From b20638756248d6fdffc6d1dc1b0ba31b57aa42f6 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Sun, 24 May 2020 22:48:41 +0200 Subject: [PATCH 001/774] Initial dump of n-fertilizer code --- .../N-fertilizer/1.aggregate_region.R | 21 + .../material/N-fertilizer/AddClimatePolicy.py | 143 +++ .../model/material/N-fertilizer/AddTrade.py | 291 +++++ .../model/material/N-fertilizer/LoadParams.py | 78 ++ ... fertil trade - FAOSTAT_data_9-25-2019.csv | 1019 +++++++++++++++++ .../N-fertilizer/ReportingToGLOBIOM.py | 186 +++ .../N-fertilizer/SetupNitrogenBase.py | 629 ++++++++++ 7 files changed, 2367 insertions(+) create mode 100644 message_ix_models/model/material/N-fertilizer/1.aggregate_region.R create mode 100644 message_ix_models/model/material/N-fertilizer/AddClimatePolicy.py create mode 100644 message_ix_models/model/material/N-fertilizer/AddTrade.py create mode 100644 message_ix_models/model/material/N-fertilizer/LoadParams.py create mode 100644 message_ix_models/model/material/N-fertilizer/N fertil trade - FAOSTAT_data_9-25-2019.csv create mode 100644 message_ix_models/model/material/N-fertilizer/ReportingToGLOBIOM.py create mode 100644 message_ix_models/model/material/N-fertilizer/SetupNitrogenBase.py diff --git a/message_ix_models/model/material/N-fertilizer/1.aggregate_region.R b/message_ix_models/model/material/N-fertilizer/1.aggregate_region.R new file mode 100644 index 0000000000..13ce6fb3a9 --- /dev/null +++ b/message_ix_models/model/material/N-fertilizer/1.aggregate_region.R @@ -0,0 +1,21 @@ +library(readxl) +library(countrycode) +library(tidyverse) + +raw.mapping <- read_xlsx('../MESSAGE_region_mapping_R14.xlsx') +raw.trade.FAO <- read.csv('../Comtrade/N fertil trade - FAOSTAT_data_9-25-2019.csv') + +trade.FAO <- raw.trade.FAO %>% mutate(ISO = countrycode(Area, 'country.name', 'iso3c')) %>% + mutate_cond(Area=='Serbia and Montenegro', ISO="SRB") %>% # Will be EEU in the end. So either SRB or MNE + left_join(raw.mapping) %>% + mutate(Element=gsub(' Quantity', '', Element)) + +trade.FAO.R14 <- trade.FAO %>% group_by(msgregion, Element, Year) %>% summarise(Value=sum(Value), Unit=first(Unit)) %>% + filter(!is.na(msgregion)) + +trade.FAO.R11 <- trade.FAO %>% mutate_cond(msgregion %in% c('RUS', 'CAS', 'SCS', 'UBM'), msgregion="FSU") %>% + group_by(msgregion, Element, Year) %>% summarise(Value=sum(Value), Unit=first(Unit)) %>% + filter(!is.na(msgregion)) + +write.csv(trade.FAO.R11, '../trade.FAO.R11.csv') +write.csv(trade.FAO.R14, '../trade.FAO.R14.csv') diff --git a/message_ix_models/model/material/N-fertilizer/AddClimatePolicy.py b/message_ix_models/model/material/N-fertilizer/AddClimatePolicy.py new file mode 100644 index 0000000000..3005bfd995 --- /dev/null +++ b/message_ix_models/model/material/N-fertilizer/AddClimatePolicy.py @@ -0,0 +1,143 @@ +# -*- coding: utf-8 -*- +""" +Created on Tue Aug 6 11:17:06 2019 + +@author: min +""" + +import time +import pandas as pd + + +#%% Add climate policies + + +mp = ix.Platform(dbprops=r'H:\MyDocuments\MESSAGE\message_ix\config\default.properties') + + + +#%% + +# new model name in ix platform +modelName = "JM_GLB_NITRO" +basescenarioName = "Baseline" # CCS now merged to the Baseline +newscenarioName = "EmBound" # '2degreeC' # + +comment = "MESSAGE_Global test for new representation of nitrogen cycle with climate policy" + +Sc_nitro = message_ix.Scenario(mp, modelName, basescenarioName) + + +#%% Clone +Sc_nitro_2C = Sc_nitro.clone(modelName, newscenarioName, comment) + +lasthistyear = 2020 # New last historical year +df_ACT = Sc_nitro_2C.var('ACT', {'year_act':lasthistyear}).groupby(['node_loc', 'technology', 'year_act']).sum().reset_index() +df_CAP_NEW = Sc_nitro_2C.var('CAP_NEW', {'year_vtg':lasthistyear}).groupby(['node_loc', 'technology', 'year_vtg']).sum().reset_index() +df_EXT = Sc_nitro_2C.var('EXT', {'year':lasthistyear}).groupby(['node', 'commodity', 'grade', 'year']).sum().reset_index() + +Sc_nitro_2C.remove_solution() +Sc_nitro_2C.check_out() + +#%% Put global emissions bound +bound = 5000 #15000 # +bound_emissions_2C = { + 'node': 'World', + 'type_emission': 'TCE', + 'type_tec': 'all', + 'type_year' : 'cumulative', #'2050', # + 'value': bound, #1076.0, # 1990 and on + 'unit' : 'tC', +} + +df = pd.DataFrame(bound_emissions_2C, index=[0]) + +Sc_nitro_2C.add_par("bound_emission", df) + +#%% Change first modeling year (dealing with the 5-year step scenario) + +df = Sc_nitro_2C.set('cat_year') +fyear = 2030 #2025 +a = df[((df.type_year=="cumulative") & (df.year 0] +a = pd.merge(df[['node_loc', 'technology', 'mode', 'time', 'unit']], + df_ACT[['node_loc', 'technology', 'year_act', 'lvl']], how='left', on=['node_loc', 'technology']).rename(columns={'lvl':'value'}) +a = a[a.value > 0] +a['unit'] = 'GWa' +a['year_act'] = a['year_act'].astype(int) +Sc_nitro_2C.add_par("historical_activity", a) + +# historical_new_capacity +df = Sc_nitro_2C.par('historical_new_capacity', {'year_vtg':lasthistyear_org}) +#df = df[df.value > 0] +a = pd.merge(df[['node_loc', 'technology', 'unit']], + df_CAP_NEW[['node_loc', 'technology', 'year_vtg', 'lvl']], how='left', on=['node_loc', 'technology']).rename(columns={'lvl':'value'}) +a = a[a.value > 0] +a['year_vtg'] = a['year_vtg'].astype(int) +Sc_nitro_2C.add_par("historical_new_capacity", a) + +# historical_extraction +df = Sc_nitro_2C.par('historical_extraction', {'year':lasthistyear_org}) +#df = df[df.value > 0] +a = pd.merge(df[['node', 'commodity', 'grade', 'unit']], + df_EXT[['node', 'commodity', 'grade', 'year', 'lvl']], how='outer', on=['node', 'commodity', 'grade']).rename(columns={'lvl':'value'}) +a = a[a.value > 0] +a['unit'] = 'GWa' +a['year'] = a['year'].astype(int) +Sc_nitro_2C.add_par("historical_extraction", a) + +# historical_land +df = Sc_nitro_2C.par('historical_land', {'year':lasthistyear_org}) +df['year'] = lasthistyear +Sc_nitro_2C.add_par("historical_land", df) + +# historical_gdp +#Sc_INDC = mp.Scenario("CD_Links_SSP2", "INDCi_1000-con-prim-dir-ncr") +#df = Sc_nitro.par('historical_gdp') +#df = df[df.year < fyear] +#Sc_nitro_2C.add_par("historical_gdp", df) + + +#%% + +#Sc_nitro_2C.commit('Hydro AllReg-Global w/ 2C constraint (starting 2020)') +Sc_nitro_2C.commit('Fertilizer Global w/ 2C constraint (starting 2030)') + +# to_gdx only +#start_time = time.time() +#Sc_nitro_2C.to_gdx(r'..\..\message_ix\message_ix\model\data', "MsgData_"+Sc_nitro_2C.model+"_"+ +# Sc_nitro_2C.scenario+"_"+str(Sc_nitro_2C.version)) +#print(".to_gdx: %s seconds taken." % (time.time() - start_time)) + +# solve +start_time = time.time() +Sc_nitro_2C.solve(model='MESSAGE', case=Sc_nitro_2C.model+"_"+ +# Sc_nitro_2C.scenario+"_"+str(Sc_nitro_2C.version)) + Sc_nitro_2C.scenario+"_"+str(bound)) + +print(".solve: %.6s seconds taken." % (time.time() - start_time)) + + +#%% +rep = Reporter.from_scenario(Sc_nitro_2C) + +# Set up filters for N tecs +rep.set_filters(t= newtechnames_ccs + newtechnames + ['NFert_imp', 'NFert_exp', 'NFert_trd']) + +# NF demand summary +NF = rep.add_product('useNF', 'land_input', 'LAND') + +print(rep.describe(rep.full_key('useNF'))) +rep.get('useNF:n-y') +rep.write('useNF:n-y', 'nf_demand_'+str(bound)+'_notrade.xlsx') +rep.write('useNF:y', 'nf_demand_total_'+str(bound)+'_notrade.xlsx') diff --git a/message_ix_models/model/material/N-fertilizer/AddTrade.py b/message_ix_models/model/material/N-fertilizer/AddTrade.py new file mode 100644 index 0000000000..163cb01453 --- /dev/null +++ b/message_ix_models/model/material/N-fertilizer/AddTrade.py @@ -0,0 +1,291 @@ +# -*- coding: utf-8 -*- +""" +Created on Tue Sep 24 14:36:18 2019 + +@author: min +""" +import numpy as np +import pandas as pd +import time + +import ixmp as ix +import message_ix + +import importlib +#import LoadParams +importlib.reload(LoadParams) + +#%% Base model load + +# launch the IX modeling platform using the local default database +mp = ix.Platform(dbprops=r'H:\MyDocuments\MESSAGE\message_ix\config\default.org.properties') + +# new model name in ix platform +modelName = "JM_GLB_NITRO" +basescenarioName = "NoPolicy" +newscenarioName = "NoPolicy_Trd" + +comment = "MESSAGE global test for new representation of nitrogen cycle with global trade" + +Sc_nitro = message_ix.Scenario(mp, modelName, basescenarioName) + +#%% Clone the model + +Sc_nitro_trd = Sc_nitro.clone(modelName, newscenarioName, comment) + +Sc_nitro_trd.remove_solution() +Sc_nitro_trd.check_out() + +#%% Add tecs to set +# Only fertilizer traded for now (NH3 trade data not yet available) +comm_for_trd = ['Fertilizer Use|Nitrogen'] +lvl_for_trd = ['material_final'] +newtechnames_trd = ['NFert_trd'] +newtechnames_imp = ['NFert_imp'] +newtechnames_exp = ['NFert_exp'] + +#comm_for_trd = comm_for_trd # = ['NH3', 'Fertilizer Use|Nitrogen'] +#newtechnames_trd = ['NH3_trd', 'NFert_trd'] +#newtechnames_imp = ['NH3_imp', 'NFert_imp'] +#newtechnames_exp = ['NH3_exp', 'NFert_exp'] + +Sc_nitro_trd.add_set("technology", newtechnames_trd + newtechnames_imp + newtechnames_exp) + +cat_add = pd.DataFrame({'type_tec': ['import', 'export'], # 'all' not need to be added here + 'technology': newtechnames_imp + newtechnames_exp}) + +Sc_nitro_trd.add_set("cat_tec", cat_add) + +#%% input & output + +for t in newtechnames_trd: + # output + df = Sc_nitro_trd.par("output", {"technology":["coal_trd"]}) + df['technology'] = t + df['commodity'] = comm_for_trd[newtechnames_trd.index(t)] + df['value'] = 1 + df['unit'] = 'Tg N/yr' + Sc_nitro_trd.add_par("output", df.copy()) + + df = Sc_nitro_trd.par("input", {"technology":["coal_trd"]}) + df['technology'] = t + df['commodity'] = comm_for_trd[newtechnames_trd.index(t)] + df['value'] = 1 + df['unit'] = 'Tg N/yr' + Sc_nitro_trd.add_par("input", df.copy()) + +reg = REGIONS.copy() +reg.remove('R11_GLB') + +for t in newtechnames_imp: + # output + df = Sc_nitro_trd.par("output", {"technology":["coal_imp"], "node_loc":['R11_CPA']}) + df['technology'] = t + df['commodity'] = comm_for_trd[newtechnames_imp.index(t)] + df['value'] = 1 + df['unit'] = 'Tg N/yr' + df['level'] = lvl_for_trd[newtechnames_imp.index(t)] + for r in reg: + df['node_loc'] = r + df['node_dest'] = r + Sc_nitro_trd.add_par("output", df.copy()) + + # input + df = Sc_nitro_trd.par("input", {"technology":["coal_imp"], "node_loc":['R11_CPA']}) + df['technology'] = t + df['commodity'] = comm_for_trd[newtechnames_imp.index(t)] + df['value'] = 1 + df['unit'] = 'Tg N/yr' + for r in reg: + df['node_loc'] = r + Sc_nitro_trd.add_par("input", df.copy()) + +for t in newtechnames_exp: + # output + df = Sc_nitro_trd.par("output", {"technology":["coal_exp"], "node_loc":['R11_CPA']}) + df['technology'] = t + df['commodity'] = comm_for_trd[newtechnames_exp.index(t)] + df['value'] = 1 + df['unit'] = 'Tg N/yr' + for r in reg: + df['node_loc'] = r + Sc_nitro_trd.add_par("output", df.copy()) + + # input + df = Sc_nitro_trd.par("input", {"technology":["coal_exp"], "node_loc":['R11_CPA']}) + df['technology'] = t + df['commodity'] = comm_for_trd[newtechnames_exp.index(t)] + df['value'] = 1 + df['unit'] = 'Tg N/yr' + df['level'] = lvl_for_trd[newtechnames_exp.index(t)] + for r in reg: + df['node_loc'] = r + df['node_origin'] = r + Sc_nitro_trd.add_par("input", df.copy()) + +# Need to incorporate the regional trade pattern + +#%% Cost + +for t in newtechnames_exp: + df = Sc_nitro_trd.par("inv_cost", {"technology":["coal_exp"]}) + df['technology'] = t + Sc_nitro_trd.add_par("inv_cost", df) + + df = Sc_nitro_trd.par("var_cost", {"technology":["coal_exp"]}) + df['technology'] = t + Sc_nitro_trd.add_par("var_cost", df) + + df = Sc_nitro_trd.par("fix_cost", {"technology":["coal_exp"]}) + df['technology'] = t + Sc_nitro_trd.add_par("fix_cost", df) + +for t in newtechnames_imp: +# No inv_cost for importing tecs +# df = Sc_nitro_trd.par("inv_cost", {"technology":["coal_imp"]}) +# df['technology'] = t +# Sc_nitro_trd.add_par("inv_cost", df) + + df = Sc_nitro_trd.par("var_cost", {"technology":["coal_imp"]}) + df['technology'] = t + Sc_nitro_trd.add_par("var_cost", df) + + df = Sc_nitro_trd.par("fix_cost", {"technology":["coal_imp"]}) + df['technology'] = t + Sc_nitro_trd.add_par("fix_cost", df) + +for t in newtechnames_trd: + df = Sc_nitro_trd.par("var_cost", {"technology":["coal_trd"]}) + df['technology'] = t + Sc_nitro_trd.add_par("var_cost", df) + + + +#%% Other background variables + +paramList_tec = [x for x in Sc_nitro_trd.par_list() if 'technology' in Sc_nitro_trd.idx_sets(x)] +#paramList_comm = [x for x in Sc_nitro.par_list() if 'commodity' in Sc_nitro.idx_sets(x)] + +def get_params_with_tech(scen, name): + result = [] + # Iterate over all parameters with a tech dimension + for par_name in paramList_tec: + if len(scen.par(par_name, filters={'technology': name})): + # Parameter has >= 1 data point with tech *name* + result.append(par_name) + return result + +params_exp = get_params_with_tech(Sc_nitro_trd, 'coal_exp') +params_imp = get_params_with_tech(Sc_nitro_trd, 'coal_imp') +params_trd = get_params_with_tech(Sc_nitro_trd, 'coal_trd') + +# Got too slow for some reason +#params_exp = [x for x in paramList_tec if 'coal_exp' in set(Sc_nitro_trd.par(x)["technology"].tolist())] +#params_imp = [x for x in paramList_tec if 'coal_imp' in set(Sc_nitro_trd.par(x)["technology"].tolist())] +#params_trd = [x for x in paramList_tec if 'coal_trd' in set(Sc_nitro_trd.par(x)["technology"].tolist())] + +a = set(params_exp + params_imp + params_trd) +suffix = ('cost', 'put') +prefix = ('historical', 'ref', 'relation') +a = a - set([x for x in a if x.endswith(suffix)] + [x for x in a if x.startswith(prefix)] + ['emission_factor']) + +for p in list(a): + for t in newtechnames_exp: + df = Sc_nitro_trd.par(p, {"technology":["coal_exp"]}) + df['technology'] = t + if df.size: + Sc_nitro_trd.add_par(p, df.copy()) + + for t in newtechnames_imp: + df = Sc_nitro_trd.par(p, {"technology":["coal_imp"]}) + df['technology'] = t + if df.size: + Sc_nitro_trd.add_par(p, df.copy()) + + for t in newtechnames_trd: + df = Sc_nitro_trd.par(p, {"technology":["coal_trd"]}) + df['technology'] = t + if df.size: + Sc_nitro_trd.add_par(p, df.copy()) + +# Found coal_exp doesn't have full cells filled for technical_lifetime. +for t in newtechnames_exp: + df = Sc_nitro_trd.par('technical_lifetime', {"technology":t, "node_loc":['R11_CPA']}) + for r in reg: + df['node_loc'] = r + Sc_nitro_trd.add_par("technical_lifetime", df.copy()) + + +#%% Histrorical trade activity +# Export capacity - understood as infrastructure enabling the trade activity (port, rail etc.) + +# historical_activity +N_trade = LoadParams.N_trade_R11.copy() + +df_histexp = N_trade.loc[(N_trade.Element=='Export') & (N_trade.year_act<2015),] +df_histexp = df_histexp.assign(mode = 'M1') +df_histexp = df_histexp.assign(technology = newtechnames_exp[0]) #t +df_histexp = df_histexp.drop(columns="Element") + +df_histimp = N_trade.loc[(N_trade.Element=='Import') & (N_trade.year_act<2015),] +df_histimp = df_histimp.assign(mode = 'M1') +df_histimp = df_histimp.assign(technology = newtechnames_imp[0]) #t +df_histimp = df_histimp.drop(columns="Element") + +# GLB trd historical activities (Now equal to the sum of imports) +dftrd = Sc_nitro_trd.par("historical_activity", {"technology":["coal_trd"]}) +dftrd = dftrd.loc[(dftrd.year_act<2015) & (dftrd.year_act>2000),] +dftrd.value = df_histimp.groupby(['year_act']).sum().values +dftrd['unit'] = 'Tg N/yr' +dftrd['technology'] = newtechnames_trd[0] +Sc_nitro_trd.add_par("historical_activity", dftrd) +Sc_nitro_trd.add_par("historical_activity", df_histexp.append(df_histimp)) + +# historical_new_capacity +trdlife = Sc_nitro_trd.par("technical_lifetime", {"technology":["NFert_exp"]}).value[0] + +df_histcap = df_histexp.drop(columns=['time', 'mode']) +df_histcap = df_histcap.rename(columns={"year_act":"year_vtg"}) +df_histcap.value = df_histcap.value.values/trdlife + +Sc_nitro_trd.add_par("historical_new_capacity", df_histcap) + + +#%% + +# Adjust i_feed demand +#demand_fs_org is the original i-feed in the reference SSP2 scenario +""" +In the scenario w/o trade, I assumed 100% NH3 demand is produced in each region. +Now with trade, I subtract the net import (of tN) from total demand, and the difference is produced regionally. +Still this does not include NH3 trade, so will not match the historical NH3 regional production. +Also, historical global production exceeds the global demand level from SSP2 scenario for the same year. +I currently ignore this excess production and only produce what is demanded. +""" +df = demand_fs_org.loc[demand_fs_org.year==2010,:].join(LoadParams.N_feed.set_index('node'), on='node') +sh = pd.DataFrame( {'node': demand_fs_org.loc[demand_fs_org.year==2010, 'node'], + 'r_feed': df.totENE / df.value}) # share of NH3 energy among total feedstock (no trade assumed) +df = demand_fs_org.join(sh.set_index('node'), on='node') +df.value *= 1 - df.r_feed # Carve out the same % from tot i_feed values +df = df.drop('r_feed', axis=1) +Sc_nitro_trd.add_par("demand", df) + + + +#%% Solve the scenario + +Sc_nitro_trd.commit('Nitrogen Fertilizer for global model with fertilizer trade via global pool') + +start_time = time.time() + +Sc_nitro_trd.solve(model='MESSAGE', case=Sc_nitro_trd.model+"_"+ + Sc_nitro_trd.scenario + "_" + str(Sc_nitro_trd.version)) + +print(".solve: %.6s seconds taken." % (time.time() - start_time)) + +#%% utils +#Sc_nitro_trd.discard_changes() + +#Sc_nitro_trd = message_ix.Scenario(mp, modelName, newscenarioName) + + diff --git a/message_ix_models/model/material/N-fertilizer/LoadParams.py b/message_ix_models/model/material/N-fertilizer/LoadParams.py new file mode 100644 index 0000000000..39c19ee6ea --- /dev/null +++ b/message_ix_models/model/material/N-fertilizer/LoadParams.py @@ -0,0 +1,78 @@ +# -*- coding: utf-8 -*- +""" +Created on Tue Jun 4 12:01:47 2019 + +@author: min + +Load parameters for the new technologies +""" + +import pandas as pd + +# Read parameters in xlsx +te_params = pd.read_excel(r'..\n-fertilizer_techno-economic.xlsx', sheet_name='Sheet1') +n_inputs_per_tech = 12 # Number of input params per technology + +inv_cost = te_params[2010][list(range(0, te_params.shape[0], n_inputs_per_tech))].reset_index(drop=True) +fix_cost = te_params[2010][list(range(1, te_params.shape[0], n_inputs_per_tech))].reset_index(drop=True) +var_cost = te_params[2010][list(range(2, te_params.shape[0], n_inputs_per_tech))].reset_index(drop=True) +technical_lifetime = te_params[2010][list(range(3, te_params.shape[0], n_inputs_per_tech))].reset_index(drop=True) +input_fuel = te_params[2010][list(range(4, te_params.shape[0], n_inputs_per_tech))].reset_index(drop=True) +input_fuel[0:5] = input_fuel[0:5] * 0.0317 # 0.0317 GWa/PJ, GJ/t = PJ/Mt NH3 +input_elec = te_params[2010][list(range(5, te_params.shape[0], n_inputs_per_tech))].reset_index(drop=True) +input_elec = input_elec * 0.0317 # 0.0317 GWa/PJ +input_water = te_params[2010][list(range(6, te_params.shape[0], n_inputs_per_tech))].reset_index(drop=True) +output_NH3 = te_params[2010][list(range(7, te_params.shape[0], n_inputs_per_tech))].reset_index(drop=True) +output_water = te_params[2010][list(range(8, te_params.shape[0], n_inputs_per_tech))].reset_index(drop=True) +output_heat = te_params[2010][list(range(9, te_params.shape[0], n_inputs_per_tech))].reset_index(drop=True) +output_heat = output_heat * 0.0317 # 0.0317 GWa/PJ +emissions = te_params[2010][list(range(10, te_params.shape[0], n_inputs_per_tech))].reset_index(drop=True) * 12 / 44 # CO2 to C +capacity_factor = te_params[2010][list(range(11, te_params.shape[0], n_inputs_per_tech))].reset_index(drop=True) + + +#%% Demand scenario [Mt N/year] from GLOBIOM +# N_demand_FSU = pd.read_excel(r'..\CD-Links SSP2 N-fertilizer demand.FSU.xlsx', sheet_name='data', nrows=3) +N_demand_GLO = pd.read_excel(r'..\CD-Links SSP2 N-fertilizer demand.Global.xlsx', sheet_name='data') + +# NH3 feedstock share by region in 2010 (from http://ietd.iipnetwork.org/content/ammonia#benchmarks) +feedshare_GLO = pd.read_excel(r'..\Ammonia feedstock share.Global.xlsx', sheet_name='Sheet2', skiprows=13) + +# Regional N demaand in 2010 +ND = N_demand_GLO.loc[N_demand_GLO.Scenario=="NoPolicy", ['Region', 2010]] +ND = ND[ND.Region!='World'] +ND.Region = 'R11_' + ND.Region +ND = ND.set_index('Region') + +# Derive total energy (GWa) of NH3 production (based on demand 2010) +N_energy = feedshare_GLO[feedshare_GLO.Region!='R11_GLB'].join(ND, on='Region') +N_energy = pd.concat([N_energy.Region, N_energy[["gas_pct", "coal_pct", "oil_pct"]].multiply(N_energy[2010], axis="index")], axis=1) +N_energy.gas_pct *= input_fuel[2] * 17/14 # NH3 / N +N_energy.coal_pct *= input_fuel[3] * 17/14 +N_energy.oil_pct *= input_fuel[4] * 17/14 +N_energy = pd.concat([N_energy.Region, N_energy.sum(axis=1)], axis=1).rename(columns={0:'totENE', 'Region':'node'}) #GWa + + +#%% Import trade data (from FAO) + +N_trade_R14 = pd.read_csv(r'..\trade.FAO.R14.csv', index_col=0) + +N_trade_R11 = pd.read_csv(r'..\trade.FAO.R11.csv', index_col=0) +N_trade_R11.msgregion = 'R11_' + N_trade_R11.msgregion +N_trade_R11.Value = N_trade_R11.Value/1e6 +N_trade_R11.Unit = 'Tg N/yr' +N_trade_R11 = N_trade_R11.assign(time = 'year') +N_trade_R11 = N_trade_R11.rename(columns={"Value":"value", "Unit":"unit", "msgregion":"node_loc", "Year":"year_act"}) + +df = N_trade_R11.loc[N_trade_R11.year_act==2010,] +df = df.pivot(index='node_loc', columns='Element', values='value') +NP = pd.DataFrame({'netimp': df.Import - df.Export, + 'demand': ND[2010]}) +NP['prod'] = NP.demand - NP.netimp + +# Derive total energy (GWa) of NH3 production (based on demand 2010) +N_feed = feedshare_GLO[feedshare_GLO.Region!='R11_GLB'].join(NP, on='Region') +N_feed = pd.concat([N_feed.Region, N_feed[["gas_pct", "coal_pct", "oil_pct"]].multiply(N_feed['prod'], axis="index")], axis=1) +N_feed.gas_pct *= input_fuel[2] * 17/14 +N_feed.coal_pct *= input_fuel[3] * 17/14 +N_feed.oil_pct *= input_fuel[4] * 17/14 +N_feed = pd.concat([N_feed.Region, N_feed.sum(axis=1)], axis=1).rename(columns={0:'totENE', 'Region':'node'}) #GWa diff --git a/message_ix_models/model/material/N-fertilizer/N fertil trade - FAOSTAT_data_9-25-2019.csv b/message_ix_models/model/material/N-fertilizer/N fertil trade - FAOSTAT_data_9-25-2019.csv new file mode 100644 index 0000000000..13d2b1b32b --- /dev/null +++ b/message_ix_models/model/material/N-fertilizer/N fertil trade - FAOSTAT_data_9-25-2019.csv @@ -0,0 +1,1019 @@ +Domain Code,Domain,Area Code,Area,Element Code,Element,Item Code,Item,Year Code,Year,Unit,Value,Flag,Flag Description +"RFN","Fertilizers by Nutrient","2","Afghanistan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","4849.98","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","2","Afghanistan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","914.39","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","2","Afghanistan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","6659.57","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","2","Afghanistan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","14.84","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","2","Afghanistan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","7.38","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","2","Afghanistan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","3","Albania","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","21325.32","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","3","Albania","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","24190.4","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","3","Albania","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","32125.1","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","3","Albania","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","271.74","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","3","Albania","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","245.24","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","3","Albania","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","7.13","Fm","Manual Estimation" +"RFN","Fertilizers by Nutrient","4","Algeria","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","33775.38","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","4","Algeria","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","58610","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","4","Algeria","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","60966.2","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","4","Algeria","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","198614.96","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","4","Algeria","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","96367.62","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","4","Algeria","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","765725.92","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","7","Angola","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","4432.93","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","7","Angola","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","10935.51","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","7","Angola","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","25468.81","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","7","Angola","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","7","Angola","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","7","Angola","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","8.58","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","8","Antigua and Barbuda","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","83.11","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","8","Antigua and Barbuda","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","6.9","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","8","Antigua and Barbuda","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","7.69","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","8","Antigua and Barbuda","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","8","Antigua and Barbuda","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","8","Antigua and Barbuda","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0.01","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","9","Argentina","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","303617.74","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","9","Argentina","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","546671.46","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","9","Argentina","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","285918.53","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","9","Argentina","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","231929.69","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","9","Argentina","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","62176.31","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","9","Argentina","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","57123.15","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","1","Armenia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","10383.26","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","1","Armenia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","10547.96","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","1","Armenia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","22147","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","1","Armenia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0.07","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","1","Armenia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","1","Armenia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","20.13","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","10","Australia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","761294","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","10","Australia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","863599.57","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","10","Australia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","1098521.25","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","10","Australia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","124387.76","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","10","Australia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","157808.99","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","10","Australia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","102064.05","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","11","Austria","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","122058.76","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","11","Austria","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","135937.05","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","11","Austria","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","174885.04","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","11","Austria","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","2403.48","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","11","Austria","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","11628.91","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","11","Austria","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","178486.11","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","52","Azerbaijan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","17891.17","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","52","Azerbaijan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","12999.09","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","52","Azerbaijan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","4659.42","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","52","Azerbaijan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","52","Azerbaijan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","52","Azerbaijan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","419.01","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","12","Bahamas","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","478.72","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","12","Bahamas","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","530.87","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","12","Bahamas","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","662.05","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","12","Bahamas","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","12","Bahamas","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0.01","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","12","Bahamas","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0.01","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","13","Bahrain","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","2807.05","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","13","Bahrain","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","2552.71","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","13","Bahrain","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","2142.06","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","13","Bahrain","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","272492.84","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","13","Bahrain","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","239847.55","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","13","Bahrain","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","330960.95","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","16","Bangladesh","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","426729.95","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","16","Bangladesh","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","651103.06","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","16","Bangladesh","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","648205.19","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","16","Bangladesh","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","356949.63","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","16","Bangladesh","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","46303.95","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","16","Bangladesh","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","14","Barbados","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","1439.38","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","14","Barbados","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","979.52","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","14","Barbados","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","558.44","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","14","Barbados","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","1.45","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","14","Barbados","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","3.59","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","14","Barbados","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","151.59","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","57","Belarus","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","13595.35","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","57","Belarus","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","167633.27","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","57","Belarus","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","28523.83","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","57","Belarus","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","232526.08","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","57","Belarus","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","276089.61","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","57","Belarus","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","582413.22","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","255","Belgium","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","533790.1","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","255","Belgium","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","649137.43","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","255","Belgium","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","367383.63","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","255","Belgium","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","921698.98","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","255","Belgium","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1009736.68","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","255","Belgium","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","731944.34","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","23","Belize","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","4765.8","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","23","Belize","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","2793.48","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","23","Belize","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","17605.12","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","23","Belize","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","23","Belize","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","23","Belize","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","1.25","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","53","Benin","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","412.85","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","53","Benin","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","4714.88","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","53","Benin","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","185.04","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","53","Benin","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","53","Benin","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","6.7","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","53","Benin","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0.18","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","17","Bermuda","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","17","Bermuda","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","79.84","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","17","Bermuda","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","41.13","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","17","Bermuda","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","17","Bermuda","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","17","Bermuda","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","18","Bhutan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","1943.3","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","18","Bhutan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1178.75","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","18","Bhutan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","1318.12","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","18","Bhutan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","1.17","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","18","Bhutan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","18","Bhutan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","19","Bolivia (Plurinational State of)","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","12232.05","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","19","Bolivia (Plurinational State of)","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","19690.64","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","19","Bolivia (Plurinational State of)","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","23120.41","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","19","Bolivia (Plurinational State of)","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0.27","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","19","Bolivia (Plurinational State of)","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0.81","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","19","Bolivia (Plurinational State of)","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","80","Bosnia and Herzegovina","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","36236.99","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","80","Bosnia and Herzegovina","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","26363.73","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","80","Bosnia and Herzegovina","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","22631.68","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","80","Bosnia and Herzegovina","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","726.22","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","80","Bosnia and Herzegovina","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","11290.08","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","80","Bosnia and Herzegovina","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","11898.34","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","20","Botswana","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","11333.2","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","20","Botswana","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","20563.96","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","20","Botswana","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","12320.6","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","20","Botswana","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","62.4","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","20","Botswana","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0.24","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","20","Botswana","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","147.68","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","21","Brazil","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","1388951.11","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","21","Brazil","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","2253801.15","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","21","Brazil","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","2761312.85","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","21","Brazil","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","105632.31","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","21","Brazil","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","89830.94","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","21","Brazil","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","90324.82","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","26","Brunei Darussalam","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","7783.48","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","26","Brunei Darussalam","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","928.66","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","26","Brunei Darussalam","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","30.99","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","26","Brunei Darussalam","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0.5","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","26","Brunei Darussalam","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","191.46","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","26","Brunei Darussalam","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","1.29","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","27","Bulgaria","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","35937.05","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","27","Bulgaria","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","120105.71","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","27","Bulgaria","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","244134.04","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","27","Bulgaria","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","196877.93","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","27","Bulgaria","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","113454.35","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","27","Bulgaria","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","196794.36","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","233","Burkina Faso","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","31826.86","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","233","Burkina Faso","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","44302.36","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","233","Burkina Faso","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","51526.59","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","233","Burkina Faso","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","1.99","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","233","Burkina Faso","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","932.48","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","233","Burkina Faso","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","440.98","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","29","Burundi","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","606.56","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","29","Burundi","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1238.28","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","29","Burundi","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","4603.82","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","29","Burundi","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","29","Burundi","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","29","Burundi","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","115","Cambodia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","11053.86","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","115","Cambodia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","22657.53","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","115","Cambodia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","80858.37","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","115","Cambodia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","115","Cambodia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","115","Cambodia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","32","Cameroon","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","23388.89","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","32","Cameroon","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","27476.68","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","32","Cameroon","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","45825.12","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","32","Cameroon","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","45.02","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","32","Cameroon","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1545.27","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","32","Cameroon","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","3191.71","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","33","Canada","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","363142.09","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","33","Canada","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","498624.98","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","33","Canada","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","889562.72","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","33","Canada","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","1237850.24","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","33","Canada","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1203560.92","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","33","Canada","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","816909.55","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","37","Central African Republic","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","245.65","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","37","Central African Republic","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","221.67","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","37","Central African Republic","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","219.25","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","37","Central African Republic","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","37","Central African Republic","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","37","Central African Republic","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","40","Chile","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","238328.78","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","40","Chile","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","286495.39","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","40","Chile","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","362671.03","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","40","Chile","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","88584.49","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","40","Chile","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","140672.89","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","40","Chile","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","132943.06","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","351","China","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","964283.07","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","351","China","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","392110.96","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","351","China","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","363731.76","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","351","China","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","1170671.82","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","351","China","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","4725630.67","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","351","China","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","9976149.17","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","96","China, Hong Kong SAR","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","2402.93","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","96","China, Hong Kong SAR","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1747.78","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","96","China, Hong Kong SAR","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","7572.22","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","96","China, Hong Kong SAR","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","1167.23","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","96","China, Hong Kong SAR","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","130.83","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","96","China, Hong Kong SAR","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","521.7","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","128","China, Macao SAR","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","5785.19","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","128","China, Macao SAR","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1296.96","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","128","China, Macao SAR","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","3028.54","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","128","China, Macao SAR","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","128","China, Macao SAR","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","128","China, Macao SAR","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","41","China, mainland","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","819579.02","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","41","China, mainland","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","257991.24","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","41","China, mainland","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","251853.44","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","41","China, mainland","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","1114808.28","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","41","China, mainland","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","4654927.65","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","41","China, mainland","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","9921758.27","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","214","China, Taiwan Province of","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","136515.93","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","214","China, Taiwan Province of","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","131074.97","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","214","China, Taiwan Province of","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","101277.56","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","214","China, Taiwan Province of","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","54696.31","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","214","China, Taiwan Province of","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","70572.18","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","214","China, Taiwan Province of","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","53869.2","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","44","Colombia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","300359.9","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","44","Colombia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","316247.76","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","44","Colombia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","414828.31","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","44","Colombia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","9640.53","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","44","Colombia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","28707.68","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","44","Colombia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","41691.58","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","46","Congo","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","99.67","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","46","Congo","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","86.81","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","46","Congo","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","298.37","Fm","Manual Estimation" +"RFN","Fertilizers by Nutrient","46","Congo","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0.09","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","46","Congo","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0.09","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","46","Congo","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","1.28","Fm","Manual Estimation" +"RFN","Fertilizers by Nutrient","47","Cook Islands","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","19.77","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","47","Cook Islands","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","7.28","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","47","Cook Islands","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","25.4","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","47","Cook Islands","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","47","Cook Islands","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","47","Cook Islands","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","48","Costa Rica","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","70118.42","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","48","Costa Rica","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","65463.12","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","48","Costa Rica","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","90040.23","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","48","Costa Rica","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","2281.6","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","48","Costa Rica","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","4678.53","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","48","Costa Rica","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","4862.69","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","107","Côte d'Ivoire","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","43778.91","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","107","Côte d'Ivoire","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","31401.99","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","107","Côte d'Ivoire","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","53158.46","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","107","Côte d'Ivoire","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","27420.9","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","107","Côte d'Ivoire","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","4311.72","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","107","Côte d'Ivoire","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","10618.98","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","98","Croatia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","14408.39","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","98","Croatia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","32163.35","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","98","Croatia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","45381.63","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","98","Croatia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","211312.75","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","98","Croatia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","269898.95","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","98","Croatia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","295571","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","49","Cuba","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","23339.2","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","49","Cuba","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","46116.87","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","49","Cuba","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","48233.07","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","49","Cuba","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","3483.08","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","49","Cuba","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1174.88","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","49","Cuba","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","26.83","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","50","Cyprus","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","8093.36","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","50","Cyprus","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","8498.45","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","50","Cyprus","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","7939.54","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","50","Cyprus","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","6.41","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","50","Cyprus","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","9.94","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","50","Cyprus","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","167","Czechia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","179025.15","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","167","Czechia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","184331.67","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","167","Czechia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","350458.67","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","167","Czechia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","151496.8","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","167","Czechia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","162496.55","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","167","Czechia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","127226.33","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","250","Democratic Republic of the Congo","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","2671.14","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","250","Democratic Republic of the Congo","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","4078.38","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","250","Democratic Republic of the Congo","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","18071.34","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","250","Democratic Republic of the Congo","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0.23","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","250","Democratic Republic of the Congo","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","7.47","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","250","Democratic Republic of the Congo","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","3.83","Fm","Manual Estimation" +"RFN","Fertilizers by Nutrient","54","Denmark","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","207018.39","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","54","Denmark","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","170956.31","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","54","Denmark","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","240107.01","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","54","Denmark","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","15627.14","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","54","Denmark","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","21362.13","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","54","Denmark","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","38188.31","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","55","Dominica","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","184.82","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","55","Dominica","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","115.56","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","55","Dominica","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","314.83","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","55","Dominica","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0.04","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","55","Dominica","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","55","Dominica","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","56","Dominican Republic","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","38400.5","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","56","Dominican Republic","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","53570.37","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","56","Dominican Republic","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","52344.26","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","56","Dominican Republic","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","1662.17","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","56","Dominican Republic","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","743.8","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","56","Dominican Republic","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","2523.6","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","58","Ecuador","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","121192.11","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","58","Ecuador","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","167078.26","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","58","Ecuador","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","177625.51","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","58","Ecuador","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","124.6","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","58","Ecuador","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1131.43","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","58","Ecuador","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","73.46","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","59","Egypt","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","367555.63","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","59","Egypt","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","2426.38","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","59","Egypt","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","57454.96","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","59","Egypt","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","32727.51","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","59","Egypt","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1888230.49","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","59","Egypt","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","417859.01","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","60","El Salvador","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","68448.08","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","60","El Salvador","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","62088.81","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","60","El Salvador","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","67105.73","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","60","El Salvador","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","5253.08","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","60","El Salvador","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","6250.9","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","60","El Salvador","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","9013.05","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","178","Eritrea","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","2036.84","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","178","Eritrea","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","274.05","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","178","Eritrea","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","1602.42","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","178","Eritrea","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","178","Eritrea","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","210.6","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","178","Eritrea","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","63","Estonia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","148928.62","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","63","Estonia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","50009.31","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","63","Estonia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","79238.35","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","63","Estonia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","177379.06","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","63","Estonia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","8626.46","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","63","Estonia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","6994.76","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","238","Ethiopia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","68336.86","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","238","Ethiopia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","153856.87","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","238","Ethiopia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","139522.48","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","238","Ethiopia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","238","Ethiopia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","238","Ethiopia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","66","Fiji","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","6695.14","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","66","Fiji","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","2452.3","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","66","Fiji","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","5827.08","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","66","Fiji","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","1.38","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","66","Fiji","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0.56","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","66","Fiji","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","1.51","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","67","Finland","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","39336.9","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","67","Finland","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","34427.83","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","67","Finland","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","90116.81","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","67","Finland","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","109062.02","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","67","Finland","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","135734.82","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","67","Finland","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","230047.99","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","68","France","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","1458910.51","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","68","France","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","777055.14","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","68","France","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","2058024.18","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","68","France","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","239029.6","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","68","France","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","92209.52","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","68","France","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","287227.29","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","70","French Polynesia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","347.99","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","70","French Polynesia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","399.17","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","70","French Polynesia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","343.26","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","70","French Polynesia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","70","French Polynesia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","70","French Polynesia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","74","Gabon","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","160.19","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","74","Gabon","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","988.04","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","74","Gabon","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","3883.69","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","74","Gabon","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","74","Gabon","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","6.17","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","74","Gabon","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","4.2","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","75","Gambia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0.12","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","75","Gambia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","7.04","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","75","Gambia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","141.24","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","75","Gambia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","75","Gambia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","75","Gambia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","73","Georgia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","441.16","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","73","Georgia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","3084.22","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","73","Georgia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","9917.49","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","73","Georgia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","88073.28","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","73","Georgia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","128955.86","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","73","Georgia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","154264.05","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","79","Germany","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","1131221.93","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","79","Germany","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1204490.62","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","79","Germany","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","1260569.66","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","79","Germany","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","697189.72","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","79","Germany","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","769700.04","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","79","Germany","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","815146.73","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","81","Ghana","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","38157.97","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","81","Ghana","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","49762.64","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","81","Ghana","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","71560.78","Fm","Manual Estimation" +"RFN","Fertilizers by Nutrient","81","Ghana","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","269.2","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","81","Ghana","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","3674.45","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","81","Ghana","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","4590.88","Fm","Manual Estimation" +"RFN","Fertilizers by Nutrient","84","Greece","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","158363.57","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","84","Greece","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","158894.64","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","84","Greece","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","202835.44","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","84","Greece","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","59326.22","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","84","Greece","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","77611.38","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","84","Greece","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","118389.21","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","89","Guatemala","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","147842.88","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","89","Guatemala","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","149517.06","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","89","Guatemala","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","189987.99","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","89","Guatemala","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","11142.38","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","89","Guatemala","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","4284.73","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","89","Guatemala","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","9809.9","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","90","Guinea","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","2074.63","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","90","Guinea","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","2794.05","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","90","Guinea","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","2463.5","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","90","Guinea","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","90","Guinea","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","90","Guinea","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","60.04","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","91","Guyana","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","6548.2","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","91","Guyana","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","14610.73","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","91","Guyana","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","17642.93","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","91","Guyana","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0.09","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","91","Guyana","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","91","Guyana","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","420.21","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","95","Honduras","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","42879.33","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","95","Honduras","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","57421.91","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","95","Honduras","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","77521.3","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","95","Honduras","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","1013.41","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","95","Honduras","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1380.24","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","95","Honduras","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","2178.28","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","97","Hungary","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","159690.08","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","97","Hungary","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","214977.39","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","97","Hungary","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","325138.94","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","97","Hungary","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","106302.26","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","97","Hungary","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","171408.4","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","97","Hungary","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","120415.46","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","99","Iceland","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","7480.39","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","99","Iceland","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","7904.89","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","99","Iceland","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","8698.58","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","99","Iceland","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0.02","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","99","Iceland","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","99","Iceland","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","2.6","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","100","India","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","1040447.01","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","100","India","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","3212884.87","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","100","India","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","5933943.45","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","100","India","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","12358.13","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","100","India","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","20778.53","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","100","India","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","36319.23","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","101","Indonesia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","175571.15","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","101","Indonesia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","279986.63","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","101","Indonesia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","504058.11","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","101","Indonesia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","312091.3","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","101","Indonesia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","450911.85","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","101","Indonesia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","411571.24","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","102","Iran (Islamic Republic of)","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","132173.26","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","102","Iran (Islamic Republic of)","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","42548.2","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","102","Iran (Islamic Republic of)","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","4328.54","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","102","Iran (Islamic Republic of)","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","14279.72","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","102","Iran (Islamic Republic of)","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","433712.26","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","102","Iran (Islamic Republic of)","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","1053550.76","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","103","Iraq","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","115.56","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","103","Iraq","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","22475.48","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","103","Iraq","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","15666.22","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","103","Iraq","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","29918.04","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","103","Iraq","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","103","Iraq","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","230.92","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","104","Ireland","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","338570.74","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","104","Ireland","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","382971.76","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","104","Ireland","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","324548.43","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","104","Ireland","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","496.26","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","104","Ireland","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","562.17","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","104","Ireland","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","2271.62","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","105","Israel","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","29856.58","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","105","Israel","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","26366.25","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","105","Israel","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","56647.52","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","105","Israel","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","60246.51","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","105","Israel","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","88677.8","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","105","Israel","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","54007.97","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","106","Italy","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","618604.02","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","106","Italy","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","464633.58","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","106","Italy","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","604744.28","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","106","Italy","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","72257.91","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","106","Italy","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","75828.46","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","106","Italy","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","140221.64","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","109","Jamaica","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","6435.24","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","109","Jamaica","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","4857.98","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","109","Jamaica","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","2644.24","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","109","Jamaica","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","27.55","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","109","Jamaica","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1.12","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","109","Jamaica","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","5.73","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","110","Japan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","315226.78","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","110","Japan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","218213.78","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","110","Japan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","251031.12","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","110","Japan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","188937.07","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","110","Japan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","180952.81","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","110","Japan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","129294.82","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","112","Jordan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","15606.58","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","112","Jordan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","16628.62","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","112","Jordan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","24588.69","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","112","Jordan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","14537.26","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","112","Jordan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","163461.93","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","112","Jordan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","64758.26","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","108","Kazakhstan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","74682.36","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","108","Kazakhstan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","72045.22","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","108","Kazakhstan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","63537.8","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","108","Kazakhstan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","21751.17","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","108","Kazakhstan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","33040","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","108","Kazakhstan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","37658.93","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","114","Kenya","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","88466.34","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","114","Kenya","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","86143.91","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","114","Kenya","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","83942.36","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","114","Kenya","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","3165.63","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","114","Kenya","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","7227.65","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","114","Kenya","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","3404.51","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","118","Kuwait","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","2095.14","Fm","Manual Estimation" +"RFN","Fertilizers by Nutrient","118","Kuwait","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","853.61","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","118","Kuwait","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","2492.5","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","118","Kuwait","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","401409.74","Fm","Manual Estimation" +"RFN","Fertilizers by Nutrient","118","Kuwait","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","540147.08","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","118","Kuwait","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","377597.14","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","113","Kyrgyzstan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","28954.64","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","113","Kyrgyzstan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","52989.72","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","113","Kyrgyzstan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","43821.86","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","113","Kyrgyzstan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","2.6","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","113","Kyrgyzstan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","21.4","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","113","Kyrgyzstan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","438.16","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","119","Latvia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","63857.82","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","119","Latvia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","89891.34","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","119","Latvia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","135459.71","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","119","Latvia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","15233.7","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","119","Latvia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","9870.41","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","119","Latvia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","23518.37","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","121","Lebanon","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","14680.32","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","121","Lebanon","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","17693.63","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","121","Lebanon","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","18985.52","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","121","Lebanon","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","73.57","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","121","Lebanon","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","86.99","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","121","Lebanon","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","61.24","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","124","Libya","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","7778.64","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","124","Libya","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","12877.52","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","124","Libya","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","7143.16","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","124","Libya","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","213471.55","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","124","Libya","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","370470.65","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","124","Libya","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","136837.76","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","126","Lithuania","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","101267.61","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","126","Lithuania","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","140283.83","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","126","Lithuania","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","316022.87","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","126","Lithuania","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","780763.79","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","126","Lithuania","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","676866.3","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","126","Lithuania","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","944438.8","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","256","Luxembourg","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","34716.32","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","256","Luxembourg","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","32635.17","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","256","Luxembourg","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","37274.93","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","256","Luxembourg","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","11442.1","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","256","Luxembourg","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","11173.67","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","256","Luxembourg","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","12776.46","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","129","Madagascar","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","7469.97","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","129","Madagascar","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","4575.16","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","129","Madagascar","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","5932.85","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","129","Madagascar","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","8.57","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","129","Madagascar","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0.27","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","129","Madagascar","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","29669.34","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","130","Malawi","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","59106.8","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","130","Malawi","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","94148.57","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","130","Malawi","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","109446.72","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","130","Malawi","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","474.72","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","130","Malawi","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","204","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","130","Malawi","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","20.1","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","131","Malaysia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","442940.28","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","131","Malaysia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","547358.45","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","131","Malaysia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","556761.56","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","131","Malaysia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","373082.13","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","131","Malaysia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","419715.4","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","131","Malaysia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","548308.45","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","132","Maldives","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","130.24","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","132","Maldives","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","164.86","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","132","Maldives","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","226.3","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","132","Maldives","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0.04","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","132","Maldives","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","132","Maldives","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","133","Mali","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","50450.28","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","133","Mali","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","75567.54","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","133","Mali","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","145603.33","Fm","Manual Estimation" +"RFN","Fertilizers by Nutrient","133","Mali","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","133","Mali","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","7909.83","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","133","Mali","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","40342.06","Fm","Manual Estimation" +"RFN","Fertilizers by Nutrient","134","Malta","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","626.17","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","134","Malta","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","391.49","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","134","Malta","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","4086.62","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","134","Malta","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0.23","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","134","Malta","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","134","Malta","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","127","Marshall Islands","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","127","Marshall Islands","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","264.81","Fm","Manual Estimation" +"RFN","Fertilizers by Nutrient","127","Marshall Islands","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0.01","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","127","Marshall Islands","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","127","Marshall Islands","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","127","Marshall Islands","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","137","Mauritius","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","11913.09","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","137","Mauritius","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","8212.81","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","137","Mauritius","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","6647.86","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","137","Mauritius","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","2137.47","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","137","Mauritius","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1444.89","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","137","Mauritius","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","1652.21","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","138","Mexico","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","877974.76","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","138","Mexico","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","789266.87","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","138","Mexico","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","1005614.06","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","138","Mexico","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","3746.34","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","138","Mexico","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","103209.85","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","138","Mexico","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","79221.99","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","141","Mongolia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","4103.35","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","141","Mongolia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","11415.97","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","141","Mongolia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","20685.73","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","141","Mongolia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","141","Mongolia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","141","Mongolia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","273","Montenegro","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1384.87","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","273","Montenegro","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","1286.24","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","273","Montenegro","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","10.28","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","273","Montenegro","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","1.08","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","143","Morocco","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","123439.62","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","143","Morocco","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","157575.47","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","143","Morocco","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","174718.48","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","143","Morocco","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","205038.12","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","143","Morocco","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","428705.32","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","143","Morocco","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","525766.94","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","144","Mozambique","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","22675.92","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","144","Mozambique","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","47210.67","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","144","Mozambique","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","13745.55","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","144","Mozambique","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","2.71","Fm","Manual Estimation" +"RFN","Fertilizers by Nutrient","144","Mozambique","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0.06","Fm","Manual Estimation" +"RFN","Fertilizers by Nutrient","144","Mozambique","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","1682.69","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","28","Myanmar","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","10239.31","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","28","Myanmar","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","3900.99","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","28","Myanmar","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","154299.74","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","28","Myanmar","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","28","Myanmar","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","28","Myanmar","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","147","Namibia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","3844.05","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","147","Namibia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","7927.63","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","147","Namibia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","10108.77","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","147","Namibia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","152.98","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","147","Namibia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","11.86","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","147","Namibia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","366.55","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","148","Nauru","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","148","Nauru","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","148","Nauru","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0.01","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","148","Nauru","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","7.71","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","148","Nauru","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","148","Nauru","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","149","Nepal","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","12766.23","Fm","Manual Estimation" +"RFN","Fertilizers by Nutrient","149","Nepal","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","56932.9","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","149","Nepal","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","108368.89","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","149","Nepal","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0.02","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","149","Nepal","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0.02","Fm","Manual Estimation" +"RFN","Fertilizers by Nutrient","149","Nepal","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","Fm","Manual Estimation" +"RFN","Fertilizers by Nutrient","150","Netherlands","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","174007.75","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","150","Netherlands","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","131080.45","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","150","Netherlands","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","502150.14","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","150","Netherlands","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","402385.85","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","150","Netherlands","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","601536.21","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","150","Netherlands","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","2002561.9","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","153","New Caledonia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","808.91","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","153","New Caledonia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","569.55","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","153","New Caledonia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","769.91","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","153","New Caledonia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","153","New Caledonia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0.01","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","153","New Caledonia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0.04","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","156","New Zealand","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","265240.54","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","156","New Zealand","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","250707.36","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","156","New Zealand","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","375184.03","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","156","New Zealand","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","1004.75","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","156","New Zealand","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1272.58","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","156","New Zealand","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","936.79","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","157","Nicaragua","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","39231.44","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","157","Nicaragua","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","40590.62","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","157","Nicaragua","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","56233.82","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","157","Nicaragua","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","302.77","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","157","Nicaragua","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","499.14","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","157","Nicaragua","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","353.48","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","158","Niger","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","2959","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","158","Niger","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","6744.29","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","158","Niger","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","6808.37","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","158","Niger","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","52","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","158","Niger","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","2.9","Fm","Manual Estimation" +"RFN","Fertilizers by Nutrient","158","Niger","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","87.33","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","159","Nigeria","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","204929.36","Fm","Manual Estimation" +"RFN","Fertilizers by Nutrient","159","Nigeria","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","18901.79","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","159","Nigeria","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","123728.68","Fm","Manual Estimation" +"RFN","Fertilizers by Nutrient","159","Nigeria","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","21.92","Fm","Manual Estimation" +"RFN","Fertilizers by Nutrient","159","Nigeria","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","910.28","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","159","Nigeria","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","55286.98","Fm","Manual Estimation" +"RFN","Fertilizers by Nutrient","154","North Macedonia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","18387.31","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","154","North Macedonia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","19775.56","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","154","North Macedonia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","19385.89","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","154","North Macedonia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0.92","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","154","North Macedonia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","8.81","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","154","North Macedonia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","24.05","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","162","Norway","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","46058.8","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","162","Norway","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","42688.4","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","162","Norway","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","71586.95","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","162","Norway","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","360695.52","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","162","Norway","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","474007.52","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","162","Norway","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","558813.34","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","221","Oman","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","8008.01","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","221","Oman","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","9630.4","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","221","Oman","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","7764.4","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","221","Oman","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","377650.3","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","221","Oman","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1507186.8","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","221","Oman","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","1192346.74","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","165","Pakistan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","430996.86","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","165","Pakistan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","460774.57","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","165","Pakistan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","567170.68","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","165","Pakistan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","5022.54","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","165","Pakistan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","223.32","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","165","Pakistan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","4.28","Fm","Manual Estimation" +"RFN","Fertilizers by Nutrient","166","Panama","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","16261.91","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","166","Panama","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","22401.84","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","166","Panama","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","16248.6","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","166","Panama","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","166","Panama","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","360.94","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","166","Panama","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","27.5","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","168","Papua New Guinea","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","15604.58","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","168","Papua New Guinea","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","18576.07","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","168","Papua New Guinea","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","20673.16","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","168","Papua New Guinea","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","168","Papua New Guinea","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","62.5","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","168","Papua New Guinea","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","2.51","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","169","Paraguay","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","42920.56","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","169","Paraguay","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","81696.16","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","169","Paraguay","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","122063.97","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","169","Paraguay","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","169","Paraguay","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","5.03","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","169","Paraguay","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","170","Peru","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","242505.52","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","170","Peru","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","145536.18","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","170","Peru","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","216790.74","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","170","Peru","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","4952.83","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","170","Peru","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","12305.56","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","170","Peru","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","12177.64","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","171","Philippines","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","370708.72","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","171","Philippines","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","415821.54","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","171","Philippines","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","481200.81","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","171","Philippines","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","72761.53","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","171","Philippines","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","63802.07","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","171","Philippines","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","28.69","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","173","Poland","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","224552.12","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","173","Poland","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","250903.81","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","173","Poland","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","521861.43","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","173","Poland","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","695095.63","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","173","Poland","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","600418.93","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","173","Poland","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","663864.18","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","174","Portugal","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","86690.74","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","174","Portugal","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","124520.39","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","174","Portugal","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","138145.56","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","174","Portugal","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","80346.55","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","174","Portugal","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","78520.11","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","174","Portugal","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","82260.16","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","179","Qatar","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","164.88","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","179","Qatar","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1944.17","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","179","Qatar","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","742.09","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","179","Qatar","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","905544.89","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","179","Qatar","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1205452.73","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","179","Qatar","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","2327518.31","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","117","Republic of Korea","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","400215.75","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","117","Republic of Korea","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","268757.52","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","117","Republic of Korea","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","362670.76","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","117","Republic of Korea","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","226342.1","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","117","Republic of Korea","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","313159.63","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","117","Republic of Korea","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","173880.97","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","146","Republic of Moldova","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","23313.84","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","146","Republic of Moldova","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","19859.36","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","146","Republic of Moldova","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","42971.46","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","146","Republic of Moldova","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","1390.63","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","146","Republic of Moldova","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","13.95","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","146","Republic of Moldova","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","3.49","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","183","Romania","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","44686.86","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","183","Romania","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","98019.62","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","183","Romania","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","264276.95","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","183","Romania","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","892654.27","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","183","Romania","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","721672.88","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","183","Romania","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","126283.68","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","185","Russian Federation","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","7592.39","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","185","Russian Federation","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","19733.63","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","185","Russian Federation","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","13249.6","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","185","Russian Federation","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","4902550.11","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","185","Russian Federation","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","5149994.93","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","185","Russian Federation","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","5641264.86","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","184","Rwanda","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","1236.56","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","184","Rwanda","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","833.97","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","184","Rwanda","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","11054.32","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","184","Rwanda","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","184","Rwanda","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","94.27","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","184","Rwanda","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0.33","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","188","Saint Kitts and Nevis","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","55.87","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","188","Saint Kitts and Nevis","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","21.47","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","188","Saint Kitts and Nevis","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","8.26","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","188","Saint Kitts and Nevis","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0.04","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","188","Saint Kitts and Nevis","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","188","Saint Kitts and Nevis","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0.03","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","189","Saint Lucia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","293.54","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","189","Saint Lucia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","414.5","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","189","Saint Lucia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","429.48","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","189","Saint Lucia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","189","Saint Lucia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","189","Saint Lucia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0.6","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","244","Samoa","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0.67","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","244","Samoa","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","2.04","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","244","Samoa","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","1.19","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","244","Samoa","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","244","Samoa","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","19.1","Fm","Manual Estimation" +"RFN","Fertilizers by Nutrient","244","Samoa","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0.01","Fm","Manual Estimation" +"RFN","Fertilizers by Nutrient","194","Saudi Arabia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","16987.73","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","194","Saudi Arabia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","17843.29","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","194","Saudi Arabia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","28743.49","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","194","Saudi Arabia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","836324.66","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","194","Saudi Arabia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1440005.8","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","194","Saudi Arabia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","2068524.82","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","195","Senegal","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","11072.85","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","195","Senegal","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","33252.01","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","195","Senegal","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","26046.75","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","195","Senegal","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","18788.92","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","195","Senegal","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","8002.94","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","195","Senegal","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","4950.8","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","272","Serbia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","105545.74","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","272","Serbia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","169744.65","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","272","Serbia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","29942.39","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","272","Serbia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","51867.81","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","186","Serbia and Montenegro","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","107019","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","186","Serbia and Montenegro","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","14579","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","196","Seychelles","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","29.52","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","196","Seychelles","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","29.06","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","196","Seychelles","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","57.59","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","196","Seychelles","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","196","Seychelles","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","196","Seychelles","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","200","Singapore","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","12456.04","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","200","Singapore","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","3835.87","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","200","Singapore","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","7107.16","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","200","Singapore","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","2804.84","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","200","Singapore","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","3361.22","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","200","Singapore","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","10185.73","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","199","Slovakia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","45952.1","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","199","Slovakia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","98177.22","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","199","Slovakia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","130713.06","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","199","Slovakia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","155564.06","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","199","Slovakia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","214140.59","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","199","Slovakia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","392670.01","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","198","Slovenia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","69078.83","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","198","Slovenia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","64436.76","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","198","Slovenia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","42409.8","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","198","Slovenia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","2615.72","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","198","Slovenia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","2917.88","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","198","Slovenia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","11334.69","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","202","South Africa","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","253726.62","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","202","South Africa","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","372114.43","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","202","South Africa","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","404538.85","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","202","South Africa","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","112599.97","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","202","South Africa","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","121551.17","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","202","South Africa","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","184291.61","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","203","Spain","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","561120.76","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","203","Spain","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","597439.58","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","203","Spain","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","874081.74","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","203","Spain","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","182961.14","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","203","Spain","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","211079.47","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","203","Spain","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","231124.97","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","38","Sri Lanka","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","153341.42","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","38","Sri Lanka","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","176530.67","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","38","Sri Lanka","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","225495.67","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","38","Sri Lanka","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","68.01","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","38","Sri Lanka","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","106.84","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","38","Sri Lanka","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","416.16","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","276","Sudan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","76863.46","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","276","Sudan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","11.09","Fm","Manual Estimation" +"RFN","Fertilizers by Nutrient","206","Sudan (former)","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","133742.96","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","206","Sudan (former)","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","52713.09","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","206","Sudan (former)","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","206","Sudan (former)","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","207","Suriname","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","4558.4","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","207","Suriname","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","10128.42","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","207","Suriname","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","10822.61","Fm","Manual Estimation" +"RFN","Fertilizers by Nutrient","207","Suriname","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","82.8","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","207","Suriname","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","2.07","Fm","Manual Estimation" +"RFN","Fertilizers by Nutrient","207","Suriname","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","7.6","Fm","Manual Estimation" +"RFN","Fertilizers by Nutrient","210","Sweden","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","131383.84","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","210","Sweden","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","229901","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","210","Sweden","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","212971.47","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","210","Sweden","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","55888.76","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","210","Sweden","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","103923.21","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","210","Sweden","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","74315.63","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","211","Switzerland","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","60099.93","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","211","Switzerland","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","57103.83","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","211","Switzerland","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","65497.71","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","211","Switzerland","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","1388.75","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","211","Switzerland","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1257.26","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","211","Switzerland","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","1519.46","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","212","Syrian Arab Republic","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","167054.09","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","212","Syrian Arab Republic","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","49007.78","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","212","Syrian Arab Republic","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","2384.47","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","212","Syrian Arab Republic","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","893.7","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","212","Syrian Arab Republic","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","19731.01","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","212","Syrian Arab Republic","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","208","Tajikistan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","748.65","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","208","Tajikistan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","598.04","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","208","Tajikistan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","16438.24","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","208","Tajikistan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","7245","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","208","Tajikistan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1232.57","Fm","Manual Estimation" +"RFN","Fertilizers by Nutrient","208","Tajikistan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","4.6","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","216","Thailand","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","1005968.1","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","216","Thailand","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1531591.65","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","216","Thailand","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","1323134.29","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","216","Thailand","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","63394.47","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","216","Thailand","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","61515.78","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","216","Thailand","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","57260.98","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","217","Togo","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","598.36","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","217","Togo","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","9588.54","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","217","Togo","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","5652.76","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","217","Togo","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","7.16","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","217","Togo","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","361.22","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","217","Togo","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","5079.48","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","219","Tonga","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","219","Tonga","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","9.56","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","219","Tonga","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","56.18","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","219","Tonga","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","219","Tonga","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","219","Tonga","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","E","Expert sources from FAO (including other divisions)" +"RFN","Fertilizers by Nutrient","220","Trinidad and Tobago","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","1317.26","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","220","Trinidad and Tobago","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","850.8","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","220","Trinidad and Tobago","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","1103.3","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","220","Trinidad and Tobago","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","279023.68","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","220","Trinidad and Tobago","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","305491.71","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","220","Trinidad and Tobago","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","575024.35","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","222","Tunisia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","11300.64","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","222","Tunisia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","12129.32","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","222","Tunisia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","16193.2","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","222","Tunisia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","179809.62","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","222","Tunisia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","206752.84","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","222","Tunisia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","44413.73","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","223","Turkey","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","1013255.23","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","223","Turkey","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1078661.84","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","223","Turkey","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","1237901.85","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","223","Turkey","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","45284.04","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","223","Turkey","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","104964.63","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","223","Turkey","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","62622.7","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","226","Uganda","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","2833.1","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","226","Uganda","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","6921.45","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","226","Uganda","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","7573.21","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","226","Uganda","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","10.48","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","226","Uganda","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","12.99","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","226","Uganda","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","5493.82","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","230","Ukraine","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","86086.51","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","230","Ukraine","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","285439.15","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","230","Ukraine","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","495178.84","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","230","Ukraine","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","2172018.81","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","230","Ukraine","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1618783.97","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","230","Ukraine","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","875235.46","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","225","United Arab Emirates","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","18913.34","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","225","United Arab Emirates","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","11618.53","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","225","United Arab Emirates","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","50296.08","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","225","United Arab Emirates","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","258315.55","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","225","United Arab Emirates","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","278117.36","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","225","United Arab Emirates","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","994023.13","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","229","United Kingdom","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","789076.96","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","229","United Kingdom","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","897211.5","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","229","United Kingdom","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","982699.83","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","229","United Kingdom","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","41941.35","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","229","United Kingdom","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","57573.48","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","229","United Kingdom","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","236151.77","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","215","United Republic of Tanzania","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","68730.38","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","215","United Republic of Tanzania","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","81414.94","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","215","United Republic of Tanzania","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","83869.56","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","215","United Republic of Tanzania","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","2008.05","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","215","United Republic of Tanzania","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","23144.74","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","215","United Republic of Tanzania","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","7353.58","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","231","United States of America","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","4007043.62","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","231","United States of America","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","3338621.62","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","231","United States of America","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","3963135.11","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","231","United States of America","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","1847901.37","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","231","United States of America","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1255457.07","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","231","United States of America","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","1435164.53","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","234","Uruguay","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","72427.8","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","234","Uruguay","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","133416.61","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","234","Uruguay","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","117585.82","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","234","Uruguay","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","3373.97","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","234","Uruguay","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","8276.54","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","234","Uruguay","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","2157.95","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","235","Uzbekistan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","553.14","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","235","Uzbekistan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1134.08","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","235","Uzbekistan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","490.87","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","235","Uzbekistan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","105159.6","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","235","Uzbekistan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","247361.66","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","235","Uzbekistan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","121776.76","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","236","Venezuela (Bolivarian Republic of)","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","54480.29","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","236","Venezuela (Bolivarian Republic of)","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","27076.74","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","236","Venezuela (Bolivarian Republic of)","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","84198.37","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","236","Venezuela (Bolivarian Republic of)","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","476143.49","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","236","Venezuela (Bolivarian Republic of)","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","74791.53","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","236","Venezuela (Bolivarian Republic of)","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","203886.4","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","237","Viet Nam","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","740500.74","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","237","Viet Nam","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","809042.94","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","237","Viet Nam","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","808290.34","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","237","Viet Nam","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","27050.17","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","237","Viet Nam","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","75372.36","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","237","Viet Nam","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","151648.93","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","249","Yemen","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","4091.6","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","249","Yemen","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","11666.05","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","249","Yemen","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","928.74","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","249","Yemen","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0.41","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","249","Yemen","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","249","Yemen","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0.51","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","251","Zambia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","141030.72","A","Aggregate, may include official, semi-official, estimated or calculated data" +"RFN","Fertilizers by Nutrient","251","Zambia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","85930.51","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","251","Zambia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","166359.71","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","251","Zambia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","897.15","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","251","Zambia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","3556.41","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","251","Zambia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","870.01","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","181","Zimbabwe","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","23230.38","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","181","Zimbabwe","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","82841.93","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","181","Zimbabwe","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","47238.87","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","181","Zimbabwe","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","5423.18","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","181","Zimbabwe","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","591.95","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" +"RFN","Fertilizers by Nutrient","181","Zimbabwe","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","2105.85","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" diff --git a/message_ix_models/model/material/N-fertilizer/ReportingToGLOBIOM.py b/message_ix_models/model/material/N-fertilizer/ReportingToGLOBIOM.py new file mode 100644 index 0000000000..7a628959f6 --- /dev/null +++ b/message_ix_models/model/material/N-fertilizer/ReportingToGLOBIOM.py @@ -0,0 +1,186 @@ +""" +Creating outputs for GLOBIOM input +""" + +import ixmp as ix +import message_ix + +from message_ix.reporting import Reporter +from pathlib import Path +import pandas as pd + +import matplotlib.pyplot as plt +import pyam +import os + +os.chdir(r'./code.Global') + +#%% Constants + +model_name = 'JM_GLB_NITRO_MACRO_TRD' + +scen_names = ["baseline", + "NPi2020-con-prim-dir-ncr", + "NPi2020_1600-con-prim-dir-ncr", + "NPi2020_400-con-prim-dir-ncr"] + +newtechnames = ['biomass_NH3', 'electr_NH3', 'gas_NH3', 'coal_NH3', 'fueloil_NH3', 'NH3_to_N_fertil'] +tec_for_ccs = list(newtechnames[i] for i in [0,2,3,4]) +newtechnames_ccs = list(map(lambda x:str(x)+'_ccs', tec_for_ccs)) #include biomass in CCS, newtechnames[2:5])) + +# Units are usually taken care of .yaml in message_data. +# In case I don't use message_data for reporting, I need to deal with the units here. +ix.reporting.configure(units={'replace': {'???': '', '-':''}}) # '???' and '-' are not handled in pyint(?) + +mp = ix.Platform(dbprops=r'H:\MyDocuments\MESSAGE\message_ix\config\default.org.properties') + +def GenerateOutput(model, scen, rep): + + rep.set_filters() + rep.set_filters(t= newtechnames_ccs + newtechnames) + + # 1. Total NF demand + rep.add_product('useNF', 'land_input', 'LAND') + + def collapse(df): + df['variable'] = 'Nitrogen demand' + df['unit'] = 'Mt N/yr' + return df + + a = rep.convert_pyam('useNF:n-y', 'y', collapse=collapse) + rep.write(a[0], Path('nf_demand_'+model+'_'+scen+'.xlsx')) + + """ + 'emi' not working with filters on NH3 technologies for now. (This was because of units '???' and '-'.) + """ + # 2. Total emissions + rep.set_filters() + rep.set_filters(t= (newtechnames_ccs + newtechnames)[:-1], e=['CO2_transformation']) # and NH3_to_N_fertil does not have emission factor + + def collapse_emi(df): + df['variable'] = 'Emissions|CO2|' +df.pop('t') + df['unit'] = 'Mt CO2' + return df + a = rep.convert_pyam('emi:nl-t-ya', 'ya', collapse=collapse_emi) + rep.write(a[0], Path('nf_emissions_CO2_'+model+'_'+scen+'.xlsx')) + + + # 3. Total inputs (incl. final energy) to fertilizer processes + rep.set_filters() + rep.set_filters(t= (newtechnames_ccs + newtechnames), c=['coal', 'gas', 'electr', 'biomass', 'fueloil']) + + def collapse_in(df): + df['variable'] = 'Final energy|'+df.pop('c') + df['unit'] = 'GWa' + return df + a = rep.convert_pyam('in:nl-ya-c', 'ya', collapse=collapse_in) + rep.write(a[0], Path('nf_input_'+model+'_'+scen+'.xlsx')) + + # 4. Commodity price + rep.set_filters() + rep.set_filters(l= ['material_final', 'material_interim']) + + def collapse_N(df): + df.loc[df['c'] == "NH3", 'unit'] = '$/tNH3' + df.loc[df['c'] == "Fertilizer Use|Nitrogen", 'unit'] = '$/tN' + df['variable'] = 'Price|' + df.pop('c') + return df + + a = rep.convert_pyam('PRICE_COMMODITY:n-c-y', 'y', collapse=collapse_N) + rep.write(a[0], Path('price_commodity_'+model+'_'+scen+'.xlsx')) + + # 5. Carbon price + if scen!="baseline": + rep.set_filters() + + a = rep.convert_pyam('PRICE_EMISSION', 'y') + rep.write(a[0], Path('price_emission_'+model+'_'+scen+'.xlsx')) + + +# Generate individual xlsx +for sc in scen_names: + Sc_ref = message_ix.Scenario(mp, model_name, sc) + repo = Reporter.from_scenario(Sc_ref) + GenerateOutput(model_name, sc, repo) + +# Combine xlsx per each output variable +for cases in ['nf_demand', 'nf_emissions_CO2', 'nf_input', 'price_commodity', 'price_emission']: + infiles = [] + for sc in scen_names: + if sc=="baseline" and cases=='price_emission': + continue + infiles.append(pd.read_excel(cases + "_"+ model_name +'_' + sc + ".xlsx")) + appended_df = pd.concat(infiles, join='outer', sort=False) + appended_df.to_excel(cases+"-"+model_name+".xlsx", index=False) + + +#%% Generate plots + +from pyam.plotting import OUTSIDE_LEGEND + +def plot_NF_result(case='nf_demand', model=model_name, scen='baseline'): + """ + - Generate PNG plots for each case of ['nf_demand', 'nf_emissions_CO2', 'nf_input', 'price_commodity'] + - Data read from the xlsx files created above + """ + if case in ['nf_demand', 'all']: + data = pyam.IamDataFrame(data='nf_demand'+'_'+model+'_'+scen+'.xlsx', encoding='utf-8') + fig, ax = plt.subplots(figsize=(12, 12)) + data.filter(level=0).stack_plot(ax=ax, stack='region') + plt.savefig('./plots/nf_demand'+'_'+model+'_'+scen+'.png') + + # Pyam currently doesn't allow stack_plot for the next two cases for unknown reasons. + # (https://github.com/IAMconsortium/pyam/issues/296) + if case in ['nf_input', 'all']: + data = pyam.IamDataFrame(data='nf_input'+'_'+model+'_'+scen+'.xlsx', encoding='utf-8') + # aggregate technologies over regions (get global sums) + for v in list(data.variables()): + data.aggregate_region(v, append=True) + + fig, ax = plt.subplots(figsize=(12, 12)) +# data.filter(region="World").stack_plot(ax=ax, legend=OUTSIDE_LEGEND['right']) + data.filter(region="World").line_plot(ax=ax, legend=OUTSIDE_LEGEND['right']) + plt.savefig('./plots/nf_input'+'_'+model+'_'+scen+'.png') + + if case in ['nf_emissions_CO2', 'all']: + data = pyam.IamDataFrame(data='nf_emissions_CO2'+'_'+model+'_'+scen+'.xlsx', encoding='utf-8') + for v in list(data.variables()): + data.aggregate_region(v, append=True) + + fig, ax = plt.subplots(figsize=(12, 12)) + data.filter(region="World").line_plot(ax=ax, legend=OUTSIDE_LEGEND['right']) + plt.savefig('./plots/nf_emissions_CO2'+'_'+model+'_'+scen+'.png') + + if case in ['price_commodity', 'all']: + # N fertilizer + data = pyam.IamDataFrame(data='price_commodity'+'_'+model+'_'+scen+'.xlsx', encoding='utf-8') + fig, ax = plt.subplots(figsize=(12, 12)) + data.filter(variable='Price|Fertilizer Use|*', region='R11_AFR').line_plot(ax=ax, legend=False) # Identical across regoins through trade + plt.savefig('./plots/price_NF'+'_'+model+'_'+scen+'.png') + + #NH3 + fig, ax = plt.subplots(figsize=(12, 12)) + data.filter(variable='Price|NH3').line_plot(ax=ax, color='region', legend=OUTSIDE_LEGEND['right']) + plt.savefig('./plots/price_NH3'+'_'+model+'_'+scen+'.png') + +""" +scen_names = ["baseline", + "NPi2020-con-prim-dir-ncr", + "NPi2020_1600-con-prim-dir-ncr", + "NPi2020_400-con-prim-dir-ncr"] +""" + +cases = ['nf_demand', 'nf_emissions_CO2', 'nf_input', 'price_commodity'] + +# Individual calls +#plot_NF_result(case='nf_demand', scen='NPi2020_1600-con-prim-dir-ncr') +#plot_NF_result(case='price_commodity', scen='NPi2020_1600-con-prim-dir-ncr') +#plot_NF_result(case='nf_input',scen='NPi2020_400-con-prim-dir-ncr') +#plot_NF_result(case='nf_emissions_CO2',scen='NPi2020_400-con-prim-dir-ncr') + +# call for all plots +for sc in scen_names: + plot_NF_result(case='all', scen=sc) + + + diff --git a/message_ix_models/model/material/N-fertilizer/SetupNitrogenBase.py b/message_ix_models/model/material/N-fertilizer/SetupNitrogenBase.py new file mode 100644 index 0000000000..e23ae0951d --- /dev/null +++ b/message_ix_models/model/material/N-fertilizer/SetupNitrogenBase.py @@ -0,0 +1,629 @@ +# -*- coding: utf-8 -*- +""" +Spyder Editor + +This is a temporary script file. +""" + +# load required packages +# import itertools +import pandas as pd +import numpy as np + +import matplotlib.pyplot as plt +plt.style.use('ggplot') + +import ixmp as ix +import message_ix + +import os +import time, re + +#os.chdir(r'H:\MyDocuments\MESSAGE\message_data') +##from tools.post_processing.iamc_report_hackathon import report as reporting +# +#from message_ix.reporting import Reporter + +os.chdir(r'H:\MyDocuments\MESSAGE\N-fertilizer\code.Global') + +#import LoadParams # Load techno-economic param values from the excel file +#exec(open(r'LoadParams.py').read()) +import importlib +#import LoadParams +importlib.reload(LoadParams) + +#%% Set up scope + +scen_names = {"baseline" : "NoPolicy", + "NPi2020-con-prim-dir-ncr" : "NPi", + "NPi2020_1000-con-prim-dir-ncr" : "NPi2020_1000", + "NPi2020_400-con-prim-dir-ncr" : "NPi2020_400"} + +run_scen = "baseline" # "NPiREF-con-prim-dir-ncr" + +# details for existing datastructure to be updated and annotation log in database +modelName = "CD_Links_SSP2" +scenarioName = run_scen + +# new model name in ix platform +newmodelName = "JM_GLB_NITRO" +newscenarioName = scen_names[run_scen] + +comment = "CD_Links_SSP2 test for new representation of nitrogen cycle" + +REGIONS = [ + 'R11_AFR', + 'R11_CPA', + 'R11_EEU', + 'R11_FSU', + 'R11_LAM', + 'R11_MEA', + 'R11_NAM', + 'R11_PAO', + 'R11_PAS', + 'R11_SAS', + 'R11_WEU', + 'R11_GLB' ] + + +#%% Load scenario + +# launch the IX modeling platform using the local default database +mp = ix.Platform(dbprops=r'H:\MyDocuments\MESSAGE\message_ix\config\default.properties') + +# Reference scenario +Sc_ref = message_ix.Scenario(mp, modelName, scenarioName) +paramList_tec = [x for x in Sc_ref.par_list() if 'technology' in Sc_ref.idx_sets(x)] +params_src = [x for x in paramList_tec if 'solar_i' in set(Sc_ref.par(x)["technology"].tolist())] + +#%% Clone +Sc_nitro = Sc_ref.clone(newmodelName, newscenarioName) +#Sc_nitro = message_ix.Scenario(mp, newmodelName, newscenarioName) + +Sc_nitro.remove_solution() +Sc_nitro.check_out() + + +#%% Add new technologies & commodities + +newtechnames = ['biomass_NH3', 'electr_NH3', 'gas_NH3', 'coal_NH3', 'fueloil_NH3', 'NH3_to_N_fertil'] +newcommnames = ['NH3', 'Fertilizer Use|Nitrogen'] #'N-fertilizer' +newlevelnames = ['material_interim', 'material_final'] + +Sc_nitro.add_set("technology", newtechnames) +Sc_nitro.add_set("commodity", newcommnames) +Sc_nitro.add_set("level", newlevelnames) +Sc_nitro.add_set("type_tec", 'industry') + +cat_add = pd.DataFrame({ + 'type_tec': 'industry', #'non_energy' not in Russia model + 'technology': newtechnames +}) + +Sc_nitro.add_set("cat_tec", cat_add) + + +#%% Connect input & output + +### NH3 production process +for t in newtechnames[0:5]: + # output + df = Sc_nitro.par("output", {"technology":["solar_i"]}) # lifetime = 15 year + df['technology'] = t + df['commodity'] = newcommnames[0] + df['level'] = newlevelnames[0] + Sc_nitro.add_par("output", df) + df['commodity'] = 'd_heat' + df['level'] = 'secondary' + df['value'] = LoadParams.output_heat[newtechnames.index(t)] + Sc_nitro.add_par("output", df) + + # Fuel input + df = df.rename(columns={'time_dest':'time_origin', 'node_dest':'node_origin'}) + if t=='biomass_NH3': + lvl = 'primary' + else: + lvl = 'secondary' + df['level'] = lvl + # Non-elec fuels + if t[:-4]!='electr': # electr has only electr input (no other fuel) + df['commodity'] = t[:-4] # removing '_NH3' + df['value'] = LoadParams.input_fuel[newtechnames.index(t)] + Sc_nitro.add_par("input", df) + # Electricity input (for any fuels) + df['commodity'] = 'electr' # All have electricity input + df['value'] = LoadParams.input_elec[newtechnames.index(t)] + df['level'] = 'secondary' + Sc_nitro.add_par("input", df) + + # Water input # Not exist in Russia model - CHECK for global model + df['level'] = 'water_supply' # final for feedstock input + df['commodity'] = 'freshwater_supply' # All have electricity input + df['value'] = LoadParams.input_water[newtechnames.index(t)] + Sc_nitro.add_par("input", df) + + df = Sc_nitro.par("technical_lifetime", {"technology":["solar_i"]}) # lifetime = 15 year + df['technology'] = t + Sc_nitro.add_par("technical_lifetime", df) + + # Costs + df = Sc_nitro.par("inv_cost", {"technology":["solar_i"]}) + df['technology'] = t + df['value'] = LoadParams.inv_cost[newtechnames.index(t)] + Sc_nitro.add_par("inv_cost", df) + + df = Sc_nitro.par("fix_cost", {"technology":["solar_i"]}) + df['technology'] = t + df['value'] = LoadParams.fix_cost[newtechnames.index(t)] + Sc_nitro.add_par("fix_cost", df) + + df = Sc_nitro.par("var_cost", {"technology":["solar_i"]}) + df['technology'] = t + df['value'] = LoadParams.var_cost[newtechnames.index(t)] + Sc_nitro.add_par("var_cost", df) + + # Emission factor + df = Sc_nitro.par("output", {"technology":["solar_i"]}) + df = df.drop(columns=['node_dest', 'commodity', 'level', 'time']) + df = df.rename(columns={'time_dest':'emission'}) + df['emission'] = 'CO2_transformation' # Check out what it is + df['value'] = LoadParams.emissions[newtechnames.index(t)] + df['technology'] = t + df['unit'] = '???' + Sc_nitro.add_par("emission_factor", df) + df['emission'] = 'CO2' # Check out what it is + df['value'] = 0 # Set the same as CO2_transformation + Sc_nitro.add_par("emission_factor", df) + + # Emission factors in relation (Currently these are more correct values than emission_factor) + df = Sc_nitro.par("relation_activity", {"relation":["CO2_cc"], "technology":["h2_smr"]}) + df['value'] = LoadParams.emissions[newtechnames.index(t)] + df['technology'] = t + df['unit'] = '???' + Sc_nitro.add_par("relation_activity", df) +# df = Sc_nitro.par("relation_activity", {"relation":["CO2_cc"], "technology":t}) +# Sc_nitro.remove_par("relation_activity", df) + + # Capacity factor + df = Sc_nitro.par("capacity_factor", {"technology":["solar_i"]}) + df['technology'] = t + df['value'] = LoadParams.capacity_factor[newtechnames.index(t)] + Sc_nitro.add_par("capacity_factor", df) + + +### N-fertilizer from NH3 (generic) +comm = newcommnames[-1] +tech = newtechnames[-1] + +# output +df = Sc_nitro.par("output", {"technology":["solar_i"]}) +df['technology'] = tech +df['commodity'] = comm #'N-fertilizer' +df['level'] = newlevelnames[-1] #'land_use_reporting' +Sc_nitro.add_par("output", df) + +# input +df = Sc_nitro.par("output", {"technology":["solar_i"]}) +df = df.rename(columns={'time_dest':'time_origin', 'node_dest':'node_origin'}) +df['technology'] = tech +df['level'] = newlevelnames[0] +df['commodity'] = newcommnames[0] #'NH3' +df['value'] = LoadParams.input_fuel[newtechnames.index(tech)] # NH3/N = 17/14 +Sc_nitro.add_par("input", df) + +df = Sc_nitro.par("technical_lifetime", {"technology":["solar_i"]}) # lifetime = 15 year +df['technology'] = tech +Sc_nitro.add_par("technical_lifetime", df) + +# Costs +df = Sc_nitro.par("inv_cost", {"technology":["solar_i"]}) +df['value'] = LoadParams.inv_cost[newtechnames.index(tech)] +df['technology'] = tech +Sc_nitro.add_par("inv_cost", df) + +df = Sc_nitro.par("fix_cost", {"technology":["solar_i"]}) +df['technology'] = tech +df['value'] = LoadParams.fix_cost[newtechnames.index(tech)] +Sc_nitro.add_par("fix_cost", df) + +df = Sc_nitro.par("var_cost", {"technology":["solar_i"]}) +df['technology'] = tech +df['value'] = LoadParams.var_cost[newtechnames.index(tech)] +Sc_nitro.add_par("var_cost", df) + +# Emission factor (<0 for this) +""" +Urea applied in the field will emit all CO2 back, so we don't need this. +Source: https://ammoniaindustry.com/urea-production-is-not-carbon-sequestration/#targetText=To%20make%20urea%2C%20fertilizer%20producers,through%20the%20production%20of%20urea. +""" +#df = Sc_nitro.par("output", {"technology":["solar_i"]}) +#df = df.drop(columns=['node_dest', 'commodity', 'level', 'time']) +#df = df.rename(columns={'time_dest':'emission'}) +#df['emission'] = 'CO2_transformation' # Check out what it is +#df['value'] = LoadParams.emissions[newtechnames.index(tech)] +#df['technology'] = tech +#df['unit'] = '???' +#Sc_nitro.add_par("emission_factor", df) +#df['emission'] = 'CO2' # Check out what it is +#Sc_nitro.add_par("emission_factor", df) + + +#%% Copy some background parameters# + +par_bgnd = [x for x in params_src if '_up' in x] + [x for x in params_src if '_lo' in x] +par_bgnd = list(set(par_bgnd) - set(['level_cost_activity_soft_lo', 'level_cost_activity_soft_up', 'growth_activity_lo'])) #, 'soft_activity_lo', 'soft_activity_up'])) +for t in par_bgnd[:-1]: + df = Sc_nitro.par(t, {"technology":["solar_i"]}) # lifetime = 15 year + for q in newtechnames: + df['technology'] = q + Sc_nitro.add_par(t, df) + +df = Sc_nitro.par('initial_activity_lo', {"technology":["gas_extr_mpen"]}) +for q in newtechnames: + df['technology'] = q + Sc_nitro.add_par('initial_activity_lo', df) + +df = Sc_nitro.par('growth_activity_lo', {"technology":["gas_extr_mpen"]}) +for q in newtechnames: + df['technology'] = q + Sc_nitro.add_par('growth_activity_lo', df) + +#%% Process the regional historical activities + +fs_GLO = LoadParams.feedshare_GLO.copy() +fs_GLO.insert(1, "bio_pct", 0) +fs_GLO.insert(2, "elec_pct", 0) +# 17/14 NH3:N ratio, to get NH3 activity based on N demand => No NH3 loss assumed during production +fs_GLO.iloc[:,1:6] = LoadParams.input_fuel[5] * fs_GLO.iloc[:,1:6] +fs_GLO.insert(6, "NH3_to_N", 1) + +# Share of feedstocks for NH3 prodution (based on 2010 => Assumed fixed for any past years) +feedshare = fs_GLO.sort_values(['Region']).set_index('Region').drop('R11_GLB') + +# Get historical N demand from SSP2-nopolicy (may need to vary for diff scenarios) +N_demand_raw = LoadParams.N_demand_GLO.copy() +N_demand = N_demand_raw[N_demand_raw.Scenario=="NoPolicy"].reset_index().loc[0:10,2010] # 2010 tot N demand +N_demand = N_demand.repeat(len(newtechnames)) + +act2010 = (feedshare.values.flatten() * N_demand).reset_index(drop=True) + + + +#%% Historical activities/capacities - Region specific + +df = Sc_nitro.par("historical_activity").iloc[0:len(newtechnames)*(len(REGIONS)-1),] # Choose whatever same number of rows +df['technology'] = newtechnames * (len(REGIONS)-1) +df['value'] = act2010 # total NH3 or N in Mt 2010 FAO Russia +df['year_act'] = 2010 +df['node_loc'] = [y for x in REGIONS[:-1] for y in (x,)*len(newtechnames)] +df['unit'] = 'Tg N/yr' # Separate unit needed for NH3? +Sc_nitro.add_par("historical_activity", df) + +# 2015 activity necessary if this is 5-year step scenario +#df['value'] = act2015 # total NH3 or N in Mt 2010 FAO Russia +#df['year_act'] = 2015 +#Sc_nitro.add_par("historical_activity", df) + +life = Sc_nitro.par("technical_lifetime", {"technology":["gas_NH3"]}).value[0] + +df = Sc_nitro.par("historical_new_capacity").iloc[0:len(newtechnames)*(len(REGIONS)-1),] # whatever +df['technology'] = newtechnames * (len(REGIONS)-1) +df['value'] = [x * 1/life/LoadParams.capacity_factor[0] for x in act2010] # Assume 1/lifetime (=15yr) is built each year +df['year_vtg'] = 2010 +df['node_loc'] = [y for x in REGIONS[:-1] for y in (x,)*len(newtechnames)] +df['unit'] = 'Tg N/yr' +Sc_nitro.add_par("historical_new_capacity", df) + + + +#%% Secure feedstock balance (foil_fs, gas_fs, coal_fs) loil_fs? + +# Select only the model years +years = set(map(int, list(Sc_nitro.set('year')))) & set(N_demand_raw) # get intersection + +#scenarios = N_demand_FSU.Scenario # Scenario names (SSP2) +N_demand = N_demand_raw.loc[N_demand_raw.Scenario=="NoPolicy",].drop(35) +N_demand = N_demand[N_demand.columns.intersection(years)] +N_demand[2110] = N_demand[2100] # Make up 2110 data (for now) in Mt/year + +# Adjust i_feed demand +demand_fs_org = Sc_nitro.par('demand', {"commodity":["i_feed"]}) +#demand_fs_org['value'] = demand_fs_org['value'] * 0.9 # (10% of total feedstock for Ammonia assumed) - REFINE +df = demand_fs_org.loc[demand_fs_org.year==2010,:].join(LoadParams.N_energy.set_index('node'), on='node') +sh = pd.DataFrame( {'node': demand_fs_org.loc[demand_fs_org.year==2010, 'node'], + 'r_feed': df.totENE / df.value}) # share of NH3 energy among total feedstock (no trade assumed) +df = demand_fs_org.join(sh.set_index('node'), on='node') +df.value *= 1 - df.r_feed # Carve out the same % from tot i_feed values +df = df.drop('r_feed', axis=1) +Sc_nitro.add_par("demand", df) + + +# Now link the GLOBIOM input (now endogenous) +df = Sc_nitro.par("land_output", {"commodity":newcommnames[-1]}) +df['level'] = newlevelnames[-1] +Sc_nitro.add_par("land_input", df) + + +#%% Add CCS tecs + +#%% Add tecs to set +tec_for_ccs = list(newtechnames[i] for i in [0,2,3,4]) +newtechnames_ccs = list(map(lambda x:str(x)+'_ccs', tec_for_ccs)) #include biomass in CCS, newtechnames[2:5])) +Sc_nitro.add_set("technology", newtechnames_ccs) + +cat_add = pd.DataFrame({ + 'type_tec': 'industry', #'non_energy' not in Russia model + 'technology': newtechnames_ccs +}) + +Sc_nitro.add_set("cat_tec", cat_add) + + + +#%% Implement technologies - only for non-elec NH3 tecs + +# input and output +# additional electricity input for CCS operation +df = Sc_nitro.par("input") +df = df[df['technology'].isin(tec_for_ccs)] +df['technology'] = df['technology'] + '_ccs' # Rename the technologies +df.loc[df['commodity']=='electr', ['value']] = df.loc[df['commodity']=='electr', ['value']] + 0.005 # TUNE THIS # Add electricity input for CCS +Sc_nitro.add_par("input", df) + +df = Sc_nitro.par("output") +df = df[df['technology'].isin(tec_for_ccs)] +df['technology'] = df['technology'] + '_ccs' # Rename the technologies +Sc_nitro.add_par("output", df) + + +# Emission factors (emission_factor) + +df = Sc_nitro.par("emission_factor") +biomass_ef = 0.942 # MtC/GWa from 109.6 kgCO2/GJ biomass (https://www.rvo.nl/sites/default/files/2013/10/Vreuls%202005%20NL%20Energiedragerlijst%20-%20Update.pdf) + +# extract vent vs. storage ratio from h2_smr tec +h2_smr_vent_ratio = -Sc_nitro.par("relation_activity", {"relation":["CO2_cc"], "technology":["h2_smr_ccs"]}).value[0] \ + / Sc_nitro.par("relation_activity", {"relation":["CO2_Emission"], "technology":["h2_smr_ccs"]}).value[0] +h2_coal_vent_ratio = -Sc_nitro.par("relation_activity", {"relation":["CO2_cc"], "technology":["h2_coal_ccs"]}).value[0] \ + / Sc_nitro.par("relation_activity", {"relation":["CO2_Emission"], "technology":["h2_coal_ccs"]}).value[0] +h2_bio_vent_ratio = 1 + Sc_nitro.par("relation_activity", {"relation":["CO2_cc"], "technology":["h2_bio_ccs"]}).value[0] \ + / (Sc_nitro.par("input", {"technology":["h2_bio_ccs"], "commodity":["biomass"]}).value[0] * biomass_ef) + +# ef for NG +df_gas = df[df['technology']=='gas_NH3'] +ef_org = np.asarray(df_gas.loc[df_gas['emission']=='CO2_transformation', ['value']]) +df_gas = df_gas.assign(technology = df_gas.technology + '_ccs') +df_gas.loc[(df.emission=='CO2_transformation'), 'value'] = ef_org*h2_smr_vent_ratio +df_gas.loc[(df.emission=='CO2'), 'value'] = ef_org*(h2_smr_vent_ratio-1) +Sc_nitro.add_par("emission_factor", df_gas) + +# ef for oil/coal +df_coal = df[df['technology'].isin(tec_for_ccs[2:])] +ef_org = np.asarray(df_coal.loc[df_coal['emission']=='CO2_transformation', ['value']]) +df_coal = df_coal.assign(technology = df_coal.technology + '_ccs') +df_coal.loc[(df.emission=='CO2_transformation'), 'value'] = ef_org*h2_coal_vent_ratio +df_coal.loc[(df.emission=='CO2'), 'value'] = ef_org*(h2_coal_vent_ratio-1) +Sc_nitro.add_par("emission_factor", df_coal) + +# ef for biomass +df_bio = df[df['technology']=='biomass_NH3'] +biomass_input = Sc_nitro.par("input", {"technology":["biomass_NH3"], "commodity":["biomass"]}).value[0] +df_bio = df_bio.assign(technology = df_bio.technology + '_ccs') +df_bio['value'] = biomass_input*(h2_bio_vent_ratio-1)*biomass_ef +Sc_nitro.add_par("emission_factor", df_bio) + + + + +# Investment cost + +df = Sc_nitro.par("inv_cost") + +# Get inv_cost ratio between std and ccs for different h2 feedstocks +a = df[df['technology'].str.contains("h2_smr")].sort_values(["technology", "year_vtg"]) # To get cost ratio for std vs CCS +r_ccs_cost_gas = a.loc[(a.technology=='h2_smr') & (a.year_vtg >=2020)]['value'].values / a.loc[(a['technology']=='h2_smr_ccs') & (a.year_vtg >=2020)]['value'].values +r_ccs_cost_gas = r_ccs_cost_gas.mean() + +a = df[df['technology'].str.contains("h2_coal")].sort_values(["technology", "year_vtg"]) # To get cost ratio for std vs CCS +r_ccs_cost_coal = a.loc[(a.technology=='h2_coal') & (a.year_vtg >=2020)]['value'].values / a.loc[(a['technology']=='h2_coal_ccs') & (a.year_vtg >=2020)]['value'].values +r_ccs_cost_coal = r_ccs_cost_coal.mean() + +a = df[df['technology'].str.contains("h2_bio")].sort_values(["technology", "year_vtg"]) # To get cost ratio for std vs CCS +a = a[a.year_vtg > 2025] # h2_bio_ccs only available from 2030 +r_ccs_cost_bio = a.loc[(a.technology=='h2_bio') & (a.year_vtg >=2020)]['value'].values / a.loc[(a['technology']=='h2_bio_ccs') & (a.year_vtg >=2020)]['value'].values +r_ccs_cost_bio = r_ccs_cost_bio.mean() + +df_gas = df[df['technology']=='gas_NH3'] +df_gas['technology'] = df_gas['technology'] + '_ccs' # Rename the technologies +df_gas['value'] = df_gas['value']/r_ccs_cost_gas +Sc_nitro.add_par("inv_cost", df_gas) + +df_coal = df[df['technology'].isin(tec_for_ccs[2:])] +df_coal['technology'] = df_coal['technology'] + '_ccs' # Rename the technologies +df_coal['value'] = df_coal['value']/r_ccs_cost_coal +Sc_nitro.add_par("inv_cost", df_coal) + +df_bio = df[df['technology']=='biomass_NH3'] +df_bio['technology'] = df_bio['technology'] + '_ccs' # Rename the technologies +df_bio['value'] = df_bio['value']/r_ccs_cost_bio +Sc_nitro.add_par("inv_cost", df_bio) + + + + +# Fixed cost + +df = Sc_nitro.par("fix_cost") +df_gas = df[df['technology']=='gas_NH3'] +df_gas['technology'] = df_gas['technology'] + '_ccs' # Rename the technologies +df_gas['value'] = df_gas['value']/r_ccs_cost_gas # Same scaling (same 4% of inv_cost in the end) +Sc_nitro.add_par("fix_cost", df_gas) + +df_coal = df[df['technology'].isin(tec_for_ccs[2:])] +df_coal['technology'] = df_coal['technology'] + '_ccs' # Rename the technologies +df_coal['value'] = df_coal['value']/r_ccs_cost_coal # Same scaling (same 4% of inv_cost in the end) +Sc_nitro.add_par("fix_cost", df_coal) + +df_bio = df[df['technology']=='biomass_NH3'] +df_bio['technology'] = df_bio['technology'] + '_ccs' # Rename the technologies +df_bio['value'] = df_bio['value']/r_ccs_cost_bio # Same scaling (same 4% of inv_cost in the end) +Sc_nitro.add_par("fix_cost", df_bio) + + + +# Emission factors (Relation) + +# Gas +df = Sc_nitro.par("relation_activity") +df_gas = df[df['technology']=='gas_NH3'] # Originally all CO2_cc (truly emitted, bottom-up) +ef_org = df_gas.value.values.copy() +df_gas.value = ef_org * h2_smr_vent_ratio +df_gas = df_gas.assign(technology = df_gas.technology + '_ccs') +Sc_nitro.add_par("relation_activity", df_gas) + +df_gas.value = ef_org * (h2_smr_vent_ratio-1) # Negative +df_gas.relation = 'CO2_Emission' +Sc_nitro.add_par("relation_activity", df_gas) + +# Coal / Oil +df_coal = df[df['technology'].isin(tec_for_ccs[2:])] # Originally all CO2_cc (truly emitted, bottom-up) +ef_org = df_coal.value.values.copy() +df_coal.value = ef_org * h2_coal_vent_ratio +df_coal = df_coal.assign(technology = df_coal.technology + '_ccs') +Sc_nitro.add_par("relation_activity", df_coal) + +df_coal.value = ef_org * (h2_coal_vent_ratio-1) # Negative +df_coal.relation = 'CO2_Emission' +Sc_nitro.add_par("relation_activity", df_coal) + +# Biomass +df_bio = df[df['technology']=='biomass_NH3'] # Originally all CO2.cc (truly emitted, bottom-up) +df_bio.value = biomass_input*(h2_bio_vent_ratio-1)*biomass_ef +df_bio = df_bio.assign(technology = df_bio.technology + '_ccs') +Sc_nitro.add_par("relation_activity", df_bio) + +df_bio.relation = 'CO2_Emission' +Sc_nitro.add_par("relation_activity", df_bio) + + +#%% Copy some bgnd parameters (values identical to _NH3 tecs) + +par_bgnd_ccs = par_bgnd + ['technical_lifetime', 'capacity_factor', 'var_cost', 'growth_activity_lo'] + +for t in par_bgnd_ccs: + df = Sc_nitro.par(t) + df = df[df['technology'].isin(tec_for_ccs)] + df['technology'] = df['technology'] + '_ccs' # Rename the technologies + Sc_nitro.add_par(t, df) + + +#%% Shift model horizon for policy scenarios + +if run_scen != "baseline": + Sc_nitro_base = message_ix.Scenario(mp, "JM_GLB_NITRO", "NoPolicy") + + import Utilities.shift_model_horizon as shift_year + if scen_names[run_scen] == "NPi": + fyear = 2040 + else: + fyear = 2030 + shift_year(Sc_nitro, Sc_nitro_base, fyear, newtechnames_ccs + newtechnames) + + +#%% Regional cost calibration + +# Scaler based on WEO 2014 (based on IGCC tec) +scaler_cost = pd.DataFrame( + {'scaler_std': [1.00, 0.81, 0.96, 0.96, 0.96, 0.88, 0.81, 0.65, 0.42, 0.65, 1.12], + 'scaler_ccs': [1.00, 0.80, 0.98, 0.92, 0.92, 0.82, 0.80, 0.70, 0.61, 0.70, 1.05], # NA values are replaced with WEO 2014 values. (LAM & MEA = 0.80) + 'node_loc': ['R11_NAM', + 'R11_LAM', + 'R11_WEU', + 'R11_EEU', + 'R11_FSU', + 'R11_AFR', + 'R11_MEA', + 'R11_SAS', + 'R11_CPA', + 'R11_PAS', + 'R11_PAO']}) + +#tec_scale = (newtechnames + newtechnames_ccs) +tec_scale = [e for e in newtechnames if e not in ('NH3_to_N_fertil', 'electr_NH3')] + +# Scale all NH3 tecs in each region with the scaler +for t in tec_scale: + for p in ['inv_cost', 'fix_cost', 'var_cost']: + df = Sc_nitro.par(p, {"technology":t}) + temp = df.join(scaler_cost.set_index('node_loc'), on='node_loc') + df.value = temp.value * temp.scaler_std + Sc_nitro.add_par(p, df) + +for t in newtechnames_ccs: + for p in ['inv_cost', 'fix_cost', 'var_cost']: + df = Sc_nitro.par(p, {"technology":t}) + temp = df.join(scaler_cost.set_index('node_loc'), on='node_loc') + df.value = temp.value * temp.scaler_ccs + Sc_nitro.add_par(p, df) + +# For CPA and SAS, experiment to make the coal_NH3 cheaper than gas_NH3 +scalers = [[0.66*0.91, 1, 0.75*0.9], [0.59, 1, 1]] # f, g, c : Scale based on the original techno-economic assumptions +reg2scale = ['R11_CPA', 'R11_SAS'] +for p in ['inv_cost', 'fix_cost']:#, 'var_cost']: + for r in reg2scale: + df_c = Sc_nitro.par(p, {"technology":'coal_NH3', "node_loc":r}) + df_g = Sc_nitro.par(p, {"technology":'gas_NH3', "node_loc":r}) + df_f = Sc_nitro.par(p, {"technology":'fueloil_NH3', "node_loc":r}) + + df_cc = Sc_nitro.par(p, {"technology":'coal_NH3_ccs', "node_loc":r}) + df_gc = Sc_nitro.par(p, {"technology":'gas_NH3_ccs', "node_loc":r}) + df_fc = Sc_nitro.par(p, {"technology":'fueloil_NH3_ccs', "node_loc":r}) + + df_fc.value *= scalers[reg2scale.index(r)][0] # Gas/fueloil cost the same as coal + df_gc.value *= scalers[reg2scale.index(r)][1] + df_cc.value *= scalers[reg2scale.index(r)][2] + + df_f.value *= scalers[reg2scale.index(r)][0] # Gas/fueloil cost the same as coal + df_g.value *= scalers[reg2scale.index(r)][1] + df_c.value *= scalers[reg2scale.index(r)][2] + + Sc_nitro.add_par(p, df_g) + Sc_nitro.add_par(p, df_c) + Sc_nitro.add_par(p, df_f) +# Sc_nitro.add_par(p, df_f.append(df_c).append(df_g)) + Sc_nitro.add_par(p, df_gc) + Sc_nitro.add_par(p, df_cc) + Sc_nitro.add_par(p, df_fc) +# Sc_nitro.add_par(p, df_fc.append(df_cc).append(df_gc)) + + + + +#%% Solve the model. + +Sc_nitro.commit('Nitrogen Fertilizer for Global model - no policy') + +start_time = time.time() + +#Sc_nitro.to_gdx(r'..\..\message_ix\message_ix\model\data', "MsgData_"+Sc_nitro.model+"_"+ +# Sc_nitro.scenario+"_"+str(Sc_nitro.version)) + +# I do this because the current set up doesn't recognize the model path correctly. +Sc_nitro.solve(model='MESSAGE', case=Sc_nitro.model+"_"+ + Sc_nitro.scenario+"_"+str(Sc_nitro.version)) +#Sc_ref.solve(model='MESSAGE', case=Sc_ref.model+"_"+Sc_ref.scenario+"_"+str(Sc_ref.version)) + + +print(".solve: %.6s seconds taken." % (time.time() - start_time)) + + + +#%% Run reference model + +# +#start_time = time.time() +#Sc_ref.remove_solution() +#Sc_ref.solve(model='MESSAGE', case=Sc_ref.model+"_"+ +# Sc_ref.scenario+"_"+str(Sc_ref.version)) +#print(".solve: %.6s seconds taken." % (time.time() - start_time)) +# From b66dc00fee5220cd09b416296833b890046e6cdf Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Tue, 26 May 2020 19:35:41 +0200 Subject: [PATCH 002/774] Add skeleton files for model.material --- message_ix_models/model/material/__init__.py | 0 message_ix_models/model/material/doc.rst | 15 +++++++++++++++ 2 files changed, 15 insertions(+) create mode 100644 message_ix_models/model/material/__init__.py create mode 100644 message_ix_models/model/material/doc.rst diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/message_ix_models/model/material/doc.rst b/message_ix_models/model/material/doc.rst new file mode 100644 index 0000000000..ca0b4a1012 --- /dev/null +++ b/message_ix_models/model/material/doc.rst @@ -0,0 +1,15 @@ +Materials accounting +******************** + +This module adds (life-cycle) accounting of materials associated with technologies and demands in MESSAGEix-GLOBIOM. + +The initial implementation supports nitrogen-based fertilizers. + + +Code reference +============== + +.. currentmodule:: message_data.model.material + +.. automodule:: message_data.model.material + :members: From 7469857cb2f0f8c977253e53f6ecf0b98467a1a3 Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Tue, 26 May 2020 20:05:52 +0200 Subject: [PATCH 003/774] Add data/material.yaml, material.get_spec(), simple tests --- message_ix_models/model/material/__init__.py | 51 ++++++++++++++++++++ message_ix_models/model/material/doc.rst | 9 ++++ 2 files changed, 60 insertions(+) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index e69de29bb2..27f9cf3d44 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -0,0 +1,51 @@ +from typing import Mapping + +from message_data.tools import ScenarioInfo, as_codes, get_context + + +def get_spec() -> Mapping[str, ScenarioInfo]: + """Return the specification for materials accounting.""" + require = ScenarioInfo() + add = ScenarioInfo() + + # Load configuration + context = read_config() + + # Update the ScenarioInfo objects with required and new set elements + for set_name, config in context["material"]["set"].items(): + # Required elements + require.set[set_name].extend(config.get("require", [])) + + # Elements to add + add.set[set_name].extend(config.get("add", [])) + + return dict(require=require, remove=ScenarioInfo(), add=add) + + +def read_config(): + """Read configuration from material.yaml.""" + # TODO this is similar to transport.utils.read_config; make a common + # function so it doesn't need to be in this file. + context = get_context() + + try: + # Check if the configuration was already loaded + context["material"]["set"] + except KeyError: + # Not yet loaded + pass + else: + # Already loaded + return context + + # Read material.yaml + context.load_config("material") + + # Convert some values to Code objects + for set_name, info in context["material"]["set"].items(): + try: + info["add"] = as_codes(info["add"]) + except KeyError: + pass + + return context diff --git a/message_ix_models/model/material/doc.rst b/message_ix_models/model/material/doc.rst index ca0b4a1012..2992c1319a 100644 --- a/message_ix_models/model/material/doc.rst +++ b/message_ix_models/model/material/doc.rst @@ -5,6 +5,8 @@ This module adds (life-cycle) accounting of materials associated with technologi The initial implementation supports nitrogen-based fertilizers. +.. contents:: + :local: Code reference ============== @@ -13,3 +15,10 @@ Code reference .. automodule:: message_data.model.material :members: + + +Configuration file (:file:`material.yaml`) +========================================== + +.. literalinclude:: ../../../data/material.yaml + :language: yaml From 80098f39dfc6ad9b4249f0ac7431cdbce4f5388c Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Tue, 26 May 2020 20:12:18 +0200 Subject: [PATCH 004/774] Add material.build and (failing) test --- message_ix_models/model/material/__init__.py | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 27f9cf3d44..83bd595708 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -1,8 +1,18 @@ from typing import Mapping +from message_data.model.build import apply_spec from message_data.tools import ScenarioInfo, as_codes, get_context +def build(scenario): + """Set up materials accounting on `scenario`.""" + # Get the specification + spec = get_spec() + + # Apply to the base scenario + apply_spec(scenario, spec) + + def get_spec() -> Mapping[str, ScenarioInfo]: """Return the specification for materials accounting.""" require = ScenarioInfo() From 0421e25b3dbe786586f68081d318dfac205d9117 Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Tue, 26 May 2020 20:18:08 +0200 Subject: [PATCH 005/774] Move material.read_config() to new material.util submodule --- message_ix_models/model/material/__init__.py | 32 ++------------------ message_ix_models/model/material/util.py | 30 ++++++++++++++++++ 2 files changed, 32 insertions(+), 30 deletions(-) create mode 100644 message_ix_models/model/material/util.py diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 83bd595708..efc1e2a5c2 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -1,7 +1,8 @@ from typing import Mapping from message_data.model.build import apply_spec -from message_data.tools import ScenarioInfo, as_codes, get_context +from message_data.tools import ScenarioInfo +from .util import read_config def build(scenario): @@ -30,32 +31,3 @@ def get_spec() -> Mapping[str, ScenarioInfo]: add.set[set_name].extend(config.get("add", [])) return dict(require=require, remove=ScenarioInfo(), add=add) - - -def read_config(): - """Read configuration from material.yaml.""" - # TODO this is similar to transport.utils.read_config; make a common - # function so it doesn't need to be in this file. - context = get_context() - - try: - # Check if the configuration was already loaded - context["material"]["set"] - except KeyError: - # Not yet loaded - pass - else: - # Already loaded - return context - - # Read material.yaml - context.load_config("material") - - # Convert some values to Code objects - for set_name, info in context["material"]["set"].items(): - try: - info["add"] = as_codes(info["add"]) - except KeyError: - pass - - return context diff --git a/message_ix_models/model/material/util.py b/message_ix_models/model/material/util.py new file mode 100644 index 0000000000..914995cf49 --- /dev/null +++ b/message_ix_models/model/material/util.py @@ -0,0 +1,30 @@ +from message_data.tools import as_codes, get_context + + +def read_config(): + """Read configuration from material.yaml.""" + # TODO this is similar to transport.utils.read_config; make a common + # function so it doesn't need to be in this file. + context = get_context() + + try: + # Check if the configuration was already loaded + context["material"]["set"] + except KeyError: + # Not yet loaded + pass + else: + # Already loaded + return context + + # Read material.yaml + context.load_config("material") + + # Convert some values to Code objects + for set_name, info in context["material"]["set"].items(): + try: + info["add"] = as_codes(info["add"]) + except KeyError: + pass + + return context From 1601e6e3d03849f6dd4fe5682a20f750f55f641c Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Tue, 26 May 2020 20:36:04 +0200 Subject: [PATCH 006/774] Add material.data.gen_data --- message_ix_models/model/material/__init__.py | 3 +- message_ix_models/model/material/data.py | 54 ++++++++++++++++++++ message_ix_models/model/material/doc.rst | 2 +- 3 files changed, 57 insertions(+), 2 deletions(-) create mode 100644 message_ix_models/model/material/data.py diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index efc1e2a5c2..1ace838c94 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -2,6 +2,7 @@ from message_data.model.build import apply_spec from message_data.tools import ScenarioInfo +from .data import gen_data from .util import read_config @@ -11,7 +12,7 @@ def build(scenario): spec = get_spec() # Apply to the base scenario - apply_spec(scenario, spec) + apply_spec(scenario, spec, gen_data) def get_spec() -> Mapping[str, ScenarioInfo]: diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py new file mode 100644 index 0000000000..5ea831735b --- /dev/null +++ b/message_ix_models/model/material/data.py @@ -0,0 +1,54 @@ +import numpy as np +import pandas as pd + +from message_data.tools import ScenarioInfo, broadcast, make_df, same_node + +from .util import read_config + + +def gen_data(scenario, dry_run=False): + """Generate data for materials representation of nitrogen fertilizers.""" + # Load configuration + config = read_config()["material"]["set"] + + # Information about scenario, e.g. node, year + s_info = ScenarioInfo(scenario) + + # List of dataframes, concatenated together at end + output = [] + + # NH3 production processes + common = dict( + year_vtg=s_info.Y, + commodity="NH3", + level="material_interim", + # TODO fill in remaining dimensions + mode="all", + ) + + # Iterate over new technologies, using the configuration + for t in config["technology"]["add"]: + # TODO describe what this is. In SetupNitrogenBase.py, the values are + # read for technology == "solar_i" but are not altered before + # being re-added to the scenario. + df = ( + make_df("output", technology=t, **common) + .pipe(broadcast, node_loc=s_info.N) + .pipe(same_node) + ) + output.append(df) + + # Heat output + output.append( + df.assign( + commodity="d_heat", + level="secondary", + value=np.nan, # LoadParams.output_heat[t] + ) + ) + + result = dict(output=pd.concat(output)) + + # Temporary: return nothing, since the data frames are incomplete + return dict() + # return result diff --git a/message_ix_models/model/material/doc.rst b/message_ix_models/model/material/doc.rst index 2992c1319a..54b2846781 100644 --- a/message_ix_models/model/material/doc.rst +++ b/message_ix_models/model/material/doc.rst @@ -14,7 +14,7 @@ Code reference .. currentmodule:: message_data.model.material .. automodule:: message_data.model.material - :members: + :members: build, get_spec, gen_data Configuration file (:file:`material.yaml`) From 0d74b98fd8df0471f8b726af383e668d09f6a9db Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Tue, 26 May 2020 20:49:48 +0200 Subject: [PATCH 007/774] Add binary/raw data files for model.material --- .../data/material/.gitattributes | 1 + ...Links SSP2 N-fertilizer demand.Global.xlsx | 3 + .../material/MESSAGE_region_mapping_R14.xlsx | 3 + ... fertil trade - FAOSTAT_data_9-25-2019.csv | 3 + message_ix_models/data/material/config.yaml | 103 ++++++++++++++++++ .../n-fertilizer_techno-economic.xlsx | 3 + message_ix_models/model/material/doc.rst | 33 +++++- message_ix_models/model/material/util.py | 5 +- 8 files changed, 150 insertions(+), 4 deletions(-) create mode 100644 message_ix_models/data/material/.gitattributes create mode 100644 message_ix_models/data/material/CD-Links SSP2 N-fertilizer demand.Global.xlsx create mode 100644 message_ix_models/data/material/MESSAGE_region_mapping_R14.xlsx create mode 100644 message_ix_models/data/material/N fertil trade - FAOSTAT_data_9-25-2019.csv create mode 100644 message_ix_models/data/material/config.yaml create mode 100644 message_ix_models/data/material/n-fertilizer_techno-economic.xlsx diff --git a/message_ix_models/data/material/.gitattributes b/message_ix_models/data/material/.gitattributes new file mode 100644 index 0000000000..87e654bb30 --- /dev/null +++ b/message_ix_models/data/material/.gitattributes @@ -0,0 +1 @@ +*.csv filter=lfs diff=lfs merge=lfs -text diff --git a/message_ix_models/data/material/CD-Links SSP2 N-fertilizer demand.Global.xlsx b/message_ix_models/data/material/CD-Links SSP2 N-fertilizer demand.Global.xlsx new file mode 100644 index 0000000000..05d78d3cda --- /dev/null +++ b/message_ix_models/data/material/CD-Links SSP2 N-fertilizer demand.Global.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0b43cf1f8140c6da5ae6c15a43028543bf9661a628396db87a8f1f7eeb7c8081 +size 16416 diff --git a/message_ix_models/data/material/MESSAGE_region_mapping_R14.xlsx b/message_ix_models/data/material/MESSAGE_region_mapping_R14.xlsx new file mode 100644 index 0000000000..d102f727ed --- /dev/null +++ b/message_ix_models/data/material/MESSAGE_region_mapping_R14.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e2d7d051d5573423d0160725d1c459da7c40d26ebfe127aa2b614584be1bf176 +size 17152 diff --git a/message_ix_models/data/material/N fertil trade - FAOSTAT_data_9-25-2019.csv b/message_ix_models/data/material/N fertil trade - FAOSTAT_data_9-25-2019.csv new file mode 100644 index 0000000000..913a696147 --- /dev/null +++ b/message_ix_models/data/material/N fertil trade - FAOSTAT_data_9-25-2019.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2a14397095785122b550b84c937529dcbddb23846ee62dbf3111aaf023912886 +size 235131 diff --git a/message_ix_models/data/material/config.yaml b/message_ix_models/data/material/config.yaml new file mode 100644 index 0000000000..bd4c8efa20 --- /dev/null +++ b/message_ix_models/data/material/config.yaml @@ -0,0 +1,103 @@ +# Configuration for message_data.model.material +# +# NB these values are currently for the nitrogen fertilizer materials +# accounting. Different sets can be created once there are 2+ different +# categories of materials. + +set: + commodity: + require: + - d_heat + - electr + - freshwater_supply + add: + - NH3 + - Fertilizer Use|Nitrogen + + level: + require: + - primary + - secondary + - water_supply + add: + - material_interim + - material_final + + # Currently only works with R11 regional aggregation + region: + # Just one is sufficient, since the RES will never be generated with a mix + require: + - R11_AFR + + technology: + require: + - h2_bio_ccs # Reference for vent-to-storage ratio + add: + - biomass_NH3 + - electr_NH3 + - gas_NH3 + - coal_NH3 + - fueloil_NH3 + - NH3_to_N_fertil + + type_tec: + add: + - industry + + +# NB this codelist added by #125 + +iron-steel: + name: Iron and Steel + description: Iron is a chemical element with the symbol Fe, while steel is an alloy of iron and carbon and, sometimes, other elements. Measured as the total mass of material. + unit: Mt + +non-ferrous: + name: Non-ferrous metals + description: Metals and alloys which do not contain iron. Measured as the total mass of material. + unit: Mt + +aluminum: + name: Aluminum + description: Aluminum (or aluminium) is a chemical element with the symbol Al. Measured as the total mass of material. + unit: Mt + parent: non-ferrous + +copper: + name: Copper + description: Copper is a chemical element with the symbol Cu. Measured as the total mass of material. + unit: Mt + parent: non-ferrous + +minerals: + name: Minerals + description: Non-metallic minerals are minerals that have no metallic luster, break easily and include, e.g., sand, limestone, marble, clay and salt. Measured as the FE-equivalent mass of material using LCA midpoint characterization factors. + unit: MtFe-eq + +cement: + name: Cement + description: Cement is a binder used for construction that sets, hardens, and adheres to other materials to bind them together. Measured as the total mass of material. + unit: Mt + parent: minerals + +chemicals: + name: Chemicals + description: Industrial chemicals that form the basis of many products. Measured as the total mass of material. + unit: Mt + +ethylene: + name: Ethylene + description: Ethylene is a hydrocarbon with the formula C2H4. Measured as the total mass of material. + unit: Mt + parent: chemicals + +ammonia: + name: Ammonia + description: Ammonia is a compound of nitrogen and hydrogen with the formula NH3. Measured as the total mass of material. + unit: Mt + parent: chemicals + +paper-pulp: + name: Paper and pulp for paper + description: Pulp is a lignocellulosic fibrous material prepared by chemically or mechanically separating cellulose fibers and the raw material for making paper. Measured as the total mass of material. + unit: Mt diff --git a/message_ix_models/data/material/n-fertilizer_techno-economic.xlsx b/message_ix_models/data/material/n-fertilizer_techno-economic.xlsx new file mode 100644 index 0000000000..aba3a88553 --- /dev/null +++ b/message_ix_models/data/material/n-fertilizer_techno-economic.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:17854387ffaa729b16da78e0ca5a506f6b7f4d748735b0ad149038b6f329d468 +size 17369 diff --git a/message_ix_models/model/material/doc.rst b/message_ix_models/model/material/doc.rst index 54b2846781..5a4f0e59ab 100644 --- a/message_ix_models/model/material/doc.rst +++ b/message_ix_models/model/material/doc.rst @@ -17,8 +17,35 @@ Code reference :members: build, get_spec, gen_data -Configuration file (:file:`material.yaml`) -========================================== +Data, metadata, and configuration +================================= -.. literalinclude:: ../../../data/material.yaml +Binary/raw data files +--------------------- + +The code relies on the following input files, stored in :file:`data/material/`: + +:file:`Ammonia feedstock share.Global.xlsx` + TODO describe the contents, original source, and layout of this file. + +:file:`CD-Links SSP2 N-fertilizer demand.Global.xlsx` + TODO describe the contents, original source, and layout of this file. + +:file:`N fertil trade - FAOSTAT_data_9-25-2019.csv` + TODO describe the contents, original source, and layout of this file. + +:file:`n-fertilizer_techno-economic.xlsx` + TODO describe the contents, original source, and layout of this file. + +:file:`trade.FAO.R11.csv` + TODO describe the contents, original source, and layout of this file. + +:file:`trade.FAO.R14.csv` + TODO describe the contents, original source, and layout of this file. + + +:file:`material/config.yaml` +---------------------------- + +.. literalinclude:: ../../../data/material/config.yaml :language: yaml diff --git a/message_ix_models/model/material/util.py b/message_ix_models/model/material/util.py index 914995cf49..9fa3fa7a7f 100644 --- a/message_ix_models/model/material/util.py +++ b/message_ix_models/model/material/util.py @@ -18,7 +18,10 @@ def read_config(): return context # Read material.yaml - context.load_config("material") + context.load_config("material", "config") + + # Use a shorter name + context["material"] = context["material config"] # Convert some values to Code objects for set_name, info in context["material"]["set"].items(): From 70debbb5b9256a86eac02df952115d7a6b67b7d7 Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Tue, 26 May 2020 20:52:49 +0200 Subject: [PATCH 008/774] Remove inline data file --- ... fertil trade - FAOSTAT_data_9-25-2019.csv | 1019 ----------------- 1 file changed, 1019 deletions(-) delete mode 100644 message_ix_models/model/material/N-fertilizer/N fertil trade - FAOSTAT_data_9-25-2019.csv diff --git a/message_ix_models/model/material/N-fertilizer/N fertil trade - FAOSTAT_data_9-25-2019.csv b/message_ix_models/model/material/N-fertilizer/N fertil trade - FAOSTAT_data_9-25-2019.csv deleted file mode 100644 index 13d2b1b32b..0000000000 --- a/message_ix_models/model/material/N-fertilizer/N fertil trade - FAOSTAT_data_9-25-2019.csv +++ /dev/null @@ -1,1019 +0,0 @@ -Domain Code,Domain,Area Code,Area,Element Code,Element,Item Code,Item,Year Code,Year,Unit,Value,Flag,Flag Description -"RFN","Fertilizers by Nutrient","2","Afghanistan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","4849.98","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","2","Afghanistan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","914.39","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","2","Afghanistan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","6659.57","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","2","Afghanistan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","14.84","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","2","Afghanistan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","7.38","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","2","Afghanistan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","3","Albania","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","21325.32","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","3","Albania","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","24190.4","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","3","Albania","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","32125.1","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","3","Albania","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","271.74","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","3","Albania","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","245.24","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","3","Albania","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","7.13","Fm","Manual Estimation" -"RFN","Fertilizers by Nutrient","4","Algeria","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","33775.38","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","4","Algeria","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","58610","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","4","Algeria","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","60966.2","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","4","Algeria","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","198614.96","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","4","Algeria","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","96367.62","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","4","Algeria","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","765725.92","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","7","Angola","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","4432.93","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","7","Angola","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","10935.51","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","7","Angola","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","25468.81","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","7","Angola","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","7","Angola","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","7","Angola","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","8.58","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","8","Antigua and Barbuda","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","83.11","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","8","Antigua and Barbuda","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","6.9","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","8","Antigua and Barbuda","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","7.69","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","8","Antigua and Barbuda","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","8","Antigua and Barbuda","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","8","Antigua and Barbuda","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0.01","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","9","Argentina","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","303617.74","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","9","Argentina","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","546671.46","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","9","Argentina","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","285918.53","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","9","Argentina","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","231929.69","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","9","Argentina","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","62176.31","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","9","Argentina","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","57123.15","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","1","Armenia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","10383.26","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","1","Armenia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","10547.96","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","1","Armenia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","22147","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","1","Armenia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0.07","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","1","Armenia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","1","Armenia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","20.13","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","10","Australia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","761294","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","10","Australia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","863599.57","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","10","Australia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","1098521.25","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","10","Australia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","124387.76","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","10","Australia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","157808.99","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","10","Australia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","102064.05","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","11","Austria","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","122058.76","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","11","Austria","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","135937.05","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","11","Austria","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","174885.04","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","11","Austria","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","2403.48","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","11","Austria","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","11628.91","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","11","Austria","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","178486.11","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","52","Azerbaijan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","17891.17","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","52","Azerbaijan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","12999.09","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","52","Azerbaijan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","4659.42","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","52","Azerbaijan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","52","Azerbaijan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","52","Azerbaijan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","419.01","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","12","Bahamas","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","478.72","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","12","Bahamas","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","530.87","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","12","Bahamas","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","662.05","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","12","Bahamas","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","12","Bahamas","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0.01","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","12","Bahamas","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0.01","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","13","Bahrain","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","2807.05","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","13","Bahrain","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","2552.71","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","13","Bahrain","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","2142.06","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","13","Bahrain","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","272492.84","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","13","Bahrain","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","239847.55","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","13","Bahrain","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","330960.95","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","16","Bangladesh","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","426729.95","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","16","Bangladesh","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","651103.06","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","16","Bangladesh","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","648205.19","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","16","Bangladesh","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","356949.63","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","16","Bangladesh","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","46303.95","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","16","Bangladesh","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","14","Barbados","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","1439.38","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","14","Barbados","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","979.52","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","14","Barbados","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","558.44","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","14","Barbados","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","1.45","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","14","Barbados","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","3.59","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","14","Barbados","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","151.59","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","57","Belarus","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","13595.35","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","57","Belarus","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","167633.27","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","57","Belarus","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","28523.83","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","57","Belarus","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","232526.08","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","57","Belarus","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","276089.61","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","57","Belarus","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","582413.22","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","255","Belgium","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","533790.1","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","255","Belgium","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","649137.43","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","255","Belgium","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","367383.63","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","255","Belgium","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","921698.98","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","255","Belgium","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1009736.68","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","255","Belgium","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","731944.34","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","23","Belize","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","4765.8","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","23","Belize","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","2793.48","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","23","Belize","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","17605.12","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","23","Belize","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","23","Belize","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","23","Belize","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","1.25","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","53","Benin","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","412.85","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","53","Benin","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","4714.88","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","53","Benin","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","185.04","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","53","Benin","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","53","Benin","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","6.7","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","53","Benin","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0.18","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","17","Bermuda","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","17","Bermuda","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","79.84","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","17","Bermuda","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","41.13","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","17","Bermuda","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","17","Bermuda","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","17","Bermuda","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","18","Bhutan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","1943.3","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","18","Bhutan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1178.75","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","18","Bhutan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","1318.12","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","18","Bhutan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","1.17","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","18","Bhutan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","18","Bhutan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","19","Bolivia (Plurinational State of)","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","12232.05","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","19","Bolivia (Plurinational State of)","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","19690.64","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","19","Bolivia (Plurinational State of)","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","23120.41","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","19","Bolivia (Plurinational State of)","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0.27","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","19","Bolivia (Plurinational State of)","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0.81","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","19","Bolivia (Plurinational State of)","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","80","Bosnia and Herzegovina","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","36236.99","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","80","Bosnia and Herzegovina","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","26363.73","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","80","Bosnia and Herzegovina","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","22631.68","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","80","Bosnia and Herzegovina","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","726.22","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","80","Bosnia and Herzegovina","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","11290.08","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","80","Bosnia and Herzegovina","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","11898.34","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","20","Botswana","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","11333.2","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","20","Botswana","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","20563.96","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","20","Botswana","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","12320.6","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","20","Botswana","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","62.4","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","20","Botswana","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0.24","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","20","Botswana","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","147.68","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","21","Brazil","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","1388951.11","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","21","Brazil","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","2253801.15","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","21","Brazil","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","2761312.85","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","21","Brazil","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","105632.31","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","21","Brazil","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","89830.94","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","21","Brazil","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","90324.82","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","26","Brunei Darussalam","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","7783.48","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","26","Brunei Darussalam","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","928.66","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","26","Brunei Darussalam","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","30.99","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","26","Brunei Darussalam","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0.5","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","26","Brunei Darussalam","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","191.46","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","26","Brunei Darussalam","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","1.29","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","27","Bulgaria","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","35937.05","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","27","Bulgaria","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","120105.71","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","27","Bulgaria","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","244134.04","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","27","Bulgaria","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","196877.93","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","27","Bulgaria","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","113454.35","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","27","Bulgaria","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","196794.36","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","233","Burkina Faso","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","31826.86","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","233","Burkina Faso","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","44302.36","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","233","Burkina Faso","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","51526.59","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","233","Burkina Faso","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","1.99","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","233","Burkina Faso","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","932.48","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","233","Burkina Faso","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","440.98","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","29","Burundi","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","606.56","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","29","Burundi","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1238.28","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","29","Burundi","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","4603.82","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","29","Burundi","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","29","Burundi","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","29","Burundi","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","115","Cambodia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","11053.86","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","115","Cambodia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","22657.53","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","115","Cambodia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","80858.37","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","115","Cambodia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","115","Cambodia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","115","Cambodia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","32","Cameroon","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","23388.89","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","32","Cameroon","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","27476.68","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","32","Cameroon","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","45825.12","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","32","Cameroon","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","45.02","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","32","Cameroon","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1545.27","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","32","Cameroon","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","3191.71","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","33","Canada","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","363142.09","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","33","Canada","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","498624.98","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","33","Canada","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","889562.72","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","33","Canada","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","1237850.24","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","33","Canada","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1203560.92","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","33","Canada","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","816909.55","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","37","Central African Republic","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","245.65","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","37","Central African Republic","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","221.67","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","37","Central African Republic","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","219.25","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","37","Central African Republic","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","37","Central African Republic","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","37","Central African Republic","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","40","Chile","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","238328.78","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","40","Chile","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","286495.39","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","40","Chile","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","362671.03","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","40","Chile","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","88584.49","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","40","Chile","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","140672.89","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","40","Chile","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","132943.06","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","351","China","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","964283.07","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","351","China","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","392110.96","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","351","China","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","363731.76","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","351","China","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","1170671.82","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","351","China","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","4725630.67","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","351","China","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","9976149.17","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","96","China, Hong Kong SAR","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","2402.93","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","96","China, Hong Kong SAR","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1747.78","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","96","China, Hong Kong SAR","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","7572.22","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","96","China, Hong Kong SAR","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","1167.23","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","96","China, Hong Kong SAR","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","130.83","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","96","China, Hong Kong SAR","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","521.7","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","128","China, Macao SAR","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","5785.19","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","128","China, Macao SAR","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1296.96","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","128","China, Macao SAR","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","3028.54","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","128","China, Macao SAR","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","128","China, Macao SAR","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","128","China, Macao SAR","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","41","China, mainland","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","819579.02","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","41","China, mainland","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","257991.24","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","41","China, mainland","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","251853.44","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","41","China, mainland","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","1114808.28","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","41","China, mainland","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","4654927.65","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","41","China, mainland","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","9921758.27","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","214","China, Taiwan Province of","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","136515.93","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","214","China, Taiwan Province of","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","131074.97","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","214","China, Taiwan Province of","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","101277.56","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","214","China, Taiwan Province of","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","54696.31","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","214","China, Taiwan Province of","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","70572.18","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","214","China, Taiwan Province of","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","53869.2","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","44","Colombia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","300359.9","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","44","Colombia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","316247.76","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","44","Colombia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","414828.31","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","44","Colombia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","9640.53","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","44","Colombia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","28707.68","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","44","Colombia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","41691.58","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","46","Congo","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","99.67","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","46","Congo","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","86.81","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","46","Congo","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","298.37","Fm","Manual Estimation" -"RFN","Fertilizers by Nutrient","46","Congo","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0.09","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","46","Congo","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0.09","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","46","Congo","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","1.28","Fm","Manual Estimation" -"RFN","Fertilizers by Nutrient","47","Cook Islands","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","19.77","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","47","Cook Islands","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","7.28","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","47","Cook Islands","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","25.4","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","47","Cook Islands","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","47","Cook Islands","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","47","Cook Islands","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","48","Costa Rica","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","70118.42","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","48","Costa Rica","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","65463.12","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","48","Costa Rica","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","90040.23","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","48","Costa Rica","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","2281.6","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","48","Costa Rica","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","4678.53","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","48","Costa Rica","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","4862.69","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","107","Côte d'Ivoire","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","43778.91","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","107","Côte d'Ivoire","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","31401.99","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","107","Côte d'Ivoire","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","53158.46","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","107","Côte d'Ivoire","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","27420.9","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","107","Côte d'Ivoire","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","4311.72","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","107","Côte d'Ivoire","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","10618.98","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","98","Croatia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","14408.39","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","98","Croatia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","32163.35","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","98","Croatia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","45381.63","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","98","Croatia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","211312.75","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","98","Croatia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","269898.95","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","98","Croatia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","295571","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","49","Cuba","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","23339.2","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","49","Cuba","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","46116.87","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","49","Cuba","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","48233.07","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","49","Cuba","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","3483.08","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","49","Cuba","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1174.88","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","49","Cuba","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","26.83","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","50","Cyprus","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","8093.36","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","50","Cyprus","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","8498.45","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","50","Cyprus","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","7939.54","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","50","Cyprus","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","6.41","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","50","Cyprus","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","9.94","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","50","Cyprus","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","167","Czechia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","179025.15","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","167","Czechia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","184331.67","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","167","Czechia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","350458.67","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","167","Czechia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","151496.8","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","167","Czechia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","162496.55","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","167","Czechia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","127226.33","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","250","Democratic Republic of the Congo","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","2671.14","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","250","Democratic Republic of the Congo","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","4078.38","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","250","Democratic Republic of the Congo","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","18071.34","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","250","Democratic Republic of the Congo","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0.23","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","250","Democratic Republic of the Congo","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","7.47","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","250","Democratic Republic of the Congo","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","3.83","Fm","Manual Estimation" -"RFN","Fertilizers by Nutrient","54","Denmark","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","207018.39","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","54","Denmark","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","170956.31","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","54","Denmark","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","240107.01","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","54","Denmark","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","15627.14","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","54","Denmark","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","21362.13","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","54","Denmark","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","38188.31","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","55","Dominica","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","184.82","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","55","Dominica","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","115.56","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","55","Dominica","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","314.83","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","55","Dominica","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0.04","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","55","Dominica","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","55","Dominica","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","56","Dominican Republic","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","38400.5","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","56","Dominican Republic","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","53570.37","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","56","Dominican Republic","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","52344.26","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","56","Dominican Republic","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","1662.17","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","56","Dominican Republic","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","743.8","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","56","Dominican Republic","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","2523.6","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","58","Ecuador","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","121192.11","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","58","Ecuador","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","167078.26","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","58","Ecuador","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","177625.51","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","58","Ecuador","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","124.6","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","58","Ecuador","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1131.43","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","58","Ecuador","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","73.46","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","59","Egypt","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","367555.63","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","59","Egypt","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","2426.38","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","59","Egypt","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","57454.96","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","59","Egypt","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","32727.51","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","59","Egypt","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1888230.49","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","59","Egypt","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","417859.01","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","60","El Salvador","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","68448.08","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","60","El Salvador","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","62088.81","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","60","El Salvador","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","67105.73","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","60","El Salvador","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","5253.08","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","60","El Salvador","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","6250.9","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","60","El Salvador","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","9013.05","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","178","Eritrea","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","2036.84","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","178","Eritrea","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","274.05","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","178","Eritrea","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","1602.42","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","178","Eritrea","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","178","Eritrea","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","210.6","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","178","Eritrea","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","63","Estonia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","148928.62","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","63","Estonia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","50009.31","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","63","Estonia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","79238.35","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","63","Estonia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","177379.06","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","63","Estonia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","8626.46","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","63","Estonia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","6994.76","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","238","Ethiopia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","68336.86","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","238","Ethiopia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","153856.87","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","238","Ethiopia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","139522.48","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","238","Ethiopia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","238","Ethiopia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","238","Ethiopia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","66","Fiji","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","6695.14","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","66","Fiji","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","2452.3","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","66","Fiji","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","5827.08","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","66","Fiji","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","1.38","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","66","Fiji","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0.56","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","66","Fiji","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","1.51","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","67","Finland","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","39336.9","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","67","Finland","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","34427.83","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","67","Finland","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","90116.81","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","67","Finland","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","109062.02","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","67","Finland","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","135734.82","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","67","Finland","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","230047.99","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","68","France","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","1458910.51","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","68","France","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","777055.14","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","68","France","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","2058024.18","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","68","France","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","239029.6","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","68","France","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","92209.52","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","68","France","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","287227.29","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","70","French Polynesia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","347.99","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","70","French Polynesia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","399.17","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","70","French Polynesia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","343.26","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","70","French Polynesia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","70","French Polynesia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","70","French Polynesia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","74","Gabon","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","160.19","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","74","Gabon","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","988.04","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","74","Gabon","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","3883.69","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","74","Gabon","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","74","Gabon","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","6.17","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","74","Gabon","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","4.2","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","75","Gambia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0.12","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","75","Gambia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","7.04","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","75","Gambia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","141.24","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","75","Gambia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","75","Gambia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","75","Gambia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","73","Georgia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","441.16","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","73","Georgia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","3084.22","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","73","Georgia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","9917.49","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","73","Georgia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","88073.28","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","73","Georgia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","128955.86","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","73","Georgia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","154264.05","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","79","Germany","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","1131221.93","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","79","Germany","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1204490.62","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","79","Germany","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","1260569.66","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","79","Germany","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","697189.72","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","79","Germany","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","769700.04","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","79","Germany","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","815146.73","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","81","Ghana","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","38157.97","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","81","Ghana","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","49762.64","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","81","Ghana","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","71560.78","Fm","Manual Estimation" -"RFN","Fertilizers by Nutrient","81","Ghana","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","269.2","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","81","Ghana","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","3674.45","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","81","Ghana","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","4590.88","Fm","Manual Estimation" -"RFN","Fertilizers by Nutrient","84","Greece","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","158363.57","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","84","Greece","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","158894.64","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","84","Greece","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","202835.44","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","84","Greece","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","59326.22","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","84","Greece","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","77611.38","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","84","Greece","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","118389.21","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","89","Guatemala","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","147842.88","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","89","Guatemala","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","149517.06","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","89","Guatemala","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","189987.99","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","89","Guatemala","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","11142.38","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","89","Guatemala","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","4284.73","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","89","Guatemala","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","9809.9","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","90","Guinea","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","2074.63","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","90","Guinea","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","2794.05","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","90","Guinea","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","2463.5","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","90","Guinea","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","90","Guinea","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","90","Guinea","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","60.04","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","91","Guyana","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","6548.2","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","91","Guyana","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","14610.73","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","91","Guyana","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","17642.93","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","91","Guyana","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0.09","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","91","Guyana","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","91","Guyana","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","420.21","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","95","Honduras","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","42879.33","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","95","Honduras","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","57421.91","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","95","Honduras","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","77521.3","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","95","Honduras","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","1013.41","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","95","Honduras","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1380.24","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","95","Honduras","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","2178.28","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","97","Hungary","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","159690.08","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","97","Hungary","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","214977.39","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","97","Hungary","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","325138.94","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","97","Hungary","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","106302.26","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","97","Hungary","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","171408.4","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","97","Hungary","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","120415.46","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","99","Iceland","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","7480.39","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","99","Iceland","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","7904.89","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","99","Iceland","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","8698.58","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","99","Iceland","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0.02","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","99","Iceland","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","99","Iceland","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","2.6","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","100","India","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","1040447.01","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","100","India","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","3212884.87","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","100","India","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","5933943.45","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","100","India","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","12358.13","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","100","India","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","20778.53","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","100","India","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","36319.23","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","101","Indonesia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","175571.15","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","101","Indonesia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","279986.63","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","101","Indonesia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","504058.11","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","101","Indonesia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","312091.3","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","101","Indonesia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","450911.85","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","101","Indonesia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","411571.24","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","102","Iran (Islamic Republic of)","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","132173.26","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","102","Iran (Islamic Republic of)","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","42548.2","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","102","Iran (Islamic Republic of)","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","4328.54","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","102","Iran (Islamic Republic of)","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","14279.72","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","102","Iran (Islamic Republic of)","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","433712.26","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","102","Iran (Islamic Republic of)","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","1053550.76","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","103","Iraq","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","115.56","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","103","Iraq","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","22475.48","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","103","Iraq","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","15666.22","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","103","Iraq","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","29918.04","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","103","Iraq","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","103","Iraq","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","230.92","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","104","Ireland","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","338570.74","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","104","Ireland","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","382971.76","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","104","Ireland","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","324548.43","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","104","Ireland","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","496.26","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","104","Ireland","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","562.17","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","104","Ireland","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","2271.62","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","105","Israel","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","29856.58","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","105","Israel","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","26366.25","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","105","Israel","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","56647.52","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","105","Israel","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","60246.51","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","105","Israel","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","88677.8","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","105","Israel","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","54007.97","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","106","Italy","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","618604.02","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","106","Italy","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","464633.58","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","106","Italy","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","604744.28","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","106","Italy","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","72257.91","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","106","Italy","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","75828.46","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","106","Italy","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","140221.64","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","109","Jamaica","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","6435.24","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","109","Jamaica","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","4857.98","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","109","Jamaica","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","2644.24","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","109","Jamaica","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","27.55","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","109","Jamaica","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1.12","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","109","Jamaica","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","5.73","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","110","Japan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","315226.78","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","110","Japan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","218213.78","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","110","Japan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","251031.12","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","110","Japan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","188937.07","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","110","Japan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","180952.81","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","110","Japan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","129294.82","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","112","Jordan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","15606.58","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","112","Jordan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","16628.62","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","112","Jordan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","24588.69","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","112","Jordan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","14537.26","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","112","Jordan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","163461.93","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","112","Jordan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","64758.26","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","108","Kazakhstan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","74682.36","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","108","Kazakhstan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","72045.22","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","108","Kazakhstan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","63537.8","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","108","Kazakhstan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","21751.17","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","108","Kazakhstan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","33040","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","108","Kazakhstan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","37658.93","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","114","Kenya","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","88466.34","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","114","Kenya","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","86143.91","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","114","Kenya","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","83942.36","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","114","Kenya","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","3165.63","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","114","Kenya","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","7227.65","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","114","Kenya","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","3404.51","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","118","Kuwait","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","2095.14","Fm","Manual Estimation" -"RFN","Fertilizers by Nutrient","118","Kuwait","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","853.61","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","118","Kuwait","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","2492.5","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","118","Kuwait","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","401409.74","Fm","Manual Estimation" -"RFN","Fertilizers by Nutrient","118","Kuwait","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","540147.08","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","118","Kuwait","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","377597.14","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","113","Kyrgyzstan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","28954.64","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","113","Kyrgyzstan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","52989.72","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","113","Kyrgyzstan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","43821.86","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","113","Kyrgyzstan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","2.6","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","113","Kyrgyzstan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","21.4","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","113","Kyrgyzstan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","438.16","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","119","Latvia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","63857.82","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","119","Latvia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","89891.34","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","119","Latvia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","135459.71","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","119","Latvia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","15233.7","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","119","Latvia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","9870.41","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","119","Latvia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","23518.37","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","121","Lebanon","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","14680.32","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","121","Lebanon","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","17693.63","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","121","Lebanon","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","18985.52","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","121","Lebanon","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","73.57","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","121","Lebanon","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","86.99","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","121","Lebanon","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","61.24","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","124","Libya","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","7778.64","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","124","Libya","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","12877.52","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","124","Libya","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","7143.16","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","124","Libya","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","213471.55","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","124","Libya","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","370470.65","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","124","Libya","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","136837.76","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","126","Lithuania","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","101267.61","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","126","Lithuania","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","140283.83","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","126","Lithuania","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","316022.87","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","126","Lithuania","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","780763.79","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","126","Lithuania","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","676866.3","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","126","Lithuania","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","944438.8","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","256","Luxembourg","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","34716.32","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","256","Luxembourg","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","32635.17","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","256","Luxembourg","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","37274.93","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","256","Luxembourg","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","11442.1","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","256","Luxembourg","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","11173.67","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","256","Luxembourg","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","12776.46","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","129","Madagascar","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","7469.97","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","129","Madagascar","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","4575.16","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","129","Madagascar","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","5932.85","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","129","Madagascar","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","8.57","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","129","Madagascar","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0.27","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","129","Madagascar","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","29669.34","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","130","Malawi","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","59106.8","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","130","Malawi","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","94148.57","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","130","Malawi","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","109446.72","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","130","Malawi","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","474.72","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","130","Malawi","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","204","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","130","Malawi","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","20.1","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","131","Malaysia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","442940.28","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","131","Malaysia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","547358.45","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","131","Malaysia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","556761.56","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","131","Malaysia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","373082.13","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","131","Malaysia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","419715.4","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","131","Malaysia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","548308.45","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","132","Maldives","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","130.24","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","132","Maldives","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","164.86","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","132","Maldives","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","226.3","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","132","Maldives","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0.04","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","132","Maldives","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","132","Maldives","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","133","Mali","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","50450.28","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","133","Mali","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","75567.54","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","133","Mali","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","145603.33","Fm","Manual Estimation" -"RFN","Fertilizers by Nutrient","133","Mali","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","133","Mali","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","7909.83","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","133","Mali","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","40342.06","Fm","Manual Estimation" -"RFN","Fertilizers by Nutrient","134","Malta","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","626.17","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","134","Malta","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","391.49","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","134","Malta","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","4086.62","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","134","Malta","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0.23","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","134","Malta","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","134","Malta","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","127","Marshall Islands","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","127","Marshall Islands","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","264.81","Fm","Manual Estimation" -"RFN","Fertilizers by Nutrient","127","Marshall Islands","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0.01","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","127","Marshall Islands","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","127","Marshall Islands","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","127","Marshall Islands","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","137","Mauritius","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","11913.09","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","137","Mauritius","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","8212.81","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","137","Mauritius","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","6647.86","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","137","Mauritius","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","2137.47","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","137","Mauritius","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1444.89","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","137","Mauritius","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","1652.21","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","138","Mexico","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","877974.76","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","138","Mexico","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","789266.87","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","138","Mexico","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","1005614.06","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","138","Mexico","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","3746.34","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","138","Mexico","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","103209.85","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","138","Mexico","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","79221.99","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","141","Mongolia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","4103.35","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","141","Mongolia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","11415.97","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","141","Mongolia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","20685.73","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","141","Mongolia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","141","Mongolia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","141","Mongolia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","273","Montenegro","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1384.87","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","273","Montenegro","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","1286.24","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","273","Montenegro","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","10.28","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","273","Montenegro","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","1.08","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","143","Morocco","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","123439.62","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","143","Morocco","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","157575.47","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","143","Morocco","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","174718.48","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","143","Morocco","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","205038.12","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","143","Morocco","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","428705.32","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","143","Morocco","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","525766.94","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","144","Mozambique","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","22675.92","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","144","Mozambique","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","47210.67","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","144","Mozambique","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","13745.55","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","144","Mozambique","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","2.71","Fm","Manual Estimation" -"RFN","Fertilizers by Nutrient","144","Mozambique","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0.06","Fm","Manual Estimation" -"RFN","Fertilizers by Nutrient","144","Mozambique","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","1682.69","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","28","Myanmar","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","10239.31","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","28","Myanmar","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","3900.99","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","28","Myanmar","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","154299.74","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","28","Myanmar","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","28","Myanmar","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","28","Myanmar","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","147","Namibia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","3844.05","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","147","Namibia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","7927.63","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","147","Namibia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","10108.77","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","147","Namibia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","152.98","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","147","Namibia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","11.86","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","147","Namibia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","366.55","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","148","Nauru","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","148","Nauru","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","148","Nauru","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0.01","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","148","Nauru","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","7.71","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","148","Nauru","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","148","Nauru","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","149","Nepal","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","12766.23","Fm","Manual Estimation" -"RFN","Fertilizers by Nutrient","149","Nepal","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","56932.9","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","149","Nepal","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","108368.89","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","149","Nepal","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0.02","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","149","Nepal","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0.02","Fm","Manual Estimation" -"RFN","Fertilizers by Nutrient","149","Nepal","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","Fm","Manual Estimation" -"RFN","Fertilizers by Nutrient","150","Netherlands","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","174007.75","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","150","Netherlands","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","131080.45","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","150","Netherlands","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","502150.14","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","150","Netherlands","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","402385.85","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","150","Netherlands","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","601536.21","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","150","Netherlands","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","2002561.9","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","153","New Caledonia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","808.91","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","153","New Caledonia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","569.55","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","153","New Caledonia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","769.91","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","153","New Caledonia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","153","New Caledonia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0.01","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","153","New Caledonia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0.04","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","156","New Zealand","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","265240.54","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","156","New Zealand","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","250707.36","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","156","New Zealand","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","375184.03","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","156","New Zealand","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","1004.75","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","156","New Zealand","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1272.58","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","156","New Zealand","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","936.79","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","157","Nicaragua","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","39231.44","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","157","Nicaragua","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","40590.62","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","157","Nicaragua","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","56233.82","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","157","Nicaragua","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","302.77","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","157","Nicaragua","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","499.14","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","157","Nicaragua","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","353.48","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","158","Niger","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","2959","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","158","Niger","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","6744.29","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","158","Niger","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","6808.37","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","158","Niger","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","52","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","158","Niger","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","2.9","Fm","Manual Estimation" -"RFN","Fertilizers by Nutrient","158","Niger","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","87.33","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","159","Nigeria","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","204929.36","Fm","Manual Estimation" -"RFN","Fertilizers by Nutrient","159","Nigeria","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","18901.79","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","159","Nigeria","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","123728.68","Fm","Manual Estimation" -"RFN","Fertilizers by Nutrient","159","Nigeria","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","21.92","Fm","Manual Estimation" -"RFN","Fertilizers by Nutrient","159","Nigeria","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","910.28","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","159","Nigeria","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","55286.98","Fm","Manual Estimation" -"RFN","Fertilizers by Nutrient","154","North Macedonia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","18387.31","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","154","North Macedonia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","19775.56","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","154","North Macedonia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","19385.89","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","154","North Macedonia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0.92","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","154","North Macedonia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","8.81","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","154","North Macedonia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","24.05","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","162","Norway","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","46058.8","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","162","Norway","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","42688.4","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","162","Norway","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","71586.95","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","162","Norway","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","360695.52","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","162","Norway","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","474007.52","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","162","Norway","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","558813.34","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","221","Oman","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","8008.01","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","221","Oman","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","9630.4","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","221","Oman","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","7764.4","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","221","Oman","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","377650.3","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","221","Oman","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1507186.8","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","221","Oman","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","1192346.74","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","165","Pakistan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","430996.86","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","165","Pakistan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","460774.57","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","165","Pakistan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","567170.68","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","165","Pakistan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","5022.54","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","165","Pakistan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","223.32","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","165","Pakistan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","4.28","Fm","Manual Estimation" -"RFN","Fertilizers by Nutrient","166","Panama","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","16261.91","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","166","Panama","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","22401.84","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","166","Panama","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","16248.6","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","166","Panama","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","166","Panama","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","360.94","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","166","Panama","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","27.5","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","168","Papua New Guinea","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","15604.58","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","168","Papua New Guinea","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","18576.07","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","168","Papua New Guinea","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","20673.16","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","168","Papua New Guinea","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","168","Papua New Guinea","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","62.5","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","168","Papua New Guinea","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","2.51","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","169","Paraguay","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","42920.56","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","169","Paraguay","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","81696.16","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","169","Paraguay","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","122063.97","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","169","Paraguay","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","169","Paraguay","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","5.03","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","169","Paraguay","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","170","Peru","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","242505.52","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","170","Peru","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","145536.18","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","170","Peru","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","216790.74","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","170","Peru","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","4952.83","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","170","Peru","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","12305.56","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","170","Peru","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","12177.64","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","171","Philippines","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","370708.72","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","171","Philippines","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","415821.54","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","171","Philippines","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","481200.81","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","171","Philippines","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","72761.53","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","171","Philippines","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","63802.07","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","171","Philippines","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","28.69","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","173","Poland","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","224552.12","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","173","Poland","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","250903.81","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","173","Poland","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","521861.43","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","173","Poland","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","695095.63","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","173","Poland","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","600418.93","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","173","Poland","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","663864.18","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","174","Portugal","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","86690.74","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","174","Portugal","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","124520.39","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","174","Portugal","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","138145.56","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","174","Portugal","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","80346.55","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","174","Portugal","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","78520.11","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","174","Portugal","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","82260.16","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","179","Qatar","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","164.88","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","179","Qatar","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1944.17","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","179","Qatar","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","742.09","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","179","Qatar","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","905544.89","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","179","Qatar","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1205452.73","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","179","Qatar","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","2327518.31","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","117","Republic of Korea","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","400215.75","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","117","Republic of Korea","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","268757.52","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","117","Republic of Korea","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","362670.76","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","117","Republic of Korea","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","226342.1","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","117","Republic of Korea","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","313159.63","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","117","Republic of Korea","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","173880.97","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","146","Republic of Moldova","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","23313.84","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","146","Republic of Moldova","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","19859.36","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","146","Republic of Moldova","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","42971.46","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","146","Republic of Moldova","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","1390.63","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","146","Republic of Moldova","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","13.95","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","146","Republic of Moldova","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","3.49","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","183","Romania","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","44686.86","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","183","Romania","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","98019.62","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","183","Romania","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","264276.95","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","183","Romania","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","892654.27","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","183","Romania","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","721672.88","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","183","Romania","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","126283.68","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","185","Russian Federation","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","7592.39","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","185","Russian Federation","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","19733.63","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","185","Russian Federation","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","13249.6","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","185","Russian Federation","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","4902550.11","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","185","Russian Federation","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","5149994.93","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","185","Russian Federation","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","5641264.86","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","184","Rwanda","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","1236.56","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","184","Rwanda","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","833.97","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","184","Rwanda","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","11054.32","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","184","Rwanda","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","184","Rwanda","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","94.27","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","184","Rwanda","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0.33","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","188","Saint Kitts and Nevis","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","55.87","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","188","Saint Kitts and Nevis","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","21.47","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","188","Saint Kitts and Nevis","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","8.26","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","188","Saint Kitts and Nevis","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0.04","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","188","Saint Kitts and Nevis","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","188","Saint Kitts and Nevis","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0.03","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","189","Saint Lucia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","293.54","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","189","Saint Lucia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","414.5","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","189","Saint Lucia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","429.48","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","189","Saint Lucia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","189","Saint Lucia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","189","Saint Lucia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0.6","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","244","Samoa","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0.67","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","244","Samoa","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","2.04","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","244","Samoa","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","1.19","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","244","Samoa","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","244","Samoa","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","19.1","Fm","Manual Estimation" -"RFN","Fertilizers by Nutrient","244","Samoa","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0.01","Fm","Manual Estimation" -"RFN","Fertilizers by Nutrient","194","Saudi Arabia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","16987.73","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","194","Saudi Arabia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","17843.29","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","194","Saudi Arabia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","28743.49","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","194","Saudi Arabia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","836324.66","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","194","Saudi Arabia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1440005.8","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","194","Saudi Arabia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","2068524.82","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","195","Senegal","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","11072.85","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","195","Senegal","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","33252.01","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","195","Senegal","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","26046.75","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","195","Senegal","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","18788.92","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","195","Senegal","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","8002.94","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","195","Senegal","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","4950.8","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","272","Serbia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","105545.74","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","272","Serbia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","169744.65","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","272","Serbia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","29942.39","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","272","Serbia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","51867.81","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","186","Serbia and Montenegro","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","107019","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","186","Serbia and Montenegro","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","14579","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","196","Seychelles","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","29.52","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","196","Seychelles","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","29.06","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","196","Seychelles","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","57.59","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","196","Seychelles","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","196","Seychelles","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","196","Seychelles","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","200","Singapore","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","12456.04","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","200","Singapore","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","3835.87","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","200","Singapore","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","7107.16","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","200","Singapore","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","2804.84","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","200","Singapore","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","3361.22","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","200","Singapore","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","10185.73","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","199","Slovakia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","45952.1","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","199","Slovakia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","98177.22","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","199","Slovakia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","130713.06","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","199","Slovakia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","155564.06","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","199","Slovakia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","214140.59","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","199","Slovakia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","392670.01","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","198","Slovenia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","69078.83","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","198","Slovenia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","64436.76","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","198","Slovenia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","42409.8","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","198","Slovenia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","2615.72","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","198","Slovenia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","2917.88","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","198","Slovenia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","11334.69","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","202","South Africa","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","253726.62","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","202","South Africa","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","372114.43","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","202","South Africa","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","404538.85","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","202","South Africa","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","112599.97","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","202","South Africa","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","121551.17","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","202","South Africa","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","184291.61","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","203","Spain","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","561120.76","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","203","Spain","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","597439.58","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","203","Spain","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","874081.74","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","203","Spain","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","182961.14","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","203","Spain","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","211079.47","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","203","Spain","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","231124.97","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","38","Sri Lanka","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","153341.42","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","38","Sri Lanka","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","176530.67","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","38","Sri Lanka","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","225495.67","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","38","Sri Lanka","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","68.01","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","38","Sri Lanka","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","106.84","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","38","Sri Lanka","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","416.16","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","276","Sudan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","76863.46","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","276","Sudan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","11.09","Fm","Manual Estimation" -"RFN","Fertilizers by Nutrient","206","Sudan (former)","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","133742.96","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","206","Sudan (former)","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","52713.09","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","206","Sudan (former)","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","206","Sudan (former)","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","207","Suriname","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","4558.4","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","207","Suriname","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","10128.42","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","207","Suriname","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","10822.61","Fm","Manual Estimation" -"RFN","Fertilizers by Nutrient","207","Suriname","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","82.8","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","207","Suriname","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","2.07","Fm","Manual Estimation" -"RFN","Fertilizers by Nutrient","207","Suriname","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","7.6","Fm","Manual Estimation" -"RFN","Fertilizers by Nutrient","210","Sweden","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","131383.84","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","210","Sweden","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","229901","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","210","Sweden","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","212971.47","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","210","Sweden","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","55888.76","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","210","Sweden","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","103923.21","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","210","Sweden","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","74315.63","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","211","Switzerland","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","60099.93","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","211","Switzerland","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","57103.83","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","211","Switzerland","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","65497.71","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","211","Switzerland","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","1388.75","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","211","Switzerland","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1257.26","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","211","Switzerland","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","1519.46","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","212","Syrian Arab Republic","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","167054.09","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","212","Syrian Arab Republic","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","49007.78","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","212","Syrian Arab Republic","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","2384.47","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","212","Syrian Arab Republic","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","893.7","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","212","Syrian Arab Republic","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","19731.01","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","212","Syrian Arab Republic","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","208","Tajikistan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","748.65","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","208","Tajikistan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","598.04","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","208","Tajikistan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","16438.24","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","208","Tajikistan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","7245","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","208","Tajikistan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1232.57","Fm","Manual Estimation" -"RFN","Fertilizers by Nutrient","208","Tajikistan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","4.6","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","216","Thailand","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","1005968.1","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","216","Thailand","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1531591.65","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","216","Thailand","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","1323134.29","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","216","Thailand","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","63394.47","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","216","Thailand","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","61515.78","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","216","Thailand","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","57260.98","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","217","Togo","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","598.36","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","217","Togo","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","9588.54","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","217","Togo","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","5652.76","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","217","Togo","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","7.16","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","217","Togo","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","361.22","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","217","Togo","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","5079.48","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","219","Tonga","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","219","Tonga","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","9.56","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","219","Tonga","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","56.18","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","219","Tonga","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","219","Tonga","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","219","Tonga","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0","E","Expert sources from FAO (including other divisions)" -"RFN","Fertilizers by Nutrient","220","Trinidad and Tobago","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","1317.26","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","220","Trinidad and Tobago","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","850.8","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","220","Trinidad and Tobago","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","1103.3","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","220","Trinidad and Tobago","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","279023.68","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","220","Trinidad and Tobago","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","305491.71","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","220","Trinidad and Tobago","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","575024.35","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","222","Tunisia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","11300.64","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","222","Tunisia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","12129.32","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","222","Tunisia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","16193.2","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","222","Tunisia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","179809.62","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","222","Tunisia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","206752.84","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","222","Tunisia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","44413.73","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","223","Turkey","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","1013255.23","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","223","Turkey","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1078661.84","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","223","Turkey","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","1237901.85","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","223","Turkey","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","45284.04","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","223","Turkey","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","104964.63","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","223","Turkey","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","62622.7","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","226","Uganda","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","2833.1","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","226","Uganda","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","6921.45","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","226","Uganda","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","7573.21","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","226","Uganda","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","10.48","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","226","Uganda","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","12.99","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","226","Uganda","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","5493.82","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","230","Ukraine","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","86086.51","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","230","Ukraine","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","285439.15","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","230","Ukraine","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","495178.84","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","230","Ukraine","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","2172018.81","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","230","Ukraine","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1618783.97","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","230","Ukraine","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","875235.46","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","225","United Arab Emirates","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","18913.34","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","225","United Arab Emirates","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","11618.53","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","225","United Arab Emirates","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","50296.08","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","225","United Arab Emirates","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","258315.55","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","225","United Arab Emirates","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","278117.36","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","225","United Arab Emirates","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","994023.13","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","229","United Kingdom","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","789076.96","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","229","United Kingdom","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","897211.5","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","229","United Kingdom","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","982699.83","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","229","United Kingdom","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","41941.35","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","229","United Kingdom","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","57573.48","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","229","United Kingdom","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","236151.77","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","215","United Republic of Tanzania","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","68730.38","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","215","United Republic of Tanzania","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","81414.94","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","215","United Republic of Tanzania","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","83869.56","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","215","United Republic of Tanzania","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","2008.05","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","215","United Republic of Tanzania","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","23144.74","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","215","United Republic of Tanzania","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","7353.58","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","231","United States of America","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","4007043.62","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","231","United States of America","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","3338621.62","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","231","United States of America","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","3963135.11","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","231","United States of America","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","1847901.37","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","231","United States of America","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1255457.07","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","231","United States of America","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","1435164.53","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","234","Uruguay","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","72427.8","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","234","Uruguay","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","133416.61","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","234","Uruguay","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","117585.82","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","234","Uruguay","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","3373.97","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","234","Uruguay","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","8276.54","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","234","Uruguay","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","2157.95","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","235","Uzbekistan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","553.14","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","235","Uzbekistan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","1134.08","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","235","Uzbekistan","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","490.87","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","235","Uzbekistan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","105159.6","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","235","Uzbekistan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","247361.66","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","235","Uzbekistan","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","121776.76","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","236","Venezuela (Bolivarian Republic of)","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","54480.29","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","236","Venezuela (Bolivarian Republic of)","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","27076.74","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","236","Venezuela (Bolivarian Republic of)","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","84198.37","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","236","Venezuela (Bolivarian Republic of)","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","476143.49","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","236","Venezuela (Bolivarian Republic of)","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","74791.53","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","236","Venezuela (Bolivarian Republic of)","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","203886.4","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","237","Viet Nam","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","740500.74","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","237","Viet Nam","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","809042.94","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","237","Viet Nam","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","808290.34","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","237","Viet Nam","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","27050.17","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","237","Viet Nam","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","75372.36","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","237","Viet Nam","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","151648.93","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","249","Yemen","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","4091.6","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","249","Yemen","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","11666.05","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","249","Yemen","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","928.74","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","249","Yemen","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","0.41","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","249","Yemen","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","0","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","249","Yemen","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","0.51","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","251","Zambia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","141030.72","A","Aggregate, may include official, semi-official, estimated or calculated data" -"RFN","Fertilizers by Nutrient","251","Zambia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","85930.51","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","251","Zambia","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","166359.71","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","251","Zambia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","897.15","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","251","Zambia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","3556.41","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","251","Zambia","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","870.01","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","181","Zimbabwe","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","23230.38","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","181","Zimbabwe","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","82841.93","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","181","Zimbabwe","5610","Import Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","47238.87","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","181","Zimbabwe","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2005","2005","tonnes","5423.18","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","181","Zimbabwe","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2010","2010","tonnes","591.95","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" -"RFN","Fertilizers by Nutrient","181","Zimbabwe","5910","Export Quantity","3102","Nutrient nitrogen N (total)","2015","2015","tonnes","2105.85","Qm","Official data from questionnaires and/or national sources and/or COMTRADE (reporters)" From dbad9b3f01127f2a1337bd77f3a9d85d5a6c7bcb Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Tue, 26 May 2020 21:45:31 +0200 Subject: [PATCH 009/774] Add material.data.read_data to read one excel input file --- message_ix_models/model/material/data.py | 93 +++++++++++++++++++++--- message_ix_models/model/material/doc.rst | 5 +- 2 files changed, 86 insertions(+), 12 deletions(-) diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index 5ea831735b..44570c5e7a 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -1,4 +1,6 @@ -import numpy as np +from collections import defaultdict +import logging + import pandas as pd from message_data.tools import ScenarioInfo, broadcast, make_df, same_node @@ -6,6 +8,9 @@ from .util import read_config +log = logging.getLogger(__name__) + + def gen_data(scenario, dry_run=False): """Generate data for materials representation of nitrogen fertilizers.""" # Load configuration @@ -14,8 +19,11 @@ def gen_data(scenario, dry_run=False): # Information about scenario, e.g. node, year s_info = ScenarioInfo(scenario) - # List of dataframes, concatenated together at end - output = [] + # Techno-economic assumptions + data = read_data() + + # List of data frames, to be concatenated together at end + results = defaultdict(list) # NH3 production processes common = dict( @@ -24,6 +32,9 @@ def gen_data(scenario, dry_run=False): level="material_interim", # TODO fill in remaining dimensions mode="all", + time="year", + time_dest="year", + time_origin="year", ) # Iterate over new technologies, using the configuration @@ -36,19 +47,79 @@ def gen_data(scenario, dry_run=False): .pipe(broadcast, node_loc=s_info.N) .pipe(same_node) ) - output.append(df) + results["output"].append(df) # Heat output - output.append( - df.assign( - commodity="d_heat", - level="secondary", - value=np.nan, # LoadParams.output_heat[t] - ) + + # Retrieve the scalar value from the data + row = data.loc[("output_heat", t.id), :] + log.info(f"Use {repr(row.Variable)} for heat output of {t}") + + # Store a modified data frame + results["output"].append( + df.assign(commodity="d_heat", level="secondary", value=row[2010]) ) - result = dict(output=pd.concat(output)) + # TODO add other variables from SetupNitrogenBase.py + + # Concatenate to one data frame per parameter + results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} # Temporary: return nothing, since the data frames are incomplete return dict() # return result + + +def read_data(): + """Read and clean data from :file:`n-fertilizer_techno-economic.xlsx`.""" + # Ensure config is loaded, get the context + context = read_config() + + # Shorter access to sets configuration + sets = context["material"]["set"] + + # Read the file + data = pd.read_excel( + context.get_path("material", "n-fertilizer_techno-economic.xlsx"), + sheet_name="Sheet1", + ) + + # Prepare contents for the "parameter" and "technology" columns + # FIXME put these in the file itself to avoid ambiguity/error + + # "Variable" column contains different values selected to match each of + # these parameters, per technology + params = [ + "inv_cost", + "fix_cost", + "var_cost", + "technical_lifetime", + "input_fuel", + "input_elec", + "input_water", + "output_NH3", + "output_water", + "output_heat", + "emissions", + "capacity_factor", + ] + + param_values = [] + tech_values = [] + for t in sets["technology"]["add"]: + param_values.extend(params) + tech_values.extend([t.id] * len(params)) + + # Clean the data + data = ( + # Insert "technology" and "parameter" columns + data.assign(technology=tech_values, parameter=param_values) + # Drop columns that don't contain useful information + .drop(["Model", "Scenario", "Region"], axis=1) + # Set the data frame index for selection + .set_index(["parameter", "technology"]) + ) + + # TODO convert units for some parameters, per LoadParams.py + + return data diff --git a/message_ix_models/model/material/doc.rst b/message_ix_models/model/material/doc.rst index 5a4f0e59ab..ccb432d5b4 100644 --- a/message_ix_models/model/material/doc.rst +++ b/message_ix_models/model/material/doc.rst @@ -14,7 +14,10 @@ Code reference .. currentmodule:: message_data.model.material .. automodule:: message_data.model.material - :members: build, get_spec, gen_data + :members: + +.. automodule:: message_data.model.material.data + :members: Data, metadata, and configuration From 264943e0cd0b332fbbedf7aa9766c7327a6ddbd7 Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Tue, 26 May 2020 22:10:57 +0200 Subject: [PATCH 010/774] Add basic cli for solving the materials model --- message_ix_models/model/material/__init__.py | 39 ++++++++++++++++++++ message_ix_models/model/material/doc.rst | 21 +++++++++++ 2 files changed, 60 insertions(+) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 1ace838c94..b288c64748 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -1,5 +1,7 @@ from typing import Mapping +import click + from message_data.model.build import apply_spec from message_data.tools import ScenarioInfo from .data import gen_data @@ -14,6 +16,8 @@ def build(scenario): # Apply to the base scenario apply_spec(scenario, spec, gen_data) + return scenario + def get_spec() -> Mapping[str, ScenarioInfo]: """Return the specification for materials accounting.""" @@ -32,3 +36,38 @@ def get_spec() -> Mapping[str, ScenarioInfo]: add.set[set_name].extend(config.get("add", [])) return dict(require=require, remove=ScenarioInfo(), add=add) + + +# Group to allow for multiple CLI subcommands under "material" +@click.group("material") +def cli(): + """Model with materials accounting.""" + + +@cli.command() +@click.pass_obj +def solve(context): + """Build and solve model. + + Use the --url option to specify the base scenario. + """ + # Determine the output scenario name based on the --url CLI option. If the + # user did not give a recognized value, this raises an error. + output_scenario_name = { + "baseline": "NoPolicy", + "NPi2020-con-prim-dir-ncr": "NPi", + "NPi2020_1000-con-prim-dir-ncr": "NPi2020_1000", + "NPi2020_400-con-prim-dir-ncr": "NPi2020_400", + }.get(context.scenario_info["scenario"]) + + if context.scenario_info["model"] != "CD_Links_SSP2": + print("WARNING: this code is not tested with this base scenario!") + + # Clone and set up + scenario = build( + context.get_scenario() + .clone(model="JM_GLB_NITRO", scenario=output_scenario_name) + ) + + # Solve + scenario.solve() diff --git a/message_ix_models/model/material/doc.rst b/message_ix_models/model/material/doc.rst index ccb432d5b4..c493bc5320 100644 --- a/message_ix_models/model/material/doc.rst +++ b/message_ix_models/model/material/doc.rst @@ -20,6 +20,27 @@ Code reference :members: +CLI usage +========= + +Use ``mix-data materials run``, giving the base scenario, e.g.:: + + $ mix-data \ + --url ixmp://ene-ixmp/CD_Links_SSP2/baseline + materials run + $ mix-data \ + --url ixmp://ene-ixmp/CD_Links_SSP2/NPi2020-con-prim-dir-ncr + materials run + $ mix-data \ + --url ixmp://ene-ixmp/CD_Links_SSP2/NPi2020_1000-con-prim-dir-ncr + materials run + $ mix-data \ + --url ixmp://ene-ixmp/CD_Links_SSP2/NPi2020_400-con-prim-dir-ncr + materials run + +The output scenario will be ``ixmp://JM_GLB_NITRO/{name}``. + + Data, metadata, and configuration ================================= From 331d859f1e9f64109fbc40a61c8d03234884bf5c Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Fri, 29 May 2020 15:21:56 +0200 Subject: [PATCH 011/774] Update comment and units for nh3 technology efficiency - during 2020-05-29 zoom call. --- message_ix_models/model/material/data.py | 8 ++++---- message_ix_models/model/material/doc.rst | 10 +++++----- 2 files changed, 9 insertions(+), 9 deletions(-) diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index 44570c5e7a..b91dfe29bb 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -39,11 +39,11 @@ def gen_data(scenario, dry_run=False): # Iterate over new technologies, using the configuration for t in config["technology"]["add"]: - # TODO describe what this is. In SetupNitrogenBase.py, the values are - # read for technology == "solar_i" but are not altered before - # being re-added to the scenario. + # Output of NH3: same efficiency for all technologies + # TODO the output commodity and level are different for + # t=NH3_to_N_fertil; use 'if' statements to fill in. df = ( - make_df("output", technology=t, **common) + make_df("output", technology=t, value=1, unit='t', **common) .pipe(broadcast, node_loc=s_info.N) .pipe(same_node) ) diff --git a/message_ix_models/model/material/doc.rst b/message_ix_models/model/material/doc.rst index c493bc5320..93d3e96274 100644 --- a/message_ix_models/model/material/doc.rst +++ b/message_ix_models/model/material/doc.rst @@ -27,18 +27,18 @@ Use ``mix-data materials run``, giving the base scenario, e.g.:: $ mix-data \ --url ixmp://ene-ixmp/CD_Links_SSP2/baseline - materials run + materials solve $ mix-data \ --url ixmp://ene-ixmp/CD_Links_SSP2/NPi2020-con-prim-dir-ncr - materials run + materials solve $ mix-data \ --url ixmp://ene-ixmp/CD_Links_SSP2/NPi2020_1000-con-prim-dir-ncr - materials run + materials solve $ mix-data \ --url ixmp://ene-ixmp/CD_Links_SSP2/NPi2020_400-con-prim-dir-ncr - materials run + materials solve -The output scenario will be ``ixmp://JM_GLB_NITRO/{name}``. +The output scenario will be ``ixmp://ene-ixmp/JM_GLB_NITRO/{name}``, where {name} is a shortened version of the input scenario name. Data, metadata, and configuration From b5c8752b72475708a076c85d64771b43ed000dad Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Wed, 5 Aug 2020 13:27:52 +0200 Subject: [PATCH 012/774] Document raw data files for model.material --- message_ix_models/model/material/doc.rst | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/message_ix_models/model/material/doc.rst b/message_ix_models/model/material/doc.rst index 93d3e96274..dbd6739b21 100644 --- a/message_ix_models/model/material/doc.rst +++ b/message_ix_models/model/material/doc.rst @@ -50,22 +50,23 @@ Binary/raw data files The code relies on the following input files, stored in :file:`data/material/`: :file:`Ammonia feedstock share.Global.xlsx` - TODO describe the contents, original source, and layout of this file. + Feedstock shares (gas/oil/coal) for NH3 production for MESSAGE R11 regions. :file:`CD-Links SSP2 N-fertilizer demand.Global.xlsx` - TODO describe the contents, original source, and layout of this file. + N-fertilizer demand time series from SSP2 scenarios (NPi2020_1000, NPi2020_400, NoPolicy) for MESSAGE R11 regions. :file:`N fertil trade - FAOSTAT_data_9-25-2019.csv` - TODO describe the contents, original source, and layout of this file. + Raw data from FAO used to generate the two :file:`trade.FAO.*.csv` files below. + Exported from `FAOSTAT `_. :file:`n-fertilizer_techno-economic.xlsx` - TODO describe the contents, original source, and layout of this file. + Techno-economic parameters from literature for various NH3 production technologies (used as direct inputs for MESSAGE parameters). :file:`trade.FAO.R11.csv` - TODO describe the contents, original source, and layout of this file. + Historical N-fertilizer trade records among R11 regions, extracted from FAO database. :file:`trade.FAO.R14.csv` - TODO describe the contents, original source, and layout of this file. + Historical N-fertilizer trade records among R14 regions, extracted from FAO database. :file:`material/config.yaml` From 6828b307781bbeb610d88f8645cfe4f5a621bafc Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Wed, 5 Aug 2020 13:35:13 +0200 Subject: [PATCH 013/774] Mark incomplete model.material methods --- message_ix_models/model/material/data.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index b91dfe29bb..faaf348c41 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -12,7 +12,11 @@ def gen_data(scenario, dry_run=False): - """Generate data for materials representation of nitrogen fertilizers.""" + """Generate data for materials representation of nitrogen fertilizers. + + .. note:: This code is only partially translated from + :file:`SetupNitrogenBase.py`. + """ # Load configuration config = read_config()["material"]["set"] From 931bec62516c70090ca370abb4c333915d2fba0e Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Fri, 18 Dec 2020 12:32:04 +0100 Subject: [PATCH 014/774] Re-save n-fertilizer_techno-economic.xlsx to remove phantom rows --- .../data/material/n-fertilizer_techno-economic.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/n-fertilizer_techno-economic.xlsx b/message_ix_models/data/material/n-fertilizer_techno-economic.xlsx index aba3a88553..a8c04c2a20 100644 --- a/message_ix_models/data/material/n-fertilizer_techno-economic.xlsx +++ b/message_ix_models/data/material/n-fertilizer_techno-economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:17854387ffaa729b16da78e0ca5a506f6b7f4d748735b0ad149038b6f329d468 -size 17369 +oid sha256:c5d5d1a84d75756b73784c7ef0ff02b8b1d49ed4a22dd201c06fa7264e481729 +size 12813 From 54769e559d6a203d0e815fcf052401b04a6fea07 Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Fri, 18 Dec 2020 12:03:08 +0100 Subject: [PATCH 015/774] Remove openpyxl from reqs (now required by ixmp); adjust material.data --- message_ix_models/model/material/data.py | 1 + 1 file changed, 1 insertion(+) diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index faaf348c41..609202e874 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -86,6 +86,7 @@ def read_data(): data = pd.read_excel( context.get_path("material", "n-fertilizer_techno-economic.xlsx"), sheet_name="Sheet1", + engine="openpyxl", ) # Prepare contents for the "parameter" and "technology" columns From 90e2d3e69ec14dc8cc3db13be59a369a6af33180 Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Mon, 22 Mar 2021 22:15:42 +0100 Subject: [PATCH 016/774] Adjust imports of migrated code --- message_ix_models/model/material/__init__.py | 9 +++++---- message_ix_models/model/material/util.py | 14 ++++++-------- 2 files changed, 11 insertions(+), 12 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index b288c64748..b7e44c5074 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -1,9 +1,9 @@ from typing import Mapping import click +from message_ix_models import ScenarioInfo +from message_ix_models.model.build import apply_spec -from message_data.model.build import apply_spec -from message_data.tools import ScenarioInfo from .data import gen_data from .util import read_config @@ -65,8 +65,9 @@ def solve(context): # Clone and set up scenario = build( - context.get_scenario() - .clone(model="JM_GLB_NITRO", scenario=output_scenario_name) + context.get_scenario().clone( + model="JM_GLB_NITRO", scenario=output_scenario_name + ) ) # Solve diff --git a/message_ix_models/model/material/util.py b/message_ix_models/model/material/util.py index 9fa3fa7a7f..7e7a516953 100644 --- a/message_ix_models/model/material/util.py +++ b/message_ix_models/model/material/util.py @@ -1,11 +1,12 @@ -from message_data.tools import as_codes, get_context +from message_ix_models import Context +from message_ix_models.util import as_codes, load_package_data -def read_config(): +def read_config(context=None): """Read configuration from material.yaml.""" # TODO this is similar to transport.utils.read_config; make a common # function so it doesn't need to be in this file. - context = get_context() + context = context or Context.get_instance(0) try: # Check if the configuration was already loaded @@ -17,11 +18,8 @@ def read_config(): # Already loaded return context - # Read material.yaml - context.load_config("material", "config") - - # Use a shorter name - context["material"] = context["material config"] + # Read material.yaml, store with a shorter name + context["material"] = load_package_data("material", "config") # Convert some values to Code objects for set_name, info in context["material"]["set"].items(): From 37ffbbaf269e79d3088f1e1dda14f53bbbe9219d Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Mon, 19 Jul 2021 21:59:07 +0200 Subject: [PATCH 017/774] Adjust migrated imports in .material.data and .transport.callback --- message_ix_models/model/material/data.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index 609202e874..0997d309e8 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -2,8 +2,9 @@ import logging import pandas as pd - -from message_data.tools import ScenarioInfo, broadcast, make_df, same_node +from message_ix import make_df +from message_ix_models import ScenarioInfo +from message_ix_models.util import broadcast, same_node from .util import read_config From d4db4b6f500b301e1e82c7993919527d32f4560a Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Wed, 25 Aug 2021 15:04:53 +0200 Subject: [PATCH 018/774] Link to #248 from .material docs --- message_ix_models/model/material/doc.rst | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/message_ix_models/model/material/doc.rst b/message_ix_models/model/material/doc.rst index dbd6739b21..1e5599a144 100644 --- a/message_ix_models/model/material/doc.rst +++ b/message_ix_models/model/material/doc.rst @@ -1,6 +1,14 @@ Materials accounting ******************** +.. warning:: + + :mod:`.material` is **under development**. + + For details, see the + `materials `_ label and + `current tracking issue (#248) `_. + This module adds (life-cycle) accounting of materials associated with technologies and demands in MESSAGEix-GLOBIOM. The initial implementation supports nitrogen-based fertilizers. From e63b90b2127b39a3750c8d59d19a4a62e16055bf Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Tue, 6 Sep 2022 17:18:58 +0200 Subject: [PATCH 019/774] Copy materials reporting files from `material-r12-rebase` full commit info: commit 4ae10cb560670143f717569a160e518f89b76fae (material-r12-rebase) author: paul natsuo kishimoto date: mon sep 5 13:36:28 2022 +0200 adjust key for message::default report --- .../model/material/report/__init__.py | 3176 +++++++++++++++++ .../model/material/report/tables.py | 2375 ++++++++++++ 2 files changed, 5551 insertions(+) create mode 100644 message_ix_models/model/material/report/__init__.py create mode 100644 message_ix_models/model/material/report/tables.py diff --git a/message_ix_models/model/material/report/__init__.py b/message_ix_models/model/material/report/__init__.py new file mode 100644 index 0000000000..ca94d26a19 --- /dev/null +++ b/message_ix_models/model/material/report/__init__.py @@ -0,0 +1,3176 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Mar 8 12:58:21 2021 +This code produces the follwoing outputs: +message_ix_reporting.xlsx: message_ix level reporting +check.xlsx: can be used for checking the filtered variables +New_Reporting_Model_Scenario.xlsx: Reporting including the material variables +Merged_Model_Scenario.xlsx: Includes all IAMC variables +Material_global_grpahs.pdf + +@author: unlu +""" + +# NOTE: Works with pyam-iamc version 0.9.0 +# Problems with the most recent versions: +# 0.11.0 --> Filtering with asterisk * does not work, dataframes are empty. +# Problems with emission and region filtering. +# https://github.com/IAMconsortium/pyam/issues/517 +# 0.10.0 --> Plotting gives the follwoing error: +# ValueError: Can not plot data that does not extend for the entire year range. +# There are various tech.s that only have data starting from 2025 or 2030. +# foil_imp AFR, frunace_h2_aluminum, h2_i... + +# PACKAGES + +from ixmp import Platform +from message_ix import Scenario +from message_ix.reporting import Reporter +from ixmp.reporting import configure +from message_ix_models import ScenarioInfo +from message_data.tools.post_processing.iamc_report_hackathon import report as reporting +from message_data.model.material.util import read_config + +import pandas as pd +import numpy as np +import pyam +import xlsxwriter +import os +import openpyxl + +import plotly.graph_objects as go +import matplotlib + +matplotlib.use("Agg") +from matplotlib import pyplot as plt + +from pyam.plotting import OUTSIDE_LEGEND +from matplotlib.backends.backend_pdf import PdfPages + + +def print_full(x): + pd.set_option("display.max_rows", len(x)) + print(x) + pd.reset_option("display.max_rows") + + +def change_names(s): + + """Change the sector names according to IMAC format.""" + + if s == "aluminum": + s = "Non-Ferrous Metals|Aluminium" + elif s == "steel": + s = "Steel" + elif s == "cement": + s = "Non-Metallic Minerals|Cement" + elif s == "petro": + s = "Chemicals|High Value Chemicals" + elif s == 'ammonia': + s = "Chemicals|Ammonia" + elif s == "BCA": + s = "BC" + elif s == "OCA": + s = "OC" + elif s == "CO2_industry": + s == "CO2" + else: + s == s + return s + + +def fix_excel(path_temp, path_new): + + """ + Fix the names of the regions or variables to be compatible + with IAMC format. This is done in the final reported excel file + (path_temp) and written to a new excel file (path_new). + + """ + # read Excel file and sheet by name + workbook = openpyxl.load_workbook(path_temp) + sheet = workbook["data"] + + new_workbook = openpyxl.Workbook() + new_sheet = new_workbook['Sheet'] + new_sheet.title = 'data' + new_sheet = new_workbook.active + + replacement = { + "CO2_industry": "CO2", + "R11_AFR|R11_AFR": "R11_AFR", + "R11_CPA|R11_CPA": "R11_CPA", + "R11_MEA|R11_MEA": "R11_MEA", + "R11_FSU|R11_FSU": "R11_FSU", + "R11_PAS|R11_PAS": "R11_PAS", + "R11_SAS|R11_SAS": "R11_SAS", + "R11_LAM|R11_LAM": "R11_LAM", + "R11_NAM|R11_NAM": "R11_NAM", + "R11_PAO|R11_PAO": "R11_PAO", + "R11_EEU|R11_EEU": "R11_EEU", + "R11_WEU|R11_WEU": "R11_WEU", + "R11_AFR": "R11_AFR", + "R11_CPA": "R11_CPA", + "R11_MEA": "R11_MEA", + "R11_FSU": "R11_FSU", + "R11_PAS": "R11_PAS", + "R11_SAS": "R11_SAS", + "R11_LAM": "R11_LAM", + "R11_NAM": "R11_NAM", + "R11_PAO": "R11_PAO", + "R11_EEU": "R11_EEU", + "R11_WEU": "R11_WEU", + "R12_AFR|R12_AFR": "R12_AFR", + "R12_RCPA|R12_RCPA": "R12_RCPA", + "R12_MEA|R12_MEA": "R12_MEA", + "R12_FSU|R12_FSU": "R12_FSU", + "R12_PAS|R12_PAS": "R12_PAS", + "R12_SAS|R12_SAS": "R12_SAS", + "R12_LAM|R12_LAM": "R12_LAM", + "R12_NAM|R12_NAM": "R12_NAM", + "R12_PAO|R12_PAO": "R12_PAO", + "R12_EEU|R12_EEU": "R12_EEU", + "R12_WEU|R12_WEU": "R12_WEU", + "R12_CHN|R12_CHN": "R12_CHN", + "R12_AFR": "R12_AFR", + "R12_RCPA": "R12_RCPA", + "R12_MEA": "R12_MEA", + "R12_FSU": "R12_FSU", + "R12_PAS": "R12_PAS", + "R12_SAS": "R12_SAS", + "R12_LAM": "R12_LAM", + "R12_NAM": "R12_NAM", + "R12_PAO": "R12_PAO", + "R12_EEU": "R12_EEU", + "R12_WEU": "R12_WEU", + "R12_CHN": "R12_CHN", + "World": "R12_GLB", + "model": "Model", + "scenario": "Scenario", + "variable": "Variable", + "region": "Region", + "unit": "Unit", + "BCA": "BC", + "OCA": "OC", + 'Final Energy|Non-Energy Use|Chemicals|Ammonia':'Final Energy|Industry|Chemicals|Ammonia', + 'Final Energy|Non-Energy Use|Chemicals|Ammonia|Gases': 'Final Energy|Industry|Chemicals|Ammonia|Gases', + 'Final Energy|Non-Energy Use|Chemicals|Ammonia|Liquids': 'Final Energy|Industry|Chemicals|Ammonia|Liquids', + 'Final Energy|Non-Energy Use|Chemicals|Ammonia|Liquids|Oil': 'Final Energy|Industry|Chemicals|Ammonia|Liquids|Oil', + 'Final Energy|Non-Energy Use|Chemicals|Ammonia|Solids': 'Final Energy|Industry|Chemicals|Ammonia|Solids' + } + # Iterate over the rows and replace + for i in range(1, ((sheet.max_row) + 1)): + data = [sheet.cell(row = i, column = col).value for col in range(1, ((sheet.max_column) + 1))] + for index, value in enumerate(data): + col_no = index + 1 + if value in replacement.keys(): + new_sheet.cell(row=i, column= col_no).value = replacement.get(value) + else: + new_sheet.cell(row=i, column= col_no).value = value + + new_workbook.save(path_new) + + +def report(context, scenario): + """Produces the material related variables.""" + + # Obtain scenario information and directory + + s_info = ScenarioInfo(scenario) + + # In order to avoid confusion in the second reporting stage there should + # no existing timeseries uploaded in the scenairo. Clear these except the + # residential and commercial ones since they should be always included. + + # Activate this part to keep the residential and commercial variables + # when the model is run with the buildigns linkage. + # df_rem = df_rem[~(df_rem["variable"].str.contains("Residential") | \ + # df_rem["variable"].str.contains("Commercial"))] + + # Temporary to add the transport variables + # transport_path = os.path.join('C:\\', 'Users','unlu','Documents','MyDocuments_IIASA','Material_Flow', + # 'jupyter_notebooks', '2022-06-02_0.xlsx') + # df_transport = pd.read_excel(transport_path) + # scenario.check_out(timeseries_only=True) + # scenario.add_timeseries(df_transport) + # scenario.commit('Added transport timeseries') + + years = s_info.Y + nodes = [] + for n in s_info.N: + n = n + "*" + nodes.append(n) + + if "R11_GLB*" in nodes: + nodes.remove("R11_GLB*") + elif "R12_GLB*" in nodes: + nodes.remove("R12_GLB*") + + # Path for materials reporting output + directory = context.get_local_path("report", "materials") + directory.mkdir(exist_ok=True) + + # Generate message_ix level reporting and dump to an excel file. + + rep = Reporter.from_scenario(scenario) + configure(units={"replace": {"-": ""}}) + df = rep.get("message::default") + name = os.path.join(directory, "message_ix_reporting.xlsx") + df.to_excel(name) + print("message_ix level reporting generated") + + # Obtain a pyam dataframe / filter / global aggregation + + path = os.path.join(directory, "message_ix_reporting.xlsx") + report = pd.read_excel(path) + report.Unit.fillna("", inplace=True) + df = pyam.IamDataFrame(report) + df.filter(region=nodes, year=years, inplace=True) + df.filter( + variable=[ + "out|new_scrap|aluminum|*", + "out|final_material|aluminum|prebake_aluminum|M1", + "out|final_material|aluminum|secondary_aluminum|M1", + "out|final_material|aluminum|soderberg_aluminum|M1", + "out|new_scrap|steel|*", + "in|new_scrap|steel|*", + "out|final_material|steel|*", + "out|useful_material|steel|import_steel|*", + "out|secondary_material|NH3|*", + "in|useful_material|steel|export_steel|*", + "out|useful_material|aluminum|import_aluminum|*", + "in|useful_material|aluminum|export_aluminum|*", + "out|final_material|*|import_petro|*", + "in|final_material|*|export_petro|*", + "out|secondary|lightoil|loil_imp|*", + "out|secondary|fueloil|foil_imp|*", + "out|tertiary_material|clinker_cement|*", + "out|demand|cement|*", + "out|final_material|BTX|*", + "out|final_material|ethylene|*", + "out|final_material|propylene|*", + "out|secondary|fueloil|agg_ref|*", + "out|secondary|lightoil|agg_ref|*", + 'out|useful|i_therm|solar_i|M1', + "in|final|*", + "in|secondary|coal|coal_NH3|M1", + "in|secondary|electr|NH3_to_N_fertil|M1", + 'in|secondary|electr|coal_NH3|M1', + 'in|secondary|electr|electr_NH3|M1', + 'in|secondary|electr|gas_NH3|M1', + 'in|secondary|fueloil|fueloil_NH3|M1', + 'in|secondary|gas|gas_NH3|M1', + "in|secondary|coal|coal_NH3_ccs|M1", + 'in|secondary|electr|coal_NH3_ccs|M1', + 'in|secondary|electr|gas_NH3_ccs|M1', + 'in|secondary|fueloil|fueloil_NH3_ccs|M1', + 'in|secondary|gas|gas_NH3_ccs|M1', + "in|primary|biomass|biomass_NH3|M1", + "in|seconday|electr|biomass_NH3|M1", + "in|primary|biomass|biomass_NH3_ccs|M1", + "in|secondary|electr|biomass_NH3_ccs|M1", + "in|desulfurized|*|steam_cracker_petro|*", + "in|secondary_material|*|steam_cracker_petro|*", + "in|product|aluminum|scrap_recovery|M1", + "in|dummy_end_of_life|aluminum|scrap_recovery_aluminum|M1", + "in|dummy_end_of_life|steel|scrap_recovery_steel|M1", + "out|dummy_end_of_life|aluminum|total_EOL_aluminum|M1", + "out|dummy_end_of_life|steel|total_EOL_steel|M1", + "out|dummy_end_of_life|cement|total_EOL_cement|M1", + "in|product|steel|scrap_recovery_steel|M1", + "out|end_of_life|*", + "emis|CO2_industry|*", + "emis|CF4|*", + "emis|SO2|*", + "emis|NOx|*", + "emis|CH4|*", + "emis|N2O|*", + "emis|BCA|*", + "emis|CO|*", + "emis|NH3|*", + "emis|NOx|*", + "emis|OCA|*", + "emis|CO2|*", + ], + inplace=True, + ) + variables = df.variable + df.aggregate_region(variables, region="World", method=sum, append=True) + name = os.path.join(directory, "check.xlsx") + df.to_excel(name) + print("Necessary variables are filtered") + + # Obtain the model and scenario name + model_name = df.model[0] + scenario_name = df.scenario[0] + + # Create an empty pyam dataframe to store the new variables + + workbook = xlsxwriter.Workbook("empty_template.xlsx") + worksheet = workbook.add_worksheet() + worksheet.write("A1", "Model") + worksheet.write("B1", "Scenario") + worksheet.write("C1", "Region") + worksheet.write("D1", "Variable") + worksheet.write("E1", "Unit") + columns = [ + "F1", + "G1", + "H1", + "I1", + "J1", + "K1", + "L1", + "M1", + "N1", + "O1", + "P1", + "Q1", + "R1", + ] + + for yr, col in zip(years, columns): + worksheet.write(col, yr) + workbook.close() + + df_final = pyam.IamDataFrame("empty_template.xlsx") + print("Empty template for new variables created") + + # Create a pdf file with figures + path = os.path.join(directory, "Material_global_graphs.pdf") + pp = PdfPages(path) + # pp = PdfPages("Material_global_graphs.pdf") + + # Reporting and Plotting + + print("Production plots and variables are being generated") + for r in nodes: + ## PRODUCTION - PLOTS + ## Needs to be checked again to see whether the graphs are correct + + fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(20, 10)) + fig.tight_layout(pad=10.0) + + # ALUMINUM + df_al = df.copy() + df_al.filter(region=r, year=years, inplace=True) + df_al.filter(variable=["out|*|aluminum|*", "in|*|aluminum|*"], inplace=True) + df_al.convert_unit('', to='Mt/yr', factor=1, inplace = True) + df_al_graph = df_al.copy() + + df_al_graph.filter( + variable=[ + "out|useful_material|aluminum|import_aluminum|*", + "out|final_material|aluminum|prebake_aluminum|*", + "out|final_material|aluminum|soderberg_aluminum|*", + "out|new_scrap|aluminum|*", + ], + inplace=True, + ) + + if r == "World": + df_al_graph.filter( + variable=[ + "out|final_material|aluminum|prebake_aluminum|*", + "out|final_material|aluminum|soderberg_aluminum|*", + "out|new_scrap|aluminum|*", + ], + inplace=True, + ) + + df_al_graph.plot.stack(ax=ax1) + ax1.legend( + [ + "Prebake", + "Soderberg", + "Newscrap", + "Oldscrap_min", + "Oldscrap_av", + "Oldscrap_max", + "import", + ], + bbox_to_anchor=(-0.4, 1), + loc="upper left", + ) + ax1.set_title("Aluminium Production_" + r) + ax1.set_xlabel("Year") + ax1.set_ylabel("Mt") + + # STEEL + + df_steel = df.copy() + df_steel.filter(region=r, year=years, inplace=True) + df_steel.filter(variable=["out|*|steel|*", "in|*|steel|*"], inplace=True) + df_steel.convert_unit('', to='Mt/yr', factor=1, inplace = True) + + df_steel_graph = df_steel.copy() + df_steel_graph.filter( + variable=[ + "out|final_material|steel|*", + "out|useful_material|steel|import_steel|*", + ], + inplace=True, + ) + + if r == "World": + df_steel.filter(variable=["out|*|steel|*", "in|*|steel|*"], inplace=True) + df_steel_graph.filter( + variable=["out|final_material|steel|*",], inplace=True, + ) + + df_steel_graph.plot.stack(ax=ax2) + ax2.legend( + ["Bof steel", "Eaf steel M1", "Eaf steel M2", "Import"], + bbox_to_anchor=(-0.4, 1), + loc="upper left", + ) + ax2.set_title("Steel Production_" + r) + ax2.set_ylabel("Mt") + + # PETRO + + df_petro = df.copy() + df_petro.filter(region=r, year=years, inplace=True) + df_petro.filter( + variable=[ + "in|final|ethanol|ethanol_to_ethylene_petro|M1", + "in|desulfurized|*|steam_cracker_petro|*", + "in|secondary_material|*|steam_cracker_petro|*", + ], + inplace=True, + ) + df_petro.convert_unit('', to='Mt/yr', factor=1, inplace = True) + + if r == "World": + + df_petro.filter( + variable=[ + "in|final|ethanol|ethanol_to_ethylene_petro|M1", + "in|desulfurized|*|steam_cracker_petro|*", + "in|secondary_material|*|steam_cracker_petro|*", + ], + inplace=True, + ) + + df_petro.plot.stack(ax=ax3) + ax3.legend( + [ + "atm_gasoil", + "naphtha", + "vacuum_gasoil", + "bioethanol", + "ethane", + "propane", + ], + bbox_to_anchor=(-0.4, 1), + loc="upper left", + ) + ax3.set_title("HVC feedstock" + r) + ax3.set_xlabel("Years") + ax3.set_ylabel("GWa") + + plt.close() + pp.savefig(fig) + + # PRODUCTION - IAMC Variables + + # ALUMINUM + + # Primary Production + primary_al_vars = [ + "out|final_material|aluminum|prebake_aluminum|M1", + "out|final_material|aluminum|soderberg_aluminum|M1", + ] + + # Secondary Production + secondary_al_vars = ["out|final_material|aluminum|secondary_aluminum|M1"] + + # Collected Scrap + collected_scrap_al_vars = [ + "out|new_scrap|aluminum|manuf_aluminum|M1", + "in|dummy_end_of_life|aluminum|scrap_recovery_aluminum|M1", + ] + + # Total Available Scrap: + # New scrap + The end of life products (exegenous assumption) + # + from power and buildings sector + + total_scrap_al_vars = [ + "out|new_scrap|aluminum|manuf_aluminum|M1", + "out|dummy_end_of_life|aluminum|total_EOL_aluminum|M1", + ] + + new_scrap_al_vars = ["out|new_scrap|aluminum|manuf_aluminum|M1"] + old_scrap_al_vars = ["out|dummy_end_of_life|aluminum|total_EOL_aluminum|M1"] + + df_al.aggregate( + "Production|Primary|Non-Ferrous Metals|Aluminium", + components=primary_al_vars, + append=True, + ) + df_al.aggregate( + "Production|Secondary|Non-Ferrous Metals|Aluminium", + components=secondary_al_vars, + append=True, + ) + + df_al.aggregate( + "Production|Non-Ferrous Metals|Aluminium", + components=secondary_al_vars + primary_al_vars, + append=True, + ) + + df_al.aggregate( + "Collected Scrap|Non-Ferrous Metals|Aluminium", + components=collected_scrap_al_vars, + append=True, + ) + + df_al.aggregate( + "Collected Scrap|Non-Ferrous Metals", + components=collected_scrap_al_vars, + append=True, + ) + + df_al.aggregate( + "Total Scrap|Non-Ferrous Metals|Aluminium", + components=total_scrap_al_vars, + append=True, + ) + + df_al.aggregate( + "Total Scrap|Non-Ferrous Metals", + components=total_scrap_al_vars, + append=True, + ) + + df_al.aggregate( + "Total Scrap|Non-Ferrous Metals|Aluminium|New Scrap", + components=new_scrap_al_vars, + append=True, + ) + + df_al.aggregate( + "Total Scrap|Non-Ferrous Metals|Aluminium|Old Scrap", + components=old_scrap_al_vars, + append=True, + ) + + df_al.aggregate( + "Production|Non-Ferrous Metals", + components=primary_al_vars + secondary_al_vars, + append=True, + ) + + df_al.filter( + variable=[ + "Production|Primary|Non-Ferrous Metals|Aluminium", + "Production|Secondary|Non-Ferrous Metals|Aluminium", + "Production|Non-Ferrous Metals|Aluminium", + "Production|Non-Ferrous Metals", + "Collected Scrap|Non-Ferrous Metals|Aluminium", + "Collected Scrap|Non-Ferrous Metals", + "Total Scrap|Non-Ferrous Metals", + "Total Scrap|Non-Ferrous Metals|Aluminium", + "Total Scrap|Non-Ferrous Metals|Aluminium|New Scrap", + "Total Scrap|Non-Ferrous Metals|Aluminium|Old Scrap", + ], + inplace=True, + ) + + # STEEL + + # bof_steel also uses a certain share of scrap but this is neglegcted in + # the reporting. + + # eaf_steel has three modes M1,M2,M3: Only M2 has scrap input. + + primary_steel_vars = ["out|final_material|steel|bof_steel|M1", + "out|final_material|steel|eaf_steel|M1", + "out|final_material|steel|eaf_steel|M3" + ] + + secondary_steel_vars = [ + "out|final_material|steel|eaf_steel|M2", + "in|new_scrap|steel|bof_steel|M1" + ] + + collected_scrap_steel_vars = [ + "in|dummy_end_of_life|steel|scrap_recovery_steel|M1" + ] + total_scrap_steel_vars = ["out|dummy_end_of_life|steel|total_EOL_steel|M1"] + + new_scrap_steel_vars = ["out|new_scrap|steel|manuf_steel|M1"] + old_scrap_steel_vars = ["out|dummy_end_of_life|steel|total_EOL_steel|M1"] + + df_steel.aggregate( + "Production|Primary|Steel (before sub.)", components=primary_steel_vars, append=True, + ) + + df_steel.subtract("Production|Primary|Steel (before sub.)", + "in|new_scrap|steel|bof_steel|M1","Production|Primary|Steel", append = True) + + df_steel.aggregate( + "Production|Secondary|Steel", components=secondary_steel_vars, append=True, + ) + + df_steel.aggregate( + "Production|Steel", + components=["Production|Primary|Steel","Production|Secondary|Steel"], + append=True, + ) + + df_steel.aggregate( + "Collected Scrap|Steel", components=collected_scrap_steel_vars, append=True, + ) + df_steel.aggregate( + "Total Scrap|Steel", components=total_scrap_steel_vars, append=True + ) + + df_steel.aggregate( + "Total Scrap|Steel|Old Scrap", components=old_scrap_steel_vars, append=True + ) + + df_steel.aggregate( + "Total Scrap|Steel|New Scrap", components=new_scrap_steel_vars, append=True + ) + + df_steel.filter( + variable=[ + "Production|Primary|Steel", + "Production|Secondary|Steel", + "Production|Steel", + "Collected Scrap|Steel", + "Total Scrap|Steel", + "Total Scrap|Steel|Old Scrap", + "Total Scrap|Steel|New Scrap", + ], + inplace=True, + ) + + # CHEMICALS + + df_chemicals = df.copy() + df_chemicals.filter(region=r, year=years, inplace=True) + df_chemicals.filter(variable=['out|secondary_material|NH3|*', + "out|final_material|ethylene|*", + "out|final_material|propylene|*", + "out|final_material|BTX|*"],inplace=True) + df_chemicals.convert_unit('', to='Mt/yr', factor=1, inplace = True) + + + # AMMONIA + + primary_ammonia_vars = [ + "out|secondary_material|NH3|gas_NH3|M1", + "out|secondary_material|NH3|coal_NH3|M1", + "out|secondary_material|NH3|biomass_NH3|M1", + "out|secondary_material|NH3|fueloil_NH3|M1", + "out|secondary_material|NH3|electr_NH3|M1" + ] + + df_chemicals.aggregate( + "Production|Primary|Chemicals|Ammonia", + components=primary_ammonia_vars, + append=True, + ) + + df_chemicals.aggregate( + "Production|Chemicals|Ammonia", + components=primary_ammonia_vars, + append=True, + ) + + # High Value Chemicals + + intermediate_petro_vars = [ + "out|final_material|ethylene|ethanol_to_ethylene_petro|M1", + "out|final_material|ethylene|steam_cracker_petro|atm_gasoil", + "out|final_material|ethylene|steam_cracker_petro|naphtha", + "out|final_material|ethylene|steam_cracker_petro|vacuum_gasoil", + "out|final_material|ethylene|steam_cracker_petro|ethane", + "out|final_material|ethylene|steam_cracker_petro|propane", + "out|final_material|propylene|steam_cracker_petro|atm_gasoil", + "out|final_material|propylene|steam_cracker_petro|naphtha", + "out|final_material|propylene|steam_cracker_petro|vacuum_gasoil", + "out|final_material|propylene|steam_cracker_petro|propane", + "out|final_material|BTX|steam_cracker_petro|atm_gasoil", + "out|final_material|BTX|steam_cracker_petro|naphtha", + "out|final_material|BTX|steam_cracker_petro|vacuum_gasoil", + ] + + df_chemicals.aggregate( + "Production|Primary|Chemicals|High Value Chemicals", + components=intermediate_petro_vars, + append=True, + ) + + df_chemicals.aggregate( + "Production|Chemicals|High Value Chemicals", + components=intermediate_petro_vars, + append=True, + ) + + # Totals + + chemicals_vars = intermediate_petro_vars + primary_ammonia_vars + df_chemicals.aggregate( + "Production|Primary|Chemicals", + components=chemicals_vars, + append=True, + ) + + df_chemicals.aggregate( + "Production|Chemicals", + components=chemicals_vars, + append=True, + ) + + df_chemicals.filter( + variable=[ + "Production|Primary|Chemicals|High Value Chemicals", + "Production|Chemicals|High Value Chemicals", + "Production|Primary|Chemicals", + "Production|Chemicals", + "Production|Primary|Chemicals|Ammonia", + "Production|Chemicals|Ammonia" + + ], + inplace=True, + ) + + + # Add to final data_frame + df_final.append(df_al, inplace=True) + df_final.append(df_steel, inplace=True) + df_final.append(df_chemicals, inplace=True) + # CEMENT + + for r in nodes: + + # PRODUCTION - PLOT + + fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 10)) + fig.tight_layout(pad=15.0) + + # Clinker cement + + df_cement_clinker = df.copy() + df_cement_clinker.filter(region=r, year=years, inplace=True) + df_cement_clinker.filter( + variable=["out|tertiary_material|clinker_cement|*"], inplace=True + ) + df_cement_clinker.plot.stack(ax=ax1) + ax1.legend( + ["Dry Clinker", "Wet Clinker"], bbox_to_anchor=(-0.5, 1), loc="upper left" + ) + ax1.set_title("Clinker Cement Production_" + r) + ax1.set_xlabel("Year") + ax1.set_ylabel("Mt") + + # Final prodcut cement + + df_cement = df.copy() + df_cement.filter(region=r, year=years, inplace=True) + df_cement.filter(variable=["out|demand|cement|*"], inplace=True) + df_cement.plot.stack(ax=ax2) + ax2.legend( + ["Ballmill Grinding", "Vertical Mill Grinding"], + bbox_to_anchor=(-0.6, 1), + loc="upper left", + ) + ax2.set_title("Final Cement Production_" + r) + ax2.set_xlabel("Year") + ax2.set_ylabel("Mt") + + plt.close() + pp.savefig(fig) + + # PRODUCTION - IAMC format + + primary_cement_vars = [ + "out|demand|cement|grinding_ballmill_cement|M1", + "out|demand|cement|grinding_vertmill_cement|M1", + ] + + total_scrap_cement_vars = ["out|dummy_end_of_life|cement|total_EOL_cement|M1"] + + df_cement.convert_unit('', to='Mt/yr', factor=1, inplace = True) + df_cement.aggregate( + "Production|Primary|Cement", components=primary_cement_vars, append=True, + ) + + df_cement.aggregate( + "Production|Non-Metallic Minerals", + components=primary_cement_vars, + append=True, + ) + + df_cement.aggregate( + "Production|Cement", components=primary_cement_vars, append=True, + ) + + df_cement.aggregate( + "Total Scrap|Non-Metallic Minerals", + components=total_scrap_cement_vars, + append=True, + ) + + df_cement.aggregate( + "Total Scrap|Non-Metallic Minerals|Cement", + components=total_scrap_cement_vars, + append=True, + ) + df_cement.filter( + variable=[ + "Production|Primary|Cement", + "Production|Cement", + "Total Scrap|Non-Metallic Minerals" + "Total Scrap|Non-Metallic Minerals|Cement", + ], + inplace=True, + ) + df_final.append(df_cement, inplace=True) + + # FINAL ENERGY BY FUELS (Only Non-Energy Use) + # Only inclides High Value Chemicals + # Ammonia is not seperately included as the model input valus are combined for + # feedstock and energy. + + + print("Final Energy by fuels only non-energy use is being printed.") + commodities = ["gas", "liquids", "solids", "all"] + + for c in commodities: + for r in nodes: + df_final_energy = df.copy() + + # GWa to EJ/yr + df_final_energy.convert_unit("", to="GWa", factor=1, inplace=True) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True) + df_final_energy.filter(region=r, year=years, inplace=True) + df_final_energy.filter( + variable=["in|final|*|cokeoven_steel|*"], keep=False, inplace=True + ) + df_final_energy.filter( + variable=[ + "in|final|atm_gasoil|*", + "in|final|vacuum_gasoil|*", + "in|final|naphtha|*", + "in|final|*|coal_fs|*", + "in|final|*|methanol_fs|*", + "in|final|*|ethanol_fs|*", + "in|final|ethanol|ethanol_to_ethylene_petro|*", + "in|final|*|foil_fs|*", + "in|final|gas|gas_processing_petro|*", + "in|final|*|loil_fs|*", + "in|final|*|gas_fs|*", + ], + inplace=True, + ) + + if c == "gas": + df_final_energy.filter(variable=["in|final|gas|*", + 'in|secondary|gas|gas_NH3|M1', + 'in|secondary|gas|gas_NH3_ccs|M1'], + inplace=True) + # Do not include gasoil and naphtha feedstock + if c == "liquids": + df_final_energy.filter( + variable=[ + "in|final|ethanol|ethanol_to_ethylene_petro|*", + "in|final|*|foil_fs|*", + "in|final|*|loil_fs|*", + "in|final|*|methanol_fs|*", + "in|final|*|ethanol_fs|*", + "in|final|atm_gasoil|*", + "in|final|vacuum_gasoil|*", + "in|final|naphtha|*", + 'in|secondary|fueloil|fueloil_NH3|M1', + 'in|secondary|fueloil|fueloil_NH3_ccs|M1', + ], + inplace=True, + ) + if c == "solids": + df_final_energy.filter( + variable=["in|final|biomass|*", "in|final|coal|*", + "in|secondary|coal|coal_NH3|M1", + "in|secondary|coal|coal_NH3_ccs|M1", + 'in|primary|biomass|biomass_NH3_ccs|M1', + 'in|primary|biomass|biomass_NH3|M1', + ],inplace=True) + + all_flows = df_final_energy.timeseries().reset_index() + splitted_vars = [v.split("|") for v in all_flows.variable] + aux1_df = pd.DataFrame( + splitted_vars, + columns=["flow_type", "level", "commodity", "technology", "mode"], + ) + aux2_df = pd.concat( + [all_flows.reset_index(drop=True), aux1_df.reset_index(drop=True)], + axis=1, + ) + + # Include only the related industry sector variables + # Aluminum, cement and steel do not have feedstock use + + var_sectors = [v for v in aux2_df["variable"].values] + + aux2_df = aux2_df[aux2_df["variable"].isin(var_sectors)] + + df_final_energy.filter(variable=var_sectors, inplace=True) + + # Aggregate + + if c == "all": + df_final_energy.aggregate( + "Final Energy|Non-Energy Use", components=var_sectors, append=True, + ) + df_final_energy.filter( + variable=["Final Energy|Non-Energy Use"], inplace=True + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + if c == "gas": + + # Can not distinguish by type Gases (natural gas, biomass, synthetic fossil, efuel) + # (coal_gas), from biomass (gas_bio), natural gas (gas_bal): All go into secondary level + # Can not be distinguished in the final level. + + df_final_energy.aggregate( + "Final Energy|Non-Energy Use|Gases", + components=var_sectors, + append=True, + ) + + df_final_energy.filter( + variable=["Final Energy|Non-Energy Use|Gases"], inplace=True, + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + + if c == "liquids": + + # All liquids + df_final_energy.aggregate( + "Final Energy|Non-Energy Use|Liquids", + components=var_sectors, + append=True, + ) + + # Only bios + + filter_vars = [ + v for v in aux2_df["variable"].values if (("ethanol" in v) + & ("methanol" not in v)) + ] + df_final_energy.aggregate( + "Final Energy|Non-Energy Use|Liquids|Biomass", + components=filter_vars, + append=True, + ) + # Fossils + + filter_vars = [ + v + for v in aux2_df["variable"].values + if ( + ("lightoil" in v) + | ("fueloil" in v) + | ("atm_gasoil" in v) + | ("vacuum_gasoil" in v) + | ("naphtha" in v) + ) + ] + + df_final_energy.aggregate( + "Final Energy|Non-Energy Use|Liquids|Oil", + components=filter_vars, + append=True, + ) + + # Other + + filter_vars = [ + v + for v in aux2_df["variable"].values + if ( + ("methanol" in v) + ) + ] + + df_final_energy.aggregate( + "Final Energy|Non-Energy Use|Liquids|Coal", + components=filter_vars, + append=True, + ) + + + df_final_energy.filter( + variable=[ + "Final Energy|Non-Energy Use|Liquids", + "Final Energy|Non-Energy Use|Liquids|Oil", + "Final Energy|Non-Energy Use|Liquids|Biomass", + "Final Energy|Non-Energy Use|Liquids|Coal", + ], + inplace=True, + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + if c == "solids": + + # All + df_final_energy.aggregate( + "Final Energy|Non-Energy Use|Solids", + components=var_sectors, + append=True, + ) + # Bio + filter_vars = [ + v for v in aux2_df["variable"].values if ("biomass" in v) + ] + df_final_energy.aggregate( + "Final Energy|Non-Energy Use|Solids|Biomass", + components=filter_vars, + append=True, + ) + + # Fossil + filter_vars = [v for v in aux2_df["variable"].values if ("coal" in v)] + df_final_energy.aggregate( + "Final Energy|Non-Energy Use|Solids|Coal", + components=filter_vars, + append=True, + ) + df_final_energy.filter( + variable=[ + "Final Energy|Non-Energy Use|Solids", + "Final Energy|Non-Energy Use|Solids|Biomass", + "Final Energy|Non-Energy Use|Solids|Coal", + ], + inplace=True, + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + + # FINAL ENERGY BY FUELS (Excluding Non-Energy Use) + # For ammonia only electricity use is included since only this has seperate + # input values in the model. + + print("Final Energy by fuels excluding non-energy use is being printed.") + commodities = ["electr", "gas", "hydrogen", "liquids", "solids", "heat", 'other',"all"] + for c in commodities: + + for r in nodes: + + df_final_energy = df.copy() + df_final_energy.convert_unit("", to="GWa", factor=1, inplace=True) + df_final_energy.filter(region=r, year=years, inplace=True) + df_final_energy.filter( + variable=["in|final|*|cokeoven_steel|*", + "in|final|co_gas|*", + "in|final|bf_gas|*"], keep=False, inplace=True + ) + + if c == "electr": + df_final_energy.filter(variable=["in|final|electr|*", + 'in|secondary|electr|NH3_to_N_fertil|M1', + 'in|secondary|electr|coal_NH3|M1', + 'in|secondary|electr|electr_NH3|M1', + 'in|secondary|electr|fueloil_NH3|M1', + 'in|secondary|electr|gas_NH3|M1', + 'in|secondary|electr|coal_NH3_ccs|M1', + 'in|secondary|electr|fueloil_NH3_ccs|M1', + 'in|secondary|electr|gas_NH3_ccs|M1', + 'in|secondary|electr|biomass_NH3_ccs|M1', + 'in|secondary|electr|biomass_NH3|M1', + ], inplace=True) + if c == "gas": + df_final_energy.filter(variable=["in|final|gas|*"], inplace=True) + + # Do not include gasoil and naphtha feedstock + if c == "liquids": + df_final_energy.filter( + variable=[ + "in|final|ethanol|*", + "in|final|fueloil|*", + "in|final|lightoil|*", + "in|final|methanol|*", + ], + inplace=True, + ) + if c == "solids": + df_final_energy.filter( + variable=[ + "in|final|biomass|*", + "in|final|coal|*", + "in|final|coke_iron|*", + ], + inplace=True, + ) + + if c == "hydrogen": + df_final_energy.filter(variable=["in|final|hydrogen|*"], inplace=True) + if c == "heat": + df_final_energy.filter(variable=["in|final|d_heat|*"], inplace=True) + if c == 'other': + df_final_energy.filter(variable=["out|useful|i_therm|*"], inplace=True) + if c == "all": + df_final_energy.filter(variable=["in|final|*", + 'in|secondary|electr|NH3_to_N_fertil|M1', + 'in|secondary|electr|coal_NH3|M1', + 'in|secondary|electr|electr_NH3|M1', + 'in|secondary|electr|fueloil_NH3|M1', + 'in|secondary|electr|gas_NH3|M1', + 'in|secondary|electr|coal_NH3_ccs|M1', + 'in|secondary|electr|fueloil_NH3_ccs|M1', + 'in|secondary|electr|gas_NH3_ccs|M1', + 'in|secondary|electr|biomass_NH3_ccs|M1', + 'in|secondary|electr|biomass_NH3|M1', + 'out|useful|i_therm|solar_i|M1' + ], inplace=True) + + all_flows = df_final_energy.timeseries().reset_index() + splitted_vars = [v.split("|") for v in all_flows.variable] + aux1_df = pd.DataFrame( + splitted_vars, + columns=["flow_type", "level", "commodity", "technology", "mode"], + ) + aux2_df = pd.concat( + [all_flows.reset_index(drop=True), aux1_df.reset_index(drop=True)], + axis=1, + ) + + # Include only the related industry sector variables + + var_sectors = [ + v + for v in aux2_df["variable"].values + if ( + ( + ("cement" in v) + | ("steel" in v) + | ("aluminum" in v) + | ("petro" in v) + | ("_i" in v) + | ("_I" in v) + | ("NH3" in v) + ) + & ( + ("eth_ic_trp" not in v) + & ("meth_ic_trp" not in v) + & ("ethanol_to_ethylene_petro" not in v) + & ("gas_processing_petro" not in v) + & ( + "in|final|atm_gasoil|steam_cracker_petro|atm_gasoil" + not in v + ) + & ( + "in|final|vacuum_gasoil|steam_cracker_petro|vacuum_gasoil" + not in v + ) + & ("in|final|naphtha|steam_cracker_petro|naphtha" not in v) + ) + ) + ] + + aux2_df = aux2_df[aux2_df["variable"].isin(var_sectors)] + + df_final_energy.filter(variable=var_sectors, inplace=True) + + # Aggregate + + if c == "all": + df_final_energy.aggregate( + "Final Energy|Industry excl Non-Energy Use", + components=var_sectors, + append=True, + ) + df_final_energy.filter( + variable=["Final Energy|Industry excl Non-Energy Use"], inplace=True + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + if c == "electr": + + df_final_energy.aggregate( + "Final Energy|Industry excl Non-Energy Use|Electricity", + components=var_sectors, + append=True, + ) + df_final_energy.filter( + variable=["Final Energy|Industry excl Non-Energy Use|Electricity"], + inplace=True, + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + if c == "gas": + + # Can not distinguish by type Gases (natural gas, biomass, synthetic fossil, efuel) + # (coal_gas), from biomass (gas_bio), natural gas (gas_bal): All go into secondary level + # Can not be distinguished in the final level. + + df_final_energy.aggregate( + "Final Energy|Industry excl Non-Energy Use|Gases", + components=var_sectors, + append=True, + ) + + df_final_energy.filter( + variable=["Final Energy|Industry excl Non-Energy Use|Gases"], + inplace=True, + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + + if c == "hydrogen": + df_final_energy.aggregate( + "Final Energy|Industry excl Non-Energy Use|Hydrogen", + components=var_sectors, + append=True, + ) + df_final_energy.filter( + variable=["Final Energy|Industry excl Non-Energy Use|Hydrogen"], + inplace=True, + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + + if c == "liquids": + + # All liquids + df_final_energy.aggregate( + "Final Energy|Industry excl Non-Energy Use|Liquids", + components=var_sectors, + append=True, + ) + + # Only bios (ethanol, methanol ?) + + filter_vars = [ + v for v in aux2_df["variable"].values if (("ethanol" in v) + & ('methanol' not in v)) + ] + df_final_energy.aggregate( + "Final Energy|Industry excl Non-Energy Use|Liquids|Biomass", + components=filter_vars, + append=True, + ) + + # Fossils + + filter_vars = [ + v for v in aux2_df["variable"].values if (("fueloil" in v) + | ("lightoil" in v)) + ] + + df_final_energy.aggregate( + "Final Energy|Industry excl Non-Energy Use|Liquids|Oil", + components=filter_vars, + append=True, + ) + + # Other + + filter_vars = [ + v for v in aux2_df["variable"].values if (("methanol" in v)) + ] + + df_final_energy.aggregate( + "Final Energy|Industry excl Non-Energy Use|Liquids|Coal", + components=filter_vars, + append=True, + ) + + df_final_energy.filter( + variable=[ + "Final Energy|Industry excl Non-Energy Use|Liquids", + "Final Energy|Industry excl Non-Energy Use|Liquids|Oil", + "Final Energy|Industry excl Non-Energy Use|Liquids|Biomass", + "Final Energy|Industry excl Non-Energy Use|Liquids|Coal", + ], + inplace=True, + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + if c == "solids": + + # All + df_final_energy.aggregate( + "Final Energy|Industry excl Non-Energy Use|Solids", + components=var_sectors, + append=True, + ) + + # Bio + filter_vars = [ + v for v in aux2_df["variable"].values if ("biomass" in v) + ] + df_final_energy.aggregate( + "Final Energy|Industry excl Non-Energy Use|Solids|Biomass", + components=filter_vars, + append=True, + ) + + # Fossil + filter_vars = [ + v for v in aux2_df["variable"].values if ("biomass" not in v) + ] + df_final_energy.aggregate( + "Final Energy|Industry excl Non-Energy Use|Solids|Coal", + components=filter_vars, + append=True, + ) + df_final_energy.filter( + variable=[ + "Final Energy|Industry excl Non-Energy Use|Solids", + "Final Energy|Industry excl Non-Energy Use|Solids|Biomass", + "Final Energy|Industry excl Non-Energy Use|Solids|Coal", + ], + inplace=True, + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + if c == "heat": + df_final_energy.aggregate( + "Final Energy|Industry excl Non-Energy Use|Heat", + components=var_sectors, + append=True, + ) + df_final_energy.filter( + variable=["Final Energy|Industry excl Non-Energy Use|Heat"], + inplace=True, + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + if c == 'other': + df_final_energy.aggregate( + "Final Energy|Industry excl Non-Energy Use|Other", + components=var_sectors, + append=True, + ) + df_final_energy.filter( + variable=["Final Energy|Industry excl Non-Energy Use|Other"], + inplace=True, + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + + # FINAL ENERGY BY FUELS (Including Non-Energy Use) + print("Final Energy by fuels including non-energy use is being printed.") + commodities = ["electr", "gas", "hydrogen", "liquids", "solids", "heat", "all",'other'] + for c in commodities: + + for r in nodes: + + df_final_energy = df.copy() + df_final_energy.convert_unit( + "", to="GWa", factor=1, inplace=True) + df_final_energy.filter(region=r, year=years, inplace=True) + df_final_energy.filter( + variable=["in|final|*|cokeoven_steel|*", + "in|final|bf_gas|*", + "in|final|co_gas|*", + ], keep=False, inplace=True + ) + + if c == 'other': + df_final_energy.filter(variable=["out|useful|i_therm|solar_i|M1"], + inplace = True) + if c == "electr": + df_final_energy.filter(variable=["in|final|electr|*", + 'in|secondary|electr|NH3_to_N_fertil|M1', + 'in|secondary|electr|coal_NH3|M1', + 'in|secondary|electr|electr_NH3|M1', + 'in|secondary|electr|fueloil_NH3|M1', + 'in|secondary|electr|gas_NH3|M1', + 'in|secondary|electr|coal_NH3_ccs|M1', + 'in|secondary|electr|fueloil_NH3_ccs|M1', + 'in|secondary|electr|gas_NH3_ccs|M1', + 'in|secondary|electr|biomass_NH3_ccs|M1', + 'in|secondary|electr|biomass_NH3|M1', + ], inplace=True) + + if c == "gas": + df_final_energy.filter(variable=["in|final|gas|*", + 'in|secondary|gas|gas_NH3|M1', + 'in|secondary|gas|gas_NH3_ccs|M1' + ], + inplace=True) + + # Include gasoil and naphtha feedstock + if c == "liquids": + df_final_energy.filter( + variable=[ + "in|final|ethanol|*", + "in|final|fueloil|*", + "in|final|lightoil|*", + "in|final|methanol|*", + "in|final|vacuum_gasoil|*", + "in|final|naphtha|*", + "in|final|atm_gasoil|*", + 'in|secondary|fueloil|fueloil_NH3|M1', + 'in|secondary|fueloil|fueloil_NH3_ccs|M1'], + inplace=True, + ) + if c == "solids": + df_final_energy.filter( + variable=[ + "in|final|biomass|*", + "in|final|coal|*", + "in|final|coke_iron|*", + 'in|secondary|coal|coal_NH3|M1', + 'in|secondary|coal|coal_NH3_ccs|M1', + 'in|primary|biomass|biomass_NH3_ccs|M1', + 'in|primary|biomass|biomass_NH3|M1' + ], + inplace=True, + ) + + if c == "hydrogen": + df_final_energy.filter(variable=["in|final|hydrogen|*"], inplace=True) + if c == "heat": + df_final_energy.filter(variable=["in|final|d_heat|*"], inplace=True) + if c == "all": + df_final_energy.filter(variable=["in|final|*", + "in|secondary|coal|coal_NH3|M1", + "in|secondary|electr|NH3_to_N_fertil|M1", + 'in|secondary|electr|coal_NH3|M1', + 'in|secondary|electr|electr_NH3|M1', + 'in|secondary|electr|gas_NH3|M1', + 'in|secondary|fueloil|fueloil_NH3|M1', + 'in|secondary|gas|gas_NH3|M1', + "in|secondary|coal|coal_NH3_ccs|M1", + 'in|secondary|electr|coal_NH3_ccs|M1', + 'in|secondary|electr|gas_NH3_ccs|M1', + 'in|secondary|fueloil|fueloil_NH3_ccs|M1', + 'in|secondary|gas|gas_NH3_ccs|M1', + "in|primary|biomass|biomass_NH3|M1", + "in|seconday|electr|biomass_NH3|M1", + "in|primary|biomass|biomass_NH3_ccs|M1", + "in|secondary|electr|biomass_NH3_ccs|M1", + "out|useful|i_therm|solar_i|M1" + ], inplace=True) + + all_flows = df_final_energy.timeseries().reset_index() + splitted_vars = [v.split("|") for v in all_flows.variable] + aux1_df = pd.DataFrame( + splitted_vars, + columns=["flow_type", "level", "commodity", "technology", "mode"], + ) + aux2_df = pd.concat( + [all_flows.reset_index(drop=True), aux1_df.reset_index(drop=True)], + axis=1, + ) + + # Include only the related industry sector variables + + var_sectors = [ + v + for v in aux2_df["variable"].values + if ( + ( + ("cement" in v) + | ("steel" in v) + | ("aluminum" in v) + | ("petro" in v) + | ("_i" in v) + | ("_I" in v) + | ("_fs" in v) + | ('NH3' in v) + ) + & (("eth_ic_trp" not in v) & ("meth_ic_trp" not in v)) + ) + ] + aux2_df = aux2_df[aux2_df["variable"].isin(var_sectors)] + + df_final_energy.filter(variable=var_sectors, inplace=True) + + # Aggregate + + if c == 'other': + df_final_energy.aggregate( + "Final Energy|Industry|Other", components=var_sectors, append=True, + ) + df_final_energy.filter(variable=["Final Energy|Industry|Other"], inplace=True) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + if c == "all": + df_final_energy.aggregate( + "Final Energy|Industry", components=var_sectors, append=True, + ) + df_final_energy.filter(variable=["Final Energy|Industry"], inplace=True) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + if c == "electr": + + df_final_energy.aggregate( + "Final Energy|Industry|Electricity", + components=var_sectors, + append=True, + ) + df_final_energy.filter( + variable=["Final Energy|Industry|Electricity"], inplace=True, + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + if c == "gas": + + # Can not distinguish by type Gases (natural gas, biomass, synthetic fossil, efuel) + # (coal_gas), from biomass (gas_bio), natural gas (gas_bal): All go into secondary level + # Can not be distinguished in the final level. + + df_final_energy.aggregate( + "Final Energy|Industry|Gases", components=var_sectors, append=True, + ) + + df_final_energy.filter( + variable=["Final Energy|Industry|Gases"], inplace=True, + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + + if c == "hydrogen": + df_final_energy.aggregate( + "Final Energy|Industry|Hydrogen", + components=var_sectors, + append=True, + ) + df_final_energy.filter( + variable=["Final Energy|Industry|Hydrogen"], inplace=True, + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + + if c == "liquids": + + # All liquids + df_final_energy.aggregate( + "Final Energy|Industry|Liquids", + components=var_sectors, + append=True, + ) + # Only bios (ethanol) + + filter_vars = [ + v for v in aux2_df["variable"].values if (("ethanol" in v) + & ("methanol" not in v)) + ] + df_final_energy.aggregate( + "Final Energy|Industry|Liquids|Biomass", + components=filter_vars, + append=True, + ) + + # Oils + + filter_vars = [ + v + for v in aux2_df["variable"].values + if ( + ("naphtha" in v) + | ("atm_gasoil" in v) + | ("vacuum_gasoil" in v) + | ("fueloil" in v) + | ("lightoil" in v) + ) + ] + + df_final_energy.aggregate( + "Final Energy|Industry|Liquids|Oil", + components=filter_vars, + append=True, + ) + + # Methanol + + filter_vars = [ + v + for v in aux2_df["variable"].values + if ( + ("methanol" in v) + ) + ] + + df_final_energy.aggregate( + "Final Energy|Industry|Liquids|Coal", + components=filter_vars, + append=True, + ) + + + df_final_energy.filter( + variable=[ + "Final Energy|Industry|Liquids", + "Final Energy|Industry|Liquids|Oil", + "Final Energy|Industry|Liquids|Biomass", + "Final Energy|Industry|Liquids|Coal", + ], + inplace=True, + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + if c == "solids": + + # All + df_final_energy.aggregate( + "Final Energy|Industry|Solids", components=var_sectors, append=True, + ) + + # Bio + filter_vars = [ + v for v in aux2_df["variable"].values if ("biomass" in v) + ] + + df_final_energy.aggregate( + "Final Energy|Industry|Solids|Biomass", + components=filter_vars, + append=True, + ) + + # Fossil + filter_vars = [ + v for v in aux2_df["variable"].values if ("biomass" not in v) + ] + + df_final_energy.aggregate( + "Final Energy|Industry|Solids|Coal", + components=filter_vars, + append=True, + ) + df_final_energy.filter( + variable=[ + "Final Energy|Industry|Solids", + "Final Energy|Industry|Solids|Biomass", + "Final Energy|Industry|Solids|Coal", + ], + inplace=True, + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + if c == "heat": + df_final_energy.aggregate( + "Final Energy|Industry|Heat", components=var_sectors, append=True, + ) + df_final_energy.filter( + variable=["Final Energy|Industry|Heat"], inplace=True, + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + + # FINAL ENERGY BY SECTOR AND FUEL + # Feedstock not included + + sectors = [ + "aluminum", + "steel", + "cement", + "petro", + "Non-Ferrous Metals", + "Non-Metallic Minerals", + "Chemicals", + 'Other Sector' + ] + print("Final Energy by sector and fuel is being printed") + for r in nodes: + for s in sectors: + df_final_energy = df.copy() + df_final_energy.convert_unit("", to="GWa", factor=1, inplace=True) + exclude = [ + "in|final|atm_gasoil|steam_cracker_petro|*", + "in|final|ethanol|ethanol_to_ethylene_petro|M1", + "in|final|gas|gas_processing_petro|M1", + "in|final|naphtha|steam_cracker_petro|*", + "in|final|vacuum_gasoil|steam_cracker_petro|*", + "in|final|*|cokeoven_steel|*", + "in|final|bf_gas|*", + "in|final|co_gas|*"] + + df_final_energy.filter(region=r, year=years, inplace=True) + df_final_energy.filter(variable=["in|final|*", 'out|useful|i_therm|solar_i|M1'], inplace=True) + df_final_energy.filter(variable=exclude, keep=False, inplace=True) + + # Decompose the pyam table into pandas data frame + + all_flows = df_final_energy.timeseries().reset_index() + + # Split the strings in the identified variables for further processing + splitted_vars = [v.split("|") for v in all_flows.variable] + + # Create auxilary dataframes for processing + aux1_df = pd.DataFrame( + splitted_vars, + columns=["flow_type", "level", "commodity", "technology", "mode"], + ) + aux2_df = pd.concat( + [all_flows.reset_index(drop=True), aux1_df.reset_index(drop=True)], + axis=1, + ) + + # To be able to report the higher level sectors. + if s == "Non-Ferrous Metals": + tec = [t for t in aux2_df["technology"].values if "aluminum" in t] + aux2_df = aux2_df[aux2_df["technology"].isin(tec)] + elif s == "Non-Metallic Minerals": + tec = [t for t in aux2_df["technology"].values if "cement" in t] + aux2_df = aux2_df[aux2_df["technology"].isin(tec)] + elif s == "Chemicals": + tec = [t for t in aux2_df["technology"].values if (("petro" in t) + | ('NH3' in t))] + aux2_df = aux2_df[aux2_df["technology"].isin(tec)] + elif s == 'Other Sector': + tec = [t for t in aux2_df["technology"].values if ((('_i' in t) + | ('_I' in t)) + & ('trp' not in t))] + aux2_df = aux2_df[aux2_df["technology"].isin(tec)] + else: + # Filter the technologies only for the certain industry sector + tec = [t for t in aux2_df["technology"].values if s in t] + aux2_df = aux2_df[aux2_df["technology"].isin(tec)] + + s = change_names(s) + + # Lists to keep commodity, aggregate and variable names. + + commodity_list = [] + aggregate_list = [] + var_list = [] + + # For the categoris below filter the required variable names, + # create a new aggregate name + + commodity_list = [ + "electr", + "gas", + "hydrogen", + "liquids", + "liquid_bio", + "liquid_fossil", + 'liquid_other', + "solids", + "solids_bio", + "solids_fossil", + "heat", + "all", + ] + + for c in commodity_list: + if c == "electr": + var = np.unique( + aux2_df.loc[aux2_df["commodity"] == "electr", "variable"].values + ).tolist() + aggregate_name = ( + "Final Energy|Industry excl Non-Energy Use|" + + s + + "|" + + "Electricity" + ) + elif c == "gas": + var = np.unique( + aux2_df.loc[aux2_df["commodity"] == "gas", "variable"].values + ).tolist() + aggregate_name = ( + "Final Energy|Industry excl Non-Energy Use|" + s + "|" + "Gases" + ) + elif c == "hydrogen": + var = np.unique( + aux2_df.loc[ + aux2_df["commodity"] == "hydrogen", "variable" + ].values + ).tolist() + aggregate_name = ( + "Final Energy|Industry excl Non-Energy Use|" + + s + + "|" + + "Hydrogen" + ) + elif c == "liquids": + var = np.unique( + aux2_df.loc[ + ( + (aux2_df["commodity"] == "fueloil") + | (aux2_df["commodity"] == "lightoil") + | (aux2_df["commodity"] == "methanol") + | (aux2_df["commodity"] == "ethanol") + ), + "variable", + ].values + ).tolist() + aggregate_name = ( + "Final Energy|Industry excl Non-Energy Use|" + + s + + "|" + + "Liquids" + ) + elif c == "liquid_bio": + var = np.unique( + aux2_df.loc[ + ((aux2_df["commodity"] == "ethanol")), "variable", + ].values + ).tolist() + aggregate_name = ( + "Final Energy|Industry excl Non-Energy Use|" + + s + + "|" + + "Liquids|Biomass" + ) + elif c == "liquid_fossil": + var = np.unique( + aux2_df.loc[ + ( + (aux2_df["commodity"] == "fueloil") + | (aux2_df["commodity"] == "lightoil") + ), + "variable", + ].values + ).tolist() + aggregate_name = ( + "Final Energy|Industry excl Non-Energy Use|" + + s + + "|" + + "Liquids|Oil" + ) + elif c == "liquid_other": + var = np.unique( + aux2_df.loc[ + ((aux2_df["commodity"] == "methanol")), + "variable", + ].values + ).tolist() + aggregate_name = ( + "Final Energy|Industry excl Non-Energy Use|" + + s + + "|" + + "Liquids|Coal" + ) + elif c == "solids": + var = np.unique( + aux2_df.loc[ + ( + (aux2_df["commodity"] == "coal") + | (aux2_df["commodity"] == "biomass") + | (aux2_df["commodity"] == "coke_iron") + ), + "variable", + ].values + ).tolist() + aggregate_name = ( + "Final Energy|Industry excl Non-Energy Use|" + + s + + "|" + + "Solids" + ) + elif c == "solids_bio": + var = np.unique( + aux2_df.loc[ + (aux2_df["commodity"] == "biomass"), "variable" + ].values + ).tolist() + aggregate_name = ( + "Final Energy|Industry excl Non-Energy Use|" + + s + + "|" + + "Solids|Biomass" + ) + elif c == "solids_fossil": + var = np.unique( + aux2_df.loc[ + ( + (aux2_df["commodity"] == "coal") + | (aux2_df["commodity"] == "coke_iron") + ), + "variable", + ].values + ).tolist() + aggregate_name = ( + "Final Energy|Industry excl Non-Energy Use|" + + s + + "|" + + "Solids|Coal" + ) + elif c == "heat": + var = np.unique( + aux2_df.loc[ + (aux2_df["commodity"] == "d_heat"), "variable" + ].values + ).tolist() + aggregate_name = ( + "Final Energy|Industry excl Non-Energy Use|" + s + "|" + "Heat" + ) + elif c == "all": + var = aux2_df["variable"].tolist() + aggregate_name = "Final Energy|Industry excl Non-Energy Use|" + s + + aggregate_list.append(aggregate_name) + var_list.append(var) + + # Obtain the iamc format dataframe again + + aux2_df.drop( + ["flow_type", "level", "commodity", "technology", "mode"], + axis=1, + inplace=True, + ) + df_final_energy = pyam.IamDataFrame(data=aux2_df) + + # Aggregate the commodities in iamc object + i = 0 + for c in commodity_list: + df_final_energy.aggregate( + aggregate_list[i], components=var_list[i], append=True + ) + i = i + 1 + + df_final_energy.filter(variable=aggregate_list, inplace=True) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + + # FINAL ENERGY NON-ENERGY USE BY SECTOR AND FUEL + # Only in chemcials sector there is non-energy use + # not in aluminum, steel, cement + + # For high value chemicals non-energy use is reported. + + # For ammonia, there is no seperation for non-energy vs. energy. Everything + # is included here and the name of the variable is later changed. + + sectors = ["petro", 'ammonia'] + print("Final Energy non-energy use by sector and fuel is being printed") + for r in nodes: + for s in sectors: + df_final_energy = df.copy() + df_final_energy.convert_unit("", to="GWa", factor=1, inplace=True) + include = [ + "in|final|atm_gasoil|steam_cracker_petro|*", + "in|final|ethanol|ethanol_to_ethylene_petro|M1", + "in|final|gas|gas_processing_petro|M1", + "in|final|naphtha|steam_cracker_petro|*", + "in|final|vacuum_gasoil|steam_cracker_petro|*", + 'in|secondary|coal|coal_NH3|M1', + 'in|secondary|coal|coal_NH3_ccs|M1', + 'in|secondary|fueloil|fueloil_NH3|M1', + 'in|secondary|fueloil|fueloil_NH3_ccs|M1', + 'in|secondary|gas|gas_NH3|M1', + 'in|secondary|gas|gas_NH3_ccs|M1', + 'in|primary|biomass|biomass_NH3|M1', + 'in|primary|biomass|biomass_NH3_ccs|M1', + "in|secondary|electr|NH3_to_N_fertil|M1", + 'in|secondary|electr|coal_NH3|M1', + 'in|secondary|electr|electr_NH3|M1', + 'in|secondary|electr|gas_NH3|M1', + 'in|secondary|electr|coal_NH3|M1', + 'in|secondary|electr|coal_NH3_ccs|M1', + 'in|secondary|electr|gas_NH3_ccs|M1', + "in|seconday|electr|biomass_NH3|M1", + "in|secondary|electr|biomass_NH3_ccs|M1", + 'in|secondary|electr|fueloil_NH3|M1', + 'in|secondary|electr|fueloil_NH3_ccs|M1', + ] + + df_final_energy.filter(region=r, year=years, inplace=True) + df_final_energy.filter(variable=include, inplace=True) + + # Decompose the pyam table into pandas data frame + + all_flows = df_final_energy.timeseries().reset_index() + + # Split the strings in the identified variables for further processing + splitted_vars = [v.split("|") for v in all_flows.variable] + + # Create auxilary dataframes for processing + aux1_df = pd.DataFrame( + splitted_vars, + columns=["flow_type", "level", "commodity", "technology", "mode"], + ) + aux2_df = pd.concat( + [all_flows.reset_index(drop=True), aux1_df.reset_index(drop=True)], + axis=1, + ) + + # Filter the technologies only for the certain industry sector + if s == 'petro': + tec = [t for t in aux2_df["technology"].values if (s in t)] + if s == 'ammonia': + tec = [t for t in aux2_df["technology"].values if ('NH3' in t)] + + aux2_df = aux2_df[aux2_df["technology"].isin(tec)] + + s = change_names(s) + + # Lists to keep commodity, aggregate and variable names. + + aggregate_list = [] + commodity_list = [] + var_list = [] + + # For the categoris below filter the required variable names, + # create a new aggregate name + + commodity_list = [ + "gas", + "liquids", + "liquid_bio", + "liquid_oil", + "all", + 'solids' + ] + + for c in commodity_list: + if c == "gas": + var = np.unique( + aux2_df.loc[aux2_df["commodity"] == "gas", "variable"].values + ).tolist() + aggregate_name = "Final Energy|Non-Energy Use|" + s + "|" + "Gases" + + elif c == "liquids": + var = np.unique( + aux2_df.loc[ + ( + (aux2_df["commodity"] == "naphtha") + | (aux2_df["commodity"] == "atm_gasoil") + | (aux2_df["commodity"] == "vacuum_gasoil") + | (aux2_df["commodity"] == "ethanol") + | (aux2_df["commodity"] == "fueloil") + ), + "variable", + ].values + ).tolist() + + aggregate_name = ( + "Final Energy|Non-Energy Use|" + s + "|" + "Liquids" + ) + elif c == "liquid_bio": + var = np.unique( + aux2_df.loc[ + ((aux2_df["commodity"] == "ethanol")), "variable", + ].values + ).tolist() + aggregate_name = ( + "Final Energy|Non-Energy Use|" + s + "|" + "Liquids|Biomass" + ) + elif c == "liquid_oil": + var = np.unique( + aux2_df.loc[ + ((aux2_df["commodity"] == "atm_gasoil") | + (aux2_df["commodity"] == "naphtha") | + (aux2_df["commodity"] == "vacuum_gasoil") | + (aux2_df["commodity"] == "fueloil") + ), "variable", + ].values + ).tolist() + + aggregate_name = ( + "Final Energy|Non-Energy Use|" + s + "|" + "Liquids|Oil" + ) + + elif c == 'solids': + var = np.unique( + aux2_df.loc[ + ((aux2_df["commodity"] == "coal") | + (aux2_df["commodity"] == "biomass")), "variable", + ].values + ).tolist() + aggregate_name = ( + "Final Energy|Non-Energy Use|" + s + "|" + "Solids" + ) + elif c == "all": + var = aux2_df["variable"].tolist() + aggregate_name = "Final Energy|Non-Energy Use|" + s + + aggregate_list.append(aggregate_name) + var_list.append(var) + + # Obtain the iamc format dataframe again + + aux2_df.drop( + ["flow_type", "level", "commodity", "technology", "mode"], + axis=1, + inplace=True, + ) + df_final_energy = pyam.IamDataFrame(data=aux2_df) + + # Aggregate the commodities in iamc object + + i = 0 + for c in commodity_list: + if var_list[i]: + df_final_energy.aggregate( + aggregate_list[i], components=var_list[i], append=True + ) + + i = i + 1 + + df_final_energy.filter(variable=aggregate_list, inplace=True) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + + # EMISSIONS + # If ammonia is used as feedstock the emissions are accounted under 'CO2_industry', + # so as 'demand'. If used as fuel, under 'CO2_transformation'. + # The CCS technologies deduct negative emissions from the overall CO2. + # If CCS technologies are used, + + sectors = [ + "aluminum", + "steel", + "petro", + "cement", + 'ammonia', + "all", + "Chemicals", + 'Other Sector' + ] + emission_type = [ + 'CO2_industry', + "CH4", + "CO2", + "NH3", + "NOx", + "CF4", + "N2O", + "BCA", + "CO", + "OCA", + ] + + print("Emissions are being printed.") + for typ in ["demand", "process"]: + for r in nodes: + for e in emission_type: + df_emi = df.copy() + df_emi.filter(region=r, year=years, inplace=True) + # CCS technologies for ammonia has both CO2 and CO2_industry + # at the same time. + if e == 'CO2_industry': + emi_filter = ["emis|CO2|biomass_NH3_ccs|*", + "emis|CO2|gas_NH3_ccs|*", + "emis|CO2|coal_NH3_ccs|*", + "emis|CO2|fueloil_NH3_ccs|*", + "emis|CO2_industry|biomass_NH3_ccs|*", + "emis|CO2_industry|gas_NH3_ccs|*", + "emis|CO2_industry|coal_NH3_ccs|*", + "emis|CO2_industry|fueloil_NH3_ccs|*", + "emis|CO2_industry|biomass_NH3|*", + "emis|CO2_industry|gas_NH3|*", + "emis|CO2_industry|coal_NH3|*", + "emis|CO2_industry|fueloil_NH3|*", + "emis|CO2_industry|electr_NH3|*", + 'emis|CO2_industry|*'] + df_emi.filter(variable=emi_filter, inplace=True) + else: + emi_filter = ["emis|" + e + "|*"] + exclude = ["emis|CO2|biomass_NH3_ccs|*", + "emis|CO2|gas_NH3_ccs|*", + "emis|CO2|coal_NH3_ccs|*", + "emis|CO2|fueloil_NH3_ccs|*"] + df_emi.filter(variable=exclude, keep = False, inplace=True) + df_emi.filter(variable=emi_filter, inplace=True) + if (e == "CO2") | (e == "CO2_industry"): + # From MtC to Mt CO2/yr + df_emi.convert_unit('', to="Mt CO2/yr", factor=44/12, + inplace=True) + elif (e == "N2O") | (e == "CF4"): + unit = "kt " + e + "/yr" + df_emi.convert_unit('', to= unit, factor=1, + inplace=True) + else: + e = change_names(e) + # From kt/yr to Mt/yr + unit = "Mt " + e + "/yr" + df_emi.convert_unit("", to= unit, factor=0.001, inplace=True) + + all_emissions = df_emi.timeseries().reset_index() + + # Split the strings in the identified variables for further processing + splitted_vars = [v.split("|") for v in all_emissions.variable] + # Lists to later keep the variables and names to aggregate + var_list = [] + aggregate_list = [] + + # Collect the same emission type for each sector + for s in sectors: + # Create auxilary dataframes for processing + aux1_df = pd.DataFrame( + splitted_vars, + columns=["emission", "type", "technology", "mode"], + ) + aux2_df = pd.concat( + [ + all_emissions.reset_index(drop=True), + aux1_df.reset_index(drop=True), + ], + axis=1, + ) + # Filter the technologies only for the sector + + if (typ == "process") & (s == "all") & (e != "CO2_industry"): + tec = [ + t + for t in aux2_df["technology"].values + if ( + ( + ( + ("cement" in t) + | ("steel" in t) + | ("aluminum" in t) + | ("petro" in t) + ) + & ("furnace" not in t) + ) + ) + ] + if (typ == "process") & (s != "all"): + tec = [ + t + for t in aux2_df["technology"].values + if ((s in t) & ("furnace" not in t) & ('NH3' not in t)) + ] + + if (typ == "demand") & (s == "Chemicals"): + tec = [ + t + for t in aux2_df["technology"].values + if (('NH3' in t) | (('petro' in t) & ('furnace' in t))) + ] + + if (typ == "demand") & (s == "Other Sector") & (e != "CO2"): + tec = [ + t + for t in aux2_df["technology"].values + if (( + ("biomass_i" in t) + | ("coal_i" in t) + | ("elec_i" in t) + | ("eth_i" in t) + | ("foil_i" in t) + | ("gas_i" in t) + | ("h2_i" in t) + | ("heat_i" in t) + | ("hp_el_i" in t) + | ("hp_gas_i" in t) + | ("loil_i" in t) + | ("meth_i" in t) + | ("sp_coal_I" in t) + | ("sp_el_I" in t) + | ("sp_eth_I" in t) + | ("sp_liq_I" in t) + | ("sp_meth_I" in t) + | ("h2_fc_I" in t) + ) + & ( + ("eth_ic_trp" not in t) + & ("meth_ic_trp" not in t) + & ("coal_imp" not in t) + & ("foil_imp" not in t) + & ("gas_imp" not in t) + & ("elec_imp" not in t) + & ("eth_imp" not in t) + & ("meth_imp" not in t) + & ("loil_imp" not in t) + ) + ) + ] + + if (typ == "demand") & (s == "all") & (e != "CO2"): + tec = [ + t + for t in aux2_df["technology"].values + if ( + ( + ( + ("cement" in t) + | ("steel" in t) + | ("aluminum" in t) + | ("petro" in t) + ) + & ("furnace" in t) + ) + + | ( + ("biomass_i" in t) + | ("coal_i" in t) + | ("elec_i" in t) + | ("eth_i" in t) + | ("foil_i" in t) + | ("gas_i" in t) + | ("h2_i" in t) + | ("heat_i" in t) + | ("hp_el_i" in t) + | ("hp_gas_i" in t) + | ("loil_i" in t) + | ("meth_i" in t) + | ("sp_coal_I" in t) + | ("sp_el_I" in t) + | ("sp_eth_I" in t) + | ("sp_liq_I" in t) + | ("sp_meth_I" in t) + | ("h2_fc_I" in t) + | ("DUMMY_limestone_supply_cement" in t) + | ("DUMMY_limestone_supply_steel" in t) + | ("eaf_steel" in t) + | ("DUMMY_coal_supply" in t) + | ("DUMMY_gas_supply" in t) + | ("NH3" in t) + ) + & ( + ("eth_ic_trp" not in t) + & ("meth_ic_trp" not in t) + & ("coal_imp" not in t) + & ("foil_imp" not in t) + & ("gas_imp" not in t) + & ("elec_imp" not in t) + & ("eth_imp" not in t) + & ("meth_imp" not in t) + & ("loil_imp" not in t) + ) + ) + ] + + if (typ == "demand") & (s != "all") & (s != "Other Sector") & (s != "Chemicals"): + + if s == "steel": + # Furnaces are not used as heat source for iron&steel + # Dummy supply technologies help accounting the emissions + # from cokeoven_steel, bf_steel, dri_steel, eaf_steel, sinter_steel. + + tec = [ + t + for t in aux2_df["technology"].values + if ( + ("DUMMY_coal_supply" in t) + | ("DUMMY_gas_supply" in t) + | ("DUMMY_limestone_supply_steel" in t) + ) + ] + + elif s == "cement": + tec = [ + t + for t in aux2_df["technology"].values + if ( + ((s in t) & ("furnace" in t)) + | ("DUMMY_limestone_supply_cement" in t) + )] + elif s == "ammonia": + tec = [ + t + for t in aux2_df["technology"].values + if ( + ('NH3' in t))] + else: + tec = [ + t + for t in aux2_df["technology"].values + if ((s in t) & ("furnace" in t)) + ] + # Adjust the sector names + s = change_names(s) + + aux2_df = aux2_df[aux2_df["technology"].isin(tec)] + # If there are no emission types for that setor skip + if aux2_df.empty: + continue + + # Add elements to lists for aggregation over emission type + # for each sector + var = aux2_df["variable"].values.tolist() + var_list.append(var) + + # Aggregate names: + if s == "all": + if (typ == "demand") & (e != "CO2"): + if e != "CO2_industry": + aggregate_name = ( + "Emissions|" + e + "|Energy|Demand|Industry" + ) + aggregate_list.append(aggregate_name) + else: + aggregate_name = ( + "Emissions|" + "CO2" + "|Energy|Demand|Industry" + ) + aggregate_list.append(aggregate_name) + if (typ == "process") & (e != "CO2_industry"): + aggregate_name = "Emissions|" + e + "|Industrial Processes" + aggregate_list.append(aggregate_name) + else: + if (typ == "demand") & (e != "CO2"): + if e != "CO2_industry": + aggregate_name = ( + "Emissions|" + e + "|Energy|Demand|Industry|" + s + ) + aggregate_list.append(aggregate_name) + else: + aggregate_name = ( + "Emissions|" + + "CO2" + + "|Energy|Demand|Industry|" + + s + ) + aggregate_list.append(aggregate_name) + if (typ == "process") & (e != "CO2_industry"): + aggregate_name = ( + "Emissions|" + e + "|Industrial Processes|" + s + ) + aggregate_list.append(aggregate_name) + + # To plot: Obtain the iamc format dataframe again + + aux2_df = pd.concat( + [ + all_emissions.reset_index(drop=True), + aux1_df.reset_index(drop=True), + ], + axis=1, + ) + aux2_df.drop( + ["emission", "type", "technology", "mode"], axis=1, inplace=True + ) + df_emi = pyam.IamDataFrame(data=aux2_df) + + # Aggregation over emission type for each sector if there are elements to aggregate + + if len(aggregate_list) != 0: + for i in range(len(aggregate_list)): + df_emi.aggregate( + aggregate_list[i], components=var_list[i], append=True + ) + + fig, ax1 = plt.subplots(1, 1, figsize=(10, 10)) + df_emi.filter(variable=aggregate_list).plot.stack(ax=ax1) + df_emi.filter(variable=aggregate_list, inplace=True) + df_final.append(df_emi, inplace=True) + ax1.set_title("Emissions_" + r + "_" + e) + ax1.set_ylabel("Mt") + ax1.legend(bbox_to_anchor=(0.3, 1)) + + plt.close() + pp.savefig(fig) + + # PLOTS + # + # # HVC Demand: See if this is correct .... + # + # for r in nodes: + # + # fig, ax1 = plt.subplots(1, 1, figsize=(10, 10)) + # + # if r != "China*": + # + # df_petro = df.copy() + # df_petro.filter(region=r, year=years, inplace=True) + # df_petro.filter( + # variable=[ + # "out|final_material|BTX|*", + # "out|final_material|ethylene|*", + # "out|final_material|propylene|*", + # ], + # inplace=True, + # ) + # + # # BTX production + # BTX_vars = [ + # "out|final_material|BTX|steam_cracker_petro|atm_gasoil", + # "out|final_material|BTX|steam_cracker_petro|naphtha", + # "out|final_material|BTX|steam_cracker_petro|vacuum_gasoil", + # ] + # df_petro.aggregate("BTX production", components=BTX_vars, append=True) + # + # # Propylene production + # + # propylene_vars = [ # "out|final_material|propylene|catalytic_cracking_ref|atm_gasoil", + # # "out|final_material|propylene|catalytic_cracking_ref|vacuum_gasoil", + # "out|final_material|propylene|steam_cracker_petro|atm_gasoil", + # "out|final_material|propylene|steam_cracker_petro|naphtha", + # "out|final_material|propylene|steam_cracker_petro|propane", + # "out|final_material|propylene|steam_cracker_petro|vacuum_gasoil", + # ] + # + # df_petro.aggregate( + # "Propylene production", components=propylene_vars, append=True + # ) + # + # ethylene_vars = [ + # "out|final_material|ethylene|ethanol_to_ethylene_petro|M1", + # "out|final_material|ethylene|steam_cracker_petro|atm_gasoil", + # "out|final_material|ethylene|steam_cracker_petro|ethane", + # "out|final_material|ethylene|steam_cracker_petro|naphtha", + # "out|final_material|ethylene|steam_cracker_petro|propane", + # "out|final_material|ethylene|steam_cracker_petro|vacuum_gasoil", + # ] + # + # df_petro.aggregate( + # "Ethylene production", components=ethylene_vars, append=True + # ) + # + # if r != "World": + # + # df_petro.filter( + # variable=[ + # "BTX production", + # "Propylene production", + # "Ethylene production", + # "out|final_material|BTX|import_petro|*", + # "out|final_material|propylene|import_petro|*", + # "out|final_material|ethylene|import_petro|*", + # ], + # inplace=True, + # ) + # else: + # df_petro.filter( + # variable=[ + # "BTX production", + # "Propylene production", + # "Ethylene production", + # ], + # inplace=True, + # ) + # + # df_petro.plot.stack(ax=ax1) + # ax1.legend( + # [ + # "BTX production", + # "Ethylene production", + # "Propylene production", + # "import_BTX", + # "import_ethylene", + # "import_propylene", + # ] + # ) + # ax1.set_title("HVC Production_" + r) + # ax1.set_xlabel("Years") + # ax1.set_ylabel("Mt") + # + # plt.close() + # pp.savefig(fig) + + # # Refinery Products - already commented out + # + # for r in nodes: + # + # fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 8)) + # # fig.tight_layout(pad=15.0) + # + # if r != "China*": + # + # # Fuel oil + # + # df_ref_fueloil = df.copy() + # df_ref_fueloil.filter(region=r, year=years, inplace=True) + # + # if r == "World": + # df_ref_fueloil.filter( + # variable=["out|secondary|fueloil|agg_ref|*"], inplace=True + # ) + # + # else: + # df_ref_fueloil.filter( + # variable=[ + # "out|secondary|fueloil|agg_ref|*", + # "out|secondary|fueloil|foil_imp|*", + # ], + # inplace=True, + # ) + # + # df_ref_fueloil.stack_plot(ax=ax1) + # + # ax1.legend( + # [ + # "Atm gas oil", + # "Atm resiude", + # "Heavy fuel oil", + # "Petroleum coke", + # "Vacuum residue", + # "import", + # ], + # bbox_to_anchor=(0.3, 1), + # ) + # ax1.set_title("Fuel oil mix_" + r) + # ax1.set_xlabel("Year") + # ax1.set_ylabel("GWa") + # + # # Light oil + # + # df_ref_lightoil = df.copy() + # df_ref_lightoil.filter(region=r, year=years, inplace=True) + # + # if r == "World": + # df_ref_lightoil.filter( + # variable=["out|secondary|lightoil|agg_ref|*"], inplace=True + # ) + # + # else: + # df_ref_lightoil.filter( + # variable=[ + # "out|secondary|lightoil|agg_ref|*", + # "out|secondary|lightoil|loil_imp|*", + # ], + # inplace=True, + # ) + # + # # ,"out|secondary|lightoil|loil_imp|*" + # df_ref_lightoil.stack_plot(ax=ax2) + # # df_final.append(df_ref_lightoil, inplace = True) + # ax2.legend( + # [ + # "Diesel", + # "Ethane", + # "Gasoline", + # "Kerosene", + # "Light fuel oil", + # "Naphtha", + # "Refinery gas", + # "import", + # ], + # bbox_to_anchor=(1, 1), + # ) + # ax2.set_title("Light oil mix_" + r) + # ax2.set_xlabel("Year") + # ax2.set_ylabel("GWa") + # + # plt.close() + # pp.savefig(fig) + # + # # Oil production World - Already commented out + # + # fig, ax1 = plt.subplots(1, 1, figsize=(8, 8)) + # + # df_all_oil = df.copy() + # df_all_oil.filter(region="World", year=years, inplace=True) + # df_all_oil.filter( + # variable=[ + # "out|secondary|fueloil|agg_ref|*", + # "out|secondary|lightoil|agg_ref|*", + # ], + # inplace=True, + # ) + # df_all_oil.stack_plot(ax=ax1) + # + # ax1.legend( + # [ + # "Atmg_gasoil", + # "atm_residue", + # "heavy_foil", + # "pet_coke", + # "vaccum_residue", + # "diesel", + # "ethane", + # "gasoline", + # "kerosne", + # "light_foil", + # "naphtha", + # "refinery_gas_a", + # "refinery_gas_b", + # ], + # bbox_to_anchor=(1, 1), + # ) + # ax1.set_title("Oil production" + r) + # ax1.set_xlabel("Year") + # ax1.set_ylabel("GWa") + # + # plt.close() + # pp.savefig(fig) + + # Final Energy by all fuels: See if this plot is correct.. + + # Select the sectors + # sectors = ["aluminum", "steel", "cement", "petro"] + # + # for r in nodes: + # + # # For each region create a figure + # + # fig, axs = plt.subplots(nrows=2, ncols=2) + # fig.set_size_inches(20, 20) + # fig.subplots_adjust(wspace=0.2) + # fig.subplots_adjust(hspace=0.5) + # fig.tight_layout(pad=20.0) + # + # if r != "China*": + # + # # Specify the position of each sector in the graph + # + # cnt = 1 + # + # for s in sectors: + # + # if cnt == 1: + # x_cor = 0 + # y_cor = 0 + # + # if cnt == 2: + # x_cor = 0 + # y_cor = 1 + # + # if cnt == 3: + # x_cor = 1 + # y_cor = 0 + # + # if cnt == 4: + # x_cor = 1 + # y_cor = 1 + # + # df_final_energy = df.copy() + # df_final_energy.convert_unit("", to="GWa", factor=1, inplace=True) + # df_final_energy.filter(region=r, year=years, inplace=True) + # df_final_energy.filter(variable=["in|final|*"], inplace=True) + # df_final_energy.filter( + # variable=["in|final|*|cokeoven_steel|*"], keep=False, inplace=True + # ) + # + # if s == "petro": + # # Exclude the feedstock ethanol and natural gas + # df_final_energy.filter( + # variable=[ + # "in|final|ethanol|ethanol_to_ethylene_petro|M1", + # "in|final|gas|gas_processing_petro|M1", + # "in|final|atm_gasoil|steam_cracker_petro|atm_gasoil", + # "in|final|vacuum_gasoil|steam_cracker_petro|vacuum_gasoil", + # "in|final|naphtha|steam_cracker_petro|naphtha", + # ], + # keep=False, + # inplace=True, + # ) + # + # all_flows = df_final_energy.timeseries().reset_index() + # + # # Split the strings in the identified variables for further processing + # splitted_vars = [v.split("|") for v in all_flows.variable] + # + # # Create auxilary dataframes for processing + # aux1_df = pd.DataFrame( + # splitted_vars, + # columns=["flow_type", "level", "commodity", "technology", "mode"], + # ) + # aux2_df = pd.concat( + # [all_flows.reset_index(drop=True), aux1_df.reset_index(drop=True)], + # axis=1, + # ) + # + # # Filter the technologies only for the certain material + # tec = [t for t in aux2_df["technology"].values if s in t] + # aux2_df = aux2_df[aux2_df["technology"].isin(tec)] + # + # # Lists to keep commodity, aggregate and variable. + # + # aggregate_list = [] + # commodity_list = [] + # var_list = [] + # + # # For each commodity collect the variable name, create an aggregate name + # s = change_names(s) + # for c in np.unique(aux2_df["commodity"].values): + # var = np.unique( + # aux2_df.loc[aux2_df["commodity"] == c, "variable"].values + # ).tolist() + # aggregate_name = "Final Energy|" + s + "|" + c + # + # aggregate_list.append(aggregate_name) + # commodity_list.append(c) + # var_list.append(var) + # + # # Obtain the iamc format dataframe again + # + # aux2_df.drop( + # ["flow_type", "level", "commodity", "technology", "mode"], + # axis=1, + # inplace=True, + # ) + # df_final_energy = pyam.IamDataFrame(data=aux2_df) + # + # # Aggregate the commodities in iamc object + # + # i = 0 + # for c in commodity_list: + # df_final_energy.aggregate( + # aggregate_list[i], components=var_list[i], append=True + # ) + # i = i + 1 + # + # df_final_energy.convert_unit( + # "GWa", to="EJ/yr", factor=0.03154, inplace=True + # ) + # df_final_energy.filter(variable=aggregate_list).plot.stack( + # ax=axs[x_cor, y_cor] + # ) + # axs[x_cor, y_cor].set_ylabel("EJ/yr") + # axs[x_cor, y_cor].set_title("Final Energy_" + s + "_" + r) + # cnt = cnt + 1 + # + # plt.close() + # pp.savefig(fig) + + # # Scrap Release: Buildings, Other and Power Sector + # # TODO: Make the code better + # # NEEDS TO BE CHECKED IF IT IS WORKING........ + # print('Scrap generated by sector') + # materials = ["aluminum","steel","cement"] + # + # for r in nodes: + # print(r) + # + # for m in materials: + # print(m) + # + # df_scrap_by_sector = df.copy() + # df_scrap_by_sector.filter(region=r, year=years, inplace=True) + # + # if m != 'cement': + # + # filt_buildings = 'out|end_of_life|' + m + '|demolition_build|M1' + # print(filt_buildings) + # filt_other = 'out|end_of_life|' + m + '|other_EOL_' + m + '|M1' + # print(filt_other) + # filt_power = ['out|end_of_life|' + m + '|other_EOL_' + m + '|M1', + # 'out|end_of_life|' + m + '|demolition_build|M1', + # 'in|end_of_life|' + m + '|total_EOL_' + m + '|M1'] + # print(filt_power) + # + # m = change_names(m) + # var_name_buildings = 'Total Scrap|Residential and Commercial|' + m + # var_name_other = 'Total Scrap|Other|' + m + # var_name_power = 'Total Scrap|Power Sector|' + m + # + # df_scrap_by_sector.aggregate(var_name_other,\ + # components=[filt_other],append=True) + # + # df_scrap_by_sector.aggregate(var_name_buildings,\ + # components=[filt_buildings],append=True) + # + # df_scrap_by_sector.subtract('in|end_of_life|' + m + '|total_EOL_'/ + # + m + '|M1', ['out|end_of_life|' + m + '|demolition_build|M1', + # 'out|end_of_life|' + m + '|other_EOL_' + m + '|M1'], + # var_name_power,axis='variable', append = True) + # + # df_scrap_by_sector.filter(variable=[var_name_buildings, + # var_name_other, var_name_power], + # inplace=True) + # + # df_scrap_by_sector["unit"] = "Mt/yr" + # df_final.append(df_scrap_by_sector, inplace=True) + # + # else: + # filt_buildings = 'out|end_of_life|' + m + '|demolition_build|M1' + # print(filt_buildings) + # filt_power = ['out|end_of_life|' + m + '|other_EOL_' + m + '|M1', + # 'out|end_of_life|' + m + '|demolition_build|M1', + # 'in|end_of_life|' + m + '|total_EOL_' + m + '|M1'] + # print(filt_power) + # m = change_names(m) + # var_name_buildings = 'Total Scrap|Residential and Commercial|' + m + # print(var_name_buildings) + # var_name_power = 'Total Scrap|Power Sector|' + m + # df_scrap_by_sector.aggregate(var_name_buildings,\ + # components=[filt_buildings],append=True) + # + # df_scrap_by_sector.subtract('in|end_of_life|' + m + '|total_EOL_'/ + # + m + '|M1', 'out|end_of_life|' + m + '|demolition_build|M1', + # var_name_power, axis = 'variable', append = True) + # + # df_scrap_by_sector.filter(variable=[var_name_buildings, + # var_name_power],inplace=True) + # + # df_scrap_by_sector["unit"] = "Mt/yr" + # df_final.append(df_scrap_by_sector, inplace=True) + + # PRICE + # + # df_final = df_final.timeseries().reset_index() + # + # commodity_type = [ + # "Non-Ferrous Metals|Aluminium", + # "Non-Ferrous Metals|Aluminium|New Scrap", + # "Non-Ferrous Metals|Aluminium|Old Scrap", + # "Non-Ferrous Metals|Bauxite", + # "Non-Ferrous Metals|Alumina", + # "Steel|Iron Ore", + # "Steel|Pig Iron", + # "Steel|New Scrap", + # "Steel|Old Scrap", + # "Steel", + # "Non-Metallic Minerals|Cement", + # "Non-Metallic Minerals|Limestone", + # "Non-Metallic Minerals|Clinker Cement", + # "Chemicals|High Value Chemicals", + # ] + # + # for c in commodity_type: + # prices = scenario.var("PRICE_COMMODITY") + # # Used for calculation of average prices for scraps + # output = scenario.par( + # "output", + # filters={"technology": ["scrap_recovery_aluminum", "scrap_recovery_steel"]}, + # ) + # # Differs per sector what to report so more flexible with conditions. + # # Store the relevant variables in prices_all + # + # # ALUMINUM + # if c == "Non-Ferrous Metals|Bauxite": + # continue + # if c == "Non-Ferrous Metals|Alumina": + # prices_all = prices[ + # (prices["level"] == "secondary_material") + # & (prices["commodity"] == "aluminum") + # ] + # if c == "Non-Ferrous Metals|Aluminium": + # prices_all = prices[ + # (prices["level"] == "final_material") + # & (prices["commodity"] == "aluminum") + # ] + # if c == "Non-Ferrous Metals|Aluminium|New Scrap": + # prices_all = prices[ + # (prices["level"] == "new_scrap") & (prices["commodity"] == "aluminum") + # ] + # + # # IRON AND STEEL + # if c == "Steel|Iron Ore": + # prices_all = prices[(prices["commodity"] == "ore_iron")] + # if c == "Steel|Pig Iron": + # prices_all = prices[(prices["commodity"] == "pig_iron")] + # if c == "Steel": + # prices_all = prices[ + # (prices["commodity"] == "steel") & (prices["level"] == "final_material") + # ] + # if c == "Steel|New Scrap": + # prices_all = prices[ + # (prices["commodity"] == "steel") & (prices["level"] == "new_scrap") + # ] + # # OLD SCRAP (For aluminum and steel) + # + # if (c == "Steel|Old Scrap") | (c == "Non-Ferrous Metals|Aluminium|Old Scrap"): + # + # prices = prices[ + # ( + # (prices["level"] == "old_scrap_1") + # | (prices["level"] == "old_scrap_2") + # | (prices["level"] == "old_scrap_3") + # ) + # ] + # + # if c == "Non-Ferrous Metals|Aluminium|Old Scrap": + # output = output[output["technology"] == "scrap_recovery_aluminum"] + # prices = prices[(prices["commodity"] == "aluminum")] + # if c == "Steel|Old Scrap": + # output = output[output["technology"] == "scrap_recovery_steel"] + # prices = prices[(prices["commodity"] == "steel")] + # + # prices.loc[prices["level"] == "old_scrap_1", "weight"] = output.loc[ + # output["level"] == "old_scrap_1", "value" + # ].values[0] + # + # prices.loc[prices["level"] == "old_scrap_2", "weight"] = output.loc[ + # output["level"] == "old_scrap_2", "value" + # ].values[0] + # + # prices.loc[prices["level"] == "old_scrap_3", "weight"] = output.loc[ + # output["level"] == "old_scrap_3", "value" + # ].values[0] + # + # prices_all = pd.DataFrame(columns=["node", "commodity", "year"]) + # prices_new = pd.DataFrame(columns=["node", "commodity", "year"]) + # + # for reg in output["node_loc"].unique(): + # for yr in output["year_act"].unique(): + # prices_temp = prices.groupby(["node", "year"]).get_group((reg, yr)) + # rate = prices_temp["weight"].values.tolist() + # amount = prices_temp["lvl"].values.tolist() + # weighted_avg = np.average(amount, weights=rate) + # prices_new = pd.DataFrame( + # {"node": reg, "year": yr, "commodity": c, "lvl": weighted_avg,}, + # index=[0], + # ) + # prices_all = pd.concat([prices_all, prices_new]) + # # CEMENT + # + # if c == "Non-Metallic Minerals|Limestone": + # prices_all = prices[(prices["commodity"] == "limestone_cement")] + # if c == "Non-Metallic Minerals|Clinker Cement": + # prices_all = prices[(prices["commodity"] == "clinker_cement")] + # if c == "Non-Metallic Minerals|Cement": + # prices_all = prices[ + # (prices["commodity"] == "cement") & (prices["level"] == "demand") + # ] + # # Petro-Chemicals + # + # if c == "Chemicals|High Value Chemicals": + # prices = prices[ + # (prices["commodity"] == "ethylene") + # | (prices["commodity"] == "propylene") + # | (prices["commodity"] == "BTX") + # ] + # prices_all = prices.groupby(by=["year", "node"]).mean().reset_index() + # + # # Convert all to IAMC format. + # for r in prices_all["node"].unique(): + # if (r == "R11_GLB") | (r == "R12_GLB"): + # continue + # df_price = pd.DataFrame( + # {"model": model_name, "scenario": scenario_name, "unit": "2010USD/Mt",}, + # index=[0], + # ) + # + # for y in prices_all["year"].unique(): + # df_price["region"] = r + # df_price["variable"] = "Price|" + c + # x = prices_all.loc[ + # ((prices_all["node"] == r) & (prices_all["year"] == y)), "lvl" + # ] + # if not x.empty: + # value = x.values[0] * 1.10774 + # else: + # value = 0 + # df_price[y] = value + # + # df.price = df_price.columns.astype(str) + # + # df_final = pd.concat([df_final, df_price]) + + # Material Demand - comment out if no power sector + # Power Sector + + # input_cap_new = scenario.par("input_cap_new") + # input_cap_new.drop( + # ["node_origin", "level", "time_origin", "unit"], axis=1, inplace=True + # ) + # input_cap_new.rename( + # columns={ + # "value": "material_intensity", + # "node_loc": "region", + # "year_vtg": "year", + # }, + # inplace=True, + # ) + # + # cap_new = scenario.var("CAP_NEW") + # cap_new.drop(["mrg"], axis=1, inplace=True) + # cap_new.rename( + # columns={"lvl": "installed_capacity", "node_loc": "region", "year_vtg": "year"}, + # inplace=True, + # ) + # + # merged_df = pd.merge(cap_new, input_cap_new) + # merged_df["Material Need"] = ( + # merged_df["installed_capacity"] * merged_df["material_intensity"] + # ) + # merged_df = merged_df[merged_df["year"] >= min(years)] + # + # final_material_needs = ( + # merged_df.groupby(["commodity", "region", "year"]) + # .sum() + # .drop(["installed_capacity", "material_intensity"], axis=1) + # ) + # + # final_material_needs = final_material_needs.reset_index(["year"]) + # final_material_needs = pd.pivot_table( + # final_material_needs, + # values="Material Need", + # columns="year", + # index=["commodity", "region"], + # ).reset_index(["commodity", "region"]) + # + # final_material_needs_global = ( + # final_material_needs.groupby("commodity").sum().reset_index() + # ) + # final_material_needs_global["region"] = "World" + # + # material_needs_all = pd.concat( + # [final_material_needs, final_material_needs_global], ignore_index=True + # ) + # + # material_needs_all["scenario"] = scenario_name + # material_needs_all["model"] = model_name + # material_needs_all["unit"] = "Mt/yr" + # material_needs_all["commodity"] = material_needs_all.apply( + # lambda x: change_names(x["commodity"]), axis=1 + # ) + # material_needs_all = material_needs_all.assign( + # variable=lambda x: "Material Demand|Power Sector|" + x["commodity"] + # ) + # + # material_needs_all = material_needs_all.drop(["commodity"], axis=1) + # df.price = df_price.columns.astype(str) + # + # print("This is the final_material_needs") + # print(material_needs_all) + # + # df_final = pd.concat([df_final, material_needs_all]) + + # Trade + # .................... + + # Print the new variables to an excel file. + # Change the naming convention and save to another excel file. + + name_temp = os.path.join(directory, "temp_new_reporting.xlsx") + df_final.to_excel(name_temp, sheet_name="data", index=False) + path_temp = os.path.join(directory, name_temp) + + excel_name_new = "New_Reporting_" + model_name + "_" + scenario_name + ".xlsx" + path_new = os.path.join(directory, excel_name_new) + + fix_excel(path_temp, path_new) + print("New reporting file generated.") + df_final = pd.read_excel(path_new) + + # df_resid = pd.read_csv(path_resid) + # df_resid["Model"] = model_name + # df_resid["Scenario"] = scenario_name + # df_comm = pd.read_csv(path_comm) + # df_comm["Model"] = model_name + # df_comm["Scenario"] = scenario_name + + scenario.check_out(timeseries_only=True) + print("Starting to upload timeseries") + print(df_final.head()) + scenario.add_timeseries(df_final) + # scenario.add_timeseries(df_resid) + # scenario.add_timeseries(df_comm) + print("Finished uploading timeseries") + scenario.commit("Reporting uploaded as timeseries") + + pp.close() + os.remove(path_temp) diff --git a/message_ix_models/model/material/report/tables.py b/message_ix_models/model/material/report/tables.py new file mode 100644 index 0000000000..98815d7cd9 --- /dev/null +++ b/message_ix_models/model/material/report/tables.py @@ -0,0 +1,2375 @@ +import pandas as pd +import numpy as np + +import message_data.tools.post_processing.pp_utils as pp_utils + +pp = None +mu = None +run_history = None +urban_perc_data = None +kyoto_hist_data = None +lu_hist_data = None + + +func_dict = {} + + +def return_func_dict(): + return func_dict + + +def _register(func): + """Function to register reporting functions. + + Parameters + ---------- + + func : str + Function name + """ + + func_dict[func.__name__] = func + return func + + +def _pe_wCCSretro(tec, scrub_tec, group, inpfilter, units, share=1): + """Calculates primary energy use of technologies with scrubbers. + + Parameters + ---------- + + tec : str + Technology name + scrub_tec : str + Name of CO2 scrubbing technology linked to `tec`. + group : list + Indexes by which results are to be grouped. + inpfilter : dict + `level` and/or `commodity` used for retrieving the input. + units : str + Units to which variables should be converted. + share : number or dataframe (default: 1) + Share of `tec` activity if multiple technologies share the same scrubber. + """ + + # The multiplication of `share` is required to determine in which years + # the powerplant is active, because the scrubber activity + # is not necessarily equal to the powerplant activity if + # there are two powerplants using the same scrubber. + + df = ( + pp.out(scrub_tec, units) + * share + / pp_utils.ppgroup( + (pp.act(tec, group=group) / pp.act(tec)).fillna(0) + * pp.eff( + tec, + inpfilter=inpfilter, + outfilter={"level": ["secondary"], "commodity": ["electr"]}, + group=group, + ) + ) + ).fillna(0) + + return df + + +def _pe_elec_woCCSretro(tec, scrub_tec, group, inpfilter, units, _Frac, share=1): + """Calculates primary energy electricity generation equivalent. + + This applies to technologies WITHOUT scrubbers. + + Parameters + ---------- + + tec : str + Technology name + scrub_tec : str + Name of CO2 scrubbing technology linked to `tec`. + inpfilter : dict + `level` and/or `commodity` used for retrieving the input. + units : str + Units to which variables should be converted. + _Frac : dataframe + Regional share of actual cogeneration (po_turbine). + share : number or dataframe (default: 1) + Share of `tec` activity if multiple technologies share the same scrubber. + """ + + df = ( + ( + pp.out(tec, units) + * (1.0 - pp.rel(tec, relfilter={"relation": ["pass_out_trb"]}) * _Frac) + - (1.0 - pp.rel(tec, relfilter={"relation": ["pass_out_trb"]}) * _Frac) + * pp.out(scrub_tec, units) + * share + ) + / pp_utils.ppgroup( + (pp.act(tec, group=group) / pp.act(tec)).fillna(0) + * pp.eff( + tec, + inpfilter=inpfilter, + outfilter={"level": ["secondary"], "commodity": ["electr"]}, + group=group, + ) + ) + ).fillna(0) + + return df + + +def _pe_elec_wCCSretro(tec, scrub_tec, group, inpfilter, units, _Frac, share=1): + """Calculates primary energy electricity generation equivalent. + + This applies to technologies WITH scrubbers. + + Parameters + ---------- + + tec : str + Technology name + scrub_tec : str + Name of CO2 scrubbing technology linked to `tec`. + group : list + Indexes by which resultsa re to be grouped. + inpfilter : dict + `level` and/or `commodity` used for retrieving the input. + units : str + Units to which variables should be converted. + _Frac : dataframe + Regional share of actual cogeneration (po_turbine). + share : number or dataframe (default: 1) + Share of `tec` activity if multiple technologies share the same scrubber. + """ + + df = ( + ( + (1.0 - pp.rel(tec, relfilter={"relation": ["pass_out_trb"]}) * _Frac) + * pp.out(scrub_tec, units) + * share + ) + / pp_utils.ppgroup( + (pp.act(tec, group=group) / pp.act(tec)).fillna(0) + * pp.eff( + tec, + inpfilter=inpfilter, + outfilter={"level": ["secondary"], "commodity": ["electr"]}, + group=group, + ) + ) + ).fillna(0) + + return df + + +def _se_elec_woCCSretro(tec, scrub_tec, units, _Frac, share=1): + """Calculates secondary energy electricity generation. + + This applies to technologies WITHOUT scrubbers. + + Parameters + ---------- + + tec : str + Technology name + scrub_tec : str + Name of CO2 scrubbing technology linked to `tec`. + units : str + Units to which variables should be converted. + _Frac : dataframe + Regional share of actual cogeneration (po_turbine). + share : number or dataframe (default: 1) + Share of `tec` activity if multiple technologies share the same scrubber. + """ + + df = ( + pp.out(tec, units) + * (1.0 - pp.rel(tec, relfilter={"relation": ["pass_out_trb"]}) * _Frac) + - (1.0 - pp.rel(tec, relfilter={"relation": ["pass_out_trb"]}) * _Frac) + * pp.out(scrub_tec, units) + * share + ) + + return df + + +def _se_elec_wCCSretro(tec, scrub_tec, units, _Frac, share=1): + """Calculates secondary energy electricity generation. + + This applies to technologies WITH scrubbers. + + Parameters + ---------- + + tec : str + Technology name + scrub_tec : str + Name of CO2 scrubbing technology linked to `tec`. + units : str + Units to which variables should be converted. + _Frac : dataframe + Regional share of actual cogeneration (po_turbine). + share : number or dataframe (default: 1) + Share of `tec` activity if multiple technologies share the same scrubber. + """ + + df = ( + (1.0 - pp.rel(tec, relfilter={"relation": ["pass_out_trb"]}) * _Frac) + * pp.out(scrub_tec, units) + * share + ) + + return df + + +def _pe_elec_po_turb(tec, group, units, _Frac, inpfilter): + """Calculates primary energy electricity equivalent generation. + + This calcualtes the amount of electricity used in primary energy equivalent used + for cogeneration (po-turbine). + + Parameters + ---------- + + tec : str + Technology name + group : list + Indexes by which resultsa re to be grouped. + units : str + Units to which variables should be converted. + _Frac : dataframe + Regional share of actual cogeneration (po_turbine). + inpfilter : dict + `level` and/or `commodity` used for retrieving the input. + """ + + df = pp_utils.ppgroup( + ( + ( + pp.out(tec, units, group=group) + * (1.0 - pp.rel(tec, relfilter={"relation": ["pass_out_trb"]}) * _Frac) + ) + / pp.eff(tec, inpfilter=inpfilter, group=group) + ).fillna(0) + ) + + return df + + +def _se_elec_po_turb(tec, units, _Frac, outfilter=None): + """Calculates secondary energy electricity generation. + + This calcualtes the amount of electricity used for cogeneration (po-turbine). + + Parameters + ---------- + + tec : str + Technology name + units : str + Units to which variables should be converted. + _Frac : dataframe + Regional share of actual cogeneration (po_turbine). + outfilter : dict + `level` and/or `commodity` used for retrieving the output. + """ + + df = pp.out(tec, units, outfilter=outfilter) * ( + 1.0 - pp.rel(tec, relfilter={"relation": ["pass_out_trb"]}) * _Frac + ) + + return df + + +def _se_heat_po_turb(tec, units, _Frac, outfilter=None): + """Calculates secondary energy heat generation. + + This calcualtes the amount of heat produced from cogeneration (po-turbine) + for a specific technology. + + Parameters + ---------- + + tec : str + Technology name + units : str + Units to which variables should be converted. + _Frac : dataframe + Regional share of actual cogeneration (po_turbine). + outfilter : dict + `level` and/or `commodity` used for retrieving the output. + """ + + df = pp.out(tec, units, outfilter=outfilter) * ( + pp.rel(tec, relfilter={"relation": ["pass_out_trb"]}) * _Frac + ) + + return df + + +def _out_div_eff(tec, group, inpfilter, outfilter): + """Calculates input based on output. + + This calculates the amount of input required based on the output. + Mainly this is used for water related reporting. + + Parameters + ---------- + + tec : str + Technology name + group : list + Indexes by which resultsa re to be grouped. + inpfilter : dict + `level` and/or `commodity` used for retrieving the input. + outfilter : dict + `level` and/or `commodity` used for retrieving the output. + """ + + tec = [tec] if type(tec) == str else tec + + dfs = [] + for t in tec: + dfs.append( + pp_utils.ppgroup( + (pp.out(t, outfilter=outfilter, group=group)) + / pp.eff(t, inpfilter=inpfilter, group=group) + ) + ) + df = pd.concat(dfs, sort=True) + + return df.groupby(df.index.name).sum() + + +# ------------------- +# Reporting functions +# ------------------- + + +@_register +def retr_SE_solids(units): + """Energy: Secondary Energy solids. + Parameters + ---------- + units : str + Units to which variables should be converted. + """ + + vars = {} + + BiomassIND_resid = pp.inp("biomass_i", units) + BiomassIND_alu = pp.inp("furnace_biomass_aluminum", units) + BiomassIND_steel = pp.inp("furnace_biomass_steel", units) + BiomassIND_petro = pp.inp("furnace_biomass_petro", units) + BiomassIND_cement = pp.inp("furnace_biomass_cement", units) + + BiomassRefining = pp.inp("furnace_biomass_refining", units) + + BiomassNC = pp.inp("biomass_nc", units) + BiomassR_cook = pp.inp("biomass_resid_cook", units) + BiomassR_heat = pp.inp("biomass_resid_heat", units) + BiomassR_water = pp.inp("biomass_resid_hotwater", units) + BiomassC_heat = pp.inp("biomass_comm_heat", units) + BiomassC_water = pp.inp("biomass_comm_hotwater", units) + + vars["Biomass"] = ( + BiomassIND_resid + + BiomassIND_alu + + BiomassIND_steel + + BiomassIND_petro + + BiomassIND_cement + + BiomassNC + + BiomassR_cook + + BiomassR_heat + + BiomassR_water + + BiomassC_heat + + BiomassC_water + + BiomassRefining + ) + + CoalIND_resid = pp.inp(["coal_i", "coal_fs"], units) + CoalIND_alu = pp.inp(["furnace_coal_aluminum"], units) + CoalIND_steel = pp.inp( + ["furnace_coal_steel", "bf_steel", "cokeoven_steel", "sinter_steel"], + units, + inpfilter={"commodity": ["coal"], "level": ["final"]}, + ) + CoalIND_petro = pp.inp(["furnace_coal_petro"], units) + CoalIND_cement = pp.inp(["furnace_coal_cement"], units) + + CoalRefining = pp.inp(["furnace_coal_refining"], units) + + CoalR_heat = pp.inp("coal_resid_heat", units) + CoalR_hotwater = pp.inp("coal_resid_hotwater", units) + CoalC_heat = pp.inp("coal_comm_heat", units) + CoalC_water = pp.inp("coal_comm_hotwater", units) + CoalTRP = pp.inp("coal_trp", units) + vars["Coal"] = ( + CoalIND_resid + + CoalIND_alu + + CoalIND_steel + + CoalIND_petro + + CoalIND_cement + + CoalR_heat + + CoalR_hotwater + + CoalC_heat + + CoalC_water + + CoalTRP + + CoalRefining + ) + df = pp_utils.make_outputdf(vars, units) + return df + + +@_register +def retr_SE_synfuels(units): + """Energy: Secondary Energy synthetic fuels. + Parameters + ---------- + units : str + Units to which variables should be converted. + """ + + vars = {} + + vars["Liquids|Oil"] = ( + pp.out( + "agg_ref", + units, + outfilter={"level": ["secondary"], "commodity": ["lightoil"]}, + ) + + pp.out( + "agg_ref", + units, + outfilter={"level": ["secondary"], "commodity": ["fueloil"]}, + ) + + pp.inp( + "steam_cracker_petro", + units, + inpfilter={ + "level": ["final"], + "commodity": ["atm_gasoil", "vacuum_gasoil", "naphtha"], + }, + ) + ) + + vars["Liquids|Biomass|w/o CCS"] = pp.out( + ["eth_bio", "liq_bio"], + units, + outfilter={"level": ["primary"], "commodity": ["ethanol"]}, + ) + + vars["Liquids|Biomass|w/ CCS"] = pp.out( + ["eth_bio_ccs", "liq_bio_ccs"], + units, + outfilter={"level": ["primary"], "commodity": ["ethanol"]}, + ) + + vars["Liquids|Coal|w/o CCS"] = pp.out( + ["meth_coal", "syn_liq"], + units, + outfilter={"level": ["primary"], "commodity": ["methanol"]}, + ) + + vars["Liquids|Coal|w/ CCS"] = pp.out( + ["meth_coal_ccs", "syn_liq_ccs"], + units, + outfilter={"level": ["primary"], "commodity": ["methanol"]}, + ) + + vars["Liquids|Gas|w/o CCS"] = pp.out( + "meth_ng", units, outfilter={"level": ["primary"], "commodity": ["methanol"]} + ) + + vars["Liquids|Gas|w/ CCS"] = pp.out( + "meth_ng_ccs", + units, + outfilter={"level": ["primary"], "commodity": ["methanol"]}, + ) + + vars["Hydrogen|Coal|w/o CCS"] = pp.out( + "h2_coal", units, outfilter={"level": ["secondary"], "commodity": ["hydrogen"]} + ) + + vars["Hydrogen|Coal|w/ CCS"] = pp.out( + "h2_coal_ccs", + units, + outfilter={"level": ["secondary"], "commodity": ["hydrogen"]}, + ) + + vars["Hydrogen|Gas|w/o CCS"] = pp.out( + "h2_smr", units, outfilter={"level": ["secondary"], "commodity": ["hydrogen"]} + ) + + vars["Hydrogen|Gas|w/ CCS"] = pp.out( + "h2_smr_ccs", + units, + outfilter={"level": ["secondary"], "commodity": ["hydrogen"]}, + ) + + vars["Hydrogen|Biomass|w/o CCS"] = pp.out( + "h2_bio", units, outfilter={"level": ["secondary"], "commodity": ["hydrogen"]} + ) + + vars["Hydrogen|Biomass|w/ CCS"] = pp.out( + "h2_bio_ccs", + units, + outfilter={"level": ["secondary"], "commodity": ["hydrogen"]}, + ) + + vars["Hydrogen|Electricity"] = pp.out( + "h2_elec", units, outfilter={"level": ["secondary"], "commodity": ["hydrogen"]} + ) + + df = pp_utils.make_outputdf(vars, units) + return df + +@_register +def retr_CO2_CCS(units_emi, units_ene): + """Carbon sequestration. + + Energy and land-use related carbon seuqestration. + + Parameters + ---------- + + units_emi : str + Units to which emission variables should be converted. + units_ene : str + Units to which energy variables should be converted. + """ + + vars = {} + + # -------------------------------- + # Calculation of helping variables + # -------------------------------- + + # Biogas share calculation + _Biogas = pp.out("gas_bio", units_ene) + + _gas_inp_tecs = [ + "gas_ppl", + "gas_cc", + "gas_cc_ccs", + "gas_ct", + "gas_htfc", + "gas_hpl", + "meth_ng", + "meth_ng_ccs", + "h2_smr", + "h2_smr_ccs", + "gas_t_d", + "gas_t_d_ch4", + ] + + _totgas = pp.inp(_gas_inp_tecs, units_ene, inpfilter={"commodity": ["gas"]}) + + _BGas_share = (_Biogas / _totgas).fillna(0) + + # Calulation of CCS components + + _CCS_coal_elec = -1.0 * pp.emi( + ["c_ppl_co2scr", "coal_adv_ccs", "igcc_ccs", "igcc_co2scr"], + "GWa", + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi, + ) + + _CCS_coal_liq = -1.0 * pp.emi( + ["syn_liq_ccs", "meth_coal_ccs"], + "GWa", + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi, + ) + + _CCS_coal_hydrogen = -1.0 * pp.emi( + "h2_coal_ccs", + "GWa", + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi, + ) + + _CCS_cement = -1.0 * pp.emi( + ["clinker_dry_ccs_cement", "clinker_wet_ccs_cement"], + "GWa", + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi, + ) + + _CCS_ammonia = -1.0 * pp.emi( + ['biomass_NH3_ccs', 'gas_NH3_ccs', 'coal_NH3_ccs', 'fueloil_NH3_ccs'], + "GWa", + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi, + ) + + _CCS_bio_elec = -1.0 * pp.emi( + ["bio_ppl_co2scr", "bio_istig_ccs"], + "GWa", + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi, + ) + + _CCS_bio_liq = -1.0 * pp.emi( + ["eth_bio_ccs", "liq_bio_ccs"], + "GWa", + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi, + ) + + _CCS_bio_hydrogen = -1.0 * pp.emi( + "h2_bio_ccs", + "GWa", + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi, + ) + + _CCS_gas_elec = ( + -1.0 + * pp.emi( + ["g_ppl_co2scr", "gas_cc_ccs"], + "GWa", + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi, + ) + * (1.0 - _BGas_share) + ) + + _CCS_gas_liq = ( + -1.0 + * pp.emi( + "meth_ng_ccs", + "GWa", + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi, + ) + * (1.0 - _BGas_share) + ) + + _CCS_gas_hydrogen = ( + -1.0 + * pp.emi( + "h2_smr_ccs", + "GWa", + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi, + ) + * (1.0 - _BGas_share) + ) + + vars["CCS|Fossil|Energy|Supply|Electricity"] = _CCS_coal_elec + _CCS_gas_elec + + vars["CCS|Fossil|Energy|Supply|Liquids"] = _CCS_coal_liq + _CCS_gas_liq + + vars["CCS|Fossil|Energy|Supply|Hydrogen"] = _CCS_coal_hydrogen + _CCS_gas_hydrogen + + vars["CCS|Biomass|Energy|Supply|Electricity"] = ( + _CCS_bio_elec + _CCS_gas_elec * _BGas_share + ) + + vars["CCS|Biomass|Energy|Supply|Liquids"] = ( + _CCS_bio_liq + _CCS_gas_liq * _BGas_share + ) + + vars["CCS|Biomass|Energy|Supply|Hydrogen"] = ( + _CCS_bio_hydrogen + _CCS_gas_hydrogen * _BGas_share + ) + + vars["CCS|Industrial Processes"] = _CCS_cement + _CCS_ammonia + + vars["Land Use"] = -pp.land_out( + lu_out_filter={ + "level": ["land_use_reporting"], + "commodity": ["Emissions|CO2|AFOLU|Negative"], + } + ) + + vars["Land Use|Afforestation"] = -pp.land_out( + lu_out_filter={ + "level": ["land_use_reporting"], + "commodity": ["Emissions|CO2|AFOLU|Afforestation"], + } + ) + + df = pp_utils.make_outputdf(vars, units_emi) + return df + +@_register +def retr_CO2emi(units_emi, units_ene_mdl): + """Emissions: CO2. + + Parameters + ---------- + + units_emi : str + Units to which emission variables should be converted. + units_ene_mdl : str + Native units of energy in the model. + """ + + dfs = [] + + vars = {} + + if run_history != "True": + group = ["Region", "Mode", "Vintage"] + else: + group = ["Region"] + + # -------------------------------- + # Calculation of helping variables + # -------------------------------- + + _inp_nonccs_gas_tecs = ( + pp.inp( + [ + "gas_rc", + "hp_gas_rc", + "gas_i", + 'furnace_gas_aluminum', + 'furnace_gas_petro', + 'furnace_gas_cement', + 'furnace_gas_refining', + "hp_gas_i", + 'hp_gas_aluminum', + 'hp_gas_petro', + 'hp_gas_refining', + "gas_trp", + "gas_fs", + "gas_ppl", + "gas_ct", + "gas_cc", + "gas_htfc", + "gas_hpl", + ], + units_ene_mdl, + inpfilter={"commodity": ["gas"]}, + ) + + pp.inp( + ["gas_t_d", "gas_t_d_ch4"], units_ene_mdl, inpfilter={"commodity": ["gas"]} + ) + - pp.out( + ["gas_t_d", "gas_t_d_ch4"], units_ene_mdl, outfilter={"commodity": ["gas"]} + ) + ) + + _inp_all_gas_tecs = _inp_nonccs_gas_tecs + pp.inp( + ["gas_cc_ccs", "meth_ng", "meth_ng_ccs", "h2_smr", "h2_smr_ccs"], + units_ene_mdl, + inpfilter={"commodity": ["gas"]}, + ) + + # Calculate shares for ppl feeding into g_ppl_co2scr (gas_cc and gas_ppl) + _gas_cc_shr = (pp.out("gas_cc") / pp.out(["gas_cc", "gas_ppl"])).fillna(0) + + _gas_ppl_shr = (pp.out("gas_ppl") / pp.out(["gas_cc", "gas_ppl"])).fillna(0) + + _inp_nonccs_gas_tecs_wo_CCSRETRO = ( + _inp_nonccs_gas_tecs + - _pe_wCCSretro( + "gas_cc", + "g_ppl_co2scr", + group, + inpfilter={"level": ["secondary"], "commodity": ["gas"]}, + units=units_ene_mdl, + share=_gas_cc_shr, + ) + - _pe_wCCSretro( + "gas_ppl", + "g_ppl_co2scr", + group, + inpfilter={"level": ["secondary"], "commodity": ["gas"]}, + units=units_ene_mdl, + share=_gas_ppl_shr, + ) + - _pe_wCCSretro( + "gas_htfc", + "gfc_co2scr", + group, + inpfilter={"level": ["secondary"], "commodity": ["gas"]}, + units=units_ene_mdl, + ) + ) + + # Helping variables required in units of Emissions + _Biogas_tot_abs = pp.out("gas_bio") + _Biogas_tot = _Biogas_tot_abs * mu["crbcnt_gas"] * mu["conv_c2co2"] + _Biogas_el = _Biogas_tot * ( + pp.inp( + ["gas_ppl", "gas_ct", "gas_cc", "gas_cc_ccs", "gas_htfc"], + units_ene_mdl, + inpfilter={"commodity": ["gas"]}, + ) + / _inp_all_gas_tecs + ).fillna(0) + + _Biogas_heat = _Biogas_tot * ( + pp.inp("gas_hpl", units_ene_mdl, inpfilter={"commodity": ["gas"]}) + / _inp_all_gas_tecs + ).fillna(0) + + _Biogas_liquids_gas_comb = _Biogas_tot * ( + pp.inp( + ["meth_ng", "meth_ng_ccs"], units_ene_mdl, inpfilter={"commodity": ["gas"]} + ) + / _inp_all_gas_tecs + ).fillna(0) + + _Biogas_gases_h2_comb = _Biogas_tot * ( + pp.inp( + ["h2_smr", "h2_smr_ccs"], units_ene_mdl, inpfilter={"commodity": ["gas"]} + ) + / _inp_all_gas_tecs + ).fillna(0) + + _Biogas_ind = _Biogas_tot * ( + pp.inp(["gas_i", "hp_gas_i", 'furnace_gas_aluminum','furnace_gas_petro', + 'furnace_gas_cement','furnace_gas_refining',"hp_gas_i",'hp_gas_aluminum', + 'hp_gas_petro','hp_gas_refining', + ], units_ene_mdl, inpfilter={"commodity": ["gas"]}) + / _inp_all_gas_tecs + ).fillna(0) + + _Biogas_fs = _Biogas_tot * ( + pp.inp("gas_fs", units_ene_mdl, inpfilter={"commodity": ["gas"]}) + / _inp_all_gas_tecs + ).fillna(0) + + _Biogas_res = _Biogas_tot * ( + pp.inp(["gas_rc", "hp_gas_rc"], units_ene_mdl, inpfilter={"commodity": ["gas"]}) + / _inp_all_gas_tecs + ).fillna(0) + + _Biogas_trp = _Biogas_tot * ( + pp.inp("gas_trp", units_ene_mdl, inpfilter={"commodity": ["gas"]}) + / _inp_all_gas_tecs + ).fillna(0) + + _Biogas_td = _Biogas_tot * ( + ( + pp.inp( + ["gas_t_d", "gas_t_d_ch4"], + units_ene_mdl, + inpfilter={"commodity": ["gas"]}, + ) + - pp.out( + ["gas_t_d", "gas_t_d_ch4"], + units_ene_mdl, + outfilter={"commodity": ["gas"]}, + ) + ) + / _inp_all_gas_tecs + ).fillna(0) + + _Hydrogen_tot = pp.emi( + "h2_mix", + units_ene_mdl, + emifilter={"relation": ["CO2_cc"]}, + emission_units=units_emi, + ) + + _Hydrogen_el = _Hydrogen_tot * ( + ( + pp.inp( + ["gas_ppl", "gas_ct", "gas_cc", "gas_htfc"], + units_ene_mdl, + inpfilter={"commodity": ["gas"]}, + ) + - _pe_wCCSretro( + "gas_cc", + "g_ppl_co2scr", + group, + inpfilter={"level": ["secondary"], "commodity": ["gas"]}, + units=units_ene_mdl, + share=_gas_cc_shr, + ) + - _pe_wCCSretro( + "gas_ppl", + "g_ppl_co2scr", + group, + inpfilter={"level": ["secondary"], "commodity": ["gas"]}, + units=units_ene_mdl, + share=_gas_ppl_shr, + ) + - _pe_wCCSretro( + "gas_htfc", + "gfc_co2scr", + group, + inpfilter={"level": ["secondary"], "commodity": ["gas"]}, + units=units_ene_mdl, + ) + ) + / _inp_nonccs_gas_tecs_wo_CCSRETRO + ).fillna(0) + + _Hydrogen_heat = _Hydrogen_tot * ( + pp.inp("gas_hpl", units_ene_mdl, inpfilter={"commodity": ["gas"]}) + / _inp_nonccs_gas_tecs_wo_CCSRETRO + ).fillna(0) + + _Hydrogen_ind = _Hydrogen_tot * ( + pp.inp(["gas_i", "hp_gas_i", + 'furnace_gas_aluminum', + 'furnace_gas_petro', + 'furnace_gas_cement', + 'furnace_gas_refining', + "hp_gas_i", + 'hp_gas_aluminum', + 'hp_gas_petro', + 'hp_gas_refining', + ], units_ene_mdl, inpfilter={"commodity": ["gas"]}) + / _inp_nonccs_gas_tecs_wo_CCSRETRO + ).fillna(0) + + _Hydrogen_fs = _Hydrogen_tot * ( + pp.inp("gas_fs", units_ene_mdl, inpfilter={"commodity": ["gas"]}) + / _inp_nonccs_gas_tecs_wo_CCSRETRO + ).fillna(0) + + _Hydrogen_res = _Hydrogen_tot * ( + pp.inp(["gas_rc", "hp_gas_rc"], units_ene_mdl, inpfilter={"commodity": ["gas"]}) + / _inp_nonccs_gas_tecs_wo_CCSRETRO + ).fillna(0) + + _Hydrogen_trp = _Hydrogen_tot * ( + pp.inp("gas_trp", units_ene_mdl, inpfilter={"commodity": ["gas"]}) + / _inp_nonccs_gas_tecs_wo_CCSRETRO + ).fillna(0) + + _Hydrogen_td = _Hydrogen_tot * ( + ( + pp.inp( + ["gas_t_d", "gas_t_d_ch4"], + units_ene_mdl, + inpfilter={"commodity": ["gas"]}, + ) + - pp.out( + ["gas_t_d", "gas_t_d_ch4"], + units_ene_mdl, + outfilter={"commodity": ["gas"]}, + ) + ) + / _inp_nonccs_gas_tecs_wo_CCSRETRO + ).fillna(0) + + _SE_Elec_gen = pp.emi( + [ + "coal_ppl_u", + "coal_ppl", + "coal_adv", + "coal_adv_ccs", + "igcc", + "igcc_ccs", + "foil_ppl", + "loil_ppl", + "loil_cc", + "oil_ppl", + "gas_ppl", + "gas_cc", + "gas_cc_ccs", + "gas_ct", + "gas_htfc", + "bio_istig", + "g_ppl_co2scr", + "c_ppl_co2scr", + "bio_ppl_co2scr", + "igcc_co2scr", + "gfc_co2scr", + "cfc_co2scr", + "bio_istig_ccs", + ], + units_ene_mdl, + emifilter={"relation": ["CO2_cc"]}, + emission_units=units_emi, + ) + + _SE_Elec_gen_woBECCS = ( + _SE_Elec_gen + - pp.emi( + ["bio_istig_ccs", "bio_ppl_co2scr"], + units_ene_mdl, + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi, + ) + - pp.emi( + ["g_ppl_co2scr", "gas_cc_ccs"], + units_ene_mdl, + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi, + ) + * (_Biogas_tot_abs / _inp_all_gas_tecs) + ) + + _SE_District_heat = pp.emi( + ["coal_hpl", "foil_hpl", "gas_hpl"], + units_ene_mdl, + emifilter={"relation": ["CO2_cc"]}, + emission_units=units_emi, + ) + + _FE_Feedstocks = pp.emi( + ["coal_fs", "foil_fs", "loil_fs", "gas_fs", "methanol_fs", + 'steam_cracker_petro', 'gas_processing_petro' + ], + units_ene_mdl, + emifilter={"relation": ["CO2_feedstocks"]}, + emission_units=units_emi, + ) + + _FE_Res_com = pp.emi( + ["coal_rc", "foil_rc", "loil_rc", "gas_rc", "meth_rc", "hp_gas_rc"], + units_ene_mdl, + emifilter={"relation": ["CO2_r_c"]}, + emission_units=units_emi, + ) + + _FE_Industry = pp.emi( + [ + "gas_i", + "hp_gas_i", + "loil_i", + "meth_i", + "coal_i", + "foil_i", + "sp_liq_I", + "sp_meth_I", + ], + units_ene_mdl, + emifilter={"relation": ["CO2_ind"]}, + emission_units=units_emi, + ) + + _FE_Transport = pp.emi( + ["gas_trp", "loil_trp", "meth_fc_trp", "meth_ic_trp", "coal_trp", "foil_trp"], + units_ene_mdl, + emifilter={"relation": ["CO2_trp"]}, + emission_units=units_emi, + ) + + _FE_total = _FE_Feedstocks + _FE_Res_com + _FE_Industry + _FE_Transport + + _Other_gases_extr_comb = pp.emi( + [ + "gas_extr_1", + "gas_extr_2", + "gas_extr_3", + "gas_extr_4", + "gas_extr_5", + "gas_extr_6", + "gas_extr_7", + "gas_extr_8", + ], + units_ene_mdl, + emifilter={"relation": ["CO2_cc"]}, + emission_units=units_emi, + ) + + _Other_gases_extr_fug = pp.emi( + "flaring_CO2", + units_ene_mdl, + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi, + ) + + # Note that this is not included in the total because + # the diff is only calcualted from CO2_TCE and doesnt include trade + _Other_gases_trans_comb_trade = pp.emi( + ["LNG_trd"], + units_ene_mdl, + emifilter={"relation": ["CO2_trade"]}, + emission_units=units_emi, + ) + + _Other_gases_trans_comb = pp.emi( + ["gas_t_d", "gas_t_d_ch4"], + units_ene_mdl, + emifilter={"relation": ["CO2_cc"]}, + emission_units=units_emi, + ) + + _Other_gases_coal_comb = pp.emi( + ["coal_gas"], + units_ene_mdl, + emifilter={"relation": ["CO2_cc"]}, + emission_units=units_emi, + ) + + # _Other_gases_biomass_comb = pp.emi(["gas_bio"], units_ene_mdl, + # emifilter={"relation": ["CO2_cc"]}, + # emission_units=units_emi) + + _Other_gases_h2_comb = pp.emi( + ["h2_smr", "h2_coal", "h2_bio", "h2_coal_ccs", "h2_smr_ccs", "h2_bio_ccs"], + units_ene_mdl, + emifilter={"relation": ["CO2_cc"]}, + emission_units=units_emi, + ) + + _Other_gases_h2_comb_woBECCS = ( + _Other_gases_h2_comb + - pp.emi( + ["h2_bio_ccs"], + units_ene_mdl, + emifilter={"relation": ["CO2_cc"]}, + emission_units=units_emi, + ) + - pp.emi( + ["h2_smr_ccs"], + units_ene_mdl, + emifilter={"relation": ["CO2_cc"]}, + emission_units=units_emi, + ) + * (_Biogas_tot_abs / _inp_all_gas_tecs) + ) + + _Other_gases_total = ( + _Other_gases_extr_comb + + _Other_gases_extr_fug + + _Other_gases_trans_comb + + _Other_gases_coal_comb + + _Other_gases_h2_comb + ) + + _Other_gases_total_woBECCS = ( + _Other_gases_extr_comb + + _Other_gases_trans_comb + + _Other_gases_coal_comb + + _Other_gases_h2_comb_woBECCS + ) + # Fugitive is not included in the total used to redistribute the difference + # + _Other_gases_extr_fug + + _Other_liquids_extr_comb = pp.emi( + [ + "oil_extr_1", + "oil_extr_2", + "oil_extr_3", + "oil_extr_4", + "oil_extr_1_ch4", + "oil_extr_2_ch4", + "oil_extr_3_ch4", + "oil_extr_4_ch4", + "oil_extr_5", + "oil_extr_6", + "oil_extr_7", + "oil_extr_8", + ], + units_ene_mdl, + emifilter={"relation": ["CO2_cc"]}, + emission_units=units_emi, + ) + + # Note that this is not included in the total because + # the diff is only calcualted from CO2_TCE and doesnt include trade + _Other_liquids_trans_comb_trade = pp.emi( + ["foil_trd", "loil_trd", "oil_trd", "meth_trd", "eth_trd"], + units_ene_mdl, + emifilter={"relation": ["CO2_trade"]}, + emission_units=units_emi, + ) + + _Other_liquids_trans_comb = pp.emi( + ["foil_t_d", "loil_t_d", "meth_t_d", "eth_t_d"], + units_ene_mdl, + emifilter={"relation": ["CO2_cc"]}, + emission_units=units_emi, + ) + + _Other_liquids_oil_comb = pp.emi( + ["furnace_coke_refining", "furnace_coal_refining", + "furnace_biomass_refining", "furnace_gas_refining", + "furnace_loil_refining", "furnace_foil_refining", + "furnace_methanol_refining", "atm_distillation_ref", + "vacuum_distillation_ref", "catalytic_cracking_ref", + "visbreaker_ref", "coking_ref", "catalytic_reforming_ref", + "hydro_cracking_ref"], + units_ene_mdl, + emifilter={"relation": ["CO2_cc"]}, + emission_units=units_emi, + ) + + _Other_liquids_gas_comb = pp.emi( + ["meth_ng", "meth_ng_ccs"], + units_ene_mdl, + emifilter={"relation": ["CO2_cc"]}, + emission_units=units_emi, + ) + + _Other_liquids_gas_comb_woBECCS = _Other_liquids_gas_comb - pp.emi( + ["meth_ng_ccs"], + units_ene_mdl, + emifilter={"relation": ["CO2_cc"]}, + emission_units=units_emi, + ) * (_Biogas_tot_abs / _inp_all_gas_tecs) + + _Other_liquids_coal_comb = pp.emi( + ["meth_coal", "syn_liq", "meth_coal_ccs", "syn_liq_ccs"], + units_ene_mdl, + emifilter={"relation": ["CO2_cc"]}, + emission_units=units_emi, + ) + + _Other_liquids_biomass_comb = pp.emi( + ["eth_bio", "liq_bio", "eth_bio_ccs", "liq_bio_ccs"], + units_ene_mdl, + emifilter={"relation": ["CO2_cc"]}, + emission_units=units_emi, + ) + + _Other_liquids_biomass_comb_woBECCS = _Other_liquids_biomass_comb - pp.emi( + ["eth_bio_ccs", "liq_bio_ccs"], + units_ene_mdl, + emifilter={"relation": ["CO2_cc"]}, + emission_units=units_emi, + ) + + _Other_liquids_total = ( + _Other_liquids_extr_comb + + _Other_liquids_trans_comb + + _Other_liquids_oil_comb + + _Other_liquids_gas_comb + + _Other_liquids_coal_comb + + _Other_liquids_biomass_comb + ) + + _Other_liquids_total_woBECCS = ( + _Other_liquids_extr_comb + + _Other_liquids_trans_comb + + _Other_liquids_oil_comb + + _Other_liquids_gas_comb_woBECCS + + _Other_liquids_coal_comb + + _Other_liquids_biomass_comb_woBECCS + ) + + _Other_solids_coal_trans_comb = pp.emi( + "coal_t_d", + units_ene_mdl, + emifilter={"relation": ["CO2_cc"]}, + emission_units=units_emi, + ) + + _Other_solids_total = _Other_solids_coal_trans_comb + + _Cement1 = pp.emi( + ["clinker_dry_cement", "clinker_wet_cement", "clinker_dry_ccs_cement", + "clinker_wet_ccs_cement"], + units_ene_mdl, + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi, + ) + + _Cement2 = pp.emi( + ['DUMMY_limestone_supply_cement','furnace_biomass_cement', + 'furnace_coal_cement','furnace_foil_cement', 'furnace_gas_cement', + 'furnace_loil_cement','furnace_methanol_cement'], + units_ene_mdl, + emifilter={"relation": ["CO2_ind"]}, + emission_units=units_emi, + ) + + _Aluminum1 = pp.emi( + ["soderberg_aluminum",'prebake_aluminum', 'vertical_stud'], + units_ene_mdl, + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi, + ) + + _Aluminum2 = pp.emi( + ['furnace_biomass_aluminum', 'furnace_coal_aluminum', + 'furnace_foil_aluminum', 'furnace_gas_aluminum', + 'furnace_loil_aluminum', 'furnace_methanol_aluminum'], + units_ene_mdl, + emifilter={"relation": ["CO2_ind"]}, + emission_units=units_emi, + ) + + _Steel = pp.emi( + ['DUMMY_coal_supply', 'DUMMY_gas_supply', 'DUMMY_limestone_supply_steel'], + units_ene_mdl, + emifilter={"relation": ["CO2_ind"]}, + emission_units=units_emi, + ) + + _Chemicals = pp.emi( + ['furnace_biomass_petro', 'furnace_coal_petro', 'furnace_coke_petro', + 'furnace_foil_petro', 'furnace_gas_petro', 'furnace_loil_petro', + 'furnace_methanol_petro'], + units_ene_mdl, + emifilter={"relation": ["CO2_ind"]}, + emission_units=units_emi, + ) + + _Ammonia = pp.emi( + ['biomass_NH3','gas_NH3', 'coal_NH3', 'fueloil_NH3'], + units_ene_mdl, + emifilter={"relation": ["CO2_cc"]}, + emission_units=units_emi, + ) + + _Ammonia_ccs = pp.emi( + ['biomass_NH3_ccs','gas_NH3_ccs', 'coal_NH3_ccs', 'fueloil_NH3_ccs'], + units_ene_mdl, + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi, + ) + + _Total = ( + _SE_Elec_gen + + _SE_District_heat + + _FE_total + + _Other_gases_total + + _Other_liquids_total + + _Other_solids_total + - _Biogas_tot + + _Hydrogen_tot + + _Cement1 + + _Cement2 + + _Aluminum1 + + _Aluminum2 + + _Steel + + _Chemicals + + _Ammonia + + _Ammonia_ccs) + + # GLOBIOM with the new lu implementation, LU_CO2 no longer writes + # into _CO2_tce1 (CO2_TCE), as these have emission factors only, + # and therefore do not write into CO2_TCE + # + _CO2_GLOBIOM) + + _Total_wo_BECCS = ( + abs(_SE_District_heat - _Biogas_heat + _Hydrogen_heat) + + abs(_SE_Elec_gen_woBECCS - _Biogas_el + _Hydrogen_el) + + abs( + _Other_gases_total_woBECCS + - _Biogas_gases_h2_comb + - _Biogas_td + + _Hydrogen_td + ) + + abs(_Other_liquids_total_woBECCS - _Biogas_liquids_gas_comb) + + abs(_Other_solids_total) + ) + + _CO2_tce1 = pp.emi( + "CO2_TCE", + units_ene_mdl, + emifilter={"relation": ["TCE_Emission"]}, + emission_units=units_emi, + ) + + _CO2_GLOBIOM = pp.land_out( + lu_out_filter={ + "level": ["land_use_reporting"], + "commodity": ["Emissions|CO2|AFOLU"], + }, + units=units_emi, + ) + + #_Diff1 = _CO2_tce1 - _Total + _Diff1 = pp_utils._make_zero() + + if run_history == "True": + df_hist = pd.read_csv(lu_hist_data).set_index("Region") + df_hist = df_hist.rename(columns={i: int(i) for i in df_hist.columns}) + _Diff1 = _Diff1 - df_hist + + # --------------------- + # Agriculture (Table 1) + # --------------------- + + AgricultureWasteBurning = pp_utils._make_zero() + vars["AFOLU|Biomass Burning"] = AgricultureWasteBurning + Agriculture = pp_utils._make_zero() + vars["AFOLU|Agriculture"] = Agriculture + + # --------------------------- + # Grassland Burning (Table 2) + # --------------------------- + + GrasslandBurning = pp_utils._make_zero() + vars["AFOLU|Land|Grassland Burning"] = GrasslandBurning + + # ------------------------ + # Forest Burning (Table 3) + # ------------------------ + + ForestBurning = pp_utils._make_zero() + vars["AFOLU|Land|Forest Burning"] = ForestBurning + + vars["AFOLU"] = _CO2_GLOBIOM + vars["AFOLU|Afforestation"] = pp.land_out( + lu_out_filter={ + "level": ["land_use_reporting"], + "commodity": ["Emissions|CO2|AFOLU|Afforestation"], + }, + units=units_emi, + ) + + vars["AFOLU|Deforestation"] = pp.land_out( + lu_out_filter={ + "level": ["land_use_reporting"], + "commodity": ["Emissions|CO2|AFOLU|Deforestation"], + }, + units=units_emi, + ) + + vars["AFOLU|Forest Management"] = pp.land_out( + lu_out_filter={ + "level": ["land_use_reporting"], + "commodity": ["Emissions|CO2|AFOLU|Forest Management"], + }, + units=units_emi, + ) + + vars["AFOLU|Negative"] = pp.land_out( + lu_out_filter={ + "level": ["land_use_reporting"], + "commodity": ["Emissions|CO2|AFOLU|Negative"], + }, + units=units_emi, + ) + + vars["AFOLU|Other LUC"] = pp.land_out( + lu_out_filter={ + "level": ["land_use_reporting"], + "commodity": ["Emissions|CO2|AFOLU|Other LUC"], + }, + units=units_emi, + ) + + vars["AFOLU|Positive"] = pp.land_out( + lu_out_filter={ + "level": ["land_use_reporting"], + "commodity": ["Emissions|CO2|AFOLU|Positive"], + }, + units=units_emi, + ) + + # vars["AFOLU|Soil Carbon"] = pp.land_out( + # lu_out_filter={"level": ["land_use_reporting"], + # "commodity": ["Emissions|CO2|AFOLU|Soil Carbon"]}, + # units=units_emi) + + # ------------------ + # Aircraft (Table 4) + # ------------------ + + Aircraft = pp_utils._make_zero() + vars["Energy|Demand|Transportation|Aviation|International"] = Aircraft + + # ----------------------------------------- + # Electricity and heat production (Table 5) + # ----------------------------------------- + + vars["Energy|Supply|Heat"] = ( + _SE_District_heat + - _Biogas_heat + + _Hydrogen_heat + + _Diff1 + * ( + abs(_SE_District_heat - _Biogas_heat + _Hydrogen_heat) / _Total_wo_BECCS + ).fillna(0) + ) + + vars["Energy|Supply|Electricity"] = ( + _SE_Elec_gen + - _Biogas_el + + _Hydrogen_el + + _Diff1 + * ( + abs(_SE_Elec_gen_woBECCS - _Biogas_el + _Hydrogen_el) / _Total_wo_BECCS + ).fillna(0) + ) + + vars["Energy|Supply|Gases|Biomass|Combustion"] = pp_utils._make_zero() + vars["Energy|Supply|Gases|Biomass|Fugitive"] = pp_utils._make_zero() + + vars["Energy|Supply|Gases|Coal|Combustion"] = _Other_gases_coal_comb + _Diff1 * ( + abs(_Other_gases_coal_comb) / _Total_wo_BECCS + ).fillna(0) + + vars["Energy|Supply|Gases|Coal|Fugitive"] = pp_utils._make_zero() + + vars[ + "Energy|Supply|Gases|Extraction|Combustion" + ] = _Other_gases_extr_comb + _Diff1 * ( + abs(_Other_gases_extr_comb) / _Total_wo_BECCS + ).fillna( + 0 + ) + + # _Diff1 is not disctributed across the variable + vars["Energy|Supply|Gases|Extraction|Fugitive"] = _Other_gases_extr_fug + vars["Energy|Supply|Gases|Hydrogen|Combustion"] = ( + _Other_gases_h2_comb + - _Biogas_gases_h2_comb + + _Diff1 + * ( + abs(_Other_gases_h2_comb_woBECCS - _Biogas_gases_h2_comb) / _Total_wo_BECCS + ).fillna(0) + ) + + vars["Energy|Supply|Gases|Hydrogen|Fugitive"] = pp_utils._make_zero() + vars["Energy|Supply|Gases|Natural Gas|Combustion"] = pp_utils._make_zero() + vars["Energy|Supply|Gases|Natural Gas|Fugitive"] = pp_utils._make_zero() + + vars["Energy|Supply|Gases|Transportation|Combustion"] = ( + _Other_gases_trans_comb + - _Biogas_td + + _Hydrogen_td + + _Diff1 + * ( + abs(_Other_gases_trans_comb - _Biogas_td + _Hydrogen_td) / _Total_wo_BECCS + ).fillna(0) + + _Other_gases_trans_comb_trade + ) + + vars["Energy|Supply|Gases|Transportation|Fugitive"] = pp_utils._make_zero() + + vars[ + "Energy|Supply|Liquids|Biomass|Combustion" + ] = _Other_liquids_biomass_comb + _Diff1 * ( + abs(_Other_liquids_biomass_comb_woBECCS) / _Total_wo_BECCS + ).fillna( + 0 + ) + + vars["Energy|Supply|Liquids|Biomass|Fugitive"] = pp_utils._make_zero() + + vars[ + "Energy|Supply|Liquids|Coal|Combustion" + ] = _Other_liquids_coal_comb + _Diff1 * ( + abs(_Other_liquids_coal_comb) / _Total_wo_BECCS + ).fillna( + 0 + ) + + vars["Energy|Supply|Liquids|Coal|Fugitive"] = pp_utils._make_zero() + + vars[ + "Energy|Supply|Liquids|Extraction|Combustion" + ] = _Other_liquids_extr_comb + _Diff1 * ( + abs(_Other_liquids_extr_comb) / _Total_wo_BECCS + ).fillna( + 0 + ) + + vars["Energy|Supply|Liquids|Extraction|Fugitive"] = pp_utils._make_zero() + + vars["Energy|Supply|Liquids|Natural Gas|Combustion"] = ( + _Other_liquids_gas_comb + - _Biogas_liquids_gas_comb + + _Diff1 + * ( + abs(_Other_liquids_gas_comb_woBECCS - _Biogas_liquids_gas_comb) + / _Total_wo_BECCS + ).fillna(0) + ) + + vars["Energy|Supply|Liquids|Natural Gas|Fugitive"] = pp_utils._make_zero() + + vars["Energy|Supply|Liquids|Oil|Combustion"] = _Other_liquids_oil_comb + _Diff1 * ( + abs(_Other_liquids_oil_comb) / _Total_wo_BECCS + ).fillna(0) + + vars["Energy|Supply|Liquids|Oil|Fugitive"] = pp_utils._make_zero() + + vars["Energy|Supply|Liquids|Transportation|Combustion"] = ( + _Other_liquids_trans_comb + + _Diff1 * (abs(_Other_liquids_trans_comb) / _Total_wo_BECCS).fillna(0) + + _Other_liquids_trans_comb_trade + ) + + vars["Energy|Supply|Liquids|Transportation|Fugitive"] = pp_utils._make_zero() + vars["Energy|Supply|Other|Combustion"] = pp_utils._make_zero() + vars["Energy|Supply|Other|Fugitive"] = pp_utils._make_zero() + vars["Energy|Supply|Solids|Biomass|Combustion"] = pp_utils._make_zero() + vars["Energy|Supply|Solids|Biomass|Fugitive"] = pp_utils._make_zero() + vars["Energy|Supply|Solids|Coal|Combustion"] = pp_utils._make_zero() + vars["Energy|Supply|Solids|Coal|Fugitive"] = pp_utils._make_zero() + + vars[ + "Energy|Supply|Solids|Extraction|Combustion" + ] = _Other_solids_coal_trans_comb + _Diff1 * ( + abs(_Other_solids_coal_trans_comb) / _Total_wo_BECCS + ).fillna( + 0 + ) + + vars["Energy|Supply|Solids|Extraction|Fugitive"] = pp_utils._make_zero() + vars["Energy|Supply|Solids|Transportation|Combustion"] = pp_utils._make_zero() + vars["Energy|Supply|Solids|Transportation|Fugitive"] = pp_utils._make_zero() + + # ------------------------------- + # Industrial Combustion (Table 7) + # ------------------------------- + + IndustrialCombustion = _FE_Industry - _Biogas_ind + _Hydrogen_ind + + vars["Energy|Demand|Industry"] = IndustrialCombustion + + # --------------------- + # Industrial Feedstocks + # --------------------- + + vars["Energy|Demand|Other Sector"] = _FE_Feedstocks - _Biogas_fs + _Hydrogen_fs + + # -------------------------------------------- + # Industrial process and product use (Table 8) + # -------------------------------------------- + + Cement = _Cement1 + vars["Industrial Processes"] = Cement + + # -------------------------------- + # International shipping (Table 9) + # -------------------------------- + + _Bunker1 = pp.act("CO2s_TCE") * mu["conv_c2co2"] + Bunker = _Bunker1 + vars["Energy|Demand|Transportation|Shipping|International"] = Bunker + + # ------------------------------------------------- + # Residential, Commercial, Other - Other (Table 11) + # ------------------------------------------------- + + ResComOth = pp_utils._make_zero() + vars["Energy|Demand|AFOFI"] = ResComOth + + # ------------------------------------------------------------------- + # Residential, Commercial, Other - Residential, Commercial (Table 12) + # ------------------------------------------------------------------- + + Res_Com = _FE_Res_com - _Biogas_res + _Hydrogen_res + vars["Energy|Demand|Residential and Commercial"] = Res_Com + + # ------------------------------ + # Road transportation (Table 13) + # ------------------------------ + + Transport = _FE_Transport - _Biogas_trp + _Hydrogen_trp + # vars["Energy|Demand|Transportation|Road"] = Transport + vars["Energy|Demand|Transportation|Road Rail and Domestic Shipping"] = Transport + + # ---------------------------------------------- + # Solvents production and application (Table 14) + # ---------------------------------------------- + + Solvents = pp_utils._make_zero() + vars["Product Use|Solvents"] = Solvents + + # ---------------- + # Waste (Table 15) + # ---------------- + + Waste = pp_utils._make_zero() + vars["Waste"] = Waste + + # Special Aggregates which cannot be treated generically + vars["Energy|Supply|Combustion"] = ( + vars["Energy|Supply|Heat"] + + vars["Energy|Supply|Electricity"] + + vars["Energy|Supply|Gases|Biomass|Combustion"] + + vars["Energy|Supply|Gases|Coal|Combustion"] + + vars["Energy|Supply|Gases|Extraction|Combustion"] + + vars["Energy|Supply|Gases|Hydrogen|Combustion"] + + vars["Energy|Supply|Gases|Natural Gas|Combustion"] + + vars["Energy|Supply|Gases|Transportation|Combustion"] + + vars["Energy|Supply|Liquids|Biomass|Combustion"] + + vars["Energy|Supply|Liquids|Coal|Combustion"] + + vars["Energy|Supply|Liquids|Extraction|Combustion"] + + vars["Energy|Supply|Liquids|Natural Gas|Combustion"] + + vars["Energy|Supply|Liquids|Oil|Combustion"] + + vars["Energy|Supply|Liquids|Transportation|Combustion"] + + vars["Energy|Supply|Other|Combustion"] + + vars["Energy|Supply|Solids|Biomass|Combustion"] + + vars["Energy|Supply|Solids|Coal|Combustion"] + + vars["Energy|Supply|Solids|Extraction|Combustion"] + + vars["Energy|Supply|Solids|Transportation|Combustion"] + ) + + vars["Energy|Combustion"] = ( + vars["Energy|Supply|Combustion"] + + vars["Energy|Demand|Transportation|Shipping|International"] + + vars["Energy|Demand|AFOFI"] + + vars["Energy|Demand|Industry"] + + vars["Energy|Demand|Residential and Commercial"] + + vars["Energy|Demand|Transportation|Road Rail and Domestic Shipping"] + + vars["Energy|Demand|Transportation|Aviation|International"] + ) + + vars["Energy|Supply|Fugitive"] = ( + vars["Energy|Supply|Gases|Biomass|Fugitive"] + + vars["Energy|Supply|Gases|Coal|Fugitive"] + + vars["Energy|Supply|Gases|Extraction|Fugitive"] + + vars["Energy|Supply|Gases|Hydrogen|Fugitive"] + + vars["Energy|Supply|Gases|Natural Gas|Fugitive"] + + vars["Energy|Supply|Gases|Transportation|Fugitive"] + + vars["Energy|Supply|Liquids|Biomass|Fugitive"] + + vars["Energy|Supply|Liquids|Coal|Fugitive"] + + vars["Energy|Supply|Liquids|Extraction|Fugitive"] + + vars["Energy|Supply|Liquids|Natural Gas|Fugitive"] + + vars["Energy|Supply|Liquids|Oil|Fugitive"] + + vars["Energy|Supply|Liquids|Transportation|Fugitive"] + + vars["Energy|Supply|Other|Fugitive"] + + vars["Energy|Supply|Solids|Biomass|Fugitive"] + + vars["Energy|Supply|Solids|Coal|Fugitive"] + + vars["Energy|Supply|Solids|Extraction|Fugitive"] + + vars["Energy|Supply|Solids|Transportation|Fugitive"] + ) + + vars["Energy|Fugitive"] = vars["Energy|Supply|Fugitive"] + + dfs.append(pp_utils.make_outputdf(vars, units_emi)) + vars = {} + + # Additional reporting to account for emission differences and accounting issues + vars["Difference|Statistical"] = _Diff1 + vars["Difference|Stock|Coal"] = pp.emi( + ["coal_imp", "coal_exp"], + units_ene_mdl, + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi, + ) + pp.emi( + ["coal_trd"], + units_ene_mdl, + emifilter={"relation": ["CO2_trade"]}, + emission_units=units_emi, + ) + # Coal cannot be used for bunkers + # + pp.emi(["coal_bunker"], units_ene_mdl, + # emifilter={"relation": ["CO2_shipping"]}, + # emission_units=units_emi) + + vars["Difference|Stock|Methanol"] = ( + pp.emi( + ["meth_imp", "meth_exp"], + units_ene_mdl, + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi, + ) + + pp.emi( + ["meth_bunker"], + units_ene_mdl, + emifilter={"relation": ["CO2_shipping"]}, + emission_units=units_emi, + ) + + pp.emi( + ["meth_trd"], + units_ene_mdl, + emifilter={"relation": ["CO2_trade"]}, + emission_units=units_emi, + ) + ) + + vars["Difference|Stock|Fueloil"] = ( + pp.emi( + ["foil_imp", "foil_exp"], + units_ene_mdl, + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi, + ) + + pp.emi( + ["foil_bunker"], + units_ene_mdl, + emifilter={"relation": ["CO2_shipping"]}, + emission_units=units_emi, + ) + + pp.emi( + ["foil_trd"], + units_ene_mdl, + emifilter={"relation": ["CO2_trade"]}, + emission_units=units_emi, + ) + ) + + vars["Difference|Stock|Lightoil"] = ( + pp.emi( + ["loil_imp", "loil_exp"], + units_ene_mdl, + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi, + ) + + pp.emi( + ["loil_bunker"], + units_ene_mdl, + emifilter={"relation": ["CO2_shipping"]}, + emission_units=units_emi, + ) + + pp.emi( + ["loil_trd"], + units_ene_mdl, + emifilter={"relation": ["CO2_trade"]}, + emission_units=units_emi, + ) + ) + + vars["Difference|Stock|Crudeoil"] = pp.emi( + ["oil_imp", "oil_exp"], + units_ene_mdl, + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi, + ) + pp.emi( + ["oil_trd"], + units_ene_mdl, + emifilter={"relation": ["CO2_trade"]}, + emission_units=units_emi, + ) + # + pp.emi(["oil_bunker"], units_ene_mdl, + # emifilter={"relation": ["CO2_shipping"]}, + # emission_units=units_emi) + + vars["Difference|Stock|LNG"] = ( + pp.emi( + ["LNG_imp", "LNG_exp"], + units_ene_mdl, + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi, + ) + + pp.emi( + ["LNG_bunker"], + units_ene_mdl, + emifilter={"relation": ["CO2_shipping"]}, + emission_units=units_emi, + ) + + pp.emi( + ["LNG_trd"], + units_ene_mdl, + emifilter={"relation": ["CO2_trade"]}, + emission_units=units_emi, + ) + ) + + vars["Difference|Stock|Natural Gas"] = pp.emi( + [ + "gas_imp", + "gas_exp_nam", + "gas_exp_weu", + "gas_exp_eeu", + "gas_exp_pao", + "gas_exp_cpa", + "gas_exp_sas", + "gas_exp_afr", + "gas_exp_scs", + "gas_exp_cas", + "gas_exp_ubm", + "gas_exp_rus", + ], + units_ene_mdl, + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi, + ) + + dfs.append(pp_utils.make_outputdf(vars, units_emi, glb=False)) + vars = {} + + vars["Difference|Stock|Activity|Coal"] = ( + pp.inp(["coal_imp"], units_ene_mdl) + - pp.inp(["coal_exp"], units_ene_mdl) + + pp.inp(["coal_trd"], units_ene_mdl) + - pp.out(["coal_trd"], units_ene_mdl) + ) + # + pp.inp(["coal_bunker"], units_ene_mdl) + + vars["Difference|Stock|Activity|Methanol"] = ( + pp.inp(["meth_imp"], units_ene_mdl) + - pp.inp(["meth_exp"], units_ene_mdl) + + pp.inp(["meth_bunker"], units_ene_mdl) + + pp.inp(["meth_trd"], units_ene_mdl) + - pp.out(["meth_trd"], units_ene_mdl) + ) + + vars["Difference|Stock|Activity|Fueloil"] = ( + pp.inp(["foil_imp"], units_ene_mdl) + - pp.inp(["foil_exp"], units_ene_mdl) + + pp.inp(["foil_bunker"], units_ene_mdl) + + pp.inp(["foil_trd"], units_ene_mdl) + - pp.out(["foil_trd"], units_ene_mdl) + ) + + vars["Difference|Stock|Activity|Lightoil"] = ( + pp.inp(["loil_imp"], units_ene_mdl) + - pp.inp(["loil_exp"], units_ene_mdl) + + pp.inp(["loil_bunker"], units_ene_mdl) + + pp.inp(["loil_trd"], units_ene_mdl) + - pp.out(["loil_trd"], units_ene_mdl) + ) + + vars["Difference|Stock|Activity|Crudeoil"] = ( + pp.inp(["oil_imp"], units_ene_mdl) + - pp.inp(["oil_exp"], units_ene_mdl) + + pp.inp(["oil_trd"], units_ene_mdl) + - pp.out(["oil_trd"], units_ene_mdl) + ) + # + pp.inp(["oil_bunker"], units_ene_mdl) + + vars["Difference|Stock|Activity|LNG"] = ( + pp.inp(["LNG_imp"], units_ene_mdl) + - pp.inp(["LNG_exp"], units_ene_mdl) + + pp.inp(["LNG_bunker"], units_ene_mdl) + + pp.inp(["LNG_trd"], units_ene_mdl) + - pp.out(["LNG_trd"], units_ene_mdl) + ) + + vars["Difference|Stock|Activity|Natural Gas"] = pp.inp( + ["gas_imp"], units_ene_mdl + ) - pp.inp( + [ + "gas_exp_nam", + "gas_exp_weu", + "gas_exp_eeu", + "gas_exp_pao", + "gas_exp_cpa", + "gas_exp_sas", + "gas_exp_afr", + "gas_exp_scs", + "gas_exp_cas", + "gas_exp_ubm", + "gas_exp_rus", + ], + units_ene_mdl, + ) + + dfs.append(pp_utils.make_outputdf(vars, units_ene_mdl, glb=False)) + return pd.concat(dfs, sort=True) + +@_register +def retr_supply_inv(units_energy, + units_emi, + units_ene_mdl): + """Technology results: Investments. + + Investments into technologies. + + Note OFR - 20.04.2017: The following has been checked between Volker + Krey, David McCollum and Oliver Fricko. + + There are fixed factors by which ccs technologies are multiplied which + equate to the share of the powerplant costs which split investments into the + share associated with the standard powerplant and the share associated + with those investments related to CCS. + + For some extraction and synfuel technologies, a certain share of the voms and + foms are attributed to the investments which is based on the GEA-Study + where the derived investment costs were partly attributed to the + voms/foms. + + Parameters + ---------- + units_energy : str + Units to which energy variables should be converted. + units_emi : str + Units to which emission variables should be converted. + units_ene_mdl : str + Native units of energy in the model. + """ + + vars = {} + + # ---------- + # Extraction + # ---------- + + # Note OFR 25.04.2017: All non-extraction costs for Coal, Gas and Oil + # have been moved to "Energy|Other" + + vars["Extraction|Coal"] = pp.investment(["coal_extr_ch4", "coal_extr", + "lignite_extr"], units=units_energy) + + vars["Extraction|Gas|Conventional"] =\ + pp.investment(["gas_extr_1", "gas_extr_2", + "gas_extr_3", "gas_extr_4"], units=units_energy) +\ + pp.act_vom(["gas_extr_1", "gas_extr_2", + "gas_extr_3", "gas_extr_4"], units=units_energy) * .5 + + vars["Extraction|Gas|Unconventional"] =\ + pp.investment(["gas_extr_5", "gas_extr_6", + "gas_extr_7", "gas_extr_8"], units=units_energy) +\ + pp.act_vom(["gas_extr_5", "gas_extr_6", + "gas_extr_7", "gas_extr_8"], units=units_energy) * .5 + + # Note OFR 25.04.2017: Any costs relating to refineries have been + # removed (compared to GEA) as these are reported under "Liquids|Oil" + + vars["Extraction|Oil|Conventional"] =\ + pp.investment(["oil_extr_1", "oil_extr_2", "oil_extr_3", + "oil_extr_1_ch4", "oil_extr_2_ch4", "oil_extr_3_ch4"], + units=units_energy) +\ + pp.act_vom(["oil_extr_1", "oil_extr_2", "oil_extr_3", + "oil_extr_1_ch4", "oil_extr_2_ch4", "oil_extr_3_ch4"], + units=units_energy) * .5 + + vars["Extraction|Oil|Unconventional"] =\ + pp.investment(["oil_extr_4", "oil_extr_4_ch4", "oil_extr_5", + "oil_extr_6", "oil_extr_7", "oil_extr_8"], + units=units_energy) +\ + pp.act_vom(["oil_extr_4", "oil_extr_4_ch4", "oil_extr_5", + "oil_extr_6", "oil_extr_7", "oil_extr_8"], units=units_energy) * .5 + + # As no mode is specified, u5-reproc will account for all 3 modes. + vars["Extraction|Uranium"] =\ + pp.investment(["uran2u5", "Uran_extr", + "u5-reproc", "plutonium_prod"], units=units_energy) +\ + pp.act_vom(["uran2u5", "Uran_extr"], units=units_energy) +\ + pp.act_vom(["u5-reproc"], actfilter={"mode": ["M1"]}, units=units_energy) +\ + pp.tic_fom(["uran2u5", "Uran_extr", "u5-reproc"], units=units_energy) + + # --------------------- + # Electricity - Fossils + # --------------------- + + vars["Electricity|Coal|w/ CCS"] =\ + pp.investment(["c_ppl_co2scr", "cfc_co2scr"], units=units_energy) +\ + pp.investment("coal_adv_ccs", units=units_energy) * 0.25 +\ + pp.investment("igcc_ccs", units=units_energy) * 0.31 + + vars["Electricity|Coal|w/o CCS"] =\ + pp.investment(["coal_ppl", "coal_ppl_u", "coal_adv"], units=units_energy) +\ + pp.investment("coal_adv_ccs", units=units_energy) * 0.75 +\ + pp.investment("igcc", units=units_energy) +\ + pp.investment("igcc_ccs", units=units_energy) * 0.69 + + vars["Electricity|Gas|w/ CCS"] =\ + pp.investment(["g_ppl_co2scr", "gfc_co2scr"], units=units_energy) +\ + pp.investment("gas_cc_ccs", units=units_energy) * 0.53 + + vars["Electricity|Gas|w/o CCS"] =\ + pp.investment(["gas_cc", "gas_ct", "gas_ppl"], units=units_energy) +\ + pp.investment("gas_cc_ccs", units=units_energy) * 0.47 + + vars["Electricity|Oil|w/o CCS"] =\ + pp.investment(["foil_ppl", "loil_ppl", "oil_ppl", "loil_cc"], + units=units_energy) + + # ------------------------ + # Electricity - Renewables + # ------------------------ + + vars["Electricity|Biomass|w/ CCS"] =\ + pp.investment("bio_ppl_co2scr", units=units_energy) +\ + pp.investment("bio_istig_ccs", units=units_energy) * 0.31 + + vars["Electricity|Biomass|w/o CCS"] =\ + pp.investment(["bio_ppl", "bio_istig"], units=units_energy) +\ + pp.investment("bio_istig_ccs", units=units_energy) * 0.69 + + vars["Electricity|Geothermal"] = pp.investment("geo_ppl", units=units_energy) + + vars["Electricity|Hydro"] = pp.investment(["hydro_hc", "hydro_lc"], + units=units_energy) + vars["Electricity|Other"] = pp.investment(["h2_fc_I", "h2_fc_RC"], + units=units_energy) + + _solar_pv_elec = pp.investment(["solar_pv_ppl", "solar_pv_I", + "solar_pv_RC"], units=units_energy) + + _solar_th_elec = pp.investment(["csp_sm1_ppl", "csp_sm3_ppl"], units=units_energy) + + vars["Electricity|Solar|PV"] = _solar_pv_elec + vars["Electricity|Solar|CSP"] = _solar_th_elec + vars["Electricity|Wind|Onshore"] = pp.investment(["wind_ppl"], units=units_energy) + vars["Electricity|Wind|Offshore"] = pp.investment(["wind_ppf"], units=units_energy) + + # ------------------- + # Electricity Nuclear + # ------------------- + + vars["Electricity|Nuclear"] = pp.investment(["nuc_hc", "nuc_lc"], + units=units_energy) + + # -------------------------------------------------- + # Electricity Storage, transmission and distribution + # -------------------------------------------------- + + vars["Electricity|Electricity Storage"] = pp.investment("stor_ppl", + units=units_energy) + + vars["Electricity|Transmission and Distribution"] =\ + pp.investment(["elec_t_d", + "elec_exp", "elec_exp_africa", + "elec_exp_america", "elec_exp_asia", + "elec_exp_eurasia", "elec_exp_eur_afr", + "elec_exp_asia_afr", + "elec_imp", "elec_imp_africa", + "elec_imp_america", "elec_imp_asia", + "elec_imp_eurasia", "elec_imp_eur_afr", + "elec_imp_asia_afr"], units=units_energy) +\ + pp.act_vom(["elec_t_d", + "elec_exp", "elec_exp_africa", + "elec_exp_america", "elec_exp_asia", + "elec_exp_eurasia", "elec_exp_eur_afr", + "elec_exp_asia_afr", + "elec_imp", "elec_imp_africa", + "elec_imp_america", "elec_imp_asia", + "elec_imp_eurasia", "elec_imp_eur_afr", + "elec_imp_asia_afr"], units=units_energy) * .5 +\ + pp.tic_fom(["elec_t_d", + "elec_exp", "elec_exp_africa", + "elec_exp_america", "elec_exp_asia", + "elec_exp_eurasia", "elec_exp_eur_afr", + "elec_exp_asia_afr", + "elec_imp", "elec_imp_africa", + "elec_imp_america", "elec_imp_asia", + "elec_imp_eurasia", "elec_imp_eur_afr", + "elec_imp_asia_afr"], units=units_energy) * .5 + + # ------------------------------------------ + # CO2 Storage, transmission and distribution + # ------------------------------------------ + + _CCS_coal_elec = -1 *\ + pp.emi(["c_ppl_co2scr", "coal_adv_ccs", + "igcc_ccs", "clinker_dry_ccs_cement",'clinker_wet_ccs_cement'], units_ene_mdl, + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi) + + _CCS_coal_synf = -1 *\ + pp.emi(["syn_liq_ccs", "h2_coal_ccs", + "meth_coal_ccs"], units_ene_mdl, + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi) + + _CCS_gas_elec = -1 *\ + pp.emi(["g_ppl_co2scr", "gas_cc_ccs"], units_ene_mdl, + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi) + + _CCS_gas_synf = -1 *\ + pp.emi(["h2_smr_ccs", "meth_ng_ccs"], units_ene_mdl, + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi) + + _CCS_bio_elec = -1 *\ + pp.emi(["bio_ppl_co2scr", "bio_istig_ccs"], units_ene_mdl, + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi) + + _CCS_bio_synf = -1 *\ + pp.emi(["eth_bio_ccs", "liq_bio_ccs", + "h2_bio_ccs"], units_ene_mdl, + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi) + + _Biogas_use_tot = pp.out("gas_bio") + + _Gas_use_tot = pp.inp(["gas_ppl", "gas_cc", "gas_cc_ccs", + "gas_ct", "gas_htfc", "gas_hpl", + "meth_ng", "meth_ng_ccs", "h2_smr", + "h2_smr_ccs", "gas_rc", "hp_gas_rc", + "gas_i", "hp_gas_i", + 'furnace_gas_aluminum', + 'furnace_gas_petro', + 'furnace_gas_cement', + 'furnace_gas_refining', + "hp_gas_i", + 'hp_gas_aluminum', + 'hp_gas_petro', + 'hp_gas_refining', + "gas_trp", + "gas_fs"]) + + _Biogas_share = (_Biogas_use_tot / _Gas_use_tot).fillna(0) + + _CCS_Foss = _CCS_coal_elec +\ + _CCS_coal_synf +\ + _CCS_gas_elec * (1 - _Biogas_share) +\ + _CCS_gas_synf * (1 - _Biogas_share) + + _CCS_Bio = _CCS_bio_elec +\ + _CCS_bio_synf -\ + (_CCS_gas_elec + _CCS_gas_synf) * _Biogas_share + + _CCS_coal_elec_shr = (_CCS_coal_elec / _CCS_Foss).fillna(0) + _CCS_coal_synf_shr = (_CCS_coal_synf / _CCS_Foss).fillna(0) + _CCS_gas_elec_shr = (_CCS_gas_elec / _CCS_Foss).fillna(0) + _CCS_gas_synf_shr = (_CCS_gas_synf / _CCS_Foss).fillna(0) + _CCS_bio_elec_shr = (_CCS_bio_elec / _CCS_Bio).fillna(0) + _CCS_bio_synf_shr = (_CCS_bio_synf / _CCS_Bio).fillna(0) + + CO2_trans_dist_elec =\ + pp.act_vom("co2_tr_dis", units=units_energy) * 0.5 * _CCS_coal_elec_shr +\ + pp.act_vom("co2_tr_dis", units=units_energy) * 0.5 * _CCS_gas_elec_shr +\ + pp.act_vom("bco2_tr_dis", units=units_energy) * 0.5 * _CCS_bio_elec_shr + + CO2_trans_dist_synf =\ + pp.act_vom("co2_tr_dis", units=units_energy) * 0.5 * _CCS_coal_synf_shr +\ + pp.act_vom("co2_tr_dis", units=units_energy) * 0.5 * _CCS_gas_synf_shr +\ + pp.act_vom("bco2_tr_dis", units=units_energy) * 0.5 * _CCS_bio_synf_shr + + vars["CO2 Transport and Storage"] = CO2_trans_dist_elec + CO2_trans_dist_synf + + # ---- + # Heat + # ---- + + vars["Heat"] = pp.investment(["coal_hpl", "foil_hpl", "gas_hpl", + "bio_hpl", "heat_t_d", "po_turbine"], + units=units_energy) + + # ------------------------- + # Synthetic fuel production + # ------------------------- + + # Note OFR 25.04.2017: XXX_synf_ccs has been split into hydrogen and + # liquids. The shares then add up to 1, but the variables are kept + # separate in order to preserve the split between CCS and non-CCS + + _Coal_synf_ccs_liq = pp.investment("meth_coal_ccs", units=units_energy) * 0.02 +\ + pp.investment("syn_liq_ccs", units=units_energy) * 0.01 + + _Gas_synf_ccs_liq = pp.investment("meth_ng_ccs", units=units_energy) * 0.08 + + _Bio_synf_ccs_liq = pp.investment("eth_bio_ccs", units=units_energy) * 0.34 + \ + pp.investment("liq_bio_ccs", units=units_energy) * 0.02 + + _Coal_synf_ccs_h2 = pp.investment("h2_coal_ccs", units=units_energy) * 0.03 + _Gas_synf_ccs_h2 = pp.investment("h2_smr_ccs", units=units_energy) * 0.17 + _Bio_synf_ccs_h2 = pp.investment("h2_bio_ccs", units=units_energy) * 0.02 + + # Note OFR 25.04.2017: "coal_gas" have been moved to "other" + vars["Liquids|Coal and Gas"] =\ + pp.investment(["meth_coal", "syn_liq", "meth_ng"], units=units_energy) +\ + pp.investment("meth_ng_ccs", units=units_energy) * 0.92 +\ + pp.investment("meth_coal_ccs", units=units_energy) * 0.98 +\ + pp.investment("syn_liq_ccs", units=units_energy) * 0.99 +\ + _Coal_synf_ccs_liq +\ + _Gas_synf_ccs_liq + + # Note OFR 25.04.2017: "gas_bio" has been moved to "other" + vars["Liquids|Biomass"] =\ + pp.investment(["eth_bio", "liq_bio"], units=units_energy) +\ + pp.investment("liq_bio_ccs", units=units_energy) * 0.98 +\ + pp.investment("eth_bio_ccs", units=units_energy) * 0.66 + _Bio_synf_ccs_liq + + # Note OFR 25.04.2017: "transport, import and exports costs related to + # liquids are only included in the total" + _Synfuel_other = pp.investment(["meth_exp", "meth_imp", "meth_t_d", + "meth_bal", "eth_exp", "eth_imp", + "eth_t_d", "eth_bal", "SO2_scrub_synf"], + units=units_energy) + + vars["Liquids|Oil"] = pp.investment(['furnace_coke_refining', + 'furnace_coal_refining', + 'furnace_foil_refining', + 'furnace_loil_refining', + 'furnace_ethanol_refining', + 'furnace_biomass_refining', + 'furnace_methanol_refining', + 'furnace_gas_refining', + 'furnace_elec_refining', + 'furnace_h2_refining', + 'hp_gas_refining', + 'hp_elec_refining', + 'fc_h2_refining', + 'solar_refining', + 'dheat_refining', + 'atm_distillation_ref', + 'vacuum_distillation_ref', + 'hydrotreating_ref', + 'catalytic_cracking_ref', + 'visbreaker_ref', + 'coking_ref', + 'catalytic_reforming_ref', + 'hydro_cracking_ref' + ], units=units_energy) +\ + pp.tic_fom(['furnace_coke_refining', + 'furnace_coal_refining', + 'furnace_foil_refining', + 'furnace_loil_refining', + 'furnace_ethanol_refining', + 'furnace_biomass_refining', + 'furnace_methanol_refining', + 'furnace_gas_refining', + 'furnace_elec_refining', + 'furnace_h2_refining', + 'hp_gas_refining', + 'hp_elec_refining', + 'fc_h2_refining', + 'solar_refining', + 'dheat_refining', + 'atm_distillation_ref', + 'vacuum_distillation_ref', + 'hydrotreating_ref', + 'catalytic_cracking_ref', + 'visbreaker_ref', + 'coking_ref', + 'catalytic_reforming_ref', + 'hydro_cracking_ref'], units=units_energy) + + vars["Liquids"] = vars["Liquids|Coal and Gas"] +\ + vars["Liquids|Biomass"] +\ + vars["Liquids|Oil"] +\ + _Synfuel_other + + # -------- + # Hydrogen + # -------- + + vars["Hydrogen|Fossil"] =\ + pp.investment(["h2_coal", "h2_smr"], units=units_energy) +\ + pp.investment("h2_coal_ccs", units=units_energy) * 0.97 +\ + pp.investment("h2_smr_ccs", units=units_energy) * 0.83 +\ + _Coal_synf_ccs_h2 +\ + _Gas_synf_ccs_h2 + + vars["Hydrogen|Renewable"] = pp.investment("h2_bio", units=units_energy) +\ + pp.investment("h2_bio_ccs", units=units_energy) * 0.98 +\ + _Bio_synf_ccs_h2 + + vars["Hydrogen|Other"] = pp.investment(["h2_elec", "h2_liq", "h2_t_d", + "lh2_exp", "lh2_imp", "lh2_bal", + "lh2_regas", "lh2_t_d"], + units=units_energy) +\ + pp.act_vom("h2_mix", units=units_energy) * 0.5 + + # ----- + # Other + # ----- + + # All questionable variables from extraction that are not related directly + # to extraction should be moved to Other + # Note OFR 25.04.2017: Any costs relating to refineries have been + # removed (compared to GEA) as these are reported under "Liquids|Oil" + + vars["Other|Liquids|Oil|Transmission and Distribution"] =\ + pp.investment(["foil_t_d", "loil_t_d"], units=units_energy) + + vars["Other|Liquids|Oil|Other"] =\ + pp.investment(["foil_exp", "loil_exp", "oil_exp", + "oil_imp", "foil_imp", "loil_imp", + "loil_std", "oil_bal", "loil_sto"], units=units_energy) + + vars["Other|Gases|Transmission and Distribution"] =\ + pp.investment(["gas_t_d", "gas_t_d_ch4"], units=units_energy) + + vars["Other|Gases|Production"] =\ + pp.investment(["gas_bio", "coal_gas"], units=units_energy) + + vars["Other|Gases|Other"] =\ + pp.investment(["LNG_bal", "LNG_prod", "LNG_regas", + "LNG_exp", "LNG_imp", "gas_bal", + "gas_std", "gas_sto", "gas_exp_eeu", + "gas_exp_nam", "gas_exp_pao", "gas_exp_weu", + "gas_exp_cpa", "gas_exp_afr", "gas_exp_sas", + "gas_exp_scs", "gas_exp_cas", "gas_exp_ubm", + "gas_exp_rus", "gas_imp"], units=units_energy) + + vars["Other|Solids|Coal|Transmission and Distribution"] =\ + pp.investment(["coal_t_d", "coal_t_d-rc-SO2", "coal_t_d-rc-06p", + "coal_t_d-in-SO2", "coal_t_d-in-06p"], units=units_energy) +\ + pp.act_vom(["coal_t_d-rc-SO2", "coal_t_d-rc-06p", "coal_t_d-in-SO2", + "coal_t_d-in-06p", "coal_t_d"], units=units_energy) * 0.5 + + vars["Other|Solids|Coal|Other"] =\ + pp.investment(["coal_exp", "coal_imp", + "coal_bal", "coal_std"], units=units_energy) + + vars["Other|Solids|Biomass|Transmission and Distribution"] =\ + pp.investment("biomass_t_d", units=units_energy) + + vars["Other|Other"] =\ + pp.investment(["SO2_scrub_ref"], units=units_energy) * 0.5 +\ + pp.investment(["SO2_scrub_ind"], units=units_energy) + + df = pp_utils.make_outputdf(vars, units_energy) + return df From a7aa4e2dd2ff4767d93eaeb6469607adab3708a1 Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Wed, 7 Sep 2022 10:17:38 +0200 Subject: [PATCH 020/774] Tidy .materials.report.tables - apply black. - sort imports and and remove unused. - imported _pe_wccsretro from default_tables.py, instead of duplicating. - remove duplicated but unused functions _out_div_eff, _pe_elec_woccsretro, _pe_elec_wccs_retro, _se_elec_wocssretro, _se_elec_wccsretro, _pe_elec_po_turb, _se_elec_po_turb, _se_heat_po_turb. --- .../model/material/report/tables.py | 1261 ++++++++--------- 1 file changed, 614 insertions(+), 647 deletions(-) diff --git a/message_ix_models/model/material/report/tables.py b/message_ix_models/model/material/report/tables.py index 98815d7cd9..29d7e8dca5 100644 --- a/message_ix_models/model/material/report/tables.py +++ b/message_ix_models/model/material/report/tables.py @@ -1,6 +1,6 @@ import pandas as pd -import numpy as np +from message_data.tools.post_processing.default_tables import _pe_wCCSretro import message_data.tools.post_processing.pp_utils as pp_utils pp = None @@ -32,315 +32,6 @@ def _register(func): return func -def _pe_wCCSretro(tec, scrub_tec, group, inpfilter, units, share=1): - """Calculates primary energy use of technologies with scrubbers. - - Parameters - ---------- - - tec : str - Technology name - scrub_tec : str - Name of CO2 scrubbing technology linked to `tec`. - group : list - Indexes by which results are to be grouped. - inpfilter : dict - `level` and/or `commodity` used for retrieving the input. - units : str - Units to which variables should be converted. - share : number or dataframe (default: 1) - Share of `tec` activity if multiple technologies share the same scrubber. - """ - - # The multiplication of `share` is required to determine in which years - # the powerplant is active, because the scrubber activity - # is not necessarily equal to the powerplant activity if - # there are two powerplants using the same scrubber. - - df = ( - pp.out(scrub_tec, units) - * share - / pp_utils.ppgroup( - (pp.act(tec, group=group) / pp.act(tec)).fillna(0) - * pp.eff( - tec, - inpfilter=inpfilter, - outfilter={"level": ["secondary"], "commodity": ["electr"]}, - group=group, - ) - ) - ).fillna(0) - - return df - - -def _pe_elec_woCCSretro(tec, scrub_tec, group, inpfilter, units, _Frac, share=1): - """Calculates primary energy electricity generation equivalent. - - This applies to technologies WITHOUT scrubbers. - - Parameters - ---------- - - tec : str - Technology name - scrub_tec : str - Name of CO2 scrubbing technology linked to `tec`. - inpfilter : dict - `level` and/or `commodity` used for retrieving the input. - units : str - Units to which variables should be converted. - _Frac : dataframe - Regional share of actual cogeneration (po_turbine). - share : number or dataframe (default: 1) - Share of `tec` activity if multiple technologies share the same scrubber. - """ - - df = ( - ( - pp.out(tec, units) - * (1.0 - pp.rel(tec, relfilter={"relation": ["pass_out_trb"]}) * _Frac) - - (1.0 - pp.rel(tec, relfilter={"relation": ["pass_out_trb"]}) * _Frac) - * pp.out(scrub_tec, units) - * share - ) - / pp_utils.ppgroup( - (pp.act(tec, group=group) / pp.act(tec)).fillna(0) - * pp.eff( - tec, - inpfilter=inpfilter, - outfilter={"level": ["secondary"], "commodity": ["electr"]}, - group=group, - ) - ) - ).fillna(0) - - return df - - -def _pe_elec_wCCSretro(tec, scrub_tec, group, inpfilter, units, _Frac, share=1): - """Calculates primary energy electricity generation equivalent. - - This applies to technologies WITH scrubbers. - - Parameters - ---------- - - tec : str - Technology name - scrub_tec : str - Name of CO2 scrubbing technology linked to `tec`. - group : list - Indexes by which resultsa re to be grouped. - inpfilter : dict - `level` and/or `commodity` used for retrieving the input. - units : str - Units to which variables should be converted. - _Frac : dataframe - Regional share of actual cogeneration (po_turbine). - share : number or dataframe (default: 1) - Share of `tec` activity if multiple technologies share the same scrubber. - """ - - df = ( - ( - (1.0 - pp.rel(tec, relfilter={"relation": ["pass_out_trb"]}) * _Frac) - * pp.out(scrub_tec, units) - * share - ) - / pp_utils.ppgroup( - (pp.act(tec, group=group) / pp.act(tec)).fillna(0) - * pp.eff( - tec, - inpfilter=inpfilter, - outfilter={"level": ["secondary"], "commodity": ["electr"]}, - group=group, - ) - ) - ).fillna(0) - - return df - - -def _se_elec_woCCSretro(tec, scrub_tec, units, _Frac, share=1): - """Calculates secondary energy electricity generation. - - This applies to technologies WITHOUT scrubbers. - - Parameters - ---------- - - tec : str - Technology name - scrub_tec : str - Name of CO2 scrubbing technology linked to `tec`. - units : str - Units to which variables should be converted. - _Frac : dataframe - Regional share of actual cogeneration (po_turbine). - share : number or dataframe (default: 1) - Share of `tec` activity if multiple technologies share the same scrubber. - """ - - df = ( - pp.out(tec, units) - * (1.0 - pp.rel(tec, relfilter={"relation": ["pass_out_trb"]}) * _Frac) - - (1.0 - pp.rel(tec, relfilter={"relation": ["pass_out_trb"]}) * _Frac) - * pp.out(scrub_tec, units) - * share - ) - - return df - - -def _se_elec_wCCSretro(tec, scrub_tec, units, _Frac, share=1): - """Calculates secondary energy electricity generation. - - This applies to technologies WITH scrubbers. - - Parameters - ---------- - - tec : str - Technology name - scrub_tec : str - Name of CO2 scrubbing technology linked to `tec`. - units : str - Units to which variables should be converted. - _Frac : dataframe - Regional share of actual cogeneration (po_turbine). - share : number or dataframe (default: 1) - Share of `tec` activity if multiple technologies share the same scrubber. - """ - - df = ( - (1.0 - pp.rel(tec, relfilter={"relation": ["pass_out_trb"]}) * _Frac) - * pp.out(scrub_tec, units) - * share - ) - - return df - - -def _pe_elec_po_turb(tec, group, units, _Frac, inpfilter): - """Calculates primary energy electricity equivalent generation. - - This calcualtes the amount of electricity used in primary energy equivalent used - for cogeneration (po-turbine). - - Parameters - ---------- - - tec : str - Technology name - group : list - Indexes by which resultsa re to be grouped. - units : str - Units to which variables should be converted. - _Frac : dataframe - Regional share of actual cogeneration (po_turbine). - inpfilter : dict - `level` and/or `commodity` used for retrieving the input. - """ - - df = pp_utils.ppgroup( - ( - ( - pp.out(tec, units, group=group) - * (1.0 - pp.rel(tec, relfilter={"relation": ["pass_out_trb"]}) * _Frac) - ) - / pp.eff(tec, inpfilter=inpfilter, group=group) - ).fillna(0) - ) - - return df - - -def _se_elec_po_turb(tec, units, _Frac, outfilter=None): - """Calculates secondary energy electricity generation. - - This calcualtes the amount of electricity used for cogeneration (po-turbine). - - Parameters - ---------- - - tec : str - Technology name - units : str - Units to which variables should be converted. - _Frac : dataframe - Regional share of actual cogeneration (po_turbine). - outfilter : dict - `level` and/or `commodity` used for retrieving the output. - """ - - df = pp.out(tec, units, outfilter=outfilter) * ( - 1.0 - pp.rel(tec, relfilter={"relation": ["pass_out_trb"]}) * _Frac - ) - - return df - - -def _se_heat_po_turb(tec, units, _Frac, outfilter=None): - """Calculates secondary energy heat generation. - - This calcualtes the amount of heat produced from cogeneration (po-turbine) - for a specific technology. - - Parameters - ---------- - - tec : str - Technology name - units : str - Units to which variables should be converted. - _Frac : dataframe - Regional share of actual cogeneration (po_turbine). - outfilter : dict - `level` and/or `commodity` used for retrieving the output. - """ - - df = pp.out(tec, units, outfilter=outfilter) * ( - pp.rel(tec, relfilter={"relation": ["pass_out_trb"]}) * _Frac - ) - - return df - - -def _out_div_eff(tec, group, inpfilter, outfilter): - """Calculates input based on output. - - This calculates the amount of input required based on the output. - Mainly this is used for water related reporting. - - Parameters - ---------- - - tec : str - Technology name - group : list - Indexes by which resultsa re to be grouped. - inpfilter : dict - `level` and/or `commodity` used for retrieving the input. - outfilter : dict - `level` and/or `commodity` used for retrieving the output. - """ - - tec = [tec] if type(tec) == str else tec - - dfs = [] - for t in tec: - dfs.append( - pp_utils.ppgroup( - (pp.out(t, outfilter=outfilter, group=group)) - / pp.eff(t, inpfilter=inpfilter, group=group) - ) - ) - df = pd.concat(dfs, sort=True) - - return df.groupby(df.index.name).sum() - - # ------------------- # Reporting functions # ------------------- @@ -524,6 +215,7 @@ def retr_SE_synfuels(units): df = pp_utils.make_outputdf(vars, units) return df + @_register def retr_CO2_CCS(units_emi, units_ene): """Carbon sequestration. @@ -598,7 +290,7 @@ def retr_CO2_CCS(units_emi, units_ene): ) _CCS_ammonia = -1.0 * pp.emi( - ['biomass_NH3_ccs', 'gas_NH3_ccs', 'coal_NH3_ccs', 'fueloil_NH3_ccs'], + ["biomass_NH3_ccs", "gas_NH3_ccs", "coal_NH3_ccs", "fueloil_NH3_ccs"], "GWa", emifilter={"relation": ["CO2_Emission"]}, emission_units=units_emi, @@ -695,6 +387,7 @@ def retr_CO2_CCS(units_emi, units_ene): df = pp_utils.make_outputdf(vars, units_emi) return df + @_register def retr_CO2emi(units_emi, units_ene_mdl): """Emissions: CO2. @@ -727,14 +420,14 @@ def retr_CO2emi(units_emi, units_ene_mdl): "gas_rc", "hp_gas_rc", "gas_i", - 'furnace_gas_aluminum', - 'furnace_gas_petro', - 'furnace_gas_cement', - 'furnace_gas_refining', + "furnace_gas_aluminum", + "furnace_gas_petro", + "furnace_gas_cement", + "furnace_gas_refining", "hp_gas_i", - 'hp_gas_aluminum', - 'hp_gas_petro', - 'hp_gas_refining', + "hp_gas_aluminum", + "hp_gas_petro", + "hp_gas_refining", "gas_trp", "gas_fs", "gas_ppl", @@ -824,10 +517,22 @@ def retr_CO2emi(units_emi, units_ene_mdl): ).fillna(0) _Biogas_ind = _Biogas_tot * ( - pp.inp(["gas_i", "hp_gas_i", 'furnace_gas_aluminum','furnace_gas_petro', - 'furnace_gas_cement','furnace_gas_refining',"hp_gas_i",'hp_gas_aluminum', - 'hp_gas_petro','hp_gas_refining', - ], units_ene_mdl, inpfilter={"commodity": ["gas"]}) + pp.inp( + [ + "gas_i", + "hp_gas_i", + "furnace_gas_aluminum", + "furnace_gas_petro", + "furnace_gas_cement", + "furnace_gas_refining", + "hp_gas_i", + "hp_gas_aluminum", + "hp_gas_petro", + "hp_gas_refining", + ], + units_ene_mdl, + inpfilter={"commodity": ["gas"]}, + ) / _inp_all_gas_tecs ).fillna(0) @@ -909,16 +614,22 @@ def retr_CO2emi(units_emi, units_ene_mdl): ).fillna(0) _Hydrogen_ind = _Hydrogen_tot * ( - pp.inp(["gas_i", "hp_gas_i", - 'furnace_gas_aluminum', - 'furnace_gas_petro', - 'furnace_gas_cement', - 'furnace_gas_refining', + pp.inp( + [ + "gas_i", + "hp_gas_i", + "furnace_gas_aluminum", + "furnace_gas_petro", + "furnace_gas_cement", + "furnace_gas_refining", "hp_gas_i", - 'hp_gas_aluminum', - 'hp_gas_petro', - 'hp_gas_refining', - ], units_ene_mdl, inpfilter={"commodity": ["gas"]}) + "hp_gas_aluminum", + "hp_gas_petro", + "hp_gas_refining", + ], + units_ene_mdl, + inpfilter={"commodity": ["gas"]}, + ) / _inp_nonccs_gas_tecs_wo_CCSRETRO ).fillna(0) @@ -1009,8 +720,14 @@ def retr_CO2emi(units_emi, units_ene_mdl): ) _FE_Feedstocks = pp.emi( - ["coal_fs", "foil_fs", "loil_fs", "gas_fs", "methanol_fs", - 'steam_cracker_petro', 'gas_processing_petro' + [ + "coal_fs", + "foil_fs", + "loil_fs", + "gas_fs", + "methanol_fs", + "steam_cracker_petro", + "gas_processing_petro", ], units_ene_mdl, emifilter={"relation": ["CO2_feedstocks"]}, @@ -1177,13 +894,22 @@ def retr_CO2emi(units_emi, units_ene_mdl): ) _Other_liquids_oil_comb = pp.emi( - ["furnace_coke_refining", "furnace_coal_refining", - "furnace_biomass_refining", "furnace_gas_refining", - "furnace_loil_refining", "furnace_foil_refining", - "furnace_methanol_refining", "atm_distillation_ref", - "vacuum_distillation_ref", "catalytic_cracking_ref", - "visbreaker_ref", "coking_ref", "catalytic_reforming_ref", - "hydro_cracking_ref"], + [ + "furnace_coke_refining", + "furnace_coal_refining", + "furnace_biomass_refining", + "furnace_gas_refining", + "furnace_loil_refining", + "furnace_foil_refining", + "furnace_methanol_refining", + "atm_distillation_ref", + "vacuum_distillation_ref", + "catalytic_cracking_ref", + "visbreaker_ref", + "coking_ref", + "catalytic_reforming_ref", + "hydro_cracking_ref", + ], units_ene_mdl, emifilter={"relation": ["CO2_cc"]}, emission_units=units_emi, @@ -1252,69 +978,91 @@ def retr_CO2emi(units_emi, units_ene_mdl): _Other_solids_total = _Other_solids_coal_trans_comb _Cement1 = pp.emi( - ["clinker_dry_cement", "clinker_wet_cement", "clinker_dry_ccs_cement", - "clinker_wet_ccs_cement"], + [ + "clinker_dry_cement", + "clinker_wet_cement", + "clinker_dry_ccs_cement", + "clinker_wet_ccs_cement", + ], units_ene_mdl, emifilter={"relation": ["CO2_Emission"]}, emission_units=units_emi, ) _Cement2 = pp.emi( - ['DUMMY_limestone_supply_cement','furnace_biomass_cement', - 'furnace_coal_cement','furnace_foil_cement', 'furnace_gas_cement', - 'furnace_loil_cement','furnace_methanol_cement'], + [ + "DUMMY_limestone_supply_cement", + "furnace_biomass_cement", + "furnace_coal_cement", + "furnace_foil_cement", + "furnace_gas_cement", + "furnace_loil_cement", + "furnace_methanol_cement", + ], units_ene_mdl, emifilter={"relation": ["CO2_ind"]}, emission_units=units_emi, ) _Aluminum1 = pp.emi( - ["soderberg_aluminum",'prebake_aluminum', 'vertical_stud'], + ["soderberg_aluminum", "prebake_aluminum", "vertical_stud"], units_ene_mdl, emifilter={"relation": ["CO2_Emission"]}, emission_units=units_emi, ) _Aluminum2 = pp.emi( - ['furnace_biomass_aluminum', 'furnace_coal_aluminum', - 'furnace_foil_aluminum', 'furnace_gas_aluminum', - 'furnace_loil_aluminum', 'furnace_methanol_aluminum'], + [ + "furnace_biomass_aluminum", + "furnace_coal_aluminum", + "furnace_foil_aluminum", + "furnace_gas_aluminum", + "furnace_loil_aluminum", + "furnace_methanol_aluminum", + ], units_ene_mdl, emifilter={"relation": ["CO2_ind"]}, emission_units=units_emi, ) _Steel = pp.emi( - ['DUMMY_coal_supply', 'DUMMY_gas_supply', 'DUMMY_limestone_supply_steel'], + ["DUMMY_coal_supply", "DUMMY_gas_supply", "DUMMY_limestone_supply_steel"], units_ene_mdl, emifilter={"relation": ["CO2_ind"]}, emission_units=units_emi, ) _Chemicals = pp.emi( - ['furnace_biomass_petro', 'furnace_coal_petro', 'furnace_coke_petro', - 'furnace_foil_petro', 'furnace_gas_petro', 'furnace_loil_petro', - 'furnace_methanol_petro'], + [ + "furnace_biomass_petro", + "furnace_coal_petro", + "furnace_coke_petro", + "furnace_foil_petro", + "furnace_gas_petro", + "furnace_loil_petro", + "furnace_methanol_petro", + ], units_ene_mdl, emifilter={"relation": ["CO2_ind"]}, emission_units=units_emi, ) _Ammonia = pp.emi( - ['biomass_NH3','gas_NH3', 'coal_NH3', 'fueloil_NH3'], + ["biomass_NH3", "gas_NH3", "coal_NH3", "fueloil_NH3"], units_ene_mdl, emifilter={"relation": ["CO2_cc"]}, emission_units=units_emi, ) _Ammonia_ccs = pp.emi( - ['biomass_NH3_ccs','gas_NH3_ccs', 'coal_NH3_ccs', 'fueloil_NH3_ccs'], + ["biomass_NH3_ccs", "gas_NH3_ccs", "coal_NH3_ccs", "fueloil_NH3_ccs"], units_ene_mdl, emifilter={"relation": ["CO2_Emission"]}, emission_units=units_emi, ) - _Total = ( + # FIXME unused; clarify purpose or remove + _Total = ( # noqa: F841 _SE_Elec_gen + _SE_District_heat + _FE_total @@ -1330,7 +1078,8 @@ def retr_CO2emi(units_emi, units_ene_mdl): + _Steel + _Chemicals + _Ammonia - + _Ammonia_ccs) + + _Ammonia_ccs + ) # GLOBIOM with the new lu implementation, LU_CO2 no longer writes # into _CO2_tce1 (CO2_TCE), as these have emission factors only, @@ -1350,7 +1099,8 @@ def retr_CO2emi(units_emi, units_ene_mdl): + abs(_Other_solids_total) ) - _CO2_tce1 = pp.emi( + # FIXME unused; clarify purpose or remove + _CO2_tce1 = pp.emi( # noqa: F841 "CO2_TCE", units_ene_mdl, emifilter={"relation": ["TCE_Emission"]}, @@ -1365,7 +1115,7 @@ def retr_CO2emi(units_emi, units_ene_mdl): units=units_emi, ) - #_Diff1 = _CO2_tce1 - _Total + # _Diff1 = _CO2_tce1 - _Total _Diff1 = pp_utils._make_zero() if run_history == "True": @@ -1933,10 +1683,9 @@ def retr_CO2emi(units_emi, units_ene_mdl): dfs.append(pp_utils.make_outputdf(vars, units_ene_mdl, glb=False)) return pd.concat(dfs, sort=True) + @_register -def retr_supply_inv(units_energy, - units_emi, - units_ene_mdl): +def retr_supply_inv(units_energy, units_emi, units_ene_mdl): """Technology results: Investments. Investments into technologies. @@ -1973,95 +1722,152 @@ def retr_supply_inv(units_energy, # Note OFR 25.04.2017: All non-extraction costs for Coal, Gas and Oil # have been moved to "Energy|Other" - vars["Extraction|Coal"] = pp.investment(["coal_extr_ch4", "coal_extr", - "lignite_extr"], units=units_energy) + vars["Extraction|Coal"] = pp.investment( + ["coal_extr_ch4", "coal_extr", "lignite_extr"], units=units_energy + ) - vars["Extraction|Gas|Conventional"] =\ - pp.investment(["gas_extr_1", "gas_extr_2", - "gas_extr_3", "gas_extr_4"], units=units_energy) +\ - pp.act_vom(["gas_extr_1", "gas_extr_2", - "gas_extr_3", "gas_extr_4"], units=units_energy) * .5 + vars["Extraction|Gas|Conventional"] = ( + pp.investment( + ["gas_extr_1", "gas_extr_2", "gas_extr_3", "gas_extr_4"], units=units_energy + ) + + pp.act_vom( + ["gas_extr_1", "gas_extr_2", "gas_extr_3", "gas_extr_4"], units=units_energy + ) + * 0.5 + ) - vars["Extraction|Gas|Unconventional"] =\ - pp.investment(["gas_extr_5", "gas_extr_6", - "gas_extr_7", "gas_extr_8"], units=units_energy) +\ - pp.act_vom(["gas_extr_5", "gas_extr_6", - "gas_extr_7", "gas_extr_8"], units=units_energy) * .5 + vars["Extraction|Gas|Unconventional"] = ( + pp.investment( + ["gas_extr_5", "gas_extr_6", "gas_extr_7", "gas_extr_8"], units=units_energy + ) + + pp.act_vom( + ["gas_extr_5", "gas_extr_6", "gas_extr_7", "gas_extr_8"], units=units_energy + ) + * 0.5 + ) # Note OFR 25.04.2017: Any costs relating to refineries have been # removed (compared to GEA) as these are reported under "Liquids|Oil" - vars["Extraction|Oil|Conventional"] =\ - pp.investment(["oil_extr_1", "oil_extr_2", "oil_extr_3", - "oil_extr_1_ch4", "oil_extr_2_ch4", "oil_extr_3_ch4"], - units=units_energy) +\ - pp.act_vom(["oil_extr_1", "oil_extr_2", "oil_extr_3", - "oil_extr_1_ch4", "oil_extr_2_ch4", "oil_extr_3_ch4"], - units=units_energy) * .5 - - vars["Extraction|Oil|Unconventional"] =\ - pp.investment(["oil_extr_4", "oil_extr_4_ch4", "oil_extr_5", - "oil_extr_6", "oil_extr_7", "oil_extr_8"], - units=units_energy) +\ - pp.act_vom(["oil_extr_4", "oil_extr_4_ch4", "oil_extr_5", - "oil_extr_6", "oil_extr_7", "oil_extr_8"], units=units_energy) * .5 + vars["Extraction|Oil|Conventional"] = ( + pp.investment( + [ + "oil_extr_1", + "oil_extr_2", + "oil_extr_3", + "oil_extr_1_ch4", + "oil_extr_2_ch4", + "oil_extr_3_ch4", + ], + units=units_energy, + ) + + pp.act_vom( + [ + "oil_extr_1", + "oil_extr_2", + "oil_extr_3", + "oil_extr_1_ch4", + "oil_extr_2_ch4", + "oil_extr_3_ch4", + ], + units=units_energy, + ) + * 0.5 + ) + + vars["Extraction|Oil|Unconventional"] = ( + pp.investment( + [ + "oil_extr_4", + "oil_extr_4_ch4", + "oil_extr_5", + "oil_extr_6", + "oil_extr_7", + "oil_extr_8", + ], + units=units_energy, + ) + + pp.act_vom( + [ + "oil_extr_4", + "oil_extr_4_ch4", + "oil_extr_5", + "oil_extr_6", + "oil_extr_7", + "oil_extr_8", + ], + units=units_energy, + ) + * 0.5 + ) # As no mode is specified, u5-reproc will account for all 3 modes. - vars["Extraction|Uranium"] =\ - pp.investment(["uran2u5", "Uran_extr", - "u5-reproc", "plutonium_prod"], units=units_energy) +\ - pp.act_vom(["uran2u5", "Uran_extr"], units=units_energy) +\ - pp.act_vom(["u5-reproc"], actfilter={"mode": ["M1"]}, units=units_energy) +\ - pp.tic_fom(["uran2u5", "Uran_extr", "u5-reproc"], units=units_energy) + vars["Extraction|Uranium"] = ( + pp.investment( + ["uran2u5", "Uran_extr", "u5-reproc", "plutonium_prod"], units=units_energy + ) + + pp.act_vom(["uran2u5", "Uran_extr"], units=units_energy) + + pp.act_vom(["u5-reproc"], actfilter={"mode": ["M1"]}, units=units_energy) + + pp.tic_fom(["uran2u5", "Uran_extr", "u5-reproc"], units=units_energy) + ) # --------------------- # Electricity - Fossils # --------------------- - vars["Electricity|Coal|w/ CCS"] =\ - pp.investment(["c_ppl_co2scr", "cfc_co2scr"], units=units_energy) +\ - pp.investment("coal_adv_ccs", units=units_energy) * 0.25 +\ - pp.investment("igcc_ccs", units=units_energy) * 0.31 + vars["Electricity|Coal|w/ CCS"] = ( + pp.investment(["c_ppl_co2scr", "cfc_co2scr"], units=units_energy) + + pp.investment("coal_adv_ccs", units=units_energy) * 0.25 + + pp.investment("igcc_ccs", units=units_energy) * 0.31 + ) - vars["Electricity|Coal|w/o CCS"] =\ - pp.investment(["coal_ppl", "coal_ppl_u", "coal_adv"], units=units_energy) +\ - pp.investment("coal_adv_ccs", units=units_energy) * 0.75 +\ - pp.investment("igcc", units=units_energy) +\ - pp.investment("igcc_ccs", units=units_energy) * 0.69 + vars["Electricity|Coal|w/o CCS"] = ( + pp.investment(["coal_ppl", "coal_ppl_u", "coal_adv"], units=units_energy) + + pp.investment("coal_adv_ccs", units=units_energy) * 0.75 + + pp.investment("igcc", units=units_energy) + + pp.investment("igcc_ccs", units=units_energy) * 0.69 + ) - vars["Electricity|Gas|w/ CCS"] =\ - pp.investment(["g_ppl_co2scr", "gfc_co2scr"], units=units_energy) +\ - pp.investment("gas_cc_ccs", units=units_energy) * 0.53 + vars["Electricity|Gas|w/ CCS"] = ( + pp.investment(["g_ppl_co2scr", "gfc_co2scr"], units=units_energy) + + pp.investment("gas_cc_ccs", units=units_energy) * 0.53 + ) - vars["Electricity|Gas|w/o CCS"] =\ - pp.investment(["gas_cc", "gas_ct", "gas_ppl"], units=units_energy) +\ - pp.investment("gas_cc_ccs", units=units_energy) * 0.47 + vars["Electricity|Gas|w/o CCS"] = ( + pp.investment(["gas_cc", "gas_ct", "gas_ppl"], units=units_energy) + + pp.investment("gas_cc_ccs", units=units_energy) * 0.47 + ) - vars["Electricity|Oil|w/o CCS"] =\ - pp.investment(["foil_ppl", "loil_ppl", "oil_ppl", "loil_cc"], - units=units_energy) + vars["Electricity|Oil|w/o CCS"] = pp.investment( + ["foil_ppl", "loil_ppl", "oil_ppl", "loil_cc"], units=units_energy + ) # ------------------------ # Electricity - Renewables # ------------------------ - vars["Electricity|Biomass|w/ CCS"] =\ - pp.investment("bio_ppl_co2scr", units=units_energy) +\ - pp.investment("bio_istig_ccs", units=units_energy) * 0.31 + vars["Electricity|Biomass|w/ CCS"] = ( + pp.investment("bio_ppl_co2scr", units=units_energy) + + pp.investment("bio_istig_ccs", units=units_energy) * 0.31 + ) - vars["Electricity|Biomass|w/o CCS"] =\ - pp.investment(["bio_ppl", "bio_istig"], units=units_energy) +\ - pp.investment("bio_istig_ccs", units=units_energy) * 0.69 + vars["Electricity|Biomass|w/o CCS"] = ( + pp.investment(["bio_ppl", "bio_istig"], units=units_energy) + + pp.investment("bio_istig_ccs", units=units_energy) * 0.69 + ) vars["Electricity|Geothermal"] = pp.investment("geo_ppl", units=units_energy) - vars["Electricity|Hydro"] = pp.investment(["hydro_hc", "hydro_lc"], - units=units_energy) - vars["Electricity|Other"] = pp.investment(["h2_fc_I", "h2_fc_RC"], - units=units_energy) + vars["Electricity|Hydro"] = pp.investment( + ["hydro_hc", "hydro_lc"], units=units_energy + ) + vars["Electricity|Other"] = pp.investment( + ["h2_fc_I", "h2_fc_RC"], units=units_energy + ) - _solar_pv_elec = pp.investment(["solar_pv_ppl", "solar_pv_I", - "solar_pv_RC"], units=units_energy) + _solar_pv_elec = pp.investment( + ["solar_pv_ppl", "solar_pv_I", "solar_pv_RC"], units=units_energy + ) _solar_th_elec = pp.investment(["csp_sm1_ppl", "csp_sm3_ppl"], units=units_energy) @@ -2074,110 +1880,178 @@ def retr_supply_inv(units_energy, # Electricity Nuclear # ------------------- - vars["Electricity|Nuclear"] = pp.investment(["nuc_hc", "nuc_lc"], - units=units_energy) + vars["Electricity|Nuclear"] = pp.investment( + ["nuc_hc", "nuc_lc"], units=units_energy + ) # -------------------------------------------------- # Electricity Storage, transmission and distribution # -------------------------------------------------- - vars["Electricity|Electricity Storage"] = pp.investment("stor_ppl", - units=units_energy) - - vars["Electricity|Transmission and Distribution"] =\ - pp.investment(["elec_t_d", - "elec_exp", "elec_exp_africa", - "elec_exp_america", "elec_exp_asia", - "elec_exp_eurasia", "elec_exp_eur_afr", - "elec_exp_asia_afr", - "elec_imp", "elec_imp_africa", - "elec_imp_america", "elec_imp_asia", - "elec_imp_eurasia", "elec_imp_eur_afr", - "elec_imp_asia_afr"], units=units_energy) +\ - pp.act_vom(["elec_t_d", - "elec_exp", "elec_exp_africa", - "elec_exp_america", "elec_exp_asia", - "elec_exp_eurasia", "elec_exp_eur_afr", - "elec_exp_asia_afr", - "elec_imp", "elec_imp_africa", - "elec_imp_america", "elec_imp_asia", - "elec_imp_eurasia", "elec_imp_eur_afr", - "elec_imp_asia_afr"], units=units_energy) * .5 +\ - pp.tic_fom(["elec_t_d", - "elec_exp", "elec_exp_africa", - "elec_exp_america", "elec_exp_asia", - "elec_exp_eurasia", "elec_exp_eur_afr", - "elec_exp_asia_afr", - "elec_imp", "elec_imp_africa", - "elec_imp_america", "elec_imp_asia", - "elec_imp_eurasia", "elec_imp_eur_afr", - "elec_imp_asia_afr"], units=units_energy) * .5 + vars["Electricity|Electricity Storage"] = pp.investment( + "stor_ppl", units=units_energy + ) + + vars["Electricity|Transmission and Distribution"] = ( + pp.investment( + [ + "elec_t_d", + "elec_exp", + "elec_exp_africa", + "elec_exp_america", + "elec_exp_asia", + "elec_exp_eurasia", + "elec_exp_eur_afr", + "elec_exp_asia_afr", + "elec_imp", + "elec_imp_africa", + "elec_imp_america", + "elec_imp_asia", + "elec_imp_eurasia", + "elec_imp_eur_afr", + "elec_imp_asia_afr", + ], + units=units_energy, + ) + + pp.act_vom( + [ + "elec_t_d", + "elec_exp", + "elec_exp_africa", + "elec_exp_america", + "elec_exp_asia", + "elec_exp_eurasia", + "elec_exp_eur_afr", + "elec_exp_asia_afr", + "elec_imp", + "elec_imp_africa", + "elec_imp_america", + "elec_imp_asia", + "elec_imp_eurasia", + "elec_imp_eur_afr", + "elec_imp_asia_afr", + ], + units=units_energy, + ) + * 0.5 + + pp.tic_fom( + [ + "elec_t_d", + "elec_exp", + "elec_exp_africa", + "elec_exp_america", + "elec_exp_asia", + "elec_exp_eurasia", + "elec_exp_eur_afr", + "elec_exp_asia_afr", + "elec_imp", + "elec_imp_africa", + "elec_imp_america", + "elec_imp_asia", + "elec_imp_eurasia", + "elec_imp_eur_afr", + "elec_imp_asia_afr", + ], + units=units_energy, + ) + * 0.5 + ) # ------------------------------------------ # CO2 Storage, transmission and distribution # ------------------------------------------ - _CCS_coal_elec = -1 *\ - pp.emi(["c_ppl_co2scr", "coal_adv_ccs", - "igcc_ccs", "clinker_dry_ccs_cement",'clinker_wet_ccs_cement'], units_ene_mdl, - emifilter={"relation": ["CO2_Emission"]}, - emission_units=units_emi) - - _CCS_coal_synf = -1 *\ - pp.emi(["syn_liq_ccs", "h2_coal_ccs", - "meth_coal_ccs"], units_ene_mdl, - emifilter={"relation": ["CO2_Emission"]}, - emission_units=units_emi) - - _CCS_gas_elec = -1 *\ - pp.emi(["g_ppl_co2scr", "gas_cc_ccs"], units_ene_mdl, - emifilter={"relation": ["CO2_Emission"]}, - emission_units=units_emi) - - _CCS_gas_synf = -1 *\ - pp.emi(["h2_smr_ccs", "meth_ng_ccs"], units_ene_mdl, - emifilter={"relation": ["CO2_Emission"]}, - emission_units=units_emi) - - _CCS_bio_elec = -1 *\ - pp.emi(["bio_ppl_co2scr", "bio_istig_ccs"], units_ene_mdl, - emifilter={"relation": ["CO2_Emission"]}, - emission_units=units_emi) - - _CCS_bio_synf = -1 *\ - pp.emi(["eth_bio_ccs", "liq_bio_ccs", - "h2_bio_ccs"], units_ene_mdl, - emifilter={"relation": ["CO2_Emission"]}, - emission_units=units_emi) + _CCS_coal_elec = -1 * pp.emi( + [ + "c_ppl_co2scr", + "coal_adv_ccs", + "igcc_ccs", + "clinker_dry_ccs_cement", + "clinker_wet_ccs_cement", + ], + units_ene_mdl, + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi, + ) + + _CCS_coal_synf = -1 * pp.emi( + ["syn_liq_ccs", "h2_coal_ccs", "meth_coal_ccs"], + units_ene_mdl, + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi, + ) + + _CCS_gas_elec = -1 * pp.emi( + ["g_ppl_co2scr", "gas_cc_ccs"], + units_ene_mdl, + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi, + ) + + _CCS_gas_synf = -1 * pp.emi( + ["h2_smr_ccs", "meth_ng_ccs"], + units_ene_mdl, + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi, + ) + + _CCS_bio_elec = -1 * pp.emi( + ["bio_ppl_co2scr", "bio_istig_ccs"], + units_ene_mdl, + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi, + ) + + _CCS_bio_synf = -1 * pp.emi( + ["eth_bio_ccs", "liq_bio_ccs", "h2_bio_ccs"], + units_ene_mdl, + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi, + ) _Biogas_use_tot = pp.out("gas_bio") - _Gas_use_tot = pp.inp(["gas_ppl", "gas_cc", "gas_cc_ccs", - "gas_ct", "gas_htfc", "gas_hpl", - "meth_ng", "meth_ng_ccs", "h2_smr", - "h2_smr_ccs", "gas_rc", "hp_gas_rc", - "gas_i", "hp_gas_i", - 'furnace_gas_aluminum', - 'furnace_gas_petro', - 'furnace_gas_cement', - 'furnace_gas_refining', - "hp_gas_i", - 'hp_gas_aluminum', - 'hp_gas_petro', - 'hp_gas_refining', - "gas_trp", - "gas_fs"]) + _Gas_use_tot = pp.inp( + [ + "gas_ppl", + "gas_cc", + "gas_cc_ccs", + "gas_ct", + "gas_htfc", + "gas_hpl", + "meth_ng", + "meth_ng_ccs", + "h2_smr", + "h2_smr_ccs", + "gas_rc", + "hp_gas_rc", + "gas_i", + "hp_gas_i", + "furnace_gas_aluminum", + "furnace_gas_petro", + "furnace_gas_cement", + "furnace_gas_refining", + "hp_gas_i", + "hp_gas_aluminum", + "hp_gas_petro", + "hp_gas_refining", + "gas_trp", + "gas_fs", + ] + ) _Biogas_share = (_Biogas_use_tot / _Gas_use_tot).fillna(0) - _CCS_Foss = _CCS_coal_elec +\ - _CCS_coal_synf +\ - _CCS_gas_elec * (1 - _Biogas_share) +\ - _CCS_gas_synf * (1 - _Biogas_share) + _CCS_Foss = ( + _CCS_coal_elec + + _CCS_coal_synf + + _CCS_gas_elec * (1 - _Biogas_share) + + _CCS_gas_synf * (1 - _Biogas_share) + ) - _CCS_Bio = _CCS_bio_elec +\ - _CCS_bio_synf -\ - (_CCS_gas_elec + _CCS_gas_synf) * _Biogas_share + _CCS_Bio = ( + _CCS_bio_elec + _CCS_bio_synf - (_CCS_gas_elec + _CCS_gas_synf) * _Biogas_share + ) _CCS_coal_elec_shr = (_CCS_coal_elec / _CCS_Foss).fillna(0) _CCS_coal_synf_shr = (_CCS_coal_synf / _CCS_Foss).fillna(0) @@ -2186,15 +2060,17 @@ def retr_supply_inv(units_energy, _CCS_bio_elec_shr = (_CCS_bio_elec / _CCS_Bio).fillna(0) _CCS_bio_synf_shr = (_CCS_bio_synf / _CCS_Bio).fillna(0) - CO2_trans_dist_elec =\ - pp.act_vom("co2_tr_dis", units=units_energy) * 0.5 * _CCS_coal_elec_shr +\ - pp.act_vom("co2_tr_dis", units=units_energy) * 0.5 * _CCS_gas_elec_shr +\ - pp.act_vom("bco2_tr_dis", units=units_energy) * 0.5 * _CCS_bio_elec_shr + CO2_trans_dist_elec = ( + pp.act_vom("co2_tr_dis", units=units_energy) * 0.5 * _CCS_coal_elec_shr + + pp.act_vom("co2_tr_dis", units=units_energy) * 0.5 * _CCS_gas_elec_shr + + pp.act_vom("bco2_tr_dis", units=units_energy) * 0.5 * _CCS_bio_elec_shr + ) - CO2_trans_dist_synf =\ - pp.act_vom("co2_tr_dis", units=units_energy) * 0.5 * _CCS_coal_synf_shr +\ - pp.act_vom("co2_tr_dis", units=units_energy) * 0.5 * _CCS_gas_synf_shr +\ - pp.act_vom("bco2_tr_dis", units=units_energy) * 0.5 * _CCS_bio_synf_shr + CO2_trans_dist_synf = ( + pp.act_vom("co2_tr_dis", units=units_energy) * 0.5 * _CCS_coal_synf_shr + + pp.act_vom("co2_tr_dis", units=units_energy) * 0.5 * _CCS_gas_synf_shr + + pp.act_vom("bco2_tr_dis", units=units_energy) * 0.5 * _CCS_bio_synf_shr + ) vars["CO2 Transport and Storage"] = CO2_trans_dist_elec + CO2_trans_dist_synf @@ -2202,9 +2078,10 @@ def retr_supply_inv(units_energy, # Heat # ---- - vars["Heat"] = pp.investment(["coal_hpl", "foil_hpl", "gas_hpl", - "bio_hpl", "heat_t_d", "po_turbine"], - units=units_energy) + vars["Heat"] = pp.investment( + ["coal_hpl", "foil_hpl", "gas_hpl", "bio_hpl", "heat_t_d", "po_turbine"], + units=units_energy, + ) # ------------------------- # Synthetic fuel production @@ -2214,113 +2091,154 @@ def retr_supply_inv(units_energy, # liquids. The shares then add up to 1, but the variables are kept # separate in order to preserve the split between CCS and non-CCS - _Coal_synf_ccs_liq = pp.investment("meth_coal_ccs", units=units_energy) * 0.02 +\ - pp.investment("syn_liq_ccs", units=units_energy) * 0.01 + _Coal_synf_ccs_liq = ( + pp.investment("meth_coal_ccs", units=units_energy) * 0.02 + + pp.investment("syn_liq_ccs", units=units_energy) * 0.01 + ) _Gas_synf_ccs_liq = pp.investment("meth_ng_ccs", units=units_energy) * 0.08 - _Bio_synf_ccs_liq = pp.investment("eth_bio_ccs", units=units_energy) * 0.34 + \ - pp.investment("liq_bio_ccs", units=units_energy) * 0.02 + _Bio_synf_ccs_liq = ( + pp.investment("eth_bio_ccs", units=units_energy) * 0.34 + + pp.investment("liq_bio_ccs", units=units_energy) * 0.02 + ) _Coal_synf_ccs_h2 = pp.investment("h2_coal_ccs", units=units_energy) * 0.03 _Gas_synf_ccs_h2 = pp.investment("h2_smr_ccs", units=units_energy) * 0.17 _Bio_synf_ccs_h2 = pp.investment("h2_bio_ccs", units=units_energy) * 0.02 # Note OFR 25.04.2017: "coal_gas" have been moved to "other" - vars["Liquids|Coal and Gas"] =\ - pp.investment(["meth_coal", "syn_liq", "meth_ng"], units=units_energy) +\ - pp.investment("meth_ng_ccs", units=units_energy) * 0.92 +\ - pp.investment("meth_coal_ccs", units=units_energy) * 0.98 +\ - pp.investment("syn_liq_ccs", units=units_energy) * 0.99 +\ - _Coal_synf_ccs_liq +\ - _Gas_synf_ccs_liq + vars["Liquids|Coal and Gas"] = ( + pp.investment(["meth_coal", "syn_liq", "meth_ng"], units=units_energy) + + pp.investment("meth_ng_ccs", units=units_energy) * 0.92 + + pp.investment("meth_coal_ccs", units=units_energy) * 0.98 + + pp.investment("syn_liq_ccs", units=units_energy) * 0.99 + + _Coal_synf_ccs_liq + + _Gas_synf_ccs_liq + ) # Note OFR 25.04.2017: "gas_bio" has been moved to "other" - vars["Liquids|Biomass"] =\ - pp.investment(["eth_bio", "liq_bio"], units=units_energy) +\ - pp.investment("liq_bio_ccs", units=units_energy) * 0.98 +\ - pp.investment("eth_bio_ccs", units=units_energy) * 0.66 + _Bio_synf_ccs_liq + vars["Liquids|Biomass"] = ( + pp.investment(["eth_bio", "liq_bio"], units=units_energy) + + pp.investment("liq_bio_ccs", units=units_energy) * 0.98 + + pp.investment("eth_bio_ccs", units=units_energy) * 0.66 + + _Bio_synf_ccs_liq + ) # Note OFR 25.04.2017: "transport, import and exports costs related to # liquids are only included in the total" - _Synfuel_other = pp.investment(["meth_exp", "meth_imp", "meth_t_d", - "meth_bal", "eth_exp", "eth_imp", - "eth_t_d", "eth_bal", "SO2_scrub_synf"], - units=units_energy) - - vars["Liquids|Oil"] = pp.investment(['furnace_coke_refining', - 'furnace_coal_refining', - 'furnace_foil_refining', - 'furnace_loil_refining', - 'furnace_ethanol_refining', - 'furnace_biomass_refining', - 'furnace_methanol_refining', - 'furnace_gas_refining', - 'furnace_elec_refining', - 'furnace_h2_refining', - 'hp_gas_refining', - 'hp_elec_refining', - 'fc_h2_refining', - 'solar_refining', - 'dheat_refining', - 'atm_distillation_ref', - 'vacuum_distillation_ref', - 'hydrotreating_ref', - 'catalytic_cracking_ref', - 'visbreaker_ref', - 'coking_ref', - 'catalytic_reforming_ref', - 'hydro_cracking_ref' - ], units=units_energy) +\ - pp.tic_fom(['furnace_coke_refining', - 'furnace_coal_refining', - 'furnace_foil_refining', - 'furnace_loil_refining', - 'furnace_ethanol_refining', - 'furnace_biomass_refining', - 'furnace_methanol_refining', - 'furnace_gas_refining', - 'furnace_elec_refining', - 'furnace_h2_refining', - 'hp_gas_refining', - 'hp_elec_refining', - 'fc_h2_refining', - 'solar_refining', - 'dheat_refining', - 'atm_distillation_ref', - 'vacuum_distillation_ref', - 'hydrotreating_ref', - 'catalytic_cracking_ref', - 'visbreaker_ref', - 'coking_ref', - 'catalytic_reforming_ref', - 'hydro_cracking_ref'], units=units_energy) - - vars["Liquids"] = vars["Liquids|Coal and Gas"] +\ - vars["Liquids|Biomass"] +\ - vars["Liquids|Oil"] +\ - _Synfuel_other + _Synfuel_other = pp.investment( + [ + "meth_exp", + "meth_imp", + "meth_t_d", + "meth_bal", + "eth_exp", + "eth_imp", + "eth_t_d", + "eth_bal", + "SO2_scrub_synf", + ], + units=units_energy, + ) + + vars["Liquids|Oil"] = pp.investment( + [ + "furnace_coke_refining", + "furnace_coal_refining", + "furnace_foil_refining", + "furnace_loil_refining", + "furnace_ethanol_refining", + "furnace_biomass_refining", + "furnace_methanol_refining", + "furnace_gas_refining", + "furnace_elec_refining", + "furnace_h2_refining", + "hp_gas_refining", + "hp_elec_refining", + "fc_h2_refining", + "solar_refining", + "dheat_refining", + "atm_distillation_ref", + "vacuum_distillation_ref", + "hydrotreating_ref", + "catalytic_cracking_ref", + "visbreaker_ref", + "coking_ref", + "catalytic_reforming_ref", + "hydro_cracking_ref", + ], + units=units_energy, + ) + pp.tic_fom( + [ + "furnace_coke_refining", + "furnace_coal_refining", + "furnace_foil_refining", + "furnace_loil_refining", + "furnace_ethanol_refining", + "furnace_biomass_refining", + "furnace_methanol_refining", + "furnace_gas_refining", + "furnace_elec_refining", + "furnace_h2_refining", + "hp_gas_refining", + "hp_elec_refining", + "fc_h2_refining", + "solar_refining", + "dheat_refining", + "atm_distillation_ref", + "vacuum_distillation_ref", + "hydrotreating_ref", + "catalytic_cracking_ref", + "visbreaker_ref", + "coking_ref", + "catalytic_reforming_ref", + "hydro_cracking_ref", + ], + units=units_energy, + ) + + vars["Liquids"] = ( + vars["Liquids|Coal and Gas"] + + vars["Liquids|Biomass"] + + vars["Liquids|Oil"] + + _Synfuel_other + ) # -------- # Hydrogen # -------- - vars["Hydrogen|Fossil"] =\ - pp.investment(["h2_coal", "h2_smr"], units=units_energy) +\ - pp.investment("h2_coal_ccs", units=units_energy) * 0.97 +\ - pp.investment("h2_smr_ccs", units=units_energy) * 0.83 +\ - _Coal_synf_ccs_h2 +\ - _Gas_synf_ccs_h2 + vars["Hydrogen|Fossil"] = ( + pp.investment(["h2_coal", "h2_smr"], units=units_energy) + + pp.investment("h2_coal_ccs", units=units_energy) * 0.97 + + pp.investment("h2_smr_ccs", units=units_energy) * 0.83 + + _Coal_synf_ccs_h2 + + _Gas_synf_ccs_h2 + ) - vars["Hydrogen|Renewable"] = pp.investment("h2_bio", units=units_energy) +\ - pp.investment("h2_bio_ccs", units=units_energy) * 0.98 +\ - _Bio_synf_ccs_h2 + vars["Hydrogen|Renewable"] = ( + pp.investment("h2_bio", units=units_energy) + + pp.investment("h2_bio_ccs", units=units_energy) * 0.98 + + _Bio_synf_ccs_h2 + ) - vars["Hydrogen|Other"] = pp.investment(["h2_elec", "h2_liq", "h2_t_d", - "lh2_exp", "lh2_imp", "lh2_bal", - "lh2_regas", "lh2_t_d"], - units=units_energy) +\ - pp.act_vom("h2_mix", units=units_energy) * 0.5 + vars["Hydrogen|Other"] = ( + pp.investment( + [ + "h2_elec", + "h2_liq", + "h2_t_d", + "lh2_exp", + "lh2_imp", + "lh2_bal", + "lh2_regas", + "lh2_t_d", + ], + units=units_energy, + ) + + pp.act_vom("h2_mix", units=units_energy) * 0.5 + ) # ----- # Other @@ -2331,45 +2249,94 @@ def retr_supply_inv(units_energy, # Note OFR 25.04.2017: Any costs relating to refineries have been # removed (compared to GEA) as these are reported under "Liquids|Oil" - vars["Other|Liquids|Oil|Transmission and Distribution"] =\ - pp.investment(["foil_t_d", "loil_t_d"], units=units_energy) - - vars["Other|Liquids|Oil|Other"] =\ - pp.investment(["foil_exp", "loil_exp", "oil_exp", - "oil_imp", "foil_imp", "loil_imp", - "loil_std", "oil_bal", "loil_sto"], units=units_energy) - - vars["Other|Gases|Transmission and Distribution"] =\ - pp.investment(["gas_t_d", "gas_t_d_ch4"], units=units_energy) - - vars["Other|Gases|Production"] =\ - pp.investment(["gas_bio", "coal_gas"], units=units_energy) - - vars["Other|Gases|Other"] =\ - pp.investment(["LNG_bal", "LNG_prod", "LNG_regas", - "LNG_exp", "LNG_imp", "gas_bal", - "gas_std", "gas_sto", "gas_exp_eeu", - "gas_exp_nam", "gas_exp_pao", "gas_exp_weu", - "gas_exp_cpa", "gas_exp_afr", "gas_exp_sas", - "gas_exp_scs", "gas_exp_cas", "gas_exp_ubm", - "gas_exp_rus", "gas_imp"], units=units_energy) - - vars["Other|Solids|Coal|Transmission and Distribution"] =\ - pp.investment(["coal_t_d", "coal_t_d-rc-SO2", "coal_t_d-rc-06p", - "coal_t_d-in-SO2", "coal_t_d-in-06p"], units=units_energy) +\ - pp.act_vom(["coal_t_d-rc-SO2", "coal_t_d-rc-06p", "coal_t_d-in-SO2", - "coal_t_d-in-06p", "coal_t_d"], units=units_energy) * 0.5 - - vars["Other|Solids|Coal|Other"] =\ - pp.investment(["coal_exp", "coal_imp", - "coal_bal", "coal_std"], units=units_energy) - - vars["Other|Solids|Biomass|Transmission and Distribution"] =\ - pp.investment("biomass_t_d", units=units_energy) - - vars["Other|Other"] =\ - pp.investment(["SO2_scrub_ref"], units=units_energy) * 0.5 +\ - pp.investment(["SO2_scrub_ind"], units=units_energy) + vars["Other|Liquids|Oil|Transmission and Distribution"] = pp.investment( + ["foil_t_d", "loil_t_d"], units=units_energy + ) + + vars["Other|Liquids|Oil|Other"] = pp.investment( + [ + "foil_exp", + "loil_exp", + "oil_exp", + "oil_imp", + "foil_imp", + "loil_imp", + "loil_std", + "oil_bal", + "loil_sto", + ], + units=units_energy, + ) + + vars["Other|Gases|Transmission and Distribution"] = pp.investment( + ["gas_t_d", "gas_t_d_ch4"], units=units_energy + ) + + vars["Other|Gases|Production"] = pp.investment( + ["gas_bio", "coal_gas"], units=units_energy + ) + + vars["Other|Gases|Other"] = pp.investment( + [ + "LNG_bal", + "LNG_prod", + "LNG_regas", + "LNG_exp", + "LNG_imp", + "gas_bal", + "gas_std", + "gas_sto", + "gas_exp_eeu", + "gas_exp_nam", + "gas_exp_pao", + "gas_exp_weu", + "gas_exp_cpa", + "gas_exp_afr", + "gas_exp_sas", + "gas_exp_scs", + "gas_exp_cas", + "gas_exp_ubm", + "gas_exp_rus", + "gas_imp", + ], + units=units_energy, + ) + + vars["Other|Solids|Coal|Transmission and Distribution"] = ( + pp.investment( + [ + "coal_t_d", + "coal_t_d-rc-SO2", + "coal_t_d-rc-06p", + "coal_t_d-in-SO2", + "coal_t_d-in-06p", + ], + units=units_energy, + ) + + pp.act_vom( + [ + "coal_t_d-rc-SO2", + "coal_t_d-rc-06p", + "coal_t_d-in-SO2", + "coal_t_d-in-06p", + "coal_t_d", + ], + units=units_energy, + ) + * 0.5 + ) + + vars["Other|Solids|Coal|Other"] = pp.investment( + ["coal_exp", "coal_imp", "coal_bal", "coal_std"], units=units_energy + ) + + vars["Other|Solids|Biomass|Transmission and Distribution"] = pp.investment( + "biomass_t_d", units=units_energy + ) + + vars["Other|Other"] = pp.investment( + ["SO2_scrub_ref"], units=units_energy + ) * 0.5 + pp.investment(["SO2_scrub_ind"], units=units_energy) df = pp_utils.make_outputdf(vars, units_energy) return df From 95809d89ca14f599ac241184eb1b954fa46fe884 Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Wed, 7 Sep 2022 10:50:11 +0200 Subject: [PATCH 021/774] Reorder .materials.report.tables to match default no functional changes; only to facilitate a diff --- .../model/material/report/tables.py | 367 +++++++++--------- 1 file changed, 186 insertions(+), 181 deletions(-) diff --git a/message_ix_models/model/material/report/tables.py b/message_ix_models/model/material/report/tables.py index 29d7e8dca5..ef51e16d25 100644 --- a/message_ix_models/model/material/report/tables.py +++ b/message_ix_models/model/material/report/tables.py @@ -10,11 +10,12 @@ kyoto_hist_data = None lu_hist_data = None - -func_dict = {} +# Dictionary where all functions defined in the script are stored. +func_dict = {} # type: dict def return_func_dict(): + """Returns functions defined in script""" return func_dict @@ -37,185 +38,6 @@ def _register(func): # ------------------- -@_register -def retr_SE_solids(units): - """Energy: Secondary Energy solids. - Parameters - ---------- - units : str - Units to which variables should be converted. - """ - - vars = {} - - BiomassIND_resid = pp.inp("biomass_i", units) - BiomassIND_alu = pp.inp("furnace_biomass_aluminum", units) - BiomassIND_steel = pp.inp("furnace_biomass_steel", units) - BiomassIND_petro = pp.inp("furnace_biomass_petro", units) - BiomassIND_cement = pp.inp("furnace_biomass_cement", units) - - BiomassRefining = pp.inp("furnace_biomass_refining", units) - - BiomassNC = pp.inp("biomass_nc", units) - BiomassR_cook = pp.inp("biomass_resid_cook", units) - BiomassR_heat = pp.inp("biomass_resid_heat", units) - BiomassR_water = pp.inp("biomass_resid_hotwater", units) - BiomassC_heat = pp.inp("biomass_comm_heat", units) - BiomassC_water = pp.inp("biomass_comm_hotwater", units) - - vars["Biomass"] = ( - BiomassIND_resid - + BiomassIND_alu - + BiomassIND_steel - + BiomassIND_petro - + BiomassIND_cement - + BiomassNC - + BiomassR_cook - + BiomassR_heat - + BiomassR_water - + BiomassC_heat - + BiomassC_water - + BiomassRefining - ) - - CoalIND_resid = pp.inp(["coal_i", "coal_fs"], units) - CoalIND_alu = pp.inp(["furnace_coal_aluminum"], units) - CoalIND_steel = pp.inp( - ["furnace_coal_steel", "bf_steel", "cokeoven_steel", "sinter_steel"], - units, - inpfilter={"commodity": ["coal"], "level": ["final"]}, - ) - CoalIND_petro = pp.inp(["furnace_coal_petro"], units) - CoalIND_cement = pp.inp(["furnace_coal_cement"], units) - - CoalRefining = pp.inp(["furnace_coal_refining"], units) - - CoalR_heat = pp.inp("coal_resid_heat", units) - CoalR_hotwater = pp.inp("coal_resid_hotwater", units) - CoalC_heat = pp.inp("coal_comm_heat", units) - CoalC_water = pp.inp("coal_comm_hotwater", units) - CoalTRP = pp.inp("coal_trp", units) - vars["Coal"] = ( - CoalIND_resid - + CoalIND_alu - + CoalIND_steel - + CoalIND_petro - + CoalIND_cement - + CoalR_heat - + CoalR_hotwater - + CoalC_heat - + CoalC_water - + CoalTRP - + CoalRefining - ) - df = pp_utils.make_outputdf(vars, units) - return df - - -@_register -def retr_SE_synfuels(units): - """Energy: Secondary Energy synthetic fuels. - Parameters - ---------- - units : str - Units to which variables should be converted. - """ - - vars = {} - - vars["Liquids|Oil"] = ( - pp.out( - "agg_ref", - units, - outfilter={"level": ["secondary"], "commodity": ["lightoil"]}, - ) - + pp.out( - "agg_ref", - units, - outfilter={"level": ["secondary"], "commodity": ["fueloil"]}, - ) - + pp.inp( - "steam_cracker_petro", - units, - inpfilter={ - "level": ["final"], - "commodity": ["atm_gasoil", "vacuum_gasoil", "naphtha"], - }, - ) - ) - - vars["Liquids|Biomass|w/o CCS"] = pp.out( - ["eth_bio", "liq_bio"], - units, - outfilter={"level": ["primary"], "commodity": ["ethanol"]}, - ) - - vars["Liquids|Biomass|w/ CCS"] = pp.out( - ["eth_bio_ccs", "liq_bio_ccs"], - units, - outfilter={"level": ["primary"], "commodity": ["ethanol"]}, - ) - - vars["Liquids|Coal|w/o CCS"] = pp.out( - ["meth_coal", "syn_liq"], - units, - outfilter={"level": ["primary"], "commodity": ["methanol"]}, - ) - - vars["Liquids|Coal|w/ CCS"] = pp.out( - ["meth_coal_ccs", "syn_liq_ccs"], - units, - outfilter={"level": ["primary"], "commodity": ["methanol"]}, - ) - - vars["Liquids|Gas|w/o CCS"] = pp.out( - "meth_ng", units, outfilter={"level": ["primary"], "commodity": ["methanol"]} - ) - - vars["Liquids|Gas|w/ CCS"] = pp.out( - "meth_ng_ccs", - units, - outfilter={"level": ["primary"], "commodity": ["methanol"]}, - ) - - vars["Hydrogen|Coal|w/o CCS"] = pp.out( - "h2_coal", units, outfilter={"level": ["secondary"], "commodity": ["hydrogen"]} - ) - - vars["Hydrogen|Coal|w/ CCS"] = pp.out( - "h2_coal_ccs", - units, - outfilter={"level": ["secondary"], "commodity": ["hydrogen"]}, - ) - - vars["Hydrogen|Gas|w/o CCS"] = pp.out( - "h2_smr", units, outfilter={"level": ["secondary"], "commodity": ["hydrogen"]} - ) - - vars["Hydrogen|Gas|w/ CCS"] = pp.out( - "h2_smr_ccs", - units, - outfilter={"level": ["secondary"], "commodity": ["hydrogen"]}, - ) - - vars["Hydrogen|Biomass|w/o CCS"] = pp.out( - "h2_bio", units, outfilter={"level": ["secondary"], "commodity": ["hydrogen"]} - ) - - vars["Hydrogen|Biomass|w/ CCS"] = pp.out( - "h2_bio_ccs", - units, - outfilter={"level": ["secondary"], "commodity": ["hydrogen"]}, - ) - - vars["Hydrogen|Electricity"] = pp.out( - "h2_elec", units, outfilter={"level": ["secondary"], "commodity": ["hydrogen"]} - ) - - df = pp_utils.make_outputdf(vars, units) - return df - - @_register def retr_CO2_CCS(units_emi, units_ene): """Carbon sequestration. @@ -1684,6 +1506,189 @@ def retr_CO2emi(units_emi, units_ene_mdl): return pd.concat(dfs, sort=True) +@_register +def retr_SE_synfuels(units): + """Energy: Secondary Energy synthetic fuels. + + Parameters + ---------- + + units : str + Units to which variables should be converted. + """ + + vars = {} + + vars["Liquids|Oil"] = ( + pp.out( + "agg_ref", + units, + outfilter={"level": ["secondary"], "commodity": ["lightoil"]}, + ) + + pp.out( + "agg_ref", + units, + outfilter={"level": ["secondary"], "commodity": ["fueloil"]}, + ) + + pp.inp( + "steam_cracker_petro", + units, + inpfilter={ + "level": ["final"], + "commodity": ["atm_gasoil", "vacuum_gasoil", "naphtha"], + }, + ) + ) + + vars["Liquids|Biomass|w/o CCS"] = pp.out( + ["eth_bio", "liq_bio"], + units, + outfilter={"level": ["primary"], "commodity": ["ethanol"]}, + ) + + vars["Liquids|Biomass|w/ CCS"] = pp.out( + ["eth_bio_ccs", "liq_bio_ccs"], + units, + outfilter={"level": ["primary"], "commodity": ["ethanol"]}, + ) + + vars["Liquids|Coal|w/o CCS"] = pp.out( + ["meth_coal", "syn_liq"], + units, + outfilter={"level": ["primary"], "commodity": ["methanol"]}, + ) + + vars["Liquids|Coal|w/ CCS"] = pp.out( + ["meth_coal_ccs", "syn_liq_ccs"], + units, + outfilter={"level": ["primary"], "commodity": ["methanol"]}, + ) + + vars["Liquids|Gas|w/o CCS"] = pp.out( + "meth_ng", units, outfilter={"level": ["primary"], "commodity": ["methanol"]} + ) + + vars["Liquids|Gas|w/ CCS"] = pp.out( + "meth_ng_ccs", + units, + outfilter={"level": ["primary"], "commodity": ["methanol"]}, + ) + + vars["Hydrogen|Coal|w/o CCS"] = pp.out( + "h2_coal", units, outfilter={"level": ["secondary"], "commodity": ["hydrogen"]} + ) + + vars["Hydrogen|Coal|w/ CCS"] = pp.out( + "h2_coal_ccs", + units, + outfilter={"level": ["secondary"], "commodity": ["hydrogen"]}, + ) + + vars["Hydrogen|Gas|w/o CCS"] = pp.out( + "h2_smr", units, outfilter={"level": ["secondary"], "commodity": ["hydrogen"]} + ) + + vars["Hydrogen|Gas|w/ CCS"] = pp.out( + "h2_smr_ccs", + units, + outfilter={"level": ["secondary"], "commodity": ["hydrogen"]}, + ) + + vars["Hydrogen|Biomass|w/o CCS"] = pp.out( + "h2_bio", units, outfilter={"level": ["secondary"], "commodity": ["hydrogen"]} + ) + + vars["Hydrogen|Biomass|w/ CCS"] = pp.out( + "h2_bio_ccs", + units, + outfilter={"level": ["secondary"], "commodity": ["hydrogen"]}, + ) + + vars["Hydrogen|Electricity"] = pp.out( + "h2_elec", units, outfilter={"level": ["secondary"], "commodity": ["hydrogen"]} + ) + + df = pp_utils.make_outputdf(vars, units) + return df + + +@_register +def retr_SE_solids(units): + """Energy: Secondary Energy solids. + + Parameters + ---------- + + units : str + Units to which variables should be converted. + """ + + vars = {} + + BiomassIND_resid = pp.inp("biomass_i", units) + BiomassIND_alu = pp.inp("furnace_biomass_aluminum", units) + BiomassIND_steel = pp.inp("furnace_biomass_steel", units) + BiomassIND_petro = pp.inp("furnace_biomass_petro", units) + BiomassIND_cement = pp.inp("furnace_biomass_cement", units) + + BiomassRefining = pp.inp("furnace_biomass_refining", units) + + BiomassNC = pp.inp("biomass_nc", units) + BiomassR_cook = pp.inp("biomass_resid_cook", units) + BiomassR_heat = pp.inp("biomass_resid_heat", units) + BiomassR_water = pp.inp("biomass_resid_hotwater", units) + BiomassC_heat = pp.inp("biomass_comm_heat", units) + BiomassC_water = pp.inp("biomass_comm_hotwater", units) + + vars["Biomass"] = ( + BiomassIND_resid + + BiomassIND_alu + + BiomassIND_steel + + BiomassIND_petro + + BiomassIND_cement + + BiomassNC + + BiomassR_cook + + BiomassR_heat + + BiomassR_water + + BiomassC_heat + + BiomassC_water + + BiomassRefining + ) + + CoalIND_resid = pp.inp(["coal_i", "coal_fs"], units) + CoalIND_alu = pp.inp(["furnace_coal_aluminum"], units) + CoalIND_steel = pp.inp( + ["furnace_coal_steel", "bf_steel", "cokeoven_steel", "sinter_steel"], + units, + inpfilter={"commodity": ["coal"], "level": ["final"]}, + ) + CoalIND_petro = pp.inp(["furnace_coal_petro"], units) + CoalIND_cement = pp.inp(["furnace_coal_cement"], units) + + CoalRefining = pp.inp(["furnace_coal_refining"], units) + + CoalR_heat = pp.inp("coal_resid_heat", units) + CoalR_hotwater = pp.inp("coal_resid_hotwater", units) + CoalC_heat = pp.inp("coal_comm_heat", units) + CoalC_water = pp.inp("coal_comm_hotwater", units) + CoalTRP = pp.inp("coal_trp", units) + vars["Coal"] = ( + CoalIND_resid + + CoalIND_alu + + CoalIND_steel + + CoalIND_petro + + CoalIND_cement + + CoalR_heat + + CoalR_hotwater + + CoalC_heat + + CoalC_water + + CoalTRP + + CoalRefining + ) + df = pp_utils.make_outputdf(vars, units) + return df + + @_register def retr_supply_inv(units_energy, units_emi, units_ene_mdl): """Technology results: Investments. From 01a23708660a4b526a69c801b82d108e2fe9d1f1 Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Wed, 7 Sep 2022 11:35:04 +0200 Subject: [PATCH 022/774] Tidy material/report/__init__.py - apply black. - sort imports and remove unused. - reflow some commented lines. --- .../model/material/report/__init__.py | 752 +++++++++++------- 1 file changed, 447 insertions(+), 305 deletions(-) diff --git a/message_ix_models/model/material/report/__init__.py b/message_ix_models/model/material/report/__init__.py index ca94d26a19..dc3e2d773e 100644 --- a/message_ix_models/model/material/report/__init__.py +++ b/message_ix_models/model/material/report/__init__.py @@ -23,29 +23,21 @@ # PACKAGES -from ixmp import Platform -from message_ix import Scenario -from message_ix.reporting import Reporter -from ixmp.reporting import configure -from message_ix_models import ScenarioInfo -from message_data.tools.post_processing.iamc_report_hackathon import report as reporting -from message_data.model.material.util import read_config - -import pandas as pd +import matplotlib import numpy as np +import openpyxl +import os +import pandas as pd import pyam import xlsxwriter -import os -import openpyxl +from ixmp.reporting import configure +from matplotlib import pyplot as plt +from matplotlib.backends.backend_pdf import PdfPages +from message_ix_models import ScenarioInfo +from message_ix.reporting import Reporter -import plotly.graph_objects as go -import matplotlib matplotlib.use("Agg") -from matplotlib import pyplot as plt - -from pyam.plotting import OUTSIDE_LEGEND -from matplotlib.backends.backend_pdf import PdfPages def print_full(x): @@ -66,7 +58,7 @@ def change_names(s): s = "Non-Metallic Minerals|Cement" elif s == "petro": s = "Chemicals|High Value Chemicals" - elif s == 'ammonia': + elif s == "ammonia": s = "Chemicals|Ammonia" elif s == "BCA": s = "BC" @@ -92,8 +84,8 @@ def fix_excel(path_temp, path_new): sheet = workbook["data"] new_workbook = openpyxl.Workbook() - new_sheet = new_workbook['Sheet'] - new_sheet.title = 'data' + new_sheet = new_workbook["Sheet"] + new_sheet.title = "data" new_sheet = new_workbook.active replacement = { @@ -152,21 +144,34 @@ def fix_excel(path_temp, path_new): "unit": "Unit", "BCA": "BC", "OCA": "OC", - 'Final Energy|Non-Energy Use|Chemicals|Ammonia':'Final Energy|Industry|Chemicals|Ammonia', - 'Final Energy|Non-Energy Use|Chemicals|Ammonia|Gases': 'Final Energy|Industry|Chemicals|Ammonia|Gases', - 'Final Energy|Non-Energy Use|Chemicals|Ammonia|Liquids': 'Final Energy|Industry|Chemicals|Ammonia|Liquids', - 'Final Energy|Non-Energy Use|Chemicals|Ammonia|Liquids|Oil': 'Final Energy|Industry|Chemicals|Ammonia|Liquids|Oil', - 'Final Energy|Non-Energy Use|Chemicals|Ammonia|Solids': 'Final Energy|Industry|Chemicals|Ammonia|Solids' + "Final Energy|Non-Energy Use|Chemicals|Ammonia": ( + "Final Energy|Industry|Chemicals|Ammonia" + ), + "Final Energy|Non-Energy Use|Chemicals|Ammonia|Gases": ( + "Final Energy|Industry|Chemicals|Ammonia|Gases" + ), + "Final Energy|Non-Energy Use|Chemicals|Ammonia|Liquids": ( + "Final Energy|Industry|Chemicals|Ammonia|Liquids" + ), + "Final Energy|Non-Energy Use|Chemicals|Ammonia|Liquids|Oil": ( + "Final Energy|Industry|Chemicals|Ammonia|Liquids|Oil" + ), + "Final Energy|Non-Energy Use|Chemicals|Ammonia|Solids": ( + "Final Energy|Industry|Chemicals|Ammonia|Solids" + ), } # Iterate over the rows and replace for i in range(1, ((sheet.max_row) + 1)): - data = [sheet.cell(row = i, column = col).value for col in range(1, ((sheet.max_column) + 1))] + data = [ + sheet.cell(row=i, column=col).value + for col in range(1, ((sheet.max_column) + 1)) + ] for index, value in enumerate(data): col_no = index + 1 if value in replacement.keys(): - new_sheet.cell(row=i, column= col_no).value = replacement.get(value) + new_sheet.cell(row=i, column=col_no).value = replacement.get(value) else: - new_sheet.cell(row=i, column= col_no).value = value + new_sheet.cell(row=i, column=col_no).value = value new_workbook.save(path_new) @@ -188,8 +193,8 @@ def report(context, scenario): # df_rem["variable"].str.contains("Commercial"))] # Temporary to add the transport variables - # transport_path = os.path.join('C:\\', 'Users','unlu','Documents','MyDocuments_IIASA','Material_Flow', - # 'jupyter_notebooks', '2022-06-02_0.xlsx') + # transport_path = os.path.join('C:\\', 'Users','unlu','Documents', + # 'MyDocuments_IIASA','Material_Flow', 'jupyter_notebooks', '2022-06-02_0.xlsx') # df_transport = pd.read_excel(transport_path) # scenario.check_out(timeseries_only=True) # scenario.add_timeseries(df_transport) @@ -251,20 +256,20 @@ def report(context, scenario): "out|final_material|propylene|*", "out|secondary|fueloil|agg_ref|*", "out|secondary|lightoil|agg_ref|*", - 'out|useful|i_therm|solar_i|M1', + "out|useful|i_therm|solar_i|M1", "in|final|*", "in|secondary|coal|coal_NH3|M1", "in|secondary|electr|NH3_to_N_fertil|M1", - 'in|secondary|electr|coal_NH3|M1', - 'in|secondary|electr|electr_NH3|M1', - 'in|secondary|electr|gas_NH3|M1', - 'in|secondary|fueloil|fueloil_NH3|M1', - 'in|secondary|gas|gas_NH3|M1', + "in|secondary|electr|coal_NH3|M1", + "in|secondary|electr|electr_NH3|M1", + "in|secondary|electr|gas_NH3|M1", + "in|secondary|fueloil|fueloil_NH3|M1", + "in|secondary|gas|gas_NH3|M1", "in|secondary|coal|coal_NH3_ccs|M1", - 'in|secondary|electr|coal_NH3_ccs|M1', - 'in|secondary|electr|gas_NH3_ccs|M1', - 'in|secondary|fueloil|fueloil_NH3_ccs|M1', - 'in|secondary|gas|gas_NH3_ccs|M1', + "in|secondary|electr|coal_NH3_ccs|M1", + "in|secondary|electr|gas_NH3_ccs|M1", + "in|secondary|fueloil|fueloil_NH3_ccs|M1", + "in|secondary|gas|gas_NH3_ccs|M1", "in|primary|biomass|biomass_NH3|M1", "in|seconday|electr|biomass_NH3|M1", "in|primary|biomass|biomass_NH3_ccs|M1", @@ -345,8 +350,8 @@ def report(context, scenario): print("Production plots and variables are being generated") for r in nodes: - ## PRODUCTION - PLOTS - ## Needs to be checked again to see whether the graphs are correct + # PRODUCTION - PLOTS + # Needs to be checked again to see whether the graphs are correct fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(20, 10)) fig.tight_layout(pad=10.0) @@ -355,7 +360,7 @@ def report(context, scenario): df_al = df.copy() df_al.filter(region=r, year=years, inplace=True) df_al.filter(variable=["out|*|aluminum|*", "in|*|aluminum|*"], inplace=True) - df_al.convert_unit('', to='Mt/yr', factor=1, inplace = True) + df_al.convert_unit("", to="Mt/yr", factor=1, inplace=True) df_al_graph = df_al.copy() df_al_graph.filter( @@ -401,7 +406,7 @@ def report(context, scenario): df_steel = df.copy() df_steel.filter(region=r, year=years, inplace=True) df_steel.filter(variable=["out|*|steel|*", "in|*|steel|*"], inplace=True) - df_steel.convert_unit('', to='Mt/yr', factor=1, inplace = True) + df_steel.convert_unit("", to="Mt/yr", factor=1, inplace=True) df_steel_graph = df_steel.copy() df_steel_graph.filter( @@ -415,7 +420,10 @@ def report(context, scenario): if r == "World": df_steel.filter(variable=["out|*|steel|*", "in|*|steel|*"], inplace=True) df_steel_graph.filter( - variable=["out|final_material|steel|*",], inplace=True, + variable=[ + "out|final_material|steel|*", + ], + inplace=True, ) df_steel_graph.plot.stack(ax=ax2) @@ -439,7 +447,7 @@ def report(context, scenario): ], inplace=True, ) - df_petro.convert_unit('', to='Mt/yr', factor=1, inplace = True) + df_petro.convert_unit("", to="Mt/yr", factor=1, inplace=True) if r == "World": @@ -585,14 +593,15 @@ def report(context, scenario): # eaf_steel has three modes M1,M2,M3: Only M2 has scrap input. - primary_steel_vars = ["out|final_material|steel|bof_steel|M1", - "out|final_material|steel|eaf_steel|M1", - "out|final_material|steel|eaf_steel|M3" + primary_steel_vars = [ + "out|final_material|steel|bof_steel|M1", + "out|final_material|steel|eaf_steel|M1", + "out|final_material|steel|eaf_steel|M3", ] secondary_steel_vars = [ "out|final_material|steel|eaf_steel|M2", - "in|new_scrap|steel|bof_steel|M1" + "in|new_scrap|steel|bof_steel|M1", ] collected_scrap_steel_vars = [ @@ -604,24 +613,34 @@ def report(context, scenario): old_scrap_steel_vars = ["out|dummy_end_of_life|steel|total_EOL_steel|M1"] df_steel.aggregate( - "Production|Primary|Steel (before sub.)", components=primary_steel_vars, append=True, + "Production|Primary|Steel (before sub.)", + components=primary_steel_vars, + append=True, ) - df_steel.subtract("Production|Primary|Steel (before sub.)", - "in|new_scrap|steel|bof_steel|M1","Production|Primary|Steel", append = True) + df_steel.subtract( + "Production|Primary|Steel (before sub.)", + "in|new_scrap|steel|bof_steel|M1", + "Production|Primary|Steel", + append=True, + ) df_steel.aggregate( - "Production|Secondary|Steel", components=secondary_steel_vars, append=True, + "Production|Secondary|Steel", + components=secondary_steel_vars, + append=True, ) df_steel.aggregate( "Production|Steel", - components=["Production|Primary|Steel","Production|Secondary|Steel"], + components=["Production|Primary|Steel", "Production|Secondary|Steel"], append=True, ) df_steel.aggregate( - "Collected Scrap|Steel", components=collected_scrap_steel_vars, append=True, + "Collected Scrap|Steel", + components=collected_scrap_steel_vars, + append=True, ) df_steel.aggregate( "Total Scrap|Steel", components=total_scrap_steel_vars, append=True @@ -652,21 +671,25 @@ def report(context, scenario): df_chemicals = df.copy() df_chemicals.filter(region=r, year=years, inplace=True) - df_chemicals.filter(variable=['out|secondary_material|NH3|*', - "out|final_material|ethylene|*", - "out|final_material|propylene|*", - "out|final_material|BTX|*"],inplace=True) - df_chemicals.convert_unit('', to='Mt/yr', factor=1, inplace = True) - + df_chemicals.filter( + variable=[ + "out|secondary_material|NH3|*", + "out|final_material|ethylene|*", + "out|final_material|propylene|*", + "out|final_material|BTX|*", + ], + inplace=True, + ) + df_chemicals.convert_unit("", to="Mt/yr", factor=1, inplace=True) # AMMONIA primary_ammonia_vars = [ - "out|secondary_material|NH3|gas_NH3|M1", - "out|secondary_material|NH3|coal_NH3|M1", - "out|secondary_material|NH3|biomass_NH3|M1", - "out|secondary_material|NH3|fueloil_NH3|M1", - "out|secondary_material|NH3|electr_NH3|M1" + "out|secondary_material|NH3|gas_NH3|M1", + "out|secondary_material|NH3|coal_NH3|M1", + "out|secondary_material|NH3|biomass_NH3|M1", + "out|secondary_material|NH3|fueloil_NH3|M1", + "out|secondary_material|NH3|electr_NH3|M1", ] df_chemicals.aggregate( @@ -733,13 +756,11 @@ def report(context, scenario): "Production|Primary|Chemicals", "Production|Chemicals", "Production|Primary|Chemicals|Ammonia", - "Production|Chemicals|Ammonia" - + "Production|Chemicals|Ammonia", ], inplace=True, ) - # Add to final data_frame df_final.append(df_al, inplace=True) df_final.append(df_steel, inplace=True) @@ -795,9 +816,11 @@ def report(context, scenario): total_scrap_cement_vars = ["out|dummy_end_of_life|cement|total_EOL_cement|M1"] - df_cement.convert_unit('', to='Mt/yr', factor=1, inplace = True) + df_cement.convert_unit("", to="Mt/yr", factor=1, inplace=True) df_cement.aggregate( - "Production|Primary|Cement", components=primary_cement_vars, append=True, + "Production|Primary|Cement", + components=primary_cement_vars, + append=True, ) df_cement.aggregate( @@ -807,7 +830,9 @@ def report(context, scenario): ) df_cement.aggregate( - "Production|Cement", components=primary_cement_vars, append=True, + "Production|Cement", + components=primary_cement_vars, + append=True, ) df_cement.aggregate( @@ -837,7 +862,6 @@ def report(context, scenario): # Ammonia is not seperately included as the model input valus are combined for # feedstock and energy. - print("Final Energy by fuels only non-energy use is being printed.") commodities = ["gas", "liquids", "solids", "all"] @@ -848,7 +872,8 @@ def report(context, scenario): # GWa to EJ/yr df_final_energy.convert_unit("", to="GWa", factor=1, inplace=True) df_final_energy.convert_unit( - "GWa", to="EJ/yr", factor=0.03154, inplace=True) + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) df_final_energy.filter(region=r, year=years, inplace=True) df_final_energy.filter( variable=["in|final|*|cokeoven_steel|*"], keep=False, inplace=True @@ -871,10 +896,14 @@ def report(context, scenario): ) if c == "gas": - df_final_energy.filter(variable=["in|final|gas|*", - 'in|secondary|gas|gas_NH3|M1', - 'in|secondary|gas|gas_NH3_ccs|M1'], - inplace=True) + df_final_energy.filter( + variable=[ + "in|final|gas|*", + "in|secondary|gas|gas_NH3|M1", + "in|secondary|gas|gas_NH3_ccs|M1", + ], + inplace=True, + ) # Do not include gasoil and naphtha feedstock if c == "liquids": df_final_energy.filter( @@ -887,19 +916,23 @@ def report(context, scenario): "in|final|atm_gasoil|*", "in|final|vacuum_gasoil|*", "in|final|naphtha|*", - 'in|secondary|fueloil|fueloil_NH3|M1', - 'in|secondary|fueloil|fueloil_NH3_ccs|M1', + "in|secondary|fueloil|fueloil_NH3|M1", + "in|secondary|fueloil|fueloil_NH3_ccs|M1", ], inplace=True, ) if c == "solids": df_final_energy.filter( - variable=["in|final|biomass|*", "in|final|coal|*", - "in|secondary|coal|coal_NH3|M1", - "in|secondary|coal|coal_NH3_ccs|M1", - 'in|primary|biomass|biomass_NH3_ccs|M1', - 'in|primary|biomass|biomass_NH3|M1', - ],inplace=True) + variable=[ + "in|final|biomass|*", + "in|final|coal|*", + "in|secondary|coal|coal_NH3|M1", + "in|secondary|coal|coal_NH3_ccs|M1", + "in|primary|biomass|biomass_NH3_ccs|M1", + "in|primary|biomass|biomass_NH3|M1", + ], + inplace=True, + ) all_flows = df_final_energy.timeseries().reset_index() splitted_vars = [v.split("|") for v in all_flows.variable] @@ -925,7 +958,9 @@ def report(context, scenario): if c == "all": df_final_energy.aggregate( - "Final Energy|Non-Energy Use", components=var_sectors, append=True, + "Final Energy|Non-Energy Use", + components=var_sectors, + append=True, ) df_final_energy.filter( variable=["Final Energy|Non-Energy Use"], inplace=True @@ -936,8 +971,9 @@ def report(context, scenario): df_final.append(df_final_energy, inplace=True) if c == "gas": - # Can not distinguish by type Gases (natural gas, biomass, synthetic fossil, efuel) - # (coal_gas), from biomass (gas_bio), natural gas (gas_bal): All go into secondary level + # Can not distinguish by type Gases (natural gas, biomass, synthetic + # fossil, efuel) (coal_gas), from biomass (gas_bio), natural gas + # (gas_bal): All go into secondary level # Can not be distinguished in the final level. df_final_energy.aggregate( @@ -947,7 +983,8 @@ def report(context, scenario): ) df_final_energy.filter( - variable=["Final Energy|Non-Energy Use|Gases"], inplace=True, + variable=["Final Energy|Non-Energy Use|Gases"], + inplace=True, ) df_final_energy.convert_unit( "GWa", to="EJ/yr", factor=0.03154, inplace=True @@ -966,8 +1003,9 @@ def report(context, scenario): # Only bios filter_vars = [ - v for v in aux2_df["variable"].values if (("ethanol" in v) - & ("methanol" not in v)) + v + for v in aux2_df["variable"].values + if (("ethanol" in v) & ("methanol" not in v)) ] df_final_energy.aggregate( "Final Energy|Non-Energy Use|Liquids|Biomass", @@ -997,11 +1035,7 @@ def report(context, scenario): # Other filter_vars = [ - v - for v in aux2_df["variable"].values - if ( - ("methanol" in v) - ) + v for v in aux2_df["variable"].values if (("methanol" in v)) ] df_final_energy.aggregate( @@ -1010,7 +1044,6 @@ def report(context, scenario): append=True, ) - df_final_energy.filter( variable=[ "Final Energy|Non-Energy Use|Liquids", @@ -1067,7 +1100,16 @@ def report(context, scenario): # input values in the model. print("Final Energy by fuels excluding non-energy use is being printed.") - commodities = ["electr", "gas", "hydrogen", "liquids", "solids", "heat", 'other',"all"] + commodities = [ + "electr", + "gas", + "hydrogen", + "liquids", + "solids", + "heat", + "other", + "all", + ] for c in commodities: for r in nodes: @@ -1076,24 +1118,32 @@ def report(context, scenario): df_final_energy.convert_unit("", to="GWa", factor=1, inplace=True) df_final_energy.filter(region=r, year=years, inplace=True) df_final_energy.filter( - variable=["in|final|*|cokeoven_steel|*", - "in|final|co_gas|*", - "in|final|bf_gas|*"], keep=False, inplace=True + variable=[ + "in|final|*|cokeoven_steel|*", + "in|final|co_gas|*", + "in|final|bf_gas|*", + ], + keep=False, + inplace=True, ) if c == "electr": - df_final_energy.filter(variable=["in|final|electr|*", - 'in|secondary|electr|NH3_to_N_fertil|M1', - 'in|secondary|electr|coal_NH3|M1', - 'in|secondary|electr|electr_NH3|M1', - 'in|secondary|electr|fueloil_NH3|M1', - 'in|secondary|electr|gas_NH3|M1', - 'in|secondary|electr|coal_NH3_ccs|M1', - 'in|secondary|electr|fueloil_NH3_ccs|M1', - 'in|secondary|electr|gas_NH3_ccs|M1', - 'in|secondary|electr|biomass_NH3_ccs|M1', - 'in|secondary|electr|biomass_NH3|M1', - ], inplace=True) + df_final_energy.filter( + variable=[ + "in|final|electr|*", + "in|secondary|electr|NH3_to_N_fertil|M1", + "in|secondary|electr|coal_NH3|M1", + "in|secondary|electr|electr_NH3|M1", + "in|secondary|electr|fueloil_NH3|M1", + "in|secondary|electr|gas_NH3|M1", + "in|secondary|electr|coal_NH3_ccs|M1", + "in|secondary|electr|fueloil_NH3_ccs|M1", + "in|secondary|electr|gas_NH3_ccs|M1", + "in|secondary|electr|biomass_NH3_ccs|M1", + "in|secondary|electr|biomass_NH3|M1", + ], + inplace=True, + ) if c == "gas": df_final_energy.filter(variable=["in|final|gas|*"], inplace=True) @@ -1122,22 +1172,26 @@ def report(context, scenario): df_final_energy.filter(variable=["in|final|hydrogen|*"], inplace=True) if c == "heat": df_final_energy.filter(variable=["in|final|d_heat|*"], inplace=True) - if c == 'other': + if c == "other": df_final_energy.filter(variable=["out|useful|i_therm|*"], inplace=True) if c == "all": - df_final_energy.filter(variable=["in|final|*", - 'in|secondary|electr|NH3_to_N_fertil|M1', - 'in|secondary|electr|coal_NH3|M1', - 'in|secondary|electr|electr_NH3|M1', - 'in|secondary|electr|fueloil_NH3|M1', - 'in|secondary|electr|gas_NH3|M1', - 'in|secondary|electr|coal_NH3_ccs|M1', - 'in|secondary|electr|fueloil_NH3_ccs|M1', - 'in|secondary|electr|gas_NH3_ccs|M1', - 'in|secondary|electr|biomass_NH3_ccs|M1', - 'in|secondary|electr|biomass_NH3|M1', - 'out|useful|i_therm|solar_i|M1' - ], inplace=True) + df_final_energy.filter( + variable=[ + "in|final|*", + "in|secondary|electr|NH3_to_N_fertil|M1", + "in|secondary|electr|coal_NH3|M1", + "in|secondary|electr|electr_NH3|M1", + "in|secondary|electr|fueloil_NH3|M1", + "in|secondary|electr|gas_NH3|M1", + "in|secondary|electr|coal_NH3_ccs|M1", + "in|secondary|electr|fueloil_NH3_ccs|M1", + "in|secondary|electr|gas_NH3_ccs|M1", + "in|secondary|electr|biomass_NH3_ccs|M1", + "in|secondary|electr|biomass_NH3|M1", + "out|useful|i_therm|solar_i|M1", + ], + inplace=True, + ) all_flows = df_final_energy.timeseries().reset_index() splitted_vars = [v.split("|") for v in all_flows.variable] @@ -1219,8 +1273,9 @@ def report(context, scenario): df_final.append(df_final_energy, inplace=True) if c == "gas": - # Can not distinguish by type Gases (natural gas, biomass, synthetic fossil, efuel) - # (coal_gas), from biomass (gas_bio), natural gas (gas_bal): All go into secondary level + # Can not distinguish by type Gases (natural gas, biomass, synthetic + # fossil, efuel) (coal_gas), from biomass (gas_bio), natural gas + # (gas_bal): All go into secondary level # Can not be distinguished in the final level. df_final_energy.aggregate( @@ -1265,8 +1320,9 @@ def report(context, scenario): # Only bios (ethanol, methanol ?) filter_vars = [ - v for v in aux2_df["variable"].values if (("ethanol" in v) - & ('methanol' not in v)) + v + for v in aux2_df["variable"].values + if (("ethanol" in v) & ("methanol" not in v)) ] df_final_energy.aggregate( "Final Energy|Industry excl Non-Energy Use|Liquids|Biomass", @@ -1277,8 +1333,9 @@ def report(context, scenario): # Fossils filter_vars = [ - v for v in aux2_df["variable"].values if (("fueloil" in v) - | ("lightoil" in v)) + v + for v in aux2_df["variable"].values + if (("fueloil" in v) | ("lightoil" in v)) ] df_final_energy.aggregate( @@ -1290,7 +1347,7 @@ def report(context, scenario): # Other filter_vars = [ - v for v in aux2_df["variable"].values if (("methanol" in v)) + v for v in aux2_df["variable"].values if (("methanol" in v)) ] df_final_energy.aggregate( @@ -1366,7 +1423,7 @@ def report(context, scenario): "GWa", to="EJ/yr", factor=0.03154, inplace=True ) df_final.append(df_final_energy, inplace=True) - if c == 'other': + if c == "other": df_final_energy.aggregate( "Final Energy|Industry excl Non-Energy Use|Other", components=var_sectors, @@ -1383,45 +1440,64 @@ def report(context, scenario): # FINAL ENERGY BY FUELS (Including Non-Energy Use) print("Final Energy by fuels including non-energy use is being printed.") - commodities = ["electr", "gas", "hydrogen", "liquids", "solids", "heat", "all",'other'] + commodities = [ + "electr", + "gas", + "hydrogen", + "liquids", + "solids", + "heat", + "all", + "other", + ] for c in commodities: for r in nodes: df_final_energy = df.copy() - df_final_energy.convert_unit( - "", to="GWa", factor=1, inplace=True) + df_final_energy.convert_unit("", to="GWa", factor=1, inplace=True) df_final_energy.filter(region=r, year=years, inplace=True) df_final_energy.filter( - variable=["in|final|*|cokeoven_steel|*", - "in|final|bf_gas|*", - "in|final|co_gas|*", - ], keep=False, inplace=True + variable=[ + "in|final|*|cokeoven_steel|*", + "in|final|bf_gas|*", + "in|final|co_gas|*", + ], + keep=False, + inplace=True, ) - if c == 'other': - df_final_energy.filter(variable=["out|useful|i_therm|solar_i|M1"], - inplace = True) + if c == "other": + df_final_energy.filter( + variable=["out|useful|i_therm|solar_i|M1"], inplace=True + ) if c == "electr": - df_final_energy.filter(variable=["in|final|electr|*", - 'in|secondary|electr|NH3_to_N_fertil|M1', - 'in|secondary|electr|coal_NH3|M1', - 'in|secondary|electr|electr_NH3|M1', - 'in|secondary|electr|fueloil_NH3|M1', - 'in|secondary|electr|gas_NH3|M1', - 'in|secondary|electr|coal_NH3_ccs|M1', - 'in|secondary|electr|fueloil_NH3_ccs|M1', - 'in|secondary|electr|gas_NH3_ccs|M1', - 'in|secondary|electr|biomass_NH3_ccs|M1', - 'in|secondary|electr|biomass_NH3|M1', - ], inplace=True) + df_final_energy.filter( + variable=[ + "in|final|electr|*", + "in|secondary|electr|NH3_to_N_fertil|M1", + "in|secondary|electr|coal_NH3|M1", + "in|secondary|electr|electr_NH3|M1", + "in|secondary|electr|fueloil_NH3|M1", + "in|secondary|electr|gas_NH3|M1", + "in|secondary|electr|coal_NH3_ccs|M1", + "in|secondary|electr|fueloil_NH3_ccs|M1", + "in|secondary|electr|gas_NH3_ccs|M1", + "in|secondary|electr|biomass_NH3_ccs|M1", + "in|secondary|electr|biomass_NH3|M1", + ], + inplace=True, + ) if c == "gas": - df_final_energy.filter(variable=["in|final|gas|*", - 'in|secondary|gas|gas_NH3|M1', - 'in|secondary|gas|gas_NH3_ccs|M1' - ], - inplace=True) + df_final_energy.filter( + variable=[ + "in|final|gas|*", + "in|secondary|gas|gas_NH3|M1", + "in|secondary|gas|gas_NH3_ccs|M1", + ], + inplace=True, + ) # Include gasoil and naphtha feedstock if c == "liquids": @@ -1434,9 +1510,10 @@ def report(context, scenario): "in|final|vacuum_gasoil|*", "in|final|naphtha|*", "in|final|atm_gasoil|*", - 'in|secondary|fueloil|fueloil_NH3|M1', - 'in|secondary|fueloil|fueloil_NH3_ccs|M1'], - inplace=True, + "in|secondary|fueloil|fueloil_NH3|M1", + "in|secondary|fueloil|fueloil_NH3_ccs|M1", + ], + inplace=True, ) if c == "solids": df_final_energy.filter( @@ -1444,10 +1521,10 @@ def report(context, scenario): "in|final|biomass|*", "in|final|coal|*", "in|final|coke_iron|*", - 'in|secondary|coal|coal_NH3|M1', - 'in|secondary|coal|coal_NH3_ccs|M1', - 'in|primary|biomass|biomass_NH3_ccs|M1', - 'in|primary|biomass|biomass_NH3|M1' + "in|secondary|coal|coal_NH3|M1", + "in|secondary|coal|coal_NH3_ccs|M1", + "in|primary|biomass|biomass_NH3_ccs|M1", + "in|primary|biomass|biomass_NH3|M1", ], inplace=True, ) @@ -1457,25 +1534,29 @@ def report(context, scenario): if c == "heat": df_final_energy.filter(variable=["in|final|d_heat|*"], inplace=True) if c == "all": - df_final_energy.filter(variable=["in|final|*", - "in|secondary|coal|coal_NH3|M1", - "in|secondary|electr|NH3_to_N_fertil|M1", - 'in|secondary|electr|coal_NH3|M1', - 'in|secondary|electr|electr_NH3|M1', - 'in|secondary|electr|gas_NH3|M1', - 'in|secondary|fueloil|fueloil_NH3|M1', - 'in|secondary|gas|gas_NH3|M1', - "in|secondary|coal|coal_NH3_ccs|M1", - 'in|secondary|electr|coal_NH3_ccs|M1', - 'in|secondary|electr|gas_NH3_ccs|M1', - 'in|secondary|fueloil|fueloil_NH3_ccs|M1', - 'in|secondary|gas|gas_NH3_ccs|M1', - "in|primary|biomass|biomass_NH3|M1", - "in|seconday|electr|biomass_NH3|M1", - "in|primary|biomass|biomass_NH3_ccs|M1", - "in|secondary|electr|biomass_NH3_ccs|M1", - "out|useful|i_therm|solar_i|M1" - ], inplace=True) + df_final_energy.filter( + variable=[ + "in|final|*", + "in|secondary|coal|coal_NH3|M1", + "in|secondary|electr|NH3_to_N_fertil|M1", + "in|secondary|electr|coal_NH3|M1", + "in|secondary|electr|electr_NH3|M1", + "in|secondary|electr|gas_NH3|M1", + "in|secondary|fueloil|fueloil_NH3|M1", + "in|secondary|gas|gas_NH3|M1", + "in|secondary|coal|coal_NH3_ccs|M1", + "in|secondary|electr|coal_NH3_ccs|M1", + "in|secondary|electr|gas_NH3_ccs|M1", + "in|secondary|fueloil|fueloil_NH3_ccs|M1", + "in|secondary|gas|gas_NH3_ccs|M1", + "in|primary|biomass|biomass_NH3|M1", + "in|seconday|electr|biomass_NH3|M1", + "in|primary|biomass|biomass_NH3_ccs|M1", + "in|secondary|electr|biomass_NH3_ccs|M1", + "out|useful|i_therm|solar_i|M1", + ], + inplace=True, + ) all_flows = df_final_energy.timeseries().reset_index() splitted_vars = [v.split("|") for v in all_flows.variable] @@ -1502,7 +1583,7 @@ def report(context, scenario): | ("_i" in v) | ("_I" in v) | ("_fs" in v) - | ('NH3' in v) + | ("NH3" in v) ) & (("eth_ic_trp" not in v) & ("meth_ic_trp" not in v)) ) @@ -1513,18 +1594,24 @@ def report(context, scenario): # Aggregate - if c == 'other': + if c == "other": df_final_energy.aggregate( - "Final Energy|Industry|Other", components=var_sectors, append=True, + "Final Energy|Industry|Other", + components=var_sectors, + append=True, + ) + df_final_energy.filter( + variable=["Final Energy|Industry|Other"], inplace=True ) - df_final_energy.filter(variable=["Final Energy|Industry|Other"], inplace=True) df_final_energy.convert_unit( "GWa", to="EJ/yr", factor=0.03154, inplace=True ) df_final.append(df_final_energy, inplace=True) if c == "all": df_final_energy.aggregate( - "Final Energy|Industry", components=var_sectors, append=True, + "Final Energy|Industry", + components=var_sectors, + append=True, ) df_final_energy.filter(variable=["Final Energy|Industry"], inplace=True) df_final_energy.convert_unit( @@ -1539,7 +1626,8 @@ def report(context, scenario): append=True, ) df_final_energy.filter( - variable=["Final Energy|Industry|Electricity"], inplace=True, + variable=["Final Energy|Industry|Electricity"], + inplace=True, ) df_final_energy.convert_unit( "GWa", to="EJ/yr", factor=0.03154, inplace=True @@ -1547,16 +1635,20 @@ def report(context, scenario): df_final.append(df_final_energy, inplace=True) if c == "gas": - # Can not distinguish by type Gases (natural gas, biomass, synthetic fossil, efuel) - # (coal_gas), from biomass (gas_bio), natural gas (gas_bal): All go into secondary level + # Can not distinguish by type Gases (natural gas, biomass, synthetic + # fossil, efuel) (coal_gas), from biomass (gas_bio), natural gas + # (gas_bal): All go into secondary level # Can not be distinguished in the final level. df_final_energy.aggregate( - "Final Energy|Industry|Gases", components=var_sectors, append=True, + "Final Energy|Industry|Gases", + components=var_sectors, + append=True, ) df_final_energy.filter( - variable=["Final Energy|Industry|Gases"], inplace=True, + variable=["Final Energy|Industry|Gases"], + inplace=True, ) df_final_energy.convert_unit( "GWa", to="EJ/yr", factor=0.03154, inplace=True @@ -1570,7 +1662,8 @@ def report(context, scenario): append=True, ) df_final_energy.filter( - variable=["Final Energy|Industry|Hydrogen"], inplace=True, + variable=["Final Energy|Industry|Hydrogen"], + inplace=True, ) df_final_energy.convert_unit( "GWa", to="EJ/yr", factor=0.03154, inplace=True @@ -1588,8 +1681,9 @@ def report(context, scenario): # Only bios (ethanol) filter_vars = [ - v for v in aux2_df["variable"].values if (("ethanol" in v) - & ("methanol" not in v)) + v + for v in aux2_df["variable"].values + if (("ethanol" in v) & ("methanol" not in v)) ] df_final_energy.aggregate( "Final Energy|Industry|Liquids|Biomass", @@ -1620,11 +1714,7 @@ def report(context, scenario): # Methanol filter_vars = [ - v - for v in aux2_df["variable"].values - if ( - ("methanol" in v) - ) + v for v in aux2_df["variable"].values if (("methanol" in v)) ] df_final_energy.aggregate( @@ -1633,7 +1723,6 @@ def report(context, scenario): append=True, ) - df_final_energy.filter( variable=[ "Final Energy|Industry|Liquids", @@ -1651,7 +1740,9 @@ def report(context, scenario): # All df_final_energy.aggregate( - "Final Energy|Industry|Solids", components=var_sectors, append=True, + "Final Energy|Industry|Solids", + components=var_sectors, + append=True, ) # Bio @@ -1689,10 +1780,13 @@ def report(context, scenario): df_final.append(df_final_energy, inplace=True) if c == "heat": df_final_energy.aggregate( - "Final Energy|Industry|Heat", components=var_sectors, append=True, + "Final Energy|Industry|Heat", + components=var_sectors, + append=True, ) df_final_energy.filter( - variable=["Final Energy|Industry|Heat"], inplace=True, + variable=["Final Energy|Industry|Heat"], + inplace=True, ) df_final_energy.convert_unit( "GWa", to="EJ/yr", factor=0.03154, inplace=True @@ -1710,7 +1804,7 @@ def report(context, scenario): "Non-Ferrous Metals", "Non-Metallic Minerals", "Chemicals", - 'Other Sector' + "Other Sector", ] print("Final Energy by sector and fuel is being printed") for r in nodes: @@ -1725,10 +1819,13 @@ def report(context, scenario): "in|final|vacuum_gasoil|steam_cracker_petro|*", "in|final|*|cokeoven_steel|*", "in|final|bf_gas|*", - "in|final|co_gas|*"] + "in|final|co_gas|*", + ] df_final_energy.filter(region=r, year=years, inplace=True) - df_final_energy.filter(variable=["in|final|*", 'out|useful|i_therm|solar_i|M1'], inplace=True) + df_final_energy.filter( + variable=["in|final|*", "out|useful|i_therm|solar_i|M1"], inplace=True + ) df_final_energy.filter(variable=exclude, keep=False, inplace=True) # Decompose the pyam table into pandas data frame @@ -1756,13 +1853,18 @@ def report(context, scenario): tec = [t for t in aux2_df["technology"].values if "cement" in t] aux2_df = aux2_df[aux2_df["technology"].isin(tec)] elif s == "Chemicals": - tec = [t for t in aux2_df["technology"].values if (("petro" in t) - | ('NH3' in t))] + tec = [ + t + for t in aux2_df["technology"].values + if (("petro" in t) | ("NH3" in t)) + ] aux2_df = aux2_df[aux2_df["technology"].isin(tec)] - elif s == 'Other Sector': - tec = [t for t in aux2_df["technology"].values if ((('_i' in t) - | ('_I' in t)) - & ('trp' not in t))] + elif s == "Other Sector": + tec = [ + t + for t in aux2_df["technology"].values + if ((("_i" in t) | ("_I" in t)) & ("trp" not in t)) + ] aux2_df = aux2_df[aux2_df["technology"].isin(tec)] else: # Filter the technologies only for the certain industry sector @@ -1787,7 +1889,7 @@ def report(context, scenario): "liquids", "liquid_bio", "liquid_fossil", - 'liquid_other', + "liquid_other", "solids", "solids_bio", "solids_fossil", @@ -1846,7 +1948,8 @@ def report(context, scenario): elif c == "liquid_bio": var = np.unique( aux2_df.loc[ - ((aux2_df["commodity"] == "ethanol")), "variable", + ((aux2_df["commodity"] == "ethanol")), + "variable", ].values ).tolist() aggregate_name = ( @@ -1977,7 +2080,7 @@ def report(context, scenario): # For ammonia, there is no seperation for non-energy vs. energy. Everything # is included here and the name of the variable is later changed. - sectors = ["petro", 'ammonia'] + sectors = ["petro", "ammonia"] print("Final Energy non-energy use by sector and fuel is being printed") for r in nodes: for s in sectors: @@ -1989,25 +2092,25 @@ def report(context, scenario): "in|final|gas|gas_processing_petro|M1", "in|final|naphtha|steam_cracker_petro|*", "in|final|vacuum_gasoil|steam_cracker_petro|*", - 'in|secondary|coal|coal_NH3|M1', - 'in|secondary|coal|coal_NH3_ccs|M1', - 'in|secondary|fueloil|fueloil_NH3|M1', - 'in|secondary|fueloil|fueloil_NH3_ccs|M1', - 'in|secondary|gas|gas_NH3|M1', - 'in|secondary|gas|gas_NH3_ccs|M1', - 'in|primary|biomass|biomass_NH3|M1', - 'in|primary|biomass|biomass_NH3_ccs|M1', + "in|secondary|coal|coal_NH3|M1", + "in|secondary|coal|coal_NH3_ccs|M1", + "in|secondary|fueloil|fueloil_NH3|M1", + "in|secondary|fueloil|fueloil_NH3_ccs|M1", + "in|secondary|gas|gas_NH3|M1", + "in|secondary|gas|gas_NH3_ccs|M1", + "in|primary|biomass|biomass_NH3|M1", + "in|primary|biomass|biomass_NH3_ccs|M1", "in|secondary|electr|NH3_to_N_fertil|M1", - 'in|secondary|electr|coal_NH3|M1', - 'in|secondary|electr|electr_NH3|M1', - 'in|secondary|electr|gas_NH3|M1', - 'in|secondary|electr|coal_NH3|M1', - 'in|secondary|electr|coal_NH3_ccs|M1', - 'in|secondary|electr|gas_NH3_ccs|M1', - "in|seconday|electr|biomass_NH3|M1", - "in|secondary|electr|biomass_NH3_ccs|M1", - 'in|secondary|electr|fueloil_NH3|M1', - 'in|secondary|electr|fueloil_NH3_ccs|M1', + "in|secondary|electr|coal_NH3|M1", + "in|secondary|electr|electr_NH3|M1", + "in|secondary|electr|gas_NH3|M1", + "in|secondary|electr|coal_NH3|M1", + "in|secondary|electr|coal_NH3_ccs|M1", + "in|secondary|electr|gas_NH3_ccs|M1", + "in|seconday|electr|biomass_NH3|M1", + "in|secondary|electr|biomass_NH3_ccs|M1", + "in|secondary|electr|fueloil_NH3|M1", + "in|secondary|electr|fueloil_NH3_ccs|M1", ] df_final_energy.filter(region=r, year=years, inplace=True) @@ -2031,10 +2134,10 @@ def report(context, scenario): ) # Filter the technologies only for the certain industry sector - if s == 'petro': + if s == "petro": tec = [t for t in aux2_df["technology"].values if (s in t)] - if s == 'ammonia': - tec = [t for t in aux2_df["technology"].values if ('NH3' in t)] + if s == "ammonia": + tec = [t for t in aux2_df["technology"].values if ("NH3" in t)] aux2_df = aux2_df[aux2_df["technology"].isin(tec)] @@ -2055,7 +2158,7 @@ def report(context, scenario): "liquid_bio", "liquid_oil", "all", - 'solids' + "solids", ] for c in commodity_list: @@ -2085,7 +2188,8 @@ def report(context, scenario): elif c == "liquid_bio": var = np.unique( aux2_df.loc[ - ((aux2_df["commodity"] == "ethanol")), "variable", + ((aux2_df["commodity"] == "ethanol")), + "variable", ].values ).tolist() aggregate_name = ( @@ -2094,11 +2198,13 @@ def report(context, scenario): elif c == "liquid_oil": var = np.unique( aux2_df.loc[ - ((aux2_df["commodity"] == "atm_gasoil") | - (aux2_df["commodity"] == "naphtha") | - (aux2_df["commodity"] == "vacuum_gasoil") | - (aux2_df["commodity"] == "fueloil") - ), "variable", + ( + (aux2_df["commodity"] == "atm_gasoil") + | (aux2_df["commodity"] == "naphtha") + | (aux2_df["commodity"] == "vacuum_gasoil") + | (aux2_df["commodity"] == "fueloil") + ), + "variable", ].values ).tolist() @@ -2106,16 +2212,17 @@ def report(context, scenario): "Final Energy|Non-Energy Use|" + s + "|" + "Liquids|Oil" ) - elif c == 'solids': + elif c == "solids": var = np.unique( aux2_df.loc[ - ((aux2_df["commodity"] == "coal") | - (aux2_df["commodity"] == "biomass")), "variable", + ( + (aux2_df["commodity"] == "coal") + | (aux2_df["commodity"] == "biomass") + ), + "variable", ].values ).tolist() - aggregate_name = ( - "Final Energy|Non-Energy Use|" + s + "|" + "Solids" - ) + aggregate_name = "Final Energy|Non-Energy Use|" + s + "|" + "Solids" elif c == "all": var = aux2_df["variable"].tolist() aggregate_name = "Final Energy|Non-Energy Use|" + s @@ -2160,13 +2267,13 @@ def report(context, scenario): "steel", "petro", "cement", - 'ammonia', + "ammonia", "all", "Chemicals", - 'Other Sector' + "Other Sector", ] emission_type = [ - 'CO2_industry', + "CO2_industry", "CH4", "CO2", "NH3", @@ -2186,43 +2293,47 @@ def report(context, scenario): df_emi.filter(region=r, year=years, inplace=True) # CCS technologies for ammonia has both CO2 and CO2_industry # at the same time. - if e == 'CO2_industry': - emi_filter = ["emis|CO2|biomass_NH3_ccs|*", - "emis|CO2|gas_NH3_ccs|*", - "emis|CO2|coal_NH3_ccs|*", - "emis|CO2|fueloil_NH3_ccs|*", - "emis|CO2_industry|biomass_NH3_ccs|*", - "emis|CO2_industry|gas_NH3_ccs|*", - "emis|CO2_industry|coal_NH3_ccs|*", - "emis|CO2_industry|fueloil_NH3_ccs|*", - "emis|CO2_industry|biomass_NH3|*", - "emis|CO2_industry|gas_NH3|*", - "emis|CO2_industry|coal_NH3|*", - "emis|CO2_industry|fueloil_NH3|*", - "emis|CO2_industry|electr_NH3|*", - 'emis|CO2_industry|*'] + if e == "CO2_industry": + emi_filter = [ + "emis|CO2|biomass_NH3_ccs|*", + "emis|CO2|gas_NH3_ccs|*", + "emis|CO2|coal_NH3_ccs|*", + "emis|CO2|fueloil_NH3_ccs|*", + "emis|CO2_industry|biomass_NH3_ccs|*", + "emis|CO2_industry|gas_NH3_ccs|*", + "emis|CO2_industry|coal_NH3_ccs|*", + "emis|CO2_industry|fueloil_NH3_ccs|*", + "emis|CO2_industry|biomass_NH3|*", + "emis|CO2_industry|gas_NH3|*", + "emis|CO2_industry|coal_NH3|*", + "emis|CO2_industry|fueloil_NH3|*", + "emis|CO2_industry|electr_NH3|*", + "emis|CO2_industry|*", + ] df_emi.filter(variable=emi_filter, inplace=True) else: emi_filter = ["emis|" + e + "|*"] - exclude = ["emis|CO2|biomass_NH3_ccs|*", - "emis|CO2|gas_NH3_ccs|*", - "emis|CO2|coal_NH3_ccs|*", - "emis|CO2|fueloil_NH3_ccs|*"] - df_emi.filter(variable=exclude, keep = False, inplace=True) + exclude = [ + "emis|CO2|biomass_NH3_ccs|*", + "emis|CO2|gas_NH3_ccs|*", + "emis|CO2|coal_NH3_ccs|*", + "emis|CO2|fueloil_NH3_ccs|*", + ] + df_emi.filter(variable=exclude, keep=False, inplace=True) df_emi.filter(variable=emi_filter, inplace=True) if (e == "CO2") | (e == "CO2_industry"): # From MtC to Mt CO2/yr - df_emi.convert_unit('', to="Mt CO2/yr", factor=44/12, - inplace=True) + df_emi.convert_unit( + "", to="Mt CO2/yr", factor=44 / 12, inplace=True + ) elif (e == "N2O") | (e == "CF4"): unit = "kt " + e + "/yr" - df_emi.convert_unit('', to= unit, factor=1, - inplace=True) + df_emi.convert_unit("", to=unit, factor=1, inplace=True) else: e = change_names(e) # From kt/yr to Mt/yr unit = "Mt " + e + "/yr" - df_emi.convert_unit("", to= unit, factor=0.001, inplace=True) + df_emi.convert_unit("", to=unit, factor=0.001, inplace=True) all_emissions = df_emi.timeseries().reset_index() @@ -2268,21 +2379,22 @@ def report(context, scenario): tec = [ t for t in aux2_df["technology"].values - if ((s in t) & ("furnace" not in t) & ('NH3' not in t)) + if ((s in t) & ("furnace" not in t) & ("NH3" not in t)) ] if (typ == "demand") & (s == "Chemicals"): tec = [ t for t in aux2_df["technology"].values - if (('NH3' in t) | (('petro' in t) & ('furnace' in t))) + if (("NH3" in t) | (("petro" in t) & ("furnace" in t))) ] if (typ == "demand") & (s == "Other Sector") & (e != "CO2"): tec = [ t for t in aux2_df["technology"].values - if (( + if ( + ( ("biomass_i" in t) | ("coal_i" in t) | ("elec_i" in t) @@ -2330,7 +2442,6 @@ def report(context, scenario): ) & ("furnace" in t) ) - | ( ("biomass_i" in t) | ("coal_i" in t) @@ -2371,12 +2482,18 @@ def report(context, scenario): ) ] - if (typ == "demand") & (s != "all") & (s != "Other Sector") & (s != "Chemicals"): + if ( + (typ == "demand") + & (s != "all") + & (s != "Other Sector") + & (s != "Chemicals") + ): if s == "steel": # Furnaces are not used as heat source for iron&steel # Dummy supply technologies help accounting the emissions - # from cokeoven_steel, bf_steel, dri_steel, eaf_steel, sinter_steel. + # from cokeoven_steel, bf_steel, dri_steel, eaf_steel, + # sinter_steel. tec = [ t @@ -2395,13 +2512,14 @@ def report(context, scenario): if ( ((s in t) & ("furnace" in t)) | ("DUMMY_limestone_supply_cement" in t) - )] + ) + ] elif s == "ammonia": tec = [ t for t in aux2_df["technology"].values - if ( - ('NH3' in t))] + if (("NH3" in t)) + ] else: tec = [ t @@ -2472,7 +2590,8 @@ def report(context, scenario): ) df_emi = pyam.IamDataFrame(data=aux2_df) - # Aggregation over emission type for each sector if there are elements to aggregate + # Aggregation over emission type for each sector if there are elements + # to aggregate if len(aggregate_list) != 0: for i in range(len(aggregate_list)): @@ -2522,7 +2641,8 @@ def report(context, scenario): # # # Propylene production # - # propylene_vars = [ # "out|final_material|propylene|catalytic_cracking_ref|atm_gasoil", + # propylene_vars = [ + # # "out|final_material|propylene|catalytic_cracking_ref|atm_gasoil", # # "out|final_material|propylene|catalytic_cracking_ref|vacuum_gasoil", # "out|final_material|propylene|steam_cracker_petro|atm_gasoil", # "out|final_material|propylene|steam_cracker_petro|naphtha", @@ -2787,7 +2907,10 @@ def report(context, scenario): # columns=["flow_type", "level", "commodity", "technology", "mode"], # ) # aux2_df = pd.concat( - # [all_flows.reset_index(drop=True), aux1_df.reset_index(drop=True)], + # [ + # all_flows.reset_index(drop=True), + # aux1_df.reset_index(drop=True), + # ], # axis=1, # ) # @@ -2801,7 +2924,8 @@ def report(context, scenario): # commodity_list = [] # var_list = [] # - # # For each commodity collect the variable name, create an aggregate name + # # For each commodity collect the variable name, create an aggregate + # # name # s = change_names(s) # for c in np.unique(aux2_df["commodity"].values): # var = np.unique( @@ -2943,7 +3067,9 @@ def report(context, scenario): # # Used for calculation of average prices for scraps # output = scenario.par( # "output", - # filters={"technology": ["scrap_recovery_aluminum", "scrap_recovery_steel"]}, + # filters={ + # "technology": ["scrap_recovery_aluminum", "scrap_recovery_steel"], + # }, # ) # # Differs per sector what to report so more flexible with conditions. # # Store the relevant variables in prices_all @@ -2973,7 +3099,8 @@ def report(context, scenario): # prices_all = prices[(prices["commodity"] == "pig_iron")] # if c == "Steel": # prices_all = prices[ - # (prices["commodity"] == "steel") & (prices["level"] == "final_material") + # (prices["commodity"] == "steel") + # & (prices["level"] == "final_material") # ] # if c == "Steel|New Scrap": # prices_all = prices[ @@ -3015,12 +3142,19 @@ def report(context, scenario): # # for reg in output["node_loc"].unique(): # for yr in output["year_act"].unique(): - # prices_temp = prices.groupby(["node", "year"]).get_group((reg, yr)) + # prices_temp = ( + # prices.groupby(["node", "year"]).get_group((reg, yr)) + # ) # rate = prices_temp["weight"].values.tolist() # amount = prices_temp["lvl"].values.tolist() # weighted_avg = np.average(amount, weights=rate) # prices_new = pd.DataFrame( - # {"node": reg, "year": yr, "commodity": c, "lvl": weighted_avg,}, + # { + # "node": reg, + # "year": yr, + # "commodity": c, + # "lvl": weighted_avg, + # }, # index=[0], # ) # prices_all = pd.concat([prices_all, prices_new]) @@ -3049,7 +3183,11 @@ def report(context, scenario): # if (r == "R11_GLB") | (r == "R12_GLB"): # continue # df_price = pd.DataFrame( - # {"model": model_name, "scenario": scenario_name, "unit": "2010USD/Mt",}, + # { + # "model": model_name, + # "scenario": scenario_name, + # "unit": "2010USD/Mt", + # }, # index=[0], # ) # @@ -3088,7 +3226,11 @@ def report(context, scenario): # cap_new = scenario.var("CAP_NEW") # cap_new.drop(["mrg"], axis=1, inplace=True) # cap_new.rename( - # columns={"lvl": "installed_capacity", "node_loc": "region", "year_vtg": "year"}, + # columns={ + # "lvl": "installed_capacity", + # "node_loc": "region", + # "year_vtg": "year" + # }, # inplace=True, # ) # From afe7772898d4dc06be285847ba35cbe774542712 Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Wed, 7 Sep 2022 12:01:08 +0200 Subject: [PATCH 023/774] Add .material.report.callback for use with general reporting cli --- .../model/material/report/__init__.py | 31 ++++++++++++++----- 1 file changed, 23 insertions(+), 8 deletions(-) diff --git a/message_ix_models/model/material/report/__init__.py b/message_ix_models/model/material/report/__init__.py index dc3e2d773e..cc6ff34395 100644 --- a/message_ix_models/model/material/report/__init__.py +++ b/message_ix_models/model/material/report/__init__.py @@ -22,19 +22,20 @@ # foil_imp AFR, frunace_h2_aluminum, h2_i... # PACKAGES +from functools import partial import matplotlib +import message_ix import numpy as np import openpyxl import os import pandas as pd import pyam import xlsxwriter -from ixmp.reporting import configure from matplotlib import pyplot as plt from matplotlib.backends.backend_pdf import PdfPages from message_ix_models import ScenarioInfo -from message_ix.reporting import Reporter +from message_ix_models import Context matplotlib.use("Agg") @@ -176,7 +177,11 @@ def fix_excel(path_temp, path_new): new_workbook.save(path_new) -def report(context, scenario): +def report( + scenario: message_ix.Scenario, + message_df: pd.DataFrame, + context: Context, +) -> None: """Produces the material related variables.""" # Obtain scenario information and directory @@ -215,11 +220,8 @@ def report(context, scenario): directory = context.get_local_path("report", "materials") directory.mkdir(exist_ok=True) - # Generate message_ix level reporting and dump to an excel file. - - rep = Reporter.from_scenario(scenario) - configure(units={"replace": {"-": ""}}) - df = rep.get("message::default") + # Dump output of message_ix built-in reporting to an Excel file + df = message_df name = os.path.join(directory, "message_ix_reporting.xlsx") df.to_excel(name) print("message_ix level reporting generated") @@ -3316,3 +3318,16 @@ def report(context, scenario): pp.close() os.remove(path_temp) + + +def callback(rep: message_ix.Reporter, context: Context) -> None: + """:meth:`.prepare_reporter` callback for MESSAGEix-Materials. + + - "materials all": invokes :func:`report`. + """ + rep.add( + "materials all", + partial(report, context=context), + "scenario", + "message::default", + ) From db6c5c415987dc2d7a3ebe2cccbdab006a3f0ae2 Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Wed, 7 Sep 2022 13:41:29 +0200 Subject: [PATCH 024/774] Remove .material.report.tables.retr_supply_inv() instead, use .default_tables.techs to control technologies used by the default tables. --- .../model/material/report/tables.py | 710 ++---------------- 1 file changed, 50 insertions(+), 660 deletions(-) diff --git a/message_ix_models/model/material/report/tables.py b/message_ix_models/model/material/report/tables.py index ef51e16d25..cc1998a702 100644 --- a/message_ix_models/model/material/report/tables.py +++ b/message_ix_models/model/material/report/tables.py @@ -1,6 +1,6 @@ import pandas as pd -from message_data.tools.post_processing.default_tables import _pe_wCCSretro +from message_data.tools.post_processing.default_tables import TECHS, _pe_wCCSretro import message_data.tools.post_processing.pp_utils as pp_utils pp = None @@ -15,10 +15,58 @@ def return_func_dict(): - """Returns functions defined in script""" + """Return functions described in script.""" + + # This is a crude hack: when iamc_report_hackathon.report(), per config, finds and + # calls this function to retrieve the list of functions in the file, we override the + # lists of technologies used by functions defined in .default_tables. + # FIXME(PNK) read these from proper hierarchical technology code lists; store on a + # Context object. + TECHS.update(_TECHS) + return func_dict +#: Lists of technologies for materials variant. +_TECHS = { + "cement with ccs": ["clinker_dry_ccs_cement", "clinker_wet_ccs_cement"], + "extra gas": [ + "furnace_gas_aluminum", + "furnace_gas_petro", + "furnace_gas_cement", + "furnace_gas_refining", + "hp_gas_aluminum", + "hp_gas_petro", + "hp_gas_refining", + ], + "refining": [ + "atm_distillation_ref", + "catalytic_cracking_ref", + "catalytic_reforming_ref", + "coking_ref", + "dheat_refining", + "fc_h2_refining", + "furnace_biomass_refining", + "furnace_coal_refining", + "furnace_coke_refining", + "furnace_elec_refining", + "furnace_ethanol_refining", + "furnace_foil_refining", + "furnace_gas_refining", + "furnace_h2_refining", + "furnace_loil_refining", + "furnace_methanol_refining", + "hp_elec_refining", + "hp_gas_refining", + "hydro_cracking_ref", + "hydrotreating_ref", + "solar_refining", + "vacuum_distillation_ref", + "visbreaker_ref", + ], +} + + def _register(func): """Function to register reporting functions. @@ -1687,661 +1735,3 @@ def retr_SE_solids(units): ) df = pp_utils.make_outputdf(vars, units) return df - - -@_register -def retr_supply_inv(units_energy, units_emi, units_ene_mdl): - """Technology results: Investments. - - Investments into technologies. - - Note OFR - 20.04.2017: The following has been checked between Volker - Krey, David McCollum and Oliver Fricko. - - There are fixed factors by which ccs technologies are multiplied which - equate to the share of the powerplant costs which split investments into the - share associated with the standard powerplant and the share associated - with those investments related to CCS. - - For some extraction and synfuel technologies, a certain share of the voms and - foms are attributed to the investments which is based on the GEA-Study - where the derived investment costs were partly attributed to the - voms/foms. - - Parameters - ---------- - units_energy : str - Units to which energy variables should be converted. - units_emi : str - Units to which emission variables should be converted. - units_ene_mdl : str - Native units of energy in the model. - """ - - vars = {} - - # ---------- - # Extraction - # ---------- - - # Note OFR 25.04.2017: All non-extraction costs for Coal, Gas and Oil - # have been moved to "Energy|Other" - - vars["Extraction|Coal"] = pp.investment( - ["coal_extr_ch4", "coal_extr", "lignite_extr"], units=units_energy - ) - - vars["Extraction|Gas|Conventional"] = ( - pp.investment( - ["gas_extr_1", "gas_extr_2", "gas_extr_3", "gas_extr_4"], units=units_energy - ) - + pp.act_vom( - ["gas_extr_1", "gas_extr_2", "gas_extr_3", "gas_extr_4"], units=units_energy - ) - * 0.5 - ) - - vars["Extraction|Gas|Unconventional"] = ( - pp.investment( - ["gas_extr_5", "gas_extr_6", "gas_extr_7", "gas_extr_8"], units=units_energy - ) - + pp.act_vom( - ["gas_extr_5", "gas_extr_6", "gas_extr_7", "gas_extr_8"], units=units_energy - ) - * 0.5 - ) - - # Note OFR 25.04.2017: Any costs relating to refineries have been - # removed (compared to GEA) as these are reported under "Liquids|Oil" - - vars["Extraction|Oil|Conventional"] = ( - pp.investment( - [ - "oil_extr_1", - "oil_extr_2", - "oil_extr_3", - "oil_extr_1_ch4", - "oil_extr_2_ch4", - "oil_extr_3_ch4", - ], - units=units_energy, - ) - + pp.act_vom( - [ - "oil_extr_1", - "oil_extr_2", - "oil_extr_3", - "oil_extr_1_ch4", - "oil_extr_2_ch4", - "oil_extr_3_ch4", - ], - units=units_energy, - ) - * 0.5 - ) - - vars["Extraction|Oil|Unconventional"] = ( - pp.investment( - [ - "oil_extr_4", - "oil_extr_4_ch4", - "oil_extr_5", - "oil_extr_6", - "oil_extr_7", - "oil_extr_8", - ], - units=units_energy, - ) - + pp.act_vom( - [ - "oil_extr_4", - "oil_extr_4_ch4", - "oil_extr_5", - "oil_extr_6", - "oil_extr_7", - "oil_extr_8", - ], - units=units_energy, - ) - * 0.5 - ) - - # As no mode is specified, u5-reproc will account for all 3 modes. - vars["Extraction|Uranium"] = ( - pp.investment( - ["uran2u5", "Uran_extr", "u5-reproc", "plutonium_prod"], units=units_energy - ) - + pp.act_vom(["uran2u5", "Uran_extr"], units=units_energy) - + pp.act_vom(["u5-reproc"], actfilter={"mode": ["M1"]}, units=units_energy) - + pp.tic_fom(["uran2u5", "Uran_extr", "u5-reproc"], units=units_energy) - ) - - # --------------------- - # Electricity - Fossils - # --------------------- - - vars["Electricity|Coal|w/ CCS"] = ( - pp.investment(["c_ppl_co2scr", "cfc_co2scr"], units=units_energy) - + pp.investment("coal_adv_ccs", units=units_energy) * 0.25 - + pp.investment("igcc_ccs", units=units_energy) * 0.31 - ) - - vars["Electricity|Coal|w/o CCS"] = ( - pp.investment(["coal_ppl", "coal_ppl_u", "coal_adv"], units=units_energy) - + pp.investment("coal_adv_ccs", units=units_energy) * 0.75 - + pp.investment("igcc", units=units_energy) - + pp.investment("igcc_ccs", units=units_energy) * 0.69 - ) - - vars["Electricity|Gas|w/ CCS"] = ( - pp.investment(["g_ppl_co2scr", "gfc_co2scr"], units=units_energy) - + pp.investment("gas_cc_ccs", units=units_energy) * 0.53 - ) - - vars["Electricity|Gas|w/o CCS"] = ( - pp.investment(["gas_cc", "gas_ct", "gas_ppl"], units=units_energy) - + pp.investment("gas_cc_ccs", units=units_energy) * 0.47 - ) - - vars["Electricity|Oil|w/o CCS"] = pp.investment( - ["foil_ppl", "loil_ppl", "oil_ppl", "loil_cc"], units=units_energy - ) - - # ------------------------ - # Electricity - Renewables - # ------------------------ - - vars["Electricity|Biomass|w/ CCS"] = ( - pp.investment("bio_ppl_co2scr", units=units_energy) - + pp.investment("bio_istig_ccs", units=units_energy) * 0.31 - ) - - vars["Electricity|Biomass|w/o CCS"] = ( - pp.investment(["bio_ppl", "bio_istig"], units=units_energy) - + pp.investment("bio_istig_ccs", units=units_energy) * 0.69 - ) - - vars["Electricity|Geothermal"] = pp.investment("geo_ppl", units=units_energy) - - vars["Electricity|Hydro"] = pp.investment( - ["hydro_hc", "hydro_lc"], units=units_energy - ) - vars["Electricity|Other"] = pp.investment( - ["h2_fc_I", "h2_fc_RC"], units=units_energy - ) - - _solar_pv_elec = pp.investment( - ["solar_pv_ppl", "solar_pv_I", "solar_pv_RC"], units=units_energy - ) - - _solar_th_elec = pp.investment(["csp_sm1_ppl", "csp_sm3_ppl"], units=units_energy) - - vars["Electricity|Solar|PV"] = _solar_pv_elec - vars["Electricity|Solar|CSP"] = _solar_th_elec - vars["Electricity|Wind|Onshore"] = pp.investment(["wind_ppl"], units=units_energy) - vars["Electricity|Wind|Offshore"] = pp.investment(["wind_ppf"], units=units_energy) - - # ------------------- - # Electricity Nuclear - # ------------------- - - vars["Electricity|Nuclear"] = pp.investment( - ["nuc_hc", "nuc_lc"], units=units_energy - ) - - # -------------------------------------------------- - # Electricity Storage, transmission and distribution - # -------------------------------------------------- - - vars["Electricity|Electricity Storage"] = pp.investment( - "stor_ppl", units=units_energy - ) - - vars["Electricity|Transmission and Distribution"] = ( - pp.investment( - [ - "elec_t_d", - "elec_exp", - "elec_exp_africa", - "elec_exp_america", - "elec_exp_asia", - "elec_exp_eurasia", - "elec_exp_eur_afr", - "elec_exp_asia_afr", - "elec_imp", - "elec_imp_africa", - "elec_imp_america", - "elec_imp_asia", - "elec_imp_eurasia", - "elec_imp_eur_afr", - "elec_imp_asia_afr", - ], - units=units_energy, - ) - + pp.act_vom( - [ - "elec_t_d", - "elec_exp", - "elec_exp_africa", - "elec_exp_america", - "elec_exp_asia", - "elec_exp_eurasia", - "elec_exp_eur_afr", - "elec_exp_asia_afr", - "elec_imp", - "elec_imp_africa", - "elec_imp_america", - "elec_imp_asia", - "elec_imp_eurasia", - "elec_imp_eur_afr", - "elec_imp_asia_afr", - ], - units=units_energy, - ) - * 0.5 - + pp.tic_fom( - [ - "elec_t_d", - "elec_exp", - "elec_exp_africa", - "elec_exp_america", - "elec_exp_asia", - "elec_exp_eurasia", - "elec_exp_eur_afr", - "elec_exp_asia_afr", - "elec_imp", - "elec_imp_africa", - "elec_imp_america", - "elec_imp_asia", - "elec_imp_eurasia", - "elec_imp_eur_afr", - "elec_imp_asia_afr", - ], - units=units_energy, - ) - * 0.5 - ) - - # ------------------------------------------ - # CO2 Storage, transmission and distribution - # ------------------------------------------ - - _CCS_coal_elec = -1 * pp.emi( - [ - "c_ppl_co2scr", - "coal_adv_ccs", - "igcc_ccs", - "clinker_dry_ccs_cement", - "clinker_wet_ccs_cement", - ], - units_ene_mdl, - emifilter={"relation": ["CO2_Emission"]}, - emission_units=units_emi, - ) - - _CCS_coal_synf = -1 * pp.emi( - ["syn_liq_ccs", "h2_coal_ccs", "meth_coal_ccs"], - units_ene_mdl, - emifilter={"relation": ["CO2_Emission"]}, - emission_units=units_emi, - ) - - _CCS_gas_elec = -1 * pp.emi( - ["g_ppl_co2scr", "gas_cc_ccs"], - units_ene_mdl, - emifilter={"relation": ["CO2_Emission"]}, - emission_units=units_emi, - ) - - _CCS_gas_synf = -1 * pp.emi( - ["h2_smr_ccs", "meth_ng_ccs"], - units_ene_mdl, - emifilter={"relation": ["CO2_Emission"]}, - emission_units=units_emi, - ) - - _CCS_bio_elec = -1 * pp.emi( - ["bio_ppl_co2scr", "bio_istig_ccs"], - units_ene_mdl, - emifilter={"relation": ["CO2_Emission"]}, - emission_units=units_emi, - ) - - _CCS_bio_synf = -1 * pp.emi( - ["eth_bio_ccs", "liq_bio_ccs", "h2_bio_ccs"], - units_ene_mdl, - emifilter={"relation": ["CO2_Emission"]}, - emission_units=units_emi, - ) - - _Biogas_use_tot = pp.out("gas_bio") - - _Gas_use_tot = pp.inp( - [ - "gas_ppl", - "gas_cc", - "gas_cc_ccs", - "gas_ct", - "gas_htfc", - "gas_hpl", - "meth_ng", - "meth_ng_ccs", - "h2_smr", - "h2_smr_ccs", - "gas_rc", - "hp_gas_rc", - "gas_i", - "hp_gas_i", - "furnace_gas_aluminum", - "furnace_gas_petro", - "furnace_gas_cement", - "furnace_gas_refining", - "hp_gas_i", - "hp_gas_aluminum", - "hp_gas_petro", - "hp_gas_refining", - "gas_trp", - "gas_fs", - ] - ) - - _Biogas_share = (_Biogas_use_tot / _Gas_use_tot).fillna(0) - - _CCS_Foss = ( - _CCS_coal_elec - + _CCS_coal_synf - + _CCS_gas_elec * (1 - _Biogas_share) - + _CCS_gas_synf * (1 - _Biogas_share) - ) - - _CCS_Bio = ( - _CCS_bio_elec + _CCS_bio_synf - (_CCS_gas_elec + _CCS_gas_synf) * _Biogas_share - ) - - _CCS_coal_elec_shr = (_CCS_coal_elec / _CCS_Foss).fillna(0) - _CCS_coal_synf_shr = (_CCS_coal_synf / _CCS_Foss).fillna(0) - _CCS_gas_elec_shr = (_CCS_gas_elec / _CCS_Foss).fillna(0) - _CCS_gas_synf_shr = (_CCS_gas_synf / _CCS_Foss).fillna(0) - _CCS_bio_elec_shr = (_CCS_bio_elec / _CCS_Bio).fillna(0) - _CCS_bio_synf_shr = (_CCS_bio_synf / _CCS_Bio).fillna(0) - - CO2_trans_dist_elec = ( - pp.act_vom("co2_tr_dis", units=units_energy) * 0.5 * _CCS_coal_elec_shr - + pp.act_vom("co2_tr_dis", units=units_energy) * 0.5 * _CCS_gas_elec_shr - + pp.act_vom("bco2_tr_dis", units=units_energy) * 0.5 * _CCS_bio_elec_shr - ) - - CO2_trans_dist_synf = ( - pp.act_vom("co2_tr_dis", units=units_energy) * 0.5 * _CCS_coal_synf_shr - + pp.act_vom("co2_tr_dis", units=units_energy) * 0.5 * _CCS_gas_synf_shr - + pp.act_vom("bco2_tr_dis", units=units_energy) * 0.5 * _CCS_bio_synf_shr - ) - - vars["CO2 Transport and Storage"] = CO2_trans_dist_elec + CO2_trans_dist_synf - - # ---- - # Heat - # ---- - - vars["Heat"] = pp.investment( - ["coal_hpl", "foil_hpl", "gas_hpl", "bio_hpl", "heat_t_d", "po_turbine"], - units=units_energy, - ) - - # ------------------------- - # Synthetic fuel production - # ------------------------- - - # Note OFR 25.04.2017: XXX_synf_ccs has been split into hydrogen and - # liquids. The shares then add up to 1, but the variables are kept - # separate in order to preserve the split between CCS and non-CCS - - _Coal_synf_ccs_liq = ( - pp.investment("meth_coal_ccs", units=units_energy) * 0.02 - + pp.investment("syn_liq_ccs", units=units_energy) * 0.01 - ) - - _Gas_synf_ccs_liq = pp.investment("meth_ng_ccs", units=units_energy) * 0.08 - - _Bio_synf_ccs_liq = ( - pp.investment("eth_bio_ccs", units=units_energy) * 0.34 - + pp.investment("liq_bio_ccs", units=units_energy) * 0.02 - ) - - _Coal_synf_ccs_h2 = pp.investment("h2_coal_ccs", units=units_energy) * 0.03 - _Gas_synf_ccs_h2 = pp.investment("h2_smr_ccs", units=units_energy) * 0.17 - _Bio_synf_ccs_h2 = pp.investment("h2_bio_ccs", units=units_energy) * 0.02 - - # Note OFR 25.04.2017: "coal_gas" have been moved to "other" - vars["Liquids|Coal and Gas"] = ( - pp.investment(["meth_coal", "syn_liq", "meth_ng"], units=units_energy) - + pp.investment("meth_ng_ccs", units=units_energy) * 0.92 - + pp.investment("meth_coal_ccs", units=units_energy) * 0.98 - + pp.investment("syn_liq_ccs", units=units_energy) * 0.99 - + _Coal_synf_ccs_liq - + _Gas_synf_ccs_liq - ) - - # Note OFR 25.04.2017: "gas_bio" has been moved to "other" - vars["Liquids|Biomass"] = ( - pp.investment(["eth_bio", "liq_bio"], units=units_energy) - + pp.investment("liq_bio_ccs", units=units_energy) * 0.98 - + pp.investment("eth_bio_ccs", units=units_energy) * 0.66 - + _Bio_synf_ccs_liq - ) - - # Note OFR 25.04.2017: "transport, import and exports costs related to - # liquids are only included in the total" - _Synfuel_other = pp.investment( - [ - "meth_exp", - "meth_imp", - "meth_t_d", - "meth_bal", - "eth_exp", - "eth_imp", - "eth_t_d", - "eth_bal", - "SO2_scrub_synf", - ], - units=units_energy, - ) - - vars["Liquids|Oil"] = pp.investment( - [ - "furnace_coke_refining", - "furnace_coal_refining", - "furnace_foil_refining", - "furnace_loil_refining", - "furnace_ethanol_refining", - "furnace_biomass_refining", - "furnace_methanol_refining", - "furnace_gas_refining", - "furnace_elec_refining", - "furnace_h2_refining", - "hp_gas_refining", - "hp_elec_refining", - "fc_h2_refining", - "solar_refining", - "dheat_refining", - "atm_distillation_ref", - "vacuum_distillation_ref", - "hydrotreating_ref", - "catalytic_cracking_ref", - "visbreaker_ref", - "coking_ref", - "catalytic_reforming_ref", - "hydro_cracking_ref", - ], - units=units_energy, - ) + pp.tic_fom( - [ - "furnace_coke_refining", - "furnace_coal_refining", - "furnace_foil_refining", - "furnace_loil_refining", - "furnace_ethanol_refining", - "furnace_biomass_refining", - "furnace_methanol_refining", - "furnace_gas_refining", - "furnace_elec_refining", - "furnace_h2_refining", - "hp_gas_refining", - "hp_elec_refining", - "fc_h2_refining", - "solar_refining", - "dheat_refining", - "atm_distillation_ref", - "vacuum_distillation_ref", - "hydrotreating_ref", - "catalytic_cracking_ref", - "visbreaker_ref", - "coking_ref", - "catalytic_reforming_ref", - "hydro_cracking_ref", - ], - units=units_energy, - ) - - vars["Liquids"] = ( - vars["Liquids|Coal and Gas"] - + vars["Liquids|Biomass"] - + vars["Liquids|Oil"] - + _Synfuel_other - ) - - # -------- - # Hydrogen - # -------- - - vars["Hydrogen|Fossil"] = ( - pp.investment(["h2_coal", "h2_smr"], units=units_energy) - + pp.investment("h2_coal_ccs", units=units_energy) * 0.97 - + pp.investment("h2_smr_ccs", units=units_energy) * 0.83 - + _Coal_synf_ccs_h2 - + _Gas_synf_ccs_h2 - ) - - vars["Hydrogen|Renewable"] = ( - pp.investment("h2_bio", units=units_energy) - + pp.investment("h2_bio_ccs", units=units_energy) * 0.98 - + _Bio_synf_ccs_h2 - ) - - vars["Hydrogen|Other"] = ( - pp.investment( - [ - "h2_elec", - "h2_liq", - "h2_t_d", - "lh2_exp", - "lh2_imp", - "lh2_bal", - "lh2_regas", - "lh2_t_d", - ], - units=units_energy, - ) - + pp.act_vom("h2_mix", units=units_energy) * 0.5 - ) - - # ----- - # Other - # ----- - - # All questionable variables from extraction that are not related directly - # to extraction should be moved to Other - # Note OFR 25.04.2017: Any costs relating to refineries have been - # removed (compared to GEA) as these are reported under "Liquids|Oil" - - vars["Other|Liquids|Oil|Transmission and Distribution"] = pp.investment( - ["foil_t_d", "loil_t_d"], units=units_energy - ) - - vars["Other|Liquids|Oil|Other"] = pp.investment( - [ - "foil_exp", - "loil_exp", - "oil_exp", - "oil_imp", - "foil_imp", - "loil_imp", - "loil_std", - "oil_bal", - "loil_sto", - ], - units=units_energy, - ) - - vars["Other|Gases|Transmission and Distribution"] = pp.investment( - ["gas_t_d", "gas_t_d_ch4"], units=units_energy - ) - - vars["Other|Gases|Production"] = pp.investment( - ["gas_bio", "coal_gas"], units=units_energy - ) - - vars["Other|Gases|Other"] = pp.investment( - [ - "LNG_bal", - "LNG_prod", - "LNG_regas", - "LNG_exp", - "LNG_imp", - "gas_bal", - "gas_std", - "gas_sto", - "gas_exp_eeu", - "gas_exp_nam", - "gas_exp_pao", - "gas_exp_weu", - "gas_exp_cpa", - "gas_exp_afr", - "gas_exp_sas", - "gas_exp_scs", - "gas_exp_cas", - "gas_exp_ubm", - "gas_exp_rus", - "gas_imp", - ], - units=units_energy, - ) - - vars["Other|Solids|Coal|Transmission and Distribution"] = ( - pp.investment( - [ - "coal_t_d", - "coal_t_d-rc-SO2", - "coal_t_d-rc-06p", - "coal_t_d-in-SO2", - "coal_t_d-in-06p", - ], - units=units_energy, - ) - + pp.act_vom( - [ - "coal_t_d-rc-SO2", - "coal_t_d-rc-06p", - "coal_t_d-in-SO2", - "coal_t_d-in-06p", - "coal_t_d", - ], - units=units_energy, - ) - * 0.5 - ) - - vars["Other|Solids|Coal|Other"] = pp.investment( - ["coal_exp", "coal_imp", "coal_bal", "coal_std"], units=units_energy - ) - - vars["Other|Solids|Biomass|Transmission and Distribution"] = pp.investment( - "biomass_t_d", units=units_energy - ) - - vars["Other|Other"] = pp.investment( - ["SO2_scrub_ref"], units=units_energy - ) * 0.5 + pp.investment(["SO2_scrub_ind"], units=units_energy) - - df = pp_utils.make_outputdf(vars, units_energy) - return df From bf2eff60313d9724931e56ca860ecedc4779350c Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Wed, 7 Sep 2022 13:52:18 +0200 Subject: [PATCH 025/774] Remove .material.report.tables.retr_co2_ccs() use .default_tables.techs["industry with ccs"] to control the default function. --- .../model/material/report/tables.py | 178 +----------------- 1 file changed, 6 insertions(+), 172 deletions(-) diff --git a/message_ix_models/model/material/report/tables.py b/message_ix_models/model/material/report/tables.py index cc1998a702..ae3caded67 100644 --- a/message_ix_models/model/material/report/tables.py +++ b/message_ix_models/model/material/report/tables.py @@ -30,6 +30,12 @@ def return_func_dict(): #: Lists of technologies for materials variant. _TECHS = { "cement with ccs": ["clinker_dry_ccs_cement", "clinker_wet_ccs_cement"], + "industry with ccs": [ + "biomass_NH3_ccs", + "coal_NH3_ccs", + "fueloil_NH3_ccs", + "gas_NH3_ccs", + ], "extra gas": [ "furnace_gas_aluminum", "furnace_gas_petro", @@ -86,178 +92,6 @@ def _register(func): # ------------------- -@_register -def retr_CO2_CCS(units_emi, units_ene): - """Carbon sequestration. - - Energy and land-use related carbon seuqestration. - - Parameters - ---------- - - units_emi : str - Units to which emission variables should be converted. - units_ene : str - Units to which energy variables should be converted. - """ - - vars = {} - - # -------------------------------- - # Calculation of helping variables - # -------------------------------- - - # Biogas share calculation - _Biogas = pp.out("gas_bio", units_ene) - - _gas_inp_tecs = [ - "gas_ppl", - "gas_cc", - "gas_cc_ccs", - "gas_ct", - "gas_htfc", - "gas_hpl", - "meth_ng", - "meth_ng_ccs", - "h2_smr", - "h2_smr_ccs", - "gas_t_d", - "gas_t_d_ch4", - ] - - _totgas = pp.inp(_gas_inp_tecs, units_ene, inpfilter={"commodity": ["gas"]}) - - _BGas_share = (_Biogas / _totgas).fillna(0) - - # Calulation of CCS components - - _CCS_coal_elec = -1.0 * pp.emi( - ["c_ppl_co2scr", "coal_adv_ccs", "igcc_ccs", "igcc_co2scr"], - "GWa", - emifilter={"relation": ["CO2_Emission"]}, - emission_units=units_emi, - ) - - _CCS_coal_liq = -1.0 * pp.emi( - ["syn_liq_ccs", "meth_coal_ccs"], - "GWa", - emifilter={"relation": ["CO2_Emission"]}, - emission_units=units_emi, - ) - - _CCS_coal_hydrogen = -1.0 * pp.emi( - "h2_coal_ccs", - "GWa", - emifilter={"relation": ["CO2_Emission"]}, - emission_units=units_emi, - ) - - _CCS_cement = -1.0 * pp.emi( - ["clinker_dry_ccs_cement", "clinker_wet_ccs_cement"], - "GWa", - emifilter={"relation": ["CO2_Emission"]}, - emission_units=units_emi, - ) - - _CCS_ammonia = -1.0 * pp.emi( - ["biomass_NH3_ccs", "gas_NH3_ccs", "coal_NH3_ccs", "fueloil_NH3_ccs"], - "GWa", - emifilter={"relation": ["CO2_Emission"]}, - emission_units=units_emi, - ) - - _CCS_bio_elec = -1.0 * pp.emi( - ["bio_ppl_co2scr", "bio_istig_ccs"], - "GWa", - emifilter={"relation": ["CO2_Emission"]}, - emission_units=units_emi, - ) - - _CCS_bio_liq = -1.0 * pp.emi( - ["eth_bio_ccs", "liq_bio_ccs"], - "GWa", - emifilter={"relation": ["CO2_Emission"]}, - emission_units=units_emi, - ) - - _CCS_bio_hydrogen = -1.0 * pp.emi( - "h2_bio_ccs", - "GWa", - emifilter={"relation": ["CO2_Emission"]}, - emission_units=units_emi, - ) - - _CCS_gas_elec = ( - -1.0 - * pp.emi( - ["g_ppl_co2scr", "gas_cc_ccs"], - "GWa", - emifilter={"relation": ["CO2_Emission"]}, - emission_units=units_emi, - ) - * (1.0 - _BGas_share) - ) - - _CCS_gas_liq = ( - -1.0 - * pp.emi( - "meth_ng_ccs", - "GWa", - emifilter={"relation": ["CO2_Emission"]}, - emission_units=units_emi, - ) - * (1.0 - _BGas_share) - ) - - _CCS_gas_hydrogen = ( - -1.0 - * pp.emi( - "h2_smr_ccs", - "GWa", - emifilter={"relation": ["CO2_Emission"]}, - emission_units=units_emi, - ) - * (1.0 - _BGas_share) - ) - - vars["CCS|Fossil|Energy|Supply|Electricity"] = _CCS_coal_elec + _CCS_gas_elec - - vars["CCS|Fossil|Energy|Supply|Liquids"] = _CCS_coal_liq + _CCS_gas_liq - - vars["CCS|Fossil|Energy|Supply|Hydrogen"] = _CCS_coal_hydrogen + _CCS_gas_hydrogen - - vars["CCS|Biomass|Energy|Supply|Electricity"] = ( - _CCS_bio_elec + _CCS_gas_elec * _BGas_share - ) - - vars["CCS|Biomass|Energy|Supply|Liquids"] = ( - _CCS_bio_liq + _CCS_gas_liq * _BGas_share - ) - - vars["CCS|Biomass|Energy|Supply|Hydrogen"] = ( - _CCS_bio_hydrogen + _CCS_gas_hydrogen * _BGas_share - ) - - vars["CCS|Industrial Processes"] = _CCS_cement + _CCS_ammonia - - vars["Land Use"] = -pp.land_out( - lu_out_filter={ - "level": ["land_use_reporting"], - "commodity": ["Emissions|CO2|AFOLU|Negative"], - } - ) - - vars["Land Use|Afforestation"] = -pp.land_out( - lu_out_filter={ - "level": ["land_use_reporting"], - "commodity": ["Emissions|CO2|AFOLU|Afforestation"], - } - ) - - df = pp_utils.make_outputdf(vars, units_emi) - return df - - @_register def retr_CO2emi(units_emi, units_ene_mdl): """Emissions: CO2. From 791d1acccc6d48fe7104247d1e7f8fc750d7346f Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Wed, 7 Sep 2022 14:29:20 +0200 Subject: [PATCH 026/774] Reduce .material.report.tables.retr_se_solids() - call the function from .default_tables, then make a small addition. - control behaviour with techs["se solids biomass extra"], techs["se solids coal extra"]. --- .../model/material/report/tables.py | 103 +++++++----------- 1 file changed, 40 insertions(+), 63 deletions(-) diff --git a/message_ix_models/model/material/report/tables.py b/message_ix_models/model/material/report/tables.py index ae3caded67..f133ae0d4e 100644 --- a/message_ix_models/model/material/report/tables.py +++ b/message_ix_models/model/material/report/tables.py @@ -1,5 +1,6 @@ import pandas as pd +from message_data.tools.post_processing import default_tables from message_data.tools.post_processing.default_tables import TECHS, _pe_wCCSretro import message_data.tools.post_processing.pp_utils as pp_utils @@ -70,6 +71,28 @@ def return_func_dict(): "vacuum_distillation_ref", "visbreaker_ref", ], + "se solids biomass extra": [ + "biomass_comm_heat", + "biomass_comm_hotwater", + "biomass_resid_cook", + "biomass_resid_heat", + "biomass_resid_hotwater", + "furnace_biomass_aluminum", + "furnace_biomass_cement", + "furnace_biomass_petro", + "furnace_biomass_refining", + "furnace_biomass_steel", + ], + "se solids coal extra": [ + "furnace_coal_aluminum", + "furnace_coal_petro", + "furnace_coal_cement", + "furnace_coal_refining", + "coal_resid_heat", + "coal_resid_hotwater", + "coal_comm_heat", + "coal_comm_hotwater", + ], } @@ -1504,68 +1527,22 @@ def retr_SE_solids(units): units : str Units to which variables should be converted. """ + # Call the function in default_tables() + base = default_tables.retr_SE_solids(units) - vars = {} - - BiomassIND_resid = pp.inp("biomass_i", units) - BiomassIND_alu = pp.inp("furnace_biomass_aluminum", units) - BiomassIND_steel = pp.inp("furnace_biomass_steel", units) - BiomassIND_petro = pp.inp("furnace_biomass_petro", units) - BiomassIND_cement = pp.inp("furnace_biomass_cement", units) - - BiomassRefining = pp.inp("furnace_biomass_refining", units) - - BiomassNC = pp.inp("biomass_nc", units) - BiomassR_cook = pp.inp("biomass_resid_cook", units) - BiomassR_heat = pp.inp("biomass_resid_heat", units) - BiomassR_water = pp.inp("biomass_resid_hotwater", units) - BiomassC_heat = pp.inp("biomass_comm_heat", units) - BiomassC_water = pp.inp("biomass_comm_hotwater", units) - - vars["Biomass"] = ( - BiomassIND_resid - + BiomassIND_alu - + BiomassIND_steel - + BiomassIND_petro - + BiomassIND_cement - + BiomassNC - + BiomassR_cook - + BiomassR_heat - + BiomassR_water - + BiomassC_heat - + BiomassC_water - + BiomassRefining - ) - - CoalIND_resid = pp.inp(["coal_i", "coal_fs"], units) - CoalIND_alu = pp.inp(["furnace_coal_aluminum"], units) - CoalIND_steel = pp.inp( - ["furnace_coal_steel", "bf_steel", "cokeoven_steel", "sinter_steel"], - units, - inpfilter={"commodity": ["coal"], "level": ["final"]}, - ) - CoalIND_petro = pp.inp(["furnace_coal_petro"], units) - CoalIND_cement = pp.inp(["furnace_coal_cement"], units) - - CoalRefining = pp.inp(["furnace_coal_refining"], units) - - CoalR_heat = pp.inp("coal_resid_heat", units) - CoalR_hotwater = pp.inp("coal_resid_hotwater", units) - CoalC_heat = pp.inp("coal_comm_heat", units) - CoalC_water = pp.inp("coal_comm_hotwater", units) - CoalTRP = pp.inp("coal_trp", units) - vars["Coal"] = ( - CoalIND_resid - + CoalIND_alu - + CoalIND_steel - + CoalIND_petro - + CoalIND_cement - + CoalR_heat - + CoalR_hotwater - + CoalC_heat - + CoalC_water - + CoalTRP - + CoalRefining + # Extra retrieval requiring special filters + vars = dict( + Coal=pp.inp( + [ + "bf_steel", + "cokeoven_steel", + "furnace_coal_steel", + "sinter_steel", + ], + units, + inpfilter={"commodity": ["coal"], "level": ["final"]}, + ) ) - df = pp_utils.make_outputdf(vars, units) - return df + + # Combine with `base` + return base + pp_utils.make_outputdf(vars, units) From 60fdb7004bfe6e960665c91e60ed454e5f75efc9 Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Wed, 7 Sep 2022 14:48:35 +0200 Subject: [PATCH 027/774] Remove .material.report.tables.retr_se_synfuels() use .default_tables.techs["synfuels oil"] to control the data retrieved for liquids|oil. --- .../model/material/report/tables.py | 118 ++---------------- 1 file changed, 12 insertions(+), 106 deletions(-) diff --git a/message_ix_models/model/material/report/tables.py b/message_ix_models/model/material/report/tables.py index f133ae0d4e..1e251857c4 100644 --- a/message_ix_models/model/material/report/tables.py +++ b/message_ix_models/model/material/report/tables.py @@ -93,6 +93,18 @@ def return_func_dict(): "coal_comm_heat", "coal_comm_hotwater", ], + "synfuels oil": [ + ( + "agg_ref", + "out", + dict(level=["secondary"], commodity=["lightoil", "fueloil"]), + ), + ( + "steam_cracker_petro", + "inp", + dict(level=["final"], commodity=["atm_gasoil", "naphtha", "vacuum_gasoil"]), + ), + ], } @@ -1411,112 +1423,6 @@ def retr_CO2emi(units_emi, units_ene_mdl): return pd.concat(dfs, sort=True) -@_register -def retr_SE_synfuels(units): - """Energy: Secondary Energy synthetic fuels. - - Parameters - ---------- - - units : str - Units to which variables should be converted. - """ - - vars = {} - - vars["Liquids|Oil"] = ( - pp.out( - "agg_ref", - units, - outfilter={"level": ["secondary"], "commodity": ["lightoil"]}, - ) - + pp.out( - "agg_ref", - units, - outfilter={"level": ["secondary"], "commodity": ["fueloil"]}, - ) - + pp.inp( - "steam_cracker_petro", - units, - inpfilter={ - "level": ["final"], - "commodity": ["atm_gasoil", "vacuum_gasoil", "naphtha"], - }, - ) - ) - - vars["Liquids|Biomass|w/o CCS"] = pp.out( - ["eth_bio", "liq_bio"], - units, - outfilter={"level": ["primary"], "commodity": ["ethanol"]}, - ) - - vars["Liquids|Biomass|w/ CCS"] = pp.out( - ["eth_bio_ccs", "liq_bio_ccs"], - units, - outfilter={"level": ["primary"], "commodity": ["ethanol"]}, - ) - - vars["Liquids|Coal|w/o CCS"] = pp.out( - ["meth_coal", "syn_liq"], - units, - outfilter={"level": ["primary"], "commodity": ["methanol"]}, - ) - - vars["Liquids|Coal|w/ CCS"] = pp.out( - ["meth_coal_ccs", "syn_liq_ccs"], - units, - outfilter={"level": ["primary"], "commodity": ["methanol"]}, - ) - - vars["Liquids|Gas|w/o CCS"] = pp.out( - "meth_ng", units, outfilter={"level": ["primary"], "commodity": ["methanol"]} - ) - - vars["Liquids|Gas|w/ CCS"] = pp.out( - "meth_ng_ccs", - units, - outfilter={"level": ["primary"], "commodity": ["methanol"]}, - ) - - vars["Hydrogen|Coal|w/o CCS"] = pp.out( - "h2_coal", units, outfilter={"level": ["secondary"], "commodity": ["hydrogen"]} - ) - - vars["Hydrogen|Coal|w/ CCS"] = pp.out( - "h2_coal_ccs", - units, - outfilter={"level": ["secondary"], "commodity": ["hydrogen"]}, - ) - - vars["Hydrogen|Gas|w/o CCS"] = pp.out( - "h2_smr", units, outfilter={"level": ["secondary"], "commodity": ["hydrogen"]} - ) - - vars["Hydrogen|Gas|w/ CCS"] = pp.out( - "h2_smr_ccs", - units, - outfilter={"level": ["secondary"], "commodity": ["hydrogen"]}, - ) - - vars["Hydrogen|Biomass|w/o CCS"] = pp.out( - "h2_bio", units, outfilter={"level": ["secondary"], "commodity": ["hydrogen"]} - ) - - vars["Hydrogen|Biomass|w/ CCS"] = pp.out( - "h2_bio_ccs", - units, - outfilter={"level": ["secondary"], "commodity": ["hydrogen"]}, - ) - - vars["Hydrogen|Electricity"] = pp.out( - "h2_elec", units, outfilter={"level": ["secondary"], "commodity": ["hydrogen"]} - ) - - df = pp_utils.make_outputdf(vars, units) - return df - - @_register def retr_SE_solids(units): """Energy: Secondary Energy solids. From 77ec8b420d434b067820cb475ce05dc317113a77 Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Wed, 7 Sep 2022 14:51:25 +0200 Subject: [PATCH 028/774] Remove duplicates of hp_gas_i in materials legacy reporting --- message_ix_models/model/material/report/tables.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/message_ix_models/model/material/report/tables.py b/message_ix_models/model/material/report/tables.py index 1e251857c4..e04bd31fa5 100644 --- a/message_ix_models/model/material/report/tables.py +++ b/message_ix_models/model/material/report/tables.py @@ -159,11 +159,11 @@ def retr_CO2emi(units_emi, units_ene_mdl): "gas_rc", "hp_gas_rc", "gas_i", + "hp_gas_i", "furnace_gas_aluminum", "furnace_gas_petro", "furnace_gas_cement", "furnace_gas_refining", - "hp_gas_i", "hp_gas_aluminum", "hp_gas_petro", "hp_gas_refining", @@ -264,7 +264,6 @@ def retr_CO2emi(units_emi, units_ene_mdl): "furnace_gas_petro", "furnace_gas_cement", "furnace_gas_refining", - "hp_gas_i", "hp_gas_aluminum", "hp_gas_petro", "hp_gas_refining", @@ -361,7 +360,6 @@ def retr_CO2emi(units_emi, units_ene_mdl): "furnace_gas_petro", "furnace_gas_cement", "furnace_gas_refining", - "hp_gas_i", "hp_gas_aluminum", "hp_gas_petro", "hp_gas_refining", From 5a33dc789a20c668a41170a02387deebd90efddf Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Wed, 7 Sep 2022 15:00:27 +0200 Subject: [PATCH 029/774] Use techs["gas extra"] in retr_co2emi() --- .../model/material/report/tables.py | 37 +++++-------------- 1 file changed, 9 insertions(+), 28 deletions(-) diff --git a/message_ix_models/model/material/report/tables.py b/message_ix_models/model/material/report/tables.py index e04bd31fa5..59aae7d360 100644 --- a/message_ix_models/model/material/report/tables.py +++ b/message_ix_models/model/material/report/tables.py @@ -37,7 +37,7 @@ def return_func_dict(): "fueloil_NH3_ccs", "gas_NH3_ccs", ], - "extra gas": [ + "gas extra": [ "furnace_gas_aluminum", "furnace_gas_petro", "furnace_gas_cement", @@ -160,13 +160,6 @@ def retr_CO2emi(units_emi, units_ene_mdl): "hp_gas_rc", "gas_i", "hp_gas_i", - "furnace_gas_aluminum", - "furnace_gas_petro", - "furnace_gas_cement", - "furnace_gas_refining", - "hp_gas_aluminum", - "hp_gas_petro", - "hp_gas_refining", "gas_trp", "gas_fs", "gas_ppl", @@ -174,7 +167,8 @@ def retr_CO2emi(units_emi, units_ene_mdl): "gas_cc", "gas_htfc", "gas_hpl", - ], + ] + + TECHS["gas extra"], units_ene_mdl, inpfilter={"commodity": ["gas"]}, ) @@ -260,14 +254,8 @@ def retr_CO2emi(units_emi, units_ene_mdl): [ "gas_i", "hp_gas_i", - "furnace_gas_aluminum", - "furnace_gas_petro", - "furnace_gas_cement", - "furnace_gas_refining", - "hp_gas_aluminum", - "hp_gas_petro", - "hp_gas_refining", - ], + ] + + TECHS["gas extra"], units_ene_mdl, inpfilter={"commodity": ["gas"]}, ) @@ -356,14 +344,8 @@ def retr_CO2emi(units_emi, units_ene_mdl): [ "gas_i", "hp_gas_i", - "furnace_gas_aluminum", - "furnace_gas_petro", - "furnace_gas_cement", - "furnace_gas_refining", - "hp_gas_aluminum", - "hp_gas_petro", - "hp_gas_refining", - ], + ] + + TECHS["gas extra"], units_ene_mdl, inpfilter={"commodity": ["gas"]}, ) @@ -718,9 +700,8 @@ def retr_CO2emi(units_emi, units_ene_mdl): [ "clinker_dry_cement", "clinker_wet_cement", - "clinker_dry_ccs_cement", - "clinker_wet_ccs_cement", - ], + ] + + TECHS["cement with ccs"], units_ene_mdl, emifilter={"relation": ["CO2_Emission"]}, emission_units=units_emi, From 12c226a718b56b32b84643276317d15ab7771236 Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Thu, 8 Sep 2022 15:35:40 +0200 Subject: [PATCH 030/774] Use existing reporter contents, pathlib.path methods --- .../model/material/report/__init__.py | 42 +++++++------------ 1 file changed, 15 insertions(+), 27 deletions(-) diff --git a/message_ix_models/model/material/report/__init__.py b/message_ix_models/model/material/report/__init__.py index cc6ff34395..d7171bcb71 100644 --- a/message_ix_models/model/material/report/__init__.py +++ b/message_ix_models/model/material/report/__init__.py @@ -23,6 +23,7 @@ # PACKAGES from functools import partial +from typing import List import matplotlib import message_ix @@ -34,7 +35,6 @@ import xlsxwriter from matplotlib import pyplot as plt from matplotlib.backends.backend_pdf import PdfPages -from message_ix_models import ScenarioInfo from message_ix_models import Context @@ -180,14 +180,12 @@ def fix_excel(path_temp, path_new): def report( scenario: message_ix.Scenario, message_df: pd.DataFrame, + years: List[int], + nodes: List[str], + config: dict, context: Context, ) -> None: """Produces the material related variables.""" - - # Obtain scenario information and directory - - s_info = ScenarioInfo(scenario) - # In order to avoid confusion in the second reporting stage there should # no existing timeseries uploaded in the scenairo. Clear these except the # residential and commercial ones since they should be always included. @@ -205,30 +203,19 @@ def report( # scenario.add_timeseries(df_transport) # scenario.commit('Added transport timeseries') - years = s_info.Y - nodes = [] - for n in s_info.N: - n = n + "*" - nodes.append(n) - - if "R11_GLB*" in nodes: - nodes.remove("R11_GLB*") - elif "R12_GLB*" in nodes: - nodes.remove("R12_GLB*") - # Path for materials reporting output - directory = context.get_local_path("report", "materials") + directory = config["output_path"].joinpath("materials") directory.mkdir(exist_ok=True) # Dump output of message_ix built-in reporting to an Excel file df = message_df - name = os.path.join(directory, "message_ix_reporting.xlsx") + name = directory.joinpath("message_ix_reporting.xlsx") df.to_excel(name) print("message_ix level reporting generated") # Obtain a pyam dataframe / filter / global aggregation - path = os.path.join(directory, "message_ix_reporting.xlsx") + path = directory.joinpath("message_ix_reporting.xlsx") report = pd.read_excel(path) report.Unit.fillna("", inplace=True) df = pyam.IamDataFrame(report) @@ -303,7 +290,7 @@ def report( ) variables = df.variable df.aggregate_region(variables, region="World", method=sum, append=True) - name = os.path.join(directory, "check.xlsx") + name = directory.joinpath("check.xlsx") df.to_excel(name) print("Necessary variables are filtered") @@ -344,7 +331,7 @@ def report( print("Empty template for new variables created") # Create a pdf file with figures - path = os.path.join(directory, "Material_global_graphs.pdf") + path = directory.joinpath("Material_global_graphs.pdf") pp = PdfPages(path) # pp = PdfPages("Material_global_graphs.pdf") @@ -3289,12 +3276,10 @@ def report( # Print the new variables to an excel file. # Change the naming convention and save to another excel file. - name_temp = os.path.join(directory, "temp_new_reporting.xlsx") - df_final.to_excel(name_temp, sheet_name="data", index=False) - path_temp = os.path.join(directory, name_temp) + path_temp = directory.joinpath("temp_new_reporting.xlsx") + path_new = directory.joinpath(f"New_Reporting_{model_name}_{scenario_name}.xlsx") - excel_name_new = "New_Reporting_" + model_name + "_" + scenario_name + ".xlsx" - path_new = os.path.join(directory, excel_name_new) + df_final.to_excel(path_temp, sheet_name="data", index=False) fix_excel(path_temp, path_new) print("New reporting file generated.") @@ -3330,4 +3315,7 @@ def callback(rep: message_ix.Reporter, context: Context) -> None: partial(report, context=context), "scenario", "message::default", + "y::model", + "n::ex world", + "config", ) From 4b12c2a8aecffbb5ccf00198bc134960841d519a Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Thu, 8 Sep 2022 15:38:50 +0200 Subject: [PATCH 031/774] Apply buildings legacy reporting config --- .../model/material/report/tables.py | 29 +++++++++---------- 1 file changed, 14 insertions(+), 15 deletions(-) diff --git a/message_ix_models/model/material/report/tables.py b/message_ix_models/model/material/report/tables.py index 59aae7d360..31a6c07d45 100644 --- a/message_ix_models/model/material/report/tables.py +++ b/message_ix_models/model/material/report/tables.py @@ -1,7 +1,11 @@ import pandas as pd from message_data.tools.post_processing import default_tables -from message_data.tools.post_processing.default_tables import TECHS, _pe_wCCSretro +from message_data.tools.post_processing.default_tables import ( + TECHS, + rc_techs, + _pe_wCCSretro, +) import message_data.tools.post_processing.pp_utils as pp_utils pp = None @@ -25,6 +29,11 @@ def return_func_dict(): # Context object. TECHS.update(_TECHS) + # Invoke similar code associated with MESSAGEix-Buildings + from message_data.model.building.report import configure_legacy_reporting + + configure_legacy_reporting(TECHS) + return func_dict @@ -72,11 +81,6 @@ def return_func_dict(): "visbreaker_ref", ], "se solids biomass extra": [ - "biomass_comm_heat", - "biomass_comm_hotwater", - "biomass_resid_cook", - "biomass_resid_heat", - "biomass_resid_hotwater", "furnace_biomass_aluminum", "furnace_biomass_cement", "furnace_biomass_petro", @@ -88,10 +92,6 @@ def return_func_dict(): "furnace_coal_petro", "furnace_coal_cement", "furnace_coal_refining", - "coal_resid_heat", - "coal_resid_hotwater", - "coal_comm_heat", - "coal_comm_hotwater", ], "synfuels oil": [ ( @@ -156,8 +156,6 @@ def retr_CO2emi(units_emi, units_ene_mdl): _inp_nonccs_gas_tecs = ( pp.inp( [ - "gas_rc", - "hp_gas_rc", "gas_i", "hp_gas_i", "gas_trp", @@ -168,6 +166,7 @@ def retr_CO2emi(units_emi, units_ene_mdl): "gas_htfc", "gas_hpl", ] + + TECHS["rc gas"] + TECHS["gas extra"], units_ene_mdl, inpfilter={"commodity": ["gas"]}, @@ -268,7 +267,7 @@ def retr_CO2emi(units_emi, units_ene_mdl): ).fillna(0) _Biogas_res = _Biogas_tot * ( - pp.inp(["gas_rc", "hp_gas_rc"], units_ene_mdl, inpfilter={"commodity": ["gas"]}) + pp.inp(TECHS["rc gas"], units_ene_mdl, inpfilter={"commodity": ["gas"]}) / _inp_all_gas_tecs ).fillna(0) @@ -358,7 +357,7 @@ def retr_CO2emi(units_emi, units_ene_mdl): ).fillna(0) _Hydrogen_res = _Hydrogen_tot * ( - pp.inp(["gas_rc", "hp_gas_rc"], units_ene_mdl, inpfilter={"commodity": ["gas"]}) + pp.inp(TECHS["rc gas"], units_ene_mdl, inpfilter={"commodity": ["gas"]}) / _inp_nonccs_gas_tecs_wo_CCSRETRO ).fillna(0) @@ -454,7 +453,7 @@ def retr_CO2emi(units_emi, units_ene_mdl): ) _FE_Res_com = pp.emi( - ["coal_rc", "foil_rc", "loil_rc", "gas_rc", "meth_rc", "hp_gas_rc"], + rc_techs("coal foil gas loil meth"), units_ene_mdl, emifilter={"relation": ["CO2_r_c"]}, emission_units=units_emi, From 126d2c98b1a952781367fac068c394ae3bb48863 Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Tue, 13 Sep 2022 10:21:41 +0200 Subject: [PATCH 032/774] Fix erroneous "r12_afr|r12_afr" region names in materials reporting - write/read "empty_template.xlsx" in the reporting output directory, rather than current working directory. - improve logging. - add comments. --- .../model/material/report/__init__.py | 43 ++++++++++++------- 1 file changed, 28 insertions(+), 15 deletions(-) diff --git a/message_ix_models/model/material/report/__init__.py b/message_ix_models/model/material/report/__init__.py index d7171bcb71..6e43979091 100644 --- a/message_ix_models/model/material/report/__init__.py +++ b/message_ix_models/model/material/report/__init__.py @@ -22,6 +22,7 @@ # foil_imp AFR, frunace_h2_aluminum, h2_i... # PACKAGES +import logging from functools import partial from typing import List @@ -37,6 +38,7 @@ from matplotlib.backends.backend_pdf import PdfPages from message_ix_models import Context +log = logging.getLogger(__name__) matplotlib.use("Agg") @@ -204,23 +206,29 @@ def report( # scenario.commit('Added transport timeseries') # Path for materials reporting output - directory = config["output_path"].joinpath("materials") + directory = config["output_path"].expanduser().joinpath("materials") directory.mkdir(exist_ok=True) + + # Replace erroneous region labels like R12_AFR|R12_AFR with simply R12_AFR + # TODO locate the cause of this upstream and fix + df = message_df.rename({"region": {f"{n}|{n}": n for n in nodes}}) + # Dump output of message_ix built-in reporting to an Excel file - df = message_df name = directory.joinpath("message_ix_reporting.xlsx") df.to_excel(name) - print("message_ix level reporting generated") + log.info(f"'message::default' report written to {name}") + log.info(f"{len(df)} rows") + log.info(f"{len(df.variable)} unique variable names") - # Obtain a pyam dataframe / filter / global aggregation + # Obtain a pyam dataframe + # FIXME(PNK) this re-reads the file above. This seems unnecessary, and can be slow. + df = pyam.IamDataFrame(pd.read_excel(name).fillna(dict(Unit=""))) - path = directory.joinpath("message_ix_reporting.xlsx") - report = pd.read_excel(path) - report.Unit.fillna("", inplace=True) - df = pyam.IamDataFrame(report) - df.filter(region=nodes, year=years, inplace=True) + # Filter variables necessary for materials reporting df.filter( + region=nodes, + year=years, variable=[ "out|new_scrap|aluminum|*", "out|final_material|aluminum|prebake_aluminum|M1", @@ -288,11 +296,14 @@ def report( ], inplace=True, ) + log.info(f"{df = }\n{len(df.variable)} unique variable names") + + # Compute global totals variables = df.variable df.aggregate_region(variables, region="World", method=sum, append=True) - name = directory.joinpath("check.xlsx") - df.to_excel(name) - print("Necessary variables are filtered") + + df.to_excel(directory.joinpath("check.xlsx")) + log.info(f"Necessary variables are filtered; {len(df)} in total") # Obtain the model and scenario name model_name = df.model[0] @@ -300,7 +311,8 @@ def report( # Create an empty pyam dataframe to store the new variables - workbook = xlsxwriter.Workbook("empty_template.xlsx") + empty_template_path = directory.joinpath("empty_template.xlsx") + workbook = xlsxwriter.Workbook(empty_template_path) worksheet = workbook.add_worksheet() worksheet.write("A1", "Model") worksheet.write("B1", "Scenario") @@ -327,7 +339,7 @@ def report( worksheet.write(col, yr) workbook.close() - df_final = pyam.IamDataFrame("empty_template.xlsx") + df_final = pyam.IamDataFrame(empty_template_path) print("Empty template for new variables created") # Create a pdf file with figures @@ -3279,8 +3291,9 @@ def report( path_temp = directory.joinpath("temp_new_reporting.xlsx") path_new = directory.joinpath(f"New_Reporting_{model_name}_{scenario_name}.xlsx") + # FIXME(PNK) manipulate the data directly instead of writing to disk, modifying, + # then re-reading df_final.to_excel(path_temp, sheet_name="data", index=False) - fix_excel(path_temp, path_new) print("New reporting file generated.") df_final = pd.read_excel(path_new) From 5c33ff5e0f6ad1c8407e79509fd161994ae287e3 Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Tue, 13 Sep 2022 10:23:42 +0200 Subject: [PATCH 033/774] =?UTF-8?q?Log=20legacy=20reporting=20configuratio?= =?UTF-8?q?n=20from=20materials=E2=80=A6return=5Ffunc=5Fdict()?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- message_ix_models/model/material/report/tables.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/message_ix_models/model/material/report/tables.py b/message_ix_models/model/material/report/tables.py index 31a6c07d45..5905c3da58 100644 --- a/message_ix_models/model/material/report/tables.py +++ b/message_ix_models/model/material/report/tables.py @@ -1,3 +1,4 @@ +import logging import pandas as pd from message_data.tools.post_processing import default_tables @@ -8,6 +9,8 @@ ) import message_data.tools.post_processing.pp_utils as pp_utils +log = logging.getLogger(__name__) + pp = None mu = None run_history = None @@ -30,10 +33,12 @@ def return_func_dict(): TECHS.update(_TECHS) # Invoke similar code associated with MESSAGEix-Buildings - from message_data.model.building.report import configure_legacy_reporting + from message_data.model.buildings.report import configure_legacy_reporting configure_legacy_reporting(TECHS) + log.info(f"Configured legacy reporting for -BM model variants:\n{TECHS = }") + return func_dict From 9495ea4686f5b2eefc93a1c05ea4ae2aaac88bf5 Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Tue, 13 Sep 2022 10:24:22 +0200 Subject: [PATCH 034/774] =?UTF-8?q?Fix=20combination=20of=20base=20&=20add?= =?UTF-8?q?itional=20data=20in=20material=E2=80=A6retr=5Fse=5Fsolids()?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit using " + " here produced incorrect labels like "baselinebaseline" --- message_ix_models/model/material/report/tables.py | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/message_ix_models/model/material/report/tables.py b/message_ix_models/model/material/report/tables.py index 5905c3da58..f28cff3d0c 100644 --- a/message_ix_models/model/material/report/tables.py +++ b/message_ix_models/model/material/report/tables.py @@ -1432,6 +1432,11 @@ def retr_SE_solids(units): inpfilter={"commodity": ["coal"], "level": ["final"]}, ) ) + df = pp_utils.make_outputdf(vars, units) - # Combine with `base` - return base + pp_utils.make_outputdf(vars, units) + # Combine with `base`; sum at matching indices + return ( + pd.concat([base, df]) + .groupby(["Model", "Scenario", "Variable", "Unit", "Region"], as_index=False) + .sum() + ) From bfd75608a5a9108891ad2a2f2f421e20c8f69448 Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Wed, 21 Sep 2022 11:22:38 +0200 Subject: [PATCH 035/774] Use product() instead of nested loops --- .../model/material/report/__init__.py | 2944 ++++++++--------- 1 file changed, 1439 insertions(+), 1505 deletions(-) diff --git a/message_ix_models/model/material/report/__init__.py b/message_ix_models/model/material/report/__init__.py index 6e43979091..4ad0f194ac 100644 --- a/message_ix_models/model/material/report/__init__.py +++ b/message_ix_models/model/material/report/__init__.py @@ -24,6 +24,7 @@ # PACKAGES import logging from functools import partial +from itertools import product from typing import List import matplotlib @@ -209,7 +210,6 @@ def report( directory = config["output_path"].expanduser().joinpath("materials") directory.mkdir(exist_ok=True) - # Replace erroneous region labels like R12_AFR|R12_AFR with simply R12_AFR # TODO locate the cause of this upstream and fix df = message_df.rename({"region": {f"{n}|{n}": n for n in nodes}}) @@ -866,235 +866,228 @@ def report( print("Final Energy by fuels only non-energy use is being printed.") commodities = ["gas", "liquids", "solids", "all"] - for c in commodities: - for r in nodes: - df_final_energy = df.copy() + for c, r in product(commodities, nodes): + df_final_energy = df.copy() - # GWa to EJ/yr - df_final_energy.convert_unit("", to="GWa", factor=1, inplace=True) - df_final_energy.convert_unit( - "GWa", to="EJ/yr", factor=0.03154, inplace=True - ) - df_final_energy.filter(region=r, year=years, inplace=True) + # GWa to EJ/yr + df_final_energy.convert_unit("", to="GWa", factor=1, inplace=True) + df_final_energy.convert_unit("GWa", to="EJ/yr", factor=0.03154, inplace=True) + df_final_energy.filter(region=r, year=years, inplace=True) + df_final_energy.filter( + variable=["in|final|*|cokeoven_steel|*"], keep=False, inplace=True + ) + df_final_energy.filter( + variable=[ + "in|final|atm_gasoil|*", + "in|final|vacuum_gasoil|*", + "in|final|naphtha|*", + "in|final|*|coal_fs|*", + "in|final|*|methanol_fs|*", + "in|final|*|ethanol_fs|*", + "in|final|ethanol|ethanol_to_ethylene_petro|*", + "in|final|*|foil_fs|*", + "in|final|gas|gas_processing_petro|*", + "in|final|*|loil_fs|*", + "in|final|*|gas_fs|*", + ], + inplace=True, + ) + + if c == "gas": df_final_energy.filter( - variable=["in|final|*|cokeoven_steel|*"], keep=False, inplace=True + variable=[ + "in|final|gas|*", + "in|secondary|gas|gas_NH3|M1", + "in|secondary|gas|gas_NH3_ccs|M1", + ], + inplace=True, ) + # Do not include gasoil and naphtha feedstock + if c == "liquids": df_final_energy.filter( variable=[ - "in|final|atm_gasoil|*", - "in|final|vacuum_gasoil|*", - "in|final|naphtha|*", - "in|final|*|coal_fs|*", - "in|final|*|methanol_fs|*", - "in|final|*|ethanol_fs|*", "in|final|ethanol|ethanol_to_ethylene_petro|*", "in|final|*|foil_fs|*", - "in|final|gas|gas_processing_petro|*", "in|final|*|loil_fs|*", - "in|final|*|gas_fs|*", + "in|final|*|methanol_fs|*", + "in|final|*|ethanol_fs|*", + "in|final|atm_gasoil|*", + "in|final|vacuum_gasoil|*", + "in|final|naphtha|*", + "in|secondary|fueloil|fueloil_NH3|M1", + "in|secondary|fueloil|fueloil_NH3_ccs|M1", + ], + inplace=True, + ) + if c == "solids": + df_final_energy.filter( + variable=[ + "in|final|biomass|*", + "in|final|coal|*", + "in|secondary|coal|coal_NH3|M1", + "in|secondary|coal|coal_NH3_ccs|M1", + "in|primary|biomass|biomass_NH3_ccs|M1", + "in|primary|biomass|biomass_NH3|M1", ], inplace=True, ) - if c == "gas": - df_final_energy.filter( - variable=[ - "in|final|gas|*", - "in|secondary|gas|gas_NH3|M1", - "in|secondary|gas|gas_NH3_ccs|M1", - ], - inplace=True, - ) - # Do not include gasoil and naphtha feedstock - if c == "liquids": - df_final_energy.filter( - variable=[ - "in|final|ethanol|ethanol_to_ethylene_petro|*", - "in|final|*|foil_fs|*", - "in|final|*|loil_fs|*", - "in|final|*|methanol_fs|*", - "in|final|*|ethanol_fs|*", - "in|final|atm_gasoil|*", - "in|final|vacuum_gasoil|*", - "in|final|naphtha|*", - "in|secondary|fueloil|fueloil_NH3|M1", - "in|secondary|fueloil|fueloil_NH3_ccs|M1", - ], - inplace=True, - ) - if c == "solids": - df_final_energy.filter( - variable=[ - "in|final|biomass|*", - "in|final|coal|*", - "in|secondary|coal|coal_NH3|M1", - "in|secondary|coal|coal_NH3_ccs|M1", - "in|primary|biomass|biomass_NH3_ccs|M1", - "in|primary|biomass|biomass_NH3|M1", - ], - inplace=True, - ) + all_flows = df_final_energy.timeseries().reset_index() + splitted_vars = [v.split("|") for v in all_flows.variable] + aux1_df = pd.DataFrame( + splitted_vars, + columns=["flow_type", "level", "commodity", "technology", "mode"], + ) + aux2_df = pd.concat( + [all_flows.reset_index(drop=True), aux1_df.reset_index(drop=True)], + axis=1, + ) - all_flows = df_final_energy.timeseries().reset_index() - splitted_vars = [v.split("|") for v in all_flows.variable] - aux1_df = pd.DataFrame( - splitted_vars, - columns=["flow_type", "level", "commodity", "technology", "mode"], - ) - aux2_df = pd.concat( - [all_flows.reset_index(drop=True), aux1_df.reset_index(drop=True)], - axis=1, - ) + # Include only the related industry sector variables + # Aluminum, cement and steel do not have feedstock use - # Include only the related industry sector variables - # Aluminum, cement and steel do not have feedstock use + var_sectors = [v for v in aux2_df["variable"].values] - var_sectors = [v for v in aux2_df["variable"].values] + aux2_df = aux2_df[aux2_df["variable"].isin(var_sectors)] - aux2_df = aux2_df[aux2_df["variable"].isin(var_sectors)] + df_final_energy.filter(variable=var_sectors, inplace=True) - df_final_energy.filter(variable=var_sectors, inplace=True) + # Aggregate - # Aggregate + if c == "all": + df_final_energy.aggregate( + "Final Energy|Non-Energy Use", + components=var_sectors, + append=True, + ) + df_final_energy.filter( + variable=["Final Energy|Non-Energy Use"], inplace=True + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + if c == "gas": - if c == "all": - df_final_energy.aggregate( - "Final Energy|Non-Energy Use", - components=var_sectors, - append=True, - ) - df_final_energy.filter( - variable=["Final Energy|Non-Energy Use"], inplace=True - ) - df_final_energy.convert_unit( - "GWa", to="EJ/yr", factor=0.03154, inplace=True - ) - df_final.append(df_final_energy, inplace=True) - if c == "gas": + # Can not distinguish by type Gases (natural gas, biomass, synthetic + # fossil, efuel) (coal_gas), from biomass (gas_bio), natural gas + # (gas_bal): All go into secondary level + # Can not be distinguished in the final level. - # Can not distinguish by type Gases (natural gas, biomass, synthetic - # fossil, efuel) (coal_gas), from biomass (gas_bio), natural gas - # (gas_bal): All go into secondary level - # Can not be distinguished in the final level. + df_final_energy.aggregate( + "Final Energy|Non-Energy Use|Gases", + components=var_sectors, + append=True, + ) - df_final_energy.aggregate( - "Final Energy|Non-Energy Use|Gases", - components=var_sectors, - append=True, - ) + df_final_energy.filter( + variable=["Final Energy|Non-Energy Use|Gases"], + inplace=True, + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) - df_final_energy.filter( - variable=["Final Energy|Non-Energy Use|Gases"], - inplace=True, - ) - df_final_energy.convert_unit( - "GWa", to="EJ/yr", factor=0.03154, inplace=True - ) - df_final.append(df_final_energy, inplace=True) + if c == "liquids": - if c == "liquids": + # All liquids + df_final_energy.aggregate( + "Final Energy|Non-Energy Use|Liquids", + components=var_sectors, + append=True, + ) - # All liquids - df_final_energy.aggregate( - "Final Energy|Non-Energy Use|Liquids", - components=var_sectors, - append=True, - ) + # Only bios - # Only bios + filter_vars = [ + v + for v in aux2_df["variable"].values + if (("ethanol" in v) & ("methanol" not in v)) + ] + df_final_energy.aggregate( + "Final Energy|Non-Energy Use|Liquids|Biomass", + components=filter_vars, + append=True, + ) + # Fossils - filter_vars = [ - v - for v in aux2_df["variable"].values - if (("ethanol" in v) & ("methanol" not in v)) - ] - df_final_energy.aggregate( - "Final Energy|Non-Energy Use|Liquids|Biomass", - components=filter_vars, - append=True, + filter_vars = [ + v + for v in aux2_df["variable"].values + if ( + ("lightoil" in v) + | ("fueloil" in v) + | ("atm_gasoil" in v) + | ("vacuum_gasoil" in v) + | ("naphtha" in v) ) - # Fossils + ] - filter_vars = [ - v - for v in aux2_df["variable"].values - if ( - ("lightoil" in v) - | ("fueloil" in v) - | ("atm_gasoil" in v) - | ("vacuum_gasoil" in v) - | ("naphtha" in v) - ) - ] + df_final_energy.aggregate( + "Final Energy|Non-Energy Use|Liquids|Oil", + components=filter_vars, + append=True, + ) - df_final_energy.aggregate( - "Final Energy|Non-Energy Use|Liquids|Oil", - components=filter_vars, - append=True, - ) + # Other - # Other + filter_vars = [v for v in aux2_df["variable"].values if (("methanol" in v))] - filter_vars = [ - v for v in aux2_df["variable"].values if (("methanol" in v)) - ] + df_final_energy.aggregate( + "Final Energy|Non-Energy Use|Liquids|Coal", + components=filter_vars, + append=True, + ) - df_final_energy.aggregate( + df_final_energy.filter( + variable=[ + "Final Energy|Non-Energy Use|Liquids", + "Final Energy|Non-Energy Use|Liquids|Oil", + "Final Energy|Non-Energy Use|Liquids|Biomass", "Final Energy|Non-Energy Use|Liquids|Coal", - components=filter_vars, - append=True, - ) + ], + inplace=True, + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + if c == "solids": - df_final_energy.filter( - variable=[ - "Final Energy|Non-Energy Use|Liquids", - "Final Energy|Non-Energy Use|Liquids|Oil", - "Final Energy|Non-Energy Use|Liquids|Biomass", - "Final Energy|Non-Energy Use|Liquids|Coal", - ], - inplace=True, - ) - df_final_energy.convert_unit( - "GWa", to="EJ/yr", factor=0.03154, inplace=True - ) - df_final.append(df_final_energy, inplace=True) - if c == "solids": + # All + df_final_energy.aggregate( + "Final Energy|Non-Energy Use|Solids", + components=var_sectors, + append=True, + ) + # Bio + filter_vars = [v for v in aux2_df["variable"].values if ("biomass" in v)] + df_final_energy.aggregate( + "Final Energy|Non-Energy Use|Solids|Biomass", + components=filter_vars, + append=True, + ) - # All - df_final_energy.aggregate( + # Fossil + filter_vars = [v for v in aux2_df["variable"].values if ("coal" in v)] + df_final_energy.aggregate( + "Final Energy|Non-Energy Use|Solids|Coal", + components=filter_vars, + append=True, + ) + df_final_energy.filter( + variable=[ "Final Energy|Non-Energy Use|Solids", - components=var_sectors, - append=True, - ) - # Bio - filter_vars = [ - v for v in aux2_df["variable"].values if ("biomass" in v) - ] - df_final_energy.aggregate( "Final Energy|Non-Energy Use|Solids|Biomass", - components=filter_vars, - append=True, - ) - - # Fossil - filter_vars = [v for v in aux2_df["variable"].values if ("coal" in v)] - df_final_energy.aggregate( "Final Energy|Non-Energy Use|Solids|Coal", - components=filter_vars, - append=True, - ) - df_final_energy.filter( - variable=[ - "Final Energy|Non-Energy Use|Solids", - "Final Energy|Non-Energy Use|Solids|Biomass", - "Final Energy|Non-Energy Use|Solids|Coal", - ], - inplace=True, - ) - df_final_energy.convert_unit( - "GWa", to="EJ/yr", factor=0.03154, inplace=True - ) - df_final.append(df_final_energy, inplace=True) + ], + inplace=True, + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) # FINAL ENERGY BY FUELS (Excluding Non-Energy Use) # For ammonia only electricity use is included since only this has seperate @@ -1111,333 +1104,323 @@ def report( "other", "all", ] - for c in commodities: - - for r in nodes: + for c, r in product(commodities, nodes): + df_final_energy = df.copy() + df_final_energy.convert_unit("", to="GWa", factor=1, inplace=True) + df_final_energy.filter(region=r, year=years, inplace=True) + df_final_energy.filter( + variable=[ + "in|final|*|cokeoven_steel|*", + "in|final|co_gas|*", + "in|final|bf_gas|*", + ], + keep=False, + inplace=True, + ) - df_final_energy = df.copy() - df_final_energy.convert_unit("", to="GWa", factor=1, inplace=True) - df_final_energy.filter(region=r, year=years, inplace=True) + if c == "electr": df_final_energy.filter( variable=[ - "in|final|*|cokeoven_steel|*", - "in|final|co_gas|*", - "in|final|bf_gas|*", + "in|final|electr|*", + "in|secondary|electr|NH3_to_N_fertil|M1", + "in|secondary|electr|coal_NH3|M1", + "in|secondary|electr|electr_NH3|M1", + "in|secondary|electr|fueloil_NH3|M1", + "in|secondary|electr|gas_NH3|M1", + "in|secondary|electr|coal_NH3_ccs|M1", + "in|secondary|electr|fueloil_NH3_ccs|M1", + "in|secondary|electr|gas_NH3_ccs|M1", + "in|secondary|electr|biomass_NH3_ccs|M1", + "in|secondary|electr|biomass_NH3|M1", ], - keep=False, inplace=True, ) + if c == "gas": + df_final_energy.filter(variable=["in|final|gas|*"], inplace=True) - if c == "electr": - df_final_energy.filter( - variable=[ - "in|final|electr|*", - "in|secondary|electr|NH3_to_N_fertil|M1", - "in|secondary|electr|coal_NH3|M1", - "in|secondary|electr|electr_NH3|M1", - "in|secondary|electr|fueloil_NH3|M1", - "in|secondary|electr|gas_NH3|M1", - "in|secondary|electr|coal_NH3_ccs|M1", - "in|secondary|electr|fueloil_NH3_ccs|M1", - "in|secondary|electr|gas_NH3_ccs|M1", - "in|secondary|electr|biomass_NH3_ccs|M1", - "in|secondary|electr|biomass_NH3|M1", - ], - inplace=True, - ) - if c == "gas": - df_final_energy.filter(variable=["in|final|gas|*"], inplace=True) - - # Do not include gasoil and naphtha feedstock - if c == "liquids": - df_final_energy.filter( - variable=[ - "in|final|ethanol|*", - "in|final|fueloil|*", - "in|final|lightoil|*", - "in|final|methanol|*", - ], - inplace=True, - ) - if c == "solids": - df_final_energy.filter( - variable=[ - "in|final|biomass|*", - "in|final|coal|*", - "in|final|coke_iron|*", - ], - inplace=True, - ) - - if c == "hydrogen": - df_final_energy.filter(variable=["in|final|hydrogen|*"], inplace=True) - if c == "heat": - df_final_energy.filter(variable=["in|final|d_heat|*"], inplace=True) - if c == "other": - df_final_energy.filter(variable=["out|useful|i_therm|*"], inplace=True) - if c == "all": - df_final_energy.filter( - variable=[ - "in|final|*", - "in|secondary|electr|NH3_to_N_fertil|M1", - "in|secondary|electr|coal_NH3|M1", - "in|secondary|electr|electr_NH3|M1", - "in|secondary|electr|fueloil_NH3|M1", - "in|secondary|electr|gas_NH3|M1", - "in|secondary|electr|coal_NH3_ccs|M1", - "in|secondary|electr|fueloil_NH3_ccs|M1", - "in|secondary|electr|gas_NH3_ccs|M1", - "in|secondary|electr|biomass_NH3_ccs|M1", - "in|secondary|electr|biomass_NH3|M1", - "out|useful|i_therm|solar_i|M1", - ], - inplace=True, - ) - - all_flows = df_final_energy.timeseries().reset_index() - splitted_vars = [v.split("|") for v in all_flows.variable] - aux1_df = pd.DataFrame( - splitted_vars, - columns=["flow_type", "level", "commodity", "technology", "mode"], + # Do not include gasoil and naphtha feedstock + if c == "liquids": + df_final_energy.filter( + variable=[ + "in|final|ethanol|*", + "in|final|fueloil|*", + "in|final|lightoil|*", + "in|final|methanol|*", + ], + inplace=True, ) - aux2_df = pd.concat( - [all_flows.reset_index(drop=True), aux1_df.reset_index(drop=True)], - axis=1, + if c == "solids": + df_final_energy.filter( + variable=[ + "in|final|biomass|*", + "in|final|coal|*", + "in|final|coke_iron|*", + ], + inplace=True, ) - # Include only the related industry sector variables + if c == "hydrogen": + df_final_energy.filter(variable=["in|final|hydrogen|*"], inplace=True) + if c == "heat": + df_final_energy.filter(variable=["in|final|d_heat|*"], inplace=True) + if c == "other": + df_final_energy.filter(variable=["out|useful|i_therm|*"], inplace=True) + if c == "all": + df_final_energy.filter( + variable=[ + "in|final|*", + "in|secondary|electr|NH3_to_N_fertil|M1", + "in|secondary|electr|coal_NH3|M1", + "in|secondary|electr|electr_NH3|M1", + "in|secondary|electr|fueloil_NH3|M1", + "in|secondary|electr|gas_NH3|M1", + "in|secondary|electr|coal_NH3_ccs|M1", + "in|secondary|electr|fueloil_NH3_ccs|M1", + "in|secondary|electr|gas_NH3_ccs|M1", + "in|secondary|electr|biomass_NH3_ccs|M1", + "in|secondary|electr|biomass_NH3|M1", + "out|useful|i_therm|solar_i|M1", + ], + inplace=True, + ) - var_sectors = [ - v - for v in aux2_df["variable"].values - if ( - ( - ("cement" in v) - | ("steel" in v) - | ("aluminum" in v) - | ("petro" in v) - | ("_i" in v) - | ("_I" in v) - | ("NH3" in v) - ) + all_flows = df_final_energy.timeseries().reset_index() + splitted_vars = [v.split("|") for v in all_flows.variable] + aux1_df = pd.DataFrame( + splitted_vars, + columns=["flow_type", "level", "commodity", "technology", "mode"], + ) + aux2_df = pd.concat( + [all_flows.reset_index(drop=True), aux1_df.reset_index(drop=True)], + axis=1, + ) + + # Include only the related industry sector variables + + var_sectors = [ + v + for v in aux2_df["variable"].values + if ( + ( + ("cement" in v) + | ("steel" in v) + | ("aluminum" in v) + | ("petro" in v) + | ("_i" in v) + | ("_I" in v) + | ("NH3" in v) + ) + & ( + ("eth_ic_trp" not in v) + & ("meth_ic_trp" not in v) + & ("ethanol_to_ethylene_petro" not in v) + & ("gas_processing_petro" not in v) + & ("in|final|atm_gasoil|steam_cracker_petro|atm_gasoil" not in v) & ( - ("eth_ic_trp" not in v) - & ("meth_ic_trp" not in v) - & ("ethanol_to_ethylene_petro" not in v) - & ("gas_processing_petro" not in v) - & ( - "in|final|atm_gasoil|steam_cracker_petro|atm_gasoil" - not in v - ) - & ( - "in|final|vacuum_gasoil|steam_cracker_petro|vacuum_gasoil" - not in v - ) - & ("in|final|naphtha|steam_cracker_petro|naphtha" not in v) + "in|final|vacuum_gasoil|steam_cracker_petro|vacuum_gasoil" + not in v ) + & ("in|final|naphtha|steam_cracker_petro|naphtha" not in v) ) - ] - - aux2_df = aux2_df[aux2_df["variable"].isin(var_sectors)] - - df_final_energy.filter(variable=var_sectors, inplace=True) + ) + ] - # Aggregate + aux2_df = aux2_df[aux2_df["variable"].isin(var_sectors)] - if c == "all": - df_final_energy.aggregate( - "Final Energy|Industry excl Non-Energy Use", - components=var_sectors, - append=True, - ) - df_final_energy.filter( - variable=["Final Energy|Industry excl Non-Energy Use"], inplace=True - ) - df_final_energy.convert_unit( - "GWa", to="EJ/yr", factor=0.03154, inplace=True - ) - df_final.append(df_final_energy, inplace=True) - if c == "electr": + df_final_energy.filter(variable=var_sectors, inplace=True) - df_final_energy.aggregate( - "Final Energy|Industry excl Non-Energy Use|Electricity", - components=var_sectors, - append=True, - ) - df_final_energy.filter( - variable=["Final Energy|Industry excl Non-Energy Use|Electricity"], - inplace=True, - ) - df_final_energy.convert_unit( - "GWa", to="EJ/yr", factor=0.03154, inplace=True - ) - df_final.append(df_final_energy, inplace=True) - if c == "gas": + # Aggregate - # Can not distinguish by type Gases (natural gas, biomass, synthetic - # fossil, efuel) (coal_gas), from biomass (gas_bio), natural gas - # (gas_bal): All go into secondary level - # Can not be distinguished in the final level. + if c == "all": + df_final_energy.aggregate( + "Final Energy|Industry excl Non-Energy Use", + components=var_sectors, + append=True, + ) + df_final_energy.filter( + variable=["Final Energy|Industry excl Non-Energy Use"], inplace=True + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + if c == "electr": - df_final_energy.aggregate( - "Final Energy|Industry excl Non-Energy Use|Gases", - components=var_sectors, - append=True, - ) + df_final_energy.aggregate( + "Final Energy|Industry excl Non-Energy Use|Electricity", + components=var_sectors, + append=True, + ) + df_final_energy.filter( + variable=["Final Energy|Industry excl Non-Energy Use|Electricity"], + inplace=True, + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + if c == "gas": - df_final_energy.filter( - variable=["Final Energy|Industry excl Non-Energy Use|Gases"], - inplace=True, - ) - df_final_energy.convert_unit( - "GWa", to="EJ/yr", factor=0.03154, inplace=True - ) - df_final.append(df_final_energy, inplace=True) + # Can not distinguish by type Gases (natural gas, biomass, synthetic + # fossil, efuel) (coal_gas), from biomass (gas_bio), natural gas + # (gas_bal): All go into secondary level + # Can not be distinguished in the final level. - if c == "hydrogen": - df_final_energy.aggregate( - "Final Energy|Industry excl Non-Energy Use|Hydrogen", - components=var_sectors, - append=True, - ) - df_final_energy.filter( - variable=["Final Energy|Industry excl Non-Energy Use|Hydrogen"], - inplace=True, - ) - df_final_energy.convert_unit( - "GWa", to="EJ/yr", factor=0.03154, inplace=True - ) - df_final.append(df_final_energy, inplace=True) + df_final_energy.aggregate( + "Final Energy|Industry excl Non-Energy Use|Gases", + components=var_sectors, + append=True, + ) - if c == "liquids": + df_final_energy.filter( + variable=["Final Energy|Industry excl Non-Energy Use|Gases"], + inplace=True, + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) - # All liquids - df_final_energy.aggregate( - "Final Energy|Industry excl Non-Energy Use|Liquids", - components=var_sectors, - append=True, - ) + if c == "hydrogen": + df_final_energy.aggregate( + "Final Energy|Industry excl Non-Energy Use|Hydrogen", + components=var_sectors, + append=True, + ) + df_final_energy.filter( + variable=["Final Energy|Industry excl Non-Energy Use|Hydrogen"], + inplace=True, + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) - # Only bios (ethanol, methanol ?) + if c == "liquids": - filter_vars = [ - v - for v in aux2_df["variable"].values - if (("ethanol" in v) & ("methanol" not in v)) - ] - df_final_energy.aggregate( - "Final Energy|Industry excl Non-Energy Use|Liquids|Biomass", - components=filter_vars, - append=True, - ) + # All liquids + df_final_energy.aggregate( + "Final Energy|Industry excl Non-Energy Use|Liquids", + components=var_sectors, + append=True, + ) - # Fossils + # Only bios (ethanol, methanol ?) - filter_vars = [ - v - for v in aux2_df["variable"].values - if (("fueloil" in v) | ("lightoil" in v)) - ] + filter_vars = [ + v + for v in aux2_df["variable"].values + if (("ethanol" in v) & ("methanol" not in v)) + ] + df_final_energy.aggregate( + "Final Energy|Industry excl Non-Energy Use|Liquids|Biomass", + components=filter_vars, + append=True, + ) - df_final_energy.aggregate( - "Final Energy|Industry excl Non-Energy Use|Liquids|Oil", - components=filter_vars, - append=True, - ) + # Fossils - # Other + filter_vars = [ + v + for v in aux2_df["variable"].values + if (("fueloil" in v) | ("lightoil" in v)) + ] - filter_vars = [ - v for v in aux2_df["variable"].values if (("methanol" in v)) - ] + df_final_energy.aggregate( + "Final Energy|Industry excl Non-Energy Use|Liquids|Oil", + components=filter_vars, + append=True, + ) - df_final_energy.aggregate( - "Final Energy|Industry excl Non-Energy Use|Liquids|Coal", - components=filter_vars, - append=True, - ) + # Other - df_final_energy.filter( - variable=[ - "Final Energy|Industry excl Non-Energy Use|Liquids", - "Final Energy|Industry excl Non-Energy Use|Liquids|Oil", - "Final Energy|Industry excl Non-Energy Use|Liquids|Biomass", - "Final Energy|Industry excl Non-Energy Use|Liquids|Coal", - ], - inplace=True, - ) - df_final_energy.convert_unit( - "GWa", to="EJ/yr", factor=0.03154, inplace=True - ) - df_final.append(df_final_energy, inplace=True) - if c == "solids": + filter_vars = [v for v in aux2_df["variable"].values if (("methanol" in v))] - # All - df_final_energy.aggregate( - "Final Energy|Industry excl Non-Energy Use|Solids", - components=var_sectors, - append=True, - ) + df_final_energy.aggregate( + "Final Energy|Industry excl Non-Energy Use|Liquids|Coal", + components=filter_vars, + append=True, + ) - # Bio - filter_vars = [ - v for v in aux2_df["variable"].values if ("biomass" in v) - ] - df_final_energy.aggregate( - "Final Energy|Industry excl Non-Energy Use|Solids|Biomass", - components=filter_vars, - append=True, - ) + df_final_energy.filter( + variable=[ + "Final Energy|Industry excl Non-Energy Use|Liquids", + "Final Energy|Industry excl Non-Energy Use|Liquids|Oil", + "Final Energy|Industry excl Non-Energy Use|Liquids|Biomass", + "Final Energy|Industry excl Non-Energy Use|Liquids|Coal", + ], + inplace=True, + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + if c == "solids": - # Fossil - filter_vars = [ - v for v in aux2_df["variable"].values if ("biomass" not in v) - ] - df_final_energy.aggregate( + # All + df_final_energy.aggregate( + "Final Energy|Industry excl Non-Energy Use|Solids", + components=var_sectors, + append=True, + ) + + # Bio + filter_vars = [v for v in aux2_df["variable"].values if ("biomass" in v)] + df_final_energy.aggregate( + "Final Energy|Industry excl Non-Energy Use|Solids|Biomass", + components=filter_vars, + append=True, + ) + + # Fossil + filter_vars = [ + v for v in aux2_df["variable"].values if ("biomass" not in v) + ] + df_final_energy.aggregate( + "Final Energy|Industry excl Non-Energy Use|Solids|Coal", + components=filter_vars, + append=True, + ) + df_final_energy.filter( + variable=[ + "Final Energy|Industry excl Non-Energy Use|Solids", + "Final Energy|Industry excl Non-Energy Use|Solids|Biomass", "Final Energy|Industry excl Non-Energy Use|Solids|Coal", - components=filter_vars, - append=True, - ) - df_final_energy.filter( - variable=[ - "Final Energy|Industry excl Non-Energy Use|Solids", - "Final Energy|Industry excl Non-Energy Use|Solids|Biomass", - "Final Energy|Industry excl Non-Energy Use|Solids|Coal", - ], - inplace=True, - ) - df_final_energy.convert_unit( - "GWa", to="EJ/yr", factor=0.03154, inplace=True - ) - df_final.append(df_final_energy, inplace=True) - if c == "heat": - df_final_energy.aggregate( - "Final Energy|Industry excl Non-Energy Use|Heat", - components=var_sectors, - append=True, - ) - df_final_energy.filter( - variable=["Final Energy|Industry excl Non-Energy Use|Heat"], - inplace=True, - ) - df_final_energy.convert_unit( - "GWa", to="EJ/yr", factor=0.03154, inplace=True - ) - df_final.append(df_final_energy, inplace=True) - if c == "other": - df_final_energy.aggregate( - "Final Energy|Industry excl Non-Energy Use|Other", - components=var_sectors, - append=True, - ) - df_final_energy.filter( - variable=["Final Energy|Industry excl Non-Energy Use|Other"], - inplace=True, - ) - df_final_energy.convert_unit( - "GWa", to="EJ/yr", factor=0.03154, inplace=True - ) - df_final.append(df_final_energy, inplace=True) + ], + inplace=True, + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + if c == "heat": + df_final_energy.aggregate( + "Final Energy|Industry excl Non-Energy Use|Heat", + components=var_sectors, + append=True, + ) + df_final_energy.filter( + variable=["Final Energy|Industry excl Non-Energy Use|Heat"], + inplace=True, + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + if c == "other": + df_final_energy.aggregate( + "Final Energy|Industry excl Non-Energy Use|Other", + components=var_sectors, + append=True, + ) + df_final_energy.filter( + variable=["Final Energy|Industry excl Non-Energy Use|Other"], + inplace=True, + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) # FINAL ENERGY BY FUELS (Including Non-Energy Use) print("Final Energy by fuels including non-energy use is being printed.") @@ -1451,348 +1434,341 @@ def report( "all", "other", ] - for c in commodities: + for c, r in product(commodities, nodes): + df_final_energy = df.copy() + df_final_energy.convert_unit("", to="GWa", factor=1, inplace=True) + df_final_energy.filter(region=r, year=years, inplace=True) + df_final_energy.filter( + variable=[ + "in|final|*|cokeoven_steel|*", + "in|final|bf_gas|*", + "in|final|co_gas|*", + ], + keep=False, + inplace=True, + ) - for r in nodes: + if c == "other": + df_final_energy.filter( + variable=["out|useful|i_therm|solar_i|M1"], inplace=True + ) + if c == "electr": + df_final_energy.filter( + variable=[ + "in|final|electr|*", + "in|secondary|electr|NH3_to_N_fertil|M1", + "in|secondary|electr|coal_NH3|M1", + "in|secondary|electr|electr_NH3|M1", + "in|secondary|electr|fueloil_NH3|M1", + "in|secondary|electr|gas_NH3|M1", + "in|secondary|electr|coal_NH3_ccs|M1", + "in|secondary|electr|fueloil_NH3_ccs|M1", + "in|secondary|electr|gas_NH3_ccs|M1", + "in|secondary|electr|biomass_NH3_ccs|M1", + "in|secondary|electr|biomass_NH3|M1", + ], + inplace=True, + ) - df_final_energy = df.copy() - df_final_energy.convert_unit("", to="GWa", factor=1, inplace=True) - df_final_energy.filter(region=r, year=years, inplace=True) + if c == "gas": df_final_energy.filter( variable=[ - "in|final|*|cokeoven_steel|*", - "in|final|bf_gas|*", - "in|final|co_gas|*", + "in|final|gas|*", + "in|secondary|gas|gas_NH3|M1", + "in|secondary|gas|gas_NH3_ccs|M1", ], - keep=False, inplace=True, ) - if c == "other": - df_final_energy.filter( - variable=["out|useful|i_therm|solar_i|M1"], inplace=True - ) - if c == "electr": - df_final_energy.filter( - variable=[ - "in|final|electr|*", - "in|secondary|electr|NH3_to_N_fertil|M1", - "in|secondary|electr|coal_NH3|M1", - "in|secondary|electr|electr_NH3|M1", - "in|secondary|electr|fueloil_NH3|M1", - "in|secondary|electr|gas_NH3|M1", - "in|secondary|electr|coal_NH3_ccs|M1", - "in|secondary|electr|fueloil_NH3_ccs|M1", - "in|secondary|electr|gas_NH3_ccs|M1", - "in|secondary|electr|biomass_NH3_ccs|M1", - "in|secondary|electr|biomass_NH3|M1", - ], - inplace=True, - ) + # Include gasoil and naphtha feedstock + if c == "liquids": + df_final_energy.filter( + variable=[ + "in|final|ethanol|*", + "in|final|fueloil|*", + "in|final|lightoil|*", + "in|final|methanol|*", + "in|final|vacuum_gasoil|*", + "in|final|naphtha|*", + "in|final|atm_gasoil|*", + "in|secondary|fueloil|fueloil_NH3|M1", + "in|secondary|fueloil|fueloil_NH3_ccs|M1", + ], + inplace=True, + ) + if c == "solids": + df_final_energy.filter( + variable=[ + "in|final|biomass|*", + "in|final|coal|*", + "in|final|coke_iron|*", + "in|secondary|coal|coal_NH3|M1", + "in|secondary|coal|coal_NH3_ccs|M1", + "in|primary|biomass|biomass_NH3_ccs|M1", + "in|primary|biomass|biomass_NH3|M1", + ], + inplace=True, + ) - if c == "gas": - df_final_energy.filter( - variable=[ - "in|final|gas|*", - "in|secondary|gas|gas_NH3|M1", - "in|secondary|gas|gas_NH3_ccs|M1", - ], - inplace=True, - ) + if c == "hydrogen": + df_final_energy.filter(variable=["in|final|hydrogen|*"], inplace=True) + if c == "heat": + df_final_energy.filter(variable=["in|final|d_heat|*"], inplace=True) + if c == "all": + df_final_energy.filter( + variable=[ + "in|final|*", + "in|secondary|coal|coal_NH3|M1", + "in|secondary|electr|NH3_to_N_fertil|M1", + "in|secondary|electr|coal_NH3|M1", + "in|secondary|electr|electr_NH3|M1", + "in|secondary|electr|gas_NH3|M1", + "in|secondary|fueloil|fueloil_NH3|M1", + "in|secondary|gas|gas_NH3|M1", + "in|secondary|coal|coal_NH3_ccs|M1", + "in|secondary|electr|coal_NH3_ccs|M1", + "in|secondary|electr|gas_NH3_ccs|M1", + "in|secondary|fueloil|fueloil_NH3_ccs|M1", + "in|secondary|gas|gas_NH3_ccs|M1", + "in|primary|biomass|biomass_NH3|M1", + "in|seconday|electr|biomass_NH3|M1", + "in|primary|biomass|biomass_NH3_ccs|M1", + "in|secondary|electr|biomass_NH3_ccs|M1", + "out|useful|i_therm|solar_i|M1", + ], + inplace=True, + ) - # Include gasoil and naphtha feedstock - if c == "liquids": - df_final_energy.filter( - variable=[ - "in|final|ethanol|*", - "in|final|fueloil|*", - "in|final|lightoil|*", - "in|final|methanol|*", - "in|final|vacuum_gasoil|*", - "in|final|naphtha|*", - "in|final|atm_gasoil|*", - "in|secondary|fueloil|fueloil_NH3|M1", - "in|secondary|fueloil|fueloil_NH3_ccs|M1", - ], - inplace=True, - ) - if c == "solids": - df_final_energy.filter( - variable=[ - "in|final|biomass|*", - "in|final|coal|*", - "in|final|coke_iron|*", - "in|secondary|coal|coal_NH3|M1", - "in|secondary|coal|coal_NH3_ccs|M1", - "in|primary|biomass|biomass_NH3_ccs|M1", - "in|primary|biomass|biomass_NH3|M1", - ], - inplace=True, - ) + all_flows = df_final_energy.timeseries().reset_index() + splitted_vars = [v.split("|") for v in all_flows.variable] + aux1_df = pd.DataFrame( + splitted_vars, + columns=["flow_type", "level", "commodity", "technology", "mode"], + ) + aux2_df = pd.concat( + [all_flows.reset_index(drop=True), aux1_df.reset_index(drop=True)], + axis=1, + ) - if c == "hydrogen": - df_final_energy.filter(variable=["in|final|hydrogen|*"], inplace=True) - if c == "heat": - df_final_energy.filter(variable=["in|final|d_heat|*"], inplace=True) - if c == "all": - df_final_energy.filter( - variable=[ - "in|final|*", - "in|secondary|coal|coal_NH3|M1", - "in|secondary|electr|NH3_to_N_fertil|M1", - "in|secondary|electr|coal_NH3|M1", - "in|secondary|electr|electr_NH3|M1", - "in|secondary|electr|gas_NH3|M1", - "in|secondary|fueloil|fueloil_NH3|M1", - "in|secondary|gas|gas_NH3|M1", - "in|secondary|coal|coal_NH3_ccs|M1", - "in|secondary|electr|coal_NH3_ccs|M1", - "in|secondary|electr|gas_NH3_ccs|M1", - "in|secondary|fueloil|fueloil_NH3_ccs|M1", - "in|secondary|gas|gas_NH3_ccs|M1", - "in|primary|biomass|biomass_NH3|M1", - "in|seconday|electr|biomass_NH3|M1", - "in|primary|biomass|biomass_NH3_ccs|M1", - "in|secondary|electr|biomass_NH3_ccs|M1", - "out|useful|i_therm|solar_i|M1", - ], - inplace=True, - ) + # Include only the related industry sector variables + + var_sectors = [ + v + for v in aux2_df["variable"].values + if ( + ( + ("cement" in v) + | ("steel" in v) + | ("aluminum" in v) + | ("petro" in v) + | ("_i" in v) + | ("_I" in v) + | ("_fs" in v) + | ("NH3" in v) + ) + & (("eth_ic_trp" not in v) & ("meth_ic_trp" not in v)) + ) + ] + aux2_df = aux2_df[aux2_df["variable"].isin(var_sectors)] - all_flows = df_final_energy.timeseries().reset_index() - splitted_vars = [v.split("|") for v in all_flows.variable] - aux1_df = pd.DataFrame( - splitted_vars, - columns=["flow_type", "level", "commodity", "technology", "mode"], + df_final_energy.filter(variable=var_sectors, inplace=True) + + # Aggregate + + if c == "other": + df_final_energy.aggregate( + "Final Energy|Industry|Other", + components=var_sectors, + append=True, ) - aux2_df = pd.concat( - [all_flows.reset_index(drop=True), aux1_df.reset_index(drop=True)], - axis=1, + df_final_energy.filter( + variable=["Final Energy|Industry|Other"], inplace=True + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True ) + df_final.append(df_final_energy, inplace=True) + if c == "all": + df_final_energy.aggregate( + "Final Energy|Industry", + components=var_sectors, + append=True, + ) + df_final_energy.filter(variable=["Final Energy|Industry"], inplace=True) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + if c == "electr": - # Include only the related industry sector variables + df_final_energy.aggregate( + "Final Energy|Industry|Electricity", + components=var_sectors, + append=True, + ) + df_final_energy.filter( + variable=["Final Energy|Industry|Electricity"], + inplace=True, + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + if c == "gas": - var_sectors = [ - v - for v in aux2_df["variable"].values - if ( - ( - ("cement" in v) - | ("steel" in v) - | ("aluminum" in v) - | ("petro" in v) - | ("_i" in v) - | ("_I" in v) - | ("_fs" in v) - | ("NH3" in v) - ) - & (("eth_ic_trp" not in v) & ("meth_ic_trp" not in v)) - ) - ] - aux2_df = aux2_df[aux2_df["variable"].isin(var_sectors)] + # Can not distinguish by type Gases (natural gas, biomass, synthetic + # fossil, efuel) (coal_gas), from biomass (gas_bio), natural gas + # (gas_bal): All go into secondary level + # Can not be distinguished in the final level. - df_final_energy.filter(variable=var_sectors, inplace=True) + df_final_energy.aggregate( + "Final Energy|Industry|Gases", + components=var_sectors, + append=True, + ) - # Aggregate + df_final_energy.filter( + variable=["Final Energy|Industry|Gases"], + inplace=True, + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) - if c == "other": - df_final_energy.aggregate( - "Final Energy|Industry|Other", - components=var_sectors, - append=True, - ) - df_final_energy.filter( - variable=["Final Energy|Industry|Other"], inplace=True - ) - df_final_energy.convert_unit( - "GWa", to="EJ/yr", factor=0.03154, inplace=True - ) - df_final.append(df_final_energy, inplace=True) - if c == "all": - df_final_energy.aggregate( - "Final Energy|Industry", - components=var_sectors, - append=True, - ) - df_final_energy.filter(variable=["Final Energy|Industry"], inplace=True) - df_final_energy.convert_unit( - "GWa", to="EJ/yr", factor=0.03154, inplace=True - ) - df_final.append(df_final_energy, inplace=True) - if c == "electr": + if c == "hydrogen": + df_final_energy.aggregate( + "Final Energy|Industry|Hydrogen", + components=var_sectors, + append=True, + ) + df_final_energy.filter( + variable=["Final Energy|Industry|Hydrogen"], + inplace=True, + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) - df_final_energy.aggregate( - "Final Energy|Industry|Electricity", - components=var_sectors, - append=True, - ) - df_final_energy.filter( - variable=["Final Energy|Industry|Electricity"], - inplace=True, - ) - df_final_energy.convert_unit( - "GWa", to="EJ/yr", factor=0.03154, inplace=True - ) - df_final.append(df_final_energy, inplace=True) - if c == "gas": + if c == "liquids": - # Can not distinguish by type Gases (natural gas, biomass, synthetic - # fossil, efuel) (coal_gas), from biomass (gas_bio), natural gas - # (gas_bal): All go into secondary level - # Can not be distinguished in the final level. + # All liquids + df_final_energy.aggregate( + "Final Energy|Industry|Liquids", + components=var_sectors, + append=True, + ) + # Only bios (ethanol) - df_final_energy.aggregate( - "Final Energy|Industry|Gases", - components=var_sectors, - append=True, - ) + filter_vars = [ + v + for v in aux2_df["variable"].values + if (("ethanol" in v) & ("methanol" not in v)) + ] + df_final_energy.aggregate( + "Final Energy|Industry|Liquids|Biomass", + components=filter_vars, + append=True, + ) - df_final_energy.filter( - variable=["Final Energy|Industry|Gases"], - inplace=True, - ) - df_final_energy.convert_unit( - "GWa", to="EJ/yr", factor=0.03154, inplace=True - ) - df_final.append(df_final_energy, inplace=True) + # Oils - if c == "hydrogen": - df_final_energy.aggregate( - "Final Energy|Industry|Hydrogen", - components=var_sectors, - append=True, - ) - df_final_energy.filter( - variable=["Final Energy|Industry|Hydrogen"], - inplace=True, - ) - df_final_energy.convert_unit( - "GWa", to="EJ/yr", factor=0.03154, inplace=True + filter_vars = [ + v + for v in aux2_df["variable"].values + if ( + ("naphtha" in v) + | ("atm_gasoil" in v) + | ("vacuum_gasoil" in v) + | ("fueloil" in v) + | ("lightoil" in v) ) - df_final.append(df_final_energy, inplace=True) + ] - if c == "liquids": + df_final_energy.aggregate( + "Final Energy|Industry|Liquids|Oil", + components=filter_vars, + append=True, + ) - # All liquids - df_final_energy.aggregate( - "Final Energy|Industry|Liquids", - components=var_sectors, - append=True, - ) - # Only bios (ethanol) + # Methanol - filter_vars = [ - v - for v in aux2_df["variable"].values - if (("ethanol" in v) & ("methanol" not in v)) - ] - df_final_energy.aggregate( - "Final Energy|Industry|Liquids|Biomass", - components=filter_vars, - append=True, - ) - - # Oils + filter_vars = [v for v in aux2_df["variable"].values if (("methanol" in v))] - filter_vars = [ - v - for v in aux2_df["variable"].values - if ( - ("naphtha" in v) - | ("atm_gasoil" in v) - | ("vacuum_gasoil" in v) - | ("fueloil" in v) - | ("lightoil" in v) - ) - ] + df_final_energy.aggregate( + "Final Energy|Industry|Liquids|Coal", + components=filter_vars, + append=True, + ) - df_final_energy.aggregate( + df_final_energy.filter( + variable=[ + "Final Energy|Industry|Liquids", "Final Energy|Industry|Liquids|Oil", - components=filter_vars, - append=True, - ) + "Final Energy|Industry|Liquids|Biomass", + "Final Energy|Industry|Liquids|Coal", + ], + inplace=True, + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + if c == "solids": - # Methanol + # All + df_final_energy.aggregate( + "Final Energy|Industry|Solids", + components=var_sectors, + append=True, + ) - filter_vars = [ - v for v in aux2_df["variable"].values if (("methanol" in v)) - ] + # Bio + filter_vars = [v for v in aux2_df["variable"].values if ("biomass" in v)] - df_final_energy.aggregate( - "Final Energy|Industry|Liquids|Coal", - components=filter_vars, - append=True, - ) + df_final_energy.aggregate( + "Final Energy|Industry|Solids|Biomass", + components=filter_vars, + append=True, + ) - df_final_energy.filter( - variable=[ - "Final Energy|Industry|Liquids", - "Final Energy|Industry|Liquids|Oil", - "Final Energy|Industry|Liquids|Biomass", - "Final Energy|Industry|Liquids|Coal", - ], - inplace=True, - ) - df_final_energy.convert_unit( - "GWa", to="EJ/yr", factor=0.03154, inplace=True - ) - df_final.append(df_final_energy, inplace=True) - if c == "solids": + # Fossil + filter_vars = [ + v for v in aux2_df["variable"].values if ("biomass" not in v) + ] - # All - df_final_energy.aggregate( + df_final_energy.aggregate( + "Final Energy|Industry|Solids|Coal", + components=filter_vars, + append=True, + ) + df_final_energy.filter( + variable=[ "Final Energy|Industry|Solids", - components=var_sectors, - append=True, - ) - - # Bio - filter_vars = [ - v for v in aux2_df["variable"].values if ("biomass" in v) - ] - - df_final_energy.aggregate( "Final Energy|Industry|Solids|Biomass", - components=filter_vars, - append=True, - ) - - # Fossil - filter_vars = [ - v for v in aux2_df["variable"].values if ("biomass" not in v) - ] - - df_final_energy.aggregate( "Final Energy|Industry|Solids|Coal", - components=filter_vars, - append=True, - ) - df_final_energy.filter( - variable=[ - "Final Energy|Industry|Solids", - "Final Energy|Industry|Solids|Biomass", - "Final Energy|Industry|Solids|Coal", - ], - inplace=True, - ) - df_final_energy.convert_unit( - "GWa", to="EJ/yr", factor=0.03154, inplace=True - ) - df_final.append(df_final_energy, inplace=True) - if c == "heat": - df_final_energy.aggregate( - "Final Energy|Industry|Heat", - components=var_sectors, - append=True, - ) - df_final_energy.filter( - variable=["Final Energy|Industry|Heat"], - inplace=True, - ) - df_final_energy.convert_unit( - "GWa", to="EJ/yr", factor=0.03154, inplace=True - ) - df_final.append(df_final_energy, inplace=True) + ], + inplace=True, + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + if c == "heat": + df_final_energy.aggregate( + "Final Energy|Industry|Heat", + components=var_sectors, + append=True, + ) + df_final_energy.filter( + variable=["Final Energy|Industry|Heat"], + inplace=True, + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) # FINAL ENERGY BY SECTOR AND FUEL # Feedstock not included @@ -1808,269 +1784,251 @@ def report( "Other Sector", ] print("Final Energy by sector and fuel is being printed") - for r in nodes: - for s in sectors: - df_final_energy = df.copy() - df_final_energy.convert_unit("", to="GWa", factor=1, inplace=True) - exclude = [ - "in|final|atm_gasoil|steam_cracker_petro|*", - "in|final|ethanol|ethanol_to_ethylene_petro|M1", - "in|final|gas|gas_processing_petro|M1", - "in|final|naphtha|steam_cracker_petro|*", - "in|final|vacuum_gasoil|steam_cracker_petro|*", - "in|final|*|cokeoven_steel|*", - "in|final|bf_gas|*", - "in|final|co_gas|*", - ] - - df_final_energy.filter(region=r, year=years, inplace=True) - df_final_energy.filter( - variable=["in|final|*", "out|useful|i_therm|solar_i|M1"], inplace=True - ) - df_final_energy.filter(variable=exclude, keep=False, inplace=True) - - # Decompose the pyam table into pandas data frame + for r, s in product(nodes, sectors): + df_final_energy = df.copy() + df_final_energy.convert_unit("", to="GWa", factor=1, inplace=True) + exclude = [ + "in|final|atm_gasoil|steam_cracker_petro|*", + "in|final|ethanol|ethanol_to_ethylene_petro|M1", + "in|final|gas|gas_processing_petro|M1", + "in|final|naphtha|steam_cracker_petro|*", + "in|final|vacuum_gasoil|steam_cracker_petro|*", + "in|final|*|cokeoven_steel|*", + "in|final|bf_gas|*", + "in|final|co_gas|*", + ] - all_flows = df_final_energy.timeseries().reset_index() + df_final_energy.filter(region=r, year=years, inplace=True) + df_final_energy.filter( + variable=["in|final|*", "out|useful|i_therm|solar_i|M1"], inplace=True + ) + df_final_energy.filter(variable=exclude, keep=False, inplace=True) - # Split the strings in the identified variables for further processing - splitted_vars = [v.split("|") for v in all_flows.variable] + # Decompose the pyam table into pandas data frame - # Create auxilary dataframes for processing - aux1_df = pd.DataFrame( - splitted_vars, - columns=["flow_type", "level", "commodity", "technology", "mode"], - ) - aux2_df = pd.concat( - [all_flows.reset_index(drop=True), aux1_df.reset_index(drop=True)], - axis=1, - ) + all_flows = df_final_energy.timeseries().reset_index() - # To be able to report the higher level sectors. - if s == "Non-Ferrous Metals": - tec = [t for t in aux2_df["technology"].values if "aluminum" in t] - aux2_df = aux2_df[aux2_df["technology"].isin(tec)] - elif s == "Non-Metallic Minerals": - tec = [t for t in aux2_df["technology"].values if "cement" in t] - aux2_df = aux2_df[aux2_df["technology"].isin(tec)] - elif s == "Chemicals": - tec = [ - t - for t in aux2_df["technology"].values - if (("petro" in t) | ("NH3" in t)) - ] - aux2_df = aux2_df[aux2_df["technology"].isin(tec)] - elif s == "Other Sector": - tec = [ - t - for t in aux2_df["technology"].values - if ((("_i" in t) | ("_I" in t)) & ("trp" not in t)) - ] - aux2_df = aux2_df[aux2_df["technology"].isin(tec)] - else: - # Filter the technologies only for the certain industry sector - tec = [t for t in aux2_df["technology"].values if s in t] - aux2_df = aux2_df[aux2_df["technology"].isin(tec)] + # Split the strings in the identified variables for further processing + splitted_vars = [v.split("|") for v in all_flows.variable] - s = change_names(s) + # Create auxilary dataframes for processing + aux1_df = pd.DataFrame( + splitted_vars, + columns=["flow_type", "level", "commodity", "technology", "mode"], + ) + aux2_df = pd.concat( + [all_flows.reset_index(drop=True), aux1_df.reset_index(drop=True)], + axis=1, + ) - # Lists to keep commodity, aggregate and variable names. - - commodity_list = [] - aggregate_list = [] - var_list = [] - - # For the categoris below filter the required variable names, - # create a new aggregate name - - commodity_list = [ - "electr", - "gas", - "hydrogen", - "liquids", - "liquid_bio", - "liquid_fossil", - "liquid_other", - "solids", - "solids_bio", - "solids_fossil", - "heat", - "all", + # To be able to report the higher level sectors. + if s == "Non-Ferrous Metals": + tec = [t for t in aux2_df["technology"].values if "aluminum" in t] + aux2_df = aux2_df[aux2_df["technology"].isin(tec)] + elif s == "Non-Metallic Minerals": + tec = [t for t in aux2_df["technology"].values if "cement" in t] + aux2_df = aux2_df[aux2_df["technology"].isin(tec)] + elif s == "Chemicals": + tec = [ + t + for t in aux2_df["technology"].values + if (("petro" in t) | ("NH3" in t)) ] + aux2_df = aux2_df[aux2_df["technology"].isin(tec)] + elif s == "Other Sector": + tec = [ + t + for t in aux2_df["technology"].values + if ((("_i" in t) | ("_I" in t)) & ("trp" not in t)) + ] + aux2_df = aux2_df[aux2_df["technology"].isin(tec)] + else: + # Filter the technologies only for the certain industry sector + tec = [t for t in aux2_df["technology"].values if s in t] + aux2_df = aux2_df[aux2_df["technology"].isin(tec)] - for c in commodity_list: - if c == "electr": - var = np.unique( - aux2_df.loc[aux2_df["commodity"] == "electr", "variable"].values - ).tolist() - aggregate_name = ( - "Final Energy|Industry excl Non-Energy Use|" - + s - + "|" - + "Electricity" - ) - elif c == "gas": - var = np.unique( - aux2_df.loc[aux2_df["commodity"] == "gas", "variable"].values - ).tolist() - aggregate_name = ( - "Final Energy|Industry excl Non-Energy Use|" + s + "|" + "Gases" - ) - elif c == "hydrogen": - var = np.unique( - aux2_df.loc[ - aux2_df["commodity"] == "hydrogen", "variable" - ].values - ).tolist() - aggregate_name = ( - "Final Energy|Industry excl Non-Energy Use|" - + s - + "|" - + "Hydrogen" - ) - elif c == "liquids": - var = np.unique( - aux2_df.loc[ - ( - (aux2_df["commodity"] == "fueloil") - | (aux2_df["commodity"] == "lightoil") - | (aux2_df["commodity"] == "methanol") - | (aux2_df["commodity"] == "ethanol") - ), - "variable", - ].values - ).tolist() - aggregate_name = ( - "Final Energy|Industry excl Non-Energy Use|" - + s - + "|" - + "Liquids" - ) - elif c == "liquid_bio": - var = np.unique( - aux2_df.loc[ - ((aux2_df["commodity"] == "ethanol")), - "variable", - ].values - ).tolist() - aggregate_name = ( - "Final Energy|Industry excl Non-Energy Use|" - + s - + "|" - + "Liquids|Biomass" - ) - elif c == "liquid_fossil": - var = np.unique( - aux2_df.loc[ - ( - (aux2_df["commodity"] == "fueloil") - | (aux2_df["commodity"] == "lightoil") - ), - "variable", - ].values - ).tolist() - aggregate_name = ( - "Final Energy|Industry excl Non-Energy Use|" - + s - + "|" - + "Liquids|Oil" - ) - elif c == "liquid_other": - var = np.unique( - aux2_df.loc[ - ((aux2_df["commodity"] == "methanol")), - "variable", - ].values - ).tolist() - aggregate_name = ( - "Final Energy|Industry excl Non-Energy Use|" - + s - + "|" - + "Liquids|Coal" - ) - elif c == "solids": - var = np.unique( - aux2_df.loc[ - ( - (aux2_df["commodity"] == "coal") - | (aux2_df["commodity"] == "biomass") - | (aux2_df["commodity"] == "coke_iron") - ), - "variable", - ].values - ).tolist() - aggregate_name = ( - "Final Energy|Industry excl Non-Energy Use|" - + s - + "|" - + "Solids" - ) - elif c == "solids_bio": - var = np.unique( - aux2_df.loc[ - (aux2_df["commodity"] == "biomass"), "variable" - ].values - ).tolist() - aggregate_name = ( - "Final Energy|Industry excl Non-Energy Use|" - + s - + "|" - + "Solids|Biomass" - ) - elif c == "solids_fossil": - var = np.unique( - aux2_df.loc[ - ( - (aux2_df["commodity"] == "coal") - | (aux2_df["commodity"] == "coke_iron") - ), - "variable", - ].values - ).tolist() - aggregate_name = ( - "Final Energy|Industry excl Non-Energy Use|" - + s - + "|" - + "Solids|Coal" - ) - elif c == "heat": - var = np.unique( - aux2_df.loc[ - (aux2_df["commodity"] == "d_heat"), "variable" - ].values - ).tolist() - aggregate_name = ( - "Final Energy|Industry excl Non-Energy Use|" + s + "|" + "Heat" - ) - elif c == "all": - var = aux2_df["variable"].tolist() - aggregate_name = "Final Energy|Industry excl Non-Energy Use|" + s - - aggregate_list.append(aggregate_name) - var_list.append(var) + s = change_names(s) + + # Lists to keep commodity, aggregate and variable names. + + commodity_list = [] + aggregate_list = [] + var_list = [] + + # For the categoris below filter the required variable names, + # create a new aggregate name + + commodity_list = [ + "electr", + "gas", + "hydrogen", + "liquids", + "liquid_bio", + "liquid_fossil", + "liquid_other", + "solids", + "solids_bio", + "solids_fossil", + "heat", + "all", + ] - # Obtain the iamc format dataframe again + for c in commodity_list: + if c == "electr": + var = np.unique( + aux2_df.loc[aux2_df["commodity"] == "electr", "variable"].values + ).tolist() + aggregate_name = ( + "Final Energy|Industry excl Non-Energy Use|" + + s + + "|" + + "Electricity" + ) + elif c == "gas": + var = np.unique( + aux2_df.loc[aux2_df["commodity"] == "gas", "variable"].values + ).tolist() + aggregate_name = ( + "Final Energy|Industry excl Non-Energy Use|" + s + "|" + "Gases" + ) + elif c == "hydrogen": + var = np.unique( + aux2_df.loc[aux2_df["commodity"] == "hydrogen", "variable"].values + ).tolist() + aggregate_name = ( + "Final Energy|Industry excl Non-Energy Use|" + s + "|" + "Hydrogen" + ) + elif c == "liquids": + var = np.unique( + aux2_df.loc[ + ( + (aux2_df["commodity"] == "fueloil") + | (aux2_df["commodity"] == "lightoil") + | (aux2_df["commodity"] == "methanol") + | (aux2_df["commodity"] == "ethanol") + ), + "variable", + ].values + ).tolist() + aggregate_name = ( + "Final Energy|Industry excl Non-Energy Use|" + s + "|" + "Liquids" + ) + elif c == "liquid_bio": + var = np.unique( + aux2_df.loc[ + ((aux2_df["commodity"] == "ethanol")), + "variable", + ].values + ).tolist() + aggregate_name = ( + "Final Energy|Industry excl Non-Energy Use|" + + s + + "|" + + "Liquids|Biomass" + ) + elif c == "liquid_fossil": + var = np.unique( + aux2_df.loc[ + ( + (aux2_df["commodity"] == "fueloil") + | (aux2_df["commodity"] == "lightoil") + ), + "variable", + ].values + ).tolist() + aggregate_name = ( + "Final Energy|Industry excl Non-Energy Use|" + + s + + "|" + + "Liquids|Oil" + ) + elif c == "liquid_other": + var = np.unique( + aux2_df.loc[ + ((aux2_df["commodity"] == "methanol")), + "variable", + ].values + ).tolist() + aggregate_name = ( + "Final Energy|Industry excl Non-Energy Use|" + + s + + "|" + + "Liquids|Coal" + ) + elif c == "solids": + var = np.unique( + aux2_df.loc[ + ( + (aux2_df["commodity"] == "coal") + | (aux2_df["commodity"] == "biomass") + | (aux2_df["commodity"] == "coke_iron") + ), + "variable", + ].values + ).tolist() + aggregate_name = ( + "Final Energy|Industry excl Non-Energy Use|" + s + "|" + "Solids" + ) + elif c == "solids_bio": + var = np.unique( + aux2_df.loc[(aux2_df["commodity"] == "biomass"), "variable"].values + ).tolist() + aggregate_name = ( + "Final Energy|Industry excl Non-Energy Use|" + + s + + "|" + + "Solids|Biomass" + ) + elif c == "solids_fossil": + var = np.unique( + aux2_df.loc[ + ( + (aux2_df["commodity"] == "coal") + | (aux2_df["commodity"] == "coke_iron") + ), + "variable", + ].values + ).tolist() + aggregate_name = ( + "Final Energy|Industry excl Non-Energy Use|" + + s + + "|" + + "Solids|Coal" + ) + elif c == "heat": + var = np.unique( + aux2_df.loc[(aux2_df["commodity"] == "d_heat"), "variable"].values + ).tolist() + aggregate_name = ( + "Final Energy|Industry excl Non-Energy Use|" + s + "|" + "Heat" + ) + elif c == "all": + var = aux2_df["variable"].tolist() + aggregate_name = "Final Energy|Industry excl Non-Energy Use|" + s + + aggregate_list.append(aggregate_name) + var_list.append(var) + + # Obtain the iamc format dataframe again + + aux2_df.drop( + ["flow_type", "level", "commodity", "technology", "mode"], + axis=1, + inplace=True, + ) + df_final_energy = pyam.IamDataFrame(data=aux2_df) - aux2_df.drop( - ["flow_type", "level", "commodity", "technology", "mode"], - axis=1, - inplace=True, + # Aggregate the commodities in iamc object + i = 0 + for c in commodity_list: + df_final_energy.aggregate( + aggregate_list[i], components=var_list[i], append=True ) - df_final_energy = pyam.IamDataFrame(data=aux2_df) + i = i + 1 - # Aggregate the commodities in iamc object - i = 0 - for c in commodity_list: - df_final_energy.aggregate( - aggregate_list[i], components=var_list[i], append=True - ) - i = i + 1 - - df_final_energy.filter(variable=aggregate_list, inplace=True) - df_final_energy.convert_unit( - "GWa", to="EJ/yr", factor=0.03154, inplace=True - ) - df_final.append(df_final_energy, inplace=True) + df_final_energy.filter(variable=aggregate_list, inplace=True) + df_final_energy.convert_unit("GWa", to="EJ/yr", factor=0.03154, inplace=True) + df_final.append(df_final_energy, inplace=True) # FINAL ENERGY NON-ENERGY USE BY SECTOR AND FUEL # Only in chemcials sector there is non-energy use @@ -2083,179 +2041,174 @@ def report( sectors = ["petro", "ammonia"] print("Final Energy non-energy use by sector and fuel is being printed") - for r in nodes: - for s in sectors: - df_final_energy = df.copy() - df_final_energy.convert_unit("", to="GWa", factor=1, inplace=True) - include = [ - "in|final|atm_gasoil|steam_cracker_petro|*", - "in|final|ethanol|ethanol_to_ethylene_petro|M1", - "in|final|gas|gas_processing_petro|M1", - "in|final|naphtha|steam_cracker_petro|*", - "in|final|vacuum_gasoil|steam_cracker_petro|*", - "in|secondary|coal|coal_NH3|M1", - "in|secondary|coal|coal_NH3_ccs|M1", - "in|secondary|fueloil|fueloil_NH3|M1", - "in|secondary|fueloil|fueloil_NH3_ccs|M1", - "in|secondary|gas|gas_NH3|M1", - "in|secondary|gas|gas_NH3_ccs|M1", - "in|primary|biomass|biomass_NH3|M1", - "in|primary|biomass|biomass_NH3_ccs|M1", - "in|secondary|electr|NH3_to_N_fertil|M1", - "in|secondary|electr|coal_NH3|M1", - "in|secondary|electr|electr_NH3|M1", - "in|secondary|electr|gas_NH3|M1", - "in|secondary|electr|coal_NH3|M1", - "in|secondary|electr|coal_NH3_ccs|M1", - "in|secondary|electr|gas_NH3_ccs|M1", - "in|seconday|electr|biomass_NH3|M1", - "in|secondary|electr|biomass_NH3_ccs|M1", - "in|secondary|electr|fueloil_NH3|M1", - "in|secondary|electr|fueloil_NH3_ccs|M1", - ] - - df_final_energy.filter(region=r, year=years, inplace=True) - df_final_energy.filter(variable=include, inplace=True) - - # Decompose the pyam table into pandas data frame + for r, s in product(nodes, sectors): + df_final_energy = df.copy() + df_final_energy.convert_unit("", to="GWa", factor=1, inplace=True) + include = [ + "in|final|atm_gasoil|steam_cracker_petro|*", + "in|final|ethanol|ethanol_to_ethylene_petro|M1", + "in|final|gas|gas_processing_petro|M1", + "in|final|naphtha|steam_cracker_petro|*", + "in|final|vacuum_gasoil|steam_cracker_petro|*", + "in|secondary|coal|coal_NH3|M1", + "in|secondary|coal|coal_NH3_ccs|M1", + "in|secondary|fueloil|fueloil_NH3|M1", + "in|secondary|fueloil|fueloil_NH3_ccs|M1", + "in|secondary|gas|gas_NH3|M1", + "in|secondary|gas|gas_NH3_ccs|M1", + "in|primary|biomass|biomass_NH3|M1", + "in|primary|biomass|biomass_NH3_ccs|M1", + "in|secondary|electr|NH3_to_N_fertil|M1", + "in|secondary|electr|coal_NH3|M1", + "in|secondary|electr|electr_NH3|M1", + "in|secondary|electr|gas_NH3|M1", + "in|secondary|electr|coal_NH3|M1", + "in|secondary|electr|coal_NH3_ccs|M1", + "in|secondary|electr|gas_NH3_ccs|M1", + "in|seconday|electr|biomass_NH3|M1", + "in|secondary|electr|biomass_NH3_ccs|M1", + "in|secondary|electr|fueloil_NH3|M1", + "in|secondary|electr|fueloil_NH3_ccs|M1", + ] - all_flows = df_final_energy.timeseries().reset_index() + df_final_energy.filter(region=r, year=years, inplace=True) + df_final_energy.filter(variable=include, inplace=True) - # Split the strings in the identified variables for further processing - splitted_vars = [v.split("|") for v in all_flows.variable] + # Decompose the pyam table into pandas data frame - # Create auxilary dataframes for processing - aux1_df = pd.DataFrame( - splitted_vars, - columns=["flow_type", "level", "commodity", "technology", "mode"], - ) - aux2_df = pd.concat( - [all_flows.reset_index(drop=True), aux1_df.reset_index(drop=True)], - axis=1, - ) + all_flows = df_final_energy.timeseries().reset_index() - # Filter the technologies only for the certain industry sector - if s == "petro": - tec = [t for t in aux2_df["technology"].values if (s in t)] - if s == "ammonia": - tec = [t for t in aux2_df["technology"].values if ("NH3" in t)] + # Split the strings in the identified variables for further processing + splitted_vars = [v.split("|") for v in all_flows.variable] - aux2_df = aux2_df[aux2_df["technology"].isin(tec)] + # Create auxilary dataframes for processing + aux1_df = pd.DataFrame( + splitted_vars, + columns=["flow_type", "level", "commodity", "technology", "mode"], + ) + aux2_df = pd.concat( + [all_flows.reset_index(drop=True), aux1_df.reset_index(drop=True)], + axis=1, + ) - s = change_names(s) + # Filter the technologies only for the certain industry sector + if s == "petro": + tec = [t for t in aux2_df["technology"].values if (s in t)] + if s == "ammonia": + tec = [t for t in aux2_df["technology"].values if ("NH3" in t)] - # Lists to keep commodity, aggregate and variable names. + aux2_df = aux2_df[aux2_df["technology"].isin(tec)] - aggregate_list = [] - commodity_list = [] - var_list = [] + s = change_names(s) - # For the categoris below filter the required variable names, - # create a new aggregate name + # Lists to keep commodity, aggregate and variable names. - commodity_list = [ - "gas", - "liquids", - "liquid_bio", - "liquid_oil", - "all", - "solids", - ] + aggregate_list = [] + commodity_list = [] + var_list = [] - for c in commodity_list: - if c == "gas": - var = np.unique( - aux2_df.loc[aux2_df["commodity"] == "gas", "variable"].values - ).tolist() - aggregate_name = "Final Energy|Non-Energy Use|" + s + "|" + "Gases" + # For the categoris below filter the required variable names, + # create a new aggregate name - elif c == "liquids": - var = np.unique( - aux2_df.loc[ - ( - (aux2_df["commodity"] == "naphtha") - | (aux2_df["commodity"] == "atm_gasoil") - | (aux2_df["commodity"] == "vacuum_gasoil") - | (aux2_df["commodity"] == "ethanol") - | (aux2_df["commodity"] == "fueloil") - ), - "variable", - ].values - ).tolist() - - aggregate_name = ( - "Final Energy|Non-Energy Use|" + s + "|" + "Liquids" - ) - elif c == "liquid_bio": - var = np.unique( - aux2_df.loc[ - ((aux2_df["commodity"] == "ethanol")), - "variable", - ].values - ).tolist() - aggregate_name = ( - "Final Energy|Non-Energy Use|" + s + "|" + "Liquids|Biomass" - ) - elif c == "liquid_oil": - var = np.unique( - aux2_df.loc[ - ( - (aux2_df["commodity"] == "atm_gasoil") - | (aux2_df["commodity"] == "naphtha") - | (aux2_df["commodity"] == "vacuum_gasoil") - | (aux2_df["commodity"] == "fueloil") - ), - "variable", - ].values - ).tolist() - - aggregate_name = ( - "Final Energy|Non-Energy Use|" + s + "|" + "Liquids|Oil" - ) + commodity_list = [ + "gas", + "liquids", + "liquid_bio", + "liquid_oil", + "all", + "solids", + ] - elif c == "solids": - var = np.unique( - aux2_df.loc[ - ( - (aux2_df["commodity"] == "coal") - | (aux2_df["commodity"] == "biomass") - ), - "variable", - ].values - ).tolist() - aggregate_name = "Final Energy|Non-Energy Use|" + s + "|" + "Solids" - elif c == "all": - var = aux2_df["variable"].tolist() - aggregate_name = "Final Energy|Non-Energy Use|" + s - - aggregate_list.append(aggregate_name) - var_list.append(var) - - # Obtain the iamc format dataframe again - - aux2_df.drop( - ["flow_type", "level", "commodity", "technology", "mode"], - axis=1, - inplace=True, - ) - df_final_energy = pyam.IamDataFrame(data=aux2_df) + for c in commodity_list: + if c == "gas": + var = np.unique( + aux2_df.loc[aux2_df["commodity"] == "gas", "variable"].values + ).tolist() + aggregate_name = "Final Energy|Non-Energy Use|" + s + "|" + "Gases" + + elif c == "liquids": + var = np.unique( + aux2_df.loc[ + ( + (aux2_df["commodity"] == "naphtha") + | (aux2_df["commodity"] == "atm_gasoil") + | (aux2_df["commodity"] == "vacuum_gasoil") + | (aux2_df["commodity"] == "ethanol") + | (aux2_df["commodity"] == "fueloil") + ), + "variable", + ].values + ).tolist() + + aggregate_name = "Final Energy|Non-Energy Use|" + s + "|" + "Liquids" + elif c == "liquid_bio": + var = np.unique( + aux2_df.loc[ + ((aux2_df["commodity"] == "ethanol")), + "variable", + ].values + ).tolist() + aggregate_name = ( + "Final Energy|Non-Energy Use|" + s + "|" + "Liquids|Biomass" + ) + elif c == "liquid_oil": + var = np.unique( + aux2_df.loc[ + ( + (aux2_df["commodity"] == "atm_gasoil") + | (aux2_df["commodity"] == "naphtha") + | (aux2_df["commodity"] == "vacuum_gasoil") + | (aux2_df["commodity"] == "fueloil") + ), + "variable", + ].values + ).tolist() + + aggregate_name = ( + "Final Energy|Non-Energy Use|" + s + "|" + "Liquids|Oil" + ) + + elif c == "solids": + var = np.unique( + aux2_df.loc[ + ( + (aux2_df["commodity"] == "coal") + | (aux2_df["commodity"] == "biomass") + ), + "variable", + ].values + ).tolist() + aggregate_name = "Final Energy|Non-Energy Use|" + s + "|" + "Solids" + elif c == "all": + var = aux2_df["variable"].tolist() + aggregate_name = "Final Energy|Non-Energy Use|" + s + + aggregate_list.append(aggregate_name) + var_list.append(var) + + # Obtain the iamc format dataframe again + + aux2_df.drop( + ["flow_type", "level", "commodity", "technology", "mode"], + axis=1, + inplace=True, + ) + df_final_energy = pyam.IamDataFrame(data=aux2_df) - # Aggregate the commodities in iamc object + # Aggregate the commodities in iamc object - i = 0 - for c in commodity_list: - if var_list[i]: - df_final_energy.aggregate( - aggregate_list[i], components=var_list[i], append=True - ) + i = 0 + for c in commodity_list: + if var_list[i]: + df_final_energy.aggregate( + aggregate_list[i], components=var_list[i], append=True + ) - i = i + 1 + i = i + 1 - df_final_energy.filter(variable=aggregate_list, inplace=True) - df_final_energy.convert_unit( - "GWa", to="EJ/yr", factor=0.03154, inplace=True - ) - df_final.append(df_final_energy, inplace=True) + df_final_energy.filter(variable=aggregate_list, inplace=True) + df_final_energy.convert_unit("GWa", to="EJ/yr", factor=0.03154, inplace=True) + df_final.append(df_final_energy, inplace=True) # EMISSIONS # If ammonia is used as feedstock the emissions are accounted under 'CO2_industry', @@ -2287,329 +2240,310 @@ def report( ] print("Emissions are being printed.") - for typ in ["demand", "process"]: - for r in nodes: - for e in emission_type: - df_emi = df.copy() - df_emi.filter(region=r, year=years, inplace=True) - # CCS technologies for ammonia has both CO2 and CO2_industry - # at the same time. - if e == "CO2_industry": - emi_filter = [ - "emis|CO2|biomass_NH3_ccs|*", - "emis|CO2|gas_NH3_ccs|*", - "emis|CO2|coal_NH3_ccs|*", - "emis|CO2|fueloil_NH3_ccs|*", - "emis|CO2_industry|biomass_NH3_ccs|*", - "emis|CO2_industry|gas_NH3_ccs|*", - "emis|CO2_industry|coal_NH3_ccs|*", - "emis|CO2_industry|fueloil_NH3_ccs|*", - "emis|CO2_industry|biomass_NH3|*", - "emis|CO2_industry|gas_NH3|*", - "emis|CO2_industry|coal_NH3|*", - "emis|CO2_industry|fueloil_NH3|*", - "emis|CO2_industry|electr_NH3|*", - "emis|CO2_industry|*", - ] - df_emi.filter(variable=emi_filter, inplace=True) - else: - emi_filter = ["emis|" + e + "|*"] - exclude = [ - "emis|CO2|biomass_NH3_ccs|*", - "emis|CO2|gas_NH3_ccs|*", - "emis|CO2|coal_NH3_ccs|*", - "emis|CO2|fueloil_NH3_ccs|*", - ] - df_emi.filter(variable=exclude, keep=False, inplace=True) - df_emi.filter(variable=emi_filter, inplace=True) - if (e == "CO2") | (e == "CO2_industry"): - # From MtC to Mt CO2/yr - df_emi.convert_unit( - "", to="Mt CO2/yr", factor=44 / 12, inplace=True - ) - elif (e == "N2O") | (e == "CF4"): - unit = "kt " + e + "/yr" - df_emi.convert_unit("", to=unit, factor=1, inplace=True) - else: - e = change_names(e) - # From kt/yr to Mt/yr - unit = "Mt " + e + "/yr" - df_emi.convert_unit("", to=unit, factor=0.001, inplace=True) - - all_emissions = df_emi.timeseries().reset_index() - - # Split the strings in the identified variables for further processing - splitted_vars = [v.split("|") for v in all_emissions.variable] - # Lists to later keep the variables and names to aggregate - var_list = [] - aggregate_list = [] - - # Collect the same emission type for each sector - for s in sectors: - # Create auxilary dataframes for processing - aux1_df = pd.DataFrame( - splitted_vars, - columns=["emission", "type", "technology", "mode"], + for typ, r, e in product(["demand", "process"], nodes, emission_type): + df_emi = df.copy() + df_emi.filter(region=r, year=years, inplace=True) + # CCS technologies for ammonia has both CO2 and CO2_industry + # at the same time. + if e == "CO2_industry": + emi_filter = [ + "emis|CO2|biomass_NH3_ccs|*", + "emis|CO2|gas_NH3_ccs|*", + "emis|CO2|coal_NH3_ccs|*", + "emis|CO2|fueloil_NH3_ccs|*", + "emis|CO2_industry|biomass_NH3_ccs|*", + "emis|CO2_industry|gas_NH3_ccs|*", + "emis|CO2_industry|coal_NH3_ccs|*", + "emis|CO2_industry|fueloil_NH3_ccs|*", + "emis|CO2_industry|biomass_NH3|*", + "emis|CO2_industry|gas_NH3|*", + "emis|CO2_industry|coal_NH3|*", + "emis|CO2_industry|fueloil_NH3|*", + "emis|CO2_industry|electr_NH3|*", + "emis|CO2_industry|*", + ] + df_emi.filter(variable=emi_filter, inplace=True) + else: + emi_filter = ["emis|" + e + "|*"] + exclude = [ + "emis|CO2|biomass_NH3_ccs|*", + "emis|CO2|gas_NH3_ccs|*", + "emis|CO2|coal_NH3_ccs|*", + "emis|CO2|fueloil_NH3_ccs|*", + ] + df_emi.filter(variable=exclude, keep=False, inplace=True) + df_emi.filter(variable=emi_filter, inplace=True) + if (e == "CO2") | (e == "CO2_industry"): + # From MtC to Mt CO2/yr + df_emi.convert_unit("", to="Mt CO2/yr", factor=44 / 12, inplace=True) + elif (e == "N2O") | (e == "CF4"): + unit = "kt " + e + "/yr" + df_emi.convert_unit("", to=unit, factor=1, inplace=True) + else: + e = change_names(e) + # From kt/yr to Mt/yr + unit = "Mt " + e + "/yr" + df_emi.convert_unit("", to=unit, factor=0.001, inplace=True) + + all_emissions = df_emi.timeseries().reset_index() + + # Split the strings in the identified variables for further processing + splitted_vars = [v.split("|") for v in all_emissions.variable] + # Lists to later keep the variables and names to aggregate + var_list = [] + aggregate_list = [] + + # Collect the same emission type for each sector + for s in sectors: + # Create auxilary dataframes for processing + aux1_df = pd.DataFrame( + splitted_vars, + columns=["emission", "type", "technology", "mode"], + ) + aux2_df = pd.concat( + [ + all_emissions.reset_index(drop=True), + aux1_df.reset_index(drop=True), + ], + axis=1, + ) + # Filter the technologies only for the sector + + if (typ == "process") & (s == "all") & (e != "CO2_industry"): + tec = [ + t + for t in aux2_df["technology"].values + if ( + ( + ( + ("cement" in t) + | ("steel" in t) + | ("aluminum" in t) + | ("petro" in t) + ) + & ("furnace" not in t) + ) ) - aux2_df = pd.concat( - [ - all_emissions.reset_index(drop=True), - aux1_df.reset_index(drop=True), - ], - axis=1, + ] + if (typ == "process") & (s != "all"): + tec = [ + t + for t in aux2_df["technology"].values + if ((s in t) & ("furnace" not in t) & ("NH3" not in t)) + ] + + if (typ == "demand") & (s == "Chemicals"): + tec = [ + t + for t in aux2_df["technology"].values + if (("NH3" in t) | (("petro" in t) & ("furnace" in t))) + ] + + if (typ == "demand") & (s == "Other Sector") & (e != "CO2"): + tec = [ + t + for t in aux2_df["technology"].values + if ( + ( + ("biomass_i" in t) + | ("coal_i" in t) + | ("elec_i" in t) + | ("eth_i" in t) + | ("foil_i" in t) + | ("gas_i" in t) + | ("h2_i" in t) + | ("heat_i" in t) + | ("hp_el_i" in t) + | ("hp_gas_i" in t) + | ("loil_i" in t) + | ("meth_i" in t) + | ("sp_coal_I" in t) + | ("sp_el_I" in t) + | ("sp_eth_I" in t) + | ("sp_liq_I" in t) + | ("sp_meth_I" in t) + | ("h2_fc_I" in t) + ) + & ( + ("eth_ic_trp" not in t) + & ("meth_ic_trp" not in t) + & ("coal_imp" not in t) + & ("foil_imp" not in t) + & ("gas_imp" not in t) + & ("elec_imp" not in t) + & ("eth_imp" not in t) + & ("meth_imp" not in t) + & ("loil_imp" not in t) + ) ) - # Filter the technologies only for the sector - - if (typ == "process") & (s == "all") & (e != "CO2_industry"): - tec = [ - t - for t in aux2_df["technology"].values - if ( - ( - ( - ("cement" in t) - | ("steel" in t) - | ("aluminum" in t) - | ("petro" in t) - ) - & ("furnace" not in t) - ) - ) - ] - if (typ == "process") & (s != "all"): - tec = [ - t - for t in aux2_df["technology"].values - if ((s in t) & ("furnace" not in t) & ("NH3" not in t)) - ] - - if (typ == "demand") & (s == "Chemicals"): - tec = [ - t - for t in aux2_df["technology"].values - if (("NH3" in t) | (("petro" in t) & ("furnace" in t))) - ] - - if (typ == "demand") & (s == "Other Sector") & (e != "CO2"): - tec = [ - t - for t in aux2_df["technology"].values - if ( - ( - ("biomass_i" in t) - | ("coal_i" in t) - | ("elec_i" in t) - | ("eth_i" in t) - | ("foil_i" in t) - | ("gas_i" in t) - | ("h2_i" in t) - | ("heat_i" in t) - | ("hp_el_i" in t) - | ("hp_gas_i" in t) - | ("loil_i" in t) - | ("meth_i" in t) - | ("sp_coal_I" in t) - | ("sp_el_I" in t) - | ("sp_eth_I" in t) - | ("sp_liq_I" in t) - | ("sp_meth_I" in t) - | ("h2_fc_I" in t) - ) - & ( - ("eth_ic_trp" not in t) - & ("meth_ic_trp" not in t) - & ("coal_imp" not in t) - & ("foil_imp" not in t) - & ("gas_imp" not in t) - & ("elec_imp" not in t) - & ("eth_imp" not in t) - & ("meth_imp" not in t) - & ("loil_imp" not in t) - ) - ) - ] - - if (typ == "demand") & (s == "all") & (e != "CO2"): - tec = [ - t - for t in aux2_df["technology"].values - if ( - ( - ( - ("cement" in t) - | ("steel" in t) - | ("aluminum" in t) - | ("petro" in t) - ) - & ("furnace" in t) - ) - | ( - ("biomass_i" in t) - | ("coal_i" in t) - | ("elec_i" in t) - | ("eth_i" in t) - | ("foil_i" in t) - | ("gas_i" in t) - | ("h2_i" in t) - | ("heat_i" in t) - | ("hp_el_i" in t) - | ("hp_gas_i" in t) - | ("loil_i" in t) - | ("meth_i" in t) - | ("sp_coal_I" in t) - | ("sp_el_I" in t) - | ("sp_eth_I" in t) - | ("sp_liq_I" in t) - | ("sp_meth_I" in t) - | ("h2_fc_I" in t) - | ("DUMMY_limestone_supply_cement" in t) - | ("DUMMY_limestone_supply_steel" in t) - | ("eaf_steel" in t) - | ("DUMMY_coal_supply" in t) - | ("DUMMY_gas_supply" in t) - | ("NH3" in t) - ) - & ( - ("eth_ic_trp" not in t) - & ("meth_ic_trp" not in t) - & ("coal_imp" not in t) - & ("foil_imp" not in t) - & ("gas_imp" not in t) - & ("elec_imp" not in t) - & ("eth_imp" not in t) - & ("meth_imp" not in t) - & ("loil_imp" not in t) - ) - ) - ] + ] + if (typ == "demand") & (s == "all") & (e != "CO2"): + tec = [ + t + for t in aux2_df["technology"].values if ( - (typ == "demand") - & (s != "all") - & (s != "Other Sector") - & (s != "Chemicals") - ): - - if s == "steel": - # Furnaces are not used as heat source for iron&steel - # Dummy supply technologies help accounting the emissions - # from cokeoven_steel, bf_steel, dri_steel, eaf_steel, - # sinter_steel. - - tec = [ - t - for t in aux2_df["technology"].values - if ( - ("DUMMY_coal_supply" in t) - | ("DUMMY_gas_supply" in t) - | ("DUMMY_limestone_supply_steel" in t) - ) - ] - - elif s == "cement": - tec = [ - t - for t in aux2_df["technology"].values - if ( - ((s in t) & ("furnace" in t)) - | ("DUMMY_limestone_supply_cement" in t) - ) - ] - elif s == "ammonia": - tec = [ - t - for t in aux2_df["technology"].values - if (("NH3" in t)) - ] - else: - tec = [ - t - for t in aux2_df["technology"].values - if ((s in t) & ("furnace" in t)) - ] - # Adjust the sector names - s = change_names(s) - - aux2_df = aux2_df[aux2_df["technology"].isin(tec)] - # If there are no emission types for that setor skip - if aux2_df.empty: - continue - - # Add elements to lists for aggregation over emission type - # for each sector - var = aux2_df["variable"].values.tolist() - var_list.append(var) - - # Aggregate names: - if s == "all": - if (typ == "demand") & (e != "CO2"): - if e != "CO2_industry": - aggregate_name = ( - "Emissions|" + e + "|Energy|Demand|Industry" - ) - aggregate_list.append(aggregate_name) - else: - aggregate_name = ( - "Emissions|" + "CO2" + "|Energy|Demand|Industry" - ) - aggregate_list.append(aggregate_name) - if (typ == "process") & (e != "CO2_industry"): - aggregate_name = "Emissions|" + e + "|Industrial Processes" - aggregate_list.append(aggregate_name) - else: - if (typ == "demand") & (e != "CO2"): - if e != "CO2_industry": - aggregate_name = ( - "Emissions|" + e + "|Energy|Demand|Industry|" + s - ) - aggregate_list.append(aggregate_name) - else: - aggregate_name = ( - "Emissions|" - + "CO2" - + "|Energy|Demand|Industry|" - + s - ) - aggregate_list.append(aggregate_name) - if (typ == "process") & (e != "CO2_industry"): - aggregate_name = ( - "Emissions|" + e + "|Industrial Processes|" + s + ( + ( + ("cement" in t) + | ("steel" in t) + | ("aluminum" in t) + | ("petro" in t) ) - aggregate_list.append(aggregate_name) - - # To plot: Obtain the iamc format dataframe again + & ("furnace" in t) + ) + | ( + ("biomass_i" in t) + | ("coal_i" in t) + | ("elec_i" in t) + | ("eth_i" in t) + | ("foil_i" in t) + | ("gas_i" in t) + | ("h2_i" in t) + | ("heat_i" in t) + | ("hp_el_i" in t) + | ("hp_gas_i" in t) + | ("loil_i" in t) + | ("meth_i" in t) + | ("sp_coal_I" in t) + | ("sp_el_I" in t) + | ("sp_eth_I" in t) + | ("sp_liq_I" in t) + | ("sp_meth_I" in t) + | ("h2_fc_I" in t) + | ("DUMMY_limestone_supply_cement" in t) + | ("DUMMY_limestone_supply_steel" in t) + | ("eaf_steel" in t) + | ("DUMMY_coal_supply" in t) + | ("DUMMY_gas_supply" in t) + | ("NH3" in t) + ) + & ( + ("eth_ic_trp" not in t) + & ("meth_ic_trp" not in t) + & ("coal_imp" not in t) + & ("foil_imp" not in t) + & ("gas_imp" not in t) + & ("elec_imp" not in t) + & ("eth_imp" not in t) + & ("meth_imp" not in t) + & ("loil_imp" not in t) + ) + ) + ] - aux2_df = pd.concat( - [ - all_emissions.reset_index(drop=True), - aux1_df.reset_index(drop=True), - ], - axis=1, - ) - aux2_df.drop( - ["emission", "type", "technology", "mode"], axis=1, inplace=True - ) - df_emi = pyam.IamDataFrame(data=aux2_df) + if ( + (typ == "demand") + & (s != "all") + & (s != "Other Sector") + & (s != "Chemicals") + ): + + if s == "steel": + # Furnaces are not used as heat source for iron&steel + # Dummy supply technologies help accounting the emissions + # from cokeoven_steel, bf_steel, dri_steel, eaf_steel, + # sinter_steel. + + tec = [ + t + for t in aux2_df["technology"].values + if ( + ("DUMMY_coal_supply" in t) + | ("DUMMY_gas_supply" in t) + | ("DUMMY_limestone_supply_steel" in t) + ) + ] - # Aggregation over emission type for each sector if there are elements - # to aggregate + elif s == "cement": + tec = [ + t + for t in aux2_df["technology"].values + if ( + ((s in t) & ("furnace" in t)) + | ("DUMMY_limestone_supply_cement" in t) + ) + ] + elif s == "ammonia": + tec = [t for t in aux2_df["technology"].values if (("NH3" in t))] + else: + tec = [ + t + for t in aux2_df["technology"].values + if ((s in t) & ("furnace" in t)) + ] + # Adjust the sector names + s = change_names(s) - if len(aggregate_list) != 0: - for i in range(len(aggregate_list)): - df_emi.aggregate( - aggregate_list[i], components=var_list[i], append=True + aux2_df = aux2_df[aux2_df["technology"].isin(tec)] + # If there are no emission types for that setor skip + if aux2_df.empty: + continue + + # Add elements to lists for aggregation over emission type + # for each sector + var = aux2_df["variable"].values.tolist() + var_list.append(var) + + # Aggregate names: + if s == "all": + if (typ == "demand") & (e != "CO2"): + if e != "CO2_industry": + aggregate_name = "Emissions|" + e + "|Energy|Demand|Industry" + aggregate_list.append(aggregate_name) + else: + aggregate_name = ( + "Emissions|" + "CO2" + "|Energy|Demand|Industry" ) + aggregate_list.append(aggregate_name) + if (typ == "process") & (e != "CO2_industry"): + aggregate_name = "Emissions|" + e + "|Industrial Processes" + aggregate_list.append(aggregate_name) + else: + if (typ == "demand") & (e != "CO2"): + if e != "CO2_industry": + aggregate_name = ( + "Emissions|" + e + "|Energy|Demand|Industry|" + s + ) + aggregate_list.append(aggregate_name) + else: + aggregate_name = ( + "Emissions|" + "CO2" + "|Energy|Demand|Industry|" + s + ) + aggregate_list.append(aggregate_name) + if (typ == "process") & (e != "CO2_industry"): + aggregate_name = "Emissions|" + e + "|Industrial Processes|" + s + aggregate_list.append(aggregate_name) - fig, ax1 = plt.subplots(1, 1, figsize=(10, 10)) - df_emi.filter(variable=aggregate_list).plot.stack(ax=ax1) - df_emi.filter(variable=aggregate_list, inplace=True) - df_final.append(df_emi, inplace=True) - ax1.set_title("Emissions_" + r + "_" + e) - ax1.set_ylabel("Mt") - ax1.legend(bbox_to_anchor=(0.3, 1)) + # To plot: Obtain the iamc format dataframe again - plt.close() - pp.savefig(fig) + aux2_df = pd.concat( + [ + all_emissions.reset_index(drop=True), + aux1_df.reset_index(drop=True), + ], + axis=1, + ) + aux2_df.drop(["emission", "type", "technology", "mode"], axis=1, inplace=True) + df_emi = pyam.IamDataFrame(data=aux2_df) + + # Aggregation over emission type for each sector if there are elements + # to aggregate + + if len(aggregate_list) != 0: + for i in range(len(aggregate_list)): + df_emi.aggregate(aggregate_list[i], components=var_list[i], append=True) + + fig, ax1 = plt.subplots(1, 1, figsize=(10, 10)) + df_emi.filter(variable=aggregate_list).plot.stack(ax=ax1) + df_emi.filter(variable=aggregate_list, inplace=True) + df_final.append(df_emi, inplace=True) + ax1.set_title("Emissions_" + r + "_" + e) + ax1.set_ylabel("Mt") + ax1.legend(bbox_to_anchor=(0.3, 1)) + + plt.close() + pp.savefig(fig) # PLOTS # From e9a31fd2848ec57279fba438045f427f3032df67 Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Wed, 21 Sep 2022 11:29:27 +0200 Subject: [PATCH 036/774] Use if/elif instead of sequential if blocks --- .../model/material/report/__init__.py | 70 +++++++++---------- 1 file changed, 34 insertions(+), 36 deletions(-) diff --git a/message_ix_models/model/material/report/__init__.py b/message_ix_models/model/material/report/__init__.py index 4ad0f194ac..cda372fd62 100644 --- a/message_ix_models/model/material/report/__init__.py +++ b/message_ix_models/model/material/report/__init__.py @@ -967,7 +967,7 @@ def report( "GWa", to="EJ/yr", factor=0.03154, inplace=True ) df_final.append(df_final_energy, inplace=True) - if c == "gas": + elif c == "gas": # Can not distinguish by type Gases (natural gas, biomass, synthetic # fossil, efuel) (coal_gas), from biomass (gas_bio), natural gas @@ -989,7 +989,7 @@ def report( ) df_final.append(df_final_energy, inplace=True) - if c == "liquids": + elif c == "liquids": # All liquids df_final_energy.aggregate( @@ -1053,7 +1053,7 @@ def report( "GWa", to="EJ/yr", factor=0.03154, inplace=True ) df_final.append(df_final_energy, inplace=True) - if c == "solids": + elif c == "solids": # All df_final_energy.aggregate( @@ -1135,11 +1135,10 @@ def report( ], inplace=True, ) - if c == "gas": + elif c == "gas": df_final_energy.filter(variable=["in|final|gas|*"], inplace=True) - - # Do not include gasoil and naphtha feedstock - if c == "liquids": + elif c == "liquids": + # Do not include gasoil and naphtha feedstock df_final_energy.filter( variable=[ "in|final|ethanol|*", @@ -1149,7 +1148,7 @@ def report( ], inplace=True, ) - if c == "solids": + elif c == "solids": df_final_energy.filter( variable=[ "in|final|biomass|*", @@ -1158,14 +1157,13 @@ def report( ], inplace=True, ) - - if c == "hydrogen": + elif c == "hydrogen": df_final_energy.filter(variable=["in|final|hydrogen|*"], inplace=True) - if c == "heat": + elif c == "heat": df_final_energy.filter(variable=["in|final|d_heat|*"], inplace=True) - if c == "other": + elif c == "other": df_final_energy.filter(variable=["out|useful|i_therm|*"], inplace=True) - if c == "all": + elif c == "all": df_final_energy.filter( variable=[ "in|final|*", @@ -1244,7 +1242,7 @@ def report( "GWa", to="EJ/yr", factor=0.03154, inplace=True ) df_final.append(df_final_energy, inplace=True) - if c == "electr": + elif c == "electr": df_final_energy.aggregate( "Final Energy|Industry excl Non-Energy Use|Electricity", @@ -1259,7 +1257,7 @@ def report( "GWa", to="EJ/yr", factor=0.03154, inplace=True ) df_final.append(df_final_energy, inplace=True) - if c == "gas": + elif c == "gas": # Can not distinguish by type Gases (natural gas, biomass, synthetic # fossil, efuel) (coal_gas), from biomass (gas_bio), natural gas @@ -1281,7 +1279,7 @@ def report( ) df_final.append(df_final_energy, inplace=True) - if c == "hydrogen": + elif c == "hydrogen": df_final_energy.aggregate( "Final Energy|Industry excl Non-Energy Use|Hydrogen", components=var_sectors, @@ -1296,7 +1294,7 @@ def report( ) df_final.append(df_final_energy, inplace=True) - if c == "liquids": + elif c == "liquids": # All liquids df_final_energy.aggregate( @@ -1355,7 +1353,7 @@ def report( "GWa", to="EJ/yr", factor=0.03154, inplace=True ) df_final.append(df_final_energy, inplace=True) - if c == "solids": + elif c == "solids": # All df_final_energy.aggregate( @@ -1393,7 +1391,7 @@ def report( "GWa", to="EJ/yr", factor=0.03154, inplace=True ) df_final.append(df_final_energy, inplace=True) - if c == "heat": + elif c == "heat": df_final_energy.aggregate( "Final Energy|Industry excl Non-Energy Use|Heat", components=var_sectors, @@ -1407,7 +1405,7 @@ def report( "GWa", to="EJ/yr", factor=0.03154, inplace=True ) df_final.append(df_final_energy, inplace=True) - if c == "other": + elif c == "other": df_final_energy.aggregate( "Final Energy|Industry excl Non-Energy Use|Other", components=var_sectors, @@ -1452,7 +1450,7 @@ def report( df_final_energy.filter( variable=["out|useful|i_therm|solar_i|M1"], inplace=True ) - if c == "electr": + elif c == "electr": df_final_energy.filter( variable=[ "in|final|electr|*", @@ -1470,7 +1468,7 @@ def report( inplace=True, ) - if c == "gas": + elif c == "gas": df_final_energy.filter( variable=[ "in|final|gas|*", @@ -1480,8 +1478,8 @@ def report( inplace=True, ) - # Include gasoil and naphtha feedstock - if c == "liquids": + elif c == "liquids": + # Include gasoil and naphtha feedstock df_final_energy.filter( variable=[ "in|final|ethanol|*", @@ -1496,7 +1494,7 @@ def report( ], inplace=True, ) - if c == "solids": + elif c == "solids": df_final_energy.filter( variable=[ "in|final|biomass|*", @@ -1510,11 +1508,11 @@ def report( inplace=True, ) - if c == "hydrogen": + elif c == "hydrogen": df_final_energy.filter(variable=["in|final|hydrogen|*"], inplace=True) - if c == "heat": + elif c == "heat": df_final_energy.filter(variable=["in|final|d_heat|*"], inplace=True) - if c == "all": + elif c == "all": df_final_energy.filter( variable=[ "in|final|*", @@ -1588,7 +1586,7 @@ def report( "GWa", to="EJ/yr", factor=0.03154, inplace=True ) df_final.append(df_final_energy, inplace=True) - if c == "all": + elif c == "all": df_final_energy.aggregate( "Final Energy|Industry", components=var_sectors, @@ -1599,7 +1597,7 @@ def report( "GWa", to="EJ/yr", factor=0.03154, inplace=True ) df_final.append(df_final_energy, inplace=True) - if c == "electr": + elif c == "electr": df_final_energy.aggregate( "Final Energy|Industry|Electricity", @@ -1614,7 +1612,7 @@ def report( "GWa", to="EJ/yr", factor=0.03154, inplace=True ) df_final.append(df_final_energy, inplace=True) - if c == "gas": + elif c == "gas": # Can not distinguish by type Gases (natural gas, biomass, synthetic # fossil, efuel) (coal_gas), from biomass (gas_bio), natural gas @@ -1636,7 +1634,7 @@ def report( ) df_final.append(df_final_energy, inplace=True) - if c == "hydrogen": + elif c == "hydrogen": df_final_energy.aggregate( "Final Energy|Industry|Hydrogen", components=var_sectors, @@ -1651,7 +1649,7 @@ def report( ) df_final.append(df_final_energy, inplace=True) - if c == "liquids": + elif c == "liquids": # All liquids df_final_energy.aggregate( @@ -1715,7 +1713,7 @@ def report( "GWa", to="EJ/yr", factor=0.03154, inplace=True ) df_final.append(df_final_energy, inplace=True) - if c == "solids": + elif c == "solids": # All df_final_energy.aggregate( @@ -1755,7 +1753,7 @@ def report( "GWa", to="EJ/yr", factor=0.03154, inplace=True ) df_final.append(df_final_energy, inplace=True) - if c == "heat": + elif c == "heat": df_final_energy.aggregate( "Final Energy|Industry|Heat", components=var_sectors, @@ -2094,7 +2092,7 @@ def report( # Filter the technologies only for the certain industry sector if s == "petro": tec = [t for t in aux2_df["technology"].values if (s in t)] - if s == "ammonia": + elif s == "ammonia": tec = [t for t in aux2_df["technology"].values if ("NH3" in t)] aux2_df = aux2_df[aux2_df["technology"].isin(tec)] From 2fb206d74f8c6e5785ba0de44a48360f4750af9d Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Wed, 21 Sep 2022 11:40:29 +0200 Subject: [PATCH 037/774] Use f-strings instead of multiple + operations --- .../model/material/report/__init__.py | 74 ++++++------------- 1 file changed, 21 insertions(+), 53 deletions(-) diff --git a/message_ix_models/model/material/report/__init__.py b/message_ix_models/model/material/report/__init__.py index cda372fd62..e296a6ffea 100644 --- a/message_ix_models/model/material/report/__init__.py +++ b/message_ix_models/model/material/report/__init__.py @@ -1877,24 +1877,19 @@ def report( aux2_df.loc[aux2_df["commodity"] == "electr", "variable"].values ).tolist() aggregate_name = ( - "Final Energy|Industry excl Non-Energy Use|" - + s - + "|" - + "Electricity" + f"Final Energy|Industry excl Non-Energy Use|{s}|Electricity" ) elif c == "gas": var = np.unique( aux2_df.loc[aux2_df["commodity"] == "gas", "variable"].values ).tolist() - aggregate_name = ( - "Final Energy|Industry excl Non-Energy Use|" + s + "|" + "Gases" - ) + aggregate_name = f"Final Energy|Industry excl Non-Energy Use|{s}|Gases" elif c == "hydrogen": var = np.unique( aux2_df.loc[aux2_df["commodity"] == "hydrogen", "variable"].values ).tolist() aggregate_name = ( - "Final Energy|Industry excl Non-Energy Use|" + s + "|" + "Hydrogen" + f"Final Energy|Industry excl Non-Energy Use|{s}|Hydrogen" ) elif c == "liquids": var = np.unique( @@ -1909,7 +1904,7 @@ def report( ].values ).tolist() aggregate_name = ( - "Final Energy|Industry excl Non-Energy Use|" + s + "|" + "Liquids" + f"Final Energy|Industry excl Non-Energy Use|{s}|Liquids" ) elif c == "liquid_bio": var = np.unique( @@ -1919,10 +1914,7 @@ def report( ].values ).tolist() aggregate_name = ( - "Final Energy|Industry excl Non-Energy Use|" - + s - + "|" - + "Liquids|Biomass" + f"Final Energy|Industry excl Non-Energy Use|{s}|Liquids|Biomass" ) elif c == "liquid_fossil": var = np.unique( @@ -1935,10 +1927,7 @@ def report( ].values ).tolist() aggregate_name = ( - "Final Energy|Industry excl Non-Energy Use|" - + s - + "|" - + "Liquids|Oil" + f"Final Energy|Industry excl Non-Energy Use|{s}|Liquids|Oil" ) elif c == "liquid_other": var = np.unique( @@ -1948,10 +1937,7 @@ def report( ].values ).tolist() aggregate_name = ( - "Final Energy|Industry excl Non-Energy Use|" - + s - + "|" - + "Liquids|Coal" + f"Final Energy|Industry excl Non-Energy Use|{s}|Liquids|Coal" ) elif c == "solids": var = np.unique( @@ -1964,18 +1950,13 @@ def report( "variable", ].values ).tolist() - aggregate_name = ( - "Final Energy|Industry excl Non-Energy Use|" + s + "|" + "Solids" - ) + aggregate_name = f"Final Energy|Industry excl Non-Energy Use|{s}|Solids" elif c == "solids_bio": var = np.unique( aux2_df.loc[(aux2_df["commodity"] == "biomass"), "variable"].values ).tolist() aggregate_name = ( - "Final Energy|Industry excl Non-Energy Use|" - + s - + "|" - + "Solids|Biomass" + f"Final Energy|Industry excl Non-Energy Use|{s}|Solids|Biomass" ) elif c == "solids_fossil": var = np.unique( @@ -1988,21 +1969,16 @@ def report( ].values ).tolist() aggregate_name = ( - "Final Energy|Industry excl Non-Energy Use|" - + s - + "|" - + "Solids|Coal" + f"Final Energy|Industry excl Non-Energy Use|{s}|Solids|Coals" ) elif c == "heat": var = np.unique( aux2_df.loc[(aux2_df["commodity"] == "d_heat"), "variable"].values ).tolist() - aggregate_name = ( - "Final Energy|Industry excl Non-Energy Use|" + s + "|" + "Heat" - ) + aggregate_name = f"Final Energy|Industry excl Non-Energy Use|{s}|Heat" elif c == "all": var = aux2_df["variable"].tolist() - aggregate_name = "Final Energy|Industry excl Non-Energy Use|" + s + aggregate_name = f"Final Energy|Industry excl Non-Energy Use|{s}" aggregate_list.append(aggregate_name) var_list.append(var) @@ -2122,7 +2098,7 @@ def report( var = np.unique( aux2_df.loc[aux2_df["commodity"] == "gas", "variable"].values ).tolist() - aggregate_name = "Final Energy|Non-Energy Use|" + s + "|" + "Gases" + aggregate_name = f"Final Energy|Non-Energy Use|{s}|Gases" elif c == "liquids": var = np.unique( @@ -2138,7 +2114,7 @@ def report( ].values ).tolist() - aggregate_name = "Final Energy|Non-Energy Use|" + s + "|" + "Liquids" + aggregate_name = f"Final Energy|Non-Energy Use|{s}|Liquids" elif c == "liquid_bio": var = np.unique( aux2_df.loc[ @@ -2146,9 +2122,7 @@ def report( "variable", ].values ).tolist() - aggregate_name = ( - "Final Energy|Non-Energy Use|" + s + "|" + "Liquids|Biomass" - ) + aggregate_name = f"Final Energy|Non-Energy Use|{s}|Liquids|Biomass" elif c == "liquid_oil": var = np.unique( aux2_df.loc[ @@ -2162,9 +2136,7 @@ def report( ].values ).tolist() - aggregate_name = ( - "Final Energy|Non-Energy Use|" + s + "|" + "Liquids|Oil" - ) + aggregate_name = f"Final Energy|Non-Energy Use|{s}|Liquids|Oil" elif c == "solids": var = np.unique( @@ -2176,10 +2148,10 @@ def report( "variable", ].values ).tolist() - aggregate_name = "Final Energy|Non-Energy Use|" + s + "|" + "Solids" + aggregate_name = f"Final Energy|Non-Energy Use|{s}|Solids" elif c == "all": var = aux2_df["variable"].tolist() - aggregate_name = "Final Energy|Non-Energy Use|" + s + aggregate_name = f"Final Energy|Non-Energy Use|{s}" aggregate_list.append(aggregate_name) var_list.append(var) @@ -2500,17 +2472,13 @@ def report( else: if (typ == "demand") & (e != "CO2"): if e != "CO2_industry": - aggregate_name = ( - "Emissions|" + e + "|Energy|Demand|Industry|" + s - ) + aggregate_name = f"Emissions|{e}|Energy|Demand|Industry|{s}" aggregate_list.append(aggregate_name) else: - aggregate_name = ( - "Emissions|" + "CO2" + "|Energy|Demand|Industry|" + s - ) + aggregate_name = f"Emissions|CO2|Energy|Demand|Industry|{s}" aggregate_list.append(aggregate_name) if (typ == "process") & (e != "CO2_industry"): - aggregate_name = "Emissions|" + e + "|Industrial Processes|" + s + aggregate_name = f"Emissions|{e}|Industrial Processes|{s}" aggregate_list.append(aggregate_name) # To plot: Obtain the iamc format dataframe again From 5d7881d11fb782a3df4e3912310b89583631367b Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Wed, 21 Sep 2022 11:48:48 +0200 Subject: [PATCH 038/774] Use dict.get instead of change_names() / long elif block --- .../model/material/report/__init__.py | 49 +++++++------------ 1 file changed, 18 insertions(+), 31 deletions(-) diff --git a/message_ix_models/model/material/report/__init__.py b/message_ix_models/model/material/report/__init__.py index e296a6ffea..26873ef53b 100644 --- a/message_ix_models/model/material/report/__init__.py +++ b/message_ix_models/model/material/report/__init__.py @@ -50,29 +50,16 @@ def print_full(x): pd.reset_option("display.max_rows") -def change_names(s): - - """Change the sector names according to IMAC format.""" - - if s == "aluminum": - s = "Non-Ferrous Metals|Aluminium" - elif s == "steel": - s = "Steel" - elif s == "cement": - s = "Non-Metallic Minerals|Cement" - elif s == "petro": - s = "Chemicals|High Value Chemicals" - elif s == "ammonia": - s = "Chemicals|Ammonia" - elif s == "BCA": - s = "BC" - elif s == "OCA": - s = "OC" - elif s == "CO2_industry": - s == "CO2" - else: - s == s - return s +NAME_MAP = { + "aluminum": "Non-Ferrous Metals|Aluminium", + "steel": "Steel", + "cement": "Non-Metallic Minerals|Cement", + "petro": "Chemicals|High Value Chemicals", + "ammonia": "Chemicals|Ammonia", + "BCA": "BC", + "OCA": "OC", + "CO2_industry": "CO2", +} def fix_excel(path_temp, path_new): @@ -1845,7 +1832,7 @@ def report( tec = [t for t in aux2_df["technology"].values if s in t] aux2_df = aux2_df[aux2_df["technology"].isin(tec)] - s = change_names(s) + s = NAME_MAP.get(s, s) # Lists to keep commodity, aggregate and variable names. @@ -2073,7 +2060,7 @@ def report( aux2_df = aux2_df[aux2_df["technology"].isin(tec)] - s = change_names(s) + s = NAME_MAP.get(s, s) # Lists to keep commodity, aggregate and variable names. @@ -2250,7 +2237,7 @@ def report( unit = "kt " + e + "/yr" df_emi.convert_unit("", to=unit, factor=1, inplace=True) else: - e = change_names(e) + e = NAME_MAP.get(e, e) # From kt/yr to Mt/yr unit = "Mt " + e + "/yr" df_emi.convert_unit("", to=unit, factor=0.001, inplace=True) @@ -2443,7 +2430,7 @@ def report( if ((s in t) & ("furnace" in t)) ] # Adjust the sector names - s = change_names(s) + s = NAME_MAP.get(s, s) aux2_df = aux2_df[aux2_df["technology"].isin(tec)] # If there are no emission types for that setor skip @@ -2827,7 +2814,7 @@ def report( # # # For each commodity collect the variable name, create an aggregate # # name - # s = change_names(s) + # s = NAME_MAP.get(s, s) # for c in np.unique(aux2_df["commodity"].values): # var = np.unique( # aux2_df.loc[aux2_df["commodity"] == c, "variable"].values @@ -2895,7 +2882,7 @@ def report( # 'in|end_of_life|' + m + '|total_EOL_' + m + '|M1'] # print(filt_power) # - # m = change_names(m) + # m = NAME_MAP.get(m, m) # var_name_buildings = 'Total Scrap|Residential and Commercial|' + m # var_name_other = 'Total Scrap|Other|' + m # var_name_power = 'Total Scrap|Power Sector|' + m @@ -2925,7 +2912,7 @@ def report( # 'out|end_of_life|' + m + '|demolition_build|M1', # 'in|end_of_life|' + m + '|total_EOL_' + m + '|M1'] # print(filt_power) - # m = change_names(m) + # m = NAME_MAP.get(m, m) # var_name_buildings = 'Total Scrap|Residential and Commercial|' + m # print(var_name_buildings) # var_name_power = 'Total Scrap|Power Sector|' + m @@ -3168,7 +3155,7 @@ def report( # material_needs_all["model"] = model_name # material_needs_all["unit"] = "Mt/yr" # material_needs_all["commodity"] = material_needs_all.apply( - # lambda x: change_names(x["commodity"]), axis=1 + # lambda x: NAME_MAP.get(x["commodity"], x["commodity"]), axis=1 # ) # material_needs_all = material_needs_all.assign( # variable=lambda x: "Material Demand|Power Sector|" + x["commodity"] From 8da785714be70e1c1da1f24fdc72696f485b14df Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Wed, 21 Sep 2022 12:17:17 +0200 Subject: [PATCH 039/774] Disentangle plotting code from reporting calculations --- .../model/material/report/__init__.py | 255 +++++++++--------- 1 file changed, 127 insertions(+), 128 deletions(-) diff --git a/message_ix_models/model/material/report/__init__.py b/message_ix_models/model/material/report/__init__.py index 26873ef53b..e1c741313b 100644 --- a/message_ix_models/model/material/report/__init__.py +++ b/message_ix_models/model/material/report/__init__.py @@ -167,6 +167,117 @@ def fix_excel(path_temp, path_new): new_workbook.save(path_new) +def plot_production_al(df: pyam.IamDataFrame, ax, r: str) -> None: + df = df.copy() + + df.filter( + variable=[ + "out|useful_material|aluminum|import_aluminum|*", + "out|final_material|aluminum|prebake_aluminum|*", + "out|final_material|aluminum|soderberg_aluminum|*", + "out|new_scrap|aluminum|*", + ], + inplace=True, + ) + + if r == "World": + df.filter( + variable=[ + "out|final_material|aluminum|prebake_aluminum|*", + "out|final_material|aluminum|soderberg_aluminum|*", + "out|new_scrap|aluminum|*", + ], + inplace=True, + ) + + df.plot.stack(ax=ax) + ax.legend( + [ + "Prebake", + "Soderberg", + "Newscrap", + "Oldscrap_min", + "Oldscrap_av", + "Oldscrap_max", + "import", + ], + bbox_to_anchor=(-0.4, 1), + loc="upper left", + ) + ax.set_title(f"Aluminium Production_{r}") + ax.set_xlabel("Year") + ax.set_ylabel("Mt") + + +def plot_production_steel(df: pyam.IamDataFrame, ax, r: str) -> None: + df = df.copy() + df.filter( + variable=[ + "out|final_material|steel|*", + "out|useful_material|steel|import_steel|*", + ], + inplace=True, + ) + + if r == "World": + df.filter(variable=["out|final_material|steel|*"], inplace=True) + + df.plot.stack(ax=ax) + ax.legend( + ["Bof steel", "Eaf steel M1", "Eaf steel M2", "Import"], + bbox_to_anchor=(-0.4, 1), + loc="upper left", + ) + ax.set_title(f"Steel Production_{r}") + ax.set_ylabel("Mt") + + +def plot_petro(df: pyam.IamDataFrame, ax, r: str) -> None: + df.plot.stack(ax=ax) + ax.legend( + ["atm_gasoil", "naphtha", "vacuum_gasoil", "bioethanol", "ethane", "propane"], + bbox_to_anchor=(-0.4, 1), + loc="upper left", + ) + ax.set_title(f"HVC feedstock {r}") + ax.set_xlabel("Years") + ax.set_ylabel("GWa") + + +def plot_production_cement(df: pyam.IamDataFrame, ax, r: str) -> None: + df.plot.stack(ax=ax) + ax.legend( + ["Ballmill Grinding", "Vertical Mill Grinding"], + bbox_to_anchor=(-0.6, 1), + loc="upper left", + ) + ax.set_title(f"Final Cement Production_{r}") + ax.set_xlabel("Year") + ax.set_ylabel("Mt") + + +def plot_production_cement_clinker(df: pyam.IamDataFrame, ax, r: str) -> None: + df.plot.stack(ax=ax) + ax.legend( + ["Dry Clinker", "Wet Clinker"], bbox_to_anchor=(-0.5, 1), loc="upper left" + ) + ax.set_title(f"Clinker Cement Production_{r}") + ax.set_xlabel("Year") + ax.set_ylabel("Mt") + + +def plot_emi_aggregates(df: pyam.IamDataFrame, pp, r: str, e: str) -> None: + fig, ax = plt.subplots(1, 1, figsize=(10, 10)) + + df.plot.stack(ax=ax) + ax.set_title(f"Emissions_{r}_{e}") + ax.set_ylabel("Mt") + ax.legend(bbox_to_anchor=(0.3, 1)) + + plt.close() + pp.savefig(fig) + + def report( scenario: message_ix.Scenario, message_df: pd.DataFrame, @@ -306,21 +417,7 @@ def report( worksheet.write("C1", "Region") worksheet.write("D1", "Variable") worksheet.write("E1", "Unit") - columns = [ - "F1", - "G1", - "H1", - "I1", - "J1", - "K1", - "L1", - "M1", - "N1", - "O1", - "P1", - "Q1", - "R1", - ] + columns = "F1 G1 H1 I1 J1 K1 L1 M1 N1 O1 P1 Q1 R1".split() for yr, col in zip(years, columns): worksheet.write(col, yr) @@ -345,49 +442,12 @@ def report( fig.tight_layout(pad=10.0) # ALUMINUM - df_al = df.copy() - df_al.filter(region=r, year=years, inplace=True) - df_al.filter(variable=["out|*|aluminum|*", "in|*|aluminum|*"], inplace=True) - df_al.convert_unit("", to="Mt/yr", factor=1, inplace=True) - df_al_graph = df_al.copy() - - df_al_graph.filter( - variable=[ - "out|useful_material|aluminum|import_aluminum|*", - "out|final_material|aluminum|prebake_aluminum|*", - "out|final_material|aluminum|soderberg_aluminum|*", - "out|new_scrap|aluminum|*", - ], - inplace=True, + df_al = df.filter( + region=r, year=years, variable=["out|*|aluminum|*", "in|*|aluminum|*"] ) + df_al.convert_unit("", to="My/yr", factor=1, inplace=True) - if r == "World": - df_al_graph.filter( - variable=[ - "out|final_material|aluminum|prebake_aluminum|*", - "out|final_material|aluminum|soderberg_aluminum|*", - "out|new_scrap|aluminum|*", - ], - inplace=True, - ) - - df_al_graph.plot.stack(ax=ax1) - ax1.legend( - [ - "Prebake", - "Soderberg", - "Newscrap", - "Oldscrap_min", - "Oldscrap_av", - "Oldscrap_max", - "import", - ], - bbox_to_anchor=(-0.4, 1), - loc="upper left", - ) - ax1.set_title("Aluminium Production_" + r) - ax1.set_xlabel("Year") - ax1.set_ylabel("Mt") + plot_production_al(df_al, ax1, r) # STEEL @@ -396,32 +456,7 @@ def report( df_steel.filter(variable=["out|*|steel|*", "in|*|steel|*"], inplace=True) df_steel.convert_unit("", to="Mt/yr", factor=1, inplace=True) - df_steel_graph = df_steel.copy() - df_steel_graph.filter( - variable=[ - "out|final_material|steel|*", - "out|useful_material|steel|import_steel|*", - ], - inplace=True, - ) - - if r == "World": - df_steel.filter(variable=["out|*|steel|*", "in|*|steel|*"], inplace=True) - df_steel_graph.filter( - variable=[ - "out|final_material|steel|*", - ], - inplace=True, - ) - - df_steel_graph.plot.stack(ax=ax2) - ax2.legend( - ["Bof steel", "Eaf steel M1", "Eaf steel M2", "Import"], - bbox_to_anchor=(-0.4, 1), - loc="upper left", - ) - ax2.set_title("Steel Production_" + r) - ax2.set_ylabel("Mt") + plot_production_steel(df_steel, ax2, r) # PETRO @@ -438,7 +473,6 @@ def report( df_petro.convert_unit("", to="Mt/yr", factor=1, inplace=True) if r == "World": - df_petro.filter( variable=[ "in|final|ethanol|ethanol_to_ethylene_petro|M1", @@ -448,22 +482,7 @@ def report( inplace=True, ) - df_petro.plot.stack(ax=ax3) - ax3.legend( - [ - "atm_gasoil", - "naphtha", - "vacuum_gasoil", - "bioethanol", - "ethane", - "propane", - ], - bbox_to_anchor=(-0.4, 1), - loc="upper left", - ) - ax3.set_title("HVC feedstock" + r) - ax3.set_xlabel("Years") - ax3.set_ylabel("GWa") + plot_petro(df_petro, ax3, r) plt.close() pp.savefig(fig) @@ -753,10 +772,9 @@ def report( df_final.append(df_al, inplace=True) df_final.append(df_steel, inplace=True) df_final.append(df_chemicals, inplace=True) - # CEMENT + # CEMENT for r in nodes: - # PRODUCTION - PLOT fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 10)) @@ -769,28 +787,16 @@ def report( df_cement_clinker.filter( variable=["out|tertiary_material|clinker_cement|*"], inplace=True ) - df_cement_clinker.plot.stack(ax=ax1) - ax1.legend( - ["Dry Clinker", "Wet Clinker"], bbox_to_anchor=(-0.5, 1), loc="upper left" - ) - ax1.set_title("Clinker Cement Production_" + r) - ax1.set_xlabel("Year") - ax1.set_ylabel("Mt") + + plot_production_cement_clinker(df_cement_clinker, ax1, r) # Final prodcut cement df_cement = df.copy() df_cement.filter(region=r, year=years, inplace=True) df_cement.filter(variable=["out|demand|cement|*"], inplace=True) - df_cement.plot.stack(ax=ax2) - ax2.legend( - ["Ballmill Grinding", "Vertical Mill Grinding"], - bbox_to_anchor=(-0.6, 1), - loc="upper left", - ) - ax2.set_title("Final Cement Production_" + r) - ax2.set_xlabel("Year") - ax2.set_ylabel("Mt") + + plot_production_cement(df_cement, ax2, r) plt.close() pp.savefig(fig) @@ -2482,21 +2488,14 @@ def report( # Aggregation over emission type for each sector if there are elements # to aggregate + for i in range(len(aggregate_list)): + df_emi.aggregate(aggregate_list[i], components=var_list[i], append=True) - if len(aggregate_list) != 0: - for i in range(len(aggregate_list)): - df_emi.aggregate(aggregate_list[i], components=var_list[i], append=True) - - fig, ax1 = plt.subplots(1, 1, figsize=(10, 10)) - df_emi.filter(variable=aggregate_list).plot.stack(ax=ax1) + if len(aggregate_list): df_emi.filter(variable=aggregate_list, inplace=True) df_final.append(df_emi, inplace=True) - ax1.set_title("Emissions_" + r + "_" + e) - ax1.set_ylabel("Mt") - ax1.legend(bbox_to_anchor=(0.3, 1)) - plt.close() - pp.savefig(fig) + plot_emi_aggregates(df_emi, pp) # PLOTS # From 2f789447667560ec4682aa39d8b786a9dbad96b0 Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Wed, 21 Sep 2022 12:18:18 +0200 Subject: [PATCH 040/774] Adjust nodes used in materials reporting --- message_ix_models/model/material/report/__init__.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/message_ix_models/model/material/report/__init__.py b/message_ix_models/model/material/report/__init__.py index e1c741313b..71e11bb9c4 100644 --- a/message_ix_models/model/material/report/__init__.py +++ b/message_ix_models/model/material/report/__init__.py @@ -308,7 +308,7 @@ def report( directory = config["output_path"].expanduser().joinpath("materials") directory.mkdir(exist_ok=True) - # Replace erroneous region labels like R12_AFR|R12_AFR with simply R12_AFR + # Replace region labels like R12_AFR|R12_AFR with simply R12_AFR # TODO locate the cause of this upstream and fix df = message_df.rename({"region": {f"{n}|{n}": n for n in nodes}}) @@ -323,6 +323,10 @@ def report( # FIXME(PNK) this re-reads the file above. This seems unnecessary, and can be slow. df = pyam.IamDataFrame(pd.read_excel(name).fillna(dict(Unit=""))) + # Subsequent code expects that the nodes set contains "World", but not "_GLB" + # NB cannot use message_ix_models.util.nodes_ex_world(), which excludes both + nodes = list(filter(lambda n: not n.endswith("_GLB"), nodes)) + # Filter variables necessary for materials reporting df.filter( region=nodes, @@ -3215,6 +3219,6 @@ def callback(rep: message_ix.Reporter, context: Context) -> None: "scenario", "message::default", "y::model", - "n::ex world", + "n", "config", ) From f1a37ea3926a8def116c420df1376debbc62d6dd Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Wed, 21 Sep 2022 12:32:52 +0200 Subject: [PATCH 041/774] Use pd.dataframe.replace instead of fix_excel() --- .../model/material/report/__init__.py | 221 ++++++++---------- 1 file changed, 91 insertions(+), 130 deletions(-) diff --git a/message_ix_models/model/material/report/__init__.py b/message_ix_models/model/material/report/__init__.py index 71e11bb9c4..6aee0a27b1 100644 --- a/message_ix_models/model/material/report/__init__.py +++ b/message_ix_models/model/material/report/__init__.py @@ -30,8 +30,6 @@ import matplotlib import message_ix import numpy as np -import openpyxl -import os import pandas as pd import pyam import xlsxwriter @@ -50,6 +48,7 @@ def print_full(x): pd.reset_option("display.max_rows") +#: Replacements applied during processing of specific quantities. NAME_MAP = { "aluminum": "Non-Ferrous Metals|Aluminium", "steel": "Steel", @@ -61,110 +60,79 @@ def print_full(x): "CO2_industry": "CO2", } - -def fix_excel(path_temp, path_new): - - """ - Fix the names of the regions or variables to be compatible - with IAMC format. This is done in the final reported excel file - (path_temp) and written to a new excel file (path_new). - - """ - # read Excel file and sheet by name - workbook = openpyxl.load_workbook(path_temp) - sheet = workbook["data"] - - new_workbook = openpyxl.Workbook() - new_sheet = new_workbook["Sheet"] - new_sheet.title = "data" - new_sheet = new_workbook.active - - replacement = { - "CO2_industry": "CO2", - "R11_AFR|R11_AFR": "R11_AFR", - "R11_CPA|R11_CPA": "R11_CPA", - "R11_MEA|R11_MEA": "R11_MEA", - "R11_FSU|R11_FSU": "R11_FSU", - "R11_PAS|R11_PAS": "R11_PAS", - "R11_SAS|R11_SAS": "R11_SAS", - "R11_LAM|R11_LAM": "R11_LAM", - "R11_NAM|R11_NAM": "R11_NAM", - "R11_PAO|R11_PAO": "R11_PAO", - "R11_EEU|R11_EEU": "R11_EEU", - "R11_WEU|R11_WEU": "R11_WEU", - "R11_AFR": "R11_AFR", - "R11_CPA": "R11_CPA", - "R11_MEA": "R11_MEA", - "R11_FSU": "R11_FSU", - "R11_PAS": "R11_PAS", - "R11_SAS": "R11_SAS", - "R11_LAM": "R11_LAM", - "R11_NAM": "R11_NAM", - "R11_PAO": "R11_PAO", - "R11_EEU": "R11_EEU", - "R11_WEU": "R11_WEU", - "R12_AFR|R12_AFR": "R12_AFR", - "R12_RCPA|R12_RCPA": "R12_RCPA", - "R12_MEA|R12_MEA": "R12_MEA", - "R12_FSU|R12_FSU": "R12_FSU", - "R12_PAS|R12_PAS": "R12_PAS", - "R12_SAS|R12_SAS": "R12_SAS", - "R12_LAM|R12_LAM": "R12_LAM", - "R12_NAM|R12_NAM": "R12_NAM", - "R12_PAO|R12_PAO": "R12_PAO", - "R12_EEU|R12_EEU": "R12_EEU", - "R12_WEU|R12_WEU": "R12_WEU", - "R12_CHN|R12_CHN": "R12_CHN", - "R12_AFR": "R12_AFR", - "R12_RCPA": "R12_RCPA", - "R12_MEA": "R12_MEA", - "R12_FSU": "R12_FSU", - "R12_PAS": "R12_PAS", - "R12_SAS": "R12_SAS", - "R12_LAM": "R12_LAM", - "R12_NAM": "R12_NAM", - "R12_PAO": "R12_PAO", - "R12_EEU": "R12_EEU", - "R12_WEU": "R12_WEU", - "R12_CHN": "R12_CHN", - "World": "R12_GLB", - "model": "Model", - "scenario": "Scenario", - "variable": "Variable", - "region": "Region", - "unit": "Unit", - "BCA": "BC", - "OCA": "OC", - "Final Energy|Non-Energy Use|Chemicals|Ammonia": ( - "Final Energy|Industry|Chemicals|Ammonia" - ), - "Final Energy|Non-Energy Use|Chemicals|Ammonia|Gases": ( - "Final Energy|Industry|Chemicals|Ammonia|Gases" - ), - "Final Energy|Non-Energy Use|Chemicals|Ammonia|Liquids": ( - "Final Energy|Industry|Chemicals|Ammonia|Liquids" - ), - "Final Energy|Non-Energy Use|Chemicals|Ammonia|Liquids|Oil": ( - "Final Energy|Industry|Chemicals|Ammonia|Liquids|Oil" - ), - "Final Energy|Non-Energy Use|Chemicals|Ammonia|Solids": ( - "Final Energy|Industry|Chemicals|Ammonia|Solids" - ), - } - # Iterate over the rows and replace - for i in range(1, ((sheet.max_row) + 1)): - data = [ - sheet.cell(row=i, column=col).value - for col in range(1, ((sheet.max_column) + 1)) - ] - for index, value in enumerate(data): - col_no = index + 1 - if value in replacement.keys(): - new_sheet.cell(row=i, column=col_no).value = replacement.get(value) - else: - new_sheet.cell(row=i, column=col_no).value = value - - new_workbook.save(path_new) +#: Replacements applied to the final results. +NAME_MAP1 = { + "CO2_industry": "CO2", + "R11_AFR|R11_AFR": "R11_AFR", + "R11_CPA|R11_CPA": "R11_CPA", + "R11_MEA|R11_MEA": "R11_MEA", + "R11_FSU|R11_FSU": "R11_FSU", + "R11_PAS|R11_PAS": "R11_PAS", + "R11_SAS|R11_SAS": "R11_SAS", + "R11_LAM|R11_LAM": "R11_LAM", + "R11_NAM|R11_NAM": "R11_NAM", + "R11_PAO|R11_PAO": "R11_PAO", + "R11_EEU|R11_EEU": "R11_EEU", + "R11_WEU|R11_WEU": "R11_WEU", + "R11_AFR": "R11_AFR", + "R11_CPA": "R11_CPA", + "R11_MEA": "R11_MEA", + "R11_FSU": "R11_FSU", + "R11_PAS": "R11_PAS", + "R11_SAS": "R11_SAS", + "R11_LAM": "R11_LAM", + "R11_NAM": "R11_NAM", + "R11_PAO": "R11_PAO", + "R11_EEU": "R11_EEU", + "R11_WEU": "R11_WEU", + "R12_AFR|R12_AFR": "R12_AFR", + "R12_RCPA|R12_RCPA": "R12_RCPA", + "R12_MEA|R12_MEA": "R12_MEA", + "R12_FSU|R12_FSU": "R12_FSU", + "R12_PAS|R12_PAS": "R12_PAS", + "R12_SAS|R12_SAS": "R12_SAS", + "R12_LAM|R12_LAM": "R12_LAM", + "R12_NAM|R12_NAM": "R12_NAM", + "R12_PAO|R12_PAO": "R12_PAO", + "R12_EEU|R12_EEU": "R12_EEU", + "R12_WEU|R12_WEU": "R12_WEU", + "R12_CHN|R12_CHN": "R12_CHN", + "R12_AFR": "R12_AFR", + "R12_RCPA": "R12_RCPA", + "R12_MEA": "R12_MEA", + "R12_FSU": "R12_FSU", + "R12_PAS": "R12_PAS", + "R12_SAS": "R12_SAS", + "R12_LAM": "R12_LAM", + "R12_NAM": "R12_NAM", + "R12_PAO": "R12_PAO", + "R12_EEU": "R12_EEU", + "R12_WEU": "R12_WEU", + "R12_CHN": "R12_CHN", + "World": "R12_GLB", + "model": "Model", + "scenario": "Scenario", + "variable": "Variable", + "region": "Region", + "unit": "Unit", + "BCA": "BC", + "OCA": "OC", + "Final Energy|Non-Energy Use|Chemicals|Ammonia": ( + "Final Energy|Industry|Chemicals|Ammonia" + ), + "Final Energy|Non-Energy Use|Chemicals|Ammonia|Gases": ( + "Final Energy|Industry|Chemicals|Ammonia|Gases" + ), + "Final Energy|Non-Energy Use|Chemicals|Ammonia|Liquids": ( + "Final Energy|Industry|Chemicals|Ammonia|Liquids" + ), + "Final Energy|Non-Energy Use|Chemicals|Ammonia|Liquids|Oil": ( + "Final Energy|Industry|Chemicals|Ammonia|Liquids|Oil" + ), + "Final Energy|Non-Energy Use|Chemicals|Ammonia|Solids": ( + "Final Energy|Industry|Chemicals|Ammonia|Solids" + ), +} def plot_production_al(df: pyam.IamDataFrame, ax, r: str) -> None: @@ -401,15 +369,14 @@ def report( log.info(f"{df = }\n{len(df.variable)} unique variable names") # Compute global totals - variables = df.variable - df.aggregate_region(variables, region="World", method=sum, append=True) + df.aggregate_region(df.variable, region="World", method=sum, append=True) df.to_excel(directory.joinpath("check.xlsx")) log.info(f"Necessary variables are filtered; {len(df)} in total") # Obtain the model and scenario name - model_name = df.model[0] - scenario_name = df.scenario[0] + model_name = scenario.model + scenario_name = scenario.scenario # Create an empty pyam dataframe to store the new variables @@ -3172,40 +3139,34 @@ def report( # # df_final = pd.concat([df_final, material_needs_all]) - # Trade - # .................... + # Plotting is complete + pp.close() - # Print the new variables to an excel file. - # Change the naming convention and save to another excel file. + # - Convert from pyam.IamDataFrame to pandas.DataFrame. + # - Apply the replacements in NAME_MAP1. + df = df_final.as_pandas().replace(NAME_MAP1) - path_temp = directory.joinpath("temp_new_reporting.xlsx") + # Store path_new = directory.joinpath(f"New_Reporting_{model_name}_{scenario_name}.xlsx") + df.to_excel(path_new, sheet_name="data") + log.info(f"Wrote output to {path_new}") - # FIXME(PNK) manipulate the data directly instead of writing to disk, modifying, - # then re-reading - df_final.to_excel(path_temp, sheet_name="data", index=False) - fix_excel(path_temp, path_new) - print("New reporting file generated.") - df_final = pd.read_excel(path_new) + scenario.check_out(timeseries_only=True) + log.info(f"Store timeseries on scenario:\n\n{df.head()}") + scenario.add_timeseries(df) + # NB(PNK) Appears to be old code that handled buildings reporting output # df_resid = pd.read_csv(path_resid) # df_resid["Model"] = model_name # df_resid["Scenario"] = scenario_name # df_comm = pd.read_csv(path_comm) # df_comm["Model"] = model_name # df_comm["Scenario"] = scenario_name - - scenario.check_out(timeseries_only=True) - print("Starting to upload timeseries") - print(df_final.head()) - scenario.add_timeseries(df_final) # scenario.add_timeseries(df_resid) # scenario.add_timeseries(df_comm) - print("Finished uploading timeseries") - scenario.commit("Reporting uploaded as timeseries") - pp.close() - os.remove(path_temp) + log.info("…finished.") + scenario.commit("material.report.report()") def callback(rep: message_ix.Reporter, context: Context) -> None: From 7f4fe9e8848ee95baa88cf84668b26c5b9fe22af Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Wed, 21 Sep 2022 12:37:20 +0200 Subject: [PATCH 042/774] Remove unused context arg from material.report.report() --- .../model/material/report/__init__.py | 31 ++++++++++--------- 1 file changed, 17 insertions(+), 14 deletions(-) diff --git a/message_ix_models/model/material/report/__init__.py b/message_ix_models/model/material/report/__init__.py index 6aee0a27b1..a5a5ad23f7 100644 --- a/message_ix_models/model/material/report/__init__.py +++ b/message_ix_models/model/material/report/__init__.py @@ -1,16 +1,18 @@ -# -*- coding: utf-8 -*- -""" -Created on Mon Mar 8 12:58:21 2021 -This code produces the follwoing outputs: -message_ix_reporting.xlsx: message_ix level reporting -check.xlsx: can be used for checking the filtered variables -New_Reporting_Model_Scenario.xlsx: Reporting including the material variables -Merged_Model_Scenario.xlsx: Includes all IAMC variables -Material_global_grpahs.pdf +"""Reporting for MESSAGEix-Materials. +Created on Mon Mar 8 12:58:21 2021 @author: unlu -""" +This code produces the following outputs: + +- message_ix_reporting.xlsx: message_ix level reporting +- check.xlsx: can be used for checking the filtered variables +- New_Reporting_Model_Scenario.xlsx: Reporting including the material variables +- Merged_Model_Scenario.xlsx: Includes all IAMC variables +- Material_global_graphs.pdf +""" +# NB(PNK) as of 2022-09-21, the following appears to be outdated; this code runs with +# pyam 1.5.0 within the NAVIGATE workflow. Check and maybe remove. # NOTE: Works with pyam-iamc version 0.9.0 # Problems with the most recent versions: # 0.11.0 --> Filtering with asterisk * does not work, dataframes are empty. @@ -23,7 +25,6 @@ # PACKAGES import logging -from functools import partial from itertools import product from typing import List @@ -252,9 +253,11 @@ def report( years: List[int], nodes: List[str], config: dict, - context: Context, ) -> None: - """Produces the material related variables.""" + """Produces the material related variables. + + .. todo:: Expand docstring. + """ # In order to avoid confusion in the second reporting stage there should # no existing timeseries uploaded in the scenairo. Clear these except the # residential and commercial ones since they should be always included. @@ -3176,7 +3179,7 @@ def callback(rep: message_ix.Reporter, context: Context) -> None: """ rep.add( "materials all", - partial(report, context=context), + report, "scenario", "message::default", "y::model", From 5af3162a1f38829ee1c4e1fd62ae132853fd411e Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Thu, 22 Sep 2022 11:28:24 +0200 Subject: [PATCH 043/774] Fix invocation of materials plotting functions --- message_ix_models/model/material/report/__init__.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/message_ix_models/model/material/report/__init__.py b/message_ix_models/model/material/report/__init__.py index a5a5ad23f7..966006f1d7 100644 --- a/message_ix_models/model/material/report/__init__.py +++ b/message_ix_models/model/material/report/__init__.py @@ -421,7 +421,7 @@ def report( ) df_al.convert_unit("", to="My/yr", factor=1, inplace=True) - plot_production_al(df_al, ax1, r) + plot_production_al(df_al.copy(), ax1, r) # STEEL @@ -430,7 +430,7 @@ def report( df_steel.filter(variable=["out|*|steel|*", "in|*|steel|*"], inplace=True) df_steel.convert_unit("", to="Mt/yr", factor=1, inplace=True) - plot_production_steel(df_steel, ax2, r) + plot_production_steel(df_steel.copy(), ax2, r) # PETRO @@ -456,7 +456,7 @@ def report( inplace=True, ) - plot_petro(df_petro, ax3, r) + plot_petro(df_petro.copy(), ax3, r) plt.close() pp.savefig(fig) @@ -2469,7 +2469,7 @@ def report( df_emi.filter(variable=aggregate_list, inplace=True) df_final.append(df_emi, inplace=True) - plot_emi_aggregates(df_emi, pp) + plot_emi_aggregates(df_emi, pp, r, e) # PLOTS # From 4f9386ef44404c39db9134091de81d6245566c34 Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Mon, 26 Sep 2022 16:44:10 +0200 Subject: [PATCH 044/774] Fix units typo in material reporting --- message_ix_models/model/material/report/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/message_ix_models/model/material/report/__init__.py b/message_ix_models/model/material/report/__init__.py index 966006f1d7..3488ef0ac4 100644 --- a/message_ix_models/model/material/report/__init__.py +++ b/message_ix_models/model/material/report/__init__.py @@ -419,7 +419,7 @@ def report( df_al = df.filter( region=r, year=years, variable=["out|*|aluminum|*", "in|*|aluminum|*"] ) - df_al.convert_unit("", to="My/yr", factor=1, inplace=True) + df_al.convert_unit("", to="Mt/yr", factor=1, inplace=True) plot_production_al(df_al.copy(), ax1, r) From 92fe0e5accab847c7f37eca4ffffb8c16a75cc8c Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Wed, 28 Sep 2022 12:22:25 +0200 Subject: [PATCH 045/774] Update variable filtering in material reporting copies 5524947a394dff71855181063334199d52b47c82 from material-r12-rebase branch, with modifications: - edit the file in its current location on main. - use regular expressions to simplify verbose conditionals. --- .../model/material/report/__init__.py | 84 +++++++------------ 1 file changed, 31 insertions(+), 53 deletions(-) diff --git a/message_ix_models/model/material/report/__init__.py b/message_ix_models/model/material/report/__init__.py index 3488ef0ac4..aefaef19de 100644 --- a/message_ix_models/model/material/report/__init__.py +++ b/message_ix_models/model/material/report/__init__.py @@ -25,6 +25,7 @@ # PACKAGES import logging +import re from itertools import product from typing import List @@ -830,6 +831,9 @@ def report( # Ammonia is not seperately included as the model input valus are combined for # feedstock and energy. + # The ammonia related variables are not included in the main filter but left in the + # remaining code to be reference for the future changes. + print("Final Energy by fuels only non-energy use is being printed.") commodities = ["gas", "liquids", "solids", "all"] @@ -936,11 +940,10 @@ def report( df_final.append(df_final_energy, inplace=True) elif c == "gas": - # Can not distinguish by type Gases (natural gas, biomass, synthetic - # fossil, efuel) (coal_gas), from biomass (gas_bio), natural gas - # (gas_bal): All go into secondary level + # Can not distinguish by type Gases (natural gas, biomass, synthetic fossil, + # efuel) (coal_gas), from biomass (gas_bio), natural gas (gas_bal): All go + # into secondary level. # Can not be distinguished in the final level. - df_final_energy.aggregate( "Final Energy|Non-Energy Use|Gases", components=var_sectors, @@ -1160,36 +1163,22 @@ def report( axis=1, ) - # Include only the related industry sector variables - - var_sectors = [ - v - for v in aux2_df["variable"].values - if ( - ( - ("cement" in v) - | ("steel" in v) - | ("aluminum" in v) - | ("petro" in v) - | ("_i" in v) - | ("_I" in v) - | ("NH3" in v) - ) - & ( - ("eth_ic_trp" not in v) - & ("meth_ic_trp" not in v) - & ("ethanol_to_ethylene_petro" not in v) - & ("gas_processing_petro" not in v) - & ("in|final|atm_gasoil|steam_cracker_petro|atm_gasoil" not in v) - & ( - "in|final|vacuum_gasoil|steam_cracker_petro|vacuum_gasoil" - not in v - ) - & ("in|final|naphtha|steam_cracker_petro|naphtha" not in v) + # Include only the related industry sector variables and state some exceptions + var_sectors = list( + filter( + # 4th element (technology) ends with one of the following + lambda v: re.match( + ".*_(cement|steel|aluminum|petro|i|I|NH3)$", v.split("|")[3] ) + # Exclude specific technologies + and not re.match("(ethanol_to_ethylene|gas_processing)_petro", v) + # Exclude inputs of 3 specific commodities to steam_cracker_petro + and not re.match( + r"^in.final.((atm|vacuum)_gasoil|naphtha).steam_cracker_petro.\1", v + ), + aux2_df["variable"].values, ) - ] - + ) aux2_df = aux2_df[aux2_df["variable"].isin(var_sectors)] df_final_energy.filter(variable=var_sectors, inplace=True) @@ -1517,23 +1506,14 @@ def report( # Include only the related industry sector variables - var_sectors = [ - v - for v in aux2_df["variable"].values - if ( - ( - ("cement" in v) - | ("steel" in v) - | ("aluminum" in v) - | ("petro" in v) - | ("_i" in v) - | ("_I" in v) - | ("_fs" in v) - | ("NH3" in v) - ) - & (("eth_ic_trp" not in v) & ("meth_ic_trp" not in v)) + var_sectors = list( + filter( + lambda v: re.match( + "_(aluminum|cement|fs|i|I|NH3|petro|steel)", v.split("|")[3] + ), + aux2_df["variable"].values, ) - ] + ) aux2_df = aux2_df[aux2_df["variable"].isin(var_sectors)] df_final_energy.filter(variable=var_sectors, inplace=True) @@ -1801,11 +1781,9 @@ def report( ] aux2_df = aux2_df[aux2_df["technology"].isin(tec)] elif s == "Other Sector": - tec = [ - t - for t in aux2_df["technology"].values - if ((("_i" in t) | ("_I" in t)) & ("trp" not in t)) - ] + tec = list( + filter(lambda t: re.match("_[iI]$", t), aux2_df["technology"].values) + ) aux2_df = aux2_df[aux2_df["technology"].isin(tec)] else: # Filter the technologies only for the certain industry sector From f6eee4f997e1ae09cb634c18811ff3981c6ab3af Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Fri, 14 Oct 2022 17:09:44 +0200 Subject: [PATCH 046/774] Use re.search() instead of re.match() in materials reporting --- .../model/material/report/__init__.py | 47 +++++++++---------- 1 file changed, 21 insertions(+), 26 deletions(-) diff --git a/message_ix_models/model/material/report/__init__.py b/message_ix_models/model/material/report/__init__.py index aefaef19de..d4a2d256ad 100644 --- a/message_ix_models/model/material/report/__init__.py +++ b/message_ix_models/model/material/report/__init__.py @@ -765,7 +765,7 @@ def report( plot_production_cement_clinker(df_cement_clinker, ax1, r) - # Final prodcut cement + # Final product cement df_cement = df.copy() df_cement.filter(region=r, year=years, inplace=True) @@ -1164,19 +1164,19 @@ def report( ) # Include only the related industry sector variables and state some exceptions - var_sectors = list( + var_sectors = sorted( filter( # 4th element (technology) ends with one of the following - lambda v: re.match( - ".*_(cement|steel|aluminum|petro|i|I|NH3)$", v.split("|")[3] + lambda v: re.search( + "_(cement|steel|aluminum|petro|i|I|NH3)$", v.split("|")[3] ) # Exclude specific technologies - and not re.match("(ethanol_to_ethylene|gas_processing)_petro", v) + and not re.search("(ethanol_to_ethylene|gas_processing)_petro", v) # Exclude inputs of 3 specific commodities to steam_cracker_petro - and not re.match( + and not re.search( r"^in.final.((atm|vacuum)_gasoil|naphtha).steam_cracker_petro.\1", v ), - aux2_df["variable"].values, + aux2_df["variable"].unique(), ) ) aux2_df = aux2_df[aux2_df["variable"].isin(var_sectors)] @@ -1505,13 +1505,12 @@ def report( ) # Include only the related industry sector variables - - var_sectors = list( + var_sectors = sorted( filter( - lambda v: re.match( + lambda v: re.search( "_(aluminum|cement|fs|i|I|NH3|petro|steel)", v.split("|")[3] ), - aux2_df["variable"].values, + aux2_df["variable"].unique(), ) ) aux2_df = aux2_df[aux2_df["variable"].isin(var_sectors)] @@ -1545,7 +1544,6 @@ def report( ) df_final.append(df_final_energy, inplace=True) elif c == "electr": - df_final_energy.aggregate( "Final Energy|Industry|Electricity", components=var_sectors, @@ -1782,7 +1780,7 @@ def report( aux2_df = aux2_df[aux2_df["technology"].isin(tec)] elif s == "Other Sector": tec = list( - filter(lambda t: re.match("_[iI]$", t), aux2_df["technology"].values) + filter(lambda t: re.search("_[iI]$", t), aux2_df["technology"].values) ) aux2_df = aux2_df[aux2_df["technology"].isin(tec)] else: @@ -1794,11 +1792,10 @@ def report( # Lists to keep commodity, aggregate and variable names. - commodity_list = [] aggregate_list = [] var_list = [] - # For the categoris below filter the required variable names, + # For the categories below filter the required variable names, # create a new aggregate name commodity_list = [ @@ -1837,17 +1834,14 @@ def report( f"Final Energy|Industry excl Non-Energy Use|{s}|Hydrogen" ) elif c == "liquids": - var = np.unique( + var = sorted( aux2_df.loc[ - ( - (aux2_df["commodity"] == "fueloil") - | (aux2_df["commodity"] == "lightoil") - | (aux2_df["commodity"] == "methanol") - | (aux2_df["commodity"] == "ethanol") + aux2_df["commodity"].isin( + ["ethanol", "fueloil", "lightoil", "methanol"] ), "variable", - ].values - ).tolist() + ].unique() + ) aggregate_name = ( f"Final Energy|Industry excl Non-Energy Use|{s}|Liquids" ) @@ -1938,12 +1932,13 @@ def report( df_final_energy = pyam.IamDataFrame(data=aux2_df) # Aggregate the commodities in iamc object - i = 0 - for c in commodity_list: + for i, c in enumerate(commodity_list): + if not var_list[i]: + log.info(f"Nothing to aggregate for '{aggregate_list[i]}'") + continue df_final_energy.aggregate( aggregate_list[i], components=var_list[i], append=True ) - i = i + 1 df_final_energy.filter(variable=aggregate_list, inplace=True) df_final_energy.convert_unit("GWa", to="EJ/yr", factor=0.03154, inplace=True) From cead39e42ff2b687febaf0f847e05e684e9c4de1 Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Mon, 24 Oct 2022 11:11:13 +0200 Subject: [PATCH 047/774] Tidy emissions calculations in .material.report.report MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - use filter(lambda t: , …) instead of [t for t in … if ]. - use f-strings. - use "and" and "or" instead of bitwise & and |. - use regular expressions instead of long lists of binary comparisons. - don't re-compute an identical variable unnecessarily. - use a dict() instead of paired lists that could be mismatched. - use if: / elif: instead of serial if: blocks to clarify intent. - move statements duplicated in every clause of an if/elif outside that block. - add comments and logging. about 100 lines are saved. --- .../model/material/report/__init__.py | 371 ++++++------------ 1 file changed, 125 insertions(+), 246 deletions(-) diff --git a/message_ix_models/model/material/report/__init__.py b/message_ix_models/model/material/report/__init__.py index d4a2d256ad..1106565b7b 100644 --- a/message_ix_models/model/material/report/__init__.py +++ b/message_ix_models/model/material/report/__init__.py @@ -2127,34 +2127,35 @@ def report( # If CCS technologies are used, sectors = [ + "all", "aluminum", - "steel", - "petro", - "cement", "ammonia", - "all", + "cement", "Chemicals", "Other Sector", + "petro", + "steel", ] emission_type = [ - "CO2_industry", + "BCA", + "CF4", "CH4", + "CO", + "CO2_industry", "CO2", + "N2O", "NH3", "NOx", - "CF4", - "N2O", - "BCA", - "CO", "OCA", ] print("Emissions are being printed.") for typ, r, e in product(["demand", "process"], nodes, emission_type): - df_emi = df.copy() - df_emi.filter(region=r, year=years, inplace=True) - # CCS technologies for ammonia has both CO2 and CO2_industry - # at the same time. + # Filter on region and years + df_emi = df.filter(region=r, year=years) + + # Identify variables to filter + # CCS technologies for ammonia have both CO2 and CO2_industry at the same time if e == "CO2_industry": emi_filter = [ "emis|CO2|biomass_NH3_ccs|*", @@ -2172,9 +2173,9 @@ def report( "emis|CO2_industry|electr_NH3|*", "emis|CO2_industry|*", ] - df_emi.filter(variable=emi_filter, inplace=True) else: - emi_filter = ["emis|" + e + "|*"] + emi_filter = [f"emis|{e}|*"] + # When e=CO2, these were already handled above exclude = [ "emis|CO2|biomass_NH3_ccs|*", "emis|CO2|gas_NH3_ccs|*", @@ -2182,264 +2183,142 @@ def report( "emis|CO2|fueloil_NH3_ccs|*", ] df_emi.filter(variable=exclude, keep=False, inplace=True) - df_emi.filter(variable=emi_filter, inplace=True) - if (e == "CO2") | (e == "CO2_industry"): + + df_emi.filter(variable=emi_filter, inplace=True) + + # Convert units + log.info(f"Units for {typ = }, {r = }, {e = }: {df_emi.unit}") + if e in {"CO2", "CO2_industry"}: # From MtC to Mt CO2/yr df_emi.convert_unit("", to="Mt CO2/yr", factor=44 / 12, inplace=True) - elif (e == "N2O") | (e == "CF4"): - unit = "kt " + e + "/yr" + elif e in {"N2O", "CF4"}: + unit = f"kt {e}/yr" df_emi.convert_unit("", to=unit, factor=1, inplace=True) else: e = NAME_MAP.get(e, e) # From kt/yr to Mt/yr - unit = "Mt " + e + "/yr" + unit = f"Mt {e}/yr" df_emi.convert_unit("", to=unit, factor=0.001, inplace=True) all_emissions = df_emi.timeseries().reset_index() - # Split the strings in the identified variables for further processing - splitted_vars = [v.split("|") for v in all_emissions.variable] - # Lists to later keep the variables and names to aggregate - var_list = [] - aggregate_list = [] + # Mapping from aggregate variable name to list of variables to be aggregated + aggregates = dict() + + # Expression for _i/_I technologies, used below in 2 places + expr_i = ( + "(biomass|coal|elec|m?eth|[fl]oil|gas|h2|heat|hp_el|hp_gas)_i" + "|(sp_(coal|el|eth|liq|meth)|h2_fc)_I" + ) + # Expression for _trp and _tmp technologies + expr_tmptrp = re.compile("m?eth_ic_trp|(coal|elec|[fl]oil|gas|m?eth)_tmp") # Collect the same emission type for each sector + # NB this loop does not modify `df_emi` or `all_emissions`; it only uses their + # contents to populate `aggregates` for s in sectors: - # Create auxilary dataframes for processing - aux1_df = pd.DataFrame( - splitted_vars, - columns=["emission", "type", "technology", "mode"], - ) - aux2_df = pd.concat( - [ - all_emissions.reset_index(drop=True), - aux1_df.reset_index(drop=True), - ], - axis=1, - ) - # Filter the technologies only for the sector - - if (typ == "process") & (s == "all") & (e != "CO2_industry"): - tec = [ - t - for t in aux2_df["technology"].values - if ( - ( - ( - ("cement" in t) - | ("steel" in t) - | ("aluminum" in t) - | ("petro" in t) - ) - & ("furnace" not in t) - ) - ) - ] - if (typ == "process") & (s != "all"): - tec = [ - t - for t in aux2_df["technology"].values - if ((s in t) & ("furnace" not in t) & ("NH3" not in t)) - ] + # Determine the aggregate name + aggregate_name = None + _e = NAME_MAP.get(e, e) # Maybe change "CO2_industry" to "CO2" + if s == "all": + if typ == "demand" and e != "CO2": + aggregate_name = f"Emissions|{_e}|Energy|Demand|Industry" + elif typ == "process" and e != "CO2_industry": + aggregate_name = f"Emissions|{e}|Industrial Processes" + else: + # Adjust the sector names + _s = NAME_MAP.get(s, s) + if typ == "demand" and e != "CO2": + aggregate_name = f"Emissions|{_e}|Energy|Demand|Industry|{_s}" + elif typ == "process" and e != "CO2_industry": + aggregate_name = f"Emissions|{e}|Industrial Processes|{_s}" + + if aggregate_name is None: + log.info(f"No aggregate name for {s = }, {typ = }, {e = }; skip") + continue - if (typ == "demand") & (s == "Chemicals"): - tec = [ - t - for t in aux2_df["technology"].values - if (("NH3" in t) | (("petro" in t) & ("furnace" in t))) + # Recover dimensions that were concatenated into the variable name + aux_df = pd.concat( + [ + all_emissions, + all_emissions.variable.str.split("|", expand=True).set_axis( + ["emission", "type", "technology", "mode"], axis=1 + ), ] + ) + # Unique list of all technologies to be filtered + all_t = aux_df["technology"].unique() - if (typ == "demand") & (s == "Other Sector") & (e != "CO2"): - tec = [ - t - for t in aux2_df["technology"].values - if ( - ( - ("biomass_i" in t) - | ("coal_i" in t) - | ("elec_i" in t) - | ("eth_i" in t) - | ("foil_i" in t) - | ("gas_i" in t) - | ("h2_i" in t) - | ("heat_i" in t) - | ("hp_el_i" in t) - | ("hp_gas_i" in t) - | ("loil_i" in t) - | ("meth_i" in t) - | ("sp_coal_I" in t) - | ("sp_el_I" in t) - | ("sp_eth_I" in t) - | ("sp_liq_I" in t) - | ("sp_meth_I" in t) - | ("h2_fc_I" in t) - ) - & ( - ("eth_ic_trp" not in t) - & ("meth_ic_trp" not in t) - & ("coal_imp" not in t) - & ("foil_imp" not in t) - & ("gas_imp" not in t) - & ("elec_imp" not in t) - & ("eth_imp" not in t) - & ("meth_imp" not in t) - & ("loil_imp" not in t) - ) + # Filter the technologies only for the sector + if typ == "process" and s == "all" and e != "CO2_industry": + tec = filter( + lambda t: re.search("aluminum|cement|steel|petro", t) + and ("furnace" not in t), + all_t, + ) + elif typ == "process" and s != "all": + tec = filter( + lambda t: s in t and not re.search("furnace|NH3", t), all_t + ) + elif typ == "demand" and s == "Chemicals": + tec = filter( + lambda t: "NH3" in t or ("petro" in t and "furnace" in t), all_t + ) + elif typ == "demand" and s == "Other Sector" and e != "CO2": + tec = filter( + lambda t: re.search(expr_i, t) and not expr_tmptrp.search(t), all_t + ) + elif typ == "demand" and s == "all" and e != "CO2": + tec = filter( + lambda t: ( + re.search("aluminum|cement|steel|petro", t) and "furnace" in t ) - ] - - if (typ == "demand") & (s == "all") & (e != "CO2"): - tec = [ - t - for t in aux2_df["technology"].values - if ( - ( - ( - ("cement" in t) - | ("steel" in t) - | ("aluminum" in t) - | ("petro" in t) - ) - & ("furnace" in t) - ) - | ( - ("biomass_i" in t) - | ("coal_i" in t) - | ("elec_i" in t) - | ("eth_i" in t) - | ("foil_i" in t) - | ("gas_i" in t) - | ("h2_i" in t) - | ("heat_i" in t) - | ("hp_el_i" in t) - | ("hp_gas_i" in t) - | ("loil_i" in t) - | ("meth_i" in t) - | ("sp_coal_I" in t) - | ("sp_el_I" in t) - | ("sp_eth_I" in t) - | ("sp_liq_I" in t) - | ("sp_meth_I" in t) - | ("h2_fc_I" in t) - | ("DUMMY_limestone_supply_cement" in t) - | ("DUMMY_limestone_supply_steel" in t) - | ("eaf_steel" in t) - | ("DUMMY_coal_supply" in t) - | ("DUMMY_gas_supply" in t) - | ("NH3" in t) - ) - & ( - ("eth_ic_trp" not in t) - & ("meth_ic_trp" not in t) - & ("coal_imp" not in t) - & ("foil_imp" not in t) - & ("gas_imp" not in t) - & ("elec_imp" not in t) - & ("eth_imp" not in t) - & ("meth_imp" not in t) - & ("loil_imp" not in t) - ) + or re.search( + expr_i + "|eaf_steel|NH3|" + "DUMMY_(limestone_supply_(cement|steel)|(coal|gas)_supply)", + t, ) - ] - - if ( - (typ == "demand") - & (s != "all") - & (s != "Other Sector") - & (s != "Chemicals") - ): - + and not expr_tmptrp.search(t), + all_t, + ) + elif typ == "demand" and s not in {"all", "Other Sector", "Chemicals"}: if s == "steel": - # Furnaces are not used as heat source for iron&steel - # Dummy supply technologies help accounting the emissions - # from cokeoven_steel, bf_steel, dri_steel, eaf_steel, - # sinter_steel. - - tec = [ - t - for t in aux2_df["technology"].values - if ( - ("DUMMY_coal_supply" in t) - | ("DUMMY_gas_supply" in t) - | ("DUMMY_limestone_supply_steel" in t) - ) - ] - + # Furnaces are not used as heat source for iron & steel. Dummy + # supply technologies help accounting the emissions from + # cokeoven_steel, bf_steel, dri_steel, eaf_steel, and sinter_steel. + tec = filter( + lambda t: re.search( + "DUMMY_((coal|gas)_supply|limestone_supply_steel)", t + ), + all_t, + ) elif s == "cement": - tec = [ - t - for t in aux2_df["technology"].values - if ( - ((s in t) & ("furnace" in t)) - | ("DUMMY_limestone_supply_cement" in t) - ) - ] + tec = filter( + lambda t: (s in t and "furnace" in t) + or ("DUMMY_limestone_supply_cement" in t), + all_t, + ) elif s == "ammonia": - tec = [t for t in aux2_df["technology"].values if (("NH3" in t))] + tec = filter(lambda t: "NH3" in t, all_t) else: - tec = [ - t - for t in aux2_df["technology"].values - if ((s in t) & ("furnace" in t)) - ] - # Adjust the sector names - s = NAME_MAP.get(s, s) + tec = filter(lambda t: s in t and "furnace" in t, all_t) - aux2_df = aux2_df[aux2_df["technology"].isin(tec)] - # If there are no emission types for that setor skip - if aux2_df.empty: - continue + aux_df = aux_df[aux_df["technology"].isin(list(tec))] - # Add elements to lists for aggregation over emission type - # for each sector - var = aux2_df["variable"].values.tolist() - var_list.append(var) - - # Aggregate names: - if s == "all": - if (typ == "demand") & (e != "CO2"): - if e != "CO2_industry": - aggregate_name = "Emissions|" + e + "|Energy|Demand|Industry" - aggregate_list.append(aggregate_name) - else: - aggregate_name = ( - "Emissions|" + "CO2" + "|Energy|Demand|Industry" - ) - aggregate_list.append(aggregate_name) - if (typ == "process") & (e != "CO2_industry"): - aggregate_name = "Emissions|" + e + "|Industrial Processes" - aggregate_list.append(aggregate_name) - else: - if (typ == "demand") & (e != "CO2"): - if e != "CO2_industry": - aggregate_name = f"Emissions|{e}|Energy|Demand|Industry|{s}" - aggregate_list.append(aggregate_name) - else: - aggregate_name = f"Emissions|CO2|Energy|Demand|Industry|{s}" - aggregate_list.append(aggregate_name) - if (typ == "process") & (e != "CO2_industry"): - aggregate_name = f"Emissions|{e}|Industrial Processes|{s}" - aggregate_list.append(aggregate_name) - - # To plot: Obtain the iamc format dataframe again + # If there are no emission types for that sector skip + if aux_df.empty: + continue - aux2_df = pd.concat( - [ - all_emissions.reset_index(drop=True), - aux1_df.reset_index(drop=True), - ], - axis=1, - ) - aux2_df.drop(["emission", "type", "technology", "mode"], axis=1, inplace=True) - df_emi = pyam.IamDataFrame(data=aux2_df) + # Add elements to lists for aggregation over emission type for each sector + aggregates[aggregate_name] = sorted(aux_df["variable"].unique()) - # Aggregation over emission type for each sector if there are elements - # to aggregate - for i in range(len(aggregate_list)): - df_emi.aggregate(aggregate_list[i], components=var_list[i], append=True) + # Aggregate over emission type for each sector if there are elements to + # aggregate + for variable, components in aggregates.items(): + df_emi.aggregate(variable, components, append=True) - if len(aggregate_list): - df_emi.filter(variable=aggregate_list, inplace=True) + if len(aggregates): + df_emi.filter(variable=aggregates.keys(), inplace=True) df_final.append(df_emi, inplace=True) plot_emi_aggregates(df_emi, pp, r, e) From 6e5dcc2c3a8d4bf2f24cfbb6e2cafbe13ab5234b Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Tue, 25 Oct 2022 14:16:59 +0200 Subject: [PATCH 048/774] Reduce logging verbosity in materials reporting --- message_ix_models/model/material/report/__init__.py | 6 +++--- message_ix_models/model/material/report/tables.py | 2 +- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/message_ix_models/model/material/report/__init__.py b/message_ix_models/model/material/report/__init__.py index 1106565b7b..85c2d2f5aa 100644 --- a/message_ix_models/model/material/report/__init__.py +++ b/message_ix_models/model/material/report/__init__.py @@ -1934,7 +1934,7 @@ def report( # Aggregate the commodities in iamc object for i, c in enumerate(commodity_list): if not var_list[i]: - log.info(f"Nothing to aggregate for '{aggregate_list[i]}'") + # log.debug(f"Nothing to aggregate for '{aggregate_list[i]}'") continue df_final_energy.aggregate( aggregate_list[i], components=var_list[i], append=True @@ -2187,7 +2187,6 @@ def report( df_emi.filter(variable=emi_filter, inplace=True) # Convert units - log.info(f"Units for {typ = }, {r = }, {e = }: {df_emi.unit}") if e in {"CO2", "CO2_industry"}: # From MtC to Mt CO2/yr df_emi.convert_unit("", to="Mt CO2/yr", factor=44 / 12, inplace=True) @@ -2234,7 +2233,7 @@ def report( aggregate_name = f"Emissions|{e}|Industrial Processes|{_s}" if aggregate_name is None: - log.info(f"No aggregate name for {s = }, {typ = }, {e = }; skip") + log.debug(f"No aggregate name for {typ = }, {e = }, {s = }; skip") continue # Recover dimensions that were concatenated into the variable name @@ -2314,6 +2313,7 @@ def report( # Aggregate over emission type for each sector if there are elements to # aggregate + log.debug(f"Compute emissions aggregates: {aggregates}") for variable, components in aggregates.items(): df_emi.aggregate(variable, components, append=True) diff --git a/message_ix_models/model/material/report/tables.py b/message_ix_models/model/material/report/tables.py index f28cff3d0c..6984c9f69c 100644 --- a/message_ix_models/model/material/report/tables.py +++ b/message_ix_models/model/material/report/tables.py @@ -37,7 +37,7 @@ def return_func_dict(): configure_legacy_reporting(TECHS) - log.info(f"Configured legacy reporting for -BM model variants:\n{TECHS = }") + log.debug(f"Configured legacy reporting for -BM model variants:\n{TECHS = }") return func_dict From fd0f088e0a60eb6ab655c0ba80a9cc0ded340b37 Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Tue, 25 Oct 2022 14:18:31 +0200 Subject: [PATCH 049/774] Remove unnecessary loop over node in material emissions reporting handle this iteration in plot_emi_aggregates(). --- .../model/material/report/__init__.py | 23 ++++++++++--------- 1 file changed, 12 insertions(+), 11 deletions(-) diff --git a/message_ix_models/model/material/report/__init__.py b/message_ix_models/model/material/report/__init__.py index 85c2d2f5aa..1e0be7bd59 100644 --- a/message_ix_models/model/material/report/__init__.py +++ b/message_ix_models/model/material/report/__init__.py @@ -236,16 +236,17 @@ def plot_production_cement_clinker(df: pyam.IamDataFrame, ax, r: str) -> None: ax.set_ylabel("Mt") -def plot_emi_aggregates(df: pyam.IamDataFrame, pp, r: str, e: str) -> None: - fig, ax = plt.subplots(1, 1, figsize=(10, 10)) +def plot_emi_aggregates(df: pyam.IamDataFrame, pp, e: str) -> None: + for r in df.region: + fig, ax = plt.subplots(1, 1, figsize=(10, 10)) - df.plot.stack(ax=ax) - ax.set_title(f"Emissions_{r}_{e}") - ax.set_ylabel("Mt") - ax.legend(bbox_to_anchor=(0.3, 1)) + df.filter(region=r).plot.stack(ax=ax) + ax.set_title(f"Emissions_{r}_{e}") + ax.set_ylabel("Mt") + ax.legend(bbox_to_anchor=(0.3, 1)) - plt.close() - pp.savefig(fig) + plt.close() + pp.savefig(fig) def report( @@ -2150,9 +2151,9 @@ def report( ] print("Emissions are being printed.") - for typ, r, e in product(["demand", "process"], nodes, emission_type): + for typ, e in product(["demand", "process"], emission_type): # Filter on region and years - df_emi = df.filter(region=r, year=years) + df_emi = df.filter(year=years) # Identify variables to filter # CCS technologies for ammonia have both CO2 and CO2_industry at the same time @@ -2321,7 +2322,7 @@ def report( df_emi.filter(variable=aggregates.keys(), inplace=True) df_final.append(df_emi, inplace=True) - plot_emi_aggregates(df_emi, pp, r, e) + plot_emi_aggregates(df_emi, pp, e) # PLOTS # From 76d8d53d563fc9e9bb5b445d737421527eabac2b Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Tue, 25 Oct 2022 14:19:57 +0200 Subject: [PATCH 050/774] Check units in materials emissions reporting --- message_ix_models/model/material/report/__init__.py | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/message_ix_models/model/material/report/__init__.py b/message_ix_models/model/material/report/__init__.py index 1e0be7bd59..baa7d3d5f4 100644 --- a/message_ix_models/model/material/report/__init__.py +++ b/message_ix_models/model/material/report/__init__.py @@ -2187,16 +2187,23 @@ def report( df_emi.filter(variable=emi_filter, inplace=True) + # Check units + log.debug(f"Units for {typ = }, {e = }: {df_emi.unit}") + assert ("",) == tuple( + df_emi.unit + ), f"Unexpected units for {typ = }, {e = }: {df_emi.unit}" + # Convert units if e in {"CO2", "CO2_industry"}: - # From MtC to Mt CO2/yr + # The model represents the in Mt (Carbon) / year df_emi.convert_unit("", to="Mt CO2/yr", factor=44 / 12, inplace=True) elif e in {"N2O", "CF4"}: + # The model represents these in kt (species) / year unit = f"kt {e}/yr" df_emi.convert_unit("", to=unit, factor=1, inplace=True) else: e = NAME_MAP.get(e, e) - # From kt/yr to Mt/yr + # The model represents these in kt (species) / year, but we report in Mt unit = f"Mt {e}/yr" df_emi.convert_unit("", to=unit, factor=0.001, inplace=True) From 53e2e581a52a95e65f054bdfc5976690b461ec97 Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Tue, 25 Oct 2022 14:20:59 +0200 Subject: [PATCH 051/774] Simplify construction of aggregate names for materials emissions --- .../model/material/report/__init__.py | 24 ++++++++----------- 1 file changed, 10 insertions(+), 14 deletions(-) diff --git a/message_ix_models/model/material/report/__init__.py b/message_ix_models/model/material/report/__init__.py index baa7d3d5f4..7ea5f5d043 100644 --- a/message_ix_models/model/material/report/__init__.py +++ b/message_ix_models/model/material/report/__init__.py @@ -2224,27 +2224,23 @@ def report( # NB this loop does not modify `df_emi` or `all_emissions`; it only uses their # contents to populate `aggregates` for s in sectors: - # Determine the aggregate name + # Determine a variable name for the aggregate aggregate_name = None - _e = NAME_MAP.get(e, e) # Maybe change "CO2_industry" to "CO2" - if s == "all": - if typ == "demand" and e != "CO2": - aggregate_name = f"Emissions|{_e}|Energy|Demand|Industry" - elif typ == "process" and e != "CO2_industry": - aggregate_name = f"Emissions|{e}|Industrial Processes" - else: - # Adjust the sector names - _s = NAME_MAP.get(s, s) - if typ == "demand" and e != "CO2": - aggregate_name = f"Emissions|{_e}|Energy|Demand|Industry|{_s}" - elif typ == "process" and e != "CO2_industry": - aggregate_name = f"Emissions|{e}|Industrial Processes|{_s}" + # Mapped sector name fragment + _s = "" if s == "all" else f"|{NAME_MAP.get(s, s)}" + if typ == "demand" and e != "CO2": + # Maybe change "CO2_industry" to "CO2" + _e = NAME_MAP.get(e, e) + aggregate_name = f"Emissions|{_e}|Energy|Demand|Industry{_s}" + elif typ == "process" and e != "CO2_industry": + aggregate_name = f"Emissions|{e}|Industrial Processes{_s}" if aggregate_name is None: log.debug(f"No aggregate name for {typ = }, {e = }, {s = }; skip") continue # Recover dimensions that were concatenated into the variable name + # NB only "technology" and "variable" are used aux_df = pd.concat( [ all_emissions, From 4b4dcd89e6f210980b85d2545b2aad3057269d45 Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Tue, 25 Oct 2022 14:21:47 +0200 Subject: [PATCH 052/774] Concatenate on axis=1 in materials emissions reporting --- message_ix_models/model/material/report/__init__.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/message_ix_models/model/material/report/__init__.py b/message_ix_models/model/material/report/__init__.py index 7ea5f5d043..bbe0f63a64 100644 --- a/message_ix_models/model/material/report/__init__.py +++ b/message_ix_models/model/material/report/__init__.py @@ -2247,10 +2247,11 @@ def report( all_emissions.variable.str.split("|", expand=True).set_axis( ["emission", "type", "technology", "mode"], axis=1 ), - ] + ], + axis=1, ) # Unique list of all technologies to be filtered - all_t = aux_df["technology"].unique() + all_t = sorted(aux_df["technology"].unique()) # Filter the technologies only for the sector if typ == "process" and s == "all" and e != "CO2_industry": From 49c9245b284d4e1a1c4bbea73915edbb7a96cc9f Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Tue, 25 Oct 2022 14:22:32 +0200 Subject: [PATCH 053/774] Use more precise matching for sector technologies in materials emissions reporting --- message_ix_models/model/material/report/__init__.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/message_ix_models/model/material/report/__init__.py b/message_ix_models/model/material/report/__init__.py index bbe0f63a64..617479964f 100644 --- a/message_ix_models/model/material/report/__init__.py +++ b/message_ix_models/model/material/report/__init__.py @@ -2266,7 +2266,7 @@ def report( ) elif typ == "demand" and s == "Chemicals": tec = filter( - lambda t: "NH3" in t or ("petro" in t and "furnace" in t), all_t + lambda t: "NH3" in t or re.search("furnace_.*_petro", t), all_t ) elif typ == "demand" and s == "Other Sector" and e != "CO2": tec = filter( @@ -2298,14 +2298,16 @@ def report( ) elif s == "cement": tec = filter( - lambda t: (s in t and "furnace" in t) + lambda t: re.search(f"furnace_.*{s}", t) or ("DUMMY_limestone_supply_cement" in t), all_t, ) elif s == "ammonia": tec = filter(lambda t: "NH3" in t, all_t) else: - tec = filter(lambda t: s in t and "furnace" in t, all_t) + tec = filter(lambda t: re.search(f"furnace_.*{s}", t), all_t) + else: + log.warning(f"No technology filters for {typ = }, {e = }, {s = }") aux_df = aux_df[aux_df["technology"].isin(list(tec))] From 35f5b154f5a562d6b033a5406b9c9e0bb67f89db Mon Sep 17 00:00:00 2001 From: Jihoon Date: Wed, 15 Jul 2020 17:39:40 +0200 Subject: [PATCH 054/774] Add commodity/level names & definitions + technology names --- message_ix_models/data/material/config.yaml | 88 ++++++++++++++++++++- 1 file changed, 87 insertions(+), 1 deletion(-) diff --git a/message_ix_models/data/material/config.yaml b/message_ix_models/data/material/config.yaml index bd4c8efa20..be8da63545 100644 --- a/message_ix_models/data/material/config.yaml +++ b/message_ix_models/data/material/config.yaml @@ -4,7 +4,93 @@ # accounting. Different sets can be created once there are 2+ different # categories of materials. -set: +aluminum: + commodity: + add: + - ht_heat + - lt_heat + - aluminum + level: + add: + - useful_aluminum + - new_scrap + - old_scrap + - final_material + - useful_material + - product + + technology: + add: + - soderberg_aluminum + - prebake_aluminum + - furnace_coal_aluminum + - furnace_hoil_aluminum + - furnace_biofuel_aluminum + - furnace_biomass_aluminum + - furnace_synfuel_aluminum + - furnace_gas_aluminum + - furnace_elec_aluminum + - furnace_h2_aluminum + - hp_gas_aluminum + - hp_elec_aluminum + - fc_h2_aluminum + - solar_aluminum + - dheat_aluminum + - secondary_aluminum + - prep_secondary_aluminum + - finishing_aluminum + - manuf_aluminum + - scrap_recovery_aluminum + +steel: + commodity: + add: + - ht_heat + - lt_heat + - steel + - pig_iron + - sponge_iron + - sinter_iron + - pellet_iron + - ore_iron + - limestone_iron + + level: + add: + - useful_steel + - new_scrap + - old_scrap + - primary_material + - secondary_material + - final_material + - useful_material + - product + + technology: + add: + - bf_steel + - dri_steel + - sr_steel + - bof_steel + - eaf_steel + - furnace_hoil_steel + - furnace_biofuel_steel + - furnace_biomass_steel + - furnace_synfuel_steel + - furnace_gas_steel + - furnace_elec_steel + - furnace_h2_steel + - hp_gas_steel + - hp_elec_steel + - fc_h2_steel + - solar_steel + - dheat_steel + - prep_secondary_steel + - finishing_steel + - manuf_steel + - scrap_recovery_steel + +fertilizer: commodity: require: - d_heat From 67b553ac6c0a72d51e2909c23494f26f6f2a08dd Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 9 Jul 2020 09:51:24 +0200 Subject: [PATCH 055/774] Addition of commodity, level and technologies --- message_ix_models/data/material/config.yaml | 58 +++++++++++++++++---- 1 file changed, 47 insertions(+), 11 deletions(-) diff --git a/message_ix_models/data/material/config.yaml b/message_ix_models/data/material/config.yaml index be8da63545..3154a5f6bb 100644 --- a/message_ix_models/data/material/config.yaml +++ b/message_ix_models/data/material/config.yaml @@ -96,35 +96,71 @@ fertilizer: - d_heat - electr - freshwater_supply + - coal + - fueloil + - ethanol + - biomass + - gas + - hydrogen add: - NH3 - Fertilizer Use|Nitrogen + - ht_heat + - lt_heat + - aluminum level: require: - primary - secondary - water_supply + - final add: - - material_interim - - material_final + # - material_interim + # - material_final + - useful_aluminum + - new_scrap + - old_scrap + - final_material + - useful_material + - product + +# should also work with regional model - # Currently only works with R11 regional aggregation region: - # Just one is sufficient, since the RES will never be generated with a mix require: - - R11_AFR technology: require: - h2_bio_ccs # Reference for vent-to-storage ratio + add: - - biomass_NH3 - - electr_NH3 - - gas_NH3 - - coal_NH3 - - fueloil_NH3 - - NH3_to_N_fertil + # - biomass_NH3 + # - electr_NH3 + # - gas_NH3 + # - coal_NH3 + # - fueloil_NH3 + # - NH3_to_N_fertil + - soderberg_aluminum + - prebake_aluminum + - furnace_coal_aluminum + - furnace_hoil_aluminum + - furnace_biofuel_aluminum + - furnace_biomass_aluminum + - furnace_synfuel_aluminum + - furnace_gas_aluminum + - furnace_elec_aluminum + - furnace_h2_aluminum + - hp_gas_aluminum + - hp_elec_aluminum + - fc_h2_aluminum + - solar_aluminum + - dheat_aluminum + - secondary_aluminum + - prep_secondary_aluminum + - finishing_aluminum + - manuf_aluminum + - scrap_recovery_aluminum type_tec: add: From 2cb6641a55e706953a3e8040c3b9ae79755d53f2 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 9 Jul 2020 10:00:37 +0200 Subject: [PATCH 056/774] Small changes to init file --- message_ix_models/model/material/__init__.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index b7e44c5074..1d4f8bb257 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -60,14 +60,15 @@ def solve(context): "NPi2020_400-con-prim-dir-ncr": "NPi2020_400", }.get(context.scenario_info["scenario"]) + # Chnage this part - the model name ?? + if context.scenario_info["model"] != "CD_Links_SSP2": print("WARNING: this code is not tested with this base scenario!") # Clone and set up scenario = build( - context.get_scenario().clone( - model="JM_GLB_NITRO", scenario=output_scenario_name - ) + context.get_scenario() + .clone(model="Material_test", scenario=output_scenario_name) ) # Solve From e1d573dfa835f1e6745ad000f94bb67640328f8b Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 9 Jul 2020 12:11:54 +0200 Subject: [PATCH 057/774] Seperate different materials --- message_ix_models/data/material/config.yaml | 109 +++----------------- 1 file changed, 15 insertions(+), 94 deletions(-) diff --git a/message_ix_models/data/material/config.yaml b/message_ix_models/data/material/config.yaml index 3154a5f6bb..825fea86d2 100644 --- a/message_ix_models/data/material/config.yaml +++ b/message_ix_models/data/material/config.yaml @@ -42,55 +42,27 @@ aluminum: - manuf_aluminum - scrap_recovery_aluminum -steel: +fertilizer: commodity: add: - - ht_heat - - lt_heat - - steel - - pig_iron - - sponge_iron - - sinter_iron - - pellet_iron - - ore_iron - - limestone_iron - + - NH3 + - Fertilizer Use|Nitrogen level: add: - - useful_steel - - new_scrap - - old_scrap - - primary_material - - secondary_material - - final_material - - useful_material - - product - + - material_interim + - material_final technology: + require: + - h2_bio_ccs # Reference for vent-to-storage ratio add: - - bf_steel - - dri_steel - - sr_steel - - bof_steel - - eaf_steel - - furnace_hoil_steel - - furnace_biofuel_steel - - furnace_biomass_steel - - furnace_synfuel_steel - - furnace_gas_steel - - furnace_elec_steel - - furnace_h2_steel - - hp_gas_steel - - hp_elec_steel - - fc_h2_steel - - solar_steel - - dheat_steel - - prep_secondary_steel - - finishing_steel - - manuf_steel - - scrap_recovery_steel - -fertilizer: + - biomass_NH3 + - electr_NH3 + - gas_NH3 + - coal_NH3 + - fueloil_NH3 + - NH3_to_N_fertil + +generic_set: commodity: require: - d_heat @@ -102,66 +74,15 @@ fertilizer: - biomass - gas - hydrogen - add: - - NH3 - - Fertilizer Use|Nitrogen - - ht_heat - - lt_heat - - aluminum - level: require: - primary - secondary - water_supply - final - add: - # - material_interim - # - material_final - - useful_aluminum - - new_scrap - - old_scrap - - final_material - - useful_material - - product - # should also work with regional model - region: require: - - technology: - require: - - h2_bio_ccs # Reference for vent-to-storage ratio - - add: - # - biomass_NH3 - # - electr_NH3 - # - gas_NH3 - # - coal_NH3 - # - fueloil_NH3 - # - NH3_to_N_fertil - - soderberg_aluminum - - prebake_aluminum - - furnace_coal_aluminum - - furnace_hoil_aluminum - - furnace_biofuel_aluminum - - furnace_biomass_aluminum - - furnace_synfuel_aluminum - - furnace_gas_aluminum - - furnace_elec_aluminum - - furnace_h2_aluminum - - hp_gas_aluminum - - hp_elec_aluminum - - fc_h2_aluminum - - solar_aluminum - - dheat_aluminum - - secondary_aluminum - - prep_secondary_aluminum - - finishing_aluminum - - manuf_aluminum - - scrap_recovery_aluminum - type_tec: add: - industry From 3dc49be6fc141f04a35d89bcb97a3a312e82a054 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 16 Jul 2020 16:46:35 +0200 Subject: [PATCH 058/774] Read and generate data functions for aluminum --- message_ix_models/model/material/data.py | 224 +++++++++++++++++------ 1 file changed, 168 insertions(+), 56 deletions(-) diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index 0997d309e8..366eea62f6 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -11,6 +11,174 @@ log = logging.getLogger(__name__) +def read_data(): + """Read and clean data from :file:`n-fertilizer_techno-economic.xlsx`.""" + # Ensure config is loaded, get the context + context = read_config() + + # Shorter access to sets configuration + sets = context["material"]["set"] + + # Read the file + data = pd.read_excel( + context.get_path("material", "n-fertilizer_techno-economic.xlsx"), + sheet_name="Sheet1", + ) + + # Prepare contents for the "parameter" and "technology" columns + # FIXME put these in the file itself to avoid ambiguity/error + + # "Variable" column contains different values selected to match each of + # these parameters, per technology + params = [ + "inv_cost", + "fix_cost", + "var_cost", + "technical_lifetime", + "input_fuel", + "input_elec", + "input_water", + "output_NH3", + "output_water", + "output_heat", + "emissions", + "capacity_factor", + ] + + param_values = [] + tech_values = [] + for t in sets["technology"]["add"]: + param_values.extend(params) + tech_values.extend([t.id] * len(params)) + + # Clean the data + data = ( + # Insert "technology" and "parameter" columns + data.assign(technology=tech_values, parameter=param_values) + # Drop columns that don't contain useful information + .drop(["Model", "Scenario", "Region"], axis=1) + # Set the data frame index for selection + .set_index(["parameter", "technology"]) + ) + + # TODO convert units for some parameters, per LoadParams.py + + return data + +# This function can also be generic for all materials +def read_data_aluminum(): + """Read and clean data from :file:`aluminum_techno_economic.xlsx`.""" + + # Ensure config is loaded, get the context + context = read_config() + + # Shorter access to sets configuration + sets = context["material"]["aluminum"] + + # Read the file + data_aluminum = pd.read_excel( + context.get_path("material", "aluminum_techno_economic.xlsx"), + sheet_name="aluminum", + ) + + # Clean the data + + data_aluminum= data_aluminum.drop(['Region', 'Source', 'Description'], axis = 1) + + # Unit conversion + + # At the moment this is done in the excel file, can be also done here + # To make sure we use the same units + + return data_aluminum + +def gen_data_aluminum(scenario, dry_run=False): + """Generate data for materials representation of aluminum.""" + # Load configuration + + config = read_config()["material"]["aluminum"] + + # Information about scenario, e.g. node, year + s_info = ScenarioInfo(scenario) + + # Techno-economic assumptions + data_aluminum = read_data() + + # List of data frames, to be concatenated together at end + results = defaultdict(list) + + # TODO: If there are differnet values for the years... + # TODO: Adding the active years to the tables + + # Iterate over technologies + + for t in technology_add: + + params = data_aluminum.loc[(data_aluminum["technology"] == t),\ + "parameter"].values.tolist() + + # Iterate over parameters + + for par in params: + + # Obtain the parameter names, commodity,level,emission + + split = par.split("|") + param_name = split[0] + + # Obtain the scalar value for the parameter + + val = data_aluminum.loc[((data_aluminum["technology"] == t) \ + & (data_aluminum["parameter"] == par)),2015].values[0] + + common = dict( + year_vtg= years, + mode="M1", + time="year", + time_origin="year", + time_dest="year",) + + # For the parameters which inlcudes index names + if len(split)> 1: + + if (param_name == "input")|(param_name == "output"): + + # Assign commodity and level names + com = split[1] + lev = split[2] + + df = (make_df(param_name, technology=t, commodity=com, \ + level=lev, value=val, unit='t', **common)\ + .pipe(broadcast, node_loc=nodes).pipe(same_node)) + + results[param_name].append(df) + + elif param_name == "emission_factor": + + # Assign the emisson type + emi = split[1] + + df = (make_df(param_name, technology=t,value=val,\ + emission=emi, unit='t', **common).pipe(broadcast, \ + node_loc=nodes)) + results[param_name].append(df) + + # Parameters with only parameter name + + else: + df = (make_df(param_name, technology=t, value=val,unit='t', \ + **common).pipe(broadcast, node_loc=nodes)) + results[param_name].append(df) + + # Concatenate to one data frame per parameter + results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} + + # Temporary: return nothing, since the data frames are incomplete + return results + +def gen_data_generic(scenario, dry_run=False): + # For furnaces and boilers + # Export this data from excel file in the model .. ? def gen_data(scenario, dry_run=False): """Generate data for materials representation of nitrogen fertilizers. @@ -73,59 +241,3 @@ def gen_data(scenario, dry_run=False): # Temporary: return nothing, since the data frames are incomplete return dict() # return result - - -def read_data(): - """Read and clean data from :file:`n-fertilizer_techno-economic.xlsx`.""" - # Ensure config is loaded, get the context - context = read_config() - - # Shorter access to sets configuration - sets = context["material"]["set"] - - # Read the file - data = pd.read_excel( - context.get_path("material", "n-fertilizer_techno-economic.xlsx"), - sheet_name="Sheet1", - engine="openpyxl", - ) - - # Prepare contents for the "parameter" and "technology" columns - # FIXME put these in the file itself to avoid ambiguity/error - - # "Variable" column contains different values selected to match each of - # these parameters, per technology - params = [ - "inv_cost", - "fix_cost", - "var_cost", - "technical_lifetime", - "input_fuel", - "input_elec", - "input_water", - "output_NH3", - "output_water", - "output_heat", - "emissions", - "capacity_factor", - ] - - param_values = [] - tech_values = [] - for t in sets["technology"]["add"]: - param_values.extend(params) - tech_values.extend([t.id] * len(params)) - - # Clean the data - data = ( - # Insert "technology" and "parameter" columns - data.assign(technology=tech_values, parameter=param_values) - # Drop columns that don't contain useful information - .drop(["Model", "Scenario", "Region"], axis=1) - # Set the data frame index for selection - .set_index(["parameter", "technology"]) - ) - - # TODO convert units for some parameters, per LoadParams.py - - return data From 17d62b574482645965afdbfab16ba3947f5a6631 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 16 Jul 2020 16:47:21 +0200 Subject: [PATCH 059/774] Generic technologies moved --- message_ix_models/data/material/config.yaml | 28 +++++++++++---------- 1 file changed, 15 insertions(+), 13 deletions(-) diff --git a/message_ix_models/data/material/config.yaml b/message_ix_models/data/material/config.yaml index 825fea86d2..888a1fd72c 100644 --- a/message_ix_models/data/material/config.yaml +++ b/message_ix_models/data/material/config.yaml @@ -23,19 +23,6 @@ aluminum: add: - soderberg_aluminum - prebake_aluminum - - furnace_coal_aluminum - - furnace_hoil_aluminum - - furnace_biofuel_aluminum - - furnace_biomass_aluminum - - furnace_synfuel_aluminum - - furnace_gas_aluminum - - furnace_elec_aluminum - - furnace_h2_aluminum - - hp_gas_aluminum - - hp_elec_aluminum - - fc_h2_aluminum - - solar_aluminum - - dheat_aluminum - secondary_aluminum - prep_secondary_aluminum - finishing_aluminum @@ -80,6 +67,21 @@ generic_set: - secondary - water_supply - final + technology: + add: + - furnace_coal_aluminum + - furnace_hoil_aluminum + - furnace_biofuel_aluminum + - furnace_biomass_aluminum + - furnace_synfuel_aluminum + - furnace_gas_aluminum + - furnace_elec_aluminum + - furnace_h2_aluminum + - hp_gas_aluminum + - hp_elec_aluminum + - fc_h2_aluminum + - solar_aluminum + - dheat_aluminum # should also work with regional model region: require: From bcc32738ffd688c72544174bfc6a2f9d50fbe880 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 28 Jul 2020 13:18:12 +0200 Subject: [PATCH 060/774] Minor fix --- message_ix_models/data/material/config.yaml | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/message_ix_models/data/material/config.yaml b/message_ix_models/data/material/config.yaml index 888a1fd72c..68be7b7111 100644 --- a/message_ix_models/data/material/config.yaml +++ b/message_ix_models/data/material/config.yaml @@ -70,10 +70,11 @@ generic_set: technology: add: - furnace_coal_aluminum - - furnace_hoil_aluminum - - furnace_biofuel_aluminum + - furnace_loil_aluminum + - furnace_foil_aluminum + - furnace_ethanol_aluminum - furnace_biomass_aluminum - - furnace_synfuel_aluminum + - furnace_methanol_aluminum - furnace_gas_aluminum - furnace_elec_aluminum - furnace_h2_aluminum @@ -82,6 +83,7 @@ generic_set: - fc_h2_aluminum - solar_aluminum - dheat_aluminum + # should also work with regional model region: require: From 4c75758fd92860451686b652b2892b911fd737ac Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 28 Jul 2020 13:19:04 +0200 Subject: [PATCH 061/774] Read and generate data for generic technologies --- message_ix_models/model/material/data.py | 110 +++++++++++++++++++++-- 1 file changed, 102 insertions(+), 8 deletions(-) diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index 366eea62f6..ea8c1abbdd 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -65,7 +65,7 @@ def read_data(): return data -# This function can also be generic for all materials +# Question: Do we need read data dunction seperately for all materials ? def read_data_aluminum(): """Read and clean data from :file:`aluminum_techno_economic.xlsx`.""" @@ -92,6 +92,36 @@ def read_data_aluminum(): return data_aluminum +def read_data_generic(): + """Read and clean data from :file:`generic_furnace_boiler_techno_economic.xlsx`.""" + + # Ensure config is loaded, get the context + context = read_config() + + # Shorter access to sets configuration + sets = context["material"]["generic_set"] + + # Read the file + data_generic = pd.read_excel( + context.get_path("material", "generic_furnace_boiler_techno_economic.xlsx"), + sheet_name="generic", + ) + + # Clean the data + # Drop columns that don't contain useful information + + data_generic= data_generic.drop(['Region', 'Source', 'Description'], axis = 1) + + # Unit conversion + + # At the moment this is done in the excel file, can be also done here + # To make sure we use the same units + + return data_generic + + +# TODO: Adding the active years to the tables +# TODO: If there are differnet values for the years. def gen_data_aluminum(scenario, dry_run=False): """Generate data for materials representation of aluminum.""" # Load configuration @@ -107,12 +137,9 @@ def gen_data_aluminum(scenario, dry_run=False): # List of data frames, to be concatenated together at end results = defaultdict(list) - # TODO: If there are differnet values for the years... - # TODO: Adding the active years to the tables - # Iterate over technologies - for t in technology_add: + for t in config["technology"]["add"]: params = data_aluminum.loc[(data_aluminum["technology"] == t),\ "parameter"].values.tolist() @@ -129,7 +156,7 @@ def gen_data_aluminum(scenario, dry_run=False): # Obtain the scalar value for the parameter val = data_aluminum.loc[((data_aluminum["technology"] == t) \ - & (data_aluminum["parameter"] == par)),2015].values[0] + & (data_aluminum["parameter"] == par)),'value'].values[0] common = dict( year_vtg= years, @@ -176,9 +203,76 @@ def gen_data_aluminum(scenario, dry_run=False): # Temporary: return nothing, since the data frames are incomplete return results +# TODO: Add active years +# TODO: Different values over the years: Add a function that modifies the values +# over the years ? def gen_data_generic(scenario, dry_run=False): - # For furnaces and boilers - # Export this data from excel file in the model .. ? + +# For each technology there are differnet input and output combinations +# Iterate over technologies + + for t in config["technology"]["add"]: + + years = s_info.Y + params = data_generic.loc[(data_generic["technology"] == t),"parameter"]\ + .values.tolist() + + # Availability year of the technology + av = data_generic.loc[(data_generic["technology"] == t),'availability'].\ + values[0] + years = [year for year in years if year >= av] + + # Iterate over parameters + for par in params: + split = par.split("|") + param_name = par.split("|")[0] + + val = data_generic.loc[((data_generic["technology"] == t) & \ + (data_generic["parameter"] == par)),'value'].values[0] + + # Common parameters for all input and output tables + # year_act is none at the moment + # node_dest and node_origin are the same as node_loc + + common = dict( + year_vtg= years, + time="year", + time_origin="year", + time_dest="year",) + + if len(split)> 1: + + if (param_name == "input")|(param_name == "output"): + + com = split[1] + lev = split[2] + mod = split[3] + + df = (make_df(param_name, technology=t, commodity=com, \ + level=lev,mode=mod, value=val, unit='t', **common).\ + pipe(broadcast, node_loc=nodes).pipe(same_node)) + + results[param_name].append(df) + + elif param_name == "emission_factor": + emi = split[1] + + df = (make_df(param_name, technology=t,value=val,\ + emission=emi, unit='t', **common).pipe(broadcast, \ + node_loc=nodes)) + + results[param_name].append(df) + + # Rest of the parameters apart from inpput, output and emission_factor + + else: + + df = (make_df(param_name, technology=t, value=val,unit='t', \ + **common).pipe(broadcast, node_loc=nodes)) + + results[param_name].append(df) + + results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} def gen_data(scenario, dry_run=False): """Generate data for materials representation of nitrogen fertilizers. From e0a64edf6a864487479f02e24f8fc9f2a72120f6 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 5 Aug 2020 16:26:50 +0200 Subject: [PATCH 062/774] Script to enable stand-alone run --- message_ix_models/model/material/bare.py | 116 +++++++++++++++++++++++ 1 file changed, 116 insertions(+) create mode 100644 message_ix_models/model/material/bare.py diff --git a/message_ix_models/model/material/bare.py b/message_ix_models/model/material/bare.py new file mode 100644 index 0000000000..5089ade0a6 --- /dev/null +++ b/message_ix_models/model/material/bare.py @@ -0,0 +1,116 @@ +# This script makes the additions to run the MESSAGE-material model stand-alone. +# without a reference energy system. + +import message_ix +import ixmp +import pandas as pd +from collections import defaultdict +from message_data.tools import broadcast, make_df, same_node + +mp = ixmp.Platform() + +# Adding a new unit to the library +mp.add_unit('Mt') + +scenario = message_ix.Scenario(mp, model='MESSAGE_material', + scenario='baseline', version='new') + +# Add model time steps + +history = [2010] +model_horizon = [2020, 2030, 2040, 2050, 2060, 2070, 2080, 2090,2100] +scenario.add_horizon({'year': history + model_horizon, + 'firstmodelyear': model_horizon[0]}) + +country = 'China' +scenario.add_spatial_sets({'country': country}) + +# Duration period + +# Create duration period + +val = [j-i for i, j in zip(model_horizon[:-1], model_horizon[1:])] +val.append(val[0]) + +duration_period = pd.DataFrame({ + 'year': model_horizon, + 'value': val, + 'unit': "y", + }) + +duration_period = duration_period["value"].values +scenario.add_par("duration_period", duration_period) + +# Add exogenous demand +# The future projection of the demand: Increases by half of the GDP growth rate. + +# TODO: Read this information from an excel file. +# Starting from 2020. Taken from global model input data. +gdp_growth = pd.Series([0.121448215899944, 0.0733079014579874, 0.0348154093342843, \ + 0.021827616787921,0.0134425983942219, 0.0108320197485592, \ + 0.00884341208063,0.00829374133206562, 0.00649794573935969],\ + index=pd.Index(model_horizon, name='Time')) + +i = 0 +values = [] +val = 36.27 * (1+ 0.147718884937996/2) ** duration_period[i] +values.append(val) + +for element in gdp_growth: + i = i + 1 + if i < len(model_horizon): + print(i) + val = val * (1+ element/2) ** duration_period[i] + values.append(val) + +aluminum_demand = pd.DataFrame({ + 'node': country, + 'commodity': 'aluminum', + 'level': 'useful_material', + 'year': model_horizon, + 'time': 'year', + 'value': values , + 'unit': 'Mt', + }) + +scenario.add_par("demand", aluminum_demand) + +# Interest rate +scenario.add_par("interestrate", model_horizon, value=0.05, unit='-') + +# Representation of energy system: +# Unlimited supply of the commodities, with a fixed cost over the years. +# Represented via variable costs. +# Variable costs are taken from PRICE_COMMODIY baseline SSP2 scenario. + +years_df = scenario.vintage_and_active_years() +vintage_years, act_years = years_df['year_vtg'], years_df['year_act'] + +# Retreive variable costs. +data_var_cost = pd.read_excel("variable_costs.xlsx",sheet_name="data") + +# TODO: We need to add the technology and mode sets here to be able to add +# variable costs. Retrieve from config.yaml file. + + + +# Add variable costs. +for row in data_var_cost.index: + data = data_var_cost.iloc[row] + + values =[] + for yr in act_years: + values.append(data[yr]) + + base_var_cost = pd.DataFrame({ + 'node_loc': country, + 'year_vtg': vintage_years.values, + 'year_act': act_years.values, + 'mode': data["mode"], + 'time': 'year', + 'unit': 'USD/GWa', + "technology": data["technology"], + "value": values + }) + + scenario.par("var_cost",base_var_cost) From 3737859800d956405f0a6be996118d3f513bbdfe Mon Sep 17 00:00:00 2001 From: Jihoon Date: Thu, 30 Jul 2020 16:53:43 +0200 Subject: [PATCH 063/774] More technologies for steel --- message_ix_models/data/material/config.yaml | 24 +++++++++++++++++++++ 1 file changed, 24 insertions(+) diff --git a/message_ix_models/data/material/config.yaml b/message_ix_models/data/material/config.yaml index 68be7b7111..82f824fd49 100644 --- a/message_ix_models/data/material/config.yaml +++ b/message_ix_models/data/material/config.yaml @@ -42,6 +42,30 @@ fertilizer: require: - h2_bio_ccs # Reference for vent-to-storage ratio add: + - cokeoven_steel + - sinter_steel + - pellet_steel + - bf_steel + - dri_steel + - sr_steel + - bof_steel + - eaf_steel + - furnace_hoil_steel + - furnace_biofuel_steel + - furnace_biomass_steel + - furnace_synfuel_steel + - furnace_gas_steel + - furnace_elec_steel + - furnace_h2_steel + - hp_gas_steel + - hp_elec_steel + - fc_h2_steel + - solar_steel + - dheat_steel + - prep_secondary_steel + - finishing_steel + - manuf_steel + - scrap_recovery_steel - biomass_NH3 - electr_NH3 - gas_NH3 From ee01727aa6b3881841d6d7a3d721377ce3facbce Mon Sep 17 00:00:00 2001 From: Jihoon Date: Wed, 5 Aug 2020 12:28:50 +0200 Subject: [PATCH 064/774] Test data read from shaohui's excel (not yet succesful) --- message_ix_models/model/material/data.py | 32 ++++++++++++++++++++++++ 1 file changed, 32 insertions(+) diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index ea8c1abbdd..611ccd80fb 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -65,6 +65,38 @@ def read_data(): return data + + +def process_china_data(): + """Read and clean data from :file:`aluminum_techno_economic.xlsx`.""" + + # Ensure config is loaded, get the context + context = read_config() + + # Shorter access to sets configuration + # sets = context["material"]["aluminum"] + + # Read the file + data_steel_china = pd.read_excel( + context.get_path("material", "China_steel_eet.xlsx"), + sheet_name="technologies", + ) + + # Clean the data + + data_steel_china= data_steel_china.drop(['Region', 'Source', 'Description'], axis = 1) + + # Unit conversion + + # At the moment this is done in the excel file, can be also done here + # To make sure we use the same units + + return data_steel_china + + + + + # Question: Do we need read data dunction seperately for all materials ? def read_data_aluminum(): """Read and clean data from :file:`aluminum_techno_economic.xlsx`.""" From d95e0fb8305137c7d6ff0c742d10754e4e0400a6 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Thu, 6 Aug 2020 10:15:14 +0200 Subject: [PATCH 065/774] Make the china data available --- message_ix_models/data/material/config.yaml | 2 +- message_ix_models/model/material/data.py | 11 ++++++++++- 2 files changed, 11 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/config.yaml b/message_ix_models/data/material/config.yaml index 82f824fd49..74f4951e95 100644 --- a/message_ix_models/data/material/config.yaml +++ b/message_ix_models/data/material/config.yaml @@ -29,7 +29,7 @@ aluminum: - manuf_aluminum - scrap_recovery_aluminum -fertilizer: +set: commodity: add: - NH3 diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index 611ccd80fb..4c39333a31 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -70,6 +70,8 @@ def read_data(): def process_china_data(): """Read and clean data from :file:`aluminum_techno_economic.xlsx`.""" + import numpy as np + # Ensure config is loaded, get the context context = read_config() @@ -84,8 +86,15 @@ def process_china_data(): # Clean the data - data_steel_china= data_steel_china.drop(['Region', 'Source', 'Description'], axis = 1) + data_steel_china = data_steel_china[['Technology', 'Parameter', 'Level', + 'Commodity', 'Species', 'Units', '2015']].replace(np.nan, '', regex=True) + + tuple_series = data_steel_china[['Parameter', 'Level', 'Commodity']].apply(tuple, axis=1) + data_steel_china['parameter'] = tuple_series.str.join('|') + # data_steel_china['parameter'] = data_steel_china.apply( + # lambda attach: attach.Parameter + "|" + attach.Commodity + "|" + attach.Level, axis=1) + # Unit conversion # At the moment this is done in the excel file, can be also done here From 5f807eb1c1d9f5b5a7d4fab0273bb0bb0379c5bd Mon Sep 17 00:00:00 2001 From: Jihoon Date: Wed, 12 Aug 2020 14:22:36 +0200 Subject: [PATCH 066/774] Add additional script files to construct china bare model --- message_ix_models/model/material/bare.py | 260 +++++++++++++--------- message_ix_models/model/material/build.py | 148 ++++++++++++ 2 files changed, 303 insertions(+), 105 deletions(-) create mode 100644 message_ix_models/model/material/build.py diff --git a/message_ix_models/model/material/bare.py b/message_ix_models/model/material/bare.py index 5089ade0a6..e7e0919d61 100644 --- a/message_ix_models/model/material/bare.py +++ b/message_ix_models/model/material/bare.py @@ -1,116 +1,166 @@ -# This script makes the additions to run the MESSAGE-material model stand-alone. -# without a reference energy system. +from functools import partial +from typing import Mapping +import logging import message_ix -import ixmp -import pandas as pd -from collections import defaultdict -from message_data.tools import broadcast, make_df, same_node -mp = ixmp.Platform() +from message_data.tools import Code, ScenarioInfo, get_context, set_info +from .build import apply_spec +from .util import read_config +from .data import get_data, gen_data_steel +import message_data -# Adding a new unit to the library -mp.add_unit('Mt') -scenario = message_ix.Scenario(mp, model='MESSAGE_material', - scenario='baseline', version='new') +log = logging.getLogger(__name__) -# Add model time steps -history = [2010] -model_horizon = [2020, 2030, 2040, 2050, 2060, 2070, 2080, 2090,2100] -scenario.add_horizon({'year': history + model_horizon, - 'firstmodelyear': model_horizon[0]}) +# Settings and valid values; the default is listed first +SETTINGS = dict( + period_start=[2010], + period_end=[2100], + regions=["China"], + res_with_dummies=[False], + time_step=[10], +) -country = 'China' -scenario.add_spatial_sets({'country': country}) -# Duration period +def create_res(context=None, quiet=True): + """Create a 'bare' MESSAGE-GLOBIOM reference energy system (RES). -# Create duration period + Parameters + ---------- + context : .Context + :attr:`.Context.scenario_info` determines the model name and scenario + name of the created Scenario. -val = [j-i for i, j in zip(model_horizon[:-1], model_horizon[1:])] -val.append(val[0]) - -duration_period = pd.DataFrame({ - 'year': model_horizon, - 'value': val, - 'unit': "y", - }) - -duration_period = duration_period["value"].values -scenario.add_par("duration_period", duration_period) - -# Add exogenous demand -# The future projection of the demand: Increases by half of the GDP growth rate. - -# TODO: Read this information from an excel file. -# Starting from 2020. Taken from global model input data. -gdp_growth = pd.Series([0.121448215899944, 0.0733079014579874, 0.0348154093342843, \ - 0.021827616787921,0.0134425983942219, 0.0108320197485592, \ - 0.00884341208063,0.00829374133206562, 0.00649794573935969],\ - index=pd.Index(model_horizon, name='Time')) - -i = 0 -values = [] -val = 36.27 * (1+ 0.147718884937996/2) ** duration_period[i] -values.append(val) - -for element in gdp_growth: - i = i + 1 - if i < len(model_horizon): - print(i) - val = val * (1+ element/2) ** duration_period[i] - values.append(val) - -aluminum_demand = pd.DataFrame({ - 'node': country, - 'commodity': 'aluminum', - 'level': 'useful_material', - 'year': model_horizon, - 'time': 'year', - 'value': values , - 'unit': 'Mt', - }) - -scenario.add_par("demand", aluminum_demand) - -# Interest rate -scenario.add_par("interestrate", model_horizon, value=0.05, unit='-') - -# Representation of energy system: -# Unlimited supply of the commodities, with a fixed cost over the years. -# Represented via variable costs. -# Variable costs are taken from PRICE_COMMODIY baseline SSP2 scenario. - -years_df = scenario.vintage_and_active_years() -vintage_years, act_years = years_df['year_vtg'], years_df['year_act'] - -# Retreive variable costs. -data_var_cost = pd.read_excel("variable_costs.xlsx",sheet_name="data") - -# TODO: We need to add the technology and mode sets here to be able to add -# variable costs. Retrieve from config.yaml file. - - - -# Add variable costs. -for row in data_var_cost.index: - data = data_var_cost.iloc[row] - - values =[] - for yr in act_years: - values.append(data[yr]) - - base_var_cost = pd.DataFrame({ - 'node_loc': country, - 'year_vtg': vintage_years.values, - 'year_act': act_years.values, - 'mode': data["mode"], - 'time': 'year', - 'unit': 'USD/GWa', - "technology": data["technology"], - "value": values - }) - - scenario.par("var_cost",base_var_cost) + Returns + ------- + message_ix.Scenario + A scenario as described by :func:`get_spec`, prepared using + :func:`.build.apply_spec`. + """ + mp = context.get_platform() + + # Model and scenario name for the RES + model_name = context.scenario_info['model'] + scenario_name = context.scenario_info['scenario'] + + # Create the Scenario + scenario = message_ix.Scenario(mp, model=model_name, + scenario=scenario_name, version='new') + + # TODO move to message_ix + scenario.init_par('MERtoPPP', ['node', 'year']) + + # Uncomment to add dummy sets and data + # context.res_with_dummies = True + + spec = get_spec(context) + apply_spec( + scenario, + spec, + # data=partial(get_data, context=context, spec=spec), + data=gen_data_steel, + quiet=quiet, + message=f"Create using message_data {message_data.__version__}", + ) + + return scenario + + +def get_spec(context=None) -> Mapping[str, ScenarioInfo]: + """Return the spec for the MESSAGE-China bare RES. + + Parameters + ---------- + context : Context, optional + If not supplied, :func:`.get_context` is used to retrieve the current + context. + + Returns + ------- + :class:`dict` of :class:`.ScenarioInfo` objects + """ + # context = context or get_context(strict=True) + context = read_config() + context.use_defaults(SETTINGS) + + # The RES is the base, so does not require/remove any elements + spec = dict(require=ScenarioInfo()) + + # JM: For China model, we need to remove the default 'World'. + remove = ScenarioInfo() + # remove.set["node"] = context["material"]["common"]["region"]["remove"] + + add = ScenarioInfo() + + + # Add technologies + # JM: try to find out a way to loop over 1st/2nd level and to just context["material"][xx]["add"] + add.set["technology"] = context["material"]["steel"]["technology"]["add"] + + # Add regions + + # # Load configuration for the specified region mapping + # nodes = set_info(f"node/{context.regions}") + # + # # Top-level "World" node + # world = nodes[nodes.index("World")] + + # Set elements: World, followed by the direct children of World + add.set["node"] = context["material"]["common"]["region"]["require"] + + # Add the time horizon + add.set['year'] = list(range( + context.period_start, context.period_end + 1, context.time_step + )) + add.set['cat_year'] = [('firstmodelyear', context.period_start)] + + # Add levels + # JM: For bare model, both 'add' & 'require' need to be added. + add.set['level'] = context["material"]["steel"]["level"]["add"] + \ + context["material"]["common"]["level"]["require"] + + # Add commodities + c_list = context["material"]["steel"]["commodity"]["add"] + \ + context["material"]["common"]["commodity"]["require"] + add.set['commodity'] = c_list + + add.set['type_tec'] = context["material"]["common"]["type_tec"]["add"] + add.set['mode'] = context["material"]["common"]["mode"]["require"] + add.set['emission'] = context["material"]["common"]["emission"]["require"] +\ + context["material"]["common"]["emission"]["add"] + + # Add units, associated with commodities + # JM: What is 'anno' + # for c in c_list: + # try: + # unit = c.anno['unit'] + # except KeyError: + # log.warning(f"Commodity {c} lacks defined units") + # continue + # + # try: + # # Check that the unit can be parsed by the pint.UnitRegistry + # context.units(unit) + # except Exception: + # log.warning(f"Unit {unit} for commodity {c} not pint compatible") + # else: + # add.set['unit'].append(unit) + + # Deduplicate by converting to a set and then back; not strictly necessary, + # but reduces duplicate log entries + add.set['unit'] = sorted(set(add.set['unit'])) + + # Manually set the first model year + add.y0 = context.period_start + + if context.res_with_dummies: + # Add dummy technologies + add.set["technology"].extend([Code("dummy"), Code("dummy source")]) + # Add a dummy commodity + add.set["commodity"].append(Code("dummy")) + + spec['add'] = add + spec['remove'] = remove + return spec diff --git a/message_ix_models/model/material/build.py b/message_ix_models/model/material/build.py new file mode 100644 index 0000000000..565aee3e96 --- /dev/null +++ b/message_ix_models/model/material/build.py @@ -0,0 +1,148 @@ +import logging +from typing import Callable, Mapping + +from ixmp.utils import maybe_check_out +from message_ix import Scenario +import pandas as pd + +from message_data.tools import Code, ScenarioInfo, add_par_data, strip_par_data + + +log = logging.getLogger(__name__) + + +def apply_spec( + scenario: Scenario, + spec: Mapping[str, ScenarioInfo], + data: Callable = None, + **options, + ): + """Apply `spec` to `scenario`. + + Parameters + ---------- + spec + A 'specification': :class:`dict` with 'require', 'remove', and 'add' + keys and :class:`.ScenarioInfo` objects as values. + data : callable, optional + Function to add data to `scenario`. `data` can either manipulate the + scenario directly, or return a :class:`dict` compatible with + :func:`.add_par_data`. + + Other parameters + ---------------- + dry_run : bool + Don't modify `scenario`; only show what would be done. Default + :obj:`False`. Exceptions will still be raised if the elements from + ``spec['required']`` are missing; this serves as a check that the + scenario has the required features for applying the spec. + fast : bool + Do not remove existing parameter data; increases speed on large + scenarios. + quiet : bool + Only show log messages at level ``ERROR`` and higher. If :obj:`False` + (default), show log messages at level ``DEBUG`` and higher. + message : str + Commit message. + + See also + -------- + .tools.add_par_data + .tools.strip_par_data + .Code + .ScenarioInfo + """ + dry_run = options.get('dry_run', False) + + log.setLevel( + logging.ERROR if options.get('quiet', False) else logging.DEBUG + ) + + if not dry_run: + try: + scenario.remove_solution() + except ValueError: + pass + maybe_check_out(scenario) + + dump = {} # Removed data + + for set_name in scenario.set_list(): + # Check whether this set is mentioned at all in the spec + check = sum(map(lambda info: len(info.set[set_name]), spec.values())) + if check == 0: + # Not mentioned; don't do anything + continue + + # Base contents of the set + base_set = scenario.set(set_name) + if isinstance(base_set, pd.DataFrame): + base = list(base_set.itertuples(index=False)) + else: + base = base_set.tolist() + log.info(f"Set {repr(set_name)}") + log.info(f" {len(base)} elements") + # log.debug(', '.join(map(repr, base))) # All elements; verbose + + # Check for required elements + require = spec['require'].set[set_name] + for element in require: + if element not in base: + log.error(f' {repr(element)} not found') + raise ValueError + if len(require): + log.info(f' Check {len(require)} required elements') + + if options.get('fast', False): + log.info(' Skip removing parameter values') + else: + # Remove elements and associated parameter values + remove = spec['remove'].set[set_name] + for element in remove: + msg = f"{repr(element)} and associated parameter elements" + if options.get('fast', False): + log.info(f" Skip removing {msg} (fast=True)") + else: + log.info(f" Remove {msg}") + strip_par_data( + scenario, + set_name, + element, + dry_run=dry_run, + dump=dump + ) + + # Add elements + add = spec['add'].set[set_name] + if not dry_run: + for element in add: + scenario.add_set( + set_name, + element.id if isinstance(element, Code) else element, + ) + + if len(add): + log.info(f" Add {len(add)} element(s)") + log.debug(' ' + ', '.join(map(repr, add))) + + log.info(' ---') + + N_removed = sum(len(d) for d in dump.values()) + log.info(f'{N_removed} parameter elements removed') + + # Add units + for unit in spec['add'].set['unit']: + unit = Code(id=unit, name=unit) if isinstance(unit, str) else unit + log.info(f'Add unit {repr(unit.id)}') + scenario.platform.add_unit(unit.id, comment=unit.name) + + # Add data + if callable(data): + result = data(scenario, dry_run=dry_run) + if result: + add_par_data(scenario, result, dry_run=dry_run) + + # Finalize + log.info('Commit results.') + if not dry_run: + scenario.commit(options.get('message', f"{__name__}.apply_spec()")) From 43d9edc0ffc8fe5bd45a8a4c734be775ce4b6da7 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Wed, 12 Aug 2020 14:24:05 +0200 Subject: [PATCH 067/774] Add set.yaml as a basis for 'spec' --- message_ix_models/data/material/set.yaml | 193 +++++++++++++++++++++++ 1 file changed, 193 insertions(+) create mode 100644 message_ix_models/data/material/set.yaml diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml new file mode 100644 index 0000000000..676fab97b3 --- /dev/null +++ b/message_ix_models/data/material/set.yaml @@ -0,0 +1,193 @@ +# Configuration for message_data.model.material +# +# NB these values are currently for the nitrogen fertilizer materials +# accounting. Different sets can be created once there are 2+ different +# categories of materials. + +common: + commodity: + require: + - coal + - gas + - electr + - ethanol + - fueloil + - hydrogen + - lh2 + - biomass + - d_heat + - water + - fresh_water + + level: + require: + - primary + - secondary + - useful + - final + - water_supply + + # Currently only works with China + region: + # Just one is sufficient, since the RES will never be generated with a mix + require: + - China + remove: + - World + + type_tec: + add: + - industry + + mode: + require: + - M1 + + emission: + require: + - CO2 + - CH4 + - N2O + - NOx + - SO2 + add: + - PM2p5 + + +steel: + commodity: + add: + - ht_heat + - lt_heat + - steel + - pig_iron + - sponge_iron + - sinter_iron + - pellet_iron + - ore_iron + - limestone_iron + - coke_iron + - slag_iron + + level: + add: + - useful_steel + - new_scrap + - old_scrap + - primary_material + - secondary_material + - tertiary_material + - final_material + - useful_material + - waste_material + - product + + technology: + add: + - cokeoven_steel + - sinter_steel + - pellet_steel + - bf_steel + - dri_steel + - sr_steel + - bof_steel + - eaf_steel + - furnace_hoil_steel + - furnace_biofuel_steel + - furnace_biomass_steel + - furnace_synfuel_steel + - furnace_gas_steel + - furnace_elec_steel + - furnace_h2_steel + - hp_gas_steel + - hp_elec_steel + - fc_h2_steel + - solar_steel + - dheat_steel + - prep_secondary_steel + - finishing_steel + - manuf_steel + - scrap_recovery_steel + +fertilizer: + commodity: + require: + - electr + - freshwater_supply + add: + - NH3 + - Fertilizer Use|Nitrogen + + level: + add: + - material_interim + - material_final + + technology: + require: + - h2_bio_ccs # Reference for vent-to-storage ratio + add: + - biomass_NH3 + - electr_NH3 + - gas_NH3 + - coal_NH3 + - fueloil_NH3 + - NH3_to_N_fertil + + +# NB this codelist added by #125 + +iron-steel: + name: Iron and Steel + description: Iron is a chemical element with the symbol Fe, while steel is an alloy of iron and carbon and, sometimes, other elements. Measured as the total mass of material. + unit: Mt + +non-ferrous: + name: Non-ferrous metals + description: Metals and alloys which do not contain iron. Measured as the total mass of material. + unit: Mt + +aluminum: + name: Aluminum + description: Aluminum (or aluminium) is a chemical element with the symbol Al. Measured as the total mass of material. + unit: Mt + parent: non-ferrous + +copper: + name: Copper + description: Copper is a chemical element with the symbol Cu. Measured as the total mass of material. + unit: Mt + parent: non-ferrous + +minerals: + name: Minerals + description: Non-metallic minerals are minerals that have no metallic luster, break easily and include, e.g., sand, limestone, marble, clay and salt. Measured as the FE-equivalent mass of material using LCA midpoint characterization factors. + unit: MtFe-eq + +cement: + name: Cement + description: Cement is a binder used for construction that sets, hardens, and adheres to other materials to bind them together. Measured as the total mass of material. + unit: Mt + parent: minerals + +chemicals: + name: Chemicals + description: Industrial chemicals that form the basis of many products. Measured as the total mass of material. + unit: Mt + +ethylene: + name: Ethylene + description: Ethylene is a hydrocarbon with the formula C2H4. Measured as the total mass of material. + unit: Mt + parent: chemicals + +ammonia: + name: Ammonia + description: Ammonia is a compound of nitrogen and hydrogen with the formula NH3. Measured as the total mass of material. + unit: Mt + parent: chemicals + +paper-pulp: + name: Paper and pulp for paper + description: Pulp is a lignocellulosic fibrous material prepared by chemically or mechanically separating cellulose fibers and the raw material for making paper. Measured as the total mass of material. + unit: Mt From 0c74557d8f71c8f3d1dc8c7b848558ea9ddb6324 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Wed, 12 Aug 2020 14:29:14 +0200 Subject: [PATCH 068/774] Add cli interface for steel bare model --- message_ix_models/model/material/__init__.py | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 1d4f8bb257..6f0f78d2d7 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -44,6 +44,24 @@ def cli(): """Model with materials accounting.""" +@cli.command("create-bare") +@click.option("--regions", type=click.Choice(["China", "R11", "R14"])) +@click.option('--dry_run', '-n', is_flag=True, + help='Only show what would be done.') +@click.pass_obj +def create_bare(context, regions, dry_run): + """Create the RES from scratch.""" + from .bare import create_res + + if regions: + context.regions = regions + + scen = create_res(context) + + # Solve + if not dry_run: + scen.solve() + @cli.command() @click.pass_obj def solve(context): From 1e74608f2fd7c1c48dfc05f1d7445b672b630eff Mon Sep 17 00:00:00 2001 From: Jihoon Date: Wed, 12 Aug 2020 14:33:40 +0200 Subject: [PATCH 069/774] Add a binary input (may be moved elsewhere later) --- message_ix_models/data/material/China_steel_renamed.xlsx | 3 +++ 1 file changed, 3 insertions(+) create mode 100644 message_ix_models/data/material/China_steel_renamed.xlsx diff --git a/message_ix_models/data/material/China_steel_renamed.xlsx b/message_ix_models/data/material/China_steel_renamed.xlsx new file mode 100644 index 0000000000..c93b1760d0 --- /dev/null +++ b/message_ix_models/data/material/China_steel_renamed.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f85620496ba6a8119895ef298c019b34178f8f1d32617e7c46f8899587a4d81c +size 43197 From 1e7ddd014ce5e9083e01450d55b1d40092f97c54 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Wed, 12 Aug 2020 14:35:34 +0200 Subject: [PATCH 070/774] Revise read_config() definition for set.yaml --- message_ix_models/model/material/util.py | 27 ++++++++++++++++-------- 1 file changed, 18 insertions(+), 9 deletions(-) diff --git a/message_ix_models/model/material/util.py b/message_ix_models/model/material/util.py index 7e7a516953..17ee4ca4b6 100644 --- a/message_ix_models/model/material/util.py +++ b/message_ix_models/model/material/util.py @@ -2,8 +2,8 @@ from message_ix_models.util import as_codes, load_package_data -def read_config(context=None): - """Read configuration from material.yaml.""" +def read_config(): + """Read configuration from set.yaml.""" # TODO this is similar to transport.utils.read_config; make a common # function so it doesn't need to be in this file. context = context or Context.get_instance(0) @@ -18,14 +18,23 @@ def read_config(context=None): # Already loaded return context - # Read material.yaml, store with a shorter name - context["material"] = load_package_data("material", "config") + # Read material.yaml + context.load_config("material", "set") + + # Use a shorter name + context["material"] = context["material set"] + + # Merge technology.yaml with set.yaml + # context["material"]["steel"]["technology"]["add"] = ( + # context.pop("transport technology") + # ) # Convert some values to Code objects - for set_name, info in context["material"]["set"].items(): - try: - info["add"] = as_codes(info["add"]) - except KeyError: - pass + # JM: Ask what this is + # for set_name, info in context["material"]["common"].items(): + # try: + # info["add"] = as_codes(info["add"]) + # except KeyError: + # pass return context From c774afb20daff8559e7d7ea181fe005459948d21 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Wed, 12 Aug 2020 14:48:43 +0200 Subject: [PATCH 071/774] Copy/paste dummy data functions for reference --- message_ix_models/model/material/data.py | 128 +++++++++++++++++++++++ 1 file changed, 128 insertions(+) diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index 4c39333a31..564e37bd99 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -376,3 +376,131 @@ def gen_data(scenario, dry_run=False): # Temporary: return nothing, since the data frames are incomplete return dict() # return result + + +def read_data(): + """Read and clean data from :file:`n-fertilizer_techno-economic.xlsx`.""" + # Ensure config is loaded, get the context + context = read_config() + + # Shorter access to sets configuration + sets = context["material"]["set"] + + # Read the file + data = pd.read_excel( + context.get_path("material", "n-fertilizer_techno-economic.xlsx"), + sheet_name="Sheet1", + ) + + # Prepare contents for the "parameter" and "technology" columns + # FIXME put these in the file itself to avoid ambiguity/error + + # "Variable" column contains different values selected to match each of + # these parameters, per technology + params = [ + "inv_cost", + "fix_cost", + "var_cost", + "technical_lifetime", + "input_fuel", + "input_elec", + "input_water", + "output_NH3", + "output_water", + "output_heat", + "emissions", + "capacity_factor", + ] + + param_values = [] + tech_values = [] + for t in sets["technology"]["add"]: + param_values.extend(params) + tech_values.extend([t.id] * len(params)) + + # Clean the data + data = ( + # Insert "technology" and "parameter" columns + data.assign(technology=tech_values, parameter=param_values) + # Drop columns that don't contain useful information + .drop(["Model", "Scenario", "Region"], axis=1) + # Set the data frame index for selection + .set_index(["parameter", "technology"]) + ) + + # TODO convert units for some parameters, per LoadParams.py + + return data + + +def get_data(scenario, context, **options): + """Data for the bare RES.""" + if context.res_with_dummies: + dt = get_dummy_data(scenario) + print(dt) + return dt + else: + return dict() + + +def get_dummy_data(scenario, **options): + """Dummy data for the bare RES. + + Currently this contains: + - A dummy 1-to-1 technology taking input from (dummy, primary) and output + to (dummy, useful). + - A dummy source technology for (dummy, primary). + - Demand for (dummy, useful). + + This ensures that the model variable ACT has some non-zero entries. + """ + info = ScenarioInfo(scenario) + + common = dict( + node=info.N[0], + node_loc=info.N[0], + node_origin=info.N[0], + node_dest=info.N[0], + technology="dummy", + year=info.Y, + year_vtg=info.Y, + year_act=info.Y, + mode="all", + # No subannual detail + time="year", + time_origin="year", + time_dest="year", + ) + + data = make_io( + src=("dummy", "primary", "GWa"), + dest=("dummy", "useful", "GWa"), + efficiency=1., + on='input', + # Other data + **common + ) + + # Source for dummy + data["output"] = data["output"].append( + data["output"].assign(technology="dummy source", level="primary") + ) + + data.update( + make_matched_dfs( + data["output"], + capacity_factor=1., + technical_lifetime=10, + var_cost=1, + ) + ) + + common.update(dict( + commodity="dummy", + level="useful", + value=1., + unit="GWa", + )) + data["demand"] = make_df("demand", **common) + + return data From e5d5681ad3dd983c776544d98d788bb1c1f02bff Mon Sep 17 00:00:00 2001 From: Jihoon Date: Wed, 12 Aug 2020 14:50:49 +0200 Subject: [PATCH 072/774] Define gen_data_steel(), providing final parameter dfs to add to message_ix.scenario --- message_ix_models/model/material/data.py | 93 ++++++++++++++++++++++++ 1 file changed, 93 insertions(+) diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index 564e37bd99..62436d0df6 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -315,6 +315,99 @@ def gen_data_generic(scenario, dry_run=False): results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} + +def gen_data_steel(scenario, dry_run=False): + """Generate data for materials representation of steel industry. + + """ + # Load configuration + # config = read_config()["material"]["steel"] + + # Information about scenario, e.g. node, year + s_info = ScenarioInfo(scenario) + + # Techno-economic assumptions + data_steel = process_china_data() + + # List of data frames, to be concatenated together at end + results = defaultdict(list) + + # For each technology there are differnet input and output combinations + # Iterate over technologies + + years = s_info.Y + nodes = s_info.N + yv_ya = s_info.yv_ya + + nodes.remove('World') # For the bare model + + for t in s_info.set['technology']: + + params = data_steel.loc[(data_steel["technology"] == t),\ + "parameter"].values.tolist() + + # Iterate over parameters + + for par in params: + + # Obtain the parameter names, commodity,level,emission + + split = par.split("|") + param_name = split[0] + + # Obtain the scalar value for the parameter + + val = data_steel.loc[((data_steel["technology"] == t) \ + & (data_steel["parameter"] == par)),'value'].values[0] + + common = dict( + year_vtg= yv_ya.year_vtg, + year_act= yv_ya.year_act, + mode="M1", + time="year", + time_origin="year", + time_dest="year",) + + # For the parameters which inlcudes index names + if len(split)> 1: + + if (param_name == "input")|(param_name == "output"): + + # Assign commodity and level names + com = split[1] + lev = split[2] + + df = (make_df(param_name, technology=t, commodity=com, \ + level=lev, value=val, unit='t', **common)\ + .pipe(broadcast, node_loc=nodes).pipe(same_node)) + + results[param_name].append(df) + + elif param_name == "emission_factor": + + # Assign the emisson type + emi = split[1] + + df = (make_df(param_name, technology=t,value=val,\ + emission=emi, unit='t', **common).pipe(broadcast, \ + node_loc=nodes)) + results[param_name].append(df) + + # Parameters with only parameter name + + else: + df = (make_df(param_name, technology=t, value=val, unit='t', \ + **common).pipe(broadcast, node_loc=nodes)) + results[param_name].append(df) + + # Concatenate to one data frame per parameter + results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} + + # Temporary: return nothing, since the data frames are incomplete + return results + + + def gen_data(scenario, dry_run=False): """Generate data for materials representation of nitrogen fertilizers. From 9fe649201e4986f5f1dadc6527e235d27c51d406 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Wed, 12 Aug 2020 14:52:35 +0200 Subject: [PATCH 073/774] Format shaohui's input (revised on a new xlsx) to our standard formatting --- message_ix_models/model/material/data.py | 32 +++++++++++++++++------- 1 file changed, 23 insertions(+), 9 deletions(-) diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index 62436d0df6..31c489e505 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -80,21 +80,35 @@ def process_china_data(): # Read the file data_steel_china = pd.read_excel( - context.get_path("material", "China_steel_eet.xlsx"), + context.get_path("material", "China_steel_renamed.xlsx"), sheet_name="technologies", ) # Clean the data - data_steel_china = data_steel_china[['Technology', 'Parameter', 'Level', - 'Commodity', 'Species', 'Units', '2015']].replace(np.nan, '', regex=True) - - tuple_series = data_steel_china[['Parameter', 'Level', 'Commodity']].apply(tuple, axis=1) - data_steel_china['parameter'] = tuple_series.str.join('|') + data_steel_china = data_steel_china \ + [['Technology', 'Parameter', 'Level', \ + 'Commodity', 'Species', 'Units', 'Value']] \ + .replace(np.nan, '', regex=True) + + tuple_series = data_steel_china[['Parameter', 'Commodity', 'Level']] \ + .apply(tuple, axis=1) + tuple_ef = data_steel_china[['Parameter', 'Species']] \ + .apply(tuple, axis=1) + + + data_steel_china['parameter'] = tuple_series.str.join('|') \ + .str.replace('\|\|', '') + data_steel_china.loc[data_steel_china['Parameter'] == "emission_factor", \ + 'parameter'] = tuple_ef.str.join('|').str.replace('\|\|', '') + + data_steel_china = data_steel_china.drop(['Parameter', 'Level', 'Commodity'] \ + , axis = 1) + data_steel_china = data_steel_china.drop( \ + data_steel_china[data_steel_china.Value==''].index) + + data_steel_china.columns = data_steel_china.columns.str.lower() - # data_steel_china['parameter'] = data_steel_china.apply( - # lambda attach: attach.Parameter + "|" + attach.Commodity + "|" + attach.Level, axis=1) - # Unit conversion # At the moment this is done in the excel file, can be also done here From c8437a3ed52c444ea33c240b318c7492f418d2cb Mon Sep 17 00:00:00 2001 From: Jihoon Date: Wed, 12 Aug 2020 14:53:31 +0200 Subject: [PATCH 074/774] Some insignificant changes --- message_ix_models/model/material/data.py | 40 ++++++++++++++++++++++-- 1 file changed, 38 insertions(+), 2 deletions(-) diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index 31c489e505..75b44fa5d5 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -116,9 +116,31 @@ def process_china_data(): return data_steel_china +def read_data_steel(): + """Read and clean data from :file:`steel_techno_economic.xlsx`.""" + # Ensure config is loaded, get the context + context = read_config() + + # Shorter access to sets configuration + sets = context["material"]["steel"] + + # Read the file + data_steel = pd.read_excel( + context.get_path("material", "steel_techno_economic.xlsx"), + sheet_name="steel", + ) + # Clean the data + + data_steel= data_steel.drop(['Region', 'Source', 'Description'], axis = 1) + + # Unit conversion + + # At the moment this is done in the excel file, can be also done here + # To make sure we use the same units + return data_steel # Question: Do we need read data dunction seperately for all materials ? def read_data_aluminum(): @@ -262,9 +284,20 @@ def gen_data_aluminum(scenario, dry_run=False): # TODO: Different values over the years: Add a function that modifies the values # over the years ? def gen_data_generic(scenario, dry_run=False): + # Load configuration + + config = read_config()["material"]["generic"] + + # Information about scenario, e.g. node, year + s_info = ScenarioInfo(scenario) + + # Techno-economic assumptions + data_generic = read_data_generic() -# For each technology there are differnet input and output combinations -# Iterate over technologies + # List of data frames, to be concatenated together at end + results = defaultdict(list) + # For each technology there are differnet input and output combinations + # Iterate over technologies for t in config["technology"]["add"]: @@ -329,6 +362,9 @@ def gen_data_generic(scenario, dry_run=False): results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} + # Temporary: return nothing, since the data frames are incomplete + return results + def gen_data_steel(scenario, dry_run=False): """Generate data for materials representation of steel industry. From 97626c6a8231f3585de66b23664db3c1f86c5b4a Mon Sep 17 00:00:00 2001 From: Jihoon Date: Wed, 12 Aug 2020 16:21:21 +0200 Subject: [PATCH 075/774] Add dummy supply technologies --- message_ix_models/data/material/China_steel_renamed.xlsx | 4 ++-- message_ix_models/data/material/set.yaml | 7 +++++++ 2 files changed, 9 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/China_steel_renamed.xlsx b/message_ix_models/data/material/China_steel_renamed.xlsx index c93b1760d0..691dfaa3d0 100644 --- a/message_ix_models/data/material/China_steel_renamed.xlsx +++ b/message_ix_models/data/material/China_steel_renamed.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:f85620496ba6a8119895ef298c019b34178f8f1d32617e7c46f8899587a4d81c -size 43197 +oid sha256:3c1bcffaa7ff6ae77c5f6bcbad3ab90ac90e3a46e6ee34b5e4ace6c703690a85 +size 42156 diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 676fab97b3..810ffd356b 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -108,6 +108,13 @@ steel: - finishing_steel - manuf_steel - scrap_recovery_steel + - DUMMY_coal_supply + - DUMMY_gas_supply + - DUMMY_ore_supply + - DUMMY_limestone_supply + - DUMMY_hydrogen_supply + - DUMMY_freshwater_supply + - DUMMY_water_supply fertilizer: commodity: From ea29abcb70ca79254c50e1a0dab3a9999bed2fdb Mon Sep 17 00:00:00 2001 From: Jihoon Date: Thu, 13 Aug 2020 15:29:10 +0200 Subject: [PATCH 076/774] Add relation as a way to specify mock demand + make the first period as historical year --- message_ix_models/model/material/bare.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/message_ix_models/model/material/bare.py b/message_ix_models/model/material/bare.py index e7e0919d61..74113a0d77 100644 --- a/message_ix_models/model/material/bare.py +++ b/message_ix_models/model/material/bare.py @@ -110,11 +110,15 @@ def get_spec(context=None) -> Mapping[str, ScenarioInfo]: # Set elements: World, followed by the direct children of World add.set["node"] = context["material"]["common"]["region"]["require"] + add.set["relation"] = context["material"]["steel"]["relation"]["add"] + # Add the time horizon add.set['year'] = list(range( context.period_start, context.period_end + 1, context.time_step )) - add.set['cat_year'] = [('firstmodelyear', context.period_start)] + + # JM: Leave the first time period as historical year + add.set['cat_year'] = [('firstmodelyear', context.period_start + context.time_step)] # Add levels # JM: For bare model, both 'add' & 'require' need to be added. @@ -152,8 +156,8 @@ def get_spec(context=None) -> Mapping[str, ScenarioInfo]: # but reduces duplicate log entries add.set['unit'] = sorted(set(add.set['unit'])) - # Manually set the first model year - add.y0 = context.period_start + # JM: Manually set the first model year + add.y0 = context.period_start + context.time_step if context.res_with_dummies: # Add dummy technologies From 2d0c5d62647c69210a8974dcb5e9b52f55608beb Mon Sep 17 00:00:00 2001 From: Jihoon Date: Thu, 13 Aug 2020 15:29:59 +0200 Subject: [PATCH 077/774] Add a relation for external demand specification --- message_ix_models/data/material/set.yaml | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 810ffd356b..6674ffc059 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -116,6 +116,10 @@ steel: - DUMMY_freshwater_supply - DUMMY_water_supply + relation: + add: + - steel_demand + fertilizer: commodity: require: From 9fa2a33c7d389d078f692f3f9ba0a4e24eb29d36 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Thu, 13 Aug 2020 15:33:30 +0200 Subject: [PATCH 078/774] Update data with historical stuff + demand relations --- message_ix_models/data/material/China_steel_renamed.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/China_steel_renamed.xlsx b/message_ix_models/data/material/China_steel_renamed.xlsx index 691dfaa3d0..5f2818eb7f 100644 --- a/message_ix_models/data/material/China_steel_renamed.xlsx +++ b/message_ix_models/data/material/China_steel_renamed.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:3c1bcffaa7ff6ae77c5f6bcbad3ab90ac90e3a46e6ee34b5e4ace6c703690a85 -size 42156 +oid sha256:05056b90eb264abab583d17ca273a098d746f276d8fbc361b3b1401c29d11ddf +size 45756 From 615698f6190c9d097effbb1d1d736a2acc7b859c Mon Sep 17 00:00:00 2001 From: Jihoon Date: Thu, 13 Aug 2020 15:35:54 +0200 Subject: [PATCH 079/774] Process relation data + generate mock china demand + handle historical params --- message_ix_models/model/material/data.py | 104 +++++++++++++++++++++-- 1 file changed, 95 insertions(+), 9 deletions(-) diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index 75b44fa5d5..e6b238cbff 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -8,6 +8,8 @@ from .util import read_config +import re + log = logging.getLogger(__name__) @@ -67,11 +69,11 @@ def read_data(): -def process_china_data(): +def process_china_data_tec(): """Read and clean data from :file:`aluminum_techno_economic.xlsx`.""" import numpy as np - + # Ensure config is loaded, get the context context = read_config() @@ -116,6 +118,27 @@ def process_china_data(): return data_steel_china + +def process_china_data_rel(): + """Read and clean data from :file:`aluminum_techno_economic.xlsx`.""" + + import numpy as np + + # Ensure config is loaded, get the context + context = read_config() + + # Shorter access to sets configuration + # sets = context["material"]["aluminum"] + + # Read the file + data_steel_china = pd.read_excel( + context.get_path("material", "China_steel_renamed.xlsx"), + sheet_name="relations", + ) + + return data_steel_china + + def read_data_steel(): """Read and clean data from :file:`steel_techno_economic.xlsx`.""" @@ -366,6 +389,17 @@ def gen_data_generic(scenario, dry_run=False): return results +def gen_mock_demand(): + # True steel use 2010 (China) = 537 Mt/year + # https://www.worldsteel.org/en/dam/jcr:0474d208-9108-4927-ace8-4ac5445c5df8/World+Steel+in+Figures+2017.pdf + gdp_growth = [0.121448215899944, \ + 0.0733079014579874, 0.0348154093342843, \ + 0.021827616787921,0.0134425983942219, 0.0108320197485592, \ + 0.00884341208063,0.00829374133206562, 0.00649794573935969] + demand = [(x+1) * 537 for x in gdp_growth] + + return demand + def gen_data_steel(scenario, dry_run=False): """Generate data for materials representation of steel industry. @@ -377,7 +411,7 @@ def gen_data_steel(scenario, dry_run=False): s_info = ScenarioInfo(scenario) # Techno-economic assumptions - data_steel = process_china_data() + data_steel = process_china_data_tec() # List of data frames, to be concatenated together at end results = defaultdict(list) @@ -385,9 +419,11 @@ def gen_data_steel(scenario, dry_run=False): # For each technology there are differnet input and output combinations # Iterate over technologies - years = s_info.Y + allyears = s_info.set['year'] #s_info.Y is only for modeling years + modelyears = s_info.Y #s_info.Y is only for modeling years nodes = s_info.N yv_ya = s_info.yv_ya + fmy = s_info.y0 nodes.remove('World') # For the bare model @@ -431,8 +467,6 @@ def gen_data_steel(scenario, dry_run=False): level=lev, value=val, unit='t', **common)\ .pipe(broadcast, node_loc=nodes).pipe(same_node)) - results[param_name].append(df) - elif param_name == "emission_factor": # Assign the emisson type @@ -441,15 +475,67 @@ def gen_data_steel(scenario, dry_run=False): df = (make_df(param_name, technology=t,value=val,\ emission=emi, unit='t', **common).pipe(broadcast, \ node_loc=nodes)) - results[param_name].append(df) + + results[param_name].append(df) # Parameters with only parameter name else: - df = (make_df(param_name, technology=t, value=val, unit='t', \ - **common).pipe(broadcast, node_loc=nodes)) + # Historical years are earlier than firstmodelyear + y_hist = [y for y in allyears if y < fmy] + # print(y_hist, fmy, years) + if re.search("historical_", param_name): + common_hist = dict( + year_vtg= y_hist, + year_act= y_hist, + mode="M1", + time="year",) + + df = (make_df(param_name, technology=t, value=val, unit='t', \ + **common_hist).pipe(broadcast, node_loc=nodes)) + # print(common_hist, param_name, t, nodes, val, y_hist) + else: + df = (make_df(param_name, technology=t, value=val, unit='t', \ + **common).pipe(broadcast, node_loc=nodes)) + results[param_name].append(df) + for r in s_info.set['relation']: + + # Read the file + rel_steel = process_china_data_rel() + + params = rel_steel.loc[(rel_steel["relation"] == r),\ + "parameter"].values.tolist() + + common_rel = dict( + year_rel = modelyears, + year_act = modelyears, + mode = 'M1', + relation = r,) + + for par_name in params: + if par_name == "relation_activity": + + val = rel_steel.loc[((rel_steel["relation"] == r) \ + & (rel_steel["parameter"] == par_name)),'value'].values[0] + tec = rel_steel.loc[((rel_steel["relation"] == r) \ + & (rel_steel["parameter"] == par_name)),'technology'].values[0] + + df = (make_df(par_name, technology=tec, value=val, unit='t',\ + **common_rel).pipe(broadcast, node_rel=nodes, node_loc=nodes)) + + results[par_name].append(df) + + elif par_name == "relation_lower": + + demand = gen_mock_demand() + + df = (make_df(par_name, value=demand, unit='t',\ + **common_rel).pipe(broadcast, node_rel=nodes)) + + results[par_name].append(df) + # Concatenate to one data frame per parameter results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} From c06934746b6eadc017f09f15e22a76d498eab829 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Tue, 18 Aug 2020 17:47:44 +0200 Subject: [PATCH 080/774] Add missing dummy supply tecs --- message_ix_models/data/material/set.yaml | 23 ++++++++++++----------- 1 file changed, 12 insertions(+), 11 deletions(-) diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 6674ffc059..16798dd06f 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -92,6 +92,18 @@ steel: - sr_steel - bof_steel - eaf_steel + - prep_secondary_steel + - finishing_steel + - manuf_steel + - scrap_recovery_steel + - DUMMY_coal_supply + - DUMMY_gas_supply + - DUMMY_electr_supply + - DUMMY_ore_supply + - DUMMY_limestone_supply + - DUMMY_hydrogen_supply + - DUMMY_freshwater_supply + - DUMMY_water_supply - furnace_hoil_steel - furnace_biofuel_steel - furnace_biomass_steel @@ -104,17 +116,6 @@ steel: - fc_h2_steel - solar_steel - dheat_steel - - prep_secondary_steel - - finishing_steel - - manuf_steel - - scrap_recovery_steel - - DUMMY_coal_supply - - DUMMY_gas_supply - - DUMMY_ore_supply - - DUMMY_limestone_supply - - DUMMY_hydrogen_supply - - DUMMY_freshwater_supply - - DUMMY_water_supply relation: add: From 95895344c24969ec2fcaf0d8397e88aa0b8cc042 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Tue, 18 Aug 2020 17:48:19 +0200 Subject: [PATCH 081/774] Update data (for the running dummy model) --- message_ix_models/data/material/China_steel_renamed.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/China_steel_renamed.xlsx b/message_ix_models/data/material/China_steel_renamed.xlsx index 5f2818eb7f..a5987e12d1 100644 --- a/message_ix_models/data/material/China_steel_renamed.xlsx +++ b/message_ix_models/data/material/China_steel_renamed.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:05056b90eb264abab583d17ca273a098d746f276d8fbc361b3b1401c29d11ddf -size 45756 +oid sha256:2a094a40af1c145be8682222db3efe3e60944964c72d75dede8a32970671e5a2 +size 47551 From d458cf02f31a355097645c7e8cca70f0d2db17d3 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 19 Aug 2020 11:36:59 +0200 Subject: [PATCH 082/774] Changes to gen_data_generic modifications on vintage and active years and modes --- message_ix_models/model/material/data.py | 75 ++++++++++++++++-------- 1 file changed, 50 insertions(+), 25 deletions(-) diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index e6b238cbff..4b81db3a63 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -1,6 +1,8 @@ from collections import defaultdict import logging - +import message_ix +import ixmp +import numpy as np import pandas as pd from message_ix import make_df from message_ix_models import ScenarioInfo @@ -197,15 +199,13 @@ def read_data_generic(): # Ensure config is loaded, get the context context = read_config() - # Shorter access to sets configuration sets = context["material"]["generic_set"] # Read the file data_generic = pd.read_excel( context.get_path("material", "generic_furnace_boiler_techno_economic.xlsx"), - sheet_name="generic", - ) + sheet_name="generic") # Clean the data # Drop columns that don't contain useful information @@ -303,13 +303,14 @@ def gen_data_aluminum(scenario, dry_run=False): # Temporary: return nothing, since the data frames are incomplete return results -# TODO: Add active years -# TODO: Different values over the years: Add a function that modifies the values -# over the years ? +# TODO: Different input values over the years: Add a function that modifies +#the values over the years + def gen_data_generic(scenario, dry_run=False): # Load configuration - config = read_config()["material"]["generic"] + # Load configuration + config = read_config()["material"]["generic_set"] # Information about scenario, e.g. node, year s_info = ScenarioInfo(scenario) @@ -319,21 +320,38 @@ def gen_data_generic(scenario, dry_run=False): # List of data frames, to be concatenated together at end results = defaultdict(list) - # For each technology there are differnet input and output combinations - # Iterate over technologies + # Iterate over technologies for t in config["technology"]["add"]: - years = s_info.Y + # Retreive the technology availability and vintage/active years + # Limit the years according to the available year + + av = data_generic.loc[(data_generic["technology"] == t),'availability']\ + .values[0] + lifetime = data_generic.loc[(data_generic["technology"] == t) & \ + (data_generic["parameter"]== "technical_lifetime"),'value'].values[0] + + # Can we use s_info.Y here ? + years_df = scenario.vintage_and_active_years() + years_df = years_df.loc[years_df["year_vtg"]>= av] + years_df_final = pd.DataFrame(columns=["year_vtg","year_act"]) + + # For each vintage adjsut the active years according to technical lifetime + for vtg in years_df["year_vtg"].unique(): + years_df_temp = years_df.loc[years_df["year_vtg"]== vtg] + years_df_temp = years_df_temp.loc[years_df["year_act"]< vtg + lifetime] + years_df_final = pd.concat([years_df_temp, years_df_final], ignore_index=True) + + vintage_years, act_years = years_df_final['year_vtg'], years_df_final['year_act'] + params = data_generic.loc[(data_generic["technology"] == t),"parameter"]\ .values.tolist() - # Availability year of the technology - av = data_generic.loc[(data_generic["technology"] == t),'availability'].\ - values[0] - years = [year for year in years if year >= av] + # Keep the available modes for a technology + mode_list = [] - # Iterate over parameters + # Iterate over parameters (e.g. input|coal|final|low_temp) for par in params: split = par.split("|") param_name = par.split("|")[0] @@ -342,11 +360,10 @@ def gen_data_generic(scenario, dry_run=False): (data_generic["parameter"] == par)),'value'].values[0] # Common parameters for all input and output tables - # year_act is none at the moment - # node_dest and node_origin are the same as node_loc common = dict( - year_vtg= years, + year_vtg= vintage_years, + year_act= act_years, time="year", time_origin="year", time_dest="year",) @@ -359,6 +376,10 @@ def gen_data_generic(scenario, dry_run=False): lev = split[2] mod = split[3] + # Store the available modes for a technology + + mode_list.append(mod) + df = (make_df(param_name, technology=t, commodity=com, \ level=lev,mode=mod, value=val, unit='t', **common).\ pipe(broadcast, node_loc=nodes).pipe(same_node)) @@ -367,15 +388,19 @@ def gen_data_generic(scenario, dry_run=False): elif param_name == "emission_factor": emi = split[1] + mod = data_generic.loc[((data_generic["technology"] == t) & (data_generic["parameter"] == par)),\ + 'value'].values[0] - df = (make_df(param_name, technology=t,value=val,\ - emission=emi, unit='t', **common).pipe(broadcast, \ - node_loc=nodes)) + # Create emission factor for existing different modes - results[param_name].append(df) + for m in np.unique(np.array(mode_list)): + df = (make_df(param_name, technology=t,value=val,\ + emission=emi,mode= m, unit='t', **common)\ + .pipe(broadcast, node_loc=nodes)) - # Rest of the parameters apart from inpput, output and emission_factor + results[param_name].append(df) + # Rest of the parameters apart from input, output and emission_factor else: df = (make_df(param_name, technology=t, value=val,unit='t', \ @@ -383,7 +408,7 @@ def gen_data_generic(scenario, dry_run=False): results[param_name].append(df) - results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} + results_generic = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} # Temporary: return nothing, since the data frames are incomplete return results From 0e98accc122041a9efe16665446f29dd809149ce Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 19 Aug 2020 13:39:25 +0200 Subject: [PATCH 083/774] Addition of vintage and active years to gen_data_aluminum --- message_ix_models/model/material/data.py | 299 +++-------------------- 1 file changed, 30 insertions(+), 269 deletions(-) diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index 4b81db3a63..0c76fc1ec0 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -69,111 +69,11 @@ def read_data(): return data - - -def process_china_data_tec(): - """Read and clean data from :file:`aluminum_techno_economic.xlsx`.""" - - import numpy as np - - # Ensure config is loaded, get the context - context = read_config() - - # Shorter access to sets configuration - # sets = context["material"]["aluminum"] - - # Read the file - data_steel_china = pd.read_excel( - context.get_path("material", "China_steel_renamed.xlsx"), - sheet_name="technologies", - ) - - # Clean the data - - data_steel_china = data_steel_china \ - [['Technology', 'Parameter', 'Level', \ - 'Commodity', 'Species', 'Units', 'Value']] \ - .replace(np.nan, '', regex=True) - - tuple_series = data_steel_china[['Parameter', 'Commodity', 'Level']] \ - .apply(tuple, axis=1) - tuple_ef = data_steel_china[['Parameter', 'Species']] \ - .apply(tuple, axis=1) - - - data_steel_china['parameter'] = tuple_series.str.join('|') \ - .str.replace('\|\|', '') - data_steel_china.loc[data_steel_china['Parameter'] == "emission_factor", \ - 'parameter'] = tuple_ef.str.join('|').str.replace('\|\|', '') - - data_steel_china = data_steel_china.drop(['Parameter', 'Level', 'Commodity'] \ - , axis = 1) - data_steel_china = data_steel_china.drop( \ - data_steel_china[data_steel_china.Value==''].index) - - data_steel_china.columns = data_steel_china.columns.str.lower() - - # Unit conversion - - # At the moment this is done in the excel file, can be also done here - # To make sure we use the same units - - return data_steel_china - - -def process_china_data_rel(): - """Read and clean data from :file:`aluminum_techno_economic.xlsx`.""" - - import numpy as np - - # Ensure config is loaded, get the context - context = read_config() - - # Shorter access to sets configuration - # sets = context["material"]["aluminum"] - - # Read the file - data_steel_china = pd.read_excel( - context.get_path("material", "China_steel_renamed.xlsx"), - sheet_name="relations", - ) - - return data_steel_china - - -def read_data_steel(): - """Read and clean data from :file:`steel_techno_economic.xlsx`.""" - - # Ensure config is loaded, get the context - context = read_config() - - # Shorter access to sets configuration - sets = context["material"]["steel"] - - # Read the file - data_steel = pd.read_excel( - context.get_path("material", "steel_techno_economic.xlsx"), - sheet_name="steel", - ) - - # Clean the data - - data_steel= data_steel.drop(['Region', 'Source', 'Description'], axis = 1) - - # Unit conversion - - # At the moment this is done in the excel file, can be also done here - # To make sure we use the same units - - return data_steel - -# Question: Do we need read data dunction seperately for all materials ? def read_data_aluminum(): """Read and clean data from :file:`aluminum_techno_economic.xlsx`.""" # Ensure config is loaded, get the context context = read_config() - # Shorter access to sets configuration sets = context["material"]["aluminum"] @@ -232,7 +132,7 @@ def gen_data_aluminum(scenario, dry_run=False): s_info = ScenarioInfo(scenario) # Techno-economic assumptions - data_aluminum = read_data() + data_aluminum = read_data_aluminum() # List of data frames, to be concatenated together at end results = defaultdict(list) @@ -241,15 +141,33 @@ def gen_data_aluminum(scenario, dry_run=False): for t in config["technology"]["add"]: + # Obtain the active and vintage years + av = data_aluminum.loc[(data_aluminum["technology"] == t),'availability']\ + .values[0] + + # For the technologies with lifetime + + if "technical_lifetime" in data_aluminum.loc[(data_aluminum["technology"] \ + == t)]["parameter"].values: + lifetime = data_aluminum.loc[(data_aluminum["technology"] == t) & \ + (data_aluminum["parameter"]== "technical_lifetime"),'value'].values[0] + years_df = scenario.vintage_and_active_years() + years_df = years_df.loc[years_df["year_vtg"]>= av] + years_df_final = pd.DataFrame(columns=["year_vtg","year_act"]) + + # For each vintage adjsut the active years according to technical lifetime + for vtg in years_df["year_vtg"].unique(): + years_df_temp = years_df.loc[years_df["year_vtg"]== vtg] + years_df_temp = years_df_temp.loc[years_df["year_act"]< vtg + lifetime] + years_df_final = pd.concat([years_df_temp, years_df_final], ignore_index=True) + + vintage_years, act_years = years_df_final['year_vtg'], years_df_final['year_act'] + params = data_aluminum.loc[(data_aluminum["technology"] == t),\ "parameter"].values.tolist() # Iterate over parameters - for par in params: - - # Obtain the parameter names, commodity,level,emission - split = par.split("|") param_name = split[0] @@ -259,8 +177,9 @@ def gen_data_aluminum(scenario, dry_run=False): & (data_aluminum["parameter"] == par)),'value'].values[0] common = dict( - year_vtg= years, - mode="M1", + year_vtg= vintage_years, + year_act = act_years, + mode="standard", time="year", time_origin="year", time_dest="year",) @@ -281,7 +200,6 @@ def gen_data_aluminum(scenario, dry_run=False): results[param_name].append(df) elif param_name == "emission_factor": - # Assign the emisson type emi = split[1] @@ -290,7 +208,7 @@ def gen_data_aluminum(scenario, dry_run=False): node_loc=nodes)) results[param_name].append(df) - # Parameters with only parameter name + # Rest of the parameters except input,output and emission_factor else: df = (make_df(param_name, technology=t, value=val,unit='t', \ @@ -298,10 +216,10 @@ def gen_data_aluminum(scenario, dry_run=False): results[param_name].append(df) # Concatenate to one data frame per parameter - results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} + results_aluminum = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} # Temporary: return nothing, since the data frames are incomplete - return results + return results_aluminum # TODO: Different input values over the years: Add a function that modifies #the values over the years @@ -410,164 +328,7 @@ def gen_data_generic(scenario, dry_run=False): results_generic = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} - # Temporary: return nothing, since the data frames are incomplete - return results - - -def gen_mock_demand(): - # True steel use 2010 (China) = 537 Mt/year - # https://www.worldsteel.org/en/dam/jcr:0474d208-9108-4927-ace8-4ac5445c5df8/World+Steel+in+Figures+2017.pdf - gdp_growth = [0.121448215899944, \ - 0.0733079014579874, 0.0348154093342843, \ - 0.021827616787921,0.0134425983942219, 0.0108320197485592, \ - 0.00884341208063,0.00829374133206562, 0.00649794573935969] - demand = [(x+1) * 537 for x in gdp_growth] - - return demand - -def gen_data_steel(scenario, dry_run=False): - """Generate data for materials representation of steel industry. - - """ - # Load configuration - # config = read_config()["material"]["steel"] - - # Information about scenario, e.g. node, year - s_info = ScenarioInfo(scenario) - - # Techno-economic assumptions - data_steel = process_china_data_tec() - - # List of data frames, to be concatenated together at end - results = defaultdict(list) - - # For each technology there are differnet input and output combinations - # Iterate over technologies - - allyears = s_info.set['year'] #s_info.Y is only for modeling years - modelyears = s_info.Y #s_info.Y is only for modeling years - nodes = s_info.N - yv_ya = s_info.yv_ya - fmy = s_info.y0 - - nodes.remove('World') # For the bare model - - for t in s_info.set['technology']: - - params = data_steel.loc[(data_steel["technology"] == t),\ - "parameter"].values.tolist() - - # Iterate over parameters - - for par in params: - - # Obtain the parameter names, commodity,level,emission - - split = par.split("|") - param_name = split[0] - - # Obtain the scalar value for the parameter - - val = data_steel.loc[((data_steel["technology"] == t) \ - & (data_steel["parameter"] == par)),'value'].values[0] - - common = dict( - year_vtg= yv_ya.year_vtg, - year_act= yv_ya.year_act, - mode="M1", - time="year", - time_origin="year", - time_dest="year",) - - # For the parameters which inlcudes index names - if len(split)> 1: - - if (param_name == "input")|(param_name == "output"): - - # Assign commodity and level names - com = split[1] - lev = split[2] - - df = (make_df(param_name, technology=t, commodity=com, \ - level=lev, value=val, unit='t', **common)\ - .pipe(broadcast, node_loc=nodes).pipe(same_node)) - - elif param_name == "emission_factor": - - # Assign the emisson type - emi = split[1] - - df = (make_df(param_name, technology=t,value=val,\ - emission=emi, unit='t', **common).pipe(broadcast, \ - node_loc=nodes)) - - results[param_name].append(df) - - # Parameters with only parameter name - - else: - # Historical years are earlier than firstmodelyear - y_hist = [y for y in allyears if y < fmy] - # print(y_hist, fmy, years) - if re.search("historical_", param_name): - common_hist = dict( - year_vtg= y_hist, - year_act= y_hist, - mode="M1", - time="year",) - - df = (make_df(param_name, technology=t, value=val, unit='t', \ - **common_hist).pipe(broadcast, node_loc=nodes)) - # print(common_hist, param_name, t, nodes, val, y_hist) - else: - df = (make_df(param_name, technology=t, value=val, unit='t', \ - **common).pipe(broadcast, node_loc=nodes)) - - results[param_name].append(df) - - for r in s_info.set['relation']: - - # Read the file - rel_steel = process_china_data_rel() - - params = rel_steel.loc[(rel_steel["relation"] == r),\ - "parameter"].values.tolist() - - common_rel = dict( - year_rel = modelyears, - year_act = modelyears, - mode = 'M1', - relation = r,) - - for par_name in params: - if par_name == "relation_activity": - - val = rel_steel.loc[((rel_steel["relation"] == r) \ - & (rel_steel["parameter"] == par_name)),'value'].values[0] - tec = rel_steel.loc[((rel_steel["relation"] == r) \ - & (rel_steel["parameter"] == par_name)),'technology'].values[0] - - df = (make_df(par_name, technology=tec, value=val, unit='t',\ - **common_rel).pipe(broadcast, node_rel=nodes, node_loc=nodes)) - - results[par_name].append(df) - - elif par_name == "relation_lower": - - demand = gen_mock_demand() - - df = (make_df(par_name, value=demand, unit='t',\ - **common_rel).pipe(broadcast, node_rel=nodes)) - - results[par_name].append(df) - - # Concatenate to one data frame per parameter - results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} - - # Temporary: return nothing, since the data frames are incomplete - return results - - + return results_generic def gen_data(scenario, dry_run=False): """Generate data for materials representation of nitrogen fertilizers. From 67590dd9b2839c5da03bee7fa145bd086cb26d8b Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 19 Aug 2020 14:08:42 +0200 Subject: [PATCH 084/774] Structural change --- message_ix_models/data/material/config.yaml | 33 +++++++-------------- message_ix_models/model/material/data.py | 4 +-- 2 files changed, 13 insertions(+), 24 deletions(-) diff --git a/message_ix_models/data/material/config.yaml b/message_ix_models/data/material/config.yaml index 74f4951e95..95b9ea0c57 100644 --- a/message_ix_models/data/material/config.yaml +++ b/message_ix_models/data/material/config.yaml @@ -1,8 +1,4 @@ # Configuration for message_data.model.material -# -# NB these values are currently for the nitrogen fertilizer materials -# accounting. Different sets can be created once there are 2+ different -# categories of materials. aluminum: commodity: @@ -10,6 +6,15 @@ aluminum: - ht_heat - lt_heat - aluminum + require: + - d_heat + - electr + - coal + - fueloil + - ethanol + - biomass + - gas + - hydrogen level: add: - useful_aluminum @@ -18,7 +23,8 @@ aluminum: - final_material - useful_material - product - + require: + - final technology: add: - soderberg_aluminum @@ -74,23 +80,6 @@ set: - NH3_to_N_fertil generic_set: - commodity: - require: - - d_heat - - electr - - freshwater_supply - - coal - - fueloil - - ethanol - - biomass - - gas - - hydrogen - level: - require: - - primary - - secondary - - water_supply - - final technology: add: - furnace_coal_aluminum diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index 0c76fc1ec0..02f3c02726 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -306,8 +306,8 @@ def gen_data_generic(scenario, dry_run=False): elif param_name == "emission_factor": emi = split[1] - mod = data_generic.loc[((data_generic["technology"] == t) & (data_generic["parameter"] == par)),\ - 'value'].values[0] + mod = data_generic.loc[((data_generic["technology"] == t) \ + & (data_generic["parameter"] == par)),'value'].values[0] # Create emission factor for existing different modes From feeadc94aeafccfd2bec47d4c1758d08320886e0 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 26 Aug 2020 17:54:19 +0200 Subject: [PATCH 085/774] Addition of temporary code to run stand alone aluminum model --- .../aluminum_techno_economic.xlsx | 3 + .../aluminum_stand_alone/generate_data_AL.py | 130 +++++++ .../generate_data_generic.py | 135 +++++++ ...eneric_furnace_boiler_techno_economic.xlsx | 3 + .../material/aluminum_stand_alone/plotting.py | 116 ++++++ .../aluminum_stand_alone/stand_alone_AL.py | 332 ++++++++++++++++++ .../material/aluminum_stand_alone/tools.py | 6 + .../material/aluminum_stand_alone/trial.py | 9 + .../aluminum_stand_alone/variable_costs.xlsx | 3 + 9 files changed, 737 insertions(+) create mode 100644 message_ix_models/model/material/aluminum_stand_alone/aluminum_techno_economic.xlsx create mode 100644 message_ix_models/model/material/aluminum_stand_alone/generate_data_AL.py create mode 100644 message_ix_models/model/material/aluminum_stand_alone/generate_data_generic.py create mode 100644 message_ix_models/model/material/aluminum_stand_alone/generic_furnace_boiler_techno_economic.xlsx create mode 100644 message_ix_models/model/material/aluminum_stand_alone/plotting.py create mode 100644 message_ix_models/model/material/aluminum_stand_alone/stand_alone_AL.py create mode 100644 message_ix_models/model/material/aluminum_stand_alone/tools.py create mode 100644 message_ix_models/model/material/aluminum_stand_alone/trial.py create mode 100644 message_ix_models/model/material/aluminum_stand_alone/variable_costs.xlsx diff --git a/message_ix_models/model/material/aluminum_stand_alone/aluminum_techno_economic.xlsx b/message_ix_models/model/material/aluminum_stand_alone/aluminum_techno_economic.xlsx new file mode 100644 index 0000000000..73dfc61cbf --- /dev/null +++ b/message_ix_models/model/material/aluminum_stand_alone/aluminum_techno_economic.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:000a5f764b194a3a3208fb872dc3d7ef1204fc80fd21b1d4923ac9b34530ec79 +size 51772 diff --git a/message_ix_models/model/material/aluminum_stand_alone/generate_data_AL.py b/message_ix_models/model/material/aluminum_stand_alone/generate_data_AL.py new file mode 100644 index 0000000000..7556d58f0f --- /dev/null +++ b/message_ix_models/model/material/aluminum_stand_alone/generate_data_AL.py @@ -0,0 +1,130 @@ +# -*- coding: utf-8 -*- +""" +Created on Wed Aug 26 14:43:41 2020 +Generate techno economic aluminum data based on aluminum_techno_economic.xlsx + +@author: unlu +""" +import pandas as pd +import numpy as np +from collections import defaultdict +from message_data.tools import broadcast, make_df, same_node +import message_ix +import ixmp + + +def gen_data_aluminum(): + + mp = ixmp.Platform() + + scenario = message_ix.Scenario(mp,"MESSAGE_material","baseline", cache=True) + + data_aluminum = pd.read_excel("aluminum_techno_economic.xlsx", + sheet_name="aluminum") + + data_aluminum_hist = pd.read_excel("aluminum_techno_economic.xlsx", + sheet_name="aluminum_historical", + usecols = "A:F") + # Clean the data + # Drop columns that don't contain useful information + data_aluminum= data_aluminum.drop(['Region', 'Source', 'Description'], + axis = 1) + # List of data frames, to be concatenated together at the end + results = defaultdict(list) + # Will come from the yaml file + technology_add = ["soderberg_aluminum", "prebake_aluminum", + "secondary_aluminum", "prep_secondary_aluminum", + "finishing_aluminum", "manuf_aluminum", + "scrap_recovery_aluminum", "alumina_supply"] + + # normally from s_info.Y + years = [2010,2020,2030,2040,2050,2060,2070,2080,2090,2100] + + # normally from s_info.N + nodes = ["CHN"] + + # Iterate over technologies + + for t in technology_add: + # Obtain the active and vintage years + av = data_aluminum.loc[(data_aluminum["technology"] == t), + 'availability'].values[0] + if "technical_lifetime" in data_aluminum.loc[ + (data_aluminum["technology"] == t)]["parameter"].values: + lifetime = data_aluminum.loc[(data_aluminum["technology"] == t) + & (data_aluminum["parameter"] == + "technical_lifetime"), 'value'].\ + values[0] + years_df = scenario.vintage_and_active_years() + years_df = years_df.loc[years_df["year_vtg"]>= av] + # Empty data_frame + years_df_final = pd.DataFrame(columns=["year_vtg","year_act"]) + + # For each vintage adjsut the active years according to technical + # lifetime + for vtg in years_df["year_vtg"].unique(): + years_df_temp = years_df.loc[years_df["year_vtg"]== vtg] + years_df_temp = years_df_temp.loc[years_df["year_act"] + < vtg + lifetime] + years_df_final = pd.concat([years_df_temp, years_df_final], + ignore_index=True) + vintage_years, act_years = years_df_final['year_vtg'], \ + years_df_final['year_act'] + + params = data_aluminum.loc[(data_aluminum["technology"] == t),\ + "parameter"].values.tolist() + # Iterate over parameters + for par in params: + split = par.split("|") + param_name = par.split("|")[0] + + val = data_aluminum.loc[((data_aluminum["technology"] == t) & + (data_aluminum["parameter"] == par)),\ + 'value'].values[0] + + # Common parameters for all input and output tables + # year_act is none at the moment + # node_dest and node_origin are the same as node_loc + + common = dict( + year_vtg= vintage_years, + year_act= act_years, + mode="standard", + time="year", + time_origin="year", + time_dest="year",) + + if len(split)> 1: + + if (param_name == "input")|(param_name == "output"): + + com = split[1] + lev = split[2] + + df = (make_df(param_name, technology=t, commodity=com, + level=lev, value=val, unit='t', **common) + .pipe(broadcast, node_loc=nodes).pipe(same_node)) + + results[param_name].append(df) + + elif param_name == "emission_factor": + emi = split[1] + + df = (make_df(param_name, technology=t,value=val, + emission=emi, unit='t', **common) + .pipe(broadcast, node_loc=nodes)) + results[param_name].append(df) + + # Rest of the parameters apart from inpput, output and + # emission_factor + else: + df = (make_df(param_name, technology=t, value=val,unit='t', + **common).pipe(broadcast, node_loc=nodes)) + results[param_name].append(df) + + results_aluminum = {par_name: pd.concat(dfs) for par_name, + dfs in results.items()} + + return results_aluminum, data_aluminum_hist + + diff --git a/message_ix_models/model/material/aluminum_stand_alone/generate_data_generic.py b/message_ix_models/model/material/aluminum_stand_alone/generate_data_generic.py new file mode 100644 index 0000000000..c14c1d615c --- /dev/null +++ b/message_ix_models/model/material/aluminum_stand_alone/generate_data_generic.py @@ -0,0 +1,135 @@ +# -*- coding: utf-8 -*- +""" +Created on Wed Aug 26 14:54:24 2020 +Generate techno economic generic furncaedata based on +generic_furnace_boiler_techno_economic.xlsx + +@author: unlu + +""" +import pandas as pd +import numpy as np +from collections import defaultdict +from message_data.tools import broadcast, make_df, same_node +import message_ix +import ixmp + +def gen_data_generic(): + + mp = ixmp.Platform() + + scenario = message_ix.Scenario(mp,"MESSAGE_material","baseline", cache=True) + + data_generic = pd.read_excel("generic_furnace_boiler_techno_economic.xlsx", + sheet_name="generic") + data_generic= data_generic.drop(['Region', 'Source', 'Description'], + axis = 1) + + # List of data frames, to be concatenated together at the end + results = defaultdict(list) + + # This comes from the config file normally + technology_add = ['furnace_coal_aluminum', 'furnace_foil_aluminum', + 'furnace_methanol_aluminum', 'furnace_biomass_aluminum', + 'furnace_ethanol_aluminum', 'furnace_gas_aluminum', + 'furnace_elec_aluminum', 'furnace_h2_aluminum', + 'hp_gas_aluminum', 'hp_elec_aluminum', 'fc_h2_aluminum', + 'solar_aluminum','dheat_aluminum',"furnace_loil_aluminum"] + + # normally from s_info.Y + years = [2010,2020,2030,2040,2050,2060,2070,2080,2090,2100] + + # normally from s_info.N + nodes = ["CHN"] + + # For each technology there are differnet input and output combinations + # Iterate over technologies + + for t in technology_add: + + # Obtain the active and vintage years + av = data_generic.loc[(data_generic["technology"] == t), + 'availability'].values[0] + lifetime = data_generic.loc[(data_generic["technology"] == t) \ + & (data_generic["parameter"]== + "technical_lifetime"),'value'].values[0] + years_df = scenario.vintage_and_active_years() + years_df = years_df.loc[years_df["year_vtg"]>= av] + years_df_final = pd.DataFrame(columns=["year_vtg","year_act"]) + + # For each vintage adjsut the active years according to technical + # lifetime + for vtg in years_df["year_vtg"].unique(): + years_df_temp = years_df.loc[years_df["year_vtg"]== vtg] + years_df_temp = years_df_temp.loc[years_df["year_act"] + < vtg + lifetime] + years_df_final = pd.concat([years_df_temp, years_df_final], + ignore_index=True) + + vintage_years, act_years = years_df_final['year_vtg'], \ + years_df_final['year_act'] + + params = data_generic.loc[(data_generic["technology"] == t), + "parameter"].values.tolist() + + # To keep the available modes and use it in the emission_factor + # parameter. + mode_list = [] + + # Iterate over parameters + for par in params: + + split = par.split("|") + param_name = par.split("|")[0] + + val = data_generic.loc[((data_generic["technology"] == t) + & (data_generic["parameter"] == par)), 'value'].values[0] + + # Common parameters for all input and output tables + common = dict( + year_vtg= vintage_years, + year_act = act_years, + time="year", + time_origin="year", + time_dest="year",) + + if len(split)> 1: + + if (param_name == "input")|(param_name == "output"): + + com = split[1] + lev = split[2] + mod = split[3] + + # Keep the available modes for a technology in a list + mode_list.append(mod) + df = (make_df(param_name, technology=t, commodity=com, + level=lev,mode=mod, value=val, unit='t', + **common).pipe(broadcast, node_loc=nodes). + pipe(same_node)) + results[param_name].append(df) + + elif param_name == "emission_factor": + emi = split[1] + mod = data_generic.loc[((data_generic["technology"] == t) + & (data_generic["parameter"] == par)), 'value'].values[0] + + # For differnet modes + for m in np.unique(np.array(mode_list)): + + df = (make_df(param_name, technology=t,value=val, + emission=emi,mode= m, unit='t', + **common).pipe(broadcast, node_loc=nodes)) + results[param_name].append(df) + + # Rest of the parameters apart from inpput, output and + # emission_factor + else: + df = (make_df(param_name, technology=t, value=val,unit='t', + **common).pipe(broadcast, node_loc=nodes)) + results[param_name].append(df) + + results_generic = {par_name: pd.concat(dfs) for par_name, + dfs in results.items()} + return results_generic + \ No newline at end of file diff --git a/message_ix_models/model/material/aluminum_stand_alone/generic_furnace_boiler_techno_economic.xlsx b/message_ix_models/model/material/aluminum_stand_alone/generic_furnace_boiler_techno_economic.xlsx new file mode 100644 index 0000000000..50a7e95e15 --- /dev/null +++ b/message_ix_models/model/material/aluminum_stand_alone/generic_furnace_boiler_techno_economic.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:65fa11feff07c45ba0a9c31543443c74147e3e9a6e902ad349913f03df7ec68b +size 47970 diff --git a/message_ix_models/model/material/aluminum_stand_alone/plotting.py b/message_ix_models/model/material/aluminum_stand_alone/plotting.py new file mode 100644 index 0000000000..74e58cb941 --- /dev/null +++ b/message_ix_models/model/material/aluminum_stand_alone/plotting.py @@ -0,0 +1,116 @@ +import pandas as pd +import matplotlib.pyplot as plt + + +class Plots(object): + def __init__(self, ds, country, firstyear=2010, input_costs='$/GWa'): + self.ds = ds + self.country = country + self.firstyear = firstyear + + if input_costs == '$/MWa': + self.cost_unit_conv = 1e3 + elif input_costs == '$/kWa': + self.cost_unit_conv = 1e6 + else: + self.cost_unit_conv = 1 + + def historic_data(self, par, year_col, subset=None): + df = self.ds.par(par) + if subset is not None: + df = df[df['technology'].isin(subset)] + idx = [year_col, 'technology'] + df = df[idx + ['value']].groupby(idx).sum().reset_index() + df = df.pivot(index=year_col, columns='technology', + values='value') + return df + + def model_data(self, var, year_col='year_act', baseyear=False, + subset=None): + df = self.ds.var(var) + if subset is not None: + df = df[df['technology'].isin(subset)] + idx = [year_col, 'technology'] + df = (df[idx + ['lvl']] + .groupby(idx) + .sum() + .reset_index() + .pivot(index=year_col, columns='technology', + values='lvl') + .rename(columns={'lvl': 'value'}) + ) + df = df[df.index >= self.firstyear] + return df + + def equ_data(self, equ, value, baseyear=False, subset=None): + df = self.ds.equ(equ) + if not baseyear: + df = df[df['year'] > self.firstyear] + if subset is not None: + df = df[df['commodity'].isin(subset)] + df = df.pivot(index='year', columns='commodity', values=value) + return df + + def plot_demand(self, light_demand, elec_demand): + fig, ax = plt.subplots() + light = light_demand['value'] + light.plot(ax=ax, label='light') + e = elec_demand['value'] + e.plot(ax=ax, label='elec') + (light + e).plot(ax=ax, label='total') + plt.ylabel('GWa') + plt.xlabel('Year') + plt.legend(loc='best') + + def plot_activity(self, baseyear=False, subset=None): + h = self.historic_data('historical_activity', + 'year_act', subset=subset) + m = self.model_data('ACT', baseyear=baseyear, subset=subset) + df = pd.concat([h, m]) if not h.empty else m + df.plot.bar(stacked=True) + plt.title('{} Energy System Activity'.format(self.country.title())) + plt.ylabel('GWa') + plt.xlabel('Year') + plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5)) + + def plot_capacity(self, baseyear=False, subset=None): + df = self.model_data('CAP', baseyear=baseyear, subset=subset) + df.plot.bar(stacked=True) + plt.title('{} Energy System Capacity'.format(self.country.title())) + plt.ylabel('GW') + plt.xlabel('Year') + plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5)) + + def plot_new_capacity(self, baseyear=False, subset=None): + h = self.historic_data('historical_new_capacity', + 'year_vtg', subset=subset) + m = self.model_data('CAP_NEW', 'year_vtg', + baseyear=baseyear, subset=subset) + df = pd.concat([h, m]) if not h.empty else m + df.plot.bar(stacked=True) + plt.title('{} Energy System New Capcity'.format(self.country.title())) + plt.ylabel('GW') + plt.xlabel('Year') + plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5)) + + def plot_prices(self, baseyear=False, subset=None): + df = self.ds.var('PRICE_COMMODITY') + if not baseyear: + df = df[df['year'] > self.firstyear] + if subset is not None: + df = df[df['commodity'].isin(subset)] + idx = ['year', 'commodity'] + df = (df[idx + ['lvl']] + .groupby(idx) + .sum(). + reset_index() + .pivot(index='year', columns='commodity', + values='lvl') + .rename(columns={'lvl': 'value'}) + ) + df = df / 8760 * 100 * self.cost_unit_conv + df.plot.bar(stacked=False) + plt.title('{} Energy System Prices'.format(self.country.title())) + plt.ylabel('cents/kWhr') + plt.xlabel('Year') + plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5)) diff --git a/message_ix_models/model/material/aluminum_stand_alone/stand_alone_AL.py b/message_ix_models/model/material/aluminum_stand_alone/stand_alone_AL.py new file mode 100644 index 0000000000..32f8604209 --- /dev/null +++ b/message_ix_models/model/material/aluminum_stand_alone/stand_alone_AL.py @@ -0,0 +1,332 @@ +# -*- coding: utf-8 -*- +""" +Created on Wed Aug 26 13:41:29 2020 + +"Aluminum stand-alone run" +"Solves and plots activity and capacity" + +@author: unlu +""" +import message_ix +import ixmp +import pandas as pd +from message_data.tools import make_df +from tools import Plots +from generate_data_AL import gen_data_aluminum as gen_data_aluminum +from generate_data_generic import gen_data_generic as gen_data_generic + +mp = ixmp.Platform() + +# Adding a new unit to the library +mp.add_unit('Mt') + +# New model +scenario = message_ix.Scenario(mp, model='MESSAGE_material', + scenario='baseline', version='new') +# Addition of basics + +history = [1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015] +model_horizon = [2020, 2030, 2040, 2050, 2060, 2070, 2080, 2090, 2100] +scenario.add_horizon({'year': history + model_horizon, + 'firstmodelyear': model_horizon[0]}) +country = 'CHN' +scenario.add_spatial_sets({'country': country}) + +# These will come from the yaml file + +commodities = ['ht_heat', 'lt_heat', 'aluminum', 'd_heat', "electr", + "coal", "fueloil", "ethanol", "biomass", "gas", "hydrogen", + "methanol", "lightoil"] + +scenario.add_set("commodity", commodities) + +levels = ['useful_aluminum', 'new_scrap', 'old_scrap', 'final_material', + 'useful_material', 'product', "secondary_material", "final", + "demand"] +scenario.add_set("level", levels) + +technologies = ['soderberg_aluminum', 'prebake_aluminum', 'secondary_aluminum', + 'prep_secondary_aluminum', 'finishing_aluminum', + 'manuf_aluminum', 'scrap_recovery_aluminum', + 'furnace_coal_aluminum', 'furnace_foil_aluminum', + 'furnace_methanol_aluminum', 'furnace_biomass_aluminum', + 'furnace_ethanol_aluminum', 'furnace_gas_aluminum', + 'furnace_elec_aluminum', 'furnace_h2_aluminum', + 'hp_gas_aluminum', 'hp_elec_aluminum', 'fc_h2_aluminum', + 'solar_aluminum', 'dheat_aluminum', 'furnace_loil_aluminum', + "alumina_supply"] + +scenario.add_set("technology", technologies) +scenario.add_set("mode", ['standard', 'low_temp', 'high_temp']) + +# Create duration period + +val = [j-i for i, j in zip(model_horizon[:-1], model_horizon[1:])] +val.append(val[0]) + +duration_period = pd.DataFrame({ + 'year': model_horizon, + 'value': val, + 'unit': "y", + }) + +scenario.add_par("duration_period", duration_period) +duration_period = duration_period["value"].values + +# Energy system: Unlimited supply of the commodities. +# Fuel costs are obtained from the PRICE_COMMODITY baseline SSP2 global model. +# PRICE_COMMODTY * input -> (2005USD/Gwa-coal)*(Gwa-coal / ACT of furnace) +# = (2005USD/ACT of furnace) + +years_df = scenario.vintage_and_active_years() +vintage_years, act_years = years_df['year_vtg'], years_df['year_act'] + +# Choose the prices in excel (baseline vs. NPi400) +data_var_cost = pd.read_excel("variable_costs.xlsx", sheet_name="data") + +for row in data_var_cost.index: + data = data_var_cost.iloc[row] + values = [] + for yr in act_years: + values.append(data[yr]) + base_var_cost = pd.DataFrame({ + 'node_loc': country, + 'year_vtg': vintage_years.values, + 'year_act': act_years.values, + 'mode': data["mode"], + 'time': 'year', + 'unit': 'USD/GWa', + "technology": data["technology"], + "value": values + }) + + scenario.add_par("var_cost",base_var_cost) + +# Add dummy technologies to represent energy system + +dummy_tech = ["electr_gen", "dist_heating", "coal_gen", "foil_gen", "eth_gen", + "biomass_gen", "gas_gen", "hydrogen_gen", "meth_gen", "loil_gen"] +scenario.add_set("technology", dummy_tech) + +commodity_tec = ["electr", "d_heat", "coal", "fueloil", "ethanol", "biomass", + "gas", "hydrogen", "methanol", "lightoil"] + +# Add output to dummy tech. + +year_df = scenario.vintage_and_active_years() +vintage_years, act_years = year_df['year_vtg'], year_df['year_act'] + +base = { + 'node_loc': country, + "node_dest": country, + "time_dest": "year", + 'year_vtg': vintage_years, + 'year_act': act_years, + 'mode': 'standard', + 'time': 'year', + "level": "final", + 'unit': '-', + "value": 1.0 +} + +t = 0 + +for tec in dummy_tech: + out = make_df("output", technology= tec, commodity = commodity_tec[t], + **base) + t = t + 1 + scenario.add_par("output", out) + +# Introduce emissions +scenario.add_set('emission', 'CO2') +scenario.add_cat('emission', 'GHG', 'CO2') + +# Run read data aluminum + +results_al, data_aluminum_hist = gen_data_aluminum() + +for k, v in results_al.items(): + scenario.add_par(k,v) + +results_generic = gen_data_generic() + +for k, v in results_generic.items(): + scenario.add_par(k,v) + +# Add temporary exogenous demand: 17.3 Mt in 2010 (IAI) +# The future projection of the demand: Increases by half of the GDP growth rate +# gdp_growth rate: SSP2 global model. Starting from 2020. +gdp_growth = pd.Series([0.121448215899944, 0.0733079014579874, + 0.0348154093342843, 0.021827616787921, + 0.0134425983942219, 0.0108320197485592, + 0.00884341208063, 0.00829374133206562, + 0.00649794573935969], + index=pd.Index(model_horizon, name='Time')) + +fin_to_useful = scenario.par("output", filters = {"technology": + "finishing_aluminum","year_act":2020})["value"][0] +useful_to_product = scenario.par("output", filters = {"technology": + "manuf_aluminum","year_act":2020})["value"][0] +i = 0 +values = [] +val = (17.3 * (1+ 0.147718884937996/2) ** duration_period[i]) +values.append(val) + +for element in gdp_growth: + i = i + 1 + if i < len(model_horizon): + val = (val * (1+ element/2) ** duration_period[i]) + values.append(val) + +# Adjust the demand to product level. + +values = [x * fin_to_useful * useful_to_product for x in values] +aluminum_demand = pd.DataFrame({ + 'node': country, + 'commodity': 'aluminum', + 'level': 'demand', + 'year': model_horizon, + 'time': 'year', + 'value': values , + 'unit': 'Mt', + }) + +scenario.add_par("demand", aluminum_demand) + +# Interest rate +scenario.add_par("interestrate", model_horizon, value=0.05, unit='-') + +# Add historical production and capacity + +for tec in data_aluminum_hist["technology"].unique(): + hist_activity = pd.DataFrame({ + 'node_loc': country, + 'year_act': history, + 'mode': data_aluminum_hist.loc[(data_aluminum_hist["technology"]== tec), + "mode"], + 'time': 'year', + 'unit': 'Mt', + "technology": tec, + "value": data_aluminum_hist.loc[(data_aluminum_hist["technology"]== tec), + "production"] + }) + scenario.add_par('historical_activity', hist_activity) + +for tec in data_aluminum_hist["technology"].unique(): + c_factor = scenario.par("capacity_factor", filters = {"technology": tec})\ + ["value"].values[0] + value = data_aluminum_hist.loc[(data_aluminum_hist["technology"]== tec), + "new_production"] / c_factor + hist_capacity = pd.DataFrame({ + 'node_loc': country, + 'year_vtg': history, + 'unit': 'Mt', + "technology": tec, + "value": value }) + scenario.add_par('historical_new_capacity', hist_capacity) + +# Historical thermal demand depending on the historical aluminum production +# This section can be revised to make shorter and generic to other materials + +scrap_recovery = scenario.par("output", filters = {"technology": + "scrap_recovery_aluminum","level":"old_scrap","year_act":2020})["value"][0] +high_th = scenario.par("input", filters = {"technology": + "secondary_aluminum","year_act":2020})["value"][0] +low_th = scenario.par("input", filters = {"technology": + "prep_secondary_aluminum","year_act":2020})["value"][0] + +historic_generation = scenario.par("historical_activity").\ +groupby("year_act").sum() + +for yr in historic_generation.index: + + total_scrap = ((historic_generation.loc[yr].value * fin_to_useful * \ + (1- useful_to_product)) + (historic_generation.loc[yr] \ + * fin_to_useful * useful_to_product * scrap_recovery)) + old_scrap = (historic_generation.loc[yr] * fin_to_useful * \ + useful_to_product * scrap_recovery) + total_hist_act = total_scrap * high_th + old_scrap * low_th + + hist_activity_h = pd.DataFrame({ + 'node_loc': country, + 'year_act': yr, + 'mode': 'high_temp', + 'time': 'year', + 'unit': 'GWa', + "technology": "furnace_gas_aluminum", + "value": total_scrap * high_th + }) + + scenario.add_par('historical_activity', hist_activity_h) + + hist_activity_l = pd.DataFrame({ + 'node_loc': country, + 'year_act': yr, + 'mode': 'low_temp', + 'time': 'year', + 'unit': 'GWa', + "technology": "furnace_gas_aluminum", + "value": old_scrap * low_th + }) + + scenario.add_par('historical_activity', hist_activity_l) + + c_fac_furnace_gas = scenario.par("capacity_factor", filters = + {"technology": "furnace_gas_aluminum"})\ + ["value"].values[0] + + hist_capacity_gas = pd.DataFrame({ + 'node_loc': country, + 'year_vtg': yr, + 'unit': 'GW', + "technology": "furnace_gas_aluminum", + "value": total_hist_act / c_fac_furnace_gas }) + + scenario.add_par('historical_new_capacity', hist_capacity_gas) + +scenario.commit("changes") +scenario.solve() + +# Be aware plots produce the same color for some technologies. + +p = Plots(scenario, country, firstyear=model_horizon[0]) + +p.plot_activity(baseyear=True, subset=['soderberg_aluminum', + 'prebake_aluminum',"secondary_aluminum"]) +p.plot_capacity(baseyear=True, subset=['soderberg_aluminum', 'prebake_aluminum']) +p.plot_activity(baseyear=True, subset=["prep_secondary_aluminum"]) +p.plot_activity(baseyear=True, subset=['furnace_coal_aluminum', + 'furnace_foil_aluminum', + 'furnace_methanol_aluminum', + 'furnace_biomass_aluminum', + 'furnace_ethanol_aluminum', + 'furnace_gas_aluminum', + 'furnace_elec_aluminum', + 'furnace_h2_aluminum', + 'hp_gas_aluminum', + 'hp_elec_aluminum','fc_h2_aluminum', + 'solar_aluminum', 'dheat_aluminum', + 'furnace_loil_aluminum']) +p.plot_capacity(baseyear=True, subset=['furnace_coal_aluminum', + 'furnace_foil_aluminum', + 'furnace_methanol_aluminum', + 'furnace_biomass_aluminum', + 'furnace_ethanol_aluminum', + 'furnace_gas_aluminum', + 'furnace_elec_aluminum', + 'furnace_h2_aluminum', + 'hp_gas_aluminum', + 'hp_elec_aluminum','fc_h2_aluminum', + 'solar_aluminum', 'dheat_aluminum', + 'furnace_loil_aluminum']) +p.plot_prices(subset=['aluminum'], baseyear=True) + + + + + + + + + + diff --git a/message_ix_models/model/material/aluminum_stand_alone/tools.py b/message_ix_models/model/material/aluminum_stand_alone/tools.py new file mode 100644 index 0000000000..2974d50e2e --- /dev/null +++ b/message_ix_models/model/material/aluminum_stand_alone/tools.py @@ -0,0 +1,6 @@ +from pathlib import Path +import sys + +sys.path.append(str(Path(__file__).parents[1] / "utils")) + +from plotting import Plots # noqa: E402, F401 diff --git a/message_ix_models/model/material/aluminum_stand_alone/trial.py b/message_ix_models/model/material/aluminum_stand_alone/trial.py new file mode 100644 index 0000000000..882f54ec5f --- /dev/null +++ b/message_ix_models/model/material/aluminum_stand_alone/trial.py @@ -0,0 +1,9 @@ +# -*- coding: utf-8 -*- +""" +Created on Wed Aug 26 16:28:28 2020 + +@author: unlu +""" + +import ixmp +mp = ixmp.Platform() \ No newline at end of file diff --git a/message_ix_models/model/material/aluminum_stand_alone/variable_costs.xlsx b/message_ix_models/model/material/aluminum_stand_alone/variable_costs.xlsx new file mode 100644 index 0000000000..06bf81e051 --- /dev/null +++ b/message_ix_models/model/material/aluminum_stand_alone/variable_costs.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8fb9ba5e09a19bd9e2d01f473533b07d082c692771110c869b6757d65049fdfe +size 38085 From 31adefad4782c6c2f6f72d552a1702220809df7c Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 27 Aug 2020 16:06:57 +0200 Subject: [PATCH 086/774] Updated temporary aluminum stand-alone --- .../aluminum_techno_economic.xlsx | 4 +- .../demand_technology.xlsx | 3 + .../aluminum_stand_alone/generate_data_AL.py | 14 +- ...eneric_furnace_boiler_techno_economic.xlsx | 4 +- .../aluminum_stand_alone/stand_alone_AL.py | 14 +- .../test_commodity_balance.py | 201 ++++++++++++++++++ .../aluminum_stand_alone/variable_costs.xlsx | 4 +- 7 files changed, 224 insertions(+), 20 deletions(-) create mode 100644 message_ix_models/model/material/aluminum_stand_alone/demand_technology.xlsx create mode 100644 message_ix_models/model/material/aluminum_stand_alone/test_commodity_balance.py diff --git a/message_ix_models/model/material/aluminum_stand_alone/aluminum_techno_economic.xlsx b/message_ix_models/model/material/aluminum_stand_alone/aluminum_techno_economic.xlsx index 73dfc61cbf..5d79787c5e 100644 --- a/message_ix_models/model/material/aluminum_stand_alone/aluminum_techno_economic.xlsx +++ b/message_ix_models/model/material/aluminum_stand_alone/aluminum_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:000a5f764b194a3a3208fb872dc3d7ef1204fc80fd21b1d4923ac9b34530ec79 -size 51772 +oid sha256:106287600beb514e90a4780f9404d59cd1ad910f52556620804c9cc15bcce6bf +size 51745 diff --git a/message_ix_models/model/material/aluminum_stand_alone/demand_technology.xlsx b/message_ix_models/model/material/aluminum_stand_alone/demand_technology.xlsx new file mode 100644 index 0000000000..8180a390b0 --- /dev/null +++ b/message_ix_models/model/material/aluminum_stand_alone/demand_technology.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ea197ac3a0ca594bf87fce7d4ec36f4c2fad802dc1472360b81941f5ab6d18c9 +size 10084 diff --git a/message_ix_models/model/material/aluminum_stand_alone/generate_data_AL.py b/message_ix_models/model/material/aluminum_stand_alone/generate_data_AL.py index 7556d58f0f..413d61d685 100644 --- a/message_ix_models/model/material/aluminum_stand_alone/generate_data_AL.py +++ b/message_ix_models/model/material/aluminum_stand_alone/generate_data_AL.py @@ -13,17 +13,16 @@ import ixmp -def gen_data_aluminum(): +def gen_data_aluminum(file): mp = ixmp.Platform() scenario = message_ix.Scenario(mp,"MESSAGE_material","baseline", cache=True) - data_aluminum = pd.read_excel("aluminum_techno_economic.xlsx", - sheet_name="aluminum") + data_aluminum = pd.read_excel(file,sheet_name="data") data_aluminum_hist = pd.read_excel("aluminum_techno_economic.xlsx", - sheet_name="aluminum_historical", + sheet_name="data_historical", usecols = "A:F") # Clean the data # Drop columns that don't contain useful information @@ -32,11 +31,7 @@ def gen_data_aluminum(): # List of data frames, to be concatenated together at the end results = defaultdict(list) # Will come from the yaml file - technology_add = ["soderberg_aluminum", "prebake_aluminum", - "secondary_aluminum", "prep_secondary_aluminum", - "finishing_aluminum", "manuf_aluminum", - "scrap_recovery_aluminum", "alumina_supply"] - + technology_add = data_aluminum["technology"].unique() # normally from s_info.Y years = [2010,2020,2030,2040,2050,2060,2070,2080,2090,2100] @@ -83,7 +78,6 @@ def gen_data_aluminum(): 'value'].values[0] # Common parameters for all input and output tables - # year_act is none at the moment # node_dest and node_origin are the same as node_loc common = dict( diff --git a/message_ix_models/model/material/aluminum_stand_alone/generic_furnace_boiler_techno_economic.xlsx b/message_ix_models/model/material/aluminum_stand_alone/generic_furnace_boiler_techno_economic.xlsx index 50a7e95e15..c6a0e2acca 100644 --- a/message_ix_models/model/material/aluminum_stand_alone/generic_furnace_boiler_techno_economic.xlsx +++ b/message_ix_models/model/material/aluminum_stand_alone/generic_furnace_boiler_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:65fa11feff07c45ba0a9c31543443c74147e3e9a6e902ad349913f03df7ec68b -size 47970 +oid sha256:bdfe832a631d0080892932af6ecb2e1ed3c1cc059adf182eb476c77de57d0ff5 +size 47993 diff --git a/message_ix_models/model/material/aluminum_stand_alone/stand_alone_AL.py b/message_ix_models/model/material/aluminum_stand_alone/stand_alone_AL.py index 32f8604209..c12cc116c8 100644 --- a/message_ix_models/model/material/aluminum_stand_alone/stand_alone_AL.py +++ b/message_ix_models/model/material/aluminum_stand_alone/stand_alone_AL.py @@ -3,17 +3,16 @@ Created on Wed Aug 26 13:41:29 2020 "Aluminum stand-alone run" -"Solves and plots activity and capacity" - +Solve and plot @author: unlu """ import message_ix import ixmp import pandas as pd from message_data.tools import make_df -from tools import Plots from generate_data_AL import gen_data_aluminum as gen_data_aluminum from generate_data_generic import gen_data_generic as gen_data_generic +from tools import Plots mp = ixmp.Platform() @@ -143,13 +142,19 @@ # Run read data aluminum -results_al, data_aluminum_hist = gen_data_aluminum() +scenario.commit("changes added") + +results_al, data_aluminum_hist = gen_data_aluminum("aluminum_techno_economic.xlsx") + +scenario.check_out() for k, v in results_al.items(): scenario.add_par(k,v) +scenario.commit("aluminum_techno_economic added") results_generic = gen_data_generic() +scenario.check_out() for k, v in results_generic.items(): scenario.add_par(k,v) @@ -285,6 +290,7 @@ scenario.add_par('historical_new_capacity', hist_capacity_gas) scenario.commit("changes") + scenario.solve() # Be aware plots produce the same color for some technologies. diff --git a/message_ix_models/model/material/aluminum_stand_alone/test_commodity_balance.py b/message_ix_models/model/material/aluminum_stand_alone/test_commodity_balance.py new file mode 100644 index 0000000000..4cb7747e7a --- /dev/null +++ b/message_ix_models/model/material/aluminum_stand_alone/test_commodity_balance.py @@ -0,0 +1,201 @@ +# -*- coding: utf-8 -*- +""" +Created on Thu Aug 27 11:00:06 2020 + +Test new commodity balance equation +Run stand_alone_AL before runing this script + +@author: unlu +""" +import message_ix +import ixmp +from generate_data_AL import gen_data_aluminum as gen_data_aluminum +import pandas as pd +from tools import Plots + +mp = ixmp.Platform() + +base = message_ix.Scenario(mp, model="MESSAGE_material", scenario='baseline') +scen = base.clone("MESSAGE_material", 'test','test commodity balance equation', + keep_solution=False) +scen.check_out() + +country = 'CHN' +model_horizon = [2020, 2030, 2040, 2050, 2060, 2070, 2080, 2090, 2100] + +# Add a new demand side technology and related commodity + +new_tec = "bulb" +scen.add_set("technology",new_tec) + +new_commodity = "light" +scen.add_set("commodity",new_commodity) + +# Add input, output,technical_lifetime, inv_cost etc. +tec_data, tec_data_hist = gen_data_aluminum("demand_technology.xlsx") + +for k, v in tec_data.items(): + scen.add_par(k,v) + +# Add dummy demand. 1 GWa initially. + +duration_period = scen.par("duration_period") +duration_period = duration_period["value"].values + +gdp_growth = pd.Series([0.121448215899944, 0.0733079014579874, + 0.0348154093342843, 0.021827616787921, + 0.0134425983942219, 0.0108320197485592, + 0.00884341208063, 0.00829374133206562, + 0.00649794573935969], + index=pd.Index(model_horizon, name='Time')) + +i = 0 +values = [] +val = (1 * (1+ 0.147718884937996/2) ** duration_period[i]) +values.append(val) + +for element in gdp_growth: + i = i + 1 + if i < len(model_horizon): + val = (val * (1+ element/2) ** duration_period[i]) + values.append(val) + +bulb_demand = pd.DataFrame({ + 'node': country, + 'commodity': 'light', + 'level': 'useful_aluminum', + 'year': model_horizon, + 'time': 'year', + 'value': values , + 'unit': 'GWa', + }) + +scen.add_par("demand", bulb_demand) + +# Adjust the active years. + +tec_lifetime = tec_data.get("technical_lifetime") + +lifetime = tec_lifetime['value'].values[0] +years_df = scen.vintage_and_active_years() +# Empty data_frame +years_df_final = pd.DataFrame(columns=["year_vtg","year_act"]) +# For each vintage adjsut the active years according to technical +# lifetime +for vtg in years_df["year_vtg"].unique(): + years_df_temp = years_df.loc[years_df["year_vtg"]== vtg] + years_df_temp = years_df_temp.loc[years_df["year_act"] + < vtg + lifetime] + years_df_final = pd.concat([years_df_temp, years_df_final], + ignore_index=True) +vintage_years, act_years = years_df_final['year_vtg'], \ +years_df_final['year_act'] + +# **** WORKS UNTIL HERE ****** + +# Add the new parameters: input_cap_ret, output_cap_ret, input_cap, output_cap +# input_cap_new, output_cap_new + +# Material release after retirement (e.g. old scrap) + +output_cap_ret = pd.DataFrame({"node_loc":country, + "technology":"bulb", + "year_vtg": vintage_years, + "node_dest":country, + "commodity":"aluminum", + "level": "useful_material", + "time":"year", + "time_dest":"year", + "value":0.15, + "unit":"-"}) +print(output_cap_ret) +scen.add_par("output_cap_ret", output_cap_ret) + +# Material need during lifetime (e.g. retrofit) + +input_cap = pd.DataFrame({"node_loc":country, + "technology":"bulb", + "year_vtg": vintage_years, + "year_act": act_years, + "node_origin":country, + "commodity":"aluminum", + "level": "useful_material", + "time":"year", + "time_origin":"year", + "value":0.01, + "unit":"-"}) +print(input_cap) +scen.add_par("input_cap", input_cap) + +# Material need for the construction + +input_cap_new = pd.DataFrame({"node_loc":country, + "technology":"bulb", + "year_vtg": vintage_years, + "node_origin":country, + "commodity":"aluminum", + "level": "useful_material", + "time":"year", + "time_origin":"year", + "value":0.25, + "unit":"-"}) +print(input_cap_new) +scen.add_par("input_cap_new",input_cap_new) + +# Material release during construction (e.g. new scrap) + +output_cap_new = pd.DataFrame({"node_loc":country, + "technology":"bulb", + "year_vtg": vintage_years, + "node_dest":country, + "commodity":"aluminum", + "level": "useful_material", + "time":"year", + "time_dest":"year", + "value":0.1, + "unit":"-"}) +print(output_cap_new) +scen.add_par("output_cap_new",output_cap_new) + +scen.commit() + +#scen.solve() + +# Be aware plots produce the same color for some technologies. + +p = Plots(scen, country, firstyear=model_horizon[0]) + +p.plot_activity(baseyear=True, subset=['soderberg_aluminum', + 'prebake_aluminum',"secondary_aluminum"]) +p.plot_capacity(baseyear=True, subset=['soderberg_aluminum', 'prebake_aluminum']) +p.plot_activity(baseyear=True, subset=["prep_secondary_aluminum"]) +p.plot_activity(baseyear=True, subset=['furnace_coal_aluminum', + 'furnace_foil_aluminum', + 'furnace_methanol_aluminum', + 'furnace_biomass_aluminum', + 'furnace_ethanol_aluminum', + 'furnace_gas_aluminum', + 'furnace_elec_aluminum', + 'furnace_h2_aluminum', + 'hp_gas_aluminum', + 'hp_elec_aluminum','fc_h2_aluminum', + 'solar_aluminum', 'dheat_aluminum', + 'furnace_loil_aluminum']) +p.plot_capacity(baseyear=True, subset=['furnace_coal_aluminum', + 'furnace_foil_aluminum', + 'furnace_methanol_aluminum', + 'furnace_biomass_aluminum', + 'furnace_ethanol_aluminum', + 'furnace_gas_aluminum', + 'furnace_elec_aluminum', + 'furnace_h2_aluminum', + 'hp_gas_aluminum', + 'hp_elec_aluminum','fc_h2_aluminum', + 'solar_aluminum', 'dheat_aluminum', + 'furnace_loil_aluminum']) +p.plot_prices(subset=['aluminum'], baseyear=True) + + + + + diff --git a/message_ix_models/model/material/aluminum_stand_alone/variable_costs.xlsx b/message_ix_models/model/material/aluminum_stand_alone/variable_costs.xlsx index 06bf81e051..03f046c956 100644 --- a/message_ix_models/model/material/aluminum_stand_alone/variable_costs.xlsx +++ b/message_ix_models/model/material/aluminum_stand_alone/variable_costs.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:8fb9ba5e09a19bd9e2d01f473533b07d082c692771110c869b6757d65049fdfe -size 38085 +oid sha256:2ecfeceb662b6bdf3d14078429f4db7d6b4997447e80eb486160ffc91d3f3695 +size 32510 From fca07875259698b088cfa108c1aaf3e7c93dfc05 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Thu, 27 Aug 2020 16:18:49 +0200 Subject: [PATCH 087/774] Take care of multiple data read functions --- message_ix_models/model/material/bare.py | 48 ++++++++++++++++++++---- 1 file changed, 40 insertions(+), 8 deletions(-) diff --git a/message_ix_models/model/material/bare.py b/message_ix_models/model/material/bare.py index 74113a0d77..1a8ad1869b 100644 --- a/message_ix_models/model/material/bare.py +++ b/message_ix_models/model/material/bare.py @@ -4,10 +4,10 @@ import message_ix -from message_data.tools import Code, ScenarioInfo, get_context, set_info +from message_data.tools import Code, ScenarioInfo, get_context, set_info, add_par_data from .build import apply_spec from .util import read_config -from .data import get_data, gen_data_steel +from .data import get_data, gen_data_steel, gen_data_generic import message_data @@ -60,7 +60,7 @@ def create_res(context=None, quiet=True): scenario, spec, # data=partial(get_data, context=context, spec=spec), - data=gen_data_steel, + data=add_data, quiet=quiet, message=f"Create using message_data {message_data.__version__}", ) @@ -68,6 +68,34 @@ def create_res(context=None, quiet=True): return scenario +DATA_FUNCTIONS = [ + gen_data_steel, + gen_data_generic, + # gen_data_aluminum, +] + + +# Try to handle multiple data input functions from different materials +def add_data(scenario, dry_run=False): + """Populate `scenario` with MESSAGE-Transport data.""" + # Information about `scenario` + info = ScenarioInfo(scenario) + + # Check for two "node" values for global data, e.g. in + # ixmp://ene-ixmp/CD_Links_SSP2_v2.1_clean/baseline + if {"World", "R11_GLB"} < set(info.set["node"]): + log.warning("Remove 'R11_GLB' from node list for data generation") + info.set["node"].remove("R11_GLB") + + for func in DATA_FUNCTIONS: + # Generate or load the data; add to the Scenario + log.info(f'from {func.__name__}()') + add_par_data(scenario, func(scenario), dry_run=dry_run) + + log.info('done') + + + def get_spec(context=None) -> Mapping[str, ScenarioInfo]: """Return the spec for the MESSAGE-China bare RES. @@ -94,10 +122,10 @@ def get_spec(context=None) -> Mapping[str, ScenarioInfo]: add = ScenarioInfo() - # Add technologies # JM: try to find out a way to loop over 1st/2nd level and to just context["material"][xx]["add"] - add.set["technology"] = context["material"]["steel"]["technology"]["add"] + add.set["technology"] = context["material"]["steel"]["technology"]["add"] + \ + context["material"]["generic"]["technology"]["add"] # Add regions @@ -123,15 +151,19 @@ def get_spec(context=None) -> Mapping[str, ScenarioInfo]: # Add levels # JM: For bare model, both 'add' & 'require' need to be added. add.set['level'] = context["material"]["steel"]["level"]["add"] + \ - context["material"]["common"]["level"]["require"] + context["material"]["common"]["level"]["require"] + \ + context["material"]["generic"]["level"]["add"] # Add commodities c_list = context["material"]["steel"]["commodity"]["add"] + \ - context["material"]["common"]["commodity"]["require"] + context["material"]["common"]["commodity"]["require"] + \ + context["material"]["generic"]["commodity"]["add"] add.set['commodity'] = c_list add.set['type_tec'] = context["material"]["common"]["type_tec"]["add"] - add.set['mode'] = context["material"]["common"]["mode"]["require"] + add.set['mode'] = context["material"]["common"]["mode"]["require"] +\ + context["material"]["generic"]["mode"]["add"] + add.set['emission'] = context["material"]["common"]["emission"]["require"] +\ context["material"]["common"]["emission"]["add"] From f264f5e9476541c8b0a1aa6dcef5b5985b84005e Mon Sep 17 00:00:00 2001 From: Jihoon Date: Thu, 27 Aug 2020 16:20:18 +0200 Subject: [PATCH 088/774] Update data --- message_ix_models/data/material/China_steel_renamed.xlsx | 4 ++-- .../data/material/generic_furnace_boiler_techno_economic.xlsx | 3 +++ 2 files changed, 5 insertions(+), 2 deletions(-) create mode 100644 message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx diff --git a/message_ix_models/data/material/China_steel_renamed.xlsx b/message_ix_models/data/material/China_steel_renamed.xlsx index a5987e12d1..4ab91666c3 100644 --- a/message_ix_models/data/material/China_steel_renamed.xlsx +++ b/message_ix_models/data/material/China_steel_renamed.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:2a094a40af1c145be8682222db3efe3e60944964c72d75dede8a32970671e5a2 -size 47551 +oid sha256:636d93984766f1743921cb4ed3a32b20460b361d28fd7e867b4725f618f02bc3 +size 52889 diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx new file mode 100644 index 0000000000..343160ac1c --- /dev/null +++ b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7cc632e60112174b475928d37b0b81823df3ddd89ce373a19aaa665ffb284988 +size 48453 From 9f364d1a44334a5a242b86d78bebf7734f6dd79c Mon Sep 17 00:00:00 2001 From: Jihoon Date: Thu, 27 Aug 2020 16:30:06 +0200 Subject: [PATCH 089/774] Add generic heat tecs and more dummy supply tects --- message_ix_models/data/material/set.yaml | 52 +++++++++++++++++------- 1 file changed, 37 insertions(+), 15 deletions(-) diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 16798dd06f..2b435cc347 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -11,7 +11,9 @@ common: - gas - electr - ethanol + - methanol - fueloil + - lightoil - hydrogen - lh2 - biomass @@ -53,12 +55,41 @@ common: add: - PM2p5 - -steel: +generic: commodity: add: - ht_heat - lt_heat + + level: + add: + - useful_steel + + technology: + add: + - furnace_foil_steel + - furnace_loil_steel + - furnace_biomass_steel + - furnace_ethanol_steel + - furnace_methanol_steel + - furnace_gas_steel + - furnace_coal_steel + - furnace_elec_steel + - furnace_h2_steel + - hp_gas_steel + - hp_elec_steel + - fc_h2_steel + - solar_steel + - dheat_steel + + mode: + add: + - low_temp + - high_temp + +steel: + commodity: + add: - steel - pig_iron - sponge_iron @@ -71,7 +102,6 @@ steel: level: add: - - useful_steel - new_scrap - old_scrap - primary_material @@ -98,24 +128,16 @@ steel: - scrap_recovery_steel - DUMMY_coal_supply - DUMMY_gas_supply + - DUMMY_loil_supply + - DUMMY_foil_supply - DUMMY_electr_supply - DUMMY_ore_supply + - DUMMY_ethanol_supply + - DUMMY_methanol_supply - DUMMY_limestone_supply - DUMMY_hydrogen_supply - DUMMY_freshwater_supply - DUMMY_water_supply - - furnace_hoil_steel - - furnace_biofuel_steel - - furnace_biomass_steel - - furnace_synfuel_steel - - furnace_gas_steel - - furnace_elec_steel - - furnace_h2_steel - - hp_gas_steel - - hp_elec_steel - - fc_h2_steel - - solar_steel - - dheat_steel relation: add: From ed5b0e35918a018ca015365538ff820c420ce184 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Thu, 27 Aug 2020 16:31:34 +0200 Subject: [PATCH 090/774] Modify for accomodating multiple data input function calls --- message_ix_models/model/material/build.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/message_ix_models/model/material/build.py b/message_ix_models/model/material/build.py index 565aee3e96..2dea7fc733 100644 --- a/message_ix_models/model/material/build.py +++ b/message_ix_models/model/material/build.py @@ -138,9 +138,9 @@ def apply_spec( # Add data if callable(data): - result = data(scenario, dry_run=dry_run) - if result: - add_par_data(scenario, result, dry_run=dry_run) + data(scenario, dry_run=dry_run) + # if result: + # add_par_data(scenario, result, dry_run=dry_run) # Finalize log.info('Commit results.') From 1e98774320e832564057b399c292d06b68812a62 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Thu, 27 Aug 2020 16:50:02 +0200 Subject: [PATCH 091/774] Read generic data & time-dependent params + some clean-up --- message_ix_models/model/material/data.py | 377 ++++++++++++++++------- 1 file changed, 261 insertions(+), 116 deletions(-) diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index 02f3c02726..4df3e00136 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -69,6 +69,92 @@ def read_data(): return data + +# Read in technology-specific parameters from input xlsx +def process_china_data_tec(): + + import numpy as np + + # Ensure config is loaded, get the context + context = read_config() + + # Read the file + data_steel_china = pd.read_excel( + context.get_path("material", "China_steel_renamed.xlsx"), + sheet_name="technologies", + ) + + # Clean the data + + data_steel_china = data_steel_china \ + [['Technology', 'Parameter', 'Level', \ + 'Commodity', 'Species', 'Units', 'Value']] \ + .replace(np.nan, '', regex=True) + + tuple_series = data_steel_china[['Parameter', 'Commodity', 'Level']] \ + .apply(tuple, axis=1) + tuple_ef = data_steel_china[['Parameter', 'Species']] \ + .apply(tuple, axis=1) + + + data_steel_china['parameter'] = tuple_series.str.join('|') \ + .str.replace('\|\|', '') + data_steel_china.loc[data_steel_china['Parameter'] == "emission_factor", \ + 'parameter'] = tuple_ef.str.join('|').str.replace('\|\|', '') + + data_steel_china = data_steel_china.drop(['Parameter', 'Level', 'Commodity'] \ + , axis = 1) + data_steel_china = data_steel_china.drop( \ + data_steel_china[data_steel_china.Value==''].index) + + data_steel_china.columns = data_steel_china.columns.str.lower() + + # Unit conversion + + # At the moment this is done in the excel file, can be also done here + # To make sure we use the same units + + return data_steel_china + +# Read in relation-specific parameters from input xlsx +def process_china_data_rel(): + + import numpy as np + + # Ensure config is loaded, get the context + context = read_config() + + # Read the file + data_steel_china = pd.read_excel( + context.get_path("material", "China_steel_renamed.xlsx"), + sheet_name="relations", + ) + + return data_steel_china + +# Read in time-dependent parameters +# Now only used to add fuel cost for bare model +def read_var_cost(): + + import numpy as np + + # Ensure config is loaded, get the context + context = read_config() + + # Read the file + df = pd.read_excel( + context.get_path("material", "China_steel_renamed.xlsx"), + sheet_name="var_cost", + ) + + df = pd.melt(df, id_vars=['technology', 'mode', 'units'], \ + value_vars=[2010, 2020, 2030, 2040, 2050, 2060, 2070, 2080, 2090, 2100], \ + var_name='year') + + return df + + +# Question: Do we need read data dunction seperately for all materials ? def read_data_aluminum(): """Read and clean data from :file:`aluminum_techno_economic.xlsx`.""" @@ -100,7 +186,7 @@ def read_data_generic(): # Ensure config is loaded, get the context context = read_config() # Shorter access to sets configuration - sets = context["material"]["generic_set"] + # sets = context["material"]["generic"] # Read the file data_generic = pd.read_excel( @@ -218,8 +304,7 @@ def gen_data_aluminum(scenario, dry_run=False): # Concatenate to one data frame per parameter results_aluminum = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} - # Temporary: return nothing, since the data frames are incomplete - return results_aluminum + return results # TODO: Different input values over the years: Add a function that modifies #the values over the years @@ -239,35 +324,30 @@ def gen_data_generic(scenario, dry_run=False): # List of data frames, to be concatenated together at end results = defaultdict(list) + # For each technology there are differnet input and output combinations # Iterate over technologies - for t in config["technology"]["add"]: - - # Retreive the technology availability and vintage/active years - # Limit the years according to the available year - av = data_generic.loc[(data_generic["technology"] == t),'availability']\ - .values[0] - lifetime = data_generic.loc[(data_generic["technology"] == t) & \ - (data_generic["parameter"]== "technical_lifetime"),'value'].values[0] + allyears = s_info.set['year'] #s_info.Y is only for modeling years + modelyears = s_info.Y #s_info.Y is only for modeling years + nodes = s_info.N + yv_ya = s_info.yv_ya + fmy = s_info.y0 - # Can we use s_info.Y here ? - years_df = scenario.vintage_and_active_years() - years_df = years_df.loc[years_df["year_vtg"]>= av] - years_df_final = pd.DataFrame(columns=["year_vtg","year_act"]) + # 'World' is included by default when creating a message_ix.Scenario(). + # Need to remove it for the China bare model + nodes.remove('World') - # For each vintage adjsut the active years according to technical lifetime - for vtg in years_df["year_vtg"].unique(): - years_df_temp = years_df.loc[years_df["year_vtg"]== vtg] - years_df_temp = years_df_temp.loc[years_df["year_act"]< vtg + lifetime] - years_df_final = pd.concat([years_df_temp, years_df_final], ignore_index=True) - - vintage_years, act_years = years_df_final['year_vtg'], years_df_final['year_act'] + for t in config["technology"]["add"]: + # years = s_info.Y params = data_generic.loc[(data_generic["technology"] == t),"parameter"]\ .values.tolist() - # Keep the available modes for a technology - mode_list = [] + # Availability year of the technology + av = data_generic.loc[(data_generic["technology"] == t),'availability'].\ + values[0] + modelyears = [year for year in modelyears if year >= av] + yva = yv_ya.loc[yv_ya.year_vtg >= av, ] # Iterate over parameters (e.g. input|coal|final|low_temp) for par in params: @@ -280,8 +360,8 @@ def gen_data_generic(scenario, dry_run=False): # Common parameters for all input and output tables common = dict( - year_vtg= vintage_years, - year_act= act_years, + year_vtg= yva.year_vtg, + year_act= yva.year_act, time="year", time_origin="year", time_dest="year",) @@ -309,7 +389,10 @@ def gen_data_generic(scenario, dry_run=False): mod = data_generic.loc[((data_generic["technology"] == t) \ & (data_generic["parameter"] == par)),'value'].values[0] - # Create emission factor for existing different modes + # TODO: Now tentatively fixed to one mode. Have values for the other mode too + df = (make_df(param_name, technology=t,value=val,\ + emission=emi, mode="low_temp", unit='t', **common).pipe(broadcast, \ + node_loc=nodes)) for m in np.unique(np.array(mode_list)): df = (make_df(param_name, technology=t,value=val,\ @@ -326,126 +409,188 @@ def gen_data_generic(scenario, dry_run=False): results[param_name].append(df) - results_generic = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} + results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} + + return results - return results_generic -def gen_data(scenario, dry_run=False): - """Generate data for materials representation of nitrogen fertilizers. +def gen_data_steel(scenario, dry_run=False): + """Generate data for materials representation of steel industry. - .. note:: This code is only partially translated from - :file:`SetupNitrogenBase.py`. """ # Load configuration - config = read_config()["material"]["set"] + config = read_config()["material"]["steel"] # Information about scenario, e.g. node, year s_info = ScenarioInfo(scenario) # Techno-economic assumptions - data = read_data() + data_steel = process_china_data_tec() + # Special treatment for time-dependent Parameters + data_steel_vc = read_var_cost() + tec_vc = set(data_steel_vc.technology) # List of data frames, to be concatenated together at end results = defaultdict(list) - # NH3 production processes - common = dict( - year_vtg=s_info.Y, - commodity="NH3", - level="material_interim", - # TODO fill in remaining dimensions - mode="all", - time="year", - time_dest="year", - time_origin="year", - ) + # For each technology there are differnet input and output combinations + # Iterate over technologies - # Iterate over new technologies, using the configuration - for t in config["technology"]["add"]: - # Output of NH3: same efficiency for all technologies - # TODO the output commodity and level are different for - # t=NH3_to_N_fertil; use 'if' statements to fill in. - df = ( - make_df("output", technology=t, value=1, unit='t', **common) - .pipe(broadcast, node_loc=s_info.N) - .pipe(same_node) - ) - results["output"].append(df) + allyears = s_info.set['year'] #s_info.Y is only for modeling years + modelyears = s_info.Y #s_info.Y is only for modeling years + nodes = s_info.N + yv_ya = s_info.yv_ya + fmy = s_info.y0 - # Heat output + nodes.remove('World') # For the bare model - # Retrieve the scalar value from the data - row = data.loc[("output_heat", t.id), :] - log.info(f"Use {repr(row.Variable)} for heat output of {t}") + # for t in s_info.set['technology']: + for t in config['technology']['add']: - # Store a modified data frame - results["output"].append( - df.assign(commodity="d_heat", level="secondary", value=row[2010]) - ) + params = data_steel.loc[(data_steel["technology"] == t),\ + "parameter"].values.tolist() - # TODO add other variables from SetupNitrogenBase.py + # Special treatment for time-varying params + if t in tec_vc: + common = dict( + time="year", + time_origin="year", + time_dest="year",) - # Concatenate to one data frame per parameter - results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} + param_name = "var_cost" + val = data_steel_vc.loc[(data_steel_vc["technology"] == t), 'value'] + units = data_steel_vc.loc[(data_steel_vc["technology"] == t), \ + 'units'].values[0] + mod = data_steel_vc.loc[(data_steel_vc["technology"] == t), 'mode'] + yr = data_steel_vc.loc[(data_steel_vc["technology"] == t), 'year'] - # Temporary: return nothing, since the data frames are incomplete - return dict() - # return result + df = (make_df(param_name, technology=t, value=val,\ + unit='t', year_vtg=yr, year_act=yr, mode=mod, **common).pipe(broadcast, \ + node_loc=nodes)) + print(param_name, df) + results[param_name].append(df) -def read_data(): - """Read and clean data from :file:`n-fertilizer_techno-economic.xlsx`.""" - # Ensure config is loaded, get the context - context = read_config() + # Iterate over parameters + for par in params: - # Shorter access to sets configuration - sets = context["material"]["set"] + # Obtain the parameter names, commodity,level,emission + split = par.split("|") + param_name = split[0] - # Read the file - data = pd.read_excel( - context.get_path("material", "n-fertilizer_techno-economic.xlsx"), - sheet_name="Sheet1", - ) + # Obtain the scalar value for the parameter + val = data_steel.loc[((data_steel["technology"] == t) \ + & (data_steel["parameter"] == par)),'value'].values[0] - # Prepare contents for the "parameter" and "technology" columns - # FIXME put these in the file itself to avoid ambiguity/error + common = dict( + year_vtg= yv_ya.year_vtg, + year_act= yv_ya.year_act, + mode="M1", + time="year", + time_origin="year", + time_dest="year",) - # "Variable" column contains different values selected to match each of - # these parameters, per technology - params = [ - "inv_cost", - "fix_cost", - "var_cost", - "technical_lifetime", - "input_fuel", - "input_elec", - "input_water", - "output_NH3", - "output_water", - "output_heat", - "emissions", - "capacity_factor", - ] + # For the parameters which inlcudes index names + if len(split)> 1: - param_values = [] - tech_values = [] - for t in sets["technology"]["add"]: - param_values.extend(params) - tech_values.extend([t.id] * len(params)) + if (param_name == "input")|(param_name == "output"): - # Clean the data - data = ( - # Insert "technology" and "parameter" columns - data.assign(technology=tech_values, parameter=param_values) - # Drop columns that don't contain useful information - .drop(["Model", "Scenario", "Region"], axis=1) - # Set the data frame index for selection - .set_index(["parameter", "technology"]) - ) + # Assign commodity and level names + com = split[1] + lev = split[2] - # TODO convert units for some parameters, per LoadParams.py + df = (make_df(param_name, technology=t, commodity=com, \ + level=lev, value=val, unit='t', **common)\ + .pipe(broadcast, node_loc=nodes).pipe(same_node)) - return data + elif param_name == "emission_factor": + + # Assign the emisson type + emi = split[1] + + df = (make_df(param_name, technology=t,value=val,\ + emission=emi, unit='t', **common).pipe(broadcast, \ + node_loc=nodes)) + + results[param_name].append(df) + + # Parameters with only parameter name + else: + # Historical years are earlier than firstmodelyear + y_hist = [y for y in allyears if y < fmy] + # print(y_hist, fmy, years) + if re.search("historical_", param_name): + common_hist = dict( + year_vtg= y_hist, + year_act= y_hist, + mode="M1", + time="year",) + + df = (make_df(param_name, technology=t, value=val, unit='t', \ + **common_hist).pipe(broadcast, node_loc=nodes)) + # print(common_hist, param_name, t, nodes, val, y_hist) + else: + df = (make_df(param_name, technology=t, value=val, unit='t', \ + **common).pipe(broadcast, node_loc=nodes)) + + results[param_name].append(df) + + # TODO: relation is used to set external demand. + # We can also have two redundant commodity outputs from manufacturing stage + # and set external demand on one of them. + for r in config['relation']['add']: + + # Read the file + rel_steel = process_china_data_rel() + + params = rel_steel.loc[(rel_steel["relation"] == r),\ + "parameter"].values.tolist() + + common_rel = dict( + year_rel = modelyears, + year_act = modelyears, + mode = 'M1', + relation = r,) + + for par_name in params: + if par_name == "relation_activity": + + val = rel_steel.loc[((rel_steel["relation"] == r) \ + & (rel_steel["parameter"] == par_name)),'value'].values[0] + tec = rel_steel.loc[((rel_steel["relation"] == r) \ + & (rel_steel["parameter"] == par_name)),'technology'].values[0] + + df = (make_df(par_name, technology=tec, value=val, unit='t',\ + **common_rel).pipe(broadcast, node_rel=nodes, node_loc=nodes)) + + results[par_name].append(df) + + elif par_name == "relation_lower": + + demand = gen_mock_demand() + + df = (make_df(par_name, value=demand, unit='t',\ + **common_rel).pipe(broadcast, node_rel=nodes)) + + results[par_name].append(df) + + # Concatenate to one data frame per parameter + results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} + + return results + + +# Generate a fake steel demand +def gen_mock_demand(): + # True steel use 2010 (China) = 537 Mt/year + # https://www.worldsteel.org/en/dam/jcr:0474d208-9108-4927-ace8-4ac5445c5df8/World+Steel+in+Figures+2017.pdf + gdp_growth = [0.121448215899944, \ + 0.0733079014579874, 0.0348154093342843, \ + 0.021827616787921,0.0134425983942219, 0.0108320197485592, \ + 0.00884341208063,0.00829374133206562, 0.00649794573935969] + demand = [(x+1) * 537 for x in gdp_growth] + + return demand def get_data(scenario, context, **options): From 13b780e5594d7fa1691fa6876c711c6933e45869 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Thu, 27 Aug 2020 16:56:13 +0200 Subject: [PATCH 092/774] Temporary test script (remove later) --- .../model/material/material_testscript.py | 74 +++++++++++++++++++ 1 file changed, 74 insertions(+) create mode 100644 message_ix_models/model/material/material_testscript.py diff --git a/message_ix_models/model/material/material_testscript.py b/message_ix_models/model/material/material_testscript.py new file mode 100644 index 0000000000..3f7d4adb5f --- /dev/null +++ b/message_ix_models/model/material/material_testscript.py @@ -0,0 +1,74 @@ +# -*- coding: utf-8 -*- +""" +Spyder Editor + +This is a temporary script file. +""" + +import ixmp +import message_ix as mix +from message_ix import Scenario + +import message_data.model.material.data as dt +import pyam + +from message_data.model.material import bare + +from message_data.tools.cli import Context + +from message_data.tools import ( + ScenarioInfo, + make_df, + make_io, + make_matched_dfs, +) + +#%% Main test run + +# Create Context obj +Context._instance = [] +ctx = Context() + +# Set default scenario/model names +ctx.scenario_info.setdefault('model', 'Material_test') +ctx.scenario_info.setdefault('scenario', 'baseline') + +# Create bare model/scenario and solve it +scen = bare.create_res(context = ctx) +scen.solve() + + + +#%% Auxiliary random test stuff + +import pandas as pd + +# Test read_data_steel <- will be in create_res if working fine +df = dt.read_data_steel() + +b = dt.read_data_generic() +b = dt.read_var_cost() +c = pd.melt(b, id_vars=['technology', 'mode', 'units'], \ + value_vars=[2010, 2020, 2030, 2040, 2050, 2060, 2070, 2080, 2090, 2100], \ + var_name='year') + +df_gen = dt.gen_data_generic(scen) +df_st = dt.gen_data_steel(scen) +a = dt.get_data(scen, ctx) + +bare.add_data(scen) + + +info = ScenarioInfo(scen) +a = bare.get_spec(ctx) + +mp_samp = ixmp.Platform(name="local") +mp_samp.scenario_list() +sample = mix.Scenario(mp_samp, model="Material_b", scenario="baseline", version="new") +sample.set_list() +sample.set('year') +sample.cat('year', 'firstmodelyear') +mp_samp.close_db() + +scen.to_excel("test.xlsx") +scen_rp = Scenario(scen) \ No newline at end of file From c50bf9b18e66d1a54429fba1d3fd719ad7a42aa6 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Fri, 28 Aug 2020 11:42:32 +0200 Subject: [PATCH 093/774] Remove unnecessary file --- .../model/material/aluminum_stand_alone/trial.py | 9 --------- 1 file changed, 9 deletions(-) delete mode 100644 message_ix_models/model/material/aluminum_stand_alone/trial.py diff --git a/message_ix_models/model/material/aluminum_stand_alone/trial.py b/message_ix_models/model/material/aluminum_stand_alone/trial.py deleted file mode 100644 index 882f54ec5f..0000000000 --- a/message_ix_models/model/material/aluminum_stand_alone/trial.py +++ /dev/null @@ -1,9 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Created on Wed Aug 26 16:28:28 2020 - -@author: unlu -""" - -import ixmp -mp = ixmp.Platform() \ No newline at end of file From 636dc4196022418a7ff33d7821f6e8594d29f682 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Mon, 14 Sep 2020 17:09:10 +0200 Subject: [PATCH 094/774] Move add_data to data.py --- message_ix_models/model/material/bare.py | 29 +------------------ message_ix_models/model/material/data.py | 36 ++++++++++++++++++++++-- 2 files changed, 34 insertions(+), 31 deletions(-) diff --git a/message_ix_models/model/material/bare.py b/message_ix_models/model/material/bare.py index 1a8ad1869b..1374c11822 100644 --- a/message_ix_models/model/material/bare.py +++ b/message_ix_models/model/material/bare.py @@ -68,33 +68,6 @@ def create_res(context=None, quiet=True): return scenario -DATA_FUNCTIONS = [ - gen_data_steel, - gen_data_generic, - # gen_data_aluminum, -] - - -# Try to handle multiple data input functions from different materials -def add_data(scenario, dry_run=False): - """Populate `scenario` with MESSAGE-Transport data.""" - # Information about `scenario` - info = ScenarioInfo(scenario) - - # Check for two "node" values for global data, e.g. in - # ixmp://ene-ixmp/CD_Links_SSP2_v2.1_clean/baseline - if {"World", "R11_GLB"} < set(info.set["node"]): - log.warning("Remove 'R11_GLB' from node list for data generation") - info.set["node"].remove("R11_GLB") - - for func in DATA_FUNCTIONS: - # Generate or load the data; add to the Scenario - log.info(f'from {func.__name__}()') - add_par_data(scenario, func(scenario), dry_run=dry_run) - - log.info('done') - - def get_spec(context=None) -> Mapping[str, ScenarioInfo]: """Return the spec for the MESSAGE-China bare RES. @@ -163,7 +136,7 @@ def get_spec(context=None) -> Mapping[str, ScenarioInfo]: add.set['type_tec'] = context["material"]["common"]["type_tec"]["add"] add.set['mode'] = context["material"]["common"]["mode"]["require"] +\ context["material"]["generic"]["mode"]["add"] - + add.set['emission'] = context["material"]["common"]["emission"]["require"] +\ context["material"]["common"]["emission"]["add"] diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index 4df3e00136..2f20fcf83a 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -428,7 +428,7 @@ def gen_data_steel(scenario, dry_run=False): data_steel = process_china_data_tec() # Special treatment for time-dependent Parameters data_steel_vc = read_var_cost() - tec_vc = set(data_steel_vc.technology) + tec_vc = set(data_steel_vc.technology) # set of tecs with var_cost # List of data frames, to be concatenated together at end results = defaultdict(list) @@ -442,6 +442,8 @@ def gen_data_steel(scenario, dry_run=False): yv_ya = s_info.yv_ya fmy = s_info.y0 + print(allyears, modelyears, fmy) + nodes.remove('World') # For the bare model # for t in s_info.set['technology']: @@ -468,7 +470,7 @@ def gen_data_steel(scenario, dry_run=False): unit='t', year_vtg=yr, year_act=yr, mode=mod, **common).pipe(broadcast, \ node_loc=nodes)) - print(param_name, df) + # print(param_name, df) results[param_name].append(df) # Iterate over parameters @@ -580,13 +582,41 @@ def gen_data_steel(scenario, dry_run=False): return results + +DATA_FUNCTIONS = [ + gen_data_steel, + gen_data_generic, + # gen_data_aluminum, +] + + +# Try to handle multiple data input functions from different materials +def add_data(scenario, dry_run=False): + """Populate `scenario` with MESSAGE-Transport data.""" + # Information about `scenario` + info = ScenarioInfo(scenario) + + # Check for two "node" values for global data, e.g. in + # ixmp://ene-ixmp/CD_Links_SSP2_v2.1_clean/baseline + if {"World", "R11_GLB"} < set(info.set["node"]): + log.warning("Remove 'R11_GLB' from node list for data generation") + info.set["node"].remove("R11_GLB") + + for func in DATA_FUNCTIONS: + # Generate or load the data; add to the Scenario + log.info(f'from {func.__name__}()') + add_par_data(scenario, func(scenario), dry_run=dry_run) + + log.info('done') + + # Generate a fake steel demand def gen_mock_demand(): # True steel use 2010 (China) = 537 Mt/year # https://www.worldsteel.org/en/dam/jcr:0474d208-9108-4927-ace8-4ac5445c5df8/World+Steel+in+Figures+2017.pdf gdp_growth = [0.121448215899944, \ 0.0733079014579874, 0.0348154093342843, \ - 0.021827616787921,0.0134425983942219, 0.0108320197485592, \ + 0.021827616787921, 0.0134425983942219, 0.0108320197485592, \ 0.00884341208063,0.00829374133206562, 0.00649794573935969] demand = [(x+1) * 537 for x in gdp_growth] From 30011af1621fc929a7c1b6d9fbba4cdddb6c7aba Mon Sep 17 00:00:00 2001 From: Jihoon Date: Mon, 14 Sep 2020 17:31:37 +0200 Subject: [PATCH 095/774] Enable reading from multiple blocks from yaml input --- message_ix_models/model/material/__init__.py | 18 ++++++++++-------- 1 file changed, 10 insertions(+), 8 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 6f0f78d2d7..0efe7cebb5 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -4,7 +4,7 @@ from message_ix_models import ScenarioInfo from message_ix_models.model.build import apply_spec -from .data import gen_data +from .data import add_data from .util import read_config @@ -14,7 +14,7 @@ def build(scenario): spec = get_spec() # Apply to the base scenario - apply_spec(scenario, spec, gen_data) + apply_spec(scenario, spec, add_data) return scenario @@ -28,12 +28,14 @@ def get_spec() -> Mapping[str, ScenarioInfo]: context = read_config() # Update the ScenarioInfo objects with required and new set elements - for set_name, config in context["material"]["set"].items(): - # Required elements - require.set[set_name].extend(config.get("require", [])) - - # Elements to add - add.set[set_name].extend(config.get("add", [])) + for type in "generic", "common", "steel",: + for set_name, config in context["material"][type].items(): + # for cat_name, detail in config.items(): + # Required elements + require.set[set_name].extend(config.get("require", [])) + + # Elements to add + add.set[set_name].extend(config.get("add", [])) return dict(require=require, remove=ScenarioInfo(), add=add) From a908288e8f9dfa60fc5eda7f05f5f4905059bee6 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Mon, 14 Sep 2020 17:33:02 +0200 Subject: [PATCH 096/774] Remove temporary node fix (need to be done properly through region definition yaml or somewhere --- message_ix_models/model/material/data.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index 2f20fcf83a..d2b4f6597f 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -335,7 +335,7 @@ def gen_data_generic(scenario, dry_run=False): # 'World' is included by default when creating a message_ix.Scenario(). # Need to remove it for the China bare model - nodes.remove('World') + # nodes.remove('World') for t in config["technology"]["add"]: @@ -444,7 +444,7 @@ def gen_data_steel(scenario, dry_run=False): print(allyears, modelyears, fmy) - nodes.remove('World') # For the bare model + # nodes.remove('World') # For the bare model # for t in s_info.set['technology']: for t in config['technology']['add']: From e20af648cb46a9331b23db77ec5d083cf6405686 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 15 Sep 2020 14:16:57 +0200 Subject: [PATCH 097/774] Addition of data files and sets --- .../material/aluminum_techno_economic.xlsx | 3 + ...eneric_furnace_boiler_techno_economic.xlsx | 3 - ...rnace_boiler_techno_economic_aluminum.xlsx | 3 + ..._furnace_boiler_techno_economic_steel.xlsx | 3 + message_ix_models/data/material/set.yaml | 78 +++++++++++++++++++ .../data/material/variable_costs.xlsx | 3 + 6 files changed, 90 insertions(+), 3 deletions(-) create mode 100644 message_ix_models/data/material/aluminum_techno_economic.xlsx delete mode 100644 message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx create mode 100644 message_ix_models/data/material/generic_furnace_boiler_techno_economic_aluminum.xlsx create mode 100644 message_ix_models/data/material/generic_furnace_boiler_techno_economic_steel.xlsx create mode 100644 message_ix_models/data/material/variable_costs.xlsx diff --git a/message_ix_models/data/material/aluminum_techno_economic.xlsx b/message_ix_models/data/material/aluminum_techno_economic.xlsx new file mode 100644 index 0000000000..5d79787c5e --- /dev/null +++ b/message_ix_models/data/material/aluminum_techno_economic.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:106287600beb514e90a4780f9404d59cd1ad910f52556620804c9cc15bcce6bf +size 51745 diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx deleted file mode 100644 index 343160ac1c..0000000000 --- a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:7cc632e60112174b475928d37b0b81823df3ddd89ce373a19aaa665ffb284988 -size 48453 diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic_aluminum.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic_aluminum.xlsx new file mode 100644 index 0000000000..c6a0e2acca --- /dev/null +++ b/message_ix_models/data/material/generic_furnace_boiler_techno_economic_aluminum.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bdfe832a631d0080892932af6ecb2e1ed3c1cc059adf182eb476c77de57d0ff5 +size 47993 diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic_steel.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic_steel.xlsx new file mode 100644 index 0000000000..9edf9afc3f --- /dev/null +++ b/message_ix_models/data/material/generic_furnace_boiler_techno_economic_steel.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f9d6d91a7c1c38ba3221d295dd2a471ffb4cc8df01da8c6014bec0dcc9e1d5bd +size 48442 diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 2b435cc347..73487d58c8 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -23,6 +23,7 @@ common: level: require: + # We do not require the secondary and primary levels ?? - primary - secondary - useful @@ -64,6 +65,7 @@ generic: level: add: - useful_steel + - useful_aluminum technology: add: @@ -81,6 +83,20 @@ generic: - fc_h2_steel - solar_steel - dheat_steel + - furnace_coal_aluminum + - furnace_foil_aluminum + - furnace_loil_aluminum + - furnace_ethanol_aluminum + - furnace_biomass_aluminum + - furnace_methanol_aluminum + - furnace_gas_aluminum + - furnace_elec_aluminum + - furnace_h2_aluminum + - hp_gas_aluminum + - hp_elec_aluminum + - fc_h2_aluminum + - solar_aluminum + - dheat_aluminum mode: add: @@ -168,6 +184,68 @@ fertilizer: - fueloil_NH3 - NH3_to_N_fertil + aluminum: + commodity: + add: + - aluminum + + level: + add: + - new_scrap + - old_scrap + - useful_aluminum + - final_material + - useful_material + - product + - secondary_material + - demand_aluminum + + technology: + add: + - soderberg_aluminum + - prebake_aluminum + - secondary_aluminum + - prep_secondary_aluminum + - finishing_aluminum + - manuf_aluminum + - scrap_recovery_aluminum + - alumina_supply + - DUMMY_coal_supply + - DUMMY_gas_supply + - DUMMY_loil_supply + - DUMMY_foil_supply + - DUMMY_electr_supply + - DUMMY_ethanol_supply + - DUMMY_methanol_supply + - DUMMY_hydrogen_supply + - DUMMY_dist_heating + - DUMMY_biomass_supply + + fertilizer: + commodity: + require: + - electr + - freshwater_supply + add: + - NH3 + - Fertilizer Use|Nitrogen + + level: + add: + - material_interim + - material_final + + technology: + require: + - h2_bio_ccs # Reference for vent-to-storage ratio + add: + - biomass_NH3 + - electr_NH3 + - gas_NH3 + - coal_NH3 + - fueloil_NH3 + - NH3_to_N_fertil + # NB this codelist added by #125 diff --git a/message_ix_models/data/material/variable_costs.xlsx b/message_ix_models/data/material/variable_costs.xlsx new file mode 100644 index 0000000000..03f046c956 --- /dev/null +++ b/message_ix_models/data/material/variable_costs.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2ecfeceb662b6bdf3d14078429f4db7d6b4997447e80eb486160ffc91d3f3695 +size 32510 From 51742d4f57ea0e0bef2bcfc52266c490c5374b65 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Tue, 15 Sep 2020 14:36:33 +0200 Subject: [PATCH 098/774] Change to be consistent with the (central) bare model. add some parts missing in bare model + setting for year configuration (historical years) --- message_ix_models/data/material/set.yaml | 20 ++++++++++++++------ 1 file changed, 14 insertions(+), 6 deletions(-) diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 73487d58c8..54e67f4682 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -16,8 +16,9 @@ common: - lightoil - hydrogen - lh2 - - biomass - d_heat + add: + - biomass - water - fresh_water @@ -31,9 +32,9 @@ common: - water_supply # Currently only works with China - region: + node: # Just one is sufficient, since the RES will never be generated with a mix - require: + add: - China remove: - World @@ -43,18 +44,25 @@ common: - industry mode: - require: + add: - M1 emission: - require: + add: - CO2 - CH4 - N2O - NOx - SO2 + - PM2p5 # Just add there since it is already in Shaohui's data + + year: + add: + - 2010 + + type_year: add: - - PM2p5 + - 2010 generic: commodity: From 76d15847744f5cbfcde5e939c6850ab8d3cea878 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Tue, 15 Sep 2020 14:41:26 +0200 Subject: [PATCH 099/774] Add missing input files for fertilizer --- .../data/material/Ammonia feedstock share.Global.xlsx | 3 +++ message_ix_models/data/material/trade.FAO.R11.csv | 3 +++ message_ix_models/data/material/trade.FAO.R14.csv | 3 +++ 3 files changed, 9 insertions(+) create mode 100644 message_ix_models/data/material/Ammonia feedstock share.Global.xlsx create mode 100644 message_ix_models/data/material/trade.FAO.R11.csv create mode 100644 message_ix_models/data/material/trade.FAO.R14.csv diff --git a/message_ix_models/data/material/Ammonia feedstock share.Global.xlsx b/message_ix_models/data/material/Ammonia feedstock share.Global.xlsx new file mode 100644 index 0000000000..8f1ff566e8 --- /dev/null +++ b/message_ix_models/data/material/Ammonia feedstock share.Global.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c5233d331d321957c7db799b114e9c5702ad7d61d932675740d56dd0f137f193 +size 16492 diff --git a/message_ix_models/data/material/trade.FAO.R11.csv b/message_ix_models/data/material/trade.FAO.R11.csv new file mode 100644 index 0000000000..740ea9e66a --- /dev/null +++ b/message_ix_models/data/material/trade.FAO.R11.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:84f9b4f5fc4d0fe59ce51289fe05f80eebdc19c2fc4b44187d7b8082934c235d +size 3054 diff --git a/message_ix_models/data/material/trade.FAO.R14.csv b/message_ix_models/data/material/trade.FAO.R14.csv new file mode 100644 index 0000000000..926ea5b1da --- /dev/null +++ b/message_ix_models/data/material/trade.FAO.R14.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:011fc5995eb1408c7fd93d977f6109436da909f920765f7903c04762856927cd +size 3173 From 830dae45041d008f85b13e00258dae0cff49b41c Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 15 Sep 2020 15:26:23 +0200 Subject: [PATCH 100/774] Additions related to aluminum --- message_ix_models/model/material/bare.py | 62 +++++++++++++++++++----- 1 file changed, 50 insertions(+), 12 deletions(-) diff --git a/message_ix_models/model/material/bare.py b/message_ix_models/model/material/bare.py index 1374c11822..67abb4172e 100644 --- a/message_ix_models/model/material/bare.py +++ b/message_ix_models/model/material/bare.py @@ -7,7 +7,7 @@ from message_data.tools import Code, ScenarioInfo, get_context, set_info, add_par_data from .build import apply_spec from .util import read_config -from .data import get_data, gen_data_steel, gen_data_generic +from .data import get_data, gen_data_steel, gen_data_generic, gen_data_aluminum import message_data @@ -15,15 +15,18 @@ # Settings and valid values; the default is listed first +# How do we add the historical period ? + SETTINGS = dict( - period_start=[2010], + #period_start=[2010], + period_start=[1980], + first_model_year = [2020], period_end=[2100], regions=["China"], res_with_dummies=[False], time_step=[10], ) - def create_res(context=None, quiet=True): """Create a 'bare' MESSAGE-GLOBIOM reference energy system (RES). @@ -53,7 +56,7 @@ def create_res(context=None, quiet=True): scenario.init_par('MERtoPPP', ['node', 'year']) # Uncomment to add dummy sets and data - # context.res_with_dummies = True + context.res_with_dummies = True spec = get_spec(context) apply_spec( @@ -68,9 +71,36 @@ def create_res(context=None, quiet=True): return scenario +DATA_FUNCTIONS = [ + gen_data_steel, + gen_data_generic, + gen_data_aluminum, +] + +# Try to handle multiple data input functions from different materials +def add_data(scenario, dry_run=False): + """Populate `scenario` with MESSAGE-Material data.""" + + # Information about `scenario` + info = ScenarioInfo(scenario) + + # Check for two "node" values for global data, e.g. in + # ixmp://ene-ixmp/CD_Links_SSP2_v2.1_clean/baseline + if {"World", "R11_GLB"} < set(info.set["node"]): + log.warning("Remove 'R11_GLB' from node list for data generation") + info.set["node"].remove("R11_GLB") + + for func in DATA_FUNCTIONS: + # Generate or load the data; add to the Scenario + log.info(f'from {func.__name__}()') + add_par_data(scenario, func(scenario), dry_run=dry_run) + + log.info('done') + + def get_spec(context=None) -> Mapping[str, ScenarioInfo]: - """Return the spec for the MESSAGE-China bare RES. + """Return the spec for the MESSAGE-Material bare RES. Parameters ---------- @@ -90,6 +120,7 @@ def get_spec(context=None) -> Mapping[str, ScenarioInfo]: spec = dict(require=ScenarioInfo()) # JM: For China model, we need to remove the default 'World'. + # GU: Remove world is already specified in set.yaml. remove = ScenarioInfo() # remove.set["node"] = context["material"]["common"]["region"]["remove"] @@ -98,7 +129,8 @@ def get_spec(context=None) -> Mapping[str, ScenarioInfo]: # Add technologies # JM: try to find out a way to loop over 1st/2nd level and to just context["material"][xx]["add"] add.set["technology"] = context["material"]["steel"]["technology"]["add"] + \ - context["material"]["generic"]["technology"]["add"] + context["material"]["generic"]["technology"]["add"] + \ + context["material"]["aluminum"]["technology"]["add"] # Add regions @@ -119,19 +151,25 @@ def get_spec(context=None) -> Mapping[str, ScenarioInfo]: )) # JM: Leave the first time period as historical year - add.set['cat_year'] = [('firstmodelyear', context.period_start + context.time_step)] + #add.set['cat_year'] = [('firstmodelyear', context.period_start + context.time_step)] + + # GU: Set 2020 as the first model year, leave the rest as historical year + add.set['cat_year'] = [('firstmodelyear', context.first_model_year)] # Add levels # JM: For bare model, both 'add' & 'require' need to be added. add.set['level'] = context["material"]["steel"]["level"]["add"] + \ context["material"]["common"]["level"]["require"] + \ - context["material"]["generic"]["level"]["add"] + context["material"]["generic"]["level"]["add"] + \ + context["material"]["aluminum"]["level"]["add"] # Add commodities - c_list = context["material"]["steel"]["commodity"]["add"] + \ + add.set['commodity'] = context["material"]["steel"]["commodity"]["add"] + \ context["material"]["common"]["commodity"]["require"] + \ - context["material"]["generic"]["commodity"]["add"] - add.set['commodity'] = c_list + context["material"]["generic"]["commodity"]["add"] + \ + context["material"]["aluminum"]["commodity"]["add"] + + # Add other sets add.set['type_tec'] = context["material"]["common"]["type_tec"]["add"] add.set['mode'] = context["material"]["common"]["mode"]["require"] +\ @@ -162,7 +200,7 @@ def get_spec(context=None) -> Mapping[str, ScenarioInfo]: add.set['unit'] = sorted(set(add.set['unit'])) # JM: Manually set the first model year - add.y0 = context.period_start + context.time_step + add.y0 = context.first_model_year if context.res_with_dummies: # Add dummy technologies From d5ec12fd39f49cc53fd44cc719f7648beb30598c Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 16 Sep 2020 11:40:29 +0200 Subject: [PATCH 101/774] Additions to integrate aluminum --- message_ix_models/model/material/data.py | 166 ++++++++++++++++++----- 1 file changed, 134 insertions(+), 32 deletions(-) diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index d2b4f6597f..d089cbd69f 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -143,7 +143,7 @@ def read_var_cost(): # Read the file df = pd.read_excel( - context.get_path("material", "China_steel_renamed.xlsx"), + context.get_path("material", "dummy_variable_costs.xlsx"), sheet_name="var_cost", ) @@ -173,12 +173,16 @@ def read_data_aluminum(): data_aluminum= data_aluminum.drop(['Region', 'Source', 'Description'], axis = 1) + data_aluminum_hist = pd.read_excel(context.get_path("material", \ + "aluminum_techno_economic.xlsx",sheet_name="data_historical", \ + usecols = "A:F") + # Unit conversion # At the moment this is done in the excel file, can be also done here # To make sure we use the same units - return data_aluminum + return data_aluminum,data_aluminum_hist def read_data_generic(): """Read and clean data from :file:`generic_furnace_boiler_techno_economic.xlsx`.""" @@ -208,6 +212,7 @@ def read_data_generic(): # TODO: Adding the active years to the tables # TODO: If there are differnet values for the years. + def gen_data_aluminum(scenario, dry_run=False): """Generate data for materials representation of aluminum.""" # Load configuration @@ -218,11 +223,19 @@ def gen_data_aluminum(scenario, dry_run=False): s_info = ScenarioInfo(scenario) # Techno-economic assumptions - data_aluminum = read_data_aluminum() + data_aluminum, data_aluminum_hist = read_data_aluminum() # List of data frames, to be concatenated together at end results = defaultdict(list) + allyears = s_info.set['year'] + modelyears = s_info.Y #s_info.Y is only for modeling years + nodes = s_info.N + yv_ya = s_info.yv_ya + fmy = s_info.y0 + + nodes.remove('World') + # Iterate over technologies for t in config["technology"]["add"]: @@ -252,6 +265,12 @@ def gen_data_aluminum(scenario, dry_run=False): params = data_aluminum.loc[(data_aluminum["technology"] == t),\ "parameter"].values.tolist() + # Availability of the technology + av = data_aluminum.loc[(data_aluminum["technology"] == t), + 'availability'].values[0] + modelyears = [year for year in modelyears if year >= av] + yva = yv_ya.loc[yv_ya.year_vtg >= av] + # Iterate over parameters for par in params: split = par.split("|") @@ -301,14 +320,23 @@ def gen_data_aluminum(scenario, dry_run=False): **common).pipe(broadcast, node_loc=nodes)) results[param_name].append(df) - # Concatenate to one data frame per parameter - results_aluminum = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} + # Add the dummy alluminum demand + + values = gen_mock_demand_aluminum() + aluminum_demand = pd.DataFrame({ + 'node': nodes, + 'commodity': 'aluminum', + 'level': 'demand', + 'year': modelyears, + 'time': 'year', + 'value': values , + 'unit': 'Mt', + }) + results["demand"].append(aluminum_demand) + results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} return results -# TODO: Different input values over the years: Add a function that modifies -#the values over the years - def gen_data_generic(scenario, dry_run=False): # Load configuration @@ -413,6 +441,47 @@ def gen_data_generic(scenario, dry_run=False): return results +def gen_variable_data(): + + # Generates variables costs for dummy technologies + + data_vc = read_var_cost() + tec_vc = set(data_vc.technology) + + # List of data frames, to be concatenated together at end + results = defaultdict(list) + + allyears = s_info.set['year'] #s_info.Y is only for modeling years + modelyears = s_info.Y #s_info.Y is only for modeling years + nodes = s_info.N + yv_ya = s_info.yv_ya + fmy = s_info.y0 + + for t in config['technology']['add']: + + # Special treatment for time-varying params + if t in tec_vc: + common = dict( + time="year", + time_origin="year", + time_dest="year",) + + param_name = "var_cost" + val = data_vc.loc[(data_vc["technology"] == t), 'value'] + units = data_vc.loc[(data_vc["technology"] == t),'units'].values[0] + mod = data_vc.loc[(data_vc["technology"] == t), 'mode'] + yr = data_vc.loc[(data_vc["technology"] == t), 'year'] + + df = (make_df(param_name, technology=t, value=val,\ + unit='t', year_vtg=yr, year_act=yr, mode=mod, **common).pipe(broadcast, \ + node_loc=nodes)) + results[param_name].append(df) + + # Concatenate to one data frame per parameter + results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} + + return results + def gen_data_steel(scenario, dry_run=False): """Generate data for materials representation of steel industry. @@ -427,8 +496,8 @@ def gen_data_steel(scenario, dry_run=False): # Techno-economic assumptions data_steel = process_china_data_tec() # Special treatment for time-dependent Parameters - data_steel_vc = read_var_cost() - tec_vc = set(data_steel_vc.technology) # set of tecs with var_cost + #data_steel_vc = read_var_cost() + #tec_vc = set(data_steel_vc.technology) # List of data frames, to be concatenated together at end results = defaultdict(list) @@ -452,26 +521,26 @@ def gen_data_steel(scenario, dry_run=False): params = data_steel.loc[(data_steel["technology"] == t),\ "parameter"].values.tolist() - # Special treatment for time-varying params - if t in tec_vc: - common = dict( - time="year", - time_origin="year", - time_dest="year",) - - param_name = "var_cost" - val = data_steel_vc.loc[(data_steel_vc["technology"] == t), 'value'] - units = data_steel_vc.loc[(data_steel_vc["technology"] == t), \ - 'units'].values[0] - mod = data_steel_vc.loc[(data_steel_vc["technology"] == t), 'mode'] - yr = data_steel_vc.loc[(data_steel_vc["technology"] == t), 'year'] - - df = (make_df(param_name, technology=t, value=val,\ - unit='t', year_vtg=yr, year_act=yr, mode=mod, **common).pipe(broadcast, \ - node_loc=nodes)) - - # print(param_name, df) - results[param_name].append(df) + # # Special treatment for time-varying params + # if t in tec_vc: + # common = dict( + # time="year", + # time_origin="year", + # time_dest="year",) + # + # param_name = "var_cost" + # val = data_steel_vc.loc[(data_steel_vc["technology"] == t), 'value'] + # units = data_steel_vc.loc[(data_steel_vc["technology"] == t), \ + # 'units'].values[0] + # mod = data_steel_vc.loc[(data_steel_vc["technology"] == t), 'mode'] + # yr = data_steel_vc.loc[(data_steel_vc["technology"] == t), 'year'] + # + # df = (make_df(param_name, technology=t, value=val,\ + # unit='t', year_vtg=yr, year_act=yr, mode=mod, **common).pipe(broadcast, \ + # node_loc=nodes)) + # + # print(param_name, df) + # results[param_name].append(df) # Iterate over parameters for par in params: @@ -569,7 +638,7 @@ def gen_data_steel(scenario, dry_run=False): elif par_name == "relation_lower": - demand = gen_mock_demand() + demand = gen_mock_demand_steel() df = (make_df(par_name, value=demand, unit='t',\ **common_rel).pipe(broadcast, node_rel=nodes)) @@ -611,7 +680,7 @@ def add_data(scenario, dry_run=False): # Generate a fake steel demand -def gen_mock_demand(): +def gen_mock_demand_steel(): # True steel use 2010 (China) = 537 Mt/year # https://www.worldsteel.org/en/dam/jcr:0474d208-9108-4927-ace8-4ac5445c5df8/World+Steel+in+Figures+2017.pdf gdp_growth = [0.121448215899944, \ @@ -622,6 +691,39 @@ def gen_mock_demand(): return demand +def gen_mock_demand_aluminum(): + + # 17.3 Mt in 2010 to match the historical production from IAI. + # This is the amount right after electrolysis. + + # The future projection of the demand: Increases by half of the GDP growth rate. + # Starting from 2020. + gdp_growth = pd.Series([0.121448215899944, 0.0733079014579874, \ + 0.0348154093342843, 0.021827616787921,\ + 0.0134425983942219, 0.0108320197485592, \ + 0.00884341208063, 0.00829374133206562, \ + 0.00649794573935969],index=pd.Index(model_horizon, \ + name='Time')) + # Values in 2010 from IAI. + fin_to_useful = 0.971 + useful_to_product = 0.866 + + i = 0 + values = [] + val = (17.3 * (1+ 0.147718884937996/2) ** duration_period[i]) + values.append(val) + + for element in gdp_growth: + i = i + 1 + if i < len(model_horizon): + val = (val * (1+ element/2) ** duration_period[i]) + values.append(val) + + # Adjust the demand according to old scrap level. + + values = [x * fin_to_useful * useful_to_product for x in values] + + return values def get_data(scenario, context, **options): """Data for the bare RES.""" From d0dcb77b0f5e91e0145e0d62f048eeea1ff3431b Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 16 Sep 2020 14:30:22 +0200 Subject: [PATCH 102/774] Add historical parameters for aluminum --- message_ix_models/model/material/data.py | 52 ++++++++++++++++++------ 1 file changed, 40 insertions(+), 12 deletions(-) diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index d089cbd69f..d6f1fe29da 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -160,8 +160,6 @@ def read_data_aluminum(): # Ensure config is loaded, get the context context = read_config() - # Shorter access to sets configuration - sets = context["material"]["aluminum"] # Read the file data_aluminum = pd.read_excel( @@ -182,7 +180,7 @@ def read_data_aluminum(): # At the moment this is done in the excel file, can be also done here # To make sure we use the same units - return data_aluminum,data_aluminum_hist + return data_aluminum, data_aluminum_hist def read_data_generic(): """Read and clean data from :file:`generic_furnace_boiler_techno_economic.xlsx`.""" @@ -209,10 +207,6 @@ def read_data_generic(): return data_generic - -# TODO: Adding the active years to the tables -# TODO: If there are differnet values for the years. - def gen_data_aluminum(scenario, dry_run=False): """Generate data for materials representation of aluminum.""" # Load configuration @@ -326,17 +320,49 @@ def gen_data_aluminum(scenario, dry_run=False): aluminum_demand = pd.DataFrame({ 'node': nodes, 'commodity': 'aluminum', - 'level': 'demand', + 'level': 'demand_aluminum', 'year': modelyears, 'time': 'year', 'value': values , 'unit': 'Mt', }) results["demand"].append(aluminum_demand) + + # Add historical data + + for tec in data_aluminum_hist["technology"].unique(): + + y_hist = [y for y in allyears if y < fmy] + + common_hist = dict( + year_vtg= y_hist, + year_act= y_hist, + mode="M1", + time="year",) + + val_act = data_aluminum_hist.\ + loc[(data_aluminum_hist["technology"]== tec), "production"] + + df_hist_act = (make_df("historical_activity", technology=tec, \ + value=val_act, unit='Mt', **common_hist).pipe(broadcast, node_loc=nodes)) + + c_factor = data_aluminum.loc[(data_aluminum["technology"]== tec) \ + & (data_aluminum["parameter"]=="capacity_factor"), "value"] + + val_cap = data_aluminum_hist.loc[(data_aluminum_hist["technology"]== tec), \ + "new_production"] / c_factor + + df_hist_cap = (make_df("historical_new_capacity", technology=tec, \ + value=val_cap, unit='Mt', **common_hist).pipe(broadcast, node_loc=nodes)) + + results["historical_activity"].append(df_hist_act) + results["historical_new_capacity""].append(df_hist_cap) + results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} return results +#TODO: Add historical data ? def gen_data_generic(scenario, dry_run=False): # Load configuration @@ -441,7 +467,7 @@ def gen_data_generic(scenario, dry_run=False): return results -def gen_variable_data(): +def gen_data_variable(): # Generates variables costs for dummy technologies @@ -451,7 +477,7 @@ def gen_variable_data(): # List of data frames, to be concatenated together at end results = defaultdict(list) - allyears = s_info.set['year'] #s_info.Y is only for modeling years + allyears = s_info.set['year'] modelyears = s_info.Y #s_info.Y is only for modeling years nodes = s_info.N yv_ya = s_info.yv_ya @@ -698,6 +724,8 @@ def gen_mock_demand_aluminum(): # The future projection of the demand: Increases by half of the GDP growth rate. # Starting from 2020. + context = read_config() + gdp_growth = pd.Series([0.121448215899944, 0.0733079014579874, \ 0.0348154093342843, 0.021827616787921,\ 0.0134425983942219, 0.0108320197485592, \ @@ -710,13 +738,13 @@ def gen_mock_demand_aluminum(): i = 0 values = [] - val = (17.3 * (1+ 0.147718884937996/2) ** duration_period[i]) + val = (17.3 * (1+ 0.147718884937996/2) ** context.time_step) values.append(val) for element in gdp_growth: i = i + 1 if i < len(model_horizon): - val = (val * (1+ element/2) ** duration_period[i]) + val = (val * (1+ element/2) ** context.time_step) values.append(val) # Adjust the demand according to old scrap level. From a3d7a666ef4736cdb22beea49dddb1bfa1c452ed Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 16 Sep 2020 14:33:36 +0200 Subject: [PATCH 103/774] Seperate generation of dummy tech and variable cost data --- message_ix_models/data/material/set.yaml | 20 ++++++++++---------- message_ix_models/model/material/bare.py | 3 ++- 2 files changed, 12 insertions(+), 11 deletions(-) diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 54e67f4682..17731f11f9 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -105,6 +105,14 @@ generic: - fc_h2_aluminum - solar_aluminum - dheat_aluminum + - DUMMY_coal_supply + - DUMMY_gas_supply + - DUMMY_loil_supply + - DUMMY_foil_supply + - DUMMY_electr_supply + - DUMMY_ethanol_supply + - DUMMY_methanol_supply + - DUMMY_hydrogen_supply mode: add: @@ -150,16 +158,8 @@ steel: - finishing_steel - manuf_steel - scrap_recovery_steel - - DUMMY_coal_supply - - DUMMY_gas_supply - - DUMMY_loil_supply - - DUMMY_foil_supply - - DUMMY_electr_supply - DUMMY_ore_supply - - DUMMY_ethanol_supply - - DUMMY_methanol_supply - DUMMY_limestone_supply - - DUMMY_hydrogen_supply - DUMMY_freshwater_supply - DUMMY_water_supply @@ -217,7 +217,7 @@ fertilizer: - finishing_aluminum - manuf_aluminum - scrap_recovery_aluminum - - alumina_supply + - DUMMY_alumina_supply - DUMMY_coal_supply - DUMMY_gas_supply - DUMMY_loil_supply @@ -228,7 +228,7 @@ fertilizer: - DUMMY_hydrogen_supply - DUMMY_dist_heating - DUMMY_biomass_supply - + fertilizer: commodity: require: diff --git a/message_ix_models/model/material/bare.py b/message_ix_models/model/material/bare.py index 67abb4172e..1ca461c752 100644 --- a/message_ix_models/model/material/bare.py +++ b/message_ix_models/model/material/bare.py @@ -7,7 +7,7 @@ from message_data.tools import Code, ScenarioInfo, get_context, set_info, add_par_data from .build import apply_spec from .util import read_config -from .data import get_data, gen_data_steel, gen_data_generic, gen_data_aluminum +from .data import get_data, gen_data_steel, gen_data_generic, gen_data_aluminum, gen_data_variable import message_data @@ -75,6 +75,7 @@ def create_res(context=None, quiet=True): gen_data_steel, gen_data_generic, gen_data_aluminum, + gen_data_variable ] # Try to handle multiple data input functions from different materials From c05295f6e4de25bda7a5012aa2de23aac69bed44 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Thu, 17 Sep 2020 10:10:02 +0200 Subject: [PATCH 104/774] Attempt to run based on different price assumptions --- .../material/China_steel_renamed - test.xlsx | 3 ++ .../data/material/China_steel_renamed.xlsx | 4 +- message_ix_models/model/material/data.py | 16 +++--- .../model/material/material_testscript.py | 51 +++++++++++++++++-- 4 files changed, 62 insertions(+), 12 deletions(-) create mode 100644 message_ix_models/data/material/China_steel_renamed - test.xlsx diff --git a/message_ix_models/data/material/China_steel_renamed - test.xlsx b/message_ix_models/data/material/China_steel_renamed - test.xlsx new file mode 100644 index 0000000000..39742e539b --- /dev/null +++ b/message_ix_models/data/material/China_steel_renamed - test.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:68875b2a8b72831063ab7a46ba1f22da807f5e14371c6ddcd695aa95496980d4 +size 54002 diff --git a/message_ix_models/data/material/China_steel_renamed.xlsx b/message_ix_models/data/material/China_steel_renamed.xlsx index 4ab91666c3..4601c377c9 100644 --- a/message_ix_models/data/material/China_steel_renamed.xlsx +++ b/message_ix_models/data/material/China_steel_renamed.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:636d93984766f1743921cb4ed3a32b20460b361d28fd7e867b4725f618f02bc3 -size 52889 +oid sha256:e48e7a16deea9672bc0f3d9854a5ddbb50b8109148f5a404ea0ddbe4ca0d6b29 +size 54442 diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index d6f1fe29da..e45325bfb5 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -80,7 +80,7 @@ def process_china_data_tec(): # Read the file data_steel_china = pd.read_excel( - context.get_path("material", "China_steel_renamed.xlsx"), + context.get_path("material", "China_steel_renamed - test.xlsx"), sheet_name="technologies", ) @@ -126,7 +126,7 @@ def process_china_data_rel(): # Read the file data_steel_china = pd.read_excel( - context.get_path("material", "China_steel_renamed.xlsx"), + context.get_path("material", "China_steel_renamed - test.xlsx"), sheet_name="relations", ) @@ -141,10 +141,14 @@ def read_var_cost(): # Ensure config is loaded, get the context context = read_config() + if context.scenario_info['scenario'] == 'NPi400': + sheet_name="var_cost_NPi400" + else: + sheet_name = "var_cost" + # Read the file df = pd.read_excel( - context.get_path("material", "dummy_variable_costs.xlsx"), - sheet_name="var_cost", + context.get_path("material", "China_steel_renamed - test.xlsx"), sheet_name ) df = pd.melt(df, id_vars=['technology', 'mode', 'units'], \ @@ -389,7 +393,7 @@ def gen_data_generic(scenario, dry_run=False): # 'World' is included by default when creating a message_ix.Scenario(). # Need to remove it for the China bare model - # nodes.remove('World') + nodes.remove('World') for t in config["technology"]["add"]: @@ -539,7 +543,7 @@ def gen_data_steel(scenario, dry_run=False): print(allyears, modelyears, fmy) - # nodes.remove('World') # For the bare model + nodes.remove('World') # For the bare model # for t in s_info.set['technology']: for t in config['technology']['add']: diff --git a/message_ix_models/model/material/material_testscript.py b/message_ix_models/model/material/material_testscript.py index 3f7d4adb5f..e49f19dc75 100644 --- a/message_ix_models/model/material/material_testscript.py +++ b/message_ix_models/model/material/material_testscript.py @@ -10,9 +10,10 @@ from message_ix import Scenario import message_data.model.material.data as dt +from message_data.model.material.plotting import Plots import pyam -from message_data.model.material import bare +# from message_data.model.material import bare from message_data.tools.cli import Context @@ -21,8 +22,15 @@ make_df, make_io, make_matched_dfs, + set_info, ) +from message_data.model.bare import create_res +# from message_data.model.create import create_res +from message_data.model.material import build, get_spec + +from message_data.model.material.util import read_config + #%% Main test run # Create Context obj @@ -32,11 +40,46 @@ # Set default scenario/model names ctx.scenario_info.setdefault('model', 'Material_test') ctx.scenario_info.setdefault('scenario', 'baseline') +ctx['period_start'] = 2020 +ctx['regions'] = 'China' + +# Use general code to create a Scenario with some stuff in it +scen = create_res(context = ctx) + +# Use material-specific code to add certain stuff +a = build(scen) -# Create bare model/scenario and solve it -scen = bare.create_res(context = ctx) +# Solve the model scen.solve() +p = Plots(scen, 'China', firstyear=2020) +p.plot_activity(baseyear=True, subset=['bf_steel', 'dri_steel', 'eaf_steel']) +p.plot_capacity(baseyear=True, subset=['bf_steel', 'dri_steel', 'eaf_steel']) + + +#%% run with NPi prices +Context._instance = [] +ctx = Context() + +# Set default scenario/model names +ctx.scenario_info.setdefault('model', 'Material_test') +ctx.scenario_info.setdefault('scenario', 'NPi400') +ctx['period_start'] = 2020 +ctx['regions'] = 'China' + +# Use general code to create a Scenario with some stuff in it +scen_np = create_res(context = ctx) + +# Use material-specific code to add certain stuff +a = build(scen_np) + +# Solve the model +scen_np.solve() + +p = Plots(scen_np, 'China', firstyear=2020) +p.plot_activity(baseyear=True, subset=['bf_steel', 'dri_steel', 'eaf_steel']) +p.plot_capacity(baseyear=True, subset=['bf_steel', 'dri_steel', 'eaf_steel']) +p.plot_new_capacity(baseyear=True, subset=['bf_steel', 'dri_steel', 'eaf_steel']) #%% Auxiliary random test stuff @@ -60,7 +103,7 @@ info = ScenarioInfo(scen) -a = bare.get_spec(ctx) +a = get_spec() mp_samp = ixmp.Platform(name="local") mp_samp.scenario_list() From 90f7db8c3fd5b94565451f632abe0941ca7b00eb Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 17 Sep 2020 11:17:23 +0200 Subject: [PATCH 105/774] Added meta_data_path manually --- message_ix_models/model/material/util.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/message_ix_models/model/material/util.py b/message_ix_models/model/material/util.py index 17ee4ca4b6..9da7c9701d 100644 --- a/message_ix_models/model/material/util.py +++ b/message_ix_models/model/material/util.py @@ -1,7 +1,7 @@ +from pathlib import Path from message_ix_models import Context from message_ix_models.util import as_codes, load_package_data - def read_config(): """Read configuration from set.yaml.""" # TODO this is similar to transport.utils.read_config; make a common @@ -19,6 +19,7 @@ def read_config(): return context # Read material.yaml + context.metadata_path=Path("C:/Users/unlu/Documents/GitHub/message_data/data") context.load_config("material", "set") # Use a shorter name From cfd7b82f9304be226d1608e45f9a80c34aee5e94 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 17 Sep 2020 11:18:22 +0200 Subject: [PATCH 106/774] Fixed the errors for aluminum data generation --- message_ix_models/model/material/data.py | 106 +++++++++++------------ 1 file changed, 52 insertions(+), 54 deletions(-) diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index e45325bfb5..9c84caa5d6 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -157,35 +157,6 @@ def read_var_cost(): return df - -# Question: Do we need read data dunction seperately for all materials ? -def read_data_aluminum(): - """Read and clean data from :file:`aluminum_techno_economic.xlsx`.""" - - # Ensure config is loaded, get the context - context = read_config() - - # Read the file - data_aluminum = pd.read_excel( - context.get_path("material", "aluminum_techno_economic.xlsx"), - sheet_name="aluminum", - ) - - # Clean the data - - data_aluminum= data_aluminum.drop(['Region', 'Source', 'Description'], axis = 1) - - data_aluminum_hist = pd.read_excel(context.get_path("material", \ - "aluminum_techno_economic.xlsx",sheet_name="data_historical", \ - usecols = "A:F") - - # Unit conversion - - # At the moment this is done in the excel file, can be also done here - # To make sure we use the same units - - return data_aluminum, data_aluminum_hist - def read_data_generic(): """Read and clean data from :file:`generic_furnace_boiler_techno_economic.xlsx`.""" @@ -211,6 +182,26 @@ def read_data_generic(): return data_generic +def read_data_aluminum(): + """Read and clean data from :file:`aluminum_techno_economic.xlsx`.""" + + # Ensure config is loaded, get the context + context = read_config() + + # Read the file + data_aluminum = pd.read_excel( + context.get_path("material", "aluminum_techno_economic.xlsx"), + sheet_name="data") + # Clean the data + + data_aluminum= data_aluminum.drop(['Region', 'Source', 'Description'], axis = 1) + + data_aluminum_hist = pd.read_excel(context.get_path("material", \ + "aluminum_techno_economic.xlsx"),sheet_name="data_historical", \ + usecols = "A:F") + + return data_aluminum,data_aluminum_hist + def gen_data_aluminum(scenario, dry_run=False): """Generate data for materials representation of aluminum.""" # Load configuration @@ -280,9 +271,9 @@ def gen_data_aluminum(scenario, dry_run=False): & (data_aluminum["parameter"] == par)),'value'].values[0] common = dict( - year_vtg= vintage_years, - year_act = act_years, - mode="standard", + year_vtg= yv_ya.year_vtg, + year_act= yv_ya.year_act, + mode="M1", time="year", time_origin="year", time_dest="year",) @@ -320,23 +311,21 @@ def gen_data_aluminum(scenario, dry_run=False): # Add the dummy alluminum demand - values = gen_mock_demand_aluminum() - aluminum_demand = pd.DataFrame({ - 'node': nodes, - 'commodity': 'aluminum', - 'level': 'demand_aluminum', - 'year': modelyears, - 'time': 'year', - 'value': values , - 'unit': 'Mt', - }) - results["demand"].append(aluminum_demand) + values = gen_mock_demand_aluminum(scenario) + + demand_al = (make_df("demand", commodity= "aluminum", \ + level= "demand_aluminum", year = modelyears, value=values, unit='Mt',\ + time= "year").pipe(broadcast, node=nodes)) + + results["demand"].append(demand_al) # Add historical data for tec in data_aluminum_hist["technology"].unique(): + print(tec) y_hist = [y for y in allyears if y < fmy] + print(y_hist) common_hist = dict( year_vtg= y_hist, @@ -350,8 +339,10 @@ def gen_data_aluminum(scenario, dry_run=False): df_hist_act = (make_df("historical_activity", technology=tec, \ value=val_act, unit='Mt', **common_hist).pipe(broadcast, node_loc=nodes)) - c_factor = data_aluminum.loc[(data_aluminum["technology"]== tec) \ - & (data_aluminum["parameter"]=="capacity_factor"), "value"] + results["historical_activity"].append(df_hist_act) + + c_factor = data_aluminum.loc[((data_aluminum["technology"]== tec) \ + & (data_aluminum["parameter"]=="capacity_factor")), "value"].values val_cap = data_aluminum_hist.loc[(data_aluminum_hist["technology"]== tec), \ "new_production"] / c_factor @@ -359,8 +350,7 @@ def gen_data_aluminum(scenario, dry_run=False): df_hist_cap = (make_df("historical_new_capacity", technology=tec, \ value=val_cap, unit='Mt', **common_hist).pipe(broadcast, node_loc=nodes)) - results["historical_activity"].append(df_hist_act) - results["historical_new_capacity""].append(df_hist_cap) + results["historical_new_capacity"].append(df_hist_cap) results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} @@ -471,7 +461,13 @@ def gen_data_generic(scenario, dry_run=False): return results -def gen_data_variable(): +def gen_data_variable(scenario, dry_run=False): + + # Load configuration + config = read_config()["material"]["generic"] + + # Information about scenario, e.g. node, year + s_info = ScenarioInfo(scenario) # Generates variables costs for dummy technologies @@ -721,7 +717,7 @@ def gen_mock_demand_steel(): return demand -def gen_mock_demand_aluminum(): +def gen_mock_demand_aluminum(scenario): # 17.3 Mt in 2010 to match the historical production from IAI. # This is the amount right after electrolysis. @@ -729,12 +725,14 @@ def gen_mock_demand_aluminum(): # The future projection of the demand: Increases by half of the GDP growth rate. # Starting from 2020. context = read_config() + s_info = ScenarioInfo(scenario) + modelyears = s_info.Y #s_info.Y is only for modeling years gdp_growth = pd.Series([0.121448215899944, 0.0733079014579874, \ 0.0348154093342843, 0.021827616787921,\ 0.0134425983942219, 0.0108320197485592, \ 0.00884341208063, 0.00829374133206562, \ - 0.00649794573935969],index=pd.Index(model_horizon, \ + 0.00649794573935969],index=pd.Index(modelyears, \ name='Time')) # Values in 2010 from IAI. fin_to_useful = 0.971 @@ -746,10 +744,10 @@ def gen_mock_demand_aluminum(): values.append(val) for element in gdp_growth: - i = i + 1 - if i < len(model_horizon): - val = (val * (1+ element/2) ** context.time_step) - values.append(val) + i = i + 1 + if i < len(modelyears): + val = (val * (1+ element/2) ** context.time_step) + values.append(val) # Adjust the demand according to old scrap level. From d4dbd5d04672c356e12406f2ea20c8ec253fb5f9 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 17 Sep 2020 11:19:06 +0200 Subject: [PATCH 107/774] Minor fixes --- message_ix_models/model/material/bare.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/message_ix_models/model/material/bare.py b/message_ix_models/model/material/bare.py index 1ca461c752..872feafd60 100644 --- a/message_ix_models/model/material/bare.py +++ b/message_ix_models/model/material/bare.py @@ -23,7 +23,7 @@ first_model_year = [2020], period_end=[2100], regions=["China"], - res_with_dummies=[False], + res_with_dummies=[True], time_step=[10], ) @@ -43,6 +43,7 @@ def create_res(context=None, quiet=True): :func:`.build.apply_spec`. """ mp = context.get_platform() + mp.add_unit('Mt') # Model and scenario name for the RES model_name = context.scenario_info['model'] From 870727a043f364ef7f051c7f6263cffe05260a7a Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 17 Sep 2020 11:20:04 +0200 Subject: [PATCH 108/774] Minor fixes (gen_data --> get_data) --- message_ix_models/model/material/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 0efe7cebb5..1765591a74 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -80,7 +80,7 @@ def solve(context): "NPi2020_400-con-prim-dir-ncr": "NPi2020_400", }.get(context.scenario_info["scenario"]) - # Chnage this part - the model name ?? + # Chnage this part - the model name ?? if context.scenario_info["model"] != "CD_Links_SSP2": print("WARNING: this code is not tested with this base scenario!") From b19c672e12f3f6df575b2239390f2ad9a1fe0b7c Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 17 Sep 2020 11:22:14 +0200 Subject: [PATCH 109/774] Error fixes in the structure --- message_ix_models/data/material/set.yaml | 69 +++++++++++------------- 1 file changed, 30 insertions(+), 39 deletions(-) diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 17731f11f9..6c518f2c77 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -192,42 +192,32 @@ fertilizer: - fueloil_NH3 - NH3_to_N_fertil - aluminum: - commodity: - add: - - aluminum +aluminum: + commodity: + add: + - aluminum - level: - add: - - new_scrap - - old_scrap - - useful_aluminum - - final_material - - useful_material - - product - - secondary_material - - demand_aluminum + level: + add: + - new_scrap + - old_scrap + - useful_aluminum + - final_material + - useful_material + - product + - secondary_material + - demand_aluminum - technology: - add: - - soderberg_aluminum - - prebake_aluminum - - secondary_aluminum - - prep_secondary_aluminum - - finishing_aluminum - - manuf_aluminum - - scrap_recovery_aluminum - - DUMMY_alumina_supply - - DUMMY_coal_supply - - DUMMY_gas_supply - - DUMMY_loil_supply - - DUMMY_foil_supply - - DUMMY_electr_supply - - DUMMY_ethanol_supply - - DUMMY_methanol_supply - - DUMMY_hydrogen_supply - - DUMMY_dist_heating - - DUMMY_biomass_supply + technology: + add: + - soderberg_aluminum + - prebake_aluminum + - secondary_aluminum + - prep_secondary_aluminum + - finishing_aluminum + - manuf_aluminum + - scrap_recovery_aluminum + - DUMMY_alumina_supply fertilizer: commodity: @@ -267,11 +257,12 @@ non-ferrous: description: Metals and alloys which do not contain iron. Measured as the total mass of material. unit: Mt -aluminum: - name: Aluminum - description: Aluminum (or aluminium) is a chemical element with the symbol Al. Measured as the total mass of material. - unit: Mt - parent: non-ferrous +# Creates an error because it is the same name with the above aluminum +# aluminum: +# name: Aluminum +# description: Aluminum (or aluminium) is a chemical element with the symbol Al. Measured as the total mass of material. +# unit: Mt +# parent: non-ferrous copper: name: Copper From 9cd8db8847dda2de45bc52f75b6d5758bf18e9fd Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 17 Sep 2020 11:23:54 +0200 Subject: [PATCH 110/774] Excel data files rearranged errors fixed --- message_ix_models/data/material/China_steel_renamed.xlsx | 5 +++++ .../data/material/aluminum_techno_economic.xlsx | 4 ++-- message_ix_models/data/material/dummy_variable_costs.xlsx | 3 +++ .../material/generic_furnace_boiler_techno_economic.xlsx | 3 +++ .../generic_furnace_boiler_techno_economic_aluminum.xlsx | 3 --- .../generic_furnace_boiler_techno_economic_steel.xlsx | 4 ++-- message_ix_models/data/material/variable_costs.xlsx | 3 --- 7 files changed, 15 insertions(+), 10 deletions(-) create mode 100644 message_ix_models/data/material/dummy_variable_costs.xlsx create mode 100644 message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx delete mode 100644 message_ix_models/data/material/generic_furnace_boiler_techno_economic_aluminum.xlsx delete mode 100644 message_ix_models/data/material/variable_costs.xlsx diff --git a/message_ix_models/data/material/China_steel_renamed.xlsx b/message_ix_models/data/material/China_steel_renamed.xlsx index 4601c377c9..7d76ad0e76 100644 --- a/message_ix_models/data/material/China_steel_renamed.xlsx +++ b/message_ix_models/data/material/China_steel_renamed.xlsx @@ -1,3 +1,8 @@ version https://git-lfs.github.com/spec/v1 +<<<<<<< master oid sha256:e48e7a16deea9672bc0f3d9854a5ddbb50b8109148f5a404ea0ddbe4ca0d6b29 size 54442 +======= +oid sha256:56d26e538005bd7249c10f35702e32f30ae081b470cc9bd2bc48ec6b66833e35 +size 51675 +>>>>>>> excel data files rearranged errors fixed diff --git a/message_ix_models/data/material/aluminum_techno_economic.xlsx b/message_ix_models/data/material/aluminum_techno_economic.xlsx index 5d79787c5e..bb2caea370 100644 --- a/message_ix_models/data/material/aluminum_techno_economic.xlsx +++ b/message_ix_models/data/material/aluminum_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:106287600beb514e90a4780f9404d59cd1ad910f52556620804c9cc15bcce6bf -size 51745 +oid sha256:06b8cd179a83aab563c341edb1b29d32d775b94412e403c2d7368401f393470d +size 55773 diff --git a/message_ix_models/data/material/dummy_variable_costs.xlsx b/message_ix_models/data/material/dummy_variable_costs.xlsx new file mode 100644 index 0000000000..9fb88afd82 --- /dev/null +++ b/message_ix_models/data/material/dummy_variable_costs.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a01c6ecfa8e91a067781ebf9698c3a060ce8e66cca92695eb21f0b7b42783d7e +size 13600 diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx new file mode 100644 index 0000000000..7320270a9f --- /dev/null +++ b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0e5f7298d17f9af324f314e17d24e71eeb6409f8bec5ec75781cc7263a2ae3e9 +size 59787 diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic_aluminum.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic_aluminum.xlsx deleted file mode 100644 index c6a0e2acca..0000000000 --- a/message_ix_models/data/material/generic_furnace_boiler_techno_economic_aluminum.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:bdfe832a631d0080892932af6ecb2e1ed3c1cc059adf182eb476c77de57d0ff5 -size 47993 diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic_steel.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic_steel.xlsx index 9edf9afc3f..f48994f31d 100644 --- a/message_ix_models/data/material/generic_furnace_boiler_techno_economic_steel.xlsx +++ b/message_ix_models/data/material/generic_furnace_boiler_techno_economic_steel.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:f9d6d91a7c1c38ba3221d295dd2a471ffb4cc8df01da8c6014bec0dcc9e1d5bd -size 48442 +oid sha256:7d537dac6568cb38e7698c25b6ebb50baf6027316553b05600fb16d1c14b0b2c +size 48197 diff --git a/message_ix_models/data/material/variable_costs.xlsx b/message_ix_models/data/material/variable_costs.xlsx deleted file mode 100644 index 03f046c956..0000000000 --- a/message_ix_models/data/material/variable_costs.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:2ecfeceb662b6bdf3d14078429f4db7d6b4997447e80eb486160ffc91d3f3695 -size 32510 From fea0307de8891a6c11a30680aecd65bd54f2d026 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Fri, 18 Sep 2020 09:08:43 +0200 Subject: [PATCH 111/774] Stop using relation as demand setup and switch to normal demand param --- message_ix_models/data/material/set.yaml | 7 +- message_ix_models/model/material/data.py | 82 +++++++++++++----------- 2 files changed, 49 insertions(+), 40 deletions(-) diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 6c518f2c77..4be100518a 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -143,6 +143,7 @@ steel: - useful_material - waste_material - product + - demand technology: add: @@ -163,9 +164,9 @@ steel: - DUMMY_freshwater_supply - DUMMY_water_supply - relation: - add: - - steel_demand + # relation: + # add: + # - steel_demand fertilizer: commodity: diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index 9c84caa5d6..c87f9963bc 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -148,13 +148,14 @@ def read_var_cost(): # Read the file df = pd.read_excel( - context.get_path("material", "China_steel_renamed - test.xlsx"), sheet_name - ) + context.get_path("material", "China_steel_renamed - test.xlsx"), sheet_name) df = pd.melt(df, id_vars=['technology', 'mode', 'units'], \ value_vars=[2010, 2020, 2030, 2040, 2050, 2060, 2070, 2080, 2090, 2100], \ var_name='year') + + return df def read_data_generic(): @@ -635,41 +636,48 @@ def gen_data_steel(scenario, dry_run=False): # TODO: relation is used to set external demand. # We can also have two redundant commodity outputs from manufacturing stage # and set external demand on one of them. - for r in config['relation']['add']: - - # Read the file - rel_steel = process_china_data_rel() - - params = rel_steel.loc[(rel_steel["relation"] == r),\ - "parameter"].values.tolist() - - common_rel = dict( - year_rel = modelyears, - year_act = modelyears, - mode = 'M1', - relation = r,) - - for par_name in params: - if par_name == "relation_activity": - - val = rel_steel.loc[((rel_steel["relation"] == r) \ - & (rel_steel["parameter"] == par_name)),'value'].values[0] - tec = rel_steel.loc[((rel_steel["relation"] == r) \ - & (rel_steel["parameter"] == par_name)),'technology'].values[0] - - df = (make_df(par_name, technology=tec, value=val, unit='t',\ - **common_rel).pipe(broadcast, node_rel=nodes, node_loc=nodes)) - - results[par_name].append(df) - - elif par_name == "relation_lower": - - demand = gen_mock_demand_steel() - - df = (make_df(par_name, value=demand, unit='t',\ - **common_rel).pipe(broadcast, node_rel=nodes)) - - results[par_name].append(df) + # for r in config['relation']['add']: + # + # # Read the file + # rel_steel = process_china_data_rel() + # + # params = rel_steel.loc[(rel_steel["relation"] == r),\ + # "parameter"].values.tolist() + # + # common_rel = dict( + # year_rel = modelyears, + # year_act = modelyears, + # mode = 'M1', + # relation = r,) + # + # for par_name in params: + # if par_name == "relation_activity": + # + # val = rel_steel.loc[((rel_steel["relation"] == r) \ + # & (rel_steel["parameter"] == par_name)),'value'].values[0] + # tec = rel_steel.loc[((rel_steel["relation"] == r) \ + # & (rel_steel["parameter"] == par_name)),'technology'].values[0] + # + # df = (make_df(par_name, technology=tec, value=val, unit='t',\ + # **common_rel).pipe(broadcast, node_rel=nodes, node_loc=nodes)) + # + # results[par_name].append(df) + # + # elif par_name == "relation_lower": + # + # demand = gen_mock_demand() + # + # df = (make_df(par_name, value=demand, unit='t',\ + # **common_rel).pipe(broadcast, node_rel=nodes)) + # + # results[par_name].append(df) + + # Create external demand param + parname = 'demand' + demand = gen_mock_demand() + df = (make_df(parname, level='demand', commodity='steel', value=demand, unit='t', \ + year=modelyears, **common).pipe(broadcast, node=nodes)) + results[parname].append(df) # Concatenate to one data frame per parameter results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} From b8ee374350a71546d777b8be4b91154f54268da9 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Fri, 18 Sep 2020 09:10:27 +0200 Subject: [PATCH 112/774] Make cli work for this rearranged structure --- message_ix_models/model/material/__init__.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 1765591a74..3b57046dca 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -53,12 +53,16 @@ def cli(): @click.pass_obj def create_bare(context, regions, dry_run): """Create the RES from scratch.""" - from .bare import create_res + from message_data.model.bare import create_res if regions: context.regions = regions + # to allow historical years + context.period_start = 2020 + scen = create_res(context) + build(scen) # Solve if not dry_run: From f773cc0c7cd75686c9325755f9adf45c6c29e010 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Mon, 21 Sep 2020 17:31:23 +0200 Subject: [PATCH 113/774] Error fixed --- message_ix_models/data/material/aluminum_techno_economic.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/aluminum_techno_economic.xlsx b/message_ix_models/data/material/aluminum_techno_economic.xlsx index bb2caea370..502097ca72 100644 --- a/message_ix_models/data/material/aluminum_techno_economic.xlsx +++ b/message_ix_models/data/material/aluminum_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:06b8cd179a83aab563c341edb1b29d32d775b94412e403c2d7368401f393470d -size 55773 +oid sha256:c390dc450e2bd92759c8b3152963edfec08a63364dd0810423163430ae2d8985 +size 55794 From 82edddf2ba0cd66b2fc3ad26e09ed84d2230e2c1 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Tue, 22 Sep 2020 09:06:42 +0200 Subject: [PATCH 114/774] Add more years as historical years --- message_ix_models/data/material/set.yaml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 4be100518a..36af7ec2a5 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -58,6 +58,8 @@ common: year: add: + - 1990 + - 2000 - 2010 type_year: From 777001ecace4746d981770514ec6f0b5ecda9fde Mon Sep 17 00:00:00 2001 From: Laura Wienpahl <57132039+LauWien@users.noreply.github.com> Date: Thu, 18 Nov 2021 19:24:27 +0100 Subject: [PATCH 115/774] Add .xlsx file to be tracked in lfs .gitattributes --- message_ix_models/data/material/.gitattributes | 1 + 1 file changed, 1 insertion(+) diff --git a/message_ix_models/data/material/.gitattributes b/message_ix_models/data/material/.gitattributes index 87e654bb30..04da74af74 100644 --- a/message_ix_models/data/material/.gitattributes +++ b/message_ix_models/data/material/.gitattributes @@ -1 +1,2 @@ *.csv filter=lfs diff=lfs merge=lfs -text +*.xlsx filter=lfs diff=lfs merge=lfs -text From bf484318f67f2906b980e0e0a1bbb0c341cc4406 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Tue, 22 Sep 2020 09:07:30 +0200 Subject: [PATCH 116/774] Fix the mock demand + go back to the normal input file --- message_ix_models/data/material/China_steel_renamed.xlsx | 9 ++------- 1 file changed, 2 insertions(+), 7 deletions(-) diff --git a/message_ix_models/data/material/China_steel_renamed.xlsx b/message_ix_models/data/material/China_steel_renamed.xlsx index 7d76ad0e76..5b9306e499 100644 --- a/message_ix_models/data/material/China_steel_renamed.xlsx +++ b/message_ix_models/data/material/China_steel_renamed.xlsx @@ -1,8 +1,3 @@ version https://git-lfs.github.com/spec/v1 -<<<<<<< master -oid sha256:e48e7a16deea9672bc0f3d9854a5ddbb50b8109148f5a404ea0ddbe4ca0d6b29 -size 54442 -======= -oid sha256:56d26e538005bd7249c10f35702e32f30ae081b470cc9bd2bc48ec6b66833e35 -size 51675 ->>>>>>> excel data files rearranged errors fixed +oid sha256:d58a350b95074ca74fc56e7d7f80d65943cf914bfbd32e1d4960267f1bf14a73 +size 289 From 7cbe96dec8a36de3ce6a22d9113ed64889d4b9d9 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 22 Sep 2020 09:57:27 +0200 Subject: [PATCH 117/774] Metadata_path error fixed --- message_ix_models/model/material/__init__.py | 3 +++ message_ix_models/model/material/util.py | 2 +- 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 3b57046dca..8c976cebfc 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -61,6 +61,9 @@ def create_bare(context, regions, dry_run): # to allow historical years context.period_start = 2020 + # Otherwise it can not find the path to read the yaml files.. + context.metadata_path = context.metadata_path /'data' + scen = create_res(context) build(scen) diff --git a/message_ix_models/model/material/util.py b/message_ix_models/model/material/util.py index 9da7c9701d..45cce26780 100644 --- a/message_ix_models/model/material/util.py +++ b/message_ix_models/model/material/util.py @@ -19,7 +19,7 @@ def read_config(): return context # Read material.yaml - context.metadata_path=Path("C:/Users/unlu/Documents/GitHub/message_data/data") + #context.metadata_path=Path("C:/Users/unlu/Documents/GitHub/message_data/data") context.load_config("material", "set") # Use a shorter name From 45dad174faae714108663a0afb6cbe5a31b4b467 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 22 Sep 2020 11:47:52 +0200 Subject: [PATCH 118/774] Addition of aluminum related parts --- message_ix_models/model/material/__init__.py | 4 ++-- message_ix_models/model/material/data.py | 23 ++++++++++++++++---- 2 files changed, 21 insertions(+), 6 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 8c976cebfc..20b7f73ead 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -28,7 +28,7 @@ def get_spec() -> Mapping[str, ScenarioInfo]: context = read_config() # Update the ScenarioInfo objects with required and new set elements - for type in "generic", "common", "steel",: + for type in "generic", "common", "steel",'aluminum': for set_name, config in context["material"][type].items(): # for cat_name, detail in config.items(): # Required elements @@ -59,7 +59,7 @@ def create_bare(context, regions, dry_run): context.regions = regions # to allow historical years - context.period_start = 2020 + context.period_start = 1980 # Otherwise it can not find the path to read the yaml files.. context.metadata_path = context.metadata_path /'data' diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index c87f9963bc..48c6928905 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -218,11 +218,20 @@ def gen_data_aluminum(scenario, dry_run=False): # List of data frames, to be concatenated together at end results = defaultdict(list) - allyears = s_info.set['year'] + #allyears = s_info.set['year'] + + # FIX: The years do not include 1980 + allyears = np.arange(1980, 2101, 10).tolist() + print('All years') + print(allyears) modelyears = s_info.Y #s_info.Y is only for modeling years + print('Model years') + print(modelyears) nodes = s_info.N yv_ya = s_info.yv_ya fmy = s_info.y0 + print('first model year') + print(fmy) nodes.remove('World') @@ -326,6 +335,9 @@ def gen_data_aluminum(scenario, dry_run=False): print(tec) y_hist = [y for y in allyears if y < fmy] + print('Second all years') + print(allyears) + print('Historical years') print(y_hist) common_hist = dict( @@ -336,6 +348,7 @@ def gen_data_aluminum(scenario, dry_run=False): val_act = data_aluminum_hist.\ loc[(data_aluminum_hist["technology"]== tec), "production"] + print(val_act) df_hist_act = (make_df("historical_activity", technology=tec, \ value=val_act, unit='Mt', **common_hist).pipe(broadcast, node_loc=nodes)) @@ -376,7 +389,7 @@ def gen_data_generic(scenario, dry_run=False): # For each technology there are differnet input and output combinations # Iterate over technologies - allyears = s_info.set['year'] #s_info.Y is only for modeling years + allyears = s_info.set['year'] modelyears = s_info.Y #s_info.Y is only for modeling years nodes = s_info.N yv_ya = s_info.yv_ya @@ -674,7 +687,7 @@ def gen_data_steel(scenario, dry_run=False): # Create external demand param parname = 'demand' - demand = gen_mock_demand() + demand = gen_mock_demand_steel() df = (make_df(parname, level='demand', commodity='steel', value=demand, unit='t', \ year=modelyears, **common).pipe(broadcast, node=nodes)) results[parname].append(df) @@ -689,7 +702,8 @@ def gen_data_steel(scenario, dry_run=False): DATA_FUNCTIONS = [ gen_data_steel, gen_data_generic, - # gen_data_aluminum, + gen_data_aluminum, + gen_data_variable ] @@ -715,6 +729,7 @@ def add_data(scenario, dry_run=False): # Generate a fake steel demand def gen_mock_demand_steel(): + import numpy as np # True steel use 2010 (China) = 537 Mt/year # https://www.worldsteel.org/en/dam/jcr:0474d208-9108-4927-ace8-4ac5445c5df8/World+Steel+in+Figures+2017.pdf gdp_growth = [0.121448215899944, \ From 0ee764339c123724f593e1599a1b26132280d2f3 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 23 Sep 2020 10:28:41 +0200 Subject: [PATCH 119/774] Remove world + add dummy biomass --- message_ix_models/data/material/set.yaml | 9 ++++++++- message_ix_models/model/material/data.py | 9 ++------- 2 files changed, 10 insertions(+), 8 deletions(-) diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 36af7ec2a5..5da899121f 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -58,6 +58,7 @@ common: year: add: + - 1980 - 1990 - 2000 - 2010 @@ -115,11 +116,17 @@ generic: - DUMMY_ethanol_supply - DUMMY_methanol_supply - DUMMY_hydrogen_supply + - DUMMY_biomass_supply + - DUMMY_dist_heating + mode: add: - low_temp - high_temp + node: + remove: + - World steel: commodity: @@ -260,7 +267,7 @@ non-ferrous: description: Metals and alloys which do not contain iron. Measured as the total mass of material. unit: Mt -# Creates an error because it is the same name with the above aluminum +# Creates an error because it is the same name with the above aluminum # aluminum: # name: Aluminum # description: Aluminum (or aluminium) is a chemical element with the symbol Al. Measured as the total mass of material. diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index 48c6928905..eed12db4c4 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -332,14 +332,8 @@ def gen_data_aluminum(scenario, dry_run=False): # Add historical data for tec in data_aluminum_hist["technology"].unique(): - print(tec) y_hist = [y for y in allyears if y < fmy] - print('Second all years') - print(allyears) - print('Historical years') - print(y_hist) - common_hist = dict( year_vtg= y_hist, year_act= y_hist, @@ -348,7 +342,6 @@ def gen_data_aluminum(scenario, dry_run=False): val_act = data_aluminum_hist.\ loc[(data_aluminum_hist["technology"]== tec), "production"] - print(val_act) df_hist_act = (make_df("historical_activity", technology=tec, \ value=val_act, unit='Mt', **common_hist).pipe(broadcast, node_loc=nodes)) @@ -497,6 +490,8 @@ def gen_data_variable(scenario, dry_run=False): yv_ya = s_info.yv_ya fmy = s_info.y0 + nodes.remove('World') + for t in config['technology']['add']: # Special treatment for time-varying params From ce03e4ccb0998aba377df8c37eb0d5b67288e3a6 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 23 Sep 2020 10:29:21 +0200 Subject: [PATCH 120/774] Fixes in data files --- message_ix_models/data/material/dummy_variable_costs.xlsx | 4 ++-- .../data/material/generic_furnace_boiler_techno_economic.xlsx | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/message_ix_models/data/material/dummy_variable_costs.xlsx b/message_ix_models/data/material/dummy_variable_costs.xlsx index 9fb88afd82..db7d7da44f 100644 --- a/message_ix_models/data/material/dummy_variable_costs.xlsx +++ b/message_ix_models/data/material/dummy_variable_costs.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:a01c6ecfa8e91a067781ebf9698c3a060ce8e66cca92695eb21f0b7b42783d7e -size 13600 +oid sha256:fbb46a75353407aede863010d530a81e0ab36f13fadb960abb3a24205fe24ce7 +size 13534 diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx index 7320270a9f..b86c7e15fa 100644 --- a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx +++ b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:0e5f7298d17f9af324f314e17d24e71eeb6409f8bec5ec75781cc7263a2ae3e9 -size 59787 +oid sha256:8596f722c703c097373228e5e184c268de802e3f31b61a0bb5de148a150e4a35 +size 59793 From af897a55cba7f505bdbe3df977cea29bb1fe6d87 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 24 Sep 2020 14:00:03 +0200 Subject: [PATCH 121/774] Extended emission types for aluminum --- message_ix_models/data/material/aluminum_techno_economic.xlsx | 4 ++-- message_ix_models/data/material/set.yaml | 1 + 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/aluminum_techno_economic.xlsx b/message_ix_models/data/material/aluminum_techno_economic.xlsx index 502097ca72..845ed0a555 100644 --- a/message_ix_models/data/material/aluminum_techno_economic.xlsx +++ b/message_ix_models/data/material/aluminum_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:c390dc450e2bd92759c8b3152963edfec08a63364dd0810423163430ae2d8985 -size 55794 +oid sha256:5fdb039c6606739ededa01347510d4cc530feac4874ada4ddf7290a3d52515d0 +size 56048 diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 5da899121f..ca4a574c26 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -55,6 +55,7 @@ common: - NOx - SO2 - PM2p5 # Just add there since it is already in Shaohui's data + - CF4 year: add: From cab877e05e4bef5ce7903f931024d873a7676207 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 24 Sep 2020 14:11:42 +0200 Subject: [PATCH 122/774] Alumina cost added --- message_ix_models/data/material/dummy_variable_costs.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/dummy_variable_costs.xlsx b/message_ix_models/data/material/dummy_variable_costs.xlsx index db7d7da44f..d02172729e 100644 --- a/message_ix_models/data/material/dummy_variable_costs.xlsx +++ b/message_ix_models/data/material/dummy_variable_costs.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:fbb46a75353407aede863010d530a81e0ab36f13fadb960abb3a24205fe24ce7 -size 13534 +oid sha256:9a5efe2669c9907589919cb5a0b38cbc067302536c5efcb1a8ccf898739d901e +size 14855 From 404e169d9563ad3dbeba960f5d34cfba6378ad14 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Thu, 1 Oct 2020 00:10:21 +0200 Subject: [PATCH 123/774] Add m2 mode for fuel/feedstock switching --- message_ix_models/data/material/set.yaml | 1 + 1 file changed, 1 insertion(+) diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index ca4a574c26..0f1773c4a4 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -46,6 +46,7 @@ common: mode: add: - M1 + - M2 emission: add: From 3cd2b3ea5c4c78c0f381bde48608659fd946c29a Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Fri, 2 Oct 2020 09:28:46 +0200 Subject: [PATCH 124/774] Variable costs fixed --- .../material/aluminum_techno_economic.xlsx | 4 +- .../data/material/dummy_variable_costs.xlsx | 3 - message_ix_models/data/material/set.yaml | 4 +- .../data/material/variable_costs.xlsx | 3 + message_ix_models/model/material/data.py | 111 ++++++++++++++---- 5 files changed, 98 insertions(+), 27 deletions(-) delete mode 100644 message_ix_models/data/material/dummy_variable_costs.xlsx create mode 100644 message_ix_models/data/material/variable_costs.xlsx diff --git a/message_ix_models/data/material/aluminum_techno_economic.xlsx b/message_ix_models/data/material/aluminum_techno_economic.xlsx index 845ed0a555..1b3261e95e 100644 --- a/message_ix_models/data/material/aluminum_techno_economic.xlsx +++ b/message_ix_models/data/material/aluminum_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:5fdb039c6606739ededa01347510d4cc530feac4874ada4ddf7290a3d52515d0 -size 56048 +oid sha256:c8ea4248665241c6bd7525f068658913577d870d9bb881a5d4e4062c2aa7d478 +size 56261 diff --git a/message_ix_models/data/material/dummy_variable_costs.xlsx b/message_ix_models/data/material/dummy_variable_costs.xlsx deleted file mode 100644 index d02172729e..0000000000 --- a/message_ix_models/data/material/dummy_variable_costs.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:9a5efe2669c9907589919cb5a0b38cbc067302536c5efcb1a8ccf898739d901e -size 14855 diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 0f1773c4a4..99f95d6804 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -225,7 +225,9 @@ aluminum: - soderberg_aluminum - prebake_aluminum - secondary_aluminum - - prep_secondary_aluminum + - prep_secondary_aluminum_1 + - prep_secondary_aluminum_2 + - prep_secondary_aluminum_3 - finishing_aluminum - manuf_aluminum - scrap_recovery_aluminum diff --git a/message_ix_models/data/material/variable_costs.xlsx b/message_ix_models/data/material/variable_costs.xlsx new file mode 100644 index 0000000000..7d405a7eaa --- /dev/null +++ b/message_ix_models/data/material/variable_costs.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:325de6ca46e98eada689e4c2cae920c64a205abc48cca7c36c89c7f56597300c +size 15112 diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index eed12db4c4..c35b66d138 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -148,7 +148,9 @@ def read_var_cost(): # Read the file df = pd.read_excel( - context.get_path("material", "China_steel_renamed - test.xlsx"), sheet_name) + context.get_path("material", "variable_costs.xlsx"), + sheet_name=sheet_name, + ) df = pd.melt(df, id_vars=['technology', 'mode', 'units'], \ value_vars=[2010, 2020, 2030, 2040, 2050, 2060, 2070, 2080, 2090, 2100], \ @@ -361,6 +363,8 @@ def gen_data_aluminum(scenario, dry_run=False): results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} + add_scrap_prices(scenario) + return results #TODO: Add historical data ? @@ -470,9 +474,6 @@ def gen_data_generic(scenario, dry_run=False): def gen_data_variable(scenario, dry_run=False): - # Load configuration - config = read_config()["material"]["generic"] - # Information about scenario, e.g. node, year s_info = ScenarioInfo(scenario) @@ -492,25 +493,25 @@ def gen_data_variable(scenario, dry_run=False): nodes.remove('World') - for t in config['technology']['add']: - - # Special treatment for time-varying params - if t in tec_vc: - common = dict( - time="year", - time_origin="year", - time_dest="year",) + #for t in config['technology']['add']: + for t in tec_vc: + # Special treatment for time-varying params + #if t in tec_vc: + common = dict( + time="year", + time_origin="year", + time_dest="year",) - param_name = "var_cost" - val = data_vc.loc[(data_vc["technology"] == t), 'value'] - units = data_vc.loc[(data_vc["technology"] == t),'units'].values[0] - mod = data_vc.loc[(data_vc["technology"] == t), 'mode'] - yr = data_vc.loc[(data_vc["technology"] == t), 'year'] + param_name = "var_cost" + val = data_vc.loc[(data_vc["technology"] == t), 'value'] + units = data_vc.loc[(data_vc["technology"] == t),'units'].values[0] + mod = data_vc.loc[(data_vc["technology"] == t), 'mode'] + yr = data_vc.loc[(data_vc["technology"] == t), 'year'] - df = (make_df(param_name, technology=t, value=val,\ - unit='t', year_vtg=yr, year_act=yr, mode=mod, **common).pipe(broadcast, \ - node_loc=nodes)) - results[param_name].append(df) + df = (make_df(param_name, technology=t, value=val,\ + unit='t', year_vtg=yr, year_act=yr, mode=mod, **common).pipe(broadcast, \ + node_loc=nodes)) + results[param_name].append(df) # Concatenate to one data frame per parameter results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} @@ -844,3 +845,71 @@ def get_dummy_data(scenario, **options): data["demand"] = make_df("demand", **common) return data + +def add_scrap_prices(scenario): + + context = read_config() + s_info = ScenarioInfo(scenario) + nodes = s_info.N + modelyears = s_info.Y + nodes.remove('World') + + # Distinguish the share and total technologies + + total = ['prep_secondary_aluminum_1', 'prep_secondary_aluminum_2', \ + 'prep_secondary_aluminum_3'] + + #total = ["scrap_recovery_aluminum"] + + # Add technology category for total + + scenario.add_cat('technology', 'type_total', total) + + no = 0 + for tech in total: + + # Add technology category for shares + + no = no + 1 + no_shr = str(no) + type_tec_shr = 'type' + no_shr + share = 'scrap_availability_' + no_shr + + scenario.add_set('shares',share) + scenario.add_cat('technology',type_tec_shr, [tech]) + + # Map shares + + map_share = pd.DataFrame({'shares': [share], + 'node_share': nodes, + 'node': nodes, + 'type_tec': type_tec_shr, + 'mode': 'M1', + 'commodity': 'aluminum', + 'level': 'old_scrap',}) + scenario.add_set('map_shares_commodity_share', map_share) + + + # Map total + + map_total = pd.DataFrame({'shares': [share], + 'node_share': nodes, + 'node': nodes, + 'type_tec': 'type_total', + 'mode': 'M1', + 'commodity': 'aluminum', + 'level': 'old_scrap',}) + scenario.add_set('map_shares_commodity_total', map_total) + + # Add the upper bound + + up_share = pd.DataFrame({'shares': share, + 'node_share': nodes[0], + 'year_act': modelyears, + 'time': 'year', + 'value': 0.333, + 'unit': '%',}) + + print(up_share) + + scenario.add_par('share_commodity_up', up_share) From 0b87a1a05a21fabddef9ed509b947370cabe078b Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Fri, 2 Oct 2020 11:45:34 +0200 Subject: [PATCH 125/774] Fixes to scrap price implementation --- .../material/aluminum_techno_economic.xlsx | 2 +- .../data/material/variable_costs.xlsx | 2 +- message_ix_models/model/material/data.py | 161 +++++++++++++----- 3 files changed, 117 insertions(+), 48 deletions(-) diff --git a/message_ix_models/data/material/aluminum_techno_economic.xlsx b/message_ix_models/data/material/aluminum_techno_economic.xlsx index 1b3261e95e..c86c376f25 100644 --- a/message_ix_models/data/material/aluminum_techno_economic.xlsx +++ b/message_ix_models/data/material/aluminum_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:c8ea4248665241c6bd7525f068658913577d870d9bb881a5d4e4062c2aa7d478 +oid sha256:09a10d2a2b788a86356fb41785bca9af545a431dc5d59aa638f2ab30623904be size 56261 diff --git a/message_ix_models/data/material/variable_costs.xlsx b/message_ix_models/data/material/variable_costs.xlsx index 7d405a7eaa..1040352c86 100644 --- a/message_ix_models/data/material/variable_costs.xlsx +++ b/message_ix_models/data/material/variable_costs.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:325de6ca46e98eada689e4c2cae920c64a205abc48cca7c36c89c7f56597300c +oid sha256:2e8371eb376ae0d5bf4d85b42339862c1333b7ea490625156f9b233cbe1d3393 size 15112 diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index c35b66d138..982baef347 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -846,6 +846,74 @@ def get_dummy_data(scenario, **options): return data +# def add_scrap_prices(scenario): +# +# context = read_config() +# s_info = ScenarioInfo(scenario) +# nodes = s_info.N +# modelyears = s_info.Y +# nodes.remove('World') +# +# # Distinguish the share and total technologies +# +# total = ['prep_secondary_aluminum_1', 'prep_secondary_aluminum_2', \ +# 'prep_secondary_aluminum_3'] +# +# #total = ["scrap_recovery_aluminum"] +# +# # Add technology category for total +# +# scenario.add_cat('technology', 'type_total', total) +# +# no = 0 +# for tech in total: +# +# # Add technology category for shares +# +# no = no + 1 +# no_shr = str(no) +# type_tec_shr = 'type' + no_shr +# share = 'scrap_availability_' + no_shr +# +# scenario.add_set('shares',share) +# scenario.add_cat('technology',type_tec_shr, [tech]) +# +# # Map shares +# +# map_share = pd.DataFrame({'shares': [share], +# 'node_share': nodes, +# 'node': nodes, +# 'type_tec': type_tec_shr, +# 'mode': 'M1', +# 'commodity': 'aluminum', +# 'level': 'old_scrap',}) +# scenario.add_set('map_shares_commodity_share', map_share) +# +# +# # Map total +# +# map_total = pd.DataFrame({'shares': [share], +# 'node_share': nodes, +# 'node': nodes, +# 'type_tec': 'type_total', +# 'mode': 'M1', +# 'commodity': 'aluminum', +# 'level': 'old_scrap',}) +# scenario.add_set('map_shares_commodity_total', map_total) +# +# # Add the upper bound +# +# up_share = pd.DataFrame({'shares': share, +# 'node_share': nodes[0], +# 'year_act': modelyears, +# 'time': 'year', +# 'value': 0.333, +# 'unit': '%',}) +# +# print(up_share) +# +# scenario.add_par('share_commodity_up', up_share) + def add_scrap_prices(scenario): context = read_config() @@ -854,62 +922,63 @@ def add_scrap_prices(scenario): modelyears = s_info.Y nodes.remove('World') - # Distinguish the share and total technologies - total = ['prep_secondary_aluminum_1', 'prep_secondary_aluminum_2', \ 'prep_secondary_aluminum_3'] - #total = ["scrap_recovery_aluminum"] - - # Add technology category for total - - scenario.add_cat('technology', 'type_total', total) - no = 0 for tech in total: # Add technology category for shares no = no + 1 - no_shr = str(no) - type_tec_shr = 'type' + no_shr - share = 'scrap_availability_' + no_shr - - scenario.add_set('shares',share) - scenario.add_cat('technology',type_tec_shr, [tech]) - - # Map shares - - map_share = pd.DataFrame({'shares': [share], - 'node_share': nodes, - 'node': nodes, - 'type_tec': type_tec_shr, - 'mode': 'M1', - 'commodity': 'aluminum', - 'level': 'old_scrap',}) - scenario.add_set('map_shares_commodity_share', map_share) - - - # Map total - - map_total = pd.DataFrame({'shares': [share], - 'node_share': nodes, - 'node': nodes, - 'type_tec': 'type_total', - 'mode': 'M1', - 'commodity': 'aluminum', - 'level': 'old_scrap',}) - scenario.add_set('map_shares_commodity_total', map_total) + relation = 'scrap_availability_' + str(no) + + scenario.add_set('relation',relation) + + # relation_activity for the technology + + nodes_new = nodes * len(modelyears) + print(relation) + print(nodes) + print(modelyears) + print(tech) + + rel_act = pd.DataFrame({ + 'relation': relation, + 'node_rel': nodes_new, + 'year_rel': modelyears, + 'node_loc': nodes_new, + 'technology': tech, + 'year_act': modelyears, + 'mode': 'M1', + "value": 1, + 'unit': '-', + }) + + # 1/3 of the old scrap is available. 0.24512 the amount of old scrap. + # Is there a way to obtain it without hard-coding ? + + rel_act_rec = pd.DataFrame({ + 'relation': relation, + 'node_rel': nodes_new, + 'year_rel': modelyears, + 'node_loc': nodes_new, + 'technology': "scrap_recovery_aluminum", + 'year_act': modelyears, + 'mode': 'M1', + "value": -1/3 * 0.24512, + 'unit': '-', + }) + + scenario.add_par("relation_activity", rel_act) + scenario.add_par("relation_activity", rel_act_rec) # Add the upper bound - up_share = pd.DataFrame({'shares': share, - 'node_share': nodes[0], - 'year_act': modelyears, - 'time': 'year', - 'value': 0.333, - 'unit': '%',}) - - print(up_share) + upper = pd.DataFrame({'relation': relation, + 'node_rel': nodes_new, + 'year_rel': modelyears, + 'value': 0, + 'unit': '???',}) - scenario.add_par('share_commodity_up', up_share) + scenario.add_par("relation_upper",upper) From ed7137795bdc3de650847f46bef11cd00bcf2c3a Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 8 Oct 2020 17:03:38 +0200 Subject: [PATCH 126/774] Scrap pirce implementation additions --- .../material/aluminum_techno_economic.xlsx | 4 ++-- .../data/material/variable_costs.xlsx | 4 ++-- message_ix_models/model/material/data.py | 24 ++++++++++++------- 3 files changed, 20 insertions(+), 12 deletions(-) diff --git a/message_ix_models/data/material/aluminum_techno_economic.xlsx b/message_ix_models/data/material/aluminum_techno_economic.xlsx index c86c376f25..148a2c614a 100644 --- a/message_ix_models/data/material/aluminum_techno_economic.xlsx +++ b/message_ix_models/data/material/aluminum_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:09a10d2a2b788a86356fb41785bca9af545a431dc5d59aa638f2ab30623904be -size 56261 +oid sha256:f13ef1813636db4024d1cffbc22d41176f003b96c137434d16a127c967699f3a +size 56375 diff --git a/message_ix_models/data/material/variable_costs.xlsx b/message_ix_models/data/material/variable_costs.xlsx index 1040352c86..3dd277a6b9 100644 --- a/message_ix_models/data/material/variable_costs.xlsx +++ b/message_ix_models/data/material/variable_costs.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:2e8371eb376ae0d5bf4d85b42339862c1333b7ea490625156f9b233cbe1d3393 -size 15112 +oid sha256:e0ec396dd16105d5429abc4769225fe8fdb56b37bc6a309083bed89b9aa95905 +size 15874 diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index 982baef347..3c8c1ed86b 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -489,6 +489,8 @@ def gen_data_variable(scenario, dry_run=False): modelyears = s_info.Y #s_info.Y is only for modeling years nodes = s_info.N yv_ya = s_info.yv_ya + print("YVYA") + print(yv_ya) fmy = s_info.y0 nodes.remove('World') @@ -497,6 +499,7 @@ def gen_data_variable(scenario, dry_run=False): for t in tec_vc: # Special treatment for time-varying params #if t in tec_vc: + common = dict( time="year", time_origin="year", @@ -508,9 +511,9 @@ def gen_data_variable(scenario, dry_run=False): mod = data_vc.loc[(data_vc["technology"] == t), 'mode'] yr = data_vc.loc[(data_vc["technology"] == t), 'year'] - df = (make_df(param_name, technology=t, value=val,\ - unit='t', year_vtg=yr, year_act=yr, mode=mod, **common).pipe(broadcast, \ - node_loc=nodes)) + df = (make_df(param_name, technology=t, value=val,unit='t', \ + year_vtg=yr, year_act=yr, mode=mod, **common).pipe(broadcast, \ + node_loc=nodes)) results[param_name].append(df) # Concatenate to one data frame per parameter @@ -938,10 +941,6 @@ def add_scrap_prices(scenario): # relation_activity for the technology nodes_new = nodes * len(modelyears) - print(relation) - print(nodes) - print(modelyears) - print(tech) rel_act = pd.DataFrame({ 'relation': relation, @@ -958,6 +957,15 @@ def add_scrap_prices(scenario): # 1/3 of the old scrap is available. 0.24512 the amount of old scrap. # Is there a way to obtain it without hard-coding ? + # Chnage the availability + + if no == 1: + val = 1/4 + if no == 2: + val = 1/4 + if no== 3: + val = 1/2 + rel_act_rec = pd.DataFrame({ 'relation': relation, 'node_rel': nodes_new, @@ -966,7 +974,7 @@ def add_scrap_prices(scenario): 'technology': "scrap_recovery_aluminum", 'year_act': modelyears, 'mode': 'M1', - "value": -1/3 * 0.24512, + "value": -val * 0.24512, 'unit': '-', }) From c4ba5d882158e211e5e9555acb3058edd2a0d1ef Mon Sep 17 00:00:00 2001 From: Jihoon Date: Fri, 9 Oct 2020 10:19:44 +0200 Subject: [PATCH 127/774] Differentiate input data for standalone and integration cases --- message_ix_models/data/material/China_steel_MESSAGE.xlsx | 3 +++ .../data/material/China_steel_renamed - test.xlsx | 3 --- message_ix_models/data/material/China_steel_renamed.xlsx | 4 ++-- .../data/material/China_steel_standalone - test.xlsx | 3 +++ 4 files changed, 8 insertions(+), 5 deletions(-) create mode 100644 message_ix_models/data/material/China_steel_MESSAGE.xlsx delete mode 100644 message_ix_models/data/material/China_steel_renamed - test.xlsx create mode 100644 message_ix_models/data/material/China_steel_standalone - test.xlsx diff --git a/message_ix_models/data/material/China_steel_MESSAGE.xlsx b/message_ix_models/data/material/China_steel_MESSAGE.xlsx new file mode 100644 index 0000000000..68e7e264b8 --- /dev/null +++ b/message_ix_models/data/material/China_steel_MESSAGE.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5b6a4f0156cee4c62825e257a31201531eed370b089cf8f552a8077912f74f98 +size 43561 diff --git a/message_ix_models/data/material/China_steel_renamed - test.xlsx b/message_ix_models/data/material/China_steel_renamed - test.xlsx deleted file mode 100644 index 39742e539b..0000000000 --- a/message_ix_models/data/material/China_steel_renamed - test.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:68875b2a8b72831063ab7a46ba1f22da807f5e14371c6ddcd695aa95496980d4 -size 54002 diff --git a/message_ix_models/data/material/China_steel_renamed.xlsx b/message_ix_models/data/material/China_steel_renamed.xlsx index 5b9306e499..cdd78a7f20 100644 --- a/message_ix_models/data/material/China_steel_renamed.xlsx +++ b/message_ix_models/data/material/China_steel_renamed.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:d58a350b95074ca74fc56e7d7f80d65943cf914bfbd32e1d4960267f1bf14a73 -size 289 +oid sha256:ed77d47df7a1735da84f0a87ed3cbdc201567d810a9e8c73174d23c035c0d33f +size 58007 diff --git a/message_ix_models/data/material/China_steel_standalone - test.xlsx b/message_ix_models/data/material/China_steel_standalone - test.xlsx new file mode 100644 index 0000000000..2360c48d52 --- /dev/null +++ b/message_ix_models/data/material/China_steel_standalone - test.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5dce2805711f59fe9d87699ac5574e123426c64af7d54af5350719daebfd39c1 +size 53054 From 5ef7d05caecbf0999d93b8d3541fa36a1ba46c53 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Fri, 9 Oct 2020 10:28:04 +0200 Subject: [PATCH 128/774] Make changes necessary for integration to messageix-china (e.g. modify demand, fix model years to the base) --- message_ix_models/model/material/data.py | 17 ++++++++++++----- 1 file changed, 12 insertions(+), 5 deletions(-) diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index 3c8c1ed86b..c29b73ee11 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -729,15 +729,22 @@ def add_data(scenario, dry_run=False): # Generate a fake steel demand def gen_mock_demand_steel(): import numpy as np + + modelyears = s_info.Y + fmy = s_info.y0 + # True steel use 2010 (China) = 537 Mt/year + demand2010 = 537 # https://www.worldsteel.org/en/dam/jcr:0474d208-9108-4927-ace8-4ac5445c5df8/World+Steel+in+Figures+2017.pdf - gdp_growth = [0.121448215899944, \ - 0.0733079014579874, 0.0348154093342843, \ + gdp_growth = [0.121448215899944, 0.0733079014579874, 0.0348154093342843, \ 0.021827616787921, 0.0134425983942219, 0.0108320197485592, \ - 0.00884341208063,0.00829374133206562, 0.00649794573935969] - demand = [(x+1) * 537 for x in gdp_growth] + 0.00884341208063,0.00829374133206562, 0.00649794573935969, 0.00649794573935969] + baseyear = list(range(2020, 2110+1, 10)) + + gr = [(x+1) for x in gdp_growth] - return demand + demand = np.cumprod(gr) * demand2010 + demand_interp = np.interp(modelyears, baseyear, demand) def gen_mock_demand_aluminum(scenario): From f4f57ae762e36a5114276c33425df2cef2e38027 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Fri, 9 Oct 2020 10:34:06 +0200 Subject: [PATCH 129/774] Improve data file interface by receiving different files for standalone vs. integration case --- message_ix_models/model/material/data.py | 160 +++++++++++------------ 1 file changed, 75 insertions(+), 85 deletions(-) diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index c29b73ee11..741f78a302 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -12,6 +12,7 @@ import re +# datafile = "China_steel_renamed - test.xlsx" # "China_steel_renamed.xlsx" # log = logging.getLogger(__name__) @@ -80,7 +81,7 @@ def process_china_data_tec(): # Read the file data_steel_china = pd.read_excel( - context.get_path("material", "China_steel_renamed - test.xlsx"), + context.get_path("material", context.datafile), sheet_name="technologies", ) @@ -88,21 +89,20 @@ def process_china_data_tec(): data_steel_china = data_steel_china \ [['Technology', 'Parameter', 'Level', \ - 'Commodity', 'Species', 'Units', 'Value']] \ + 'Commodity', 'Mode', 'Species', 'Units', 'Value']] \ .replace(np.nan, '', regex=True) - tuple_series = data_steel_china[['Parameter', 'Commodity', 'Level']] \ - .apply(tuple, axis=1) - tuple_ef = data_steel_china[['Parameter', 'Species']] \ - .apply(tuple, axis=1) + # Combine columns and remove '' + list_series = data_steel_china[['Parameter', 'Commodity', 'Level', 'Mode']] \ + .apply(list, axis=1).apply(lambda x: list(filter(lambda a: a != '', x))) + list_ef = data_steel_china[['Parameter', 'Species', 'Mode']] \ + .apply(list, axis=1) - - data_steel_china['parameter'] = tuple_series.str.join('|') \ - .str.replace('\|\|', '') + data_steel_china['parameter'] = list_series.str.join('|') data_steel_china.loc[data_steel_china['Parameter'] == "emission_factor", \ - 'parameter'] = tuple_ef.str.join('|').str.replace('\|\|', '') + 'parameter'] = list_ef.str.join('|') - data_steel_china = data_steel_china.drop(['Parameter', 'Level', 'Commodity'] \ + data_steel_china = data_steel_china.drop(['Parameter', 'Level', 'Commodity', 'Mode'] \ , axis = 1) data_steel_china = data_steel_china.drop( \ data_steel_china[data_steel_china.Value==''].index) @@ -126,7 +126,7 @@ def process_china_data_rel(): # Read the file data_steel_china = pd.read_excel( - context.get_path("material", "China_steel_renamed - test.xlsx"), + context.get_path("material", context.datafile), sheet_name="relations", ) @@ -134,7 +134,7 @@ def process_china_data_rel(): # Read in time-dependent parameters # Now only used to add fuel cost for bare model -def read_var_cost(): +def read_timeseries(): import numpy as np @@ -142,21 +142,21 @@ def read_var_cost(): context = read_config() if context.scenario_info['scenario'] == 'NPi400': - sheet_name="var_cost_NPi400" + sheet_name="timeseries_NPi400" else: - sheet_name = "var_cost" + sheet_name = "timeseries" # Read the file df = pd.read_excel( - context.get_path("material", "variable_costs.xlsx"), - sheet_name=sheet_name, - ) - - df = pd.melt(df, id_vars=['technology', 'mode', 'units'], \ - value_vars=[2010, 2020, 2030, 2040, 2050, 2060, 2070, 2080, 2090, 2100], \ - var_name='year') + context.get_path("material", context.datafile), sheet_name) + import numbers + # Take only existing years in the data + datayears = [x for x in list(df) if isinstance(x, numbers.Number)] + df = pd.melt(df, id_vars=['parameter', 'technology', 'mode', 'units'], \ + value_vars = datayears, \ + var_name ='year') return df @@ -438,7 +438,7 @@ def gen_data_generic(scenario, dry_run=False): mode_list.append(mod) df = (make_df(param_name, technology=t, commodity=com, \ - level=lev,mode=mod, value=val, unit='t', **common).\ + level=lev, mode=mod, value=val, unit='t', **common).\ pipe(broadcast, node_loc=nodes).pipe(same_node)) results[param_name].append(df) @@ -535,8 +535,8 @@ def gen_data_steel(scenario, dry_run=False): # Techno-economic assumptions data_steel = process_china_data_tec() # Special treatment for time-dependent Parameters - #data_steel_vc = read_var_cost() - #tec_vc = set(data_steel_vc.technology) + data_steel_vc = read_timeseries() + tec_vc = set(data_steel_vc.technology) # set of tecs with var_cost # List of data frames, to be concatenated together at end results = defaultdict(list) @@ -581,13 +581,31 @@ def gen_data_steel(scenario, dry_run=False): # print(param_name, df) # results[param_name].append(df) + param_name = data_steel_vc.loc[(data_steel_vc["technology"] == t), 'parameter'] + + for p in set(param_name): + val = data_steel_vc.loc[(data_steel_vc["technology"] == t) \ + & (data_steel_vc["parameter"] == p), 'value'] + units = data_steel_vc.loc[(data_steel_vc["technology"] == t) \ + & (data_steel_vc["parameter"] == p), 'units'].values[0] + mod = data_steel_vc.loc[(data_steel_vc["technology"] == t) \ + & (data_steel_vc["parameter"] == p), 'mode'] + yr = data_steel_vc.loc[(data_steel_vc["technology"] == t) \ + & (data_steel_vc["parameter"] == p), 'year'] + + df = (make_df(p, technology=t, value=val,\ + unit='t', year_vtg=yr, year_act=yr, mode=mod, **common).pipe(broadcast, \ + node_loc=nodes)) + + print("time-dependent::", p, df) + results[p].append(df) + # Iterate over parameters for par in params: # Obtain the parameter names, commodity,level,emission split = par.split("|") param_name = split[0] - # Obtain the scalar value for the parameter val = data_steel.loc[((data_steel["technology"] == t) \ & (data_steel["parameter"] == par)),'value'].values[0] @@ -595,7 +613,7 @@ def gen_data_steel(scenario, dry_run=False): common = dict( year_vtg= yv_ya.year_vtg, year_act= yv_ya.year_act, - mode="M1", + # mode="M1", time="year", time_origin="year", time_dest="year",) @@ -603,87 +621,58 @@ def gen_data_steel(scenario, dry_run=False): # For the parameters which inlcudes index names if len(split)> 1: + print('1.param_name:', param_name, t) if (param_name == "input")|(param_name == "output"): # Assign commodity and level names com = split[1] lev = split[2] + mod = split[3] df = (make_df(param_name, technology=t, commodity=com, \ - level=lev, value=val, unit='t', **common)\ + level=lev, value=val, mode=mod, unit='t', **common)\ .pipe(broadcast, node_loc=nodes).pipe(same_node)) elif param_name == "emission_factor": # Assign the emisson type emi = split[1] + mod = split[2] - df = (make_df(param_name, technology=t,value=val,\ - emission=emi, unit='t', **common).pipe(broadcast, \ + df = (make_df(param_name, technology=t, value=val,\ + emission=emi, mode=mod, unit='t', **common).pipe(broadcast, \ node_loc=nodes)) + else: # time-independent var_cost + mod = split[1] + df = (make_df(param_name, technology=t, value=val, \ + mode=mod, unit='t', \ + **common).pipe(broadcast, node_loc=nodes)) + results[param_name].append(df) # Parameters with only parameter name else: - # Historical years are earlier than firstmodelyear - y_hist = [y for y in allyears if y < fmy] - # print(y_hist, fmy, years) - if re.search("historical_", param_name): - common_hist = dict( - year_vtg= y_hist, - year_act= y_hist, - mode="M1", - time="year",) - - df = (make_df(param_name, technology=t, value=val, unit='t', \ - **common_hist).pipe(broadcast, node_loc=nodes)) - # print(common_hist, param_name, t, nodes, val, y_hist) - else: - df = (make_df(param_name, technology=t, value=val, unit='t', \ - **common).pipe(broadcast, node_loc=nodes)) + print('2.param_name:', param_name) + # # Historical years are earlier than firstmodelyear + # y_hist = [y for y in allyears if y < fmy] + # # print(y_hist, fmy, years) + # if re.search("historical_", param_name): + # common_hist = dict( + # year_vtg= y_hist, + # year_act= y_hist, + # # mode="M1", + # time="year",) + # + # df = (make_df(param_name, technology=t, value=val, unit='t', \ + # **common_hist).pipe(broadcast, node_loc=nodes)) + # # print(common_hist, param_name, t, nodes, val, y_hist) + # else: + df = (make_df(param_name, technology=t, value=val, unit='t', \ + **common).pipe(broadcast, node_loc=nodes)) results[param_name].append(df) - # TODO: relation is used to set external demand. - # We can also have two redundant commodity outputs from manufacturing stage - # and set external demand on one of them. - # for r in config['relation']['add']: - # - # # Read the file - # rel_steel = process_china_data_rel() - # - # params = rel_steel.loc[(rel_steel["relation"] == r),\ - # "parameter"].values.tolist() - # - # common_rel = dict( - # year_rel = modelyears, - # year_act = modelyears, - # mode = 'M1', - # relation = r,) - # - # for par_name in params: - # if par_name == "relation_activity": - # - # val = rel_steel.loc[((rel_steel["relation"] == r) \ - # & (rel_steel["parameter"] == par_name)),'value'].values[0] - # tec = rel_steel.loc[((rel_steel["relation"] == r) \ - # & (rel_steel["parameter"] == par_name)),'technology'].values[0] - # - # df = (make_df(par_name, technology=tec, value=val, unit='t',\ - # **common_rel).pipe(broadcast, node_rel=nodes, node_loc=nodes)) - # - # results[par_name].append(df) - # - # elif par_name == "relation_lower": - # - # demand = gen_mock_demand() - # - # df = (make_df(par_name, value=demand, unit='t',\ - # **common_rel).pipe(broadcast, node_rel=nodes)) - # - # results[par_name].append(df) - # Create external demand param parname = 'demand' demand = gen_mock_demand_steel() @@ -746,6 +735,7 @@ def gen_mock_demand_steel(): demand = np.cumprod(gr) * demand2010 demand_interp = np.interp(modelyears, baseyear, demand) + return demand.tolist() def gen_mock_demand_aluminum(scenario): # 17.3 Mt in 2010 to match the historical production from IAI. From fd674137c2c2328426e95251406354b09dbebc05 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Fri, 9 Oct 2020 10:38:52 +0200 Subject: [PATCH 130/774] Add necessary sets for cement --- message_ix_models/data/material/set.yaml | 37 ++++++++++++++++++++++++ 1 file changed, 37 insertions(+) diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 99f95d6804..fe56f56ce9 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -175,6 +175,43 @@ steel: - DUMMY_freshwater_supply - DUMMY_water_supply +cement: + commodity: + add: + - cement + - clinker_cement + - raw_meal_cement + + level: + add: + - primary_material + - secondary_material + - tertiary_material + - final_material + - useful_material + - demand + + technology: + add: + - raw_meal_prep_cement + - clinker_dry_cement + - clinker_dry_ccs_cement + - clinker_wet_cement + - grinding_ballmill_cement + - grinding_vertmill_cement + - DUMMY_coal_supply + - DUMMY_gas_supply + - DUMMY_loil_supply + - DUMMY_foil_supply + - DUMMY_electr_supply + - DUMMY_ore_supply + - DUMMY_ethanol_supply + - DUMMY_methanol_supply + - DUMMY_limestone_supply + - DUMMY_hydrogen_supply + - DUMMY_freshwater_supply + - DUMMY_water_supply + # relation: # add: # - steel_demand From 1be3d5b108954018ebe3ebd961c9e33578e6ecbd Mon Sep 17 00:00:00 2001 From: Jihoon Date: Mon, 12 Oct 2020 23:13:13 +0200 Subject: [PATCH 131/774] Test run with china model --- .../data/material/China_steel_MESSAGE.xlsx | 4 +- .../data/material/China_steel_renamed.xlsx | 4 +- .../model/material/material_testscript.py | 58 ++++++++++++++++--- 3 files changed, 54 insertions(+), 12 deletions(-) diff --git a/message_ix_models/data/material/China_steel_MESSAGE.xlsx b/message_ix_models/data/material/China_steel_MESSAGE.xlsx index 68e7e264b8..10ed32c55e 100644 --- a/message_ix_models/data/material/China_steel_MESSAGE.xlsx +++ b/message_ix_models/data/material/China_steel_MESSAGE.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:5b6a4f0156cee4c62825e257a31201531eed370b089cf8f552a8077912f74f98 -size 43561 +oid sha256:b4251932332a9045bc287264aaec6519ca885f5ddc304302217b5bd84f43690b +size 43671 diff --git a/message_ix_models/data/material/China_steel_renamed.xlsx b/message_ix_models/data/material/China_steel_renamed.xlsx index cdd78a7f20..e6fab8b572 100644 --- a/message_ix_models/data/material/China_steel_renamed.xlsx +++ b/message_ix_models/data/material/China_steel_renamed.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:ed77d47df7a1735da84f0a87ed3cbdc201567d810a9e8c73174d23c035c0d33f -size 58007 +oid sha256:efd5a2101f72a51c555ce341fc38ffe279ee688db7178012098725d913c045dc +size 58684 diff --git a/message_ix_models/model/material/material_testscript.py b/message_ix_models/model/material/material_testscript.py index e49f19dc75..319b4912c9 100644 --- a/message_ix_models/model/material/material_testscript.py +++ b/message_ix_models/model/material/material_testscript.py @@ -15,7 +15,7 @@ # from message_data.model.material import bare -from message_data.tools.cli import Context +from message_data.tools import Context from message_data.tools import ( ScenarioInfo, @@ -31,8 +31,9 @@ from message_data.model.material.util import read_config -#%% Main test run +#%% Main test run from bare +from message_data.model.bare import create_res # Create Context obj Context._instance = [] ctx = Context() @@ -42,6 +43,7 @@ ctx.scenario_info.setdefault('scenario', 'baseline') ctx['period_start'] = 2020 ctx['regions'] = 'China' +ctx['datafile'] = 'China_steel_standalone - test.xlsx' # Use general code to create a Scenario with some stuff in it scen = create_res(context = ctx) @@ -53,11 +55,15 @@ scen.solve() p = Plots(scen, 'China', firstyear=2020) -p.plot_activity(baseyear=True, subset=['bf_steel', 'dri_steel', 'eaf_steel']) -p.plot_capacity(baseyear=True, subset=['bf_steel', 'dri_steel', 'eaf_steel']) +p.plot_activity(baseyear=False, subset=['bf_steel', 'bof_steel', 'dri_steel', 'eaf_steel']) +p.plot_activity(baseyear=False, subset=['bof_steel', 'eaf_steel']) +p.plot_capacity(baseyear=True, subset=['bf_steel', 'bof_steel', 'dri_steel', 'eaf_steel']) +p.plot_new_capacity(baseyear=True, subset=['bf_steel', 'bof_steel', 'dri_steel', 'eaf_steel']) +p.plot_activity(baseyear=True, subset=['manuf_steel', 'prep_secondary_steel']) -#%% run with NPi prices +#%% run with NPi prices (bare) +from message_data.model.bare import create_res Context._instance = [] ctx = Context() @@ -66,6 +72,7 @@ ctx.scenario_info.setdefault('scenario', 'NPi400') ctx['period_start'] = 2020 ctx['regions'] = 'China' +ctx['datafile'] = 'China_steel_standalone - test.xlsx' # Use general code to create a Scenario with some stuff in it scen_np = create_res(context = ctx) @@ -77,9 +84,43 @@ scen_np.solve() p = Plots(scen_np, 'China', firstyear=2020) -p.plot_activity(baseyear=True, subset=['bf_steel', 'dri_steel', 'eaf_steel']) -p.plot_capacity(baseyear=True, subset=['bf_steel', 'dri_steel', 'eaf_steel']) -p.plot_new_capacity(baseyear=True, subset=['bf_steel', 'dri_steel', 'eaf_steel']) +p.plot_activity(baseyear=True, subset=['bf_steel', 'bof_steel', 'dri_steel', 'eaf_steel']) +p.plot_capacity(baseyear=True, subset=['bf_steel', 'bof_steel', 'dri_steel', 'eaf_steel']) +p.plot_new_capacity(baseyear=True, subset=['bf_steel', 'bof_steel', 'dri_steel', 'eaf_steel']) + + +#%% Main test run based on a MESSAGE scenario + +from message_data.model.create import create_res + +# Create Context obj +Context._instance = [] +ctx = Context() + +# Set default scenario/model names +ctx.scenario_info.setdefault('model', 'Material_test_MESSAGE_China') +ctx.scenario_info.setdefault('scenario', 'baseline') +# ctx['period_start'] = 2020 +# ctx['regions'] = 'China' +# ctx['ssp'] = 'SSP2' # placeholder +ctx['scentype'] = 'C30-const' +ctx['datafile'] = 'China_steel_MESSAGE.xlsx' + +# Use general code to create a Scenario with some stuff in it +scen = create_res(context = ctx) + +# Use material-specific code to add certain stuff +a = build(scen) + +# Solve the model +scen.solve() + +p = Plots(scen, 'China', firstyear=2020) +p.plot_activity(baseyear=False, subset=['bf_steel', 'bof_steel', 'dri_steel', 'eaf_steel']) +p.plot_activity(baseyear=False, subset=['bof_steel', 'eaf_steel']) +p.plot_capacity(baseyear=True, subset=['bf_steel', 'bof_steel', 'dri_steel', 'eaf_steel']) +p.plot_new_capacity(baseyear=True, subset=['bf_steel', 'bof_steel', 'dri_steel', 'eaf_steel']) +p.plot_activity(baseyear=True, subset=['manuf_steel', 'prep_secondary_steel']) #%% Auxiliary random test stuff @@ -91,6 +132,7 @@ b = dt.read_data_generic() b = dt.read_var_cost() +bb = dt.process_china_data_tec() c = pd.melt(b, id_vars=['technology', 'mode', 'units'], \ value_vars=[2010, 2020, 2030, 2040, 2050, 2060, 2070, 2080, 2090, 2100], \ var_name='year') From 5b442153d33ce80cff3e98f094c575e2bfeef7fe Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 15 Oct 2020 16:05:42 +0200 Subject: [PATCH 132/774] Data addition --- .../data/material/petrochemicals_techno_economic.xlsx | 3 +++ 1 file changed, 3 insertions(+) create mode 100644 message_ix_models/data/material/petrochemicals_techno_economic.xlsx diff --git a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx new file mode 100644 index 0000000000..8352857b58 --- /dev/null +++ b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:54bc96285f60b11093aeb3b196ae7da2345ab2903541ff15e2e21454168e72ac +size 382674 From b23f989102db91195b91100297c9738d3b2b627b Mon Sep 17 00:00:00 2001 From: Jihoon Date: Mon, 19 Oct 2020 09:24:24 +0200 Subject: [PATCH 133/774] Update data with cement --- message_ix_models/data/material/China_steel_MESSAGE.xlsx | 4 ++-- message_ix_models/data/material/China_steel_renamed.xlsx | 4 ++-- .../data/material/China_steel_standalone - test.xlsx | 4 ++-- .../material/generic_furnace_boiler_techno_economic.xlsx | 6 +++--- 4 files changed, 9 insertions(+), 9 deletions(-) diff --git a/message_ix_models/data/material/China_steel_MESSAGE.xlsx b/message_ix_models/data/material/China_steel_MESSAGE.xlsx index 10ed32c55e..0386466ed4 100644 --- a/message_ix_models/data/material/China_steel_MESSAGE.xlsx +++ b/message_ix_models/data/material/China_steel_MESSAGE.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:b4251932332a9045bc287264aaec6519ca885f5ddc304302217b5bd84f43690b -size 43671 +oid sha256:555d866fc3e62a9cb3f4464c65178017da972af9323fb33e355cb75803612344 +size 47414 diff --git a/message_ix_models/data/material/China_steel_renamed.xlsx b/message_ix_models/data/material/China_steel_renamed.xlsx index e6fab8b572..5db89dd314 100644 --- a/message_ix_models/data/material/China_steel_renamed.xlsx +++ b/message_ix_models/data/material/China_steel_renamed.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:efd5a2101f72a51c555ce341fc38ffe279ee688db7178012098725d913c045dc -size 58684 +oid sha256:cf86b142cb48b90d5469d6247455ad4b8252b3264038a1d9c566881163d06ee7 +size 61177 diff --git a/message_ix_models/data/material/China_steel_standalone - test.xlsx b/message_ix_models/data/material/China_steel_standalone - test.xlsx index 2360c48d52..67f6a72077 100644 --- a/message_ix_models/data/material/China_steel_standalone - test.xlsx +++ b/message_ix_models/data/material/China_steel_standalone - test.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:5dce2805711f59fe9d87699ac5574e123426c64af7d54af5350719daebfd39c1 -size 53054 +oid sha256:42c031d8af13d71b2ad5a1ec5b6f14b973105a5164494552968710a014908fd8 +size 56730 diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx index b86c7e15fa..fc86d1f1f7 100644 --- a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx +++ b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx @@ -1,3 +1,3 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:8596f722c703c097373228e5e184c268de802e3f31b61a0bb5de148a150e4a35 -size 59793 +version https://git-lfs.github.com/spec/v1 +oid sha256:91b263b3f172b7ee9f17d85d682d5128b54ec8290c041eb3538c63f8549b9733 +size 51396 From a8aae0edba7dfd908bcffb05416facd9c4f6dd68 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Mon, 19 Oct 2020 09:25:52 +0200 Subject: [PATCH 134/774] Add missing parts for cement in set.yaml --- message_ix_models/data/material/set.yaml | 88 +++++++++++++++++++----- 1 file changed, 71 insertions(+), 17 deletions(-) diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index fe56f56ce9..276bc42895 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -36,8 +36,8 @@ common: # Just one is sufficient, since the RES will never be generated with a mix add: - China - remove: - - World + # remove: + # - World type_tec: add: @@ -79,6 +79,7 @@ generic: add: - useful_steel - useful_aluminum + - useful_cement technology: add: @@ -120,6 +121,15 @@ generic: - DUMMY_hydrogen_supply - DUMMY_biomass_supply - DUMMY_dist_heating + - furnace_foil_cement + - furnace_loil_cement + - furnace_biomass_cement + - furnace_ethanol_cement + - furnace_methanol_cement + - furnace_gas_cement + - furnace_coal_cement + - furnace_elec_cement + - furnace_h2_cement mode: @@ -181,6 +191,7 @@ cement: - cement - clinker_cement - raw_meal_cement + - limestone_cement level: add: @@ -199,18 +210,11 @@ cement: - clinker_wet_cement - grinding_ballmill_cement - grinding_vertmill_cement - - DUMMY_coal_supply - - DUMMY_gas_supply - - DUMMY_loil_supply - - DUMMY_foil_supply - - DUMMY_electr_supply - - DUMMY_ore_supply - - DUMMY_ethanol_supply - - DUMMY_methanol_supply - DUMMY_limestone_supply - - DUMMY_hydrogen_supply - - DUMMY_freshwater_supply - - DUMMY_water_supply + + remove: + - cement_co2scr + - cement_CO2 # relation: # add: @@ -298,10 +302,60 @@ aluminum: # NB this codelist added by #125 -iron-steel: - name: Iron and Steel - description: Iron is a chemical element with the symbol Fe, while steel is an alloy of iron and carbon and, sometimes, other elements. Measured as the total mass of material. - unit: Mt +# iron-steel: +# name: Iron and Steel +# description: Iron is a chemical element with the symbol Fe, while steel is an alloy of iron and carbon and, sometimes, other elements. Measured as the total mass of material. +# unit: Mt +# +# non-ferrous: +# name: Non-ferrous metals +# description: Metals and alloys which do not contain iron. Measured as the total mass of material. +# unit: Mt +# +# aluminum: +# name: Aluminum +# description: Aluminum (or aluminium) is a chemical element with the symbol Al. Measured as the total mass of material. +# unit: Mt +# parent: non-ferrous +# +# copper: +# name: Copper +# description: Copper is a chemical element with the symbol Cu. Measured as the total mass of material. +# unit: Mt +# parent: non-ferrous +# +# minerals: +# name: Minerals +# description: Non-metallic minerals are minerals that have no metallic luster, break easily and include, e.g., sand, limestone, marble, clay and salt. Measured as the FE-equivalent mass of material using LCA midpoint characterization factors. +# unit: MtFe-eq +# +# cement: +# name: Cement +# description: Cement is a binder used for construction that sets, hardens, and adheres to other materials to bind them together. Measured as the total mass of material. +# unit: Mt +# parent: minerals +# +# chemicals: +# name: Chemicals +# description: Industrial chemicals that form the basis of many products. Measured as the total mass of material. +# unit: Mt +# +# ethylene: +# name: Ethylene +# description: Ethylene is a hydrocarbon with the formula C2H4. Measured as the total mass of material. +# unit: Mt +# parent: chemicals +# +# ammonia: +# name: Ammonia +# description: Ammonia is a compound of nitrogen and hydrogen with the formula NH3. Measured as the total mass of material. +# unit: Mt +# parent: chemicals +# +# paper-pulp: +# name: Paper and pulp for paper +# description: Pulp is a lignocellulosic fibrous material prepared by chemically or mechanically separating cellulose fibers and the raw material for making paper. Measured as the total mass of material. +# unit: Mt non-ferrous: name: Non-ferrous metals From a4855afcdd7f7fd11ea13085e7be63f547b69d1c Mon Sep 17 00:00:00 2001 From: Jihoon Date: Mon, 19 Oct 2020 09:26:46 +0200 Subject: [PATCH 135/774] Need 'remove' part in spec --- message_ix_models/model/material/__init__.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 20b7f73ead..00016f7ed6 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -23,12 +23,13 @@ def get_spec() -> Mapping[str, ScenarioInfo]: """Return the specification for materials accounting.""" require = ScenarioInfo() add = ScenarioInfo() + remove = ScenarioInfo() # Load configuration context = read_config() # Update the ScenarioInfo objects with required and new set elements - for type in "generic", "common", "steel",'aluminum': + for type in "generic", "common", "steel", "cement",: for set_name, config in context["material"][type].items(): # for cat_name, detail in config.items(): # Required elements @@ -37,7 +38,10 @@ def get_spec() -> Mapping[str, ScenarioInfo]: # Elements to add add.set[set_name].extend(config.get("add", [])) - return dict(require=require, remove=ScenarioInfo(), add=add) + # Elements to add + remove.set[set_name].extend(config.get("remove", [])) + + return dict(require=require, add=add, remove=remove) # Group to allow for multiple CLI subcommands under "material" From 3a5788ac659e9614268c3b2310574b93c90e792c Mon Sep 17 00:00:00 2001 From: Jihoon Date: Mon, 19 Oct 2020 09:28:03 +0200 Subject: [PATCH 136/774] Now have separate data input functions for diff materials --- message_ix_models/model/material/data.py | 199 ++++++++++++++++++++--- 1 file changed, 180 insertions(+), 19 deletions(-) diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index 741f78a302..4883f0dec4 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -84,37 +84,43 @@ def process_china_data_tec(): context.get_path("material", context.datafile), sheet_name="technologies", ) + data_cement_china = pd.read_excel( + context.get_path("material", context.datafile), + sheet_name="tec_cement", + ) + + data_df = data_steel_china.append(data_cement_china, ignore_index=True) # Clean the data - data_steel_china = data_steel_china \ + data_df = data_df \ [['Technology', 'Parameter', 'Level', \ 'Commodity', 'Mode', 'Species', 'Units', 'Value']] \ .replace(np.nan, '', regex=True) # Combine columns and remove '' - list_series = data_steel_china[['Parameter', 'Commodity', 'Level', 'Mode']] \ + list_series = data_df[['Parameter', 'Commodity', 'Level', 'Mode']] \ .apply(list, axis=1).apply(lambda x: list(filter(lambda a: a != '', x))) - list_ef = data_steel_china[['Parameter', 'Species', 'Mode']] \ + list_ef = data_df[['Parameter', 'Species', 'Mode']] \ .apply(list, axis=1) - data_steel_china['parameter'] = list_series.str.join('|') - data_steel_china.loc[data_steel_china['Parameter'] == "emission_factor", \ + data_df['parameter'] = list_series.str.join('|') + data_df.loc[data_df['Parameter'] == "emission_factor", \ 'parameter'] = list_ef.str.join('|') - data_steel_china = data_steel_china.drop(['Parameter', 'Level', 'Commodity', 'Mode'] \ + data_df = data_df.drop(['Parameter', 'Level', 'Commodity', 'Mode'] \ , axis = 1) - data_steel_china = data_steel_china.drop( \ - data_steel_china[data_steel_china.Value==''].index) + data_df = data_df.drop( \ + data_df[data_df.Value==''].index) - data_steel_china.columns = data_steel_china.columns.str.lower() + data_df.columns = data_df.columns.str.lower() # Unit conversion # At the moment this is done in the excel file, can be also done here # To make sure we use the same units - return data_steel_china + return data_df # Read in relation-specific parameters from input xlsx def process_china_data_rel(): @@ -533,6 +539,7 @@ def gen_data_steel(scenario, dry_run=False): s_info = ScenarioInfo(scenario) # Techno-economic assumptions + # TEMP: now add cement sector as well => Need to separate those since now I have get_data_steel and cement data_steel = process_china_data_tec() # Special treatment for time-dependent Parameters data_steel_vc = read_timeseries() @@ -675,7 +682,7 @@ def gen_data_steel(scenario, dry_run=False): # Create external demand param parname = 'demand' - demand = gen_mock_demand_steel() + demand = gen_mock_demand_steel(s_info) df = (make_df(parname, level='demand', commodity='steel', value=demand, unit='t', \ year=modelyears, **common).pipe(broadcast, node=nodes)) results[parname].append(df) @@ -686,9 +693,146 @@ def gen_data_steel(scenario, dry_run=False): return results +def gen_data_cement(scenario, dry_run=False): + """Generate data for materials representation of steel industry. + + """ + # Load configuration + config = read_config()["material"]["cement"] + + # Information about scenario, e.g. node, year + s_info = ScenarioInfo(scenario) + + # Techno-economic assumptions + # TEMP: now add cement sector as well + data_cement = process_china_data_tec() + # Special treatment for time-dependent Parameters + # data_cement_vc = read_timeseries() + # tec_vc = set(data_cement_vc.technology) # set of tecs with var_cost + + # List of data frames, to be concatenated together at end + results = defaultdict(list) + + # For each technology there are differnet input and output combinations + # Iterate over technologies + + allyears = s_info.set['year'] #s_info.Y is only for modeling years + modelyears = s_info.Y #s_info.Y is only for modeling years + nodes = s_info.N + yv_ya = s_info.yv_ya + fmy = s_info.y0 + + print(allyears, modelyears, fmy) + + nodes.remove('World') # For the bare model + + # for t in s_info.set['technology']: + for t in config['technology']['add']: + + params = data_cement.loc[(data_cement["technology"] == t),\ + "parameter"].values.tolist() + + # # Special treatment for time-varying params + # if t in tec_vc: + # common = dict( + # time="year", + # time_origin="year", + # time_dest="year",) + # + # param_name = data_cement_vc.loc[(data_cement_vc["technology"] == t), 'parameter'] + # + # for p in set(param_name): + # val = data_cement_vc.loc[(data_cement_vc["technology"] == t) \ + # & (data_cement_vc["parameter"] == p), 'value'] + # units = data_cement_vc.loc[(data_cement_vc["technology"] == t) \ + # & (data_cement_vc["parameter"] == p), 'units'].values[0] + # mod = data_cement_vc.loc[(data_cement_vc["technology"] == t) \ + # & (data_cement_vc["parameter"] == p), 'mode'] + # yr = data_cement_vc.loc[(data_cement_vc["technology"] == t) \ + # & (data_cement_vc["parameter"] == p), 'year'] + # + # df = (make_df(p, technology=t, value=val,\ + # unit='t', year_vtg=yr, year_act=yr, mode=mod, **common).pipe(broadcast, \ + # node_loc=nodes)) + # + # print("time-dependent::", p, df) + # results[p].append(df) + + # Iterate over parameters + for par in params: + + # Obtain the parameter names, commodity,level,emission + split = par.split("|") + param_name = split[0] + # Obtain the scalar value for the parameter + val = data_cement.loc[((data_cement["technology"] == t) \ + & (data_cement["parameter"] == par)),'value'].values[0] + + common = dict( + year_vtg= yv_ya.year_vtg, + year_act= yv_ya.year_act, + # mode="M1", + time="year", + time_origin="year", + time_dest="year",) + + # For the parameters which inlcudes index names + if len(split)> 1: + + print('1.param_name:', param_name, t) + if (param_name == "input")|(param_name == "output"): + + # Assign commodity and level names + com = split[1] + lev = split[2] + mod = split[3] + + df = (make_df(param_name, technology=t, commodity=com, \ + level=lev, value=val, mode=mod, unit='t', **common)\ + .pipe(broadcast, node_loc=nodes).pipe(same_node)) + + elif param_name == "emission_factor": + + # Assign the emisson type + emi = split[1] + mod = split[2] + + df = (make_df(param_name, technology=t, value=val,\ + emission=emi, mode=mod, unit='t', **common).pipe(broadcast, \ + node_loc=nodes)) + + else: # time-independent var_cost + mod = split[1] + df = (make_df(param_name, technology=t, value=val, \ + mode=mod, unit='t', \ + **common).pipe(broadcast, node_loc=nodes)) + + results[param_name].append(df) + + # Parameters with only parameter name + else: + print('2.param_name:', param_name) + df = (make_df(param_name, technology=t, value=val, unit='t', \ + **common).pipe(broadcast, node_loc=nodes)) + + results[param_name].append(df) + + # Create external demand param + parname = 'demand' + demand = gen_mock_demand_cement(s_info) + df = (make_df(parname, level='demand', commodity='cement', value=demand, unit='t', \ + year=modelyears, **common).pipe(broadcast, node=nodes)) + results[parname].append(df) + + # Concatenate to one data frame per parameter + results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} + + return results + DATA_FUNCTIONS = [ gen_data_steel, + gen_data_cement, gen_data_generic, gen_data_aluminum, gen_data_variable @@ -715,24 +859,41 @@ def add_data(scenario, dry_run=False): log.info('done') +import numpy as np +gdp_growth = [0.121448215899944, 0.0733079014579874, 0.0348154093342843, \ + 0.021827616787921, 0.0134425983942219, 0.0108320197485592, \ + 0.00884341208063,0.00829374133206562, 0.00649794573935969, 0.00649794573935969] +gr = np.cumprod([(x+1) for x in gdp_growth]) + # Generate a fake steel demand -def gen_mock_demand_steel(): - import numpy as np +def gen_mock_demand_steel(s_info): modelyears = s_info.Y fmy = s_info.y0 # True steel use 2010 (China) = 537 Mt/year - demand2010 = 537 + demand2010_steel = 537 # https://www.worldsteel.org/en/dam/jcr:0474d208-9108-4927-ace8-4ac5445c5df8/World+Steel+in+Figures+2017.pdf - gdp_growth = [0.121448215899944, 0.0733079014579874, 0.0348154093342843, \ - 0.021827616787921, 0.0134425983942219, 0.0108320197485592, \ - 0.00884341208063,0.00829374133206562, 0.00649794573935969, 0.00649794573935969] + baseyear = list(range(2020, 2110+1, 10)) - gr = [(x+1) for x in gdp_growth] + demand = gr * demand2010_steel + demand_interp = np.interp(modelyears, baseyear, demand) + + return demand_interp.tolist() + +# Generate a fake cement demand +def gen_mock_demand_cement(s_info): + + modelyears = s_info.Y + fmy = s_info.y0 + + # True cement use 2011 (China) = 2100 Mt/year (ADVANCE) + demand2010_cement = 2100 + + baseyear = list(range(2020, 2110+1, 10)) - demand = np.cumprod(gr) * demand2010 + demand = gr * demand2010_cement demand_interp = np.interp(modelyears, baseyear, demand) return demand.tolist() From 788a6e4eb36e77802ed334017f0ad5783e8e3594 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Mon, 19 Oct 2020 09:28:42 +0200 Subject: [PATCH 137/774] Some more testing stuff --- message_ix_models/model/material/material_testscript.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/message_ix_models/model/material/material_testscript.py b/message_ix_models/model/material/material_testscript.py index 319b4912c9..1e50b16875 100644 --- a/message_ix_models/model/material/material_testscript.py +++ b/message_ix_models/model/material/material_testscript.py @@ -140,7 +140,8 @@ df_gen = dt.gen_data_generic(scen) df_st = dt.gen_data_steel(scen) a = dt.get_data(scen, ctx) - +dt.gen_mock_demand_cement(ScenarioInfo(scen)) +dt.read_data_generic() bare.add_data(scen) From b393658bfde9ce3be2909b98f86bd5624cd7711c Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 22 Oct 2020 16:52:33 +0200 Subject: [PATCH 138/774] Data updated --- .../material/generic_furnace_boiler_techno_economic.xlsx | 6 +++--- .../data/material/petrochemicals_techno_economic.xlsx | 4 ++-- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx index fc86d1f1f7..95ea7112f7 100644 --- a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx +++ b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx @@ -1,3 +1,3 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:91b263b3f172b7ee9f17d85d682d5128b54ec8290c041eb3538c63f8549b9733 -size 51396 +version https://git-lfs.github.com/spec/v1 +oid sha256:91b263b3f172b7ee9f17d85d682d5128b54ec8290c041eb3538c63f8549b9733 +size 51396 diff --git a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx index 8352857b58..c3143421cc 100644 --- a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx +++ b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:54bc96285f60b11093aeb3b196ae7da2345ab2903541ff15e2e21454168e72ac -size 382674 +oid sha256:baef402f8288c71c2dc158cdfb4f7a0f57a10876ac261ec5cff495ac51af6d1f +size 463218 From b5eb5997e9c7538ca319c797d8d5bf1e19d5eed3 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Fri, 23 Oct 2020 10:10:32 +0200 Subject: [PATCH 139/774] More adjustment for cement --- .../data/material/China_steel_MESSAGE.xlsx | 4 +-- ...eneric_furnace_boiler_techno_economic.xlsx | 4 +-- message_ix_models/model/material/data.py | 26 ------------------- 3 files changed, 4 insertions(+), 30 deletions(-) diff --git a/message_ix_models/data/material/China_steel_MESSAGE.xlsx b/message_ix_models/data/material/China_steel_MESSAGE.xlsx index 0386466ed4..3940f8824b 100644 --- a/message_ix_models/data/material/China_steel_MESSAGE.xlsx +++ b/message_ix_models/data/material/China_steel_MESSAGE.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:555d866fc3e62a9cb3f4464c65178017da972af9323fb33e355cb75803612344 -size 47414 +oid sha256:42dce06aa810859dfe162641a5fd38ebd1e8b1251af6d273cb51935034b9feb1 +size 47457 diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx index 95ea7112f7..a77c8f97e6 100644 --- a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx +++ b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:91b263b3f172b7ee9f17d85d682d5128b54ec8290c041eb3538c63f8549b9733 -size 51396 +oid sha256:0a2709165e1951bf4e543ce09abbed101edec7a12a58403e04c34639883ec349 +size 364 diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index 4883f0dec4..41b9080690 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -732,32 +732,6 @@ def gen_data_cement(scenario, dry_run=False): params = data_cement.loc[(data_cement["technology"] == t),\ "parameter"].values.tolist() - # # Special treatment for time-varying params - # if t in tec_vc: - # common = dict( - # time="year", - # time_origin="year", - # time_dest="year",) - # - # param_name = data_cement_vc.loc[(data_cement_vc["technology"] == t), 'parameter'] - # - # for p in set(param_name): - # val = data_cement_vc.loc[(data_cement_vc["technology"] == t) \ - # & (data_cement_vc["parameter"] == p), 'value'] - # units = data_cement_vc.loc[(data_cement_vc["technology"] == t) \ - # & (data_cement_vc["parameter"] == p), 'units'].values[0] - # mod = data_cement_vc.loc[(data_cement_vc["technology"] == t) \ - # & (data_cement_vc["parameter"] == p), 'mode'] - # yr = data_cement_vc.loc[(data_cement_vc["technology"] == t) \ - # & (data_cement_vc["parameter"] == p), 'year'] - # - # df = (make_df(p, technology=t, value=val,\ - # unit='t', year_vtg=yr, year_act=yr, mode=mod, **common).pipe(broadcast, \ - # node_loc=nodes)) - # - # print("time-dependent::", p, df) - # results[p].append(df) - # Iterate over parameters for par in params: From 65a6d72a60f0e3ddb98df345a4d6bce672b81376 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 29 Oct 2020 14:31:29 +0100 Subject: [PATCH 140/774] Sets added --- .../petrochemicals_techno_economic.xlsx | 4 +- message_ix_models/data/material/set.yaml | 65 +++++++++++++++++++ 2 files changed, 67 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx index c3143421cc..3a285b5330 100644 --- a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx +++ b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:baef402f8288c71c2dc158cdfb4f7a0f57a10876ac261ec5cff495ac51af6d1f -size 463218 +oid sha256:1523b27941193f4869bf141eabd8ac34d69f634f9158e1725740e225b9d4f1f0 +size 467548 diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 276bc42895..791bc904ec 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -4,6 +4,71 @@ # accounting. Different sets can be created once there are 2+ different # categories of materials. +petro_chemicals: + commodity: + require: + - crudeoil + - hydrogen + - elctr + - ethanol + - fueloil + - lightoil + add: + - naphtha + - kerosene + - diesel + - atm_residue + - refinery_gas + - atm_gasoil + - atm_residue + - vacuum_gasoil + - vacuum_residue + - desulf_gasoil + - desulf_naphtha + - desulf_kerosene + - desulf_diesel + - desful_gasoil + - gasoline + - heavy_foil + - light_foil + - propylene + - coke + - ethane + - propane + - ethylene + - BTX + + level: + require: + - secondary + - final + add: + - pre_intermediate + - useful_refining + - desulfurized + - intermediate + - secondary_material + - useful_petro + - final_material + + mode: + add: + - atm_gasoil + - vacuum_gasoil + - naphtha + - kerosene + - diesel + - cracking_gasoline + - cracking_loil + - gasoil + - ethane + - propane + + emission: + add: + - CO2 + - SO2 + common: commodity: require: From 4c89760a3f2602481738e038af6447b9370c4b51 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 29 Oct 2020 17:26:07 +0100 Subject: [PATCH 141/774] Addition of petrochemicals (in progress) --- .../petrochemicals_techno_economic.xlsx | 4 +- message_ix_models/data/material/set.yaml | 5 - message_ix_models/model/material/__init__.py | 2 +- message_ix_models/model/material/bare.py | 13 +- message_ix_models/model/material/data.py | 210 +++++++++++++++++- 5 files changed, 219 insertions(+), 15 deletions(-) diff --git a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx index 3a285b5330..6f4a396db5 100644 --- a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx +++ b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:1523b27941193f4869bf141eabd8ac34d69f634f9158e1725740e225b9d4f1f0 -size 467548 +oid sha256:ea7575232cd911bf707eb42e255d0ff9642715a2d0ace9a05c58140f35b9370e +size 468124 diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 791bc904ec..51e2eaea94 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -64,11 +64,6 @@ petro_chemicals: - ethane - propane - emission: - add: - - CO2 - - SO2 - common: commodity: require: diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 00016f7ed6..401a20cf36 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -29,7 +29,7 @@ def get_spec() -> Mapping[str, ScenarioInfo]: context = read_config() # Update the ScenarioInfo objects with required and new set elements - for type in "generic", "common", "steel", "cement",: + for type in "generic", "common", "steel",'aluminum',"petro_chemicals": for set_name, config in context["material"][type].items(): # for cat_name, detail in config.items(): # Required elements diff --git a/message_ix_models/model/material/bare.py b/message_ix_models/model/material/bare.py index 872feafd60..c8fa119914 100644 --- a/message_ix_models/model/material/bare.py +++ b/message_ix_models/model/material/bare.py @@ -7,7 +7,8 @@ from message_data.tools import Code, ScenarioInfo, get_context, set_info, add_par_data from .build import apply_spec from .util import read_config -from .data import get_data, gen_data_steel, gen_data_generic, gen_data_aluminum, gen_data_variable +from .data import get_data, gen_data_steel, gen_data_generic, gen_data_aluminum, \ +gen_data_variable, gen_data_petro_chemicals import message_data @@ -132,7 +133,8 @@ def get_spec(context=None) -> Mapping[str, ScenarioInfo]: # JM: try to find out a way to loop over 1st/2nd level and to just context["material"][xx]["add"] add.set["technology"] = context["material"]["steel"]["technology"]["add"] + \ context["material"]["generic"]["technology"]["add"] + \ - context["material"]["aluminum"]["technology"]["add"] + context["material"]["aluminum"]["technology"]["add"] + \ + context["material"]["petro_chemicals"]["technology"]["add"] # Add regions @@ -163,19 +165,22 @@ def get_spec(context=None) -> Mapping[str, ScenarioInfo]: add.set['level'] = context["material"]["steel"]["level"]["add"] + \ context["material"]["common"]["level"]["require"] + \ context["material"]["generic"]["level"]["add"] + \ - context["material"]["aluminum"]["level"]["add"] + context["material"]["aluminum"]["level"]["add"] +\ + context["material"]["petro_chemicals"]["level"]["add"] # Add commodities add.set['commodity'] = context["material"]["steel"]["commodity"]["add"] + \ context["material"]["common"]["commodity"]["require"] + \ context["material"]["generic"]["commodity"]["add"] + \ context["material"]["aluminum"]["commodity"]["add"] + context["material"]["petro_chemicals"]["commodity"]["add"] # Add other sets add.set['type_tec'] = context["material"]["common"]["type_tec"]["add"] add.set['mode'] = context["material"]["common"]["mode"]["require"] +\ - context["material"]["generic"]["mode"]["add"] + context["material"]["generic"]["mode"]["add"] + \ + context["material"]["petro_chemicals"]["mode"]["add"] add.set['emission'] = context["material"]["common"]["emission"]["require"] +\ context["material"]["common"]["emission"]["add"] diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index 41b9080690..80c09ae1d3 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -211,6 +211,26 @@ def read_data_aluminum(): return data_aluminum,data_aluminum_hist +def read_data_petrochemicals(): + """Read and clean data from :file:`petrochemicals_techno_economic.xlsx`.""" + + # Ensure config is loaded, get the context + context = read_config() + + # Read the file + data_petro = pd.read_excel( + context.get_path("material", "petrochemicals_techno_economic.xlsx"), + sheet_name="data") + # Clean the data + + data_petro= data_petro.drop(['Region', 'Source', 'Description'], axis = 1) + + data_petro_hist = pd.read_excel(context.get_path("material", \ + "petrochemicals_techno_economic.xlsx"),sheet_name="data_historical", \ + usecols = "A:F") + + return data_petro,data_petro_hist + def gen_data_aluminum(scenario, dry_run=False): """Generate data for materials representation of aluminum.""" # Load configuration @@ -478,6 +498,151 @@ def gen_data_generic(scenario, dry_run=False): return results +def gen_data_petro_chemicals(scenario, dry_run=False): + # Load configuration + + config = read_config()["material"]["petro_chemicals"] + + # Information about scenario, e.g. node, year + s_info = ScenarioInfo(scenario) + + # Techno-economic assumptions + data_petro = read_data_petrochemicals() + + # List of data frames, to be concatenated together at end + results = defaultdict(list) + + # For each technology there are differnet input and output combinations + # Iterate over technologies + + allyears = s_info.set['year'] + modelyears = s_info.Y #s_info.Y is only for modeling years + nodes = s_info.N + yv_ya = s_info.yv_ya + fmy = s_info.y0 + + # 'World' is included by default when creating a message_ix.Scenario(). + # Need to remove it for the China bare model + nodes.remove('World') + + for t in config["technology"]["add"]: + + # years = s_info.Y + params = data_petro.loc[(data_petro["technology"] == t),"parameter"]\ + .values.tolist() + + # Availability year of the technology + av = data_petro.loc[(data_petro["technology"] == t),'availability'].\ + values[0] + modelyears = [year for year in modelyears if year >= av] + yva = yv_ya.loc[yv_ya.year_vtg >= av, ] + + # Iterate over parameters + for par in params: + split = par.split("|") + param_name = par.split("|")[0] + + val = data_petro.loc[((data_petro["technology"] == t) & \ + (data_petro["parameter"] == par)),'value'].values[0] + + # Common parameters for all input and output tables + # year_act is none at the moment + # node_dest and node_origin are the same as node_loc + + common = dict( + year_vtg= yva.year_vtg, + year_act= yva.year_act, + time="year", + time_origin="year", + time_dest="year",) + + if len(split)> 1: + + if (param_name == "input")|(param_name == "output"): + + com = split[1] + lev = split[2] + mod = split[3] + + df = (make_df(param_name, technology=t, commodity=com, \ + level=lev,mode=mod, value=val, unit='t', **common).\ + pipe(broadcast, node_loc=nodes).pipe(same_node)) + + results[param_name].append(df) + + elif param_name == "emission_factor": + emi = split[1] + + # TODO: Now tentatively fixed to one mode. Have values for the other mode too + df = (make_df(param_name, technology=t,value=val,\ + emission=emi, mode="low_temp", unit='t', **common).pipe(broadcast, \ + node_loc=nodes)) + + results[param_name].append(df) + + # Rest of the parameters apart from inpput, output and emission_factor + + else: + + df = (make_df(param_name, technology=t, value=val,unit='t', \ + **common).pipe(broadcast, node_loc=nodes)) + + results[param_name].append(df) + + # Add demand + + values_e, values_p, values_BTX = gen_mock_demand_petro(scenario) + + demand_ethylene = (make_df("demand", commodity= "ethylene", \ + level= "demand_ethylene", year = modelyears, value=values_e, unit='Mt',\ + time= "year").pipe(broadcast, node=nodes)) + + demand_propylene = (make_df("demand", commodity= "propylene", \ + level= "demand_propylene", year = modelyears, value=values_p, unit='Mt',\ + time= "year").pipe(broadcast, node=nodes)) + + demand_BTX = (make_df("demand", commodity= "BTX", \ + level= "demand_BTX", year = modelyears, value=values_p, unit='Mt',\ + time= "year").pipe(broadcast, node=nodes)) + + results["demand"].append(demand_ethylene) + results["demand"].append(demand_propylene) + results["demand"].append(demand_BTX) + + # Add historical data + + for tec in data_petro_hist["technology"].unique(): + + y_hist = [y for y in allyears if y < fmy] + common_hist = dict( + year_vtg= y_hist, + year_act= y_hist, + mode="M1", + time="year",) + + val_act = data_petro_hist.\ + loc[(data_petro_hist["technology"]== tec), "production"] + + df_hist_act = (make_df("historical_activity", technology=tec, \ + value=val_act, unit='Mt', **common_hist).pipe(broadcast, node_loc=nodes)) + + results["historical_activity"].append(df_hist_act) + + c_factor = data_petro.loc[((data_petro["technology"]== tec) \ + & (data_petro["parameter"]=="capacity_factor")), "value"].values + + val_cap = data_petro_hist.loc[(data_petro_hist["technology"]== tec), \ + "new_production"] / c_factor + + df_hist_cap = (make_df("historical_new_capacity", technology=tec, \ + value=val_cap, unit='Mt', **common_hist).pipe(broadcast, node_loc=nodes)) + + results["historical_new_capacity"].append(df_hist_cap) + + results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} + + return results + def gen_data_variable(scenario, dry_run=False): # Information about scenario, e.g. node, year @@ -803,16 +968,15 @@ def gen_data_cement(scenario, dry_run=False): return results - DATA_FUNCTIONS = [ gen_data_steel, gen_data_cement, gen_data_generic, gen_data_aluminum, - gen_data_variable + gen_data_variable, + gen_data_petro_chemicals ] - # Try to handle multiple data input functions from different materials def add_data(scenario, dry_run=False): """Populate `scenario` with MESSAGE-Transport data.""" @@ -909,6 +1073,46 @@ def gen_mock_demand_aluminum(scenario): return values +def gen_mock_demand_petro(scenario): + + # China 2006: 22 kg/cap HVC demand. 2006 population: 1.311 billion + # This makes 28.842 Mt. (Energy Technology Transitions for Industry) + # Distribution: 6:5:6 (ethylene,propylene,BTX) + # Future of Petrochemicals Methodological Annex + + # The future projection of the demand: Increases by half of the GDP growth rate. + # Starting from 2020. + context = read_config() + s_info = ScenarioInfo(scenario) + modelyears = s_info.Y #s_info.Y is only for modeling years + + gdp_growth = pd.Series([0.121448215899944, 0.0733079014579874, \ + 0.0348154093342843, 0.021827616787921,\ + 0.0134425983942219, 0.0108320197485592, \ + 0.00884341208063, 0.00829374133206562, \ + 0.00649794573935969],index=pd.Index(modelyears, \ + name='Time')) + # Values in 2010 from IAI. + fin_to_useful = 0.971 + useful_to_product = 0.866 + + i = 0 + values = [] + val = (17.3 * (1+ 0.147718884937996/2) ** context.time_step) + values.append(val) + + for element in gdp_growth: + i = i + 1 + if i < len(modelyears): + val = (val * (1+ element/2) ** context.time_step) + values.append(val) + + # Adjust the demand according to old scrap level. + + values = [x * fin_to_useful * useful_to_product for x in values] + + return values + def get_data(scenario, context, **options): """Data for the bare RES.""" if context.res_with_dummies: From 9a44e77211c7d7e31c9e9dde3b7d71aac82f6fdd Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Fri, 30 Oct 2020 13:15:27 +0100 Subject: [PATCH 142/774] Additions for petrochemicals --- .../data/material/oil_demand.xlsx | 3 + .../petrochemicals_techno_economic.xlsx | 4 +- message_ix_models/data/material/set.yaml | 27 ++++++++ .../data/material/variable_costs.xlsx | 4 +- message_ix_models/model/material/data.py | 65 ++++++++++++++----- 5 files changed, 83 insertions(+), 20 deletions(-) create mode 100644 message_ix_models/data/material/oil_demand.xlsx diff --git a/message_ix_models/data/material/oil_demand.xlsx b/message_ix_models/data/material/oil_demand.xlsx new file mode 100644 index 0000000000..e6948b203c --- /dev/null +++ b/message_ix_models/data/material/oil_demand.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0b4d6c59daae641cdc2baa0b293cc52e2f7b9558f9009d53f8778e1b3ce6f8dc +size 12698 diff --git a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx index 6f4a396db5..48440e0eb4 100644 --- a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx +++ b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:ea7575232cd911bf707eb42e255d0ff9642715a2d0ace9a05c58140f35b9370e -size 468124 +oid sha256:6e876ea8f3119ba1fdadadc9bef92c5babb194b2488e17cdd4a1bda6c71b3826 +size 468123 diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 51e2eaea94..dfcad9bb00 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -50,6 +50,11 @@ petro_chemicals: - secondary_material - useful_petro - final_material + - demnad_ethylene + - demand_propylene + - demand_BTX + - demand_foil + - demand_loil mode: add: @@ -64,6 +69,27 @@ petro_chemicals: - ethane - propane + technology: + add: + - atm_distillation_ref + - vacuum_distillation_ref + - hydrotreating_ref + - catalytic_cracking_ref + - visbreaker_ref + - coking_ref + - catalytic_reforming_ref + - hydro_cracking_ref + - steam_cracker_petro + - ethanol_to_ethylene_petro + - blend_loil_ref + - blend_foil_ref + - gas_processing_petro + + remove: + # Any representation of refinery. + - ref_hil + - ref_lol + common: commodity: require: @@ -181,6 +207,7 @@ generic: - DUMMY_hydrogen_supply - DUMMY_biomass_supply - DUMMY_dist_heating + - DUMMY_crudeoil_supply - furnace_foil_cement - furnace_loil_cement - furnace_biomass_cement diff --git a/message_ix_models/data/material/variable_costs.xlsx b/message_ix_models/data/material/variable_costs.xlsx index 3dd277a6b9..c0d08c659b 100644 --- a/message_ix_models/data/material/variable_costs.xlsx +++ b/message_ix_models/data/material/variable_costs.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:e0ec396dd16105d5429abc4769225fe8fdb56b37bc6a309083bed89b9aa95905 -size 15874 +oid sha256:fe6e090926a73592830e98fa787ff2e8eccae2e7cb8987b43d000ced3bdfd36a +size 16184 diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index 80c09ae1d3..86dd106f74 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -591,7 +591,8 @@ def gen_data_petro_chemicals(scenario, dry_run=False): # Add demand - values_e, values_p, values_BTX = gen_mock_demand_petro(scenario) + values_e, values_p, values_BTX, values_foil, values_loil = \ + gen_mock_demand_petro(scenario) demand_ethylene = (make_df("demand", commodity= "ethylene", \ level= "demand_ethylene", year = modelyears, value=values_e, unit='Mt',\ @@ -605,9 +606,19 @@ def gen_data_petro_chemicals(scenario, dry_run=False): level= "demand_BTX", year = modelyears, value=values_p, unit='Mt',\ time= "year").pipe(broadcast, node=nodes)) + demand_foil = (make_df("demand", commodity= "fueloil", \ + level= "demand_foil", year = modelyears, value=values_foil, unit='GWyr',\ + time= "year").pipe(broadcast, node=nodes)) + + demand_loil = (make_df("demand", commodity= "lightoil", \ + level= "demand_loil", year = modelyears, value=values_loil, unit='GWyr',\ + time= "year").pipe(broadcast, node=nodes)) + results["demand"].append(demand_ethylene) results["demand"].append(demand_propylene) results["demand"].append(demand_BTX) + results["demand"].append(demand_loil) + results["demand"].append(demand_foil) # Add historical data @@ -1076,9 +1087,10 @@ def gen_mock_demand_aluminum(scenario): def gen_mock_demand_petro(scenario): # China 2006: 22 kg/cap HVC demand. 2006 population: 1.311 billion - # This makes 28.842 Mt. (Energy Technology Transitions for Industry) - # Distribution: 6:5:6 (ethylene,propylene,BTX) + # This makes 28.842 Mt. (IEA Energy Technology Transitions for Industry) + # Distribution in 2015 for China: 6:6:5 (ethylene,propylene,BTX) # Future of Petrochemicals Methodological Annex + # This can be verified by other sources. # The future projection of the demand: Increases by half of the GDP growth rate. # Starting from 2020. @@ -1092,26 +1104,48 @@ def gen_mock_demand_petro(scenario): 0.00884341208063, 0.00829374133206562, \ 0.00649794573935969],index=pd.Index(modelyears, \ name='Time')) - # Values in 2010 from IAI. - fin_to_useful = 0.971 - useful_to_product = 0.866 - i = 0 - values = [] - val = (17.3 * (1+ 0.147718884937996/2) ** context.time_step) - values.append(val) + values_e = [] + values_p = [] + values_BTX = [] + + val_e = (10 * (1+ 0.147718884937996/2) ** context.time_step) + values_e.append(val_e) + + val_p = (10 * (1+ 0.147718884937996/2) ** context.time_step) + values_p.append(val_p) + + val_BTX = (8 * (1+ 0.147718884937996/2) ** context.time_step) + values_BTX.append(val_BTX) for element in gdp_growth: i = i + 1 if i < len(modelyears): - val = (val * (1+ element/2) ** context.time_step) - values.append(val) + val_e = (val_e * (1+ element/2) ** context.time_step) + values_e.append(val_e) - # Adjust the demand according to old scrap level. + val_p = (val_p * (1+ element/2) ** context.time_step) + values_p.append(val_p) - values = [x * fin_to_useful * useful_to_product for x in values] + val_BTX = (val_BTX * (1+ element/2) ** context.time_step) + values_BTX.append(val_BTX) - return values + pd.read_excel('oil_demand.xlsx', index_col=0) + + if context.scenario_info['scenario'] == 'NPi400': + sheet_name="demand_NPi400" + else: + sheet_name = "demand_baseline" + # Read the file + df = pd.read_excel( + context.get_path("material", "oil_demand.xlsx"), + sheet_name=sheet_name, + ) + + values_foil = df["Total_foil"].tolist() + values_loil = df["Total_loil"].tolist() + + return values_e, values_p, values_BTX, values_foil, values_loil def get_data(scenario, context, **options): """Data for the bare RES.""" @@ -1122,7 +1156,6 @@ def get_data(scenario, context, **options): else: return dict() - def get_dummy_data(scenario, **options): """Dummy data for the bare RES. From 8299d9295aa2c054b1be4aeca08bc628f70d4be3 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Sun, 1 Nov 2020 12:47:24 +0100 Subject: [PATCH 143/774] Fixes --- ...eneric_furnace_boiler_techno_economic.xlsx | 4 +-- .../petrochemicals_techno_economic.xlsx | 4 +-- message_ix_models/data/material/set.yaml | 7 +---- message_ix_models/model/material/data.py | 27 ++++++++++++------- 4 files changed, 22 insertions(+), 20 deletions(-) diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx index a77c8f97e6..a0fa8b7110 100644 --- a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx +++ b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:0a2709165e1951bf4e543ce09abbed101edec7a12a58403e04c34639883ec349 -size 364 +oid sha256:5e0a1562998fc99dcca7de592d9c636da2360a712c97cc2917621a59347d5057 +size 261 diff --git a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx index 48440e0eb4..6d33183ceb 100644 --- a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx +++ b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:6e876ea8f3119ba1fdadadc9bef92c5babb194b2488e17cdd4a1bda6c71b3826 -size 468123 +oid sha256:8de9970ad3bac74e7e8eb3bf3c5822815004e4a10e231ade3933410ad063949c +size 468524 diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index dfcad9bb00..3135c31631 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -8,11 +8,6 @@ petro_chemicals: commodity: require: - crudeoil - - hydrogen - - elctr - - ethanol - - fueloil - - lightoil add: - naphtha - kerosene @@ -50,7 +45,7 @@ petro_chemicals: - secondary_material - useful_petro - final_material - - demnad_ethylene + - demand_ethylene - demand_propylene - demand_BTX - demand_foil diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index 86dd106f74..f5b6a6ad0b 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -424,13 +424,14 @@ def gen_data_generic(scenario, dry_run=False): for t in config["technology"]["add"]: + print(t) + # years = s_info.Y params = data_generic.loc[(data_generic["technology"] == t),"parameter"]\ .values.tolist() # Availability year of the technology - av = data_generic.loc[(data_generic["technology"] == t),'availability'].\ - values[0] + av = data_generic.loc[(data_generic["technology"] == t),'availability'].values[0] modelyears = [year for year in modelyears if year >= av] yva = yv_ya.loc[yv_ya.year_vtg >= av, ] @@ -507,8 +508,7 @@ def gen_data_petro_chemicals(scenario, dry_run=False): s_info = ScenarioInfo(scenario) # Techno-economic assumptions - data_petro = read_data_petrochemicals() - + data_petro, data_petro_hist = read_data_petrochemicals() # List of data frames, to be concatenated together at end results = defaultdict(list) @@ -572,12 +572,21 @@ def gen_data_petro_chemicals(scenario, dry_run=False): elif param_name == "emission_factor": emi = split[1] + mod = split[2] - # TODO: Now tentatively fixed to one mode. Have values for the other mode too df = (make_df(param_name, technology=t,value=val,\ - emission=emi, mode="low_temp", unit='t', **common).pipe(broadcast, \ + emission=emi, mode=mod, unit='t', **common).pipe(broadcast, \ node_loc=nodes)) + elif param_name == "var_cost": + mod = split[1] + + df = (make_df(param_name, technology=t, commodity=com, \ + level=lev,mode=mod, value=val, unit='t', **common).\ + pipe(broadcast, node_loc=nodes).pipe(same_node)) + + + results[param_name].append(df) # Rest of the parameters apart from inpput, output and emission_factor @@ -607,11 +616,11 @@ def gen_data_petro_chemicals(scenario, dry_run=False): time= "year").pipe(broadcast, node=nodes)) demand_foil = (make_df("demand", commodity= "fueloil", \ - level= "demand_foil", year = modelyears, value=values_foil, unit='GWyr',\ + level= "demand_foil", year = modelyears, value=values_foil, unit='GWa',\ time= "year").pipe(broadcast, node=nodes)) demand_loil = (make_df("demand", commodity= "lightoil", \ - level= "demand_loil", year = modelyears, value=values_loil, unit='GWyr',\ + level= "demand_loil", year = modelyears, value=values_loil, unit='GWa',\ time= "year").pipe(broadcast, node=nodes)) results["demand"].append(demand_ethylene) @@ -1130,8 +1139,6 @@ def gen_mock_demand_petro(scenario): val_BTX = (val_BTX * (1+ element/2) ** context.time_step) values_BTX.append(val_BTX) - pd.read_excel('oil_demand.xlsx', index_col=0) - if context.scenario_info['scenario'] == 'NPi400': sheet_name="demand_NPi400" else: From 3d36e0a5f13da4616b06999bbc99eb30c51ee7a4 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Mon, 2 Nov 2020 10:34:26 +0100 Subject: [PATCH 144/774] Revised /corrected data --- message_ix_models/data/material/China_steel_MESSAGE.xlsx | 4 ++-- .../data/material/China_steel_standalone - test.xlsx | 4 ++-- .../data/material/generic_furnace_boiler_techno_economic.xlsx | 4 ++-- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/message_ix_models/data/material/China_steel_MESSAGE.xlsx b/message_ix_models/data/material/China_steel_MESSAGE.xlsx index 3940f8824b..8e2420208b 100644 --- a/message_ix_models/data/material/China_steel_MESSAGE.xlsx +++ b/message_ix_models/data/material/China_steel_MESSAGE.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:42dce06aa810859dfe162641a5fd38ebd1e8b1251af6d273cb51935034b9feb1 -size 47457 +oid sha256:1fbe43b846e3a545c0e481eefa11fc2011defbde5f6cb8f48c546d53cad30dec +size 48766 diff --git a/message_ix_models/data/material/China_steel_standalone - test.xlsx b/message_ix_models/data/material/China_steel_standalone - test.xlsx index 67f6a72077..5d78113fcd 100644 --- a/message_ix_models/data/material/China_steel_standalone - test.xlsx +++ b/message_ix_models/data/material/China_steel_standalone - test.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:42c031d8af13d71b2ad5a1ec5b6f14b973105a5164494552968710a014908fd8 -size 56730 +oid sha256:21ea535be0559bdab3626ec1e2c8f8a8f326d1c808421566b85644a2793999f4 +size 59248 diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx index a0fa8b7110..d8921813e6 100644 --- a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx +++ b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:5e0a1562998fc99dcca7de592d9c636da2360a712c97cc2917621a59347d5057 -size 261 +oid sha256:8e3d326d1abed964447c3cce4c07b1a5f301af27d693d9f2d9cde0d0c6d1c6a7 +size 279 From 82f0ff25fabc4bdab7c78577b245e776bf702185 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Mon, 2 Nov 2020 10:44:13 +0100 Subject: [PATCH 145/774] Add add-on tecs and remove old cement relations (not just old cement tecs) --- message_ix_models/data/material/set.yaml | 23 +++++++++++++++++++++-- 1 file changed, 21 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 3135c31631..ebb7535525 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -288,16 +288,35 @@ cement: add: - raw_meal_prep_cement - clinker_dry_cement - - clinker_dry_ccs_cement - clinker_wet_cement + - clinker_dry_ccs_cement + - clinker_wet_ccs_cement - grinding_ballmill_cement - grinding_vertmill_cement - DUMMY_limestone_supply - remove: - cement_co2scr - cement_CO2 + relation: + remove: + - cement_pro + - cement_scrub_lim + + addon: + add: + - clinker_dry_ccs_cement + - clinker_wet_ccs_cement + + type_addon: + add: + - ccs_cement + + cat_addon: + add: + - [ccs_cement, clinker_dry_ccs_cement] + - [ccs_cement, clinker_wet_ccs_cement] + # relation: # add: # - steel_demand From 375155652341a6adaca22a0df095632ceabf72c4 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Tue, 3 Nov 2020 14:36:07 +0100 Subject: [PATCH 146/774] Include 'cement' spec + fix cement yaml items --- message_ix_models/model/material/__init__.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 401a20cf36..0087ced8c3 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -14,10 +14,11 @@ def build(scenario): spec = get_spec() # Apply to the base scenario - apply_spec(scenario, spec, add_data) + apply_spec(scenario, spec, add_data) # dry_run=True return scenario +SPEC_LIST = ["generic", "common", "steel", "cement"] # add as needed/implemented def get_spec() -> Mapping[str, ScenarioInfo]: """Return the specification for materials accounting.""" @@ -29,7 +30,7 @@ def get_spec() -> Mapping[str, ScenarioInfo]: context = read_config() # Update the ScenarioInfo objects with required and new set elements - for type in "generic", "common", "steel",'aluminum',"petro_chemicals": + for type in SPEC_LIST: for set_name, config in context["material"][type].items(): # for cat_name, detail in config.items(): # Required elements @@ -38,7 +39,7 @@ def get_spec() -> Mapping[str, ScenarioInfo]: # Elements to add add.set[set_name].extend(config.get("add", [])) - # Elements to add + # Elements to remove remove.set[set_name].extend(config.get("remove", [])) return dict(require=require, add=add, remove=remove) From cdf5a3825f1d774abcdf030b4557e2700ced3d4d Mon Sep 17 00:00:00 2001 From: Jihoon Date: Tue, 3 Nov 2020 14:38:07 +0100 Subject: [PATCH 147/774] Fix cement yaml items + remove debug print --- message_ix_models/data/material/set.yaml | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index ebb7535525..ecd5de4dd7 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -312,6 +312,11 @@ cement: add: - ccs_cement + map_tec_addon: + add: + - [clinker_dry_cement, ccs_cement] + - [clinker_wet_cement, ccs_cement] + cat_addon: add: - [ccs_cement, clinker_dry_ccs_cement] From 707687fd286e2d071cae96a77ef20ab04aae1f21 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 3 Nov 2020 14:42:28 +0100 Subject: [PATCH 148/774] Aggregation of fuels as light oil and fuel oil --- .../data/material/petrochemicals_techno_economic.xlsx | 4 ++-- message_ix_models/data/material/set.yaml | 11 +++++++++-- 2 files changed, 11 insertions(+), 4 deletions(-) diff --git a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx index 6d33183ceb..89be1356c1 100644 --- a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx +++ b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:8de9970ad3bac74e7e8eb3bf3c5822815004e4a10e231ade3933410ad063949c -size 468524 +oid sha256:399e05f9a9eee556eaf33798038580bbbb49888f5a32879f48e1349cc70194e5 +size 469184 diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index ecd5de4dd7..45d979c948 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -22,12 +22,13 @@ petro_chemicals: - desulf_naphtha - desulf_kerosene - desulf_diesel - - desful_gasoil + - desful_atm_gasoil + - desful_vacuum_gasoil - gasoline - heavy_foil - light_foil - propylene - - coke + - pet_coke - ethane - propane - ethylene @@ -63,6 +64,12 @@ petro_chemicals: - gasoil - ethane - propane + - light_foil + - refinery_gas + - heavy_foil + - pet_coke + - atm_residue + - vacuum_residue technology: add: From 54b81de2060edc23156d3c9d8dc9c05c8c39804c Mon Sep 17 00:00:00 2001 From: Jihoon Date: Tue, 3 Nov 2020 14:42:52 +0100 Subject: [PATCH 149/774] Update data functions (separate 'process_china_data_tec' for different materials, remove debug prints, add cement mock demand) --- message_ix_models/model/material/data.py | 73 ++++++++++--------- .../model/material/material_testscript.py | 2 +- 2 files changed, 40 insertions(+), 35 deletions(-) diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index f5b6a6ad0b..4ce096c1a1 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -71,8 +71,15 @@ def read_data(): return data +# def read_data_cement(): +# return process_china_data_tec("cement") +# +# def read_data_steel(): +# return process_china_data_tec("steel") + + # Read in technology-specific parameters from input xlsx -def process_china_data_tec(): +def process_china_data_tec(sectname): import numpy as np @@ -80,16 +87,20 @@ def process_china_data_tec(): context = read_config() # Read the file - data_steel_china = pd.read_excel( + # data_steel_china = pd.read_excel( + # context.get_path("material", context.datafile), + # sheet_name="technologies", + # ) + # data_cement_china = pd.read_excel( + # context.get_path("material", context.datafile), + # sheet_name="tec_cement", + # ) + + # data_df = data_steel_china.append(data_cement_china, ignore_index=True) + data_df = pd.read_excel( context.get_path("material", context.datafile), - sheet_name="technologies", + sheet_name=sectname, ) - data_cement_china = pd.read_excel( - context.get_path("material", context.datafile), - sheet_name="tec_cement", - ) - - data_df = data_steel_china.append(data_cement_china, ignore_index=True) # Clean the data @@ -725,7 +736,7 @@ def gen_data_steel(scenario, dry_run=False): # Techno-economic assumptions # TEMP: now add cement sector as well => Need to separate those since now I have get_data_steel and cement - data_steel = process_china_data_tec() + data_steel = process_china_data_tec("steel") # Special treatment for time-dependent Parameters data_steel_vc = read_timeseries() tec_vc = set(data_steel_vc.technology) # set of tecs with var_cost @@ -742,7 +753,7 @@ def gen_data_steel(scenario, dry_run=False): yv_ya = s_info.yv_ya fmy = s_info.y0 - print(allyears, modelyears, fmy) + #print(allyears, modelyears, fmy) nodes.remove('World') # For the bare model @@ -789,7 +800,7 @@ def gen_data_steel(scenario, dry_run=False): unit='t', year_vtg=yr, year_act=yr, mode=mod, **common).pipe(broadcast, \ node_loc=nodes)) - print("time-dependent::", p, df) + #print("time-dependent::", p, df) results[p].append(df) # Iterate over parameters @@ -813,7 +824,7 @@ def gen_data_steel(scenario, dry_run=False): # For the parameters which inlcudes index names if len(split)> 1: - print('1.param_name:', param_name, t) + #print('1.param_name:', param_name, t) if (param_name == "input")|(param_name == "output"): # Assign commodity and level names @@ -845,21 +856,7 @@ def gen_data_steel(scenario, dry_run=False): # Parameters with only parameter name else: - print('2.param_name:', param_name) - # # Historical years are earlier than firstmodelyear - # y_hist = [y for y in allyears if y < fmy] - # # print(y_hist, fmy, years) - # if re.search("historical_", param_name): - # common_hist = dict( - # year_vtg= y_hist, - # year_act= y_hist, - # # mode="M1", - # time="year",) - # - # df = (make_df(param_name, technology=t, value=val, unit='t', \ - # **common_hist).pipe(broadcast, node_loc=nodes)) - # # print(common_hist, param_name, t, nodes, val, y_hist) - # else: + #print('2.param_name:', param_name) df = (make_df(param_name, technology=t, value=val, unit='t', \ **common).pipe(broadcast, node_loc=nodes)) @@ -890,7 +887,7 @@ def gen_data_cement(scenario, dry_run=False): # Techno-economic assumptions # TEMP: now add cement sector as well - data_cement = process_china_data_tec() + data_cement = process_china_data_tec("cement") # Special treatment for time-dependent Parameters # data_cement_vc = read_timeseries() # tec_vc = set(data_cement_vc.technology) # set of tecs with var_cost @@ -907,7 +904,7 @@ def gen_data_cement(scenario, dry_run=False): yv_ya = s_info.yv_ya fmy = s_info.y0 - print(allyears, modelyears, fmy) + #print(allyears, modelyears, fmy) nodes.remove('World') # For the bare model @@ -938,7 +935,7 @@ def gen_data_cement(scenario, dry_run=False): # For the parameters which inlcudes index names if len(split)> 1: - print('1.param_name:', param_name, t) + #print('1.param_name:', param_name, t) if (param_name == "input")|(param_name == "output"): # Assign commodity and level names @@ -970,7 +967,7 @@ def gen_data_cement(scenario, dry_run=False): # Parameters with only parameter name else: - print('2.param_name:', param_name) + #print('2.param_name:', param_name) df = (make_df(param_name, technology=t, value=val, unit='t', \ **common).pipe(broadcast, node_loc=nodes)) @@ -979,10 +976,18 @@ def gen_data_cement(scenario, dry_run=False): # Create external demand param parname = 'demand' demand = gen_mock_demand_cement(s_info) - df = (make_df(parname, level='demand', commodity='cement', value=demand, unit='t', \ - year=modelyears, **common).pipe(broadcast, node=nodes)) + df = (make_df(parname, level='demand', commodity='cement', value=demand, \ + unit='t', year=modelyears, **common).pipe(broadcast, node=nodes)) results[parname].append(df) + # Add CCS as addon + # parname = 'addon_conversion' + # ccs_tec = ['clinker_wet_cement', 'clinker_dry_cement'] + # df = (make_df(parname, technology=ccs_tec, mode='M1', \ + # type_addon='ccs_cement', \ + # value=1, unit='-', **common).pipe(broadcast, node=nodes)) + # results[parname].append(df) + # Concatenate to one data frame per parameter results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} diff --git a/message_ix_models/model/material/material_testscript.py b/message_ix_models/model/material/material_testscript.py index 1e50b16875..2c79bedc86 100644 --- a/message_ix_models/model/material/material_testscript.py +++ b/message_ix_models/model/material/material_testscript.py @@ -150,7 +150,7 @@ mp_samp = ixmp.Platform(name="local") mp_samp.scenario_list() -sample = mix.Scenario(mp_samp, model="Material_b", scenario="baseline", version="new") +sample = mix.Scenario(mp_samp, model="Material_test", scenario="baseline") sample.set_list() sample.set('year') sample.cat('year', 'firstmodelyear') From 52fb4cf58e62e3cd64dca22c4c8624ca017b7b3d Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 3 Nov 2020 15:10:48 +0100 Subject: [PATCH 150/774] Addition of furnace technologies --- ...eneric_furnace_boiler_techno_economic.xlsx | 4 ++-- message_ix_models/data/material/set.yaml | 21 +++++++++++++++++-- 2 files changed, 21 insertions(+), 4 deletions(-) diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx index d8921813e6..4be328d3f7 100644 --- a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx +++ b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:8e3d326d1abed964447c3cce4c07b1a5f301af27d693d9f2d9cde0d0c6d1c6a7 -size 279 +oid sha256:5c5868be0accd7c084e5bf25468e48026592df86d79d1e2e58ab0f8287017e91 +size 288 diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 45d979c948..88e6f27e76 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -83,8 +83,8 @@ petro_chemicals: - hydro_cracking_ref - steam_cracker_petro - ethanol_to_ethylene_petro - - blend_loil_ref - - blend_foil_ref + - loil_ref + - foil_ref - gas_processing_petro remove: @@ -171,6 +171,7 @@ generic: technology: add: + - furnace_coke_steel - furnace_foil_steel - furnace_loil_steel - furnace_biomass_steel @@ -185,6 +186,7 @@ generic: - fc_h2_steel - solar_steel - dheat_steel + - furnace_coke_aluminum - furnace_coal_aluminum - furnace_foil_aluminum - furnace_loil_aluminum @@ -199,6 +201,21 @@ generic: - fc_h2_aluminum - solar_aluminum - dheat_aluminum + - furnace_coke_petro + - furnace_coal_petro + - furnace_foil_petro + - furnace_loil_petro + - furnace_ethanol_petro + - furnace_biomass_petro + - furnace_methanol_petro + - furnace_gas_petro + - furnace_elec_petro + - furnace_h2_petro + - hp_gas_petro + - hp_elec_petro + - fc_h2_petro + - solar_petro + - dheat_petro - DUMMY_coal_supply - DUMMY_gas_supply - DUMMY_loil_supply From 14274898352a803d03d5b5e39d79ce9ebd166633 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 3 Nov 2020 15:34:33 +0100 Subject: [PATCH 151/774] Generic furnaces added for refining --- ...eneric_furnace_boiler_techno_economic.xlsx | 4 ++-- .../petrochemicals_techno_economic.xlsx | 4 ++-- message_ix_models/data/material/set.yaml | 22 +++++++++++++++---- 3 files changed, 22 insertions(+), 8 deletions(-) diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx index 4be328d3f7..75d525e4cf 100644 --- a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx +++ b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:5c5868be0accd7c084e5bf25468e48026592df86d79d1e2e58ab0f8287017e91 -size 288 +oid sha256:435bb0b7b9d92872de093543d560ec685f5f954ddb31be85b167056c18095ee2 +size 291 diff --git a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx index 89be1356c1..feb81c9206 100644 --- a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx +++ b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:399e05f9a9eee556eaf33798038580bbbb49888f5a32879f48e1349cc70194e5 -size 469184 +oid sha256:e61c66ddd602a22a8f1e12ff95182661450b188b7b617903ab851d5cd6c64737 +size 469501 diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 88e6f27e76..34d402268f 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -18,12 +18,11 @@ petro_chemicals: - atm_residue - vacuum_gasoil - vacuum_residue - - desulf_gasoil - desulf_naphtha - desulf_kerosene - desulf_diesel - - desful_atm_gasoil - - desful_vacuum_gasoil + - desulf_atm_gasoil + - desulf_vacuum_gasoil - gasoline - heavy_foil - light_foil @@ -61,7 +60,6 @@ petro_chemicals: - diesel - cracking_gasoline - cracking_loil - - gasoil - ethane - propane - light_foil @@ -70,6 +68,7 @@ petro_chemicals: - pet_coke - atm_residue - vacuum_residue + - gasoline technology: add: @@ -216,6 +215,21 @@ generic: - fc_h2_petro - solar_petro - dheat_petro + - furnace_coke_refining + - furnace_coal_refining + - furnace_foil_refining + - furnace_loil_refining + - furnace_ethanol_refining + - furnace_biomass_refining + - furnace_methanol_refining + - furnace_gas_refining + - furnace_elec_refining + - furnace_h2_refining + - hp_gas_refining + - hp_elec_refining + - fc_h2_refining + - solar_refining + - dheat_refining - DUMMY_coal_supply - DUMMY_gas_supply - DUMMY_loil_supply From 646e52039e10877459f310af7de617d3cdeb6a18 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 4 Nov 2020 15:33:15 +0100 Subject: [PATCH 152/774] Fixes to run the model --- ...eneric_furnace_boiler_techno_economic.xlsx | 4 ++-- .../petrochemicals_techno_economic.xlsx | 4 ++-- message_ix_models/data/material/set.yaml | 17 +++++++++++--- .../data/material/variable_costs.xlsx | 4 ++-- message_ix_models/model/material/data.py | 23 +++++++++++++++---- 5 files changed, 39 insertions(+), 13 deletions(-) diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx index 75d525e4cf..a3e056d0fa 100644 --- a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx +++ b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:435bb0b7b9d92872de093543d560ec685f5f954ddb31be85b167056c18095ee2 -size 291 +oid sha256:794c99a6e3924b72e622870108da61bbd3fb12b6b1dce346238d59da6998441c +size 278 diff --git a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx index feb81c9206..6bc80a6f22 100644 --- a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx +++ b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:e61c66ddd602a22a8f1e12ff95182661450b188b7b617903ab851d5cd6c64737 -size 469501 +oid sha256:a47326faccfabfa3fc1f40c44a2a630f7c5d47035e758162cc9e76379f51f9e5 +size 436155 diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 34d402268f..7138932784 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -82,8 +82,18 @@ petro_chemicals: - hydro_cracking_ref - steam_cracker_petro - ethanol_to_ethylene_petro - - loil_ref - - foil_ref + - loil_ref_naphtha + - loil_ref_gasoline + - loil_ref_lightfoil + - loil_ref_ethane + - loil_ref_kerosene + - loil_ref_diesel + - loil_ref_refinerygas + - foil_ref_heavyfoil + - foil_ref_petcoke + - foil_ref_atmgasoil + - foil_ref_atmresidue + - foil_ref_vacuumresidue - gas_processing_petro remove: @@ -241,6 +251,8 @@ generic: - DUMMY_biomass_supply - DUMMY_dist_heating - DUMMY_crudeoil_supply + - DUMMY_ethanol_supply_secondary + - DUMMY_gas_supply_secondary - furnace_foil_cement - furnace_loil_cement - furnace_biomass_cement @@ -251,7 +263,6 @@ generic: - furnace_elec_cement - furnace_h2_cement - mode: add: - low_temp diff --git a/message_ix_models/data/material/variable_costs.xlsx b/message_ix_models/data/material/variable_costs.xlsx index c0d08c659b..447419409a 100644 --- a/message_ix_models/data/material/variable_costs.xlsx +++ b/message_ix_models/data/material/variable_costs.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:fe6e090926a73592830e98fa787ff2e8eccae2e7cb8987b43d000ced3bdfd36a -size 16184 +oid sha256:40805bde40f6bca765da57f162eafc7030157d32c9d70d58c9ebc47c3c7d4204 +size 16862 diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index 4ce096c1a1..b9fdec1077 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -617,14 +617,20 @@ def gen_data_petro_chemicals(scenario, dry_run=False): demand_ethylene = (make_df("demand", commodity= "ethylene", \ level= "demand_ethylene", year = modelyears, value=values_e, unit='Mt',\ time= "year").pipe(broadcast, node=nodes)) + print("Ethylene demand") + print(demand_ethylene) demand_propylene = (make_df("demand", commodity= "propylene", \ level= "demand_propylene", year = modelyears, value=values_p, unit='Mt',\ time= "year").pipe(broadcast, node=nodes)) + print("Propylene demand") + print(demand_propylene) demand_BTX = (make_df("demand", commodity= "BTX", \ - level= "demand_BTX", year = modelyears, value=values_p, unit='Mt',\ + level= "demand_BTX", year = modelyears, value=values_BTX, unit='Mt',\ time= "year").pipe(broadcast, node=nodes)) + print("BTX demand") + print(demand_BTX) demand_foil = (make_df("demand", commodity= "fueloil", \ level= "demand_foil", year = modelyears, value=values_foil, unit='GWa',\ @@ -691,8 +697,6 @@ def gen_data_variable(scenario, dry_run=False): modelyears = s_info.Y #s_info.Y is only for modeling years nodes = s_info.N yv_ya = s_info.yv_ya - print("YVYA") - print(yv_ya) fmy = s_info.y0 nodes.remove('World') @@ -1092,7 +1096,7 @@ def gen_mock_demand_aluminum(scenario): val = (val * (1+ element/2) ** context.time_step) values.append(val) - # Adjust the demand according to old scrap level. + # Adjust the demand according to old scrap level. x is in final level. values = [x * fin_to_useful * useful_to_product for x in values] @@ -1123,14 +1127,25 @@ def gen_mock_demand_petro(scenario): values_p = [] values_BTX = [] + # 10-10-8 is the ratio + val_e = (10 * (1+ 0.147718884937996/2) ** context.time_step) + print("val_e") + print(val_e) values_e.append(val_e) + print(values_e) val_p = (10 * (1+ 0.147718884937996/2) ** context.time_step) + print("val_p") + print(val_p) values_p.append(val_p) + print(values_p) val_BTX = (8 * (1+ 0.147718884937996/2) ** context.time_step) + print("val_BTX") + print(val_BTX) values_BTX.append(val_BTX) + print(values_BTX) for element in gdp_growth: i = i + 1 From 747b5bc81be909e4f65178acd06e0e4c3022fd10 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Fri, 6 Nov 2020 09:24:50 +0100 Subject: [PATCH 153/774] Modify emission category to 'co2_industry' for cement --- message_ix_models/data/material/China_steel_MESSAGE.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/China_steel_MESSAGE.xlsx b/message_ix_models/data/material/China_steel_MESSAGE.xlsx index 8e2420208b..54c681cd0d 100644 --- a/message_ix_models/data/material/China_steel_MESSAGE.xlsx +++ b/message_ix_models/data/material/China_steel_MESSAGE.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:1fbe43b846e3a545c0e481eefa11fc2011defbde5f6cb8f48c546d53cad30dec -size 48766 +oid sha256:f748b1513d90bd52d42598c971fab442ffc48f03be5beab59f5425cc2ebcc682 +size 52030 From d93654ce5aaa91334be291939c8d4c3569df8f55 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Fri, 6 Nov 2020 09:28:32 +0100 Subject: [PATCH 154/774] Add params for cement (bound_emission for test) + test plotting for cement --- message_ix_models/model/material/data.py | 19 ++++++++++----- .../model/material/material_testscript.py | 24 ++++++++++++------- 2 files changed, 29 insertions(+), 14 deletions(-) diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index b9fdec1077..a8757a062b 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -985,12 +985,19 @@ def gen_data_cement(scenario, dry_run=False): results[parname].append(df) # Add CCS as addon - # parname = 'addon_conversion' - # ccs_tec = ['clinker_wet_cement', 'clinker_dry_cement'] - # df = (make_df(parname, technology=ccs_tec, mode='M1', \ - # type_addon='ccs_cement', \ - # value=1, unit='-', **common).pipe(broadcast, node=nodes)) - # results[parname].append(df) + parname = 'addon_conversion' + ccs_tec = ['clinker_wet_cement', 'clinker_dry_cement'] + df = (make_df(parname, mode='M1', \ + type_addon='ccs_cement', \ + value=1, unit='-', **common).pipe(broadcast, node=nodes, technology=ccs_tec)) + results[parname].append(df) + + # Test emission bound + parname = 'bound_emission' + df = (make_df(parname, type_tec='all', type_year='cumulative', \ + type_emission='CO2_industry', \ + value=200, unit='-').pipe(broadcast, node=nodes)) + results[parname].append(df) # Concatenate to one data frame per parameter results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} diff --git a/message_ix_models/model/material/material_testscript.py b/message_ix_models/model/material/material_testscript.py index 2c79bedc86..44b3f85b6b 100644 --- a/message_ix_models/model/material/material_testscript.py +++ b/message_ix_models/model/material/material_testscript.py @@ -84,9 +84,12 @@ scen_np.solve() p = Plots(scen_np, 'China', firstyear=2020) -p.plot_activity(baseyear=True, subset=['bf_steel', 'bof_steel', 'dri_steel', 'eaf_steel']) -p.plot_capacity(baseyear=True, subset=['bf_steel', 'bof_steel', 'dri_steel', 'eaf_steel']) -p.plot_new_capacity(baseyear=True, subset=['bf_steel', 'bof_steel', 'dri_steel', 'eaf_steel']) +p.plot_activity(baseyear=True, subset=['bf_steel', 'bof_steel', 'dri_steel', \ + 'eaf_steel']) +p.plot_capacity(baseyear=True, subset=['bf_steel', 'bof_steel', 'dri_steel', \ + 'eaf_steel']) +p.plot_new_capacity(baseyear=True, subset=['bf_steel', 'bof_steel', \ + 'dri_steel', 'eaf_steel']) #%% Main test run based on a MESSAGE scenario @@ -116,11 +119,16 @@ scen.solve() p = Plots(scen, 'China', firstyear=2020) -p.plot_activity(baseyear=False, subset=['bf_steel', 'bof_steel', 'dri_steel', 'eaf_steel']) -p.plot_activity(baseyear=False, subset=['bof_steel', 'eaf_steel']) -p.plot_capacity(baseyear=True, subset=['bf_steel', 'bof_steel', 'dri_steel', 'eaf_steel']) -p.plot_new_capacity(baseyear=True, subset=['bf_steel', 'bof_steel', 'dri_steel', 'eaf_steel']) -p.plot_activity(baseyear=True, subset=['manuf_steel', 'prep_secondary_steel']) +p.plot_activity(baseyear=False, subset=['clinker_dry_cement', \ + 'clinker_wet_cement']) +p.plot_activity(baseyear=False, subset=['grinding_ballmill_cement', \ + 'grinding_vertmill_cement']) +# p.plot_capacity(baseyear=True, subset=['bf_steel', 'bof_steel', 'dri_steel', 'eaf_steel']) +# p.plot_new_capacity(baseyear=True, subset=['bf_steel', 'bof_steel', 'dri_steel', 'eaf_steel']) +p.plot_activity(baseyear=False, subset=['clinker_dry_cement', \ + 'clinker_dry_ccs_cement', \ + 'clinker_wet_cement', \ + 'clinker_wet_ccs_cement']) #%% Auxiliary random test stuff From 36aef480e3dd1d472f7f952ec1076ca14909662f Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Fri, 6 Nov 2020 12:22:39 +0100 Subject: [PATCH 155/774] Modifications --- .../data/material/oil_demand.xlsx | 4 +-- .../petrochemicals_techno_economic.xlsx | 4 +-- message_ix_models/data/material/set.yaml | 27 ++++++++++--------- 3 files changed, 19 insertions(+), 16 deletions(-) diff --git a/message_ix_models/data/material/oil_demand.xlsx b/message_ix_models/data/material/oil_demand.xlsx index e6948b203c..12b0031dfd 100644 --- a/message_ix_models/data/material/oil_demand.xlsx +++ b/message_ix_models/data/material/oil_demand.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:0b4d6c59daae641cdc2baa0b293cc52e2f7b9558f9009d53f8778e1b3ce6f8dc -size 12698 +oid sha256:1cde570d735ef2f3c67ebaf7a748877369aacb42c5aa6872dd9a7e2369d3f297 +size 13332 diff --git a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx index 6bc80a6f22..e72e9aeaab 100644 --- a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx +++ b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:a47326faccfabfa3fc1f40c44a2a630f7c5d47035e758162cc9e76379f51f9e5 -size 436155 +oid sha256:5ab38ff71da98b21c4bb176661737010ebc2a8ba932dcedb760b45316bc7e3c5 +size 438700 diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 7138932784..b6a076bcd3 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -82,18 +82,21 @@ petro_chemicals: - hydro_cracking_ref - steam_cracker_petro - ethanol_to_ethylene_petro - - loil_ref_naphtha - - loil_ref_gasoline - - loil_ref_lightfoil - - loil_ref_ethane - - loil_ref_kerosene - - loil_ref_diesel - - loil_ref_refinerygas - - foil_ref_heavyfoil - - foil_ref_petcoke - - foil_ref_atmgasoil - - foil_ref_atmresidue - - foil_ref_vacuumresidue + # - loil_ref_naphtha + # - loil_ref_gasoline + # - loil_ref_lightfoil + # - loil_ref_ethane + # - loil_ref_kerosene + # - loil_ref_diesel + # - loil_ref_refinerygas + # - loil_ref + # - foil_ref + # - foil_ref_heavyfoil + # - foil_ref_petcoke + # - foil_ref_atmgasoil + # - foil_ref_atmresidue + # - foil_ref_vacuumresidue + - agg_ref - gas_processing_petro remove: From 676b0e1c308a67ef1dd078e75b613c030416ba02 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Fri, 6 Nov 2020 15:35:48 +0100 Subject: [PATCH 156/774] Break down data input scripts by material --- message_ix_models/model/material/data.py | 1374 +---------------- .../model/material/data_aluminum.py | 122 ++ .../model/material/data_cement.py | 167 ++ .../model/material/data_generic.py | 148 ++ .../model/material/data_steel.py | 179 +++ message_ix_models/model/material/data_util.py | 81 + 6 files changed, 712 insertions(+), 1359 deletions(-) create mode 100644 message_ix_models/model/material/data_aluminum.py create mode 100644 message_ix_models/model/material/data_cement.py create mode 100644 message_ix_models/model/material/data_generic.py create mode 100644 message_ix_models/model/material/data_steel.py create mode 100644 message_ix_models/model/material/data_util.py diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index a8757a062b..4c837c0ed8 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -4,1013 +4,31 @@ import ixmp import numpy as np import pandas as pd -from message_ix import make_df -from message_ix_models import ScenarioInfo -from message_ix_models.util import broadcast, same_node +from .data_cement import gen_data_cement +from .data_steel import gen_data_steel +from .data_aluminum import gen_data_aluminum +from .data_generic import gen_data_generic + +from message_data.tools import ( + ScenarioInfo, + broadcast, + make_df, + make_io, + make_matched_dfs, + same_node, + add_par_data +) from .util import read_config - import re -# datafile = "China_steel_renamed - test.xlsx" # "China_steel_renamed.xlsx" # - log = logging.getLogger(__name__) -def read_data(): - """Read and clean data from :file:`n-fertilizer_techno-economic.xlsx`.""" - # Ensure config is loaded, get the context - context = read_config() - - # Shorter access to sets configuration - sets = context["material"]["set"] - - # Read the file - data = pd.read_excel( - context.get_path("material", "n-fertilizer_techno-economic.xlsx"), - sheet_name="Sheet1", - ) - - # Prepare contents for the "parameter" and "technology" columns - # FIXME put these in the file itself to avoid ambiguity/error - - # "Variable" column contains different values selected to match each of - # these parameters, per technology - params = [ - "inv_cost", - "fix_cost", - "var_cost", - "technical_lifetime", - "input_fuel", - "input_elec", - "input_water", - "output_NH3", - "output_water", - "output_heat", - "emissions", - "capacity_factor", - ] - - param_values = [] - tech_values = [] - for t in sets["technology"]["add"]: - param_values.extend(params) - tech_values.extend([t.id] * len(params)) - - # Clean the data - data = ( - # Insert "technology" and "parameter" columns - data.assign(technology=tech_values, parameter=param_values) - # Drop columns that don't contain useful information - .drop(["Model", "Scenario", "Region"], axis=1) - # Set the data frame index for selection - .set_index(["parameter", "technology"]) - ) - - # TODO convert units for some parameters, per LoadParams.py - - return data - - -# def read_data_cement(): -# return process_china_data_tec("cement") -# -# def read_data_steel(): -# return process_china_data_tec("steel") - - -# Read in technology-specific parameters from input xlsx -def process_china_data_tec(sectname): - - import numpy as np - - # Ensure config is loaded, get the context - context = read_config() - - # Read the file - # data_steel_china = pd.read_excel( - # context.get_path("material", context.datafile), - # sheet_name="technologies", - # ) - # data_cement_china = pd.read_excel( - # context.get_path("material", context.datafile), - # sheet_name="tec_cement", - # ) - - # data_df = data_steel_china.append(data_cement_china, ignore_index=True) - data_df = pd.read_excel( - context.get_path("material", context.datafile), - sheet_name=sectname, - ) - - # Clean the data - - data_df = data_df \ - [['Technology', 'Parameter', 'Level', \ - 'Commodity', 'Mode', 'Species', 'Units', 'Value']] \ - .replace(np.nan, '', regex=True) - - # Combine columns and remove '' - list_series = data_df[['Parameter', 'Commodity', 'Level', 'Mode']] \ - .apply(list, axis=1).apply(lambda x: list(filter(lambda a: a != '', x))) - list_ef = data_df[['Parameter', 'Species', 'Mode']] \ - .apply(list, axis=1) - - data_df['parameter'] = list_series.str.join('|') - data_df.loc[data_df['Parameter'] == "emission_factor", \ - 'parameter'] = list_ef.str.join('|') - - data_df = data_df.drop(['Parameter', 'Level', 'Commodity', 'Mode'] \ - , axis = 1) - data_df = data_df.drop( \ - data_df[data_df.Value==''].index) - - data_df.columns = data_df.columns.str.lower() - - # Unit conversion - - # At the moment this is done in the excel file, can be also done here - # To make sure we use the same units - - return data_df - -# Read in relation-specific parameters from input xlsx -def process_china_data_rel(): - - import numpy as np - - # Ensure config is loaded, get the context - context = read_config() - - # Read the file - data_steel_china = pd.read_excel( - context.get_path("material", context.datafile), - sheet_name="relations", - ) - - return data_steel_china - -# Read in time-dependent parameters -# Now only used to add fuel cost for bare model -def read_timeseries(): - - import numpy as np - - # Ensure config is loaded, get the context - context = read_config() - - if context.scenario_info['scenario'] == 'NPi400': - sheet_name="timeseries_NPi400" - else: - sheet_name = "timeseries" - - # Read the file - df = pd.read_excel( - context.get_path("material", context.datafile), sheet_name) - - import numbers - # Take only existing years in the data - datayears = [x for x in list(df) if isinstance(x, numbers.Number)] - - df = pd.melt(df, id_vars=['parameter', 'technology', 'mode', 'units'], \ - value_vars = datayears, \ - var_name ='year') - - return df - -def read_data_generic(): - """Read and clean data from :file:`generic_furnace_boiler_techno_economic.xlsx`.""" - - # Ensure config is loaded, get the context - context = read_config() - # Shorter access to sets configuration - # sets = context["material"]["generic"] - - # Read the file - data_generic = pd.read_excel( - context.get_path("material", "generic_furnace_boiler_techno_economic.xlsx"), - sheet_name="generic") - - # Clean the data - # Drop columns that don't contain useful information - - data_generic= data_generic.drop(['Region', 'Source', 'Description'], axis = 1) - - # Unit conversion - - # At the moment this is done in the excel file, can be also done here - # To make sure we use the same units - - return data_generic - -def read_data_aluminum(): - """Read and clean data from :file:`aluminum_techno_economic.xlsx`.""" - - # Ensure config is loaded, get the context - context = read_config() - - # Read the file - data_aluminum = pd.read_excel( - context.get_path("material", "aluminum_techno_economic.xlsx"), - sheet_name="data") - # Clean the data - - data_aluminum= data_aluminum.drop(['Region', 'Source', 'Description'], axis = 1) - - data_aluminum_hist = pd.read_excel(context.get_path("material", \ - "aluminum_techno_economic.xlsx"),sheet_name="data_historical", \ - usecols = "A:F") - - return data_aluminum,data_aluminum_hist - -def read_data_petrochemicals(): - """Read and clean data from :file:`petrochemicals_techno_economic.xlsx`.""" - - # Ensure config is loaded, get the context - context = read_config() - - # Read the file - data_petro = pd.read_excel( - context.get_path("material", "petrochemicals_techno_economic.xlsx"), - sheet_name="data") - # Clean the data - - data_petro= data_petro.drop(['Region', 'Source', 'Description'], axis = 1) - - data_petro_hist = pd.read_excel(context.get_path("material", \ - "petrochemicals_techno_economic.xlsx"),sheet_name="data_historical", \ - usecols = "A:F") - - return data_petro,data_petro_hist - -def gen_data_aluminum(scenario, dry_run=False): - """Generate data for materials representation of aluminum.""" - # Load configuration - - config = read_config()["material"]["aluminum"] - - # Information about scenario, e.g. node, year - s_info = ScenarioInfo(scenario) - - # Techno-economic assumptions - data_aluminum, data_aluminum_hist = read_data_aluminum() - - # List of data frames, to be concatenated together at end - results = defaultdict(list) - - #allyears = s_info.set['year'] - - # FIX: The years do not include 1980 - allyears = np.arange(1980, 2101, 10).tolist() - print('All years') - print(allyears) - modelyears = s_info.Y #s_info.Y is only for modeling years - print('Model years') - print(modelyears) - nodes = s_info.N - yv_ya = s_info.yv_ya - fmy = s_info.y0 - print('first model year') - print(fmy) - - nodes.remove('World') - - # Iterate over technologies - - for t in config["technology"]["add"]: - - # Obtain the active and vintage years - av = data_aluminum.loc[(data_aluminum["technology"] == t),'availability']\ - .values[0] - - # For the technologies with lifetime - - if "technical_lifetime" in data_aluminum.loc[(data_aluminum["technology"] \ - == t)]["parameter"].values: - lifetime = data_aluminum.loc[(data_aluminum["technology"] == t) & \ - (data_aluminum["parameter"]== "technical_lifetime"),'value'].values[0] - years_df = scenario.vintage_and_active_years() - years_df = years_df.loc[years_df["year_vtg"]>= av] - years_df_final = pd.DataFrame(columns=["year_vtg","year_act"]) - - # For each vintage adjsut the active years according to technical lifetime - for vtg in years_df["year_vtg"].unique(): - years_df_temp = years_df.loc[years_df["year_vtg"]== vtg] - years_df_temp = years_df_temp.loc[years_df["year_act"]< vtg + lifetime] - years_df_final = pd.concat([years_df_temp, years_df_final], ignore_index=True) - - vintage_years, act_years = years_df_final['year_vtg'], years_df_final['year_act'] - - params = data_aluminum.loc[(data_aluminum["technology"] == t),\ - "parameter"].values.tolist() - - # Availability of the technology - av = data_aluminum.loc[(data_aluminum["technology"] == t), - 'availability'].values[0] - modelyears = [year for year in modelyears if year >= av] - yva = yv_ya.loc[yv_ya.year_vtg >= av] - - # Iterate over parameters - for par in params: - split = par.split("|") - param_name = split[0] - - # Obtain the scalar value for the parameter - - val = data_aluminum.loc[((data_aluminum["technology"] == t) \ - & (data_aluminum["parameter"] == par)),'value'].values[0] - - common = dict( - year_vtg= yv_ya.year_vtg, - year_act= yv_ya.year_act, - mode="M1", - time="year", - time_origin="year", - time_dest="year",) - - # For the parameters which inlcudes index names - if len(split)> 1: - - if (param_name == "input")|(param_name == "output"): - - # Assign commodity and level names - com = split[1] - lev = split[2] - - df = (make_df(param_name, technology=t, commodity=com, \ - level=lev, value=val, unit='t', **common)\ - .pipe(broadcast, node_loc=nodes).pipe(same_node)) - - results[param_name].append(df) - - elif param_name == "emission_factor": - # Assign the emisson type - emi = split[1] - - df = (make_df(param_name, technology=t,value=val,\ - emission=emi, unit='t', **common).pipe(broadcast, \ - node_loc=nodes)) - results[param_name].append(df) - - # Rest of the parameters except input,output and emission_factor - - else: - df = (make_df(param_name, technology=t, value=val,unit='t', \ - **common).pipe(broadcast, node_loc=nodes)) - results[param_name].append(df) - - # Add the dummy alluminum demand - - values = gen_mock_demand_aluminum(scenario) - - demand_al = (make_df("demand", commodity= "aluminum", \ - level= "demand_aluminum", year = modelyears, value=values, unit='Mt',\ - time= "year").pipe(broadcast, node=nodes)) - - results["demand"].append(demand_al) - - # Add historical data - - for tec in data_aluminum_hist["technology"].unique(): - - y_hist = [y for y in allyears if y < fmy] - common_hist = dict( - year_vtg= y_hist, - year_act= y_hist, - mode="M1", - time="year",) - - val_act = data_aluminum_hist.\ - loc[(data_aluminum_hist["technology"]== tec), "production"] - - df_hist_act = (make_df("historical_activity", technology=tec, \ - value=val_act, unit='Mt', **common_hist).pipe(broadcast, node_loc=nodes)) - - results["historical_activity"].append(df_hist_act) - - c_factor = data_aluminum.loc[((data_aluminum["technology"]== tec) \ - & (data_aluminum["parameter"]=="capacity_factor")), "value"].values - - val_cap = data_aluminum_hist.loc[(data_aluminum_hist["technology"]== tec), \ - "new_production"] / c_factor - - df_hist_cap = (make_df("historical_new_capacity", technology=tec, \ - value=val_cap, unit='Mt', **common_hist).pipe(broadcast, node_loc=nodes)) - - results["historical_new_capacity"].append(df_hist_cap) - - results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} - - add_scrap_prices(scenario) - - return results - -#TODO: Add historical data ? -def gen_data_generic(scenario, dry_run=False): - # Load configuration - - # Load configuration - config = read_config()["material"]["generic_set"] - - # Information about scenario, e.g. node, year - s_info = ScenarioInfo(scenario) - - # Techno-economic assumptions - data_generic = read_data_generic() - - # List of data frames, to be concatenated together at end - results = defaultdict(list) - - # For each technology there are differnet input and output combinations - # Iterate over technologies - - allyears = s_info.set['year'] - modelyears = s_info.Y #s_info.Y is only for modeling years - nodes = s_info.N - yv_ya = s_info.yv_ya - fmy = s_info.y0 - - # 'World' is included by default when creating a message_ix.Scenario(). - # Need to remove it for the China bare model - nodes.remove('World') - - for t in config["technology"]["add"]: - - print(t) - - # years = s_info.Y - params = data_generic.loc[(data_generic["technology"] == t),"parameter"]\ - .values.tolist() - - # Availability year of the technology - av = data_generic.loc[(data_generic["technology"] == t),'availability'].values[0] - modelyears = [year for year in modelyears if year >= av] - yva = yv_ya.loc[yv_ya.year_vtg >= av, ] - - # Iterate over parameters (e.g. input|coal|final|low_temp) - for par in params: - split = par.split("|") - param_name = par.split("|")[0] - - val = data_generic.loc[((data_generic["technology"] == t) & \ - (data_generic["parameter"] == par)),'value'].values[0] - - # Common parameters for all input and output tables - - common = dict( - year_vtg= yva.year_vtg, - year_act= yva.year_act, - time="year", - time_origin="year", - time_dest="year",) - - if len(split)> 1: - - if (param_name == "input")|(param_name == "output"): - - com = split[1] - lev = split[2] - mod = split[3] - - # Store the available modes for a technology - - mode_list.append(mod) - - df = (make_df(param_name, technology=t, commodity=com, \ - level=lev, mode=mod, value=val, unit='t', **common).\ - pipe(broadcast, node_loc=nodes).pipe(same_node)) - - results[param_name].append(df) - - elif param_name == "emission_factor": - emi = split[1] - mod = data_generic.loc[((data_generic["technology"] == t) \ - & (data_generic["parameter"] == par)),'value'].values[0] - - # TODO: Now tentatively fixed to one mode. Have values for the other mode too - df = (make_df(param_name, technology=t,value=val,\ - emission=emi, mode="low_temp", unit='t', **common).pipe(broadcast, \ - node_loc=nodes)) - - for m in np.unique(np.array(mode_list)): - df = (make_df(param_name, technology=t,value=val,\ - emission=emi,mode= m, unit='t', **common)\ - .pipe(broadcast, node_loc=nodes)) - - results[param_name].append(df) - - # Rest of the parameters apart from input, output and emission_factor - else: - - df = (make_df(param_name, technology=t, value=val,unit='t', \ - **common).pipe(broadcast, node_loc=nodes)) - - results[param_name].append(df) - - results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} - - return results - -def gen_data_petro_chemicals(scenario, dry_run=False): - # Load configuration - - config = read_config()["material"]["petro_chemicals"] - - # Information about scenario, e.g. node, year - s_info = ScenarioInfo(scenario) - - # Techno-economic assumptions - data_petro, data_petro_hist = read_data_petrochemicals() - # List of data frames, to be concatenated together at end - results = defaultdict(list) - - # For each technology there are differnet input and output combinations - # Iterate over technologies - - allyears = s_info.set['year'] - modelyears = s_info.Y #s_info.Y is only for modeling years - nodes = s_info.N - yv_ya = s_info.yv_ya - fmy = s_info.y0 - - # 'World' is included by default when creating a message_ix.Scenario(). - # Need to remove it for the China bare model - nodes.remove('World') - - for t in config["technology"]["add"]: - - # years = s_info.Y - params = data_petro.loc[(data_petro["technology"] == t),"parameter"]\ - .values.tolist() - - # Availability year of the technology - av = data_petro.loc[(data_petro["technology"] == t),'availability'].\ - values[0] - modelyears = [year for year in modelyears if year >= av] - yva = yv_ya.loc[yv_ya.year_vtg >= av, ] - - # Iterate over parameters - for par in params: - split = par.split("|") - param_name = par.split("|")[0] - - val = data_petro.loc[((data_petro["technology"] == t) & \ - (data_petro["parameter"] == par)),'value'].values[0] - - # Common parameters for all input and output tables - # year_act is none at the moment - # node_dest and node_origin are the same as node_loc - - common = dict( - year_vtg= yva.year_vtg, - year_act= yva.year_act, - time="year", - time_origin="year", - time_dest="year",) - - if len(split)> 1: - - if (param_name == "input")|(param_name == "output"): - - com = split[1] - lev = split[2] - mod = split[3] - - df = (make_df(param_name, technology=t, commodity=com, \ - level=lev,mode=mod, value=val, unit='t', **common).\ - pipe(broadcast, node_loc=nodes).pipe(same_node)) - - results[param_name].append(df) - - elif param_name == "emission_factor": - emi = split[1] - mod = split[2] - - df = (make_df(param_name, technology=t,value=val,\ - emission=emi, mode=mod, unit='t', **common).pipe(broadcast, \ - node_loc=nodes)) - - elif param_name == "var_cost": - mod = split[1] - - df = (make_df(param_name, technology=t, commodity=com, \ - level=lev,mode=mod, value=val, unit='t', **common).\ - pipe(broadcast, node_loc=nodes).pipe(same_node)) - - - - results[param_name].append(df) - - # Rest of the parameters apart from inpput, output and emission_factor - - else: - - df = (make_df(param_name, technology=t, value=val,unit='t', \ - **common).pipe(broadcast, node_loc=nodes)) - - results[param_name].append(df) - - # Add demand - - values_e, values_p, values_BTX, values_foil, values_loil = \ - gen_mock_demand_petro(scenario) - - demand_ethylene = (make_df("demand", commodity= "ethylene", \ - level= "demand_ethylene", year = modelyears, value=values_e, unit='Mt',\ - time= "year").pipe(broadcast, node=nodes)) - print("Ethylene demand") - print(demand_ethylene) - - demand_propylene = (make_df("demand", commodity= "propylene", \ - level= "demand_propylene", year = modelyears, value=values_p, unit='Mt',\ - time= "year").pipe(broadcast, node=nodes)) - print("Propylene demand") - print(demand_propylene) - - demand_BTX = (make_df("demand", commodity= "BTX", \ - level= "demand_BTX", year = modelyears, value=values_BTX, unit='Mt',\ - time= "year").pipe(broadcast, node=nodes)) - print("BTX demand") - print(demand_BTX) - - demand_foil = (make_df("demand", commodity= "fueloil", \ - level= "demand_foil", year = modelyears, value=values_foil, unit='GWa',\ - time= "year").pipe(broadcast, node=nodes)) - - demand_loil = (make_df("demand", commodity= "lightoil", \ - level= "demand_loil", year = modelyears, value=values_loil, unit='GWa',\ - time= "year").pipe(broadcast, node=nodes)) - - results["demand"].append(demand_ethylene) - results["demand"].append(demand_propylene) - results["demand"].append(demand_BTX) - results["demand"].append(demand_loil) - results["demand"].append(demand_foil) - - # Add historical data - - for tec in data_petro_hist["technology"].unique(): - - y_hist = [y for y in allyears if y < fmy] - common_hist = dict( - year_vtg= y_hist, - year_act= y_hist, - mode="M1", - time="year",) - - val_act = data_petro_hist.\ - loc[(data_petro_hist["technology"]== tec), "production"] - - df_hist_act = (make_df("historical_activity", technology=tec, \ - value=val_act, unit='Mt', **common_hist).pipe(broadcast, node_loc=nodes)) - - results["historical_activity"].append(df_hist_act) - - c_factor = data_petro.loc[((data_petro["technology"]== tec) \ - & (data_petro["parameter"]=="capacity_factor")), "value"].values - - val_cap = data_petro_hist.loc[(data_petro_hist["technology"]== tec), \ - "new_production"] / c_factor - - df_hist_cap = (make_df("historical_new_capacity", technology=tec, \ - value=val_cap, unit='Mt', **common_hist).pipe(broadcast, node_loc=nodes)) - - results["historical_new_capacity"].append(df_hist_cap) - - results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} - - return results - -def gen_data_variable(scenario, dry_run=False): - - # Information about scenario, e.g. node, year - s_info = ScenarioInfo(scenario) - - # Generates variables costs for dummy technologies - - data_vc = read_var_cost() - tec_vc = set(data_vc.technology) - - # List of data frames, to be concatenated together at end - results = defaultdict(list) - - allyears = s_info.set['year'] - modelyears = s_info.Y #s_info.Y is only for modeling years - nodes = s_info.N - yv_ya = s_info.yv_ya - fmy = s_info.y0 - - nodes.remove('World') - - #for t in config['technology']['add']: - for t in tec_vc: - # Special treatment for time-varying params - #if t in tec_vc: - - common = dict( - time="year", - time_origin="year", - time_dest="year",) - - param_name = "var_cost" - val = data_vc.loc[(data_vc["technology"] == t), 'value'] - units = data_vc.loc[(data_vc["technology"] == t),'units'].values[0] - mod = data_vc.loc[(data_vc["technology"] == t), 'mode'] - yr = data_vc.loc[(data_vc["technology"] == t), 'year'] - - df = (make_df(param_name, technology=t, value=val,unit='t', \ - year_vtg=yr, year_act=yr, mode=mod, **common).pipe(broadcast, \ - node_loc=nodes)) - results[param_name].append(df) - - # Concatenate to one data frame per parameter - results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} - - return results - - -def gen_data_steel(scenario, dry_run=False): - """Generate data for materials representation of steel industry. - - """ - # Load configuration - config = read_config()["material"]["steel"] - - # Information about scenario, e.g. node, year - s_info = ScenarioInfo(scenario) - - # Techno-economic assumptions - # TEMP: now add cement sector as well => Need to separate those since now I have get_data_steel and cement - data_steel = process_china_data_tec("steel") - # Special treatment for time-dependent Parameters - data_steel_vc = read_timeseries() - tec_vc = set(data_steel_vc.technology) # set of tecs with var_cost - - # List of data frames, to be concatenated together at end - results = defaultdict(list) - - # For each technology there are differnet input and output combinations - # Iterate over technologies - - allyears = s_info.set['year'] #s_info.Y is only for modeling years - modelyears = s_info.Y #s_info.Y is only for modeling years - nodes = s_info.N - yv_ya = s_info.yv_ya - fmy = s_info.y0 - - #print(allyears, modelyears, fmy) - - nodes.remove('World') # For the bare model - - # for t in s_info.set['technology']: - for t in config['technology']['add']: - - params = data_steel.loc[(data_steel["technology"] == t),\ - "parameter"].values.tolist() - - # # Special treatment for time-varying params - # if t in tec_vc: - # common = dict( - # time="year", - # time_origin="year", - # time_dest="year",) - # - # param_name = "var_cost" - # val = data_steel_vc.loc[(data_steel_vc["technology"] == t), 'value'] - # units = data_steel_vc.loc[(data_steel_vc["technology"] == t), \ - # 'units'].values[0] - # mod = data_steel_vc.loc[(data_steel_vc["technology"] == t), 'mode'] - # yr = data_steel_vc.loc[(data_steel_vc["technology"] == t), 'year'] - # - # df = (make_df(param_name, technology=t, value=val,\ - # unit='t', year_vtg=yr, year_act=yr, mode=mod, **common).pipe(broadcast, \ - # node_loc=nodes)) - # - # print(param_name, df) - # results[param_name].append(df) - - param_name = data_steel_vc.loc[(data_steel_vc["technology"] == t), 'parameter'] - - for p in set(param_name): - val = data_steel_vc.loc[(data_steel_vc["technology"] == t) \ - & (data_steel_vc["parameter"] == p), 'value'] - units = data_steel_vc.loc[(data_steel_vc["technology"] == t) \ - & (data_steel_vc["parameter"] == p), 'units'].values[0] - mod = data_steel_vc.loc[(data_steel_vc["technology"] == t) \ - & (data_steel_vc["parameter"] == p), 'mode'] - yr = data_steel_vc.loc[(data_steel_vc["technology"] == t) \ - & (data_steel_vc["parameter"] == p), 'year'] - - df = (make_df(p, technology=t, value=val,\ - unit='t', year_vtg=yr, year_act=yr, mode=mod, **common).pipe(broadcast, \ - node_loc=nodes)) - - #print("time-dependent::", p, df) - results[p].append(df) - - # Iterate over parameters - for par in params: - - # Obtain the parameter names, commodity,level,emission - split = par.split("|") - param_name = split[0] - # Obtain the scalar value for the parameter - val = data_steel.loc[((data_steel["technology"] == t) \ - & (data_steel["parameter"] == par)),'value'].values[0] - - common = dict( - year_vtg= yv_ya.year_vtg, - year_act= yv_ya.year_act, - # mode="M1", - time="year", - time_origin="year", - time_dest="year",) - - # For the parameters which inlcudes index names - if len(split)> 1: - - #print('1.param_name:', param_name, t) - if (param_name == "input")|(param_name == "output"): - - # Assign commodity and level names - com = split[1] - lev = split[2] - mod = split[3] - - df = (make_df(param_name, technology=t, commodity=com, \ - level=lev, value=val, mode=mod, unit='t', **common)\ - .pipe(broadcast, node_loc=nodes).pipe(same_node)) - - elif param_name == "emission_factor": - - # Assign the emisson type - emi = split[1] - mod = split[2] - - df = (make_df(param_name, technology=t, value=val,\ - emission=emi, mode=mod, unit='t', **common).pipe(broadcast, \ - node_loc=nodes)) - - else: # time-independent var_cost - mod = split[1] - df = (make_df(param_name, technology=t, value=val, \ - mode=mod, unit='t', \ - **common).pipe(broadcast, node_loc=nodes)) - - results[param_name].append(df) - - # Parameters with only parameter name - else: - #print('2.param_name:', param_name) - df = (make_df(param_name, technology=t, value=val, unit='t', \ - **common).pipe(broadcast, node_loc=nodes)) - - results[param_name].append(df) - - # Create external demand param - parname = 'demand' - demand = gen_mock_demand_steel(s_info) - df = (make_df(parname, level='demand', commodity='steel', value=demand, unit='t', \ - year=modelyears, **common).pipe(broadcast, node=nodes)) - results[parname].append(df) - - # Concatenate to one data frame per parameter - results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} - - return results - - -def gen_data_cement(scenario, dry_run=False): - """Generate data for materials representation of steel industry. - - """ - # Load configuration - config = read_config()["material"]["cement"] - - # Information about scenario, e.g. node, year - s_info = ScenarioInfo(scenario) - - # Techno-economic assumptions - # TEMP: now add cement sector as well - data_cement = process_china_data_tec("cement") - # Special treatment for time-dependent Parameters - # data_cement_vc = read_timeseries() - # tec_vc = set(data_cement_vc.technology) # set of tecs with var_cost - - # List of data frames, to be concatenated together at end - results = defaultdict(list) - - # For each technology there are differnet input and output combinations - # Iterate over technologies - - allyears = s_info.set['year'] #s_info.Y is only for modeling years - modelyears = s_info.Y #s_info.Y is only for modeling years - nodes = s_info.N - yv_ya = s_info.yv_ya - fmy = s_info.y0 - - #print(allyears, modelyears, fmy) - - nodes.remove('World') # For the bare model - - # for t in s_info.set['technology']: - for t in config['technology']['add']: - - params = data_cement.loc[(data_cement["technology"] == t),\ - "parameter"].values.tolist() - - # Iterate over parameters - for par in params: - - # Obtain the parameter names, commodity,level,emission - split = par.split("|") - param_name = split[0] - # Obtain the scalar value for the parameter - val = data_cement.loc[((data_cement["technology"] == t) \ - & (data_cement["parameter"] == par)),'value'].values[0] - - common = dict( - year_vtg= yv_ya.year_vtg, - year_act= yv_ya.year_act, - # mode="M1", - time="year", - time_origin="year", - time_dest="year",) - - # For the parameters which inlcudes index names - if len(split)> 1: - - #print('1.param_name:', param_name, t) - if (param_name == "input")|(param_name == "output"): - - # Assign commodity and level names - com = split[1] - lev = split[2] - mod = split[3] - - df = (make_df(param_name, technology=t, commodity=com, \ - level=lev, value=val, mode=mod, unit='t', **common)\ - .pipe(broadcast, node_loc=nodes).pipe(same_node)) - - elif param_name == "emission_factor": - - # Assign the emisson type - emi = split[1] - mod = split[2] - - df = (make_df(param_name, technology=t, value=val,\ - emission=emi, mode=mod, unit='t', **common).pipe(broadcast, \ - node_loc=nodes)) - - else: # time-independent var_cost - mod = split[1] - df = (make_df(param_name, technology=t, value=val, \ - mode=mod, unit='t', \ - **common).pipe(broadcast, node_loc=nodes)) - - results[param_name].append(df) - - # Parameters with only parameter name - else: - #print('2.param_name:', param_name) - df = (make_df(param_name, technology=t, value=val, unit='t', \ - **common).pipe(broadcast, node_loc=nodes)) - - results[param_name].append(df) - - # Create external demand param - parname = 'demand' - demand = gen_mock_demand_cement(s_info) - df = (make_df(parname, level='demand', commodity='cement', value=demand, \ - unit='t', year=modelyears, **common).pipe(broadcast, node=nodes)) - results[parname].append(df) - - # Add CCS as addon - parname = 'addon_conversion' - ccs_tec = ['clinker_wet_cement', 'clinker_dry_cement'] - df = (make_df(parname, mode='M1', \ - type_addon='ccs_cement', \ - value=1, unit='-', **common).pipe(broadcast, node=nodes, technology=ccs_tec)) - results[parname].append(df) - - # Test emission bound - parname = 'bound_emission' - df = (make_df(parname, type_tec='all', type_year='cumulative', \ - type_emission='CO2_industry', \ - value=200, unit='-').pipe(broadcast, node=nodes)) - results[parname].append(df) - - # Concatenate to one data frame per parameter - results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} - - return results - DATA_FUNCTIONS = [ gen_data_steel, gen_data_cement, - gen_data_generic, gen_data_aluminum, - gen_data_variable, - gen_data_petro_chemicals + gen_data_generic, ] # Try to handle multiple data input functions from different materials @@ -1031,365 +49,3 @@ def add_data(scenario, dry_run=False): add_par_data(scenario, func(scenario), dry_run=dry_run) log.info('done') - - -import numpy as np -gdp_growth = [0.121448215899944, 0.0733079014579874, 0.0348154093342843, \ - 0.021827616787921, 0.0134425983942219, 0.0108320197485592, \ - 0.00884341208063,0.00829374133206562, 0.00649794573935969, 0.00649794573935969] -gr = np.cumprod([(x+1) for x in gdp_growth]) - -# Generate a fake steel demand -def gen_mock_demand_steel(s_info): - - modelyears = s_info.Y - fmy = s_info.y0 - - # True steel use 2010 (China) = 537 Mt/year - demand2010_steel = 537 - # https://www.worldsteel.org/en/dam/jcr:0474d208-9108-4927-ace8-4ac5445c5df8/World+Steel+in+Figures+2017.pdf - - baseyear = list(range(2020, 2110+1, 10)) - - demand = gr * demand2010_steel - demand_interp = np.interp(modelyears, baseyear, demand) - - return demand_interp.tolist() - -# Generate a fake cement demand -def gen_mock_demand_cement(s_info): - - modelyears = s_info.Y - fmy = s_info.y0 - - # True cement use 2011 (China) = 2100 Mt/year (ADVANCE) - demand2010_cement = 2100 - - baseyear = list(range(2020, 2110+1, 10)) - - demand = gr * demand2010_cement - demand_interp = np.interp(modelyears, baseyear, demand) - - return demand.tolist() -def gen_mock_demand_aluminum(scenario): - - # 17.3 Mt in 2010 to match the historical production from IAI. - # This is the amount right after electrolysis. - - # The future projection of the demand: Increases by half of the GDP growth rate. - # Starting from 2020. - context = read_config() - s_info = ScenarioInfo(scenario) - modelyears = s_info.Y #s_info.Y is only for modeling years - - gdp_growth = pd.Series([0.121448215899944, 0.0733079014579874, \ - 0.0348154093342843, 0.021827616787921,\ - 0.0134425983942219, 0.0108320197485592, \ - 0.00884341208063, 0.00829374133206562, \ - 0.00649794573935969],index=pd.Index(modelyears, \ - name='Time')) - # Values in 2010 from IAI. - fin_to_useful = 0.971 - useful_to_product = 0.866 - - i = 0 - values = [] - val = (17.3 * (1+ 0.147718884937996/2) ** context.time_step) - values.append(val) - - for element in gdp_growth: - i = i + 1 - if i < len(modelyears): - val = (val * (1+ element/2) ** context.time_step) - values.append(val) - - # Adjust the demand according to old scrap level. x is in final level. - - values = [x * fin_to_useful * useful_to_product for x in values] - - return values - -def gen_mock_demand_petro(scenario): - - # China 2006: 22 kg/cap HVC demand. 2006 population: 1.311 billion - # This makes 28.842 Mt. (IEA Energy Technology Transitions for Industry) - # Distribution in 2015 for China: 6:6:5 (ethylene,propylene,BTX) - # Future of Petrochemicals Methodological Annex - # This can be verified by other sources. - - # The future projection of the demand: Increases by half of the GDP growth rate. - # Starting from 2020. - context = read_config() - s_info = ScenarioInfo(scenario) - modelyears = s_info.Y #s_info.Y is only for modeling years - - gdp_growth = pd.Series([0.121448215899944, 0.0733079014579874, \ - 0.0348154093342843, 0.021827616787921,\ - 0.0134425983942219, 0.0108320197485592, \ - 0.00884341208063, 0.00829374133206562, \ - 0.00649794573935969],index=pd.Index(modelyears, \ - name='Time')) - i = 0 - values_e = [] - values_p = [] - values_BTX = [] - - # 10-10-8 is the ratio - - val_e = (10 * (1+ 0.147718884937996/2) ** context.time_step) - print("val_e") - print(val_e) - values_e.append(val_e) - print(values_e) - - val_p = (10 * (1+ 0.147718884937996/2) ** context.time_step) - print("val_p") - print(val_p) - values_p.append(val_p) - print(values_p) - - val_BTX = (8 * (1+ 0.147718884937996/2) ** context.time_step) - print("val_BTX") - print(val_BTX) - values_BTX.append(val_BTX) - print(values_BTX) - - for element in gdp_growth: - i = i + 1 - if i < len(modelyears): - val_e = (val_e * (1+ element/2) ** context.time_step) - values_e.append(val_e) - - val_p = (val_p * (1+ element/2) ** context.time_step) - values_p.append(val_p) - - val_BTX = (val_BTX * (1+ element/2) ** context.time_step) - values_BTX.append(val_BTX) - - if context.scenario_info['scenario'] == 'NPi400': - sheet_name="demand_NPi400" - else: - sheet_name = "demand_baseline" - # Read the file - df = pd.read_excel( - context.get_path("material", "oil_demand.xlsx"), - sheet_name=sheet_name, - ) - - values_foil = df["Total_foil"].tolist() - values_loil = df["Total_loil"].tolist() - - return values_e, values_p, values_BTX, values_foil, values_loil - -def get_data(scenario, context, **options): - """Data for the bare RES.""" - if context.res_with_dummies: - dt = get_dummy_data(scenario) - print(dt) - return dt - else: - return dict() - -def get_dummy_data(scenario, **options): - """Dummy data for the bare RES. - - Currently this contains: - - A dummy 1-to-1 technology taking input from (dummy, primary) and output - to (dummy, useful). - - A dummy source technology for (dummy, primary). - - Demand for (dummy, useful). - - This ensures that the model variable ACT has some non-zero entries. - """ - info = ScenarioInfo(scenario) - - common = dict( - node=info.N[0], - node_loc=info.N[0], - node_origin=info.N[0], - node_dest=info.N[0], - technology="dummy", - year=info.Y, - year_vtg=info.Y, - year_act=info.Y, - mode="all", - # No subannual detail - time="year", - time_origin="year", - time_dest="year", - ) - - data = make_io( - src=("dummy", "primary", "GWa"), - dest=("dummy", "useful", "GWa"), - efficiency=1., - on='input', - # Other data - **common - ) - - # Source for dummy - data["output"] = data["output"].append( - data["output"].assign(technology="dummy source", level="primary") - ) - - data.update( - make_matched_dfs( - data["output"], - capacity_factor=1., - technical_lifetime=10, - var_cost=1, - ) - ) - - common.update(dict( - commodity="dummy", - level="useful", - value=1., - unit="GWa", - )) - data["demand"] = make_df("demand", **common) - - return data - -# def add_scrap_prices(scenario): -# -# context = read_config() -# s_info = ScenarioInfo(scenario) -# nodes = s_info.N -# modelyears = s_info.Y -# nodes.remove('World') -# -# # Distinguish the share and total technologies -# -# total = ['prep_secondary_aluminum_1', 'prep_secondary_aluminum_2', \ -# 'prep_secondary_aluminum_3'] -# -# #total = ["scrap_recovery_aluminum"] -# -# # Add technology category for total -# -# scenario.add_cat('technology', 'type_total', total) -# -# no = 0 -# for tech in total: -# -# # Add technology category for shares -# -# no = no + 1 -# no_shr = str(no) -# type_tec_shr = 'type' + no_shr -# share = 'scrap_availability_' + no_shr -# -# scenario.add_set('shares',share) -# scenario.add_cat('technology',type_tec_shr, [tech]) -# -# # Map shares -# -# map_share = pd.DataFrame({'shares': [share], -# 'node_share': nodes, -# 'node': nodes, -# 'type_tec': type_tec_shr, -# 'mode': 'M1', -# 'commodity': 'aluminum', -# 'level': 'old_scrap',}) -# scenario.add_set('map_shares_commodity_share', map_share) -# -# -# # Map total -# -# map_total = pd.DataFrame({'shares': [share], -# 'node_share': nodes, -# 'node': nodes, -# 'type_tec': 'type_total', -# 'mode': 'M1', -# 'commodity': 'aluminum', -# 'level': 'old_scrap',}) -# scenario.add_set('map_shares_commodity_total', map_total) -# -# # Add the upper bound -# -# up_share = pd.DataFrame({'shares': share, -# 'node_share': nodes[0], -# 'year_act': modelyears, -# 'time': 'year', -# 'value': 0.333, -# 'unit': '%',}) -# -# print(up_share) -# -# scenario.add_par('share_commodity_up', up_share) - -def add_scrap_prices(scenario): - - context = read_config() - s_info = ScenarioInfo(scenario) - nodes = s_info.N - modelyears = s_info.Y - nodes.remove('World') - - total = ['prep_secondary_aluminum_1', 'prep_secondary_aluminum_2', \ - 'prep_secondary_aluminum_3'] - - no = 0 - for tech in total: - - # Add technology category for shares - - no = no + 1 - relation = 'scrap_availability_' + str(no) - - scenario.add_set('relation',relation) - - # relation_activity for the technology - - nodes_new = nodes * len(modelyears) - - rel_act = pd.DataFrame({ - 'relation': relation, - 'node_rel': nodes_new, - 'year_rel': modelyears, - 'node_loc': nodes_new, - 'technology': tech, - 'year_act': modelyears, - 'mode': 'M1', - "value": 1, - 'unit': '-', - }) - - # 1/3 of the old scrap is available. 0.24512 the amount of old scrap. - # Is there a way to obtain it without hard-coding ? - - # Chnage the availability - - if no == 1: - val = 1/4 - if no == 2: - val = 1/4 - if no== 3: - val = 1/2 - - rel_act_rec = pd.DataFrame({ - 'relation': relation, - 'node_rel': nodes_new, - 'year_rel': modelyears, - 'node_loc': nodes_new, - 'technology': "scrap_recovery_aluminum", - 'year_act': modelyears, - 'mode': 'M1', - "value": -val * 0.24512, - 'unit': '-', - }) - - scenario.add_par("relation_activity", rel_act) - scenario.add_par("relation_activity", rel_act_rec) - - # Add the upper bound - - upper = pd.DataFrame({'relation': relation, - 'node_rel': nodes_new, - 'year_rel': modelyears, - 'value': 0, - 'unit': '???',}) - - scenario.add_par("relation_upper",upper) diff --git a/message_ix_models/model/material/data_aluminum.py b/message_ix_models/model/material/data_aluminum.py new file mode 100644 index 0000000000..2576d0cb6d --- /dev/null +++ b/message_ix_models/model/material/data_aluminum.py @@ -0,0 +1,122 @@ +# -*- coding: utf-8 -*- +""" +Created on Wed Aug 26 14:43:41 2020 +Generate techno economic aluminum data based on aluminum_techno_economic.xlsx + +@author: unlu +""" +import pandas as pd +import numpy as np +from collections import defaultdict +from message_data.tools import broadcast, make_df, same_node +import message_ix +import ixmp + + +def gen_data_aluminum(file): + + mp = ixmp.Platform() + + scenario = message_ix.Scenario(mp,"MESSAGE_material","baseline", cache=True) + + data_aluminum = pd.read_excel(file,sheet_name="data") + + data_aluminum_hist = pd.read_excel("aluminum_techno_economic.xlsx", + sheet_name="data_historical", + usecols = "A:F") + # Clean the data + # Drop columns that don't contain useful information + data_aluminum= data_aluminum.drop(['Region', 'Source', 'Description'], + axis = 1) + # List of data frames, to be concatenated together at the end + results = defaultdict(list) + # Will come from the yaml file + technology_add = data_aluminum["technology"].unique() + # normally from s_info.Y + years = [2010,2020,2030,2040,2050,2060,2070,2080,2090,2100] + + # normally from s_info.N + nodes = ["CHN"] + + # Iterate over technologies + + for t in technology_add: + # Obtain the active and vintage years + av = data_aluminum.loc[(data_aluminum["technology"] == t), + 'availability'].values[0] + if "technical_lifetime" in data_aluminum.loc[ + (data_aluminum["technology"] == t)]["parameter"].values: + lifetime = data_aluminum.loc[(data_aluminum["technology"] == t) + & (data_aluminum["parameter"] == + "technical_lifetime"), 'value'].\ + values[0] + years_df = scenario.vintage_and_active_years() + years_df = years_df.loc[years_df["year_vtg"]>= av] + # Empty data_frame + years_df_final = pd.DataFrame(columns=["year_vtg","year_act"]) + + # For each vintage adjsut the active years according to technical + # lifetime + for vtg in years_df["year_vtg"].unique(): + years_df_temp = years_df.loc[years_df["year_vtg"]== vtg] + years_df_temp = years_df_temp.loc[years_df["year_act"] + < vtg + lifetime] + years_df_final = pd.concat([years_df_temp, years_df_final], + ignore_index=True) + vintage_years, act_years = years_df_final['year_vtg'], \ + years_df_final['year_act'] + + params = data_aluminum.loc[(data_aluminum["technology"] == t),\ + "parameter"].values.tolist() + # Iterate over parameters + for par in params: + split = par.split("|") + param_name = par.split("|")[0] + + val = data_aluminum.loc[((data_aluminum["technology"] == t) & + (data_aluminum["parameter"] == par)),\ + 'value'].values[0] + + # Common parameters for all input and output tables + # node_dest and node_origin are the same as node_loc + + common = dict( + year_vtg= vintage_years, + year_act= act_years, + mode="standard", + time="year", + time_origin="year", + time_dest="year",) + + if len(split)> 1: + + if (param_name == "input")|(param_name == "output"): + + com = split[1] + lev = split[2] + + df = (make_df(param_name, technology=t, commodity=com, + level=lev, value=val, unit='t', **common) + .pipe(broadcast, node_loc=nodes).pipe(same_node)) + + results[param_name].append(df) + + elif param_name == "emission_factor": + emi = split[1] + + df = (make_df(param_name, technology=t,value=val, + emission=emi, unit='t', **common) + .pipe(broadcast, node_loc=nodes)) + results[param_name].append(df) + + # Rest of the parameters apart from inpput, output and + # emission_factor + else: + df = (make_df(param_name, technology=t, value=val,unit='t', + **common).pipe(broadcast, node_loc=nodes)) + results[param_name].append(df) + + results_aluminum = {par_name: pd.concat(dfs) for par_name, + dfs in results.items()} + + return results_aluminum, data_aluminum_hist diff --git a/message_ix_models/model/material/data_cement.py b/message_ix_models/model/material/data_cement.py new file mode 100644 index 0000000000..5732dcd73f --- /dev/null +++ b/message_ix_models/model/material/data_cement.py @@ -0,0 +1,167 @@ +from .data_util import process_china_data_tec, read_timeseries + +import numpy as np +from collections import defaultdict +import logging + +import pandas as pd + +from .util import read_config +from message_data.tools import ( + ScenarioInfo, + broadcast, + make_df, + make_io, + make_matched_dfs, + same_node, + add_par_data +) + + +gdp_growth = [0.121448215899944, 0.0733079014579874, 0.0348154093342843, \ + 0.021827616787921, 0.0134425983942219, 0.0108320197485592, \ + 0.00884341208063,0.00829374133206562, 0.00649794573935969, 0.00649794573935969] +gr = np.cumprod([(x+1) for x in gdp_growth]) + + +# Generate a fake cement demand +def gen_mock_demand_cement(s_info): + + modelyears = s_info.Y + fmy = s_info.y0 + + # True cement use 2011 (China) = 2100 Mt/year (ADVANCE) + demand2010_cement = 2100 + + baseyear = list(range(2020, 2110+1, 10)) + + demand = gr * demand2010_cement + demand_interp = np.interp(modelyears, baseyear, demand) + + return demand_interp.tolist() + + +def gen_data_cement(scenario, dry_run=False): + """Generate data for materials representation of steel industry. + + """ + # Load configuration + config = read_config()["material"]["cement"] + + # Information about scenario, e.g. node, year + s_info = ScenarioInfo(scenario) + + # Techno-economic assumptions + # TEMP: now add cement sector as well + data_cement = process_china_data_tec("cement") + # Special treatment for time-dependent Parameters + # data_cement_vc = read_timeseries() + # tec_vc = set(data_cement_vc.technology) # set of tecs with var_cost + + # List of data frames, to be concatenated together at end + results = defaultdict(list) + + # For each technology there are differnet input and output combinations + # Iterate over technologies + + allyears = s_info.set['year'] #s_info.Y is only for modeling years + modelyears = s_info.Y #s_info.Y is only for modeling years + nodes = s_info.N + yv_ya = s_info.yv_ya + fmy = s_info.y0 + + #print(allyears, modelyears, fmy) + + nodes.remove('World') # For the bare model + + # for t in s_info.set['technology']: + for t in config['technology']['add']: + + params = data_cement.loc[(data_cement["technology"] == t),\ + "parameter"].values.tolist() + + # Iterate over parameters + for par in params: + + # Obtain the parameter names, commodity,level,emission + split = par.split("|") + param_name = split[0] + # Obtain the scalar value for the parameter + val = data_cement.loc[((data_cement["technology"] == t) \ + & (data_cement["parameter"] == par)),'value'].values[0] + + common = dict( + year_vtg= yv_ya.year_vtg, + year_act= yv_ya.year_act, + # mode="M1", + time="year", + time_origin="year", + time_dest="year",) + + # For the parameters which inlcudes index names + if len(split)> 1: + + #print('1.param_name:', param_name, t) + if (param_name == "input")|(param_name == "output"): + + # Assign commodity and level names + com = split[1] + lev = split[2] + mod = split[3] + + df = (make_df(param_name, technology=t, commodity=com, \ + level=lev, value=val, mode=mod, unit='t', **common)\ + .pipe(broadcast, node_loc=nodes).pipe(same_node)) + + elif param_name == "emission_factor": + + # Assign the emisson type + emi = split[1] + mod = split[2] + + df = (make_df(param_name, technology=t, value=val,\ + emission=emi, mode=mod, unit='t', **common).pipe(broadcast, \ + node_loc=nodes)) + + else: # time-independent var_cost + mod = split[1] + df = (make_df(param_name, technology=t, value=val, \ + mode=mod, unit='t', \ + **common).pipe(broadcast, node_loc=nodes)) + + results[param_name].append(df) + + # Parameters with only parameter name + else: + #print('2.param_name:', param_name) + df = (make_df(param_name, technology=t, value=val, unit='t', \ + **common).pipe(broadcast, node_loc=nodes)) + + results[param_name].append(df) + + # Create external demand param + parname = 'demand' + demand = gen_mock_demand_cement(s_info) + df = (make_df(parname, level='demand', commodity='cement', value=demand, \ + unit='t', year=modelyears, **common).pipe(broadcast, node=nodes)) + results[parname].append(df) + + # Add CCS as addon + parname = 'addon_conversion' + ccs_tec = ['clinker_wet_cement', 'clinker_dry_cement'] + df = (make_df(parname, mode='M1', \ + type_addon='ccs_cement', \ + value=1, unit='-', **common).pipe(broadcast, node=nodes, technology=ccs_tec)) + results[parname].append(df) + + # Test emission bound + parname = 'bound_emission' + df = (make_df(parname, type_tec='all', type_year='cumulative', \ + type_emission='CO2_industry', \ + value=200, unit='-').pipe(broadcast, node=nodes)) + results[parname].append(df) + + # Concatenate to one data frame per parameter + results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} + + return results diff --git a/message_ix_models/model/material/data_generic.py b/message_ix_models/model/material/data_generic.py new file mode 100644 index 0000000000..7153b59aef --- /dev/null +++ b/message_ix_models/model/material/data_generic.py @@ -0,0 +1,148 @@ +# -*- coding: utf-8 -*- +""" +Created on Wed Aug 26 14:54:24 2020 +Generate techno economic generic furncaedata based on +generic_furnace_boiler_techno_economic.xlsx + +@author: unlu + +""" +from collections import defaultdict +import logging + +import pandas as pd + +from .util import read_config +from message_data.tools import ( + ScenarioInfo, + broadcast, + make_df, + make_io, + make_matched_dfs, + same_node, + add_par_data +) + + +def read_data_generic(): + """Read and clean data from :file:`generic_furnace_boiler_techno_economic.xlsx`.""" + + # Ensure config is loaded, get the context + context = read_config() + + # Shorter access to sets configuration + # sets = context["material"]["generic"] + + # Read the file + data_generic = pd.read_excel( + context.get_path("material", "generic_furnace_boiler_techno_economic.xlsx"), + sheet_name="generic", + ) + + # Clean the data + # Drop columns that don't contain useful information + + data_generic= data_generic.drop(['Region', 'Source', 'Description'], axis = 1) + + # Unit conversion + + # At the moment this is done in the excel file, can be also done here + # To make sure we use the same units + + return data_generic + + +def gen_data_generic(scenario, dry_run=False): + # Load configuration + + config = read_config()["material"]["generic"] + + # Information about scenario, e.g. node, year + s_info = ScenarioInfo(scenario) + + # Techno-economic assumptions + data_generic = read_data_generic() + + # List of data frames, to be concatenated together at end + results = defaultdict(list) + + # For each technology there are differnet input and output combinations + # Iterate over technologies + + allyears = s_info.set['year'] #s_info.Y is only for modeling years + modelyears = s_info.Y #s_info.Y is only for modeling years + nodes = s_info.N + yv_ya = s_info.yv_ya + fmy = s_info.y0 + + # 'World' is included by default when creating a message_ix.Scenario(). + # Need to remove it for the China bare model + nodes.remove('World') + + for t in config["technology"]["add"]: + + # years = s_info.Y + params = data_generic.loc[(data_generic["technology"] == t),"parameter"]\ + .values.tolist() + + # Availability year of the technology + av = data_generic.loc[(data_generic["technology"] == t),'availability'].\ + values[0] + modelyears = [year for year in modelyears if year >= av] + yva = yv_ya.loc[yv_ya.year_vtg >= av, ] + + # Iterate over parameters + for par in params: + split = par.split("|") + param_name = par.split("|")[0] + + val = data_generic.loc[((data_generic["technology"] == t) & \ + (data_generic["parameter"] == par)),'value'].values[0] + + # Common parameters for all input and output tables + # year_act is none at the moment + # node_dest and node_origin are the same as node_loc + + common = dict( + year_vtg= yva.year_vtg, + year_act= yva.year_act, + time="year", + time_origin="year", + time_dest="year",) + + if len(split)> 1: + + if (param_name == "input")|(param_name == "output"): + + com = split[1] + lev = split[2] + mod = split[3] + + df = (make_df(param_name, technology=t, commodity=com, \ + level=lev, mode=mod, value=val, unit='t', **common).\ + pipe(broadcast, node_loc=nodes).pipe(same_node)) + + results[param_name].append(df) + + elif param_name == "emission_factor": + emi = split[1] + + # TODO: Now tentatively fixed to one mode. Have values for the other mode too + df = (make_df(param_name, technology=t,value=val,\ + emission=emi, mode="low_temp", unit='t', **common).pipe(broadcast, \ + node_loc=nodes)) + + results[param_name].append(df) + + # Rest of the parameters apart from inpput, output and emission_factor + + else: + + df = (make_df(param_name, technology=t, value=val,unit='t', \ + **common).pipe(broadcast, node_loc=nodes)) + + results[param_name].append(df) + + results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} + + return results diff --git a/message_ix_models/model/material/data_steel.py b/message_ix_models/model/material/data_steel.py new file mode 100644 index 0000000000..270ef2e6d5 --- /dev/null +++ b/message_ix_models/model/material/data_steel.py @@ -0,0 +1,179 @@ +from .data_util import process_china_data_tec, read_timeseries + +import numpy as np +from collections import defaultdict +import logging + +import pandas as pd + +from .util import read_config +from message_data.tools import ( + ScenarioInfo, + broadcast, + make_df, + make_io, + make_matched_dfs, + same_node, + add_par_data +) + + +gdp_growth = [0.121448215899944, 0.0733079014579874, 0.0348154093342843, \ + 0.021827616787921, 0.0134425983942219, 0.0108320197485592, \ + 0.00884341208063,0.00829374133206562, 0.00649794573935969, 0.00649794573935969] +gr = np.cumprod([(x+1) for x in gdp_growth]) + + +# Generate a fake steel demand +def gen_mock_demand_steel(s_info): + + modelyears = s_info.Y + fmy = s_info.y0 + + # True steel use 2010 (China) = 537 Mt/year + demand2010_steel = 537 + # https://www.worldsteel.org/en/dam/jcr:0474d208-9108-4927-ace8-4ac5445c5df8/World+Steel+in+Figures+2017.pdf + + baseyear = list(range(2020, 2110+1, 10)) + + demand = gr * demand2010_steel + demand_interp = np.interp(modelyears, baseyear, demand) + + return demand_interp.tolist() + + +def gen_data_steel(scenario, dry_run=False): + """Generate data for materials representation of steel industry. + + """ + # Load configuration + config = read_config()["material"]["steel"] + + # Information about scenario, e.g. node, year + s_info = ScenarioInfo(scenario) + + # Techno-economic assumptions + # TEMP: now add cement sector as well => Need to separate those since now I have get_data_steel and cement + data_steel = process_china_data_tec("steel") + # Special treatment for time-dependent Parameters + data_steel_vc = read_timeseries() + tec_vc = set(data_steel_vc.technology) # set of tecs with var_cost + + # List of data frames, to be concatenated together at end + results = defaultdict(list) + + # For each technology there are differnet input and output combinations + # Iterate over technologies + + allyears = s_info.set['year'] #s_info.Y is only for modeling years + modelyears = s_info.Y #s_info.Y is only for modeling years + nodes = s_info.N + yv_ya = s_info.yv_ya + fmy = s_info.y0 + + #print(allyears, modelyears, fmy) + + nodes.remove('World') # For the bare model + + # for t in s_info.set['technology']: + for t in config['technology']['add']: + + params = data_steel.loc[(data_steel["technology"] == t),\ + "parameter"].values.tolist() + + # Special treatment for time-varying params + if t in tec_vc: + common = dict( + time="year", + time_origin="year", + time_dest="year",) + + param_name = data_steel_vc.loc[(data_steel_vc["technology"] == t), 'parameter'] + + for p in set(param_name): + val = data_steel_vc.loc[(data_steel_vc["technology"] == t) \ + & (data_steel_vc["parameter"] == p), 'value'] + units = data_steel_vc.loc[(data_steel_vc["technology"] == t) \ + & (data_steel_vc["parameter"] == p), 'units'].values[0] + mod = data_steel_vc.loc[(data_steel_vc["technology"] == t) \ + & (data_steel_vc["parameter"] == p), 'mode'] + yr = data_steel_vc.loc[(data_steel_vc["technology"] == t) \ + & (data_steel_vc["parameter"] == p), 'year'] + + df = (make_df(p, technology=t, value=val,\ + unit='t', year_vtg=yr, year_act=yr, mode=mod, **common).pipe(broadcast, \ + node_loc=nodes)) + + #print("time-dependent::", p, df) + results[p].append(df) + + # Iterate over parameters + for par in params: + + # Obtain the parameter names, commodity,level,emission + split = par.split("|") + param_name = split[0] + # Obtain the scalar value for the parameter + val = data_steel.loc[((data_steel["technology"] == t) \ + & (data_steel["parameter"] == par)),'value'].values[0] + + common = dict( + year_vtg= yv_ya.year_vtg, + year_act= yv_ya.year_act, + # mode="M1", + time="year", + time_origin="year", + time_dest="year",) + + # For the parameters which inlcudes index names + if len(split)> 1: + + #print('1.param_name:', param_name, t) + if (param_name == "input")|(param_name == "output"): + + # Assign commodity and level names + com = split[1] + lev = split[2] + mod = split[3] + + df = (make_df(param_name, technology=t, commodity=com, \ + level=lev, value=val, mode=mod, unit='t', **common)\ + .pipe(broadcast, node_loc=nodes).pipe(same_node)) + + elif param_name == "emission_factor": + + # Assign the emisson type + emi = split[1] + mod = split[2] + + df = (make_df(param_name, technology=t, value=val,\ + emission=emi, mode=mod, unit='t', **common).pipe(broadcast, \ + node_loc=nodes)) + + else: # time-independent var_cost + mod = split[1] + df = (make_df(param_name, technology=t, value=val, \ + mode=mod, unit='t', \ + **common).pipe(broadcast, node_loc=nodes)) + + results[param_name].append(df) + + # Parameters with only parameter name + else: + #print('2.param_name:', param_name) + df = (make_df(param_name, technology=t, value=val, unit='t', \ + **common).pipe(broadcast, node_loc=nodes)) + + results[param_name].append(df) + + # Create external demand param + parname = 'demand' + demand = gen_mock_demand_steel(s_info) + df = (make_df(parname, level='demand', commodity='steel', value=demand, unit='t', \ + year=modelyears, **common).pipe(broadcast, node=nodes)) + results[parname].append(df) + + # Concatenate to one data frame per parameter + results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} + + return results diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py new file mode 100644 index 0000000000..98e37eb1bc --- /dev/null +++ b/message_ix_models/model/material/data_util.py @@ -0,0 +1,81 @@ +from collections import defaultdict + +import pandas as pd + +from .util import read_config +import re + + +# Read in technology-specific parameters from input xlsx +def process_china_data_tec(sectname): + + import numpy as np + + # Ensure config is loaded, get the context + context = read_config() + + # data_df = data_steel_china.append(data_cement_china, ignore_index=True) + data_df = pd.read_excel( + context.get_path("material", context.datafile), + sheet_name=sectname, + ) + + # Clean the data + data_df = data_df \ + [['Technology', 'Parameter', 'Level', \ + 'Commodity', 'Mode', 'Species', 'Units', 'Value']] \ + .replace(np.nan, '', regex=True) + + # Combine columns and remove '' + list_series = data_df[['Parameter', 'Commodity', 'Level', 'Mode']] \ + .apply(list, axis=1).apply(lambda x: list(filter(lambda a: a != '', x))) + list_ef = data_df[['Parameter', 'Species', 'Mode']] \ + .apply(list, axis=1) + + data_df['parameter'] = list_series.str.join('|') + data_df.loc[data_df['Parameter'] == "emission_factor", \ + 'parameter'] = list_ef.str.join('|') + + data_df = data_df.drop(['Parameter', 'Level', 'Commodity', 'Mode'] \ + , axis = 1) + data_df = data_df.drop( \ + data_df[data_df.Value==''].index) + + data_df.columns = data_df.columns.str.lower() + + # Unit conversion + + # At the moment this is done in the excel file, can be also done here + # To make sure we use the same units + + return data_df + + +# Read in time-dependent parameters +# Now only used to add fuel cost for bare model +def read_timeseries(): + + import numpy as np + + # Ensure config is loaded, get the context + context = read_config() + + if context.scenario_info['scenario'] == 'NPi400': + sheet_name="timeseries_NPi400" + else: + sheet_name = "timeseries" + + # Read the file + df = pd.read_excel( + context.get_path("material", context.datafile), sheet_name) + + import numbers + # Take only existing years in the data + datayears = [x for x in list(df) if isinstance(x, numbers.Number)] + + df = pd.melt(df, id_vars=['parameter', 'technology', 'mode', 'units'], \ + value_vars = datayears, \ + var_name ='year') + + df = df.drop(df[np.isnan(df.value)].index) + return df From 9cc942e8ee3a79dc0fbe55319cc89f0d07d86062 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Fri, 6 Nov 2020 15:37:32 +0100 Subject: [PATCH 157/774] Add set elements for aluminum and generic --- message_ix_models/data/material/set.yaml | 58 +++++++++++++++++++- message_ix_models/model/material/__init__.py | 2 +- 2 files changed, 56 insertions(+), 4 deletions(-) diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index b6a076bcd3..134336a77b 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -180,6 +180,7 @@ generic: - useful_steel - useful_aluminum - useful_cement + - useful_aluminum technology: add: @@ -265,6 +266,32 @@ generic: - furnace_coal_cement - furnace_elec_cement - furnace_h2_cement + - furnace_coal_aluminum + - furnace_foil_aluminum + - furnace_loil_aluminum + - furnace_ethanol_aluminum + - furnace_biomass_aluminum + - furnace_methanol_aluminum + - furnace_gas_aluminum + - furnace_elec_aluminum + - furnace_h2_aluminum + - hp_gas_aluminum + - hp_elec_aluminum + - fc_h2_aluminum + - solar_aluminum + - dheat_aluminum + - DUMMY_coal_supply + - DUMMY_gas_supply + - DUMMY_loil_supply + - DUMMY_foil_supply + - DUMMY_electr_supply + - DUMMY_ethanol_supply + - DUMMY_methanol_supply + - DUMMY_hydrogen_supply + - DUMMY_freshwater_supply + - DUMMY_water_supply + - DUMMY_biomass_supply + - DUMMY_dist_heating mode: add: @@ -374,9 +401,34 @@ cement: - [ccs_cement, clinker_dry_ccs_cement] - [ccs_cement, clinker_wet_ccs_cement] - # relation: - # add: - # - steel_demand +aluminum: + commodity: + add: + - aluminum + + level: + add: + - new_scrap + - old_scrap + - useful_aluminum + - final_material + - useful_material + - product + - secondary_material + - demand_aluminum + + technology: + add: + - soderberg_aluminum + - prebake_aluminum + - secondary_aluminum + - prep_secondary_aluminum_1 + - prep_secondary_aluminum_2 + - prep_secondary_aluminum_3 + - finishing_aluminum + - manuf_aluminum + - scrap_recovery_aluminum + - DUMMY_alumina_supply fertilizer: commodity: diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 0087ced8c3..fe0ac356dc 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -18,7 +18,7 @@ def build(scenario): return scenario -SPEC_LIST = ["generic", "common", "steel", "cement"] # add as needed/implemented +SPEC_LIST = ["generic", "common", "steel", "cement", "aluminum"] # add as needed/implemented def get_spec() -> Mapping[str, ScenarioInfo]: """Return the specification for materials accounting.""" From ef2a5e1ef4d9d8c200b8825654819e6f7cb3d6cb Mon Sep 17 00:00:00 2001 From: Jihoon Date: Fri, 6 Nov 2020 15:39:46 +0100 Subject: [PATCH 158/774] Organize data files --- ...ina_steel_MESSAGE.xlsx => China_steel_cement_MESSAGE.xlsx} | 0 message_ix_models/data/material/China_steel_renamed.xlsx | 3 --- message_ix_models/data/material/aluminum_techno_economic.xlsx | 4 ++-- .../data/material/generic_furnace_boiler_techno_economic.xlsx | 4 ++-- message_ix_models/data/material/variable_costs.xlsx | 4 ++-- 5 files changed, 6 insertions(+), 9 deletions(-) rename message_ix_models/data/material/{China_steel_MESSAGE.xlsx => China_steel_cement_MESSAGE.xlsx} (100%) delete mode 100644 message_ix_models/data/material/China_steel_renamed.xlsx diff --git a/message_ix_models/data/material/China_steel_MESSAGE.xlsx b/message_ix_models/data/material/China_steel_cement_MESSAGE.xlsx similarity index 100% rename from message_ix_models/data/material/China_steel_MESSAGE.xlsx rename to message_ix_models/data/material/China_steel_cement_MESSAGE.xlsx diff --git a/message_ix_models/data/material/China_steel_renamed.xlsx b/message_ix_models/data/material/China_steel_renamed.xlsx deleted file mode 100644 index 5db89dd314..0000000000 --- a/message_ix_models/data/material/China_steel_renamed.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:cf86b142cb48b90d5469d6247455ad4b8252b3264038a1d9c566881163d06ee7 -size 61177 diff --git a/message_ix_models/data/material/aluminum_techno_economic.xlsx b/message_ix_models/data/material/aluminum_techno_economic.xlsx index 148a2c614a..5d79787c5e 100644 --- a/message_ix_models/data/material/aluminum_techno_economic.xlsx +++ b/message_ix_models/data/material/aluminum_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:f13ef1813636db4024d1cffbc22d41176f003b96c137434d16a127c967699f3a -size 56375 +oid sha256:106287600beb514e90a4780f9404d59cd1ad910f52556620804c9cc15bcce6bf +size 51745 diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx index a3e056d0fa..7025dae040 100644 --- a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx +++ b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:794c99a6e3924b72e622870108da61bbd3fb12b6b1dce346238d59da6998441c -size 278 +oid sha256:09068788ce98db8295a28a9fef5e3306d3a5986d9c71b27e6d49317d994cc0cd +size 275 diff --git a/message_ix_models/data/material/variable_costs.xlsx b/message_ix_models/data/material/variable_costs.xlsx index 447419409a..03f046c956 100644 --- a/message_ix_models/data/material/variable_costs.xlsx +++ b/message_ix_models/data/material/variable_costs.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:40805bde40f6bca765da57f162eafc7030157d32c9d70d58c9ebc47c3c7d4204 -size 16862 +oid sha256:2ecfeceb662b6bdf3d14078429f4db7d6b4997447e80eb486160ffc91d3f3695 +size 32510 From b357c22e889dcc2744b6bd36b3137d4aa1555998 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Fri, 6 Nov 2020 15:40:55 +0100 Subject: [PATCH 159/774] Minor removal of unnecessary lines + new config --- .../model/material/material_testscript.py | 64 +------------------ 1 file changed, 1 insertion(+), 63 deletions(-) diff --git a/message_ix_models/model/material/material_testscript.py b/message_ix_models/model/material/material_testscript.py index 44b3f85b6b..3f1cd27cd5 100644 --- a/message_ix_models/model/material/material_testscript.py +++ b/message_ix_models/model/material/material_testscript.py @@ -13,7 +13,6 @@ from message_data.model.material.plotting import Plots import pyam -# from message_data.model.material import bare from message_data.tools import Context @@ -25,72 +24,11 @@ set_info, ) -from message_data.model.bare import create_res -# from message_data.model.create import create_res +from message_data.model.create import create_res from message_data.model.material import build, get_spec from message_data.model.material.util import read_config -#%% Main test run from bare - -from message_data.model.bare import create_res -# Create Context obj -Context._instance = [] -ctx = Context() - -# Set default scenario/model names -ctx.scenario_info.setdefault('model', 'Material_test') -ctx.scenario_info.setdefault('scenario', 'baseline') -ctx['period_start'] = 2020 -ctx['regions'] = 'China' -ctx['datafile'] = 'China_steel_standalone - test.xlsx' - -# Use general code to create a Scenario with some stuff in it -scen = create_res(context = ctx) - -# Use material-specific code to add certain stuff -a = build(scen) - -# Solve the model -scen.solve() - -p = Plots(scen, 'China', firstyear=2020) -p.plot_activity(baseyear=False, subset=['bf_steel', 'bof_steel', 'dri_steel', 'eaf_steel']) -p.plot_activity(baseyear=False, subset=['bof_steel', 'eaf_steel']) -p.plot_capacity(baseyear=True, subset=['bf_steel', 'bof_steel', 'dri_steel', 'eaf_steel']) -p.plot_new_capacity(baseyear=True, subset=['bf_steel', 'bof_steel', 'dri_steel', 'eaf_steel']) -p.plot_activity(baseyear=True, subset=['manuf_steel', 'prep_secondary_steel']) - - -#%% run with NPi prices (bare) -from message_data.model.bare import create_res -Context._instance = [] -ctx = Context() - -# Set default scenario/model names -ctx.scenario_info.setdefault('model', 'Material_test') -ctx.scenario_info.setdefault('scenario', 'NPi400') -ctx['period_start'] = 2020 -ctx['regions'] = 'China' -ctx['datafile'] = 'China_steel_standalone - test.xlsx' - -# Use general code to create a Scenario with some stuff in it -scen_np = create_res(context = ctx) - -# Use material-specific code to add certain stuff -a = build(scen_np) - -# Solve the model -scen_np.solve() - -p = Plots(scen_np, 'China', firstyear=2020) -p.plot_activity(baseyear=True, subset=['bf_steel', 'bof_steel', 'dri_steel', \ - 'eaf_steel']) -p.plot_capacity(baseyear=True, subset=['bf_steel', 'bof_steel', 'dri_steel', \ - 'eaf_steel']) -p.plot_new_capacity(baseyear=True, subset=['bf_steel', 'bof_steel', \ - 'dri_steel', 'eaf_steel']) - #%% Main test run based on a MESSAGE scenario From 97f212fc513be432ac87ab99d704906c754b7f99 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Tue, 10 Nov 2020 08:57:58 +0100 Subject: [PATCH 160/774] Data update for aluminum --- message_ix_models/data/material/aluminum_techno_economic.xlsx | 4 ++-- message_ix_models/data/material/variable_costs.xlsx | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/message_ix_models/data/material/aluminum_techno_economic.xlsx b/message_ix_models/data/material/aluminum_techno_economic.xlsx index 5d79787c5e..37088de3c9 100644 --- a/message_ix_models/data/material/aluminum_techno_economic.xlsx +++ b/message_ix_models/data/material/aluminum_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:106287600beb514e90a4780f9404d59cd1ad910f52556620804c9cc15bcce6bf -size 51745 +oid sha256:6816b9d3e5f329f73c73200b15dac36dfde17c786a0cfca88ed72d8c65684547 +size 55937 diff --git a/message_ix_models/data/material/variable_costs.xlsx b/message_ix_models/data/material/variable_costs.xlsx index 03f046c956..3dd277a6b9 100644 --- a/message_ix_models/data/material/variable_costs.xlsx +++ b/message_ix_models/data/material/variable_costs.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:2ecfeceb662b6bdf3d14078429f4db7d6b4997447e80eb486160ffc91d3f3695 -size 32510 +oid sha256:e0ec396dd16105d5429abc4769225fe8fdb56b37bc6a309083bed89b9aa95905 +size 15874 From 30721c913e2e025444b460cfcae0b7410983cea5 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Tue, 10 Nov 2020 09:01:34 +0100 Subject: [PATCH 161/774] Some renaming/reshuffling data routines --- message_ix_models/model/material/data.py | 6 ++- .../model/material/data_cement.py | 9 ++-- .../model/material/data_steel.py | 24 ++++----- message_ix_models/model/material/data_util.py | 54 +++++++++++++++++-- 4 files changed, 71 insertions(+), 22 deletions(-) diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index 4c837c0ed8..44063a9107 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -6,7 +6,7 @@ import pandas as pd from .data_cement import gen_data_cement from .data_steel import gen_data_steel -from .data_aluminum import gen_data_aluminum +from .data_aluminum import gen_data_aluminum, add_other_data_aluminum from .data_generic import gen_data_generic from message_data.tools import ( @@ -29,6 +29,7 @@ gen_data_cement, gen_data_aluminum, gen_data_generic, + add_other_data_aluminum, # Directly writing to some additional params ] # Try to handle multiple data input functions from different materials @@ -37,6 +38,9 @@ def add_data(scenario, dry_run=False): # Information about `scenario` info = ScenarioInfo(scenario) + # Need to modify demand specification to accomodate material endogenization + # modify_demand(scenario) + # Check for two "node" values for global data, e.g. in # ixmp://ene-ixmp/CD_Links_SSP2_v2.1_clean/baseline if {"World", "R11_GLB"} < set(info.set["node"]): diff --git a/message_ix_models/model/material/data_cement.py b/message_ix_models/model/material/data_cement.py index 5732dcd73f..6dabacb99b 100644 --- a/message_ix_models/model/material/data_cement.py +++ b/message_ix_models/model/material/data_cement.py @@ -1,4 +1,4 @@ -from .data_util import process_china_data_tec, read_timeseries +from .data_util import read_sector_data, read_timeseries import numpy as np from collections import defaultdict @@ -53,7 +53,7 @@ def gen_data_cement(scenario, dry_run=False): # Techno-economic assumptions # TEMP: now add cement sector as well - data_cement = process_china_data_tec("cement") + data_cement = read_sector_data("cement") # Special treatment for time-dependent Parameters # data_cement_vc = read_timeseries() # tec_vc = set(data_cement_vc.technology) # set of tecs with var_cost @@ -69,10 +69,7 @@ def gen_data_cement(scenario, dry_run=False): nodes = s_info.N yv_ya = s_info.yv_ya fmy = s_info.y0 - - #print(allyears, modelyears, fmy) - - nodes.remove('World') # For the bare model + nodes.remove('World') # for t in s_info.set['technology']: for t in config['technology']['add']: diff --git a/message_ix_models/model/material/data_steel.py b/message_ix_models/model/material/data_steel.py index 270ef2e6d5..3dc630ce0d 100644 --- a/message_ix_models/model/material/data_steel.py +++ b/message_ix_models/model/material/data_steel.py @@ -1,4 +1,4 @@ -from .data_util import process_china_data_tec, read_timeseries +from .data_util import read_sector_data, read_timeseries import numpy as np from collections import defaultdict @@ -17,10 +17,12 @@ add_par_data ) - -gdp_growth = [0.121448215899944, 0.0733079014579874, 0.0348154093342843, \ - 0.021827616787921, 0.0134425983942219, 0.0108320197485592, \ - 0.00884341208063,0.00829374133206562, 0.00649794573935969, 0.00649794573935969] +# Decadal growth rate (2020-2110) +gdp_growth = [0.121448215899944, 0.0733079014579874, + 0.0348154093342843, 0.021827616787921, \ + 0.0134425983942219, 0.0108320197485592, \ + 0.00884341208063, 0.00829374133206562, \ + 0.00649794573935969, 0.00649794573935969] gr = np.cumprod([(x+1) for x in gdp_growth]) @@ -47,16 +49,17 @@ def gen_data_steel(scenario, dry_run=False): """ # Load configuration - config = read_config()["material"]["steel"] + context = read_config() + config = context["material"]["steel"] # Information about scenario, e.g. node, year s_info = ScenarioInfo(scenario) # Techno-economic assumptions # TEMP: now add cement sector as well => Need to separate those since now I have get_data_steel and cement - data_steel = process_china_data_tec("steel") + data_steel = read_sector_data("steel") # Special treatment for time-dependent Parameters - data_steel_vc = read_timeseries() + data_steel_vc = read_timeseries(context.datafile) tec_vc = set(data_steel_vc.technology) # set of tecs with var_cost # List of data frames, to be concatenated together at end @@ -70,10 +73,7 @@ def gen_data_steel(scenario, dry_run=False): nodes = s_info.N yv_ya = s_info.yv_ya fmy = s_info.y0 - - #print(allyears, modelyears, fmy) - - nodes.remove('World') # For the bare model + nodes.remove('World') # for t in s_info.set['technology']: for t in config['technology']['add']: diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index 98e37eb1bc..28e51eb34c 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -6,8 +6,42 @@ import re + +def modify_demand(scen): + """Take care of demand changes due to the introduction of material parents + + Shed industrial energy demand properly. + + Also need take care of remove dynamic constraints for certain energy carriers + """ + + # NOTE Temporarily modifying industrial energy demand + # (30% of non-elec industrial energy for steel) + df = scen.par('demand', filters={'commodity':'i_therm'}) + df.value = df.value * 0.45 #(30% steel, 25% cement) + + scen.check_out() + scen.add_par('demand', df) + scen.commit(comment = 'modify i_therm demand') + + # NOTE Aggregate industrial coal demand need to adjust to + # the sudden intro of steel setor in the first model year + + t_i = ['coal_i','elec_i','gas_i','heat_i','loil_i','solar_i'] + + for t in t_i: + df = scen.par('growth_activity_lo', \ + filters={'technology':t, 'year_act':2020}) + + scen.check_out() + scen.remove_par('growth_activity_lo', df) + scen.commit(comment = 'remove growth_lo constraints') + + + # Read in technology-specific parameters from input xlsx -def process_china_data_tec(sectname): +# Now used for steel and cement, which are in one file +def read_sector_data(sectname): import numpy as np @@ -53,7 +87,7 @@ def process_china_data_tec(sectname): # Read in time-dependent parameters # Now only used to add fuel cost for bare model -def read_timeseries(): +def read_timeseries(filename): import numpy as np @@ -67,7 +101,7 @@ def read_timeseries(): # Read the file df = pd.read_excel( - context.get_path("material", context.datafile), sheet_name) + context.get_path("material", filename), sheet_name) import numbers # Take only existing years in the data @@ -79,3 +113,17 @@ def read_timeseries(): df = df.drop(df[np.isnan(df.value)].index) return df + +def read_rel(filename): + + import numpy as np + + # Ensure config is loaded, get the context + context = read_config() + + # Read the file + data_rel = pd.read_excel( + context.get_path("material", filename), sheet_name="relations", + ) + + return data_rel From 48ead7e8835db7d0ad5bee01b1309ba52b132f9a Mon Sep 17 00:00:00 2001 From: Jihoon Date: Tue, 10 Nov 2020 09:03:47 +0100 Subject: [PATCH 162/774] First attempt of aluminum part + modify set.yaml + move modify_demand to the very beginning of build() --- message_ix_models/data/material/set.yaml | 30 +- message_ix_models/model/material/__init__.py | 3 + message_ix_models/model/material/build.py | 1 + .../model/material/data_aluminum.py | 532 +++++++++++++++++- .../model/material/material_testscript.py | 7 +- 5 files changed, 536 insertions(+), 37 deletions(-) diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 134336a77b..a4d27b23bb 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -280,18 +280,18 @@ generic: - fc_h2_aluminum - solar_aluminum - dheat_aluminum - - DUMMY_coal_supply - - DUMMY_gas_supply - - DUMMY_loil_supply - - DUMMY_foil_supply - - DUMMY_electr_supply - - DUMMY_ethanol_supply - - DUMMY_methanol_supply - - DUMMY_hydrogen_supply - - DUMMY_freshwater_supply - - DUMMY_water_supply - - DUMMY_biomass_supply - - DUMMY_dist_heating + # - DUMMY_coal_supply + # - DUMMY_gas_supply + # - DUMMY_loil_supply + # - DUMMY_foil_supply + # - DUMMY_electr_supply + # - DUMMY_ethanol_supply + # - DUMMY_methanol_supply + # - DUMMY_hydrogen_supply + # - DUMMY_freshwater_supply + # - DUMMY_water_supply + # - DUMMY_biomass_supply + # - DUMMY_dist_heating mode: add: @@ -430,6 +430,12 @@ aluminum: - scrap_recovery_aluminum - DUMMY_alumina_supply + relation: + add: + - scrap_availability_1 + - scrap_availability_2 + - scrap_availability_3 + fertilizer: commodity: require: diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index fe0ac356dc..caf0bebd07 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -5,6 +5,7 @@ from message_ix_models.model.build import apply_spec from .data import add_data +from .data_util import modify_demand from .util import read_config @@ -13,6 +14,8 @@ def build(scenario): # Get the specification spec = get_spec() + modify_demand(scenario) + # Apply to the base scenario apply_spec(scenario, spec, add_data) # dry_run=True diff --git a/message_ix_models/model/material/build.py b/message_ix_models/model/material/build.py index 2dea7fc733..04577e8a44 100644 --- a/message_ix_models/model/material/build.py +++ b/message_ix_models/model/material/build.py @@ -139,6 +139,7 @@ def apply_spec( # Add data if callable(data): data(scenario, dry_run=dry_run) + # JM: call add_par_data at add_data in data.py # if result: # add_par_data(scenario, result, dry_run=dry_run) diff --git a/message_ix_models/model/material/data_aluminum.py b/message_ix_models/model/material/data_aluminum.py index 2576d0cb6d..d95c2b7f4d 100644 --- a/message_ix_models/model/material/data_aluminum.py +++ b/message_ix_models/model/material/data_aluminum.py @@ -8,40 +8,117 @@ import pandas as pd import numpy as np from collections import defaultdict -from message_data.tools import broadcast, make_df, same_node + import message_ix import ixmp +from .util import read_config +from .data_util import read_rel +from message_data.tools import ( + ScenarioInfo, + broadcast, + make_df, + make_io, + make_matched_dfs, + same_node, + add_par_data +) + + -def gen_data_aluminum(file): +def read_data_aluminum(): + """Read and clean data from :file:`aluminum_techno_economic.xlsx`.""" - mp = ixmp.Platform() + # Ensure config is loaded, get the context + context = read_config() - scenario = message_ix.Scenario(mp,"MESSAGE_material","baseline", cache=True) + # Shorter access to sets configuration + # sets = context["material"]["generic"] - data_aluminum = pd.read_excel(file,sheet_name="data") + fname = "aluminum_techno_economic.xlsx" + # Read the file + data_alu = pd.read_excel( + context.get_path("material", fname), + sheet_name="data", + ) - data_aluminum_hist = pd.read_excel("aluminum_techno_economic.xlsx", - sheet_name="data_historical", - usecols = "A:F") - # Clean the data # Drop columns that don't contain useful information - data_aluminum= data_aluminum.drop(['Region', 'Source', 'Description'], - axis = 1) - # List of data frames, to be concatenated together at the end - results = defaultdict(list) - # Will come from the yaml file - technology_add = data_aluminum["technology"].unique() - # normally from s_info.Y - years = [2010,2020,2030,2040,2050,2060,2070,2080,2090,2100] + data_alu= data_alu.drop(['Region', 'Source', 'Description'], axis = 1) + + data_alu_hist = pd.read_excel( + context.get_path("material", fname), + sheet_name="data_historical", + usecols = "A:G") + + data_alu_rel = read_rel(fname) + # Unit conversion + + # At the moment this is done in the excel file, can be also done here + # To make sure we use the same units + + return data_alu, data_alu_hist, data_alu_rel + +def gen_data_aluminum(scenario, dry_run=False): - # normally from s_info.N - nodes = ["CHN"] + config = read_config()["material"]["aluminum"] + # Information about scenario, e.g. node, year + s_info = ScenarioInfo(scenario) + + # Techno-economic assumptions + data_aluminum, data_aluminum_hist, data_aluminum_rel= read_data_aluminum() + tec_tv = set(data_aluminum_hist.technology) # set of tecs with time-varying values + + # List of data frames, to be concatenated together at end + results = defaultdict(list) + + # For each technology there are differnet input and output combinations # Iterate over technologies - for t in technology_add: + allyears = s_info.set['year'] #s_info.Y is only for modeling years + modelyears = s_info.Y #s_info.Y is only for modeling years + nodes = s_info.N + yv_ya = s_info.yv_ya + fmy = s_info.y0 + + # 'World' is included by default when creating a message_ix.Scenario(). + # Need to remove it for the China bare model + nodes.remove('World') + + for t in config["technology"]["add"]: + + # years = s_info.Y + params = data_aluminum.loc[(data_aluminum["technology"] == t),"parameter"]\ + .values.tolist() + + # Special treatment for time-varying params + if t in tec_tv: + common = dict( + time="year", + time_origin="year", + time_dest="year",) + + param_name = data_aluminum_hist.loc[(data_aluminum_hist["technology"] == t), 'parameter'] + + for p in set(param_name): + val = data_aluminum_hist.loc[(data_aluminum_hist["technology"] == t) \ + & (data_aluminum_hist["parameter"] == p), 'value'] + units = data_aluminum_hist.loc[(data_aluminum_hist["technology"] == t) \ + & (data_aluminum_hist["parameter"] == p), 'unit'].values[0] + mod = data_aluminum_hist.loc[(data_aluminum_hist["technology"] == t) \ + & (data_aluminum_hist["parameter"] == p), 'mode'] + yr = data_aluminum_hist.loc[(data_aluminum_hist["technology"] == t) \ + & (data_aluminum_hist["parameter"] == p), 'year'] + + df = (make_df(p, technology=t, value=val,\ + unit='t', year_vtg=yr, year_act=yr, mode=mod, **common).pipe(broadcast, \ + node_loc=nodes)) + + #print("time-dependent::", p, df) + results[p].append(df) + # Obtain the active and vintage years + print("aluminum", t) av = data_aluminum.loc[(data_aluminum["technology"] == t), 'availability'].values[0] if "technical_lifetime" in data_aluminum.loc[ @@ -83,7 +160,7 @@ def gen_data_aluminum(file): common = dict( year_vtg= vintage_years, year_act= act_years, - mode="standard", + mode="M1", time="year", time_origin="year", time_dest="year",) @@ -116,7 +193,418 @@ def gen_data_aluminum(file): **common).pipe(broadcast, node_loc=nodes)) results[param_name].append(df) + for r in config['relation']['add']: + + params = data_aluminum_rel.loc[(data_aluminum_rel["relation"] == r),\ + "parameter"].values.tolist() + + common_rel = dict( + year_rel = modelyears, + year_act = modelyears, + mode = 'M1', + relation = r,) + + for par_name in params: + if par_name == "relation_activity": + + val = data_aluminum_rel.loc[((data_aluminum_rel["relation"] == r) \ + & (data_aluminum_rel["parameter"] == par_name)),'value'].values[0] + tec = data_aluminum_rel.loc[((data_aluminum_rel["relation"] == r) \ + & (data_aluminum_rel["parameter"] == par_name)),'technology'].values[0] + + df = (make_df(par_name, technology=tec, value=val, unit='-',\ + **common_rel).pipe(broadcast, node_rel=nodes, node_loc=nodes)) + + results[par_name].append(df) + + elif par_name == "relation_upper": + + val = data_aluminum_rel.loc[((data_aluminum_rel["relation"] == r) \ + & (data_aluminum_rel["parameter"] == par_name)),'value'].values[0] + + df = (make_df(par_name, value=val, unit='-',\ + **common_rel).pipe(broadcast, node_rel=nodes)) + + results[par_name].append(df) + + # print(par_name, df) + results_aluminum = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} - return results_aluminum, data_aluminum_hist + return results_aluminum#, data_aluminum_hist + + +def add_other_data_aluminum(scenario, dry_run=False): + # # Adding a new unit to the library + # mp.add_unit('Mt') + # + # # New model + # scenario = message_ix.Scenario(mp, model='MESSAGE_material', + # scenario='baseline', version='new') + # # Addition of basics + # + # history = [1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015] + # model_horizon = [2020, 2030, 2040, 2050, 2060, 2070, 2080, 2090, 2100] + # scenario.add_horizon({'year': history + model_horizon, + # 'firstmodelyear': model_horizon[0]}) + # country = 'CHN' + # scenario.add_spatial_sets({'country': country}) + # + # # These will come from the yaml file + # + # commodities = ['ht_heat', 'lt_heat', 'aluminum', 'd_heat', "electr", + # "coal", "fueloil", "ethanol", "biomass", "gas", "hydrogen", + # "methanol", "lightoil"] + # + # scenario.add_set("commodity", commodities) + # + # levels = ['useful_aluminum', 'new_scrap', 'old_scrap', 'final_material', + # 'useful_material', 'product', "secondary_material", "final", + # "demand"] + # scenario.add_set("level", levels) + # + # technologies = ['soderberg_aluminum', 'prebake_aluminum', 'secondary_aluminum', + # 'prep_secondary_aluminum', 'finishing_aluminum', + # 'manuf_aluminum', 'scrap_recovery_aluminum', + # 'furnace_coal_aluminum', 'furnace_foil_aluminum', + # 'furnace_methanol_aluminum', 'furnace_biomass_aluminum', + # 'furnace_ethanol_aluminum', 'furnace_gas_aluminum', + # 'furnace_elec_aluminum', 'furnace_h2_aluminum', + # 'hp_gas_aluminum', 'hp_elec_aluminum', 'fc_h2_aluminum', + # 'solar_aluminum', 'dheat_aluminum', 'furnace_loil_aluminum', + # "alumina_supply"] + # + # scenario.add_set("technology", technologies) + # scenario.add_set("mode", ['standard', 'low_temp', 'high_temp']) + # + # # Create duration period + # + # val = [j-i for i, j in zip(model_horizon[:-1], model_horizon[1:])] + # val.append(val[0]) + # + # duration_period = pd.DataFrame({ + # 'year': model_horizon, + # 'value': val, + # 'unit': "y", + # }) + # + # scenario.add_par("duration_period", duration_period) + # duration_period = duration_period["value"].values + # + # # Energy system: Unlimited supply of the commodities. + # # Fuel costs are obtained from the PRICE_COMMODITY baseline SSP2 global model. + # # PRICE_COMMODTY * input -> (2005USD/Gwa-coal)*(Gwa-coal / ACT of furnace) + # # = (2005USD/ACT of furnace) + # + # years_df = scenario.vintage_and_active_years() + # vintage_years, act_years = years_df['year_vtg'], years_df['year_act'] + # + # # Choose the prices in excel (baseline vs. NPi400) + # data_var_cost = pd.read_excel("variable_costs.xlsx", sheet_name="data") + # + # for row in data_var_cost.index: + # data = data_var_cost.iloc[row] + # values = [] + # for yr in act_years: + # values.append(data[yr]) + # base_var_cost = pd.DataFrame({ + # 'node_loc': country, + # 'year_vtg': vintage_years.values, + # 'year_act': act_years.values, + # 'mode': data["mode"], + # 'time': 'year', + # 'unit': 'USD/GWa', + # "technology": data["technology"], + # "value": values + # }) + # + # scenario.add_par("var_cost",base_var_cost) + # + # # Add dummy technologies to represent energy system + # + # dummy_tech = ["electr_gen", "dist_heating", "coal_gen", "foil_gen", "eth_gen", + # "biomass_gen", "gas_gen", "hydrogen_gen", "meth_gen", "loil_gen"] + # scenario.add_set("technology", dummy_tech) + # + # commodity_tec = ["electr", "d_heat", "coal", "fueloil", "ethanol", "biomass", + # "gas", "hydrogen", "methanol", "lightoil"] + # + # # Add output to dummy tech. + # + # year_df = scenario.vintage_and_active_years() + # vintage_years, act_years = year_df['year_vtg'], year_df['year_act'] + # + # base = { + # 'node_loc': country, + # "node_dest": country, + # "time_dest": "year", + # 'year_vtg': vintage_years, + # 'year_act': act_years, + # 'mode': 'standard', + # 'time': 'year', + # "level": "final", + # 'unit': '-', + # "value": 1.0 + # } + # + # t = 0 + # + # for tec in dummy_tech: + # out = make_df("output", technology= tec, commodity = commodity_tec[t], + # **base) + # t = t + 1 + # scenario.add_par("output", out) + # + # # Introduce emissions + # scenario.add_set('emission', 'CO2') + # scenario.add_cat('emission', 'GHG', 'CO2') + # + # # Run read data aluminum + # + # scenario.commit("changes added") + # + # results_al, data_aluminum_hist = gen_data_aluminum("aluminum_techno_economic.xlsx") + # + # scenario.check_out() + # + # for k, v in results_al.items(): + # scenario.add_par(k,v) + # + # scenario.commit("aluminum_techno_economic added") + # results_generic = gen_data_generic() + # + # scenario.check_out() + # for k, v in results_generic.items(): + # scenario.add_par(k,v) + + # Information about scenario, e.g. node, year + s_info = ScenarioInfo(scenario) + + # List of data frames, to be concatenated together at end + results = defaultdict(list) + + # For each technology there are differnet input and output combinations + # Iterate over technologies + + allyears = s_info.set['year'] #s_info.Y is only for modeling years + modelyears = s_info.Y #s_info.Y is only for modeling years + nodes = s_info.N + yv_ya = s_info.yv_ya + fmy = s_info.y0 + nodes.remove('World') + + # Add temporary exogenous demand: 17.3 Mt in 2010 (IAI) + demand2010_aluminum = 17.3 + + # The future projection of the demand: Increases by half of the GDP growth rate + # gdp_growth rate: SSP2 global model. Starting from 2020. + gdp_growth = [0.121448215899944, 0.0733079014579874, + 0.0348154093342843, 0.021827616787921, + 0.0134425983942219, 0.0108320197485592, + 0.00884341208063, 0.00829374133206562, + # Add one more element since model is until 2110 normally + 0.00649794573935969, 0.00649794573935969] + baseyear = list(range(2020, 2110+1, 10)) # Index for above vector + gdp_growth_interp = np.interp(modelyears, baseyear, gdp_growth) + + fin_to_useful = scenario.par("output", filters = {"technology": + "finishing_aluminum","year_act":2020})["value"][0] + useful_to_product = scenario.par("output", filters = {"technology": + "manuf_aluminum","year_act":2020})["value"][0] + i = 0 + values = [] + + # Assume 5 year duration at the beginning + duration_period = (pd.Series(modelyears) - \ + pd.Series(modelyears).shift(1)).tolist() + duration_period[0] = 5 + + # print('modelyears', modelyears) + # print('duration_period', duration_period) + + val = (demand2010_aluminum * (1+ 0.147718884937996/2) ** duration_period[i]) + values.append(val) + + for element in gdp_growth_interp: + i = i + 1 + if i < len(modelyears): + val = (val * (1+ element/2) ** duration_period[i]) + values.append(val) + # print('val', val) + # Adjust the demand to product level. + + values = [x * fin_to_useful * useful_to_product for x in values] + + aluminum_demand = (make_df('demand', level='demand', commodity='aluminum', value=values, \ + unit='Mt', time = 'year', year=modelyears).pipe(broadcast, node=nodes)) + # results[parname].append(df) + + # print('demand', aluminum_demand) + # aluminum_demand = pd.DataFrame({ + # 'node': nodes, + # 'commodity': 'aluminum', + # 'level': 'demand', + # 'year': modelyears, + # 'time': 'year', + # 'value': values, + # 'unit': 'Mt', + # }) + + results['demand'].append(aluminum_demand) + + # scenario.add_par("demand", aluminum_demand) + + # # Interest rate + # scenario.add_par("interestrate", model_horizon, value=0.05, unit='-') + + # Add historical production and capacity + # + # for tec in data_aluminum_hist["technology"].unique(): + # hist_activity = pd.DataFrame({ + # 'node_loc': country, + # 'year_act': history, + # 'mode': data_aluminum_hist.loc[(data_aluminum_hist["technology"]== tec), + # "mode"], + # 'time': 'year', + # 'unit': 'Mt', + # "technology": tec, + # "value": data_aluminum_hist.loc[(data_aluminum_hist["technology"]== tec), + # "production"] + # }) + # scenario.add_par('historical_activity', hist_activity) + + # for tec in data_aluminum_hist["technology"].unique(): + # c_factor = scenario.par("capacity_factor", filters = {"technology": tec})\ + # ["value"].values[0] + # value = data_aluminum_hist.loc[(data_aluminum_hist["technology"]== tec), + # "new_production"] / c_factor + # hist_capacity = pd.DataFrame({ + # 'node_loc': country, + # 'year_vtg': history, + # 'unit': 'Mt', + # "technology": tec, + # "value": value }) + # scenario.add_par('historical_new_capacity', hist_capacity) + # + # Historical thermal demand depending on the historical aluminum production + # This section can be revised to make shorter and generic to other materials + + scrap_recovery = scenario.par("output", filters = {"technology": + "scrap_recovery_aluminum","level":"old_scrap","year_act":2020})["value"][0] + high_th = scenario.par("input", filters = {"technology": + "secondary_aluminum","year_act":2020})["value"][0] + low_th = scenario.par("input", filters = {"technology": + "prep_secondary_aluminum_1","year_act":2020})["value"][0] #JM: tec name? + + # What is this? Aggregate activity of alu tecs? + historic_generation = scenario.par("historical_activity").\ + groupby("year_act").sum() + + for yr in historic_generation.index: + + total_scrap = ((historic_generation.loc[yr].value * fin_to_useful * \ + (1- useful_to_product)) + (historic_generation.loc[yr] \ + * fin_to_useful * useful_to_product * scrap_recovery)) + old_scrap = (historic_generation.loc[yr] * fin_to_useful * \ + useful_to_product * scrap_recovery) + total_hist_act = total_scrap * high_th + old_scrap * low_th + + # hist_activity_h = pd.DataFrame({ + # 'node_loc': nodes, + # 'year_act': yr, + # 'mode': 'high_temp', + # 'time': 'year', + # 'unit': 'GWa', + # "technology": "furnace_gas_aluminum", + # "value": total_scrap * high_th + # }) + + hist_activity_h = make_df('historical_activity', year_act= yr, mode= 'high_temp', \ + time= 'year', unit= 'GWa', \ + technology= "furnace_gas_aluminum", \ + value= total_scrap * high_th).pipe(broadcast, node_loc=nodes) + + print('historical_activity 1', hist_activity_h) + # scenario.add_par('historical_activity', hist_activity_h) + results['historical_activity'].append(hist_activity_h) + + # hist_activity_l = pd.DataFrame({ + # 'node_loc': nodes, + # 'year_act': yr, + # 'mode': 'low_temp', + # 'time': 'year', + # 'unit': 'GWa', + # "technology": "furnace_gas_aluminum", + # "value": old_scrap * low_th + # }) + hist_activity_l = make_df('historical_activity', year_act= yr, mode= 'low_temp', \ + time= 'year', unit= 'GWa', \ + technology= 'furnace_gas_aluminum', \ + value= old_scrap * low_th).pipe(broadcast, node_loc=nodes) + + print('historical_activity 2', hist_activity_l) + # scenario.add_par('historical_activity', hist_activity_l) + results['historical_activity'].append(hist_activity_l) + + c_fac_furnace_gas = scenario.par("capacity_factor", filters = + {"technology": "furnace_gas_aluminum"})\ + ["value"].values[0] + + # hist_capacity_gas = pd.DataFrame({ + # 'node_loc': nodes, + # 'year_vtg': yr, + # 'unit': 'GW', + # "technology": "furnace_gas_aluminum", + # "value": total_hist_act / c_fac_furnace_gas }) + hist_capacity_gas = make_df('historical_new_capacity', year_vtg = yr, unit = 'GW', \ + technology = "furnace_gas_aluminum", \ + value = total_hist_act / c_fac_furnace_gas).pipe(broadcast, node_loc=nodes) + print('historical_new_capacity', hist_capacity_gas) + # scenario.add_par('historical_new_capacity', hist_capacity_gas) + results['historical_new_capacity'].append(hist_capacity_gas) + + # scenario.commit('aluminum other data added') + + # Concatenate to one data frame per parameter + results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} + + return results + # + # scenario.commit("changes") + # + # scenario.solve() + # + # # Be aware plots produce the same color for some technologies. + # + # p = Plots(scenario, country, firstyear=model_horizon[0]) + # + # p.plot_activity(baseyear=True, subset=['soderberg_aluminum', + # 'prebake_aluminum',"secondary_aluminum"]) + # p.plot_capacity(baseyear=True, subset=['soderberg_aluminum', 'prebake_aluminum']) + # p.plot_activity(baseyear=True, subset=["prep_secondary_aluminum"]) + # p.plot_activity(baseyear=True, subset=['furnace_coal_aluminum', + # 'furnace_foil_aluminum', + # 'furnace_methanol_aluminum', + # 'furnace_biomass_aluminum', + # 'furnace_ethanol_aluminum', + # 'furnace_gas_aluminum', + # 'furnace_elec_aluminum', + # 'furnace_h2_aluminum', + # 'hp_gas_aluminum', + # 'hp_elec_aluminum','fc_h2_aluminum', + # 'solar_aluminum', 'dheat_aluminum', + # 'furnace_loil_aluminum']) + # p.plot_capacity(baseyear=True, subset=['furnace_coal_aluminum', + # 'furnace_foil_aluminum', + # 'furnace_methanol_aluminum', + # 'furnace_biomass_aluminum', + # 'furnace_ethanol_aluminum', + # 'furnace_gas_aluminum', + # 'furnace_elec_aluminum', + # 'furnace_h2_aluminum', + # 'hp_gas_aluminum', + # 'hp_elec_aluminum','fc_h2_aluminum', + # 'solar_aluminum', 'dheat_aluminum', + # 'furnace_loil_aluminum']) + # p.plot_prices(subset=['aluminum'], baseyear=True) diff --git a/message_ix_models/model/material/material_testscript.py b/message_ix_models/model/material/material_testscript.py index 3f1cd27cd5..db8d800bb1 100644 --- a/message_ix_models/model/material/material_testscript.py +++ b/message_ix_models/model/material/material_testscript.py @@ -39,13 +39,14 @@ ctx = Context() # Set default scenario/model names +ctx.platform_info.setdefault('name', 'ixmp_dev') ctx.scenario_info.setdefault('model', 'Material_test_MESSAGE_China') ctx.scenario_info.setdefault('scenario', 'baseline') # ctx['period_start'] = 2020 # ctx['regions'] = 'China' # ctx['ssp'] = 'SSP2' # placeholder -ctx['scentype'] = 'C30-const' -ctx['datafile'] = 'China_steel_MESSAGE.xlsx' +ctx['scentype'] = 'C30-const_E414' +ctx['datafile'] = 'China_steel_cement_MESSAGE.xlsx' # Use general code to create a Scenario with some stuff in it scen = create_res(context = ctx) @@ -78,7 +79,7 @@ b = dt.read_data_generic() b = dt.read_var_cost() -bb = dt.process_china_data_tec() +bb = dt.read_sector_data() c = pd.melt(b, id_vars=['technology', 'mode', 'units'], \ value_vars=[2010, 2020, 2030, 2040, 2050, 2060, 2070, 2080, 2090, 2100], \ var_name='year') From 68f1ef748b1c5af52b8729986aae5813a2b7a605 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 10 Nov 2020 09:55:11 +0100 Subject: [PATCH 163/774] Unit fixes --- .../data/material/petrochemicals_techno_economic.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx index e72e9aeaab..93a4262e88 100644 --- a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx +++ b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:5ab38ff71da98b21c4bb176661737010ebc2a8ba932dcedb760b45316bc7e3c5 -size 438700 +oid sha256:3d1d71bd055a82b208dcdc310db01a5ed86dd0e5e2257e53e4ab51819db7e477 +size 439471 From b574051b4893beb3d50a62095883b17d819915f1 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Tue, 10 Nov 2020 10:53:15 +0100 Subject: [PATCH 164/774] Add back gamze's original historical data sheets and follow her approach in aluminum part --- message_ix_models/data/material/aluminum_techno_economic.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/aluminum_techno_economic.xlsx b/message_ix_models/data/material/aluminum_techno_economic.xlsx index 37088de3c9..ac74bad0ad 100644 --- a/message_ix_models/data/material/aluminum_techno_economic.xlsx +++ b/message_ix_models/data/material/aluminum_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:6816b9d3e5f329f73c73200b15dac36dfde17c786a0cfca88ed72d8c65684547 -size 55937 +oid sha256:71ffcc6e26925c3ea71d28f2c6a6da8f00f8f20c846fdd15f99c2942b669ef36 +size 64959 From 1e5f70e861c417007d55b4c38c6eb8aa31793d08 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Tue, 10 Nov 2020 10:56:27 +0100 Subject: [PATCH 165/774] Fix data interface to make the model run with all three material sectors --- message_ix_models/data/material/set.yaml | 12 - message_ix_models/model/material/data.py | 6 +- .../model/material/data_aluminum.py | 491 +++--------------- 3 files changed, 84 insertions(+), 425 deletions(-) diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index a4d27b23bb..a6274f03e9 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -280,18 +280,6 @@ generic: - fc_h2_aluminum - solar_aluminum - dheat_aluminum - # - DUMMY_coal_supply - # - DUMMY_gas_supply - # - DUMMY_loil_supply - # - DUMMY_foil_supply - # - DUMMY_electr_supply - # - DUMMY_ethanol_supply - # - DUMMY_methanol_supply - # - DUMMY_hydrogen_supply - # - DUMMY_freshwater_supply - # - DUMMY_water_supply - # - DUMMY_biomass_supply - # - DUMMY_dist_heating mode: add: diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index 44063a9107..4c837c0ed8 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -6,7 +6,7 @@ import pandas as pd from .data_cement import gen_data_cement from .data_steel import gen_data_steel -from .data_aluminum import gen_data_aluminum, add_other_data_aluminum +from .data_aluminum import gen_data_aluminum from .data_generic import gen_data_generic from message_data.tools import ( @@ -29,7 +29,6 @@ gen_data_cement, gen_data_aluminum, gen_data_generic, - add_other_data_aluminum, # Directly writing to some additional params ] # Try to handle multiple data input functions from different materials @@ -38,9 +37,6 @@ def add_data(scenario, dry_run=False): # Information about `scenario` info = ScenarioInfo(scenario) - # Need to modify demand specification to accomodate material endogenization - # modify_demand(scenario) - # Check for two "node" values for global data, e.g. in # ixmp://ene-ixmp/CD_Links_SSP2_v2.1_clean/baseline if {"World", "R11_GLB"} < set(info.set["node"]): diff --git a/message_ix_models/model/material/data_aluminum.py b/message_ix_models/model/material/data_aluminum.py index d95c2b7f4d..0a35f7c6fc 100644 --- a/message_ix_models/model/material/data_aluminum.py +++ b/message_ix_models/model/material/data_aluminum.py @@ -47,8 +47,8 @@ def read_data_aluminum(): data_alu_hist = pd.read_excel( context.get_path("material", fname), - sheet_name="data_historical", - usecols = "A:G") + sheet_name="data_historical_5year", + usecols = "A:F") data_alu_rel = read_rel(fname) # Unit conversion @@ -91,108 +91,107 @@ def gen_data_aluminum(scenario, dry_run=False): params = data_aluminum.loc[(data_aluminum["technology"] == t),"parameter"]\ .values.tolist() - # Special treatment for time-varying params - if t in tec_tv: - common = dict( - time="year", - time_origin="year", - time_dest="year",) - - param_name = data_aluminum_hist.loc[(data_aluminum_hist["technology"] == t), 'parameter'] - - for p in set(param_name): - val = data_aluminum_hist.loc[(data_aluminum_hist["technology"] == t) \ - & (data_aluminum_hist["parameter"] == p), 'value'] - units = data_aluminum_hist.loc[(data_aluminum_hist["technology"] == t) \ - & (data_aluminum_hist["parameter"] == p), 'unit'].values[0] - mod = data_aluminum_hist.loc[(data_aluminum_hist["technology"] == t) \ - & (data_aluminum_hist["parameter"] == p), 'mode'] - yr = data_aluminum_hist.loc[(data_aluminum_hist["technology"] == t) \ - & (data_aluminum_hist["parameter"] == p), 'year'] - - df = (make_df(p, technology=t, value=val,\ - unit='t', year_vtg=yr, year_act=yr, mode=mod, **common).pipe(broadcast, \ - node_loc=nodes)) - - #print("time-dependent::", p, df) - results[p].append(df) - # Obtain the active and vintage years - print("aluminum", t) av = data_aluminum.loc[(data_aluminum["technology"] == t), 'availability'].values[0] - if "technical_lifetime" in data_aluminum.loc[ - (data_aluminum["technology"] == t)]["parameter"].values: - lifetime = data_aluminum.loc[(data_aluminum["technology"] == t) - & (data_aluminum["parameter"] == - "technical_lifetime"), 'value'].\ - values[0] - years_df = scenario.vintage_and_active_years() - years_df = years_df.loc[years_df["year_vtg"]>= av] - # Empty data_frame - years_df_final = pd.DataFrame(columns=["year_vtg","year_act"]) - - # For each vintage adjsut the active years according to technical - # lifetime - for vtg in years_df["year_vtg"].unique(): - years_df_temp = years_df.loc[years_df["year_vtg"]== vtg] - years_df_temp = years_df_temp.loc[years_df["year_act"] - < vtg + lifetime] - years_df_final = pd.concat([years_df_temp, years_df_final], - ignore_index=True) - vintage_years, act_years = years_df_final['year_vtg'], \ - years_df_final['year_act'] - - params = data_aluminum.loc[(data_aluminum["technology"] == t),\ - "parameter"].values.tolist() + modelyears = [year for year in modelyears if year >= av] + yva = yv_ya.loc[yv_ya.year_vtg >= av] + # Iterate over parameters for par in params: + + # Obtain the parameter names, commodity,level,emission + split = par.split("|") - param_name = par.split("|")[0] + param_name = split[0] - val = data_aluminum.loc[((data_aluminum["technology"] == t) & - (data_aluminum["parameter"] == par)),\ - 'value'].values[0] + # Obtain the scalar value for the parameter - # Common parameters for all input and output tables - # node_dest and node_origin are the same as node_loc + val = data_aluminum.loc[((data_aluminum["technology"] == t) \ + & (data_aluminum["parameter"] == par)),'value'].values[0] common = dict( - year_vtg= vintage_years, - year_act= act_years, + year_vtg= yv_ya.year_vtg, + year_act= yv_ya.year_act, mode="M1", time="year", time_origin="year", time_dest="year",) + # For the parameters which inlcudes index names if len(split)> 1: if (param_name == "input")|(param_name == "output"): + # Assign commodity and level names com = split[1] lev = split[2] - df = (make_df(param_name, technology=t, commodity=com, - level=lev, value=val, unit='t', **common) + df = (make_df(param_name, technology=t, commodity=com, \ + level=lev, value=val, unit='t', **common)\ .pipe(broadcast, node_loc=nodes).pipe(same_node)) results[param_name].append(df) elif param_name == "emission_factor": + + # Assign the emisson type emi = split[1] - df = (make_df(param_name, technology=t,value=val, - emission=emi, unit='t', **common) - .pipe(broadcast, node_loc=nodes)) + df = (make_df(param_name, technology=t,value=val,\ + emission=emi, unit='t', **common).pipe(broadcast, \ + node_loc=nodes)) results[param_name].append(df) - # Rest of the parameters apart from inpput, output and - # emission_factor + # Parameters with only parameter name + else: - df = (make_df(param_name, technology=t, value=val,unit='t', - **common).pipe(broadcast, node_loc=nodes)) + df = (make_df(param_name, technology=t, value=val,unit='t', \ + **common).pipe(broadcast, node_loc=nodes)) results[param_name].append(df) + # Add the dummy alluminum demand + + values = gen_mock_demand_aluminum(scenario) + + demand_al = (make_df("demand", commodity= "aluminum", \ + level= "demand_aluminum", year = modelyears, value=values, unit='Mt',\ + time= "year").pipe(broadcast, node=nodes)) + + results["demand"].append(demand_al) + + # Add historical data + + for tec in data_aluminum_hist["technology"].unique(): + + y_hist = [1980,1985,1990,1995,2000,2005,2010,2015] #length need to match what's in the xls + common_hist = dict( + year_vtg= y_hist, + year_act= y_hist, + mode="M1", + time="year",) + + val_act = data_aluminum_hist.\ + loc[(data_aluminum_hist["technology"]== tec), "production"] + + df_hist_act = (make_df("historical_activity", technology=tec, \ + value=val_act, unit='Mt', **common_hist).pipe(broadcast, node_loc=nodes)) + + results["historical_activity"].append(df_hist_act) + + c_factor = data_aluminum.loc[((data_aluminum["technology"]== tec) \ + & (data_aluminum["parameter"]=="capacity_factor")), "value"].values + + val_cap = data_aluminum_hist.loc[(data_aluminum_hist["technology"]== tec), \ + "new_production"] / c_factor + + df_hist_cap = (make_df("historical_new_capacity", technology=tec, \ + value=val_cap, unit='Mt', **common_hist).pipe(broadcast, node_loc=nodes)) + + results["historical_new_capacity"].append(df_hist_cap) + + # Add relations for scrap grades and availability + for r in config['relation']['add']: params = data_aluminum_rel.loc[(data_aluminum_rel["relation"] == r),\ @@ -227,172 +226,27 @@ def gen_data_aluminum(scenario, dry_run=False): results[par_name].append(df) - # print(par_name, df) - results_aluminum = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} - return results_aluminum#, data_aluminum_hist - - -def add_other_data_aluminum(scenario, dry_run=False): - # # Adding a new unit to the library - # mp.add_unit('Mt') - # - # # New model - # scenario = message_ix.Scenario(mp, model='MESSAGE_material', - # scenario='baseline', version='new') - # # Addition of basics - # - # history = [1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015] - # model_horizon = [2020, 2030, 2040, 2050, 2060, 2070, 2080, 2090, 2100] - # scenario.add_horizon({'year': history + model_horizon, - # 'firstmodelyear': model_horizon[0]}) - # country = 'CHN' - # scenario.add_spatial_sets({'country': country}) - # - # # These will come from the yaml file - # - # commodities = ['ht_heat', 'lt_heat', 'aluminum', 'd_heat', "electr", - # "coal", "fueloil", "ethanol", "biomass", "gas", "hydrogen", - # "methanol", "lightoil"] - # - # scenario.add_set("commodity", commodities) - # - # levels = ['useful_aluminum', 'new_scrap', 'old_scrap', 'final_material', - # 'useful_material', 'product', "secondary_material", "final", - # "demand"] - # scenario.add_set("level", levels) - # - # technologies = ['soderberg_aluminum', 'prebake_aluminum', 'secondary_aluminum', - # 'prep_secondary_aluminum', 'finishing_aluminum', - # 'manuf_aluminum', 'scrap_recovery_aluminum', - # 'furnace_coal_aluminum', 'furnace_foil_aluminum', - # 'furnace_methanol_aluminum', 'furnace_biomass_aluminum', - # 'furnace_ethanol_aluminum', 'furnace_gas_aluminum', - # 'furnace_elec_aluminum', 'furnace_h2_aluminum', - # 'hp_gas_aluminum', 'hp_elec_aluminum', 'fc_h2_aluminum', - # 'solar_aluminum', 'dheat_aluminum', 'furnace_loil_aluminum', - # "alumina_supply"] - # - # scenario.add_set("technology", technologies) - # scenario.add_set("mode", ['standard', 'low_temp', 'high_temp']) - # - # # Create duration period - # - # val = [j-i for i, j in zip(model_horizon[:-1], model_horizon[1:])] - # val.append(val[0]) - # - # duration_period = pd.DataFrame({ - # 'year': model_horizon, - # 'value': val, - # 'unit': "y", - # }) - # - # scenario.add_par("duration_period", duration_period) - # duration_period = duration_period["value"].values - # - # # Energy system: Unlimited supply of the commodities. - # # Fuel costs are obtained from the PRICE_COMMODITY baseline SSP2 global model. - # # PRICE_COMMODTY * input -> (2005USD/Gwa-coal)*(Gwa-coal / ACT of furnace) - # # = (2005USD/ACT of furnace) - # - # years_df = scenario.vintage_and_active_years() - # vintage_years, act_years = years_df['year_vtg'], years_df['year_act'] - # - # # Choose the prices in excel (baseline vs. NPi400) - # data_var_cost = pd.read_excel("variable_costs.xlsx", sheet_name="data") - # - # for row in data_var_cost.index: - # data = data_var_cost.iloc[row] - # values = [] - # for yr in act_years: - # values.append(data[yr]) - # base_var_cost = pd.DataFrame({ - # 'node_loc': country, - # 'year_vtg': vintage_years.values, - # 'year_act': act_years.values, - # 'mode': data["mode"], - # 'time': 'year', - # 'unit': 'USD/GWa', - # "technology": data["technology"], - # "value": values - # }) - # - # scenario.add_par("var_cost",base_var_cost) - # - # # Add dummy technologies to represent energy system - # - # dummy_tech = ["electr_gen", "dist_heating", "coal_gen", "foil_gen", "eth_gen", - # "biomass_gen", "gas_gen", "hydrogen_gen", "meth_gen", "loil_gen"] - # scenario.add_set("technology", dummy_tech) - # - # commodity_tec = ["electr", "d_heat", "coal", "fueloil", "ethanol", "biomass", - # "gas", "hydrogen", "methanol", "lightoil"] - # - # # Add output to dummy tech. - # - # year_df = scenario.vintage_and_active_years() - # vintage_years, act_years = year_df['year_vtg'], year_df['year_act'] - # - # base = { - # 'node_loc': country, - # "node_dest": country, - # "time_dest": "year", - # 'year_vtg': vintage_years, - # 'year_act': act_years, - # 'mode': 'standard', - # 'time': 'year', - # "level": "final", - # 'unit': '-', - # "value": 1.0 - # } - # - # t = 0 - # - # for tec in dummy_tech: - # out = make_df("output", technology= tec, commodity = commodity_tec[t], - # **base) - # t = t + 1 - # scenario.add_par("output", out) - # - # # Introduce emissions - # scenario.add_set('emission', 'CO2') - # scenario.add_cat('emission', 'GHG', 'CO2') - # - # # Run read data aluminum - # - # scenario.commit("changes added") - # - # results_al, data_aluminum_hist = gen_data_aluminum("aluminum_techno_economic.xlsx") - # - # scenario.check_out() - # - # for k, v in results_al.items(): - # scenario.add_par(k,v) - # - # scenario.commit("aluminum_techno_economic added") - # results_generic = gen_data_generic() - # - # scenario.check_out() - # for k, v in results_generic.items(): - # scenario.add_par(k,v) + return results_aluminum - # Information about scenario, e.g. node, year - s_info = ScenarioInfo(scenario) - # List of data frames, to be concatenated together at end - results = defaultdict(list) - # For each technology there are differnet input and output combinations - # Iterate over technologies +def gen_mock_demand_aluminum(scenario): - allyears = s_info.set['year'] #s_info.Y is only for modeling years + # 17.3 Mt in 2010 to match the historical production from IAI. + # This is the amount right after electrolysis. + + # The future projection of the demand: Increases by half of the GDP growth rate. + # Starting from 2020. + context = read_config() + s_info = ScenarioInfo(scenario) modelyears = s_info.Y #s_info.Y is only for modeling years - nodes = s_info.N - yv_ya = s_info.yv_ya - fmy = s_info.y0 - nodes.remove('World') + + # Values in 2010 from IAI. + fin_to_useful = 0.971 + useful_to_product = 0.866 # Add temporary exogenous demand: 17.3 Mt in 2010 (IAI) demand2010_aluminum = 17.3 @@ -408,10 +262,6 @@ def add_other_data_aluminum(scenario, dry_run=False): baseyear = list(range(2020, 2110+1, 10)) # Index for above vector gdp_growth_interp = np.interp(modelyears, baseyear, gdp_growth) - fin_to_useful = scenario.par("output", filters = {"technology": - "finishing_aluminum","year_act":2020})["value"][0] - useful_to_product = scenario.par("output", filters = {"technology": - "manuf_aluminum","year_act":2020})["value"][0] i = 0 values = [] @@ -420,9 +270,6 @@ def add_other_data_aluminum(scenario, dry_run=False): pd.Series(modelyears).shift(1)).tolist() duration_period[0] = 5 - # print('modelyears', modelyears) - # print('duration_period', duration_period) - val = (demand2010_aluminum * (1+ 0.147718884937996/2) ** duration_period[i]) values.append(val) @@ -431,180 +278,8 @@ def add_other_data_aluminum(scenario, dry_run=False): if i < len(modelyears): val = (val * (1+ element/2) ** duration_period[i]) values.append(val) - # print('val', val) # Adjust the demand to product level. values = [x * fin_to_useful * useful_to_product for x in values] - aluminum_demand = (make_df('demand', level='demand', commodity='aluminum', value=values, \ - unit='Mt', time = 'year', year=modelyears).pipe(broadcast, node=nodes)) - # results[parname].append(df) - - # print('demand', aluminum_demand) - # aluminum_demand = pd.DataFrame({ - # 'node': nodes, - # 'commodity': 'aluminum', - # 'level': 'demand', - # 'year': modelyears, - # 'time': 'year', - # 'value': values, - # 'unit': 'Mt', - # }) - - results['demand'].append(aluminum_demand) - - # scenario.add_par("demand", aluminum_demand) - - # # Interest rate - # scenario.add_par("interestrate", model_horizon, value=0.05, unit='-') - - # Add historical production and capacity - # - # for tec in data_aluminum_hist["technology"].unique(): - # hist_activity = pd.DataFrame({ - # 'node_loc': country, - # 'year_act': history, - # 'mode': data_aluminum_hist.loc[(data_aluminum_hist["technology"]== tec), - # "mode"], - # 'time': 'year', - # 'unit': 'Mt', - # "technology": tec, - # "value": data_aluminum_hist.loc[(data_aluminum_hist["technology"]== tec), - # "production"] - # }) - # scenario.add_par('historical_activity', hist_activity) - - # for tec in data_aluminum_hist["technology"].unique(): - # c_factor = scenario.par("capacity_factor", filters = {"technology": tec})\ - # ["value"].values[0] - # value = data_aluminum_hist.loc[(data_aluminum_hist["technology"]== tec), - # "new_production"] / c_factor - # hist_capacity = pd.DataFrame({ - # 'node_loc': country, - # 'year_vtg': history, - # 'unit': 'Mt', - # "technology": tec, - # "value": value }) - # scenario.add_par('historical_new_capacity', hist_capacity) - # - # Historical thermal demand depending on the historical aluminum production - # This section can be revised to make shorter and generic to other materials - - scrap_recovery = scenario.par("output", filters = {"technology": - "scrap_recovery_aluminum","level":"old_scrap","year_act":2020})["value"][0] - high_th = scenario.par("input", filters = {"technology": - "secondary_aluminum","year_act":2020})["value"][0] - low_th = scenario.par("input", filters = {"technology": - "prep_secondary_aluminum_1","year_act":2020})["value"][0] #JM: tec name? - - # What is this? Aggregate activity of alu tecs? - historic_generation = scenario.par("historical_activity").\ - groupby("year_act").sum() - - for yr in historic_generation.index: - - total_scrap = ((historic_generation.loc[yr].value * fin_to_useful * \ - (1- useful_to_product)) + (historic_generation.loc[yr] \ - * fin_to_useful * useful_to_product * scrap_recovery)) - old_scrap = (historic_generation.loc[yr] * fin_to_useful * \ - useful_to_product * scrap_recovery) - total_hist_act = total_scrap * high_th + old_scrap * low_th - - # hist_activity_h = pd.DataFrame({ - # 'node_loc': nodes, - # 'year_act': yr, - # 'mode': 'high_temp', - # 'time': 'year', - # 'unit': 'GWa', - # "technology": "furnace_gas_aluminum", - # "value": total_scrap * high_th - # }) - - hist_activity_h = make_df('historical_activity', year_act= yr, mode= 'high_temp', \ - time= 'year', unit= 'GWa', \ - technology= "furnace_gas_aluminum", \ - value= total_scrap * high_th).pipe(broadcast, node_loc=nodes) - - print('historical_activity 1', hist_activity_h) - # scenario.add_par('historical_activity', hist_activity_h) - results['historical_activity'].append(hist_activity_h) - - # hist_activity_l = pd.DataFrame({ - # 'node_loc': nodes, - # 'year_act': yr, - # 'mode': 'low_temp', - # 'time': 'year', - # 'unit': 'GWa', - # "technology": "furnace_gas_aluminum", - # "value": old_scrap * low_th - # }) - hist_activity_l = make_df('historical_activity', year_act= yr, mode= 'low_temp', \ - time= 'year', unit= 'GWa', \ - technology= 'furnace_gas_aluminum', \ - value= old_scrap * low_th).pipe(broadcast, node_loc=nodes) - - print('historical_activity 2', hist_activity_l) - # scenario.add_par('historical_activity', hist_activity_l) - results['historical_activity'].append(hist_activity_l) - - c_fac_furnace_gas = scenario.par("capacity_factor", filters = - {"technology": "furnace_gas_aluminum"})\ - ["value"].values[0] - - # hist_capacity_gas = pd.DataFrame({ - # 'node_loc': nodes, - # 'year_vtg': yr, - # 'unit': 'GW', - # "technology": "furnace_gas_aluminum", - # "value": total_hist_act / c_fac_furnace_gas }) - hist_capacity_gas = make_df('historical_new_capacity', year_vtg = yr, unit = 'GW', \ - technology = "furnace_gas_aluminum", \ - value = total_hist_act / c_fac_furnace_gas).pipe(broadcast, node_loc=nodes) - print('historical_new_capacity', hist_capacity_gas) - # scenario.add_par('historical_new_capacity', hist_capacity_gas) - results['historical_new_capacity'].append(hist_capacity_gas) - - # scenario.commit('aluminum other data added') - - # Concatenate to one data frame per parameter - results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} - - return results - # - # scenario.commit("changes") - # - # scenario.solve() - # - # # Be aware plots produce the same color for some technologies. - # - # p = Plots(scenario, country, firstyear=model_horizon[0]) - # - # p.plot_activity(baseyear=True, subset=['soderberg_aluminum', - # 'prebake_aluminum',"secondary_aluminum"]) - # p.plot_capacity(baseyear=True, subset=['soderberg_aluminum', 'prebake_aluminum']) - # p.plot_activity(baseyear=True, subset=["prep_secondary_aluminum"]) - # p.plot_activity(baseyear=True, subset=['furnace_coal_aluminum', - # 'furnace_foil_aluminum', - # 'furnace_methanol_aluminum', - # 'furnace_biomass_aluminum', - # 'furnace_ethanol_aluminum', - # 'furnace_gas_aluminum', - # 'furnace_elec_aluminum', - # 'furnace_h2_aluminum', - # 'hp_gas_aluminum', - # 'hp_elec_aluminum','fc_h2_aluminum', - # 'solar_aluminum', 'dheat_aluminum', - # 'furnace_loil_aluminum']) - # p.plot_capacity(baseyear=True, subset=['furnace_coal_aluminum', - # 'furnace_foil_aluminum', - # 'furnace_methanol_aluminum', - # 'furnace_biomass_aluminum', - # 'furnace_ethanol_aluminum', - # 'furnace_gas_aluminum', - # 'furnace_elec_aluminum', - # 'furnace_h2_aluminum', - # 'hp_gas_aluminum', - # 'hp_elec_aluminum','fc_h2_aluminum', - # 'solar_aluminum', 'dheat_aluminum', - # 'furnace_loil_aluminum']) - # p.plot_prices(subset=['aluminum'], baseyear=True) + return values From e75075310a245ed5c4f44d5bd26f24d561239e88 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Tue, 10 Nov 2020 15:40:48 +0100 Subject: [PATCH 166/774] Fixes to make the cli work --- message_ix_models/model/material/__init__.py | 16 ++++++++++------ .../model/material/material_testscript.py | 4 ++-- 2 files changed, 12 insertions(+), 8 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index caf0bebd07..40c0522cdd 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -14,11 +14,12 @@ def build(scenario): # Get the specification spec = get_spec() - modify_demand(scenario) - # Apply to the base scenario apply_spec(scenario, spec, add_data) # dry_run=True + # Adjust exogenous energy demand to incorporate the endogenized sectors + modify_demand(scenario) + return scenario SPEC_LIST = ["generic", "common", "steel", "cement", "aluminum"] # add as needed/implemented @@ -79,9 +80,11 @@ def create_bare(context, regions, dry_run): if not dry_run: scen.solve() -@cli.command() +@cli.command("solve") +@click.option('--datafile', default='China_steel_cement_MESSAGE.xlsx', + metavar='INPUT', help='File name for external data input') @click.pass_obj -def solve(context): +def solve(context, datafile): """Build and solve model. Use the --url option to specify the base scenario. @@ -93,9 +96,10 @@ def solve(context): "NPi2020-con-prim-dir-ncr": "NPi", "NPi2020_1000-con-prim-dir-ncr": "NPi2020_1000", "NPi2020_400-con-prim-dir-ncr": "NPi2020_400", + "DIAG-C30-const_E414": "baseline_test", }.get(context.scenario_info["scenario"]) - # Chnage this part - the model name ?? + context.datafile = datafile if context.scenario_info["model"] != "CD_Links_SSP2": print("WARNING: this code is not tested with this base scenario!") @@ -103,7 +107,7 @@ def solve(context): # Clone and set up scenario = build( context.get_scenario() - .clone(model="Material_test", scenario=output_scenario_name) + .clone(model="Material_China", scenario=output_scenario_name) ) # Solve diff --git a/message_ix_models/model/material/material_testscript.py b/message_ix_models/model/material/material_testscript.py index db8d800bb1..9f0e13e999 100644 --- a/message_ix_models/model/material/material_testscript.py +++ b/message_ix_models/model/material/material_testscript.py @@ -38,7 +38,7 @@ Context._instance = [] ctx = Context() -# Set default scenario/model names +# Set default scenario/model names - Later coming from CLI ctx.platform_info.setdefault('name', 'ixmp_dev') ctx.scenario_info.setdefault('model', 'Material_test_MESSAGE_China') ctx.scenario_info.setdefault('scenario', 'baseline') @@ -95,7 +95,7 @@ info = ScenarioInfo(scen) a = get_spec() -mp_samp = ixmp.Platform(name="local") +mp_samp = ixmp.Platform(name="ene_ixmp") mp_samp.scenario_list() sample = mix.Scenario(mp_samp, model="Material_test", scenario="baseline") sample.set_list() From c3178dc8ea2d09209aea258f16c75fd4973ed150 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 10 Nov 2020 16:14:23 +0100 Subject: [PATCH 167/774] Final changes --- .../data/material/petrochemicals_techno_economic.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx index 93a4262e88..75b34ef834 100644 --- a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx +++ b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:3d1d71bd055a82b208dcdc310db01a5ed86dd0e5e2257e53e4ab51819db7e477 -size 439471 +oid sha256:2b052ef566073918ff9fcd2df9c0cea48b4e3f1c6aae2e1731c128fb3e7b82b0 +size 439381 From 94160ff053b6e53934cc3edebd5fb70766f55195 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 11 Nov 2020 15:21:56 +0100 Subject: [PATCH 168/774] Changes to integrate petro_chemicals --- ...eneric_furnace_boiler_techno_economic.xlsx | 4 +- ..._furnace_boiler_techno_economic_steel.xlsx | 3 - message_ix_models/data/material/set.yaml | 115 +++++++ .../data/material/variable_costs.xlsx | 4 +- message_ix_models/model/material/__init__.py | 2 +- message_ix_models/model/material/data.py | 1 + .../model/material/data_petro.py | 280 ++++++++++++++++++ 7 files changed, 401 insertions(+), 8 deletions(-) delete mode 100644 message_ix_models/data/material/generic_furnace_boiler_techno_economic_steel.xlsx create mode 100644 message_ix_models/model/material/data_petro.py diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx index 7025dae040..6ed81249a1 100644 --- a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx +++ b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:09068788ce98db8295a28a9fef5e3306d3a5986d9c71b27e6d49317d994cc0cd -size 275 +oid sha256:be4c50851bc9d019c1d5baedc1098d6ad65c657741d0b69209bbda424ad65657 +size 292 diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic_steel.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic_steel.xlsx deleted file mode 100644 index f48994f31d..0000000000 --- a/message_ix_models/data/material/generic_furnace_boiler_techno_economic_steel.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:7d537dac6568cb38e7698c25b6ebb50baf6027316553b05600fb16d1c14b0b2c -size 48197 diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index a6274f03e9..d6b80056af 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -181,6 +181,8 @@ generic: - useful_aluminum - useful_cement - useful_aluminum + - useful_refining + - useful_petro technology: add: @@ -280,6 +282,36 @@ generic: - fc_h2_aluminum - solar_aluminum - dheat_aluminum + - furnace_coke_petro + - furnace_coal_petro + - furnace_foil_petro + - furnace_loil_petro + - furnace_ethanol_petro + - furnace_biomass_petro + - furnace_methanol_petro + - furnace_gas_petro + - furnace_elec_petro + - furnace_h2_petro + - hp_gas_petro + - hp_elec_petro + - fc_h2_petro + - solar_petro + - dheat_petro + - furnace_coke_refining + - furnace_coal_refining + - furnace_foil_refining + - furnace_loil_refining + - furnace_ethanol_refining + - furnace_biomass_refining + - furnace_methanol_refining + - furnace_gas_refining + - furnace_elec_refining + - furnace_h2_refining + - hp_gas_refining + - hp_elec_refining + - fc_h2_refining + - solar_refining + - dheat_refining mode: add: @@ -289,6 +321,89 @@ generic: remove: - World +petro_chemicals: + commodity: + require: + - crudeoil + add: + - naphtha + - kerosene + - diesel + - atm_residue + - refinery_gas + - atm_gasoil + - atm_residue + - vacuum_gasoil + - vacuum_residue + - desulf_naphtha + - desulf_kerosene + - desulf_diesel + - desulf_atm_gasoil + - desulf_vacuum_gasoil + - gasoline + - heavy_foil + - light_foil + - propylene + - pet_coke + - ethane + - propane + - ethylene + - BTX + + level: + require: + - secondary + - final + add: + - pre_intermediate + - desulfurized + - intermediate + - secondary_material + - final_material + - demand_ethylene + - demand_propylene + - demand_BTX + - demand_foil + - demand_loil + mode: + add: + - atm_gasoil + - vacuum_gasoil + - naphtha + - kerosene + - diesel + - cracking_gasoline + - cracking_loil + - ethane + - propane + - light_foil + - refinery_gas + - heavy_foil + - pet_coke + - atm_residue + - vacuum_residue + - gasoline + + technology: + add: + - atm_distillation_ref + - vacuum_distillation_ref + - hydrotreating_ref + - catalytic_cracking_ref + - visbreaker_ref + - coking_ref + - catalytic_reforming_ref + - hydro_cracking_ref + - steam_cracker_petro + - ethanol_to_ethylene_petro + - agg_ref + - gas_processing_petro + + remove: + # Any representation of refinery. + - ref_hil + - ref_lol + steel: commodity: add: diff --git a/message_ix_models/data/material/variable_costs.xlsx b/message_ix_models/data/material/variable_costs.xlsx index 3dd277a6b9..376c539c3e 100644 --- a/message_ix_models/data/material/variable_costs.xlsx +++ b/message_ix_models/data/material/variable_costs.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:e0ec396dd16105d5429abc4769225fe8fdb56b37bc6a309083bed89b9aa95905 -size 15874 +oid sha256:f997925caf15d670c02359826b4d5e9ea5da66188fe6c81e31a9470074460d3a +size 16382 diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 40c0522cdd..fe678f556e 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -22,7 +22,7 @@ def build(scenario): return scenario -SPEC_LIST = ["generic", "common", "steel", "cement", "aluminum"] # add as needed/implemented +SPEC_LIST = ["generic", "common", "steel", "cement", "aluminum", "petro_chemicals"] # add as needed/implemented def get_spec() -> Mapping[str, ScenarioInfo]: """Return the specification for materials accounting.""" diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index 4c837c0ed8..98d77c1f42 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -29,6 +29,7 @@ gen_data_cement, gen_data_aluminum, gen_data_generic, + gen_data_petro_chemicals ] # Try to handle multiple data input functions from different materials diff --git a/message_ix_models/model/material/data_petro.py b/message_ix_models/model/material/data_petro.py new file mode 100644 index 0000000000..8fb5adc398 --- /dev/null +++ b/message_ix_models/model/material/data_petro.py @@ -0,0 +1,280 @@ +import pandas as pd +import numpy as np +from collections import defaultdict + +import message_ix +import ixmp + +from .util import read_config +from .data_util import read_rel +from message_data.tools import ( + ScenarioInfo, + broadcast, + make_df, + make_io, + make_matched_dfs, + same_node, + add_par_data +) + +def read_data_petrochemicals(): + """Read and clean data from :file:`petrochemicals_techno_economic.xlsx`.""" + + # Ensure config is loaded, get the context + context = read_config() + + # Read the file + data_petro = pd.read_excel( + context.get_path("material", "petrochemicals_techno_economic.xlsx"), + sheet_name="data") + # Clean the data + + data_petro= data_petro.drop(['Region', 'Source', 'Description'], axis = 1) + + data_petro_hist = pd.read_excel(context.get_path("material", \ + "petrochemicals_techno_economic.xlsx"),sheet_name="data_historical", \ + usecols = "A:F") + + return data_petro,data_petro_hist + +def gen_data_petro_chemicals(scenario, dry_run=False): + # Load configuration + + config = read_config()["material"]["petro_chemicals"] + + # Information about scenario, e.g. node, year + s_info = ScenarioInfo(scenario) + + # Techno-economic assumptions + data_petro, data_petro_hist = read_data_petrochemicals() + # List of data frames, to be concatenated together at end + results = defaultdict(list) + + # For each technology there are differnet input and output combinations + # Iterate over technologies + + allyears = s_info.set['year'] + modelyears = s_info.Y #s_info.Y is only for modeling years + nodes = s_info.N + yv_ya = s_info.yv_ya + fmy = s_info.y0 + + # 'World' is included by default when creating a message_ix.Scenario(). + # Need to remove it for the China bare model + nodes.remove('World') + + for t in config["technology"]["add"]: + + # years = s_info.Y + params = data_petro.loc[(data_petro["technology"] == t),"parameter"]\ + .values.tolist() + + # Availability year of the technology + av = data_petro.loc[(data_petro["technology"] == t),'availability'].\ + values[0] + modelyears = [year for year in modelyears if year >= av] + yva = yv_ya.loc[yv_ya.year_vtg >= av, ] + + # Iterate over parameters + for par in params: + split = par.split("|") + param_name = par.split("|")[0] + + val = data_petro.loc[((data_petro["technology"] == t) & \ + (data_petro["parameter"] == par)),'value'].values[0] + + # Common parameters for all input and output tables + # year_act is none at the moment + # node_dest and node_origin are the same as node_loc + + common = dict( + year_vtg= yva.year_vtg, + year_act= yva.year_act, + time="year", + time_origin="year", + time_dest="year",) + + if len(split)> 1: + + if (param_name == "input")|(param_name == "output"): + + com = split[1] + lev = split[2] + mod = split[3] + + df = (make_df(param_name, technology=t, commodity=com, \ + level=lev,mode=mod, value=val, unit='t', **common).\ + pipe(broadcast, node_loc=nodes).pipe(same_node)) + + results[param_name].append(df) + + elif param_name == "emission_factor": + emi = split[1] + mod = split[2] + + df = (make_df(param_name, technology=t,value=val,\ + emission=emi, mode=mod, unit='t', **common).pipe(broadcast, \ + node_loc=nodes)) + + elif param_name == "var_cost": + mod = split[1] + + df = (make_df(param_name, technology=t, commodity=com, \ + level=lev,mode=mod, value=val, unit='t', **common).\ + pipe(broadcast, node_loc=nodes).pipe(same_node)) + + + + results[param_name].append(df) + + # Rest of the parameters apart from inpput, output and emission_factor + + else: + + df = (make_df(param_name, technology=t, value=val,unit='t', \ + **common).pipe(broadcast, node_loc=nodes)) + + results[param_name].append(df) + + # Add demand + + values_e, values_p, values_BTX, values_foil, values_loil = \ + gen_mock_demand_petro(scenario) + + demand_ethylene = (make_df("demand", commodity= "ethylene", \ + level= "demand_ethylene", year = modelyears, value=values_e, unit='Mt',\ + time= "year").pipe(broadcast, node=nodes)) + print("Ethylene demand") + print(demand_ethylene) + + demand_propylene = (make_df("demand", commodity= "propylene", \ + level= "demand_propylene", year = modelyears, value=values_p, unit='Mt',\ + time= "year").pipe(broadcast, node=nodes)) + print("Propylene demand") + print(demand_propylene) + + demand_BTX = (make_df("demand", commodity= "BTX", \ + level= "demand_BTX", year = modelyears, value=values_BTX, unit='Mt',\ + time= "year").pipe(broadcast, node=nodes)) + print("BTX demand") + print(demand_BTX) + + demand_foil = (make_df("demand", commodity= "fueloil", \ + level= "demand_foil", year = modelyears, value=values_foil, unit='GWa',\ + time= "year").pipe(broadcast, node=nodes)) + + demand_loil = (make_df("demand", commodity= "lightoil", \ + level= "demand_loil", year = modelyears, value=values_loil, unit='GWa',\ + time= "year").pipe(broadcast, node=nodes)) + + results["demand"].append(demand_ethylene) + results["demand"].append(demand_propylene) + results["demand"].append(demand_BTX) + results["demand"].append(demand_loil) + results["demand"].append(demand_foil) + + # Add historical data + + for tec in data_petro_hist["technology"].unique(): + + y_hist = [y for y in allyears if y < fmy] + common_hist = dict( + year_vtg= y_hist, + year_act= y_hist, + mode="M1", + time="year",) + + val_act = data_petro_hist.\ + loc[(data_petro_hist["technology"]== tec), "production"] + + df_hist_act = (make_df("historical_activity", technology=tec, \ + value=val_act, unit='Mt', **common_hist).pipe(broadcast, node_loc=nodes)) + + results["historical_activity"].append(df_hist_act) + + c_factor = data_petro.loc[((data_petro["technology"]== tec) \ + & (data_petro["parameter"]=="capacity_factor")), "value"].values + + val_cap = data_petro_hist.loc[(data_petro_hist["technology"]== tec), \ + "new_production"] / c_factor + + df_hist_cap = (make_df("historical_new_capacity", technology=tec, \ + value=val_cap, unit='Mt', **common_hist).pipe(broadcast, node_loc=nodes)) + + results["historical_new_capacity"].append(df_hist_cap) + + results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} + + return results + + def gen_mock_demand_petro(scenario): + + # China 2006: 22 kg/cap HVC demand. 2006 population: 1.311 billion + # This makes 28.842 Mt. (IEA Energy Technology Transitions for Industry) + # Distribution in 2015 for China: 6:6:5 (ethylene,propylene,BTX) + # Future of Petrochemicals Methodological Annex + # This can be verified by other sources. + + # The future projection of the demand: Increases by half of the GDP growth rate. + # Starting from 2020. + context = read_config() + s_info = ScenarioInfo(scenario) + modelyears = s_info.Y #s_info.Y is only for modeling years + + gdp_growth = pd.Series([0.121448215899944, 0.0733079014579874, \ + 0.0348154093342843, 0.021827616787921,\ + 0.0134425983942219, 0.0108320197485592, \ + 0.00884341208063, 0.00829374133206562, \ + 0.00649794573935969],index=pd.Index(modelyears, \ + name='Time')) + i = 0 + values_e = [] + values_p = [] + values_BTX = [] + + # 10-10-8 is the ratio + + val_e = (10 * (1+ 0.147718884937996/2) ** context.time_step) + print("val_e") + print(val_e) + values_e.append(val_e) + print(values_e) + + val_p = (10 * (1+ 0.147718884937996/2) ** context.time_step) + print("val_p") + print(val_p) + values_p.append(val_p) + print(values_p) + + val_BTX = (8 * (1+ 0.147718884937996/2) ** context.time_step) + print("val_BTX") + print(val_BTX) + values_BTX.append(val_BTX) + print(values_BTX) + + for element in gdp_growth: + i = i + 1 + if i < len(modelyears): + val_e = (val_e * (1+ element/2) ** context.time_step) + values_e.append(val_e) + + val_p = (val_p * (1+ element/2) ** context.time_step) + values_p.append(val_p) + + val_BTX = (val_BTX * (1+ element/2) ** context.time_step) + values_BTX.append(val_BTX) + + if context.scenario_info['scenario'] == 'NPi400': + sheet_name="demand_NPi400" + else: + sheet_name = "demand_baseline" + # Read the file + df = pd.read_excel( + context.get_path("material", "oil_demand.xlsx"), + sheet_name=sheet_name, + ) + + values_foil = df["Total_foil"].tolist() + values_loil = df["Total_loil"].tolist() + + return values_e, values_p, values_BTX, values_foil, values_loil From 71453cbed2e57d981860d9050a65b7e0f8e1ffd8 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 12 Nov 2020 09:34:27 +0100 Subject: [PATCH 169/774] Fixes to run the code --- .../petrochemicals_techno_economic.xlsx | 4 +- message_ix_models/model/material/__init__.py | 1 + message_ix_models/model/material/data.py | 1 + .../model/material/data_petro.py | 161 +++++++++--------- 4 files changed, 80 insertions(+), 87 deletions(-) diff --git a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx index 75b34ef834..a5f6ae012b 100644 --- a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx +++ b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:2b052ef566073918ff9fcd2df9c0cea48b4e3f1c6aae2e1731c128fb3e7b82b0 -size 439381 +oid sha256:c7c82985a0905983801c2019f86eb98c725164d320f93f70866c8bf5219967e7 +size 439601 diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index fe678f556e..aae731504f 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -99,6 +99,7 @@ def solve(context, datafile): "DIAG-C30-const_E414": "baseline_test", }.get(context.scenario_info["scenario"]) + context.metadata_path = context.metadata_path /'data' context.datafile = datafile if context.scenario_info["model"] != "CD_Links_SSP2": diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index 98d77c1f42..171476ac15 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -8,6 +8,7 @@ from .data_steel import gen_data_steel from .data_aluminum import gen_data_aluminum from .data_generic import gen_data_generic +from .data_petro import gen_data_petro_chemicals from message_data.tools import ( ScenarioInfo, diff --git a/message_ix_models/model/material/data_petro.py b/message_ix_models/model/material/data_petro.py index 8fb5adc398..fd6ed6e174 100644 --- a/message_ix_models/model/material/data_petro.py +++ b/message_ix_models/model/material/data_petro.py @@ -37,6 +37,78 @@ def read_data_petrochemicals(): return data_petro,data_petro_hist +def gen_mock_demand_petro(scenario): + + # China 2006: 22 kg/cap HVC demand. 2006 population: 1.311 billion + # This makes 28.842 Mt. (IEA Energy Technology Transitions for Industry) + # Distribution in 2015 for China: 6:6:5 (ethylene,propylene,BTX) + # Future of Petrochemicals Methodological Annex + # This can be verified by other sources. + + # The future projection of the demand: Increases by half of the GDP growth rate. + # Starting from 2020. + context = read_config() + s_info = ScenarioInfo(scenario) + modelyears = s_info.Y #s_info.Y is only for modeling years + + gdp_growth = [0.121448215899944, 0.0733079014579874, + 0.0348154093342843, 0.021827616787921, + 0.0134425983942219, 0.0108320197485592, + 0.00884341208063, 0.00829374133206562, + # Add one more element since model is until 2110 normally + 0.00649794573935969, 0.00649794573935969] + baseyear = list(range(2020, 2110+1, 10)) # Index for above vector + gdp_growth_interp = np.interp(modelyears, baseyear, gdp_growth) + + i = 0 + values_e = [] + values_p = [] + values_BTX = [] + + # Assume 5 year duration at the beginning + duration_period = (pd.Series(modelyears) - \ + pd.Series(modelyears).shift(1)).tolist() + duration_period[0] = 5 + + # 10-10-8 is the ratio + + val_e = (10 * (1+ 0.147718884937996/2) ** duration_period[i]) + print("val_e") + print(val_e) + values_e.append(val_e) + print(values_e) + + val_p = (10 * (1+ 0.147718884937996/2) ** duration_period[i]) + print("val_p") + print(val_p) + values_p.append(val_p) + print(values_p) + + val_BTX = (8 * (1+ 0.147718884937996/2) ** duration_period[i]) + print("val_BTX") + print(val_BTX) + values_BTX.append(val_BTX) + print(values_BTX) + + for element in gdp_growth_interp: + i = i + 1 + if i < len(modelyears): + val_e = (val_e * (1+ element/2) ** duration_period[i]) + values_e.append(val_e) + + val_p = (val_p * (1+ element/2) ** duration_period[i]) + values_p.append(val_p) + + val_BTX = (val_BTX * (1+ element/2) ** duration_period[i]) + values_BTX.append(val_BTX) + + if context.scenario_info['scenario'] == 'NPi400': + sheet_name="demand_NPi400" + else: + sheet_name = "demand_baseline" + + return values_e, values_p, values_BTX + def gen_data_petro_chemicals(scenario, dry_run=False): # Load configuration @@ -138,8 +210,7 @@ def gen_data_petro_chemicals(scenario, dry_run=False): # Add demand - values_e, values_p, values_BTX, values_foil, values_loil = \ - gen_mock_demand_petro(scenario) + values_e, values_p, values_BTX = gen_mock_demand_petro(scenario) demand_ethylene = (make_df("demand", commodity= "ethylene", \ level= "demand_ethylene", year = modelyears, value=values_e, unit='Mt',\ @@ -159,33 +230,25 @@ def gen_data_petro_chemicals(scenario, dry_run=False): print("BTX demand") print(demand_BTX) - demand_foil = (make_df("demand", commodity= "fueloil", \ - level= "demand_foil", year = modelyears, value=values_foil, unit='GWa',\ - time= "year").pipe(broadcast, node=nodes)) - - demand_loil = (make_df("demand", commodity= "lightoil", \ - level= "demand_loil", year = modelyears, value=values_loil, unit='GWa',\ - time= "year").pipe(broadcast, node=nodes)) - results["demand"].append(demand_ethylene) results["demand"].append(demand_propylene) results["demand"].append(demand_BTX) - results["demand"].append(demand_loil) - results["demand"].append(demand_foil) # Add historical data for tec in data_petro_hist["technology"].unique(): - y_hist = [y for y in allyears if y < fmy] + y_hist = [1980,1985,1990,1995,2000,2005,2010,2015] #length need to match what's in the xls common_hist = dict( year_vtg= y_hist, year_act= y_hist, mode="M1", time="year",) + print(y_hist) val_act = data_petro_hist.\ loc[(data_petro_hist["technology"]== tec), "production"] + print(val_act) df_hist_act = (make_df("historical_activity", technology=tec, \ value=val_act, unit='Mt', **common_hist).pipe(broadcast, node_loc=nodes)) @@ -206,75 +269,3 @@ def gen_data_petro_chemicals(scenario, dry_run=False): results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} return results - - def gen_mock_demand_petro(scenario): - - # China 2006: 22 kg/cap HVC demand. 2006 population: 1.311 billion - # This makes 28.842 Mt. (IEA Energy Technology Transitions for Industry) - # Distribution in 2015 for China: 6:6:5 (ethylene,propylene,BTX) - # Future of Petrochemicals Methodological Annex - # This can be verified by other sources. - - # The future projection of the demand: Increases by half of the GDP growth rate. - # Starting from 2020. - context = read_config() - s_info = ScenarioInfo(scenario) - modelyears = s_info.Y #s_info.Y is only for modeling years - - gdp_growth = pd.Series([0.121448215899944, 0.0733079014579874, \ - 0.0348154093342843, 0.021827616787921,\ - 0.0134425983942219, 0.0108320197485592, \ - 0.00884341208063, 0.00829374133206562, \ - 0.00649794573935969],index=pd.Index(modelyears, \ - name='Time')) - i = 0 - values_e = [] - values_p = [] - values_BTX = [] - - # 10-10-8 is the ratio - - val_e = (10 * (1+ 0.147718884937996/2) ** context.time_step) - print("val_e") - print(val_e) - values_e.append(val_e) - print(values_e) - - val_p = (10 * (1+ 0.147718884937996/2) ** context.time_step) - print("val_p") - print(val_p) - values_p.append(val_p) - print(values_p) - - val_BTX = (8 * (1+ 0.147718884937996/2) ** context.time_step) - print("val_BTX") - print(val_BTX) - values_BTX.append(val_BTX) - print(values_BTX) - - for element in gdp_growth: - i = i + 1 - if i < len(modelyears): - val_e = (val_e * (1+ element/2) ** context.time_step) - values_e.append(val_e) - - val_p = (val_p * (1+ element/2) ** context.time_step) - values_p.append(val_p) - - val_BTX = (val_BTX * (1+ element/2) ** context.time_step) - values_BTX.append(val_BTX) - - if context.scenario_info['scenario'] == 'NPi400': - sheet_name="demand_NPi400" - else: - sheet_name = "demand_baseline" - # Read the file - df = pd.read_excel( - context.get_path("material", "oil_demand.xlsx"), - sheet_name=sheet_name, - ) - - values_foil = df["Total_foil"].tolist() - values_loil = df["Total_loil"].tolist() - - return values_e, values_p, values_BTX, values_foil, values_loil From 9ef5060b5bf4c6359550f374d4131acefaee3dc3 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 12 Nov 2020 14:32:21 +0100 Subject: [PATCH 170/774] Fixing historical activity and capacity values --- .../material/aluminum_techno_economic.xlsx | 4 +-- .../petrochemicals_techno_economic.xlsx | 4 +-- message_ix_models/data/material/set.yaml | 9 +------ .../model/material/data_petro.py | 26 ++++++++++++++----- 4 files changed, 24 insertions(+), 19 deletions(-) diff --git a/message_ix_models/data/material/aluminum_techno_economic.xlsx b/message_ix_models/data/material/aluminum_techno_economic.xlsx index ac74bad0ad..eae1ad0cff 100644 --- a/message_ix_models/data/material/aluminum_techno_economic.xlsx +++ b/message_ix_models/data/material/aluminum_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:71ffcc6e26925c3ea71d28f2c6a6da8f00f8f20c846fdd15f99c2942b669ef36 -size 64959 +oid sha256:dd4ce2a0a15c0252288d655f88edb6d861531d3ea9c4f4bc8cd486dfd16fe6af +size 65039 diff --git a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx index a5f6ae012b..e786cf2724 100644 --- a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx +++ b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:c7c82985a0905983801c2019f86eb98c725164d320f93f70866c8bf5219967e7 -size 439601 +oid sha256:0ec4893bf8569c6c77be55dd4555fdaf5231d7c751f0c48db8e75e2361d70aad +size 480838 diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index d6b80056af..efa92ed5b0 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -335,11 +335,6 @@ petro_chemicals: - atm_residue - vacuum_gasoil - vacuum_residue - - desulf_naphtha - - desulf_kerosene - - desulf_diesel - - desulf_atm_gasoil - - desulf_vacuum_gasoil - gasoline - heavy_foil - light_foil @@ -363,8 +358,7 @@ petro_chemicals: - demand_ethylene - demand_propylene - demand_BTX - - demand_foil - - demand_loil + mode: add: - atm_gasoil @@ -398,7 +392,6 @@ petro_chemicals: - ethanol_to_ethylene_petro - agg_ref - gas_processing_petro - remove: # Any representation of refinery. - ref_hil diff --git a/message_ix_models/model/material/data_petro.py b/message_ix_models/model/material/data_petro.py index fd6ed6e174..3611eecc8b 100644 --- a/message_ix_models/model/material/data_petro.py +++ b/message_ix_models/model/material/data_petro.py @@ -33,16 +33,22 @@ def read_data_petrochemicals(): data_petro_hist = pd.read_excel(context.get_path("material", \ "petrochemicals_techno_economic.xlsx"),sheet_name="data_historical", \ - usecols = "A:F") - + usecols = "A:G", nrows= 16) + print("data petro hist") + print(data_petro_hist) return data_petro,data_petro_hist + def gen_mock_demand_petro(scenario): # China 2006: 22 kg/cap HVC demand. 2006 population: 1.311 billion # This makes 28.842 Mt. (IEA Energy Technology Transitions for Industry) + # In 2010: 43.263 Mt (1.5 times of 2006) + # In 2015 72.105 Mt (1.56 times of 2010) + # Grwoth rates are from CECDATA (assuming same growth rate as ethylene). # Distribution in 2015 for China: 6:6:5 (ethylene,propylene,BTX) # Future of Petrochemicals Methodological Annex + # This makes 25 Mt ethylene, 25 Mt propylene, 21 Mt BTX # This can be verified by other sources. # The future projection of the demand: Increases by half of the GDP growth rate. @@ -59,6 +65,7 @@ def gen_mock_demand_petro(scenario): 0.00649794573935969, 0.00649794573935969] baseyear = list(range(2020, 2110+1, 10)) # Index for above vector gdp_growth_interp = np.interp(modelyears, baseyear, gdp_growth) + print(gdp_growth_interp) i = 0 values_e = [] @@ -70,21 +77,19 @@ def gen_mock_demand_petro(scenario): pd.Series(modelyears).shift(1)).tolist() duration_period[0] = 5 - # 10-10-8 is the ratio - - val_e = (10 * (1+ 0.147718884937996/2) ** duration_period[i]) + val_e = (25 * (1+ 0.147718884937996/2) ** duration_period[i]) print("val_e") print(val_e) values_e.append(val_e) print(values_e) - val_p = (10 * (1+ 0.147718884937996/2) ** duration_period[i]) + val_p = (25 * (1+ 0.147718884937996/2) ** duration_period[i]) print("val_p") print(val_p) values_p.append(val_p) print(values_p) - val_BTX = (8 * (1+ 0.147718884937996/2) ** duration_period[i]) + val_BTX = (21 * (1+ 0.147718884937996/2) ** duration_period[i]) print("val_BTX") print(val_BTX) values_BTX.append(val_BTX) @@ -236,7 +241,9 @@ def gen_data_petro_chemicals(scenario, dry_run=False): # Add historical data + print(data_petro_hist["technology"].unique()) for tec in data_petro_hist["technology"].unique(): + print(tec) y_hist = [1980,1985,1990,1995,2000,2005,2010,2015] #length need to match what's in the xls common_hist = dict( @@ -245,9 +252,11 @@ def gen_data_petro_chemicals(scenario, dry_run=False): mode="M1", time="year",) + print("historical years") print(y_hist) val_act = data_petro_hist.\ loc[(data_petro_hist["technology"]== tec), "production"] + print("value activity") print(val_act) df_hist_act = (make_df("historical_activity", technology=tec, \ @@ -261,6 +270,9 @@ def gen_data_petro_chemicals(scenario, dry_run=False): val_cap = data_petro_hist.loc[(data_petro_hist["technology"]== tec), \ "new_production"] / c_factor + print("This is capacity value") + print(val_cap) + df_hist_cap = (make_df("historical_new_capacity", technology=tec, \ value=val_cap, unit='Mt', **common_hist).pipe(broadcast, node_loc=nodes)) From 64bb05592f25b260a066d6f9f66bc81555550461 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 12 Nov 2020 15:27:24 +0100 Subject: [PATCH 171/774] Adjusting demand --- .../data/material/petrochemicals_techno_economic.xlsx | 4 ++-- message_ix_models/data/material/set.yaml | 6 ++---- message_ix_models/model/material/data_aluminum.py | 2 +- message_ix_models/model/material/data_petro.py | 6 +++--- message_ix_models/model/material/data_util.py | 4 ++++ 5 files changed, 12 insertions(+), 10 deletions(-) diff --git a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx index e786cf2724..147ead5350 100644 --- a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx +++ b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:0ec4893bf8569c6c77be55dd4555fdaf5231d7c751f0c48db8e75e2361d70aad -size 480838 +oid sha256:84f348ec7048ff1faded751c4dda4c9c14e732b2986977e65b8a1625976d3cff +size 480843 diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index efa92ed5b0..7a69976020 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -355,9 +355,7 @@ petro_chemicals: - intermediate - secondary_material - final_material - - demand_ethylene - - demand_propylene - - demand_BTX + - demand mode: add: @@ -511,7 +509,7 @@ aluminum: - useful_material - product - secondary_material - - demand_aluminum + - demand technology: add: diff --git a/message_ix_models/model/material/data_aluminum.py b/message_ix_models/model/material/data_aluminum.py index 0a35f7c6fc..d79b33679b 100644 --- a/message_ix_models/model/material/data_aluminum.py +++ b/message_ix_models/model/material/data_aluminum.py @@ -155,7 +155,7 @@ def gen_data_aluminum(scenario, dry_run=False): values = gen_mock_demand_aluminum(scenario) demand_al = (make_df("demand", commodity= "aluminum", \ - level= "demand_aluminum", year = modelyears, value=values, unit='Mt',\ + level= "demand", year = modelyears, value=values, unit='Mt',\ time= "year").pipe(broadcast, node=nodes)) results["demand"].append(demand_al) diff --git a/message_ix_models/model/material/data_petro.py b/message_ix_models/model/material/data_petro.py index 3611eecc8b..906eddc39d 100644 --- a/message_ix_models/model/material/data_petro.py +++ b/message_ix_models/model/material/data_petro.py @@ -218,19 +218,19 @@ def gen_data_petro_chemicals(scenario, dry_run=False): values_e, values_p, values_BTX = gen_mock_demand_petro(scenario) demand_ethylene = (make_df("demand", commodity= "ethylene", \ - level= "demand_ethylene", year = modelyears, value=values_e, unit='Mt',\ + level= "demand", year = modelyears, value=values_e, unit='Mt',\ time= "year").pipe(broadcast, node=nodes)) print("Ethylene demand") print(demand_ethylene) demand_propylene = (make_df("demand", commodity= "propylene", \ - level= "demand_propylene", year = modelyears, value=values_p, unit='Mt',\ + level= "demand", year = modelyears, value=values_p, unit='Mt',\ time= "year").pipe(broadcast, node=nodes)) print("Propylene demand") print(demand_propylene) demand_BTX = (make_df("demand", commodity= "BTX", \ - level= "demand_BTX", year = modelyears, value=values_BTX, unit='Mt',\ + level= "demand", year = modelyears, value=values_BTX, unit='Mt',\ time= "year").pipe(broadcast, node=nodes)) print("BTX demand") print(demand_BTX) diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index 28e51eb34c..1218cd7724 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -17,6 +17,8 @@ def modify_demand(scen): # NOTE Temporarily modifying industrial energy demand # (30% of non-elec industrial energy for steel) + # Add aluminum and petro-chemicals + df = scen.par('demand', filters={'commodity':'i_therm'}) df.value = df.value * 0.45 #(30% steel, 25% cement) @@ -24,6 +26,8 @@ def modify_demand(scen): scen.add_par('demand', df) scen.commit(comment = 'modify i_therm demand') + # Also adjust the i_spec. + # NOTE Aggregate industrial coal demand need to adjust to # the sudden intro of steel setor in the first model year From 69733d4cb79811aaad167ad8b656c3a8c011b4de Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 12 Nov 2020 15:27:24 +0100 Subject: [PATCH 172/774] Adjusting demand --- message_ix_models/data/material/aluminum_techno_economic.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/aluminum_techno_economic.xlsx b/message_ix_models/data/material/aluminum_techno_economic.xlsx index eae1ad0cff..ef17dbb114 100644 --- a/message_ix_models/data/material/aluminum_techno_economic.xlsx +++ b/message_ix_models/data/material/aluminum_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:dd4ce2a0a15c0252288d655f88edb6d861531d3ea9c4f4bc8cd486dfd16fe6af -size 65039 +oid sha256:68ba244fd8b01b47b8170a538bf9e4a8695b4f349131c7eea29ca21f4cb4c306 +size 65004 From 9a27642cd3eeb576a8846179c02e485379d7e89a Mon Sep 17 00:00:00 2001 From: Jihoon Date: Fri, 13 Nov 2020 17:43:01 +0100 Subject: [PATCH 173/774] Remove hydrogen input to dri_steel (revisit later with different mode) --- .../data/material/China_steel_cement_MESSAGE.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/China_steel_cement_MESSAGE.xlsx b/message_ix_models/data/material/China_steel_cement_MESSAGE.xlsx index 54c681cd0d..ffc154048e 100644 --- a/message_ix_models/data/material/China_steel_cement_MESSAGE.xlsx +++ b/message_ix_models/data/material/China_steel_cement_MESSAGE.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:f748b1513d90bd52d42598c971fab442ffc48f03be5beab59f5425cc2ebcc682 -size 52030 +oid sha256:190f5f5802233fe091a8dd582eb1092a819611ef558a09804ac6209547102975 +size 52036 From 0511e05a009420b9fd920a093215066b18384fbc Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Sun, 15 Nov 2020 11:29:28 +0100 Subject: [PATCH 174/774] Emission factors added --- .../data/material/petrochemicals_techno_economic.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx index 147ead5350..e2543b8a60 100644 --- a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx +++ b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:84f348ec7048ff1faded751c4dda4c9c14e732b2986977e65b8a1625976d3cff -size 480843 +oid sha256:eb450d8bfa409b78d79c4b04b8e6e49b400ee9a0f9a350ce6c57083d536f7f72 +size 481230 From abc62bdeec1b16cdc45b9dd127422e48f946375c Mon Sep 17 00:00:00 2001 From: Jihoon Date: Mon, 16 Nov 2020 10:09:35 +0100 Subject: [PATCH 175/774] Fix mock demand for steel & cement and remove emission bound for now --- .../model/material/data_cement.py | 40 ++++++++++++++----- .../model/material/data_petro.py | 5 --- .../model/material/data_steel.py | 34 ++++++++++++---- 3 files changed, 55 insertions(+), 24 deletions(-) diff --git a/message_ix_models/model/material/data_cement.py b/message_ix_models/model/material/data_cement.py index 6dabacb99b..916e89e6d8 100644 --- a/message_ix_models/model/material/data_cement.py +++ b/message_ix_models/model/material/data_cement.py @@ -25,20 +25,38 @@ # Generate a fake cement demand -def gen_mock_demand_cement(s_info): +def gen_mock_demand_cement(scenario): - modelyears = s_info.Y + context = read_config() + s_info = ScenarioInfo(scenario) + modelyears = s_info.Y #s_info.Y is only for modeling years fmy = s_info.y0 # True cement use 2011 (China) = 2100 Mt/year (ADVANCE) demand2010_cement = 2100 baseyear = list(range(2020, 2110+1, 10)) + gdp_growth_interp = np.interp(modelyears, baseyear, gdp_growth) + + i = 0 + values = [] + + # Assume 5 year duration at the beginning + duration_period = (pd.Series(modelyears) - \ + pd.Series(modelyears).shift(1)).tolist() + duration_period[0] = 5 - demand = gr * demand2010_cement - demand_interp = np.interp(modelyears, baseyear, demand) + val = (demand2010_cement * (1+ 0.147718884937996/2) ** duration_period[i]) + values.append(val) + + for element in gdp_growth_interp: + i = i + 1 + if i < len(modelyears): + val = (val * (1+ element/2) ** duration_period[i]) + values.append(val) + + return values - return demand_interp.tolist() def gen_data_cement(scenario, dry_run=False): @@ -138,7 +156,7 @@ def gen_data_cement(scenario, dry_run=False): # Create external demand param parname = 'demand' - demand = gen_mock_demand_cement(s_info) + demand = gen_mock_demand_cement(scenario) df = (make_df(parname, level='demand', commodity='cement', value=demand, \ unit='t', year=modelyears, **common).pipe(broadcast, node=nodes)) results[parname].append(df) @@ -152,11 +170,11 @@ def gen_data_cement(scenario, dry_run=False): results[parname].append(df) # Test emission bound - parname = 'bound_emission' - df = (make_df(parname, type_tec='all', type_year='cumulative', \ - type_emission='CO2_industry', \ - value=200, unit='-').pipe(broadcast, node=nodes)) - results[parname].append(df) + # parname = 'bound_emission' + # df = (make_df(parname, type_tec='all', type_year='cumulative', \ + # type_emission='CO2_industry', \ + # value=200, unit='-').pipe(broadcast, node=nodes)) + # results[parname].append(df) # Concatenate to one data frame per parameter results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} diff --git a/message_ix_models/model/material/data_petro.py b/message_ix_models/model/material/data_petro.py index 906eddc39d..8046294a83 100644 --- a/message_ix_models/model/material/data_petro.py +++ b/message_ix_models/model/material/data_petro.py @@ -107,11 +107,6 @@ def gen_mock_demand_petro(scenario): val_BTX = (val_BTX * (1+ element/2) ** duration_period[i]) values_BTX.append(val_BTX) - if context.scenario_info['scenario'] == 'NPi400': - sheet_name="demand_NPi400" - else: - sheet_name = "demand_baseline" - return values_e, values_p, values_BTX def gen_data_petro_chemicals(scenario, dry_run=False): diff --git a/message_ix_models/model/material/data_steel.py b/message_ix_models/model/material/data_steel.py index 3dc630ce0d..f2413f2014 100644 --- a/message_ix_models/model/material/data_steel.py +++ b/message_ix_models/model/material/data_steel.py @@ -17,19 +17,21 @@ add_par_data ) -# Decadal growth rate (2020-2110) +# annual average growth rate by decade (2020-2110) gdp_growth = [0.121448215899944, 0.0733079014579874, 0.0348154093342843, 0.021827616787921, \ 0.0134425983942219, 0.0108320197485592, \ 0.00884341208063, 0.00829374133206562, \ 0.00649794573935969, 0.00649794573935969] -gr = np.cumprod([(x+1) for x in gdp_growth]) +# gr = np.cumprod([(x+1) for x in gdp_growth]) # Generate a fake steel demand -def gen_mock_demand_steel(s_info): +def gen_mock_demand_steel(scenario): - modelyears = s_info.Y + context = read_config() + s_info = ScenarioInfo(scenario) + modelyears = s_info.Y #s_info.Y is only for modeling years fmy = s_info.y0 # True steel use 2010 (China) = 537 Mt/year @@ -37,11 +39,27 @@ def gen_mock_demand_steel(s_info): # https://www.worldsteel.org/en/dam/jcr:0474d208-9108-4927-ace8-4ac5445c5df8/World+Steel+in+Figures+2017.pdf baseyear = list(range(2020, 2110+1, 10)) + gdp_growth_interp = np.interp(modelyears, baseyear, gdp_growth) + + i = 0 + values = [] + + # Assume 5 year duration at the beginning + duration_period = (pd.Series(modelyears) - \ + pd.Series(modelyears).shift(1)).tolist() + duration_period[0] = 5 + + val = (demand2010_steel * (1+ 0.147718884937996/2) ** duration_period[i]) + values.append(val) + + for element in gdp_growth_interp: + i = i + 1 + if i < len(modelyears): + val = (val * (1+ element/2) ** duration_period[i]) + values.append(val) - demand = gr * demand2010_steel - demand_interp = np.interp(modelyears, baseyear, demand) + return values - return demand_interp.tolist() def gen_data_steel(scenario, dry_run=False): @@ -168,7 +186,7 @@ def gen_data_steel(scenario, dry_run=False): # Create external demand param parname = 'demand' - demand = gen_mock_demand_steel(s_info) + demand = gen_mock_demand_steel(scenario) df = (make_df(parname, level='demand', commodity='steel', value=demand, unit='t', \ year=modelyears, **common).pipe(broadcast, node=nodes)) results[parname].append(df) From 46acf27c8efe1e3b0d0a091332c4b3e84fc14059 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 18 Nov 2020 15:24:03 +0100 Subject: [PATCH 176/774] Industrial demand adjustment --- message_ix_models/model/material/data_util.py | 31 +++++++++++++++++-- 1 file changed, 28 insertions(+), 3 deletions(-) diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index 1218cd7724..15c06465a0 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -17,16 +17,41 @@ def modify_demand(scen): # NOTE Temporarily modifying industrial energy demand # (30% of non-elec industrial energy for steel) - # Add aluminum and petro-chemicals + + # For aluminum there is no significant deduction required + # (refining process not included and thermal energy required from + # recycling is not a significant share.) + # For petro: based on 13.1 GJ/tonne of ethylene and the demand in the model df = scen.par('demand', filters={'commodity':'i_therm'}) - df.value = df.value * 0.45 #(30% steel, 25% cement) + df.value = df.value * 0.38 #(30% steel, 25% cement, 7% petro) scen.check_out() scen.add_par('demand', df) scen.commit(comment = 'modify i_therm demand') - # Also adjust the i_spec. + # Adjust the i_spec. + # Electricity usage seems negligable in the production of HVCs. + # Aluminum: based on IAI China data 20%. + + df = scen.par('demand', filters={'commodity':'i_spec'}) + df.value = df.value * 0.80 #(15% aluminum) + + scen.check_out() + scen.add_par('demand', df) + scen.commit(comment = 'modify i_spec demand') + + # Adjust the i_feedstock. + # 45 GJ/tonne of ethylene or propylene or BTX + # 2020 demand of one of these: 35.7 Mt + # Makes up around 30% of total feedstock demand. + + df = scen.par('demand', filters={'commodity':'i_feed'}) + df.value = df.value * 0.7 #(70% HVCs) + + scen.check_out() + scen.add_par('demand', df) + scen.commit(comment = 'modify i_feed demand') # NOTE Aggregate industrial coal demand need to adjust to # the sudden intro of steel setor in the first model year From 0e626e784412015efa1dec2d01e96087bee12f55 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 18 Nov 2020 15:59:57 +0100 Subject: [PATCH 177/774] Aluminum scrap grades fix --- .../model/material/data_aluminum.py | 24 +++++++++++++------ 1 file changed, 17 insertions(+), 7 deletions(-) diff --git a/message_ix_models/model/material/data_aluminum.py b/message_ix_models/model/material/data_aluminum.py index d79b33679b..51e21e75e3 100644 --- a/message_ix_models/model/material/data_aluminum.py +++ b/message_ix_models/model/material/data_aluminum.py @@ -193,6 +193,7 @@ def gen_data_aluminum(scenario, dry_run=False): # Add relations for scrap grades and availability for r in config['relation']['add']: + print(r) params = data_aluminum_rel.loc[(data_aluminum_rel["relation"] == r),\ "parameter"].values.tolist() @@ -206,15 +207,24 @@ def gen_data_aluminum(scenario, dry_run=False): for par_name in params: if par_name == "relation_activity": - val = data_aluminum_rel.loc[((data_aluminum_rel["relation"] == r) \ - & (data_aluminum_rel["parameter"] == par_name)),'value'].values[0] - tec = data_aluminum_rel.loc[((data_aluminum_rel["relation"] == r) \ - & (data_aluminum_rel["parameter"] == par_name)),'technology'].values[0] + tec_list = data_aluminum_rel.loc[((data_aluminum_rel["relation"] == r) \ + & (data_aluminum_rel["parameter"] == par_name)) ,'technology'] - df = (make_df(par_name, technology=tec, value=val, unit='-',\ - **common_rel).pipe(broadcast, node_rel=nodes, node_loc=nodes)) + for tec in tec_list.unique(): + print("tec list") + print(data_aluminum_rel["technology"].unique()) + print(tec) - results[par_name].append(df) + val = data_aluminum_rel.loc[((data_aluminum_rel["relation"] == r) \ + & (data_aluminum_rel["parameter"] == par_name) & \ + (data_aluminum_rel["technology"]==tec)),'value'].values[0] + print("relation value") + print(val) + + df = (make_df(par_name, technology=tec, value=val, unit='-',\ + **common_rel).pipe(broadcast, node_rel=nodes, node_loc=nodes)) + + results[par_name].append(df) elif par_name == "relation_upper": From 06843719349b864057ae4c61ffb280a97119c3ae Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 19 Nov 2020 09:28:18 +0100 Subject: [PATCH 178/774] Petro chemicals data update --- .../data/material/petrochemicals_techno_economic.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx index e2543b8a60..4b8792cc03 100644 --- a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx +++ b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:eb450d8bfa409b78d79c4b04b8e6e49b400ee9a0f9a350ce6c57083d536f7f72 -size 481230 +oid sha256:f89fbd29a532e91694067ef18739bf501dd16e1076aea66af098e233ccdb3a8a +size 481677 From 8d2e642ba61b9b75ca2b69c041a540c225944349 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 19 Nov 2020 09:37:57 +0100 Subject: [PATCH 179/774] Data update --- message_ix_models/data/material/aluminum_techno_economic.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/aluminum_techno_economic.xlsx b/message_ix_models/data/material/aluminum_techno_economic.xlsx index ef17dbb114..93c88b0ccf 100644 --- a/message_ix_models/data/material/aluminum_techno_economic.xlsx +++ b/message_ix_models/data/material/aluminum_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:68ba244fd8b01b47b8170a538bf9e4a8695b4f349131c7eea29ca21f4cb4c306 -size 65004 +oid sha256:3579c2bf807a5a0c266f403f1f51c5a35da0afec5586304085c491ee7c66d5dc +size 65018 From 19323ba483f825ba71dbac31e858d0e6e16c1f4a Mon Sep 17 00:00:00 2001 From: Jihoon Date: Thu, 19 Nov 2020 15:08:56 +0100 Subject: [PATCH 180/774] Add the scrap relations for steel and fix a bug --- .../material/China_steel_cement_MESSAGE.xlsx | 4 +- message_ix_models/data/material/set.yaml | 10 ++- .../model/material/data_steel.py | 69 +++++++++++++++---- .../model/material/material_testscript.py | 9 ++- 4 files changed, 76 insertions(+), 16 deletions(-) diff --git a/message_ix_models/data/material/China_steel_cement_MESSAGE.xlsx b/message_ix_models/data/material/China_steel_cement_MESSAGE.xlsx index ffc154048e..762fdc2a89 100644 --- a/message_ix_models/data/material/China_steel_cement_MESSAGE.xlsx +++ b/message_ix_models/data/material/China_steel_cement_MESSAGE.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:190f5f5802233fe091a8dd582eb1092a819611ef558a09804ac6209547102975 -size 52036 +oid sha256:b7912ac95b5eddac6c6b789e66f517040728a381466904527c43fcbcafa6f8ee +size 54803 diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 7a69976020..092a1a464e 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -431,7 +431,9 @@ steel: - sr_steel - bof_steel - eaf_steel - - prep_secondary_steel + - prep_secondary_steel_1 + - prep_secondary_steel_2 + - prep_secondary_steel_3 - finishing_steel - manuf_steel - scrap_recovery_steel @@ -440,6 +442,12 @@ steel: - DUMMY_freshwater_supply - DUMMY_water_supply + relation: + add: + - scrap_availability_steel_1 + - scrap_availability_steel_2 + - scrap_availability_steel_3 + cement: commodity: add: diff --git a/message_ix_models/model/material/data_steel.py b/message_ix_models/model/material/data_steel.py index f2413f2014..feb58c7a11 100644 --- a/message_ix_models/model/material/data_steel.py +++ b/message_ix_models/model/material/data_steel.py @@ -7,6 +7,7 @@ import pandas as pd from .util import read_config +from .data_util import read_rel from message_data.tools import ( ScenarioInfo, broadcast, @@ -14,6 +15,7 @@ make_io, make_matched_dfs, same_node, + copy_column, add_par_data ) @@ -77,8 +79,10 @@ def gen_data_steel(scenario, dry_run=False): # TEMP: now add cement sector as well => Need to separate those since now I have get_data_steel and cement data_steel = read_sector_data("steel") # Special treatment for time-dependent Parameters - data_steel_vc = read_timeseries(context.datafile) - tec_vc = set(data_steel_vc.technology) # set of tecs with var_cost + data_steel_ts = read_timeseries(context.datafile) + data_steel_rel = read_rel(context.datafile) + + tec_ts = set(data_steel_ts.technology) # set of tecs with var_cost # List of data frames, to be concatenated together at end results = defaultdict(list) @@ -100,23 +104,23 @@ def gen_data_steel(scenario, dry_run=False): "parameter"].values.tolist() # Special treatment for time-varying params - if t in tec_vc: + if t in tec_ts: common = dict( time="year", time_origin="year", time_dest="year",) - param_name = data_steel_vc.loc[(data_steel_vc["technology"] == t), 'parameter'] + param_name = data_steel_ts.loc[(data_steel_ts["technology"] == t), 'parameter'] for p in set(param_name): - val = data_steel_vc.loc[(data_steel_vc["technology"] == t) \ - & (data_steel_vc["parameter"] == p), 'value'] - units = data_steel_vc.loc[(data_steel_vc["technology"] == t) \ - & (data_steel_vc["parameter"] == p), 'units'].values[0] - mod = data_steel_vc.loc[(data_steel_vc["technology"] == t) \ - & (data_steel_vc["parameter"] == p), 'mode'] - yr = data_steel_vc.loc[(data_steel_vc["technology"] == t) \ - & (data_steel_vc["parameter"] == p), 'year'] + val = data_steel_ts.loc[(data_steel_ts["technology"] == t) \ + & (data_steel_ts["parameter"] == p), 'value'] + units = data_steel_ts.loc[(data_steel_ts["technology"] == t) \ + & (data_steel_ts["parameter"] == p), 'units'].values[0] + mod = data_steel_ts.loc[(data_steel_ts["technology"] == t) \ + & (data_steel_ts["parameter"] == p), 'mode'] + yr = data_steel_ts.loc[(data_steel_ts["technology"] == t) \ + & (data_steel_ts["parameter"] == p), 'year'] df = (make_df(p, technology=t, value=val,\ unit='t', year_vtg=yr, year_act=yr, mode=mod, **common).pipe(broadcast, \ @@ -184,6 +188,47 @@ def gen_data_steel(scenario, dry_run=False): results[param_name].append(df) + # Add relations for scrap grades and availability + + for r in config['relation']['add']: + + params = set(data_steel_rel.loc[(data_steel_rel["relation"] == r),\ + "parameter"].values) + + common_rel = dict( + year_rel = modelyears, + year_act = modelyears, + mode = 'M1', + relation = r,) + + for par_name in params: + if par_name == "relation_activity": + + val = data_steel_rel.loc[((data_steel_rel["relation"] == r) \ + & (data_steel_rel["parameter"] == par_name)),'value'].values + tec = data_steel_rel.loc[((data_steel_rel["relation"] == r) \ + & (data_steel_rel["parameter"] == par_name)),'technology'].values + + print(par_name, "val", val, "tec", tec) + + df = (make_df(par_name, technology=tec, \ + value=val, unit='-', mode = 'M1', relation = r)\ + .pipe(broadcast, node_rel=nodes, \ + node_loc=nodes, year_rel = modelyears))\ + .assign(year_act=copy_column('year_rel')) + + results[par_name].append(df) + + elif par_name == "relation_upper": + + val = data_steel_rel.loc[((data_steel_rel["relation"] == r) \ + & (data_steel_rel["parameter"] == par_name)),'value'].values[0] + + df = (make_df(par_name, value=val, unit='-',\ + **common_rel).pipe(broadcast, node_rel=nodes)) + + results[par_name].append(df) + # Create external demand param parname = 'demand' demand = gen_mock_demand_steel(scenario) diff --git a/message_ix_models/model/material/material_testscript.py b/message_ix_models/model/material/material_testscript.py index 9f0e13e999..6a9c9304ee 100644 --- a/message_ix_models/model/material/material_testscript.py +++ b/message_ix_models/model/material/material_testscript.py @@ -19,7 +19,9 @@ from message_data.tools import ( ScenarioInfo, make_df, + broadcast, make_io, + copy_column, make_matched_dfs, set_info, ) @@ -69,13 +71,18 @@ 'clinker_wet_cement', \ 'clinker_wet_ccs_cement']) +p.plot_activity(baseyear=False, subset=['dri_steel', \ + 'bf_steel']) #%% Auxiliary random test stuff import pandas as pd +import message_data.model.material.data_util as du + # Test read_data_steel <- will be in create_res if working fine df = dt.read_data_steel() +df = du.read_rel(ctx.datafile) b = dt.read_data_generic() b = dt.read_var_cost() @@ -95,7 +102,7 @@ info = ScenarioInfo(scen) a = get_spec() -mp_samp = ixmp.Platform(name="ene_ixmp") +mp_samp = ixmp.Platform(name="ixmp_dev") mp_samp.scenario_list() sample = mix.Scenario(mp_samp, model="Material_test", scenario="baseline") sample.set_list() From 1d2124155f94cd2a879fcff4a88f8dd0e65fe08e Mon Sep 17 00:00:00 2001 From: Jihoon Date: Thu, 26 Nov 2020 17:22:05 +0100 Subject: [PATCH 181/774] Get region input from the data inputs + change node requirement --- message_ix_models/data/material/set.yaml | 7 ++----- message_ix_models/model/material/data_util.py | 2 +- 2 files changed, 3 insertions(+), 6 deletions(-) diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 092a1a464e..c46f22424c 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -131,13 +131,10 @@ common: - final - water_supply - # Currently only works with China node: # Just one is sufficient, since the RES will never be generated with a mix - add: - - China - # remove: - # - World + require: + - R11_AFR type_tec: add: diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index 15c06465a0..de51cc63f3 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -85,7 +85,7 @@ def read_sector_data(sectname): # Clean the data data_df = data_df \ - [['Technology', 'Parameter', 'Level', \ + [['Region', 'Technology', 'Parameter', 'Level', \ 'Commodity', 'Mode', 'Species', 'Units', 'Value']] \ .replace(np.nan, '', regex=True) From 4c1b3b5f9eff86266b97fabff0ada1ae8b114a60 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Thu, 26 Nov 2020 17:27:07 +0100 Subject: [PATCH 182/774] Set starting point as one of engage scenarios --- message_ix_models/model/material/__init__.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index aae731504f..70060e5c96 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -21,8 +21,9 @@ def build(scenario): modify_demand(scenario) return scenario - -SPEC_LIST = ["generic", "common", "steel", "cement", "aluminum", "petro_chemicals"] # add as needed/implemented + +# add as needed/implemented +SPEC_LIST = ["generic", "common", "steel", "cement", "aluminum", "petro_chemicals"] def get_spec() -> Mapping[str, ScenarioInfo]: """Return the specification for materials accounting.""" @@ -99,7 +100,7 @@ def solve(context, datafile): "DIAG-C30-const_E414": "baseline_test", }.get(context.scenario_info["scenario"]) - context.metadata_path = context.metadata_path /'data' + #context.metadata_path = context.metadata_path /'data' context.datafile = datafile if context.scenario_info["model"] != "CD_Links_SSP2": From 2f0413ecdd301cd799c8c81a27254ebaaf0c0e75 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Fri, 27 Nov 2020 10:55:57 +0100 Subject: [PATCH 183/774] Revise steel data input routines to accomodate region-specific parametrization --- .../model/material/data_steel.py | 98 +++++++++++-------- 1 file changed, 58 insertions(+), 40 deletions(-) diff --git a/message_ix_models/model/material/data_steel.py b/message_ix_models/model/material/data_steel.py index feb58c7a11..d18255e8c2 100644 --- a/message_ix_models/model/material/data_steel.py +++ b/message_ix_models/model/material/data_steel.py @@ -25,7 +25,6 @@ 0.0134425983942219, 0.0108320197485592, \ 0.00884341208063, 0.00829374133206562, \ 0.00649794573935969, 0.00649794573935969] -# gr = np.cumprod([(x+1) for x in gdp_growth]) # Generate a fake steel demand @@ -97,6 +96,10 @@ def gen_data_steel(scenario, dry_run=False): fmy = s_info.y0 nodes.remove('World') + # Do not parametrize GLB region the same way + if "R11_GLB" in nodes: + nodes.remove("R11_GLB") + # for t in s_info.set['technology']: for t in config['technology']['add']: @@ -134,10 +137,13 @@ def gen_data_steel(scenario, dry_run=False): # Obtain the parameter names, commodity,level,emission split = par.split("|") + print(split) param_name = split[0] # Obtain the scalar value for the parameter val = data_steel.loc[((data_steel["technology"] == t) \ - & (data_steel["parameter"] == par)),'value'].values[0] + & (data_steel["parameter"] == par)),'value']#.values + regions = data_steel.loc[((data_steel["technology"] == t) \ + & (data_steel["parameter"] == par)),'region']#.values common = dict( year_vtg= yv_ya.year_vtg, @@ -147,44 +153,56 @@ def gen_data_steel(scenario, dry_run=False): time_origin="year", time_dest="year",) - # For the parameters which inlcudes index names - if len(split)> 1: - - #print('1.param_name:', param_name, t) - if (param_name == "input")|(param_name == "output"): - - # Assign commodity and level names - com = split[1] - lev = split[2] - mod = split[3] - - df = (make_df(param_name, technology=t, commodity=com, \ - level=lev, value=val, mode=mod, unit='t', **common)\ - .pipe(broadcast, node_loc=nodes).pipe(same_node)) - - elif param_name == "emission_factor": - - # Assign the emisson type - emi = split[1] - mod = split[2] - - df = (make_df(param_name, technology=t, value=val,\ - emission=emi, mode=mod, unit='t', **common).pipe(broadcast, \ - node_loc=nodes)) - - else: # time-independent var_cost - mod = split[1] - df = (make_df(param_name, technology=t, value=val, \ - mode=mod, unit='t', \ - **common).pipe(broadcast, node_loc=nodes)) - - results[param_name].append(df) - - # Parameters with only parameter name - else: - #print('2.param_name:', param_name) - df = (make_df(param_name, technology=t, value=val, unit='t', \ - **common).pipe(broadcast, node_loc=nodes)) + for rg in regions: + + # For the parameters which inlcudes index names + if len(split)> 1: + + print('1.param_name:', param_name, t) + if (param_name == "input")|(param_name == "output"): + + print(rg, par, regions, val) + # Assign commodity and level names + com = split[1] + lev = split[2] + mod = split[3] + + df = (make_df(param_name, technology=t, commodity=com, \ + level=lev, \ + value=val[regions[regions==rg].index[0]], mode=mod, unit='t', \ + node_loc=rg, **common)\ + .pipe(same_node)) + + elif param_name == "emission_factor": + + # Assign the emisson type + emi = split[1] + mod = split[2] + + df = make_df(param_name, technology=t, \ + value=val[regions[regions==rg].index[0]],\ + emission=emi, mode=mod, unit='t', \ + node_loc=rg, **common)#.pipe(broadcast, \ + #node_loc=nodes)) + + else: # time-independent var_cost + mod = split[1] + df = make_df(param_name, technology=t, \ + value=val[regions[regions==rg].index[0]], \ + mode=mod, unit='t', node_loc=rg, \ + **common)#.pipe(broadcast, node_loc=nodes)) + + # Parameters with only parameter name + else: + print('2.param_name:', param_name) + df = make_df(param_name, technology=t, \ + value=val[regions[regions==rg].index[0]], unit='t', \ + node_loc=rg, **common)#.pipe(broadcast, node_loc=nodes)) + + if len(regions) == 1: + print(df) + df['node_loc'] = None + df = df.pipe(broadcast, node_loc=nodes).pipe(same_node) results[param_name].append(df) From 21076e2ffe0e629d0ec5192bd5114606cf5c5a45 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Tue, 1 Dec 2020 21:04:26 +0100 Subject: [PATCH 184/774] Incorporate regional mock demands (scaled based on gdp) for steel and cement --- .../model/material/data_cement.py | 189 +++++++++++------- .../model/material/data_steel.py | 84 +++++--- 2 files changed, 175 insertions(+), 98 deletions(-) diff --git a/message_ix_models/model/material/data_cement.py b/message_ix_models/model/material/data_cement.py index 916e89e6d8..087bfb74e3 100644 --- a/message_ix_models/model/material/data_cement.py +++ b/message_ix_models/model/material/data_cement.py @@ -18,10 +18,10 @@ ) -gdp_growth = [0.121448215899944, 0.0733079014579874, 0.0348154093342843, \ - 0.021827616787921, 0.0134425983942219, 0.0108320197485592, \ - 0.00884341208063,0.00829374133206562, 0.00649794573935969, 0.00649794573935969] -gr = np.cumprod([(x+1) for x in gdp_growth]) +# gdp_growth = [0.121448215899944, 0.0733079014579874, 0.0348154093342843, \ +# 0.021827616787921, 0.0134425983942219, 0.0108320197485592, \ +# 0.00884341208063,0.00829374133206562, 0.00649794573935969, 0.00649794573935969] +# gr = np.cumprod([(x+1) for x in gdp_growth]) # Generate a fake cement demand @@ -32,30 +32,62 @@ def gen_mock_demand_cement(scenario): modelyears = s_info.Y #s_info.Y is only for modeling years fmy = s_info.y0 - # True cement use 2011 (China) = 2100 Mt/year (ADVANCE) - demand2010_cement = 2100 - - baseyear = list(range(2020, 2110+1, 10)) - gdp_growth_interp = np.interp(modelyears, baseyear, gdp_growth) - - i = 0 - values = [] - - # Assume 5 year duration at the beginning - duration_period = (pd.Series(modelyears) - \ - pd.Series(modelyears).shift(1)).tolist() - duration_period[0] = 5 - - val = (demand2010_cement * (1+ 0.147718884937996/2) ** duration_period[i]) - values.append(val) - - for element in gdp_growth_interp: - i = i + 1 - if i < len(modelyears): - val = (val * (1+ element/2) ** duration_period[i]) - values.append(val) - - return values + # SSP2 R11 baseline GDP projection + gdp_growth = pd.read_excel( + context.get_path("material", "iamc_db ENGAGE baseline GDP PPP.xlsx"), + sheet_name="data", + ) + + gdp_growth = gdp_growth.loc[(gdp_growth['Scenario']=='baseline') & (gdp_growth['Region']!='World')].\ + drop(['Model', 'Variable', 'Unit', 'Notes', 2000, 2005, 2010, 2015], axis = 1) + + gdp_growth['Region'] = 'R11_'+ gdp_growth['Region'] + + # Regions setting for IMAGE + region_cement = pd.read_excel( + context.get_path("material", "CEMENT.BvR2010.xlsx"), + sheet_name="Timer_Regions", skiprows=range(0,3))[['Region #', 'Name']]\ + .drop_duplicates().sort_values(by='Region #') + + region_cement = region_cement.loc[region_cement['Region #'] < 999] + region_cement['node'] = \ + ['R11_NAM', 'R11_NAM', + 'R11_LAM', 'R11_LAM', + 'R11_LAM', 'R11_LAM', + 'R11_AFR', 'R11_AFR', + 'R11_AFR', 'R11_AFR', + 'R11_WEU', 'R11_EEU', + 'R11_EEU', 'R11_FSU', + 'R11_FSU', 'R11_FSU', + 'R11_MEA', 'R11_SAS', + 'R11_PAS', 'R11_CPA', + 'R11_PAS', 'R11_PAS', + 'R11_PAO', 'R11_PAO', + 'R11_SAS', 'R11_AFR'] + + # Cement demand 2010 [Mt/year] (IMAGE) + demand2010_cement = pd.read_excel( + context.get_path("material", "CEMENT.BvR2010.xlsx"), + sheet_name="Domestic Consumption", skiprows=range(0,3)).\ + groupby(by=["Region #"]).sum()[[2010]].\ + join(region_cement.set_index('Region #'), on='Region #').\ + rename(columns={2010:'value'}) + + demand2010_cement = demand2010_cement.groupby(by=['node']).sum().reset_index() + demand2010_cement['value'] = demand2010_cement['value'] / 1e9 # kg to Mt + + demand2010_cement = demand2010_cement.\ + join(gdp_growth.rename(columns={'Region':'node'}).set_index('node'), on='node') + + demand2010_cement.iloc[:,3:] = demand2010_cement.iloc[:,3:].\ + div(demand2010_cement[2020], axis=0).\ + multiply(demand2010_cement["value"], axis=0) + + demand2010_cement = pd.melt(demand2010_cement.drop(['value', 'Scenario'], axis=1),\ + id_vars=['node'], \ + var_name='year', value_name = 'value') + + return demand2010_cement @@ -89,6 +121,10 @@ def gen_data_cement(scenario, dry_run=False): fmy = s_info.y0 nodes.remove('World') + # Do not parametrize GLB region the same way + if "R11_GLB" in nodes: + nodes.remove("R11_GLB") + # for t in s_info.set['technology']: for t in config['technology']['add']: @@ -100,10 +136,13 @@ def gen_data_cement(scenario, dry_run=False): # Obtain the parameter names, commodity,level,emission split = par.split("|") + print(split) param_name = split[0] # Obtain the scalar value for the parameter val = data_cement.loc[((data_cement["technology"] == t) \ - & (data_cement["parameter"] == par)),'value'].values[0] + & (data_cement["parameter"] == par)),'value']#.values + regions = data_cement.loc[((data_cement["technology"] == t) \ + & (data_cement["parameter"] == par)),'region']#.values common = dict( year_vtg= yv_ya.year_vtg, @@ -113,52 +152,64 @@ def gen_data_cement(scenario, dry_run=False): time_origin="year", time_dest="year",) - # For the parameters which inlcudes index names - if len(split)> 1: - - #print('1.param_name:', param_name, t) - if (param_name == "input")|(param_name == "output"): - - # Assign commodity and level names - com = split[1] - lev = split[2] - mod = split[3] - - df = (make_df(param_name, technology=t, commodity=com, \ - level=lev, value=val, mode=mod, unit='t', **common)\ - .pipe(broadcast, node_loc=nodes).pipe(same_node)) - - elif param_name == "emission_factor": - - # Assign the emisson type - emi = split[1] - mod = split[2] - - df = (make_df(param_name, technology=t, value=val,\ - emission=emi, mode=mod, unit='t', **common).pipe(broadcast, \ - node_loc=nodes)) - - else: # time-independent var_cost - mod = split[1] - df = (make_df(param_name, technology=t, value=val, \ - mode=mod, unit='t', \ - **common).pipe(broadcast, node_loc=nodes)) - - results[param_name].append(df) - - # Parameters with only parameter name - else: - #print('2.param_name:', param_name) - df = (make_df(param_name, technology=t, value=val, unit='t', \ - **common).pipe(broadcast, node_loc=nodes)) + for rg in regions: + + # For the parameters which inlcudes index names + if len(split)> 1: + + print('1.param_name:', param_name, t) + if (param_name == "input")|(param_name == "output"): + + print(rg, par, regions, val) + # Assign commodity and level names + com = split[1] + lev = split[2] + mod = split[3] + + df = (make_df(param_name, technology=t, commodity=com, \ + level=lev, \ + value=val[regions[regions==rg].index[0]], mode=mod, unit='t', \ + node_loc=rg, **common)\ + .pipe(same_node)) + + elif param_name == "emission_factor": + + # Assign the emisson type + emi = split[1] + mod = split[2] + + df = make_df(param_name, technology=t, \ + value=val[regions[regions==rg].index[0]],\ + emission=emi, mode=mod, unit='t', \ + node_loc=rg, **common)#.pipe(broadcast, \ + #node_loc=nodes)) + + else: # time-independent var_cost + mod = split[1] + df = make_df(param_name, technology=t, \ + value=val[regions[regions==rg].index[0]], \ + mode=mod, unit='t', node_loc=rg, \ + **common)#.pipe(broadcast, node_loc=nodes)) + + # Parameters with only parameter name + else: + print('2.param_name:', param_name) + df = make_df(param_name, technology=t, \ + value=val[regions[regions==rg].index[0]], unit='t', \ + node_loc=rg, **common)#.pipe(broadcast, node_loc=nodes)) + + if len(regions) == 1: + print(df) + df['node_loc'] = None + df = df.pipe(broadcast, node_loc=nodes).pipe(same_node) results[param_name].append(df) # Create external demand param parname = 'demand' demand = gen_mock_demand_cement(scenario) - df = (make_df(parname, level='demand', commodity='cement', value=demand, \ - unit='t', year=modelyears, **common).pipe(broadcast, node=nodes)) + df = make_df(parname, level='demand', commodity='cement', value=demand.value, unit='t', \ + year=demand.year, time='year', node=demand.node) results[parname].append(df) # Add CCS as addon diff --git a/message_ix_models/model/material/data_steel.py b/message_ix_models/model/material/data_steel.py index d18255e8c2..3f0e60287c 100644 --- a/message_ix_models/model/material/data_steel.py +++ b/message_ix_models/model/material/data_steel.py @@ -20,11 +20,12 @@ ) # annual average growth rate by decade (2020-2110) -gdp_growth = [0.121448215899944, 0.0733079014579874, - 0.0348154093342843, 0.021827616787921, \ - 0.0134425983942219, 0.0108320197485592, \ - 0.00884341208063, 0.00829374133206562, \ - 0.00649794573935969, 0.00649794573935969] +# gdp_growth = [0.121448215899944, 0.0733079014579874, +# 0.0348154093342843, 0.021827616787921, \ +# 0.0134425983942219, 0.0108320197485592, \ +# 0.00884341208063, 0.00829374133206562, \ +# 0.00649794573935969, 0.00649794573935969] + # Generate a fake steel demand @@ -35,31 +36,56 @@ def gen_mock_demand_steel(scenario): modelyears = s_info.Y #s_info.Y is only for modeling years fmy = s_info.y0 - # True steel use 2010 (China) = 537 Mt/year - demand2010_steel = 537 - # https://www.worldsteel.org/en/dam/jcr:0474d208-9108-4927-ace8-4ac5445c5df8/World+Steel+in+Figures+2017.pdf - - baseyear = list(range(2020, 2110+1, 10)) - gdp_growth_interp = np.interp(modelyears, baseyear, gdp_growth) + # SSP2 R11 baseline GDP projection + gdp_growth = pd.read_excel( + context.get_path("material", "iamc_db ENGAGE baseline GDP PPP.xlsx"), + sheet_name="data", + ) - i = 0 - values = [] + gdp_growth = gdp_growth.loc[(gdp_growth['Scenario']=='baseline') & (gdp_growth['Region']!='World')].\ + drop(['Model', 'Variable', 'Unit', 'Notes', 2000, 2005, 2010, 2015], axis = 1) - # Assume 5 year duration at the beginning - duration_period = (pd.Series(modelyears) - \ - pd.Series(modelyears).shift(1)).tolist() - duration_period[0] = 5 + gdp_growth['Region'] = 'R11_'+ gdp_growth['Region'] - val = (demand2010_steel * (1+ 0.147718884937996/2) ** duration_period[i]) - values.append(val) - - for element in gdp_growth_interp: - i = i + 1 - if i < len(modelyears): - val = (val * (1+ element/2) ** duration_period[i]) - values.append(val) - - return values + # True steel use 2010 [Mt/year] + # https://www.worldsteel.org/en/dam/jcr:0474d208-9108-4927-ace8-4ac5445c5df8/World+Steel+in+Figures+2017.pdf + r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', \ + 'R11_MEA', 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU'] + d = [35, 537, 70, 53, 49, \ + 39, 130, 80, 45, 96, 100] + + demand2010_steel = pd.DataFrame({'Region':r, 'Val':d}).\ + join(gdp_growth.set_index('Region'), on='Region').rename(columns={'Region':'node'}) + + demand2010_steel.iloc[:,3:] = demand2010_steel.iloc[:,3:].\ + div(demand2010_steel[2020], axis=0).\ + multiply(demand2010_steel["Val"], axis=0) + + demand2010_steel = pd.melt(demand2010_steel.drop(['Val', 'Scenario'], axis=1),\ + id_vars=['node'], \ + var_name='year', value_name = 'value') + # + # baseyear = list(range(2020, 2110+1, 10)) + # gdp_growth_interp = np.interp(modelyears, baseyear, gdp_growth) + # + # i = 0 + # values = [] + # + # # Assume 5 year duration at the beginning + # duration_period = (pd.Series(modelyears) - \ + # pd.Series(modelyears).shift(1)).tolist() + # duration_period[0] = 5 + # + # val = (demand2010_steel.val * (1+ 0.147718884937996/2) ** duration_period[i]) + # values.append(val) + # + # for element in gdp_growth_interp: + # i = i + 1 + # if i < len(modelyears): + # val = (val * (1+ element/2) ** duration_period[i]) + # values.append(val) + + return demand2010_steel @@ -250,8 +276,8 @@ def gen_data_steel(scenario, dry_run=False): # Create external demand param parname = 'demand' demand = gen_mock_demand_steel(scenario) - df = (make_df(parname, level='demand', commodity='steel', value=demand, unit='t', \ - year=modelyears, **common).pipe(broadcast, node=nodes)) + df = make_df(parname, level='demand', commodity='steel', value=demand.value, unit='t', \ + year=demand.year, time='year', node=demand.node)#.pipe(broadcast, node=nodes) results[parname].append(df) # Concatenate to one data frame per parameter From 0ae35b5bce56ccc5e37f920c87f9809f498a7778 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Tue, 1 Dec 2020 21:05:25 +0100 Subject: [PATCH 185/774] Exclude r11_glb from material parametrization --- message_ix_models/model/material/data_aluminum.py | 4 ++++ message_ix_models/model/material/data_generic.py | 4 ++++ message_ix_models/model/material/data_petro.py | 4 ++++ 3 files changed, 12 insertions(+) diff --git a/message_ix_models/model/material/data_aluminum.py b/message_ix_models/model/material/data_aluminum.py index 51e21e75e3..e9ddc6a920 100644 --- a/message_ix_models/model/material/data_aluminum.py +++ b/message_ix_models/model/material/data_aluminum.py @@ -81,6 +81,10 @@ def gen_data_aluminum(scenario, dry_run=False): yv_ya = s_info.yv_ya fmy = s_info.y0 + # Do not parametrize GLB region the same way + if "R11_GLB" in nodes: + nodes.remove("R11_GLB") + # 'World' is included by default when creating a message_ix.Scenario(). # Need to remove it for the China bare model nodes.remove('World') diff --git a/message_ix_models/model/material/data_generic.py b/message_ix_models/model/material/data_generic.py index 7153b59aef..766225d54e 100644 --- a/message_ix_models/model/material/data_generic.py +++ b/message_ix_models/model/material/data_generic.py @@ -75,6 +75,10 @@ def gen_data_generic(scenario, dry_run=False): yv_ya = s_info.yv_ya fmy = s_info.y0 + # Do not parametrize GLB region the same way + if "R11_GLB" in nodes: + nodes.remove("R11_GLB") + # 'World' is included by default when creating a message_ix.Scenario(). # Need to remove it for the China bare model nodes.remove('World') diff --git a/message_ix_models/model/material/data_petro.py b/message_ix_models/model/material/data_petro.py index 8046294a83..8c94b6b5c2 100644 --- a/message_ix_models/model/material/data_petro.py +++ b/message_ix_models/model/material/data_petro.py @@ -131,6 +131,10 @@ def gen_data_petro_chemicals(scenario, dry_run=False): yv_ya = s_info.yv_ya fmy = s_info.y0 + # Do not parametrize GLB region the same way + if "R11_GLB" in nodes: + nodes.remove("R11_GLB") + # 'World' is included by default when creating a message_ix.Scenario(). # Need to remove it for the China bare model nodes.remove('World') From a46fee7f32308809d026d8c10b374d56fcda5a5c Mon Sep 17 00:00:00 2001 From: Jihoon Date: Tue, 1 Dec 2020 21:06:20 +0100 Subject: [PATCH 186/774] More data for global expansion --- message_ix_models/data/material/CEMENT.BvR2010.xlsx | 3 +++ .../data/material/Global_steel_cement_MESSAGE.xlsx | 3 +++ .../data/material/iamc_db ENGAGE baseline GDP PPP.xlsx | 3 +++ 3 files changed, 9 insertions(+) create mode 100644 message_ix_models/data/material/CEMENT.BvR2010.xlsx create mode 100644 message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx create mode 100644 message_ix_models/data/material/iamc_db ENGAGE baseline GDP PPP.xlsx diff --git a/message_ix_models/data/material/CEMENT.BvR2010.xlsx b/message_ix_models/data/material/CEMENT.BvR2010.xlsx new file mode 100644 index 0000000000..3de75f8692 --- /dev/null +++ b/message_ix_models/data/material/CEMENT.BvR2010.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f29cc85cf609901ea736c293d3448da88a6c83b2601c9c4aed6b192695050950 +size 1586050 diff --git a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx new file mode 100644 index 0000000000..e0ea9ba480 --- /dev/null +++ b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:466d0b340bda99b32cdfce967d0142c8b013f63845e1cee43c9c64ffec8a412d +size 50058 diff --git a/message_ix_models/data/material/iamc_db ENGAGE baseline GDP PPP.xlsx b/message_ix_models/data/material/iamc_db ENGAGE baseline GDP PPP.xlsx new file mode 100644 index 0000000000..fa9288b058 --- /dev/null +++ b/message_ix_models/data/material/iamc_db ENGAGE baseline GDP PPP.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b4f202d56a88750e792b68d3514f72d90341179bd6b8c90f5e8d4a655e165ce4 +size 17501 From 2862026f721866fb87c10a81963606f3241bf6fa Mon Sep 17 00:00:00 2001 From: Jihoon Date: Tue, 1 Dec 2020 21:07:01 +0100 Subject: [PATCH 187/774] Testing global setup --- .../model/material/material_testscript.py | 34 +++++++++++++++++-- 1 file changed, 32 insertions(+), 2 deletions(-) diff --git a/message_ix_models/model/material/material_testscript.py b/message_ix_models/model/material/material_testscript.py index 6a9c9304ee..fc858f7f02 100644 --- a/message_ix_models/model/material/material_testscript.py +++ b/message_ix_models/model/material/material_testscript.py @@ -74,16 +74,45 @@ p.plot_activity(baseyear=False, subset=['dri_steel', \ 'bf_steel']) +#%% Global test +from message_data.model.create import create_res + +# Create Context obj +Context._instance = [] +ctx = Context() + +# Set default scenario/model names - Later coming from CLI +ctx.platform_info.setdefault('name', 'ixmp_dev') +ctx.platform_info.setdefault('jvmargs', ['-Xmx12G']) # To avoid java heap space error +ctx.scenario_info.setdefault('model', 'Material_test_MESSAGE_global') +ctx.scenario_info.setdefault('scenario', 'baseline') +ctx['ssp'] = 'SSP2' +ctx['datafile'] = 'Global_steel_cement_MESSAGE.xlsx' + +# Use general code to create a Scenario with some stuff in it +scen = create_res(context = ctx) + +# Use material-specific code to add certain stuff +a = build(scen) + +# Solve the model +import time +start_time = time.time() +scen.solve() +print(".solve: %.6s seconds taken." % (time.time() - start_time)) #%% Auxiliary random test stuff import pandas as pd import message_data.model.material.data_util as du +import message_data.model.material.data_cement as dc # Test read_data_steel <- will be in create_res if working fine -df = dt.read_data_steel() +df = du.read_sector_data('steel') df = du.read_rel(ctx.datafile) +mp = ctx.get_platform() + b = dt.read_data_generic() b = dt.read_var_cost() bb = dt.read_sector_data() @@ -93,8 +122,9 @@ df_gen = dt.gen_data_generic(scen) df_st = dt.gen_data_steel(scen) +df_st = dt.gen_data_cement(scen) a = dt.get_data(scen, ctx) -dt.gen_mock_demand_cement(ScenarioInfo(scen)) +dc.gen_mock_demand_cement(scen) dt.read_data_generic() bare.add_data(scen) From 52655eb685c57a0293da7505732bc3c16c298105 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Wed, 2 Dec 2020 14:24:43 +0100 Subject: [PATCH 188/774] Adjust mea steel demand to temporarily avoid infeasibility --- message_ix_models/model/material/data_steel.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/message_ix_models/model/material/data_steel.py b/message_ix_models/model/material/data_steel.py index 3f0e60287c..42a15786ca 100644 --- a/message_ix_models/model/material/data_steel.py +++ b/message_ix_models/model/material/data_steel.py @@ -52,7 +52,7 @@ def gen_mock_demand_steel(scenario): r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', \ 'R11_MEA', 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU'] d = [35, 537, 70, 53, 49, \ - 39, 130, 80, 45, 96, 100] + 9, 130, 80, 45, 96, 100] # MEA change from 39 to 9 to make it feasible (coal supply bound) demand2010_steel = pd.DataFrame({'Region':r, 'Val':d}).\ join(gdp_growth.set_index('Region'), on='Region').rename(columns={'Region':'node'}) From c541c9d88116679166e934793f24c73759709516 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Fri, 4 Dec 2020 10:28:19 +0100 Subject: [PATCH 189/774] Extended aluminum and petro chemicals demand for other regions (not yet tested) --- .../model/material/data_aluminum.py | 189 +++++++----- .../model/material/data_petro.py | 290 +++++++++++------- 2 files changed, 279 insertions(+), 200 deletions(-) diff --git a/message_ix_models/model/material/data_aluminum.py b/message_ix_models/model/material/data_aluminum.py index e9ddc6a920..f2667a566d 100644 --- a/message_ix_models/model/material/data_aluminum.py +++ b/message_ix_models/model/material/data_aluminum.py @@ -84,7 +84,7 @@ def gen_data_aluminum(scenario, dry_run=False): # Do not parametrize GLB region the same way if "R11_GLB" in nodes: nodes.remove("R11_GLB") - + # 'World' is included by default when creating a message_ix.Scenario(). # Need to remove it for the China bare model nodes.remove('World') @@ -154,45 +154,42 @@ def gen_data_aluminum(scenario, dry_run=False): **common).pipe(broadcast, node_loc=nodes)) results[param_name].append(df) - # Add the dummy alluminum demand - - values = gen_mock_demand_aluminum(scenario) - - demand_al = (make_df("demand", commodity= "aluminum", \ - level= "demand", year = modelyears, value=values, unit='Mt',\ - time= "year").pipe(broadcast, node=nodes)) - - results["demand"].append(demand_al) - - # Add historical data - - for tec in data_aluminum_hist["technology"].unique(): - - y_hist = [1980,1985,1990,1995,2000,2005,2010,2015] #length need to match what's in the xls - common_hist = dict( - year_vtg= y_hist, - year_act= y_hist, - mode="M1", - time="year",) - - val_act = data_aluminum_hist.\ - loc[(data_aluminum_hist["technology"]== tec), "production"] - - df_hist_act = (make_df("historical_activity", technology=tec, \ - value=val_act, unit='Mt', **common_hist).pipe(broadcast, node_loc=nodes)) - - results["historical_activity"].append(df_hist_act) - - c_factor = data_aluminum.loc[((data_aluminum["technology"]== tec) \ - & (data_aluminum["parameter"]=="capacity_factor")), "value"].values - - val_cap = data_aluminum_hist.loc[(data_aluminum_hist["technology"]== tec), \ - "new_production"] / c_factor - - df_hist_cap = (make_df("historical_new_capacity", technology=tec, \ - value=val_cap, unit='Mt', **common_hist).pipe(broadcast, node_loc=nodes)) - - results["historical_new_capacity"].append(df_hist_cap) + # Create external demand param + parname = 'demand' + demand = gen_mock_demand_aluminum(scenario) + df = make_df(parname, level='demand', commodity='aluminum', value=demand.value, unit='t', \ + year=demand.year, time='year', node=demand.node)#.pipe(broadcast, node=nodes) + results[parname].append(df) + + # # Add historical data + # + # for tec in data_aluminum_hist["technology"].unique(): + # + # y_hist = [1980,1985,1990,1995,2000,2005,2010,2015] #length need to match what's in the xls + # common_hist = dict( + # year_vtg= y_hist, + # year_act= y_hist, + # mode="M1", + # time="year",) + # + # val_act = data_aluminum_hist.\ + # loc[(data_aluminum_hist["technology"]== tec), "production"] + # + # df_hist_act = (make_df("historical_activity", technology=tec, \ + # value=val_act, unit='Mt', **common_hist).pipe(broadcast, node_loc=nodes)) + # + # results["historical_activity"].append(df_hist_act) + # + # c_factor = data_aluminum.loc[((data_aluminum["technology"]== tec) \ + # & (data_aluminum["parameter"]=="capacity_factor")), "value"].values + # + # val_cap = data_aluminum_hist.loc[(data_aluminum_hist["technology"]== tec), \ + # "new_production"] / c_factor + # + # df_hist_cap = (make_df("historical_new_capacity", technology=tec, \ + # value=val_cap, unit='Mt', **common_hist).pipe(broadcast, node_loc=nodes)) + # + # results["historical_new_capacity"].append(df_hist_cap) # Add relations for scrap grades and availability @@ -249,51 +246,81 @@ def gen_data_aluminum(scenario, dry_run=False): def gen_mock_demand_aluminum(scenario): - # 17.3 Mt in 2010 to match the historical production from IAI. - # This is the amount right after electrolysis. - - # The future projection of the demand: Increases by half of the GDP growth rate. - # Starting from 2020. context = read_config() s_info = ScenarioInfo(scenario) modelyears = s_info.Y #s_info.Y is only for modeling years + fmy = s_info.y0 + + # SSP2 R11 baseline GDP projection + gdp_growth = pd.read_excel( + context.get_path("material", "iamc_db ENGAGE baseline GDP PPP.xlsx"), + sheet_name="data",) + + gdp_growth = gdp_growth.loc[(gdp_growth['Scenario']=='baseline') & \ + (gdp_growth['Region']!='World')].drop(['Model', 'Variable', 'Unit', 'Notes',\ + 2000, 2005, 2010, 2015], axis = 1) + + gdp_growth['Region'] = 'R11_'+ gdp_growth['Region'] + + # Aluminum 2015 + # https://www.world-aluminium.org/statistics/#data + # Not all the regions match here. Some assumptions needed to be made. + # MEA, PAS, SAS, EEU, FSU + + # Values in 2010 from IAI for China. Slightly varies for other regions. + # Can be adjusted: https://alucycle.world-aluminium.org/public-access/ - # Values in 2010 from IAI. fin_to_useful = 0.971 useful_to_product = 0.866 - # Add temporary exogenous demand: 17.3 Mt in 2010 (IAI) - demand2010_aluminum = 17.3 - - # The future projection of the demand: Increases by half of the GDP growth rate - # gdp_growth rate: SSP2 global model. Starting from 2020. - gdp_growth = [0.121448215899944, 0.0733079014579874, - 0.0348154093342843, 0.021827616787921, - 0.0134425983942219, 0.0108320197485592, - 0.00884341208063, 0.00829374133206562, - # Add one more element since model is until 2110 normally - 0.00649794573935969, 0.00649794573935969] - baseyear = list(range(2020, 2110+1, 10)) # Index for above vector - gdp_growth_interp = np.interp(modelyears, baseyear, gdp_growth) - - i = 0 - values = [] - - # Assume 5 year duration at the beginning - duration_period = (pd.Series(modelyears) - \ - pd.Series(modelyears).shift(1)).tolist() - duration_period[0] = 5 - - val = (demand2010_aluminum * (1+ 0.147718884937996/2) ** duration_period[i]) - values.append(val) - - for element in gdp_growth_interp: - i = i + 1 - if i < len(modelyears): - val = (val * (1+ element/2) ** duration_period[i]) - values.append(val) - # Adjust the demand to product level. - - values = [x * fin_to_useful * useful_to_product for x in values] - - return values + r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', \ + 'R11_MEA', 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU'] + d = [1.7, 31.5, 1.8, 2, 1.3, 6.1, 4.5, 2,1,1,3.7] + d = [x * fin_to_useful * useful_to_product for x in d] + + demand2015_al = pd.DataFrame({'Region':r, 'Val':d}).\ + join(gdp_growth.set_index('Region'), on='Region').\ + rename(columns={'Region':'node'}) + + demand2015_al.iloc[:,3:] = demand2015_al.iloc[:,3:].\ + div(demand2015_al[2020], axis=0).\ + multiply(demand2015_al["Val"], axis=0) + + demand2015_al = pd.melt(demand2015_al.drop(['Val', 'Scenario'], axis=1),\ + id_vars=['node'], var_name='year', value_name = 'value') + + # # Add temporary exogenous demand: 17.3 Mt in 2010 (IAI) + # demand2010_aluminum = 17.3 + # + # # The future projection of the demand: Increases by half of the GDP growth rate + # # gdp_growth rate: SSP2 global model. Starting from 2020. + # gdp_growth = [0.121448215899944, 0.0733079014579874, + # 0.0348154093342843, 0.021827616787921, + # 0.0134425983942219, 0.0108320197485592, + # 0.00884341208063, 0.00829374133206562, + # # Add one more element since model is until 2110 normally + # 0.00649794573935969, 0.00649794573935969] + # baseyear = list(range(2020, 2110+1, 10)) # Index for above vector + # gdp_growth_interp = np.interp(modelyears, baseyear, gdp_growth) + # + # i = 0 + # values = [] + # + # # Assume 5 year duration at the beginning + # duration_period = (pd.Series(modelyears) - \ + # pd.Series(modelyears).shift(1)).tolist() + # duration_period[0] = 5 + # + # val = (demand2010_aluminum * (1+ 0.147718884937996/2) ** duration_period[i]) + # values.append(val) + # + # for element in gdp_growth_interp: + # i = i + 1 + # if i < len(modelyears): + # val = (val * (1+ element/2) ** duration_period[i]) + # values.append(val) + # # Adjust the demand to product level. + # + # values = [x * fin_to_useful * useful_to_product for x in values] + + return demand2015_al diff --git a/message_ix_models/model/material/data_petro.py b/message_ix_models/model/material/data_petro.py index 8c94b6b5c2..e1e7a749a8 100644 --- a/message_ix_models/model/material/data_petro.py +++ b/message_ix_models/model/material/data_petro.py @@ -41,6 +41,63 @@ def read_data_petrochemicals(): def gen_mock_demand_petro(scenario): + context = read_config() + s_info = ScenarioInfo(scenario) + modelyears = s_info.Y #s_info.Y is only for modeling years + fmy = s_info.y0 + + # SSP2 R11 baseline GDP projection + gdp_growth = pd.read_excel( + context.get_path("material", "iamc_db ENGAGE baseline GDP PPP.xlsx"), + sheet_name="data", + ) + + gdp_growth = gdp_growth.loc[(gdp_growth['Scenario']=='baseline') & \ + (gdp_growth['Region']!='World')].drop(['Model', 'Variable', 'Unit', \ + 'Notes', 2000, 2005, 2010, 2015], axis = 1) + + gdp_growth['Region'] = 'R11_'+ gdp_growth['Region'] + + # 2015 demand + # The Future of Petrochemicals Methodological Annex + # Projections here do not show too much growth until 2050 for some regions. + # For division of some regions assumptions made: + # PAO, PAS, SAS, EEU,WEU + + r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', \ + 'R11_MEA', 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU'] + d_ethylene = [1,25,10,3,5,20,25,12,10,13,15] + d_propylene = [0.5,25, 10, 0.5, 3, 10, 15, 10,7,8, 10] + d_BTX = [0.5,25, 10, 0.5, 3, 10, 15, 10,7,8, 10] + list = [] + + for e in ["ethylene","propylene","BTX"]: + if e == "ethylene": + demand2015 = pd.DataFrame({'Region':r, 'Val':d_ethylene}).\ + join(gdp_growth.set_index('Region'), on='Region').\ + rename(columns={'Region':'node'}) + + if e == "propylene": + demand2015 = pd.DataFrame({'Region':r, 'Val':d_propylene}).\ + join(gdp_growth.set_index('Region'), on='Region').\ + rename(columns={'Region':'node'}) + + if e == "BTX": + demand2015 = pd.DataFrame({'Region':r, 'Val':d_BTX}).\ + join(gdp_growth.set_index('Region'), on='Region').\ + rename(columns={'Region':'node'}) + + demand2015.iloc[:,3:] = demand2015.iloc[:,3:].div(demand2015[2020], axis=0).\ + multiply(demand2015["Val"], axis=0) + + demand2015 = pd.melt(demand2015.drop(['Val', 'Scenario'], axis=1),\ + id_vars=['node'], var_name='year', value_name = 'value') + + list.append(demand2015) + + return demand2015[0], demand2015[1], demand2015[2] + + # China 2006: 22 kg/cap HVC demand. 2006 population: 1.311 billion # This makes 28.842 Mt. (IEA Energy Technology Transitions for Industry) # In 2010: 43.263 Mt (1.5 times of 2006) @@ -53,61 +110,61 @@ def gen_mock_demand_petro(scenario): # The future projection of the demand: Increases by half of the GDP growth rate. # Starting from 2020. - context = read_config() - s_info = ScenarioInfo(scenario) - modelyears = s_info.Y #s_info.Y is only for modeling years - - gdp_growth = [0.121448215899944, 0.0733079014579874, - 0.0348154093342843, 0.021827616787921, - 0.0134425983942219, 0.0108320197485592, - 0.00884341208063, 0.00829374133206562, - # Add one more element since model is until 2110 normally - 0.00649794573935969, 0.00649794573935969] - baseyear = list(range(2020, 2110+1, 10)) # Index for above vector - gdp_growth_interp = np.interp(modelyears, baseyear, gdp_growth) - print(gdp_growth_interp) - - i = 0 - values_e = [] - values_p = [] - values_BTX = [] - - # Assume 5 year duration at the beginning - duration_period = (pd.Series(modelyears) - \ - pd.Series(modelyears).shift(1)).tolist() - duration_period[0] = 5 - - val_e = (25 * (1+ 0.147718884937996/2) ** duration_period[i]) - print("val_e") - print(val_e) - values_e.append(val_e) - print(values_e) - - val_p = (25 * (1+ 0.147718884937996/2) ** duration_period[i]) - print("val_p") - print(val_p) - values_p.append(val_p) - print(values_p) - - val_BTX = (21 * (1+ 0.147718884937996/2) ** duration_period[i]) - print("val_BTX") - print(val_BTX) - values_BTX.append(val_BTX) - print(values_BTX) - - for element in gdp_growth_interp: - i = i + 1 - if i < len(modelyears): - val_e = (val_e * (1+ element/2) ** duration_period[i]) - values_e.append(val_e) - - val_p = (val_p * (1+ element/2) ** duration_period[i]) - values_p.append(val_p) - - val_BTX = (val_BTX * (1+ element/2) ** duration_period[i]) - values_BTX.append(val_BTX) - - return values_e, values_p, values_BTX + # context = read_config() + # s_info = ScenarioInfo(scenario) + # modelyears = s_info.Y #s_info.Y is only for modeling years + # + # gdp_growth = [0.121448215899944, 0.0733079014579874, + # 0.0348154093342843, 0.021827616787921, + # 0.0134425983942219, 0.0108320197485592, + # 0.00884341208063, 0.00829374133206562, + # # Add one more element since model is until 2110 normally + # 0.00649794573935969, 0.00649794573935969] + # baseyear = list(range(2020, 2110+1, 10)) # Index for above vector + # gdp_growth_interp = np.interp(modelyears, baseyear, gdp_growth) + # print(gdp_growth_interp) + + # i = 0 + # values_e = [] + # values_p = [] + # values_BTX = [] + # + # # Assume 5 year duration at the beginning + # duration_period = (pd.Series(modelyears) - \ + # pd.Series(modelyears).shift(1)).tolist() + # duration_period[0] = 5 + # + # val_e = (25 * (1+ 0.147718884937996/2) ** duration_period[i]) + # print("val_e") + # print(val_e) + # values_e.append(val_e) + # print(values_e) + # + # val_p = (25 * (1+ 0.147718884937996/2) ** duration_period[i]) + # print("val_p") + # print(val_p) + # values_p.append(val_p) + # print(values_p) + # + # val_BTX = (21 * (1+ 0.147718884937996/2) ** duration_period[i]) + # print("val_BTX") + # print(val_BTX) + # values_BTX.append(val_BTX) + # print(values_BTX) + # + # for element in gdp_growth_interp: + # i = i + 1 + # if i < len(modelyears): + # val_e = (val_e * (1+ element/2) ** duration_period[i]) + # values_e.append(val_e) + # + # val_p = (val_p * (1+ element/2) ** duration_period[i]) + # values_p.append(val_p) + # + # val_BTX = (val_BTX * (1+ element/2) ** duration_period[i]) + # values_BTX.append(val_BTX) + # + # return values_e, values_p, values_BTX def gen_data_petro_chemicals(scenario, dry_run=False): # Load configuration @@ -134,7 +191,7 @@ def gen_data_petro_chemicals(scenario, dry_run=False): # Do not parametrize GLB region the same way if "R11_GLB" in nodes: nodes.remove("R11_GLB") - + # 'World' is included by default when creating a message_ix.Scenario(). # Need to remove it for the China bare model nodes.remove('World') @@ -213,69 +270,64 @@ def gen_data_petro_chemicals(scenario, dry_run=False): results[param_name].append(df) # Add demand - - values_e, values_p, values_BTX = gen_mock_demand_petro(scenario) - - demand_ethylene = (make_df("demand", commodity= "ethylene", \ - level= "demand", year = modelyears, value=values_e, unit='Mt',\ - time= "year").pipe(broadcast, node=nodes)) - print("Ethylene demand") - print(demand_ethylene) - - demand_propylene = (make_df("demand", commodity= "propylene", \ - level= "demand", year = modelyears, value=values_p, unit='Mt',\ - time= "year").pipe(broadcast, node=nodes)) - print("Propylene demand") - print(demand_propylene) - - demand_BTX = (make_df("demand", commodity= "BTX", \ - level= "demand", year = modelyears, value=values_BTX, unit='Mt',\ - time= "year").pipe(broadcast, node=nodes)) - print("BTX demand") - print(demand_BTX) - - results["demand"].append(demand_ethylene) - results["demand"].append(demand_propylene) - results["demand"].append(demand_BTX) - - # Add historical data - - print(data_petro_hist["technology"].unique()) - for tec in data_petro_hist["technology"].unique(): - print(tec) - - y_hist = [1980,1985,1990,1995,2000,2005,2010,2015] #length need to match what's in the xls - common_hist = dict( - year_vtg= y_hist, - year_act= y_hist, - mode="M1", - time="year",) - - print("historical years") - print(y_hist) - val_act = data_petro_hist.\ - loc[(data_petro_hist["technology"]== tec), "production"] - print("value activity") - print(val_act) - - df_hist_act = (make_df("historical_activity", technology=tec, \ - value=val_act, unit='Mt', **common_hist).pipe(broadcast, node_loc=nodes)) - - results["historical_activity"].append(df_hist_act) - - c_factor = data_petro.loc[((data_petro["technology"]== tec) \ - & (data_petro["parameter"]=="capacity_factor")), "value"].values - - val_cap = data_petro_hist.loc[(data_petro_hist["technology"]== tec), \ - "new_production"] / c_factor - - print("This is capacity value") - print(val_cap) - - df_hist_cap = (make_df("historical_new_capacity", technology=tec, \ - value=val_cap, unit='Mt', **common_hist).pipe(broadcast, node_loc=nodes)) - - results["historical_new_capacity"].append(df_hist_cap) + # Create external demand param + + demand_e,demand_p,demand_BTX = gen_mock_demand_petro(scenario) + paramname = "demand" + + df_e = make_df(parname, level='demand', commodity="ethylene", \ + value=demand_e.value, unit='t',year=demand.year, time='year', \ + node=demand.node)#.pipe(broadcast, node=nodes) + results["demand"].append(df) + + df_p = make_df(parname, level='demand', commodity="propylene", \ + value=demand_p.value, unit='t',year=demand.year, time='year', \ + node=demand.node)#.pipe(broadcast, node=nodes) + results["demand"].append(df_p) + + df_BTX = make_df(parname, level='demand', commodity="BTX", \ + value=demand_BTX.value, unit='t',year=demand.year, time='year', \ + node=demand.node)#.pipe(broadcast, node=nodes) + results["demand"].append(df_BTX) + + # # Add historical data + # + # print(data_petro_hist["technology"].unique()) + # for tec in data_petro_hist["technology"].unique(): + # print(tec) + # + # y_hist = [1980,1985,1990,1995,2000,2005,2010,2015] #length need to match what's in the xls + # common_hist = dict( + # year_vtg= y_hist, + # year_act= y_hist, + # mode="M1", + # time="year",) + # + # print("historical years") + # print(y_hist) + # val_act = data_petro_hist.\ + # loc[(data_petro_hist["technology"]== tec), "production"] + # print("value activity") + # print(val_act) + # + # df_hist_act = (make_df("historical_activity", technology=tec, \ + # value=val_act, unit='Mt', **common_hist).pipe(broadcast, node_loc=nodes)) + # + # results["historical_activity"].append(df_hist_act) + # + # c_factor = data_petro.loc[((data_petro["technology"]== tec) \ + # & (data_petro["parameter"]=="capacity_factor")), "value"].values + # + # val_cap = data_petro_hist.loc[(data_petro_hist["technology"]== tec), \ + # "new_production"] / c_factor + # + # print("This is capacity value") + # print(val_cap) + # + # df_hist_cap = (make_df("historical_new_capacity", technology=tec, \ + # value=val_cap, unit='Mt', **common_hist).pipe(broadcast, node_loc=nodes)) + # + # results["historical_new_capacity"].append(df_hist_cap) results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} From 1419055de13c1c24e423bbe3901af30ef81bf7d9 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Fri, 4 Dec 2020 12:53:23 +0100 Subject: [PATCH 190/774] Alu and petro demand fixed --- .../model/material/data_aluminum.py | 16 +++++---- .../model/material/data_petro.py | 36 ++++++++++++------- 2 files changed, 33 insertions(+), 19 deletions(-) diff --git a/message_ix_models/model/material/data_aluminum.py b/message_ix_models/model/material/data_aluminum.py index f2667a566d..07e25958c3 100644 --- a/message_ix_models/model/material/data_aluminum.py +++ b/message_ix_models/model/material/data_aluminum.py @@ -159,6 +159,8 @@ def gen_data_aluminum(scenario, dry_run=False): demand = gen_mock_demand_aluminum(scenario) df = make_df(parname, level='demand', commodity='aluminum', value=demand.value, unit='t', \ year=demand.year, time='year', node=demand.node)#.pipe(broadcast, node=nodes) + print("aluminum node") + print(demand.node) results[parname].append(df) # # Add historical data @@ -194,7 +196,6 @@ def gen_data_aluminum(scenario, dry_run=False): # Add relations for scrap grades and availability for r in config['relation']['add']: - print(r) params = data_aluminum_rel.loc[(data_aluminum_rel["relation"] == r),\ "parameter"].values.tolist() @@ -212,15 +213,9 @@ def gen_data_aluminum(scenario, dry_run=False): & (data_aluminum_rel["parameter"] == par_name)) ,'technology'] for tec in tec_list.unique(): - print("tec list") - print(data_aluminum_rel["technology"].unique()) - print(tec) - val = data_aluminum_rel.loc[((data_aluminum_rel["relation"] == r) \ & (data_aluminum_rel["parameter"] == par_name) & \ (data_aluminum_rel["technology"]==tec)),'value'].values[0] - print("relation value") - print(val) df = (make_df(par_name, technology=tec, value=val, unit='-',\ **common_rel).pipe(broadcast, node_rel=nodes, node_loc=nodes)) @@ -275,9 +270,14 @@ def gen_mock_demand_aluminum(scenario): r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', \ 'R11_MEA', 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU'] + + # Domestic production d = [1.7, 31.5, 1.8, 2, 1.3, 6.1, 4.5, 2,1,1,3.7] d = [x * fin_to_useful * useful_to_product for x in d] + # Demand at product level. (IAI) + #d = [1.7, 28.2, 4.5, 2, 2.5, 2, 14.1, 3.5, 5.5,6,8 ] + demand2015_al = pd.DataFrame({'Region':r, 'Val':d}).\ join(gdp_growth.set_index('Region'), on='Region').\ rename(columns={'Region':'node'}) @@ -289,6 +289,8 @@ def gen_mock_demand_aluminum(scenario): demand2015_al = pd.melt(demand2015_al.drop(['Val', 'Scenario'], axis=1),\ id_vars=['node'], var_name='year', value_name = 'value') + print(demand2015_al) + # # Add temporary exogenous demand: 17.3 Mt in 2010 (IAI) # demand2010_aluminum = 17.3 # diff --git a/message_ix_models/model/material/data_petro.py b/message_ix_models/model/material/data_petro.py index e1e7a749a8..89a8d3ee28 100644 --- a/message_ix_models/model/material/data_petro.py +++ b/message_ix_models/model/material/data_petro.py @@ -72,6 +72,7 @@ def gen_mock_demand_petro(scenario): list = [] for e in ["ethylene","propylene","BTX"]: + print(e) if e == "ethylene": demand2015 = pd.DataFrame({'Region':r, 'Val':d_ethylene}).\ join(gdp_growth.set_index('Region'), on='Region').\ @@ -93,9 +94,13 @@ def gen_mock_demand_petro(scenario): demand2015 = pd.melt(demand2015.drop(['Val', 'Scenario'], axis=1),\ id_vars=['node'], var_name='year', value_name = 'value') + print(demand2015) + list.append(demand2015) + print("list") + print(list) - return demand2015[0], demand2015[1], demand2015[2] + return list[0], list[1], list[2] # China 2006: 22 kg/cap HVC demand. 2006 population: 1.311 billion @@ -275,21 +280,28 @@ def gen_data_petro_chemicals(scenario, dry_run=False): demand_e,demand_p,demand_BTX = gen_mock_demand_petro(scenario) paramname = "demand" - df_e = make_df(parname, level='demand', commodity="ethylene", \ - value=demand_e.value, unit='t',year=demand.year, time='year', \ - node=demand.node)#.pipe(broadcast, node=nodes) - results["demand"].append(df) - - df_p = make_df(parname, level='demand', commodity="propylene", \ - value=demand_p.value, unit='t',year=demand.year, time='year', \ - node=demand.node)#.pipe(broadcast, node=nodes) + df_e = make_df(paramname, level='demand', commodity="ethylene", \ + value=demand_e.value, unit='t',year=demand_e.year, time='year', \ + node=demand_e.node)#.pipe(broadcast, node=nodes) + print("demand ethylene") + print(demand_e.node) + results["demand"].append(df_e) + + df_p = make_df(paramname, level='demand', commodity="propylene", \ + value=demand_p.value, unit='t',year=demand_p.year, time='year', \ + node=demand_p.node)#.pipe(broadcast, node=nodes) + print("demand propylene") + print(demand_p.node) results["demand"].append(df_p) - df_BTX = make_df(parname, level='demand', commodity="BTX", \ - value=demand_BTX.value, unit='t',year=demand.year, time='year', \ - node=demand.node)#.pipe(broadcast, node=nodes) + df_BTX = make_df(paramname, level='demand', commodity="BTX", \ + value=demand_BTX.value, unit='t',year=demand_BTX.year, time='year', \ + node=demand_BTX.node)#.pipe(broadcast, node=nodes) + print("demand BTX") + print(demand_BTX.node) results["demand"].append(df_BTX) + # # Add historical data # # print(data_petro_hist["technology"].unique()) From 297c7d910f126d2aa80fe06a7dc35cee3b72a336 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Fri, 4 Dec 2020 16:31:40 +0100 Subject: [PATCH 191/774] Add trade network for steel --- message_ix_models/data/material/set.yaml | 7 +++- .../model/material/data_steel.py | 41 +++++++++++++------ 2 files changed, 33 insertions(+), 15 deletions(-) diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index c46f22424c..d5726bd1fb 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -130,6 +130,8 @@ common: - useful - final - water_supply + - export + - import node: # Just one is sufficient, since the RES will never be generated with a mix @@ -436,8 +438,9 @@ steel: - scrap_recovery_steel - DUMMY_ore_supply - DUMMY_limestone_supply - - DUMMY_freshwater_supply - - DUMMY_water_supply + - trade_steel + - import_steel + - export_steel relation: add: diff --git a/message_ix_models/model/material/data_steel.py b/message_ix_models/model/material/data_steel.py index 42a15786ca..c327fc86c1 100644 --- a/message_ix_models/model/material/data_steel.py +++ b/message_ix_models/model/material/data_steel.py @@ -52,7 +52,7 @@ def gen_mock_demand_steel(scenario): r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', \ 'R11_MEA', 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU'] d = [35, 537, 70, 53, 49, \ - 9, 130, 80, 45, 96, 100] # MEA change from 39 to 9 to make it feasible (coal supply bound) + 39, 130, 80, 45, 96, 100] # MEA change from 39 to 9 to make it feasible (coal supply bound) demand2010_steel = pd.DataFrame({'Region':r, 'Val':d}).\ join(gdp_growth.set_index('Region'), on='Region').rename(columns={'Region':'node'}) @@ -187,17 +187,37 @@ def gen_data_steel(scenario, dry_run=False): print('1.param_name:', param_name, t) if (param_name == "input")|(param_name == "output"): - print(rg, par, regions, val) # Assign commodity and level names com = split[1] lev = split[2] mod = split[3] - - df = (make_df(param_name, technology=t, commodity=com, \ - level=lev, \ - value=val[regions[regions==rg].index[0]], mode=mod, unit='t', \ - node_loc=rg, **common)\ - .pipe(same_node)) + print(rg, par, lev) + + if (param_name == "input") and (lev == "import"): + df = make_df(param_name, technology=t, commodity=com, \ + level=lev, \ + value=val[regions[regions==rg].index[0]], mode=mod, unit='t', \ + node_loc=rg, node_origin="R11_GLB", **common) + elif (param_name == "output") and (lev == "export"): + df = make_df(param_name, technology=t, commodity=com, \ + level=lev, \ + value=val[regions[regions==rg].index[0]], mode=mod, unit='t', \ + node_loc=rg, node_dest="R11_GLB", **common) + else: + df = (make_df(param_name, technology=t, commodity=com, \ + level=lev, \ + value=val[regions[regions==rg].index[0]], mode=mod, unit='t', \ + node_loc=rg, **common)\ + .pipe(same_node)) + + # Copy parameters to all regions, when node_loc is not GLB + if (len(regions) == 1) and (rg != "R11_GLB"): + print("copying to all R11", rg, lev) + df['node_loc'] = None + df = df.pipe(broadcast, node_loc=nodes)#.pipe(same_node) + # Use same_node only for non-trade technologies + if (lev != "import") and (lev != "export"): + df = df.pipe(same_node) elif param_name == "emission_factor": @@ -225,11 +245,6 @@ def gen_data_steel(scenario, dry_run=False): value=val[regions[regions==rg].index[0]], unit='t', \ node_loc=rg, **common)#.pipe(broadcast, node_loc=nodes)) - if len(regions) == 1: - print(df) - df['node_loc'] = None - df = df.pipe(broadcast, node_loc=nodes).pipe(same_node) - results[param_name].append(df) # Add relations for scrap grades and availability From bc42b68a4f6f91e1d743fcbd06ed5dd743fae28b Mon Sep 17 00:00:00 2001 From: Jihoon Date: Fri, 4 Dec 2020 16:32:05 +0100 Subject: [PATCH 192/774] Add parameterization for trade tecs --- .../data/material/Global_steel_cement_MESSAGE.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx index e0ea9ba480..a56a15dc0c 100644 --- a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx +++ b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:466d0b340bda99b32cdfce967d0142c8b013f63845e1cee43c9c64ffec8a412d -size 50058 +oid sha256:d9bc7ef707f4de3720ba30b25f697640431d0cd4d796caea17c680e7e904838c +size 51987 From 5e582af6f526cf1c365cb518ccb09c2707b28b9f Mon Sep 17 00:00:00 2001 From: Jihoon Date: Fri, 4 Dec 2020 16:32:29 +0100 Subject: [PATCH 193/774] Minor testing --- message_ix_models/model/material/material_testscript.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/message_ix_models/model/material/material_testscript.py b/message_ix_models/model/material/material_testscript.py index fc858f7f02..865b1ba135 100644 --- a/message_ix_models/model/material/material_testscript.py +++ b/message_ix_models/model/material/material_testscript.py @@ -122,6 +122,9 @@ df_gen = dt.gen_data_generic(scen) df_st = dt.gen_data_steel(scen) +a = df_st['input'] +b=a.loc[a['level']=="export"] + df_st = dt.gen_data_cement(scen) a = dt.get_data(scen, ctx) dc.gen_mock_demand_cement(scen) From ec0cfcc2530a72f89e8a122488cb69afaa2bb1ce Mon Sep 17 00:00:00 2001 From: Jihoon Date: Mon, 7 Dec 2020 10:18:21 +0100 Subject: [PATCH 194/774] Fix the handling of parametrization of glb (especially for trade network) --- message_ix_models/model/material/data_steel.py | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) diff --git a/message_ix_models/model/material/data_steel.py b/message_ix_models/model/material/data_steel.py index c327fc86c1..592878d23c 100644 --- a/message_ix_models/model/material/data_steel.py +++ b/message_ix_models/model/material/data_steel.py @@ -228,22 +228,27 @@ def gen_data_steel(scenario, dry_run=False): df = make_df(param_name, technology=t, \ value=val[regions[regions==rg].index[0]],\ emission=emi, mode=mod, unit='t', \ - node_loc=rg, **common)#.pipe(broadcast, \ - #node_loc=nodes)) + node_loc=rg, **common) else: # time-independent var_cost mod = split[1] df = make_df(param_name, technology=t, \ value=val[regions[regions==rg].index[0]], \ mode=mod, unit='t', node_loc=rg, \ - **common)#.pipe(broadcast, node_loc=nodes)) + **common) # Parameters with only parameter name else: print('2.param_name:', param_name) df = make_df(param_name, technology=t, \ value=val[regions[regions==rg].index[0]], unit='t', \ - node_loc=rg, **common)#.pipe(broadcast, node_loc=nodes)) + node_loc=rg, **common) + + # Copy parameters to all regions + if len(set(df['node_loc'])) == 1 and list(set(df['node_loc']))[0]!='R11_GLB': + print("Copying to all R11") + df['node_loc'] = None + df = df.pipe(broadcast, node_loc=nodes) results[param_name].append(df) From a0abaa9802f956dc47d0a3b1e241be29915c92b8 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Mon, 7 Dec 2020 10:24:31 +0100 Subject: [PATCH 195/774] Data update for trade network for steel --- .../data/material/Global_steel_cement_MESSAGE.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx index a56a15dc0c..1ad62587dd 100644 --- a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx +++ b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:d9bc7ef707f4de3720ba30b25f697640431d0cd4d796caea17c680e7e904838c -size 51987 +oid sha256:228526a9668537fd4cf1243cb41b15b65e69ff6be5c6096555581dfee93ff560 +size 52101 From efc2eaf65763261300f6eb38f3ebf06ea760a36b Mon Sep 17 00:00:00 2001 From: Jihoon Date: Mon, 7 Dec 2020 11:32:07 +0100 Subject: [PATCH 196/774] Some clean-up of the input file (remove unneccesary tab/columns) --- .../data/material/Global_steel_cement_MESSAGE - org.xlsx | 3 +++ .../data/material/Global_steel_cement_MESSAGE.xlsx | 4 ++-- 2 files changed, 5 insertions(+), 2 deletions(-) create mode 100644 message_ix_models/data/material/Global_steel_cement_MESSAGE - org.xlsx diff --git a/message_ix_models/data/material/Global_steel_cement_MESSAGE - org.xlsx b/message_ix_models/data/material/Global_steel_cement_MESSAGE - org.xlsx new file mode 100644 index 0000000000..1ad62587dd --- /dev/null +++ b/message_ix_models/data/material/Global_steel_cement_MESSAGE - org.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:228526a9668537fd4cf1243cb41b15b65e69ff6be5c6096555581dfee93ff560 +size 52101 diff --git a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx index 1ad62587dd..ddc5e8c211 100644 --- a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx +++ b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:228526a9668537fd4cf1243cb41b15b65e69ff6be5c6096555581dfee93ff560 -size 52101 +oid sha256:2e1db67ffad30b761bd32049a99230a84a69c682c47bc0175d39a52e37574669 +size 38635 From a7dd8ae41b230b89cc4a33b89add07695b4bb367 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 8 Dec 2020 16:36:24 +0100 Subject: [PATCH 197/774] Unnecessary printings deleted --- .../data/material/aluminum_techno_economic.xlsx | 4 ++-- message_ix_models/model/material/data_petro.py | 8 ++------ 2 files changed, 4 insertions(+), 8 deletions(-) diff --git a/message_ix_models/data/material/aluminum_techno_economic.xlsx b/message_ix_models/data/material/aluminum_techno_economic.xlsx index 93c88b0ccf..eefd3d515f 100644 --- a/message_ix_models/data/material/aluminum_techno_economic.xlsx +++ b/message_ix_models/data/material/aluminum_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:3579c2bf807a5a0c266f403f1f51c5a35da0afec5586304085c491ee7c66d5dc -size 65018 +oid sha256:49c4811fdddfed773548918568d6e9332492a9a73f14d1e0801dc812333e2b9c +size 68115 diff --git a/message_ix_models/model/material/data_petro.py b/message_ix_models/model/material/data_petro.py index 89a8d3ee28..f74ced4798 100644 --- a/message_ix_models/model/material/data_petro.py +++ b/message_ix_models/model/material/data_petro.py @@ -64,6 +64,8 @@ def gen_mock_demand_petro(scenario): # For division of some regions assumptions made: # PAO, PAS, SAS, EEU,WEU + # Domestic production (not demand !!!) + r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', \ 'R11_MEA', 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU'] d_ethylene = [1,25,10,3,5,20,25,12,10,13,15] @@ -283,22 +285,16 @@ def gen_data_petro_chemicals(scenario, dry_run=False): df_e = make_df(paramname, level='demand', commodity="ethylene", \ value=demand_e.value, unit='t',year=demand_e.year, time='year', \ node=demand_e.node)#.pipe(broadcast, node=nodes) - print("demand ethylene") - print(demand_e.node) results["demand"].append(df_e) df_p = make_df(paramname, level='demand', commodity="propylene", \ value=demand_p.value, unit='t',year=demand_p.year, time='year', \ node=demand_p.node)#.pipe(broadcast, node=nodes) - print("demand propylene") - print(demand_p.node) results["demand"].append(df_p) df_BTX = make_df(paramname, level='demand', commodity="BTX", \ value=demand_BTX.value, unit='t',year=demand_BTX.year, time='year', \ node=demand_BTX.node)#.pipe(broadcast, node=nodes) - print("demand BTX") - print(demand_BTX.node) results["demand"].append(df_BTX) From c87f0af5d69d42a98fe0fcbe6b99cb17880c19f8 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 10 Dec 2020 09:37:47 +0100 Subject: [PATCH 198/774] Aluminum variable costs added --- .../material/aluminum_techno_economic.xlsx | 4 +- .../model/material/data_aluminum.py | 146 +++++++++--------- 2 files changed, 79 insertions(+), 71 deletions(-) diff --git a/message_ix_models/data/material/aluminum_techno_economic.xlsx b/message_ix_models/data/material/aluminum_techno_economic.xlsx index eefd3d515f..83a085419b 100644 --- a/message_ix_models/data/material/aluminum_techno_economic.xlsx +++ b/message_ix_models/data/material/aluminum_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:49c4811fdddfed773548918568d6e9332492a9a73f14d1e0801dc812333e2b9c -size 68115 +oid sha256:60804cbd6ee73da1089915451b38c90c4dd07712733e66772dfba788da16d561 +size 67087 diff --git a/message_ix_models/model/material/data_aluminum.py b/message_ix_models/model/material/data_aluminum.py index 07e25958c3..1a1f318c9d 100644 --- a/message_ix_models/model/material/data_aluminum.py +++ b/message_ix_models/model/material/data_aluminum.py @@ -51,12 +51,16 @@ def read_data_aluminum(): usecols = "A:F") data_alu_rel = read_rel(fname) + + data_alu_var = pd.read_excel( + context.get_path("material", fname), + sheet_name="variable_data") # Unit conversion # At the moment this is done in the excel file, can be also done here # To make sure we use the same units - return data_alu, data_alu_hist, data_alu_rel + return data_alu, data_alu_hist, data_alu_rel, data_alu_var def gen_data_aluminum(scenario, dry_run=False): @@ -66,7 +70,7 @@ def gen_data_aluminum(scenario, dry_run=False): s_info = ScenarioInfo(scenario) # Techno-economic assumptions - data_aluminum, data_aluminum_hist, data_aluminum_rel= read_data_aluminum() + data_aluminum, data_aluminum_hist, data_aluminum_rel, data_aluminum_var= read_data_aluminum() tec_tv = set(data_aluminum_hist.technology) # set of tecs with time-varying values # List of data frames, to be concatenated together at end @@ -163,35 +167,77 @@ def gen_data_aluminum(scenario, dry_run=False): print(demand.node) results[parname].append(df) - # # Add historical data - # - # for tec in data_aluminum_hist["technology"].unique(): - # - # y_hist = [1980,1985,1990,1995,2000,2005,2010,2015] #length need to match what's in the xls - # common_hist = dict( - # year_vtg= y_hist, - # year_act= y_hist, - # mode="M1", - # time="year",) - # - # val_act = data_aluminum_hist.\ - # loc[(data_aluminum_hist["technology"]== tec), "production"] - # - # df_hist_act = (make_df("historical_activity", technology=tec, \ - # value=val_act, unit='Mt', **common_hist).pipe(broadcast, node_loc=nodes)) - # - # results["historical_activity"].append(df_hist_act) - # - # c_factor = data_aluminum.loc[((data_aluminum["technology"]== tec) \ - # & (data_aluminum["parameter"]=="capacity_factor")), "value"].values - # - # val_cap = data_aluminum_hist.loc[(data_aluminum_hist["technology"]== tec), \ - # "new_production"] / c_factor - # - # df_hist_cap = (make_df("historical_new_capacity", technology=tec, \ - # value=val_cap, unit='Mt', **common_hist).pipe(broadcast, node_loc=nodes)) - # - # results["historical_new_capacity"].append(df_hist_cap) + # Add historical data + + for tec in data_aluminum_hist["technology"].unique(): + + y_hist = [1980,1985,1990,1995,2000,2005,2010,2015] #length need to match what's in the xls + common_hist = dict( + year_vtg= y_hist, + year_act= y_hist, + mode="M1", + time="year",) + + val_act = data_aluminum_hist.\ + loc[(data_aluminum_hist["technology"]== tec), "production"] + + df_hist_act = (make_df("historical_activity", technology=tec, \ + value=val_act, unit='Mt', **common_hist).pipe(broadcast, node_loc=nodes)) + + results["historical_activity"].append(df_hist_act) + + c_factor = data_aluminum.loc[((data_aluminum["technology"]== tec) \ + & (data_aluminum["parameter"]=="capacity_factor")), "value"].values + + val_cap = data_aluminum_hist.loc[(data_aluminum_hist["technology"]== tec), \ + "new_production"] / c_factor + + df_hist_cap = (make_df("historical_new_capacity", technology=tec, \ + value=val_cap, unit='Mt', **common_hist).pipe(broadcast, node_loc=nodes)) + + results["historical_new_capacity"].append(df_hist_cap) + + + # Add variable costs + + data_aluminum_var = pd.melt(data_aluminum_var, id_vars=['technology', 'mode', 'units',\ + "parameter","region"], value_vars=[2020, 2025,2030,2035, 2040,2045, 2050,2055,2060, 2070, 2080, 2090, 2100], var_name='year') + + tec_vc = set(data_aluminum_var.technology) + print(tec_vc) + param_name = set(data_aluminum_var.parameter) + + + for p in param_name: + for t in tec_vc: + + print("V_technology") + print(t) + + common = dict( + time="year", + time_origin="year", + time_dest="year", + ) + + param_name = p + print(param_name) + val = data_aluminum_var.loc[((data_aluminum_var["technology"] == t) \ + & (data_aluminum_var["parameter"] == p)), 'value'].values + print(val) + units = data_aluminum_var.loc[((data_aluminum_var["technology"] == t) \ + & (data_aluminum_var["parameter"] == p)),'units'].values + print(units) + mod = data_aluminum_var.loc[((data_aluminum_var["technology"] == t) \ + & (data_aluminum_var["parameter"] == p)), 'mode'].values + print(mod) + yr = data_aluminum_var.loc[((data_aluminum_var["technology"] == t) \ + & (data_aluminum_var["parameter"] == p)), 'year'].values + print(yr) + + df = (make_df(param_name, technology=t, value=val,unit='t', \ + mode=mod, year_vtg=yr, year_act=yr, **common).pipe(broadcast,node_loc=nodes)) + results[param_name].append(df) # Add relations for scrap grades and availability @@ -237,8 +283,6 @@ def gen_data_aluminum(scenario, dry_run=False): return results_aluminum - - def gen_mock_demand_aluminum(scenario): context = read_config() @@ -289,40 +333,4 @@ def gen_mock_demand_aluminum(scenario): demand2015_al = pd.melt(demand2015_al.drop(['Val', 'Scenario'], axis=1),\ id_vars=['node'], var_name='year', value_name = 'value') - print(demand2015_al) - - # # Add temporary exogenous demand: 17.3 Mt in 2010 (IAI) - # demand2010_aluminum = 17.3 - # - # # The future projection of the demand: Increases by half of the GDP growth rate - # # gdp_growth rate: SSP2 global model. Starting from 2020. - # gdp_growth = [0.121448215899944, 0.0733079014579874, - # 0.0348154093342843, 0.021827616787921, - # 0.0134425983942219, 0.0108320197485592, - # 0.00884341208063, 0.00829374133206562, - # # Add one more element since model is until 2110 normally - # 0.00649794573935969, 0.00649794573935969] - # baseyear = list(range(2020, 2110+1, 10)) # Index for above vector - # gdp_growth_interp = np.interp(modelyears, baseyear, gdp_growth) - # - # i = 0 - # values = [] - # - # # Assume 5 year duration at the beginning - # duration_period = (pd.Series(modelyears) - \ - # pd.Series(modelyears).shift(1)).tolist() - # duration_period[0] = 5 - # - # val = (demand2010_aluminum * (1+ 0.147718884937996/2) ** duration_period[i]) - # values.append(val) - # - # for element in gdp_growth_interp: - # i = i + 1 - # if i < len(modelyears): - # val = (val * (1+ element/2) ** duration_period[i]) - # values.append(val) - # # Adjust the demand to product level. - # - # values = [x * fin_to_useful * useful_to_product for x in values] - return demand2015_al From cb0de0e1044e25482e110f5fbb4f9d2856893428 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Thu, 10 Dec 2020 22:40:55 +0100 Subject: [PATCH 199/774] Small changes for global adaptation --- message_ix_models/model/material/__init__.py | 8 ++++---- message_ix_models/model/material/material_testscript.py | 4 ++-- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 70060e5c96..256fa56eea 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -21,7 +21,7 @@ def build(scenario): modify_demand(scenario) return scenario - + # add as needed/implemented SPEC_LIST = ["generic", "common", "steel", "cement", "aluminum", "petro_chemicals"] @@ -82,7 +82,7 @@ def create_bare(context, regions, dry_run): scen.solve() @cli.command("solve") -@click.option('--datafile', default='China_steel_cement_MESSAGE.xlsx', +@click.option('--datafile', default='Global_steel_cement_MESSAGE.xlsx', metavar='INPUT', help='File name for external data input') @click.pass_obj def solve(context, datafile): @@ -97,7 +97,7 @@ def solve(context, datafile): "NPi2020-con-prim-dir-ncr": "NPi", "NPi2020_1000-con-prim-dir-ncr": "NPi2020_1000", "NPi2020_400-con-prim-dir-ncr": "NPi2020_400", - "DIAG-C30-const_E414": "baseline_test", + # "DIAG-C30-const_E414": "baseline_test", }.get(context.scenario_info["scenario"]) #context.metadata_path = context.metadata_path /'data' @@ -109,7 +109,7 @@ def solve(context, datafile): # Clone and set up scenario = build( context.get_scenario() - .clone(model="Material_China", scenario=output_scenario_name) + .clone(model="Material_Global", scenario=output_scenario_name) ) # Solve diff --git a/message_ix_models/model/material/material_testscript.py b/message_ix_models/model/material/material_testscript.py index 865b1ba135..c4729b33f9 100644 --- a/message_ix_models/model/material/material_testscript.py +++ b/message_ix_models/model/material/material_testscript.py @@ -11,7 +11,7 @@ import message_data.model.material.data as dt from message_data.model.material.plotting import Plots -import pyam +# import pyam from message_data.tools import Context @@ -29,7 +29,7 @@ from message_data.model.create import create_res from message_data.model.material import build, get_spec -from message_data.model.material.util import read_config +# from message_data.model.material.util import read_config #%% Main test run based on a MESSAGE scenario From e8b15c3739d334cca049d0058a0e7a26bb629c68 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Mon, 14 Dec 2020 12:26:59 +0100 Subject: [PATCH 200/774] Error fixed (emission factors were not added) --- message_ix_models/model/material/data_petro.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/model/material/data_petro.py b/message_ix_models/model/material/data_petro.py index f74ced4798..4a7c706263 100644 --- a/message_ix_models/model/material/data_petro.py +++ b/message_ix_models/model/material/data_petro.py @@ -256,6 +256,8 @@ def gen_data_petro_chemicals(scenario, dry_run=False): emission=emi, mode=mod, unit='t', **common).pipe(broadcast, \ node_loc=nodes)) + results[param_name].append(df) + elif param_name == "var_cost": mod = split[1] @@ -263,8 +265,6 @@ def gen_data_petro_chemicals(scenario, dry_run=False): level=lev,mode=mod, value=val, unit='t', **common).\ pipe(broadcast, node_loc=nodes).pipe(same_node)) - - results[param_name].append(df) # Rest of the parameters apart from inpput, output and emission_factor From cd22e4ff0ba2b63800482ac5b8a8655db71dfad6 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Fri, 11 Dec 2020 10:39:09 +0100 Subject: [PATCH 201/774] Test script update --- message_ix_models/model/material/material_testscript.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/message_ix_models/model/material/material_testscript.py b/message_ix_models/model/material/material_testscript.py index c4729b33f9..4dfe008f3e 100644 --- a/message_ix_models/model/material/material_testscript.py +++ b/message_ix_models/model/material/material_testscript.py @@ -84,8 +84,8 @@ # Set default scenario/model names - Later coming from CLI ctx.platform_info.setdefault('name', 'ixmp_dev') ctx.platform_info.setdefault('jvmargs', ['-Xmx12G']) # To avoid java heap space error -ctx.scenario_info.setdefault('model', 'Material_test_MESSAGE_global') -ctx.scenario_info.setdefault('scenario', 'baseline') +ctx.scenario_info.setdefault('model', 'Material_Global') +ctx.scenario_info.setdefault('scenario', 'NoPolicy') ctx['ssp'] = 'SSP2' ctx['datafile'] = 'Global_steel_cement_MESSAGE.xlsx' @@ -135,8 +135,9 @@ info = ScenarioInfo(scen) a = get_spec() -mp_samp = ixmp.Platform(name="ixmp_dev") -mp_samp.scenario_list() +mp = ixmp.Platform(name="ixmp_dev") +a = mp.scenario_list() +b=a.loc[a['cre_user']=="min"] sample = mix.Scenario(mp_samp, model="Material_test", scenario="baseline") sample.set_list() sample.set('year') From c7d786a46c1b094fd4eb461672aad37f46179ed0 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Tue, 15 Dec 2020 13:03:39 +0100 Subject: [PATCH 202/774] Data update for better historical data for steel + diffusion constraints on total production --- .../data/material/Global_steel_cement_MESSAGE.xlsx | 4 ++-- .../data/material/Steel_capacity_historical.OECD.xlsx | 3 +++ 2 files changed, 5 insertions(+), 2 deletions(-) create mode 100644 message_ix_models/data/material/Steel_capacity_historical.OECD.xlsx diff --git a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx index ddc5e8c211..ee3c605c29 100644 --- a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx +++ b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:2e1db67ffad30b761bd32049a99230a84a69c682c47bc0175d39a52e37574669 -size 38635 +oid sha256:e6d95a532e4b234dcae79cdc8fe50eb671ab0db0a8e7ca5dff5f3e3d34a99d25 +size 44539 diff --git a/message_ix_models/data/material/Steel_capacity_historical.OECD.xlsx b/message_ix_models/data/material/Steel_capacity_historical.OECD.xlsx new file mode 100644 index 0000000000..fba1df826e --- /dev/null +++ b/message_ix_models/data/material/Steel_capacity_historical.OECD.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:80ef345807e44e9c06c0f3520128be0d680d643a3da029f971ddb159094e7785 +size 38935 From 8977fed33c479d0f84b1a49d3fef2dc55f271308 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Tue, 15 Dec 2020 13:04:59 +0100 Subject: [PATCH 203/774] Handle region-specific historical data inputs --- message_ix_models/model/material/data_steel.py | 13 +++++++++---- message_ix_models/model/material/data_util.py | 2 +- 2 files changed, 10 insertions(+), 5 deletions(-) diff --git a/message_ix_models/model/material/data_steel.py b/message_ix_models/model/material/data_steel.py index 592878d23c..e807fa2711 100644 --- a/message_ix_models/model/material/data_steel.py +++ b/message_ix_models/model/material/data_steel.py @@ -151,11 +151,16 @@ def gen_data_steel(scenario, dry_run=False): yr = data_steel_ts.loc[(data_steel_ts["technology"] == t) \ & (data_steel_ts["parameter"] == p), 'year'] - df = (make_df(p, technology=t, value=val,\ - unit='t', year_vtg=yr, year_act=yr, mode=mod, **common).pipe(broadcast, \ - node_loc=nodes)) + if p=="var_cost": + df = (make_df(p, technology=t, value=val,\ + unit='t', year_vtg=yr, year_act=yr, mode=mod, **common).pipe(broadcast, \ + node_loc=nodes)) + else: + rg = data_steel_ts.loc[(data_steel_ts["technology"] == t) \ + & (data_steel_ts["parameter"] == p), 'region'] + df = make_df(p, technology=t, value=val,\ + unit='t', year_vtg=yr, year_act=yr, mode=mod, node_loc=rg, **common) - #print("time-dependent::", p, df) results[p].append(df) # Iterate over parameters diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index de51cc63f3..cfaeef1858 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -136,7 +136,7 @@ def read_timeseries(filename): # Take only existing years in the data datayears = [x for x in list(df) if isinstance(x, numbers.Number)] - df = pd.melt(df, id_vars=['parameter', 'technology', 'mode', 'units'], \ + df = pd.melt(df, id_vars=['parameter', 'region', 'technology', 'mode', 'units'], \ value_vars = datayears, \ var_name ='year') From 0ce00c172d4e3c6013b85c95bba02a97233e7a43 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 16 Dec 2020 11:05:54 +0100 Subject: [PATCH 204/774] Minor chnages --- .../data/material/Global_steel_cement_MESSAGE - org.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/Global_steel_cement_MESSAGE - org.xlsx b/message_ix_models/data/material/Global_steel_cement_MESSAGE - org.xlsx index 1ad62587dd..e3901a6676 100644 --- a/message_ix_models/data/material/Global_steel_cement_MESSAGE - org.xlsx +++ b/message_ix_models/data/material/Global_steel_cement_MESSAGE - org.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:228526a9668537fd4cf1243cb41b15b65e69ff6be5c6096555581dfee93ff560 -size 52101 +oid sha256:df34371c0b00f946c347facaf43f85cf6d86745719d5ffb3b2b14c0733fc45d1 +size 52337 From 17b564f8dc6615482f0c648de623e8f09758aab4 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 16 Dec 2020 11:06:33 +0100 Subject: [PATCH 205/774] Fix emission factors --- .../data/material/Global_steel_cement_MESSAGE.xlsx | 4 ++-- .../generic_furnace_boiler_techno_economic.xlsx | 4 ++-- message_ix_models/model/material/__init__.py | 2 +- message_ix_models/model/material/data_generic.py | 11 ++++++++--- 4 files changed, 13 insertions(+), 8 deletions(-) diff --git a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx index ee3c605c29..a51e4ac3b2 100644 --- a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx +++ b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:e6d95a532e4b234dcae79cdc8fe50eb671ab0db0a8e7ca5dff5f3e3d34a99d25 -size 44539 +oid sha256:295e6c8a526851d98263216d1f8f4b4cf016e1f706ce29c806575adf41556c30 +size 41153 diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx index 6ed81249a1..575c44b095 100644 --- a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx +++ b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:be4c50851bc9d019c1d5baedc1098d6ad65c657741d0b69209bbda424ad65657 -size 292 +oid sha256:45b963809f1a3b262b0ecedcdf92df8be4d7134dbd1498967b0bea2fc8ebaa54 +size 276 diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 256fa56eea..5f94bf3d19 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -100,7 +100,7 @@ def solve(context, datafile): # "DIAG-C30-const_E414": "baseline_test", }.get(context.scenario_info["scenario"]) - #context.metadata_path = context.metadata_path /'data' + context.metadata_path = context.metadata_path /'data' context.datafile = datafile if context.scenario_info["model"] != "CD_Links_SSP2": diff --git a/message_ix_models/model/material/data_generic.py b/message_ix_models/model/material/data_generic.py index 766225d54e..d08c44ce28 100644 --- a/message_ix_models/model/material/data_generic.py +++ b/message_ix_models/model/material/data_generic.py @@ -78,7 +78,7 @@ def gen_data_generic(scenario, dry_run=False): # Do not parametrize GLB region the same way if "R11_GLB" in nodes: nodes.remove("R11_GLB") - + # 'World' is included by default when creating a message_ix.Scenario(). # Need to remove it for the China bare model nodes.remove('World') @@ -132,11 +132,16 @@ def gen_data_generic(scenario, dry_run=False): emi = split[1] # TODO: Now tentatively fixed to one mode. Have values for the other mode too - df = (make_df(param_name, technology=t,value=val,\ + df_low = (make_df(param_name, technology=t,value=val,\ emission=emi, mode="low_temp", unit='t', **common).pipe(broadcast, \ node_loc=nodes)) - results[param_name].append(df) + df_high = (make_df(param_name, technology=t,value=val,\ + emission=emi, mode="high_temp", unit='t', **common).pipe(broadcast, \ + node_loc=nodes)) + + results[param_name].append(df_low) + results[param_name].append(df_high) # Rest of the parameters apart from inpput, output and emission_factor From 7d26f5d29a94f90a48e8923414236a34c25d70d3 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 16 Dec 2020 11:21:32 +0100 Subject: [PATCH 206/774] Update the cement and steel file to resolve the conflict --- .../data/material/Global_steel_cement_MESSAGE.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx index a51e4ac3b2..ee3c605c29 100644 --- a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx +++ b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:295e6c8a526851d98263216d1f8f4b4cf016e1f706ce29c806575adf41556c30 -size 41153 +oid sha256:e6d95a532e4b234dcae79cdc8fe50eb671ab0db0a8e7ca5dff5f3e3d34a99d25 +size 44539 From dfce85b1ae4e87932c1994784b9471a6dfef4e32 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Mon, 21 Dec 2020 17:05:27 +0100 Subject: [PATCH 207/774] Petrochemicals addition of historical data --- .../petrochemicals_techno_economic.xlsx | 4 +- message_ix_models/data/material/set.yaml | 1 + .../model/material/data_petro.py | 49 ++++++++++++++----- 3 files changed, 40 insertions(+), 14 deletions(-) diff --git a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx index 4b8792cc03..18ad2391bf 100644 --- a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx +++ b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:f89fbd29a532e91694067ef18739bf501dd16e1076aea66af098e233ccdb3a8a -size 481677 +oid sha256:e826eb77a159f27f7ba02a6670e5a8438d2ec5c4324e8159254ddbfb369c896c +size 575549 diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index d5726bd1fb..8b5f701eff 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -369,6 +369,7 @@ petro_chemicals: - propane - light_foil - refinery_gas + - refinery_gas_int - heavy_foil - pet_coke - atm_residue diff --git a/message_ix_models/model/material/data_petro.py b/message_ix_models/model/material/data_petro.py index 4a7c706263..9a940ef6c4 100644 --- a/message_ix_models/model/material/data_petro.py +++ b/message_ix_models/model/material/data_petro.py @@ -1,6 +1,7 @@ import pandas as pd import numpy as np from collections import defaultdict +from .data_util import read_timeseries import message_ix import ixmp @@ -31,12 +32,7 @@ def read_data_petrochemicals(): data_petro= data_petro.drop(['Region', 'Source', 'Description'], axis = 1) - data_petro_hist = pd.read_excel(context.get_path("material", \ - "petrochemicals_techno_economic.xlsx"),sheet_name="data_historical", \ - usecols = "A:G", nrows= 16) - print("data petro hist") - print(data_petro_hist) - return data_petro,data_petro_hist + return data_petro def gen_mock_demand_petro(scenario): @@ -74,7 +70,6 @@ def gen_mock_demand_petro(scenario): list = [] for e in ["ethylene","propylene","BTX"]: - print(e) if e == "ethylene": demand2015 = pd.DataFrame({'Region':r, 'Val':d_ethylene}).\ join(gdp_growth.set_index('Region'), on='Region').\ @@ -96,11 +91,7 @@ def gen_mock_demand_petro(scenario): demand2015 = pd.melt(demand2015.drop(['Val', 'Scenario'], axis=1),\ id_vars=['node'], var_name='year', value_name = 'value') - print(demand2015) - list.append(demand2015) - print("list") - print(list) return list[0], list[1], list[2] @@ -182,7 +173,8 @@ def gen_data_petro_chemicals(scenario, dry_run=False): s_info = ScenarioInfo(scenario) # Techno-economic assumptions - data_petro, data_petro_hist = read_data_petrochemicals() + data_petro = read_data_petrochemicals() + data_petro_ts = read_timeseries("petrochemicals_techno_economic.xlsx") # List of data frames, to be concatenated together at end results = defaultdict(list) @@ -297,6 +289,39 @@ def gen_data_petro_chemicals(scenario, dry_run=False): node=demand_BTX.node)#.pipe(broadcast, node=nodes) results["demand"].append(df_BTX) + # Special treatment for time-varying params + + tec_ts = set(data_petro_ts.technology) # set of tecs in timeseries sheet + + for t in tec_ts: + common = dict( + time="year", + time_origin="year", + time_dest="year",) + + param_name = data_petro_ts.loc[(data_petro_ts["technology"] == t), 'parameter'] + + for p in set(param_name): + val = data_petro_ts.loc[(data_petro_ts["technology"] == t) \ + & (data_petro_ts["parameter"] == p), 'value'] + units = data_petro_ts.loc[(data_petro_ts["technology"] == t) \ + & (data_petro_ts["parameter"] == p), 'units'].values[0] + mod = data_petro_ts.loc[(data_petro_ts["technology"] == t) \ + & (data_petro_ts["parameter"] == p), 'mode'] + yr = data_petro_ts.loc[(data_petro_ts["technology"] == t) \ + & (data_petro_ts["parameter"] == p), 'year'] + + if p=="var_cost": + df = (make_df(p, technology=t, value=val,\ + unit='t', year_vtg=yr, year_act=yr, mode=mod, **common).pipe(broadcast, \ + node_loc=nodes)) + else: + rg = data_petro_ts.loc[(data_petro_ts["technology"] == t) \ + & (data_petro_ts["parameter"] == p), 'region'] + df = make_df(p, technology=t, value=val,\ + unit='t', year_vtg=yr, year_act=yr, mode=mod, node_loc=rg, **common) + + results[p].append(df) # # Add historical data # From 67cf7f50d6b581337b1488a9ebf366646e7f0076 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Mon, 21 Dec 2020 17:05:48 +0100 Subject: [PATCH 208/774] Aluminum updates --- .../data/material/aluminum_techno_economic.xlsx | 4 ++-- message_ix_models/model/material/data_aluminum.py | 15 +-------------- 2 files changed, 3 insertions(+), 16 deletions(-) diff --git a/message_ix_models/data/material/aluminum_techno_economic.xlsx b/message_ix_models/data/material/aluminum_techno_economic.xlsx index 83a085419b..d180ac33ad 100644 --- a/message_ix_models/data/material/aluminum_techno_economic.xlsx +++ b/message_ix_models/data/material/aluminum_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:60804cbd6ee73da1089915451b38c90c4dd07712733e66772dfba788da16d561 -size 67087 +oid sha256:9c694d5ddf5b1833bb2b575cfe2fae351edc51d587856639c948c82d701ae44d +size 66999 diff --git a/message_ix_models/model/material/data_aluminum.py b/message_ix_models/model/material/data_aluminum.py index 1a1f318c9d..5981a46b1e 100644 --- a/message_ix_models/model/material/data_aluminum.py +++ b/message_ix_models/model/material/data_aluminum.py @@ -163,8 +163,6 @@ def gen_data_aluminum(scenario, dry_run=False): demand = gen_mock_demand_aluminum(scenario) df = make_df(parname, level='demand', commodity='aluminum', value=demand.value, unit='t', \ year=demand.year, time='year', node=demand.node)#.pipe(broadcast, node=nodes) - print("aluminum node") - print(demand.node) results[parname].append(df) # Add historical data @@ -196,7 +194,7 @@ def gen_data_aluminum(scenario, dry_run=False): value=val_cap, unit='Mt', **common_hist).pipe(broadcast, node_loc=nodes)) results["historical_new_capacity"].append(df_hist_cap) - + # Add variable costs @@ -204,16 +202,12 @@ def gen_data_aluminum(scenario, dry_run=False): "parameter","region"], value_vars=[2020, 2025,2030,2035, 2040,2045, 2050,2055,2060, 2070, 2080, 2090, 2100], var_name='year') tec_vc = set(data_aluminum_var.technology) - print(tec_vc) param_name = set(data_aluminum_var.parameter) for p in param_name: for t in tec_vc: - print("V_technology") - print(t) - common = dict( time="year", time_origin="year", @@ -221,19 +215,14 @@ def gen_data_aluminum(scenario, dry_run=False): ) param_name = p - print(param_name) val = data_aluminum_var.loc[((data_aluminum_var["technology"] == t) \ & (data_aluminum_var["parameter"] == p)), 'value'].values - print(val) units = data_aluminum_var.loc[((data_aluminum_var["technology"] == t) \ & (data_aluminum_var["parameter"] == p)),'units'].values - print(units) mod = data_aluminum_var.loc[((data_aluminum_var["technology"] == t) \ & (data_aluminum_var["parameter"] == p)), 'mode'].values - print(mod) yr = data_aluminum_var.loc[((data_aluminum_var["technology"] == t) \ & (data_aluminum_var["parameter"] == p)), 'year'].values - print(yr) df = (make_df(param_name, technology=t, value=val,unit='t', \ mode=mod, year_vtg=yr, year_act=yr, **common).pipe(broadcast,node_loc=nodes)) @@ -266,8 +255,6 @@ def gen_data_aluminum(scenario, dry_run=False): df = (make_df(par_name, technology=tec, value=val, unit='-',\ **common_rel).pipe(broadcast, node_rel=nodes, node_loc=nodes)) - results[par_name].append(df) - elif par_name == "relation_upper": val = data_aluminum_rel.loc[((data_aluminum_rel["relation"] == r) \ From 11a528c1d86e01f646f0e4b0c0c52624967b973e Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Mon, 4 Jan 2021 10:07:25 +0100 Subject: [PATCH 209/774] Trade and regional historical data added for aluminum&petro --- .../material/aluminum_techno_economic.xlsx | 4 +- .../petrochemicals_techno_economic.xlsx | 4 +- message_ix_models/data/material/set.yaml | 9 + .../model/material/data_aluminum.py | 295 +++++++++++------- .../model/material/data_petro.py | 198 ++++-------- 5 files changed, 250 insertions(+), 260 deletions(-) diff --git a/message_ix_models/data/material/aluminum_techno_economic.xlsx b/message_ix_models/data/material/aluminum_techno_economic.xlsx index d180ac33ad..6a81e8c1d6 100644 --- a/message_ix_models/data/material/aluminum_techno_economic.xlsx +++ b/message_ix_models/data/material/aluminum_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:9c694d5ddf5b1833bb2b575cfe2fae351edc51d587856639c948c82d701ae44d -size 66999 +oid sha256:f71eb8cdd92af846ff876208e799527afc348b85701b03ad57c3a8d6a92f73b1 +size 74309 diff --git a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx index 18ad2391bf..be9509b6b8 100644 --- a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx +++ b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:e826eb77a159f27f7ba02a6670e5a8438d2ec5c4324e8159254ddbfb369c896c -size 575549 +oid sha256:d6c17d76af7e8e9e58c040fa0068da0f70377e52bf7f00ff74cc743d79f8f4e8 +size 576258 diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 8b5f701eff..9ee86796e6 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -375,6 +375,9 @@ petro_chemicals: - atm_residue - vacuum_residue - gasoline + - ethylene + - propylene + - BTX technology: add: @@ -390,6 +393,9 @@ petro_chemicals: - ethanol_to_ethylene_petro - agg_ref - gas_processing_petro + - trade_petro + - import_petro + - export_petro remove: # Any representation of refinery. - ref_hil @@ -532,6 +538,9 @@ aluminum: - manuf_aluminum - scrap_recovery_aluminum - DUMMY_alumina_supply + - trade_aluminum + - import_aluminum + - export_aluminum relation: add: diff --git a/message_ix_models/model/material/data_aluminum.py b/message_ix_models/model/material/data_aluminum.py index 5981a46b1e..1ff04524b3 100644 --- a/message_ix_models/model/material/data_aluminum.py +++ b/message_ix_models/model/material/data_aluminum.py @@ -8,6 +8,7 @@ import pandas as pd import numpy as np from collections import defaultdict +from .data_util import read_timeseries import message_ix import ixmp @@ -43,24 +44,15 @@ def read_data_aluminum(): ) # Drop columns that don't contain useful information - data_alu= data_alu.drop(['Region', 'Source', 'Description'], axis = 1) - - data_alu_hist = pd.read_excel( - context.get_path("material", fname), - sheet_name="data_historical_5year", - usecols = "A:F") + data_alu= data_alu.drop(["Source", 'Description'], axis = 1) data_alu_rel = read_rel(fname) - - data_alu_var = pd.read_excel( - context.get_path("material", fname), - sheet_name="variable_data") # Unit conversion # At the moment this is done in the excel file, can be also done here # To make sure we use the same units - return data_alu, data_alu_hist, data_alu_rel, data_alu_var + return data_alu, data_alu_rel def gen_data_aluminum(scenario, dry_run=False): @@ -70,8 +62,8 @@ def gen_data_aluminum(scenario, dry_run=False): s_info = ScenarioInfo(scenario) # Techno-economic assumptions - data_aluminum, data_aluminum_hist, data_aluminum_rel, data_aluminum_var= read_data_aluminum() - tec_tv = set(data_aluminum_hist.technology) # set of tecs with time-varying values + data_aluminum, data_aluminum_rel= read_data_aluminum() + data_aluminum_ts = read_timeseries("aluminum_techno_economic.xlsx") # List of data frames, to be concatenated together at end results = defaultdict(list) @@ -84,18 +76,14 @@ def gen_data_aluminum(scenario, dry_run=False): nodes = s_info.N yv_ya = s_info.yv_ya fmy = s_info.y0 + nodes.remove('World') # Do not parametrize GLB region the same way if "R11_GLB" in nodes: nodes.remove("R11_GLB") - # 'World' is included by default when creating a message_ix.Scenario(). - # Need to remove it for the China bare model - nodes.remove('World') - for t in config["technology"]["add"]: - # years = s_info.Y params = data_aluminum.loc[(data_aluminum["technology"] == t),"parameter"]\ .values.tolist() @@ -116,7 +104,11 @@ def gen_data_aluminum(scenario, dry_run=False): # Obtain the scalar value for the parameter val = data_aluminum.loc[((data_aluminum["technology"] == t) \ - & (data_aluminum["parameter"] == par)),'value'].values[0] + & (data_aluminum["parameter"] == par)),'value'] + + regions = data_aluminum.loc[((data_aluminum["technology"] == t) \ + & (data_aluminum["parameter"] == par)),'region'] + common = dict( year_vtg= yv_ya.year_vtg, @@ -126,36 +118,61 @@ def gen_data_aluminum(scenario, dry_run=False): time_origin="year", time_dest="year",) - # For the parameters which inlcudes index names - if len(split)> 1: - - if (param_name == "input")|(param_name == "output"): - - # Assign commodity and level names - com = split[1] - lev = split[2] - - df = (make_df(param_name, technology=t, commodity=com, \ - level=lev, value=val, unit='t', **common)\ - .pipe(broadcast, node_loc=nodes).pipe(same_node)) + for rg in regions: + # For the parameters which inlcudes index names + if len(split)> 1: + + if (param_name == "input")|(param_name == "output"): + + # Assign commodity and level names + # Later mod can be added + com = split[1] + lev = split[2] + + if (param_name == "input") and (lev == "import"): + df = make_df(param_name, technology=t, commodity=com, \ + level=lev, value=val[regions[regions==rg].index[0]], unit='t', \ + node_loc=rg, node_origin="R11_GLB", **common) + + elif (param_name == "output") and (lev == "export"): + df = make_df(param_name, technology=t, commodity=com, \ + level=lev, value=val[regions[regions==rg].index[0]], unit='t', \ + node_loc=rg, node_dest="R11_GLB", **common) + else: + df = (make_df(param_name, technology=t, commodity=com, \ + level=lev, value=val[regions[regions==rg].index[0]], unit='t', \ + node_loc=rg, **common).pipe(same_node)) + + # Copy parameters to all regions, when node_loc is not GLB + if (len(regions) == 1) and (rg != "R11_GLB"): + print("copying to all R11", rg, lev) + df['node_loc'] = None + df = df.pipe(broadcast, node_loc=nodes)#.pipe(same_node) + # Use same_node only for non-trade technologies + if (lev != "import") and (lev != "export"): + df = df.pipe(same_node) + + elif param_name == "emission_factor": + + # Assign the emisson type + emi = split[1] + + df = make_df(param_name, technology=t, \ + value=val[regions[regions==rg].index[0]],emission=emi,\ + unit='t', node_loc=rg, **common) + + # Parameters with only parameter name + else: + df = make_df(param_name, technology=t, \ + value=val[regions[regions==rg].index[0]], unit='t', \ + node_loc=rg, **common) + + # Copy parameters to all regions + if len(set(df['node_loc'])) == 1 and list(set(df['node_loc']))[0]!='R11_GLB': + print("Copying to all R11") + df['node_loc'] = None + df = df.pipe(broadcast, node_loc=nodes) - results[param_name].append(df) - - elif param_name == "emission_factor": - - # Assign the emisson type - emi = split[1] - - df = (make_df(param_name, technology=t,value=val,\ - emission=emi, unit='t', **common).pipe(broadcast, \ - node_loc=nodes)) - results[param_name].append(df) - - # Parameters with only parameter name - - else: - df = (make_df(param_name, technology=t, value=val,unit='t', \ - **common).pipe(broadcast, node_loc=nodes)) results[param_name].append(df) # Create external demand param @@ -165,68 +182,102 @@ def gen_data_aluminum(scenario, dry_run=False): year=demand.year, time='year', node=demand.node)#.pipe(broadcast, node=nodes) results[parname].append(df) - # Add historical data - - for tec in data_aluminum_hist["technology"].unique(): - - y_hist = [1980,1985,1990,1995,2000,2005,2010,2015] #length need to match what's in the xls - common_hist = dict( - year_vtg= y_hist, - year_act= y_hist, - mode="M1", - time="year",) - - val_act = data_aluminum_hist.\ - loc[(data_aluminum_hist["technology"]== tec), "production"] - - df_hist_act = (make_df("historical_activity", technology=tec, \ - value=val_act, unit='Mt', **common_hist).pipe(broadcast, node_loc=nodes)) + # Special treatment for time-varying params - results["historical_activity"].append(df_hist_act) + tec_ts = set(data_aluminum_ts.technology) # set of tecs in timeseries sheet - c_factor = data_aluminum.loc[((data_aluminum["technology"]== tec) \ - & (data_aluminum["parameter"]=="capacity_factor")), "value"].values - - val_cap = data_aluminum_hist.loc[(data_aluminum_hist["technology"]== tec), \ - "new_production"] / c_factor - - df_hist_cap = (make_df("historical_new_capacity", technology=tec, \ - value=val_cap, unit='Mt', **common_hist).pipe(broadcast, node_loc=nodes)) - - results["historical_new_capacity"].append(df_hist_cap) - - - # Add variable costs - - data_aluminum_var = pd.melt(data_aluminum_var, id_vars=['technology', 'mode', 'units',\ - "parameter","region"], value_vars=[2020, 2025,2030,2035, 2040,2045, 2050,2055,2060, 2070, 2080, 2090, 2100], var_name='year') - - tec_vc = set(data_aluminum_var.technology) - param_name = set(data_aluminum_var.parameter) - - - for p in param_name: - for t in tec_vc: + for t in tec_ts: + common = dict( + time="year", + time_origin="year", + time_dest="year",) - common = dict( - time="year", - time_origin="year", - time_dest="year", - ) - - param_name = p - val = data_aluminum_var.loc[((data_aluminum_var["technology"] == t) \ - & (data_aluminum_var["parameter"] == p)), 'value'].values - units = data_aluminum_var.loc[((data_aluminum_var["technology"] == t) \ - & (data_aluminum_var["parameter"] == p)),'units'].values - mod = data_aluminum_var.loc[((data_aluminum_var["technology"] == t) \ - & (data_aluminum_var["parameter"] == p)), 'mode'].values - yr = data_aluminum_var.loc[((data_aluminum_var["technology"] == t) \ - & (data_aluminum_var["parameter"] == p)), 'year'].values - - df = (make_df(param_name, technology=t, value=val,unit='t', \ - mode=mod, year_vtg=yr, year_act=yr, **common).pipe(broadcast,node_loc=nodes)) - results[param_name].append(df) + param_name = data_aluminum_ts.loc[(data_aluminum_ts["technology"] == t), 'parameter'] + + for p in set(param_name): + val = data_aluminum_ts.loc[(data_aluminum_ts["technology"] == t) \ + & (data_aluminum_ts["parameter"] == p), 'value'] + units = data_aluminum_ts.loc[(data_aluminum_ts["technology"] == t) \ + & (data_aluminum_ts["parameter"] == p), 'units'].values[0] + mod = data_aluminum_ts.loc[(data_aluminum_ts["technology"] == t) \ + & (data_aluminum_ts["parameter"] == p), 'mode'] + yr = data_aluminum_ts.loc[(data_aluminum_ts["technology"] == t) \ + & (data_aluminum_ts["parameter"] == p), 'year'] + + if p=="var_cost": + df = (make_df(p, technology=t, value=val,\ + unit='t', year_vtg=yr, year_act=yr, mode=mod, **common).pipe(broadcast, \ + node_loc=nodes)) + else: + rg = data_aluminum_ts.loc[(data_aluminum_ts["technology"] == t) \ + & (data_aluminum_ts["parameter"] == p), 'region'] + df = make_df(p, technology=t, value=val,\ + unit='t', year_vtg=yr, year_act=yr, mode=mod, node_loc=rg, **common) + + results[p].append(df) + + # # Add historical data + # + # for tec in data_aluminum_hist["technology"].unique(): + # + # y_hist = [1980,1985,1990,1995,2000,2005,2010,2015] #length need to match what's in the xls + # common_hist = dict( + # year_vtg= y_hist, + # year_act= y_hist, + # mode="M1", + # time="year",) + # + # val_act = data_aluminum_hist.\ + # loc[(data_aluminum_hist["technology"]== tec), "production"] + # + # df_hist_act = (make_df("historical_activity", technology=tec, \ + # value=val_act, unit='Mt', **common_hist).pipe(broadcast, node_loc=nodes)) + # + # results["historical_activity"].append(df_hist_act) + # + # c_factor = data_aluminum.loc[((data_aluminum["technology"]== tec) \ + # & (data_aluminum["parameter"]=="capacity_factor")), "value"].values + # + # val_cap = data_aluminum_hist.loc[(data_aluminum_hist["technology"]== tec), \ + # "new_production"] / c_factor + # + # df_hist_cap = (make_df("historical_new_capacity", technology=tec, \ + # value=val_cap, unit='Mt', **common_hist).pipe(broadcast, node_loc=nodes)) + # + # results["historical_new_capacity"].append(df_hist_cap) + # + # + # # Add variable costs + # + # data_aluminum_var = pd.melt(data_aluminum_var, id_vars=['technology', 'mode', 'units',\ + # "parameter","region"], value_vars=[2020, 2025,2030,2035, 2040,2045, 2050,2055,2060, 2070, 2080, 2090, 2100], var_name='year') + # + # tec_vc = set(data_aluminum_var.technology) + # param_name = set(data_aluminum_var.parameter) + # + # + # for p in param_name: + # for t in tec_vc: + # + # common = dict( + # time="year", + # time_origin="year", + # time_dest="year", + # ) + # + # param_name = p + # val = data_aluminum_var.loc[((data_aluminum_var["technology"] == t) \ + # & (data_aluminum_var["parameter"] == p)), 'value'].values + # units = data_aluminum_var.loc[((data_aluminum_var["technology"] == t) \ + # & (data_aluminum_var["parameter"] == p)),'units'].values + # mod = data_aluminum_var.loc[((data_aluminum_var["technology"] == t) \ + # & (data_aluminum_var["parameter"] == p)), 'mode'].values + # yr = data_aluminum_var.loc[((data_aluminum_var["technology"] == t) \ + # & (data_aluminum_var["parameter"] == p)), 'year'].values + # + # df = (make_df(param_name, technology=t, value=val,unit='t', \ + # mode=mod, year_vtg=yr, year_act=yr, **common).pipe(broadcast,node_loc=nodes)) + # results[param_name].append(df) # Add relations for scrap grades and availability @@ -255,6 +306,8 @@ def gen_data_aluminum(scenario, dry_run=False): df = (make_df(par_name, technology=tec, value=val, unit='-',\ **common_rel).pipe(broadcast, node_rel=nodes, node_loc=nodes)) + results[par_name].append(df) + elif par_name == "relation_upper": val = data_aluminum_rel.loc[((data_aluminum_rel["relation"] == r) \ @@ -288,26 +341,28 @@ def gen_mock_demand_aluminum(scenario): gdp_growth['Region'] = 'R11_'+ gdp_growth['Region'] + r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', \ + 'R11_MEA', 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU'] + # Aluminum 2015 # https://www.world-aluminium.org/statistics/#data # Not all the regions match here. Some assumptions needed to be made. # MEA, PAS, SAS, EEU, FSU + # Domestic production (final material level) + #fin_to_useful = 0.971 + #useful_to_product = 0.866 + #d = [1.7, 31.5, 1.8, 2, 1.3, 6.1, 4.5, 2,1,1,3.7] + #d = [x * fin_to_useful * useful_to_product for x in d] - # Values in 2010 from IAI for China. Slightly varies for other regions. - # Can be adjusted: https://alucycle.world-aluminium.org/public-access/ - - fin_to_useful = 0.971 - useful_to_product = 0.866 - - r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', \ - 'R11_MEA', 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU'] + # Demand at product level + # Material efficiency in clean energy transitions (2017) + # Europe is divided between WEU and EEU + # FUS: Eurasia + # PAO, PAS, SAS: IAI Alu cycle - # Domestic production - d = [1.7, 31.5, 1.8, 2, 1.3, 6.1, 4.5, 2,1,1,3.7] - d = [x * fin_to_useful * useful_to_product for x in d] + # Below matrix is for 2020 - # Demand at product level. (IAI) - #d = [1.7, 28.2, 4.5, 2, 2.5, 2, 14.1, 3.5, 5.5,6,8 ] + d = [3,55, 4, 7, 5, 6, 15, 3.5, 5.5,6,6 ] demand2015_al = pd.DataFrame({'Region':r, 'Val':d}).\ join(gdp_growth.set_index('Region'), on='Region').\ diff --git a/message_ix_models/model/material/data_petro.py b/message_ix_models/model/material/data_petro.py index 9a940ef6c4..212be55b5b 100644 --- a/message_ix_models/model/material/data_petro.py +++ b/message_ix_models/model/material/data_petro.py @@ -30,7 +30,7 @@ def read_data_petrochemicals(): sheet_name="data") # Clean the data - data_petro= data_petro.drop(['Region', 'Source', 'Description'], axis = 1) + data_petro= data_petro.drop(['Source', 'Description'], axis = 1) return data_petro @@ -54,14 +54,12 @@ def gen_mock_demand_petro(scenario): gdp_growth['Region'] = 'R11_'+ gdp_growth['Region'] - # 2015 demand + # 2015 production # The Future of Petrochemicals Methodological Annex # Projections here do not show too much growth until 2050 for some regions. # For division of some regions assumptions made: # PAO, PAS, SAS, EEU,WEU - # Domestic production (not demand !!!) - r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', \ 'R11_MEA', 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU'] d_ethylene = [1,25,10,3,5,20,25,12,10,13,15] @@ -106,64 +104,6 @@ def gen_mock_demand_petro(scenario): # This makes 25 Mt ethylene, 25 Mt propylene, 21 Mt BTX # This can be verified by other sources. - # The future projection of the demand: Increases by half of the GDP growth rate. - # Starting from 2020. - # context = read_config() - # s_info = ScenarioInfo(scenario) - # modelyears = s_info.Y #s_info.Y is only for modeling years - # - # gdp_growth = [0.121448215899944, 0.0733079014579874, - # 0.0348154093342843, 0.021827616787921, - # 0.0134425983942219, 0.0108320197485592, - # 0.00884341208063, 0.00829374133206562, - # # Add one more element since model is until 2110 normally - # 0.00649794573935969, 0.00649794573935969] - # baseyear = list(range(2020, 2110+1, 10)) # Index for above vector - # gdp_growth_interp = np.interp(modelyears, baseyear, gdp_growth) - # print(gdp_growth_interp) - - # i = 0 - # values_e = [] - # values_p = [] - # values_BTX = [] - # - # # Assume 5 year duration at the beginning - # duration_period = (pd.Series(modelyears) - \ - # pd.Series(modelyears).shift(1)).tolist() - # duration_period[0] = 5 - # - # val_e = (25 * (1+ 0.147718884937996/2) ** duration_period[i]) - # print("val_e") - # print(val_e) - # values_e.append(val_e) - # print(values_e) - # - # val_p = (25 * (1+ 0.147718884937996/2) ** duration_period[i]) - # print("val_p") - # print(val_p) - # values_p.append(val_p) - # print(values_p) - # - # val_BTX = (21 * (1+ 0.147718884937996/2) ** duration_period[i]) - # print("val_BTX") - # print(val_BTX) - # values_BTX.append(val_BTX) - # print(values_BTX) - # - # for element in gdp_growth_interp: - # i = i + 1 - # if i < len(modelyears): - # val_e = (val_e * (1+ element/2) ** duration_period[i]) - # values_e.append(val_e) - # - # val_p = (val_p * (1+ element/2) ** duration_period[i]) - # values_p.append(val_p) - # - # val_BTX = (val_BTX * (1+ element/2) ** duration_period[i]) - # values_BTX.append(val_BTX) - # - # return values_e, values_p, values_BTX - def gen_data_petro_chemicals(scenario, dry_run=False): # Load configuration @@ -186,15 +126,12 @@ def gen_data_petro_chemicals(scenario, dry_run=False): nodes = s_info.N yv_ya = s_info.yv_ya fmy = s_info.y0 + nodes.remove('World') # Do not parametrize GLB region the same way if "R11_GLB" in nodes: nodes.remove("R11_GLB") - # 'World' is included by default when creating a message_ix.Scenario(). - # Need to remove it for the China bare model - nodes.remove('World') - for t in config["technology"]["add"]: # years = s_info.Y @@ -213,10 +150,13 @@ def gen_data_petro_chemicals(scenario, dry_run=False): param_name = par.split("|")[0] val = data_petro.loc[((data_petro["technology"] == t) & \ - (data_petro["parameter"] == par)),'value'].values[0] + (data_petro["parameter"] == par)),'value'] + + regions = data_petro.loc[((data_petro["technology"] == t) \ + & (data_petro["parameter"] == par)),'Region'] + # Common parameters for all input and output tables - # year_act is none at the moment # node_dest and node_origin are the same as node_loc common = dict( @@ -226,45 +166,70 @@ def gen_data_petro_chemicals(scenario, dry_run=False): time_origin="year", time_dest="year",) - if len(split)> 1: + for rg in regions: + if len(split)> 1: - if (param_name == "input")|(param_name == "output"): + if (param_name == "input")|(param_name == "output"): - com = split[1] - lev = split[2] - mod = split[3] + com = split[1] + lev = split[2] + mod = split[3] - df = (make_df(param_name, technology=t, commodity=com, \ - level=lev,mode=mod, value=val, unit='t', **common).\ - pipe(broadcast, node_loc=nodes).pipe(same_node)) + if (param_name == "input") and (lev == "import"): + df = make_df(param_name, technology=t, commodity=com, \ + level=lev, mode= mod, \ + value=val[regions[regions==rg].index[0]], unit='t', \ + node_loc=rg, node_origin="R11_GLB", **common) + elif (param_name == "output") and (lev == "export"): + df = make_df(param_name, technology=t, commodity=com, \ + level=lev, mode=mod, \ + value=val[regions[regions==rg].index[0]], unit='t', \ + node_loc=rg, node_dest="R11_GLB", **common) + else: + df = (make_df(param_name, technology=t, commodity=com, \ + level=lev, mode=mod, \ + value=val[regions[regions==rg].index[0]], unit='t', \ + node_loc=rg, **common).pipe(same_node)) - results[param_name].append(df) + # Copy parameters to all regions, when node_loc is not GLB + if (len(regions) == 1) and (rg != "R11_GLB"): + print("copying to all R11", rg, lev) + df['node_loc'] = None + df = df.pipe(broadcast, node_loc=nodes)#.pipe(same_node) + # Use same_node only for non-trade technologies + if (lev != "import") and (lev != "export"): + df = df.pipe(same_node) - elif param_name == "emission_factor": - emi = split[1] - mod = split[2] - df = (make_df(param_name, technology=t,value=val,\ - emission=emi, mode=mod, unit='t', **common).pipe(broadcast, \ - node_loc=nodes)) - results[param_name].append(df) + elif param_name == "emission_factor": + emi = split[1] + mod = split[2] - elif param_name == "var_cost": - mod = split[1] + df = make_df(param_name, technology=t, \ + value=val[regions[regions==rg].index[0]],emission=emi,\ + mode=mod, unit='t', node_loc=rg, **common) - df = (make_df(param_name, technology=t, commodity=com, \ - level=lev,mode=mod, value=val, unit='t', **common).\ - pipe(broadcast, node_loc=nodes).pipe(same_node)) + elif param_name == "var_cost": + mod = split[1] - results[param_name].append(df) + df = (make_df(param_name, technology=t, commodity=com, \ + level=lev,mode=mod, value=val[regions[regions==rg].index[0]],\ + unit='t', **common).pipe(broadcast, node_loc=nodes).pipe(same_node)) - # Rest of the parameters apart from inpput, output and emission_factor + # Rest of the parameters apart from inpput, output and emission_factor - else: + else: - df = (make_df(param_name, technology=t, value=val,unit='t', \ - **common).pipe(broadcast, node_loc=nodes)) + df = make_df(param_name, technology=t, \ + value=val[regions[regions==rg].index[0]], unit='t', \ + node_loc=rg, **common) + + # Copy parameters to all regions + if len(set(df['node_loc'])) == 1 and list(set(df['node_loc']))[0]!='R11_GLB': + print("Copying to all R11") + df['node_loc'] = None + df = df.pipe(broadcast, node_loc=nodes) results[param_name].append(df) @@ -274,17 +239,17 @@ def gen_data_petro_chemicals(scenario, dry_run=False): demand_e,demand_p,demand_BTX = gen_mock_demand_petro(scenario) paramname = "demand" - df_e = make_df(paramname, level='demand', commodity="ethylene", \ + df_e = make_df(paramname, level='final_material', commodity="ethylene", \ value=demand_e.value, unit='t',year=demand_e.year, time='year', \ node=demand_e.node)#.pipe(broadcast, node=nodes) results["demand"].append(df_e) - df_p = make_df(paramname, level='demand', commodity="propylene", \ + df_p = make_df(paramname, level='final_material', commodity="propylene", \ value=demand_p.value, unit='t',year=demand_p.year, time='year', \ node=demand_p.node)#.pipe(broadcast, node=nodes) results["demand"].append(df_p) - df_BTX = make_df(paramname, level='demand', commodity="BTX", \ + df_BTX = make_df(paramname, level='final_material', commodity="BTX", \ value=demand_BTX.value, unit='t',year=demand_BTX.year, time='year', \ node=demand_BTX.node)#.pipe(broadcast, node=nodes) results["demand"].append(df_BTX) @@ -323,45 +288,6 @@ def gen_data_petro_chemicals(scenario, dry_run=False): results[p].append(df) - # # Add historical data - # - # print(data_petro_hist["technology"].unique()) - # for tec in data_petro_hist["technology"].unique(): - # print(tec) - # - # y_hist = [1980,1985,1990,1995,2000,2005,2010,2015] #length need to match what's in the xls - # common_hist = dict( - # year_vtg= y_hist, - # year_act= y_hist, - # mode="M1", - # time="year",) - # - # print("historical years") - # print(y_hist) - # val_act = data_petro_hist.\ - # loc[(data_petro_hist["technology"]== tec), "production"] - # print("value activity") - # print(val_act) - # - # df_hist_act = (make_df("historical_activity", technology=tec, \ - # value=val_act, unit='Mt', **common_hist).pipe(broadcast, node_loc=nodes)) - # - # results["historical_activity"].append(df_hist_act) - # - # c_factor = data_petro.loc[((data_petro["technology"]== tec) \ - # & (data_petro["parameter"]=="capacity_factor")), "value"].values - # - # val_cap = data_petro_hist.loc[(data_petro_hist["technology"]== tec), \ - # "new_production"] / c_factor - # - # print("This is capacity value") - # print(val_cap) - # - # df_hist_cap = (make_df("historical_new_capacity", technology=tec, \ - # value=val_cap, unit='Mt', **common_hist).pipe(broadcast, node_loc=nodes)) - # - # results["historical_new_capacity"].append(df_hist_cap) - results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} return results From dad5ba5bac537a04743ef524c89a1f14bbe031c9 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Mon, 11 Jan 2021 14:35:03 +0100 Subject: [PATCH 210/774] Update of data for petro and aluminum (trade, diffusion parameters..) --- .../data/material/aluminum_techno_economic.xlsx | 4 ++-- .../data/material/petrochemicals_techno_economic.xlsx | 4 ++-- message_ix_models/model/material/data_petro.py | 9 +++++---- 3 files changed, 9 insertions(+), 8 deletions(-) diff --git a/message_ix_models/data/material/aluminum_techno_economic.xlsx b/message_ix_models/data/material/aluminum_techno_economic.xlsx index 6a81e8c1d6..b735dde05c 100644 --- a/message_ix_models/data/material/aluminum_techno_economic.xlsx +++ b/message_ix_models/data/material/aluminum_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:f71eb8cdd92af846ff876208e799527afc348b85701b03ad57c3a8d6a92f73b1 -size 74309 +oid sha256:a064cbad9018687cab793ab554fd53c3fa3c9dc8515c899044207b2a5b382ab2 +size 80298 diff --git a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx index be9509b6b8..d52daca18f 100644 --- a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx +++ b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:d6c17d76af7e8e9e58c040fa0068da0f70377e52bf7f00ff74cc743d79f8f4e8 -size 576258 +oid sha256:875bdd3a0c7e78801fddc3fb0ae532f116d060774152f27f8a8b8011e6db8c2b +size 618936 diff --git a/message_ix_models/model/material/data_petro.py b/message_ix_models/model/material/data_petro.py index 212be55b5b..1191ef81ed 100644 --- a/message_ix_models/model/material/data_petro.py +++ b/message_ix_models/model/material/data_petro.py @@ -226,10 +226,11 @@ def gen_data_petro_chemicals(scenario, dry_run=False): node_loc=rg, **common) # Copy parameters to all regions - if len(set(df['node_loc'])) == 1 and list(set(df['node_loc']))[0]!='R11_GLB': - print("Copying to all R11") - df['node_loc'] = None - df = df.pipe(broadcast, node_loc=nodes) + if (len(regions) == 1) and (rg != "R11_GLB"): + if len(set(df['node_loc'])) == 1 and list(set(df['node_loc']))[0]!='R11_GLB': + print("Copying to all R11") + df['node_loc'] = None + df = df.pipe(broadcast, node_loc=nodes) results[param_name].append(df) From 0190e032fa1e1dd9e655a354a1bcea52562b75c3 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Mon, 11 Jan 2021 14:36:09 +0100 Subject: [PATCH 211/774] Small revision on the scrap implementation --- message_ix_models/data/material/set.yaml | 4 +- .../model/material/data_aluminum.py | 143 +++++------------- 2 files changed, 40 insertions(+), 107 deletions(-) diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 9ee86796e6..d880e061d9 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -518,7 +518,9 @@ aluminum: level: add: - new_scrap - - old_scrap + - old_scrap_1 + - old_scrap_2 + - old_scrap_3 - useful_aluminum - final_material - useful_material diff --git a/message_ix_models/model/material/data_aluminum.py b/message_ix_models/model/material/data_aluminum.py index 1ff04524b3..1b40f5a1a5 100644 --- a/message_ix_models/model/material/data_aluminum.py +++ b/message_ix_models/model/material/data_aluminum.py @@ -25,8 +25,6 @@ add_par_data ) - - def read_data_aluminum(): """Read and clean data from :file:`aluminum_techno_economic.xlsx`.""" @@ -54,6 +52,11 @@ def read_data_aluminum(): return data_alu, data_alu_rel +def print_full(x): + pd.set_option('display.max_rows', len(x)) + print(x) + pd.reset_option('display.max_rows') + def gen_data_aluminum(scenario, dry_run=False): config = read_config()["material"]["aluminum"] @@ -95,7 +98,6 @@ def gen_data_aluminum(scenario, dry_run=False): # Iterate over parameters for par in params: - # Obtain the parameter names, commodity,level,emission split = par.split("|") @@ -109,7 +111,6 @@ def gen_data_aluminum(scenario, dry_run=False): regions = data_aluminum.loc[((data_aluminum["technology"] == t) \ & (data_aluminum["parameter"] == par)),'region'] - common = dict( year_vtg= yv_ya.year_vtg, year_act= yv_ya.year_act, @@ -145,7 +146,6 @@ def gen_data_aluminum(scenario, dry_run=False): # Copy parameters to all regions, when node_loc is not GLB if (len(regions) == 1) and (rg != "R11_GLB"): - print("copying to all R11", rg, lev) df['node_loc'] = None df = df.pipe(broadcast, node_loc=nodes)#.pipe(same_node) # Use same_node only for non-trade technologies @@ -168,8 +168,7 @@ def gen_data_aluminum(scenario, dry_run=False): node_loc=rg, **common) # Copy parameters to all regions - if len(set(df['node_loc'])) == 1 and list(set(df['node_loc']))[0]!='R11_GLB': - print("Copying to all R11") + if (len(regions) == 1) and len(set(df['node_loc'])) == 1 and list(set(df['node_loc']))[0]!='R11_GLB': df['node_loc'] = None df = df.pipe(broadcast, node_loc=nodes) @@ -216,107 +215,50 @@ def gen_data_aluminum(scenario, dry_run=False): results[p].append(df) - # # Add historical data - # - # for tec in data_aluminum_hist["technology"].unique(): - # - # y_hist = [1980,1985,1990,1995,2000,2005,2010,2015] #length need to match what's in the xls - # common_hist = dict( - # year_vtg= y_hist, - # year_act= y_hist, - # mode="M1", - # time="year",) - # - # val_act = data_aluminum_hist.\ - # loc[(data_aluminum_hist["technology"]== tec), "production"] - # - # df_hist_act = (make_df("historical_activity", technology=tec, \ - # value=val_act, unit='Mt', **common_hist).pipe(broadcast, node_loc=nodes)) - # - # results["historical_activity"].append(df_hist_act) - # - # c_factor = data_aluminum.loc[((data_aluminum["technology"]== tec) \ - # & (data_aluminum["parameter"]=="capacity_factor")), "value"].values - # - # val_cap = data_aluminum_hist.loc[(data_aluminum_hist["technology"]== tec), \ - # "new_production"] / c_factor - # - # df_hist_cap = (make_df("historical_new_capacity", technology=tec, \ - # value=val_cap, unit='Mt', **common_hist).pipe(broadcast, node_loc=nodes)) - # - # results["historical_new_capacity"].append(df_hist_cap) - # - # - # # Add variable costs - # - # data_aluminum_var = pd.melt(data_aluminum_var, id_vars=['technology', 'mode', 'units',\ - # "parameter","region"], value_vars=[2020, 2025,2030,2035, 2040,2045, 2050,2055,2060, 2070, 2080, 2090, 2100], var_name='year') - # - # tec_vc = set(data_aluminum_var.technology) - # param_name = set(data_aluminum_var.parameter) - # - # - # for p in param_name: - # for t in tec_vc: - # - # common = dict( - # time="year", - # time_origin="year", - # time_dest="year", - # ) - # - # param_name = p - # val = data_aluminum_var.loc[((data_aluminum_var["technology"] == t) \ - # & (data_aluminum_var["parameter"] == p)), 'value'].values - # units = data_aluminum_var.loc[((data_aluminum_var["technology"] == t) \ - # & (data_aluminum_var["parameter"] == p)),'units'].values - # mod = data_aluminum_var.loc[((data_aluminum_var["technology"] == t) \ - # & (data_aluminum_var["parameter"] == p)), 'mode'].values - # yr = data_aluminum_var.loc[((data_aluminum_var["technology"] == t) \ - # & (data_aluminum_var["parameter"] == p)), 'year'].values - # - # df = (make_df(param_name, technology=t, value=val,unit='t', \ - # mode=mod, year_vtg=yr, year_act=yr, **common).pipe(broadcast,node_loc=nodes)) - # results[param_name].append(df) - # Add relations for scrap grades and availability - for r in config['relation']['add']: + regions = set(data_aluminum_rel["Region"].values) - params = data_aluminum_rel.loc[(data_aluminum_rel["relation"] == r),\ - "parameter"].values.tolist() + for reg in regions: - common_rel = dict( - year_rel = modelyears, - year_act = modelyears, - mode = 'M1', - relation = r,) + for r in config['relation']['add']: - for par_name in params: - if par_name == "relation_activity": + params = set(data_aluminum_rel.loc[(data_aluminum_rel["relation"] == r),\ + "parameter"].values) - tec_list = data_aluminum_rel.loc[((data_aluminum_rel["relation"] == r) \ - & (data_aluminum_rel["parameter"] == par_name)) ,'technology'] + common_rel = dict( + year_rel = modelyears, + year_act = modelyears, + mode = 'M1', + relation = r,) - for tec in tec_list.unique(): - val = data_aluminum_rel.loc[((data_aluminum_rel["relation"] == r) \ - & (data_aluminum_rel["parameter"] == par_name) & \ - (data_aluminum_rel["technology"]==tec)),'value'].values[0] + for par_name in params: + if par_name == "relation_activity": - df = (make_df(par_name, technology=tec, value=val, unit='-',\ - **common_rel).pipe(broadcast, node_rel=nodes, node_loc=nodes)) + tec_list = data_aluminum_rel.loc[((data_aluminum_rel["relation"] == r) \ + & (data_aluminum_rel["parameter"] == par_name)) ,'technology'] - results[par_name].append(df) + for tec in tec_list.unique(): + + val = data_aluminum_rel.loc[((data_aluminum_rel["relation"] == r) \ + & (data_aluminum_rel["parameter"] == par_name) & \ + (data_aluminum_rel["technology"]==tec) & \ + (data_aluminum_rel["Region"]==reg)),'value'].values[0] - elif par_name == "relation_upper": + df = (make_df(par_name, technology=tec, value=val, unit='-',\ + node_loc = reg, node_rel= reg, **common_rel).pipe(same_node)) - val = data_aluminum_rel.loc[((data_aluminum_rel["relation"] == r) \ - & (data_aluminum_rel["parameter"] == par_name)),'value'].values[0] + results[par_name].append(df) + + elif (par_name == "relation_upper") | (par_name == "relation_lower"): + val = data_aluminum_rel.loc[((data_aluminum_rel["relation"] == r) \ + & (data_aluminum_rel["parameter"] == par_name) & \ + (data_aluminum_rel["Region"]==reg)),'value'].values[0] - df = (make_df(par_name, value=val, unit='-',\ - **common_rel).pipe(broadcast, node_rel=nodes)) + df = (make_df(par_name, value=val, unit='-',node_rel=reg, + **common_rel)) - results[par_name].append(df) + results[par_name].append(df) results_aluminum = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} @@ -344,22 +286,11 @@ def gen_mock_demand_aluminum(scenario): r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', \ 'R11_MEA', 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU'] - # Aluminum 2015 - # https://www.world-aluminium.org/statistics/#data - # Not all the regions match here. Some assumptions needed to be made. - # MEA, PAS, SAS, EEU, FSU - # Domestic production (final material level) - #fin_to_useful = 0.971 - #useful_to_product = 0.866 - #d = [1.7, 31.5, 1.8, 2, 1.3, 6.1, 4.5, 2,1,1,3.7] - #d = [x * fin_to_useful * useful_to_product for x in d] - # Demand at product level # Material efficiency in clean energy transitions (2017) # Europe is divided between WEU and EEU # FUS: Eurasia # PAO, PAS, SAS: IAI Alu cycle - # Below matrix is for 2020 d = [3,55, 4, 7, 5, 6, 15, 3.5, 5.5,6,6 ] From 12809b9730e81b6e6be1e743396768fe8fe2e37c Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 14 Jan 2021 16:27:23 +0100 Subject: [PATCH 212/774] Aluminum historical data changes (to improve trade representation..) --- message_ix_models/data/material/aluminum_techno_economic.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/aluminum_techno_economic.xlsx b/message_ix_models/data/material/aluminum_techno_economic.xlsx index b735dde05c..45140da5fb 100644 --- a/message_ix_models/data/material/aluminum_techno_economic.xlsx +++ b/message_ix_models/data/material/aluminum_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:a064cbad9018687cab793ab554fd53c3fa3c9dc8515c899044207b2a5b382ab2 -size 80298 +oid sha256:969ffcc1db717d71cfe5a2b23f564c555153d7e2c090e4fff79949525513ee28 +size 81325 From e585dd66d34f02ac25b63311bb1f1a6a42c92585 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 14 Jan 2021 16:28:19 +0100 Subject: [PATCH 213/774] Set the default scenario --- message_ix_models/model/material/__init__.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 5f94bf3d19..5af6cc61d1 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -112,5 +112,8 @@ def solve(context, datafile): .clone(model="Material_Global", scenario=output_scenario_name) ) + # Set the latest version as default + scenario.set_as_default() + # Solve scenario.solve() From d85a778e965f33cf0426c26124fdb099125908cb Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Fri, 15 Jan 2021 17:22:37 +0100 Subject: [PATCH 214/774] Historical activity of the industry technologies that supply to useful level adjusted --- message_ix_models/model/material/__init__.py | 6 ++- message_ix_models/model/material/data_util.py | 51 +++++++++++++++++-- 2 files changed, 53 insertions(+), 4 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 5af6cc61d1..46737ebba8 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -6,6 +6,7 @@ from .data import add_data from .data_util import modify_demand +from .data_util import modify_historical_activity from .util import read_config @@ -20,6 +21,9 @@ def build(scenario): # Adjust exogenous energy demand to incorporate the endogenized sectors modify_demand(scenario) + # Adjust the historical activity of the usefyl level industry technologies + modify_historical_activity(scenario) + return scenario # add as needed/implemented @@ -112,7 +116,7 @@ def solve(context, datafile): .clone(model="Material_Global", scenario=output_scenario_name) ) - # Set the latest version as default + # Set the latest version as default scenario.set_as_default() # Solve diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index cfaeef1858..517b21c0fb 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -5,7 +5,54 @@ from .util import read_config import re +def modify_historical_activity(scen): + """Adjust the historical activity of the related industry technologies + + that provide output to different categories of industrial demand (e.g. + + i_therm, i_spec, i_feed). The historical activity is reduced the same % + + as the industrial demand is reduced in modfiy_demand function. + """ + + # Relted technologies that have outputs to useful industry level. + + tec_therm = ["biomass_i","coal_i","elec_i","eth_i","foil_i","gas_i", + "h2_i","heat_i","hp_el_i", "hp_gas_i","loil_i", + "meth_i","solar_i"] + tec_fs = ["coal_fs","ethanol_fs","foil_fs","gas_fs","loil_fs","methanol_fs",] + tec_sp = ["sp_coal_I","sp_el_I","sp_eth_I","sp_liq_I","sp_meth_I", "h2_fc_I"] + + # Thermal industrial technologies + thermal_df = scen.par('historical_activity', filters={'technology':tec_therm}) + # Specific industrial technologies + spec_df = scen.par('historical_activity', filters={'technology':tec_sp}) + # Specific industrial technologies + feed_df = scen.par('historical_activity', filters={'technology':tec_fs}) + + #i_therm adjsutment (30% steel, 25% cement, 7% petro) + thermal_df.value = thermal_df.value * 0.38 + # i_spec adjustment (15% aluminum) + spec_df.value = spec_df.value * 0.8 + # i_feed adjustment (30% HVCs) + feed_df.value = feed_df.value * 0.7 + + # Infeasibility R11_NAM 2020 act_dynamic_up: 0.0409422121702949) + # Modify initial activity up: + + initial_activity_up_df = scen.par('initial_activity_up', filters = \ + {'node_loc': 'R11_NAM', 'technology':'biomass_i', 'year_act':2020}) + + initial_activity_up_df.value = initial_activity_up_df.value + 0.041 + + scen.check_out() + scen.add_par('historical_activity', thermal_df) + scen.add_par('historical_activity', spec_df) + scen.add_par('historical_activity', feed_df) + scen.add_par("initial_activity_up", initial_activity_up_df) + scen.commit(comment = 'historical activity for useful level industry \ + technologies adjusted') def modify_demand(scen): """Take care of demand changes due to the introduction of material parents @@ -47,7 +94,7 @@ def modify_demand(scen): # Makes up around 30% of total feedstock demand. df = scen.par('demand', filters={'commodity':'i_feed'}) - df.value = df.value * 0.7 #(70% HVCs) + df.value = df.value * 0.7 #(30% HVCs) scen.check_out() scen.add_par('demand', df) @@ -66,8 +113,6 @@ def modify_demand(scen): scen.remove_par('growth_activity_lo', df) scen.commit(comment = 'remove growth_lo constraints') - - # Read in technology-specific parameters from input xlsx # Now used for steel and cement, which are in one file def read_sector_data(sectname): From 0ed41f7059d9ae18cc1c7d7f28b01c1ebbd5c25d Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 20 Jan 2021 16:11:48 +0100 Subject: [PATCH 215/774] Addition of script to adjsut the historical activity and demand values --- ...AGEix-Materials_final_energy_industry.xlsx | 3 + message_ix_models/model/material/__init__.py | 8 +- message_ix_models/model/material/data_util.py | 230 +++++++++++++----- 3 files changed, 179 insertions(+), 62 deletions(-) create mode 100644 message_ix_models/data/material/MESSAGEix-Materials_final_energy_industry.xlsx diff --git a/message_ix_models/data/material/MESSAGEix-Materials_final_energy_industry.xlsx b/message_ix_models/data/material/MESSAGEix-Materials_final_energy_industry.xlsx new file mode 100644 index 0000000000..8338706477 --- /dev/null +++ b/message_ix_models/data/material/MESSAGEix-Materials_final_energy_industry.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b222682c9cbe45494c26474c99ad1c71dda86afa5e04f874ca1f0c0f2c74215e +size 344589 diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 46737ebba8..a4487176aa 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -5,11 +5,9 @@ from message_ix_models.model.build import apply_spec from .data import add_data -from .data_util import modify_demand -from .data_util import modify_historical_activity +from .data_util import modify_demand_and_hist_activity from .util import read_config - def build(scenario): """Set up materials accounting on `scenario`.""" # Get the specification @@ -19,10 +17,8 @@ def build(scenario): apply_spec(scenario, spec, add_data) # dry_run=True # Adjust exogenous energy demand to incorporate the endogenized sectors - modify_demand(scenario) - # Adjust the historical activity of the usefyl level industry technologies - modify_historical_activity(scenario) + modify_demand_and_hist_activity(scenario) return scenario diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index 517b21c0fb..63a9770ac1 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -5,9 +5,14 @@ from .util import read_config import re -def modify_historical_activity(scen): +def modify_demand_and_hist_activity(scen): + """Take care of demand changes due to the introduction of material parents + + Shed industrial energy demand properly. + + Also need take care of remove dynamic constraints for certain energy carriers. - """Adjust the historical activity of the related industry technologies + Adjust the historical activity of the related industry technologies that provide output to different categories of industrial demand (e.g. @@ -16,89 +21,202 @@ def modify_historical_activity(scen): as the industrial demand is reduced in modfiy_demand function. """ - # Relted technologies that have outputs to useful industry level. + # NOTE Temporarily modifying industrial energy demand + # From IEA database (dumped to an excel) + + context = read_config() + fname = "MESSAGEix-Materials_final_energy_industry.xlsx" + df = pd.read_excel(context.get_path("material", fname),\ + sheet_name="Export Worksheet") + + # Filter the necessary variables + df = df[(df["SECTOR"]== "feedstock (petrochemical industry)")| + (df["SECTOR"]== "feedstock (total)") | \ + (df["SECTOR"]== "industry (chemicals)") | + (df["SECTOR"]== "industry (iron and steel)") | \ + (df["SECTOR"]== "industry (non-ferrous metals)") | \ + (df["SECTOR"]== "industry (non-metallic minerals)") | \ + (df["SECTOR"]== "industry (total)")] + df = df[df["RYEAR"]==2015] + + # Retreive data for i_spec (Excludes petrochemicals as the share is negligable) + # Aluminum, cement and steel included. + # NOTE: Steel has high shares (previously it was not inlcuded in i_spec) + + df_spec = df[(df["FUEL"]=="electricity") & \ + (df["SECTOR"] != "industry (total)") \ + & (df["SECTOR"]!= "feedstock (petrochemical industry)") \ + & (df["SECTOR"]!= "feedstock (total)")] + df_spec_total = df[(df["SECTOR"]=="industry (total)") \ + & (df["FUEL"]=="electricity") ] + + df_spec_new = pd.DataFrame(columns=["REGION","SECTOR","FUEL","RYEAR",\ + "UNIT_OUT","RESULT"]) + for r in df_spec["REGION"].unique(): + df_spec_temp = df_spec[df_spec["REGION"]==r] + df_spec_total_temp = df_spec_total[df_spec_total["REGION"] == r] + df_spec_temp["i_spec"] = df_spec_temp["RESULT"]/df_spec_total_temp["RESULT"].values[0] + df_spec_new = pd.concat([df_spec_temp,df_spec_new],ignore_index = True) + + df_spec_new.drop(["FUEL","RYEAR","UNIT_OUT","RESULT"],axis=1, inplace=True) + df_spec_new.loc[df_spec_new["SECTOR"]=="industry (chemicals)","i_spec"] = \ + df_spec_new.loc[df_spec_new["SECTOR"]=="industry (chemicals)","i_spec"] * 0.5 + + df_spec_new = df_spec_new.groupby(["REGION"]).sum().reset_index() + + # Retreive data for i_feed: Only for petrochemicals + + df_feed = df[(df["SECTOR"]== "feedstock (petrochemical industry)") & \ + (df["FUEL"]== "total") ] + df_feed_total = df[(df["SECTOR"]== "feedstock (total)") \ + & (df["FUEL"]== "total")] + + df_feed_new = pd.DataFrame(columns=["REGION","SECTOR","FUEL",\ + "RYEAR","UNIT_OUT","RESULT"]) + for r in df_feed["REGION"].unique(): + df_feed_temp = df_feed[df_feed["REGION"]==r] + df_feed_total_temp = df_feed_total[df_feed_total["REGION"] == r] + df_feed_temp["i_feed"] = df_feed_temp["RESULT"]/df_feed_total_temp["RESULT"].values[0] + df_feed_new = pd.concat([df_feed_temp,df_feed_new],ignore_index = True) + + df_feed_new.drop(["FUEL","RYEAR","UNIT_OUT","RESULT"],axis=1, inplace=True) + df_feed_new = df_feed_new.groupby(["REGION"]).sum().reset_index() + + # Retreive data for i_therm + # NOTE: It is assumped that 50% of the chemicals category is HVCs + # NOTE: Aluminum is excluded since refining process is not explicitly represented + # NOTE: CPA has a 3% share while it used to be 30% previosuly ?? + + df_therm = df[(df["FUEL"]!="electricity") & (df["FUEL"]!="total") \ + & (df["SECTOR"] != "industry (total)") \ + & (df["SECTOR"]!= "feedstock (petrochemical industry)") \ + & (df["SECTOR"]!= "feedstock (total)") \ + & (df["SECTOR"]!= "industry (non-ferrous metals)") ] + df_therm_total = df[(df["SECTOR"]=="industry (total)") \ + & (df["FUEL"]!="total") & (df["FUEL"]!="electricity")] + df_therm_total = df_therm_total.groupby(by="REGION").\ + sum().drop(["RYEAR"],axis=1).reset_index() + df_therm = df_therm.groupby(by=["REGION","SECTOR"]).\ + sum().drop(["RYEAR"],axis=1).reset_index() + df_therm_new = pd.DataFrame(columns=["REGION","SECTOR","FUEL",\ + "RYEAR","UNIT_OUT","RESULT"]) + + for r in df_therm["REGION"].unique(): + df_therm_temp = df_therm[df_therm["REGION"]==r] + df_therm_total_temp = df_therm_total[df_therm_total["REGION"] == r] + df_therm_temp["i_therm"] = df_therm_temp["RESULT"]/df_therm_total_temp["RESULT"].values[0] + df_therm_new = pd.concat([df_therm_temp,df_therm_new],\ + ignore_index = True) + df_therm_new = df_therm_new.drop(["RESULT"],axis=1) + + df_therm_new.drop(["FUEL","RYEAR","UNIT_OUT"],axis=1,inplace=True) + df_therm_new.loc[df_therm_new["SECTOR"]=="industry (chemicals)","i_therm"] = \ + df_therm_new.loc[df_therm_new["SECTOR"]=="industry (chemicals)","i_therm"] * 0.5 + + # Modify CPA based on https://www.iea.org/sankey/#?c=Japan&s=Final%20consumption. + # Since the value did not allign with the one in the IEA website. + index = ((df_therm_new["SECTOR"]=="industry (iron and steel)") \ + & (df_therm_new["REGION"]=="CPA")) + df_therm_new.loc[index,"i_therm"] = 0.2 + + df_therm_new = df_therm_new.groupby(["REGION"]).sum().reset_index() + + # TODO: Useful technology efficiencies will also be included + + # Add the modified demand and historical activity to the scenario + # Relted technologies that have outputs to useful industry level. + # Historical activity of theese will be adjusted tec_therm = ["biomass_i","coal_i","elec_i","eth_i","foil_i","gas_i", "h2_i","heat_i","hp_el_i", "hp_gas_i","loil_i", "meth_i","solar_i"] tec_fs = ["coal_fs","ethanol_fs","foil_fs","gas_fs","loil_fs","methanol_fs",] tec_sp = ["sp_coal_I","sp_el_I","sp_eth_I","sp_liq_I","sp_meth_I", "h2_fc_I"] - # Thermal industrial technologies - thermal_df = scen.par('historical_activity', filters={'technology':tec_therm}) - # Specific industrial technologies - spec_df = scen.par('historical_activity', filters={'technology':tec_sp}) - # Specific industrial technologies - feed_df = scen.par('historical_activity', filters={'technology':tec_fs}) - - #i_therm adjsutment (30% steel, 25% cement, 7% petro) - thermal_df.value = thermal_df.value * 0.38 - # i_spec adjustment (15% aluminum) - spec_df.value = spec_df.value * 0.8 - # i_feed adjustment (30% HVCs) - feed_df.value = feed_df.value * 0.7 - - # Infeasibility R11_NAM 2020 act_dynamic_up: 0.0409422121702949) - # Modify initial activity up: + thermal_df_hist = scen.par('historical_activity', filters={'technology':tec_therm}) + spec_df_hist = scen.par('historical_activity', filters={'technology':tec_sp}) + feed_df_hist = scen.par('historical_activity', filters={'technology':tec_fs}) + useful_thermal = scen.par('demand', filters={'commodity':'i_therm'}) + useful_spec = scen.par('demand', filters={'commodity':'i_spec'}) + useful_feed = scen.par('demand', filters={'commodity':'i_feed'}) + + for r in df_therm_new["REGION"]: + r_MESSAGE = "R11_" + r + useful_thermal.loc[useful_thermal["node"]== r_MESSAGE ,"value"] = \ + useful_thermal.loc[useful_thermal["node"]== r_MESSAGE,"value"] * \ + (1 - df_therm_new.loc[df_therm_new["REGION"]==r,"i_therm"].values[0]) + + thermal_df_hist.loc[thermal_df_hist["node_loc"]== r_MESSAGE ,"value"] = \ + thermal_df_hist.loc[thermal_df_hist["node_loc"]== r_MESSAGE,"value"] * \ + (1 - df_therm_new.loc[df_therm_new["REGION"]==r,"i_therm"].values[0]) + + for r in df_spec_new["REGION"]: + r_MESSAGE = "R11_" + r + useful_spec.loc[useful_spec["node"]== r_MESSAGE ,"value"] = \ + useful_spec.loc[useful_spec["node"]== r_MESSAGE,"value"] * \ + (1 - df_spec_new.loc[df_spec_new["REGION"]==r,"i_spec"].values[0]) + + spec_df_hist.loc[spec_df_hist["node_loc"]== r_MESSAGE ,"value"] = \ + spec_df_hist.loc[spec_df_hist["node_loc"]== r_MESSAGE,"value"] * \ + (1 - df_spec_new.loc[df_spec_new["REGION"]==r,"i_spec"].values[0]) + + for r in df_feed_new["REGION"]: + r_MESSAGE = "R11_" + r + useful_feed.loc[useful_feed["node"]== r_MESSAGE ,"value"] = \ + useful_feed.loc[useful_feed["node"]== r_MESSAGE,"value"] * \ + (1 - df_feed_new.loc[df_feed_new["REGION"]==r,"i_feed"].values[0]) + + feed_df_hist.loc[feed_df_hist["node_loc"]== r_MESSAGE ,"value"] = \ + feed_df_hist.loc[feed_df_hist["node_loc"]== r_MESSAGE,"value"] * \ + (1 - df_feed_new.loc[df_feed_new["REGION"]==r,"i_feed"].values[0]) - initial_activity_up_df = scen.par('initial_activity_up', filters = \ - {'node_loc': 'R11_NAM', 'technology':'biomass_i', 'year_act':2020}) - - initial_activity_up_df.value = initial_activity_up_df.value + 0.041 + scen.check_out() + scen.add_par("demand",useful_thermal) + scen.add_par("demand",useful_spec) + scen.add_par("demand",useful_feed) + scen.commit("Demand values adjusted") scen.check_out() - scen.add_par('historical_activity', thermal_df) - scen.add_par('historical_activity', spec_df) - scen.add_par('historical_activity', feed_df) - scen.add_par("initial_activity_up", initial_activity_up_df) + scen.add_par('historical_activity', thermal_df_hist) + scen.add_par('historical_activity', spec_df_hist) + scen.add_par('historical_activity', feed_df_hist) scen.commit(comment = 'historical activity for useful level industry \ technologies adjusted') -def modify_demand(scen): - """Take care of demand changes due to the introduction of material parents - - Shed industrial energy demand properly. - - Also need take care of remove dynamic constraints for certain energy carriers - """ - - # NOTE Temporarily modifying industrial energy demand - # (30% of non-elec industrial energy for steel) - # For aluminum there is no significant deduction required # (refining process not included and thermal energy required from # recycling is not a significant share.) # For petro: based on 13.1 GJ/tonne of ethylene and the demand in the model - df = scen.par('demand', filters={'commodity':'i_therm'}) - df.value = df.value * 0.38 #(30% steel, 25% cement, 7% petro) - - scen.check_out() - scen.add_par('demand', df) - scen.commit(comment = 'modify i_therm demand') + # df = scen.par('demand', filters={'commodity':'i_therm'}) + # df.value = df.value * 0.38 #(30% steel, 25% cement, 7% petro) + # + # scen.check_out() + # scen.add_par('demand', df) + # scen.commit(comment = 'modify i_therm demand') # Adjust the i_spec. # Electricity usage seems negligable in the production of HVCs. # Aluminum: based on IAI China data 20%. - df = scen.par('demand', filters={'commodity':'i_spec'}) - df.value = df.value * 0.80 #(15% aluminum) - - scen.check_out() - scen.add_par('demand', df) - scen.commit(comment = 'modify i_spec demand') + # df = scen.par('demand', filters={'commodity':'i_spec'}) + # df.value = df.value * 0.80 #(20% aluminum) + # + # scen.check_out() + # scen.add_par('demand', df) + # scen.commit(comment = 'modify i_spec demand') # Adjust the i_feedstock. # 45 GJ/tonne of ethylene or propylene or BTX # 2020 demand of one of these: 35.7 Mt # Makes up around 30% of total feedstock demand. - df = scen.par('demand', filters={'commodity':'i_feed'}) - df.value = df.value * 0.7 #(30% HVCs) - - scen.check_out() - scen.add_par('demand', df) - scen.commit(comment = 'modify i_feed demand') + # df = scen.par('demand', filters={'commodity':'i_feed'}) + # df.value = df.value * 0.7 #(30% HVCs) + # + # scen.check_out() + # scen.add_par('demand', df) + # scen.commit(comment = 'modify i_feed demand') # NOTE Aggregate industrial coal demand need to adjust to # the sudden intro of steel setor in the first model year From 02c2a298916d54843a3bc3af7c62eec5d465931a Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 21 Jan 2021 15:36:00 +0100 Subject: [PATCH 216/774] Changing the thermal energy need of refinery technologies to secondary level --- .../data/material/generic_furnace_boiler_techno_economic.xlsx | 4 ++-- .../data/material/petrochemicals_techno_economic.xlsx | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx index 575c44b095..87808f0e53 100644 --- a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx +++ b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:45b963809f1a3b262b0ecedcdf92df8be4d7134dbd1498967b0bea2fc8ebaa54 -size 276 +oid sha256:35212763daafd18fde4448ddb6d7bcc5ae4d0a2e3a9d623e51019cf2daa55ed3 +size 332 diff --git a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx index d52daca18f..4203da66b3 100644 --- a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx +++ b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:875bdd3a0c7e78801fddc3fb0ae532f116d060774152f27f8a8b8011e6db8c2b -size 618936 +oid sha256:2b6bf6ebfbee26719a44508ca9869ae2fe0e5c03ac41611bcc53a82e46c8d810 +size 618901 From df35fafcf8bbdb87a973c750d452480bfd5849dc Mon Sep 17 00:00:00 2001 From: Jihoon Min Date: Mon, 25 Jan 2021 10:58:33 +0100 Subject: [PATCH 217/774] Led input data --- message_ix_models/data/material/LED_LED_report_IAMC.csv | 3 +++ 1 file changed, 3 insertions(+) create mode 100644 message_ix_models/data/material/LED_LED_report_IAMC.csv diff --git a/message_ix_models/data/material/LED_LED_report_IAMC.csv b/message_ix_models/data/material/LED_LED_report_IAMC.csv new file mode 100644 index 0000000000..595ff9296b --- /dev/null +++ b/message_ix_models/data/material/LED_LED_report_IAMC.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2eee76b0e25eeb360997f69e64b4092280dc51dc62ae4b94bb7957867b7c1564 +size 63673 From 3b48aa3ed5928e83681873c5d20880169b4077bb Mon Sep 17 00:00:00 2001 From: Jihoon Min Date: Thu, 21 Jan 2021 14:58:10 +0100 Subject: [PATCH 218/774] Build up buildings script not tested. wanted to have this in a separate feature branch, but some git situation doesn't allow me to do so. --- message_ix_models/data/material/set.yaml | 54 +-- message_ix_models/model/material/data.py | 3 +- .../model/material/data_buildings.py | 374 ++++++++++++++++++ message_ix_models/model/material/data_util.py | 1 + 4 files changed, 383 insertions(+), 49 deletions(-) create mode 100644 message_ix_models/model/material/data_buildings.py diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index d880e061d9..e697fb4c5f 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -575,60 +575,18 @@ fertilizer: - fueloil_NH3 - NH3_to_N_fertil -aluminum: - commodity: +buildings: + level: add: - - aluminum + - service - level: + commodity: add: - - new_scrap - - old_scrap - - useful_aluminum - - final_material - - useful_material - - product - - secondary_material - - demand_aluminum + - floor_area technology: add: - - soderberg_aluminum - - prebake_aluminum - - secondary_aluminum - - prep_secondary_aluminum_1 - - prep_secondary_aluminum_2 - - prep_secondary_aluminum_3 - - finishing_aluminum - - manuf_aluminum - - scrap_recovery_aluminum - - DUMMY_alumina_supply - - fertilizer: - commodity: - require: - - electr - - freshwater_supply - add: - - NH3 - - Fertilizer Use|Nitrogen - - level: - add: - - material_interim - - material_final - - technology: - require: - - h2_bio_ccs # Reference for vent-to-storage ratio - add: - - biomass_NH3 - - electr_NH3 - - gas_NH3 - - coal_NH3 - - fueloil_NH3 - - NH3_to_N_fertil - + - buildings # NB this codelist added by #125 diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index 171476ac15..b36ef6c7ca 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -30,7 +30,8 @@ gen_data_cement, gen_data_aluminum, gen_data_generic, - gen_data_petro_chemicals + gen_data_petro_chemicals, + gen_data_buildings ] # Try to handle multiple data input functions from different materials diff --git a/message_ix_models/model/material/data_buildings.py b/message_ix_models/model/material/data_buildings.py new file mode 100644 index 0000000000..eb85420670 --- /dev/null +++ b/message_ix_models/model/material/data_buildings.py @@ -0,0 +1,374 @@ +from .data_util import read_sector_data, read_timeseries_buildings + +import numpy as np +from collections import defaultdict +import logging + +import pandas as pd + +from .util import read_config +from .data_util import read_rel +from message_data.tools import ( + ScenarioInfo, + broadcast, + make_df, + make_io, + make_matched_dfs, + same_node, + copy_column, + add_par_data +) + + +def read_timeseries_buildings(filename): + + import numpy as np + + # Ensure config is loaded, get the context + context = read_config() + + # Read the file + bld_input_raw = pd.read_csv( + context.get_path("material", filename)) + + bld_input_mat = bld_input_raw[bld_input_raw['Variable'].\ + str.contains("Floor Space|Aluminum|Cement|Steel")] + + bld_input_pivot = \ + bld_input_mat.melt(id_vars=['Region','Variable'], var_name='Year', \ + value_vars=list(map(str, range(2015, 2101, 5)))).\ + set_index(['Region','Year','Variable'])\ + .squeeze()\ + .unstack()\ + .reset_index() + + # Calculate material intensities + bld_input_pivot['Material Demand|Residential|Buildings|Cement|Intensity'] =\ + bld_input_pivot['Material Demand|Residential|Buildings|Cement']/\ + bld_input_pivot['Energy Service|Residential|Floor Space'] + bld_input_pivot['Material Demand|Residential|Buildings|Steel|Intensity'] =\ + bld_input_pivot['Material Demand|Residential|Buildings|Steel']/\ + bld_input_pivot['Energy Service|Residential|Floor Space'] + bld_input_pivot['Material Demand|Residential|Buildings|Aluminum|Intensity'] =\ + bld_input_pivot['Material Demand|Residential|Buildings|Aluminum']/\ + bld_input_pivot['Energy Service|Residential|Floor Space'] + bld_input_pivot['Scrap Release|Residential|Buildings|Cement|Intensity'] =\ + bld_input_pivot['Scrap Release|Residential|Buildings|Cement']/\ + bld_input_pivot['Energy Service|Residential|Floor Space'] + bld_input_pivot['Scrap Release|Residential|Buildings|Steel|Intensity'] =\ + bld_input_pivot['Scrap Release|Residential|Buildings|Steel']/\ + bld_input_pivot['Energy Service|Residential|Floor Space'] + bld_input_pivot['Scrap Release|Residential|Buildings|Aluminum|Intensity'] =\ + bld_input_pivot['Scrap Release|Residential|Buildings|Aluminum']/\ + bld_input_pivot['Energy Service|Residential|Floor Space'] + + # Material intensities are in kg/m2 + bld_data_long = bld_input_pivot.melt(id_vars=['Region','Year'], var_name='Variable')\ + .rename(columns={"Region": "node", "Year": "year"}) + bld_intensity_long = bld_data_long[bld_data_long['Variable'].\ + str.contains("Intensity")]\ + .reset_index(drop=True) + bld_area_long = bld_data_long[bld_data_long['Variable'].\ + str.contains("Floor Space")]\ + .reset_index(drop=True) + + tmp = bld_intensity_long.Variable.str.split("|", expand=True) + + bld_intensity_long['commodity'] = tmp[3] + bld_intensity_long['type'] = tmp[0] + bld_intensity_long['unit'] = "kg/m2" + + bld_intensity_long = bld_intensity_long.drop(columns='Variable') + bld_area_long = bld_area_long.drop(columns='Variable') + + bld_intensity_long = bld_intensity_long\ + .drop(bld_intensity_long[np.isnan(bld_intensity_long.value)].index) + + return bld_intensity_long, bld_area_long + + +def gen_data_buildings(scenario, dry_run=False): + """Generate data for materials representation of steel industry. + + """ + # Load configuration + context = read_config() + config = context["material"]["buildings"] + + lev = config['level']['add'] + comm = config['commodity']['add'] + tec = config['technology']['add'] # "buildings" + + print(lev, comm, tec, type(tec)) + + # Information about scenario, e.g. node, year + s_info = ScenarioInfo(scenario) + + # Buildings raw data (from Alessio) + data_buildings, data_buildings_demand = read_timeseries_buildings('LED_LED_report_IAMC.csv') + + # List of data frames, to be concatenated together at end + results = defaultdict(list) + + # For each technology there are differnet input and output combinations + # Iterate over technologies + + allyears = s_info.set['year'] #s_info.Y is only for modeling years + modelyears = s_info.Y #s_info.Y is only for modeling years + nodes = s_info.N + yv_ya = s_info.yv_ya + fmy = s_info.y0 + nodes.remove('World') + + # R11 + regions = set(data_buildings.node) + comms = set(data_buildings.commodity) + types = set(data_buildings.type) + # ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', \ + # 'R11_MEA', 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU'] + + common = dict( + time="year", + time_origin="year", + time_dest="year", + mode="M1") + + for rg in regions: + for comm in comms: + # for typ in types: + + val_mat = data_buildings.loc[(data_buildings["type"] == types[0]) \ + & (data_buildings["commodity"] == comm), ] + val_scr = data_buildings.loc[(data_buildings["type"] == types[1]) \ + & (data_buildings["commodity"] == comm), ] + + # Material input to buildings + df = make_df('input', technology=tec, commodity=comm, \ + level="product", year_vtg = val_mat.year, \ + value=val_mat.value, unit='t', \ + node_loc = rg, **common)\ + .pipe(same_node))\ + .assign(year_act=copy_column('year_vtg')) + results[param_name].append(df) + + # Scrap output back to industry + df = make_df('output', technology=tec, commodity=comm, \ + level='old_scrap', year_vtg = val_mat.year, \ + value=val_scr.value, unit='t', \ + node_loc = rg, **common)\ + .pipe(same_node))\ + .assign(year_act=copy_column('year_vtg')) + results[param_name].append(df) + + # Service output to buildings demand + df = make_df('output', technology=tec, commodity='floor_area', \ + level=lev, year_vtg = val_mat.year, \ + value=1, unit='t', \ + node_dest=rg, **common)\ + .pipe(same_node))\ + .assign(year_act=copy_column('year_vtg')) + results[param_name].append(df) + + # Create external demand param + parname = 'demand' + demand = data_buildings_demand + df = make_df(parname, level='demand', commodity='steel', value=demand.value, unit='t', \ + year=demand.year, time='year', node=demand.node) + results[parname].append(df) + + # Concatenate to one data frame per parameter + results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} + + return results + + + + + + + + + + # for t in s_info.set['technology']: + for t in config['technology']['add']: + + params = data_steel.loc[(data_steel["technology"] == t),\ + "parameter"].values.tolist() + + # Special treatment for time-varying params + if t in tec_ts: + common = dict( + time="year", + time_origin="year", + time_dest="year",) + + param_name = data_steel_ts.loc[(data_steel_ts["technology"] == t), 'parameter'] + + for p in set(param_name): + val = data_steel_ts.loc[(data_steel_ts["technology"] == t) \ + & (data_steel_ts["parameter"] == p), 'value'] + units = data_steel_ts.loc[(data_steel_ts["technology"] == t) \ + & (data_steel_ts["parameter"] == p), 'units'].values[0] + mod = data_steel_ts.loc[(data_steel_ts["technology"] == t) \ + & (data_steel_ts["parameter"] == p), 'mode'] + yr = data_steel_ts.loc[(data_steel_ts["technology"] == t) \ + & (data_steel_ts["parameter"] == p), 'year'] + + if p=="var_cost": + df = (make_df(p, technology=t, value=val,\ + unit='t', year_vtg=yr, year_act=yr, mode=mod, **common).pipe(broadcast, \ + node_loc=nodes)) + else: + rg = data_steel_ts.loc[(data_steel_ts["technology"] == t) \ + & (data_steel_ts["parameter"] == p), 'region'] + df = make_df(p, technology=t, value=val,\ + unit='t', year_vtg=yr, year_act=yr, mode=mod, node_loc=rg, **common) + + results[p].append(df) + + # Iterate over parameters + for par in params: + + # Obtain the parameter names, commodity,level,emission + split = par.split("|") + print(split) + param_name = split[0] + # Obtain the scalar value for the parameter + val = data_steel.loc[((data_steel["technology"] == t) \ + & (data_steel["parameter"] == par)),'value']#.values + regions = data_steel.loc[((data_steel["technology"] == t) \ + & (data_steel["parameter"] == par)),'region']#.values + + common = dict( + year_vtg= yv_ya.year_vtg, + year_act= yv_ya.year_act, + # mode="M1", + time="year", + time_origin="year", + time_dest="year",) + + for rg in regions: + + # For the parameters which inlcudes index names + if len(split)> 1: + + print('1.param_name:', param_name, t) + if (param_name == "input")|(param_name == "output"): + + # Assign commodity and level names + com = split[1] + lev = split[2] + mod = split[3] + print(rg, par, lev) + + if (param_name == "input") and (lev == "import"): + df = make_df(param_name, technology=t, commodity=com, \ + level=lev, \ + value=val[regions[regions==rg].index[0]], mode=mod, unit='t', \ + node_loc=rg, node_origin="R11_GLB", **common) + elif (param_name == "output") and (lev == "export"): + df = make_df(param_name, technology=t, commodity=com, \ + level=lev, \ + value=val[regions[regions==rg].index[0]], mode=mod, unit='t', \ + node_loc=rg, node_dest="R11_GLB", **common) + else: + df = (make_df(param_name, technology=t, commodity=com, \ + level=lev, \ + value=val[regions[regions==rg].index[0]], mode=mod, unit='t', \ + node_loc=rg, **common)\ + .pipe(same_node)) + + # Copy parameters to all regions, when node_loc is not GLB + if (len(regions) == 1) and (rg != "R11_GLB"): + print("copying to all R11", rg, lev) + df['node_loc'] = None + df = df.pipe(broadcast, node_loc=nodes)#.pipe(same_node) + # Use same_node only for non-trade technologies + if (lev != "import") and (lev != "export"): + df = df.pipe(same_node) + + elif param_name == "emission_factor": + + # Assign the emisson type + emi = split[1] + mod = split[2] + + df = make_df(param_name, technology=t, \ + value=val[regions[regions==rg].index[0]],\ + emission=emi, mode=mod, unit='t', \ + node_loc=rg, **common) + + else: # time-independent var_cost + mod = split[1] + df = make_df(param_name, technology=t, \ + value=val[regions[regions==rg].index[0]], \ + mode=mod, unit='t', node_loc=rg, \ + **common) + + # Parameters with only parameter name + else: + print('2.param_name:', param_name) + df = make_df(param_name, technology=t, \ + value=val[regions[regions==rg].index[0]], unit='t', \ + node_loc=rg, **common) + + # Copy parameters to all regions + if len(set(df['node_loc'])) == 1 and list(set(df['node_loc']))[0]!='R11_GLB': + print("Copying to all R11") + df['node_loc'] = None + df = df.pipe(broadcast, node_loc=nodes) + + results[param_name].append(df) + + # Add relations for scrap grades and availability + + for r in config['relation']['add']: + + params = set(data_steel_rel.loc[(data_steel_rel["relation"] == r),\ + "parameter"].values) + + common_rel = dict( + year_rel = modelyears, + year_act = modelyears, + mode = 'M1', + relation = r,) + + for par_name in params: + if par_name == "relation_activity": + + val = data_steel_rel.loc[((data_steel_rel["relation"] == r) \ + & (data_steel_rel["parameter"] == par_name)),'value'].values + tec = data_steel_rel.loc[((data_steel_rel["relation"] == r) \ + & (data_steel_rel["parameter"] == par_name)),'technology'].values + + print(par_name, "val", val, "tec", tec) + + df = (make_df(par_name, technology=tec, \ + value=val, unit='-', mode = 'M1', relation = r)\ + .pipe(broadcast, node_rel=nodes, \ + node_loc=nodes, year_rel = modelyears))\ + .assign(year_act=copy_column('year_rel')) + + results[par_name].append(df) + + elif par_name == "relation_upper": + + val = data_steel_rel.loc[((data_steel_rel["relation"] == r) \ + & (data_steel_rel["parameter"] == par_name)),'value'].values[0] + + df = (make_df(par_name, value=val, unit='-',\ + **common_rel).pipe(broadcast, node_rel=nodes)) + + results[par_name].append(df) + + # Create external demand param + parname = 'demand' + demand = gen_mock_demand_steel(scenario) + df = make_df(parname, level='demand', commodity='steel', value=demand.value, unit='t', \ + year=demand.year, time='year', node=demand.node)#.pipe(broadcast, node=nodes) + results[parname].append(df) + + # Concatenate to one data frame per parameter + results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} + + return results diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index 63a9770ac1..19f0621c14 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -306,6 +306,7 @@ def read_timeseries(filename): df = df.drop(df[np.isnan(df.value)].index) return df + def read_rel(filename): import numpy as np From d4b5c28c5f701dfc5334df7e504016a47a41ea89 Mon Sep 17 00:00:00 2001 From: Jihoon Min Date: Mon, 25 Jan 2021 11:39:45 +0100 Subject: [PATCH 219/774] Add buildings part --- message_ix_models/model/material/__init__.py | 6 +++--- message_ix_models/model/material/data.py | 1 + 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index a4487176aa..9886bf075b 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -5,7 +5,7 @@ from message_ix_models.model.build import apply_spec from .data import add_data -from .data_util import modify_demand_and_hist_activity +from .data_util import modify_historical_activity from .util import read_config def build(scenario): @@ -18,12 +18,12 @@ def build(scenario): # Adjust exogenous energy demand to incorporate the endogenized sectors # Adjust the historical activity of the usefyl level industry technologies - modify_demand_and_hist_activity(scenario) + modify_historical_activity(scenario) return scenario # add as needed/implemented -SPEC_LIST = ["generic", "common", "steel", "cement", "aluminum", "petro_chemicals"] +SPEC_LIST = ["generic", "common", "steel", "cement", "aluminum", "petro_chemicals", "buildings"] def get_spec() -> Mapping[str, ScenarioInfo]: """Return the specification for materials accounting.""" diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index b36ef6c7ca..24a31a971a 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -9,6 +9,7 @@ from .data_aluminum import gen_data_aluminum from .data_generic import gen_data_generic from .data_petro import gen_data_petro_chemicals +from .data_buildings import gen_data_buildings from message_data.tools import ( ScenarioInfo, From 9908734fee89450a5f12cebfd2f3032f2d06a74b Mon Sep 17 00:00:00 2001 From: Jihoon Min Date: Mon, 25 Jan 2021 11:41:00 +0100 Subject: [PATCH 220/774] Fixes to make it error-free (year, node, etc) --- .../model/material/data_buildings.py | 114 +++++++++++------- 1 file changed, 69 insertions(+), 45 deletions(-) diff --git a/message_ix_models/model/material/data_buildings.py b/message_ix_models/model/material/data_buildings.py index eb85420670..448c210585 100644 --- a/message_ix_models/model/material/data_buildings.py +++ b/message_ix_models/model/material/data_buildings.py @@ -1,4 +1,4 @@ -from .data_util import read_sector_data, read_timeseries_buildings +from .data_util import read_sector_data import numpy as np from collections import defaultdict @@ -32,7 +32,9 @@ def read_timeseries_buildings(filename): context.get_path("material", filename)) bld_input_mat = bld_input_raw[bld_input_raw['Variable'].\ - str.contains("Floor Space|Aluminum|Cement|Steel")] + # str.contains("Floor Space|Aluminum|Cement|Steel|Final Energy")] + str.contains("Floor Space|Aluminum|Cement|Steel")] # Final Energy - Later. Need to figure out carving out + bld_input_mat['Region'] = 'R11_' + bld_input_mat['Region'] bld_input_pivot = \ bld_input_mat.melt(id_vars=['Region','Variable'], var_name='Year', \ @@ -42,40 +44,53 @@ def read_timeseries_buildings(filename): .unstack()\ .reset_index() + # Divide by floor area to get energy/material intensities + bld_intensity_ene_mat = bld_input_pivot.iloc[:,2:].div(bld_input_pivot['Energy Service|Residential|Floor Space'], axis=0) + bld_intensity_ene_mat.columns = [s + "|Intensity" for s in bld_intensity_ene_mat.columns] + bld_intensity_ene_mat = pd.concat([bld_input_pivot[['Region', 'Year']], \ + bld_intensity_ene_mat.reindex(bld_input_pivot.index)], axis=1).\ + drop(columns = ['Energy Service|Residential|Floor Space|Intensity']) + + bld_intensity_ene_mat['Energy Service|Residential|Floor Space'] = bld_input_pivot['Energy Service|Residential|Floor Space'] + + # bld_intensity_ene = bld_intensity_ene_mat.iloc[:, 1:7] + # bld_intensity_mat = bld_intensity_ene_mat.iloc[:, 8:] + # Calculate material intensities - bld_input_pivot['Material Demand|Residential|Buildings|Cement|Intensity'] =\ - bld_input_pivot['Material Demand|Residential|Buildings|Cement']/\ - bld_input_pivot['Energy Service|Residential|Floor Space'] - bld_input_pivot['Material Demand|Residential|Buildings|Steel|Intensity'] =\ - bld_input_pivot['Material Demand|Residential|Buildings|Steel']/\ - bld_input_pivot['Energy Service|Residential|Floor Space'] - bld_input_pivot['Material Demand|Residential|Buildings|Aluminum|Intensity'] =\ - bld_input_pivot['Material Demand|Residential|Buildings|Aluminum']/\ - bld_input_pivot['Energy Service|Residential|Floor Space'] - bld_input_pivot['Scrap Release|Residential|Buildings|Cement|Intensity'] =\ - bld_input_pivot['Scrap Release|Residential|Buildings|Cement']/\ - bld_input_pivot['Energy Service|Residential|Floor Space'] - bld_input_pivot['Scrap Release|Residential|Buildings|Steel|Intensity'] =\ - bld_input_pivot['Scrap Release|Residential|Buildings|Steel']/\ - bld_input_pivot['Energy Service|Residential|Floor Space'] - bld_input_pivot['Scrap Release|Residential|Buildings|Aluminum|Intensity'] =\ - bld_input_pivot['Scrap Release|Residential|Buildings|Aluminum']/\ - bld_input_pivot['Energy Service|Residential|Floor Space'] + # bld_input_pivot['Material Demand|Residential|Buildings|Cement|Intensity'] =\ + # bld_input_pivot['Material Demand|Residential|Buildings|Cement']/\ + # bld_input_pivot['Energy Service|Residential|Floor Space'] + # bld_input_pivot['Material Demand|Residential|Buildings|Steel|Intensity'] =\ + # bld_input_pivot['Material Demand|Residential|Buildings|Steel']/\ + # bld_input_pivot['Energy Service|Residential|Floor Space'] + # bld_input_pivot['Material Demand|Residential|Buildings|Aluminum|Intensity'] =\ + # bld_input_pivot['Material Demand|Residential|Buildings|Aluminum']/\ + # bld_input_pivot['Energy Service|Residential|Floor Space'] + # bld_input_pivot['Scrap Release|Residential|Buildings|Cement|Intensity'] =\ + # bld_input_pivot['Scrap Release|Residential|Buildings|Cement']/\ + # bld_input_pivot['Energy Service|Residential|Floor Space'] + # bld_input_pivot['Scrap Release|Residential|Buildings|Steel|Intensity'] =\ + # bld_input_pivot['Scrap Release|Residential|Buildings|Steel']/\ + # bld_input_pivot['Energy Service|Residential|Floor Space'] + # bld_input_pivot['Scrap Release|Residential|Buildings|Aluminum|Intensity'] =\ + # bld_input_pivot['Scrap Release|Residential|Buildings|Aluminum']/\ + # bld_input_pivot['Energy Service|Residential|Floor Space'] # Material intensities are in kg/m2 - bld_data_long = bld_input_pivot.melt(id_vars=['Region','Year'], var_name='Variable')\ + bld_data_long = bld_intensity_ene_mat.melt(id_vars=['Region','Year'], var_name='Variable')\ .rename(columns={"Region": "node", "Year": "year"}) + # Both for energy and material bld_intensity_long = bld_data_long[bld_data_long['Variable'].\ str.contains("Intensity")]\ .reset_index(drop=True) - bld_area_long = bld_data_long[bld_data_long['Variable'].\ - str.contains("Floor Space")]\ + bld_area_long = bld_data_long[bld_data_long['Variable']==\ + 'Energy Service|Residential|Floor Space']\ .reset_index(drop=True) tmp = bld_intensity_long.Variable.str.split("|", expand=True) - bld_intensity_long['commodity'] = tmp[3] - bld_intensity_long['type'] = tmp[0] + bld_intensity_long['commodity'] = tmp[3].str.lower() # Material type + bld_intensity_long['type'] = tmp[0] # 'Material Demand' or 'Scrap Release' bld_intensity_long['unit'] = "kg/m2" bld_intensity_long = bld_intensity_long.drop(columns='Variable') @@ -95,9 +110,9 @@ def gen_data_buildings(scenario, dry_run=False): context = read_config() config = context["material"]["buildings"] - lev = config['level']['add'] - comm = config['commodity']['add'] - tec = config['technology']['add'] # "buildings" + lev = config['level']['add'][0] + comm = config['commodity']['add'][0] + tec = config['technology']['add'][0] # "buildings" print(lev, comm, tec, type(tec)) @@ -113,17 +128,17 @@ def gen_data_buildings(scenario, dry_run=False): # For each technology there are differnet input and output combinations # Iterate over technologies - allyears = s_info.set['year'] #s_info.Y is only for modeling years + # allyears = s_info.set['year'] #s_info.Y is only for modeling years modelyears = s_info.Y #s_info.Y is only for modeling years nodes = s_info.N yv_ya = s_info.yv_ya - fmy = s_info.y0 + # fmy = s_info.y0 nodes.remove('World') - # R11 - regions = set(data_buildings.node) - comms = set(data_buildings.commodity) - types = set(data_buildings.type) + # Read field values from the buildings input data + regions = list(set(data_buildings.node)) + comms = list(set(data_buildings.commodity)) + types = list(set(data_buildings.type)) # # ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', \ # 'R11_MEA', 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU'] @@ -132,42 +147,51 @@ def gen_data_buildings(scenario, dry_run=False): time_origin="year", time_dest="year", mode="M1") - + + # Filter only the years in the base scenario + data_buildings['year'] = data_buildings['year'].astype(int) + data_buildings_demand['year'] = data_buildings_demand['year'].astype(int) + data_buildings = data_buildings[data_buildings['year'].isin(modelyears)] + data_buildings_demand = data_buildings_demand[data_buildings_demand['year'].isin(modelyears)] + for rg in regions: for comm in comms: # for typ in types: val_mat = data_buildings.loc[(data_buildings["type"] == types[0]) \ - & (data_buildings["commodity"] == comm), ] + & (data_buildings["commodity"] == comm)\ + & (data_buildings["node"] == rg), ] val_scr = data_buildings.loc[(data_buildings["type"] == types[1]) \ - & (data_buildings["commodity"] == comm), ] + & (data_buildings["commodity"] == comm)\ + & (data_buildings["node"] == rg), ] + # Material input to buildings df = make_df('input', technology=tec, commodity=comm, \ level="product", year_vtg = val_mat.year, \ value=val_mat.value, unit='t', \ node_loc = rg, **common)\ - .pipe(same_node))\ + .pipe(same_node)\ .assign(year_act=copy_column('year_vtg')) - results[param_name].append(df) + results['input'].append(df) # Scrap output back to industry df = make_df('output', technology=tec, commodity=comm, \ - level='old_scrap', year_vtg = val_mat.year, \ + level='old_scrap', year_vtg = val_scr.year, \ value=val_scr.value, unit='t', \ node_loc = rg, **common)\ - .pipe(same_node))\ + .pipe(same_node)\ .assign(year_act=copy_column('year_vtg')) - results[param_name].append(df) + results['output'].append(df) # Service output to buildings demand df = make_df('output', technology=tec, commodity='floor_area', \ level=lev, year_vtg = val_mat.year, \ value=1, unit='t', \ - node_dest=rg, **common)\ - .pipe(same_node))\ + node_loc=rg, **common)\ + .pipe(same_node)\ .assign(year_act=copy_column('year_vtg')) - results[param_name].append(df) + results['output'].append(df) # Create external demand param parname = 'demand' From df5b474453b8d497ca7a0ac3582e5a92020483d4 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Mon, 25 Jan 2021 16:19:44 +0100 Subject: [PATCH 221/774] Debug the data interfact (fix level/comm names) --- .../model/material/data_buildings.py | 220 ++---------------- 1 file changed, 13 insertions(+), 207 deletions(-) diff --git a/message_ix_models/model/material/data_buildings.py b/message_ix_models/model/material/data_buildings.py index 448c210585..e26b3e984b 100644 --- a/message_ix_models/model/material/data_buildings.py +++ b/message_ix_models/model/material/data_buildings.py @@ -110,11 +110,12 @@ def gen_data_buildings(scenario, dry_run=False): context = read_config() config = context["material"]["buildings"] - lev = config['level']['add'][0] - comm = config['commodity']['add'][0] - tec = config['technology']['add'][0] # "buildings" + # New element names for buildings integrations + lev_new = config['level']['add'][0] + comm_new = config['commodity']['add'][0] + tec_new = config['technology']['add'][0] # "buildings" - print(lev, comm, tec, type(tec)) + print(lev_new, comm_new, tec_new, type(tec_new)) # Information about scenario, e.g. node, year s_info = ScenarioInfo(scenario) @@ -138,22 +139,20 @@ def gen_data_buildings(scenario, dry_run=False): # Read field values from the buildings input data regions = list(set(data_buildings.node)) comms = list(set(data_buildings.commodity)) - types = list(set(data_buildings.type)) # - # ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', \ - # 'R11_MEA', 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU'] + types = list(set(data_buildings.type)) common = dict( time="year", time_origin="year", time_dest="year", mode="M1") - + # Filter only the years in the base scenario data_buildings['year'] = data_buildings['year'].astype(int) data_buildings_demand['year'] = data_buildings_demand['year'].astype(int) data_buildings = data_buildings[data_buildings['year'].isin(modelyears)] data_buildings_demand = data_buildings_demand[data_buildings_demand['year'].isin(modelyears)] - + for rg in regions: for comm in comms: # for typ in types: @@ -164,10 +163,9 @@ def gen_data_buildings(scenario, dry_run=False): val_scr = data_buildings.loc[(data_buildings["type"] == types[1]) \ & (data_buildings["commodity"] == comm)\ & (data_buildings["node"] == rg), ] - # Material input to buildings - df = make_df('input', technology=tec, commodity=comm, \ + df = make_df('input', technology=tec_new, commodity=comm, \ level="product", year_vtg = val_mat.year, \ value=val_mat.value, unit='t', \ node_loc = rg, **common)\ @@ -176,7 +174,7 @@ def gen_data_buildings(scenario, dry_run=False): results['input'].append(df) # Scrap output back to industry - df = make_df('output', technology=tec, commodity=comm, \ + df = make_df('output', technology=tec_new, commodity=comm, \ level='old_scrap', year_vtg = val_scr.year, \ value=val_scr.value, unit='t', \ node_loc = rg, **common)\ @@ -185,8 +183,8 @@ def gen_data_buildings(scenario, dry_run=False): results['output'].append(df) # Service output to buildings demand - df = make_df('output', technology=tec, commodity='floor_area', \ - level=lev, year_vtg = val_mat.year, \ + df = make_df('output', technology=tec_new, commodity=comm_new, \ + level='demand', year_vtg = val_mat.year, \ value=1, unit='t', \ node_loc=rg, **common)\ .pipe(same_node)\ @@ -196,7 +194,7 @@ def gen_data_buildings(scenario, dry_run=False): # Create external demand param parname = 'demand' demand = data_buildings_demand - df = make_df(parname, level='demand', commodity='steel', value=demand.value, unit='t', \ + df = make_df(parname, level='demand', commodity=comm_new, value=demand.value, unit='t', \ year=demand.year, time='year', node=demand.node) results[parname].append(df) @@ -204,195 +202,3 @@ def gen_data_buildings(scenario, dry_run=False): results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} return results - - - - - - - - - - # for t in s_info.set['technology']: - for t in config['technology']['add']: - - params = data_steel.loc[(data_steel["technology"] == t),\ - "parameter"].values.tolist() - - # Special treatment for time-varying params - if t in tec_ts: - common = dict( - time="year", - time_origin="year", - time_dest="year",) - - param_name = data_steel_ts.loc[(data_steel_ts["technology"] == t), 'parameter'] - - for p in set(param_name): - val = data_steel_ts.loc[(data_steel_ts["technology"] == t) \ - & (data_steel_ts["parameter"] == p), 'value'] - units = data_steel_ts.loc[(data_steel_ts["technology"] == t) \ - & (data_steel_ts["parameter"] == p), 'units'].values[0] - mod = data_steel_ts.loc[(data_steel_ts["technology"] == t) \ - & (data_steel_ts["parameter"] == p), 'mode'] - yr = data_steel_ts.loc[(data_steel_ts["technology"] == t) \ - & (data_steel_ts["parameter"] == p), 'year'] - - if p=="var_cost": - df = (make_df(p, technology=t, value=val,\ - unit='t', year_vtg=yr, year_act=yr, mode=mod, **common).pipe(broadcast, \ - node_loc=nodes)) - else: - rg = data_steel_ts.loc[(data_steel_ts["technology"] == t) \ - & (data_steel_ts["parameter"] == p), 'region'] - df = make_df(p, technology=t, value=val,\ - unit='t', year_vtg=yr, year_act=yr, mode=mod, node_loc=rg, **common) - - results[p].append(df) - - # Iterate over parameters - for par in params: - - # Obtain the parameter names, commodity,level,emission - split = par.split("|") - print(split) - param_name = split[0] - # Obtain the scalar value for the parameter - val = data_steel.loc[((data_steel["technology"] == t) \ - & (data_steel["parameter"] == par)),'value']#.values - regions = data_steel.loc[((data_steel["technology"] == t) \ - & (data_steel["parameter"] == par)),'region']#.values - - common = dict( - year_vtg= yv_ya.year_vtg, - year_act= yv_ya.year_act, - # mode="M1", - time="year", - time_origin="year", - time_dest="year",) - - for rg in regions: - - # For the parameters which inlcudes index names - if len(split)> 1: - - print('1.param_name:', param_name, t) - if (param_name == "input")|(param_name == "output"): - - # Assign commodity and level names - com = split[1] - lev = split[2] - mod = split[3] - print(rg, par, lev) - - if (param_name == "input") and (lev == "import"): - df = make_df(param_name, technology=t, commodity=com, \ - level=lev, \ - value=val[regions[regions==rg].index[0]], mode=mod, unit='t', \ - node_loc=rg, node_origin="R11_GLB", **common) - elif (param_name == "output") and (lev == "export"): - df = make_df(param_name, technology=t, commodity=com, \ - level=lev, \ - value=val[regions[regions==rg].index[0]], mode=mod, unit='t', \ - node_loc=rg, node_dest="R11_GLB", **common) - else: - df = (make_df(param_name, technology=t, commodity=com, \ - level=lev, \ - value=val[regions[regions==rg].index[0]], mode=mod, unit='t', \ - node_loc=rg, **common)\ - .pipe(same_node)) - - # Copy parameters to all regions, when node_loc is not GLB - if (len(regions) == 1) and (rg != "R11_GLB"): - print("copying to all R11", rg, lev) - df['node_loc'] = None - df = df.pipe(broadcast, node_loc=nodes)#.pipe(same_node) - # Use same_node only for non-trade technologies - if (lev != "import") and (lev != "export"): - df = df.pipe(same_node) - - elif param_name == "emission_factor": - - # Assign the emisson type - emi = split[1] - mod = split[2] - - df = make_df(param_name, technology=t, \ - value=val[regions[regions==rg].index[0]],\ - emission=emi, mode=mod, unit='t', \ - node_loc=rg, **common) - - else: # time-independent var_cost - mod = split[1] - df = make_df(param_name, technology=t, \ - value=val[regions[regions==rg].index[0]], \ - mode=mod, unit='t', node_loc=rg, \ - **common) - - # Parameters with only parameter name - else: - print('2.param_name:', param_name) - df = make_df(param_name, technology=t, \ - value=val[regions[regions==rg].index[0]], unit='t', \ - node_loc=rg, **common) - - # Copy parameters to all regions - if len(set(df['node_loc'])) == 1 and list(set(df['node_loc']))[0]!='R11_GLB': - print("Copying to all R11") - df['node_loc'] = None - df = df.pipe(broadcast, node_loc=nodes) - - results[param_name].append(df) - - # Add relations for scrap grades and availability - - for r in config['relation']['add']: - - params = set(data_steel_rel.loc[(data_steel_rel["relation"] == r),\ - "parameter"].values) - - common_rel = dict( - year_rel = modelyears, - year_act = modelyears, - mode = 'M1', - relation = r,) - - for par_name in params: - if par_name == "relation_activity": - - val = data_steel_rel.loc[((data_steel_rel["relation"] == r) \ - & (data_steel_rel["parameter"] == par_name)),'value'].values - tec = data_steel_rel.loc[((data_steel_rel["relation"] == r) \ - & (data_steel_rel["parameter"] == par_name)),'technology'].values - - print(par_name, "val", val, "tec", tec) - - df = (make_df(par_name, technology=tec, \ - value=val, unit='-', mode = 'M1', relation = r)\ - .pipe(broadcast, node_rel=nodes, \ - node_loc=nodes, year_rel = modelyears))\ - .assign(year_act=copy_column('year_rel')) - - results[par_name].append(df) - - elif par_name == "relation_upper": - - val = data_steel_rel.loc[((data_steel_rel["relation"] == r) \ - & (data_steel_rel["parameter"] == par_name)),'value'].values[0] - - df = (make_df(par_name, value=val, unit='-',\ - **common_rel).pipe(broadcast, node_rel=nodes)) - - results[par_name].append(df) - - # Create external demand param - parname = 'demand' - demand = gen_mock_demand_steel(scenario) - df = make_df(parname, level='demand', commodity='steel', value=demand.value, unit='t', \ - year=demand.year, time='year', node=demand.node)#.pipe(broadcast, node=nodes) - results[parname].append(df) - - # Concatenate to one data frame per parameter - results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} - - return results From 1dd00ee1d76f844b24900f9c5b01029368218928 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Tue, 26 Jan 2021 00:18:26 +0100 Subject: [PATCH 222/774] Subtract the endogenous material demand from the demand parameter --- message_ix_models/model/material/data.py | 4 +- .../model/material/data_aluminum.py | 11 +++++ .../model/material/data_buildings.py | 45 +++++++++---------- .../model/material/data_cement.py | 12 +++++ .../model/material/data_steel.py | 11 +++++ 5 files changed, 56 insertions(+), 27 deletions(-) diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index 24a31a971a..6300faef5b 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -27,12 +27,12 @@ log = logging.getLogger(__name__) DATA_FUNCTIONS = [ + gen_data_buildings, gen_data_steel, gen_data_cement, gen_data_aluminum, - gen_data_generic, gen_data_petro_chemicals, - gen_data_buildings + gen_data_generic ] # Try to handle multiple data input functions from different materials diff --git a/message_ix_models/model/material/data_aluminum.py b/message_ix_models/model/material/data_aluminum.py index 1b40f5a1a5..712815c05b 100644 --- a/message_ix_models/model/material/data_aluminum.py +++ b/message_ix_models/model/material/data_aluminum.py @@ -24,6 +24,9 @@ same_node, add_par_data ) +# Get endogenous material demand from buildings interface +from .data_buildings import get_baseyear_mat_demand +from . import get_spec def read_data_aluminum(): """Read and clean data from :file:`aluminum_techno_economic.xlsx`.""" @@ -295,6 +298,14 @@ def gen_mock_demand_aluminum(scenario): d = [3,55, 4, 7, 5, 6, 15, 3.5, 5.5,6,6 ] + # Do this if we have 2020 demand values for buildings + sp = get_spec() + if 'buildings' in sp['add'].set['technology']: + val = get_baseyear_mat_demand("steel") + print("Base year demand of {}:".format("aluminum"), val) + d = d - val.value + print("UPDATE {} demand for 2020!".format("aluminum")) + demand2015_al = pd.DataFrame({'Region':r, 'Val':d}).\ join(gdp_growth.set_index('Region'), on='Region').\ rename(columns={'Region':'node'}) diff --git a/message_ix_models/model/material/data_buildings.py b/message_ix_models/model/material/data_buildings.py index e26b3e984b..f5a3f3fb27 100644 --- a/message_ix_models/model/material/data_buildings.py +++ b/message_ix_models/model/material/data_buildings.py @@ -19,7 +19,6 @@ add_par_data ) - def read_timeseries_buildings(filename): import numpy as np @@ -53,28 +52,6 @@ def read_timeseries_buildings(filename): bld_intensity_ene_mat['Energy Service|Residential|Floor Space'] = bld_input_pivot['Energy Service|Residential|Floor Space'] - # bld_intensity_ene = bld_intensity_ene_mat.iloc[:, 1:7] - # bld_intensity_mat = bld_intensity_ene_mat.iloc[:, 8:] - - # Calculate material intensities - # bld_input_pivot['Material Demand|Residential|Buildings|Cement|Intensity'] =\ - # bld_input_pivot['Material Demand|Residential|Buildings|Cement']/\ - # bld_input_pivot['Energy Service|Residential|Floor Space'] - # bld_input_pivot['Material Demand|Residential|Buildings|Steel|Intensity'] =\ - # bld_input_pivot['Material Demand|Residential|Buildings|Steel']/\ - # bld_input_pivot['Energy Service|Residential|Floor Space'] - # bld_input_pivot['Material Demand|Residential|Buildings|Aluminum|Intensity'] =\ - # bld_input_pivot['Material Demand|Residential|Buildings|Aluminum']/\ - # bld_input_pivot['Energy Service|Residential|Floor Space'] - # bld_input_pivot['Scrap Release|Residential|Buildings|Cement|Intensity'] =\ - # bld_input_pivot['Scrap Release|Residential|Buildings|Cement']/\ - # bld_input_pivot['Energy Service|Residential|Floor Space'] - # bld_input_pivot['Scrap Release|Residential|Buildings|Steel|Intensity'] =\ - # bld_input_pivot['Scrap Release|Residential|Buildings|Steel']/\ - # bld_input_pivot['Energy Service|Residential|Floor Space'] - # bld_input_pivot['Scrap Release|Residential|Buildings|Aluminum|Intensity'] =\ - # bld_input_pivot['Scrap Release|Residential|Buildings|Aluminum']/\ - # bld_input_pivot['Energy Service|Residential|Floor Space'] # Material intensities are in kg/m2 bld_data_long = bld_intensity_ene_mat.melt(id_vars=['Region','Year'], var_name='Variable')\ @@ -99,7 +76,23 @@ def read_timeseries_buildings(filename): bld_intensity_long = bld_intensity_long\ .drop(bld_intensity_long[np.isnan(bld_intensity_long.value)].index) - return bld_intensity_long, bld_area_long + # Derive baseyear material demand (Mt/year in 2020) + bld_demand_long = bld_input_pivot.melt(id_vars=['Region','Year'], var_name='Variable')\ + .rename(columns={"Region": "node", "Year": "year"}) + tmp = bld_demand_long.Variable.str.split("|", expand=True) + bld_demand_long['commodity'] = tmp[3].str.lower() # Material type + bld_demand_long = bld_demand_long[bld_demand_long['year']=="2020"].\ + dropna(how='any') + bld_demand_long = bld_demand_long[bld_demand_long['Variable'].str.contains("Material Demand")].drop(columns='Variable') + + return bld_intensity_long, bld_area_long, bld_demand_long + + +INPUTFILE = 'LED_LED_report_IAMC.csv' + +def get_baseyear_mat_demand(commod): + a, b, c = read_timeseries_buildings(INPUTFILE) + return c[c.commodity==commod].reset_index() def gen_data_buildings(scenario, dry_run=False): @@ -121,7 +114,7 @@ def gen_data_buildings(scenario, dry_run=False): s_info = ScenarioInfo(scenario) # Buildings raw data (from Alessio) - data_buildings, data_buildings_demand = read_timeseries_buildings('LED_LED_report_IAMC.csv') + data_buildings, data_buildings_demand, data_buildings_mat_demand = read_timeseries_buildings(INPUTFILE) # List of data frames, to be concatenated together at end results = defaultdict(list) @@ -153,6 +146,8 @@ def gen_data_buildings(scenario, dry_run=False): data_buildings = data_buildings[data_buildings['year'].isin(modelyears)] data_buildings_demand = data_buildings_demand[data_buildings_demand['year'].isin(modelyears)] + # historical demands + for rg in regions: for comm in comms: # for typ in types: diff --git a/message_ix_models/model/material/data_cement.py b/message_ix_models/model/material/data_cement.py index 087bfb74e3..4e513fc0d2 100644 --- a/message_ix_models/model/material/data_cement.py +++ b/message_ix_models/model/material/data_cement.py @@ -16,6 +16,9 @@ same_node, add_par_data ) +# Get endogenous material demand from buildings interface +from .data_buildings import get_baseyear_mat_demand +from . import get_spec # gdp_growth = [0.121448215899944, 0.0733079014579874, 0.0348154093342843, \ @@ -76,6 +79,15 @@ def gen_mock_demand_cement(scenario): demand2010_cement = demand2010_cement.groupby(by=['node']).sum().reset_index() demand2010_cement['value'] = demand2010_cement['value'] / 1e9 # kg to Mt + # Do this if we have 2020 demand values for buildings + sp = get_spec() + if 'buildings' in sp['add'].set['technology']: + val = get_baseyear_mat_demand("cement") + print("Base year demand of {}:".format("cement"), val) + demand2010_cement['value'] = demand2010_cement['value'] - val['value'] + print("UPDATE {} demand for 2020!".format("cement")) + + demand2010_cement = demand2010_cement.\ join(gdp_growth.rename(columns={'Region':'node'}).set_index('node'), on='node') diff --git a/message_ix_models/model/material/data_steel.py b/message_ix_models/model/material/data_steel.py index e807fa2711..8d267ff421 100644 --- a/message_ix_models/model/material/data_steel.py +++ b/message_ix_models/model/material/data_steel.py @@ -8,6 +8,8 @@ from .util import read_config from .data_util import read_rel +# Get endogenous material demand from buildings interface +from .data_buildings import get_baseyear_mat_demand from message_data.tools import ( ScenarioInfo, broadcast, @@ -18,6 +20,7 @@ copy_column, add_par_data ) +from . import get_spec # annual average growth rate by decade (2020-2110) # gdp_growth = [0.121448215899944, 0.0733079014579874, @@ -54,6 +57,14 @@ def gen_mock_demand_steel(scenario): d = [35, 537, 70, 53, 49, \ 39, 130, 80, 45, 96, 100] # MEA change from 39 to 9 to make it feasible (coal supply bound) + # Do this if we have 2020 demand values for buildings + sp = get_spec() + if 'buildings' in sp['add'].set['technology']: + val = get_baseyear_mat_demand("steel") + print("Base year demand of {}:".format("steel"), val) + d = d - val.value + print("UPDATE {} demand for 2020!".format("steel")) + demand2010_steel = pd.DataFrame({'Region':r, 'Val':d}).\ join(gdp_growth.set_index('Region'), on='Region').rename(columns={'Region':'node'}) From dc3f1f9096ea210c6a83925758014b7cc3a641ef Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 26 Jan 2021 13:39:41 +0100 Subject: [PATCH 223/774] Adjusting 2020 demand for steel and cement --- .../iamc_db ENGAGE baseline GDP PPP.xlsx | 4 +-- .../model/material/data_aluminum.py | 28 ++++++++----------- .../model/material/data_cement.py | 11 ++------ .../model/material/data_petro.py | 25 +++++++++-------- .../model/material/data_steel.py | 14 ++-------- 5 files changed, 30 insertions(+), 52 deletions(-) diff --git a/message_ix_models/data/material/iamc_db ENGAGE baseline GDP PPP.xlsx b/message_ix_models/data/material/iamc_db ENGAGE baseline GDP PPP.xlsx index fa9288b058..99f6179200 100644 --- a/message_ix_models/data/material/iamc_db ENGAGE baseline GDP PPP.xlsx +++ b/message_ix_models/data/material/iamc_db ENGAGE baseline GDP PPP.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:b4f202d56a88750e792b68d3514f72d90341179bd6b8c90f5e8d4a655e165ce4 -size 17501 +oid sha256:2ad3f395efa20edf0ee9ab74cb2a4f510a65ab29f5f4bf6f183d46c2ed72a7f8 +size 20762 diff --git a/message_ix_models/model/material/data_aluminum.py b/message_ix_models/model/material/data_aluminum.py index 712815c05b..bb877f95a1 100644 --- a/message_ix_models/model/material/data_aluminum.py +++ b/message_ix_models/model/material/data_aluminum.py @@ -282,39 +282,33 @@ def gen_mock_demand_aluminum(scenario): gdp_growth = gdp_growth.loc[(gdp_growth['Scenario']=='baseline') & \ (gdp_growth['Region']!='World')].drop(['Model', 'Variable', 'Unit', 'Notes',\ - 2000, 2005, 2010, 2015], axis = 1) + 2000, 2005], axis = 1) gdp_growth['Region'] = 'R11_'+ gdp_growth['Region'] + print("This is gdp growth table") + print(gdp_growth) r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', \ 'R11_MEA', 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU'] # Demand at product level - # Material efficiency in clean energy transitions (2017) + # Material efficiency in clean energy transitions (2018) + # This is assumed as 2020. # Europe is divided between WEU and EEU # FUS: Eurasia # PAO, PAS, SAS: IAI Alu cycle - # Below matrix is for 2020 d = [3,55, 4, 7, 5, 6, 15, 3.5, 5.5,6,6 ] - # Do this if we have 2020 demand values for buildings - sp = get_spec() - if 'buildings' in sp['add'].set['technology']: - val = get_baseyear_mat_demand("steel") - print("Base year demand of {}:".format("aluminum"), val) - d = d - val.value - print("UPDATE {} demand for 2020!".format("aluminum")) - - demand2015_al = pd.DataFrame({'Region':r, 'Val':d}).\ + demand2020_al = pd.DataFrame({'Region':r, 'Val':d}).\ join(gdp_growth.set_index('Region'), on='Region').\ rename(columns={'Region':'node'}) - demand2015_al.iloc[:,3:] = demand2015_al.iloc[:,3:].\ - div(demand2015_al[2020], axis=0).\ - multiply(demand2015_al["Val"], axis=0) + demand2020_al.iloc[:,3:] = demand2020_al.iloc[:,3:].\ + div(demand2020_al[2020], axis=0).\ + multiply(demand2020_al["Val"], axis=0) - demand2015_al = pd.melt(demand2015_al.drop(['Val', 'Scenario'], axis=1),\ + demand2020_al = pd.melt(demand2020_al.drop(['Val', 'Scenario'], axis=1),\ id_vars=['node'], var_name='year', value_name = 'value') - return demand2015_al + return demand2020_al diff --git a/message_ix_models/model/material/data_cement.py b/message_ix_models/model/material/data_cement.py index 4e513fc0d2..8a97dee8b1 100644 --- a/message_ix_models/model/material/data_cement.py +++ b/message_ix_models/model/material/data_cement.py @@ -42,7 +42,7 @@ def gen_mock_demand_cement(scenario): ) gdp_growth = gdp_growth.loc[(gdp_growth['Scenario']=='baseline') & (gdp_growth['Region']!='World')].\ - drop(['Model', 'Variable', 'Unit', 'Notes', 2000, 2005, 2010, 2015], axis = 1) + drop(['Model', 'Variable', 'Unit', 'Notes', 2000, 2005], axis = 1) gdp_growth['Region'] = 'R11_'+ gdp_growth['Region'] @@ -92,7 +92,7 @@ def gen_mock_demand_cement(scenario): join(gdp_growth.rename(columns={'Region':'node'}).set_index('node'), on='node') demand2010_cement.iloc[:,3:] = demand2010_cement.iloc[:,3:].\ - div(demand2010_cement[2020], axis=0).\ + div(demand2010_cement[2010], axis=0).\ multiply(demand2010_cement["value"], axis=0) demand2010_cement = pd.melt(demand2010_cement.drop(['value', 'Scenario'], axis=1),\ @@ -148,7 +148,6 @@ def gen_data_cement(scenario, dry_run=False): # Obtain the parameter names, commodity,level,emission split = par.split("|") - print(split) param_name = split[0] # Obtain the scalar value for the parameter val = data_cement.loc[((data_cement["technology"] == t) \ @@ -168,11 +167,7 @@ def gen_data_cement(scenario, dry_run=False): # For the parameters which inlcudes index names if len(split)> 1: - - print('1.param_name:', param_name, t) if (param_name == "input")|(param_name == "output"): - - print(rg, par, regions, val) # Assign commodity and level names com = split[1] lev = split[2] @@ -205,13 +200,11 @@ def gen_data_cement(scenario, dry_run=False): # Parameters with only parameter name else: - print('2.param_name:', param_name) df = make_df(param_name, technology=t, \ value=val[regions[regions==rg].index[0]], unit='t', \ node_loc=rg, **common)#.pipe(broadcast, node_loc=nodes)) if len(regions) == 1: - print(df) df['node_loc'] = None df = df.pipe(broadcast, node_loc=nodes).pipe(same_node) diff --git a/message_ix_models/model/material/data_petro.py b/message_ix_models/model/material/data_petro.py index 1191ef81ed..d54a428033 100644 --- a/message_ix_models/model/material/data_petro.py +++ b/message_ix_models/model/material/data_petro.py @@ -50,11 +50,12 @@ def gen_mock_demand_petro(scenario): gdp_growth = gdp_growth.loc[(gdp_growth['Scenario']=='baseline') & \ (gdp_growth['Region']!='World')].drop(['Model', 'Variable', 'Unit', \ - 'Notes', 2000, 2005, 2010, 2015], axis = 1) + 'Notes', 2000, 2005], axis = 1) gdp_growth['Region'] = 'R11_'+ gdp_growth['Region'] - # 2015 production + # 2018 production + # Use as 2020 # The Future of Petrochemicals Methodological Annex # Projections here do not show too much growth until 2050 for some regions. # For division of some regions assumptions made: @@ -62,34 +63,34 @@ def gen_mock_demand_petro(scenario): r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', \ 'R11_MEA', 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU'] - d_ethylene = [1,25,10,3,5,20,25,12,10,13,15] - d_propylene = [0.5,25, 10, 0.5, 3, 10, 15, 10,7,8, 10] - d_BTX = [0.5,25, 10, 0.5, 3, 10, 15, 10,7,8, 10] + d_ethylene = [1,25,10,3,5,20,30,12,10,13,15] + d_propylene = [0.5,25, 10, 0.5, 3, 10, 15, 10,10,8, 10] + d_BTX = [0.5,25, 10, 0.5, 3, 12, 15, 10,10,8, 10] list = [] for e in ["ethylene","propylene","BTX"]: if e == "ethylene": - demand2015 = pd.DataFrame({'Region':r, 'Val':d_ethylene}).\ + demand2020 = pd.DataFrame({'Region':r, 'Val':d_ethylene}).\ join(gdp_growth.set_index('Region'), on='Region').\ rename(columns={'Region':'node'}) if e == "propylene": - demand2015 = pd.DataFrame({'Region':r, 'Val':d_propylene}).\ + demand2020 = pd.DataFrame({'Region':r, 'Val':d_propylene}).\ join(gdp_growth.set_index('Region'), on='Region').\ rename(columns={'Region':'node'}) if e == "BTX": - demand2015 = pd.DataFrame({'Region':r, 'Val':d_BTX}).\ + demand2020 = pd.DataFrame({'Region':r, 'Val':d_BTX}).\ join(gdp_growth.set_index('Region'), on='Region').\ rename(columns={'Region':'node'}) - demand2015.iloc[:,3:] = demand2015.iloc[:,3:].div(demand2015[2020], axis=0).\ - multiply(demand2015["Val"], axis=0) + demand2020.iloc[:,3:] = demand2020.iloc[:,3:].div(demand2020[2020], axis=0).\ + multiply(demand2020["Val"], axis=0) - demand2015 = pd.melt(demand2015.drop(['Val', 'Scenario'], axis=1),\ + demand2020 = pd.melt(demand2020.drop(['Val', 'Scenario'], axis=1),\ id_vars=['node'], var_name='year', value_name = 'value') - list.append(demand2015) + list.append(demand2020) return list[0], list[1], list[2] diff --git a/message_ix_models/model/material/data_steel.py b/message_ix_models/model/material/data_steel.py index 8d267ff421..326a40b6dc 100644 --- a/message_ix_models/model/material/data_steel.py +++ b/message_ix_models/model/material/data_steel.py @@ -46,7 +46,7 @@ def gen_mock_demand_steel(scenario): ) gdp_growth = gdp_growth.loc[(gdp_growth['Scenario']=='baseline') & (gdp_growth['Region']!='World')].\ - drop(['Model', 'Variable', 'Unit', 'Notes', 2000, 2005, 2010, 2015], axis = 1) + drop(['Model', 'Variable', 'Unit', 'Notes', 2000, 2005], axis = 1) gdp_growth['Region'] = 'R11_'+ gdp_growth['Region'] @@ -69,7 +69,7 @@ def gen_mock_demand_steel(scenario): join(gdp_growth.set_index('Region'), on='Region').rename(columns={'Region':'node'}) demand2010_steel.iloc[:,3:] = demand2010_steel.iloc[:,3:].\ - div(demand2010_steel[2020], axis=0).\ + div(demand2010_steel[2010], axis=0).\ multiply(demand2010_steel["Val"], axis=0) demand2010_steel = pd.melt(demand2010_steel.drop(['Val', 'Scenario'], axis=1),\ @@ -179,7 +179,6 @@ def gen_data_steel(scenario, dry_run=False): # Obtain the parameter names, commodity,level,emission split = par.split("|") - print(split) param_name = split[0] # Obtain the scalar value for the parameter val = data_steel.loc[((data_steel["technology"] == t) \ @@ -199,16 +198,12 @@ def gen_data_steel(scenario, dry_run=False): # For the parameters which inlcudes index names if len(split)> 1: - - print('1.param_name:', param_name, t) if (param_name == "input")|(param_name == "output"): # Assign commodity and level names com = split[1] lev = split[2] mod = split[3] - print(rg, par, lev) - if (param_name == "input") and (lev == "import"): df = make_df(param_name, technology=t, commodity=com, \ level=lev, \ @@ -228,7 +223,6 @@ def gen_data_steel(scenario, dry_run=False): # Copy parameters to all regions, when node_loc is not GLB if (len(regions) == 1) and (rg != "R11_GLB"): - print("copying to all R11", rg, lev) df['node_loc'] = None df = df.pipe(broadcast, node_loc=nodes)#.pipe(same_node) # Use same_node only for non-trade technologies @@ -255,14 +249,12 @@ def gen_data_steel(scenario, dry_run=False): # Parameters with only parameter name else: - print('2.param_name:', param_name) df = make_df(param_name, technology=t, \ value=val[regions[regions==rg].index[0]], unit='t', \ node_loc=rg, **common) # Copy parameters to all regions if len(set(df['node_loc'])) == 1 and list(set(df['node_loc']))[0]!='R11_GLB': - print("Copying to all R11") df['node_loc'] = None df = df.pipe(broadcast, node_loc=nodes) @@ -289,8 +281,6 @@ def gen_data_steel(scenario, dry_run=False): tec = data_steel_rel.loc[((data_steel_rel["relation"] == r) \ & (data_steel_rel["parameter"] == par_name)),'technology'].values - print(par_name, "val", val, "tec", tec) - df = (make_df(par_name, technology=tec, \ value=val, unit='-', mode = 'M1', relation = r)\ .pipe(broadcast, node_rel=nodes, \ From 105edfbcea7383b59eb8c9fa22c854d2c43e8b31 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 26 Jan 2021 13:40:39 +0100 Subject: [PATCH 224/774] Shift feedstock to final level for simpler reporting --- .../data/material/petrochemicals_techno_economic.xlsx | 4 ++-- message_ix_models/data/material/set.yaml | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx index 4203da66b3..5b0171f325 100644 --- a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx +++ b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:2b6bf6ebfbee26719a44508ca9869ae2fe0e5c03ac41611bcc53a82e46c8d810 -size 618901 +oid sha256:621dad8ea2d9ab7169a988523817e413c3c168da71b2a19f8052bfcf5d717ceb +size 619289 diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index e697fb4c5f..51b50dfad6 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -311,7 +311,6 @@ generic: - fc_h2_refining - solar_refining - dheat_refining - mode: add: - low_temp @@ -396,6 +395,7 @@ petro_chemicals: - trade_petro - import_petro - export_petro + - feedstock_t/d remove: # Any representation of refinery. - ref_hil From 0058c094cc56a618931030495e05d73e723a82b2 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Tue, 26 Jan 2021 17:14:55 +0100 Subject: [PATCH 225/774] Fixes while trying to resolve an infeasibility --- message_ix_models/model/material/data_aluminum.py | 10 +++++++++- message_ix_models/model/material/data_buildings.py | 5 +++-- 2 files changed, 12 insertions(+), 3 deletions(-) diff --git a/message_ix_models/model/material/data_aluminum.py b/message_ix_models/model/material/data_aluminum.py index bb877f95a1..88f84b3742 100644 --- a/message_ix_models/model/material/data_aluminum.py +++ b/message_ix_models/model/material/data_aluminum.py @@ -300,7 +300,15 @@ def gen_mock_demand_aluminum(scenario): d = [3,55, 4, 7, 5, 6, 15, 3.5, 5.5,6,6 ] - demand2020_al = pd.DataFrame({'Region':r, 'Val':d}).\ + # Do this if we have 2020 demand values for buildings + sp = get_spec() + if 'buildings' in sp['add'].set['technology']: + val = get_baseyear_mat_demand("aluminum") + print("Base year demand of {}:".format("aluminum"), val) + d = d - val.value + print("UPDATE {} demand for 2020!".format("aluminum")) + + demand2015_al = pd.DataFrame({'Region':r, 'Val':d}).\ join(gdp_growth.set_index('Region'), on='Region').\ rename(columns={'Region':'node'}) diff --git a/message_ix_models/model/material/data_buildings.py b/message_ix_models/model/material/data_buildings.py index f5a3f3fb27..ee341947ff 100644 --- a/message_ix_models/model/material/data_buildings.py +++ b/message_ix_models/model/material/data_buildings.py @@ -132,7 +132,8 @@ def gen_data_buildings(scenario, dry_run=False): # Read field values from the buildings input data regions = list(set(data_buildings.node)) comms = list(set(data_buildings.commodity)) - types = list(set(data_buildings.type)) + # types = list(set(data_buildings.type)) + types = ['Material Demand', 'Scrap Release'] # Order matters common = dict( time="year", @@ -161,7 +162,7 @@ def gen_data_buildings(scenario, dry_run=False): # Material input to buildings df = make_df('input', technology=tec_new, commodity=comm, \ - level="product", year_vtg = val_mat.year, \ + level="demand", year_vtg = val_mat.year, \ value=val_mat.value, unit='t', \ node_loc = rg, **common)\ .pipe(same_node)\ From 952db8e5f717b3a3c6dae87e3756797a039a19f2 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Tue, 26 Jan 2021 17:15:45 +0100 Subject: [PATCH 226/774] Move a functino from data.py to avoid circular import --- message_ix_models/model/material/__init__.py | 39 ++++++++++++++++++++ 1 file changed, 39 insertions(+) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 9886bf075b..a012b17586 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -117,3 +117,42 @@ def solve(context, datafile): # Solve scenario.solve() + +import logging +log = logging.getLogger(__name__) + +from .data_cement import gen_data_cement +from .data_steel import gen_data_steel +from .data_aluminum import gen_data_aluminum +from .data_generic import gen_data_generic +from .data_petro import gen_data_petro_chemicals +from .data_buildings import gen_data_buildings +from message_data.tools import add_par_data + +DATA_FUNCTIONS = [ + gen_data_buildings, + gen_data_steel, + gen_data_cement, + gen_data_aluminum, + gen_data_petro_chemicals, + gen_data_generic +] + +# Try to handle multiple data input functions from different materials +def add_data(scenario, dry_run=False): + """Populate `scenario` with MESSAGE-Transport data.""" + # Information about `scenario` + info = ScenarioInfo(scenario) + + # Check for two "node" values for global data, e.g. in + # ixmp://ene-ixmp/CD_Links_SSP2_v2.1_clean/baseline + if {"World", "R11_GLB"} < set(info.set["node"]): + log.warning("Remove 'R11_GLB' from node list for data generation") + info.set["node"].remove("R11_GLB") + + for func in DATA_FUNCTIONS: + # Generate or load the data; add to the Scenario + log.info(f'from {func.__name__}()') + add_par_data(scenario, func(scenario), dry_run=dry_run) + + log.info('done') From af8d736b3dbe18271818863c03354dcd7ebc0cd8 Mon Sep 17 00:00:00 2001 From: Jihoon Min Date: Thu, 21 Jan 2021 16:09:49 +0100 Subject: [PATCH 227/774] Add initial scripts and data for buildings work --- .../data/material/LED_LED_report_IAMC_sensitivity.csv | 3 +++ 1 file changed, 3 insertions(+) create mode 100644 message_ix_models/data/material/LED_LED_report_IAMC_sensitivity.csv diff --git a/message_ix_models/data/material/LED_LED_report_IAMC_sensitivity.csv b/message_ix_models/data/material/LED_LED_report_IAMC_sensitivity.csv new file mode 100644 index 0000000000..62e8d7e260 --- /dev/null +++ b/message_ix_models/data/material/LED_LED_report_IAMC_sensitivity.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1e5b051f314c528cb01655810bef81c2900b63472ffeb92547907e574315b688 +size 243446 From 04aaeefdcf4f8453cae2bef00e7ee8dc128bf298 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Wed, 27 Jan 2021 01:29:22 +0100 Subject: [PATCH 228/774] Modify demand carving (set the anchor year at 2020) --- .../model/material/data_aluminum.py | 22 +++++++++++-------- .../model/material/data_cement.py | 21 ++++++++++-------- .../model/material/data_steel.py | 20 ++++++++++------- 3 files changed, 37 insertions(+), 26 deletions(-) diff --git a/message_ix_models/model/material/data_aluminum.py b/message_ix_models/model/material/data_aluminum.py index 88f84b3742..b22255fcd2 100644 --- a/message_ix_models/model/material/data_aluminum.py +++ b/message_ix_models/model/material/data_aluminum.py @@ -300,15 +300,7 @@ def gen_mock_demand_aluminum(scenario): d = [3,55, 4, 7, 5, 6, 15, 3.5, 5.5,6,6 ] - # Do this if we have 2020 demand values for buildings - sp = get_spec() - if 'buildings' in sp['add'].set['technology']: - val = get_baseyear_mat_demand("aluminum") - print("Base year demand of {}:".format("aluminum"), val) - d = d - val.value - print("UPDATE {} demand for 2020!".format("aluminum")) - - demand2015_al = pd.DataFrame({'Region':r, 'Val':d}).\ + demand2020_al = pd.DataFrame({'Region':r, 'Val':d}).\ join(gdp_growth.set_index('Region'), on='Region').\ rename(columns={'Region':'node'}) @@ -316,6 +308,18 @@ def gen_mock_demand_aluminum(scenario): div(demand2020_al[2020], axis=0).\ multiply(demand2020_al["Val"], axis=0) + # Do this if we have 2020 demand values for buildings + sp = get_spec() + if 'buildings' in sp['add'].set['technology']: + val = get_baseyear_mat_demand("aluminum") + print("Base year demand of {}:".format("aluminum"), val) + # d = d - val.value + # Scale down all years' demand values by the 2020 ratio + demand2020_al.iloc[:,3:] = demand2020_al.iloc[:,3:].\ + multiply(demand2020_al[2020]- val['value'], axis=0).\ + div(demand2020_al[2020], axis=0) + print("UPDATE {} demand for 2020!".format("aluminum")) + demand2020_al = pd.melt(demand2020_al.drop(['Val', 'Scenario'], axis=1),\ id_vars=['node'], var_name='year', value_name = 'value') diff --git a/message_ix_models/model/material/data_cement.py b/message_ix_models/model/material/data_cement.py index 8a97dee8b1..97406274e9 100644 --- a/message_ix_models/model/material/data_cement.py +++ b/message_ix_models/model/material/data_cement.py @@ -79,15 +79,6 @@ def gen_mock_demand_cement(scenario): demand2010_cement = demand2010_cement.groupby(by=['node']).sum().reset_index() demand2010_cement['value'] = demand2010_cement['value'] / 1e9 # kg to Mt - # Do this if we have 2020 demand values for buildings - sp = get_spec() - if 'buildings' in sp['add'].set['technology']: - val = get_baseyear_mat_demand("cement") - print("Base year demand of {}:".format("cement"), val) - demand2010_cement['value'] = demand2010_cement['value'] - val['value'] - print("UPDATE {} demand for 2020!".format("cement")) - - demand2010_cement = demand2010_cement.\ join(gdp_growth.rename(columns={'Region':'node'}).set_index('node'), on='node') @@ -95,6 +86,18 @@ def gen_mock_demand_cement(scenario): div(demand2010_cement[2010], axis=0).\ multiply(demand2010_cement["value"], axis=0) + # Do this if we have 2020 demand values for buildings + sp = get_spec() + if 'buildings' in sp['add'].set['technology']: + val = get_baseyear_mat_demand("cement") # Mt in 2020 + print("Base year demand of {}:".format("cement"), val) + # demand2010_cement['value'] = demand2010_cement['value'] - val['value'] + # Scale down all years' demand values by the 2020 ratio + demand2010_cement.iloc[:,3:] = demand2010_cement.iloc[:,3:].\ + multiply(demand2010_cement[2020]- val['value'], axis=0).\ + div(demand2010_cement[2020], axis=0) + print("UPDATE {} demand for 2020!".format("cement")) + demand2010_cement = pd.melt(demand2010_cement.drop(['value', 'Scenario'], axis=1),\ id_vars=['node'], \ var_name='year', value_name = 'value') diff --git a/message_ix_models/model/material/data_steel.py b/message_ix_models/model/material/data_steel.py index 326a40b6dc..390aa53926 100644 --- a/message_ix_models/model/material/data_steel.py +++ b/message_ix_models/model/material/data_steel.py @@ -57,14 +57,6 @@ def gen_mock_demand_steel(scenario): d = [35, 537, 70, 53, 49, \ 39, 130, 80, 45, 96, 100] # MEA change from 39 to 9 to make it feasible (coal supply bound) - # Do this if we have 2020 demand values for buildings - sp = get_spec() - if 'buildings' in sp['add'].set['technology']: - val = get_baseyear_mat_demand("steel") - print("Base year demand of {}:".format("steel"), val) - d = d - val.value - print("UPDATE {} demand for 2020!".format("steel")) - demand2010_steel = pd.DataFrame({'Region':r, 'Val':d}).\ join(gdp_growth.set_index('Region'), on='Region').rename(columns={'Region':'node'}) @@ -72,6 +64,18 @@ def gen_mock_demand_steel(scenario): div(demand2010_steel[2010], axis=0).\ multiply(demand2010_steel["Val"], axis=0) + # Do this if we have 2020 demand values for buildings + sp = get_spec() + if 'buildings' in sp['add'].set['technology']: + val = get_baseyear_mat_demand("steel") + print("Base year demand of {}:".format("steel"), val) + # d = d - val.value + # Scale down all years' demand values by the 2020 ratio + demand2010_steel.iloc[:,3:] = demand2010_steel.iloc[:,3:].\ + multiply(demand2010_steel[2020]- val['value'], axis=0).\ + div(demand2010_steel[2020], axis=0) + print("UPDATE {} demand for 2020!".format("steel")) + demand2010_steel = pd.melt(demand2010_steel.drop(['Val', 'Scenario'], axis=1),\ id_vars=['node'], \ var_name='year', value_name = 'value') From 0209f4184bb93bfe0d36cb5a12be6ab77198b1c7 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Wed, 27 Jan 2021 01:30:19 +0100 Subject: [PATCH 229/774] Restore gamze's 'modify_demand_and_hist_activity' function and its call --- message_ix_models/model/material/__init__.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index a012b17586..6d1ae768f5 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -4,8 +4,10 @@ from message_ix_models import ScenarioInfo from message_ix_models.model.build import apply_spec -from .data import add_data -from .data_util import modify_historical_activity +from message_data.model.build import apply_spec +from message_data.tools import ScenarioInfo +# from .data import add_data +from .data_util import modify_demand_and_hist_activity from .util import read_config def build(scenario): @@ -18,7 +20,7 @@ def build(scenario): # Adjust exogenous energy demand to incorporate the endogenized sectors # Adjust the historical activity of the usefyl level industry technologies - modify_historical_activity(scenario) + modify_demand_and_hist_activity(scenario) return scenario From f7618cbd2e08609b1359dd1f974d6d0f18c39db1 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 27 Jan 2021 13:25:20 +0100 Subject: [PATCH 230/774] Emission factor corrections --- .../data/material/China_steel_cement_MESSAGE.xlsx | 4 ++-- message_ix_models/data/material/aluminum_techno_economic.xlsx | 4 ++-- .../data/material/generic_furnace_boiler_techno_economic.xlsx | 4 ++-- .../data/material/petrochemicals_techno_economic.xlsx | 4 ++-- 4 files changed, 8 insertions(+), 8 deletions(-) diff --git a/message_ix_models/data/material/China_steel_cement_MESSAGE.xlsx b/message_ix_models/data/material/China_steel_cement_MESSAGE.xlsx index 762fdc2a89..4464f2f668 100644 --- a/message_ix_models/data/material/China_steel_cement_MESSAGE.xlsx +++ b/message_ix_models/data/material/China_steel_cement_MESSAGE.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:b7912ac95b5eddac6c6b789e66f517040728a381466904527c43fcbcafa6f8ee -size 54803 +oid sha256:e02c30da21a7ca123ceacdf7728b5d282340d04c5f612ab4a6efe792cf6cbe25 +size 54952 diff --git a/message_ix_models/data/material/aluminum_techno_economic.xlsx b/message_ix_models/data/material/aluminum_techno_economic.xlsx index 45140da5fb..10b276a157 100644 --- a/message_ix_models/data/material/aluminum_techno_economic.xlsx +++ b/message_ix_models/data/material/aluminum_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:969ffcc1db717d71cfe5a2b23f564c555153d7e2c090e4fff79949525513ee28 -size 81325 +oid sha256:9c6b601f43774cdd6b00dde69f8cd9936830e6570cfbc97d5e5ed86177b2e2d5 +size 81394 diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx index 87808f0e53..ca8f52a99f 100644 --- a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx +++ b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:35212763daafd18fde4448ddb6d7bcc5ae4d0a2e3a9d623e51019cf2daa55ed3 -size 332 +oid sha256:ce1add437cbdb32bb82e4c148097f997253c835b5050e09a838e7987d6c0d923 +size 283 diff --git a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx index 5b0171f325..c0c07d3468 100644 --- a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx +++ b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:621dad8ea2d9ab7169a988523817e413c3c168da71b2a19f8052bfcf5d717ceb -size 619289 +oid sha256:81f3ee0c2998e6fc979c689026b37b41d1661291bac8e325a66cd53d79a46028 +size 619379 From 002248723121d427f16a9a1a6726ee2de437f6ad Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 27 Jan 2021 13:26:15 +0100 Subject: [PATCH 231/774] Petro-chemical share updated to 0.7 --- message_ix_models/model/material/data_util.py | 37 +++++++++++++------ 1 file changed, 26 insertions(+), 11 deletions(-) diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index 19f0621c14..702d7c1d21 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -60,27 +60,42 @@ def modify_demand_and_hist_activity(scen): df_spec_new.drop(["FUEL","RYEAR","UNIT_OUT","RESULT"],axis=1, inplace=True) df_spec_new.loc[df_spec_new["SECTOR"]=="industry (chemicals)","i_spec"] = \ - df_spec_new.loc[df_spec_new["SECTOR"]=="industry (chemicals)","i_spec"] * 0.5 + df_spec_new.loc[df_spec_new["SECTOR"]=="industry (chemicals)","i_spec"] * 0.7 df_spec_new = df_spec_new.groupby(["REGION"]).sum().reset_index() # Retreive data for i_feed: Only for petrochemicals + # It is assumed that the sectors that are explicitly covered in MESSAGE are + # 50% of the total feedstock. df_feed = df[(df["SECTOR"]== "feedstock (petrochemical industry)") & \ (df["FUEL"]== "total") ] - df_feed_total = df[(df["SECTOR"]== "feedstock (total)") \ - & (df["FUEL"]== "total")] + df_feed_total = df[(df["SECTOR"]== "feedstock (total)") & (df["FUEL"]== "total")] + df_feed_temp = pd.DataFrame(columns= ["REGION","i_feed"]) - df_feed_new = pd.DataFrame(columns=["REGION","SECTOR","FUEL",\ - "RYEAR","UNIT_OUT","RESULT"]) for r in df_feed["REGION"].unique(): - df_feed_temp = df_feed[df_feed["REGION"]==r] - df_feed_total_temp = df_feed_total[df_feed_total["REGION"] == r] - df_feed_temp["i_feed"] = df_feed_temp["RESULT"]/df_feed_total_temp["RESULT"].values[0] + + i = 0 + df_feed_temp.at[i,"REGION"] = r + df_feed_temp.at[i,"i_feed"] = 0.7 + i = i + 1 df_feed_new = pd.concat([df_feed_temp,df_feed_new],ignore_index = True) - df_feed_new.drop(["FUEL","RYEAR","UNIT_OUT","RESULT"],axis=1, inplace=True) - df_feed_new = df_feed_new.groupby(["REGION"]).sum().reset_index() + # df_feed = df[(df["SECTOR"]== "feedstock (petrochemical industry)") & \ + # (df["FUEL"]== "total") ] + # df_feed_total = df[(df["SECTOR"]== "feedstock (total)") \ + # & (df["FUEL"]== "total")] + # + # df_feed_new = pd.DataFrame(columns=["REGION","SECTOR","FUEL",\ + # "RYEAR","UNIT_OUT","RESULT"]) + # for r in df_feed["REGION"].unique(): + # df_feed_temp = df_feed[df_feed["REGION"]==r] + # df_feed_total_temp = df_feed_total[df_feed_total["REGION"] == r] + # df_feed_temp["i_feed"] = df_feed_temp["RESULT"]/df_feed_total_temp["RESULT"].values[0] + # df_feed_new = pd.concat([df_feed_temp,df_feed_new],ignore_index = True) + # + # df_feed_new.drop(["FUEL","RYEAR","UNIT_OUT","RESULT"],axis=1, inplace=True) + # df_feed_new = df_feed_new.groupby(["REGION"]).sum().reset_index() # Retreive data for i_therm # NOTE: It is assumped that 50% of the chemicals category is HVCs @@ -111,7 +126,7 @@ def modify_demand_and_hist_activity(scen): df_therm_new.drop(["FUEL","RYEAR","UNIT_OUT"],axis=1,inplace=True) df_therm_new.loc[df_therm_new["SECTOR"]=="industry (chemicals)","i_therm"] = \ - df_therm_new.loc[df_therm_new["SECTOR"]=="industry (chemicals)","i_therm"] * 0.5 + df_therm_new.loc[df_therm_new["SECTOR"]=="industry (chemicals)","i_therm"] * 0.7 # Modify CPA based on https://www.iea.org/sankey/#?c=Japan&s=Final%20consumption. # Since the value did not allign with the one in the IEA website. From fe04b0d69417a7ea7d9a5ba144fd0ca84be790d7 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Wed, 27 Jan 2021 15:24:01 +0100 Subject: [PATCH 232/774] Update 2020 (2019) historical production --- .../model/material/data_cement.py | 106 ++++++++++-------- 1 file changed, 61 insertions(+), 45 deletions(-) diff --git a/message_ix_models/model/material/data_cement.py b/message_ix_models/model/material/data_cement.py index 97406274e9..829576ed4b 100644 --- a/message_ix_models/model/material/data_cement.py +++ b/message_ix_models/model/material/data_cement.py @@ -46,63 +46,79 @@ def gen_mock_demand_cement(scenario): gdp_growth['Region'] = 'R11_'+ gdp_growth['Region'] - # Regions setting for IMAGE - region_cement = pd.read_excel( - context.get_path("material", "CEMENT.BvR2010.xlsx"), - sheet_name="Timer_Regions", skiprows=range(0,3))[['Region #', 'Name']]\ - .drop_duplicates().sort_values(by='Region #') - - region_cement = region_cement.loc[region_cement['Region #'] < 999] - region_cement['node'] = \ - ['R11_NAM', 'R11_NAM', - 'R11_LAM', 'R11_LAM', - 'R11_LAM', 'R11_LAM', - 'R11_AFR', 'R11_AFR', - 'R11_AFR', 'R11_AFR', - 'R11_WEU', 'R11_EEU', - 'R11_EEU', 'R11_FSU', - 'R11_FSU', 'R11_FSU', - 'R11_MEA', 'R11_SAS', - 'R11_PAS', 'R11_CPA', - 'R11_PAS', 'R11_PAS', - 'R11_PAO', 'R11_PAO', - 'R11_SAS', 'R11_AFR'] - - # Cement demand 2010 [Mt/year] (IMAGE) - demand2010_cement = pd.read_excel( - context.get_path("material", "CEMENT.BvR2010.xlsx"), - sheet_name="Domestic Consumption", skiprows=range(0,3)).\ - groupby(by=["Region #"]).sum()[[2010]].\ - join(region_cement.set_index('Region #'), on='Region #').\ - rename(columns={2010:'value'}) - - demand2010_cement = demand2010_cement.groupby(by=['node']).sum().reset_index() - demand2010_cement['value'] = demand2010_cement['value'] / 1e9 # kg to Mt - - demand2010_cement = demand2010_cement.\ - join(gdp_growth.rename(columns={'Region':'node'}).set_index('node'), on='node') - - demand2010_cement.iloc[:,3:] = demand2010_cement.iloc[:,3:].\ - div(demand2010_cement[2010], axis=0).\ - multiply(demand2010_cement["value"], axis=0) + # # Regions setting for IMAGE + # region_cement = pd.read_excel( + # context.get_path("material", "CEMENT.BvR2010.xlsx"), + # sheet_name="Timer_Regions", skiprows=range(0,3))[['Region #', 'Name']]\ + # .drop_duplicates().sort_values(by='Region #') + # + # region_cement = region_cement.loc[region_cement['Region #'] < 999] + # region_cement['node'] = \ + # ['R11_NAM', 'R11_NAM', + # 'R11_LAM', 'R11_LAM', + # 'R11_LAM', 'R11_LAM', + # 'R11_AFR', 'R11_AFR', + # 'R11_AFR', 'R11_AFR', + # 'R11_WEU', 'R11_EEU', + # 'R11_EEU', 'R11_FSU', + # 'R11_FSU', 'R11_FSU', + # 'R11_MEA', 'R11_SAS', + # 'R11_PAS', 'R11_CPA', + # 'R11_PAS', 'R11_PAS', + # 'R11_PAO', 'R11_PAO', + # 'R11_SAS', 'R11_AFR'] + # + # # Cement demand 2010 [Mt/year] (IMAGE) + # demand2010_cement = pd.read_excel( + # context.get_path("material", "CEMENT.BvR2010.xlsx"), + # sheet_name="Domestic Consumption", skiprows=range(0,3)).\ + # groupby(by=["Region #"]).sum()[[2010]].\ + # join(region_cement.set_index('Region #'), on='Region #').\ + # rename(columns={2010:'value'}) + # + # demand2010_cement = demand2010_cement.groupby(by=['node']).sum().reset_index() + # demand2010_cement['value'] = demand2010_cement['value'] / 1e9 # kg to Mt + + # 2019 production by country (USGS) + # p43 of https://pubs.usgs.gov/periodicals/mcs2020/mcs2020-cement.pdf + r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', \ + 'R11_MEA', 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU'] + + # Directly assigned countries from the table on p43 + demand2020_top = [76, 2295, 0, 57, 55, \ + 60, 89, 54, 129, 320, 51] + # the rest (~900 Mt) allocated by % values in http://www.cembureau.eu/media/clkdda45/activity-report-2019.pdf + demand2020_rest = [4100*0.051-76, (4100*0.14-155)*0.2, 4100*0.064*0.5, 4100*0.026-57, 4100*0.046*0.5-55, \ + (4100*0.14-155)*0.2, 4100*0.046*0.5, 12, 4100*0.003, (4100*0.14-155)*0.6, 4100*0.064*0.5 - 51] + d = [a + b for a, b in zip(demand2020_top, demand2020_rest)] + + demand2020_cement = pd.DataFrame({'Region':r, 'value':d}).\ + join(gdp_growth.set_index('Region'), on='Region').rename(columns={'Region':'node'}) + + # demand2010_cement = demand2010_cement.\ + # join(gdp_growth.rename(columns={'Region':'node'}).set_index('node'), on='node') + + demand2020_cement.iloc[:,3:] = demand2020_cement.iloc[:,3:].\ + div(demand2020_cement[2020], axis=0).\ + multiply(demand2020_cement["value"], axis=0) # Do this if we have 2020 demand values for buildings sp = get_spec() if 'buildings' in sp['add'].set['technology']: val = get_baseyear_mat_demand("cement") # Mt in 2020 print("Base year demand of {}:".format("cement"), val) - # demand2010_cement['value'] = demand2010_cement['value'] - val['value'] + # demand2020_cement['value'] = demand2020_cement['value'] - val['value'] # Scale down all years' demand values by the 2020 ratio - demand2010_cement.iloc[:,3:] = demand2010_cement.iloc[:,3:].\ - multiply(demand2010_cement[2020]- val['value'], axis=0).\ - div(demand2010_cement[2020], axis=0) + demand2020_cement.iloc[:,3:] = demand2020_cement.iloc[:,3:].\ + multiply(demand2020_cement[2020]- val['value'], axis=0).\ + div(demand2020_cement[2020], axis=0) print("UPDATE {} demand for 2020!".format("cement")) - demand2010_cement = pd.melt(demand2010_cement.drop(['value', 'Scenario'], axis=1),\ + demand2020_cement = pd.melt(demand2020_cement.drop(['value', 'Scenario'], axis=1),\ id_vars=['node'], \ var_name='year', value_name = 'value') - return demand2010_cement + return demand2020_cement From 9507577dce31825b79de51adbc2d0ce5df0876b9 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 27 Jan 2021 15:29:28 +0100 Subject: [PATCH 233/774] Steel and cement emission factors adjsuted cement: co2_industry --> co2 for both coefficients are multiplied by 12/44 --- .../data/material/China_steel_cement_MESSAGE.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/China_steel_cement_MESSAGE.xlsx b/message_ix_models/data/material/China_steel_cement_MESSAGE.xlsx index 4464f2f668..750b43688a 100644 --- a/message_ix_models/data/material/China_steel_cement_MESSAGE.xlsx +++ b/message_ix_models/data/material/China_steel_cement_MESSAGE.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:e02c30da21a7ca123ceacdf7728b5d282340d04c5f612ab4a6efe792cf6cbe25 -size 54952 +oid sha256:c399584ab299ee1dff535c7f51e42d0b8697647f114dc6e2c11643230bd9b7e1 +size 54926 From 1d634058a104ed088bcdea66dcfc93d7ad7cf294 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Wed, 27 Jan 2021 15:42:16 +0100 Subject: [PATCH 234/774] Test scripts --- .../model/material/material_testscript.py | 28 ++++++++++++++----- 1 file changed, 21 insertions(+), 7 deletions(-) diff --git a/message_ix_models/model/material/material_testscript.py b/message_ix_models/model/material/material_testscript.py index 4dfe008f3e..a8c03ea03a 100644 --- a/message_ix_models/model/material/material_testscript.py +++ b/message_ix_models/model/material/material_testscript.py @@ -9,8 +9,8 @@ import message_ix as mix from message_ix import Scenario -import message_data.model.material.data as dt -from message_data.model.material.plotting import Plots +# import message_data.model.material.data as dt +# from message_data.model.material.plotting import Plots # import pyam @@ -29,7 +29,7 @@ from message_data.model.create import create_res from message_data.model.material import build, get_spec -# from message_data.model.material.util import read_config +from message_data.model.material.util import read_config #%% Main test run based on a MESSAGE scenario @@ -85,7 +85,7 @@ ctx.platform_info.setdefault('name', 'ixmp_dev') ctx.platform_info.setdefault('jvmargs', ['-Xmx12G']) # To avoid java heap space error ctx.scenario_info.setdefault('model', 'Material_Global') -ctx.scenario_info.setdefault('scenario', 'NoPolicy') +ctx.scenario_info.setdefault('scenario', 'NoPolicy-Buildings') ctx['ssp'] = 'SSP2' ctx['datafile'] = 'Global_steel_cement_MESSAGE.xlsx' @@ -104,13 +104,26 @@ import pandas as pd -import message_data.model.material.data_util as du +import message_data.model.material.data_aluminum as da +import message_data.model.material.data_steel as ds import message_data.model.material.data_cement as dc +import message_data.model.material.data_buildings as db +from message_data.model.material.data_buildings import BLD_MAT_USE_2020 as bld_demand2020 + +mp = ixmp.Platform(name="ixmp_dev") +sample = mix.Scenario(mp, model="Material_Global", scenario="NoPolicy") +cem_demand = sample.par('demand', {"commodity":"cement", "year":2010}) # Test read_data_steel <- will be in create_res if working fine df = du.read_sector_data('steel') df = du.read_rel(ctx.datafile) +# Buildings scripts +a,b,c = db.read_timeseries_buildings('LED_LED_report_IAMC.csv') +r = db.gen_data_buildings(scen) + + + mp = ctx.get_platform() b = dt.read_data_generic() @@ -127,7 +140,9 @@ df_st = dt.gen_data_cement(scen) a = dt.get_data(scen, ctx) -dc.gen_mock_demand_cement(scen) +dc.gen_mock_demand_cement(sample) +ds.gen_mock_demand_steel(sample) +da.gen_mock_demand_aluminum(sample) dt.read_data_generic() bare.add_data(scen) @@ -135,7 +150,6 @@ info = ScenarioInfo(scen) a = get_spec() -mp = ixmp.Platform(name="ixmp_dev") a = mp.scenario_list() b=a.loc[a['cre_user']=="min"] sample = mix.Scenario(mp_samp, model="Material_test", scenario="baseline") From b7673321d810da86c05dd967d99a8ed0a08178e6 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 27 Jan 2021 16:38:50 +0100 Subject: [PATCH 235/774] Update generic furnaces emission factors --- .../data/material/generic_furnace_boiler_techno_economic.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx index ca8f52a99f..27b353de4c 100644 --- a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx +++ b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:ce1add437cbdb32bb82e4c148097f997253c835b5050e09a838e7987d6c0d923 -size 283 +oid sha256:97f0f5f2e131c5e2e15c0c15dc9221054d75dee416846d3c781e30dd754da21c +size 296 From ee6d54749e2f47fbe7be063ca2569929ea6e305d Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 27 Jan 2021 17:20:52 +0100 Subject: [PATCH 236/774] Bug fix --- message_ix_models/model/material/data_util.py | 1 + 1 file changed, 1 insertion(+) diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index 702d7c1d21..dc1c38bedf 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -72,6 +72,7 @@ def modify_demand_and_hist_activity(scen): (df["FUEL"]== "total") ] df_feed_total = df[(df["SECTOR"]== "feedstock (total)") & (df["FUEL"]== "total")] df_feed_temp = pd.DataFrame(columns= ["REGION","i_feed"]) + df_feed_new = pd.DataFrame(columns= ["REGION","i_feed"]) for r in df_feed["REGION"].unique(): From f8868b58fe41d85b7516b188fca9de3b6c69ffd3 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Wed, 27 Jan 2021 23:27:08 +0100 Subject: [PATCH 237/774] Change the function name to incorporate ex-post building material adjustment through 'adjust_demand_param' --- message_ix_models/model/material/data_aluminum.py | 4 ++-- message_ix_models/model/material/data_cement.py | 4 ++-- message_ix_models/model/material/data_steel.py | 4 ++-- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/message_ix_models/model/material/data_aluminum.py b/message_ix_models/model/material/data_aluminum.py index b22255fcd2..00370dccef 100644 --- a/message_ix_models/model/material/data_aluminum.py +++ b/message_ix_models/model/material/data_aluminum.py @@ -25,7 +25,7 @@ add_par_data ) # Get endogenous material demand from buildings interface -from .data_buildings import get_baseyear_mat_demand +from .data_buildings import get_scen_mat_demand from . import get_spec def read_data_aluminum(): @@ -311,7 +311,7 @@ def gen_mock_demand_aluminum(scenario): # Do this if we have 2020 demand values for buildings sp = get_spec() if 'buildings' in sp['add'].set['technology']: - val = get_baseyear_mat_demand("aluminum") + val = get_scen_mat_demand("aluminum") print("Base year demand of {}:".format("aluminum"), val) # d = d - val.value # Scale down all years' demand values by the 2020 ratio diff --git a/message_ix_models/model/material/data_cement.py b/message_ix_models/model/material/data_cement.py index 829576ed4b..a19e27f5a4 100644 --- a/message_ix_models/model/material/data_cement.py +++ b/message_ix_models/model/material/data_cement.py @@ -17,7 +17,7 @@ add_par_data ) # Get endogenous material demand from buildings interface -from .data_buildings import get_baseyear_mat_demand +from .data_buildings import get_scen_mat_demand from . import get_spec @@ -105,7 +105,7 @@ def gen_mock_demand_cement(scenario): # Do this if we have 2020 demand values for buildings sp = get_spec() if 'buildings' in sp['add'].set['technology']: - val = get_baseyear_mat_demand("cement") # Mt in 2020 + val = get_scen_mat_demand("cement") # Mt in 2020 print("Base year demand of {}:".format("cement"), val) # demand2020_cement['value'] = demand2020_cement['value'] - val['value'] # Scale down all years' demand values by the 2020 ratio diff --git a/message_ix_models/model/material/data_steel.py b/message_ix_models/model/material/data_steel.py index 390aa53926..3188e53e9d 100644 --- a/message_ix_models/model/material/data_steel.py +++ b/message_ix_models/model/material/data_steel.py @@ -9,7 +9,7 @@ from .util import read_config from .data_util import read_rel # Get endogenous material demand from buildings interface -from .data_buildings import get_baseyear_mat_demand +from .data_buildings import get_scen_mat_demand from message_data.tools import ( ScenarioInfo, broadcast, @@ -67,7 +67,7 @@ def gen_mock_demand_steel(scenario): # Do this if we have 2020 demand values for buildings sp = get_spec() if 'buildings' in sp['add'].set['technology']: - val = get_baseyear_mat_demand("steel") + val = get_scen_mat_demand("steel") print("Base year demand of {}:".format("steel"), val) # d = d - val.value # Scale down all years' demand values by the 2020 ratio From 76c6831e5cc97a74dc3bec9e7d65ed51d73a8c88 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Wed, 27 Jan 2021 23:56:44 +0100 Subject: [PATCH 238/774] Add a function for ex-post demand adjustment --- .../model/material/data_buildings.py | 60 ++++++++++++++++--- 1 file changed, 51 insertions(+), 9 deletions(-) diff --git a/message_ix_models/model/material/data_buildings.py b/message_ix_models/model/material/data_buildings.py index ee341947ff..50db6fb2a7 100644 --- a/message_ix_models/model/material/data_buildings.py +++ b/message_ix_models/model/material/data_buildings.py @@ -19,16 +19,21 @@ add_par_data ) -def read_timeseries_buildings(filename): +INPUTFILE = 'LED_LED_report_IAMC_sensitivity.csv' #'LED_LED_report_IAMC.csv' +CASE_SENS = 'ref' # 'min', 'max' + + +def read_timeseries_buildings(filename, case=CASE_SENS): import numpy as np # Ensure config is loaded, get the context context = read_config() - # Read the file + # Read the file and filter the given sensitivity case bld_input_raw = pd.read_csv( context.get_path("material", filename)) + bld_input_raw = bld_input_raw.loc[bld_input_raw.Sensitivity==case] bld_input_mat = bld_input_raw[bld_input_raw['Variable'].\ # str.contains("Floor Space|Aluminum|Cement|Steel|Final Energy")] @@ -81,18 +86,51 @@ def read_timeseries_buildings(filename): .rename(columns={"Region": "node", "Year": "year"}) tmp = bld_demand_long.Variable.str.split("|", expand=True) bld_demand_long['commodity'] = tmp[3].str.lower() # Material type - bld_demand_long = bld_demand_long[bld_demand_long['year']=="2020"].\ - dropna(how='any') + # bld_demand_long = bld_demand_long[bld_demand_long['year']=="2020"].\ + # dropna(how='any') + bld_demand_long = bld_demand_long.dropna(how='any') bld_demand_long = bld_demand_long[bld_demand_long['Variable'].str.contains("Material Demand")].drop(columns='Variable') return bld_intensity_long, bld_area_long, bld_demand_long -INPUTFILE = 'LED_LED_report_IAMC.csv' +def get_scen_mat_demand(commod, year = "2020", inputfile = INPUTFILE, case=CASE_SENS): + a, b, c = read_timeseries_buildings(inputfile, case) + if not year == "all": # specific year + cc = c[(c.commodity==commod) & (c.year==year)].reset_index(drop=True) + else: # all years + cc = c[(c.commodity==commod)].reset_index(drop=True) + return cc + + +def adjust_demand_param(scen): + + s_info = ScenarioInfo(scen) + modelyears = s_info.Y #s_info.Y is only for modeling years + + # scen.clone(model=scen.model, scenario=scen.scenario+"_building") + + scen_mat_demand = scen.par('demand', {"level":"demand"}) # mat demand without buildings considered + + scen.check_out() + comms = ["steel", "cement", "aluminum"] + for c in comms: + mat_building = get_scen_mat_demand(c, year="all").rename(columns={"value":"bld_demand"}) # mat demand (timeseries) from buildings model (Alessio) + mat_building['year'] = mat_building['year'].astype(int) + + sub_mat_demand = scen_mat_demand.loc[scen_mat_demand.commodity == c] + # print("old", sub_mat_demand.loc[sub_mat_demand.year >=2025]) + sub_mat_demand = sub_mat_demand.join(mat_building.set_index(["node", "year", "commodity"]), \ + on = ["node", "year", "commodity"], how='left') + sub_mat_demand['value'] = sub_mat_demand['value'] - sub_mat_demand['bld_demand'] + sub_mat_demand = sub_mat_demand.drop(columns=["bld_demand"]).dropna(how="any") + + # Only replace for year >= 2025 + scen.add_par('demand', sub_mat_demand.loc[sub_mat_demand.year >=2025]) + # print("new", sub_mat_demand.loc[sub_mat_demand.year >=2025]) + scen.commit("Building material demand subtracted") + -def get_baseyear_mat_demand(commod): - a, b, c = read_timeseries_buildings(INPUTFILE) - return c[c.commodity==commod].reset_index() def gen_data_buildings(scenario, dry_run=False): @@ -114,7 +152,7 @@ def gen_data_buildings(scenario, dry_run=False): s_info = ScenarioInfo(scenario) # Buildings raw data (from Alessio) - data_buildings, data_buildings_demand, data_buildings_mat_demand = read_timeseries_buildings(INPUTFILE) + data_buildings, data_buildings_demand, data_buildings_mat_demand = read_timeseries_buildings(INPUTFILE, CASE_SENS) # List of data frames, to be concatenated together at end results = defaultdict(list) @@ -197,4 +235,8 @@ def gen_data_buildings(scenario, dry_run=False): # Concatenate to one data frame per parameter results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} + # TODO: check the starting model/scenario, if not ENGAGE, call adjust_demand_param + if scenario.scenario == "LEDXXXX": + adjust_demand_param(scenario) + return results From 817a783742cf974eb57b8ada0c4aa868a735b615 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Wed, 27 Jan 2021 23:57:11 +0100 Subject: [PATCH 239/774] Tests --- message_ix_models/model/material/material_testscript.py | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/message_ix_models/model/material/material_testscript.py b/message_ix_models/model/material/material_testscript.py index a8c03ea03a..da0d7f634b 100644 --- a/message_ix_models/model/material/material_testscript.py +++ b/message_ix_models/model/material/material_testscript.py @@ -85,7 +85,7 @@ ctx.platform_info.setdefault('name', 'ixmp_dev') ctx.platform_info.setdefault('jvmargs', ['-Xmx12G']) # To avoid java heap space error ctx.scenario_info.setdefault('model', 'Material_Global') -ctx.scenario_info.setdefault('scenario', 'NoPolicy-Buildings') +ctx.scenario_info.setdefault('scenario', 'NoPolicy') ctx['ssp'] = 'SSP2' ctx['datafile'] = 'Global_steel_cement_MESSAGE.xlsx' @@ -111,6 +111,9 @@ from message_data.model.material.data_buildings import BLD_MAT_USE_2020 as bld_demand2020 mp = ixmp.Platform(name="ixmp_dev") +sl = mp.scenario_list() +sl = sl.loc[sl.model == "Material_Global"] # "ENGAGE_SSP2_v4.1.4"] + sample = mix.Scenario(mp, model="Material_Global", scenario="NoPolicy") cem_demand = sample.par('demand', {"commodity":"cement", "year":2010}) @@ -119,7 +122,9 @@ df = du.read_rel(ctx.datafile) # Buildings scripts -a,b,c = db.read_timeseries_buildings('LED_LED_report_IAMC.csv') +a,b,cc = db.read_timeseries_buildings('LED_LED_report_IAMC_sensitivity.csv', 'ref') +a1,b1,cc1 = db.read_timeseries_buildings('LED_LED_report_IAMC_sensitivity.csv', 'min') +c = db.get_scen_mat_demand(commod='steel', year="all") r = db.gen_data_buildings(scen) From b6e63366279b02a2215ebfc3de52095831988c71 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 28 Jan 2021 09:16:27 +0100 Subject: [PATCH 240/774] Steel and cement emission factors corrected --- .../data/material/Global_steel_cement_MESSAGE.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx index ee3c605c29..9516042d49 100644 --- a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx +++ b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:e6d95a532e4b234dcae79cdc8fe50eb671ab0db0a8e7ca5dff5f3e3d34a99d25 -size 44539 +oid sha256:182c82700a904d0dde6338896a9da0fb17711049989f5e5950af8fb3a4abca34 +size 43505 From 96b1743af493e44bf1d434131b3a4a1afa1895ee Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Fri, 5 Mar 2021 10:20:05 +0100 Subject: [PATCH 241/774] Recycling section revisions --- .../material/Global_steel_cement_MESSAGE.xlsx | 4 +- .../material/aluminum_techno_economic.xlsx | 4 +- message_ix_models/data/material/set.yaml | 36 ++++++++--- .../model/material/data_aluminum.py | 30 +++++---- .../model/material/data_buildings.py | 2 +- .../model/material/data_steel.py | 61 +++++++++++-------- 6 files changed, 87 insertions(+), 50 deletions(-) diff --git a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx index 9516042d49..f2782e113a 100644 --- a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx +++ b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:182c82700a904d0dde6338896a9da0fb17711049989f5e5950af8fb3a4abca34 -size 43505 +oid sha256:2cfffe41f59c4b988206e73b265192bd37c8f0e29dc07b947c1c1893fb00b18f +size 46071 diff --git a/message_ix_models/data/material/aluminum_techno_economic.xlsx b/message_ix_models/data/material/aluminum_techno_economic.xlsx index 10b276a157..9e2d407ad1 100644 --- a/message_ix_models/data/material/aluminum_techno_economic.xlsx +++ b/message_ix_models/data/material/aluminum_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:9c6b601f43774cdd6b00dde69f8cd9936830e6570cfbc97d5e5ed86177b2e2d5 -size 81394 +oid sha256:e6050ac3b69c1020ac7cf82c60e3f33af3cb3828bf93b125ff4e511735e0437d +size 93938 diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 51b50dfad6..1dba140044 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -417,7 +417,9 @@ steel: level: add: - new_scrap - - old_scrap + - old_scrap_1 + - old_scrap_2 + - old_scrap_3 - primary_material - secondary_material - tertiary_material @@ -426,6 +428,8 @@ steel: - waste_material - product - demand + - end_of_life + - dummy_end_of_life technology: add: @@ -448,12 +452,20 @@ steel: - trade_steel - import_steel - export_steel + - other_EOL_steel + - total_EOL_steel relation: add: - - scrap_availability_steel_1 - - scrap_availability_steel_2 - - scrap_availability_steel_3 + - minimum_recycling_steel + + balance_equality: + add: + - ["steel","old_scrap_1"] + - ["steel","old_scrap_2"] + - ["steel","old_scrap_3"] + - ["steel","end_of_life"] + - ["steel","product"] cement: commodity: @@ -527,6 +539,8 @@ aluminum: - product - secondary_material - demand + - end_of_life + - dummy_end_of_life technology: add: @@ -543,12 +557,20 @@ aluminum: - trade_aluminum - import_aluminum - export_aluminum + - other_EOL_aluminum + - total_EOL_aluminum relation: add: - - scrap_availability_1 - - scrap_availability_2 - - scrap_availability_3 + - minimum_recycling_aluminum + + balance_equality: + add: + - ["aluminum","old_scrap_1"] + - ["aluminum","old_scrap_2"] + - ["aluminum","old_scrap_3"] + - ["aluminum","end_of_life"] + - ["aluminum","product"] fertilizer: commodity: diff --git a/message_ix_models/model/material/data_aluminum.py b/message_ix_models/model/material/data_aluminum.py index 00370dccef..6023d547c1 100644 --- a/message_ix_models/model/material/data_aluminum.py +++ b/message_ix_models/model/material/data_aluminum.py @@ -223,8 +223,9 @@ def gen_data_aluminum(scenario, dry_run=False): regions = set(data_aluminum_rel["Region"].values) for reg in regions: - - for r in config['relation']['add']: + for r in data_aluminum_rel["relation"]: + if r is None: + break params = set(data_aluminum_rel.loc[(data_aluminum_rel["relation"] == r),\ "parameter"].values) @@ -265,7 +266,6 @@ def gen_data_aluminum(scenario, dry_run=False): results_aluminum = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} - return results_aluminum def gen_mock_demand_aluminum(scenario): @@ -291,14 +291,22 @@ def gen_mock_demand_aluminum(scenario): r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', \ 'R11_MEA', 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU'] - # Demand at product level - # Material efficiency in clean energy transitions (2018) - # This is assumed as 2020. - # Europe is divided between WEU and EEU - # FUS: Eurasia - # PAO, PAS, SAS: IAI Alu cycle - - d = [3,55, 4, 7, 5, 6, 15, 3.5, 5.5,6,6 ] + # Demand at product level (IAI Global Aluminum Cycle 2018) + # Globally: 82.4 Mt + # Domestic production + Import + # AFR: No Data + # CPA - China: 28.2 Mt + # EEU / 2 + WEU / 2 = Europe 12.5 Mt + # FSU: No data + # LAM: South America: 2.5 Mt + # MEA: Middle East: 2 + # NAM: North America: 14.1 + # PAO: Japan: 3 + # PAS/2 + SAS /2: Other Asia: 11.5 Mt + # Remaining 8.612 Mt shared between AFR and FSU + # This is used as 2020 data. + + d = [3,28.2, 6.25,5,2.5,2,14.1,3,5.75,5.75,6.25] demand2020_al = pd.DataFrame({'Region':r, 'Val':d}).\ join(gdp_growth.set_index('Region'), on='Region').\ diff --git a/message_ix_models/model/material/data_buildings.py b/message_ix_models/model/material/data_buildings.py index 50db6fb2a7..858fc396d4 100644 --- a/message_ix_models/model/material/data_buildings.py +++ b/message_ix_models/model/material/data_buildings.py @@ -209,7 +209,7 @@ def gen_data_buildings(scenario, dry_run=False): # Scrap output back to industry df = make_df('output', technology=tec_new, commodity=comm, \ - level='old_scrap', year_vtg = val_scr.year, \ + level='end_of_life', year_vtg = val_scr.year, \ value=val_scr.value, unit='t', \ node_loc = rg, **common)\ .pipe(same_node)\ diff --git a/message_ix_models/model/material/data_steel.py b/message_ix_models/model/material/data_steel.py index 3188e53e9d..25ce9fafd4 100644 --- a/message_ix_models/model/material/data_steel.py +++ b/message_ix_models/model/material/data_steel.py @@ -266,42 +266,50 @@ def gen_data_steel(scenario, dry_run=False): # Add relations for scrap grades and availability - for r in config['relation']['add']: + regions = set(data_steel_rel["Region"].values) - params = set(data_steel_rel.loc[(data_steel_rel["relation"] == r),\ - "parameter"].values) + for reg in regions: + for r in data_steel_rel["relation"]: + if r is None: + break - common_rel = dict( - year_rel = modelyears, - year_act = modelyears, - mode = 'M1', - relation = r,) + params = set(data_steel_rel.loc[(data_steel_rel["relation"] == r),\ + "parameter"].values) - for par_name in params: - if par_name == "relation_activity": + common_rel = dict( + year_rel = modelyears, + year_act = modelyears, + mode = 'M1', + relation = r,) - val = data_steel_rel.loc[((data_steel_rel["relation"] == r) \ - & (data_steel_rel["parameter"] == par_name)),'value'].values - tec = data_steel_rel.loc[((data_steel_rel["relation"] == r) \ - & (data_steel_rel["parameter"] == par_name)),'technology'].values + for par_name in params: + if par_name == "relation_activity": - df = (make_df(par_name, technology=tec, \ - value=val, unit='-', mode = 'M1', relation = r)\ - .pipe(broadcast, node_rel=nodes, \ - node_loc=nodes, year_rel = modelyears))\ - .assign(year_act=copy_column('year_rel')) + tec_list = data_steel_rel.loc[((data_steel_rel["relation"] == r) \ + & (data_steel_rel["parameter"] == par_name)) ,'technology'] - results[par_name].append(df) + for tec in tec_list.unique(): - elif par_name == "relation_upper": + val = data_steel_rel.loc[((data_steel_rel["relation"] == r) \ + & (data_steel_rel["parameter"] == par_name) & \ + (data_steel_rel["technology"]==tec) & \ + (data_steel_rel["Region"]==reg)),'value'].values[0] - val = data_steel_rel.loc[((data_steel_rel["relation"] == r) \ - & (data_steel_rel["parameter"] == par_name)),'value'].values[0] + df = (make_df(par_name, technology=tec, value=val, unit='-',\ + node_loc = reg, node_rel = reg, **common_rel).pipe(same_node)) - df = (make_df(par_name, value=val, unit='-',\ - **common_rel).pipe(broadcast, node_rel=nodes)) + results[par_name].append(df) - results[par_name].append(df) + elif (par_name == "relation_upper") | (par_name == "relation_lower"): + + val = data_steel_rel.loc[((data_steel_rel["relation"] == r) \ + & (data_steel_rel["parameter"] == par_name) & \ + (data_steel_rel["Region"]==reg)),'value'].values[0] + + df = (make_df(par_name, value=val, unit='-', node_rel = reg,\ + **common_rel)) + + results[par_name].append(df) # Create external demand param parname = 'demand' @@ -312,5 +320,4 @@ def gen_data_steel(scenario, dry_run=False): # Concatenate to one data frame per parameter results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} - return results From 8cfcd1d6cbb127704e0b391c122e4e62bf20a1c8 Mon Sep 17 00:00:00 2001 From: Unknown Date: Mon, 11 Jan 2021 22:08:12 +0100 Subject: [PATCH 242/774] R script for adding material intensities to electricity technologies plus data files --- .../data/material/LCA_commodity_mapping.xlsx | 3 + .../data/material/LCA_region_mapping.xlsx | 3 + .../MESSAGE_global_model_technologies.xlsx | 3 + .../data/material/NTNU_LCA_coefficients.xlsx | 3 + .../ADVANCE_LCA_coefficients.R | 195 ++++++++++++++++++ 5 files changed, 207 insertions(+) create mode 100644 message_ix_models/data/material/LCA_commodity_mapping.xlsx create mode 100644 message_ix_models/data/material/LCA_region_mapping.xlsx create mode 100644 message_ix_models/data/material/MESSAGE_global_model_technologies.xlsx create mode 100644 message_ix_models/data/material/NTNU_LCA_coefficients.xlsx create mode 100644 message_ix_models/model/material/material_intensity/ADVANCE_LCA_coefficients.R diff --git a/message_ix_models/data/material/LCA_commodity_mapping.xlsx b/message_ix_models/data/material/LCA_commodity_mapping.xlsx new file mode 100644 index 0000000000..e6d4b79297 --- /dev/null +++ b/message_ix_models/data/material/LCA_commodity_mapping.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f4e208c48b1bb2f49f9c9d9e35f710c5e999fc972e18969b541679cd24a65440 +size 9883 diff --git a/message_ix_models/data/material/LCA_region_mapping.xlsx b/message_ix_models/data/material/LCA_region_mapping.xlsx new file mode 100644 index 0000000000..09a101ab08 --- /dev/null +++ b/message_ix_models/data/material/LCA_region_mapping.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e1dcb26f2486b5486dc98ec5a87c542f87b64563db3db40358b1c41cf946f8a6 +size 9084 diff --git a/message_ix_models/data/material/MESSAGE_global_model_technologies.xlsx b/message_ix_models/data/material/MESSAGE_global_model_technologies.xlsx new file mode 100644 index 0000000000..7f3c050cc6 --- /dev/null +++ b/message_ix_models/data/material/MESSAGE_global_model_technologies.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1127f59d56aebb43c981d4bb15ed45850056f363f2658c9fc0032d906e36e1a4 +size 40700 diff --git a/message_ix_models/data/material/NTNU_LCA_coefficients.xlsx b/message_ix_models/data/material/NTNU_LCA_coefficients.xlsx new file mode 100644 index 0000000000..2b2a5ec959 --- /dev/null +++ b/message_ix_models/data/material/NTNU_LCA_coefficients.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:362360e9edb499d889d7402ac2ca84c9dec46965489d5dbd3fd10bf0883811bb +size 3560471 diff --git a/message_ix_models/model/material/material_intensity/ADVANCE_LCA_coefficients.R b/message_ix_models/model/material/material_intensity/ADVANCE_LCA_coefficients.R new file mode 100644 index 0000000000..f82369c397 --- /dev/null +++ b/message_ix_models/model/material/material_intensity/ADVANCE_LCA_coefficients.R @@ -0,0 +1,195 @@ +################################################################################ +# include libraries +################################################################################ + +options(java.parameters = "-Xmx8g") + +library(dplyr) +library(tidyr) +library(readxl) +library(imputeTS) # for time series interpolation of NA + +data.path = "C:/Users/krey/Documents/git/message_data_https/data/material" + +################################################################################ +# read data +################################################################################ + +setwd(data.path) + +# read LCA data from ADVANCE LCA tool +col.types = c(rep("text", 9), rep("numeric", 3)) +data.lca = read_xlsx(path = "NTNU_LCA_coefficients.xlsx", sheet = "environmentalImpacts", col_types = col.types) + +# technology mapping +technology.mapping = read_xlsx('MESSAGE_global_model_technologies.xlsx', sheet = 'technology') + +# region mapping +region.mapping = read_xlsx('LCA_region_mapping.xlsx', sheet = 'region') + +# commodity mapping +commodity.mapping = read_xlsx('LCA_commodity_mapping.xlsx', sheet = 'commodity') + +################################################################################ +# process data +################################################################################ + +# filter relevant scenario, technology variant and remove operation phase (and remove duplicates) +data.lca = data.lca %>% filter(scenario == 'Baseline' & `technology variant` == 'mix' & phase != 'Operation') + +# add intermediate time steps and turn into long table format +data.lca = data.lca %>% mutate(`2015` = NA, `2020` = NA, `2025` = NA, `2035` = NA, `2040` = NA, `2045` = NA) %>% pivot_longer(cols = c("2010":"2045"), names_to = 'year', values_to = 'value') %>% mutate(value = as.numeric(value)) +#data.lca$year = factor(data.lca$year, levels = as.character(seq(2010, 2050, 5))) + +# apply technology, commodity/impact and region mappings to MESSAGE +data.lca = data.lca %>% inner_join(region.mapping, by = c("region" = "THEMIS")) %>% inner_join(technology.mapping, by = c("technology" = "LCA mapping")) %>% + inner_join(commodity.mapping, by = c("impact" = "impact")) %>% + filter(!is.na(`MESSAGEix-GLOBIOM_1.1`)) %>% + select(node = `MESSAGEix-GLOBIOM_1.1`, technology = `Type of Technology`, phase, commodity, level, year, unit, value) #%>% unique() + +temp = c() +for (n in unique(data.lca$node)) for (t in unique(data.lca$technology)) for (c in unique(data.lca$commodity)) for (p in unique(data.lca$phase)){ + temp = rbind(temp, data.lca %>% filter(node == n & technology == t & commodity == c & phase == p) %>% na_interpolation()) +} +data.lca = temp + +################################################################################ +# ixmp setup +################################################################################ + +# existing model and scenario name in ixmp to add material intensities to +modelName <- "ENGAGE_SSP2_v4.1.5" +scenarioName <- "baseline" + +# new model and scenario name in ixmp +newmodelName <- "ENGAGE_SSP2_v4.1.5" +newscenarioName <- "baseline_lca_material" + +# comment for commit +comment <- "adding LCA-based material intensity coefficients to electricity generation technologies in MESSAGEix-GLOBIOM" + +# load required packages +library(rmessageix) + +# specify python binaries and environment under which messageix is installed +use_python("C:/Users/krey/anaconda3/envs/message/") +use_condaenv("message") + +# launch the IX modeling platform using the default database +mp <- ixmp$Platform() + +################################################################################ +# load and clone scenario and extract structural information +################################################################################ + +# load existing policy baseline scenario +ixScenarioOriginal = message_ix$Scenario(mp, modelName, scenarioName) + +# clone original policy baseline scenario with new scenario name +ixScenario = ixScenarioOriginal$clone(newmodelName, newscenarioName, comment, keep_solution=FALSE) + +# checkout scenario +ixScenario$check_out() + +# read inv.cost data +inv.cost = ixScenarioOriginal$par('inv_cost') + +# read node, technology, commodity and level from existing scenario +node = ixScenarioOriginal$set('node') %>% data.frame() +year = ixScenarioOriginal$set('year') %>% data.frame() +technology = ixScenarioOriginal$set('technology') %>% data.frame() +commodity = ixScenarioOriginal$set('commodity') %>% data.frame() +level = ixScenarioOriginal$set('level') %>% data.frame() + +# extract node, technology, commodity, level, and year list from LCA data set +node.list = unique(data.lca$node) +year.list = unique(data.lca$year) +tec.list = unique(data.lca$technology) +com.list = unique(data.lca$commodity) +lev.list = unique(data.lca$level) +# add scrap as commodity level +lev.list = c(lev.list, "old_scrap") + +# check whether set members exist in scenario and add in case not +for (n in 1:length(node.list)) if (!node.list[n] %in% node$.) ixScenario$add_set('node', node.list[n]) +for (n in 1:length(year.list)) if (!year.list[n] %in% year$.) ixScenario$add_set('year', year.list[n]) +for (n in 1:length(tec.list)) if (!tec.list[n] %in% technology$.) ixScenario$add_set('technology', tec.list[n]) +for (n in 1:length(com.list)) if (!com.list[n] %in% commodity$.) ixScenario$add_set('commodity', com.list[n]) +for (n in 1:length(lev.list)) if (!lev.list[n] %in% level$.) ixScenario$add_set('level', lev.list[n]) + +# check whether needed units are registered on ixmp and add if not the case +unit = mp$units() %>% data.frame() +if (!('t/kW' %in% unit$.)) mp$add_unit('t/kW', 'tonnes (of commodity) per kW of capacity') + +################################################################################ +# create data frames for material intensitty input/output parameters +################################################################################ + +# new data frames for parameters +input_cap_new <- input_cap_ret <- output_cap_ret <- data.frame() + +for (n in node.list) for (t in tec.list) for (c in com.list) { + year_vtg.list = filter(inv.cost, node_loc == n & technology == t)$year_vtg %>% unique() # & year_vtg >= min(as.numeric(year.list)) + for (y in year_vtg.list) { + # for years after maximum year in data set use values for maximum year, similarly for years before minimum year in data set use values for minimum year + if (y > max(year.list)) yeff = max(year.list) else if (y < min(year.list)) yeff = min(year.list) else yeff = y + input_cap_new = rbind(input_cap_new, data.frame(node_loc = n, technology = t, year_vtg = as.character(y), node_origin = n, commodity = c, level = "product", time_origin = "year", value = filter(data.lca, node == n & technology == t & phase == 'Construction' & commodity == c & year == yeff)$value * 1e-3, unit = 't/kW')) + input_cap_ret = rbind(input_cap_ret, data.frame(node_loc = n, technology = t, year_vtg = as.character(y), node_origin = n, commodity = c, level = "product", time_origin = "year", value = filter(data.lca, node == n & technology == t & phase == 'End-of-life' & commodity == c & year == yeff)$value * 1e-3, unit = 't/kW')) + output_cap_ret = rbind(output_cap_ret, data.frame(node_loc = n, technology = t, year_vtg = as.character(y), node_dest = n, commodity = c, level = "old_scrap", time_dest = "year", value = filter(data.lca, node == n & technology == t & phase == 'Construction' & commodity == c & year == yeff)$value * 1e-3, unit = 't/kW')) + } +} + +################################################################################ +# create new parameters and add new data to scenario +################################################################################ + +# create new parameters input_cap_new, output_cap_new, input_cap_ret, output_cap_ret, input_cap and output_cap if they don't exist +if (!ixScenario$has_par('input_cap_new')) + ixScenario$init_par('input_cap_new', idx_sets = c('node', 'technology', 'year', 'node', 'commodity', 'level', 'time'), idx_names = c('node_loc', 'technology', 'year_vtg', 'node_origin', 'commodity', 'level', 'time_origin')) +if (!ixScenario$has_par('output_cap_new')) + ixScenario$init_par('output_cap_new', idx_sets = c('node', 'technology', 'year', 'node', 'commodity', 'level', 'time'), idx_names = c('node_loc', 'technology', 'year_vtg', 'node_dest', 'commodity', 'level', 'time_dest')) +if (!ixScenario$has_par('input_cap_ret')) + ixScenario$init_par('input_cap_ret', idx_sets = c('node', 'technology', 'year', 'node', 'commodity', 'level', 'time'), idx_names = c('node_loc', 'technology', 'year_vtg', 'node_origin', 'commodity', 'level', 'time_origin')) +if (!ixScenario$has_par('output_cap_ret')) + ixScenario$init_par('output_cap_ret', idx_sets = c('node', 'technology', 'year', 'node', 'commodity', 'level', 'time'), idx_names = c('node_loc', 'technology', 'year_vtg', 'node_dest', 'commodity', 'level', 'time_dest')) +if (!ixScenario$has_par('input_cap')) + ixScenario$init_par('input_cap', idx_sets = c('node', 'technology', 'year', 'year', 'node', 'commodity', 'level', 'time'), idx_names = c('node_loc', 'technology', 'year_vtg', 'year_act', 'node_origin', 'commodity', 'level', 'time_origin')) +if (!ixScenario$has_par('output_cap')) + ixScenario$init_par('output_cap', idx_sets = c('node', 'technology', 'year', 'year', 'node', 'commodity', 'level', 'time'), idx_names = c('node_loc', 'technology', 'year_vtg', 'year_act', 'node_dest', 'commodity', 'level', 'time_dest')) + +ixScenario$add_par('input_cap_new', input_cap_new) +ixScenario$add_par('input_cap_ret', input_cap_ret) +ixScenario$add_par('output_cap_ret', output_cap_ret) + +################################################################################ +# add dummy material production technologies (only needed for model variants without material sector) +################################################################################ + +technology.material = data.frame(tec = c("material_aluminum", "material_cement", "material_steel", "scrap_aluminum", "scrap_cement", "scrap_steel"), com = c("aluminum", "cement", "steel", "aluminum", "cement", "steel"), lev = c("product", "product", "product", "old_scrap", "old_scrap", "old_scrap")) + +for (n in 1:length(technology.material$tec)) if (!technology.material$tec[n] %in% technology$.) ixScenario$add_set('technology', technology.material$tec[n]) + +# new data frames for parameters +output <- var_cost <- data.frame() + +for (n in node.list) for (t in 1:length(technology.material$tec)) { + year_vtg.list = filter(year, . > 2010)$. + for (y in year_vtg.list) { + output = rbind(output, data.frame(node_loc = n, technology = technology.material$tec[t], year_vtg = as.character(y), year_act = as.character(y), mode = 'M1', node_dest = n, commodity = technology.material$com[t], level = technology.material$lev[t], time = 'year', time_dest = "year", value = 1.0, unit = 't')) + var_cost = rbind(var_cost, data.frame(node_loc = n, technology = technology.material$tec[t], year_vtg = as.character(y), year_act = as.character(y), mode = 'M1', time = "year", value = 1.0, unit = 'USD')) + } +} + +ixScenario$add_par('output', output) +ixScenario$add_par('var_cost', var_cost) + +################################################################################ +# commit new scenario and solve model +################################################################################ + +# commit scenario to platform and set as default +ixScenario$commit(comment) +ixScenario$set_as_default() + +# run MESSAGE scenario in GAMS and import results in ix platform +ixScenario$solve("MESSAGE") From 7e287bab25d6a10f84bb3e922bf46c7f374b4642 Mon Sep 17 00:00:00 2001 From: Unknown Date: Thu, 14 Jan 2021 14:22:43 +0100 Subject: [PATCH 243/774] Adjusted paths and scenario names to material model --- .../material_intensity/ADVANCE_LCA_coefficients.R | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/message_ix_models/model/material/material_intensity/ADVANCE_LCA_coefficients.R b/message_ix_models/model/material/material_intensity/ADVANCE_LCA_coefficients.R index f82369c397..52c64efae2 100644 --- a/message_ix_models/model/material/material_intensity/ADVANCE_LCA_coefficients.R +++ b/message_ix_models/model/material/material_intensity/ADVANCE_LCA_coefficients.R @@ -58,15 +58,15 @@ data.lca = temp ################################################################################ # existing model and scenario name in ixmp to add material intensities to -modelName <- "ENGAGE_SSP2_v4.1.5" -scenarioName <- "baseline" +modelName <- "Material_Global" +scenarioName <- "NoPolicy" # new model and scenario name in ixmp -newmodelName <- "ENGAGE_SSP2_v4.1.5" -newscenarioName <- "baseline_lca_material" +newmodelName <- "Material_Global" +newscenarioName <- "NoPolicy_lca_material" # comment for commit -comment <- "adding LCA-based material intensity coefficients to electricity generation technologies in MESSAGEix-GLOBIOM" +comment <- "adding LCA-based material intensity coefficients to electricity generation technologies in MESSAGEix-Materials" # load required packages library(rmessageix) From e9ec519966d8c38cca26a00ac86982514ab14514 Mon Sep 17 00:00:00 2001 From: Unknown Date: Thu, 14 Jan 2021 21:14:08 +0100 Subject: [PATCH 244/774] Added 'residue' technology variant for biomass technologies previously biomass power plants were omitted due to missing data in 'mix' variant --- .../material/material_intensity/ADVANCE_LCA_coefficients.R | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/model/material/material_intensity/ADVANCE_LCA_coefficients.R b/message_ix_models/model/material/material_intensity/ADVANCE_LCA_coefficients.R index 52c64efae2..682c761519 100644 --- a/message_ix_models/model/material/material_intensity/ADVANCE_LCA_coefficients.R +++ b/message_ix_models/model/material/material_intensity/ADVANCE_LCA_coefficients.R @@ -34,8 +34,8 @@ commodity.mapping = read_xlsx('LCA_commodity_mapping.xlsx', sheet = 'commodity') # process data ################################################################################ -# filter relevant scenario, technology variant and remove operation phase (and remove duplicates) -data.lca = data.lca %>% filter(scenario == 'Baseline' & `technology variant` == 'mix' & phase != 'Operation') +# filter relevant scenario, technology variant (residue for biomass, mix for others) and remove operation phase (and remove duplicates) +data.lca = data.lca %>% filter(scenario == 'Baseline' & `technology variant` %in% c('mix', 'residue') & phase != 'Operation') # add intermediate time steps and turn into long table format data.lca = data.lca %>% mutate(`2015` = NA, `2020` = NA, `2025` = NA, `2035` = NA, `2040` = NA, `2045` = NA) %>% pivot_longer(cols = c("2010":"2045"), names_to = 'year', values_to = 'value') %>% mutate(value = as.numeric(value)) From 9c71f6f5583f027e341d55bd2746376590eddecd Mon Sep 17 00:00:00 2001 From: Unknown Date: Tue, 26 Jan 2021 09:29:02 +0100 Subject: [PATCH 245/774] Corrected mapping to lca wind technology data --- .../data/material/MESSAGE_global_model_technologies.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/MESSAGE_global_model_technologies.xlsx b/message_ix_models/data/material/MESSAGE_global_model_technologies.xlsx index 7f3c050cc6..99948e7f65 100644 --- a/message_ix_models/data/material/MESSAGE_global_model_technologies.xlsx +++ b/message_ix_models/data/material/MESSAGE_global_model_technologies.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:1127f59d56aebb43c981d4bb15ed45850056f363f2658c9fc0032d906e36e1a4 -size 40700 +oid sha256:0648585da99d2c7b4038a9da223ad483fb9cfc9165e9d32379794634c0f1a975 +size 40721 From 645ac8e5acd9b67dd6dd8b83048a808f19aa9b28 Mon Sep 17 00:00:00 2001 From: Unknown Date: Tue, 26 Jan 2021 10:02:36 +0100 Subject: [PATCH 246/774] Added visualization of material intensities for alps report --- .../material_intensity/ADVANCE_LCA_coefficients.R | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/message_ix_models/model/material/material_intensity/ADVANCE_LCA_coefficients.R b/message_ix_models/model/material/material_intensity/ADVANCE_LCA_coefficients.R index 682c761519..009c1c3bd0 100644 --- a/message_ix_models/model/material/material_intensity/ADVANCE_LCA_coefficients.R +++ b/message_ix_models/model/material/material_intensity/ADVANCE_LCA_coefficients.R @@ -193,3 +193,16 @@ ixScenario$set_as_default() # run MESSAGE scenario in GAMS and import results in ix platform ixScenario$solve("MESSAGE") + +################################################################################ +# illustrative visualization of material intensities +################################################################################ + +tec.figure = data.frame(tec = c("coal_adv", "coal_adv_ccs", "gas_cc", "nuc_hc", "bio_ppl", "solar_pv_ppl", "csp_sm1_ppl", "wind_ppl"), label = c("Coal w/o CCS", "Coal w/ CCS", " Gas CC", "Nuclear", "Biomass", "Solar PV", "CSP", "Wind onshore")) +data.figure = input_cap_new %>% filter(year_vtg == 2030 & node_loc == 'R11_WEU') %>% inner_join(tec.figure, by = c("technology" = "tec")) + +library(ggplot2) +setwd("H:/Projects/ENE/ALPS/material_integration/figures") +png(paste("Material_intensity_electricity.png", sep = ''), width = 10, height = 5, units = "in", res = 300) + ggplot(data.figure) + geom_bar(aes(x = label, y = value, fill = commodity), stat = 'identity', position="dodge") + scale_y_continuous(limits = c(0, 0.5)) + ylab("Material Intensity [t/kW]") + xlab("") +dev.off() From 15b440f0add3ed3ed2431f20c7c70a7b226cd1f7 Mon Sep 17 00:00:00 2001 From: Unknown Date: Wed, 10 Mar 2021 14:39:19 +0100 Subject: [PATCH 247/774] Adjusted level "old_scrap" to "end_of_life" for compatibility with latest changes --- .../material_intensity/ADVANCE_LCA_coefficients.R | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/message_ix_models/model/material/material_intensity/ADVANCE_LCA_coefficients.R b/message_ix_models/model/material/material_intensity/ADVANCE_LCA_coefficients.R index 009c1c3bd0..8244972461 100644 --- a/message_ix_models/model/material/material_intensity/ADVANCE_LCA_coefficients.R +++ b/message_ix_models/model/material/material_intensity/ADVANCE_LCA_coefficients.R @@ -108,7 +108,7 @@ tec.list = unique(data.lca$technology) com.list = unique(data.lca$commodity) lev.list = unique(data.lca$level) # add scrap as commodity level -lev.list = c(lev.list, "old_scrap") +lev.list = c(lev.list, 'end_of_life') # check whether set members exist in scenario and add in case not for (n in 1:length(node.list)) if (!node.list[n] %in% node$.) ixScenario$add_set('node', node.list[n]) @@ -133,9 +133,9 @@ for (n in node.list) for (t in tec.list) for (c in com.list) { for (y in year_vtg.list) { # for years after maximum year in data set use values for maximum year, similarly for years before minimum year in data set use values for minimum year if (y > max(year.list)) yeff = max(year.list) else if (y < min(year.list)) yeff = min(year.list) else yeff = y - input_cap_new = rbind(input_cap_new, data.frame(node_loc = n, technology = t, year_vtg = as.character(y), node_origin = n, commodity = c, level = "product", time_origin = "year", value = filter(data.lca, node == n & technology == t & phase == 'Construction' & commodity == c & year == yeff)$value * 1e-3, unit = 't/kW')) - input_cap_ret = rbind(input_cap_ret, data.frame(node_loc = n, technology = t, year_vtg = as.character(y), node_origin = n, commodity = c, level = "product", time_origin = "year", value = filter(data.lca, node == n & technology == t & phase == 'End-of-life' & commodity == c & year == yeff)$value * 1e-3, unit = 't/kW')) - output_cap_ret = rbind(output_cap_ret, data.frame(node_loc = n, technology = t, year_vtg = as.character(y), node_dest = n, commodity = c, level = "old_scrap", time_dest = "year", value = filter(data.lca, node == n & technology == t & phase == 'Construction' & commodity == c & year == yeff)$value * 1e-3, unit = 't/kW')) + input_cap_new = rbind(input_cap_new, data.frame(node_loc = n, technology = t, year_vtg = as.character(y), node_origin = n, commodity = c, level = 'product', time_origin = "year", value = filter(data.lca, node == n & technology == t & phase == 'Construction' & commodity == c & year == yeff)$value * 1e-3, unit = 't/kW')) + input_cap_ret = rbind(input_cap_ret, data.frame(node_loc = n, technology = t, year_vtg = as.character(y), node_origin = n, commodity = c, level = 'product', time_origin = "year", value = filter(data.lca, node == n & technology == t & phase == 'End-of-life' & commodity == c & year == yeff)$value * 1e-3, unit = 't/kW')) + output_cap_ret = rbind(output_cap_ret, data.frame(node_loc = n, technology = t, year_vtg = as.character(y), node_dest = n, commodity = c, level = 'end_of_life', time_dest = "year", value = filter(data.lca, node == n & technology == t & phase == 'Construction' & commodity == c & year == yeff)$value * 1e-3, unit = 't/kW')) } } @@ -165,7 +165,7 @@ ixScenario$add_par('output_cap_ret', output_cap_ret) # add dummy material production technologies (only needed for model variants without material sector) ################################################################################ -technology.material = data.frame(tec = c("material_aluminum", "material_cement", "material_steel", "scrap_aluminum", "scrap_cement", "scrap_steel"), com = c("aluminum", "cement", "steel", "aluminum", "cement", "steel"), lev = c("product", "product", "product", "old_scrap", "old_scrap", "old_scrap")) +technology.material = data.frame(tec = c("material_aluminum", "material_cement", "material_steel", "scrap_aluminum", "scrap_cement", "scrap_steel"), com = c("aluminum", "cement", "steel", "aluminum", "cement", "steel"), lev = c("product", "product", "product", "end_of_life", "end_of_life", "end_of_life")) for (n in 1:length(technology.material$tec)) if (!technology.material$tec[n] %in% technology$.) ixScenario$add_set('technology', technology.material$tec[n]) From 08d59f29eeb1a23e3d8a479c9720f5688e1fec11 Mon Sep 17 00:00:00 2001 From: Unknown Date: Thu, 11 Mar 2021 13:11:49 +0100 Subject: [PATCH 248/774] Adjusted material intensity r code to be called from python via rpy2 package --- .../ADVANCE_lca_coefficients_embedded.R | 92 +++++++++++++++++++ .../data_material_intensities_rpy2.py | 55 +++++++++++ 2 files changed, 147 insertions(+) create mode 100644 message_ix_models/model/material/material_intensity/ADVANCE_lca_coefficients_embedded.R create mode 100644 message_ix_models/model/material/material_intensity/data_material_intensities_rpy2.py diff --git a/message_ix_models/model/material/material_intensity/ADVANCE_lca_coefficients_embedded.R b/message_ix_models/model/material/material_intensity/ADVANCE_lca_coefficients_embedded.R new file mode 100644 index 0000000000..5a2c8202d0 --- /dev/null +++ b/message_ix_models/model/material/material_intensity/ADVANCE_lca_coefficients_embedded.R @@ -0,0 +1,92 @@ +################################################################################ +# include libraries +################################################################################ + +options(java.parameters = "-Xmx8g") + +library(dplyr) +library(tidyr) +library(readxl) +library(imputeTS) # for time series interpolation of NA + +################################################################################ +# functions +################################################################################ + +read_material_intensities <- function(data_path, node, year, technology, commodity, level, inv_cost) { + + ################################################################################ + # read data + ################################################################################ + + # read LCA data from ADVANCE LCA tool + col_types = c(rep("text", 9), rep("numeric", 3)) + data_lca = read_xlsx(path = paste(data_path, "/NTNU_LCA_coefficients.xlsx", sep = ''), sheet = "environmentalImpacts", col_types = col_types) + + # read technology, region and commodity mappings + technology_mapping = read_xlsx(paste(data_path, "/MESSAGE_global_model_technologies.xlsx", sep = ''), sheet = 'technology') + region_mapping = read_xlsx(paste(data_path, "/LCA_region_mapping.xlsx", sep = ''), sheet = 'region') + commodity_mapping = read_xlsx(paste(data_path, "/LCA_commodity_mapping.xlsx", sep = ''), sheet = 'commodity') + + ################################################################################ + # process data + ################################################################################ + + # filter relevant scenario, technology variant (residue for biomass, mix for others) and remove operation phase (and remove duplicates) + data_lca = data_lca %>% filter(scenario == 'Baseline' & `technology variant` %in% c('mix', 'residue') & phase != 'Operation') + + # add intermediate time steps and turn into long table format + data_lca = data_lca %>% mutate(`2015` = NA, `2020` = NA, `2025` = NA, `2035` = NA, `2040` = NA, `2045` = NA) %>% pivot_longer(cols = c("2010":"2045"), names_to = 'year', values_to = 'value') %>% mutate(value = as.numeric(value)) + #data_lca$year = factor(data_lca$year, levels = as.character(seq(2010, 2050, 5))) + + # apply technology, commodity/impact and region mappings to MESSAGEix + data_lca = data_lca %>% inner_join(region_mapping, by = c("region" = "THEMIS")) %>% inner_join(technology_mapping, by = c("technology" = "LCA mapping")) %>% + inner_join(commodity_mapping, by = c("impact" = "impact")) %>% + filter(!is.na(`MESSAGEix-GLOBIOM_1.1`)) %>% + select(node = `MESSAGEix-GLOBIOM_1.1`, technology = `Type of Technology`, phase, commodity, level, year, unit, value) #%>% unique() + + temp = c() + for (n in unique(data_lca$node)) for (t in unique(data_lca$technology)) for (c in unique(data_lca$commodity)) for (p in unique(data_lca$phase)){ + temp = rbind(temp, data_lca %>% filter(node == n & technology == t & commodity == c & phase == p) %>% na_interpolation()) + } + # return data frame + data_lca = temp + + # extract node, technology, commodity, level, and year list from LCA data set + node_list = unique(data_lca$node) + year_list = unique(data_lca$year) + tec_list = unique(data_lca$technology) + com_list = unique(data_lca$commodity) + lev_list = unique(data_lca$level) + # add scrap as commodity level + lev_list = c(lev_list, 'end_of_life') + + # check whether set members exist in scenario and add in case not + #for (n in 1:length(node.list)) if (!node.list[n] %in% node$.) ixScenario$add_set('node', node.list[n]) + #for (n in 1:length(year.list)) if (!year.list[n] %in% year$.) ixScenario$add_set('year', year.list[n]) + #for (n in 1:length(tec.list)) if (!tec.list[n] %in% technology$.) ixScenario$add_set('technology', tec.list[n]) + #for (n in 1:length(com.list)) if (!com.list[n] %in% commodity$.) ixScenario$add_set('commodity', com.list[n]) + #for (n in 1:length(lev.list)) if (!lev.list[n] %in% level$.) ixScenario$add_set('level', lev.list[n]) + + ################################################################################ + # create data frames for material intensity input/output parameters + ################################################################################ + + # new data frames for parameters + input_cap_new <- input_cap_ret <- output_cap_ret <- data.frame() + + for (n in node_list) for (t in tec_list) for (c in com_list) { + year_vtg_list = filter(inv_cost, node_loc == n & technology == t)$year_vtg %>% unique() # & year_vtg >= min(as.numeric(year.list)) + for (y in year_vtg_list) { + # for years after maximum year in data set use values for maximum year, similarly for years before minimum year in data set use values for minimum year + if (y > max(year_list)) yeff = max(year_list) else if (y < min(year_list)) yeff = min(year_list) else yeff = y + input_cap_new = rbind(input_cap_new, data.frame(node_loc = n, technology = t, year_vtg = as.character(y), node_origin = n, commodity = c, level = 'product', time_origin = "year", value = filter(data_lca, node == n & technology == t & phase == 'Construction' & commodity == c & year == yeff)$value * 1e-3, unit = 't/kW')) + input_cap_ret = rbind(input_cap_ret, data.frame(node_loc = n, technology = t, year_vtg = as.character(y), node_origin = n, commodity = c, level = 'product', time_origin = "year", value = filter(data_lca, node == n & technology == t & phase == 'End-of-life' & commodity == c & year == yeff)$value * 1e-3, unit = 't/kW')) + output_cap_ret = rbind(output_cap_ret, data.frame(node_loc = n, technology = t, year_vtg = as.character(y), node_dest = n, commodity = c, level = 'end_of_life', time_dest = "year", value = filter(data_lca, node == n & technology == t & phase == 'Construction' & commodity == c & year == yeff)$value * 1e-3, unit = 't/kW')) + } + } + + # return parameter (other parameters currently not returned) + input_cap_new + +} diff --git a/message_ix_models/model/material/material_intensity/data_material_intensities_rpy2.py b/message_ix_models/model/material/material_intensity/data_material_intensities_rpy2.py new file mode 100644 index 0000000000..ebc2152dfb --- /dev/null +++ b/message_ix_models/model/material/material_intensity/data_material_intensities_rpy2.py @@ -0,0 +1,55 @@ +# -*- coding: utf-8 -*- +""" +This script adds material intensity coefficients (prototype) +""" + +# check Python and R environments (for debugging) +import rpy2.situation +for row in rpy2.situation.iter_info(): + print(row) + +# load rpy2 modules +import rpy2.robjects as ro +#from rpy2.robjects.packages import importr +from rpy2.robjects import pandas2ri +from rpy2.robjects.conversion import localconverter + +# paths to r code and lca data +rcode_path="C:/Users/krey/Documents/git/message_data/message_data/model/material/material_intensity/" +data_path = "C:/Users/krey/Documents/git/message_data/data/material" + +# import MESSAGEix +import ixmp +import message_ix + +# launch the IX modeling platform using the local default databases +mp = ixmp.Platform(name='default', jvmargs=['-Xmx12G']) + +# model and scenario names of baseline scenario as basis for diagnostic analysis +model = "Material_Global" +scen = "NoPolicy" + +# load existing scenario +scenario = message_ix.Scenario(mp, model, scen) + +# read node, technology, commodity and level from existing scenario +node = scenario.set('node') +year = scenario.set('year') +technology = scenario.set('technology') +commodity = scenario.set('commodity') +level = scenario.set('level') +# read inv.cost data +inv_cost = scenario.par('inv_cost') + +# check whether needed units are registered on ixmp and add if not the case +unit = mp.units() +#if (!('t/kW' %in% unit$.)) mp$add_unit('t/kW', 'tonnes (of commodity) per kW of capacity') + +# source R code +r=ro.r +r.source(rcode_path+"ADVANCE_lca_coefficients_embedded.R") + +# call R function with type conversion +with localconverter(ro.default_converter + pandas2ri.converter): + p=r.read_material_intensities(data_path, node, year, technology, commodity, level, inv_cost) + From 443e9ce4b7333041f696cd2a3d6165963d532567 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Fri, 12 Mar 2021 11:54:13 +0100 Subject: [PATCH 249/774] Reporting code added (can be run from the command line..) --- message_ix_models/model/material/__init__.py | 18 +++++++++++++++++- 1 file changed, 17 insertions(+), 1 deletion(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 6d1ae768f5..cec418bbca 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -111,7 +111,7 @@ def solve(context, datafile): # Clone and set up scenario = build( context.get_scenario() - .clone(model="Material_Global", scenario=output_scenario_name) + .clone(model="MESSAGEix-Materials", scenario=output_scenario_name) ) # Set the latest version as default @@ -120,6 +120,22 @@ def solve(context, datafile): # Solve scenario.solve() +@cli.command("report") +@click.option('--old_reporting', default= True, + help='If True old reporting is merged with the new variables.') +@click.option('--scenario_name', default= "NoPolicy") +@click.option('--model_name', default= "MESSAGEix-Materials") +#@click.pass_obj +def run_reporting(old_reporting, scenario_name, model_name): + from message_data.reporting.materials.reporting import report + from message_ix import Scenario + from ixmp import Platform + + print(model_name) + mp = Platform() + scenario = Scenario(mp, model_name, scenario_name) + report(scenario, old_reporting) + import logging log = logging.getLogger(__name__) From 7e1fe1251979a7e9534fa3b43436af33c4d70924 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Fri, 12 Mar 2021 12:56:39 +0100 Subject: [PATCH 250/774] Missing emission factors lightoil, fueloil, methanol added --- .../data/material/generic_furnace_boiler_techno_economic.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx index 27b353de4c..25b2accb7f 100644 --- a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx +++ b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:97f0f5f2e131c5e2e15c0c15dc9221054d75dee416846d3c781e30dd754da21c -size 296 +oid sha256:1c112aff79b5c9bb6b91760be12b42693c60ab9f72a6993bb4b1cf02d6c384a2 +size 314 From b94213185f67cec160c8d156ff6a0b8e7ed412fe Mon Sep 17 00:00:00 2001 From: Jihoon Date: Fri, 12 Mar 2021 13:11:18 +0100 Subject: [PATCH 251/774] Add sectoral emission accounting for steel sector --- .../data/material/Global_steel_cement_MESSAGE.xlsx | 4 ++-- message_ix_models/data/material/set.yaml | 3 +++ 2 files changed, 5 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx index f2782e113a..ef404bfda9 100644 --- a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx +++ b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:2cfffe41f59c4b988206e73b265192bd37c8f0e29dc07b947c1c1893fb00b18f -size 46071 +oid sha256:0d13e4848c45ab0579939b7c2b30bc31ca164db88ea316113210e5ef1cb4e7a4 +size 46147 diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 1dba140044..d730a4ed14 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -428,6 +428,7 @@ steel: - waste_material - product - demand + - dummy_emission - end_of_life - dummy_end_of_life @@ -449,6 +450,8 @@ steel: - scrap_recovery_steel - DUMMY_ore_supply - DUMMY_limestone_supply + - DUMMY_coal_supply + - DUMMY_gas_supply - trade_steel - import_steel - export_steel From f78eecfc54687a7850cf7aeb1e4f509718e8664b Mon Sep 17 00:00:00 2001 From: Jihoon Date: Fri, 12 Mar 2021 17:18:47 +0100 Subject: [PATCH 252/774] Use own apply_spec --- message_ix_models/model/material/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index cec418bbca..a70a08dbe0 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -4,7 +4,7 @@ from message_ix_models import ScenarioInfo from message_ix_models.model.build import apply_spec -from message_data.model.build import apply_spec +from .build import apply_spec from message_data.tools import ScenarioInfo # from .data import add_data from .data_util import modify_demand_and_hist_activity From 53fb260f61d34965fc977940da7f726a495a5d82 Mon Sep 17 00:00:00 2001 From: Unknown Date: Tue, 16 Mar 2021 14:36:05 +0100 Subject: [PATCH 253/774] Adjusted code for compatibility with structure in python (not everything tested) --- .../ADVANCE_lca_coefficients_embedded.R | 117 ++++++++++-------- .../data_material_intensities_rpy2.py | 35 ++++-- 2 files changed, 91 insertions(+), 61 deletions(-) diff --git a/message_ix_models/model/material/material_intensity/ADVANCE_lca_coefficients_embedded.R b/message_ix_models/model/material/material_intensity/ADVANCE_lca_coefficients_embedded.R index 5a2c8202d0..05dfcc9835 100644 --- a/message_ix_models/model/material/material_intensity/ADVANCE_lca_coefficients_embedded.R +++ b/message_ix_models/model/material/material_intensity/ADVANCE_lca_coefficients_embedded.R @@ -13,53 +13,55 @@ library(imputeTS) # for time series interpolation of NA # functions ################################################################################ -read_material_intensities <- function(data_path, node, year, technology, commodity, level, inv_cost) { +read_material_intensities <- function(parameter, data_path, node, year, technology, commodity, level, inv_cost) { - ################################################################################ - # read data - ################################################################################ + if (parameter %in% c("input_cap_new", "input_cap_ret", "output_cap_ret")) { + + ################################################################################ + # read data + ################################################################################ - # read LCA data from ADVANCE LCA tool - col_types = c(rep("text", 9), rep("numeric", 3)) - data_lca = read_xlsx(path = paste(data_path, "/NTNU_LCA_coefficients.xlsx", sep = ''), sheet = "environmentalImpacts", col_types = col_types) + # read LCA data from ADVANCE LCA tool + col_types = c(rep("text", 9), rep("numeric", 3)) + data_lca = read_xlsx(path = paste(data_path, "/NTNU_LCA_coefficients.xlsx", sep = ''), sheet = "environmentalImpacts", col_types = col_types) - # read technology, region and commodity mappings - technology_mapping = read_xlsx(paste(data_path, "/MESSAGE_global_model_technologies.xlsx", sep = ''), sheet = 'technology') - region_mapping = read_xlsx(paste(data_path, "/LCA_region_mapping.xlsx", sep = ''), sheet = 'region') - commodity_mapping = read_xlsx(paste(data_path, "/LCA_commodity_mapping.xlsx", sep = ''), sheet = 'commodity') + # read technology, region and commodity mappings + technology_mapping = read_xlsx(paste(data_path, "/MESSAGE_global_model_technologies.xlsx", sep = ''), sheet = 'technology') + region_mapping = read_xlsx(paste(data_path, "/LCA_region_mapping.xlsx", sep = ''), sheet = 'region') + commodity_mapping = read_xlsx(paste(data_path, "/LCA_commodity_mapping.xlsx", sep = ''), sheet = 'commodity') - ################################################################################ - # process data - ################################################################################ + ################################################################################ + # process data + ################################################################################ - # filter relevant scenario, technology variant (residue for biomass, mix for others) and remove operation phase (and remove duplicates) - data_lca = data_lca %>% filter(scenario == 'Baseline' & `technology variant` %in% c('mix', 'residue') & phase != 'Operation') + # filter relevant scenario, technology variant (residue for biomass, mix for others) and remove operation phase (and remove duplicates) + data_lca = data_lca %>% filter(scenario == 'Baseline' & `technology variant` %in% c('mix', 'residue') & phase != 'Operation') - # add intermediate time steps and turn into long table format - data_lca = data_lca %>% mutate(`2015` = NA, `2020` = NA, `2025` = NA, `2035` = NA, `2040` = NA, `2045` = NA) %>% pivot_longer(cols = c("2010":"2045"), names_to = 'year', values_to = 'value') %>% mutate(value = as.numeric(value)) - #data_lca$year = factor(data_lca$year, levels = as.character(seq(2010, 2050, 5))) + # add intermediate time steps and turn into long table format + data_lca = data_lca %>% mutate(`2015` = NA, `2020` = NA, `2025` = NA, `2035` = NA, `2040` = NA, `2045` = NA) %>% pivot_longer(cols = c("2010":"2045"), names_to = 'year', values_to = 'value') %>% mutate(value = as.numeric(value)) + #data_lca$year = factor(data_lca$year, levels = as.character(seq(2010, 2050, 5))) - # apply technology, commodity/impact and region mappings to MESSAGEix - data_lca = data_lca %>% inner_join(region_mapping, by = c("region" = "THEMIS")) %>% inner_join(technology_mapping, by = c("technology" = "LCA mapping")) %>% - inner_join(commodity_mapping, by = c("impact" = "impact")) %>% - filter(!is.na(`MESSAGEix-GLOBIOM_1.1`)) %>% - select(node = `MESSAGEix-GLOBIOM_1.1`, technology = `Type of Technology`, phase, commodity, level, year, unit, value) #%>% unique() + # apply technology, commodity/impact and region mappings to MESSAGEix + data_lca = data_lca %>% inner_join(region_mapping, by = c("region" = "THEMIS")) %>% inner_join(technology_mapping, by = c("technology" = "LCA mapping")) %>% + inner_join(commodity_mapping, by = c("impact" = "impact")) %>% + filter(!is.na(`MESSAGEix-GLOBIOM_1.1`)) %>% + select(node = `MESSAGEix-GLOBIOM_1.1`, technology = `Type of Technology`, phase, commodity, level, year, unit, value) #%>% unique() - temp = c() - for (n in unique(data_lca$node)) for (t in unique(data_lca$technology)) for (c in unique(data_lca$commodity)) for (p in unique(data_lca$phase)){ - temp = rbind(temp, data_lca %>% filter(node == n & technology == t & commodity == c & phase == p) %>% na_interpolation()) - } - # return data frame - data_lca = temp + temp = c() + for (n in unique(data_lca$node)) for (t in unique(data_lca$technology)) for (c in unique(data_lca$commodity)) for (p in unique(data_lca$phase)){ + temp = rbind(temp, data_lca %>% filter(node == n & technology == t & commodity == c & phase == p) %>% na_interpolation()) + } + # return data frame + data_lca = temp - # extract node, technology, commodity, level, and year list from LCA data set - node_list = unique(data_lca$node) - year_list = unique(data_lca$year) - tec_list = unique(data_lca$technology) - com_list = unique(data_lca$commodity) - lev_list = unique(data_lca$level) - # add scrap as commodity level - lev_list = c(lev_list, 'end_of_life') + # extract node, technology, commodity, level, and year list from LCA data set + node_list = unique(data_lca$node) + year_list = unique(data_lca$year) + tec_list = unique(data_lca$technology) + com_list = unique(data_lca$commodity) + lev_list = unique(data_lca$level) + # add scrap as commodity level + lev_list = c(lev_list, 'end_of_life') # check whether set members exist in scenario and add in case not #for (n in 1:length(node.list)) if (!node.list[n] %in% node$.) ixScenario$add_set('node', node.list[n]) @@ -68,25 +70,32 @@ read_material_intensities <- function(data_path, node, year, technology, commodi #for (n in 1:length(com.list)) if (!com.list[n] %in% commodity$.) ixScenario$add_set('commodity', com.list[n]) #for (n in 1:length(lev.list)) if (!lev.list[n] %in% level$.) ixScenario$add_set('level', lev.list[n]) - ################################################################################ - # create data frames for material intensity input/output parameters - ################################################################################ + ################################################################################ + # create data frames for material intensity input/output parameters + ################################################################################ - # new data frames for parameters - input_cap_new <- input_cap_ret <- output_cap_ret <- data.frame() + # new data frames for parameters + input_cap_new <- input_cap_ret <- output_cap_ret <- data.frame() - for (n in node_list) for (t in tec_list) for (c in com_list) { - year_vtg_list = filter(inv_cost, node_loc == n & technology == t)$year_vtg %>% unique() # & year_vtg >= min(as.numeric(year.list)) - for (y in year_vtg_list) { - # for years after maximum year in data set use values for maximum year, similarly for years before minimum year in data set use values for minimum year - if (y > max(year_list)) yeff = max(year_list) else if (y < min(year_list)) yeff = min(year_list) else yeff = y - input_cap_new = rbind(input_cap_new, data.frame(node_loc = n, technology = t, year_vtg = as.character(y), node_origin = n, commodity = c, level = 'product', time_origin = "year", value = filter(data_lca, node == n & technology == t & phase == 'Construction' & commodity == c & year == yeff)$value * 1e-3, unit = 't/kW')) - input_cap_ret = rbind(input_cap_ret, data.frame(node_loc = n, technology = t, year_vtg = as.character(y), node_origin = n, commodity = c, level = 'product', time_origin = "year", value = filter(data_lca, node == n & technology == t & phase == 'End-of-life' & commodity == c & year == yeff)$value * 1e-3, unit = 't/kW')) - output_cap_ret = rbind(output_cap_ret, data.frame(node_loc = n, technology = t, year_vtg = as.character(y), node_dest = n, commodity = c, level = 'end_of_life', time_dest = "year", value = filter(data_lca, node == n & technology == t & phase == 'Construction' & commodity == c & year == yeff)$value * 1e-3, unit = 't/kW')) + for (n in node_list) for (t in tec_list) for (c in com_list) { + year_vtg_list = filter(inv_cost, node_loc == n & technology == t)$year_vtg %>% unique() # & year_vtg >= min(as.numeric(year.list)) + for (y in year_vtg_list) { + # for years after maximum year in data set use values for maximum year, similarly for years before minimum year in data set use values for minimum year + if (y > max(year_list)) yeff = max(year_list) else if (y < min(year_list)) yeff = min(year_list) else yeff = y + input_cap_new = rbind(input_cap_new, data.frame(node_loc = n, technology = t, year_vtg = as.character(y), node_origin = n, commodity = c, level = 'product', time_origin = "year", value = filter(data_lca, node == n & technology == t & phase == 'Construction' & commodity == c & year == yeff)$value * 1e-3, unit = 't/kW')) + input_cap_ret = rbind(input_cap_ret, data.frame(node_loc = n, technology = t, year_vtg = as.character(y), node_origin = n, commodity = c, level = 'product', time_origin = "year", value = filter(data_lca, node == n & technology == t & phase == 'End-of-life' & commodity == c & year == yeff)$value * 1e-3, unit = 't/kW')) + output_cap_ret = rbind(output_cap_ret, data.frame(node_loc = n, technology = t, year_vtg = as.character(y), node_dest = n, commodity = c, level = 'end_of_life', time_dest = "year", value = filter(data_lca, node == n & technology == t & phase == 'Construction' & commodity == c & year == yeff)$value * 1e-3, unit = 't/kW')) + } } } - # return parameter (other parameters currently not returned) - input_cap_new - + # return parameter + if (parameter == "input_cap_new") + input_cap_new + if (parameter == "input_cap_ret") + input_cap_ret + if (parameter == "output_cap_ret") + output_cap_ret + else + NA } diff --git a/message_ix_models/model/material/material_intensity/data_material_intensities_rpy2.py b/message_ix_models/model/material/material_intensity/data_material_intensities_rpy2.py index ebc2152dfb..1eba5814e1 100644 --- a/message_ix_models/model/material/material_intensity/data_material_intensities_rpy2.py +++ b/message_ix_models/model/material/material_intensity/data_material_intensities_rpy2.py @@ -21,6 +21,12 @@ # import MESSAGEix import ixmp import message_ix +from message_data.tools import ScenarioInfo + +#def gen_data_material_intensities(scenario, dry_run=False): +# """Generate data for endogenous materials demand of power sector. + +# """ # launch the IX modeling platform using the local default databases mp = ixmp.Platform(name='default', jvmargs=['-Xmx12G']) @@ -32,12 +38,15 @@ # load existing scenario scenario = message_ix.Scenario(mp, model, scen) +# information about scenario, e.g. node, year +s_info = ScenarioInfo(scenario) + # read node, technology, commodity and level from existing scenario -node = scenario.set('node') -year = scenario.set('year') -technology = scenario.set('technology') -commodity = scenario.set('commodity') -level = scenario.set('level') +node = s_info.N +year = s_info.set['year'] #s_info.Y is only for modeling years +technology = s_info.set['technology'] +commodity = s_info.set['commodity'] +level = s_info.set['level'] # read inv.cost data inv_cost = scenario.par('inv_cost') @@ -45,11 +54,23 @@ unit = mp.units() #if (!('t/kW' %in% unit$.)) mp$add_unit('t/kW', 'tonnes (of commodity) per kW of capacity') +# List of data frames, to be concatenated together at end +results = defaultdict(list) + +param_name = ["input_cap_new", "input_cap_ret", "output_cap_ret",] # source R code r=ro.r r.source(rcode_path+"ADVANCE_lca_coefficients_embedded.R") # call R function with type conversion -with localconverter(ro.default_converter + pandas2ri.converter): - p=r.read_material_intensities(data_path, node, year, technology, commodity, level, inv_cost) +for p in set(param_name): + with localconverter(ro.default_converter + pandas2ri.converter): + df = r.read_material_intensities(p, data_path, node, year, technology, commodity, level, inv_cost) + + +results[parname].append(df) + + # Concatenate to one data frame per parameter + results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} + return results From 1c29858ada0941c1f5e614f4cccbd41dc6de5483 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Tue, 16 Mar 2021 19:12:57 +0100 Subject: [PATCH 254/774] Changes to incorporate power sector intensity params and unit --- message_ix_models/data/material/set.yaml | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index d730a4ed14..53a69ce051 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -613,6 +613,11 @@ buildings: add: - buildings +power_sector: + unit: + add: + - t/kW + # NB this codelist added by #125 # iron-steel: From 3f084e2c1901643811369aacb57bf2e440c17482 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Wed, 17 Mar 2021 09:09:04 +0100 Subject: [PATCH 255/774] Add components for power sector (comment out others for test) --- message_ix_models/model/material/__init__.py | 25 +++++++++++++------- 1 file changed, 16 insertions(+), 9 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index a70a08dbe0..088cff56dc 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -25,7 +25,10 @@ def build(scenario): return scenario # add as needed/implemented -SPEC_LIST = ["generic", "common", "steel", "cement", "aluminum", "petro_chemicals", "buildings"] +SPEC_LIST = [ + # "generic", "common", "steel", "cement", "aluminum", + # "petro_chemicals", "buildings", + "power_sector"] def get_spec() -> Mapping[str, ScenarioInfo]: """Return the specification for materials accounting.""" @@ -86,8 +89,10 @@ def create_bare(context, regions, dry_run): @cli.command("solve") @click.option('--datafile', default='Global_steel_cement_MESSAGE.xlsx', metavar='INPUT', help='File name for external data input') +@click.option('--tag', default='', + help='Suffix to the scenario name') @click.pass_obj -def solve(context, datafile): +def solve(context, datafile, tag): """Build and solve model. Use the --url option to specify the base scenario. @@ -111,7 +116,7 @@ def solve(context, datafile): # Clone and set up scenario = build( context.get_scenario() - .clone(model="MESSAGEix-Materials", scenario=output_scenario_name) + .clone(model="MESSAGEix-Materials", scenario=output_scenario_name + tag) ) # Set the latest version as default @@ -145,15 +150,17 @@ def run_reporting(old_reporting, scenario_name, model_name): from .data_generic import gen_data_generic from .data_petro import gen_data_petro_chemicals from .data_buildings import gen_data_buildings +from .data_power_sector import gen_data_power_sector from message_data.tools import add_par_data DATA_FUNCTIONS = [ - gen_data_buildings, - gen_data_steel, - gen_data_cement, - gen_data_aluminum, - gen_data_petro_chemicals, - gen_data_generic + # gen_data_buildings, + # gen_data_steel, + # gen_data_cement, + # gen_data_aluminum, + # gen_data_petro_chemicals, + # gen_data_generic, + gen_data_power_sector ] # Try to handle multiple data input functions from different materials From f710f47bbeb2a59463e1a351c7ee164b630984ca Mon Sep 17 00:00:00 2001 From: Jihoon Date: Wed, 17 Mar 2021 09:09:48 +0100 Subject: [PATCH 256/774] Add a dummy data file for power sector (doing nothing) --- .../model/material/data_power_sector.py | 81 +++++++++++++++++++ 1 file changed, 81 insertions(+) create mode 100644 message_ix_models/model/material/data_power_sector.py diff --git a/message_ix_models/model/material/data_power_sector.py b/message_ix_models/model/material/data_power_sector.py new file mode 100644 index 0000000000..973792a475 --- /dev/null +++ b/message_ix_models/model/material/data_power_sector.py @@ -0,0 +1,81 @@ +from .data_util import read_sector_data, read_timeseries + +import numpy as np +from collections import defaultdict +import logging + +import pandas as pd + +from .util import read_config +from message_data.tools import ( + ScenarioInfo, + broadcast, + make_df, + make_io, + make_matched_dfs, + same_node, + copy_column, + add_par_data +) +from . import get_spec + + +def gen_data_power_sector(scenario, dry_run=False): + """Generate data for materials representation of power industry. + + """ + # Load configuration + context = read_config() + config = context["material"]["power_sector"] + + # Information about scenario, e.g. node, year + s_info = ScenarioInfo(scenario) + + modelyears = s_info.Y #s_info.Y is only for modeling years + nodes = s_info.N + tecs = s_info.set['technology'] + lvls = s_info.set['level'] + comms = s_info.set['commodity'] + + params = ['input_cap_new', 'input_cap_ret', 'output_cap_ret'] + + # List of data frames, to be concatenated together at end + results = defaultdict(list) + + # Create external demand param + for p in params: + df = import_intensity_r(p, s_info) + results[p].append(df) + + # Concatenate to one data frame per parameter + results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} + + print(results) + # return results + + +# Dummy definition - LINK THIS +def import_intensity_r(param_name, info): + print("Get {} from R...".format(param_name)) + + common = dict( + node=info.N[0], + node_loc=info.N[0], + node_origin=info.N[0], + node_dest=info.N[0], + technology="dummy", + year=info.Y, + year_vtg=info.Y, + year_act=info.Y, + mode="all", + # No subannual detail + time="year", + time_origin="year", + time_dest="year", + ) + + # Dummy DF + data = make_df(param_name, + **common) + + return data From aaf4209bc46bca7b07580256cba8479ed643c461 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 17 Mar 2021 10:09:07 +0100 Subject: [PATCH 257/774] Reporting code formatted and prices section updated --- .../data/material/petrochemicals_techno_economic.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx index c0c07d3468..0702fa7f28 100644 --- a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx +++ b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:81f3ee0c2998e6fc979c689026b37b41d1661291bac8e325a66cd53d79a46028 -size 619379 +oid sha256:c02c08dc0885b531fc625270f3d9aa62a291ccdcfe769fb3dc680823e2492cff +size 619446 From 553e7f2ebc4b4ccfb19bb87185774e00500baa1f Mon Sep 17 00:00:00 2001 From: Jihoon Date: Wed, 17 Mar 2021 14:57:18 +0100 Subject: [PATCH 258/774] Bring in the r interface (error: returned df is not a right format - boolvector) --- .../model/material/data_power_sector.py | 75 ++++++++++--------- 1 file changed, 38 insertions(+), 37 deletions(-) diff --git a/message_ix_models/model/material/data_power_sector.py b/message_ix_models/model/material/data_power_sector.py index 973792a475..9c136bf250 100644 --- a/message_ix_models/model/material/data_power_sector.py +++ b/message_ix_models/model/material/data_power_sector.py @@ -3,6 +3,18 @@ import numpy as np from collections import defaultdict import logging +from pathlib import Path + +# check Python and R environments (for debugging) +import rpy2.situation +for row in rpy2.situation.iter_info(): + print(row) + +# load rpy2 modules +import rpy2.robjects as ro +#from rpy2.robjects.packages import importr +from rpy2.robjects import pandas2ri +from rpy2.robjects.conversion import localconverter import pandas as pd @@ -28,54 +40,43 @@ def gen_data_power_sector(scenario, dry_run=False): context = read_config() config = context["material"]["power_sector"] + # paths to r code and lca data + rcode_path= Path(__file__).parents[0] / 'material_intensity' + data_path = context.get_path("material") + # Information about scenario, e.g. node, year s_info = ScenarioInfo(scenario) - modelyears = s_info.Y #s_info.Y is only for modeling years - nodes = s_info.N - tecs = s_info.set['technology'] - lvls = s_info.set['level'] - comms = s_info.set['commodity'] + # read node, technology, commodity and level from existing scenario + node = s_info.N + year = s_info.set['year'] #s_info.Y is only for modeling years + technology = s_info.set['technology'] + commodity = s_info.set['commodity'] + level = s_info.set['level'] - params = ['input_cap_new', 'input_cap_ret', 'output_cap_ret'] + # read inv.cost data + inv_cost = scenario.par('inv_cost') + + # source R code + r=ro.r + r.source(str(rcode_path / "ADVANCE_lca_coefficients_embedded.R")) + + param_name = ['input_cap_new']#, 'input_cap_ret', 'output_cap_ret'] # List of data frames, to be concatenated together at end results = defaultdict(list) - # Create external demand param - for p in params: - df = import_intensity_r(p, s_info) + # call R function with type conversion + for p in set(param_name): + with localconverter(ro.default_converter + pandas2ri.converter): + df = r.read_material_intensities(p, str(data_path), node, year, technology, commodity, level, inv_cost) + print('type df:', type(df)) + print(df.head()) + results[p].append(df) # Concatenate to one data frame per parameter results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} print(results) - # return results - - -# Dummy definition - LINK THIS -def import_intensity_r(param_name, info): - print("Get {} from R...".format(param_name)) - - common = dict( - node=info.N[0], - node_loc=info.N[0], - node_origin=info.N[0], - node_dest=info.N[0], - technology="dummy", - year=info.Y, - year_vtg=info.Y, - year_act=info.Y, - mode="all", - # No subannual detail - time="year", - time_origin="year", - time_dest="year", - ) - - # Dummy DF - data = make_df(param_name, - **common) - - return data + return results From cbb12a8aa16359335ca5fd70d295d0443dc7d4a9 Mon Sep 17 00:00:00 2001 From: Unknown Date: Thu, 18 Mar 2021 15:35:30 +0100 Subject: [PATCH 259/774] Correct if statement for return value of r function --- .../ADVANCE_lca_coefficients_embedded.R | 14 +++++--------- 1 file changed, 5 insertions(+), 9 deletions(-) diff --git a/message_ix_models/model/material/material_intensity/ADVANCE_lca_coefficients_embedded.R b/message_ix_models/model/material/material_intensity/ADVANCE_lca_coefficients_embedded.R index 05dfcc9835..0974a967ec 100644 --- a/message_ix_models/model/material/material_intensity/ADVANCE_lca_coefficients_embedded.R +++ b/message_ix_models/model/material/material_intensity/ADVANCE_lca_coefficients_embedded.R @@ -88,14 +88,10 @@ read_material_intensities <- function(parameter, data_path, node, year, technolo } } } - + # return parameter - if (parameter == "input_cap_new") - input_cap_new - if (parameter == "input_cap_ret") - input_cap_ret - if (parameter == "output_cap_ret") - output_cap_ret - else - NA + if (parameter == "input_cap_new") {input_cap_new + } else if (parameter == "input_cap_ret") {input_cap_ret + } else if (parameter == "output_cap_ret") {output_cap_ret + } else NA } From 2eb043942f7d21b7480df9169159ab477ef6cdf4 Mon Sep 17 00:00:00 2001 From: Unknown Date: Thu, 18 Mar 2021 18:01:02 +0100 Subject: [PATCH 260/774] Added basic documentation of r code integration and related data files --- message_ix_models/model/material/doc.rst | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) diff --git a/message_ix_models/model/material/doc.rst b/message_ix_models/model/material/doc.rst index 1e5599a144..1bc9294fd2 100644 --- a/message_ix_models/model/material/doc.rst +++ b/message_ix_models/model/material/doc.rst @@ -76,9 +76,29 @@ The code relies on the following input files, stored in :file:`data/material/`: :file:`trade.FAO.R14.csv` Historical N-fertilizer trade records among R14 regions, extracted from FAO database. +:file:`NTNU_LCA_coefficients.xlsx` + Material intensity (and other) and other coefficients for power plants based on lifecycle assessment (LCA) data from the THEMIS database, compiled in the `ADVANCE project` `_. + +:file:`MESSAGE_global_model_technologies.xlsx` + Technology list of global MESSAGEix-GLOBIOM model with mapping to LCA technology dataset. + +:file:`LCA_region_mapping.xlsx` + Region mapping of global 11-regional MESSAGEix-GLOBIOM model to regions of THEMIS LCA dataset. + +:file:`LCA_commodity_mapping.xlsx` + Commodity mapping (for materials) of global 11-regional MESSAGEix-GLOBIOM model to commodities of THEMIS LCA dataset. :file:`material/config.yaml` ---------------------------- .. literalinclude:: ../../../data/material/config.yaml :language: yaml + +R code and dependencies +----------------------- + +:file:`ADVANCE_lca_coefficients_embedded.R` +The code processing the material intensity coefficients of power plants is written in R and integrated into the Python workflow via the Python package `rpy2`. +R code is called from the Python data module `data_power_sector.py`. +Depending on the local R installation(s), the environment variables `R_HOME` and `R_USER` may need to be set for the installation to work (see `stackoverflow `_). +Additional dependencies include the R packages `dplyr`, `tidyr`, `readxl` and `imputeTS` that need to be installed in the R environment. From 1fe80ec2096ca6ccdd6bcd859936ba9e2b5a5c07 Mon Sep 17 00:00:00 2001 From: Unknown Date: Sun, 21 Mar 2021 22:05:30 +0100 Subject: [PATCH 261/774] Adding new capacity-related input/output parameters if they don't exist code not tested! --- .../model/material/data_power_sector.py | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/message_ix_models/model/material/data_power_sector.py b/message_ix_models/model/material/data_power_sector.py index 9c136bf250..27ad65a964 100644 --- a/message_ix_models/model/material/data_power_sector.py +++ b/message_ix_models/model/material/data_power_sector.py @@ -75,6 +75,20 @@ def gen_data_power_sector(scenario, dry_run=False): results[p].append(df) + # create new parameters input_cap_new, output_cap_new, input_cap_ret, output_cap_ret, input_cap and output_cap if they don't exist + if not scenario.has_par('input_cap_new'): + scenario.init_par('input_cap_new', idx_sets = ['node', 'technology', 'year', 'node', 'commodity', 'level', 'time'], idx_names = ['node_loc', 'technology', 'year_vtg', 'node_origin', 'commodity', 'level', 'time_origin']) + if not scenario.has_par('output_cap_new'): + scenario.init_par('output_cap_new', idx_sets = ['node', 'technology', 'year', 'node', 'commodity', 'level', 'time'], idx_names = ['node_loc', 'technology', 'year_vtg', 'node_dest', 'commodity', 'level', 'time_dest']) + if not scenario.has_par('input_cap_ret'): + scenario.init_par('input_cap_ret', idx_sets = ['node', 'technology', 'year', 'node', 'commodity', 'level', 'time'], idx_names = ['node_loc', 'technology', 'year_vtg', 'node_origin', 'commodity', 'level', 'time_origin']) + if not scenario.has_par('output_cap_ret'): + scenario.init_par('output_cap_ret', idx_sets = ['node', 'technology', 'year', 'node', 'commodity', 'level', 'time'], idx_names = ['node_loc', 'technology', 'year_vtg', 'node_dest', 'commodity', 'level', 'time_dest']) + if not scenario.has_par('input_cap'): + scenario.init_par('input_cap', idx_sets = ['node', 'technology', 'year', 'year', 'node', 'commodity', 'level', 'time'], idx_names = ['node_loc', 'technology', 'year_vtg', 'year_act', 'node_origin', 'commodity', 'level', 'time_origin']) + if not scenario.has_par('output_cap'): + scenario.init_par('output_cap', idx_sets = ['node', 'technology', 'year', 'year', 'node', 'commodity', 'level', 'time'], idx_names = ['node_loc', 'technology', 'year_vtg', 'year_act', 'node_dest', 'commodity', 'level', 'time_dest']) + # Concatenate to one data frame per parameter results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} From 3d26e7445e959b5cf013d967761578745fe43dcd Mon Sep 17 00:00:00 2001 From: Jihoon Date: Tue, 30 Mar 2021 10:03:06 +0200 Subject: [PATCH 262/774] Reduce the debug log print --- message_ix_models/model/material/data_petro.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/model/material/data_petro.py b/message_ix_models/model/material/data_petro.py index d54a428033..fb851fd649 100644 --- a/message_ix_models/model/material/data_petro.py +++ b/message_ix_models/model/material/data_petro.py @@ -194,7 +194,7 @@ def gen_data_petro_chemicals(scenario, dry_run=False): # Copy parameters to all regions, when node_loc is not GLB if (len(regions) == 1) and (rg != "R11_GLB"): - print("copying to all R11", rg, lev) + # print("copying to all R11", rg, lev) df['node_loc'] = None df = df.pipe(broadcast, node_loc=nodes)#.pipe(same_node) # Use same_node only for non-trade technologies @@ -229,7 +229,7 @@ def gen_data_petro_chemicals(scenario, dry_run=False): # Copy parameters to all regions if (len(regions) == 1) and (rg != "R11_GLB"): if len(set(df['node_loc'])) == 1 and list(set(df['node_loc']))[0]!='R11_GLB': - print("Copying to all R11") + # print("Copying to all R11") df['node_loc'] = None df = df.pipe(broadcast, node_loc=nodes) From 229f2bb3f35697b258d5f236560c7e90ed91b4ba Mon Sep 17 00:00:00 2001 From: Jihoon Date: Tue, 30 Mar 2021 10:16:46 +0200 Subject: [PATCH 263/774] Add all power sector parameters and also buildings component --- message_ix_models/model/material/__init__.py | 18 +++++++++--------- .../model/material/data_power_sector.py | 14 ++++++++------ 2 files changed, 17 insertions(+), 15 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 088cff56dc..e8393561c8 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -26,8 +26,8 @@ def build(scenario): # add as needed/implemented SPEC_LIST = [ - # "generic", "common", "steel", "cement", "aluminum", - # "petro_chemicals", "buildings", + "generic", "common", "steel", "cement", "aluminum", + "petro_chemicals", "buildings", "power_sector"] def get_spec() -> Mapping[str, ScenarioInfo]: @@ -116,7 +116,7 @@ def solve(context, datafile, tag): # Clone and set up scenario = build( context.get_scenario() - .clone(model="MESSAGEix-Materials", scenario=output_scenario_name + tag) + .clone(model="MESSAGEix-Materials", scenario=output_scenario_name + '_' + tag) ) # Set the latest version as default @@ -154,12 +154,12 @@ def run_reporting(old_reporting, scenario_name, model_name): from message_data.tools import add_par_data DATA_FUNCTIONS = [ - # gen_data_buildings, - # gen_data_steel, - # gen_data_cement, - # gen_data_aluminum, - # gen_data_petro_chemicals, - # gen_data_generic, + gen_data_buildings, + gen_data_steel, + gen_data_cement, + gen_data_aluminum, + gen_data_petro_chemicals, + gen_data_generic, gen_data_power_sector ] diff --git a/message_ix_models/model/material/data_power_sector.py b/message_ix_models/model/material/data_power_sector.py index 27ad65a964..ebcccd6d12 100644 --- a/message_ix_models/model/material/data_power_sector.py +++ b/message_ix_models/model/material/data_power_sector.py @@ -61,7 +61,7 @@ def gen_data_power_sector(scenario, dry_run=False): r=ro.r r.source(str(rcode_path / "ADVANCE_lca_coefficients_embedded.R")) - param_name = ['input_cap_new']#, 'input_cap_ret', 'output_cap_ret'] + param_name = ['input_cap_new', 'input_cap_ret', 'output_cap_ret'] # List of data frames, to be concatenated together at end results = defaultdict(list) @@ -75,18 +75,20 @@ def gen_data_power_sector(scenario, dry_run=False): results[p].append(df) - # create new parameters input_cap_new, output_cap_new, input_cap_ret, output_cap_ret, input_cap and output_cap if they don't exist - if not scenario.has_par('input_cap_new'): + # import pdb; pdb.set_trace() + + # create new parameters input_cap_new, output_cap_new, input_cap_ret, output_cap_ret, input_cap and output_cap if they don't exist + if not scenario.has_par('input_cap_new'): scenario.init_par('input_cap_new', idx_sets = ['node', 'technology', 'year', 'node', 'commodity', 'level', 'time'], idx_names = ['node_loc', 'technology', 'year_vtg', 'node_origin', 'commodity', 'level', 'time_origin']) if not scenario.has_par('output_cap_new'): scenario.init_par('output_cap_new', idx_sets = ['node', 'technology', 'year', 'node', 'commodity', 'level', 'time'], idx_names = ['node_loc', 'technology', 'year_vtg', 'node_dest', 'commodity', 'level', 'time_dest']) - if not scenario.has_par('input_cap_ret'): + if not scenario.has_par('input_cap_ret'): scenario.init_par('input_cap_ret', idx_sets = ['node', 'technology', 'year', 'node', 'commodity', 'level', 'time'], idx_names = ['node_loc', 'technology', 'year_vtg', 'node_origin', 'commodity', 'level', 'time_origin']) if not scenario.has_par('output_cap_ret'): scenario.init_par('output_cap_ret', idx_sets = ['node', 'technology', 'year', 'node', 'commodity', 'level', 'time'], idx_names = ['node_loc', 'technology', 'year_vtg', 'node_dest', 'commodity', 'level', 'time_dest']) - if not scenario.has_par('input_cap'): + if not scenario.has_par('input_cap'): scenario.init_par('input_cap', idx_sets = ['node', 'technology', 'year', 'year', 'node', 'commodity', 'level', 'time'], idx_names = ['node_loc', 'technology', 'year_vtg', 'year_act', 'node_origin', 'commodity', 'level', 'time_origin']) - if not scenario.has_par('output_cap'): + if not scenario.has_par('output_cap'): scenario.init_par('output_cap', idx_sets = ['node', 'technology', 'year', 'year', 'node', 'commodity', 'level', 'time'], idx_names = ['node_loc', 'technology', 'year_vtg', 'year_act', 'node_dest', 'commodity', 'level', 'time_dest']) # Concatenate to one data frame per parameter From 859f7857f39c46b6b3eadd377186595b81245639 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Fri, 2 Apr 2021 15:01:42 +0200 Subject: [PATCH 264/774] Update base year parameters to match the statistics --- message_ix_models/data/material/aluminum_techno_economic.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/aluminum_techno_economic.xlsx b/message_ix_models/data/material/aluminum_techno_economic.xlsx index 9e2d407ad1..494a302015 100644 --- a/message_ix_models/data/material/aluminum_techno_economic.xlsx +++ b/message_ix_models/data/material/aluminum_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:e6050ac3b69c1020ac7cf82c60e3f33af3cb3828bf93b125ff4e511735e0437d -size 93938 +oid sha256:d87f9f271fe2598cb233cd89edbb8d81e561825dd1d196c15de27b450740b851 +size 96054 From b9bf6e7560fe371310408eda9b6ee7129fbdcd8d Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Fri, 2 Apr 2021 15:02:26 +0200 Subject: [PATCH 265/774] Convert emission factors to kt/mt --- .../data/material/Global_steel_cement_MESSAGE.xlsx | 4 ++-- .../data/material/generic_furnace_boiler_techno_economic.xlsx | 4 ++-- .../data/material/petrochemicals_techno_economic.xlsx | 4 ++-- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx index ef404bfda9..1fc945d544 100644 --- a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx +++ b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:0d13e4848c45ab0579939b7c2b30bc31ca164db88ea316113210e5ef1cb4e7a4 -size 46147 +oid sha256:81391e36391728ce45c06d206485852066783bb1184824612d6ce55123ff4a1e +size 46575 diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx index 25b2accb7f..5637a0c94e 100644 --- a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx +++ b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:1c112aff79b5c9bb6b91760be12b42693c60ab9f72a6993bb4b1cf02d6c384a2 -size 314 +oid sha256:dce3d954381967c7f3695f21a5a083c19e099cfc939d2a5e44e0b5df01bbec33 +size 289 diff --git a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx index 0702fa7f28..ae8ed09988 100644 --- a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx +++ b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:c02c08dc0885b531fc625270f3d9aa62a291ccdcfe769fb3dc680823e2492cff -size 619446 +oid sha256:d3f45615e1798fda29be9cd3824418e3788f3bbcdfceb4bc430be33aa840d59d +size 619314 From 4840fa5c3002b420fd6c6d4b63e75bbbe1084be3 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Fri, 2 Apr 2021 15:05:58 +0200 Subject: [PATCH 266/774] Differentiate technology efficiencies over the years and remove 2020 minimum recycling limit --- .../model/material/data_aluminum.py | 54 ++++++++++++++++--- 1 file changed, 46 insertions(+), 8 deletions(-) diff --git a/message_ix_models/model/material/data_aluminum.py b/message_ix_models/model/material/data_aluminum.py index 6023d547c1..12486bd302 100644 --- a/message_ix_models/model/material/data_aluminum.py +++ b/message_ix_models/model/material/data_aluminum.py @@ -142,6 +142,33 @@ def gen_data_aluminum(scenario, dry_run=False): df = make_df(param_name, technology=t, commodity=com, \ level=lev, value=val[regions[regions==rg].index[0]], unit='t', \ node_loc=rg, node_dest="R11_GLB", **common) + + # Assign higher efficiency to younger plants + elif (((t == "soderberg_aluminum") or (t == "prebake_aluminum")) \ + & (com == "electr") & (param_name == "input")): + # All the vıntage years + year_vtg = sorted(set(yv_ya.year_vtg.values)) + # Collect the values for the combination of vintage and + # active years. + input_values_all = [] + for yr_v in year_vtg: + # The initial year efficiency value + input_values_temp = [val[regions[regions==rg].index[0]]] + # Reduction after the vintage year + year_vtg_filtered = list(filter(lambda op: op>=yr_v, year_vtg)) + # Filter the active model years + year_act = yv_ya.loc[yv_ya["year_vtg"]== yr_v,"year_act"].values + for i in range(len(year_vtg_filtered) - 1): + input_values_temp.append(input_values_temp[i] * 1.1) + + act_year_no = len(year_act) + input_values_temp = input_values_temp[-act_year_no:] + input_values_all = input_values_all + input_values_temp + + df = (make_df(param_name, technology=t, commodity=com, \ + level=lev, value= input_values_all, unit='t', \ + node_loc=rg, **common).pipe(same_node)) + else: df = (make_df(param_name, technology=t, commodity=com, \ level=lev, value=val[regions[regions==rg].index[0]], unit='t', \ @@ -230,11 +257,24 @@ def gen_data_aluminum(scenario, dry_run=False): params = set(data_aluminum_rel.loc[(data_aluminum_rel["relation"] == r),\ "parameter"].values) - common_rel = dict( - year_rel = modelyears, - year_act = modelyears, - mode = 'M1', - relation = r,) + # This relation should start from 2020... + if r == "minimum_recycling_aluminum": + modelyears_copy = modelyears[:] + modelyears_copy.remove(2020) + + common_rel = dict( + year_rel = modelyears_copy, + year_act = modelyears_copy, + mode = 'M1', + relation = r,) + else: + + # Use all the model years for other relations... + common_rel = dict( + year_rel = modelyears, + year_act = modelyears, + mode = 'M1', + relation = r,) for par_name in params: if par_name == "relation_activity": @@ -285,8 +325,6 @@ def gen_mock_demand_aluminum(scenario): 2000, 2005], axis = 1) gdp_growth['Region'] = 'R11_'+ gdp_growth['Region'] - print("This is gdp growth table") - print(gdp_growth) r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', \ 'R11_MEA', 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU'] @@ -306,7 +344,7 @@ def gen_mock_demand_aluminum(scenario): # Remaining 8.612 Mt shared between AFR and FSU # This is used as 2020 data. - d = [3,28.2, 6.25,5,2.5,2,14.1,3,5.75,5.75,6.25] + d = [3,28, 6,5,2.5,2,13.6,3,4.8,4.8,6] demand2020_al = pd.DataFrame({'Region':r, 'Val':d}).\ join(gdp_growth.set_index('Region'), on='Region').\ From 084508cef2d1ebf09048de93170826ac6f29540f Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Fri, 2 Apr 2021 15:07:45 +0200 Subject: [PATCH 267/774] Seperate dummy_limestone_supply for steel and cement --- message_ix_models/data/material/set.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 53a69ce051..caa313c550 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -449,7 +449,7 @@ steel: - manuf_steel - scrap_recovery_steel - DUMMY_ore_supply - - DUMMY_limestone_supply + - DUMMY_limestone_supply_steel - DUMMY_coal_supply - DUMMY_gas_supply - trade_steel @@ -496,7 +496,7 @@ cement: - clinker_wet_ccs_cement - grinding_ballmill_cement - grinding_vertmill_cement - - DUMMY_limestone_supply + - DUMMY_limestone_supply_cement remove: - cement_co2scr - cement_CO2 From 7545ca4491c9c5bc0bc22b2323f39a2c7a27438f Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Mon, 19 Apr 2021 13:58:35 +0200 Subject: [PATCH 268/774] Update petrocehmicals demand --- message_ix_models/model/material/data_petro.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/message_ix_models/model/material/data_petro.py b/message_ix_models/model/material/data_petro.py index fb851fd649..fdcf262ae7 100644 --- a/message_ix_models/model/material/data_petro.py +++ b/message_ix_models/model/material/data_petro.py @@ -63,9 +63,13 @@ def gen_mock_demand_petro(scenario): r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', \ 'R11_MEA', 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU'] - d_ethylene = [1,25,10,3,5,20,30,12,10,13,15] - d_propylene = [0.5,25, 10, 0.5, 3, 10, 15, 10,10,8, 10] - d_BTX = [0.5,25, 10, 0.5, 3, 12, 15, 10,10,8, 10] + + d_ethylene = [0.853064, 32.04327, 2.88788, 8.780442, 8.831229,21.58509, + 32.54942, 8.94036, 7.497867, 28.12818, 16.65209] + d_propylene = [0.426532, 32.04327, 2.88788, 1.463407, 5.298738, 10.79255, + 16.27471, 7.4503, 7.497867, 17.30965, 11.1014] + d_BTX = [0.426532, 32.04327, 2.88788, 1.463407, 5.298738, 12.95105, 16.27471, + 7.4503, 7.497867, 17.30965, 11.1014] list = [] for e in ["ethylene","propylene","BTX"]: From 0b252e33b98d8fdb2498bad8091233587ab7a8ec Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Mon, 26 Apr 2021 12:02:32 +0200 Subject: [PATCH 269/774] Add end of life technologies for cement --- .../data/material/Global_steel_cement_MESSAGE.xlsx | 4 ++-- message_ix_models/data/material/set.yaml | 4 ++++ 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx index 1fc945d544..97a37fa497 100644 --- a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx +++ b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:81391e36391728ce45c06d206485852066783bb1184824612d6ce55123ff4a1e -size 46575 +oid sha256:e402bae903442ae82042606042218e8eed46813512f09bcfba28c51c096c8e65 +size 47116 diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index caa313c550..4006c5490b 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -486,6 +486,8 @@ cement: - final_material - useful_material - demand + - dummy_end_of_life + - end_of_life technology: add: @@ -497,6 +499,8 @@ cement: - grinding_ballmill_cement - grinding_vertmill_cement - DUMMY_limestone_supply_cement + - total_EOL_cement + - scrap_recovery_cement remove: - cement_co2scr - cement_CO2 From d2b04414a81d537e06888cc3168b92039f31a04f Mon Sep 17 00:00:00 2001 From: Jihoon Date: Mon, 3 May 2021 16:03:44 +0200 Subject: [PATCH 270/774] Separate build and solve from the cli --- message_ix_models/model/material/__init__.py | 96 ++++++++++++++----- .../model/material/material_testscript.py | 49 +++++++++- 2 files changed, 118 insertions(+), 27 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index e8393561c8..90bed78b1a 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -6,17 +6,19 @@ from .build import apply_spec from message_data.tools import ScenarioInfo + # from .data import add_data from .data_util import modify_demand_and_hist_activity from .util import read_config + def build(scenario): """Set up materials accounting on `scenario`.""" # Get the specification spec = get_spec() # Apply to the base scenario - apply_spec(scenario, spec, add_data) # dry_run=True + apply_spec(scenario, spec, add_data) # dry_run=True # Adjust exogenous energy demand to incorporate the endogenized sectors # Adjust the historical activity of the usefyl level industry technologies @@ -24,11 +26,19 @@ def build(scenario): return scenario + # add as needed/implemented SPEC_LIST = [ - "generic", "common", "steel", "cement", "aluminum", - "petro_chemicals", "buildings", - "power_sector"] + "generic", + "common", + "steel", + "cement", + "aluminum", + "petro_chemicals", + "buildings", + "power_sector", +] + def get_spec() -> Mapping[str, ScenarioInfo]: """Return the specification for materials accounting.""" @@ -63,8 +73,7 @@ def cli(): @cli.command("create-bare") @click.option("--regions", type=click.Choice(["China", "R11", "R14"])) -@click.option('--dry_run', '-n', is_flag=True, - help='Only show what would be done.') +@click.option("--dry_run", "-n", is_flag=True, help="Only show what would be done.") @click.pass_obj def create_bare(context, regions, dry_run): """Create the RES from scratch.""" @@ -77,7 +86,7 @@ def create_bare(context, regions, dry_run): context.period_start = 1980 # Otherwise it can not find the path to read the yaml files.. - context.metadata_path = context.metadata_path /'data' + context.metadata_path = context.metadata_path / "data" scen = create_res(context) build(scen) @@ -86,14 +95,18 @@ def create_bare(context, regions, dry_run): if not dry_run: scen.solve() -@cli.command("solve") -@click.option('--datafile', default='Global_steel_cement_MESSAGE.xlsx', - metavar='INPUT', help='File name for external data input') -@click.option('--tag', default='', - help='Suffix to the scenario name') + +@cli.command("build") +@click.option( + "--datafile", + default="Global_steel_cement_MESSAGE.xlsx", + metavar="INPUT", + help="File name for external data input", +) +@click.option("--tag", default="", help="Suffix to the scenario name") @click.pass_obj def solve(context, datafile, tag): - """Build and solve model. + """Build model. Use the --url option to specify the base scenario. """ @@ -107,7 +120,7 @@ def solve(context, datafile, tag): # "DIAG-C30-const_E414": "baseline_test", }.get(context.scenario_info["scenario"]) - context.metadata_path = context.metadata_path /'data' + context.metadata_path = context.metadata_path / "data" context.datafile = datafile if context.scenario_info["model"] != "CD_Links_SSP2": @@ -115,22 +128,55 @@ def solve(context, datafile, tag): # Clone and set up scenario = build( - context.get_scenario() - .clone(model="MESSAGEix-Materials", scenario=output_scenario_name + '_' + tag) + context.get_scenario().clone( + model="MESSAGEix-Materials", scenario=output_scenario_name + "_" + tag + ) ) # Set the latest version as default scenario.set_as_default() + # Solve + # scenario.solve() + + +@cli.command("solve") +@click.option("--scenario_name", default="NoPolicy") +@click.option("--model_name", default="MESSAGEix-Materials") +@click.option( + "--datafile", + default="Global_steel_cement_MESSAGE.xlsx", + metavar="INPUT", + help="File name for external data input", +) +@click.pass_obj +# @click.pass_obj +def solve_scen(context, datafile, model_name, scenario_name): + """Build model. + + Use the --url option to specify the base scenario. + """ + # Clone and set up + from message_ix import Scenario + + scenario = Scenario(context.get_platform(), model_name, scenario_name) + + if scenario.has_solution(): + scenario.remove_solution() + # Solve scenario.solve() + @cli.command("report") -@click.option('--old_reporting', default= True, - help='If True old reporting is merged with the new variables.') -@click.option('--scenario_name', default= "NoPolicy") -@click.option('--model_name', default= "MESSAGEix-Materials") -#@click.pass_obj +@click.option( + "--old_reporting", + default=True, + help="If True old reporting is merged with the new variables.", +) +@click.option("--scenario_name", default="NoPolicy") +@click.option("--model_name", default="MESSAGEix-Materials") +# @click.pass_obj def run_reporting(old_reporting, scenario_name, model_name): from message_data.reporting.materials.reporting import report from message_ix import Scenario @@ -141,7 +187,9 @@ def run_reporting(old_reporting, scenario_name, model_name): scenario = Scenario(mp, model_name, scenario_name) report(scenario, old_reporting) + import logging + log = logging.getLogger(__name__) from .data_cement import gen_data_cement @@ -160,7 +208,7 @@ def run_reporting(old_reporting, scenario_name, model_name): gen_data_aluminum, gen_data_petro_chemicals, gen_data_generic, - gen_data_power_sector + gen_data_power_sector, ] # Try to handle multiple data input functions from different materials @@ -177,7 +225,7 @@ def add_data(scenario, dry_run=False): for func in DATA_FUNCTIONS: # Generate or load the data; add to the Scenario - log.info(f'from {func.__name__}()') + log.info(f"from {func.__name__}()") add_par_data(scenario, func(scenario), dry_run=dry_run) - log.info('done') + log.info("done") diff --git a/message_ix_models/model/material/material_testscript.py b/message_ix_models/model/material/material_testscript.py index da0d7f634b..036c55d5ff 100644 --- a/message_ix_models/model/material/material_testscript.py +++ b/message_ix_models/model/material/material_testscript.py @@ -112,9 +112,9 @@ mp = ixmp.Platform(name="ixmp_dev") sl = mp.scenario_list() -sl = sl.loc[sl.model == "Material_Global"] # "ENGAGE_SSP2_v4.1.4"] +sl = sl.loc[sl.model == "MESSAGEix-Materials"] # "ENGAGE_SSP2_v4.1.4"] -sample = mix.Scenario(mp, model="Material_Global", scenario="NoPolicy") +sample = mix.Scenario(mp, model="MESSAGEix-Materials", scenario="NoPolicy") cem_demand = sample.par('demand', {"commodity":"cement", "year":2010}) # Test read_data_steel <- will be in create_res if working fine @@ -164,4 +164,47 @@ mp_samp.close_db() scen.to_excel("test.xlsx") -scen_rp = Scenario(scen) \ No newline at end of file +scen_rp = Scenario(scen) + +#%% + +s_info = ScenarioInfo(sample) +years = s_info.Y + +import os +import pyam +import pandas as pd +import matplotlib.pyplot as plt +msg_data_path = os.environ["MESSAGE_DATA_PATH"] +directory = os.path.join(msg_data_path, "message_data", "reporting", "materials") +path = os.path.join(directory, "message_ix_reporting.xlsx") +report = pd.read_excel(path) +report.Unit.fillna("", inplace=True) +df = pyam.IamDataFrame(report) + +df_ref_fueloil = df.copy() +r = 'R11_CPA|R11_CPA' +df_ref_fueloil.filter(region=r, year=years, inplace=True) + +df_ref_fueloil.filter( + variable=["out|secondary|fueloil|agg_ref|*"], inplace=True +) + + +fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 8)) + +df_ref_fueloil.stack_plot(ax=ax1) +ax1.legend( + [ + "Atm gas oil", + "Atm resiude", + "Heavy fuel oil", + "Petroleum coke", + "Vacuum residue", + "import", + ], + bbox_to_anchor=(0.3, 1), +) +ax1.set_title("Fuel oil mix_" + r) +ax1.set_xlabel("Year") +ax1.set_ylabel("GWa") \ No newline at end of file From 15caf874305c085ac59733b4b2e1322db03f1535 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Mon, 17 May 2021 10:30:31 +0200 Subject: [PATCH 271/774] Replicate image material demand model for cement and steel --- .../data/material/CEMENT.BvR2010.xlsx | 4 +- .../data/material/STEEL_database_2012.xlsx | 3 + .../model/material/material_demand/init.R | 68 +++++++++++++++++++ 3 files changed, 73 insertions(+), 2 deletions(-) create mode 100644 message_ix_models/data/material/STEEL_database_2012.xlsx create mode 100644 message_ix_models/model/material/material_demand/init.R diff --git a/message_ix_models/data/material/CEMENT.BvR2010.xlsx b/message_ix_models/data/material/CEMENT.BvR2010.xlsx index 3de75f8692..d73aa3dd3f 100644 --- a/message_ix_models/data/material/CEMENT.BvR2010.xlsx +++ b/message_ix_models/data/material/CEMENT.BvR2010.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:f29cc85cf609901ea736c293d3448da88a6c83b2601c9c4aed6b192695050950 -size 1586050 +oid sha256:56e7a31bab30f55d47bf7ebdafe7cd5059d81c1744b33d92f9a4965684dca55e +size 1586591 diff --git a/message_ix_models/data/material/STEEL_database_2012.xlsx b/message_ix_models/data/material/STEEL_database_2012.xlsx new file mode 100644 index 0000000000..1a580e5f38 --- /dev/null +++ b/message_ix_models/data/material/STEEL_database_2012.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0e22badd6aa7f4e686f704ac1b449a306dab2f06a20e71069e32a99f5d1a3844 +size 504346 diff --git a/message_ix_models/model/material/material_demand/init.R b/message_ix_models/model/material/material_demand/init.R new file mode 100644 index 0000000000..ef61c0a096 --- /dev/null +++ b/message_ix_models/model/material/material_demand/init.R @@ -0,0 +1,68 @@ +library(tidyverse) +library(readxl) + +# Data file names and path +datapath = '../../../../data/material/' + +file_cement = "CEMENT.BvR2010.xlsx" +file_steel = "STEEL_database_2012.xlsx" + +#### Import raw data (from Bas) - Cement & Steel #### +# Apparent consumption +# GDP per dap +# Population +df_raw_steel_consumption = read_excel(paste0(datapath, file_steel), + sheet="Consumption regions", n_max=27) %>% # kt + select(-2) %>% + pivot_longer(cols="1970":"2012", + values_to='consumption', names_to='year') +df_raw_cement_consumption = read_excel(paste0(datapath, file_cement), + sheet="Regions", skip=122, n_max=27) %>% # kt + pivot_longer(cols="1970":"2010", values_to='consumption', names_to='year') +df_population = read_excel(paste0(datapath, file_cement), + sheet="Timer_POP", skip=3, n_max=27) %>% # million + pivot_longer(cols="1970":"2100", values_to='pop', names_to='year') +df_gdp = read_excel(paste0(datapath, file_cement), + sheet="Timer_GDPCAP", skip=3, n_max=27) %>% # million + pivot_longer(cols="1970":"2100", values_to='gdp.pcap', names_to='year') + +#### Organize data #### +names(df_raw_steel_consumption)[1] = names(df_raw_cement_consumption)[1] = + names(df_population)[1] = names(df_gdp)[1] = "reg_no" +names(df_raw_steel_consumption)[2] = names(df_raw_cement_consumption)[2] = + names(df_population)[2] = names(df_gdp)[2] = "region" +df_steel_consumption = df_raw_steel_consumption %>% + left_join(df_population %>% select(-region)) %>% + left_join(df_gdp %>% select(-region)) %>% + mutate(cons.pcap = consumption/pop, del.t = as.numeric(year)-2010) %>% #kg/cap + drop_na() %>% + filter(cons.pcap > 0) +df_cement_consumption = df_raw_cement_consumption %>% + left_join(df_population %>% select(-region)) %>% + left_join(df_gdp %>% select(-region)) %>% + mutate(cons.pcap = consumption/pop/1e6, del.t= as.numeric(year) - 2010) %>% #kg/cap + drop_na() %>% + filter(cons.pcap > 0) + +#### Fit models #### +# . Linear ==== +lni.c = lm(log(cons.pcap) ~ I(1/gdp.pcap), data=df_cement_consumption) +lni.s = lm(log(cons.pcap) ~ I(1/gdp.pcap), data=df_steel_consumption) +summary(lni.c) +summary(lni.s) + +lnit.c = lm(log(cons.pcap) ~ I(1/gdp.pcap)+del.t, data=df_cement_consumption) +lnit.s = lm(log(cons.pcap) ~ I(1/gdp.pcap)+del.t, data=df_steel_consumption) +summary(lnit.c) +summary(lnit.s) + +# . Non-linear ==== +nlni.c = nls(cons.pcap ~ a * exp(b/gdp.pcap), data=df_cement_consumption, start=list(a=500, b=-3000)) +nlni.s = nls(cons.pcap ~ a * exp(b/gdp.pcap), data=df_steel_consumption, start=list(a=600, b=-10000)) +summary(nlni.c) +summary(nlni.s) + +nlnit.c = nls(cons.pcap ~ a * exp(b/gdp.pcap) * (1-m)^del.t, data=df_cement_consumption, start=list(a=500, b=-3000, m=0)) +nlnit.s = nls(cons.pcap ~ a * exp(b/gdp.pcap) * (1-m)^del.t, data=df_steel_consumption, start=list(a=600, b=-10000, m=0)) +summary(nlnit.c) +summary(nlnit.s) From a5cb21a1f8ec140a6771860664a05ceacbe44bb3 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 20 May 2021 10:35:58 +0200 Subject: [PATCH 272/774] Aluminum adjusted for r12 --- .../material/aluminum_techno_economic.xlsx | 4 +- .../data/material/demand_aluminum.xlsx | 3 + .../iamc_db ENGAGE baseline GDP PPP.xlsx | 4 +- .../model/material/data_aluminum.py | 70 ++++++++++++------- 4 files changed, 53 insertions(+), 28 deletions(-) create mode 100644 message_ix_models/data/material/demand_aluminum.xlsx diff --git a/message_ix_models/data/material/aluminum_techno_economic.xlsx b/message_ix_models/data/material/aluminum_techno_economic.xlsx index 494a302015..dd50aa68b2 100644 --- a/message_ix_models/data/material/aluminum_techno_economic.xlsx +++ b/message_ix_models/data/material/aluminum_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:d87f9f271fe2598cb233cd89edbb8d81e561825dd1d196c15de27b450740b851 -size 96054 +oid sha256:ad35e8909f8620dedce8862468bb20e99955e30576ae12c20f8e8ad3c573f15d +size 124429 diff --git a/message_ix_models/data/material/demand_aluminum.xlsx b/message_ix_models/data/material/demand_aluminum.xlsx new file mode 100644 index 0000000000..2eaeda3721 --- /dev/null +++ b/message_ix_models/data/material/demand_aluminum.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8873192d337aa884f7a6aab437a8bbd8b0a3e438ce6e201d56487235f51abb6b +size 155698 diff --git a/message_ix_models/data/material/iamc_db ENGAGE baseline GDP PPP.xlsx b/message_ix_models/data/material/iamc_db ENGAGE baseline GDP PPP.xlsx index 99f6179200..c692d83b7d 100644 --- a/message_ix_models/data/material/iamc_db ENGAGE baseline GDP PPP.xlsx +++ b/message_ix_models/data/material/iamc_db ENGAGE baseline GDP PPP.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:2ad3f395efa20edf0ee9ab74cb2a4f510a65ab29f5f4bf6f183d46c2ed72a7f8 -size 20762 +oid sha256:d58e7fd25cca4af945b8b57fd1b16d19e7b722c3a86b5c86e344310085540956 +size 31046 diff --git a/message_ix_models/model/material/data_aluminum.py b/message_ix_models/model/material/data_aluminum.py index 12486bd302..ee2b8d5548 100644 --- a/message_ix_models/model/material/data_aluminum.py +++ b/message_ix_models/model/material/data_aluminum.py @@ -28,32 +28,43 @@ from .data_buildings import get_scen_mat_demand from . import get_spec -def read_data_aluminum(): +def read_data_aluminum(scenario): """Read and clean data from :file:`aluminum_techno_economic.xlsx`.""" # Ensure config is loaded, get the context context = read_config() + s_info = ScenarioInfo(scenario) # Shorter access to sets configuration # sets = context["material"]["generic"] fname = "aluminum_techno_economic.xlsx" + + if "R11_CHN" in s_info.N: + sheet_n = "data_R12" + sheet_n_relations = "relations_R12" + else: + sheet_n = "data_R11" + sheet_n_relations = "relations_R11" + # Read the file - data_alu = pd.read_excel( - context.get_path("material", fname), - sheet_name="data", - ) + data_alu = pd.read_excel(context.get_path("material", fname), + sheet_name=sheet_n,) # Drop columns that don't contain useful information data_alu= data_alu.drop(["Source", 'Description'], axis = 1) - data_alu_rel = read_rel(fname) + data_alu_rel = pd.read_excel(context.get_path("material", fname), + sheet_name=sheet_n_relations,) + + data_aluminum_ts = read_timeseries("aluminum_techno_economic.xlsx") + # Unit conversion # At the moment this is done in the excel file, can be also done here # To make sure we use the same units - return data_alu, data_alu_rel + return data_alu, data_alu_rel, data_aluminum_ts def print_full(x): pd.set_option('display.max_rows', len(x)) @@ -68,8 +79,7 @@ def gen_data_aluminum(scenario, dry_run=False): s_info = ScenarioInfo(scenario) # Techno-economic assumptions - data_aluminum, data_aluminum_rel= read_data_aluminum() - data_aluminum_ts = read_timeseries("aluminum_techno_economic.xlsx") + data_aluminum, data_aluminum_rel, data_aluminum_ts = read_data_aluminum() # List of data frames, to be concatenated together at end results = defaultdict(list) @@ -314,20 +324,7 @@ def gen_mock_demand_aluminum(scenario): s_info = ScenarioInfo(scenario) modelyears = s_info.Y #s_info.Y is only for modeling years fmy = s_info.y0 - - # SSP2 R11 baseline GDP projection - gdp_growth = pd.read_excel( - context.get_path("material", "iamc_db ENGAGE baseline GDP PPP.xlsx"), - sheet_name="data",) - - gdp_growth = gdp_growth.loc[(gdp_growth['Scenario']=='baseline') & \ - (gdp_growth['Region']!='World')].drop(['Model', 'Variable', 'Unit', 'Notes',\ - 2000, 2005], axis = 1) - - gdp_growth['Region'] = 'R11_'+ gdp_growth['Region'] - - r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', \ - 'R11_MEA', 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU'] + nodes = s_info.N # Demand at product level (IAI Global Aluminum Cycle 2018) # Globally: 82.4 Mt @@ -344,7 +341,32 @@ def gen_mock_demand_aluminum(scenario): # Remaining 8.612 Mt shared between AFR and FSU # This is used as 2020 data. - d = [3,28, 6,5,2.5,2,13.6,3,4.8,4.8,6] + if "R11_CHN" in s_info.N: + sheet_n = "data_R11" + + r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', \ + 'R11_MEA', 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU'] + + d = [3,28, 6,5,2.5,2,13.6,3,4.8,4.8,6] + + else: + sheet_n = "data_12" + + r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', 'R11_MEA',\ + 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU',"R11_CHN"] + + d = [3,26, 6,5,2.5,2,13.6,3,4.8,4.8,6,2] + + # SSP2 R11 baseline GDP projection + gdp_growth = pd.read_excel( + context.get_path("material", "iamc_db ENGAGE baseline GDP PPP.xlsx"), + sheet_name=sheet_n,) + + gdp_growth = gdp_growth.loc[(gdp_growth['Scenario']=='baseline') & \ + (gdp_growth['Region']!='World')].drop(['Model', 'Variable', 'Unit', 'Notes',\ + 2000, 2005], axis = 1) + + gdp_growth['Region'] = 'R11_'+ gdp_growth['Region'] demand2020_al = pd.DataFrame({'Region':r, 'Val':d}).\ join(gdp_growth.set_index('Region'), on='Region').\ From 6ff44bb6ddd19123abce1018e238d9f062d54e23 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 20 May 2021 14:57:21 +0200 Subject: [PATCH 273/774] Adjust petrochemicals for r12 --- .../petrochemicals_techno_economic.xlsx | 4 +- .../model/material/data_petro.py | 59 ++++++++++++++----- 2 files changed, 46 insertions(+), 17 deletions(-) diff --git a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx index ae8ed09988..d208bd9935 100644 --- a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx +++ b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:d3f45615e1798fda29be9cd3824418e3788f3bbcdfceb4bc430be33aa840d59d -size 619314 +oid sha256:8705afb5edc4f58a5ce2cf3e36245d98562b87db1c83d6059a87f169c480ede3 +size 664303 diff --git a/message_ix_models/model/material/data_petro.py b/message_ix_models/model/material/data_petro.py index fdcf262ae7..b26cf173ce 100644 --- a/message_ix_models/model/material/data_petro.py +++ b/message_ix_models/model/material/data_petro.py @@ -23,11 +23,17 @@ def read_data_petrochemicals(): # Ensure config is loaded, get the context context = read_config() + s_info = ScenarioInfo(scenario) + fname = "petrochemicals_techno_economic.xlsx" + + if "R11_CHN" in s_info.N: + sheet_n = "data_R12" + else: + sheet_n = "data_R11" # Read the file data_petro = pd.read_excel( - context.get_path("material", "petrochemicals_techno_economic.xlsx"), - sheet_name="data") + context.get_path("material", fname),sheet_name=sheet_n) # Clean the data data_petro= data_petro.drop(['Source', 'Description'], axis = 1) @@ -41,6 +47,41 @@ def gen_mock_demand_petro(scenario): s_info = ScenarioInfo(scenario) modelyears = s_info.Y #s_info.Y is only for modeling years fmy = s_info.y0 + nodes = s_info.N + + # 2018 production + # Use as 2020 + # The Future of Petrochemicals Methodological Annex + # Projections here do not show too much growth until 2050 for some regions. + # For division of some regions assumptions made: + # PAO, PAS, SAS, EEU,WEU + + if "R11_CHN" in s_info.N: + sheet_n = "data_R11" + + r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', \ + 'R11_MEA', 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU'] + + d_ethylene = [0.853064, 32.04327, 2.88788, 8.780442, 8.831229,21.58509, + 32.54942, 8.94036, 7.497867, 28.12818, 16.65209] + d_propylene = [0.426532, 32.04327, 2.88788, 1.463407, 5.298738, 10.79255, + 16.27471, 7.4503, 7.497867, 17.30965, 11.1014] + d_BTX = [0.426532, 32.04327, 2.88788, 1.463407, 5.298738, 12.95105, 16.27471, + 7.4503, 7.497867, 17.30965, 11.1014] + + else: + sheet_n = "data_12" + + r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', 'R11_MEA',\ + 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU',"R11_CHN"] + + d_ethylene = [0.853064, 28.84327, 2.88788, 8.780442, 8.831229,21.58509, + 32.54942, 8.94036, 28.84327, 28.12818, 16.65209,3.2] + d_propylene = [0.426532, 28.84327, 2.88788, 1.463407, 5.298738, 10.79255, + 16.27471, 7.4503, 7.497867, 17.30965, 11.1014,3.2] + d_BTX = [0.426532, 28.84327, 2.88788, 1.463407, 5.298738, 12.95105, 16.27471, + 7.4503, 7.497867, 17.30965, 11.1014, 3.2] + # SSP2 R11 baseline GDP projection gdp_growth = pd.read_excel( @@ -54,22 +95,10 @@ def gen_mock_demand_petro(scenario): gdp_growth['Region'] = 'R11_'+ gdp_growth['Region'] - # 2018 production - # Use as 2020 - # The Future of Petrochemicals Methodological Annex - # Projections here do not show too much growth until 2050 for some regions. - # For division of some regions assumptions made: - # PAO, PAS, SAS, EEU,WEU - r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', \ 'R11_MEA', 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU'] - d_ethylene = [0.853064, 32.04327, 2.88788, 8.780442, 8.831229,21.58509, - 32.54942, 8.94036, 7.497867, 28.12818, 16.65209] - d_propylene = [0.426532, 32.04327, 2.88788, 1.463407, 5.298738, 10.79255, - 16.27471, 7.4503, 7.497867, 17.30965, 11.1014] - d_BTX = [0.426532, 32.04327, 2.88788, 1.463407, 5.298738, 12.95105, 16.27471, - 7.4503, 7.497867, 17.30965, 11.1014] + list = [] for e in ["ethylene","propylene","BTX"]: From 76af6e6f495f88e767eb3a4358fbe0a04c430e3a Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 20 May 2021 16:38:21 +0200 Subject: [PATCH 274/774] Adjust steel and cement for r12 --- .../material/Global_steel_cement_MESSAGE.xlsx | 4 +- .../model/material/data_cement.py | 41 ++++++++++++++----- .../model/material/data_steel.py | 29 +++++++++---- 3 files changed, 52 insertions(+), 22 deletions(-) diff --git a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx index 97a37fa497..8f955969b6 100644 --- a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx +++ b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:e402bae903442ae82042606042218e8eed46813512f09bcfba28c51c096c8e65 -size 47116 +oid sha256:292467355f3eff2ab93b1bcef28e84756564d3daad4e5d6595786345d42f0041 +size 81056 diff --git a/message_ix_models/model/material/data_cement.py b/message_ix_models/model/material/data_cement.py index a19e27f5a4..6b93d96f38 100644 --- a/message_ix_models/model/material/data_cement.py +++ b/message_ix_models/model/material/data_cement.py @@ -34,6 +34,35 @@ def gen_mock_demand_cement(scenario): s_info = ScenarioInfo(scenario) modelyears = s_info.Y #s_info.Y is only for modeling years fmy = s_info.y0 + nodes = s_info.N + + # 2019 production by country (USGS) + # p43 of https://pubs.usgs.gov/periodicals/mcs2020/mcs2020-cement.pdf + + if "R11_CHN" in s_info.N: + sheet_n = "data_R11" + + r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', \ + 'R11_MEA', 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU'] + + demand2020_top = [76, 2295, 0, 57, 55, 60, 89, 54, 129, 320, 51] + # the rest (~900 Mt) allocated by % values in http://www.cembureau.eu/media/clkdda45/activity-report-2019.pdf + demand2020_rest = [4100*0.051-76, (4100*0.14-155)*0.2, 4100*0.064*0.5, 4100*0.026-57, 4100*0.046*0.5-55, \ + (4100*0.14-155)*0.2, 4100*0.046*0.5, 12, 4100*0.003, (4100*0.14-155)*0.6, 4100*0.064*0.5 - 51] + d = [a + b for a, b in zip(demand2020_top, demand2020_rest)] + + else: + sheet_n = "data_R12" + + r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', 'R11_MEA',\ + 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU',"R11_CHN"] + + demand2020_top = [76, 229.5, 0, 57, 55, 60, 89, 54, 129, 320, 51,2065.5] + # the rest (~900 Mt) allocated by % values in http://www.cembureau.eu/media/clkdda45/activity-report-2019.pdf + demand2020_rest = [4100*0.051-76, (4100*0.14-155)*0.2*0.1, 4100*0.064*0.5, 4100*0.026-57, 4100*0.046*0.5-55, \ + (4100*0.14-155)*0.2, 4100*0.046*0.5, 12, 4100*0.003, (4100*0.14-155)*0.6, 4100*0.064*0.5 - 51, + (4100*0.14-155)*0.2*0.9] + d = [a + b for a, b in zip(demand2020_top, demand2020_rest)] # SSP2 R11 baseline GDP projection gdp_growth = pd.read_excel( @@ -79,18 +108,8 @@ def gen_mock_demand_cement(scenario): # demand2010_cement = demand2010_cement.groupby(by=['node']).sum().reset_index() # demand2010_cement['value'] = demand2010_cement['value'] / 1e9 # kg to Mt - # 2019 production by country (USGS) - # p43 of https://pubs.usgs.gov/periodicals/mcs2020/mcs2020-cement.pdf - r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', \ - 'R11_MEA', 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU'] - # Directly assigned countries from the table on p43 - demand2020_top = [76, 2295, 0, 57, 55, \ - 60, 89, 54, 129, 320, 51] - # the rest (~900 Mt) allocated by % values in http://www.cembureau.eu/media/clkdda45/activity-report-2019.pdf - demand2020_rest = [4100*0.051-76, (4100*0.14-155)*0.2, 4100*0.064*0.5, 4100*0.026-57, 4100*0.046*0.5-55, \ - (4100*0.14-155)*0.2, 4100*0.046*0.5, 12, 4100*0.003, (4100*0.14-155)*0.6, 4100*0.064*0.5 - 51] - d = [a + b for a, b in zip(demand2020_top, demand2020_rest)] + demand2020_cement = pd.DataFrame({'Region':r, 'value':d}).\ join(gdp_growth.set_index('Region'), on='Region').rename(columns={'Region':'node'}) diff --git a/message_ix_models/model/material/data_steel.py b/message_ix_models/model/material/data_steel.py index 25ce9fafd4..cb27f4f001 100644 --- a/message_ix_models/model/material/data_steel.py +++ b/message_ix_models/model/material/data_steel.py @@ -38,6 +38,26 @@ def gen_mock_demand_steel(scenario): s_info = ScenarioInfo(scenario) modelyears = s_info.Y #s_info.Y is only for modeling years fmy = s_info.y0 + nodes = s_info.N + + # True steel use 2010 [Mt/year] + # https://www.worldsteel.org/en/dam/jcr:0474d208-9108-4927-ace8-4ac5445c5df8/World+Steel+in+Figures+2017.pdf + + if "R11_CHN" in s_info.N: + sheet_n = "data_R11" + + r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', \ + 'R11_MEA', 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU'] + + d = [35, 537, 70, 53, 49, 39, 130, 80, 45, 96, 100] + # MEA change from 39 to 9 to make it feasible (coal supply bound) + else: + sheet_n = "data_R12" + + r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', 'R11_MEA',\ + 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU',"R11_CHN"] + + d = [35,5.37, 70, 53, 49, 39, 130, 80, 45, 96, 100,531.63] # SSP2 R11 baseline GDP projection gdp_growth = pd.read_excel( @@ -50,13 +70,6 @@ def gen_mock_demand_steel(scenario): gdp_growth['Region'] = 'R11_'+ gdp_growth['Region'] - # True steel use 2010 [Mt/year] - # https://www.worldsteel.org/en/dam/jcr:0474d208-9108-4927-ace8-4ac5445c5df8/World+Steel+in+Figures+2017.pdf - r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', \ - 'R11_MEA', 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU'] - d = [35, 537, 70, 53, 49, \ - 39, 130, 80, 45, 96, 100] # MEA change from 39 to 9 to make it feasible (coal supply bound) - demand2010_steel = pd.DataFrame({'Region':r, 'Val':d}).\ join(gdp_growth.set_index('Region'), on='Region').rename(columns={'Region':'node'}) @@ -102,8 +115,6 @@ def gen_mock_demand_steel(scenario): return demand2010_steel - - def gen_data_steel(scenario, dry_run=False): """Generate data for materials representation of steel industry. From 2d6ff3b8e6c92185ee994104dcc73d551ffc3252 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 20 May 2021 16:39:58 +0200 Subject: [PATCH 275/774] Fix minor issues --- .../model/material/data_aluminum.py | 4 ++-- message_ix_models/model/material/data_petro.py | 17 ++++++----------- 2 files changed, 8 insertions(+), 13 deletions(-) diff --git a/message_ix_models/model/material/data_aluminum.py b/message_ix_models/model/material/data_aluminum.py index ee2b8d5548..92ae97b457 100644 --- a/message_ix_models/model/material/data_aluminum.py +++ b/message_ix_models/model/material/data_aluminum.py @@ -350,12 +350,12 @@ def gen_mock_demand_aluminum(scenario): d = [3,28, 6,5,2.5,2,13.6,3,4.8,4.8,6] else: - sheet_n = "data_12" + sheet_n = "data_R12" r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', 'R11_MEA',\ 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU',"R11_CHN"] - d = [3,26, 6,5,2.5,2,13.6,3,4.8,4.8,6,2] + d = [3,2,6,5,2.5,2,13.6,3,4.8,4.8,6,26] # SSP2 R11 baseline GDP projection gdp_growth = pd.read_excel( diff --git a/message_ix_models/model/material/data_petro.py b/message_ix_models/model/material/data_petro.py index b26cf173ce..8cc235a4f9 100644 --- a/message_ix_models/model/material/data_petro.py +++ b/message_ix_models/model/material/data_petro.py @@ -75,13 +75,12 @@ def gen_mock_demand_petro(scenario): r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', 'R11_MEA',\ 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU',"R11_CHN"] - d_ethylene = [0.853064, 28.84327, 2.88788, 8.780442, 8.831229,21.58509, - 32.54942, 8.94036, 28.84327, 28.12818, 16.65209,3.2] - d_propylene = [0.426532, 28.84327, 2.88788, 1.463407, 5.298738, 10.79255, - 16.27471, 7.4503, 7.497867, 17.30965, 11.1014,3.2] - d_BTX = [0.426532, 28.84327, 2.88788, 1.463407, 5.298738, 12.95105, 16.27471, - 7.4503, 7.497867, 17.30965, 11.1014, 3.2] - + d_ethylene = [0.853064, 3.2, 2.88788, 8.780442, 8.831229,21.58509, + 32.54942, 8.94036, 28.84327, 28.12818, 16.65209, 28.84327] + d_propylene = [0.426532, 3.2, 2.88788, 1.463407, 5.298738, 10.79255, + 16.27471, 7.4503, 7.497867, 17.30965, 11.1014,28.84327] + d_BTX = [0.426532, 3.2, 2.88788, 1.463407, 5.298738, 12.95105, 16.27471, + 7.4503, 7.497867, 17.30965, 11.1014, 28.84327] # SSP2 R11 baseline GDP projection gdp_growth = pd.read_excel( @@ -95,10 +94,6 @@ def gen_mock_demand_petro(scenario): gdp_growth['Region'] = 'R11_'+ gdp_growth['Region'] - r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', \ - 'R11_MEA', 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU'] - - list = [] for e in ["ethylene","propylene","BTX"]: From fe39576f56ac82e8ec926c2ece8d8320c4bc5935 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 20 May 2021 16:40:46 +0200 Subject: [PATCH 276/774] Adjust utility functions for r12 --- message_ix_models/model/material/data_util.py | 36 ++++++++++++++----- 1 file changed, 28 insertions(+), 8 deletions(-) diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index dc1c38bedf..307e0d2e6f 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -249,17 +249,24 @@ def modify_demand_and_hist_activity(scen): # Read in technology-specific parameters from input xlsx # Now used for steel and cement, which are in one file -def read_sector_data(sectname): +def read_sector_data(sectname,scenario): import numpy as np # Ensure config is loaded, get the context context = read_config() + s_info = ScenarioInfo(scenario) + + if "R11_CHN" in s_info.N: + sheet_n = sect_name + "_R12" + else: + sheet_n = sect_name + "_R11" + # data_df = data_steel_china.append(data_cement_china, ignore_index=True) data_df = pd.read_excel( context.get_path("material", context.datafile), - sheet_name=sectname, + sheet_name=sheet_n, ) # Clean the data @@ -295,21 +302,27 @@ def read_sector_data(sectname): # Read in time-dependent parameters # Now only used to add fuel cost for bare model -def read_timeseries(filename): +def read_timeseries(filename,scenario): import numpy as np # Ensure config is loaded, get the context context = read_config() + s_info = ScenarioInfo(scenario) + + # if context.scenario_info['scenario'] == 'NPi400': + # sheet_name="timeseries_NPi400" + # else: + # sheet_name = "timeseries" - if context.scenario_info['scenario'] == 'NPi400': - sheet_name="timeseries_NPi400" + if "R11_CHN" in s_info.N: + sheet_n = timeseries_R12 else: - sheet_name = "timeseries" + sheet_n = timeseries_R11 # Read the file df = pd.read_excel( - context.get_path("material", filename), sheet_name) + context.get_path("material", filename), sheet_name=sheet_n) import numbers # Take only existing years in the data @@ -330,9 +343,16 @@ def read_rel(filename): # Ensure config is loaded, get the context context = read_config() + s_info = ScenarioInfo(scenario) + + if "R11_CHN" in s_info.N: + sheet_n = relations_R12 + else: + sheet_n = relations_R11 + # Read the file data_rel = pd.read_excel( - context.get_path("material", filename), sheet_name="relations", + context.get_path("material", filename), sheet_name=sheet_n, ) return data_rel From ea147c8228e165dc2c01da51840d7369f23ff90e Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Fri, 21 May 2021 15:49:17 +0200 Subject: [PATCH 277/774] Omit water supply --- message_ix_models/data/material/set.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 4006c5490b..2c481464be 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -129,7 +129,7 @@ common: - secondary - useful - final - - water_supply + #- water_supply - export - import From fe91cd04b53edcd98a2058af20f36a20ed06b5ca Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Fri, 21 May 2021 15:49:49 +0200 Subject: [PATCH 278/774] Fix error messages --- message_ix_models/model/material/__init__.py | 1 + message_ix_models/model/material/data_aluminum.py | 10 +++++----- message_ix_models/model/material/data_cement.py | 12 +++++++----- message_ix_models/model/material/data_petro.py | 10 ++++++---- message_ix_models/model/material/data_steel.py | 10 ++++++---- 5 files changed, 25 insertions(+), 18 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 90bed78b1a..536ec46de1 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -114,6 +114,7 @@ def solve(context, datafile, tag): # user did not give a recognized value, this raises an error. output_scenario_name = { "baseline": "NoPolicy", + "baseline_macro":"NoPolicy", "NPi2020-con-prim-dir-ncr": "NPi", "NPi2020_1000-con-prim-dir-ncr": "NPi2020_1000", "NPi2020_400-con-prim-dir-ncr": "NPi2020_400", diff --git a/message_ix_models/model/material/data_aluminum.py b/message_ix_models/model/material/data_aluminum.py index 92ae97b457..e15a5aba9e 100644 --- a/message_ix_models/model/material/data_aluminum.py +++ b/message_ix_models/model/material/data_aluminum.py @@ -54,10 +54,9 @@ def read_data_aluminum(scenario): # Drop columns that don't contain useful information data_alu= data_alu.drop(["Source", 'Description'], axis = 1) - data_alu_rel = pd.read_excel(context.get_path("material", fname), - sheet_name=sheet_n_relations,) + data_alu_rel = read_rel(scenario, "aluminum_techno_economic.xlsx") - data_aluminum_ts = read_timeseries("aluminum_techno_economic.xlsx") + data_aluminum_ts = read_timeseries(scenario,"aluminum_techno_economic.xlsx") # Unit conversion @@ -79,8 +78,7 @@ def gen_data_aluminum(scenario, dry_run=False): s_info = ScenarioInfo(scenario) # Techno-economic assumptions - data_aluminum, data_aluminum_rel, data_aluminum_ts = read_data_aluminum() - + data_aluminum, data_aluminum_rel, data_aluminum_ts = read_data_aluminum(scenario) # List of data frames, to be concatenated together at end results = defaultdict(list) @@ -341,6 +339,8 @@ def gen_mock_demand_aluminum(scenario): # Remaining 8.612 Mt shared between AFR and FSU # This is used as 2020 data. + # For R12: China and CPA demand divided by 0.1 and 0.9. + if "R11_CHN" in s_info.N: sheet_n = "data_R11" diff --git a/message_ix_models/model/material/data_cement.py b/message_ix_models/model/material/data_cement.py index 6b93d96f38..858490605e 100644 --- a/message_ix_models/model/material/data_cement.py +++ b/message_ix_models/model/material/data_cement.py @@ -39,6 +39,8 @@ def gen_mock_demand_cement(scenario): # 2019 production by country (USGS) # p43 of https://pubs.usgs.gov/periodicals/mcs2020/mcs2020-cement.pdf + # For R12: China and CPA demand divided by 0.1 and 0.9. + if "R11_CHN" in s_info.N: sheet_n = "data_R11" @@ -57,17 +59,17 @@ def gen_mock_demand_cement(scenario): r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', 'R11_MEA',\ 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU',"R11_CHN"] - demand2020_top = [76, 229.5, 0, 57, 55, 60, 89, 54, 129, 320, 51,2065.5] + demand2020_top = [76, 229.5, 0, 57, 55, 60, 89, 54, 129, 320, 51,2065.5] # the rest (~900 Mt) allocated by % values in http://www.cembureau.eu/media/clkdda45/activity-report-2019.pdf - demand2020_rest = [4100*0.051-76, (4100*0.14-155)*0.2*0.1, 4100*0.064*0.5, 4100*0.026-57, 4100*0.046*0.5-55, \ + demand2020_rest = [4100*0.051-76, (4100*0.14-155)*0.2*0.1, 4100*0.064*0.5, 4100*0.026-57, 4100*0.046*0.5-55, \ (4100*0.14-155)*0.2, 4100*0.046*0.5, 12, 4100*0.003, (4100*0.14-155)*0.6, 4100*0.064*0.5 - 51, (4100*0.14-155)*0.2*0.9] - d = [a + b for a, b in zip(demand2020_top, demand2020_rest)] + d = [a + b for a, b in zip(demand2020_top, demand2020_rest)] # SSP2 R11 baseline GDP projection gdp_growth = pd.read_excel( context.get_path("material", "iamc_db ENGAGE baseline GDP PPP.xlsx"), - sheet_name="data", + sheet_name=sheet_n, ) gdp_growth = gdp_growth.loc[(gdp_growth['Scenario']=='baseline') & (gdp_growth['Region']!='World')].\ @@ -153,7 +155,7 @@ def gen_data_cement(scenario, dry_run=False): # Techno-economic assumptions # TEMP: now add cement sector as well - data_cement = read_sector_data("cement") + data_cement = read_sector_data(scenario,"cement") # Special treatment for time-dependent Parameters # data_cement_vc = read_timeseries() # tec_vc = set(data_cement_vc.technology) # set of tecs with var_cost diff --git a/message_ix_models/model/material/data_petro.py b/message_ix_models/model/material/data_petro.py index 8cc235a4f9..021d5d42df 100644 --- a/message_ix_models/model/material/data_petro.py +++ b/message_ix_models/model/material/data_petro.py @@ -18,7 +18,7 @@ add_par_data ) -def read_data_petrochemicals(): +def read_data_petrochemicals(scenario): """Read and clean data from :file:`petrochemicals_techno_economic.xlsx`.""" # Ensure config is loaded, get the context @@ -56,6 +56,8 @@ def gen_mock_demand_petro(scenario): # For division of some regions assumptions made: # PAO, PAS, SAS, EEU,WEU + # For R12: China and CPA demand divided by 0.1 and 0.9. + if "R11_CHN" in s_info.N: sheet_n = "data_R11" @@ -85,7 +87,7 @@ def gen_mock_demand_petro(scenario): # SSP2 R11 baseline GDP projection gdp_growth = pd.read_excel( context.get_path("material", "iamc_db ENGAGE baseline GDP PPP.xlsx"), - sheet_name="data", + sheet_name=sheet_n, ) gdp_growth = gdp_growth.loc[(gdp_growth['Scenario']=='baseline') & \ @@ -142,8 +144,8 @@ def gen_data_petro_chemicals(scenario, dry_run=False): s_info = ScenarioInfo(scenario) # Techno-economic assumptions - data_petro = read_data_petrochemicals() - data_petro_ts = read_timeseries("petrochemicals_techno_economic.xlsx") + data_petro = read_data_petrochemicals(scenario) + data_petro_ts = read_timeseries(scenario,"petrochemicals_techno_economic.xlsx") # List of data frames, to be concatenated together at end results = defaultdict(list) diff --git a/message_ix_models/model/material/data_steel.py b/message_ix_models/model/material/data_steel.py index cb27f4f001..2d07ac1772 100644 --- a/message_ix_models/model/material/data_steel.py +++ b/message_ix_models/model/material/data_steel.py @@ -43,6 +43,8 @@ def gen_mock_demand_steel(scenario): # True steel use 2010 [Mt/year] # https://www.worldsteel.org/en/dam/jcr:0474d208-9108-4927-ace8-4ac5445c5df8/World+Steel+in+Figures+2017.pdf + # For R12: China and CPA demand divided by 0.1 and 0.9. + if "R11_CHN" in s_info.N: sheet_n = "data_R11" @@ -62,7 +64,7 @@ def gen_mock_demand_steel(scenario): # SSP2 R11 baseline GDP projection gdp_growth = pd.read_excel( context.get_path("material", "iamc_db ENGAGE baseline GDP PPP.xlsx"), - sheet_name="data", + sheet_name=sheet_n, ) gdp_growth = gdp_growth.loc[(gdp_growth['Scenario']=='baseline') & (gdp_growth['Region']!='World')].\ @@ -128,10 +130,10 @@ def gen_data_steel(scenario, dry_run=False): # Techno-economic assumptions # TEMP: now add cement sector as well => Need to separate those since now I have get_data_steel and cement - data_steel = read_sector_data("steel") + data_steel = read_sector_data(scenario,"steel") # Special treatment for time-dependent Parameters - data_steel_ts = read_timeseries(context.datafile) - data_steel_rel = read_rel(context.datafile) + data_steel_ts = read_timeseries(scenario, context.datafile) + data_steel_rel = read_rel(scenario, context.datafile) tec_ts = set(data_steel_ts.technology) # set of tecs with var_cost From 32aa953a262530bc904b55f2697e99f682c2c44e Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 27 May 2021 11:28:31 +0200 Subject: [PATCH 279/774] Fix minor issues and disable buildings --- message_ix_models/data/material/set.yaml | 2 +- message_ix_models/model/material/__init__.py | 3 +- message_ix_models/model/material/data.py | 2 +- .../model/material/data_aluminum.py | 38 ++++++++------- .../model/material/data_buildings.py | 20 +++++--- .../model/material/data_cement.py | 48 ++++++++++--------- .../model/material/data_generic.py | 1 + .../model/material/data_petro.py | 27 ++++++----- .../model/material/data_steel.py | 43 +++++++++-------- 9 files changed, 101 insertions(+), 83 deletions(-) diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 2c481464be..4006c5490b 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -129,7 +129,7 @@ common: - secondary - useful - final - #- water_supply + - water_supply - export - import diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 536ec46de1..bdc6931a3b 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -115,6 +115,7 @@ def solve(context, datafile, tag): output_scenario_name = { "baseline": "NoPolicy", "baseline_macro":"NoPolicy", + "baseline_new":"NoPolicy", "NPi2020-con-prim-dir-ncr": "NPi", "NPi2020_1000-con-prim-dir-ncr": "NPi2020_1000", "NPi2020_400-con-prim-dir-ncr": "NPi2020_400", @@ -203,7 +204,7 @@ def run_reporting(old_reporting, scenario_name, model_name): from message_data.tools import add_par_data DATA_FUNCTIONS = [ - gen_data_buildings, + #gen_data_buildings, gen_data_steel, gen_data_cement, gen_data_aluminum, diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index 6300faef5b..40ad2656b1 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -27,7 +27,7 @@ log = logging.getLogger(__name__) DATA_FUNCTIONS = [ - gen_data_buildings, + #gen_data_buildings, gen_data_steel, gen_data_cement, gen_data_aluminum, diff --git a/message_ix_models/model/material/data_aluminum.py b/message_ix_models/model/material/data_aluminum.py index e15a5aba9e..fa73dff6fc 100644 --- a/message_ix_models/model/material/data_aluminum.py +++ b/message_ix_models/model/material/data_aluminum.py @@ -91,6 +91,7 @@ def gen_data_aluminum(scenario, dry_run=False): yv_ya = s_info.yv_ya fmy = s_info.y0 nodes.remove('World') + nodes.remove("R11_RCPA") # Do not parametrize GLB region the same way if "R11_GLB" in nodes: @@ -342,6 +343,14 @@ def gen_mock_demand_aluminum(scenario): # For R12: China and CPA demand divided by 0.1 and 0.9. if "R11_CHN" in s_info.N: + sheet_n = "data_R12" + + r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', 'R11_MEA',\ + 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU',"R11_CHN"] + + d = [3,2,6,5,2.5,2,13.6,3,4.8,4.8,6,26] + + else: sheet_n = "data_R11" r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', \ @@ -349,13 +358,6 @@ def gen_mock_demand_aluminum(scenario): d = [3,28, 6,5,2.5,2,13.6,3,4.8,4.8,6] - else: - sheet_n = "data_R12" - - r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', 'R11_MEA',\ - 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU',"R11_CHN"] - - d = [3,2,6,5,2.5,2,13.6,3,4.8,4.8,6,26] # SSP2 R11 baseline GDP projection gdp_growth = pd.read_excel( @@ -377,17 +379,17 @@ def gen_mock_demand_aluminum(scenario): multiply(demand2020_al["Val"], axis=0) # Do this if we have 2020 demand values for buildings - sp = get_spec() - if 'buildings' in sp['add'].set['technology']: - val = get_scen_mat_demand("aluminum") - print("Base year demand of {}:".format("aluminum"), val) - # d = d - val.value - # Scale down all years' demand values by the 2020 ratio - demand2020_al.iloc[:,3:] = demand2020_al.iloc[:,3:].\ - multiply(demand2020_al[2020]- val['value'], axis=0).\ - div(demand2020_al[2020], axis=0) - print("UPDATE {} demand for 2020!".format("aluminum")) - + # sp = get_spec() + # if 'buildings' in sp['add'].set['technology']: + # val = get_scen_mat_demand("aluminum",scenario) + # print("Base year demand of {}:".format("aluminum"), val) + # # d = d - val.value + # # Scale down all years' demand values by the 2020 ratio + # demand2020_al.iloc[:,3:] = demand2020_al.iloc[:,3:].\ + # multiply(demand2020_al[2020]- val['value'], axis=0).\ + # div(demand2020_al[2020], axis=0) + # print("UPDATE {} demand for 2020!".format("aluminum")) + # demand2020_al = pd.melt(demand2020_al.drop(['Val', 'Scenario'], axis=1),\ id_vars=['node'], var_name='year', value_name = 'value') diff --git a/message_ix_models/model/material/data_buildings.py b/message_ix_models/model/material/data_buildings.py index 858fc396d4..bd6d068d77 100644 --- a/message_ix_models/model/material/data_buildings.py +++ b/message_ix_models/model/material/data_buildings.py @@ -19,16 +19,18 @@ add_par_data ) -INPUTFILE = 'LED_LED_report_IAMC_sensitivity.csv' #'LED_LED_report_IAMC.csv' CASE_SENS = 'ref' # 'min', 'max' +INPUTFILE = 'LED_LED_report_IAMC_sensitivity_R12.csv' +#INPUTFILE = 'LED_LED_report_IAMC_sensitivity_R11.csv' - -def read_timeseries_buildings(filename, case=CASE_SENS): +def read_timeseries_buildings(filename, scenario, case=CASE_SENS): import numpy as np # Ensure config is loaded, get the context context = read_config() + s_info = ScenarioInfo(scenario) + nodes = s_info.N # Read the file and filter the given sensitivity case bld_input_raw = pd.read_csv( @@ -39,6 +41,8 @@ def read_timeseries_buildings(filename, case=CASE_SENS): # str.contains("Floor Space|Aluminum|Cement|Steel|Final Energy")] str.contains("Floor Space|Aluminum|Cement|Steel")] # Final Energy - Later. Need to figure out carving out bld_input_mat['Region'] = 'R11_' + bld_input_mat['Region'] + print("Check the year values") + print(bld_input_mat) bld_input_pivot = \ bld_input_mat.melt(id_vars=['Region','Variable'], var_name='Year', \ @@ -94,8 +98,8 @@ def read_timeseries_buildings(filename, case=CASE_SENS): return bld_intensity_long, bld_area_long, bld_demand_long -def get_scen_mat_demand(commod, year = "2020", inputfile = INPUTFILE, case=CASE_SENS): - a, b, c = read_timeseries_buildings(inputfile, case) +def get_scen_mat_demand(commod, scenario, year = "2020", inputfile = INPUTFILE, case=CASE_SENS): + a, b, c = read_timeseries_buildings(inputfile, scenario, case) if not year == "all": # specific year cc = c[(c.commodity==commod) & (c.year==year)].reset_index(drop=True) else: # all years @@ -115,7 +119,7 @@ def adjust_demand_param(scen): scen.check_out() comms = ["steel", "cement", "aluminum"] for c in comms: - mat_building = get_scen_mat_demand(c, year="all").rename(columns={"value":"bld_demand"}) # mat demand (timeseries) from buildings model (Alessio) + mat_building = get_scen_mat_demand(c, scen, year="all").rename(columns={"value":"bld_demand"}) # mat demand (timeseries) from buildings model (Alessio) mat_building['year'] = mat_building['year'].astype(int) sub_mat_demand = scen_mat_demand.loc[scen_mat_demand.commodity == c] @@ -152,7 +156,8 @@ def gen_data_buildings(scenario, dry_run=False): s_info = ScenarioInfo(scenario) # Buildings raw data (from Alessio) - data_buildings, data_buildings_demand, data_buildings_mat_demand = read_timeseries_buildings(INPUTFILE, CASE_SENS) + data_buildings, data_buildings_demand, data_buildings_mat_demand = \ + read_timeseries_buildings(INPUTFILE, scenario, CASE_SENS) # List of data frames, to be concatenated together at end results = defaultdict(list) @@ -166,6 +171,7 @@ def gen_data_buildings(scenario, dry_run=False): yv_ya = s_info.yv_ya # fmy = s_info.y0 nodes.remove('World') + nodes.remove("R11_RCPA") # Read field values from the buildings input data regions = list(set(data_buildings.node)) diff --git a/message_ix_models/model/material/data_cement.py b/message_ix_models/model/material/data_cement.py index 858490605e..906d186d2e 100644 --- a/message_ix_models/model/material/data_cement.py +++ b/message_ix_models/model/material/data_cement.py @@ -42,18 +42,6 @@ def gen_mock_demand_cement(scenario): # For R12: China and CPA demand divided by 0.1 and 0.9. if "R11_CHN" in s_info.N: - sheet_n = "data_R11" - - r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', \ - 'R11_MEA', 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU'] - - demand2020_top = [76, 2295, 0, 57, 55, 60, 89, 54, 129, 320, 51] - # the rest (~900 Mt) allocated by % values in http://www.cembureau.eu/media/clkdda45/activity-report-2019.pdf - demand2020_rest = [4100*0.051-76, (4100*0.14-155)*0.2, 4100*0.064*0.5, 4100*0.026-57, 4100*0.046*0.5-55, \ - (4100*0.14-155)*0.2, 4100*0.046*0.5, 12, 4100*0.003, (4100*0.14-155)*0.6, 4100*0.064*0.5 - 51] - d = [a + b for a, b in zip(demand2020_top, demand2020_rest)] - - else: sheet_n = "data_R12" r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', 'R11_MEA',\ @@ -66,6 +54,19 @@ def gen_mock_demand_cement(scenario): (4100*0.14-155)*0.2*0.9] d = [a + b for a, b in zip(demand2020_top, demand2020_rest)] + else: + sheet_n = "data_R11" + + r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', \ + 'R11_MEA', 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU'] + + demand2020_top = [76, 2295, 0, 57, 55, 60, 89, 54, 129, 320, 51] + # the rest (~900 Mt) allocated by % values in http://www.cembureau.eu/media/clkdda45/activity-report-2019.pdf + demand2020_rest = [4100*0.051-76, (4100*0.14-155)*0.2, 4100*0.064*0.5, 4100*0.026-57, 4100*0.046*0.5-55, \ + (4100*0.14-155)*0.2, 4100*0.046*0.5, 12, 4100*0.003, (4100*0.14-155)*0.6, 4100*0.064*0.5 - 51] + d = [a + b for a, b in zip(demand2020_top, demand2020_rest)] + + # SSP2 R11 baseline GDP projection gdp_growth = pd.read_excel( context.get_path("material", "iamc_db ENGAGE baseline GDP PPP.xlsx"), @@ -124,17 +125,17 @@ def gen_mock_demand_cement(scenario): multiply(demand2020_cement["value"], axis=0) # Do this if we have 2020 demand values for buildings - sp = get_spec() - if 'buildings' in sp['add'].set['technology']: - val = get_scen_mat_demand("cement") # Mt in 2020 - print("Base year demand of {}:".format("cement"), val) - # demand2020_cement['value'] = demand2020_cement['value'] - val['value'] - # Scale down all years' demand values by the 2020 ratio - demand2020_cement.iloc[:,3:] = demand2020_cement.iloc[:,3:].\ - multiply(demand2020_cement[2020]- val['value'], axis=0).\ - div(demand2020_cement[2020], axis=0) - print("UPDATE {} demand for 2020!".format("cement")) - + # sp = get_spec() + # if 'buildings' in sp['add'].set['technology']: + # val = get_scen_mat_demand("cement",scenario) # Mt in 2020 + # print("Base year demand of {}:".format("cement"), val) + # # demand2020_cement['value'] = demand2020_cement['value'] - val['value'] + # # Scale down all years' demand values by the 2020 ratio + # demand2020_cement.iloc[:,3:] = demand2020_cement.iloc[:,3:].\ + # multiply(demand2020_cement[2020]- val['value'], axis=0).\ + # div(demand2020_cement[2020], axis=0) + # print("UPDATE {} demand for 2020!".format("cement")) + # demand2020_cement = pd.melt(demand2020_cement.drop(['value', 'Scenario'], axis=1),\ id_vars=['node'], \ var_name='year', value_name = 'value') @@ -172,6 +173,7 @@ def gen_data_cement(scenario, dry_run=False): yv_ya = s_info.yv_ya fmy = s_info.y0 nodes.remove('World') + nodes.remove("R11_RCPA") # Do not parametrize GLB region the same way if "R11_GLB" in nodes: diff --git a/message_ix_models/model/material/data_generic.py b/message_ix_models/model/material/data_generic.py index d08c44ce28..3c92b25e08 100644 --- a/message_ix_models/model/material/data_generic.py +++ b/message_ix_models/model/material/data_generic.py @@ -82,6 +82,7 @@ def gen_data_generic(scenario, dry_run=False): # 'World' is included by default when creating a message_ix.Scenario(). # Need to remove it for the China bare model nodes.remove('World') + nodes.remove("R11_RCPA") for t in config["technology"]["add"]: diff --git a/message_ix_models/model/material/data_petro.py b/message_ix_models/model/material/data_petro.py index 021d5d42df..b6d8a18822 100644 --- a/message_ix_models/model/material/data_petro.py +++ b/message_ix_models/model/material/data_petro.py @@ -59,7 +59,19 @@ def gen_mock_demand_petro(scenario): # For R12: China and CPA demand divided by 0.1 and 0.9. if "R11_CHN" in s_info.N: - sheet_n = "data_R11" + sheet_n = "data_R12" + + r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', 'R11_MEA',\ + 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU',"R11_CHN"] + + d_ethylene = [0.853064, 3.2, 2.88788, 8.780442, 8.831229,21.58509, + 32.54942, 8.94036, 28.84327, 28.12818, 16.65209, 28.84327] + d_propylene = [0.426532, 3.2, 2.88788, 1.463407, 5.298738, 10.79255, + 16.27471, 7.4503, 7.497867, 17.30965, 11.1014,28.84327] + d_BTX = [0.426532, 3.2, 2.88788, 1.463407, 5.298738, 12.95105, 16.27471, + 7.4503, 7.497867, 17.30965, 11.1014, 28.84327] + + else: r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', \ 'R11_MEA', 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU'] @@ -71,18 +83,6 @@ def gen_mock_demand_petro(scenario): d_BTX = [0.426532, 32.04327, 2.88788, 1.463407, 5.298738, 12.95105, 16.27471, 7.4503, 7.497867, 17.30965, 11.1014] - else: - sheet_n = "data_12" - - r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', 'R11_MEA',\ - 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU',"R11_CHN"] - - d_ethylene = [0.853064, 3.2, 2.88788, 8.780442, 8.831229,21.58509, - 32.54942, 8.94036, 28.84327, 28.12818, 16.65209, 28.84327] - d_propylene = [0.426532, 3.2, 2.88788, 1.463407, 5.298738, 10.79255, - 16.27471, 7.4503, 7.497867, 17.30965, 11.1014,28.84327] - d_BTX = [0.426532, 3.2, 2.88788, 1.463407, 5.298738, 12.95105, 16.27471, - 7.4503, 7.497867, 17.30965, 11.1014, 28.84327] # SSP2 R11 baseline GDP projection gdp_growth = pd.read_excel( @@ -158,6 +158,7 @@ def gen_data_petro_chemicals(scenario, dry_run=False): yv_ya = s_info.yv_ya fmy = s_info.y0 nodes.remove('World') + nodes.remove("R11_RCPA") # Do not parametrize GLB region the same way if "R11_GLB" in nodes: diff --git a/message_ix_models/model/material/data_steel.py b/message_ix_models/model/material/data_steel.py index 2d07ac1772..92d5a9a009 100644 --- a/message_ix_models/model/material/data_steel.py +++ b/message_ix_models/model/material/data_steel.py @@ -45,7 +45,15 @@ def gen_mock_demand_steel(scenario): # For R12: China and CPA demand divided by 0.1 and 0.9. - if "R11_CHN" in s_info.N: + if "R11_CHN" in nodes: + sheet_n = "data_R12" + + r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', 'R11_MEA',\ + 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU',"R11_CHN"] + + d = [35,5.37, 70, 53, 49, 39, 130, 80, 45, 96, 100,531.63] + + else: sheet_n = "data_R11" r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', \ @@ -53,13 +61,6 @@ def gen_mock_demand_steel(scenario): d = [35, 537, 70, 53, 49, 39, 130, 80, 45, 96, 100] # MEA change from 39 to 9 to make it feasible (coal supply bound) - else: - sheet_n = "data_R12" - - r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', 'R11_MEA',\ - 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU',"R11_CHN"] - - d = [35,5.37, 70, 53, 49, 39, 130, 80, 45, 96, 100,531.63] # SSP2 R11 baseline GDP projection gdp_growth = pd.read_excel( @@ -80,21 +81,24 @@ def gen_mock_demand_steel(scenario): multiply(demand2010_steel["Val"], axis=0) # Do this if we have 2020 demand values for buildings - sp = get_spec() - if 'buildings' in sp['add'].set['technology']: - val = get_scen_mat_demand("steel") - print("Base year demand of {}:".format("steel"), val) - # d = d - val.value - # Scale down all years' demand values by the 2020 ratio - demand2010_steel.iloc[:,3:] = demand2010_steel.iloc[:,3:].\ - multiply(demand2010_steel[2020]- val['value'], axis=0).\ - div(demand2010_steel[2020], axis=0) - print("UPDATE {} demand for 2020!".format("steel")) - + # sp = get_spec() + # if 'buildings' in sp['add'].set['technology']: + # val = get_scen_mat_demand("steel",scenario) + # print("Base year demand of {}:".format("steel"), val) + # # d = d - val.value + # # Scale down all years' demand values by the 2020 ratio + # demand2010_steel.iloc[:,3:] = demand2010_steel.iloc[:,3:].\ + # multiply(demand2010_steel[2020]- val['value'], axis=0).\ + # div(demand2010_steel[2020], axis=0) + # print("UPDATE {} demand for 2020!".format("steel")) + # demand2010_steel = pd.melt(demand2010_steel.drop(['Val', 'Scenario'], axis=1),\ id_vars=['node'], \ var_name='year', value_name = 'value') # + # print("This is steel demand") + # print(demand2010_steel) + # # baseyear = list(range(2020, 2110+1, 10)) # gdp_growth_interp = np.interp(modelyears, baseyear, gdp_growth) # @@ -149,6 +153,7 @@ def gen_data_steel(scenario, dry_run=False): yv_ya = s_info.yv_ya fmy = s_info.y0 nodes.remove('World') + nodes.remove("R11_RCPA") # Do not parametrize GLB region the same way if "R11_GLB" in nodes: From aec5cf996e47fd4cba4b4e2fbcd40fb308cb96bf Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 27 May 2021 11:29:34 +0200 Subject: [PATCH 280/774] Fix data files --- .../data/material/LED_LED_report_IAMC_sensitivity_R11.csv | 3 +++ .../data/material/LED_LED_report_IAMC_sensitivity_R12.csv | 3 +++ .../data/material/petrochemicals_techno_economic.xlsx | 4 ++-- 3 files changed, 8 insertions(+), 2 deletions(-) create mode 100644 message_ix_models/data/material/LED_LED_report_IAMC_sensitivity_R11.csv create mode 100644 message_ix_models/data/material/LED_LED_report_IAMC_sensitivity_R12.csv diff --git a/message_ix_models/data/material/LED_LED_report_IAMC_sensitivity_R11.csv b/message_ix_models/data/material/LED_LED_report_IAMC_sensitivity_R11.csv new file mode 100644 index 0000000000..a8853545a7 --- /dev/null +++ b/message_ix_models/data/material/LED_LED_report_IAMC_sensitivity_R11.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:08652e54fca937ec1fb2da8e068020c61eb33eafbc233186a5dfdc4d4e1cbb15 +size 189976 diff --git a/message_ix_models/data/material/LED_LED_report_IAMC_sensitivity_R12.csv b/message_ix_models/data/material/LED_LED_report_IAMC_sensitivity_R12.csv new file mode 100644 index 0000000000..899669bfc3 --- /dev/null +++ b/message_ix_models/data/material/LED_LED_report_IAMC_sensitivity_R12.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:851e957ee94fe451416f7c09e36d7c863b34039606b11cd535f879ee1185cf65 +size 207524 diff --git a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx index d208bd9935..694babc5b4 100644 --- a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx +++ b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:8705afb5edc4f58a5ce2cf3e36245d98562b87db1c83d6059a87f169c480ede3 -size 664303 +oid sha256:0c540d1d84ec3ac20782f311ff7a108514ee9b4a57c0937a3bfff4f011f2d4bf +size 663309 From 5f87191ac919b066c6dcc81e20918fd562c5b79c Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 8 Jun 2021 11:46:55 +0200 Subject: [PATCH 281/774] Update region names in the data files for r12 --- .../data/material/iamc_db ENGAGE baseline GDP PPP.xlsx | 4 ++-- .../data/material/petrochemicals_techno_economic.xlsx | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/message_ix_models/data/material/iamc_db ENGAGE baseline GDP PPP.xlsx b/message_ix_models/data/material/iamc_db ENGAGE baseline GDP PPP.xlsx index c692d83b7d..542468ec8d 100644 --- a/message_ix_models/data/material/iamc_db ENGAGE baseline GDP PPP.xlsx +++ b/message_ix_models/data/material/iamc_db ENGAGE baseline GDP PPP.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:d58e7fd25cca4af945b8b57fd1b16d19e7b722c3a86b5c86e344310085540956 -size 31046 +oid sha256:63f801d7590e7cdf9fed7e50e5bf0a896968e7c435b5a93612e6cdefae4dc2c9 +size 31033 diff --git a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx index 694babc5b4..3ef87fb74e 100644 --- a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx +++ b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:0c540d1d84ec3ac20782f311ff7a108514ee9b4a57c0937a3bfff4f011f2d4bf -size 663309 +oid sha256:c6de2ea8201dc4de429b843b010ca58e045c74bbfd05d8a0d1deb8040a51db7c +size 663310 From 85701d6d1e343eb12d09406bcba529702bc4f65e Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 9 Jun 2021 10:11:52 +0200 Subject: [PATCH 282/774] Update the region names to r12 --- message_ix_models/data/material/set.yaml | 5 -- message_ix_models/model/material/__init__.py | 3 ++ message_ix_models/model/material/data.py | 4 ++ .../model/material/data_aluminum.py | 39 ++++++++------- .../model/material/data_cement.py | 32 ++++++------- .../model/material/data_generic.py | 3 +- .../model/material/data_petro.py | 47 +++++++++++-------- .../model/material/data_steel.py | 36 +++++++------- 8 files changed, 92 insertions(+), 77 deletions(-) diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 4006c5490b..eab03e8e74 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -133,11 +133,6 @@ common: - export - import - node: - # Just one is sufficient, since the RES will never be generated with a mix - require: - - R11_AFR - type_tec: add: - industry diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index bdc6931a3b..2112fdf8a5 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -224,6 +224,9 @@ def add_data(scenario, dry_run=False): if {"World", "R11_GLB"} < set(info.set["node"]): log.warning("Remove 'R11_GLB' from node list for data generation") info.set["node"].remove("R11_GLB") + if {"World", "R12_GLB"} < set(info.set["node"]): + log.warning("Remove 'R12_GLB' from node list for data generation") + info.set["node"].remove("R12_GLB") for func in DATA_FUNCTIONS: # Generate or load the data; add to the Scenario diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index 40ad2656b1..ad7141d7a5 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -47,6 +47,10 @@ def add_data(scenario, dry_run=False): log.warning("Remove 'R11_GLB' from node list for data generation") info.set["node"].remove("R11_GLB") + if {"World", "R12_GLB"} < set(info.set["node"]): + log.warning("Remove 'R12_GLB' from node list for data generation") + info.set["node"].remove("R12_GLB") + for func in DATA_FUNCTIONS: # Generate or load the data; add to the Scenario log.info(f'from {func.__name__}()') diff --git a/message_ix_models/model/material/data_aluminum.py b/message_ix_models/model/material/data_aluminum.py index fa73dff6fc..211e8ae3cc 100644 --- a/message_ix_models/model/material/data_aluminum.py +++ b/message_ix_models/model/material/data_aluminum.py @@ -40,7 +40,7 @@ def read_data_aluminum(scenario): fname = "aluminum_techno_economic.xlsx" - if "R11_CHN" in s_info.N: + if "R12_CHN" in s_info.N: sheet_n = "data_R12" sheet_n_relations = "relations_R12" else: @@ -91,11 +91,14 @@ def gen_data_aluminum(scenario, dry_run=False): yv_ya = s_info.yv_ya fmy = s_info.y0 nodes.remove('World') - nodes.remove("R11_RCPA") # Do not parametrize GLB region the same way if "R11_GLB" in nodes: nodes.remove("R11_GLB") + global_region = "R11_GLB" + if "R12_GLB" in nodes: + nodes.remove("R12_GLB") + global_region = "R12_GLB" for t in config["technology"]["add"]: @@ -145,12 +148,12 @@ def gen_data_aluminum(scenario, dry_run=False): if (param_name == "input") and (lev == "import"): df = make_df(param_name, technology=t, commodity=com, \ level=lev, value=val[regions[regions==rg].index[0]], unit='t', \ - node_loc=rg, node_origin="R11_GLB", **common) + node_loc=rg, node_origin=global_region, **common) elif (param_name == "output") and (lev == "export"): df = make_df(param_name, technology=t, commodity=com, \ level=lev, value=val[regions[regions==rg].index[0]], unit='t', \ - node_loc=rg, node_dest="R11_GLB", **common) + node_loc=rg, node_dest=global_region, **common) # Assign higher efficiency to younger plants elif (((t == "soderberg_aluminum") or (t == "prebake_aluminum")) \ @@ -184,7 +187,7 @@ def gen_data_aluminum(scenario, dry_run=False): node_loc=rg, **common).pipe(same_node)) # Copy parameters to all regions, when node_loc is not GLB - if (len(regions) == 1) and (rg != "R11_GLB"): + if (len(regions) == 1) and (rg != global_region): df['node_loc'] = None df = df.pipe(broadcast, node_loc=nodes)#.pipe(same_node) # Use same_node only for non-trade technologies @@ -207,7 +210,7 @@ def gen_data_aluminum(scenario, dry_run=False): node_loc=rg, **common) # Copy parameters to all regions - if (len(regions) == 1) and len(set(df['node_loc'])) == 1 and list(set(df['node_loc']))[0]!='R11_GLB': + if (len(regions) == 1) and len(set(df['node_loc'])) == 1 and list(set(df['node_loc']))[0]!=global_region: df['node_loc'] = None df = df.pipe(broadcast, node_loc=nodes) @@ -324,6 +327,7 @@ def gen_mock_demand_aluminum(scenario): modelyears = s_info.Y #s_info.Y is only for modeling years fmy = s_info.y0 nodes = s_info.N + nodes.remove('World') # Demand at product level (IAI Global Aluminum Cycle 2018) # Globally: 82.4 Mt @@ -342,23 +346,22 @@ def gen_mock_demand_aluminum(scenario): # For R12: China and CPA demand divided by 0.1 and 0.9. - if "R11_CHN" in s_info.N: - sheet_n = "data_R12" - - r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', 'R11_MEA',\ - 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU',"R11_CHN"] + # The order: + #r = ['R12_AFR', 'R12_RCPA', 'R12_EEU', 'R12_FSU', 'R12_LAM', 'R12_MEA',\ + #'R12_NAM', 'R12_PAO', 'R12_PAS', 'R12_SAS', 'R12_WEU',"R12_CHN"] + if "R12_CHN" in nodes: + nodes.remove('R12_GLB') + sheet_n = "data_R12" + region_set = 'R12_' d = [3,2,6,5,2.5,2,13.6,3,4.8,4.8,6,26] else: + nodes.remove('R11_GLB') sheet_n = "data_R11" - - r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', \ - 'R11_MEA', 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU'] - + region_set = 'R11_' d = [3,28, 6,5,2.5,2,13.6,3,4.8,4.8,6] - # SSP2 R11 baseline GDP projection gdp_growth = pd.read_excel( context.get_path("material", "iamc_db ENGAGE baseline GDP PPP.xlsx"), @@ -368,9 +371,9 @@ def gen_mock_demand_aluminum(scenario): (gdp_growth['Region']!='World')].drop(['Model', 'Variable', 'Unit', 'Notes',\ 2000, 2005], axis = 1) - gdp_growth['Region'] = 'R11_'+ gdp_growth['Region'] + gdp_growth['Region'] = region_set + gdp_growth['Region'] - demand2020_al = pd.DataFrame({'Region':r, 'Val':d}).\ + demand2020_al = pd.DataFrame({'Region':nodes, 'Val':d}).\ join(gdp_growth.set_index('Region'), on='Region').\ rename(columns={'Region':'node'}) diff --git a/message_ix_models/model/material/data_cement.py b/message_ix_models/model/material/data_cement.py index 906d186d2e..d2e7dba1c6 100644 --- a/message_ix_models/model/material/data_cement.py +++ b/message_ix_models/model/material/data_cement.py @@ -35,37 +35,36 @@ def gen_mock_demand_cement(scenario): modelyears = s_info.Y #s_info.Y is only for modeling years fmy = s_info.y0 nodes = s_info.N + nodes.remove('World') # 2019 production by country (USGS) # p43 of https://pubs.usgs.gov/periodicals/mcs2020/mcs2020-cement.pdf # For R12: China and CPA demand divided by 0.1 and 0.9. - if "R11_CHN" in s_info.N: - sheet_n = "data_R12" + # The order: + #r = ['R12_AFR', 'R12_RCPA', 'R12_EEU', 'R12_FSU', 'R12_LAM', 'R12_MEA',\ + #'R12_NAM', 'R12_PAO', 'R12_PAS', 'R12_SAS', 'R12_WEU',"R12_CHN"] - r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', 'R11_MEA',\ - 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU',"R11_CHN"] + if "R12_CHN" in nodes: + nodes.remove('R12_GLB') + sheet_n = "data_R12" + region_set = 'R12_' demand2020_top = [76, 229.5, 0, 57, 55, 60, 89, 54, 129, 320, 51,2065.5] # the rest (~900 Mt) allocated by % values in http://www.cembureau.eu/media/clkdda45/activity-report-2019.pdf demand2020_rest = [4100*0.051-76, (4100*0.14-155)*0.2*0.1, 4100*0.064*0.5, 4100*0.026-57, 4100*0.046*0.5-55, \ (4100*0.14-155)*0.2, 4100*0.046*0.5, 12, 4100*0.003, (4100*0.14-155)*0.6, 4100*0.064*0.5 - 51, (4100*0.14-155)*0.2*0.9] - d = [a + b for a, b in zip(demand2020_top, demand2020_rest)] - else: + nodes.remove('R11_GLB') sheet_n = "data_R11" - - r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', \ - 'R11_MEA', 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU'] + region_set = 'R11_' demand2020_top = [76, 2295, 0, 57, 55, 60, 89, 54, 129, 320, 51] # the rest (~900 Mt) allocated by % values in http://www.cembureau.eu/media/clkdda45/activity-report-2019.pdf demand2020_rest = [4100*0.051-76, (4100*0.14-155)*0.2, 4100*0.064*0.5, 4100*0.026-57, 4100*0.046*0.5-55, \ (4100*0.14-155)*0.2, 4100*0.046*0.5, 12, 4100*0.003, (4100*0.14-155)*0.6, 4100*0.064*0.5 - 51] - d = [a + b for a, b in zip(demand2020_top, demand2020_rest)] - # SSP2 R11 baseline GDP projection gdp_growth = pd.read_excel( @@ -76,7 +75,8 @@ def gen_mock_demand_cement(scenario): gdp_growth = gdp_growth.loc[(gdp_growth['Scenario']=='baseline') & (gdp_growth['Region']!='World')].\ drop(['Model', 'Variable', 'Unit', 'Notes', 2000, 2005], axis = 1) - gdp_growth['Region'] = 'R11_'+ gdp_growth['Region'] + d = [a + b for a, b in zip(demand2020_top, demand2020_rest)] + gdp_growth['Region'] = region_set + gdp_growth['Region'] # # Regions setting for IMAGE # region_cement = pd.read_excel( @@ -113,8 +113,7 @@ def gen_mock_demand_cement(scenario): # Directly assigned countries from the table on p43 - - demand2020_cement = pd.DataFrame({'Region':r, 'value':d}).\ + demand2020_cement = pd.DataFrame({'Region':nodes, 'value':d}).\ join(gdp_growth.set_index('Region'), on='Region').rename(columns={'Region':'node'}) # demand2010_cement = demand2010_cement.\ @@ -142,8 +141,6 @@ def gen_mock_demand_cement(scenario): return demand2020_cement - - def gen_data_cement(scenario, dry_run=False): """Generate data for materials representation of steel industry. @@ -173,11 +170,12 @@ def gen_data_cement(scenario, dry_run=False): yv_ya = s_info.yv_ya fmy = s_info.y0 nodes.remove('World') - nodes.remove("R11_RCPA") # Do not parametrize GLB region the same way if "R11_GLB" in nodes: nodes.remove("R11_GLB") + if "R12_GLB" in nodes: + nodes.remove("R12_GLB") # for t in s_info.set['technology']: for t in config['technology']['add']: diff --git a/message_ix_models/model/material/data_generic.py b/message_ix_models/model/material/data_generic.py index 3c92b25e08..74311a5d1c 100644 --- a/message_ix_models/model/material/data_generic.py +++ b/message_ix_models/model/material/data_generic.py @@ -78,11 +78,12 @@ def gen_data_generic(scenario, dry_run=False): # Do not parametrize GLB region the same way if "R11_GLB" in nodes: nodes.remove("R11_GLB") + if "R12_GLB" in nodes: + nodes.remove("R12_GLB") # 'World' is included by default when creating a message_ix.Scenario(). # Need to remove it for the China bare model nodes.remove('World') - nodes.remove("R11_RCPA") for t in config["technology"]["add"]: diff --git a/message_ix_models/model/material/data_petro.py b/message_ix_models/model/material/data_petro.py index b6d8a18822..eb0de17430 100644 --- a/message_ix_models/model/material/data_petro.py +++ b/message_ix_models/model/material/data_petro.py @@ -26,7 +26,7 @@ def read_data_petrochemicals(scenario): s_info = ScenarioInfo(scenario) fname = "petrochemicals_techno_economic.xlsx" - if "R11_CHN" in s_info.N: + if "R12_CHN" in s_info.N: sheet_n = "data_R12" else: sheet_n = "data_R11" @@ -48,6 +48,7 @@ def gen_mock_demand_petro(scenario): modelyears = s_info.Y #s_info.Y is only for modeling years fmy = s_info.y0 nodes = s_info.N + nodes.remove('World') # 2018 production # Use as 2020 @@ -58,11 +59,16 @@ def gen_mock_demand_petro(scenario): # For R12: China and CPA demand divided by 0.1 and 0.9. - if "R11_CHN" in s_info.N: - sheet_n = "data_R12" + # SSP2 R11 baseline GDP projection - r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', 'R11_MEA',\ - 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU',"R11_CHN"] + # The orders of the regions + #r = ['R12_AFR', 'R12_RCPA', 'R12_EEU', 'R12_FSU', 'R12_LAM', 'R12_MEA',\ + # 'R12_NAM', 'R12_PAO', 'R12_PAS', 'R12_SAS', 'R12_WEU',"R12_CHN"] + + if "R12_CHN" in nodes: + nodes.remove('R12_GLB') + sheet_n = "data_R12" + region_set = 'R12_' d_ethylene = [0.853064, 3.2, 2.88788, 8.780442, 8.831229,21.58509, 32.54942, 8.94036, 28.84327, 28.12818, 16.65209, 28.84327] @@ -72,9 +78,9 @@ def gen_mock_demand_petro(scenario): 7.4503, 7.497867, 17.30965, 11.1014, 28.84327] else: - - r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', \ - 'R11_MEA', 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU'] + nodes.remove('R11_GLB') + sheet_n = "data_R11" + region_set = 'R11_' d_ethylene = [0.853064, 32.04327, 2.88788, 8.780442, 8.831229,21.58509, 32.54942, 8.94036, 7.497867, 28.12818, 16.65209] @@ -83,8 +89,6 @@ def gen_mock_demand_petro(scenario): d_BTX = [0.426532, 32.04327, 2.88788, 1.463407, 5.298738, 12.95105, 16.27471, 7.4503, 7.497867, 17.30965, 11.1014] - - # SSP2 R11 baseline GDP projection gdp_growth = pd.read_excel( context.get_path("material", "iamc_db ENGAGE baseline GDP PPP.xlsx"), sheet_name=sheet_n, @@ -94,23 +98,23 @@ def gen_mock_demand_petro(scenario): (gdp_growth['Region']!='World')].drop(['Model', 'Variable', 'Unit', \ 'Notes', 2000, 2005], axis = 1) - gdp_growth['Region'] = 'R11_'+ gdp_growth['Region'] + gdp_growth['Region'] = region_set + gdp_growth['Region'] list = [] for e in ["ethylene","propylene","BTX"]: if e == "ethylene": - demand2020 = pd.DataFrame({'Region':r, 'Val':d_ethylene}).\ + demand2020 = pd.DataFrame({'Region':nodes, 'Val':d_ethylene}).\ join(gdp_growth.set_index('Region'), on='Region').\ rename(columns={'Region':'node'}) if e == "propylene": - demand2020 = pd.DataFrame({'Region':r, 'Val':d_propylene}).\ + demand2020 = pd.DataFrame({'Region':nodes, 'Val':d_propylene}).\ join(gdp_growth.set_index('Region'), on='Region').\ rename(columns={'Region':'node'}) if e == "BTX": - demand2020 = pd.DataFrame({'Region':r, 'Val':d_BTX}).\ + demand2020 = pd.DataFrame({'Region':nodes, 'Val':d_BTX}).\ join(gdp_growth.set_index('Region'), on='Region').\ rename(columns={'Region':'node'}) @@ -158,11 +162,14 @@ def gen_data_petro_chemicals(scenario, dry_run=False): yv_ya = s_info.yv_ya fmy = s_info.y0 nodes.remove('World') - nodes.remove("R11_RCPA") # Do not parametrize GLB region the same way if "R11_GLB" in nodes: nodes.remove("R11_GLB") + global_region = "R11_GLB" + if "R12_GLB" in nodes: + nodes.remove("R12_GLB") + global_region = "R12_GLB" for t in config["technology"]["add"]: @@ -211,12 +218,12 @@ def gen_data_petro_chemicals(scenario, dry_run=False): df = make_df(param_name, technology=t, commodity=com, \ level=lev, mode= mod, \ value=val[regions[regions==rg].index[0]], unit='t', \ - node_loc=rg, node_origin="R11_GLB", **common) + node_loc=rg, node_origin=global_region, **common) elif (param_name == "output") and (lev == "export"): df = make_df(param_name, technology=t, commodity=com, \ level=lev, mode=mod, \ value=val[regions[regions==rg].index[0]], unit='t', \ - node_loc=rg, node_dest="R11_GLB", **common) + node_loc=rg, node_dest=global_region, **common) else: df = (make_df(param_name, technology=t, commodity=com, \ level=lev, mode=mod, \ @@ -224,7 +231,7 @@ def gen_data_petro_chemicals(scenario, dry_run=False): node_loc=rg, **common).pipe(same_node)) # Copy parameters to all regions, when node_loc is not GLB - if (len(regions) == 1) and (rg != "R11_GLB"): + if (len(regions) == 1) and (rg != global_region): # print("copying to all R11", rg, lev) df['node_loc'] = None df = df.pipe(broadcast, node_loc=nodes)#.pipe(same_node) @@ -258,8 +265,8 @@ def gen_data_petro_chemicals(scenario, dry_run=False): node_loc=rg, **common) # Copy parameters to all regions - if (len(regions) == 1) and (rg != "R11_GLB"): - if len(set(df['node_loc'])) == 1 and list(set(df['node_loc']))[0]!='R11_GLB': + if (len(regions) == 1) and (rg != global_region): + if len(set(df['node_loc'])) == 1 and list(set(df['node_loc']))[0]!= global_region: # print("Copying to all R11") df['node_loc'] = None df = df.pipe(broadcast, node_loc=nodes) diff --git a/message_ix_models/model/material/data_steel.py b/message_ix_models/model/material/data_steel.py index 92d5a9a009..2a6df4fb42 100644 --- a/message_ix_models/model/material/data_steel.py +++ b/message_ix_models/model/material/data_steel.py @@ -39,26 +39,27 @@ def gen_mock_demand_steel(scenario): modelyears = s_info.Y #s_info.Y is only for modeling years fmy = s_info.y0 nodes = s_info.N + nodes.remove('World') + + # The order: + #r = ['R12_AFR', 'R12_RCPA', 'R12_EEU', 'R12_FSU', 'R12_LAM', 'R12_MEA',\ + #'R12_NAM', 'R12_PAO', 'R12_PAS', 'R12_SAS', 'R12_WEU',"R12_CHN"] # True steel use 2010 [Mt/year] # https://www.worldsteel.org/en/dam/jcr:0474d208-9108-4927-ace8-4ac5445c5df8/World+Steel+in+Figures+2017.pdf # For R12: China and CPA demand divided by 0.1 and 0.9. - if "R11_CHN" in nodes: + if "R12_CHN" in nodes: + nodes.remove('R12_GLB') sheet_n = "data_R12" - - r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', 'R11_MEA',\ - 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU',"R11_CHN"] - + region_set = 'R12_' d = [35,5.37, 70, 53, 49, 39, 130, 80, 45, 96, 100,531.63] else: + nodes.remove('R11_GLB') sheet_n = "data_R11" - - r = ['R11_AFR', 'R11_CPA', 'R11_EEU', 'R11_FSU', 'R11_LAM', \ - 'R11_MEA', 'R11_NAM', 'R11_PAO', 'R11_PAS', 'R11_SAS', 'R11_WEU'] - + region_set = 'R11_' d = [35, 537, 70, 53, 49, 39, 130, 80, 45, 96, 100] # MEA change from 39 to 9 to make it feasible (coal supply bound) @@ -71,9 +72,9 @@ def gen_mock_demand_steel(scenario): gdp_growth = gdp_growth.loc[(gdp_growth['Scenario']=='baseline') & (gdp_growth['Region']!='World')].\ drop(['Model', 'Variable', 'Unit', 'Notes', 2000, 2005], axis = 1) - gdp_growth['Region'] = 'R11_'+ gdp_growth['Region'] + gdp_growth['Region'] = region_set + gdp_growth['Region'] - demand2010_steel = pd.DataFrame({'Region':r, 'Val':d}).\ + demand2010_steel = pd.DataFrame({'Region':nodes, 'Val':d}).\ join(gdp_growth.set_index('Region'), on='Region').rename(columns={'Region':'node'}) demand2010_steel.iloc[:,3:] = demand2010_steel.iloc[:,3:].\ @@ -153,11 +154,14 @@ def gen_data_steel(scenario, dry_run=False): yv_ya = s_info.yv_ya fmy = s_info.y0 nodes.remove('World') - nodes.remove("R11_RCPA") # Do not parametrize GLB region the same way if "R11_GLB" in nodes: nodes.remove("R11_GLB") + global_region = "R11_GLB" + if "R12_GLB" in nodes: + nodes.remove("R12_GLB") + global_region = "R12_GLB" # for t in s_info.set['technology']: for t in config['technology']['add']: @@ -230,12 +234,12 @@ def gen_data_steel(scenario, dry_run=False): df = make_df(param_name, technology=t, commodity=com, \ level=lev, \ value=val[regions[regions==rg].index[0]], mode=mod, unit='t', \ - node_loc=rg, node_origin="R11_GLB", **common) + node_loc=rg, node_origin=global_region, **common) elif (param_name == "output") and (lev == "export"): df = make_df(param_name, technology=t, commodity=com, \ level=lev, \ value=val[regions[regions==rg].index[0]], mode=mod, unit='t', \ - node_loc=rg, node_dest="R11_GLB", **common) + node_loc=rg, node_dest=global_region, **common) else: df = (make_df(param_name, technology=t, commodity=com, \ level=lev, \ @@ -244,7 +248,7 @@ def gen_data_steel(scenario, dry_run=False): .pipe(same_node)) # Copy parameters to all regions, when node_loc is not GLB - if (len(regions) == 1) and (rg != "R11_GLB"): + if (len(regions) == 1) and (rg != global_region): df['node_loc'] = None df = df.pipe(broadcast, node_loc=nodes)#.pipe(same_node) # Use same_node only for non-trade technologies @@ -276,7 +280,7 @@ def gen_data_steel(scenario, dry_run=False): node_loc=rg, **common) # Copy parameters to all regions - if len(set(df['node_loc'])) == 1 and list(set(df['node_loc']))[0]!='R11_GLB': + if len(set(df['node_loc'])) == 1 and list(set(df['node_loc']))[0]!=global_region: df['node_loc'] = None df = df.pipe(broadcast, node_loc=nodes) From 4eda1b5509a131e8abb6db6b5ae34cb5e463109e Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Mon, 14 Jun 2021 14:59:57 +0200 Subject: [PATCH 283/774] Extend the gp values until 2110 --- .../data/material/iamc_db ENGAGE baseline GDP PPP.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/iamc_db ENGAGE baseline GDP PPP.xlsx b/message_ix_models/data/material/iamc_db ENGAGE baseline GDP PPP.xlsx index 542468ec8d..e321e82967 100644 --- a/message_ix_models/data/material/iamc_db ENGAGE baseline GDP PPP.xlsx +++ b/message_ix_models/data/material/iamc_db ENGAGE baseline GDP PPP.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:63f801d7590e7cdf9fed7e50e5bf0a896968e7c435b5a93612e6cdefae4dc2c9 -size 31033 +oid sha256:58ed30fb35f06d2f6d109bd6f4411158a3f5bb32b9191f743953e7ea9c163270 +size 34879 From 1a8df77bba1dfd91e8bd225cc099d2d4d5b77dc2 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Mon, 14 Jun 2021 15:02:16 +0200 Subject: [PATCH 284/774] Add high value chemicals as cumulative of ethylene, propylene, btx when ethylene, propylene, btx are seperate, commodity prices can be zero which created errors in macro calibration for these sectors. --- .../petrochemicals_techno_economic.xlsx | 4 +- message_ix_models/data/material/set.yaml | 2 + .../model/material/data_petro.py | 115 ++++++++++-------- 3 files changed, 70 insertions(+), 51 deletions(-) diff --git a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx index 3ef87fb74e..3365995aa8 100644 --- a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx +++ b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:c6de2ea8201dc4de429b843b010ca58e045c74bbfd05d8a0d1deb8040a51db7c -size 663310 +oid sha256:b4ad793b75f2bdbabeff6032086b973c087b75a71af228f9bb58efcdfa3d216e +size 662318 diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index eab03e8e74..6c4ab10fef 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -319,6 +319,7 @@ petro_chemicals: require: - crudeoil add: + - HVC - naphtha - kerosene - diesel @@ -391,6 +392,7 @@ petro_chemicals: - import_petro - export_petro - feedstock_t/d + - demand_HVC remove: # Any representation of refinery. - ref_hil diff --git a/message_ix_models/model/material/data_petro.py b/message_ix_models/model/material/data_petro.py index eb0de17430..ac7fdc0773 100644 --- a/message_ix_models/model/material/data_petro.py +++ b/message_ix_models/model/material/data_petro.py @@ -70,24 +70,30 @@ def gen_mock_demand_petro(scenario): sheet_n = "data_R12" region_set = 'R12_' - d_ethylene = [0.853064, 3.2, 2.88788, 8.780442, 8.831229,21.58509, - 32.54942, 8.94036, 28.84327, 28.12818, 16.65209, 28.84327] - d_propylene = [0.426532, 3.2, 2.88788, 1.463407, 5.298738, 10.79255, - 16.27471, 7.4503, 7.497867, 17.30965, 11.1014,28.84327] - d_BTX = [0.426532, 3.2, 2.88788, 1.463407, 5.298738, 12.95105, 16.27471, - 7.4503, 7.497867, 17.30965, 11.1014, 28.84327] + d_HVC = [1.706128, 9.6, 8.66364, 11.707256, 19.428705, 47.48719, 65.09884, + 23.84096, 43.839004, 62.74748, 38.85489, 86.52981] + + # d_ethylene = [0.853064, 3.2, 2.88788, 8.780442, 8.831229,21.58509, + # 32.54942, 8.94036, 28.84327, 28.12818, 16.65209, 28.84327] + # d_propylene = [0.426532, 3.2, 2.88788, 1.463407, 5.298738, 12.95105, + # 16.27471, 7.4503, 7.497867, 17.30965, 11.1014,28.84327] + # d_BTX = [0.426532, 3.2, 2.88788, 1.463407, 5.298738, 12.95105, 16.27471, + # 7.4503, 7.497867, 17.30965, 11.1014, 28.84327] else: nodes.remove('R11_GLB') sheet_n = "data_R11" region_set = 'R11_' - d_ethylene = [0.853064, 32.04327, 2.88788, 8.780442, 8.831229,21.58509, - 32.54942, 8.94036, 7.497867, 28.12818, 16.65209] - d_propylene = [0.426532, 32.04327, 2.88788, 1.463407, 5.298738, 10.79255, - 16.27471, 7.4503, 7.497867, 17.30965, 11.1014] - d_BTX = [0.426532, 32.04327, 2.88788, 1.463407, 5.298738, 12.95105, 16.27471, - 7.4503, 7.497867, 17.30965, 11.1014] + d_HVC = [1.706128, 96.12981, 8.66364, 11.707256, 19.428705, 47.48719, 65.09884, + 23.84096, 43.839004, 62.74748, 38.85489] + + # d_ethylene = [0.853064, 32.04327, 2.88788, 8.780442, 8.831229,21.58509, + # 32.54942, 8.94036, 7.497867, 28.12818, 16.65209] + # d_propylene = [0.426532, 32.04327, 2.88788, 1.463407, 5.298738, 10.79255, + # 16.27471, 7.4503, 7.497867, 17.30965, 11.1014] + # d_BTX = [0.426532, 32.04327, 2.88788, 1.463407, 5.298738, 12.95105, 16.27471, + # 7.4503, 7.497867, 17.30965, 11.1014] gdp_growth = pd.read_excel( context.get_path("material", "iamc_db ENGAGE baseline GDP PPP.xlsx"), @@ -100,33 +106,38 @@ def gen_mock_demand_petro(scenario): gdp_growth['Region'] = region_set + gdp_growth['Region'] - list = [] - - for e in ["ethylene","propylene","BTX"]: - if e == "ethylene": - demand2020 = pd.DataFrame({'Region':nodes, 'Val':d_ethylene}).\ - join(gdp_growth.set_index('Region'), on='Region').\ - rename(columns={'Region':'node'}) + # list = [] + # + # for e in ["ethylene","propylene","BTX"]: + # if e == "ethylene": + # demand2020 = pd.DataFrame({'Region':nodes, 'Val':d_ethylene}).\ + # join(gdp_growth.set_index('Region'), on='Region').\ + # rename(columns={'Region':'node'}) + # + # if e == "propylene": + # demand2020 = pd.DataFrame({'Region':nodes, 'Val':d_propylene}).\ + # join(gdp_growth.set_index('Region'), on='Region').\ + # rename(columns={'Region':'node'}) + # + # if e == "BTX": + # demand2020 = pd.DataFrame({'Region':nodes, 'Val':d_BTX}).\ + # join(gdp_growth.set_index('Region'), on='Region').\ + # rename(columns={'Region':'node'}) - if e == "propylene": - demand2020 = pd.DataFrame({'Region':nodes, 'Val':d_propylene}).\ - join(gdp_growth.set_index('Region'), on='Region').\ - rename(columns={'Region':'node'}) + demand2020 = pd.DataFrame({'Region':nodes, 'Val':d_HVC}).\ + join(gdp_growth.set_index('Region'), on='Region').\ + rename(columns={'Region':'node'}) - if e == "BTX": - demand2020 = pd.DataFrame({'Region':nodes, 'Val':d_BTX}).\ - join(gdp_growth.set_index('Region'), on='Region').\ - rename(columns={'Region':'node'}) + demand2020.iloc[:,3:] = demand2020.iloc[:,3:].div(demand2020[2020], axis=0).\ + multiply(demand2020["Val"], axis=0) - demand2020.iloc[:,3:] = demand2020.iloc[:,3:].div(demand2020[2020], axis=0).\ - multiply(demand2020["Val"], axis=0) + demand2020 = pd.melt(demand2020.drop(['Val', 'Scenario'], axis=1),\ + id_vars=['node'], var_name='year', value_name = 'value') - demand2020 = pd.melt(demand2020.drop(['Val', 'Scenario'], axis=1),\ - id_vars=['node'], var_name='year', value_name = 'value') + #list.append(demand2020) - list.append(demand2020) - - return list[0], list[1], list[2] + #return list[0], list[1], list[2] + return demand2020 # China 2006: 22 kg/cap HVC demand. 2006 population: 1.311 billion @@ -276,23 +287,29 @@ def gen_data_petro_chemicals(scenario, dry_run=False): # Add demand # Create external demand param - demand_e,demand_p,demand_BTX = gen_mock_demand_petro(scenario) + #demand_e,demand_p,demand_BTX = gen_mock_demand_petro(scenario) + demand_HVC = gen_mock_demand_petro(scenario) paramname = "demand" - df_e = make_df(paramname, level='final_material', commodity="ethylene", \ - value=demand_e.value, unit='t',year=demand_e.year, time='year', \ - node=demand_e.node)#.pipe(broadcast, node=nodes) - results["demand"].append(df_e) - - df_p = make_df(paramname, level='final_material', commodity="propylene", \ - value=demand_p.value, unit='t',year=demand_p.year, time='year', \ - node=demand_p.node)#.pipe(broadcast, node=nodes) - results["demand"].append(df_p) - - df_BTX = make_df(paramname, level='final_material', commodity="BTX", \ - value=demand_BTX.value, unit='t',year=demand_BTX.year, time='year', \ - node=demand_BTX.node)#.pipe(broadcast, node=nodes) - results["demand"].append(df_BTX) + df_HVC = make_df(paramname, level='demand', commodity="HVC", \ + value=demand_HVC.value, unit='t',year=demand_HVC.year, time='year', \ + node=demand_HVC.node)#.pipe(broadcast, node=nodes) + results["demand"].append(df_HVC) + + # df_e = make_df(paramname, level='final_material', commodity="ethylene", \ + # value=demand_e.value, unit='t',year=demand_e.year, time='year', \ + # node=demand_e.node)#.pipe(broadcast, node=nodes) + # results["demand"].append(df_e) + # + # df_p = make_df(paramname, level='final_material', commodity="propylene", \ + # value=demand_p.value, unit='t',year=demand_p.year, time='year', \ + # node=demand_p.node)#.pipe(broadcast, node=nodes) + # results["demand"].append(df_p) + # + # df_BTX = make_df(paramname, level='final_material', commodity="BTX", \ + # value=demand_BTX.value, unit='t',year=demand_BTX.year, time='year', \ + # node=demand_BTX.node)#.pipe(broadcast, node=nodes) + # results["demand"].append(df_BTX) # Special treatment for time-varying params From 5a69bb7084c25b088f0640e358938fedf49aa372 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Mon, 28 Jun 2021 18:54:50 +0200 Subject: [PATCH 285/774] Revert petro chemicals demand --- .../data/material/aluminum_techno_economic.xlsx | 4 ++-- message_ix_models/model/material/data_petro.py | 13 ++----------- 2 files changed, 4 insertions(+), 13 deletions(-) diff --git a/message_ix_models/data/material/aluminum_techno_economic.xlsx b/message_ix_models/data/material/aluminum_techno_economic.xlsx index dd50aa68b2..a8d8f6f6cc 100644 --- a/message_ix_models/data/material/aluminum_techno_economic.xlsx +++ b/message_ix_models/data/material/aluminum_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:ad35e8909f8620dedce8862468bb20e99955e30576ae12c20f8e8ad3c573f15d -size 124429 +oid sha256:81323ac954c9d459eec37b7d10663afad0cf2530f5361d1dd6ff7c373a5bfd2d +size 120640 diff --git a/message_ix_models/model/material/data_petro.py b/message_ix_models/model/material/data_petro.py index ac7fdc0773..06722113d9 100644 --- a/message_ix_models/model/material/data_petro.py +++ b/message_ix_models/model/material/data_petro.py @@ -70,23 +70,14 @@ def gen_mock_demand_petro(scenario): sheet_n = "data_R12" region_set = 'R12_' - d_HVC = [1.706128, 9.6, 8.66364, 11.707256, 19.428705, 47.48719, 65.09884, - 23.84096, 43.839004, 62.74748, 38.85489, 86.52981] - - # d_ethylene = [0.853064, 3.2, 2.88788, 8.780442, 8.831229,21.58509, - # 32.54942, 8.94036, 28.84327, 28.12818, 16.65209, 28.84327] - # d_propylene = [0.426532, 3.2, 2.88788, 1.463407, 5.298738, 12.95105, - # 16.27471, 7.4503, 7.497867, 17.30965, 11.1014,28.84327] - # d_BTX = [0.426532, 3.2, 2.88788, 1.463407, 5.298738, 12.95105, 16.27471, - # 7.4503, 7.497867, 17.30965, 11.1014, 28.84327] + d_HVC = [2, 7.5, 30, 4, 11, 42, 60, 32, 30, 29, 35, 67.5] else: nodes.remove('R11_GLB') sheet_n = "data_R11" region_set = 'R11_' - d_HVC = [1.706128, 96.12981, 8.66364, 11.707256, 19.428705, 47.48719, 65.09884, - 23.84096, 43.839004, 62.74748, 38.85489] + d_HVC = [2, 75, 30, 4, 11, 42, 60, 32, 30, 29, 35] # d_ethylene = [0.853064, 32.04327, 2.88788, 8.780442, 8.831229,21.58509, # 32.54942, 8.94036, 7.497867, 28.12818, 16.65209] From 20acef938cc984af6b0b7605f25ec384bb39a5a3 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 1 Jul 2021 16:29:34 +0200 Subject: [PATCH 286/774] Fix an error adjusting the historical activity and demand --- message_ix_models/model/material/data_util.py | 112 ++++++++++++++---- 1 file changed, 88 insertions(+), 24 deletions(-) diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index 307e0d2e6f..70bb3aa6ca 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -5,29 +5,41 @@ from .util import read_config import re +from message_data.tools import ( + ScenarioInfo) + def modify_demand_and_hist_activity(scen): """Take care of demand changes due to the introduction of material parents - Shed industrial energy demand properly. - Also need take care of remove dynamic constraints for certain energy carriers. - Adjust the historical activity of the related industry technologies - that provide output to different categories of industrial demand (e.g. - i_therm, i_spec, i_feed). The historical activity is reduced the same % - - as the industrial demand is reduced in modfiy_demand function. + as the industrial demand is reduced. """ # NOTE Temporarily modifying industrial energy demand # From IEA database (dumped to an excel) context = read_config() + s_info = ScenarioInfo(scen) fname = "MESSAGEix-Materials_final_energy_industry.xlsx" + + if "R12_CHN" in s_info.N: + sheet_n = "R12" + region_type = 'R12_' + region_name_CPA = "RCPA" + region_name_CHN = "CHN" + else: + sheet_n = "R11" + region_type = 'R11_' + region_name_CPA = "CPA" + df = pd.read_excel(context.get_path("material", fname),\ - sheet_name="Export Worksheet") + sheet_name=sheet_n, usecols = "A:F") + + print("Are the correct numbers read?") + print(df) # Filter the necessary variables df = df[(df["SECTOR"]== "feedstock (petrochemical industry)")| @@ -39,6 +51,9 @@ def modify_demand_and_hist_activity(scen): (df["SECTOR"]== "industry (total)")] df = df[df["RYEAR"]==2015] + print("Is the filter correct?") + print(df) + # Retreive data for i_spec (Excludes petrochemicals as the share is negligable) # Aluminum, cement and steel included. # NOTE: Steel has high shares (previously it was not inlcuded in i_spec) @@ -63,6 +78,8 @@ def modify_demand_and_hist_activity(scen): df_spec_new.loc[df_spec_new["SECTOR"]=="industry (chemicals)","i_spec"] * 0.7 df_spec_new = df_spec_new.groupby(["REGION"]).sum().reset_index() + print("spec") + print(df_spec_new) # Retreive data for i_feed: Only for petrochemicals # It is assumed that the sectors that are explicitly covered in MESSAGE are @@ -82,6 +99,9 @@ def modify_demand_and_hist_activity(scen): i = i + 1 df_feed_new = pd.concat([df_feed_temp,df_feed_new],ignore_index = True) + print("feed") + print(df_feed_new) + # df_feed = df[(df["SECTOR"]== "feedstock (petrochemical industry)") & \ # (df["FUEL"]== "total") ] # df_feed_total = df[(df["SECTOR"]== "feedstock (total)") \ @@ -132,11 +152,16 @@ def modify_demand_and_hist_activity(scen): # Modify CPA based on https://www.iea.org/sankey/#?c=Japan&s=Final%20consumption. # Since the value did not allign with the one in the IEA website. index = ((df_therm_new["SECTOR"]=="industry (iron and steel)") \ - & (df_therm_new["REGION"]=="CPA")) + & ((df_therm_new["REGION"]==region_name_CPA) | \ + (df_therm_new["REGION"]==region_name_CHN))) + df_therm_new.loc[index,"i_therm"] = 0.2 df_therm_new = df_therm_new.groupby(["REGION"]).sum().reset_index() + print("therm") + print(df_therm_new) + # TODO: Useful technology efficiencies will also be included # Add the modified demand and historical activity to the scenario @@ -157,7 +182,14 @@ def modify_demand_and_hist_activity(scen): useful_feed = scen.par('demand', filters={'commodity':'i_feed'}) for r in df_therm_new["REGION"]: - r_MESSAGE = "R11_" + r + r_MESSAGE = region_type + r + + if ((r_MESSAGE == "R12_RCPA") | (r_MESSAGE == "R12_CHN")): + print(r_MESSAGE) + print("Thermal before multiplication") + print(useful_thermal.loc[useful_thermal["node"]== r_MESSAGE]) + print(thermal_df_hist.loc[thermal_df_hist["node_loc"]== r_MESSAGE]) + useful_thermal.loc[useful_thermal["node"]== r_MESSAGE ,"value"] = \ useful_thermal.loc[useful_thermal["node"]== r_MESSAGE,"value"] * \ (1 - df_therm_new.loc[df_therm_new["REGION"]==r,"i_therm"].values[0]) @@ -166,8 +198,21 @@ def modify_demand_and_hist_activity(scen): thermal_df_hist.loc[thermal_df_hist["node_loc"]== r_MESSAGE,"value"] * \ (1 - df_therm_new.loc[df_therm_new["REGION"]==r,"i_therm"].values[0]) + if ((r_MESSAGE == "R12_RCPA") | (r_MESSAGE == "R12_CHN")): + print(r_MESSAGE) + print("Thermal after multiplication") + print(useful_thermal.loc[useful_thermal["node"]== r_MESSAGE]) + print(thermal_df_hist.loc[thermal_df_hist["node_loc"]== r_MESSAGE]) + for r in df_spec_new["REGION"]: - r_MESSAGE = "R11_" + r + r_MESSAGE = region_type + r + + if ((r_MESSAGE == "R12_RCPA") | (r_MESSAGE == "R12_CHN")): + print(r_MESSAGE) + print("Spec before multiplication") + print(useful_spec.loc[useful_spec["node"]== r_MESSAGE]) + print(spec_df_hist.loc[spec_df_hist["node_loc"]== r_MESSAGE]) + useful_spec.loc[useful_spec["node"]== r_MESSAGE ,"value"] = \ useful_spec.loc[useful_spec["node"]== r_MESSAGE,"value"] * \ (1 - df_spec_new.loc[df_spec_new["REGION"]==r,"i_spec"].values[0]) @@ -176,8 +221,21 @@ def modify_demand_and_hist_activity(scen): spec_df_hist.loc[spec_df_hist["node_loc"]== r_MESSAGE,"value"] * \ (1 - df_spec_new.loc[df_spec_new["REGION"]==r,"i_spec"].values[0]) + if ((r_MESSAGE == "R12_RCPA") | (r_MESSAGE == "R12_CHN")): + print(r_MESSAGE) + print("Spec after multiplication") + print(useful_spec.loc[useful_spec["node"]== r_MESSAGE]) + print(spec_df_hist.loc[spec_df_hist["node_loc"]== r_MESSAGE]) + for r in df_feed_new["REGION"]: - r_MESSAGE = "R11_" + r + r_MESSAGE = region_type + r + + if ((r_MESSAGE == "R12_RCPA") | (r_MESSAGE == "R12_CHN")): + print(r_MESSAGE) + print("Feedstock before multiplication") + print(useful_feed.loc[useful_feed["node"]== r_MESSAGE]) + print(feed_df_hist.loc[feed_df_hist["node_loc"]== r_MESSAGE]) + useful_feed.loc[useful_feed["node"]== r_MESSAGE ,"value"] = \ useful_feed.loc[useful_feed["node"]== r_MESSAGE,"value"] * \ (1 - df_feed_new.loc[df_feed_new["REGION"]==r,"i_feed"].values[0]) @@ -186,6 +244,12 @@ def modify_demand_and_hist_activity(scen): feed_df_hist.loc[feed_df_hist["node_loc"]== r_MESSAGE,"value"] * \ (1 - df_feed_new.loc[df_feed_new["REGION"]==r,"i_feed"].values[0]) + if ((r_MESSAGE == "R12_RCPA") | (r_MESSAGE == "R12_CHN")): + print(r_MESSAGE) + print("Feedstock after multiplication") + print(useful_feed.loc[useful_feed["node"]== r_MESSAGE]) + print(feed_df_hist.loc[feed_df_hist["node_loc"]== r_MESSAGE]) + scen.check_out() scen.add_par("demand",useful_thermal) scen.add_par("demand",useful_spec) @@ -249,7 +313,7 @@ def modify_demand_and_hist_activity(scen): # Read in technology-specific parameters from input xlsx # Now used for steel and cement, which are in one file -def read_sector_data(sectname,scenario): +def read_sector_data(scenario,sectname): import numpy as np @@ -258,10 +322,10 @@ def read_sector_data(sectname,scenario): s_info = ScenarioInfo(scenario) - if "R11_CHN" in s_info.N: - sheet_n = sect_name + "_R12" + if "R12_CHN" in s_info.N: + sheet_n = sectname + "_R12" else: - sheet_n = sect_name + "_R11" + sheet_n = sectname + "_R11" # data_df = data_steel_china.append(data_cement_china, ignore_index=True) data_df = pd.read_excel( @@ -302,7 +366,7 @@ def read_sector_data(sectname,scenario): # Read in time-dependent parameters # Now only used to add fuel cost for bare model -def read_timeseries(filename,scenario): +def read_timeseries(scenario,filename): import numpy as np @@ -315,10 +379,10 @@ def read_timeseries(filename,scenario): # else: # sheet_name = "timeseries" - if "R11_CHN" in s_info.N: - sheet_n = timeseries_R12 + if "R12_CHN" in s_info.N: + sheet_n = "timeseries_R12" else: - sheet_n = timeseries_R11 + sheet_n = "timeseries_R11" # Read the file df = pd.read_excel( @@ -336,7 +400,7 @@ def read_timeseries(filename,scenario): return df -def read_rel(filename): +def read_rel(scenario,filename): import numpy as np @@ -345,10 +409,10 @@ def read_rel(filename): s_info = ScenarioInfo(scenario) - if "R11_CHN" in s_info.N: - sheet_n = relations_R12 + if "R12_CHN" in s_info.N: + sheet_n = "relations_R12" else: - sheet_n = relations_R11 + sheet_n = "relations_R11" # Read the file data_rel = pd.read_excel( From e023ea06a5162eeef694dba8a61f999146f75846 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 13 Jul 2021 11:51:24 +0200 Subject: [PATCH 287/774] Carry the new reporting changes --- message_ix_models/model/material/data_util.py | 1 + 1 file changed, 1 insertion(+) diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index 70bb3aa6ca..095809ce83 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -34,6 +34,7 @@ def modify_demand_and_hist_activity(scen): sheet_n = "R11" region_type = 'R11_' region_name_CPA = "CPA" + region_name_CHN = '' df = pd.read_excel(context.get_path("material", fname),\ sheet_name=sheet_n, usecols = "A:F") From bdff4e420b01b73c12a721973679b2610b54ea55 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Fri, 16 Jul 2021 13:30:18 +0200 Subject: [PATCH 288/774] Set old reproting false --- message_ix_models/model/material/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 2112fdf8a5..b137159d2e 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -173,7 +173,7 @@ def solve_scen(context, datafile, model_name, scenario_name): @cli.command("report") @click.option( "--old_reporting", - default=True, + default=False, help="If True old reporting is merged with the new variables.", ) @click.option("--scenario_name", default="NoPolicy") From 265ccc109535cb418d8511fc4d9a7a24844ced01 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Fri, 6 Aug 2021 15:01:05 +0200 Subject: [PATCH 289/774] Update other scrap and minimum recycling rate --- .../data/material/Global_steel_cement_MESSAGE.xlsx | 4 ++-- message_ix_models/data/material/aluminum_techno_economic.xlsx | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx index 8f955969b6..c301bd2d67 100644 --- a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx +++ b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:292467355f3eff2ab93b1bcef28e84756564d3daad4e5d6595786345d42f0041 -size 81056 +oid sha256:b495e5b368f8fb59e3af24e33fbda584cef9993055c2846a2f10a677aba4ba8a +size 91153 diff --git a/message_ix_models/data/material/aluminum_techno_economic.xlsx b/message_ix_models/data/material/aluminum_techno_economic.xlsx index a8d8f6f6cc..3a73c3f24b 100644 --- a/message_ix_models/data/material/aluminum_techno_economic.xlsx +++ b/message_ix_models/data/material/aluminum_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:81323ac954c9d459eec37b7d10663afad0cf2530f5361d1dd6ff7c373a5bfd2d -size 120640 +oid sha256:02560c2866deeebf2603ef5ef04ba42c7c1a01786559538fa0ffa66be60203ec +size 120761 From a6a07d14ec2bd76d5faf38cbfaba4e071692656a Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Fri, 6 Aug 2021 15:01:41 +0200 Subject: [PATCH 290/774] Make sure all collected scrap is used in secondary production --- message_ix_models/data/material/set.yaml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 6c4ab10fef..07b7a98ac4 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -466,6 +466,7 @@ steel: - ["steel","old_scrap_3"] - ["steel","end_of_life"] - ["steel","product"] + - ["steel","new_scrap"] cement: commodity: @@ -575,6 +576,7 @@ aluminum: - ["aluminum","old_scrap_3"] - ["aluminum","end_of_life"] - ["aluminum","product"] + - ["aluminum","new_scrap"] fertilizer: commodity: From 46a648bd7ba9107138e1b70216f2bbaa300b7fc8 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Fri, 6 Aug 2021 15:14:03 +0200 Subject: [PATCH 291/774] Fix merge conflict (minimum recycling rate steel) --- .../data/material/Global_steel_cement_MESSAGE.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx index c301bd2d67..9ad3e49f14 100644 --- a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx +++ b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:b495e5b368f8fb59e3af24e33fbda584cef9993055c2846a2f10a677aba4ba8a -size 91153 +oid sha256:8e98e4dc1b64a97074fa972799433820d916ad6f94c93a226ae41f6d8549a4c6 +size 94974 From 4489cb8b86758153a40259456ca7dec595039ad7 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Mon, 9 Aug 2021 11:09:42 +0200 Subject: [PATCH 292/774] Fix other scrap for steel --- .../data/material/Global_steel_cement_MESSAGE.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx index 9ad3e49f14..25b32bdcb0 100644 --- a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx +++ b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:8e98e4dc1b64a97074fa972799433820d916ad6f94c93a226ae41f6d8549a4c6 -size 94974 +oid sha256:adbe50ad07ada49776626b24c92e73e4916edee50c58ec32b90b11e5b1adc14e +size 94987 From f94fca42b00dcf2424a87d742f612064ac0f7de2 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Fri, 6 Aug 2021 16:10:05 +0200 Subject: [PATCH 293/774] Add draft image demand projections for steel/cement --- .../model/material/material_demand/init.R | 14 ++ .../material_demand/init_modularized.R | 129 ++++++++++++++++++ 2 files changed, 143 insertions(+) create mode 100644 message_ix_models/model/material/material_demand/init_modularized.R diff --git a/message_ix_models/model/material/material_demand/init.R b/message_ix_models/model/material/material_demand/init.R index ef61c0a096..1c79fd400d 100644 --- a/message_ix_models/model/material/material_demand/init.R +++ b/message_ix_models/model/material/material_demand/init.R @@ -45,6 +45,9 @@ df_cement_consumption = df_raw_cement_consumption %>% filter(cons.pcap > 0) #### Fit models #### + +# Note: IMAGE adopts NLI for cement, NLIT for steel. + # . Linear ==== lni.c = lm(log(cons.pcap) ~ I(1/gdp.pcap), data=df_cement_consumption) lni.s = lm(log(cons.pcap) ~ I(1/gdp.pcap), data=df_steel_consumption) @@ -66,3 +69,14 @@ nlnit.c = nls(cons.pcap ~ a * exp(b/gdp.pcap) * (1-m)^del.t, data=df_cement_cons nlnit.s = nls(cons.pcap ~ a * exp(b/gdp.pcap) * (1-m)^del.t, data=df_steel_consumption, start=list(a=600, b=-10000, m=0)) summary(nlnit.c) summary(nlnit.s) + + +#### Prediction #### + +year = seq(2020, 2100, 10) +df = data.frame(gdp.pcap = seq(3000, 50000, length.out = length(year)), year) %>% mutate(del.t = year - 2010) +df2 = df %>% mutate(gdp.pcap = 2*gdp.pcap) +predict(nlnit.s, df) +predict(nlnit.s, df2) +predict(nlni.c, df) +predict(nlni.c, df2) diff --git a/message_ix_models/model/material/material_demand/init_modularized.R b/message_ix_models/model/material/material_demand/init_modularized.R new file mode 100644 index 0000000000..8425ba4384 --- /dev/null +++ b/message_ix_models/model/material/material_demand/init_modularized.R @@ -0,0 +1,129 @@ +library(tidyverse) +library(readxl) +library(sitools) + +# Data file names and path +# datapath = '../../../../data/material/' +datapath = 'H:/MyDocuments/MESSAGE/message_data/data/material/' + +file_cement = "CEMENT.BvR2010.xlsx" +file_steel = "STEEL_database_2012.xlsx" +file_gdp = "iamc_db ENGAGE baseline GDP PPP.xlsx" +# file_pop = "iamc_db ENGAGE baseline population.xlsx" +# +gdp.ppp = read_excel(paste0(datapath, file_gdp), sheet="data_R12") %>% filter(Scenario == "baseline") %>% + select(region=Region, `2020`:`2100`) %>% + pivot_longer(cols=`2020`:`2100`, names_to="year", values_to="gdp.ppp") %>% # billion US$2010/yr OR local currency + filter(region != "World") %>% + mutate(year = as.integer(year), + region = paste0('R12_', region)) + +# popul = read_excel(paste0(datapath, file_pop), sheet="data_R12") %>% +# select(Region, `2020`:`2100`) %>% +# pivot_longer(cols=`2020`:`2100`, names_to="year", values_to="pop.mil") +# df_input = gdp.ppp %>% left_join(popul, by=c("Region", "year")) %>% +# filter(Region != "World") %>% +# mutate(gdp.pcap = gdp.ppp*giga/pop.mil/mega, # USD/cap +# year = as.integer(year), +# Region = paste0('R12_', Region)) + +derive_steel_demand <- function(df_pop, df_demand) { + # df_in will have columns: + # region + # year + # gdp.pcap + # population + + df_raw_steel_consumption = read_excel(paste0(datapath, file_steel), + sheet="Consumption regions", n_max=27) %>% # kt + select(-2) %>% + pivot_longer(cols="1970":"2012", + values_to='consumption', names_to='year') + df_population = read_excel(paste0(datapath, file_cement), + sheet="Timer_POP", skip=3, n_max=27) %>% # million + pivot_longer(cols="1970":"2100", values_to='pop', names_to='year') + df_gdp = read_excel(paste0(datapath, file_cement), + sheet="Timer_GDPCAP", skip=3, n_max=27) %>% # million + pivot_longer(cols="1970":"2100", values_to='gdp.pcap', names_to='year') + + #### Organize data #### + names(df_raw_steel_consumption)[1] = + names(df_population)[1] = names(df_gdp)[1] = "reg_no" + names(df_raw_steel_consumption)[2] = + names(df_population)[2] = names(df_gdp)[2] = "region" + df_steel_consumption = df_raw_steel_consumption %>% + left_join(df_population %>% select(-region)) %>% + left_join(df_gdp %>% select(-region)) %>% + mutate(cons.pcap = consumption/pop, del.t = as.numeric(year)-2010) %>% #kg/cap + drop_na() %>% + filter(cons.pcap > 0) + + nlnit.s = nls(cons.pcap ~ a * exp(b/gdp.pcap) * (1-m)^del.t, data=df_steel_consumption, start=list(a=600, b=-10000, m=0)) + + df_in = df_pop %>% left_join(df_demand %>% select(-year)) %>% # df_demand is only for 2020 + inner_join(gdp.ppp) %>% mutate(del.t = year - 2010, gdp.pcap = gdp.ppp*giga/pop.mil/mega) + demand = df_in %>% + mutate(demand.pcap0 = predict(nlnit.s, .)) %>% #kg/cap + group_by(region) %>% + mutate(demand.pcap.base = first(demand.tot.base*giga/pop.mil/mega)) %>% + mutate(gap.base = first(demand.pcap.base - demand.pcap0)) %>% + mutate(demand.pcap = demand.pcap0 + gap.base * (1-exp(-9*exp(-0.1*(year - 2010))))) %>% # Bas' equation + mutate(demand.tot = demand.pcap * pop.mil * mega / giga) # Mt + + return(demand %>% select(region, year, demand.tot)) # Mt +} + + + +derive_cement_demand <- function(df_pop, df_demand) { + # df_in will have columns: + # region + # year + # gdp.pcap + # population (in mil.) + + df_raw_cement_consumption = read_excel(paste0(datapath, file_cement), + sheet="Regions", skip=122, n_max=27) %>% # kt + pivot_longer(cols="1970":"2010", values_to='consumption', names_to='year') + df_population = read_excel(paste0(datapath, file_cement), + sheet="Timer_POP", skip=3, n_max=27) %>% # million + pivot_longer(cols="1970":"2100", values_to='pop', names_to='year') + df_gdp = read_excel(paste0(datapath, file_cement), + sheet="Timer_GDPCAP", skip=3, n_max=27) %>% # million + pivot_longer(cols="1970":"2100", values_to='gdp.pcap', names_to='year') + + #### Organize data #### + names(df_raw_cement_consumption)[1] = + names(df_population)[1] = names(df_gdp)[1] = "reg_no" + names(df_raw_cement_consumption)[2] = + names(df_population)[2] = names(df_gdp)[2] = "region" + df_cement_consumption = df_raw_cement_consumption %>% + left_join(df_population %>% select(-region)) %>% + left_join(df_gdp %>% select(-region)) %>% + mutate(cons.pcap = consumption/pop/1e6, del.t= as.numeric(year) - 2010) %>% #kg/cap + drop_na() %>% + filter(cons.pcap > 0) + + nlni.c = nls(cons.pcap ~ a * exp(b/gdp.pcap), data=df_cement_consumption, start=list(a=500, b=-3000)) + + df_in = df_pop %>% left_join(df_demand %>% select(-year)) %>% # df_demand is only for 2020 + inner_join(gdp.ppp) %>% mutate(gdp.pcap = gdp.ppp*giga/pop.mil/mega) + demand = df_in %>% + mutate(demand.pcap0 = predict(nlni.c, .)) %>% #kg/cap + group_by(region) %>% + mutate(demand.pcap.base = first(demand.tot.base*giga/pop.mil/mega)) %>% + mutate(gap.base = first(demand.pcap.base - demand.pcap0)) %>% + mutate(demand.pcap = demand.pcap0 + gap.base * (1-exp(-10*exp(-0.1*(year - 2010))))) %>% # Bas' equation + mutate(demand.tot = demand.pcap * pop.mil * mega / giga) # Mt + + return(demand %>% select(region, year, demand.tot)) # Mt +} + + + +#### test +# year = seq(2020, 2100, 10) +# df = data.frame(region = "Korea", gdp.pcap = seq(3000, 50000, length.out = length(year)), year, +# population = seq(300, 500, length.out = length(year))) +# a = derive_cement_demand(df) +# b = derive_steel_demand(df) From 4f86a3186dcfcff2d73ac7296a619564cc5a2521 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Fri, 6 Aug 2021 16:15:22 +0200 Subject: [PATCH 294/774] Add functions to call the demand projection script in r --- .../model/material/data_cement.py | 251 +++++++---- .../model/material/data_steel.py | 389 +++++++++++++----- 2 files changed, 454 insertions(+), 186 deletions(-) diff --git a/message_ix_models/model/material/data_cement.py b/message_ix_models/model/material/data_cement.py index d2e7dba1c6..77ed00b49c 100644 --- a/message_ix_models/model/material/data_cement.py +++ b/message_ix_models/model/material/data_cement.py @@ -14,8 +14,9 @@ make_io, make_matched_dfs, same_node, - add_par_data + add_par_data, ) + # Get endogenous material demand from buildings interface from .data_buildings import get_scen_mat_demand from . import get_spec @@ -32,10 +33,10 @@ def gen_mock_demand_cement(scenario): context = read_config() s_info = ScenarioInfo(scenario) - modelyears = s_info.Y #s_info.Y is only for modeling years + modelyears = s_info.Y # s_info.Y is only for modeling years fmy = s_info.y0 nodes = s_info.N - nodes.remove('World') + nodes.remove("World") # 2019 production by country (USGS) # p43 of https://pubs.usgs.gov/periodicals/mcs2020/mcs2020-cement.pdf @@ -43,28 +44,50 @@ def gen_mock_demand_cement(scenario): # For R12: China and CPA demand divided by 0.1 and 0.9. # The order: - #r = ['R12_AFR', 'R12_RCPA', 'R12_EEU', 'R12_FSU', 'R12_LAM', 'R12_MEA',\ + # r = ['R12_AFR', 'R12_RCPA', 'R12_EEU', 'R12_FSU', 'R12_LAM', 'R12_MEA',\ #'R12_NAM', 'R12_PAO', 'R12_PAS', 'R12_SAS', 'R12_WEU',"R12_CHN"] if "R12_CHN" in nodes: - nodes.remove('R12_GLB') + nodes.remove("R12_GLB") sheet_n = "data_R12" - region_set = 'R12_' + region_set = "R12_" - demand2020_top = [76, 229.5, 0, 57, 55, 60, 89, 54, 129, 320, 51,2065.5] - # the rest (~900 Mt) allocated by % values in http://www.cembureau.eu/media/clkdda45/activity-report-2019.pdf - demand2020_rest = [4100*0.051-76, (4100*0.14-155)*0.2*0.1, 4100*0.064*0.5, 4100*0.026-57, 4100*0.046*0.5-55, \ - (4100*0.14-155)*0.2, 4100*0.046*0.5, 12, 4100*0.003, (4100*0.14-155)*0.6, 4100*0.064*0.5 - 51, - (4100*0.14-155)*0.2*0.9] + demand2020_top = [76, 229.5, 0, 57, 55, 60, 89, 54, 129, 320, 51, 2065.5] + # the rest (~900 Mt) allocated by % values in http://www.cembureau.eu/media/clkdda45/activity-report-2019.pdf + demand2020_rest = [ + 4100 * 0.051 - 76, + (4100 * 0.14 - 155) * 0.2 * 0.1, + 4100 * 0.064 * 0.5, + 4100 * 0.026 - 57, + 4100 * 0.046 * 0.5 - 55, + (4100 * 0.14 - 155) * 0.2, + 4100 * 0.046 * 0.5, + 12, + 4100 * 0.003, + (4100 * 0.14 - 155) * 0.6, + 4100 * 0.064 * 0.5 - 51, + (4100 * 0.14 - 155) * 0.2 * 0.9, + ] else: - nodes.remove('R11_GLB') + nodes.remove("R11_GLB") sheet_n = "data_R11" - region_set = 'R11_' + region_set = "R11_" demand2020_top = [76, 2295, 0, 57, 55, 60, 89, 54, 129, 320, 51] # the rest (~900 Mt) allocated by % values in http://www.cembureau.eu/media/clkdda45/activity-report-2019.pdf - demand2020_rest = [4100*0.051-76, (4100*0.14-155)*0.2, 4100*0.064*0.5, 4100*0.026-57, 4100*0.046*0.5-55, \ - (4100*0.14-155)*0.2, 4100*0.046*0.5, 12, 4100*0.003, (4100*0.14-155)*0.6, 4100*0.064*0.5 - 51] + demand2020_rest = [ + 4100 * 0.051 - 76, + (4100 * 0.14 - 155) * 0.2, + 4100 * 0.064 * 0.5, + 4100 * 0.026 - 57, + 4100 * 0.046 * 0.5 - 55, + (4100 * 0.14 - 155) * 0.2, + 4100 * 0.046 * 0.5, + 12, + 4100 * 0.003, + (4100 * 0.14 - 155) * 0.6, + 4100 * 0.064 * 0.5 - 51, + ] # SSP2 R11 baseline GDP projection gdp_growth = pd.read_excel( @@ -72,11 +95,12 @@ def gen_mock_demand_cement(scenario): sheet_name=sheet_n, ) - gdp_growth = gdp_growth.loc[(gdp_growth['Scenario']=='baseline') & (gdp_growth['Region']!='World')].\ - drop(['Model', 'Variable', 'Unit', 'Notes', 2000, 2005], axis = 1) + gdp_growth = gdp_growth.loc[ + (gdp_growth["Scenario"] == "baseline") & (gdp_growth["Region"] != "World") + ].drop(["Model", "Variable", "Unit", "Notes", 2000, 2005], axis=1) d = [a + b for a, b in zip(demand2020_top, demand2020_rest)] - gdp_growth['Region'] = region_set + gdp_growth['Region'] + gdp_growth["Region"] = region_set + gdp_growth["Region"] # # Regions setting for IMAGE # region_cement = pd.read_excel( @@ -113,15 +137,20 @@ def gen_mock_demand_cement(scenario): # Directly assigned countries from the table on p43 - demand2020_cement = pd.DataFrame({'Region':nodes, 'value':d}).\ - join(gdp_growth.set_index('Region'), on='Region').rename(columns={'Region':'node'}) + demand2020_cement = ( + pd.DataFrame({"Region": nodes, "value": d}) + .join(gdp_growth.set_index("Region"), on="Region") + .rename(columns={"Region": "node"}) + ) # demand2010_cement = demand2010_cement.\ # join(gdp_growth.rename(columns={'Region':'node'}).set_index('node'), on='node') - demand2020_cement.iloc[:,3:] = demand2020_cement.iloc[:,3:].\ - div(demand2020_cement[2020], axis=0).\ - multiply(demand2020_cement["value"], axis=0) + demand2020_cement.iloc[:, 3:] = ( + demand2020_cement.iloc[:, 3:] + .div(demand2020_cement[2020], axis=0) + .multiply(demand2020_cement["value"], axis=0) + ) # Do this if we have 2020 demand values for buildings # sp = get_spec() @@ -135,16 +164,18 @@ def gen_mock_demand_cement(scenario): # div(demand2020_cement[2020], axis=0) # print("UPDATE {} demand for 2020!".format("cement")) # - demand2020_cement = pd.melt(demand2020_cement.drop(['value', 'Scenario'], axis=1),\ - id_vars=['node'], \ - var_name='year', value_name = 'value') + demand2020_cement = pd.melt( + demand2020_cement.drop(["value", "Scenario"], axis=1), + id_vars=["node"], + var_name="year", + value_name="value", + ) return demand2020_cement -def gen_data_cement(scenario, dry_run=False): - """Generate data for materials representation of steel industry. - """ +def gen_data_cement(scenario, dry_run=False): + """Generate data for materials representation of steel industry.""" # Load configuration config = read_config()["material"]["cement"] @@ -153,7 +184,7 @@ def gen_data_cement(scenario, dry_run=False): # Techno-economic assumptions # TEMP: now add cement sector as well - data_cement = read_sector_data(scenario,"cement") + data_cement = read_sector_data(scenario, "cement") # Special treatment for time-dependent Parameters # data_cement_vc = read_timeseries() # tec_vc = set(data_cement_vc.technology) # set of tecs with var_cost @@ -164,12 +195,12 @@ def gen_data_cement(scenario, dry_run=False): # For each technology there are differnet input and output combinations # Iterate over technologies - allyears = s_info.set['year'] #s_info.Y is only for modeling years - modelyears = s_info.Y #s_info.Y is only for modeling years + allyears = s_info.set["year"] # s_info.Y is only for modeling years + modelyears = s_info.Y # s_info.Y is only for modeling years nodes = s_info.N yv_ya = s_info.yv_ya fmy = s_info.y0 - nodes.remove('World') + nodes.remove("World") # Do not parametrize GLB region the same way if "R11_GLB" in nodes: @@ -178,10 +209,11 @@ def gen_data_cement(scenario, dry_run=False): nodes.remove("R12_GLB") # for t in s_info.set['technology']: - for t in config['technology']['add']: + for t in config["technology"]["add"]: - params = data_cement.loc[(data_cement["technology"] == t),\ - "parameter"].values.tolist() + params = data_cement.loc[ + (data_cement["technology"] == t), "parameter" + ].values.tolist() # Iterate over parameters for par in params: @@ -190,34 +222,45 @@ def gen_data_cement(scenario, dry_run=False): split = par.split("|") param_name = split[0] # Obtain the scalar value for the parameter - val = data_cement.loc[((data_cement["technology"] == t) \ - & (data_cement["parameter"] == par)),'value']#.values - regions = data_cement.loc[((data_cement["technology"] == t) \ - & (data_cement["parameter"] == par)),'region']#.values + val = data_cement.loc[ + ((data_cement["technology"] == t) & (data_cement["parameter"] == par)), + "value", + ] # .values + regions = data_cement.loc[ + ((data_cement["technology"] == t) & (data_cement["parameter"] == par)), + "region", + ] # .values common = dict( - year_vtg= yv_ya.year_vtg, - year_act= yv_ya.year_act, + year_vtg=yv_ya.year_vtg, + year_act=yv_ya.year_act, # mode="M1", time="year", time_origin="year", - time_dest="year",) + time_dest="year", + ) for rg in regions: # For the parameters which inlcudes index names - if len(split)> 1: - if (param_name == "input")|(param_name == "output"): + if len(split) > 1: + if (param_name == "input") | (param_name == "output"): # Assign commodity and level names com = split[1] lev = split[2] mod = split[3] - df = (make_df(param_name, technology=t, commodity=com, \ - level=lev, \ - value=val[regions[regions==rg].index[0]], mode=mod, unit='t', \ - node_loc=rg, **common)\ - .pipe(same_node)) + df = make_df( + param_name, + technology=t, + commodity=com, + level=lev, + value=val[regions[regions == rg].index[0]], + mode=mod, + unit="t", + node_loc=rg, + **common + ).pipe(same_node) elif param_name == "emission_factor": @@ -225,44 +268,68 @@ def gen_data_cement(scenario, dry_run=False): emi = split[1] mod = split[2] - df = make_df(param_name, technology=t, \ - value=val[regions[regions==rg].index[0]],\ - emission=emi, mode=mod, unit='t', \ - node_loc=rg, **common)#.pipe(broadcast, \ - #node_loc=nodes)) - - else: # time-independent var_cost + df = make_df( + param_name, + technology=t, + value=val[regions[regions == rg].index[0]], + emission=emi, + mode=mod, + unit="t", + node_loc=rg, + **common + ) # .pipe(broadcast, \ + # node_loc=nodes)) + + else: # time-independent var_cost mod = split[1] - df = make_df(param_name, technology=t, \ - value=val[regions[regions==rg].index[0]], \ - mode=mod, unit='t', node_loc=rg, \ - **common)#.pipe(broadcast, node_loc=nodes)) + df = make_df( + param_name, + technology=t, + value=val[regions[regions == rg].index[0]], + mode=mod, + unit="t", + node_loc=rg, + **common + ) # .pipe(broadcast, node_loc=nodes)) # Parameters with only parameter name else: - df = make_df(param_name, technology=t, \ - value=val[regions[regions==rg].index[0]], unit='t', \ - node_loc=rg, **common)#.pipe(broadcast, node_loc=nodes)) + df = make_df( + param_name, + technology=t, + value=val[regions[regions == rg].index[0]], + unit="t", + node_loc=rg, + **common + ) # .pipe(broadcast, node_loc=nodes)) if len(regions) == 1: - df['node_loc'] = None + df["node_loc"] = None df = df.pipe(broadcast, node_loc=nodes).pipe(same_node) results[param_name].append(df) # Create external demand param - parname = 'demand' + parname = "demand" demand = gen_mock_demand_cement(scenario) - df = make_df(parname, level='demand', commodity='cement', value=demand.value, unit='t', \ - year=demand.year, time='year', node=demand.node) + df = make_df( + parname, + level="demand", + commodity="cement", + value=demand.value, + unit="t", + year=demand.year, + time="year", + node=demand.node, + ) results[parname].append(df) # Add CCS as addon - parname = 'addon_conversion' - ccs_tec = ['clinker_wet_cement', 'clinker_dry_cement'] - df = (make_df(parname, mode='M1', \ - type_addon='ccs_cement', \ - value=1, unit='-', **common).pipe(broadcast, node=nodes, technology=ccs_tec)) + parname = "addon_conversion" + ccs_tec = ["clinker_wet_cement", "clinker_dry_cement"] + df = make_df( + parname, mode="M1", type_addon="ccs_cement", value=1, unit="-", **common + ).pipe(broadcast, node=nodes, technology=ccs_tec) results[parname].append(df) # Test emission bound @@ -276,3 +343,41 @@ def gen_data_cement(scenario, dry_run=False): results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} return results + + +# load rpy2 modules +import rpy2.robjects as ro +from rpy2.robjects import pandas2ri +from rpy2.robjects.conversion import localconverter + + +# This returns a df with columns ["region", "year", "demand.tot"] +def derive_cement_demand(scenario, dry_run=False): + """Generate data for materials representation of power industry.""" + # paths to r code and lca data + rcode_path = Path(__file__).parents[0] / "material_demand" + + # source R code + r = ro.r + r.source(str(rcode_path / "init_modularized.R")) + + # Read population and baseline demand for materials + pop = scenario.var("ACT", {"technology": "Population"}) + pop = pop.loc[pop.year_act >= 2020].rename( + columns={"year_act": "year", "lvl": "pop.mil", "node_loc": "region"} + ) + pop = pop[["region", "year", "pop.mil"]] + base_demand = scenario.par("demand", {"commodity": "steel", "year": 2020}) + base_demand = base_demand.loc[base_demand.year >= 2020].rename( + columns={"value": "demand.tot.base", "node": "region"} + ) + base_demand = base_demand[["region", "year", "demand.tot.base"]] + + # call R function with type conversion + with localconverter(ro.default_converter + pandas2ri.converter): + # GDP is only in MER in scenario. + # To get PPP GDP, it is read externally from the R side + df = r.derive_steel_demand(pop, base_demand) + + +return df diff --git a/message_ix_models/model/material/data_steel.py b/message_ix_models/model/material/data_steel.py index 2a6df4fb42..2411fed423 100644 --- a/message_ix_models/model/material/data_steel.py +++ b/message_ix_models/model/material/data_steel.py @@ -3,11 +3,13 @@ import numpy as np from collections import defaultdict import logging +from pathlib import Path import pandas as pd from .util import read_config from .data_util import read_rel + # Get endogenous material demand from buildings interface from .data_buildings import get_scen_mat_demand from message_data.tools import ( @@ -18,7 +20,7 @@ make_matched_dfs, same_node, copy_column, - add_par_data + add_par_data, ) from . import get_spec @@ -30,19 +32,18 @@ # 0.00649794573935969, 0.00649794573935969] - # Generate a fake steel demand def gen_mock_demand_steel(scenario): context = read_config() s_info = ScenarioInfo(scenario) - modelyears = s_info.Y #s_info.Y is only for modeling years + modelyears = s_info.Y # s_info.Y is only for modeling years fmy = s_info.y0 nodes = s_info.N - nodes.remove('World') + nodes.remove("World") # The order: - #r = ['R12_AFR', 'R12_RCPA', 'R12_EEU', 'R12_FSU', 'R12_LAM', 'R12_MEA',\ + # r = ['R12_AFR', 'R12_RCPA', 'R12_EEU', 'R12_FSU', 'R12_LAM', 'R12_MEA',\ #'R12_NAM', 'R12_PAO', 'R12_PAS', 'R12_SAS', 'R12_WEU',"R12_CHN"] # True steel use 2010 [Mt/year] @@ -51,15 +52,15 @@ def gen_mock_demand_steel(scenario): # For R12: China and CPA demand divided by 0.1 and 0.9. if "R12_CHN" in nodes: - nodes.remove('R12_GLB') + nodes.remove("R12_GLB") sheet_n = "data_R12" - region_set = 'R12_' - d = [35,5.37, 70, 53, 49, 39, 130, 80, 45, 96, 100,531.63] + region_set = "R12_" + d = [35, 5.37, 70, 53, 49, 39, 130, 80, 45, 96, 100, 531.63] else: - nodes.remove('R11_GLB') + nodes.remove("R11_GLB") sheet_n = "data_R11" - region_set = 'R11_' + region_set = "R11_" d = [35, 537, 70, 53, 49, 39, 130, 80, 45, 96, 100] # MEA change from 39 to 9 to make it feasible (coal supply bound) @@ -69,17 +70,23 @@ def gen_mock_demand_steel(scenario): sheet_name=sheet_n, ) - gdp_growth = gdp_growth.loc[(gdp_growth['Scenario']=='baseline') & (gdp_growth['Region']!='World')].\ - drop(['Model', 'Variable', 'Unit', 'Notes', 2000, 2005], axis = 1) + gdp_growth = gdp_growth.loc[ + (gdp_growth["Scenario"] == "baseline") & (gdp_growth["Region"] != "World") + ].drop(["Model", "Variable", "Unit", "Notes", 2000, 2005], axis=1) - gdp_growth['Region'] = region_set + gdp_growth['Region'] + gdp_growth["Region"] = region_set + gdp_growth["Region"] - demand2010_steel = pd.DataFrame({'Region':nodes, 'Val':d}).\ - join(gdp_growth.set_index('Region'), on='Region').rename(columns={'Region':'node'}) + demand2010_steel = ( + pd.DataFrame({"Region": nodes, "Val": d}) + .join(gdp_growth.set_index("Region"), on="Region") + .rename(columns={"Region": "node"}) + ) - demand2010_steel.iloc[:,3:] = demand2010_steel.iloc[:,3:].\ - div(demand2010_steel[2010], axis=0).\ - multiply(demand2010_steel["Val"], axis=0) + demand2010_steel.iloc[:, 3:] = ( + demand2010_steel.iloc[:, 3:] + .div(demand2010_steel[2010], axis=0) + .multiply(demand2010_steel["Val"], axis=0) + ) # Do this if we have 2020 demand values for buildings # sp = get_spec() @@ -93,9 +100,12 @@ def gen_mock_demand_steel(scenario): # div(demand2010_steel[2020], axis=0) # print("UPDATE {} demand for 2020!".format("steel")) # - demand2010_steel = pd.melt(demand2010_steel.drop(['Val', 'Scenario'], axis=1),\ - id_vars=['node'], \ - var_name='year', value_name = 'value') + demand2010_steel = pd.melt( + demand2010_steel.drop(["Val", "Scenario"], axis=1), + id_vars=["node"], + var_name="year", + value_name="value", + ) # # print("This is steel demand") # print(demand2010_steel) @@ -122,10 +132,9 @@ def gen_mock_demand_steel(scenario): return demand2010_steel -def gen_data_steel(scenario, dry_run=False): - """Generate data for materials representation of steel industry. - """ +def gen_data_steel(scenario, dry_run=False): + """Generate data for materials representation of steel industry.""" # Load configuration context = read_config() config = context["material"]["steel"] @@ -135,12 +144,12 @@ def gen_data_steel(scenario, dry_run=False): # Techno-economic assumptions # TEMP: now add cement sector as well => Need to separate those since now I have get_data_steel and cement - data_steel = read_sector_data(scenario,"steel") + data_steel = read_sector_data(scenario, "steel") # Special treatment for time-dependent Parameters data_steel_ts = read_timeseries(scenario, context.datafile) data_steel_rel = read_rel(scenario, context.datafile) - tec_ts = set(data_steel_ts.technology) # set of tecs with var_cost + tec_ts = set(data_steel_ts.technology) # set of tecs with var_cost # List of data frames, to be concatenated together at end results = defaultdict(list) @@ -148,12 +157,12 @@ def gen_data_steel(scenario, dry_run=False): # For each technology there are differnet input and output combinations # Iterate over technologies - allyears = s_info.set['year'] #s_info.Y is only for modeling years - modelyears = s_info.Y #s_info.Y is only for modeling years + allyears = s_info.set["year"] # s_info.Y is only for modeling years + modelyears = s_info.Y # s_info.Y is only for modeling years nodes = s_info.N yv_ya = s_info.yv_ya fmy = s_info.y0 - nodes.remove('World') + nodes.remove("World") # Do not parametrize GLB region the same way if "R11_GLB" in nodes: @@ -164,39 +173,74 @@ def gen_data_steel(scenario, dry_run=False): global_region = "R12_GLB" # for t in s_info.set['technology']: - for t in config['technology']['add']: + for t in config["technology"]["add"]: - params = data_steel.loc[(data_steel["technology"] == t),\ - "parameter"].values.tolist() + params = data_steel.loc[ + (data_steel["technology"] == t), "parameter" + ].values.tolist() # Special treatment for time-varying params if t in tec_ts: common = dict( time="year", time_origin="year", - time_dest="year",) + time_dest="year", + ) - param_name = data_steel_ts.loc[(data_steel_ts["technology"] == t), 'parameter'] + param_name = data_steel_ts.loc[ + (data_steel_ts["technology"] == t), "parameter" + ] for p in set(param_name): - val = data_steel_ts.loc[(data_steel_ts["technology"] == t) \ - & (data_steel_ts["parameter"] == p), 'value'] - units = data_steel_ts.loc[(data_steel_ts["technology"] == t) \ - & (data_steel_ts["parameter"] == p), 'units'].values[0] - mod = data_steel_ts.loc[(data_steel_ts["technology"] == t) \ - & (data_steel_ts["parameter"] == p), 'mode'] - yr = data_steel_ts.loc[(data_steel_ts["technology"] == t) \ - & (data_steel_ts["parameter"] == p), 'year'] - - if p=="var_cost": - df = (make_df(p, technology=t, value=val,\ - unit='t', year_vtg=yr, year_act=yr, mode=mod, **common).pipe(broadcast, \ - node_loc=nodes)) + val = data_steel_ts.loc[ + (data_steel_ts["technology"] == t) + & (data_steel_ts["parameter"] == p), + "value", + ] + units = data_steel_ts.loc[ + (data_steel_ts["technology"] == t) + & (data_steel_ts["parameter"] == p), + "units", + ].values[0] + mod = data_steel_ts.loc[ + (data_steel_ts["technology"] == t) + & (data_steel_ts["parameter"] == p), + "mode", + ] + yr = data_steel_ts.loc[ + (data_steel_ts["technology"] == t) + & (data_steel_ts["parameter"] == p), + "year", + ] + + if p == "var_cost": + df = make_df( + p, + technology=t, + value=val, + unit="t", + year_vtg=yr, + year_act=yr, + mode=mod, + **common + ).pipe(broadcast, node_loc=nodes) else: - rg = data_steel_ts.loc[(data_steel_ts["technology"] == t) \ - & (data_steel_ts["parameter"] == p), 'region'] - df = make_df(p, technology=t, value=val,\ - unit='t', year_vtg=yr, year_act=yr, mode=mod, node_loc=rg, **common) + rg = data_steel_ts.loc[ + (data_steel_ts["technology"] == t) + & (data_steel_ts["parameter"] == p), + "region", + ] + df = make_df( + p, + technology=t, + value=val, + unit="t", + year_vtg=yr, + year_act=yr, + mode=mod, + node_loc=rg, + **common + ) results[p].append(df) @@ -207,50 +251,77 @@ def gen_data_steel(scenario, dry_run=False): split = par.split("|") param_name = split[0] # Obtain the scalar value for the parameter - val = data_steel.loc[((data_steel["technology"] == t) \ - & (data_steel["parameter"] == par)),'value']#.values - regions = data_steel.loc[((data_steel["technology"] == t) \ - & (data_steel["parameter"] == par)),'region']#.values + val = data_steel.loc[ + ((data_steel["technology"] == t) & (data_steel["parameter"] == par)), + "value", + ] # .values + regions = data_steel.loc[ + ((data_steel["technology"] == t) & (data_steel["parameter"] == par)), + "region", + ] # .values common = dict( - year_vtg= yv_ya.year_vtg, - year_act= yv_ya.year_act, + year_vtg=yv_ya.year_vtg, + year_act=yv_ya.year_act, # mode="M1", time="year", time_origin="year", - time_dest="year",) + time_dest="year", + ) for rg in regions: # For the parameters which inlcudes index names - if len(split)> 1: - if (param_name == "input")|(param_name == "output"): + if len(split) > 1: + if (param_name == "input") | (param_name == "output"): # Assign commodity and level names com = split[1] lev = split[2] mod = split[3] if (param_name == "input") and (lev == "import"): - df = make_df(param_name, technology=t, commodity=com, \ - level=lev, \ - value=val[regions[regions==rg].index[0]], mode=mod, unit='t', \ - node_loc=rg, node_origin=global_region, **common) + df = make_df( + param_name, + technology=t, + commodity=com, + level=lev, + value=val[regions[regions == rg].index[0]], + mode=mod, + unit="t", + node_loc=rg, + node_origin=global_region, + **common + ) elif (param_name == "output") and (lev == "export"): - df = make_df(param_name, technology=t, commodity=com, \ - level=lev, \ - value=val[regions[regions==rg].index[0]], mode=mod, unit='t', \ - node_loc=rg, node_dest=global_region, **common) + df = make_df( + param_name, + technology=t, + commodity=com, + level=lev, + value=val[regions[regions == rg].index[0]], + mode=mod, + unit="t", + node_loc=rg, + node_dest=global_region, + **common + ) else: - df = (make_df(param_name, technology=t, commodity=com, \ - level=lev, \ - value=val[regions[regions==rg].index[0]], mode=mod, unit='t', \ - node_loc=rg, **common)\ - .pipe(same_node)) + df = make_df( + param_name, + technology=t, + commodity=com, + level=lev, + value=val[regions[regions == rg].index[0]], + mode=mod, + unit="t", + node_loc=rg, + **common + ).pipe(same_node) # Copy parameters to all regions, when node_loc is not GLB if (len(regions) == 1) and (rg != global_region): - df['node_loc'] = None - df = df.pipe(broadcast, node_loc=nodes)#.pipe(same_node) + df["node_loc"] = None + df = df.pipe(broadcast, node_loc=nodes) # .pipe(same_node) # Use same_node only for non-trade technologies if (lev != "import") and (lev != "export"): df = df.pipe(same_node) @@ -261,27 +332,46 @@ def gen_data_steel(scenario, dry_run=False): emi = split[1] mod = split[2] - df = make_df(param_name, technology=t, \ - value=val[regions[regions==rg].index[0]],\ - emission=emi, mode=mod, unit='t', \ - node_loc=rg, **common) - - else: # time-independent var_cost + df = make_df( + param_name, + technology=t, + value=val[regions[regions == rg].index[0]], + emission=emi, + mode=mod, + unit="t", + node_loc=rg, + **common + ) + + else: # time-independent var_cost mod = split[1] - df = make_df(param_name, technology=t, \ - value=val[regions[regions==rg].index[0]], \ - mode=mod, unit='t', node_loc=rg, \ - **common) + df = make_df( + param_name, + technology=t, + value=val[regions[regions == rg].index[0]], + mode=mod, + unit="t", + node_loc=rg, + **common + ) # Parameters with only parameter name else: - df = make_df(param_name, technology=t, \ - value=val[regions[regions==rg].index[0]], unit='t', \ - node_loc=rg, **common) + df = make_df( + param_name, + technology=t, + value=val[regions[regions == rg].index[0]], + unit="t", + node_loc=rg, + **common + ) # Copy parameters to all regions - if len(set(df['node_loc'])) == 1 and list(set(df['node_loc']))[0]!=global_region: - df['node_loc'] = None + if ( + len(set(df["node_loc"])) == 1 + and list(set(df["node_loc"]))[0] != global_region + ): + df["node_loc"] = None df = df.pipe(broadcast, node_loc=nodes) results[param_name].append(df) @@ -295,51 +385,124 @@ def gen_data_steel(scenario, dry_run=False): if r is None: break - params = set(data_steel_rel.loc[(data_steel_rel["relation"] == r),\ - "parameter"].values) + params = set( + data_steel_rel.loc[ + (data_steel_rel["relation"] == r), "parameter" + ].values + ) common_rel = dict( - year_rel = modelyears, - year_act = modelyears, - mode = 'M1', - relation = r,) + year_rel=modelyears, + year_act=modelyears, + mode="M1", + relation=r, + ) for par_name in params: if par_name == "relation_activity": - tec_list = data_steel_rel.loc[((data_steel_rel["relation"] == r) \ - & (data_steel_rel["parameter"] == par_name)) ,'technology'] + tec_list = data_steel_rel.loc[ + ( + (data_steel_rel["relation"] == r) + & (data_steel_rel["parameter"] == par_name) + ), + "technology", + ] for tec in tec_list.unique(): - val = data_steel_rel.loc[((data_steel_rel["relation"] == r) \ - & (data_steel_rel["parameter"] == par_name) & \ - (data_steel_rel["technology"]==tec) & \ - (data_steel_rel["Region"]==reg)),'value'].values[0] - - df = (make_df(par_name, technology=tec, value=val, unit='-',\ - node_loc = reg, node_rel = reg, **common_rel).pipe(same_node)) + val = data_steel_rel.loc[ + ( + (data_steel_rel["relation"] == r) + & (data_steel_rel["parameter"] == par_name) + & (data_steel_rel["technology"] == tec) + & (data_steel_rel["Region"] == reg) + ), + "value", + ].values[0] + + df = make_df( + par_name, + technology=tec, + value=val, + unit="-", + node_loc=reg, + node_rel=reg, + **common_rel + ).pipe(same_node) results[par_name].append(df) elif (par_name == "relation_upper") | (par_name == "relation_lower"): - val = data_steel_rel.loc[((data_steel_rel["relation"] == r) \ - & (data_steel_rel["parameter"] == par_name) & \ - (data_steel_rel["Region"]==reg)),'value'].values[0] + val = data_steel_rel.loc[ + ( + (data_steel_rel["relation"] == r) + & (data_steel_rel["parameter"] == par_name) + & (data_steel_rel["Region"] == reg) + ), + "value", + ].values[0] - df = (make_df(par_name, value=val, unit='-', node_rel = reg,\ - **common_rel)) + df = make_df( + par_name, value=val, unit="-", node_rel=reg, **common_rel + ) results[par_name].append(df) # Create external demand param - parname = 'demand' + parname = "demand" demand = gen_mock_demand_steel(scenario) - df = make_df(parname, level='demand', commodity='steel', value=demand.value, unit='t', \ - year=demand.year, time='year', node=demand.node)#.pipe(broadcast, node=nodes) + df = make_df( + parname, + level="demand", + commodity="steel", + value=demand.value, + unit="t", + year=demand.year, + time="year", + node=demand.node, + ) # .pipe(broadcast, node=nodes) results[parname].append(df) # Concatenate to one data frame per parameter results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} return results + + +# load rpy2 modules +import rpy2.robjects as ro +from rpy2.robjects import pandas2ri +from rpy2.robjects.conversion import localconverter + + +# This returns a df with columns ["region", "year", "demand.tot"] +def derive_steel_demand(scenario, dry_run=False): + """Generate data for materials representation of power industry.""" + # paths to r code and lca data + rcode_path = Path(__file__).parents[0] / "material_demand" + + # source R code + r = ro.r + r.source(str(rcode_path / "init_modularized.R")) + + # Read population and baseline demand for materials + pop = scenario.var("ACT", {"technology": "Population"}) + pop = pop.loc[pop.year_act >= 2020].rename( + columns={"year_act": "year", "lvl": "pop.mil", "node_loc": "region"} + ) + pop = pop[["region", "year", "pop.mil"]] + base_demand = scenario.par("demand", {"commodity": "steel", "year": 2020}) + base_demand = base_demand.loc[base_demand.year >= 2020].rename( + columns={"value": "demand.tot.base", "node": "region"} + ) + base_demand = base_demand[["region", "year", "demand.tot.base"]] + + # call R function with type conversion + with localconverter(ro.default_converter + pandas2ri.converter): + # GDP is only in MER in scenario. + # To get PPP GDP, it is read externally from the R side + df = r.derive_steel_demand(pop, base_demand) + + +return df From 27aed757f0604e67905634edf0eac30e81be91e9 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Mon, 9 Aug 2021 11:15:34 +0200 Subject: [PATCH 295/774] Make files 'blacked' --- .../model/material/data_power_sector.py | 180 +++++++++++++++--- 1 file changed, 153 insertions(+), 27 deletions(-) diff --git a/message_ix_models/model/material/data_power_sector.py b/message_ix_models/model/material/data_power_sector.py index ebcccd6d12..2163660a74 100644 --- a/message_ix_models/model/material/data_power_sector.py +++ b/message_ix_models/model/material/data_power_sector.py @@ -7,12 +7,14 @@ # check Python and R environments (for debugging) import rpy2.situation + for row in rpy2.situation.iter_info(): print(row) # load rpy2 modules import rpy2.robjects as ro -#from rpy2.robjects.packages import importr + +# from rpy2.robjects.packages import importr from rpy2.robjects import pandas2ri from rpy2.robjects.conversion import localconverter @@ -27,21 +29,19 @@ make_matched_dfs, same_node, copy_column, - add_par_data + add_par_data, ) from . import get_spec def gen_data_power_sector(scenario, dry_run=False): - """Generate data for materials representation of power industry. - - """ + """Generate data for materials representation of power industry.""" # Load configuration context = read_config() config = context["material"]["power_sector"] # paths to r code and lca data - rcode_path= Path(__file__).parents[0] / 'material_intensity' + rcode_path = Path(__file__).parents[0] / "material_intensity" data_path = context.get_path("material") # Information about scenario, e.g. node, year @@ -49,19 +49,19 @@ def gen_data_power_sector(scenario, dry_run=False): # read node, technology, commodity and level from existing scenario node = s_info.N - year = s_info.set['year'] #s_info.Y is only for modeling years - technology = s_info.set['technology'] - commodity = s_info.set['commodity'] - level = s_info.set['level'] + year = s_info.set["year"] # s_info.Y is only for modeling years + technology = s_info.set["technology"] + commodity = s_info.set["commodity"] + level = s_info.set["level"] # read inv.cost data - inv_cost = scenario.par('inv_cost') + inv_cost = scenario.par("inv_cost") # source R code - r=ro.r + r = ro.r r.source(str(rcode_path / "ADVANCE_lca_coefficients_embedded.R")) - param_name = ['input_cap_new', 'input_cap_ret', 'output_cap_ret'] + param_name = ["input_cap_new", "input_cap_ret", "output_cap_ret"] # List of data frames, to be concatenated together at end results = defaultdict(list) @@ -69,8 +69,10 @@ def gen_data_power_sector(scenario, dry_run=False): # call R function with type conversion for p in set(param_name): with localconverter(ro.default_converter + pandas2ri.converter): - df = r.read_material_intensities(p, str(data_path), node, year, technology, commodity, level, inv_cost) - print('type df:', type(df)) + df = r.read_material_intensities( + p, str(data_path), node, year, technology, commodity, level, inv_cost + ) + print("type df:", type(df)) print(df.head()) results[p].append(df) @@ -78,18 +80,142 @@ def gen_data_power_sector(scenario, dry_run=False): # import pdb; pdb.set_trace() # create new parameters input_cap_new, output_cap_new, input_cap_ret, output_cap_ret, input_cap and output_cap if they don't exist - if not scenario.has_par('input_cap_new'): - scenario.init_par('input_cap_new', idx_sets = ['node', 'technology', 'year', 'node', 'commodity', 'level', 'time'], idx_names = ['node_loc', 'technology', 'year_vtg', 'node_origin', 'commodity', 'level', 'time_origin']) - if not scenario.has_par('output_cap_new'): - scenario.init_par('output_cap_new', idx_sets = ['node', 'technology', 'year', 'node', 'commodity', 'level', 'time'], idx_names = ['node_loc', 'technology', 'year_vtg', 'node_dest', 'commodity', 'level', 'time_dest']) - if not scenario.has_par('input_cap_ret'): - scenario.init_par('input_cap_ret', idx_sets = ['node', 'technology', 'year', 'node', 'commodity', 'level', 'time'], idx_names = ['node_loc', 'technology', 'year_vtg', 'node_origin', 'commodity', 'level', 'time_origin']) - if not scenario.has_par('output_cap_ret'): - scenario.init_par('output_cap_ret', idx_sets = ['node', 'technology', 'year', 'node', 'commodity', 'level', 'time'], idx_names = ['node_loc', 'technology', 'year_vtg', 'node_dest', 'commodity', 'level', 'time_dest']) - if not scenario.has_par('input_cap'): - scenario.init_par('input_cap', idx_sets = ['node', 'technology', 'year', 'year', 'node', 'commodity', 'level', 'time'], idx_names = ['node_loc', 'technology', 'year_vtg', 'year_act', 'node_origin', 'commodity', 'level', 'time_origin']) - if not scenario.has_par('output_cap'): - scenario.init_par('output_cap', idx_sets = ['node', 'technology', 'year', 'year', 'node', 'commodity', 'level', 'time'], idx_names = ['node_loc', 'technology', 'year_vtg', 'year_act', 'node_dest', 'commodity', 'level', 'time_dest']) + if not scenario.has_par("input_cap_new"): + scenario.init_par( + "input_cap_new", + idx_sets=[ + "node", + "technology", + "year", + "node", + "commodity", + "level", + "time", + ], + idx_names=[ + "node_loc", + "technology", + "year_vtg", + "node_origin", + "commodity", + "level", + "time_origin", + ], + ) + if not scenario.has_par("output_cap_new"): + scenario.init_par( + "output_cap_new", + idx_sets=[ + "node", + "technology", + "year", + "node", + "commodity", + "level", + "time", + ], + idx_names=[ + "node_loc", + "technology", + "year_vtg", + "node_dest", + "commodity", + "level", + "time_dest", + ], + ) + if not scenario.has_par("input_cap_ret"): + scenario.init_par( + "input_cap_ret", + idx_sets=[ + "node", + "technology", + "year", + "node", + "commodity", + "level", + "time", + ], + idx_names=[ + "node_loc", + "technology", + "year_vtg", + "node_origin", + "commodity", + "level", + "time_origin", + ], + ) + if not scenario.has_par("output_cap_ret"): + scenario.init_par( + "output_cap_ret", + idx_sets=[ + "node", + "technology", + "year", + "node", + "commodity", + "level", + "time", + ], + idx_names=[ + "node_loc", + "technology", + "year_vtg", + "node_dest", + "commodity", + "level", + "time_dest", + ], + ) + if not scenario.has_par("input_cap"): + scenario.init_par( + "input_cap", + idx_sets=[ + "node", + "technology", + "year", + "year", + "node", + "commodity", + "level", + "time", + ], + idx_names=[ + "node_loc", + "technology", + "year_vtg", + "year_act", + "node_origin", + "commodity", + "level", + "time_origin", + ], + ) + if not scenario.has_par("output_cap"): + scenario.init_par( + "output_cap", + idx_sets=[ + "node", + "technology", + "year", + "year", + "node", + "commodity", + "level", + "time", + ], + idx_names=[ + "node_loc", + "technology", + "year_vtg", + "year_act", + "node_dest", + "commodity", + "level", + "time_dest", + ], + ) # Concatenate to one data frame per parameter results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} From f9c89f0138413a0d0e108cc163aa9a843471f568 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Mon, 9 Aug 2021 16:59:48 +0200 Subject: [PATCH 296/774] Harmonize column names with mock demand function - and add a dummy year 2110 for consistency --- .../model/material/material_demand/init_modularized.R | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/message_ix_models/model/material/material_demand/init_modularized.R b/message_ix_models/model/material/material_demand/init_modularized.R index 8425ba4384..fba92115f6 100644 --- a/message_ix_models/model/material/material_demand/init_modularized.R +++ b/message_ix_models/model/material/material_demand/init_modularized.R @@ -70,7 +70,10 @@ derive_steel_demand <- function(df_pop, df_demand) { mutate(demand.pcap = demand.pcap0 + gap.base * (1-exp(-9*exp(-0.1*(year - 2010))))) %>% # Bas' equation mutate(demand.tot = demand.pcap * pop.mil * mega / giga) # Mt - return(demand %>% select(region, year, demand.tot)) # Mt + # Add 2110 spaceholder + demand = demand %>% rbind(demand %>% filter(year==2100) %>% mutate(year = 2110)) + + return(demand %>% select(node=region, year, value=demand.tot) %>% arrange(year, node)) # Mt } @@ -116,7 +119,10 @@ derive_cement_demand <- function(df_pop, df_demand) { mutate(demand.pcap = demand.pcap0 + gap.base * (1-exp(-10*exp(-0.1*(year - 2010))))) %>% # Bas' equation mutate(demand.tot = demand.pcap * pop.mil * mega / giga) # Mt - return(demand %>% select(region, year, demand.tot)) # Mt + # Add 2110 spaceholder + demand = demand %>% rbind(demand %>% filter(year==2100) %>% mutate(year = 2110)) + + return(demand %>% select(node=region, year, value=demand.tot) %>% arrange(year, node)) # Mt } From f74d5563c6be927f3a07973359bcd8c7dce1679e Mon Sep 17 00:00:00 2001 From: Jihoon Date: Mon, 9 Aug 2021 17:03:53 +0200 Subject: [PATCH 297/774] Changes to make it work from cli command - population information source - base year demand still from mock functions - needed to harmonize return column type (integer) - some minor 'black' changes --- message_ix_models/model/material/__init__.py | 6 ++-- .../model/material/data_cement.py | 28 +++++++++++++------ .../model/material/data_steel.py | 26 +++++++++++------ 3 files changed, 40 insertions(+), 20 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index b137159d2e..e88edbb638 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -114,8 +114,8 @@ def solve(context, datafile, tag): # user did not give a recognized value, this raises an error. output_scenario_name = { "baseline": "NoPolicy", - "baseline_macro":"NoPolicy", - "baseline_new":"NoPolicy", + "baseline_macro": "NoPolicy", + "baseline_new": "NoPolicy", "NPi2020-con-prim-dir-ncr": "NPi", "NPi2020_1000-con-prim-dir-ncr": "NPi2020_1000", "NPi2020_400-con-prim-dir-ncr": "NPi2020_400", @@ -204,7 +204,7 @@ def run_reporting(old_reporting, scenario_name, model_name): from message_data.tools import add_par_data DATA_FUNCTIONS = [ - #gen_data_buildings, + # gen_data_buildings, gen_data_steel, gen_data_cement, gen_data_aluminum, diff --git a/message_ix_models/model/material/data_cement.py b/message_ix_models/model/material/data_cement.py index 77ed00b49c..fe75d18b2f 100644 --- a/message_ix_models/model/material/data_cement.py +++ b/message_ix_models/model/material/data_cement.py @@ -3,6 +3,7 @@ import numpy as np from collections import defaultdict import logging +from pathlib import Path import pandas as pd @@ -311,7 +312,8 @@ def gen_data_cement(scenario, dry_run=False): # Create external demand param parname = "demand" - demand = gen_mock_demand_cement(scenario) + # demand = gen_mock_demand_cement(scenario) + demand = derive_cement_demand(scenario) df = make_df( parname, level="demand", @@ -362,22 +364,30 @@ def derive_cement_demand(scenario, dry_run=False): r.source(str(rcode_path / "init_modularized.R")) # Read population and baseline demand for materials - pop = scenario.var("ACT", {"technology": "Population"}) + pop = scenario.par("bound_activity_up", {"technology": "Population"}) pop = pop.loc[pop.year_act >= 2020].rename( - columns={"year_act": "year", "lvl": "pop.mil", "node_loc": "region"} + columns={"year_act": "year", "value": "pop.mil", "node_loc": "region"} ) pop = pop[["region", "year", "pop.mil"]] - base_demand = scenario.par("demand", {"commodity": "steel", "year": 2020}) - base_demand = base_demand.loc[base_demand.year >= 2020].rename( + + base_demand = gen_mock_demand_cement(scenario) + base_demand = base_demand.loc[base_demand.year == 2020].rename( columns={"value": "demand.tot.base", "node": "region"} ) - base_demand = base_demand[["region", "year", "demand.tot.base"]] + + # import pdb; pdb.set_trace() + + # base_demand = scenario.par("demand", {"commodity": "steel", "year": 2020}) + # base_demand = base_demand.loc[base_demand.year >= 2020].rename( + # columns={"value": "demand.tot.base", "node": "region"} + # ) + # base_demand = base_demand[["region", "year", "demand.tot.base"]] # call R function with type conversion with localconverter(ro.default_converter + pandas2ri.converter): # GDP is only in MER in scenario. # To get PPP GDP, it is read externally from the R side - df = r.derive_steel_demand(pop, base_demand) + df = r.derive_cement_demand(pop, base_demand) + df.year = df.year.astype(int) - -return df + return df diff --git a/message_ix_models/model/material/data_steel.py b/message_ix_models/model/material/data_steel.py index 2411fed423..5a5380f7b9 100644 --- a/message_ix_models/model/material/data_steel.py +++ b/message_ix_models/model/material/data_steel.py @@ -452,7 +452,8 @@ def gen_data_steel(scenario, dry_run=False): # Create external demand param parname = "demand" - demand = gen_mock_demand_steel(scenario) + demand = derive_steel_demand(scenario) + df = make_df( parname, level="demand", @@ -487,22 +488,31 @@ def derive_steel_demand(scenario, dry_run=False): r.source(str(rcode_path / "init_modularized.R")) # Read population and baseline demand for materials - pop = scenario.var("ACT", {"technology": "Population"}) + pop = scenario.par("bound_activity_up", {"technology": "Population"}) pop = pop.loc[pop.year_act >= 2020].rename( - columns={"year_act": "year", "lvl": "pop.mil", "node_loc": "region"} + columns={"year_act": "year", "value": "pop.mil", "node_loc": "region"} ) + + # import pdb; pdb.set_trace() + pop = pop[["region", "year", "pop.mil"]] - base_demand = scenario.par("demand", {"commodity": "steel", "year": 2020}) - base_demand = base_demand.loc[base_demand.year >= 2020].rename( + + base_demand = gen_mock_demand_steel(scenario) + base_demand = base_demand.loc[base_demand.year == 2020].rename( columns={"value": "demand.tot.base", "node": "region"} ) - base_demand = base_demand[["region", "year", "demand.tot.base"]] + + # base_demand = scenario.par("demand", {"commodity": "steel", "year": 2020}) + # base_demand = base_demand.loc[base_demand.year >= 2020].rename( + # columns={"value": "demand.tot.base", "node": "region"} + # ) + # base_demand = base_demand[["region", "year", "demand.tot.base"]] # call R function with type conversion with localconverter(ro.default_converter + pandas2ri.converter): # GDP is only in MER in scenario. # To get PPP GDP, it is read externally from the R side df = r.derive_steel_demand(pop, base_demand) + df.year = df.year.astype(int) - -return df + return df From 6ce2677c01b6def21082946dd92fed8215cc691f Mon Sep 17 00:00:00 2001 From: Jihoon Date: Mon, 9 Aug 2021 18:21:01 +0200 Subject: [PATCH 298/774] Add aluminum material demand functions also 'black'ed --- .../model/material/material_demand/init.R | 43 +++++++++++++------ 1 file changed, 31 insertions(+), 12 deletions(-) diff --git a/message_ix_models/model/material/material_demand/init.R b/message_ix_models/model/material/material_demand/init.R index 1c79fd400d..2f0983aa74 100644 --- a/message_ix_models/model/material/material_demand/init.R +++ b/message_ix_models/model/material/material_demand/init.R @@ -6,69 +6,88 @@ datapath = '../../../../data/material/' file_cement = "CEMENT.BvR2010.xlsx" file_steel = "STEEL_database_2012.xlsx" +file_al = "demand_aluminum.xlsx" #### Import raw data (from Bas) - Cement & Steel #### # Apparent consumption # GDP per dap # Population -df_raw_steel_consumption = read_excel(paste0(datapath, file_steel), +df_raw_steel_consumption = read_excel(paste0(datapath, file_steel), sheet="Consumption regions", n_max=27) %>% # kt - select(-2) %>% - pivot_longer(cols="1970":"2012", - values_to='consumption', names_to='year') -df_raw_cement_consumption = read_excel(paste0(datapath, file_cement), + select(-2) %>% + pivot_longer(cols="1970":"2012", + values_to='consumption', names_to='year') +df_raw_cement_consumption = read_excel(paste0(datapath, file_cement), sheet="Regions", skip=122, n_max=27) %>% # kt pivot_longer(cols="1970":"2010", values_to='consumption', names_to='year') -df_population = read_excel(paste0(datapath, file_cement), +df_population = read_excel(paste0(datapath, file_cement), sheet="Timer_POP", skip=3, n_max=27) %>% # million pivot_longer(cols="1970":"2100", values_to='pop', names_to='year') -df_gdp = read_excel(paste0(datapath, file_cement), +df_gdp = read_excel(paste0(datapath, file_cement), sheet="Timer_GDPCAP", skip=3, n_max=27) %>% # million pivot_longer(cols="1970":"2100", values_to='gdp.pcap', names_to='year') +df_raw_aluminum_consumption = read_excel(paste0(datapath, file_al), + sheet="final_table", n_max = 378) # kt + #### Organize data #### -names(df_raw_steel_consumption)[1] = names(df_raw_cement_consumption)[1] = +names(df_raw_steel_consumption)[1] = names(df_raw_cement_consumption)[1] = names(df_population)[1] = names(df_gdp)[1] = "reg_no" -names(df_raw_steel_consumption)[2] = names(df_raw_cement_consumption)[2] = +names(df_raw_steel_consumption)[2] = names(df_raw_cement_consumption)[2] = names(df_population)[2] = names(df_gdp)[2] = "region" -df_steel_consumption = df_raw_steel_consumption %>% +df_steel_consumption = df_raw_steel_consumption %>% left_join(df_population %>% select(-region)) %>% left_join(df_gdp %>% select(-region)) %>% mutate(cons.pcap = consumption/pop, del.t = as.numeric(year)-2010) %>% #kg/cap drop_na() %>% filter(cons.pcap > 0) -df_cement_consumption = df_raw_cement_consumption %>% +df_cement_consumption = df_raw_cement_consumption %>% left_join(df_population %>% select(-region)) %>% left_join(df_gdp %>% select(-region)) %>% mutate(cons.pcap = consumption/pop/1e6, del.t= as.numeric(year) - 2010) %>% #kg/cap drop_na() %>% filter(cons.pcap > 0) +df_aluminum_consumption = df_raw_aluminum_consumption %>% + mutate(cons.pcap = consumption/pop, del.t= as.numeric(year) - 2010) %>% #kg/cap + drop_na() %>% + filter(cons.pcap > 0) + #### Fit models #### # Note: IMAGE adopts NLI for cement, NLIT for steel. -# . Linear ==== +# . Linear ==== lni.c = lm(log(cons.pcap) ~ I(1/gdp.pcap), data=df_cement_consumption) lni.s = lm(log(cons.pcap) ~ I(1/gdp.pcap), data=df_steel_consumption) +lni.a = lm(log(cons.pcap) ~ I(1/gdp.pcap), data=df_aluminum_consumption) summary(lni.c) summary(lni.s) +summary(lni.a) lnit.c = lm(log(cons.pcap) ~ I(1/gdp.pcap)+del.t, data=df_cement_consumption) lnit.s = lm(log(cons.pcap) ~ I(1/gdp.pcap)+del.t, data=df_steel_consumption) +lnit.a = lm(log(cons.pcap) ~ I(1/gdp.pcap)+del.t, data=df_aluminum_consumption) summary(lnit.c) summary(lnit.s) +summary(lnit.a) # . Non-linear ==== nlni.c = nls(cons.pcap ~ a * exp(b/gdp.pcap), data=df_cement_consumption, start=list(a=500, b=-3000)) nlni.s = nls(cons.pcap ~ a * exp(b/gdp.pcap), data=df_steel_consumption, start=list(a=600, b=-10000)) +nlni.a = nls(cons.pcap ~ a * exp(b/gdp.pcap), data=df_aluminum_consumption) + summary(nlni.c) summary(nlni.s) +summary(nlni.a) nlnit.c = nls(cons.pcap ~ a * exp(b/gdp.pcap) * (1-m)^del.t, data=df_cement_consumption, start=list(a=500, b=-3000, m=0)) nlnit.s = nls(cons.pcap ~ a * exp(b/gdp.pcap) * (1-m)^del.t, data=df_steel_consumption, start=list(a=600, b=-10000, m=0)) +nlnit.a = nls(cons.pcap ~ a * exp(b/gdp.pcap) * (1-m)^del.t, data=df_aluminum_consumption,start=list(a=600, b=-10000, m=0)) + summary(nlnit.c) summary(nlnit.s) +summary(nlnit.a) #### Prediction #### From 378509ad1f2963d088fe5b8bfff804329185be11 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Mon, 9 Aug 2021 19:41:13 +0200 Subject: [PATCH 299/774] Add draft aluminum demand adjustment --- .../model/material/data_aluminum.py | 478 ++++++++++++------ .../model/material/material_demand/init.R | 17 +- .../material_demand/init_modularized.R | 45 ++ 3 files changed, 394 insertions(+), 146 deletions(-) diff --git a/message_ix_models/model/material/data_aluminum.py b/message_ix_models/model/material/data_aluminum.py index 211e8ae3cc..8d3b37fc80 100644 --- a/message_ix_models/model/material/data_aluminum.py +++ b/message_ix_models/model/material/data_aluminum.py @@ -9,6 +9,7 @@ import numpy as np from collections import defaultdict from .data_util import read_timeseries +from pathlib import Path import message_ix import ixmp @@ -22,12 +23,14 @@ make_io, make_matched_dfs, same_node, - add_par_data + add_par_data, ) + # Get endogenous material demand from buildings interface from .data_buildings import get_scen_mat_demand from . import get_spec + def read_data_aluminum(scenario): """Read and clean data from :file:`aluminum_techno_economic.xlsx`.""" @@ -48,15 +51,17 @@ def read_data_aluminum(scenario): sheet_n_relations = "relations_R11" # Read the file - data_alu = pd.read_excel(context.get_path("material", fname), - sheet_name=sheet_n,) + data_alu = pd.read_excel( + context.get_path("material", fname), + sheet_name=sheet_n, + ) # Drop columns that don't contain useful information - data_alu= data_alu.drop(["Source", 'Description'], axis = 1) + data_alu = data_alu.drop(["Source", "Description"], axis=1) data_alu_rel = read_rel(scenario, "aluminum_techno_economic.xlsx") - data_aluminum_ts = read_timeseries(scenario,"aluminum_techno_economic.xlsx") + data_aluminum_ts = read_timeseries(scenario, "aluminum_techno_economic.xlsx") # Unit conversion @@ -65,10 +70,12 @@ def read_data_aluminum(scenario): return data_alu, data_alu_rel, data_aluminum_ts + def print_full(x): - pd.set_option('display.max_rows', len(x)) + pd.set_option("display.max_rows", len(x)) print(x) - pd.reset_option('display.max_rows') + pd.reset_option("display.max_rows") + def gen_data_aluminum(scenario, dry_run=False): @@ -85,12 +92,12 @@ def gen_data_aluminum(scenario, dry_run=False): # For each technology there are differnet input and output combinations # Iterate over technologies - allyears = s_info.set['year'] #s_info.Y is only for modeling years - modelyears = s_info.Y #s_info.Y is only for modeling years + allyears = s_info.set["year"] # s_info.Y is only for modeling years + modelyears = s_info.Y # s_info.Y is only for modeling years nodes = s_info.N yv_ya = s_info.yv_ya fmy = s_info.y0 - nodes.remove('World') + nodes.remove("World") # Do not parametrize GLB region the same way if "R11_GLB" in nodes: @@ -102,12 +109,14 @@ def gen_data_aluminum(scenario, dry_run=False): for t in config["technology"]["add"]: - params = data_aluminum.loc[(data_aluminum["technology"] == t),"parameter"]\ - .values.tolist() + params = data_aluminum.loc[ + (data_aluminum["technology"] == t), "parameter" + ].values.tolist() # Obtain the active and vintage years - av = data_aluminum.loc[(data_aluminum["technology"] == t), - 'availability'].values[0] + av = data_aluminum.loc[ + (data_aluminum["technology"] == t), "availability" + ].values[0] modelyears = [year for year in modelyears if year >= av] yva = yv_ya.loc[yv_ya.year_vtg >= av] @@ -120,25 +129,36 @@ def gen_data_aluminum(scenario, dry_run=False): # Obtain the scalar value for the parameter - val = data_aluminum.loc[((data_aluminum["technology"] == t) \ - & (data_aluminum["parameter"] == par)),'value'] - - regions = data_aluminum.loc[((data_aluminum["technology"] == t) \ - & (data_aluminum["parameter"] == par)),'region'] + val = data_aluminum.loc[ + ( + (data_aluminum["technology"] == t) + & (data_aluminum["parameter"] == par) + ), + "value", + ] + + regions = data_aluminum.loc[ + ( + (data_aluminum["technology"] == t) + & (data_aluminum["parameter"] == par) + ), + "region", + ] common = dict( - year_vtg= yv_ya.year_vtg, - year_act= yv_ya.year_act, - mode="M1", - time="year", - time_origin="year", - time_dest="year",) + year_vtg=yv_ya.year_vtg, + year_act=yv_ya.year_act, + mode="M1", + time="year", + time_origin="year", + time_dest="year", + ) for rg in regions: # For the parameters which inlcudes index names - if len(split)> 1: + if len(split) > 1: - if (param_name == "input")|(param_name == "output"): + if (param_name == "input") | (param_name == "output"): # Assign commodity and level names # Later mod can be added @@ -146,50 +166,89 @@ def gen_data_aluminum(scenario, dry_run=False): lev = split[2] if (param_name == "input") and (lev == "import"): - df = make_df(param_name, technology=t, commodity=com, \ - level=lev, value=val[regions[regions==rg].index[0]], unit='t', \ - node_loc=rg, node_origin=global_region, **common) + df = make_df( + param_name, + technology=t, + commodity=com, + level=lev, + value=val[regions[regions == rg].index[0]], + unit="t", + node_loc=rg, + node_origin=global_region, + **common + ) elif (param_name == "output") and (lev == "export"): - df = make_df(param_name, technology=t, commodity=com, \ - level=lev, value=val[regions[regions==rg].index[0]], unit='t', \ - node_loc=rg, node_dest=global_region, **common) + df = make_df( + param_name, + technology=t, + commodity=com, + level=lev, + value=val[regions[regions == rg].index[0]], + unit="t", + node_loc=rg, + node_dest=global_region, + **common + ) # Assign higher efficiency to younger plants - elif (((t == "soderberg_aluminum") or (t == "prebake_aluminum")) \ - & (com == "electr") & (param_name == "input")): - # All the vıntage years - year_vtg = sorted(set(yv_ya.year_vtg.values)) - # Collect the values for the combination of vintage and - # active years. - input_values_all = [] - for yr_v in year_vtg: - # The initial year efficiency value - input_values_temp = [val[regions[regions==rg].index[0]]] - # Reduction after the vintage year - year_vtg_filtered = list(filter(lambda op: op>=yr_v, year_vtg)) - # Filter the active model years - year_act = yv_ya.loc[yv_ya["year_vtg"]== yr_v,"year_act"].values - for i in range(len(year_vtg_filtered) - 1): - input_values_temp.append(input_values_temp[i] * 1.1) - - act_year_no = len(year_act) - input_values_temp = input_values_temp[-act_year_no:] - input_values_all = input_values_all + input_values_temp - - df = (make_df(param_name, technology=t, commodity=com, \ - level=lev, value= input_values_all, unit='t', \ - node_loc=rg, **common).pipe(same_node)) + elif ( + ((t == "soderberg_aluminum") or (t == "prebake_aluminum")) + & (com == "electr") + & (param_name == "input") + ): + # All the vıntage years + year_vtg = sorted(set(yv_ya.year_vtg.values)) + # Collect the values for the combination of vintage and + # active years. + input_values_all = [] + for yr_v in year_vtg: + # The initial year efficiency value + input_values_temp = [ + val[regions[regions == rg].index[0]] + ] + # Reduction after the vintage year + year_vtg_filtered = list( + filter(lambda op: op >= yr_v, year_vtg) + ) + # Filter the active model years + year_act = yv_ya.loc[ + yv_ya["year_vtg"] == yr_v, "year_act" + ].values + for i in range(len(year_vtg_filtered) - 1): + input_values_temp.append(input_values_temp[i] * 1.1) + + act_year_no = len(year_act) + input_values_temp = input_values_temp[-act_year_no:] + input_values_all = input_values_all + input_values_temp + + df = make_df( + param_name, + technology=t, + commodity=com, + level=lev, + value=input_values_all, + unit="t", + node_loc=rg, + **common + ).pipe(same_node) else: - df = (make_df(param_name, technology=t, commodity=com, \ - level=lev, value=val[regions[regions==rg].index[0]], unit='t', \ - node_loc=rg, **common).pipe(same_node)) + df = make_df( + param_name, + technology=t, + commodity=com, + level=lev, + value=val[regions[regions == rg].index[0]], + unit="t", + node_loc=rg, + **common + ).pipe(same_node) # Copy parameters to all regions, when node_loc is not GLB if (len(regions) == 1) and (rg != global_region): - df['node_loc'] = None - df = df.pipe(broadcast, node_loc=nodes)#.pipe(same_node) + df["node_loc"] = None + df = df.pipe(broadcast, node_loc=nodes) # .pipe(same_node) # Use same_node only for non-trade technologies if (lev != "import") and (lev != "export"): df = df.pipe(same_node) @@ -199,61 +258,118 @@ def gen_data_aluminum(scenario, dry_run=False): # Assign the emisson type emi = split[1] - df = make_df(param_name, technology=t, \ - value=val[regions[regions==rg].index[0]],emission=emi,\ - unit='t', node_loc=rg, **common) + df = make_df( + param_name, + technology=t, + value=val[regions[regions == rg].index[0]], + emission=emi, + unit="t", + node_loc=rg, + **common + ) # Parameters with only parameter name else: - df = make_df(param_name, technology=t, \ - value=val[regions[regions==rg].index[0]], unit='t', \ - node_loc=rg, **common) + df = make_df( + param_name, + technology=t, + value=val[regions[regions == rg].index[0]], + unit="t", + node_loc=rg, + **common + ) # Copy parameters to all regions - if (len(regions) == 1) and len(set(df['node_loc'])) == 1 and list(set(df['node_loc']))[0]!=global_region: - df['node_loc'] = None + if ( + (len(regions) == 1) + and len(set(df["node_loc"])) == 1 + and list(set(df["node_loc"]))[0] != global_region + ): + df["node_loc"] = None df = df.pipe(broadcast, node_loc=nodes) results[param_name].append(df) # Create external demand param - parname = 'demand' + parname = "demand" demand = gen_mock_demand_aluminum(scenario) - df = make_df(parname, level='demand', commodity='aluminum', value=demand.value, unit='t', \ - year=demand.year, time='year', node=demand.node)#.pipe(broadcast, node=nodes) + df = make_df( + parname, + level="demand", + commodity="aluminum", + value=demand.value, + unit="t", + year=demand.year, + time="year", + node=demand.node, + ) # .pipe(broadcast, node=nodes) results[parname].append(df) # Special treatment for time-varying params - tec_ts = set(data_aluminum_ts.technology) # set of tecs in timeseries sheet + tec_ts = set(data_aluminum_ts.technology) # set of tecs in timeseries sheet for t in tec_ts: common = dict( time="year", time_origin="year", - time_dest="year",) + time_dest="year", + ) - param_name = data_aluminum_ts.loc[(data_aluminum_ts["technology"] == t), 'parameter'] + param_name = data_aluminum_ts.loc[ + (data_aluminum_ts["technology"] == t), "parameter" + ] for p in set(param_name): - val = data_aluminum_ts.loc[(data_aluminum_ts["technology"] == t) \ - & (data_aluminum_ts["parameter"] == p), 'value'] - units = data_aluminum_ts.loc[(data_aluminum_ts["technology"] == t) \ - & (data_aluminum_ts["parameter"] == p), 'units'].values[0] - mod = data_aluminum_ts.loc[(data_aluminum_ts["technology"] == t) \ - & (data_aluminum_ts["parameter"] == p), 'mode'] - yr = data_aluminum_ts.loc[(data_aluminum_ts["technology"] == t) \ - & (data_aluminum_ts["parameter"] == p), 'year'] - - if p=="var_cost": - df = (make_df(p, technology=t, value=val,\ - unit='t', year_vtg=yr, year_act=yr, mode=mod, **common).pipe(broadcast, \ - node_loc=nodes)) + val = data_aluminum_ts.loc[ + (data_aluminum_ts["technology"] == t) + & (data_aluminum_ts["parameter"] == p), + "value", + ] + units = data_aluminum_ts.loc[ + (data_aluminum_ts["technology"] == t) + & (data_aluminum_ts["parameter"] == p), + "units", + ].values[0] + mod = data_aluminum_ts.loc[ + (data_aluminum_ts["technology"] == t) + & (data_aluminum_ts["parameter"] == p), + "mode", + ] + yr = data_aluminum_ts.loc[ + (data_aluminum_ts["technology"] == t) + & (data_aluminum_ts["parameter"] == p), + "year", + ] + + if p == "var_cost": + df = make_df( + p, + technology=t, + value=val, + unit="t", + year_vtg=yr, + year_act=yr, + mode=mod, + **common + ).pipe(broadcast, node_loc=nodes) else: - rg = data_aluminum_ts.loc[(data_aluminum_ts["technology"] == t) \ - & (data_aluminum_ts["parameter"] == p), 'region'] - df = make_df(p, technology=t, value=val,\ - unit='t', year_vtg=yr, year_act=yr, mode=mod, node_loc=rg, **common) + rg = data_aluminum_ts.loc[ + (data_aluminum_ts["technology"] == t) + & (data_aluminum_ts["parameter"] == p), + "region", + ] + df = make_df( + p, + technology=t, + value=val, + unit="t", + year_vtg=yr, + year_act=yr, + mode=mod, + node_loc=rg, + **common + ) results[p].append(df) @@ -266,8 +382,11 @@ def gen_data_aluminum(scenario, dry_run=False): if r is None: break - params = set(data_aluminum_rel.loc[(data_aluminum_rel["relation"] == r),\ - "parameter"].values) + params = set( + data_aluminum_rel.loc[ + (data_aluminum_rel["relation"] == r), "parameter" + ].values + ) # This relation should start from 2020... if r == "minimum_recycling_aluminum": @@ -275,59 +394,84 @@ def gen_data_aluminum(scenario, dry_run=False): modelyears_copy.remove(2020) common_rel = dict( - year_rel = modelyears_copy, - year_act = modelyears_copy, - mode = 'M1', - relation = r,) + year_rel=modelyears_copy, + year_act=modelyears_copy, + mode="M1", + relation=r, + ) else: # Use all the model years for other relations... common_rel = dict( - year_rel = modelyears, - year_act = modelyears, - mode = 'M1', - relation = r,) + year_rel=modelyears, + year_act=modelyears, + mode="M1", + relation=r, + ) for par_name in params: if par_name == "relation_activity": - tec_list = data_aluminum_rel.loc[((data_aluminum_rel["relation"] == r) \ - & (data_aluminum_rel["parameter"] == par_name)) ,'technology'] + tec_list = data_aluminum_rel.loc[ + ( + (data_aluminum_rel["relation"] == r) + & (data_aluminum_rel["parameter"] == par_name) + ), + "technology", + ] for tec in tec_list.unique(): - val = data_aluminum_rel.loc[((data_aluminum_rel["relation"] == r) \ - & (data_aluminum_rel["parameter"] == par_name) & \ - (data_aluminum_rel["technology"]==tec) & \ - (data_aluminum_rel["Region"]==reg)),'value'].values[0] - - df = (make_df(par_name, technology=tec, value=val, unit='-',\ - node_loc = reg, node_rel= reg, **common_rel).pipe(same_node)) + val = data_aluminum_rel.loc[ + ( + (data_aluminum_rel["relation"] == r) + & (data_aluminum_rel["parameter"] == par_name) + & (data_aluminum_rel["technology"] == tec) + & (data_aluminum_rel["Region"] == reg) + ), + "value", + ].values[0] + + df = make_df( + par_name, + technology=tec, + value=val, + unit="-", + node_loc=reg, + node_rel=reg, + **common_rel + ).pipe(same_node) results[par_name].append(df) elif (par_name == "relation_upper") | (par_name == "relation_lower"): - val = data_aluminum_rel.loc[((data_aluminum_rel["relation"] == r) \ - & (data_aluminum_rel["parameter"] == par_name) & \ - (data_aluminum_rel["Region"]==reg)),'value'].values[0] - - df = (make_df(par_name, value=val, unit='-',node_rel=reg, - **common_rel)) + val = data_aluminum_rel.loc[ + ( + (data_aluminum_rel["relation"] == r) + & (data_aluminum_rel["parameter"] == par_name) + & (data_aluminum_rel["Region"] == reg) + ), + "value", + ].values[0] + + df = make_df( + par_name, value=val, unit="-", node_rel=reg, **common_rel + ) results[par_name].append(df) - results_aluminum = {par_name: pd.concat(dfs) for par_name, - dfs in results.items()} + results_aluminum = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} return results_aluminum + def gen_mock_demand_aluminum(scenario): context = read_config() s_info = ScenarioInfo(scenario) - modelyears = s_info.Y #s_info.Y is only for modeling years + modelyears = s_info.Y # s_info.Y is only for modeling years fmy = s_info.y0 nodes = s_info.N - nodes.remove('World') + nodes.remove("World") # Demand at product level (IAI Global Aluminum Cycle 2018) # Globally: 82.4 Mt @@ -347,39 +491,44 @@ def gen_mock_demand_aluminum(scenario): # For R12: China and CPA demand divided by 0.1 and 0.9. # The order: - #r = ['R12_AFR', 'R12_RCPA', 'R12_EEU', 'R12_FSU', 'R12_LAM', 'R12_MEA',\ + # r = ['R12_AFR', 'R12_RCPA', 'R12_EEU', 'R12_FSU', 'R12_LAM', 'R12_MEA',\ #'R12_NAM', 'R12_PAO', 'R12_PAS', 'R12_SAS', 'R12_WEU',"R12_CHN"] if "R12_CHN" in nodes: - nodes.remove('R12_GLB') + nodes.remove("R12_GLB") sheet_n = "data_R12" - region_set = 'R12_' - d = [3,2,6,5,2.5,2,13.6,3,4.8,4.8,6,26] + region_set = "R12_" + d = [3, 2, 6, 5, 2.5, 2, 13.6, 3, 4.8, 4.8, 6, 26] else: - nodes.remove('R11_GLB') + nodes.remove("R11_GLB") sheet_n = "data_R11" - region_set = 'R11_' - d = [3,28, 6,5,2.5,2,13.6,3,4.8,4.8,6] + region_set = "R11_" + d = [3, 28, 6, 5, 2.5, 2, 13.6, 3, 4.8, 4.8, 6] # SSP2 R11 baseline GDP projection gdp_growth = pd.read_excel( context.get_path("material", "iamc_db ENGAGE baseline GDP PPP.xlsx"), - sheet_name=sheet_n,) + sheet_name=sheet_n, + ) - gdp_growth = gdp_growth.loc[(gdp_growth['Scenario']=='baseline') & \ - (gdp_growth['Region']!='World')].drop(['Model', 'Variable', 'Unit', 'Notes',\ - 2000, 2005], axis = 1) + gdp_growth = gdp_growth.loc[ + (gdp_growth["Scenario"] == "baseline") & (gdp_growth["Region"] != "World") + ].drop(["Model", "Variable", "Unit", "Notes", 2000, 2005], axis=1) - gdp_growth['Region'] = region_set + gdp_growth['Region'] + gdp_growth["Region"] = region_set + gdp_growth["Region"] - demand2020_al = pd.DataFrame({'Region':nodes, 'Val':d}).\ - join(gdp_growth.set_index('Region'), on='Region').\ - rename(columns={'Region':'node'}) + demand2020_al = ( + pd.DataFrame({"Region": nodes, "Val": d}) + .join(gdp_growth.set_index("Region"), on="Region") + .rename(columns={"Region": "node"}) + ) - demand2020_al.iloc[:,3:] = demand2020_al.iloc[:,3:].\ - div(demand2020_al[2020], axis=0).\ - multiply(demand2020_al["Val"], axis=0) + demand2020_al.iloc[:, 3:] = ( + demand2020_al.iloc[:, 3:] + .div(demand2020_al[2020], axis=0) + .multiply(demand2020_al["Val"], axis=0) + ) # Do this if we have 2020 demand values for buildings # sp = get_spec() @@ -393,7 +542,52 @@ def gen_mock_demand_aluminum(scenario): # div(demand2020_al[2020], axis=0) # print("UPDATE {} demand for 2020!".format("aluminum")) # - demand2020_al = pd.melt(demand2020_al.drop(['Val', 'Scenario'], axis=1),\ - id_vars=['node'], var_name='year', value_name = 'value') + demand2020_al = pd.melt( + demand2020_al.drop(["Val", "Scenario"], axis=1), + id_vars=["node"], + var_name="year", + value_name="value", + ) return demand2020_al + + +# This returns a df with columns ["region", "year", "demand.tot"] +def derive_aluminum_demand(scenario, dry_run=False): + """Generate data for materials representation of power industry.""" + # paths to r code and lca data + rcode_path = Path(__file__).parents[0] / "material_demand" + + # source R code + r = ro.r + r.source(str(rcode_path / "init_modularized.R")) + + # Read population and baseline demand for materials + pop = scenario.par("bound_activity_up", {"technology": "Population"}) + pop = pop.loc[pop.year_act >= 2020].rename( + columns={"year_act": "year", "value": "pop.mil", "node_loc": "region"} + ) + + # import pdb; pdb.set_trace() + + pop = pop[["region", "year", "pop.mil"]] + + base_demand = gen_mock_demand_aluminum(scenario) + base_demand = base_demand.loc[base_demand.year == 2020].rename( + columns={"value": "demand.tot.base", "node": "region"} + ) + + # base_demand = scenario.par("demand", {"commodity": "steel", "year": 2020}) + # base_demand = base_demand.loc[base_demand.year >= 2020].rename( + # columns={"value": "demand.tot.base", "node": "region"} + # ) + # base_demand = base_demand[["region", "year", "demand.tot.base"]] + + # call R function with type conversion + with localconverter(ro.default_converter + pandas2ri.converter): + # GDP is only in MER in scenario. + # To get PPP GDP, it is read externally from the R side + df = r.derive_aluminum_demand(pop, base_demand) + df.year = df.year.astype(int) + + return df diff --git a/message_ix_models/model/material/material_demand/init.R b/message_ix_models/model/material/material_demand/init.R index 2f0983aa74..8eb5c3e81e 100644 --- a/message_ix_models/model/material/material_demand/init.R +++ b/message_ix_models/model/material/material_demand/init.R @@ -70,12 +70,12 @@ lnit.s = lm(log(cons.pcap) ~ I(1/gdp.pcap)+del.t, data=df_steel_consumption) lnit.a = lm(log(cons.pcap) ~ I(1/gdp.pcap)+del.t, data=df_aluminum_consumption) summary(lnit.c) summary(lnit.s) -summary(lnit.a) +summary(lnit.a) # better in linear # . Non-linear ==== nlni.c = nls(cons.pcap ~ a * exp(b/gdp.pcap), data=df_cement_consumption, start=list(a=500, b=-3000)) nlni.s = nls(cons.pcap ~ a * exp(b/gdp.pcap), data=df_steel_consumption, start=list(a=600, b=-10000)) -nlni.a = nls(cons.pcap ~ a * exp(b/gdp.pcap), data=df_aluminum_consumption) +nlni.a = nls(cons.pcap ~ a * exp(b/gdp.pcap), data=df_aluminum_consumption, start=list(a=600, b=-10000)) summary(nlni.c) summary(nlni.s) @@ -83,7 +83,7 @@ summary(nlni.a) nlnit.c = nls(cons.pcap ~ a * exp(b/gdp.pcap) * (1-m)^del.t, data=df_cement_consumption, start=list(a=500, b=-3000, m=0)) nlnit.s = nls(cons.pcap ~ a * exp(b/gdp.pcap) * (1-m)^del.t, data=df_steel_consumption, start=list(a=600, b=-10000, m=0)) -nlnit.a = nls(cons.pcap ~ a * exp(b/gdp.pcap) * (1-m)^del.t, data=df_aluminum_consumption,start=list(a=600, b=-10000, m=0)) +nlnit.a = nls(cons.pcap ~ a * exp(b/gdp.pcap) * (1-m)^del.t, data=df_aluminum_consumption, start=list(a=600, b=-10000, m=0)) summary(nlnit.c) summary(nlnit.s) @@ -93,9 +93,18 @@ summary(nlnit.a) #### Prediction #### year = seq(2020, 2100, 10) -df = data.frame(gdp.pcap = seq(3000, 50000, length.out = length(year)), year) %>% mutate(del.t = year - 2010) +df = data.frame(gdp.pcap = seq(3000, 70000, length.out = length(year)), year) %>% mutate(del.t = year - 2010) df2 = df %>% mutate(gdp.pcap = 2*gdp.pcap) predict(nlnit.s, df) +predict(nlni.s, df) +exp(predict(lnit.s, df)) +exp(predict(lni.s, df)) predict(nlnit.s, df2) + predict(nlni.c, df) predict(nlni.c, df2) + +predict(nlni.a, df) +predict(nlnit.a, df) +exp(predict(lni.a, df)) +exp(predict(lnit.a, df)) diff --git a/message_ix_models/model/material/material_demand/init_modularized.R b/message_ix_models/model/material/material_demand/init_modularized.R index fba92115f6..f9b9dd5044 100644 --- a/message_ix_models/model/material/material_demand/init_modularized.R +++ b/message_ix_models/model/material/material_demand/init_modularized.R @@ -127,6 +127,51 @@ derive_cement_demand <- function(df_pop, df_demand) { + +derive_aluminum_demand <- function(df_pop, df_demand) { + # df_in will have columns: + # region + # year + # gdp.pcap + # population (in mil.) + df_raw_aluminum_consumption = read_excel(paste0(datapath, file_al), + sheet="final_table", n_max = 378) # kt + # df_population = read_excel(paste0(datapath, file_cement), + # sheet="Timer_POP", skip=3, n_max=27) %>% # million + # pivot_longer(cols="1970":"2100", values_to='pop', names_to='year') + # df_gdp = read_excel(paste0(datapath, file_cement), + # sheet="Timer_GDPCAP", skip=3, n_max=27) %>% # million + # pivot_longer(cols="1970":"2100", values_to='gdp.pcap', names_to='year') + + #### Organize data #### + # names(df_raw_aluminum_consumption)[1] = + # names(df_population)[1] = names(df_gdp)[1] = "reg_no" + # names(df_raw_aluminum_consumption)[2] = + # names(df_population)[2] = names(df_gdp)[2] = "region" + df_aluminum_consumption = df_raw_aluminum_consumption %>% + mutate(cons.pcap = consumption/pop, del.t= as.numeric(year) - 2010) %>% #kg/cap + drop_na() %>% + filter(cons.pcap > 0) + + nlnit.a = nls(cons.pcap ~ a * exp(b/gdp.pcap) * (1-m)^del.t, data=df_aluminum_consumption, start=list(a=600, b=-10000, m=0)) + + df_in = df_pop %>% left_join(df_demand %>% select(-year)) %>% # df_demand is only for 2020 + inner_join(gdp.ppp) %>% mutate(del.t = year - 2010, gdp.pcap = gdp.ppp*giga/pop.mil/mega) + demand = df_in %>% + mutate(demand.pcap0 = predict(nlnit.a, .)) %>% #kg/cap + group_by(region) %>% + mutate(demand.pcap.base = first(demand.tot.base*giga/pop.mil/mega)) %>% + mutate(gap.base = first(demand.pcap.base - demand.pcap0)) %>% + mutate(demand.pcap = demand.pcap0 + gap.base * (1-exp(-9*exp(-0.1*(year - 2010))))) %>% # Bas' equation + mutate(demand.tot = demand.pcap * pop.mil * mega / giga) # Mt + + # Add 2110 spaceholder + demand = demand %>% rbind(demand %>% filter(year==2100) %>% mutate(year = 2110)) + + return(demand %>% select(node=region, year, value=demand.tot) %>% arrange(year, node)) # Mt +} + + #### test # year = seq(2020, 2100, 10) # df = data.frame(region = "Korea", gdp.pcap = seq(3000, 50000, length.out = length(year)), year, From 214fbe1a9e79bb763e13e1988f934bc774a3db8b Mon Sep 17 00:00:00 2001 From: Jihoon Date: Tue, 10 Aug 2021 14:01:39 +0200 Subject: [PATCH 300/774] Make changes for aluminum demand aluminum now uses nli regression (saturation at ~20kg/cap). --- .../model/material/data_aluminum.py | 7 +++- .../model/material/material_demand/init.R | 7 +++- .../material_demand/init_modularized.R | 35 ++++++++----------- 3 files changed, 26 insertions(+), 23 deletions(-) diff --git a/message_ix_models/model/material/data_aluminum.py b/message_ix_models/model/material/data_aluminum.py index 8d3b37fc80..a34bdf603c 100644 --- a/message_ix_models/model/material/data_aluminum.py +++ b/message_ix_models/model/material/data_aluminum.py @@ -292,7 +292,7 @@ def gen_data_aluminum(scenario, dry_run=False): # Create external demand param parname = "demand" - demand = gen_mock_demand_aluminum(scenario) + demand = derive_aluminum_demand(scenario) df = make_df( parname, level="demand", @@ -552,6 +552,11 @@ def gen_mock_demand_aluminum(scenario): return demand2020_al +# load rpy2 modules +import rpy2.robjects as ro +from rpy2.robjects import pandas2ri +from rpy2.robjects.conversion import localconverter + # This returns a df with columns ["region", "year", "demand.tot"] def derive_aluminum_demand(scenario, dry_run=False): """Generate data for materials representation of power industry.""" diff --git a/message_ix_models/model/material/material_demand/init.R b/message_ix_models/model/material/material_demand/init.R index 8eb5c3e81e..c7248f3853 100644 --- a/message_ix_models/model/material/material_demand/init.R +++ b/message_ix_models/model/material/material_demand/init.R @@ -93,7 +93,7 @@ summary(nlnit.a) #### Prediction #### year = seq(2020, 2100, 10) -df = data.frame(gdp.pcap = seq(3000, 70000, length.out = length(year)), year) %>% mutate(del.t = year - 2010) +df = data.frame(gdp.pcap = seq(3000, 90000, length.out = length(year)), year) %>% mutate(del.t = year - 2010) df2 = df %>% mutate(gdp.pcap = 2*gdp.pcap) predict(nlnit.s, df) predict(nlni.s, df) @@ -108,3 +108,8 @@ predict(nlni.a, df) predict(nlnit.a, df) exp(predict(lni.a, df)) exp(predict(lnit.a, df)) +predict(nlni.a, df2) +predict(nlnit.a, df2) +exp(predict(lni.a, df2)) +exp(predict(lnit.a, df2)) + diff --git a/message_ix_models/model/material/material_demand/init_modularized.R b/message_ix_models/model/material/material_demand/init_modularized.R index f9b9dd5044..ffc85e97d4 100644 --- a/message_ix_models/model/material/material_demand/init_modularized.R +++ b/message_ix_models/model/material/material_demand/init_modularized.R @@ -8,6 +8,7 @@ datapath = 'H:/MyDocuments/MESSAGE/message_data/data/material/' file_cement = "CEMENT.BvR2010.xlsx" file_steel = "STEEL_database_2012.xlsx" +file_al = "demand_aluminum.xlsx" file_gdp = "iamc_db ENGAGE baseline GDP PPP.xlsx" # file_pop = "iamc_db ENGAGE baseline population.xlsx" # @@ -67,7 +68,7 @@ derive_steel_demand <- function(df_pop, df_demand) { group_by(region) %>% mutate(demand.pcap.base = first(demand.tot.base*giga/pop.mil/mega)) %>% mutate(gap.base = first(demand.pcap.base - demand.pcap0)) %>% - mutate(demand.pcap = demand.pcap0 + gap.base * (1-exp(-9*exp(-0.1*(year - 2010))))) %>% # Bas' equation + mutate(demand.pcap = demand.pcap0 + gap.base * gompertz(9, 0.1, y=year)) %>% # Bas' equation mutate(demand.tot = demand.pcap * pop.mil * mega / giga) # Mt # Add 2110 spaceholder @@ -116,7 +117,7 @@ derive_cement_demand <- function(df_pop, df_demand) { group_by(region) %>% mutate(demand.pcap.base = first(demand.tot.base*giga/pop.mil/mega)) %>% mutate(gap.base = first(demand.pcap.base - demand.pcap0)) %>% - mutate(demand.pcap = demand.pcap0 + gap.base * (1-exp(-10*exp(-0.1*(year - 2010))))) %>% # Bas' equation + mutate(demand.pcap = demand.pcap0 + gap.base * gompertz(10, 0.1, y=year)) %>% # Bas' equation mutate(demand.tot = demand.pcap * pop.mil * mega / giga) # Mt # Add 2110 spaceholder @@ -129,40 +130,28 @@ derive_cement_demand <- function(df_pop, df_demand) { derive_aluminum_demand <- function(df_pop, df_demand) { - # df_in will have columns: - # region - # year - # gdp.pcap - # population (in mil.) + + # Aluminum xls input already has population and gdp df_raw_aluminum_consumption = read_excel(paste0(datapath, file_al), sheet="final_table", n_max = 378) # kt - # df_population = read_excel(paste0(datapath, file_cement), - # sheet="Timer_POP", skip=3, n_max=27) %>% # million - # pivot_longer(cols="1970":"2100", values_to='pop', names_to='year') - # df_gdp = read_excel(paste0(datapath, file_cement), - # sheet="Timer_GDPCAP", skip=3, n_max=27) %>% # million - # pivot_longer(cols="1970":"2100", values_to='gdp.pcap', names_to='year') #### Organize data #### - # names(df_raw_aluminum_consumption)[1] = - # names(df_population)[1] = names(df_gdp)[1] = "reg_no" - # names(df_raw_aluminum_consumption)[2] = - # names(df_population)[2] = names(df_gdp)[2] = "region" df_aluminum_consumption = df_raw_aluminum_consumption %>% mutate(cons.pcap = consumption/pop, del.t= as.numeric(year) - 2010) %>% #kg/cap drop_na() %>% filter(cons.pcap > 0) - nlnit.a = nls(cons.pcap ~ a * exp(b/gdp.pcap) * (1-m)^del.t, data=df_aluminum_consumption, start=list(a=600, b=-10000, m=0)) + # nlnit.a = nls(cons.pcap ~ a * exp(b/gdp.pcap) * (1-m)^del.t, data=df_aluminum_consumption, start=list(a=600, b=-10000, m=0)) + nlni.a = nls(cons.pcap ~ a * exp(b/gdp.pcap), data=df_aluminum_consumption, start=list(a=600, b=-10000)) df_in = df_pop %>% left_join(df_demand %>% select(-year)) %>% # df_demand is only for 2020 - inner_join(gdp.ppp) %>% mutate(del.t = year - 2010, gdp.pcap = gdp.ppp*giga/pop.mil/mega) + inner_join(gdp.ppp) %>% mutate(gdp.pcap = gdp.ppp*giga/pop.mil/mega) demand = df_in %>% - mutate(demand.pcap0 = predict(nlnit.a, .)) %>% #kg/cap + mutate(demand.pcap0 = predict(nlni.a, .)) %>% #kg/cap group_by(region) %>% mutate(demand.pcap.base = first(demand.tot.base*giga/pop.mil/mega)) %>% mutate(gap.base = first(demand.pcap.base - demand.pcap0)) %>% - mutate(demand.pcap = demand.pcap0 + gap.base * (1-exp(-9*exp(-0.1*(year - 2010))))) %>% # Bas' equation + mutate(demand.pcap = demand.pcap0 + gap.base * gompertz(9, 0.1, y=year)) %>% # Bas' equation mutate(demand.tot = demand.pcap * pop.mil * mega / giga) # Mt # Add 2110 spaceholder @@ -172,6 +161,10 @@ derive_aluminum_demand <- function(df_pop, df_demand) { } +gompertz <- function(phi, mu, y, baseyear=2020) { + return (1-exp(-phi*exp(-mu*(y - baseyear)))) +} + #### test # year = seq(2020, 2100, 10) # df = data.frame(region = "Korea", gdp.pcap = seq(3000, 50000, length.out = length(year)), year, From c51ae459792318b7b4dfd5d205e5fbd13f580f29 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 11 Aug 2021 13:59:20 +0200 Subject: [PATCH 301/774] Fix error --- message_ix_models/model/material/data_cement.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/message_ix_models/model/material/data_cement.py b/message_ix_models/model/material/data_cement.py index fe75d18b2f..249de27ace 100644 --- a/message_ix_models/model/material/data_cement.py +++ b/message_ix_models/model/material/data_cement.py @@ -391,3 +391,5 @@ def derive_cement_demand(scenario, dry_run=False): df.year = df.year.astype(int) return df + + return df From e86a51e87f2d0f02e2c203b7e6e8b66f65e55e86 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Thu, 23 Sep 2021 17:20:05 +0200 Subject: [PATCH 302/774] Changes for dealing with inconsistencies with the new message_data interfaces --- message_ix_models/model/material/__init__.py | 14 +- message_ix_models/model/material/bare.py | 2 +- message_ix_models/model/material/build.py | 54 ++- message_ix_models/model/material/data.py | 22 +- .../model/material/data_aluminum.py | 10 +- .../model/material/data_buildings.py | 297 ++++++++------ .../model/material/data_cement.py | 14 +- .../model/material/data_generic.py | 106 +++-- .../model/material/data_petro.py | 301 +++++++++----- .../model/material/data_power_sector.py | 8 +- .../model/material/data_steel.py | 8 +- message_ix_models/model/material/data_util.py | 376 +++++++++++------- .../model/material/material_testscript.py | 122 +++--- message_ix_models/model/material/util.py | 46 ++- 14 files changed, 826 insertions(+), 554 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index e88edbb638..7f69f0f2a1 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -4,8 +4,11 @@ from message_ix_models import ScenarioInfo from message_ix_models.model.build import apply_spec -from .build import apply_spec -from message_data.tools import ScenarioInfo +# from .build import apply_spec +# from message_data.tools import ScenarioInfo +from message_ix_models import ScenarioInfo +from message_ix_models.model.build import apply_spec +from message_ix_models.util.context import Context # from .data import add_data from .data_util import modify_demand_and_hist_activity @@ -47,6 +50,7 @@ def get_spec() -> Mapping[str, ScenarioInfo]: remove = ScenarioInfo() # Load configuration + # context = Context.get_instance(-1) context = read_config() # Update the ScenarioInfo objects with required and new set elements @@ -86,7 +90,7 @@ def create_bare(context, regions, dry_run): context.period_start = 1980 # Otherwise it can not find the path to read the yaml files.. - context.metadata_path = context.metadata_path / "data" + # context.metadata_path = context.metadata_path / "data" scen = create_res(context) build(scen) @@ -122,7 +126,7 @@ def solve(context, datafile, tag): # "DIAG-C30-const_E414": "baseline_test", }.get(context.scenario_info["scenario"]) - context.metadata_path = context.metadata_path / "data" + # context.metadata_path = context.metadata_path / "data" context.datafile = datafile if context.scenario_info["model"] != "CD_Links_SSP2": @@ -201,7 +205,7 @@ def run_reporting(old_reporting, scenario_name, model_name): from .data_petro import gen_data_petro_chemicals from .data_buildings import gen_data_buildings from .data_power_sector import gen_data_power_sector -from message_data.tools import add_par_data +from message_ix_models.util import add_par_data DATA_FUNCTIONS = [ # gen_data_buildings, diff --git a/message_ix_models/model/material/bare.py b/message_ix_models/model/material/bare.py index c8fa119914..624a479f00 100644 --- a/message_ix_models/model/material/bare.py +++ b/message_ix_models/model/material/bare.py @@ -4,7 +4,7 @@ import message_ix -from message_data.tools import Code, ScenarioInfo, get_context, set_info, add_par_data +from message_ix_models import Code, ScenarioInfo, get_context, set_info, add_par_data from .build import apply_spec from .util import read_config from .data import get_data, gen_data_steel, gen_data_generic, gen_data_aluminum, \ diff --git a/message_ix_models/model/material/build.py b/message_ix_models/model/material/build.py index 04577e8a44..0692a96687 100644 --- a/message_ix_models/model/material/build.py +++ b/message_ix_models/model/material/build.py @@ -5,18 +5,18 @@ from message_ix import Scenario import pandas as pd -from message_data.tools import Code, ScenarioInfo, add_par_data, strip_par_data +from message_ix_models import Code, ScenarioInfo, add_par_data, strip_par_data log = logging.getLogger(__name__) def apply_spec( - scenario: Scenario, - spec: Mapping[str, ScenarioInfo], - data: Callable = None, - **options, - ): + scenario: Scenario, + spec: Mapping[str, ScenarioInfo], + data: Callable = None, + **options, +): """Apply `spec` to `scenario`. Parameters @@ -52,11 +52,9 @@ def apply_spec( .Code .ScenarioInfo """ - dry_run = options.get('dry_run', False) + dry_run = options.get("dry_run", False) - log.setLevel( - logging.ERROR if options.get('quiet', False) else logging.DEBUG - ) + log.setLevel(logging.ERROR if options.get("quiet", False) else logging.DEBUG) if not dry_run: try: @@ -85,35 +83,31 @@ def apply_spec( # log.debug(', '.join(map(repr, base))) # All elements; verbose # Check for required elements - require = spec['require'].set[set_name] + require = spec["require"].set[set_name] for element in require: if element not in base: - log.error(f' {repr(element)} not found') + log.error(f" {repr(element)} not found") raise ValueError if len(require): - log.info(f' Check {len(require)} required elements') + log.info(f" Check {len(require)} required elements") - if options.get('fast', False): - log.info(' Skip removing parameter values') + if options.get("fast", False): + log.info(" Skip removing parameter values") else: # Remove elements and associated parameter values - remove = spec['remove'].set[set_name] + remove = spec["remove"].set[set_name] for element in remove: msg = f"{repr(element)} and associated parameter elements" - if options.get('fast', False): + if options.get("fast", False): log.info(f" Skip removing {msg} (fast=True)") else: log.info(f" Remove {msg}") strip_par_data( - scenario, - set_name, - element, - dry_run=dry_run, - dump=dump + scenario, set_name, element, dry_run=dry_run, dump=dump ) # Add elements - add = spec['add'].set[set_name] + add = spec["add"].set[set_name] if not dry_run: for element in add: scenario.add_set( @@ -123,17 +117,17 @@ def apply_spec( if len(add): log.info(f" Add {len(add)} element(s)") - log.debug(' ' + ', '.join(map(repr, add))) + log.debug(" " + ", ".join(map(repr, add))) - log.info(' ---') + log.info(" ---") N_removed = sum(len(d) for d in dump.values()) - log.info(f'{N_removed} parameter elements removed') + log.info(f"{N_removed} parameter elements removed") # Add units - for unit in spec['add'].set['unit']: + for unit in spec["add"].set["unit"]: unit = Code(id=unit, name=unit) if isinstance(unit, str) else unit - log.info(f'Add unit {repr(unit.id)}') + log.info(f"Add unit {repr(unit.id)}") scenario.platform.add_unit(unit.id, comment=unit.name) # Add data @@ -144,6 +138,6 @@ def apply_spec( # add_par_data(scenario, result, dry_run=dry_run) # Finalize - log.info('Commit results.') + log.info("Commit results.") if not dry_run: - scenario.commit(options.get('message', f"{__name__}.apply_spec()")) + scenario.commit(options.get("message", f"{__name__}.apply_spec()")) diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index ad7141d7a5..2b0828bd26 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -8,17 +8,15 @@ from .data_steel import gen_data_steel from .data_aluminum import gen_data_aluminum from .data_generic import gen_data_generic -from .data_petro import gen_data_petro_chemicals -from .data_buildings import gen_data_buildings -from message_data.tools import ( - ScenarioInfo, +from message_ix_models import ScenarioInfo +from message_ix import make_df +from message_ix_models.util import ( broadcast, - make_df, make_io, make_matched_dfs, same_node, - add_par_data + add_par_data, ) from .util import read_config @@ -27,12 +25,10 @@ log = logging.getLogger(__name__) DATA_FUNCTIONS = [ - #gen_data_buildings, gen_data_steel, gen_data_cement, gen_data_aluminum, - gen_data_petro_chemicals, - gen_data_generic + gen_data_generic, ] # Try to handle multiple data input functions from different materials @@ -47,13 +43,9 @@ def add_data(scenario, dry_run=False): log.warning("Remove 'R11_GLB' from node list for data generation") info.set["node"].remove("R11_GLB") - if {"World", "R12_GLB"} < set(info.set["node"]): - log.warning("Remove 'R12_GLB' from node list for data generation") - info.set["node"].remove("R12_GLB") - for func in DATA_FUNCTIONS: # Generate or load the data; add to the Scenario - log.info(f'from {func.__name__}()') + log.info(f"from {func.__name__}()") add_par_data(scenario, func(scenario), dry_run=dry_run) - log.info('done') + log.info("done") diff --git a/message_ix_models/model/material/data_aluminum.py b/message_ix_models/model/material/data_aluminum.py index a34bdf603c..7a19d6c744 100644 --- a/message_ix_models/model/material/data_aluminum.py +++ b/message_ix_models/model/material/data_aluminum.py @@ -16,10 +16,10 @@ from .util import read_config from .data_util import read_rel -from message_data.tools import ( - ScenarioInfo, +from message_ix_models import ScenarioInfo +from message_ix import make_df +from message_ix_models.util import ( broadcast, - make_df, make_io, make_matched_dfs, same_node, @@ -52,7 +52,7 @@ def read_data_aluminum(scenario): # Read the file data_alu = pd.read_excel( - context.get_path("material", fname), + context.get_local_path("material", fname), sheet_name=sheet_n, ) @@ -508,7 +508,7 @@ def gen_mock_demand_aluminum(scenario): # SSP2 R11 baseline GDP projection gdp_growth = pd.read_excel( - context.get_path("material", "iamc_db ENGAGE baseline GDP PPP.xlsx"), + context.get_local_path("material", "iamc_db ENGAGE baseline GDP PPP.xlsx"), sheet_name=sheet_n, ) diff --git a/message_ix_models/model/material/data_buildings.py b/message_ix_models/model/material/data_buildings.py index bd6d068d77..493026a198 100644 --- a/message_ix_models/model/material/data_buildings.py +++ b/message_ix_models/model/material/data_buildings.py @@ -8,20 +8,21 @@ from .util import read_config from .data_util import read_rel -from message_data.tools import ( - ScenarioInfo, +from message_ix_models import ScenarioInfo +from message_ix import make_df +from message_ix_models.util import ( broadcast, - make_df, make_io, make_matched_dfs, same_node, copy_column, - add_par_data + add_par_data, ) -CASE_SENS = 'ref' # 'min', 'max' -INPUTFILE = 'LED_LED_report_IAMC_sensitivity_R12.csv' -#INPUTFILE = 'LED_LED_report_IAMC_sensitivity_R11.csv' +CASE_SENS = "ref" # 'min', 'max' +INPUTFILE = "LED_LED_report_IAMC_sensitivity_R12.csv" +# INPUTFILE = 'LED_LED_report_IAMC_sensitivity_R11.csv' + def read_timeseries_buildings(filename, scenario, case=CASE_SENS): @@ -33,122 +34,148 @@ def read_timeseries_buildings(filename, scenario, case=CASE_SENS): nodes = s_info.N # Read the file and filter the given sensitivity case - bld_input_raw = pd.read_csv( - context.get_path("material", filename)) - bld_input_raw = bld_input_raw.loc[bld_input_raw.Sensitivity==case] - - bld_input_mat = bld_input_raw[bld_input_raw['Variable'].\ - # str.contains("Floor Space|Aluminum|Cement|Steel|Final Energy")] - str.contains("Floor Space|Aluminum|Cement|Steel")] # Final Energy - Later. Need to figure out carving out - bld_input_mat['Region'] = 'R11_' + bld_input_mat['Region'] + bld_input_raw = pd.read_csv(context.get_local_path("material", filename)) + bld_input_raw = bld_input_raw.loc[bld_input_raw.Sensitivity == case] + + bld_input_mat = bld_input_raw[ + bld_input_raw[ + "Variable" + ].str.contains( # str.contains("Floor Space|Aluminum|Cement|Steel|Final Energy")] + "Floor Space|Aluminum|Cement|Steel" + ) + ] # Final Energy - Later. Need to figure out carving out + bld_input_mat["Region"] = "R11_" + bld_input_mat["Region"] print("Check the year values") print(bld_input_mat) - bld_input_pivot = \ - bld_input_mat.melt(id_vars=['Region','Variable'], var_name='Year', \ - value_vars=list(map(str, range(2015, 2101, 5)))).\ - set_index(['Region','Year','Variable'])\ - .squeeze()\ - .unstack()\ - .reset_index() + bld_input_pivot = ( + bld_input_mat.melt( + id_vars=["Region", "Variable"], + var_name="Year", + value_vars=list(map(str, range(2015, 2101, 5))), + ) + .set_index(["Region", "Year", "Variable"]) + .squeeze() + .unstack() + .reset_index() + ) # Divide by floor area to get energy/material intensities - bld_intensity_ene_mat = bld_input_pivot.iloc[:,2:].div(bld_input_pivot['Energy Service|Residential|Floor Space'], axis=0) - bld_intensity_ene_mat.columns = [s + "|Intensity" for s in bld_intensity_ene_mat.columns] - bld_intensity_ene_mat = pd.concat([bld_input_pivot[['Region', 'Year']], \ - bld_intensity_ene_mat.reindex(bld_input_pivot.index)], axis=1).\ - drop(columns = ['Energy Service|Residential|Floor Space|Intensity']) - - bld_intensity_ene_mat['Energy Service|Residential|Floor Space'] = bld_input_pivot['Energy Service|Residential|Floor Space'] - + bld_intensity_ene_mat = bld_input_pivot.iloc[:, 2:].div( + bld_input_pivot["Energy Service|Residential|Floor Space"], axis=0 + ) + bld_intensity_ene_mat.columns = [ + s + "|Intensity" for s in bld_intensity_ene_mat.columns + ] + bld_intensity_ene_mat = pd.concat( + [ + bld_input_pivot[["Region", "Year"]], + bld_intensity_ene_mat.reindex(bld_input_pivot.index), + ], + axis=1, + ).drop(columns=["Energy Service|Residential|Floor Space|Intensity"]) + + bld_intensity_ene_mat["Energy Service|Residential|Floor Space"] = bld_input_pivot[ + "Energy Service|Residential|Floor Space" + ] # Material intensities are in kg/m2 - bld_data_long = bld_intensity_ene_mat.melt(id_vars=['Region','Year'], var_name='Variable')\ - .rename(columns={"Region": "node", "Year": "year"}) + bld_data_long = bld_intensity_ene_mat.melt( + id_vars=["Region", "Year"], var_name="Variable" + ).rename(columns={"Region": "node", "Year": "year"}) # Both for energy and material - bld_intensity_long = bld_data_long[bld_data_long['Variable'].\ - str.contains("Intensity")]\ - .reset_index(drop=True) - bld_area_long = bld_data_long[bld_data_long['Variable']==\ - 'Energy Service|Residential|Floor Space']\ - .reset_index(drop=True) + bld_intensity_long = bld_data_long[ + bld_data_long["Variable"].str.contains("Intensity") + ].reset_index(drop=True) + bld_area_long = bld_data_long[ + bld_data_long["Variable"] == "Energy Service|Residential|Floor Space" + ].reset_index(drop=True) tmp = bld_intensity_long.Variable.str.split("|", expand=True) - bld_intensity_long['commodity'] = tmp[3].str.lower() # Material type - bld_intensity_long['type'] = tmp[0] # 'Material Demand' or 'Scrap Release' - bld_intensity_long['unit'] = "kg/m2" + bld_intensity_long["commodity"] = tmp[3].str.lower() # Material type + bld_intensity_long["type"] = tmp[0] # 'Material Demand' or 'Scrap Release' + bld_intensity_long["unit"] = "kg/m2" - bld_intensity_long = bld_intensity_long.drop(columns='Variable') - bld_area_long = bld_area_long.drop(columns='Variable') + bld_intensity_long = bld_intensity_long.drop(columns="Variable") + bld_area_long = bld_area_long.drop(columns="Variable") - bld_intensity_long = bld_intensity_long\ - .drop(bld_intensity_long[np.isnan(bld_intensity_long.value)].index) + bld_intensity_long = bld_intensity_long.drop( + bld_intensity_long[np.isnan(bld_intensity_long.value)].index + ) # Derive baseyear material demand (Mt/year in 2020) - bld_demand_long = bld_input_pivot.melt(id_vars=['Region','Year'], var_name='Variable')\ - .rename(columns={"Region": "node", "Year": "year"}) + bld_demand_long = bld_input_pivot.melt( + id_vars=["Region", "Year"], var_name="Variable" + ).rename(columns={"Region": "node", "Year": "year"}) tmp = bld_demand_long.Variable.str.split("|", expand=True) - bld_demand_long['commodity'] = tmp[3].str.lower() # Material type + bld_demand_long["commodity"] = tmp[3].str.lower() # Material type # bld_demand_long = bld_demand_long[bld_demand_long['year']=="2020"].\ # dropna(how='any') - bld_demand_long = bld_demand_long.dropna(how='any') - bld_demand_long = bld_demand_long[bld_demand_long['Variable'].str.contains("Material Demand")].drop(columns='Variable') + bld_demand_long = bld_demand_long.dropna(how="any") + bld_demand_long = bld_demand_long[ + bld_demand_long["Variable"].str.contains("Material Demand") + ].drop(columns="Variable") return bld_intensity_long, bld_area_long, bld_demand_long -def get_scen_mat_demand(commod, scenario, year = "2020", inputfile = INPUTFILE, case=CASE_SENS): +def get_scen_mat_demand( + commod, scenario, year="2020", inputfile=INPUTFILE, case=CASE_SENS +): a, b, c = read_timeseries_buildings(inputfile, scenario, case) - if not year == "all": # specific year - cc = c[(c.commodity==commod) & (c.year==year)].reset_index(drop=True) - else: # all years - cc = c[(c.commodity==commod)].reset_index(drop=True) + if not year == "all": # specific year + cc = c[(c.commodity == commod) & (c.year == year)].reset_index(drop=True) + else: # all years + cc = c[(c.commodity == commod)].reset_index(drop=True) return cc def adjust_demand_param(scen): s_info = ScenarioInfo(scen) - modelyears = s_info.Y #s_info.Y is only for modeling years + modelyears = s_info.Y # s_info.Y is only for modeling years # scen.clone(model=scen.model, scenario=scen.scenario+"_building") - scen_mat_demand = scen.par('demand', {"level":"demand"}) # mat demand without buildings considered + scen_mat_demand = scen.par( + "demand", {"level": "demand"} + ) # mat demand without buildings considered scen.check_out() comms = ["steel", "cement", "aluminum"] for c in comms: - mat_building = get_scen_mat_demand(c, scen, year="all").rename(columns={"value":"bld_demand"}) # mat demand (timeseries) from buildings model (Alessio) - mat_building['year'] = mat_building['year'].astype(int) + mat_building = get_scen_mat_demand(c, scen, year="all").rename( + columns={"value": "bld_demand"} + ) # mat demand (timeseries) from buildings model (Alessio) + mat_building["year"] = mat_building["year"].astype(int) sub_mat_demand = scen_mat_demand.loc[scen_mat_demand.commodity == c] # print("old", sub_mat_demand.loc[sub_mat_demand.year >=2025]) - sub_mat_demand = sub_mat_demand.join(mat_building.set_index(["node", "year", "commodity"]), \ - on = ["node", "year", "commodity"], how='left') - sub_mat_demand['value'] = sub_mat_demand['value'] - sub_mat_demand['bld_demand'] + sub_mat_demand = sub_mat_demand.join( + mat_building.set_index(["node", "year", "commodity"]), + on=["node", "year", "commodity"], + how="left", + ) + sub_mat_demand["value"] = sub_mat_demand["value"] - sub_mat_demand["bld_demand"] sub_mat_demand = sub_mat_demand.drop(columns=["bld_demand"]).dropna(how="any") # Only replace for year >= 2025 - scen.add_par('demand', sub_mat_demand.loc[sub_mat_demand.year >=2025]) + scen.add_par("demand", sub_mat_demand.loc[sub_mat_demand.year >= 2025]) # print("new", sub_mat_demand.loc[sub_mat_demand.year >=2025]) scen.commit("Building material demand subtracted") - - def gen_data_buildings(scenario, dry_run=False): - """Generate data for materials representation of steel industry. - - """ + """Generate data for materials representation of steel industry.""" # Load configuration context = read_config() config = context["material"]["buildings"] # New element names for buildings integrations - lev_new = config['level']['add'][0] - comm_new = config['commodity']['add'][0] - tec_new = config['technology']['add'][0] # "buildings" + lev_new = config["level"]["add"][0] + comm_new = config["commodity"]["add"][0] + tec_new = config["technology"]["add"][0] # "buildings" print(lev_new, comm_new, tec_new, type(tec_new)) @@ -156,8 +183,11 @@ def gen_data_buildings(scenario, dry_run=False): s_info = ScenarioInfo(scenario) # Buildings raw data (from Alessio) - data_buildings, data_buildings_demand, data_buildings_mat_demand = \ - read_timeseries_buildings(INPUTFILE, scenario, CASE_SENS) + ( + data_buildings, + data_buildings_demand, + data_buildings_mat_demand, + ) = read_timeseries_buildings(INPUTFILE, scenario, CASE_SENS) # List of data frames, to be concatenated together at end results = defaultdict(list) @@ -166,30 +196,28 @@ def gen_data_buildings(scenario, dry_run=False): # Iterate over technologies # allyears = s_info.set['year'] #s_info.Y is only for modeling years - modelyears = s_info.Y #s_info.Y is only for modeling years + modelyears = s_info.Y # s_info.Y is only for modeling years nodes = s_info.N yv_ya = s_info.yv_ya # fmy = s_info.y0 - nodes.remove('World') + nodes.remove("World") nodes.remove("R11_RCPA") # Read field values from the buildings input data regions = list(set(data_buildings.node)) comms = list(set(data_buildings.commodity)) # types = list(set(data_buildings.type)) - types = ['Material Demand', 'Scrap Release'] # Order matters + types = ["Material Demand", "Scrap Release"] # Order matters - common = dict( - time="year", - time_origin="year", - time_dest="year", - mode="M1") + common = dict(time="year", time_origin="year", time_dest="year", mode="M1") # Filter only the years in the base scenario - data_buildings['year'] = data_buildings['year'].astype(int) - data_buildings_demand['year'] = data_buildings_demand['year'].astype(int) - data_buildings = data_buildings[data_buildings['year'].isin(modelyears)] - data_buildings_demand = data_buildings_demand[data_buildings_demand['year'].isin(modelyears)] + data_buildings["year"] = data_buildings["year"].astype(int) + data_buildings_demand["year"] = data_buildings_demand["year"].astype(int) + data_buildings = data_buildings[data_buildings["year"].isin(modelyears)] + data_buildings_demand = data_buildings_demand[ + data_buildings_demand["year"].isin(modelyears) + ] # historical demands @@ -197,45 +225,84 @@ def gen_data_buildings(scenario, dry_run=False): for comm in comms: # for typ in types: - val_mat = data_buildings.loc[(data_buildings["type"] == types[0]) \ - & (data_buildings["commodity"] == comm)\ - & (data_buildings["node"] == rg), ] - val_scr = data_buildings.loc[(data_buildings["type"] == types[1]) \ - & (data_buildings["commodity"] == comm)\ - & (data_buildings["node"] == rg), ] + val_mat = data_buildings.loc[ + (data_buildings["type"] == types[0]) + & (data_buildings["commodity"] == comm) + & (data_buildings["node"] == rg), + ] + val_scr = data_buildings.loc[ + (data_buildings["type"] == types[1]) + & (data_buildings["commodity"] == comm) + & (data_buildings["node"] == rg), + ] # Material input to buildings - df = make_df('input', technology=tec_new, commodity=comm, \ - level="demand", year_vtg = val_mat.year, \ - value=val_mat.value, unit='t', \ - node_loc = rg, **common)\ - .pipe(same_node)\ - .assign(year_act=copy_column('year_vtg')) - results['input'].append(df) + df = ( + make_df( + "input", + technology=tec_new, + commodity=comm, + level="demand", + year_vtg=val_mat.year, + value=val_mat.value, + unit="t", + node_loc=rg, + **common + ) + .pipe(same_node) + .assign(year_act=copy_column("year_vtg")) + ) + results["input"].append(df) # Scrap output back to industry - df = make_df('output', technology=tec_new, commodity=comm, \ - level='end_of_life', year_vtg = val_scr.year, \ - value=val_scr.value, unit='t', \ - node_loc = rg, **common)\ - .pipe(same_node)\ - .assign(year_act=copy_column('year_vtg')) - results['output'].append(df) + df = ( + make_df( + "output", + technology=tec_new, + commodity=comm, + level="end_of_life", + year_vtg=val_scr.year, + value=val_scr.value, + unit="t", + node_loc=rg, + **common + ) + .pipe(same_node) + .assign(year_act=copy_column("year_vtg")) + ) + results["output"].append(df) # Service output to buildings demand - df = make_df('output', technology=tec_new, commodity=comm_new, \ - level='demand', year_vtg = val_mat.year, \ - value=1, unit='t', \ - node_loc=rg, **common)\ - .pipe(same_node)\ - .assign(year_act=copy_column('year_vtg')) - results['output'].append(df) + df = ( + make_df( + "output", + technology=tec_new, + commodity=comm_new, + level="demand", + year_vtg=val_mat.year, + value=1, + unit="t", + node_loc=rg, + **common + ) + .pipe(same_node) + .assign(year_act=copy_column("year_vtg")) + ) + results["output"].append(df) # Create external demand param - parname = 'demand' + parname = "demand" demand = data_buildings_demand - df = make_df(parname, level='demand', commodity=comm_new, value=demand.value, unit='t', \ - year=demand.year, time='year', node=demand.node) + df = make_df( + parname, + level="demand", + commodity=comm_new, + value=demand.value, + unit="t", + year=demand.year, + time="year", + node=demand.node, + ) results[parname].append(df) # Concatenate to one data frame per parameter diff --git a/message_ix_models/model/material/data_cement.py b/message_ix_models/model/material/data_cement.py index 249de27ace..c139155d6c 100644 --- a/message_ix_models/model/material/data_cement.py +++ b/message_ix_models/model/material/data_cement.py @@ -8,10 +8,10 @@ import pandas as pd from .util import read_config -from message_data.tools import ( - ScenarioInfo, +from message_ix_models import ScenarioInfo +from message_ix import make_df +from message_ix_models.util import ( broadcast, - make_df, make_io, make_matched_dfs, same_node, @@ -92,7 +92,7 @@ def gen_mock_demand_cement(scenario): # SSP2 R11 baseline GDP projection gdp_growth = pd.read_excel( - context.get_path("material", "iamc_db ENGAGE baseline GDP PPP.xlsx"), + context.get_local_path("material", "iamc_db ENGAGE baseline GDP PPP.xlsx"), sheet_name=sheet_n, ) @@ -105,7 +105,7 @@ def gen_mock_demand_cement(scenario): # # Regions setting for IMAGE # region_cement = pd.read_excel( - # context.get_path("material", "CEMENT.BvR2010.xlsx"), + # context.get_local_path("material", "CEMENT.BvR2010.xlsx"), # sheet_name="Timer_Regions", skiprows=range(0,3))[['Region #', 'Name']]\ # .drop_duplicates().sort_values(by='Region #') # @@ -127,7 +127,7 @@ def gen_mock_demand_cement(scenario): # # # Cement demand 2010 [Mt/year] (IMAGE) # demand2010_cement = pd.read_excel( - # context.get_path("material", "CEMENT.BvR2010.xlsx"), + # context.get_local_path("material", "CEMENT.BvR2010.xlsx"), # sheet_name="Domestic Consumption", skiprows=range(0,3)).\ # groupby(by=["Region #"]).sum()[[2010]].\ # join(region_cement.set_index('Region #'), on='Region #').\ @@ -391,5 +391,3 @@ def derive_cement_demand(scenario, dry_run=False): df.year = df.year.astype(int) return df - - return df diff --git a/message_ix_models/model/material/data_generic.py b/message_ix_models/model/material/data_generic.py index 74311a5d1c..3ab4604fa2 100644 --- a/message_ix_models/model/material/data_generic.py +++ b/message_ix_models/model/material/data_generic.py @@ -13,14 +13,14 @@ import pandas as pd from .util import read_config -from message_data.tools import ( - ScenarioInfo, +from message_ix_models import ScenarioInfo +from message_ix import make_df +from message_ix_models.util import ( broadcast, - make_df, make_io, make_matched_dfs, same_node, - add_par_data + add_par_data, ) @@ -35,14 +35,16 @@ def read_data_generic(): # Read the file data_generic = pd.read_excel( - context.get_path("material", "generic_furnace_boiler_techno_economic.xlsx"), + context.get_local_path( + "material", "generic_furnace_boiler_techno_economic.xlsx" + ), sheet_name="generic", ) # Clean the data # Drop columns that don't contain useful information - data_generic= data_generic.drop(['Region', 'Source', 'Description'], axis = 1) + data_generic = data_generic.drop(["Region", "Source", "Description"], axis=1) # Unit conversion @@ -69,8 +71,8 @@ def gen_data_generic(scenario, dry_run=False): # For each technology there are differnet input and output combinations # Iterate over technologies - allyears = s_info.set['year'] #s_info.Y is only for modeling years - modelyears = s_info.Y #s_info.Y is only for modeling years + allyears = s_info.set["year"] # s_info.Y is only for modeling years + modelyears = s_info.Y # s_info.Y is only for modeling years nodes = s_info.N yv_ya = s_info.yv_ya fmy = s_info.y0 @@ -83,50 +85,71 @@ def gen_data_generic(scenario, dry_run=False): # 'World' is included by default when creating a message_ix.Scenario(). # Need to remove it for the China bare model - nodes.remove('World') + nodes.remove("World") for t in config["technology"]["add"]: # years = s_info.Y - params = data_generic.loc[(data_generic["technology"] == t),"parameter"]\ - .values.tolist() + params = data_generic.loc[ + (data_generic["technology"] == t), "parameter" + ].values.tolist() # Availability year of the technology - av = data_generic.loc[(data_generic["technology"] == t),'availability'].\ - values[0] + av = data_generic.loc[(data_generic["technology"] == t), "availability"].values[ + 0 + ] modelyears = [year for year in modelyears if year >= av] - yva = yv_ya.loc[yv_ya.year_vtg >= av, ] + yva = yv_ya.loc[ + yv_ya.year_vtg >= av, + ] # Iterate over parameters for par in params: split = par.split("|") param_name = par.split("|")[0] - val = data_generic.loc[((data_generic["technology"] == t) & \ - (data_generic["parameter"] == par)),'value'].values[0] + val = data_generic.loc[ + ( + (data_generic["technology"] == t) + & (data_generic["parameter"] == par) + ), + "value", + ].values[0] # Common parameters for all input and output tables # year_act is none at the moment # node_dest and node_origin are the same as node_loc common = dict( - year_vtg= yva.year_vtg, - year_act= yva.year_act, - time="year", - time_origin="year", - time_dest="year",) + year_vtg=yva.year_vtg, + year_act=yva.year_act, + time="year", + time_origin="year", + time_dest="year", + ) - if len(split)> 1: + if len(split) > 1: - if (param_name == "input")|(param_name == "output"): + if (param_name == "input") | (param_name == "output"): com = split[1] lev = split[2] mod = split[3] - df = (make_df(param_name, technology=t, commodity=com, \ - level=lev, mode=mod, value=val, unit='t', **common).\ - pipe(broadcast, node_loc=nodes).pipe(same_node)) + df = ( + make_df( + param_name, + technology=t, + commodity=com, + level=lev, + mode=mod, + value=val, + unit="t", + **common + ) + .pipe(broadcast, node_loc=nodes) + .pipe(same_node) + ) results[param_name].append(df) @@ -134,13 +157,25 @@ def gen_data_generic(scenario, dry_run=False): emi = split[1] # TODO: Now tentatively fixed to one mode. Have values for the other mode too - df_low = (make_df(param_name, technology=t,value=val,\ - emission=emi, mode="low_temp", unit='t', **common).pipe(broadcast, \ - node_loc=nodes)) - - df_high = (make_df(param_name, technology=t,value=val,\ - emission=emi, mode="high_temp", unit='t', **common).pipe(broadcast, \ - node_loc=nodes)) + df_low = make_df( + param_name, + technology=t, + value=val, + emission=emi, + mode="low_temp", + unit="t", + **common + ).pipe(broadcast, node_loc=nodes) + + df_high = make_df( + param_name, + technology=t, + value=val, + emission=emi, + mode="high_temp", + unit="t", + **common + ).pipe(broadcast, node_loc=nodes) results[param_name].append(df_low) results[param_name].append(df_high) @@ -149,8 +184,9 @@ def gen_data_generic(scenario, dry_run=False): else: - df = (make_df(param_name, technology=t, value=val,unit='t', \ - **common).pipe(broadcast, node_loc=nodes)) + df = make_df( + param_name, technology=t, value=val, unit="t", **common + ).pipe(broadcast, node_loc=nodes) results[param_name].append(df) diff --git a/message_ix_models/model/material/data_petro.py b/message_ix_models/model/material/data_petro.py index 06722113d9..fbd9ecf35f 100644 --- a/message_ix_models/model/material/data_petro.py +++ b/message_ix_models/model/material/data_petro.py @@ -8,23 +8,24 @@ from .util import read_config from .data_util import read_rel -from message_data.tools import ( - ScenarioInfo, +from message_ix_models import ScenarioInfo +from message_ix import make_df +from message_ix_models.util import ( broadcast, - make_df, make_io, make_matched_dfs, same_node, - add_par_data + add_par_data, ) + def read_data_petrochemicals(scenario): """Read and clean data from :file:`petrochemicals_techno_economic.xlsx`.""" # Ensure config is loaded, get the context context = read_config() s_info = ScenarioInfo(scenario) - fname = "petrochemicals_techno_economic.xlsx" + fname = "petrochemicals_techno_economic.xlsx" if "R12_CHN" in s_info.N: sheet_n = "data_R12" @@ -33,10 +34,11 @@ def read_data_petrochemicals(scenario): # Read the file data_petro = pd.read_excel( - context.get_path("material", fname),sheet_name=sheet_n) + context.get_local_path("material", fname), sheet_name=sheet_n + ) # Clean the data - data_petro= data_petro.drop(['Source', 'Description'], axis = 1) + data_petro = data_petro.drop(["Source", "Description"], axis=1) return data_petro @@ -45,10 +47,10 @@ def gen_mock_demand_petro(scenario): context = read_config() s_info = ScenarioInfo(scenario) - modelyears = s_info.Y #s_info.Y is only for modeling years + modelyears = s_info.Y # s_info.Y is only for modeling years fmy = s_info.y0 nodes = s_info.N - nodes.remove('World') + nodes.remove("World") # 2018 production # Use as 2020 @@ -62,20 +64,20 @@ def gen_mock_demand_petro(scenario): # SSP2 R11 baseline GDP projection # The orders of the regions - #r = ['R12_AFR', 'R12_RCPA', 'R12_EEU', 'R12_FSU', 'R12_LAM', 'R12_MEA',\ + # r = ['R12_AFR', 'R12_RCPA', 'R12_EEU', 'R12_FSU', 'R12_LAM', 'R12_MEA',\ # 'R12_NAM', 'R12_PAO', 'R12_PAS', 'R12_SAS', 'R12_WEU',"R12_CHN"] if "R12_CHN" in nodes: - nodes.remove('R12_GLB') + nodes.remove("R12_GLB") sheet_n = "data_R12" - region_set = 'R12_' + region_set = "R12_" d_HVC = [2, 7.5, 30, 4, 11, 42, 60, 32, 30, 29, 35, 67.5] else: - nodes.remove('R11_GLB') + nodes.remove("R11_GLB") sheet_n = "data_R11" - region_set = 'R11_' + region_set = "R11_" d_HVC = [2, 75, 30, 4, 11, 42, 60, 32, 30, 29, 35] @@ -87,15 +89,15 @@ def gen_mock_demand_petro(scenario): # 7.4503, 7.497867, 17.30965, 11.1014] gdp_growth = pd.read_excel( - context.get_path("material", "iamc_db ENGAGE baseline GDP PPP.xlsx"), + context.get_local_path("material", "iamc_db ENGAGE baseline GDP PPP.xlsx"), sheet_name=sheet_n, ) - gdp_growth = gdp_growth.loc[(gdp_growth['Scenario']=='baseline') & \ - (gdp_growth['Region']!='World')].drop(['Model', 'Variable', 'Unit', \ - 'Notes', 2000, 2005], axis = 1) + gdp_growth = gdp_growth.loc[ + (gdp_growth["Scenario"] == "baseline") & (gdp_growth["Region"] != "World") + ].drop(["Model", "Variable", "Unit", "Notes", 2000, 2005], axis=1) - gdp_growth['Region'] = region_set + gdp_growth['Region'] + gdp_growth["Region"] = region_set + gdp_growth["Region"] # list = [] # @@ -115,22 +117,30 @@ def gen_mock_demand_petro(scenario): # join(gdp_growth.set_index('Region'), on='Region').\ # rename(columns={'Region':'node'}) - demand2020 = pd.DataFrame({'Region':nodes, 'Val':d_HVC}).\ - join(gdp_growth.set_index('Region'), on='Region').\ - rename(columns={'Region':'node'}) + demand2020 = ( + pd.DataFrame({"Region": nodes, "Val": d_HVC}) + .join(gdp_growth.set_index("Region"), on="Region") + .rename(columns={"Region": "node"}) + ) - demand2020.iloc[:,3:] = demand2020.iloc[:,3:].div(demand2020[2020], axis=0).\ - multiply(demand2020["Val"], axis=0) + demand2020.iloc[:, 3:] = ( + demand2020.iloc[:, 3:] + .div(demand2020[2020], axis=0) + .multiply(demand2020["Val"], axis=0) + ) - demand2020 = pd.melt(demand2020.drop(['Val', 'Scenario'], axis=1),\ - id_vars=['node'], var_name='year', value_name = 'value') + demand2020 = pd.melt( + demand2020.drop(["Val", "Scenario"], axis=1), + id_vars=["node"], + var_name="year", + value_name="value", + ) - #list.append(demand2020) + # list.append(demand2020) - #return list[0], list[1], list[2] + # return list[0], list[1], list[2] return demand2020 - # China 2006: 22 kg/cap HVC demand. 2006 population: 1.311 billion # This makes 28.842 Mt. (IEA Energy Technology Transitions for Industry) # In 2010: 43.263 Mt (1.5 times of 2006) @@ -141,6 +151,7 @@ def gen_mock_demand_petro(scenario): # This makes 25 Mt ethylene, 25 Mt propylene, 21 Mt BTX # This can be verified by other sources. + def gen_data_petro_chemicals(scenario, dry_run=False): # Load configuration @@ -151,19 +162,19 @@ def gen_data_petro_chemicals(scenario, dry_run=False): # Techno-economic assumptions data_petro = read_data_petrochemicals(scenario) - data_petro_ts = read_timeseries(scenario,"petrochemicals_techno_economic.xlsx") + data_petro_ts = read_timeseries(scenario, "petrochemicals_techno_economic.xlsx") # List of data frames, to be concatenated together at end results = defaultdict(list) # For each technology there are differnet input and output combinations # Iterate over technologies - allyears = s_info.set['year'] - modelyears = s_info.Y #s_info.Y is only for modeling years + allyears = s_info.set["year"] + modelyears = s_info.Y # s_info.Y is only for modeling years nodes = s_info.N yv_ya = s_info.yv_ya fmy = s_info.y0 - nodes.remove('World') + nodes.remove("World") # Do not parametrize GLB region the same way if "R11_GLB" in nodes: @@ -176,101 +187,154 @@ def gen_data_petro_chemicals(scenario, dry_run=False): for t in config["technology"]["add"]: # years = s_info.Y - params = data_petro.loc[(data_petro["technology"] == t),"parameter"]\ - .values.tolist() + params = data_petro.loc[ + (data_petro["technology"] == t), "parameter" + ].values.tolist() # Availability year of the technology - av = data_petro.loc[(data_petro["technology"] == t),'availability'].\ - values[0] + av = data_petro.loc[(data_petro["technology"] == t), "availability"].values[0] modelyears = [year for year in modelyears if year >= av] - yva = yv_ya.loc[yv_ya.year_vtg >= av, ] + yva = yv_ya.loc[ + yv_ya.year_vtg >= av, + ] # Iterate over parameters for par in params: split = par.split("|") param_name = par.split("|")[0] - val = data_petro.loc[((data_petro["technology"] == t) & \ - (data_petro["parameter"] == par)),'value'] - - regions = data_petro.loc[((data_petro["technology"] == t) \ - & (data_petro["parameter"] == par)),'Region'] + val = data_petro.loc[ + ((data_petro["technology"] == t) & (data_petro["parameter"] == par)), + "value", + ] + regions = data_petro.loc[ + ((data_petro["technology"] == t) & (data_petro["parameter"] == par)), + "Region", + ] # Common parameters for all input and output tables # node_dest and node_origin are the same as node_loc common = dict( - year_vtg= yva.year_vtg, - year_act= yva.year_act, - time="year", - time_origin="year", - time_dest="year",) + year_vtg=yva.year_vtg, + year_act=yva.year_act, + time="year", + time_origin="year", + time_dest="year", + ) for rg in regions: - if len(split)> 1: + if len(split) > 1: - if (param_name == "input")|(param_name == "output"): + if (param_name == "input") | (param_name == "output"): com = split[1] lev = split[2] mod = split[3] if (param_name == "input") and (lev == "import"): - df = make_df(param_name, technology=t, commodity=com, \ - level=lev, mode= mod, \ - value=val[regions[regions==rg].index[0]], unit='t', \ - node_loc=rg, node_origin=global_region, **common) + df = make_df( + param_name, + technology=t, + commodity=com, + level=lev, + mode=mod, + value=val[regions[regions == rg].index[0]], + unit="t", + node_loc=rg, + node_origin=global_region, + **common + ) elif (param_name == "output") and (lev == "export"): - df = make_df(param_name, technology=t, commodity=com, \ - level=lev, mode=mod, \ - value=val[regions[regions==rg].index[0]], unit='t', \ - node_loc=rg, node_dest=global_region, **common) + df = make_df( + param_name, + technology=t, + commodity=com, + level=lev, + mode=mod, + value=val[regions[regions == rg].index[0]], + unit="t", + node_loc=rg, + node_dest=global_region, + **common + ) else: - df = (make_df(param_name, technology=t, commodity=com, \ - level=lev, mode=mod, \ - value=val[regions[regions==rg].index[0]], unit='t', \ - node_loc=rg, **common).pipe(same_node)) + df = make_df( + param_name, + technology=t, + commodity=com, + level=lev, + mode=mod, + value=val[regions[regions == rg].index[0]], + unit="t", + node_loc=rg, + **common + ).pipe(same_node) # Copy parameters to all regions, when node_loc is not GLB if (len(regions) == 1) and (rg != global_region): # print("copying to all R11", rg, lev) - df['node_loc'] = None - df = df.pipe(broadcast, node_loc=nodes)#.pipe(same_node) + df["node_loc"] = None + df = df.pipe(broadcast, node_loc=nodes) # .pipe(same_node) # Use same_node only for non-trade technologies if (lev != "import") and (lev != "export"): df = df.pipe(same_node) - - elif param_name == "emission_factor": emi = split[1] mod = split[2] - df = make_df(param_name, technology=t, \ - value=val[regions[regions==rg].index[0]],emission=emi,\ - mode=mod, unit='t', node_loc=rg, **common) + df = make_df( + param_name, + technology=t, + value=val[regions[regions == rg].index[0]], + emission=emi, + mode=mod, + unit="t", + node_loc=rg, + **common + ) elif param_name == "var_cost": mod = split[1] - df = (make_df(param_name, technology=t, commodity=com, \ - level=lev,mode=mod, value=val[regions[regions==rg].index[0]],\ - unit='t', **common).pipe(broadcast, node_loc=nodes).pipe(same_node)) + df = ( + make_df( + param_name, + technology=t, + commodity=com, + level=lev, + mode=mod, + value=val[regions[regions == rg].index[0]], + unit="t", + **common + ) + .pipe(broadcast, node_loc=nodes) + .pipe(same_node) + ) # Rest of the parameters apart from inpput, output and emission_factor else: - df = make_df(param_name, technology=t, \ - value=val[regions[regions==rg].index[0]], unit='t', \ - node_loc=rg, **common) + df = make_df( + param_name, + technology=t, + value=val[regions[regions == rg].index[0]], + unit="t", + node_loc=rg, + **common + ) # Copy parameters to all regions if (len(regions) == 1) and (rg != global_region): - if len(set(df['node_loc'])) == 1 and list(set(df['node_loc']))[0]!= global_region: + if ( + len(set(df["node_loc"])) == 1 + and list(set(df["node_loc"]))[0] != global_region + ): # print("Copying to all R11") - df['node_loc'] = None + df["node_loc"] = None df = df.pipe(broadcast, node_loc=nodes) results[param_name].append(df) @@ -278,13 +342,20 @@ def gen_data_petro_chemicals(scenario, dry_run=False): # Add demand # Create external demand param - #demand_e,demand_p,demand_BTX = gen_mock_demand_petro(scenario) + # demand_e,demand_p,demand_BTX = gen_mock_demand_petro(scenario) demand_HVC = gen_mock_demand_petro(scenario) paramname = "demand" - df_HVC = make_df(paramname, level='demand', commodity="HVC", \ - value=demand_HVC.value, unit='t',year=demand_HVC.year, time='year', \ - node=demand_HVC.node)#.pipe(broadcast, node=nodes) + df_HVC = make_df( + paramname, + level="demand", + commodity="HVC", + value=demand_HVC.value, + unit="t", + year=demand_HVC.year, + time="year", + node=demand_HVC.node, + ) # .pipe(broadcast, node=nodes) results["demand"].append(df_HVC) # df_e = make_df(paramname, level='final_material', commodity="ethylene", \ @@ -304,35 +375,63 @@ def gen_data_petro_chemicals(scenario, dry_run=False): # Special treatment for time-varying params - tec_ts = set(data_petro_ts.technology) # set of tecs in timeseries sheet + tec_ts = set(data_petro_ts.technology) # set of tecs in timeseries sheet for t in tec_ts: common = dict( time="year", time_origin="year", - time_dest="year",) + time_dest="year", + ) - param_name = data_petro_ts.loc[(data_petro_ts["technology"] == t), 'parameter'] + param_name = data_petro_ts.loc[(data_petro_ts["technology"] == t), "parameter"] for p in set(param_name): - val = data_petro_ts.loc[(data_petro_ts["technology"] == t) \ - & (data_petro_ts["parameter"] == p), 'value'] - units = data_petro_ts.loc[(data_petro_ts["technology"] == t) \ - & (data_petro_ts["parameter"] == p), 'units'].values[0] - mod = data_petro_ts.loc[(data_petro_ts["technology"] == t) \ - & (data_petro_ts["parameter"] == p), 'mode'] - yr = data_petro_ts.loc[(data_petro_ts["technology"] == t) \ - & (data_petro_ts["parameter"] == p), 'year'] - - if p=="var_cost": - df = (make_df(p, technology=t, value=val,\ - unit='t', year_vtg=yr, year_act=yr, mode=mod, **common).pipe(broadcast, \ - node_loc=nodes)) + val = data_petro_ts.loc[ + (data_petro_ts["technology"] == t) & (data_petro_ts["parameter"] == p), + "value", + ] + units = data_petro_ts.loc[ + (data_petro_ts["technology"] == t) & (data_petro_ts["parameter"] == p), + "units", + ].values[0] + mod = data_petro_ts.loc[ + (data_petro_ts["technology"] == t) & (data_petro_ts["parameter"] == p), + "mode", + ] + yr = data_petro_ts.loc[ + (data_petro_ts["technology"] == t) & (data_petro_ts["parameter"] == p), + "year", + ] + + if p == "var_cost": + df = make_df( + p, + technology=t, + value=val, + unit="t", + year_vtg=yr, + year_act=yr, + mode=mod, + **common + ).pipe(broadcast, node_loc=nodes) else: - rg = data_petro_ts.loc[(data_petro_ts["technology"] == t) \ - & (data_petro_ts["parameter"] == p), 'region'] - df = make_df(p, technology=t, value=val,\ - unit='t', year_vtg=yr, year_act=yr, mode=mod, node_loc=rg, **common) + rg = data_petro_ts.loc[ + (data_petro_ts["technology"] == t) + & (data_petro_ts["parameter"] == p), + "region", + ] + df = make_df( + p, + technology=t, + value=val, + unit="t", + year_vtg=yr, + year_act=yr, + mode=mod, + node_loc=rg, + **common + ) results[p].append(df) diff --git a/message_ix_models/model/material/data_power_sector.py b/message_ix_models/model/material/data_power_sector.py index 2163660a74..8c1b972d94 100644 --- a/message_ix_models/model/material/data_power_sector.py +++ b/message_ix_models/model/material/data_power_sector.py @@ -21,10 +21,10 @@ import pandas as pd from .util import read_config -from message_data.tools import ( - ScenarioInfo, +from message_ix_models import ScenarioInfo +from message_ix import make_df +from message_ix_models.util import ( broadcast, - make_df, make_io, make_matched_dfs, same_node, @@ -42,7 +42,7 @@ def gen_data_power_sector(scenario, dry_run=False): # paths to r code and lca data rcode_path = Path(__file__).parents[0] / "material_intensity" - data_path = context.get_path("material") + data_path = context.get_local_path("material") # Information about scenario, e.g. node, year s_info = ScenarioInfo(scenario) diff --git a/message_ix_models/model/material/data_steel.py b/message_ix_models/model/material/data_steel.py index 5a5380f7b9..ab907c8541 100644 --- a/message_ix_models/model/material/data_steel.py +++ b/message_ix_models/model/material/data_steel.py @@ -12,10 +12,10 @@ # Get endogenous material demand from buildings interface from .data_buildings import get_scen_mat_demand -from message_data.tools import ( - ScenarioInfo, +from message_ix_models import ScenarioInfo +from message_ix import make_df +from message_ix_models.util import ( broadcast, - make_df, make_io, make_matched_dfs, same_node, @@ -66,7 +66,7 @@ def gen_mock_demand_steel(scenario): # SSP2 R11 baseline GDP projection gdp_growth = pd.read_excel( - context.get_path("material", "iamc_db ENGAGE baseline GDP PPP.xlsx"), + context.get_local_path("material", "iamc_db ENGAGE baseline GDP PPP.xlsx"), sheet_name=sheet_n, ) diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index 095809ce83..ec68e6e41b 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -5,8 +5,8 @@ from .util import read_config import re -from message_data.tools import ( - ScenarioInfo) +from message_ix_models import ScenarioInfo + def modify_demand_and_hist_activity(scen): """Take care of demand changes due to the introduction of material parents @@ -27,30 +27,33 @@ def modify_demand_and_hist_activity(scen): if "R12_CHN" in s_info.N: sheet_n = "R12" - region_type = 'R12_' + region_type = "R12_" region_name_CPA = "RCPA" region_name_CHN = "CHN" else: sheet_n = "R11" - region_type = 'R11_' + region_type = "R11_" region_name_CPA = "CPA" - region_name_CHN = '' + region_name_CHN = "" - df = pd.read_excel(context.get_path("material", fname),\ - sheet_name=sheet_n, usecols = "A:F") + df = pd.read_excel( + context.get_local_path("material", fname), sheet_name=sheet_n, usecols="A:F" + ) print("Are the correct numbers read?") print(df) # Filter the necessary variables - df = df[(df["SECTOR"]== "feedstock (petrochemical industry)")| - (df["SECTOR"]== "feedstock (total)") | \ - (df["SECTOR"]== "industry (chemicals)") | - (df["SECTOR"]== "industry (iron and steel)") | \ - (df["SECTOR"]== "industry (non-ferrous metals)") | \ - (df["SECTOR"]== "industry (non-metallic minerals)") | \ - (df["SECTOR"]== "industry (total)")] - df = df[df["RYEAR"]==2015] + df = df[ + (df["SECTOR"] == "feedstock (petrochemical industry)") + | (df["SECTOR"] == "feedstock (total)") + | (df["SECTOR"] == "industry (chemicals)") + | (df["SECTOR"] == "industry (iron and steel)") + | (df["SECTOR"] == "industry (non-ferrous metals)") + | (df["SECTOR"] == "industry (non-metallic minerals)") + | (df["SECTOR"] == "industry (total)") + ] + df = df[df["RYEAR"] == 2015] print("Is the filter correct?") print(df) @@ -59,24 +62,31 @@ def modify_demand_and_hist_activity(scen): # Aluminum, cement and steel included. # NOTE: Steel has high shares (previously it was not inlcuded in i_spec) - df_spec = df[(df["FUEL"]=="electricity") & \ - (df["SECTOR"] != "industry (total)") \ - & (df["SECTOR"]!= "feedstock (petrochemical industry)") \ - & (df["SECTOR"]!= "feedstock (total)")] - df_spec_total = df[(df["SECTOR"]=="industry (total)") \ - & (df["FUEL"]=="electricity") ] - - df_spec_new = pd.DataFrame(columns=["REGION","SECTOR","FUEL","RYEAR",\ - "UNIT_OUT","RESULT"]) + df_spec = df[ + (df["FUEL"] == "electricity") + & (df["SECTOR"] != "industry (total)") + & (df["SECTOR"] != "feedstock (petrochemical industry)") + & (df["SECTOR"] != "feedstock (total)") + ] + df_spec_total = df[ + (df["SECTOR"] == "industry (total)") & (df["FUEL"] == "electricity") + ] + + df_spec_new = pd.DataFrame( + columns=["REGION", "SECTOR", "FUEL", "RYEAR", "UNIT_OUT", "RESULT"] + ) for r in df_spec["REGION"].unique(): - df_spec_temp = df_spec[df_spec["REGION"]==r] + df_spec_temp = df_spec[df_spec["REGION"] == r] df_spec_total_temp = df_spec_total[df_spec_total["REGION"] == r] - df_spec_temp["i_spec"] = df_spec_temp["RESULT"]/df_spec_total_temp["RESULT"].values[0] - df_spec_new = pd.concat([df_spec_temp,df_spec_new],ignore_index = True) - - df_spec_new.drop(["FUEL","RYEAR","UNIT_OUT","RESULT"],axis=1, inplace=True) - df_spec_new.loc[df_spec_new["SECTOR"]=="industry (chemicals)","i_spec"] = \ - df_spec_new.loc[df_spec_new["SECTOR"]=="industry (chemicals)","i_spec"] * 0.7 + df_spec_temp["i_spec"] = ( + df_spec_temp["RESULT"] / df_spec_total_temp["RESULT"].values[0] + ) + df_spec_new = pd.concat([df_spec_temp, df_spec_new], ignore_index=True) + + df_spec_new.drop(["FUEL", "RYEAR", "UNIT_OUT", "RESULT"], axis=1, inplace=True) + df_spec_new.loc[df_spec_new["SECTOR"] == "industry (chemicals)", "i_spec"] = ( + df_spec_new.loc[df_spec_new["SECTOR"] == "industry (chemicals)", "i_spec"] * 0.7 + ) df_spec_new = df_spec_new.groupby(["REGION"]).sum().reset_index() print("spec") @@ -86,19 +96,20 @@ def modify_demand_and_hist_activity(scen): # It is assumed that the sectors that are explicitly covered in MESSAGE are # 50% of the total feedstock. - df_feed = df[(df["SECTOR"]== "feedstock (petrochemical industry)") & \ - (df["FUEL"]== "total") ] - df_feed_total = df[(df["SECTOR"]== "feedstock (total)") & (df["FUEL"]== "total")] - df_feed_temp = pd.DataFrame(columns= ["REGION","i_feed"]) - df_feed_new = pd.DataFrame(columns= ["REGION","i_feed"]) + df_feed = df[ + (df["SECTOR"] == "feedstock (petrochemical industry)") & (df["FUEL"] == "total") + ] + df_feed_total = df[(df["SECTOR"] == "feedstock (total)") & (df["FUEL"] == "total")] + df_feed_temp = pd.DataFrame(columns=["REGION", "i_feed"]) + df_feed_new = pd.DataFrame(columns=["REGION", "i_feed"]) for r in df_feed["REGION"].unique(): i = 0 - df_feed_temp.at[i,"REGION"] = r - df_feed_temp.at[i,"i_feed"] = 0.7 + df_feed_temp.at[i, "REGION"] = r + df_feed_temp.at[i, "i_feed"] = 0.7 i = i + 1 - df_feed_new = pd.concat([df_feed_temp,df_feed_new],ignore_index = True) + df_feed_new = pd.concat([df_feed_temp, df_feed_new], ignore_index=True) print("feed") print(df_feed_new) @@ -124,39 +135,55 @@ def modify_demand_and_hist_activity(scen): # NOTE: Aluminum is excluded since refining process is not explicitly represented # NOTE: CPA has a 3% share while it used to be 30% previosuly ?? - df_therm = df[(df["FUEL"]!="electricity") & (df["FUEL"]!="total") \ - & (df["SECTOR"] != "industry (total)") \ - & (df["SECTOR"]!= "feedstock (petrochemical industry)") \ - & (df["SECTOR"]!= "feedstock (total)") \ - & (df["SECTOR"]!= "industry (non-ferrous metals)") ] - df_therm_total = df[(df["SECTOR"]=="industry (total)") \ - & (df["FUEL"]!="total") & (df["FUEL"]!="electricity")] - df_therm_total = df_therm_total.groupby(by="REGION").\ - sum().drop(["RYEAR"],axis=1).reset_index() - df_therm = df_therm.groupby(by=["REGION","SECTOR"]).\ - sum().drop(["RYEAR"],axis=1).reset_index() - df_therm_new = pd.DataFrame(columns=["REGION","SECTOR","FUEL",\ - "RYEAR","UNIT_OUT","RESULT"]) + df_therm = df[ + (df["FUEL"] != "electricity") + & (df["FUEL"] != "total") + & (df["SECTOR"] != "industry (total)") + & (df["SECTOR"] != "feedstock (petrochemical industry)") + & (df["SECTOR"] != "feedstock (total)") + & (df["SECTOR"] != "industry (non-ferrous metals)") + ] + df_therm_total = df[ + (df["SECTOR"] == "industry (total)") + & (df["FUEL"] != "total") + & (df["FUEL"] != "electricity") + ] + df_therm_total = ( + df_therm_total.groupby(by="REGION").sum().drop(["RYEAR"], axis=1).reset_index() + ) + df_therm = ( + df_therm.groupby(by=["REGION", "SECTOR"]) + .sum() + .drop(["RYEAR"], axis=1) + .reset_index() + ) + df_therm_new = pd.DataFrame( + columns=["REGION", "SECTOR", "FUEL", "RYEAR", "UNIT_OUT", "RESULT"] + ) for r in df_therm["REGION"].unique(): - df_therm_temp = df_therm[df_therm["REGION"]==r] + df_therm_temp = df_therm[df_therm["REGION"] == r] df_therm_total_temp = df_therm_total[df_therm_total["REGION"] == r] - df_therm_temp["i_therm"] = df_therm_temp["RESULT"]/df_therm_total_temp["RESULT"].values[0] - df_therm_new = pd.concat([df_therm_temp,df_therm_new],\ - ignore_index = True) - df_therm_new = df_therm_new.drop(["RESULT"],axis=1) - - df_therm_new.drop(["FUEL","RYEAR","UNIT_OUT"],axis=1,inplace=True) - df_therm_new.loc[df_therm_new["SECTOR"]=="industry (chemicals)","i_therm"] = \ - df_therm_new.loc[df_therm_new["SECTOR"]=="industry (chemicals)","i_therm"] * 0.7 + df_therm_temp["i_therm"] = ( + df_therm_temp["RESULT"] / df_therm_total_temp["RESULT"].values[0] + ) + df_therm_new = pd.concat([df_therm_temp, df_therm_new], ignore_index=True) + df_therm_new = df_therm_new.drop(["RESULT"], axis=1) + + df_therm_new.drop(["FUEL", "RYEAR", "UNIT_OUT"], axis=1, inplace=True) + df_therm_new.loc[df_therm_new["SECTOR"] == "industry (chemicals)", "i_therm"] = ( + df_therm_new.loc[df_therm_new["SECTOR"] == "industry (chemicals)", "i_therm"] + * 0.7 + ) # Modify CPA based on https://www.iea.org/sankey/#?c=Japan&s=Final%20consumption. # Since the value did not allign with the one in the IEA website. - index = ((df_therm_new["SECTOR"]=="industry (iron and steel)") \ - & ((df_therm_new["REGION"]==region_name_CPA) | \ - (df_therm_new["REGION"]==region_name_CHN))) + index = (df_therm_new["SECTOR"] == "industry (iron and steel)") & ( + (df_therm_new["REGION"] == region_name_CPA) + | (df_therm_new["REGION"] == region_name_CHN) + ) - df_therm_new.loc[index,"i_therm"] = 0.2 + df_therm_new.loc[index, "i_therm"] = 0.2 df_therm_new = df_therm_new.groupby(["REGION"]).sum().reset_index() @@ -169,100 +196,129 @@ def modify_demand_and_hist_activity(scen): # Relted technologies that have outputs to useful industry level. # Historical activity of theese will be adjusted - tec_therm = ["biomass_i","coal_i","elec_i","eth_i","foil_i","gas_i", - "h2_i","heat_i","hp_el_i", "hp_gas_i","loil_i", - "meth_i","solar_i"] - tec_fs = ["coal_fs","ethanol_fs","foil_fs","gas_fs","loil_fs","methanol_fs",] - tec_sp = ["sp_coal_I","sp_el_I","sp_eth_I","sp_liq_I","sp_meth_I", "h2_fc_I"] - - thermal_df_hist = scen.par('historical_activity', filters={'technology':tec_therm}) - spec_df_hist = scen.par('historical_activity', filters={'technology':tec_sp}) - feed_df_hist = scen.par('historical_activity', filters={'technology':tec_fs}) - useful_thermal = scen.par('demand', filters={'commodity':'i_therm'}) - useful_spec = scen.par('demand', filters={'commodity':'i_spec'}) - useful_feed = scen.par('demand', filters={'commodity':'i_feed'}) + tec_therm = [ + "biomass_i", + "coal_i", + "elec_i", + "eth_i", + "foil_i", + "gas_i", + "h2_i", + "heat_i", + "hp_el_i", + "hp_gas_i", + "loil_i", + "meth_i", + "solar_i", + ] + tec_fs = [ + "coal_fs", + "ethanol_fs", + "foil_fs", + "gas_fs", + "loil_fs", + "methanol_fs", + ] + tec_sp = ["sp_coal_I", "sp_el_I", "sp_eth_I", "sp_liq_I", "sp_meth_I", "h2_fc_I"] + + thermal_df_hist = scen.par("historical_activity", filters={"technology": tec_therm}) + spec_df_hist = scen.par("historical_activity", filters={"technology": tec_sp}) + feed_df_hist = scen.par("historical_activity", filters={"technology": tec_fs}) + useful_thermal = scen.par("demand", filters={"commodity": "i_therm"}) + useful_spec = scen.par("demand", filters={"commodity": "i_spec"}) + useful_feed = scen.par("demand", filters={"commodity": "i_feed"}) for r in df_therm_new["REGION"]: r_MESSAGE = region_type + r - if ((r_MESSAGE == "R12_RCPA") | (r_MESSAGE == "R12_CHN")): + if (r_MESSAGE == "R12_RCPA") | (r_MESSAGE == "R12_CHN"): print(r_MESSAGE) print("Thermal before multiplication") - print(useful_thermal.loc[useful_thermal["node"]== r_MESSAGE]) - print(thermal_df_hist.loc[thermal_df_hist["node_loc"]== r_MESSAGE]) - - useful_thermal.loc[useful_thermal["node"]== r_MESSAGE ,"value"] = \ - useful_thermal.loc[useful_thermal["node"]== r_MESSAGE,"value"] * \ - (1 - df_therm_new.loc[df_therm_new["REGION"]==r,"i_therm"].values[0]) - - thermal_df_hist.loc[thermal_df_hist["node_loc"]== r_MESSAGE ,"value"] = \ - thermal_df_hist.loc[thermal_df_hist["node_loc"]== r_MESSAGE,"value"] * \ - (1 - df_therm_new.loc[df_therm_new["REGION"]==r,"i_therm"].values[0]) - - if ((r_MESSAGE == "R12_RCPA") | (r_MESSAGE == "R12_CHN")): + print(useful_thermal.loc[useful_thermal["node"] == r_MESSAGE]) + print(thermal_df_hist.loc[thermal_df_hist["node_loc"] == r_MESSAGE]) + + useful_thermal.loc[ + useful_thermal["node"] == r_MESSAGE, "value" + ] = useful_thermal.loc[useful_thermal["node"] == r_MESSAGE, "value"] * ( + 1 - df_therm_new.loc[df_therm_new["REGION"] == r, "i_therm"].values[0] + ) + + thermal_df_hist.loc[ + thermal_df_hist["node_loc"] == r_MESSAGE, "value" + ] = thermal_df_hist.loc[thermal_df_hist["node_loc"] == r_MESSAGE, "value"] * ( + 1 - df_therm_new.loc[df_therm_new["REGION"] == r, "i_therm"].values[0] + ) + + if (r_MESSAGE == "R12_RCPA") | (r_MESSAGE == "R12_CHN"): print(r_MESSAGE) print("Thermal after multiplication") - print(useful_thermal.loc[useful_thermal["node"]== r_MESSAGE]) - print(thermal_df_hist.loc[thermal_df_hist["node_loc"]== r_MESSAGE]) + print(useful_thermal.loc[useful_thermal["node"] == r_MESSAGE]) + print(thermal_df_hist.loc[thermal_df_hist["node_loc"] == r_MESSAGE]) for r in df_spec_new["REGION"]: r_MESSAGE = region_type + r - if ((r_MESSAGE == "R12_RCPA") | (r_MESSAGE == "R12_CHN")): + if (r_MESSAGE == "R12_RCPA") | (r_MESSAGE == "R12_CHN"): print(r_MESSAGE) print("Spec before multiplication") - print(useful_spec.loc[useful_spec["node"]== r_MESSAGE]) - print(spec_df_hist.loc[spec_df_hist["node_loc"]== r_MESSAGE]) + print(useful_spec.loc[useful_spec["node"] == r_MESSAGE]) + print(spec_df_hist.loc[spec_df_hist["node_loc"] == r_MESSAGE]) - useful_spec.loc[useful_spec["node"]== r_MESSAGE ,"value"] = \ - useful_spec.loc[useful_spec["node"]== r_MESSAGE,"value"] * \ - (1 - df_spec_new.loc[df_spec_new["REGION"]==r,"i_spec"].values[0]) + useful_spec.loc[useful_spec["node"] == r_MESSAGE, "value"] = useful_spec.loc[ + useful_spec["node"] == r_MESSAGE, "value" + ] * (1 - df_spec_new.loc[df_spec_new["REGION"] == r, "i_spec"].values[0]) - spec_df_hist.loc[spec_df_hist["node_loc"]== r_MESSAGE ,"value"] = \ - spec_df_hist.loc[spec_df_hist["node_loc"]== r_MESSAGE,"value"] * \ - (1 - df_spec_new.loc[df_spec_new["REGION"]==r,"i_spec"].values[0]) + spec_df_hist.loc[ + spec_df_hist["node_loc"] == r_MESSAGE, "value" + ] = spec_df_hist.loc[spec_df_hist["node_loc"] == r_MESSAGE, "value"] * ( + 1 - df_spec_new.loc[df_spec_new["REGION"] == r, "i_spec"].values[0] + ) - if ((r_MESSAGE == "R12_RCPA") | (r_MESSAGE == "R12_CHN")): + if (r_MESSAGE == "R12_RCPA") | (r_MESSAGE == "R12_CHN"): print(r_MESSAGE) print("Spec after multiplication") - print(useful_spec.loc[useful_spec["node"]== r_MESSAGE]) - print(spec_df_hist.loc[spec_df_hist["node_loc"]== r_MESSAGE]) + print(useful_spec.loc[useful_spec["node"] == r_MESSAGE]) + print(spec_df_hist.loc[spec_df_hist["node_loc"] == r_MESSAGE]) for r in df_feed_new["REGION"]: r_MESSAGE = region_type + r - if ((r_MESSAGE == "R12_RCPA") | (r_MESSAGE == "R12_CHN")): + if (r_MESSAGE == "R12_RCPA") | (r_MESSAGE == "R12_CHN"): print(r_MESSAGE) print("Feedstock before multiplication") - print(useful_feed.loc[useful_feed["node"]== r_MESSAGE]) - print(feed_df_hist.loc[feed_df_hist["node_loc"]== r_MESSAGE]) + print(useful_feed.loc[useful_feed["node"] == r_MESSAGE]) + print(feed_df_hist.loc[feed_df_hist["node_loc"] == r_MESSAGE]) - useful_feed.loc[useful_feed["node"]== r_MESSAGE ,"value"] = \ - useful_feed.loc[useful_feed["node"]== r_MESSAGE,"value"] * \ - (1 - df_feed_new.loc[df_feed_new["REGION"]==r,"i_feed"].values[0]) + useful_feed.loc[useful_feed["node"] == r_MESSAGE, "value"] = useful_feed.loc[ + useful_feed["node"] == r_MESSAGE, "value" + ] * (1 - df_feed_new.loc[df_feed_new["REGION"] == r, "i_feed"].values[0]) - feed_df_hist.loc[feed_df_hist["node_loc"]== r_MESSAGE ,"value"] = \ - feed_df_hist.loc[feed_df_hist["node_loc"]== r_MESSAGE,"value"] * \ - (1 - df_feed_new.loc[df_feed_new["REGION"]==r,"i_feed"].values[0]) + feed_df_hist.loc[ + feed_df_hist["node_loc"] == r_MESSAGE, "value" + ] = feed_df_hist.loc[feed_df_hist["node_loc"] == r_MESSAGE, "value"] * ( + 1 - df_feed_new.loc[df_feed_new["REGION"] == r, "i_feed"].values[0] + ) - if ((r_MESSAGE == "R12_RCPA") | (r_MESSAGE == "R12_CHN")): + if (r_MESSAGE == "R12_RCPA") | (r_MESSAGE == "R12_CHN"): print(r_MESSAGE) print("Feedstock after multiplication") - print(useful_feed.loc[useful_feed["node"]== r_MESSAGE]) - print(feed_df_hist.loc[feed_df_hist["node_loc"]== r_MESSAGE]) + print(useful_feed.loc[useful_feed["node"] == r_MESSAGE]) + print(feed_df_hist.loc[feed_df_hist["node_loc"] == r_MESSAGE]) scen.check_out() - scen.add_par("demand",useful_thermal) - scen.add_par("demand",useful_spec) - scen.add_par("demand",useful_feed) + scen.add_par("demand", useful_thermal) + scen.add_par("demand", useful_spec) + scen.add_par("demand", useful_feed) scen.commit("Demand values adjusted") scen.check_out() - scen.add_par('historical_activity', thermal_df_hist) - scen.add_par('historical_activity', spec_df_hist) - scen.add_par('historical_activity', feed_df_hist) - scen.commit(comment = 'historical activity for useful level industry \ - technologies adjusted') + scen.add_par("historical_activity", thermal_df_hist) + scen.add_par("historical_activity", spec_df_hist) + scen.add_par("historical_activity", feed_df_hist) + scen.commit( + comment="historical activity for useful level industry \ + technologies adjusted" + ) # For aluminum there is no significant deduction required # (refining process not included and thermal energy required from @@ -302,19 +358,19 @@ def modify_demand_and_hist_activity(scen): # NOTE Aggregate industrial coal demand need to adjust to # the sudden intro of steel setor in the first model year - t_i = ['coal_i','elec_i','gas_i','heat_i','loil_i','solar_i'] + t_i = ["coal_i", "elec_i", "gas_i", "heat_i", "loil_i", "solar_i"] for t in t_i: - df = scen.par('growth_activity_lo', \ - filters={'technology':t, 'year_act':2020}) + df = scen.par("growth_activity_lo", filters={"technology": t, "year_act": 2020}) scen.check_out() - scen.remove_par('growth_activity_lo', df) - scen.commit(comment = 'remove growth_lo constraints') + scen.remove_par("growth_activity_lo", df) + scen.commit(comment="remove growth_lo constraints") + # Read in technology-specific parameters from input xlsx # Now used for steel and cement, which are in one file -def read_sector_data(scenario,sectname): +def read_sector_data(scenario, sectname): import numpy as np @@ -330,30 +386,40 @@ def read_sector_data(scenario,sectname): # data_df = data_steel_china.append(data_cement_china, ignore_index=True) data_df = pd.read_excel( - context.get_path("material", context.datafile), + context.get_local_path("material", context.datafile), sheet_name=sheet_n, ) # Clean the data - data_df = data_df \ - [['Region', 'Technology', 'Parameter', 'Level', \ - 'Commodity', 'Mode', 'Species', 'Units', 'Value']] \ - .replace(np.nan, '', regex=True) + data_df = data_df[ + [ + "Region", + "Technology", + "Parameter", + "Level", + "Commodity", + "Mode", + "Species", + "Units", + "Value", + ] + ].replace(np.nan, "", regex=True) # Combine columns and remove '' - list_series = data_df[['Parameter', 'Commodity', 'Level', 'Mode']] \ - .apply(list, axis=1).apply(lambda x: list(filter(lambda a: a != '', x))) - list_ef = data_df[['Parameter', 'Species', 'Mode']] \ + list_series = ( + data_df[["Parameter", "Commodity", "Level", "Mode"]] .apply(list, axis=1) + .apply(lambda x: list(filter(lambda a: a != "", x))) + ) + list_ef = data_df[["Parameter", "Species", "Mode"]].apply(list, axis=1) - data_df['parameter'] = list_series.str.join('|') - data_df.loc[data_df['Parameter'] == "emission_factor", \ - 'parameter'] = list_ef.str.join('|') + data_df["parameter"] = list_series.str.join("|") + data_df.loc[ + data_df["Parameter"] == "emission_factor", "parameter" + ] = list_ef.str.join("|") - data_df = data_df.drop(['Parameter', 'Level', 'Commodity', 'Mode'] \ - , axis = 1) - data_df = data_df.drop( \ - data_df[data_df.Value==''].index) + data_df = data_df.drop(["Parameter", "Level", "Commodity", "Mode"], axis=1) + data_df = data_df.drop(data_df[data_df.Value == ""].index) data_df.columns = data_df.columns.str.lower() @@ -367,7 +433,7 @@ def read_sector_data(scenario,sectname): # Read in time-dependent parameters # Now only used to add fuel cost for bare model -def read_timeseries(scenario,filename): +def read_timeseries(scenario, filename): import numpy as np @@ -386,22 +452,25 @@ def read_timeseries(scenario,filename): sheet_n = "timeseries_R11" # Read the file - df = pd.read_excel( - context.get_path("material", filename), sheet_name=sheet_n) + df = pd.read_excel(context.get_local_path("material", filename), sheet_name=sheet_n) import numbers + # Take only existing years in the data datayears = [x for x in list(df) if isinstance(x, numbers.Number)] - df = pd.melt(df, id_vars=['parameter', 'region', 'technology', 'mode', 'units'], \ - value_vars = datayears, \ - var_name ='year') + df = pd.melt( + df, + id_vars=["parameter", "region", "technology", "mode", "units"], + value_vars=datayears, + var_name="year", + ) df = df.drop(df[np.isnan(df.value)].index) return df -def read_rel(scenario,filename): +def read_rel(scenario, filename): import numpy as np @@ -417,7 +486,8 @@ def read_rel(scenario,filename): # Read the file data_rel = pd.read_excel( - context.get_path("material", filename), sheet_name=sheet_n, + context.get_local_path("material", filename), + sheet_name=sheet_n, ) return data_rel diff --git a/message_ix_models/model/material/material_testscript.py b/message_ix_models/model/material/material_testscript.py index 036c55d5ff..7ff8f6453e 100644 --- a/message_ix_models/model/material/material_testscript.py +++ b/message_ix_models/model/material/material_testscript.py @@ -16,9 +16,9 @@ from message_data.tools import Context -from message_data.tools import ( - ScenarioInfo, - make_df, +from message_ix_models import ScenarioInfo +from message_ix import make_df +from message_ix_models.util import ( broadcast, make_io, copy_column, @@ -41,38 +41,42 @@ ctx = Context() # Set default scenario/model names - Later coming from CLI -ctx.platform_info.setdefault('name', 'ixmp_dev') -ctx.scenario_info.setdefault('model', 'Material_test_MESSAGE_China') -ctx.scenario_info.setdefault('scenario', 'baseline') +ctx.platform_info.setdefault("name", "ixmp_dev") +ctx.scenario_info.setdefault("model", "Material_test_MESSAGE_China") +ctx.scenario_info.setdefault("scenario", "baseline") # ctx['period_start'] = 2020 # ctx['regions'] = 'China' # ctx['ssp'] = 'SSP2' # placeholder -ctx['scentype'] = 'C30-const_E414' -ctx['datafile'] = 'China_steel_cement_MESSAGE.xlsx' +ctx["scentype"] = "C30-const_E414" +ctx["datafile"] = "China_steel_cement_MESSAGE.xlsx" # Use general code to create a Scenario with some stuff in it -scen = create_res(context = ctx) - +scen = create_res(context=ctx) + # Use material-specific code to add certain stuff a = build(scen) # Solve the model scen.solve() -p = Plots(scen, 'China', firstyear=2020) -p.plot_activity(baseyear=False, subset=['clinker_dry_cement', \ - 'clinker_wet_cement']) -p.plot_activity(baseyear=False, subset=['grinding_ballmill_cement', \ - 'grinding_vertmill_cement']) +p = Plots(scen, "China", firstyear=2020) +p.plot_activity(baseyear=False, subset=["clinker_dry_cement", "clinker_wet_cement"]) +p.plot_activity( + baseyear=False, subset=["grinding_ballmill_cement", "grinding_vertmill_cement"] +) # p.plot_capacity(baseyear=True, subset=['bf_steel', 'bof_steel', 'dri_steel', 'eaf_steel']) # p.plot_new_capacity(baseyear=True, subset=['bf_steel', 'bof_steel', 'dri_steel', 'eaf_steel']) -p.plot_activity(baseyear=False, subset=['clinker_dry_cement', \ - 'clinker_dry_ccs_cement', \ - 'clinker_wet_cement', \ - 'clinker_wet_ccs_cement']) +p.plot_activity( + baseyear=False, + subset=[ + "clinker_dry_cement", + "clinker_dry_ccs_cement", + "clinker_wet_cement", + "clinker_wet_ccs_cement", + ], +) -p.plot_activity(baseyear=False, subset=['dri_steel', \ - 'bf_steel']) +p.plot_activity(baseyear=False, subset=["dri_steel", "bf_steel"]) #%% Global test from message_data.model.create import create_res @@ -82,21 +86,22 @@ ctx = Context() # Set default scenario/model names - Later coming from CLI -ctx.platform_info.setdefault('name', 'ixmp_dev') -ctx.platform_info.setdefault('jvmargs', ['-Xmx12G']) # To avoid java heap space error -ctx.scenario_info.setdefault('model', 'Material_Global') -ctx.scenario_info.setdefault('scenario', 'NoPolicy') -ctx['ssp'] = 'SSP2' -ctx['datafile'] = 'Global_steel_cement_MESSAGE.xlsx' +ctx.platform_info.setdefault("name", "ixmp_dev") +ctx.platform_info.setdefault("jvmargs", ["-Xmx12G"]) # To avoid java heap space error +ctx.scenario_info.setdefault("model", "Material_Global") +ctx.scenario_info.setdefault("scenario", "NoPolicy") +ctx["ssp"] = "SSP2" +ctx["datafile"] = "Global_steel_cement_MESSAGE.xlsx" # Use general code to create a Scenario with some stuff in it -scen = create_res(context = ctx) - +scen = create_res(context=ctx) + # Use material-specific code to add certain stuff a = build(scen) # Solve the model import time + start_time = time.time() scen.solve() print(".solve: %.6s seconds taken." % (time.time() - start_time)) @@ -108,42 +113,46 @@ import message_data.model.material.data_steel as ds import message_data.model.material.data_cement as dc import message_data.model.material.data_buildings as db -from message_data.model.material.data_buildings import BLD_MAT_USE_2020 as bld_demand2020 +from message_data.model.material.data_buildings import ( + BLD_MAT_USE_2020 as bld_demand2020, +) mp = ixmp.Platform(name="ixmp_dev") sl = mp.scenario_list() -sl = sl.loc[sl.model == "MESSAGEix-Materials"] # "ENGAGE_SSP2_v4.1.4"] +sl = sl.loc[sl.model == "MESSAGEix-Materials"] # "ENGAGE_SSP2_v4.1.4"] sample = mix.Scenario(mp, model="MESSAGEix-Materials", scenario="NoPolicy") -cem_demand = sample.par('demand', {"commodity":"cement", "year":2010}) +cem_demand = sample.par("demand", {"commodity": "cement", "year": 2010}) # Test read_data_steel <- will be in create_res if working fine -df = du.read_sector_data('steel') +df = du.read_sector_data("steel") df = du.read_rel(ctx.datafile) # Buildings scripts -a,b,cc = db.read_timeseries_buildings('LED_LED_report_IAMC_sensitivity.csv', 'ref') -a1,b1,cc1 = db.read_timeseries_buildings('LED_LED_report_IAMC_sensitivity.csv', 'min') -c = db.get_scen_mat_demand(commod='steel', year="all") +a, b, cc = db.read_timeseries_buildings("LED_LED_report_IAMC_sensitivity.csv", "ref") +a1, b1, cc1 = db.read_timeseries_buildings("LED_LED_report_IAMC_sensitivity.csv", "min") +c = db.get_scen_mat_demand(commod="steel", year="all") r = db.gen_data_buildings(scen) - mp = ctx.get_platform() b = dt.read_data_generic() -b = dt.read_var_cost() +b = dt.read_var_cost() bb = dt.read_sector_data() -c = pd.melt(b, id_vars=['technology', 'mode', 'units'], \ - value_vars=[2010, 2020, 2030, 2040, 2050, 2060, 2070, 2080, 2090, 2100], \ - var_name='year') - -df_gen = dt.gen_data_generic(scen) -df_st = dt.gen_data_steel(scen) -a = df_st['input'] -b=a.loc[a['level']=="export"] - -df_st = dt.gen_data_cement(scen) +c = pd.melt( + b, + id_vars=["technology", "mode", "units"], + value_vars=[2010, 2020, 2030, 2040, 2050, 2060, 2070, 2080, 2090, 2100], + var_name="year", +) + +df_gen = dt.gen_data_generic(scen) +df_st = dt.gen_data_steel(scen) +a = df_st["input"] +b = a.loc[a["level"] == "export"] + +df_st = dt.gen_data_cement(scen) a = dt.get_data(scen, ctx) dc.gen_mock_demand_cement(sample) ds.gen_mock_demand_steel(sample) @@ -156,11 +165,11 @@ a = get_spec() a = mp.scenario_list() -b=a.loc[a['cre_user']=="min"] +b = a.loc[a["cre_user"] == "min"] sample = mix.Scenario(mp_samp, model="Material_test", scenario="baseline") sample.set_list() -sample.set('year') -sample.cat('year', 'firstmodelyear') +sample.set("year") +sample.cat("year", "firstmodelyear") mp_samp.close_db() scen.to_excel("test.xlsx") @@ -175,6 +184,7 @@ import pyam import pandas as pd import matplotlib.pyplot as plt + msg_data_path = os.environ["MESSAGE_DATA_PATH"] directory = os.path.join(msg_data_path, "message_data", "reporting", "materials") path = os.path.join(directory, "message_ix_reporting.xlsx") @@ -183,16 +193,14 @@ df = pyam.IamDataFrame(report) df_ref_fueloil = df.copy() -r = 'R11_CPA|R11_CPA' +r = "R11_CPA|R11_CPA" df_ref_fueloil.filter(region=r, year=years, inplace=True) -df_ref_fueloil.filter( - variable=["out|secondary|fueloil|agg_ref|*"], inplace=True -) +df_ref_fueloil.filter(variable=["out|secondary|fueloil|agg_ref|*"], inplace=True) fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 8)) - + df_ref_fueloil.stack_plot(ax=ax1) ax1.legend( [ @@ -207,4 +215,4 @@ ) ax1.set_title("Fuel oil mix_" + r) ax1.set_xlabel("Year") -ax1.set_ylabel("GWa") \ No newline at end of file +ax1.set_ylabel("GWa") diff --git a/message_ix_models/model/material/util.py b/message_ix_models/model/material/util.py index 45cce26780..74ed21a48f 100644 --- a/message_ix_models/model/material/util.py +++ b/message_ix_models/model/material/util.py @@ -1,26 +1,38 @@ -from pathlib import Path from message_ix_models import Context -from message_ix_models.util import as_codes, load_package_data +from pathlib import Path +from message_ix_models.util import load_private_data + +# Configuration files +METADATA = [ + # ("material", "config"), + ("material", "set"), + # ("material", "technology"), +] def read_config(): """Read configuration from set.yaml.""" # TODO this is similar to transport.utils.read_config; make a common # function so it doesn't need to be in this file. - context = context or Context.get_instance(0) - - try: - # Check if the configuration was already loaded - context["material"]["set"] - except KeyError: - # Not yet loaded - pass - else: + context = Context.get_instance(-1) + + if "material set" in context: # Already loaded return context + # Load material configuration + for parts in METADATA: + # Key for storing in the context + key = " ".join(parts) + + # Actual filename parts; ends with YAML + _parts = list(parts) + _parts[-1] += ".yaml" + + context[key] = load_private_data(*_parts) + # Read material.yaml - #context.metadata_path=Path("C:/Users/unlu/Documents/GitHub/message_data/data") - context.load_config("material", "set") + # context.metadata_path=Path("C:/Users/unlu/Documents/GitHub/message_data/data") + # context.load_config("material", "set") # Use a shorter name context["material"] = context["material set"] @@ -30,12 +42,4 @@ def read_config(): # context.pop("transport technology") # ) - # Convert some values to Code objects - # JM: Ask what this is - # for set_name, info in context["material"]["common"].items(): - # try: - # info["add"] = as_codes(info["add"]) - # except KeyError: - # pass - return context From 19892e20ba4b96ed2ee8885f17b57c6baa3102b8 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Thu, 7 Oct 2021 17:20:40 +0200 Subject: [PATCH 303/774] Cater to the r12 scenario name change --- message_ix_models/model/material/__init__.py | 1 + 1 file changed, 1 insertion(+) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 7f69f0f2a1..ffd79761f6 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -120,6 +120,7 @@ def solve(context, datafile, tag): "baseline": "NoPolicy", "baseline_macro": "NoPolicy", "baseline_new": "NoPolicy", + "baseline_new_macro": "NoPolicy", "NPi2020-con-prim-dir-ncr": "NPi", "NPi2020_1000-con-prim-dir-ncr": "NPi2020_1000", "NPi2020_400-con-prim-dir-ncr": "NPi2020_400", From db4eb0a84f3c2ae5c694e6ae1bb5735dd6f1b35f Mon Sep 17 00:00:00 2001 From: Jihoon Date: Mon, 8 Nov 2021 16:02:52 +0100 Subject: [PATCH 304/774] Update the .rst documentation remove unused data files --- .../China_steel_standalone - test.xlsx | 3 - .../Global_steel_cement_MESSAGE - org.xlsx | 3 - .../Steel_capacity_historical.OECD.xlsx | 3 - message_ix_models/data/material/config.yaml | 163 ------------------ .../data/material/oil_demand.xlsx | 3 - .../data/material/variable_costs.xlsx | 3 - message_ix_models/model/material/doc.rst | 100 +++++++++-- 7 files changed, 81 insertions(+), 197 deletions(-) delete mode 100644 message_ix_models/data/material/China_steel_standalone - test.xlsx delete mode 100644 message_ix_models/data/material/Global_steel_cement_MESSAGE - org.xlsx delete mode 100644 message_ix_models/data/material/Steel_capacity_historical.OECD.xlsx delete mode 100644 message_ix_models/data/material/config.yaml delete mode 100644 message_ix_models/data/material/oil_demand.xlsx delete mode 100644 message_ix_models/data/material/variable_costs.xlsx diff --git a/message_ix_models/data/material/China_steel_standalone - test.xlsx b/message_ix_models/data/material/China_steel_standalone - test.xlsx deleted file mode 100644 index 5d78113fcd..0000000000 --- a/message_ix_models/data/material/China_steel_standalone - test.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:21ea535be0559bdab3626ec1e2c8f8a8f326d1c808421566b85644a2793999f4 -size 59248 diff --git a/message_ix_models/data/material/Global_steel_cement_MESSAGE - org.xlsx b/message_ix_models/data/material/Global_steel_cement_MESSAGE - org.xlsx deleted file mode 100644 index e3901a6676..0000000000 --- a/message_ix_models/data/material/Global_steel_cement_MESSAGE - org.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:df34371c0b00f946c347facaf43f85cf6d86745719d5ffb3b2b14c0733fc45d1 -size 52337 diff --git a/message_ix_models/data/material/Steel_capacity_historical.OECD.xlsx b/message_ix_models/data/material/Steel_capacity_historical.OECD.xlsx deleted file mode 100644 index fba1df826e..0000000000 --- a/message_ix_models/data/material/Steel_capacity_historical.OECD.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:80ef345807e44e9c06c0f3520128be0d680d643a3da029f971ddb159094e7785 -size 38935 diff --git a/message_ix_models/data/material/config.yaml b/message_ix_models/data/material/config.yaml deleted file mode 100644 index 95b9ea0c57..0000000000 --- a/message_ix_models/data/material/config.yaml +++ /dev/null @@ -1,163 +0,0 @@ -# Configuration for message_data.model.material - -aluminum: - commodity: - add: - - ht_heat - - lt_heat - - aluminum - require: - - d_heat - - electr - - coal - - fueloil - - ethanol - - biomass - - gas - - hydrogen - level: - add: - - useful_aluminum - - new_scrap - - old_scrap - - final_material - - useful_material - - product - require: - - final - technology: - add: - - soderberg_aluminum - - prebake_aluminum - - secondary_aluminum - - prep_secondary_aluminum - - finishing_aluminum - - manuf_aluminum - - scrap_recovery_aluminum - -set: - commodity: - add: - - NH3 - - Fertilizer Use|Nitrogen - level: - add: - - material_interim - - material_final - technology: - require: - - h2_bio_ccs # Reference for vent-to-storage ratio - add: - - cokeoven_steel - - sinter_steel - - pellet_steel - - bf_steel - - dri_steel - - sr_steel - - bof_steel - - eaf_steel - - furnace_hoil_steel - - furnace_biofuel_steel - - furnace_biomass_steel - - furnace_synfuel_steel - - furnace_gas_steel - - furnace_elec_steel - - furnace_h2_steel - - hp_gas_steel - - hp_elec_steel - - fc_h2_steel - - solar_steel - - dheat_steel - - prep_secondary_steel - - finishing_steel - - manuf_steel - - scrap_recovery_steel - - biomass_NH3 - - electr_NH3 - - gas_NH3 - - coal_NH3 - - fueloil_NH3 - - NH3_to_N_fertil - -generic_set: - technology: - add: - - furnace_coal_aluminum - - furnace_loil_aluminum - - furnace_foil_aluminum - - furnace_ethanol_aluminum - - furnace_biomass_aluminum - - furnace_methanol_aluminum - - furnace_gas_aluminum - - furnace_elec_aluminum - - furnace_h2_aluminum - - hp_gas_aluminum - - hp_elec_aluminum - - fc_h2_aluminum - - solar_aluminum - - dheat_aluminum - -# should also work with regional model - region: - require: - type_tec: - add: - - industry - - -# NB this codelist added by #125 - -iron-steel: - name: Iron and Steel - description: Iron is a chemical element with the symbol Fe, while steel is an alloy of iron and carbon and, sometimes, other elements. Measured as the total mass of material. - unit: Mt - -non-ferrous: - name: Non-ferrous metals - description: Metals and alloys which do not contain iron. Measured as the total mass of material. - unit: Mt - -aluminum: - name: Aluminum - description: Aluminum (or aluminium) is a chemical element with the symbol Al. Measured as the total mass of material. - unit: Mt - parent: non-ferrous - -copper: - name: Copper - description: Copper is a chemical element with the symbol Cu. Measured as the total mass of material. - unit: Mt - parent: non-ferrous - -minerals: - name: Minerals - description: Non-metallic minerals are minerals that have no metallic luster, break easily and include, e.g., sand, limestone, marble, clay and salt. Measured as the FE-equivalent mass of material using LCA midpoint characterization factors. - unit: MtFe-eq - -cement: - name: Cement - description: Cement is a binder used for construction that sets, hardens, and adheres to other materials to bind them together. Measured as the total mass of material. - unit: Mt - parent: minerals - -chemicals: - name: Chemicals - description: Industrial chemicals that form the basis of many products. Measured as the total mass of material. - unit: Mt - -ethylene: - name: Ethylene - description: Ethylene is a hydrocarbon with the formula C2H4. Measured as the total mass of material. - unit: Mt - parent: chemicals - -ammonia: - name: Ammonia - description: Ammonia is a compound of nitrogen and hydrogen with the formula NH3. Measured as the total mass of material. - unit: Mt - parent: chemicals - -paper-pulp: - name: Paper and pulp for paper - description: Pulp is a lignocellulosic fibrous material prepared by chemically or mechanically separating cellulose fibers and the raw material for making paper. Measured as the total mass of material. - unit: Mt diff --git a/message_ix_models/data/material/oil_demand.xlsx b/message_ix_models/data/material/oil_demand.xlsx deleted file mode 100644 index 12b0031dfd..0000000000 --- a/message_ix_models/data/material/oil_demand.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:1cde570d735ef2f3c67ebaf7a748877369aacb42c5aa6872dd9a7e2369d3f297 -size 13332 diff --git a/message_ix_models/data/material/variable_costs.xlsx b/message_ix_models/data/material/variable_costs.xlsx deleted file mode 100644 index 376c539c3e..0000000000 --- a/message_ix_models/data/material/variable_costs.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:f997925caf15d670c02359826b4d5e9ea5da66188fe6c81e31a9470074460d3a -size 16382 diff --git a/message_ix_models/model/material/doc.rst b/message_ix_models/model/material/doc.rst index 1bc9294fd2..d4d0b872d6 100644 --- a/message_ix_models/model/material/doc.rst +++ b/message_ix_models/model/material/doc.rst @@ -11,7 +11,8 @@ Materials accounting This module adds (life-cycle) accounting of materials associated with technologies and demands in MESSAGEix-GLOBIOM. -The initial implementation supports nitrogen-based fertilizers. +The implementation currently supports four key energy/emission-intensive material industries: Steel, Aluminum, Cement, and Petrochemical. +The petrochemical sector will soon expand to cover plastic production processes, and ammonia and nitrogen-based fertilizer process will be added too. .. contents:: :local: @@ -22,31 +23,59 @@ Code reference .. currentmodule:: message_data.model.material .. automodule:: message_data.model.material - :members: + :members: .. automodule:: message_data.model.material.data - :members: + :members: + +.. automodule:: message_data.model.material.util + :members: + +Data preparation +################ + +These modules do the necessary parametrization for each sector, which can be turned on and off individually under `DATA_FUNCTIONS` in `__init__.py`. +For example, the buildings module (`data_buildings.py`) is only used when the buildings model outputs are given explicitly without linking the CHILLED/STURM model through a soft link. + +.. automodule:: message_data.model.material.data_aluminum + :members: + +.. automodule:: message_data.model.material.data_steel + :members: + +.. automodule:: message_data.model.material.data_cement + :members: + +.. automodule:: message_data.model.material.data_petro + :members: + +.. automodule:: message_data.model.material.data_power_sector + :members: + +.. automodule:: message_data.model.material.data_buildings + :members: + +.. automodule:: message_data.model.material.data_generic + :members: CLI usage ========= -Use ``mix-data materials run``, giving the base scenario, e.g.:: +Use ``mix-models materials build`` to add the material implementation on top of existing standard global (R12) scenarios, also giving the base scenario and indicating the relevant data location, e.g.:: + + $ mix-models \ + --url="ixmp://ixmp_dev/MESSAGEix-GLOBIOM_R12_CHN/baseline_new_macro#8" \ + --local-data "./data" material build + +Currently, a set of given base scenario names will be translated to own scenario names. +The output scenario will be ``ixmp://ixmp_dev-ixmp/MESSAGEix-Materials/{name}``, where {name} is a shortened version of the input scenario name. +Using an additional tag `--tag` can be used to add a suffix to the new scenario name. +This command line only builds the scenario but does not run it. To run the scenario, use ``mix-models materials solve``, giving the scenario name, e.g.:: + + $ mix-models material solve --scenario_name NoPolicy_R12 - $ mix-data \ - --url ixmp://ene-ixmp/CD_Links_SSP2/baseline - materials solve - $ mix-data \ - --url ixmp://ene-ixmp/CD_Links_SSP2/NPi2020-con-prim-dir-ncr - materials solve - $ mix-data \ - --url ixmp://ene-ixmp/CD_Links_SSP2/NPi2020_1000-con-prim-dir-ncr - materials solve - $ mix-data \ - --url ixmp://ene-ixmp/CD_Links_SSP2/NPi2020_400-con-prim-dir-ncr - materials solve -The output scenario will be ``ixmp://ene-ixmp/JM_GLB_NITRO/{name}``, where {name} is a shortened version of the input scenario name. Data, metadata, and configuration @@ -57,6 +86,39 @@ Binary/raw data files The code relies on the following input files, stored in :file:`data/material/`: +:file:`CEMENT.BvR2010.xlsx` + Historical cement demand data + +:file:`STEEL_database_2012.xlsx` + Historical steel demand data + +:file:`demand_aluminum.xlsx` + Historical aluminum demand data + +:file:`demand_aluminum.xlsx` + Historical aluminum demand data + +:file:`aluminum_techno_economic.xlsx` + Techno-economic parametrization data for aluminum sector + +:file:`Global_steel_cement_MESSAGE.xlsx` + Techno-economic parametrization data for steel and cement sector combined (R12) + +:file:`China_steel_cement_MESSAGE.xlsx` + Techno-economic parametrization data for steel and cement sector combined (China standalone) + +:file:`generic_furnace_boiler_techno_economic.xlsx` + Techno-economic parametrization data for generic furnace technologies + +:file:`LED_LED_report_IAMC*.csv` + Output from buildings model on the sector's energy/material/floor space demand. It was used for ALPS2020 report, when the linkage to the buildings model is not yet set up. + +:file:`MESSAGE_region_mapping_R14.xlsx` + MESSAGE region mapping used for fertilizer trade mapping + +:file:`iamc_db ENGAGE baseline GDP PPP.xlsx` + SSP GDP projection used for material demand projections + :file:`Ammonia feedstock share.Global.xlsx` Feedstock shares (gas/oil/coal) for NH3 production for MESSAGE R11 regions. @@ -88,10 +150,10 @@ The code relies on the following input files, stored in :file:`data/material/`: :file:`LCA_commodity_mapping.xlsx` Commodity mapping (for materials) of global 11-regional MESSAGEix-GLOBIOM model to commodities of THEMIS LCA dataset. -:file:`material/config.yaml` +:file:`material/set.yaml` ---------------------------- -.. literalinclude:: ../../../data/material/config.yaml +.. literalinclude:: ../../../data/material/set.yaml :language: yaml R code and dependencies From b8e57661818001ad98357523f1ecdb07cf5a3f69 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Fri, 12 Nov 2021 15:30:35 +0100 Subject: [PATCH 305/774] Update documentation to add recycling section as well --- message_ix_models/model/material/doc.rst | 26 +++++++++++++++++++++++- 1 file changed, 25 insertions(+), 1 deletion(-) diff --git a/message_ix_models/model/material/doc.rst b/message_ix_models/model/material/doc.rst index d4d0b872d6..ddc40368f9 100644 --- a/message_ix_models/model/material/doc.rst +++ b/message_ix_models/model/material/doc.rst @@ -12,7 +12,31 @@ Materials accounting This module adds (life-cycle) accounting of materials associated with technologies and demands in MESSAGEix-GLOBIOM. The implementation currently supports four key energy/emission-intensive material industries: Steel, Aluminum, Cement, and Petrochemical. -The petrochemical sector will soon expand to cover plastic production processes, and ammonia and nitrogen-based fertilizer process will be added too. +The petrochemical sector will soon expand to cover production processes of plastics, ammonia and nitrogen-based fertilizers. + +The technologies to represent for the primary production processes of the materials are chosen based on their emission mitigation potential and the degree +of commercialization. + +After the primary production stages of the materials, finishing and manufacturing processes are carried out which results in a complete product. +For metals, during the manufacturing process new scrap is formed as residue. This type of scrap requires less preparation before recycling and has a higher quality +as it is the direct product of the manufacturing unlike the old scrap which is formed at the end of the life cycle of a product. +The percentage of the new scrap is an exogenous fixed ratio in the model. + +The products that are produced are used in different end-use sectors as stocks and therefore they are not immediately available for recycling until the end of their lifetime. +In the model, each year only certain quantity of products are available for recycling and this ratio is exogenously determined based on historical values. +The end-of-life products coming from buildings and power sector can be endogenously determined in case the relevant links are turned on. + +In the model, there is a minimum recycling rate specified for different materials and it is based on the historical recycling rates. This parameter can also be used to represent +regulations in different regions. In the end recycling rate is a model decision which can be higher than the minimum rate depending on the economic attractiveness. + +The end-of-life products that are collected as old scrap are classified in three different grade/quality. This reflects the degree of difficulty of recycling process in terms of +labor and energy. Different initial designs and final use conditions determine the ease of recycling which is also reflected in costs. +The availability of different scrap is set with 1-2-1 ratio as default for high, medium and low scrap quality. + +Three different scrap preparation technologies have different variable costs and energy inputs to process the old scraps with different grades. At the end of the preparation process +these scraps are returned as new scrap all with the same quality. The quality differences in the end product are neglected. All of the old scrap that is collected is used in the model +assuming scrap availability and collection are the main bottlenecks of the recycling process. All the scraps are sent to a secondary melter where they are turned into final materials. +During this process there are also recycling losses. .. contents:: :local: From 9b1881e139822630c7235f0f6f2b87e7de924dd1 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Sun, 14 Nov 2021 14:40:49 +0100 Subject: [PATCH 306/774] Add emissions accounting for industry technologies --- message_ix_models/model/material/__init__.py | 2 ++ message_ix_models/model/material/data_util.py | 34 +++++++++++++++++-- 2 files changed, 33 insertions(+), 3 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index ffd79761f6..066487389e 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -12,6 +12,7 @@ # from .data import add_data from .data_util import modify_demand_and_hist_activity +from .data_util import add_emission_accounting from .util import read_config @@ -26,6 +27,7 @@ def build(scenario): # Adjust exogenous energy demand to incorporate the endogenized sectors # Adjust the historical activity of the usefyl level industry technologies modify_demand_and_hist_activity(scenario) + add_emission_accounting(scenario) return scenario diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index ec68e6e41b..4fe56f7c50 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -6,7 +6,7 @@ import re from message_ix_models import ScenarioInfo - +from message_data.tools.utilities.get_optimization_years import main as get_optimization_years def modify_demand_and_hist_activity(scen): """Take care of demand changes due to the introduction of material parents @@ -367,11 +367,39 @@ def modify_demand_and_hist_activity(scen): scen.remove_par("growth_activity_lo", df) scen.commit(comment="remove growth_lo constraints") +def add_emission_accounting(scen): + context = read_config() + s_info = ScenarioInfo(scen) + + # Obtain the emission factors only for material related technologies + # TODO: Also residential and commercial technologies should be added to this list. + tec_list = scen.par("emission_factor")["technology"].unique() + tec_list_materials = [i for i in tec_list if (("steel" in i) | ("aluminum" in i) \ + | ("petro" in i) | ("cement" in i) | ("ref" in i))] + tec_list_materials.remove("refrigerant_recovery") + tec_list_materials.remove("replacement_so2") + tec_list_materials.remove("SO2_scrub_ref") + emission_factors = scen.par("emission_factor",filters = {"technology":tec_list_materials}) + + # Note: Emission factors for non-CO2 gases are in kt/ACT. For CO2 MtC/ACT. + + relation_activity = emission_factors.assign(relation = lambda x: (x['emission'] + \ + '_Emission')) + relation_activity["node_rel"] = relation_activity["node_loc"] + relation_activity.drop(["year_vtg","emission"],axis = 1,inplace = True) + relation_activity["year_rel"] = relation_activity["year_act"] + relation_activity = relation_activity[(relation_activity["relation"] != + 'PM2p5_Emission') & (relation_activity["relation"] != 'CO2_industry_Emission')] + + scen.check_out() + scen.add_par("relation_activity",relation_activity) + scen.commit("Emissions accounting for industry technologies added.") -# Read in technology-specific parameters from input xlsx -# Now used for steel and cement, which are in one file def read_sector_data(scenario, sectname): + # Read in technology-specific parameters from input xlsx + # Now used for steel and cement, which are in one file + import numpy as np # Ensure config is loaded, get the context From b831884f3afdb2b40fae66291e1ff142cb84eb20 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 16 Nov 2021 11:38:42 +0100 Subject: [PATCH 307/774] Update reporting code, documentation and cli --- message_ix_models/model/material/__init__.py | 25 +++++++++++++++++-- message_ix_models/model/material/doc.rst | 26 +++++++++++++++++++- 2 files changed, 48 insertions(+), 3 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 066487389e..9c897cc74c 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -15,7 +15,6 @@ from .data_util import add_emission_accounting from .util import read_config - def build(scenario): """Set up materials accounting on `scenario`.""" # Get the specification @@ -177,7 +176,7 @@ def solve_scen(context, datafile, model_name, scenario_name): scenario.solve() -@cli.command("report") +@cli.command("report-1") @click.option( "--old_reporting", default=False, @@ -196,6 +195,28 @@ def run_reporting(old_reporting, scenario_name, model_name): scenario = Scenario(mp, model_name, scenario_name) report(scenario, old_reporting) +@cli.command("report-2") +@click.option("--scenario_name", default="NoPolicy") +@click.option("--model_name", default="MESSAGEix-Materials") +# @click.pass_obj +def run_old_reporting(scenario_name, model_name): + from message_ix import Scenario + from ixmp import Platform + from message_data.tools.post_processing.iamc_report_hackathon import ( + report as reporting + ) + + base_model = model_name + scen_name = scenario_name + + print(model_name) + print(scenario_name) + mp = Platform() + scenario = Scenario(mp, model_name, scenario_name) + + reporting(mp, scenario, 'False', base_model, + scen_name, merge_hist=True, merge_ts= True) + import logging diff --git a/message_ix_models/model/material/doc.rst b/message_ix_models/model/material/doc.rst index ddc40368f9..db797b5acc 100644 --- a/message_ix_models/model/material/doc.rst +++ b/message_ix_models/model/material/doc.rst @@ -82,6 +82,29 @@ For example, the buildings module (`data_buildings.py`) is only used when the bu .. automodule:: message_data.model.material.data_generic :members: +Reporting +########## + +.. automodule:: message_data.reporting.materials.reporting + :members: + +.. automodule:: message_data.tools.post_processing.iamc_report_hackathon + :members: + +The reporting of the scenarios involves two steps. First step generates the specific +variables that are related to materials and industry sectors. At the end of this step +all the varibles are uploaded as ixmp timeseries objects. The reporting file is +generated under \...\message_data\message_data\reporting\materials with the name +“New_Reporting_MESSAGEix-Materials_scenario_name.xls”. The required versions of +pyam and pyam-iamc are as follows respectively: 0.2.1a0 and 0.9.0. + +If the model is ran with buildings and appliances linkage, the related extra variables +are uploaded as ixmp timeseries object to the scenario as well. + +In the second step, rest of the default reporting variables are obtained by running +the general reporting code. This step combines all the variables that were uploaded +as timeseries to the scenario together with the generic IAMC variables. It also +correctly reports the aggregate variables such as Final Energy and Emissions. CLI usage ========= @@ -99,8 +122,9 @@ This command line only builds the scenario but does not run it. To run the scena $ mix-models material solve --scenario_name NoPolicy_R12 +To run the first step of the reporting use $ mix-models material report-1 --model_name MESSAGEix-Materials --scenario_name xxxx - +To run the second step of the reporting use $ mix-models material report-2 --model_name MESSAGEix-Materials --scenario_name xxxx Data, metadata, and configuration ================================= From 7aee8149bf78f44a748e5a70804e200e2fa2df27 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Tue, 16 Nov 2021 12:00:41 +0100 Subject: [PATCH 308/774] Add a minimal test line also with minor change in import statement order --- message_ix_models/model/material/__init__.py | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 9c897cc74c..235efb014b 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -1,20 +1,24 @@ from typing import Mapping import click -from message_ix_models import ScenarioInfo -from message_ix_models.model.build import apply_spec +import logging # from .build import apply_spec # from message_data.tools import ScenarioInfo from message_ix_models import ScenarioInfo from message_ix_models.model.build import apply_spec from message_ix_models.util.context import Context +from message_ix_models.util import add_par_data # from .data import add_data from .data_util import modify_demand_and_hist_activity from .data_util import add_emission_accounting from .util import read_config + +log = logging.getLogger(__name__) + + def build(scenario): """Set up materials accounting on `scenario`.""" # Get the specification @@ -218,10 +222,6 @@ def run_old_reporting(scenario_name, model_name): scen_name, merge_hist=True, merge_ts= True) -import logging - -log = logging.getLogger(__name__) - from .data_cement import gen_data_cement from .data_steel import gen_data_steel from .data_aluminum import gen_data_aluminum @@ -229,7 +229,7 @@ def run_old_reporting(scenario_name, model_name): from .data_petro import gen_data_petro_chemicals from .data_buildings import gen_data_buildings from .data_power_sector import gen_data_power_sector -from message_ix_models.util import add_par_data + DATA_FUNCTIONS = [ # gen_data_buildings, @@ -241,6 +241,7 @@ def run_old_reporting(scenario_name, model_name): gen_data_power_sector, ] + # Try to handle multiple data input functions from different materials def add_data(scenario, dry_run=False): """Populate `scenario` with MESSAGE-Transport data.""" From 773ff9d1322224b2dba5898a23fab9062aa86472 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Mon, 22 Nov 2021 17:36:42 +0100 Subject: [PATCH 309/774] Overwrite two files - tools/__init__.py directly from master - set.yaml from the working branch (pr#261) --- message_ix_models/data/material/set.yaml | 215 ----------------------- 1 file changed, 215 deletions(-) diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 07b7a98ac4..2f25150370 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -4,106 +4,6 @@ # accounting. Different sets can be created once there are 2+ different # categories of materials. -petro_chemicals: - commodity: - require: - - crudeoil - add: - - naphtha - - kerosene - - diesel - - atm_residue - - refinery_gas - - atm_gasoil - - atm_residue - - vacuum_gasoil - - vacuum_residue - - desulf_naphtha - - desulf_kerosene - - desulf_diesel - - desulf_atm_gasoil - - desulf_vacuum_gasoil - - gasoline - - heavy_foil - - light_foil - - propylene - - pet_coke - - ethane - - propane - - ethylene - - BTX - - level: - require: - - secondary - - final - add: - - pre_intermediate - - useful_refining - - desulfurized - - intermediate - - secondary_material - - useful_petro - - final_material - - demand_ethylene - - demand_propylene - - demand_BTX - - demand_foil - - demand_loil - - mode: - add: - - atm_gasoil - - vacuum_gasoil - - naphtha - - kerosene - - diesel - - cracking_gasoline - - cracking_loil - - ethane - - propane - - light_foil - - refinery_gas - - heavy_foil - - pet_coke - - atm_residue - - vacuum_residue - - gasoline - - technology: - add: - - atm_distillation_ref - - vacuum_distillation_ref - - hydrotreating_ref - - catalytic_cracking_ref - - visbreaker_ref - - coking_ref - - catalytic_reforming_ref - - hydro_cracking_ref - - steam_cracker_petro - - ethanol_to_ethylene_petro - # - loil_ref_naphtha - # - loil_ref_gasoline - # - loil_ref_lightfoil - # - loil_ref_ethane - # - loil_ref_kerosene - # - loil_ref_diesel - # - loil_ref_refinerygas - # - loil_ref - # - foil_ref - # - foil_ref_heavyfoil - # - foil_ref_petcoke - # - foil_ref_atmgasoil - # - foil_ref_atmresidue - # - foil_ref_vacuumresidue - - agg_ref - - gas_processing_petro - - remove: - # Any representation of refinery. - - ref_hil - - ref_lol - common: commodity: require: @@ -124,7 +24,6 @@ common: level: require: - # We do not require the secondary and primary levels ?? - primary - secondary - useful @@ -172,7 +71,6 @@ generic: level: add: - useful_steel - - useful_aluminum - useful_cement - useful_aluminum - useful_refining @@ -180,7 +78,6 @@ generic: technology: add: - - furnace_coke_steel - furnace_foil_steel - furnace_loil_steel - furnace_biomass_steel @@ -195,64 +92,6 @@ generic: - fc_h2_steel - solar_steel - dheat_steel - - furnace_coke_aluminum - - furnace_coal_aluminum - - furnace_foil_aluminum - - furnace_loil_aluminum - - furnace_ethanol_aluminum - - furnace_biomass_aluminum - - furnace_methanol_aluminum - - furnace_gas_aluminum - - furnace_elec_aluminum - - furnace_h2_aluminum - - hp_gas_aluminum - - hp_elec_aluminum - - fc_h2_aluminum - - solar_aluminum - - dheat_aluminum - - furnace_coke_petro - - furnace_coal_petro - - furnace_foil_petro - - furnace_loil_petro - - furnace_ethanol_petro - - furnace_biomass_petro - - furnace_methanol_petro - - furnace_gas_petro - - furnace_elec_petro - - furnace_h2_petro - - hp_gas_petro - - hp_elec_petro - - fc_h2_petro - - solar_petro - - dheat_petro - - furnace_coke_refining - - furnace_coal_refining - - furnace_foil_refining - - furnace_loil_refining - - furnace_ethanol_refining - - furnace_biomass_refining - - furnace_methanol_refining - - furnace_gas_refining - - furnace_elec_refining - - furnace_h2_refining - - hp_gas_refining - - hp_elec_refining - - fc_h2_refining - - solar_refining - - dheat_refining - - DUMMY_coal_supply - - DUMMY_gas_supply - - DUMMY_loil_supply - - DUMMY_foil_supply - - DUMMY_electr_supply - - DUMMY_ethanol_supply - - DUMMY_methanol_supply - - DUMMY_hydrogen_supply - - DUMMY_biomass_supply - - DUMMY_dist_heating - - DUMMY_crudeoil_supply - - DUMMY_ethanol_supply_secondary - - DUMMY_gas_supply_secondary - furnace_foil_cement - furnace_loil_cement - furnace_biomass_cement @@ -310,9 +149,6 @@ generic: add: - low_temp - high_temp - node: - remove: - - World petro_chemicals: commodity: @@ -677,54 +513,3 @@ power_sector: # name: Paper and pulp for paper # description: Pulp is a lignocellulosic fibrous material prepared by chemically or mechanically separating cellulose fibers and the raw material for making paper. Measured as the total mass of material. # unit: Mt - -non-ferrous: - name: Non-ferrous metals - description: Metals and alloys which do not contain iron. Measured as the total mass of material. - unit: Mt - -# Creates an error because it is the same name with the above aluminum -# aluminum: -# name: Aluminum -# description: Aluminum (or aluminium) is a chemical element with the symbol Al. Measured as the total mass of material. -# unit: Mt -# parent: non-ferrous - -copper: - name: Copper - description: Copper is a chemical element with the symbol Cu. Measured as the total mass of material. - unit: Mt - parent: non-ferrous - -minerals: - name: Minerals - description: Non-metallic minerals are minerals that have no metallic luster, break easily and include, e.g., sand, limestone, marble, clay and salt. Measured as the FE-equivalent mass of material using LCA midpoint characterization factors. - unit: MtFe-eq - -cement: - name: Cement - description: Cement is a binder used for construction that sets, hardens, and adheres to other materials to bind them together. Measured as the total mass of material. - unit: Mt - parent: minerals - -chemicals: - name: Chemicals - description: Industrial chemicals that form the basis of many products. Measured as the total mass of material. - unit: Mt - -ethylene: - name: Ethylene - description: Ethylene is a hydrocarbon with the formula C2H4. Measured as the total mass of material. - unit: Mt - parent: chemicals - -ammonia: - name: Ammonia - description: Ammonia is a compound of nitrogen and hydrogen with the formula NH3. Measured as the total mass of material. - unit: Mt - parent: chemicals - -paper-pulp: - name: Paper and pulp for paper - description: Pulp is a lignocellulosic fibrous material prepared by chemically or mechanically separating cellulose fibers and the raw material for making paper. Measured as the total mass of material. - unit: Mt From 57e4c47b3efc131f2fa27d14ce95055c95043988 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Mon, 29 Nov 2021 13:31:32 +0100 Subject: [PATCH 310/774] Apply black for the formatting of the code --- .../material/N-fertilizer/AddClimatePolicy.py | 176 ++-- .../model/material/N-fertilizer/AddTrade.py | 377 ++++---- .../model/material/N-fertilizer/LoadParams.py | 160 +++- .../N-fertilizer/ReportingToGLOBIOM.py | 237 +++-- .../N-fertilizer/SetupNitrogenBase.py | 883 ++++++++++-------- message_ix_models/model/material/__init__.py | 8 +- .../aluminum_stand_alone/generate_data_AL.py | 194 ++-- .../generate_data_generic.py | 237 +++-- .../material/aluminum_stand_alone/plotting.py | 129 ++- .../aluminum_stand_alone/stand_alone_AL.py | 542 ++++++----- .../test_commodity_balance.py | 293 +++--- message_ix_models/model/material/build.py | 3 +- .../model/material/data_aluminum.py | 14 +- .../model/material/data_cement.py | 6 +- .../model/material/data_petro.py | 6 +- .../model/material/data_steel.py | 21 +- message_ix_models/model/material/data_util.py | 45 +- message_ix_models/model/material/util.py | 1 + 18 files changed, 1924 insertions(+), 1408 deletions(-) diff --git a/message_ix_models/model/material/N-fertilizer/AddClimatePolicy.py b/message_ix_models/model/material/N-fertilizer/AddClimatePolicy.py index 3005bfd995..0264a31b4d 100644 --- a/message_ix_models/model/material/N-fertilizer/AddClimatePolicy.py +++ b/message_ix_models/model/material/N-fertilizer/AddClimatePolicy.py @@ -7,23 +7,24 @@ import time import pandas as pd - - + + #%% Add climate policies - -mp = ix.Platform(dbprops=r'H:\MyDocuments\MESSAGE\message_ix\config\default.properties') +mp = ix.Platform(dbprops=r"H:\MyDocuments\MESSAGE\message_ix\config\default.properties") #%% # new model name in ix platform modelName = "JM_GLB_NITRO" -basescenarioName = "Baseline" # CCS now merged to the Baseline -newscenarioName = "EmBound" # '2degreeC' # +basescenarioName = "Baseline" # CCS now merged to the Baseline +newscenarioName = "EmBound" # '2degreeC' # -comment = "MESSAGE_Global test for new representation of nitrogen cycle with climate policy" +comment = ( + "MESSAGE_Global test for new representation of nitrogen cycle with climate policy" +) Sc_nitro = message_ix.Scenario(mp, modelName, basescenarioName) @@ -31,99 +32,132 @@ #%% Clone Sc_nitro_2C = Sc_nitro.clone(modelName, newscenarioName, comment) -lasthistyear = 2020 # New last historical year -df_ACT = Sc_nitro_2C.var('ACT', {'year_act':lasthistyear}).groupby(['node_loc', 'technology', 'year_act']).sum().reset_index() -df_CAP_NEW = Sc_nitro_2C.var('CAP_NEW', {'year_vtg':lasthistyear}).groupby(['node_loc', 'technology', 'year_vtg']).sum().reset_index() -df_EXT = Sc_nitro_2C.var('EXT', {'year':lasthistyear}).groupby(['node', 'commodity', 'grade', 'year']).sum().reset_index() +lasthistyear = 2020 # New last historical year +df_ACT = ( + Sc_nitro_2C.var("ACT", {"year_act": lasthistyear}) + .groupby(["node_loc", "technology", "year_act"]) + .sum() + .reset_index() +) +df_CAP_NEW = ( + Sc_nitro_2C.var("CAP_NEW", {"year_vtg": lasthistyear}) + .groupby(["node_loc", "technology", "year_vtg"]) + .sum() + .reset_index() +) +df_EXT = ( + Sc_nitro_2C.var("EXT", {"year": lasthistyear}) + .groupby(["node", "commodity", "grade", "year"]) + .sum() + .reset_index() +) Sc_nitro_2C.remove_solution() Sc_nitro_2C.check_out() -#%% Put global emissions bound -bound = 5000 #15000 # +#%% Put global emissions bound +bound = 5000 # 15000 # bound_emissions_2C = { - 'node': 'World', - 'type_emission': 'TCE', - 'type_tec': 'all', - 'type_year' : 'cumulative', #'2050', # - 'value': bound, #1076.0, # 1990 and on - 'unit' : 'tC', + "node": "World", + "type_emission": "TCE", + "type_tec": "all", + "type_year": "cumulative", #'2050', # + "value": bound, # 1076.0, # 1990 and on + "unit": "tC", } - + df = pd.DataFrame(bound_emissions_2C, index=[0]) - -Sc_nitro_2C.add_par("bound_emission", df) + +Sc_nitro_2C.add_par("bound_emission", df) #%% Change first modeling year (dealing with the 5-year step scenario) -df = Sc_nitro_2C.set('cat_year') -fyear = 2030 #2025 -a = df[((df.type_year=="cumulative") & (df.year 0] -a = pd.merge(df[['node_loc', 'technology', 'mode', 'time', 'unit']], - df_ACT[['node_loc', 'technology', 'year_act', 'lvl']], how='left', on=['node_loc', 'technology']).rename(columns={'lvl':'value'}) +df = Sc_nitro_2C.par("historical_activity", {"year_act": lasthistyear_org}) +# df = df[df.value > 0] +a = pd.merge( + df[["node_loc", "technology", "mode", "time", "unit"]], + df_ACT[["node_loc", "technology", "year_act", "lvl"]], + how="left", + on=["node_loc", "technology"], +).rename(columns={"lvl": "value"}) a = a[a.value > 0] -a['unit'] = 'GWa' -a['year_act'] = a['year_act'].astype(int) -Sc_nitro_2C.add_par("historical_activity", a) +a["unit"] = "GWa" +a["year_act"] = a["year_act"].astype(int) +Sc_nitro_2C.add_par("historical_activity", a) # historical_new_capacity -df = Sc_nitro_2C.par('historical_new_capacity', {'year_vtg':lasthistyear_org}) -#df = df[df.value > 0] -a = pd.merge(df[['node_loc', 'technology', 'unit']], - df_CAP_NEW[['node_loc', 'technology', 'year_vtg', 'lvl']], how='left', on=['node_loc', 'technology']).rename(columns={'lvl':'value'}) +df = Sc_nitro_2C.par("historical_new_capacity", {"year_vtg": lasthistyear_org}) +# df = df[df.value > 0] +a = pd.merge( + df[["node_loc", "technology", "unit"]], + df_CAP_NEW[["node_loc", "technology", "year_vtg", "lvl"]], + how="left", + on=["node_loc", "technology"], +).rename(columns={"lvl": "value"}) a = a[a.value > 0] -a['year_vtg'] = a['year_vtg'].astype(int) -Sc_nitro_2C.add_par("historical_new_capacity", a) +a["year_vtg"] = a["year_vtg"].astype(int) +Sc_nitro_2C.add_par("historical_new_capacity", a) # historical_extraction -df = Sc_nitro_2C.par('historical_extraction', {'year':lasthistyear_org}) -#df = df[df.value > 0] -a = pd.merge(df[['node', 'commodity', 'grade', 'unit']], - df_EXT[['node', 'commodity', 'grade', 'year', 'lvl']], how='outer', on=['node', 'commodity', 'grade']).rename(columns={'lvl':'value'}) +df = Sc_nitro_2C.par("historical_extraction", {"year": lasthistyear_org}) +# df = df[df.value > 0] +a = pd.merge( + df[["node", "commodity", "grade", "unit"]], + df_EXT[["node", "commodity", "grade", "year", "lvl"]], + how="outer", + on=["node", "commodity", "grade"], +).rename(columns={"lvl": "value"}) a = a[a.value > 0] -a['unit'] = 'GWa' -a['year'] = a['year'].astype(int) -Sc_nitro_2C.add_par("historical_extraction", a) +a["unit"] = "GWa" +a["year"] = a["year"].astype(int) +Sc_nitro_2C.add_par("historical_extraction", a) # historical_land -df = Sc_nitro_2C.par('historical_land', {'year':lasthistyear_org}) -df['year'] = lasthistyear -Sc_nitro_2C.add_par("historical_land", df) +df = Sc_nitro_2C.par("historical_land", {"year": lasthistyear_org}) +df["year"] = lasthistyear +Sc_nitro_2C.add_par("historical_land", df) # historical_gdp -#Sc_INDC = mp.Scenario("CD_Links_SSP2", "INDCi_1000-con-prim-dir-ncr") -#df = Sc_nitro.par('historical_gdp') -#df = df[df.year < fyear] -#Sc_nitro_2C.add_par("historical_gdp", df) +# Sc_INDC = mp.Scenario("CD_Links_SSP2", "INDCi_1000-con-prim-dir-ncr") +# df = Sc_nitro.par('historical_gdp') +# df = df[df.year < fyear] +# Sc_nitro_2C.add_par("historical_gdp", df) #%% -#Sc_nitro_2C.commit('Hydro AllReg-Global w/ 2C constraint (starting 2020)') -Sc_nitro_2C.commit('Fertilizer Global w/ 2C constraint (starting 2030)') +# Sc_nitro_2C.commit('Hydro AllReg-Global w/ 2C constraint (starting 2020)') +Sc_nitro_2C.commit("Fertilizer Global w/ 2C constraint (starting 2030)") # to_gdx only -#start_time = time.time() -#Sc_nitro_2C.to_gdx(r'..\..\message_ix\message_ix\model\data', "MsgData_"+Sc_nitro_2C.model+"_"+ +# start_time = time.time() +# Sc_nitro_2C.to_gdx(r'..\..\message_ix\message_ix\model\data', "MsgData_"+Sc_nitro_2C.model+"_"+ # Sc_nitro_2C.scenario+"_"+str(Sc_nitro_2C.version)) -#print(".to_gdx: %s seconds taken." % (time.time() - start_time)) +# print(".to_gdx: %s seconds taken." % (time.time() - start_time)) # solve start_time = time.time() -Sc_nitro_2C.solve(model='MESSAGE', case=Sc_nitro_2C.model+"_"+ -# Sc_nitro_2C.scenario+"_"+str(Sc_nitro_2C.version)) - Sc_nitro_2C.scenario+"_"+str(bound)) +Sc_nitro_2C.solve( + model="MESSAGE", + case=Sc_nitro_2C.model + "_" + + # Sc_nitro_2C.scenario+"_"+str(Sc_nitro_2C.version)) + Sc_nitro_2C.scenario + "_" + str(bound), +) print(".solve: %.6s seconds taken." % (time.time() - start_time)) @@ -132,12 +166,14 @@ rep = Reporter.from_scenario(Sc_nitro_2C) # Set up filters for N tecs -rep.set_filters(t= newtechnames_ccs + newtechnames + ['NFert_imp', 'NFert_exp', 'NFert_trd']) +rep.set_filters( + t=newtechnames_ccs + newtechnames + ["NFert_imp", "NFert_exp", "NFert_trd"] +) # NF demand summary -NF = rep.add_product('useNF', 'land_input', 'LAND') +NF = rep.add_product("useNF", "land_input", "LAND") -print(rep.describe(rep.full_key('useNF'))) -rep.get('useNF:n-y') -rep.write('useNF:n-y', 'nf_demand_'+str(bound)+'_notrade.xlsx') -rep.write('useNF:y', 'nf_demand_total_'+str(bound)+'_notrade.xlsx') +print(rep.describe(rep.full_key("useNF"))) +rep.get("useNF:n-y") +rep.write("useNF:n-y", "nf_demand_" + str(bound) + "_notrade.xlsx") +rep.write("useNF:y", "nf_demand_total_" + str(bound) + "_notrade.xlsx") diff --git a/message_ix_models/model/material/N-fertilizer/AddTrade.py b/message_ix_models/model/material/N-fertilizer/AddTrade.py index 163cb01453..750268358c 100644 --- a/message_ix_models/model/material/N-fertilizer/AddTrade.py +++ b/message_ix_models/model/material/N-fertilizer/AddTrade.py @@ -12,20 +12,25 @@ import message_ix import importlib -#import LoadParams + +# import LoadParams importlib.reload(LoadParams) #%% Base model load -# launch the IX modeling platform using the local default database -mp = ix.Platform(dbprops=r'H:\MyDocuments\MESSAGE\message_ix\config\default.org.properties') +# launch the IX modeling platform using the local default database +mp = ix.Platform( + dbprops=r"H:\MyDocuments\MESSAGE\message_ix\config\default.org.properties" +) # new model name in ix platform modelName = "JM_GLB_NITRO" -basescenarioName = "NoPolicy" -newscenarioName = "NoPolicy_Trd" +basescenarioName = "NoPolicy" +newscenarioName = "NoPolicy_Trd" -comment = "MESSAGE global test for new representation of nitrogen cycle with global trade" +comment = ( + "MESSAGE global test for new representation of nitrogen cycle with global trade" +) Sc_nitro = message_ix.Scenario(mp, modelName, basescenarioName) @@ -38,21 +43,27 @@ #%% Add tecs to set # Only fertilizer traded for now (NH3 trade data not yet available) -comm_for_trd = ['Fertilizer Use|Nitrogen'] -lvl_for_trd = ['material_final'] -newtechnames_trd = ['NFert_trd'] -newtechnames_imp = ['NFert_imp'] -newtechnames_exp = ['NFert_exp'] - -#comm_for_trd = comm_for_trd # = ['NH3', 'Fertilizer Use|Nitrogen'] -#newtechnames_trd = ['NH3_trd', 'NFert_trd'] -#newtechnames_imp = ['NH3_imp', 'NFert_imp'] -#newtechnames_exp = ['NH3_exp', 'NFert_exp'] - -Sc_nitro_trd.add_set("technology", newtechnames_trd + newtechnames_imp + newtechnames_exp) - -cat_add = pd.DataFrame({'type_tec': ['import', 'export'], # 'all' not need to be added here - 'technology': newtechnames_imp + newtechnames_exp}) +comm_for_trd = ["Fertilizer Use|Nitrogen"] +lvl_for_trd = ["material_final"] +newtechnames_trd = ["NFert_trd"] +newtechnames_imp = ["NFert_imp"] +newtechnames_exp = ["NFert_exp"] + +# comm_for_trd = comm_for_trd # = ['NH3', 'Fertilizer Use|Nitrogen'] +# newtechnames_trd = ['NH3_trd', 'NFert_trd'] +# newtechnames_imp = ['NH3_imp', 'NFert_imp'] +# newtechnames_exp = ['NH3_exp', 'NFert_exp'] + +Sc_nitro_trd.add_set( + "technology", newtechnames_trd + newtechnames_imp + newtechnames_exp +) + +cat_add = pd.DataFrame( + { + "type_tec": ["import", "export"], # 'all' not need to be added here + "technology": newtechnames_imp + newtechnames_exp, + } +) Sc_nitro_trd.add_set("cat_tec", cat_add) @@ -60,201 +71,224 @@ for t in newtechnames_trd: # output - df = Sc_nitro_trd.par("output", {"technology":["coal_trd"]}) - df['technology'] = t - df['commodity'] = comm_for_trd[newtechnames_trd.index(t)] - df['value'] = 1 - df['unit'] = 'Tg N/yr' - Sc_nitro_trd.add_par("output", df.copy()) - - df = Sc_nitro_trd.par("input", {"technology":["coal_trd"]}) - df['technology'] = t - df['commodity'] = comm_for_trd[newtechnames_trd.index(t)] - df['value'] = 1 - df['unit'] = 'Tg N/yr' - Sc_nitro_trd.add_par("input", df.copy()) - + df = Sc_nitro_trd.par("output", {"technology": ["coal_trd"]}) + df["technology"] = t + df["commodity"] = comm_for_trd[newtechnames_trd.index(t)] + df["value"] = 1 + df["unit"] = "Tg N/yr" + Sc_nitro_trd.add_par("output", df.copy()) + + df = Sc_nitro_trd.par("input", {"technology": ["coal_trd"]}) + df["technology"] = t + df["commodity"] = comm_for_trd[newtechnames_trd.index(t)] + df["value"] = 1 + df["unit"] = "Tg N/yr" + Sc_nitro_trd.add_par("input", df.copy()) + reg = REGIONS.copy() -reg.remove('R11_GLB') +reg.remove("R11_GLB") for t in newtechnames_imp: # output - df = Sc_nitro_trd.par("output", {"technology":["coal_imp"], "node_loc":['R11_CPA']}) - df['technology'] = t - df['commodity'] = comm_for_trd[newtechnames_imp.index(t)] - df['value'] = 1 - df['unit'] = 'Tg N/yr' - df['level'] = lvl_for_trd[newtechnames_imp.index(t)] + df = Sc_nitro_trd.par( + "output", {"technology": ["coal_imp"], "node_loc": ["R11_CPA"]} + ) + df["technology"] = t + df["commodity"] = comm_for_trd[newtechnames_imp.index(t)] + df["value"] = 1 + df["unit"] = "Tg N/yr" + df["level"] = lvl_for_trd[newtechnames_imp.index(t)] for r in reg: - df['node_loc'] = r - df['node_dest'] = r - Sc_nitro_trd.add_par("output", df.copy()) - + df["node_loc"] = r + df["node_dest"] = r + Sc_nitro_trd.add_par("output", df.copy()) + # input - df = Sc_nitro_trd.par("input", {"technology":["coal_imp"], "node_loc":['R11_CPA']}) - df['technology'] = t - df['commodity'] = comm_for_trd[newtechnames_imp.index(t)] - df['value'] = 1 - df['unit'] = 'Tg N/yr' + df = Sc_nitro_trd.par( + "input", {"technology": ["coal_imp"], "node_loc": ["R11_CPA"]} + ) + df["technology"] = t + df["commodity"] = comm_for_trd[newtechnames_imp.index(t)] + df["value"] = 1 + df["unit"] = "Tg N/yr" for r in reg: - df['node_loc'] = r - Sc_nitro_trd.add_par("input", df.copy()) - + df["node_loc"] = r + Sc_nitro_trd.add_par("input", df.copy()) + for t in newtechnames_exp: # output - df = Sc_nitro_trd.par("output", {"technology":["coal_exp"], "node_loc":['R11_CPA']}) - df['technology'] = t - df['commodity'] = comm_for_trd[newtechnames_exp.index(t)] - df['value'] = 1 - df['unit'] = 'Tg N/yr' + df = Sc_nitro_trd.par( + "output", {"technology": ["coal_exp"], "node_loc": ["R11_CPA"]} + ) + df["technology"] = t + df["commodity"] = comm_for_trd[newtechnames_exp.index(t)] + df["value"] = 1 + df["unit"] = "Tg N/yr" for r in reg: - df['node_loc'] = r - Sc_nitro_trd.add_par("output", df.copy()) - + df["node_loc"] = r + Sc_nitro_trd.add_par("output", df.copy()) + # input - df = Sc_nitro_trd.par("input", {"technology":["coal_exp"], "node_loc":['R11_CPA']}) - df['technology'] = t - df['commodity'] = comm_for_trd[newtechnames_exp.index(t)] - df['value'] = 1 - df['unit'] = 'Tg N/yr' - df['level'] = lvl_for_trd[newtechnames_exp.index(t)] + df = Sc_nitro_trd.par( + "input", {"technology": ["coal_exp"], "node_loc": ["R11_CPA"]} + ) + df["technology"] = t + df["commodity"] = comm_for_trd[newtechnames_exp.index(t)] + df["value"] = 1 + df["unit"] = "Tg N/yr" + df["level"] = lvl_for_trd[newtechnames_exp.index(t)] for r in reg: - df['node_loc'] = r - df['node_origin'] = r - Sc_nitro_trd.add_par("input", df.copy()) + df["node_loc"] = r + df["node_origin"] = r + Sc_nitro_trd.add_par("input", df.copy()) # Need to incorporate the regional trade pattern #%% Cost - + for t in newtechnames_exp: - df = Sc_nitro_trd.par("inv_cost", {"technology":["coal_exp"]}) - df['technology'] = t - Sc_nitro_trd.add_par("inv_cost", df) - - df = Sc_nitro_trd.par("var_cost", {"technology":["coal_exp"]}) - df['technology'] = t - Sc_nitro_trd.add_par("var_cost", df) - - df = Sc_nitro_trd.par("fix_cost", {"technology":["coal_exp"]}) - df['technology'] = t - Sc_nitro_trd.add_par("fix_cost", df) - + df = Sc_nitro_trd.par("inv_cost", {"technology": ["coal_exp"]}) + df["technology"] = t + Sc_nitro_trd.add_par("inv_cost", df) + + df = Sc_nitro_trd.par("var_cost", {"technology": ["coal_exp"]}) + df["technology"] = t + Sc_nitro_trd.add_par("var_cost", df) + + df = Sc_nitro_trd.par("fix_cost", {"technology": ["coal_exp"]}) + df["technology"] = t + Sc_nitro_trd.add_par("fix_cost", df) + for t in newtechnames_imp: -# No inv_cost for importing tecs -# df = Sc_nitro_trd.par("inv_cost", {"technology":["coal_imp"]}) -# df['technology'] = t -# Sc_nitro_trd.add_par("inv_cost", df) - - df = Sc_nitro_trd.par("var_cost", {"technology":["coal_imp"]}) - df['technology'] = t - Sc_nitro_trd.add_par("var_cost", df) - - df = Sc_nitro_trd.par("fix_cost", {"technology":["coal_imp"]}) - df['technology'] = t - Sc_nitro_trd.add_par("fix_cost", df) - -for t in newtechnames_trd: - df = Sc_nitro_trd.par("var_cost", {"technology":["coal_trd"]}) - df['technology'] = t - Sc_nitro_trd.add_par("var_cost", df) - - - + # No inv_cost for importing tecs + # df = Sc_nitro_trd.par("inv_cost", {"technology":["coal_imp"]}) + # df['technology'] = t + # Sc_nitro_trd.add_par("inv_cost", df) + + df = Sc_nitro_trd.par("var_cost", {"technology": ["coal_imp"]}) + df["technology"] = t + Sc_nitro_trd.add_par("var_cost", df) + + df = Sc_nitro_trd.par("fix_cost", {"technology": ["coal_imp"]}) + df["technology"] = t + Sc_nitro_trd.add_par("fix_cost", df) + +for t in newtechnames_trd: + df = Sc_nitro_trd.par("var_cost", {"technology": ["coal_trd"]}) + df["technology"] = t + Sc_nitro_trd.add_par("var_cost", df) + + #%% Other background variables - -paramList_tec = [x for x in Sc_nitro_trd.par_list() if 'technology' in Sc_nitro_trd.idx_sets(x)] -#paramList_comm = [x for x in Sc_nitro.par_list() if 'commodity' in Sc_nitro.idx_sets(x)] + +paramList_tec = [ + x for x in Sc_nitro_trd.par_list() if "technology" in Sc_nitro_trd.idx_sets(x) +] +# paramList_comm = [x for x in Sc_nitro.par_list() if 'commodity' in Sc_nitro.idx_sets(x)] + def get_params_with_tech(scen, name): result = [] # Iterate over all parameters with a tech dimension for par_name in paramList_tec: - if len(scen.par(par_name, filters={'technology': name})): + if len(scen.par(par_name, filters={"technology": name})): # Parameter has >= 1 data point with tech *name* result.append(par_name) return result -params_exp = get_params_with_tech(Sc_nitro_trd, 'coal_exp') -params_imp = get_params_with_tech(Sc_nitro_trd, 'coal_imp') -params_trd = get_params_with_tech(Sc_nitro_trd, 'coal_trd') -# Got too slow for some reason -#params_exp = [x for x in paramList_tec if 'coal_exp' in set(Sc_nitro_trd.par(x)["technology"].tolist())] -#params_imp = [x for x in paramList_tec if 'coal_imp' in set(Sc_nitro_trd.par(x)["technology"].tolist())] -#params_trd = [x for x in paramList_tec if 'coal_trd' in set(Sc_nitro_trd.par(x)["technology"].tolist())] +params_exp = get_params_with_tech(Sc_nitro_trd, "coal_exp") +params_imp = get_params_with_tech(Sc_nitro_trd, "coal_imp") +params_trd = get_params_with_tech(Sc_nitro_trd, "coal_trd") -a = set(params_exp + params_imp + params_trd) -suffix = ('cost', 'put') -prefix = ('historical', 'ref', 'relation') -a = a - set([x for x in a if x.endswith(suffix)] + [x for x in a if x.startswith(prefix)] + ['emission_factor']) +# Got too slow for some reason +# params_exp = [x for x in paramList_tec if 'coal_exp' in set(Sc_nitro_trd.par(x)["technology"].tolist())] +# params_imp = [x for x in paramList_tec if 'coal_imp' in set(Sc_nitro_trd.par(x)["technology"].tolist())] +# params_trd = [x for x in paramList_tec if 'coal_trd' in set(Sc_nitro_trd.par(x)["technology"].tolist())] + +a = set(params_exp + params_imp + params_trd) +suffix = ("cost", "put") +prefix = ("historical", "ref", "relation") +a = a - set( + [x for x in a if x.endswith(suffix)] + + [x for x in a if x.startswith(prefix)] + + ["emission_factor"] +) for p in list(a): for t in newtechnames_exp: - df = Sc_nitro_trd.par(p, {"technology":["coal_exp"]}) - df['technology'] = t + df = Sc_nitro_trd.par(p, {"technology": ["coal_exp"]}) + df["technology"] = t if df.size: Sc_nitro_trd.add_par(p, df.copy()) - + for t in newtechnames_imp: - df = Sc_nitro_trd.par(p, {"technology":["coal_imp"]}) - df['technology'] = t + df = Sc_nitro_trd.par(p, {"technology": ["coal_imp"]}) + df["technology"] = t if df.size: Sc_nitro_trd.add_par(p, df.copy()) - + for t in newtechnames_trd: - df = Sc_nitro_trd.par(p, {"technology":["coal_trd"]}) - df['technology'] = t + df = Sc_nitro_trd.par(p, {"technology": ["coal_trd"]}) + df["technology"] = t if df.size: Sc_nitro_trd.add_par(p, df.copy()) - + # Found coal_exp doesn't have full cells filled for technical_lifetime. for t in newtechnames_exp: - df = Sc_nitro_trd.par('technical_lifetime', {"technology":t, "node_loc":['R11_CPA']}) + df = Sc_nitro_trd.par( + "technical_lifetime", {"technology": t, "node_loc": ["R11_CPA"]} + ) for r in reg: - df['node_loc'] = r - Sc_nitro_trd.add_par("technical_lifetime", df.copy()) + df["node_loc"] = r + Sc_nitro_trd.add_par("technical_lifetime", df.copy()) #%% Histrorical trade activity # Export capacity - understood as infrastructure enabling the trade activity (port, rail etc.) - -# historical_activity -N_trade = LoadParams.N_trade_R11.copy() - -df_histexp = N_trade.loc[(N_trade.Element=='Export') & (N_trade.year_act<2015),] -df_histexp = df_histexp.assign(mode = 'M1') -df_histexp = df_histexp.assign(technology = newtechnames_exp[0]) #t + +# historical_activity +N_trade = LoadParams.N_trade_R11.copy() + +df_histexp = N_trade.loc[ + (N_trade.Element == "Export") & (N_trade.year_act < 2015), +] +df_histexp = df_histexp.assign(mode="M1") +df_histexp = df_histexp.assign(technology=newtechnames_exp[0]) # t df_histexp = df_histexp.drop(columns="Element") -df_histimp = N_trade.loc[(N_trade.Element=='Import') & (N_trade.year_act<2015),] -df_histimp = df_histimp.assign(mode = 'M1') -df_histimp = df_histimp.assign(technology = newtechnames_imp[0]) #t +df_histimp = N_trade.loc[ + (N_trade.Element == "Import") & (N_trade.year_act < 2015), +] +df_histimp = df_histimp.assign(mode="M1") +df_histimp = df_histimp.assign(technology=newtechnames_imp[0]) # t df_histimp = df_histimp.drop(columns="Element") # GLB trd historical activities (Now equal to the sum of imports) -dftrd = Sc_nitro_trd.par("historical_activity", {"technology":["coal_trd"]}) -dftrd = dftrd.loc[(dftrd.year_act<2015) & (dftrd.year_act>2000),] -dftrd.value = df_histimp.groupby(['year_act']).sum().values -dftrd['unit'] = 'Tg N/yr' -dftrd['technology'] = newtechnames_trd[0] +dftrd = Sc_nitro_trd.par("historical_activity", {"technology": ["coal_trd"]}) +dftrd = dftrd.loc[ + (dftrd.year_act < 2015) & (dftrd.year_act > 2000), +] +dftrd.value = df_histimp.groupby(["year_act"]).sum().values +dftrd["unit"] = "Tg N/yr" +dftrd["technology"] = newtechnames_trd[0] Sc_nitro_trd.add_par("historical_activity", dftrd) Sc_nitro_trd.add_par("historical_activity", df_histexp.append(df_histimp)) # historical_new_capacity -trdlife = Sc_nitro_trd.par("technical_lifetime", {"technology":["NFert_exp"]}).value[0] +trdlife = Sc_nitro_trd.par("technical_lifetime", {"technology": ["NFert_exp"]}).value[0] -df_histcap = df_histexp.drop(columns=['time', 'mode']) -df_histcap = df_histcap.rename(columns={"year_act":"year_vtg"}) -df_histcap.value = df_histcap.value.values/trdlife +df_histcap = df_histexp.drop(columns=["time", "mode"]) +df_histcap = df_histcap.rename(columns={"year_act": "year_vtg"}) +df_histcap.value = df_histcap.value.values / trdlife Sc_nitro_trd.add_par("historical_new_capacity", df_histcap) #%% -# Adjust i_feed demand -#demand_fs_org is the original i-feed in the reference SSP2 scenario +# Adjust i_feed demand +# demand_fs_org is the original i-feed in the reference SSP2 scenario """ In the scenario w/o trade, I assumed 100% NH3 demand is produced in each region. Now with trade, I subtract the net import (of tN) from total demand, and the difference is produced regionally. @@ -262,30 +296,41 @@ def get_params_with_tech(scen, name): Also, historical global production exceeds the global demand level from SSP2 scenario for the same year. I currently ignore this excess production and only produce what is demanded. """ -df = demand_fs_org.loc[demand_fs_org.year==2010,:].join(LoadParams.N_feed.set_index('node'), on='node') -sh = pd.DataFrame( {'node': demand_fs_org.loc[demand_fs_org.year==2010, 'node'], - 'r_feed': df.totENE / df.value}) # share of NH3 energy among total feedstock (no trade assumed) -df = demand_fs_org.join(sh.set_index('node'), on='node') -df.value *= 1 - df.r_feed # Carve out the same % from tot i_feed values -df = df.drop('r_feed', axis=1) +df = demand_fs_org.loc[demand_fs_org.year == 2010, :].join( + LoadParams.N_feed.set_index("node"), on="node" +) +sh = pd.DataFrame( + { + "node": demand_fs_org.loc[demand_fs_org.year == 2010, "node"], + "r_feed": df.totENE / df.value, + } +) # share of NH3 energy among total feedstock (no trade assumed) +df = demand_fs_org.join(sh.set_index("node"), on="node") +df.value *= 1 - df.r_feed # Carve out the same % from tot i_feed values +df = df.drop("r_feed", axis=1) Sc_nitro_trd.add_par("demand", df) - #%% Solve the scenario -Sc_nitro_trd.commit('Nitrogen Fertilizer for global model with fertilizer trade via global pool') +Sc_nitro_trd.commit( + "Nitrogen Fertilizer for global model with fertilizer trade via global pool" +) start_time = time.time() -Sc_nitro_trd.solve(model='MESSAGE', case=Sc_nitro_trd.model+"_"+ - Sc_nitro_trd.scenario + "_" + str(Sc_nitro_trd.version)) +Sc_nitro_trd.solve( + model="MESSAGE", + case=Sc_nitro_trd.model + + "_" + + Sc_nitro_trd.scenario + + "_" + + str(Sc_nitro_trd.version), +) print(".solve: %.6s seconds taken." % (time.time() - start_time)) #%% utils -#Sc_nitro_trd.discard_changes() - -#Sc_nitro_trd = message_ix.Scenario(mp, modelName, newscenarioName) - +# Sc_nitro_trd.discard_changes() +# Sc_nitro_trd = message_ix.Scenario(mp, modelName, newscenarioName) diff --git a/message_ix_models/model/material/N-fertilizer/LoadParams.py b/message_ix_models/model/material/N-fertilizer/LoadParams.py index 39c19ee6ea..5777e7692a 100644 --- a/message_ix_models/model/material/N-fertilizer/LoadParams.py +++ b/message_ix_models/model/material/N-fertilizer/LoadParams.py @@ -10,69 +10,129 @@ import pandas as pd # Read parameters in xlsx -te_params = pd.read_excel(r'..\n-fertilizer_techno-economic.xlsx', sheet_name='Sheet1') -n_inputs_per_tech = 12 # Number of input params per technology - -inv_cost = te_params[2010][list(range(0, te_params.shape[0], n_inputs_per_tech))].reset_index(drop=True) -fix_cost = te_params[2010][list(range(1, te_params.shape[0], n_inputs_per_tech))].reset_index(drop=True) -var_cost = te_params[2010][list(range(2, te_params.shape[0], n_inputs_per_tech))].reset_index(drop=True) -technical_lifetime = te_params[2010][list(range(3, te_params.shape[0], n_inputs_per_tech))].reset_index(drop=True) -input_fuel = te_params[2010][list(range(4, te_params.shape[0], n_inputs_per_tech))].reset_index(drop=True) -input_fuel[0:5] = input_fuel[0:5] * 0.0317 # 0.0317 GWa/PJ, GJ/t = PJ/Mt NH3 -input_elec = te_params[2010][list(range(5, te_params.shape[0], n_inputs_per_tech))].reset_index(drop=True) -input_elec = input_elec * 0.0317 # 0.0317 GWa/PJ -input_water = te_params[2010][list(range(6, te_params.shape[0], n_inputs_per_tech))].reset_index(drop=True) -output_NH3 = te_params[2010][list(range(7, te_params.shape[0], n_inputs_per_tech))].reset_index(drop=True) -output_water = te_params[2010][list(range(8, te_params.shape[0], n_inputs_per_tech))].reset_index(drop=True) -output_heat = te_params[2010][list(range(9, te_params.shape[0], n_inputs_per_tech))].reset_index(drop=True) -output_heat = output_heat * 0.0317 # 0.0317 GWa/PJ -emissions = te_params[2010][list(range(10, te_params.shape[0], n_inputs_per_tech))].reset_index(drop=True) * 12 / 44 # CO2 to C -capacity_factor = te_params[2010][list(range(11, te_params.shape[0], n_inputs_per_tech))].reset_index(drop=True) +te_params = pd.read_excel(r"..\n-fertilizer_techno-economic.xlsx", sheet_name="Sheet1") +n_inputs_per_tech = 12 # Number of input params per technology + +inv_cost = te_params[2010][ + list(range(0, te_params.shape[0], n_inputs_per_tech)) +].reset_index(drop=True) +fix_cost = te_params[2010][ + list(range(1, te_params.shape[0], n_inputs_per_tech)) +].reset_index(drop=True) +var_cost = te_params[2010][ + list(range(2, te_params.shape[0], n_inputs_per_tech)) +].reset_index(drop=True) +technical_lifetime = te_params[2010][ + list(range(3, te_params.shape[0], n_inputs_per_tech)) +].reset_index(drop=True) +input_fuel = te_params[2010][ + list(range(4, te_params.shape[0], n_inputs_per_tech)) +].reset_index(drop=True) +input_fuel[0:5] = input_fuel[0:5] * 0.0317 # 0.0317 GWa/PJ, GJ/t = PJ/Mt NH3 +input_elec = te_params[2010][ + list(range(5, te_params.shape[0], n_inputs_per_tech)) +].reset_index(drop=True) +input_elec = input_elec * 0.0317 # 0.0317 GWa/PJ +input_water = te_params[2010][ + list(range(6, te_params.shape[0], n_inputs_per_tech)) +].reset_index(drop=True) +output_NH3 = te_params[2010][ + list(range(7, te_params.shape[0], n_inputs_per_tech)) +].reset_index(drop=True) +output_water = te_params[2010][ + list(range(8, te_params.shape[0], n_inputs_per_tech)) +].reset_index(drop=True) +output_heat = te_params[2010][ + list(range(9, te_params.shape[0], n_inputs_per_tech)) +].reset_index(drop=True) +output_heat = output_heat * 0.0317 # 0.0317 GWa/PJ +emissions = ( + te_params[2010][list(range(10, te_params.shape[0], n_inputs_per_tech))].reset_index( + drop=True + ) + * 12 + / 44 +) # CO2 to C +capacity_factor = te_params[2010][ + list(range(11, te_params.shape[0], n_inputs_per_tech)) +].reset_index(drop=True) #%% Demand scenario [Mt N/year] from GLOBIOM # N_demand_FSU = pd.read_excel(r'..\CD-Links SSP2 N-fertilizer demand.FSU.xlsx', sheet_name='data', nrows=3) -N_demand_GLO = pd.read_excel(r'..\CD-Links SSP2 N-fertilizer demand.Global.xlsx', sheet_name='data') +N_demand_GLO = pd.read_excel( + r"..\CD-Links SSP2 N-fertilizer demand.Global.xlsx", sheet_name="data" +) # NH3 feedstock share by region in 2010 (from http://ietd.iipnetwork.org/content/ammonia#benchmarks) -feedshare_GLO = pd.read_excel(r'..\Ammonia feedstock share.Global.xlsx', sheet_name='Sheet2', skiprows=13) +feedshare_GLO = pd.read_excel( + r"..\Ammonia feedstock share.Global.xlsx", sheet_name="Sheet2", skiprows=13 +) # Regional N demaand in 2010 -ND = N_demand_GLO.loc[N_demand_GLO.Scenario=="NoPolicy", ['Region', 2010]] -ND = ND[ND.Region!='World'] -ND.Region = 'R11_' + ND.Region -ND = ND.set_index('Region') +ND = N_demand_GLO.loc[N_demand_GLO.Scenario == "NoPolicy", ["Region", 2010]] +ND = ND[ND.Region != "World"] +ND.Region = "R11_" + ND.Region +ND = ND.set_index("Region") # Derive total energy (GWa) of NH3 production (based on demand 2010) -N_energy = feedshare_GLO[feedshare_GLO.Region!='R11_GLB'].join(ND, on='Region') -N_energy = pd.concat([N_energy.Region, N_energy[["gas_pct", "coal_pct", "oil_pct"]].multiply(N_energy[2010], axis="index")], axis=1) -N_energy.gas_pct *= input_fuel[2] * 17/14 # NH3 / N -N_energy.coal_pct *= input_fuel[3] * 17/14 -N_energy.oil_pct *= input_fuel[4] * 17/14 -N_energy = pd.concat([N_energy.Region, N_energy.sum(axis=1)], axis=1).rename(columns={0:'totENE', 'Region':'node'}) #GWa +N_energy = feedshare_GLO[feedshare_GLO.Region != "R11_GLB"].join(ND, on="Region") +N_energy = pd.concat( + [ + N_energy.Region, + N_energy[["gas_pct", "coal_pct", "oil_pct"]].multiply( + N_energy[2010], axis="index" + ), + ], + axis=1, +) +N_energy.gas_pct *= input_fuel[2] * 17 / 14 # NH3 / N +N_energy.coal_pct *= input_fuel[3] * 17 / 14 +N_energy.oil_pct *= input_fuel[4] * 17 / 14 +N_energy = pd.concat([N_energy.Region, N_energy.sum(axis=1)], axis=1).rename( + columns={0: "totENE", "Region": "node"} +) # GWa #%% Import trade data (from FAO) -N_trade_R14 = pd.read_csv(r'..\trade.FAO.R14.csv', index_col=0) - -N_trade_R11 = pd.read_csv(r'..\trade.FAO.R11.csv', index_col=0) -N_trade_R11.msgregion = 'R11_' + N_trade_R11.msgregion -N_trade_R11.Value = N_trade_R11.Value/1e6 -N_trade_R11.Unit = 'Tg N/yr' -N_trade_R11 = N_trade_R11.assign(time = 'year') -N_trade_R11 = N_trade_R11.rename(columns={"Value":"value", "Unit":"unit", "msgregion":"node_loc", "Year":"year_act"}) - -df = N_trade_R11.loc[N_trade_R11.year_act==2010,] -df = df.pivot(index='node_loc', columns='Element', values='value') -NP = pd.DataFrame({'netimp': df.Import - df.Export, - 'demand': ND[2010]}) -NP['prod'] = NP.demand - NP.netimp +N_trade_R14 = pd.read_csv(r"..\trade.FAO.R14.csv", index_col=0) + +N_trade_R11 = pd.read_csv(r"..\trade.FAO.R11.csv", index_col=0) +N_trade_R11.msgregion = "R11_" + N_trade_R11.msgregion +N_trade_R11.Value = N_trade_R11.Value / 1e6 +N_trade_R11.Unit = "Tg N/yr" +N_trade_R11 = N_trade_R11.assign(time="year") +N_trade_R11 = N_trade_R11.rename( + columns={ + "Value": "value", + "Unit": "unit", + "msgregion": "node_loc", + "Year": "year_act", + } +) + +df = N_trade_R11.loc[ + N_trade_R11.year_act == 2010, +] +df = df.pivot(index="node_loc", columns="Element", values="value") +NP = pd.DataFrame({"netimp": df.Import - df.Export, "demand": ND[2010]}) +NP["prod"] = NP.demand - NP.netimp # Derive total energy (GWa) of NH3 production (based on demand 2010) -N_feed = feedshare_GLO[feedshare_GLO.Region!='R11_GLB'].join(NP, on='Region') -N_feed = pd.concat([N_feed.Region, N_feed[["gas_pct", "coal_pct", "oil_pct"]].multiply(N_feed['prod'], axis="index")], axis=1) -N_feed.gas_pct *= input_fuel[2] * 17/14 -N_feed.coal_pct *= input_fuel[3] * 17/14 -N_feed.oil_pct *= input_fuel[4] * 17/14 -N_feed = pd.concat([N_feed.Region, N_feed.sum(axis=1)], axis=1).rename(columns={0:'totENE', 'Region':'node'}) #GWa +N_feed = feedshare_GLO[feedshare_GLO.Region != "R11_GLB"].join(NP, on="Region") +N_feed = pd.concat( + [ + N_feed.Region, + N_feed[["gas_pct", "coal_pct", "oil_pct"]].multiply( + N_feed["prod"], axis="index" + ), + ], + axis=1, +) +N_feed.gas_pct *= input_fuel[2] * 17 / 14 +N_feed.coal_pct *= input_fuel[3] * 17 / 14 +N_feed.oil_pct *= input_fuel[4] * 17 / 14 +N_feed = pd.concat([N_feed.Region, N_feed.sum(axis=1)], axis=1).rename( + columns={0: "totENE", "Region": "node"} +) # GWa diff --git a/message_ix_models/model/material/N-fertilizer/ReportingToGLOBIOM.py b/message_ix_models/model/material/N-fertilizer/ReportingToGLOBIOM.py index 7a628959f6..4cd10e4f73 100644 --- a/message_ix_models/model/material/N-fertilizer/ReportingToGLOBIOM.py +++ b/message_ix_models/model/material/N-fertilizer/ReportingToGLOBIOM.py @@ -13,88 +13,110 @@ import pyam import os -os.chdir(r'./code.Global') +os.chdir(r"./code.Global") #%% Constants -model_name = 'JM_GLB_NITRO_MACRO_TRD' - -scen_names = ["baseline", - "NPi2020-con-prim-dir-ncr", - "NPi2020_1600-con-prim-dir-ncr", - "NPi2020_400-con-prim-dir-ncr"] - -newtechnames = ['biomass_NH3', 'electr_NH3', 'gas_NH3', 'coal_NH3', 'fueloil_NH3', 'NH3_to_N_fertil'] -tec_for_ccs = list(newtechnames[i] for i in [0,2,3,4]) -newtechnames_ccs = list(map(lambda x:str(x)+'_ccs', tec_for_ccs)) #include biomass in CCS, newtechnames[2:5])) +model_name = "JM_GLB_NITRO_MACRO_TRD" + +scen_names = [ + "baseline", + "NPi2020-con-prim-dir-ncr", + "NPi2020_1600-con-prim-dir-ncr", + "NPi2020_400-con-prim-dir-ncr", +] + +newtechnames = [ + "biomass_NH3", + "electr_NH3", + "gas_NH3", + "coal_NH3", + "fueloil_NH3", + "NH3_to_N_fertil", +] +tec_for_ccs = list(newtechnames[i] for i in [0, 2, 3, 4]) +newtechnames_ccs = list( + map(lambda x: str(x) + "_ccs", tec_for_ccs) +) # include biomass in CCS, newtechnames[2:5])) # Units are usually taken care of .yaml in message_data. # In case I don't use message_data for reporting, I need to deal with the units here. -ix.reporting.configure(units={'replace': {'???': '', '-':''}}) # '???' and '-' are not handled in pyint(?) +ix.reporting.configure( + units={"replace": {"???": "", "-": ""}} +) # '???' and '-' are not handled in pyint(?) + +mp = ix.Platform( + dbprops=r"H:\MyDocuments\MESSAGE\message_ix\config\default.org.properties" +) -mp = ix.Platform(dbprops=r'H:\MyDocuments\MESSAGE\message_ix\config\default.org.properties') def GenerateOutput(model, scen, rep): - + rep.set_filters() - rep.set_filters(t= newtechnames_ccs + newtechnames) + rep.set_filters(t=newtechnames_ccs + newtechnames) # 1. Total NF demand - rep.add_product('useNF', 'land_input', 'LAND') - + rep.add_product("useNF", "land_input", "LAND") + def collapse(df): - df['variable'] = 'Nitrogen demand' - df['unit'] = 'Mt N/yr' + df["variable"] = "Nitrogen demand" + df["unit"] = "Mt N/yr" return df - - a = rep.convert_pyam('useNF:n-y', 'y', collapse=collapse) - rep.write(a[0], Path('nf_demand_'+model+'_'+scen+'.xlsx')) + + a = rep.convert_pyam("useNF:n-y", "y", collapse=collapse) + rep.write(a[0], Path("nf_demand_" + model + "_" + scen + ".xlsx")) """ 'emi' not working with filters on NH3 technologies for now. (This was because of units '???' and '-'.) """ # 2. Total emissions rep.set_filters() - rep.set_filters(t= (newtechnames_ccs + newtechnames)[:-1], e=['CO2_transformation']) # and NH3_to_N_fertil does not have emission factor + rep.set_filters( + t=(newtechnames_ccs + newtechnames)[:-1], e=["CO2_transformation"] + ) # and NH3_to_N_fertil does not have emission factor def collapse_emi(df): - df['variable'] = 'Emissions|CO2|' +df.pop('t') - df['unit'] = 'Mt CO2' + df["variable"] = "Emissions|CO2|" + df.pop("t") + df["unit"] = "Mt CO2" return df - a = rep.convert_pyam('emi:nl-t-ya', 'ya', collapse=collapse_emi) - rep.write(a[0], Path('nf_emissions_CO2_'+model+'_'+scen+'.xlsx')) + a = rep.convert_pyam("emi:nl-t-ya", "ya", collapse=collapse_emi) + rep.write(a[0], Path("nf_emissions_CO2_" + model + "_" + scen + ".xlsx")) # 3. Total inputs (incl. final energy) to fertilizer processes rep.set_filters() - rep.set_filters(t= (newtechnames_ccs + newtechnames), c=['coal', 'gas', 'electr', 'biomass', 'fueloil']) - + rep.set_filters( + t=(newtechnames_ccs + newtechnames), + c=["coal", "gas", "electr", "biomass", "fueloil"], + ) + def collapse_in(df): - df['variable'] = 'Final energy|'+df.pop('c') - df['unit'] = 'GWa' + df["variable"] = "Final energy|" + df.pop("c") + df["unit"] = "GWa" return df - a = rep.convert_pyam('in:nl-ya-c', 'ya', collapse=collapse_in) - rep.write(a[0], Path('nf_input_'+model+'_'+scen+'.xlsx')) - + + a = rep.convert_pyam("in:nl-ya-c", "ya", collapse=collapse_in) + rep.write(a[0], Path("nf_input_" + model + "_" + scen + ".xlsx")) + # 4. Commodity price rep.set_filters() - rep.set_filters(l= ['material_final', 'material_interim']) - + rep.set_filters(l=["material_final", "material_interim"]) + def collapse_N(df): - df.loc[df['c'] == "NH3", 'unit'] = '$/tNH3' - df.loc[df['c'] == "Fertilizer Use|Nitrogen", 'unit'] = '$/tN' - df['variable'] = 'Price|' + df.pop('c') + df.loc[df["c"] == "NH3", "unit"] = "$/tNH3" + df.loc[df["c"] == "Fertilizer Use|Nitrogen", "unit"] = "$/tN" + df["variable"] = "Price|" + df.pop("c") return df - - a = rep.convert_pyam('PRICE_COMMODITY:n-c-y', 'y', collapse=collapse_N) - rep.write(a[0], Path('price_commodity_'+model+'_'+scen+'.xlsx')) + + a = rep.convert_pyam("PRICE_COMMODITY:n-c-y", "y", collapse=collapse_N) + rep.write(a[0], Path("price_commodity_" + model + "_" + scen + ".xlsx")) # 5. Carbon price - if scen!="baseline": + if scen != "baseline": rep.set_filters() - - a = rep.convert_pyam('PRICE_EMISSION', 'y') - rep.write(a[0], Path('price_emission_'+model+'_'+scen+'.xlsx')) + + a = rep.convert_pyam("PRICE_EMISSION", "y") + rep.write(a[0], Path("price_emission_" + model + "_" + scen + ".xlsx")) # Generate individual xlsx @@ -102,67 +124,89 @@ def collapse_N(df): Sc_ref = message_ix.Scenario(mp, model_name, sc) repo = Reporter.from_scenario(Sc_ref) GenerateOutput(model_name, sc, repo) - -# Combine xlsx per each output variable -for cases in ['nf_demand', 'nf_emissions_CO2', 'nf_input', 'price_commodity', 'price_emission']: + +# Combine xlsx per each output variable +for cases in [ + "nf_demand", + "nf_emissions_CO2", + "nf_input", + "price_commodity", + "price_emission", +]: infiles = [] for sc in scen_names: - if sc=="baseline" and cases=='price_emission': + if sc == "baseline" and cases == "price_emission": continue - infiles.append(pd.read_excel(cases + "_"+ model_name +'_' + sc + ".xlsx")) - appended_df = pd.concat(infiles, join='outer', sort=False) - appended_df.to_excel(cases+"-"+model_name+".xlsx", index=False) - - + infiles.append(pd.read_excel(cases + "_" + model_name + "_" + sc + ".xlsx")) + appended_df = pd.concat(infiles, join="outer", sort=False) + appended_df.to_excel(cases + "-" + model_name + ".xlsx", index=False) + + #%% Generate plots from pyam.plotting import OUTSIDE_LEGEND -def plot_NF_result(case='nf_demand', model=model_name, scen='baseline'): + +def plot_NF_result(case="nf_demand", model=model_name, scen="baseline"): """ - Generate PNG plots for each case of ['nf_demand', 'nf_emissions_CO2', 'nf_input', 'price_commodity'] - Data read from the xlsx files created above - """ - if case in ['nf_demand', 'all']: - data = pyam.IamDataFrame(data='nf_demand'+'_'+model+'_'+scen+'.xlsx', encoding='utf-8') - fig, ax = plt.subplots(figsize=(12, 12)) - data.filter(level=0).stack_plot(ax=ax, stack='region') - plt.savefig('./plots/nf_demand'+'_'+model+'_'+scen+'.png') + """ + if case in ["nf_demand", "all"]: + data = pyam.IamDataFrame( + data="nf_demand" + "_" + model + "_" + scen + ".xlsx", encoding="utf-8" + ) + fig, ax = plt.subplots(figsize=(12, 12)) + data.filter(level=0).stack_plot(ax=ax, stack="region") + plt.savefig("./plots/nf_demand" + "_" + model + "_" + scen + ".png") # Pyam currently doesn't allow stack_plot for the next two cases for unknown reasons. - # (https://github.com/IAMconsortium/pyam/issues/296) - if case in ['nf_input', 'all']: - data = pyam.IamDataFrame(data='nf_input'+'_'+model+'_'+scen+'.xlsx', encoding='utf-8') + # (https://github.com/IAMconsortium/pyam/issues/296) + if case in ["nf_input", "all"]: + data = pyam.IamDataFrame( + data="nf_input" + "_" + model + "_" + scen + ".xlsx", encoding="utf-8" + ) # aggregate technologies over regions (get global sums) for v in list(data.variables()): data.aggregate_region(v, append=True) - - fig, ax = plt.subplots(figsize=(12, 12)) -# data.filter(region="World").stack_plot(ax=ax, legend=OUTSIDE_LEGEND['right']) - data.filter(region="World").line_plot(ax=ax, legend=OUTSIDE_LEGEND['right']) - plt.savefig('./plots/nf_input'+'_'+model+'_'+scen+'.png') - - if case in ['nf_emissions_CO2', 'all']: - data = pyam.IamDataFrame(data='nf_emissions_CO2'+'_'+model+'_'+scen+'.xlsx', encoding='utf-8') + + fig, ax = plt.subplots(figsize=(12, 12)) + # data.filter(region="World").stack_plot(ax=ax, legend=OUTSIDE_LEGEND['right']) + data.filter(region="World").line_plot(ax=ax, legend=OUTSIDE_LEGEND["right"]) + plt.savefig("./plots/nf_input" + "_" + model + "_" + scen + ".png") + + if case in ["nf_emissions_CO2", "all"]: + data = pyam.IamDataFrame( + data="nf_emissions_CO2" + "_" + model + "_" + scen + ".xlsx", + encoding="utf-8", + ) for v in list(data.variables()): data.aggregate_region(v, append=True) - - fig, ax = plt.subplots(figsize=(12, 12)) - data.filter(region="World").line_plot(ax=ax, legend=OUTSIDE_LEGEND['right']) - plt.savefig('./plots/nf_emissions_CO2'+'_'+model+'_'+scen+'.png') - - if case in ['price_commodity', 'all']: + + fig, ax = plt.subplots(figsize=(12, 12)) + data.filter(region="World").line_plot(ax=ax, legend=OUTSIDE_LEGEND["right"]) + plt.savefig("./plots/nf_emissions_CO2" + "_" + model + "_" + scen + ".png") + + if case in ["price_commodity", "all"]: # N fertilizer - data = pyam.IamDataFrame(data='price_commodity'+'_'+model+'_'+scen+'.xlsx', encoding='utf-8') - fig, ax = plt.subplots(figsize=(12, 12)) - data.filter(variable='Price|Fertilizer Use|*', region='R11_AFR').line_plot(ax=ax, legend=False) # Identical across regoins through trade - plt.savefig('./plots/price_NF'+'_'+model+'_'+scen+'.png') - - #NH3 - fig, ax = plt.subplots(figsize=(12, 12)) - data.filter(variable='Price|NH3').line_plot(ax=ax, color='region', legend=OUTSIDE_LEGEND['right']) - plt.savefig('./plots/price_NH3'+'_'+model+'_'+scen+'.png') - + data = pyam.IamDataFrame( + data="price_commodity" + "_" + model + "_" + scen + ".xlsx", + encoding="utf-8", + ) + fig, ax = plt.subplots(figsize=(12, 12)) + data.filter(variable="Price|Fertilizer Use|*", region="R11_AFR").line_plot( + ax=ax, legend=False + ) # Identical across regoins through trade + plt.savefig("./plots/price_NF" + "_" + model + "_" + scen + ".png") + + # NH3 + fig, ax = plt.subplots(figsize=(12, 12)) + data.filter(variable="Price|NH3").line_plot( + ax=ax, color="region", legend=OUTSIDE_LEGEND["right"] + ) + plt.savefig("./plots/price_NH3" + "_" + model + "_" + scen + ".png") + + """ scen_names = ["baseline", "NPi2020-con-prim-dir-ncr", @@ -170,17 +214,14 @@ def plot_NF_result(case='nf_demand', model=model_name, scen='baseline'): "NPi2020_400-con-prim-dir-ncr"] """ -cases = ['nf_demand', 'nf_emissions_CO2', 'nf_input', 'price_commodity'] +cases = ["nf_demand", "nf_emissions_CO2", "nf_input", "price_commodity"] # Individual calls -#plot_NF_result(case='nf_demand', scen='NPi2020_1600-con-prim-dir-ncr') -#plot_NF_result(case='price_commodity', scen='NPi2020_1600-con-prim-dir-ncr') -#plot_NF_result(case='nf_input',scen='NPi2020_400-con-prim-dir-ncr') -#plot_NF_result(case='nf_emissions_CO2',scen='NPi2020_400-con-prim-dir-ncr') +# plot_NF_result(case='nf_demand', scen='NPi2020_1600-con-prim-dir-ncr') +# plot_NF_result(case='price_commodity', scen='NPi2020_1600-con-prim-dir-ncr') +# plot_NF_result(case='nf_input',scen='NPi2020_400-con-prim-dir-ncr') +# plot_NF_result(case='nf_emissions_CO2',scen='NPi2020_400-con-prim-dir-ncr') # call for all plots for sc in scen_names: - plot_NF_result(case='all', scen=sc) - - - + plot_NF_result(case="all", scen=sc) diff --git a/message_ix_models/model/material/N-fertilizer/SetupNitrogenBase.py b/message_ix_models/model/material/N-fertilizer/SetupNitrogenBase.py index e23ae0951d..b3195db3eb 100644 --- a/message_ix_models/model/material/N-fertilizer/SetupNitrogenBase.py +++ b/message_ix_models/model/material/N-fertilizer/SetupNitrogenBase.py @@ -5,13 +5,14 @@ This is a temporary script file. """ -# load required packages +# load required packages # import itertools import pandas as pd import numpy as np import matplotlib.pyplot as plt -plt.style.use('ggplot') + +plt.style.use("ggplot") import ixmp as ix import message_ix @@ -19,30 +20,33 @@ import os import time, re -#os.chdir(r'H:\MyDocuments\MESSAGE\message_data') +# os.chdir(r'H:\MyDocuments\MESSAGE\message_data') ##from tools.post_processing.iamc_report_hackathon import report as reporting # -#from message_ix.reporting import Reporter +# from message_ix.reporting import Reporter -os.chdir(r'H:\MyDocuments\MESSAGE\N-fertilizer\code.Global') +os.chdir(r"H:\MyDocuments\MESSAGE\N-fertilizer\code.Global") -#import LoadParams # Load techno-economic param values from the excel file -#exec(open(r'LoadParams.py').read()) +# import LoadParams # Load techno-economic param values from the excel file +# exec(open(r'LoadParams.py').read()) import importlib -#import LoadParams + +# import LoadParams importlib.reload(LoadParams) #%% Set up scope -scen_names = {"baseline" : "NoPolicy", - "NPi2020-con-prim-dir-ncr" : "NPi", - "NPi2020_1000-con-prim-dir-ncr" : "NPi2020_1000", - "NPi2020_400-con-prim-dir-ncr" : "NPi2020_400"} +scen_names = { + "baseline": "NoPolicy", + "NPi2020-con-prim-dir-ncr": "NPi", + "NPi2020_1000-con-prim-dir-ncr": "NPi2020_1000", + "NPi2020_400-con-prim-dir-ncr": "NPi2020_400", +} -run_scen = "baseline" # "NPiREF-con-prim-dir-ncr" +run_scen = "baseline" # "NPiREF-con-prim-dir-ncr" -# details for existing datastructure to be updated and annotation log in database -modelName = "CD_Links_SSP2" +# details for existing datastructure to be updated and annotation log in database +modelName = "CD_Links_SSP2" scenarioName = run_scen # new model name in ix platform @@ -52,53 +56,65 @@ comment = "CD_Links_SSP2 test for new representation of nitrogen cycle" REGIONS = [ - 'R11_AFR', - 'R11_CPA', - 'R11_EEU', - 'R11_FSU', - 'R11_LAM', - 'R11_MEA', - 'R11_NAM', - 'R11_PAO', - 'R11_PAS', - 'R11_SAS', - 'R11_WEU', - 'R11_GLB' ] + "R11_AFR", + "R11_CPA", + "R11_EEU", + "R11_FSU", + "R11_LAM", + "R11_MEA", + "R11_NAM", + "R11_PAO", + "R11_PAS", + "R11_SAS", + "R11_WEU", + "R11_GLB", +] #%% Load scenario -# launch the IX modeling platform using the local default database -mp = ix.Platform(dbprops=r'H:\MyDocuments\MESSAGE\message_ix\config\default.properties') +# launch the IX modeling platform using the local default database +mp = ix.Platform(dbprops=r"H:\MyDocuments\MESSAGE\message_ix\config\default.properties") # Reference scenario Sc_ref = message_ix.Scenario(mp, modelName, scenarioName) -paramList_tec = [x for x in Sc_ref.par_list() if 'technology' in Sc_ref.idx_sets(x)] -params_src = [x for x in paramList_tec if 'solar_i' in set(Sc_ref.par(x)["technology"].tolist())] +paramList_tec = [x for x in Sc_ref.par_list() if "technology" in Sc_ref.idx_sets(x)] +params_src = [ + x for x in paramList_tec if "solar_i" in set(Sc_ref.par(x)["technology"].tolist()) +] #%% Clone Sc_nitro = Sc_ref.clone(newmodelName, newscenarioName) -#Sc_nitro = message_ix.Scenario(mp, newmodelName, newscenarioName) +# Sc_nitro = message_ix.Scenario(mp, newmodelName, newscenarioName) Sc_nitro.remove_solution() Sc_nitro.check_out() - + #%% Add new technologies & commodities -newtechnames = ['biomass_NH3', 'electr_NH3', 'gas_NH3', 'coal_NH3', 'fueloil_NH3', 'NH3_to_N_fertil'] -newcommnames = ['NH3', 'Fertilizer Use|Nitrogen'] #'N-fertilizer' -newlevelnames = ['material_interim', 'material_final'] +newtechnames = [ + "biomass_NH3", + "electr_NH3", + "gas_NH3", + "coal_NH3", + "fueloil_NH3", + "NH3_to_N_fertil", +] +newcommnames = ["NH3", "Fertilizer Use|Nitrogen"] #'N-fertilizer' +newlevelnames = ["material_interim", "material_final"] Sc_nitro.add_set("technology", newtechnames) Sc_nitro.add_set("commodity", newcommnames) Sc_nitro.add_set("level", newlevelnames) -Sc_nitro.add_set("type_tec", 'industry') +Sc_nitro.add_set("type_tec", "industry") -cat_add = pd.DataFrame({ - 'type_tec': 'industry', #'non_energy' not in Russia model - 'technology': newtechnames -}) +cat_add = pd.DataFrame( + { + "type_tec": "industry", #'non_energy' not in Russia model + "technology": newtechnames, + } +) Sc_nitro.add_set("cat_tec", cat_add) @@ -108,311 +124,367 @@ ### NH3 production process for t in newtechnames[0:5]: # output - df = Sc_nitro.par("output", {"technology":["solar_i"]}) # lifetime = 15 year - df['technology'] = t - df['commodity'] = newcommnames[0] - df['level'] = newlevelnames[0] - Sc_nitro.add_par("output", df) - df['commodity'] = 'd_heat' - df['level'] = 'secondary' - df['value'] = LoadParams.output_heat[newtechnames.index(t)] - Sc_nitro.add_par("output", df) - + df = Sc_nitro.par("output", {"technology": ["solar_i"]}) # lifetime = 15 year + df["technology"] = t + df["commodity"] = newcommnames[0] + df["level"] = newlevelnames[0] + Sc_nitro.add_par("output", df) + df["commodity"] = "d_heat" + df["level"] = "secondary" + df["value"] = LoadParams.output_heat[newtechnames.index(t)] + Sc_nitro.add_par("output", df) + # Fuel input - df = df.rename(columns={'time_dest':'time_origin', 'node_dest':'node_origin'}) - if t=='biomass_NH3': - lvl = 'primary' + df = df.rename(columns={"time_dest": "time_origin", "node_dest": "node_origin"}) + if t == "biomass_NH3": + lvl = "primary" else: - lvl = 'secondary' - df['level'] = lvl + lvl = "secondary" + df["level"] = lvl # Non-elec fuels - if t[:-4]!='electr': # electr has only electr input (no other fuel) - df['commodity'] = t[:-4] # removing '_NH3' - df['value'] = LoadParams.input_fuel[newtechnames.index(t)] - Sc_nitro.add_par("input", df) + if t[:-4] != "electr": # electr has only electr input (no other fuel) + df["commodity"] = t[:-4] # removing '_NH3' + df["value"] = LoadParams.input_fuel[newtechnames.index(t)] + Sc_nitro.add_par("input", df) # Electricity input (for any fuels) - df['commodity'] = 'electr' # All have electricity input - df['value'] = LoadParams.input_elec[newtechnames.index(t)] - df['level'] = 'secondary' - Sc_nitro.add_par("input", df) - + df["commodity"] = "electr" # All have electricity input + df["value"] = LoadParams.input_elec[newtechnames.index(t)] + df["level"] = "secondary" + Sc_nitro.add_par("input", df) + # Water input # Not exist in Russia model - CHECK for global model - df['level'] = 'water_supply' # final for feedstock input - df['commodity'] = 'freshwater_supply' # All have electricity input - df['value'] = LoadParams.input_water[newtechnames.index(t)] - Sc_nitro.add_par("input", df) - - df = Sc_nitro.par("technical_lifetime", {"technology":["solar_i"]}) # lifetime = 15 year - df['technology'] = t - Sc_nitro.add_par("technical_lifetime", df) - + df["level"] = "water_supply" # final for feedstock input + df["commodity"] = "freshwater_supply" # All have electricity input + df["value"] = LoadParams.input_water[newtechnames.index(t)] + Sc_nitro.add_par("input", df) + + df = Sc_nitro.par( + "technical_lifetime", {"technology": ["solar_i"]} + ) # lifetime = 15 year + df["technology"] = t + Sc_nitro.add_par("technical_lifetime", df) + # Costs - df = Sc_nitro.par("inv_cost", {"technology":["solar_i"]}) - df['technology'] = t - df['value'] = LoadParams.inv_cost[newtechnames.index(t)] - Sc_nitro.add_par("inv_cost", df) - - df = Sc_nitro.par("fix_cost", {"technology":["solar_i"]}) - df['technology'] = t - df['value'] = LoadParams.fix_cost[newtechnames.index(t)] - Sc_nitro.add_par("fix_cost", df) - - df = Sc_nitro.par("var_cost", {"technology":["solar_i"]}) - df['technology'] = t - df['value'] = LoadParams.var_cost[newtechnames.index(t)] - Sc_nitro.add_par("var_cost", df) - + df = Sc_nitro.par("inv_cost", {"technology": ["solar_i"]}) + df["technology"] = t + df["value"] = LoadParams.inv_cost[newtechnames.index(t)] + Sc_nitro.add_par("inv_cost", df) + + df = Sc_nitro.par("fix_cost", {"technology": ["solar_i"]}) + df["technology"] = t + df["value"] = LoadParams.fix_cost[newtechnames.index(t)] + Sc_nitro.add_par("fix_cost", df) + + df = Sc_nitro.par("var_cost", {"technology": ["solar_i"]}) + df["technology"] = t + df["value"] = LoadParams.var_cost[newtechnames.index(t)] + Sc_nitro.add_par("var_cost", df) + # Emission factor - df = Sc_nitro.par("output", {"technology":["solar_i"]}) - df = df.drop(columns=['node_dest', 'commodity', 'level', 'time']) - df = df.rename(columns={'time_dest':'emission'}) - df['emission'] = 'CO2_transformation' # Check out what it is - df['value'] = LoadParams.emissions[newtechnames.index(t)] - df['technology'] = t - df['unit'] = '???' - Sc_nitro.add_par("emission_factor", df) - df['emission'] = 'CO2' # Check out what it is - df['value'] = 0 # Set the same as CO2_transformation - Sc_nitro.add_par("emission_factor", df) - + df = Sc_nitro.par("output", {"technology": ["solar_i"]}) + df = df.drop(columns=["node_dest", "commodity", "level", "time"]) + df = df.rename(columns={"time_dest": "emission"}) + df["emission"] = "CO2_transformation" # Check out what it is + df["value"] = LoadParams.emissions[newtechnames.index(t)] + df["technology"] = t + df["unit"] = "???" + Sc_nitro.add_par("emission_factor", df) + df["emission"] = "CO2" # Check out what it is + df["value"] = 0 # Set the same as CO2_transformation + Sc_nitro.add_par("emission_factor", df) + # Emission factors in relation (Currently these are more correct values than emission_factor) - df = Sc_nitro.par("relation_activity", {"relation":["CO2_cc"], "technology":["h2_smr"]}) - df['value'] = LoadParams.emissions[newtechnames.index(t)] - df['technology'] = t - df['unit'] = '???' - Sc_nitro.add_par("relation_activity", df) -# df = Sc_nitro.par("relation_activity", {"relation":["CO2_cc"], "technology":t}) -# Sc_nitro.remove_par("relation_activity", df) + df = Sc_nitro.par( + "relation_activity", {"relation": ["CO2_cc"], "technology": ["h2_smr"]} + ) + df["value"] = LoadParams.emissions[newtechnames.index(t)] + df["technology"] = t + df["unit"] = "???" + Sc_nitro.add_par("relation_activity", df) + # df = Sc_nitro.par("relation_activity", {"relation":["CO2_cc"], "technology":t}) + # Sc_nitro.remove_par("relation_activity", df) # Capacity factor - df = Sc_nitro.par("capacity_factor", {"technology":["solar_i"]}) - df['technology'] = t - df['value'] = LoadParams.capacity_factor[newtechnames.index(t)] - Sc_nitro.add_par("capacity_factor", df) + df = Sc_nitro.par("capacity_factor", {"technology": ["solar_i"]}) + df["technology"] = t + df["value"] = LoadParams.capacity_factor[newtechnames.index(t)] + Sc_nitro.add_par("capacity_factor", df) ### N-fertilizer from NH3 (generic) comm = newcommnames[-1] tech = newtechnames[-1] -# output -df = Sc_nitro.par("output", {"technology":["solar_i"]}) -df['technology'] = tech -df['commodity'] = comm #'N-fertilizer' -df['level'] = newlevelnames[-1] #'land_use_reporting' -Sc_nitro.add_par("output", df) - +# output +df = Sc_nitro.par("output", {"technology": ["solar_i"]}) +df["technology"] = tech +df["commodity"] = comm #'N-fertilizer' +df["level"] = newlevelnames[-1] #'land_use_reporting' +Sc_nitro.add_par("output", df) + # input -df = Sc_nitro.par("output", {"technology":["solar_i"]}) -df = df.rename(columns={'time_dest':'time_origin', 'node_dest':'node_origin'}) -df['technology'] = tech -df['level'] = newlevelnames[0] -df['commodity'] = newcommnames[0] #'NH3' -df['value'] = LoadParams.input_fuel[newtechnames.index(tech)] # NH3/N = 17/14 -Sc_nitro.add_par("input", df) - -df = Sc_nitro.par("technical_lifetime", {"technology":["solar_i"]}) # lifetime = 15 year -df['technology'] = tech -Sc_nitro.add_par("technical_lifetime", df) +df = Sc_nitro.par("output", {"technology": ["solar_i"]}) +df = df.rename(columns={"time_dest": "time_origin", "node_dest": "node_origin"}) +df["technology"] = tech +df["level"] = newlevelnames[0] +df["commodity"] = newcommnames[0] #'NH3' +df["value"] = LoadParams.input_fuel[newtechnames.index(tech)] # NH3/N = 17/14 +Sc_nitro.add_par("input", df) + +df = Sc_nitro.par( + "technical_lifetime", {"technology": ["solar_i"]} +) # lifetime = 15 year +df["technology"] = tech +Sc_nitro.add_par("technical_lifetime", df) # Costs -df = Sc_nitro.par("inv_cost", {"technology":["solar_i"]}) -df['value'] = LoadParams.inv_cost[newtechnames.index(tech)] -df['technology'] = tech -Sc_nitro.add_par("inv_cost", df) +df = Sc_nitro.par("inv_cost", {"technology": ["solar_i"]}) +df["value"] = LoadParams.inv_cost[newtechnames.index(tech)] +df["technology"] = tech +Sc_nitro.add_par("inv_cost", df) -df = Sc_nitro.par("fix_cost", {"technology":["solar_i"]}) -df['technology'] = tech -df['value'] = LoadParams.fix_cost[newtechnames.index(tech)] -Sc_nitro.add_par("fix_cost", df) +df = Sc_nitro.par("fix_cost", {"technology": ["solar_i"]}) +df["technology"] = tech +df["value"] = LoadParams.fix_cost[newtechnames.index(tech)] +Sc_nitro.add_par("fix_cost", df) -df = Sc_nitro.par("var_cost", {"technology":["solar_i"]}) -df['technology'] = tech -df['value'] = LoadParams.var_cost[newtechnames.index(tech)] -Sc_nitro.add_par("var_cost", df) +df = Sc_nitro.par("var_cost", {"technology": ["solar_i"]}) +df["technology"] = tech +df["value"] = LoadParams.var_cost[newtechnames.index(tech)] +Sc_nitro.add_par("var_cost", df) # Emission factor (<0 for this) """ Urea applied in the field will emit all CO2 back, so we don't need this. Source: https://ammoniaindustry.com/urea-production-is-not-carbon-sequestration/#targetText=To%20make%20urea%2C%20fertilizer%20producers,through%20the%20production%20of%20urea. """ -#df = Sc_nitro.par("output", {"technology":["solar_i"]}) -#df = df.drop(columns=['node_dest', 'commodity', 'level', 'time']) -#df = df.rename(columns={'time_dest':'emission'}) -#df['emission'] = 'CO2_transformation' # Check out what it is -#df['value'] = LoadParams.emissions[newtechnames.index(tech)] -#df['technology'] = tech -#df['unit'] = '???' -#Sc_nitro.add_par("emission_factor", df) -#df['emission'] = 'CO2' # Check out what it is -#Sc_nitro.add_par("emission_factor", df) - - -#%% Copy some background parameters# - -par_bgnd = [x for x in params_src if '_up' in x] + [x for x in params_src if '_lo' in x] -par_bgnd = list(set(par_bgnd) - set(['level_cost_activity_soft_lo', 'level_cost_activity_soft_up', 'growth_activity_lo'])) #, 'soft_activity_lo', 'soft_activity_up'])) +# df = Sc_nitro.par("output", {"technology":["solar_i"]}) +# df = df.drop(columns=['node_dest', 'commodity', 'level', 'time']) +# df = df.rename(columns={'time_dest':'emission'}) +# df['emission'] = 'CO2_transformation' # Check out what it is +# df['value'] = LoadParams.emissions[newtechnames.index(tech)] +# df['technology'] = tech +# df['unit'] = '???' +# Sc_nitro.add_par("emission_factor", df) +# df['emission'] = 'CO2' # Check out what it is +# Sc_nitro.add_par("emission_factor", df) + + +#%% Copy some background parameters# + +par_bgnd = [x for x in params_src if "_up" in x] + [x for x in params_src if "_lo" in x] +par_bgnd = list( + set(par_bgnd) + - set( + [ + "level_cost_activity_soft_lo", + "level_cost_activity_soft_up", + "growth_activity_lo", + ] + ) +) # , 'soft_activity_lo', 'soft_activity_up'])) for t in par_bgnd[:-1]: - df = Sc_nitro.par(t, {"technology":["solar_i"]}) # lifetime = 15 year + df = Sc_nitro.par(t, {"technology": ["solar_i"]}) # lifetime = 15 year for q in newtechnames: - df['technology'] = q - Sc_nitro.add_par(t, df) - -df = Sc_nitro.par('initial_activity_lo', {"technology":["gas_extr_mpen"]}) + df["technology"] = q + Sc_nitro.add_par(t, df) + +df = Sc_nitro.par("initial_activity_lo", {"technology": ["gas_extr_mpen"]}) for q in newtechnames: - df['technology'] = q - Sc_nitro.add_par('initial_activity_lo', df) - -df = Sc_nitro.par('growth_activity_lo', {"technology":["gas_extr_mpen"]}) + df["technology"] = q + Sc_nitro.add_par("initial_activity_lo", df) + +df = Sc_nitro.par("growth_activity_lo", {"technology": ["gas_extr_mpen"]}) for q in newtechnames: - df['technology'] = q - Sc_nitro.add_par('growth_activity_lo', df) + df["technology"] = q + Sc_nitro.add_par("growth_activity_lo", df) #%% Process the regional historical activities - -fs_GLO = LoadParams.feedshare_GLO.copy() + +fs_GLO = LoadParams.feedshare_GLO.copy() fs_GLO.insert(1, "bio_pct", 0) fs_GLO.insert(2, "elec_pct", 0) # 17/14 NH3:N ratio, to get NH3 activity based on N demand => No NH3 loss assumed during production -fs_GLO.iloc[:,1:6] = LoadParams.input_fuel[5] * fs_GLO.iloc[:,1:6] +fs_GLO.iloc[:, 1:6] = LoadParams.input_fuel[5] * fs_GLO.iloc[:, 1:6] fs_GLO.insert(6, "NH3_to_N", 1) # Share of feedstocks for NH3 prodution (based on 2010 => Assumed fixed for any past years) -feedshare = fs_GLO.sort_values(['Region']).set_index('Region').drop('R11_GLB') - +feedshare = fs_GLO.sort_values(["Region"]).set_index("Region").drop("R11_GLB") + # Get historical N demand from SSP2-nopolicy (may need to vary for diff scenarios) N_demand_raw = LoadParams.N_demand_GLO.copy() -N_demand = N_demand_raw[N_demand_raw.Scenario=="NoPolicy"].reset_index().loc[0:10,2010] # 2010 tot N demand +N_demand = ( + N_demand_raw[N_demand_raw.Scenario == "NoPolicy"].reset_index().loc[0:10, 2010] +) # 2010 tot N demand N_demand = N_demand.repeat(len(newtechnames)) act2010 = (feedshare.values.flatten() * N_demand).reset_index(drop=True) - #%% Historical activities/capacities - Region specific -df = Sc_nitro.par("historical_activity").iloc[0:len(newtechnames)*(len(REGIONS)-1),] # Choose whatever same number of rows -df['technology'] = newtechnames * (len(REGIONS)-1) -df['value'] = act2010 # total NH3 or N in Mt 2010 FAO Russia -df['year_act'] = 2010 -df['node_loc'] = [y for x in REGIONS[:-1] for y in (x,)*len(newtechnames)] -df['unit'] = 'Tg N/yr' # Separate unit needed for NH3? +df = Sc_nitro.par("historical_activity").iloc[ + 0 : len(newtechnames) * (len(REGIONS) - 1), +] # Choose whatever same number of rows +df["technology"] = newtechnames * (len(REGIONS) - 1) +df["value"] = act2010 # total NH3 or N in Mt 2010 FAO Russia +df["year_act"] = 2010 +df["node_loc"] = [y for x in REGIONS[:-1] for y in (x,) * len(newtechnames)] +df["unit"] = "Tg N/yr" # Separate unit needed for NH3? Sc_nitro.add_par("historical_activity", df) # 2015 activity necessary if this is 5-year step scenario -#df['value'] = act2015 # total NH3 or N in Mt 2010 FAO Russia -#df['year_act'] = 2015 -#Sc_nitro.add_par("historical_activity", df) - -life = Sc_nitro.par("technical_lifetime", {"technology":["gas_NH3"]}).value[0] - -df = Sc_nitro.par("historical_new_capacity").iloc[0:len(newtechnames)*(len(REGIONS)-1),] # whatever -df['technology'] = newtechnames * (len(REGIONS)-1) -df['value'] = [x * 1/life/LoadParams.capacity_factor[0] for x in act2010] # Assume 1/lifetime (=15yr) is built each year -df['year_vtg'] = 2010 -df['node_loc'] = [y for x in REGIONS[:-1] for y in (x,)*len(newtechnames)] -df['unit'] = 'Tg N/yr' +# df['value'] = act2015 # total NH3 or N in Mt 2010 FAO Russia +# df['year_act'] = 2015 +# Sc_nitro.add_par("historical_activity", df) + +life = Sc_nitro.par("technical_lifetime", {"technology": ["gas_NH3"]}).value[0] + +df = Sc_nitro.par("historical_new_capacity").iloc[ + 0 : len(newtechnames) * (len(REGIONS) - 1), +] # whatever +df["technology"] = newtechnames * (len(REGIONS) - 1) +df["value"] = [ + x * 1 / life / LoadParams.capacity_factor[0] for x in act2010 +] # Assume 1/lifetime (=15yr) is built each year +df["year_vtg"] = 2010 +df["node_loc"] = [y for x in REGIONS[:-1] for y in (x,) * len(newtechnames)] +df["unit"] = "Tg N/yr" Sc_nitro.add_par("historical_new_capacity", df) - #%% Secure feedstock balance (foil_fs, gas_fs, coal_fs) loil_fs? # Select only the model years -years = set(map(int, list(Sc_nitro.set('year')))) & set(N_demand_raw) # get intersection +years = set(map(int, list(Sc_nitro.set("year")))) & set( + N_demand_raw +) # get intersection -#scenarios = N_demand_FSU.Scenario # Scenario names (SSP2) -N_demand = N_demand_raw.loc[N_demand_raw.Scenario=="NoPolicy",].drop(35) +# scenarios = N_demand_FSU.Scenario # Scenario names (SSP2) +N_demand = N_demand_raw.loc[N_demand_raw.Scenario == "NoPolicy",].drop(35) N_demand = N_demand[N_demand.columns.intersection(years)] -N_demand[2110] = N_demand[2100] # Make up 2110 data (for now) in Mt/year - -# Adjust i_feed demand -demand_fs_org = Sc_nitro.par('demand', {"commodity":["i_feed"]}) -#demand_fs_org['value'] = demand_fs_org['value'] * 0.9 # (10% of total feedstock for Ammonia assumed) - REFINE -df = demand_fs_org.loc[demand_fs_org.year==2010,:].join(LoadParams.N_energy.set_index('node'), on='node') -sh = pd.DataFrame( {'node': demand_fs_org.loc[demand_fs_org.year==2010, 'node'], - 'r_feed': df.totENE / df.value}) # share of NH3 energy among total feedstock (no trade assumed) -df = demand_fs_org.join(sh.set_index('node'), on='node') -df.value *= 1 - df.r_feed # Carve out the same % from tot i_feed values -df = df.drop('r_feed', axis=1) +N_demand[2110] = N_demand[2100] # Make up 2110 data (for now) in Mt/year + +# Adjust i_feed demand +demand_fs_org = Sc_nitro.par("demand", {"commodity": ["i_feed"]}) +# demand_fs_org['value'] = demand_fs_org['value'] * 0.9 # (10% of total feedstock for Ammonia assumed) - REFINE +df = demand_fs_org.loc[demand_fs_org.year == 2010, :].join( + LoadParams.N_energy.set_index("node"), on="node" +) +sh = pd.DataFrame( + { + "node": demand_fs_org.loc[demand_fs_org.year == 2010, "node"], + "r_feed": df.totENE / df.value, + } +) # share of NH3 energy among total feedstock (no trade assumed) +df = demand_fs_org.join(sh.set_index("node"), on="node") +df.value *= 1 - df.r_feed # Carve out the same % from tot i_feed values +df = df.drop("r_feed", axis=1) Sc_nitro.add_par("demand", df) # Now link the GLOBIOM input (now endogenous) -df = Sc_nitro.par("land_output", {"commodity":newcommnames[-1]}) -df['level'] = newlevelnames[-1] -Sc_nitro.add_par("land_input", df) +df = Sc_nitro.par("land_output", {"commodity": newcommnames[-1]}) +df["level"] = newlevelnames[-1] +Sc_nitro.add_par("land_input", df) #%% Add CCS tecs #%% Add tecs to set -tec_for_ccs = list(newtechnames[i] for i in [0,2,3,4]) -newtechnames_ccs = list(map(lambda x:str(x)+'_ccs', tec_for_ccs)) #include biomass in CCS, newtechnames[2:5])) +tec_for_ccs = list(newtechnames[i] for i in [0, 2, 3, 4]) +newtechnames_ccs = list( + map(lambda x: str(x) + "_ccs", tec_for_ccs) +) # include biomass in CCS, newtechnames[2:5])) Sc_nitro.add_set("technology", newtechnames_ccs) -cat_add = pd.DataFrame({ - 'type_tec': 'industry', #'non_energy' not in Russia model - 'technology': newtechnames_ccs -}) +cat_add = pd.DataFrame( + { + "type_tec": "industry", #'non_energy' not in Russia model + "technology": newtechnames_ccs, + } +) Sc_nitro.add_set("cat_tec", cat_add) - #%% Implement technologies - only for non-elec NH3 tecs # input and output # additional electricity input for CCS operation df = Sc_nitro.par("input") -df = df[df['technology'].isin(tec_for_ccs)] -df['technology'] = df['technology'] + '_ccs' # Rename the technologies -df.loc[df['commodity']=='electr', ['value']] = df.loc[df['commodity']=='electr', ['value']] + 0.005 # TUNE THIS # Add electricity input for CCS -Sc_nitro.add_par("input", df) +df = df[df["technology"].isin(tec_for_ccs)] +df["technology"] = df["technology"] + "_ccs" # Rename the technologies +df.loc[df["commodity"] == "electr", ["value"]] = ( + df.loc[df["commodity"] == "electr", ["value"]] + 0.005 +) # TUNE THIS # Add electricity input for CCS +Sc_nitro.add_par("input", df) df = Sc_nitro.par("output") -df = df[df['technology'].isin(tec_for_ccs)] -df['technology'] = df['technology'] + '_ccs' # Rename the technologies -Sc_nitro.add_par("output", df) +df = df[df["technology"].isin(tec_for_ccs)] +df["technology"] = df["technology"] + "_ccs" # Rename the technologies +Sc_nitro.add_par("output", df) # Emission factors (emission_factor) df = Sc_nitro.par("emission_factor") -biomass_ef = 0.942 # MtC/GWa from 109.6 kgCO2/GJ biomass (https://www.rvo.nl/sites/default/files/2013/10/Vreuls%202005%20NL%20Energiedragerlijst%20-%20Update.pdf) +biomass_ef = 0.942 # MtC/GWa from 109.6 kgCO2/GJ biomass (https://www.rvo.nl/sites/default/files/2013/10/Vreuls%202005%20NL%20Energiedragerlijst%20-%20Update.pdf) # extract vent vs. storage ratio from h2_smr tec -h2_smr_vent_ratio = -Sc_nitro.par("relation_activity", {"relation":["CO2_cc"], "technology":["h2_smr_ccs"]}).value[0] \ - / Sc_nitro.par("relation_activity", {"relation":["CO2_Emission"], "technology":["h2_smr_ccs"]}).value[0] -h2_coal_vent_ratio = -Sc_nitro.par("relation_activity", {"relation":["CO2_cc"], "technology":["h2_coal_ccs"]}).value[0] \ - / Sc_nitro.par("relation_activity", {"relation":["CO2_Emission"], "technology":["h2_coal_ccs"]}).value[0] -h2_bio_vent_ratio = 1 + Sc_nitro.par("relation_activity", {"relation":["CO2_cc"], "technology":["h2_bio_ccs"]}).value[0] \ - / (Sc_nitro.par("input", {"technology":["h2_bio_ccs"], "commodity":["biomass"]}).value[0] * biomass_ef) +h2_smr_vent_ratio = ( + -Sc_nitro.par( + "relation_activity", {"relation": ["CO2_cc"], "technology": ["h2_smr_ccs"]} + ).value[0] + / Sc_nitro.par( + "relation_activity", + {"relation": ["CO2_Emission"], "technology": ["h2_smr_ccs"]}, + ).value[0] +) +h2_coal_vent_ratio = ( + -Sc_nitro.par( + "relation_activity", {"relation": ["CO2_cc"], "technology": ["h2_coal_ccs"]} + ).value[0] + / Sc_nitro.par( + "relation_activity", + {"relation": ["CO2_Emission"], "technology": ["h2_coal_ccs"]}, + ).value[0] +) +h2_bio_vent_ratio = 1 + Sc_nitro.par( + "relation_activity", {"relation": ["CO2_cc"], "technology": ["h2_bio_ccs"]} +).value[0] / ( + Sc_nitro.par( + "input", {"technology": ["h2_bio_ccs"], "commodity": ["biomass"]} + ).value[0] + * biomass_ef +) # ef for NG -df_gas = df[df['technology']=='gas_NH3'] -ef_org = np.asarray(df_gas.loc[df_gas['emission']=='CO2_transformation', ['value']]) -df_gas = df_gas.assign(technology = df_gas.technology + '_ccs') -df_gas.loc[(df.emission=='CO2_transformation'), 'value'] = ef_org*h2_smr_vent_ratio -df_gas.loc[(df.emission=='CO2'), 'value'] = ef_org*(h2_smr_vent_ratio-1) -Sc_nitro.add_par("emission_factor", df_gas) +df_gas = df[df["technology"] == "gas_NH3"] +ef_org = np.asarray(df_gas.loc[df_gas["emission"] == "CO2_transformation", ["value"]]) +df_gas = df_gas.assign(technology=df_gas.technology + "_ccs") +df_gas.loc[(df.emission == "CO2_transformation"), "value"] = ef_org * h2_smr_vent_ratio +df_gas.loc[(df.emission == "CO2"), "value"] = ef_org * (h2_smr_vent_ratio - 1) +Sc_nitro.add_par("emission_factor", df_gas) # ef for oil/coal -df_coal = df[df['technology'].isin(tec_for_ccs[2:])] -ef_org = np.asarray(df_coal.loc[df_coal['emission']=='CO2_transformation', ['value']]) -df_coal = df_coal.assign(technology = df_coal.technology + '_ccs') -df_coal.loc[(df.emission=='CO2_transformation'), 'value'] = ef_org*h2_coal_vent_ratio -df_coal.loc[(df.emission=='CO2'), 'value'] = ef_org*(h2_coal_vent_ratio-1) -Sc_nitro.add_par("emission_factor", df_coal) +df_coal = df[df["technology"].isin(tec_for_ccs[2:])] +ef_org = np.asarray(df_coal.loc[df_coal["emission"] == "CO2_transformation", ["value"]]) +df_coal = df_coal.assign(technology=df_coal.technology + "_ccs") +df_coal.loc[(df.emission == "CO2_transformation"), "value"] = ( + ef_org * h2_coal_vent_ratio +) +df_coal.loc[(df.emission == "CO2"), "value"] = ef_org * (h2_coal_vent_ratio - 1) +Sc_nitro.add_par("emission_factor", df_coal) # ef for biomass -df_bio = df[df['technology']=='biomass_NH3'] -biomass_input = Sc_nitro.par("input", {"technology":["biomass_NH3"], "commodity":["biomass"]}).value[0] -df_bio = df_bio.assign(technology = df_bio.technology + '_ccs') -df_bio['value'] = biomass_input*(h2_bio_vent_ratio-1)*biomass_ef -Sc_nitro.add_par("emission_factor", df_bio) - - +df_bio = df[df["technology"] == "biomass_NH3"] +biomass_input = Sc_nitro.par( + "input", {"technology": ["biomass_NH3"], "commodity": ["biomass"]} +).value[0] +df_bio = df_bio.assign(technology=df_bio.technology + "_ccs") +df_bio["value"] = biomass_input * (h2_bio_vent_ratio - 1) * biomass_ef +Sc_nitro.add_par("emission_factor", df_bio) # Investment cost @@ -420,210 +492,275 @@ df = Sc_nitro.par("inv_cost") # Get inv_cost ratio between std and ccs for different h2 feedstocks -a = df[df['technology'].str.contains("h2_smr")].sort_values(["technology", "year_vtg"]) # To get cost ratio for std vs CCS -r_ccs_cost_gas = a.loc[(a.technology=='h2_smr') & (a.year_vtg >=2020)]['value'].values / a.loc[(a['technology']=='h2_smr_ccs') & (a.year_vtg >=2020)]['value'].values +a = df[df["technology"].str.contains("h2_smr")].sort_values( + ["technology", "year_vtg"] +) # To get cost ratio for std vs CCS +r_ccs_cost_gas = ( + a.loc[(a.technology == "h2_smr") & (a.year_vtg >= 2020)]["value"].values + / a.loc[(a["technology"] == "h2_smr_ccs") & (a.year_vtg >= 2020)]["value"].values +) r_ccs_cost_gas = r_ccs_cost_gas.mean() -a = df[df['technology'].str.contains("h2_coal")].sort_values(["technology", "year_vtg"]) # To get cost ratio for std vs CCS -r_ccs_cost_coal = a.loc[(a.technology=='h2_coal') & (a.year_vtg >=2020)]['value'].values / a.loc[(a['technology']=='h2_coal_ccs') & (a.year_vtg >=2020)]['value'].values +a = df[df["technology"].str.contains("h2_coal")].sort_values( + ["technology", "year_vtg"] +) # To get cost ratio for std vs CCS +r_ccs_cost_coal = ( + a.loc[(a.technology == "h2_coal") & (a.year_vtg >= 2020)]["value"].values + / a.loc[(a["technology"] == "h2_coal_ccs") & (a.year_vtg >= 2020)]["value"].values +) r_ccs_cost_coal = r_ccs_cost_coal.mean() -a = df[df['technology'].str.contains("h2_bio")].sort_values(["technology", "year_vtg"]) # To get cost ratio for std vs CCS -a = a[a.year_vtg > 2025] # h2_bio_ccs only available from 2030 -r_ccs_cost_bio = a.loc[(a.technology=='h2_bio') & (a.year_vtg >=2020)]['value'].values / a.loc[(a['technology']=='h2_bio_ccs') & (a.year_vtg >=2020)]['value'].values +a = df[df["technology"].str.contains("h2_bio")].sort_values( + ["technology", "year_vtg"] +) # To get cost ratio for std vs CCS +a = a[a.year_vtg > 2025] # h2_bio_ccs only available from 2030 +r_ccs_cost_bio = ( + a.loc[(a.technology == "h2_bio") & (a.year_vtg >= 2020)]["value"].values + / a.loc[(a["technology"] == "h2_bio_ccs") & (a.year_vtg >= 2020)]["value"].values +) r_ccs_cost_bio = r_ccs_cost_bio.mean() -df_gas = df[df['technology']=='gas_NH3'] -df_gas['technology'] = df_gas['technology'] + '_ccs' # Rename the technologies -df_gas['value'] = df_gas['value']/r_ccs_cost_gas -Sc_nitro.add_par("inv_cost", df_gas) - -df_coal = df[df['technology'].isin(tec_for_ccs[2:])] -df_coal['technology'] = df_coal['technology'] + '_ccs' # Rename the technologies -df_coal['value'] = df_coal['value']/r_ccs_cost_coal -Sc_nitro.add_par("inv_cost", df_coal) - -df_bio = df[df['technology']=='biomass_NH3'] -df_bio['technology'] = df_bio['technology'] + '_ccs' # Rename the technologies -df_bio['value'] = df_bio['value']/r_ccs_cost_bio -Sc_nitro.add_par("inv_cost", df_bio) +df_gas = df[df["technology"] == "gas_NH3"] +df_gas["technology"] = df_gas["technology"] + "_ccs" # Rename the technologies +df_gas["value"] = df_gas["value"] / r_ccs_cost_gas +Sc_nitro.add_par("inv_cost", df_gas) +df_coal = df[df["technology"].isin(tec_for_ccs[2:])] +df_coal["technology"] = df_coal["technology"] + "_ccs" # Rename the technologies +df_coal["value"] = df_coal["value"] / r_ccs_cost_coal +Sc_nitro.add_par("inv_cost", df_coal) +df_bio = df[df["technology"] == "biomass_NH3"] +df_bio["technology"] = df_bio["technology"] + "_ccs" # Rename the technologies +df_bio["value"] = df_bio["value"] / r_ccs_cost_bio +Sc_nitro.add_par("inv_cost", df_bio) # Fixed cost df = Sc_nitro.par("fix_cost") -df_gas = df[df['technology']=='gas_NH3'] -df_gas['technology'] = df_gas['technology'] + '_ccs' # Rename the technologies -df_gas['value'] = df_gas['value']/r_ccs_cost_gas # Same scaling (same 4% of inv_cost in the end) -Sc_nitro.add_par("fix_cost", df_gas) - -df_coal = df[df['technology'].isin(tec_for_ccs[2:])] -df_coal['technology'] = df_coal['technology'] + '_ccs' # Rename the technologies -df_coal['value'] = df_coal['value']/r_ccs_cost_coal # Same scaling (same 4% of inv_cost in the end) -Sc_nitro.add_par("fix_cost", df_coal) - -df_bio = df[df['technology']=='biomass_NH3'] -df_bio['technology'] = df_bio['technology'] + '_ccs' # Rename the technologies -df_bio['value'] = df_bio['value']/r_ccs_cost_bio # Same scaling (same 4% of inv_cost in the end) -Sc_nitro.add_par("fix_cost", df_bio) - +df_gas = df[df["technology"] == "gas_NH3"] +df_gas["technology"] = df_gas["technology"] + "_ccs" # Rename the technologies +df_gas["value"] = ( + df_gas["value"] / r_ccs_cost_gas +) # Same scaling (same 4% of inv_cost in the end) +Sc_nitro.add_par("fix_cost", df_gas) + +df_coal = df[df["technology"].isin(tec_for_ccs[2:])] +df_coal["technology"] = df_coal["technology"] + "_ccs" # Rename the technologies +df_coal["value"] = ( + df_coal["value"] / r_ccs_cost_coal +) # Same scaling (same 4% of inv_cost in the end) +Sc_nitro.add_par("fix_cost", df_coal) + +df_bio = df[df["technology"] == "biomass_NH3"] +df_bio["technology"] = df_bio["technology"] + "_ccs" # Rename the technologies +df_bio["value"] = ( + df_bio["value"] / r_ccs_cost_bio +) # Same scaling (same 4% of inv_cost in the end) +Sc_nitro.add_par("fix_cost", df_bio) # Emission factors (Relation) - + # Gas df = Sc_nitro.par("relation_activity") -df_gas = df[df['technology']=='gas_NH3'] # Originally all CO2_cc (truly emitted, bottom-up) +df_gas = df[ + df["technology"] == "gas_NH3" +] # Originally all CO2_cc (truly emitted, bottom-up) ef_org = df_gas.value.values.copy() df_gas.value = ef_org * h2_smr_vent_ratio -df_gas = df_gas.assign(technology = df_gas.technology + '_ccs') -Sc_nitro.add_par("relation_activity", df_gas) +df_gas = df_gas.assign(technology=df_gas.technology + "_ccs") +Sc_nitro.add_par("relation_activity", df_gas) -df_gas.value = ef_org * (h2_smr_vent_ratio-1) # Negative -df_gas.relation = 'CO2_Emission' -Sc_nitro.add_par("relation_activity", df_gas) +df_gas.value = ef_org * (h2_smr_vent_ratio - 1) # Negative +df_gas.relation = "CO2_Emission" +Sc_nitro.add_par("relation_activity", df_gas) # Coal / Oil -df_coal = df[df['technology'].isin(tec_for_ccs[2:])] # Originally all CO2_cc (truly emitted, bottom-up) +df_coal = df[ + df["technology"].isin(tec_for_ccs[2:]) +] # Originally all CO2_cc (truly emitted, bottom-up) ef_org = df_coal.value.values.copy() df_coal.value = ef_org * h2_coal_vent_ratio -df_coal = df_coal.assign(technology = df_coal.technology + '_ccs') -Sc_nitro.add_par("relation_activity", df_coal) +df_coal = df_coal.assign(technology=df_coal.technology + "_ccs") +Sc_nitro.add_par("relation_activity", df_coal) -df_coal.value = ef_org * (h2_coal_vent_ratio-1) # Negative -df_coal.relation = 'CO2_Emission' -Sc_nitro.add_par("relation_activity", df_coal) +df_coal.value = ef_org * (h2_coal_vent_ratio - 1) # Negative +df_coal.relation = "CO2_Emission" +Sc_nitro.add_par("relation_activity", df_coal) # Biomass -df_bio = df[df['technology']=='biomass_NH3'] # Originally all CO2.cc (truly emitted, bottom-up) -df_bio.value = biomass_input*(h2_bio_vent_ratio-1)*biomass_ef -df_bio = df_bio.assign(technology = df_bio.technology + '_ccs') -Sc_nitro.add_par("relation_activity", df_bio) +df_bio = df[ + df["technology"] == "biomass_NH3" +] # Originally all CO2.cc (truly emitted, bottom-up) +df_bio.value = biomass_input * (h2_bio_vent_ratio - 1) * biomass_ef +df_bio = df_bio.assign(technology=df_bio.technology + "_ccs") +Sc_nitro.add_par("relation_activity", df_bio) -df_bio.relation = 'CO2_Emission' -Sc_nitro.add_par("relation_activity", df_bio) +df_bio.relation = "CO2_Emission" +Sc_nitro.add_par("relation_activity", df_bio) #%% Copy some bgnd parameters (values identical to _NH3 tecs) -par_bgnd_ccs = par_bgnd + ['technical_lifetime', 'capacity_factor', 'var_cost', 'growth_activity_lo'] +par_bgnd_ccs = par_bgnd + [ + "technical_lifetime", + "capacity_factor", + "var_cost", + "growth_activity_lo", +] for t in par_bgnd_ccs: df = Sc_nitro.par(t) - df = df[df['technology'].isin(tec_for_ccs)] - df['technology'] = df['technology'] + '_ccs' # Rename the technologies - Sc_nitro.add_par(t, df) + df = df[df["technology"].isin(tec_for_ccs)] + df["technology"] = df["technology"] + "_ccs" # Rename the technologies + Sc_nitro.add_par(t, df) #%% Shift model horizon for policy scenarios - + if run_scen != "baseline": Sc_nitro_base = message_ix.Scenario(mp, "JM_GLB_NITRO", "NoPolicy") - + import Utilities.shift_model_horizon as shift_year + if scen_names[run_scen] == "NPi": fyear = 2040 else: fyear = 2030 - shift_year(Sc_nitro, Sc_nitro_base, fyear, newtechnames_ccs + newtechnames) - + shift_year(Sc_nitro, Sc_nitro_base, fyear, newtechnames_ccs + newtechnames) + + +#%% Regional cost calibration -#%% Regional cost calibration - -# Scaler based on WEO 2014 (based on IGCC tec) +# Scaler based on WEO 2014 (based on IGCC tec) scaler_cost = pd.DataFrame( - {'scaler_std': [1.00, 0.81, 0.96, 0.96, 0.96, 0.88, 0.81, 0.65, 0.42, 0.65, 1.12], - 'scaler_ccs': [1.00, 0.80, 0.98, 0.92, 0.92, 0.82, 0.80, 0.70, 0.61, 0.70, 1.05], # NA values are replaced with WEO 2014 values. (LAM & MEA = 0.80) - 'node_loc': ['R11_NAM', - 'R11_LAM', - 'R11_WEU', - 'R11_EEU', - 'R11_FSU', - 'R11_AFR', - 'R11_MEA', - 'R11_SAS', - 'R11_CPA', - 'R11_PAS', - 'R11_PAO']}) - -#tec_scale = (newtechnames + newtechnames_ccs) -tec_scale = [e for e in newtechnames if e not in ('NH3_to_N_fertil', 'electr_NH3')] + { + "scaler_std": [ + 1.00, + 0.81, + 0.96, + 0.96, + 0.96, + 0.88, + 0.81, + 0.65, + 0.42, + 0.65, + 1.12, + ], + "scaler_ccs": [ + 1.00, + 0.80, + 0.98, + 0.92, + 0.92, + 0.82, + 0.80, + 0.70, + 0.61, + 0.70, + 1.05, + ], # NA values are replaced with WEO 2014 values. (LAM & MEA = 0.80) + "node_loc": [ + "R11_NAM", + "R11_LAM", + "R11_WEU", + "R11_EEU", + "R11_FSU", + "R11_AFR", + "R11_MEA", + "R11_SAS", + "R11_CPA", + "R11_PAS", + "R11_PAO", + ], + } +) + +# tec_scale = (newtechnames + newtechnames_ccs) +tec_scale = [e for e in newtechnames if e not in ("NH3_to_N_fertil", "electr_NH3")] # Scale all NH3 tecs in each region with the scaler -for t in tec_scale: - for p in ['inv_cost', 'fix_cost', 'var_cost']: - df = Sc_nitro.par(p, {"technology":t}) - temp = df.join(scaler_cost.set_index('node_loc'), on='node_loc') +for t in tec_scale: + for p in ["inv_cost", "fix_cost", "var_cost"]: + df = Sc_nitro.par(p, {"technology": t}) + temp = df.join(scaler_cost.set_index("node_loc"), on="node_loc") df.value = temp.value * temp.scaler_std - Sc_nitro.add_par(p, df) + Sc_nitro.add_par(p, df) -for t in newtechnames_ccs: - for p in ['inv_cost', 'fix_cost', 'var_cost']: - df = Sc_nitro.par(p, {"technology":t}) - temp = df.join(scaler_cost.set_index('node_loc'), on='node_loc') +for t in newtechnames_ccs: + for p in ["inv_cost", "fix_cost", "var_cost"]: + df = Sc_nitro.par(p, {"technology": t}) + temp = df.join(scaler_cost.set_index("node_loc"), on="node_loc") df.value = temp.value * temp.scaler_ccs - Sc_nitro.add_par(p, df) - + Sc_nitro.add_par(p, df) + # For CPA and SAS, experiment to make the coal_NH3 cheaper than gas_NH3 -scalers = [[0.66*0.91, 1, 0.75*0.9], [0.59, 1, 1]] # f, g, c : Scale based on the original techno-economic assumptions -reg2scale = ['R11_CPA', 'R11_SAS'] -for p in ['inv_cost', 'fix_cost']:#, 'var_cost']: +scalers = [ + [0.66 * 0.91, 1, 0.75 * 0.9], + [0.59, 1, 1], +] # f, g, c : Scale based on the original techno-economic assumptions +reg2scale = ["R11_CPA", "R11_SAS"] +for p in ["inv_cost", "fix_cost"]: # , 'var_cost']: for r in reg2scale: - df_c = Sc_nitro.par(p, {"technology":'coal_NH3', "node_loc":r}) - df_g = Sc_nitro.par(p, {"technology":'gas_NH3', "node_loc":r}) - df_f = Sc_nitro.par(p, {"technology":'fueloil_NH3', "node_loc":r}) - - df_cc = Sc_nitro.par(p, {"technology":'coal_NH3_ccs', "node_loc":r}) - df_gc = Sc_nitro.par(p, {"technology":'gas_NH3_ccs', "node_loc":r}) - df_fc = Sc_nitro.par(p, {"technology":'fueloil_NH3_ccs', "node_loc":r}) - - df_fc.value *= scalers[reg2scale.index(r)][0] # Gas/fueloil cost the same as coal + df_c = Sc_nitro.par(p, {"technology": "coal_NH3", "node_loc": r}) + df_g = Sc_nitro.par(p, {"technology": "gas_NH3", "node_loc": r}) + df_f = Sc_nitro.par(p, {"technology": "fueloil_NH3", "node_loc": r}) + + df_cc = Sc_nitro.par(p, {"technology": "coal_NH3_ccs", "node_loc": r}) + df_gc = Sc_nitro.par(p, {"technology": "gas_NH3_ccs", "node_loc": r}) + df_fc = Sc_nitro.par(p, {"technology": "fueloil_NH3_ccs", "node_loc": r}) + + df_fc.value *= scalers[reg2scale.index(r)][ + 0 + ] # Gas/fueloil cost the same as coal df_gc.value *= scalers[reg2scale.index(r)][1] df_cc.value *= scalers[reg2scale.index(r)][2] - - df_f.value *= scalers[reg2scale.index(r)][0] # Gas/fueloil cost the same as coal + + df_f.value *= scalers[reg2scale.index(r)][ + 0 + ] # Gas/fueloil cost the same as coal df_g.value *= scalers[reg2scale.index(r)][1] df_c.value *= scalers[reg2scale.index(r)][2] - + Sc_nitro.add_par(p, df_g) Sc_nitro.add_par(p, df_c) Sc_nitro.add_par(p, df_f) -# Sc_nitro.add_par(p, df_f.append(df_c).append(df_g)) + # Sc_nitro.add_par(p, df_f.append(df_c).append(df_g)) Sc_nitro.add_par(p, df_gc) Sc_nitro.add_par(p, df_cc) Sc_nitro.add_par(p, df_fc) # Sc_nitro.add_par(p, df_fc.append(df_cc).append(df_gc)) - - #%% Solve the model. -Sc_nitro.commit('Nitrogen Fertilizer for Global model - no policy') +Sc_nitro.commit("Nitrogen Fertilizer for Global model - no policy") start_time = time.time() -#Sc_nitro.to_gdx(r'..\..\message_ix\message_ix\model\data', "MsgData_"+Sc_nitro.model+"_"+ +# Sc_nitro.to_gdx(r'..\..\message_ix\message_ix\model\data', "MsgData_"+Sc_nitro.model+"_"+ # Sc_nitro.scenario+"_"+str(Sc_nitro.version)) # I do this because the current set up doesn't recognize the model path correctly. -Sc_nitro.solve(model='MESSAGE', case=Sc_nitro.model+"_"+ - Sc_nitro.scenario+"_"+str(Sc_nitro.version)) -#Sc_ref.solve(model='MESSAGE', case=Sc_ref.model+"_"+Sc_ref.scenario+"_"+str(Sc_ref.version)) +Sc_nitro.solve( + model="MESSAGE", + case=Sc_nitro.model + "_" + Sc_nitro.scenario + "_" + str(Sc_nitro.version), +) +# Sc_ref.solve(model='MESSAGE', case=Sc_ref.model+"_"+Sc_ref.scenario+"_"+str(Sc_ref.version)) print(".solve: %.6s seconds taken." % (time.time() - start_time)) - #%% Run reference model # -#start_time = time.time() -#Sc_ref.remove_solution() -#Sc_ref.solve(model='MESSAGE', case=Sc_ref.model+"_"+ +# start_time = time.time() +# Sc_ref.remove_solution() +# Sc_ref.solve(model='MESSAGE', case=Sc_ref.model+"_"+ # Sc_ref.scenario+"_"+str(Sc_ref.version)) -#print(".solve: %.6s seconds taken." % (time.time() - start_time)) +# print(".solve: %.6s seconds taken." % (time.time() - start_time)) # diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 235efb014b..cb3461195e 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -199,6 +199,7 @@ def run_reporting(old_reporting, scenario_name, model_name): scenario = Scenario(mp, model_name, scenario_name) report(scenario, old_reporting) + @cli.command("report-2") @click.option("--scenario_name", default="NoPolicy") @click.option("--model_name", default="MESSAGEix-Materials") @@ -207,7 +208,7 @@ def run_old_reporting(scenario_name, model_name): from message_ix import Scenario from ixmp import Platform from message_data.tools.post_processing.iamc_report_hackathon import ( - report as reporting + report as reporting, ) base_model = model_name @@ -218,8 +219,9 @@ def run_old_reporting(scenario_name, model_name): mp = Platform() scenario = Scenario(mp, model_name, scenario_name) - reporting(mp, scenario, 'False', base_model, - scen_name, merge_hist=True, merge_ts= True) + reporting( + mp, scenario, "False", base_model, scen_name, merge_hist=True, merge_ts=True + ) from .data_cement import gen_data_cement diff --git a/message_ix_models/model/material/aluminum_stand_alone/generate_data_AL.py b/message_ix_models/model/material/aluminum_stand_alone/generate_data_AL.py index 413d61d685..5ed05aae15 100644 --- a/message_ix_models/model/material/aluminum_stand_alone/generate_data_AL.py +++ b/message_ix_models/model/material/aluminum_stand_alone/generate_data_AL.py @@ -14,111 +14,135 @@ def gen_data_aluminum(file): - + mp = ixmp.Platform() - - scenario = message_ix.Scenario(mp,"MESSAGE_material","baseline", cache=True) - - data_aluminum = pd.read_excel(file,sheet_name="data") - - data_aluminum_hist = pd.read_excel("aluminum_techno_economic.xlsx", - sheet_name="data_historical", - usecols = "A:F") + + scenario = message_ix.Scenario(mp, "MESSAGE_material", "baseline", cache=True) + + data_aluminum = pd.read_excel(file, sheet_name="data") + + data_aluminum_hist = pd.read_excel( + "aluminum_techno_economic.xlsx", sheet_name="data_historical", usecols="A:F" + ) # Clean the data # Drop columns that don't contain useful information - data_aluminum= data_aluminum.drop(['Region', 'Source', 'Description'], - axis = 1) + data_aluminum = data_aluminum.drop(["Region", "Source", "Description"], axis=1) # List of data frames, to be concatenated together at the end - results = defaultdict(list) - # Will come from the yaml file + results = defaultdict(list) + # Will come from the yaml file technology_add = data_aluminum["technology"].unique() # normally from s_info.Y - years = [2010,2020,2030,2040,2050,2060,2070,2080,2090,2100] - + years = [2010, 2020, 2030, 2040, 2050, 2060, 2070, 2080, 2090, 2100] + # normally from s_info.N nodes = ["CHN"] - - # Iterate over technologies - - for t in technology_add: - # Obtain the active and vintage years - av = data_aluminum.loc[(data_aluminum["technology"] == t), - 'availability'].values[0] - if "technical_lifetime" in data_aluminum.loc[ - (data_aluminum["technology"] == t)]["parameter"].values: - lifetime = data_aluminum.loc[(data_aluminum["technology"] == t) - & (data_aluminum["parameter"] == - "technical_lifetime"), 'value'].\ - values[0] + + # Iterate over technologies + + for t in technology_add: + # Obtain the active and vintage years + av = data_aluminum.loc[ + (data_aluminum["technology"] == t), "availability" + ].values[0] + if ( + "technical_lifetime" + in data_aluminum.loc[(data_aluminum["technology"] == t)]["parameter"].values + ): + lifetime = data_aluminum.loc[ + (data_aluminum["technology"] == t) + & (data_aluminum["parameter"] == "technical_lifetime"), + "value", + ].values[0] years_df = scenario.vintage_and_active_years() - years_df = years_df.loc[years_df["year_vtg"]>= av] - # Empty data_frame - years_df_final = pd.DataFrame(columns=["year_vtg","year_act"]) - - # For each vintage adjsut the active years according to technical + years_df = years_df.loc[years_df["year_vtg"] >= av] + # Empty data_frame + years_df_final = pd.DataFrame(columns=["year_vtg", "year_act"]) + + # For each vintage adjsut the active years according to technical # lifetime for vtg in years_df["year_vtg"].unique(): - years_df_temp = years_df.loc[years_df["year_vtg"]== vtg] - years_df_temp = years_df_temp.loc[years_df["year_act"] - < vtg + lifetime] - years_df_final = pd.concat([years_df_temp, years_df_final], - ignore_index=True) - vintage_years, act_years = years_df_final['year_vtg'], \ - years_df_final['year_act'] - - params = data_aluminum.loc[(data_aluminum["technology"] == t),\ - "parameter"].values.tolist() - # Iterate over parameters + years_df_temp = years_df.loc[years_df["year_vtg"] == vtg] + years_df_temp = years_df_temp.loc[years_df["year_act"] < vtg + lifetime] + years_df_final = pd.concat( + [years_df_temp, years_df_final], ignore_index=True + ) + vintage_years, act_years = ( + years_df_final["year_vtg"], + years_df_final["year_act"], + ) + + params = data_aluminum.loc[ + (data_aluminum["technology"] == t), "parameter" + ].values.tolist() + # Iterate over parameters for par in params: split = par.split("|") param_name = par.split("|")[0] - - val = data_aluminum.loc[((data_aluminum["technology"] == t) & - (data_aluminum["parameter"] == par)),\ - 'value'].values[0] - - # Common parameters for all input and output tables + + val = data_aluminum.loc[ + ( + (data_aluminum["technology"] == t) + & (data_aluminum["parameter"] == par) + ), + "value", + ].values[0] + + # Common parameters for all input and output tables # node_dest and node_origin are the same as node_loc - + common = dict( - year_vtg= vintage_years, - year_act= act_years, - mode="standard", - time="year", - time_origin="year", - time_dest="year",) - - if len(split)> 1: - - if (param_name == "input")|(param_name == "output"): - + year_vtg=vintage_years, + year_act=act_years, + mode="standard", + time="year", + time_origin="year", + time_dest="year", + ) + + if len(split) > 1: + + if (param_name == "input") | (param_name == "output"): + com = split[1] lev = split[2] - - df = (make_df(param_name, technology=t, commodity=com, - level=lev, value=val, unit='t', **common) - .pipe(broadcast, node_loc=nodes).pipe(same_node)) - - results[param_name].append(df) - + + df = ( + make_df( + param_name, + technology=t, + commodity=com, + level=lev, + value=val, + unit="t", + **common + ) + .pipe(broadcast, node_loc=nodes) + .pipe(same_node) + ) + + results[param_name].append(df) + elif param_name == "emission_factor": emi = split[1] - - df = (make_df(param_name, technology=t,value=val, - emission=emi, unit='t', **common) - .pipe(broadcast, node_loc=nodes)) - results[param_name].append(df) - - # Rest of the parameters apart from inpput, output and - # emission_factor - else: - df = (make_df(param_name, technology=t, value=val,unit='t', - **common).pipe(broadcast, node_loc=nodes)) - results[param_name].append(df) - results_aluminum = {par_name: pd.concat(dfs) for par_name, - dfs in results.items()} - - return results_aluminum, data_aluminum_hist + df = make_df( + param_name, + technology=t, + value=val, + emission=emi, + unit="t", + **common + ).pipe(broadcast, node_loc=nodes) + results[param_name].append(df) + + # Rest of the parameters apart from inpput, output and + # emission_factor + else: + df = make_df( + param_name, technology=t, value=val, unit="t", **common + ).pipe(broadcast, node_loc=nodes) + results[param_name].append(df) + results_aluminum = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} + return results_aluminum, data_aluminum_hist diff --git a/message_ix_models/model/material/aluminum_stand_alone/generate_data_generic.py b/message_ix_models/model/material/aluminum_stand_alone/generate_data_generic.py index c14c1d615c..25095a711d 100644 --- a/message_ix_models/model/material/aluminum_stand_alone/generate_data_generic.py +++ b/message_ix_models/model/material/aluminum_stand_alone/generate_data_generic.py @@ -14,122 +14,165 @@ import message_ix import ixmp + def gen_data_generic(): - + mp = ixmp.Platform() - - scenario = message_ix.Scenario(mp,"MESSAGE_material","baseline", cache=True) - - data_generic = pd.read_excel("generic_furnace_boiler_techno_economic.xlsx", - sheet_name="generic") - data_generic= data_generic.drop(['Region', 'Source', 'Description'], - axis = 1) - + + scenario = message_ix.Scenario(mp, "MESSAGE_material", "baseline", cache=True) + + data_generic = pd.read_excel( + "generic_furnace_boiler_techno_economic.xlsx", sheet_name="generic" + ) + data_generic = data_generic.drop(["Region", "Source", "Description"], axis=1) + # List of data frames, to be concatenated together at the end results = defaultdict(list) - - # This comes from the config file normally - technology_add = ['furnace_coal_aluminum', 'furnace_foil_aluminum', - 'furnace_methanol_aluminum', 'furnace_biomass_aluminum', - 'furnace_ethanol_aluminum', 'furnace_gas_aluminum', - 'furnace_elec_aluminum', 'furnace_h2_aluminum', - 'hp_gas_aluminum', 'hp_elec_aluminum', 'fc_h2_aluminum', - 'solar_aluminum','dheat_aluminum',"furnace_loil_aluminum"] - + + # This comes from the config file normally + technology_add = [ + "furnace_coal_aluminum", + "furnace_foil_aluminum", + "furnace_methanol_aluminum", + "furnace_biomass_aluminum", + "furnace_ethanol_aluminum", + "furnace_gas_aluminum", + "furnace_elec_aluminum", + "furnace_h2_aluminum", + "hp_gas_aluminum", + "hp_elec_aluminum", + "fc_h2_aluminum", + "solar_aluminum", + "dheat_aluminum", + "furnace_loil_aluminum", + ] + # normally from s_info.Y - years = [2010,2020,2030,2040,2050,2060,2070,2080,2090,2100] - + years = [2010, 2020, 2030, 2040, 2050, 2060, 2070, 2080, 2090, 2100] + # normally from s_info.N nodes = ["CHN"] - - # For each technology there are differnet input and output combinations - # Iterate over technologies - - for t in technology_add: - - # Obtain the active and vintage years - av = data_generic.loc[(data_generic["technology"] == t), - 'availability'].values[0] - lifetime = data_generic.loc[(data_generic["technology"] == t) \ - & (data_generic["parameter"]== - "technical_lifetime"),'value'].values[0] + + # For each technology there are differnet input and output combinations + # Iterate over technologies + + for t in technology_add: + + # Obtain the active and vintage years + av = data_generic.loc[(data_generic["technology"] == t), "availability"].values[ + 0 + ] + lifetime = data_generic.loc[ + (data_generic["technology"] == t) + & (data_generic["parameter"] == "technical_lifetime"), + "value", + ].values[0] years_df = scenario.vintage_and_active_years() - years_df = years_df.loc[years_df["year_vtg"]>= av] - years_df_final = pd.DataFrame(columns=["year_vtg","year_act"]) - - # For each vintage adjsut the active years according to technical + years_df = years_df.loc[years_df["year_vtg"] >= av] + years_df_final = pd.DataFrame(columns=["year_vtg", "year_act"]) + + # For each vintage adjsut the active years according to technical # lifetime for vtg in years_df["year_vtg"].unique(): - years_df_temp = years_df.loc[years_df["year_vtg"]== vtg] - years_df_temp = years_df_temp.loc[years_df["year_act"] - < vtg + lifetime] - years_df_final = pd.concat([years_df_temp, years_df_final], - ignore_index=True) - - vintage_years, act_years = years_df_final['year_vtg'], \ - years_df_final['year_act'] - - params = data_generic.loc[(data_generic["technology"] == t), - "parameter"].values.tolist() - - # To keep the available modes and use it in the emission_factor - # parameter. + years_df_temp = years_df.loc[years_df["year_vtg"] == vtg] + years_df_temp = years_df_temp.loc[years_df["year_act"] < vtg + lifetime] + years_df_final = pd.concat( + [years_df_temp, years_df_final], ignore_index=True + ) + + vintage_years, act_years = ( + years_df_final["year_vtg"], + years_df_final["year_act"], + ) + + params = data_generic.loc[ + (data_generic["technology"] == t), "parameter" + ].values.tolist() + + # To keep the available modes and use it in the emission_factor + # parameter. mode_list = [] - - # Iterate over parameters + + # Iterate over parameters for par in params: - + split = par.split("|") param_name = par.split("|")[0] - - val = data_generic.loc[((data_generic["technology"] == t) - & (data_generic["parameter"] == par)), 'value'].values[0] - - # Common parameters for all input and output tables + + val = data_generic.loc[ + ( + (data_generic["technology"] == t) + & (data_generic["parameter"] == par) + ), + "value", + ].values[0] + + # Common parameters for all input and output tables common = dict( - year_vtg= vintage_years, - year_act = act_years, - time="year", - time_origin="year", - time_dest="year",) - - if len(split)> 1: - - if (param_name == "input")|(param_name == "output"): - + year_vtg=vintage_years, + year_act=act_years, + time="year", + time_origin="year", + time_dest="year", + ) + + if len(split) > 1: + + if (param_name == "input") | (param_name == "output"): + com = split[1] lev = split[2] mod = split[3] - - # Keep the available modes for a technology in a list - mode_list.append(mod) - df = (make_df(param_name, technology=t, commodity=com, - level=lev,mode=mod, value=val, unit='t', - **common).pipe(broadcast, node_loc=nodes). - pipe(same_node)) - results[param_name].append(df) - + + # Keep the available modes for a technology in a list + mode_list.append(mod) + df = ( + make_df( + param_name, + technology=t, + commodity=com, + level=lev, + mode=mod, + value=val, + unit="t", + **common + ) + .pipe(broadcast, node_loc=nodes) + .pipe(same_node) + ) + results[param_name].append(df) + elif param_name == "emission_factor": emi = split[1] - mod = data_generic.loc[((data_generic["technology"] == t) - & (data_generic["parameter"] == par)), 'value'].values[0] - - # For differnet modes + mod = data_generic.loc[ + ( + (data_generic["technology"] == t) + & (data_generic["parameter"] == par) + ), + "value", + ].values[0] + + # For differnet modes for m in np.unique(np.array(mode_list)): - - df = (make_df(param_name, technology=t,value=val, - emission=emi,mode= m, unit='t', - **common).pipe(broadcast, node_loc=nodes)) - results[param_name].append(df) - - # Rest of the parameters apart from inpput, output and + + df = make_df( + param_name, + technology=t, + value=val, + emission=emi, + mode=m, + unit="t", + **common + ).pipe(broadcast, node_loc=nodes) + results[param_name].append(df) + + # Rest of the parameters apart from inpput, output and # emission_factor - else: - df = (make_df(param_name, technology=t, value=val,unit='t', - **common).pipe(broadcast, node_loc=nodes)) - results[param_name].append(df) - - results_generic = {par_name: pd.concat(dfs) for par_name, - dfs in results.items()} - return results_generic - \ No newline at end of file + else: + df = make_df( + param_name, technology=t, value=val, unit="t", **common + ).pipe(broadcast, node_loc=nodes) + results[param_name].append(df) + + results_generic = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} + return results_generic diff --git a/message_ix_models/model/material/aluminum_stand_alone/plotting.py b/message_ix_models/model/material/aluminum_stand_alone/plotting.py index 74e58cb941..73e620d760 100644 --- a/message_ix_models/model/material/aluminum_stand_alone/plotting.py +++ b/message_ix_models/model/material/aluminum_stand_alone/plotting.py @@ -3,14 +3,14 @@ class Plots(object): - def __init__(self, ds, country, firstyear=2010, input_costs='$/GWa'): + def __init__(self, ds, country, firstyear=2010, input_costs="$/GWa"): self.ds = ds self.country = country self.firstyear = firstyear - if input_costs == '$/MWa': + if input_costs == "$/MWa": self.cost_unit_conv = 1e3 - elif input_costs == '$/kWa': + elif input_costs == "$/kWa": self.cost_unit_conv = 1e6 else: self.cost_unit_conv = 1 @@ -18,99 +18,94 @@ def __init__(self, ds, country, firstyear=2010, input_costs='$/GWa'): def historic_data(self, par, year_col, subset=None): df = self.ds.par(par) if subset is not None: - df = df[df['technology'].isin(subset)] - idx = [year_col, 'technology'] - df = df[idx + ['value']].groupby(idx).sum().reset_index() - df = df.pivot(index=year_col, columns='technology', - values='value') + df = df[df["technology"].isin(subset)] + idx = [year_col, "technology"] + df = df[idx + ["value"]].groupby(idx).sum().reset_index() + df = df.pivot(index=year_col, columns="technology", values="value") return df - def model_data(self, var, year_col='year_act', baseyear=False, - subset=None): + def model_data(self, var, year_col="year_act", baseyear=False, subset=None): df = self.ds.var(var) if subset is not None: - df = df[df['technology'].isin(subset)] - idx = [year_col, 'technology'] - df = (df[idx + ['lvl']] - .groupby(idx) - .sum() - .reset_index() - .pivot(index=year_col, columns='technology', - values='lvl') - .rename(columns={'lvl': 'value'}) - ) + df = df[df["technology"].isin(subset)] + idx = [year_col, "technology"] + df = ( + df[idx + ["lvl"]] + .groupby(idx) + .sum() + .reset_index() + .pivot(index=year_col, columns="technology", values="lvl") + .rename(columns={"lvl": "value"}) + ) df = df[df.index >= self.firstyear] return df def equ_data(self, equ, value, baseyear=False, subset=None): df = self.ds.equ(equ) if not baseyear: - df = df[df['year'] > self.firstyear] + df = df[df["year"] > self.firstyear] if subset is not None: - df = df[df['commodity'].isin(subset)] - df = df.pivot(index='year', columns='commodity', values=value) + df = df[df["commodity"].isin(subset)] + df = df.pivot(index="year", columns="commodity", values=value) return df def plot_demand(self, light_demand, elec_demand): fig, ax = plt.subplots() - light = light_demand['value'] - light.plot(ax=ax, label='light') - e = elec_demand['value'] - e.plot(ax=ax, label='elec') - (light + e).plot(ax=ax, label='total') - plt.ylabel('GWa') - plt.xlabel('Year') - plt.legend(loc='best') + light = light_demand["value"] + light.plot(ax=ax, label="light") + e = elec_demand["value"] + e.plot(ax=ax, label="elec") + (light + e).plot(ax=ax, label="total") + plt.ylabel("GWa") + plt.xlabel("Year") + plt.legend(loc="best") def plot_activity(self, baseyear=False, subset=None): - h = self.historic_data('historical_activity', - 'year_act', subset=subset) - m = self.model_data('ACT', baseyear=baseyear, subset=subset) + h = self.historic_data("historical_activity", "year_act", subset=subset) + m = self.model_data("ACT", baseyear=baseyear, subset=subset) df = pd.concat([h, m]) if not h.empty else m df.plot.bar(stacked=True) - plt.title('{} Energy System Activity'.format(self.country.title())) - plt.ylabel('GWa') - plt.xlabel('Year') - plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5)) + plt.title("{} Energy System Activity".format(self.country.title())) + plt.ylabel("GWa") + plt.xlabel("Year") + plt.legend(loc="center left", bbox_to_anchor=(1.0, 0.5)) def plot_capacity(self, baseyear=False, subset=None): - df = self.model_data('CAP', baseyear=baseyear, subset=subset) + df = self.model_data("CAP", baseyear=baseyear, subset=subset) df.plot.bar(stacked=True) - plt.title('{} Energy System Capacity'.format(self.country.title())) - plt.ylabel('GW') - plt.xlabel('Year') - plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5)) + plt.title("{} Energy System Capacity".format(self.country.title())) + plt.ylabel("GW") + plt.xlabel("Year") + plt.legend(loc="center left", bbox_to_anchor=(1.0, 0.5)) def plot_new_capacity(self, baseyear=False, subset=None): - h = self.historic_data('historical_new_capacity', - 'year_vtg', subset=subset) - m = self.model_data('CAP_NEW', 'year_vtg', - baseyear=baseyear, subset=subset) + h = self.historic_data("historical_new_capacity", "year_vtg", subset=subset) + m = self.model_data("CAP_NEW", "year_vtg", baseyear=baseyear, subset=subset) df = pd.concat([h, m]) if not h.empty else m df.plot.bar(stacked=True) - plt.title('{} Energy System New Capcity'.format(self.country.title())) - plt.ylabel('GW') - plt.xlabel('Year') - plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5)) + plt.title("{} Energy System New Capcity".format(self.country.title())) + plt.ylabel("GW") + plt.xlabel("Year") + plt.legend(loc="center left", bbox_to_anchor=(1.0, 0.5)) def plot_prices(self, baseyear=False, subset=None): - df = self.ds.var('PRICE_COMMODITY') + df = self.ds.var("PRICE_COMMODITY") if not baseyear: - df = df[df['year'] > self.firstyear] + df = df[df["year"] > self.firstyear] if subset is not None: - df = df[df['commodity'].isin(subset)] - idx = ['year', 'commodity'] - df = (df[idx + ['lvl']] - .groupby(idx) - .sum(). - reset_index() - .pivot(index='year', columns='commodity', - values='lvl') - .rename(columns={'lvl': 'value'}) - ) + df = df[df["commodity"].isin(subset)] + idx = ["year", "commodity"] + df = ( + df[idx + ["lvl"]] + .groupby(idx) + .sum() + .reset_index() + .pivot(index="year", columns="commodity", values="lvl") + .rename(columns={"lvl": "value"}) + ) df = df / 8760 * 100 * self.cost_unit_conv df.plot.bar(stacked=False) - plt.title('{} Energy System Prices'.format(self.country.title())) - plt.ylabel('cents/kWhr') - plt.xlabel('Year') - plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5)) + plt.title("{} Energy System Prices".format(self.country.title())) + plt.ylabel("cents/kWhr") + plt.xlabel("Year") + plt.legend(loc="center left", bbox_to_anchor=(1.0, 0.5)) diff --git a/message_ix_models/model/material/aluminum_stand_alone/stand_alone_AL.py b/message_ix_models/model/material/aluminum_stand_alone/stand_alone_AL.py index c12cc116c8..66cd0c90c2 100644 --- a/message_ix_models/model/material/aluminum_stand_alone/stand_alone_AL.py +++ b/message_ix_models/model/material/aluminum_stand_alone/stand_alone_AL.py @@ -17,68 +17,100 @@ mp = ixmp.Platform() # Adding a new unit to the library -mp.add_unit('Mt') +mp.add_unit("Mt") # New model -scenario = message_ix.Scenario(mp, model='MESSAGE_material', - scenario='baseline', version='new') +scenario = message_ix.Scenario( + mp, model="MESSAGE_material", scenario="baseline", version="new" +) # Addition of basics history = [1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015] model_horizon = [2020, 2030, 2040, 2050, 2060, 2070, 2080, 2090, 2100] -scenario.add_horizon({'year': history + model_horizon, - 'firstmodelyear': model_horizon[0]}) -country = 'CHN' -scenario.add_spatial_sets({'country': country}) +scenario.add_horizon( + {"year": history + model_horizon, "firstmodelyear": model_horizon[0]} +) +country = "CHN" +scenario.add_spatial_sets({"country": country}) # These will come from the yaml file -commodities = ['ht_heat', 'lt_heat', 'aluminum', 'd_heat', "electr", - "coal", "fueloil", "ethanol", "biomass", "gas", "hydrogen", - "methanol", "lightoil"] +commodities = [ + "ht_heat", + "lt_heat", + "aluminum", + "d_heat", + "electr", + "coal", + "fueloil", + "ethanol", + "biomass", + "gas", + "hydrogen", + "methanol", + "lightoil", +] scenario.add_set("commodity", commodities) -levels = ['useful_aluminum', 'new_scrap', 'old_scrap', 'final_material', - 'useful_material', 'product', "secondary_material", "final", - "demand"] +levels = [ + "useful_aluminum", + "new_scrap", + "old_scrap", + "final_material", + "useful_material", + "product", + "secondary_material", + "final", + "demand", +] scenario.add_set("level", levels) -technologies = ['soderberg_aluminum', 'prebake_aluminum', 'secondary_aluminum', - 'prep_secondary_aluminum', 'finishing_aluminum', - 'manuf_aluminum', 'scrap_recovery_aluminum', - 'furnace_coal_aluminum', 'furnace_foil_aluminum', - 'furnace_methanol_aluminum', 'furnace_biomass_aluminum', - 'furnace_ethanol_aluminum', 'furnace_gas_aluminum', - 'furnace_elec_aluminum', 'furnace_h2_aluminum', - 'hp_gas_aluminum', 'hp_elec_aluminum', 'fc_h2_aluminum', - 'solar_aluminum', 'dheat_aluminum', 'furnace_loil_aluminum', - "alumina_supply"] +technologies = [ + "soderberg_aluminum", + "prebake_aluminum", + "secondary_aluminum", + "prep_secondary_aluminum", + "finishing_aluminum", + "manuf_aluminum", + "scrap_recovery_aluminum", + "furnace_coal_aluminum", + "furnace_foil_aluminum", + "furnace_methanol_aluminum", + "furnace_biomass_aluminum", + "furnace_ethanol_aluminum", + "furnace_gas_aluminum", + "furnace_elec_aluminum", + "furnace_h2_aluminum", + "hp_gas_aluminum", + "hp_elec_aluminum", + "fc_h2_aluminum", + "solar_aluminum", + "dheat_aluminum", + "furnace_loil_aluminum", + "alumina_supply", +] scenario.add_set("technology", technologies) -scenario.add_set("mode", ['standard', 'low_temp', 'high_temp']) +scenario.add_set("mode", ["standard", "low_temp", "high_temp"]) # Create duration period -val = [j-i for i, j in zip(model_horizon[:-1], model_horizon[1:])] +val = [j - i for i, j in zip(model_horizon[:-1], model_horizon[1:])] val.append(val[0]) -duration_period = pd.DataFrame({ - 'year': model_horizon, - 'value': val, - 'unit': "y", - }) +duration_period = pd.DataFrame({"year": model_horizon, "value": val, "unit": "y",}) scenario.add_par("duration_period", duration_period) duration_period = duration_period["value"].values -# Energy system: Unlimited supply of the commodities. -# Fuel costs are obtained from the PRICE_COMMODITY baseline SSP2 global model. -# PRICE_COMMODTY * input -> (2005USD/Gwa-coal)*(Gwa-coal / ACT of furnace) +# Energy system: Unlimited supply of the commodities. +# Fuel costs are obtained from the PRICE_COMMODITY baseline SSP2 global model. +# PRICE_COMMODTY * input -> (2005USD/Gwa-coal)*(Gwa-coal / ACT of furnace) # = (2005USD/ACT of furnace) years_df = scenario.vintage_and_active_years() -vintage_years, act_years = years_df['year_vtg'], years_df['year_act'] +vintage_years, act_years = years_df["year_vtg"], years_df["year_act"] # Choose the prices in excel (baseline vs. NPi400) data_var_cost = pd.read_excel("variable_costs.xlsx", sheet_name="data") @@ -88,59 +120,80 @@ values = [] for yr in act_years: values.append(data[yr]) - base_var_cost = pd.DataFrame({ - 'node_loc': country, - 'year_vtg': vintage_years.values, - 'year_act': act_years.values, - 'mode': data["mode"], - 'time': 'year', - 'unit': 'USD/GWa', - "technology": data["technology"], - "value": values - }) - - scenario.add_par("var_cost",base_var_cost) - -# Add dummy technologies to represent energy system - -dummy_tech = ["electr_gen", "dist_heating", "coal_gen", "foil_gen", "eth_gen", - "biomass_gen", "gas_gen", "hydrogen_gen", "meth_gen", "loil_gen"] -scenario.add_set("technology", dummy_tech) - -commodity_tec = ["electr", "d_heat", "coal", "fueloil", "ethanol", "biomass", - "gas", "hydrogen", "methanol", "lightoil"] + base_var_cost = pd.DataFrame( + { + "node_loc": country, + "year_vtg": vintage_years.values, + "year_act": act_years.values, + "mode": data["mode"], + "time": "year", + "unit": "USD/GWa", + "technology": data["technology"], + "value": values, + } + ) + + scenario.add_par("var_cost", base_var_cost) + +# Add dummy technologies to represent energy system + +dummy_tech = [ + "electr_gen", + "dist_heating", + "coal_gen", + "foil_gen", + "eth_gen", + "biomass_gen", + "gas_gen", + "hydrogen_gen", + "meth_gen", + "loil_gen", +] +scenario.add_set("technology", dummy_tech) + +commodity_tec = [ + "electr", + "d_heat", + "coal", + "fueloil", + "ethanol", + "biomass", + "gas", + "hydrogen", + "methanol", + "lightoil", +] # Add output to dummy tech. year_df = scenario.vintage_and_active_years() -vintage_years, act_years = year_df['year_vtg'], year_df['year_act'] +vintage_years, act_years = year_df["year_vtg"], year_df["year_act"] base = { - 'node_loc': country, + "node_loc": country, "node_dest": country, "time_dest": "year", - 'year_vtg': vintage_years, - 'year_act': act_years, - 'mode': 'standard', - 'time': 'year', + "year_vtg": vintage_years, + "year_act": act_years, + "mode": "standard", + "time": "year", "level": "final", - 'unit': '-', - "value": 1.0 + "unit": "-", + "value": 1.0, } t = 0 for tec in dummy_tech: - out = make_df("output", technology= tec, commodity = commodity_tec[t], - **base) + out = make_df("output", technology=tec, commodity=commodity_tec[t], **base) t = t + 1 scenario.add_par("output", out) - + # Introduce emissions -scenario.add_set('emission', 'CO2') -scenario.add_cat('emission', 'GHG', 'CO2') +scenario.add_set("emission", "CO2") +scenario.add_cat("emission", "GHG", "CO2") -# Run read data aluminum +# Run read data aluminum scenario.commit("changes added") @@ -149,190 +202,237 @@ scenario.check_out() for k, v in results_al.items(): - scenario.add_par(k,v) - + scenario.add_par(k, v) + scenario.commit("aluminum_techno_economic added") -results_generic = gen_data_generic() +results_generic = gen_data_generic() scenario.check_out() for k, v in results_generic.items(): - scenario.add_par(k,v) - + scenario.add_par(k, v) + # Add temporary exogenous demand: 17.3 Mt in 2010 (IAI) # The future projection of the demand: Increases by half of the GDP growth rate # gdp_growth rate: SSP2 global model. Starting from 2020. -gdp_growth = pd.Series([0.121448215899944, 0.0733079014579874, - 0.0348154093342843, 0.021827616787921, - 0.0134425983942219, 0.0108320197485592, - 0.00884341208063, 0.00829374133206562, - 0.00649794573935969], - index=pd.Index(model_horizon, name='Time')) - -fin_to_useful = scenario.par("output", filters = {"technology": - "finishing_aluminum","year_act":2020})["value"][0] -useful_to_product = scenario.par("output", filters = {"technology": - "manuf_aluminum","year_act":2020})["value"][0] +gdp_growth = pd.Series( + [ + 0.121448215899944, + 0.0733079014579874, + 0.0348154093342843, + 0.021827616787921, + 0.0134425983942219, + 0.0108320197485592, + 0.00884341208063, + 0.00829374133206562, + 0.00649794573935969, + ], + index=pd.Index(model_horizon, name="Time"), +) + +fin_to_useful = scenario.par( + "output", filters={"technology": "finishing_aluminum", "year_act": 2020} +)["value"][0] +useful_to_product = scenario.par( + "output", filters={"technology": "manuf_aluminum", "year_act": 2020} +)["value"][0] i = 0 values = [] -val = (17.3 * (1+ 0.147718884937996/2) ** duration_period[i]) +val = 17.3 * (1 + 0.147718884937996 / 2) ** duration_period[i] values.append(val) for element in gdp_growth: - i = i + 1 + i = i + 1 if i < len(model_horizon): - val = (val * (1+ element/2) ** duration_period[i]) + val = val * (1 + element / 2) ** duration_period[i] values.append(val) - -# Adjust the demand to product level. + +# Adjust the demand to product level. values = [x * fin_to_useful * useful_to_product for x in values] -aluminum_demand = pd.DataFrame({ - 'node': country, - 'commodity': 'aluminum', - 'level': 'demand', - 'year': model_horizon, - 'time': 'year', - 'value': values , - 'unit': 'Mt', - }) - +aluminum_demand = pd.DataFrame( + { + "node": country, + "commodity": "aluminum", + "level": "demand", + "year": model_horizon, + "time": "year", + "value": values, + "unit": "Mt", + } +) + scenario.add_par("demand", aluminum_demand) # Interest rate -scenario.add_par("interestrate", model_horizon, value=0.05, unit='-') +scenario.add_par("interestrate", model_horizon, value=0.05, unit="-") # Add historical production and capacity for tec in data_aluminum_hist["technology"].unique(): - hist_activity = pd.DataFrame({ - 'node_loc': country, - 'year_act': history, - 'mode': data_aluminum_hist.loc[(data_aluminum_hist["technology"]== tec), - "mode"], - 'time': 'year', - 'unit': 'Mt', - "technology": tec, - "value": data_aluminum_hist.loc[(data_aluminum_hist["technology"]== tec), - "production"] - }) - scenario.add_par('historical_activity', hist_activity) - + hist_activity = pd.DataFrame( + { + "node_loc": country, + "year_act": history, + "mode": data_aluminum_hist.loc[ + (data_aluminum_hist["technology"] == tec), "mode" + ], + "time": "year", + "unit": "Mt", + "technology": tec, + "value": data_aluminum_hist.loc[ + (data_aluminum_hist["technology"] == tec), "production" + ], + } + ) + scenario.add_par("historical_activity", hist_activity) + for tec in data_aluminum_hist["technology"].unique(): - c_factor = scenario.par("capacity_factor", filters = {"technology": tec})\ - ["value"].values[0] - value = data_aluminum_hist.loc[(data_aluminum_hist["technology"]== tec), - "new_production"] / c_factor - hist_capacity = pd.DataFrame({ - 'node_loc': country, - 'year_vtg': history, - 'unit': 'Mt', - "technology": tec, - "value": value }) - scenario.add_par('historical_new_capacity', hist_capacity) - + c_factor = scenario.par("capacity_factor", filters={"technology": tec})[ + "value" + ].values[0] + value = ( + data_aluminum_hist.loc[ + (data_aluminum_hist["technology"] == tec), "new_production" + ] + / c_factor + ) + hist_capacity = pd.DataFrame( + { + "node_loc": country, + "year_vtg": history, + "unit": "Mt", + "technology": tec, + "value": value, + } + ) + scenario.add_par("historical_new_capacity", hist_capacity) + # Historical thermal demand depending on the historical aluminum production # This section can be revised to make shorter and generic to other materials -scrap_recovery = scenario.par("output", filters = {"technology": - "scrap_recovery_aluminum","level":"old_scrap","year_act":2020})["value"][0] -high_th = scenario.par("input", filters = {"technology": - "secondary_aluminum","year_act":2020})["value"][0] -low_th = scenario.par("input", filters = {"technology": - "prep_secondary_aluminum","year_act":2020})["value"][0] - -historic_generation = scenario.par("historical_activity").\ -groupby("year_act").sum() - -for yr in historic_generation.index: - - total_scrap = ((historic_generation.loc[yr].value * fin_to_useful * \ - (1- useful_to_product)) + (historic_generation.loc[yr] \ - * fin_to_useful * useful_to_product * scrap_recovery)) - old_scrap = (historic_generation.loc[yr] * fin_to_useful * \ - useful_to_product * scrap_recovery) - total_hist_act = total_scrap * high_th + old_scrap * low_th - - hist_activity_h = pd.DataFrame({ - 'node_loc': country, - 'year_act': yr, - 'mode': 'high_temp', - 'time': 'year', - 'unit': 'GWa', - "technology": "furnace_gas_aluminum", - "value": total_scrap * high_th - }) - - scenario.add_par('historical_activity', hist_activity_h) - - hist_activity_l = pd.DataFrame({ - 'node_loc': country, - 'year_act': yr, - 'mode': 'low_temp', - 'time': 'year', - 'unit': 'GWa', - "technology": "furnace_gas_aluminum", - "value": old_scrap * low_th - }) - - scenario.add_par('historical_activity', hist_activity_l) - - c_fac_furnace_gas = scenario.par("capacity_factor", filters = - {"technology": "furnace_gas_aluminum"})\ - ["value"].values[0] - - hist_capacity_gas = pd.DataFrame({ - 'node_loc': country, - 'year_vtg': yr, - 'unit': 'GW', - "technology": "furnace_gas_aluminum", - "value": total_hist_act / c_fac_furnace_gas }) - - scenario.add_par('historical_new_capacity', hist_capacity_gas) - +scrap_recovery = scenario.par( + "output", + filters={ + "technology": "scrap_recovery_aluminum", + "level": "old_scrap", + "year_act": 2020, + }, +)["value"][0] +high_th = scenario.par( + "input", filters={"technology": "secondary_aluminum", "year_act": 2020} +)["value"][0] +low_th = scenario.par( + "input", filters={"technology": "prep_secondary_aluminum", "year_act": 2020} +)["value"][0] + +historic_generation = scenario.par("historical_activity").groupby("year_act").sum() + +for yr in historic_generation.index: + + total_scrap = ( + historic_generation.loc[yr].value * fin_to_useful * (1 - useful_to_product) + ) + ( + historic_generation.loc[yr] * fin_to_useful * useful_to_product * scrap_recovery + ) + old_scrap = ( + historic_generation.loc[yr] * fin_to_useful * useful_to_product * scrap_recovery + ) + total_hist_act = total_scrap * high_th + old_scrap * low_th + + hist_activity_h = pd.DataFrame( + { + "node_loc": country, + "year_act": yr, + "mode": "high_temp", + "time": "year", + "unit": "GWa", + "technology": "furnace_gas_aluminum", + "value": total_scrap * high_th, + } + ) + + scenario.add_par("historical_activity", hist_activity_h) + + hist_activity_l = pd.DataFrame( + { + "node_loc": country, + "year_act": yr, + "mode": "low_temp", + "time": "year", + "unit": "GWa", + "technology": "furnace_gas_aluminum", + "value": old_scrap * low_th, + } + ) + + scenario.add_par("historical_activity", hist_activity_l) + + c_fac_furnace_gas = scenario.par( + "capacity_factor", filters={"technology": "furnace_gas_aluminum"} + )["value"].values[0] + + hist_capacity_gas = pd.DataFrame( + { + "node_loc": country, + "year_vtg": yr, + "unit": "GW", + "technology": "furnace_gas_aluminum", + "value": total_hist_act / c_fac_furnace_gas, + } + ) + + scenario.add_par("historical_new_capacity", hist_capacity_gas) + scenario.commit("changes") scenario.solve() -# Be aware plots produce the same color for some technologies. +# Be aware plots produce the same color for some technologies. p = Plots(scenario, country, firstyear=model_horizon[0]) -p.plot_activity(baseyear=True, subset=['soderberg_aluminum', - 'prebake_aluminum',"secondary_aluminum"]) -p.plot_capacity(baseyear=True, subset=['soderberg_aluminum', 'prebake_aluminum']) +p.plot_activity( + baseyear=True, + subset=["soderberg_aluminum", "prebake_aluminum", "secondary_aluminum"], +) +p.plot_capacity(baseyear=True, subset=["soderberg_aluminum", "prebake_aluminum"]) p.plot_activity(baseyear=True, subset=["prep_secondary_aluminum"]) -p.plot_activity(baseyear=True, subset=['furnace_coal_aluminum', - 'furnace_foil_aluminum', - 'furnace_methanol_aluminum', - 'furnace_biomass_aluminum', - 'furnace_ethanol_aluminum', - 'furnace_gas_aluminum', - 'furnace_elec_aluminum', - 'furnace_h2_aluminum', - 'hp_gas_aluminum', - 'hp_elec_aluminum','fc_h2_aluminum', - 'solar_aluminum', 'dheat_aluminum', - 'furnace_loil_aluminum']) -p.plot_capacity(baseyear=True, subset=['furnace_coal_aluminum', - 'furnace_foil_aluminum', - 'furnace_methanol_aluminum', - 'furnace_biomass_aluminum', - 'furnace_ethanol_aluminum', - 'furnace_gas_aluminum', - 'furnace_elec_aluminum', - 'furnace_h2_aluminum', - 'hp_gas_aluminum', - 'hp_elec_aluminum','fc_h2_aluminum', - 'solar_aluminum', 'dheat_aluminum', - 'furnace_loil_aluminum']) -p.plot_prices(subset=['aluminum'], baseyear=True) - - - - - - - - - - +p.plot_activity( + baseyear=True, + subset=[ + "furnace_coal_aluminum", + "furnace_foil_aluminum", + "furnace_methanol_aluminum", + "furnace_biomass_aluminum", + "furnace_ethanol_aluminum", + "furnace_gas_aluminum", + "furnace_elec_aluminum", + "furnace_h2_aluminum", + "hp_gas_aluminum", + "hp_elec_aluminum", + "fc_h2_aluminum", + "solar_aluminum", + "dheat_aluminum", + "furnace_loil_aluminum", + ], +) +p.plot_capacity( + baseyear=True, + subset=[ + "furnace_coal_aluminum", + "furnace_foil_aluminum", + "furnace_methanol_aluminum", + "furnace_biomass_aluminum", + "furnace_ethanol_aluminum", + "furnace_gas_aluminum", + "furnace_elec_aluminum", + "furnace_h2_aluminum", + "hp_gas_aluminum", + "hp_elec_aluminum", + "fc_h2_aluminum", + "solar_aluminum", + "dheat_aluminum", + "furnace_loil_aluminum", + ], +) +p.plot_prices(subset=["aluminum"], baseyear=True) diff --git a/message_ix_models/model/material/aluminum_stand_alone/test_commodity_balance.py b/message_ix_models/model/material/aluminum_stand_alone/test_commodity_balance.py index 4cb7747e7a..a251733849 100644 --- a/message_ix_models/model/material/aluminum_stand_alone/test_commodity_balance.py +++ b/message_ix_models/model/material/aluminum_stand_alone/test_commodity_balance.py @@ -15,187 +15,222 @@ mp = ixmp.Platform() -base = message_ix.Scenario(mp, model="MESSAGE_material", scenario='baseline') -scen = base.clone("MESSAGE_material", 'test','test commodity balance equation', - keep_solution=False) +base = message_ix.Scenario(mp, model="MESSAGE_material", scenario="baseline") +scen = base.clone( + "MESSAGE_material", "test", "test commodity balance equation", keep_solution=False +) scen.check_out() -country = 'CHN' +country = "CHN" model_horizon = [2020, 2030, 2040, 2050, 2060, 2070, 2080, 2090, 2100] # Add a new demand side technology and related commodity - + new_tec = "bulb" -scen.add_set("technology",new_tec) +scen.add_set("technology", new_tec) new_commodity = "light" -scen.add_set("commodity",new_commodity) +scen.add_set("commodity", new_commodity) -# Add input, output,technical_lifetime, inv_cost etc. +# Add input, output,technical_lifetime, inv_cost etc. tec_data, tec_data_hist = gen_data_aluminum("demand_technology.xlsx") for k, v in tec_data.items(): - scen.add_par(k,v) - -# Add dummy demand. 1 GWa initially. + scen.add_par(k, v) + +# Add dummy demand. 1 GWa initially. duration_period = scen.par("duration_period") duration_period = duration_period["value"].values - -gdp_growth = pd.Series([0.121448215899944, 0.0733079014579874, - 0.0348154093342843, 0.021827616787921, - 0.0134425983942219, 0.0108320197485592, - 0.00884341208063, 0.00829374133206562, - 0.00649794573935969], - index=pd.Index(model_horizon, name='Time')) + +gdp_growth = pd.Series( + [ + 0.121448215899944, + 0.0733079014579874, + 0.0348154093342843, + 0.021827616787921, + 0.0134425983942219, + 0.0108320197485592, + 0.00884341208063, + 0.00829374133206562, + 0.00649794573935969, + ], + index=pd.Index(model_horizon, name="Time"), +) i = 0 values = [] -val = (1 * (1+ 0.147718884937996/2) ** duration_period[i]) +val = 1 * (1 + 0.147718884937996 / 2) ** duration_period[i] values.append(val) for element in gdp_growth: - i = i + 1 + i = i + 1 if i < len(model_horizon): - val = (val * (1+ element/2) ** duration_period[i]) + val = val * (1 + element / 2) ** duration_period[i] values.append(val) - -bulb_demand = pd.DataFrame({ - 'node': country, - 'commodity': 'light', - 'level': 'useful_aluminum', - 'year': model_horizon, - 'time': 'year', - 'value': values , - 'unit': 'GWa', - }) - + +bulb_demand = pd.DataFrame( + { + "node": country, + "commodity": "light", + "level": "useful_aluminum", + "year": model_horizon, + "time": "year", + "value": values, + "unit": "GWa", + } +) + scen.add_par("demand", bulb_demand) - -# Adjust the active years. + +# Adjust the active years. tec_lifetime = tec_data.get("technical_lifetime") -lifetime = tec_lifetime['value'].values[0] +lifetime = tec_lifetime["value"].values[0] years_df = scen.vintage_and_active_years() -# Empty data_frame -years_df_final = pd.DataFrame(columns=["year_vtg","year_act"]) -# For each vintage adjsut the active years according to technical +# Empty data_frame +years_df_final = pd.DataFrame(columns=["year_vtg", "year_act"]) +# For each vintage adjsut the active years according to technical # lifetime for vtg in years_df["year_vtg"].unique(): - years_df_temp = years_df.loc[years_df["year_vtg"]== vtg] - years_df_temp = years_df_temp.loc[years_df["year_act"] - < vtg + lifetime] - years_df_final = pd.concat([years_df_temp, years_df_final], - ignore_index=True) -vintage_years, act_years = years_df_final['year_vtg'], \ -years_df_final['year_act'] + years_df_temp = years_df.loc[years_df["year_vtg"] == vtg] + years_df_temp = years_df_temp.loc[years_df["year_act"] < vtg + lifetime] + years_df_final = pd.concat([years_df_temp, years_df_final], ignore_index=True) +vintage_years, act_years = years_df_final["year_vtg"], years_df_final["year_act"] -# **** WORKS UNTIL HERE ****** +# **** WORKS UNTIL HERE ****** # Add the new parameters: input_cap_ret, output_cap_ret, input_cap, output_cap # input_cap_new, output_cap_new # Material release after retirement (e.g. old scrap) -output_cap_ret = pd.DataFrame({"node_loc":country, - "technology":"bulb", - "year_vtg": vintage_years, - "node_dest":country, - "commodity":"aluminum", - "level": "useful_material", - "time":"year", - "time_dest":"year", - "value":0.15, - "unit":"-"}) -print(output_cap_ret) +output_cap_ret = pd.DataFrame( + { + "node_loc": country, + "technology": "bulb", + "year_vtg": vintage_years, + "node_dest": country, + "commodity": "aluminum", + "level": "useful_material", + "time": "year", + "time_dest": "year", + "value": 0.15, + "unit": "-", + } +) +print(output_cap_ret) scen.add_par("output_cap_ret", output_cap_ret) - + # Material need during lifetime (e.g. retrofit) - -input_cap = pd.DataFrame({"node_loc":country, - "technology":"bulb", - "year_vtg": vintage_years, - "year_act": act_years, - "node_origin":country, - "commodity":"aluminum", - "level": "useful_material", - "time":"year", - "time_origin":"year", - "value":0.01, - "unit":"-"}) + +input_cap = pd.DataFrame( + { + "node_loc": country, + "technology": "bulb", + "year_vtg": vintage_years, + "year_act": act_years, + "node_origin": country, + "commodity": "aluminum", + "level": "useful_material", + "time": "year", + "time_origin": "year", + "value": 0.01, + "unit": "-", + } +) print(input_cap) scen.add_par("input_cap", input_cap) # Material need for the construction -input_cap_new = pd.DataFrame({"node_loc":country, - "technology":"bulb", - "year_vtg": vintage_years, - "node_origin":country, - "commodity":"aluminum", - "level": "useful_material", - "time":"year", - "time_origin":"year", - "value":0.25, - "unit":"-"}) -print(input_cap_new) -scen.add_par("input_cap_new",input_cap_new) +input_cap_new = pd.DataFrame( + { + "node_loc": country, + "technology": "bulb", + "year_vtg": vintage_years, + "node_origin": country, + "commodity": "aluminum", + "level": "useful_material", + "time": "year", + "time_origin": "year", + "value": 0.25, + "unit": "-", + } +) +print(input_cap_new) +scen.add_par("input_cap_new", input_cap_new) # Material release during construction (e.g. new scrap) - -output_cap_new = pd.DataFrame({"node_loc":country, - "technology":"bulb", - "year_vtg": vintage_years, - "node_dest":country, - "commodity":"aluminum", - "level": "useful_material", - "time":"year", - "time_dest":"year", - "value":0.1, - "unit":"-"}) + +output_cap_new = pd.DataFrame( + { + "node_loc": country, + "technology": "bulb", + "year_vtg": vintage_years, + "node_dest": country, + "commodity": "aluminum", + "level": "useful_material", + "time": "year", + "time_dest": "year", + "value": 0.1, + "unit": "-", + } +) print(output_cap_new) -scen.add_par("output_cap_new",output_cap_new) +scen.add_par("output_cap_new", output_cap_new) scen.commit() -#scen.solve() +# scen.solve() -# Be aware plots produce the same color for some technologies. +# Be aware plots produce the same color for some technologies. p = Plots(scen, country, firstyear=model_horizon[0]) -p.plot_activity(baseyear=True, subset=['soderberg_aluminum', - 'prebake_aluminum',"secondary_aluminum"]) -p.plot_capacity(baseyear=True, subset=['soderberg_aluminum', 'prebake_aluminum']) +p.plot_activity( + baseyear=True, + subset=["soderberg_aluminum", "prebake_aluminum", "secondary_aluminum"], +) +p.plot_capacity(baseyear=True, subset=["soderberg_aluminum", "prebake_aluminum"]) p.plot_activity(baseyear=True, subset=["prep_secondary_aluminum"]) -p.plot_activity(baseyear=True, subset=['furnace_coal_aluminum', - 'furnace_foil_aluminum', - 'furnace_methanol_aluminum', - 'furnace_biomass_aluminum', - 'furnace_ethanol_aluminum', - 'furnace_gas_aluminum', - 'furnace_elec_aluminum', - 'furnace_h2_aluminum', - 'hp_gas_aluminum', - 'hp_elec_aluminum','fc_h2_aluminum', - 'solar_aluminum', 'dheat_aluminum', - 'furnace_loil_aluminum']) -p.plot_capacity(baseyear=True, subset=['furnace_coal_aluminum', - 'furnace_foil_aluminum', - 'furnace_methanol_aluminum', - 'furnace_biomass_aluminum', - 'furnace_ethanol_aluminum', - 'furnace_gas_aluminum', - 'furnace_elec_aluminum', - 'furnace_h2_aluminum', - 'hp_gas_aluminum', - 'hp_elec_aluminum','fc_h2_aluminum', - 'solar_aluminum', 'dheat_aluminum', - 'furnace_loil_aluminum']) -p.plot_prices(subset=['aluminum'], baseyear=True) - - - - - +p.plot_activity( + baseyear=True, + subset=[ + "furnace_coal_aluminum", + "furnace_foil_aluminum", + "furnace_methanol_aluminum", + "furnace_biomass_aluminum", + "furnace_ethanol_aluminum", + "furnace_gas_aluminum", + "furnace_elec_aluminum", + "furnace_h2_aluminum", + "hp_gas_aluminum", + "hp_elec_aluminum", + "fc_h2_aluminum", + "solar_aluminum", + "dheat_aluminum", + "furnace_loil_aluminum", + ], +) +p.plot_capacity( + baseyear=True, + subset=[ + "furnace_coal_aluminum", + "furnace_foil_aluminum", + "furnace_methanol_aluminum", + "furnace_biomass_aluminum", + "furnace_ethanol_aluminum", + "furnace_gas_aluminum", + "furnace_elec_aluminum", + "furnace_h2_aluminum", + "hp_gas_aluminum", + "hp_elec_aluminum", + "fc_h2_aluminum", + "solar_aluminum", + "dheat_aluminum", + "furnace_loil_aluminum", + ], +) +p.plot_prices(subset=["aluminum"], baseyear=True) diff --git a/message_ix_models/model/material/build.py b/message_ix_models/model/material/build.py index 0692a96687..9d3e8c5325 100644 --- a/message_ix_models/model/material/build.py +++ b/message_ix_models/model/material/build.py @@ -111,8 +111,7 @@ def apply_spec( if not dry_run: for element in add: scenario.add_set( - set_name, - element.id if isinstance(element, Code) else element, + set_name, element.id if isinstance(element, Code) else element, ) if len(add): diff --git a/message_ix_models/model/material/data_aluminum.py b/message_ix_models/model/material/data_aluminum.py index 7a19d6c744..67eca8889a 100644 --- a/message_ix_models/model/material/data_aluminum.py +++ b/message_ix_models/model/material/data_aluminum.py @@ -52,8 +52,7 @@ def read_data_aluminum(scenario): # Read the file data_alu = pd.read_excel( - context.get_local_path("material", fname), - sheet_name=sheet_n, + context.get_local_path("material", fname), sheet_name=sheet_n, ) # Drop columns that don't contain useful information @@ -310,11 +309,7 @@ def gen_data_aluminum(scenario, dry_run=False): tec_ts = set(data_aluminum_ts.technology) # set of tecs in timeseries sheet for t in tec_ts: - common = dict( - time="year", - time_origin="year", - time_dest="year", - ) + common = dict(time="year", time_origin="year", time_dest="year",) param_name = data_aluminum_ts.loc[ (data_aluminum_ts["technology"] == t), "parameter" @@ -403,10 +398,7 @@ def gen_data_aluminum(scenario, dry_run=False): # Use all the model years for other relations... common_rel = dict( - year_rel=modelyears, - year_act=modelyears, - mode="M1", - relation=r, + year_rel=modelyears, year_act=modelyears, mode="M1", relation=r, ) for par_name in params: diff --git a/message_ix_models/model/material/data_cement.py b/message_ix_models/model/material/data_cement.py index c139155d6c..cb3baa324a 100644 --- a/message_ix_models/model/material/data_cement.py +++ b/message_ix_models/model/material/data_cement.py @@ -369,14 +369,14 @@ def derive_cement_demand(scenario, dry_run=False): columns={"year_act": "year", "value": "pop.mil", "node_loc": "region"} ) pop = pop[["region", "year", "pop.mil"]] - + base_demand = gen_mock_demand_cement(scenario) base_demand = base_demand.loc[base_demand.year == 2020].rename( columns={"value": "demand.tot.base", "node": "region"} ) - + # import pdb; pdb.set_trace() - + # base_demand = scenario.par("demand", {"commodity": "steel", "year": 2020}) # base_demand = base_demand.loc[base_demand.year >= 2020].rename( # columns={"value": "demand.tot.base", "node": "region"} diff --git a/message_ix_models/model/material/data_petro.py b/message_ix_models/model/material/data_petro.py index fbd9ecf35f..265d272fe9 100644 --- a/message_ix_models/model/material/data_petro.py +++ b/message_ix_models/model/material/data_petro.py @@ -378,11 +378,7 @@ def gen_data_petro_chemicals(scenario, dry_run=False): tec_ts = set(data_petro_ts.technology) # set of tecs in timeseries sheet for t in tec_ts: - common = dict( - time="year", - time_origin="year", - time_dest="year", - ) + common = dict(time="year", time_origin="year", time_dest="year",) param_name = data_petro_ts.loc[(data_petro_ts["technology"] == t), "parameter"] diff --git a/message_ix_models/model/material/data_steel.py b/message_ix_models/model/material/data_steel.py index ab907c8541..3a9dfe3f54 100644 --- a/message_ix_models/model/material/data_steel.py +++ b/message_ix_models/model/material/data_steel.py @@ -181,11 +181,7 @@ def gen_data_steel(scenario, dry_run=False): # Special treatment for time-varying params if t in tec_ts: - common = dict( - time="year", - time_origin="year", - time_dest="year", - ) + common = dict(time="year", time_origin="year", time_dest="year",) param_name = data_steel_ts.loc[ (data_steel_ts["technology"] == t), "parameter" @@ -392,10 +388,7 @@ def gen_data_steel(scenario, dry_run=False): ) common_rel = dict( - year_rel=modelyears, - year_act=modelyears, - mode="M1", - relation=r, + year_rel=modelyears, year_act=modelyears, mode="M1", relation=r, ) for par_name in params: @@ -453,7 +446,7 @@ def gen_data_steel(scenario, dry_run=False): # Create external demand param parname = "demand" demand = derive_steel_demand(scenario) - + df = make_df( parname, level="demand", @@ -492,16 +485,16 @@ def derive_steel_demand(scenario, dry_run=False): pop = pop.loc[pop.year_act >= 2020].rename( columns={"year_act": "year", "value": "pop.mil", "node_loc": "region"} ) - + # import pdb; pdb.set_trace() - + pop = pop[["region", "year", "pop.mil"]] - + base_demand = gen_mock_demand_steel(scenario) base_demand = base_demand.loc[base_demand.year == 2020].rename( columns={"value": "demand.tot.base", "node": "region"} ) - + # base_demand = scenario.par("demand", {"commodity": "steel", "year": 2020}) # base_demand = base_demand.loc[base_demand.year >= 2020].rename( # columns={"value": "demand.tot.base", "node": "region"} diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index 4fe56f7c50..58ea3d200e 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -6,7 +6,10 @@ import re from message_ix_models import ScenarioInfo -from message_data.tools.utilities.get_optimization_years import main as get_optimization_years +from message_data.tools.utilities.get_optimization_years import ( + main as get_optimization_years, +) + def modify_demand_and_hist_activity(scen): """Take care of demand changes due to the introduction of material parents @@ -367,6 +370,7 @@ def modify_demand_and_hist_activity(scen): scen.remove_par("growth_activity_lo", df) scen.commit(comment="remove growth_lo constraints") + def add_emission_accounting(scen): context = read_config() s_info = ScenarioInfo(scen) @@ -374,27 +378,42 @@ def add_emission_accounting(scen): # Obtain the emission factors only for material related technologies # TODO: Also residential and commercial technologies should be added to this list. tec_list = scen.par("emission_factor")["technology"].unique() - tec_list_materials = [i for i in tec_list if (("steel" in i) | ("aluminum" in i) \ - | ("petro" in i) | ("cement" in i) | ("ref" in i))] + tec_list_materials = [ + i + for i in tec_list + if ( + ("steel" in i) + | ("aluminum" in i) + | ("petro" in i) + | ("cement" in i) + | ("ref" in i) + ) + ] tec_list_materials.remove("refrigerant_recovery") tec_list_materials.remove("replacement_so2") tec_list_materials.remove("SO2_scrub_ref") - emission_factors = scen.par("emission_factor",filters = {"technology":tec_list_materials}) + emission_factors = scen.par( + "emission_factor", filters={"technology": tec_list_materials} + ) # Note: Emission factors for non-CO2 gases are in kt/ACT. For CO2 MtC/ACT. - relation_activity = emission_factors.assign(relation = lambda x: (x['emission'] + \ - '_Emission')) + relation_activity = emission_factors.assign( + relation=lambda x: (x["emission"] + "_Emission") + ) relation_activity["node_rel"] = relation_activity["node_loc"] - relation_activity.drop(["year_vtg","emission"],axis = 1,inplace = True) + relation_activity.drop(["year_vtg", "emission"], axis=1, inplace=True) relation_activity["year_rel"] = relation_activity["year_act"] - relation_activity = relation_activity[(relation_activity["relation"] != - 'PM2p5_Emission') & (relation_activity["relation"] != 'CO2_industry_Emission')] + relation_activity = relation_activity[ + (relation_activity["relation"] != "PM2p5_Emission") + & (relation_activity["relation"] != "CO2_industry_Emission") + ] scen.check_out() - scen.add_par("relation_activity",relation_activity) + scen.add_par("relation_activity", relation_activity) scen.commit("Emissions accounting for industry technologies added.") + def read_sector_data(scenario, sectname): # Read in technology-specific parameters from input xlsx @@ -414,8 +433,7 @@ def read_sector_data(scenario, sectname): # data_df = data_steel_china.append(data_cement_china, ignore_index=True) data_df = pd.read_excel( - context.get_local_path("material", context.datafile), - sheet_name=sheet_n, + context.get_local_path("material", context.datafile), sheet_name=sheet_n, ) # Clean the data @@ -514,8 +532,7 @@ def read_rel(scenario, filename): # Read the file data_rel = pd.read_excel( - context.get_local_path("material", filename), - sheet_name=sheet_n, + context.get_local_path("material", filename), sheet_name=sheet_n, ) return data_rel diff --git a/message_ix_models/model/material/util.py b/message_ix_models/model/material/util.py index 74ed21a48f..915aee386f 100644 --- a/message_ix_models/model/material/util.py +++ b/message_ix_models/model/material/util.py @@ -9,6 +9,7 @@ # ("material", "technology"), ] + def read_config(): """Read configuration from set.yaml.""" # TODO this is similar to transport.utils.read_config; make a common From 4199d4afd8353bed7adedbf02865cc2519827cba Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Mon, 29 Nov 2021 14:26:56 +0100 Subject: [PATCH 311/774] Fix documentation related issues --- message_ix_models/model/material/__init__.py | 2 +- message_ix_models/model/material/data.py | 2 +- .../model/material/data_aluminum.py | 9 +--- .../model/material/data_cement.py | 4 +- .../model/material/data_generic.py | 9 ---- .../model/material/data_steel.py | 2 +- message_ix_models/model/material/doc.rst | 54 +++++++++---------- 7 files changed, 31 insertions(+), 51 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index cb3461195e..e393a31918 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -246,7 +246,7 @@ def run_old_reporting(scenario_name, model_name): # Try to handle multiple data input functions from different materials def add_data(scenario, dry_run=False): - """Populate `scenario` with MESSAGE-Transport data.""" + """Populate `scenario` with MESSAGEix-Materials data.""" # Information about `scenario` info = ScenarioInfo(scenario) diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index 2b0828bd26..13c7683994 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -33,7 +33,7 @@ # Try to handle multiple data input functions from different materials def add_data(scenario, dry_run=False): - """Populate `scenario` with MESSAGE-Transport data.""" + """Populate `scenario` with MESSAGEix-Materials data.""" # Information about `scenario` info = ScenarioInfo(scenario) diff --git a/message_ix_models/model/material/data_aluminum.py b/message_ix_models/model/material/data_aluminum.py index 67eca8889a..7ac08dbaf2 100644 --- a/message_ix_models/model/material/data_aluminum.py +++ b/message_ix_models/model/material/data_aluminum.py @@ -1,10 +1,3 @@ -# -*- coding: utf-8 -*- -""" -Created on Wed Aug 26 14:43:41 2020 -Generate techno economic aluminum data based on aluminum_techno_economic.xlsx - -@author: unlu -""" import pandas as pd import numpy as np from collections import defaultdict @@ -551,7 +544,7 @@ def gen_mock_demand_aluminum(scenario): # This returns a df with columns ["region", "year", "demand.tot"] def derive_aluminum_demand(scenario, dry_run=False): - """Generate data for materials representation of power industry.""" + """Generate aluminum demand.""" # paths to r code and lca data rcode_path = Path(__file__).parents[0] / "material_demand" diff --git a/message_ix_models/model/material/data_cement.py b/message_ix_models/model/material/data_cement.py index cb3baa324a..1124ffd540 100644 --- a/message_ix_models/model/material/data_cement.py +++ b/message_ix_models/model/material/data_cement.py @@ -176,7 +176,7 @@ def gen_mock_demand_cement(scenario): def gen_data_cement(scenario, dry_run=False): - """Generate data for materials representation of steel industry.""" + """Generate data for materials representation of cement industry.""" # Load configuration config = read_config()["material"]["cement"] @@ -355,7 +355,7 @@ def gen_data_cement(scenario, dry_run=False): # This returns a df with columns ["region", "year", "demand.tot"] def derive_cement_demand(scenario, dry_run=False): - """Generate data for materials representation of power industry.""" + """Generate cement demand.""" # paths to r code and lca data rcode_path = Path(__file__).parents[0] / "material_demand" diff --git a/message_ix_models/model/material/data_generic.py b/message_ix_models/model/material/data_generic.py index 3ab4604fa2..949291da7e 100644 --- a/message_ix_models/model/material/data_generic.py +++ b/message_ix_models/model/material/data_generic.py @@ -1,12 +1,3 @@ -# -*- coding: utf-8 -*- -""" -Created on Wed Aug 26 14:54:24 2020 -Generate techno economic generic furncaedata based on -generic_furnace_boiler_techno_economic.xlsx - -@author: unlu - -""" from collections import defaultdict import logging diff --git a/message_ix_models/model/material/data_steel.py b/message_ix_models/model/material/data_steel.py index 3a9dfe3f54..3f48bc5655 100644 --- a/message_ix_models/model/material/data_steel.py +++ b/message_ix_models/model/material/data_steel.py @@ -472,7 +472,7 @@ def gen_data_steel(scenario, dry_run=False): # This returns a df with columns ["region", "year", "demand.tot"] def derive_steel_demand(scenario, dry_run=False): - """Generate data for materials representation of power industry.""" + """Generate steel demand.""" # paths to r code and lca data rcode_path = Path(__file__).parents[0] / "material_demand" diff --git a/message_ix_models/model/material/doc.rst b/message_ix_models/model/material/doc.rst index db797b5acc..85c39f2973 100644 --- a/message_ix_models/model/material/doc.rst +++ b/message_ix_models/model/material/doc.rst @@ -1,4 +1,4 @@ -Materials accounting +MESSAGEix-Materials ******************** .. warning:: @@ -44,8 +44,6 @@ During this process there are also recycling losses. Code reference ============== -.. currentmodule:: message_data.model.material - .. automodule:: message_data.model.material :members: @@ -58,7 +56,7 @@ Code reference Data preparation ################ -These modules do the necessary parametrization for each sector, which can be turned on and off individually under `DATA_FUNCTIONS` in `__init__.py`. +These modules are not necessary for the parametrization for each sector and they can be turned on and off individually under `DATA_FUNCTIONS` in `__init__.py`. For example, the buildings module (`data_buildings.py`) is only used when the buildings model outputs are given explicitly without linking the CHILLED/STURM model through a soft link. .. automodule:: message_data.model.material.data_aluminum @@ -82,19 +80,29 @@ For example, the buildings module (`data_buildings.py`) is only used when the bu .. automodule:: message_data.model.material.data_generic :members: -Reporting -########## +Build and Solve the model from CLI +================================== -.. automodule:: message_data.reporting.materials.reporting - :members: +Use ``mix-models materials build`` to add the material implementation on top of existing standard global (R12) scenarios, also giving the base scenario and indicating the relevant data location, e.g.:: + + $ mix-models \ + --url="ixmp://ixmp_dev/MESSAGEix-GLOBIOM_R12_CHN/baseline_new_macro#8" \ + --local-data "./data" material build + +Currently, a set of given base scenario names will be translated to own scenario names. +The output scenario will be ``ixmp://ixmp_dev-ixmp/MESSAGEix-Materials/{name}``, where {name} is a shortened version of the input scenario name. +Using an additional tag `--tag` can be used to add a suffix to the new scenario name. +This command line only builds the scenario but does not run it. To run the scenario, use ``mix-models materials solve``, giving the scenario name, e.g.:: + + $ mix-models material solve --scenario_name NoPolicy_R12 -.. automodule:: message_data.tools.post_processing.iamc_report_hackathon - :members: +Reporting +========= The reporting of the scenarios involves two steps. First step generates the specific variables that are related to materials and industry sectors. At the end of this step all the varibles are uploaded as ixmp timeseries objects. The reporting file is -generated under \...\message_data\message_data\reporting\materials with the name +generated under message_data\\reporting\\materials with the name “New_Reporting_MESSAGEix-Materials_scenario_name.xls”. The required versions of pyam and pyam-iamc are as follows respectively: 0.2.1a0 and 0.9.0. @@ -106,31 +114,19 @@ the general reporting code. This step combines all the variables that were uploa as timeseries to the scenario together with the generic IAMC variables. It also correctly reports the aggregate variables such as Final Energy and Emissions. -CLI usage -========= - -Use ``mix-models materials build`` to add the material implementation on top of existing standard global (R12) scenarios, also giving the base scenario and indicating the relevant data location, e.g.:: +To run the first step of the reporting use :: - $ mix-models \ - --url="ixmp://ixmp_dev/MESSAGEix-GLOBIOM_R12_CHN/baseline_new_macro#8" \ - --local-data "./data" material build - -Currently, a set of given base scenario names will be translated to own scenario names. -The output scenario will be ``ixmp://ixmp_dev-ixmp/MESSAGEix-Materials/{name}``, where {name} is a shortened version of the input scenario name. -Using an additional tag `--tag` can be used to add a suffix to the new scenario name. -This command line only builds the scenario but does not run it. To run the scenario, use ``mix-models materials solve``, giving the scenario name, e.g.:: - - $ mix-models material solve --scenario_name NoPolicy_R12 +$ mix-models material report-1 --model_name MESSAGEix-Materials --scenario_name xxxx -To run the first step of the reporting use $ mix-models material report-1 --model_name MESSAGEix-Materials --scenario_name xxxx +To run the second step of the reporting use :: -To run the second step of the reporting use $ mix-models material report-2 --model_name MESSAGEix-Materials --scenario_name xxxx +$ mix-models material report-2 --model_name MESSAGEix-Materials --scenario_name xxxx Data, metadata, and configuration ================================= Binary/raw data files ---------------------- +##################### The code relies on the following input files, stored in :file:`data/material/`: @@ -205,7 +201,7 @@ The code relies on the following input files, stored in :file:`data/material/`: :language: yaml R code and dependencies ------------------------ +####################### :file:`ADVANCE_lca_coefficients_embedded.R` The code processing the material intensity coefficients of power plants is written in R and integrated into the Python workflow via the Python package `rpy2`. From bc03dfaa179b953c2c207cf7525fa0fb382d4320 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Mon, 29 Nov 2021 22:59:18 +0100 Subject: [PATCH 312/774] Rename a function with a confusing name revise the documentation minor formatting --- message_ix_models/model/material/__init__.py | 12 ++++-------- message_ix_models/model/material/doc.rst | 9 +++++++++ 2 files changed, 13 insertions(+), 8 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index e393a31918..aef8f9da63 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -114,8 +114,8 @@ def create_bare(context, regions, dry_run): ) @click.option("--tag", default="", help="Suffix to the scenario name") @click.pass_obj -def solve(context, datafile, tag): - """Build model. +def build_scen(context, datafile, tag): + """Build a scenario. Use the --url option to specify the base scenario. """ @@ -129,7 +129,6 @@ def solve(context, datafile, tag): "NPi2020-con-prim-dir-ncr": "NPi", "NPi2020_1000-con-prim-dir-ncr": "NPi2020_1000", "NPi2020_400-con-prim-dir-ncr": "NPi2020_400", - # "DIAG-C30-const_E414": "baseline_test", }.get(context.scenario_info["scenario"]) # context.metadata_path = context.metadata_path / "data" @@ -148,9 +147,6 @@ def solve(context, datafile, tag): # Set the latest version as default scenario.set_as_default() - # Solve - # scenario.solve() - @cli.command("solve") @click.option("--scenario_name", default="NoPolicy") @@ -164,9 +160,9 @@ def solve(context, datafile, tag): @click.pass_obj # @click.pass_obj def solve_scen(context, datafile, model_name, scenario_name): - """Build model. + """Solve a scenario. - Use the --url option to specify the base scenario. + Use the --model_name and --scenario_name option to specify the scenario to solve. """ # Clone and set up from message_ix import Scenario diff --git a/message_ix_models/model/material/doc.rst b/message_ix_models/model/material/doc.rst index 85c39f2973..42cf930594 100644 --- a/message_ix_models/model/material/doc.rst +++ b/message_ix_models/model/material/doc.rst @@ -9,6 +9,9 @@ MESSAGEix-Materials `materials `_ label and `current tracking issue (#248) `_. +Description +============== + This module adds (life-cycle) accounting of materials associated with technologies and demands in MESSAGEix-GLOBIOM. The implementation currently supports four key energy/emission-intensive material industries: Steel, Aluminum, Cement, and Petrochemical. @@ -17,6 +20,9 @@ The petrochemical sector will soon expand to cover production processes of plast The technologies to represent for the primary production processes of the materials are chosen based on their emission mitigation potential and the degree of commercialization. +Processing secondary materials +----------------------------- + After the primary production stages of the materials, finishing and manufacturing processes are carried out which results in a complete product. For metals, during the manufacturing process new scrap is formed as residue. This type of scrap requires less preparation before recycling and has a higher quality as it is the direct product of the manufacturing unlike the old scrap which is formed at the end of the life cycle of a product. @@ -26,6 +32,9 @@ The products that are produced are used in different end-use sectors as stocks a In the model, each year only certain quantity of products are available for recycling and this ratio is exogenously determined based on historical values. The end-of-life products coming from buildings and power sector can be endogenously determined in case the relevant links are turned on. +Modeling recycling decisions +~~~~~~~~~~~~~~~~~~~~ + In the model, there is a minimum recycling rate specified for different materials and it is based on the historical recycling rates. This parameter can also be used to represent regulations in different regions. In the end recycling rate is a model decision which can be higher than the minimum rate depending on the economic attractiveness. From fb22e559fb4487524fdbfc4b6cb89bfc20d9d9bf Mon Sep 17 00:00:00 2001 From: Jihoon Date: Mon, 29 Nov 2021 23:13:47 +0100 Subject: [PATCH 313/774] Fix .rst formatting --- message_ix_models/model/material/doc.rst | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/message_ix_models/model/material/doc.rst b/message_ix_models/model/material/doc.rst index 42cf930594..8b87b62dfc 100644 --- a/message_ix_models/model/material/doc.rst +++ b/message_ix_models/model/material/doc.rst @@ -10,7 +10,7 @@ MESSAGEix-Materials `current tracking issue (#248) `_. Description -============== +=========== This module adds (life-cycle) accounting of materials associated with technologies and demands in MESSAGEix-GLOBIOM. @@ -21,7 +21,7 @@ The technologies to represent for the primary production processes of the materi of commercialization. Processing secondary materials ------------------------------ +------------------------------ After the primary production stages of the materials, finishing and manufacturing processes are carried out which results in a complete product. For metals, during the manufacturing process new scrap is formed as residue. This type of scrap requires less preparation before recycling and has a higher quality @@ -33,7 +33,7 @@ In the model, each year only certain quantity of products are available for recy The end-of-life products coming from buildings and power sector can be endogenously determined in case the relevant links are turned on. Modeling recycling decisions -~~~~~~~~~~~~~~~~~~~~ +############################ In the model, there is a minimum recycling rate specified for different materials and it is based on the historical recycling rates. This parameter can also be used to represent regulations in different regions. In the end recycling rate is a model decision which can be higher than the minimum rate depending on the economic attractiveness. From 7b7144176f7129493af792ed14b913dd24d1910a Mon Sep 17 00:00:00 2001 From: Jihoon Date: Mon, 29 Nov 2021 23:21:15 +0100 Subject: [PATCH 314/774] Fix .rst headers --- message_ix_models/model/material/doc.rst | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/message_ix_models/model/material/doc.rst b/message_ix_models/model/material/doc.rst index 8b87b62dfc..2ac0cd07d6 100644 --- a/message_ix_models/model/material/doc.rst +++ b/message_ix_models/model/material/doc.rst @@ -33,7 +33,7 @@ In the model, each year only certain quantity of products are available for recy The end-of-life products coming from buildings and power sector can be endogenously determined in case the relevant links are turned on. Modeling recycling decisions -############################ +---------------------------- In the model, there is a minimum recycling rate specified for different materials and it is based on the historical recycling rates. This parameter can also be used to represent regulations in different regions. In the end recycling rate is a model decision which can be higher than the minimum rate depending on the economic attractiveness. @@ -63,7 +63,7 @@ Code reference :members: Data preparation -################ +---------------- These modules are not necessary for the parametrization for each sector and they can be turned on and off individually under `DATA_FUNCTIONS` in `__init__.py`. For example, the buildings module (`data_buildings.py`) is only used when the buildings model outputs are given explicitly without linking the CHILLED/STURM model through a soft link. @@ -135,7 +135,7 @@ Data, metadata, and configuration ================================= Binary/raw data files -##################### +--------------------- The code relies on the following input files, stored in :file:`data/material/`: @@ -210,7 +210,7 @@ The code relies on the following input files, stored in :file:`data/material/`: :language: yaml R code and dependencies -####################### +======================= :file:`ADVANCE_lca_coefficients_embedded.R` The code processing the material intensity coefficients of power plants is written in R and integrated into the Python workflow via the Python package `rpy2`. From a51c4db569a9bf708b62161450920ccec7fa85d4 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Mon, 6 Dec 2021 16:13:02 +0100 Subject: [PATCH 315/774] A small update to the doc.rst about the output scenario name --- message_ix_models/model/material/__init__.py | 3 +++ message_ix_models/model/material/doc.rst | 8 +++++--- 2 files changed, 8 insertions(+), 3 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index aef8f9da63..f9d4e03f7f 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -131,6 +131,9 @@ def build_scen(context, datafile, tag): "NPi2020_400-con-prim-dir-ncr": "NPi2020_400", }.get(context.scenario_info["scenario"]) + if type(output_scenario_name).__name__ == "NoneType": + output_scenario_name = context.scenario_info["scenario"] + # context.metadata_path = context.metadata_path / "data" context.datafile = datafile diff --git a/message_ix_models/model/material/doc.rst b/message_ix_models/model/material/doc.rst index 2ac0cd07d6..4f066e09a7 100644 --- a/message_ix_models/model/material/doc.rst +++ b/message_ix_models/model/material/doc.rst @@ -98,9 +98,11 @@ Use ``mix-models materials build`` to add the material implementation on top of --url="ixmp://ixmp_dev/MESSAGEix-GLOBIOM_R12_CHN/baseline_new_macro#8" \ --local-data "./data" material build -Currently, a set of given base scenario names will be translated to own scenario names. -The output scenario will be ``ixmp://ixmp_dev-ixmp/MESSAGEix-Materials/{name}``, where {name} is a shortened version of the input scenario name. -Using an additional tag `--tag` can be used to add a suffix to the new scenario name. +It can be helpful to note that this command will not work for all R12 scenarios because of dependencies on certain levels or commodities described in :file:`set.yaml`. +Currently, a set of pre-defined base scenario names will be translated to own scenario names. But when an unknown base scenario name is given, we reuse it for the output scenario name. +The output scenario will be ``ixmp://ixmp_dev-ixmp/MESSAGEix-Materials/{name}``, where {name} is the output scenario name. + +Using an additional tag `--tag` can be used to add an additional suffix to the new scenario name. This command line only builds the scenario but does not run it. To run the scenario, use ``mix-models materials solve``, giving the scenario name, e.g.:: $ mix-models material solve --scenario_name NoPolicy_R12 From de9a9b44b23c30e8530283983e0d58c24e728233 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Mon, 13 Dec 2021 17:13:03 +0100 Subject: [PATCH 316/774] Fix the absolute path problem in the r demand projection script --- .../model/material/data_aluminum.py | 37 ++--- .../model/material/data_cement.py | 5 +- .../model/material/data_steel.py | 68 ++------- .../material_demand/init_modularized.R | 135 +++++++++--------- 4 files changed, 102 insertions(+), 143 deletions(-) diff --git a/message_ix_models/model/material/data_aluminum.py b/message_ix_models/model/material/data_aluminum.py index 7ac08dbaf2..6a61f0f7f5 100644 --- a/message_ix_models/model/material/data_aluminum.py +++ b/message_ix_models/model/material/data_aluminum.py @@ -45,7 +45,8 @@ def read_data_aluminum(scenario): # Read the file data_alu = pd.read_excel( - context.get_local_path("material", fname), sheet_name=sheet_n, + context.get_local_path("material", fname), + sheet_name=sheet_n, ) # Drop columns that don't contain useful information @@ -302,7 +303,11 @@ def gen_data_aluminum(scenario, dry_run=False): tec_ts = set(data_aluminum_ts.technology) # set of tecs in timeseries sheet for t in tec_ts: - common = dict(time="year", time_origin="year", time_dest="year",) + common = dict( + time="year", + time_origin="year", + time_dest="year", + ) param_name = data_aluminum_ts.loc[ (data_aluminum_ts["technology"] == t), "parameter" @@ -391,7 +396,10 @@ def gen_data_aluminum(scenario, dry_run=False): # Use all the model years for other relations... common_rel = dict( - year_rel=modelyears, year_act=modelyears, mode="M1", relation=r, + year_rel=modelyears, + year_act=modelyears, + mode="M1", + relation=r, ) for par_name in params: @@ -515,18 +523,6 @@ def gen_mock_demand_aluminum(scenario): .multiply(demand2020_al["Val"], axis=0) ) - # Do this if we have 2020 demand values for buildings - # sp = get_spec() - # if 'buildings' in sp['add'].set['technology']: - # val = get_scen_mat_demand("aluminum",scenario) - # print("Base year demand of {}:".format("aluminum"), val) - # # d = d - val.value - # # Scale down all years' demand values by the 2020 ratio - # demand2020_al.iloc[:,3:] = demand2020_al.iloc[:,3:].\ - # multiply(demand2020_al[2020]- val['value'], axis=0).\ - # div(demand2020_al[2020], axis=0) - # print("UPDATE {} demand for 2020!".format("aluminum")) - # demand2020_al = pd.melt( demand2020_al.drop(["Val", "Scenario"], axis=1), id_vars=["node"], @@ -547,6 +543,7 @@ def derive_aluminum_demand(scenario, dry_run=False): """Generate aluminum demand.""" # paths to r code and lca data rcode_path = Path(__file__).parents[0] / "material_demand" + context = read_config() # source R code r = ro.r @@ -567,17 +564,13 @@ def derive_aluminum_demand(scenario, dry_run=False): columns={"value": "demand.tot.base", "node": "region"} ) - # base_demand = scenario.par("demand", {"commodity": "steel", "year": 2020}) - # base_demand = base_demand.loc[base_demand.year >= 2020].rename( - # columns={"value": "demand.tot.base", "node": "region"} - # ) - # base_demand = base_demand[["region", "year", "demand.tot.base"]] - # call R function with type conversion with localconverter(ro.default_converter + pandas2ri.converter): # GDP is only in MER in scenario. # To get PPP GDP, it is read externally from the R side - df = r.derive_aluminum_demand(pop, base_demand) + df = r.derive_aluminum_demand( + pop, base_demand, str(context.get_local_path("material")) + ) df.year = df.year.astype(int) return df diff --git a/message_ix_models/model/material/data_cement.py b/message_ix_models/model/material/data_cement.py index 1124ffd540..03a6b7bfc2 100644 --- a/message_ix_models/model/material/data_cement.py +++ b/message_ix_models/model/material/data_cement.py @@ -358,6 +358,7 @@ def derive_cement_demand(scenario, dry_run=False): """Generate cement demand.""" # paths to r code and lca data rcode_path = Path(__file__).parents[0] / "material_demand" + context = read_config() # source R code r = ro.r @@ -387,7 +388,9 @@ def derive_cement_demand(scenario, dry_run=False): with localconverter(ro.default_converter + pandas2ri.converter): # GDP is only in MER in scenario. # To get PPP GDP, it is read externally from the R side - df = r.derive_cement_demand(pop, base_demand) + df = r.derive_cement_demand( + pop, base_demand, str(context.get_local_path("material")) + ) df.year = df.year.astype(int) return df diff --git a/message_ix_models/model/material/data_steel.py b/message_ix_models/model/material/data_steel.py index 3f48bc5655..e844f51973 100644 --- a/message_ix_models/model/material/data_steel.py +++ b/message_ix_models/model/material/data_steel.py @@ -24,13 +24,6 @@ ) from . import get_spec -# annual average growth rate by decade (2020-2110) -# gdp_growth = [0.121448215899944, 0.0733079014579874, -# 0.0348154093342843, 0.021827616787921, \ -# 0.0134425983942219, 0.0108320197485592, \ -# 0.00884341208063, 0.00829374133206562, \ -# 0.00649794573935969, 0.00649794573935969] - # Generate a fake steel demand def gen_mock_demand_steel(scenario): @@ -88,47 +81,12 @@ def gen_mock_demand_steel(scenario): .multiply(demand2010_steel["Val"], axis=0) ) - # Do this if we have 2020 demand values for buildings - # sp = get_spec() - # if 'buildings' in sp['add'].set['technology']: - # val = get_scen_mat_demand("steel",scenario) - # print("Base year demand of {}:".format("steel"), val) - # # d = d - val.value - # # Scale down all years' demand values by the 2020 ratio - # demand2010_steel.iloc[:,3:] = demand2010_steel.iloc[:,3:].\ - # multiply(demand2010_steel[2020]- val['value'], axis=0).\ - # div(demand2010_steel[2020], axis=0) - # print("UPDATE {} demand for 2020!".format("steel")) - # demand2010_steel = pd.melt( demand2010_steel.drop(["Val", "Scenario"], axis=1), id_vars=["node"], var_name="year", value_name="value", ) - # - # print("This is steel demand") - # print(demand2010_steel) - # - # baseyear = list(range(2020, 2110+1, 10)) - # gdp_growth_interp = np.interp(modelyears, baseyear, gdp_growth) - # - # i = 0 - # values = [] - # - # # Assume 5 year duration at the beginning - # duration_period = (pd.Series(modelyears) - \ - # pd.Series(modelyears).shift(1)).tolist() - # duration_period[0] = 5 - # - # val = (demand2010_steel.val * (1+ 0.147718884937996/2) ** duration_period[i]) - # values.append(val) - # - # for element in gdp_growth_interp: - # i = i + 1 - # if i < len(modelyears): - # val = (val * (1+ element/2) ** duration_period[i]) - # values.append(val) return demand2010_steel @@ -181,7 +139,11 @@ def gen_data_steel(scenario, dry_run=False): # Special treatment for time-varying params if t in tec_ts: - common = dict(time="year", time_origin="year", time_dest="year",) + common = dict( + time="year", + time_origin="year", + time_dest="year", + ) param_name = data_steel_ts.loc[ (data_steel_ts["technology"] == t), "parameter" @@ -317,7 +279,7 @@ def gen_data_steel(scenario, dry_run=False): # Copy parameters to all regions, when node_loc is not GLB if (len(regions) == 1) and (rg != global_region): df["node_loc"] = None - df = df.pipe(broadcast, node_loc=nodes) # .pipe(same_node) + df = df.pipe(broadcast, node_loc=nodes) # Use same_node only for non-trade technologies if (lev != "import") and (lev != "export"): df = df.pipe(same_node) @@ -388,7 +350,10 @@ def gen_data_steel(scenario, dry_run=False): ) common_rel = dict( - year_rel=modelyears, year_act=modelyears, mode="M1", relation=r, + year_rel=modelyears, + year_act=modelyears, + mode="M1", + relation=r, ) for par_name in params: @@ -475,11 +440,12 @@ def derive_steel_demand(scenario, dry_run=False): """Generate steel demand.""" # paths to r code and lca data rcode_path = Path(__file__).parents[0] / "material_demand" + context = read_config() # source R code r = ro.r r.source(str(rcode_path / "init_modularized.R")) - + context.get_local_path("material") # Read population and baseline demand for materials pop = scenario.par("bound_activity_up", {"technology": "Population"}) pop = pop.loc[pop.year_act >= 2020].rename( @@ -495,17 +461,13 @@ def derive_steel_demand(scenario, dry_run=False): columns={"value": "demand.tot.base", "node": "region"} ) - # base_demand = scenario.par("demand", {"commodity": "steel", "year": 2020}) - # base_demand = base_demand.loc[base_demand.year >= 2020].rename( - # columns={"value": "demand.tot.base", "node": "region"} - # ) - # base_demand = base_demand[["region", "year", "demand.tot.base"]] - # call R function with type conversion with localconverter(ro.default_converter + pandas2ri.converter): # GDP is only in MER in scenario. # To get PPP GDP, it is read externally from the R side - df = r.derive_steel_demand(pop, base_demand) + df = r.derive_steel_demand( + pop, base_demand, str(context.get_local_path("material")) + ) df.year = df.year.astype(int) return df diff --git a/message_ix_models/model/material/material_demand/init_modularized.R b/message_ix_models/model/material/material_demand/init_modularized.R index ffc85e97d4..91ab66cd6f 100644 --- a/message_ix_models/model/material/material_demand/init_modularized.R +++ b/message_ix_models/model/material/material_demand/init_modularized.R @@ -4,161 +4,162 @@ library(sitools) # Data file names and path # datapath = '../../../../data/material/' -datapath = 'H:/MyDocuments/MESSAGE/message_data/data/material/' - -file_cement = "CEMENT.BvR2010.xlsx" -file_steel = "STEEL_database_2012.xlsx" -file_al = "demand_aluminum.xlsx" -file_gdp = "iamc_db ENGAGE baseline GDP PPP.xlsx" -# file_pop = "iamc_db ENGAGE baseline population.xlsx" -# -gdp.ppp = read_excel(paste0(datapath, file_gdp), sheet="data_R12") %>% filter(Scenario == "baseline") %>% - select(region=Region, `2020`:`2100`) %>% - pivot_longer(cols=`2020`:`2100`, names_to="year", values_to="gdp.ppp") %>% # billion US$2010/yr OR local currency - filter(region != "World") %>% - mutate(year = as.integer(year), - region = paste0('R12_', region)) - -# popul = read_excel(paste0(datapath, file_pop), sheet="data_R12") %>% -# select(Region, `2020`:`2100`) %>% -# pivot_longer(cols=`2020`:`2100`, names_to="year", values_to="pop.mil") -# df_input = gdp.ppp %>% left_join(popul, by=c("Region", "year")) %>% -# filter(Region != "World") %>% -# mutate(gdp.pcap = gdp.ppp*giga/pop.mil/mega, # USD/cap -# year = as.integer(year), -# Region = paste0('R12_', Region)) - -derive_steel_demand <- function(df_pop, df_demand) { +# datapath = 'H:/MyDocuments/MESSAGE/message_data/data/material/' + +file_cement = "/CEMENT.BvR2010.xlsx" +file_steel = "/STEEL_database_2012.xlsx" +file_al = "/demand_aluminum.xlsx" +file_gdp = "/iamc_db ENGAGE baseline GDP PPP.xlsx" + +derive_steel_demand <- function(df_pop, df_demand, datapath) { # df_in will have columns: # region # year # gdp.pcap # population - - df_raw_steel_consumption = read_excel(paste0(datapath, file_steel), + + gdp.ppp = read_excel(paste0(datapath, file_gdp), sheet="data_R12") %>% filter(Scenario == "baseline") %>% + select(region=Region, `2020`:`2100`) %>% + pivot_longer(cols=`2020`:`2100`, names_to="year", values_to="gdp.ppp") %>% # billion US$2010/yr OR local currency + filter(region != "World") %>% + mutate(year = as.integer(year), region = paste0('R12_', region)) + + df_raw_steel_consumption = read_excel(paste0(datapath, file_steel), sheet="Consumption regions", n_max=27) %>% # kt - select(-2) %>% - pivot_longer(cols="1970":"2012", - values_to='consumption', names_to='year') - df_population = read_excel(paste0(datapath, file_cement), + select(-2) %>% + pivot_longer(cols="1970":"2012", + values_to='consumption', names_to='year') + df_population = read_excel(paste0(datapath, file_cement), sheet="Timer_POP", skip=3, n_max=27) %>% # million pivot_longer(cols="1970":"2100", values_to='pop', names_to='year') - df_gdp = read_excel(paste0(datapath, file_cement), + df_gdp = read_excel(paste0(datapath, file_cement), sheet="Timer_GDPCAP", skip=3, n_max=27) %>% # million pivot_longer(cols="1970":"2100", values_to='gdp.pcap', names_to='year') - + #### Organize data #### names(df_raw_steel_consumption)[1] = names(df_population)[1] = names(df_gdp)[1] = "reg_no" - names(df_raw_steel_consumption)[2] = + names(df_raw_steel_consumption)[2] = names(df_population)[2] = names(df_gdp)[2] = "region" - df_steel_consumption = df_raw_steel_consumption %>% + df_steel_consumption = df_raw_steel_consumption %>% left_join(df_population %>% select(-region)) %>% left_join(df_gdp %>% select(-region)) %>% mutate(cons.pcap = consumption/pop, del.t = as.numeric(year)-2010) %>% #kg/cap drop_na() %>% filter(cons.pcap > 0) - + nlnit.s = nls(cons.pcap ~ a * exp(b/gdp.pcap) * (1-m)^del.t, data=df_steel_consumption, start=list(a=600, b=-10000, m=0)) - + df_in = df_pop %>% left_join(df_demand %>% select(-year)) %>% # df_demand is only for 2020 inner_join(gdp.ppp) %>% mutate(del.t = year - 2010, gdp.pcap = gdp.ppp*giga/pop.mil/mega) - demand = df_in %>% + demand = df_in %>% mutate(demand.pcap0 = predict(nlnit.s, .)) %>% #kg/cap group_by(region) %>% mutate(demand.pcap.base = first(demand.tot.base*giga/pop.mil/mega)) %>% mutate(gap.base = first(demand.pcap.base - demand.pcap0)) %>% mutate(demand.pcap = demand.pcap0 + gap.base * gompertz(9, 0.1, y=year)) %>% # Bas' equation mutate(demand.tot = demand.pcap * pop.mil * mega / giga) # Mt - + # Add 2110 spaceholder demand = demand %>% rbind(demand %>% filter(year==2100) %>% mutate(year = 2110)) - + return(demand %>% select(node=region, year, value=demand.tot) %>% arrange(year, node)) # Mt -} +} -derive_cement_demand <- function(df_pop, df_demand) { +derive_cement_demand <- function(df_pop, df_demand, datapath) { # df_in will have columns: # region # year # gdp.pcap # population (in mil.) - - df_raw_cement_consumption = read_excel(paste0(datapath, file_cement), + + gdp.ppp = read_excel(paste0(datapath, file_gdp), sheet="data_R12") %>% filter(Scenario == "baseline") %>% + select(region=Region, `2020`:`2100`) %>% + pivot_longer(cols=`2020`:`2100`, names_to="year", values_to="gdp.ppp") %>% # billion US$2010/yr OR local currency + filter(region != "World") %>% + mutate(year = as.integer(year), region = paste0('R12_', region)) + + df_raw_cement_consumption = read_excel(paste0(datapath, file_cement), sheet="Regions", skip=122, n_max=27) %>% # kt pivot_longer(cols="1970":"2010", values_to='consumption', names_to='year') - df_population = read_excel(paste0(datapath, file_cement), + df_population = read_excel(paste0(datapath, file_cement), sheet="Timer_POP", skip=3, n_max=27) %>% # million pivot_longer(cols="1970":"2100", values_to='pop', names_to='year') - df_gdp = read_excel(paste0(datapath, file_cement), + df_gdp = read_excel(paste0(datapath, file_cement), sheet="Timer_GDPCAP", skip=3, n_max=27) %>% # million pivot_longer(cols="1970":"2100", values_to='gdp.pcap', names_to='year') - + #### Organize data #### - names(df_raw_cement_consumption)[1] = + names(df_raw_cement_consumption)[1] = names(df_population)[1] = names(df_gdp)[1] = "reg_no" - names(df_raw_cement_consumption)[2] = + names(df_raw_cement_consumption)[2] = names(df_population)[2] = names(df_gdp)[2] = "region" - df_cement_consumption = df_raw_cement_consumption %>% + df_cement_consumption = df_raw_cement_consumption %>% left_join(df_population %>% select(-region)) %>% left_join(df_gdp %>% select(-region)) %>% mutate(cons.pcap = consumption/pop/1e6, del.t= as.numeric(year) - 2010) %>% #kg/cap drop_na() %>% filter(cons.pcap > 0) - + nlni.c = nls(cons.pcap ~ a * exp(b/gdp.pcap), data=df_cement_consumption, start=list(a=500, b=-3000)) - + df_in = df_pop %>% left_join(df_demand %>% select(-year)) %>% # df_demand is only for 2020 inner_join(gdp.ppp) %>% mutate(gdp.pcap = gdp.ppp*giga/pop.mil/mega) - demand = df_in %>% + demand = df_in %>% mutate(demand.pcap0 = predict(nlni.c, .)) %>% #kg/cap group_by(region) %>% mutate(demand.pcap.base = first(demand.tot.base*giga/pop.mil/mega)) %>% mutate(gap.base = first(demand.pcap.base - demand.pcap0)) %>% mutate(demand.pcap = demand.pcap0 + gap.base * gompertz(10, 0.1, y=year)) %>% # Bas' equation mutate(demand.tot = demand.pcap * pop.mil * mega / giga) # Mt - + # Add 2110 spaceholder demand = demand %>% rbind(demand %>% filter(year==2100) %>% mutate(year = 2110)) - + return(demand %>% select(node=region, year, value=demand.tot) %>% arrange(year, node)) # Mt -} +} + +derive_aluminum_demand <- function(df_pop, df_demand, datapath) { -derive_aluminum_demand <- function(df_pop, df_demand) { + gdp.ppp = read_excel(paste0(datapath, file_gdp), sheet="data_R12") %>% filter(Scenario == "baseline") %>% + select(region=Region, `2020`:`2100`) %>% + pivot_longer(cols=`2020`:`2100`, names_to="year", values_to="gdp.ppp") %>% # billion US$2010/yr OR local currency + filter(region != "World") %>% + mutate(year = as.integer(year), region = paste0('R12_', region)) # Aluminum xls input already has population and gdp df_raw_aluminum_consumption = read_excel(paste0(datapath, file_al), sheet="final_table", n_max = 378) # kt - + #### Organize data #### df_aluminum_consumption = df_raw_aluminum_consumption %>% mutate(cons.pcap = consumption/pop, del.t= as.numeric(year) - 2010) %>% #kg/cap drop_na() %>% filter(cons.pcap > 0) - + # nlnit.a = nls(cons.pcap ~ a * exp(b/gdp.pcap) * (1-m)^del.t, data=df_aluminum_consumption, start=list(a=600, b=-10000, m=0)) nlni.a = nls(cons.pcap ~ a * exp(b/gdp.pcap), data=df_aluminum_consumption, start=list(a=600, b=-10000)) - + df_in = df_pop %>% left_join(df_demand %>% select(-year)) %>% # df_demand is only for 2020 inner_join(gdp.ppp) %>% mutate(gdp.pcap = gdp.ppp*giga/pop.mil/mega) - demand = df_in %>% + demand = df_in %>% mutate(demand.pcap0 = predict(nlni.a, .)) %>% #kg/cap group_by(region) %>% mutate(demand.pcap.base = first(demand.tot.base*giga/pop.mil/mega)) %>% mutate(gap.base = first(demand.pcap.base - demand.pcap0)) %>% mutate(demand.pcap = demand.pcap0 + gap.base * gompertz(9, 0.1, y=year)) %>% # Bas' equation mutate(demand.tot = demand.pcap * pop.mil * mega / giga) # Mt - + # Add 2110 spaceholder demand = demand %>% rbind(demand %>% filter(year==2100) %>% mutate(year = 2110)) - + return(demand %>% select(node=region, year, value=demand.tot) %>% arrange(year, node)) # Mt -} +} gompertz <- function(phi, mu, y, baseyear=2020) { @@ -167,7 +168,7 @@ gompertz <- function(phi, mu, y, baseyear=2020) { #### test # year = seq(2020, 2100, 10) -# df = data.frame(region = "Korea", gdp.pcap = seq(3000, 50000, length.out = length(year)), year, -# population = seq(300, 500, length.out = length(year))) -# a = derive_cement_demand(df) +# df = data.frame(region = "Korea", gdp.pcap = seq(3000, 50000, length.out = length(year)), year, +# population = seq(300, 500, length.out = length(year))) +# a = derive_cement_demand(df) # b = derive_steel_demand(df) From 2736d40bc137b5ec77cc977f87352ab482ea324b Mon Sep 17 00:00:00 2001 From: Jihoon Date: Mon, 13 Dec 2021 17:17:36 +0100 Subject: [PATCH 317/774] Add the two offgas commodities and update the input/output params - mainly for the future use, but doesn't affect the behavior much - at the same time, the coke input value is updated based on the iea global share of coke among the total coal/coke/bfg final consumption at the steel sector --- .../data/material/Global_steel_cement_MESSAGE.xlsx | 4 ++-- message_ix_models/data/material/set.yaml | 2 ++ 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx index 25b32bdcb0..f1d915875b 100644 --- a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx +++ b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:adbe50ad07ada49776626b24c92e73e4916edee50c58ec32b90b11e5b1adc14e -size 94987 +oid sha256:eb9fc82954b548b9b83860ed351132de4bc3e5a9ebd1bbffdfef89f4678d5835 +size 96926 diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 2f25150370..006050bd84 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -246,6 +246,8 @@ steel: - limestone_iron - coke_iron - slag_iron + - co_gas + - bf_gas level: add: From 6c86b61637890382ced1c8e330ce57a8388d541e Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Mon, 24 Jan 2022 10:44:01 +0100 Subject: [PATCH 318/774] Organize aluminum and petrochemicals data files --- message_ix_models/data/material/aluminum_techno_economic.xlsx | 4 ++-- .../data/material/petrochemicals_techno_economic.xlsx | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/message_ix_models/data/material/aluminum_techno_economic.xlsx b/message_ix_models/data/material/aluminum_techno_economic.xlsx index 3a73c3f24b..dd56542de4 100644 --- a/message_ix_models/data/material/aluminum_techno_economic.xlsx +++ b/message_ix_models/data/material/aluminum_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:02560c2866deeebf2603ef5ef04ba42c7c1a01786559538fa0ffa66be60203ec -size 120761 +oid sha256:32dcbc45de9bd5d208e8f3a9489f5933aa4360dfb645cdb3080e3351ca2cea05 +size 117334 diff --git a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx index 3365995aa8..2e3c757668 100644 --- a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx +++ b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:b4ad793b75f2bdbabeff6032086b973c087b75a71af228f9bb58efcdfa3d216e -size 662318 +oid sha256:e65937c154232d33241e8e7446e6e06f65bfcce3afbce5663de0882ee3f7a07d +size 661861 From d9d4c7c6cff3cf1bac927ce1b9118224675a0df9 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 3 Feb 2022 15:26:03 +0100 Subject: [PATCH 319/774] Delete material_testscript.py --- .../model/material/material_testscript.py | 218 ------------------ 1 file changed, 218 deletions(-) delete mode 100644 message_ix_models/model/material/material_testscript.py diff --git a/message_ix_models/model/material/material_testscript.py b/message_ix_models/model/material/material_testscript.py deleted file mode 100644 index 7ff8f6453e..0000000000 --- a/message_ix_models/model/material/material_testscript.py +++ /dev/null @@ -1,218 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Spyder Editor - -This is a temporary script file. -""" - -import ixmp -import message_ix as mix -from message_ix import Scenario - -# import message_data.model.material.data as dt -# from message_data.model.material.plotting import Plots -# import pyam - - -from message_data.tools import Context - -from message_ix_models import ScenarioInfo -from message_ix import make_df -from message_ix_models.util import ( - broadcast, - make_io, - copy_column, - make_matched_dfs, - set_info, -) - -from message_data.model.create import create_res -from message_data.model.material import build, get_spec - -from message_data.model.material.util import read_config - - -#%% Main test run based on a MESSAGE scenario - -from message_data.model.create import create_res - -# Create Context obj -Context._instance = [] -ctx = Context() - -# Set default scenario/model names - Later coming from CLI -ctx.platform_info.setdefault("name", "ixmp_dev") -ctx.scenario_info.setdefault("model", "Material_test_MESSAGE_China") -ctx.scenario_info.setdefault("scenario", "baseline") -# ctx['period_start'] = 2020 -# ctx['regions'] = 'China' -# ctx['ssp'] = 'SSP2' # placeholder -ctx["scentype"] = "C30-const_E414" -ctx["datafile"] = "China_steel_cement_MESSAGE.xlsx" - -# Use general code to create a Scenario with some stuff in it -scen = create_res(context=ctx) - -# Use material-specific code to add certain stuff -a = build(scen) - -# Solve the model -scen.solve() - -p = Plots(scen, "China", firstyear=2020) -p.plot_activity(baseyear=False, subset=["clinker_dry_cement", "clinker_wet_cement"]) -p.plot_activity( - baseyear=False, subset=["grinding_ballmill_cement", "grinding_vertmill_cement"] -) -# p.plot_capacity(baseyear=True, subset=['bf_steel', 'bof_steel', 'dri_steel', 'eaf_steel']) -# p.plot_new_capacity(baseyear=True, subset=['bf_steel', 'bof_steel', 'dri_steel', 'eaf_steel']) -p.plot_activity( - baseyear=False, - subset=[ - "clinker_dry_cement", - "clinker_dry_ccs_cement", - "clinker_wet_cement", - "clinker_wet_ccs_cement", - ], -) - -p.plot_activity(baseyear=False, subset=["dri_steel", "bf_steel"]) - -#%% Global test -from message_data.model.create import create_res - -# Create Context obj -Context._instance = [] -ctx = Context() - -# Set default scenario/model names - Later coming from CLI -ctx.platform_info.setdefault("name", "ixmp_dev") -ctx.platform_info.setdefault("jvmargs", ["-Xmx12G"]) # To avoid java heap space error -ctx.scenario_info.setdefault("model", "Material_Global") -ctx.scenario_info.setdefault("scenario", "NoPolicy") -ctx["ssp"] = "SSP2" -ctx["datafile"] = "Global_steel_cement_MESSAGE.xlsx" - -# Use general code to create a Scenario with some stuff in it -scen = create_res(context=ctx) - -# Use material-specific code to add certain stuff -a = build(scen) - -# Solve the model -import time - -start_time = time.time() -scen.solve() -print(".solve: %.6s seconds taken." % (time.time() - start_time)) -#%% Auxiliary random test stuff - -import pandas as pd - -import message_data.model.material.data_aluminum as da -import message_data.model.material.data_steel as ds -import message_data.model.material.data_cement as dc -import message_data.model.material.data_buildings as db -from message_data.model.material.data_buildings import ( - BLD_MAT_USE_2020 as bld_demand2020, -) - -mp = ixmp.Platform(name="ixmp_dev") -sl = mp.scenario_list() -sl = sl.loc[sl.model == "MESSAGEix-Materials"] # "ENGAGE_SSP2_v4.1.4"] - -sample = mix.Scenario(mp, model="MESSAGEix-Materials", scenario="NoPolicy") -cem_demand = sample.par("demand", {"commodity": "cement", "year": 2010}) - -# Test read_data_steel <- will be in create_res if working fine -df = du.read_sector_data("steel") -df = du.read_rel(ctx.datafile) - -# Buildings scripts -a, b, cc = db.read_timeseries_buildings("LED_LED_report_IAMC_sensitivity.csv", "ref") -a1, b1, cc1 = db.read_timeseries_buildings("LED_LED_report_IAMC_sensitivity.csv", "min") -c = db.get_scen_mat_demand(commod="steel", year="all") -r = db.gen_data_buildings(scen) - - -mp = ctx.get_platform() - -b = dt.read_data_generic() -b = dt.read_var_cost() -bb = dt.read_sector_data() -c = pd.melt( - b, - id_vars=["technology", "mode", "units"], - value_vars=[2010, 2020, 2030, 2040, 2050, 2060, 2070, 2080, 2090, 2100], - var_name="year", -) - -df_gen = dt.gen_data_generic(scen) -df_st = dt.gen_data_steel(scen) -a = df_st["input"] -b = a.loc[a["level"] == "export"] - -df_st = dt.gen_data_cement(scen) -a = dt.get_data(scen, ctx) -dc.gen_mock_demand_cement(sample) -ds.gen_mock_demand_steel(sample) -da.gen_mock_demand_aluminum(sample) -dt.read_data_generic() -bare.add_data(scen) - - -info = ScenarioInfo(scen) -a = get_spec() - -a = mp.scenario_list() -b = a.loc[a["cre_user"] == "min"] -sample = mix.Scenario(mp_samp, model="Material_test", scenario="baseline") -sample.set_list() -sample.set("year") -sample.cat("year", "firstmodelyear") -mp_samp.close_db() - -scen.to_excel("test.xlsx") -scen_rp = Scenario(scen) - -#%% - -s_info = ScenarioInfo(sample) -years = s_info.Y - -import os -import pyam -import pandas as pd -import matplotlib.pyplot as plt - -msg_data_path = os.environ["MESSAGE_DATA_PATH"] -directory = os.path.join(msg_data_path, "message_data", "reporting", "materials") -path = os.path.join(directory, "message_ix_reporting.xlsx") -report = pd.read_excel(path) -report.Unit.fillna("", inplace=True) -df = pyam.IamDataFrame(report) - -df_ref_fueloil = df.copy() -r = "R11_CPA|R11_CPA" -df_ref_fueloil.filter(region=r, year=years, inplace=True) - -df_ref_fueloil.filter(variable=["out|secondary|fueloil|agg_ref|*"], inplace=True) - - -fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 8)) - -df_ref_fueloil.stack_plot(ax=ax1) -ax1.legend( - [ - "Atm gas oil", - "Atm resiude", - "Heavy fuel oil", - "Petroleum coke", - "Vacuum residue", - "import", - ], - bbox_to_anchor=(0.3, 1), -) -ax1.set_title("Fuel oil mix_" + r) -ax1.set_xlabel("Year") -ax1.set_ylabel("GWa") From d2129a6acb1a861038b40731bac5b65effdc2cad Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Fri, 4 Feb 2022 14:03:28 +0100 Subject: [PATCH 320/774] Delete unused folder --- .../aluminum_techno_economic.xlsx | 3 - .../demand_technology.xlsx | 3 - .../aluminum_stand_alone/generate_data_AL.py | 148 ------ .../generate_data_generic.py | 178 ------- ...eneric_furnace_boiler_techno_economic.xlsx | 3 - .../material/aluminum_stand_alone/plotting.py | 111 ----- .../aluminum_stand_alone/stand_alone_AL.py | 438 ------------------ .../test_commodity_balance.py | 236 ---------- .../material/aluminum_stand_alone/tools.py | 6 - .../aluminum_stand_alone/variable_costs.xlsx | 3 - 10 files changed, 1129 deletions(-) delete mode 100644 message_ix_models/model/material/aluminum_stand_alone/aluminum_techno_economic.xlsx delete mode 100644 message_ix_models/model/material/aluminum_stand_alone/demand_technology.xlsx delete mode 100644 message_ix_models/model/material/aluminum_stand_alone/generate_data_AL.py delete mode 100644 message_ix_models/model/material/aluminum_stand_alone/generate_data_generic.py delete mode 100644 message_ix_models/model/material/aluminum_stand_alone/generic_furnace_boiler_techno_economic.xlsx delete mode 100644 message_ix_models/model/material/aluminum_stand_alone/plotting.py delete mode 100644 message_ix_models/model/material/aluminum_stand_alone/stand_alone_AL.py delete mode 100644 message_ix_models/model/material/aluminum_stand_alone/test_commodity_balance.py delete mode 100644 message_ix_models/model/material/aluminum_stand_alone/tools.py delete mode 100644 message_ix_models/model/material/aluminum_stand_alone/variable_costs.xlsx diff --git a/message_ix_models/model/material/aluminum_stand_alone/aluminum_techno_economic.xlsx b/message_ix_models/model/material/aluminum_stand_alone/aluminum_techno_economic.xlsx deleted file mode 100644 index 5d79787c5e..0000000000 --- a/message_ix_models/model/material/aluminum_stand_alone/aluminum_techno_economic.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:106287600beb514e90a4780f9404d59cd1ad910f52556620804c9cc15bcce6bf -size 51745 diff --git a/message_ix_models/model/material/aluminum_stand_alone/demand_technology.xlsx b/message_ix_models/model/material/aluminum_stand_alone/demand_technology.xlsx deleted file mode 100644 index 8180a390b0..0000000000 --- a/message_ix_models/model/material/aluminum_stand_alone/demand_technology.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:ea197ac3a0ca594bf87fce7d4ec36f4c2fad802dc1472360b81941f5ab6d18c9 -size 10084 diff --git a/message_ix_models/model/material/aluminum_stand_alone/generate_data_AL.py b/message_ix_models/model/material/aluminum_stand_alone/generate_data_AL.py deleted file mode 100644 index 5ed05aae15..0000000000 --- a/message_ix_models/model/material/aluminum_stand_alone/generate_data_AL.py +++ /dev/null @@ -1,148 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Created on Wed Aug 26 14:43:41 2020 -Generate techno economic aluminum data based on aluminum_techno_economic.xlsx - -@author: unlu -""" -import pandas as pd -import numpy as np -from collections import defaultdict -from message_data.tools import broadcast, make_df, same_node -import message_ix -import ixmp - - -def gen_data_aluminum(file): - - mp = ixmp.Platform() - - scenario = message_ix.Scenario(mp, "MESSAGE_material", "baseline", cache=True) - - data_aluminum = pd.read_excel(file, sheet_name="data") - - data_aluminum_hist = pd.read_excel( - "aluminum_techno_economic.xlsx", sheet_name="data_historical", usecols="A:F" - ) - # Clean the data - # Drop columns that don't contain useful information - data_aluminum = data_aluminum.drop(["Region", "Source", "Description"], axis=1) - # List of data frames, to be concatenated together at the end - results = defaultdict(list) - # Will come from the yaml file - technology_add = data_aluminum["technology"].unique() - # normally from s_info.Y - years = [2010, 2020, 2030, 2040, 2050, 2060, 2070, 2080, 2090, 2100] - - # normally from s_info.N - nodes = ["CHN"] - - # Iterate over technologies - - for t in technology_add: - # Obtain the active and vintage years - av = data_aluminum.loc[ - (data_aluminum["technology"] == t), "availability" - ].values[0] - if ( - "technical_lifetime" - in data_aluminum.loc[(data_aluminum["technology"] == t)]["parameter"].values - ): - lifetime = data_aluminum.loc[ - (data_aluminum["technology"] == t) - & (data_aluminum["parameter"] == "technical_lifetime"), - "value", - ].values[0] - years_df = scenario.vintage_and_active_years() - years_df = years_df.loc[years_df["year_vtg"] >= av] - # Empty data_frame - years_df_final = pd.DataFrame(columns=["year_vtg", "year_act"]) - - # For each vintage adjsut the active years according to technical - # lifetime - for vtg in years_df["year_vtg"].unique(): - years_df_temp = years_df.loc[years_df["year_vtg"] == vtg] - years_df_temp = years_df_temp.loc[years_df["year_act"] < vtg + lifetime] - years_df_final = pd.concat( - [years_df_temp, years_df_final], ignore_index=True - ) - vintage_years, act_years = ( - years_df_final["year_vtg"], - years_df_final["year_act"], - ) - - params = data_aluminum.loc[ - (data_aluminum["technology"] == t), "parameter" - ].values.tolist() - # Iterate over parameters - for par in params: - split = par.split("|") - param_name = par.split("|")[0] - - val = data_aluminum.loc[ - ( - (data_aluminum["technology"] == t) - & (data_aluminum["parameter"] == par) - ), - "value", - ].values[0] - - # Common parameters for all input and output tables - # node_dest and node_origin are the same as node_loc - - common = dict( - year_vtg=vintage_years, - year_act=act_years, - mode="standard", - time="year", - time_origin="year", - time_dest="year", - ) - - if len(split) > 1: - - if (param_name == "input") | (param_name == "output"): - - com = split[1] - lev = split[2] - - df = ( - make_df( - param_name, - technology=t, - commodity=com, - level=lev, - value=val, - unit="t", - **common - ) - .pipe(broadcast, node_loc=nodes) - .pipe(same_node) - ) - - results[param_name].append(df) - - elif param_name == "emission_factor": - emi = split[1] - - df = make_df( - param_name, - technology=t, - value=val, - emission=emi, - unit="t", - **common - ).pipe(broadcast, node_loc=nodes) - results[param_name].append(df) - - # Rest of the parameters apart from inpput, output and - # emission_factor - else: - df = make_df( - param_name, technology=t, value=val, unit="t", **common - ).pipe(broadcast, node_loc=nodes) - results[param_name].append(df) - - results_aluminum = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} - - return results_aluminum, data_aluminum_hist diff --git a/message_ix_models/model/material/aluminum_stand_alone/generate_data_generic.py b/message_ix_models/model/material/aluminum_stand_alone/generate_data_generic.py deleted file mode 100644 index 25095a711d..0000000000 --- a/message_ix_models/model/material/aluminum_stand_alone/generate_data_generic.py +++ /dev/null @@ -1,178 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Created on Wed Aug 26 14:54:24 2020 -Generate techno economic generic furncaedata based on -generic_furnace_boiler_techno_economic.xlsx - -@author: unlu - -""" -import pandas as pd -import numpy as np -from collections import defaultdict -from message_data.tools import broadcast, make_df, same_node -import message_ix -import ixmp - - -def gen_data_generic(): - - mp = ixmp.Platform() - - scenario = message_ix.Scenario(mp, "MESSAGE_material", "baseline", cache=True) - - data_generic = pd.read_excel( - "generic_furnace_boiler_techno_economic.xlsx", sheet_name="generic" - ) - data_generic = data_generic.drop(["Region", "Source", "Description"], axis=1) - - # List of data frames, to be concatenated together at the end - results = defaultdict(list) - - # This comes from the config file normally - technology_add = [ - "furnace_coal_aluminum", - "furnace_foil_aluminum", - "furnace_methanol_aluminum", - "furnace_biomass_aluminum", - "furnace_ethanol_aluminum", - "furnace_gas_aluminum", - "furnace_elec_aluminum", - "furnace_h2_aluminum", - "hp_gas_aluminum", - "hp_elec_aluminum", - "fc_h2_aluminum", - "solar_aluminum", - "dheat_aluminum", - "furnace_loil_aluminum", - ] - - # normally from s_info.Y - years = [2010, 2020, 2030, 2040, 2050, 2060, 2070, 2080, 2090, 2100] - - # normally from s_info.N - nodes = ["CHN"] - - # For each technology there are differnet input and output combinations - # Iterate over technologies - - for t in technology_add: - - # Obtain the active and vintage years - av = data_generic.loc[(data_generic["technology"] == t), "availability"].values[ - 0 - ] - lifetime = data_generic.loc[ - (data_generic["technology"] == t) - & (data_generic["parameter"] == "technical_lifetime"), - "value", - ].values[0] - years_df = scenario.vintage_and_active_years() - years_df = years_df.loc[years_df["year_vtg"] >= av] - years_df_final = pd.DataFrame(columns=["year_vtg", "year_act"]) - - # For each vintage adjsut the active years according to technical - # lifetime - for vtg in years_df["year_vtg"].unique(): - years_df_temp = years_df.loc[years_df["year_vtg"] == vtg] - years_df_temp = years_df_temp.loc[years_df["year_act"] < vtg + lifetime] - years_df_final = pd.concat( - [years_df_temp, years_df_final], ignore_index=True - ) - - vintage_years, act_years = ( - years_df_final["year_vtg"], - years_df_final["year_act"], - ) - - params = data_generic.loc[ - (data_generic["technology"] == t), "parameter" - ].values.tolist() - - # To keep the available modes and use it in the emission_factor - # parameter. - mode_list = [] - - # Iterate over parameters - for par in params: - - split = par.split("|") - param_name = par.split("|")[0] - - val = data_generic.loc[ - ( - (data_generic["technology"] == t) - & (data_generic["parameter"] == par) - ), - "value", - ].values[0] - - # Common parameters for all input and output tables - common = dict( - year_vtg=vintage_years, - year_act=act_years, - time="year", - time_origin="year", - time_dest="year", - ) - - if len(split) > 1: - - if (param_name == "input") | (param_name == "output"): - - com = split[1] - lev = split[2] - mod = split[3] - - # Keep the available modes for a technology in a list - mode_list.append(mod) - df = ( - make_df( - param_name, - technology=t, - commodity=com, - level=lev, - mode=mod, - value=val, - unit="t", - **common - ) - .pipe(broadcast, node_loc=nodes) - .pipe(same_node) - ) - results[param_name].append(df) - - elif param_name == "emission_factor": - emi = split[1] - mod = data_generic.loc[ - ( - (data_generic["technology"] == t) - & (data_generic["parameter"] == par) - ), - "value", - ].values[0] - - # For differnet modes - for m in np.unique(np.array(mode_list)): - - df = make_df( - param_name, - technology=t, - value=val, - emission=emi, - mode=m, - unit="t", - **common - ).pipe(broadcast, node_loc=nodes) - results[param_name].append(df) - - # Rest of the parameters apart from inpput, output and - # emission_factor - else: - df = make_df( - param_name, technology=t, value=val, unit="t", **common - ).pipe(broadcast, node_loc=nodes) - results[param_name].append(df) - - results_generic = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} - return results_generic diff --git a/message_ix_models/model/material/aluminum_stand_alone/generic_furnace_boiler_techno_economic.xlsx b/message_ix_models/model/material/aluminum_stand_alone/generic_furnace_boiler_techno_economic.xlsx deleted file mode 100644 index c6a0e2acca..0000000000 --- a/message_ix_models/model/material/aluminum_stand_alone/generic_furnace_boiler_techno_economic.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:bdfe832a631d0080892932af6ecb2e1ed3c1cc059adf182eb476c77de57d0ff5 -size 47993 diff --git a/message_ix_models/model/material/aluminum_stand_alone/plotting.py b/message_ix_models/model/material/aluminum_stand_alone/plotting.py deleted file mode 100644 index 73e620d760..0000000000 --- a/message_ix_models/model/material/aluminum_stand_alone/plotting.py +++ /dev/null @@ -1,111 +0,0 @@ -import pandas as pd -import matplotlib.pyplot as plt - - -class Plots(object): - def __init__(self, ds, country, firstyear=2010, input_costs="$/GWa"): - self.ds = ds - self.country = country - self.firstyear = firstyear - - if input_costs == "$/MWa": - self.cost_unit_conv = 1e3 - elif input_costs == "$/kWa": - self.cost_unit_conv = 1e6 - else: - self.cost_unit_conv = 1 - - def historic_data(self, par, year_col, subset=None): - df = self.ds.par(par) - if subset is not None: - df = df[df["technology"].isin(subset)] - idx = [year_col, "technology"] - df = df[idx + ["value"]].groupby(idx).sum().reset_index() - df = df.pivot(index=year_col, columns="technology", values="value") - return df - - def model_data(self, var, year_col="year_act", baseyear=False, subset=None): - df = self.ds.var(var) - if subset is not None: - df = df[df["technology"].isin(subset)] - idx = [year_col, "technology"] - df = ( - df[idx + ["lvl"]] - .groupby(idx) - .sum() - .reset_index() - .pivot(index=year_col, columns="technology", values="lvl") - .rename(columns={"lvl": "value"}) - ) - df = df[df.index >= self.firstyear] - return df - - def equ_data(self, equ, value, baseyear=False, subset=None): - df = self.ds.equ(equ) - if not baseyear: - df = df[df["year"] > self.firstyear] - if subset is not None: - df = df[df["commodity"].isin(subset)] - df = df.pivot(index="year", columns="commodity", values=value) - return df - - def plot_demand(self, light_demand, elec_demand): - fig, ax = plt.subplots() - light = light_demand["value"] - light.plot(ax=ax, label="light") - e = elec_demand["value"] - e.plot(ax=ax, label="elec") - (light + e).plot(ax=ax, label="total") - plt.ylabel("GWa") - plt.xlabel("Year") - plt.legend(loc="best") - - def plot_activity(self, baseyear=False, subset=None): - h = self.historic_data("historical_activity", "year_act", subset=subset) - m = self.model_data("ACT", baseyear=baseyear, subset=subset) - df = pd.concat([h, m]) if not h.empty else m - df.plot.bar(stacked=True) - plt.title("{} Energy System Activity".format(self.country.title())) - plt.ylabel("GWa") - plt.xlabel("Year") - plt.legend(loc="center left", bbox_to_anchor=(1.0, 0.5)) - - def plot_capacity(self, baseyear=False, subset=None): - df = self.model_data("CAP", baseyear=baseyear, subset=subset) - df.plot.bar(stacked=True) - plt.title("{} Energy System Capacity".format(self.country.title())) - plt.ylabel("GW") - plt.xlabel("Year") - plt.legend(loc="center left", bbox_to_anchor=(1.0, 0.5)) - - def plot_new_capacity(self, baseyear=False, subset=None): - h = self.historic_data("historical_new_capacity", "year_vtg", subset=subset) - m = self.model_data("CAP_NEW", "year_vtg", baseyear=baseyear, subset=subset) - df = pd.concat([h, m]) if not h.empty else m - df.plot.bar(stacked=True) - plt.title("{} Energy System New Capcity".format(self.country.title())) - plt.ylabel("GW") - plt.xlabel("Year") - plt.legend(loc="center left", bbox_to_anchor=(1.0, 0.5)) - - def plot_prices(self, baseyear=False, subset=None): - df = self.ds.var("PRICE_COMMODITY") - if not baseyear: - df = df[df["year"] > self.firstyear] - if subset is not None: - df = df[df["commodity"].isin(subset)] - idx = ["year", "commodity"] - df = ( - df[idx + ["lvl"]] - .groupby(idx) - .sum() - .reset_index() - .pivot(index="year", columns="commodity", values="lvl") - .rename(columns={"lvl": "value"}) - ) - df = df / 8760 * 100 * self.cost_unit_conv - df.plot.bar(stacked=False) - plt.title("{} Energy System Prices".format(self.country.title())) - plt.ylabel("cents/kWhr") - plt.xlabel("Year") - plt.legend(loc="center left", bbox_to_anchor=(1.0, 0.5)) diff --git a/message_ix_models/model/material/aluminum_stand_alone/stand_alone_AL.py b/message_ix_models/model/material/aluminum_stand_alone/stand_alone_AL.py deleted file mode 100644 index 66cd0c90c2..0000000000 --- a/message_ix_models/model/material/aluminum_stand_alone/stand_alone_AL.py +++ /dev/null @@ -1,438 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Created on Wed Aug 26 13:41:29 2020 - -"Aluminum stand-alone run" -Solve and plot -@author: unlu -""" -import message_ix -import ixmp -import pandas as pd -from message_data.tools import make_df -from generate_data_AL import gen_data_aluminum as gen_data_aluminum -from generate_data_generic import gen_data_generic as gen_data_generic -from tools import Plots - -mp = ixmp.Platform() - -# Adding a new unit to the library -mp.add_unit("Mt") - -# New model -scenario = message_ix.Scenario( - mp, model="MESSAGE_material", scenario="baseline", version="new" -) -# Addition of basics - -history = [1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015] -model_horizon = [2020, 2030, 2040, 2050, 2060, 2070, 2080, 2090, 2100] -scenario.add_horizon( - {"year": history + model_horizon, "firstmodelyear": model_horizon[0]} -) -country = "CHN" -scenario.add_spatial_sets({"country": country}) - -# These will come from the yaml file - -commodities = [ - "ht_heat", - "lt_heat", - "aluminum", - "d_heat", - "electr", - "coal", - "fueloil", - "ethanol", - "biomass", - "gas", - "hydrogen", - "methanol", - "lightoil", -] - -scenario.add_set("commodity", commodities) - -levels = [ - "useful_aluminum", - "new_scrap", - "old_scrap", - "final_material", - "useful_material", - "product", - "secondary_material", - "final", - "demand", -] -scenario.add_set("level", levels) - -technologies = [ - "soderberg_aluminum", - "prebake_aluminum", - "secondary_aluminum", - "prep_secondary_aluminum", - "finishing_aluminum", - "manuf_aluminum", - "scrap_recovery_aluminum", - "furnace_coal_aluminum", - "furnace_foil_aluminum", - "furnace_methanol_aluminum", - "furnace_biomass_aluminum", - "furnace_ethanol_aluminum", - "furnace_gas_aluminum", - "furnace_elec_aluminum", - "furnace_h2_aluminum", - "hp_gas_aluminum", - "hp_elec_aluminum", - "fc_h2_aluminum", - "solar_aluminum", - "dheat_aluminum", - "furnace_loil_aluminum", - "alumina_supply", -] - -scenario.add_set("technology", technologies) -scenario.add_set("mode", ["standard", "low_temp", "high_temp"]) - -# Create duration period - -val = [j - i for i, j in zip(model_horizon[:-1], model_horizon[1:])] -val.append(val[0]) - -duration_period = pd.DataFrame({"year": model_horizon, "value": val, "unit": "y",}) - -scenario.add_par("duration_period", duration_period) -duration_period = duration_period["value"].values - -# Energy system: Unlimited supply of the commodities. -# Fuel costs are obtained from the PRICE_COMMODITY baseline SSP2 global model. -# PRICE_COMMODTY * input -> (2005USD/Gwa-coal)*(Gwa-coal / ACT of furnace) -# = (2005USD/ACT of furnace) - -years_df = scenario.vintage_and_active_years() -vintage_years, act_years = years_df["year_vtg"], years_df["year_act"] - -# Choose the prices in excel (baseline vs. NPi400) -data_var_cost = pd.read_excel("variable_costs.xlsx", sheet_name="data") - -for row in data_var_cost.index: - data = data_var_cost.iloc[row] - values = [] - for yr in act_years: - values.append(data[yr]) - base_var_cost = pd.DataFrame( - { - "node_loc": country, - "year_vtg": vintage_years.values, - "year_act": act_years.values, - "mode": data["mode"], - "time": "year", - "unit": "USD/GWa", - "technology": data["technology"], - "value": values, - } - ) - - scenario.add_par("var_cost", base_var_cost) - -# Add dummy technologies to represent energy system - -dummy_tech = [ - "electr_gen", - "dist_heating", - "coal_gen", - "foil_gen", - "eth_gen", - "biomass_gen", - "gas_gen", - "hydrogen_gen", - "meth_gen", - "loil_gen", -] -scenario.add_set("technology", dummy_tech) - -commodity_tec = [ - "electr", - "d_heat", - "coal", - "fueloil", - "ethanol", - "biomass", - "gas", - "hydrogen", - "methanol", - "lightoil", -] - -# Add output to dummy tech. - -year_df = scenario.vintage_and_active_years() -vintage_years, act_years = year_df["year_vtg"], year_df["year_act"] - -base = { - "node_loc": country, - "node_dest": country, - "time_dest": "year", - "year_vtg": vintage_years, - "year_act": act_years, - "mode": "standard", - "time": "year", - "level": "final", - "unit": "-", - "value": 1.0, -} - -t = 0 - -for tec in dummy_tech: - out = make_df("output", technology=tec, commodity=commodity_tec[t], **base) - t = t + 1 - scenario.add_par("output", out) - -# Introduce emissions -scenario.add_set("emission", "CO2") -scenario.add_cat("emission", "GHG", "CO2") - -# Run read data aluminum - -scenario.commit("changes added") - -results_al, data_aluminum_hist = gen_data_aluminum("aluminum_techno_economic.xlsx") - -scenario.check_out() - -for k, v in results_al.items(): - scenario.add_par(k, v) - -scenario.commit("aluminum_techno_economic added") -results_generic = gen_data_generic() - -scenario.check_out() -for k, v in results_generic.items(): - scenario.add_par(k, v) - -# Add temporary exogenous demand: 17.3 Mt in 2010 (IAI) -# The future projection of the demand: Increases by half of the GDP growth rate -# gdp_growth rate: SSP2 global model. Starting from 2020. -gdp_growth = pd.Series( - [ - 0.121448215899944, - 0.0733079014579874, - 0.0348154093342843, - 0.021827616787921, - 0.0134425983942219, - 0.0108320197485592, - 0.00884341208063, - 0.00829374133206562, - 0.00649794573935969, - ], - index=pd.Index(model_horizon, name="Time"), -) - -fin_to_useful = scenario.par( - "output", filters={"technology": "finishing_aluminum", "year_act": 2020} -)["value"][0] -useful_to_product = scenario.par( - "output", filters={"technology": "manuf_aluminum", "year_act": 2020} -)["value"][0] -i = 0 -values = [] -val = 17.3 * (1 + 0.147718884937996 / 2) ** duration_period[i] -values.append(val) - -for element in gdp_growth: - i = i + 1 - if i < len(model_horizon): - val = val * (1 + element / 2) ** duration_period[i] - values.append(val) - -# Adjust the demand to product level. - -values = [x * fin_to_useful * useful_to_product for x in values] -aluminum_demand = pd.DataFrame( - { - "node": country, - "commodity": "aluminum", - "level": "demand", - "year": model_horizon, - "time": "year", - "value": values, - "unit": "Mt", - } -) - -scenario.add_par("demand", aluminum_demand) - -# Interest rate -scenario.add_par("interestrate", model_horizon, value=0.05, unit="-") - -# Add historical production and capacity - -for tec in data_aluminum_hist["technology"].unique(): - hist_activity = pd.DataFrame( - { - "node_loc": country, - "year_act": history, - "mode": data_aluminum_hist.loc[ - (data_aluminum_hist["technology"] == tec), "mode" - ], - "time": "year", - "unit": "Mt", - "technology": tec, - "value": data_aluminum_hist.loc[ - (data_aluminum_hist["technology"] == tec), "production" - ], - } - ) - scenario.add_par("historical_activity", hist_activity) - -for tec in data_aluminum_hist["technology"].unique(): - c_factor = scenario.par("capacity_factor", filters={"technology": tec})[ - "value" - ].values[0] - value = ( - data_aluminum_hist.loc[ - (data_aluminum_hist["technology"] == tec), "new_production" - ] - / c_factor - ) - hist_capacity = pd.DataFrame( - { - "node_loc": country, - "year_vtg": history, - "unit": "Mt", - "technology": tec, - "value": value, - } - ) - scenario.add_par("historical_new_capacity", hist_capacity) - -# Historical thermal demand depending on the historical aluminum production -# This section can be revised to make shorter and generic to other materials - -scrap_recovery = scenario.par( - "output", - filters={ - "technology": "scrap_recovery_aluminum", - "level": "old_scrap", - "year_act": 2020, - }, -)["value"][0] -high_th = scenario.par( - "input", filters={"technology": "secondary_aluminum", "year_act": 2020} -)["value"][0] -low_th = scenario.par( - "input", filters={"technology": "prep_secondary_aluminum", "year_act": 2020} -)["value"][0] - -historic_generation = scenario.par("historical_activity").groupby("year_act").sum() - -for yr in historic_generation.index: - - total_scrap = ( - historic_generation.loc[yr].value * fin_to_useful * (1 - useful_to_product) - ) + ( - historic_generation.loc[yr] * fin_to_useful * useful_to_product * scrap_recovery - ) - old_scrap = ( - historic_generation.loc[yr] * fin_to_useful * useful_to_product * scrap_recovery - ) - total_hist_act = total_scrap * high_th + old_scrap * low_th - - hist_activity_h = pd.DataFrame( - { - "node_loc": country, - "year_act": yr, - "mode": "high_temp", - "time": "year", - "unit": "GWa", - "technology": "furnace_gas_aluminum", - "value": total_scrap * high_th, - } - ) - - scenario.add_par("historical_activity", hist_activity_h) - - hist_activity_l = pd.DataFrame( - { - "node_loc": country, - "year_act": yr, - "mode": "low_temp", - "time": "year", - "unit": "GWa", - "technology": "furnace_gas_aluminum", - "value": old_scrap * low_th, - } - ) - - scenario.add_par("historical_activity", hist_activity_l) - - c_fac_furnace_gas = scenario.par( - "capacity_factor", filters={"technology": "furnace_gas_aluminum"} - )["value"].values[0] - - hist_capacity_gas = pd.DataFrame( - { - "node_loc": country, - "year_vtg": yr, - "unit": "GW", - "technology": "furnace_gas_aluminum", - "value": total_hist_act / c_fac_furnace_gas, - } - ) - - scenario.add_par("historical_new_capacity", hist_capacity_gas) - -scenario.commit("changes") - -scenario.solve() - -# Be aware plots produce the same color for some technologies. - -p = Plots(scenario, country, firstyear=model_horizon[0]) - -p.plot_activity( - baseyear=True, - subset=["soderberg_aluminum", "prebake_aluminum", "secondary_aluminum"], -) -p.plot_capacity(baseyear=True, subset=["soderberg_aluminum", "prebake_aluminum"]) -p.plot_activity(baseyear=True, subset=["prep_secondary_aluminum"]) -p.plot_activity( - baseyear=True, - subset=[ - "furnace_coal_aluminum", - "furnace_foil_aluminum", - "furnace_methanol_aluminum", - "furnace_biomass_aluminum", - "furnace_ethanol_aluminum", - "furnace_gas_aluminum", - "furnace_elec_aluminum", - "furnace_h2_aluminum", - "hp_gas_aluminum", - "hp_elec_aluminum", - "fc_h2_aluminum", - "solar_aluminum", - "dheat_aluminum", - "furnace_loil_aluminum", - ], -) -p.plot_capacity( - baseyear=True, - subset=[ - "furnace_coal_aluminum", - "furnace_foil_aluminum", - "furnace_methanol_aluminum", - "furnace_biomass_aluminum", - "furnace_ethanol_aluminum", - "furnace_gas_aluminum", - "furnace_elec_aluminum", - "furnace_h2_aluminum", - "hp_gas_aluminum", - "hp_elec_aluminum", - "fc_h2_aluminum", - "solar_aluminum", - "dheat_aluminum", - "furnace_loil_aluminum", - ], -) -p.plot_prices(subset=["aluminum"], baseyear=True) diff --git a/message_ix_models/model/material/aluminum_stand_alone/test_commodity_balance.py b/message_ix_models/model/material/aluminum_stand_alone/test_commodity_balance.py deleted file mode 100644 index a251733849..0000000000 --- a/message_ix_models/model/material/aluminum_stand_alone/test_commodity_balance.py +++ /dev/null @@ -1,236 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Created on Thu Aug 27 11:00:06 2020 - -Test new commodity balance equation -Run stand_alone_AL before runing this script - -@author: unlu -""" -import message_ix -import ixmp -from generate_data_AL import gen_data_aluminum as gen_data_aluminum -import pandas as pd -from tools import Plots - -mp = ixmp.Platform() - -base = message_ix.Scenario(mp, model="MESSAGE_material", scenario="baseline") -scen = base.clone( - "MESSAGE_material", "test", "test commodity balance equation", keep_solution=False -) -scen.check_out() - -country = "CHN" -model_horizon = [2020, 2030, 2040, 2050, 2060, 2070, 2080, 2090, 2100] - -# Add a new demand side technology and related commodity - -new_tec = "bulb" -scen.add_set("technology", new_tec) - -new_commodity = "light" -scen.add_set("commodity", new_commodity) - -# Add input, output,technical_lifetime, inv_cost etc. -tec_data, tec_data_hist = gen_data_aluminum("demand_technology.xlsx") - -for k, v in tec_data.items(): - scen.add_par(k, v) - -# Add dummy demand. 1 GWa initially. - -duration_period = scen.par("duration_period") -duration_period = duration_period["value"].values - -gdp_growth = pd.Series( - [ - 0.121448215899944, - 0.0733079014579874, - 0.0348154093342843, - 0.021827616787921, - 0.0134425983942219, - 0.0108320197485592, - 0.00884341208063, - 0.00829374133206562, - 0.00649794573935969, - ], - index=pd.Index(model_horizon, name="Time"), -) - -i = 0 -values = [] -val = 1 * (1 + 0.147718884937996 / 2) ** duration_period[i] -values.append(val) - -for element in gdp_growth: - i = i + 1 - if i < len(model_horizon): - val = val * (1 + element / 2) ** duration_period[i] - values.append(val) - -bulb_demand = pd.DataFrame( - { - "node": country, - "commodity": "light", - "level": "useful_aluminum", - "year": model_horizon, - "time": "year", - "value": values, - "unit": "GWa", - } -) - -scen.add_par("demand", bulb_demand) - -# Adjust the active years. - -tec_lifetime = tec_data.get("technical_lifetime") - -lifetime = tec_lifetime["value"].values[0] -years_df = scen.vintage_and_active_years() -# Empty data_frame -years_df_final = pd.DataFrame(columns=["year_vtg", "year_act"]) -# For each vintage adjsut the active years according to technical -# lifetime -for vtg in years_df["year_vtg"].unique(): - years_df_temp = years_df.loc[years_df["year_vtg"] == vtg] - years_df_temp = years_df_temp.loc[years_df["year_act"] < vtg + lifetime] - years_df_final = pd.concat([years_df_temp, years_df_final], ignore_index=True) -vintage_years, act_years = years_df_final["year_vtg"], years_df_final["year_act"] - -# **** WORKS UNTIL HERE ****** - -# Add the new parameters: input_cap_ret, output_cap_ret, input_cap, output_cap -# input_cap_new, output_cap_new - -# Material release after retirement (e.g. old scrap) - -output_cap_ret = pd.DataFrame( - { - "node_loc": country, - "technology": "bulb", - "year_vtg": vintage_years, - "node_dest": country, - "commodity": "aluminum", - "level": "useful_material", - "time": "year", - "time_dest": "year", - "value": 0.15, - "unit": "-", - } -) -print(output_cap_ret) -scen.add_par("output_cap_ret", output_cap_ret) - -# Material need during lifetime (e.g. retrofit) - -input_cap = pd.DataFrame( - { - "node_loc": country, - "technology": "bulb", - "year_vtg": vintage_years, - "year_act": act_years, - "node_origin": country, - "commodity": "aluminum", - "level": "useful_material", - "time": "year", - "time_origin": "year", - "value": 0.01, - "unit": "-", - } -) -print(input_cap) -scen.add_par("input_cap", input_cap) - -# Material need for the construction - -input_cap_new = pd.DataFrame( - { - "node_loc": country, - "technology": "bulb", - "year_vtg": vintage_years, - "node_origin": country, - "commodity": "aluminum", - "level": "useful_material", - "time": "year", - "time_origin": "year", - "value": 0.25, - "unit": "-", - } -) -print(input_cap_new) -scen.add_par("input_cap_new", input_cap_new) - -# Material release during construction (e.g. new scrap) - -output_cap_new = pd.DataFrame( - { - "node_loc": country, - "technology": "bulb", - "year_vtg": vintage_years, - "node_dest": country, - "commodity": "aluminum", - "level": "useful_material", - "time": "year", - "time_dest": "year", - "value": 0.1, - "unit": "-", - } -) -print(output_cap_new) -scen.add_par("output_cap_new", output_cap_new) - -scen.commit() - -# scen.solve() - -# Be aware plots produce the same color for some technologies. - -p = Plots(scen, country, firstyear=model_horizon[0]) - -p.plot_activity( - baseyear=True, - subset=["soderberg_aluminum", "prebake_aluminum", "secondary_aluminum"], -) -p.plot_capacity(baseyear=True, subset=["soderberg_aluminum", "prebake_aluminum"]) -p.plot_activity(baseyear=True, subset=["prep_secondary_aluminum"]) -p.plot_activity( - baseyear=True, - subset=[ - "furnace_coal_aluminum", - "furnace_foil_aluminum", - "furnace_methanol_aluminum", - "furnace_biomass_aluminum", - "furnace_ethanol_aluminum", - "furnace_gas_aluminum", - "furnace_elec_aluminum", - "furnace_h2_aluminum", - "hp_gas_aluminum", - "hp_elec_aluminum", - "fc_h2_aluminum", - "solar_aluminum", - "dheat_aluminum", - "furnace_loil_aluminum", - ], -) -p.plot_capacity( - baseyear=True, - subset=[ - "furnace_coal_aluminum", - "furnace_foil_aluminum", - "furnace_methanol_aluminum", - "furnace_biomass_aluminum", - "furnace_ethanol_aluminum", - "furnace_gas_aluminum", - "furnace_elec_aluminum", - "furnace_h2_aluminum", - "hp_gas_aluminum", - "hp_elec_aluminum", - "fc_h2_aluminum", - "solar_aluminum", - "dheat_aluminum", - "furnace_loil_aluminum", - ], -) -p.plot_prices(subset=["aluminum"], baseyear=True) diff --git a/message_ix_models/model/material/aluminum_stand_alone/tools.py b/message_ix_models/model/material/aluminum_stand_alone/tools.py deleted file mode 100644 index 2974d50e2e..0000000000 --- a/message_ix_models/model/material/aluminum_stand_alone/tools.py +++ /dev/null @@ -1,6 +0,0 @@ -from pathlib import Path -import sys - -sys.path.append(str(Path(__file__).parents[1] / "utils")) - -from plotting import Plots # noqa: E402, F401 diff --git a/message_ix_models/model/material/aluminum_stand_alone/variable_costs.xlsx b/message_ix_models/model/material/aluminum_stand_alone/variable_costs.xlsx deleted file mode 100644 index 03f046c956..0000000000 --- a/message_ix_models/model/material/aluminum_stand_alone/variable_costs.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:2ecfeceb662b6bdf3d14078429f4db7d6b4997447e80eb486160ffc91d3f3695 -size 32510 From 261264a61245ec26f4fb78bec0f6ef5d2cc6a556 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Mon, 7 Mar 2022 10:27:39 +0100 Subject: [PATCH 321/774] Activate verbose mode --- message_ix_models/model/material/__init__.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index f9d4e03f7f..d21b7cd76e 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -219,7 +219,8 @@ def run_old_reporting(scenario_name, model_name): scenario = Scenario(mp, model_name, scenario_name) reporting( - mp, scenario, "False", base_model, scen_name, merge_hist=True, merge_ts=True + mp, scenario, "False", base_model, scen_name, merge_hist=True, merge_ts=True, + verbose= True ) From 9938330b31da558f4d2cdbe05909cedb765f1a95 Mon Sep 17 00:00:00 2001 From: Jihoon Date: Mon, 20 Dec 2021 18:23:11 +0100 Subject: [PATCH 322/774] Add the run_config argument in the report call --- message_ix_models/model/material/__init__.py | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index d21b7cd76e..d0711f62fd 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -219,8 +219,15 @@ def run_old_reporting(scenario_name, model_name): scenario = Scenario(mp, model_name, scenario_name) reporting( - mp, scenario, "False", base_model, scen_name, merge_hist=True, merge_ts=True, - verbose= True + mp, + scenario, + "False", + base_model, + scen_name, + merge_hist=True, + merge_ts=True, + run_config="materials_run_config.yaml", + verbose = True ) From ab693fd8d3ab9fc0febd130318464c476c0a6bee Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 15 Feb 2022 14:55:46 +0100 Subject: [PATCH 323/774] Add missing input file to version control --- message_ix_models/data/material/GLOBIOM_Fertilizer_use_N.xlsx | 3 +++ 1 file changed, 3 insertions(+) create mode 100644 message_ix_models/data/material/GLOBIOM_Fertilizer_use_N.xlsx diff --git a/message_ix_models/data/material/GLOBIOM_Fertilizer_use_N.xlsx b/message_ix_models/data/material/GLOBIOM_Fertilizer_use_N.xlsx new file mode 100644 index 0000000000..67e59cc10e --- /dev/null +++ b/message_ix_models/data/material/GLOBIOM_Fertilizer_use_N.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fe7d802340e0b545133153b183d44a3fb4121bc25ffb961b0ce1f5b2e1a91b45 +size 582147 From 77a5db4125485fc48591fbe6da3eb2f3a4b2e271 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 15 Feb 2022 15:58:03 +0100 Subject: [PATCH 324/774] Add ammonia to data_functions and specs --- message_ix_models/model/material/__init__.py | 16 ++++++++++------ 1 file changed, 10 insertions(+), 6 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index d0711f62fd..5b0d9c35a2 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -45,6 +45,7 @@ def build(scenario): "petro_chemicals", "buildings", "power_sector", + "fertilizer" ] @@ -238,16 +239,19 @@ def run_old_reporting(scenario_name, model_name): from .data_petro import gen_data_petro_chemicals from .data_buildings import gen_data_buildings from .data_power_sector import gen_data_power_sector +from .data_ammonia import gen_data_ammonia DATA_FUNCTIONS = [ # gen_data_buildings, - gen_data_steel, - gen_data_cement, - gen_data_aluminum, - gen_data_petro_chemicals, - gen_data_generic, - gen_data_power_sector, + #gen_data_steel, + #gen_data_cement, + #gen_data_aluminum, + #gen_data_petro_chemicals, + #gen_data_generic, + #gen_data_power_sector, + gen_data_ammonia, + ] From bd69c411fa63db85b0a2c95ea230b65f1e6dec33 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 16 Feb 2022 16:01:36 +0100 Subject: [PATCH 325/774] Fix waste water --- message_ix_models/data/material/set.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 006050bd84..b55037a7d9 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -424,12 +424,12 @@ fertilizer: add: - NH3 - Fertilizer Use|Nitrogen - + - wastewater level: add: - material_interim - material_final - + - wastewater technology: require: - h2_bio_ccs # Reference for vent-to-storage ratio From 5f3366484593865b30ec24e6e786f9c1edd765fd Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 23 Feb 2022 16:56:45 +0100 Subject: [PATCH 326/774] Activate all sectors --- message_ix_models/model/material/__init__.py | 15 +++++++-------- 1 file changed, 7 insertions(+), 8 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 5b0d9c35a2..ca982ebb4a 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -243,18 +243,17 @@ def run_old_reporting(scenario_name, model_name): DATA_FUNCTIONS = [ - # gen_data_buildings, - #gen_data_steel, - #gen_data_cement, - #gen_data_aluminum, - #gen_data_petro_chemicals, - #gen_data_generic, - #gen_data_power_sector, + #gen_data_buildings, + gen_data_steel, + gen_data_cement, + gen_data_aluminum, + gen_data_petro_chemicals, + gen_data_generic, + gen_data_power_sector, gen_data_ammonia, ] - # Try to handle multiple data input functions from different materials def add_data(scenario, dry_run=False): """Populate `scenario` with MESSAGEix-Materials data.""" From 00841b7fabb3c2496db81e44001b9e3a16014afb Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Mon, 28 Feb 2022 14:27:19 +0100 Subject: [PATCH 327/774] Reactivate commented out add_set() --- .../model/material/data_ammonia.py | 763 ++++++++++++++++++ 1 file changed, 763 insertions(+) create mode 100644 message_ix_models/model/material/data_ammonia.py diff --git a/message_ix_models/model/material/data_ammonia.py b/message_ix_models/model/material/data_ammonia.py new file mode 100644 index 0000000000..18bca3a54f --- /dev/null +++ b/message_ix_models/model/material/data_ammonia.py @@ -0,0 +1,763 @@ +from collections import defaultdict +import logging +from pathlib import Path + +import pandas as pd +import numpy as np +from message_ix import make_df +from message_ix_models import ScenarioInfo +from message_ix_models.util import ( + broadcast, + same_node, +) + +from .util import read_config + +log = logging.getLogger(__name__) + +CONVERSION_FACTOR_CO2_C = 12 / 44 +CONVERSION_FACTOR_NH3_N = 17 / 14 +CONVERSION_FACTOR_PJ_GWa = 0.0317 + +def gen_data_ammonia(scenario, dry_run=False): + """Generate data for materials representation of nitrogen fertilizers. + + .. note:: This code is only partially translated from + :file:`SetupNitrogenBase.py`. + """ + # Load configuration + config = read_config()["material"]["fertilizer"] + context = read_config() + #print(config_.get_local_path("material", "test.xlsx")) + # Information about scenario, e.g. node, year + s_info = ScenarioInfo(scenario) + nodes = s_info.N + if (("World" in nodes) | ("R12_GLB" in nodes)): + nodes.pop(nodes.index("World")) + nodes.pop(nodes.index("R12_GLB")) + + # Techno-economic assumptions + data = read_data() + + # List of data frames, to be concatenated together at end + results = defaultdict(list) + + input_commodity_dict = { + "input_water": "freshwater_supply", + "input_elec": "electr", + "input_fuel": "" + } + output_commodity_dict = { + "output_NH3": "NH3", + "output_heat": "d_heat", + "output_water": "wastewater" # ask Jihoon how to name + } + commodity_dict = { + "output": output_commodity_dict, + "input": input_commodity_dict + } + input_level_dict = { + "input_water": "water_supply", + "input_fuel": "secondary", + "input_elec": "secondary" + } + output_level_dict = { + "output_water": "wastewater", + "output_heat": "secondary", + "output_NH3": "material_interim" + } + level_cat_dict = { + "output": output_level_dict, + "input": input_level_dict + } + + + vtg_years = s_info.yv_ya[s_info.yv_ya.year_vtg > 2000]["year_vtg"] + act_years = s_info.yv_ya[s_info.yv_ya.year_vtg > 2000]["year_act"] + + # NH3 production processes + common = dict( + year_act=act_years, # confirm if correct?? + year_vtg=vtg_years, + commodity="NH3", + level="material_interim", + mode="all", + time="year", + time_dest="year", + time_origin="year", + emission="CO2" # confirm if correct + ) + + # Iterate over new technologies, using the configuration + for t in config["technology"]["add"]: # : refactor to adjust to yaml structure + # Output of NH3: same efficiency for all technologies + # the output commodity and level are different for + + for param in data['parameter'].unique(): + if (t == "electr_NH3") & (param == "input_fuel"): + continue + unit = "t" + cat = data['param_cat'][data['parameter'] == param].iloc[0] + if cat in ["input", "output"]: + common["commodity"] = commodity_dict[cat][param] + common["level"] = level_cat_dict[cat][param] + if (t == "biomass_NH3") & (cat == "input"): + common["level"] = "primary" + if (str(t) == "NH3_to_N_fertil") & (param == "output_NH3"): + common['commodity'] = "Fertilizer Use|Nitrogen" + common['level'] = "material_final" + df = ( + make_df(cat, technology=t, value=1, unit="-", **common) + .pipe(broadcast, node_loc=nodes) + .pipe(same_node) + ) + + row = data[(data['technology'] == t) & + (data['parameter'] == param)] + df = df.assign(value=row[2010].values[0]) + + if param == "input_fuel": + comm = data['technology'][(data['parameter'] == param) & + (data["technology"] == t)].iloc[0].split("_")[0] + df = df.assign(commodity=comm) + + results[cat].append(df) + + # Historical activities/capacities - Region specific + common = dict( + commodity="NH3", + level="material_interim", + mode="all", + time="year", + time_dest="year", + time_origin="year", + ) + act2010 = read_demand()['act2010'] + df = ( + make_df("historical_activity", + technology=[t for t in config["technology"]["add"]], #], TODO: maybe reintroduce std/ccs in yaml + value=1, unit='t', years_act=s_info.Y, **common) + .pipe(broadcast, node_loc=nodes) + .pipe(same_node) + ) + row = act2010 + + # Unit is Tg N/yr + results["historical_activity"].append( + df.assign(value=row, unit="t", year_act=2010) + ) + # 2015 activity necessary if this is 5-year step scenario + # df['value'] = act2015 # total NH3 or N in Mt 2010 FAO Russia + # df['year_act'] = 2015 + # Sc_nitro.add_par("historical_activity", df) + + df = ( + make_df("historical_new_capacity", + technology=[t for t in config["technology"]["add"]], # ], refactor to adjust to yaml structure + value=1, unit='t', **common) + .pipe(broadcast, node_loc=nodes) + .pipe(same_node) + ) + + # modifying act2010 values by assuming 1/lifetime (=15yr) is built each year and account for capacity factor + capacity_factor = read_demand()['capacity_factor'] + row = act2010 * 1 / 15 / capacity_factor[0] + + # Unit is Tg N/yr + results["historical_new_capacity"].append( + df.assign(value=row, unit='t', year_vtg=2010) + ) + + # %% Secure feedstock balance (foil_fs, gas_fs, coal_fs) loil_fs? + + # Adjust i_feed demand + N_energy = read_demand()['N_energy'] + N_energy = read_demand()['N_feed'] # updated feed with imports accounted + + demand_fs_org = pd.read_excel(context.get_local_path('material','demand_i_feed_R12.xlsx')) + + df = demand_fs_org.loc[demand_fs_org.year == 2010, :].join(N_energy.set_index('node'), on='node') + sh = pd.DataFrame({'node': demand_fs_org.loc[demand_fs_org.year == 2010, 'node'], + 'r_feed': df.totENE / df.value}) # share of NH3 energy among total feedstock (no trade assumed) + df = demand_fs_org.join(sh.set_index('node'), on='node') + df.value *= 1 - df.r_feed # Carve out the same % from tot i_feed values + df = df.drop('r_feed', axis=1) + df = df.drop('Unnamed: 0', axis=1) + # TODO: refactor with a more sophisticated solution to reduce i_feed + df.loc[df["value"] < 0, "value"] = 0 # temporary solution to avoid negative values + results["demand"].append(df) + + # Globiom land input + """ + df = pd.read_excel(context.get_local_path('material','GLOBIOM_Fertilizer_use_N.xlsx')) + df = df.replace(regex=r'^R11', value="R12").replace(regex=r'^R12_CPA', value="R12_CHN") + df["unit"] = "t" + df.loc[df["node"] == "R12_CHN", "value"] *= 0.93 # hotfix to adjust to R12 + df_rcpa = df.loc[df["node"] == "R12_CHN"].copy(deep=True) + df_rcpa["node"] = "R12_RCPA" + df_rcpa["value"] *= 0.07 + df = df.append(df_rcpa) + df = df.drop("Unnamed: 0", axis=1) + results["land_input"].append(df) + """ + + df = scenario.par("land_output", {"commodity": "Fertilizer Use|Nitrogen"}) + df["level"] = "material_final" + results["land_input"].append(df) + #scenario.add_par("land_input", df) + + # add background parameters (growth rates and bounds) + + df = scenario.par('initial_activity_lo', {"technology": ["gas_extr_mpen"]}) + for q in config["technology"]["add"]: + df['technology'] = q + results["initial_activity_lo"].append(df) + + df = scenario.par('growth_activity_lo', {"technology": ["gas_extr_mpen"]}) + for q in config["technology"]["add"]: + df['technology'] = q + results["growth_activity_lo"].append(df) + + # TODO add regional cost scaling for ccs + """ + # tec_scale = (newtechnames + newtechnames_ccs) + tec_scale = [e for e in newtechnames if e not in ('NH3_to_N_fertil', 'electr_NH3')] + + # Scale all NH3 tecs in each region with the scaler + for t in tec_scale: + for p in ['inv_cost', 'fix_cost', 'var_cost']: + df = Sc_nitro.par(p, {"technology": t}) + df = results[p][results[p]["technology"]==t] + temp = df.join(scaler_cost.set_index('node_loc'), on='node_loc') + df.value = temp.value * temp.scaler_std + Sc_nitro.add_par(p, df) + + for t in newtechnames_ccs: + for p in ['inv_cost', 'fix_cost', 'var_cost']: + df = Sc_nitro.par(p, {"technology": t}) + temp = df.join(scaler_cost.set_index('node_loc'), on='node_loc') + df.value = temp.value * temp.scaler_ccs + Sc_nitro.add_par(p, df) + """ + + cost_scaler = pd.read_excel( + context.get_local_path('material','regional_cost_scaler_R12.xlsx'), index_col=0).T + + scalers_dict = { + "R12_CHN": {"coal_NH3": 0.75 * 0.91, # gas/coal price ratio * discount + "fueloil_NH3": 0.66 * 0.91}, # gas/oil price ratio * discount + "R12_SAS": {"fueloil_NH3": 0.59, + "coal_NH3": 1} + } + + params = ["inv_cost", "fix_cost", "var_cost"] + for param in params: + for i in range(len(results[param])): + df = results[param][i] + if df["technology"].any() in ('NH3_to_N_fertil', 'electr_NH3'): # skip those techs + continue + regs = df.set_index("node_loc").join(cost_scaler, on="node_loc") + regs.value = regs.value * regs["standard"] + regs = regs.reset_index() + if df["technology"].any() in ("coal_NH3", "fueloil_NH3"): # additional scaling to make coal/oil cheaper + regs.loc[regs["node_loc"] == "R12_CHN", "value"] = \ + regs.loc[regs["node_loc"] == "R12_CHN", "value"] * \ + scalers_dict["R12_CHN"][df.technology[0]] + regs.loc[regs["node_loc"] == "R12_SAS", "value"] = \ + regs.loc[regs["node_loc"] == "R12_SAS", "value"] * \ + scalers_dict["R12_SAS"][df.technology[0]] + results[param][i] = regs.drop(["standard", "ccs"], axis="columns") + + # add trade tecs (exp, imp, trd) + + newtechnames_trd = ["trade_NFert"] + newtechnames_imp = ["import_NFert"] + newtechnames_exp = ["export_NFert"] + + scenario.add_set( + "technology", + newtechnames_trd + newtechnames_imp + newtechnames_exp + ) + cat_add = pd.DataFrame( + { + "type_tec": ["import", "export"], # 'all' not need to be added here + "technology": newtechnames_imp + newtechnames_exp, + } + ) + scenario.add_set("cat_tec", cat_add) + #scenario.commit("add trade tec sets") + + yv_ya_exp = s_info.yv_ya + yv_ya_exp = yv_ya_exp[(yv_ya_exp["year_act"] - yv_ya_exp["year_vtg"] < 30) & (yv_ya_exp["year_vtg"] > 2000)] + yv_ya_same = yv_ya_exp[(yv_ya_exp["year_act"] - yv_ya_exp["year_vtg"] == 0) & ( yv_ya_exp["year_vtg"] == 2000)] + + common = dict( + year_act=yv_ya_same.year_act, + year_vtg=yv_ya_same.year_vtg, + commodity="Fertilizer Use|Nitrogen", + level="material_final", + mode="M1", + time="year", + time_dest="year", + time_origin="year", + ) + + data = read_trade_data(context) + + for i in data["var_name"].unique(): + for tec in data["technology"].unique(): + row = data[(data["var_name"] == i) & (data["technology"] == tec)] + if len(row): + if row["technology"].values[0] == "trade_NFert": + node = ["R12_GLB"] + else: + node = s_info.N[1:] + if tec == "export_NFert": + common_exp = common + common_exp["year_act"] = yv_ya_exp.year_act + common_exp["year_vtg"] = yv_ya_exp.year_vtg + df = make_df(i, technology=tec, value=row[2010].values[0], + unit="-", **common_exp).pipe(broadcast, node_loc=node).pipe(same_node) + else: + df = make_df(i, technology=tec, value=row[2010].values[0], + unit="-", **common).pipe(broadcast, node_loc=node).pipe(same_node) + if (tec == "export_NFert") & (i == "output"): + df["node_dest"] = "R12_GLB" + df["level"] = "export" + elif (tec == "import_NFert") & (i == "input"): + df["node_origin"] = "R12_GLB" + df["level"] = "import" + else: + df.pipe(same_node) + results[i].append(df) + + common = dict( + commodity="Fertilizer Use|Nitrogen", + level="material_final", + mode="M1", + time="year", + time_dest="year", + time_origin="year", + unit="t" + ) + + N_trade_R12 = read_demand()["N_trade_R12"].assign(mode="M1") + N_trade_R12["technology"] = N_trade_R12["Element"].apply( + lambda x: "export_NFert" if x == "Export" else "import_NFert") + df_exp_imp_act = N_trade_R12.drop("Element", axis=1) + + trd_act_years = N_trade_R12["year_act"].unique() + values = N_trade_R12.groupby(["year_act"]).sum().values.flatten() + fert_trd_hist = make_df("historical_activity", technology="trade_NFert", + year_act=trd_act_years, value=values, + node_loc="R12_GLB", **common) + results["historical_activity"].append(pd.concat([df_exp_imp_act, fert_trd_hist])) + + df_hist_cap_new = N_trade_R12[N_trade_R12["technology"] == "export_NFert"].drop(columns=["time", "mode", "Element"]) + df_hist_cap_new = df_hist_cap_new.rename(columns={"year_act": "year_vtg"}) + # divide by export lifetime derived from coal_exp + df_hist_cap_new = df_hist_cap_new.assign(value=lambda x: x["value"] / 30) + results["historical_new_capacity"].append(df_hist_cap_new) + + # Concatenate to one dataframe per parameter + results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} + + return results + + +def gen_data_ccs(scenario, dry_run=False): + """Generate data for materials representation of nitrogen fertilizers. + + .. note:: This code is only partially translated from + :file:`SetupNitrogenBase.py`. + """ + config = read_config()["material"]["set"] + context = read_config() + + # Information about scenario, e.g. node, year + s_info = ScenarioInfo(scenario) + nodes = s_info.N + if "World" in nodes: + nodes.pop(nodes.index("World")) + + # Techno-economic assumptions + data = read_data_ccs() + + # List of data frames, to be concatenated together at end + results = defaultdict(list) + + # NH3 production processes + common = dict( + year_vtg=s_info.Y, + year_act=s_info.Y, # confirm if correct?? + commodity="NH3", + level="material_interim", + # TODO fill in remaining dimensions + mode="all", + time="year", + time_dest="year", + time_origin="year", + emission="CO2" # confirm if correct + # node_loc='node' + ) + + input_commodity_dict = { + "input_water": "freshwater_supply", + "input_elec": "electr", + "input_fuel": "" + } + output_commodity_dict = { + "output_NH3": "NH3", + "output_heat": "d_heat", + "output_water": "" # ask Jihoon how to name + } + commodity_dict = { + "output": output_commodity_dict, + "input": input_commodity_dict + } + input_level_dict = { + "input_water": "water_supply", + "input_fuel": "secondary", + "input_elec": "secondary" + } + output_level_dict = { + "output_water": "wastewater", + "output_heat": "secondary", + "output_NH3": "material_interim" + } + level_cat_dict = { + "output": output_level_dict, + "input": input_level_dict + } + + # Iterate over new technologies, using the configuration + for t in config["technology"]["add"]["ccs"]: + # Output of NH3: same efficiency for all technologies + # TODO the output commodity and level are different for + # t=NH3_to_N_fertil; use 'if' statements to fill in. + + for param in data['parameter'].unique(): + unit = "t" + cat = data['param_cat'][data['parameter'] == param].iloc[0] + if cat in ["input", "output"]: + common["commodity"] = commodity_dict[cat][param] + common["level"] = level_cat_dict[cat][param] + df = ( + make_df(cat, technology=t, value=1, unit="-", **common) + .pipe(broadcast, node_loc=nodes) + .pipe(same_node) + ) + row = data[(data['technology'] == str(t)) & + (data['parameter'] == param)] + df = df.assign(value=row[2010].values[0]) + + if param == "input_fuel": + comm = data['technology'][(data['parameter'] == param) & + (data["technology"] == t)].iloc[0].split("_")[0] + df = df.assign(commodity=comm) + + results[cat].append(df) + + + # add background parameters (growth rates and bounds) + + df = scenario.par('initial_activity_lo', {"technology": ["gas_extr_mpen"]}) + for q in config["technology"]["add"]: + df['technology'] = q + results["initial_activity_lo"].append(df) + + df = scenario.par('growth_activity_lo', {"technology": ["gas_extr_mpen"]}) + for q in config["technology"]["add"]: + df['technology'] = q + results["growth_activity_lo"].append(df) + + cost_scaler = pd.read_excel( + context.get_local_path('material','regional_cost_scaler_R12.xlsx'), index_col=0).T + + scalers_dict = { + "R12_CHN": {"coal_NH3": 0.75 * 0.91, # gas/coal price ratio * discount + "fueloil_NH3": 0.66 * 0.91}, # gas/oil price ratio * discount + "R12_SAS": {"fueloil_NH3": 0.59, + "coal_NH3": 1} + } + + params = ["inv_cost", "fix_cost", "var_cost"] + for param in params: + for i in range(len(results[param])): + df = results[param][i] + if df["technology"].any() in ('NH3_to_N_fertil', 'electr_NH3'): # skip those techs + continue + regs = df.set_index("node_loc").join(cost_scaler, on="node_loc") + regs.value = regs.value * regs["ccs"] + regs = regs.reset_index() + if df["technology"].any() in ("coal_NH3", "fueloil_NH3"): # additional scaling to make coal/oil cheaper + regs.loc[regs["node_loc"] == "R12_CHN", "value"] = \ + regs.loc[regs["node_loc"] == "R12_CHN", "value"] * \ + scalers_dict["R12_CHN"][df.technology[0].values[0].name] + regs.loc[regs["node_loc"] == "R12_SAS", "value"] = \ + regs.loc[regs["node_loc"] == "R12_SAS", "value"] * \ + scalers_dict["R12_SAS"][df.technology[0].values[0].name] + results[param][i] = regs.drop(["standard", "ccs"], axis="columns") + + # Concatenate to one dataframe per parameter + results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} + + return results + + +def read_demand(): + """Read and clean data from :file:`CD-Links SSP2 N-fertilizer demand.Global.xlsx`.""" + # Demand scenario [Mt N/year] from GLOBIOM + context = read_config() + + + N_demand_GLO = pd.read_excel(context.get_local_path('material','CD-Links SSP2 N-fertilizer demand.Global_R12_adaption.xlsx'), sheet_name='data') + + # NH3 feedstock share by region in 2010 (from http://ietd.iipnetwork.org/content/ammonia#benchmarks) + feedshare_GLO = pd.read_excel(context.get_local_path('material','Ammonia feedstock share.Global_R12.xlsx'), sheet_name='Sheet2', skiprows=14) + + # Read parameters in xlsx + te_params = data = pd.read_excel( + context.get_local_path("material", "n-fertilizer_techno-economic.xlsx"), + sheet_name="Sheet1", engine="openpyxl", nrows=72 + ) + n_inputs_per_tech = 12 # Number of input params per technology + + input_fuel = te_params[2010][list(range(4, te_params.shape[0], n_inputs_per_tech))].reset_index(drop=True) + input_fuel[0:5] = input_fuel[0:5] * CONVERSION_FACTOR_PJ_GWa # 0.0317 GWa/PJ, GJ/t = PJ/Mt NH3 + + capacity_factor = te_params[2010][list(range(11, te_params.shape[0], n_inputs_per_tech))].reset_index(drop=True) + + # Regional N demaand in 2010 + ND = N_demand_GLO.loc[N_demand_GLO.Scenario == "NoPolicy", ['Region', 2010]] + ND = ND[ND.Region != 'World'] + ND.Region = 'R12_' + ND.Region + ND = ND.set_index('Region') + + # Derive total energy (GWa) of NH3 production (based on demand 2010) + N_energy = feedshare_GLO[feedshare_GLO.Region != 'R12_GLB'].join(ND, on='Region') + N_energy = pd.concat( + [N_energy.Region, N_energy[["gas_pct", "coal_pct", "oil_pct"]].multiply(N_energy[2010], axis="index")], axis=1) + N_energy.gas_pct *= input_fuel[2] * CONVERSION_FACTOR_NH3_N # NH3 / N + N_energy.coal_pct *= input_fuel[3] * CONVERSION_FACTOR_NH3_N + N_energy.oil_pct *= input_fuel[4] * CONVERSION_FACTOR_NH3_N + N_energy = pd.concat([N_energy.Region, N_energy.sum(axis=1)], axis=1).rename( + columns={0: 'totENE', 'Region': 'node'}) # GWa + + N_trade_R12 = pd.read_csv(context.get_local_path("material","trade.FAO.R12.csv"), index_col=0) + N_trade_R12.msgregion = "R12_" + N_trade_R12.msgregion + N_trade_R12.Value = N_trade_R12.Value / 1e6 + N_trade_R12.Unit = "t" + N_trade_R12 = N_trade_R12.assign(time="year") + N_trade_R12 = N_trade_R12.rename( + columns={ + "Value": "value", + "Unit": "unit", + "msgregion": "node_loc", + "Year": "year_act", + } + ) + + df = N_trade_R12.loc[ + N_trade_R12.year_act == 2010, + ] + df = df.pivot(index="node_loc", columns="Element", values="value") + NP = pd.DataFrame({"netimp": df.Import - df.Export, "demand": ND[2010]}) + NP["prod"] = NP.demand - NP.netimp + + + # Derive total energy (GWa) of NH3 production (based on demand 2010) + N_feed = feedshare_GLO[feedshare_GLO.Region != "R11_GLB"].join(NP, on="Region") + N_feed = pd.concat( + [ + N_feed.Region, + N_feed[["gas_pct", "coal_pct", "oil_pct"]].multiply( + N_feed["prod"], axis="index" + ), + ], + axis=1, + ) + N_feed.gas_pct *= input_fuel[2] * 17 / 14 + N_feed.coal_pct *= input_fuel[3] * 17 / 14 + N_feed.oil_pct *= input_fuel[4] * 17 / 14 + N_feed = pd.concat([N_feed.Region, N_feed.sum(axis=1)], axis=1).rename( + columns={0: "totENE", "Region": "node"}) + + # Process the regional historical activities + + fs_GLO = feedshare_GLO.copy() + fs_GLO.insert(1, "bio_pct", 0) + fs_GLO.insert(2, "elec_pct", 0) + # 17/14 NH3:N ratio, to get NH3 activity based on N demand => No NH3 loss assumed during production + fs_GLO.iloc[:, 1:6] = input_fuel[5] * fs_GLO.iloc[:, 1:6] + fs_GLO.insert(6, "NH3_to_N", 1) + + # Share of feedstocks for NH3 prodution (based on 2010 => Assumed fixed for any past years) + feedshare = fs_GLO.sort_values(['Region']).set_index('Region').drop('R12_GLB') + + # Get historical N demand from SSP2-nopolicy (may need to vary for diff scenarios) + N_demand_raw = N_demand_GLO.copy() + N_demand = N_demand_raw[(N_demand_raw.Scenario == "NoPolicy") & + (N_demand_raw.Region != "World")].reset_index().loc[:, 2010] # 2010 tot N demand + N_demand = N_demand.repeat(6) + + act2010 = (feedshare.values.flatten() * N_demand).reset_index(drop=True) + + return {"feedshare_GLO": feedshare_GLO, "ND": ND, "N_energy": N_energy, "feedshare": feedshare, 'act2010': act2010, + 'capacity_factor': capacity_factor, "N_feed":N_feed, "N_trade_R12":N_trade_R12} + + +def read_trade_data(context): + data = pd.read_excel( + context.get_local_path("material", "n-fertilizer_techno-economic_new.xlsx"), + sheet_name="Trade", engine="openpyxl", usecols=np.linspace(0, 7, 8, dtype=int)) + data = data.assign(technology=lambda x: set_trade_tec(x["Variable"])) + return data + + +def set_trade_tec(x): + arr=[] + for i in x: + if "Import" in i: + arr.append("import_NFert") + if "Export" in i: + arr.append("export_NFert") + if "Trade" in i: + arr.append("trade_NFert") + return arr + + +def read_data(): + """Read and clean data from :file:`n-fertilizer_techno-economic.xlsx`.""" + # Ensure config is loaded, get the context + context = read_config() + print(context.get_local_path()) + #print(Path(__file__).parents[3]/"data"/"material") + context.handle_cli_args(local_data=Path(__file__).parents[3]/"data") + print(context.get_local_path()) + # Shorter access to sets configuration + sets = context["material"]["fertilizer"] + + # Read the file + data = pd.read_excel( + context.get_local_path("material", "n-fertilizer_techno-economic_new.xlsx"), + sheet_name="Sheet1", engine="openpyxl", nrows=72 + ) + + # Prepare contents for the "parameter" and "technology" columns + # FIXME put these in the file itself to avoid ambiguity/error + + # "Variable" column contains different values selected to match each of + # these parameters, per technology + params = [ + "inv_cost", + "fix_cost", + "var_cost", + "technical_lifetime", + "input_fuel", + "input_elec", + "input_water", + "output_NH3", + "output_water", + "output_heat", + "emission_factor", + "capacity_factor", + ] + + param_values = [] + tech_values = [] + param_cat = [split.split('_')[0] if + (split.startswith('input') or split.startswith('output')) + else split for split in params] + + param_cat2 = [] + # print(param_cat) + for t in sets["technology"]["add"]: # : refactor to adjust to yaml structure + # print(t) + param_values.extend(params) + tech_values.extend([t] * len(params)) + param_cat2.extend(param_cat) + + # Clean the data + data = ( + # Insert "technology" and "parameter" columns + data.assign(technology=tech_values, + parameter=param_values, + param_cat=param_cat2) + # Drop columns that don't contain useful information + .drop(["Model", "Scenario", "Region"], axis=1) + # Set the data frame index for selection + ) + data.loc[data['parameter'] == 'emission_factor', 2010] = \ + data.loc[data['parameter'] == 'emission_factor', 2010] * CONVERSION_FACTOR_CO2_C + data.loc[data['parameter'] == 'input_elec', 2010] = \ + data.loc[data['parameter'] == 'input_elec', 2010] * CONVERSION_FACTOR_PJ_GWa + + # TODO convert units for some parameters, per LoadParams.py + return data + + +def read_data_ccs(): + """Read and clean data from :file:`n-fertilizer_techno-economic.xlsx`.""" + # Ensure config is loaded, get the context + context = read_config() + + # Shorter access to sets configuration + sets = context["material"]["set"] + + # Read the file + data = pd.read_excel( + context.get_local_path("material", "n-fertilizer_techno-economic.xlsx"), + sheet_name="CCS", + ) + + # Prepare contents for the "parameter" and "technology" columns + # FIXME put these in the file itself to avoid ambiguity/error + + # "Variable" column contains different values selected to match each of + # these parameters, per technology + params = [ + "inv_cost", + "fix_cost", + "var_cost", + "technical_lifetime", + "input_fuel", + "input_elec", + "input_water", + "output_NH3", + "output_water", + "output_heat", + "emission_factor", + "emission_factor", + "capacity_factor", + ] + + param_values = [] + tech_values = [] + param_cat = [split.split('_')[0] if (split.startswith('input') or split.startswith('output')) else split for split + in params] + + param_cat2 = [] + + for t in sets["technology"]["add"]["ccs"]: + param_values.extend(params) + tech_values.extend([t] * len(params)) + param_cat2.extend(param_cat) + + # Clean the data + data = ( + # Insert "technology" and "parameter" columns + data.assign(technology=tech_values, parameter=param_values, param_cat=param_cat2) + # , param_cat=param_cat2) + # Drop columns that don't contain useful information + .drop(["Model", "Scenario", "Region"], axis=1) + # Set the data frame index for selection + ) + #unit conversions and extra electricity for CCS process + data.loc[data['parameter'] == 'emission_factor', 2010] = \ + data.loc[data['parameter'] == 'emission_factor', 2010] * CONVERSION_FACTOR_CO2_C + data.loc[data['parameter'] == 'input_elec', 2010] = \ + data.loc[data['parameter'] == 'input_elec', 2010] * CONVERSION_FACTOR_PJ_GWa + 0.005 + + # TODO convert units for some parameters, per LoadParams.py + return data From 5397a4422d62a12298be9caa41889f16d604d4db Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Mon, 28 Feb 2022 14:37:55 +0100 Subject: [PATCH 328/774] Comment out power sector --- message_ix_models/model/material/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index ca982ebb4a..1c0c817b90 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -249,7 +249,7 @@ def run_old_reporting(scenario_name, model_name): gen_data_aluminum, gen_data_petro_chemicals, gen_data_generic, - gen_data_power_sector, + #gen_data_power_sector, gen_data_ammonia, ] From 852daa16c5e38841e5676e271401d840e66944ed Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 1 Mar 2022 11:43:39 +0100 Subject: [PATCH 329/774] Increase memory --- message_ix_models/model/material/__init__.py | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 1c0c817b90..c31b38eeaa 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -195,7 +195,7 @@ def run_reporting(old_reporting, scenario_name, model_name): from ixmp import Platform print(model_name) - mp = Platform() + mp = Platform(jvmargs=['-Xmx12G']) scenario = Scenario(mp, model_name, scenario_name) report(scenario, old_reporting) @@ -216,7 +216,7 @@ def run_old_reporting(scenario_name, model_name): print(model_name) print(scenario_name) - mp = Platform() + mp = Platform(jvmargs=['-Xmx12G']) scenario = Scenario(mp, model_name, scenario_name) reporting( @@ -244,14 +244,13 @@ def run_old_reporting(scenario_name, model_name): DATA_FUNCTIONS = [ #gen_data_buildings, + gen_data_ammonia, + gen_data_generic, gen_data_steel, gen_data_cement, - gen_data_aluminum, gen_data_petro_chemicals, - gen_data_generic, #gen_data_power_sector, - gen_data_ammonia, - + gen_data_aluminum, ] # Try to handle multiple data input functions from different materials From 3401ccda5f2b7386ce500552df0c7f5a05c44447 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 9 Mar 2022 17:11:19 +0100 Subject: [PATCH 330/774] Fix the function name --- message_ix_models/model/material/data_ammonia.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/message_ix_models/model/material/data_ammonia.py b/message_ix_models/model/material/data_ammonia.py index 18bca3a54f..8e7aa54962 100644 --- a/message_ix_models/model/material/data_ammonia.py +++ b/message_ix_models/model/material/data_ammonia.py @@ -19,7 +19,7 @@ CONVERSION_FACTOR_NH3_N = 17 / 14 CONVERSION_FACTOR_PJ_GWa = 0.0317 -def gen_data_ammonia(scenario, dry_run=False): +def gen_data_ammonia(scenario, dry_run=False, add_ccs: bool = False): """Generate data for materials representation of nitrogen fertilizers. .. note:: This code is only partially translated from From ae9cd40907bd25844f2812cbf87bcabe40f962d1 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 15 Mar 2022 16:23:23 +0100 Subject: [PATCH 331/774] Change back to secondary level and add explanation --- .../model/material/data_ammonia.py | 284 +++++++++++++----- 1 file changed, 214 insertions(+), 70 deletions(-) diff --git a/message_ix_models/model/material/data_ammonia.py b/message_ix_models/model/material/data_ammonia.py index 8e7aa54962..e47f00c1e4 100644 --- a/message_ix_models/model/material/data_ammonia.py +++ b/message_ix_models/model/material/data_ammonia.py @@ -19,7 +19,7 @@ CONVERSION_FACTOR_NH3_N = 17 / 14 CONVERSION_FACTOR_PJ_GWa = 0.0317 -def gen_data_ammonia(scenario, dry_run=False, add_ccs: bool = False): +def gen_data_ammonia(scenario, dry_run=False, add_ccs: bool = True): """Generate data for materials representation of nitrogen fertilizers. .. note:: This code is only partially translated from @@ -64,7 +64,7 @@ def gen_data_ammonia(scenario, dry_run=False, add_ccs: bool = False): output_level_dict = { "output_water": "wastewater", "output_heat": "secondary", - "output_NH3": "material_interim" + "output_NH3": "secondary_material" } level_cat_dict = { "output": output_level_dict, @@ -80,16 +80,17 @@ def gen_data_ammonia(scenario, dry_run=False, add_ccs: bool = False): year_act=act_years, # confirm if correct?? year_vtg=vtg_years, commodity="NH3", - level="material_interim", - mode="all", + level="secondary_material", + mode="M1", time="year", time_dest="year", time_origin="year", - emission="CO2" # confirm if correct + emission="CO2_transformation" # confirm if correct ) # Iterate over new technologies, using the configuration - for t in config["technology"]["add"]: # : refactor to adjust to yaml structure + for t in config["technology"]["add"][:6]: + # TODO: refactor to adjust to yaml structure # Output of NH3: same efficiency for all technologies # the output commodity and level are different for @@ -105,7 +106,10 @@ def gen_data_ammonia(scenario, dry_run=False, add_ccs: bool = False): common["level"] = "primary" if (str(t) == "NH3_to_N_fertil") & (param == "output_NH3"): common['commodity'] = "Fertilizer Use|Nitrogen" - common['level'] = "material_final" + common['level'] = "final_material" + if (str(t) == "NH3_to_N_fertil") & (param == "input_fuel"): + common['level'] = "secondary_material" + df = ( make_df(cat, technology=t, value=1, unit="-", **common) .pipe(broadcast, node_loc=nodes) @@ -126,8 +130,8 @@ def gen_data_ammonia(scenario, dry_run=False, add_ccs: bool = False): # Historical activities/capacities - Region specific common = dict( commodity="NH3", - level="material_interim", - mode="all", + level="secondary_material", + mode="M1", time="year", time_dest="year", time_origin="year", @@ -135,7 +139,7 @@ def gen_data_ammonia(scenario, dry_run=False, add_ccs: bool = False): act2010 = read_demand()['act2010'] df = ( make_df("historical_activity", - technology=[t for t in config["technology"]["add"]], #], TODO: maybe reintroduce std/ccs in yaml + technology=[t for t in config["technology"]["add"][:6]], #], TODO: maybe reintroduce std/ccs in yaml value=1, unit='t', years_act=s_info.Y, **common) .pipe(broadcast, node_loc=nodes) .pipe(same_node) @@ -153,7 +157,7 @@ def gen_data_ammonia(scenario, dry_run=False, add_ccs: bool = False): df = ( make_df("historical_new_capacity", - technology=[t for t in config["technology"]["add"]], # ], refactor to adjust to yaml structure + technology=[t for t in config["technology"]["add"][:6]], # ], refactor to adjust to yaml structure value=1, unit='t', **common) .pipe(broadcast, node_loc=nodes) .pipe(same_node) @@ -202,19 +206,19 @@ def gen_data_ammonia(scenario, dry_run=False, add_ccs: bool = False): """ df = scenario.par("land_output", {"commodity": "Fertilizer Use|Nitrogen"}) - df["level"] = "material_final" + df["level"] = "final_material" results["land_input"].append(df) #scenario.add_par("land_input", df) # add background parameters (growth rates and bounds) df = scenario.par('initial_activity_lo', {"technology": ["gas_extr_mpen"]}) - for q in config["technology"]["add"]: + for q in config["technology"]["add"][:6]: df['technology'] = q results["initial_activity_lo"].append(df) df = scenario.par('growth_activity_lo', {"technology": ["gas_extr_mpen"]}) - for q in config["technology"]["add"]: + for q in config["technology"]["add"][:6]: df['technology'] = q results["growth_activity_lo"].append(df) @@ -274,36 +278,22 @@ def gen_data_ammonia(scenario, dry_run=False, add_ccs: bool = False): newtechnames_imp = ["import_NFert"] newtechnames_exp = ["export_NFert"] - scenario.add_set( - "technology", - newtechnames_trd + newtechnames_imp + newtechnames_exp - ) - cat_add = pd.DataFrame( - { - "type_tec": ["import", "export"], # 'all' not need to be added here - "technology": newtechnames_imp + newtechnames_exp, - } - ) - scenario.add_set("cat_tec", cat_add) - #scenario.commit("add trade tec sets") - yv_ya_exp = s_info.yv_ya yv_ya_exp = yv_ya_exp[(yv_ya_exp["year_act"] - yv_ya_exp["year_vtg"] < 30) & (yv_ya_exp["year_vtg"] > 2000)] - yv_ya_same = yv_ya_exp[(yv_ya_exp["year_act"] - yv_ya_exp["year_vtg"] == 0) & ( yv_ya_exp["year_vtg"] == 2000)] + yv_ya_same = s_info.yv_ya[(s_info.yv_ya["year_act"] - s_info.yv_ya["year_vtg"] == 0) & ( s_info.yv_ya["year_vtg"] > 2000)] common = dict( year_act=yv_ya_same.year_act, year_vtg=yv_ya_same.year_vtg, commodity="Fertilizer Use|Nitrogen", - level="material_final", + level="final_material", mode="M1", time="year", time_dest="year", time_origin="year", ) - data = read_trade_data(context) - + data = read_trade_data(context, comm="NFert") for i in data["var_name"].unique(): for tec in data["technology"].unique(): row = data[(data["var_name"] == i) & (data["technology"] == tec)] @@ -311,7 +301,7 @@ def gen_data_ammonia(scenario, dry_run=False, add_ccs: bool = False): if row["technology"].values[0] == "trade_NFert": node = ["R12_GLB"] else: - node = s_info.N[1:] + node = nodes if tec == "export_NFert": common_exp = common common_exp["year_act"] = yv_ya_exp.year_act @@ -327,13 +317,17 @@ def gen_data_ammonia(scenario, dry_run=False, add_ccs: bool = False): elif (tec == "import_NFert") & (i == "input"): df["node_origin"] = "R12_GLB" df["level"] = "import" + elif (tec == "trade_NFert") & (i == "input"): + df["level"] = "export" + elif (tec == "trade_NFert") & (i == "output"): + df["level"] = "import" else: df.pipe(same_node) results[i].append(df) common = dict( commodity="Fertilizer Use|Nitrogen", - level="material_final", + level="final_material", mode="M1", time="year", time_dest="year", @@ -359,9 +353,113 @@ def gen_data_ammonia(scenario, dry_run=False, add_ccs: bool = False): df_hist_cap_new = df_hist_cap_new.assign(value=lambda x: x["value"] / 30) results["historical_new_capacity"].append(df_hist_cap_new) + #NH3 trade + #_______________________________________________ + + common = dict( + year_act=yv_ya_same.year_act, + year_vtg=yv_ya_same.year_vtg, + commodity="NH3", + level="secondary_material", + mode="M1", + time="year", + time_dest="year", + time_origin="year", + ) + + data = read_trade_data(context, comm="NH3") + + for i in data["var_name"].unique(): + for tec in data["technology"].unique(): + row = data[(data["var_name"] == i) & (data["technology"] == tec)] + if len(row): + if row["technology"].values[0] == "trade_NH3": + node = ["R12_GLB"] + else: + node = nodes + if tec == "export_NH3": + common_exp = common + common_exp["year_act"] = yv_ya_exp.year_act + common_exp["year_vtg"] = yv_ya_exp.year_vtg + df = make_df(i, technology=tec, value=row[2010].values[0], + unit="-", **common_exp).pipe(broadcast, node_loc=node).pipe(same_node) + else: + df = make_df(i, technology=tec, value=row[2010].values[0], + unit="-", **common).pipe(broadcast, node_loc=node).pipe(same_node) + if (tec == "export_NH3") & (i == "output"): + df["node_dest"] = "R12_GLB" + df["level"] = "export" + elif (tec == "import_NH3") & (i == "input"): + df["node_origin"] = "R12_GLB" + df["level"] = "import" + elif (tec == "trade_NH3") & (i == "input"): + df["level"] = "export" + elif (tec == "trade_NH3") & (i == "output"): + df["level"] = "import" + else: + df.pipe(same_node) + results[i].append(df) + + common = dict( + commodity="NH3", + level="secondary_material", + mode="M1", + time="year", + time_dest="year", + time_origin="year", + unit="t" + ) + + N_trade_R12 = read_demand()["N_trade_R12"].assign(mode="M1") + N_trade_R12["technology"] = N_trade_R12["Element"].apply( + lambda x: "export_NH3" if x == "Export" else "import_NH3") + df_exp_imp_act = N_trade_R12.drop("Element", axis=1) + + trd_act_years = N_trade_R12["year_act"].unique() + values = N_trade_R12.groupby(["year_act"]).sum().values.flatten() + fert_trd_hist = make_df("historical_activity", technology="trade_NH3", + year_act=trd_act_years, value=values, + node_loc="R12_GLB", **common) + results["historical_activity"].append(pd.concat([df_exp_imp_act, fert_trd_hist])) + + df_hist_cap_new = N_trade_R12[N_trade_R12["technology"] == "export_NH3"].drop(columns=["time", "mode", "Element"]) + df_hist_cap_new = df_hist_cap_new.rename(columns={"year_act": "year_vtg"}) + # divide by export lifetime derived from coal_exp + df_hist_cap_new = df_hist_cap_new.assign(value=lambda x: x["value"] / 30) + results["historical_new_capacity"].append(df_hist_cap_new) + #___________________________________________________________________________ + + if add_ccs: + for k, v in gen_data_ccs(scenario).items(): + results[k].append(v) + # Concatenate to one dataframe per parameter results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} + par = "emission_factor" + rel_df_cc = results[par] + rel_df_cc = rel_df_cc[rel_df_cc["emission"] == "CO2_transformation"] + rel_df_cc = rel_df_cc.assign(year_rel=rel_df_cc["year_act"], + node_rel=rel_df_cc["node_loc"], + relation="CO2_cc").drop(["emission", "year_vtg"], axis=1) + rel_df_cc = rel_df_cc[rel_df_cc["technology"] != "NH3_to_N_fertil"] + rel_df_cc = rel_df_cc[rel_df_cc["year_rel"] == rel_df_cc["year_act"]].drop_duplicates() + rel_df_cc[rel_df_cc["technology"] == "biomass_NH3_ccs"] = rel_df_cc[ + rel_df_cc["technology"] == "biomass_NH3_ccs"].assign( + value=results[par][results[par]["technology"] == "biomass_NH3_ccs"]["value"].values[0]) + + + rel_df_em = results[par] + rel_df_em = rel_df_em[rel_df_em["emission"] == "CO2"] + rel_df_em = rel_df_em.assign(year_rel=rel_df_em["year_act"], + node_rel=rel_df_em["node_loc"], + relation="CO2_Emission").drop(["emission", "year_vtg"], axis=1) + rel_df_em = rel_df_em[rel_df_em["technology"] != "NH3_to_N_fertil"] + rel_df_em[rel_df_em["year_rel"] == rel_df_em["year_act"]].drop_duplicates() + results["relation_activity"] = pd.concat([rel_df_cc, rel_df_em]) + + results["emission_factor"] = results["emission_factor"][results["emission_factor"]["technology"]!="NH3_to_N_fertil"] + return results @@ -371,7 +469,7 @@ def gen_data_ccs(scenario, dry_run=False): .. note:: This code is only partially translated from :file:`SetupNitrogenBase.py`. """ - config = read_config()["material"]["set"] + config = read_config()["material"]["fertilizer"] context = read_config() # Information about scenario, e.g. node, year @@ -379,6 +477,8 @@ def gen_data_ccs(scenario, dry_run=False): nodes = s_info.N if "World" in nodes: nodes.pop(nodes.index("World")) + if "R12_GLB" in nodes: + nodes.pop(nodes.index("R12_GLB")) # Techno-economic assumptions data = read_data_ccs() @@ -386,14 +486,26 @@ def gen_data_ccs(scenario, dry_run=False): # List of data frames, to be concatenated together at end results = defaultdict(list) + vtg_years = s_info.yv_ya[s_info.yv_ya.year_vtg > 2000]["year_vtg"] + act_years = s_info.yv_ya[s_info.yv_ya.year_vtg > 2000]["year_act"] + # NH3 production processes + # NOTE: The energy required for NH3 production process is retreived + # from secondary energy level for the moment. However, the energy that is used to + # produce ammonia as feedstock is accounted in Final Energy in reporting. + # The energy that is used to produce ammonia as fuel should be accounted in + # Secondary energy. At the moment all ammonia is used as feedstock. + # Later we can track the shares of feedstock vs fuel use of ammonia and + # divide final energy. Or create another set of technologies (only for fuel + # use vs. only for feedstock use). + common = dict( - year_vtg=s_info.Y, - year_act=s_info.Y, # confirm if correct?? + year_vtg=vtg_years, + year_act=act_years, # confirm if correct?? commodity="NH3", - level="material_interim", + level="secondary_material", # TODO fill in remaining dimensions - mode="all", + mode="M1", time="year", time_dest="year", time_origin="year", @@ -409,7 +521,7 @@ def gen_data_ccs(scenario, dry_run=False): output_commodity_dict = { "output_NH3": "NH3", "output_heat": "d_heat", - "output_water": "" # ask Jihoon how to name + "output_water": "wastewater" } commodity_dict = { "output": output_commodity_dict, @@ -423,7 +535,7 @@ def gen_data_ccs(scenario, dry_run=False): output_level_dict = { "output_water": "wastewater", "output_heat": "secondary", - "output_NH3": "material_interim" + "output_NH3": "secondary_material" } level_cat_dict = { "output": output_level_dict, @@ -431,7 +543,7 @@ def gen_data_ccs(scenario, dry_run=False): } # Iterate over new technologies, using the configuration - for t in config["technology"]["add"]["ccs"]: + for t in config["technology"]["add"][12:]: # Output of NH3: same efficiency for all technologies # TODO the output commodity and level are different for # t=NH3_to_N_fertil; use 'if' statements to fill in. @@ -442,32 +554,41 @@ def gen_data_ccs(scenario, dry_run=False): if cat in ["input", "output"]: common["commodity"] = commodity_dict[cat][param] common["level"] = level_cat_dict[cat][param] - df = ( - make_df(cat, technology=t, value=1, unit="-", **common) - .pipe(broadcast, node_loc=nodes) - .pipe(same_node) - ) + if param == "emission_factor_trans": + _common = common.copy() + _common["emission"] = "CO2_transformation" + cat = "emission_factor" + df = ( + make_df(cat, technology=t, value=1, unit="-", **_common) + .pipe(broadcast, node_loc=nodes) + .pipe(same_node) + ) + + else: + df = ( + make_df(cat, technology=t, value=1, unit="-", **common) + .pipe(broadcast, node_loc=nodes) + .pipe(same_node) + ) row = data[(data['technology'] == str(t)) & (data['parameter'] == param)] df = df.assign(value=row[2010].values[0]) - if param == "input_fuel": comm = data['technology'][(data['parameter'] == param) & (data["technology"] == t)].iloc[0].split("_")[0] df = df.assign(commodity=comm) - results[cat].append(df) # add background parameters (growth rates and bounds) df = scenario.par('initial_activity_lo', {"technology": ["gas_extr_mpen"]}) - for q in config["technology"]["add"]: + for q in config["technology"]["add"][12:]: df['technology'] = q results["initial_activity_lo"].append(df) df = scenario.par('growth_activity_lo', {"technology": ["gas_extr_mpen"]}) - for q in config["technology"]["add"]: + for q in config["technology"]["add"][12:]: df['technology'] = q results["growth_activity_lo"].append(df) @@ -502,6 +623,9 @@ def gen_data_ccs(scenario, dry_run=False): # Concatenate to one dataframe per parameter results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} + #results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} + + return results @@ -524,7 +648,7 @@ def read_demand(): n_inputs_per_tech = 12 # Number of input params per technology input_fuel = te_params[2010][list(range(4, te_params.shape[0], n_inputs_per_tech))].reset_index(drop=True) - input_fuel[0:5] = input_fuel[0:5] * CONVERSION_FACTOR_PJ_GWa # 0.0317 GWa/PJ, GJ/t = PJ/Mt NH3 + #input_fuel[0:5] = input_fuel[0:5] * CONVERSION_FACTOR_PJ_GWa # 0.0317 GWa/PJ, GJ/t = PJ/Mt NH3 capacity_factor = te_params[2010][list(range(11, te_params.shape[0], n_inputs_per_tech))].reset_index(drop=True) @@ -607,11 +731,17 @@ def read_demand(): 'capacity_factor': capacity_factor, "N_feed":N_feed, "N_trade_R12":N_trade_R12} -def read_trade_data(context): - data = pd.read_excel( - context.get_local_path("material", "n-fertilizer_techno-economic_new.xlsx"), - sheet_name="Trade", engine="openpyxl", usecols=np.linspace(0, 7, 8, dtype=int)) - data = data.assign(technology=lambda x: set_trade_tec(x["Variable"])) +def read_trade_data(context, comm): + if comm == "NFert": + data = pd.read_excel( + context.get_local_path("material", "n-fertilizer_techno-economic_new.xlsx"), + sheet_name="Trade", engine="openpyxl", usecols=np.linspace(0, 7, 8, dtype=int)) + data = data.assign(technology=lambda x: set_trade_tec(x["Variable"])) + if comm == "NH3": + data = pd.read_excel( + context.get_local_path("material", "n-fertilizer_techno-economic_new.xlsx"), + sheet_name="Trade_NH3", engine="openpyxl", usecols=np.linspace(0, 7, 8, dtype=int)) + data = data.assign(technology=lambda x: set_trade_tec_NH3(x["Variable"])) return data @@ -627,14 +757,26 @@ def set_trade_tec(x): return arr +def set_trade_tec_NH3(x): + arr=[] + for i in x: + if "Import" in i: + arr.append("import_NH3") + if "Export" in i: + arr.append("export_NH3") + if "Trade" in i: + arr.append("trade_NH3") + return arr + + def read_data(): """Read and clean data from :file:`n-fertilizer_techno-economic.xlsx`.""" # Ensure config is loaded, get the context context = read_config() - print(context.get_local_path()) + #print(context.get_local_path()) #print(Path(__file__).parents[3]/"data"/"material") context.handle_cli_args(local_data=Path(__file__).parents[3]/"data") - print(context.get_local_path()) + #print(context.get_local_path()) # Shorter access to sets configuration sets = context["material"]["fertilizer"] @@ -672,7 +814,7 @@ def read_data(): param_cat2 = [] # print(param_cat) - for t in sets["technology"]["add"]: # : refactor to adjust to yaml structure + for t in sets["technology"]["add"][:6]: # : refactor to adjust to yaml structure # print(t) param_values.extend(params) tech_values.extend([t] * len(params)) @@ -689,9 +831,9 @@ def read_data(): # Set the data frame index for selection ) data.loc[data['parameter'] == 'emission_factor', 2010] = \ - data.loc[data['parameter'] == 'emission_factor', 2010] * CONVERSION_FACTOR_CO2_C - data.loc[data['parameter'] == 'input_elec', 2010] = \ - data.loc[data['parameter'] == 'input_elec', 2010] * CONVERSION_FACTOR_PJ_GWa + data.loc[data['parameter'] == 'emission_factor', 2010]# * CONVERSION_FACTOR_CO2_C + #data.loc[data['parameter'] == 'input_elec', 2010] = \ + # data.loc[data['parameter'] == 'input_elec', 2010] * CONVERSION_FACTOR_PJ_GWa # TODO convert units for some parameters, per LoadParams.py return data @@ -703,11 +845,11 @@ def read_data_ccs(): context = read_config() # Shorter access to sets configuration - sets = context["material"]["set"] + sets = context["material"]["fertilizer"] # Read the file data = pd.read_excel( - context.get_local_path("material", "n-fertilizer_techno-economic.xlsx"), + context.get_local_path("material", "n-fertilizer_techno-economic_new.xlsx"), sheet_name="CCS", ) @@ -728,7 +870,7 @@ def read_data_ccs(): "output_water", "output_heat", "emission_factor", - "emission_factor", + "emission_factor_trans", "capacity_factor", ] @@ -739,11 +881,11 @@ def read_data_ccs(): param_cat2 = [] - for t in sets["technology"]["add"]["ccs"]: + for t in sets["technology"]["add"][12:]: param_values.extend(params) tech_values.extend([t] * len(params)) param_cat2.extend(param_cat) - + #print(sets["technology"]["add"][12:]) # Clean the data data = ( # Insert "technology" and "parameter" columns @@ -755,9 +897,11 @@ def read_data_ccs(): ) #unit conversions and extra electricity for CCS process data.loc[data['parameter'] == 'emission_factor', 2010] = \ - data.loc[data['parameter'] == 'emission_factor', 2010] * CONVERSION_FACTOR_CO2_C + data.loc[data['parameter'] == 'emission_factor', 2010]# * CONVERSION_FACTOR_CO2_C + #data.loc[data['parameter'] == 'input_elec', 2010] = \ + # data.loc[data['parameter'] == 'input_elec', 2010] * CONVERSION_FACTOR_PJ_GWa + 0.005 data.loc[data['parameter'] == 'input_elec', 2010] = \ - data.loc[data['parameter'] == 'input_elec', 2010] * CONVERSION_FACTOR_PJ_GWa + 0.005 - + data.loc[data['parameter'] == 'input_elec', 2010] + (CONVERSION_FACTOR_PJ_GWa * 0.005) + # TODO: check this 0.005 hardcoded value for ccs elec input and move to excel # TODO convert units for some parameters, per LoadParams.py return data From 8715d89b3230f4c2fe882c87980854340cd2c045 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 22 Mar 2022 13:53:48 +0100 Subject: [PATCH 332/774] Add note to emissions accounting --- message_ix_models/model/material/data_util.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index 58ea3d200e..45dc820a66 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -377,6 +377,10 @@ def add_emission_accounting(scen): # Obtain the emission factors only for material related technologies # TODO: Also residential and commercial technologies should be added to this list. + # We dont need to add ammonia/fertilier production here. Because there are + # no extra process emissions that need to be accounted in emissions relations. + # CCS negative emission_factor are already handled in gen_data_ammonia.py. + tec_list = scen.par("emission_factor")["technology"].unique() tec_list_materials = [ i From 0723505629ee9ac9304e39937a60ec836ef90907 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Fri, 25 Mar 2022 14:25:54 +0100 Subject: [PATCH 333/774] Add option to run 2 degrees scenario and merge reporting --- message_ix_models/model/material/__init__.py | 112 +++++++++++++++---- 1 file changed, 88 insertions(+), 24 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index c31b38eeaa..893d289ab9 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -5,8 +5,8 @@ # from .build import apply_spec # from message_data.tools import ScenarioInfo +from message_data.model.material.build import apply_spec from message_ix_models import ScenarioInfo -from message_ix_models.model.build import apply_spec from message_ix_models.util.context import Context from message_ix_models.util import add_par_data @@ -22,10 +22,13 @@ def build(scenario): """Set up materials accounting on `scenario`.""" # Get the specification - spec = get_spec() + # Apply to the base scenario - apply_spec(scenario, spec, add_data) # dry_run=True + spec = get_spec() + apply_spec(scenario,spec, add_data_2) + spec = None + apply_spec(scenario, spec,add_data_1) # dry_run=True # Adjust exogenous energy demand to incorporate the endogenized sectors # Adjust the historical activity of the usefyl level industry technologies @@ -114,25 +117,38 @@ def create_bare(context, regions, dry_run): help="File name for external data input", ) @click.option("--tag", default="", help="Suffix to the scenario name") +@click.option("--mode", default = 'by_url') @click.pass_obj -def build_scen(context, datafile, tag): +def build_scen(context, datafile, tag, mode): """Build a scenario. Use the --url option to specify the base scenario. """ - # Determine the output scenario name based on the --url CLI option. If the - # user did not give a recognized value, this raises an error. - output_scenario_name = { - "baseline": "NoPolicy", - "baseline_macro": "NoPolicy", - "baseline_new": "NoPolicy", - "baseline_new_macro": "NoPolicy", - "NPi2020-con-prim-dir-ncr": "NPi", - "NPi2020_1000-con-prim-dir-ncr": "NPi2020_1000", - "NPi2020_400-con-prim-dir-ncr": "NPi2020_400", - }.get(context.scenario_info["scenario"]) - - if type(output_scenario_name).__name__ == "NoneType": + from ixmp import Platform + import message_ix + + mp = Platform(name='ixmp_dev', jvmargs=['-Xmx16G']) + + if mode == 'by_url': + # Determine the output scenario name based on the --url CLI option. If the + # user did not give a recognized value, this raises an error. + output_scenario_name = { + "baseline": "NoPolicy", + "baseline_macro": "NoPolicy", + "baseline_new": "NoPolicy", + "baseline_new_macro": "NoPolicy", + "NPi2020-con-prim-dir-ncr": "NPi", + "NPi2020_1000-con-prim-dir-ncr": "NPi2020_1000", + "NPi2020_400-con-prim-dir-ncr": "NPi2020_400", + }.get(context.scenario_info["scenario"]) + # Create a two degrees scenario by copying carbon prices from another scenario. + elif mode == 'by_copy': + output_scenario_name = '2degrees' + mod_mitig = 'ENGAGE_SSP2_v4.1.8' + scen_mitig = 'EN_NPi2020_1000f' + scen_mitig_prices = message_ix.Scenario(mp, mod_mitig, scen_mitig) + tax_emission_new = scen_mitig_prices.var("PRICE_EMISSION") + elif type(output_scenario_name).__name__ == "NoneType": output_scenario_name = context.scenario_info["scenario"] # context.metadata_path = context.metadata_path / "data" @@ -148,6 +164,13 @@ def build_scen(context, datafile, tag): ) ) + if mode == 'by_copy': + scenario.check_out() + tax_emission_new.columns = scenario.par("tax_emission").columns + tax_emission_new["unit"] = "USD/tCO2" + scenario.add_par("tax_emission", tax_emission_new) + scenario.commit('2 degree prices are added') + # Set the latest version as default scenario.set_as_default() @@ -179,8 +202,8 @@ def solve_scen(context, datafile, model_name, scenario_name): # Solve scenario.solve() - -@cli.command("report-1") +@cli.command("report") +# @cli.command("report-1") @click.option( "--old_reporting", default=False, @@ -191,14 +214,29 @@ def solve_scen(context, datafile, model_name, scenario_name): # @click.pass_obj def run_reporting(old_reporting, scenario_name, model_name): from message_data.reporting.materials.reporting import report + from message_data.tools.post_processing.iamc_report_hackathon import report as reporting from message_ix import Scenario from ixmp import Platform print(model_name) - mp = Platform(jvmargs=['-Xmx12G']) + mp = Platform() + + # Remove existing timeseries and add material timeseries + print("Reporting material-specific variables") scenario = Scenario(mp, model_name, scenario_name) report(scenario, old_reporting) + print("Reporting standard variables") + reporting( + mp, + scenario, + "False", + model_name, + scenario_name, + merge_hist=True, + merge_ts=True, + run_config="materials_run_config.yaml", + ) @cli.command("report-2") @click.option("--scenario_name", default="NoPolicy") @@ -216,7 +254,7 @@ def run_old_reporting(scenario_name, model_name): print(model_name) print(scenario_name) - mp = Platform(jvmargs=['-Xmx12G']) + mp = Platform(name='ixmp_dev', jvmargs=['-Xmx16G']) scenario = Scenario(mp, model_name, scenario_name) reporting( @@ -242,11 +280,16 @@ def run_old_reporting(scenario_name, model_name): from .data_ammonia import gen_data_ammonia -DATA_FUNCTIONS = [ +DATA_FUNCTIONS_1 = [ #gen_data_buildings, gen_data_ammonia, gen_data_generic, gen_data_steel, + #gen_data_power_sector, +] + +DATA_FUNCTIONS_2 = [ + #gen_data_buildings, gen_data_cement, gen_data_petro_chemicals, #gen_data_power_sector, @@ -254,7 +297,28 @@ def run_old_reporting(scenario_name, model_name): ] # Try to handle multiple data input functions from different materials -def add_data(scenario, dry_run=False): +def add_data_1(scenario, dry_run=False): + """Populate `scenario` with MESSAGEix-Materials data.""" + # Information about `scenario` + info = ScenarioInfo(scenario) + + # Check for two "node" values for global data, e.g. in + # ixmp://ene-ixmp/CD_Links_SSP2_v2.1_clean/baseline + if {"World", "R11_GLB"} < set(info.set["node"]): + log.warning("Remove 'R11_GLB' from node list for data generation") + info.set["node"].remove("R11_GLB") + if {"World", "R12_GLB"} < set(info.set["node"]): + log.warning("Remove 'R12_GLB' from node list for data generation") + info.set["node"].remove("R12_GLB") + + for func in DATA_FUNCTIONS_1: + # Generate or load the data; add to the Scenario + log.info(f"from {func.__name__}()") + add_par_data(scenario, func(scenario), dry_run=dry_run) + + log.info("done") + +def add_data_2(scenario, dry_run=False): """Populate `scenario` with MESSAGEix-Materials data.""" # Information about `scenario` info = ScenarioInfo(scenario) @@ -268,7 +332,7 @@ def add_data(scenario, dry_run=False): log.warning("Remove 'R12_GLB' from node list for data generation") info.set["node"].remove("R12_GLB") - for func in DATA_FUNCTIONS: + for func in DATA_FUNCTIONS_2: # Generate or load the data; add to the Scenario log.info(f"from {func.__name__}()") add_par_data(scenario, func(scenario), dry_run=dry_run) From 9f230bcf5dd4e097996cd27f01d8f1cada200d86 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Fri, 25 Mar 2022 14:26:18 +0100 Subject: [PATCH 334/774] Modify reporting documentation --- message_ix_models/model/material/doc.rst | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/message_ix_models/model/material/doc.rst b/message_ix_models/model/material/doc.rst index 4f066e09a7..6e5bf8d4dd 100644 --- a/message_ix_models/model/material/doc.rst +++ b/message_ix_models/model/material/doc.rst @@ -127,11 +127,7 @@ correctly reports the aggregate variables such as Final Energy and Emissions. To run the first step of the reporting use :: -$ mix-models material report-1 --model_name MESSAGEix-Materials --scenario_name xxxx - -To run the second step of the reporting use :: - -$ mix-models material report-2 --model_name MESSAGEix-Materials --scenario_name xxxx +$ mix-models material report --model_name MESSAGEix-Materials --scenario_name xxxx Data, metadata, and configuration ================================= From fe2e607e8786761832e3304d0ea57c4ee0394846 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 29 Mar 2022 17:44:25 +0200 Subject: [PATCH 335/774] Move demand and historical_activity adjustment due to ammonia --- .../model/material/data_ammonia.py | 30 +++++++++---------- message_ix_models/model/material/data_util.py | 5 +++- 2 files changed, 19 insertions(+), 16 deletions(-) diff --git a/message_ix_models/model/material/data_ammonia.py b/message_ix_models/model/material/data_ammonia.py index e47f00c1e4..cbc4e74dad 100644 --- a/message_ix_models/model/material/data_ammonia.py +++ b/message_ix_models/model/material/data_ammonia.py @@ -175,21 +175,21 @@ def gen_data_ammonia(scenario, dry_run=False, add_ccs: bool = True): # %% Secure feedstock balance (foil_fs, gas_fs, coal_fs) loil_fs? # Adjust i_feed demand - N_energy = read_demand()['N_energy'] - N_energy = read_demand()['N_feed'] # updated feed with imports accounted - - demand_fs_org = pd.read_excel(context.get_local_path('material','demand_i_feed_R12.xlsx')) - - df = demand_fs_org.loc[demand_fs_org.year == 2010, :].join(N_energy.set_index('node'), on='node') - sh = pd.DataFrame({'node': demand_fs_org.loc[demand_fs_org.year == 2010, 'node'], - 'r_feed': df.totENE / df.value}) # share of NH3 energy among total feedstock (no trade assumed) - df = demand_fs_org.join(sh.set_index('node'), on='node') - df.value *= 1 - df.r_feed # Carve out the same % from tot i_feed values - df = df.drop('r_feed', axis=1) - df = df.drop('Unnamed: 0', axis=1) - # TODO: refactor with a more sophisticated solution to reduce i_feed - df.loc[df["value"] < 0, "value"] = 0 # temporary solution to avoid negative values - results["demand"].append(df) + # N_energy = read_demand()['N_energy'] + # N_energy = read_demand()['N_feed'] # updated feed with imports accounted + # + # demand_fs_org = pd.read_excel(context.get_local_path('material','demand_i_feed_R12.xlsx')) + # + # df = demand_fs_org.loc[demand_fs_org.year == 2010, :].join(N_energy.set_index('node'), on='node') + # sh = pd.DataFrame({'node': demand_fs_org.loc[demand_fs_org.year == 2010, 'node'], + # 'r_feed': df.totENE / df.value}) # share of NH3 energy among total feedstock (no trade assumed) + # df = demand_fs_org.join(sh.set_index('node'), on='node') + # df.value *= 1 - df.r_feed # Carve out the same % from tot i_feed values + # df = df.drop('r_feed', axis=1) + # df = df.drop('Unnamed: 0', axis=1) + # # TODO: refactor with a more sophisticated solution to reduce i_feed + # df.loc[df["value"] < 0, "value"] = 0 # temporary solution to avoid negative values + # results["demand"].append(df) # Globiom land input """ diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index 45dc820a66..5b52d346e8 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -108,9 +108,12 @@ def modify_demand_and_hist_activity(scen): for r in df_feed["REGION"].unique(): + # Temporary solution. With the addition of ammonia the residual demand + # becomes negative or zero. We assume all of the feedstock production + # is endogenously covered. i = 0 df_feed_temp.at[i, "REGION"] = r - df_feed_temp.at[i, "i_feed"] = 0.7 + df_feed_temp.at[i, "i_feed"] = 1 i = i + 1 df_feed_new = pd.concat([df_feed_temp, df_feed_new], ignore_index=True) From 0d822adf530feba81e598fd3e40823935eda458b Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 30 Mar 2022 16:30:46 +0200 Subject: [PATCH 336/774] Fix unit issue in n-fertilizer_techno-economic_new.xslx --- .../data/material/n-fertilizer_techno-economic_new.xlsx | 3 +++ 1 file changed, 3 insertions(+) create mode 100644 message_ix_models/data/material/n-fertilizer_techno-economic_new.xlsx diff --git a/message_ix_models/data/material/n-fertilizer_techno-economic_new.xlsx b/message_ix_models/data/material/n-fertilizer_techno-economic_new.xlsx new file mode 100644 index 0000000000..9270b147f2 --- /dev/null +++ b/message_ix_models/data/material/n-fertilizer_techno-economic_new.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ea9d6a21f13ff6aa8c6be141985a4ae8fba3a32a1a0a0c44b3160248cffe7f84 +size 30103 From 1a4c0eb0c9e5c655bbbcf3909920d792ab229341 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 30 Mar 2022 17:42:35 +0200 Subject: [PATCH 337/774] Fix ccs units --- .../data/material/n-fertilizer_techno-economic_new.xlsx | 4 ++-- message_ix_models/model/material/data_ammonia.py | 3 +-- 2 files changed, 3 insertions(+), 4 deletions(-) diff --git a/message_ix_models/data/material/n-fertilizer_techno-economic_new.xlsx b/message_ix_models/data/material/n-fertilizer_techno-economic_new.xlsx index 9270b147f2..ca4d266fd1 100644 --- a/message_ix_models/data/material/n-fertilizer_techno-economic_new.xlsx +++ b/message_ix_models/data/material/n-fertilizer_techno-economic_new.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:ea9d6a21f13ff6aa8c6be141985a4ae8fba3a32a1a0a0c44b3160248cffe7f84 -size 30103 +oid sha256:608501fb2633a32c407722af8433dcf79d81619fd5364df7df79195e877a9ce7 +size 30230 diff --git a/message_ix_models/model/material/data_ammonia.py b/message_ix_models/model/material/data_ammonia.py index cbc4e74dad..095d3fb247 100644 --- a/message_ix_models/model/material/data_ammonia.py +++ b/message_ix_models/model/material/data_ammonia.py @@ -901,7 +901,6 @@ def read_data_ccs(): #data.loc[data['parameter'] == 'input_elec', 2010] = \ # data.loc[data['parameter'] == 'input_elec', 2010] * CONVERSION_FACTOR_PJ_GWa + 0.005 data.loc[data['parameter'] == 'input_elec', 2010] = \ - data.loc[data['parameter'] == 'input_elec', 2010] + (CONVERSION_FACTOR_PJ_GWa * 0.005) - # TODO: check this 0.005 hardcoded value for ccs elec input and move to excel + data.loc[data['parameter'] == 'input_elec', 2010] # TODO convert units for some parameters, per LoadParams.py return data From b4640f39ec5c86586e544c6466f58298bbb48e58 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Fri, 8 Apr 2022 09:11:16 +0200 Subject: [PATCH 338/774] Adjust i_therm to include ammonia --- message_ix_models/model/material/data_util.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index 5b52d346e8..bb681b4648 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -137,7 +137,7 @@ def modify_demand_and_hist_activity(scen): # df_feed_new = df_feed_new.groupby(["REGION"]).sum().reset_index() # Retreive data for i_therm - # NOTE: It is assumped that 50% of the chemicals category is HVCs + # NOTE: It is assumped that 80% of i_therm is from ammonia and HVCs. # NOTE: Aluminum is excluded since refining process is not explicitly represented # NOTE: CPA has a 3% share while it used to be 30% previosuly ?? @@ -179,7 +179,7 @@ def modify_demand_and_hist_activity(scen): df_therm_new.drop(["FUEL", "RYEAR", "UNIT_OUT"], axis=1, inplace=True) df_therm_new.loc[df_therm_new["SECTOR"] == "industry (chemicals)", "i_therm"] = ( df_therm_new.loc[df_therm_new["SECTOR"] == "industry (chemicals)", "i_therm"] - * 0.7 + * 0.8 ) # Modify CPA based on https://www.iea.org/sankey/#?c=Japan&s=Final%20consumption. From 455917d0bf78b1da5ae551e5b1f16831a2023711 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 14 Apr 2022 15:55:36 +0200 Subject: [PATCH 339/774] Change hvc demand -wip --- .../data/material/demand_petro.xlsx | 3 ++ .../model/material/data_petro.py | 48 ++++++++++++++++++- .../model/material/material_demand/init.R | 29 ++++++++++- .../material_demand/init_modularized.R | 45 +++++++++++++++-- 4 files changed, 120 insertions(+), 5 deletions(-) create mode 100644 message_ix_models/data/material/demand_petro.xlsx diff --git a/message_ix_models/data/material/demand_petro.xlsx b/message_ix_models/data/material/demand_petro.xlsx new file mode 100644 index 0000000000..f35f17b83a --- /dev/null +++ b/message_ix_models/data/material/demand_petro.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cda2c19b06cd40354967315fbe2619dd09aac74d39ff2daea62e658d403a8c6d +size 35653 diff --git a/message_ix_models/model/material/data_petro.py b/message_ix_models/model/material/data_petro.py index 265d272fe9..1e861f22bd 100644 --- a/message_ix_models/model/material/data_petro.py +++ b/message_ix_models/model/material/data_petro.py @@ -2,6 +2,7 @@ import numpy as np from collections import defaultdict from .data_util import read_timeseries +from pathlib import Path import message_ix import ixmp @@ -151,6 +152,51 @@ def gen_mock_demand_petro(scenario): # This makes 25 Mt ethylene, 25 Mt propylene, 21 Mt BTX # This can be verified by other sources. +# load rpy2 modules +# import rpy2.robjects as ro +# from rpy2.robjects import pandas2ri +# from rpy2.robjects.conversion import localconverter + +# This returns a df with columns ["region", "year", "demand.tot"] +# def derive_petro_demand(scenario, dry_run=False): +# """Generate HVC demand.""" +# # paths to r code and lca data +# rcode_path = Path(__file__).parents[0] / "material_demand" +# context = read_config() +# +# # source R code +# r = ro.r +# r.source(str(rcode_path / "init_modularized.R")) +# +# # Read population and baseline demand for materials +# pop = scenario.par("bound_activity_up", {"technology": "Population"}) +# pop = pop.loc[pop.year_act >= 2020].rename( +# columns={"year_act": "year", "value": "pop.mil", "node_loc": "region"} +# ) +# +# # import pdb; pdb.set_trace() +# +# pop = pop[["region", "year", "pop.mil"]] +# +# base_demand = gen_mock_demand_petro(scenario) +# base_demand = base_demand.loc[base_demand.year == 2020].rename( +# columns={"value": "demand.tot.base", "node": "region"} +# ) +# +# print("base demand") +# print(base_demand) +# +# # call R function with type conversion +# with localconverter(ro.default_converter + pandas2ri.converter): +# # GDP is only in MER in scenario. +# # To get PPP GDP, it is read externally from the R side +# df = r.derive_petro_demand( +# pop, base_demand, str(context.get_local_path("material")) +# ) +# df.year = df.year.astype(int) +# +# return df +# def gen_data_petro_chemicals(scenario, dry_run=False): # Load configuration @@ -342,7 +388,7 @@ def gen_data_petro_chemicals(scenario, dry_run=False): # Add demand # Create external demand param - # demand_e,demand_p,demand_BTX = gen_mock_demand_petro(scenario) + #demand_HVC = derive_petro_demand(scenario) demand_HVC = gen_mock_demand_petro(scenario) paramname = "demand" diff --git a/message_ix_models/model/material/material_demand/init.R b/message_ix_models/model/material/material_demand/init.R index c7248f3853..9379b140f0 100644 --- a/message_ix_models/model/material/material_demand/init.R +++ b/message_ix_models/model/material/material_demand/init.R @@ -2,11 +2,13 @@ library(tidyverse) library(readxl) # Data file names and path -datapath = '../../../../data/material/' +# datapath = '../../../../data/material/' +datapath = 'H:/GitHub/message_data/data/material/' file_cement = "CEMENT.BvR2010.xlsx" file_steel = "STEEL_database_2012.xlsx" file_al = "demand_aluminum.xlsx" +file_petro = "/demand_petro.xlsx" #### Import raw data (from Bas) - Cement & Steel #### # Apparent consumption @@ -30,6 +32,9 @@ df_gdp = read_excel(paste0(datapath, file_cement), df_raw_aluminum_consumption = read_excel(paste0(datapath, file_al), sheet="final_table", n_max = 378) # kt +df_raw_petro_consumption = read_excel(paste0(datapath, file_petro), + sheet="final_table", n_max = 362) #kg/cap + #### Organize data #### names(df_raw_steel_consumption)[1] = names(df_raw_cement_consumption)[1] = names(df_population)[1] = names(df_gdp)[1] = "reg_no" @@ -53,6 +58,11 @@ df_aluminum_consumption = df_raw_aluminum_consumption %>% drop_na() %>% filter(cons.pcap > 0) +df_petro_consumption = df_raw_petro_consumption %>% + mutate(del.t= as.numeric(year) - 2010) %>% + drop_na() %>% + filter(cons.pcap > 0) + #### Fit models #### # Note: IMAGE adopts NLI for cement, NLIT for steel. @@ -61,33 +71,41 @@ df_aluminum_consumption = df_raw_aluminum_consumption %>% lni.c = lm(log(cons.pcap) ~ I(1/gdp.pcap), data=df_cement_consumption) lni.s = lm(log(cons.pcap) ~ I(1/gdp.pcap), data=df_steel_consumption) lni.a = lm(log(cons.pcap) ~ I(1/gdp.pcap), data=df_aluminum_consumption) +lni.p = lm(log(cons.pcap) ~ I(1/gdp.pcap), data=df_petro_consumption) summary(lni.c) summary(lni.s) summary(lni.a) +summary(lni.p) lnit.c = lm(log(cons.pcap) ~ I(1/gdp.pcap)+del.t, data=df_cement_consumption) lnit.s = lm(log(cons.pcap) ~ I(1/gdp.pcap)+del.t, data=df_steel_consumption) lnit.a = lm(log(cons.pcap) ~ I(1/gdp.pcap)+del.t, data=df_aluminum_consumption) +lnit.p = lm(log(cons.pcap) ~ I(1/gdp.pcap)+del.t, data=df_petro_consumption) summary(lnit.c) summary(lnit.s) summary(lnit.a) # better in linear +summary(lnit.p) # . Non-linear ==== nlni.c = nls(cons.pcap ~ a * exp(b/gdp.pcap), data=df_cement_consumption, start=list(a=500, b=-3000)) nlni.s = nls(cons.pcap ~ a * exp(b/gdp.pcap), data=df_steel_consumption, start=list(a=600, b=-10000)) nlni.a = nls(cons.pcap ~ a * exp(b/gdp.pcap), data=df_aluminum_consumption, start=list(a=600, b=-10000)) +nlni.p = nls(cons.pcap ~ a * exp(b/gdp.pcap), data=df_petro_consumption, start=list(a=600, b=-10000)) summary(nlni.c) summary(nlni.s) summary(nlni.a) +summary(nlni.p) nlnit.c = nls(cons.pcap ~ a * exp(b/gdp.pcap) * (1-m)^del.t, data=df_cement_consumption, start=list(a=500, b=-3000, m=0)) nlnit.s = nls(cons.pcap ~ a * exp(b/gdp.pcap) * (1-m)^del.t, data=df_steel_consumption, start=list(a=600, b=-10000, m=0)) nlnit.a = nls(cons.pcap ~ a * exp(b/gdp.pcap) * (1-m)^del.t, data=df_aluminum_consumption, start=list(a=600, b=-10000, m=0)) +nlnit.p = nls(cons.pcap ~ a * exp(b/gdp.pcap) * (1-m)^del.t, data=df_petro_consumption, start=list(a=600, b=-10000, m=0)) summary(nlnit.c) summary(nlnit.s) summary(nlnit.a) +summary(nlnit.p) #### Prediction #### @@ -113,3 +131,12 @@ predict(nlnit.a, df2) exp(predict(lni.a, df2)) exp(predict(lnit.a, df2)) +predict(nlni.p, df) +predict(nlnit.p, df) +exp(predict(lni.p, df)) +exp(predict(lnit.p, df)) +predict(nlni.p, df2) +predict(nlnit.p, df2) +exp(predict(lni.p, df2)) +exp(predict(lnit.p, df2)) + diff --git a/message_ix_models/model/material/material_demand/init_modularized.R b/message_ix_models/model/material/material_demand/init_modularized.R index 91ab66cd6f..3d60aeaa70 100644 --- a/message_ix_models/model/material/material_demand/init_modularized.R +++ b/message_ix_models/model/material/material_demand/init_modularized.R @@ -4,11 +4,12 @@ library(sitools) # Data file names and path # datapath = '../../../../data/material/' -# datapath = 'H:/MyDocuments/MESSAGE/message_data/data/material/' +# datapath = 'H:/GitHub/message_data/data/material/' file_cement = "/CEMENT.BvR2010.xlsx" file_steel = "/STEEL_database_2012.xlsx" file_al = "/demand_aluminum.xlsx" +#file_petro = "/demand_petro.xlsx" file_gdp = "/iamc_db ENGAGE baseline GDP PPP.xlsx" derive_steel_demand <- function(df_pop, df_demand, datapath) { @@ -161,14 +162,52 @@ derive_aluminum_demand <- function(df_pop, df_demand, datapath) { return(demand %>% select(node=region, year, value=demand.tot) %>% arrange(year, node)) # Mt } +#derive_petro_demand <- function(df_pop, df_demand, datapath) { + +# gdp.ppp = read_excel(paste0(datapath, file_gdp), sheet="data_R12") %>% filter(Scenario == "baseline") %>% +# select(region=Region, `2020`:`2100`) %>% +# pivot_longer(cols=`2020`:`2100`, names_to="year", values_to="gdp.ppp") %>% # billion US$2010/yr OR local currency +# filter(region != "World") %>% +# mutate(year = as.integer(year), region = paste0('R12_', region)) +# +# df_raw_petro_consumption = read_excel(paste0(datapath, file_petro), +# sheet="final_table", n_max = 362) #kg/cap + + #### Organize data #### +# df_petro_consumption = df_raw_petro_consumption %>% +# mutate(del.t= as.numeric(year) - 2010) %>% +# drop_na() %>% +# filter(cons.pcap > 0) + + # nlnit.p = nls(cons.pcap ~ a * exp(b/gdp.pcap) * (1-m)^del.t, data=df_petro_consumption, start=list(a=600, b=-10000, m=0)) +#nlni.p = nls(cons.pcap ~ a * exp(b/gdp.pcap), data=df_petro_consumption, start=list(a=600, b=-10000)) + # lni.p = lm(log(cons.pcap) ~ I(1/gdp.pcap), data=df_petro_consumption) + +# df_in = df_pop %>% left_join(df_demand %>% select(-year)) %>% # df_demand is only for 2020 +# inner_join(gdp.ppp) %>% mutate(gdp.pcap = gdp.ppp*giga/pop.mil/mega) +# demand = df_in %>% +# mutate(demand.pcap0 = predict(nlni.p, .)) %>% #kg/cap +# group_by(region) %>% +# mutate(demand.pcap.base = first(demand.tot.base*giga/pop.mil/mega)) %>% +# mutate(gap.base = first(demand.pcap.base - demand.pcap0)) %>% +# mutate(demand.pcap = demand.pcap0 + gap.base * gompertz(9, 0.1, y=year)) %>% # Bas' equation +# mutate(demand.tot = demand.pcap * pop.mil * mega / giga) # Mt + + # Add 2110 spaceholder +# demand = demand %>% rbind(demand %>% filter(year==2100) %>% mutate(year = 2110)) + +# return(demand %>% select(node=region, year, value=demand.tot) %>% arrange(year, node)) # Mt +#} + gompertz <- function(phi, mu, y, baseyear=2020) { return (1-exp(-phi*exp(-mu*(y - baseyear)))) } #### test + # year = seq(2020, 2100, 10) # df = data.frame(region = "Korea", gdp.pcap = seq(3000, 50000, length.out = length(year)), year, # population = seq(300, 500, length.out = length(year))) -# a = derive_cement_demand(df) -# b = derive_steel_demand(df) +# a = derive_cement_demand(datapath = 'H:/GitHub/message_data/data/material/',df) +# b = derive_petro_demand(datapath = 'H:/GitHub/message_data/data/material/',df) From 7f3636684d294efe8a4d8c181d8b709504ad9c70 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Mon, 25 Apr 2022 16:45:29 +0200 Subject: [PATCH 340/774] Update data to match base year statistics --- .../generic_furnace_boiler_techno_economic.xlsx | 4 ++-- .../material/n-fertilizer_techno-economic_new.xlsx | 4 ++-- .../data/material/petrochemicals_techno_economic.xlsx | 4 ++-- message_ix_models/model/material/data_util.py | 10 ++++++---- 4 files changed, 12 insertions(+), 10 deletions(-) diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx index 5637a0c94e..f99265ba31 100644 --- a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx +++ b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:dce3d954381967c7f3695f21a5a083c19e099cfc939d2a5e44e0b5df01bbec33 -size 289 +oid sha256:cbe691b7f76fa8376ec25ad4f6639e64c8fb15f3ead2e6b6e9d9d4b97a392b0b +size 298 diff --git a/message_ix_models/data/material/n-fertilizer_techno-economic_new.xlsx b/message_ix_models/data/material/n-fertilizer_techno-economic_new.xlsx index ca4d266fd1..76fe9e9c6b 100644 --- a/message_ix_models/data/material/n-fertilizer_techno-economic_new.xlsx +++ b/message_ix_models/data/material/n-fertilizer_techno-economic_new.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:608501fb2633a32c407722af8433dcf79d81619fd5364df7df79195e877a9ce7 -size 30230 +oid sha256:bb80d7e89adb97881ee09a557c8130ca5b2ef6236d28f7220cd99f418b58177b +size 30333 diff --git a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx index 2e3c757668..18bbc7b7a4 100644 --- a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx +++ b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:e65937c154232d33241e8e7446e6e06f65bfcce3afbce5663de0882ee3f7a07d -size 661861 +oid sha256:e86dd2efa40114d9ffd384a843c216632ddd454c5bc3eea7e21a14696a59707a +size 663459 diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index bb681b4648..2398ff7882 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -378,11 +378,12 @@ def add_emission_accounting(scen): context = read_config() s_info = ScenarioInfo(scen) - # Obtain the emission factors only for material related technologies + # Obtain the CO2 emission factors and add to CO2_Emission relation. # TODO: Also residential and commercial technologies should be added to this list. # We dont need to add ammonia/fertilier production here. Because there are - # no extra process emissions that need to be accounted in emissions relations. - # CCS negative emission_factor are already handled in gen_data_ammonia.py. + # no extra process emissions that need to be accounted in emissions relation. + # CCS negative emission_factor are added to this relation in gen_data_ammonia.py. + # Emissions from refining sector are categorized as 'CO2_transformation'. tec_list = scen.par("emission_factor")["technology"].unique() tec_list_materials = [ @@ -393,7 +394,7 @@ def add_emission_accounting(scen): | ("aluminum" in i) | ("petro" in i) | ("cement" in i) - | ("ref" in i) + | ('ref' in i) ) ] tec_list_materials.remove("refrigerant_recovery") @@ -414,6 +415,7 @@ def add_emission_accounting(scen): relation_activity = relation_activity[ (relation_activity["relation"] != "PM2p5_Emission") & (relation_activity["relation"] != "CO2_industry_Emission") + & (relation_activity["relation"] != "CO2_transformation_Emission") ] scen.check_out() From 3677da3fd6f5a16a522b83b93769c8132dfee647 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 27 Apr 2022 10:20:16 +0200 Subject: [PATCH 341/774] Implement dynamic growth rates for ccs cement --- .../data/material/Global_steel_cement_MESSAGE.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx index f1d915875b..eacfd68388 100644 --- a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx +++ b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:eb9fc82954b548b9b83860ed351132de4bc3e5a9ebd1bbffdfef89f4678d5835 -size 96926 +oid sha256:82bee0eaca307e42db768f7557b3439f9ce0d45b3e4f99df72b281a7496dbbb2 +size 103774 From cff1d0e1f02f022fd3891037d1d2a2e39b24833d Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 27 Apr 2022 14:50:29 +0200 Subject: [PATCH 342/774] Update steel demand --- .../model/material/data_steel.py | 28 +++++++++---------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/message_ix_models/model/material/data_steel.py b/message_ix_models/model/material/data_steel.py index e844f51973..5307380e6f 100644 --- a/message_ix_models/model/material/data_steel.py +++ b/message_ix_models/model/material/data_steel.py @@ -39,22 +39,22 @@ def gen_mock_demand_steel(scenario): # r = ['R12_AFR', 'R12_RCPA', 'R12_EEU', 'R12_FSU', 'R12_LAM', 'R12_MEA',\ #'R12_NAM', 'R12_PAO', 'R12_PAS', 'R12_SAS', 'R12_WEU',"R12_CHN"] - # True steel use 2010 [Mt/year] - # https://www.worldsteel.org/en/dam/jcr:0474d208-9108-4927-ace8-4ac5445c5df8/World+Steel+in+Figures+2017.pdf - - # For R12: China and CPA demand divided by 0.1 and 0.9. + # Finished steel demand from: https://www.oecd.org/industry/ind/Item_4b_Worldsteel.pdf + # For region definitions: https://worldsteel.org/wp-content/uploads/2021-World-Steel-in-Figures.pdf + # For detailed assumptions and calculation see: steel_demand_calculation.xlsx + # under https://iiasahub.sharepoint.com/sites/eceprog?cid=75ea8244-8757-44f1-83fd-d34f94ffd06a if "R12_CHN" in nodes: nodes.remove("R12_GLB") sheet_n = "data_R12" region_set = "R12_" - d = [35, 5.37, 70, 53, 49, 39, 130, 80, 45, 96, 100, 531.63] + d = [20.04, 12.08, 56.55, 56.5, 64.94, 54.26, 97.76, 91.3, 65.2, 164.28, 131.95, 980.1] else: nodes.remove("R11_GLB") sheet_n = "data_R11" region_set = "R11_" - d = [35, 537, 70, 53, 49, 39, 130, 80, 45, 96, 100] + d = [20.04, 992.18, 56.55, 56.5, 64.94, 54.26, 97.76, 91.3, 65.2, 164.28, 131.95] # MEA change from 39 to 9 to make it feasible (coal supply bound) # SSP2 R11 baseline GDP projection @@ -69,26 +69,26 @@ def gen_mock_demand_steel(scenario): gdp_growth["Region"] = region_set + gdp_growth["Region"] - demand2010_steel = ( + demand2020_steel = ( pd.DataFrame({"Region": nodes, "Val": d}) .join(gdp_growth.set_index("Region"), on="Region") .rename(columns={"Region": "node"}) ) - demand2010_steel.iloc[:, 3:] = ( - demand2010_steel.iloc[:, 3:] - .div(demand2010_steel[2010], axis=0) - .multiply(demand2010_steel["Val"], axis=0) + demand2020_steel.iloc[:, 3:] = ( + demand2020_steel.iloc[:, 3:] + .div(demand2020_steel[2020], axis=0) + .multiply(demand2020_steel["Val"], axis=0) ) - demand2010_steel = pd.melt( - demand2010_steel.drop(["Val", "Scenario"], axis=1), + demand2020_steel = pd.melt( + demand2020_steel.drop(["Val", "Scenario"], axis=1), id_vars=["node"], var_name="year", value_name="value", ) - return demand2010_steel + return demand2020_steel def gen_data_steel(scenario, dry_run=False): From 4f1815468aca3a0411f2488d12d329310eb907d3 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 28 Apr 2022 13:34:35 +0200 Subject: [PATCH 343/774] Fix cf4 emissions --- message_ix_models/model/material/data_util.py | 28 +++++++++++++++++++ 1 file changed, 28 insertions(+) diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index 2398ff7882..4a502b5f03 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -422,6 +422,34 @@ def add_emission_accounting(scen): scen.add_par("relation_activity", relation_activity) scen.commit("Emissions accounting for industry technologies added.") + # Correct CF4 Emission relations + # Remove transport related technologies from CF4_Emissions + + scen.check_out() + + CF4_trp_Emissions = scen.par("relation_activity", filters = {"relation": "CF4_Emission"}) + list_tec_trp = [l for l in CF4_trp_Emissions["technology"].unique() if "trp" in l] + CF4_trp_Emissions = CF4_trp_Emissions[CF4_trp_Emissions["technology"].isin(list_tec_trp)] + + scen.remove_par("relation_activity",CF4_trp_Emissions) + + # Remove transport related technologies from CF4_alm_red and add aluminum tecs. + + CF4_red = scen.par("relation_activity", filters = {"relation": "CF4_alm_red"}) + list_tec_trp = [l for l in CF4_red["technology"].unique() if "trp" in l] + CF4_red = CF4_red[CF4_red["technology"].isin(list_tec_trp)] + + scen.remove_par("relation_activity",CF4_red) + + CF4_red_add = scen.par("emission_factor", filters = {"technology": ["soderberg_aluminum","prebake_aluminum"], "emission":"CF4"}) + CF4_red_add.drop(["year_vtg","emission"], axis = 1, inplace = True) + CF4_red_add["relation"] = "CF4_alm_red" + CF4_red_add["unit"] = "???" + CF4_red_add["year_rel"] = CF4_red_add["year_act"] + CF4_red_add["node_rel"] = CF4_red_add["node_loc"] + + scen.add_par("relation_activity",CF4_red_add) + scen.commit("CF4 relations corrected.") def read_sector_data(scenario, sectname): From 36d8913009ce3a95e686cd2d497e13ccba4b3c74 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 3 May 2022 20:15:14 +0200 Subject: [PATCH 344/774] Add furnaces to co2_ind relation --- message_ix_models/model/material/data_util.py | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index 4a502b5f03..66f5cc7b46 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -418,6 +418,23 @@ def add_emission_accounting(scen): & (relation_activity["relation"] != "CO2_transformation_Emission") ] + # Add thermal industry technologies to CO2_ind relation + + relation_activity_furnaces = scen.par( + "emission_factor", filters={"emission": 'CO2_industry', + "technology": tec_list_materials}) + relation_activity_furnaces['relation'] = 'CO2_ind' + relation_activity_furnaces["node_rel"] = relation_activity_furnaces["node_loc"] + relation_activity_furnaces.drop(["year_vtg", "emission"], axis=1, inplace=True) + relation_activity_furnaces["year_rel"] = relation_activity_furnaces["year_act"] + relation_activity_furnaces = relation_activity_furnaces[~relation_activity_furnaces['technology'].str.contains('_refining')] + + scen.check_out() + scen.add_par("relation_activity", relation_activity) + scen.add_par("relation_activity", relation_activity_furnaces) + scen.commit("Emissions accounting for industry technologies added.") + + scen.check_out() scen.add_par("relation_activity", relation_activity) scen.commit("Emissions accounting for industry technologies added.") From 24a5fdcf8ab389ab8d349f2124b37900bc9ece0d Mon Sep 17 00:00:00 2001 From: Jihoon Date: Mon, 2 May 2022 18:02:36 +0200 Subject: [PATCH 345/774] Add time-varying cement ccs initial_new_capacity_up --- .../data/material/Global_steel_cement_MESSAGE.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx index eacfd68388..cf7f586bdf 100644 --- a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx +++ b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:82bee0eaca307e42db768f7557b3439f9ce0d45b3e4f99df72b281a7496dbbb2 -size 103774 +oid sha256:1cd3935d8853286421958e6da47a0e155cecee958278089a07888da917033da7 +size 108702 From b874090bf351f0cbe7bfdbad1fbe2c0c95b076da Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 4 May 2022 12:43:32 +0200 Subject: [PATCH 346/774] Change cement ccs growth constraints --- .../material/Global_steel_cement_MESSAGE.xlsx | 4 +- .../model/material/data_cement.py | 71 ++++++++++++++++++- 2 files changed, 71 insertions(+), 4 deletions(-) diff --git a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx index cf7f586bdf..4c39adbbfc 100644 --- a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx +++ b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:1cd3935d8853286421958e6da47a0e155cecee958278089a07888da917033da7 -size 108702 +oid sha256:b2f8ee5ab7dd6a8599e090d57f83c164292b1cd2a925b7c6929b5761ababc9df +size 107552 diff --git a/message_ix_models/model/material/data_cement.py b/message_ix_models/model/material/data_cement.py index 03a6b7bfc2..80577877a5 100644 --- a/message_ix_models/model/material/data_cement.py +++ b/message_ix_models/model/material/data_cement.py @@ -178,6 +178,7 @@ def gen_mock_demand_cement(scenario): def gen_data_cement(scenario, dry_run=False): """Generate data for materials representation of cement industry.""" # Load configuration + context = read_config() config = read_config()["material"]["cement"] # Information about scenario, e.g. node, year @@ -187,8 +188,8 @@ def gen_data_cement(scenario, dry_run=False): # TEMP: now add cement sector as well data_cement = read_sector_data(scenario, "cement") # Special treatment for time-dependent Parameters - # data_cement_vc = read_timeseries() - # tec_vc = set(data_cement_vc.technology) # set of tecs with var_cost + data_cement_ts = read_timeseries(scenario,context.datafile) + tec_ts = set(data_cement_ts.technology) # set of tecs with var_cost # List of data frames, to be concatenated together at end results = defaultdict(list) @@ -216,6 +217,72 @@ def gen_data_cement(scenario, dry_run=False): (data_cement["technology"] == t), "parameter" ].values.tolist() + # Special treatment for time-varying params + if t in tec_ts: + common = dict( + time="year", + time_origin="year", + time_dest="year", + ) + + param_name = data_cement_ts.loc[ + (data_cement_ts["technology"] == t), "parameter" + ] + + for p in set(param_name): + val = data_cement_ts.loc[ + (data_cement_ts["technology"] == t) + & (data_cement_ts["parameter"] == p), + "value", + ] + units = data_cement_ts.loc[ + (data_cement_ts["technology"] == t) + & (data_cement_ts["parameter"] == p), + "units", + ].values[0] + mod = data_cement_ts.loc[ + (data_cement_ts["technology"] == t) + & (data_cement_ts["parameter"] == p), + "mode", + ] + yr = data_cement_ts.loc[ + (data_cement_ts["technology"] == t) + & (data_cement_ts["parameter"] == p), + "year", + ] + + if p == "var_cost": + df = make_df( + p, + technology=t, + value=val, + unit="t", + year_vtg=yr, + year_act=yr, + mode=mod, + **common + ).pipe(broadcast, node_loc=nodes) + else: + rg = data_cement_ts.loc[ + (data_cement_ts["technology"] == t) + & (data_cement_ts["parameter"] == p), + "region", + ] + df = make_df( + p, + technology=t, + value=val, + unit="t", + year_vtg=yr, + year_act=yr, + mode=mod, + node_loc=rg, + **common + ) + + results[p].append(df) + + # Iterate over parameters for par in params: From d93617bbb06c515ff4c147bb2fb736395fa589c5 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 4 May 2022 12:46:23 +0200 Subject: [PATCH 347/774] Add new refinery technologies to co2 reporting --- .../generic_furnace_boiler_techno_economic.xlsx | 4 ++-- message_ix_models/model/material/data_util.py | 12 ++++++++++++ 2 files changed, 14 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx index f99265ba31..e2e4967f6f 100644 --- a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx +++ b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:cbe691b7f76fa8376ec25ad4f6639e64c8fb15f3ead2e6b6e9d9d4b97a392b0b -size 298 +oid sha256:c5f9ffb5625c44568f1615e912ecdb3dba4dd0cd84b37ae2a3b66846e6cd7a63 +size 303 diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index 66f5cc7b46..fb6992c4c1 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -434,9 +434,21 @@ def add_emission_accounting(scen): scen.add_par("relation_activity", relation_activity_furnaces) scen.commit("Emissions accounting for industry technologies added.") + # Add refinery technologies to CO2_cc + + relation_activity_ref = scen.par( + "emission_factor", filters={"emission": 'CO2_transformation', + "technology": tec_list_materials}) + relation_activity_ref['relation'] = 'CO2_cc' + relation_activity_ref["node_rel"] = relation_activity_ref["node_loc"] + relation_activity_ref.drop(["year_vtg", "emission"], axis=1, inplace=True) + relation_activity_ref["year_rel"] = relation_activity_ref["year_act"] + scen.check_out() scen.add_par("relation_activity", relation_activity) + scen.add_par("relation_activity", relation_activity_furnaces) + scen.add_par("relation_activity", relation_activity_ref) scen.commit("Emissions accounting for industry technologies added.") # Correct CF4 Emission relations From 621a34b23c9da4646ed0a12be2a3d1e3e2607c1d Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 4 May 2022 14:35:44 +0200 Subject: [PATCH 348/774] Fix a bug --- .../data/material/generic_furnace_boiler_techno_economic.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx index e2e4967f6f..92807a639e 100644 --- a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx +++ b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:c5f9ffb5625c44568f1615e912ecdb3dba4dd0cd84b37ae2a3b66846e6cd7a63 -size 303 +oid sha256:8bd0d3c0b60ac2f0fa4331c377017c1ed1fc11a42ae6940a0f4bcaa3402f23bd +size 266 From 29270e3eca004c97585d319728e083288a8652a0 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 5 May 2022 11:02:59 +0200 Subject: [PATCH 349/774] Add new feedstock technologies to co2 emission rep. --- message_ix_models/model/material/data_util.py | 41 +++++++++++++++++++ 1 file changed, 41 insertions(+) diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index fb6992c4c1..b932a3cd67 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -386,6 +386,7 @@ def add_emission_accounting(scen): # Emissions from refining sector are categorized as 'CO2_transformation'. tec_list = scen.par("emission_factor")["technology"].unique() + tec_list_materials = [ i for i in tec_list @@ -451,6 +452,46 @@ def add_emission_accounting(scen): scen.add_par("relation_activity", relation_activity_ref) scen.commit("Emissions accounting for industry technologies added.") + # Add feedstock using technologies to CO2_feedstocks + nodes = scen.par("relation_activity", filters = {'relation':'CO2_feedstocks'})["node_rel"].unique() + years = scen.par("relation_activity", filters = {'relation':'CO2_feedstocks'})["year_rel"].unique() + + for n in nodes: + for t in ['steam_cracker_petro','gas_processing_petro']: + for m in ['atm_gasoil', 'vacuum_gasoil', 'naphtha']: + if t == 'steam_cracker_petro': + if (m == 'atm_gasoil') | (m == 'vacuum_gasoil'): + # fueloil emission factor * input + val = 0.665 * 1.17683105 + else: + val = 0.665 * 1.537442922 + + co2_feedstocks = pd.DataFrame({'relation': 'CO2_feedstocks', + 'node_rel': n, + 'year_rel': years, + 'node_loc': n, + 'technology': t, + 'year_act': years, + 'mode': m, + 'value': val, + 'unit': 't'}) + else: + # gas emission factor * gas input + val = 0.482 * 1.331811263 + + co2_feedstocks = pd.DataFrame({'relation': 'CO2_feedstocks', + 'node_rel': n, + 'year_rel': years, + 'node_loc': n, + 'technology': t, + 'year_act': years, + 'mode': 'M1', + 'value': val, + 'unit': 't'}) + scen.check_out() + scen.add_par('relation_activity', co2_feedstocks) + scen.commit('co2_feedstocks updated') + # Correct CF4 Emission relations # Remove transport related technologies from CF4_Emissions From 9ccb8d6fb39eacf49cd7643def665f885ad84e53 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Fri, 13 May 2022 17:22:36 +0200 Subject: [PATCH 350/774] Add 2020 calibration --- .../data/material/residual_industry_2019.csv | 3 ++ message_ix_models/model/material/__init__.py | 6 ++- message_ix_models/model/material/data_util.py | 50 +++++++++++++++++++ 3 files changed, 58 insertions(+), 1 deletion(-) create mode 100644 message_ix_models/data/material/residual_industry_2019.csv diff --git a/message_ix_models/data/material/residual_industry_2019.csv b/message_ix_models/data/material/residual_industry_2019.csv new file mode 100644 index 0000000000..782d0ac7e8 --- /dev/null +++ b/message_ix_models/data/material/residual_industry_2019.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d3f38754b7509f2c000106c674023b2083593e61cdd98cbd1be86a42df8cb28e +size 22895 diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 893d289ab9..03081bec54 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -13,6 +13,7 @@ # from .data import add_data from .data_util import modify_demand_and_hist_activity from .data_util import add_emission_accounting +from .data_util import add_coal_lowerbound_2020 from .util import read_config @@ -31,9 +32,11 @@ def build(scenario): apply_spec(scenario, spec,add_data_1) # dry_run=True # Adjust exogenous energy demand to incorporate the endogenized sectors - # Adjust the historical activity of the usefyl level industry technologies + # Adjust the historical activity of the useful level industry technologies + # Coal calibration 2020 modify_demand_and_hist_activity(scenario) add_emission_accounting(scenario) + add_coal_lowerbound_2020(scenario) return scenario @@ -124,6 +127,7 @@ def build_scen(context, datafile, tag, mode): Use the --url option to specify the base scenario. """ + from ixmp import Platform import message_ix diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index b932a3cd67..283dc71104 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -521,6 +521,56 @@ def add_emission_accounting(scen): scen.add_par("relation_activity",CF4_red_add) scen.commit("CF4 relations corrected.") +def add_coal_lowerbound_2020(scen): + '''Set lower bounds for coal and i_spec as a calibration for 2020''' + final_resid = pd.read_csv(private_data_path("projects", "ngfs", "residual_industry_2019.csv")) + + # read input parameters for relevant technology/commodity combinations for converting betwen final and useful energy + input_residual_coal = sc.par('input',filters={"technology": "coal_i", "year_vtg": "2020", "year_act": "2020"}) + input_cement_coal = sc.par('input',filters={"technology": "furnace_coal_cement", "year_vtg": "2020", "year_act": "2020", "mode": "high_temp"}) + input_residual_electricity = sc.par('input',filters={"technology": "sp_el_I", "year_vtg": "2020", "year_act": "2020"}) + + # downselect needed fuels and sectors + final_residual_coal = final_resid.query('MESSAGE_fuel=="coal" & MESSAGE_sector=="industry_residual"') + final_cement_coal = final_resid.query('MESSAGE_fuel=="coal" & MESSAGE_sector=="cement"') + final_residual_electricity = final_resid.query('MESSAGE_fuel=="electr" & MESSAGE_sector=="industry_residual"') + + # join final energy data from IEA energy balances and input coefficients from final-to-useful technologies from MESSAGEix + bound_coal = pd.merge(input_residual_coal, final_residual_coal, left_on = "node_loc", right_on = "MESSAGE_region", how = "inner") + bound_cement_coal = pd.merge(input_cement_coal, final_cement_coal, left_on = "node_loc", right_on = "MESSAGE_region", how = "inner") + bound_residual_electricity = pd.merge(input_residual_electricity, final_residual_electricity, left_on = "node_loc", right_on = "MESSAGE_region", how = "inner") + + # derive useful energy values by dividing final energy by input coefficient from final-to-useful technologies + bound_coal["value"] = bound_coal["Value"] / bound_coal["value"] + bound_cement_coal["value"] = bound_cement_coal["Value"] / bound_cement_coal["value"] + bound_residual_electricity["value"] = bound_residual_electricity["Value"] / bound_residual_electricity["value"] + + # downselect dataframe columns for MESSAGEix parameters + bound_coal = bound_coal.filter(items=['node_loc', 'technology', 'year_act', 'mode', 'time', 'value', 'unit_x']) + bound_cement_coal = bound_cement_coal.filter(items=['node_loc', 'technology', 'year_act', 'mode', 'time', 'value', 'unit_x']) + bound_residual_electricity = bound_residual_electricity.filter(items=['node_loc', 'technology', 'year_act', 'mode', 'time', 'value', 'unit_x']) + + # rename columns if necessary + bound_coal.columns = ['node_loc', 'technology', 'year_act', 'mode', 'time', 'value', 'unit'] + bound_cement_coal.columns = ['node_loc', 'technology', 'year_act', 'mode', 'time', 'value', 'unit'] + bound_residual_electricity.columns = ['node_loc', 'technology', 'year_act', 'mode', 'time', 'value', 'unit'] + + # (Artificially) lower bounds when i_spec act is too close to the bounds (avoid 0-price for macro calibration) + more = ["R12_MEA", "R12_EEU", "R12_SAS", "R12_PAS"] + # import pdb; pdb.set_trace() + bound_residual_electricity.loc[bound_residual_electricity.node_loc.isin(["R12_PAO"]), 'value'] *= 0.80 + bound_residual_electricity.loc[bound_residual_electricity.node_loc.isin(more), 'value'] *= 0.85 + + sc.check_out() + + # add parameter dataframes to ixmp + sc.add_par('bound_activity_lo', bound_coal) + sc.add_par('bound_activity_lo', bound_cement_coal) + sc.add_par('bound_activity_lo', bound_residual_electricity) + + # commit scenario to ixmp backend + sc.commit("added lower bound for activity of residual industrial coal and cement coal furnace technologies and adjusted 2020 residual industrial electricity demand") + def read_sector_data(scenario, sectname): # Read in technology-specific parameters from input xlsx From 2a96da07c46239de9009981d2bac4fbd8e7ca622 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 17 May 2022 11:24:31 +0200 Subject: [PATCH 351/774] Fix add_coal_lowerbound --- message_ix_models/model/material/data_util.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index 283dc71104..302557c1dc 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -521,9 +521,11 @@ def add_emission_accounting(scen): scen.add_par("relation_activity",CF4_red_add) scen.commit("CF4 relations corrected.") -def add_coal_lowerbound_2020(scen): +def add_coal_lowerbound_2020(sc): '''Set lower bounds for coal and i_spec as a calibration for 2020''' - final_resid = pd.read_csv(private_data_path("projects", "ngfs", "residual_industry_2019.csv")) + + context = read_config() + final_resid = pd.read_csv(context.get_local_path("material", "residual_industry_2019.csv")) # read input parameters for relevant technology/commodity combinations for converting betwen final and useful energy input_residual_coal = sc.par('input',filters={"technology": "coal_i", "year_vtg": "2020", "year_act": "2020"}) From 6f5782df75f5b68e1f9352ab5b80b0d09cde4824 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Fri, 20 May 2022 09:56:54 +0200 Subject: [PATCH 352/774] Add macro calibration option --- message_ix_models/model/material/__init__.py | 10 +++ message_ix_models/model/material/data_util.py | 82 +++++++++++++++++++ 2 files changed, 92 insertions(+) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 03081bec54..d42d7cc7b3 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -14,6 +14,7 @@ from .data_util import modify_demand_and_hist_activity from .data_util import add_emission_accounting from .data_util import add_coal_lowerbound_2020 +from .data_util import add_macro_COVID from .util import read_config @@ -206,6 +207,15 @@ def solve_scen(context, datafile, model_name, scenario_name): # Solve scenario.solve() +@cli.command("add_calibration") +@click.option("--scenario_name", default="NoPolicy") +@click.option("--model_name", default="MESSAGEix-Materials") +@click.pass_obj +def add_MACRO(): + mp = Platform() + scenario = Scenario(mp, model_name, scenario_name) + add_macro_COVID(scenario,'R12-CHN-5y_materials_macro_data.xlsx') + @cli.command("report") # @cli.command("report-1") @click.option( diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index 302557c1dc..f5ee13bf78 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -1,6 +1,9 @@ from collections import defaultdict import pandas as pd +import os +import message_ix +import ixmp from .util import read_config import re @@ -10,6 +13,85 @@ main as get_optimization_years, ) +def load_GDP_COVID(): + + context = read_config() + # Obtain 2015 and 2020 GDP values from NGFS baseline. These values are COVID corrected. (GDP MER) + + mp = ixmp.Platform() + scen_NGFS = message_ix.Scenario(mp,'MESSAGEix-GLOBIOM 1.1-M-R12-NGFS','baseline', cache=True) + gdp_covid_2015 = scen_NGFS.par('gdp_calibrate', filters = {'year': 2015}) + + gdp_covid_2020 = scen_NGFS.par('gdp_calibrate', filters = {'year': 2020}) + gdp_covid_2020 = gdp_covid_2020.drop(['year', 'unit'],axis = 1) + + # Obtain SSP2 GDP growth rates after 2020 (from ENGAGE baseline) + + f_name = 'iamc_db ENGAGE baseline GDP PPP.xlsx' + + gdp_ssp2 = pd.read_excel(context.get_local_path('material', f_name), + sheet_name = 'data_R12') + gdp_ssp2 = gdp_ssp2[gdp_ssp2['Scenario'] == 'baseline'] + regions = 'R12_' + gdp_ssp2['Region'] + gdp_ssp2 = gdp_ssp2.drop(['Model','Scenario','Unit','Region','Variable','Notes'],axis = 1) + gdp_ssp2 = gdp_ssp2.loc[:, 2020:] + gdp_ssp2 = gdp_ssp2.divide(gdp_ssp2[2020], axis = 0) + gdp_ssp2['node'] = regions + gdp_ssp2 = gdp_ssp2[gdp_ssp2['node'] != 'R12_World'] + + # Multiply 2020 COVID corrrected values with SSP2 growth rates + + df_new = pd.DataFrame(columns = ['node','year','value']) + + for ind in gdp_ssp2.index: + + df_temp = pd.DataFrame(columns = ['node','year','value']) + region = gdp_ssp2.loc[ind, 'node'] + mult_value = gdp_covid_2020.loc[gdp_covid_2020['node']== region, 'value'].values[0] + temp = gdp_ssp2.loc[ind, 2020:2110] * mult_value + region_list = [region] * temp.size + + df_temp['node'] = region_list + df_temp['year'] = temp.index + df_temp['value'] = temp.values + + df_new = pd.concat([df_new, df_temp]) + + df_new['unit'] = 'T$' + df_new = pd.concat([df_new,gdp_covid_2015]) + + return df_new + +def add_macro_COVID(scen, filename, check_converge=False): + + context = read_config() + info = ScenarioInfo(scen) + + # Excel file for calibration data + xls_file = os.path.join('P:', 'ene.model', 'MACRO', 'python', filename) + + # Making a dictionary from the MACRO Excel file + xls = pd.ExcelFile(xls_file) + data = {} + for s in xls.sheet_names: + data[s] = xls.parse(s) + + # Load the new GDP values + df_gdp = load_GDP_COVID() + + # substitute the gdp_calibrate + parname = 'gdp_calibrate' + + # keep the historical GDP to pass the GDP check at add_macro() + df_gdphist = data[parname] + df_gdphist = df_gdphist.loc[df_gdphist.year < info.y0] + data[parname] = df_gdphist.append( + df_gdp.loc[df_gdp.year >= info.y0], + ignore_index=True + ) + + # Calibration + scen = scen.add_macro(data, check_convergence=check_converge) def modify_demand_and_hist_activity(scen): """Take care of demand changes due to the introduction of material parents From 85d7b1fd574a4cdcedcaa403431f3b4f229f4750 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 25 May 2022 11:18:56 +0200 Subject: [PATCH 353/774] Add add_macro --- message_ix_models/model/material/__init__.py | 32 +++++++++++++------- 1 file changed, 21 insertions(+), 11 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index d42d7cc7b3..c4b78099ff 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -9,6 +9,9 @@ from message_ix_models import ScenarioInfo from message_ix_models.util.context import Context from message_ix_models.util import add_par_data +from message_data.tools.utilities import calibrate_UE_gr_to_demand +from message_data.tools.utilities import calibrate_UE_share_constraints +from message_ix_models.util import private_data_path # from .data import add_data from .data_util import modify_demand_and_hist_activity @@ -17,17 +20,16 @@ from .data_util import add_macro_COVID from .util import read_config - log = logging.getLogger(__name__) def build(scenario): """Set up materials accounting on `scenario`.""" - # Get the specification - + # Get the specification # Apply to the base scenario spec = get_spec() + apply_spec(scenario,spec, add_data_2) spec = None apply_spec(scenario, spec,add_data_1) # dry_run=True @@ -39,8 +41,14 @@ def build(scenario): add_emission_accounting(scenario) add_coal_lowerbound_2020(scenario) - return scenario + # Market penetration adjustments + # NOTE: changing demand affects the market penetration levels for the enduse technologies. + # Note: context.ssp doesnt work + calibrate_UE_gr_to_demand(scenario, data_path=private_data_path(), ssp='SSP2') + calibrate_UE_share_constraints(scenario) + print('calibrate_UE') + return scenario # add as needed/implemented SPEC_LIST = [ @@ -211,10 +219,14 @@ def solve_scen(context, datafile, model_name, scenario_name): @click.option("--scenario_name", default="NoPolicy") @click.option("--model_name", default="MESSAGEix-Materials") @click.pass_obj -def add_MACRO(): - mp = Platform() - scenario = Scenario(mp, model_name, scenario_name) - add_macro_COVID(scenario,'R12-CHN-5y_materials_macro_data.xlsx') +def add_MACRO(context, model_name, scenario_name): + from message_ix import Scenario + scenario = Scenario(context.get_platform(), model_name, scenario_name) + print(model_name) + print(scenario_name) + + # Use the same calibration that was used in NGFS project rc and ind demand adjusted + add_macro_COVID(scenario,'R12-CHN-5y_macro_data_NGFS_w_rc_ind_adj.xlsx') @cli.command("report") # @cli.command("report-1") @@ -299,14 +311,12 @@ def run_old_reporting(scenario_name, model_name): gen_data_ammonia, gen_data_generic, gen_data_steel, - #gen_data_power_sector, ] DATA_FUNCTIONS_2 = [ - #gen_data_buildings, gen_data_cement, gen_data_petro_chemicals, - #gen_data_power_sector, + gen_data_power_sector, gen_data_aluminum, ] From 83a79a550d639c4441dcbd0baf01d9c85b71b875 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 25 May 2022 11:19:17 +0200 Subject: [PATCH 354/774] Delete unnecessary lines --- message_ix_models/model/material/data_util.py | 53 +------------------ 1 file changed, 1 insertion(+), 52 deletions(-) diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index f5ee13bf78..d24a0cc15e 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -29,7 +29,7 @@ def load_GDP_COVID(): f_name = 'iamc_db ENGAGE baseline GDP PPP.xlsx' - gdp_ssp2 = pd.read_excel(context.get_local_path('material', f_name), + gdp_ssp2 = pd.read_excel(context.get_local_path('data','material', f_name), sheet_name = 'data_R12') gdp_ssp2 = gdp_ssp2[gdp_ssp2['Scenario'] == 'baseline'] regions = 'R12_' + gdp_ssp2['Region'] @@ -89,7 +89,6 @@ def add_macro_COVID(scen, filename, check_converge=False): df_gdp.loc[df_gdp.year >= info.y0], ignore_index=True ) - # Calibration scen = scen.add_macro(data, check_convergence=check_converge) @@ -125,9 +124,6 @@ def modify_demand_and_hist_activity(scen): context.get_local_path("material", fname), sheet_name=sheet_n, usecols="A:F" ) - print("Are the correct numbers read?") - print(df) - # Filter the necessary variables df = df[ (df["SECTOR"] == "feedstock (petrochemical industry)") @@ -140,9 +136,6 @@ def modify_demand_and_hist_activity(scen): ] df = df[df["RYEAR"] == 2015] - print("Is the filter correct?") - print(df) - # Retreive data for i_spec (Excludes petrochemicals as the share is negligable) # Aluminum, cement and steel included. # NOTE: Steel has high shares (previously it was not inlcuded in i_spec) @@ -174,8 +167,6 @@ def modify_demand_and_hist_activity(scen): ) df_spec_new = df_spec_new.groupby(["REGION"]).sum().reset_index() - print("spec") - print(df_spec_new) # Retreive data for i_feed: Only for petrochemicals # It is assumed that the sectors that are explicitly covered in MESSAGE are @@ -199,9 +190,6 @@ def modify_demand_and_hist_activity(scen): i = i + 1 df_feed_new = pd.concat([df_feed_temp, df_feed_new], ignore_index=True) - print("feed") - print(df_feed_new) - # df_feed = df[(df["SECTOR"]== "feedstock (petrochemical industry)") & \ # (df["FUEL"]== "total") ] # df_feed_total = df[(df["SECTOR"]== "feedstock (total)") \ @@ -275,9 +263,6 @@ def modify_demand_and_hist_activity(scen): df_therm_new = df_therm_new.groupby(["REGION"]).sum().reset_index() - print("therm") - print(df_therm_new) - # TODO: Useful technology efficiencies will also be included # Add the modified demand and historical activity to the scenario @@ -319,12 +304,6 @@ def modify_demand_and_hist_activity(scen): for r in df_therm_new["REGION"]: r_MESSAGE = region_type + r - if (r_MESSAGE == "R12_RCPA") | (r_MESSAGE == "R12_CHN"): - print(r_MESSAGE) - print("Thermal before multiplication") - print(useful_thermal.loc[useful_thermal["node"] == r_MESSAGE]) - print(thermal_df_hist.loc[thermal_df_hist["node_loc"] == r_MESSAGE]) - useful_thermal.loc[ useful_thermal["node"] == r_MESSAGE, "value" ] = useful_thermal.loc[useful_thermal["node"] == r_MESSAGE, "value"] * ( @@ -337,21 +316,9 @@ def modify_demand_and_hist_activity(scen): 1 - df_therm_new.loc[df_therm_new["REGION"] == r, "i_therm"].values[0] ) - if (r_MESSAGE == "R12_RCPA") | (r_MESSAGE == "R12_CHN"): - print(r_MESSAGE) - print("Thermal after multiplication") - print(useful_thermal.loc[useful_thermal["node"] == r_MESSAGE]) - print(thermal_df_hist.loc[thermal_df_hist["node_loc"] == r_MESSAGE]) - for r in df_spec_new["REGION"]: r_MESSAGE = region_type + r - if (r_MESSAGE == "R12_RCPA") | (r_MESSAGE == "R12_CHN"): - print(r_MESSAGE) - print("Spec before multiplication") - print(useful_spec.loc[useful_spec["node"] == r_MESSAGE]) - print(spec_df_hist.loc[spec_df_hist["node_loc"] == r_MESSAGE]) - useful_spec.loc[useful_spec["node"] == r_MESSAGE, "value"] = useful_spec.loc[ useful_spec["node"] == r_MESSAGE, "value" ] * (1 - df_spec_new.loc[df_spec_new["REGION"] == r, "i_spec"].values[0]) @@ -362,21 +329,9 @@ def modify_demand_and_hist_activity(scen): 1 - df_spec_new.loc[df_spec_new["REGION"] == r, "i_spec"].values[0] ) - if (r_MESSAGE == "R12_RCPA") | (r_MESSAGE == "R12_CHN"): - print(r_MESSAGE) - print("Spec after multiplication") - print(useful_spec.loc[useful_spec["node"] == r_MESSAGE]) - print(spec_df_hist.loc[spec_df_hist["node_loc"] == r_MESSAGE]) - for r in df_feed_new["REGION"]: r_MESSAGE = region_type + r - if (r_MESSAGE == "R12_RCPA") | (r_MESSAGE == "R12_CHN"): - print(r_MESSAGE) - print("Feedstock before multiplication") - print(useful_feed.loc[useful_feed["node"] == r_MESSAGE]) - print(feed_df_hist.loc[feed_df_hist["node_loc"] == r_MESSAGE]) - useful_feed.loc[useful_feed["node"] == r_MESSAGE, "value"] = useful_feed.loc[ useful_feed["node"] == r_MESSAGE, "value" ] * (1 - df_feed_new.loc[df_feed_new["REGION"] == r, "i_feed"].values[0]) @@ -387,12 +342,6 @@ def modify_demand_and_hist_activity(scen): 1 - df_feed_new.loc[df_feed_new["REGION"] == r, "i_feed"].values[0] ) - if (r_MESSAGE == "R12_RCPA") | (r_MESSAGE == "R12_CHN"): - print(r_MESSAGE) - print("Feedstock after multiplication") - print(useful_feed.loc[useful_feed["node"] == r_MESSAGE]) - print(feed_df_hist.loc[feed_df_hist["node_loc"] == r_MESSAGE]) - scen.check_out() scen.add_par("demand", useful_thermal) scen.add_par("demand", useful_spec) From 47e10c4258271911d146831be20bf35822585e8b Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 31 May 2022 16:58:49 +0200 Subject: [PATCH 355/774] Adjustments to solve macro calibration issues --- message_ix_models/model/material/__init__.py | 44 +++++++++------ message_ix_models/model/material/data_util.py | 56 +++++++++++++++++++ 2 files changed, 84 insertions(+), 16 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index c4b78099ff..cc5731580a 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -18,6 +18,7 @@ from .data_util import add_emission_accounting from .data_util import add_coal_lowerbound_2020 from .data_util import add_macro_COVID +from .data_util import add_elec_lowerbound_2020 from .util import read_config log = logging.getLogger(__name__) @@ -34,6 +35,9 @@ def build(scenario): spec = None apply_spec(scenario, spec,add_data_1) # dry_run=True + s_info = ScenarioInfo(scenario) + nodes = s_info.N + # Adjust exogenous energy demand to incorporate the endogenized sectors # Adjust the historical activity of the useful level industry technologies # Coal calibration 2020 @@ -46,7 +50,15 @@ def build(scenario): # Note: context.ssp doesnt work calibrate_UE_gr_to_demand(scenario, data_path=private_data_path(), ssp='SSP2') calibrate_UE_share_constraints(scenario) - print('calibrate_UE') + + # Electricity calibration to avoid zero prices for CHN. + if 'R12_CHN' in nodes: + add_elec_lowerbound_2020(scenario) + + # i_feed demand is zero creating a zero division error during MACRO calibration + scenario.check_out() + scenario.remove_set('sector','i_feed') + scenario.commit('i_feed removed from sectors.') return scenario @@ -197,9 +209,10 @@ def build_scen(context, datafile, tag, mode): metavar="INPUT", help="File name for external data input", ) +@click.option("--add_macro", default=True) @click.pass_obj # @click.pass_obj -def solve_scen(context, datafile, model_name, scenario_name): +def solve_scen(context, datafile, model_name, scenario_name, add_macro): """Solve a scenario. Use the --model_name and --scenario_name option to specify the scenario to solve. @@ -213,20 +226,19 @@ def solve_scen(context, datafile, model_name, scenario_name): scenario.remove_solution() # Solve - scenario.solve() - -@cli.command("add_calibration") -@click.option("--scenario_name", default="NoPolicy") -@click.option("--model_name", default="MESSAGEix-Materials") -@click.pass_obj -def add_MACRO(context, model_name, scenario_name): - from message_ix import Scenario - scenario = Scenario(context.get_platform(), model_name, scenario_name) - print(model_name) - print(scenario_name) + print('Solving the scenario without MACRO') + scenario.solve(model="MESSAGE", solve_options={'lpmethod': '4'}) + scenario.set_as_default() - # Use the same calibration that was used in NGFS project rc and ind demand adjusted - add_macro_COVID(scenario,'R12-CHN-5y_macro_data_NGFS_w_rc_ind_adj.xlsx') + if add_macro: + # After solving, add macro calibration + print('Scenario solved, now adding MACRO calibration') + scenario_macro = add_macro_COVID(scenario,'R12-CHN-5y_macro_data_NGFS_w_rc_ind_adj_mat.xlsx') + print('Scenario calibrated.') + macro_scenario_name = scenario_name + '_macro' + scenario_macro = scenario_macro.clone(scenario= macro_scenario_name) + scenario_macro.solve(model="MESSAGE-MACRO", solve_options={"lpmethod": "4"}) + scenario_macro.set_as_default() @cli.command("report") # @cli.command("report-1") @@ -316,7 +328,7 @@ def run_old_reporting(scenario_name, model_name): DATA_FUNCTIONS_2 = [ gen_data_cement, gen_data_petro_chemicals, - gen_data_power_sector, + # gen_data_power_sector, gen_data_aluminum, ] diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index d24a0cc15e..d7182287fd 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -66,6 +66,7 @@ def add_macro_COVID(scen, filename, check_converge=False): context = read_config() info = ScenarioInfo(scen) + nodes = info.N # Excel file for calibration data xls_file = os.path.join('P:', 'ene.model', 'MACRO', 'python', filename) @@ -89,9 +90,12 @@ def add_macro_COVID(scen, filename, check_converge=False): df_gdp.loc[df_gdp.year >= info.y0], ignore_index=True ) + # Calibration scen = scen.add_macro(data, check_convergence=check_converge) + return scen + def modify_demand_and_hist_activity(scen): """Take care of demand changes due to the introduction of material parents Shed industrial energy demand properly. @@ -552,6 +556,58 @@ def add_emission_accounting(scen): scen.add_par("relation_activity",CF4_red_add) scen.commit("CF4 relations corrected.") +def add_elec_lowerbound_2020(scen): + + # To avoid zero i_spec prices only for R12_CHN, add the below section. + # read input parameters for relevant technology/commodity combinations for + # converting betwen final and useful energy + + context = read_config() + + input_residual_electricity = scen.par('input',filters={"technology": + "sp_el_I", "year_vtg": "2020", "year_act": "2020"}) + + # read processed final energy data from IEA extended energy balances + # that is aggregated to MESSAGEix regions, fuels and (industry) sectors + + final = pd.read_csv(context.get_local_path("material", 'residual_industry_2019.csv')) + + # downselect needed fuels and sectors + final_residual_electricity = final.query('MESSAGE_fuel=="electr" & MESSAGE_sector=="industry_residual"') + + # join final energy data from IEA energy balances and input coefficients + # from final-to-useful technologies from MESSAGEix + bound_residual_electricity = pd.merge(input_residual_electricity, + final_residual_electricity, left_on = "node_loc", + right_on = "MESSAGE_region", how = "inner") + + # derive useful energy values by dividing final energy by + # input coefficient from final-to-useful technologies + bound_residual_electricity["value"] = bound_residual_electricity["Value"] / bound_residual_electricity["value"] + + # downselect dataframe columns for MESSAGEix parameters + bound_residual_electricity = bound_residual_electricity.filter(items=['node_loc', + 'technology', 'year_act', 'mode', 'time', 'value', 'unit_x']) + # rename columns if necessary + bound_residual_electricity.columns = ['node_loc', 'technology', 'year_act', + 'mode', 'time', 'value', 'unit'] + + # Decrease 20% to aviod zero prices (the issue continiues otherwise) + bound_residual_electricity['value'] = bound_residual_electricity['value'] * 0.8 + bound_residual_electricity = bound_residual_electricity[bound_residual_electricity['node_loc'] == 'R12_CHN'] + + scen.check_out() + + # add parameter dataframes to ixmp + scen.add_par('bound_activity_lo', bound_residual_electricity) + + # Remove the previous bounds + remove_par_lo = scen.par('growth_activity_lo', filters = {'technology':'sp_el_I','year_act':2020,'node_loc':'R12_CHN'}) + scen.remove_par('growth_activity_lo',remove_par_lo) + + scen.commit("added lower bound for activity of residual electricity technologies") + + def add_coal_lowerbound_2020(sc): '''Set lower bounds for coal and i_spec as a calibration for 2020''' From e01c788a7ba8b58719c230937d7555bb6095ac16 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 2 Jun 2022 13:09:25 +0200 Subject: [PATCH 356/774] Update the cli commands --- message_ix_models/model/material/__init__.py | 81 ++++++++++++-------- message_ix_models/model/material/doc.rst | 9 ++- 2 files changed, 56 insertions(+), 34 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index cc5731580a..6bb94b7cb1 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -142,8 +142,9 @@ def create_bare(context, regions, dry_run): ) @click.option("--tag", default="", help="Suffix to the scenario name") @click.option("--mode", default = 'by_url') +@click.option("--scenario_name", default = 'NoPolicy_3105_macro') @click.pass_obj -def build_scen(context, datafile, tag, mode): +def build_scen(context, datafile, tag, mode, scenario_name): """Build a scenario. Use the --url option to specify the base scenario. @@ -166,39 +167,46 @@ def build_scen(context, datafile, tag, mode): "NPi2020_1000-con-prim-dir-ncr": "NPi2020_1000", "NPi2020_400-con-prim-dir-ncr": "NPi2020_400", }.get(context.scenario_info["scenario"]) + + if type(output_scenario_name).__name__ == "NoneType": + output_scenario_name = context.scenario_info["scenario"] + + # context.metadata_path = context.metadata_path / "data" + context.datafile = datafile + + if context.scenario_info["model"] != "CD_Links_SSP2": + print("WARNING: this code is not tested with this base scenario!") + + # Clone and set up + scenario = build( + context.get_scenario().clone( + model="MESSAGEix-Materials", scenario=output_scenario_name + "_" + tag + ) + ) + # Set the latest version as default + scenario.set_as_default() + # Create a two degrees scenario by copying carbon prices from another scenario. - elif mode == 'by_copy': + if mode == 'by_copy': output_scenario_name = '2degrees' mod_mitig = 'ENGAGE_SSP2_v4.1.8' scen_mitig = 'EN_NPi2020_1000f' + print('Loading ' + mod_mitig + ' ' + scen_mitig + ' to retreive carbon prices.') scen_mitig_prices = message_ix.Scenario(mp, mod_mitig, scen_mitig) tax_emission_new = scen_mitig_prices.var("PRICE_EMISSION") - elif type(output_scenario_name).__name__ == "NoneType": - output_scenario_name = context.scenario_info["scenario"] - - # context.metadata_path = context.metadata_path / "data" - context.datafile = datafile - if context.scenario_info["model"] != "CD_Links_SSP2": - print("WARNING: this code is not tested with this base scenario!") - - # Clone and set up - scenario = build( - context.get_scenario().clone( - model="MESSAGEix-Materials", scenario=output_scenario_name + "_" + tag - ) - ) - - if mode == 'by_copy': + scenario = message_ix.Scenario(mp, 'MESSAGEix-Materials', scenario_name) + print('Base scenario is ' + scenario_name) + output_scenario_name = output_scenario_name + '_' + tag + scenario = scenario.clone('MESSAGEix-Materials',output_scenario_name , keep_solution=False) scenario.check_out() tax_emission_new.columns = scenario.par("tax_emission").columns tax_emission_new["unit"] = "USD/tCO2" scenario.add_par("tax_emission", tax_emission_new) scenario.commit('2 degree prices are added') - - # Set the latest version as default - scenario.set_as_default() - + print('New carbon prices added') + print('New scenario name is ' + output_scenario_name) + scenario.set_as_default() @cli.command("solve") @click.option("--scenario_name", default="NoPolicy") @@ -210,9 +218,10 @@ def build_scen(context, datafile, tag, mode): help="File name for external data input", ) @click.option("--add_macro", default=True) +@click.option("--add_calibration", default=False) @click.pass_obj # @click.pass_obj -def solve_scen(context, datafile, model_name, scenario_name, add_macro): +def solve_scen(context, datafile, model_name, scenario_name, add_calibration, add_macro): """Solve a scenario. Use the --model_name and --scenario_name option to specify the scenario to solve. @@ -225,20 +234,26 @@ def solve_scen(context, datafile, model_name, scenario_name, add_macro): if scenario.has_solution(): scenario.remove_solution() - # Solve - print('Solving the scenario without MACRO') - scenario.solve(model="MESSAGE", solve_options={'lpmethod': '4'}) - scenario.set_as_default() + if add_calibration: + # Solve + print('Solving the scenario without MACRO') + scenario.solve(model="MESSAGE", solve_options={'lpmethod': '4'}) + scenario.set_as_default() - if add_macro: # After solving, add macro calibration print('Scenario solved, now adding MACRO calibration') - scenario_macro = add_macro_COVID(scenario,'R12-CHN-5y_macro_data_NGFS_w_rc_ind_adj_mat.xlsx') + scenario = add_macro_COVID(scenario,'R12-CHN-5y_macro_data_NGFS_w_rc_ind_adj_mat.xlsx') print('Scenario calibrated.') - macro_scenario_name = scenario_name + '_macro' - scenario_macro = scenario_macro.clone(scenario= macro_scenario_name) - scenario_macro.solve(model="MESSAGE-MACRO", solve_options={"lpmethod": "4"}) - scenario_macro.set_as_default() + + if add_macro: + scenario.solve(model="MESSAGE-MACRO", solve_options={"lpmethod": "4"}) + scenario.set_as_default() + + if add_macro == False: + # Solve + print('Solving the scenario without MACRO') + scenario.solve(model="MESSAGE", solve_options={'lpmethod': '4'}) + scenario.set_as_default() @cli.command("report") # @cli.command("report-1") diff --git a/message_ix_models/model/material/doc.rst b/message_ix_models/model/material/doc.rst index 6e5bf8d4dd..1c7672b545 100644 --- a/message_ix_models/model/material/doc.rst +++ b/message_ix_models/model/material/doc.rst @@ -102,11 +102,18 @@ It can be helpful to note that this command will not work for all R12 scenarios Currently, a set of pre-defined base scenario names will be translated to own scenario names. But when an unknown base scenario name is given, we reuse it for the output scenario name. The output scenario will be ``ixmp://ixmp_dev-ixmp/MESSAGEix-Materials/{name}``, where {name} is the output scenario name. -Using an additional tag `--tag` can be used to add an additional suffix to the new scenario name. +An additional tag `--tag` can be used to add an additional suffix to the new scenario name. +The mode option `--mode` has two different inputs 'by_url' (by default) or 'by_copy'. The first one uses the provided url to add the materials implementation on top of the scenario from the url. +The latter is used to create a 2 degree mitigation scenario with materials by copying relevant carbon prices to the scenario that is specified by `--scenario_name`. + + $ mix-models material build --tag test --mode by_copy --scenario_name NoPolicy_R12 + This command line only builds the scenario but does not run it. To run the scenario, use ``mix-models materials solve``, giving the scenario name, e.g.:: $ mix-models material solve --scenario_name NoPolicy_R12 +The solve command has the `--add_calibration` option to add MACRO calibration to a baseline scenario. `--add_macro` option solves the scenario with MACRO. + Reporting ========= From 7ae611a526afcc7188bbc1152c4958c1e5050dbd Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 9 Jun 2022 12:04:50 +0200 Subject: [PATCH 357/774] Inital methanol commit --- .../material/meth_bio_techno_economic.xlsx | 3 ++ .../material/meth_h2_techno_economic.xlsx | 3 ++ .../model/material/data_methanol.py | 29 +++++++++++++++++++ 3 files changed, 35 insertions(+) create mode 100644 message_ix_models/data/material/meth_bio_techno_economic.xlsx create mode 100644 message_ix_models/data/material/meth_h2_techno_economic.xlsx create mode 100644 message_ix_models/model/material/data_methanol.py diff --git a/message_ix_models/data/material/meth_bio_techno_economic.xlsx b/message_ix_models/data/material/meth_bio_techno_economic.xlsx new file mode 100644 index 0000000000..04d9d3e8e9 --- /dev/null +++ b/message_ix_models/data/material/meth_bio_techno_economic.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:388a9cdf5a461f528ea4659eda3a3eb044f85a53f91ba3f190e3bc9e3f4b3f5d +size 47985 diff --git a/message_ix_models/data/material/meth_h2_techno_economic.xlsx b/message_ix_models/data/material/meth_h2_techno_economic.xlsx new file mode 100644 index 0000000000..df82ad3157 --- /dev/null +++ b/message_ix_models/data/material/meth_h2_techno_economic.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8fb13e2a05376ed342d71e4eeb310741e0a82e0ec241177ff145cf2c06e9019c +size 200893 diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py new file mode 100644 index 0000000000..4e37c90a9a --- /dev/null +++ b/message_ix_models/model/material/data_methanol.py @@ -0,0 +1,29 @@ +import message_ix +import message_data +import ixmp as ix + +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt + +from message_ix_models import ScenarioInfo +from message_ix import make_df +from message_ix_models.util import broadcast, same_node +from util import read_config + + +def gen_data_methanol(scenario): + return {**gen_data_meth_h2(scenario), **gen_data_meth_bio(scenario)} + + +def gen_data_meth_h2(scenario): + context = read_config() + df_h2 = pd.read_excel(context.get_local_path("material", "meth_h2_techno_economic.xlsx"), sheet_name=None) + return df_h2 + + +def gen_data_meth_bio(scenario): + context = read_config() + context.get_local_path("material", "meth_bio_techno_economic.xlsx") + df_h2 = pd.read_excel(context.get_local_path("material", "meth_bio_techno_economic.xlsx"), sheet_name=None) + return df_h2 From 3166be6ebc87df203bbb3397165c641d979adce8 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 21 Jul 2022 23:31:27 +0200 Subject: [PATCH 358/774] Fix par_dict merging + add sets --- message_ix_models/data/material/set.yaml | 18 ++++++++++++++++++ .../model/material/data_methanol.py | 13 +++++++++++-- 2 files changed, 29 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index b55037a7d9..a268bea185 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -441,6 +441,24 @@ fertilizer: - fueloil_NH3 - NH3_to_N_fertil +methanol: + commodity: + require: + - electr + - biomass + - hydrogen + - d_heat + - ht_heat + add: + - methanol + level: + add: + technology: + require: + add: + - meth_bio + - meth_h2 + buildings: level: add: diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index 4e37c90a9a..7b67e4d4a8 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -9,11 +9,20 @@ from message_ix_models import ScenarioInfo from message_ix import make_df from message_ix_models.util import broadcast, same_node -from util import read_config +from .util import read_config def gen_data_methanol(scenario): - return {**gen_data_meth_h2(scenario), **gen_data_meth_bio(scenario)} + dict1 = gen_data_meth_h2(scenario) + dict2 = gen_data_meth_bio(scenario) + keys = set(list(dict1.keys())+list(dict2.keys())) + new_dict = {} + for i in keys: + if (i in dict2.keys()) & (i in dict1.keys()): + new_dict[i] = dict1[i].append(dict2[i]) + else: + new_dict[i] = dict1[i] + return new_dict def gen_data_meth_h2(scenario): From 9684a1b0b8c6f66128621f7dd03f8383c611b011 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Wed, 27 Jul 2022 17:07:51 +0200 Subject: [PATCH 359/774] Add addon tec sets --- message_ix_models/data/material/set.yaml | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index a268bea185..8d56fad397 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -459,6 +459,22 @@ methanol: - meth_bio - meth_h2 + addon: + add: + - meth_h2 + + type_addon: + add: + - methanol_synthesis_addon + + map_tec_addon: + add: + - [h2_elec, methanol_synthesis_addon] + + cat_addon: + add: + - [methanol_synthesis_addon, meth_h2] + buildings: level: add: From 29249b0841f2c564e421f0b7df7e091ffd37ad61 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Wed, 27 Jul 2022 17:11:11 +0200 Subject: [PATCH 360/774] Add meth_bal parameters --- message_ix_models/model/material/__init__.py | 11 +++++---- .../model/material/data_methanol.py | 24 +++++++++++++++---- 2 files changed, 26 insertions(+), 9 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 6bb94b7cb1..cb808e9ffd 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -72,7 +72,8 @@ def build(scenario): "petro_chemicals", "buildings", "power_sector", - "fertilizer" + "fertilizer", + "methanol" ] @@ -331,13 +332,15 @@ def run_old_reporting(scenario_name, model_name): from .data_buildings import gen_data_buildings from .data_power_sector import gen_data_power_sector from .data_ammonia import gen_data_ammonia +from .data_methanol import gen_data_methanol DATA_FUNCTIONS_1 = [ #gen_data_buildings, - gen_data_ammonia, - gen_data_generic, - gen_data_steel, + gen_data_methanol + #gen_data_ammonia, + #gen_data_generic, + #gen_data_steel, ] DATA_FUNCTIONS_2 = [ diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index 7b67e4d4a8..fc1d50e78c 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -13,8 +13,8 @@ def gen_data_methanol(scenario): - dict1 = gen_data_meth_h2(scenario) - dict2 = gen_data_meth_bio(scenario) + dict1 = gen_data_meth_h2() + dict2 = gen_data_meth_bio() keys = set(list(dict1.keys())+list(dict2.keys())) new_dict = {} for i in keys: @@ -22,16 +22,30 @@ def gen_data_methanol(scenario): new_dict[i] = dict1[i].append(dict2[i]) else: new_dict[i] = dict1[i] - return new_dict + + context = read_config() + dict3 = pd.read_excel(context.get_local_path("material", "meth_bal_pars.xlsx"), sheet_name=None) + + keys = set(list(dict3.keys())+list(new_dict.keys())) + new_dict2 = {} + for i in keys: + if (i in dict3.keys()) & (i in new_dict.keys()): + new_dict2[i] = new_dict[i].append(dict3[i]) + if (i in dict3.keys()) & ~(i in new_dict.keys()): + new_dict2[i] = dict3[i] + if ~(i in dict3.keys()) & (i in new_dict.keys()): + new_dict2[i] = new_dict[i] + + return new_dict2 -def gen_data_meth_h2(scenario): +def gen_data_meth_h2(): context = read_config() df_h2 = pd.read_excel(context.get_local_path("material", "meth_h2_techno_economic.xlsx"), sheet_name=None) return df_h2 -def gen_data_meth_bio(scenario): +def gen_data_meth_bio(): context = read_config() context.get_local_path("material", "meth_bio_techno_economic.xlsx") df_h2 = pd.read_excel(context.get_local_path("material", "meth_bio_techno_economic.xlsx"), sheet_name=None) From 597ca3a2f595d63f9ea2d8800acdb25501dc6248 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 28 Jul 2022 16:42:27 +0200 Subject: [PATCH 361/774] Add meth_bal input data file --- message_ix_models/data/material/meth_bal_pars.xlsx | 3 +++ 1 file changed, 3 insertions(+) create mode 100644 message_ix_models/data/material/meth_bal_pars.xlsx diff --git a/message_ix_models/data/material/meth_bal_pars.xlsx b/message_ix_models/data/material/meth_bal_pars.xlsx new file mode 100644 index 0000000000..3377b22a2d --- /dev/null +++ b/message_ix_models/data/material/meth_bal_pars.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:80d98cab6477300c2e4770c55b0e63b0d4cf17b56d50bb758f6ef827110712e6 +size 51475 From 4717e5e05d155da073bfab43ef35b964fc08881f Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 28 Jul 2022 18:03:37 +0200 Subject: [PATCH 362/774] Add missing methanol sets --- message_ix_models/data/material/set.yaml | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 8d56fad397..6268c98ead 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -448,16 +448,19 @@ methanol: - biomass - hydrogen - d_heat - - ht_heat add: - methanol + - ht_heat + level: add: + - final_material + technology: - require: add: - meth_bio - meth_h2 + - meth_bal_material addon: add: @@ -475,6 +478,10 @@ methanol: add: - [methanol_synthesis_addon, meth_h2] + relation: + add: + - CO2_PtX_trans_disp_split + buildings: level: add: From 4ff97214c100a1433b87b7528e3215c889510cf8 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Mon, 22 Aug 2022 22:13:24 +0200 Subject: [PATCH 363/774] Change input data units --- message_ix_models/data/material/meth_bal_pars.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/meth_bal_pars.xlsx b/message_ix_models/data/material/meth_bal_pars.xlsx index 3377b22a2d..0087ac896f 100644 --- a/message_ix_models/data/material/meth_bal_pars.xlsx +++ b/message_ix_models/data/material/meth_bal_pars.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:80d98cab6477300c2e4770c55b0e63b0d4cf17b56d50bb758f6ef827110712e6 -size 51475 +oid sha256:1d05a1a29ae335ce237dc31923f0ccdc17159a3835f092f5a32a06407d202f6e +size 65316 From 232046f75fabf1d21433d5d0e104817ced7dcb54 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Mon, 22 Aug 2022 22:50:07 +0200 Subject: [PATCH 364/774] Add methanol demand --- message_ix_models/model/material/data_methanol.py | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index fc1d50e78c..705b8959d3 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -9,7 +9,7 @@ from message_ix_models import ScenarioInfo from message_ix import make_df from message_ix_models.util import broadcast, same_node -from .util import read_config +from util import read_config def gen_data_methanol(scenario): @@ -35,7 +35,15 @@ def gen_data_methanol(scenario): new_dict2[i] = dict3[i] if ~(i in dict3.keys()) & (i in new_dict.keys()): new_dict2[i] = new_dict[i] - + # still contains ref_activity parameter! remove! + df = pd.read_excel(context.get_local_path("material", "methanol demand.xlsx"), sheet_name="methanol_demand") + df = df[(~df["Region"].isna()) & (df["Region"] != "World")] + df = df.dropna(axis=1) + df_melt = df.melt(id_vars=["Region"], value_vars=df.columns[5:], var_name="year") + df_final = message_ix.make_df("demand", unit="Mt", level="demand", value=df_melt.value, time="year", + commodity="methanol", year=df_melt.year, node=df_melt["Region"]) + df_final["value"] = df_final["value"].apply(lambda x: x * 0.75) + new_dict2["demand"] = df_final return new_dict2 From 0ed03cc5c7cc0169dd3b85e5701831300238eb4b Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Mon, 22 Aug 2022 23:01:16 +0200 Subject: [PATCH 365/774] Add methanol demand input file --- message_ix_models/data/material/methanol demand.xlsx | 3 +++ 1 file changed, 3 insertions(+) create mode 100644 message_ix_models/data/material/methanol demand.xlsx diff --git a/message_ix_models/data/material/methanol demand.xlsx b/message_ix_models/data/material/methanol demand.xlsx new file mode 100644 index 0000000000..2b40f64293 --- /dev/null +++ b/message_ix_models/data/material/methanol demand.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:44a4307d1a60f703bc8cc38e1486608079006d68d17f9206c5f8557e02d92733 +size 70758 From 772c9f81475d1e44c78384349b95bc9df4db8dfe Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Mon, 22 Aug 2022 23:15:48 +0200 Subject: [PATCH 366/774] Change units to units registered in db --- message_ix_models/data/material/meth_bal_pars.xlsx | 4 ++-- message_ix_models/model/material/data_methanol.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/message_ix_models/data/material/meth_bal_pars.xlsx b/message_ix_models/data/material/meth_bal_pars.xlsx index 0087ac896f..82c971b89a 100644 --- a/message_ix_models/data/material/meth_bal_pars.xlsx +++ b/message_ix_models/data/material/meth_bal_pars.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:1d05a1a29ae335ce237dc31923f0ccdc17159a3835f092f5a32a06407d202f6e -size 65316 +oid sha256:ad7498da83aaa8ae53b43f6029172a9cac8344bbe59c1af2ef6c43ccca021bae +size 65306 diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index 705b8959d3..4a33fffdee 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -40,7 +40,7 @@ def gen_data_methanol(scenario): df = df[(~df["Region"].isna()) & (df["Region"] != "World")] df = df.dropna(axis=1) df_melt = df.melt(id_vars=["Region"], value_vars=df.columns[5:], var_name="year") - df_final = message_ix.make_df("demand", unit="Mt", level="demand", value=df_melt.value, time="year", + df_final = message_ix.make_df("demand", unit="t", level="demand", value=df_melt.value, time="year", commodity="methanol", year=df_melt.year, node=df_melt["Region"]) df_final["value"] = df_final["value"].apply(lambda x: x * 0.75) new_dict2["demand"] = df_final From 271b88e0930fa79f5700224ed83275e1fc66ae43 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Wed, 31 Aug 2022 21:43:56 +0200 Subject: [PATCH 367/774] Add meth_coal historical activity --- .../model/material/data_methanol.py | 21 ++++++++++++++----- 1 file changed, 16 insertions(+), 5 deletions(-) diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index 4a33fffdee..93afd729dc 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -9,7 +9,7 @@ from message_ix_models import ScenarioInfo from message_ix import make_df from message_ix_models.util import broadcast, same_node -from util import read_config +from .util import read_config def gen_data_methanol(scenario): @@ -19,7 +19,7 @@ def gen_data_methanol(scenario): new_dict = {} for i in keys: if (i in dict2.keys()) & (i in dict1.keys()): - new_dict[i] = dict1[i].append(dict2[i]) + new_dict[i] = pd.concat([dict1[i], dict2[i]]) else: new_dict[i] = dict1[i] @@ -30,7 +30,7 @@ def gen_data_methanol(scenario): new_dict2 = {} for i in keys: if (i in dict3.keys()) & (i in new_dict.keys()): - new_dict2[i] = new_dict[i].append(dict3[i]) + new_dict2[i] = pd.concat([new_dict[i], dict3[i]]) if (i in dict3.keys()) & ~(i in new_dict.keys()): new_dict2[i] = dict3[i] if ~(i in dict3.keys()) & (i in new_dict.keys()): @@ -40,10 +40,21 @@ def gen_data_methanol(scenario): df = df[(~df["Region"].isna()) & (df["Region"] != "World")] df = df.dropna(axis=1) df_melt = df.melt(id_vars=["Region"], value_vars=df.columns[5:], var_name="year") - df_final = message_ix.make_df("demand", unit="t", level="demand", value=df_melt.value, time="year", - commodity="methanol", year=df_melt.year, node=df_melt["Region"]) + df_final = message_ix.make_df("demand", unit="t", level="final_material", value=df_melt.value, time="year", + commodity="methanol", year=df_melt.year, node=("R12_"+df_melt["Region"])) df_final["value"] = df_final["value"].apply(lambda x: x * 0.75) new_dict2["demand"] = df_final + + # fix demand infeasibility + act = scenario.par("historical_activity") + row = act[act["technology"].str.startswith("meth")].sort_values("value", ascending=False).iloc[0] + # china meth_coal production (90% coal share on 2015 47 Mt total; 1.348 = Mt to GWa ) + row["value"] = (47 / 1.3498) * 0.9 + new_dict2["historical_activity"] = pd.concat([new_dict2["historical_activity"], pd.DataFrame(row).T]) + # derived from graphic in "Methanol production statstics.xlsx/China demand split" diagram + hist_cap = message_ix.make_df("historical_new_capacity", node_loc="R12_CHN", technology="meth_coal", year_vtg=2015, value=9.6, + unit="GW") + new_dict2["historical_new_capacity"] = hist_cap return new_dict2 From cb167eeb101a085c9c9500579f6b4bb565e8f44e Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Wed, 31 Aug 2022 21:52:27 +0200 Subject: [PATCH 368/774] Calibrate meth_ng historical activity --- message_ix_models/model/material/data_methanol.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index 93afd729dc..902027876f 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -55,6 +55,13 @@ def gen_data_methanol(scenario): hist_cap = message_ix.make_df("historical_new_capacity", node_loc="R12_CHN", technology="meth_coal", year_vtg=2015, value=9.6, unit="GW") new_dict2["historical_new_capacity"] = hist_cap + # fix demand infeasibility + act = scenario.par("historical_activity") + row = act[act["technology"].str.startswith("meth")].sort_values("value", ascending=False).iloc[0] + row["value"] = 0.0 + new_dict2["historical_activity"] = pd.concat([new_dict2["historical_activity"], pd.DataFrame(row).T]) + df_ng = pd.read_excel(context.get_local_path("material", "meth_ng_techno_economic.xlsx")) + new_dict2["historical_activity"] = new_dict2["historical_activity"].append(df_ng) return new_dict2 From e0a8b83d075557dbf61f80cf4923ea5213dd2880 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Wed, 31 Aug 2022 22:08:33 +0200 Subject: [PATCH 369/774] Add missing input file --- message_ix_models/data/material/meth_ng_techno_economic.xlsx | 3 +++ 1 file changed, 3 insertions(+) create mode 100644 message_ix_models/data/material/meth_ng_techno_economic.xlsx diff --git a/message_ix_models/data/material/meth_ng_techno_economic.xlsx b/message_ix_models/data/material/meth_ng_techno_economic.xlsx new file mode 100644 index 0000000000..8bc9b41f6d --- /dev/null +++ b/message_ix_models/data/material/meth_ng_techno_economic.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:67b898630b70bd73af3d44c06993587d05ce6c21a4a5b7e018b9abffeae11df8 +size 5935 From af7818a586a3880a7a01567e98eb48eb498c6664 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 4 Oct 2022 10:53:50 +0200 Subject: [PATCH 370/774] Add meth_h2 relation constraints --- message_ix_models/data/material/meth_h2_techno_economic.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/meth_h2_techno_economic.xlsx b/message_ix_models/data/material/meth_h2_techno_economic.xlsx index df82ad3157..18cc380dfb 100644 --- a/message_ix_models/data/material/meth_h2_techno_economic.xlsx +++ b/message_ix_models/data/material/meth_h2_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:8fb13e2a05376ed342d71e4eeb310741e0a82e0ec241177ff145cf2c06e9019c -size 200893 +oid sha256:1b9c8a9ea75bfc9c180ae3023990b5cb9337870963bb569f68bc539654236be2 +size 248102 From 3a5c14276c59b64f40b91de269e72da74b5c83c2 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 4 Oct 2022 10:56:44 +0200 Subject: [PATCH 371/774] Add mto and resin technologies --- .../data/material/MTO data collection.xlsx | 3 + ...nol production statistics (version 1).xlsx | 3 + .../material/results_material_SHAPE_comm.csv | 3 + .../results_material_SHAPE_residential.csv | 3 + message_ix_models/data/material/set.yaml | 7 + .../model/material/data_methanol.py | 167 +++++++++++++++++- 6 files changed, 181 insertions(+), 5 deletions(-) create mode 100644 message_ix_models/data/material/MTO data collection.xlsx create mode 100644 message_ix_models/data/material/Methanol production statistics (version 1).xlsx create mode 100644 message_ix_models/data/material/results_material_SHAPE_comm.csv create mode 100644 message_ix_models/data/material/results_material_SHAPE_residential.csv diff --git a/message_ix_models/data/material/MTO data collection.xlsx b/message_ix_models/data/material/MTO data collection.xlsx new file mode 100644 index 0000000000..a6ae61a7a2 --- /dev/null +++ b/message_ix_models/data/material/MTO data collection.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bf832d356358fce3ae1be472b0097b917d4abbb8dd8d61de6043092aaf59b930 +size 170418 diff --git a/message_ix_models/data/material/Methanol production statistics (version 1).xlsx b/message_ix_models/data/material/Methanol production statistics (version 1).xlsx new file mode 100644 index 0000000000..7d79a896e7 --- /dev/null +++ b/message_ix_models/data/material/Methanol production statistics (version 1).xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a86f38bed888551367be25bec135d77522b3a1001e1ecfb893d6651ba6c9c794 +size 3587789 diff --git a/message_ix_models/data/material/results_material_SHAPE_comm.csv b/message_ix_models/data/material/results_material_SHAPE_comm.csv new file mode 100644 index 0000000000..d2a9423483 --- /dev/null +++ b/message_ix_models/data/material/results_material_SHAPE_comm.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:957d0b80959d4564524517f5dd003e2f8b3ebd8e182217659c11d13481be5b3d +size 416073 diff --git a/message_ix_models/data/material/results_material_SHAPE_residential.csv b/message_ix_models/data/material/results_material_SHAPE_residential.csv new file mode 100644 index 0000000000..f291ff222b --- /dev/null +++ b/message_ix_models/data/material/results_material_SHAPE_residential.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a59e1c215692bf575bac373f368326f9d89891d2860e6e0ba3ba320aef076e29 +size 429432 diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 6268c98ead..ed5a234b97 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -451,6 +451,10 @@ methanol: add: - methanol - ht_heat + - formaldehyde + - ethylene + - propylene + - fcoh_resin level: add: @@ -461,6 +465,9 @@ methanol: - meth_bio - meth_h2 - meth_bal_material + - MTO + - CH2O_synth + - CH2O_to_resin addon: add: diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index 902027876f..3c542e0bab 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -23,7 +23,6 @@ def gen_data_methanol(scenario): else: new_dict[i] = dict1[i] - context = read_config() dict3 = pd.read_excel(context.get_local_path("material", "meth_bal_pars.xlsx"), sheet_name=None) keys = set(list(dict3.keys())+list(new_dict.keys())) @@ -42,7 +41,7 @@ def gen_data_methanol(scenario): df_melt = df.melt(id_vars=["Region"], value_vars=df.columns[5:], var_name="year") df_final = message_ix.make_df("demand", unit="t", level="final_material", value=df_melt.value, time="year", commodity="methanol", year=df_melt.year, node=("R12_"+df_melt["Region"])) - df_final["value"] = df_final["value"].apply(lambda x: x * 0.75) + df_final["value"] = df_final["value"].apply(lambda x: x * 0.5) new_dict2["demand"] = df_final # fix demand infeasibility @@ -56,12 +55,50 @@ def gen_data_methanol(scenario): unit="GW") new_dict2["historical_new_capacity"] = hist_cap # fix demand infeasibility - act = scenario.par("historical_activity") - row = act[act["technology"].str.startswith("meth")].sort_values("value", ascending=False).iloc[0] - row["value"] = 0.0 + #act = scenario.par("historical_activity") + #row = act[act["technology"].str.startswith("meth")].sort_values("value", ascending=False).iloc[0] + #row["value"] = 0.0 new_dict2["historical_activity"] = pd.concat([new_dict2["historical_activity"], pd.DataFrame(row).T]) df_ng = pd.read_excel(context.get_local_path("material", "meth_ng_techno_economic.xlsx")) new_dict2["historical_activity"] = new_dict2["historical_activity"].append(df_ng) + + mto_dict = gen_data_mto(scenario, "MTO") + keys = set(list(new_dict2.keys()) + list(mto_dict.keys())) + for i in keys: + if (i in new_dict2.keys()) & (i in mto_dict.keys()): + new_dict2[i] = pd.concat([new_dict2[i], mto_dict[i]]) + if ~(i in new_dict2.keys()) & (i in mto_dict.keys()): + new_dict2[i] = mto_dict[i] + + ch2o_dict = gen_data_mto(scenario, "Formaldehyde") + keys = set(list(new_dict2.keys()) + list(ch2o_dict.keys())) + for i in keys: + if (i in new_dict2.keys()) & (i in ch2o_dict.keys()): + new_dict2[i] = pd.concat([new_dict2[i], ch2o_dict[i]]) + if ~(i in new_dict2.keys()) & (i in ch2o_dict.keys()): + new_dict2[i] = ch2o_dict[i] + + resin_dict = gen_data_mto(scenario, "Resins") + keys = set(list(new_dict2.keys()) + list(resin_dict.keys())) + for i in keys: + if (i in new_dict2.keys()) & (i in resin_dict.keys()): + new_dict2[i] = pd.concat([new_dict2[i], resin_dict[i]]) + if ~(i in new_dict2.keys()) & (i in resin_dict.keys()): + new_dict2[i] = resin_dict[i] + + new_dict2["demand"] = new_dict2["demand"].append(gen_resin_demand(scenario, 0.03, "residential")) + new_dict2["demand"] = new_dict2["demand"].append(gen_resin_demand(scenario, 0.03, "comm")) + new_dict2["input"].append(add_methanol_trp_additives(scenario)) + + emission_dict = { + "node": "World", + "type_emission": "TCE_CO2", + "type_tec": "all", + "type_year": "cumulative", + "unit": "???" + } + new_dict2["bound_emission"] = make_df("bound_emission", value=3667, **emission_dict) + return new_dict2 @@ -76,3 +113,123 @@ def gen_data_meth_bio(): context.get_local_path("material", "meth_bio_techno_economic.xlsx") df_h2 = pd.read_excel(context.get_local_path("material", "meth_bio_techno_economic.xlsx"), sheet_name=None) return df_h2 + + +def gen_data_mto(scenario, chemical): + df = pd.read_excel(context.get_local_path("material", "MTO data collection.xlsx"), + sheet_name=chemical, + usecols=[1, 2, 3, 4, 6, 7]) + #exclude emissions for now + if chemical == "MTO": + df = df.iloc[:10, ] + if chemical == "Formaldehyde": + df = df.iloc[:9, ] + + common = dict( + # commodity="NH3", + # level="secondary_material", + mode="M1", + time="year", + time_dest="year", + time_origin="year", + emission="CO2_industry", # confirm if correct + relation="CO2_cc" + ) + + all_years = scenario.vintage_and_active_years() + all_years = all_years[all_years["year_vtg"] > 1990] + + nodes = scenario.set("node")[1:] + nodes = nodes.drop(5).reset_index(drop=True) + + par_dict = {k: pd.DataFrame() for k in (df["parameter"])} + for i in df["parameter"]: + for index, row in df[df["parameter"] == i].iterrows(): + par_dict[i] = par_dict[i].append( + make_df(i, **all_years.to_dict(orient="list"), **row, **common).pipe(broadcast, + node_loc=nodes).pipe( + same_node)) + + if i == "relation_activity": + par_dict[i]["year_rel"] = par_dict[i]["year_act"] + par_dict[i]["node_rel"] = par_dict[i]["node_loc"] + + if "unit" in par_dict[i].columns: + par_dict[i]["unit"] = "???" + + hist_dict = { + "node_loc": "R12_CHN", + "technology": "MTO", + "mode": "M1", + "time": "year", + "unit": "???" + } + if chemical == "MTO": + par_dict["historical_activity"] = make_df("historical_activity", value=[4.5, 11], year_act=[2015, 2020], **hist_dict) + #par_dict["historical_new_capacity"] = make_df("historical_new_capacity", value=[1.2, 1.2], year_vtg=[2015, 2020], **hist_dict) + par_dict["historical_new_capacity"] = make_df("historical_new_capacity", value=1.2, year_vtg=2015, + **hist_dict) + return par_dict + + +def add_methanol_trp_additives(scenario): + df_loil = scenario.par("input") + df_loil = df_loil[df_loil["technology"] == "loil_trp"] + + df_mtbe = pd.read_excel( + context.get_local_path("material", "Methanol production statistics (version 1).xlsx"), + # usecols=[1,2,3,4,6,7], + skiprows=np.linspace(0, 65, 66), sheet_name="MTBE calc") + df_mtbe = df_mtbe.iloc[1:13, ] + df_mtbe["node_loc"] = "R12_" + df_mtbe["node_loc"] + df_mtbe = df_mtbe[["node_loc", "methanol energy%"]] + df_biodiesel = pd.read_excel( + context.get_local_path("material", "Methanol production statistics (version 1).xlsx"), + skiprows=np.linspace(0, 37, 38), + usecols=[1, 2], + sheet_name="Biodiesel") + df_biodiesel["node_loc"] = "R12_" + df_biodiesel["node_loc"] + df_total = df_biodiesel.merge(df_mtbe) + df_total = df_total.assign(value=lambda x: x["methanol energy %"] + x["methanol energy%"]) + + def get_meth_share(df, node): + return df[df["node_loc"] == node["node_loc"]]["value"].values[0] + + df_loil_meth = df_loil.copy(deep=True) + df_loil_meth["value"] = df_loil.apply(lambda x: get_meth_share(df_total, x), axis=1) + df_loil_meth["commodity"] = "methanol" + df_loil["value"] = df_loil["value"] - df_loil_meth["value"] + + return pd.concat([df_loil, df_loil_meth]) + + +def gen_resin_demand(scenario, resin_share, sector): + df = pd.read_csv( + context.get_local_path("material", "results_material_SHAPE_"+sector+".csv")) + resin_intensity = resin_share + df = df[df["scenario"] == "SH2"] + df = df[df["material"] == "wood"].assign(resin_demand=df["mat_demand_Mt"] * resin_intensity) + df["R12"] = "R12_" + df["R12"] + + common = dict( + mode="M1", + time="year", + time_dest="year", + time_origin="year", + emission="CO2_industry", # confirm if correct, + relation="CO2_cc", + commodity="fcoh_resin", + unit="???", + level="final_material" + ) + all_years = scenario.vintage_and_active_years() + all_years = all_years[all_years["year_vtg"] > 1990] + nodes = scenario.set("node")[1:] + nodes = nodes.drop(5).reset_index(drop=True) + + df_demand = make_df("demand", year=all_years["year_act"].unique()[:-1], **common).pipe(broadcast, + node=nodes).merge( + df[["R12", "year", "resin_demand"]], left_on=["node", "year"], right_on=["R12", "year"]) + df_demand["value"] = df_demand["resin_demand"] + df_demand = make_df("demand", **df_demand) + return df_demand \ No newline at end of file From fdf2d2e0c4e0860d3df3bfbf47046ce1fda132f1 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 4 Oct 2022 14:22:14 +0200 Subject: [PATCH 372/774] Change ht_heat level to "useful_petro" --- message_ix_models/data/material/MTO data collection.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/MTO data collection.xlsx b/message_ix_models/data/material/MTO data collection.xlsx index a6ae61a7a2..db7f9fc3b9 100644 --- a/message_ix_models/data/material/MTO data collection.xlsx +++ b/message_ix_models/data/material/MTO data collection.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:bf832d356358fce3ae1be472b0097b917d4abbb8dd8d61de6043092aaf59b930 -size 170418 +oid sha256:5487566b72995f5fee734c609c382c1632223b66aaabe7c6c6628de171e9f711 +size 170424 From 3f42072821182a5cd2f14315a167110d26d93eb4 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 4 Oct 2022 16:37:52 +0200 Subject: [PATCH 373/774] Code formatting commit --- message_ix_models/model/material/__init__.py | 1 + message_ix_models/model/material/build.py | 170 ++++++++++--------- 2 files changed, 95 insertions(+), 76 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index cb808e9ffd..08cf2ffcec 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -372,6 +372,7 @@ def add_data_1(scenario, dry_run=False): log.info("done") + def add_data_2(scenario, dry_run=False): """Populate `scenario` with MESSAGEix-Materials data.""" # Information about `scenario` diff --git a/message_ix_models/model/material/build.py b/message_ix_models/model/material/build.py index 9d3e8c5325..927549d7f6 100644 --- a/message_ix_models/model/material/build.py +++ b/message_ix_models/model/material/build.py @@ -1,19 +1,34 @@ import logging -from typing import Callable, Mapping +from typing import Callable, Dict, List, Mapping, Union -from ixmp.utils import maybe_check_out +from ixmp.utils import maybe_check_out, maybe_commit from message_ix import Scenario import pandas as pd -from message_ix_models import Code, ScenarioInfo, add_par_data, strip_par_data +from sdmx.model import Code + +from message_ix_models.util import add_par_data, strip_par_data +from message_ix_models.util.scenarioinfo import ScenarioInfo, Spec log = logging.getLogger(__name__) +def ellipsize(elements: List) -> str: + """Generate a short string representation of `elements`. + + If the list has more than 5 elements, only the first two and last two are shown, + with "..." between. + """ + if len(elements) > 5: + return ", ".join(map(str, elements[:2] + ["..."] + elements[-2:])) + else: + return ", ".join(map(str, elements)) + + def apply_spec( scenario: Scenario, - spec: Mapping[str, ScenarioInfo], + spec: Union[Spec, Mapping[str, ScenarioInfo]] = None, data: Callable = None, **options, ): @@ -21,34 +36,31 @@ def apply_spec( Parameters ---------- - spec - A 'specification': :class:`dict` with 'require', 'remove', and 'add' - keys and :class:`.ScenarioInfo` objects as values. + spec : .Spec + Specification of changes to make to `scenario`. data : callable, optional - Function to add data to `scenario`. `data` can either manipulate the - scenario directly, or return a :class:`dict` compatible with - :func:`.add_par_data`. + Function to add data to `scenario`. `data` can either manipulate the scenario + directly, or return a :class:`dict` compatible with :func:`.add_par_data`. Other parameters ---------------- dry_run : bool - Don't modify `scenario`; only show what would be done. Default - :obj:`False`. Exceptions will still be raised if the elements from - ``spec['required']`` are missing; this serves as a check that the - scenario has the required features for applying the spec. + Don't modify `scenario`; only show what would be done. Default :obj:`False`. + Exceptions will still be raised if the elements from ``spec['required']`` are + missing; this serves as a check that the scenario has the required features for + applying the spec. fast : bool - Do not remove existing parameter data; increases speed on large - scenarios. + Do not remove existing parameter data; increases speed on large scenarios. quiet : bool - Only show log messages at level ``ERROR`` and higher. If :obj:`False` - (default), show log messages at level ``DEBUG`` and higher. + Only show log messages at level ``ERROR`` and higher. If :obj:`False` (default), + show log messages at level ``DEBUG`` and higher. message : str Commit message. See also -------- - .tools.add_par_data - .tools.strip_par_data + .add_par_data + .strip_par_data .Code .ScenarioInfo """ @@ -63,80 +75,86 @@ def apply_spec( pass maybe_check_out(scenario) - dump = {} # Removed data - - for set_name in scenario.set_list(): - # Check whether this set is mentioned at all in the spec - check = sum(map(lambda info: len(info.set[set_name]), spec.values())) - if check == 0: - # Not mentioned; don't do anything - continue - - # Base contents of the set - base_set = scenario.set(set_name) - if isinstance(base_set, pd.DataFrame): - base = list(base_set.itertuples(index=False)) - else: - base = base_set.tolist() - log.info(f"Set {repr(set_name)}") - log.info(f" {len(base)} elements") - # log.debug(', '.join(map(repr, base))) # All elements; verbose - - # Check for required elements - require = spec["require"].set[set_name] - for element in require: - if element not in base: - log.error(f" {repr(element)} not found") - raise ValueError - if len(require): + if spec: + + dump: Dict[str, pd.DataFrame] = {} # Removed data + + for set_name in scenario.set_list(): + # Check whether this set is mentioned at all in the spec + if 0 == sum(map(lambda info: len(info.set[set_name]), spec.values())): + # Not mentioned; don't do anything + continue + + log.info(f"Set {repr(set_name)}") + + # Base contents of the set + base_set = scenario.set(set_name) + # Unpack a multi-dimensional/indexed set to a list of tuples + base = ( + list(base_set.itertuples(index=False)) + if isinstance(base_set, pd.DataFrame) + else base_set.tolist() + ) + + log.info(f" {len(base)} elements") + # log.debug(', '.join(map(repr, base))) # All elements; verbose + + # Check for required elements + require = spec["require"].set[set_name] log.info(f" Check {len(require)} required elements") - if options.get("fast", False): - log.info(" Skip removing parameter values") - else: + # Raise an exception about the first missing element + missing = list(filter(lambda e: e not in base, require)) + if len(missing): + log.error(f" {len(missing)} elements not found: {repr(missing)}") + raise ValueError + # Remove elements and associated parameter values remove = spec["remove"].set[set_name] for element in remove: msg = f"{repr(element)} and associated parameter elements" + if options.get("fast", False): log.info(f" Skip removing {msg} (fast=True)") - else: - log.info(f" Remove {msg}") - strip_par_data( - scenario, set_name, element, dry_run=dry_run, dump=dump - ) - - # Add elements - add = spec["add"].set[set_name] - if not dry_run: + continue + + log.info(f" Remove {msg}") + strip_par_data(scenario, set_name, element, dry_run=dry_run, dump=dump) + + # Add elements + add = [] if dry_run else spec["add"].set[set_name] for element in add: scenario.add_set( - set_name, element.id if isinstance(element, Code) else element, + set_name, + element.id if isinstance(element, Code) else element, ) - if len(add): - log.info(f" Add {len(add)} element(s)") - log.debug(" " + ", ".join(map(repr, add))) + if len(add): + log.info(f" Add {len(add)} element(s)") + log.debug(" " + ellipsize(add)) - log.info(" ---") + log.info(" ---") - N_removed = sum(len(d) for d in dump.values()) - log.info(f"{N_removed} parameter elements removed") + N_removed = sum(len(d) for d in dump.values()) + log.info(f"{N_removed} parameter elements removed") - # Add units - for unit in spec["add"].set["unit"]: - unit = Code(id=unit, name=unit) if isinstance(unit, str) else unit - log.info(f"Add unit {repr(unit.id)}") - scenario.platform.add_unit(unit.id, comment=unit.name) + # Add units to the Platform before adding data + for unit in spec["add"].set["unit"]: + unit = unit if isinstance(unit, Code) else Code(id=unit, name=unit) + log.info(f"Add unit {repr(unit)}") + scenario.platform.add_unit(unit.id, comment=str(unit.name)) # Add data if callable(data): - data(scenario, dry_run=dry_run) - # JM: call add_par_data at add_data in data.py - # if result: - # add_par_data(scenario, result, dry_run=dry_run) + result = data(scenario, dry_run=dry_run) + if result: + # `data` function returned some data; use add_par_data() + add_par_data(scenario, result, dry_run=dry_run) # Finalize log.info("Commit results.") - if not dry_run: - scenario.commit(options.get("message", f"{__name__}.apply_spec()")) + maybe_commit( + scenario, + condition=not dry_run, + message=options.get("message", f"{__name__}.apply_spec()"), + ) From ad8b278a40278032c0419d8b441d1401be05b8d5 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 4 Oct 2022 16:38:54 +0200 Subject: [PATCH 374/774] Silence pandas warning --- message_ix_models/model/material/data_util.py | 19 +++++++++++-------- 1 file changed, 11 insertions(+), 8 deletions(-) diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index d7182287fd..33e17e25a2 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -13,6 +13,9 @@ main as get_optimization_years, ) +pd.options.mode.chained_assignment = None + + def load_GDP_COVID(): context = read_config() @@ -158,10 +161,10 @@ def modify_demand_and_hist_activity(scen): columns=["REGION", "SECTOR", "FUEL", "RYEAR", "UNIT_OUT", "RESULT"] ) for r in df_spec["REGION"].unique(): - df_spec_temp = df_spec[df_spec["REGION"] == r] - df_spec_total_temp = df_spec_total[df_spec_total["REGION"] == r] - df_spec_temp["i_spec"] = ( - df_spec_temp["RESULT"] / df_spec_total_temp["RESULT"].values[0] + df_spec_temp = df_spec.loc[df_spec["REGION"] == r] + df_spec_total_temp = df_spec_total.loc[df_spec_total["REGION"] == r] + df_spec_temp.loc[:,"i_spec"] = ( + df_spec_temp.loc[:,"RESULT"] / df_spec_total_temp.loc[:,"RESULT"].values[0] ) df_spec_new = pd.concat([df_spec_temp, df_spec_new], ignore_index=True) @@ -242,10 +245,10 @@ def modify_demand_and_hist_activity(scen): ) for r in df_therm["REGION"].unique(): - df_therm_temp = df_therm[df_therm["REGION"] == r] - df_therm_total_temp = df_therm_total[df_therm_total["REGION"] == r] - df_therm_temp["i_therm"] = ( - df_therm_temp["RESULT"] / df_therm_total_temp["RESULT"].values[0] + df_therm_temp = df_therm.loc[df_therm["REGION"] == r] + df_therm_total_temp = df_therm_total.loc[df_therm_total["REGION"] == r] + df_therm_temp.loc[:,"i_therm"] = ( + df_therm_temp.loc[:,"RESULT"] / df_therm_total_temp.loc[:,"RESULT"].values[0] ) df_therm_new = pd.concat([df_therm_temp, df_therm_new], ignore_index=True) df_therm_new = df_therm_new.drop(["RESULT"], axis=1) From baa5d05e51382f3925b6f53e8d869f0f1717d866 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 4 Oct 2022 16:40:21 +0200 Subject: [PATCH 375/774] Add global context variable --- message_ix_models/model/material/data_methanol.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index 3c542e0bab..345a946e9a 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -11,6 +11,8 @@ from message_ix_models.util import broadcast, same_node from .util import read_config +context = read_config() + def gen_data_methanol(scenario): dict1 = gen_data_meth_h2() From b85cfe48e57cd99b591ed4f2d897f47a1d15eb45 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 29 Jun 2022 17:56:41 +0200 Subject: [PATCH 376/774] Add dynamic growth constraints to furnaces --- .../data/material/generic_furnace_boiler_techno_economic.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx index 92807a639e..4e7ad5afef 100644 --- a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx +++ b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:8bd0d3c0b60ac2f0fa4331c377017c1ed1fc11a42ae6940a0f4bcaa3402f23bd -size 266 +oid sha256:8aa9b6c0761cd2eeb0e221f8d4a69a00a9fe45cc2516f853b621b5b3ee37fcab +size 299 From 7e31a3dbbc59a4f275517c04a32873ceda623935 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 29 Jun 2022 17:57:24 +0200 Subject: [PATCH 377/774] Update total scrap availability for steel --- .../data/material/Global_steel_cement_MESSAGE.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx index 4c39adbbfc..92d2b529d0 100644 --- a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx +++ b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:b2f8ee5ab7dd6a8599e090d57f83c164292b1cd2a925b7c6929b5761ababc9df -size 107552 +oid sha256:42415fc631bc7a5f49a8d3548531990be50b8166aa9a7b7f929a902773d172ae +size 107751 From e0b048199ed469d4294ad454c768e90de593445f Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 29 Jun 2022 17:58:57 +0200 Subject: [PATCH 378/774] Add timeseries option to data_generic --- .../model/material/data_generic.py | 108 +++++++++++++++++- 1 file changed, 105 insertions(+), 3 deletions(-) diff --git a/message_ix_models/model/material/data_generic.py b/message_ix_models/model/material/data_generic.py index 949291da7e..19627a4d2e 100644 --- a/message_ix_models/model/material/data_generic.py +++ b/message_ix_models/model/material/data_generic.py @@ -6,6 +6,7 @@ from .util import read_config from message_ix_models import ScenarioInfo from message_ix import make_df +from .data_util import read_timeseries from message_ix_models.util import ( broadcast, make_io, @@ -15,11 +16,12 @@ ) -def read_data_generic(): +def read_data_generic(scenario): """Read and clean data from :file:`generic_furnace_boiler_techno_economic.xlsx`.""" # Ensure config is loaded, get the context context = read_config() + s_info = ScenarioInfo(scenario) # Shorter access to sets configuration # sets = context["material"]["generic"] @@ -36,13 +38,14 @@ def read_data_generic(): # Drop columns that don't contain useful information data_generic = data_generic.drop(["Region", "Source", "Description"], axis=1) + data_generic_ts = read_timeseries(scenario, "generic_furnace_boiler_techno_economic.xlsx") # Unit conversion # At the moment this is done in the excel file, can be also done here # To make sure we use the same units - return data_generic + return data_generic, data_generic_ts def gen_data_generic(scenario, dry_run=False): @@ -54,7 +57,7 @@ def gen_data_generic(scenario, dry_run=False): s_info = ScenarioInfo(scenario) # Techno-economic assumptions - data_generic = read_data_generic() + data_generic, data_generic_ts = read_data_generic(scenario) # List of data frames, to be concatenated together at end results = defaultdict(list) @@ -68,11 +71,14 @@ def gen_data_generic(scenario, dry_run=False): yv_ya = s_info.yv_ya fmy = s_info.y0 + # Do not parametrize GLB region the same way if "R11_GLB" in nodes: nodes.remove("R11_GLB") + global_region = 'R11_GLB' if "R12_GLB" in nodes: nodes.remove("R12_GLB") + global_region = "R12_GLB" # 'World' is included by default when creating a message_ix.Scenario(). # Need to remove it for the China bare model @@ -181,6 +187,102 @@ def gen_data_generic(scenario, dry_run=False): results[param_name].append(df) + # Special treatment for time-varying params + + tec_ts = set(data_generic_ts.technology) # set of tecs in timeseries sheet + + for t in tec_ts: + print(t) + common = dict( + time="year", + time_origin="year", + time_dest="year", + ) + + param_name = data_generic_ts.loc[ + (data_generic_ts["technology"] == t), "parameter" + ] + + for p in set(param_name): + print(p) + val = data_generic_ts.loc[ + (data_generic_ts["technology"] == t) + & (data_generic_ts["parameter"] == p), + "value", + ] + regions = data_generic_ts.loc[ + ( + (data_generic_ts["technology"] == t) + & (data_generic_ts["parameter"] == p) + ), + "region", + ] + print('Regions') + print(regions) + print(len(regions)) + units = data_generic_ts.loc[ + (data_generic_ts["technology"] == t) + & (data_generic_ts["parameter"] == p), + "units", + ].values[0] + mod = data_generic_ts.loc[ + (data_generic_ts["technology"] == t) + & (data_generic_ts["parameter"] == p), + "mode", + ] + yr = data_generic_ts.loc[ + (data_generic_ts["technology"] == t) + & (data_generic_ts["parameter"] == p), + "year", + ] + + if p == "var_cost": + df = make_df( + p, + technology=t, + value=val, + unit="t", + year_vtg=yr, + year_act=yr, + mode=mod, + **common + ).pipe(broadcast, node_loc=nodes) + else: + rg = data_generic_ts.loc[ + (data_generic_ts["technology"] == t) + & (data_generic_ts["parameter"] == p), + "region", + ] + df = make_df( + p, + technology=t, + value=val, + unit="t", + year_vtg=yr, + year_act=yr, + mode=mod, + node_loc=rg, + **common + ) + + print('df node_loc') + print(df['node_loc']) + + # Copy parameters to all regions + if ( + (len(set(regions)) == 1) + and len(set(df["node_loc"])) == 1 + and list(set(df["node_loc"]))[0] != global_region + ): + print('This is the length of the regions') + print(regions) + print(len(regions)) + df["node_loc"] = None + df = df.pipe(broadcast, node_loc=nodes) + + results[p].append(df) + + results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} return results From a9bcf61c9841344ee4d80146c3f64c9fd6e039e8 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Fri, 8 Jul 2022 14:13:13 +0200 Subject: [PATCH 379/774] Update the reporting workflow --- message_ix_models/model/material/__init__.py | 54 +++++++++++++------- 1 file changed, 36 insertions(+), 18 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 08cf2ffcec..5440ccd490 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -238,7 +238,7 @@ def solve_scen(context, datafile, model_name, scenario_name, add_calibration, ad if add_calibration: # Solve print('Solving the scenario without MACRO') - scenario.solve(model="MESSAGE", solve_options={'lpmethod': '4'}) + scenario.solve(model="MESSAGE", solve_options={'lpmethod': '4', 'barcrossalg':'2'}) scenario.set_as_default() # After solving, add macro calibration @@ -246,7 +246,7 @@ def solve_scen(context, datafile, model_name, scenario_name, add_calibration, ad scenario = add_macro_COVID(scenario,'R12-CHN-5y_macro_data_NGFS_w_rc_ind_adj_mat.xlsx') print('Scenario calibrated.') - if add_macro: + if add_macro: # Default True scenario.solve(model="MESSAGE-MACRO", solve_options={"lpmethod": "4"}) scenario.set_as_default() @@ -263,10 +263,15 @@ def solve_scen(context, datafile, model_name, scenario_name, add_calibration, ad default=False, help="If True old reporting is merged with the new variables.", ) +@click.option( + "--remove_ts", + default=False, + help="If True the existing timeseries in the scenario is removed.", +) @click.option("--scenario_name", default="NoPolicy") @click.option("--model_name", default="MESSAGEix-Materials") # @click.pass_obj -def run_reporting(old_reporting, scenario_name, model_name): +def run_reporting(old_reporting, scenario_name, model_name, remove_ts): from message_data.reporting.materials.reporting import report from message_data.tools.post_processing.iamc_report_hackathon import report as reporting from message_ix import Scenario @@ -275,22 +280,35 @@ def run_reporting(old_reporting, scenario_name, model_name): print(model_name) mp = Platform() - # Remove existing timeseries and add material timeseries - print("Reporting material-specific variables") scenario = Scenario(mp, model_name, scenario_name) - report(scenario, old_reporting) - print("Reporting standard variables") - reporting( - mp, - scenario, - "False", - model_name, - scenario_name, - merge_hist=True, - merge_ts=True, - run_config="materials_run_config.yaml", - ) + if remove_ts: + + df_rem = scenario.timeseries() + + if not df_rem.empty: + scenario.check_out(timeseries_only=True) + scenario.remove_timeseries(df_rem) + scenario.commit("Existing timeseries removed.") + print('Existing timeseries are removed.') + else: + print('There are no timeseries to be removed.') + + if not remove_ts: + # Remove existing timeseries and add material timeseries + print("Reporting material-specific variables") + report(scenario) + print("Reporting standard variables") + reporting( + mp, + scenario, + "False", + model_name, + scenario_name, + merge_hist=True, + merge_ts=True, + run_config="materials_run_config.yaml", + ) @cli.command("report-2") @click.option("--scenario_name", default="NoPolicy") @@ -346,7 +364,7 @@ def run_old_reporting(scenario_name, model_name): DATA_FUNCTIONS_2 = [ gen_data_cement, gen_data_petro_chemicals, - # gen_data_power_sector, + gen_data_power_sector, gen_data_aluminum, ] From 67629c21134df639a5c6fa3b2010532628ca25b0 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Fri, 8 Jul 2022 14:13:44 +0200 Subject: [PATCH 380/774] Update the reporting documentation --- message_ix_models/model/material/doc.rst | 25 +++++++++++++----------- 1 file changed, 14 insertions(+), 11 deletions(-) diff --git a/message_ix_models/model/material/doc.rst b/message_ix_models/model/material/doc.rst index 1c7672b545..77778c4ea6 100644 --- a/message_ix_models/model/material/doc.rst +++ b/message_ix_models/model/material/doc.rst @@ -117,25 +117,28 @@ The solve command has the `--add_calibration` option to add MACRO calibration to Reporting ========= -The reporting of the scenarios involves two steps. First step generates the specific -variables that are related to materials and industry sectors. At the end of this step -all the varibles are uploaded as ixmp timeseries objects. The reporting file is -generated under message_data\\reporting\\materials with the name -“New_Reporting_MESSAGEix-Materials_scenario_name.xls”. The required versions of -pyam and pyam-iamc are as follows respectively: 0.2.1a0 and 0.9.0. - -If the model is ran with buildings and appliances linkage, the related extra variables -are uploaded as ixmp timeseries object to the scenario as well. - +The reporting of the scenarios involves two steps combined in one command. +First step generates the specific variables that are related to materials and +industry sectors. At the end of this step all the variables are uploaded as ixmp +timeseries objects to the scenario. The reporting file is generated under +message_data\\reporting\\materials with the name “New_Reporting_MESSAGEix-Materials_scenario_name.xls”. In the second step, rest of the default reporting variables are obtained by running the general reporting code. This step combines all the variables that were uploaded as timeseries to the scenario together with the generic IAMC variables. It also correctly reports the aggregate variables such as Final Energy and Emissions. -To run the first step of the reporting use :: +If the model is ran with other end-use modules such as buildings/appliances or +transport, the new reporting variables from these should be uploaded to the scenario +as timeseries object before running the below reporting command: $ mix-models material report --model_name MESSAGEix-Materials --scenario_name xxxx +There should be no other existing timeseries (other than the ones coming from the end-use modules) +in the scenario when running the reporting command to obtain correct results. +To remove any existing timeseries in the scenario the following command can be used: + +$ mix-models material report --model_name MESSAGEix-Materials --scenario_name xxxx --remove_ts True + Data, metadata, and configuration ================================= From 56bc39065101394705c5431cee3003a0b86cdcad Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Fri, 8 Jul 2022 14:46:43 +0200 Subject: [PATCH 381/774] Add a command to add building timeseries --- message_ix_models/model/material/__init__.py | 22 ++++++++++++++------ 1 file changed, 16 insertions(+), 6 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 5440ccd490..d40033ffb4 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -256,13 +256,23 @@ def solve_scen(context, datafile, model_name, scenario_name, add_calibration, ad scenario.solve(model="MESSAGE", solve_options={'lpmethod': '4'}) scenario.set_as_default() +@cli.command("add_buildings_ts") +@click.option("--scenario_name", default="NoPolicy") +@click.option("--model_name", default="MESSAGEix-Materials") +def add_building_ts(scenario_name, model_name): + from message_data.reporting.materials.add_buildings_ts import add_building_timeseries + from message_ix import Scenario + from ixmp import Platform + + print(model_name) + mp = Platform() + + scenario = Scenario(mp, model_name, scenario_name) + + add_building_timeseries(scenario) + @cli.command("report") # @cli.command("report-1") -@click.option( - "--old_reporting", - default=False, - help="If True old reporting is merged with the new variables.", -) @click.option( "--remove_ts", default=False, @@ -271,7 +281,7 @@ def solve_scen(context, datafile, model_name, scenario_name, add_calibration, ad @click.option("--scenario_name", default="NoPolicy") @click.option("--model_name", default="MESSAGEix-Materials") # @click.pass_obj -def run_reporting(old_reporting, scenario_name, model_name, remove_ts): +def run_reporting(old_reporting, scenario_name, model_name, remove_ts,key): from message_data.reporting.materials.reporting import report from message_data.tools.post_processing.iamc_report_hackathon import report as reporting from message_ix import Scenario From 4a8cae4a21ad3eb5a89784f6e5e89cfb34b279af Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Fri, 8 Jul 2022 14:54:04 +0200 Subject: [PATCH 382/774] Add buildings timeseries --- .../data/material/buildings/report_IRP_SSP2_BL_comm_R12.csv | 3 +++ .../data/material/buildings/report_IRP_SSP2_BL_resid_R12.csv | 3 +++ 2 files changed, 6 insertions(+) create mode 100644 message_ix_models/data/material/buildings/report_IRP_SSP2_BL_comm_R12.csv create mode 100644 message_ix_models/data/material/buildings/report_IRP_SSP2_BL_resid_R12.csv diff --git a/message_ix_models/data/material/buildings/report_IRP_SSP2_BL_comm_R12.csv b/message_ix_models/data/material/buildings/report_IRP_SSP2_BL_comm_R12.csv new file mode 100644 index 0000000000..eb87133efb --- /dev/null +++ b/message_ix_models/data/material/buildings/report_IRP_SSP2_BL_comm_R12.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7014fa0eefbb39baa8c6a4dbe7d504fcf67fbfb78cab1e805ea54f839ad40e2a +size 60443 diff --git a/message_ix_models/data/material/buildings/report_IRP_SSP2_BL_resid_R12.csv b/message_ix_models/data/material/buildings/report_IRP_SSP2_BL_resid_R12.csv new file mode 100644 index 0000000000..bf6de4985f --- /dev/null +++ b/message_ix_models/data/material/buildings/report_IRP_SSP2_BL_resid_R12.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6b5efc1a7105ec64ede177811389876bc5e394d949e59029931549e1c6fbb8dc +size 74613 From 043fd00993db269521478cc368f1e40b9c0f3234 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Fri, 8 Jul 2022 15:04:15 +0200 Subject: [PATCH 383/774] Update documentation --- message_ix_models/model/material/doc.rst | 21 ++++++++++++++------- 1 file changed, 14 insertions(+), 7 deletions(-) diff --git a/message_ix_models/model/material/doc.rst b/message_ix_models/model/material/doc.rst index 77778c4ea6..20312e895b 100644 --- a/message_ix_models/model/material/doc.rst +++ b/message_ix_models/model/material/doc.rst @@ -117,26 +117,33 @@ The solve command has the `--add_calibration` option to add MACRO calibration to Reporting ========= -The reporting of the scenarios involves two steps combined in one command. -First step generates the specific variables that are related to materials and -industry sectors. At the end of this step all the variables are uploaded as ixmp +The reporting of the scenarios that include materials representation mainly involves +two steps. In the first step the material specific variables are generated. +At the end of this step all the variables are uploaded as ixmp timeseries objects to the scenario. The reporting file is generated under message_data\\reporting\\materials with the name “New_Reporting_MESSAGEix-Materials_scenario_name.xls”. In the second step, rest of the default reporting variables are obtained by running the general reporting code. This step combines all the variables that were uploaded as timeseries to the scenario together with the generic IAMC variables. It also correctly reports the aggregate variables such as Final Energy and Emissions. +The reporting is executed by the following command: + +$ mix-models material report --model_name MESSAGEix-Materials --scenario_name xxxx If the model is ran with other end-use modules such as buildings/appliances or transport, the new reporting variables from these should be uploaded to the scenario -as timeseries object before running the below reporting command: +as timeseries object before running the above command. -$ mix-models material report --model_name MESSAGEix-Materials --scenario_name xxxx +It is possible to add reporting variables from the Buildings model results by using +the following command: +$ mix-models material add_buildings_ts --model_name xxxx --scenario_name xxxx +The reporting output files in csv form should be located under +message_data\\data\\material\\buildings. -There should be no other existing timeseries (other than the ones coming from the end-use modules) +There should be no other existing timeseries (other than the ones from the end-use modules) in the scenario when running the reporting command to obtain correct results. -To remove any existing timeseries in the scenario the following command can be used: +To remove any existing timeseries in the scenario the following command can be used: $ mix-models material report --model_name MESSAGEix-Materials --scenario_name xxxx --remove_ts True Data, metadata, and configuration From ebdfaf6c8355bbe368f80e5dfbee2983ea8f037d Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 12 Jul 2022 16:44:12 +0200 Subject: [PATCH 384/774] Add new ccs technologies to co2 trans relations --- message_ix_models/model/material/data_util.py | 38 +++++++++++++++++++ 1 file changed, 38 insertions(+) diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index 33e17e25a2..b5582e8796 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -725,6 +725,44 @@ def read_sector_data(scenario, sectname): return data_df +# Add the relevant ccs technologies to the co2_trans_disp and bco2_trans_disp +# relations +def add_ccs_technologies(scen): + + context = read_config() + s_info = ScenarioInfo(scen) + + # The relation coefficients for CO2_Emision and bco2_trans_disp and + # co2_trans_disp are both MtC. The emission factor for CCS add_ccs_technologies + # are specified in MtC as well. + bco2_trans_relation = scen.par('emission_factor', + filters = {'technology':'biomass_NH3_ccs', + 'emission':'CO2'}) + co2_trans_relation = scen.par('emission_factor', + filters = {'technology':['clinker_dry_ccs_cement', + 'clinker_wet_ccs_cement', + 'gas_NH3_ccs', + 'coal_NH3_ccs', + 'fueloil_NH3_ccs'], + 'emission':'CO2'}) + + bco2_trans_relation.drop(['year_vtg','emission','unit'], axis = 1, + inplace = True) + bco2_trans_relation['relation'] = 'bco2_trans_disp' + bco2_trans_relation['node_rel'] = bco2_trans_relation['node_loc'] + bco2_trans_relation['year_rel'] = bco2_trans_relation['year_act'] + bco2_trans_relation['unit'] = '???' + + co2_trans_relation.drop(['year_vtg','emission','unit'], axis = 1, inplace = True) + co2_trans_relation['relation'] = 'co2_trans_disp' + co2_trans_relation['node_rel'] = co2_trans_relation['node_loc'] + co2_trans_relation['year_rel'] = co2_trans_relation['year_act'] + co2_trans_relation['unit'] = '???' + + scen.check_out() + scen.add_par('relation_activity', bco2_trans_relation) + scen.add_par('relation_activity', co2_trans_relation) + scen.commit('New CCS technologies added to the CO2 accounting relations.') # Read in time-dependent parameters # Now only used to add fuel cost for bare model From ddc6d626813c4ca89fb90df0d6e2ffaaf90d8d8b Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 13 Jul 2022 14:45:33 +0200 Subject: [PATCH 385/774] Add_ccs_technologies function --- message_ix_models/model/material/__init__.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index d40033ffb4..06730d28be 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -19,6 +19,7 @@ from .data_util import add_coal_lowerbound_2020 from .data_util import add_macro_COVID from .data_util import add_elec_lowerbound_2020 +from .data_util import add_ccs_technologies from .util import read_config log = logging.getLogger(__name__) @@ -41,6 +42,7 @@ def build(scenario): # Adjust exogenous energy demand to incorporate the endogenized sectors # Adjust the historical activity of the useful level industry technologies # Coal calibration 2020 + add_ccs_technologies(scenario) modify_demand_and_hist_activity(scenario) add_emission_accounting(scenario) add_coal_lowerbound_2020(scenario) From 8a97f5a3097968f6080d4d2e54507c302035e052 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Fri, 22 Jul 2022 15:24:57 +0200 Subject: [PATCH 386/774] Update __init__.py --- message_ix_models/model/material/__init__.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 06730d28be..6bf31a32fb 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -283,7 +283,7 @@ def add_building_ts(scenario_name, model_name): @click.option("--scenario_name", default="NoPolicy") @click.option("--model_name", default="MESSAGEix-Materials") # @click.pass_obj -def run_reporting(old_reporting, scenario_name, model_name, remove_ts,key): +def run_reporting(scenario_name, model_name, remove_ts): from message_data.reporting.materials.reporting import report from message_data.tools.post_processing.iamc_report_hackathon import report as reporting from message_ix import Scenario @@ -302,6 +302,7 @@ def run_reporting(old_reporting, scenario_name, model_name, remove_ts,key): scenario.check_out(timeseries_only=True) scenario.remove_timeseries(df_rem) scenario.commit("Existing timeseries removed.") + scenario.set_as_default() print('Existing timeseries are removed.') else: print('There are no timeseries to be removed.') From 64d847945dabcff70efe42aec8cb733600375aed Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 27 Jul 2022 14:32:18 +0200 Subject: [PATCH 387/774] Update balance_equality for steel --- message_ix_models/data/material/set.yaml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index ed5a234b97..74a98f785a 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -305,6 +305,8 @@ steel: - ["steel","end_of_life"] - ["steel","product"] - ["steel","new_scrap"] + - ["steel","useful_material"] + - ["steel","final_material"] cement: commodity: From e00cd4035159066b1775c6cff38e4353b77e524b Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 27 Jul 2022 14:33:00 +0200 Subject: [PATCH 388/774] Update steel data update minimum recycling rates remove the dynamic bound for finishing_steel --- .../data/material/Global_steel_cement_MESSAGE.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx index 92d2b529d0..206862f933 100644 --- a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx +++ b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:42415fc631bc7a5f49a8d3548531990be50b8166aa9a7b7f929a902773d172ae -size 107751 +oid sha256:ad07ef8e0aa5a0e85904eb9b48ccc449c45531ab6283c98ad853b93aaf6f0ce2 +size 107723 From df570ae033a1d4ac8b31e29f3d662d8a3471de0b Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 3 Aug 2022 10:23:18 +0200 Subject: [PATCH 389/774] Update global_steel_cement_message.xlsx - change the historical_activity and capacity for mea (by using oecd data) and put a lower bound to match regional 2020 steel production - put an upper bound to steel production in lam in 2020 - add initial_activity_up to afr and mea for eaf adoption in later years --- .../data/material/Global_steel_cement_MESSAGE.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx index 206862f933..9bbc790dde 100644 --- a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx +++ b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:ad07ef8e0aa5a0e85904eb9b48ccc449c45531ab6283c98ad853b93aaf6f0ce2 -size 107723 +oid sha256:e9d2a4c73894876e70186dab3333d774a674bd3523c50b80b77e640c2decf709 +size 108642 From 9e4ca5e30a18cf5a2789baac08434584b20e6a25 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 4 Aug 2022 09:56:35 +0200 Subject: [PATCH 390/774] Change the name of demand_hvc to production_hvc --- .../data/material/petrochemicals_techno_economic.xlsx | 4 ++-- message_ix_models/data/material/set.yaml | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx index 18bbc7b7a4..b1d79c235d 100644 --- a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx +++ b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:e86dd2efa40114d9ffd384a843c216632ddd454c5bc3eea7e21a14696a59707a -size 663459 +oid sha256:7dfbc79999a063ae70c0558954f9d4e74009ea04dd0d4226ccc953aa5117e60e +size 663470 diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 74a98f785a..3fadbecc7e 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -228,7 +228,7 @@ petro_chemicals: - import_petro - export_petro - feedstock_t/d - - demand_HVC + - production_HVC remove: # Any representation of refinery. - ref_hil From e08861a25cd381038a5a8d328fb507957d8b0df4 Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Thu, 4 Aug 2022 12:51:11 +0200 Subject: [PATCH 391/774] Use context.get_platform() in "mix-models material build" this retrieves the same platform indicated by --url, and avoids the need to hard-code a platform name that may vary by user/system. --- message_ix_models/model/material/__init__.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 6bf31a32fb..a21fe47f3f 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -150,13 +150,15 @@ def create_bare(context, regions, dry_run): def build_scen(context, datafile, tag, mode, scenario_name): """Build a scenario. - Use the --url option to specify the base scenario. + Use the --url option to specify the base scenario. If this scenario is on a + Platform stored with ixmp.JDBCBackend, it should be configured with >16 GB of + memory, i.e. ``jvmargs=["-Xmx16G"]``. """ from ixmp import Platform import message_ix - mp = Platform(name='ixmp_dev', jvmargs=['-Xmx16G']) + mp = context.get_platform() if mode == 'by_url': # Determine the output scenario name based on the --url CLI option. If the From 032182d63e768a1baaf7d0cb43ca54dc66c1d351 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Fri, 5 Aug 2022 11:53:57 +0200 Subject: [PATCH 392/774] Limit furnace_biomass_aluminum --- .../data/material/generic_furnace_boiler_techno_economic.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx index 4e7ad5afef..95118ce4f6 100644 --- a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx +++ b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:8aa9b6c0761cd2eeb0e221f8d4a69a00a9fe45cc2516f853b621b5b3ee37fcab -size 299 +oid sha256:497aefc2b200fc63a0ebc0ad7f8e57252c89866a688282cb48ac0768e2a98963 +size 287 From 6faec62529fb96b2b0a9c3800b88d330842f8af8 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 9 Aug 2022 15:32:40 +0200 Subject: [PATCH 393/774] Add biomass furnace limit to petro in 2020, no biomass is used for thermal energy --- .../data/material/generic_furnace_boiler_techno_economic.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx index 95118ce4f6..5c97bb79ea 100644 --- a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx +++ b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:497aefc2b200fc63a0ebc0ad7f8e57252c89866a688282cb48ac0768e2a98963 -size 287 +oid sha256:0c9f5aec81721d7f3b42e56581aa231b7c8c1dad7c2ab6da3bd7b48d27cbb418 +size 291 From 03a20decae9700f20a4e7dc00d56d6b6c269ecf5 Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Wed, 10 Aug 2022 11:48:16 +0200 Subject: [PATCH 394/774] Use common --url option for "mix-models material report" this allows to specify the scenario version. --- message_ix_models/model/material/__init__.py | 30 +++++++++----------- 1 file changed, 14 insertions(+), 16 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index a21fe47f3f..0e9d7aa3e7 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -213,6 +213,7 @@ def build_scen(context, datafile, tag, mode, scenario_name): print('New scenario name is ' + output_scenario_name) scenario.set_as_default() + @cli.command("solve") @click.option("--scenario_name", default="NoPolicy") @click.option("--model_name", default="MESSAGEix-Materials") @@ -225,7 +226,6 @@ def build_scen(context, datafile, tag, mode, scenario_name): @click.option("--add_macro", default=True) @click.option("--add_calibration", default=False) @click.pass_obj -# @click.pass_obj def solve_scen(context, datafile, model_name, scenario_name, add_calibration, add_macro): """Solve a scenario. @@ -275,6 +275,7 @@ def add_building_ts(scenario_name, model_name): add_building_timeseries(scenario) + @cli.command("report") # @cli.command("report-1") @click.option( @@ -282,22 +283,17 @@ def add_building_ts(scenario_name, model_name): default=False, help="If True the existing timeseries in the scenario is removed.", ) -@click.option("--scenario_name", default="NoPolicy") -@click.option("--model_name", default="MESSAGEix-Materials") -# @click.pass_obj -def run_reporting(scenario_name, model_name, remove_ts): +@click.pass_obj +def run_reporting(context, remove_ts): + """Run materials, then legacy reporting.""" from message_data.reporting.materials.reporting import report from message_data.tools.post_processing.iamc_report_hackathon import report as reporting - from message_ix import Scenario - from ixmp import Platform - - print(model_name) - mp = Platform() - scenario = Scenario(mp, model_name, scenario_name) + # Retrieve the scenario given by the --url option + scenario = context.get_scenario() + mp = scenario.platform if remove_ts: - df_rem = scenario.timeseries() if not df_rem.empty: @@ -308,8 +304,7 @@ def run_reporting(scenario_name, model_name, remove_ts): print('Existing timeseries are removed.') else: print('There are no timeseries to be removed.') - - if not remove_ts: + else: # Remove existing timeseries and add material timeseries print("Reporting material-specific variables") report(scenario) @@ -317,14 +312,17 @@ def run_reporting(scenario_name, model_name, remove_ts): reporting( mp, scenario, + # NB(PNK) this is not an error; .iamc_report_hackathon.report() expects a + # string containing "True" or "False" instead of an actual bool. "False", - model_name, - scenario_name, + scenario.model, + scenario.scenario, merge_hist=True, merge_ts=True, run_config="materials_run_config.yaml", ) + @cli.command("report-2") @click.option("--scenario_name", default="NoPolicy") @click.option("--model_name", default="MESSAGEix-Materials") From 2c1926e20b42a4ed4e0a72f542bb7e6a811441bc Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Wed, 10 Aug 2022 16:55:55 +0200 Subject: [PATCH 395/774] Use context.get_local_path() for materials reporting output directory --- message_ix_models/model/material/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 0e9d7aa3e7..3a79be534b 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -307,7 +307,7 @@ def run_reporting(context, remove_ts): else: # Remove existing timeseries and add material timeseries print("Reporting material-specific variables") - report(scenario) + report(context, scenario) print("Reporting standard variables") reporting( mp, From 3826b1c20afe2915a5196cb51786153bd06e77bd Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 16 Aug 2022 18:13:14 +0200 Subject: [PATCH 396/774] Remove the growth bound 2020 for petro --- .../data/material/generic_furnace_boiler_techno_economic.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx index 5c97bb79ea..7d9a1a3701 100644 --- a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx +++ b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:0c9f5aec81721d7f3b42e56581aa231b7c8c1dad7c2ab6da3bd7b48d27cbb418 -size 291 +oid sha256:4f5152f5fcae55cb6fdf863d753ba7469299d496f1048fada8b10670289dc4e9 +size 295 From b0fc1b6c9445f47196a246162a5f9bfc9c7ffb61 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 16 Aug 2022 18:13:34 +0200 Subject: [PATCH 397/774] Add soft constraints to eaf_steel --- .../data/material/Global_steel_cement_MESSAGE.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx index 9bbc790dde..cb01b1cf37 100644 --- a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx +++ b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:e9d2a4c73894876e70186dab3333d774a674bd3523c50b80b77e640c2decf709 -size 108642 +oid sha256:4841d59b04e02c609423f9ca75634b67163bd0571b1d8390de820aa97f3353bb +size 110590 From 7e4ee794c926a49915b7c14e8510f0763d0a0143 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 16 Aug 2022 18:14:11 +0200 Subject: [PATCH 398/774] Update doc.rst --- message_ix_models/model/material/doc.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/model/material/doc.rst b/message_ix_models/model/material/doc.rst index 20312e895b..974baf514f 100644 --- a/message_ix_models/model/material/doc.rst +++ b/message_ix_models/model/material/doc.rst @@ -128,7 +128,7 @@ as timeseries to the scenario together with the generic IAMC variables. It also correctly reports the aggregate variables such as Final Energy and Emissions. The reporting is executed by the following command: -$ mix-models material report --model_name MESSAGEix-Materials --scenario_name xxxx +$ mix-models --url="ixmp://ixmp_dev/MESSAGEix-Materials/scenario_name" material report If the model is ran with other end-use modules such as buildings/appliances or transport, the new reporting variables from these should be uploaded to the scenario @@ -144,7 +144,7 @@ There should be no other existing timeseries (other than the ones from the end-u in the scenario when running the reporting command to obtain correct results. To remove any existing timeseries in the scenario the following command can be used: -$ mix-models material report --model_name MESSAGEix-Materials --scenario_name xxxx --remove_ts True +$ mix-models --url="ixmp://ixmp_dev/MESSAGEix-Materials/scenario_name" material report --remove_ts True Data, metadata, and configuration ================================= From 993ba0f40edcd186072180a370babbfe1f247ee8 Mon Sep 17 00:00:00 2001 From: Paul Natsuo Kishimoto Date: Wed, 5 Oct 2022 18:13:21 +0200 Subject: [PATCH 399/774] Use private_data_path() in materials data code also apply black to modified files. --- .../model/material/data_aluminum.py | 10 +- .../model/material/data_ammonia.py | 566 +++++++++++------- .../model/material/data_buildings.py | 3 +- .../model/material/data_cement.py | 12 +- .../model/material/data_petro.py | 17 +- .../model/material/data_steel.py | 32 +- message_ix_models/model/material/data_util.py | 427 ++++++++----- 7 files changed, 698 insertions(+), 369 deletions(-) diff --git a/message_ix_models/model/material/data_aluminum.py b/message_ix_models/model/material/data_aluminum.py index 6a61f0f7f5..36aca46564 100644 --- a/message_ix_models/model/material/data_aluminum.py +++ b/message_ix_models/model/material/data_aluminum.py @@ -17,6 +17,7 @@ make_matched_dfs, same_node, add_par_data, + private_data_path, ) # Get endogenous material demand from buildings interface @@ -44,10 +45,7 @@ def read_data_aluminum(scenario): sheet_n_relations = "relations_R11" # Read the file - data_alu = pd.read_excel( - context.get_local_path("material", fname), - sheet_name=sheet_n, - ) + data_alu = pd.read_excel(private_data_path("material", fname), sheet_name=sheet_n) # Drop columns that don't contain useful information data_alu = data_alu.drop(["Source", "Description"], axis=1) @@ -501,7 +499,7 @@ def gen_mock_demand_aluminum(scenario): # SSP2 R11 baseline GDP projection gdp_growth = pd.read_excel( - context.get_local_path("material", "iamc_db ENGAGE baseline GDP PPP.xlsx"), + private_data_path("material", "iamc_db ENGAGE baseline GDP PPP.xlsx"), sheet_name=sheet_n, ) @@ -569,7 +567,7 @@ def derive_aluminum_demand(scenario, dry_run=False): # GDP is only in MER in scenario. # To get PPP GDP, it is read externally from the R side df = r.derive_aluminum_demand( - pop, base_demand, str(context.get_local_path("material")) + pop, base_demand, str(private_data_path("material")) ) df.year = df.year.astype(int) diff --git a/message_ix_models/model/material/data_ammonia.py b/message_ix_models/model/material/data_ammonia.py index 095d3fb247..2e825f5d38 100644 --- a/message_ix_models/model/material/data_ammonia.py +++ b/message_ix_models/model/material/data_ammonia.py @@ -9,6 +9,7 @@ from message_ix_models.util import ( broadcast, same_node, + private_data_path, ) from .util import read_config @@ -19,6 +20,7 @@ CONVERSION_FACTOR_NH3_N = 17 / 14 CONVERSION_FACTOR_PJ_GWa = 0.0317 + def gen_data_ammonia(scenario, dry_run=False, add_ccs: bool = True): """Generate data for materials representation of nitrogen fertilizers. @@ -28,11 +30,11 @@ def gen_data_ammonia(scenario, dry_run=False, add_ccs: bool = True): # Load configuration config = read_config()["material"]["fertilizer"] context = read_config() - #print(config_.get_local_path("material", "test.xlsx")) + # print(config_.get_local_path("material", "test.xlsx")) # Information about scenario, e.g. node, year s_info = ScenarioInfo(scenario) nodes = s_info.N - if (("World" in nodes) | ("R12_GLB" in nodes)): + if ("World" in nodes) | ("R12_GLB" in nodes): nodes.pop(nodes.index("World")) nodes.pop(nodes.index("R12_GLB")) @@ -45,32 +47,25 @@ def gen_data_ammonia(scenario, dry_run=False, add_ccs: bool = True): input_commodity_dict = { "input_water": "freshwater_supply", "input_elec": "electr", - "input_fuel": "" + "input_fuel": "", } output_commodity_dict = { "output_NH3": "NH3", "output_heat": "d_heat", - "output_water": "wastewater" # ask Jihoon how to name - } - commodity_dict = { - "output": output_commodity_dict, - "input": input_commodity_dict + "output_water": "wastewater", # ask Jihoon how to name } + commodity_dict = {"output": output_commodity_dict, "input": input_commodity_dict} input_level_dict = { "input_water": "water_supply", "input_fuel": "secondary", - "input_elec": "secondary" + "input_elec": "secondary", } output_level_dict = { "output_water": "wastewater", "output_heat": "secondary", - "output_NH3": "secondary_material" + "output_NH3": "secondary_material", } - level_cat_dict = { - "output": output_level_dict, - "input": input_level_dict - } - + level_cat_dict = {"output": output_level_dict, "input": input_level_dict} vtg_years = s_info.yv_ya[s_info.yv_ya.year_vtg > 2000]["year_vtg"] act_years = s_info.yv_ya[s_info.yv_ya.year_vtg > 2000]["year_act"] @@ -85,7 +80,7 @@ def gen_data_ammonia(scenario, dry_run=False, add_ccs: bool = True): time="year", time_dest="year", time_origin="year", - emission="CO2_transformation" # confirm if correct + emission="CO2_industry", # confirm if correct ) # Iterate over new technologies, using the configuration @@ -94,35 +89,39 @@ def gen_data_ammonia(scenario, dry_run=False, add_ccs: bool = True): # Output of NH3: same efficiency for all technologies # the output commodity and level are different for - for param in data['parameter'].unique(): + for param in data["parameter"].unique(): if (t == "electr_NH3") & (param == "input_fuel"): continue unit = "t" - cat = data['param_cat'][data['parameter'] == param].iloc[0] + cat = data["param_cat"][data["parameter"] == param].iloc[0] if cat in ["input", "output"]: common["commodity"] = commodity_dict[cat][param] common["level"] = level_cat_dict[cat][param] - if (t == "biomass_NH3") & (cat == "input"): + if (t == "biomass_NH3") & (param == "input_fuel"): common["level"] = "primary" if (str(t) == "NH3_to_N_fertil") & (param == "output_NH3"): - common['commodity'] = "Fertilizer Use|Nitrogen" - common['level'] = "final_material" + common["commodity"] = "Fertilizer Use|Nitrogen" + common["level"] = "final_material" if (str(t) == "NH3_to_N_fertil") & (param == "input_fuel"): - common['level'] = "secondary_material" + common["level"] = "secondary_material" df = ( make_df(cat, technology=t, value=1, unit="-", **common) - .pipe(broadcast, node_loc=nodes) - .pipe(same_node) + .pipe(broadcast, node_loc=nodes) + .pipe(same_node) ) - row = data[(data['technology'] == t) & - (data['parameter'] == param)] + row = data[(data["technology"] == t) & (data["parameter"] == param)] df = df.assign(value=row[2010].values[0]) if param == "input_fuel": - comm = data['technology'][(data['parameter'] == param) & - (data["technology"] == t)].iloc[0].split("_")[0] + comm = ( + data["technology"][ + (data["parameter"] == param) & (data["technology"] == t) + ] + .iloc[0] + .split("_")[0] + ) df = df.assign(commodity=comm) results[cat].append(df) @@ -136,40 +135,51 @@ def gen_data_ammonia(scenario, dry_run=False, add_ccs: bool = True): time_dest="year", time_origin="year", ) - act2010 = read_demand()['act2010'] + act2010 = read_demand()["act2010"] df = ( - make_df("historical_activity", - technology=[t for t in config["technology"]["add"][:6]], #], TODO: maybe reintroduce std/ccs in yaml - value=1, unit='t', years_act=s_info.Y, **common) - .pipe(broadcast, node_loc=nodes) - .pipe(same_node) + make_df( + "historical_activity", + technology=[ + t for t in config["technology"]["add"][:6] + ], # ], TODO: maybe reintroduce std/ccs in yaml + value=1, + unit="t", + years_act=s_info.Y, + **common + ) + .pipe(broadcast, node_loc=nodes) + .pipe(same_node) ) row = act2010 # Unit is Tg N/yr - results["historical_activity"].append( - df.assign(value=row, unit="t", year_act=2010) - ) + results["historical_activity"].append(df.assign(value=row, unit="t", year_act=2010)) # 2015 activity necessary if this is 5-year step scenario # df['value'] = act2015 # total NH3 or N in Mt 2010 FAO Russia # df['year_act'] = 2015 # Sc_nitro.add_par("historical_activity", df) df = ( - make_df("historical_new_capacity", - technology=[t for t in config["technology"]["add"][:6]], # ], refactor to adjust to yaml structure - value=1, unit='t', **common) - .pipe(broadcast, node_loc=nodes) - .pipe(same_node) + make_df( + "historical_new_capacity", + technology=[ + t for t in config["technology"]["add"][:6] + ], # ], refactor to adjust to yaml structure + value=1, + unit="t", + **common + ) + .pipe(broadcast, node_loc=nodes) + .pipe(same_node) ) # modifying act2010 values by assuming 1/lifetime (=15yr) is built each year and account for capacity factor - capacity_factor = read_demand()['capacity_factor'] + capacity_factor = read_demand()["capacity_factor"] row = act2010 * 1 / 15 / capacity_factor[0] # Unit is Tg N/yr results["historical_new_capacity"].append( - df.assign(value=row, unit='t', year_vtg=2010) + df.assign(value=row, unit="t", year_vtg=2010) ) # %% Secure feedstock balance (foil_fs, gas_fs, coal_fs) loil_fs? @@ -208,18 +218,18 @@ def gen_data_ammonia(scenario, dry_run=False, add_ccs: bool = True): df = scenario.par("land_output", {"commodity": "Fertilizer Use|Nitrogen"}) df["level"] = "final_material" results["land_input"].append(df) - #scenario.add_par("land_input", df) + # scenario.add_par("land_input", df) # add background parameters (growth rates and bounds) - df = scenario.par('initial_activity_lo', {"technology": ["gas_extr_mpen"]}) + df = scenario.par("initial_activity_lo", {"technology": ["gas_extr_mpen"]}) for q in config["technology"]["add"][:6]: - df['technology'] = q + df["technology"] = q results["initial_activity_lo"].append(df) - df = scenario.par('growth_activity_lo', {"technology": ["gas_extr_mpen"]}) + df = scenario.par("growth_activity_lo", {"technology": ["gas_extr_mpen"]}) for q in config["technology"]["add"][:6]: - df['technology'] = q + df["technology"] = q results["growth_activity_lo"].append(df) # TODO add regional cost scaling for ccs @@ -245,31 +255,41 @@ def gen_data_ammonia(scenario, dry_run=False, add_ccs: bool = True): """ cost_scaler = pd.read_excel( - context.get_local_path('material','regional_cost_scaler_R12.xlsx'), index_col=0).T + context.get_local_path("material", "regional_cost_scaler_R12.xlsx"), index_col=0 + ).T scalers_dict = { - "R12_CHN": {"coal_NH3": 0.75 * 0.91, # gas/coal price ratio * discount - "fueloil_NH3": 0.66 * 0.91}, # gas/oil price ratio * discount - "R12_SAS": {"fueloil_NH3": 0.59, - "coal_NH3": 1} + "R12_CHN": { + "coal_NH3": 0.75 * 0.91, # gas/coal price ratio * discount + "fueloil_NH3": 0.66 * 0.91, + }, # gas/oil price ratio * discount + "R12_SAS": {"fueloil_NH3": 0.59, "coal_NH3": 1}, } params = ["inv_cost", "fix_cost", "var_cost"] for param in params: for i in range(len(results[param])): df = results[param][i] - if df["technology"].any() in ('NH3_to_N_fertil', 'electr_NH3'): # skip those techs + if df["technology"].any() in ( + "NH3_to_N_fertil", + "electr_NH3", + ): # skip those techs continue regs = df.set_index("node_loc").join(cost_scaler, on="node_loc") regs.value = regs.value * regs["standard"] regs = regs.reset_index() - if df["technology"].any() in ("coal_NH3", "fueloil_NH3"): # additional scaling to make coal/oil cheaper - regs.loc[regs["node_loc"] == "R12_CHN", "value"] = \ - regs.loc[regs["node_loc"] == "R12_CHN", "value"] * \ - scalers_dict["R12_CHN"][df.technology[0]] - regs.loc[regs["node_loc"] == "R12_SAS", "value"] = \ - regs.loc[regs["node_loc"] == "R12_SAS", "value"] * \ - scalers_dict["R12_SAS"][df.technology[0]] + if df["technology"].any() in ( + "coal_NH3", + "fueloil_NH3", + ): # additional scaling to make coal/oil cheaper + regs.loc[regs["node_loc"] == "R12_CHN", "value"] = ( + regs.loc[regs["node_loc"] == "R12_CHN", "value"] + * scalers_dict["R12_CHN"][df.technology[0]] + ) + regs.loc[regs["node_loc"] == "R12_SAS", "value"] = ( + regs.loc[regs["node_loc"] == "R12_SAS", "value"] + * scalers_dict["R12_SAS"][df.technology[0]] + ) results[param][i] = regs.drop(["standard", "ccs"], axis="columns") # add trade tecs (exp, imp, trd) @@ -279,8 +299,14 @@ def gen_data_ammonia(scenario, dry_run=False, add_ccs: bool = True): newtechnames_exp = ["export_NFert"] yv_ya_exp = s_info.yv_ya - yv_ya_exp = yv_ya_exp[(yv_ya_exp["year_act"] - yv_ya_exp["year_vtg"] < 30) & (yv_ya_exp["year_vtg"] > 2000)] - yv_ya_same = s_info.yv_ya[(s_info.yv_ya["year_act"] - s_info.yv_ya["year_vtg"] == 0) & ( s_info.yv_ya["year_vtg"] > 2000)] + yv_ya_exp = yv_ya_exp[ + (yv_ya_exp["year_act"] - yv_ya_exp["year_vtg"] < 30) + & (yv_ya_exp["year_vtg"] > 2000) + ] + yv_ya_same = s_info.yv_ya[ + (s_info.yv_ya["year_act"] - s_info.yv_ya["year_vtg"] == 0) + & (s_info.yv_ya["year_vtg"] > 2000) + ] common = dict( year_act=yv_ya_same.year_act, @@ -306,11 +332,29 @@ def gen_data_ammonia(scenario, dry_run=False, add_ccs: bool = True): common_exp = common common_exp["year_act"] = yv_ya_exp.year_act common_exp["year_vtg"] = yv_ya_exp.year_vtg - df = make_df(i, technology=tec, value=row[2010].values[0], - unit="-", **common_exp).pipe(broadcast, node_loc=node).pipe(same_node) + df = ( + make_df( + i, + technology=tec, + value=row[2010].values[0], + unit="-", + **common_exp + ) + .pipe(broadcast, node_loc=node) + .pipe(same_node) + ) else: - df = make_df(i, technology=tec, value=row[2010].values[0], - unit="-", **common).pipe(broadcast, node_loc=node).pipe(same_node) + df = ( + make_df( + i, + technology=tec, + value=row[2010].values[0], + unit="-", + **common + ) + .pipe(broadcast, node_loc=node) + .pipe(same_node) + ) if (tec == "export_NFert") & (i == "output"): df["node_dest"] = "R12_GLB" df["level"] = "export" @@ -332,29 +376,37 @@ def gen_data_ammonia(scenario, dry_run=False, add_ccs: bool = True): time="year", time_dest="year", time_origin="year", - unit="t" + unit="t", ) N_trade_R12 = read_demand()["N_trade_R12"].assign(mode="M1") N_trade_R12["technology"] = N_trade_R12["Element"].apply( - lambda x: "export_NFert" if x == "Export" else "import_NFert") + lambda x: "export_NFert" if x == "Export" else "import_NFert" + ) df_exp_imp_act = N_trade_R12.drop("Element", axis=1) trd_act_years = N_trade_R12["year_act"].unique() values = N_trade_R12.groupby(["year_act"]).sum().values.flatten() - fert_trd_hist = make_df("historical_activity", technology="trade_NFert", - year_act=trd_act_years, value=values, - node_loc="R12_GLB", **common) + fert_trd_hist = make_df( + "historical_activity", + technology="trade_NFert", + year_act=trd_act_years, + value=values, + node_loc="R12_GLB", + **common + ) results["historical_activity"].append(pd.concat([df_exp_imp_act, fert_trd_hist])) - df_hist_cap_new = N_trade_R12[N_trade_R12["technology"] == "export_NFert"].drop(columns=["time", "mode", "Element"]) + df_hist_cap_new = N_trade_R12[N_trade_R12["technology"] == "export_NFert"].drop( + columns=["time", "mode", "Element"] + ) df_hist_cap_new = df_hist_cap_new.rename(columns={"year_act": "year_vtg"}) # divide by export lifetime derived from coal_exp df_hist_cap_new = df_hist_cap_new.assign(value=lambda x: x["value"] / 30) results["historical_new_capacity"].append(df_hist_cap_new) - #NH3 trade - #_______________________________________________ + # NH3 trade + # _______________________________________________ common = dict( year_act=yv_ya_same.year_act, @@ -381,11 +433,29 @@ def gen_data_ammonia(scenario, dry_run=False, add_ccs: bool = True): common_exp = common common_exp["year_act"] = yv_ya_exp.year_act common_exp["year_vtg"] = yv_ya_exp.year_vtg - df = make_df(i, technology=tec, value=row[2010].values[0], - unit="-", **common_exp).pipe(broadcast, node_loc=node).pipe(same_node) + df = ( + make_df( + i, + technology=tec, + value=row[2010].values[0], + unit="-", + **common_exp + ) + .pipe(broadcast, node_loc=node) + .pipe(same_node) + ) else: - df = make_df(i, technology=tec, value=row[2010].values[0], - unit="-", **common).pipe(broadcast, node_loc=node).pipe(same_node) + df = ( + make_df( + i, + technology=tec, + value=row[2010].values[0], + unit="-", + **common + ) + .pipe(broadcast, node_loc=node) + .pipe(same_node) + ) if (tec == "export_NH3") & (i == "output"): df["node_dest"] = "R12_GLB" df["level"] = "export" @@ -407,27 +477,35 @@ def gen_data_ammonia(scenario, dry_run=False, add_ccs: bool = True): time="year", time_dest="year", time_origin="year", - unit="t" + unit="t", ) N_trade_R12 = read_demand()["N_trade_R12"].assign(mode="M1") N_trade_R12["technology"] = N_trade_R12["Element"].apply( - lambda x: "export_NH3" if x == "Export" else "import_NH3") + lambda x: "export_NH3" if x == "Export" else "import_NH3" + ) df_exp_imp_act = N_trade_R12.drop("Element", axis=1) trd_act_years = N_trade_R12["year_act"].unique() values = N_trade_R12.groupby(["year_act"]).sum().values.flatten() - fert_trd_hist = make_df("historical_activity", technology="trade_NH3", - year_act=trd_act_years, value=values, - node_loc="R12_GLB", **common) + fert_trd_hist = make_df( + "historical_activity", + technology="trade_NH3", + year_act=trd_act_years, + value=values, + node_loc="R12_GLB", + **common + ) results["historical_activity"].append(pd.concat([df_exp_imp_act, fert_trd_hist])) - df_hist_cap_new = N_trade_R12[N_trade_R12["technology"] == "export_NH3"].drop(columns=["time", "mode", "Element"]) + df_hist_cap_new = N_trade_R12[N_trade_R12["technology"] == "export_NH3"].drop( + columns=["time", "mode", "Element"] + ) df_hist_cap_new = df_hist_cap_new.rename(columns={"year_act": "year_vtg"}) # divide by export lifetime derived from coal_exp df_hist_cap_new = df_hist_cap_new.assign(value=lambda x: x["value"] / 30) results["historical_new_capacity"].append(df_hist_cap_new) - #___________________________________________________________________________ + # ___________________________________________________________________________ if add_ccs: for k, v in gen_data_ccs(scenario).items(): @@ -438,27 +516,38 @@ def gen_data_ammonia(scenario, dry_run=False, add_ccs: bool = True): par = "emission_factor" rel_df_cc = results[par] - rel_df_cc = rel_df_cc[rel_df_cc["emission"] == "CO2_transformation"] - rel_df_cc = rel_df_cc.assign(year_rel=rel_df_cc["year_act"], - node_rel=rel_df_cc["node_loc"], - relation="CO2_cc").drop(["emission", "year_vtg"], axis=1) + rel_df_cc = rel_df_cc[rel_df_cc["emission"] == "CO2_industry"] + rel_df_cc = rel_df_cc.assign( + year_rel=rel_df_cc["year_act"], + node_rel=rel_df_cc["node_loc"], + relation="CO2_cc", + ).drop(["emission", "year_vtg"], axis=1) rel_df_cc = rel_df_cc[rel_df_cc["technology"] != "NH3_to_N_fertil"] - rel_df_cc = rel_df_cc[rel_df_cc["year_rel"] == rel_df_cc["year_act"]].drop_duplicates() + rel_df_cc = rel_df_cc[ + rel_df_cc["year_rel"] == rel_df_cc["year_act"] + ].drop_duplicates() rel_df_cc[rel_df_cc["technology"] == "biomass_NH3_ccs"] = rel_df_cc[ - rel_df_cc["technology"] == "biomass_NH3_ccs"].assign( - value=results[par][results[par]["technology"] == "biomass_NH3_ccs"]["value"].values[0]) - + rel_df_cc["technology"] == "biomass_NH3_ccs" + ].assign( + value=results[par][results[par]["technology"] == "biomass_NH3_ccs"][ + "value" + ].values[0] + ) rel_df_em = results[par] rel_df_em = rel_df_em[rel_df_em["emission"] == "CO2"] - rel_df_em = rel_df_em.assign(year_rel=rel_df_em["year_act"], - node_rel=rel_df_em["node_loc"], - relation="CO2_Emission").drop(["emission", "year_vtg"], axis=1) + rel_df_em = rel_df_em.assign( + year_rel=rel_df_em["year_act"], + node_rel=rel_df_em["node_loc"], + relation="CO2_Emission", + ).drop(["emission", "year_vtg"], axis=1) rel_df_em = rel_df_em[rel_df_em["technology"] != "NH3_to_N_fertil"] rel_df_em[rel_df_em["year_rel"] == rel_df_em["year_act"]].drop_duplicates() results["relation_activity"] = pd.concat([rel_df_cc, rel_df_em]) - results["emission_factor"] = results["emission_factor"][results["emission_factor"]["technology"]!="NH3_to_N_fertil"] + results["emission_factor"] = results["emission_factor"][ + results["emission_factor"]["technology"] != "NH3_to_N_fertil" + ] return results @@ -501,7 +590,7 @@ def gen_data_ccs(scenario, dry_run=False): common = dict( year_vtg=vtg_years, - year_act=act_years, # confirm if correct?? + year_act=act_years, # confirm if correct?? commodity="NH3", level="secondary_material", # TODO fill in remaining dimensions @@ -509,38 +598,32 @@ def gen_data_ccs(scenario, dry_run=False): time="year", time_dest="year", time_origin="year", - emission="CO2" # confirm if correct + emission="CO2" # confirm if correct # node_loc='node' ) input_commodity_dict = { "input_water": "freshwater_supply", "input_elec": "electr", - "input_fuel": "" + "input_fuel": "", } output_commodity_dict = { "output_NH3": "NH3", "output_heat": "d_heat", - "output_water": "wastewater" - } - commodity_dict = { - "output": output_commodity_dict, - "input": input_commodity_dict + "output_water": "wastewater", } + commodity_dict = {"output": output_commodity_dict, "input": input_commodity_dict} input_level_dict = { "input_water": "water_supply", "input_fuel": "secondary", - "input_elec": "secondary" + "input_elec": "secondary", } output_level_dict = { "output_water": "wastewater", "output_heat": "secondary", - "output_NH3": "secondary_material" - } - level_cat_dict = { - "output": output_level_dict, - "input": input_level_dict + "output_NH3": "secondary_material", } + level_cat_dict = {"output": output_level_dict, "input": input_level_dict} # Iterate over new technologies, using the configuration for t in config["technology"]["add"][12:]: @@ -548,83 +631,97 @@ def gen_data_ccs(scenario, dry_run=False): # TODO the output commodity and level are different for # t=NH3_to_N_fertil; use 'if' statements to fill in. - for param in data['parameter'].unique(): + for param in data["parameter"].unique(): unit = "t" - cat = data['param_cat'][data['parameter'] == param].iloc[0] + cat = data["param_cat"][data["parameter"] == param].iloc[0] if cat in ["input", "output"]: common["commodity"] = commodity_dict[cat][param] common["level"] = level_cat_dict[cat][param] + if (t == "biomass_NH3_ccs") & (param == "input_fuel"): + common["level"] = "primary" if param == "emission_factor_trans": _common = common.copy() - _common["emission"] = "CO2_transformation" + _common["emission"] = "CO2_industry" cat = "emission_factor" df = ( make_df(cat, technology=t, value=1, unit="-", **_common) - .pipe(broadcast, node_loc=nodes) - .pipe(same_node) + .pipe(broadcast, node_loc=nodes) + .pipe(same_node) ) else: df = ( make_df(cat, technology=t, value=1, unit="-", **common) - .pipe(broadcast, node_loc=nodes) - .pipe(same_node) + .pipe(broadcast, node_loc=nodes) + .pipe(same_node) ) - row = data[(data['technology'] == str(t)) & - (data['parameter'] == param)] + row = data[(data["technology"] == str(t)) & (data["parameter"] == param)] df = df.assign(value=row[2010].values[0]) if param == "input_fuel": - comm = data['technology'][(data['parameter'] == param) & - (data["technology"] == t)].iloc[0].split("_")[0] + comm = ( + data["technology"][ + (data["parameter"] == param) & (data["technology"] == t) + ] + .iloc[0] + .split("_")[0] + ) df = df.assign(commodity=comm) results[cat].append(df) - # add background parameters (growth rates and bounds) - df = scenario.par('initial_activity_lo', {"technology": ["gas_extr_mpen"]}) + df = scenario.par("initial_activity_lo", {"technology": ["gas_extr_mpen"]}) for q in config["technology"]["add"][12:]: - df['technology'] = q + df["technology"] = q results["initial_activity_lo"].append(df) - df = scenario.par('growth_activity_lo', {"technology": ["gas_extr_mpen"]}) + df = scenario.par("growth_activity_lo", {"technology": ["gas_extr_mpen"]}) for q in config["technology"]["add"][12:]: - df['technology'] = q + df["technology"] = q results["growth_activity_lo"].append(df) cost_scaler = pd.read_excel( - context.get_local_path('material','regional_cost_scaler_R12.xlsx'), index_col=0).T + context.get_local_path("material", "regional_cost_scaler_R12.xlsx"), index_col=0 + ).T scalers_dict = { - "R12_CHN": {"coal_NH3": 0.75 * 0.91, # gas/coal price ratio * discount - "fueloil_NH3": 0.66 * 0.91}, # gas/oil price ratio * discount - "R12_SAS": {"fueloil_NH3": 0.59, - "coal_NH3": 1} + "R12_CHN": { + "coal_NH3": 0.75 * 0.91, # gas/coal price ratio * discount + "fueloil_NH3": 0.66 * 0.91, + }, # gas/oil price ratio * discount + "R12_SAS": {"fueloil_NH3": 0.59, "coal_NH3": 1}, } params = ["inv_cost", "fix_cost", "var_cost"] for param in params: for i in range(len(results[param])): df = results[param][i] - if df["technology"].any() in ('NH3_to_N_fertil', 'electr_NH3'): # skip those techs + if df["technology"].any() in ( + "NH3_to_N_fertil", + "electr_NH3", + ): # skip those techs continue regs = df.set_index("node_loc").join(cost_scaler, on="node_loc") regs.value = regs.value * regs["ccs"] regs = regs.reset_index() - if df["technology"].any() in ("coal_NH3", "fueloil_NH3"): # additional scaling to make coal/oil cheaper - regs.loc[regs["node_loc"] == "R12_CHN", "value"] = \ - regs.loc[regs["node_loc"] == "R12_CHN", "value"] * \ - scalers_dict["R12_CHN"][df.technology[0].values[0].name] - regs.loc[regs["node_loc"] == "R12_SAS", "value"] = \ - regs.loc[regs["node_loc"] == "R12_SAS", "value"] * \ - scalers_dict["R12_SAS"][df.technology[0].values[0].name] + if df["technology"].any() in ( + "coal_NH3", + "fueloil_NH3", + ): # additional scaling to make coal/oil cheaper + regs.loc[regs["node_loc"] == "R12_CHN", "value"] = ( + regs.loc[regs["node_loc"] == "R12_CHN", "value"] + * scalers_dict["R12_CHN"][df.technology[0].values[0].name] + ) + regs.loc[regs["node_loc"] == "R12_SAS", "value"] = ( + regs.loc[regs["node_loc"] == "R12_SAS", "value"] + * scalers_dict["R12_SAS"][df.technology[0].values[0].name] + ) results[param][i] = regs.drop(["standard", "ccs"], axis="columns") # Concatenate to one dataframe per parameter results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} - #results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} - + # results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} return results @@ -634,41 +731,64 @@ def read_demand(): # Demand scenario [Mt N/year] from GLOBIOM context = read_config() - - N_demand_GLO = pd.read_excel(context.get_local_path('material','CD-Links SSP2 N-fertilizer demand.Global_R12_adaption.xlsx'), sheet_name='data') + N_demand_GLO = pd.read_excel( + context.get_local_path( + "material", "CD-Links SSP2 N-fertilizer demand.Global_R12_adaption.xlsx" + ), + sheet_name="data", + ) # NH3 feedstock share by region in 2010 (from http://ietd.iipnetwork.org/content/ammonia#benchmarks) - feedshare_GLO = pd.read_excel(context.get_local_path('material','Ammonia feedstock share.Global_R12.xlsx'), sheet_name='Sheet2', skiprows=14) + feedshare_GLO = pd.read_excel( + context.get_local_path("material", "Ammonia feedstock share.Global_R12.xlsx"), + sheet_name="Sheet2", + skiprows=14, + ) # Read parameters in xlsx te_params = data = pd.read_excel( - context.get_local_path("material", "n-fertilizer_techno-economic.xlsx"), - sheet_name="Sheet1", engine="openpyxl", nrows=72 + private_data_path("material", "n-fertilizer_techno-economic.xlsx"), + sheet_name="Sheet1", + engine="openpyxl", + nrows=72, ) n_inputs_per_tech = 12 # Number of input params per technology - input_fuel = te_params[2010][list(range(4, te_params.shape[0], n_inputs_per_tech))].reset_index(drop=True) - #input_fuel[0:5] = input_fuel[0:5] * CONVERSION_FACTOR_PJ_GWa # 0.0317 GWa/PJ, GJ/t = PJ/Mt NH3 + input_fuel = te_params[2010][ + list(range(4, te_params.shape[0], n_inputs_per_tech)) + ].reset_index(drop=True) + # input_fuel[0:5] = input_fuel[0:5] * CONVERSION_FACTOR_PJ_GWa # 0.0317 GWa/PJ, GJ/t = PJ/Mt NH3 - capacity_factor = te_params[2010][list(range(11, te_params.shape[0], n_inputs_per_tech))].reset_index(drop=True) + capacity_factor = te_params[2010][ + list(range(11, te_params.shape[0], n_inputs_per_tech)) + ].reset_index(drop=True) # Regional N demaand in 2010 - ND = N_demand_GLO.loc[N_demand_GLO.Scenario == "NoPolicy", ['Region', 2010]] - ND = ND[ND.Region != 'World'] - ND.Region = 'R12_' + ND.Region - ND = ND.set_index('Region') + ND = N_demand_GLO.loc[N_demand_GLO.Scenario == "NoPolicy", ["Region", 2010]] + ND = ND[ND.Region != "World"] + ND.Region = "R12_" + ND.Region + ND = ND.set_index("Region") # Derive total energy (GWa) of NH3 production (based on demand 2010) - N_energy = feedshare_GLO[feedshare_GLO.Region != 'R12_GLB'].join(ND, on='Region') + N_energy = feedshare_GLO[feedshare_GLO.Region != "R12_GLB"].join(ND, on="Region") N_energy = pd.concat( - [N_energy.Region, N_energy[["gas_pct", "coal_pct", "oil_pct"]].multiply(N_energy[2010], axis="index")], axis=1) + [ + N_energy.Region, + N_energy[["gas_pct", "coal_pct", "oil_pct"]].multiply( + N_energy[2010], axis="index" + ), + ], + axis=1, + ) N_energy.gas_pct *= input_fuel[2] * CONVERSION_FACTOR_NH3_N # NH3 / N N_energy.coal_pct *= input_fuel[3] * CONVERSION_FACTOR_NH3_N N_energy.oil_pct *= input_fuel[4] * CONVERSION_FACTOR_NH3_N - N_energy = pd.concat([N_energy.Region, N_energy.sum(axis=1)], axis=1).rename( + N_energy = pd.concat([N_energy.Region, N_energy.sum(axis=1, numeric_only=True)], axis=1).rename( columns={0: 'totENE', 'Region': 'node'}) # GWa - N_trade_R12 = pd.read_csv(context.get_local_path("material","trade.FAO.R12.csv"), index_col=0) + N_trade_R12 = pd.read_csv( + private_data_path("material", "trade.FAO.R12.csv"), index_col=0 + ) N_trade_R12.msgregion = "R12_" + N_trade_R12.msgregion N_trade_R12.Value = N_trade_R12.Value / 1e6 N_trade_R12.Unit = "t" @@ -689,7 +809,6 @@ def read_demand(): NP = pd.DataFrame({"netimp": df.Import - df.Export, "demand": ND[2010]}) NP["prod"] = NP.demand - NP.netimp - # Derive total energy (GWa) of NH3 production (based on demand 2010) N_feed = feedshare_GLO[feedshare_GLO.Region != "R11_GLB"].join(NP, on="Region") N_feed = pd.concat( @@ -704,7 +823,7 @@ def read_demand(): N_feed.gas_pct *= input_fuel[2] * 17 / 14 N_feed.coal_pct *= input_fuel[3] * 17 / 14 N_feed.oil_pct *= input_fuel[4] * 17 / 14 - N_feed = pd.concat([N_feed.Region, N_feed.sum(axis=1)], axis=1).rename( + N_feed = pd.concat([N_feed.Region, N_feed.sum(axis=1, numeric_only=True)], axis=1).rename( columns={0: "totENE", "Region": "node"}) # Process the regional historical activities @@ -717,55 +836,74 @@ def read_demand(): fs_GLO.insert(6, "NH3_to_N", 1) # Share of feedstocks for NH3 prodution (based on 2010 => Assumed fixed for any past years) - feedshare = fs_GLO.sort_values(['Region']).set_index('Region').drop('R12_GLB') + feedshare = fs_GLO.sort_values(["Region"]).set_index("Region").drop("R12_GLB") # Get historical N demand from SSP2-nopolicy (may need to vary for diff scenarios) N_demand_raw = N_demand_GLO.copy() - N_demand = N_demand_raw[(N_demand_raw.Scenario == "NoPolicy") & - (N_demand_raw.Region != "World")].reset_index().loc[:, 2010] # 2010 tot N demand + N_demand = ( + N_demand_raw[ + (N_demand_raw.Scenario == "NoPolicy") & (N_demand_raw.Region != "World") + ] + .reset_index() + .loc[:, 2010] + ) # 2010 tot N demand N_demand = N_demand.repeat(6) act2010 = (feedshare.values.flatten() * N_demand).reset_index(drop=True) - return {"feedshare_GLO": feedshare_GLO, "ND": ND, "N_energy": N_energy, "feedshare": feedshare, 'act2010': act2010, - 'capacity_factor': capacity_factor, "N_feed":N_feed, "N_trade_R12":N_trade_R12} + return { + "feedshare_GLO": feedshare_GLO, + "ND": ND, + "N_energy": N_energy, + "feedshare": feedshare, + "act2010": act2010, + "capacity_factor": capacity_factor, + "N_feed": N_feed, + "N_trade_R12": N_trade_R12, + } def read_trade_data(context, comm): if comm == "NFert": data = pd.read_excel( - context.get_local_path("material", "n-fertilizer_techno-economic_new.xlsx"), - sheet_name="Trade", engine="openpyxl", usecols=np.linspace(0, 7, 8, dtype=int)) + private_data_path("material", "n-fertilizer_techno-economic_new.xlsx"), + sheet_name="Trade", + engine="openpyxl", + usecols=np.linspace(0, 7, 8, dtype=int), + ) data = data.assign(technology=lambda x: set_trade_tec(x["Variable"])) if comm == "NH3": data = pd.read_excel( - context.get_local_path("material", "n-fertilizer_techno-economic_new.xlsx"), - sheet_name="Trade_NH3", engine="openpyxl", usecols=np.linspace(0, 7, 8, dtype=int)) + private_data_path("material", "n-fertilizer_techno-economic_new.xlsx"), + sheet_name="Trade_NH3", + engine="openpyxl", + usecols=np.linspace(0, 7, 8, dtype=int), + ) data = data.assign(technology=lambda x: set_trade_tec_NH3(x["Variable"])) return data def set_trade_tec(x): - arr=[] + arr = [] for i in x: if "Import" in i: arr.append("import_NFert") if "Export" in i: arr.append("export_NFert") if "Trade" in i: - arr.append("trade_NFert") + arr.append("trade_NFert") return arr def set_trade_tec_NH3(x): - arr=[] + arr = [] for i in x: if "Import" in i: arr.append("import_NH3") if "Export" in i: arr.append("export_NH3") if "Trade" in i: - arr.append("trade_NH3") + arr.append("trade_NH3") return arr @@ -773,17 +911,19 @@ def read_data(): """Read and clean data from :file:`n-fertilizer_techno-economic.xlsx`.""" # Ensure config is loaded, get the context context = read_config() - #print(context.get_local_path()) - #print(Path(__file__).parents[3]/"data"/"material") - context.handle_cli_args(local_data=Path(__file__).parents[3]/"data") - #print(context.get_local_path()) + # print(context.get_local_path()) + # print(Path(__file__).parents[3]/"data"/"material") + context.handle_cli_args(local_data=Path(__file__).parents[3] / "data") + # print(context.get_local_path()) # Shorter access to sets configuration sets = context["material"]["fertilizer"] # Read the file data = pd.read_excel( - context.get_local_path("material", "n-fertilizer_techno-economic_new.xlsx"), - sheet_name="Sheet1", engine="openpyxl", nrows=72 + private_data_path("material", "n-fertilizer_techno-economic_new.xlsx"), + sheet_name="Sheet1", + engine="openpyxl", + nrows=72, ) # Prepare contents for the "parameter" and "technology" columns @@ -808,13 +948,16 @@ def read_data(): param_values = [] tech_values = [] - param_cat = [split.split('_')[0] if - (split.startswith('input') or split.startswith('output')) - else split for split in params] + param_cat = [ + split.split("_")[0] + if (split.startswith("input") or split.startswith("output")) + else split + for split in params + ] param_cat2 = [] # print(param_cat) - for t in sets["technology"]["add"][:6]: # : refactor to adjust to yaml structure + for t in sets["technology"]["add"][:6]: # : refactor to adjust to yaml structure # print(t) param_values.extend(params) tech_values.extend([t] * len(params)) @@ -823,16 +966,17 @@ def read_data(): # Clean the data data = ( # Insert "technology" and "parameter" columns - data.assign(technology=tech_values, - parameter=param_values, - param_cat=param_cat2) - # Drop columns that don't contain useful information - .drop(["Model", "Scenario", "Region"], axis=1) + data.assign( + technology=tech_values, parameter=param_values, param_cat=param_cat2 + ) + # Drop columns that don't contain useful information + .drop(["Model", "Scenario", "Region"], axis=1) # Set the data frame index for selection ) - data.loc[data['parameter'] == 'emission_factor', 2010] = \ - data.loc[data['parameter'] == 'emission_factor', 2010]# * CONVERSION_FACTOR_CO2_C - #data.loc[data['parameter'] == 'input_elec', 2010] = \ + data.loc[data["parameter"] == "emission_factor", 2010] = data.loc[ + data["parameter"] == "emission_factor", 2010 + ] # * CONVERSION_FACTOR_CO2_C + # data.loc[data['parameter'] == 'input_elec', 2010] = \ # data.loc[data['parameter'] == 'input_elec', 2010] * CONVERSION_FACTOR_PJ_GWa # TODO convert units for some parameters, per LoadParams.py @@ -849,7 +993,7 @@ def read_data_ccs(): # Read the file data = pd.read_excel( - context.get_local_path("material", "n-fertilizer_techno-economic_new.xlsx"), + private_data_path("material", "n-fertilizer_techno-economic_new.xlsx"), sheet_name="CCS", ) @@ -876,8 +1020,12 @@ def read_data_ccs(): param_values = [] tech_values = [] - param_cat = [split.split('_')[0] if (split.startswith('input') or split.startswith('output')) else split for split - in params] + param_cat = [ + split.split("_")[0] + if (split.startswith("input") or split.startswith("output")) + else split + for split in params + ] param_cat2 = [] @@ -885,22 +1033,26 @@ def read_data_ccs(): param_values.extend(params) tech_values.extend([t] * len(params)) param_cat2.extend(param_cat) - #print(sets["technology"]["add"][12:]) + # print(sets["technology"]["add"][12:]) # Clean the data data = ( # Insert "technology" and "parameter" columns - data.assign(technology=tech_values, parameter=param_values, param_cat=param_cat2) - # , param_cat=param_cat2) - # Drop columns that don't contain useful information - .drop(["Model", "Scenario", "Region"], axis=1) + data.assign( + technology=tech_values, parameter=param_values, param_cat=param_cat2 + ) + # , param_cat=param_cat2) + # Drop columns that don't contain useful information + .drop(["Model", "Scenario", "Region"], axis=1) # Set the data frame index for selection ) - #unit conversions and extra electricity for CCS process - data.loc[data['parameter'] == 'emission_factor', 2010] = \ - data.loc[data['parameter'] == 'emission_factor', 2010]# * CONVERSION_FACTOR_CO2_C - #data.loc[data['parameter'] == 'input_elec', 2010] = \ + # unit conversions and extra electricity for CCS process + data.loc[data["parameter"] == "emission_factor", 2010] = data.loc[ + data["parameter"] == "emission_factor", 2010 + ] # * CONVERSION_FACTOR_CO2_C + # data.loc[data['parameter'] == 'input_elec', 2010] = \ # data.loc[data['parameter'] == 'input_elec', 2010] * CONVERSION_FACTOR_PJ_GWa + 0.005 - data.loc[data['parameter'] == 'input_elec', 2010] = \ - data.loc[data['parameter'] == 'input_elec', 2010] + data.loc[data["parameter"] == "input_elec", 2010] = data.loc[ + data["parameter"] == "input_elec", 2010 + ] # TODO convert units for some parameters, per LoadParams.py return data diff --git a/message_ix_models/model/material/data_buildings.py b/message_ix_models/model/material/data_buildings.py index 493026a198..995c0d24ca 100644 --- a/message_ix_models/model/material/data_buildings.py +++ b/message_ix_models/model/material/data_buildings.py @@ -17,6 +17,7 @@ same_node, copy_column, add_par_data, + private_data_path, ) CASE_SENS = "ref" # 'min', 'max' @@ -34,7 +35,7 @@ def read_timeseries_buildings(filename, scenario, case=CASE_SENS): nodes = s_info.N # Read the file and filter the given sensitivity case - bld_input_raw = pd.read_csv(context.get_local_path("material", filename)) + bld_input_raw = pd.read_csv(private_data_path("material", filename)) bld_input_raw = bld_input_raw.loc[bld_input_raw.Sensitivity == case] bld_input_mat = bld_input_raw[ diff --git a/message_ix_models/model/material/data_cement.py b/message_ix_models/model/material/data_cement.py index 80577877a5..503534661c 100644 --- a/message_ix_models/model/material/data_cement.py +++ b/message_ix_models/model/material/data_cement.py @@ -16,6 +16,7 @@ make_matched_dfs, same_node, add_par_data, + private_data_path, ) # Get endogenous material demand from buildings interface @@ -92,7 +93,7 @@ def gen_mock_demand_cement(scenario): # SSP2 R11 baseline GDP projection gdp_growth = pd.read_excel( - context.get_local_path("material", "iamc_db ENGAGE baseline GDP PPP.xlsx"), + private_data_path("material", "iamc_db ENGAGE baseline GDP PPP.xlsx"), sheet_name=sheet_n, ) @@ -105,7 +106,7 @@ def gen_mock_demand_cement(scenario): # # Regions setting for IMAGE # region_cement = pd.read_excel( - # context.get_local_path("material", "CEMENT.BvR2010.xlsx"), + # private_data_path("material", "CEMENT.BvR2010.xlsx"), # sheet_name="Timer_Regions", skiprows=range(0,3))[['Region #', 'Name']]\ # .drop_duplicates().sort_values(by='Region #') # @@ -127,7 +128,7 @@ def gen_mock_demand_cement(scenario): # # # Cement demand 2010 [Mt/year] (IMAGE) # demand2010_cement = pd.read_excel( - # context.get_local_path("material", "CEMENT.BvR2010.xlsx"), + # private_data_path("material", "CEMENT.BvR2010.xlsx"), # sheet_name="Domestic Consumption", skiprows=range(0,3)).\ # groupby(by=["Region #"]).sum()[[2010]].\ # join(region_cement.set_index('Region #'), on='Region #').\ @@ -188,8 +189,8 @@ def gen_data_cement(scenario, dry_run=False): # TEMP: now add cement sector as well data_cement = read_sector_data(scenario, "cement") # Special treatment for time-dependent Parameters - data_cement_ts = read_timeseries(scenario,context.datafile) - tec_ts = set(data_cement_ts.technology) # set of tecs with var_cost + data_cement_ts = read_timeseries(scenario, context.datafile) + tec_ts = set(data_cement_ts.technology) # set of tecs with var_cost # List of data frames, to be concatenated together at end results = defaultdict(list) @@ -282,7 +283,6 @@ def gen_data_cement(scenario, dry_run=False): results[p].append(df) - # Iterate over parameters for par in params: diff --git a/message_ix_models/model/material/data_petro.py b/message_ix_models/model/material/data_petro.py index 1e861f22bd..9c0f6f3bd0 100644 --- a/message_ix_models/model/material/data_petro.py +++ b/message_ix_models/model/material/data_petro.py @@ -17,6 +17,7 @@ make_matched_dfs, same_node, add_par_data, + private_data_path, ) @@ -34,9 +35,7 @@ def read_data_petrochemicals(scenario): sheet_n = "data_R11" # Read the file - data_petro = pd.read_excel( - context.get_local_path("material", fname), sheet_name=sheet_n - ) + data_petro = pd.read_excel(private_data_path("material", fname), sheet_name=sheet_n) # Clean the data data_petro = data_petro.drop(["Source", "Description"], axis=1) @@ -90,7 +89,7 @@ def gen_mock_demand_petro(scenario): # 7.4503, 7.497867, 17.30965, 11.1014] gdp_growth = pd.read_excel( - context.get_local_path("material", "iamc_db ENGAGE baseline GDP PPP.xlsx"), + private_data_path("material", "iamc_db ENGAGE baseline GDP PPP.xlsx"), sheet_name=sheet_n, ) @@ -152,6 +151,7 @@ def gen_mock_demand_petro(scenario): # This makes 25 Mt ethylene, 25 Mt propylene, 21 Mt BTX # This can be verified by other sources. + # load rpy2 modules # import rpy2.robjects as ro # from rpy2.robjects import pandas2ri @@ -198,6 +198,7 @@ def gen_mock_demand_petro(scenario): # return df # + def gen_data_petro_chemicals(scenario, dry_run=False): # Load configuration @@ -388,7 +389,7 @@ def gen_data_petro_chemicals(scenario, dry_run=False): # Add demand # Create external demand param - #demand_HVC = derive_petro_demand(scenario) + # demand_HVC = derive_petro_demand(scenario) demand_HVC = gen_mock_demand_petro(scenario) paramname = "demand" @@ -424,7 +425,11 @@ def gen_data_petro_chemicals(scenario, dry_run=False): tec_ts = set(data_petro_ts.technology) # set of tecs in timeseries sheet for t in tec_ts: - common = dict(time="year", time_origin="year", time_dest="year",) + common = dict( + time="year", + time_origin="year", + time_dest="year", + ) param_name = data_petro_ts.loc[(data_petro_ts["technology"] == t), "parameter"] diff --git a/message_ix_models/model/material/data_steel.py b/message_ix_models/model/material/data_steel.py index 5307380e6f..1b89e5183d 100644 --- a/message_ix_models/model/material/data_steel.py +++ b/message_ix_models/model/material/data_steel.py @@ -21,6 +21,7 @@ same_node, copy_column, add_par_data, + private_data_path, ) from . import get_spec @@ -48,18 +49,43 @@ def gen_mock_demand_steel(scenario): nodes.remove("R12_GLB") sheet_n = "data_R12" region_set = "R12_" - d = [20.04, 12.08, 56.55, 56.5, 64.94, 54.26, 97.76, 91.3, 65.2, 164.28, 131.95, 980.1] + d = [ + 20.04, + 12.08, + 56.55, + 56.5, + 64.94, + 54.26, + 97.76, + 91.3, + 65.2, + 164.28, + 131.95, + 980.1, + ] else: nodes.remove("R11_GLB") sheet_n = "data_R11" region_set = "R11_" - d = [20.04, 992.18, 56.55, 56.5, 64.94, 54.26, 97.76, 91.3, 65.2, 164.28, 131.95] + d = [ + 20.04, + 992.18, + 56.55, + 56.5, + 64.94, + 54.26, + 97.76, + 91.3, + 65.2, + 164.28, + 131.95, + ] # MEA change from 39 to 9 to make it feasible (coal supply bound) # SSP2 R11 baseline GDP projection gdp_growth = pd.read_excel( - context.get_local_path("material", "iamc_db ENGAGE baseline GDP PPP.xlsx"), + private_data_path("material", "iamc_db ENGAGE baseline GDP PPP.xlsx"), sheet_name=sheet_n, ) diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index b5582e8796..7584966b64 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -9,6 +9,8 @@ import re from message_ix_models import ScenarioInfo +from message_ix_models.util import private_data_path + from message_data.tools.utilities.get_optimization_years import ( main as get_optimization_years, ) @@ -22,49 +24,57 @@ def load_GDP_COVID(): # Obtain 2015 and 2020 GDP values from NGFS baseline. These values are COVID corrected. (GDP MER) mp = ixmp.Platform() - scen_NGFS = message_ix.Scenario(mp,'MESSAGEix-GLOBIOM 1.1-M-R12-NGFS','baseline', cache=True) - gdp_covid_2015 = scen_NGFS.par('gdp_calibrate', filters = {'year': 2015}) + scen_NGFS = message_ix.Scenario( + mp, "MESSAGEix-GLOBIOM 1.1-M-R12-NGFS", "baseline", cache=True + ) + gdp_covid_2015 = scen_NGFS.par("gdp_calibrate", filters={"year": 2015}) - gdp_covid_2020 = scen_NGFS.par('gdp_calibrate', filters = {'year': 2020}) - gdp_covid_2020 = gdp_covid_2020.drop(['year', 'unit'],axis = 1) + gdp_covid_2020 = scen_NGFS.par("gdp_calibrate", filters={"year": 2020}) + gdp_covid_2020 = gdp_covid_2020.drop(["year", "unit"], axis=1) # Obtain SSP2 GDP growth rates after 2020 (from ENGAGE baseline) - f_name = 'iamc_db ENGAGE baseline GDP PPP.xlsx' + f_name = "iamc_db ENGAGE baseline GDP PPP.xlsx" - gdp_ssp2 = pd.read_excel(context.get_local_path('data','material', f_name), - sheet_name = 'data_R12') - gdp_ssp2 = gdp_ssp2[gdp_ssp2['Scenario'] == 'baseline'] - regions = 'R12_' + gdp_ssp2['Region'] - gdp_ssp2 = gdp_ssp2.drop(['Model','Scenario','Unit','Region','Variable','Notes'],axis = 1) + gdp_ssp2 = pd.read_excel( + context.get_local_path("data", "material", f_name), sheet_name="data_R12" + ) + gdp_ssp2 = gdp_ssp2[gdp_ssp2["Scenario"] == "baseline"] + regions = "R12_" + gdp_ssp2["Region"] + gdp_ssp2 = gdp_ssp2.drop( + ["Model", "Scenario", "Unit", "Region", "Variable", "Notes"], axis=1 + ) gdp_ssp2 = gdp_ssp2.loc[:, 2020:] - gdp_ssp2 = gdp_ssp2.divide(gdp_ssp2[2020], axis = 0) - gdp_ssp2['node'] = regions - gdp_ssp2 = gdp_ssp2[gdp_ssp2['node'] != 'R12_World'] + gdp_ssp2 = gdp_ssp2.divide(gdp_ssp2[2020], axis=0) + gdp_ssp2["node"] = regions + gdp_ssp2 = gdp_ssp2[gdp_ssp2["node"] != "R12_World"] # Multiply 2020 COVID corrrected values with SSP2 growth rates - df_new = pd.DataFrame(columns = ['node','year','value']) + df_new = pd.DataFrame(columns=["node", "year", "value"]) for ind in gdp_ssp2.index: - df_temp = pd.DataFrame(columns = ['node','year','value']) - region = gdp_ssp2.loc[ind, 'node'] - mult_value = gdp_covid_2020.loc[gdp_covid_2020['node']== region, 'value'].values[0] + df_temp = pd.DataFrame(columns=["node", "year", "value"]) + region = gdp_ssp2.loc[ind, "node"] + mult_value = gdp_covid_2020.loc[ + gdp_covid_2020["node"] == region, "value" + ].values[0] temp = gdp_ssp2.loc[ind, 2020:2110] * mult_value region_list = [region] * temp.size - df_temp['node'] = region_list - df_temp['year'] = temp.index - df_temp['value'] = temp.values + df_temp["node"] = region_list + df_temp["year"] = temp.index + df_temp["value"] = temp.values df_new = pd.concat([df_new, df_temp]) - df_new['unit'] = 'T$' - df_new = pd.concat([df_new,gdp_covid_2015]) + df_new["unit"] = "T$" + df_new = pd.concat([df_new, gdp_covid_2015]) return df_new + def add_macro_COVID(scen, filename, check_converge=False): context = read_config() @@ -72,7 +82,7 @@ def add_macro_COVID(scen, filename, check_converge=False): nodes = info.N # Excel file for calibration data - xls_file = os.path.join('P:', 'ene.model', 'MACRO', 'python', filename) + xls_file = os.path.join("P:", "ene.model", "MACRO", "python", filename) # Making a dictionary from the MACRO Excel file xls = pd.ExcelFile(xls_file) @@ -84,14 +94,13 @@ def add_macro_COVID(scen, filename, check_converge=False): df_gdp = load_GDP_COVID() # substitute the gdp_calibrate - parname = 'gdp_calibrate' + parname = "gdp_calibrate" # keep the historical GDP to pass the GDP check at add_macro() df_gdphist = data[parname] df_gdphist = df_gdphist.loc[df_gdphist.year < info.y0] data[parname] = df_gdphist.append( - df_gdp.loc[df_gdp.year >= info.y0], - ignore_index=True + df_gdp.loc[df_gdp.year >= info.y0], ignore_index=True ) # Calibration @@ -99,6 +108,7 @@ def add_macro_COVID(scen, filename, check_converge=False): return scen + def modify_demand_and_hist_activity(scen): """Take care of demand changes due to the introduction of material parents Shed industrial energy demand properly. @@ -128,7 +138,7 @@ def modify_demand_and_hist_activity(scen): region_name_CHN = "" df = pd.read_excel( - context.get_local_path("material", fname), sheet_name=sheet_n, usecols="A:F" + private_data_path("material", fname), sheet_name=sheet_n, usecols="A:F" ) # Filter the necessary variables @@ -433,7 +443,7 @@ def add_emission_accounting(scen): | ("aluminum" in i) | ("petro" in i) | ("cement" in i) - | ('ref' in i) + | ("ref" in i) ) ] tec_list_materials.remove("refrigerant_recovery") @@ -460,13 +470,16 @@ def add_emission_accounting(scen): # Add thermal industry technologies to CO2_ind relation relation_activity_furnaces = scen.par( - "emission_factor", filters={"emission": 'CO2_industry', - "technology": tec_list_materials}) - relation_activity_furnaces['relation'] = 'CO2_ind' + "emission_factor", + filters={"emission": "CO2_industry", "technology": tec_list_materials}, + ) + relation_activity_furnaces["relation"] = "CO2_ind" relation_activity_furnaces["node_rel"] = relation_activity_furnaces["node_loc"] relation_activity_furnaces.drop(["year_vtg", "emission"], axis=1, inplace=True) relation_activity_furnaces["year_rel"] = relation_activity_furnaces["year_act"] - relation_activity_furnaces = relation_activity_furnaces[~relation_activity_furnaces['technology'].str.contains('_refining')] + relation_activity_furnaces = relation_activity_furnaces[ + ~relation_activity_furnaces["technology"].str.contains("_refining") + ] scen.check_out() scen.add_par("relation_activity", relation_activity) @@ -476,14 +489,14 @@ def add_emission_accounting(scen): # Add refinery technologies to CO2_cc relation_activity_ref = scen.par( - "emission_factor", filters={"emission": 'CO2_transformation', - "technology": tec_list_materials}) - relation_activity_ref['relation'] = 'CO2_cc' + "emission_factor", + filters={"emission": "CO2_transformation", "technology": tec_list_materials}, + ) + relation_activity_ref["relation"] = "CO2_cc" relation_activity_ref["node_rel"] = relation_activity_ref["node_loc"] relation_activity_ref.drop(["year_vtg", "emission"], axis=1, inplace=True) relation_activity_ref["year_rel"] = relation_activity_ref["year_act"] - scen.check_out() scen.add_par("relation_activity", relation_activity) scen.add_par("relation_activity", relation_activity_furnaces) @@ -491,74 +504,97 @@ def add_emission_accounting(scen): scen.commit("Emissions accounting for industry technologies added.") # Add feedstock using technologies to CO2_feedstocks - nodes = scen.par("relation_activity", filters = {'relation':'CO2_feedstocks'})["node_rel"].unique() - years = scen.par("relation_activity", filters = {'relation':'CO2_feedstocks'})["year_rel"].unique() + nodes = scen.par("relation_activity", filters={"relation": "CO2_feedstocks"})[ + "node_rel" + ].unique() + years = scen.par("relation_activity", filters={"relation": "CO2_feedstocks"})[ + "year_rel" + ].unique() for n in nodes: - for t in ['steam_cracker_petro','gas_processing_petro']: - for m in ['atm_gasoil', 'vacuum_gasoil', 'naphtha']: - if t == 'steam_cracker_petro': - if (m == 'atm_gasoil') | (m == 'vacuum_gasoil'): + for t in ["steam_cracker_petro", "gas_processing_petro"]: + for m in ["atm_gasoil", "vacuum_gasoil", "naphtha"]: + if t == "steam_cracker_petro": + if (m == "atm_gasoil") | (m == "vacuum_gasoil"): # fueloil emission factor * input val = 0.665 * 1.17683105 else: val = 0.665 * 1.537442922 - co2_feedstocks = pd.DataFrame({'relation': 'CO2_feedstocks', - 'node_rel': n, - 'year_rel': years, - 'node_loc': n, - 'technology': t, - 'year_act': years, - 'mode': m, - 'value': val, - 'unit': 't'}) + co2_feedstocks = pd.DataFrame( + { + "relation": "CO2_feedstocks", + "node_rel": n, + "year_rel": years, + "node_loc": n, + "technology": t, + "year_act": years, + "mode": m, + "value": val, + "unit": "t", + } + ) else: # gas emission factor * gas input val = 0.482 * 1.331811263 - co2_feedstocks = pd.DataFrame({'relation': 'CO2_feedstocks', - 'node_rel': n, - 'year_rel': years, - 'node_loc': n, - 'technology': t, - 'year_act': years, - 'mode': 'M1', - 'value': val, - 'unit': 't'}) + co2_feedstocks = pd.DataFrame( + { + "relation": "CO2_feedstocks", + "node_rel": n, + "year_rel": years, + "node_loc": n, + "technology": t, + "year_act": years, + "mode": "M1", + "value": val, + "unit": "t", + } + ) scen.check_out() - scen.add_par('relation_activity', co2_feedstocks) - scen.commit('co2_feedstocks updated') + scen.add_par("relation_activity", co2_feedstocks) + scen.commit("co2_feedstocks updated") # Correct CF4 Emission relations # Remove transport related technologies from CF4_Emissions scen.check_out() - CF4_trp_Emissions = scen.par("relation_activity", filters = {"relation": "CF4_Emission"}) + CF4_trp_Emissions = scen.par( + "relation_activity", filters={"relation": "CF4_Emission"} + ) list_tec_trp = [l for l in CF4_trp_Emissions["technology"].unique() if "trp" in l] - CF4_trp_Emissions = CF4_trp_Emissions[CF4_trp_Emissions["technology"].isin(list_tec_trp)] + CF4_trp_Emissions = CF4_trp_Emissions[ + CF4_trp_Emissions["technology"].isin(list_tec_trp) + ] - scen.remove_par("relation_activity",CF4_trp_Emissions) + scen.remove_par("relation_activity", CF4_trp_Emissions) # Remove transport related technologies from CF4_alm_red and add aluminum tecs. - CF4_red = scen.par("relation_activity", filters = {"relation": "CF4_alm_red"}) + CF4_red = scen.par("relation_activity", filters={"relation": "CF4_alm_red"}) list_tec_trp = [l for l in CF4_red["technology"].unique() if "trp" in l] CF4_red = CF4_red[CF4_red["technology"].isin(list_tec_trp)] - scen.remove_par("relation_activity",CF4_red) + scen.remove_par("relation_activity", CF4_red) - CF4_red_add = scen.par("emission_factor", filters = {"technology": ["soderberg_aluminum","prebake_aluminum"], "emission":"CF4"}) - CF4_red_add.drop(["year_vtg","emission"], axis = 1, inplace = True) + CF4_red_add = scen.par( + "emission_factor", + filters={ + "technology": ["soderberg_aluminum", "prebake_aluminum"], + "emission": "CF4", + }, + ) + CF4_red_add.drop(["year_vtg", "emission"], axis=1, inplace=True) CF4_red_add["relation"] = "CF4_alm_red" CF4_red_add["unit"] = "???" CF4_red_add["year_rel"] = CF4_red_add["year_act"] CF4_red_add["node_rel"] = CF4_red_add["node_loc"] - scen.add_par("relation_activity",CF4_red_add) + scen.add_par("relation_activity", CF4_red_add) scen.commit("CF4 relations corrected.") + def add_elec_lowerbound_2020(scen): # To avoid zero i_spec prices only for R12_CHN, add the below section. @@ -567,101 +603,203 @@ def add_elec_lowerbound_2020(scen): context = read_config() - input_residual_electricity = scen.par('input',filters={"technology": - "sp_el_I", "year_vtg": "2020", "year_act": "2020"}) + input_residual_electricity = scen.par( + "input", + filters={"technology": "sp_el_I", "year_vtg": "2020", "year_act": "2020"}, + ) # read processed final energy data from IEA extended energy balances # that is aggregated to MESSAGEix regions, fuels and (industry) sectors - final = pd.read_csv(context.get_local_path("material", 'residual_industry_2019.csv')) + final = pd.read_csv(private_data_path("material", "residual_industry_2019.csv")) # downselect needed fuels and sectors - final_residual_electricity = final.query('MESSAGE_fuel=="electr" & MESSAGE_sector=="industry_residual"') + final_residual_electricity = final.query( + 'MESSAGE_fuel=="electr" & MESSAGE_sector=="industry_residual"' + ) # join final energy data from IEA energy balances and input coefficients # from final-to-useful technologies from MESSAGEix - bound_residual_electricity = pd.merge(input_residual_electricity, - final_residual_electricity, left_on = "node_loc", - right_on = "MESSAGE_region", how = "inner") + bound_residual_electricity = pd.merge( + input_residual_electricity, + final_residual_electricity, + left_on="node_loc", + right_on="MESSAGE_region", + how="inner", + ) # derive useful energy values by dividing final energy by # input coefficient from final-to-useful technologies - bound_residual_electricity["value"] = bound_residual_electricity["Value"] / bound_residual_electricity["value"] + bound_residual_electricity["value"] = ( + bound_residual_electricity["Value"] / bound_residual_electricity["value"] + ) # downselect dataframe columns for MESSAGEix parameters - bound_residual_electricity = bound_residual_electricity.filter(items=['node_loc', - 'technology', 'year_act', 'mode', 'time', 'value', 'unit_x']) + bound_residual_electricity = bound_residual_electricity.filter( + items=["node_loc", "technology", "year_act", "mode", "time", "value", "unit_x"] + ) # rename columns if necessary - bound_residual_electricity.columns = ['node_loc', 'technology', 'year_act', - 'mode', 'time', 'value', 'unit'] + bound_residual_electricity.columns = [ + "node_loc", + "technology", + "year_act", + "mode", + "time", + "value", + "unit", + ] # Decrease 20% to aviod zero prices (the issue continiues otherwise) - bound_residual_electricity['value'] = bound_residual_electricity['value'] * 0.8 - bound_residual_electricity = bound_residual_electricity[bound_residual_electricity['node_loc'] == 'R12_CHN'] + bound_residual_electricity["value"] = bound_residual_electricity["value"] * 0.8 + bound_residual_electricity = bound_residual_electricity[ + bound_residual_electricity["node_loc"] == "R12_CHN" + ] scen.check_out() # add parameter dataframes to ixmp - scen.add_par('bound_activity_lo', bound_residual_electricity) + scen.add_par("bound_activity_lo", bound_residual_electricity) # Remove the previous bounds - remove_par_lo = scen.par('growth_activity_lo', filters = {'technology':'sp_el_I','year_act':2020,'node_loc':'R12_CHN'}) - scen.remove_par('growth_activity_lo',remove_par_lo) + remove_par_lo = scen.par( + "growth_activity_lo", + filters={"technology": "sp_el_I", "year_act": 2020, "node_loc": "R12_CHN"}, + ) + scen.remove_par("growth_activity_lo", remove_par_lo) scen.commit("added lower bound for activity of residual electricity technologies") def add_coal_lowerbound_2020(sc): - '''Set lower bounds for coal and i_spec as a calibration for 2020''' + """Set lower bounds for coal and i_spec as a calibration for 2020""" context = read_config() - final_resid = pd.read_csv(context.get_local_path("material", "residual_industry_2019.csv")) + final_resid = pd.read_csv( + private_data_path("material", "residual_industry_2019.csv") + ) # read input parameters for relevant technology/commodity combinations for converting betwen final and useful energy - input_residual_coal = sc.par('input',filters={"technology": "coal_i", "year_vtg": "2020", "year_act": "2020"}) - input_cement_coal = sc.par('input',filters={"technology": "furnace_coal_cement", "year_vtg": "2020", "year_act": "2020", "mode": "high_temp"}) - input_residual_electricity = sc.par('input',filters={"technology": "sp_el_I", "year_vtg": "2020", "year_act": "2020"}) + input_residual_coal = sc.par( + "input", + filters={"technology": "coal_i", "year_vtg": "2020", "year_act": "2020"}, + ) + input_cement_coal = sc.par( + "input", + filters={ + "technology": "furnace_coal_cement", + "year_vtg": "2020", + "year_act": "2020", + "mode": "high_temp", + }, + ) + input_residual_electricity = sc.par( + "input", + filters={"technology": "sp_el_I", "year_vtg": "2020", "year_act": "2020"}, + ) # downselect needed fuels and sectors - final_residual_coal = final_resid.query('MESSAGE_fuel=="coal" & MESSAGE_sector=="industry_residual"') - final_cement_coal = final_resid.query('MESSAGE_fuel=="coal" & MESSAGE_sector=="cement"') - final_residual_electricity = final_resid.query('MESSAGE_fuel=="electr" & MESSAGE_sector=="industry_residual"') + final_residual_coal = final_resid.query( + 'MESSAGE_fuel=="coal" & MESSAGE_sector=="industry_residual"' + ) + final_cement_coal = final_resid.query( + 'MESSAGE_fuel=="coal" & MESSAGE_sector=="cement"' + ) + final_residual_electricity = final_resid.query( + 'MESSAGE_fuel=="electr" & MESSAGE_sector=="industry_residual"' + ) # join final energy data from IEA energy balances and input coefficients from final-to-useful technologies from MESSAGEix - bound_coal = pd.merge(input_residual_coal, final_residual_coal, left_on = "node_loc", right_on = "MESSAGE_region", how = "inner") - bound_cement_coal = pd.merge(input_cement_coal, final_cement_coal, left_on = "node_loc", right_on = "MESSAGE_region", how = "inner") - bound_residual_electricity = pd.merge(input_residual_electricity, final_residual_electricity, left_on = "node_loc", right_on = "MESSAGE_region", how = "inner") + bound_coal = pd.merge( + input_residual_coal, + final_residual_coal, + left_on="node_loc", + right_on="MESSAGE_region", + how="inner", + ) + bound_cement_coal = pd.merge( + input_cement_coal, + final_cement_coal, + left_on="node_loc", + right_on="MESSAGE_region", + how="inner", + ) + bound_residual_electricity = pd.merge( + input_residual_electricity, + final_residual_electricity, + left_on="node_loc", + right_on="MESSAGE_region", + how="inner", + ) # derive useful energy values by dividing final energy by input coefficient from final-to-useful technologies bound_coal["value"] = bound_coal["Value"] / bound_coal["value"] bound_cement_coal["value"] = bound_cement_coal["Value"] / bound_cement_coal["value"] - bound_residual_electricity["value"] = bound_residual_electricity["Value"] / bound_residual_electricity["value"] + bound_residual_electricity["value"] = ( + bound_residual_electricity["Value"] / bound_residual_electricity["value"] + ) # downselect dataframe columns for MESSAGEix parameters - bound_coal = bound_coal.filter(items=['node_loc', 'technology', 'year_act', 'mode', 'time', 'value', 'unit_x']) - bound_cement_coal = bound_cement_coal.filter(items=['node_loc', 'technology', 'year_act', 'mode', 'time', 'value', 'unit_x']) - bound_residual_electricity = bound_residual_electricity.filter(items=['node_loc', 'technology', 'year_act', 'mode', 'time', 'value', 'unit_x']) + bound_coal = bound_coal.filter( + items=["node_loc", "technology", "year_act", "mode", "time", "value", "unit_x"] + ) + bound_cement_coal = bound_cement_coal.filter( + items=["node_loc", "technology", "year_act", "mode", "time", "value", "unit_x"] + ) + bound_residual_electricity = bound_residual_electricity.filter( + items=["node_loc", "technology", "year_act", "mode", "time", "value", "unit_x"] + ) # rename columns if necessary - bound_coal.columns = ['node_loc', 'technology', 'year_act', 'mode', 'time', 'value', 'unit'] - bound_cement_coal.columns = ['node_loc', 'technology', 'year_act', 'mode', 'time', 'value', 'unit'] - bound_residual_electricity.columns = ['node_loc', 'technology', 'year_act', 'mode', 'time', 'value', 'unit'] + bound_coal.columns = [ + "node_loc", + "technology", + "year_act", + "mode", + "time", + "value", + "unit", + ] + bound_cement_coal.columns = [ + "node_loc", + "technology", + "year_act", + "mode", + "time", + "value", + "unit", + ] + bound_residual_electricity.columns = [ + "node_loc", + "technology", + "year_act", + "mode", + "time", + "value", + "unit", + ] # (Artificially) lower bounds when i_spec act is too close to the bounds (avoid 0-price for macro calibration) more = ["R12_MEA", "R12_EEU", "R12_SAS", "R12_PAS"] # import pdb; pdb.set_trace() - bound_residual_electricity.loc[bound_residual_electricity.node_loc.isin(["R12_PAO"]), 'value'] *= 0.80 - bound_residual_electricity.loc[bound_residual_electricity.node_loc.isin(more), 'value'] *= 0.85 + bound_residual_electricity.loc[ + bound_residual_electricity.node_loc.isin(["R12_PAO"]), "value" + ] *= 0.80 + bound_residual_electricity.loc[ + bound_residual_electricity.node_loc.isin(more), "value" + ] *= 0.85 sc.check_out() # add parameter dataframes to ixmp - sc.add_par('bound_activity_lo', bound_coal) - sc.add_par('bound_activity_lo', bound_cement_coal) - sc.add_par('bound_activity_lo', bound_residual_electricity) + sc.add_par("bound_activity_lo", bound_coal) + sc.add_par("bound_activity_lo", bound_cement_coal) + sc.add_par("bound_activity_lo", bound_residual_electricity) # commit scenario to ixmp backend - sc.commit("added lower bound for activity of residual industrial coal and cement coal furnace technologies and adjusted 2020 residual industrial electricity demand") + sc.commit( + "added lower bound for activity of residual industrial coal and cement coal furnace technologies and adjusted 2020 residual industrial electricity demand" + ) + def read_sector_data(scenario, sectname): @@ -682,7 +820,8 @@ def read_sector_data(scenario, sectname): # data_df = data_steel_china.append(data_cement_china, ignore_index=True) data_df = pd.read_excel( - context.get_local_path("material", context.datafile), sheet_name=sheet_n, + private_data_path("material", context.datafile), + sheet_name=sheet_n, ) # Clean the data @@ -725,6 +864,7 @@ def read_sector_data(scenario, sectname): return data_df + # Add the relevant ccs technologies to the co2_trans_disp and bco2_trans_disp # relations def add_ccs_technologies(scen): @@ -735,34 +875,40 @@ def add_ccs_technologies(scen): # The relation coefficients for CO2_Emision and bco2_trans_disp and # co2_trans_disp are both MtC. The emission factor for CCS add_ccs_technologies # are specified in MtC as well. - bco2_trans_relation = scen.par('emission_factor', - filters = {'technology':'biomass_NH3_ccs', - 'emission':'CO2'}) - co2_trans_relation = scen.par('emission_factor', - filters = {'technology':['clinker_dry_ccs_cement', - 'clinker_wet_ccs_cement', - 'gas_NH3_ccs', - 'coal_NH3_ccs', - 'fueloil_NH3_ccs'], - 'emission':'CO2'}) - - bco2_trans_relation.drop(['year_vtg','emission','unit'], axis = 1, - inplace = True) - bco2_trans_relation['relation'] = 'bco2_trans_disp' - bco2_trans_relation['node_rel'] = bco2_trans_relation['node_loc'] - bco2_trans_relation['year_rel'] = bco2_trans_relation['year_act'] - bco2_trans_relation['unit'] = '???' - - co2_trans_relation.drop(['year_vtg','emission','unit'], axis = 1, inplace = True) - co2_trans_relation['relation'] = 'co2_trans_disp' - co2_trans_relation['node_rel'] = co2_trans_relation['node_loc'] - co2_trans_relation['year_rel'] = co2_trans_relation['year_act'] - co2_trans_relation['unit'] = '???' + bco2_trans_relation = scen.par( + "emission_factor", filters={"technology": "biomass_NH3_ccs", "emission": "CO2"} + ) + co2_trans_relation = scen.par( + "emission_factor", + filters={ + "technology": [ + "clinker_dry_ccs_cement", + "clinker_wet_ccs_cement", + "gas_NH3_ccs", + "coal_NH3_ccs", + "fueloil_NH3_ccs", + ], + "emission": "CO2", + }, + ) + + bco2_trans_relation.drop(["year_vtg", "emission", "unit"], axis=1, inplace=True) + bco2_trans_relation["relation"] = "bco2_trans_disp" + bco2_trans_relation["node_rel"] = bco2_trans_relation["node_loc"] + bco2_trans_relation["year_rel"] = bco2_trans_relation["year_act"] + bco2_trans_relation["unit"] = "???" + + co2_trans_relation.drop(["year_vtg", "emission", "unit"], axis=1, inplace=True) + co2_trans_relation["relation"] = "co2_trans_disp" + co2_trans_relation["node_rel"] = co2_trans_relation["node_loc"] + co2_trans_relation["year_rel"] = co2_trans_relation["year_act"] + co2_trans_relation["unit"] = "???" scen.check_out() - scen.add_par('relation_activity', bco2_trans_relation) - scen.add_par('relation_activity', co2_trans_relation) - scen.commit('New CCS technologies added to the CO2 accounting relations.') + scen.add_par("relation_activity", bco2_trans_relation) + scen.add_par("relation_activity", co2_trans_relation) + scen.commit("New CCS technologies added to the CO2 accounting relations.") + # Read in time-dependent parameters # Now only used to add fuel cost for bare model @@ -785,7 +931,7 @@ def read_timeseries(scenario, filename): sheet_n = "timeseries_R11" # Read the file - df = pd.read_excel(context.get_local_path("material", filename), sheet_name=sheet_n) + df = pd.read_excel(private_data_path("material", filename), sheet_name=sheet_n) import numbers @@ -819,7 +965,8 @@ def read_rel(scenario, filename): # Read the file data_rel = pd.read_excel( - context.get_local_path("material", filename), sheet_name=sheet_n, + private_data_path("material", filename), + sheet_name=sheet_n, ) return data_rel From 0e0e3e324794457ebf791c7e2d55b09743f6e93f Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 11 Oct 2022 12:38:23 +0200 Subject: [PATCH 400/774] Modify methanol output level of new meth tecs --- message_ix_models/data/material/meth_bio_techno_economic.xlsx | 4 ++-- message_ix_models/data/material/meth_h2_techno_economic.xlsx | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/message_ix_models/data/material/meth_bio_techno_economic.xlsx b/message_ix_models/data/material/meth_bio_techno_economic.xlsx index 04d9d3e8e9..f9bea3ca1c 100644 --- a/message_ix_models/data/material/meth_bio_techno_economic.xlsx +++ b/message_ix_models/data/material/meth_bio_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:388a9cdf5a461f528ea4659eda3a3eb044f85a53f91ba3f190e3bc9e3f4b3f5d -size 47985 +oid sha256:81d201109358d1247122c8673d8a027de29095b28dc95449bbf2a78a2a4247bc +size 59516 diff --git a/message_ix_models/data/material/meth_h2_techno_economic.xlsx b/message_ix_models/data/material/meth_h2_techno_economic.xlsx index 18cc380dfb..2b5b952464 100644 --- a/message_ix_models/data/material/meth_h2_techno_economic.xlsx +++ b/message_ix_models/data/material/meth_h2_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:1b9c8a9ea75bfc9c180ae3023990b5cb9337870963bb569f68bc539654236be2 -size 248102 +oid sha256:4a2e2f87fc6c23b48c933373684a2e6c0d030646b9ea14fcf8d8a6088b219422 +size 249040 From 94f3d516e80595a6b54cba104f4871340e719d6f Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 11 Oct 2022 12:39:38 +0200 Subject: [PATCH 401/774] Convert mto co2 emissions to mt c --- message_ix_models/data/material/MTO data collection.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/MTO data collection.xlsx b/message_ix_models/data/material/MTO data collection.xlsx index db7f9fc3b9..8c34e66adc 100644 --- a/message_ix_models/data/material/MTO data collection.xlsx +++ b/message_ix_models/data/material/MTO data collection.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:5487566b72995f5fee734c609c382c1632223b66aaabe7c6c6628de171e9f711 -size 170424 +oid sha256:cdaaf64a313bbb318f5531f1f0f3851ccb94b2e1cdc84916a684453dd97712bf +size 174565 From e7919cd987997846ead46c267c5e78602f66a175 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 11 Oct 2022 12:40:40 +0200 Subject: [PATCH 402/774] Rename meth_bal_material --- message_ix_models/data/material/meth_bal_pars.xlsx | 3 --- message_ix_models/data/material/meth_t_d_material_pars.xlsx | 3 +++ 2 files changed, 3 insertions(+), 3 deletions(-) delete mode 100644 message_ix_models/data/material/meth_bal_pars.xlsx create mode 100644 message_ix_models/data/material/meth_t_d_material_pars.xlsx diff --git a/message_ix_models/data/material/meth_bal_pars.xlsx b/message_ix_models/data/material/meth_bal_pars.xlsx deleted file mode 100644 index 82c971b89a..0000000000 --- a/message_ix_models/data/material/meth_bal_pars.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:ad7498da83aaa8ae53b43f6029172a9cac8344bbe59c1af2ef6c43ccca021bae -size 65306 diff --git a/message_ix_models/data/material/meth_t_d_material_pars.xlsx b/message_ix_models/data/material/meth_t_d_material_pars.xlsx new file mode 100644 index 0000000000..7fa38208a8 --- /dev/null +++ b/message_ix_models/data/material/meth_t_d_material_pars.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:87f82c6235212e4d5525b4f939c683f469b256d4a4a48e4832600db53650c066 +size 56679 From 344dcb6505683dcd93a868ee392c74fa3cbf459b Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 11 Oct 2022 12:45:05 +0200 Subject: [PATCH 403/774] Adapt data_methanol.py for sensitivity analysis --- .../model/material/data_methanol.py | 25 +++++++++++-------- 1 file changed, 15 insertions(+), 10 deletions(-) diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index 345a946e9a..e48cebce30 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -15,6 +15,10 @@ def gen_data_methanol(scenario): + df_pars = pd.read_excel(context.get_local_path("material", "methanol_sensitivity_pars.xlsx"), + sheet_name="Sheet1", dtype=object) + pars = df_pars.set_index("par").to_dict()["value"] + dict1 = gen_data_meth_h2() dict2 = gen_data_meth_bio() keys = set(list(dict1.keys())+list(dict2.keys())) @@ -88,18 +92,19 @@ def gen_data_methanol(scenario): if ~(i in new_dict2.keys()) & (i in resin_dict.keys()): new_dict2[i] = resin_dict[i] - new_dict2["demand"] = new_dict2["demand"].append(gen_resin_demand(scenario, 0.03, "residential")) - new_dict2["demand"] = new_dict2["demand"].append(gen_resin_demand(scenario, 0.03, "comm")) + new_dict2["demand"] = new_dict2["demand"].append(gen_resin_demand(scenario, pars["resin_share"], "residential")) + new_dict2["demand"] = new_dict2["demand"].append(gen_resin_demand(scenario, pars["resin_share"], "comm")) new_dict2["input"].append(add_methanol_trp_additives(scenario)) - emission_dict = { - "node": "World", - "type_emission": "TCE_CO2", - "type_tec": "all", - "type_year": "cumulative", - "unit": "???" - } - new_dict2["bound_emission"] = make_df("bound_emission", value=3667, **emission_dict) + if pars["cbudget"]: + emission_dict = { + "node": "World", + "type_emission": "TCE_CO2", + "type_tec": "all", + "type_year": "cumulative", + "unit": "???" + } + new_dict2["bound_emission"] = make_df("bound_emission", value=3667, **emission_dict) return new_dict2 From eed1e2f59c169cb30c72c1c7c3d74089fcdac316 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 11 Oct 2022 12:46:03 +0200 Subject: [PATCH 404/774] Fix meth_t_d_material read-in --- message_ix_models/model/material/data_methanol.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index e48cebce30..07beea7d15 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -29,7 +29,7 @@ def gen_data_methanol(scenario): else: new_dict[i] = dict1[i] - dict3 = pd.read_excel(context.get_local_path("material", "meth_bal_pars.xlsx"), sheet_name=None) + dict3 = pd.read_excel(context.get_local_path("material", "meth_t_d_material_pars.xlsx"), sheet_name=None) keys = set(list(dict3.keys())+list(new_dict.keys())) new_dict2 = {} From 738aef3de37de8d92e4673a2b3ecfe117fb22e46 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 11 Oct 2022 12:47:58 +0200 Subject: [PATCH 405/774] Move demand calculation from excel to python function --- .../data/material/methanol demand.xlsx | 4 +- .../model/material/data_methanol.py | 48 +++++++++++++++---- 2 files changed, 42 insertions(+), 10 deletions(-) diff --git a/message_ix_models/data/material/methanol demand.xlsx b/message_ix_models/data/material/methanol demand.xlsx index 2b40f64293..dd45ea6f1d 100644 --- a/message_ix_models/data/material/methanol demand.xlsx +++ b/message_ix_models/data/material/methanol demand.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:44a4307d1a60f703bc8cc38e1486608079006d68d17f9206c5f8557e02d92733 -size 70758 +oid sha256:5397faa6297815cd7d3f8e7897c6e8d21b8a3a0a2cc47e6a8c962fc7fda6c2eb +size 51010 diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index 07beea7d15..508a7778c8 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -40,13 +40,8 @@ def gen_data_methanol(scenario): new_dict2[i] = dict3[i] if ~(i in dict3.keys()) & (i in new_dict.keys()): new_dict2[i] = new_dict[i] - # still contains ref_activity parameter! remove! - df = pd.read_excel(context.get_local_path("material", "methanol demand.xlsx"), sheet_name="methanol_demand") - df = df[(~df["Region"].isna()) & (df["Region"] != "World")] - df = df.dropna(axis=1) - df_melt = df.melt(id_vars=["Region"], value_vars=df.columns[5:], var_name="year") - df_final = message_ix.make_df("demand", unit="t", level="final_material", value=df_melt.value, time="year", - commodity="methanol", year=df_melt.year, node=("R12_"+df_melt["Region"])) + + df_final = gen_meth_residual_demand(pars["methanol_elasticity"]) df_final["value"] = df_final["value"].apply(lambda x: x * 0.5) new_dict2["demand"] = df_final @@ -239,4 +234,41 @@ def gen_resin_demand(scenario, resin_share, sector): df[["R12", "year", "resin_demand"]], left_on=["node", "year"], right_on=["R12", "year"]) df_demand["value"] = df_demand["resin_demand"] df_demand = make_df("demand", **df_demand) - return df_demand \ No newline at end of file + return df_demand + + +def gen_meth_residual_demand(gdp_elasticity): + def get_demand_t1_with_income_elasticity(demand_t0, income_t0, income_t1, elasticity): + return (elasticity * demand_t0 * ((income_t1 - income_t0) / income_t0)) + demand_t0 + + df_gdp = pd.read_excel( + context.get_local_path("material", "methanol demand.xlsx"), + sheet_name="GDP_baseline") + + df = df_gdp[(~df_gdp["Region"].isna()) & (df_gdp["Region"] != "World")] + df = df.dropna(axis=1) + + df_demand_meth = pd.read_excel(context.get_local_path("material", "methanol demand.xlsx"), + sheet_name="methanol_demand", skiprows=[12]) + df_demand_meth = df_demand_meth[(~df_demand_meth["Region"].isna()) & (df_demand_meth["Region"] != "World")] + df_demand_meth = df_demand_meth.dropna(axis=1) + + df_demand = df.copy(deep=True) + df_demand = df_demand.drop([2010, 2015, 2020], axis=1) + + years = list(df_demand_meth.columns[5:]) + dem_2020 = df_demand_meth[2020].values + for i in range(len(years) - 1): + income_year1 = years[i] + income_year2 = years[i + 1] + + dem_2020 = get_demand_t1_with_income_elasticity(dem_2020, + df[income_year1], + df[income_year2], + gdp_elasticity) + df_demand[income_year2] = dem_2020 + + df_melt = df_demand.melt(id_vars=["Region"], value_vars=df_demand.columns[5:], var_name="year") + return message_ix.make_df("demand", unit="t", level="final_material", value=df_melt.value, + time="year", commodity="methanol", year=df_melt.year, + node=("R12_" + df_melt["Region"])) From 17dd8e21bbbe97a7d09a24191cb2a4d81b0d3852 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 11 Oct 2022 12:49:11 +0200 Subject: [PATCH 406/774] Add activity bound to mto in china 2020 --- message_ix_models/model/material/data_methanol.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index 508a7778c8..c83b1a87b3 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -167,10 +167,12 @@ def gen_data_mto(scenario, chemical): "unit": "???" } if chemical == "MTO": - par_dict["historical_activity"] = make_df("historical_activity", value=[4.5, 11], year_act=[2015, 2020], **hist_dict) - #par_dict["historical_new_capacity"] = make_df("historical_new_capacity", value=[1.2, 1.2], year_vtg=[2015, 2020], **hist_dict) + par_dict["historical_activity"] = make_df("historical_activity", value=4.5, year_act=2015, **hist_dict) + # par_dict["historical_new_capacity"] = make_df("historical_new_capacity", value=[1.2, 1.2], year_vtg=[2015, 2020], **hist_dict) par_dict["historical_new_capacity"] = make_df("historical_new_capacity", value=1.2, year_vtg=2015, - **hist_dict) + **hist_dict) + par_dict["bound_total_capacity_lo"] = make_df("bound_total_capacity_lo", year_act=2020, value=9, **hist_dict) + return par_dict From 2b8d58dc93cba4d35f87bf2a8863c1491b7fb4d3 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 11 Oct 2022 12:50:31 +0200 Subject: [PATCH 407/774] Rename mto function and format file --- .../model/material/data_methanol.py | 29 ++++++++++--------- 1 file changed, 15 insertions(+), 14 deletions(-) diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index c83b1a87b3..5a201ace18 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -21,7 +21,7 @@ def gen_data_methanol(scenario): dict1 = gen_data_meth_h2() dict2 = gen_data_meth_bio() - keys = set(list(dict1.keys())+list(dict2.keys())) + keys = set(list(dict1.keys()) + list(dict2.keys())) new_dict = {} for i in keys: if (i in dict2.keys()) & (i in dict1.keys()): @@ -31,7 +31,7 @@ def gen_data_methanol(scenario): dict3 = pd.read_excel(context.get_local_path("material", "meth_t_d_material_pars.xlsx"), sheet_name=None) - keys = set(list(dict3.keys())+list(new_dict.keys())) + keys = set(list(dict3.keys()) + list(new_dict.keys())) new_dict2 = {} for i in keys: if (i in dict3.keys()) & (i in new_dict.keys()): @@ -52,18 +52,19 @@ def gen_data_methanol(scenario): row["value"] = (47 / 1.3498) * 0.9 new_dict2["historical_activity"] = pd.concat([new_dict2["historical_activity"], pd.DataFrame(row).T]) # derived from graphic in "Methanol production statstics.xlsx/China demand split" diagram - hist_cap = message_ix.make_df("historical_new_capacity", node_loc="R12_CHN", technology="meth_coal", year_vtg=2015, value=9.6, - unit="GW") + hist_cap = message_ix.make_df("historical_new_capacity", node_loc="R12_CHN", technology="meth_coal", year_vtg=2015, + value=9.6, + unit="GW") new_dict2["historical_new_capacity"] = hist_cap # fix demand infeasibility - #act = scenario.par("historical_activity") - #row = act[act["technology"].str.startswith("meth")].sort_values("value", ascending=False).iloc[0] - #row["value"] = 0.0 + # act = scenario.par("historical_activity") + # row = act[act["technology"].str.startswith("meth")].sort_values("value", ascending=False).iloc[0] + # row["value"] = 0.0 new_dict2["historical_activity"] = pd.concat([new_dict2["historical_activity"], pd.DataFrame(row).T]) df_ng = pd.read_excel(context.get_local_path("material", "meth_ng_techno_economic.xlsx")) new_dict2["historical_activity"] = new_dict2["historical_activity"].append(df_ng) - mto_dict = gen_data_mto(scenario, "MTO") + mto_dict = gen_data_meth_chemicals(scenario, "MTO") keys = set(list(new_dict2.keys()) + list(mto_dict.keys())) for i in keys: if (i in new_dict2.keys()) & (i in mto_dict.keys()): @@ -71,7 +72,7 @@ def gen_data_methanol(scenario): if ~(i in new_dict2.keys()) & (i in mto_dict.keys()): new_dict2[i] = mto_dict[i] - ch2o_dict = gen_data_mto(scenario, "Formaldehyde") + ch2o_dict = gen_data_meth_chemicals(scenario, "Formaldehyde") keys = set(list(new_dict2.keys()) + list(ch2o_dict.keys())) for i in keys: if (i in new_dict2.keys()) & (i in ch2o_dict.keys()): @@ -79,7 +80,7 @@ def gen_data_methanol(scenario): if ~(i in new_dict2.keys()) & (i in ch2o_dict.keys()): new_dict2[i] = ch2o_dict[i] - resin_dict = gen_data_mto(scenario, "Resins") + resin_dict = gen_data_meth_chemicals(scenario, "Resins") keys = set(list(new_dict2.keys()) + list(resin_dict.keys())) for i in keys: if (i in new_dict2.keys()) & (i in resin_dict.keys()): @@ -117,11 +118,11 @@ def gen_data_meth_bio(): return df_h2 -def gen_data_mto(scenario, chemical): +def gen_data_meth_chemicals(scenario, chemical): df = pd.read_excel(context.get_local_path("material", "MTO data collection.xlsx"), sheet_name=chemical, usecols=[1, 2, 3, 4, 6, 7]) - #exclude emissions for now + # exclude emissions for now if chemical == "MTO": df = df.iloc[:10, ] if chemical == "Formaldehyde": @@ -209,7 +210,7 @@ def get_meth_share(df, node): def gen_resin_demand(scenario, resin_share, sector): df = pd.read_csv( - context.get_local_path("material", "results_material_SHAPE_"+sector+".csv")) + context.get_local_path("material", "results_material_SHAPE_" + sector + ".csv")) resin_intensity = resin_share df = df[df["scenario"] == "SH2"] df = df[df["material"] == "wood"].assign(resin_demand=df["mat_demand_Mt"] * resin_intensity) @@ -232,7 +233,7 @@ def gen_resin_demand(scenario, resin_share, sector): nodes = nodes.drop(5).reset_index(drop=True) df_demand = make_df("demand", year=all_years["year_act"].unique()[:-1], **common).pipe(broadcast, - node=nodes).merge( + node=nodes).merge( df[["R12", "year", "resin_demand"]], left_on=["node", "year"], right_on=["R12", "year"]) df_demand["value"] = df_demand["resin_demand"] df_demand = make_df("demand", **df_demand) From df0b356b0dc273a23b33fa6c0aa6a73b4356de88 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 11 Oct 2022 12:52:13 +0200 Subject: [PATCH 408/774] Add methanol sensivity analysis input file --- message_ix_models/data/material/methanol_sensitivity_pars.xlsx | 3 +++ 1 file changed, 3 insertions(+) create mode 100644 message_ix_models/data/material/methanol_sensitivity_pars.xlsx diff --git a/message_ix_models/data/material/methanol_sensitivity_pars.xlsx b/message_ix_models/data/material/methanol_sensitivity_pars.xlsx new file mode 100644 index 0000000000..8650c03e57 --- /dev/null +++ b/message_ix_models/data/material/methanol_sensitivity_pars.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:62d314c435cd4f024ad5e0a40f6aa80c192f8715c03fbbb856fe0433cc061902 +size 9717 From 28c5eb72b6246808c236856cbf69e65d42e743d0 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 11 Oct 2022 14:20:49 +0200 Subject: [PATCH 409/774] Add sensitivity parameters in all functions --- message_ix_models/model/material/data_methanol.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index 5a201ace18..e328f5cdb8 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -88,8 +88,8 @@ def gen_data_methanol(scenario): if ~(i in new_dict2.keys()) & (i in resin_dict.keys()): new_dict2[i] = resin_dict[i] - new_dict2["demand"] = new_dict2["demand"].append(gen_resin_demand(scenario, pars["resin_share"], "residential")) - new_dict2["demand"] = new_dict2["demand"].append(gen_resin_demand(scenario, pars["resin_share"], "comm")) + new_dict2["demand"] = new_dict2["demand"].append(gen_resin_demand(scenario, pars["resin_share"], "residential", pars["wood_scenario"])) + new_dict2["demand"] = new_dict2["demand"].append(gen_resin_demand(scenario, pars["resin_share"], "comm", pars["wood_scenario"])) new_dict2["input"].append(add_methanol_trp_additives(scenario)) if pars["cbudget"]: @@ -208,11 +208,11 @@ def get_meth_share(df, node): return pd.concat([df_loil, df_loil_meth]) -def gen_resin_demand(scenario, resin_share, sector): +def gen_resin_demand(scenario, resin_share, sector, buildings_scen): df = pd.read_csv( context.get_local_path("material", "results_material_SHAPE_" + sector + ".csv")) resin_intensity = resin_share - df = df[df["scenario"] == "SH2"] + df = df[df["scenario"] == buildings_scen] df = df[df["material"] == "wood"].assign(resin_demand=df["mat_demand_Mt"] * resin_intensity) df["R12"] = "R12_" + df["R12"] From 9c992ff91cefb469c4f3c87a18961351a57b5ef7 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 11 Oct 2022 14:23:18 +0200 Subject: [PATCH 410/774] Apply black to data_methanol.py --- .../model/material/data_methanol.py | 197 +++++++++++++----- 1 file changed, 141 insertions(+), 56 deletions(-) diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index e328f5cdb8..21eb24db44 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -15,8 +15,11 @@ def gen_data_methanol(scenario): - df_pars = pd.read_excel(context.get_local_path("material", "methanol_sensitivity_pars.xlsx"), - sheet_name="Sheet1", dtype=object) + df_pars = pd.read_excel( + context.get_local_path("material", "methanol_sensitivity_pars.xlsx"), + sheet_name="Sheet1", + dtype=object, + ) pars = df_pars.set_index("par").to_dict()["value"] dict1 = gen_data_meth_h2() @@ -29,7 +32,10 @@ def gen_data_methanol(scenario): else: new_dict[i] = dict1[i] - dict3 = pd.read_excel(context.get_local_path("material", "meth_t_d_material_pars.xlsx"), sheet_name=None) + dict3 = pd.read_excel( + context.get_local_path("material", "meth_t_d_material_pars.xlsx"), + sheet_name=None, + ) keys = set(list(dict3.keys()) + list(new_dict.keys())) new_dict2 = {} @@ -47,21 +53,36 @@ def gen_data_methanol(scenario): # fix demand infeasibility act = scenario.par("historical_activity") - row = act[act["technology"].str.startswith("meth")].sort_values("value", ascending=False).iloc[0] + row = ( + act[act["technology"].str.startswith("meth")] + .sort_values("value", ascending=False) + .iloc[0] + ) # china meth_coal production (90% coal share on 2015 47 Mt total; 1.348 = Mt to GWa ) row["value"] = (47 / 1.3498) * 0.9 - new_dict2["historical_activity"] = pd.concat([new_dict2["historical_activity"], pd.DataFrame(row).T]) + new_dict2["historical_activity"] = pd.concat( + [new_dict2["historical_activity"], pd.DataFrame(row).T] + ) # derived from graphic in "Methanol production statstics.xlsx/China demand split" diagram - hist_cap = message_ix.make_df("historical_new_capacity", node_loc="R12_CHN", technology="meth_coal", year_vtg=2015, - value=9.6, - unit="GW") + hist_cap = message_ix.make_df( + "historical_new_capacity", + node_loc="R12_CHN", + technology="meth_coal", + year_vtg=2015, + value=9.6, + unit="GW", + ) new_dict2["historical_new_capacity"] = hist_cap # fix demand infeasibility # act = scenario.par("historical_activity") # row = act[act["technology"].str.startswith("meth")].sort_values("value", ascending=False).iloc[0] # row["value"] = 0.0 - new_dict2["historical_activity"] = pd.concat([new_dict2["historical_activity"], pd.DataFrame(row).T]) - df_ng = pd.read_excel(context.get_local_path("material", "meth_ng_techno_economic.xlsx")) + new_dict2["historical_activity"] = pd.concat( + [new_dict2["historical_activity"], pd.DataFrame(row).T] + ) + df_ng = pd.read_excel( + context.get_local_path("material", "meth_ng_techno_economic.xlsx") + ) new_dict2["historical_activity"] = new_dict2["historical_activity"].append(df_ng) mto_dict = gen_data_meth_chemicals(scenario, "MTO") @@ -88,8 +109,14 @@ def gen_data_methanol(scenario): if ~(i in new_dict2.keys()) & (i in resin_dict.keys()): new_dict2[i] = resin_dict[i] - new_dict2["demand"] = new_dict2["demand"].append(gen_resin_demand(scenario, pars["resin_share"], "residential", pars["wood_scenario"])) - new_dict2["demand"] = new_dict2["demand"].append(gen_resin_demand(scenario, pars["resin_share"], "comm", pars["wood_scenario"])) + new_dict2["demand"] = new_dict2["demand"].append( + gen_resin_demand( + scenario, pars["resin_share"], "residential", pars["wood_scenario"] + ) + ) + new_dict2["demand"] = new_dict2["demand"].append( + gen_resin_demand(scenario, pars["resin_share"], "comm", pars["wood_scenario"]) + ) new_dict2["input"].append(add_methanol_trp_additives(scenario)) if pars["cbudget"]: @@ -98,35 +125,49 @@ def gen_data_methanol(scenario): "type_emission": "TCE_CO2", "type_tec": "all", "type_year": "cumulative", - "unit": "???" + "unit": "???", } - new_dict2["bound_emission"] = make_df("bound_emission", value=3667, **emission_dict) + new_dict2["bound_emission"] = make_df( + "bound_emission", value=3667, **emission_dict + ) return new_dict2 def gen_data_meth_h2(): context = read_config() - df_h2 = pd.read_excel(context.get_local_path("material", "meth_h2_techno_economic.xlsx"), sheet_name=None) + df_h2 = pd.read_excel( + context.get_local_path("material", "meth_h2_techno_economic.xlsx"), + sheet_name=None, + ) return df_h2 def gen_data_meth_bio(): context = read_config() context.get_local_path("material", "meth_bio_techno_economic.xlsx") - df_h2 = pd.read_excel(context.get_local_path("material", "meth_bio_techno_economic.xlsx"), sheet_name=None) + df_h2 = pd.read_excel( + context.get_local_path("material", "meth_bio_techno_economic.xlsx"), + sheet_name=None, + ) return df_h2 def gen_data_meth_chemicals(scenario, chemical): - df = pd.read_excel(context.get_local_path("material", "MTO data collection.xlsx"), - sheet_name=chemical, - usecols=[1, 2, 3, 4, 6, 7]) + df = pd.read_excel( + context.get_local_path("material", "MTO data collection.xlsx"), + sheet_name=chemical, + usecols=[1, 2, 3, 4, 6, 7], + ) # exclude emissions for now if chemical == "MTO": - df = df.iloc[:10, ] + df = df.iloc[ + :10, + ] if chemical == "Formaldehyde": - df = df.iloc[:9, ] + df = df.iloc[ + :9, + ] common = dict( # commodity="NH3", @@ -136,7 +177,7 @@ def gen_data_meth_chemicals(scenario, chemical): time_dest="year", time_origin="year", emission="CO2_industry", # confirm if correct - relation="CO2_cc" + relation="CO2_cc", ) all_years = scenario.vintage_and_active_years() @@ -149,9 +190,10 @@ def gen_data_meth_chemicals(scenario, chemical): for i in df["parameter"]: for index, row in df[df["parameter"] == i].iterrows(): par_dict[i] = par_dict[i].append( - make_df(i, **all_years.to_dict(orient="list"), **row, **common).pipe(broadcast, - node_loc=nodes).pipe( - same_node)) + make_df(i, **all_years.to_dict(orient="list"), **row, **common) + .pipe(broadcast, node_loc=nodes) + .pipe(same_node) + ) if i == "relation_activity": par_dict[i]["year_rel"] = par_dict[i]["year_act"] @@ -165,14 +207,19 @@ def gen_data_meth_chemicals(scenario, chemical): "technology": "MTO", "mode": "M1", "time": "year", - "unit": "???" + "unit": "???", } if chemical == "MTO": - par_dict["historical_activity"] = make_df("historical_activity", value=4.5, year_act=2015, **hist_dict) + par_dict["historical_activity"] = make_df( + "historical_activity", value=4.5, year_act=2015, **hist_dict + ) # par_dict["historical_new_capacity"] = make_df("historical_new_capacity", value=[1.2, 1.2], year_vtg=[2015, 2020], **hist_dict) - par_dict["historical_new_capacity"] = make_df("historical_new_capacity", value=1.2, year_vtg=2015, - **hist_dict) - par_dict["bound_total_capacity_lo"] = make_df("bound_total_capacity_lo", year_act=2020, value=9, **hist_dict) + par_dict["historical_new_capacity"] = make_df( + "historical_new_capacity", value=1.2, year_vtg=2015, **hist_dict + ) + par_dict["bound_total_capacity_lo"] = make_df( + "bound_total_capacity_lo", year_act=2020, value=9, **hist_dict + ) return par_dict @@ -182,20 +229,31 @@ def add_methanol_trp_additives(scenario): df_loil = df_loil[df_loil["technology"] == "loil_trp"] df_mtbe = pd.read_excel( - context.get_local_path("material", "Methanol production statistics (version 1).xlsx"), + context.get_local_path( + "material", "Methanol production statistics (version 1).xlsx" + ), # usecols=[1,2,3,4,6,7], - skiprows=np.linspace(0, 65, 66), sheet_name="MTBE calc") - df_mtbe = df_mtbe.iloc[1:13, ] + skiprows=np.linspace(0, 65, 66), + sheet_name="MTBE calc", + ) + df_mtbe = df_mtbe.iloc[ + 1:13, + ] df_mtbe["node_loc"] = "R12_" + df_mtbe["node_loc"] df_mtbe = df_mtbe[["node_loc", "methanol energy%"]] df_biodiesel = pd.read_excel( - context.get_local_path("material", "Methanol production statistics (version 1).xlsx"), + context.get_local_path( + "material", "Methanol production statistics (version 1).xlsx" + ), skiprows=np.linspace(0, 37, 38), usecols=[1, 2], - sheet_name="Biodiesel") + sheet_name="Biodiesel", + ) df_biodiesel["node_loc"] = "R12_" + df_biodiesel["node_loc"] df_total = df_biodiesel.merge(df_mtbe) - df_total = df_total.assign(value=lambda x: x["methanol energy %"] + x["methanol energy%"]) + df_total = df_total.assign( + value=lambda x: x["methanol energy %"] + x["methanol energy%"] + ) def get_meth_share(df, node): return df[df["node_loc"] == node["node_loc"]]["value"].values[0] @@ -210,10 +268,13 @@ def get_meth_share(df, node): def gen_resin_demand(scenario, resin_share, sector, buildings_scen): df = pd.read_csv( - context.get_local_path("material", "results_material_SHAPE_" + sector + ".csv")) + context.get_local_path("material", "results_material_SHAPE_" + sector + ".csv") + ) resin_intensity = resin_share df = df[df["scenario"] == buildings_scen] - df = df[df["material"] == "wood"].assign(resin_demand=df["mat_demand_Mt"] * resin_intensity) + df = df[df["material"] == "wood"].assign( + resin_demand=df["mat_demand_Mt"] * resin_intensity + ) df["R12"] = "R12_" + df["R12"] common = dict( @@ -225,35 +286,51 @@ def gen_resin_demand(scenario, resin_share, sector, buildings_scen): relation="CO2_cc", commodity="fcoh_resin", unit="???", - level="final_material" + level="final_material", ) all_years = scenario.vintage_and_active_years() all_years = all_years[all_years["year_vtg"] > 1990] nodes = scenario.set("node")[1:] nodes = nodes.drop(5).reset_index(drop=True) - df_demand = make_df("demand", year=all_years["year_act"].unique()[:-1], **common).pipe(broadcast, - node=nodes).merge( - df[["R12", "year", "resin_demand"]], left_on=["node", "year"], right_on=["R12", "year"]) + df_demand = ( + make_df("demand", year=all_years["year_act"].unique()[:-1], **common) + .pipe(broadcast, node=nodes) + .merge( + df[["R12", "year", "resin_demand"]], + left_on=["node", "year"], + right_on=["R12", "year"], + ) + ) df_demand["value"] = df_demand["resin_demand"] df_demand = make_df("demand", **df_demand) return df_demand def gen_meth_residual_demand(gdp_elasticity): - def get_demand_t1_with_income_elasticity(demand_t0, income_t0, income_t1, elasticity): - return (elasticity * demand_t0 * ((income_t1 - income_t0) / income_t0)) + demand_t0 + def get_demand_t1_with_income_elasticity( + demand_t0, income_t0, income_t1, elasticity + ): + return ( + elasticity * demand_t0 * ((income_t1 - income_t0) / income_t0) + ) + demand_t0 df_gdp = pd.read_excel( context.get_local_path("material", "methanol demand.xlsx"), - sheet_name="GDP_baseline") + sheet_name="GDP_baseline", + ) df = df_gdp[(~df_gdp["Region"].isna()) & (df_gdp["Region"] != "World")] df = df.dropna(axis=1) - df_demand_meth = pd.read_excel(context.get_local_path("material", "methanol demand.xlsx"), - sheet_name="methanol_demand", skiprows=[12]) - df_demand_meth = df_demand_meth[(~df_demand_meth["Region"].isna()) & (df_demand_meth["Region"] != "World")] + df_demand_meth = pd.read_excel( + context.get_local_path("material", "methanol demand.xlsx"), + sheet_name="methanol_demand", + skiprows=[12], + ) + df_demand_meth = df_demand_meth[ + (~df_demand_meth["Region"].isna()) & (df_demand_meth["Region"] != "World") + ] df_demand_meth = df_demand_meth.dropna(axis=1) df_demand = df.copy(deep=True) @@ -265,13 +342,21 @@ def get_demand_t1_with_income_elasticity(demand_t0, income_t0, income_t1, elasti income_year1 = years[i] income_year2 = years[i + 1] - dem_2020 = get_demand_t1_with_income_elasticity(dem_2020, - df[income_year1], - df[income_year2], - gdp_elasticity) + dem_2020 = get_demand_t1_with_income_elasticity( + dem_2020, df[income_year1], df[income_year2], gdp_elasticity + ) df_demand[income_year2] = dem_2020 - df_melt = df_demand.melt(id_vars=["Region"], value_vars=df_demand.columns[5:], var_name="year") - return message_ix.make_df("demand", unit="t", level="final_material", value=df_melt.value, - time="year", commodity="methanol", year=df_melt.year, - node=("R12_" + df_melt["Region"])) + df_melt = df_demand.melt( + id_vars=["Region"], value_vars=df_demand.columns[5:], var_name="year" + ) + return message_ix.make_df( + "demand", + unit="t", + level="final_material", + value=df_melt.value, + time="year", + commodity="methanol", + year=df_melt.year, + node=("R12_" + df_melt["Region"]), + ) From fb5f5f6be9bdd05b75a37666126e0f310cffbe5d Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Wed, 12 Oct 2022 12:56:15 +0200 Subject: [PATCH 411/774] Add missing ptx relation values --- message_ix_models/data/material/meth_h2_techno_economic.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/meth_h2_techno_economic.xlsx b/message_ix_models/data/material/meth_h2_techno_economic.xlsx index 2b5b952464..b5a9aebfd4 100644 --- a/message_ix_models/data/material/meth_h2_techno_economic.xlsx +++ b/message_ix_models/data/material/meth_h2_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:4a2e2f87fc6c23b48c933373684a2e6c0d030646b9ea14fcf8d8a6088b219422 -size 249040 +oid sha256:ce87fdff9930f36f5e1df9de12248575a1843cbb7edd864eecf37667d1460285 +size 260906 From d8ace74c898861942f7d417bfcb0a8c4c7ce78d0 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Wed, 12 Oct 2022 12:56:34 +0200 Subject: [PATCH 412/774] Add meth_t_d_material to yaml --- message_ix_models/data/material/set.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 3fadbecc7e..caefe6767e 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -466,7 +466,7 @@ methanol: add: - meth_bio - meth_h2 - - meth_bal_material + - meth_t_d_material - MTO - CH2O_synth - CH2O_to_resin From 923d21654a9129e0eebe58fe00beb8b0a88573e5 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 11 Oct 2022 15:49:07 +0200 Subject: [PATCH 413/774] Add furnace set for resin production --- .../data/material/generic_furnace_boiler_techno_economic.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx index 7d9a1a3701..cb3a21ce8d 100644 --- a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx +++ b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:4f5152f5fcae55cb6fdf863d753ba7469299d496f1048fada8b10670289dc4e9 -size 295 +oid sha256:971cbf651a00a7ebac8eb13899144cbe7da95bb78488440965ba8a3c7e209bda +size 293 From 94d925cdce4c4fcae92f76c28ca35e748e3937e0 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Wed, 12 Oct 2022 10:48:43 +0200 Subject: [PATCH 414/774] Change electricity input level to final --- message_ix_models/data/material/MTO data collection.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/MTO data collection.xlsx b/message_ix_models/data/material/MTO data collection.xlsx index 8c34e66adc..0931cb4496 100644 --- a/message_ix_models/data/material/MTO data collection.xlsx +++ b/message_ix_models/data/material/MTO data collection.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:cdaaf64a313bbb318f5531f1f0f3851ccb94b2e1cdc84916a684453dd97712bf -size 174565 +oid sha256:8ce2c09cae61e8e56770385a785e4e1bdf20749295f02285e81a853ca8f80dfb +size 299 From 6f7274941e0cc0d5d0b70d96b73cd9bbd8966a4b Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Wed, 12 Oct 2022 13:00:18 +0200 Subject: [PATCH 415/774] Add negative emissions for methanol used as feedstock --- message_ix_models/data/material/meth_t_d_material_pars.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/meth_t_d_material_pars.xlsx b/message_ix_models/data/material/meth_t_d_material_pars.xlsx index 7fa38208a8..18e72add50 100644 --- a/message_ix_models/data/material/meth_t_d_material_pars.xlsx +++ b/message_ix_models/data/material/meth_t_d_material_pars.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:87f82c6235212e4d5525b4f939c683f469b256d4a4a48e4832600db53650c066 -size 56679 +oid sha256:851a8c62acdc467d6a13643479631266398fe047d1944be77d2e8278ae90aaad +size 311 From 3e5840d7acb6c8ff7e6cf72e1afc84e4d3fcac18 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Wed, 12 Oct 2022 15:06:37 +0200 Subject: [PATCH 416/774] Rename mto tec --- message_ix_models/data/material/MTO data collection.xlsx | 4 ++-- message_ix_models/data/material/meth_t_d_material_pars.xlsx | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/message_ix_models/data/material/MTO data collection.xlsx b/message_ix_models/data/material/MTO data collection.xlsx index 0931cb4496..f0d6cec0a8 100644 --- a/message_ix_models/data/material/MTO data collection.xlsx +++ b/message_ix_models/data/material/MTO data collection.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:8ce2c09cae61e8e56770385a785e4e1bdf20749295f02285e81a853ca8f80dfb -size 299 +oid sha256:3f4f7006cdce4a47d5fb674f0460d4222969242c6b8274ad69adebaa18be6fc1 +size 271 diff --git a/message_ix_models/data/material/meth_t_d_material_pars.xlsx b/message_ix_models/data/material/meth_t_d_material_pars.xlsx index 18e72add50..d7399df317 100644 --- a/message_ix_models/data/material/meth_t_d_material_pars.xlsx +++ b/message_ix_models/data/material/meth_t_d_material_pars.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:851a8c62acdc467d6a13643479631266398fe047d1944be77d2e8278ae90aaad -size 311 +oid sha256:3ddfe5e662c1bb20286812f5b26ae73a1f1aa29b4d409393918ee75a85ffda2b +size 270 From 4e7fe42f16ad36804eb5d687dafd00da30dcacf8 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Wed, 12 Oct 2022 15:26:21 +0200 Subject: [PATCH 417/774] Fix meth_t_d_material relation tec column --- message_ix_models/data/material/meth_t_d_material_pars.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/meth_t_d_material_pars.xlsx b/message_ix_models/data/material/meth_t_d_material_pars.xlsx index d7399df317..d78fd79d46 100644 --- a/message_ix_models/data/material/meth_t_d_material_pars.xlsx +++ b/message_ix_models/data/material/meth_t_d_material_pars.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:3ddfe5e662c1bb20286812f5b26ae73a1f1aa29b4d409393918ee75a85ffda2b -size 270 +oid sha256:1f09dc2bd277c2dd02864baee5160c011bd959117b0c4f24f461e7e6842302aa +size 297 From 79d7803f66d8589db117b6c0051977752a384c49 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Fri, 14 Oct 2022 09:38:33 +0200 Subject: [PATCH 418/774] Change deprecated df.append() to pd.concat() --- .../model/material/data_methanol.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index 21eb24db44..ac8c40b9a5 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -83,7 +83,7 @@ def gen_data_methanol(scenario): df_ng = pd.read_excel( context.get_local_path("material", "meth_ng_techno_economic.xlsx") ) - new_dict2["historical_activity"] = new_dict2["historical_activity"].append(df_ng) + new_dict2["historical_activity"] = pd.concat([new_dict2["historical_activity"], df_ng]) mto_dict = gen_data_meth_chemicals(scenario, "MTO") keys = set(list(new_dict2.keys()) + list(mto_dict.keys())) @@ -109,15 +109,15 @@ def gen_data_methanol(scenario): if ~(i in new_dict2.keys()) & (i in resin_dict.keys()): new_dict2[i] = resin_dict[i] - new_dict2["demand"] = new_dict2["demand"].append( + new_dict2["demand"] = pd.concat([new_dict2["demand"], gen_resin_demand( - scenario, pars["resin_share"], "residential", pars["wood_scenario"] - ) + scenario, pars["resin_share"], "residential", pars["wood_scenario"]) + ] ) - new_dict2["demand"] = new_dict2["demand"].append( + new_dict2["demand"] = pd.concat([new_dict2["demand"], gen_resin_demand(scenario, pars["resin_share"], "comm", pars["wood_scenario"]) - ) - new_dict2["input"].append(add_methanol_trp_additives(scenario)) + ]) + new_dict2["input"] = pd.concat([new_dict2["input"], add_methanol_trp_additives(scenario)]) if pars["cbudget"]: emission_dict = { @@ -189,10 +189,10 @@ def gen_data_meth_chemicals(scenario, chemical): par_dict = {k: pd.DataFrame() for k in (df["parameter"])} for i in df["parameter"]: for index, row in df[df["parameter"] == i].iterrows(): - par_dict[i] = par_dict[i].append( + par_dict[i] = pd.concat([par_dict[i], make_df(i, **all_years.to_dict(orient="list"), **row, **common) .pipe(broadcast, node_loc=nodes) - .pipe(same_node) + .pipe(same_node)] ) if i == "relation_activity": From c30fd5cac3e327d279e101ecb724d52526e39cb4 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Fri, 14 Oct 2022 11:40:23 +0200 Subject: [PATCH 419/774] Add co2_industry for methanol technologies --- .../data/material/MTO data collection.xlsx | 4 ++-- message_ix_models/model/material/data_util.py | 15 +++++++++++++++ 2 files changed, 17 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/MTO data collection.xlsx b/message_ix_models/data/material/MTO data collection.xlsx index f0d6cec0a8..10fffe74bb 100644 --- a/message_ix_models/data/material/MTO data collection.xlsx +++ b/message_ix_models/data/material/MTO data collection.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:3f4f7006cdce4a47d5fb674f0460d4222969242c6b8274ad69adebaa18be6fc1 -size 271 +oid sha256:6831d9207dfefadd51fb2c01122687580f3a14f0bb5b086b33c0068cb6cc980e +size 299 diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index 7584966b64..1fba3cab94 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -594,6 +594,21 @@ def add_emission_accounting(scen): scen.add_par("relation_activity", CF4_red_add) scen.commit("CF4 relations corrected.") + # copy CO2_cc values to CO2_industry for conventional methanol tecs + scen.check_out() + meth_arr = ["meth_ng", "meth_coal"] + df = scen.par("relation_activity", filters={"relation": "CO2_cc", "technology": meth_arr}) + df = df.rename({"year_rel": "year_vtg"}, axis=1) + values = dict(zip(df["technology"], df["value"])) + + df_em = scen.par("emission_factor", filters={"emission": "CO2_transformation", "technology": meth_arr}) + for i in meth_arr: + df_em.loc[df_em["technology"] == i, "value"] = values[i] + df_em["emission"] = "CO2_industry" + + scen.add_par("emission_factor", df_em) + scen.commit("add methanol CO2_industry") + def add_elec_lowerbound_2020(scen): From 10e4db690678f04c0cfc70dcdad80160bf7f0fc8 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Sat, 15 Oct 2022 11:54:35 +0200 Subject: [PATCH 420/774] Add methanol ccs technologies co2_industry value --- message_ix_models/model/material/data_util.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index 1fba3cab94..c14e134062 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -596,7 +596,7 @@ def add_emission_accounting(scen): # copy CO2_cc values to CO2_industry for conventional methanol tecs scen.check_out() - meth_arr = ["meth_ng", "meth_coal"] + meth_arr = ["meth_ng", "meth_coal", "meth_coal_ccs", "meth_ng_ccs"] df = scen.par("relation_activity", filters={"relation": "CO2_cc", "technology": meth_arr}) df = df.rename({"year_rel": "year_vtg"}, axis=1) values = dict(zip(df["technology"], df["value"])) From a5ef9c45e8fdede8181e8e02258579ccd5cb3da8 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Sat, 15 Oct 2022 12:09:05 +0200 Subject: [PATCH 421/774] Add missing 2020 methanol residual demand --- message_ix_models/model/material/data_methanol.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index ac8c40b9a5..b98e833be6 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -338,6 +338,7 @@ def get_demand_t1_with_income_elasticity( years = list(df_demand_meth.columns[5:]) dem_2020 = df_demand_meth[2020].values + df_demand[2020] = dem_2020 for i in range(len(years) - 1): income_year1 = years[i] income_year2 = years[i + 1] @@ -347,6 +348,8 @@ def get_demand_t1_with_income_elasticity( ) df_demand[income_year2] = dem_2020 + + df_melt = df_demand.melt( id_vars=["Region"], value_vars=df_demand.columns[5:], var_name="year" ) From 0ee1dc097595a4a2fc0c7aee0e1f0bfe38bdd6a3 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Sat, 15 Oct 2022 12:14:29 +0200 Subject: [PATCH 422/774] Normalize meth_t_d_material to output=1 --- message_ix_models/data/material/meth_t_d_material_pars.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/meth_t_d_material_pars.xlsx b/message_ix_models/data/material/meth_t_d_material_pars.xlsx index d78fd79d46..13ce78c622 100644 --- a/message_ix_models/data/material/meth_t_d_material_pars.xlsx +++ b/message_ix_models/data/material/meth_t_d_material_pars.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:1f09dc2bd277c2dd02864baee5160c011bd959117b0c4f24f461e7e6842302aa -size 297 +oid sha256:44917c7b193626c3af2bd777c393d572cc3e34f9770c180ced68fc683e05389a +size 295 From 498fd87017b296a8a008c2128e1f02c7d98ede0e Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Sun, 16 Oct 2022 18:21:10 +0200 Subject: [PATCH 423/774] Add regional prices ratio function --- message_ix_models/model/material/data_methanol.py | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index b98e833be6..385f2616ba 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -363,3 +363,18 @@ def get_demand_t1_with_income_elasticity( year=df_melt.year, node=("R12_" + df_melt["Region"]), ) + + +def get_meth_bio_cost_ratio(scenario, tec_name, cost_type): + + df = scenario.par(cost_type) + df = df[df["technology"]==tec_name] + df= df[df["year_vtg"]>=2020] + if cost_type in ["fix_cost", "var_cost"]: + df = df[df["year_vtg"]==df["year_act"]] + + df_nam = df[df["node_loc"]=="R12_NAM"].sort_values("year_vtg") + df = df.merge(df_nam[["value", "year_vtg"]],left_on="year_vtg", right_on="year_vtg") + df["ratio"] = df["value_x"]/df["value_y"] + + return df["ratio"] \ No newline at end of file From 3359d337368826ba679c6adf0da401d0c93aadad Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Sun, 16 Oct 2022 18:22:03 +0200 Subject: [PATCH 424/774] Customize click commands --- message_ix_models/model/material/__init__.py | 16 ++++++++++------ 1 file changed, 10 insertions(+), 6 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 3a79be534b..989d852d91 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -3,6 +3,8 @@ import click import logging +import ixmp + # from .build import apply_spec # from message_data.tools import ScenarioInfo from message_data.model.material.build import apply_spec @@ -234,7 +236,9 @@ def solve_scen(context, datafile, model_name, scenario_name, add_calibration, ad # Clone and set up from message_ix import Scenario - scenario = Scenario(context.get_platform(), model_name, scenario_name) + mp = ixmp.Platform("ixmp_dev") + scenario = Scenario(mp, model_name, scenario_name) + #scenario = Scenario(context.get_platform(), model_name, scenario_name) if scenario.has_solution(): scenario.remove_solution() @@ -368,16 +372,16 @@ def run_old_reporting(scenario_name, model_name): DATA_FUNCTIONS_1 = [ #gen_data_buildings, - gen_data_methanol - #gen_data_ammonia, - #gen_data_generic, - #gen_data_steel, + gen_data_methanol, + gen_data_ammonia, + gen_data_generic, + gen_data_steel, ] DATA_FUNCTIONS_2 = [ gen_data_cement, gen_data_petro_chemicals, - gen_data_power_sector, + #gen_data_power_sector, gen_data_aluminum, ] From 4f22f86d4978b9536f51f4acb11c9fcac5a76c9f Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 8 Feb 2022 16:05:31 +0100 Subject: [PATCH 425/774] Add input files and small fix to input filepath --- .../n-fertilizer_techno-economic_new.xlsx | 4 +- .../model/material/data_ammonia.py | 870 ++++-------------- 2 files changed, 201 insertions(+), 673 deletions(-) diff --git a/message_ix_models/data/material/n-fertilizer_techno-economic_new.xlsx b/message_ix_models/data/material/n-fertilizer_techno-economic_new.xlsx index 76fe9e9c6b..9c4edb7ce9 100644 --- a/message_ix_models/data/material/n-fertilizer_techno-economic_new.xlsx +++ b/message_ix_models/data/material/n-fertilizer_techno-economic_new.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:bb80d7e89adb97881ee09a557c8130ca5b2ef6236d28f7220cd99f418b58177b -size 30333 +oid sha256:11c11bed1bc58064062118012b157a76c25562b0cc5cf954843d9f25fbb553bd +size 305 diff --git a/message_ix_models/model/material/data_ammonia.py b/message_ix_models/model/material/data_ammonia.py index 2e825f5d38..727c240056 100644 --- a/message_ix_models/model/material/data_ammonia.py +++ b/message_ix_models/model/material/data_ammonia.py @@ -1,19 +1,14 @@ from collections import defaultdict import logging -from pathlib import Path import pandas as pd -import numpy as np from message_ix import make_df from message_ix_models import ScenarioInfo -from message_ix_models.util import ( - broadcast, - same_node, - private_data_path, -) +from message_ix_models.util import broadcast, same_node from .util import read_config + log = logging.getLogger(__name__) CONVERSION_FACTOR_CO2_C = 12 / 44 @@ -21,23 +16,21 @@ CONVERSION_FACTOR_PJ_GWa = 0.0317 -def gen_data_ammonia(scenario, dry_run=False, add_ccs: bool = True): +def gen_data(scenario, dry_run=False): """Generate data for materials representation of nitrogen fertilizers. .. note:: This code is only partially translated from :file:`SetupNitrogenBase.py`. """ # Load configuration - config = read_config()["material"]["fertilizer"] + config = read_config()["material"]["set"] context = read_config() - # print(config_.get_local_path("material", "test.xlsx")) + #print(config_.get_local_path("material", "test.xlsx")) # Information about scenario, e.g. node, year s_info = ScenarioInfo(scenario) nodes = s_info.N - if ("World" in nodes) | ("R12_GLB" in nodes): + if "World" in nodes: nodes.pop(nodes.index("World")) - nodes.pop(nodes.index("R12_GLB")) - # Techno-economic assumptions data = read_data() @@ -47,81 +40,77 @@ def gen_data_ammonia(scenario, dry_run=False, add_ccs: bool = True): input_commodity_dict = { "input_water": "freshwater_supply", "input_elec": "electr", - "input_fuel": "", + "input_fuel": "" } output_commodity_dict = { "output_NH3": "NH3", "output_heat": "d_heat", - "output_water": "wastewater", # ask Jihoon how to name + "output_water": "" # ask Jihoon how to name + } + commodity_dict = { + "output": output_commodity_dict, + "input": input_commodity_dict } - commodity_dict = {"output": output_commodity_dict, "input": input_commodity_dict} input_level_dict = { "input_water": "water_supply", "input_fuel": "secondary", - "input_elec": "secondary", + "input_elec": "secondary" } output_level_dict = { "output_water": "wastewater", "output_heat": "secondary", - "output_NH3": "secondary_material", + "output_NH3": "material_interim" + } + level_cat_dict = { + "output": output_level_dict, + "input": input_level_dict } - level_cat_dict = {"output": output_level_dict, "input": input_level_dict} - - vtg_years = s_info.yv_ya[s_info.yv_ya.year_vtg > 2000]["year_vtg"] - act_years = s_info.yv_ya[s_info.yv_ya.year_vtg > 2000]["year_act"] # NH3 production processes common = dict( - year_act=act_years, # confirm if correct?? - year_vtg=vtg_years, + year_act=s_info.Y, # confirm if correct?? + year_vtg=s_info.Y, commodity="NH3", - level="secondary_material", - mode="M1", + level="material_interim", + mode="all", time="year", time_dest="year", time_origin="year", - emission="CO2_industry", # confirm if correct + emission="CO2" # confirm if correct ) # Iterate over new technologies, using the configuration - for t in config["technology"]["add"][:6]: - # TODO: refactor to adjust to yaml structure + for t in config["technology"]["add"]["std"]: # Output of NH3: same efficiency for all technologies # the output commodity and level are different for + # t=NH3_to_N_fertil; use 'if' statements to fill in. - for param in data["parameter"].unique(): + for param in data['parameter'].unique(): if (t == "electr_NH3") & (param == "input_fuel"): continue - unit = "t" - cat = data["param_cat"][data["parameter"] == param].iloc[0] + unit = data['Unit'][data['parameter'] == param].iloc[0] + cat = data['param_cat'][data['parameter'] == param].iloc[0] if cat in ["input", "output"]: common["commodity"] = commodity_dict[cat][param] common["level"] = level_cat_dict[cat][param] - if (t == "biomass_NH3") & (param == "input_fuel"): + if t == "biomass_NH3": common["level"] = "primary" if (str(t) == "NH3_to_N_fertil") & (param == "output_NH3"): - common["commodity"] = "Fertilizer Use|Nitrogen" - common["level"] = "final_material" - if (str(t) == "NH3_to_N_fertil") & (param == "input_fuel"): - common["level"] = "secondary_material" - + common['commodity'] = "Fertilizer Use|Nitrogen" + common['level'] = "material_final" df = ( - make_df(cat, technology=t, value=1, unit="-", **common) - .pipe(broadcast, node_loc=nodes) - .pipe(same_node) + make_df(cat, technology=t, value=1, unit=unit, **common) + .pipe(broadcast, node_loc=nodes) + .pipe(same_node) ) - row = data[(data["technology"] == t) & (data["parameter"] == param)] + row = data[(data['technology'] == t) & + (data['parameter'] == param)] df = df.assign(value=row[2010].values[0]) if param == "input_fuel": - comm = ( - data["technology"][ - (data["parameter"] == param) & (data["technology"] == t) - ] - .iloc[0] - .split("_")[0] - ) + comm = data['technology'][(data['parameter'] == param) & + (data["technology"] == t)].iloc[0].split("_")[0] df = df.assign(commodity=comm) results[cat].append(df) @@ -129,107 +118,80 @@ def gen_data_ammonia(scenario, dry_run=False, add_ccs: bool = True): # Historical activities/capacities - Region specific common = dict( commodity="NH3", - level="secondary_material", - mode="M1", + level="material_interim", + mode="all", time="year", time_dest="year", time_origin="year", ) - act2010 = read_demand()["act2010"] + act2010 = read_demand()['act2010'] df = ( - make_df( - "historical_activity", - technology=[ - t for t in config["technology"]["add"][:6] - ], # ], TODO: maybe reintroduce std/ccs in yaml - value=1, - unit="t", - years_act=s_info.Y, - **common - ) - .pipe(broadcast, node_loc=nodes) - .pipe(same_node) + make_df("historical_activity", + technology=[t for t in config["technology"]["add"]["std"]], + value=1, unit='t', years_act=s_info.Y, **common) + .pipe(broadcast, node_loc=nodes) + .pipe(same_node) ) row = act2010 - # Unit is Tg N/yr - results["historical_activity"].append(df.assign(value=row, unit="t", year_act=2010)) + results["historical_activity"].append( + df.assign(value=row, unit='Tg N/yr', year_act=2010) + ) # 2015 activity necessary if this is 5-year step scenario # df['value'] = act2015 # total NH3 or N in Mt 2010 FAO Russia # df['year_act'] = 2015 # Sc_nitro.add_par("historical_activity", df) df = ( - make_df( - "historical_new_capacity", - technology=[ - t for t in config["technology"]["add"][:6] - ], # ], refactor to adjust to yaml structure - value=1, - unit="t", - **common - ) - .pipe(broadcast, node_loc=nodes) - .pipe(same_node) + make_df("historical_new_capacity", + technology=[t for t in config["technology"]["add"]["std"]], + value=1, unit='t', years_act=s_info.Y, **common) + .pipe(broadcast, node_loc=nodes) + .pipe(same_node) ) # modifying act2010 values by assuming 1/lifetime (=15yr) is built each year and account for capacity factor - capacity_factor = read_demand()["capacity_factor"] + capacity_factor = read_demand()['capacity_factor'] row = act2010 * 1 / 15 / capacity_factor[0] - # Unit is Tg N/yr results["historical_new_capacity"].append( - df.assign(value=row, unit="t", year_vtg=2010) + df.assign(value=row, unit='Tg N/yr', year_act=2010) ) # %% Secure feedstock balance (foil_fs, gas_fs, coal_fs) loil_fs? # Adjust i_feed demand - # N_energy = read_demand()['N_energy'] - # N_energy = read_demand()['N_feed'] # updated feed with imports accounted - # - # demand_fs_org = pd.read_excel(context.get_local_path('material','demand_i_feed_R12.xlsx')) - # - # df = demand_fs_org.loc[demand_fs_org.year == 2010, :].join(N_energy.set_index('node'), on='node') - # sh = pd.DataFrame({'node': demand_fs_org.loc[demand_fs_org.year == 2010, 'node'], - # 'r_feed': df.totENE / df.value}) # share of NH3 energy among total feedstock (no trade assumed) - # df = demand_fs_org.join(sh.set_index('node'), on='node') - # df.value *= 1 - df.r_feed # Carve out the same % from tot i_feed values - # df = df.drop('r_feed', axis=1) - # df = df.drop('Unnamed: 0', axis=1) - # # TODO: refactor with a more sophisticated solution to reduce i_feed - # df.loc[df["value"] < 0, "value"] = 0 # temporary solution to avoid negative values - # results["demand"].append(df) + N_energy = read_demand()['N_energy'] + + demand_fs_org = pd.read_excel(context.get_path('material','demand_i_feed_R12.xlsx')) + + df = demand_fs_org.loc[demand_fs_org.year == 2010, :].join(N_energy.set_index('node'), on='node') + sh = pd.DataFrame({'node': demand_fs_org.loc[demand_fs_org.year == 2010, 'node'], + 'r_feed': df.totENE / df.value}) # share of NH3 energy among total feedstock (no trade assumed) + df = demand_fs_org.join(sh.set_index('node'), on='node') + df.value *= 1 - df.r_feed # Carve out the same % from tot i_feed values + df = df.drop('r_feed', axis=1) + df = df.drop('Unnamed: 0', axis=1) + # TODO: refactor with a more sophisticated solution to reduce i_feed + df.loc[df["value"] < 0, "value"] = 0 # tempoary solution to avoid negative values + results["demand"].append(df) # Globiom land input - """ - df = pd.read_excel(context.get_local_path('material','GLOBIOM_Fertilizer_use_N.xlsx')) - df = df.replace(regex=r'^R11', value="R12").replace(regex=r'^R12_CPA', value="R12_CHN") - df["unit"] = "t" - df.loc[df["node"] == "R12_CHN", "value"] *= 0.93 # hotfix to adjust to R12 - df_rcpa = df.loc[df["node"] == "R12_CHN"].copy(deep=True) - df_rcpa["node"] = "R12_RCPA" - df_rcpa["value"] *= 0.07 - df = df.append(df_rcpa) - df = df.drop("Unnamed: 0", axis=1) - results["land_input"].append(df) - """ + df = pd.read_excel(context.get_path('material','GLOBIOM_Fertilizer_use_N.xlsx')) - df = scenario.par("land_output", {"commodity": "Fertilizer Use|Nitrogen"}) - df["level"] = "final_material" + df = df.drop("Unnamed: 0", axis=1) results["land_input"].append(df) - # scenario.add_par("land_input", df) # add background parameters (growth rates and bounds) - df = scenario.par("initial_activity_lo", {"technology": ["gas_extr_mpen"]}) - for q in config["technology"]["add"][:6]: - df["technology"] = q + df = scenario.par('initial_activity_lo', {"technology": ["gas_extr_mpen"]}) + for q in config["technology"]["add"]["std"]: + df['technology'] = q results["initial_activity_lo"].append(df) - df = scenario.par("growth_activity_lo", {"technology": ["gas_extr_mpen"]}) - for q in config["technology"]["add"][:6]: - df["technology"] = q + df = scenario.par('growth_activity_lo', {"technology": ["gas_extr_mpen"]}) + for q in config["technology"]["add"]["std"]: + df['technology'] = q results["growth_activity_lo"].append(df) # TODO add regional cost scaling for ccs @@ -255,300 +217,36 @@ def gen_data_ammonia(scenario, dry_run=False, add_ccs: bool = True): """ cost_scaler = pd.read_excel( - context.get_local_path("material", "regional_cost_scaler_R12.xlsx"), index_col=0 - ).T + context.get_path('material','regional_cost_scaler_R12.xlsx'), index_col=0).T scalers_dict = { - "R12_CHN": { - "coal_NH3": 0.75 * 0.91, # gas/coal price ratio * discount - "fueloil_NH3": 0.66 * 0.91, - }, # gas/oil price ratio * discount - "R12_SAS": {"fueloil_NH3": 0.59, "coal_NH3": 1}, + "R12_CHN": {"coal_NH3": 0.75 * 0.91, # gas/coal price ratio * discount + "fueloil_NH3": 0.66 * 0.91}, # gas/oil price ratio * discount + "R12_SAS": {"fueloil_NH3": 0.59, + "coal_NH3": 1} } params = ["inv_cost", "fix_cost", "var_cost"] for param in params: for i in range(len(results[param])): df = results[param][i] - if df["technology"].any() in ( - "NH3_to_N_fertil", - "electr_NH3", - ): # skip those techs + if df["technology"].any() in ('NH3_to_N_fertil', 'electr_NH3'): # skip those techs continue regs = df.set_index("node_loc").join(cost_scaler, on="node_loc") regs.value = regs.value * regs["standard"] regs = regs.reset_index() - if df["technology"].any() in ( - "coal_NH3", - "fueloil_NH3", - ): # additional scaling to make coal/oil cheaper - regs.loc[regs["node_loc"] == "R12_CHN", "value"] = ( - regs.loc[regs["node_loc"] == "R12_CHN", "value"] - * scalers_dict["R12_CHN"][df.technology[0]] - ) - regs.loc[regs["node_loc"] == "R12_SAS", "value"] = ( - regs.loc[regs["node_loc"] == "R12_SAS", "value"] - * scalers_dict["R12_SAS"][df.technology[0]] - ) + if df["technology"].any() in ("coal_NH3", "fueloil_NH3"): # additional scaling to make coal/oil cheaper + regs.loc[regs["node_loc"] == "R12_CHN", "value"] = \ + regs.loc[regs["node_loc"] == "R12_CHN", "value"] * \ + scalers_dict["R12_CHN"][df.technology[0].values[0].name] + regs.loc[regs["node_loc"] == "R12_SAS", "value"] = \ + regs.loc[regs["node_loc"] == "R12_SAS", "value"] * \ + scalers_dict["R12_SAS"][df.technology[0].values[0].name] results[param][i] = regs.drop(["standard", "ccs"], axis="columns") - # add trade tecs (exp, imp, trd) - - newtechnames_trd = ["trade_NFert"] - newtechnames_imp = ["import_NFert"] - newtechnames_exp = ["export_NFert"] - - yv_ya_exp = s_info.yv_ya - yv_ya_exp = yv_ya_exp[ - (yv_ya_exp["year_act"] - yv_ya_exp["year_vtg"] < 30) - & (yv_ya_exp["year_vtg"] > 2000) - ] - yv_ya_same = s_info.yv_ya[ - (s_info.yv_ya["year_act"] - s_info.yv_ya["year_vtg"] == 0) - & (s_info.yv_ya["year_vtg"] > 2000) - ] - - common = dict( - year_act=yv_ya_same.year_act, - year_vtg=yv_ya_same.year_vtg, - commodity="Fertilizer Use|Nitrogen", - level="final_material", - mode="M1", - time="year", - time_dest="year", - time_origin="year", - ) - - data = read_trade_data(context, comm="NFert") - for i in data["var_name"].unique(): - for tec in data["technology"].unique(): - row = data[(data["var_name"] == i) & (data["technology"] == tec)] - if len(row): - if row["technology"].values[0] == "trade_NFert": - node = ["R12_GLB"] - else: - node = nodes - if tec == "export_NFert": - common_exp = common - common_exp["year_act"] = yv_ya_exp.year_act - common_exp["year_vtg"] = yv_ya_exp.year_vtg - df = ( - make_df( - i, - technology=tec, - value=row[2010].values[0], - unit="-", - **common_exp - ) - .pipe(broadcast, node_loc=node) - .pipe(same_node) - ) - else: - df = ( - make_df( - i, - technology=tec, - value=row[2010].values[0], - unit="-", - **common - ) - .pipe(broadcast, node_loc=node) - .pipe(same_node) - ) - if (tec == "export_NFert") & (i == "output"): - df["node_dest"] = "R12_GLB" - df["level"] = "export" - elif (tec == "import_NFert") & (i == "input"): - df["node_origin"] = "R12_GLB" - df["level"] = "import" - elif (tec == "trade_NFert") & (i == "input"): - df["level"] = "export" - elif (tec == "trade_NFert") & (i == "output"): - df["level"] = "import" - else: - df.pipe(same_node) - results[i].append(df) - - common = dict( - commodity="Fertilizer Use|Nitrogen", - level="final_material", - mode="M1", - time="year", - time_dest="year", - time_origin="year", - unit="t", - ) - - N_trade_R12 = read_demand()["N_trade_R12"].assign(mode="M1") - N_trade_R12["technology"] = N_trade_R12["Element"].apply( - lambda x: "export_NFert" if x == "Export" else "import_NFert" - ) - df_exp_imp_act = N_trade_R12.drop("Element", axis=1) - - trd_act_years = N_trade_R12["year_act"].unique() - values = N_trade_R12.groupby(["year_act"]).sum().values.flatten() - fert_trd_hist = make_df( - "historical_activity", - technology="trade_NFert", - year_act=trd_act_years, - value=values, - node_loc="R12_GLB", - **common - ) - results["historical_activity"].append(pd.concat([df_exp_imp_act, fert_trd_hist])) - - df_hist_cap_new = N_trade_R12[N_trade_R12["technology"] == "export_NFert"].drop( - columns=["time", "mode", "Element"] - ) - df_hist_cap_new = df_hist_cap_new.rename(columns={"year_act": "year_vtg"}) - # divide by export lifetime derived from coal_exp - df_hist_cap_new = df_hist_cap_new.assign(value=lambda x: x["value"] / 30) - results["historical_new_capacity"].append(df_hist_cap_new) - - # NH3 trade - # _______________________________________________ - - common = dict( - year_act=yv_ya_same.year_act, - year_vtg=yv_ya_same.year_vtg, - commodity="NH3", - level="secondary_material", - mode="M1", - time="year", - time_dest="year", - time_origin="year", - ) - - data = read_trade_data(context, comm="NH3") - - for i in data["var_name"].unique(): - for tec in data["technology"].unique(): - row = data[(data["var_name"] == i) & (data["technology"] == tec)] - if len(row): - if row["technology"].values[0] == "trade_NH3": - node = ["R12_GLB"] - else: - node = nodes - if tec == "export_NH3": - common_exp = common - common_exp["year_act"] = yv_ya_exp.year_act - common_exp["year_vtg"] = yv_ya_exp.year_vtg - df = ( - make_df( - i, - technology=tec, - value=row[2010].values[0], - unit="-", - **common_exp - ) - .pipe(broadcast, node_loc=node) - .pipe(same_node) - ) - else: - df = ( - make_df( - i, - technology=tec, - value=row[2010].values[0], - unit="-", - **common - ) - .pipe(broadcast, node_loc=node) - .pipe(same_node) - ) - if (tec == "export_NH3") & (i == "output"): - df["node_dest"] = "R12_GLB" - df["level"] = "export" - elif (tec == "import_NH3") & (i == "input"): - df["node_origin"] = "R12_GLB" - df["level"] = "import" - elif (tec == "trade_NH3") & (i == "input"): - df["level"] = "export" - elif (tec == "trade_NH3") & (i == "output"): - df["level"] = "import" - else: - df.pipe(same_node) - results[i].append(df) - - common = dict( - commodity="NH3", - level="secondary_material", - mode="M1", - time="year", - time_dest="year", - time_origin="year", - unit="t", - ) - - N_trade_R12 = read_demand()["N_trade_R12"].assign(mode="M1") - N_trade_R12["technology"] = N_trade_R12["Element"].apply( - lambda x: "export_NH3" if x == "Export" else "import_NH3" - ) - df_exp_imp_act = N_trade_R12.drop("Element", axis=1) - - trd_act_years = N_trade_R12["year_act"].unique() - values = N_trade_R12.groupby(["year_act"]).sum().values.flatten() - fert_trd_hist = make_df( - "historical_activity", - technology="trade_NH3", - year_act=trd_act_years, - value=values, - node_loc="R12_GLB", - **common - ) - results["historical_activity"].append(pd.concat([df_exp_imp_act, fert_trd_hist])) - - df_hist_cap_new = N_trade_R12[N_trade_R12["technology"] == "export_NH3"].drop( - columns=["time", "mode", "Element"] - ) - df_hist_cap_new = df_hist_cap_new.rename(columns={"year_act": "year_vtg"}) - # divide by export lifetime derived from coal_exp - df_hist_cap_new = df_hist_cap_new.assign(value=lambda x: x["value"] / 30) - results["historical_new_capacity"].append(df_hist_cap_new) - # ___________________________________________________________________________ - - if add_ccs: - for k, v in gen_data_ccs(scenario).items(): - results[k].append(v) - # Concatenate to one dataframe per parameter results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} - par = "emission_factor" - rel_df_cc = results[par] - rel_df_cc = rel_df_cc[rel_df_cc["emission"] == "CO2_industry"] - rel_df_cc = rel_df_cc.assign( - year_rel=rel_df_cc["year_act"], - node_rel=rel_df_cc["node_loc"], - relation="CO2_cc", - ).drop(["emission", "year_vtg"], axis=1) - rel_df_cc = rel_df_cc[rel_df_cc["technology"] != "NH3_to_N_fertil"] - rel_df_cc = rel_df_cc[ - rel_df_cc["year_rel"] == rel_df_cc["year_act"] - ].drop_duplicates() - rel_df_cc[rel_df_cc["technology"] == "biomass_NH3_ccs"] = rel_df_cc[ - rel_df_cc["technology"] == "biomass_NH3_ccs" - ].assign( - value=results[par][results[par]["technology"] == "biomass_NH3_ccs"][ - "value" - ].values[0] - ) - - rel_df_em = results[par] - rel_df_em = rel_df_em[rel_df_em["emission"] == "CO2"] - rel_df_em = rel_df_em.assign( - year_rel=rel_df_em["year_act"], - node_rel=rel_df_em["node_loc"], - relation="CO2_Emission", - ).drop(["emission", "year_vtg"], axis=1) - rel_df_em = rel_df_em[rel_df_em["technology"] != "NH3_to_N_fertil"] - rel_df_em[rel_df_em["year_rel"] == rel_df_em["year_act"]].drop_duplicates() - results["relation_activity"] = pd.concat([rel_df_cc, rel_df_em]) - - results["emission_factor"] = results["emission_factor"][ - results["emission_factor"]["technology"] != "NH3_to_N_fertil" - ] - return results @@ -558,7 +256,7 @@ def gen_data_ccs(scenario, dry_run=False): .. note:: This code is only partially translated from :file:`SetupNitrogenBase.py`. """ - config = read_config()["material"]["fertilizer"] + config = read_config()["material"]["set"] context = read_config() # Information about scenario, e.g. node, year @@ -566,8 +264,6 @@ def gen_data_ccs(scenario, dry_run=False): nodes = s_info.N if "World" in nodes: nodes.pop(nodes.index("World")) - if "R12_GLB" in nodes: - nodes.pop(nodes.index("R12_GLB")) # Techno-economic assumptions data = read_data_ccs() @@ -575,258 +271,165 @@ def gen_data_ccs(scenario, dry_run=False): # List of data frames, to be concatenated together at end results = defaultdict(list) - vtg_years = s_info.yv_ya[s_info.yv_ya.year_vtg > 2000]["year_vtg"] - act_years = s_info.yv_ya[s_info.yv_ya.year_vtg > 2000]["year_act"] - # NH3 production processes - # NOTE: The energy required for NH3 production process is retreived - # from secondary energy level for the moment. However, the energy that is used to - # produce ammonia as feedstock is accounted in Final Energy in reporting. - # The energy that is used to produce ammonia as fuel should be accounted in - # Secondary energy. At the moment all ammonia is used as feedstock. - # Later we can track the shares of feedstock vs fuel use of ammonia and - # divide final energy. Or create another set of technologies (only for fuel - # use vs. only for feedstock use). - common = dict( - year_vtg=vtg_years, - year_act=act_years, # confirm if correct?? + year_vtg=s_info.Y, + year_act=s_info.Y, # confirm if correct?? commodity="NH3", - level="secondary_material", + level="material_interim", # TODO fill in remaining dimensions - mode="M1", + mode="all", time="year", time_dest="year", time_origin="year", - emission="CO2" # confirm if correct + emission="CO2" # confirm if correct # node_loc='node' ) input_commodity_dict = { "input_water": "freshwater_supply", "input_elec": "electr", - "input_fuel": "", + "input_fuel": "" } output_commodity_dict = { "output_NH3": "NH3", "output_heat": "d_heat", - "output_water": "wastewater", + "output_water": "" # ask Jihoon how to name + } + commodity_dict = { + "output": output_commodity_dict, + "input": input_commodity_dict } - commodity_dict = {"output": output_commodity_dict, "input": input_commodity_dict} input_level_dict = { "input_water": "water_supply", "input_fuel": "secondary", - "input_elec": "secondary", + "input_elec": "secondary" } output_level_dict = { "output_water": "wastewater", "output_heat": "secondary", - "output_NH3": "secondary_material", + "output_NH3": "material_interim" + } + level_cat_dict = { + "output": output_level_dict, + "input": input_level_dict } - level_cat_dict = {"output": output_level_dict, "input": input_level_dict} # Iterate over new technologies, using the configuration - for t in config["technology"]["add"][12:]: + for t in config["technology"]["add"]["ccs"]: # Output of NH3: same efficiency for all technologies # TODO the output commodity and level are different for # t=NH3_to_N_fertil; use 'if' statements to fill in. - for param in data["parameter"].unique(): - unit = "t" - cat = data["param_cat"][data["parameter"] == param].iloc[0] + for param in data['parameter'].unique(): + unit = data['Unit'][data['parameter'] == param].iloc[0] + cat = data['param_cat'][data['parameter'] == param].iloc[0] if cat in ["input", "output"]: common["commodity"] = commodity_dict[cat][param] common["level"] = level_cat_dict[cat][param] - if (t == "biomass_NH3_ccs") & (param == "input_fuel"): - common["level"] = "primary" - if param == "emission_factor_trans": - _common = common.copy() - _common["emission"] = "CO2_industry" - cat = "emission_factor" - df = ( - make_df(cat, technology=t, value=1, unit="-", **_common) - .pipe(broadcast, node_loc=nodes) - .pipe(same_node) - ) - - else: - df = ( - make_df(cat, technology=t, value=1, unit="-", **common) + df = ( + make_df(cat, technology=t, value=1, unit=unit, **common) .pipe(broadcast, node_loc=nodes) .pipe(same_node) - ) - row = data[(data["technology"] == str(t)) & (data["parameter"] == param)] + ) + row = data[(data['technology'] == str(t)) & + (data['parameter'] == param)] df = df.assign(value=row[2010].values[0]) + if param == "input_fuel": - comm = ( - data["technology"][ - (data["parameter"] == param) & (data["technology"] == t) - ] - .iloc[0] - .split("_")[0] - ) + comm = data['technology'][(data['parameter'] == param) & + (data["technology"] == t)].iloc[0].split("_")[0] df = df.assign(commodity=comm) + results[cat].append(df) + # add background parameters (growth rates and bounds) - df = scenario.par("initial_activity_lo", {"technology": ["gas_extr_mpen"]}) - for q in config["technology"]["add"][12:]: - df["technology"] = q + df = scenario.par('initial_activity_lo', {"technology": ["gas_extr_mpen"]}) + for q in config["technology"]["add"]["std"]: + df['technology'] = q results["initial_activity_lo"].append(df) - df = scenario.par("growth_activity_lo", {"technology": ["gas_extr_mpen"]}) - for q in config["technology"]["add"][12:]: - df["technology"] = q + df = scenario.par('growth_activity_lo', {"technology": ["gas_extr_mpen"]}) + for q in config["technology"]["add"]["std"]: + df['technology'] = q results["growth_activity_lo"].append(df) cost_scaler = pd.read_excel( - context.get_local_path("material", "regional_cost_scaler_R12.xlsx"), index_col=0 - ).T + context.get_path('material','regional_cost_scaler_R12.xlsx'), index_col=0).T scalers_dict = { - "R12_CHN": { - "coal_NH3": 0.75 * 0.91, # gas/coal price ratio * discount - "fueloil_NH3": 0.66 * 0.91, - }, # gas/oil price ratio * discount - "R12_SAS": {"fueloil_NH3": 0.59, "coal_NH3": 1}, + "R12_CHN": {"coal_NH3": 0.75 * 0.91, # gas/coal price ratio * discount + "fueloil_NH3": 0.66 * 0.91}, # gas/oil price ratio * discount + "R12_SAS": {"fueloil_NH3": 0.59, + "coal_NH3": 1} } params = ["inv_cost", "fix_cost", "var_cost"] for param in params: for i in range(len(results[param])): df = results[param][i] - if df["technology"].any() in ( - "NH3_to_N_fertil", - "electr_NH3", - ): # skip those techs + if df["technology"].any() in ('NH3_to_N_fertil', 'electr_NH3'): # skip those techs continue regs = df.set_index("node_loc").join(cost_scaler, on="node_loc") regs.value = regs.value * regs["ccs"] regs = regs.reset_index() - if df["technology"].any() in ( - "coal_NH3", - "fueloil_NH3", - ): # additional scaling to make coal/oil cheaper - regs.loc[regs["node_loc"] == "R12_CHN", "value"] = ( - regs.loc[regs["node_loc"] == "R12_CHN", "value"] - * scalers_dict["R12_CHN"][df.technology[0].values[0].name] - ) - regs.loc[regs["node_loc"] == "R12_SAS", "value"] = ( - regs.loc[regs["node_loc"] == "R12_SAS", "value"] - * scalers_dict["R12_SAS"][df.technology[0].values[0].name] - ) + if df["technology"].any() in ("coal_NH3", "fueloil_NH3"): # additional scaling to make coal/oil cheaper + regs.loc[regs["node_loc"] == "R12_CHN", "value"] = \ + regs.loc[regs["node_loc"] == "R12_CHN", "value"] * \ + scalers_dict["R12_CHN"][df.technology[0].values[0].name] + regs.loc[regs["node_loc"] == "R12_SAS", "value"] = \ + regs.loc[regs["node_loc"] == "R12_SAS", "value"] * \ + scalers_dict["R12_SAS"][df.technology[0].values[0].name] results[param][i] = regs.drop(["standard", "ccs"], axis="columns") # Concatenate to one dataframe per parameter results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} - # results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} - return results def read_demand(): """Read and clean data from :file:`CD-Links SSP2 N-fertilizer demand.Global.xlsx`.""" - # Demand scenario [Mt N/year] from GLOBIOM + # %% Demand scenario [Mt N/year] from GLOBIOM context = read_config() - N_demand_GLO = pd.read_excel( - context.get_local_path( - "material", "CD-Links SSP2 N-fertilizer demand.Global_R12_adaption.xlsx" - ), - sheet_name="data", - ) + + N_demand_GLO = pd.read_excel(context.get_path('material','CD-Links SSP2 N-fertilizer demand.Global_R12_adaption.xlsx'), sheet_name='data') # NH3 feedstock share by region in 2010 (from http://ietd.iipnetwork.org/content/ammonia#benchmarks) - feedshare_GLO = pd.read_excel( - context.get_local_path("material", "Ammonia feedstock share.Global_R12.xlsx"), - sheet_name="Sheet2", - skiprows=14, - ) + feedshare_GLO = pd.read_excel(context.get_path('material','Ammonia feedstock share.Global_R12.xlsx'), sheet_name='Sheet2', skiprows=14) # Read parameters in xlsx te_params = data = pd.read_excel( - private_data_path("material", "n-fertilizer_techno-economic.xlsx"), - sheet_name="Sheet1", - engine="openpyxl", - nrows=72, + context.get_path("material", "n-fertilizer_techno-economic.xlsx"), + sheet_name="Sheet1", engine="openpyxl", nrows=72 ) n_inputs_per_tech = 12 # Number of input params per technology - input_fuel = te_params[2010][ - list(range(4, te_params.shape[0], n_inputs_per_tech)) - ].reset_index(drop=True) - # input_fuel[0:5] = input_fuel[0:5] * CONVERSION_FACTOR_PJ_GWa # 0.0317 GWa/PJ, GJ/t = PJ/Mt NH3 + input_fuel = te_params[2010][list(range(4, te_params.shape[0], n_inputs_per_tech))].reset_index(drop=True) + input_fuel[0:5] = input_fuel[0:5] * CONVERSION_FACTOR_PJ_GWa # 0.0317 GWa/PJ, GJ/t = PJ/Mt NH3 - capacity_factor = te_params[2010][ - list(range(11, te_params.shape[0], n_inputs_per_tech)) - ].reset_index(drop=True) + capacity_factor = te_params[2010][list(range(11, te_params.shape[0], n_inputs_per_tech))].reset_index(drop=True) # Regional N demaand in 2010 - ND = N_demand_GLO.loc[N_demand_GLO.Scenario == "NoPolicy", ["Region", 2010]] - ND = ND[ND.Region != "World"] - ND.Region = "R12_" + ND.Region - ND = ND.set_index("Region") + ND = N_demand_GLO.loc[N_demand_GLO.Scenario == "NoPolicy", ['Region', 2010]] + ND = ND[ND.Region != 'World'] + ND.Region = 'R12_' + ND.Region + ND = ND.set_index('Region') # Derive total energy (GWa) of NH3 production (based on demand 2010) - N_energy = feedshare_GLO[feedshare_GLO.Region != "R12_GLB"].join(ND, on="Region") + N_energy = feedshare_GLO[feedshare_GLO.Region != 'R12_GLB'].join(ND, on='Region') N_energy = pd.concat( - [ - N_energy.Region, - N_energy[["gas_pct", "coal_pct", "oil_pct"]].multiply( - N_energy[2010], axis="index" - ), - ], - axis=1, - ) + [N_energy.Region, N_energy[["gas_pct", "coal_pct", "oil_pct"]].multiply(N_energy[2010], axis="index")], axis=1) N_energy.gas_pct *= input_fuel[2] * CONVERSION_FACTOR_NH3_N # NH3 / N N_energy.coal_pct *= input_fuel[3] * CONVERSION_FACTOR_NH3_N N_energy.oil_pct *= input_fuel[4] * CONVERSION_FACTOR_NH3_N - N_energy = pd.concat([N_energy.Region, N_energy.sum(axis=1, numeric_only=True)], axis=1).rename( + N_energy = pd.concat([N_energy.Region, N_energy.sum(axis=1)], axis=1).rename( columns={0: 'totENE', 'Region': 'node'}) # GWa - N_trade_R12 = pd.read_csv( - private_data_path("material", "trade.FAO.R12.csv"), index_col=0 - ) - N_trade_R12.msgregion = "R12_" + N_trade_R12.msgregion - N_trade_R12.Value = N_trade_R12.Value / 1e6 - N_trade_R12.Unit = "t" - N_trade_R12 = N_trade_R12.assign(time="year") - N_trade_R12 = N_trade_R12.rename( - columns={ - "Value": "value", - "Unit": "unit", - "msgregion": "node_loc", - "Year": "year_act", - } - ) - - df = N_trade_R12.loc[ - N_trade_R12.year_act == 2010, - ] - df = df.pivot(index="node_loc", columns="Element", values="value") - NP = pd.DataFrame({"netimp": df.Import - df.Export, "demand": ND[2010]}) - NP["prod"] = NP.demand - NP.netimp - - # Derive total energy (GWa) of NH3 production (based on demand 2010) - N_feed = feedshare_GLO[feedshare_GLO.Region != "R11_GLB"].join(NP, on="Region") - N_feed = pd.concat( - [ - N_feed.Region, - N_feed[["gas_pct", "coal_pct", "oil_pct"]].multiply( - N_feed["prod"], axis="index" - ), - ], - axis=1, - ) - N_feed.gas_pct *= input_fuel[2] * 17 / 14 - N_feed.coal_pct *= input_fuel[3] * 17 / 14 - N_feed.oil_pct *= input_fuel[4] * 17 / 14 - N_feed = pd.concat([N_feed.Region, N_feed.sum(axis=1, numeric_only=True)], axis=1).rename( - columns={0: "totENE", "Region": "node"}) - - # Process the regional historical activities + # %% Process the regional historical activities fs_GLO = feedshare_GLO.copy() fs_GLO.insert(1, "bio_pct", 0) @@ -836,94 +439,32 @@ def read_demand(): fs_GLO.insert(6, "NH3_to_N", 1) # Share of feedstocks for NH3 prodution (based on 2010 => Assumed fixed for any past years) - feedshare = fs_GLO.sort_values(["Region"]).set_index("Region").drop("R12_GLB") + feedshare = fs_GLO.sort_values(['Region']).set_index('Region').drop('R12_GLB') # Get historical N demand from SSP2-nopolicy (may need to vary for diff scenarios) N_demand_raw = N_demand_GLO.copy() - N_demand = ( - N_demand_raw[ - (N_demand_raw.Scenario == "NoPolicy") & (N_demand_raw.Region != "World") - ] - .reset_index() - .loc[:, 2010] - ) # 2010 tot N demand + N_demand = N_demand_raw[(N_demand_raw.Scenario == "NoPolicy") & (N_demand_raw.Region != "World")].reset_index().loc[ + :, 2010] # 2010 tot N demand N_demand = N_demand.repeat(6) act2010 = (feedshare.values.flatten() * N_demand).reset_index(drop=True) - return { - "feedshare_GLO": feedshare_GLO, - "ND": ND, - "N_energy": N_energy, - "feedshare": feedshare, - "act2010": act2010, - "capacity_factor": capacity_factor, - "N_feed": N_feed, - "N_trade_R12": N_trade_R12, - } - - -def read_trade_data(context, comm): - if comm == "NFert": - data = pd.read_excel( - private_data_path("material", "n-fertilizer_techno-economic_new.xlsx"), - sheet_name="Trade", - engine="openpyxl", - usecols=np.linspace(0, 7, 8, dtype=int), - ) - data = data.assign(technology=lambda x: set_trade_tec(x["Variable"])) - if comm == "NH3": - data = pd.read_excel( - private_data_path("material", "n-fertilizer_techno-economic_new.xlsx"), - sheet_name="Trade_NH3", - engine="openpyxl", - usecols=np.linspace(0, 7, 8, dtype=int), - ) - data = data.assign(technology=lambda x: set_trade_tec_NH3(x["Variable"])) - return data - - -def set_trade_tec(x): - arr = [] - for i in x: - if "Import" in i: - arr.append("import_NFert") - if "Export" in i: - arr.append("export_NFert") - if "Trade" in i: - arr.append("trade_NFert") - return arr - - -def set_trade_tec_NH3(x): - arr = [] - for i in x: - if "Import" in i: - arr.append("import_NH3") - if "Export" in i: - arr.append("export_NH3") - if "Trade" in i: - arr.append("trade_NH3") - return arr + return {"feedshare_GLO": feedshare_GLO, "ND": ND, "N_energy": N_energy, "feedshare": feedshare, 'act2010': act2010, + 'capacity_factor': capacity_factor} def read_data(): """Read and clean data from :file:`n-fertilizer_techno-economic.xlsx`.""" # Ensure config is loaded, get the context context = read_config() - # print(context.get_local_path()) - # print(Path(__file__).parents[3]/"data"/"material") - context.handle_cli_args(local_data=Path(__file__).parents[3] / "data") - # print(context.get_local_path()) + # Shorter access to sets configuration - sets = context["material"]["fertilizer"] + sets = context["material"]["set"] # Read the file data = pd.read_excel( - private_data_path("material", "n-fertilizer_techno-economic_new.xlsx"), - sheet_name="Sheet1", - engine="openpyxl", - nrows=72, + context.get_path("material", "n-fertilizer_techno-economic_new.xlsx"), + sheet_name="Sheet1", engine="openpyxl", nrows=72 ) # Prepare contents for the "parameter" and "technology" columns @@ -948,36 +489,32 @@ def read_data(): param_values = [] tech_values = [] - param_cat = [ - split.split("_")[0] - if (split.startswith("input") or split.startswith("output")) - else split - for split in params - ] + param_cat = [split.split('_')[0] if + (split.startswith('input') or split.startswith('output')) + else split for split in params] param_cat2 = [] # print(param_cat) - for t in sets["technology"]["add"][:6]: # : refactor to adjust to yaml structure + for t in sets["technology"]["add"]["std"]: # print(t) param_values.extend(params) - tech_values.extend([t] * len(params)) + tech_values.extend([t.id] * len(params)) param_cat2.extend(param_cat) # Clean the data data = ( # Insert "technology" and "parameter" columns - data.assign( - technology=tech_values, parameter=param_values, param_cat=param_cat2 - ) - # Drop columns that don't contain useful information - .drop(["Model", "Scenario", "Region"], axis=1) + data.assign(technology=tech_values, + parameter=param_values, + param_cat=param_cat2) + # Drop columns that don't contain useful information + .drop(["Model", "Scenario", "Region"], axis=1) # Set the data frame index for selection ) - data.loc[data["parameter"] == "emission_factor", 2010] = data.loc[ - data["parameter"] == "emission_factor", 2010 - ] # * CONVERSION_FACTOR_CO2_C - # data.loc[data['parameter'] == 'input_elec', 2010] = \ - # data.loc[data['parameter'] == 'input_elec', 2010] * CONVERSION_FACTOR_PJ_GWa + data.loc[data['parameter'] == 'emission_factor', 2010] = \ + data.loc[data['parameter'] == 'emission_factor', 2010] * CONVERSION_FACTOR_CO2_C + data.loc[data['parameter'] == 'input_elec', 2010] = \ + data.loc[data['parameter'] == 'input_elec', 2010] * CONVERSION_FACTOR_PJ_GWa # TODO convert units for some parameters, per LoadParams.py return data @@ -989,11 +526,11 @@ def read_data_ccs(): context = read_config() # Shorter access to sets configuration - sets = context["material"]["fertilizer"] + sets = context["material"]["set"] # Read the file data = pd.read_excel( - private_data_path("material", "n-fertilizer_techno-economic_new.xlsx"), + context.get_path("material", "n-fertilizer_techno-economic.xlsx"), sheet_name="CCS", ) @@ -1014,45 +551,36 @@ def read_data_ccs(): "output_water", "output_heat", "emission_factor", - "emission_factor_trans", + "emission_factor", "capacity_factor", ] param_values = [] tech_values = [] - param_cat = [ - split.split("_")[0] - if (split.startswith("input") or split.startswith("output")) - else split - for split in params - ] + param_cat = [split.split('_')[0] if (split.startswith('input') or split.startswith('output')) else split for split + in params] param_cat2 = [] - for t in sets["technology"]["add"][12:]: + for t in sets["technology"]["add"]["ccs"]: param_values.extend(params) tech_values.extend([t] * len(params)) param_cat2.extend(param_cat) - # print(sets["technology"]["add"][12:]) + # Clean the data data = ( # Insert "technology" and "parameter" columns - data.assign( - technology=tech_values, parameter=param_values, param_cat=param_cat2 - ) - # , param_cat=param_cat2) - # Drop columns that don't contain useful information - .drop(["Model", "Scenario", "Region"], axis=1) + data.assign(technology=tech_values, parameter=param_values, param_cat=param_cat2) + # , param_cat=param_cat2) + # Drop columns that don't contain useful information + .drop(["Model", "Scenario", "Region"], axis=1) # Set the data frame index for selection ) - # unit conversions and extra electricity for CCS process - data.loc[data["parameter"] == "emission_factor", 2010] = data.loc[ - data["parameter"] == "emission_factor", 2010 - ] # * CONVERSION_FACTOR_CO2_C - # data.loc[data['parameter'] == 'input_elec', 2010] = \ - # data.loc[data['parameter'] == 'input_elec', 2010] * CONVERSION_FACTOR_PJ_GWa + 0.005 - data.loc[data["parameter"] == "input_elec", 2010] = data.loc[ - data["parameter"] == "input_elec", 2010 - ] + #unit conversions and extra electricity for CCS process + data.loc[data['parameter'] == 'emission_factor', 2010] = \ + data.loc[data['parameter'] == 'emission_factor', 2010] * CONVERSION_FACTOR_CO2_C + data.loc[data['parameter'] == 'input_elec', 2010] = \ + data.loc[data['parameter'] == 'input_elec', 2010] * CONVERSION_FACTOR_PJ_GWa + 0.005 + # TODO convert units for some parameters, per LoadParams.py return data From 13e92b966997118a3256becb285519beeda5b0c4 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Wed, 9 Feb 2022 11:17:16 +0100 Subject: [PATCH 426/774] Resolve changed dependencies --- .../model/material/data_ammonia.py | 50 ++++++++++--------- 1 file changed, 27 insertions(+), 23 deletions(-) diff --git a/message_ix_models/model/material/data_ammonia.py b/message_ix_models/model/material/data_ammonia.py index 727c240056..1161de3118 100644 --- a/message_ix_models/model/material/data_ammonia.py +++ b/message_ix_models/model/material/data_ammonia.py @@ -1,5 +1,6 @@ from collections import defaultdict import logging +from pathlib import Path import pandas as pd from message_ix import make_df @@ -23,7 +24,7 @@ def gen_data(scenario, dry_run=False): :file:`SetupNitrogenBase.py`. """ # Load configuration - config = read_config()["material"]["set"] + config = read_config()["material"]["fertilizer"] context = read_config() #print(config_.get_local_path("material", "test.xlsx")) # Information about scenario, e.g. node, year @@ -80,7 +81,7 @@ def gen_data(scenario, dry_run=False): ) # Iterate over new technologies, using the configuration - for t in config["technology"]["add"]["std"]: + for t in config["technology"]["add"]: # : refactor to adjust to yaml structure # Output of NH3: same efficiency for all technologies # the output commodity and level are different for # t=NH3_to_N_fertil; use 'if' statements to fill in. @@ -127,7 +128,7 @@ def gen_data(scenario, dry_run=False): act2010 = read_demand()['act2010'] df = ( make_df("historical_activity", - technology=[t for t in config["technology"]["add"]["std"]], + technology=[t for t in config["technology"]["add"]], #], TODO: maybe reintroduce std/ccs in yaml value=1, unit='t', years_act=s_info.Y, **common) .pipe(broadcast, node_loc=nodes) .pipe(same_node) @@ -144,7 +145,7 @@ def gen_data(scenario, dry_run=False): df = ( make_df("historical_new_capacity", - technology=[t for t in config["technology"]["add"]["std"]], + technology=[t for t in config["technology"]["add"]], # ], refactor to adjust to yaml structure value=1, unit='t', years_act=s_info.Y, **common) .pipe(broadcast, node_loc=nodes) .pipe(same_node) @@ -163,7 +164,7 @@ def gen_data(scenario, dry_run=False): # Adjust i_feed demand N_energy = read_demand()['N_energy'] - demand_fs_org = pd.read_excel(context.get_path('material','demand_i_feed_R12.xlsx')) + demand_fs_org = pd.read_excel(context.get_local_path('material','demand_i_feed_R12.xlsx')) df = demand_fs_org.loc[demand_fs_org.year == 2010, :].join(N_energy.set_index('node'), on='node') sh = pd.DataFrame({'node': demand_fs_org.loc[demand_fs_org.year == 2010, 'node'], @@ -177,7 +178,7 @@ def gen_data(scenario, dry_run=False): results["demand"].append(df) # Globiom land input - df = pd.read_excel(context.get_path('material','GLOBIOM_Fertilizer_use_N.xlsx')) + df = pd.read_excel(context.get_local_path('material','GLOBIOM_Fertilizer_use_N.xlsx')) df = df.drop("Unnamed: 0", axis=1) results["land_input"].append(df) @@ -185,12 +186,12 @@ def gen_data(scenario, dry_run=False): # add background parameters (growth rates and bounds) df = scenario.par('initial_activity_lo', {"technology": ["gas_extr_mpen"]}) - for q in config["technology"]["add"]["std"]: + for q in config["technology"]["add"]: df['technology'] = q results["initial_activity_lo"].append(df) df = scenario.par('growth_activity_lo', {"technology": ["gas_extr_mpen"]}) - for q in config["technology"]["add"]["std"]: + for q in config["technology"]["add"]: df['technology'] = q results["growth_activity_lo"].append(df) @@ -217,7 +218,7 @@ def gen_data(scenario, dry_run=False): """ cost_scaler = pd.read_excel( - context.get_path('material','regional_cost_scaler_R12.xlsx'), index_col=0).T + context.get_local_path('material','regional_cost_scaler_R12.xlsx'), index_col=0).T scalers_dict = { "R12_CHN": {"coal_NH3": 0.75 * 0.91, # gas/coal price ratio * discount @@ -238,10 +239,10 @@ def gen_data(scenario, dry_run=False): if df["technology"].any() in ("coal_NH3", "fueloil_NH3"): # additional scaling to make coal/oil cheaper regs.loc[regs["node_loc"] == "R12_CHN", "value"] = \ regs.loc[regs["node_loc"] == "R12_CHN", "value"] * \ - scalers_dict["R12_CHN"][df.technology[0].values[0].name] + scalers_dict["R12_CHN"][df.technology[0]] regs.loc[regs["node_loc"] == "R12_SAS", "value"] = \ regs.loc[regs["node_loc"] == "R12_SAS", "value"] * \ - scalers_dict["R12_SAS"][df.technology[0].values[0].name] + scalers_dict["R12_SAS"][df.technology[0]] results[param][i] = regs.drop(["standard", "ccs"], axis="columns") # Concatenate to one dataframe per parameter @@ -347,17 +348,17 @@ def gen_data_ccs(scenario, dry_run=False): # add background parameters (growth rates and bounds) df = scenario.par('initial_activity_lo', {"technology": ["gas_extr_mpen"]}) - for q in config["technology"]["add"]["std"]: + for q in config["technology"]["add"]: df['technology'] = q results["initial_activity_lo"].append(df) df = scenario.par('growth_activity_lo', {"technology": ["gas_extr_mpen"]}) - for q in config["technology"]["add"]["std"]: + for q in config["technology"]["add"]: df['technology'] = q results["growth_activity_lo"].append(df) cost_scaler = pd.read_excel( - context.get_path('material','regional_cost_scaler_R12.xlsx'), index_col=0).T + context.get_local_path('material','regional_cost_scaler_R12.xlsx'), index_col=0).T scalers_dict = { "R12_CHN": {"coal_NH3": 0.75 * 0.91, # gas/coal price ratio * discount @@ -396,14 +397,14 @@ def read_demand(): context = read_config() - N_demand_GLO = pd.read_excel(context.get_path('material','CD-Links SSP2 N-fertilizer demand.Global_R12_adaption.xlsx'), sheet_name='data') + N_demand_GLO = pd.read_excel(context.get_local_path('material','CD-Links SSP2 N-fertilizer demand.Global_R12_adaption.xlsx'), sheet_name='data') # NH3 feedstock share by region in 2010 (from http://ietd.iipnetwork.org/content/ammonia#benchmarks) - feedshare_GLO = pd.read_excel(context.get_path('material','Ammonia feedstock share.Global_R12.xlsx'), sheet_name='Sheet2', skiprows=14) + feedshare_GLO = pd.read_excel(context.get_local_path('material','Ammonia feedstock share.Global_R12.xlsx'), sheet_name='Sheet2', skiprows=14) # Read parameters in xlsx te_params = data = pd.read_excel( - context.get_path("material", "n-fertilizer_techno-economic.xlsx"), + context.get_local_path("material", "n-fertilizer_techno-economic.xlsx"), sheet_name="Sheet1", engine="openpyxl", nrows=72 ) n_inputs_per_tech = 12 # Number of input params per technology @@ -457,13 +458,16 @@ def read_data(): """Read and clean data from :file:`n-fertilizer_techno-economic.xlsx`.""" # Ensure config is loaded, get the context context = read_config() - + print(context.get_local_path()) + #print(Path(__file__).parents[3]/"data"/"material") + context.handle_cli_args(local_data=Path(__file__).parents[3]/"data") + print(context.get_local_path()) # Shorter access to sets configuration - sets = context["material"]["set"] + sets = context["material"]["fertilizer"] # Read the file data = pd.read_excel( - context.get_path("material", "n-fertilizer_techno-economic_new.xlsx"), + context.get_local_path("material", "n-fertilizer_techno-economic_new.xlsx"), sheet_name="Sheet1", engine="openpyxl", nrows=72 ) @@ -495,10 +499,10 @@ def read_data(): param_cat2 = [] # print(param_cat) - for t in sets["technology"]["add"]["std"]: + for t in sets["technology"]["add"]: # : refactor to adjust to yaml structure # print(t) param_values.extend(params) - tech_values.extend([t.id] * len(params)) + tech_values.extend([t] * len(params)) param_cat2.extend(param_cat) # Clean the data @@ -530,7 +534,7 @@ def read_data_ccs(): # Read the file data = pd.read_excel( - context.get_path("material", "n-fertilizer_techno-economic.xlsx"), + context.get_local_path("material", "n-fertilizer_techno-economic.xlsx"), sheet_name="CCS", ) From 6eaf7ebc027d86595855c3418f3a67bd511be566 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 15 Feb 2022 12:27:10 +0100 Subject: [PATCH 427/774] Add gen_data_ammonia --- message_ix_models/model/material/data_ammonia.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/message_ix_models/model/material/data_ammonia.py b/message_ix_models/model/material/data_ammonia.py index 1161de3118..9b5afd294c 100644 --- a/message_ix_models/model/material/data_ammonia.py +++ b/message_ix_models/model/material/data_ammonia.py @@ -17,7 +17,7 @@ CONVERSION_FACTOR_PJ_GWa = 0.0317 -def gen_data(scenario, dry_run=False): +def gen_data_ammonia(scenario, dry_run=False): """Generate data for materials representation of nitrogen fertilizers. .. note:: This code is only partially translated from From 18454dd0fe3d7faf0a3fc50c3e0f480d0d256286 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 15 Feb 2022 15:58:31 +0100 Subject: [PATCH 428/774] Fix units --- message_ix_models/model/material/data_ammonia.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/message_ix_models/model/material/data_ammonia.py b/message_ix_models/model/material/data_ammonia.py index 9b5afd294c..d673d37746 100644 --- a/message_ix_models/model/material/data_ammonia.py +++ b/message_ix_models/model/material/data_ammonia.py @@ -89,7 +89,7 @@ def gen_data_ammonia(scenario, dry_run=False): for param in data['parameter'].unique(): if (t == "electr_NH3") & (param == "input_fuel"): continue - unit = data['Unit'][data['parameter'] == param].iloc[0] + unit = "t" cat = data['param_cat'][data['parameter'] == param].iloc[0] if cat in ["input", "output"]: common["commodity"] = commodity_dict[cat][param] @@ -135,8 +135,9 @@ def gen_data_ammonia(scenario, dry_run=False): ) row = act2010 + # Unit is Tg N/yr results["historical_activity"].append( - df.assign(value=row, unit='Tg N/yr', year_act=2010) + df.assign(value=row, unit="t", year_act=2010) ) # 2015 activity necessary if this is 5-year step scenario # df['value'] = act2015 # total NH3 or N in Mt 2010 FAO Russia @@ -155,8 +156,9 @@ def gen_data_ammonia(scenario, dry_run=False): capacity_factor = read_demand()['capacity_factor'] row = act2010 * 1 / 15 / capacity_factor[0] + # Unit is Tg N/yr results["historical_new_capacity"].append( - df.assign(value=row, unit='Tg N/yr', year_act=2010) + df.assign(value=row, unit="t", year_act=2010) ) # %% Secure feedstock balance (foil_fs, gas_fs, coal_fs) loil_fs? @@ -323,7 +325,7 @@ def gen_data_ccs(scenario, dry_run=False): # t=NH3_to_N_fertil; use 'if' statements to fill in. for param in data['parameter'].unique(): - unit = data['Unit'][data['parameter'] == param].iloc[0] + unit = "t" cat = data['param_cat'][data['parameter'] == param].iloc[0] if cat in ["input", "output"]: common["commodity"] = commodity_dict[cat][param] From cea0a28d748e5d3185ae909e141b0bd8b8c2bcf6 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 16 Feb 2022 14:07:18 +0100 Subject: [PATCH 429/774] Remove the region r12_glb --- message_ix_models/model/material/data_ammonia.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/message_ix_models/model/material/data_ammonia.py b/message_ix_models/model/material/data_ammonia.py index d673d37746..c72bf571a9 100644 --- a/message_ix_models/model/material/data_ammonia.py +++ b/message_ix_models/model/material/data_ammonia.py @@ -30,8 +30,9 @@ def gen_data_ammonia(scenario, dry_run=False): # Information about scenario, e.g. node, year s_info = ScenarioInfo(scenario) nodes = s_info.N - if "World" in nodes: + if (("World" in nodes) | ("R12_GLB" in nodes)): nodes.pop(nodes.index("World")) + nodes.pop(nodes.index("R12_GLB")) # Techno-economic assumptions data = read_data() From 8eb44f9374f719c8be36c85ccab65ddc074ff8f5 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Wed, 16 Feb 2022 12:52:45 +0100 Subject: [PATCH 430/774] Add hotfix for r12 adjusted land_input --- message_ix_models/model/material/data_ammonia.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/message_ix_models/model/material/data_ammonia.py b/message_ix_models/model/material/data_ammonia.py index c72bf571a9..b5e1304ab7 100644 --- a/message_ix_models/model/material/data_ammonia.py +++ b/message_ix_models/model/material/data_ammonia.py @@ -182,7 +182,13 @@ def gen_data_ammonia(scenario, dry_run=False): # Globiom land input df = pd.read_excel(context.get_local_path('material','GLOBIOM_Fertilizer_use_N.xlsx')) - + df = df.replace(regex=r'^R11', value="R12").replace(regex=r'^R12_CPA', value="R12_CHN") + df["unit"] = "t" + df.loc[df["node"] == "R12_CHN", "value"] *= 0.93 # hotfix to adjust to R12 + df_rcpa = df.loc[df["node"] == "R12_CHN"].copy(deep=True) + df_rcpa["node"] = "R12_RCPA" + df_rcpa["value"] *= 0.07 + df = df.append(df_rcpa) df = df.drop("Unnamed: 0", axis=1) results["land_input"].append(df) From 3dcf17e0766f18e1e161c1f2a2cb74fcd60871e7 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Wed, 16 Feb 2022 14:03:55 +0100 Subject: [PATCH 431/774] Add wastewater level and years_vtg for hist_new_cap --- message_ix_models/model/material/data_ammonia.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/message_ix_models/model/material/data_ammonia.py b/message_ix_models/model/material/data_ammonia.py index b5e1304ab7..4bedc3cc0c 100644 --- a/message_ix_models/model/material/data_ammonia.py +++ b/message_ix_models/model/material/data_ammonia.py @@ -47,7 +47,7 @@ def gen_data_ammonia(scenario, dry_run=False): output_commodity_dict = { "output_NH3": "NH3", "output_heat": "d_heat", - "output_water": "" # ask Jihoon how to name + "output_water": "wastewater" # ask Jihoon how to name } commodity_dict = { "output": output_commodity_dict, @@ -95,7 +95,7 @@ def gen_data_ammonia(scenario, dry_run=False): if cat in ["input", "output"]: common["commodity"] = commodity_dict[cat][param] common["level"] = level_cat_dict[cat][param] - if t == "biomass_NH3": + if (t == "biomass_NH3") & (cat == "input"): common["level"] = "primary" if (str(t) == "NH3_to_N_fertil") & (param == "output_NH3"): common['commodity'] = "Fertilizer Use|Nitrogen" @@ -148,7 +148,7 @@ def gen_data_ammonia(scenario, dry_run=False): df = ( make_df("historical_new_capacity", technology=[t for t in config["technology"]["add"]], # ], refactor to adjust to yaml structure - value=1, unit='t', years_act=s_info.Y, **common) + value=1, unit='t', years_act=s_info.Y, years_vtg=s_info.Y, **common) .pipe(broadcast, node_loc=nodes) .pipe(same_node) ) From 29a982df2fd4171d06c0e4aacad1167bcbc81748 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 17 Feb 2022 16:09:30 +0100 Subject: [PATCH 432/774] Fix land_input, remove r12_glb, fix units --- .../model/material/data_ammonia.py | 33 +++++++++++-------- 1 file changed, 20 insertions(+), 13 deletions(-) diff --git a/message_ix_models/model/material/data_ammonia.py b/message_ix_models/model/material/data_ammonia.py index 4bedc3cc0c..afc659d8c0 100644 --- a/message_ix_models/model/material/data_ammonia.py +++ b/message_ix_models/model/material/data_ammonia.py @@ -17,7 +17,7 @@ CONVERSION_FACTOR_PJ_GWa = 0.0317 -def gen_data_ammonia(scenario, dry_run=False): +def gen_data(scenario, dry_run=False): """Generate data for materials representation of nitrogen fertilizers. .. note:: This code is only partially translated from @@ -30,9 +30,11 @@ def gen_data_ammonia(scenario, dry_run=False): # Information about scenario, e.g. node, year s_info = ScenarioInfo(scenario) nodes = s_info.N - if (("World" in nodes) | ("R12_GLB" in nodes)): + if "World" in nodes: nodes.pop(nodes.index("World")) + if "R12_GLB" in nodes: nodes.pop(nodes.index("R12_GLB")) + # Techno-economic assumptions data = read_data() @@ -90,7 +92,7 @@ def gen_data_ammonia(scenario, dry_run=False): for param in data['parameter'].unique(): if (t == "electr_NH3") & (param == "input_fuel"): continue - unit = "t" + unit = data['Unit'][data['parameter'] == param].iloc[0] cat = data['param_cat'][data['parameter'] == param].iloc[0] if cat in ["input", "output"]: common["commodity"] = commodity_dict[cat][param] @@ -101,7 +103,7 @@ def gen_data_ammonia(scenario, dry_run=False): common['commodity'] = "Fertilizer Use|Nitrogen" common['level'] = "material_final" df = ( - make_df(cat, technology=t, value=1, unit=unit, **common) + make_df(cat, technology=t, value=1, unit="-", **common) .pipe(broadcast, node_loc=nodes) .pipe(same_node) ) @@ -136,9 +138,8 @@ def gen_data_ammonia(scenario, dry_run=False): ) row = act2010 - # Unit is Tg N/yr results["historical_activity"].append( - df.assign(value=row, unit="t", year_act=2010) + df.assign(value=row, unit='t', year_act=2010) ) # 2015 activity necessary if this is 5-year step scenario # df['value'] = act2015 # total NH3 or N in Mt 2010 FAO Russia @@ -148,7 +149,7 @@ def gen_data_ammonia(scenario, dry_run=False): df = ( make_df("historical_new_capacity", technology=[t for t in config["technology"]["add"]], # ], refactor to adjust to yaml structure - value=1, unit='t', years_act=s_info.Y, years_vtg=s_info.Y, **common) + value=1, unit='t', **common) .pipe(broadcast, node_loc=nodes) .pipe(same_node) ) @@ -157,9 +158,8 @@ def gen_data_ammonia(scenario, dry_run=False): capacity_factor = read_demand()['capacity_factor'] row = act2010 * 1 / 15 / capacity_factor[0] - # Unit is Tg N/yr results["historical_new_capacity"].append( - df.assign(value=row, unit="t", year_act=2010) + df.assign(value=row, unit='t', year_vtg=2010) ) # %% Secure feedstock balance (foil_fs, gas_fs, coal_fs) loil_fs? @@ -181,6 +181,7 @@ def gen_data_ammonia(scenario, dry_run=False): results["demand"].append(df) # Globiom land input + """ df = pd.read_excel(context.get_local_path('material','GLOBIOM_Fertilizer_use_N.xlsx')) df = df.replace(regex=r'^R11', value="R12").replace(regex=r'^R12_CPA', value="R12_CHN") df["unit"] = "t" @@ -191,6 +192,12 @@ def gen_data_ammonia(scenario, dry_run=False): df = df.append(df_rcpa) df = df.drop("Unnamed: 0", axis=1) results["land_input"].append(df) + """ + + df = scenario.par("land_output", {"commodity": "Fertilizer Use|Nitrogen"}) + df["level"] = "material_final" + results["land_input"].append(df) + #scenario.add_par("land_input", df) # add background parameters (growth rates and bounds) @@ -332,13 +339,13 @@ def gen_data_ccs(scenario, dry_run=False): # t=NH3_to_N_fertil; use 'if' statements to fill in. for param in data['parameter'].unique(): - unit = "t" + unit = data['Unit'][data['parameter'] == param].iloc[0] cat = data['param_cat'][data['parameter'] == param].iloc[0] if cat in ["input", "output"]: common["commodity"] = commodity_dict[cat][param] common["level"] = level_cat_dict[cat][param] df = ( - make_df(cat, technology=t, value=1, unit=unit, **common) + make_df(cat, technology=t, value=1, unit="-", **common) .pipe(broadcast, node_loc=nodes) .pipe(same_node) ) @@ -453,8 +460,8 @@ def read_demand(): # Get historical N demand from SSP2-nopolicy (may need to vary for diff scenarios) N_demand_raw = N_demand_GLO.copy() - N_demand = N_demand_raw[(N_demand_raw.Scenario == "NoPolicy") & (N_demand_raw.Region != "World")].reset_index().loc[ - :, 2010] # 2010 tot N demand + N_demand = N_demand_raw[(N_demand_raw.Scenario == "NoPolicy") & + (N_demand_raw.Region != "World")].reset_index().loc[:, 2010] # 2010 tot N demand N_demand = N_demand.repeat(6) act2010 = (feedshare.values.flatten() * N_demand).reset_index(drop=True) From 5933f7a460e44dd8cb8fdea07384a64d209c26a7 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 24 Feb 2022 20:48:58 +0100 Subject: [PATCH 433/774] Add trade integration --- .../n-fertilizer_techno-economic_new.xlsx | 4 +- .../model/material/data_ammonia.py | 148 +++++++++++++++++- 2 files changed, 143 insertions(+), 9 deletions(-) diff --git a/message_ix_models/data/material/n-fertilizer_techno-economic_new.xlsx b/message_ix_models/data/material/n-fertilizer_techno-economic_new.xlsx index 9c4edb7ce9..268c90ae48 100644 --- a/message_ix_models/data/material/n-fertilizer_techno-economic_new.xlsx +++ b/message_ix_models/data/material/n-fertilizer_techno-economic_new.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:11c11bed1bc58064062118012b157a76c25562b0cc5cf954843d9f25fbb553bd -size 305 +oid sha256:d153add8b83c71776e127708a9eacc1310b1ff71c2634aa24304506942395f5c +size 277 diff --git a/message_ix_models/model/material/data_ammonia.py b/message_ix_models/model/material/data_ammonia.py index afc659d8c0..c54804da6a 100644 --- a/message_ix_models/model/material/data_ammonia.py +++ b/message_ix_models/model/material/data_ammonia.py @@ -3,6 +3,7 @@ from pathlib import Path import pandas as pd +import numpy as np from message_ix import make_df from message_ix_models import ScenarioInfo from message_ix_models.util import broadcast, same_node @@ -72,8 +73,8 @@ def gen_data(scenario, dry_run=False): # NH3 production processes common = dict( - year_act=s_info.Y, # confirm if correct?? - year_vtg=s_info.Y, + year_act=s_info.yv_ya.year_act, # confirm if correct?? + year_vtg=s_info.yv_ya.year_vtg, commodity="NH3", level="material_interim", mode="all", @@ -87,7 +88,6 @@ def gen_data(scenario, dry_run=False): for t in config["technology"]["add"]: # : refactor to adjust to yaml structure # Output of NH3: same efficiency for all technologies # the output commodity and level are different for - # t=NH3_to_N_fertil; use 'if' statements to fill in. for param in data['parameter'].unique(): if (t == "electr_NH3") & (param == "input_fuel"): @@ -166,6 +166,7 @@ def gen_data(scenario, dry_run=False): # Adjust i_feed demand N_energy = read_demand()['N_energy'] + N_energy = read_demand()['N_feed'] # updated feed with imports accounted demand_fs_org = pd.read_excel(context.get_local_path('material','demand_i_feed_R12.xlsx')) @@ -177,7 +178,7 @@ def gen_data(scenario, dry_run=False): df = df.drop('r_feed', axis=1) df = df.drop('Unnamed: 0', axis=1) # TODO: refactor with a more sophisticated solution to reduce i_feed - df.loc[df["value"] < 0, "value"] = 0 # tempoary solution to avoid negative values + df.loc[df["value"] < 0, "value"] = 0 # temporary solution to avoid negative values results["demand"].append(df) # Globiom land input @@ -261,6 +262,80 @@ def gen_data(scenario, dry_run=False): scalers_dict["R12_SAS"][df.technology[0]] results[param][i] = regs.drop(["standard", "ccs"], axis="columns") + # add trade tecs (exp, imp, trd) + + yv_ya_exp = s_info.yv_ya + yv_ya_exp = yv_ya_exp[yv_ya_exp["year_act"] - yv_ya_exp["year_vtg"] < 30] + yv_ya_same = yv_ya_exp[yv_ya_exp["year_act"] - yv_ya_exp["year_vtg"] == 0] + + common = dict( + year_act=yv_ya_same.year_act, + year_vtg=yv_ya_same.year_vtg, + commodity="Fertilizer Use|Nitrogen", + level="material_final", + mode="M1", + time="year", + time_dest="year", + time_origin="year", + ) + + data = read_trade_data(context) + + for i in data["var_name"].unique(): + for tec in data["technology"].unique(): + row = data[(data["var_name"] == i) & (data["technology"] == tec)] + if len(row): + if row["technology"].values[0] == "trade_NFert": + node = ["R12_GLB"] + else: + node = s_info.N[1:] + if tec == "export_NFert": + common_exp = common + common_exp["year_act"] = yv_ya_exp.year_act + common_exp["year_vtg"] = yv_ya_exp.year_vtg + df = make_df(i, technology=tec, value=row[2010].values[0], + unit="-", **common_exp).pipe(broadcast, node_loc=node).pipe(same_node) + else: + df = make_df(i, technology=tec, value=row[2010].values[0], + unit="-", **common).pipe(broadcast, node_loc=node).pipe(same_node) + if (tec == "export_NFert") & (i == "output"): + df["node_dest"] = "R12_GLB" + df["level"] = "export" + elif (tec == "import_NFert") & (i == "input"): + df["node_origin"] = "R12_GLB" + df["level"] = "import" + else: + df.pipe(same_node) + results[i].append(df) + + common = dict( + commodity="Fertilizer Use|Nitrogen", + level="material_final", + mode="M1", + time="year", + time_dest="year", + time_origin="year", + unit="t" + ) + + N_trade_R12 = read_demand()["N_trade_R12"].assign(mode="M1") + N_trade_R12["technology"] = N_trade_R12["Element"].apply( + lambda x: "export_NFert" if x == "Export" else "import_NFert") + df_exp_imp_act = N_trade_R12.drop("Element", axis=1) + + trd_act_years = N_trade_R12["year_act"].unique() + values = N_trade_R12.groupby(["year_act"]).sum().values.flatten() + fert_trd_hist = make_df("historical_activity", technology="trade_NFert", + year_act=trd_act_years, value=values, + node_loc="R12_GLB", **common) + results["historical_activity"].append(pd.concat([df_exp_imp_act, fert_trd_hist])) + + df_hist_cap_new = N_trade_R12[N_trade_R12["technology"] == "export_NFert"].drop(columns=["time", "mode", "Element"]) + df_hist_cap_new = df_hist_cap_new.rename(columns={"year_act": "year_vtg"}) + # divide by export lifetime derived from coal_exp + df_hist_cap_new = df_hist_cap_new.assign(value=lambda x: x["value"] / 30) + results["historical_new_capacity"].append(df_hist_cap_new) + # Concatenate to one dataframe per parameter results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} @@ -409,7 +484,7 @@ def gen_data_ccs(scenario, dry_run=False): def read_demand(): """Read and clean data from :file:`CD-Links SSP2 N-fertilizer demand.Global.xlsx`.""" - # %% Demand scenario [Mt N/year] from GLOBIOM + # Demand scenario [Mt N/year] from GLOBIOM context = read_config() @@ -446,7 +521,46 @@ def read_demand(): N_energy = pd.concat([N_energy.Region, N_energy.sum(axis=1)], axis=1).rename( columns={0: 'totENE', 'Region': 'node'}) # GWa - # %% Process the regional historical activities + N_trade_R12 = pd.read_csv(context.get_local_path("material","trade.FAO.R12.csv"), index_col=0) + N_trade_R12.msgregion = "R12_" + N_trade_R12.msgregion + N_trade_R12.Value = N_trade_R12.Value / 1e6 + N_trade_R12.Unit = "t" + N_trade_R12 = N_trade_R12.assign(time="year") + N_trade_R12 = N_trade_R12.rename( + columns={ + "Value": "value", + "Unit": "unit", + "msgregion": "node_loc", + "Year": "year_act", + } + ) + + df = N_trade_R12.loc[ + N_trade_R12.year_act == 2010, + ] + df = df.pivot(index="node_loc", columns="Element", values="value") + NP = pd.DataFrame({"netimp": df.Import - df.Export, "demand": ND[2010]}) + NP["prod"] = NP.demand - NP.netimp + + + # Derive total energy (GWa) of NH3 production (based on demand 2010) + N_feed = feedshare_GLO[feedshare_GLO.Region != "R11_GLB"].join(NP, on="Region") + N_feed = pd.concat( + [ + N_feed.Region, + N_feed[["gas_pct", "coal_pct", "oil_pct"]].multiply( + N_feed["prod"], axis="index" + ), + ], + axis=1, + ) + N_feed.gas_pct *= input_fuel[2] * 17 / 14 + N_feed.coal_pct *= input_fuel[3] * 17 / 14 + N_feed.oil_pct *= input_fuel[4] * 17 / 14 + N_feed = pd.concat([N_feed.Region, N_feed.sum(axis=1)], axis=1).rename( + columns={0: "totENE", "Region": "node"}) + + # Process the regional historical activities fs_GLO = feedshare_GLO.copy() fs_GLO.insert(1, "bio_pct", 0) @@ -467,7 +581,27 @@ def read_demand(): act2010 = (feedshare.values.flatten() * N_demand).reset_index(drop=True) return {"feedshare_GLO": feedshare_GLO, "ND": ND, "N_energy": N_energy, "feedshare": feedshare, 'act2010': act2010, - 'capacity_factor': capacity_factor} + 'capacity_factor': capacity_factor, "N_feed":N_feed, "N_trade_R12":N_trade_R12} + + +def read_trade_data(context): + data = pd.read_excel( + context.get_local_path("material", "n-fertilizer_techno-economic_new.xlsx"), + sheet_name="Trade", engine="openpyxl", usecols=np.linspace(0, 7, 8, dtype=int)) + data = data.assign(technology=lambda x: set_trade_tec(x["Variable"])) + return data + + +def set_trade_tec(x): + arr=[] + for i in x: + if "Import" in i: + arr.append("import_NFert") + if "Export" in i: + arr.append("export_NFert") + if "Trade" in i: + arr.append("trade_NFert") + return arr def read_data(): From 40618a208ee14bd057875123db9af1f3a3dbef08 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 24 Feb 2022 20:56:52 +0100 Subject: [PATCH 434/774] Add trade missing sets --- .../model/material/data_ammonia.py | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) diff --git a/message_ix_models/model/material/data_ammonia.py b/message_ix_models/model/material/data_ammonia.py index c54804da6a..c7033cc8c9 100644 --- a/message_ix_models/model/material/data_ammonia.py +++ b/message_ix_models/model/material/data_ammonia.py @@ -264,6 +264,23 @@ def gen_data(scenario, dry_run=False): # add trade tecs (exp, imp, trd) + newtechnames_trd = ["trade_NFert"] + newtechnames_imp = ["import_NFert"] + newtechnames_exp = ["export_NFert"] + scenario.check_out() + scenario.add_set( + "technology", + newtechnames_trd + newtechnames_imp + newtechnames_exp + ) + cat_add = pd.DataFrame( + { + "type_tec": ["import", "export"], # 'all' not need to be added here + "technology": newtechnames_imp + newtechnames_exp, + } + ) + scenario.add_set("cat_tec", cat_add) + scenario.commit("add trade tec sets") + yv_ya_exp = s_info.yv_ya yv_ya_exp = yv_ya_exp[yv_ya_exp["year_act"] - yv_ya_exp["year_vtg"] < 30] yv_ya_same = yv_ya_exp[yv_ya_exp["year_act"] - yv_ya_exp["year_vtg"] == 0] From a45cd7725c858b817ac24d4f91d9d4bef011b79a Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Mon, 28 Feb 2022 14:35:55 +0100 Subject: [PATCH 435/774] Remove unnecessary checkout and commit --- message_ix_models/model/material/data_ammonia.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/message_ix_models/model/material/data_ammonia.py b/message_ix_models/model/material/data_ammonia.py index c7033cc8c9..fc196857fc 100644 --- a/message_ix_models/model/material/data_ammonia.py +++ b/message_ix_models/model/material/data_ammonia.py @@ -267,7 +267,7 @@ def gen_data(scenario, dry_run=False): newtechnames_trd = ["trade_NFert"] newtechnames_imp = ["import_NFert"] newtechnames_exp = ["export_NFert"] - scenario.check_out() + scenario.add_set( "technology", newtechnames_trd + newtechnames_imp + newtechnames_exp @@ -279,7 +279,6 @@ def gen_data(scenario, dry_run=False): } ) scenario.add_set("cat_tec", cat_add) - scenario.commit("add trade tec sets") yv_ya_exp = s_info.yv_ya yv_ya_exp = yv_ya_exp[yv_ya_exp["year_act"] - yv_ya_exp["year_vtg"] < 30] From d82c591b5b1debe035ceb3f91d1d875e0631709f Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Mon, 28 Feb 2022 14:25:59 +0100 Subject: [PATCH 436/774] Remove redundant rows from parameter dfs --- message_ix_models/model/material/data_ammonia.py | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/message_ix_models/model/material/data_ammonia.py b/message_ix_models/model/material/data_ammonia.py index fc196857fc..d10f4a8514 100644 --- a/message_ix_models/model/material/data_ammonia.py +++ b/message_ix_models/model/material/data_ammonia.py @@ -71,10 +71,14 @@ def gen_data(scenario, dry_run=False): "input": input_level_dict } + + vtg_years = s_info.yv_ya[s_info.yv_ya.year_vtg > 2000]["year_vtg"] + act_years = s_info.yv_ya[s_info.yv_ya.year_vtg > 2000]["year_act"] + # NH3 production processes common = dict( - year_act=s_info.yv_ya.year_act, # confirm if correct?? - year_vtg=s_info.yv_ya.year_vtg, + year_act=act_years, # confirm if correct?? + year_vtg=vtg_years, commodity="NH3", level="material_interim", mode="all", @@ -281,8 +285,8 @@ def gen_data(scenario, dry_run=False): scenario.add_set("cat_tec", cat_add) yv_ya_exp = s_info.yv_ya - yv_ya_exp = yv_ya_exp[yv_ya_exp["year_act"] - yv_ya_exp["year_vtg"] < 30] - yv_ya_same = yv_ya_exp[yv_ya_exp["year_act"] - yv_ya_exp["year_vtg"] == 0] + yv_ya_exp = yv_ya_exp[(yv_ya_exp["year_act"] - yv_ya_exp["year_vtg"] < 30) & (yv_ya_exp["year_vtg"] > 2000)] + yv_ya_same = yv_ya_exp[(yv_ya_exp["year_act"] - yv_ya_exp["year_vtg"] == 0) & ( yv_ya_exp["year_vtg"] == 2000)] common = dict( year_act=yv_ya_same.year_act, From ad8b028da6ae4accbbc62df6421b09af438e5e58 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 1 Mar 2022 11:43:17 +0100 Subject: [PATCH 437/774] Remove unnecessary vintage years --- message_ix_models/data/material/aluminum_techno_economic.xlsx | 4 ++-- message_ix_models/model/material/data_aluminum.py | 1 - 2 files changed, 2 insertions(+), 3 deletions(-) diff --git a/message_ix_models/data/material/aluminum_techno_economic.xlsx b/message_ix_models/data/material/aluminum_techno_economic.xlsx index dd56542de4..5e0b413262 100644 --- a/message_ix_models/data/material/aluminum_techno_economic.xlsx +++ b/message_ix_models/data/material/aluminum_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:32dcbc45de9bd5d208e8f3a9489f5933aa4360dfb645cdb3080e3351ca2cea05 -size 117334 +oid sha256:ccfc80ef9c6e27255438a4e801355a1e59e8eb0c82cd652ff97400708a2394e0 +size 292 diff --git a/message_ix_models/model/material/data_aluminum.py b/message_ix_models/model/material/data_aluminum.py index 36aca46564..197106223b 100644 --- a/message_ix_models/model/material/data_aluminum.py +++ b/message_ix_models/model/material/data_aluminum.py @@ -454,7 +454,6 @@ def gen_data_aluminum(scenario, dry_run=False): results_aluminum = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} return results_aluminum - def gen_mock_demand_aluminum(scenario): context = read_config() From 6d59b2bc516934b3e46c529df0f077904b56fbf1 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 1 Mar 2022 15:30:38 +0100 Subject: [PATCH 438/774] Adjust add_spec function to perform two commits --- message_ix_models/model/material/build.py | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/message_ix_models/model/material/build.py b/message_ix_models/model/material/build.py index 927549d7f6..c925705894 100644 --- a/message_ix_models/model/material/build.py +++ b/message_ix_models/model/material/build.py @@ -13,6 +13,16 @@ log = logging.getLogger(__name__) +def ellipsize(elements: List) -> str: + """Generate a short string representation of `elements`. + + If the list has more than 5 elements, only the first two and last two are shown, + with "..." between. + """ + if len(elements) > 5: + return ", ".join(map(str, elements[:2] + ["..."] + elements[-2:])) + else: + return ", ".join(map(str, elements)) def ellipsize(elements: List) -> str: """Generate a short string representation of `elements`. From b28d9ce61715e2e022e86223188ee9a1c64129f7 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 3 Mar 2022 10:14:59 +0100 Subject: [PATCH 439/774] Update units in n-fertilizer-techno_economic --- .../data/material/n-fertilizer_techno-economic_new.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/n-fertilizer_techno-economic_new.xlsx b/message_ix_models/data/material/n-fertilizer_techno-economic_new.xlsx index 268c90ae48..246e5c8114 100644 --- a/message_ix_models/data/material/n-fertilizer_techno-economic_new.xlsx +++ b/message_ix_models/data/material/n-fertilizer_techno-economic_new.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:d153add8b83c71776e127708a9eacc1310b1ff71c2634aa24304506942395f5c -size 277 +oid sha256:991c95cd945caf3437bf39698b7df88ccde12d05431121b7e11a47e364edf8b3 +size 300 From 406785a2472b5dee46a24ade1e3e83b7ea77d1ce Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 3 Mar 2022 10:29:40 +0100 Subject: [PATCH 440/774] Update n-fertilizer_techno-economic_new.xlsx --- .../data/material/n-fertilizer_techno-economic_new.xlsx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/message_ix_models/data/material/n-fertilizer_techno-economic_new.xlsx b/message_ix_models/data/material/n-fertilizer_techno-economic_new.xlsx index 246e5c8114..91c98b8e95 100644 --- a/message_ix_models/data/material/n-fertilizer_techno-economic_new.xlsx +++ b/message_ix_models/data/material/n-fertilizer_techno-economic_new.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:991c95cd945caf3437bf39698b7df88ccde12d05431121b7e11a47e364edf8b3 +oid sha256:afc69e27b4c9c2e48023eb00ddaa14b1dda78ee69b99e2002d9896b729f7af2f size 300 From 5ade4660ca8a05f01aef1f5436790d4a8e5593c0 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 3 Mar 2022 17:12:53 +0100 Subject: [PATCH 441/774] Change level names --- .../model/material/data_ammonia.py | 24 +++++++++---------- 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/message_ix_models/model/material/data_ammonia.py b/message_ix_models/model/material/data_ammonia.py index d10f4a8514..b32868b97d 100644 --- a/message_ix_models/model/material/data_ammonia.py +++ b/message_ix_models/model/material/data_ammonia.py @@ -64,7 +64,7 @@ def gen_data(scenario, dry_run=False): output_level_dict = { "output_water": "wastewater", "output_heat": "secondary", - "output_NH3": "material_interim" + "output_NH3": "secondary_material" } level_cat_dict = { "output": output_level_dict, @@ -80,8 +80,8 @@ def gen_data(scenario, dry_run=False): year_act=act_years, # confirm if correct?? year_vtg=vtg_years, commodity="NH3", - level="material_interim", - mode="all", + level="secondary_material", + mode="M1", time="year", time_dest="year", time_origin="year", @@ -105,7 +105,7 @@ def gen_data(scenario, dry_run=False): common["level"] = "primary" if (str(t) == "NH3_to_N_fertil") & (param == "output_NH3"): common['commodity'] = "Fertilizer Use|Nitrogen" - common['level'] = "material_final" + common['level'] = "final_material" df = ( make_df(cat, technology=t, value=1, unit="-", **common) .pipe(broadcast, node_loc=nodes) @@ -126,8 +126,8 @@ def gen_data(scenario, dry_run=False): # Historical activities/capacities - Region specific common = dict( commodity="NH3", - level="material_interim", - mode="all", + level="secondary_material", + mode="M1", time="year", time_dest="year", time_origin="year", @@ -200,7 +200,7 @@ def gen_data(scenario, dry_run=False): """ df = scenario.par("land_output", {"commodity": "Fertilizer Use|Nitrogen"}) - df["level"] = "material_final" + df["level"] = "final_material" results["land_input"].append(df) #scenario.add_par("land_input", df) @@ -292,7 +292,7 @@ def gen_data(scenario, dry_run=False): year_act=yv_ya_same.year_act, year_vtg=yv_ya_same.year_vtg, commodity="Fertilizer Use|Nitrogen", - level="material_final", + level="final_material", mode="M1", time="year", time_dest="year", @@ -330,7 +330,7 @@ def gen_data(scenario, dry_run=False): common = dict( commodity="Fertilizer Use|Nitrogen", - level="material_final", + level="final_material", mode="M1", time="year", time_dest="year", @@ -388,9 +388,9 @@ def gen_data_ccs(scenario, dry_run=False): year_vtg=s_info.Y, year_act=s_info.Y, # confirm if correct?? commodity="NH3", - level="material_interim", + level="secondary_material", # TODO fill in remaining dimensions - mode="all", + mode="M1", time="year", time_dest="year", time_origin="year", @@ -420,7 +420,7 @@ def gen_data_ccs(scenario, dry_run=False): output_level_dict = { "output_water": "wastewater", "output_heat": "secondary", - "output_NH3": "material_interim" + "output_NH3": "secondary_material" } level_cat_dict = { "output": output_level_dict, From 80642aa3062e00815c143dad1230b4e228bdf83f Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 9 Mar 2022 16:13:03 +0100 Subject: [PATCH 442/774] Fix a wrong level --- message_ix_models/model/material/data_ammonia.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/message_ix_models/model/material/data_ammonia.py b/message_ix_models/model/material/data_ammonia.py index b32868b97d..9b9088d4c1 100644 --- a/message_ix_models/model/material/data_ammonia.py +++ b/message_ix_models/model/material/data_ammonia.py @@ -106,6 +106,8 @@ def gen_data(scenario, dry_run=False): if (str(t) == "NH3_to_N_fertil") & (param == "output_NH3"): common['commodity'] = "Fertilizer Use|Nitrogen" common['level'] = "final_material" + if (str(t) == "NH3_to_N_fertil") & (param == "input_fuel"): + common['level'] = "secondary_material" df = ( make_df(cat, technology=t, value=1, unit="-", **common) .pipe(broadcast, node_loc=nodes) From c5bb4e2d9f99e42bf16c0176db3a5eb754660dae Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Wed, 2 Mar 2022 16:34:11 +0100 Subject: [PATCH 443/774] Fix trade nodes and years and add trade and ccs to set.yaml --- .../model/material/data_ammonia.py | 29 ++++++++++++------- 1 file changed, 18 insertions(+), 11 deletions(-) diff --git a/message_ix_models/model/material/data_ammonia.py b/message_ix_models/model/material/data_ammonia.py index 9b9088d4c1..004aa0b175 100644 --- a/message_ix_models/model/material/data_ammonia.py +++ b/message_ix_models/model/material/data_ammonia.py @@ -89,7 +89,8 @@ def gen_data(scenario, dry_run=False): ) # Iterate over new technologies, using the configuration - for t in config["technology"]["add"]: # : refactor to adjust to yaml structure + for t in config["technology"]["add"][:6]: + # TODO: refactor to adjust to yaml structure # Output of NH3: same efficiency for all technologies # the output commodity and level are different for @@ -288,7 +289,7 @@ def gen_data(scenario, dry_run=False): yv_ya_exp = s_info.yv_ya yv_ya_exp = yv_ya_exp[(yv_ya_exp["year_act"] - yv_ya_exp["year_vtg"] < 30) & (yv_ya_exp["year_vtg"] > 2000)] - yv_ya_same = yv_ya_exp[(yv_ya_exp["year_act"] - yv_ya_exp["year_vtg"] == 0) & ( yv_ya_exp["year_vtg"] == 2000)] + yv_ya_same = s_info.yv_ya[(s_info.yv_ya["year_act"] - s_info.yv_ya["year_vtg"] == 0) & ( s_info.yv_ya["year_vtg"] > 2000)] common = dict( year_act=yv_ya_same.year_act, @@ -310,7 +311,7 @@ def gen_data(scenario, dry_run=False): if row["technology"].values[0] == "trade_NFert": node = ["R12_GLB"] else: - node = s_info.N[1:] + node = nodes if tec == "export_NFert": common_exp = common common_exp["year_act"] = yv_ya_exp.year_act @@ -370,7 +371,7 @@ def gen_data_ccs(scenario, dry_run=False): .. note:: This code is only partially translated from :file:`SetupNitrogenBase.py`. """ - config = read_config()["material"]["set"] + config = read_config()["material"]["fertilizer"] context = read_config() # Information about scenario, e.g. node, year @@ -378,6 +379,8 @@ def gen_data_ccs(scenario, dry_run=False): nodes = s_info.N if "World" in nodes: nodes.pop(nodes.index("World")) + if "R12_GLB" in nodes: + nodes.pop(nodes.index("R12_GLB")) # Techno-economic assumptions data = read_data_ccs() @@ -385,10 +388,13 @@ def gen_data_ccs(scenario, dry_run=False): # List of data frames, to be concatenated together at end results = defaultdict(list) + vtg_years = s_info.yv_ya[s_info.yv_ya.year_vtg > 2000]["year_vtg"] + act_years = s_info.yv_ya[s_info.yv_ya.year_vtg > 2000]["year_act"] + # NH3 production processes common = dict( - year_vtg=s_info.Y, - year_act=s_info.Y, # confirm if correct?? + year_vtg=vtg_years, + year_act=act_years, # confirm if correct?? commodity="NH3", level="secondary_material", # TODO fill in remaining dimensions @@ -430,7 +436,7 @@ def gen_data_ccs(scenario, dry_run=False): } # Iterate over new technologies, using the configuration - for t in config["technology"]["add"]["ccs"]: + for t in config["technology"]["add"][9:]: # Output of NH3: same efficiency for all technologies # TODO the output commodity and level are different for # t=NH3_to_N_fertil; use 'if' statements to fill in. @@ -671,7 +677,7 @@ def read_data(): param_cat2 = [] # print(param_cat) - for t in sets["technology"]["add"]: # : refactor to adjust to yaml structure + for t in sets["technology"]["add"][:6]: # : refactor to adjust to yaml structure # print(t) param_values.extend(params) tech_values.extend([t] * len(params)) @@ -702,11 +708,11 @@ def read_data_ccs(): context = read_config() # Shorter access to sets configuration - sets = context["material"]["set"] + sets = context["material"]["fertilizer"] # Read the file data = pd.read_excel( - context.get_local_path("material", "n-fertilizer_techno-economic.xlsx"), + context.get_local_path("material", "n-fertilizer_techno-economic_new.xlsx"), sheet_name="CCS", ) @@ -738,7 +744,8 @@ def read_data_ccs(): param_cat2 = [] - for t in sets["technology"]["add"]["ccs"]: + + for t in sets["technology"]["add"][9:]: param_values.extend(params) tech_values.extend([t] * len(params)) param_cat2.extend(param_cat) From e6e000257c6a2a178d3066b215792d8557450891 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 3 Mar 2022 11:31:24 +0100 Subject: [PATCH 444/774] Remove unit conversions --- message_ix_models/model/material/data_ammonia.py | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/message_ix_models/model/material/data_ammonia.py b/message_ix_models/model/material/data_ammonia.py index 004aa0b175..b22e7e552c 100644 --- a/message_ix_models/model/material/data_ammonia.py +++ b/message_ix_models/model/material/data_ammonia.py @@ -529,7 +529,7 @@ def read_demand(): n_inputs_per_tech = 12 # Number of input params per technology input_fuel = te_params[2010][list(range(4, te_params.shape[0], n_inputs_per_tech))].reset_index(drop=True) - input_fuel[0:5] = input_fuel[0:5] * CONVERSION_FACTOR_PJ_GWa # 0.0317 GWa/PJ, GJ/t = PJ/Mt NH3 + #input_fuel[0:5] = input_fuel[0:5] * CONVERSION_FACTOR_PJ_GWa # 0.0317 GWa/PJ, GJ/t = PJ/Mt NH3 capacity_factor = te_params[2010][list(range(11, te_params.shape[0], n_inputs_per_tech))].reset_index(drop=True) @@ -694,9 +694,9 @@ def read_data(): # Set the data frame index for selection ) data.loc[data['parameter'] == 'emission_factor', 2010] = \ - data.loc[data['parameter'] == 'emission_factor', 2010] * CONVERSION_FACTOR_CO2_C - data.loc[data['parameter'] == 'input_elec', 2010] = \ - data.loc[data['parameter'] == 'input_elec', 2010] * CONVERSION_FACTOR_PJ_GWa + data.loc[data['parameter'] == 'emission_factor', 2010]# * CONVERSION_FACTOR_CO2_C + #data.loc[data['parameter'] == 'input_elec', 2010] = \ + # data.loc[data['parameter'] == 'input_elec', 2010] * CONVERSION_FACTOR_PJ_GWa # TODO convert units for some parameters, per LoadParams.py return data @@ -761,9 +761,11 @@ def read_data_ccs(): ) #unit conversions and extra electricity for CCS process data.loc[data['parameter'] == 'emission_factor', 2010] = \ - data.loc[data['parameter'] == 'emission_factor', 2010] * CONVERSION_FACTOR_CO2_C + data.loc[data['parameter'] == 'emission_factor', 2010]# * CONVERSION_FACTOR_CO2_C + #data.loc[data['parameter'] == 'input_elec', 2010] = \ + # data.loc[data['parameter'] == 'input_elec', 2010] * CONVERSION_FACTOR_PJ_GWa + 0.005 data.loc[data['parameter'] == 'input_elec', 2010] = \ - data.loc[data['parameter'] == 'input_elec', 2010] * CONVERSION_FACTOR_PJ_GWa + 0.005 - + data.loc[data['parameter'] == 'input_elec', 2010] + (CONVERSION_FACTOR_PJ_GWa * 0.005) + # TODO: check this 0.005 hardcoded value for ccs elec input and move to excel # TODO convert units for some parameters, per LoadParams.py return data From 0c113ea4b3f1891376e760595c6fa900979ad840 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 3 Mar 2022 12:04:10 +0100 Subject: [PATCH 445/774] Add add_ccs option for gen_data() --- message_ix_models/model/material/data_ammonia.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/message_ix_models/model/material/data_ammonia.py b/message_ix_models/model/material/data_ammonia.py index b22e7e552c..41a7564d6f 100644 --- a/message_ix_models/model/material/data_ammonia.py +++ b/message_ix_models/model/material/data_ammonia.py @@ -18,7 +18,7 @@ CONVERSION_FACTOR_PJ_GWa = 0.0317 -def gen_data(scenario, dry_run=False): +def gen_data(scenario, dry_run=False, add_ccs: bool = True): """Generate data for materials representation of nitrogen fertilizers. .. note:: This code is only partially translated from @@ -359,6 +359,10 @@ def gen_data(scenario, dry_run=False): df_hist_cap_new = df_hist_cap_new.assign(value=lambda x: x["value"] / 30) results["historical_new_capacity"].append(df_hist_cap_new) + if add_ccs: + for k, v in gen_data_ccs(scenario).items(): + results[k].append(v) + # Concatenate to one dataframe per parameter results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} From a3d6958f4ade1d694b80840835c93b638644506a Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Wed, 9 Mar 2022 16:07:50 +0100 Subject: [PATCH 446/774] Fix trade_nfert ouput input level --- message_ix_models/model/material/data_ammonia.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/message_ix_models/model/material/data_ammonia.py b/message_ix_models/model/material/data_ammonia.py index 41a7564d6f..9e265a719b 100644 --- a/message_ix_models/model/material/data_ammonia.py +++ b/message_ix_models/model/material/data_ammonia.py @@ -327,6 +327,10 @@ def gen_data(scenario, dry_run=False, add_ccs: bool = True): elif (tec == "import_NFert") & (i == "input"): df["node_origin"] = "R12_GLB" df["level"] = "import" + elif (tec == "trade_NFert") & (i == "input"): + df["level"] = "import" + elif (tec == "trade_NFert") & (i == "output"): + df["level"] = "export" else: df.pipe(same_node) results[i].append(df) From 581acc10999c0864942ea8d682ef85a0941080fd Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Wed, 9 Mar 2022 16:39:25 +0100 Subject: [PATCH 447/774] Remove nan issue --- message_ix_models/model/material/data_ammonia.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/message_ix_models/model/material/data_ammonia.py b/message_ix_models/model/material/data_ammonia.py index 9e265a719b..09a5c3c2a8 100644 --- a/message_ix_models/model/material/data_ammonia.py +++ b/message_ix_models/model/material/data_ammonia.py @@ -138,7 +138,7 @@ def gen_data(scenario, dry_run=False, add_ccs: bool = True): act2010 = read_demand()['act2010'] df = ( make_df("historical_activity", - technology=[t for t in config["technology"]["add"]], #], TODO: maybe reintroduce std/ccs in yaml + technology=[t for t in config["technology"]["add"][:6]], #], TODO: maybe reintroduce std/ccs in yaml value=1, unit='t', years_act=s_info.Y, **common) .pipe(broadcast, node_loc=nodes) .pipe(same_node) @@ -155,7 +155,7 @@ def gen_data(scenario, dry_run=False, add_ccs: bool = True): df = ( make_df("historical_new_capacity", - technology=[t for t in config["technology"]["add"]], # ], refactor to adjust to yaml structure + technology=[t for t in config["technology"]["add"][:6]], # ], refactor to adjust to yaml structure value=1, unit='t', **common) .pipe(broadcast, node_loc=nodes) .pipe(same_node) @@ -210,12 +210,12 @@ def gen_data(scenario, dry_run=False, add_ccs: bool = True): # add background parameters (growth rates and bounds) df = scenario.par('initial_activity_lo', {"technology": ["gas_extr_mpen"]}) - for q in config["technology"]["add"]: + for q in config["technology"]["add"][:6]: df['technology'] = q results["initial_activity_lo"].append(df) df = scenario.par('growth_activity_lo', {"technology": ["gas_extr_mpen"]}) - for q in config["technology"]["add"]: + for q in config["technology"]["add"][:6]: df['technology'] = q results["growth_activity_lo"].append(df) @@ -475,12 +475,12 @@ def gen_data_ccs(scenario, dry_run=False): # add background parameters (growth rates and bounds) df = scenario.par('initial_activity_lo', {"technology": ["gas_extr_mpen"]}) - for q in config["technology"]["add"]: + for q in config["technology"]["add"][9:]: df['technology'] = q results["initial_activity_lo"].append(df) df = scenario.par('growth_activity_lo', {"technology": ["gas_extr_mpen"]}) - for q in config["technology"]["add"]: + for q in config["technology"]["add"][9:]: df['technology'] = q results["growth_activity_lo"].append(df) From a5294a43022c7d2ddeeaedde203d6bd64c87ba48 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Wed, 9 Mar 2022 17:51:49 +0100 Subject: [PATCH 448/774] Switch trade_nfert input output levels --- message_ix_models/model/material/data_ammonia.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/model/material/data_ammonia.py b/message_ix_models/model/material/data_ammonia.py index 09a5c3c2a8..ba43b8e810 100644 --- a/message_ix_models/model/material/data_ammonia.py +++ b/message_ix_models/model/material/data_ammonia.py @@ -328,9 +328,9 @@ def gen_data(scenario, dry_run=False, add_ccs: bool = True): df["node_origin"] = "R12_GLB" df["level"] = "import" elif (tec == "trade_NFert") & (i == "input"): - df["level"] = "import" - elif (tec == "trade_NFert") & (i == "output"): df["level"] = "export" + elif (tec == "trade_NFert") & (i == "output"): + df["level"] = "import" else: df.pipe(same_node) results[i].append(df) From 1f286fea8b590236b191ae09017e0ee9891acca4 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 10 Mar 2022 15:57:25 +0100 Subject: [PATCH 449/774] Add nh3 trade and relation_activity for emission accounting --- .../n-fertilizer_techno-economic_new.xlsx | 4 +- .../model/material/data_ammonia.py | 176 +++++++++++++++--- 2 files changed, 154 insertions(+), 26 deletions(-) diff --git a/message_ix_models/data/material/n-fertilizer_techno-economic_new.xlsx b/message_ix_models/data/material/n-fertilizer_techno-economic_new.xlsx index 91c98b8e95..0dbcfc7b0a 100644 --- a/message_ix_models/data/material/n-fertilizer_techno-economic_new.xlsx +++ b/message_ix_models/data/material/n-fertilizer_techno-economic_new.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:afc69e27b4c9c2e48023eb00ddaa14b1dda78ee69b99e2002d9896b729f7af2f -size 300 +oid sha256:c799872e689fc07315578426b189f86e6dd9bce02f8d96bc1cb03f9b82a03f0e +size 315 diff --git a/message_ix_models/model/material/data_ammonia.py b/message_ix_models/model/material/data_ammonia.py index ba43b8e810..ca8cb3c802 100644 --- a/message_ix_models/model/material/data_ammonia.py +++ b/message_ix_models/model/material/data_ammonia.py @@ -85,7 +85,7 @@ def gen_data(scenario, dry_run=False, add_ccs: bool = True): time="year", time_dest="year", time_origin="year", - emission="CO2" # confirm if correct + emission="CO2_transformation" # confirm if correct ) # Iterate over new technologies, using the configuration @@ -302,8 +302,7 @@ def gen_data(scenario, dry_run=False, add_ccs: bool = True): time_origin="year", ) - data = read_trade_data(context) - + data = read_trade_data(context, comm="NFert") for i in data["var_name"].unique(): for tec in data["technology"].unique(): row = data[(data["var_name"] == i) & (data["technology"] == tec)] @@ -363,6 +362,82 @@ def gen_data(scenario, dry_run=False, add_ccs: bool = True): df_hist_cap_new = df_hist_cap_new.assign(value=lambda x: x["value"] / 30) results["historical_new_capacity"].append(df_hist_cap_new) + #NH3 trade + #_______________________________________________ + + common = dict( + year_act=yv_ya_same.year_act, + year_vtg=yv_ya_same.year_vtg, + commodity="NH3", + level="secondary_material", + mode="M1", + time="year", + time_dest="year", + time_origin="year", + ) + + data = read_trade_data(context, comm="NH3") + + for i in data["var_name"].unique(): + for tec in data["technology"].unique(): + row = data[(data["var_name"] == i) & (data["technology"] == tec)] + if len(row): + if row["technology"].values[0] == "trade_NH3": + node = ["R12_GLB"] + else: + node = nodes + if tec == "export_NH3": + common_exp = common + common_exp["year_act"] = yv_ya_exp.year_act + common_exp["year_vtg"] = yv_ya_exp.year_vtg + df = make_df(i, technology=tec, value=row[2010].values[0], + unit="-", **common_exp).pipe(broadcast, node_loc=node).pipe(same_node) + else: + df = make_df(i, technology=tec, value=row[2010].values[0], + unit="-", **common).pipe(broadcast, node_loc=node).pipe(same_node) + if (tec == "export_NH3") & (i == "output"): + df["node_dest"] = "R12_GLB" + df["level"] = "export" + elif (tec == "import_NH3") & (i == "input"): + df["node_origin"] = "R12_GLB" + df["level"] = "import" + elif (tec == "trade_NH3") & (i == "input"): + df["level"] = "export" + elif (tec == "trade_NH3") & (i == "output"): + df["level"] = "import" + else: + df.pipe(same_node) + results[i].append(df) + + common = dict( + commodity="NH3", + level="secondary_material", + mode="M1", + time="year", + time_dest="year", + time_origin="year", + unit="t" + ) + + N_trade_R12 = read_demand()["N_trade_R12"].assign(mode="M1") + N_trade_R12["technology"] = N_trade_R12["Element"].apply( + lambda x: "export_NH3" if x == "Export" else "import_NH3") + df_exp_imp_act = N_trade_R12.drop("Element", axis=1) + + trd_act_years = N_trade_R12["year_act"].unique() + values = N_trade_R12.groupby(["year_act"]).sum().values.flatten() + fert_trd_hist = make_df("historical_activity", technology="trade_NH3", + year_act=trd_act_years, value=values, + node_loc="R12_GLB", **common) + results["historical_activity"].append(pd.concat([df_exp_imp_act, fert_trd_hist])) + + df_hist_cap_new = N_trade_R12[N_trade_R12["technology"] == "export_NH3"].drop(columns=["time", "mode", "Element"]) + df_hist_cap_new = df_hist_cap_new.rename(columns={"year_act": "year_vtg"}) + # divide by export lifetime derived from coal_exp + df_hist_cap_new = df_hist_cap_new.assign(value=lambda x: x["value"] / 30) + results["historical_new_capacity"].append(df_hist_cap_new) + #___________________________________________________________________________ + if add_ccs: for k, v in gen_data_ccs(scenario).items(): results[k].append(v) @@ -370,6 +445,30 @@ def gen_data(scenario, dry_run=False, add_ccs: bool = True): # Concatenate to one dataframe per parameter results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} + par = "emission_factor" + rel_df_cc = results[par] + rel_df_cc = rel_df_cc[rel_df_cc["emission"] == "CO2_transformation"] + rel_df_cc = rel_df_cc.assign(year_rel=rel_df_cc["year_act"], + node_rel=rel_df_cc["node_loc"], + relation="CO2_cc").drop(["emission", "year_vtg"], axis=1) + rel_df_cc = rel_df_cc[rel_df_cc["technology"] != "NH3_to_N_fertil"] + rel_df_cc = rel_df_cc[rel_df_cc["year_rel"] == rel_df_cc["year_act"]].drop_duplicates() + rel_df_cc[rel_df_cc["technology"] == "biomass_NH3_ccs"] = rel_df_cc[ + rel_df_cc["technology"] == "biomass_NH3_ccs"].assign( + value=results[par][results[par]["technology"] == "biomass_NH3_ccs"]["value"].values[0]) + + + rel_df_em = results[par] + rel_df_em = rel_df_em[rel_df_em["emission"] == "CO2"] + rel_df_em = rel_df_em.assign(year_rel=rel_df_em["year_act"], + node_rel=rel_df_em["node_loc"], + relation="CO2_Emission").drop(["emission", "year_vtg"], axis=1) + rel_df_em = rel_df_em[rel_df_em["technology"] != "NH3_to_N_fertil"] + rel_df_em[rel_df_em["year_rel"] == rel_df_em["year_act"]].drop_duplicates() + results["relation_activity"] = pd.concat([rel_df_cc, rel_df_em]) + + results["emission_factor"] = results["emission_factor"][results["emission_factor"]["technology"]!="NH3_to_N_fertil"] + return results @@ -444,7 +543,7 @@ def gen_data_ccs(scenario, dry_run=False): } # Iterate over new technologies, using the configuration - for t in config["technology"]["add"][9:]: + for t in config["technology"]["add"][12:]: # Output of NH3: same efficiency for all technologies # TODO the output commodity and level are different for # t=NH3_to_N_fertil; use 'if' statements to fill in. @@ -455,32 +554,41 @@ def gen_data_ccs(scenario, dry_run=False): if cat in ["input", "output"]: common["commodity"] = commodity_dict[cat][param] common["level"] = level_cat_dict[cat][param] - df = ( - make_df(cat, technology=t, value=1, unit="-", **common) - .pipe(broadcast, node_loc=nodes) - .pipe(same_node) - ) + if param == "emission_factor_trans": + _common = common.copy() + _common["emission"] = "CO2_transformation" + cat = "emission_factor" + df = ( + make_df(cat, technology=t, value=1, unit="-", **_common) + .pipe(broadcast, node_loc=nodes) + .pipe(same_node) + ) + + else: + df = ( + make_df(cat, technology=t, value=1, unit="-", **common) + .pipe(broadcast, node_loc=nodes) + .pipe(same_node) + ) row = data[(data['technology'] == str(t)) & (data['parameter'] == param)] df = df.assign(value=row[2010].values[0]) - if param == "input_fuel": comm = data['technology'][(data['parameter'] == param) & (data["technology"] == t)].iloc[0].split("_")[0] df = df.assign(commodity=comm) - results[cat].append(df) # add background parameters (growth rates and bounds) df = scenario.par('initial_activity_lo', {"technology": ["gas_extr_mpen"]}) - for q in config["technology"]["add"][9:]: + for q in config["technology"]["add"][12:]: df['technology'] = q results["initial_activity_lo"].append(df) df = scenario.par('growth_activity_lo', {"technology": ["gas_extr_mpen"]}) - for q in config["technology"]["add"][9:]: + for q in config["technology"]["add"][12:]: df['technology'] = q results["growth_activity_lo"].append(df) @@ -515,6 +623,9 @@ def gen_data_ccs(scenario, dry_run=False): # Concatenate to one dataframe per parameter results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} + #results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} + + return results @@ -620,11 +731,17 @@ def read_demand(): 'capacity_factor': capacity_factor, "N_feed":N_feed, "N_trade_R12":N_trade_R12} -def read_trade_data(context): - data = pd.read_excel( - context.get_local_path("material", "n-fertilizer_techno-economic_new.xlsx"), - sheet_name="Trade", engine="openpyxl", usecols=np.linspace(0, 7, 8, dtype=int)) - data = data.assign(technology=lambda x: set_trade_tec(x["Variable"])) +def read_trade_data(context, comm): + if comm == "NFert": + data = pd.read_excel( + context.get_local_path("material", "n-fertilizer_techno-economic_new.xlsx"), + sheet_name="Trade", engine="openpyxl", usecols=np.linspace(0, 7, 8, dtype=int)) + data = data.assign(technology=lambda x: set_trade_tec(x["Variable"])) + if comm == "NH3": + data = pd.read_excel( + context.get_local_path("material", "n-fertilizer_techno-economic_new.xlsx"), + sheet_name="Trade_NH3", engine="openpyxl", usecols=np.linspace(0, 7, 8, dtype=int)) + data = data.assign(technology=lambda x: set_trade_tec_NH3(x["Variable"])) return data @@ -640,14 +757,26 @@ def set_trade_tec(x): return arr +def set_trade_tec_NH3(x): + arr=[] + for i in x: + if "Import" in i: + arr.append("import_NH3") + if "Export" in i: + arr.append("export_NH3") + if "Trade" in i: + arr.append("trade_NH3") + return arr + + def read_data(): """Read and clean data from :file:`n-fertilizer_techno-economic.xlsx`.""" # Ensure config is loaded, get the context context = read_config() - print(context.get_local_path()) + #print(context.get_local_path()) #print(Path(__file__).parents[3]/"data"/"material") context.handle_cli_args(local_data=Path(__file__).parents[3]/"data") - print(context.get_local_path()) + #print(context.get_local_path()) # Shorter access to sets configuration sets = context["material"]["fertilizer"] @@ -741,7 +870,7 @@ def read_data_ccs(): "output_water", "output_heat", "emission_factor", - "emission_factor", + "emission_factor_trans", "capacity_factor", ] @@ -752,12 +881,11 @@ def read_data_ccs(): param_cat2 = [] - - for t in sets["technology"]["add"][9:]: + for t in sets["technology"]["add"][12:]: param_values.extend(params) tech_values.extend([t] * len(params)) param_cat2.extend(param_cat) - + #print(sets["technology"]["add"][12:]) # Clean the data data = ( # Insert "technology" and "parameter" columns From acc7e1e5d122c0f9b360a299f2e5b79e1043d342 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Wed, 16 Mar 2022 12:46:03 +0100 Subject: [PATCH 450/774] Fix biomass input level --- message_ix_models/model/material/data_ammonia.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/message_ix_models/model/material/data_ammonia.py b/message_ix_models/model/material/data_ammonia.py index ca8cb3c802..8a6f1d8792 100644 --- a/message_ix_models/model/material/data_ammonia.py +++ b/message_ix_models/model/material/data_ammonia.py @@ -102,7 +102,7 @@ def gen_data(scenario, dry_run=False, add_ccs: bool = True): if cat in ["input", "output"]: common["commodity"] = commodity_dict[cat][param] common["level"] = level_cat_dict[cat][param] - if (t == "biomass_NH3") & (cat == "input"): + if (t == "biomass_NH3") & (param == "input_fuel"): common["level"] = "primary" if (str(t) == "NH3_to_N_fertil") & (param == "output_NH3"): common['commodity'] = "Fertilizer Use|Nitrogen" @@ -554,6 +554,8 @@ def gen_data_ccs(scenario, dry_run=False): if cat in ["input", "output"]: common["commodity"] = commodity_dict[cat][param] common["level"] = level_cat_dict[cat][param] + if (t == "biomass_NH3_ccs") & (param == "input_fuel"): + common["level"] = "primary" if param == "emission_factor_trans": _common = common.copy() _common["emission"] = "CO2_transformation" From 161aeb1b8a18d6954b0c3da9fc2882554741eb29 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Fri, 8 Apr 2022 13:14:08 +0200 Subject: [PATCH 451/774] Add historical data for nh3 trade --- .../NH3_trade_BACI_R12_aggregation.csv | 3 ++ .../model/material/data_ammonia.py | 33 ++++++++++++------- 2 files changed, 25 insertions(+), 11 deletions(-) create mode 100644 message_ix_models/data/material/NH3_trade_BACI_R12_aggregation.csv diff --git a/message_ix_models/data/material/NH3_trade_BACI_R12_aggregation.csv b/message_ix_models/data/material/NH3_trade_BACI_R12_aggregation.csv new file mode 100644 index 0000000000..d64a4e89c7 --- /dev/null +++ b/message_ix_models/data/material/NH3_trade_BACI_R12_aggregation.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:03eaae03904c5575acba7bfc6d29c96c5d7e8e0891dd21f5ad33a696cf2d3c7f +size 3816 diff --git a/message_ix_models/model/material/data_ammonia.py b/message_ix_models/model/material/data_ammonia.py index 8a6f1d8792..48ec2548b5 100644 --- a/message_ix_models/model/material/data_ammonia.py +++ b/message_ix_models/model/material/data_ammonia.py @@ -138,7 +138,7 @@ def gen_data(scenario, dry_run=False, add_ccs: bool = True): act2010 = read_demand()['act2010'] df = ( make_df("historical_activity", - technology=[t for t in config["technology"]["add"][:6]], #], TODO: maybe reintroduce std/ccs in yaml + technology=[t for t in config["technology"]["add"][:6]], value=1, unit='t', years_act=s_info.Y, **common) .pipe(broadcast, node_loc=nodes) .pipe(same_node) @@ -419,19 +419,19 @@ def gen_data(scenario, dry_run=False, add_ccs: bool = True): unit="t" ) - N_trade_R12 = read_demand()["N_trade_R12"].assign(mode="M1") - N_trade_R12["technology"] = N_trade_R12["Element"].apply( - lambda x: "export_NH3" if x == "Export" else "import_NH3") - df_exp_imp_act = N_trade_R12.drop("Element", axis=1) + NH3_trade_R12 = read_demand()["NH3_trade_R12"].assign(mode="M1") + NH3_trade_R12["technology"] = NH3_trade_R12["type"].apply( + lambda x: "export_NH3" if x == "export" else "import_NH3") + df_exp_imp_act = NH3_trade_R12.drop("type", axis=1) - trd_act_years = N_trade_R12["year_act"].unique() - values = N_trade_R12.groupby(["year_act"]).sum().values.flatten() + trd_act_years = NH3_trade_R12["year_act"].unique() + values = NH3_trade_R12.groupby(["year_act"]).sum().values.flatten() fert_trd_hist = make_df("historical_activity", technology="trade_NH3", year_act=trd_act_years, value=values, node_loc="R12_GLB", **common) results["historical_activity"].append(pd.concat([df_exp_imp_act, fert_trd_hist])) - df_hist_cap_new = N_trade_R12[N_trade_R12["technology"] == "export_NH3"].drop(columns=["time", "mode", "Element"]) + df_hist_cap_new = NH3_trade_R12[NH3_trade_R12["technology"] == "export_NH3"].drop(columns=["time", "mode", "type"]) df_hist_cap_new = df_hist_cap_new.rename(columns={"year_act": "year_vtg"}) # divide by export lifetime derived from coal_exp df_hist_cap_new = df_hist_cap_new.assign(value=lambda x: x["value"] / 30) @@ -691,6 +691,19 @@ def read_demand(): NP = pd.DataFrame({"netimp": df.Import - df.Export, "demand": ND[2010]}) NP["prod"] = NP.demand - NP.netimp + NH3_trade_R12 = pd.read_csv(context.get_local_path("material","NH3_trade_BACI_R12_aggregation.csv"))#, index_col=0) + NH3_trade_R12.region = "R12_" + NH3_trade_R12.region + NH3_trade_R12.quantity = NH3_trade_R12.quantity / 1e6 + NH3_trade_R12.unit = "t" + NH3_trade_R12 = NH3_trade_R12.assign(time="year") + NH3_trade_R12 = NH3_trade_R12.rename( + columns={ + "quantity": "value", + "region": "node_loc", + "year": "year_act", + } + ) + # Derive total energy (GWa) of NH3 production (based on demand 2010) N_feed = feedshare_GLO[feedshare_GLO.Region != "R11_GLB"].join(NP, on="Region") @@ -730,7 +743,7 @@ def read_demand(): act2010 = (feedshare.values.flatten() * N_demand).reset_index(drop=True) return {"feedshare_GLO": feedshare_GLO, "ND": ND, "N_energy": N_energy, "feedshare": feedshare, 'act2010': act2010, - 'capacity_factor': capacity_factor, "N_feed":N_feed, "N_trade_R12":N_trade_R12} + 'capacity_factor': capacity_factor, "N_feed":N_feed, "N_trade_R12":N_trade_R12, "NH3_trade_R12":NH3_trade_R12} def read_trade_data(context, comm): @@ -887,12 +900,10 @@ def read_data_ccs(): param_values.extend(params) tech_values.extend([t] * len(params)) param_cat2.extend(param_cat) - #print(sets["technology"]["add"][12:]) # Clean the data data = ( # Insert "technology" and "parameter" columns data.assign(technology=tech_values, parameter=param_values, param_cat=param_cat2) - # , param_cat=param_cat2) # Drop columns that don't contain useful information .drop(["Model", "Scenario", "Region"], axis=1) # Set the data frame index for selection From 6fb9b07e43108e35e30a033e8664a2641c46fdb6 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Sun, 29 May 2022 22:09:32 +0200 Subject: [PATCH 452/774] Clean up data files --- .../Ammonia feedstock share.Global.xlsx | 3 - ...Links SSP2 N-fertilizer demand.Global.xlsx | 3 - .../material/GLOBIOM_Fertilizer_use_N.xlsx | 3 - .../NH3_trade_BACI_R12_aggregation.csv | 3 - .../n-fertilizer_techno-economic.xlsx | 3 - .../data/material/trade.FAO.R11.csv | 3 - .../data/material/trade.FAO.R14.csv | 3 - .../model/material/data_ammonia.py | 72 +++++++------------ 8 files changed, 27 insertions(+), 66 deletions(-) delete mode 100644 message_ix_models/data/material/Ammonia feedstock share.Global.xlsx delete mode 100644 message_ix_models/data/material/CD-Links SSP2 N-fertilizer demand.Global.xlsx delete mode 100644 message_ix_models/data/material/GLOBIOM_Fertilizer_use_N.xlsx delete mode 100644 message_ix_models/data/material/NH3_trade_BACI_R12_aggregation.csv delete mode 100644 message_ix_models/data/material/n-fertilizer_techno-economic.xlsx delete mode 100644 message_ix_models/data/material/trade.FAO.R11.csv delete mode 100644 message_ix_models/data/material/trade.FAO.R14.csv diff --git a/message_ix_models/data/material/Ammonia feedstock share.Global.xlsx b/message_ix_models/data/material/Ammonia feedstock share.Global.xlsx deleted file mode 100644 index 8f1ff566e8..0000000000 --- a/message_ix_models/data/material/Ammonia feedstock share.Global.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:c5233d331d321957c7db799b114e9c5702ad7d61d932675740d56dd0f137f193 -size 16492 diff --git a/message_ix_models/data/material/CD-Links SSP2 N-fertilizer demand.Global.xlsx b/message_ix_models/data/material/CD-Links SSP2 N-fertilizer demand.Global.xlsx deleted file mode 100644 index 05d78d3cda..0000000000 --- a/message_ix_models/data/material/CD-Links SSP2 N-fertilizer demand.Global.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:0b43cf1f8140c6da5ae6c15a43028543bf9661a628396db87a8f1f7eeb7c8081 -size 16416 diff --git a/message_ix_models/data/material/GLOBIOM_Fertilizer_use_N.xlsx b/message_ix_models/data/material/GLOBIOM_Fertilizer_use_N.xlsx deleted file mode 100644 index 67e59cc10e..0000000000 --- a/message_ix_models/data/material/GLOBIOM_Fertilizer_use_N.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:fe7d802340e0b545133153b183d44a3fb4121bc25ffb961b0ce1f5b2e1a91b45 -size 582147 diff --git a/message_ix_models/data/material/NH3_trade_BACI_R12_aggregation.csv b/message_ix_models/data/material/NH3_trade_BACI_R12_aggregation.csv deleted file mode 100644 index d64a4e89c7..0000000000 --- a/message_ix_models/data/material/NH3_trade_BACI_R12_aggregation.csv +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:03eaae03904c5575acba7bfc6d29c96c5d7e8e0891dd21f5ad33a696cf2d3c7f -size 3816 diff --git a/message_ix_models/data/material/n-fertilizer_techno-economic.xlsx b/message_ix_models/data/material/n-fertilizer_techno-economic.xlsx deleted file mode 100644 index a8c04c2a20..0000000000 --- a/message_ix_models/data/material/n-fertilizer_techno-economic.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:c5d5d1a84d75756b73784c7ef0ff02b8b1d49ed4a22dd201c06fa7264e481729 -size 12813 diff --git a/message_ix_models/data/material/trade.FAO.R11.csv b/message_ix_models/data/material/trade.FAO.R11.csv deleted file mode 100644 index 740ea9e66a..0000000000 --- a/message_ix_models/data/material/trade.FAO.R11.csv +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:84f9b4f5fc4d0fe59ce51289fe05f80eebdc19c2fc4b44187d7b8082934c235d -size 3054 diff --git a/message_ix_models/data/material/trade.FAO.R14.csv b/message_ix_models/data/material/trade.FAO.R14.csv deleted file mode 100644 index 926ea5b1da..0000000000 --- a/message_ix_models/data/material/trade.FAO.R14.csv +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:011fc5995eb1408c7fd93d977f6109436da909f920765f7903c04762856927cd -size 3173 diff --git a/message_ix_models/model/material/data_ammonia.py b/message_ix_models/model/material/data_ammonia.py index 48ec2548b5..74e636b69f 100644 --- a/message_ix_models/model/material/data_ammonia.py +++ b/message_ix_models/model/material/data_ammonia.py @@ -27,7 +27,7 @@ def gen_data(scenario, dry_run=False, add_ccs: bool = True): # Load configuration config = read_config()["material"]["fertilizer"] context = read_config() - #print(config_.get_local_path("material", "test.xlsx")) + #print(config_.get_local_path("material", "ammonia", "test.xlsx")) # Information about scenario, e.g. node, year s_info = ScenarioInfo(scenario) nodes = s_info.N @@ -175,7 +175,7 @@ def gen_data(scenario, dry_run=False, add_ccs: bool = True): N_energy = read_demand()['N_energy'] N_energy = read_demand()['N_feed'] # updated feed with imports accounted - demand_fs_org = pd.read_excel(context.get_local_path('material','demand_i_feed_R12.xlsx')) + demand_fs_org = pd.read_excel(context.get_local_path("material", "ammonia",'demand_i_feed_R12.xlsx')) df = demand_fs_org.loc[demand_fs_org.year == 2010, :].join(N_energy.set_index('node'), on='node') sh = pd.DataFrame({'node': demand_fs_org.loc[demand_fs_org.year == 2010, 'node'], @@ -188,9 +188,9 @@ def gen_data(scenario, dry_run=False, add_ccs: bool = True): df.loc[df["value"] < 0, "value"] = 0 # temporary solution to avoid negative values results["demand"].append(df) - # Globiom land input + # Globiom land input DEPRECATED """ - df = pd.read_excel(context.get_local_path('material','GLOBIOM_Fertilizer_use_N.xlsx')) + df = pd.read_excel(context.get_local_path("material", "ammonia",'GLOBIOM_Fertilizer_use_N.xlsx')) df = df.replace(regex=r'^R11', value="R12").replace(regex=r'^R12_CPA', value="R12_CHN") df["unit"] = "t" df.loc[df["node"] == "R12_CHN", "value"] *= 0.93 # hotfix to adjust to R12 @@ -211,38 +211,18 @@ def gen_data(scenario, dry_run=False, add_ccs: bool = True): df = scenario.par('initial_activity_lo', {"technology": ["gas_extr_mpen"]}) for q in config["technology"]["add"][:6]: - df['technology'] = q - results["initial_activity_lo"].append(df) + df1 = df.copy() + df1['technology'] = q + results["initial_activity_lo"].append(df1) df = scenario.par('growth_activity_lo', {"technology": ["gas_extr_mpen"]}) for q in config["technology"]["add"][:6]: - df['technology'] = q - results["growth_activity_lo"].append(df) - - # TODO add regional cost scaling for ccs - """ - # tec_scale = (newtechnames + newtechnames_ccs) - tec_scale = [e for e in newtechnames if e not in ('NH3_to_N_fertil', 'electr_NH3')] - - # Scale all NH3 tecs in each region with the scaler - for t in tec_scale: - for p in ['inv_cost', 'fix_cost', 'var_cost']: - df = Sc_nitro.par(p, {"technology": t}) - df = results[p][results[p]["technology"]==t] - temp = df.join(scaler_cost.set_index('node_loc'), on='node_loc') - df.value = temp.value * temp.scaler_std - Sc_nitro.add_par(p, df) - - for t in newtechnames_ccs: - for p in ['inv_cost', 'fix_cost', 'var_cost']: - df = Sc_nitro.par(p, {"technology": t}) - temp = df.join(scaler_cost.set_index('node_loc'), on='node_loc') - df.value = temp.value * temp.scaler_ccs - Sc_nitro.add_par(p, df) - """ + df1 = df.copy() + df1['technology'] = q + results["growth_activity_lo"].append(df1) cost_scaler = pd.read_excel( - context.get_local_path('material','regional_cost_scaler_R12.xlsx'), index_col=0).T + context.get_local_path("material", "ammonia",'regional_cost_scaler_R12.xlsx'), index_col=0).T scalers_dict = { "R12_CHN": {"coal_NH3": 0.75 * 0.91, # gas/coal price ratio * discount @@ -586,16 +566,18 @@ def gen_data_ccs(scenario, dry_run=False): df = scenario.par('initial_activity_lo', {"technology": ["gas_extr_mpen"]}) for q in config["technology"]["add"][12:]: - df['technology'] = q - results["initial_activity_lo"].append(df) + df1 = df.copy() + df1['technology'] = q + results["initial_activity_lo"].append(df1) df = scenario.par('growth_activity_lo', {"technology": ["gas_extr_mpen"]}) for q in config["technology"]["add"][12:]: - df['technology'] = q - results["growth_activity_lo"].append(df) + df1 = df.copy() + df1['technology'] = q + results["growth_activity_lo"].append(df1) cost_scaler = pd.read_excel( - context.get_local_path('material','regional_cost_scaler_R12.xlsx'), index_col=0).T + context.get_local_path("material", "ammonia",'regional_cost_scaler_R12.xlsx'), index_col=0).T scalers_dict = { "R12_CHN": {"coal_NH3": 0.75 * 0.91, # gas/coal price ratio * discount @@ -637,14 +619,14 @@ def read_demand(): context = read_config() - N_demand_GLO = pd.read_excel(context.get_local_path('material','CD-Links SSP2 N-fertilizer demand.Global_R12_adaption.xlsx'), sheet_name='data') + N_demand_GLO = pd.read_excel(context.get_local_path("material", "ammonia",'CD-Links SSP2 N-fertilizer demand_R12.xlsx'), sheet_name='data') # NH3 feedstock share by region in 2010 (from http://ietd.iipnetwork.org/content/ammonia#benchmarks) - feedshare_GLO = pd.read_excel(context.get_local_path('material','Ammonia feedstock share.Global_R12.xlsx'), sheet_name='Sheet2', skiprows=14) + feedshare_GLO = pd.read_excel(context.get_local_path("material", "ammonia",'Ammonia feedstock share_R12.xlsx'), sheet_name='Sheet2', skiprows=14) # Read parameters in xlsx te_params = data = pd.read_excel( - context.get_local_path("material", "n-fertilizer_techno-economic.xlsx"), + context.get_local_path("material", "ammonia", "n-fertilizer_techno-economic_new.xlsx"), sheet_name="Sheet1", engine="openpyxl", nrows=72 ) n_inputs_per_tech = 12 # Number of input params per technology @@ -670,7 +652,7 @@ def read_demand(): N_energy = pd.concat([N_energy.Region, N_energy.sum(axis=1)], axis=1).rename( columns={0: 'totENE', 'Region': 'node'}) # GWa - N_trade_R12 = pd.read_csv(context.get_local_path("material","trade.FAO.R12.csv"), index_col=0) + N_trade_R12 = pd.read_csv(context.get_local_path("material", "ammonia","trade.FAO.R12.csv"), index_col=0) N_trade_R12.msgregion = "R12_" + N_trade_R12.msgregion N_trade_R12.Value = N_trade_R12.Value / 1e6 N_trade_R12.Unit = "t" @@ -691,7 +673,7 @@ def read_demand(): NP = pd.DataFrame({"netimp": df.Import - df.Export, "demand": ND[2010]}) NP["prod"] = NP.demand - NP.netimp - NH3_trade_R12 = pd.read_csv(context.get_local_path("material","NH3_trade_BACI_R12_aggregation.csv"))#, index_col=0) + NH3_trade_R12 = pd.read_csv(context.get_local_path("material", "ammonia","NH3_trade_BACI_R12_aggregation.csv"))#, index_col=0) NH3_trade_R12.region = "R12_" + NH3_trade_R12.region NH3_trade_R12.quantity = NH3_trade_R12.quantity / 1e6 NH3_trade_R12.unit = "t" @@ -749,12 +731,12 @@ def read_demand(): def read_trade_data(context, comm): if comm == "NFert": data = pd.read_excel( - context.get_local_path("material", "n-fertilizer_techno-economic_new.xlsx"), + context.get_local_path("material", "ammonia", "n-fertilizer_techno-economic_new.xlsx"), sheet_name="Trade", engine="openpyxl", usecols=np.linspace(0, 7, 8, dtype=int)) data = data.assign(technology=lambda x: set_trade_tec(x["Variable"])) if comm == "NH3": data = pd.read_excel( - context.get_local_path("material", "n-fertilizer_techno-economic_new.xlsx"), + context.get_local_path("material", "ammonia", "n-fertilizer_techno-economic_new.xlsx"), sheet_name="Trade_NH3", engine="openpyxl", usecols=np.linspace(0, 7, 8, dtype=int)) data = data.assign(technology=lambda x: set_trade_tec_NH3(x["Variable"])) return data @@ -797,7 +779,7 @@ def read_data(): # Read the file data = pd.read_excel( - context.get_local_path("material", "n-fertilizer_techno-economic_new.xlsx"), + context.get_local_path("material", "ammonia", "n-fertilizer_techno-economic_new.xlsx"), sheet_name="Sheet1", engine="openpyxl", nrows=72 ) @@ -864,7 +846,7 @@ def read_data_ccs(): # Read the file data = pd.read_excel( - context.get_local_path("material", "n-fertilizer_techno-economic_new.xlsx"), + context.get_local_path("material", "ammonia", "n-fertilizer_techno-economic_new.xlsx"), sheet_name="CCS", ) From 17398a5cfdb3ab56a1dea0c3e4b14619f7ece61b Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Wed, 1 Jun 2022 21:24:02 +0200 Subject: [PATCH 453/774] Fix small things --- message_ix_models/model/material/data_ammonia.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/message_ix_models/model/material/data_ammonia.py b/message_ix_models/model/material/data_ammonia.py index 74e636b69f..6d5642f8dd 100644 --- a/message_ix_models/model/material/data_ammonia.py +++ b/message_ix_models/model/material/data_ammonia.py @@ -433,9 +433,10 @@ def gen_data(scenario, dry_run=False, add_ccs: bool = True): relation="CO2_cc").drop(["emission", "year_vtg"], axis=1) rel_df_cc = rel_df_cc[rel_df_cc["technology"] != "NH3_to_N_fertil"] rel_df_cc = rel_df_cc[rel_df_cc["year_rel"] == rel_df_cc["year_act"]].drop_duplicates() - rel_df_cc[rel_df_cc["technology"] == "biomass_NH3_ccs"] = rel_df_cc[ - rel_df_cc["technology"] == "biomass_NH3_ccs"].assign( - value=results[par][results[par]["technology"] == "biomass_NH3_ccs"]["value"].values[0]) + if add_ccs: + rel_df_cc[rel_df_cc["technology"] == "biomass_NH3_ccs"] = rel_df_cc[ + rel_df_cc["technology"] == "biomass_NH3_ccs"].assign( + value=results[par][results[par]["technology"] == "biomass_NH3_ccs"]["value"].values[0]) rel_df_em = results[par] @@ -501,7 +502,7 @@ def gen_data_ccs(scenario, dry_run=False): output_commodity_dict = { "output_NH3": "NH3", "output_heat": "d_heat", - "output_water": "" # ask Jihoon how to name + "output_water": "wastewater" # ask Jihoon how to name } commodity_dict = { "output": output_commodity_dict, From 94c456407281e87c1e6f99d0752a27062df000c9 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Fri, 11 Mar 2022 10:09:56 +0100 Subject: [PATCH 454/774] Change secondary level to final for fuel input --- message_ix_models/model/material/data_ammonia.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/message_ix_models/model/material/data_ammonia.py b/message_ix_models/model/material/data_ammonia.py index 6d5642f8dd..24af5adad8 100644 --- a/message_ix_models/model/material/data_ammonia.py +++ b/message_ix_models/model/material/data_ammonia.py @@ -58,12 +58,12 @@ def gen_data(scenario, dry_run=False, add_ccs: bool = True): } input_level_dict = { "input_water": "water_supply", - "input_fuel": "secondary", - "input_elec": "secondary" + "input_fuel": "final", + "input_elec": "final" } output_level_dict = { "output_water": "wastewater", - "output_heat": "secondary", + "output_heat": "final", "output_NH3": "secondary_material" } level_cat_dict = { @@ -510,12 +510,12 @@ def gen_data_ccs(scenario, dry_run=False): } input_level_dict = { "input_water": "water_supply", - "input_fuel": "secondary", - "input_elec": "secondary" + "input_fuel": "final", + "input_elec": "final" } output_level_dict = { "output_water": "wastewater", - "output_heat": "secondary", + "output_heat": "final", "output_NH3": "secondary_material" } level_cat_dict = { From c402d05958d503780fbe26a853cb7b336ef59acd Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 15 Mar 2022 16:23:23 +0100 Subject: [PATCH 455/774] Change back to secondary level and add explanation --- .../model/material/data_ammonia.py | 21 +++++++++++++------ 1 file changed, 15 insertions(+), 6 deletions(-) diff --git a/message_ix_models/model/material/data_ammonia.py b/message_ix_models/model/material/data_ammonia.py index 24af5adad8..6e0ffe2bb2 100644 --- a/message_ix_models/model/material/data_ammonia.py +++ b/message_ix_models/model/material/data_ammonia.py @@ -58,12 +58,12 @@ def gen_data(scenario, dry_run=False, add_ccs: bool = True): } input_level_dict = { "input_water": "water_supply", - "input_fuel": "final", - "input_elec": "final" + "input_fuel": "secondary", + "input_elec": "secondary" } output_level_dict = { "output_water": "wastewater", - "output_heat": "final", + "output_heat": "secondary", "output_NH3": "secondary_material" } level_cat_dict = { @@ -480,6 +480,15 @@ def gen_data_ccs(scenario, dry_run=False): act_years = s_info.yv_ya[s_info.yv_ya.year_vtg > 2000]["year_act"] # NH3 production processes + # NOTE: The energy required for NH3 production process is retreived + # from secondary energy level for the moment. However, the energy that is used to + # produce ammonia as feedstock is accounted in Final Energy in reporting. + # The energy that is used to produce ammonia as fuel should be accounted in + # Secondary energy. At the moment all ammonia is used as feedstock. + # Later we can track the shares of feedstock vs fuel use of ammonia and + # divide final energy. Or create another set of technologies (only for fuel + # use vs. only for feedstock use). + common = dict( year_vtg=vtg_years, year_act=act_years, # confirm if correct?? @@ -510,12 +519,12 @@ def gen_data_ccs(scenario, dry_run=False): } input_level_dict = { "input_water": "water_supply", - "input_fuel": "final", - "input_elec": "final" + "input_fuel": "secondary", + "input_elec": "secondary" } output_level_dict = { "output_water": "wastewater", - "output_heat": "final", + "output_heat": "secondary", "output_NH3": "secondary_material" } level_cat_dict = { From b413217631d5689b43fd9a22916c290deb96369b Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 22 Mar 2022 13:50:59 +0100 Subject: [PATCH 456/774] Change dummy_limestone_supply_steel emission_factor --- .../data/material/Global_steel_cement_MESSAGE.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx index cb01b1cf37..f6d5e6cf21 100644 --- a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx +++ b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:4841d59b04e02c609423f9ca75634b67163bd0571b1d8390de820aa97f3353bb -size 110590 +oid sha256:f08c2c85810ec6effd8772326140f13ef5de8a91f77c7137c080f5cf6da3846c +size 310 From 7d8ad07f03eeb8bb8d47350509b6fd1e174df226 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 22 Mar 2022 13:52:39 +0100 Subject: [PATCH 457/774] Change ammonia emissions to co2_industry --- message_ix_models/model/material/data_ammonia.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/message_ix_models/model/material/data_ammonia.py b/message_ix_models/model/material/data_ammonia.py index 6e0ffe2bb2..2d40e5c216 100644 --- a/message_ix_models/model/material/data_ammonia.py +++ b/message_ix_models/model/material/data_ammonia.py @@ -85,7 +85,7 @@ def gen_data(scenario, dry_run=False, add_ccs: bool = True): time="year", time_dest="year", time_origin="year", - emission="CO2_transformation" # confirm if correct + emission="CO2_industry" # confirm if correct ) # Iterate over new technologies, using the configuration @@ -427,7 +427,7 @@ def gen_data(scenario, dry_run=False, add_ccs: bool = True): par = "emission_factor" rel_df_cc = results[par] - rel_df_cc = rel_df_cc[rel_df_cc["emission"] == "CO2_transformation"] + rel_df_cc = rel_df_cc[rel_df_cc["emission"] == "CO2_industry"] rel_df_cc = rel_df_cc.assign(year_rel=rel_df_cc["year_act"], node_rel=rel_df_cc["node_loc"], relation="CO2_cc").drop(["emission", "year_vtg"], axis=1) @@ -548,7 +548,7 @@ def gen_data_ccs(scenario, dry_run=False): common["level"] = "primary" if param == "emission_factor_trans": _common = common.copy() - _common["emission"] = "CO2_transformation" + _common["emission"] = "CO2_industry" cat = "emission_factor" df = ( make_df(cat, technology=t, value=1, unit="-", **_common) From b870736ee10e62d5e86025e93b3cedaab75bc4eb Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Wed, 8 Jun 2022 23:08:35 +0200 Subject: [PATCH 458/774] Create new ammonia/fert script for harmonized input data --- .../model/material/data_ammonia_new.py | 390 ++++++++++++++++++ 1 file changed, 390 insertions(+) create mode 100644 message_ix_models/model/material/data_ammonia_new.py diff --git a/message_ix_models/model/material/data_ammonia_new.py b/message_ix_models/model/material/data_ammonia_new.py new file mode 100644 index 0000000000..c5cb15511c --- /dev/null +++ b/message_ix_models/model/material/data_ammonia_new.py @@ -0,0 +1,390 @@ +import message_ix +import message_data +import ixmp as ix + +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt + +from message_ix_models import ScenarioInfo +from message_ix import make_df +from message_ix_models.util import broadcast, same_node +from util import read_config + +CONVERSION_FACTOR_NH3_N = 17 / 14 + + +def gen_all_NH3_fert(scenario): + return { + **gen_data(scenario), + **gen_data_rel(scenario), + **gen_data_ts(scenario), + **gen_demand(), + **gen_land_input(scenario), + } + + +def gen_data(scenario, dry_run=False, add_ccs: bool = True): + s_info = ScenarioInfo(scenario) + # s_info.yv_ya + nodes = s_info.N + if "World" in nodes: + nodes.pop(nodes.index("World")) + if "R12_GLB" in nodes: + nodes.pop(nodes.index("R12_GLB")) + + df = pd.read_excel("fert_techno_economic.xlsx", sheet_name="data_R12") + df.groupby("parameter") + par_dict = {key: value for (key, value) in df.groupby("parameter")} + for i in par_dict.keys(): + par_dict[i] = par_dict[i].dropna(axis=1) + + vtg_years = s_info.yv_ya[s_info.yv_ya.year_vtg > 2000]["year_vtg"] + act_years = s_info.yv_ya[s_info.yv_ya.year_vtg > 2000]["year_act"] + + try: + max_lt = par_dict["technical_lifetime"].value.max() + except KeyError: + print("lifetime not in xlsx") + + vtg_years = vtg_years.drop_duplicates() + act_years = act_years.drop_duplicates() + + for par_name in par_dict.keys(): + + df = par_dict[par_name] + # remove "default" node name to broadcast with all scenario regions later + df["node_loc"] = df["node_loc"].apply(lambda x: None if x == "default" else x) + df = df.to_dict() + + df_new = make_df(par_name, **df) + # split df into df with default values and df with regionalized values + df_new_no_reg = df_new[df_new["node_loc"].isna()] + df_new_reg = df_new[~df_new["node_loc"].isna()] + # broadcast regions to default parameter values + df_new_no_reg = df_new_no_reg.pipe(broadcast, node_loc=nodes) + df_new = pd.concat([df_new_reg, df_new_no_reg]) + + # broadcast scenario years + if "year_act" in df_new.columns: + df_new = df_new.pipe(same_node).pipe(broadcast, year_act=act_years) + + if "year_vtg" in df_new.columns: + df_new = df_new.pipe( + broadcast, + year_vtg=np.linspace( + 0, int(max_lt / 5), int(max_lt / 5 + 1), dtype=int + ), + ) + df_new["year_vtg"] = df_new["year_act"] - 5 * df_new["year_vtg"] + else: + if "year_vtg" in df_new.columns: + df_new = df_new.pipe(same_node).pipe(broadcast, year_vtg=vtg_years) + + # set import/export node_dest/origin to GLB for input/output + set_exp_imp_nodes(df_new) + par_dict[par_name] = df_new + + return par_dict + + +def gen_data_rel(scenario, dry_run=False, add_ccs: bool = True): + s_info = ScenarioInfo(scenario) + # s_info.yv_ya + nodes = s_info.N + if "World" in nodes: + nodes.pop(nodes.index("World")) + if "R12_GLB" in nodes: + nodes.pop(nodes.index("R12_GLB")) + + df = pd.read_excel("fert_techno_economic.xlsx", sheet_name="relations_R12") + df.groupby("parameter") + par_dict = {key: value for (key, value) in df.groupby("parameter")} + for i in par_dict.keys(): + par_dict[i] = par_dict[i].dropna(axis=1) + + act_years = s_info.yv_ya[s_info.yv_ya.year_vtg > 2000]["year_act"] + act_years = act_years.drop_duplicates() + + for par_name in par_dict.keys(): + + df = par_dict[par_name] + # remove "default" node name to broadcast with all scenario regions later + df["node_loc"] = df["node_loc"].apply(lambda x: None if x == "default" else x) + df = df.to_dict() + + df_new = make_df(par_name, **df) + # split df into df with default values and df with regionalized values + df_new_no_reg = df_new[df_new["node_loc"].isna()] + df_new_reg = df_new[~df_new["node_loc"].isna()] + # broadcast regions to default parameter values + df_new_no_reg = df_new_no_reg.pipe(broadcast, node_rel=nodes) + if "node_loc" in df_new_no_reg.columns: + df_new_no_reg["node_loc"] = df_new_no_reg["node_rel"] + df_new = pd.concat([df_new_reg, df_new_no_reg]) + + # broadcast scenario years + if "year_rel" in df_new.columns: + df_new = df_new.pipe(same_node).pipe(broadcast, year_rel=act_years) + + if "year_act" in df_new.columns: + df_new["year_act"] = df_new["year_rel"] + + # set import/export node_dest/origin to GLB for input/output + set_exp_imp_nodes(df_new) + par_dict[par_name] = df_new + + return par_dict + + +def gen_data_ts(scenario, dry_run=False, add_ccs: bool = True): + s_info = ScenarioInfo(scenario) + # s_info.yv_ya + nodes = s_info.N + if "World" in nodes: + nodes.pop(nodes.index("World")) + if "R12_GLB" in nodes: + nodes.pop(nodes.index("R12_GLB")) + + df = pd.read_excel("fert_techno_economic.xlsx", sheet_name="timeseries_R12") + df.groupby("parameter") + par_dict = {key: value for (key, value) in df.groupby("parameter")} + for i in par_dict.keys(): + par_dict[i] = par_dict[i].dropna(axis=1) + + for par_name in par_dict.keys(): + + df = par_dict[par_name] + # remove "default" node name to broadcast with all scenario regions later + df["node_loc"] = df["node_loc"].apply(lambda x: None if x == "default" else x) + df = df.to_dict() + + df_new = make_df(par_name, **df) + # split df into df with default values and df with regionalized values + df_new_no_reg = df_new[df_new["node_loc"].isna()] + df_new_reg = df_new[~df_new["node_loc"].isna()] + # broadcast regions to default parameter values + df_new_no_reg = df_new_no_reg.pipe(broadcast, node_loc=nodes) + df_new = pd.concat([df_new_reg, df_new_no_reg]) + + # set import/export node_dest/origin to GLB for input/output + set_exp_imp_nodes(df_new) + par_dict[par_name] = df_new + + return par_dict + + +def set_exp_imp_nodes(df): + if "node_dest" in df.columns: + df.loc[df["technology"].str.contains("export"), "node_dest"] = "R12_GLB" + if "node_origin" in df.columns: + df.loc[df["technology"].str.contains("import"), "node_origin"] = "R12_GLB" + + +def read_demand(): + """Read and clean data from :file:`CD-Links SSP2 N-fertilizer demand.Global.xlsx`.""" + # Demand scenario [Mt N/year] from GLOBIOM + context = read_config() + + N_demand_GLO = pd.read_excel( + context.get_local_path( + "material", "ammonia", "CD-Links SSP2 N-fertilizer demand_R12.xlsx" + ), + sheet_name="data", + ) + N_demand_GLO = pd.read_excel( + "nh3_fertilizer_demand.xlsx", sheet_name="NFertilizer_demand" + ) + + # NH3 feedstock share by region in 2010 (from http://ietd.iipnetwork.org/content/ammonia#benchmarks) + feedshare_GLO = pd.read_excel( + context.get_local_path( + "material", "ammonia", "Ammonia feedstock share_R12.xlsx" + ), + sheet_name="Sheet2", + skiprows=14, + ) + feedshare_GLO = pd.read_excel( + "nh3_fertilizer_demand.xlsx", sheet_name="NH3_feedstock_share", skiprows=14 + ) + + # Read parameters in xlsx + te_params = data = pd.read_excel( + context.get_local_path( + "material", "ammonia", "n-fertilizer_techno-economic_new.xlsx" + ), + sheet_name="Sheet1", + engine="openpyxl", + nrows=72, + ) + + n_inputs_per_tech = 12 # Number of input params per technology + + input_fuel = te_params[2010][ + list(range(4, te_params.shape[0], n_inputs_per_tech)) + ].reset_index(drop=True) + # input_fuel[0:5] = input_fuel[0:5] * CONVERSION_FACTOR_PJ_GWa # 0.0317 GWa/PJ, GJ/t = PJ/Mt NH3 + + capacity_factor = te_params[2010][ + list(range(11, te_params.shape[0], n_inputs_per_tech)) + ].reset_index(drop=True) + + # Regional N demaand in 2010 + ND = N_demand_GLO.loc[N_demand_GLO.Scenario == "NoPolicy", ["Region", 2010]] + ND = ND[ND.Region != "World"] + ND.Region = "R12_" + ND.Region + ND = ND.set_index("Region") + + # Derive total energy (GWa) of NH3 production (based on demand 2010) + N_energy = feedshare_GLO[feedshare_GLO.Region != "R12_GLB"].join(ND, on="Region") + N_energy = pd.concat( + [ + N_energy.Region, + N_energy[["gas_pct", "coal_pct", "oil_pct"]].multiply( + N_energy[2010], axis="index" + ), + ], + axis=1, + ) + N_energy.gas_pct *= input_fuel[2] * CONVERSION_FACTOR_NH3_N # NH3 / N + N_energy.coal_pct *= input_fuel[3] * CONVERSION_FACTOR_NH3_N + N_energy.oil_pct *= input_fuel[4] * CONVERSION_FACTOR_NH3_N + N_energy = pd.concat([N_energy.Region, N_energy.sum(axis=1)], axis=1).rename( + columns={0: "totENE", "Region": "node"} + ) # GWa + + N_trade_R12 = pd.read_csv( + context.get_local_path("material", "ammonia", "trade.FAO.R12.csv"), index_col=0 + ) + N_trade_R12 = pd.read_excel( + "nh3_fertilizer_demand.xlsx", sheet_name="NFertilizer_trade" + ) # , index_col=0) + + N_trade_R12.msgregion = "R12_" + N_trade_R12.msgregion + N_trade_R12.Value = N_trade_R12.Value / 1e6 + N_trade_R12.Unit = "t" + N_trade_R12 = N_trade_R12.assign(time="year") + N_trade_R12 = N_trade_R12.rename( + columns={ + "Value": "value", + "Unit": "unit", + "msgregion": "node_loc", + "Year": "year_act", + } + ) + + df = N_trade_R12.loc[ + N_trade_R12.year_act == 2010, + ] + df = df.pivot(index="node_loc", columns="Element", values="value") + NP = pd.DataFrame({"netimp": df.Import - df.Export, "demand": ND[2010]}) + NP["prod"] = NP.demand - NP.netimp + + NH3_trade_R12 = pd.read_csv( + context.get_local_path( + "material", "ammonia", "NH3_trade_BACI_R12_aggregation.csv" + ) + ) # , index_col=0) + NH3_trade_R12 = pd.read_excel( + "nh3_fertilizer_demand.xlsx", sheet_name="NH3_trade_R12_aggregated" + ) + + NH3_trade_R12.region = "R12_" + NH3_trade_R12.region + NH3_trade_R12.quantity = NH3_trade_R12.quantity / 1e6 + NH3_trade_R12.unit = "t" + NH3_trade_R12 = NH3_trade_R12.assign(time="year") + NH3_trade_R12 = NH3_trade_R12.rename( + columns={ + "quantity": "value", + "region": "node_loc", + "year": "year_act", + } + ) + + # Derive total energy (GWa) of NH3 production (based on demand 2010) + N_feed = feedshare_GLO[feedshare_GLO.Region != "R11_GLB"].join(NP, on="Region") + N_feed = pd.concat( + [ + N_feed.Region, + N_feed[["gas_pct", "coal_pct", "oil_pct"]].multiply( + N_feed["prod"], axis="index" + ), + ], + axis=1, + ) + N_feed.gas_pct *= input_fuel[2] * 17 / 14 + N_feed.coal_pct *= input_fuel[3] * 17 / 14 + N_feed.oil_pct *= input_fuel[4] * 17 / 14 + N_feed = pd.concat([N_feed.Region, N_feed.sum(axis=1)], axis=1).rename( + columns={0: "totENE", "Region": "node"} + ) + + # Process the regional historical activities + + fs_GLO = feedshare_GLO.copy() + fs_GLO.insert(1, "bio_pct", 0) + fs_GLO.insert(2, "elec_pct", 0) + # 17/14 NH3:N ratio, to get NH3 activity based on N demand => No NH3 loss assumed during production + fs_GLO.iloc[:, 1:6] = input_fuel[5] * fs_GLO.iloc[:, 1:6] + fs_GLO.insert(6, "NH3_to_N", 1) + + # Share of feedstocks for NH3 prodution (based on 2010 => Assumed fixed for any past years) + feedshare = fs_GLO.sort_values(["Region"]).set_index("Region").drop("R12_GLB") + + # Get historical N demand from SSP2-nopolicy (may need to vary for diff scenarios) + N_demand_raw = N_demand_GLO.copy() + N_demand = ( + N_demand_raw[ + (N_demand_raw.Scenario == "NoPolicy") & (N_demand_raw.Region != "World") + ] + .reset_index() + .loc[:, 2010] + ) # 2010 tot N demand + N_demand = N_demand.repeat(6) + + act2010 = (feedshare.values.flatten() * N_demand).reset_index(drop=True) + + return { + "feedshare_GLO": feedshare_GLO, + "ND": ND, + "N_energy": N_energy, + "feedshare": feedshare, + "act2010": act2010, + "capacity_factor": capacity_factor, + "N_feed": N_feed, + "N_trade_R12": N_trade_R12, + "NH3_trade_R12": NH3_trade_R12, + } + + +def gen_demand(): + context = read_config() + + N_energy = read_demand()["N_feed"] # updated feed with imports accounted + + demand_fs_org = pd.read_excel( + context.get_local_path("material", "ammonia", "demand_i_feed_R12.xlsx") + ) + + df = demand_fs_org.loc[demand_fs_org.year == 2010, :].join( + N_energy.set_index("node"), on="node" + ) + sh = pd.DataFrame( + { + "node": demand_fs_org.loc[demand_fs_org.year == 2010, "node"], + "r_feed": df.totENE / df.value, + } + ) # share of NH3 energy among total feedstock (no trade assumed) + df = demand_fs_org.join(sh.set_index("node"), on="node") + df.value *= 1 - df.r_feed # Carve out the same % from tot i_feed values + df = df.drop("r_feed", axis=1) + df = df.drop("Unnamed: 0", axis=1) + # TODO: refactor with a more sophisticated solution to reduce i_feed + df.loc[df["value"] < 0, "value"] = 0 # temporary solution to avoid negative values + return df + + +def gen_land_input(scenario): + df = scenario.par("land_output", {"commodity": "Fertilizer Use|Nitrogen"}) + df["level"] = "final_material" + return df From 27f35806a4b9bcba81d5e727435a71e5e665ab58 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Wed, 13 Jul 2022 19:11:33 +0200 Subject: [PATCH 459/774] Update input file dir --- .../model/material/data_ammonia_new.py | 67 ++++++++++++++----- 1 file changed, 52 insertions(+), 15 deletions(-) diff --git a/message_ix_models/model/material/data_ammonia_new.py b/message_ix_models/model/material/data_ammonia_new.py index c5cb15511c..7983cad69d 100644 --- a/message_ix_models/model/material/data_ammonia_new.py +++ b/message_ix_models/model/material/data_ammonia_new.py @@ -12,6 +12,7 @@ from util import read_config CONVERSION_FACTOR_NH3_N = 17 / 14 +context = read_config() def gen_all_NH3_fert(scenario): @@ -33,7 +34,15 @@ def gen_data(scenario, dry_run=False, add_ccs: bool = True): if "R12_GLB" in nodes: nodes.pop(nodes.index("R12_GLB")) - df = pd.read_excel("fert_techno_economic.xlsx", sheet_name="data_R12") + df = pd.read_excel( + context.get_local_path( + "material", + "ammonia", + "new concise input files", + "fert_techno_economic.xlsx", + ), + sheet_name="data_R12", + ) df.groupby("parameter") par_dict = {key: value for (key, value) in df.groupby("parameter")} for i in par_dict.keys(): @@ -97,7 +106,15 @@ def gen_data_rel(scenario, dry_run=False, add_ccs: bool = True): if "R12_GLB" in nodes: nodes.pop(nodes.index("R12_GLB")) - df = pd.read_excel("fert_techno_economic.xlsx", sheet_name="relations_R12") + df = pd.read_excel( + context.get_local_path( + "material", + "ammonia", + "new concise input files", + "fert_techno_economic.xlsx", + ), + sheet_name="relations_R12", + ) df.groupby("parameter") par_dict = {key: value for (key, value) in df.groupby("parameter")} for i in par_dict.keys(): @@ -146,7 +163,15 @@ def gen_data_ts(scenario, dry_run=False, add_ccs: bool = True): if "R12_GLB" in nodes: nodes.pop(nodes.index("R12_GLB")) - df = pd.read_excel("fert_techno_economic.xlsx", sheet_name="timeseries_R12") + df = pd.read_excel( + context.get_local_path( + "material", + "ammonia", + "new concise input files", + "fert_techno_economic.xlsx", + ), + sheet_name="timeseries_R12", + ) df.groupby("parameter") par_dict = {key: value for (key, value) in df.groupby("parameter")} for i in par_dict.keys(): @@ -188,25 +213,25 @@ def read_demand(): N_demand_GLO = pd.read_excel( context.get_local_path( - "material", "ammonia", "CD-Links SSP2 N-fertilizer demand_R12.xlsx" + "material", + "ammonia", + "new concise input files", + "nh3_fertilizer_demand.xlsx", ), - sheet_name="data", - ) - N_demand_GLO = pd.read_excel( - "nh3_fertilizer_demand.xlsx", sheet_name="NFertilizer_demand" + sheet_name="NFertilizer_demand", ) # NH3 feedstock share by region in 2010 (from http://ietd.iipnetwork.org/content/ammonia#benchmarks) feedshare_GLO = pd.read_excel( context.get_local_path( - "material", "ammonia", "Ammonia feedstock share_R12.xlsx" + "material", + "ammonia", + "new concise input files", + "nh3_fertilizer_demand.xlsx", ), - sheet_name="Sheet2", + sheet_name="NH3_feedstock_share", skiprows=14, ) - feedshare_GLO = pd.read_excel( - "nh3_fertilizer_demand.xlsx", sheet_name="NH3_feedstock_share", skiprows=14 - ) # Read parameters in xlsx te_params = data = pd.read_excel( @@ -257,7 +282,13 @@ def read_demand(): context.get_local_path("material", "ammonia", "trade.FAO.R12.csv"), index_col=0 ) N_trade_R12 = pd.read_excel( - "nh3_fertilizer_demand.xlsx", sheet_name="NFertilizer_trade" + context.get_local_path( + "material", + "ammonia", + "new concise input files", + "nh3_fertilizer_demand.xlsx", + ), + sheet_name="NFertilizer_trade", ) # , index_col=0) N_trade_R12.msgregion = "R12_" + N_trade_R12.msgregion @@ -286,7 +317,13 @@ def read_demand(): ) ) # , index_col=0) NH3_trade_R12 = pd.read_excel( - "nh3_fertilizer_demand.xlsx", sheet_name="NH3_trade_R12_aggregated" + context.get_local_path( + "material", + "ammonia", + "new concise input files", + "nh3_fertilizer_demand.xlsx", + ), + sheet_name="NH3_trade_R12_aggregated", ) NH3_trade_R12.region = "R12_" + NH3_trade_R12.region From db9f0ad638d6a81c46cb896b9368d89ea0270670 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Wed, 13 Jul 2022 19:12:25 +0200 Subject: [PATCH 460/774] Update input file dir - add files --- .../ammonia/new concise input files/fert_techno_economic.xlsx | 3 +++ .../ammonia/new concise input files/nh3_fertilizer_demand.xlsx | 3 +++ 2 files changed, 6 insertions(+) create mode 100644 message_ix_models/data/material/ammonia/new concise input files/fert_techno_economic.xlsx create mode 100644 message_ix_models/data/material/ammonia/new concise input files/nh3_fertilizer_demand.xlsx diff --git a/message_ix_models/data/material/ammonia/new concise input files/fert_techno_economic.xlsx b/message_ix_models/data/material/ammonia/new concise input files/fert_techno_economic.xlsx new file mode 100644 index 0000000000..ba1d366483 --- /dev/null +++ b/message_ix_models/data/material/ammonia/new concise input files/fert_techno_economic.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6b893abdba91d6d769d746355af6da88b63d566640bfdec40f52dec8aa4f9431 +size 34841 diff --git a/message_ix_models/data/material/ammonia/new concise input files/nh3_fertilizer_demand.xlsx b/message_ix_models/data/material/ammonia/new concise input files/nh3_fertilizer_demand.xlsx new file mode 100644 index 0000000000..c01b549335 --- /dev/null +++ b/message_ix_models/data/material/ammonia/new concise input files/nh3_fertilizer_demand.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:514a9ea2b1b20bfd1b14f9818809727562ebfa95313040bca19c1e01ea4b5d24 +size 94418 From 45ee46242680c1dfc5d3de901ddb034cbfbea157 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Fri, 15 Jul 2022 13:05:35 +0200 Subject: [PATCH 461/774] Fix ammonia build problems --- .../model/material/data_ammonia_new.py | 22 +++++++++++-------- 1 file changed, 13 insertions(+), 9 deletions(-) diff --git a/message_ix_models/model/material/data_ammonia_new.py b/message_ix_models/model/material/data_ammonia_new.py index 7983cad69d..11a13c64f9 100644 --- a/message_ix_models/model/material/data_ammonia_new.py +++ b/message_ix_models/model/material/data_ammonia_new.py @@ -4,18 +4,17 @@ import pandas as pd import numpy as np -import matplotlib.pyplot as plt from message_ix_models import ScenarioInfo from message_ix import make_df from message_ix_models.util import broadcast, same_node -from util import read_config +from .util import read_config CONVERSION_FACTOR_NH3_N = 17 / 14 context = read_config() -def gen_all_NH3_fert(scenario): +def gen_all_NH3_fert(scenario, dry_run=False): return { **gen_data(scenario), **gen_data_rel(scenario), @@ -86,6 +85,8 @@ def gen_data(scenario, dry_run=False, add_ccs: bool = True): ), ) df_new["year_vtg"] = df_new["year_act"] - 5 * df_new["year_vtg"] + #remove years that are not in scenario set + df_new = df_new[~df_new["year_vtg"].isin([2065, 2075, 2085, 2095, 2105])] else: if "year_vtg" in df_new.columns: df_new = df_new.pipe(same_node).pipe(broadcast, year_vtg=vtg_years) @@ -178,7 +179,6 @@ def gen_data_ts(scenario, dry_run=False, add_ccs: bool = True): par_dict[i] = par_dict[i].dropna(axis=1) for par_name in par_dict.keys(): - df = par_dict[par_name] # remove "default" node name to broadcast with all scenario regions later df["node_loc"] = df["node_loc"].apply(lambda x: None if x == "default" else x) @@ -196,6 +196,10 @@ def gen_data_ts(scenario, dry_run=False, add_ccs: bool = True): set_exp_imp_nodes(df_new) par_dict[par_name] = df_new + #convert floats + par_dict["historical_activity"] = par_dict["historical_activity"].astype({'year_act': 'int32'}) + par_dict["historical_new_capacity"] = par_dict["historical_new_capacity"].astype({'year_vtg': 'int32'}) + return par_dict @@ -373,9 +377,9 @@ def read_demand(): N_demand = ( N_demand_raw[ (N_demand_raw.Scenario == "NoPolicy") & (N_demand_raw.Region != "World") - ] - .reset_index() - .loc[:, 2010] + ] + .reset_index() + .loc[:, 2010] ) # 2010 tot N demand N_demand = N_demand.repeat(6) @@ -418,10 +422,10 @@ def gen_demand(): df = df.drop("Unnamed: 0", axis=1) # TODO: refactor with a more sophisticated solution to reduce i_feed df.loc[df["value"] < 0, "value"] = 0 # temporary solution to avoid negative values - return df + return {"demand": df} def gen_land_input(scenario): df = scenario.par("land_output", {"commodity": "Fertilizer Use|Nitrogen"}) df["level"] = "final_material" - return df + return {"land_input": df} From 2ae4af36e6749f620697f986f80461f4904038c0 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Sun, 16 Oct 2022 16:58:32 +0200 Subject: [PATCH 462/774] Correct emissions parameters --- .../ammonia/new concise input files/fert_techno_economic.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/ammonia/new concise input files/fert_techno_economic.xlsx b/message_ix_models/data/material/ammonia/new concise input files/fert_techno_economic.xlsx index ba1d366483..e4dd2d897d 100644 --- a/message_ix_models/data/material/ammonia/new concise input files/fert_techno_economic.xlsx +++ b/message_ix_models/data/material/ammonia/new concise input files/fert_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:6b893abdba91d6d769d746355af6da88b63d566640bfdec40f52dec8aa4f9431 -size 34841 +oid sha256:5b930daad21e6af4ba21842805b93325420fd765dfe7e51ab8e34104a7083ada +size 44972 From 5940fde2adf4330b3c8497beb912cfd8a9abeb5d Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Sun, 16 Oct 2022 18:03:02 +0200 Subject: [PATCH 463/774] Correct historic trade activities --- .../new concise input files/nh3_fertilizer_demand.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/ammonia/new concise input files/nh3_fertilizer_demand.xlsx b/message_ix_models/data/material/ammonia/new concise input files/nh3_fertilizer_demand.xlsx index c01b549335..9c9e057f5e 100644 --- a/message_ix_models/data/material/ammonia/new concise input files/nh3_fertilizer_demand.xlsx +++ b/message_ix_models/data/material/ammonia/new concise input files/nh3_fertilizer_demand.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:514a9ea2b1b20bfd1b14f9818809727562ebfa95313040bca19c1e01ea4b5d24 -size 94418 +oid sha256:97f5430552bc764e6c40818b8b0d6675aaebcaabe5c81c9f8be2e3167a20d5bb +size 94310 From feff272a5f763753b812f3c26670df3fffcaba4c Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Sun, 16 Oct 2022 18:03:24 +0200 Subject: [PATCH 464/774] Correct historic trade and emissions parameters --- .../ammonia/new concise input files/fert_techno_economic.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/ammonia/new concise input files/fert_techno_economic.xlsx b/message_ix_models/data/material/ammonia/new concise input files/fert_techno_economic.xlsx index e4dd2d897d..8205efee51 100644 --- a/message_ix_models/data/material/ammonia/new concise input files/fert_techno_economic.xlsx +++ b/message_ix_models/data/material/ammonia/new concise input files/fert_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:5b930daad21e6af4ba21842805b93325420fd765dfe7e51ab8e34104a7083ada -size 44972 +oid sha256:5399db9c086ec937ca2de8b664236aa30eee3013bf92c40db3b0ab7b61b5f1f3 +size 44491 From 2790b944c9abdaf1e830db01d5400b52c9787f26 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Sun, 16 Oct 2022 18:04:11 +0200 Subject: [PATCH 465/774] Adjust code to trade input changes --- .../model/material/data_ammonia_new.py | 17 ++++++++--------- 1 file changed, 8 insertions(+), 9 deletions(-) diff --git a/message_ix_models/model/material/data_ammonia_new.py b/message_ix_models/model/material/data_ammonia_new.py index 11a13c64f9..3113779726 100644 --- a/message_ix_models/model/material/data_ammonia_new.py +++ b/message_ix_models/model/material/data_ammonia_new.py @@ -295,24 +295,23 @@ def read_demand(): sheet_name="NFertilizer_trade", ) # , index_col=0) - N_trade_R12.msgregion = "R12_" + N_trade_R12.msgregion - N_trade_R12.Value = N_trade_R12.Value / 1e6 - N_trade_R12.Unit = "t" + N_trade_R12.region = "R12_" + N_trade_R12.region + N_trade_R12.quantity = N_trade_R12.quantity + N_trade_R12.unit = "t" N_trade_R12 = N_trade_R12.assign(time="year") N_trade_R12 = N_trade_R12.rename( columns={ - "Value": "value", - "Unit": "unit", - "msgregion": "node_loc", - "Year": "year_act", + "quantity": "value", + "region": "node_loc", + "year": "year_act", } ) df = N_trade_R12.loc[ N_trade_R12.year_act == 2010, ] - df = df.pivot(index="node_loc", columns="Element", values="value") - NP = pd.DataFrame({"netimp": df.Import - df.Export, "demand": ND[2010]}) + df = df.pivot(index="node_loc", columns="type", values="value") + NP = pd.DataFrame({"netimp": df["import"] - df.export, "demand": ND[2010]}) NP["prod"] = NP.demand - NP.netimp NH3_trade_R12 = pd.read_csv( From 2826a1a60573861b8d96f02f1e092e698c7dd7cc Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Sun, 16 Oct 2022 18:59:49 +0200 Subject: [PATCH 466/774] Comment out deprecated code --- .../model/material/data_ammonia_new.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/message_ix_models/model/material/data_ammonia_new.py b/message_ix_models/model/material/data_ammonia_new.py index 3113779726..5ae0798948 100644 --- a/message_ix_models/model/material/data_ammonia_new.py +++ b/message_ix_models/model/material/data_ammonia_new.py @@ -258,7 +258,7 @@ def read_demand(): list(range(11, te_params.shape[0], n_inputs_per_tech)) ].reset_index(drop=True) - # Regional N demaand in 2010 + # Regional N demand in 2010 ND = N_demand_GLO.loc[N_demand_GLO.Scenario == "NoPolicy", ["Region", 2010]] ND = ND[ND.Region != "World"] ND.Region = "R12_" + ND.Region @@ -282,9 +282,9 @@ def read_demand(): columns={0: "totENE", "Region": "node"} ) # GWa - N_trade_R12 = pd.read_csv( - context.get_local_path("material", "ammonia", "trade.FAO.R12.csv"), index_col=0 - ) + #N_trade_R12 = pd.read_csv( + # context.get_local_path("material", "ammonia", "trade.FAO.R12.csv"), index_col=0 + #) N_trade_R12 = pd.read_excel( context.get_local_path( "material", @@ -314,11 +314,11 @@ def read_demand(): NP = pd.DataFrame({"netimp": df["import"] - df.export, "demand": ND[2010]}) NP["prod"] = NP.demand - NP.netimp - NH3_trade_R12 = pd.read_csv( - context.get_local_path( - "material", "ammonia", "NH3_trade_BACI_R12_aggregation.csv" - ) - ) # , index_col=0) + #NH3_trade_R12 = pd.read_csv( + # context.get_local_path( + # "material", "ammonia", "NH3_trade_BACI_R12_aggregation.csv" + # ) + #) # , index_col=0) NH3_trade_R12 = pd.read_excel( context.get_local_path( "material", From 5edf09573657c59d300d64eb9701cf999ee360b8 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Sun, 16 Oct 2022 19:00:06 +0200 Subject: [PATCH 467/774] Modify init for ammonia only build --- message_ix_models/model/material/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 989d852d91..11d7bbdebe 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -382,7 +382,7 @@ def run_old_reporting(scenario_name, model_name): gen_data_cement, gen_data_petro_chemicals, #gen_data_power_sector, - gen_data_aluminum, + #gen_data_aluminum, ] # Try to handle multiple data input functions from different materials From 0424d6bc72b20aee86db0288ac2cff26a200eed3 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 18 Oct 2022 12:41:51 +0200 Subject: [PATCH 468/774] Harmonize input values with old input file --- .../ammonia/new concise input files/fert_techno_economic.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/ammonia/new concise input files/fert_techno_economic.xlsx b/message_ix_models/data/material/ammonia/new concise input files/fert_techno_economic.xlsx index 8205efee51..3d8c03bda6 100644 --- a/message_ix_models/data/material/ammonia/new concise input files/fert_techno_economic.xlsx +++ b/message_ix_models/data/material/ammonia/new concise input files/fert_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:5399db9c086ec937ca2de8b664236aa30eee3013bf92c40db3b0ab7b61b5f1f3 -size 44491 +oid sha256:1c0ad82a1fc12a41f59b6d8add8a6ad4bf968322972a997cfdbf9f465e2ef328 +size 46078 From 0ced8ce2cf02848fe71a5444c58ab227bcedb3a6 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 18 Oct 2022 12:44:14 +0200 Subject: [PATCH 469/774] Add code to remove rows with year_act-year_vtg >= lifetime (new code) --- message_ix_models/model/material/data_ammonia_new.py | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/message_ix_models/model/material/data_ammonia_new.py b/message_ix_models/model/material/data_ammonia_new.py index 5ae0798948..0386d96f0e 100644 --- a/message_ix_models/model/material/data_ammonia_new.py +++ b/message_ix_models/model/material/data_ammonia_new.py @@ -95,9 +95,19 @@ def gen_data(scenario, dry_run=False, add_ccs: bool = True): set_exp_imp_nodes(df_new) par_dict[par_name] = df_new + df = par_dict.get("technical_lifetime") + dict_lifetime = df.loc[:,["technology", "value"]].set_index("technology").to_dict()[ + "value"] + for i in par_dict.keys(): + if ("year_vtg" in par_dict[i].columns) & ("year_act" in par_dict[i].columns): + df_temp = par_dict[i] + df_temp["lifetime"] = df_temp["technology"].map(dict_lifetime) + df_temp = df_temp[(df_temp["year_act"] - df_temp["year_vtg"]) < df_temp["lifetime"]] + par_dict[i] = df_temp.drop("lifetime", axis="columns") return par_dict + def gen_data_rel(scenario, dry_run=False, add_ccs: bool = True): s_info = ScenarioInfo(scenario) # s_info.yv_ya From 8f7d38d79f6b25e96a658e3d8a588204cf849b5a Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 18 Oct 2022 12:45:41 +0200 Subject: [PATCH 470/774] Remove dependency on old input file --- .../model/material/data_ammonia_new.py | 34 +++++++++++++++---- 1 file changed, 28 insertions(+), 6 deletions(-) diff --git a/message_ix_models/model/material/data_ammonia_new.py b/message_ix_models/model/material/data_ammonia_new.py index 0386d96f0e..b90ad40fed 100644 --- a/message_ix_models/model/material/data_ammonia_new.py +++ b/message_ix_models/model/material/data_ammonia_new.py @@ -247,7 +247,7 @@ def read_demand(): skiprows=14, ) - # Read parameters in xlsx + """# Read parameters in xlsx te_params = data = pd.read_excel( context.get_local_path( "material", "ammonia", "n-fertilizer_techno-economic_new.xlsx" @@ -261,12 +261,34 @@ def read_demand(): input_fuel = te_params[2010][ list(range(4, te_params.shape[0], n_inputs_per_tech)) - ].reset_index(drop=True) + ].reset_index(drop=True)""" # input_fuel[0:5] = input_fuel[0:5] * CONVERSION_FACTOR_PJ_GWa # 0.0317 GWa/PJ, GJ/t = PJ/Mt NH3 - capacity_factor = te_params[2010][ - list(range(11, te_params.shape[0], n_inputs_per_tech)) - ].reset_index(drop=True) + te_params_new = pd.read_excel( + context.get_local_path( + "material", + "ammonia", + "new concise input files", + "fert_techno_economic.xlsx"), + sheet_name="data_R12") + + tec_dict = [ + "biomass_NH3", + "electr_NH3", + "gas_NH3", + "coal_NH3", + "fueloil_NH3", + "NH3_to_N_fertil", + ] + + input_fuel = te_params_new[ + (te_params_new["parameter"] == "input") & ~(te_params_new["commodity"].isin(["freshwater_supply"])) & ( + te_params_new["technology"].isin(tec_dict))] + input_fuel = input_fuel.iloc[[0, 2, 4, 5, 7, 9]].set_index("technology").loc[tec_dict, "value"] + + #capacity_factor = te_params[2010][ + # list(range(11, te_params.shape[0], n_inputs_per_tech)) + #].reset_index(drop=True) # Regional N demand in 2010 ND = N_demand_GLO.loc[N_demand_GLO.Scenario == "NoPolicy", ["Region", 2010]] @@ -400,7 +422,7 @@ def read_demand(): "N_energy": N_energy, "feedshare": feedshare, "act2010": act2010, - "capacity_factor": capacity_factor, + #"capacity_factor": capacity_factor, "N_feed": N_feed, "N_trade_R12": N_trade_R12, "NH3_trade_R12": NH3_trade_R12, From daa4280f871bb0163748b1782fa6b1aa6bf4949d Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 18 Oct 2022 12:52:25 +0200 Subject: [PATCH 471/774] Add demand file --- message_ix_models/data/material/ammonia/demand_i_feed_R12.xlsx | 3 +++ 1 file changed, 3 insertions(+) create mode 100644 message_ix_models/data/material/ammonia/demand_i_feed_R12.xlsx diff --git a/message_ix_models/data/material/ammonia/demand_i_feed_R12.xlsx b/message_ix_models/data/material/ammonia/demand_i_feed_R12.xlsx new file mode 100644 index 0000000000..5cf8edc3fc --- /dev/null +++ b/message_ix_models/data/material/ammonia/demand_i_feed_R12.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:78a07b53c8831bcd10235a4caf194d24cb4a3ff654b69a6be60e4edbe796eb54 +size 14414 From 19b063e912df2c527e2e355a46247e08e999262a Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Fri, 28 Oct 2022 22:16:04 +0200 Subject: [PATCH 472/774] Update fertilizer parameters --- .../ammonia/new concise input files/fert_techno_economic.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/ammonia/new concise input files/fert_techno_economic.xlsx b/message_ix_models/data/material/ammonia/new concise input files/fert_techno_economic.xlsx index 3d8c03bda6..ed5439fa6d 100644 --- a/message_ix_models/data/material/ammonia/new concise input files/fert_techno_economic.xlsx +++ b/message_ix_models/data/material/ammonia/new concise input files/fert_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:1c0ad82a1fc12a41f59b6d8add8a6ad4bf968322972a997cfdbf9f465e2ef328 -size 46078 +oid sha256:38b26bab05ea74acf24e3a6cfa247e7fea51298b2baf84f59625b7148396fc38 +size 286 From d9cfd8d808bac6b9ddf2cae9801fd6436c448181 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Fri, 28 Oct 2022 22:16:21 +0200 Subject: [PATCH 473/774] Addd i_feed demand to xlsx --- .../new concise input files/nh3_fertilizer_demand.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/ammonia/new concise input files/nh3_fertilizer_demand.xlsx b/message_ix_models/data/material/ammonia/new concise input files/nh3_fertilizer_demand.xlsx index 9c9e057f5e..5e579cbf9e 100644 --- a/message_ix_models/data/material/ammonia/new concise input files/nh3_fertilizer_demand.xlsx +++ b/message_ix_models/data/material/ammonia/new concise input files/nh3_fertilizer_demand.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:97f5430552bc764e6c40818b8b0d6675aaebcaabe5c81c9f8be2e3167a20d5bb -size 94310 +oid sha256:d9a106f5975f7cff278c8015b830992400d0f855a84071f2b755878b33519206 +size 105842 From c534806f0840013bfd5a671a183b9d4f9a49e7e7 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Fri, 28 Oct 2022 22:17:14 +0200 Subject: [PATCH 474/774] Change i_feed demand path --- message_ix_models/model/material/data_ammonia_new.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/message_ix_models/model/material/data_ammonia_new.py b/message_ix_models/model/material/data_ammonia_new.py index b90ad40fed..9531ffe074 100644 --- a/message_ix_models/model/material/data_ammonia_new.py +++ b/message_ix_models/model/material/data_ammonia_new.py @@ -435,7 +435,10 @@ def gen_demand(): N_energy = read_demand()["N_feed"] # updated feed with imports accounted demand_fs_org = pd.read_excel( - context.get_local_path("material", "ammonia", "demand_i_feed_R12.xlsx") + context.get_local_path("material", "ammonia", + "new concise input files", + "nh3_fertilizer_demand.xlsx"), + sheet_name="demand_i_feed_R12" ) df = demand_fs_org.loc[demand_fs_org.year == 2010, :].join( From 8d434992e1daaa8f04ebdd91964247878f0db008 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Fri, 28 Oct 2022 22:19:26 +0200 Subject: [PATCH 475/774] Refactor historic activity calculation --- .../model/material/data_ammonia_new.py | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/message_ix_models/model/material/data_ammonia_new.py b/message_ix_models/model/material/data_ammonia_new.py index 9531ffe074..fa552d34d3 100644 --- a/message_ix_models/model/material/data_ammonia_new.py +++ b/message_ix_models/model/material/data_ammonia_new.py @@ -404,19 +404,21 @@ def read_demand(): feedshare = fs_GLO.sort_values(["Region"]).set_index("Region").drop("R12_GLB") # Get historical N demand from SSP2-nopolicy (may need to vary for diff scenarios) - N_demand_raw = N_demand_GLO.copy() + N_demand_raw = N_demand_GLO[N_demand_GLO["Region"]!="World"].copy() + N_demand_raw["Region"] = "R12_" + N_demand_raw["Region"] + N_demand_raw = N_demand_raw.set_index("Region") N_demand = ( - N_demand_raw[ - (N_demand_raw.Scenario == "NoPolicy") & (N_demand_raw.Region != "World") + N_demand_raw.loc[ + (N_demand_raw.Scenario == "NoPolicy")# & (N_demand_raw.Region != "World") ] - .reset_index() + #.reset_index() .loc[:, 2010] ) # 2010 tot N demand - N_demand = N_demand.repeat(6) - - act2010 = (feedshare.values.flatten() * N_demand).reset_index(drop=True) + #N_demand = N_demand.repeat(6) + #act2010 = (feedshare.values.flatten() * N_demand).reset_index(drop=True) return { + "act2010": feedshare.mul(N_demand, axis=0), "feedshare_GLO": feedshare_GLO, "ND": ND, "N_energy": N_energy, From 281bd6ece044cd9a9ea5d9db4d2c0345c4ad09a9 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 18 Oct 2022 15:15:27 +0200 Subject: [PATCH 476/774] Fix meth_t_d efficiency and add region cost scaling function --- message_ix_models/model/material/data_methanol.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index 385f2616ba..e5ac0c8cb0 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -131,6 +131,10 @@ def gen_data_methanol(scenario): "bound_emission", value=3667, **emission_dict ) + df = scenario.par("input", filters={"technology": "meth_t_d"}) + df["value"] = 1 + new_dict2["input"] = pd.concat([new_dict2["input"], df]) + return new_dict2 From fd0dc62ad540808c5b3e028ba8d240372e6bce28 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Wed, 19 Oct 2022 11:05:35 +0200 Subject: [PATCH 477/774] Fix meth_bio input level --- message_ix_models/data/material/meth_bio_techno_economic.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/meth_bio_techno_economic.xlsx b/message_ix_models/data/material/meth_bio_techno_economic.xlsx index f9bea3ca1c..39993396e3 100644 --- a/message_ix_models/data/material/meth_bio_techno_economic.xlsx +++ b/message_ix_models/data/material/meth_bio_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:81d201109358d1247122c8673d8a027de29095b28dc95449bbf2a78a2a4247bc -size 59516 +oid sha256:c39870e71246a696c14e429044fafd2b34c69912719a087e8a284b0b3873b488 +size 59550 From a42837cca202a338c7e0e780b5e3e1186a137df6 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Wed, 19 Oct 2022 11:07:53 +0200 Subject: [PATCH 478/774] Combine residential and commercial resin demand --- .../model/material/data_methanol.py | 16 ++++++---------- 1 file changed, 6 insertions(+), 10 deletions(-) diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index e5ac0c8cb0..52a742409e 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -109,14 +109,12 @@ def gen_data_methanol(scenario): if ~(i in new_dict2.keys()) & (i in resin_dict.keys()): new_dict2[i] = resin_dict[i] - new_dict2["demand"] = pd.concat([new_dict2["demand"], - gen_resin_demand( - scenario, pars["resin_share"], "residential", pars["wood_scenario"]) - ] - ) - new_dict2["demand"] = pd.concat([new_dict2["demand"], - gen_resin_demand(scenario, pars["resin_share"], "comm", pars["wood_scenario"]) - ]) + df_comm = gen_resin_demand(scenario, pars["resin_share"], "comm", pars["wood_scenario"]) + df_resid = gen_resin_demand(scenario, pars["resin_share"], "residential", pars["wood_scenario"]) + df_resin_demand = df_comm.copy(deep=True) + df_resin_demand["value"] = df_comm["value"] + df_resid["value"] + new_dict2["demand"] = pd.concat([new_dict2["demand"], df_resin_demand]) + new_dict2["input"] = pd.concat([new_dict2["input"], add_methanol_trp_additives(scenario)]) if pars["cbudget"]: @@ -352,8 +350,6 @@ def get_demand_t1_with_income_elasticity( ) df_demand[income_year2] = dem_2020 - - df_melt = df_demand.melt( id_vars=["Region"], value_vars=df_demand.columns[5:], var_name="year" ) From f7f471afbce9387502ae3b3706e0a302689e3cd3 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Wed, 19 Oct 2022 11:08:24 +0200 Subject: [PATCH 479/774] Add regional cost scaling util function --- message_ix_models/model/material/data_methanol.py | 15 ++++++++++++++- 1 file changed, 14 insertions(+), 1 deletion(-) diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index 52a742409e..bd64329f3b 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -377,4 +377,17 @@ def get_meth_bio_cost_ratio(scenario, tec_name, cost_type): df = df.merge(df_nam[["value", "year_vtg"]],left_on="year_vtg", right_on="year_vtg") df["ratio"] = df["value_x"]/df["value_y"] - return df["ratio"] \ No newline at end of file + return df[["node_loc","year_vtg", "ratio"]] + +def get_meth_bio_cost_ratio_2020(scenario, tec_name, cost_type): + + df = scenario.par(cost_type) + df = df[df["technology"]==tec_name] + df= df[df["year_vtg"]>=2020] + if cost_type in ["fix_cost", "var_cost"]: + df = df[df["year_vtg"]==df["year_act"]] + + val_nam_2020 = df.loc[(df["node_loc"]=="R12_NAM") & (df["year_vtg"]==2020), "value"].iloc[0] + df["ratio"] = df["value"]/ val_nam_2020 + + return df[["node_loc","year_vtg", "ratio"]] \ No newline at end of file From 1fa088b5d6ec95eb1e585bd6195a514bf0c03ec7 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 20 Oct 2022 10:54:46 +0200 Subject: [PATCH 480/774] Add missing vtg years inv_cost --- message_ix_models/data/material/meth_bio_techno_economic.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/meth_bio_techno_economic.xlsx b/message_ix_models/data/material/meth_bio_techno_economic.xlsx index 39993396e3..337a799e99 100644 --- a/message_ix_models/data/material/meth_bio_techno_economic.xlsx +++ b/message_ix_models/data/material/meth_bio_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:c39870e71246a696c14e429044fafd2b34c69912719a087e8a284b0b3873b488 -size 59550 +oid sha256:2920e61f9aa01022b12280fc5a0d5ed0f06b19b6c09234f9824a81a18959b9ae +size 914718 From e32e19ddb6104c70d32efa1eb9d96b1c4f2508eb Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 20 Oct 2022 10:55:48 +0200 Subject: [PATCH 481/774] Add building resin demand scenario parameter --- .../data/material/methanol_sensitivity_pars.xlsx | 4 ++-- message_ix_models/model/material/data_methanol.py | 8 ++++---- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/message_ix_models/data/material/methanol_sensitivity_pars.xlsx b/message_ix_models/data/material/methanol_sensitivity_pars.xlsx index 8650c03e57..1377bcc323 100644 --- a/message_ix_models/data/material/methanol_sensitivity_pars.xlsx +++ b/message_ix_models/data/material/methanol_sensitivity_pars.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:62d314c435cd4f024ad5e0a40f6aa80c192f8715c03fbbb856fe0433cc061902 -size 9717 +oid sha256:8552a3c15174cd529ebbd72924b4b20390e0d8f29ad6cd8cf7afda27d16cb1c3 +size 300 diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index bd64329f3b..d59f56217a 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -109,8 +109,8 @@ def gen_data_methanol(scenario): if ~(i in new_dict2.keys()) & (i in resin_dict.keys()): new_dict2[i] = resin_dict[i] - df_comm = gen_resin_demand(scenario, pars["resin_share"], "comm", pars["wood_scenario"]) - df_resid = gen_resin_demand(scenario, pars["resin_share"], "residential", pars["wood_scenario"]) + df_comm = gen_resin_demand(scenario, pars["resin_share"], "comm", pars["wood_scenario"], pars["pathway"]) + df_resid = gen_resin_demand(scenario, pars["resin_share"], "residential", pars["wood_scenario"], pars["pathway"]) df_resin_demand = df_comm.copy(deep=True) df_resin_demand["value"] = df_comm["value"] + df_resid["value"] new_dict2["demand"] = pd.concat([new_dict2["demand"], df_resin_demand]) @@ -268,9 +268,9 @@ def get_meth_share(df, node): return pd.concat([df_loil, df_loil_meth]) -def gen_resin_demand(scenario, resin_share, sector, buildings_scen): +def gen_resin_demand(scenario, resin_share, sector, buildings_scen, pathway="SHAPE"): df = pd.read_csv( - context.get_local_path("material", "results_material_SHAPE_" + sector + ".csv") + context.get_local_path("material", "results_material_"+ pathway +"_" + sector + ".csv") ) resin_intensity = resin_share df = df[df["scenario"] == buildings_scen] From d82f9c5a850ea8bcc3d420237328a65164dfad8f Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Fri, 21 Oct 2022 17:32:13 +0200 Subject: [PATCH 482/774] Add missing addon parameters to meth_h2 input file --- message_ix_models/data/material/meth_h2_techno_economic.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/meth_h2_techno_economic.xlsx b/message_ix_models/data/material/meth_h2_techno_economic.xlsx index b5a9aebfd4..d66ede6526 100644 --- a/message_ix_models/data/material/meth_h2_techno_economic.xlsx +++ b/message_ix_models/data/material/meth_h2_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:ce87fdff9930f36f5e1df9de12248575a1843cbb7edd864eecf37667d1460285 -size 260906 +oid sha256:59feabb443b701294543a1d5e2564a1d4edbe66017b6b39694c394b4cebbc6a8 +size 421735 From 5b23de60d70018850a124c5f7721c2d6294185ae Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Sun, 23 Oct 2022 15:05:44 +0200 Subject: [PATCH 483/774] Add new cost scaling func --- message_ix_models/model/material/data_methanol.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index d59f56217a..c3fd8af6b6 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -389,5 +389,12 @@ def get_meth_bio_cost_ratio_2020(scenario, tec_name, cost_type): val_nam_2020 = df.loc[(df["node_loc"]=="R12_NAM") & (df["year_vtg"]==2020), "value"].iloc[0] df["ratio"] = df["value"]/ val_nam_2020 + return df[["node_loc","year_vtg", "ratio"]] + - return df[["node_loc","year_vtg", "ratio"]] \ No newline at end of file +def get_scaled_cost_from_proxy_tec(value, scenario, proxy_tec, cost_type, new_tec): + df = get_meth_bio_cost_ratio_2020(scenario, proxy_tec, cost_type) + df["value"] = value * df["ratio"] + df["technology"] = new_tec + df["unit"] = "-" + return message_ix.make_df(cost_type, **df) \ No newline at end of file From 7e6a8514fec3080f4401ce3f5f0c94aaa476cf21 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 25 Oct 2022 11:57:16 +0200 Subject: [PATCH 484/774] Remove unused addon tec params from francesco --- message_ix_models/data/material/meth_h2_techno_economic.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/meth_h2_techno_economic.xlsx b/message_ix_models/data/material/meth_h2_techno_economic.xlsx index d66ede6526..d978a40df6 100644 --- a/message_ix_models/data/material/meth_h2_techno_economic.xlsx +++ b/message_ix_models/data/material/meth_h2_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:59feabb443b701294543a1d5e2564a1d4edbe66017b6b39694c394b4cebbc6a8 -size 421735 +oid sha256:026434a25df20d472a2be02d3e75845bf704c075ded388e1cedc48937654892f +size 342719 From 26046bbe855420f6d9249350d26d33424224aea1 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Wed, 26 Oct 2022 14:01:32 +0200 Subject: [PATCH 485/774] Extend meth_bio parameter timeseries --- message_ix_models/data/material/meth_bio_techno_economic.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/meth_bio_techno_economic.xlsx b/message_ix_models/data/material/meth_bio_techno_economic.xlsx index 337a799e99..711cfcbbe9 100644 --- a/message_ix_models/data/material/meth_bio_techno_economic.xlsx +++ b/message_ix_models/data/material/meth_bio_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:2920e61f9aa01022b12280fc5a0d5ed0f06b19b6c09234f9824a81a18959b9ae -size 914718 +oid sha256:45cfcda946b5359e3dae3e5c8d2f7b7596c013f89df6564076d4c52733a5c663 +size 103918 From 1917ec109eb17ee6718f41428efeb994da875324 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Wed, 26 Oct 2022 14:03:41 +0200 Subject: [PATCH 486/774] Scale meth_bio costs regionally --- .../model/material/data_methanol.py | 94 ++++++++++++++----- 1 file changed, 70 insertions(+), 24 deletions(-) diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index c3fd8af6b6..a0d53c33d9 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -145,14 +145,23 @@ def gen_data_meth_h2(): return df_h2 -def gen_data_meth_bio(): +def gen_data_meth_bio(scenario): context = read_config() - context.get_local_path("material", "meth_bio_techno_economic.xlsx") - df_h2 = pd.read_excel( + df_bio = pd.read_excel( context.get_local_path("material", "meth_bio_techno_economic.xlsx"), sheet_name=None, ) - return df_h2 + coal_ratio = get_meth_bio_cost_ratio_2020(scenario, "meth_coal", "fix_cost") + merge = df_bio["fix_cost"].merge(on=["node_loc", "year_vtg", "year_act"], + right=coal_ratio.drop(["technology", "unit", "value"], axis=1)) + df_bio["fix_cost"] = merge.assign(value=lambda x: x["value"] * x["ratio"]).drop("ratio", axis=1) + + coal_ratio = get_meth_bio_cost_ratio_2020(scenario, "meth_coal", "inv_cost") + merge = df_bio["inv_cost"].merge(on=["node_loc", "year_vtg"], + right=coal_ratio.drop(["technology", "unit", "value"], axis=1)) + df_bio["inv_cost"] = merge.assign(value=lambda x: x["value"] * x["ratio"]).drop("ratio", axis=1) + + return df_bio def gen_data_meth_chemicals(scenario, chemical): @@ -365,31 +374,17 @@ def get_demand_t1_with_income_elasticity( ) -def get_meth_bio_cost_ratio(scenario, tec_name, cost_type): - - df = scenario.par(cost_type) - df = df[df["technology"]==tec_name] - df= df[df["year_vtg"]>=2020] - if cost_type in ["fix_cost", "var_cost"]: - df = df[df["year_vtg"]==df["year_act"]] - - df_nam = df[df["node_loc"]=="R12_NAM"].sort_values("year_vtg") - df = df.merge(df_nam[["value", "year_vtg"]],left_on="year_vtg", right_on="year_vtg") - df["ratio"] = df["value_x"]/df["value_y"] - - return df[["node_loc","year_vtg", "ratio"]] - def get_meth_bio_cost_ratio_2020(scenario, tec_name, cost_type): df = scenario.par(cost_type) - df = df[df["technology"]==tec_name] - df= df[df["year_vtg"]>=2020] - if cost_type in ["fix_cost", "var_cost"]: - df = df[df["year_vtg"]==df["year_act"]] + df = df[df["technology"] == tec_name] + df= df[df["year_vtg"] >= 2020] + #if cost_type in ["fix_cost", "var_cost"]: + # df = df[df["year_vtg"] == df["year_act"]] val_nam_2020 = df.loc[(df["node_loc"]=="R12_NAM") & (df["year_vtg"]==2020), "value"].iloc[0] df["ratio"] = df["value"]/ val_nam_2020 - return df[["node_loc","year_vtg", "ratio"]] + return df#[["node_loc","year_vtg", "ratio"]] def get_scaled_cost_from_proxy_tec(value, scenario, proxy_tec, cost_type, new_tec): @@ -397,4 +392,55 @@ def get_scaled_cost_from_proxy_tec(value, scenario, proxy_tec, cost_type, new_te df["value"] = value * df["ratio"] df["technology"] = new_tec df["unit"] = "-" - return message_ix.make_df(cost_type, **df) \ No newline at end of file + return message_ix.make_df(cost_type, **df) + + +def update_methanol_costs(scenario): + df_inv = pd.concat([ + get_scaled_cost_from_proxy_tec(842, scenario, "meth_coal", "inv_cost", "meth_coal"), + get_scaled_cost_from_proxy_tec(350, scenario, "meth_ng", "inv_cost", "meth_ng")]) + #get_scaled_cost_from_proxy_tec(4800, scenario, "meth_ng", "inv_cost", "meth_bio"), + df_fix = pd.concat([ + get_scaled_cost_from_proxy_tec(42.1, scenario, "meth_coal", "fix_cost", "meth_coal"), + get_scaled_cost_from_proxy_tec(8.75, scenario, "meth_ng", "fix_cost", "meth_ng")]) + #get_scaled_cost_from_proxy_tec(290, scenario, "meth_ng", "fix_cost", "meth_bio")]) + return {"inv_cost": df_inv, "fix_cost": df_fix} + + +def gen_meth_bio_ccs(scenario): + par_dict = gen_data_meth_bio(scenario) + for k in par_dict.keys(): + par_dict[k]["technology"] = "meth_bio_ccs" + + df_bio = par_dict["inv_cost"] + df = scenario.par("inv_cost") + df_std = df[df["technology"] == "meth_coal"] + df_ccs = df[df["technology"] == "meth_coal_ccs"] + merge = df_std.merge(on=["year_vtg", "node_loc"], right=df_ccs.drop(["technology", "unit"], axis=1)) + ratio = merge.assign(ratio=lambda x: x["value_x"] / x["value_y"]).drop(["value_x", "value_y"], axis=1) + merge = df_bio.merge(on=["year_vtg", "node_loc"], right=ratio.drop(["technology", "unit"], axis=1)) + merge = merge.assign(value=lambda x: x["value"] * x["ratio"]).drop(["ratio"], axis=1) + par_dict["inv_cost"] = merge + + df_bio = par_dict["fix_cost"] + df = scenario.par("fix_cost") + df_std = df[df["technology"] == "meth_coal"] + df_ccs = df[df["technology"] == "meth_coal_ccs"] + merge = df_std.merge(on=["node_loc", "year_vtg", "year_act"], right=df_ccs.drop(["technology", "unit"], axis=1)) + ratio = merge.assign(ratio=lambda x: x["value_x"] / x["value_y"]).drop(["value_x", "value_y"], axis=1) + merge = df_bio.merge(on=["node_loc", "year_vtg", "year_act"], right=ratio.drop(["technology", "unit"], axis=1)) + merge = merge.assign(value=lambda x: x["value"] * x["ratio"]).drop(["ratio"], axis=1) + par_dict["fix_cost"] = merge + + par_dict["output"].loc[par_dict["output"]["commodity"]=="electr", "value"] = \ + par_dict["output"].loc[par_dict["output"]["commodity"]=="electr", "value"] - 0.019231 # from meth_coal_ccs + + return par_dict + + +def combine_df_dictionaries(dict1, dict2): + keys = set(list(dict1.keys()) + list(dict2.keys())) + new_dict = {} + for i in keys: + new_dict[i] = pd.concat([dict1.get(i), dict2.get(i)]) + return new_dict From 7c678f092d2f4ec766a8949e04b676b0078fc660 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Wed, 26 Oct 2022 14:04:22 +0200 Subject: [PATCH 487/774] Simplify dictionary merging --- .../model/material/data_methanol.py | 44 ++++--------------- 1 file changed, 8 insertions(+), 36 deletions(-) diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index a0d53c33d9..728579da95 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -23,29 +23,17 @@ def gen_data_methanol(scenario): pars = df_pars.set_index("par").to_dict()["value"] dict1 = gen_data_meth_h2() - dict2 = gen_data_meth_bio() - keys = set(list(dict1.keys()) + list(dict2.keys())) - new_dict = {} - for i in keys: - if (i in dict2.keys()) & (i in dict1.keys()): - new_dict[i] = pd.concat([dict1[i], dict2[i]]) - else: - new_dict[i] = dict1[i] + dict2 = gen_data_meth_bio(scenario) + new_dict = combine_df_dictionaries(dict1, dict2) + dict3 = gen_meth_bio_ccs(scenario) + new_dict = combine_df_dictionaries(new_dict, dict3) dict3 = pd.read_excel( context.get_local_path("material", "meth_t_d_material_pars.xlsx"), sheet_name=None, ) - keys = set(list(dict3.keys()) + list(new_dict.keys())) - new_dict2 = {} - for i in keys: - if (i in dict3.keys()) & (i in new_dict.keys()): - new_dict2[i] = pd.concat([new_dict[i], dict3[i]]) - if (i in dict3.keys()) & ~(i in new_dict.keys()): - new_dict2[i] = dict3[i] - if ~(i in dict3.keys()) & (i in new_dict.keys()): - new_dict2[i] = new_dict[i] + new_dict2 = combine_df_dictionaries(new_dict, dict3) df_final = gen_meth_residual_demand(pars["methanol_elasticity"]) df_final["value"] = df_final["value"].apply(lambda x: x * 0.5) @@ -86,28 +74,12 @@ def gen_data_methanol(scenario): new_dict2["historical_activity"] = pd.concat([new_dict2["historical_activity"], df_ng]) mto_dict = gen_data_meth_chemicals(scenario, "MTO") - keys = set(list(new_dict2.keys()) + list(mto_dict.keys())) - for i in keys: - if (i in new_dict2.keys()) & (i in mto_dict.keys()): - new_dict2[i] = pd.concat([new_dict2[i], mto_dict[i]]) - if ~(i in new_dict2.keys()) & (i in mto_dict.keys()): - new_dict2[i] = mto_dict[i] + new_dict2 = combine_df_dictionaries(new_dict2, mto_dict) ch2o_dict = gen_data_meth_chemicals(scenario, "Formaldehyde") - keys = set(list(new_dict2.keys()) + list(ch2o_dict.keys())) - for i in keys: - if (i in new_dict2.keys()) & (i in ch2o_dict.keys()): - new_dict2[i] = pd.concat([new_dict2[i], ch2o_dict[i]]) - if ~(i in new_dict2.keys()) & (i in ch2o_dict.keys()): - new_dict2[i] = ch2o_dict[i] - + new_dict2 = combine_df_dictionaries(new_dict2, ch2o_dict) resin_dict = gen_data_meth_chemicals(scenario, "Resins") - keys = set(list(new_dict2.keys()) + list(resin_dict.keys())) - for i in keys: - if (i in new_dict2.keys()) & (i in resin_dict.keys()): - new_dict2[i] = pd.concat([new_dict2[i], resin_dict[i]]) - if ~(i in new_dict2.keys()) & (i in resin_dict.keys()): - new_dict2[i] = resin_dict[i] + new_dict2 = combine_df_dictionaries(new_dict2, resin_dict) df_comm = gen_resin_demand(scenario, pars["resin_share"], "comm", pars["wood_scenario"], pars["pathway"]) df_resid = gen_resin_demand(scenario, pars["resin_share"], "residential", pars["wood_scenario"], pars["pathway"]) From a0a83d0a0e13f1353c36eb28b0f8819ff4c22af6 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Wed, 26 Oct 2022 14:07:07 +0200 Subject: [PATCH 488/774] Update conventional methanol tecs costs --- message_ix_models/model/material/data_methanol.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index 728579da95..23ff4766ca 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -105,6 +105,9 @@ def gen_data_methanol(scenario): df["value"] = 1 new_dict2["input"] = pd.concat([new_dict2["input"], df]) + cost_dict = update_methanol_costs(scenario) + new_dict2 = combine_df_dictionaries(new_dict2, cost_dict) + return new_dict2 From 79f439e9c36b99bbc8cfb0d0a009c0aee4b0139e Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Wed, 26 Oct 2022 18:10:27 +0200 Subject: [PATCH 489/774] Add cost update option to sensitivity file --- .../data/material/methanol_sensitivity_pars.xlsx | 4 ++-- message_ix_models/model/material/data_methanol.py | 5 +++-- 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/message_ix_models/data/material/methanol_sensitivity_pars.xlsx b/message_ix_models/data/material/methanol_sensitivity_pars.xlsx index 1377bcc323..6444aed086 100644 --- a/message_ix_models/data/material/methanol_sensitivity_pars.xlsx +++ b/message_ix_models/data/material/methanol_sensitivity_pars.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:8552a3c15174cd529ebbd72924b4b20390e0d8f29ad6cd8cf7afda27d16cb1c3 -size 300 +oid sha256:72a6cb39af205141790bf679c3b45d2fe3260bfd06826bf08d6c1f757d2ca098 +size 298 diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index 23ff4766ca..c038bf7a68 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -105,8 +105,9 @@ def gen_data_methanol(scenario): df["value"] = 1 new_dict2["input"] = pd.concat([new_dict2["input"], df]) - cost_dict = update_methanol_costs(scenario) - new_dict2 = combine_df_dictionaries(new_dict2, cost_dict) + if pars["update_old_tecs"]: + cost_dict = update_methanol_costs(scenario) + new_dict2 = combine_df_dictionaries(new_dict2, cost_dict) return new_dict2 From b15961052a65c633f33d1c095729c628a22d0b5b Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Wed, 26 Oct 2022 18:11:36 +0200 Subject: [PATCH 490/774] Add meth_bio_ccs to set --- message_ix_models/data/material/set.yaml | 1 + 1 file changed, 1 insertion(+) diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index caefe6767e..e28becf177 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -465,6 +465,7 @@ methanol: technology: add: - meth_bio + - meth_bio_ccs - meth_h2 - meth_t_d_material - MTO From d11a88c23f8457184b92df73e762f97ed1dc12a1 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Fri, 28 Oct 2022 17:34:25 +0200 Subject: [PATCH 491/774] Add methanol trade historic data --- message_ix_models/data/material/meth_trd_pars.xlsx | 3 +++ message_ix_models/model/material/data_methanol.py | 10 ++++++++++ 2 files changed, 13 insertions(+) create mode 100644 message_ix_models/data/material/meth_trd_pars.xlsx diff --git a/message_ix_models/data/material/meth_trd_pars.xlsx b/message_ix_models/data/material/meth_trd_pars.xlsx new file mode 100644 index 0000000000..a74cb099b8 --- /dev/null +++ b/message_ix_models/data/material/meth_trd_pars.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:54e303f8887902b1241da714ab94e9f886378b3513021ec3afd171152885a70c +size 15004 diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index c038bf7a68..6833a68a5b 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -109,6 +109,8 @@ def gen_data_methanol(scenario): cost_dict = update_methanol_costs(scenario) new_dict2 = combine_df_dictionaries(new_dict2, cost_dict) + new_dict2 = combine_df_dictionaries(new_dict2, add_meth_trade_historic()) + return new_dict2 @@ -414,6 +416,14 @@ def gen_meth_bio_ccs(scenario): return par_dict +def add_meth_trade_historic(): + par_dict_trade = pd.read_excel( + context.get_local_path("material", "meth_trd_pars.xlsx"), + sheet_name=None, + ) + return par_dict_trade + + def combine_df_dictionaries(dict1, dict2): keys = set(list(dict1.keys()) + list(dict2.keys())) new_dict = {} From 8ad734dffa10c11b0619a3c5f9c9750f2bb6ff24 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Fri, 28 Oct 2022 17:34:58 +0200 Subject: [PATCH 492/774] Fill meth_bio missing years --- message_ix_models/data/material/meth_bio_techno_economic.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/meth_bio_techno_economic.xlsx b/message_ix_models/data/material/meth_bio_techno_economic.xlsx index 711cfcbbe9..e22d97ecca 100644 --- a/message_ix_models/data/material/meth_bio_techno_economic.xlsx +++ b/message_ix_models/data/material/meth_bio_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:45cfcda946b5359e3dae3e5c8d2f7b7596c013f89df6564076d4c52733a5c663 -size 103918 +oid sha256:15e7e2751524e080ebc1d635ebfc68c2460254ce7f489be3bdc6086f1c4c033e +size 177317 From 0ea2b2f81ed6ced0aee523ebf738be7d625854d7 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Sat, 29 Oct 2022 19:48:03 +0200 Subject: [PATCH 493/774] Add lower cost option for sas and cpa & remove rows with wrong vtg year --- .../ammonia/new concise input files/fert_techno_economic.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/ammonia/new concise input files/fert_techno_economic.xlsx b/message_ix_models/data/material/ammonia/new concise input files/fert_techno_economic.xlsx index ed5439fa6d..abe6bd11a7 100644 --- a/message_ix_models/data/material/ammonia/new concise input files/fert_techno_economic.xlsx +++ b/message_ix_models/data/material/ammonia/new concise input files/fert_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:38b26bab05ea74acf24e3a6cfa247e7fea51298b2baf84f59625b7148396fc38 -size 286 +oid sha256:0dd48af4cc558b66f482f443b68248ac7a9aa4dcef08688986562f470bbc3c42 +size 327 From 4fd39771082108d34ab1d5f3b4a98080830ca566 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Sat, 29 Oct 2022 21:23:47 +0200 Subject: [PATCH 494/774] Remove dependency on old te input file --- .../new concise input files/nh3_fertilizer_demand.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/ammonia/new concise input files/nh3_fertilizer_demand.xlsx b/message_ix_models/data/material/ammonia/new concise input files/nh3_fertilizer_demand.xlsx index 5e579cbf9e..096e0c74b3 100644 --- a/message_ix_models/data/material/ammonia/new concise input files/nh3_fertilizer_demand.xlsx +++ b/message_ix_models/data/material/ammonia/new concise input files/nh3_fertilizer_demand.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:d9a106f5975f7cff278c8015b830992400d0f855a84071f2b755878b33519206 -size 105842 +oid sha256:a2e5934ef4420e3734ad15365b0cd209cfcd0c75619862e8d56d75e43ee38b75 +size 114818 From 91671b2c1a47f3c723dbfb80da3202f3ccc2db34 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Sat, 29 Oct 2022 21:37:01 +0200 Subject: [PATCH 495/774] Move ammonia input files --- message_ix_models/data/material/ammonia/demand_i_feed_R12.xlsx | 3 --- .../{new concise input files => }/fert_techno_economic.xlsx | 0 .../{new concise input files => }/nh3_fertilizer_demand.xlsx | 0 3 files changed, 3 deletions(-) delete mode 100644 message_ix_models/data/material/ammonia/demand_i_feed_R12.xlsx rename message_ix_models/data/material/ammonia/{new concise input files => }/fert_techno_economic.xlsx (100%) rename message_ix_models/data/material/ammonia/{new concise input files => }/nh3_fertilizer_demand.xlsx (100%) diff --git a/message_ix_models/data/material/ammonia/demand_i_feed_R12.xlsx b/message_ix_models/data/material/ammonia/demand_i_feed_R12.xlsx deleted file mode 100644 index 5cf8edc3fc..0000000000 --- a/message_ix_models/data/material/ammonia/demand_i_feed_R12.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:78a07b53c8831bcd10235a4caf194d24cb4a3ff654b69a6be60e4edbe796eb54 -size 14414 diff --git a/message_ix_models/data/material/ammonia/new concise input files/fert_techno_economic.xlsx b/message_ix_models/data/material/ammonia/fert_techno_economic.xlsx similarity index 100% rename from message_ix_models/data/material/ammonia/new concise input files/fert_techno_economic.xlsx rename to message_ix_models/data/material/ammonia/fert_techno_economic.xlsx diff --git a/message_ix_models/data/material/ammonia/new concise input files/nh3_fertilizer_demand.xlsx b/message_ix_models/data/material/ammonia/nh3_fertilizer_demand.xlsx similarity index 100% rename from message_ix_models/data/material/ammonia/new concise input files/nh3_fertilizer_demand.xlsx rename to message_ix_models/data/material/ammonia/nh3_fertilizer_demand.xlsx From 25bf63c588176c0d106e128e749ae59e6d2403d9 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Sat, 29 Oct 2022 21:51:27 +0200 Subject: [PATCH 496/774] Move methanol input files --- .../{ => methanol}/MTO data collection.xlsx | 0 ...nol production statistics (version 1).xlsx | 0 .../meth_bio_techno_economic.xlsx | 0 .../meth_h2_techno_economic.xlsx | 0 .../meth_ng_techno_economic.xlsx | 0 .../meth_t_d_material_pars.xlsx | 0 .../{ => methanol}/meth_trd_pars.xlsx | 0 .../{ => methanol}/methanol demand.xlsx | 0 .../methanol_sensitivity_pars.xlsx | 0 .../results_material_SHAPE_comm.csv | 0 .../results_material_SHAPE_residential.csv | 0 .../methanol/results_material_SSP2_comm.csv | 3 +++ .../results_material_SSP2_residential.csv | 3 +++ .../model/material/data_methanol.py | 26 +++++++++---------- .../material/meth_rc_all_unique_par_vals.xlsx | 3 +++ 15 files changed, 22 insertions(+), 13 deletions(-) rename message_ix_models/data/material/{ => methanol}/MTO data collection.xlsx (100%) rename message_ix_models/data/material/{ => methanol}/Methanol production statistics (version 1).xlsx (100%) rename message_ix_models/data/material/{ => methanol}/meth_bio_techno_economic.xlsx (100%) rename message_ix_models/data/material/{ => methanol}/meth_h2_techno_economic.xlsx (100%) rename message_ix_models/data/material/{ => methanol}/meth_ng_techno_economic.xlsx (100%) rename message_ix_models/data/material/{ => methanol}/meth_t_d_material_pars.xlsx (100%) rename message_ix_models/data/material/{ => methanol}/meth_trd_pars.xlsx (100%) rename message_ix_models/data/material/{ => methanol}/methanol demand.xlsx (100%) rename message_ix_models/data/material/{ => methanol}/methanol_sensitivity_pars.xlsx (100%) rename message_ix_models/data/material/{ => methanol}/results_material_SHAPE_comm.csv (100%) rename message_ix_models/data/material/{ => methanol}/results_material_SHAPE_residential.csv (100%) create mode 100644 message_ix_models/data/material/methanol/results_material_SSP2_comm.csv create mode 100644 message_ix_models/data/material/methanol/results_material_SSP2_residential.csv create mode 100644 message_ix_models/model/material/meth_rc_all_unique_par_vals.xlsx diff --git a/message_ix_models/data/material/MTO data collection.xlsx b/message_ix_models/data/material/methanol/MTO data collection.xlsx similarity index 100% rename from message_ix_models/data/material/MTO data collection.xlsx rename to message_ix_models/data/material/methanol/MTO data collection.xlsx diff --git a/message_ix_models/data/material/Methanol production statistics (version 1).xlsx b/message_ix_models/data/material/methanol/Methanol production statistics (version 1).xlsx similarity index 100% rename from message_ix_models/data/material/Methanol production statistics (version 1).xlsx rename to message_ix_models/data/material/methanol/Methanol production statistics (version 1).xlsx diff --git a/message_ix_models/data/material/meth_bio_techno_economic.xlsx b/message_ix_models/data/material/methanol/meth_bio_techno_economic.xlsx similarity index 100% rename from message_ix_models/data/material/meth_bio_techno_economic.xlsx rename to message_ix_models/data/material/methanol/meth_bio_techno_economic.xlsx diff --git a/message_ix_models/data/material/meth_h2_techno_economic.xlsx b/message_ix_models/data/material/methanol/meth_h2_techno_economic.xlsx similarity index 100% rename from message_ix_models/data/material/meth_h2_techno_economic.xlsx rename to message_ix_models/data/material/methanol/meth_h2_techno_economic.xlsx diff --git a/message_ix_models/data/material/meth_ng_techno_economic.xlsx b/message_ix_models/data/material/methanol/meth_ng_techno_economic.xlsx similarity index 100% rename from message_ix_models/data/material/meth_ng_techno_economic.xlsx rename to message_ix_models/data/material/methanol/meth_ng_techno_economic.xlsx diff --git a/message_ix_models/data/material/meth_t_d_material_pars.xlsx b/message_ix_models/data/material/methanol/meth_t_d_material_pars.xlsx similarity index 100% rename from message_ix_models/data/material/meth_t_d_material_pars.xlsx rename to message_ix_models/data/material/methanol/meth_t_d_material_pars.xlsx diff --git a/message_ix_models/data/material/meth_trd_pars.xlsx b/message_ix_models/data/material/methanol/meth_trd_pars.xlsx similarity index 100% rename from message_ix_models/data/material/meth_trd_pars.xlsx rename to message_ix_models/data/material/methanol/meth_trd_pars.xlsx diff --git a/message_ix_models/data/material/methanol demand.xlsx b/message_ix_models/data/material/methanol/methanol demand.xlsx similarity index 100% rename from message_ix_models/data/material/methanol demand.xlsx rename to message_ix_models/data/material/methanol/methanol demand.xlsx diff --git a/message_ix_models/data/material/methanol_sensitivity_pars.xlsx b/message_ix_models/data/material/methanol/methanol_sensitivity_pars.xlsx similarity index 100% rename from message_ix_models/data/material/methanol_sensitivity_pars.xlsx rename to message_ix_models/data/material/methanol/methanol_sensitivity_pars.xlsx diff --git a/message_ix_models/data/material/results_material_SHAPE_comm.csv b/message_ix_models/data/material/methanol/results_material_SHAPE_comm.csv similarity index 100% rename from message_ix_models/data/material/results_material_SHAPE_comm.csv rename to message_ix_models/data/material/methanol/results_material_SHAPE_comm.csv diff --git a/message_ix_models/data/material/results_material_SHAPE_residential.csv b/message_ix_models/data/material/methanol/results_material_SHAPE_residential.csv similarity index 100% rename from message_ix_models/data/material/results_material_SHAPE_residential.csv rename to message_ix_models/data/material/methanol/results_material_SHAPE_residential.csv diff --git a/message_ix_models/data/material/methanol/results_material_SSP2_comm.csv b/message_ix_models/data/material/methanol/results_material_SSP2_comm.csv new file mode 100644 index 0000000000..27ca014012 --- /dev/null +++ b/message_ix_models/data/material/methanol/results_material_SSP2_comm.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a7de4b6d78bc2df7f028d9edf81e54e2ab5dccf483dd59e223a56b852c3ea57c +size 139700 diff --git a/message_ix_models/data/material/methanol/results_material_SSP2_residential.csv b/message_ix_models/data/material/methanol/results_material_SSP2_residential.csv new file mode 100644 index 0000000000..706337d76a --- /dev/null +++ b/message_ix_models/data/material/methanol/results_material_SSP2_residential.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2b6dce2f9fd2da6817ec69a002b9b69610e42aa6089bbf4d9d9c1f894366e120 +size 287785 diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index 6833a68a5b..cfec17cd91 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -9,14 +9,14 @@ from message_ix_models import ScenarioInfo from message_ix import make_df from message_ix_models.util import broadcast, same_node -from .util import read_config +from util import read_config context = read_config() def gen_data_methanol(scenario): df_pars = pd.read_excel( - context.get_local_path("material", "methanol_sensitivity_pars.xlsx"), + context.get_local_path("material", "methanol", "methanol_sensitivity_pars.xlsx"), sheet_name="Sheet1", dtype=object, ) @@ -29,7 +29,7 @@ def gen_data_methanol(scenario): new_dict = combine_df_dictionaries(new_dict, dict3) dict3 = pd.read_excel( - context.get_local_path("material", "meth_t_d_material_pars.xlsx"), + context.get_local_path("material", "methanol", "meth_t_d_material_pars.xlsx"), sheet_name=None, ) @@ -69,7 +69,7 @@ def gen_data_methanol(scenario): [new_dict2["historical_activity"], pd.DataFrame(row).T] ) df_ng = pd.read_excel( - context.get_local_path("material", "meth_ng_techno_economic.xlsx") + context.get_local_path("material", "methanol", "meth_ng_techno_economic.xlsx") ) new_dict2["historical_activity"] = pd.concat([new_dict2["historical_activity"], df_ng]) @@ -117,7 +117,7 @@ def gen_data_methanol(scenario): def gen_data_meth_h2(): context = read_config() df_h2 = pd.read_excel( - context.get_local_path("material", "meth_h2_techno_economic.xlsx"), + context.get_local_path("material", "methanol", "meth_h2_techno_economic.xlsx"), sheet_name=None, ) return df_h2 @@ -126,7 +126,7 @@ def gen_data_meth_h2(): def gen_data_meth_bio(scenario): context = read_config() df_bio = pd.read_excel( - context.get_local_path("material", "meth_bio_techno_economic.xlsx"), + context.get_local_path("material", "methanol", "meth_bio_techno_economic.xlsx"), sheet_name=None, ) coal_ratio = get_meth_bio_cost_ratio_2020(scenario, "meth_coal", "fix_cost") @@ -144,7 +144,7 @@ def gen_data_meth_bio(scenario): def gen_data_meth_chemicals(scenario, chemical): df = pd.read_excel( - context.get_local_path("material", "MTO data collection.xlsx"), + context.get_local_path("material", "methanol", "MTO data collection.xlsx"), sheet_name=chemical, usecols=[1, 2, 3, 4, 6, 7], ) @@ -219,7 +219,7 @@ def add_methanol_trp_additives(scenario): df_mtbe = pd.read_excel( context.get_local_path( - "material", "Methanol production statistics (version 1).xlsx" + "material", "methanol", "Methanol production statistics (version 1).xlsx" ), # usecols=[1,2,3,4,6,7], skiprows=np.linspace(0, 65, 66), @@ -232,7 +232,7 @@ def add_methanol_trp_additives(scenario): df_mtbe = df_mtbe[["node_loc", "methanol energy%"]] df_biodiesel = pd.read_excel( context.get_local_path( - "material", "Methanol production statistics (version 1).xlsx" + "material", "methanol", "Methanol production statistics (version 1).xlsx" ), skiprows=np.linspace(0, 37, 38), usecols=[1, 2], @@ -257,7 +257,7 @@ def get_meth_share(df, node): def gen_resin_demand(scenario, resin_share, sector, buildings_scen, pathway="SHAPE"): df = pd.read_csv( - context.get_local_path("material", "results_material_"+ pathway +"_" + sector + ".csv") + context.get_local_path("material", "methanol", "results_material_"+ pathway +"_" + sector + ".csv") ) resin_intensity = resin_share df = df[df["scenario"] == buildings_scen] @@ -305,7 +305,7 @@ def get_demand_t1_with_income_elasticity( ) + demand_t0 df_gdp = pd.read_excel( - context.get_local_path("material", "methanol demand.xlsx"), + context.get_local_path("material", "methanol", "methanol demand.xlsx"), sheet_name="GDP_baseline", ) @@ -313,7 +313,7 @@ def get_demand_t1_with_income_elasticity( df = df.dropna(axis=1) df_demand_meth = pd.read_excel( - context.get_local_path("material", "methanol demand.xlsx"), + context.get_local_path("material", "methanol", "methanol demand.xlsx"), sheet_name="methanol_demand", skiprows=[12], ) @@ -418,7 +418,7 @@ def gen_meth_bio_ccs(scenario): def add_meth_trade_historic(): par_dict_trade = pd.read_excel( - context.get_local_path("material", "meth_trd_pars.xlsx"), + context.get_local_path("material", "methanol", "meth_trd_pars.xlsx"), sheet_name=None, ) return par_dict_trade diff --git a/message_ix_models/model/material/meth_rc_all_unique_par_vals.xlsx b/message_ix_models/model/material/meth_rc_all_unique_par_vals.xlsx new file mode 100644 index 0000000000..fd67bf5bcb --- /dev/null +++ b/message_ix_models/model/material/meth_rc_all_unique_par_vals.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ae007002c8b593170db49e6968d42e5c5b6a53b614b11e62918704f812f434d0 +size 103431 From 89f66565079678cc09adae124f232851fa21b0c4 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Sat, 29 Oct 2022 22:21:22 +0200 Subject: [PATCH 497/774] Apply black formatting and reorder functions --- .../model/material/data_methanol.py | 209 ++++++++++++------ 1 file changed, 137 insertions(+), 72 deletions(-) diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index cfec17cd91..f4db24910a 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -16,7 +16,9 @@ def gen_data_methanol(scenario): df_pars = pd.read_excel( - context.get_local_path("material", "methanol", "methanol_sensitivity_pars.xlsx"), + context.get_local_path( + "material", "methanol", "methanol_sensitivity_pars.xlsx" + ), sheet_name="Sheet1", dtype=object, ) @@ -71,7 +73,9 @@ def gen_data_methanol(scenario): df_ng = pd.read_excel( context.get_local_path("material", "methanol", "meth_ng_techno_economic.xlsx") ) - new_dict2["historical_activity"] = pd.concat([new_dict2["historical_activity"], df_ng]) + new_dict2["historical_activity"] = pd.concat( + [new_dict2["historical_activity"], df_ng] + ) mto_dict = gen_data_meth_chemicals(scenario, "MTO") new_dict2 = combine_df_dictionaries(new_dict2, mto_dict) @@ -81,13 +85,23 @@ def gen_data_methanol(scenario): resin_dict = gen_data_meth_chemicals(scenario, "Resins") new_dict2 = combine_df_dictionaries(new_dict2, resin_dict) - df_comm = gen_resin_demand(scenario, pars["resin_share"], "comm", pars["wood_scenario"], pars["pathway"]) - df_resid = gen_resin_demand(scenario, pars["resin_share"], "residential", pars["wood_scenario"], pars["pathway"]) + df_comm = gen_resin_demand( + scenario, pars["resin_share"], "comm", pars["wood_scenario"], pars["pathway"] + ) + df_resid = gen_resin_demand( + scenario, + pars["resin_share"], + "residential", + pars["wood_scenario"], + pars["pathway"], + ) df_resin_demand = df_comm.copy(deep=True) df_resin_demand["value"] = df_comm["value"] + df_resid["value"] new_dict2["demand"] = pd.concat([new_dict2["demand"], df_resin_demand]) - new_dict2["input"] = pd.concat([new_dict2["input"], add_methanol_trp_additives(scenario)]) + new_dict2["input"] = pd.concat( + [new_dict2["input"], add_methanol_trp_additives(scenario)] + ) if pars["cbudget"]: emission_dict = { @@ -130,18 +144,77 @@ def gen_data_meth_bio(scenario): sheet_name=None, ) coal_ratio = get_meth_bio_cost_ratio_2020(scenario, "meth_coal", "fix_cost") - merge = df_bio["fix_cost"].merge(on=["node_loc", "year_vtg", "year_act"], - right=coal_ratio.drop(["technology", "unit", "value"], axis=1)) - df_bio["fix_cost"] = merge.assign(value=lambda x: x["value"] * x["ratio"]).drop("ratio", axis=1) + merge = df_bio["fix_cost"].merge( + on=["node_loc", "year_vtg", "year_act"], + right=coal_ratio.drop(["technology", "unit", "value"], axis=1), + ) + df_bio["fix_cost"] = merge.assign(value=lambda x: x["value"] * x["ratio"]).drop( + "ratio", axis=1 + ) coal_ratio = get_meth_bio_cost_ratio_2020(scenario, "meth_coal", "inv_cost") - merge = df_bio["inv_cost"].merge(on=["node_loc", "year_vtg"], - right=coal_ratio.drop(["technology", "unit", "value"], axis=1)) - df_bio["inv_cost"] = merge.assign(value=lambda x: x["value"] * x["ratio"]).drop("ratio", axis=1) + merge = df_bio["inv_cost"].merge( + on=["node_loc", "year_vtg"], + right=coal_ratio.drop(["technology", "unit", "value"], axis=1), + ) + df_bio["inv_cost"] = merge.assign(value=lambda x: x["value"] * x["ratio"]).drop( + "ratio", axis=1 + ) return df_bio +def gen_meth_bio_ccs(scenario): + par_dict = gen_data_meth_bio(scenario) + for k in par_dict.keys(): + par_dict[k]["technology"] = "meth_bio_ccs" + + df_bio = par_dict["inv_cost"] + df = scenario.par("inv_cost") + df_std = df[df["technology"] == "meth_coal"] + df_ccs = df[df["technology"] == "meth_coal_ccs"] + merge = df_std.merge( + on=["year_vtg", "node_loc"], right=df_ccs.drop(["technology", "unit"], axis=1) + ) + ratio = merge.assign(ratio=lambda x: x["value_x"] / x["value_y"]).drop( + ["value_x", "value_y"], axis=1 + ) + merge = df_bio.merge( + on=["year_vtg", "node_loc"], right=ratio.drop(["technology", "unit"], axis=1) + ) + merge = merge.assign(value=lambda x: x["value"] * x["ratio"]).drop( + ["ratio"], axis=1 + ) + par_dict["inv_cost"] = merge + + df_bio = par_dict["fix_cost"] + df = scenario.par("fix_cost") + df_std = df[df["technology"] == "meth_coal"] + df_ccs = df[df["technology"] == "meth_coal_ccs"] + merge = df_std.merge( + on=["node_loc", "year_vtg", "year_act"], + right=df_ccs.drop(["technology", "unit"], axis=1), + ) + ratio = merge.assign(ratio=lambda x: x["value_x"] / x["value_y"]).drop( + ["value_x", "value_y"], axis=1 + ) + merge = df_bio.merge( + on=["node_loc", "year_vtg", "year_act"], + right=ratio.drop(["technology", "unit"], axis=1), + ) + merge = merge.assign(value=lambda x: x["value"] * x["ratio"]).drop( + ["ratio"], axis=1 + ) + par_dict["fix_cost"] = merge + + par_dict["output"].loc[par_dict["output"]["commodity"] == "electr", "value"] = ( + par_dict["output"].loc[par_dict["output"]["commodity"] == "electr", "value"] + - 0.019231 + ) # from meth_coal_ccs + + return par_dict + + def gen_data_meth_chemicals(scenario, chemical): df = pd.read_excel( context.get_local_path("material", "methanol", "MTO data collection.xlsx"), @@ -178,10 +251,13 @@ def gen_data_meth_chemicals(scenario, chemical): par_dict = {k: pd.DataFrame() for k in (df["parameter"])} for i in df["parameter"]: for index, row in df[df["parameter"] == i].iterrows(): - par_dict[i] = pd.concat([par_dict[i], - make_df(i, **all_years.to_dict(orient="list"), **row, **common) - .pipe(broadcast, node_loc=nodes) - .pipe(same_node)] + par_dict[i] = pd.concat( + [ + par_dict[i], + make_df(i, **all_years.to_dict(orient="list"), **row, **common) + .pipe(broadcast, node_loc=nodes) + .pipe(same_node), + ] ) if i == "relation_activity": @@ -255,9 +331,47 @@ def get_meth_share(df, node): return pd.concat([df_loil, df_loil_meth]) +def add_meth_trade_historic(): + par_dict_trade = pd.read_excel( + context.get_local_path("material", "methanol", "meth_trd_pars.xlsx"), + sheet_name=None, + ) + return par_dict_trade + + +def update_methanol_costs(scenario): + df_inv = pd.concat( + [ + get_scaled_cost_from_proxy_tec( + 842, scenario, "meth_coal", "inv_cost", "meth_coal" + ), + get_scaled_cost_from_proxy_tec( + 350, scenario, "meth_ng", "inv_cost", "meth_ng" + ), + ] + ) + # get_scaled_cost_from_proxy_tec(4800, scenario, "meth_ng", "inv_cost", "meth_bio"), + df_fix = pd.concat( + [ + get_scaled_cost_from_proxy_tec( + 42.1, scenario, "meth_coal", "fix_cost", "meth_coal" + ), + get_scaled_cost_from_proxy_tec( + 8.75, scenario, "meth_ng", "fix_cost", "meth_ng" + ), + ] + ) + # get_scaled_cost_from_proxy_tec(290, scenario, "meth_ng", "fix_cost", "meth_bio")]) + return {"inv_cost": df_inv, "fix_cost": df_fix} + + def gen_resin_demand(scenario, resin_share, sector, buildings_scen, pathway="SHAPE"): df = pd.read_csv( - context.get_local_path("material", "methanol", "results_material_"+ pathway +"_" + sector + ".csv") + context.get_local_path( + "material", + "methanol", + "results_material_" + pathway + "_" + sector + ".csv", + ) ) resin_intensity = resin_share df = df[df["scenario"] == buildings_scen] @@ -356,13 +470,15 @@ def get_meth_bio_cost_ratio_2020(scenario, tec_name, cost_type): df = scenario.par(cost_type) df = df[df["technology"] == tec_name] - df= df[df["year_vtg"] >= 2020] - #if cost_type in ["fix_cost", "var_cost"]: + df = df[df["year_vtg"] >= 2020] + # if cost_type in ["fix_cost", "var_cost"]: # df = df[df["year_vtg"] == df["year_act"]] - val_nam_2020 = df.loc[(df["node_loc"]=="R12_NAM") & (df["year_vtg"]==2020), "value"].iloc[0] - df["ratio"] = df["value"]/ val_nam_2020 - return df#[["node_loc","year_vtg", "ratio"]] + val_nam_2020 = df.loc[ + (df["node_loc"] == "R12_NAM") & (df["year_vtg"] == 2020), "value" + ].iloc[0] + df["ratio"] = df["value"] / val_nam_2020 + return df # [["node_loc","year_vtg", "ratio"]] def get_scaled_cost_from_proxy_tec(value, scenario, proxy_tec, cost_type, new_tec): @@ -373,57 +489,6 @@ def get_scaled_cost_from_proxy_tec(value, scenario, proxy_tec, cost_type, new_te return message_ix.make_df(cost_type, **df) -def update_methanol_costs(scenario): - df_inv = pd.concat([ - get_scaled_cost_from_proxy_tec(842, scenario, "meth_coal", "inv_cost", "meth_coal"), - get_scaled_cost_from_proxy_tec(350, scenario, "meth_ng", "inv_cost", "meth_ng")]) - #get_scaled_cost_from_proxy_tec(4800, scenario, "meth_ng", "inv_cost", "meth_bio"), - df_fix = pd.concat([ - get_scaled_cost_from_proxy_tec(42.1, scenario, "meth_coal", "fix_cost", "meth_coal"), - get_scaled_cost_from_proxy_tec(8.75, scenario, "meth_ng", "fix_cost", "meth_ng")]) - #get_scaled_cost_from_proxy_tec(290, scenario, "meth_ng", "fix_cost", "meth_bio")]) - return {"inv_cost": df_inv, "fix_cost": df_fix} - - -def gen_meth_bio_ccs(scenario): - par_dict = gen_data_meth_bio(scenario) - for k in par_dict.keys(): - par_dict[k]["technology"] = "meth_bio_ccs" - - df_bio = par_dict["inv_cost"] - df = scenario.par("inv_cost") - df_std = df[df["technology"] == "meth_coal"] - df_ccs = df[df["technology"] == "meth_coal_ccs"] - merge = df_std.merge(on=["year_vtg", "node_loc"], right=df_ccs.drop(["technology", "unit"], axis=1)) - ratio = merge.assign(ratio=lambda x: x["value_x"] / x["value_y"]).drop(["value_x", "value_y"], axis=1) - merge = df_bio.merge(on=["year_vtg", "node_loc"], right=ratio.drop(["technology", "unit"], axis=1)) - merge = merge.assign(value=lambda x: x["value"] * x["ratio"]).drop(["ratio"], axis=1) - par_dict["inv_cost"] = merge - - df_bio = par_dict["fix_cost"] - df = scenario.par("fix_cost") - df_std = df[df["technology"] == "meth_coal"] - df_ccs = df[df["technology"] == "meth_coal_ccs"] - merge = df_std.merge(on=["node_loc", "year_vtg", "year_act"], right=df_ccs.drop(["technology", "unit"], axis=1)) - ratio = merge.assign(ratio=lambda x: x["value_x"] / x["value_y"]).drop(["value_x", "value_y"], axis=1) - merge = df_bio.merge(on=["node_loc", "year_vtg", "year_act"], right=ratio.drop(["technology", "unit"], axis=1)) - merge = merge.assign(value=lambda x: x["value"] * x["ratio"]).drop(["ratio"], axis=1) - par_dict["fix_cost"] = merge - - par_dict["output"].loc[par_dict["output"]["commodity"]=="electr", "value"] = \ - par_dict["output"].loc[par_dict["output"]["commodity"]=="electr", "value"] - 0.019231 # from meth_coal_ccs - - return par_dict - - -def add_meth_trade_historic(): - par_dict_trade = pd.read_excel( - context.get_local_path("material", "methanol", "meth_trd_pars.xlsx"), - sheet_name=None, - ) - return par_dict_trade - - def combine_df_dictionaries(dict1, dict2): keys = set(list(dict1.keys()) + list(dict2.keys())) new_dict = {} From 1a0c6c99015bd93c817f59643415c04985428320 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Sat, 29 Oct 2022 22:29:43 +0200 Subject: [PATCH 498/774] Resolve pycharm warnings --- message_ix_models/model/material/data_methanol.py | 11 ++--------- 1 file changed, 2 insertions(+), 9 deletions(-) diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index f4db24910a..d877c584e7 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -1,12 +1,7 @@ import message_ix -import message_data -import ixmp as ix - import pandas as pd import numpy as np -import matplotlib.pyplot as plt -from message_ix_models import ScenarioInfo from message_ix import make_df from message_ix_models.util import broadcast, same_node from util import read_config @@ -71,7 +66,8 @@ def gen_data_methanol(scenario): [new_dict2["historical_activity"], pd.DataFrame(row).T] ) df_ng = pd.read_excel( - context.get_local_path("material", "methanol", "meth_ng_techno_economic.xlsx") + context.get_local_path("material", "methanol", "meth_ng_techno_economic.xlsx"), + sheet_name="Sheet1" ) new_dict2["historical_activity"] = pd.concat( [new_dict2["historical_activity"], df_ng] @@ -129,7 +125,6 @@ def gen_data_methanol(scenario): def gen_data_meth_h2(): - context = read_config() df_h2 = pd.read_excel( context.get_local_path("material", "methanol", "meth_h2_techno_economic.xlsx"), sheet_name=None, @@ -138,7 +133,6 @@ def gen_data_meth_h2(): def gen_data_meth_bio(scenario): - context = read_config() df_bio = pd.read_excel( context.get_local_path("material", "methanol", "meth_bio_techno_economic.xlsx"), sheet_name=None, @@ -278,7 +272,6 @@ def gen_data_meth_chemicals(scenario, chemical): par_dict["historical_activity"] = make_df( "historical_activity", value=4.5, year_act=2015, **hist_dict ) - # par_dict["historical_new_capacity"] = make_df("historical_new_capacity", value=[1.2, 1.2], year_vtg=[2015, 2020], **hist_dict) par_dict["historical_new_capacity"] = make_df( "historical_new_capacity", value=1.2, year_vtg=2015, **hist_dict ) From d1b54f54d9a51620d1b49140013d495838d1cdb0 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Sun, 30 Oct 2022 16:14:06 +0100 Subject: [PATCH 499/774] Add new ammonia function to build --- message_ix_models/model/material/__init__.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 11d7bbdebe..118caafeaa 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -373,7 +373,8 @@ def run_old_reporting(scenario_name, model_name): DATA_FUNCTIONS_1 = [ #gen_data_buildings, gen_data_methanol, - gen_data_ammonia, + gen_all_NH3_fert, + #gen_data_ammonia, gen_data_generic, gen_data_steel, ] From e5bc5c8ef6872d113b7f15b758e8c677f4f400e4 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Sun, 30 Oct 2022 16:14:54 +0100 Subject: [PATCH 500/774] Set meth tecs cost update to false --- .../data/material/methanol/methanol_sensitivity_pars.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/methanol/methanol_sensitivity_pars.xlsx b/message_ix_models/data/material/methanol/methanol_sensitivity_pars.xlsx index 6444aed086..4d4fa6f38a 100644 --- a/message_ix_models/data/material/methanol/methanol_sensitivity_pars.xlsx +++ b/message_ix_models/data/material/methanol/methanol_sensitivity_pars.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:72a6cb39af205141790bf679c3b45d2fe3260bfd06826bf08d6c1f757d2ca098 -size 298 +oid sha256:b8652519f4d545b61704a677660a590ac46d1856fd2cbcf2db05f286c4b0ff69 +size 290 From 288c40e0c4f84564cca9fd982deac21087b1a7ff Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Mon, 31 Oct 2022 14:54:35 +0100 Subject: [PATCH 501/774] Fix meth_bio(_ccs) parametrization --- .../methanol/meth_bio_techno_economic.xlsx | 4 +- .../model/material/data_methanol.py | 57 +++++++++++-------- 2 files changed, 34 insertions(+), 27 deletions(-) diff --git a/message_ix_models/data/material/methanol/meth_bio_techno_economic.xlsx b/message_ix_models/data/material/methanol/meth_bio_techno_economic.xlsx index e22d97ecca..c7238d058b 100644 --- a/message_ix_models/data/material/methanol/meth_bio_techno_economic.xlsx +++ b/message_ix_models/data/material/methanol/meth_bio_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:15e7e2751524e080ebc1d635ebfc68c2460254ce7f489be3bdc6086f1c4c033e -size 177317 +oid sha256:e506a5d87c5cc200e7fd312bf89ad0a844467dfcbb4f2520bdb9db7df1cacf92 +size 1049812 diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index d877c584e7..d0945fa533 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -137,7 +137,7 @@ def gen_data_meth_bio(scenario): context.get_local_path("material", "methanol", "meth_bio_techno_economic.xlsx"), sheet_name=None, ) - coal_ratio = get_meth_bio_cost_ratio_2020(scenario, "meth_coal", "fix_cost") + coal_ratio = get_cost_ratio_2020(scenario, "meth_coal", "fix_cost")#.drop("value", axis=1) merge = df_bio["fix_cost"].merge( on=["node_loc", "year_vtg", "year_act"], right=coal_ratio.drop(["technology", "unit", "value"], axis=1), @@ -145,16 +145,17 @@ def gen_data_meth_bio(scenario): df_bio["fix_cost"] = merge.assign(value=lambda x: x["value"] * x["ratio"]).drop( "ratio", axis=1 ) - - coal_ratio = get_meth_bio_cost_ratio_2020(scenario, "meth_coal", "inv_cost") - merge = df_bio["inv_cost"].merge( - on=["node_loc", "year_vtg"], - right=coal_ratio.drop(["technology", "unit", "value"], axis=1), - ) - df_bio["inv_cost"] = merge.assign(value=lambda x: x["value"] * x["ratio"]).drop( - "ratio", axis=1 - ) - + df_bio_inv = df_bio["inv_cost"] + for y in df_bio["inv_cost"]["year_vtg"].unique(): + coal_ratio = get_cost_ratio_2020(scenario, "meth_coal", "inv_cost", ref_reg="R12_WEU", year=y).drop("value", axis=1) + merge = df_bio_inv.loc[df_bio_inv["year_vtg"] == y].merge( + on=["node_loc", "year_vtg"], + right=coal_ratio.drop(["technology", "unit"], axis=1), + ) + df_bio_inv.loc[df_bio_inv["year_vtg"] == y, "value"] = merge.assign(value=lambda x: x["value"] * x["ratio"]).drop( + "ratio", axis=1 + )["value"].values + df_bio["inv_cost"] = df_bio_inv return df_bio @@ -176,7 +177,7 @@ def gen_meth_bio_ccs(scenario): merge = df_bio.merge( on=["year_vtg", "node_loc"], right=ratio.drop(["technology", "unit"], axis=1) ) - merge = merge.assign(value=lambda x: x["value"] * x["ratio"]).drop( + merge = merge.assign(value=lambda x: x["value"] / x["ratio"]).drop( ["ratio"], axis=1 ) par_dict["inv_cost"] = merge @@ -196,7 +197,7 @@ def gen_meth_bio_ccs(scenario): on=["node_loc", "year_vtg", "year_act"], right=ratio.drop(["technology", "unit"], axis=1), ) - merge = merge.assign(value=lambda x: x["value"] * x["ratio"]).drop( + merge = merge.assign(value=lambda x: x["value"] / x["ratio"]).drop( ["ratio"], axis=1 ) par_dict["fix_cost"] = merge @@ -205,7 +206,9 @@ def gen_meth_bio_ccs(scenario): par_dict["output"].loc[par_dict["output"]["commodity"] == "electr", "value"] - 0.019231 ) # from meth_coal_ccs - + for par in ["input", "output"]: + df = par_dict[par] + par_dict[par] = df[df["year_act"] > 2025] return par_dict @@ -459,23 +462,27 @@ def get_demand_t1_with_income_elasticity( ) -def get_meth_bio_cost_ratio_2020(scenario, tec_name, cost_type): +def get_cost_ratio_2020(scenario, tec_name, cost_type, ref_reg="R12_NAM", year="all"): - df = scenario.par(cost_type) - df = df[df["technology"] == tec_name] - df = df[df["year_vtg"] >= 2020] - # if cost_type in ["fix_cost", "var_cost"]: - # df = df[df["year_vtg"] == df["year_act"]] + df = scenario.par(cost_type, filters={"technology": tec_name}) + if year == "all": + df = df[df["year_vtg"] >= 2020] + val_nam_2020 = df.loc[ + (df["node_loc"] == ref_reg) & (df["year_vtg"] == 2020), "value" + ].iloc[0] + df["ratio"] = df["value"] / val_nam_2020 + else: + df = df[df["year_vtg"] == year] + val_nam_2020 = df.loc[ + (df["node_loc"] == ref_reg) & (df["year_vtg"] == year), "value" + ].iloc[0] + df["ratio"] = df["value"] / val_nam_2020 - val_nam_2020 = df.loc[ - (df["node_loc"] == "R12_NAM") & (df["year_vtg"] == 2020), "value" - ].iloc[0] - df["ratio"] = df["value"] / val_nam_2020 return df # [["node_loc","year_vtg", "ratio"]] def get_scaled_cost_from_proxy_tec(value, scenario, proxy_tec, cost_type, new_tec): - df = get_meth_bio_cost_ratio_2020(scenario, proxy_tec, cost_type) + df = get_cost_ratio_2020(scenario, proxy_tec, cost_type) df["value"] = value * df["ratio"] df["technology"] = new_tec df["unit"] = "-" From a2d05062a755e056f4db906a9a4c2afb76902f13 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Mon, 31 Oct 2022 14:55:35 +0100 Subject: [PATCH 502/774] Fix fuel additives calculation --- .../Methanol production statistics (version 1).xlsx | 4 ++-- message_ix_models/model/material/data_methanol.py | 13 ++++++++----- 2 files changed, 10 insertions(+), 7 deletions(-) diff --git a/message_ix_models/data/material/methanol/Methanol production statistics (version 1).xlsx b/message_ix_models/data/material/methanol/Methanol production statistics (version 1).xlsx index 7d79a896e7..de93d5987b 100644 --- a/message_ix_models/data/material/methanol/Methanol production statistics (version 1).xlsx +++ b/message_ix_models/data/material/methanol/Methanol production statistics (version 1).xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:a86f38bed888551367be25bec135d77522b3a1001e1ecfb893d6651ba6c9c794 -size 3587789 +oid sha256:35ae3ccaa7b3a9682cf655e708da7552399edb53d82bf37f5c086429af3a1c3f +size 4620708 diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index d0945fa533..dfe59a2057 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -4,7 +4,7 @@ from message_ix import make_df from message_ix_models.util import broadcast, same_node -from util import read_config +from .util import read_config context = read_config() @@ -82,7 +82,7 @@ def gen_data_methanol(scenario): new_dict2 = combine_df_dictionaries(new_dict2, resin_dict) df_comm = gen_resin_demand( - scenario, pars["resin_share"], "comm", pars["wood_scenario"], pars["pathway"] + scenario, pars["resin_share"], "comm", "SH2", "SHAPE", #pars["wood_scenario"], #pars["pathway"] ) df_resid = gen_resin_demand( scenario, @@ -301,7 +301,8 @@ def add_methanol_trp_additives(scenario): 1:13, ] df_mtbe["node_loc"] = "R12_" + df_mtbe["node_loc"] - df_mtbe = df_mtbe[["node_loc", "methanol energy%"]] + #df_mtbe = df_mtbe[["node_loc", "methanol energy%"]] + df_mtbe = df_mtbe[["node_loc", "% share on trp"]] df_biodiesel = pd.read_excel( context.get_local_path( "material", "methanol", "Methanol production statistics (version 1).xlsx" @@ -312,10 +313,12 @@ def add_methanol_trp_additives(scenario): ) df_biodiesel["node_loc"] = "R12_" + df_biodiesel["node_loc"] df_total = df_biodiesel.merge(df_mtbe) + #df_total = df_total.assign( + # value=lambda x: (x["methanol energy %"] + x["methanol energy%"]) + #) df_total = df_total.assign( - value=lambda x: x["methanol energy %"] + x["methanol energy%"] + value=lambda x: (x["methanol energy %"] + x["% share on trp"]) ) - def get_meth_share(df, node): return df[df["node_loc"] == node["node_loc"]]["value"].values[0] From 95bdf969a61e28bc5a30112ff73695e6de153b51 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 3 Nov 2022 11:34:03 +0100 Subject: [PATCH 503/774] Set sensitivity parameters --- .../data/material/methanol/methanol_sensitivity_pars.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/methanol/methanol_sensitivity_pars.xlsx b/message_ix_models/data/material/methanol/methanol_sensitivity_pars.xlsx index 4d4fa6f38a..a8bc01dee1 100644 --- a/message_ix_models/data/material/methanol/methanol_sensitivity_pars.xlsx +++ b/message_ix_models/data/material/methanol/methanol_sensitivity_pars.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:b8652519f4d545b61704a677660a590ac46d1856fd2cbcf2db05f286c4b0ff69 -size 290 +oid sha256:77f4304ae7f973b0c49b9d130c4012ee05ced1bc4dd2613dc9cfbd9a8f1adde0 +size 282 From 7cbb1ab74c3c83889846a9b21177ffdeafe1f083 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 3 Nov 2022 12:44:34 +0100 Subject: [PATCH 504/774] Update hvc technology costs --- .../data/material/petrochemicals_techno_economic.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx index b1d79c235d..92407bd0f1 100644 --- a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx +++ b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:7dfbc79999a063ae70c0558954f9d4e74009ea04dd0d4226ccc953aa5117e60e -size 663470 +oid sha256:cbdf2868113b755bebb809e385ba9cd34d079519d56d68a7ed628ac5dd129f27 +size 663700 From 08073c43c223921861f00bef33fe4c7fe0ed2f6b Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 10 Nov 2022 16:51:21 +0100 Subject: [PATCH 505/774] Update methanol costs --- .../model/material/data_methanol.py | 33 ++++++++++++++----- 1 file changed, 25 insertions(+), 8 deletions(-) diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index dfe59a2057..e076a1e7d4 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -22,13 +22,14 @@ def gen_data_methanol(scenario): dict1 = gen_data_meth_h2() dict2 = gen_data_meth_bio(scenario) new_dict = combine_df_dictionaries(dict1, dict2) - dict3 = gen_meth_bio_ccs(scenario) - new_dict = combine_df_dictionaries(new_dict, dict3) + #dict3 = gen_meth_bio_ccs(scenario) + #new_dict = combine_df_dictionaries(new_dict, dict3) dict3 = pd.read_excel( context.get_local_path("material", "methanol", "meth_t_d_material_pars.xlsx"), sheet_name=None, ) + dict3.pop("relation_activity") # remove negative emissions for now new_dict2 = combine_df_dictionaries(new_dict, dict3) @@ -102,7 +103,7 @@ def gen_data_methanol(scenario): if pars["cbudget"]: emission_dict = { "node": "World", - "type_emission": "TCE_CO2", + "type_emission": "TCE", "type_tec": "all", "type_year": "cumulative", "unit": "???", @@ -347,9 +348,14 @@ def update_methanol_costs(scenario): get_scaled_cost_from_proxy_tec( 350, scenario, "meth_ng", "inv_cost", "meth_ng" ), + get_scaled_cost_from_proxy_tec( + 500, scenario, "meth_ng_ccs", "inv_cost", "meth_ng_ccs" + ), + get_scaled_cost_from_proxy_tec( + 1430, scenario, "meth_coal_ccs", "inv_cost", "meth_coal_ccs" + ), ] ) - # get_scaled_cost_from_proxy_tec(4800, scenario, "meth_ng", "inv_cost", "meth_bio"), df_fix = pd.concat( [ get_scaled_cost_from_proxy_tec( @@ -358,8 +364,15 @@ def update_methanol_costs(scenario): get_scaled_cost_from_proxy_tec( 8.75, scenario, "meth_ng", "fix_cost", "meth_ng" ), + get_scaled_cost_from_proxy_tec( + 12.5, scenario, "meth_ng_ccs", "fix_cost", "meth_ng_ccs" + ), + get_scaled_cost_from_proxy_tec( + 67, scenario, "meth_coal_ccs", "fix_cost", "meth_coal_ccs" + ), ] ) + df_inv["unit"] = "-" # get_scaled_cost_from_proxy_tec(290, scenario, "meth_ng", "fix_cost", "meth_bio")]) return {"inv_cost": df_inv, "fix_cost": df_fix} @@ -469,9 +482,13 @@ def get_cost_ratio_2020(scenario, tec_name, cost_type, ref_reg="R12_NAM", year=" df = scenario.par(cost_type, filters={"technology": tec_name}) if year == "all": - df = df[df["year_vtg"] >= 2020] + if 2020 in df.year_vtg.unique(): + ref_year = 2020 + else: + ref_year = min(df.year_vtg.unique()) + df = df[df["year_vtg"] >= ref_year] val_nam_2020 = df.loc[ - (df["node_loc"] == ref_reg) & (df["year_vtg"] == 2020), "value" + (df["node_loc"] == ref_reg) & (df["year_vtg"] == ref_year), "value" ].iloc[0] df["ratio"] = df["value"] / val_nam_2020 else: @@ -484,8 +501,8 @@ def get_cost_ratio_2020(scenario, tec_name, cost_type, ref_reg="R12_NAM", year=" return df # [["node_loc","year_vtg", "ratio"]] -def get_scaled_cost_from_proxy_tec(value, scenario, proxy_tec, cost_type, new_tec): - df = get_cost_ratio_2020(scenario, proxy_tec, cost_type) +def get_scaled_cost_from_proxy_tec(value, scenario, proxy_tec, cost_type, new_tec, year="all"): + df = get_cost_ratio_2020(scenario, proxy_tec, cost_type, year=year) df["value"] = value * df["ratio"] df["technology"] = new_tec df["unit"] = "-" From 8185c3331b7a8c31e353005f344e6071ae077c01 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 10 Nov 2022 16:51:45 +0100 Subject: [PATCH 506/774] Set sensitivity pars --- .../data/material/methanol/methanol_sensitivity_pars.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/methanol/methanol_sensitivity_pars.xlsx b/message_ix_models/data/material/methanol/methanol_sensitivity_pars.xlsx index a8bc01dee1..4368219cf5 100644 --- a/message_ix_models/data/material/methanol/methanol_sensitivity_pars.xlsx +++ b/message_ix_models/data/material/methanol/methanol_sensitivity_pars.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:77f4304ae7f973b0c49b9d130c4012ee05ced1bc4dd2613dc9cfbd9a8f1adde0 -size 282 +oid sha256:5b88e2f78961d59ac69103b4ec9f29c96cbf80209d639eda552bc087dd4abc7c +size 276 From 54ce596cbd706f4a315f038c7e414aa0b563a03f Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 17 Nov 2022 22:39:38 +0100 Subject: [PATCH 507/774] Add reporting formatting for scenario upload --- message_ix_models/model/material/__init__.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 118caafeaa..4be68f03be 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -2,9 +2,9 @@ import click import logging - import ixmp - +import os +from pathlib import Path # from .build import apply_spec # from message_data.tools import ScenarioInfo from message_data.model.material.build import apply_spec @@ -325,6 +325,9 @@ def run_reporting(context, remove_ts): merge_ts=True, run_config="materials_run_config.yaml", ) + util.prepare_xlsx_for_explorer( + Path(os.getcwd()).parents[3].joinpath( + "reporting_output", scenario.model+scenario.scenario+".xlsx")) @cli.command("report-2") From f6e7cf45791f8f8b234e5ca7c28276d1f9a0c2c6 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 17 Nov 2022 22:40:43 +0100 Subject: [PATCH 508/774] Add diffusion constraints for mto --- .../data/material/methanol/MTO data collection.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/methanol/MTO data collection.xlsx b/message_ix_models/data/material/methanol/MTO data collection.xlsx index 10fffe74bb..a87b0dcc6f 100644 --- a/message_ix_models/data/material/methanol/MTO data collection.xlsx +++ b/message_ix_models/data/material/methanol/MTO data collection.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:6831d9207dfefadd51fb2c01122687580f3a14f0bb5b086b33c0068cb6cc980e -size 299 +oid sha256:c0839e30217c36d84cd06239c432d6d67c81d4790da110c14165cd8905197886 +size 290 From 3eadfb4269fdf8c24f47341c69b5b8ba17db92ce Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 17 Nov 2022 22:41:38 +0100 Subject: [PATCH 509/774] Add reporting formatting function --- message_ix_models/model/material/util.py | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/message_ix_models/model/material/util.py b/message_ix_models/model/material/util.py index 915aee386f..d8335dc876 100644 --- a/message_ix_models/model/material/util.py +++ b/message_ix_models/model/material/util.py @@ -44,3 +44,18 @@ def read_config(): # ) return context + + +def prepare_xlsx_for_explorer(filepath): + import pandas as pd + df = pd.read_excel(filepath) + + def add_R12(str): + if len(str) < 5: + return "R12_" + str + else: + return str + + df = df[~df["Region"].isna()] + df["Region"] = df["Region"].map(add_R12) + df.to_excel(filepath, index=False) From 926fa8729a43c4b270e6100092093713ec12a2e9 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 17 Nov 2022 22:42:14 +0100 Subject: [PATCH 510/774] Add missing years to methanol trade --- .../data/material/methanol/meth_trade_additions.xlsx | 3 +++ message_ix_models/model/material/data_methanol.py | 5 +++++ 2 files changed, 8 insertions(+) create mode 100644 message_ix_models/data/material/methanol/meth_trade_additions.xlsx diff --git a/message_ix_models/data/material/methanol/meth_trade_additions.xlsx b/message_ix_models/data/material/methanol/meth_trade_additions.xlsx new file mode 100644 index 0000000000..9657515eac --- /dev/null +++ b/message_ix_models/data/material/methanol/meth_trade_additions.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:149ba30dd92d91ec3dc28f353a28c5c9b51e327ef9a395d28a61ae0e9ebb6961 +size 200702 diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index e076a1e7d4..5acb1c63c7 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -336,6 +336,11 @@ def add_meth_trade_historic(): context.get_local_path("material", "methanol", "meth_trd_pars.xlsx"), sheet_name=None, ) + par_dict_trade2 = pd.read_excel( + context.get_local_path("material", "methanol", "meth_trade_additions.xlsx"), + sheet_name=None, + ) + par_dict_trade = combine_df_dictionaries(par_dict_trade, par_dict_trade2) return par_dict_trade From d66a97f84bc92bec8280e3849238227cb5b14381 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 17 Nov 2022 22:42:35 +0100 Subject: [PATCH 511/774] Fix unit issue with methanol tecs --- message_ix_models/model/material/data_methanol.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index 5acb1c63c7..6eecd04f03 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -377,7 +377,8 @@ def update_methanol_costs(scenario): ), ] ) - df_inv["unit"] = "-" + df_inv["unit"] = "USD/GWa" + df_fix["unit"] = "USD/GWa" # get_scaled_cost_from_proxy_tec(290, scenario, "meth_ng", "fix_cost", "meth_bio")]) return {"inv_cost": df_inv, "fix_cost": df_fix} From 1322c32602c4294293a4a3ee174febf18c574e44 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 24 Nov 2022 16:06:06 +0100 Subject: [PATCH 512/774] Remove cbudget option from methanol_sensitivity_pars.xlsx --- .../data/material/methanol/methanol_sensitivity_pars.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/methanol/methanol_sensitivity_pars.xlsx b/message_ix_models/data/material/methanol/methanol_sensitivity_pars.xlsx index 4368219cf5..63e1117eac 100644 --- a/message_ix_models/data/material/methanol/methanol_sensitivity_pars.xlsx +++ b/message_ix_models/data/material/methanol/methanol_sensitivity_pars.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:5b88e2f78961d59ac69103b4ec9f29c96cbf80209d639eda552bc087dd4abc7c -size 276 +oid sha256:0b37730d9ebf976f33d634c9cdfaae74c3d35998d24559ee42a3eb577364a50b +size 313 From 6cf8d01b412d849aa4ac4eebba2b690ca1aa2352 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 24 Nov 2022 16:07:10 +0100 Subject: [PATCH 513/774] Remove duplicate rows in meth_trd_pars.xlsx --- message_ix_models/data/material/methanol/meth_trd_pars.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/methanol/meth_trd_pars.xlsx b/message_ix_models/data/material/methanol/meth_trd_pars.xlsx index a74cb099b8..0d00b31670 100644 --- a/message_ix_models/data/material/methanol/meth_trd_pars.xlsx +++ b/message_ix_models/data/material/methanol/meth_trd_pars.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:54e303f8887902b1241da714ab94e9f886378b3513021ec3afd171152885a70c -size 15004 +oid sha256:c145135ef3b4f7e14c3bce6080adb29d1816d4c66cc747171afff0cbf7aa4e72 +size 19065 From 54c37ed893f9303fab94279f70f708ec2f48345b Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 24 Nov 2022 16:24:10 +0100 Subject: [PATCH 514/774] Fix china mto baseyear activity --- .../model/material/data_methanol.py | 24 +++++++++---------- 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index 6eecd04f03..c93f29a7ed 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -100,18 +100,6 @@ def gen_data_methanol(scenario): [new_dict2["input"], add_methanol_trp_additives(scenario)] ) - if pars["cbudget"]: - emission_dict = { - "node": "World", - "type_emission": "TCE", - "type_tec": "all", - "type_year": "cumulative", - "unit": "???", - } - new_dict2["bound_emission"] = make_df( - "bound_emission", value=3667, **emission_dict - ) - df = scenario.par("input", filters={"technology": "meth_t_d"}) df["value"] = 1 new_dict2["input"] = pd.concat([new_dict2["input"], df]) @@ -282,6 +270,15 @@ def gen_data_meth_chemicals(scenario, chemical): par_dict["bound_total_capacity_lo"] = make_df( "bound_total_capacity_lo", year_act=2020, value=9, **hist_dict ) + par_dict["bound_activity_lo"] = make_df( + "bound_activity_lo", year_act=2020, value=8, **hist_dict + ) + df = par_dict["growth_activity_lo"] + par_dict["growth_activity_lo"] = df[~((df["node_loc"] == "R12_CHN") & ((df["year_act"] == 2020)))] + df = par_dict["growth_activity_up"] + par_dict["growth_activity_up"] = df[~((df["node_loc"] == "R12_CHN") & ((df["year_act"] == 2020)))] + #par_dict.pop("growth_activity_up") + #par_dict.pop("growth_activity_lo") return par_dict @@ -340,6 +337,9 @@ def add_meth_trade_historic(): context.get_local_path("material", "methanol", "meth_trade_additions.xlsx"), sheet_name=None, ) + df = par_dict_trade["historical_new_capacity"] + df.loc[df["value"] < 0, "value"] = 0 + par_dict_trade["historical_new_capacity"] = df[(df["technology"] != "meth_imp")] par_dict_trade = combine_df_dictionaries(par_dict_trade, par_dict_trade2) return par_dict_trade From 6aedd2a309c5970c62283b2dc0b5d60978715138 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 24 Nov 2022 16:24:59 +0100 Subject: [PATCH 515/774] Add missing years of meth_ng and meth_coal --- message_ix_models/model/material/data_methanol.py | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index c93f29a7ed..4c2b62d4f8 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -521,3 +521,15 @@ def combine_df_dictionaries(dict1, dict2): for i in keys: new_dict[i] = pd.concat([dict1.get(i), dict2.get(i)]) return new_dict + + +def add_meth_tec_vintages(): + par_dict_trade = pd.read_excel( + context.get_local_path("material", "methanol", "meth_ng_additions.xlsx"), + sheet_name=None, + ) + par_dict_trade2 = pd.read_excel( + context.get_local_path("material", "methanol", "meth_coal_additions.xlsx"), + sheet_name=None, + ) + return combine_df_dictionaries(par_dict_trade, par_dict_trade2) \ No newline at end of file From 3eda40ad10885536ddd96c7b71ebc35b95e9101b Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 24 Nov 2022 16:27:16 +0100 Subject: [PATCH 516/774] Add new input files --- .../data/material/methanol/meth_bio_techno_economic_new.xlsx | 3 +++ .../material/methanol/meth_bio_techno_economic_new_ccs.xlsx | 3 +++ .../data/material/methanol/meth_coal_additions.xlsx | 3 +++ .../data/material/methanol/meth_ng_additions.xlsx | 3 +++ message_ix_models/model/material/meth_trade_additions.xlsx | 3 +++ 5 files changed, 15 insertions(+) create mode 100644 message_ix_models/data/material/methanol/meth_bio_techno_economic_new.xlsx create mode 100644 message_ix_models/data/material/methanol/meth_bio_techno_economic_new_ccs.xlsx create mode 100644 message_ix_models/data/material/methanol/meth_coal_additions.xlsx create mode 100644 message_ix_models/data/material/methanol/meth_ng_additions.xlsx create mode 100644 message_ix_models/model/material/meth_trade_additions.xlsx diff --git a/message_ix_models/data/material/methanol/meth_bio_techno_economic_new.xlsx b/message_ix_models/data/material/methanol/meth_bio_techno_economic_new.xlsx new file mode 100644 index 0000000000..8bcf6de21d --- /dev/null +++ b/message_ix_models/data/material/methanol/meth_bio_techno_economic_new.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5beb6e994ff03624851f0fabc556cb5efe0dd2f64ce1f438c73be6d3a42e5a4b +size 1085779 diff --git a/message_ix_models/data/material/methanol/meth_bio_techno_economic_new_ccs.xlsx b/message_ix_models/data/material/methanol/meth_bio_techno_economic_new_ccs.xlsx new file mode 100644 index 0000000000..ec558a32fd --- /dev/null +++ b/message_ix_models/data/material/methanol/meth_bio_techno_economic_new_ccs.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f73e4c12ad52ddc9ca100f73a8139f2b05733f4b1d735b766596dca5c4c785b5 +size 1064016 diff --git a/message_ix_models/data/material/methanol/meth_coal_additions.xlsx b/message_ix_models/data/material/methanol/meth_coal_additions.xlsx new file mode 100644 index 0000000000..3b93d7dbdf --- /dev/null +++ b/message_ix_models/data/material/methanol/meth_coal_additions.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9731c1f8ccffbcd5e7c5584d7faeddb7b0d41a3ddb1885cfc5491f4079afe270 +size 93875 diff --git a/message_ix_models/data/material/methanol/meth_ng_additions.xlsx b/message_ix_models/data/material/methanol/meth_ng_additions.xlsx new file mode 100644 index 0000000000..4cd3e233e3 --- /dev/null +++ b/message_ix_models/data/material/methanol/meth_ng_additions.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:100ae351c706f53225182a82e2da14c60120deea1b1e29a1bf57b8afe705543d +size 97255 diff --git a/message_ix_models/model/material/meth_trade_additions.xlsx b/message_ix_models/model/material/meth_trade_additions.xlsx new file mode 100644 index 0000000000..9657515eac --- /dev/null +++ b/message_ix_models/model/material/meth_trade_additions.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:149ba30dd92d91ec3dc28f353a28c5c9b51e327ef9a395d28a61ae0e9ebb6961 +size 200702 From e478aa66ba785645dcaa07b80b0ec83ed93cd893 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Sat, 26 Nov 2022 15:53:46 +0100 Subject: [PATCH 517/774] Add meth_bio_ccs technology --- .../meth_bio_techno_economic_new_ccs.xlsx | 4 +- .../model/material/data_methanol.py | 70 ++++++------------- 2 files changed, 24 insertions(+), 50 deletions(-) diff --git a/message_ix_models/data/material/methanol/meth_bio_techno_economic_new_ccs.xlsx b/message_ix_models/data/material/methanol/meth_bio_techno_economic_new_ccs.xlsx index ec558a32fd..c9670fd85a 100644 --- a/message_ix_models/data/material/methanol/meth_bio_techno_economic_new_ccs.xlsx +++ b/message_ix_models/data/material/methanol/meth_bio_techno_economic_new_ccs.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:f73e4c12ad52ddc9ca100f73a8139f2b05733f4b1d735b766596dca5c4c785b5 -size 1064016 +oid sha256:51aaf4ed1cc7ee675ad1c7e3eee48cebf00986e1341c095fabc06cd928f2372a +size 1079654 diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index 4c2b62d4f8..148ca30689 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -22,8 +22,8 @@ def gen_data_methanol(scenario): dict1 = gen_data_meth_h2() dict2 = gen_data_meth_bio(scenario) new_dict = combine_df_dictionaries(dict1, dict2) - #dict3 = gen_meth_bio_ccs(scenario) - #new_dict = combine_df_dictionaries(new_dict, dict3) + dict_bio_ccs = gen_meth_bio_ccs(scenario) + new_dict = combine_df_dictionaries(new_dict, dict_bio_ccs) dict3 = pd.read_excel( context.get_local_path("material", "methanol", "meth_t_d_material_pars.xlsx"), @@ -149,56 +149,30 @@ def gen_data_meth_bio(scenario): def gen_meth_bio_ccs(scenario): - par_dict = gen_data_meth_bio(scenario) - for k in par_dict.keys(): - par_dict[k]["technology"] = "meth_bio_ccs" - - df_bio = par_dict["inv_cost"] - df = scenario.par("inv_cost") - df_std = df[df["technology"] == "meth_coal"] - df_ccs = df[df["technology"] == "meth_coal_ccs"] - merge = df_std.merge( - on=["year_vtg", "node_loc"], right=df_ccs.drop(["technology", "unit"], axis=1) - ) - ratio = merge.assign(ratio=lambda x: x["value_x"] / x["value_y"]).drop( - ["value_x", "value_y"], axis=1 - ) - merge = df_bio.merge( - on=["year_vtg", "node_loc"], right=ratio.drop(["technology", "unit"], axis=1) - ) - merge = merge.assign(value=lambda x: x["value"] / x["ratio"]).drop( - ["ratio"], axis=1 - ) - par_dict["inv_cost"] = merge - - df_bio = par_dict["fix_cost"] - df = scenario.par("fix_cost") - df_std = df[df["technology"] == "meth_coal"] - df_ccs = df[df["technology"] == "meth_coal_ccs"] - merge = df_std.merge( - on=["node_loc", "year_vtg", "year_act"], - right=df_ccs.drop(["technology", "unit"], axis=1), - ) - ratio = merge.assign(ratio=lambda x: x["value_x"] / x["value_y"]).drop( - ["value_x", "value_y"], axis=1 + df_bio = pd.read_excel( + context.get_local_path("material", "methanol", "meth_bio_techno_economic_new_ccs.xlsx"), + sheet_name=None, ) - merge = df_bio.merge( + coal_ratio = get_cost_ratio_2020(scenario, "meth_coal_ccs", "fix_cost")#.drop("value", axis=1) + merge = df_bio["fix_cost"].merge( on=["node_loc", "year_vtg", "year_act"], - right=ratio.drop(["technology", "unit"], axis=1), + right=coal_ratio.drop(["technology", "unit", "value"], axis=1), ) - merge = merge.assign(value=lambda x: x["value"] / x["ratio"]).drop( - ["ratio"], axis=1 + df_bio["fix_cost"] = merge.assign(value=lambda x: x["value"] * x["ratio"]).drop( + "ratio", axis=1 ) - par_dict["fix_cost"] = merge - - par_dict["output"].loc[par_dict["output"]["commodity"] == "electr", "value"] = ( - par_dict["output"].loc[par_dict["output"]["commodity"] == "electr", "value"] - - 0.019231 - ) # from meth_coal_ccs - for par in ["input", "output"]: - df = par_dict[par] - par_dict[par] = df[df["year_act"] > 2025] - return par_dict + df_bio_inv = df_bio["inv_cost"] + for y in df_bio["inv_cost"]["year_vtg"].unique(): + coal_ratio = get_cost_ratio_2020(scenario, "meth_coal_ccs", "inv_cost", ref_reg="R12_WEU", year=y).drop("value", axis=1) + merge = df_bio_inv.loc[df_bio_inv["year_vtg"] == y].merge( + on=["node_loc", "year_vtg"], + right=coal_ratio.drop(["technology", "unit"], axis=1), + ) + df_bio_inv.loc[df_bio_inv["year_vtg"] == y, "value"] = merge.assign(value=lambda x: x["value"] * x["ratio"]).drop( + "ratio", axis=1 + )["value"].values + df_bio["inv_cost"] = df_bio_inv + return df_bio def gen_data_meth_chemicals(scenario, chemical): From 0ca1bb05232732d82dc23bcb8ad07231517b795d Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Sat, 26 Nov 2022 15:55:02 +0100 Subject: [PATCH 518/774] Add embodied emissions for chemicals --- .../material/methanol/meth_t_d_material_pars.xlsx | 4 ++-- message_ix_models/model/material/data_methanol.py | 11 ++++++++++- 2 files changed, 12 insertions(+), 3 deletions(-) diff --git a/message_ix_models/data/material/methanol/meth_t_d_material_pars.xlsx b/message_ix_models/data/material/methanol/meth_t_d_material_pars.xlsx index 13ce78c622..378b5de563 100644 --- a/message_ix_models/data/material/methanol/meth_t_d_material_pars.xlsx +++ b/message_ix_models/data/material/methanol/meth_t_d_material_pars.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:44917c7b193626c3af2bd777c393d572cc3e34f9770c180ced68fc683e05389a -size 295 +oid sha256:17aa27097b99ee35e45344f74232e3c9609868962d960ea924d7b14f146837bc +size 292 diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index 148ca30689..4a67ff1b55 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -29,8 +29,17 @@ def gen_data_methanol(scenario): context.get_local_path("material", "methanol", "meth_t_d_material_pars.xlsx"), sheet_name=None, ) - dict3.pop("relation_activity") # remove negative emissions for now + df_rel = dict3["relation_activity"] + def get_embodied_emi(row, pars): + if row["year_act"] < pars["incin_trend_end"]: + share = pars["incin_rate"] + pars["incin_trend"] * (row["year_act"] - 2020) + else: + share = 0.5 + return row["value"] * (1 - share) * pars["hvc_plastics_share"] + + df_rel["value"] = df_rel.apply(lambda x: get_embodied_emi(x, pars), axis=1) + # dict3.pop("relation_activity") # remove negative emissions for now new_dict2 = combine_df_dictionaries(new_dict, dict3) df_final = gen_meth_residual_demand(pars["methanol_elasticity"]) From 132da34a2c26bc16ce245b018fbb7866d9608dd4 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Sat, 26 Nov 2022 15:56:16 +0100 Subject: [PATCH 519/774] Remove old end use methanol tecs and add conventional meth tec missing years --- message_ix_models/model/material/data_methanol.py | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index 4a67ff1b55..87e1601bcd 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -118,6 +118,19 @@ def get_embodied_emi(row, pars): new_dict2 = combine_df_dictionaries(new_dict2, cost_dict) new_dict2 = combine_df_dictionaries(new_dict2, add_meth_trade_historic()) + new_dict2 = combine_df_dictionaries(add_meth_tec_vintages(), new_dict2) + + for i in new_dict2.keys(): + new_dict2[i] = new_dict2[i].drop_duplicates() + + pars = ["input", "output"] + #scenario.check_out() + for i in pars: + df = scenario.par(i, filters={"technology": ["sp_meth_I", "meth_rc", + "meth_ic_trp", "meth_fc_trp", + "meth_i"]}) + scenario.remove_par(i, df) + #scenario.commit("remove old methanol end use tecs") return new_dict2 From 3defdddfe24d23eab8a0f7f6d0e2d3de2aa8b9da Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 1 Dec 2022 19:02:00 +0100 Subject: [PATCH 520/774] Add cbudget option to build cli command --- message_ix_models/model/material/__init__.py | 21 ++++++++++++++++++++ 1 file changed, 21 insertions(+) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 4be68f03be..773c1fe1ac 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -215,6 +215,27 @@ def build_scen(context, datafile, tag, mode, scenario_name): print('New scenario name is ' + output_scenario_name) scenario.set_as_default() + if mode == 'cbudget': + scenario = context.get_scenario() + print('Base scenario is: ' + scenario.scenario + ", version: " + scenario.version) + output_scenario_name = scenario.scenario + '_' + tag + scenario_new = scenario.clone('MESSAGEix-Materials', output_scenario_name, + keep_solution=False, shift_first_model_year=2025) + emission_dict = { + "node": "World", + "type_emission": "TCE", + "type_tec": "all", + "type_year": "cumulative", + "unit": "???", + } + df = message_ix.make_df("bound_emission", value=3667, **emission_dict) + scenario_new.check_out() + scenario_new.add_par("bound_emission", df) + scenario_new.commit("add emission bound") + print('New carbon budget added') + print('New scenario name is ' + output_scenario_name) + scenario_new.set_as_default() + @cli.command("solve") @click.option("--scenario_name", default="NoPolicy") From 2dd481bbfd0ead6b8eab391d54c08e4222d430ff Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 1 Dec 2022 19:03:28 +0100 Subject: [PATCH 521/774] Fix reporting formatting file path --- message_ix_models/model/material/__init__.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 773c1fe1ac..cef11c60b0 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -347,8 +347,8 @@ def run_reporting(context, remove_ts): run_config="materials_run_config.yaml", ) util.prepare_xlsx_for_explorer( - Path(os.getcwd()).parents[3].joinpath( - "reporting_output", scenario.model+scenario.scenario+".xlsx")) + Path(os.getcwd()).parents[0].joinpath( + "reporting_output", scenario.model+"_"+scenario.scenario+".xlsx")) @cli.command("report-2") From 1aaa574952d060e82a3b3b677972907a16289d8a Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 1 Dec 2022 19:08:38 +0100 Subject: [PATCH 522/774] Use new parameter values for meth_bio_ccs --- .../data/material/methanol/methanol_sensitivity_pars.xlsx | 4 ++-- message_ix_models/model/material/data_methanol.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/message_ix_models/data/material/methanol/methanol_sensitivity_pars.xlsx b/message_ix_models/data/material/methanol/methanol_sensitivity_pars.xlsx index 63e1117eac..15acbd7d87 100644 --- a/message_ix_models/data/material/methanol/methanol_sensitivity_pars.xlsx +++ b/message_ix_models/data/material/methanol/methanol_sensitivity_pars.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:0b37730d9ebf976f33d634c9cdfaae74c3d35998d24559ee42a3eb577364a50b -size 313 +oid sha256:ea7a64eefb6164801bb661d019c9dfb470cf305b2c4011654ba0d204bed36eb9 +size 297 diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index 87e1601bcd..a632c27ecb 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -145,7 +145,7 @@ def gen_data_meth_h2(): def gen_data_meth_bio(scenario): df_bio = pd.read_excel( - context.get_local_path("material", "methanol", "meth_bio_techno_economic.xlsx"), + context.get_local_path("material", "methanol", "meth_bio_techno_economic_new.xlsx"), sheet_name=None, ) coal_ratio = get_cost_ratio_2020(scenario, "meth_coal", "fix_cost")#.drop("value", axis=1) From 6f89708c6ec2a26ce18a350b7621bd3beed7acb3 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 1 Dec 2022 23:11:24 +0100 Subject: [PATCH 523/774] Update plastics share of base chemicals --- .../material/methanol/methanol_sensitivity_pars.xlsx | 4 ++-- message_ix_models/model/material/data_methanol.py | 12 ++++++++---- 2 files changed, 10 insertions(+), 6 deletions(-) diff --git a/message_ix_models/data/material/methanol/methanol_sensitivity_pars.xlsx b/message_ix_models/data/material/methanol/methanol_sensitivity_pars.xlsx index 15acbd7d87..4bee489417 100644 --- a/message_ix_models/data/material/methanol/methanol_sensitivity_pars.xlsx +++ b/message_ix_models/data/material/methanol/methanol_sensitivity_pars.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:ea7a64eefb6164801bb661d019c9dfb470cf305b2c4011654ba0d204bed36eb9 -size 297 +oid sha256:1509243fee498ef69c6c94d26e17f995de7568584a0bbd0189c61f8c04c10e72 +size 295 diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index a632c27ecb..a6b320f1af 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -4,7 +4,7 @@ from message_ix import make_df from message_ix_models.util import broadcast, same_node -from .util import read_config +from util import read_config context = read_config() @@ -31,14 +31,18 @@ def gen_data_methanol(scenario): ) df_rel = dict3["relation_activity"] - def get_embodied_emi(row, pars): + def get_embodied_emi(row, pars, share_par): if row["year_act"] < pars["incin_trend_end"]: share = pars["incin_rate"] + pars["incin_trend"] * (row["year_act"] - 2020) else: share = 0.5 - return row["value"] * (1 - share) * pars["hvc_plastics_share"] + return row["value"] * (1 - share) * pars[share_par] - df_rel["value"] = df_rel.apply(lambda x: get_embodied_emi(x, pars), axis=1) + df_rel_meth = df_rel.loc[df_rel["technology"] == "meth_t_d_material",:] + df_rel_meth["value"] = df_rel_meth.apply(lambda x: get_embodied_emi(x, pars, "meth_plastics_share"), axis=1) + df_rel_hvc = df_rel.loc[df_rel["technology"] == "steam_cracker_petro",:] + df_rel_hvc["value"] = df_rel_hvc.apply(lambda x: get_embodied_emi(x, pars, "hvc_plastics_share"), axis=1) + dict3["relation_activity"] = pd.concat([df_rel_meth, df_rel_hvc]) # dict3.pop("relation_activity") # remove negative emissions for now new_dict2 = combine_df_dictionaries(new_dict, dict3) From b1a9d0f38a29480da5c1d72dd85113d8608b7e6e Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Mon, 19 Dec 2022 16:17:11 +0100 Subject: [PATCH 524/774] Remove unused files --- .../data/material/methanol/meth_bio_techno_economic.xlsx | 3 --- .../data/material/methanol/meth_ng_additions.xlsx | 3 --- .../data/material/methanol/meth_ng_techno_economic.xlsx | 4 ++-- .../data/material/methanol/meth_trade_additions.xlsx | 3 --- message_ix_models/data/material/methanol/meth_trd_pars.xlsx | 3 --- 5 files changed, 2 insertions(+), 14 deletions(-) delete mode 100644 message_ix_models/data/material/methanol/meth_bio_techno_economic.xlsx delete mode 100644 message_ix_models/data/material/methanol/meth_ng_additions.xlsx delete mode 100644 message_ix_models/data/material/methanol/meth_trade_additions.xlsx delete mode 100644 message_ix_models/data/material/methanol/meth_trd_pars.xlsx diff --git a/message_ix_models/data/material/methanol/meth_bio_techno_economic.xlsx b/message_ix_models/data/material/methanol/meth_bio_techno_economic.xlsx deleted file mode 100644 index c7238d058b..0000000000 --- a/message_ix_models/data/material/methanol/meth_bio_techno_economic.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:e506a5d87c5cc200e7fd312bf89ad0a844467dfcbb4f2520bdb9db7df1cacf92 -size 1049812 diff --git a/message_ix_models/data/material/methanol/meth_ng_additions.xlsx b/message_ix_models/data/material/methanol/meth_ng_additions.xlsx deleted file mode 100644 index 4cd3e233e3..0000000000 --- a/message_ix_models/data/material/methanol/meth_ng_additions.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:100ae351c706f53225182a82e2da14c60120deea1b1e29a1bf57b8afe705543d -size 97255 diff --git a/message_ix_models/data/material/methanol/meth_ng_techno_economic.xlsx b/message_ix_models/data/material/methanol/meth_ng_techno_economic.xlsx index 8bc9b41f6d..1ff4ba56c2 100644 --- a/message_ix_models/data/material/methanol/meth_ng_techno_economic.xlsx +++ b/message_ix_models/data/material/methanol/meth_ng_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:67b898630b70bd73af3d44c06993587d05ce6c21a4a5b7e018b9abffeae11df8 -size 5935 +oid sha256:05562ca0b40fb546516bcb2bfca114d7fa5ba73a9326b2c8027439514d1bc590 +size 103018 diff --git a/message_ix_models/data/material/methanol/meth_trade_additions.xlsx b/message_ix_models/data/material/methanol/meth_trade_additions.xlsx deleted file mode 100644 index 9657515eac..0000000000 --- a/message_ix_models/data/material/methanol/meth_trade_additions.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:149ba30dd92d91ec3dc28f353a28c5c9b51e327ef9a395d28a61ae0e9ebb6961 -size 200702 diff --git a/message_ix_models/data/material/methanol/meth_trd_pars.xlsx b/message_ix_models/data/material/methanol/meth_trd_pars.xlsx deleted file mode 100644 index 0d00b31670..0000000000 --- a/message_ix_models/data/material/methanol/meth_trd_pars.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:c145135ef3b4f7e14c3bce6080adb29d1816d4c66cc747171afff0cbf7aa4e72 -size 19065 From 5815e013d39bc65d040b0eefce0fee092b40a039 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Mon, 19 Dec 2022 16:18:27 +0100 Subject: [PATCH 525/774] Update china transport methanol share --- .../methanol/Methanol production statistics (version 1).xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/methanol/Methanol production statistics (version 1).xlsx b/message_ix_models/data/material/methanol/Methanol production statistics (version 1).xlsx index de93d5987b..fc1b10a459 100644 --- a/message_ix_models/data/material/methanol/Methanol production statistics (version 1).xlsx +++ b/message_ix_models/data/material/methanol/Methanol production statistics (version 1).xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:35ae3ccaa7b3a9682cf655e708da7552399edb53d82bf37f5c086429af3a1c3f -size 4620708 +oid sha256:6e853af49a291d6e42e1ef9e25ad339960bb73b5c341f649fc197ae01e76e7df +size 4621962 From 2c2fda0059b1b93fef3471bc77e3752dbb5c6273 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Mon, 19 Dec 2022 16:20:57 +0100 Subject: [PATCH 526/774] Refactor dictionary merge util func --- .../model/material/data_methanol.py | 67 +++++-------------- 1 file changed, 15 insertions(+), 52 deletions(-) diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index a6b320f1af..19b67008ab 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -19,11 +19,10 @@ def gen_data_methanol(scenario): ) pars = df_pars.set_index("par").to_dict()["value"] - dict1 = gen_data_meth_h2() - dict2 = gen_data_meth_bio(scenario) - new_dict = combine_df_dictionaries(dict1, dict2) - dict_bio_ccs = gen_meth_bio_ccs(scenario) - new_dict = combine_df_dictionaries(new_dict, dict_bio_ccs) + meth_h2_dict = gen_data_meth_h2() + meth_bio_dict = gen_data_meth_bio(scenario) + meth_bio_ccs_dict = gen_meth_bio_ccs(scenario) + new_dict = combine_df_dictionaries(meth_h2_dict, meth_bio_dict, meth_bio_ccs_dict) dict3 = pd.read_excel( context.get_local_path("material", "methanol", "meth_t_d_material_pars.xlsx"), @@ -50,50 +49,12 @@ def get_embodied_emi(row, pars, share_par): df_final["value"] = df_final["value"].apply(lambda x: x * 0.5) new_dict2["demand"] = df_final - # fix demand infeasibility - act = scenario.par("historical_activity") - row = ( - act[act["technology"].str.startswith("meth")] - .sort_values("value", ascending=False) - .iloc[0] - ) - # china meth_coal production (90% coal share on 2015 47 Mt total; 1.348 = Mt to GWa ) - row["value"] = (47 / 1.3498) * 0.9 - new_dict2["historical_activity"] = pd.concat( - [new_dict2["historical_activity"], pd.DataFrame(row).T] - ) - # derived from graphic in "Methanol production statstics.xlsx/China demand split" diagram - hist_cap = message_ix.make_df( - "historical_new_capacity", - node_loc="R12_CHN", - technology="meth_coal", - year_vtg=2015, - value=9.6, - unit="GW", - ) - new_dict2["historical_new_capacity"] = hist_cap - # fix demand infeasibility - # act = scenario.par("historical_activity") - # row = act[act["technology"].str.startswith("meth")].sort_values("value", ascending=False).iloc[0] - # row["value"] = 0.0 - new_dict2["historical_activity"] = pd.concat( - [new_dict2["historical_activity"], pd.DataFrame(row).T] - ) - df_ng = pd.read_excel( - context.get_local_path("material", "methanol", "meth_ng_techno_economic.xlsx"), - sheet_name="Sheet1" - ) - new_dict2["historical_activity"] = pd.concat( - [new_dict2["historical_activity"], df_ng] - ) + new_dict2 = combine_df_dictionaries(new_dict2, add_meth_hist_act(scenario)) mto_dict = gen_data_meth_chemicals(scenario, "MTO") - new_dict2 = combine_df_dictionaries(new_dict2, mto_dict) - ch2o_dict = gen_data_meth_chemicals(scenario, "Formaldehyde") - new_dict2 = combine_df_dictionaries(new_dict2, ch2o_dict) resin_dict = gen_data_meth_chemicals(scenario, "Resins") - new_dict2 = combine_df_dictionaries(new_dict2, resin_dict) + chemicals_dict = combine_df_dictionaries(mto_dict, ch2o_dict, resin_dict) df_comm = gen_resin_demand( scenario, pars["resin_share"], "comm", "SH2", "SHAPE", #pars["wood_scenario"], #pars["pathway"] @@ -121,8 +82,8 @@ def get_embodied_emi(row, pars, share_par): cost_dict = update_methanol_costs(scenario) new_dict2 = combine_df_dictionaries(new_dict2, cost_dict) - new_dict2 = combine_df_dictionaries(new_dict2, add_meth_trade_historic()) - new_dict2 = combine_df_dictionaries(add_meth_tec_vintages(), new_dict2) + new_dict2 = combine_df_dictionaries(new_dict2, add_meth_trade_historic(), + chemicals_dict, add_meth_tec_vintages()) for i in new_dict2.keys(): new_dict2[i] = new_dict2[i].drop_duplicates() @@ -515,12 +476,14 @@ def get_scaled_cost_from_proxy_tec(value, scenario, proxy_tec, cost_type, new_te return message_ix.make_df(cost_type, **df) -def combine_df_dictionaries(dict1, dict2): - keys = set(list(dict1.keys()) + list(dict2.keys())) - new_dict = {} +def combine_df_dictionaries(*args): + keys = set([key for tup in args for key in tup]) + #keys = set(list(dict1.keys()) + list(dict2.keys())) + comb_dict = {} for i in keys: - new_dict[i] = pd.concat([dict1.get(i), dict2.get(i)]) - return new_dict + #comb_dict[i] = pd.concat([dict1.get(i), dict2.get(i)]) + comb_dict[i] = pd.concat([j.get(i) for j in args]) + return comb_dict def add_meth_tec_vintages(): From 79d8617e57cda2078fdb298e6b94f88bf09cc48b Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Mon, 19 Dec 2022 16:23:47 +0100 Subject: [PATCH 527/774] Update historical activity of chn --- .../model/material/data_methanol.py | 50 ++++++++++++++++--- 1 file changed, 44 insertions(+), 6 deletions(-) diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index 19b67008ab..da48cf0d2f 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -229,10 +229,10 @@ def gen_data_meth_chemicals(scenario, chemical): "historical_new_capacity", value=1.2, year_vtg=2015, **hist_dict ) par_dict["bound_total_capacity_lo"] = make_df( - "bound_total_capacity_lo", year_act=2020, value=9, **hist_dict + "bound_total_capacity_lo", year_act=2020, value=11, **hist_dict ) par_dict["bound_activity_lo"] = make_df( - "bound_activity_lo", year_act=2020, value=8, **hist_dict + "bound_activity_lo", year_act=2020, value=10, **hist_dict ) df = par_dict["growth_activity_lo"] par_dict["growth_activity_lo"] = df[~((df["node_loc"] == "R12_CHN") & ((df["year_act"] == 2020)))] @@ -487,12 +487,50 @@ def combine_df_dictionaries(*args): def add_meth_tec_vintages(): - par_dict_trade = pd.read_excel( - context.get_local_path("material", "methanol", "meth_ng_additions.xlsx"), + par_dict_ng = pd.read_excel( + context.get_local_path("material", "methanol", "meth_ng_techno_economic.xlsx"), sheet_name=None, ) - par_dict_trade2 = pd.read_excel( + par_dict_ng.pop("historical_activity") + par_dict_coal = pd.read_excel( context.get_local_path("material", "methanol", "meth_coal_additions.xlsx"), sheet_name=None, ) - return combine_df_dictionaries(par_dict_trade, par_dict_trade2) \ No newline at end of file + return combine_df_dictionaries(par_dict_ng, par_dict_coal) + + +def add_meth_hist_act(scenario): + # fix demand infeasibility + par_dict = {} + act = scenario.par("historical_activity") + row = ( + act[act["technology"].str.startswith("meth")] + .sort_values("value", ascending=False) + .iloc[0] + ) + # china meth_coal production (90% coal share on 2015 47 Mt total; 1.348 = Mt to GWa ) + row["value"] = (47 * 0.6976) * 0.9 + row["technology"] = "meth_coal" + par_dict["historical_activity"] = pd.DataFrame(row).T + # derived from graphic in "Methanol production statstics.xlsx/China demand split" diagram + hist_cap = message_ix.make_df( + "historical_new_capacity", + node_loc="R12_CHN", + technology="meth_coal", + year_vtg=2015, + value=9.6, + unit="GW", + ) + par_dict["historical_new_capacity"] = hist_cap + # fix demand infeasibility + # act = scenario.par("historical_activity") + # row = act[act["technology"].str.startswith("meth")].sort_values("value", ascending=False).iloc[0] + # row["value"] = 0.0 + df_ng = pd.read_excel( + context.get_local_path("material", "methanol", "meth_ng_techno_economic.xlsx"), + sheet_name="historical_activity" + ) + par_dict["historical_activity"] = pd.concat( + [par_dict["historical_activity"], df_ng] + ) + return par_dict From f0d4269f7caedb73a3b217011aaa7b1fa2f60e14 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Mon, 19 Dec 2022 16:24:50 +0100 Subject: [PATCH 528/774] Fix mtbe loil share --- message_ix_models/model/material/data_methanol.py | 13 ++++--------- 1 file changed, 4 insertions(+), 9 deletions(-) diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index da48cf0d2f..f0c688893a 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -245,8 +245,7 @@ def gen_data_meth_chemicals(scenario, chemical): def add_methanol_trp_additives(scenario): - df_loil = scenario.par("input") - df_loil = df_loil[df_loil["technology"] == "loil_trp"] + df_loil = scenario.par("input", filters={"technology": "loil_trp"}) df_mtbe = pd.read_excel( context.get_local_path( @@ -260,8 +259,8 @@ def add_methanol_trp_additives(scenario): 1:13, ] df_mtbe["node_loc"] = "R12_" + df_mtbe["node_loc"] - #df_mtbe = df_mtbe[["node_loc", "methanol energy%"]] df_mtbe = df_mtbe[["node_loc", "% share on trp"]] + df_biodiesel = pd.read_excel( context.get_local_path( "material", "methanol", "Methanol production statistics (version 1).xlsx" @@ -272,20 +271,16 @@ def add_methanol_trp_additives(scenario): ) df_biodiesel["node_loc"] = "R12_" + df_biodiesel["node_loc"] df_total = df_biodiesel.merge(df_mtbe) - #df_total = df_total.assign( - # value=lambda x: (x["methanol energy %"] + x["methanol energy%"]) - #) df_total = df_total.assign( value=lambda x: (x["methanol energy %"] + x["% share on trp"]) ) + def get_meth_share(df, node): return df[df["node_loc"] == node["node_loc"]]["value"].values[0] - df_loil_meth = df_loil.copy(deep=True) - df_loil_meth["value"] = df_loil.apply(lambda x: get_meth_share(df_total, x), axis=1) + df_loil_meth["value"] = df_loil_meth.apply(lambda x: get_meth_share(df_total, x), axis=1) df_loil_meth["commodity"] = "methanol" df_loil["value"] = df_loil["value"] - df_loil_meth["value"] - return pd.concat([df_loil, df_loil_meth]) From 45c4792609fb26af405cea2976b399fd344b29bc Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Mon, 19 Dec 2022 16:25:06 +0100 Subject: [PATCH 529/774] Fix util import --- message_ix_models/model/material/data_methanol.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index f0c688893a..d988784da2 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -4,7 +4,7 @@ from message_ix import make_df from message_ix_models.util import broadcast, same_node -from util import read_config +from .util import read_config context = read_config() From c0ae2691a434207d9e85ec2652c2b7eb3d14142f Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Mon, 19 Dec 2022 16:25:41 +0100 Subject: [PATCH 530/774] Fix methanol trade file name --- message_ix_models/model/material/data_methanol.py | 7 +------ 1 file changed, 1 insertion(+), 6 deletions(-) diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index d988784da2..4ee40f473c 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -286,17 +286,12 @@ def get_meth_share(df, node): def add_meth_trade_historic(): par_dict_trade = pd.read_excel( - context.get_local_path("material", "methanol", "meth_trd_pars.xlsx"), - sheet_name=None, - ) - par_dict_trade2 = pd.read_excel( - context.get_local_path("material", "methanol", "meth_trade_additions.xlsx"), + context.get_local_path("material", "methanol", "meth_trade_techno_economic.xlsx"), sheet_name=None, ) df = par_dict_trade["historical_new_capacity"] df.loc[df["value"] < 0, "value"] = 0 par_dict_trade["historical_new_capacity"] = df[(df["technology"] != "meth_imp")] - par_dict_trade = combine_df_dictionaries(par_dict_trade, par_dict_trade2) return par_dict_trade From 1bbef901865381fa11c34e1c3dacabb88f8b427f Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Mon, 19 Dec 2022 16:26:11 +0100 Subject: [PATCH 531/774] Update emissions factor of chemicals --- .../data/material/methanol/meth_t_d_material_pars.xlsx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/message_ix_models/data/material/methanol/meth_t_d_material_pars.xlsx b/message_ix_models/data/material/methanol/meth_t_d_material_pars.xlsx index 378b5de563..71e860b85d 100644 --- a/message_ix_models/data/material/methanol/meth_t_d_material_pars.xlsx +++ b/message_ix_models/data/material/methanol/meth_t_d_material_pars.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:17aa27097b99ee35e45344f74232e3c9609868962d960ea924d7b14f146837bc +oid sha256:fe971a3528bca7dddc5ca849209477c71f04ac428352acfaf70aff6fe545a2bd size 292 From c32f710ba6e7a3cb687b66968c9248a6f04e0c5d Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 11 Oct 2022 15:06:15 +0200 Subject: [PATCH 532/774] Add explanations to generic_furnaces data --- .../data/material/generic_furnace_boiler_techno_economic.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx index cb3a21ce8d..b4a6b69a8e 100644 --- a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx +++ b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:971cbf651a00a7ebac8eb13899144cbe7da95bb78488440965ba8a3c7e209bda -size 293 +oid sha256:3b3254089b6cc0af867073c2c410600a90a9ea629a7fda4e9ed4e19665127eba +size 298 From 8573073e17b8e7b01a3b21f690fa7b80be6a8c7d Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 6 Dec 2022 14:41:04 +0100 Subject: [PATCH 533/774] Change hvc demand (add income elasticity) --- .../material/methanol/methanol demand.xlsx | 4 +- .../model/material/data_petro.py | 197 +++++------------- 2 files changed, 59 insertions(+), 142 deletions(-) diff --git a/message_ix_models/data/material/methanol/methanol demand.xlsx b/message_ix_models/data/material/methanol/methanol demand.xlsx index dd45ea6f1d..74614b0079 100644 --- a/message_ix_models/data/material/methanol/methanol demand.xlsx +++ b/message_ix_models/data/material/methanol/methanol demand.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:5397faa6297815cd7d3f8e7897c6e8d21b8a3a0a2cc47e6a8c962fc7fda6c2eb -size 51010 +oid sha256:be170202af8c876b5a4eb8e5bda6d1ceb7458c8ce4b64f9f3d205c18c59836f7 +size 299 diff --git a/message_ix_models/model/material/data_petro.py b/message_ix_models/model/material/data_petro.py index 9c0f6f3bd0..f54fcbde51 100644 --- a/message_ix_models/model/material/data_petro.py +++ b/message_ix_models/model/material/data_petro.py @@ -42,15 +42,30 @@ def read_data_petrochemicals(scenario): return data_petro - -def gen_mock_demand_petro(scenario): +def gen_mock_demand_petro(scenario, gdp_elasticity): context = read_config() s_info = ScenarioInfo(scenario) - modelyears = s_info.Y # s_info.Y is only for modeling years - fmy = s_info.y0 + modelyears = s_info.Y #s_info.Y is only for modeling years nodes = s_info.N - nodes.remove("World") + + def get_demand_t1_with_income_elasticity( + demand_t0, income_t0, income_t1, elasticity + ): + return ( + elasticity * demand_t0 * ((income_t1 - income_t0) / income_t0) + ) + demand_t0 + + df_gdp = pd.read_excel( + context.get_local_path("material", "methanol", "methanol demand.xlsx"), + sheet_name="GDP_baseline", + ) + + df = df_gdp[(~df_gdp["Region"].isna()) & (df_gdp["Region"] != "World")] + df = df.dropna(axis=1) + + df_demand = df.copy(deep=True) + df_demand = df_demand.drop([2010, 2015, 2020], axis=1) # 2018 production # Use as 2020 @@ -58,147 +73,60 @@ def gen_mock_demand_petro(scenario): # Projections here do not show too much growth until 2050 for some regions. # For division of some regions assumptions made: # PAO, PAS, SAS, EEU,WEU - # For R12: China and CPA demand divided by 0.1 and 0.9. - # SSP2 R11 baseline GDP projection - # The orders of the regions # r = ['R12_AFR', 'R12_RCPA', 'R12_EEU', 'R12_FSU', 'R12_LAM', 'R12_MEA',\ # 'R12_NAM', 'R12_PAO', 'R12_PAS', 'R12_SAS', 'R12_WEU',"R12_CHN"] if "R12_CHN" in nodes: nodes.remove("R12_GLB") - sheet_n = "data_R12" - region_set = "R12_" - - d_HVC = [2, 7.5, 30, 4, 11, 42, 60, 32, 30, 29, 35, 67.5] + region_set = 'R12_' + dem_2020 = np.array([2, 7.5, 30, 4, 11, 42, 60, 32, 30, 29, 35, 67.5]) + dem_2020 = pd.Series(dem_2020) else: nodes.remove("R11_GLB") - sheet_n = "data_R11" - region_set = "R11_" + region_set = 'R11_' + dem_2020 = np.array([2, 75, 30, 4, 11, 42, 60, 32, 30, 29, 35]) + dem_2020 = pd.Series(dem_2020) - d_HVC = [2, 75, 30, 4, 11, 42, 60, 32, 30, 29, 35] + df_demand[2020] = dem_2020 - # d_ethylene = [0.853064, 32.04327, 2.88788, 8.780442, 8.831229,21.58509, - # 32.54942, 8.94036, 7.497867, 28.12818, 16.65209] - # d_propylene = [0.426532, 32.04327, 2.88788, 1.463407, 5.298738, 10.79255, - # 16.27471, 7.4503, 7.497867, 17.30965, 11.1014] - # d_BTX = [0.426532, 32.04327, 2.88788, 1.463407, 5.298738, 12.95105, 16.27471, - # 7.4503, 7.497867, 17.30965, 11.1014] + for i in range(len(modelyears) - 1): + income_year1 = modelyears[i] + income_year2 = modelyears[i + 1] - gdp_growth = pd.read_excel( - private_data_path("material", "iamc_db ENGAGE baseline GDP PPP.xlsx"), - sheet_name=sheet_n, - ) - - gdp_growth = gdp_growth.loc[ - (gdp_growth["Scenario"] == "baseline") & (gdp_growth["Region"] != "World") - ].drop(["Model", "Variable", "Unit", "Notes", 2000, 2005], axis=1) - - gdp_growth["Region"] = region_set + gdp_growth["Region"] + dem_2020 = get_demand_t1_with_income_elasticity( + dem_2020, df[income_year1], df[income_year2], gdp_elasticity + ) + df_demand[income_year2] = dem_2020 - # list = [] - # - # for e in ["ethylene","propylene","BTX"]: - # if e == "ethylene": - # demand2020 = pd.DataFrame({'Region':nodes, 'Val':d_ethylene}).\ - # join(gdp_growth.set_index('Region'), on='Region').\ - # rename(columns={'Region':'node'}) - # - # if e == "propylene": - # demand2020 = pd.DataFrame({'Region':nodes, 'Val':d_propylene}).\ - # join(gdp_growth.set_index('Region'), on='Region').\ - # rename(columns={'Region':'node'}) - # - # if e == "BTX": - # demand2020 = pd.DataFrame({'Region':nodes, 'Val':d_BTX}).\ - # join(gdp_growth.set_index('Region'), on='Region').\ - # rename(columns={'Region':'node'}) - - demand2020 = ( - pd.DataFrame({"Region": nodes, "Val": d_HVC}) - .join(gdp_growth.set_index("Region"), on="Region") - .rename(columns={"Region": "node"}) + df_melt = df_demand.melt( + id_vars=["Region"], value_vars=df_demand.columns[5:], var_name="year" ) - demand2020.iloc[:, 3:] = ( - demand2020.iloc[:, 3:] - .div(demand2020[2020], axis=0) - .multiply(demand2020["Val"], axis=0) - ) + df_final = message_ix.make_df( + "demand", + unit="t", + level="demand", + value=df_melt.value, + time="year", + commodity="HVC", + year=df_melt.year, + node=(region_set + df_melt["Region"])) - demand2020 = pd.melt( - demand2020.drop(["Val", "Scenario"], axis=1), - id_vars=["node"], - var_name="year", - value_name="value", + return message_ix.make_df( + "demand", + unit="t", + level="demand", + value=df_melt.value, + time="year", + commodity="HVC", + year=df_melt.year, + node=(region_set + df_melt["Region"]), ) - # list.append(demand2020) - - # return list[0], list[1], list[2] - return demand2020 - - # China 2006: 22 kg/cap HVC demand. 2006 population: 1.311 billion - # This makes 28.842 Mt. (IEA Energy Technology Transitions for Industry) - # In 2010: 43.263 Mt (1.5 times of 2006) - # In 2015 72.105 Mt (1.56 times of 2010) - # Grwoth rates are from CECDATA (assuming same growth rate as ethylene). - # Distribution in 2015 for China: 6:6:5 (ethylene,propylene,BTX) - # Future of Petrochemicals Methodological Annex - # This makes 25 Mt ethylene, 25 Mt propylene, 21 Mt BTX - # This can be verified by other sources. - - -# load rpy2 modules -# import rpy2.robjects as ro -# from rpy2.robjects import pandas2ri -# from rpy2.robjects.conversion import localconverter - -# This returns a df with columns ["region", "year", "demand.tot"] -# def derive_petro_demand(scenario, dry_run=False): -# """Generate HVC demand.""" -# # paths to r code and lca data -# rcode_path = Path(__file__).parents[0] / "material_demand" -# context = read_config() -# -# # source R code -# r = ro.r -# r.source(str(rcode_path / "init_modularized.R")) -# -# # Read population and baseline demand for materials -# pop = scenario.par("bound_activity_up", {"technology": "Population"}) -# pop = pop.loc[pop.year_act >= 2020].rename( -# columns={"year_act": "year", "value": "pop.mil", "node_loc": "region"} -# ) -# -# # import pdb; pdb.set_trace() -# -# pop = pop[["region", "year", "pop.mil"]] -# -# base_demand = gen_mock_demand_petro(scenario) -# base_demand = base_demand.loc[base_demand.year == 2020].rename( -# columns={"value": "demand.tot.base", "node": "region"} -# ) -# -# print("base demand") -# print(base_demand) -# -# # call R function with type conversion -# with localconverter(ro.default_converter + pandas2ri.converter): -# # GDP is only in MER in scenario. -# # To get PPP GDP, it is read externally from the R side -# df = r.derive_petro_demand( -# pop, base_demand, str(context.get_local_path("material")) -# ) -# df.year = df.year.astype(int) -# -# return df -# - - def gen_data_petro_chemicals(scenario, dry_run=False): # Load configuration @@ -390,20 +318,9 @@ def gen_data_petro_chemicals(scenario, dry_run=False): # Create external demand param # demand_HVC = derive_petro_demand(scenario) - demand_HVC = gen_mock_demand_petro(scenario) - paramname = "demand" - - df_HVC = make_df( - paramname, - level="demand", - commodity="HVC", - value=demand_HVC.value, - unit="t", - year=demand_HVC.year, - time="year", - node=demand_HVC.node, - ) # .pipe(broadcast, node=nodes) - results["demand"].append(df_HVC) + default_gdp_elasticity = float(0.93) + demand_HVC = gen_mock_demand_petro(scenario, default_gdp_elasticity) + results["demand"].append(demand_HVC) # df_e = make_df(paramname, level='final_material', commodity="ethylene", \ # value=demand_e.value, unit='t',year=demand_e.year, time='year', \ From 05818d5cae1ade36fc1c4b57e2f6fea68af1441a Mon Sep 17 00:00:00 2001 From: Jihoon Min Date: Wed, 7 Dec 2022 18:00:30 +0100 Subject: [PATCH 534/774] Add dummy fuel inputs to steel sector to the co2 emission relation 'co2_ind' --- message_ix_models/model/material/data_util.py | 20 ++++++++++++------- 1 file changed, 13 insertions(+), 7 deletions(-) diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index c14e134062..dc2a430b8c 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -198,9 +198,7 @@ def modify_demand_and_hist_activity(scen): for r in df_feed["REGION"].unique(): - # Temporary solution. With the addition of ammonia the residual demand - # becomes negative or zero. We assume all of the feedstock production - # is endogenously covered. + i = 0 df_feed_temp.at[i, "REGION"] = r df_feed_temp.at[i, "i_feed"] = 1 @@ -481,10 +479,17 @@ def add_emission_accounting(scen): ~relation_activity_furnaces["technology"].str.contains("_refining") ] - scen.check_out() - scen.add_par("relation_activity", relation_activity) - scen.add_par("relation_activity", relation_activity_furnaces) - scen.commit("Emissions accounting for industry technologies added.") + # Add steel energy input technologies to CO2_ind relation + + relation_activity_steel = scen.par( + "emission_factor", + filters={"emission": "CO2_industry", + "technology": ["DUMMY_coal_supply", "DUMMY_gas_supply"]}, + ) + relation_activity_steel["relation"] = "CO2_ind" + relation_activity_steel["node_rel"] = relation_activity_steel["node_loc"] + relation_activity_steel.drop(["year_vtg", "emission"], axis=1, inplace=True) + relation_activity_steel["year_rel"] = relation_activity_steel["year_act"] # Add refinery technologies to CO2_cc @@ -500,6 +505,7 @@ def add_emission_accounting(scen): scen.check_out() scen.add_par("relation_activity", relation_activity) scen.add_par("relation_activity", relation_activity_furnaces) + scen.add_par("relation_activity", relation_activity_steel) scen.add_par("relation_activity", relation_activity_ref) scen.commit("Emissions accounting for industry technologies added.") From 11fdf0772d907111ab71248090d633c1800e4657 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 8 Dec 2022 16:44:30 +0100 Subject: [PATCH 535/774] Add non-co2 emission accounting --- ...eneric_furnace_boiler_techno_economic.xlsx | 4 +- message_ix_models/data/material/set.yaml | 1 + message_ix_models/model/material/data_util.py | 122 +++++++++++++++--- 3 files changed, 109 insertions(+), 18 deletions(-) diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx index b4a6b69a8e..4c28d6ac8f 100644 --- a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx +++ b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:3b3254089b6cc0af867073c2c410600a90a9ea629a7fda4e9ed4e19665127eba -size 298 +oid sha256:3a37c04e4684723556ac39630f194e68d6d1a44a0f0ae1d8c0ec84e6055b1285 +size 288 diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index e28becf177..1d1a03da06 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -101,6 +101,7 @@ generic: - furnace_coal_cement - furnace_elec_cement - furnace_h2_cement + - hp_gas_cement - furnace_coal_aluminum - furnace_foil_aluminum - furnace_loil_aluminum diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index dc2a430b8c..705c4a7be4 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -424,6 +424,111 @@ def add_emission_accounting(scen): context = read_config() s_info = ScenarioInfo(scen) + # Add non-CO2 gases to the relevant relations + + tec_list_residual = scen.par("emission_factor")["technology"].unique() + tec_list_input = scen.par("input")["technology"].unique() + + # The technology list to retrieve the input values + tec_list_input = [ + i + for i in tec_list_input + if ( + ("furnace" in i) | + ("hp_gas_" in i) + + ) + ] + tec_list_input.remove('hp_gas_i') + tec_list_input.remove('hp_gas_rc') + + # The technology list to retreive the emission_factors + tec_list_residual = [ + i + for i in tec_list_residual + if ( + (('biomass_i' in i) | + ('coal_i' in i) | + ('foil_i' in i) | + ('gas_i' in i) | + ('hp_gas_i' in i) | + ('loil_i' in i) | + ('meth_i' in i)) & + ('imp' not in i) & + ('trp' not in i) + ) + ] + + # Retrieve the input values + input_df = scen.par('input', filters = {'technology':tec_list_input}) + input_df.drop(['node_origin','commodity','level','time','time_origin','unit'], + axis = 1, inplace = True) + input_df.drop_duplicates(inplace = True) + input_df = input_df[input_df['year_act']>=2020] + + # Retrieve the emission factors + + emission_df = scen.par("emission_factor", filters={"technology": tec_list_residual}) + emission_df.drop(['unit','mode'], axis = 1, inplace = True) + emission_df = emission_df[emission_df['year_act']>=2020] + emission_df.drop_duplicates(inplace = True) + + # Mapping to multiply the emission_factor with the corresponding input values from new indsutry technologies + + dic = {"foil_i" : ['furnace_foil_steel', 'furnace_foil_aluminum', + 'furnace_foil_cement', 'furnace_foil_petro', 'furnace_foil_refining'], + "biomass_i" : ['furnace_biomass_steel', 'furnace_biomass_aluminum', + 'furnace_biomass_cement', 'furnace_biomass_petro', + 'furnace_biomass_refining'], + "coal_i": ['furnace_coal_steel', 'furnace_coal_aluminum', + 'furnace_coal_cement', 'furnace_coal_petro', 'furnace_coal_refining', + 'furnace_coke_petro', 'furnace_coke_refining'], + "loil_i" : ['furnace_loil_steel', 'furnace_loil_aluminum', + 'furnace_loil_cement', 'furnace_loil_petro', 'furnace_loil_refining'], + "gas_i":['furnace_gas_steel', 'furnace_gas_aluminum', + 'furnace_gas_cement', 'furnace_gas_petro', 'furnace_gas_refining'], + 'meth_i': ['furnace_methanol_steel', 'furnace_methanol_aluminum', + 'furnace_methanol_cement', 'furnace_methanol_petro', 'furnace_methanol_refining'], + 'hp_gas_i': ['hp_gas_steel', 'hp_gas_aluminum','hp_gas_cement', + 'hp_gas_petro', 'hp_gas_refining']} + + # Create an empty dataframe + df_non_co2_emissions = pd.DataFrame() + + # Find the technology, year_act, year_vtg, emission, node_loc combination + emissions = [e for e in emission_df['emission'].unique()] + emissions.remove('CO2_industry') + emissions.remove('CO2_res_com') + + for t in emission_df['technology'].unique(): + for e in emissions: + # This should be a dataframe + emission_df_filt = emission_df.loc[((emission_df['technology'] == t)\ + & (emission_df['emission'] == e))] + # Filter the technologies that we need the input value + # This should be a dataframe + input_df_filt = input_df[input_df['technology'].isin(dic[t])] + if ((emission_df_filt.empty) | (input_df_filt.empty)): + continue + else: + df_merged = pd.merge(emission_df_filt, input_df_filt, on = \ + ['year_act', 'year_vtg', 'node_loc']) + df_merged['value'] = df_merged['value_x'] * df_merged['value_y'] + df_merged.drop(['technology_x','value_x','value_y', 'year_vtg', + 'emission'], axis= 1, inplace = True) + df_merged.rename(columns={'technology_y':'technology'}, + inplace = True) + relation_name = e + '_Emission' + df_merged['relation'] = relation_name + df_merged['node_rel'] = df_merged['node_loc'] + df_merged['year_rel'] = df_merged['year_act'] + df_merged['unit'] = '???' + df_non_co2_emissions = pd.concat([df_non_co2_emissions,df_merged]) + + scen.check_out() + scen.add_par("relation_activity", df_non_co2_emissions) + scen.commit("Non-CO2 Emissions accounting for industry technologies added.") + # Obtain the CO2 emission factors and add to CO2_Emission relation. # TODO: Also residential and commercial technologies should be added to this list. # We dont need to add ammonia/fertilier production here. Because there are @@ -451,20 +556,6 @@ def add_emission_accounting(scen): "emission_factor", filters={"technology": tec_list_materials} ) - # Note: Emission factors for non-CO2 gases are in kt/ACT. For CO2 MtC/ACT. - - relation_activity = emission_factors.assign( - relation=lambda x: (x["emission"] + "_Emission") - ) - relation_activity["node_rel"] = relation_activity["node_loc"] - relation_activity.drop(["year_vtg", "emission"], axis=1, inplace=True) - relation_activity["year_rel"] = relation_activity["year_act"] - relation_activity = relation_activity[ - (relation_activity["relation"] != "PM2p5_Emission") - & (relation_activity["relation"] != "CO2_industry_Emission") - & (relation_activity["relation"] != "CO2_transformation_Emission") - ] - # Add thermal industry technologies to CO2_ind relation relation_activity_furnaces = scen.par( @@ -483,7 +574,7 @@ def add_emission_accounting(scen): relation_activity_steel = scen.par( "emission_factor", - filters={"emission": "CO2_industry", + filters={"emission": "CO2_industry", "technology": ["DUMMY_coal_supply", "DUMMY_gas_supply"]}, ) relation_activity_steel["relation"] = "CO2_ind" @@ -503,7 +594,6 @@ def add_emission_accounting(scen): relation_activity_ref["year_rel"] = relation_activity_ref["year_act"] scen.check_out() - scen.add_par("relation_activity", relation_activity) scen.add_par("relation_activity", relation_activity_furnaces) scen.add_par("relation_activity", relation_activity_steel) scen.add_par("relation_activity", relation_activity_ref) From 6dcb522f1fab175b4e7e6565f0afc2cead49c2a3 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Fri, 9 Dec 2022 10:31:37 +0100 Subject: [PATCH 536/774] Fix co2_emission relation --- message_ix_models/model/material/data_util.py | 36 +++++++++++++------ 1 file changed, 25 insertions(+), 11 deletions(-) diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index 705c4a7be4..1e9e99ae31 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -424,7 +424,9 @@ def add_emission_accounting(scen): context = read_config() s_info = ScenarioInfo(scen) - # Add non-CO2 gases to the relevant relations + # (1) ******* Add non-CO2 gases to the relevant relations. ******** + # This is done by multiplying the input values and emission_factor + # per year,region and technology. tec_list_residual = scen.par("emission_factor")["technology"].unique() tec_list_input = scen.par("input")["technology"].unique() @@ -473,7 +475,8 @@ def add_emission_accounting(scen): emission_df = emission_df[emission_df['year_act']>=2020] emission_df.drop_duplicates(inplace = True) - # Mapping to multiply the emission_factor with the corresponding input values from new indsutry technologies + # Mapping to multiply the emission_factor with the corresponding + # input values from new indsutry technologies dic = {"foil_i" : ['furnace_foil_steel', 'furnace_foil_aluminum', 'furnace_foil_cement', 'furnace_foil_petro', 'furnace_foil_refining'], @@ -529,15 +532,13 @@ def add_emission_accounting(scen): scen.add_par("relation_activity", df_non_co2_emissions) scen.commit("Non-CO2 Emissions accounting for industry technologies added.") - # Obtain the CO2 emission factors and add to CO2_Emission relation. - # TODO: Also residential and commercial technologies should be added to this list. + # ***** (2) Add the CO2 emission factors to CO2_Emission relation. ****** # We dont need to add ammonia/fertilier production here. Because there are # no extra process emissions that need to be accounted in emissions relation. # CCS negative emission_factor are added to this relation in gen_data_ammonia.py. # Emissions from refining sector are categorized as 'CO2_transformation'. tec_list = scen.par("emission_factor")["technology"].unique() - tec_list_materials = [ i for i in tec_list @@ -553,10 +554,22 @@ def add_emission_accounting(scen): tec_list_materials.remove("replacement_so2") tec_list_materials.remove("SO2_scrub_ref") emission_factors = scen.par( - "emission_factor", filters={"technology": tec_list_materials} + "emission_factor", filters={"technology": tec_list_materials, 'emission':'CO2'} + ) + # Note: Emission for CO2 MtC/ACT. + relation_activity = emission_factors.assign( + relation=lambda x: (x["emission"] + "_Emission") ) + relation_activity["node_rel"] = relation_activity["node_loc"] + relation_activity.drop(["year_vtg", "emission"], axis=1, inplace=True) + relation_activity["year_rel"] = relation_activity["year_act"] + relation_activity_co2 = relation_activity[ + (relation_activity["relation"] != "PM2p5_Emission") + & (relation_activity["relation"] != "CO2_industry_Emission") + & (relation_activity["relation"] != "CO2_transformation_Emission") + ] - # Add thermal industry technologies to CO2_ind relation + # ***** (3) Add thermal industry technologies to CO2_ind relation ****** relation_activity_furnaces = scen.par( "emission_factor", @@ -570,7 +583,7 @@ def add_emission_accounting(scen): ~relation_activity_furnaces["technology"].str.contains("_refining") ] - # Add steel energy input technologies to CO2_ind relation + # ***** (4) Add steel energy input technologies to CO2_ind relation **** relation_activity_steel = scen.par( "emission_factor", @@ -582,7 +595,7 @@ def add_emission_accounting(scen): relation_activity_steel.drop(["year_vtg", "emission"], axis=1, inplace=True) relation_activity_steel["year_rel"] = relation_activity_steel["year_act"] - # Add refinery technologies to CO2_cc + # ***** (5) Add refinery technologies to CO2_cc ****** relation_activity_ref = scen.par( "emission_factor", @@ -594,12 +607,13 @@ def add_emission_accounting(scen): relation_activity_ref["year_rel"] = relation_activity_ref["year_act"] scen.check_out() + scen.add_par("relation_activity", relation_activity_co2) scen.add_par("relation_activity", relation_activity_furnaces) scen.add_par("relation_activity", relation_activity_steel) scen.add_par("relation_activity", relation_activity_ref) scen.commit("Emissions accounting for industry technologies added.") - # Add feedstock using technologies to CO2_feedstocks + # ***** (6) Add feedstock using technologies to CO2_feedstocks ***** nodes = scen.par("relation_activity", filters={"relation": "CO2_feedstocks"})[ "node_rel" ].unique() @@ -651,7 +665,7 @@ def add_emission_accounting(scen): scen.add_par("relation_activity", co2_feedstocks) scen.commit("co2_feedstocks updated") - # Correct CF4 Emission relations + # **** (7) Correct CF4 Emission relations ***** # Remove transport related technologies from CF4_Emissions scen.check_out() From d03ae54fd1b567a29c47cce00ed29a56026ec01e Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 15 Dec 2022 11:00:01 +0100 Subject: [PATCH 537/774] Add 2020 scrap calibration for steel in addition growth_activity_lo for bof_steel is set. --- .../material/Global_steel_cement_MESSAGE.xlsx | 4 +- message_ix_models/data/material/set.yaml | 1 + .../model/material/data_steel.py | 49 ++++++++++++++++--- 3 files changed, 45 insertions(+), 9 deletions(-) diff --git a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx index f6d5e6cf21..32eba2601b 100644 --- a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx +++ b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:f08c2c85810ec6effd8772326140f13ef5de8a91f77c7137c080f5cf6da3846c -size 310 +oid sha256:d353614a326f6bbce34a19918e78f5fa320c5718cf9d2596f37c9b0392f89f6a +size 293 diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 1d1a03da06..996bb97a5a 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -297,6 +297,7 @@ steel: relation: add: - minimum_recycling_steel + - max_global_recycling_steel balance_equality: add: diff --git a/message_ix_models/model/material/data_steel.py b/message_ix_models/model/material/data_steel.py index 1b89e5183d..20bb304cf7 100644 --- a/message_ix_models/model/material/data_steel.py +++ b/message_ix_models/model/material/data_steel.py @@ -360,14 +360,51 @@ def gen_data_steel(scenario, dry_run=False): results[param_name].append(df) - # Add relations for scrap grades and availability + # Add relation for the maximum global scrap use in 2020 + + df_max_recycling = pd.DataFrame({'relation': 'max_global_recycling_steel', + 'node_rel': 'R12_GLB', + 'year_rel': 2020, + 'year_act': 2020, + 'node_loc': nodes, + 'technology': 'scrap_recovery_steel', + 'mode': 'M1', + 'unit': '???', + 'value': data_steel_rel.loc[((data_steel_rel['relation'] \ + == 'max_global_recycling_steel') & (data_steel_rel['parameter'] \ + == 'relation_activity')), 'value'].values[0]}) + + df_max_recycling_upper = pd.DataFrame({'relation': 'max_global_recycling_steel', + 'node_rel': 'R12_GLB', + 'year_rel': 2020, + 'unit': '???', + 'value': data_steel_rel.loc[((data_steel_rel['relation'] \ + == 'max_global_recycling_steel') & (data_steel_rel['parameter'] \ + == 'relation_upper')), 'value'].values[0]}, index = [0]) + df_max_recycling_lower = pd.DataFrame({'relation': 'max_global_recycling_steel', + 'node_rel': 'R12_GLB', + 'year_rel': 2020, + 'unit': '???', + 'value': data_steel_rel.loc[((data_steel_rel['relation'] \ + == 'max_global_recycling_steel') & (data_steel_rel['parameter'] \ + == 'relation_lower')), 'value'].values[0]}, index = [0]) + + results['relation_activity'].append(df_max_recycling) + results['relation_upper'].append(df_max_recycling_upper) + results['relation_lower'].append(df_max_recycling_lower) + # Add relations for scrap grades and availability regions = set(data_steel_rel["Region"].values) - for reg in regions: for r in data_steel_rel["relation"]: + model_years_rel = modelyears.copy() if r is None: break + if r == 'max_global_recycling_steel': + continue + if r == 'minimum_recycling_steel': + # Do not implement the minimum recycling rate for the year 2020 + model_years_rel.remove(2020) params = set( data_steel_rel.loc[ @@ -376,12 +413,12 @@ def gen_data_steel(scenario, dry_run=False): ) common_rel = dict( - year_rel=modelyears, - year_act=modelyears, + year_rel=model_years_rel, + year_act=model_years_rel, mode="M1", relation=r, ) - + for par_name in params: if par_name == "relation_activity": @@ -394,7 +431,6 @@ def gen_data_steel(scenario, dry_run=False): ] for tec in tec_list.unique(): - val = data_steel_rel.loc[ ( (data_steel_rel["relation"] == r) @@ -418,7 +454,6 @@ def gen_data_steel(scenario, dry_run=False): results[par_name].append(df) elif (par_name == "relation_upper") | (par_name == "relation_lower"): - val = data_steel_rel.loc[ ( (data_steel_rel["relation"] == r) From 09814c7a74aa8196591c850e624c1e45263ef4b6 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Fri, 16 Dec 2022 10:15:29 +0100 Subject: [PATCH 538/774] Change demand connections --- .../data/material/Global_steel_cement_MESSAGE.xlsx | 4 ++-- message_ix_models/data/material/set.yaml | 1 + 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx index 32eba2601b..07d1a91bd4 100644 --- a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx +++ b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:d353614a326f6bbce34a19918e78f5fa320c5718cf9d2596f37c9b0392f89f6a -size 293 +oid sha256:0e3f2412b5d64e4128722e242914fc328173ccb7169b9369b9e3e88a6f9d51a0 +size 282 diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 996bb97a5a..61ccf931cd 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -340,6 +340,7 @@ cement: - grinding_vertmill_cement - DUMMY_limestone_supply_cement - total_EOL_cement + - other_EOL_cement - scrap_recovery_cement remove: - cement_co2scr From ec70aab1a1bc50fcc4278f8e3a78a2cf945710b2 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 20 Dec 2022 11:36:26 +0100 Subject: [PATCH 539/774] Manual merge of furnace input file --- .../data/material/generic_furnace_boiler_techno_economic.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx index 4c28d6ac8f..7e82c576ea 100644 --- a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx +++ b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:3a37c04e4684723556ac39630f194e68d6d1a44a0f0ae1d8c0ec84e6055b1285 -size 288 +oid sha256:721f426425d712cf83d25f8c6acc2b96a65941bccc1b054305d8ed30fc6eafc7 +size 291 From 6a8f3e5b7107c5951ed1ab4abc05980f021fbee4 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 20 Dec 2022 11:38:09 +0100 Subject: [PATCH 540/774] Fix small bug --- message_ix_models/model/material/__init__.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index cef11c60b0..bb8fe1dd4c 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -217,7 +217,8 @@ def build_scen(context, datafile, tag, mode, scenario_name): if mode == 'cbudget': scenario = context.get_scenario() - print('Base scenario is: ' + scenario.scenario + ", version: " + scenario.version) + print(scenario.version) + #print('Base scenario is: ' + scenario.scenario + ", version: " + scenario.version) output_scenario_name = scenario.scenario + '_' + tag scenario_new = scenario.clone('MESSAGEix-Materials', output_scenario_name, keep_solution=False, shift_first_model_year=2025) From 775ac8a2fd95d5d77bbda4fff39b978e949b3dc0 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 20 Dec 2022 14:54:04 +0100 Subject: [PATCH 541/774] Add residual ammonia demand --- message_ix_models/data/material/set.yaml | 32 +++- .../model/material/data_ammonia.py | 137 +++++++++++++++++- 2 files changed, 161 insertions(+), 8 deletions(-) diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 61ccf931cd..3fa67cad3c 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -75,6 +75,7 @@ generic: - useful_aluminum - useful_refining - useful_petro + - useful_resins technology: add: @@ -146,6 +147,20 @@ generic: - fc_h2_refining - solar_refining - dheat_refining + - furnace_coal_resins + - furnace_foil_resins + - furnace_loil_resins + - furnace_ethanol_resins + - furnace_biomass_resins + - furnace_methanol_resins + - furnace_gas_resins + - furnace_elec_resins + - furnace_h2_resins + - hp_gas_resins + - hp_elec_resins + - fc_h2_resins + - solar_resins + - dheat_resins mode: add: - low_temp @@ -432,8 +447,8 @@ fertilizer: - wastewater level: add: - - material_interim - - material_final + - secondary_material + - final_material - wastewater technology: require: @@ -445,6 +460,17 @@ fertilizer: - coal_NH3 - fueloil_NH3 - NH3_to_N_fertil + - trade_NFert + - export_NFert + - import_NFert + - trade_NH3 + - export_NH3 + - import_NH3 + - residual_NH3 + - biomass_NH3_ccs + - gas_NH3_ccs + - coal_NH3_ccs + - fueloil_NH3_ccs methanol: commodity: @@ -471,7 +497,7 @@ methanol: - meth_bio_ccs - meth_h2 - meth_t_d_material - - MTO + - MTO_petro - CH2O_synth - CH2O_to_resin diff --git a/message_ix_models/model/material/data_ammonia.py b/message_ix_models/model/material/data_ammonia.py index 2d40e5c216..6c86a73b5c 100644 --- a/message_ix_models/model/material/data_ammonia.py +++ b/message_ix_models/model/material/data_ammonia.py @@ -126,6 +126,52 @@ def gen_data(scenario, dry_run=False, add_ccs: bool = True): results[cat].append(df) + # Create residual_NH3 technology input and outputs + + common = dict( + commodity="NH3", + technology = 'residual_NH3', + mode="M1", + year_act=act_years, + year_vtg=vtg_years, + time="year", + time_dest="year", + time_origin="year", + ) + + df_input_resid = ( + make_df( + "input", + value=1, + unit="t", + level = 'secondary_material', + **common + ) + .pipe(broadcast, node_loc=nodes) + .pipe(same_node) + ) + + df_output_resid = ( + make_df( + "output", + value=1, + unit="t", + level = 'final_material', + **common + ) + .pipe(broadcast, node_loc=nodes) + .pipe(same_node) + ) + + results['input'].append(df_input_resid) + results['output'].append(df_output_resid) + + # Add residual NH3 demand + + default_gdp_elasticity = float(0.65) + demand_resid_NH3 = gen_resid_demand_NH3(scenario, default_gdp_elasticity) + results["demand"].append(demand_resid_NH3) + # Historical activities/capacities - Region specific common = dict( commodity="NH3", @@ -533,7 +579,7 @@ def gen_data_ccs(scenario, dry_run=False): } # Iterate over new technologies, using the configuration - for t in config["technology"]["add"][12:]: + for t in config["technology"]["add"][13:]: # Output of NH3: same efficiency for all technologies # TODO the output commodity and level are different for # t=NH3_to_N_fertil; use 'if' statements to fill in. @@ -578,13 +624,17 @@ def gen_data_ccs(scenario, dry_run=False): for q in config["technology"]["add"][12:]: df1 = df.copy() df1['technology'] = q - results["initial_activity_lo"].append(df1) + if not q == 'residual_NH3': + df["technology"] = q + results["initial_activity_lo"].append(df1) df = scenario.par('growth_activity_lo', {"technology": ["gas_extr_mpen"]}) for q in config["technology"]["add"][12:]: df1 = df.copy() df1['technology'] = q - results["growth_activity_lo"].append(df1) + if not q == 'residual_NH3': + df["technology"] = q + results["growth_activity_lo"].append(df1) cost_scaler = pd.read_excel( context.get_local_path("material", "ammonia",'regional_cost_scaler_R12.xlsx'), index_col=0).T @@ -622,6 +672,83 @@ def gen_data_ccs(scenario, dry_run=False): return results +def gen_resid_demand_NH3(scenario, gdp_elasticity): + + context = read_config() + s_info = ScenarioInfo(scenario) + modelyears = s_info.Y #s_info.Y is only for modeling years + nodes = s_info.N + + def get_demand_t1_with_income_elasticity( + demand_t0, income_t0, income_t1, elasticity + ): + return ( + elasticity * demand_t0 * ((income_t1 - income_t0) / income_t0) + ) + demand_t0 + + df_gdp = pd.read_excel( + context.get_local_path("material", "methanol", "methanol demand.xlsx"), + sheet_name="GDP_baseline", + ) + + df = df_gdp[(~df_gdp["Region"].isna()) & (df_gdp["Region"] != "World")] + df = df.dropna(axis=1) + + df_demand = df.copy(deep=True) + df_demand = df_demand.drop([2010, 2015, 2020], axis=1) + + # Ammonia Technology Roadmap IEA. 2019 Global NH3 production = 182 Mt. + # 70% is used for nitrogen fertilizer production. Rest is 54.7 Mt. + # Approxiamte regional shares are from Future of Petrochemicals + # Methodological Annex page 7. Total production for regions: + # Asia Pacific (RCPA, CHN, SAS, PAS, PAO) = 90 Mt + # Eurasia (FSU) = 20 Mt, Middle East (MEA) = 15, Africa (AFR) = 5 + # Europe (WEU, EEU) = 25 Mt, Central&South America (LAM) = 5 + # North America (NAM) = 20 Mt. + # Regional shares are derived. They are based on production values not demand. + # Some assumptions made for the regions that are not explicitly covered in IEA. + # (CHN produces the 30% of the ammonia globaly and India 10%.) + # The orders of the regions + # r = ['R12_AFR', 'R12_RCPA', 'R12_EEU', 'R12_FSU', 'R12_LAM', 'R12_MEA',\ + # 'R12_NAM', 'R12_PAO', 'R12_PAS', 'R12_SAS', 'R12_WEU',"R12_CHN"] + + if "R12_CHN" in nodes: + nodes.remove("R12_GLB") + region_set = 'R12_' + dem_2020 = np.array([1.5, 1.5, 3, 6, 1.5, 4.6, 6, 1.5, 1.5, 6, 4.6, 17]) + dem_2020 = pd.Series(dem_2020) + + else: + nodes.remove("R11_GLB") + region_set = 'R11_' + dem_2020 = np.array([1.5, 18.5, 3, 6, 1.5, 4.6, 6, 1.5, 1.5, 6, 4.6]) + dem_2020 = pd.Series(dem_2020) + + df_demand[2020] = dem_2020 + + for i in range(len(modelyears) - 1): + income_year1 = modelyears[i] + income_year2 = modelyears[i + 1] + + dem_2020 = get_demand_t1_with_income_elasticity( + dem_2020, df[income_year1], df[income_year2], gdp_elasticity + ) + df_demand[income_year2] = dem_2020 + + df_melt = df_demand.melt( + id_vars=["Region"], value_vars=df_demand.columns[5:], var_name="year" + ) + + return make_df( + "demand", + unit="t", + level="final_material", + value=df_melt.value, + time="year", + commodity="NH3", + year=df_melt.year, + node=(region_set + df_melt["Region"]), + ) def read_demand(): """Read and clean data from :file:`CD-Links SSP2 N-fertilizer demand.Global.xlsx`.""" @@ -888,9 +1015,9 @@ def read_data_ccs(): param_cat2 = [] - for t in sets["technology"]["add"][12:]: - param_values.extend(params) + for t in sets["technology"]["add"][13:]: tech_values.extend([t] * len(params)) + param_values.extend(params) param_cat2.extend(param_cat) # Clean the data data = ( From 965f05e3bc56e882250c3236e42f7a4c598de157 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 20 Dec 2022 14:56:10 +0100 Subject: [PATCH 542/774] Fix small issues (nh3 reporting, petro demand) --- message_ix_models/model/material/data_petro.py | 10 ---------- 1 file changed, 10 deletions(-) diff --git a/message_ix_models/model/material/data_petro.py b/message_ix_models/model/material/data_petro.py index f54fcbde51..133bde10ce 100644 --- a/message_ix_models/model/material/data_petro.py +++ b/message_ix_models/model/material/data_petro.py @@ -106,16 +106,6 @@ def get_demand_t1_with_income_elasticity( id_vars=["Region"], value_vars=df_demand.columns[5:], var_name="year" ) - df_final = message_ix.make_df( - "demand", - unit="t", - level="demand", - value=df_melt.value, - time="year", - commodity="HVC", - year=df_melt.year, - node=(region_set + df_melt["Region"])) - return message_ix.make_df( "demand", unit="t", From 2d4a573c4ce86207109cac27b79a24c4c9b59839 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 22 Dec 2022 14:44:39 +0100 Subject: [PATCH 543/774] Fix generic furnace data after rebase --- .../data/material/generic_furnace_boiler_techno_economic.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx index 7e82c576ea..e3b984d02b 100644 --- a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx +++ b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:721f426425d712cf83d25f8c6acc2b96a65941bccc1b054305d8ed30fc6eafc7 -size 291 +oid sha256:8ae4c3830d94b46f1619c27bb2f1204e88f5c16bbe8f22bdfb8c51970ccb077c +size 149161 From e83592e22e93ff3587155bc06459a4f022096535 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 22 Dec 2022 16:05:59 +0100 Subject: [PATCH 544/774] Fix all the data files after rebase --- .../data/material/Ammonia feedstock share.Global_R12.xlsx | 3 +++ ...CD-Links SSP2 N-fertilizer demand.Global_R12_adaption.xlsx | 3 +++ message_ix_models/data/material/GLOBIOM_Fertilizer_use_N.xlsx | 3 +++ .../data/material/Global_steel_cement_MESSAGE.xlsx | 4 ++-- .../data/material/N fertil trade - FAOSTAT_data_9-25-2019.csv | 3 --- message_ix_models/data/material/aluminum_techno_economic.xlsx | 4 ++-- .../data/material/ammonia/fert_techno_economic.xlsx | 4 ++-- message_ix_models/data/material/demand_i_feed_R12.xlsx | 3 +++ .../data/material/methanol/MTO data collection.xlsx | 4 ++-- .../data/material/methanol/meth_t_d_material_pars.xlsx | 4 ++-- message_ix_models/data/material/methanol/methanol demand.xlsx | 4 ++-- .../data/material/methanol/methanol_sensitivity_pars.xlsx | 4 ++-- .../data/material/n-fertilizer_techno-economic_new.xlsx | 4 ++-- message_ix_models/data/material/regional_cost_scaler_R12.xlsx | 3 +++ message_ix_models/data/material/trade.FAO.R12.csv | 3 +++ 15 files changed, 34 insertions(+), 19 deletions(-) create mode 100644 message_ix_models/data/material/Ammonia feedstock share.Global_R12.xlsx create mode 100644 message_ix_models/data/material/CD-Links SSP2 N-fertilizer demand.Global_R12_adaption.xlsx create mode 100644 message_ix_models/data/material/GLOBIOM_Fertilizer_use_N.xlsx delete mode 100644 message_ix_models/data/material/N fertil trade - FAOSTAT_data_9-25-2019.csv create mode 100644 message_ix_models/data/material/demand_i_feed_R12.xlsx create mode 100644 message_ix_models/data/material/regional_cost_scaler_R12.xlsx create mode 100644 message_ix_models/data/material/trade.FAO.R12.csv diff --git a/message_ix_models/data/material/Ammonia feedstock share.Global_R12.xlsx b/message_ix_models/data/material/Ammonia feedstock share.Global_R12.xlsx new file mode 100644 index 0000000000..7b0a310bbb --- /dev/null +++ b/message_ix_models/data/material/Ammonia feedstock share.Global_R12.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cd8c36e378c361a5241f80dfec1ae79349ee6426d955ab61f35fecf62f2d1196 +size 16572 diff --git a/message_ix_models/data/material/CD-Links SSP2 N-fertilizer demand.Global_R12_adaption.xlsx b/message_ix_models/data/material/CD-Links SSP2 N-fertilizer demand.Global_R12_adaption.xlsx new file mode 100644 index 0000000000..8c2086b537 --- /dev/null +++ b/message_ix_models/data/material/CD-Links SSP2 N-fertilizer demand.Global_R12_adaption.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:70f88990b90fd7222c570c2091d6b6811673da08a18782dd46c886def1037e4f +size 17012 diff --git a/message_ix_models/data/material/GLOBIOM_Fertilizer_use_N.xlsx b/message_ix_models/data/material/GLOBIOM_Fertilizer_use_N.xlsx new file mode 100644 index 0000000000..67e59cc10e --- /dev/null +++ b/message_ix_models/data/material/GLOBIOM_Fertilizer_use_N.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fe7d802340e0b545133153b183d44a3fb4121bc25ffb961b0ce1f5b2e1a91b45 +size 582147 diff --git a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx index 07d1a91bd4..a19fc189e9 100644 --- a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx +++ b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:0e3f2412b5d64e4128722e242914fc328173ccb7169b9369b9e3e88a6f9d51a0 -size 282 +oid sha256:1b353e40d5a0926de224f4b3af6d95109416b6b104cf70b8895b6ed5196052a5 +size 111240 diff --git a/message_ix_models/data/material/N fertil trade - FAOSTAT_data_9-25-2019.csv b/message_ix_models/data/material/N fertil trade - FAOSTAT_data_9-25-2019.csv deleted file mode 100644 index 913a696147..0000000000 --- a/message_ix_models/data/material/N fertil trade - FAOSTAT_data_9-25-2019.csv +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:2a14397095785122b550b84c937529dcbddb23846ee62dbf3111aaf023912886 -size 235131 diff --git a/message_ix_models/data/material/aluminum_techno_economic.xlsx b/message_ix_models/data/material/aluminum_techno_economic.xlsx index 5e0b413262..da1f0db1cf 100644 --- a/message_ix_models/data/material/aluminum_techno_economic.xlsx +++ b/message_ix_models/data/material/aluminum_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:ccfc80ef9c6e27255438a4e801355a1e59e8eb0c82cd652ff97400708a2394e0 -size 292 +oid sha256:ee05dc52a6c6a8935ceabaa5180cde72612874fdd13d420ccf4001ad5af9c7d5 +size 117451 diff --git a/message_ix_models/data/material/ammonia/fert_techno_economic.xlsx b/message_ix_models/data/material/ammonia/fert_techno_economic.xlsx index abe6bd11a7..6021dad752 100644 --- a/message_ix_models/data/material/ammonia/fert_techno_economic.xlsx +++ b/message_ix_models/data/material/ammonia/fert_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:0dd48af4cc558b66f482f443b68248ac7a9aa4dcef08688986562f470bbc3c42 -size 327 +oid sha256:c305aa19f207694b5e8bf724e902575e0f38dac8e59868be9f5ba61a0abca127 +size 47495 diff --git 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a/message_ix_models/data/material/methanol/meth_t_d_material_pars.xlsx b/message_ix_models/data/material/methanol/meth_t_d_material_pars.xlsx index 71e860b85d..fbabb084ab 100644 --- a/message_ix_models/data/material/methanol/meth_t_d_material_pars.xlsx +++ b/message_ix_models/data/material/methanol/meth_t_d_material_pars.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:fe971a3528bca7dddc5ca849209477c71f04ac428352acfaf70aff6fe545a2bd -size 292 +oid sha256:f0b9aaf0e665f7f82f80c54b484a3c4e27026b106ddddc0802e07c4d1aba4c56 +size 91560 diff --git a/message_ix_models/data/material/methanol/methanol demand.xlsx b/message_ix_models/data/material/methanol/methanol demand.xlsx index 74614b0079..cad4ed6ecf 100644 --- a/message_ix_models/data/material/methanol/methanol demand.xlsx +++ b/message_ix_models/data/material/methanol/methanol demand.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid 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a/message_ix_models/data/material/n-fertilizer_techno-economic_new.xlsx +++ b/message_ix_models/data/material/n-fertilizer_techno-economic_new.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:c799872e689fc07315578426b189f86e6dd9bce02f8d96bc1cb03f9b82a03f0e -size 315 +oid sha256:bb80d7e89adb97881ee09a557c8130ca5b2ef6236d28f7220cd99f418b58177b +size 30333 diff --git a/message_ix_models/data/material/regional_cost_scaler_R12.xlsx b/message_ix_models/data/material/regional_cost_scaler_R12.xlsx new file mode 100644 index 0000000000..7fa9cefec7 --- /dev/null +++ b/message_ix_models/data/material/regional_cost_scaler_R12.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:74aa9b84801d0f5752a59917f1c6c56da50fe2f8d8545409d04003281b7563b8 +size 9680 diff --git a/message_ix_models/data/material/trade.FAO.R12.csv b/message_ix_models/data/material/trade.FAO.R12.csv new file mode 100644 index 0000000000..68020a1d8d --- /dev/null +++ b/message_ix_models/data/material/trade.FAO.R12.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8fe4337e315536cfe4971caed24f22f55b999382109303fd295db85202685831 +size 2737 From 22309c06351f3133c1d092b6195aebec230e18a5 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 22 Dec 2022 17:35:14 +0100 Subject: [PATCH 545/774] Fix missing things after the rebase --- .../ammonia/fert_techno_economic.xlsx | 4 +- message_ix_models/model/material/__init__.py | 4 +- .../model/material/data_ammonia.py | 1039 ----------------- .../model/material/data_ammonia_new.py | 196 +++- 4 files changed, 142 insertions(+), 1101 deletions(-) delete mode 100644 message_ix_models/model/material/data_ammonia.py diff --git a/message_ix_models/data/material/ammonia/fert_techno_economic.xlsx b/message_ix_models/data/material/ammonia/fert_techno_economic.xlsx index 6021dad752..47f7a37753 100644 --- a/message_ix_models/data/material/ammonia/fert_techno_economic.xlsx +++ b/message_ix_models/data/material/ammonia/fert_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:c305aa19f207694b5e8bf724e902575e0f38dac8e59868be9f5ba61a0abca127 -size 47495 +oid sha256:a464621f464d44aed998dd40dcae772417027578b1d899b73f2127d0daa8afda +size 47645 diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index bb8fe1dd4c..368022f003 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -393,13 +393,13 @@ def run_old_reporting(scenario_name, model_name): from .data_power_sector import gen_data_power_sector from .data_ammonia import gen_data_ammonia from .data_methanol import gen_data_methanol +from .data_ammonia_new import gen_all_NH3_fert DATA_FUNCTIONS_1 = [ #gen_data_buildings, gen_data_methanol, gen_all_NH3_fert, - #gen_data_ammonia, gen_data_generic, gen_data_steel, ] @@ -408,7 +408,7 @@ def run_old_reporting(scenario_name, model_name): gen_data_cement, gen_data_petro_chemicals, #gen_data_power_sector, - #gen_data_aluminum, + gen_data_aluminum ] # Try to handle multiple data input functions from different materials diff --git a/message_ix_models/model/material/data_ammonia.py b/message_ix_models/model/material/data_ammonia.py deleted file mode 100644 index 6c86a73b5c..0000000000 --- a/message_ix_models/model/material/data_ammonia.py +++ /dev/null @@ -1,1039 +0,0 @@ -from collections import defaultdict -import logging -from pathlib import Path - -import pandas as pd -import numpy as np -from message_ix import make_df -from message_ix_models import ScenarioInfo -from message_ix_models.util import broadcast, same_node - -from .util import read_config - - -log = logging.getLogger(__name__) - -CONVERSION_FACTOR_CO2_C = 12 / 44 -CONVERSION_FACTOR_NH3_N = 17 / 14 -CONVERSION_FACTOR_PJ_GWa = 0.0317 - - -def gen_data(scenario, dry_run=False, add_ccs: bool = True): - """Generate data for materials representation of nitrogen fertilizers. - - .. note:: This code is only partially translated from - :file:`SetupNitrogenBase.py`. - """ - # Load configuration - config = read_config()["material"]["fertilizer"] - context = read_config() - #print(config_.get_local_path("material", "ammonia", "test.xlsx")) - # Information about scenario, e.g. node, year - s_info = ScenarioInfo(scenario) - nodes = s_info.N - if "World" in nodes: - nodes.pop(nodes.index("World")) - if "R12_GLB" in nodes: - nodes.pop(nodes.index("R12_GLB")) - - # Techno-economic assumptions - data = read_data() - - # List of data frames, to be concatenated together at end - results = defaultdict(list) - - input_commodity_dict = { - "input_water": "freshwater_supply", - "input_elec": "electr", - "input_fuel": "" - } - output_commodity_dict = { - "output_NH3": "NH3", - "output_heat": "d_heat", - "output_water": "wastewater" # ask Jihoon how to name - } - commodity_dict = { - "output": output_commodity_dict, - "input": input_commodity_dict - } - input_level_dict = { - "input_water": "water_supply", - "input_fuel": "secondary", - "input_elec": "secondary" - } - output_level_dict = { - "output_water": "wastewater", - "output_heat": "secondary", - "output_NH3": "secondary_material" - } - level_cat_dict = { - "output": output_level_dict, - "input": input_level_dict - } - - - vtg_years = s_info.yv_ya[s_info.yv_ya.year_vtg > 2000]["year_vtg"] - act_years = s_info.yv_ya[s_info.yv_ya.year_vtg > 2000]["year_act"] - - # NH3 production processes - common = dict( - year_act=act_years, # confirm if correct?? - year_vtg=vtg_years, - commodity="NH3", - level="secondary_material", - mode="M1", - time="year", - time_dest="year", - time_origin="year", - emission="CO2_industry" # confirm if correct - ) - - # Iterate over new technologies, using the configuration - for t in config["technology"]["add"][:6]: - # TODO: refactor to adjust to yaml structure - # Output of NH3: same efficiency for all technologies - # the output commodity and level are different for - - for param in data['parameter'].unique(): - if (t == "electr_NH3") & (param == "input_fuel"): - continue - unit = data['Unit'][data['parameter'] == param].iloc[0] - cat = data['param_cat'][data['parameter'] == param].iloc[0] - if cat in ["input", "output"]: - common["commodity"] = commodity_dict[cat][param] - common["level"] = level_cat_dict[cat][param] - if (t == "biomass_NH3") & (param == "input_fuel"): - common["level"] = "primary" - if (str(t) == "NH3_to_N_fertil") & (param == "output_NH3"): - common['commodity'] = "Fertilizer Use|Nitrogen" - common['level'] = "final_material" - if (str(t) == "NH3_to_N_fertil") & (param == "input_fuel"): - common['level'] = "secondary_material" - df = ( - make_df(cat, technology=t, value=1, unit="-", **common) - .pipe(broadcast, node_loc=nodes) - .pipe(same_node) - ) - - row = data[(data['technology'] == t) & - (data['parameter'] == param)] - df = df.assign(value=row[2010].values[0]) - - if param == "input_fuel": - comm = data['technology'][(data['parameter'] == param) & - (data["technology"] == t)].iloc[0].split("_")[0] - df = df.assign(commodity=comm) - - results[cat].append(df) - - # Create residual_NH3 technology input and outputs - - common = dict( - commodity="NH3", - technology = 'residual_NH3', - mode="M1", - year_act=act_years, - year_vtg=vtg_years, - time="year", - time_dest="year", - time_origin="year", - ) - - df_input_resid = ( - make_df( - "input", - value=1, - unit="t", - level = 'secondary_material', - **common - ) - .pipe(broadcast, node_loc=nodes) - .pipe(same_node) - ) - - df_output_resid = ( - make_df( - "output", - value=1, - unit="t", - level = 'final_material', - **common - ) - .pipe(broadcast, node_loc=nodes) - .pipe(same_node) - ) - - results['input'].append(df_input_resid) - results['output'].append(df_output_resid) - - # Add residual NH3 demand - - default_gdp_elasticity = float(0.65) - demand_resid_NH3 = gen_resid_demand_NH3(scenario, default_gdp_elasticity) - results["demand"].append(demand_resid_NH3) - - # Historical activities/capacities - Region specific - common = dict( - commodity="NH3", - level="secondary_material", - mode="M1", - time="year", - time_dest="year", - time_origin="year", - ) - act2010 = read_demand()['act2010'] - df = ( - make_df("historical_activity", - technology=[t for t in config["technology"]["add"][:6]], - value=1, unit='t', years_act=s_info.Y, **common) - .pipe(broadcast, node_loc=nodes) - .pipe(same_node) - ) - row = act2010 - - results["historical_activity"].append( - df.assign(value=row, unit='t', year_act=2010) - ) - # 2015 activity necessary if this is 5-year step scenario - # df['value'] = act2015 # total NH3 or N in Mt 2010 FAO Russia - # df['year_act'] = 2015 - # Sc_nitro.add_par("historical_activity", df) - - df = ( - make_df("historical_new_capacity", - technology=[t for t in config["technology"]["add"][:6]], # ], refactor to adjust to yaml structure - value=1, unit='t', **common) - .pipe(broadcast, node_loc=nodes) - .pipe(same_node) - ) - - # modifying act2010 values by assuming 1/lifetime (=15yr) is built each year and account for capacity factor - capacity_factor = read_demand()['capacity_factor'] - row = act2010 * 1 / 15 / capacity_factor[0] - - results["historical_new_capacity"].append( - df.assign(value=row, unit='t', year_vtg=2010) - ) - - # %% Secure feedstock balance (foil_fs, gas_fs, coal_fs) loil_fs? - - # Adjust i_feed demand - N_energy = read_demand()['N_energy'] - N_energy = read_demand()['N_feed'] # updated feed with imports accounted - - demand_fs_org = pd.read_excel(context.get_local_path("material", "ammonia",'demand_i_feed_R12.xlsx')) - - df = demand_fs_org.loc[demand_fs_org.year == 2010, :].join(N_energy.set_index('node'), on='node') - sh = pd.DataFrame({'node': demand_fs_org.loc[demand_fs_org.year == 2010, 'node'], - 'r_feed': df.totENE / df.value}) # share of NH3 energy among total feedstock (no trade assumed) - df = demand_fs_org.join(sh.set_index('node'), on='node') - df.value *= 1 - df.r_feed # Carve out the same % from tot i_feed values - df = df.drop('r_feed', axis=1) - df = df.drop('Unnamed: 0', axis=1) - # TODO: refactor with a more sophisticated solution to reduce i_feed - df.loc[df["value"] < 0, "value"] = 0 # temporary solution to avoid negative values - results["demand"].append(df) - - # Globiom land input DEPRECATED - """ - df = pd.read_excel(context.get_local_path("material", "ammonia",'GLOBIOM_Fertilizer_use_N.xlsx')) - df = df.replace(regex=r'^R11', value="R12").replace(regex=r'^R12_CPA', value="R12_CHN") - df["unit"] = "t" - df.loc[df["node"] == "R12_CHN", "value"] *= 0.93 # hotfix to adjust to R12 - df_rcpa = df.loc[df["node"] == "R12_CHN"].copy(deep=True) - df_rcpa["node"] = "R12_RCPA" - df_rcpa["value"] *= 0.07 - df = df.append(df_rcpa) - df = df.drop("Unnamed: 0", axis=1) - results["land_input"].append(df) - """ - - df = scenario.par("land_output", {"commodity": "Fertilizer Use|Nitrogen"}) - df["level"] = "final_material" - results["land_input"].append(df) - #scenario.add_par("land_input", df) - - # add background parameters (growth rates and bounds) - - df = scenario.par('initial_activity_lo', {"technology": ["gas_extr_mpen"]}) - for q in config["technology"]["add"][:6]: - df1 = df.copy() - df1['technology'] = q - results["initial_activity_lo"].append(df1) - - df = scenario.par('growth_activity_lo', {"technology": ["gas_extr_mpen"]}) - for q in config["technology"]["add"][:6]: - df1 = df.copy() - df1['technology'] = q - results["growth_activity_lo"].append(df1) - - cost_scaler = pd.read_excel( - context.get_local_path("material", "ammonia",'regional_cost_scaler_R12.xlsx'), index_col=0).T - - scalers_dict = { - "R12_CHN": {"coal_NH3": 0.75 * 0.91, # gas/coal price ratio * discount - "fueloil_NH3": 0.66 * 0.91}, # gas/oil price ratio * discount - "R12_SAS": {"fueloil_NH3": 0.59, - "coal_NH3": 1} - } - - params = ["inv_cost", "fix_cost", "var_cost"] - for param in params: - for i in range(len(results[param])): - df = results[param][i] - if df["technology"].any() in ('NH3_to_N_fertil', 'electr_NH3'): # skip those techs - continue - regs = df.set_index("node_loc").join(cost_scaler, on="node_loc") - regs.value = regs.value * regs["standard"] - regs = regs.reset_index() - if df["technology"].any() in ("coal_NH3", "fueloil_NH3"): # additional scaling to make coal/oil cheaper - regs.loc[regs["node_loc"] == "R12_CHN", "value"] = \ - regs.loc[regs["node_loc"] == "R12_CHN", "value"] * \ - scalers_dict["R12_CHN"][df.technology[0]] - regs.loc[regs["node_loc"] == "R12_SAS", "value"] = \ - regs.loc[regs["node_loc"] == "R12_SAS", "value"] * \ - scalers_dict["R12_SAS"][df.technology[0]] - results[param][i] = regs.drop(["standard", "ccs"], axis="columns") - - # add trade tecs (exp, imp, trd) - - newtechnames_trd = ["trade_NFert"] - newtechnames_imp = ["import_NFert"] - newtechnames_exp = ["export_NFert"] - - scenario.add_set( - "technology", - newtechnames_trd + newtechnames_imp + newtechnames_exp - ) - cat_add = pd.DataFrame( - { - "type_tec": ["import", "export"], # 'all' not need to be added here - "technology": newtechnames_imp + newtechnames_exp, - } - ) - scenario.add_set("cat_tec", cat_add) - - yv_ya_exp = s_info.yv_ya - yv_ya_exp = yv_ya_exp[(yv_ya_exp["year_act"] - yv_ya_exp["year_vtg"] < 30) & (yv_ya_exp["year_vtg"] > 2000)] - yv_ya_same = s_info.yv_ya[(s_info.yv_ya["year_act"] - s_info.yv_ya["year_vtg"] == 0) & ( s_info.yv_ya["year_vtg"] > 2000)] - - common = dict( - year_act=yv_ya_same.year_act, - year_vtg=yv_ya_same.year_vtg, - commodity="Fertilizer Use|Nitrogen", - level="final_material", - mode="M1", - time="year", - time_dest="year", - time_origin="year", - ) - - data = read_trade_data(context, comm="NFert") - for i in data["var_name"].unique(): - for tec in data["technology"].unique(): - row = data[(data["var_name"] == i) & (data["technology"] == tec)] - if len(row): - if row["technology"].values[0] == "trade_NFert": - node = ["R12_GLB"] - else: - node = nodes - if tec == "export_NFert": - common_exp = common - common_exp["year_act"] = yv_ya_exp.year_act - common_exp["year_vtg"] = yv_ya_exp.year_vtg - df = make_df(i, technology=tec, value=row[2010].values[0], - unit="-", **common_exp).pipe(broadcast, node_loc=node).pipe(same_node) - else: - df = make_df(i, technology=tec, value=row[2010].values[0], - unit="-", **common).pipe(broadcast, node_loc=node).pipe(same_node) - if (tec == "export_NFert") & (i == "output"): - df["node_dest"] = "R12_GLB" - df["level"] = "export" - elif (tec == "import_NFert") & (i == "input"): - df["node_origin"] = "R12_GLB" - df["level"] = "import" - elif (tec == "trade_NFert") & (i == "input"): - df["level"] = "export" - elif (tec == "trade_NFert") & (i == "output"): - df["level"] = "import" - else: - df.pipe(same_node) - results[i].append(df) - - common = dict( - commodity="Fertilizer Use|Nitrogen", - level="final_material", - mode="M1", - time="year", - time_dest="year", - time_origin="year", - unit="t" - ) - - N_trade_R12 = read_demand()["N_trade_R12"].assign(mode="M1") - N_trade_R12["technology"] = N_trade_R12["Element"].apply( - lambda x: "export_NFert" if x == "Export" else "import_NFert") - df_exp_imp_act = N_trade_R12.drop("Element", axis=1) - - trd_act_years = N_trade_R12["year_act"].unique() - values = N_trade_R12.groupby(["year_act"]).sum().values.flatten() - fert_trd_hist = make_df("historical_activity", technology="trade_NFert", - year_act=trd_act_years, value=values, - node_loc="R12_GLB", **common) - results["historical_activity"].append(pd.concat([df_exp_imp_act, fert_trd_hist])) - - df_hist_cap_new = N_trade_R12[N_trade_R12["technology"] == "export_NFert"].drop(columns=["time", "mode", "Element"]) - df_hist_cap_new = df_hist_cap_new.rename(columns={"year_act": "year_vtg"}) - # divide by export lifetime derived from coal_exp - df_hist_cap_new = df_hist_cap_new.assign(value=lambda x: x["value"] / 30) - results["historical_new_capacity"].append(df_hist_cap_new) - - #NH3 trade - #_______________________________________________ - - common = dict( - year_act=yv_ya_same.year_act, - year_vtg=yv_ya_same.year_vtg, - commodity="NH3", - level="secondary_material", - mode="M1", - time="year", - time_dest="year", - time_origin="year", - ) - - data = read_trade_data(context, comm="NH3") - - for i in data["var_name"].unique(): - for tec in data["technology"].unique(): - row = data[(data["var_name"] == i) & (data["technology"] == tec)] - if len(row): - if row["technology"].values[0] == "trade_NH3": - node = ["R12_GLB"] - else: - node = nodes - if tec == "export_NH3": - common_exp = common - common_exp["year_act"] = yv_ya_exp.year_act - common_exp["year_vtg"] = yv_ya_exp.year_vtg - df = make_df(i, technology=tec, value=row[2010].values[0], - unit="-", **common_exp).pipe(broadcast, node_loc=node).pipe(same_node) - else: - df = make_df(i, technology=tec, value=row[2010].values[0], - unit="-", **common).pipe(broadcast, node_loc=node).pipe(same_node) - if (tec == "export_NH3") & (i == "output"): - df["node_dest"] = "R12_GLB" - df["level"] = "export" - elif (tec == "import_NH3") & (i == "input"): - df["node_origin"] = "R12_GLB" - df["level"] = "import" - elif (tec == "trade_NH3") & (i == "input"): - df["level"] = "export" - elif (tec == "trade_NH3") & (i == "output"): - df["level"] = "import" - else: - df.pipe(same_node) - results[i].append(df) - - common = dict( - commodity="NH3", - level="secondary_material", - mode="M1", - time="year", - time_dest="year", - time_origin="year", - unit="t" - ) - - NH3_trade_R12 = read_demand()["NH3_trade_R12"].assign(mode="M1") - NH3_trade_R12["technology"] = NH3_trade_R12["type"].apply( - lambda x: "export_NH3" if x == "export" else "import_NH3") - df_exp_imp_act = NH3_trade_R12.drop("type", axis=1) - - trd_act_years = NH3_trade_R12["year_act"].unique() - values = NH3_trade_R12.groupby(["year_act"]).sum().values.flatten() - fert_trd_hist = make_df("historical_activity", technology="trade_NH3", - year_act=trd_act_years, value=values, - node_loc="R12_GLB", **common) - results["historical_activity"].append(pd.concat([df_exp_imp_act, fert_trd_hist])) - - df_hist_cap_new = NH3_trade_R12[NH3_trade_R12["technology"] == "export_NH3"].drop(columns=["time", "mode", "type"]) - df_hist_cap_new = df_hist_cap_new.rename(columns={"year_act": "year_vtg"}) - # divide by export lifetime derived from coal_exp - df_hist_cap_new = df_hist_cap_new.assign(value=lambda x: x["value"] / 30) - results["historical_new_capacity"].append(df_hist_cap_new) - #___________________________________________________________________________ - - if add_ccs: - for k, v in gen_data_ccs(scenario).items(): - results[k].append(v) - - # Concatenate to one dataframe per parameter - results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} - - par = "emission_factor" - rel_df_cc = results[par] - rel_df_cc = rel_df_cc[rel_df_cc["emission"] == "CO2_industry"] - rel_df_cc = rel_df_cc.assign(year_rel=rel_df_cc["year_act"], - node_rel=rel_df_cc["node_loc"], - relation="CO2_cc").drop(["emission", "year_vtg"], axis=1) - rel_df_cc = rel_df_cc[rel_df_cc["technology"] != "NH3_to_N_fertil"] - rel_df_cc = rel_df_cc[rel_df_cc["year_rel"] == rel_df_cc["year_act"]].drop_duplicates() - if add_ccs: - rel_df_cc[rel_df_cc["technology"] == "biomass_NH3_ccs"] = rel_df_cc[ - rel_df_cc["technology"] == "biomass_NH3_ccs"].assign( - value=results[par][results[par]["technology"] == "biomass_NH3_ccs"]["value"].values[0]) - - - rel_df_em = results[par] - rel_df_em = rel_df_em[rel_df_em["emission"] == "CO2"] - rel_df_em = rel_df_em.assign(year_rel=rel_df_em["year_act"], - node_rel=rel_df_em["node_loc"], - relation="CO2_Emission").drop(["emission", "year_vtg"], axis=1) - rel_df_em = rel_df_em[rel_df_em["technology"] != "NH3_to_N_fertil"] - rel_df_em[rel_df_em["year_rel"] == rel_df_em["year_act"]].drop_duplicates() - results["relation_activity"] = pd.concat([rel_df_cc, rel_df_em]) - - results["emission_factor"] = results["emission_factor"][results["emission_factor"]["technology"]!="NH3_to_N_fertil"] - - return results - - -def gen_data_ccs(scenario, dry_run=False): - """Generate data for materials representation of nitrogen fertilizers. - - .. note:: This code is only partially translated from - :file:`SetupNitrogenBase.py`. - """ - config = read_config()["material"]["fertilizer"] - context = read_config() - - # Information about scenario, e.g. node, year - s_info = ScenarioInfo(scenario) - nodes = s_info.N - if "World" in nodes: - nodes.pop(nodes.index("World")) - if "R12_GLB" in nodes: - nodes.pop(nodes.index("R12_GLB")) - - # Techno-economic assumptions - data = read_data_ccs() - - # List of data frames, to be concatenated together at end - results = defaultdict(list) - - vtg_years = s_info.yv_ya[s_info.yv_ya.year_vtg > 2000]["year_vtg"] - act_years = s_info.yv_ya[s_info.yv_ya.year_vtg > 2000]["year_act"] - - # NH3 production processes - # NOTE: The energy required for NH3 production process is retreived - # from secondary energy level for the moment. However, the energy that is used to - # produce ammonia as feedstock is accounted in Final Energy in reporting. - # The energy that is used to produce ammonia as fuel should be accounted in - # Secondary energy. At the moment all ammonia is used as feedstock. - # Later we can track the shares of feedstock vs fuel use of ammonia and - # divide final energy. Or create another set of technologies (only for fuel - # use vs. only for feedstock use). - - common = dict( - year_vtg=vtg_years, - year_act=act_years, # confirm if correct?? - commodity="NH3", - level="secondary_material", - # TODO fill in remaining dimensions - mode="M1", - time="year", - time_dest="year", - time_origin="year", - emission="CO2" # confirm if correct - # node_loc='node' - ) - - input_commodity_dict = { - "input_water": "freshwater_supply", - "input_elec": "electr", - "input_fuel": "" - } - output_commodity_dict = { - "output_NH3": "NH3", - "output_heat": "d_heat", - "output_water": "wastewater" # ask Jihoon how to name - } - commodity_dict = { - "output": output_commodity_dict, - "input": input_commodity_dict - } - input_level_dict = { - "input_water": "water_supply", - "input_fuel": "secondary", - "input_elec": "secondary" - } - output_level_dict = { - "output_water": "wastewater", - "output_heat": "secondary", - "output_NH3": "secondary_material" - } - level_cat_dict = { - "output": output_level_dict, - "input": input_level_dict - } - - # Iterate over new technologies, using the configuration - for t in config["technology"]["add"][13:]: - # Output of NH3: same efficiency for all technologies - # TODO the output commodity and level are different for - # t=NH3_to_N_fertil; use 'if' statements to fill in. - - for param in data['parameter'].unique(): - unit = data['Unit'][data['parameter'] == param].iloc[0] - cat = data['param_cat'][data['parameter'] == param].iloc[0] - if cat in ["input", "output"]: - common["commodity"] = commodity_dict[cat][param] - common["level"] = level_cat_dict[cat][param] - if (t == "biomass_NH3_ccs") & (param == "input_fuel"): - common["level"] = "primary" - if param == "emission_factor_trans": - _common = common.copy() - _common["emission"] = "CO2_industry" - cat = "emission_factor" - df = ( - make_df(cat, technology=t, value=1, unit="-", **_common) - .pipe(broadcast, node_loc=nodes) - .pipe(same_node) - ) - - else: - df = ( - make_df(cat, technology=t, value=1, unit="-", **common) - .pipe(broadcast, node_loc=nodes) - .pipe(same_node) - ) - row = data[(data['technology'] == str(t)) & - (data['parameter'] == param)] - df = df.assign(value=row[2010].values[0]) - if param == "input_fuel": - comm = data['technology'][(data['parameter'] == param) & - (data["technology"] == t)].iloc[0].split("_")[0] - df = df.assign(commodity=comm) - results[cat].append(df) - - - # add background parameters (growth rates and bounds) - - df = scenario.par('initial_activity_lo', {"technology": ["gas_extr_mpen"]}) - for q in config["technology"]["add"][12:]: - df1 = df.copy() - df1['technology'] = q - if not q == 'residual_NH3': - df["technology"] = q - results["initial_activity_lo"].append(df1) - - df = scenario.par('growth_activity_lo', {"technology": ["gas_extr_mpen"]}) - for q in config["technology"]["add"][12:]: - df1 = df.copy() - df1['technology'] = q - if not q == 'residual_NH3': - df["technology"] = q - results["growth_activity_lo"].append(df1) - - cost_scaler = pd.read_excel( - context.get_local_path("material", "ammonia",'regional_cost_scaler_R12.xlsx'), index_col=0).T - - scalers_dict = { - "R12_CHN": {"coal_NH3": 0.75 * 0.91, # gas/coal price ratio * discount - "fueloil_NH3": 0.66 * 0.91}, # gas/oil price ratio * discount - "R12_SAS": {"fueloil_NH3": 0.59, - "coal_NH3": 1} - } - - params = ["inv_cost", "fix_cost", "var_cost"] - for param in params: - for i in range(len(results[param])): - df = results[param][i] - if df["technology"].any() in ('NH3_to_N_fertil', 'electr_NH3'): # skip those techs - continue - regs = df.set_index("node_loc").join(cost_scaler, on="node_loc") - regs.value = regs.value * regs["ccs"] - regs = regs.reset_index() - if df["technology"].any() in ("coal_NH3", "fueloil_NH3"): # additional scaling to make coal/oil cheaper - regs.loc[regs["node_loc"] == "R12_CHN", "value"] = \ - regs.loc[regs["node_loc"] == "R12_CHN", "value"] * \ - scalers_dict["R12_CHN"][df.technology[0].values[0].name] - regs.loc[regs["node_loc"] == "R12_SAS", "value"] = \ - regs.loc[regs["node_loc"] == "R12_SAS", "value"] * \ - scalers_dict["R12_SAS"][df.technology[0].values[0].name] - results[param][i] = regs.drop(["standard", "ccs"], axis="columns") - - # Concatenate to one dataframe per parameter - results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} - - #results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} - - - return results - -def gen_resid_demand_NH3(scenario, gdp_elasticity): - - context = read_config() - s_info = ScenarioInfo(scenario) - modelyears = s_info.Y #s_info.Y is only for modeling years - nodes = s_info.N - - def get_demand_t1_with_income_elasticity( - demand_t0, income_t0, income_t1, elasticity - ): - return ( - elasticity * demand_t0 * ((income_t1 - income_t0) / income_t0) - ) + demand_t0 - - df_gdp = pd.read_excel( - context.get_local_path("material", "methanol", "methanol demand.xlsx"), - sheet_name="GDP_baseline", - ) - - df = df_gdp[(~df_gdp["Region"].isna()) & (df_gdp["Region"] != "World")] - df = df.dropna(axis=1) - - df_demand = df.copy(deep=True) - df_demand = df_demand.drop([2010, 2015, 2020], axis=1) - - # Ammonia Technology Roadmap IEA. 2019 Global NH3 production = 182 Mt. - # 70% is used for nitrogen fertilizer production. Rest is 54.7 Mt. - # Approxiamte regional shares are from Future of Petrochemicals - # Methodological Annex page 7. Total production for regions: - # Asia Pacific (RCPA, CHN, SAS, PAS, PAO) = 90 Mt - # Eurasia (FSU) = 20 Mt, Middle East (MEA) = 15, Africa (AFR) = 5 - # Europe (WEU, EEU) = 25 Mt, Central&South America (LAM) = 5 - # North America (NAM) = 20 Mt. - # Regional shares are derived. They are based on production values not demand. - # Some assumptions made for the regions that are not explicitly covered in IEA. - # (CHN produces the 30% of the ammonia globaly and India 10%.) - # The orders of the regions - # r = ['R12_AFR', 'R12_RCPA', 'R12_EEU', 'R12_FSU', 'R12_LAM', 'R12_MEA',\ - # 'R12_NAM', 'R12_PAO', 'R12_PAS', 'R12_SAS', 'R12_WEU',"R12_CHN"] - - if "R12_CHN" in nodes: - nodes.remove("R12_GLB") - region_set = 'R12_' - dem_2020 = np.array([1.5, 1.5, 3, 6, 1.5, 4.6, 6, 1.5, 1.5, 6, 4.6, 17]) - dem_2020 = pd.Series(dem_2020) - - else: - nodes.remove("R11_GLB") - region_set = 'R11_' - dem_2020 = np.array([1.5, 18.5, 3, 6, 1.5, 4.6, 6, 1.5, 1.5, 6, 4.6]) - dem_2020 = pd.Series(dem_2020) - - df_demand[2020] = dem_2020 - - for i in range(len(modelyears) - 1): - income_year1 = modelyears[i] - income_year2 = modelyears[i + 1] - - dem_2020 = get_demand_t1_with_income_elasticity( - dem_2020, df[income_year1], df[income_year2], gdp_elasticity - ) - df_demand[income_year2] = dem_2020 - - df_melt = df_demand.melt( - id_vars=["Region"], value_vars=df_demand.columns[5:], var_name="year" - ) - - return make_df( - "demand", - unit="t", - level="final_material", - value=df_melt.value, - time="year", - commodity="NH3", - year=df_melt.year, - node=(region_set + df_melt["Region"]), - ) - -def read_demand(): - """Read and clean data from :file:`CD-Links SSP2 N-fertilizer demand.Global.xlsx`.""" - # Demand scenario [Mt N/year] from GLOBIOM - context = read_config() - - - N_demand_GLO = pd.read_excel(context.get_local_path("material", "ammonia",'CD-Links SSP2 N-fertilizer demand_R12.xlsx'), sheet_name='data') - - # NH3 feedstock share by region in 2010 (from http://ietd.iipnetwork.org/content/ammonia#benchmarks) - feedshare_GLO = pd.read_excel(context.get_local_path("material", "ammonia",'Ammonia feedstock share_R12.xlsx'), sheet_name='Sheet2', skiprows=14) - - # Read parameters in xlsx - te_params = data = pd.read_excel( - context.get_local_path("material", "ammonia", "n-fertilizer_techno-economic_new.xlsx"), - sheet_name="Sheet1", engine="openpyxl", nrows=72 - ) - n_inputs_per_tech = 12 # Number of input params per technology - - input_fuel = te_params[2010][list(range(4, te_params.shape[0], n_inputs_per_tech))].reset_index(drop=True) - #input_fuel[0:5] = input_fuel[0:5] * CONVERSION_FACTOR_PJ_GWa # 0.0317 GWa/PJ, GJ/t = PJ/Mt NH3 - - capacity_factor = te_params[2010][list(range(11, te_params.shape[0], n_inputs_per_tech))].reset_index(drop=True) - - # Regional N demaand in 2010 - ND = N_demand_GLO.loc[N_demand_GLO.Scenario == "NoPolicy", ['Region', 2010]] - ND = ND[ND.Region != 'World'] - ND.Region = 'R12_' + ND.Region - ND = ND.set_index('Region') - - # Derive total energy (GWa) of NH3 production (based on demand 2010) - N_energy = feedshare_GLO[feedshare_GLO.Region != 'R12_GLB'].join(ND, on='Region') - N_energy = pd.concat( - [N_energy.Region, N_energy[["gas_pct", "coal_pct", "oil_pct"]].multiply(N_energy[2010], axis="index")], axis=1) - N_energy.gas_pct *= input_fuel[2] * CONVERSION_FACTOR_NH3_N # NH3 / N - N_energy.coal_pct *= input_fuel[3] * CONVERSION_FACTOR_NH3_N - N_energy.oil_pct *= input_fuel[4] * CONVERSION_FACTOR_NH3_N - N_energy = pd.concat([N_energy.Region, N_energy.sum(axis=1)], axis=1).rename( - columns={0: 'totENE', 'Region': 'node'}) # GWa - - N_trade_R12 = pd.read_csv(context.get_local_path("material", "ammonia","trade.FAO.R12.csv"), index_col=0) - N_trade_R12.msgregion = "R12_" + N_trade_R12.msgregion - N_trade_R12.Value = N_trade_R12.Value / 1e6 - N_trade_R12.Unit = "t" - N_trade_R12 = N_trade_R12.assign(time="year") - N_trade_R12 = N_trade_R12.rename( - columns={ - "Value": "value", - "Unit": "unit", - "msgregion": "node_loc", - "Year": "year_act", - } - ) - - df = N_trade_R12.loc[ - N_trade_R12.year_act == 2010, - ] - df = df.pivot(index="node_loc", columns="Element", values="value") - NP = pd.DataFrame({"netimp": df.Import - df.Export, "demand": ND[2010]}) - NP["prod"] = NP.demand - NP.netimp - - NH3_trade_R12 = pd.read_csv(context.get_local_path("material", "ammonia","NH3_trade_BACI_R12_aggregation.csv"))#, index_col=0) - NH3_trade_R12.region = "R12_" + NH3_trade_R12.region - NH3_trade_R12.quantity = NH3_trade_R12.quantity / 1e6 - NH3_trade_R12.unit = "t" - NH3_trade_R12 = NH3_trade_R12.assign(time="year") - NH3_trade_R12 = NH3_trade_R12.rename( - columns={ - "quantity": "value", - "region": "node_loc", - "year": "year_act", - } - ) - - - # Derive total energy (GWa) of NH3 production (based on demand 2010) - N_feed = feedshare_GLO[feedshare_GLO.Region != "R11_GLB"].join(NP, on="Region") - N_feed = pd.concat( - [ - N_feed.Region, - N_feed[["gas_pct", "coal_pct", "oil_pct"]].multiply( - N_feed["prod"], axis="index" - ), - ], - axis=1, - ) - N_feed.gas_pct *= input_fuel[2] * 17 / 14 - N_feed.coal_pct *= input_fuel[3] * 17 / 14 - N_feed.oil_pct *= input_fuel[4] * 17 / 14 - N_feed = pd.concat([N_feed.Region, N_feed.sum(axis=1)], axis=1).rename( - columns={0: "totENE", "Region": "node"}) - - # Process the regional historical activities - - fs_GLO = feedshare_GLO.copy() - fs_GLO.insert(1, "bio_pct", 0) - fs_GLO.insert(2, "elec_pct", 0) - # 17/14 NH3:N ratio, to get NH3 activity based on N demand => No NH3 loss assumed during production - fs_GLO.iloc[:, 1:6] = input_fuel[5] * fs_GLO.iloc[:, 1:6] - fs_GLO.insert(6, "NH3_to_N", 1) - - # Share of feedstocks for NH3 prodution (based on 2010 => Assumed fixed for any past years) - feedshare = fs_GLO.sort_values(['Region']).set_index('Region').drop('R12_GLB') - - # Get historical N demand from SSP2-nopolicy (may need to vary for diff scenarios) - N_demand_raw = N_demand_GLO.copy() - N_demand = N_demand_raw[(N_demand_raw.Scenario == "NoPolicy") & - (N_demand_raw.Region != "World")].reset_index().loc[:, 2010] # 2010 tot N demand - N_demand = N_demand.repeat(6) - - act2010 = (feedshare.values.flatten() * N_demand).reset_index(drop=True) - - return {"feedshare_GLO": feedshare_GLO, "ND": ND, "N_energy": N_energy, "feedshare": feedshare, 'act2010': act2010, - 'capacity_factor': capacity_factor, "N_feed":N_feed, "N_trade_R12":N_trade_R12, "NH3_trade_R12":NH3_trade_R12} - - -def read_trade_data(context, comm): - if comm == "NFert": - data = pd.read_excel( - context.get_local_path("material", "ammonia", "n-fertilizer_techno-economic_new.xlsx"), - sheet_name="Trade", engine="openpyxl", usecols=np.linspace(0, 7, 8, dtype=int)) - data = data.assign(technology=lambda x: set_trade_tec(x["Variable"])) - if comm == "NH3": - data = pd.read_excel( - context.get_local_path("material", "ammonia", "n-fertilizer_techno-economic_new.xlsx"), - sheet_name="Trade_NH3", engine="openpyxl", usecols=np.linspace(0, 7, 8, dtype=int)) - data = data.assign(technology=lambda x: set_trade_tec_NH3(x["Variable"])) - return data - - -def set_trade_tec(x): - arr=[] - for i in x: - if "Import" in i: - arr.append("import_NFert") - if "Export" in i: - arr.append("export_NFert") - if "Trade" in i: - arr.append("trade_NFert") - return arr - - -def set_trade_tec_NH3(x): - arr=[] - for i in x: - if "Import" in i: - arr.append("import_NH3") - if "Export" in i: - arr.append("export_NH3") - if "Trade" in i: - arr.append("trade_NH3") - return arr - - -def read_data(): - """Read and clean data from :file:`n-fertilizer_techno-economic.xlsx`.""" - # Ensure config is loaded, get the context - context = read_config() - #print(context.get_local_path()) - #print(Path(__file__).parents[3]/"data"/"material") - context.handle_cli_args(local_data=Path(__file__).parents[3]/"data") - #print(context.get_local_path()) - # Shorter access to sets configuration - sets = context["material"]["fertilizer"] - - # Read the file - data = pd.read_excel( - context.get_local_path("material", "ammonia", "n-fertilizer_techno-economic_new.xlsx"), - sheet_name="Sheet1", engine="openpyxl", nrows=72 - ) - - # Prepare contents for the "parameter" and "technology" columns - # FIXME put these in the file itself to avoid ambiguity/error - - # "Variable" column contains different values selected to match each of - # these parameters, per technology - params = [ - "inv_cost", - "fix_cost", - "var_cost", - "technical_lifetime", - "input_fuel", - "input_elec", - "input_water", - "output_NH3", - "output_water", - "output_heat", - "emission_factor", - "capacity_factor", - ] - - param_values = [] - tech_values = [] - param_cat = [split.split('_')[0] if - (split.startswith('input') or split.startswith('output')) - else split for split in params] - - param_cat2 = [] - # print(param_cat) - for t in sets["technology"]["add"][:6]: # : refactor to adjust to yaml structure - # print(t) - param_values.extend(params) - tech_values.extend([t] * len(params)) - param_cat2.extend(param_cat) - - # Clean the data - data = ( - # Insert "technology" and "parameter" columns - data.assign(technology=tech_values, - parameter=param_values, - param_cat=param_cat2) - # Drop columns that don't contain useful information - .drop(["Model", "Scenario", "Region"], axis=1) - # Set the data frame index for selection - ) - data.loc[data['parameter'] == 'emission_factor', 2010] = \ - data.loc[data['parameter'] == 'emission_factor', 2010]# * CONVERSION_FACTOR_CO2_C - #data.loc[data['parameter'] == 'input_elec', 2010] = \ - # data.loc[data['parameter'] == 'input_elec', 2010] * CONVERSION_FACTOR_PJ_GWa - - # TODO convert units for some parameters, per LoadParams.py - return data - - -def read_data_ccs(): - """Read and clean data from :file:`n-fertilizer_techno-economic.xlsx`.""" - # Ensure config is loaded, get the context - context = read_config() - - # Shorter access to sets configuration - sets = context["material"]["fertilizer"] - - # Read the file - data = pd.read_excel( - context.get_local_path("material", "ammonia", "n-fertilizer_techno-economic_new.xlsx"), - sheet_name="CCS", - ) - - # Prepare contents for the "parameter" and "technology" columns - # FIXME put these in the file itself to avoid ambiguity/error - - # "Variable" column contains different values selected to match each of - # these parameters, per technology - params = [ - "inv_cost", - "fix_cost", - "var_cost", - "technical_lifetime", - "input_fuel", - "input_elec", - "input_water", - "output_NH3", - "output_water", - "output_heat", - "emission_factor", - "emission_factor_trans", - "capacity_factor", - ] - - param_values = [] - tech_values = [] - param_cat = [split.split('_')[0] if (split.startswith('input') or split.startswith('output')) else split for split - in params] - - param_cat2 = [] - - for t in sets["technology"]["add"][13:]: - tech_values.extend([t] * len(params)) - param_values.extend(params) - param_cat2.extend(param_cat) - # Clean the data - data = ( - # Insert "technology" and "parameter" columns - data.assign(technology=tech_values, parameter=param_values, param_cat=param_cat2) - # Drop columns that don't contain useful information - .drop(["Model", "Scenario", "Region"], axis=1) - # Set the data frame index for selection - ) - #unit conversions and extra electricity for CCS process - data.loc[data['parameter'] == 'emission_factor', 2010] = \ - data.loc[data['parameter'] == 'emission_factor', 2010]# * CONVERSION_FACTOR_CO2_C - #data.loc[data['parameter'] == 'input_elec', 2010] = \ - # data.loc[data['parameter'] == 'input_elec', 2010] * CONVERSION_FACTOR_PJ_GWa + 0.005 - data.loc[data['parameter'] == 'input_elec', 2010] = \ - data.loc[data['parameter'] == 'input_elec', 2010] + (CONVERSION_FACTOR_PJ_GWa * 0.005) - # TODO: check this 0.005 hardcoded value for ccs elec input and move to excel - # TODO convert units for some parameters, per LoadParams.py - return data diff --git a/message_ix_models/model/material/data_ammonia_new.py b/message_ix_models/model/material/data_ammonia_new.py index fa552d34d3..44ff5e0b2c 100644 --- a/message_ix_models/model/material/data_ammonia_new.py +++ b/message_ix_models/model/material/data_ammonia_new.py @@ -12,7 +12,7 @@ CONVERSION_FACTOR_NH3_N = 17 / 14 context = read_config() - +default_gdp_elasticity = float(0.65) def gen_all_NH3_fert(scenario, dry_run=False): return { @@ -21,10 +21,11 @@ def gen_all_NH3_fert(scenario, dry_run=False): **gen_data_ts(scenario), **gen_demand(), **gen_land_input(scenario), + **gen_resid_demand_NH3(scenario, default_gdp_elasticity) } -def gen_data(scenario, dry_run=False, add_ccs: bool = True): +def gen_data(scenario, dry_run=False, add_ccs: bool = True, lower_costs = False): s_info = ScenarioInfo(scenario) # s_info.yv_ya nodes = s_info.N @@ -37,7 +38,6 @@ def gen_data(scenario, dry_run=False, add_ccs: bool = True): context.get_local_path( "material", "ammonia", - "new concise input files", "fert_techno_economic.xlsx", ), sheet_name="data_R12", @@ -49,12 +49,7 @@ def gen_data(scenario, dry_run=False, add_ccs: bool = True): vtg_years = s_info.yv_ya[s_info.yv_ya.year_vtg > 2000]["year_vtg"] act_years = s_info.yv_ya[s_info.yv_ya.year_vtg > 2000]["year_act"] - - try: - max_lt = par_dict["technical_lifetime"].value.max() - except KeyError: - print("lifetime not in xlsx") - + max_lt = par_dict["technical_lifetime"].value.max() vtg_years = vtg_years.drop_duplicates() act_years = act_years.drop_duplicates() @@ -95,9 +90,19 @@ def gen_data(scenario, dry_run=False, add_ccs: bool = True): set_exp_imp_nodes(df_new) par_dict[par_name] = df_new - df = par_dict.get("technical_lifetime") - dict_lifetime = df.loc[:,["technology", "value"]].set_index("technology").to_dict()[ - "value"] + if lower_costs: + par_doct = experiment_lower_CPA_SAS_costs(par_dict) + + df_lifetime = par_dict.get("technical_lifetime") + dict_lifetime = ( df_lifetime.loc[:,["technology", "value"]] + .set_index("technology") + .to_dict()["value"] ) + + class missingdict(dict): + def __missing__(self,key): + return 1 + + dict_lifetime = missingdict(dict_lifetime) for i in par_dict.keys(): if ("year_vtg" in par_dict[i].columns) & ("year_act" in par_dict[i].columns): df_temp = par_dict[i] @@ -121,7 +126,6 @@ def gen_data_rel(scenario, dry_run=False, add_ccs: bool = True): context.get_local_path( "material", "ammonia", - "new concise input files", "fert_techno_economic.xlsx", ), sheet_name="relations_R12", @@ -178,7 +182,6 @@ def gen_data_ts(scenario, dry_run=False, add_ccs: bool = True): context.get_local_path( "material", "ammonia", - "new concise input files", "fert_techno_economic.xlsx", ), sheet_name="timeseries_R12", @@ -247,7 +250,7 @@ def read_demand(): skiprows=14, ) - """# Read parameters in xlsx + # Read parameters in xlsx te_params = data = pd.read_excel( context.get_local_path( "material", "ammonia", "n-fertilizer_techno-economic_new.xlsx" @@ -261,34 +264,12 @@ def read_demand(): input_fuel = te_params[2010][ list(range(4, te_params.shape[0], n_inputs_per_tech)) - ].reset_index(drop=True)""" + ].reset_index(drop=True) # input_fuel[0:5] = input_fuel[0:5] * CONVERSION_FACTOR_PJ_GWa # 0.0317 GWa/PJ, GJ/t = PJ/Mt NH3 - te_params_new = pd.read_excel( - context.get_local_path( - "material", - "ammonia", - "new concise input files", - "fert_techno_economic.xlsx"), - sheet_name="data_R12") - - tec_dict = [ - "biomass_NH3", - "electr_NH3", - "gas_NH3", - "coal_NH3", - "fueloil_NH3", - "NH3_to_N_fertil", - ] - - input_fuel = te_params_new[ - (te_params_new["parameter"] == "input") & ~(te_params_new["commodity"].isin(["freshwater_supply"])) & ( - te_params_new["technology"].isin(tec_dict))] - input_fuel = input_fuel.iloc[[0, 2, 4, 5, 7, 9]].set_index("technology").loc[tec_dict, "value"] - - #capacity_factor = te_params[2010][ - # list(range(11, te_params.shape[0], n_inputs_per_tech)) - #].reset_index(drop=True) + capacity_factor = te_params[2010][ + list(range(11, te_params.shape[0], n_inputs_per_tech)) + ].reset_index(drop=True) # Regional N demand in 2010 ND = N_demand_GLO.loc[N_demand_GLO.Scenario == "NoPolicy", ["Region", 2010]] @@ -314,14 +295,13 @@ def read_demand(): columns={0: "totENE", "Region": "node"} ) # GWa - #N_trade_R12 = pd.read_csv( + # N_trade_R12 = pd.read_csv( # context.get_local_path("material", "ammonia", "trade.FAO.R12.csv"), index_col=0 - #) + # ) N_trade_R12 = pd.read_excel( context.get_local_path( "material", "ammonia", - "new concise input files", "nh3_fertilizer_demand.xlsx", ), sheet_name="NFertilizer_trade", @@ -346,16 +326,15 @@ def read_demand(): NP = pd.DataFrame({"netimp": df["import"] - df.export, "demand": ND[2010]}) NP["prod"] = NP.demand - NP.netimp - #NH3_trade_R12 = pd.read_csv( + # NH3_trade_R12 = pd.read_csv( # context.get_local_path( # "material", "ammonia", "NH3_trade_BACI_R12_aggregation.csv" # ) - #) # , index_col=0) + # ) # , index_col=0) NH3_trade_R12 = pd.read_excel( context.get_local_path( "material", "ammonia", - "new concise input files", "nh3_fertilizer_demand.xlsx", ), sheet_name="NH3_trade_R12_aggregated", @@ -404,18 +383,18 @@ def read_demand(): feedshare = fs_GLO.sort_values(["Region"]).set_index("Region").drop("R12_GLB") # Get historical N demand from SSP2-nopolicy (may need to vary for diff scenarios) - N_demand_raw = N_demand_GLO[N_demand_GLO["Region"]!="World"].copy() + N_demand_raw = N_demand_GLO[N_demand_GLO["Region"] != "World"].copy() N_demand_raw["Region"] = "R12_" + N_demand_raw["Region"] N_demand_raw = N_demand_raw.set_index("Region") N_demand = ( N_demand_raw.loc[ - (N_demand_raw.Scenario == "NoPolicy")# & (N_demand_raw.Region != "World") - ] - #.reset_index() - .loc[:, 2010] + (N_demand_raw.Scenario == "NoPolicy") # & (N_demand_raw.Region != "World") + ] + # .reset_index() + .loc[:, 2010] ) # 2010 tot N demand - #N_demand = N_demand.repeat(6) - #act2010 = (feedshare.values.flatten() * N_demand).reset_index(drop=True) + # N_demand = N_demand.repeat(6) + # act2010 = (feedshare.values.flatten() * N_demand).reset_index(drop=True) return { "act2010": feedshare.mul(N_demand, axis=0), @@ -423,14 +402,13 @@ def read_demand(): "ND": ND, "N_energy": N_energy, "feedshare": feedshare, - "act2010": act2010, - #"capacity_factor": capacity_factor, + # "act2010": act2010, + "capacity_factor": capacity_factor, "N_feed": N_feed, "N_trade_R12": N_trade_R12, "NH3_trade_R12": NH3_trade_R12, } - def gen_demand(): context = read_config() @@ -438,7 +416,6 @@ def gen_demand(): demand_fs_org = pd.read_excel( context.get_local_path("material", "ammonia", - "new concise input files", "nh3_fertilizer_demand.xlsx"), sheet_name="demand_i_feed_R12" ) @@ -460,8 +437,111 @@ def gen_demand(): df.loc[df["value"] < 0, "value"] = 0 # temporary solution to avoid negative values return {"demand": df} +def gen_resid_demand_NH3(scenario, gdp_elasticity): + + context = read_config() + s_info = ScenarioInfo(scenario) + modelyears = s_info.Y #s_info.Y is only for modeling years + nodes = s_info.N + + def get_demand_t1_with_income_elasticity( + demand_t0, income_t0, income_t1, elasticity + ): + return ( + elasticity * demand_t0 * ((income_t1 - income_t0) / income_t0) + ) + demand_t0 + + df_gdp = pd.read_excel( + context.get_local_path("material", "methanol", "methanol demand.xlsx"), + sheet_name="GDP_baseline", + ) + + df = df_gdp[(~df_gdp["Region"].isna()) & (df_gdp["Region"] != "World")] + df = df.dropna(axis=1) + + df_demand = df.copy(deep=True) + df_demand = df_demand.drop([2010, 2015, 2020], axis=1) + + # Ammonia Technology Roadmap IEA. 2019 Global NH3 production = 182 Mt. + # 70% is used for nitrogen fertilizer production. Rest is 54.7 Mt. + # Approxiamte regional shares are from Future of Petrochemicals + # Methodological Annex page 7. Total production for regions: + # Asia Pacific (RCPA, CHN, SAS, PAS, PAO) = 90 Mt + # Eurasia (FSU) = 20 Mt, Middle East (MEA) = 15, Africa (AFR) = 5 + # Europe (WEU, EEU) = 25 Mt, Central&South America (LAM) = 5 + # North America (NAM) = 20 Mt. + # Regional shares are derived. They are based on production values not demand. + # Some assumptions made for the regions that are not explicitly covered in IEA. + # (CHN produces the 30% of the ammonia globaly and India 10%.) + # The orders of the regions + # r = ['R12_AFR', 'R12_RCPA', 'R12_EEU', 'R12_FSU', 'R12_LAM', 'R12_MEA',\ + # 'R12_NAM', 'R12_PAO', 'R12_PAS', 'R12_SAS', 'R12_WEU',"R12_CHN"] + + if "R12_CHN" in nodes: + nodes.remove("R12_GLB") + region_set = 'R12_' + dem_2020 = np.array([1.5, 1.5, 3, 6, 1.5, 4.6, 6, 1.5, 1.5, 6, 4.6, 17]) + dem_2020 = pd.Series(dem_2020) + + else: + nodes.remove("R11_GLB") + region_set = 'R11_' + dem_2020 = np.array([1.5, 18.5, 3, 6, 1.5, 4.6, 6, 1.5, 1.5, 6, 4.6]) + dem_2020 = pd.Series(dem_2020) + + df_demand[2020] = dem_2020 + + for i in range(len(modelyears) - 1): + income_year1 = modelyears[i] + income_year2 = modelyears[i + 1] + + dem_2020 = get_demand_t1_with_income_elasticity( + dem_2020, df[income_year1], df[income_year2], gdp_elasticity + ) + df_demand[income_year2] = dem_2020 + + df_melt = df_demand.melt( + id_vars=["Region"], value_vars=df_demand.columns[5:], var_name="year" + ) + + df_residual = make_df( + "demand", + unit="t", + level="final_material", + value=df_melt.value, + time="year", + commodity="NH3", + year=df_melt.year, + node=(region_set + df_melt["Region"]), + ) + + return {"demand": df_residual} def gen_land_input(scenario): df = scenario.par("land_output", {"commodity": "Fertilizer Use|Nitrogen"}) df["level"] = "final_material" return {"land_input": df} + +def experiment_lower_CPA_SAS_costs(par_dict): + cost_list = ["inv_cost", "fix_cost"] + scaler = { + "R12_RCPA": [0.66 * 0.91, 0.75 * 0.9], + "R12_CHN": [0.66 * 0.91, 0.75 * 0.9], + "R12_SAS": [0.59, 1], + } + tec_list = ["fueloil_NH3", "coal_NH3"] + for c in cost_list: + df = par_dict[c] + for k in scaler.keys(): + df_tmp = df.loc[df["node_loc"] == k] + for e, t in enumerate(tec_list): + df_tmp.loc[df_tmp["technology"] == t, "value"] = df_tmp.loc[ + df_tmp["technology"] == t, "value" + ].mul(scaler.get(k)[e]) + df_tmp.loc[df_tmp["technology"] == t + "_ccs", "value"] = df_tmp.loc[ + df_tmp["technology"] == t + "_ccs", "value" + ].mul(scaler.get(k)[e]) + df.loc[df["node_loc"] == k] = df_tmp + par_dict[c] = df + + return par_dict From fa940c2ec2f5c39faa7dcf0d818fd22bd7351013 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Fri, 23 Dec 2022 13:38:41 +0100 Subject: [PATCH 546/774] Update files lost in rebase --- .../data/material/ammonia/fert_techno_economic.xlsx | 4 ++-- .../data/material/methanol/meth_trade_techno_economic.xlsx | 3 +++ 2 files changed, 5 insertions(+), 2 deletions(-) create mode 100644 message_ix_models/data/material/methanol/meth_trade_techno_economic.xlsx diff --git a/message_ix_models/data/material/ammonia/fert_techno_economic.xlsx b/message_ix_models/data/material/ammonia/fert_techno_economic.xlsx index 47f7a37753..a3222ebd6b 100644 --- a/message_ix_models/data/material/ammonia/fert_techno_economic.xlsx +++ b/message_ix_models/data/material/ammonia/fert_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:a464621f464d44aed998dd40dcae772417027578b1d899b73f2127d0daa8afda -size 47645 +oid sha256:71f63a74c2bd8a5dd91d9b759aad77e9aebb8e1d62806e2382c9a3cc8a79b535 +size 47600 diff --git a/message_ix_models/data/material/methanol/meth_trade_techno_economic.xlsx b/message_ix_models/data/material/methanol/meth_trade_techno_economic.xlsx new file mode 100644 index 0000000000..03d045ebfb --- /dev/null +++ b/message_ix_models/data/material/methanol/meth_trade_techno_economic.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:86b218ef477631f18440b78fbfa6c6e0c2c3de25d55e6a6e8617d3385f98f269 +size 211792 From ad25bb4a327bbfa77af408188d58d2628a53d2c5 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Fri, 23 Dec 2022 13:39:20 +0100 Subject: [PATCH 547/774] Fix issues after rebase --- message_ix_models/model/material/__init__.py | 3 +-- message_ix_models/model/material/data_ammonia_new.py | 6 ++---- 2 files changed, 3 insertions(+), 6 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 368022f003..f213be3252 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -52,7 +52,7 @@ def build(scenario): # Market penetration adjustments # NOTE: changing demand affects the market penetration levels for the enduse technologies. # Note: context.ssp doesnt work - calibrate_UE_gr_to_demand(scenario, data_path=private_data_path(), ssp='SSP2') + calibrate_UE_gr_to_demand(scenario, data_path=private_data_path(), ssp='SSP2', region = 'R12') calibrate_UE_share_constraints(scenario) # Electricity calibration to avoid zero prices for CHN. @@ -391,7 +391,6 @@ def run_old_reporting(scenario_name, model_name): from .data_petro import gen_data_petro_chemicals from .data_buildings import gen_data_buildings from .data_power_sector import gen_data_power_sector -from .data_ammonia import gen_data_ammonia from .data_methanol import gen_data_methanol from .data_ammonia_new import gen_all_NH3_fert diff --git a/message_ix_models/model/material/data_ammonia_new.py b/message_ix_models/model/material/data_ammonia_new.py index 44ff5e0b2c..2f7e124f65 100644 --- a/message_ix_models/model/material/data_ammonia_new.py +++ b/message_ix_models/model/material/data_ammonia_new.py @@ -232,7 +232,6 @@ def read_demand(): context.get_local_path( "material", "ammonia", - "new concise input files", "nh3_fertilizer_demand.xlsx", ), sheet_name="NFertilizer_demand", @@ -243,7 +242,6 @@ def read_demand(): context.get_local_path( "material", "ammonia", - "new concise input files", "nh3_fertilizer_demand.xlsx", ), sheet_name="NH3_feedstock_share", @@ -253,9 +251,9 @@ def read_demand(): # Read parameters in xlsx te_params = data = pd.read_excel( context.get_local_path( - "material", "ammonia", "n-fertilizer_techno-economic_new.xlsx" + "material", "ammonia", "nh3_fertilizer_demand.xlsx" ), - sheet_name="Sheet1", + sheet_name="old_TE_sheet", engine="openpyxl", nrows=72, ) From 301075bc32bd5e2527278a30912c8842823ad941 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Fri, 23 Dec 2022 13:39:52 +0100 Subject: [PATCH 548/774] Adjust demand carve out --- message_ix_models/model/material/data_util.py | 64 ++++++++----------- 1 file changed, 28 insertions(+), 36 deletions(-) diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index 1e9e99ae31..7c84ca5f2f 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -153,7 +153,14 @@ def modify_demand_and_hist_activity(scen): ] df = df[df["RYEAR"] == 2015] - # Retreive data for i_spec (Excludes petrochemicals as the share is negligable) + # NOTE: Total cehmical industry energy: 27% thermal, 8% electricity, 65% feedstock + # SOURCE: IEA Sankey 2020: https://www.iea.org/sankey/#?c=World&s=Final%20consumption + # 67% of total chemicals energy is used for primary chemicals (ammonia,methnol,HVCs) + # SOURCE: https://www.iea.org/data-and-statistics/charts/primary-chemical-production-in-the-sustainable-development-scenario-2000-2030 + + # Retreive data for i_spec + # 67% of total chemcials electricity demand comes from primary chemicals (IEA) + # (Excludes petrochemicals as the share is negligable) # Aluminum, cement and steel included. # NOTE: Steel has high shares (previously it was not inlcuded in i_spec) @@ -180,49 +187,34 @@ def modify_demand_and_hist_activity(scen): df_spec_new.drop(["FUEL", "RYEAR", "UNIT_OUT", "RESULT"], axis=1, inplace=True) df_spec_new.loc[df_spec_new["SECTOR"] == "industry (chemicals)", "i_spec"] = ( - df_spec_new.loc[df_spec_new["SECTOR"] == "industry (chemicals)", "i_spec"] * 0.7 + df_spec_new.loc[df_spec_new["SECTOR"] == "industry (chemicals)", "i_spec"] * 0.67 ) df_spec_new = df_spec_new.groupby(["REGION"]).sum().reset_index() - # Retreive data for i_feed: Only for petrochemicals - # It is assumed that the sectors that are explicitly covered in MESSAGE are - # 50% of the total feedstock. - - df_feed = df[ - (df["SECTOR"] == "feedstock (petrochemical industry)") & (df["FUEL"] == "total") - ] - df_feed_total = df[(df["SECTOR"] == "feedstock (total)") & (df["FUEL"] == "total")] - df_feed_temp = pd.DataFrame(columns=["REGION", "i_feed"]) - df_feed_new = pd.DataFrame(columns=["REGION", "i_feed"]) - - for r in df_feed["REGION"].unique(): - - - i = 0 - df_feed_temp.at[i, "REGION"] = r - df_feed_temp.at[i, "i_feed"] = 1 - i = i + 1 - df_feed_new = pd.concat([df_feed_temp, df_feed_new], ignore_index=True) - - # df_feed = df[(df["SECTOR"]== "feedstock (petrochemical industry)") & \ - # (df["FUEL"]== "total") ] - # df_feed_total = df[(df["SECTOR"]== "feedstock (total)") \ - # & (df["FUEL"]== "total")] + # Already set to zero: ammonia, methanol, HVCs cover most of the feedstock + # # Retreive data for i_feed: Only for petrochemicals + # # It is assumed that the sectors that are explicitly covered in MESSAGE are + # # 50% of the total feedstock. + # + # df_feed = df[ + # (df["SECTOR"] == "feedstock (petrochemical industry)") & (df["FUEL"] == "total") + # ] + # df_feed_total = df[(df["SECTOR"] == "feedstock (total)") & (df["FUEL"] == "total")] + # df_feed_temp = pd.DataFrame(columns=["REGION", "i_feed"]) + # df_feed_new = pd.DataFrame(columns=["REGION", "i_feed"]) # - # df_feed_new = pd.DataFrame(columns=["REGION","SECTOR","FUEL",\ - # "RYEAR","UNIT_OUT","RESULT"]) # for r in df_feed["REGION"].unique(): - # df_feed_temp = df_feed[df_feed["REGION"]==r] - # df_feed_total_temp = df_feed_total[df_feed_total["REGION"] == r] - # df_feed_temp["i_feed"] = df_feed_temp["RESULT"]/df_feed_total_temp["RESULT"].values[0] - # df_feed_new = pd.concat([df_feed_temp,df_feed_new],ignore_index = True) # - # df_feed_new.drop(["FUEL","RYEAR","UNIT_OUT","RESULT"],axis=1, inplace=True) - # df_feed_new = df_feed_new.groupby(["REGION"]).sum().reset_index() + # + # i = 0 + # df_feed_temp.at[i, "REGION"] = r + # df_feed_temp.at[i, "i_feed"] = 1 + # i = i + 1 + # df_feed_new = pd.concat([df_feed_temp, df_feed_new], ignore_index=True) # Retreive data for i_therm - # NOTE: It is assumped that 80% of i_therm is from ammonia and HVCs. + # 67% of chemical thermal energy chemicals comes from primary chemicals. (IEA) # NOTE: Aluminum is excluded since refining process is not explicitly represented # NOTE: CPA has a 3% share while it used to be 30% previosuly ?? @@ -264,7 +256,7 @@ def modify_demand_and_hist_activity(scen): df_therm_new.drop(["FUEL", "RYEAR", "UNIT_OUT"], axis=1, inplace=True) df_therm_new.loc[df_therm_new["SECTOR"] == "industry (chemicals)", "i_therm"] = ( df_therm_new.loc[df_therm_new["SECTOR"] == "industry (chemicals)", "i_therm"] - * 0.8 + * 0.67 ) # Modify CPA based on https://www.iea.org/sankey/#?c=Japan&s=Final%20consumption. From ef683d8627d8d3dc6a4e38bb9de30330491da531 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Fri, 23 Dec 2022 16:38:26 +0100 Subject: [PATCH 549/774] Update clinker cement ratio --- .../data/material/Global_steel_cement_MESSAGE.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx index a19fc189e9..f3eab1c2a4 100644 --- a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx +++ b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:1b353e40d5a0926de224f4b3af6d95109416b6b104cf70b8895b6ed5196052a5 -size 111240 +oid sha256:d226ca58dbd251e3e76e24cd1eedd2784462383a35cc4098b0006104cb765583 +size 111390 From e53e08780bd167310f14aae58fb62ccf0d9405ab Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Mon, 16 Jan 2023 15:07:26 +0100 Subject: [PATCH 550/774] Adjust ammonia demand --- .../model/material/data_ammonia_new.py | 6 ++-- message_ix_models/model/material/data_util.py | 33 ++++++++----------- 2 files changed, 18 insertions(+), 21 deletions(-) diff --git a/message_ix_models/model/material/data_ammonia_new.py b/message_ix_models/model/material/data_ammonia_new.py index 2f7e124f65..a47d320626 100644 --- a/message_ix_models/model/material/data_ammonia_new.py +++ b/message_ix_models/model/material/data_ammonia_new.py @@ -462,6 +462,8 @@ def get_demand_t1_with_income_elasticity( # Ammonia Technology Roadmap IEA. 2019 Global NH3 production = 182 Mt. # 70% is used for nitrogen fertilizer production. Rest is 54.7 Mt. + # 12 Mt is missing from nitrogen fertilizer part. Possibly due to low demand + # coming from GLOBIOM updates. Also add this to the residual demand. (66.7 Mt) # Approxiamte regional shares are from Future of Petrochemicals # Methodological Annex page 7. Total production for regions: # Asia Pacific (RCPA, CHN, SAS, PAS, PAO) = 90 Mt @@ -478,13 +480,13 @@ def get_demand_t1_with_income_elasticity( if "R12_CHN" in nodes: nodes.remove("R12_GLB") region_set = 'R12_' - dem_2020 = np.array([1.5, 1.5, 3, 6, 1.5, 4.6, 6, 1.5, 1.5, 6, 4.6, 17]) + dem_2020 = np.array([2.5, 2.5, 4, 7, 2.5, 5.6, 7, 2.5, 2.5, 7, 5.6, 18]) dem_2020 = pd.Series(dem_2020) else: nodes.remove("R11_GLB") region_set = 'R11_' - dem_2020 = np.array([1.5, 18.5, 3, 6, 1.5, 4.6, 6, 1.5, 1.5, 6, 4.6]) + dem_2020 = np.array([2.5, 19.5, 4, 7, 2.5, 5.6, 7, 2.5, 2.5, 7, 5.6]) dem_2020 = pd.Series(dem_2020) df_demand[2020] = dem_2020 diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index 7c84ca5f2f..4a4b09316d 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -193,25 +193,20 @@ def modify_demand_and_hist_activity(scen): df_spec_new = df_spec_new.groupby(["REGION"]).sum().reset_index() # Already set to zero: ammonia, methanol, HVCs cover most of the feedstock - # # Retreive data for i_feed: Only for petrochemicals - # # It is assumed that the sectors that are explicitly covered in MESSAGE are - # # 50% of the total feedstock. - # - # df_feed = df[ - # (df["SECTOR"] == "feedstock (petrochemical industry)") & (df["FUEL"] == "total") - # ] - # df_feed_total = df[(df["SECTOR"] == "feedstock (total)") & (df["FUEL"] == "total")] - # df_feed_temp = pd.DataFrame(columns=["REGION", "i_feed"]) - # df_feed_new = pd.DataFrame(columns=["REGION", "i_feed"]) - # - # for r in df_feed["REGION"].unique(): - # - # - # i = 0 - # df_feed_temp.at[i, "REGION"] = r - # df_feed_temp.at[i, "i_feed"] = 1 - # i = i + 1 - # df_feed_new = pd.concat([df_feed_temp, df_feed_new], ignore_index=True) + + df_feed = df[ + (df["SECTOR"] == "feedstock (petrochemical industry)") & (df["FUEL"] == "total") + ] + df_feed_total = df[(df["SECTOR"] == "feedstock (total)") & (df["FUEL"] == "total")] + df_feed_temp = pd.DataFrame(columns=["REGION", "i_feed"]) + df_feed_new = pd.DataFrame(columns=["REGION", "i_feed"]) + + for r in df_feed["REGION"].unique(): + i = 0 + df_feed_temp.at[i, "REGION"] = r + df_feed_temp.at[i, "i_feed"] = 1 + i = i + 1 + df_feed_new = pd.concat([df_feed_temp, df_feed_new], ignore_index=True) # Retreive data for i_therm # 67% of chemical thermal energy chemicals comes from primary chemicals. (IEA) From 06b418be450638f8be724750afe31ee75e29e813 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 18 Jan 2023 14:54:09 +0100 Subject: [PATCH 551/774] Update tables.py --- .../model/material/report/tables.py | 1631 ++++++++++++++--- 1 file changed, 1375 insertions(+), 256 deletions(-) diff --git a/message_ix_models/model/material/report/tables.py b/message_ix_models/model/material/report/tables.py index 6984c9f69c..fb28d54855 100644 --- a/message_ix_models/model/material/report/tables.py +++ b/message_ix_models/model/material/report/tables.py @@ -131,14 +131,284 @@ def _register(func): # Reporting functions # ------------------- +@_register +def retr_SE_synfuels(units): + """Energy: Secondary Energy synthetic fuels. + Parameters + ---------- + units : str + Units to which variables should be converted. + """ + + vars = {} + + vars["Liquids|Oil"] = ( + pp.out( + "agg_ref", + units, + outfilter={"level": ["secondary"], "commodity": ["lightoil"]}, + ) + + pp.out( + "agg_ref", + units, + outfilter={"level": ["secondary"], "commodity": ["fueloil"]}, + ) + + pp.inp( + "steam_cracker_petro", + units, + inpfilter={ + "level": ["final"], + "commodity": ["atm_gasoil", "vacuum_gasoil", "naphtha"], + }, + ) + ) + + vars["Liquids|Biomass|w/o CCS"] = pp.out( + ["eth_bio", "liq_bio"], + units, + outfilter={"level": ["primary"], "commodity": ["ethanol"]}, + ) + + vars["Liquids|Biomass|w/ CCS"] = pp.out( + ["eth_bio_ccs", "liq_bio_ccs"], + units, + outfilter={"level": ["primary"], "commodity": ["ethanol"]}, + ) + + vars["Liquids|Coal|w/o CCS"] = pp.out( + ["meth_coal", "syn_liq"], + units, + outfilter={"level": ["primary"], "commodity": ["methanol"]}, + ) + vars["Liquids|Coal|w/ CCS"] = pp.out( + ["meth_coal_ccs", "syn_liq_ccs"], + units, + outfilter={"level": ["primary"], "commodity": ["methanol"]}, + ) + + vars["Liquids|Gas|w/o CCS"] = pp.out( + "meth_ng", units, outfilter={"level": ["primary"], "commodity": ["methanol"]} + ) + + vars["Liquids|Gas|w/ CCS"] = pp.out( + "meth_ng_ccs", + units, + outfilter={"level": ["primary"], "commodity": ["methanol"]}, + ) + + vars["Hydrogen|Coal|w/o CCS"] = pp.out( + "h2_coal", units, outfilter={"level": ["secondary"], "commodity": ["hydrogen"]} + ) + + vars["Hydrogen|Coal|w/ CCS"] = pp.out( + "h2_coal_ccs", + units, + outfilter={"level": ["secondary"], "commodity": ["hydrogen"]}, + ) + + vars["Hydrogen|Gas|w/o CCS"] = pp.out( + "h2_smr", units, outfilter={"level": ["secondary"], "commodity": ["hydrogen"]} + ) + + vars["Hydrogen|Gas|w/ CCS"] = pp.out( + "h2_smr_ccs", + units, + outfilter={"level": ["secondary"], "commodity": ["hydrogen"]}, + ) + + vars["Hydrogen|Biomass|w/o CCS"] = pp.out( + "h2_bio", units, outfilter={"level": ["secondary"], "commodity": ["hydrogen"]} + ) + + vars["Hydrogen|Biomass|w/ CCS"] = pp.out( + "h2_bio_ccs", + units, + outfilter={"level": ["secondary"], "commodity": ["hydrogen"]}, + ) + + vars["Hydrogen|Electricity"] = pp.out( + "h2_elec", units, outfilter={"level": ["secondary"], "commodity": ["hydrogen"]} + ) + + df = pp_utils.make_outputdf(vars, units) + return df @_register -def retr_CO2emi(units_emi, units_ene_mdl): - """Emissions: CO2. +def retr_CO2_CCS(units_emi, units_ene): + """Carbon sequestration. + + Energy and land-use related carbon seuqestration. Parameters ---------- + units_emi : str + Units to which emission variables should be converted. + units_ene : str + Units to which energy variables should be converted. + """ + + vars = {} + + # -------------------------------- + # Calculation of helping variables + # -------------------------------- + + # Biogas share calculation + _Biogas = pp.out("gas_bio", units_ene) + + _gas_inp_tecs = [ + "gas_ppl", + "gas_cc", + "gas_cc_ccs", + "gas_ct", + "gas_htfc", + "gas_hpl", + "meth_ng", + "meth_ng_ccs", + "h2_smr", + "h2_smr_ccs", + "gas_t_d", + "gas_t_d_ch4", + ] + + _totgas = pp.inp(_gas_inp_tecs, units_ene, inpfilter={"commodity": ["gas"]}) + + _BGas_share = (_Biogas / _totgas).fillna(0) + + # Calulation of CCS components + + _CCS_coal_elec = -1.0 * pp.emi( + ["c_ppl_co2scr", "coal_adv_ccs", "igcc_ccs", "igcc_co2scr"], + "GWa", + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi, + ) + + _CCS_coal_liq = -1.0 * pp.emi( + ["syn_liq_ccs", "meth_coal_ccs"], + "GWa", + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi, + ) + + _CCS_coal_hydrogen = -1.0 * pp.emi( + "h2_coal_ccs", + "GWa", + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi, + ) + + _CCS_cement = -1.0 * pp.emi( + ["clinker_dry_ccs_cement", "clinker_wet_ccs_cement"], + "GWa", + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi, + ) + + _CCS_ammonia = -1.0 * pp.emi( + ['biomass_NH3_ccs', 'gas_NH3_ccs', 'coal_NH3_ccs', 'fueloil_NH3_ccs'], + "GWa", + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi, + ) + + _CCS_bio_elec = -1.0 * pp.emi( + ["bio_ppl_co2scr", "bio_istig_ccs"], + "GWa", + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi, + ) + + _CCS_bio_liq = -1.0 * pp.emi( + ["eth_bio_ccs", "liq_bio_ccs"], + "GWa", + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi, + ) + + _CCS_bio_hydrogen = -1.0 * pp.emi( + "h2_bio_ccs", + "GWa", + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi, + ) + + _CCS_gas_elec = ( + -1.0 + * pp.emi( + ["g_ppl_co2scr", "gas_cc_ccs"], + "GWa", + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi, + ) + * (1.0 - _BGas_share) + ) + + _CCS_gas_liq = ( + -1.0 + * pp.emi( + "meth_ng_ccs", + "GWa", + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi, + ) + * (1.0 - _BGas_share) + ) + + _CCS_gas_hydrogen = ( + -1.0 + * pp.emi( + "h2_smr_ccs", + "GWa", + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi, + ) + * (1.0 - _BGas_share) + ) + + vars["CCS|Fossil|Energy|Supply|Electricity"] = _CCS_coal_elec + _CCS_gas_elec + + vars["CCS|Fossil|Energy|Supply|Liquids"] = _CCS_coal_liq + _CCS_gas_liq + + vars["CCS|Fossil|Energy|Supply|Hydrogen"] = _CCS_coal_hydrogen + _CCS_gas_hydrogen + + vars["CCS|Biomass|Energy|Supply|Electricity"] = ( + _CCS_bio_elec + _CCS_gas_elec * _BGas_share + ) + + vars["CCS|Biomass|Energy|Supply|Liquids"] = ( + _CCS_bio_liq + _CCS_gas_liq * _BGas_share + ) + + vars["CCS|Biomass|Energy|Supply|Hydrogen"] = ( + _CCS_bio_hydrogen + _CCS_gas_hydrogen * _BGas_share + ) + + vars["CCS|Industrial Processes"] = _CCS_cement + _CCS_ammonia + + vars["Land Use"] = -pp.land_out( + lu_out_filter={ + "level": ["land_use_reporting"], + "commodity": ["Emissions|CO2|AFOLU|Negative"], + } + ) + + vars["Land Use|Afforestation"] = -pp.land_out( + lu_out_filter={ + "level": ["land_use_reporting"], + "commodity": ["Emissions|CO2|AFOLU|Afforestation"], + } + ) + + df = pp_utils.make_outputdf(vars, units_emi) + return df + +@_register +def retr_CO2emi(units_emi, units_ene_mdl): + """Emissions: CO2. + Parameters + ---------- units_emi : str Units to which emission variables should be converted. units_ene_mdl : str @@ -154,45 +424,46 @@ def retr_CO2emi(units_emi, units_ene_mdl): else: group = ["Region"] - # -------------------------------- - # Calculation of helping variables - # -------------------------------- + # ---------------------------------------------------------------------------------- + # Step 1. Calculate variables used for distribution of hydrogen and biogas related + # emissions + # ---------------------------------------------------------------------------------- + + # ---------------------------------------------------------------------------------- + # Step 1.1. Calculate total got HYDROGEN distribution + # + # Calculate all non-ccs gas use which is used for the distribution of HYDROGEN + # related emissions which need to be subtracted from various sectors. + # ---------------------------------------------------------------------------------- _inp_nonccs_gas_tecs = ( pp.inp( [ - "gas_i", - "hp_gas_i", - "gas_trp", - "gas_fs", - "gas_ppl", - "gas_ct", - "gas_cc", - "gas_htfc", - "gas_hpl", + "gas_cc", # _Biogas_el, _Hydrogen_el + "gas_ct", # _Biogas_el, _Hydrogen_el + "gas_fs", # _Biogas_fs, _Hydrogen_fs + "gas_hpl", # _Biogas_heat, _Hydrogen_heat + "gas_htfc", # _Biogas_el, _Hydrogen_el + "gas_i", # _Biogas_ind_rest, _Hydrogen_ind_rest + "gas_ppl", # _Biogas_el, _Hydrogen_el + "gas_trp", # _Biogas_trp, _Hydrogen_trp + "hp_gas_i", # _Biogas_ind_rest, _Hydrogen_ind_rest ] - + TECHS["rc gas"] - + TECHS["gas extra"], + + TECHS["rc gas"] # _Biogas_res, _Hydrogen_res + + TECHS["gas extra"], # See above units_ene_mdl, inpfilter={"commodity": ["gas"]}, ) + pp.inp( ["gas_t_d", "gas_t_d_ch4"], units_ene_mdl, inpfilter={"commodity": ["gas"]} - ) + ) # _Biogas_td, _Hydrogen_td - pp.out( ["gas_t_d", "gas_t_d_ch4"], units_ene_mdl, outfilter={"commodity": ["gas"]} - ) - ) - - _inp_all_gas_tecs = _inp_nonccs_gas_tecs + pp.inp( - ["gas_cc_ccs", "meth_ng", "meth_ng_ccs", "h2_smr", "h2_smr_ccs"], - units_ene_mdl, - inpfilter={"commodity": ["gas"]}, + ) # _Biogas_td, _Hydrogen_td ) # Calculate shares for ppl feeding into g_ppl_co2scr (gas_cc and gas_ppl) _gas_cc_shr = (pp.out("gas_cc") / pp.out(["gas_cc", "gas_ppl"])).fillna(0) - _gas_ppl_shr = (pp.out("gas_ppl") / pp.out(["gas_cc", "gas_ppl"])).fillna(0) _inp_nonccs_gas_tecs_wo_CCSRETRO = ( @@ -222,9 +493,42 @@ def retr_CO2emi(units_emi, units_ene_mdl): ) ) - # Helping variables required in units of Emissions + # ---------------------------------------------------------------------------------- + # Step 1.2. Calculate total for BIOGAS distribution + # + # Calculate all gas use which is used to for the distribution of BIOGAS related + # emissions which need to be subtracted from various sectors. + # ---------------------------------------------------------------------------------- + + _inp_all_gas_tecs = _inp_nonccs_gas_tecs + pp.inp( + [ + "gas_cc_ccs", # _Biogas_el, no-hydrogen via hlim + "meth_ng", # _Biogas_liquids_gas_comb, no-hydrogen via ? + "meth_ng_ccs", # _Biogas_liquids_gas_comb, no-hydrogen via ? + "h2_smr", # _Biogas_gases_h2_comb, no-hydrogen via ? + "h2_smr_ccs", # _Biogas_gases_h2_comb, no-hydrogen via hlim + "gas_NH3_ccs" # _Biogas_fs, no-hydrogen needs entry to hlim entry + # equivalent to CO2 entry (-) + ], + units_ene_mdl, + inpfilter={"commodity": ["gas"]}, + ) + + # ---------------------------------------------------------------------------------- + # Step 2. Distribute BIOGAS emissions + # + # - Calculate biogas-emission attribution to sector + # - Based on: sector_specfific_gas_consumption / total_gas_use * total_biogas_use + # - These emissions will later be subtracted from the respective sectors. + # ---------------------------------------------------------------------------------- + + # Retrieve total bio-gas quantity procuded _Biogas_tot_abs = pp.out("gas_bio") + + # Multiply quantity by emission factor _Biogas_tot = _Biogas_tot_abs * mu["crbcnt_gas"] * mu["conv_c2co2"] + + # Variable: "Energy|Supply|Electricity" _Biogas_el = _Biogas_tot * ( pp.inp( ["gas_ppl", "gas_ct", "gas_cc", "gas_cc_ccs", "gas_htfc"], @@ -234,11 +538,13 @@ def retr_CO2emi(units_emi, units_ene_mdl): / _inp_all_gas_tecs ).fillna(0) + # Variable: "Energy|Supply|Heat" _Biogas_heat = _Biogas_tot * ( pp.inp("gas_hpl", units_ene_mdl, inpfilter={"commodity": ["gas"]}) / _inp_all_gas_tecs ).fillna(0) + # Variable: "Energy|Supply|Liquids|Natural Gas|Combustion" _Biogas_liquids_gas_comb = _Biogas_tot * ( pp.inp( ["meth_ng", "meth_ng_ccs"], units_ene_mdl, inpfilter={"commodity": ["gas"]} @@ -246,6 +552,7 @@ def retr_CO2emi(units_emi, units_ene_mdl): / _inp_all_gas_tecs ).fillna(0) + # Variable: "Energy|Supply|Gases|Hydrogen|Combustion" _Biogas_gases_h2_comb = _Biogas_tot * ( pp.inp( ["h2_smr", "h2_smr_ccs"], units_ene_mdl, inpfilter={"commodity": ["gas"]} @@ -253,50 +560,165 @@ def retr_CO2emi(units_emi, units_ene_mdl): / _inp_all_gas_tecs ).fillna(0) - _Biogas_ind = _Biogas_tot * ( + # Variable: "Energy|Supply|Gases|Transportation|Combustion" + _Biogas_td = _Biogas_tot * ( + ( + pp.inp( + ["gas_t_d", "gas_t_d_ch4"], + units_ene_mdl, + inpfilter={"commodity": ["gas"]}, + ) + - pp.out( + ["gas_t_d", "gas_t_d_ch4"], + units_ene_mdl, + outfilter={"commodity": ["gas"]}, + ) + ) + / _inp_all_gas_tecs + ).fillna(0) + + # Variable: "Energy|Demand|Industry|Other Sector" + _Biogas_ind_rest = _Biogas_tot * ( pp.inp( [ "gas_i", "hp_gas_i", - ] - + TECHS["gas extra"], + ], + units_ene_mdl, + inpfilter={"commodity": ["gas"]}, + ) + / _inp_all_gas_tecs + ).fillna(0) + + # Variable: "Energy|Demand|Industry|Steel" + _Biogas_ind_steel = _Biogas_tot * ( + pp.inp( + ["dri_steel", "eaf_steel", "furnace_gas_steel", "hp_gas_steel"], + units_ene_mdl, + inpfilter={"commodity": ["gas"]}, + ) + / _inp_all_gas_tecs + ).fillna(0) + + # Variable: "Energy|Demand|Industry|Non-Metallic Minerals|Cement" + _Biogas_ind_cement = _Biogas_tot * ( + pp.inp( + [ + "furnace_gas_cement", + "hp_gas_cement", + ], + units_ene_mdl, + inpfilter={"commodity": ["gas"]}, + ) + / _inp_all_gas_tecs + ).fillna(0) + + # Variable: "Energy|Demand|Industry|Non-Ferrous Metals|Aluminium" + _Biogas_ind_alu = _Biogas_tot * ( + pp.inp( + [ + "furnace_gas_aluminum", + "hp_gas_aluminum", + ], + units_ene_mdl, + inpfilter={"commodity": ["gas"]}, + ) + / _inp_all_gas_tecs + ).fillna(0) + + # Variable: "Energy|Demand|Industry|Chemicals|High Value Chemicals" + _Biogas_ind_petro = _Biogas_tot * ( + pp.inp( + [ + "furnace_gas_petro", + "gas_processing_petro", + "hp_gas_petro", + ], units_ene_mdl, inpfilter={"commodity": ["gas"]}, ) / _inp_all_gas_tecs ).fillna(0) + # Variable: "Energy|Demand|Industry|Chemicals|Ammonia" + _Biogas_ind_ammonia = _Biogas_tot * ( + pp.inp( + ["gas_NH3", "gas_NH3_ccs"], + units_ene_mdl, + inpfilter={"commodity": ["gas"]}, + ) + / _inp_all_gas_tecs + ).fillna(0) + + # Variable: "Energy|Demand|Other Sector" _Biogas_fs = _Biogas_tot * ( - pp.inp("gas_fs", units_ene_mdl, inpfilter={"commodity": ["gas"]}) + pp.inp( + ["gas_fs"], + units_ene_mdl, + inpfilter={"commodity": ["gas"]}, + ) / _inp_all_gas_tecs ).fillna(0) + # Variable: "Energy|Supply|Liquids|Oil|Combustion" + _Biogas_ref = _Biogas_tot * ( + pp.inp( + ["furnace_gas_refining", "hp_gas_refining"], + units_ene_mdl, + inpfilter={"commodity": ["gas"]}, + ) + / _inp_all_gas_tecs + ).fillna(0) + + # Variable: "Energy|Demand|Residential and Commercial" _Biogas_res = _Biogas_tot * ( pp.inp(TECHS["rc gas"], units_ene_mdl, inpfilter={"commodity": ["gas"]}) / _inp_all_gas_tecs ).fillna(0) + # Variable: "Energy|Demand|Transportation|Road Rail and Domestic Shipping" _Biogas_trp = _Biogas_tot * ( pp.inp("gas_trp", units_ene_mdl, inpfilter={"commodity": ["gas"]}) / _inp_all_gas_tecs ).fillna(0) - _Biogas_td = _Biogas_tot * ( - ( - pp.inp( - ["gas_t_d", "gas_t_d_ch4"], - units_ene_mdl, - inpfilter={"commodity": ["gas"]}, - ) - - pp.out( - ["gas_t_d", "gas_t_d_ch4"], - units_ene_mdl, - outfilter={"commodity": ["gas"]}, - ) - ) - / _inp_all_gas_tecs - ).fillna(0) - + # Finally check to ensure that the sum of all the varaibles onto which biogas + # emissions are distributed on, matches the total. + # FIXME this variable is assigned but never used + check_biogas = _Biogas_tot - ( # noqa: F841 + _Biogas_el + + _Biogas_heat + + _Biogas_liquids_gas_comb + + _Biogas_gases_h2_comb + + _Biogas_td + + _Biogas_ind_rest + + _Biogas_ind_steel + + _Biogas_ind_cement + + _Biogas_ind_alu + + _Biogas_ind_petro + + _Biogas_ind_ammonia + + _Biogas_fs + + _Biogas_ref + + _Biogas_res + + _Biogas_trp + ) + + # ---------------------------------------------------------------------------------- + # Step 3. Distribute HYDROGEN emissions + # + # - Calculate hydrogen-emissions for each sector. + # - Based on: sector_specific_gas_consumption / total_NONCCS_gas_use + # * total hydrogen-emissions + # - All CCS excluded as hydrogen cannot be used in CCS applications, hence also + # division by _inp_nonccs_gas_tecs_wo_CCSRETRO. + # - Emissions that will later be added to the respective sectors, as the + # emission-entry of `h2_mix` is negative, hence the derived values are negative. + # - All FE-technologies listed here, must have an entry into the relation + # `h2mix_direct` + # ---------------------------------------------------------------------------------- + + # Retrieve total hydrogen emissions + # Note, these are negative! _Hydrogen_tot = pp.emi( "h2_mix", units_ene_mdl, @@ -304,6 +726,7 @@ def retr_CO2emi(units_emi, units_ene_mdl): emission_units=units_emi, ) + # Variable: "Energy|Supply|Electricity" _Hydrogen_el = _Hydrogen_tot * ( ( pp.inp( @@ -338,110 +761,358 @@ def retr_CO2emi(units_emi, units_ene_mdl): / _inp_nonccs_gas_tecs_wo_CCSRETRO ).fillna(0) + # Variable: "Energy|Supply|Heat" _Hydrogen_heat = _Hydrogen_tot * ( pp.inp("gas_hpl", units_ene_mdl, inpfilter={"commodity": ["gas"]}) / _inp_nonccs_gas_tecs_wo_CCSRETRO ).fillna(0) - _Hydrogen_ind = _Hydrogen_tot * ( + # Variable: "Energy|Supply|Gases|Transportation|Combustion" + _Hydrogen_td = _Hydrogen_tot * ( + ( + pp.inp( + ["gas_t_d", "gas_t_d_ch4"], + units_ene_mdl, + inpfilter={"commodity": ["gas"]}, + ) + - pp.out( + ["gas_t_d", "gas_t_d_ch4"], + units_ene_mdl, + outfilter={"commodity": ["gas"]}, + ) + ) + / _inp_nonccs_gas_tecs_wo_CCSRETRO + ).fillna(0) + + # Variable: "Energy|Demand|Industry|Other Sector" + _Hydrogen_ind_rest = _Hydrogen_tot * ( pp.inp( [ "gas_i", "hp_gas_i", - ] - + TECHS["gas extra"], + ], + units_ene_mdl, + inpfilter={"commodity": ["gas"]}, + ) + / _inp_nonccs_gas_tecs_wo_CCSRETRO + ).fillna(0) + + # Variable: "Energy|Demand|Industry|Steel" + _Hydrogen_ind_steel = _Hydrogen_tot * ( + pp.inp( + ["dri_steel", "eaf_steel", "furnace_gas_steel", "hp_gas_steel"], + units_ene_mdl, + inpfilter={"commodity": ["gas"]}, + ) + / _inp_nonccs_gas_tecs_wo_CCSRETRO + ).fillna(0) + + # Variable: "Energy|Demand|Industry|Non-Metallic Minerals|Cement" + _Hydrogen_ind_cement = _Hydrogen_tot * ( + pp.inp( + [ + "furnace_gas_cement", + "hp_gas_cement", + ], + units_ene_mdl, + inpfilter={"commodity": ["gas"]}, + ) + / _inp_nonccs_gas_tecs_wo_CCSRETRO + ).fillna(0) + + # Variable: "Energy|Demand|Industry|Non-Ferrous Metals|Aluminium" + _Hydrogen_ind_alu = _Hydrogen_tot * ( + pp.inp( + [ + "furnace_gas_aluminum", + "hp_gas_aluminum", + ], + units_ene_mdl, + inpfilter={"commodity": ["gas"]}, + ) + / _inp_nonccs_gas_tecs_wo_CCSRETRO + ).fillna(0) + + # Variable: "Energy|Demand|Industry|Chemicals|High Value Chemicals" + _Hydrogen_ind_petro = _Hydrogen_tot * ( + pp.inp( + [ + "furnace_gas_petro", + "gas_processing_petro", + "hp_gas_petro", + ], + units_ene_mdl, + inpfilter={"commodity": ["gas"]}, + ) + / _inp_nonccs_gas_tecs_wo_CCSRETRO + ).fillna(0) + + # Variable: "Energy|Demand|Industry|Chemicals|Ammonia" + _Hydrogen_ind_ammonia = _Hydrogen_tot * ( + pp.inp( + ["gas_NH3"], units_ene_mdl, inpfilter={"commodity": ["gas"]}, ) / _inp_nonccs_gas_tecs_wo_CCSRETRO ).fillna(0) + # Variable: "Energy|Demand|Other Sector" _Hydrogen_fs = _Hydrogen_tot * ( - pp.inp("gas_fs", units_ene_mdl, inpfilter={"commodity": ["gas"]}) + pp.inp( + ["gas_fs"], + units_ene_mdl, + inpfilter={"commodity": ["gas"]}, + ) / _inp_nonccs_gas_tecs_wo_CCSRETRO ).fillna(0) + # Variable: "Energy|Supply|Liquids|Oil|Combustion" + _Hydrogen_ref = _Hydrogen_tot * ( + pp.inp( + ["furnace_gas_refining", "hp_gas_refining"], + units_ene_mdl, + inpfilter={"commodity": ["gas"]}, + ) + / _inp_nonccs_gas_tecs_wo_CCSRETRO + ).fillna(0) + + # Variable: "Energy|Demand|Residential and Commercial" _Hydrogen_res = _Hydrogen_tot * ( pp.inp(TECHS["rc gas"], units_ene_mdl, inpfilter={"commodity": ["gas"]}) / _inp_nonccs_gas_tecs_wo_CCSRETRO ).fillna(0) - _Hydrogen_trp = _Hydrogen_tot * ( - pp.inp("gas_trp", units_ene_mdl, inpfilter={"commodity": ["gas"]}) - / _inp_nonccs_gas_tecs_wo_CCSRETRO - ).fillna(0) + # Variable: "Energy|Demand|Transportation|Road Rail and Domestic Shipping" + _Hydrogen_trp = _Hydrogen_tot * ( + pp.inp("gas_trp", units_ene_mdl, inpfilter={"commodity": ["gas"]}) + / _inp_nonccs_gas_tecs_wo_CCSRETRO + ).fillna(0) + + # Finally check to ensure that the sum of all the variables onto which hydrogen + # emissions are distributed on, matches the total. + # FIXME this variable is assigned but never used + check_hydrogen = _Hydrogen_tot - ( # noqa: F841 + _Hydrogen_el + + _Hydrogen_heat + + _Hydrogen_td + + _Hydrogen_ind_rest + + _Hydrogen_ind_steel + + _Hydrogen_ind_cement + + _Hydrogen_ind_alu + + _Hydrogen_ind_petro + + _Hydrogen_ind_ammonia + + _Hydrogen_fs + + _Hydrogen_ref + + _Hydrogen_res + + _Hydrogen_trp + ) + + # ---------------------------------------------------------------------------------- + # Step 4. Calculate total sector emissions + # + # - For all biomass-CCS or gas-CCS based technologies, total emissions with their + # contributions to emission abatement need to be calculated. + # ---------------------------------------------------------------------------------- + + # ---------------------------------------------------------------------------------- + # Step 4.1 Energy Supply - Heat and Electricity + # ---------------------------------------------------------------------------------- + + # Variable: "Energy|Supply|Electricity" + _SE_Elec_gen = pp.emi( + [ + "coal_ppl_u", + "coal_ppl", + "coal_adv", + "coal_adv_ccs", + "igcc", + "igcc_ccs", + "foil_ppl", + "loil_ppl", + "loil_cc", + "oil_ppl", + "gas_ppl", + "gas_cc", + "gas_cc_ccs", + "gas_ct", + "gas_htfc", + "bio_istig", + "g_ppl_co2scr", + "c_ppl_co2scr", + "bio_ppl_co2scr", + "igcc_co2scr", + "gfc_co2scr", + "cfc_co2scr", + "bio_istig_ccs", + ], + units_ene_mdl, + emifilter={"relation": ["CO2_cc"]}, + emission_units=units_emi, + ) + + # Variable: "Energy|Supply|Electricity" + # Used for reallocation of Diff. + _SE_Elec_gen_woBECCS = ( + _SE_Elec_gen + - pp.emi( + ["bio_istig_ccs", "bio_ppl_co2scr"], + units_ene_mdl, + emifilter={"relation": ["CO2_cc"]}, + emission_units=units_emi, + ) + - pp.emi( + ["g_ppl_co2scr", "gas_cc_ccs"], + units_ene_mdl, + emifilter={"relation": ["CO2_cc"]}, + emission_units=units_emi, + ) + * (_Biogas_tot_abs / _inp_all_gas_tecs) + ) + + # Variable: "Energy|Supply|Heat" + _SE_District_heat = pp.emi( + ["coal_hpl", "foil_hpl", "gas_hpl"], + units_ene_mdl, + emifilter={"relation": ["CO2_cc"]}, + emission_units=units_emi, + ) + + # ---------------------------------------------------------------------------------- + # Step 4.2 Final Energy + # ---------------------------------------------------------------------------------- + + # Variable: "Energy|Demand|Industry|Other Sector" + _FE_Industry_rest = pp.emi( + [ + "coal_i", + "foil_i", + "gas_i", + "hp_gas_i", + "loil_i", + "meth_i", + "sp_liq_I", + "sp_meth_I", + ], + units_ene_mdl, + emifilter={"relation": ["CO2_ind"]}, + emission_units=units_emi, + ) + + # Variable: "Energy|Demand|Industry|Steel" + _FE_Industry_steel = pp.emi( + ["DUMMY_coal_supply", "DUMMY_gas_supply", "DUMMY_limestone_supply_steel"], + units_ene_mdl, + emifilter={"relation": ["CO2_ind"]}, + emission_units=units_emi, + ) + + # Variable: "Energy|Demand|Industry|Non-Metallic Minerals|Cement" + _FE_Industry_cement = pp.emi( + [ + "DUMMY_limestone_supply_cement", + "furnace_coal_cement", + "furnace_foil_cement", + "furnace_gas_cement", + "furnace_loil_cement", + "furnace_methanol_cement", + ], + units_ene_mdl, + emifilter={"relation": ["CO2_ind"]}, + emission_units=units_emi, + ) + + # Variable: "Industrial Processes|Non-Metallic Minerals|Cement" + _FE_IndustryProcess_alu = pp.emi( + ["prebake_aluminum", "soderberg_aluminum", "vertical_stud"], + units_ene_mdl, + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi, + ) + + # Variable: "Energy|Demand|Industry|Non-Ferrous Metals|Aluminium" + _FE_Industry_alu = pp.emi( + [ + "furnace_coal_aluminum", + "furnace_foil_aluminum", + "furnace_gas_aluminum", + "furnace_loil_aluminum", + "furnace_methanol_aluminum", + ], + units_ene_mdl, + emifilter={"relation": ["CO2_ind"]}, + emission_units=units_emi, + ) + + # Variable: "Industrial Processes|Non-Ferrous Metals|Aluminium" + _FE_IndustryProcess_cement = pp.emi( + [ + "clinker_dry_ccs_cement", + "clinker_dry_cement", + "clinker_wet_ccs_cement", + "clinker_wet_cement", + ], + units_ene_mdl, + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi, + ) - _Hydrogen_td = _Hydrogen_tot * ( - ( - pp.inp( - ["gas_t_d", "gas_t_d_ch4"], - units_ene_mdl, - inpfilter={"commodity": ["gas"]}, - ) - - pp.out( - ["gas_t_d", "gas_t_d_ch4"], - units_ene_mdl, - outfilter={"commodity": ["gas"]}, - ) - ) - / _inp_nonccs_gas_tecs_wo_CCSRETRO - ).fillna(0) + # Variable: "Energy|Demand|Industry|Chemicals|High Value Chemicals" + _FE_Industry_petro = pp.emi( + [ + "furnace_coal_petro", + "furnace_coke_petro", + "furnace_foil_petro", + "furnace_gas_petro", + "furnace_loil_petro", + "furnace_methanol_petro", + "gas_processing_petro", + "steam_cracker_petro", + ], + units_ene_mdl, + emifilter={"relation": ["CO2_ind"]}, + emission_units=units_emi, + ) - _SE_Elec_gen = pp.emi( + # Variable: "Energy|Demand|Industry|Chemicals|Ammonia" + _FE_Industry_ammonia = pp.emi( [ - "coal_ppl_u", - "coal_ppl", - "coal_adv", - "coal_adv_ccs", - "igcc", - "igcc_ccs", - "foil_ppl", - "loil_ppl", - "loil_cc", - "oil_ppl", - "gas_ppl", - "gas_cc", - "gas_cc_ccs", - "gas_ct", - "gas_htfc", - "bio_istig", - "g_ppl_co2scr", - "c_ppl_co2scr", - "bio_ppl_co2scr", - "igcc_co2scr", - "gfc_co2scr", - "cfc_co2scr", - "bio_istig_ccs", + "biomass_NH3_ccs", + "biomass_NH3", + "coal_NH3_ccs", + "coal_NH3", + "fueloil_NH3_ccs", + "fueloil_NH3", + "gas_NH3_ccs", + "gas_NH3", ], units_ene_mdl, emifilter={"relation": ["CO2_cc"]}, emission_units=units_emi, ) - _SE_Elec_gen_woBECCS = ( - _SE_Elec_gen + # Variable: "Energy|Demand|Industry|Chemicals|Ammonia" + # Used for reallocation of Diff. - this is an exception. Normally the Diff. is only + # reallocated across SE-conversion technologies, which is where the NH3 technologies + # are located despite being assigned to `Demand|Industry` emissions. + _FE_Industry_ammonia_woBECCS = ( + _FE_Industry_ammonia - pp.emi( - ["bio_istig_ccs", "bio_ppl_co2scr"], + ["biomass_NH3_ccs"], units_ene_mdl, - emifilter={"relation": ["CO2_Emission"]}, + emifilter={"relation": ["CO2_cc"]}, emission_units=units_emi, ) - pp.emi( - ["g_ppl_co2scr", "gas_cc_ccs"], + ["gas_NH3_ccs"], units_ene_mdl, - emifilter={"relation": ["CO2_Emission"]}, + emifilter={"relation": ["CO2_cc"]}, emission_units=units_emi, ) * (_Biogas_tot_abs / _inp_all_gas_tecs) ) - _SE_District_heat = pp.emi( - ["coal_hpl", "foil_hpl", "gas_hpl"], - units_ene_mdl, - emifilter={"relation": ["CO2_cc"]}, - emission_units=units_emi, - ) - + # Variable: "Energy|Demand|Other Sector" _FE_Feedstocks = pp.emi( [ "coal_fs", @@ -449,14 +1120,13 @@ def retr_CO2emi(units_emi, units_ene_mdl): "loil_fs", "gas_fs", "methanol_fs", - "steam_cracker_petro", - "gas_processing_petro", ], units_ene_mdl, emifilter={"relation": ["CO2_feedstocks"]}, emission_units=units_emi, ) + # Variable: "Energy|Demand|Residential and Commercial" _FE_Res_com = pp.emi( rc_techs("coal foil gas loil meth"), units_ene_mdl, @@ -464,22 +1134,7 @@ def retr_CO2emi(units_emi, units_ene_mdl): emission_units=units_emi, ) - _FE_Industry = pp.emi( - [ - "gas_i", - "hp_gas_i", - "loil_i", - "meth_i", - "coal_i", - "foil_i", - "sp_liq_I", - "sp_meth_I", - ], - units_ene_mdl, - emifilter={"relation": ["CO2_ind"]}, - emission_units=units_emi, - ) - + # Variable: "Energy|Demand|Transportation|Road Rail and Domestic Shipping" _FE_Transport = pp.emi( ["gas_trp", "loil_trp", "meth_fc_trp", "meth_ic_trp", "coal_trp", "foil_trp"], units_ene_mdl, @@ -487,8 +1142,23 @@ def retr_CO2emi(units_emi, units_ene_mdl): emission_units=units_emi, ) + _FE_Industry = ( + _FE_Industry_rest + + _FE_Industry_steel + + _FE_IndustryProcess_cement + + _FE_Industry_cement + + _FE_IndustryProcess_alu + + _FE_Industry_alu + + _FE_Industry_petro + + _FE_Industry_ammonia + ) _FE_total = _FE_Feedstocks + _FE_Res_com + _FE_Industry + _FE_Transport + # ---------------------------------------------------------------------------------- + # Step 4.3 Energy Supply - Gases + # ---------------------------------------------------------------------------------- + + # Variable: "Energy|Supply|Gases|Extraction|Combustion" _Other_gases_extr_comb = pp.emi( [ "gas_extr_1", @@ -505,15 +1175,17 @@ def retr_CO2emi(units_emi, units_ene_mdl): emission_units=units_emi, ) + # Variable: "Energy|Supply|Gases|Extraction|Fugitive" _Other_gases_extr_fug = pp.emi( "flaring_CO2", units_ene_mdl, - emifilter={"relation": ["CO2_Emission"]}, + emifilter={"relation": ["CO2_cc"]}, emission_units=units_emi, ) - # Note that this is not included in the total because - # the diff is only calcualted from CO2_TCE and doesnt include trade + # Variable: "Energy|Supply|Gases|Transportation|Combustion" + # Note that this is not included in the total because the diff is only calculated + # from CO2_TCE and doesn't include trade. _Other_gases_trans_comb_trade = pp.emi( ["LNG_trd"], units_ene_mdl, @@ -521,6 +1193,7 @@ def retr_CO2emi(units_emi, units_ene_mdl): emission_units=units_emi, ) + # Variable: "Energy|Supply|Gases|Transportation|Combustion" _Other_gases_trans_comb = pp.emi( ["gas_t_d", "gas_t_d_ch4"], units_ene_mdl, @@ -528,6 +1201,7 @@ def retr_CO2emi(units_emi, units_ene_mdl): emission_units=units_emi, ) + # Variable: "Energy|Supply|Gases|Coal|Combustion" _Other_gases_coal_comb = pp.emi( ["coal_gas"], units_ene_mdl, @@ -539,6 +1213,7 @@ def retr_CO2emi(units_emi, units_ene_mdl): # emifilter={"relation": ["CO2_cc"]}, # emission_units=units_emi) + # Varpible: "Energy|Supply|Gases|Hydrogen|Combustion" _Other_gases_h2_comb = pp.emi( ["h2_smr", "h2_coal", "h2_bio", "h2_coal_ccs", "h2_smr_ccs", "h2_bio_ccs"], units_ene_mdl, @@ -546,6 +1221,7 @@ def retr_CO2emi(units_emi, units_ene_mdl): emission_units=units_emi, ) + # Variable: "Energy|Supply|Gases|Hydrogen|Combustion" _Other_gases_h2_comb_woBECCS = ( _Other_gases_h2_comb - pp.emi( @@ -563,6 +1239,7 @@ def retr_CO2emi(units_emi, units_ene_mdl): * (_Biogas_tot_abs / _inp_all_gas_tecs) ) + # Summation of all gases to be added to total _Other_gases_total = ( _Other_gases_extr_comb + _Other_gases_extr_fug @@ -571,6 +1248,8 @@ def retr_CO2emi(units_emi, units_ene_mdl): + _Other_gases_h2_comb ) + # Summation of all gases without BECCS used to reallocate the difference between + # top-down and bottom up accounting. _Other_gases_total_woBECCS = ( _Other_gases_extr_comb + _Other_gases_trans_comb @@ -580,6 +1259,11 @@ def retr_CO2emi(units_emi, units_ene_mdl): # Fugitive is not included in the total used to redistribute the difference # + _Other_gases_extr_fug + # ---------------------------------------------------------------------------------- + # Step 4.4 Energy Supply - Liquids + # ---------------------------------------------------------------------------------- + + # Variable: "Energy|Supply|Liquids|Extraction|Combustion" _Other_liquids_extr_comb = pp.emi( [ "oil_extr_1", @@ -600,8 +1284,9 @@ def retr_CO2emi(units_emi, units_ene_mdl): emission_units=units_emi, ) - # Note that this is not included in the total because - # the diff is only calcualted from CO2_TCE and doesnt include trade + # Variable: "Energy|Supply|Liquids|Transportation|Combustion" + # Note that this is not included in the total because the diff is only calculated + # from CO2_TCE and doesn't include trade. _Other_liquids_trans_comb_trade = pp.emi( ["foil_trd", "loil_trd", "oil_trd", "meth_trd", "eth_trd"], units_ene_mdl, @@ -609,6 +1294,7 @@ def retr_CO2emi(units_emi, units_ene_mdl): emission_units=units_emi, ) + # Variable: "Energy|Supply|Liquids|Transportation|Combustion" _Other_liquids_trans_comb = pp.emi( ["foil_t_d", "loil_t_d", "meth_t_d", "eth_t_d"], units_ene_mdl, @@ -616,6 +1302,7 @@ def retr_CO2emi(units_emi, units_ene_mdl): emission_units=units_emi, ) + # Variable: "Energy|Supply|Liquids|Oil|Combustion" _Other_liquids_oil_comb = pp.emi( [ "furnace_coke_refining", @@ -638,6 +1325,7 @@ def retr_CO2emi(units_emi, units_ene_mdl): emission_units=units_emi, ) + # Variable: "Energy|Supply|Liquids|Natural Gas|Combustion" _Other_liquids_gas_comb = pp.emi( ["meth_ng", "meth_ng_ccs"], units_ene_mdl, @@ -645,6 +1333,8 @@ def retr_CO2emi(units_emi, units_ene_mdl): emission_units=units_emi, ) + # Variable: "Energy|Supply|Liquids|Natural Gas|Combustion" + # For Diff allocation _Other_liquids_gas_comb_woBECCS = _Other_liquids_gas_comb - pp.emi( ["meth_ng_ccs"], units_ene_mdl, @@ -652,6 +1342,7 @@ def retr_CO2emi(units_emi, units_ene_mdl): emission_units=units_emi, ) * (_Biogas_tot_abs / _inp_all_gas_tecs) + # Variable: "Energy|Supply|Liquids|Coal|Combustion" _Other_liquids_coal_comb = pp.emi( ["meth_coal", "syn_liq", "meth_coal_ccs", "syn_liq_ccs"], units_ene_mdl, @@ -659,6 +1350,7 @@ def retr_CO2emi(units_emi, units_ene_mdl): emission_units=units_emi, ) + # Variable: "Energy|Supply|Liquids|Biomass|Combustion" _Other_liquids_biomass_comb = pp.emi( ["eth_bio", "liq_bio", "eth_bio_ccs", "liq_bio_ccs"], units_ene_mdl, @@ -666,6 +1358,8 @@ def retr_CO2emi(units_emi, units_ene_mdl): emission_units=units_emi, ) + # Variable: "Energy|Supply|Liquids|Biomass|Combustion" + # For Diff allocation _Other_liquids_biomass_comb_woBECCS = _Other_liquids_biomass_comb - pp.emi( ["eth_bio_ccs", "liq_bio_ccs"], units_ene_mdl, @@ -691,6 +1385,11 @@ def retr_CO2emi(units_emi, units_ene_mdl): + _Other_liquids_biomass_comb_woBECCS ) + # ---------------------------------------------------------------------------------- + # Step 4.5 Energy Supply - Solids + # ---------------------------------------------------------------------------------- + + # Variable: "Energy|Supply|Solids|Extraction|Combustion" _Other_solids_coal_trans_comb = pp.emi( "coal_t_d", units_ene_mdl, @@ -700,91 +1399,27 @@ def retr_CO2emi(units_emi, units_ene_mdl): _Other_solids_total = _Other_solids_coal_trans_comb - _Cement1 = pp.emi( - [ - "clinker_dry_cement", - "clinker_wet_cement", - ] - + TECHS["cement with ccs"], - units_ene_mdl, - emifilter={"relation": ["CO2_Emission"]}, - emission_units=units_emi, - ) - - _Cement2 = pp.emi( - [ - "DUMMY_limestone_supply_cement", - "furnace_biomass_cement", - "furnace_coal_cement", - "furnace_foil_cement", - "furnace_gas_cement", - "furnace_loil_cement", - "furnace_methanol_cement", - ], - units_ene_mdl, - emifilter={"relation": ["CO2_ind"]}, - emission_units=units_emi, - ) - - _Aluminum1 = pp.emi( - ["soderberg_aluminum", "prebake_aluminum", "vertical_stud"], - units_ene_mdl, - emifilter={"relation": ["CO2_Emission"]}, - emission_units=units_emi, - ) - - _Aluminum2 = pp.emi( - [ - "furnace_biomass_aluminum", - "furnace_coal_aluminum", - "furnace_foil_aluminum", - "furnace_gas_aluminum", - "furnace_loil_aluminum", - "furnace_methanol_aluminum", - ], - units_ene_mdl, - emifilter={"relation": ["CO2_ind"]}, - emission_units=units_emi, - ) - - _Steel = pp.emi( - ["DUMMY_coal_supply", "DUMMY_gas_supply", "DUMMY_limestone_supply_steel"], - units_ene_mdl, - emifilter={"relation": ["CO2_ind"]}, - emission_units=units_emi, - ) - - _Chemicals = pp.emi( - [ - "furnace_biomass_petro", - "furnace_coal_petro", - "furnace_coke_petro", - "furnace_foil_petro", - "furnace_gas_petro", - "furnace_loil_petro", - "furnace_methanol_petro", - ], - units_ene_mdl, - emifilter={"relation": ["CO2_ind"]}, - emission_units=units_emi, - ) + # ---------------------------------------------------------------------------------- + # Step 4.6 Land-use + # ---------------------------------------------------------------------------------- - _Ammonia = pp.emi( - ["biomass_NH3", "gas_NH3", "coal_NH3", "fueloil_NH3"], - units_ene_mdl, - emifilter={"relation": ["CO2_cc"]}, - emission_units=units_emi, + # Variable: "AFOLU" + _CO2_GLOBIOM = pp.land_out( + lu_out_filter={ + "level": ["land_use_reporting"], + "commodity": ["Emissions|CO2|AFOLU"], + }, + units=units_emi, ) - _Ammonia_ccs = pp.emi( - ["biomass_NH3_ccs", "gas_NH3_ccs", "coal_NH3_ccs", "fueloil_NH3_ccs"], - units_ene_mdl, - emifilter={"relation": ["CO2_Emission"]}, - emission_units=units_emi, - ) + # ---------------------------------------------------------------------------------- + # Step 5. Calculate Difference in top-down vs bottom-up + # + # TODO What about _FE_Feedstocks_woBECCS? Should they be included in + # _Total_wo_BECCS? + # ---------------------------------------------------------------------------------- - # FIXME unused; clarify purpose or remove - _Total = ( # noqa: F841 + _Total = ( # noqa: F841 due to modified calculation of _Diff1, below _SE_Elec_gen + _SE_District_heat + _FE_total @@ -793,14 +1428,6 @@ def retr_CO2emi(units_emi, units_ene_mdl): + _Other_solids_total - _Biogas_tot + _Hydrogen_tot - + _Cement1 - + _Cement2 - + _Aluminum1 - + _Aluminum2 - + _Steel - + _Chemicals - + _Ammonia - + _Ammonia_ccs ) # GLOBIOM with the new lu implementation, LU_CO2 no longer writes @@ -819,24 +1446,20 @@ def retr_CO2emi(units_emi, units_ene_mdl): ) + abs(_Other_liquids_total_woBECCS - _Biogas_liquids_gas_comb) + abs(_Other_solids_total) + # Exception made for ammonia as it is as SecondaryEnergy level not at final. + + abs( + _FE_Industry_ammonia_woBECCS - _Biogas_ind_ammonia + _Hydrogen_ind_ammonia + ) ) - # FIXME unused; clarify purpose or remove - _CO2_tce1 = pp.emi( # noqa: F841 + _CO2_tce1 = pp.emi( # noqa: F841 due to modified calculation of _Diff1, below "CO2_TCE", units_ene_mdl, emifilter={"relation": ["TCE_Emission"]}, emission_units=units_emi, ) - _CO2_GLOBIOM = pp.land_out( - lu_out_filter={ - "level": ["land_use_reporting"], - "commodity": ["Emissions|CO2|AFOLU"], - }, - units=units_emi, - ) - + # This was temporarily zero, since the difference was still not zero. # _Diff1 = _CO2_tce1 - _Total _Diff1 = pp_utils._make_zero() @@ -845,6 +1468,10 @@ def retr_CO2emi(units_emi, units_ene_mdl): df_hist = df_hist.rename(columns={i: int(i) for i in df_hist.columns}) _Diff1 = _Diff1 - df_hist + # ---------------------------------------------------------------------------------- + # Step 6. Assign values to IAMC Reporting Variables + # ---------------------------------------------------------------------------------- + # --------------------- # Agriculture (Table 1) # --------------------- @@ -922,26 +1549,16 @@ def retr_CO2emi(units_emi, units_ene_mdl): # "commodity": ["Emissions|CO2|AFOLU|Soil Carbon"]}, # units=units_emi) - # ------------------ - # Aircraft (Table 4) - # ------------------ + # ---------------------------------------------------------------------------------- + # Aircraft + # ---------------------------------------------------------------------------------- Aircraft = pp_utils._make_zero() vars["Energy|Demand|Transportation|Aviation|International"] = Aircraft - # ----------------------------------------- - # Electricity and heat production (Table 5) - # ----------------------------------------- - - vars["Energy|Supply|Heat"] = ( - _SE_District_heat - - _Biogas_heat - + _Hydrogen_heat - + _Diff1 - * ( - abs(_SE_District_heat - _Biogas_heat + _Hydrogen_heat) / _Total_wo_BECCS - ).fillna(0) - ) + # ---------------------------------------------------------------------------------- + # Energy Supply - Heat and Electricity + # ---------------------------------------------------------------------------------- vars["Energy|Supply|Electricity"] = ( _SE_Elec_gen @@ -953,6 +1570,16 @@ def retr_CO2emi(units_emi, units_ene_mdl): ).fillna(0) ) + vars["Energy|Supply|Heat"] = ( + _SE_District_heat + - _Biogas_heat + + _Hydrogen_heat + + _Diff1 + * ( + abs(_SE_District_heat - _Biogas_heat + _Hydrogen_heat) / _Total_wo_BECCS + ).fillna(0) + ) + vars["Energy|Supply|Gases|Biomass|Combustion"] = pp_utils._make_zero() vars["Energy|Supply|Gases|Biomass|Fugitive"] = pp_utils._make_zero() @@ -1040,9 +1667,15 @@ def retr_CO2emi(units_emi, units_ene_mdl): vars["Energy|Supply|Liquids|Natural Gas|Fugitive"] = pp_utils._make_zero() - vars["Energy|Supply|Liquids|Oil|Combustion"] = _Other_liquids_oil_comb + _Diff1 * ( - abs(_Other_liquids_oil_comb) / _Total_wo_BECCS - ).fillna(0) + vars["Energy|Supply|Liquids|Oil|Combustion"] = ( + _Other_liquids_oil_comb + - _Biogas_ref + + _Hydrogen_ref + + _Diff1 + * ( + abs(_Other_liquids_oil_comb - _Biogas_ref + _Hydrogen_ref) / _Total_wo_BECCS + ).fillna(0) + ) vars["Energy|Supply|Liquids|Oil|Fugitive"] = pp_utils._make_zero() @@ -1072,13 +1705,58 @@ def retr_CO2emi(units_emi, units_ene_mdl): vars["Energy|Supply|Solids|Transportation|Combustion"] = pp_utils._make_zero() vars["Energy|Supply|Solids|Transportation|Fugitive"] = pp_utils._make_zero() - # ------------------------------- - # Industrial Combustion (Table 7) - # ------------------------------- + # ---------------------------------------------------------------------------------- + # Final Energy - Industrial Combustion + # ---------------------------------------------------------------------------------- + + vars["Energy|Demand|Industry|Other Sector"] = ( + _FE_Industry_rest - _Biogas_ind_rest + _Hydrogen_ind_rest + ) + vars["Energy|Demand|Industry|Steel"] = ( + _FE_Industry_steel - _Biogas_ind_steel + _Hydrogen_ind_steel + ) + vars["Energy|Demand|Industry|Non-Metallic Minerals|Cement"] = ( + _FE_Industry_cement - _Biogas_ind_cement + _Hydrogen_ind_cement + ) + vars["Energy|Demand|Industry|Non-Metallic Minerals"] = ( + _FE_Industry_cement - _Biogas_ind_cement + _Hydrogen_ind_cement + ) + vars["Energy|Demand|Industry|Non-Ferrous Metals|Aluminium"] = ( + _FE_Industry_alu - _Biogas_ind_alu + _Hydrogen_ind_alu + ) + vars["Energy|Demand|Industry|Non-Ferrous Metals"] = ( + _FE_Industry_alu - _Biogas_ind_alu + _Hydrogen_ind_alu + ) + vars["Energy|Demand|Industry|Chemicals|High Value Chemicals"] = ( + _FE_Industry_petro - _Biogas_ind_petro + _Hydrogen_ind_petro + ) + vars["Energy|Demand|Industry|Chemicals|Ammonia"] = ( + _FE_Industry_ammonia + - _Biogas_ind_ammonia + + _Hydrogen_ind_ammonia + + _Diff1 + * ( + abs( + _FE_Industry_ammonia_woBECCS + - _Biogas_ind_ammonia + + _Hydrogen_ind_ammonia + ) + / _Total_wo_BECCS + ).fillna(0) + ) - IndustrialCombustion = _FE_Industry - _Biogas_ind + _Hydrogen_ind + vars["Energy|Demand|Industry|Chemicals"] = ( + vars["Energy|Demand|Industry|Chemicals|High Value Chemicals"] + + vars["Energy|Demand|Industry|Chemicals|Ammonia"] ) - vars["Energy|Demand|Industry"] = IndustrialCombustion + # Intermediate total, used below in calculation of "Energy|Combustion" + vars["Energy|Demand|Industry"] = ( + vars["Energy|Demand|Industry|Other Sector"] + + vars["Energy|Demand|Industry|Steel"] + + vars["Energy|Demand|Industry|Non-Metallic Minerals|Cement"] + + vars["Energy|Demand|Industry|Non-Ferrous Metals|Aluminium"] + + vars["Energy|Demand|Industry|Chemicals"] + ) # --------------------- # Industrial Feedstocks @@ -1090,8 +1768,10 @@ def retr_CO2emi(units_emi, units_ene_mdl): # Industrial process and product use (Table 8) # -------------------------------------------- - Cement = _Cement1 - vars["Industrial Processes"] = Cement + vars[ + "Industrial Processes|Non-Ferrous Metals|Aluminium" + ] = _FE_IndustryProcess_alu + vars["Industrial Processes|Non-Metallic Minerals|Cement"] = _FE_IndustryProcess_cement # -------------------------------- # International shipping (Table 9) @@ -1120,7 +1800,6 @@ def retr_CO2emi(units_emi, units_ene_mdl): # ------------------------------ Transport = _FE_Transport - _Biogas_trp + _Hydrogen_trp - # vars["Energy|Demand|Transportation|Road"] = Transport vars["Energy|Demand|Transportation|Road Rail and Domestic Shipping"] = Transport # ---------------------------------------------- @@ -1405,7 +2084,6 @@ def retr_CO2emi(units_emi, units_ene_mdl): dfs.append(pp_utils.make_outputdf(vars, units_ene_mdl, glb=False)) return pd.concat(dfs, sort=True) - @_register def retr_SE_solids(units): """Energy: Secondary Energy solids. @@ -1440,3 +2118,444 @@ def retr_SE_solids(units): .groupby(["Model", "Scenario", "Variable", "Unit", "Region"], as_index=False) .sum() ) + +@_register +def retr_supply_inv(units_energy, + units_emi, + units_ene_mdl): + """Technology results: Investments. + + Investments into technologies. + + Note OFR - 20.04.2017: The following has been checked between Volker + Krey, David McCollum and Oliver Fricko. + + There are fixed factors by which ccs technologies are multiplied which + equate to the share of the powerplant costs which split investments into the + share associated with the standard powerplant and the share associated + with those investments related to CCS. + + For some extraction and synfuel technologies, a certain share of the voms and + foms are attributed to the investments which is based on the GEA-Study + where the derived investment costs were partly attributed to the + voms/foms. + + Parameters + ---------- + units_energy : str + Units to which energy variables should be converted. + units_emi : str + Units to which emission variables should be converted. + units_ene_mdl : str + Native units of energy in the model. + """ + + vars = {} + + # ---------- + # Extraction + # ---------- + + # Note OFR 25.04.2017: All non-extraction costs for Coal, Gas and Oil + # have been moved to "Energy|Other" + + vars["Extraction|Coal"] = pp.investment(["coal_extr_ch4", "coal_extr", + "lignite_extr"], units=units_energy) + + vars["Extraction|Gas|Conventional"] =\ + pp.investment(["gas_extr_1", "gas_extr_2", + "gas_extr_3", "gas_extr_4"], units=units_energy) +\ + pp.act_vom(["gas_extr_1", "gas_extr_2", + "gas_extr_3", "gas_extr_4"], units=units_energy) * .5 + + vars["Extraction|Gas|Unconventional"] =\ + pp.investment(["gas_extr_5", "gas_extr_6", + "gas_extr_7", "gas_extr_8"], units=units_energy) +\ + pp.act_vom(["gas_extr_5", "gas_extr_6", + "gas_extr_7", "gas_extr_8"], units=units_energy) * .5 + + # Note OFR 25.04.2017: Any costs relating to refineries have been + # removed (compared to GEA) as these are reported under "Liquids|Oil" + + vars["Extraction|Oil|Conventional"] =\ + pp.investment(["oil_extr_1", "oil_extr_2", "oil_extr_3", + "oil_extr_1_ch4", "oil_extr_2_ch4", "oil_extr_3_ch4"], + units=units_energy) +\ + pp.act_vom(["oil_extr_1", "oil_extr_2", "oil_extr_3", + "oil_extr_1_ch4", "oil_extr_2_ch4", "oil_extr_3_ch4"], + units=units_energy) * .5 + + vars["Extraction|Oil|Unconventional"] =\ + pp.investment(["oil_extr_4", "oil_extr_4_ch4", "oil_extr_5", + "oil_extr_6", "oil_extr_7", "oil_extr_8"], + units=units_energy) +\ + pp.act_vom(["oil_extr_4", "oil_extr_4_ch4", "oil_extr_5", + "oil_extr_6", "oil_extr_7", "oil_extr_8"], units=units_energy) * .5 + + # As no mode is specified, u5-reproc will account for all 3 modes. + vars["Extraction|Uranium"] =\ + pp.investment(["uran2u5", "Uran_extr", + "u5-reproc", "plutonium_prod"], units=units_energy) +\ + pp.act_vom(["uran2u5", "Uran_extr"], units=units_energy) +\ + pp.act_vom(["u5-reproc"], actfilter={"mode": ["M1"]}, units=units_energy) +\ + pp.tic_fom(["uran2u5", "Uran_extr", "u5-reproc"], units=units_energy) + + # --------------------- + # Electricity - Fossils + # --------------------- + + vars["Electricity|Coal|w/ CCS"] =\ + pp.investment(["c_ppl_co2scr", "cfc_co2scr"], units=units_energy) +\ + pp.investment("coal_adv_ccs", units=units_energy) * 0.25 +\ + pp.investment("igcc_ccs", units=units_energy) * 0.31 + + vars["Electricity|Coal|w/o CCS"] =\ + pp.investment(["coal_ppl", "coal_ppl_u", "coal_adv"], units=units_energy) +\ + pp.investment("coal_adv_ccs", units=units_energy) * 0.75 +\ + pp.investment("igcc", units=units_energy) +\ + pp.investment("igcc_ccs", units=units_energy) * 0.69 + + vars["Electricity|Gas|w/ CCS"] =\ + pp.investment(["g_ppl_co2scr", "gfc_co2scr"], units=units_energy) +\ + pp.investment("gas_cc_ccs", units=units_energy) * 0.53 + + vars["Electricity|Gas|w/o CCS"] =\ + pp.investment(["gas_cc", "gas_ct", "gas_ppl"], units=units_energy) +\ + pp.investment("gas_cc_ccs", units=units_energy) * 0.47 + + vars["Electricity|Oil|w/o CCS"] =\ + pp.investment(["foil_ppl", "loil_ppl", "oil_ppl", "loil_cc"], + units=units_energy) + + # ------------------------ + # Electricity - Renewables + # ------------------------ + + vars["Electricity|Biomass|w/ CCS"] =\ + pp.investment("bio_ppl_co2scr", units=units_energy) +\ + pp.investment("bio_istig_ccs", units=units_energy) * 0.31 + + vars["Electricity|Biomass|w/o CCS"] =\ + pp.investment(["bio_ppl", "bio_istig"], units=units_energy) +\ + pp.investment("bio_istig_ccs", units=units_energy) * 0.69 + + vars["Electricity|Geothermal"] = pp.investment("geo_ppl", units=units_energy) + + vars["Electricity|Hydro"] = pp.investment(["hydro_hc", "hydro_lc"], + units=units_energy) + vars["Electricity|Other"] = pp.investment(["h2_fc_I", "h2_fc_RC"], + units=units_energy) + + _solar_pv_elec = pp.investment(["solar_pv_ppl", "solar_pv_I", + "solar_pv_RC"], units=units_energy) + + _solar_th_elec = pp.investment(["csp_sm1_ppl", "csp_sm3_ppl"], units=units_energy) + + vars["Electricity|Solar|PV"] = _solar_pv_elec + vars["Electricity|Solar|CSP"] = _solar_th_elec + vars["Electricity|Wind|Onshore"] = pp.investment(["wind_ppl"], units=units_energy) + vars["Electricity|Wind|Offshore"] = pp.investment(["wind_ppf"], units=units_energy) + + # ------------------- + # Electricity Nuclear + # ------------------- + + vars["Electricity|Nuclear"] = pp.investment(["nuc_hc", "nuc_lc"], + units=units_energy) + + # -------------------------------------------------- + # Electricity Storage, transmission and distribution + # -------------------------------------------------- + + vars["Electricity|Electricity Storage"] = pp.investment("stor_ppl", + units=units_energy) + + vars["Electricity|Transmission and Distribution"] =\ + pp.investment(["elec_t_d", + "elec_exp", "elec_exp_africa", + "elec_exp_america", "elec_exp_asia", + "elec_exp_eurasia", "elec_exp_eur_afr", + "elec_exp_asia_afr", + "elec_imp", "elec_imp_africa", + "elec_imp_america", "elec_imp_asia", + "elec_imp_eurasia", "elec_imp_eur_afr", + "elec_imp_asia_afr"], units=units_energy) +\ + pp.act_vom(["elec_t_d", + "elec_exp", "elec_exp_africa", + "elec_exp_america", "elec_exp_asia", + "elec_exp_eurasia", "elec_exp_eur_afr", + "elec_exp_asia_afr", + "elec_imp", "elec_imp_africa", + "elec_imp_america", "elec_imp_asia", + "elec_imp_eurasia", "elec_imp_eur_afr", + "elec_imp_asia_afr"], units=units_energy) * .5 +\ + pp.tic_fom(["elec_t_d", + "elec_exp", "elec_exp_africa", + "elec_exp_america", "elec_exp_asia", + "elec_exp_eurasia", "elec_exp_eur_afr", + "elec_exp_asia_afr", + "elec_imp", "elec_imp_africa", + "elec_imp_america", "elec_imp_asia", + "elec_imp_eurasia", "elec_imp_eur_afr", + "elec_imp_asia_afr"], units=units_energy) * .5 + + # ------------------------------------------ + # CO2 Storage, transmission and distribution + # ------------------------------------------ + + _CCS_coal_elec = -1 *\ + pp.emi(["c_ppl_co2scr", "coal_adv_ccs", + "igcc_ccs", "clinker_dry_ccs_cement",'clinker_wet_ccs_cement'], units_ene_mdl, + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi) + + _CCS_coal_synf = -1 *\ + pp.emi(["syn_liq_ccs", "h2_coal_ccs", + "meth_coal_ccs"], units_ene_mdl, + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi) + + _CCS_gas_elec = -1 *\ + pp.emi(["g_ppl_co2scr", "gas_cc_ccs"], units_ene_mdl, + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi) + + _CCS_gas_synf = -1 *\ + pp.emi(["h2_smr_ccs", "meth_ng_ccs"], units_ene_mdl, + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi) + + _CCS_bio_elec = -1 *\ + pp.emi(["bio_ppl_co2scr", "bio_istig_ccs"], units_ene_mdl, + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi) + + _CCS_bio_synf = -1 *\ + pp.emi(["eth_bio_ccs", "liq_bio_ccs", + "h2_bio_ccs"], units_ene_mdl, + emifilter={"relation": ["CO2_Emission"]}, + emission_units=units_emi) + + _Biogas_use_tot = pp.out("gas_bio") + + _Gas_use_tot = pp.inp(["gas_ppl", "gas_cc", "gas_cc_ccs", + "gas_ct", "gas_htfc", "gas_hpl", + "meth_ng", "meth_ng_ccs", "h2_smr", + "h2_smr_ccs", "gas_rc", "hp_gas_rc", + "gas_i", "hp_gas_i", + 'furnace_gas_aluminum', + 'furnace_gas_petro', + 'furnace_gas_cement', + 'furnace_gas_refining', + "hp_gas_i", + 'hp_gas_aluminum', + 'hp_gas_petro', + 'hp_gas_refining', + "gas_trp", + "gas_fs"]) + + _Biogas_share = (_Biogas_use_tot / _Gas_use_tot).fillna(0) + + _CCS_Foss = _CCS_coal_elec +\ + _CCS_coal_synf +\ + _CCS_gas_elec * (1 - _Biogas_share) +\ + _CCS_gas_synf * (1 - _Biogas_share) + + _CCS_Bio = _CCS_bio_elec +\ + _CCS_bio_synf -\ + (_CCS_gas_elec + _CCS_gas_synf) * _Biogas_share + + _CCS_coal_elec_shr = (_CCS_coal_elec / _CCS_Foss).fillna(0) + _CCS_coal_synf_shr = (_CCS_coal_synf / _CCS_Foss).fillna(0) + _CCS_gas_elec_shr = (_CCS_gas_elec / _CCS_Foss).fillna(0) + _CCS_gas_synf_shr = (_CCS_gas_synf / _CCS_Foss).fillna(0) + _CCS_bio_elec_shr = (_CCS_bio_elec / _CCS_Bio).fillna(0) + _CCS_bio_synf_shr = (_CCS_bio_synf / _CCS_Bio).fillna(0) + + CO2_trans_dist_elec =\ + pp.act_vom("co2_tr_dis", units=units_energy) * 0.5 * _CCS_coal_elec_shr +\ + pp.act_vom("co2_tr_dis", units=units_energy) * 0.5 * _CCS_gas_elec_shr +\ + pp.act_vom("bco2_tr_dis", units=units_energy) * 0.5 * _CCS_bio_elec_shr + + CO2_trans_dist_synf =\ + pp.act_vom("co2_tr_dis", units=units_energy) * 0.5 * _CCS_coal_synf_shr +\ + pp.act_vom("co2_tr_dis", units=units_energy) * 0.5 * _CCS_gas_synf_shr +\ + pp.act_vom("bco2_tr_dis", units=units_energy) * 0.5 * _CCS_bio_synf_shr + + vars["CO2 Transport and Storage"] = CO2_trans_dist_elec + CO2_trans_dist_synf + + # ---- + # Heat + # ---- + + vars["Heat"] = pp.investment(["coal_hpl", "foil_hpl", "gas_hpl", + "bio_hpl", "heat_t_d", "po_turbine"], + units=units_energy) + + # ------------------------- + # Synthetic fuel production + # ------------------------- + + # Note OFR 25.04.2017: XXX_synf_ccs has been split into hydrogen and + # liquids. The shares then add up to 1, but the variables are kept + # separate in order to preserve the split between CCS and non-CCS + + _Coal_synf_ccs_liq = pp.investment("meth_coal_ccs", units=units_energy) * 0.02 +\ + pp.investment("syn_liq_ccs", units=units_energy) * 0.01 + + _Gas_synf_ccs_liq = pp.investment("meth_ng_ccs", units=units_energy) * 0.08 + + _Bio_synf_ccs_liq = pp.investment("eth_bio_ccs", units=units_energy) * 0.34 + \ + pp.investment("liq_bio_ccs", units=units_energy) * 0.02 + + _Coal_synf_ccs_h2 = pp.investment("h2_coal_ccs", units=units_energy) * 0.03 + _Gas_synf_ccs_h2 = pp.investment("h2_smr_ccs", units=units_energy) * 0.17 + _Bio_synf_ccs_h2 = pp.investment("h2_bio_ccs", units=units_energy) * 0.02 + + # Note OFR 25.04.2017: "coal_gas" have been moved to "other" + vars["Liquids|Coal and Gas"] =\ + pp.investment(["meth_coal", "syn_liq", "meth_ng"], units=units_energy) +\ + pp.investment("meth_ng_ccs", units=units_energy) * 0.92 +\ + pp.investment("meth_coal_ccs", units=units_energy) * 0.98 +\ + pp.investment("syn_liq_ccs", units=units_energy) * 0.99 +\ + _Coal_synf_ccs_liq +\ + _Gas_synf_ccs_liq + + # Note OFR 25.04.2017: "gas_bio" has been moved to "other" + vars["Liquids|Biomass"] =\ + pp.investment(["eth_bio", "liq_bio"], units=units_energy) +\ + pp.investment("liq_bio_ccs", units=units_energy) * 0.98 +\ + pp.investment("eth_bio_ccs", units=units_energy) * 0.66 + _Bio_synf_ccs_liq + + # Note OFR 25.04.2017: "transport, import and exports costs related to + # liquids are only included in the total" + _Synfuel_other = pp.investment(["meth_exp", "meth_imp", "meth_t_d", + "meth_bal", "eth_exp", "eth_imp", + "eth_t_d", "eth_bal", "SO2_scrub_synf"], + units=units_energy) + + vars["Liquids|Oil"] = pp.investment(['furnace_coke_refining', + 'furnace_coal_refining', + 'furnace_foil_refining', + 'furnace_loil_refining', + 'furnace_ethanol_refining', + 'furnace_biomass_refining', + 'furnace_methanol_refining', + 'furnace_gas_refining', + 'furnace_elec_refining', + 'furnace_h2_refining', + 'hp_gas_refining', + 'hp_elec_refining', + 'fc_h2_refining', + 'solar_refining', + 'dheat_refining', + 'atm_distillation_ref', + 'vacuum_distillation_ref', + 'hydrotreating_ref', + 'catalytic_cracking_ref', + 'visbreaker_ref', + 'coking_ref', + 'catalytic_reforming_ref', + 'hydro_cracking_ref' + ], units=units_energy) +\ + pp.tic_fom(['furnace_coke_refining', + 'furnace_coal_refining', + 'furnace_foil_refining', + 'furnace_loil_refining', + 'furnace_ethanol_refining', + 'furnace_biomass_refining', + 'furnace_methanol_refining', + 'furnace_gas_refining', + 'furnace_elec_refining', + 'furnace_h2_refining', + 'hp_gas_refining', + 'hp_elec_refining', + 'fc_h2_refining', + 'solar_refining', + 'dheat_refining', + 'atm_distillation_ref', + 'vacuum_distillation_ref', + 'hydrotreating_ref', + 'catalytic_cracking_ref', + 'visbreaker_ref', + 'coking_ref', + 'catalytic_reforming_ref', + 'hydro_cracking_ref'], units=units_energy) + + vars["Liquids"] = vars["Liquids|Coal and Gas"] +\ + vars["Liquids|Biomass"] +\ + vars["Liquids|Oil"] +\ + _Synfuel_other + + # -------- + # Hydrogen + # -------- + + vars["Hydrogen|Fossil"] =\ + pp.investment(["h2_coal", "h2_smr"], units=units_energy) +\ + pp.investment("h2_coal_ccs", units=units_energy) * 0.97 +\ + pp.investment("h2_smr_ccs", units=units_energy) * 0.83 +\ + _Coal_synf_ccs_h2 +\ + _Gas_synf_ccs_h2 + + vars["Hydrogen|Renewable"] = pp.investment("h2_bio", units=units_energy) +\ + pp.investment("h2_bio_ccs", units=units_energy) * 0.98 +\ + _Bio_synf_ccs_h2 + + vars["Hydrogen|Other"] = pp.investment(["h2_elec", "h2_liq", "h2_t_d", + "lh2_exp", "lh2_imp", "lh2_bal", + "lh2_regas", "lh2_t_d"], + units=units_energy) +\ + pp.act_vom("h2_mix", units=units_energy) * 0.5 + + # ----- + # Other + # ----- + + # All questionable variables from extraction that are not related directly + # to extraction should be moved to Other + # Note OFR 25.04.2017: Any costs relating to refineries have been + # removed (compared to GEA) as these are reported under "Liquids|Oil" + + vars["Other|Liquids|Oil|Transmission and Distribution"] =\ + pp.investment(["foil_t_d", "loil_t_d"], units=units_energy) + + vars["Other|Liquids|Oil|Other"] =\ + pp.investment(["foil_exp", "loil_exp", "oil_exp", + "oil_imp", "foil_imp", "loil_imp", + "loil_std", "oil_bal", "loil_sto"], units=units_energy) + + vars["Other|Gases|Transmission and Distribution"] =\ + pp.investment(["gas_t_d", "gas_t_d_ch4"], units=units_energy) + + vars["Other|Gases|Production"] =\ + pp.investment(["gas_bio", "coal_gas"], units=units_energy) + + vars["Other|Gases|Other"] =\ + pp.investment(["LNG_bal", "LNG_prod", "LNG_regas", + "LNG_exp", "LNG_imp", "gas_bal", + "gas_std", "gas_sto", "gas_exp_eeu", + "gas_exp_nam", "gas_exp_pao", "gas_exp_weu", + "gas_exp_cpa", "gas_exp_afr", "gas_exp_sas", + "gas_exp_scs", "gas_exp_cas", "gas_exp_ubm", + "gas_exp_rus", "gas_imp"], units=units_energy) + + vars["Other|Solids|Coal|Transmission and Distribution"] =\ + pp.investment(["coal_t_d", "coal_t_d-rc-SO2", "coal_t_d-rc-06p", + "coal_t_d-in-SO2", "coal_t_d-in-06p"], units=units_energy) +\ + pp.act_vom(["coal_t_d-rc-SO2", "coal_t_d-rc-06p", "coal_t_d-in-SO2", + "coal_t_d-in-06p", "coal_t_d"], units=units_energy) * 0.5 + + vars["Other|Solids|Coal|Other"] =\ + pp.investment(["coal_exp", "coal_imp", + "coal_bal", "coal_std"], units=units_energy) + + vars["Other|Solids|Biomass|Transmission and Distribution"] =\ + pp.investment("biomass_t_d", units=units_energy) + + vars["Other|Other"] =\ + pp.investment(["SO2_scrub_ref"], units=units_energy) * 0.5 +\ + pp.investment(["SO2_scrub_ind"], units=units_energy) + + df = pp_utils.make_outputdf(vars, units_energy) + return df From d94c5a84f091f35abaa1bee93600dc8c10b7a5af Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 18 Jan 2023 14:56:01 +0100 Subject: [PATCH 552/774] Update material report-2 command --- message_ix_models/model/material/__init__.py | 24 ++++++++------------ 1 file changed, 9 insertions(+), 15 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index f213be3252..12a88f7c1b 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -353,37 +353,31 @@ def run_reporting(context, remove_ts): @cli.command("report-2") -@click.option("--scenario_name", default="NoPolicy") -@click.option("--model_name", default="MESSAGEix-Materials") -# @click.pass_obj -def run_old_reporting(scenario_name, model_name): +@click.pass_obj +def run_old_reporting(context): from message_ix import Scenario from ixmp import Platform from message_data.tools.post_processing.iamc_report_hackathon import ( report as reporting, ) - base_model = model_name - scen_name = scenario_name - - print(model_name) - print(scenario_name) - mp = Platform(name='ixmp_dev', jvmargs=['-Xmx16G']) - scenario = Scenario(mp, model_name, scenario_name) + # Retrieve the scenario given by the --url option + scenario = context.get_scenario() + mp = scenario.platform reporting( mp, scenario, + # NB(PNK) this is not an error; .iamc_report_hackathon.report() expects a + # string containing "True" or "False" instead of an actual bool. "False", - base_model, - scen_name, + scenario.model, + scenario.scenario, merge_hist=True, merge_ts=True, run_config="materials_run_config.yaml", - verbose = True ) - from .data_cement import gen_data_cement from .data_steel import gen_data_steel from .data_aluminum import gen_data_aluminum From 920a78d69c1b8ecd9dadd3fbf0858d1b9e728d9b Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 24 Jan 2023 13:36:30 +0100 Subject: [PATCH 553/774] Add missing cement technologies --- message_ix_models/data/material/set.yaml | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 3fa67cad3c..3e7a0dca36 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -103,6 +103,10 @@ generic: - furnace_elec_cement - furnace_h2_cement - hp_gas_cement + - hp_elec_cement + - fc_h2_cement + - solar_cement + - dheat_cement - furnace_coal_aluminum - furnace_foil_aluminum - furnace_loil_aluminum From 9f7547f3efccc9081f628c43caaef25687c68758 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 25 Jan 2023 09:30:00 +0100 Subject: [PATCH 554/774] Add methanol reporting changes - not tested yet --- message_ix_models/model/material/__init__.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 12a88f7c1b..988c2e2218 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -347,9 +347,9 @@ def run_reporting(context, remove_ts): merge_ts=True, run_config="materials_run_config.yaml", ) - util.prepare_xlsx_for_explorer( - Path(os.getcwd()).parents[0].joinpath( - "reporting_output", scenario.model+"_"+scenario.scenario+".xlsx")) + # util.prepare_xlsx_for_explorer( + # Path(os.getcwd()).parents[0].joinpath( + # "reporting_output", scenario.model+"_"+scenario.scenario+".xlsx")) @cli.command("report-2") From edf66286b0cee76fa9e1054be1122c40a328613b Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 24 Jan 2023 17:04:16 +0100 Subject: [PATCH 555/774] Add feedstock mode for methanol production --- .../methanol/meth_coal_additions.xlsx | 4 +- .../methanol/meth_coal_additions_fs.xlsx | 3 + .../methanol/meth_ng_techno_economic.xlsx | 4 +- .../methanol/meth_ng_techno_economic_fs.xlsx | 3 + .../data/material/methanol/meth_t_d_fuel.xlsx | 3 + .../methanol/meth_t_d_material_pars.xlsx | 4 +- .../methanol/meth_trade_techno_economic.xlsx | 4 +- .../meth_trade_techno_economic_fs.xlsx | 3 + message_ix_models/data/material/set.yaml | 9 + .../model/material/data_methanol.py | 154 +++++++++++++++++- 10 files changed, 175 insertions(+), 16 deletions(-) create mode 100644 message_ix_models/data/material/methanol/meth_coal_additions_fs.xlsx create mode 100644 message_ix_models/data/material/methanol/meth_ng_techno_economic_fs.xlsx create mode 100644 message_ix_models/data/material/methanol/meth_t_d_fuel.xlsx create mode 100644 message_ix_models/data/material/methanol/meth_trade_techno_economic_fs.xlsx diff --git a/message_ix_models/data/material/methanol/meth_coal_additions.xlsx b/message_ix_models/data/material/methanol/meth_coal_additions.xlsx index 3b93d7dbdf..a6f0eb1f55 100644 --- a/message_ix_models/data/material/methanol/meth_coal_additions.xlsx +++ b/message_ix_models/data/material/methanol/meth_coal_additions.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:9731c1f8ccffbcd5e7c5584d7faeddb7b0d41a3ddb1885cfc5491f4079afe270 -size 93875 +oid sha256:e3a826c7be981b00c27798200d1cf3c3867785d3f27f22f3f1def8a22c12543f +size 94157 diff --git a/message_ix_models/data/material/methanol/meth_coal_additions_fs.xlsx b/message_ix_models/data/material/methanol/meth_coal_additions_fs.xlsx new file mode 100644 index 0000000000..621d8eec56 --- /dev/null +++ b/message_ix_models/data/material/methanol/meth_coal_additions_fs.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3e48bb9ea42383ce32fb0b3c3aa8d24dbb1eb920abd8096ae1caa05d9e099497 +size 94214 diff --git a/message_ix_models/data/material/methanol/meth_ng_techno_economic.xlsx b/message_ix_models/data/material/methanol/meth_ng_techno_economic.xlsx index 1ff4ba56c2..70e6f8e226 100644 --- a/message_ix_models/data/material/methanol/meth_ng_techno_economic.xlsx +++ b/message_ix_models/data/material/methanol/meth_ng_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:05562ca0b40fb546516bcb2bfca114d7fa5ba73a9326b2c8027439514d1bc590 -size 103018 +oid sha256:c28f1c27ac2b5b851f6d535524cfd59f3585e11acdd126cd13cd5411a1430ba6 +size 103677 diff --git a/message_ix_models/data/material/methanol/meth_ng_techno_economic_fs.xlsx b/message_ix_models/data/material/methanol/meth_ng_techno_economic_fs.xlsx new file mode 100644 index 0000000000..d79f572f49 --- /dev/null +++ b/message_ix_models/data/material/methanol/meth_ng_techno_economic_fs.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:451a1439b6dbaf5f6f0175c033cc7f3732733b3d512e5f751973bbde2cb6db26 +size 103586 diff --git a/message_ix_models/data/material/methanol/meth_t_d_fuel.xlsx b/message_ix_models/data/material/methanol/meth_t_d_fuel.xlsx new file mode 100644 index 0000000000..3284a7a1d2 --- /dev/null +++ b/message_ix_models/data/material/methanol/meth_t_d_fuel.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a6d532d71686bfceb0146340b7d1bbbf301a52ccc0c9779bb30e9771b45a7bb2 +size 91798 diff --git a/message_ix_models/data/material/methanol/meth_t_d_material_pars.xlsx b/message_ix_models/data/material/methanol/meth_t_d_material_pars.xlsx index fbabb084ab..628d1f4f43 100644 --- a/message_ix_models/data/material/methanol/meth_t_d_material_pars.xlsx +++ b/message_ix_models/data/material/methanol/meth_t_d_material_pars.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:f0b9aaf0e665f7f82f80c54b484a3c4e27026b106ddddc0802e07c4d1aba4c56 -size 91560 +oid sha256:2fd5f1d4eeaca36ac79e73cfb093580d7d00dc7d840f1d3f09532d1e2f96f515 +size 91812 diff --git a/message_ix_models/data/material/methanol/meth_trade_techno_economic.xlsx b/message_ix_models/data/material/methanol/meth_trade_techno_economic.xlsx index 03d045ebfb..2e84e91ffc 100644 --- a/message_ix_models/data/material/methanol/meth_trade_techno_economic.xlsx +++ b/message_ix_models/data/material/methanol/meth_trade_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:86b218ef477631f18440b78fbfa6c6e0c2c3de25d55e6a6e8617d3385f98f269 -size 211792 +oid sha256:d7fb799a8b5c6faf2a8da6e9d847a18e1a624b4aeb0dcc20fe8013c7a5dd1a2a +size 302 diff --git a/message_ix_models/data/material/methanol/meth_trade_techno_economic_fs.xlsx b/message_ix_models/data/material/methanol/meth_trade_techno_economic_fs.xlsx new file mode 100644 index 0000000000..1544a04c51 --- /dev/null +++ b/message_ix_models/data/material/methanol/meth_trade_techno_economic_fs.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:871838da796e854fd76812cc286b254315d1d54e76a51ecd972608ae772f10fd +size 211546 diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 3e7a0dca36..d70bd3b2fb 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -494,6 +494,15 @@ methanol: level: add: - final_material + - secondary_material + - primary_material + - export_fs + - import_fs + + mode: + add: + - feedstock + - fuel technology: add: diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index 4ee40f473c..23b09d7207 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -19,16 +19,126 @@ def gen_data_methanol(scenario): ) pars = df_pars.set_index("par").to_dict()["value"] - meth_h2_dict = gen_data_meth_h2() + + meth_h2_fs_dict = gen_data_meth_h2() + meth_h2_fuel_dict = gen_data_meth_h2() meth_bio_dict = gen_data_meth_bio(scenario) meth_bio_ccs_dict = gen_meth_bio_ccs(scenario) - new_dict = combine_df_dictionaries(meth_h2_dict, meth_bio_dict, meth_bio_ccs_dict) + meth_fs_dic = combine_df_dictionaries(meth_h2_fs_dict, gen_data_meth_bio(scenario), + gen_meth_bio_ccs(scenario)) + meth_fuel_dic = combine_df_dictionaries(meth_h2_fuel_dict, meth_bio_dict, meth_bio_ccs_dict) + for k in meth_h2_fuel_dict.keys(): + df_fuel = meth_fuel_dic[k] + df_fs = meth_fs_dic[k] + if "mode" in df_fuel.columns: + df_fuel["mode"] = "fuel" + df_fs["mode"] = "feedstock" + + df = meth_fs_dic["output"] + df.loc[df["commodity"] == "methanol", "level"] = "primary_material" + meth_fs_dic["output"] = df + + + #add meth prod fs mode + par_dict_fs = {} + for i in scenario.par_list(): + try: + df = scenario.par(i, filters={"technology": ["meth_ng", + "meth_coal", "meth_ng_ccs", + "meth_coal_ccs"]}) + if df.size != 0: + par_dict_fs[i] = df + except: + pass + for i in par_dict_fs.keys(): + if "mode" in par_dict_fs[i].columns: + par_dict_fs[i]["mode"] = "feedstock" + df = par_dict_fs["output"] + df.loc[df["commodity"] == "methanol", "level"] = "primary_material" + par_dict_fs["output"] = df - dict3 = pd.read_excel( + par_dict = {} + for i in scenario.par_list(): + try: + df = scenario.par(i, filters={"technology": ["meth_ng", + "meth_coal", "meth_ng_ccs", + "meth_coal_ccs"]}) + if df.size != 0: + par_dict[i] = df + except: + pass + for i in par_dict.keys(): + if "mode" in par_dict[i].columns: + par_dict[i]["mode"] = "fuel" + df = par_dict["output"] + par_dict["output"] = df + + + #add meth_bal fs mode + bal_fs_dict = {} + bal_fuel_dict = {} + for i in scenario.par_list(): + try: + df = scenario.par(i, filters={"technology": "meth_bal"}) + if df.size != 0: + bal_fs_dict[i] = df + bal_fuel_dict[i] = df.copy(deep=True) + except: + pass + for i in bal_fs_dict.keys(): + if "mode" in bal_fs_dict[i].columns: + bal_fs_dict[i]["mode"] = "feedstock" + bal_fuel_dict[i]["mode"] = "fuel" + + df = bal_fs_dict["input"] + df.loc[df["commodity"] == "methanol", "level"] = "primary_material" + bal_fs_dict["input"] = df + df = bal_fs_dict["output"] + df.loc[df["commodity"] == "methanol", "level"] = "secondary_material" + bal_fs_dict["output"] = df + + + # add meth trade fs mode + trade_dict_fs = {} + trade_dict_fuel = {} + for i in scenario.par_list(): + try: + df = scenario.par(i, filters={"technology": ["meth_imp", + "meth_exp", + "meth_trd"]}) + if df.size != 0: + trade_dict_fs[i] = df + trade_dict_fuel[i] = df.copy(deep=True) + except: + pass + for i in trade_dict_fs.keys(): + if "mode" in trade_dict_fs[i].columns: + trade_dict_fs[i]["mode"] = "feedstock" + trade_dict_fuel[i]["mode"] = "fuel" + + df = trade_dict_fs["output"] + df.loc[df["technology"]=="meth_imp", "level"] = "secondary_material" + df.loc[df["technology"] == "meth_exp", "level"] = "export_fs" + df.loc[df["technology"] == "meth_trd", "level"] = "import_fs" + trade_dict_fs["output"] = df + + df = trade_dict_fs["input"] + df.loc[df["technology"] == "meth_imp", "level"] = "import_fs" + df.loc[df["technology"] == "meth_trd", "level"] = "export_fs" + df.loc[df["technology"] == "meth_exp", "level"] = "primary_material" + trade_dict_fs["input"] = df + + + dict_t_d_fs = pd.read_excel( context.get_local_path("material", "methanol", "meth_t_d_material_pars.xlsx"), sheet_name=None, ) - df_rel = dict3["relation_activity"] + dict_t_d_fuel = pd.read_excel( + context.get_local_path("material", "methanol", "meth_t_d_fuel.xlsx"), + sheet_name=None, + ) + + df_rel = dict_t_d_fs["relation_activity"] def get_embodied_emi(row, pars, share_par): if row["year_act"] < pars["incin_trend_end"]: @@ -37,13 +147,32 @@ def get_embodied_emi(row, pars, share_par): share = 0.5 return row["value"] * (1 - share) * pars[share_par] - df_rel_meth = df_rel.loc[df_rel["technology"] == "meth_t_d_material",:] + df_rel_meth = df_rel.loc[df_rel["technology"] == "meth_t_d",:] df_rel_meth["value"] = df_rel_meth.apply(lambda x: get_embodied_emi(x, pars, "meth_plastics_share"), axis=1) df_rel_hvc = df_rel.loc[df_rel["technology"] == "steam_cracker_petro",:] df_rel_hvc["value"] = df_rel_hvc.apply(lambda x: get_embodied_emi(x, pars, "hvc_plastics_share"), axis=1) - dict3["relation_activity"] = pd.concat([df_rel_meth, df_rel_hvc]) + dict_t_d_fs["relation_activity"] = pd.concat([df_rel_meth, df_rel_hvc]) # dict3.pop("relation_activity") # remove negative emissions for now - new_dict2 = combine_df_dictionaries(new_dict, dict3) + new_dict2 = combine_df_dictionaries(meth_fuel_dic, meth_fs_dic, + par_dict_fs, par_dict, + bal_fs_dict, bal_fuel_dict, + trade_dict_fuel, trade_dict_fs, + dict_t_d_fs, dict_t_d_fuel) + + + for i in scenario.par_list(): + try: + df = scenario.par(i, filters={"technology": ["meth_ng", + "meth_coal", "meth_ng_ccs", + "meth_coal_ccs", "meth_exp", + "meth_imp", "meth_trd", "meth_bal", "meth_t_d"], + "mode": "M1"}) + if df.size != 0: + scenario.remove_par(i, df) + except: + pass + + df_final = gen_meth_residual_demand(pars["methanol_elasticity"]) df_final["value"] = df_final["value"].apply(lambda x: x * 0.5) @@ -481,12 +610,21 @@ def add_meth_tec_vintages(): context.get_local_path("material", "methanol", "meth_ng_techno_economic.xlsx"), sheet_name=None, ) + par_dict_ng_fs = pd.read_excel( + context.get_local_path("material", "methanol", "meth_ng_techno_economic_fs.xlsx"), + sheet_name=None, + ) par_dict_ng.pop("historical_activity") + par_dict_ng_fs.pop("historical_activity") par_dict_coal = pd.read_excel( context.get_local_path("material", "methanol", "meth_coal_additions.xlsx"), sheet_name=None, ) - return combine_df_dictionaries(par_dict_ng, par_dict_coal) + par_dict_coal_fs = pd.read_excel( + context.get_local_path("material", "methanol", "meth_coal_additions_fs.xlsx"), + sheet_name=None, + ) + return combine_df_dictionaries(par_dict_ng, par_dict_ng_fs, par_dict_coal, par_dict_coal_fs) def add_meth_hist_act(scenario): From 4629ab70928b6ffc9464db3b1b4626f7bb83b28c Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 24 Jan 2023 17:09:27 +0100 Subject: [PATCH 556/774] Fix meth_t_d efficiency --- message_ix_models/data/material/methanol/meth_t_d_fuel.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/methanol/meth_t_d_fuel.xlsx b/message_ix_models/data/material/methanol/meth_t_d_fuel.xlsx index 3284a7a1d2..2ae924c3b2 100644 --- a/message_ix_models/data/material/methanol/meth_t_d_fuel.xlsx +++ b/message_ix_models/data/material/methanol/meth_t_d_fuel.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:a6d532d71686bfceb0146340b7d1bbbf301a52ccc0c9779bb30e9771b45a7bb2 -size 91798 +oid sha256:60f5e542f45f9928a6887f350d1b675241727afdab0e8045284caa096e6a8562 +size 91785 From 8453ba1052a62448246217bdff8fa8de9e6798b7 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 24 Jan 2023 18:55:12 +0100 Subject: [PATCH 557/774] Fix methanol historical activities --- .../methanol/meth_coal_additions.xlsx | 4 +-- .../methanol/meth_coal_additions_fs.xlsx | 4 +-- .../methanol/meth_ng_techno_economic.xlsx | 4 +-- .../methanol/meth_ng_techno_economic_fs.xlsx | 4 +-- .../model/material/data_methanol.py | 30 +++++++++++-------- 5 files changed, 26 insertions(+), 20 deletions(-) diff --git a/message_ix_models/data/material/methanol/meth_coal_additions.xlsx b/message_ix_models/data/material/methanol/meth_coal_additions.xlsx index a6f0eb1f55..1229c1dc36 100644 --- a/message_ix_models/data/material/methanol/meth_coal_additions.xlsx +++ b/message_ix_models/data/material/methanol/meth_coal_additions.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:e3a826c7be981b00c27798200d1cf3c3867785d3f27f22f3f1def8a22c12543f -size 94157 +oid sha256:c50a527d0b9acc2e8db8fc686a474a0ffbc81be9498fe0e0a9dc3a730df5d01d +size 98666 diff --git a/message_ix_models/data/material/methanol/meth_coal_additions_fs.xlsx b/message_ix_models/data/material/methanol/meth_coal_additions_fs.xlsx index 621d8eec56..d648734d44 100644 --- a/message_ix_models/data/material/methanol/meth_coal_additions_fs.xlsx +++ b/message_ix_models/data/material/methanol/meth_coal_additions_fs.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:3e48bb9ea42383ce32fb0b3c3aa8d24dbb1eb920abd8096ae1caa05d9e099497 -size 94214 +oid sha256:4610622a608061180ba2809b7a628616b60026d67d04830f394b5cee530a8be1 +size 98719 diff --git a/message_ix_models/data/material/methanol/meth_ng_techno_economic.xlsx b/message_ix_models/data/material/methanol/meth_ng_techno_economic.xlsx index 70e6f8e226..df22abd283 100644 --- a/message_ix_models/data/material/methanol/meth_ng_techno_economic.xlsx +++ b/message_ix_models/data/material/methanol/meth_ng_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:c28f1c27ac2b5b851f6d535524cfd59f3585e11acdd126cd13cd5411a1430ba6 -size 103677 +oid sha256:380e5b4244385e536dc3a38e4ba969324151658c144bbc145db96535094a2c79 +size 103769 diff --git a/message_ix_models/data/material/methanol/meth_ng_techno_economic_fs.xlsx b/message_ix_models/data/material/methanol/meth_ng_techno_economic_fs.xlsx index d79f572f49..0cf88e66e5 100644 --- a/message_ix_models/data/material/methanol/meth_ng_techno_economic_fs.xlsx +++ b/message_ix_models/data/material/methanol/meth_ng_techno_economic_fs.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:451a1439b6dbaf5f6f0175c033cc7f3732733b3d512e5f751973bbde2cb6db26 -size 103586 +oid sha256:ef09dd09a43c4dfdb1bc555e3d6ab5b5666e72e12ea396a6a493df19bf77904c +size 103651 diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index 23b09d7207..98752705b9 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -4,7 +4,7 @@ from message_ix import make_df from message_ix_models.util import broadcast, same_node -from .util import read_config +from util import read_config context = read_config() @@ -616,6 +616,7 @@ def add_meth_tec_vintages(): ) par_dict_ng.pop("historical_activity") par_dict_ng_fs.pop("historical_activity") + par_dict_coal = pd.read_excel( context.get_local_path("material", "methanol", "meth_coal_additions.xlsx"), sheet_name=None, @@ -624,22 +625,23 @@ def add_meth_tec_vintages(): context.get_local_path("material", "methanol", "meth_coal_additions_fs.xlsx"), sheet_name=None, ) + par_dict_coal.pop("historical_activity") + par_dict_coal_fs.pop("historical_activity") return combine_df_dictionaries(par_dict_ng, par_dict_ng_fs, par_dict_coal, par_dict_coal_fs) def add_meth_hist_act(scenario): # fix demand infeasibility par_dict = {} - act = scenario.par("historical_activity") - row = ( - act[act["technology"].str.startswith("meth")] - .sort_values("value", ascending=False) - .iloc[0] - ) - # china meth_coal production (90% coal share on 2015 47 Mt total; 1.348 = Mt to GWa ) - row["value"] = (47 * 0.6976) * 0.9 - row["technology"] = "meth_coal" - par_dict["historical_activity"] = pd.DataFrame(row).T + df_fs = pd.read_excel( + context.get_local_path("material", "methanol", "meth_coal_additions_fs.xlsx"), + sheet_name="historical_activity" + ) + df_fuel = pd.read_excel( + context.get_local_path("material", "methanol", "meth_coal_additions.xlsx"), + sheet_name="historical_activity" + ) + par_dict["historical_activity"] = pd.concat([df_fs, df_fuel]) # derived from graphic in "Methanol production statstics.xlsx/China demand split" diagram hist_cap = message_ix.make_df( "historical_new_capacity", @@ -658,7 +660,11 @@ def add_meth_hist_act(scenario): context.get_local_path("material", "methanol", "meth_ng_techno_economic.xlsx"), sheet_name="historical_activity" ) + df_ng_fs = pd.read_excel( + context.get_local_path("material", "methanol", "meth_ng_techno_economic_fs.xlsx"), + sheet_name="historical_activity" + ) par_dict["historical_activity"] = pd.concat( - [par_dict["historical_activity"], df_ng] + [par_dict["historical_activity"], df_ng, df_ng_fs] ) return par_dict From 74585cd2e0a77139ec7c4e63ab3281557eedcfeb Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 25 Jan 2023 15:34:23 +0100 Subject: [PATCH 558/774] Update meth_trade_techno_economic.xlsx --- .../data/material/methanol/meth_trade_techno_economic.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/methanol/meth_trade_techno_economic.xlsx b/message_ix_models/data/material/methanol/meth_trade_techno_economic.xlsx index 2e84e91ffc..b7fe1acce4 100644 --- a/message_ix_models/data/material/methanol/meth_trade_techno_economic.xlsx +++ b/message_ix_models/data/material/methanol/meth_trade_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:d7fb799a8b5c6faf2a8da6e9d847a18e1a624b4aeb0dcc20fe8013c7a5dd1a2a -size 302 +oid sha256:f707453eeedfe80af8ba40725281b11a27aa41f3de3d6eccbc3df0fcafc47a95 +size 211262 From 31e5eda28e56c43027907c3048f776de0172ab02 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 25 Jan 2023 15:35:42 +0100 Subject: [PATCH 559/774] Update reporting based on new methanol setup --- message_ix_models/model/material/data_methanol.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index 98752705b9..6eb7cc7b41 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -4,7 +4,7 @@ from message_ix import make_df from message_ix_models.util import broadcast, same_node -from util import read_config +from .util import read_config context = read_config() From 671396cff83a0fa336d157a39ca83cbf290683db Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 23 Feb 2023 15:48:17 +0100 Subject: [PATCH 560/774] Remove methanol end use tecs from technology set --- message_ix_models/model/material/data_methanol.py | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index 6eb7cc7b41..d3dbe9b803 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -217,13 +217,11 @@ def get_embodied_emi(row, pars, share_par): for i in new_dict2.keys(): new_dict2[i] = new_dict2[i].drop_duplicates() - pars = ["input", "output"] + # remove old methanol end use tecs #scenario.check_out() - for i in pars: - df = scenario.par(i, filters={"technology": ["sp_meth_I", "meth_rc", - "meth_ic_trp", "meth_fc_trp", - "meth_i"]}) - scenario.remove_par(i, df) + scenario.remove_set("technology", ["sp_meth_I", "meth_rc", + "meth_ic_trp", "meth_fc_trp", + "meth_i"]) #scenario.commit("remove old methanol end use tecs") return new_dict2 From 48ee18ae3fb1da9d791c40be77e910afe965cf7b Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Fri, 24 Feb 2023 11:37:20 +0100 Subject: [PATCH 561/774] Fix the bugs in furnace excel file - also efficiency of the petro furnaces are increased --- .../data/material/generic_furnace_boiler_techno_economic.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx index e3b984d02b..c44bd05570 100644 --- a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx +++ b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:8ae4c3830d94b46f1619c27bb2f1204e88f5c16bbe8f22bdfb8c51970ccb077c -size 149161 +oid sha256:c9a81dbb7e303f5a3cbde631cf3496d8b4adfcd1bd65191a2c28b103531e8d38 +size 157073 From c386523a83d9aacda26da25758be6e4f1e90724b Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Fri, 24 Feb 2023 14:10:50 +0100 Subject: [PATCH 562/774] Fix the ammonia technology emission factors --- .../data/material/ammonia/fert_techno_economic.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/ammonia/fert_techno_economic.xlsx b/message_ix_models/data/material/ammonia/fert_techno_economic.xlsx index a3222ebd6b..ebb2a7e21b 100644 --- a/message_ix_models/data/material/ammonia/fert_techno_economic.xlsx +++ b/message_ix_models/data/material/ammonia/fert_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:71f63a74c2bd8a5dd91d9b759aad77e9aebb8e1d62806e2382c9a3cc8a79b535 -size 47600 +oid sha256:92c63774f1c00127a2add13a2c4ed7ca0a24f0019c8a4cd6951174a35c9e27d3 +size 47986 From 076581d71018728892fbfb9920cb2158b088488f Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Fri, 24 Feb 2023 14:42:45 +0100 Subject: [PATCH 563/774] Remove methanol fuel technologies --- message_ix_models/data/material/set.yaml | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index d70bd3b2fb..03719d3a6e 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -513,6 +513,12 @@ methanol: - MTO_petro - CH2O_synth - CH2O_to_resin + remove: + - sp_meth_I + - meth_rc + - meth_ic_trp + - meth_fc_trp + - meth_i addon: add: From a4e947789d5eb1f2d4431b54afaf44066c8cf749 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Fri, 24 Feb 2023 14:42:59 +0100 Subject: [PATCH 564/774] Update data_methanol.py --- message_ix_models/model/material/data_methanol.py | 4 ---- 1 file changed, 4 deletions(-) diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index d3dbe9b803..0794cd461d 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -217,11 +217,7 @@ def get_embodied_emi(row, pars, share_par): for i in new_dict2.keys(): new_dict2[i] = new_dict2[i].drop_duplicates() - # remove old methanol end use tecs #scenario.check_out() - scenario.remove_set("technology", ["sp_meth_I", "meth_rc", - "meth_ic_trp", "meth_fc_trp", - "meth_i"]) #scenario.commit("remove old methanol end use tecs") return new_dict2 From 03783c784b7a2b000a900717d51286926a7cde3d Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Fri, 24 Mar 2023 16:45:47 +0100 Subject: [PATCH 565/774] Convert power sector r script to python --- .../model/material/data_power_sector.py | 219 +++++++++++++++--- 1 file changed, 189 insertions(+), 30 deletions(-) diff --git a/message_ix_models/model/material/data_power_sector.py b/message_ix_models/model/material/data_power_sector.py index 8c1b972d94..b87d1ad39b 100644 --- a/message_ix_models/model/material/data_power_sector.py +++ b/message_ix_models/model/material/data_power_sector.py @@ -5,19 +5,6 @@ import logging from pathlib import Path -# check Python and R environments (for debugging) -import rpy2.situation - -for row in rpy2.situation.iter_info(): - print(row) - -# load rpy2 modules -import rpy2.robjects as ro - -# from rpy2.robjects.packages import importr -from rpy2.robjects import pandas2ri -from rpy2.robjects.conversion import localconverter - import pandas as pd from .util import read_config @@ -33,6 +20,186 @@ ) from . import get_spec +def read_material_intensities(parameter, data_path, node, year, technology, + commodity, level, inv_cost): + if parameter in ["input_cap_new", "input_cap_ret", "output_cap_ret"]: + #################################################################### + # read data + #################################################################### + + # read LCA data from ADVANCE LCA tool + data_path_lca = data_path + '/NTNU_LCA_coefficients.xlsx' + data_lca = pd.read_excel(data_path_lca, sheet_name = "environmentalImpacts") + + # read technology, region and commodity mappings + data_path_tec_map = data_path + '/MESSAGE_global_model_technologies.xlsx' + technology_mapping = pd.read_excel(data_path_tec_map, sheet_name = "technology") + + data_path_reg_map = data_path + '/LCA_region_mapping.xlsx' + region_mapping = pd.read_excel(data_path_reg_map, sheet_name = "region") + + data_path_com_map = data_path + '/LCA_commodity_mapping.xlsx' + commodity_mapping = pd.read_excel(data_path_com_map, sheet_name = "commodity") + + #################################################################### + # process data + #################################################################### + + # filter relevant scenario, technology variant (residue for biomass, + # mix for others) and remove operation phase (and remove duplicates) + data_lca = data_lca.loc[((data_lca['scenario']=='Baseline') + & (data_lca['technology variant'].isin(['mix', + 'residue'])) & (data_lca['phase'] != 'Operation'))] + + data_lca[2015] = None + data_lca[2020] = None + data_lca[2025] = None + data_lca[2035] = None + data_lca[2040] = None + data_lca[2045] = None + + # add intermediate time steps and turn into long table format + years = [2010,2015,2020,2025,2030,2035,2040,2045] + data_lca = pd.melt(data_lca, id_vars = ['source','scenario','region','variable', + 'technology','technology variant', 'impact', 'phase', 'unit'], value_vars = years, + var_name = 'year') + # Make sure the values are numeric. + data_lca[['value']] = data_lca[['value']].astype(float) + + # apply technology, commodity/impact and region mappings to MESSAGEix + data_lca_merged_1 = pd.merge(data_lca, region_mapping, how = 'inner', left_on = 'region', + right_on = 'THEMIS') + data_lca_merged_2 = pd.merge(data_lca_merged_1, technology_mapping, how = 'inner', left_on = 'technology', + right_on = 'LCA mapping') + data_lca_merged_final = pd.merge(data_lca_merged_2, commodity_mapping, how = 'inner', left_on = 'impact', + right_on = 'impact') + + data_lca = data_lca_merged_final[~data_lca_merged_final['MESSAGEix-GLOBIOM_1.1'].isnull()] + + # Drop technology column that has LCA style names + # Instead MESSAGE technology names will be used in the technology column + data_lca = data_lca.drop(['technology'], axis = 1) + + data_lca.rename(columns={'MESSAGEix-GLOBIOM_1.1':'node', + 'Type of Technology':'technology'}, inplace = True) + keep_columns = ['node','technology','phase', 'commodity', 'level', + 'year', 'unit', 'value'] + data_lca = data_lca[keep_columns] + + data_lca_final = pd.DataFrame() + + for n in data_lca['node'].unique(): + for t in data_lca['technology'].unique(): + for c in data_lca['commodity'].unique(): + for p in data_lca['phase'].unique(): + temp = data_lca.loc[((data_lca['node'] == n) & + (data_lca['technology'] == t) & + (data_lca['commodity'] == c) & + (data_lca['phase'] == p))] + temp['value'] = temp['value'].interpolate(method='linear', limit_direction= 'forward', axis=0) + data_lca_final = pd.concat([temp, data_lca_final]) + + # extract node, technology, commodity, level, and year list from LCA + # data set + node_list = data_lca_final['node'].unique() + year_list = data_lca_final['year'].unique() + tec_list = data_lca_final['technology'].unique() + com_list = data_lca_final['commodity'].unique() + lev_list = data_lca_final['level'].unique() + # add scrap as commodity level + lev_list = lev_list + ['end_of_life'] + + #################################################################### + # create data frames for material intensity input/output parameters + #################################################################### + + # new data frames for parameters + input_cap_new = pd.DataFrame() + input_cap_ret = pd.DataFrame() + output_cap_ret = pd.DataFrame() + + for n in node_list: + for t in tec_list: + for c in com_list: + year_vtg_list = inv_cost.loc[((inv_cost['node_loc']==n) + & (inv_cost['technology']==t))]['year_vtg'].unique() + for y in year_vtg_list: + # for years after maximum year in data set use + # values for maximum year, similarly for years + # before minimum year in data set use values for + # minimum year + if y > max(year_list): + yeff = max(year_list) + elif y< min(year_list): + yeff = min(year_list) + else: + yeff = y + val_cap_new = data_lca_final.loc[(data_lca_final['node']==n) & + (data_lca_final['technology'] == t) & + (data_lca_final['phase'] == 'Construction') & + (data_lca_final['commodity'] == c) & + (data_lca_final['year'] == yeff)]['value'].values[0] + val_cap_new = val_cap_new * 0.001 + + input_cap_new = pd.concat([input_cap_new,pd.DataFrame({ + 'node_loc': n, + 'technology': t, + 'year_vtg': str(y), + 'node_origin': n, + 'commodity':c, + 'level': 'product', + 'time_origin': 'year', + 'value': val_cap_new, + 'unit': 't/kW' + }, index = [0])]) + + val_cap_input_ret = data_lca_final.loc[(data_lca_final['node']==n) & + (data_lca_final['technology'] == t) & + (data_lca_final['phase'] == 'End-of-life') & + (data_lca_final['commodity'] == c) & + (data_lca_final['year'] == yeff)]['value'].values[0] + val_cap_input_ret = val_cap_input_ret * 0.001 + + input_cap_ret = pd.concat([input_cap_ret,pd.DataFrame({ + 'node_loc': n, + 'technology': t, + 'year_vtg': str(y), + 'node_origin': n, + 'commodity':c, + 'level': 'product', + 'time_origin': 'year', + 'value': val_cap_input_ret, + 'unit': 't/kW' + },index = [0])]) + + val_cap_output_ret = data_lca_final.loc[(data_lca_final['node']==n) & + (data_lca_final['technology'] == t) & + (data_lca_final['phase'] == 'Construction') & + (data_lca_final['commodity'] == c) & + (data_lca_final['year'] == yeff)]['value'].values[0] + val_cap_output_ret = val_cap_output_ret * 0.001 + + output_cap_ret = pd.concat([output_cap_ret,pd.DataFrame({ + 'node_loc': n, + 'technology': t, + 'year_vtg': str(y), + 'node_dest': n, + 'commodity':c, + 'level': 'end_of_life', + 'time_dest': 'year', + 'value': val_cap_output_ret, + 'unit': 't/kW' + }, index = [0])]) + + # return parameter + if (parameter == "input_cap_new"): + return input_cap_new + elif (parameter == "input_cap_ret"): + return input_cap_ret + elif (parameter == "output_cap_ret"): + return output_cap_ret + else: + return def gen_data_power_sector(scenario, dry_run=False): """Generate data for materials representation of power industry.""" @@ -40,8 +207,8 @@ def gen_data_power_sector(scenario, dry_run=False): context = read_config() config = context["material"]["power_sector"] - # paths to r code and lca data - rcode_path = Path(__file__).parents[0] / "material_intensity" + # paths to lca data + code_path = Path(__file__).parents[0] / "material_intensity" data_path = context.get_local_path("material") # Information about scenario, e.g. node, year @@ -57,29 +224,22 @@ def gen_data_power_sector(scenario, dry_run=False): # read inv.cost data inv_cost = scenario.par("inv_cost") - # source R code - r = ro.r - r.source(str(rcode_path / "ADVANCE_lca_coefficients_embedded.R")) - param_name = ["input_cap_new", "input_cap_ret", "output_cap_ret"] # List of data frames, to be concatenated together at end results = defaultdict(list) - # call R function with type conversion for p in set(param_name): - with localconverter(ro.default_converter + pandas2ri.converter): - df = r.read_material_intensities( - p, str(data_path), node, year, technology, commodity, level, inv_cost - ) - print("type df:", type(df)) - print(df.head()) + df = read_material_intensities( + p, str(data_path), node, year, technology, commodity, level, inv_cost + ) + print("type df:", type(df)) + print(df.head()) results[p].append(df) - # import pdb; pdb.set_trace() - - # create new parameters input_cap_new, output_cap_new, input_cap_ret, output_cap_ret, input_cap and output_cap if they don't exist + # create new parameters input_cap_new, output_cap_new, input_cap_ret, + # output_cap_ret, input_cap and output_cap if they don't exist if not scenario.has_par("input_cap_new"): scenario.init_par( "input_cap_new", @@ -220,5 +380,4 @@ def gen_data_power_sector(scenario, dry_run=False): # Concatenate to one data frame per parameter results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} - print(results) return results From 4200b3139acb66521764784485a520d860362bf0 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 28 Mar 2023 17:24:27 +0200 Subject: [PATCH 566/774] Remove loil_trp dependency --- message_ix_models/model/material/data_methanol.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index 0794cd461d..74e80383ad 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -369,6 +369,8 @@ def gen_data_meth_chemicals(scenario, chemical): def add_methanol_trp_additives(scenario): df_loil = scenario.par("input", filters={"technology": "loil_trp"}) + if df_loil.index.size == 0: + return pd.DataFrame() df_mtbe = pd.read_excel( context.get_local_path( From 3ba282801d8f1178dc82acc5745f100be30f769f Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 11 Apr 2023 10:56:22 +0200 Subject: [PATCH 567/774] Add warning for macro calibration --- message_ix_models/model/material/__init__.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 988c2e2218..0862c53f07 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -277,6 +277,9 @@ def solve_scen(context, datafile, model_name, scenario_name, add_calibration, ad print('Scenario calibrated.') if add_macro: # Default True + print('After macro calibration a new scneario with the suffix _macro is created.') + print('Make sure to use this scenario to solve with MACRO iterations.') + scenario.solve(model="MESSAGE-MACRO", solve_options={"lpmethod": "4"}) scenario.set_as_default() From 9f86c133094c5f40a5b15596c7a606a480d77cd4 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Wed, 12 Apr 2023 16:14:07 +0200 Subject: [PATCH 568/774] Remove duplicated relation values --- message_ix_models/data/material/methanol/meth_t_d_fuel.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/methanol/meth_t_d_fuel.xlsx b/message_ix_models/data/material/methanol/meth_t_d_fuel.xlsx index 2ae924c3b2..71275cc9be 100644 --- a/message_ix_models/data/material/methanol/meth_t_d_fuel.xlsx +++ b/message_ix_models/data/material/methanol/meth_t_d_fuel.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:60f5e542f45f9928a6887f350d1b675241727afdab0e8045284caa096e6a8562 -size 91785 +oid sha256:53bfd60c089ccf0b374d10aa3c5830db658db8925deea06c7a0b0b68efbd7871 +size 42561 From 1e4acd465809e2be80980a565d389cddb0ff9dc9 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Mon, 24 Apr 2023 16:44:57 +0200 Subject: [PATCH 569/774] Add limestone_dummy_supply to emissions accounting --- .../data/material/Global_steel_cement_MESSAGE.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx index f3eab1c2a4..ff1ee21d2b 100644 --- a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx +++ b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:d226ca58dbd251e3e76e24cd1eedd2784462383a35cc4098b0006104cb765583 -size 111390 +oid sha256:555f7f10a0bf9698a4cf3424903e2746aff3b9d611e4ad021b0c63de36dfd287 +size 111382 From b2fad8cc26ede24e1c53bf17617775621d5bdbf0 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Mon, 24 Apr 2023 16:46:07 +0200 Subject: [PATCH 570/774] Remove unnecessary printing --- message_ix_models/model/material/data_generic.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/message_ix_models/model/material/data_generic.py b/message_ix_models/model/material/data_generic.py index 19627a4d2e..9beec17599 100644 --- a/message_ix_models/model/material/data_generic.py +++ b/message_ix_models/model/material/data_generic.py @@ -274,9 +274,6 @@ def gen_data_generic(scenario, dry_run=False): and len(set(df["node_loc"])) == 1 and list(set(df["node_loc"]))[0] != global_region ): - print('This is the length of the regions') - print(regions) - print(len(regions)) df["node_loc"] = None df = df.pipe(broadcast, node_loc=nodes) From 29d596904e6747ba27ce091788781b1a0c8a094d Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Mon, 24 Apr 2023 16:47:16 +0200 Subject: [PATCH 571/774] Update reporting for embodied emissions --- message_ix_models/model/material/data_methanol.py | 1 - message_ix_models/model/material/data_util.py | 8 ++++---- 2 files changed, 4 insertions(+), 5 deletions(-) diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index 74e80383ad..910c032d2d 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -152,7 +152,6 @@ def get_embodied_emi(row, pars, share_par): df_rel_hvc = df_rel.loc[df_rel["technology"] == "steam_cracker_petro",:] df_rel_hvc["value"] = df_rel_hvc.apply(lambda x: get_embodied_emi(x, pars, "hvc_plastics_share"), axis=1) dict_t_d_fs["relation_activity"] = pd.concat([df_rel_meth, df_rel_hvc]) - # dict3.pop("relation_activity") # remove negative emissions for now new_dict2 = combine_df_dictionaries(meth_fuel_dic, meth_fs_dic, par_dict_fs, par_dict, bal_fs_dict, bal_fuel_dict, diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index 4a4b09316d..aa1bf121ea 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -613,10 +613,10 @@ def add_emission_accounting(scen): for m in ["atm_gasoil", "vacuum_gasoil", "naphtha"]: if t == "steam_cracker_petro": if (m == "atm_gasoil") | (m == "vacuum_gasoil"): - # fueloil emission factor * input - val = 0.665 * 1.17683105 - else: - val = 0.665 * 1.537442922 + # crudeoil emission factor * input to steam cracker + val = 0.631 * 1.17683105 + elif (m == "naphtha"): + val = 0.631 * 1.537442922 co2_feedstocks = pd.DataFrame( { From b4065bcdd8bde81df429b326e012c54f34ccd8e1 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 23 Feb 2023 14:19:45 +0100 Subject: [PATCH 572/774] Clean up deprecated methanol code and add inline comments --- .../model/material/data_methanol.py | 44 +++++++++---------- 1 file changed, 21 insertions(+), 23 deletions(-) diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index 910c032d2d..02fa17df47 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -1,15 +1,15 @@ -import message_ix +import copy import pandas as pd -import numpy as np from message_ix import make_df from message_ix_models.util import broadcast, same_node -from .util import read_config +from .util import read_config, combine_df_dictionaries context = read_config() def gen_data_methanol(scenario): + # read sensitivity file df_pars = pd.read_excel( context.get_local_path( "material", "methanol", "methanol_sensitivity_pars.xlsx" @@ -20,6 +20,7 @@ def gen_data_methanol(scenario): pars = df_pars.set_index("par").to_dict()["value"] + # read new meth tec parameters meth_h2_fs_dict = gen_data_meth_h2() meth_h2_fuel_dict = gen_data_meth_h2() meth_bio_dict = gen_data_meth_bio(scenario) @@ -184,6 +185,8 @@ def get_embodied_emi(row, pars, share_par): resin_dict = gen_data_meth_chemicals(scenario, "Resins") chemicals_dict = combine_df_dictionaries(mto_dict, ch2o_dict, resin_dict) + + # generate resin demand from STURM results df_comm = gen_resin_demand( scenario, pars["resin_share"], "comm", "SH2", "SHAPE", #pars["wood_scenario"], #pars["pathway"] ) @@ -198,14 +201,17 @@ def get_embodied_emi(row, pars, share_par): df_resin_demand["value"] = df_comm["value"] + df_resid["value"] new_dict2["demand"] = pd.concat([new_dict2["demand"], df_resin_demand]) - new_dict2["input"] = pd.concat( - [new_dict2["input"], add_methanol_trp_additives(scenario)] - ) + # modify loil_trp inputs + new_dict2 = combine_df_dictionaries(new_dict2, + add_methanol_fuel_additives(scenario)) + + # fix efficiency of meth_t_d df = scenario.par("input", filters={"technology": "meth_t_d"}) df["value"] = 1 new_dict2["input"] = pd.concat([new_dict2["input"], df]) + # update production costs with IEA data if pars["update_old_tecs"]: cost_dict = update_methanol_costs(scenario) new_dict2 = combine_df_dictionaries(new_dict2, cost_dict) @@ -227,6 +233,8 @@ def gen_data_meth_h2(): context.get_local_path("material", "methanol", "meth_h2_techno_economic.xlsx"), sheet_name=None, ) + df_h2["inv_cost"] = update_costs_with_loc_factor(df_h2["inv_cost"]) + df_h2["inv_cost"] = update_costs_with_loc_factor(df_h2["fix_cost"]) return df_h2 @@ -376,7 +384,7 @@ def add_methanol_trp_additives(scenario): "material", "methanol", "Methanol production statistics (version 1).xlsx" ), # usecols=[1,2,3,4,6,7], - skiprows=np.linspace(0, 65, 66), + skiprows=[i for i in range(66)], sheet_name="MTBE calc", ) df_mtbe = df_mtbe.iloc[ @@ -389,7 +397,7 @@ def add_methanol_trp_additives(scenario): context.get_local_path( "material", "methanol", "Methanol production statistics (version 1).xlsx" ), - skiprows=np.linspace(0, 37, 38), + skiprows=[i for i in range(38)], usecols=[1, 2], sheet_name="Biodiesel", ) @@ -547,7 +555,7 @@ def get_demand_t1_with_income_elasticity( df_melt = df_demand.melt( id_vars=["Region"], value_vars=df_demand.columns[5:], var_name="year" ) - return message_ix.make_df( + return make_df( "demand", unit="t", level="final_material", @@ -579,7 +587,7 @@ def get_cost_ratio_2020(scenario, tec_name, cost_type, ref_reg="R12_NAM", year=" ].iloc[0] df["ratio"] = df["value"] / val_nam_2020 - return df # [["node_loc","year_vtg", "ratio"]] + return df def get_scaled_cost_from_proxy_tec(value, scenario, proxy_tec, cost_type, new_tec, year="all"): @@ -587,17 +595,7 @@ def get_scaled_cost_from_proxy_tec(value, scenario, proxy_tec, cost_type, new_te df["value"] = value * df["ratio"] df["technology"] = new_tec df["unit"] = "-" - return message_ix.make_df(cost_type, **df) - - -def combine_df_dictionaries(*args): - keys = set([key for tup in args for key in tup]) - #keys = set(list(dict1.keys()) + list(dict2.keys())) - comb_dict = {} - for i in keys: - #comb_dict[i] = pd.concat([dict1.get(i), dict2.get(i)]) - comb_dict[i] = pd.concat([j.get(i) for j in args]) - return comb_dict + return make_df(cost_type, **df) def add_meth_tec_vintages(): @@ -625,7 +623,7 @@ def add_meth_tec_vintages(): return combine_df_dictionaries(par_dict_ng, par_dict_ng_fs, par_dict_coal, par_dict_coal_fs) -def add_meth_hist_act(scenario): +def add_meth_hist_act(): # fix demand infeasibility par_dict = {} df_fs = pd.read_excel( @@ -638,7 +636,7 @@ def add_meth_hist_act(scenario): ) par_dict["historical_activity"] = pd.concat([df_fs, df_fuel]) # derived from graphic in "Methanol production statstics.xlsx/China demand split" diagram - hist_cap = message_ix.make_df( + hist_cap = make_df( "historical_new_capacity", node_loc="R12_CHN", technology="meth_coal", From 26c5d9ff092b6de5651b09c78601e42eaf798fdd Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 23 Feb 2023 14:33:51 +0100 Subject: [PATCH 573/774] Fix typos, line breaks, imports --- message_ix_models/model/material/__init__.py | 8 +++++--- .../model/material/data_ammonia_new.py | 17 ++++++++--------- .../model/material/data_generic.py | 4 ++-- message_ix_models/model/material/data_petro.py | 6 ++---- 4 files changed, 17 insertions(+), 18 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 0862c53f07..3cde8cc851 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -34,9 +34,9 @@ def build(scenario): # Apply to the base scenario spec = get_spec() - apply_spec(scenario,spec, add_data_2) + apply_spec(scenario, spec, add_data_2) spec = None - apply_spec(scenario, spec,add_data_1) # dry_run=True + apply_spec(scenario, spec, add_data_1) # dry_run=True s_info = ScenarioInfo(scenario) nodes = s_info.N @@ -61,7 +61,7 @@ def build(scenario): # i_feed demand is zero creating a zero division error during MACRO calibration scenario.check_out() - scenario.remove_set('sector','i_feed') + scenario.remove_set('sector', 'i_feed') scenario.commit('i_feed removed from sectors.') return scenario @@ -396,6 +396,7 @@ def run_old_reporting(context): #gen_data_buildings, gen_data_methanol, gen_all_NH3_fert, + #gen_data_ammonia, ## deprecated module! gen_data_generic, gen_data_steel, ] @@ -407,6 +408,7 @@ def run_old_reporting(context): gen_data_aluminum ] + # Try to handle multiple data input functions from different materials def add_data_1(scenario, dry_run=False): """Populate `scenario` with MESSAGEix-Materials data.""" diff --git a/message_ix_models/model/material/data_ammonia_new.py b/message_ix_models/model/material/data_ammonia_new.py index a47d320626..b12773ccec 100644 --- a/message_ix_models/model/material/data_ammonia_new.py +++ b/message_ix_models/model/material/data_ammonia_new.py @@ -1,7 +1,3 @@ -import message_ix -import message_data -import ixmp as ix - import pandas as pd import numpy as np @@ -25,7 +21,7 @@ def gen_all_NH3_fert(scenario, dry_run=False): } -def gen_data(scenario, dry_run=False, add_ccs: bool = True, lower_costs = False): +def gen_data(scenario, dry_run=False, add_ccs: bool = True, lower_costs=False): s_info = ScenarioInfo(scenario) # s_info.yv_ya nodes = s_info.N @@ -91,7 +87,7 @@ def gen_data(scenario, dry_run=False, add_ccs: bool = True, lower_costs = False) par_dict[par_name] = df_new if lower_costs: - par_doct = experiment_lower_CPA_SAS_costs(par_dict) + par_dict = experiment_lower_CPA_SAS_costs(par_dict) df_lifetime = par_dict.get("technical_lifetime") dict_lifetime = ( df_lifetime.loc[:,["technology", "value"]] @@ -112,7 +108,6 @@ def __missing__(self,key): return par_dict - def gen_data_rel(scenario, dry_run=False, add_ccs: bool = True): s_info = ScenarioInfo(scenario) # s_info.yv_ya @@ -407,6 +402,7 @@ def read_demand(): "NH3_trade_R12": NH3_trade_R12, } + def gen_demand(): context = read_config() @@ -435,6 +431,7 @@ def gen_demand(): df.loc[df["value"] < 0, "value"] = 0 # temporary solution to avoid negative values return {"demand": df} + def gen_resid_demand_NH3(scenario, gdp_elasticity): context = read_config() @@ -464,7 +461,7 @@ def get_demand_t1_with_income_elasticity( # 70% is used for nitrogen fertilizer production. Rest is 54.7 Mt. # 12 Mt is missing from nitrogen fertilizer part. Possibly due to low demand # coming from GLOBIOM updates. Also add this to the residual demand. (66.7 Mt) - # Approxiamte regional shares are from Future of Petrochemicals + # Approximate regional shares are from Future of Petrochemicals # Methodological Annex page 7. Total production for regions: # Asia Pacific (RCPA, CHN, SAS, PAS, PAO) = 90 Mt # Eurasia (FSU) = 20 Mt, Middle East (MEA) = 15, Africa (AFR) = 5 @@ -472,7 +469,7 @@ def get_demand_t1_with_income_elasticity( # North America (NAM) = 20 Mt. # Regional shares are derived. They are based on production values not demand. # Some assumptions made for the regions that are not explicitly covered in IEA. - # (CHN produces the 30% of the ammonia globaly and India 10%.) + # (CHN produces the 30% of the ammonia globally and India 10%.) # The orders of the regions # r = ['R12_AFR', 'R12_RCPA', 'R12_EEU', 'R12_FSU', 'R12_LAM', 'R12_MEA',\ # 'R12_NAM', 'R12_PAO', 'R12_PAS', 'R12_SAS', 'R12_WEU',"R12_CHN"] @@ -517,11 +514,13 @@ def get_demand_t1_with_income_elasticity( return {"demand": df_residual} + def gen_land_input(scenario): df = scenario.par("land_output", {"commodity": "Fertilizer Use|Nitrogen"}) df["level"] = "final_material" return {"land_input": df} + def experiment_lower_CPA_SAS_costs(par_dict): cost_list = ["inv_cost", "fix_cost"] scaler = { diff --git a/message_ix_models/model/material/data_generic.py b/message_ix_models/model/material/data_generic.py index 9beec17599..84d5fea33c 100644 --- a/message_ix_models/model/material/data_generic.py +++ b/message_ix_models/model/material/data_generic.py @@ -103,7 +103,7 @@ def gen_data_generic(scenario, dry_run=False): # Iterate over parameters for par in params: split = par.split("|") - param_name = par.split("|")[0] + param_name = split[0] val = data_generic.loc[ ( @@ -177,7 +177,7 @@ def gen_data_generic(scenario, dry_run=False): results[param_name].append(df_low) results[param_name].append(df_high) - # Rest of the parameters apart from inpput, output and emission_factor + # Rest of the parameters apart from input, output and emission_factor else: diff --git a/message_ix_models/model/material/data_petro.py b/message_ix_models/model/material/data_petro.py index 133bde10ce..5d8aa20e51 100644 --- a/message_ix_models/model/material/data_petro.py +++ b/message_ix_models/model/material/data_petro.py @@ -1,14 +1,11 @@ import pandas as pd import numpy as np from collections import defaultdict -from .data_util import read_timeseries -from pathlib import Path +from .data_util import read_timeseries, read_rel import message_ix -import ixmp from .util import read_config -from .data_util import read_rel from message_ix_models import ScenarioInfo from message_ix import make_df from message_ix_models.util import ( @@ -117,6 +114,7 @@ def get_demand_t1_with_income_elasticity( node=(region_set + df_melt["Region"]), ) + def gen_data_petro_chemicals(scenario, dry_run=False): # Load configuration From 982837afc30263292521c58bbb242394b27aec20 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 23 Feb 2023 14:40:55 +0100 Subject: [PATCH 574/774] Add coal_nh3 base year bound and fix emission factors --- .../data/material/ammonia/fert_techno_economic.xlsx | 4 ++-- message_ix_models/model/material/data_ammonia_new.py | 1 + 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/ammonia/fert_techno_economic.xlsx b/message_ix_models/data/material/ammonia/fert_techno_economic.xlsx index ebb2a7e21b..7290e193f0 100644 --- a/message_ix_models/data/material/ammonia/fert_techno_economic.xlsx +++ b/message_ix_models/data/material/ammonia/fert_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:92c63774f1c00127a2add13a2c4ed7ca0a24f0019c8a4cd6951174a35c9e27d3 -size 47986 +oid sha256:ba4d65b5ffb3111cf9139269e9a2212c60dace46bb0ecfd9ba49a8998f878d8d +size 329 diff --git a/message_ix_models/model/material/data_ammonia_new.py b/message_ix_models/model/material/data_ammonia_new.py index b12773ccec..c6fc618bdc 100644 --- a/message_ix_models/model/material/data_ammonia_new.py +++ b/message_ix_models/model/material/data_ammonia_new.py @@ -207,6 +207,7 @@ def gen_data_ts(scenario, dry_run=False, add_ccs: bool = True): #convert floats par_dict["historical_activity"] = par_dict["historical_activity"].astype({'year_act': 'int32'}) par_dict["historical_new_capacity"] = par_dict["historical_new_capacity"].astype({'year_vtg': 'int32'}) + par_dict["bound_activity_lo"] = par_dict["bound_activity_lo"].astype({'year_act': 'int32'}) return par_dict From d86e08d66ddf3a2a0d3706ec8ab62e46bc01a0fe Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 23 Feb 2023 14:42:40 +0100 Subject: [PATCH 575/774] Add ethane mode constraint, correct steam_cracker technical data --- .../petrochemicals_techno_economic.xlsx | 4 +-- .../model/material/data_petro.py | 34 ++++++++++++++----- 2 files changed, 28 insertions(+), 10 deletions(-) diff --git a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx index 92407bd0f1..7fae63fa40 100644 --- a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx +++ b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:cbdf2868113b755bebb809e385ba9cd34d079519d56d68a7ed628ac5dd129f27 -size 663700 +oid sha256:433d900bf720bc058438d57caa2afd693f17731d1eac3636cef48f0482316b0c +size 664245 diff --git a/message_ix_models/model/material/data_petro.py b/message_ix_models/model/material/data_petro.py index 5d8aa20e51..c8881a5de5 100644 --- a/message_ix_models/model/material/data_petro.py +++ b/message_ix_models/model/material/data_petro.py @@ -277,7 +277,24 @@ def gen_data_petro_chemicals(scenario, dry_run=False): .pipe(same_node) ) - # Rest of the parameters apart from inpput, output and emission_factor + elif param_name == "share_mode_up": + mod = split[1] + + df = ( + make_df( + param_name, + technology=t, + mode=mod, + shares="steam_cracker", + value=val[regions[regions == rg].index[0]], + unit="-", + **common + ) + .pipe(broadcast, node_share=nodes) + .pipe(same_node) + ) + + # Rest of the parameters apart from input, output and emission_factor else: @@ -292,13 +309,14 @@ def gen_data_petro_chemicals(scenario, dry_run=False): # Copy parameters to all regions if (len(regions) == 1) and (rg != global_region): - if ( - len(set(df["node_loc"])) == 1 - and list(set(df["node_loc"]))[0] != global_region - ): - # print("Copying to all R11") - df["node_loc"] = None - df = df.pipe(broadcast, node_loc=nodes) + if "node_loc" in df.columns: + if ( + len(set(df["node_loc"])) == 1 + and list(set(df["node_loc"]))[0] != global_region + ): + # print("Copying to all R11") + df["node_loc"] = None + df = df.pipe(broadcast, node_loc=nodes) results[param_name].append(df) From 574126dcf6d034efc39f2567016904feb04ac987 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 23 Feb 2023 14:53:35 +0100 Subject: [PATCH 576/774] Add dictionary merge util func --- message_ix_models/model/material/util.py | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/message_ix_models/model/material/util.py b/message_ix_models/model/material/util.py index d8335dc876..a60c734da5 100644 --- a/message_ix_models/model/material/util.py +++ b/message_ix_models/model/material/util.py @@ -1,6 +1,7 @@ from message_ix_models import Context from pathlib import Path from message_ix_models.util import load_private_data +import pandas as pd # Configuration files METADATA = [ @@ -59,3 +60,11 @@ def add_R12(str): df = df[~df["Region"].isna()] df["Region"] = df["Region"].map(add_R12) df.to_excel(filepath, index=False) + + +def combine_df_dictionaries(*args): + keys = set([key for tup in args for key in tup]) + comb_dict = {} + for i in keys: + comb_dict[i] = pd.concat([j.get(i) for j in args]) + return comb_dict From 5ca9e8a826f05acfa406134042ac5f181c93e38b Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 23 Feb 2023 14:54:50 +0100 Subject: [PATCH 577/774] Change default sturm input file --- message_ix_models/model/material/data_methanol.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index 02fa17df47..d7f832e21b 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -188,14 +188,14 @@ def get_embodied_emi(row, pars, share_par): # generate resin demand from STURM results df_comm = gen_resin_demand( - scenario, pars["resin_share"], "comm", "SH2", "SHAPE", #pars["wood_scenario"], #pars["pathway"] + scenario, pars["resin_share"], "comm", "REF", "SSP2", #pars["wood_scenario"], #pars["pathway"] ) df_resid = gen_resin_demand( scenario, pars["resin_share"], "residential", pars["wood_scenario"], - pars["pathway"], + "SSP2", ) df_resin_demand = df_comm.copy(deep=True) df_resin_demand["value"] = df_comm["value"] + df_resid["value"] From 3f2bcde41a2498224b01bb0d51073b34eba973f6 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 23 Feb 2023 14:58:00 +0100 Subject: [PATCH 578/774] Add ethanol mtbe alternative, update costs with loc-factor --- .../methanol/location factor collection.xlsx | 3 + .../model/material/data_methanol.py | 76 +++++++++++++++++-- 2 files changed, 72 insertions(+), 7 deletions(-) create mode 100644 message_ix_models/data/material/methanol/location factor collection.xlsx diff --git a/message_ix_models/data/material/methanol/location factor collection.xlsx b/message_ix_models/data/material/methanol/location factor collection.xlsx new file mode 100644 index 0000000000..fd1e4ff56f --- /dev/null +++ b/message_ix_models/data/material/methanol/location factor collection.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4ff92231d590f733c825f9536f4b2bc090fa118d8321484e490797b4c5edd06b +size 47890 diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index d7f832e21b..f6aad33d87 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -222,6 +222,11 @@ def get_embodied_emi(row, pars, share_par): for i in new_dict2.keys(): new_dict2[i] = new_dict2[i].drop_duplicates() + # model MTBE phase out legislation + if pars["mtbe_scenario"] == "phase_out": + new_dict2 = combine_df_dictionaries(new_dict2, add_mtbe_act_bound(scenario)) + + # remove old methanol end use tecs #scenario.check_out() #scenario.commit("remove old methanol end use tecs") @@ -370,14 +375,23 @@ def gen_data_meth_chemicals(scenario, chemical): par_dict["growth_activity_up"] = df[~((df["node_loc"] == "R12_CHN") & ((df["year_act"] == 2020)))] #par_dict.pop("growth_activity_up") #par_dict.pop("growth_activity_lo") + par_dict["inv_cost"] = update_costs_with_loc_factor(par_dict["inv_cost"]) + par_dict["fix_cost"] = update_costs_with_loc_factor(par_dict["fix_cost"]) return par_dict -def add_methanol_trp_additives(scenario): - df_loil = scenario.par("input", filters={"technology": "loil_trp"}) - if df_loil.index.size == 0: - return pd.DataFrame() +def add_methanol_fuel_additives(scenario): + par_dict_loil = {} + pars = ['output', 'var_cost', 'relation_activity', 'input', 'emission_factor'] + for i in pars: + try: + df = scenario.par(i, filters={"technology": "loil_trp", "mode": "M1"}) + if df.size != 0: + par_dict_loil[i] = df.copy(deep=True) + except: + pass + par_dict_loil["input"] = par_dict_loil["input"][par_dict_loil["input"]["commodity"] == "lightoil"] df_mtbe = pd.read_excel( context.get_local_path( @@ -409,11 +423,27 @@ def add_methanol_trp_additives(scenario): def get_meth_share(df, node): return df[df["node_loc"] == node["node_loc"]]["value"].values[0] - df_loil_meth = df_loil.copy(deep=True) + df_loil_meth = par_dict_loil["input"].copy(deep=True) df_loil_meth["value"] = df_loil_meth.apply(lambda x: get_meth_share(df_total, x), axis=1) df_loil_meth["commodity"] = "methanol" - df_loil["value"] = df_loil["value"] - df_loil_meth["value"] - return pd.concat([df_loil, df_loil_meth]) + par_dict_loil["input"]["value"] = par_dict_loil["input"]["value"] - df_loil_meth["value"] + + df_loil_eth = df_loil_meth.copy(deep=True) + df_loil_eth["commodity"] = "ethanol" + df_loil_eth["mode"] = "ethanol" + df_loil_eth["value"] = df_loil_eth["value"] * 1.6343 # energy ratio methanol/ethanol in MTBE vs ETBE + + df_loil_eth_mode = par_dict_loil["input"].copy(deep=True) + df_loil_eth_mode["mode"] = "ethanol" + df_loil_eth_mode["value"] = df_loil_eth_mode["value"] - df_loil_eth["value"] + + par_dict_loil_eth = copy.deepcopy(par_dict_loil) + par_dict_loil_eth.pop("input") + for i in par_dict_loil_eth.keys(): + par_dict_loil_eth[i]["mode"] = "ethanol" + + df_input = pd.concat([par_dict_loil["input"], df_loil_meth, df_loil_eth, df_loil_eth_mode]) + return combine_df_dictionaries(par_dict_loil_eth, {"input": df_input}) def add_meth_trade_historic(): @@ -661,3 +691,35 @@ def add_meth_hist_act(): [par_dict["historical_activity"], df_ng, df_ng_fs] ) return par_dict + + +def update_costs_with_loc_factor(df): + loc_fact = pd.read_excel( + "C:/Users\maczek\PycharmProjects\message_data\data\material\methanol/location factor collection.xlsx", + sheet_name="comparison", index_col=0) + cost_conv = pd.read_excel( + "C:/Users\maczek\PycharmProjects\message_data\data\material\methanol/location factor collection.xlsx", + sheet_name="cost_convergence", index_col=0) + loc_fact = loc_fact.loc["WEU normalized"] + loc_fact.index = ("R12_" + loc_fact.index) + df = df.merge(loc_fact, left_on="node_loc", right_index=True) + df = df.merge(cost_conv, left_on="year_vtg", right_index=True) + df["value"] = df["value"] - (1 - df["WEU normalized"]) * df["value"] * df["convergence"] + return df[df.columns[:-2]] + + +def add_mtbe_act_bound(scenario): + mtbe_dict = {} + year = [i for i in range(2030, 2056, 5)] + [i for i in range(2060, 2111, 10)] + par_dict = { + "technology": "loil_trp", + "mode": "M1", + "unit": "-", + "time": "year", + "year_act": year, + "value": 0 + } + nodes = scenario.set("node").drop(0).drop(5) + mtbe_dict["bound_activity_up"] = \ + make_df("bound_activity_up", **par_dict).pipe(broadcast, node_loc=nodes) + return mtbe_dict From 5e9163b24ee3062d86e94c27ccebb5c2ba8fb73d Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 23 Feb 2023 14:59:27 +0100 Subject: [PATCH 579/774] Add fuel/feedstock modes for all methanol tecs --- .../methanol/Methanol production statistics (version 1).xlsx | 4 ++-- .../data/material/methanol/meth_bio_techno_economic_new.xlsx | 4 ++-- .../data/material/methanol/meth_h2_techno_economic.xlsx | 4 ++-- .../data/material/methanol/meth_t_d_material_pars.xlsx | 4 ++-- message_ix_models/data/material/set.yaml | 5 +++++ 5 files changed, 13 insertions(+), 8 deletions(-) diff --git a/message_ix_models/data/material/methanol/Methanol production statistics (version 1).xlsx b/message_ix_models/data/material/methanol/Methanol production statistics (version 1).xlsx index fc1b10a459..0b26f63885 100644 --- a/message_ix_models/data/material/methanol/Methanol production statistics (version 1).xlsx +++ b/message_ix_models/data/material/methanol/Methanol production statistics (version 1).xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:6e853af49a291d6e42e1ef9e25ad339960bb73b5c341f649fc197ae01e76e7df -size 4621962 +oid sha256:229bfc7b27e56da90586446bcc8cd0e814639d0c1b412c3f8e91315fe310be37 +size 4624970 diff --git a/message_ix_models/data/material/methanol/meth_bio_techno_economic_new.xlsx b/message_ix_models/data/material/methanol/meth_bio_techno_economic_new.xlsx index 8bcf6de21d..49f06ea3f3 100644 --- a/message_ix_models/data/material/methanol/meth_bio_techno_economic_new.xlsx +++ b/message_ix_models/data/material/methanol/meth_bio_techno_economic_new.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:5beb6e994ff03624851f0fabc556cb5efe0dd2f64ce1f438c73be6d3a42e5a4b -size 1085779 +oid sha256:3b15f544856151a81e54f984d2045e6f2aa467268fcccc315eccf34d1bb2da99 +size 1096326 diff --git a/message_ix_models/data/material/methanol/meth_h2_techno_economic.xlsx b/message_ix_models/data/material/methanol/meth_h2_techno_economic.xlsx index d978a40df6..e0af71ff8c 100644 --- a/message_ix_models/data/material/methanol/meth_h2_techno_economic.xlsx +++ b/message_ix_models/data/material/methanol/meth_h2_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:026434a25df20d472a2be02d3e75845bf704c075ded388e1cedc48937654892f -size 342719 +oid sha256:a016ca940c6d67e84afc07ab5588a8a3d995a7d5804fb7586eaa111851395482 +size 326455 diff --git a/message_ix_models/data/material/methanol/meth_t_d_material_pars.xlsx b/message_ix_models/data/material/methanol/meth_t_d_material_pars.xlsx index 628d1f4f43..2f2da7fa3f 100644 --- a/message_ix_models/data/material/methanol/meth_t_d_material_pars.xlsx +++ b/message_ix_models/data/material/methanol/meth_t_d_material_pars.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:2fd5f1d4eeaca36ac79e73cfb093580d7d00dc7d840f1d3f09532d1e2f96f515 -size 91812 +oid sha256:e43dbb591afd1fc53416e6fda653d48a70633d14606c28ee26855c302907cc30 +size 91805 diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 03719d3a6e..ea1e295c80 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -254,6 +254,10 @@ petro_chemicals: - ref_hil - ref_lol + shares: + add: + - steam_cracker + steel: commodity: add: @@ -503,6 +507,7 @@ methanol: add: - feedstock - fuel + - ethanol technology: add: From 5d6cc7569f140692fd820be5f9b6ea8a8ad52b16 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 23 Feb 2023 15:00:16 +0100 Subject: [PATCH 580/774] Add mto base year upper bound --- message_ix_models/model/material/data_methanol.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index f6aad33d87..03663825bb 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -369,6 +369,9 @@ def gen_data_meth_chemicals(scenario, chemical): par_dict["bound_activity_lo"] = make_df( "bound_activity_lo", year_act=2020, value=10, **hist_dict ) + par_dict["bound_activity_up"] = make_df( + "bound_activity_up", year_act=2020, value=12, **hist_dict + ) df = par_dict["growth_activity_lo"] par_dict["growth_activity_lo"] = df[~((df["node_loc"] == "R12_CHN") & ((df["year_act"] == 2020)))] df = par_dict["growth_activity_up"] From d2b2eae6c82fe81e92ce96d4362f7506c84523c6 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 23 Feb 2023 15:02:46 +0100 Subject: [PATCH 581/774] Add bounds to biomass/coal petro furnaces --- .../data/material/generic_furnace_boiler_techno_economic.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx index c44bd05570..2f4c6640e6 100644 --- a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx +++ b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:c9a81dbb7e303f5a3cbde631cf3496d8b4adfcd1bd65191a2c28b103531e8d38 -size 157073 +oid sha256:0cd180721b06b7eaad01a71e1926c37c3eab90279e3ef45b5ec7a0924b5e43a5 +size 320 From 6ac53d2cc325434aa7a2a372b7afbd1f569ff5a8 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 23 Feb 2023 15:07:20 +0100 Subject: [PATCH 582/774] Shorten import statements --- message_ix_models/model/material/__init__.py | 20 ++++++-------------- 1 file changed, 6 insertions(+), 14 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 3cde8cc851..568e619029 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -3,26 +3,19 @@ import click import logging import ixmp -import os -from pathlib import Path # from .build import apply_spec # from message_data.tools import ScenarioInfo from message_data.model.material.build import apply_spec from message_ix_models import ScenarioInfo from message_ix_models.util.context import Context -from message_ix_models.util import add_par_data -from message_data.tools.utilities import calibrate_UE_gr_to_demand -from message_data.tools.utilities import calibrate_UE_share_constraints -from message_ix_models.util import private_data_path +from message_ix_models.util import add_par_data, private_data_path +from message_data.tools.utilities import calibrate_UE_gr_to_demand, add_globiom,\ + calibrate_UE_share_constraints # from .data import add_data -from .data_util import modify_demand_and_hist_activity -from .data_util import add_emission_accounting -from .data_util import add_coal_lowerbound_2020 -from .data_util import add_macro_COVID -from .data_util import add_elec_lowerbound_2020 -from .data_util import add_ccs_technologies -from .util import read_config +from .data_util import modify_demand_and_hist_activity, add_emission_accounting +from .data_util import add_coal_lowerbound_2020, add_macro_COVID +from .data_util import add_elec_lowerbound_2020, add_ccs_technologies, read_config log = logging.getLogger(__name__) @@ -400,7 +393,6 @@ def run_old_reporting(context): gen_data_generic, gen_data_steel, ] - DATA_FUNCTIONS_2 = [ gen_data_cement, gen_data_petro_chemicals, From fbf8e011d8b2423d8ff4d4b6b27adc056d6664f7 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 23 Feb 2023 17:57:42 +0100 Subject: [PATCH 583/774] Fix furnace emission factors --- .../data/material/generic_furnace_boiler_techno_economic.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx index 2f4c6640e6..afd583ecba 100644 --- a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx +++ b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:0cd180721b06b7eaad01a71e1926c37c3eab90279e3ef45b5ec7a0924b5e43a5 -size 320 +oid sha256:486dbd536a5eb2103c3edb3d5bd78f97c5d2e5c0bcb3d2a6002c31b0089ae204 +size 301 From d8d57284831017cbfa0cfde1a122f15c1c0a3bcf Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 9 Mar 2023 08:28:04 +0100 Subject: [PATCH 584/774] Add sc ethane minimum share for mea and nam --- message_ix_models/model/material/data_petro.py | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/message_ix_models/model/material/data_petro.py b/message_ix_models/model/material/data_petro.py index c8881a5de5..cac080c8b6 100644 --- a/message_ix_models/model/material/data_petro.py +++ b/message_ix_models/model/material/data_petro.py @@ -320,6 +320,18 @@ def gen_data_petro_chemicals(scenario, dry_run=False): results[param_name].append(df) + share_dict = { + "shares": "steam_cracker", + "node_share": ["R12_MEA", "R12_NAM"], + "technology": "steam_cracker_petro", + "mode": "ethane", + "year_act": "2020", + "time": "year", + "value": [0.4, 0.4], + "unit": "-" + } + results["share_mode_lo"].append(message_ix.make_df("share_mode_lo", **share_dict)) + # Add demand # Create external demand param From a69fb6f2512e5c4664775ab09d198b192d48bcd3 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 9 Mar 2023 11:13:25 +0100 Subject: [PATCH 585/774] Add profiling option to run_reporting() --- message_ix_models/model/material/__init__.py | 73 +++++++++++++++----- 1 file changed, 55 insertions(+), 18 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 568e619029..b4faa1ebea 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -305,8 +305,9 @@ def add_building_ts(scenario_name, model_name): default=False, help="If True the existing timeseries in the scenario is removed.", ) +@click.option("--profile", default=False) @click.pass_obj -def run_reporting(context, remove_ts): +def run_reporting(context, remove_ts, profile): """Run materials, then legacy reporting.""" from message_data.reporting.materials.reporting import report from message_data.tools.post_processing.iamc_report_hackathon import report as reporting @@ -327,23 +328,59 @@ def run_reporting(context, remove_ts): else: print('There are no timeseries to be removed.') else: - # Remove existing timeseries and add material timeseries - print("Reporting material-specific variables") - report(context, scenario) - print("Reporting standard variables") - reporting( - mp, - scenario, - # NB(PNK) this is not an error; .iamc_report_hackathon.report() expects a - # string containing "True" or "False" instead of an actual bool. - "False", - scenario.model, - scenario.scenario, - merge_hist=True, - merge_ts=True, - run_config="materials_run_config.yaml", - ) - # util.prepare_xlsx_for_explorer( + + if profile: + import cProfile + import pstats + import io + import atexit + + print("Profiling...") + pr = cProfile.Profile() + pr.enable() + print("Reporting material-specific variables") + report(context, scenario) + print("Reporting standard variables") + reporting( + mp, + scenario, + # NB(PNK) this is not an error; .iamc_report_hackathon.report() expects a + # string containing "True" or "False" instead of an actual bool. + "False", + scenario.model, + scenario.scenario, + merge_hist=True, + merge_ts=True, + run_config="materials_run_config.yaml", + ) + + def exit(): + pr.disable() + print("Profiling completed") + s = io.StringIO() + pstats.Stats(pr, stream=s).sort_stats("cumulative").dump_stats("profiling.dmp") + print(s.getvalue()) + + atexit.register(exit) + else: + + # Remove existing timeseries and add material timeseries + print("Reporting material-specific variables") + report(context, scenario) + print("Reporting standard variables") + reporting( + mp, + scenario, + # NB(PNK) this is not an error; .iamc_report_hackathon.report() expects a + # string containing "True" or "False" instead of an actual bool. + "False", + scenario.model, + scenario.scenario, + merge_hist=True, + merge_ts=True, + run_config="materials_run_config.yaml", + ) + #util.prepare_xlsx_for_explorer( # Path(os.getcwd()).parents[0].joinpath( # "reporting_output", scenario.model+"_"+scenario.scenario+".xlsx")) From eb10d79807dc0a0b3c99c138c824420f3aaae98f Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 9 Mar 2023 11:14:15 +0100 Subject: [PATCH 586/774] Adjust petro furnaces and sc --- .../data/material/generic_furnace_boiler_techno_economic.xlsx | 2 +- .../data/material/methanol/methanol_sensitivity_pars.xlsx | 4 ++-- .../data/material/petrochemicals_techno_economic.xlsx | 4 ++-- 3 files changed, 5 insertions(+), 5 deletions(-) diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx index afd583ecba..3a6d7941aa 100644 --- a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx +++ b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:486dbd536a5eb2103c3edb3d5bd78f97c5d2e5c0bcb3d2a6002c31b0089ae204 +oid sha256:1a4306f98c08ef0967b9a8d098e5f88f055ab91d4057a42b414dbe80cd35ec22 size 301 diff --git a/message_ix_models/data/material/methanol/methanol_sensitivity_pars.xlsx b/message_ix_models/data/material/methanol/methanol_sensitivity_pars.xlsx index 2c9297a2fb..a3a1426f62 100644 --- a/message_ix_models/data/material/methanol/methanol_sensitivity_pars.xlsx +++ b/message_ix_models/data/material/methanol/methanol_sensitivity_pars.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:f3be058d1262928a88c44963c03c92ae89f2234576b9348508102e3c7d806620 -size 10158 +oid sha256:ff24351004675500b0c655ec20a272679811cb1aaab051fc681767ccf2a0bf76 +size 302 diff --git a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx index 7fae63fa40..87b3ef5112 100644 --- a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx +++ b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:433d900bf720bc058438d57caa2afd693f17731d1eac3636cef48f0482316b0c -size 664245 +oid sha256:8bdb2a4ac3249256a36e3d46f47f064699b59b3de1e71d4f1b197445232e35e2 +size 664262 From c84d875c61d5da9600754114873fbf781c1088a8 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Mon, 13 Mar 2023 22:40:16 +0100 Subject: [PATCH 587/774] Refine excel formulas --- .../data/material/petrochemicals_techno_economic.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx index 87b3ef5112..c03a6362f8 100644 --- a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx +++ b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:8bdb2a4ac3249256a36e3d46f47f064699b59b3de1e71d4f1b197445232e35e2 -size 664262 +oid sha256:480619d5c78518f1801e0cf24a8f73a8d23e9363e990d1d309d055422ab6555e +size 664491 From f9887209283699dfa36da3e86f180ec4f5ce2254 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 28 Mar 2023 16:35:55 +0200 Subject: [PATCH 588/774] Add methanol feedstock trade --- .../material/methanol/meth_trade_techno_economic_fs.xlsx | 4 ++-- message_ix_models/model/material/data_methanol.py | 5 +++++ 2 files changed, 7 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/methanol/meth_trade_techno_economic_fs.xlsx b/message_ix_models/data/material/methanol/meth_trade_techno_economic_fs.xlsx index 1544a04c51..656d7f6d4d 100644 --- a/message_ix_models/data/material/methanol/meth_trade_techno_economic_fs.xlsx +++ b/message_ix_models/data/material/methanol/meth_trade_techno_economic_fs.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:871838da796e854fd76812cc286b254315d1d54e76a51ecd972608ae772f10fd -size 211546 +oid sha256:1fd06b590bd48f2f3bb787c6121e5261a5e63be61ac1172adefd60b5004dcc11 +size 135679 diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index 03663825bb..7e62fc45fc 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -454,6 +454,11 @@ def add_meth_trade_historic(): context.get_local_path("material", "methanol", "meth_trade_techno_economic.xlsx"), sheet_name=None, ) + par_dict_trade_fs = pd.read_excel( + context.get_local_path("material", "methanol", "meth_trade_techno_economic_fs.xlsx"), + sheet_name=None, + ) + par_dict_trade = combine_df_dictionaries(par_dict_trade_fs, par_dict_trade) df = par_dict_trade["historical_new_capacity"] df.loc[df["value"] < 0, "value"] = 0 par_dict_trade["historical_new_capacity"] = df[(df["technology"] != "meth_imp")] From c78106775edbbd1758572b7423f74af4c600bfc7 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 28 Mar 2023 16:37:49 +0200 Subject: [PATCH 589/774] Add meth exp relation to harmonize 2020 --- .../data/material/methanol/meth_trade_techno_economic.xlsx | 4 ++-- message_ix_models/data/material/set.yaml | 1 + 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/methanol/meth_trade_techno_economic.xlsx b/message_ix_models/data/material/methanol/meth_trade_techno_economic.xlsx index b7fe1acce4..112b159768 100644 --- a/message_ix_models/data/material/methanol/meth_trade_techno_economic.xlsx +++ b/message_ix_models/data/material/methanol/meth_trade_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:f707453eeedfe80af8ba40725281b11a27aa41f3de3d6eccbc3df0fcafc47a95 -size 211262 +oid sha256:9e6a864c4be9578b4653c0a2f28ec75b3742c966832aa201df7b4c3840665139 +size 214990 diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index ea1e295c80..e22e4c3b7b 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -544,6 +544,7 @@ methanol: relation: add: - CO2_PtX_trans_disp_split + - meth_exp_tot buildings: level: From b67acde7e00f39b75dabcf5af6cfb89f2fa3351e Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 28 Mar 2023 16:54:56 +0200 Subject: [PATCH 590/774] Remove unnecessary meth_coal additions & fix hist new cap and fix_cost --- .../material/methanol/meth_coal_additions.xlsx | 4 ++-- .../methanol/meth_coal_additions_fs.xlsx | 4 ++-- .../model/material/data_methanol.py | 18 +++++++++--------- 3 files changed, 13 insertions(+), 13 deletions(-) diff --git a/message_ix_models/data/material/methanol/meth_coal_additions.xlsx b/message_ix_models/data/material/methanol/meth_coal_additions.xlsx index 1229c1dc36..8861432937 100644 --- a/message_ix_models/data/material/methanol/meth_coal_additions.xlsx +++ b/message_ix_models/data/material/methanol/meth_coal_additions.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:c50a527d0b9acc2e8db8fc686a474a0ffbc81be9498fe0e0a9dc3a730df5d01d -size 98666 +oid sha256:41f00449ceea43d46533e33fad5747f8e2693baf0abff0222fced475902ca5a5 +size 100037 diff --git a/message_ix_models/data/material/methanol/meth_coal_additions_fs.xlsx b/message_ix_models/data/material/methanol/meth_coal_additions_fs.xlsx index d648734d44..b32b9c0a8d 100644 --- a/message_ix_models/data/material/methanol/meth_coal_additions_fs.xlsx +++ b/message_ix_models/data/material/methanol/meth_coal_additions_fs.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:4610622a608061180ba2809b7a628616b60026d67d04830f394b5cee530a8be1 -size 98719 +oid sha256:2d1b3f1949b9918a04651602299f6e015c13a9db1ed38d83cfcfa66cbb1c3a3d +size 81510 diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index 7e62fc45fc..fdcc32209d 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -674,15 +674,15 @@ def add_meth_hist_act(): ) par_dict["historical_activity"] = pd.concat([df_fs, df_fuel]) # derived from graphic in "Methanol production statstics.xlsx/China demand split" diagram - hist_cap = make_df( - "historical_new_capacity", - node_loc="R12_CHN", - technology="meth_coal", - year_vtg=2015, - value=9.6, - unit="GW", - ) - par_dict["historical_new_capacity"] = hist_cap + # hist_cap = make_df( + # "historical_new_capacity", + # node_loc="R12_CHN", + # technology="meth_coal", + # year_vtg=2015, + # value=9.6, + # unit="GW", + # ) + # par_dict["historical_new_capacity"] = hist_cap # fix demand infeasibility # act = scenario.par("historical_activity") # row = act[act["technology"].str.startswith("meth")].sort_values("value", ascending=False).iloc[0] From feaf77fd7ff038edb3c6c9d5255463364f094f5b Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 28 Mar 2023 16:58:15 +0200 Subject: [PATCH 591/774] Fix meth_ng historical_activity with new modes --- .../data/material/methanol/meth_ng_techno_economic.xlsx | 4 ++-- .../data/material/methanol/meth_ng_techno_economic_fs.xlsx | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/message_ix_models/data/material/methanol/meth_ng_techno_economic.xlsx b/message_ix_models/data/material/methanol/meth_ng_techno_economic.xlsx index df22abd283..efe084480a 100644 --- a/message_ix_models/data/material/methanol/meth_ng_techno_economic.xlsx +++ b/message_ix_models/data/material/methanol/meth_ng_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:380e5b4244385e536dc3a38e4ba969324151658c144bbc145db96535094a2c79 -size 103769 +oid sha256:75947901238ee89930612efeba0c6120ba08feaaf0aff9bb026f9ff427f14319 +size 102245 diff --git a/message_ix_models/data/material/methanol/meth_ng_techno_economic_fs.xlsx b/message_ix_models/data/material/methanol/meth_ng_techno_economic_fs.xlsx index 0cf88e66e5..da2c3fd2db 100644 --- a/message_ix_models/data/material/methanol/meth_ng_techno_economic_fs.xlsx +++ b/message_ix_models/data/material/methanol/meth_ng_techno_economic_fs.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:ef09dd09a43c4dfdb1bc555e3d6ab5b5666e72e12ea396a6a493df19bf77904c -size 103651 +oid sha256:94a32dc9efebb8c54be4df14f07721827e6ea38a4533165170e3060a97956980 +size 89037 From f49c7fa31d4536160d919c1b9923f6cd2288a996 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 28 Mar 2023 17:02:47 +0200 Subject: [PATCH 592/774] Update to meti statistics --- .../petrochemicals_techno_economic.xlsx | 4 +- .../model/material/data_petro.py | 38 ++++++++++++++++++- 2 files changed, 39 insertions(+), 3 deletions(-) diff --git a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx index c03a6362f8..8735ab1d87 100644 --- a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx +++ b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:480619d5c78518f1801e0cf24a8f73a8d23e9363e990d1d309d055422ab6555e -size 664491 +oid sha256:4d47888caa1b80271539f0548a4268d5a9fee0cb0fa77a8555388e12fcbde24e +size 667905 diff --git a/message_ix_models/model/material/data_petro.py b/message_ix_models/model/material/data_petro.py index cac080c8b6..3ab9f23542 100644 --- a/message_ix_models/model/material/data_petro.py +++ b/message_ix_models/model/material/data_petro.py @@ -79,7 +79,7 @@ def get_demand_t1_with_income_elasticity( if "R12_CHN" in nodes: nodes.remove("R12_GLB") region_set = 'R12_' - dem_2020 = np.array([2, 7.5, 30, 4, 11, 42, 60, 32, 30, 29, 35, 67.5]) + dem_2020 = np.array([2.4, 0.44, 3, 5, 11, 40.3, 49.8, 11, 37.5, 10.7, 29.2, 50.5]) dem_2020 = pd.Series(dem_2020) else: @@ -306,6 +306,7 @@ def gen_data_petro_chemicals(scenario, dry_run=False): node_loc=rg, **common ) + df = df.drop_duplicates() # Copy parameters to all regions if (len(regions) == 1) and (rg != global_region): @@ -419,4 +420,39 @@ def gen_data_petro_chemicals(scenario, dry_run=False): results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} + # modify steam cracker hist data (naphtha -> gasoil) to make model feasible + df_cap = pd.read_csv(context.get_local_path( + "material", "methanol", "steam_cracking_hist_new_cap.csv" + )) + df_act = pd.read_csv(context.get_local_path( + "material", "methanol", "steam_cracking_hist_act.csv" + )) + df_act.loc[df_act["mode"]=="naphtha", "mode"] = "vacuum_gasoil" + df = results["historical_activity"] + results["historical_activity"] = pd.concat([df.loc[df["technology"]!="steam_cracker_petro"], df_act]) + df = results["historical_new_capacity"] + results["historical_new_capacity"] = pd.concat([df.loc[df["technology"]!="steam_cracker_petro"], df_cap]) + + # remove growth constraint for R12_AFR to make trade constraints feasible + df = results["growth_activity_up"] + results["growth_activity_up"] = df[~((df["technology"]=="steam_cracker_petro") & + (df["node_loc"]=="R12_AFR") & + (df["year_act"]==2020))] + + # add 25% total trade bound + df_dem = results["demand"] + df_dem = df_dem.groupby("year").sum() * 0.25 + df_dem = df_dem.reset_index() + df_dem = df_dem.rename({"year": "year_act"}, axis=1) + + par_dict = { + "node_loc": "R12_GLB", + "technology": "trade_petro", + "mode": "M1", + "time": "year", + "unit": "-", + } + results["bound_activity_up"] = pd.concat([results["bound_activity_up"], + message_ix.make_df("bound_activity_up", **df_dem, **par_dict)]) + return results From 2fa9159606139f0e0f3fa378b6cc5d51fde25118 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 28 Mar 2023 17:04:44 +0200 Subject: [PATCH 593/774] Allow year columns to be nan --- message_ix_models/model/material/data_ammonia_new.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/message_ix_models/model/material/data_ammonia_new.py b/message_ix_models/model/material/data_ammonia_new.py index c6fc618bdc..b8e82e7557 100644 --- a/message_ix_models/model/material/data_ammonia_new.py +++ b/message_ix_models/model/material/data_ammonia_new.py @@ -181,7 +181,8 @@ def gen_data_ts(scenario, dry_run=False, add_ccs: bool = True): ), sheet_name="timeseries_R12", ) - df.groupby("parameter") + df["year_act"] = df["year_act"].astype("Int64") + df["year_vtg"] = df["year_vtg"].astype("Int64") par_dict = {key: value for (key, value) in df.groupby("parameter")} for i in par_dict.keys(): par_dict[i] = par_dict[i].dropna(axis=1) From a118deb752b2ff8a5317fde5b9f404633a8e9e7d Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 28 Mar 2023 17:06:25 +0200 Subject: [PATCH 594/774] Harmonize base year trade with bounds --- .../data/material/ammonia/fert_techno_economic.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/ammonia/fert_techno_economic.xlsx b/message_ix_models/data/material/ammonia/fert_techno_economic.xlsx index 7290e193f0..2b2957983e 100644 --- a/message_ix_models/data/material/ammonia/fert_techno_economic.xlsx +++ b/message_ix_models/data/material/ammonia/fert_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:ba4d65b5ffb3111cf9139269e9a2212c60dace46bb0ecfd9ba49a8998f878d8d -size 329 +oid sha256:f89c9e3932cadea0206fda577a57734527a3d6c48a0efe4f79ec5b9bfb037aa4 +size 307 From 50277f9327e9d5d329ce579a68fe7a35952afdea Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 28 Mar 2023 17:08:10 +0200 Subject: [PATCH 595/774] Remove end-use technologies dependencies --- message_ix_models/model/material/data_methanol.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index fdcc32209d..0108d272eb 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -226,10 +226,6 @@ def get_embodied_emi(row, pars, share_par): if pars["mtbe_scenario"] == "phase_out": new_dict2 = combine_df_dictionaries(new_dict2, add_mtbe_act_bound(scenario)) - # remove old methanol end use tecs - #scenario.check_out() - #scenario.commit("remove old methanol end use tecs") - return new_dict2 @@ -387,6 +383,10 @@ def gen_data_meth_chemicals(scenario, chemical): def add_methanol_fuel_additives(scenario): par_dict_loil = {} pars = ['output', 'var_cost', 'relation_activity', 'input', 'emission_factor'] + if "loil_trp" not in scenario.set("technology"): + print( + "It seems that the selected scenario does not contain the technology: loil_trp. Methanol fuel blending could not be calculated and was skipped") + return par_dict_loil for i in pars: try: df = scenario.par(i, filters={"technology": "loil_trp", "mode": "M1"}) @@ -394,6 +394,7 @@ def add_methanol_fuel_additives(scenario): par_dict_loil[i] = df.copy(deep=True) except: pass + par_dict_loil["input"] = par_dict_loil["input"][par_dict_loil["input"]["commodity"] == "lightoil"] df_mtbe = pd.read_excel( From a5bcc251e4985465eedb28bb9e7fc30fab652dcd Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 4 Apr 2023 13:27:21 +0200 Subject: [PATCH 596/774] Refactor nh3 relations read-in --- .../model/material/data_ammonia_new.py | 47 ++++++++++++------- 1 file changed, 30 insertions(+), 17 deletions(-) diff --git a/message_ix_models/model/material/data_ammonia_new.py b/message_ix_models/model/material/data_ammonia_new.py index b8e82e7557..4999a35d82 100644 --- a/message_ix_models/model/material/data_ammonia_new.py +++ b/message_ix_models/model/material/data_ammonia_new.py @@ -127,8 +127,8 @@ def gen_data_rel(scenario, dry_run=False, add_ccs: bool = True): ) df.groupby("parameter") par_dict = {key: value for (key, value) in df.groupby("parameter")} - for i in par_dict.keys(): - par_dict[i] = par_dict[i].dropna(axis=1) + #for i in par_dict.keys(): + #par_dict[i] = par_dict[i].dropna(axis=1) act_years = s_info.yv_ya[s_info.yv_ya.year_vtg > 2000]["year_act"] act_years = act_years.drop_duplicates() @@ -137,29 +137,42 @@ def gen_data_rel(scenario, dry_run=False, add_ccs: bool = True): df = par_dict[par_name] # remove "default" node name to broadcast with all scenario regions later - df["node_loc"] = df["node_loc"].apply(lambda x: None if x == "default" else x) + df["node_rel"] = df["node_rel"].apply(lambda x: None if x == "all" else x) df = df.to_dict() - - df_new = make_df(par_name, **df) + df = make_df(par_name, **df) # split df into df with default values and df with regionalized values - df_new_no_reg = df_new[df_new["node_loc"].isna()] - df_new_reg = df_new[~df_new["node_loc"].isna()] + df_all_regs = df[df["node_rel"].isna()] + df_single_regs = df[~df["node_rel"].isna()] + # broadcast regions to default parameter values - df_new_no_reg = df_new_no_reg.pipe(broadcast, node_rel=nodes) - if "node_loc" in df_new_no_reg.columns: - df_new_no_reg["node_loc"] = df_new_no_reg["node_rel"] - df_new = pd.concat([df_new_reg, df_new_no_reg]) + df_all_regs = df_all_regs.pipe(broadcast, node_rel=nodes) + + def same_node_if_nan(df): + if df["node_loc"] == "same": + df["node_loc"] = df["node_rel"] + return df + + if "node_loc" in df_all_regs.columns: + df_all_regs = df_all_regs.apply(lambda x: same_node_if_nan(x), axis=1) + + if "node_loc" in df_single_regs.columns: + df_single_regs["node_loc"] = df_single_regs["node_loc"].apply(lambda x: None if x == "all" else x) + df_new_reg_all_regs = df_single_regs[df_single_regs["node_loc"].isna()] + df_new_reg_all_regs = df_new_reg_all_regs.pipe(broadcast, node_loc=nodes) + df_single_regs = pd.concat([df_single_regs[~df_single_regs["node_loc"].isna()], df_new_reg_all_regs]) + + df = pd.concat([df_single_regs, df_all_regs]) # broadcast scenario years - if "year_rel" in df_new.columns: - df_new = df_new.pipe(same_node).pipe(broadcast, year_rel=act_years) + if "year_rel" in df.columns: + df = df.pipe(same_node).pipe(broadcast, year_rel=act_years) - if "year_act" in df_new.columns: - df_new["year_act"] = df_new["year_rel"] + if "year_act" in df.columns: + df["year_act"] = df["year_rel"] # set import/export node_dest/origin to GLB for input/output - set_exp_imp_nodes(df_new) - par_dict[par_name] = df_new + set_exp_imp_nodes(df) + par_dict[par_name] = df return par_dict From bb520fb3a109d17d9558d4f966881e600c27b90c Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 4 Apr 2023 13:28:01 +0200 Subject: [PATCH 597/774] Add regional cost convergence for nh3 production --- .../model/material/data_ammonia_new.py | 33 +++++++++++++++++++ 1 file changed, 33 insertions(+) diff --git a/message_ix_models/model/material/data_ammonia_new.py b/message_ix_models/model/material/data_ammonia_new.py index 4999a35d82..a32245a4a2 100644 --- a/message_ix_models/model/material/data_ammonia_new.py +++ b/message_ix_models/model/material/data_ammonia_new.py @@ -105,6 +105,39 @@ def __missing__(self,key): df_temp["lifetime"] = df_temp["technology"].map(dict_lifetime) df_temp = df_temp[(df_temp["year_act"] - df_temp["year_vtg"]) < df_temp["lifetime"]] par_dict[i] = df_temp.drop("lifetime", axis="columns") + pars = ["inv_cost", "fix_cost"] + tec_list = ["biomass_NH3", + "electr_NH3", + "gas_NH3", + "coal_NH3", + "fueloil_NH3", + "biomass_NH3_ccs", + "gas_NH3_ccs", + "coal_NH3_ccs", + "fueloil_NH3_ccs"] + cost_conv = pd.read_excel( + "C:/Users\maczek\PycharmProjects\message_data\data\material\methanol/location factor collection.xlsx", + sheet_name="cost_convergence", index_col=0) + for p in pars: + conv_cost_df = pd.DataFrame() + df = par_dict[p] + for tec in tec_list: + if p == "inv_cost": + year_col = "year_vtg" + else: + year_col = "year_act" + + df_tecs = df[df["technology"]==tec] + df_tecs = df_tecs.merge(cost_conv, left_on=year_col, right_index=True) + df_tecs_nam = df_tecs[df_tecs["node_loc"] == "R12_NAM"] + df_tecs = df_tecs.merge(df_tecs_nam[[year_col, "value"]], left_on=year_col, right_on=year_col) + df_tecs["diff"] = df_tecs["value_x"] - df_tecs["value_y"] + df_tecs["diff"] = df_tecs["diff"] * (1 - df_tecs["convergence"]) + df_tecs["new_val"] = df_tecs["value_x"] - df_tecs["diff"] + df_tecs["value"] = df_tecs["new_val"] + conv_cost_df = pd.concat([conv_cost_df, make_df(p, **df_tecs)]) + par_dict[p] = pd.concat([df[~df["technology"].isin(tec_list)], conv_cost_df]) + return par_dict From 3be1ce51b1c36d3185703e2f0bb319f8dcc0f2eb Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 4 Apr 2023 13:28:51 +0200 Subject: [PATCH 598/774] Fix trade_petro var_cost region --- .../model/material/data_petro.py | 43 +++++++++++++------ 1 file changed, 29 insertions(+), 14 deletions(-) diff --git a/message_ix_models/model/material/data_petro.py b/message_ix_models/model/material/data_petro.py index 3ab9f23542..1cd9461ce3 100644 --- a/message_ix_models/model/material/data_petro.py +++ b/message_ix_models/model/material/data_petro.py @@ -261,21 +261,36 @@ def gen_data_petro_chemicals(scenario, dry_run=False): elif param_name == "var_cost": mod = split[1] - - df = ( - make_df( - param_name, - technology=t, - commodity=com, - level=lev, - mode=mod, - value=val[regions[regions == rg].index[0]], - unit="t", - **common + if rg != global_region: + df = ( + make_df( + param_name, + technology=t, + commodity=com, + level=lev, + mode=mod, + value=val[regions[regions == rg].index[0]], + unit="t", + **common + ) + .pipe(broadcast, node_loc=nodes) + .pipe(same_node) + ) + else: + df = ( + make_df( + param_name, + technology=t, + commodity=com, + level=lev, + mode=mod, + value=val[regions[regions == rg].index[0]], + node_loc=rg, + unit="t", + **common + ) + .pipe(same_node) ) - .pipe(broadcast, node_loc=nodes) - .pipe(same_node) - ) elif param_name == "share_mode_up": mod = split[1] From f2bc2c6b0dab0307eff0fa5af83925d38abb7dc2 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 4 Apr 2023 13:29:34 +0200 Subject: [PATCH 599/774] Add trade bound for nh3 and fertilizer --- .../data/material/ammonia/fert_techno_economic.xlsx | 4 ++-- message_ix_models/data/material/set.yaml | 4 ++++ 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/ammonia/fert_techno_economic.xlsx b/message_ix_models/data/material/ammonia/fert_techno_economic.xlsx index 2b2957983e..c1ee10fb4f 100644 --- a/message_ix_models/data/material/ammonia/fert_techno_economic.xlsx +++ b/message_ix_models/data/material/ammonia/fert_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:f89c9e3932cadea0206fda577a57734527a3d6c48a0efe4f79ec5b9bfb037aa4 -size 307 +oid sha256:9477a8ee897330f611ebbb43c719160c34a1568edc26b9bfd76cbd4194d2e69b +size 306 diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index e22e4c3b7b..5af99d2d70 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -479,6 +479,10 @@ fertilizer: - gas_NH3_ccs - coal_NH3_ccs - fueloil_NH3_ccs + relation: + add: + - NH3_trd_cap + - NFert_trd_cap methanol: commodity: From fc55e79a4ec3d6bc29fb66f329dfcae64e0c4329 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 13 Apr 2023 15:21:49 +0200 Subject: [PATCH 600/774] Add ini_act_up for mto and ethanol-ethylene in all regions --- .../data/material/methanol/MTO data collection.xlsx | 4 ++-- .../data/material/petrochemicals_techno_economic.xlsx | 4 ++-- message_ix_models/model/material/data_methanol.py | 10 +++++----- 3 files changed, 9 insertions(+), 9 deletions(-) diff --git a/message_ix_models/data/material/methanol/MTO data collection.xlsx b/message_ix_models/data/material/methanol/MTO data collection.xlsx index 107910646f..08db1064cb 100644 --- a/message_ix_models/data/material/methanol/MTO data collection.xlsx +++ b/message_ix_models/data/material/methanol/MTO data collection.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:fe9027d036a7a13c16767d58b6214bc8a21b6645235b8189e30e171edcbd3731 -size 175109 +oid sha256:573c3cbe403ea101bd248d5b92d6e3fc40f5092603142ba216857628cb83c17f +size 175150 diff --git a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx index 8735ab1d87..affb6f74e0 100644 --- a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx +++ b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:4d47888caa1b80271539f0548a4268d5a9fee0cb0fa77a8555388e12fcbde24e -size 667905 +oid sha256:04834850e3a6a3cff7c2b58f80eaa2a6e0ceb8fd79cef9d7c826e64b709c8244 +size 668098 diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index 0108d272eb..accaebae2c 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -302,11 +302,11 @@ def gen_data_meth_chemicals(scenario, chemical): # exclude emissions for now if chemical == "MTO": df = df.iloc[ - :10, + :15, ] if chemical == "Formaldehyde": df = df.iloc[ - :9, + :10, ] common = dict( @@ -347,7 +347,7 @@ def gen_data_meth_chemicals(scenario, chemical): hist_dict = { "node_loc": "R12_CHN", - "technology": "MTO", + "technology": "MTO_petro", "mode": "M1", "time": "year", "unit": "???", @@ -369,9 +369,9 @@ def gen_data_meth_chemicals(scenario, chemical): "bound_activity_up", year_act=2020, value=12, **hist_dict ) df = par_dict["growth_activity_lo"] - par_dict["growth_activity_lo"] = df[~((df["node_loc"] == "R12_CHN") & ((df["year_act"] == 2020)))] + par_dict["growth_activity_lo"] = df[~((df["node_loc"] == "R12_CHN") & (df["year_act"] == 2020))] df = par_dict["growth_activity_up"] - par_dict["growth_activity_up"] = df[~((df["node_loc"] == "R12_CHN") & ((df["year_act"] == 2020)))] + par_dict["growth_activity_up"] = df[~((df["node_loc"] == "R12_CHN") & (df["year_act"] == 2020))] #par_dict.pop("growth_activity_up") #par_dict.pop("growth_activity_lo") par_dict["inv_cost"] = update_costs_with_loc_factor(par_dict["inv_cost"]) From 9ab70760cb6694230fc55c674769ea10f65f08e1 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 13 Apr 2023 15:23:22 +0200 Subject: [PATCH 601/774] Fix meth_h2 addon constraint --- .../meth_bio_techno_economic_new.xlsx | 4 +-- .../methanol/meth_h2_techno_economic.xlsx | 4 +-- .../model/material/data_methanol.py | 30 +++++++++++-------- 3 files changed, 21 insertions(+), 17 deletions(-) diff --git a/message_ix_models/data/material/methanol/meth_bio_techno_economic_new.xlsx b/message_ix_models/data/material/methanol/meth_bio_techno_economic_new.xlsx index 49f06ea3f3..1226e95245 100644 --- a/message_ix_models/data/material/methanol/meth_bio_techno_economic_new.xlsx +++ b/message_ix_models/data/material/methanol/meth_bio_techno_economic_new.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:3b15f544856151a81e54f984d2045e6f2aa467268fcccc315eccf34d1bb2da99 -size 1096326 +oid sha256:81e22009ff06260d0fa4081043539df375a20ef01442a6cd742db755d231e4b1 +size 1096592 diff --git a/message_ix_models/data/material/methanol/meth_h2_techno_economic.xlsx b/message_ix_models/data/material/methanol/meth_h2_techno_economic.xlsx index e0af71ff8c..412c743c49 100644 --- a/message_ix_models/data/material/methanol/meth_h2_techno_economic.xlsx +++ b/message_ix_models/data/material/methanol/meth_h2_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:a016ca940c6d67e84afc07ab5588a8a3d995a7d5804fb7586eaa111851395482 -size 326455 +oid sha256:d48aacccef01d9fa4ea8d1d81af118c54c3c012099381c83b76bd1611c81a88d +size 288681 diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index accaebae2c..eee890802e 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -21,22 +21,25 @@ def gen_data_methanol(scenario): # read new meth tec parameters - meth_h2_fs_dict = gen_data_meth_h2() - meth_h2_fuel_dict = gen_data_meth_h2() meth_bio_dict = gen_data_meth_bio(scenario) meth_bio_ccs_dict = gen_meth_bio_ccs(scenario) - meth_fs_dic = combine_df_dictionaries(meth_h2_fs_dict, gen_data_meth_bio(scenario), + meth_fs_dic = combine_df_dictionaries(gen_data_meth_bio(scenario), gen_meth_bio_ccs(scenario)) - meth_fuel_dic = combine_df_dictionaries(meth_h2_fuel_dict, meth_bio_dict, meth_bio_ccs_dict) - for k in meth_h2_fuel_dict.keys(): + meth_fuel_dic = combine_df_dictionaries(meth_bio_dict, meth_bio_ccs_dict) + for k in meth_fs_dic.keys(): df_fuel = meth_fuel_dic[k] df_fs = meth_fs_dic[k] if "mode" in df_fuel.columns: df_fuel["mode"] = "fuel" df_fs["mode"] = "feedstock" + meth_h2_fs_dict = gen_data_meth_h2("fs") + meth_h2_fuel_dict = gen_data_meth_h2("fuel") + meth_fuel_dic = combine_df_dictionaries(meth_fuel_dic, meth_h2_fuel_dict) + meth_fs_dic = combine_df_dictionaries(meth_fs_dic, meth_h2_fs_dict) + df = meth_fs_dic["output"] - df.loc[df["commodity"] == "methanol", "level"] = "primary_material" + df.loc[(df["commodity"] == "methanol") & (df["level"] == "primary"), "level"] = "primary_material" meth_fs_dic["output"] = df @@ -229,14 +232,15 @@ def get_embodied_emi(row, pars, share_par): return new_dict2 -def gen_data_meth_h2(): - df_h2 = pd.read_excel( - context.get_local_path("material", "methanol", "meth_h2_techno_economic.xlsx"), +def gen_data_meth_h2(mode): + h2_par_dict = pd.read_excel( + context.get_local_path("material", "methanol", f"meth_h2_techno_economic_{mode}.xlsx"), sheet_name=None, ) - df_h2["inv_cost"] = update_costs_with_loc_factor(df_h2["inv_cost"]) - df_h2["inv_cost"] = update_costs_with_loc_factor(df_h2["fix_cost"]) - return df_h2 + if "inv_cost" in h2_par_dict.keys(): + h2_par_dict["inv_cost"] = update_costs_with_loc_factor(h2_par_dict["inv_cost"]) + h2_par_dict["fix_cost"] = update_costs_with_loc_factor(h2_par_dict["fix_cost"]) + return h2_par_dict def gen_data_meth_bio(scenario): @@ -383,7 +387,7 @@ def gen_data_meth_chemicals(scenario, chemical): def add_methanol_fuel_additives(scenario): par_dict_loil = {} pars = ['output', 'var_cost', 'relation_activity', 'input', 'emission_factor'] - if "loil_trp" not in scenario.set("technology"): + if "loil_trp" not in scenario.set("technology").to_list(): print( "It seems that the selected scenario does not contain the technology: loil_trp. Methanol fuel blending could not be calculated and was skipped") return par_dict_loil From 366385da74894cdfd275ae806d3536a02941a79c Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Fri, 14 Apr 2023 11:54:45 +0200 Subject: [PATCH 602/774] Add scenario version flag for solve command --- message_ix_models/model/material/__init__.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index b4faa1ebea..24f979cee4 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -233,6 +233,7 @@ def build_scen(context, datafile, tag, mode, scenario_name): @cli.command("solve") @click.option("--scenario_name", default="NoPolicy") +@click.option("--version", default=None) @click.option("--model_name", default="MESSAGEix-Materials") @click.option( "--datafile", @@ -243,7 +244,7 @@ def build_scen(context, datafile, tag, mode, scenario_name): @click.option("--add_macro", default=True) @click.option("--add_calibration", default=False) @click.pass_obj -def solve_scen(context, datafile, model_name, scenario_name, add_calibration, add_macro): +def solve_scen(context, datafile, model_name, scenario_name, add_calibration, add_macro, version): """Solve a scenario. Use the --model_name and --scenario_name option to specify the scenario to solve. @@ -252,7 +253,10 @@ def solve_scen(context, datafile, model_name, scenario_name, add_calibration, ad from message_ix import Scenario mp = ixmp.Platform("ixmp_dev") - scenario = Scenario(mp, model_name, scenario_name) + if version: + scenario = Scenario(mp, model_name, scenario_name, version=int(version)) + else: + scenario = Scenario(mp, model_name, scenario_name) #scenario = Scenario(context.get_platform(), model_name, scenario_name) if scenario.has_solution(): From 72873c2028dd96f1e49832bb1b2088915113b19d Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Fri, 14 Apr 2023 11:55:59 +0200 Subject: [PATCH 603/774] Fix ini_act_up parameter of ethanol/ethylene and mto for regions with hist_act --- .../data/material/petrochemicals_techno_economic.xlsx | 4 ++-- message_ix_models/model/material/data_methanol.py | 2 ++ 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx index affb6f74e0..f143960fc5 100644 --- a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx +++ b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:04834850e3a6a3cff7c2b58f80eaa2a6e0ceb8fd79cef9d7c826e64b709c8244 -size 668098 +oid sha256:ec851cf6d927b6a1d0d05864cbfb13ea2c31ca7a8aa523c4e4f259c858f840c1 +size 668645 diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index eee890802e..2d14422567 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -376,6 +376,8 @@ def gen_data_meth_chemicals(scenario, chemical): par_dict["growth_activity_lo"] = df[~((df["node_loc"] == "R12_CHN") & (df["year_act"] == 2020))] df = par_dict["growth_activity_up"] par_dict["growth_activity_up"] = df[~((df["node_loc"] == "R12_CHN") & (df["year_act"] == 2020))] + df = par_dict["initial_activity_up"] + par_dict["initial_activity_up"] = df[~((df["node_loc"] == "R12_CHN"))]# & (df["year_act"] == 2020))] #par_dict.pop("growth_activity_up") #par_dict.pop("growth_activity_lo") par_dict["inv_cost"] = update_costs_with_loc_factor(par_dict["inv_cost"]) From a821ea8074ef2c169b62d6666a8130a51816dd29 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 25 Apr 2023 10:28:51 +0200 Subject: [PATCH 604/774] Small style and typo correction --- message_ix_models/model/material/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 24f979cee4..cb7b92bf1b 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -280,7 +280,7 @@ def solve_scen(context, datafile, model_name, scenario_name, add_calibration, ad scenario.solve(model="MESSAGE-MACRO", solve_options={"lpmethod": "4"}) scenario.set_as_default() - if add_macro == False: + if not add_macro: # Solve print('Solving the scenario without MACRO') scenario.solve(model="MESSAGE", solve_options={'lpmethod': '4'}) From e136fa9b25ee15676b6ebab8fe8bfbc422664369 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 25 Apr 2023 10:31:19 +0200 Subject: [PATCH 605/774] Add fs/fuel modes for h2_elec --- message_ix_models/data/material/methanol/h2_elec_fs.xlsx | 3 +++ .../data/material/methanol/h2_elec_fuel.xlsx | 3 +++ message_ix_models/model/material/data_methanol.py | 8 ++++++++ 3 files changed, 14 insertions(+) create mode 100644 message_ix_models/data/material/methanol/h2_elec_fs.xlsx create mode 100644 message_ix_models/data/material/methanol/h2_elec_fuel.xlsx diff --git a/message_ix_models/data/material/methanol/h2_elec_fs.xlsx b/message_ix_models/data/material/methanol/h2_elec_fs.xlsx new file mode 100644 index 0000000000..3fea1c447f --- /dev/null +++ b/message_ix_models/data/material/methanol/h2_elec_fs.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:15c8b80c4346f66e38565493374dee4ce68435c160795b81ead4fb5529edbf9a +size 462513 diff --git a/message_ix_models/data/material/methanol/h2_elec_fuel.xlsx b/message_ix_models/data/material/methanol/h2_elec_fuel.xlsx new file mode 100644 index 0000000000..e43c856fca --- /dev/null +++ b/message_ix_models/data/material/methanol/h2_elec_fuel.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6296e379d3d35af74ac789642f7db69bb7f1c3a1eab5a69d39e4cce709e2d34e +size 462800 diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index 2d14422567..55846db13f 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -229,6 +229,14 @@ def get_embodied_emi(row, pars, share_par): if pars["mtbe_scenario"] == "phase_out": new_dict2 = combine_df_dictionaries(new_dict2, add_mtbe_act_bound(scenario)) + + h2_modes = ["fs", "fuel"] + for mode in h2_modes: + new_dict2 = combine_df_dictionaries(new_dict2, pd.read_excel( + context.get_local_path("material", "methanol", f"h2_elec_{mode}.xlsx"), + sheet_name=None, + )) + return new_dict2 From 358b37a6b95f5ef8b83bc6a7f070aad749cb19fe Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Wed, 26 Apr 2023 10:22:28 +0200 Subject: [PATCH 606/774] Add missing meth_h2 input files --- .../data/material/methanol/meth_h2_techno_economic_fs.xlsx | 3 +++ .../data/material/methanol/meth_h2_techno_economic_fuel.xlsx | 3 +++ 2 files changed, 6 insertions(+) create mode 100644 message_ix_models/data/material/methanol/meth_h2_techno_economic_fs.xlsx create mode 100644 message_ix_models/data/material/methanol/meth_h2_techno_economic_fuel.xlsx diff --git a/message_ix_models/data/material/methanol/meth_h2_techno_economic_fs.xlsx b/message_ix_models/data/material/methanol/meth_h2_techno_economic_fs.xlsx new file mode 100644 index 0000000000..1e22cfeed7 --- /dev/null +++ b/message_ix_models/data/material/methanol/meth_h2_techno_economic_fs.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1fbd1d2270a9512e098c42854ca94bc139135a03338d132448deb32ad8220f29 +size 182626 diff --git a/message_ix_models/data/material/methanol/meth_h2_techno_economic_fuel.xlsx b/message_ix_models/data/material/methanol/meth_h2_techno_economic_fuel.xlsx new file mode 100644 index 0000000000..86e95e8521 --- /dev/null +++ b/message_ix_models/data/material/methanol/meth_h2_techno_economic_fuel.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:316586f8672167a7a481140a8205b9ba8d112eeb4b630bce568283a4d5ef12f5 +size 239194 From 6903253cdab2b0457a8881678b9392953dea20c0 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Wed, 26 Apr 2023 17:16:15 +0200 Subject: [PATCH 607/774] Add missing steam cracker historic input data --- message_ix_models/model/material/data_petro.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/model/material/data_petro.py b/message_ix_models/model/material/data_petro.py index 1cd9461ce3..5ca123f217 100644 --- a/message_ix_models/model/material/data_petro.py +++ b/message_ix_models/model/material/data_petro.py @@ -437,10 +437,10 @@ def gen_data_petro_chemicals(scenario, dry_run=False): # modify steam cracker hist data (naphtha -> gasoil) to make model feasible df_cap = pd.read_csv(context.get_local_path( - "material", "methanol", "steam_cracking_hist_new_cap.csv" + "material", "steam_cracking_hist_new_cap.csv" )) df_act = pd.read_csv(context.get_local_path( - "material", "methanol", "steam_cracking_hist_act.csv" + "material", "steam_cracking_hist_act.csv" )) df_act.loc[df_act["mode"]=="naphtha", "mode"] = "vacuum_gasoil" df = results["historical_activity"] From 33e66854235dc7537910fe24b57c1e47dd204087 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Wed, 26 Apr 2023 17:16:41 +0200 Subject: [PATCH 608/774] Add missing steam cracker historic input data2 --- message_ix_models/data/material/steam_cracking_hist_act.csv | 3 +++ .../data/material/steam_cracking_hist_new_cap.csv | 3 +++ 2 files changed, 6 insertions(+) create mode 100644 message_ix_models/data/material/steam_cracking_hist_act.csv create mode 100644 message_ix_models/data/material/steam_cracking_hist_new_cap.csv diff --git a/message_ix_models/data/material/steam_cracking_hist_act.csv b/message_ix_models/data/material/steam_cracking_hist_act.csv new file mode 100644 index 0000000000..d32544f02d --- /dev/null +++ b/message_ix_models/data/material/steam_cracking_hist_act.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3554a5e366cdd40b4bd25628490a1798c782c332ae9a4f8c7a1ebab84ff45326 +size 6303 diff --git a/message_ix_models/data/material/steam_cracking_hist_new_cap.csv b/message_ix_models/data/material/steam_cracking_hist_new_cap.csv new file mode 100644 index 0000000000..e73b1bb2a5 --- /dev/null +++ b/message_ix_models/data/material/steam_cracking_hist_new_cap.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8a7d995b2b5d9bad6fddf9ba0bd52a5da59ab738598ca34a4213a4696eb771d3 +size 2965 From 283ac2cbb86bcdb597d74091f70ffe784c396845 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 2 May 2023 14:11:48 +0200 Subject: [PATCH 609/774] Fix the excel and csv files --- .../data/material/ammonia/fert_techno_economic.xlsx | 4 ++-- .../data/material/generic_furnace_boiler_techno_economic.xlsx | 4 ++-- .../data/material/methanol/meth_t_d_material_pars.xlsx | 4 ++-- .../data/material/methanol/meth_trade_techno_economic_fs.xlsx | 4 ++-- .../data/material/methanol/methanol_sensitivity_pars.xlsx | 4 ++-- .../data/material/petrochemicals_techno_economic.xlsx | 4 ++-- 6 files changed, 12 insertions(+), 12 deletions(-) diff --git a/message_ix_models/data/material/ammonia/fert_techno_economic.xlsx b/message_ix_models/data/material/ammonia/fert_techno_economic.xlsx index c1ee10fb4f..fff7166ac8 100644 --- a/message_ix_models/data/material/ammonia/fert_techno_economic.xlsx +++ b/message_ix_models/data/material/ammonia/fert_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:9477a8ee897330f611ebbb43c719160c34a1568edc26b9bfd76cbd4194d2e69b -size 306 +oid sha256:afb15eae27fca8ef45be1592bab3e269b9799bc2d3a25286b730eaddf9256e7f +size 53059 diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx index 3a6d7941aa..b04c151d2a 100644 --- a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx +++ b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:1a4306f98c08ef0967b9a8d098e5f88f055ab91d4057a42b414dbe80cd35ec22 -size 301 +oid sha256:c65df1b518c122786f3ad0c4cb2dd57ac5c613adcb3fe2776cc0b4bf7e66cbef +size 158251 diff --git a/message_ix_models/data/material/methanol/meth_t_d_material_pars.xlsx b/message_ix_models/data/material/methanol/meth_t_d_material_pars.xlsx index 2f2da7fa3f..de6c8362d8 100644 --- a/message_ix_models/data/material/methanol/meth_t_d_material_pars.xlsx +++ b/message_ix_models/data/material/methanol/meth_t_d_material_pars.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:e43dbb591afd1fc53416e6fda653d48a70633d14606c28ee26855c302907cc30 -size 91805 +oid sha256:ec088063f2504ef6dfcd0e7dea4e25c04639cfcc4106a8ab2f257aff2745ae79 +size 92278 diff --git a/message_ix_models/data/material/methanol/meth_trade_techno_economic_fs.xlsx b/message_ix_models/data/material/methanol/meth_trade_techno_economic_fs.xlsx index 656d7f6d4d..d9dd9b5e01 100644 --- a/message_ix_models/data/material/methanol/meth_trade_techno_economic_fs.xlsx +++ b/message_ix_models/data/material/methanol/meth_trade_techno_economic_fs.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:1fd06b590bd48f2f3bb787c6121e5261a5e63be61ac1172adefd60b5004dcc11 -size 135679 +oid sha256:f0fcb1e766fd787c3ca120cd71eab1486c5d20d48e28a8b69e21dbb7dcb33f74 +size 135362 diff --git a/message_ix_models/data/material/methanol/methanol_sensitivity_pars.xlsx b/message_ix_models/data/material/methanol/methanol_sensitivity_pars.xlsx index a3a1426f62..6c68d78751 100644 --- a/message_ix_models/data/material/methanol/methanol_sensitivity_pars.xlsx +++ b/message_ix_models/data/material/methanol/methanol_sensitivity_pars.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:ff24351004675500b0c655ec20a272679811cb1aaab051fc681767ccf2a0bf76 -size 302 +oid sha256:43dac9b00496c6d00500f4d14c95ad3bb6678c756039b46ea4db90d27642960f +size 10543 diff --git a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx index f143960fc5..7467a87cf4 100644 --- a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx +++ b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:ec851cf6d927b6a1d0d05864cbfb13ea2c31ca7a8aa523c4e4f259c858f840c1 -size 668645 +oid sha256:fc7ef4d73643fb94c8b262ed1806e2dcd345ce2bbf277399b4c1a290fd094a39 +size 668481 From 2cbc8cddb456fe9bb52cdc1f0f4a9bc9ac037d18 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 23 Feb 2023 14:52:33 +0100 Subject: [PATCH 610/774] Update income elasticity demand model --- .../material/methanol/methanol demand.xlsx | 4 ++-- .../methanol/methanol_sensitivity_pars.xlsx | 4 ++-- .../model/material/data_methanol.py | 22 +++++++++++-------- 3 files changed, 17 insertions(+), 13 deletions(-) diff --git a/message_ix_models/data/material/methanol/methanol demand.xlsx b/message_ix_models/data/material/methanol/methanol demand.xlsx index cad4ed6ecf..2f3e2dbc13 100644 --- a/message_ix_models/data/material/methanol/methanol demand.xlsx +++ b/message_ix_models/data/material/methanol/methanol demand.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:99a477b942772b6dc5f04b7a8e33a8204750e1b6294ed6cc01a7dd08c2ce8b4d -size 50717 +oid sha256:47686b74930a007377578da04d0fba92c51ad5a74e974504e71b453070194ca1 +size 86629 diff --git a/message_ix_models/data/material/methanol/methanol_sensitivity_pars.xlsx b/message_ix_models/data/material/methanol/methanol_sensitivity_pars.xlsx index 6c68d78751..b85cbad041 100644 --- a/message_ix_models/data/material/methanol/methanol_sensitivity_pars.xlsx +++ b/message_ix_models/data/material/methanol/methanol_sensitivity_pars.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:43dac9b00496c6d00500f4d14c95ad3bb6678c756039b46ea4db90d27642960f -size 10543 +oid sha256:5a4644662496c9d31b1b8f6e3a4b09191098ae5869d302ae61a73fa1284a817c +size 314 diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index 55846db13f..76eae9d166 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -176,12 +176,12 @@ def get_embodied_emi(row, pars, share_par): pass - - df_final = gen_meth_residual_demand(pars["methanol_elasticity"]) - df_final["value"] = df_final["value"].apply(lambda x: x * 0.5) + df_final = gen_meth_residual_demand(pars["methanol_elasticity_2020"], pars["methanol_elasticity_2030"]) + df_final["value"] = df_final["value"].apply( + lambda x: x * pars["methanol_resid_demand_share"]) new_dict2["demand"] = df_final - new_dict2 = combine_df_dictionaries(new_dict2, add_meth_hist_act(scenario)) + new_dict2 = combine_df_dictionaries(new_dict2, add_meth_hist_act()) mto_dict = gen_data_meth_chemicals(scenario, "MTO") ch2o_dict = gen_data_meth_chemicals(scenario, "Formaldehyde") @@ -564,7 +564,7 @@ def gen_resin_demand(scenario, resin_share, sector, buildings_scen, pathway="SHA return df_demand -def gen_meth_residual_demand(gdp_elasticity): +def gen_meth_residual_demand(gdp_elasticity_2020, gdp_elasticity_2030): def get_demand_t1_with_income_elasticity( demand_t0, income_t0, income_t1, elasticity ): @@ -599,10 +599,14 @@ def get_demand_t1_with_income_elasticity( for i in range(len(years) - 1): income_year1 = years[i] income_year2 = years[i + 1] - - dem_2020 = get_demand_t1_with_income_elasticity( - dem_2020, df[income_year1], df[income_year2], gdp_elasticity - ) + if income_year2 >= 2030: + dem_2020 = get_demand_t1_with_income_elasticity( + dem_2020, df[income_year1], df[income_year2], gdp_elasticity_2030 + ) + else: + dem_2020 = get_demand_t1_with_income_elasticity( + dem_2020, df[income_year1], df[income_year2], gdp_elasticity_2020 + ) df_demand[income_year2] = dem_2020 df_melt = df_demand.melt( From d3a2cea097616269ff24458b4513226c634a3229 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 2 May 2023 14:16:47 +0200 Subject: [PATCH 611/774] Update methanol_sensitivity_pars.xlsx --- .../data/material/methanol/methanol_sensitivity_pars.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/methanol/methanol_sensitivity_pars.xlsx b/message_ix_models/data/material/methanol/methanol_sensitivity_pars.xlsx index b85cbad041..6c68d78751 100644 --- a/message_ix_models/data/material/methanol/methanol_sensitivity_pars.xlsx +++ b/message_ix_models/data/material/methanol/methanol_sensitivity_pars.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:5a4644662496c9d31b1b8f6e3a4b09191098ae5869d302ae61a73fa1284a817c -size 314 +oid sha256:43dac9b00496c6d00500f4d14c95ad3bb6678c756039b46ea4db90d27642960f +size 10543 From 774560a9675d2361c724e1fb60563a9935a9113e Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 2 May 2023 17:30:14 +0200 Subject: [PATCH 612/774] Implement small fixes --- message_ix_models/model/material/data_ammonia_new.py | 2 +- message_ix_models/model/material/data_generic.py | 8 -------- message_ix_models/model/material/data_methanol.py | 8 ++++---- message_ix_models/model/material/data_petro.py | 1 + 4 files changed, 6 insertions(+), 13 deletions(-) diff --git a/message_ix_models/model/material/data_ammonia_new.py b/message_ix_models/model/material/data_ammonia_new.py index a32245a4a2..cb043f6733 100644 --- a/message_ix_models/model/material/data_ammonia_new.py +++ b/message_ix_models/model/material/data_ammonia_new.py @@ -116,7 +116,7 @@ def __missing__(self,key): "coal_NH3_ccs", "fueloil_NH3_ccs"] cost_conv = pd.read_excel( - "C:/Users\maczek\PycharmProjects\message_data\data\material\methanol/location factor collection.xlsx", + context.get_local_path("material","methanol","location factor collection.xlsx"), sheet_name="cost_convergence", index_col=0) for p in pars: conv_cost_df = pd.DataFrame() diff --git a/message_ix_models/model/material/data_generic.py b/message_ix_models/model/material/data_generic.py index 84d5fea33c..62ce47cbe9 100644 --- a/message_ix_models/model/material/data_generic.py +++ b/message_ix_models/model/material/data_generic.py @@ -192,7 +192,6 @@ def gen_data_generic(scenario, dry_run=False): tec_ts = set(data_generic_ts.technology) # set of tecs in timeseries sheet for t in tec_ts: - print(t) common = dict( time="year", time_origin="year", @@ -204,7 +203,6 @@ def gen_data_generic(scenario, dry_run=False): ] for p in set(param_name): - print(p) val = data_generic_ts.loc[ (data_generic_ts["technology"] == t) & (data_generic_ts["parameter"] == p), @@ -217,9 +215,6 @@ def gen_data_generic(scenario, dry_run=False): ), "region", ] - print('Regions') - print(regions) - print(len(regions)) units = data_generic_ts.loc[ (data_generic_ts["technology"] == t) & (data_generic_ts["parameter"] == p), @@ -265,9 +260,6 @@ def gen_data_generic(scenario, dry_run=False): **common ) - print('df node_loc') - print(df['node_loc']) - # Copy parameters to all regions if ( (len(set(regions)) == 1) diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index 76eae9d166..28deb42686 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -722,11 +722,11 @@ def add_meth_hist_act(): def update_costs_with_loc_factor(df): loc_fact = pd.read_excel( - "C:/Users\maczek\PycharmProjects\message_data\data\material\methanol/location factor collection.xlsx", - sheet_name="comparison", index_col=0) + context.get_local_path("material","methanol","location factor collection.xlsx"), + sheet_name="comparison", index_col=0) cost_conv = pd.read_excel( - "C:/Users\maczek\PycharmProjects\message_data\data\material\methanol/location factor collection.xlsx", - sheet_name="cost_convergence", index_col=0) + context.get_local_path("material","methanol","location factor collection.xlsx"), + sheet_name="cost_convergence", index_col=0) loc_fact = loc_fact.loc["WEU normalized"] loc_fact.index = ("R12_" + loc_fact.index) df = df.merge(loc_fact, left_on="node_loc", right_index=True) diff --git a/message_ix_models/model/material/data_petro.py b/message_ix_models/model/material/data_petro.py index 5ca123f217..4756ad5394 100644 --- a/message_ix_models/model/material/data_petro.py +++ b/message_ix_models/model/material/data_petro.py @@ -353,6 +353,7 @@ def gen_data_petro_chemicals(scenario, dry_run=False): # demand_HVC = derive_petro_demand(scenario) default_gdp_elasticity = float(0.93) + context = read_config() demand_HVC = gen_mock_demand_petro(scenario, default_gdp_elasticity) results["demand"].append(demand_HVC) From b6d0213cb4fb0f32619165bca613c4955b687f9f Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 3 May 2023 13:40:10 +0200 Subject: [PATCH 613/774] Cement update to allign emissions --- .../data/material/Global_steel_cement_MESSAGE.xlsx | 4 ++-- .../data/material/generic_furnace_boiler_techno_economic.xlsx | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx index ff1ee21d2b..fb9c4f604a 100644 --- a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx +++ b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:555f7f10a0bf9698a4cf3424903e2746aff3b9d611e4ad021b0c63de36dfd287 -size 111382 +oid sha256:0c94437a51d5564c17485aa01a8198fa74e3fa6d572b883b6b9681101e29ec1f +size 111190 diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx index b04c151d2a..a818af88a7 100644 --- a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx +++ b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:c65df1b518c122786f3ad0c4cb2dd57ac5c613adcb3fe2776cc0b4bf7e66cbef -size 158251 +oid sha256:0ed37813e9737d428edf1de71288e744d8a4bb9e636628f19cb6b2a01611c306 +size 158016 From 7a317854e47d675053ca12917e591ad934367c78 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 3 May 2023 13:40:41 +0200 Subject: [PATCH 614/774] Add note about gas_processing_petro --- .../data/material/petrochemicals_techno_economic.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx index 7467a87cf4..d3e11dbff3 100644 --- a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx +++ b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:fc7ef4d73643fb94c8b262ed1806e2dcd345ce2bbf277399b4c1a290fd094a39 -size 668481 +oid sha256:18478b06939d8cbb4eaaad897155f2e2f2f6f27735912e63cf06a82411e292f2 +size 668548 From 231b975769da57efdc9500f8d81449cdbd6ae526 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 9 May 2023 12:59:43 +0200 Subject: [PATCH 615/774] Enable addition of timeseries data for input/output --- message_ix_models/data/material/aluminum_techno_economic.xlsx | 4 ++-- .../data/material/generic_furnace_boiler_techno_economic.xlsx | 4 ++-- .../data/material/petrochemicals_techno_economic.xlsx | 4 ++-- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/message_ix_models/data/material/aluminum_techno_economic.xlsx b/message_ix_models/data/material/aluminum_techno_economic.xlsx index da1f0db1cf..1358a38b90 100644 --- a/message_ix_models/data/material/aluminum_techno_economic.xlsx +++ b/message_ix_models/data/material/aluminum_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:ee05dc52a6c6a8935ceabaa5180cde72612874fdd13d420ccf4001ad5af9c7d5 -size 117451 +oid sha256:8e220fc8a5d65c4caa7b4f49e3f1293f270be9d9d86947f6075c7e2f18823f42 +size 117931 diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx index a818af88a7..af347ad4e2 100644 --- a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx +++ b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:0ed37813e9737d428edf1de71288e744d8a4bb9e636628f19cb6b2a01611c306 -size 158016 +oid sha256:5e5b6b1b7485f115ddb0a785fedbd5f78da11ea6e051fa581faca77c507e5981 +size 158206 diff --git a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx index d3e11dbff3..249e7d8e75 100644 --- a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx +++ b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:18478b06939d8cbb4eaaad897155f2e2f2f6f27735912e63cf06a82411e292f2 -size 668548 +oid sha256:fa239b6452d1edd60f157a85e3cc7857f1f9424c82de3448a99e650f5bd83761 +size 668612 From c0301a3d6b28a7254dd8723369df7e2b480a090a Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 9 May 2023 13:01:55 +0200 Subject: [PATCH 616/774] Update steel sector - add soft constraints for eaf and bof - revise total scrap assumption in the future - add maximum recycling/recovery limit --- .../material/Global_steel_cement_MESSAGE.xlsx | 4 +- message_ix_models/data/material/set.yaml | 1 + .../model/material/data_steel.py | 39 ++++++++++++++++++- message_ix_models/model/material/data_util.py | 2 +- 4 files changed, 41 insertions(+), 5 deletions(-) diff --git a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx index fb9c4f604a..b0ed94de32 100644 --- a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx +++ b/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:0c94437a51d5564c17485aa01a8198fa74e3fa6d572b883b6b9681101e29ec1f -size 111190 +oid sha256:46c81f9778ab244e470f1262c918ce6662ffdb4067b82c14a5e80f1d613da3ff +size 118713 diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 5af99d2d70..42641427ee 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -321,6 +321,7 @@ steel: add: - minimum_recycling_steel - max_global_recycling_steel + - max_regional_recycling_steel balance_equality: add: diff --git a/message_ix_models/model/material/data_steel.py b/message_ix_models/model/material/data_steel.py index 20bb304cf7..764f76a1ec 100644 --- a/message_ix_models/model/material/data_steel.py +++ b/message_ix_models/model/material/data_steel.py @@ -196,7 +196,6 @@ def gen_data_steel(scenario, dry_run=False): & (data_steel_ts["parameter"] == p), "year", ] - if p == "var_cost": df = make_df( p, @@ -208,6 +207,39 @@ def gen_data_steel(scenario, dry_run=False): mode=mod, **common ).pipe(broadcast, node_loc=nodes) + if p == "output": + comm = data_steel_ts.loc[ + (data_steel_ts["technology"] == t) + & (data_steel_ts["parameter"] == p), + "commodity", + ] + + lev = data_steel_ts.loc[ + (data_steel_ts["technology"] == t) + & (data_steel_ts["parameter"] == p), + "level", + ] + + rg = data_steel_ts.loc[ + (data_steel_ts["technology"] == t) + & (data_steel_ts["parameter"] == p), + "region", + ] + + df = make_df( + p, + technology=t, + value=val, + unit="t", + year_vtg=yr, + year_act=yr, + mode=mod, + node_loc=rg, + node_dest=rg, + commodity = comm, + level = lev, + **common + ) else: rg = data_steel_ts.loc[ (data_steel_ts["technology"] == t) @@ -405,6 +437,9 @@ def gen_data_steel(scenario, dry_run=False): if r == 'minimum_recycling_steel': # Do not implement the minimum recycling rate for the year 2020 model_years_rel.remove(2020) + if r == 'max_regional_recycling_steel': + # Do not implement the minimum recycling rate for the year 2020 + model_years_rel.remove(2020) params = set( data_steel_rel.loc[ @@ -418,7 +453,7 @@ def gen_data_steel(scenario, dry_run=False): mode="M1", relation=r, ) - + for par_name in params: if par_name == "relation_activity": diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index aa1bf121ea..f62d13b622 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -1052,7 +1052,7 @@ def read_timeseries(scenario, filename): df = pd.melt( df, - id_vars=["parameter", "region", "technology", "mode", "units"], + id_vars=["parameter", "region", "technology", "mode", "units","commodity","level"], value_vars=datayears, var_name="year", ) From 2e64884960c021d99f7e9817f68c74cd809c1b68 Mon Sep 17 00:00:00 2001 From: Jihoon Min Date: Wed, 17 May 2023 16:29:05 +0200 Subject: [PATCH 617/774] Add changes to reflect pandas 2.0 upgrade and message-ix-models update --- message_ix_models/model/material/data_ammonia_new.py | 6 +++--- message_ix_models/model/material/data_petro.py | 2 +- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/message_ix_models/model/material/data_ammonia_new.py b/message_ix_models/model/material/data_ammonia_new.py index cb043f6733..a8deb85c21 100644 --- a/message_ix_models/model/material/data_ammonia_new.py +++ b/message_ix_models/model/material/data_ammonia_new.py @@ -198,7 +198,7 @@ def same_node_if_nan(df): # broadcast scenario years if "year_rel" in df.columns: - df = df.pipe(same_node).pipe(broadcast, year_rel=act_years) + df = df.pipe(broadcast, year_rel=act_years) if "year_act" in df.columns: df["year_act"] = df["year_rel"] @@ -332,7 +332,7 @@ def read_demand(): N_energy.gas_pct *= input_fuel[2] * CONVERSION_FACTOR_NH3_N # NH3 / N N_energy.coal_pct *= input_fuel[3] * CONVERSION_FACTOR_NH3_N N_energy.oil_pct *= input_fuel[4] * CONVERSION_FACTOR_NH3_N - N_energy = pd.concat([N_energy.Region, N_energy.sum(axis=1)], axis=1).rename( + N_energy = pd.concat([N_energy.Region, N_energy.sum(axis=1, numeric_only=True)], axis=1).rename( columns={0: "totENE", "Region": "node"} ) # GWa @@ -407,7 +407,7 @@ def read_demand(): N_feed.gas_pct *= input_fuel[2] * 17 / 14 N_feed.coal_pct *= input_fuel[3] * 17 / 14 N_feed.oil_pct *= input_fuel[4] * 17 / 14 - N_feed = pd.concat([N_feed.Region, N_feed.sum(axis=1)], axis=1).rename( + N_feed = pd.concat([N_feed.Region, N_feed.sum(axis=1, numeric_only=True)], axis=1).rename( columns={0: "totENE", "Region": "node"} ) diff --git a/message_ix_models/model/material/data_petro.py b/message_ix_models/model/material/data_petro.py index 4756ad5394..705f617ce4 100644 --- a/message_ix_models/model/material/data_petro.py +++ b/message_ix_models/model/material/data_petro.py @@ -457,7 +457,7 @@ def gen_data_petro_chemicals(scenario, dry_run=False): # add 25% total trade bound df_dem = results["demand"] - df_dem = df_dem.groupby("year").sum() * 0.25 + df_dem = df_dem.groupby("year").sum(numeric_only=True) * 0.25 df_dem = df_dem.reset_index() df_dem = df_dem.rename({"year": "year_act"}, axis=1) From 179e91409fd41400c598e40a68a1d0bb59439e1b Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 31 May 2023 16:27:11 +0200 Subject: [PATCH 618/774] Update aluminum industry - revise the scrap recovery system (most costly one is used the last) - add maximum recycling limit - update reporting based on these changes - revise the sector efficiency to allign with the 2020 emission statistics - adjust furnace_biomass parametrization (initial_activity_up and cost) --- .../data/material/aluminum_techno_economic.xlsx | 4 ++-- .../data/material/extra/aluminum_techno_economic.xlsx | 3 +++ .../generic_furnace_boiler_techno_economic.xlsx | 4 ++-- message_ix_models/data/material/set.yaml | 10 ++++++++-- 4 files changed, 15 insertions(+), 6 deletions(-) create mode 100644 message_ix_models/data/material/extra/aluminum_techno_economic.xlsx diff --git a/message_ix_models/data/material/aluminum_techno_economic.xlsx b/message_ix_models/data/material/aluminum_techno_economic.xlsx index 1358a38b90..6dd704a1dc 100644 --- a/message_ix_models/data/material/aluminum_techno_economic.xlsx +++ b/message_ix_models/data/material/aluminum_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:8e220fc8a5d65c4caa7b4f49e3f1293f270be9d9d86947f6075c7e2f18823f42 -size 117931 +oid sha256:fb2bdb6289d5151adabdd80c0758bea5393955d81f698f267254278b4b60df5b +size 120465 diff --git a/message_ix_models/data/material/extra/aluminum_techno_economic.xlsx b/message_ix_models/data/material/extra/aluminum_techno_economic.xlsx new file mode 100644 index 0000000000..6e06b4ec29 --- /dev/null +++ b/message_ix_models/data/material/extra/aluminum_techno_economic.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0678f39d6fc46798cbe3f65580d530b72c04dbee63eda8db8e3b202e42713fc2 +size 119328 diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx index af347ad4e2..6c957bff96 100644 --- a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx +++ b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:5e5b6b1b7485f115ddb0a785fedbd5f78da11ea6e051fa581faca77c507e5981 -size 158206 +oid sha256:d030907bdea1e597416eb77778885a514c3837f5adc5d978c8c94377dab061ee +size 163887 diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 42641427ee..a8db464d21 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -412,7 +412,10 @@ aluminum: - secondary_material - demand - end_of_life - - dummy_end_of_life + - dummy_end_of_life_1 + - dummy_end_of_life_2 + - dummy_end_of_life_3 + technology: add: @@ -424,7 +427,9 @@ aluminum: - prep_secondary_aluminum_3 - finishing_aluminum - manuf_aluminum - - scrap_recovery_aluminum + - scrap_recovery_aluminum_1 + - scrap_recovery_aluminum_2 + - scrap_recovery_aluminum_3 - DUMMY_alumina_supply - trade_aluminum - import_aluminum @@ -435,6 +440,7 @@ aluminum: relation: add: - minimum_recycling_aluminum + - maximum_recycling_aluminum balance_equality: add: From b00e1add687e71cc3fb71861e68bf0b74155c4cf Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 1 Jun 2023 14:53:02 +0200 Subject: [PATCH 619/774] Add the missing refinery oil furnace input --- .../data/material/generic_furnace_boiler_techno_economic.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx index 6c957bff96..a392deaecb 100644 --- a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx +++ b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:d030907bdea1e597416eb77778885a514c3837f5adc5d978c8c94377dab061ee -size 163887 +oid sha256:90c22015363377a2106e304ee628e2ac24f129a04d65a770da4003b15c9fb3d2 +size 163894 From 564135c1a0e3065f0a713aabb7cfc489c757d30f Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 1 Jun 2023 15:00:43 +0200 Subject: [PATCH 620/774] Remove unnecessary relation_activity entries --- .../data/material/methanol/meth_trade_techno_economic.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/methanol/meth_trade_techno_economic.xlsx b/message_ix_models/data/material/methanol/meth_trade_techno_economic.xlsx index 112b159768..8f1ee7cbdc 100644 --- a/message_ix_models/data/material/methanol/meth_trade_techno_economic.xlsx +++ b/message_ix_models/data/material/methanol/meth_trade_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:9e6a864c4be9578b4653c0a2f28ec75b3742c966832aa201df7b4c3840665139 -size 214990 +oid sha256:6a1ca37c2c8c951396582613a57597e935f82fca5292adbd0711a570e6320f16 +size 180987 From 6e3fcc06912708d135b447bd55e89f37ed385391 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 9 May 2023 09:43:41 +0200 Subject: [PATCH 621/774] Update steam_cracker emisssion factors with new input value --- message_ix_models/model/material/data_util.py | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index f62d13b622..40b5a038ab 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -612,11 +612,13 @@ def add_emission_accounting(scen): for t in ["steam_cracker_petro", "gas_processing_petro"]: for m in ["atm_gasoil", "vacuum_gasoil", "naphtha"]: if t == "steam_cracker_petro": - if (m == "atm_gasoil") | (m == "vacuum_gasoil"): - # crudeoil emission factor * input to steam cracker - val = 0.631 * 1.17683105 - elif (m == "naphtha"): - val = 0.631 * 1.537442922 + if m == "vacuum_gasoil": + # fueloil emission factor * input + val = 0.665 * 1.339 + elif m == "atm_gasoil": + val = 0.665 * 1.435 + else: + val = 0.665 * 1.537442922 co2_feedstocks = pd.DataFrame( { From f525ab49c3adbb6a600ecca6ee466b90026651ba Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 1 Jun 2023 15:19:42 +0200 Subject: [PATCH 622/774] Add new meth tecs to meth_exp_limit relation --- .../data/material/methanol/meth_bio_techno_economic_new.xlsx | 4 ++-- .../material/methanol/meth_bio_techno_economic_new_ccs.xlsx | 4 ++-- .../data/material/methanol/meth_h2_techno_economic.xlsx | 3 --- .../data/material/methanol/meth_h2_techno_economic_fs.xlsx | 4 ++-- .../data/material/methanol/meth_h2_techno_economic_fuel.xlsx | 4 ++-- .../data/material/methanol/meth_trade_techno_economic_fs.xlsx | 4 ++-- 6 files changed, 10 insertions(+), 13 deletions(-) delete mode 100644 message_ix_models/data/material/methanol/meth_h2_techno_economic.xlsx diff --git a/message_ix_models/data/material/methanol/meth_bio_techno_economic_new.xlsx b/message_ix_models/data/material/methanol/meth_bio_techno_economic_new.xlsx index 1226e95245..f18acbee33 100644 --- a/message_ix_models/data/material/methanol/meth_bio_techno_economic_new.xlsx +++ b/message_ix_models/data/material/methanol/meth_bio_techno_economic_new.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:81e22009ff06260d0fa4081043539df375a20ef01442a6cd742db755d231e4b1 -size 1096592 +oid sha256:15d994a022f9de1d51a747c7bc05a3824e73f18f9682e2e2c4c2fbdc2b508220 +size 1103249 diff --git a/message_ix_models/data/material/methanol/meth_bio_techno_economic_new_ccs.xlsx b/message_ix_models/data/material/methanol/meth_bio_techno_economic_new_ccs.xlsx index c9670fd85a..506b32e690 100644 --- a/message_ix_models/data/material/methanol/meth_bio_techno_economic_new_ccs.xlsx +++ b/message_ix_models/data/material/methanol/meth_bio_techno_economic_new_ccs.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:51aaf4ed1cc7ee675ad1c7e3eee48cebf00986e1341c095fabc06cd928f2372a -size 1079654 +oid sha256:22e1e4c4aae4d106c0aca710ab64fcac02eba25d7e7625969ea0d5e6f0a5b13a +size 1085207 diff --git a/message_ix_models/data/material/methanol/meth_h2_techno_economic.xlsx b/message_ix_models/data/material/methanol/meth_h2_techno_economic.xlsx deleted file mode 100644 index 412c743c49..0000000000 --- a/message_ix_models/data/material/methanol/meth_h2_techno_economic.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:d48aacccef01d9fa4ea8d1d81af118c54c3c012099381c83b76bd1611c81a88d -size 288681 diff --git a/message_ix_models/data/material/methanol/meth_h2_techno_economic_fs.xlsx b/message_ix_models/data/material/methanol/meth_h2_techno_economic_fs.xlsx index 1e22cfeed7..a75fc7ab93 100644 --- a/message_ix_models/data/material/methanol/meth_h2_techno_economic_fs.xlsx +++ b/message_ix_models/data/material/methanol/meth_h2_techno_economic_fs.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:1fbd1d2270a9512e098c42854ca94bc139135a03338d132448deb32ad8220f29 -size 182626 +oid sha256:e599481c293c6f0b560bcb2c5f0cc2022fe4bceaf71dce5808eae646407f6489 +size 188715 diff --git a/message_ix_models/data/material/methanol/meth_h2_techno_economic_fuel.xlsx b/message_ix_models/data/material/methanol/meth_h2_techno_economic_fuel.xlsx index 86e95e8521..6ff9d1338b 100644 --- a/message_ix_models/data/material/methanol/meth_h2_techno_economic_fuel.xlsx +++ b/message_ix_models/data/material/methanol/meth_h2_techno_economic_fuel.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:316586f8672167a7a481140a8205b9ba8d112eeb4b630bce568283a4d5ef12f5 -size 239194 +oid sha256:d6cc27f87251bd333bf01b1efa66332e304d6fcf5e43efe9e89a177306255206 +size 245253 diff --git a/message_ix_models/data/material/methanol/meth_trade_techno_economic_fs.xlsx b/message_ix_models/data/material/methanol/meth_trade_techno_economic_fs.xlsx index d9dd9b5e01..2c7cdaf807 100644 --- a/message_ix_models/data/material/methanol/meth_trade_techno_economic_fs.xlsx +++ b/message_ix_models/data/material/methanol/meth_trade_techno_economic_fs.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:f0fcb1e766fd787c3ca120cd71eab1486c5d20d48e28a8b69e21dbb7dcb33f74 -size 135362 +oid sha256:cc6ac6404810dda477ab6669821c2c1509334952b9a197be2ecc504e019a2f3a +size 99973 From e36ed0c3958651fb4c3210dc89303254b92d5e49 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 23 Feb 2023 14:37:46 +0100 Subject: [PATCH 623/774] Read nh3/hvc income elasticity from external file --- .../model/material/data_ammonia_new.py | 11 +++++++- .../model/material/data_petro.py | 27 +++++++++++++++---- 2 files changed, 32 insertions(+), 6 deletions(-) diff --git a/message_ix_models/model/material/data_ammonia_new.py b/message_ix_models/model/material/data_ammonia_new.py index a8deb85c21..031050651c 100644 --- a/message_ix_models/model/material/data_ammonia_new.py +++ b/message_ix_models/model/material/data_ammonia_new.py @@ -8,7 +8,16 @@ CONVERSION_FACTOR_NH3_N = 17 / 14 context = read_config() -default_gdp_elasticity = float(0.65) +default_gdp_elasticity = pd.read_excel( + context.get_local_path( + "data", + "material", + "methanol", + "methanol_sensitivity_pars.xlsx", + ) + ).set_index("par").to_dict()["value"]["nh3_elasticity"] +# float(0.65) # old default value + def gen_all_NH3_fert(scenario, dry_run=False): return { diff --git a/message_ix_models/model/material/data_petro.py b/message_ix_models/model/material/data_petro.py index 705f617ce4..2a6b821350 100644 --- a/message_ix_models/model/material/data_petro.py +++ b/message_ix_models/model/material/data_petro.py @@ -39,7 +39,8 @@ def read_data_petrochemicals(scenario): return data_petro -def gen_mock_demand_petro(scenario, gdp_elasticity): + +def gen_mock_demand_petro(scenario, gdp_elasticity_2020, gdp_elasticity_2030): context = read_config() s_info = ScenarioInfo(scenario) @@ -94,9 +95,14 @@ def get_demand_t1_with_income_elasticity( income_year1 = modelyears[i] income_year2 = modelyears[i + 1] - dem_2020 = get_demand_t1_with_income_elasticity( - dem_2020, df[income_year1], df[income_year2], gdp_elasticity - ) + if income_year2 >= 2030: + dem_2020 = get_demand_t1_with_income_elasticity( + dem_2020, df[income_year1], df[income_year2], gdp_elasticity_2030 + ) + else: + dem_2020 = get_demand_t1_with_income_elasticity( + dem_2020, df[income_year1], df[income_year2], gdp_elasticity_2020 + ) df_demand[income_year2] = dem_2020 df_melt = df_demand.melt( @@ -354,7 +360,18 @@ def gen_data_petro_chemicals(scenario, dry_run=False): # demand_HVC = derive_petro_demand(scenario) default_gdp_elasticity = float(0.93) context = read_config() - demand_HVC = gen_mock_demand_petro(scenario, default_gdp_elasticity) + + df_pars = pd.read_excel( + context.get_local_path( + "material", "methanol", "methanol_sensitivity_pars.xlsx" + ), + sheet_name="Sheet1", + dtype=object, + ) + pars = df_pars.set_index("par").to_dict()["value"] + default_gdp_elasticity_2020 = pars["hvc_elasticity_2020"] + default_gdp_elasticity_2030 = pars["hvc_elasticity_2030"] + demand_HVC = gen_mock_demand_petro(scenario, default_gdp_elasticity_2020, default_gdp_elasticity_2030) results["demand"].append(demand_HVC) # df_e = make_df(paramname, level='final_material', commodity="ethylene", \ From 4bbfb1c0a46d54e2f6245e6ba9b56918f3c9de76 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 13 Jun 2023 12:30:31 +0200 Subject: [PATCH 624/774] Shift model year to 2025 for 2 degree scenario --- message_ix_models/model/material/__init__.py | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index cb7b92bf1b..2bf85f0d78 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -198,7 +198,8 @@ def build_scen(context, datafile, tag, mode, scenario_name): scenario = message_ix.Scenario(mp, 'MESSAGEix-Materials', scenario_name) print('Base scenario is ' + scenario_name) output_scenario_name = output_scenario_name + '_' + tag - scenario = scenario.clone('MESSAGEix-Materials',output_scenario_name , keep_solution=False) + scenario = scenario.clone('MESSAGEix-Materials',output_scenario_name , keep_solution=False, + shift_first_model_year=2025) scenario.check_out() tax_emission_new.columns = scenario.par("tax_emission").columns tax_emission_new["unit"] = "USD/tCO2" @@ -265,7 +266,7 @@ def solve_scen(context, datafile, model_name, scenario_name, add_calibration, ad if add_calibration: # Solve print('Solving the scenario without MACRO') - scenario.solve(model="MESSAGE", solve_options={'lpmethod': '4', 'barcrossalg':'2'}) + scenario.solve(model="MESSAGE", solve_options={'lpmethod': '4', 'scaind':'-1'}) scenario.set_as_default() # After solving, add macro calibration @@ -277,13 +278,13 @@ def solve_scen(context, datafile, model_name, scenario_name, add_calibration, ad print('After macro calibration a new scneario with the suffix _macro is created.') print('Make sure to use this scenario to solve with MACRO iterations.') - scenario.solve(model="MESSAGE-MACRO", solve_options={"lpmethod": "4"}) + scenario.solve(model="MESSAGE-MACRO", solve_options={'lpmethod': '4', 'scaind':'-1'}) scenario.set_as_default() if not add_macro: # Solve print('Solving the scenario without MACRO') - scenario.solve(model="MESSAGE", solve_options={'lpmethod': '4'}) + scenario.solve(model="MESSAGE", solve_options={'lpmethod': '4', 'scaind':'-1'}) scenario.set_as_default() @cli.command("add_buildings_ts") @@ -437,7 +438,7 @@ def run_old_reporting(context): DATA_FUNCTIONS_2 = [ gen_data_cement, gen_data_petro_chemicals, - #gen_data_power_sector, + gen_data_power_sector, gen_data_aluminum ] From 8482849f30658234654542ad0258d77185421564 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 15 Jun 2023 17:30:32 +0200 Subject: [PATCH 625/774] Change co2_cc to co2_feedstocks --- .../data/material/methanol/MTO data collection.xlsx | 4 ++-- message_ix_models/model/material/data_methanol.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/message_ix_models/data/material/methanol/MTO data collection.xlsx b/message_ix_models/data/material/methanol/MTO data collection.xlsx index 08db1064cb..cffeb7be3f 100644 --- a/message_ix_models/data/material/methanol/MTO data collection.xlsx +++ b/message_ix_models/data/material/methanol/MTO data collection.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:573c3cbe403ea101bd248d5b92d6e3fc40f5092603142ba216857628cb83c17f -size 175150 +oid sha256:d602a93ee02929b4052b534311dd3d9437064ad147429b126ae061041d79e307 +size 175139 diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index 28deb42686..763453eec0 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -329,7 +329,7 @@ def gen_data_meth_chemicals(scenario, chemical): time_dest="year", time_origin="year", emission="CO2_industry", # confirm if correct - relation="CO2_cc", + relation="CO2_feedstocks", ) all_years = scenario.vintage_and_active_years() @@ -540,7 +540,7 @@ def gen_resin_demand(scenario, resin_share, sector, buildings_scen, pathway="SHA time_dest="year", time_origin="year", emission="CO2_industry", # confirm if correct, - relation="CO2_cc", + relation="CO2_feedstocks", commodity="fcoh_resin", unit="???", level="final_material", From 0bbc32694aff646ed3ddc6b363367d04c10475b5 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 15 Jun 2023 17:30:50 +0200 Subject: [PATCH 626/774] Update petrochemicals_techno_economic.xlsx --- .../data/material/petrochemicals_techno_economic.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx index 249e7d8e75..6d3f2e33b3 100644 --- a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx +++ b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:fa239b6452d1edd60f157a85e3cc7857f1f9424c82de3448a99e650f5bd83761 -size 668612 +oid sha256:c9993bb1d6d7c9a9b08e1461e46212a448e9ade53d145c1b3cc26ff0033cbd95 +size 668549 From 276e5773052bb8c7fa357ea8863e49376d015417 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 20 Jun 2023 11:41:34 +0200 Subject: [PATCH 627/774] Organize data structure --- .../material/MESSAGE_region_mapping_R14.xlsx | 3 -- .../aluminum_techno_economic.xlsx | 0 .../{ => aluminum}/demand_aluminum.xlsx | 0 .../ammonia/fert_techno_economic.xlsx | 4 +-- .../Ammonia feedstock share.Global_R12.xlsx | 0 ...fertilizer demand.Global_R12_adaption.xlsx | 0 .../GLOBIOM_Fertilizer_use_N.xlsx | 0 .../{ => deprecated}/LED_LED_report_IAMC.csv | 0 .../LED_LED_report_IAMC_sensitivity.csv | 0 .../LED_LED_report_IAMC_sensitivity_R11.csv | 0 .../LED_LED_report_IAMC_sensitivity_R12.csv | 0 .../{ => deprecated}/demand_i_feed_R12.xlsx | 0 .../{ => deprecated}/demand_petro.xlsx | 0 .../n-fertilizer_techno-economic_new.xlsx | 0 .../regional_cost_scaler_R12.xlsx | 0 .../{ => deprecated}/trade.FAO.R12.csv | 0 .../extra/aluminum_techno_economic.xlsx | 3 -- ...eneric_furnace_boiler_techno_economic.xlsx | 3 -- .../MTO data collection.xlsx | 0 ...nol production statistics (version 1).xlsx | 0 ...AGEix-Materials_final_energy_industry.xlsx | 0 ...eneric_furnace_boiler_techno_economic.xlsx | 3 ++ .../iamc_db ENGAGE baseline GDP PPP.xlsx | 0 .../{ => other}/residual_industry_2019.csv | 0 .../petrochemicals_techno_economic.xlsx | 3 ++ .../steam_cracking_hist_act.csv | 0 .../steam_cracking_hist_new_cap.csv | 0 .../petrochemicals_techno_economic.xlsx | 3 -- .../LCA_commodity_mapping.xlsx | 0 .../LCA_region_mapping.xlsx | 0 .../MESSAGE_global_model_technologies.xlsx | 0 .../NTNU_LCA_coefficients.xlsx | 0 .../{ => steel_cement}/CEMENT.BvR2010.xlsx | 0 .../China_steel_cement_MESSAGE.xlsx | 0 .../Global_steel_cement_MESSAGE.xlsx | 0 .../STEEL_database_2012.xlsx | 0 .../model/material/data_aluminum.py | 8 ++--- .../model/material/data_cement.py | 6 ++-- .../model/material/data_generic.py | 11 +++--- .../model/material/data_methanol.py | 23 ++++++++++-- .../model/material/data_petro.py | 14 ++++---- .../model/material/data_power_sector.py | 8 ++--- .../model/material/data_steel.py | 6 ++-- message_ix_models/model/material/data_util.py | 21 +++++------ .../material_demand/init_modularized.R | 36 +++++++++---------- 45 files changed, 81 insertions(+), 74 deletions(-) delete mode 100644 message_ix_models/data/material/MESSAGE_region_mapping_R14.xlsx rename message_ix_models/data/material/{ => aluminum}/aluminum_techno_economic.xlsx (100%) rename message_ix_models/data/material/{ => aluminum}/demand_aluminum.xlsx (100%) rename message_ix_models/data/material/{ => deprecated}/Ammonia feedstock share.Global_R12.xlsx (100%) rename message_ix_models/data/material/{ => deprecated}/CD-Links SSP2 N-fertilizer demand.Global_R12_adaption.xlsx (100%) rename message_ix_models/data/material/{ => deprecated}/GLOBIOM_Fertilizer_use_N.xlsx (100%) rename message_ix_models/data/material/{ => deprecated}/LED_LED_report_IAMC.csv (100%) rename message_ix_models/data/material/{ => deprecated}/LED_LED_report_IAMC_sensitivity.csv (100%) rename message_ix_models/data/material/{ => deprecated}/LED_LED_report_IAMC_sensitivity_R11.csv (100%) rename message_ix_models/data/material/{ => deprecated}/LED_LED_report_IAMC_sensitivity_R12.csv (100%) rename message_ix_models/data/material/{ => deprecated}/demand_i_feed_R12.xlsx (100%) rename message_ix_models/data/material/{ => deprecated}/demand_petro.xlsx (100%) rename message_ix_models/data/material/{ => deprecated}/n-fertilizer_techno-economic_new.xlsx (100%) rename message_ix_models/data/material/{ => deprecated}/regional_cost_scaler_R12.xlsx (100%) rename message_ix_models/data/material/{ => deprecated}/trade.FAO.R12.csv (100%) delete mode 100644 message_ix_models/data/material/extra/aluminum_techno_economic.xlsx delete mode 100644 message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx rename message_ix_models/data/material/methanol/{ => collection files}/MTO data collection.xlsx (100%) rename message_ix_models/data/material/methanol/{ => collection files}/Methanol production statistics (version 1).xlsx (100%) rename message_ix_models/data/material/{ => other}/MESSAGEix-Materials_final_energy_industry.xlsx (100%) create mode 100644 message_ix_models/data/material/other/generic_furnace_boiler_techno_economic.xlsx rename message_ix_models/data/material/{ => other}/iamc_db ENGAGE baseline GDP PPP.xlsx (100%) rename message_ix_models/data/material/{ => other}/residual_industry_2019.csv (100%) create mode 100644 message_ix_models/data/material/petrochemicals/petrochemicals_techno_economic.xlsx rename message_ix_models/data/material/{ => petrochemicals}/steam_cracking_hist_act.csv (100%) rename message_ix_models/data/material/{ => petrochemicals}/steam_cracking_hist_new_cap.csv (100%) delete mode 100644 message_ix_models/data/material/petrochemicals_techno_economic.xlsx rename message_ix_models/data/material/{ => power sector}/LCA_commodity_mapping.xlsx (100%) rename message_ix_models/data/material/{ => power sector}/LCA_region_mapping.xlsx (100%) rename message_ix_models/data/material/{ => power sector}/MESSAGE_global_model_technologies.xlsx (100%) rename message_ix_models/data/material/{ => power sector}/NTNU_LCA_coefficients.xlsx (100%) rename message_ix_models/data/material/{ => steel_cement}/CEMENT.BvR2010.xlsx (100%) rename message_ix_models/data/material/{ => steel_cement}/China_steel_cement_MESSAGE.xlsx (100%) rename message_ix_models/data/material/{ => steel_cement}/Global_steel_cement_MESSAGE.xlsx (100%) rename message_ix_models/data/material/{ => steel_cement}/STEEL_database_2012.xlsx (100%) diff --git a/message_ix_models/data/material/MESSAGE_region_mapping_R14.xlsx b/message_ix_models/data/material/MESSAGE_region_mapping_R14.xlsx deleted file mode 100644 index d102f727ed..0000000000 --- a/message_ix_models/data/material/MESSAGE_region_mapping_R14.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:e2d7d051d5573423d0160725d1c459da7c40d26ebfe127aa2b614584be1bf176 -size 17152 diff --git a/message_ix_models/data/material/aluminum_techno_economic.xlsx b/message_ix_models/data/material/aluminum/aluminum_techno_economic.xlsx similarity index 100% rename from message_ix_models/data/material/aluminum_techno_economic.xlsx rename to message_ix_models/data/material/aluminum/aluminum_techno_economic.xlsx diff --git a/message_ix_models/data/material/demand_aluminum.xlsx b/message_ix_models/data/material/aluminum/demand_aluminum.xlsx similarity index 100% rename from message_ix_models/data/material/demand_aluminum.xlsx rename to message_ix_models/data/material/aluminum/demand_aluminum.xlsx diff --git a/message_ix_models/data/material/ammonia/fert_techno_economic.xlsx b/message_ix_models/data/material/ammonia/fert_techno_economic.xlsx index fff7166ac8..09d1e4ddc5 100644 --- a/message_ix_models/data/material/ammonia/fert_techno_economic.xlsx +++ b/message_ix_models/data/material/ammonia/fert_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:afb15eae27fca8ef45be1592bab3e269b9799bc2d3a25286b730eaddf9256e7f -size 53059 +oid sha256:a064be30dcd244f823805250bd5e095c5af5917420c2be35fec0c19e8ac24c14 +size 58668 diff --git a/message_ix_models/data/material/Ammonia feedstock share.Global_R12.xlsx b/message_ix_models/data/material/deprecated/Ammonia feedstock share.Global_R12.xlsx similarity index 100% rename from message_ix_models/data/material/Ammonia feedstock share.Global_R12.xlsx rename to message_ix_models/data/material/deprecated/Ammonia feedstock share.Global_R12.xlsx diff --git a/message_ix_models/data/material/CD-Links SSP2 N-fertilizer demand.Global_R12_adaption.xlsx b/message_ix_models/data/material/deprecated/CD-Links SSP2 N-fertilizer demand.Global_R12_adaption.xlsx similarity index 100% rename from message_ix_models/data/material/CD-Links SSP2 N-fertilizer demand.Global_R12_adaption.xlsx rename to message_ix_models/data/material/deprecated/CD-Links SSP2 N-fertilizer demand.Global_R12_adaption.xlsx diff --git a/message_ix_models/data/material/GLOBIOM_Fertilizer_use_N.xlsx b/message_ix_models/data/material/deprecated/GLOBIOM_Fertilizer_use_N.xlsx similarity index 100% rename from message_ix_models/data/material/GLOBIOM_Fertilizer_use_N.xlsx rename to message_ix_models/data/material/deprecated/GLOBIOM_Fertilizer_use_N.xlsx diff --git a/message_ix_models/data/material/LED_LED_report_IAMC.csv b/message_ix_models/data/material/deprecated/LED_LED_report_IAMC.csv similarity index 100% rename from message_ix_models/data/material/LED_LED_report_IAMC.csv rename to message_ix_models/data/material/deprecated/LED_LED_report_IAMC.csv diff --git a/message_ix_models/data/material/LED_LED_report_IAMC_sensitivity.csv b/message_ix_models/data/material/deprecated/LED_LED_report_IAMC_sensitivity.csv similarity index 100% rename from message_ix_models/data/material/LED_LED_report_IAMC_sensitivity.csv rename to message_ix_models/data/material/deprecated/LED_LED_report_IAMC_sensitivity.csv diff --git a/message_ix_models/data/material/LED_LED_report_IAMC_sensitivity_R11.csv b/message_ix_models/data/material/deprecated/LED_LED_report_IAMC_sensitivity_R11.csv similarity index 100% rename from message_ix_models/data/material/LED_LED_report_IAMC_sensitivity_R11.csv rename to message_ix_models/data/material/deprecated/LED_LED_report_IAMC_sensitivity_R11.csv diff --git a/message_ix_models/data/material/LED_LED_report_IAMC_sensitivity_R12.csv b/message_ix_models/data/material/deprecated/LED_LED_report_IAMC_sensitivity_R12.csv similarity index 100% rename from message_ix_models/data/material/LED_LED_report_IAMC_sensitivity_R12.csv rename to message_ix_models/data/material/deprecated/LED_LED_report_IAMC_sensitivity_R12.csv diff --git a/message_ix_models/data/material/demand_i_feed_R12.xlsx b/message_ix_models/data/material/deprecated/demand_i_feed_R12.xlsx similarity index 100% rename from message_ix_models/data/material/demand_i_feed_R12.xlsx rename to message_ix_models/data/material/deprecated/demand_i_feed_R12.xlsx diff --git a/message_ix_models/data/material/demand_petro.xlsx b/message_ix_models/data/material/deprecated/demand_petro.xlsx similarity index 100% rename from message_ix_models/data/material/demand_petro.xlsx rename to message_ix_models/data/material/deprecated/demand_petro.xlsx diff --git a/message_ix_models/data/material/n-fertilizer_techno-economic_new.xlsx b/message_ix_models/data/material/deprecated/n-fertilizer_techno-economic_new.xlsx similarity index 100% rename from message_ix_models/data/material/n-fertilizer_techno-economic_new.xlsx rename to message_ix_models/data/material/deprecated/n-fertilizer_techno-economic_new.xlsx diff --git a/message_ix_models/data/material/regional_cost_scaler_R12.xlsx b/message_ix_models/data/material/deprecated/regional_cost_scaler_R12.xlsx similarity index 100% rename from message_ix_models/data/material/regional_cost_scaler_R12.xlsx rename to message_ix_models/data/material/deprecated/regional_cost_scaler_R12.xlsx diff --git a/message_ix_models/data/material/trade.FAO.R12.csv b/message_ix_models/data/material/deprecated/trade.FAO.R12.csv similarity index 100% rename from message_ix_models/data/material/trade.FAO.R12.csv rename to message_ix_models/data/material/deprecated/trade.FAO.R12.csv diff --git a/message_ix_models/data/material/extra/aluminum_techno_economic.xlsx b/message_ix_models/data/material/extra/aluminum_techno_economic.xlsx deleted file mode 100644 index 6e06b4ec29..0000000000 --- a/message_ix_models/data/material/extra/aluminum_techno_economic.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:0678f39d6fc46798cbe3f65580d530b72c04dbee63eda8db8e3b202e42713fc2 -size 119328 diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx deleted file mode 100644 index a392deaecb..0000000000 --- a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:90c22015363377a2106e304ee628e2ac24f129a04d65a770da4003b15c9fb3d2 -size 163894 diff --git a/message_ix_models/data/material/methanol/MTO data collection.xlsx b/message_ix_models/data/material/methanol/collection files/MTO data collection.xlsx similarity index 100% rename from message_ix_models/data/material/methanol/MTO data collection.xlsx rename to message_ix_models/data/material/methanol/collection files/MTO data collection.xlsx diff --git a/message_ix_models/data/material/methanol/Methanol production statistics (version 1).xlsx b/message_ix_models/data/material/methanol/collection files/Methanol production statistics (version 1).xlsx similarity index 100% rename from message_ix_models/data/material/methanol/Methanol production statistics (version 1).xlsx rename to message_ix_models/data/material/methanol/collection files/Methanol production statistics (version 1).xlsx diff --git a/message_ix_models/data/material/MESSAGEix-Materials_final_energy_industry.xlsx b/message_ix_models/data/material/other/MESSAGEix-Materials_final_energy_industry.xlsx similarity index 100% rename from message_ix_models/data/material/MESSAGEix-Materials_final_energy_industry.xlsx rename to message_ix_models/data/material/other/MESSAGEix-Materials_final_energy_industry.xlsx diff --git a/message_ix_models/data/material/other/generic_furnace_boiler_techno_economic.xlsx b/message_ix_models/data/material/other/generic_furnace_boiler_techno_economic.xlsx new file mode 100644 index 0000000000..a5c494cd99 --- /dev/null +++ b/message_ix_models/data/material/other/generic_furnace_boiler_techno_economic.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f91735eb23ce7df1372d82fc360b3d829f6e5852b026bfda26499142b2d94b35 +size 163912 diff --git a/message_ix_models/data/material/iamc_db ENGAGE baseline GDP PPP.xlsx b/message_ix_models/data/material/other/iamc_db ENGAGE baseline GDP PPP.xlsx similarity index 100% rename from message_ix_models/data/material/iamc_db ENGAGE baseline GDP PPP.xlsx rename to message_ix_models/data/material/other/iamc_db ENGAGE baseline GDP PPP.xlsx diff --git a/message_ix_models/data/material/residual_industry_2019.csv b/message_ix_models/data/material/other/residual_industry_2019.csv similarity index 100% rename from message_ix_models/data/material/residual_industry_2019.csv rename to message_ix_models/data/material/other/residual_industry_2019.csv diff --git a/message_ix_models/data/material/petrochemicals/petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/petrochemicals/petrochemicals_techno_economic.xlsx new file mode 100644 index 0000000000..a66b701be2 --- /dev/null +++ b/message_ix_models/data/material/petrochemicals/petrochemicals_techno_economic.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:120a611fcd3a6325233b3c2275e16bd03d974c0f9b5c8f7f88832091264c8d97 +size 668550 diff --git a/message_ix_models/data/material/steam_cracking_hist_act.csv b/message_ix_models/data/material/petrochemicals/steam_cracking_hist_act.csv similarity index 100% rename from message_ix_models/data/material/steam_cracking_hist_act.csv rename to message_ix_models/data/material/petrochemicals/steam_cracking_hist_act.csv diff --git a/message_ix_models/data/material/steam_cracking_hist_new_cap.csv b/message_ix_models/data/material/petrochemicals/steam_cracking_hist_new_cap.csv similarity index 100% rename from message_ix_models/data/material/steam_cracking_hist_new_cap.csv rename to message_ix_models/data/material/petrochemicals/steam_cracking_hist_new_cap.csv diff --git a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx deleted file mode 100644 index 6d3f2e33b3..0000000000 --- a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:c9993bb1d6d7c9a9b08e1461e46212a448e9ade53d145c1b3cc26ff0033cbd95 -size 668549 diff --git a/message_ix_models/data/material/LCA_commodity_mapping.xlsx b/message_ix_models/data/material/power sector/LCA_commodity_mapping.xlsx similarity index 100% rename from message_ix_models/data/material/LCA_commodity_mapping.xlsx rename to message_ix_models/data/material/power sector/LCA_commodity_mapping.xlsx diff --git a/message_ix_models/data/material/LCA_region_mapping.xlsx b/message_ix_models/data/material/power sector/LCA_region_mapping.xlsx similarity index 100% rename from message_ix_models/data/material/LCA_region_mapping.xlsx rename to message_ix_models/data/material/power sector/LCA_region_mapping.xlsx diff --git a/message_ix_models/data/material/MESSAGE_global_model_technologies.xlsx b/message_ix_models/data/material/power sector/MESSAGE_global_model_technologies.xlsx similarity index 100% rename from message_ix_models/data/material/MESSAGE_global_model_technologies.xlsx rename to message_ix_models/data/material/power sector/MESSAGE_global_model_technologies.xlsx diff --git a/message_ix_models/data/material/NTNU_LCA_coefficients.xlsx b/message_ix_models/data/material/power sector/NTNU_LCA_coefficients.xlsx similarity index 100% rename from message_ix_models/data/material/NTNU_LCA_coefficients.xlsx rename to message_ix_models/data/material/power sector/NTNU_LCA_coefficients.xlsx diff --git a/message_ix_models/data/material/CEMENT.BvR2010.xlsx b/message_ix_models/data/material/steel_cement/CEMENT.BvR2010.xlsx similarity index 100% rename from message_ix_models/data/material/CEMENT.BvR2010.xlsx rename to message_ix_models/data/material/steel_cement/CEMENT.BvR2010.xlsx diff --git a/message_ix_models/data/material/China_steel_cement_MESSAGE.xlsx b/message_ix_models/data/material/steel_cement/China_steel_cement_MESSAGE.xlsx similarity index 100% rename from message_ix_models/data/material/China_steel_cement_MESSAGE.xlsx rename to message_ix_models/data/material/steel_cement/China_steel_cement_MESSAGE.xlsx diff --git a/message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx b/message_ix_models/data/material/steel_cement/Global_steel_cement_MESSAGE.xlsx similarity index 100% rename from message_ix_models/data/material/Global_steel_cement_MESSAGE.xlsx rename to message_ix_models/data/material/steel_cement/Global_steel_cement_MESSAGE.xlsx diff --git a/message_ix_models/data/material/STEEL_database_2012.xlsx b/message_ix_models/data/material/steel_cement/STEEL_database_2012.xlsx similarity index 100% rename from message_ix_models/data/material/STEEL_database_2012.xlsx rename to message_ix_models/data/material/steel_cement/STEEL_database_2012.xlsx diff --git a/message_ix_models/model/material/data_aluminum.py b/message_ix_models/model/material/data_aluminum.py index 197106223b..c12bd655d7 100644 --- a/message_ix_models/model/material/data_aluminum.py +++ b/message_ix_models/model/material/data_aluminum.py @@ -45,14 +45,14 @@ def read_data_aluminum(scenario): sheet_n_relations = "relations_R11" # Read the file - data_alu = pd.read_excel(private_data_path("material", fname), sheet_name=sheet_n) + data_alu = pd.read_excel(private_data_path("material", "aluminum", fname), sheet_name=sheet_n) # Drop columns that don't contain useful information data_alu = data_alu.drop(["Source", "Description"], axis=1) - data_alu_rel = read_rel(scenario, "aluminum_techno_economic.xlsx") + data_alu_rel = read_rel(scenario, "aluminum", "aluminum_techno_economic.xlsx") - data_aluminum_ts = read_timeseries(scenario, "aluminum_techno_economic.xlsx") + data_aluminum_ts = read_timeseries(scenario, "aluminum", "aluminum_techno_economic.xlsx") # Unit conversion @@ -498,7 +498,7 @@ def gen_mock_demand_aluminum(scenario): # SSP2 R11 baseline GDP projection gdp_growth = pd.read_excel( - private_data_path("material", "iamc_db ENGAGE baseline GDP PPP.xlsx"), + private_data_path("material", "other", "iamc_db ENGAGE baseline GDP PPP.xlsx"), sheet_name=sheet_n, ) diff --git a/message_ix_models/model/material/data_cement.py b/message_ix_models/model/material/data_cement.py index 503534661c..10027a3fff 100644 --- a/message_ix_models/model/material/data_cement.py +++ b/message_ix_models/model/material/data_cement.py @@ -93,7 +93,7 @@ def gen_mock_demand_cement(scenario): # SSP2 R11 baseline GDP projection gdp_growth = pd.read_excel( - private_data_path("material", "iamc_db ENGAGE baseline GDP PPP.xlsx"), + private_data_path("material", "other", "iamc_db ENGAGE baseline GDP PPP.xlsx"), sheet_name=sheet_n, ) @@ -189,7 +189,7 @@ def gen_data_cement(scenario, dry_run=False): # TEMP: now add cement sector as well data_cement = read_sector_data(scenario, "cement") # Special treatment for time-dependent Parameters - data_cement_ts = read_timeseries(scenario, context.datafile) + data_cement_ts = read_timeseries(scenario, "steel_cement", context.datafile) tec_ts = set(data_cement_ts.technology) # set of tecs with var_cost # List of data frames, to be concatenated together at end @@ -456,7 +456,7 @@ def derive_cement_demand(scenario, dry_run=False): # GDP is only in MER in scenario. # To get PPP GDP, it is read externally from the R side df = r.derive_cement_demand( - pop, base_demand, str(context.get_local_path("material")) + pop, base_demand, str(private_data_path("material")) ) df.year = df.year.astype(int) diff --git a/message_ix_models/model/material/data_generic.py b/message_ix_models/model/material/data_generic.py index 62ce47cbe9..7046bb7d7a 100644 --- a/message_ix_models/model/material/data_generic.py +++ b/message_ix_models/model/material/data_generic.py @@ -27,18 +27,15 @@ def read_data_generic(scenario): # sets = context["material"]["generic"] # Read the file - data_generic = pd.read_excel( - context.get_local_path( - "material", "generic_furnace_boiler_techno_economic.xlsx" - ), - sheet_name="generic", - ) + data_generic = pd.read_excel(context.get_local_path( + "material", "other", "generic_furnace_boiler_techno_economic.xlsx" + ), sheet_name="generic",) # Clean the data # Drop columns that don't contain useful information data_generic = data_generic.drop(["Region", "Source", "Description"], axis=1) - data_generic_ts = read_timeseries(scenario, "generic_furnace_boiler_techno_economic.xlsx") + data_generic_ts = read_timeseries(scenario, "other", "generic_furnace_boiler_techno_economic.xlsx") # Unit conversion diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index 763453eec0..edfe6bc5c6 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -307,7 +307,7 @@ def gen_meth_bio_ccs(scenario): def gen_data_meth_chemicals(scenario, chemical): df = pd.read_excel( - context.get_local_path("material", "methanol", "MTO data collection.xlsx"), + context.get_local_path("material", "methanol", "collection files", "MTO data collection.xlsx"), sheet_name=chemical, usecols=[1, 2, 3, 4, 6, 7], ) @@ -364,6 +364,17 @@ def gen_data_meth_chemicals(scenario, chemical): "time": "year", "unit": "???", } + nodes_ex_chn = nodes.tolist() + nodes_ex_chn.remove("R12_CHN") + + bound_dict = { + "node_loc": nodes_ex_chn, + "technology": "MTO_petro", + "mode": "M1", + "time": "year", + "unit": "???", + } + if chemical == "MTO": par_dict["historical_activity"] = make_df( "historical_activity", value=4.5, year_act=2015, **hist_dict @@ -380,6 +391,12 @@ def gen_data_meth_chemicals(scenario, chemical): par_dict["bound_activity_up"] = make_df( "bound_activity_up", year_act=2020, value=12, **hist_dict ) + par_dict["bound_activity_lo"] = make_df( + "bound_activity_lo", year_act=2020, value=0, **bound_dict + ) + par_dict["bound_activity_up"] = make_df( + "bound_activity_up", year_act=2020, value=0, **bound_dict + ) df = par_dict["growth_activity_lo"] par_dict["growth_activity_lo"] = df[~((df["node_loc"] == "R12_CHN") & (df["year_act"] == 2020))] df = par_dict["growth_activity_up"] @@ -413,7 +430,7 @@ def add_methanol_fuel_additives(scenario): df_mtbe = pd.read_excel( context.get_local_path( - "material", "methanol", "Methanol production statistics (version 1).xlsx" + "material", "methanol", "collection files","Methanol production statistics (version 1).xlsx" ), # usecols=[1,2,3,4,6,7], skiprows=[i for i in range(66)], @@ -427,7 +444,7 @@ def add_methanol_fuel_additives(scenario): df_biodiesel = pd.read_excel( context.get_local_path( - "material", "methanol", "Methanol production statistics (version 1).xlsx" + "material", "methanol", "collection files","Methanol production statistics (version 1).xlsx" ), skiprows=[i for i in range(38)], usecols=[1, 2], diff --git a/message_ix_models/model/material/data_petro.py b/message_ix_models/model/material/data_petro.py index 2a6b821350..b61003efbe 100644 --- a/message_ix_models/model/material/data_petro.py +++ b/message_ix_models/model/material/data_petro.py @@ -32,7 +32,8 @@ def read_data_petrochemicals(scenario): sheet_n = "data_R11" # Read the file - data_petro = pd.read_excel(private_data_path("material", fname), sheet_name=sheet_n) + data_petro = pd.read_excel(private_data_path("material", "petrochemicals", fname), + sheet_name=sheet_n) # Clean the data data_petro = data_petro.drop(["Source", "Description"], axis=1) @@ -131,7 +132,8 @@ def gen_data_petro_chemicals(scenario, dry_run=False): # Techno-economic assumptions data_petro = read_data_petrochemicals(scenario) - data_petro_ts = read_timeseries(scenario, "petrochemicals_techno_economic.xlsx") + data_petro_ts = read_timeseries(scenario, "petrochemicals", + "petrochemicals_techno_economic.xlsx") # List of data frames, to be concatenated together at end results = defaultdict(list) @@ -454,11 +456,11 @@ def gen_data_petro_chemicals(scenario, dry_run=False): results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} # modify steam cracker hist data (naphtha -> gasoil) to make model feasible - df_cap = pd.read_csv(context.get_local_path( - "material", "steam_cracking_hist_new_cap.csv" + df_cap = pd.read_csv(private_data_path( + "material", "petrochemicals", "steam_cracking_hist_new_cap.csv" )) - df_act = pd.read_csv(context.get_local_path( - "material", "steam_cracking_hist_act.csv" + df_act = pd.read_csv(private_data_path( + "material", "petrochemicals", "steam_cracking_hist_act.csv" )) df_act.loc[df_act["mode"]=="naphtha", "mode"] = "vacuum_gasoil" df = results["historical_activity"] diff --git a/message_ix_models/model/material/data_power_sector.py b/message_ix_models/model/material/data_power_sector.py index b87d1ad39b..67efcf1005 100644 --- a/message_ix_models/model/material/data_power_sector.py +++ b/message_ix_models/model/material/data_power_sector.py @@ -28,17 +28,17 @@ def read_material_intensities(parameter, data_path, node, year, technology, #################################################################### # read LCA data from ADVANCE LCA tool - data_path_lca = data_path + '/NTNU_LCA_coefficients.xlsx' + data_path_lca = data_path + '/power sector/NTNU_LCA_coefficients.xlsx' data_lca = pd.read_excel(data_path_lca, sheet_name = "environmentalImpacts") # read technology, region and commodity mappings - data_path_tec_map = data_path + '/MESSAGE_global_model_technologies.xlsx' + data_path_tec_map = data_path + '/power sector/MESSAGE_global_model_technologies.xlsx' technology_mapping = pd.read_excel(data_path_tec_map, sheet_name = "technology") - data_path_reg_map = data_path + '/LCA_region_mapping.xlsx' + data_path_reg_map = data_path + '/power sector/LCA_region_mapping.xlsx' region_mapping = pd.read_excel(data_path_reg_map, sheet_name = "region") - data_path_com_map = data_path + '/LCA_commodity_mapping.xlsx' + data_path_com_map = data_path + '/power sector/LCA_commodity_mapping.xlsx' commodity_mapping = pd.read_excel(data_path_com_map, sheet_name = "commodity") #################################################################### diff --git a/message_ix_models/model/material/data_steel.py b/message_ix_models/model/material/data_steel.py index 764f76a1ec..110c3190f3 100644 --- a/message_ix_models/model/material/data_steel.py +++ b/message_ix_models/model/material/data_steel.py @@ -85,7 +85,7 @@ def gen_mock_demand_steel(scenario): # SSP2 R11 baseline GDP projection gdp_growth = pd.read_excel( - private_data_path("material", "iamc_db ENGAGE baseline GDP PPP.xlsx"), + private_data_path("material", "other", "iamc_db ENGAGE baseline GDP PPP.xlsx"), sheet_name=sheet_n, ) @@ -130,8 +130,8 @@ def gen_data_steel(scenario, dry_run=False): # TEMP: now add cement sector as well => Need to separate those since now I have get_data_steel and cement data_steel = read_sector_data(scenario, "steel") # Special treatment for time-dependent Parameters - data_steel_ts = read_timeseries(scenario, context.datafile) - data_steel_rel = read_rel(scenario, context.datafile) + data_steel_ts = read_timeseries(scenario, "steel_cement", context.datafile) + data_steel_rel = read_rel(scenario, "steel_cement", context.datafile) tec_ts = set(data_steel_ts.technology) # set of tecs with var_cost diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index 40b5a038ab..06157132b7 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -1,5 +1,3 @@ -from collections import defaultdict - import pandas as pd import os import message_ix @@ -37,7 +35,7 @@ def load_GDP_COVID(): f_name = "iamc_db ENGAGE baseline GDP PPP.xlsx" gdp_ssp2 = pd.read_excel( - context.get_local_path("data", "material", f_name), sheet_name="data_R12" + private_data_path("material", "other", f_name), sheet_name="data_R12" ) gdp_ssp2 = gdp_ssp2[gdp_ssp2["Scenario"] == "baseline"] regions = "R12_" + gdp_ssp2["Region"] @@ -138,7 +136,7 @@ def modify_demand_and_hist_activity(scen): region_name_CHN = "" df = pd.read_excel( - private_data_path("material", fname), sheet_name=sheet_n, usecols="A:F" + private_data_path("material", "other", fname), sheet_name=sheet_n, usecols="A:F" ) # Filter the necessary variables @@ -708,7 +706,6 @@ def add_emission_accounting(scen): scen.add_par("emission_factor", df_em) scen.commit("add methanol CO2_industry") - def add_elec_lowerbound_2020(scen): # To avoid zero i_spec prices only for R12_CHN, add the below section. @@ -725,7 +722,7 @@ def add_elec_lowerbound_2020(scen): # read processed final energy data from IEA extended energy balances # that is aggregated to MESSAGEix regions, fuels and (industry) sectors - final = pd.read_csv(private_data_path("material", "residual_industry_2019.csv")) + final = pd.read_csv(private_data_path("material", "other", "residual_industry_2019.csv")) # downselect needed fuels and sectors final_residual_electricity = final.query( @@ -789,7 +786,7 @@ def add_coal_lowerbound_2020(sc): context = read_config() final_resid = pd.read_csv( - private_data_path("material", "residual_industry_2019.csv") + private_data_path("material", "other","residual_industry_2019.csv") ) # read input parameters for relevant technology/commodity combinations for converting betwen final and useful energy @@ -934,7 +931,7 @@ def read_sector_data(scenario, sectname): # data_df = data_steel_china.append(data_cement_china, ignore_index=True) data_df = pd.read_excel( - private_data_path("material", context.datafile), + private_data_path("material", "steel_cement", context.datafile), sheet_name=sheet_n, ) @@ -1026,7 +1023,7 @@ def add_ccs_technologies(scen): # Read in time-dependent parameters # Now only used to add fuel cost for bare model -def read_timeseries(scenario, filename): +def read_timeseries(scenario, material, filename): import numpy as np @@ -1045,7 +1042,7 @@ def read_timeseries(scenario, filename): sheet_n = "timeseries_R11" # Read the file - df = pd.read_excel(private_data_path("material", filename), sheet_name=sheet_n) + df = pd.read_excel(private_data_path("material", material, filename), sheet_name=sheet_n) import numbers @@ -1063,7 +1060,7 @@ def read_timeseries(scenario, filename): return df -def read_rel(scenario, filename): +def read_rel(scenario, material, filename): import numpy as np @@ -1079,7 +1076,7 @@ def read_rel(scenario, filename): # Read the file data_rel = pd.read_excel( - private_data_path("material", filename), + private_data_path("material", material, filename), sheet_name=sheet_n, ) diff --git a/message_ix_models/model/material/material_demand/init_modularized.R b/message_ix_models/model/material/material_demand/init_modularized.R index 3d60aeaa70..537fe471a1 100644 --- a/message_ix_models/model/material/material_demand/init_modularized.R +++ b/message_ix_models/model/material/material_demand/init_modularized.R @@ -19,21 +19,21 @@ derive_steel_demand <- function(df_pop, df_demand, datapath) { # gdp.pcap # population - gdp.ppp = read_excel(paste0(datapath, file_gdp), sheet="data_R12") %>% filter(Scenario == "baseline") %>% + gdp.ppp = read_excel(paste0(datapath, "/other", file_gdp), sheet="data_R12") %>% filter(Scenario == "baseline") %>% select(region=Region, `2020`:`2100`) %>% pivot_longer(cols=`2020`:`2100`, names_to="year", values_to="gdp.ppp") %>% # billion US$2010/yr OR local currency filter(region != "World") %>% mutate(year = as.integer(year), region = paste0('R12_', region)) - df_raw_steel_consumption = read_excel(paste0(datapath, file_steel), + df_raw_steel_consumption = read_excel(paste0(datapath, "/steel_cement", file_steel), sheet="Consumption regions", n_max=27) %>% # kt select(-2) %>% pivot_longer(cols="1970":"2012", values_to='consumption', names_to='year') - df_population = read_excel(paste0(datapath, file_cement), + df_population = read_excel(paste0(datapath, "/steel_cement" ,file_cement), sheet="Timer_POP", skip=3, n_max=27) %>% # million pivot_longer(cols="1970":"2100", values_to='pop', names_to='year') - df_gdp = read_excel(paste0(datapath, file_cement), + df_gdp = read_excel(paste0(datapath, "/steel_cement", file_cement), sheet="Timer_GDPCAP", skip=3, n_max=27) %>% # million pivot_longer(cols="1970":"2100", values_to='gdp.pcap', names_to='year') @@ -76,19 +76,19 @@ derive_cement_demand <- function(df_pop, df_demand, datapath) { # gdp.pcap # population (in mil.) - gdp.ppp = read_excel(paste0(datapath, file_gdp), sheet="data_R12") %>% filter(Scenario == "baseline") %>% + gdp.ppp = read_excel(paste0(datapath, "/other", file_gdp), sheet="data_R12") %>% filter(Scenario == "baseline") %>% select(region=Region, `2020`:`2100`) %>% pivot_longer(cols=`2020`:`2100`, names_to="year", values_to="gdp.ppp") %>% # billion US$2010/yr OR local currency filter(region != "World") %>% mutate(year = as.integer(year), region = paste0('R12_', region)) - df_raw_cement_consumption = read_excel(paste0(datapath, file_cement), + df_raw_cement_consumption = read_excel(paste0(datapath, "/steel_cement", file_cement), sheet="Regions", skip=122, n_max=27) %>% # kt pivot_longer(cols="1970":"2010", values_to='consumption', names_to='year') - df_population = read_excel(paste0(datapath, file_cement), + df_population = read_excel(paste0(datapath, "/steel_cement", file_cement), sheet="Timer_POP", skip=3, n_max=27) %>% # million pivot_longer(cols="1970":"2100", values_to='pop', names_to='year') - df_gdp = read_excel(paste0(datapath, file_cement), + df_gdp = read_excel(paste0(datapath, "/steel_cement", file_cement), sheet="Timer_GDPCAP", skip=3, n_max=27) %>% # million pivot_longer(cols="1970":"2100", values_to='gdp.pcap', names_to='year') @@ -127,14 +127,14 @@ derive_cement_demand <- function(df_pop, df_demand, datapath) { derive_aluminum_demand <- function(df_pop, df_demand, datapath) { - gdp.ppp = read_excel(paste0(datapath, file_gdp), sheet="data_R12") %>% filter(Scenario == "baseline") %>% + gdp.ppp = read_excel(paste0(datapath, "/other", file_gdp), sheet="data_R12") %>% filter(Scenario == "baseline") %>% select(region=Region, `2020`:`2100`) %>% pivot_longer(cols=`2020`:`2100`, names_to="year", values_to="gdp.ppp") %>% # billion US$2010/yr OR local currency filter(region != "World") %>% mutate(year = as.integer(year), region = paste0('R12_', region)) # Aluminum xls input already has population and gdp - df_raw_aluminum_consumption = read_excel(paste0(datapath, file_al), + df_raw_aluminum_consumption = read_excel(paste0(datapath, "/aluminum", file_al), sheet="final_table", n_max = 378) # kt #### Organize data #### @@ -163,26 +163,26 @@ derive_aluminum_demand <- function(df_pop, df_demand, datapath) { } #derive_petro_demand <- function(df_pop, df_demand, datapath) { - + # gdp.ppp = read_excel(paste0(datapath, file_gdp), sheet="data_R12") %>% filter(Scenario == "baseline") %>% # select(region=Region, `2020`:`2100`) %>% # pivot_longer(cols=`2020`:`2100`, names_to="year", values_to="gdp.ppp") %>% # billion US$2010/yr OR local currency # filter(region != "World") %>% # mutate(year = as.integer(year), region = paste0('R12_', region)) -# +# # df_raw_petro_consumption = read_excel(paste0(datapath, file_petro), # sheet="final_table", n_max = 362) #kg/cap - + #### Organize data #### # df_petro_consumption = df_raw_petro_consumption %>% -# mutate(del.t= as.numeric(year) - 2010) %>% +# mutate(del.t= as.numeric(year) - 2010) %>% # drop_na() %>% # filter(cons.pcap > 0) - + # nlnit.p = nls(cons.pcap ~ a * exp(b/gdp.pcap) * (1-m)^del.t, data=df_petro_consumption, start=list(a=600, b=-10000, m=0)) #nlni.p = nls(cons.pcap ~ a * exp(b/gdp.pcap), data=df_petro_consumption, start=list(a=600, b=-10000)) # lni.p = lm(log(cons.pcap) ~ I(1/gdp.pcap), data=df_petro_consumption) - + # df_in = df_pop %>% left_join(df_demand %>% select(-year)) %>% # df_demand is only for 2020 # inner_join(gdp.ppp) %>% mutate(gdp.pcap = gdp.ppp*giga/pop.mil/mega) # demand = df_in %>% @@ -192,10 +192,10 @@ derive_aluminum_demand <- function(df_pop, df_demand, datapath) { # mutate(gap.base = first(demand.pcap.base - demand.pcap0)) %>% # mutate(demand.pcap = demand.pcap0 + gap.base * gompertz(9, 0.1, y=year)) %>% # Bas' equation # mutate(demand.tot = demand.pcap * pop.mil * mega / giga) # Mt - + # Add 2110 spaceholder # demand = demand %>% rbind(demand %>% filter(year==2100) %>% mutate(year = 2110)) - + # return(demand %>% select(node=region, year, value=demand.tot) %>% arrange(year, node)) # Mt #} From d2717acb807724897654a95a596eca8c141943d0 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 21 Jun 2023 09:21:58 +0200 Subject: [PATCH 628/774] Add trade balance for light_oil and methanol using balance_equality --- message_ix_models/data/material/set.yaml | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index a8db464d21..268a56060f 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -207,6 +207,12 @@ petro_chemicals: - final_material - demand + balance_equality: + add: + - ["lightoil", "import"] + - ["lightoil", "export"] + - ["fueloil", "import"] + - ["fueloil", "export"] mode: add: - atm_gasoil @@ -557,6 +563,14 @@ methanol: - CO2_PtX_trans_disp_split - meth_exp_tot + balance_equality: + add: + - ["methanol","export"] + - ["methanol","export_fs"] + - ["methanol","import"] + - ["methanol","import_fs"] + - ["methanol","final"] + buildings: level: add: From 9d5177677bfa9c135ca4e58d94af1ad2bda9781a Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 21 Jun 2023 09:35:02 +0200 Subject: [PATCH 629/774] Update diffusion constraints for petro and ammonia - ethanol_to_ethylene initial_activity_up increased to 0.1 (similar to mto) - nh3 technologies initial_activity_up decreased to 0.5 (similar to methanol tecs) - initial_activity_lo deleted for nh3 tecs (to be consistent with all other chemical sector technologies) --- .../data/material/ammonia/fert_techno_economic.xlsx | 4 ++-- .../petrochemicals/petrochemicals_techno_economic.xlsx | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/message_ix_models/data/material/ammonia/fert_techno_economic.xlsx b/message_ix_models/data/material/ammonia/fert_techno_economic.xlsx index 09d1e4ddc5..d762452476 100644 --- a/message_ix_models/data/material/ammonia/fert_techno_economic.xlsx +++ b/message_ix_models/data/material/ammonia/fert_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:a064be30dcd244f823805250bd5e095c5af5917420c2be35fec0c19e8ac24c14 -size 58668 +oid sha256:9b0b2b1f7f5f17bfa868168bd6d86f0382dceca995c9e113c80b6f9817167297 +size 55580 diff --git a/message_ix_models/data/material/petrochemicals/petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/petrochemicals/petrochemicals_techno_economic.xlsx index a66b701be2..e3afda7df9 100644 --- a/message_ix_models/data/material/petrochemicals/petrochemicals_techno_economic.xlsx +++ b/message_ix_models/data/material/petrochemicals/petrochemicals_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:120a611fcd3a6325233b3c2275e16bd03d974c0f9b5c8f7f88832091264c8d97 -size 668550 +oid sha256:bfdc475ff9adf45b90fa0a215e47e9b1609de3091a743667cd148a16a1902b55 +size 668994 From 0cef84c39ac3233fb49128ed144355743e45fa39 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 13 Jul 2023 10:58:55 +0200 Subject: [PATCH 630/774] Calibrate cement 2020 fuel use --- message_ix_models/model/material/__init__.py | 3 +- message_ix_models/model/material/data_util.py | 247 ++++++++++++++++++ 2 files changed, 249 insertions(+), 1 deletion(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 2bf85f0d78..fcedc39051 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -14,7 +14,7 @@ # from .data import add_data from .data_util import modify_demand_and_hist_activity, add_emission_accounting -from .data_util import add_coal_lowerbound_2020, add_macro_COVID +from .data_util import add_coal_lowerbound_2020, add_macro_COVID, add_cement_bounds_2020 from .data_util import add_elec_lowerbound_2020, add_ccs_technologies, read_config log = logging.getLogger(__name__) @@ -41,6 +41,7 @@ def build(scenario): modify_demand_and_hist_activity(scenario) add_emission_accounting(scenario) add_coal_lowerbound_2020(scenario) + add_cement_bounds_2020(scenario) # Market penetration adjustments # NOTE: changing demand affects the market penetration levels for the enduse technologies. diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index 06157132b7..163b999c13 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -911,6 +911,253 @@ def add_coal_lowerbound_2020(sc): "added lower bound for activity of residual industrial coal and cement coal furnace technologies and adjusted 2020 residual industrial electricity demand" ) +def add_cement_bounds_2020(sc): + + """Set lower and upper bounds for gas and oil as a calibration for 2020""" + + context = read_config() + final_resid = pd.read_csv( + private_data_path("material", "other","residual_industry_2019.csv") + ) + + input_cement_foil = sc.par( + "input", + filters={ + "technology": "furnace_foil_cement", + "year_vtg": "2020", + "year_act": "2020", + "mode": "high_temp", + }, + ) + + input_cement_loil = sc.par( + "input", + filters={ + "technology": "furnace_loil_cement", + "year_vtg": "2020", + "year_act": "2020", + "mode": "high_temp", + }, + ) + + input_cement_gas = sc.par( + "input", + filters={ + "technology": "furnace_gas_cement", + "year_vtg": "2020", + "year_act": "2020", + "mode": "high_temp", + }, + ) + + input_cement_biomass = sc.par( + "input", + filters={ + "technology": "furnace_biomass_cement", + "year_vtg": "2020", + "year_act": "2020", + "mode": "high_temp", + }, + ) + + input_cement_coal = sc.par( + "input", + filters={ + "technology": "furnace_coal_cement", + "year_vtg": "2020", + "year_act": "2020", + "mode": "high_temp", + }, + ) + + # downselect needed fuels and sectors + final_cement_foil = final_resid.query( + 'MESSAGE_fuel=="foil" & MESSAGE_sector=="cement"' + ) + + final_cement_loil = final_resid.query( + 'MESSAGE_fuel=="loil" & MESSAGE_sector=="cement"' + ) + + final_cement_gas = final_resid.query( + 'MESSAGE_fuel=="gas" & MESSAGE_sector=="cement"' + ) + + final_cement_biomass = final_resid.query( + 'MESSAGE_fuel=="biomass" & MESSAGE_sector=="cement"' + ) + + final_cement_coal = final_resid.query( + 'MESSAGE_fuel=="coal" & MESSAGE_sector=="cement"' + ) + + + # join final energy data from IEA energy balances and input coefficients from final-to-useful technologies from MESSAGEix + bound_cement_loil = pd.merge( + input_cement_loil, + final_cement_loil, + left_on="node_loc", + right_on="MESSAGE_region", + how="inner", + ) + + bound_cement_foil = pd.merge( + input_cement_foil, + final_cement_foil, + left_on="node_loc", + right_on="MESSAGE_region", + how="inner", + ) + + bound_cement_gas = pd.merge( + input_cement_gas, + final_cement_gas, + left_on="node_loc", + right_on="MESSAGE_region", + how="inner", + ) + + bound_cement_biomass = pd.merge( + input_cement_biomass, + final_cement_biomass, + left_on="node_loc", + right_on="MESSAGE_region", + how="inner", + ) + + bound_cement_coal = pd.merge( + input_cement_coal, + final_cement_coal, + left_on="node_loc", + right_on="MESSAGE_region", + how="inner", + ) + + # derive useful energy values by dividing final energy by input coefficient from final-to-useful technologies + bound_cement_loil["value"] = bound_cement_loil["Value"] / bound_cement_loil["value"] + bound_cement_foil["value"] = bound_cement_foil["Value"] / bound_cement_foil["value"] + bound_cement_gas["value"] = bound_cement_gas["Value"] / bound_cement_gas["value"] + bound_cement_biomass["value"] = bound_cement_biomass["Value"] / bound_cement_biomass["value"] + bound_cement_coal["value"] = bound_cement_coal["Value"] / bound_cement_coal["value"] + + + # downselect dataframe columns for MESSAGEix parameters + bound_cement_loil = bound_cement_loil.filter( + items=["node_loc", "technology", "year_act", "mode", "time", "value", "unit_x"] + ) + + bound_cement_foil = bound_cement_foil.filter( + items=["node_loc", "technology", "year_act", "mode", "time", "value", "unit_x"] + ) + + bound_cement_gas = bound_cement_gas.filter( + items=["node_loc", "technology", "year_act", "mode", "time", "value", "unit_x"] + ) + + bound_cement_biomass = bound_cement_biomass.filter( + items=["node_loc", "technology", "year_act", "mode", "time", "value", "unit_x"] + ) + + bound_cement_coal = bound_cement_coal.filter( + items=["node_loc", "technology", "year_act", "mode", "time", "value", "unit_x"] + ) + + + # rename columns if necessary + bound_cement_loil.columns = [ + "node_loc", + "technology", + "year_act", + "mode", + "time", + "value", + "unit", + ] + + bound_cement_foil.columns = [ + "node_loc", + "technology", + "year_act", + "mode", + "time", + "value", + "unit", + ] + + bound_cement_gas.columns = [ + "node_loc", + "technology", + "year_act", + "mode", + "time", + "value", + "unit", + ] + + bound_cement_biomass.columns = [ + "node_loc", + "technology", + "year_act", + "mode", + "time", + "value", + "unit", + ] + + bound_cement_coal.columns = [ + "node_loc", + "technology", + "year_act", + "mode", + "time", + "value", + "unit", + ] + + sc.check_out() + nodes = bound_cement_loil["node_loc"].values + years = bound_cement_loil["year_act"].values + + # add parameter dataframes to ixmp + sc.add_par("bound_activity_up", bound_cement_loil) + sc.add_par("bound_activity_up", bound_cement_foil) + sc.add_par("bound_activity_lo", bound_cement_gas) + sc.add_par("bound_activity_up", bound_cement_gas) + sc.add_par("bound_activity_up", bound_cement_biomass) + sc.add_par("bound_activity_up", bound_cement_coal) + + for n in nodes: + bound_cement_meth = pd.DataFrame({ + "node_loc": n, + "technology": "furnace_methanol_cement", + "year_act": years, + "mode":"high_temp", + "time":"year", + "value":0, + "unit": "???" + }) + + sc.add_par("bound_activity_lo", bound_cement_meth) + sc.add_par("bound_activity_up", bound_cement_meth) + + for n in nodes: + bound_cement_eth = pd.DataFrame({ + "node_loc": n, + "technology": "furnace_ethanol_cement", + "year_act": years, + "mode":"high_temp", + "time":"year", + "value":0, + "unit": "???" + }) + + sc.add_par("bound_activity_lo", bound_cement_eth) + sc.add_par("bound_activity_up", bound_cement_eth) + + # commit scenario to ixmp backend + sc.commit( + "added lower and upper bound for fuels for cement 2020." + ) def read_sector_data(scenario, sectname): From 22e87e1890e56ca1ecb89a435504d56ed2db8178 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 18 Jul 2023 13:30:54 +0200 Subject: [PATCH 631/774] Add balance_equality for cement end_of_life to enable the reporting of cement scrap (otherwise total_eol_cement has not all the available scrap as its act) --- message_ix_models/data/material/set.yaml | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 268a56060f..93691abaf3 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -372,6 +372,11 @@ cement: - total_EOL_cement - other_EOL_cement - scrap_recovery_cement + + balance_equality: + add: + - ["cement","end_of_life"] + remove: - cement_co2scr - cement_CO2 From 7309cf73982e85cfd01cac070e375a51dabae42c Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 25 Jul 2023 17:35:38 +0200 Subject: [PATCH 632/774] Use concise methanol input files --- .../data/material/methanol/h2_elec_fs.xlsx | 3 - .../data/material/methanol/h2_elec_fuel.xlsx | 3 - .../methanol/location factor collection.xlsx | 3 - .../meth_bio_techno_economic_new.xlsx | 3 - .../meth_bio_techno_economic_new_ccs.xlsx | 3 - .../methanol/meth_coal_additions.xlsx | 3 - .../methanol/meth_coal_additions_fs.xlsx | 3 - .../methanol/meth_h2_techno_economic_fs.xlsx | 3 - .../meth_h2_techno_economic_fuel.xlsx | 3 - .../methanol/meth_ng_techno_economic.xlsx | 3 - .../methanol/meth_ng_techno_economic_fs.xlsx | 3 - .../data/material/methanol/meth_t_d_fuel.xlsx | 3 - .../methanol/meth_t_d_material_pars.xlsx | 3 - .../methanol/meth_trade_techno_economic.xlsx | 3 - .../meth_trade_techno_economic_fs.xlsx | 3 - .../methanol/methanol_techno_economic.xlsx | 3 + .../methanol/results_material_SHAPE_comm.csv | 3 - .../results_material_SHAPE_residential.csv | 3 - .../methanol/results_material_SSP2_comm.csv | 3 - .../results_material_SSP2_residential.csv | 3 - message_ix_models/data/material/set.yaml | 8 +- message_ix_models/model/material/__init__.py | 4 +- .../model/material/data_methanol_new.py | 123 ++++++++++++++++++ 23 files changed, 132 insertions(+), 63 deletions(-) delete mode 100644 message_ix_models/data/material/methanol/h2_elec_fs.xlsx delete mode 100644 message_ix_models/data/material/methanol/h2_elec_fuel.xlsx delete mode 100644 message_ix_models/data/material/methanol/location factor collection.xlsx delete mode 100644 message_ix_models/data/material/methanol/meth_bio_techno_economic_new.xlsx delete mode 100644 message_ix_models/data/material/methanol/meth_bio_techno_economic_new_ccs.xlsx delete mode 100644 message_ix_models/data/material/methanol/meth_coal_additions.xlsx delete mode 100644 message_ix_models/data/material/methanol/meth_coal_additions_fs.xlsx delete mode 100644 message_ix_models/data/material/methanol/meth_h2_techno_economic_fs.xlsx delete mode 100644 message_ix_models/data/material/methanol/meth_h2_techno_economic_fuel.xlsx delete mode 100644 message_ix_models/data/material/methanol/meth_ng_techno_economic.xlsx delete mode 100644 message_ix_models/data/material/methanol/meth_ng_techno_economic_fs.xlsx delete mode 100644 message_ix_models/data/material/methanol/meth_t_d_fuel.xlsx delete mode 100644 message_ix_models/data/material/methanol/meth_t_d_material_pars.xlsx delete mode 100644 message_ix_models/data/material/methanol/meth_trade_techno_economic.xlsx delete mode 100644 message_ix_models/data/material/methanol/meth_trade_techno_economic_fs.xlsx create mode 100644 message_ix_models/data/material/methanol/methanol_techno_economic.xlsx delete mode 100644 message_ix_models/data/material/methanol/results_material_SHAPE_comm.csv delete mode 100644 message_ix_models/data/material/methanol/results_material_SHAPE_residential.csv delete mode 100644 message_ix_models/data/material/methanol/results_material_SSP2_comm.csv delete mode 100644 message_ix_models/data/material/methanol/results_material_SSP2_residential.csv create mode 100644 message_ix_models/model/material/data_methanol_new.py diff --git a/message_ix_models/data/material/methanol/h2_elec_fs.xlsx b/message_ix_models/data/material/methanol/h2_elec_fs.xlsx deleted file mode 100644 index 3fea1c447f..0000000000 --- a/message_ix_models/data/material/methanol/h2_elec_fs.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:15c8b80c4346f66e38565493374dee4ce68435c160795b81ead4fb5529edbf9a -size 462513 diff --git a/message_ix_models/data/material/methanol/h2_elec_fuel.xlsx b/message_ix_models/data/material/methanol/h2_elec_fuel.xlsx deleted file mode 100644 index e43c856fca..0000000000 --- a/message_ix_models/data/material/methanol/h2_elec_fuel.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:6296e379d3d35af74ac789642f7db69bb7f1c3a1eab5a69d39e4cce709e2d34e -size 462800 diff --git a/message_ix_models/data/material/methanol/location factor collection.xlsx b/message_ix_models/data/material/methanol/location factor collection.xlsx deleted file mode 100644 index fd1e4ff56f..0000000000 --- a/message_ix_models/data/material/methanol/location factor collection.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:4ff92231d590f733c825f9536f4b2bc090fa118d8321484e490797b4c5edd06b -size 47890 diff --git a/message_ix_models/data/material/methanol/meth_bio_techno_economic_new.xlsx b/message_ix_models/data/material/methanol/meth_bio_techno_economic_new.xlsx deleted file mode 100644 index f18acbee33..0000000000 --- a/message_ix_models/data/material/methanol/meth_bio_techno_economic_new.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:15d994a022f9de1d51a747c7bc05a3824e73f18f9682e2e2c4c2fbdc2b508220 -size 1103249 diff --git a/message_ix_models/data/material/methanol/meth_bio_techno_economic_new_ccs.xlsx b/message_ix_models/data/material/methanol/meth_bio_techno_economic_new_ccs.xlsx deleted file mode 100644 index 506b32e690..0000000000 --- a/message_ix_models/data/material/methanol/meth_bio_techno_economic_new_ccs.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:22e1e4c4aae4d106c0aca710ab64fcac02eba25d7e7625969ea0d5e6f0a5b13a -size 1085207 diff --git a/message_ix_models/data/material/methanol/meth_coal_additions.xlsx b/message_ix_models/data/material/methanol/meth_coal_additions.xlsx deleted file mode 100644 index 8861432937..0000000000 --- a/message_ix_models/data/material/methanol/meth_coal_additions.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:41f00449ceea43d46533e33fad5747f8e2693baf0abff0222fced475902ca5a5 -size 100037 diff --git a/message_ix_models/data/material/methanol/meth_coal_additions_fs.xlsx b/message_ix_models/data/material/methanol/meth_coal_additions_fs.xlsx deleted file mode 100644 index b32b9c0a8d..0000000000 --- a/message_ix_models/data/material/methanol/meth_coal_additions_fs.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:2d1b3f1949b9918a04651602299f6e015c13a9db1ed38d83cfcfa66cbb1c3a3d -size 81510 diff --git a/message_ix_models/data/material/methanol/meth_h2_techno_economic_fs.xlsx b/message_ix_models/data/material/methanol/meth_h2_techno_economic_fs.xlsx deleted file mode 100644 index a75fc7ab93..0000000000 --- a/message_ix_models/data/material/methanol/meth_h2_techno_economic_fs.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:e599481c293c6f0b560bcb2c5f0cc2022fe4bceaf71dce5808eae646407f6489 -size 188715 diff --git a/message_ix_models/data/material/methanol/meth_h2_techno_economic_fuel.xlsx b/message_ix_models/data/material/methanol/meth_h2_techno_economic_fuel.xlsx deleted file mode 100644 index 6ff9d1338b..0000000000 --- a/message_ix_models/data/material/methanol/meth_h2_techno_economic_fuel.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:d6cc27f87251bd333bf01b1efa66332e304d6fcf5e43efe9e89a177306255206 -size 245253 diff --git a/message_ix_models/data/material/methanol/meth_ng_techno_economic.xlsx b/message_ix_models/data/material/methanol/meth_ng_techno_economic.xlsx deleted file mode 100644 index efe084480a..0000000000 --- a/message_ix_models/data/material/methanol/meth_ng_techno_economic.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:75947901238ee89930612efeba0c6120ba08feaaf0aff9bb026f9ff427f14319 -size 102245 diff --git a/message_ix_models/data/material/methanol/meth_ng_techno_economic_fs.xlsx b/message_ix_models/data/material/methanol/meth_ng_techno_economic_fs.xlsx deleted file mode 100644 index da2c3fd2db..0000000000 --- a/message_ix_models/data/material/methanol/meth_ng_techno_economic_fs.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:94a32dc9efebb8c54be4df14f07721827e6ea38a4533165170e3060a97956980 -size 89037 diff --git a/message_ix_models/data/material/methanol/meth_t_d_fuel.xlsx b/message_ix_models/data/material/methanol/meth_t_d_fuel.xlsx deleted file mode 100644 index 71275cc9be..0000000000 --- a/message_ix_models/data/material/methanol/meth_t_d_fuel.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:53bfd60c089ccf0b374d10aa3c5830db658db8925deea06c7a0b0b68efbd7871 -size 42561 diff --git a/message_ix_models/data/material/methanol/meth_t_d_material_pars.xlsx b/message_ix_models/data/material/methanol/meth_t_d_material_pars.xlsx deleted file mode 100644 index de6c8362d8..0000000000 --- a/message_ix_models/data/material/methanol/meth_t_d_material_pars.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version 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sha256:cc6ac6404810dda477ab6669821c2c1509334952b9a197be2ecc504e019a2f3a -size 99973 diff --git a/message_ix_models/data/material/methanol/methanol_techno_economic.xlsx b/message_ix_models/data/material/methanol/methanol_techno_economic.xlsx new file mode 100644 index 0000000000..e75be39949 --- /dev/null +++ b/message_ix_models/data/material/methanol/methanol_techno_economic.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e337cf41b5100c1cfebaab3d222628af23efc71c02dd46127c3834da2e0fb0f7 +size 619406 diff --git a/message_ix_models/data/material/methanol/results_material_SHAPE_comm.csv b/message_ix_models/data/material/methanol/results_material_SHAPE_comm.csv deleted file mode 100644 index d2a9423483..0000000000 --- a/message_ix_models/data/material/methanol/results_material_SHAPE_comm.csv +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:957d0b80959d4564524517f5dd003e2f8b3ebd8e182217659c11d13481be5b3d -size 416073 diff --git a/message_ix_models/data/material/methanol/results_material_SHAPE_residential.csv b/message_ix_models/data/material/methanol/results_material_SHAPE_residential.csv deleted file mode 100644 index f291ff222b..0000000000 --- a/message_ix_models/data/material/methanol/results_material_SHAPE_residential.csv +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:a59e1c215692bf575bac373f368326f9d89891d2860e6e0ba3ba320aef076e29 -size 429432 diff --git a/message_ix_models/data/material/methanol/results_material_SSP2_comm.csv b/message_ix_models/data/material/methanol/results_material_SSP2_comm.csv deleted file mode 100644 index 27ca014012..0000000000 --- a/message_ix_models/data/material/methanol/results_material_SSP2_comm.csv +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:a7de4b6d78bc2df7f028d9edf81e54e2ab5dccf483dd59e223a56b852c3ea57c -size 139700 diff --git a/message_ix_models/data/material/methanol/results_material_SSP2_residential.csv b/message_ix_models/data/material/methanol/results_material_SSP2_residential.csv deleted file mode 100644 index 706337d76a..0000000000 --- a/message_ix_models/data/material/methanol/results_material_SSP2_residential.csv +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:2b6dce2f9fd2da6817ec69a002b9b69610e42aa6089bbf4d9d9c1f894366e120 -size 287785 diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 93691abaf3..903c3356fe 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -373,10 +373,6 @@ cement: - other_EOL_cement - scrap_recovery_cement - balance_equality: - add: - - ["cement","end_of_life"] - remove: - cement_co2scr - cement_CO2 @@ -386,6 +382,10 @@ cement: - cement_pro - cement_scrub_lim + balance_equality: + add: + - ["cement","end_of_life"] + addon: add: - clinker_dry_ccs_cement diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index fcedc39051..65125b0693 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -424,13 +424,13 @@ def run_old_reporting(context): from .data_petro import gen_data_petro_chemicals from .data_buildings import gen_data_buildings from .data_power_sector import gen_data_power_sector -from .data_methanol import gen_data_methanol +from .data_methanol_new import gen_data_methanol_new from .data_ammonia_new import gen_all_NH3_fert DATA_FUNCTIONS_1 = [ #gen_data_buildings, - gen_data_methanol, + gen_data_methanol_new, gen_all_NH3_fert, #gen_data_ammonia, ## deprecated module! gen_data_generic, diff --git a/message_ix_models/model/material/data_methanol_new.py b/message_ix_models/model/material/data_methanol_new.py new file mode 100644 index 0000000000..62040fb3eb --- /dev/null +++ b/message_ix_models/model/material/data_methanol_new.py @@ -0,0 +1,123 @@ +import message_ix_models.util +import pandas as pd + +from message_ix import make_df +from message_ix_models.util import broadcast, same_node +from message_data.model.material.util import read_config +from ast import literal_eval + +context = read_config() + + +def gen_data_methanol_new(scenario): + df_pars = pd.read_excel( + context.get_local_path( + "material", "methanol", "methanol_sensitivity_pars.xlsx" + ), + sheet_name="Sheet1", + dtype=object, + ) + pars = df_pars.set_index("par").to_dict()["value"] + if pars["mtbe_scenario"] == "phase-out": + pars_dict = pd.read_excel( + message_ix_models.util.private_data_path( + "material", "methanol", "methanol_techno_economic.xlsx" + ), + sheet_name=None, + dtype=object, + ) + else: + pars_dict = pd.read_excel( + message_ix_models.util.private_data_path( + "material", "methanol", "methanol_techno_economic_high_demand.xlsx" + ), + sheet_name=None, + dtype=object, + ) + + for i in pars_dict.keys(): + pars_dict[i] = broadcast_reduced_df(pars_dict[i], i) + + return pars_dict + + +def broadcast_reduced_df(df, par_name): + df_final = df + df_final_full = pd.DataFrame() + + for i in df_final.index: + remove = ["'", "[", "]", " "] + node_cols = [i for i in df_final.columns if "node" in i] + node_cols_codes = {} + for col in node_cols: + node_cols_codes[col] = pd.Series(''.join(x for x in df_final.loc[i][col] if not x in remove).split(",")) + + df_bc_node = make_df(par_name, **df_final.loc[i]) + # brodcast in year dimensions + yr_cols_codes = {} + yr_col_inp = [i for i in df_final.columns if "year" in i] + yr_col_out = [i for i in df_bc_node.columns if "year" in i] + df_bc_node[yr_col_inp] = df_final.loc[i][yr_col_inp] + + for colname in node_cols: + df_bc_node[colname] = None + # broadcast in node dimensions + if len(node_cols) == 1: + if "node_loc" in node_cols: + df_bc_node = df_bc_node.pipe(broadcast, node_loc=node_cols_codes["node_loc"]) + if "node_vtg" in node_cols: + df_bc_node = df_bc_node.pipe(broadcast, node_vtg=node_cols_codes["node_vtg"]) + if "node_rel" in node_cols: + df_bc_node = df_bc_node.pipe(broadcast, node_rel=node_cols_codes["node_rel"]) + if "node" in node_cols: + df_bc_node = df_bc_node.pipe(broadcast, node=node_cols_codes["node"]) + if "node_share" in node_cols: + df_bc_node = df_bc_node.pipe(broadcast, node_share=node_cols_codes["node_share"]) + else: + df_bc_node = df_bc_node.pipe(broadcast, node_loc=node_cols_codes["node_loc"]) + if len(df_final.loc[i][node_cols].T.unique()) == 1: + # df_bc_node["node_rel"] = df_bc_node["node_loc"] + df_bc_node = df_bc_node.pipe( + same_node) # not working for node_rel in installed message_ix_models version + else: + if "node_rel" in list(df_bc_node.columns): + df_bc_node = df_bc_node.pipe(broadcast, node_rel=node_cols_codes["node_rel"]) + if "node_origin" in list(df_bc_node.columns): + df_bc_node = df_bc_node.pipe(broadcast, node_origin=node_cols_codes["node_origin"]) + if "node_dest" in list(df_bc_node.columns): + df_bc_node = df_bc_node.pipe(broadcast, node_dest=node_cols_codes["node_dest"]) + + for col in yr_col_inp: + yr_cols_codes[col] = literal_eval(df_bc_node[col].values[0]) + if len(yr_col_out) == 1: + yr_list = [i[0] for i in yr_cols_codes[col]] + # print(yr_list) + if "year_act" in yr_col_out: + df_bc_node = df_bc_node.pipe(broadcast, year_act=yr_list) + if "year_vtg" in yr_col_out: + df_bc_node = df_bc_node.pipe(broadcast, year_vtg=yr_list) + if "year_rel" in yr_col_out: + df_bc_node = df_bc_node.pipe(broadcast, year_rel=yr_list) + if "year" in yr_col_out: + df_bc_node = df_bc_node.pipe(broadcast, year=yr_list) + df_bc_node[yr_col_out] = df_bc_node[yr_col_out].astype(int) + else: + if "year_vtg" in yr_col_out: + y_v = [str(i) for i in yr_cols_codes[col]] + df_bc_node = df_bc_node.pipe(broadcast, year_vtg=y_v) + df_bc_node["year_act"] = [literal_eval(i)[1] for i in df_bc_node["year_vtg"]] + df_bc_node["year_vtg"] = [literal_eval(i)[0] for i in df_bc_node["year_vtg"]] + if "year_rel" in yr_col_out: + if "year_act" in yr_col_out: + df_bc_node = df_bc_node.pipe(broadcast, year_act=[i[0] for i in yr_cols_codes[col]]) + df_bc_node["year_rel"] = df_bc_node["year_act"] + # return df_bc_node + # df_bc_node["year_rel"] = df_bc_node["year_act"] + df_bc_node[yr_col_out] = df_bc_node[yr_col_out].astype(int) + + df_final_full = pd.concat([df_final_full, df_bc_node]) + df_final_full = df_final_full.drop_duplicates().reset_index(drop=True) + if par_name == "relation_activity": + df_final_full = df_final_full.drop(df_final_full[(df_final_full.node_rel.values != "R12_GLB") & + (df_final_full.node_rel.values != df_final_full.node_loc.values)].index) + return make_df(par_name, **df_final_full) From d2ac9abe3bb5be0def00f878435bf70879fd3d75 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Fri, 28 Jul 2023 13:24:02 +0200 Subject: [PATCH 633/774] Move cost convergence input data to separate file --- .../data/material/ammonia/cost_conv_nh3.xlsx | 3 +++ message_ix_models/model/material/data_ammonia_new.py | 9 ++++++--- 2 files changed, 9 insertions(+), 3 deletions(-) create mode 100644 message_ix_models/data/material/ammonia/cost_conv_nh3.xlsx diff --git a/message_ix_models/data/material/ammonia/cost_conv_nh3.xlsx b/message_ix_models/data/material/ammonia/cost_conv_nh3.xlsx new file mode 100644 index 0000000000..f2c0f009d2 --- /dev/null +++ b/message_ix_models/data/material/ammonia/cost_conv_nh3.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7de516008c80cdf55bb3b7403cbe72e2cc85f6c751d971498619ddb69e7ee450 +size 8879 diff --git a/message_ix_models/model/material/data_ammonia_new.py b/message_ix_models/model/material/data_ammonia_new.py index 031050651c..ade24a556d 100644 --- a/message_ix_models/model/material/data_ammonia_new.py +++ b/message_ix_models/model/material/data_ammonia_new.py @@ -3,7 +3,7 @@ from message_ix_models import ScenarioInfo from message_ix import make_df -from message_ix_models.util import broadcast, same_node +from message_ix_models.util import broadcast, same_node, private_data_path from .util import read_config CONVERSION_FACTOR_NH3_N = 17 / 14 @@ -125,8 +125,11 @@ def __missing__(self,key): "coal_NH3_ccs", "fueloil_NH3_ccs"] cost_conv = pd.read_excel( - context.get_local_path("material","methanol","location factor collection.xlsx"), - sheet_name="cost_convergence", index_col=0) + private_data_path( + "material", + "ammonia", + "cost_conv_nh3.xlsx"), + sheet_name="Sheet1", index_col=0) for p in pars: conv_cost_df = pd.DataFrame() df = par_dict[p] From db2397f4575ae22e5b76cb54c7e049077b57a116 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Mon, 31 Jul 2023 17:15:39 +0200 Subject: [PATCH 634/774] Remove and add methanol technologies --- message_ix_models/data/material/set.yaml | 28 +++++++++++++++++++----- 1 file changed, 23 insertions(+), 5 deletions(-) diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 903c3356fe..e4968f68c8 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -540,12 +540,30 @@ methanol: - MTO_petro - CH2O_synth - CH2O_to_resin + - meth_coal + - meth_coal_ccs + - meth_ng + - meth_ng_ccs + - meth_t_d + - meth_bal + - meth_trd + - meth_exp + - meth_imp remove: - - sp_meth_I - - meth_rc - - meth_ic_trp - - meth_fc_trp - - meth_i + - sp_meth_I + - meth_rc + - meth_ic_trp + - meth_fc_trp + - meth_i + - meth_coal + - meth_coal_ccs + - meth_ng + - meth_ng_ccs + - meth_t_d + - meth_bal + - meth_trd + - meth_exp + - meth_imp addon: add: From 4386aee31ee7d7dc1d45e8ff8fd32a973597b337 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Mon, 31 Jul 2023 17:49:58 +0200 Subject: [PATCH 635/774] Remove meth_trade_balance and loil_trade_balance --- .../data/material/methanol/methanol_techno_economic.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/methanol/methanol_techno_economic.xlsx b/message_ix_models/data/material/methanol/methanol_techno_economic.xlsx index e75be39949..54bb4a9871 100644 --- a/message_ix_models/data/material/methanol/methanol_techno_economic.xlsx +++ b/message_ix_models/data/material/methanol/methanol_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:e337cf41b5100c1cfebaab3d222628af23efc71c02dd46127c3834da2e0fb0f7 -size 619406 +oid sha256:80d5afca0619923f9dabed695969ee27e8cfe78589604779928d0d4020a01b78 +size 619049 From 4607a4f10519039813472064b9d0a45042c433d2 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 1 Aug 2023 11:24:12 +0200 Subject: [PATCH 636/774] Fix syntax issue --- message_ix_models/data/material/set.yaml | 28 ++++++++++++------------ 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index e4968f68c8..8be913ecc6 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -550,20 +550,20 @@ methanol: - meth_exp - meth_imp remove: - - sp_meth_I - - meth_rc - - meth_ic_trp - - meth_fc_trp - - meth_i - - meth_coal - - meth_coal_ccs - - meth_ng - - meth_ng_ccs - - meth_t_d - - meth_bal - - meth_trd - - meth_exp - - meth_imp + - sp_meth_I + - meth_rc + - meth_ic_trp + - meth_fc_trp + - meth_i + - meth_coal + - meth_coal_ccs + - meth_ng + - meth_ng_ccs + - meth_t_d + - meth_bal + - meth_trd + - meth_exp + - meth_imp addon: add: From b61d56a48748a4d1446857e375200efa3e531adb Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 1 Aug 2023 13:35:21 +0200 Subject: [PATCH 637/774] Update data_util.py --- message_ix_models/model/material/data_util.py | 26 +++++++++---------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index 163b999c13..2db0f6512f 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -692,19 +692,19 @@ def add_emission_accounting(scen): scen.commit("CF4 relations corrected.") # copy CO2_cc values to CO2_industry for conventional methanol tecs - scen.check_out() - meth_arr = ["meth_ng", "meth_coal", "meth_coal_ccs", "meth_ng_ccs"] - df = scen.par("relation_activity", filters={"relation": "CO2_cc", "technology": meth_arr}) - df = df.rename({"year_rel": "year_vtg"}, axis=1) - values = dict(zip(df["technology"], df["value"])) - - df_em = scen.par("emission_factor", filters={"emission": "CO2_transformation", "technology": meth_arr}) - for i in meth_arr: - df_em.loc[df_em["technology"] == i, "value"] = values[i] - df_em["emission"] = "CO2_industry" - - scen.add_par("emission_factor", df_em) - scen.commit("add methanol CO2_industry") + # scen.check_out() + # meth_arr = ["meth_ng", "meth_coal", "meth_coal_ccs", "meth_ng_ccs"] + # df = scen.par("relation_activity", filters={"relation": "CO2_cc", "technology": meth_arr}) + # df = df.rename({"year_rel": "year_vtg"}, axis=1) + # values = dict(zip(df["technology"], df["value"])) + # + # df_em = scen.par("emission_factor", filters={"emission": "CO2_transformation", "technology": meth_arr}) + # for i in meth_arr: + # df_em.loc[df_em["technology"] == i, "value"] = values[i] + # df_em["emission"] = "CO2_industry" + # + # scen.add_par("emission_factor", df_em) + # scen.commit("add methanol CO2_industry") def add_elec_lowerbound_2020(scen): From 2bb89e3192efd0c819ba268cd8ee6192785321cc Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Mon, 14 Aug 2023 16:13:19 +0200 Subject: [PATCH 638/774] Relocate reporting files --- ...orical Power Sector Stock Reporting-.ipynb | 301 ++ .../model/material/report/__init__.py | 3047 ------------- .../model/material/report/reporting.py | 3908 +++++++++++++++++ .../model/material/report/tables.py | 2561 ----------- 4 files changed, 4209 insertions(+), 5608 deletions(-) create mode 100644 message_ix_models/model/material/report/Historical Power Sector Stock Reporting-.ipynb delete mode 100644 message_ix_models/model/material/report/__init__.py create mode 100644 message_ix_models/model/material/report/reporting.py delete mode 100644 message_ix_models/model/material/report/tables.py diff --git a/message_ix_models/model/material/report/Historical Power Sector Stock Reporting-.ipynb b/message_ix_models/model/material/report/Historical Power Sector Stock Reporting-.ipynb new file mode 100644 index 0000000000..5f8c3a7403 --- /dev/null +++ b/message_ix_models/model/material/report/Historical Power Sector Stock Reporting-.ipynb @@ -0,0 +1,301 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1b58b637", + "metadata": {}, + "source": [ + "This notebook only includes stock reporting of the histroical years for power sector by using historical_new_capacity. The model periods can be integrated as well by using CAP variable." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "4faf48c4", + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "if (typeof IPython !== 'undefined') { IPython.OutputArea.prototype._should_scroll = function(lines){ return false; }}" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Import the necessary packages \n", + "\n", + "import ixmp\n", + "import message_ix\n", + "import pandas as pd\n", + "import numpy as np\n", + "from message_data.tools.utilities.get_optimization_years import main as get_optimization_years" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "500df0af", + "metadata": {}, + "outputs": [], + "source": [ + "# Load the platform\n", + "\n", + "mp = ixmp.Platform()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "6b81bf6c", + "metadata": {}, + "outputs": [], + "source": [ + "# Enter the relevant model and scenario name \n", + "\n", + "base_scen = 'NoPolicy_2206_macro'\n", + "base_model = 'MESSAGEix-Materials'\n", + "scen = message_ix.Scenario(mp, base_model, base_scen, cache=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "67d06ae3", + "metadata": {}, + "outputs": [], + "source": [ + "# Retreive output material intensities and relevant technology names\n", + "\n", + "output_cap_ret = scen.par('output_cap_ret')\n", + "technologies = output_cap_ret['technology'].unique()\n", + "technologies = pd.Series(technologies)\n", + "technologies = technologies.to_list()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "64e4047f", + "metadata": {}, + "outputs": [], + "source": [ + "# Retreive duration_period, historical_new_capacity, technical_lifetime and create a historical_total_capacity dataframe\n", + "\n", + "duration_period = scen.par('duration_period')\n", + "historical_new_capacity = scen.par('historical_new_capacity', filters = {'technology': technologies})\n", + "technical_lifetime = scen.par('technical_lifetime', filters = {'technology': technologies})\n", + "historical_total_capacity = pd.DataFrame(columns = [\"node_loc\",\"technology\",\"year_vtg\",\"value\",\"unit\",\"year_act\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "bb0deae6", + "metadata": {}, + "outputs": [], + "source": [ + "# Create the historical_total_capacity dataframe based on the lifetime. \n", + "# historical_total_capacity = historial_new_capacity * duration_period\n", + "\n", + "for y in historical_new_capacity['year_vtg'].unique():\n", + " for n in historical_new_capacity['node_loc'].unique():\n", + " for t in historical_new_capacity['technology'].unique():\n", + " lifetime = technical_lifetime.loc[(technical_lifetime['year_vtg']==y) & (technical_lifetime['node_loc']==n) & \\\n", + " (technical_lifetime['technology']==t), 'value'].values\n", + " capacity = historical_new_capacity.loc[(historical_new_capacity['year_vtg']==y) & (historical_new_capacity['node_loc']==n) & \\\n", + " (historical_new_capacity['technology']==t), 'value'].values\n", + " \n", + " if ((lifetime.size != 0) & (capacity.size != 0)):\n", + " lifetime = lifetime[0]\n", + " lifetime = lifetime.astype(np.int32)\n", + " \n", + " capacity = capacity[0]\n", + " period = duration_period.loc[(duration_period['year']==y), 'value'].values[0]\n", + " period = period.astype(np.int32)\n", + " val = capacity * period\n", + "\n", + " until = y + lifetime\n", + " df_temp = pd.DataFrame({\n", + " \"node_loc\": n,\n", + " \"technology\": t,\n", + " \"year_vtg\": y,\n", + " \"value\": val,\n", + " \"unit\": \"GW\",\n", + " \"year_act\": list(range(y,until, 5))\n", + "\n", + " })\n", + "\n", + " historical_total_capacity = pd.concat([df_temp, historical_total_capacity], ignore_index=True)\n", + " else:\n", + " continue " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "545f32f1", + "metadata": {}, + "outputs": [], + "source": [ + "# Filter the dataframe for historical years. For the model years CAP variable should be used. \n", + "\n", + "first_year = 2020\n", + "historical_total_capacity = historical_total_capacity[historical_total_capacity['year_act'] < first_year] " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "a57a133a", + "metadata": {}, + "outputs": [], + "source": [ + "# Modify the dataframes and merge to calculate the stocks \n", + "\n", + "historical_total_capacity.rename(columns={'value':'capacity'},inplace = True)\n", + "historical_total_capacity = historical_total_capacity.drop(['unit'], axis = 1)\n", + "\n", + "output_cap_ret = output_cap_ret.drop(['unit'],axis = 1)\n", + "output_cap_ret.rename(columns={'value':'material_intensity'},inplace = True)\n", + "\n", + "merged_df = pd.merge(historical_total_capacity, output_cap_ret, how = 'inner')\n", + "\n", + "# This way we can consider different material intensities for differnet vintage years. E.g. for year_vtg 2015 \n", + "# material intensities change\n", + "\n", + "merged_df['material_stock'] = merged_df['material_intensity'] * merged_df['capacity']" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "1fdffe3d", + "metadata": {}, + "outputs": [], + "source": [ + "name = base_model + '_' + base_scen + '_detailed_output1.xlsx'\n", + "merged_df.to_excel(name)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "e98b4051", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\unlu\\AppData\\Local\\Temp\\ipykernel_8564\\3669579720.py:2: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n", + " merged_df_sum = merged_df.groupby(['year_act','node_loc','technology','node_dest','commodity','level'])['capacity','material_stock'].sum()\n" + ] + } + ], + "source": [ + "merged_df.drop(['time_dest','material_intensity'], axis = 1, inplace = True)\n", + "merged_df_sum = merged_df.groupby(['year_act','node_loc','technology','node_dest','commodity','level'])['capacity','material_stock'].sum()\n", + "merged_df_sum.reset_index(inplace = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "71300784", + "metadata": {}, + "outputs": [], + "source": [ + "name = base_model + '_' + base_scen + '_detailed_output2.xlsx'\n", + "merged_df_sum.to_excel(name)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "889b9a0e", + "metadata": {}, + "outputs": [], + "source": [ + "# Sum over the differnet power plants \n", + "\n", + "merged_df_sum.drop(['node_dest','level','capacity'],axis = 1, inplace = True)\n", + "\n", + "merged_df_sum_material = merged_df_sum.groupby(['year_act','node_loc','commodity'])['material_stock'].sum()\n", + "merged_df_sum_material = merged_df_sum_material.to_frame()\n", + "merged_df_sum_material.reset_index(inplace = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "4c03fa24", + "metadata": {}, + "outputs": [], + "source": [ + "name = base_model + '_' + base_scen + '_detailed_output3.xlsx'\n", + "merged_df_sum_material.to_excel(name)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "1973a911", + "metadata": {}, + "outputs": [], + "source": [ + "# Prepare data to be dumped out as IAMC excel format\n", + "\n", + "final_output = pd.pivot_table(merged_df_sum, values = 'material_stock', columns = ['year_act'], index = ['node_loc','commodity'])\n", + "final_output.reset_index(inplace = True)\n", + "final_output.rename(columns = {'node_loc':'Region'}, inplace = True)\n", + "final_output = final_output.replace(['aluminum','cement','steel'], ['Non-Ferrous Metlals|Aluminum', 'Non-Metallic Minerals|Cement','Steel'])\n", + "final_output = final_output.assign(Variable = lambda x: 'Material Stock|Power Sector|' + x['commodity'])\n", + "final_output.drop(['commodity'],axis = 1, inplace = True)\n", + "final_output['Model'] = base_model\n", + "final_output['Scenario'] = base_scen\n", + "final_output['Unit'] = 'Mt'\n", + "year_cols = final_output.select_dtypes([np.number]).columns.to_list()\n", + "initial_columns = ['Model','Scenario','Region','Variable','Unit']\n", + "reorder = initial_columns + year_cols\n", + "final_output = final_output[reorder]\n", + "output_name = base_model + '_' + base_scen + '_power_sector_material.xlsx'\n", + "final_output.to_excel(output_name, index = False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dc9c75d1", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/message_ix_models/model/material/report/__init__.py b/message_ix_models/model/material/report/__init__.py deleted file mode 100644 index 617479964f..0000000000 --- a/message_ix_models/model/material/report/__init__.py +++ /dev/null @@ -1,3047 +0,0 @@ -"""Reporting for MESSAGEix-Materials. - -Created on Mon Mar 8 12:58:21 2021 -@author: unlu - -This code produces the following outputs: - -- message_ix_reporting.xlsx: message_ix level reporting -- check.xlsx: can be used for checking the filtered variables -- New_Reporting_Model_Scenario.xlsx: Reporting including the material variables -- Merged_Model_Scenario.xlsx: Includes all IAMC variables -- Material_global_graphs.pdf -""" -# NB(PNK) as of 2022-09-21, the following appears to be outdated; this code runs with -# pyam 1.5.0 within the NAVIGATE workflow. Check and maybe remove. -# NOTE: Works with pyam-iamc version 0.9.0 -# Problems with the most recent versions: -# 0.11.0 --> Filtering with asterisk * does not work, dataframes are empty. -# Problems with emission and region filtering. -# https://github.com/IAMconsortium/pyam/issues/517 -# 0.10.0 --> Plotting gives the follwoing error: -# ValueError: Can not plot data that does not extend for the entire year range. -# There are various tech.s that only have data starting from 2025 or 2030. -# foil_imp AFR, frunace_h2_aluminum, h2_i... - -# PACKAGES -import logging -import re -from itertools import product -from typing import List - -import matplotlib -import message_ix -import numpy as np -import pandas as pd -import pyam -import xlsxwriter -from matplotlib import pyplot as plt -from matplotlib.backends.backend_pdf import PdfPages -from message_ix_models import Context - -log = logging.getLogger(__name__) - -matplotlib.use("Agg") - - -def print_full(x): - pd.set_option("display.max_rows", len(x)) - print(x) - pd.reset_option("display.max_rows") - - -#: Replacements applied during processing of specific quantities. -NAME_MAP = { - "aluminum": "Non-Ferrous Metals|Aluminium", - "steel": "Steel", - "cement": "Non-Metallic Minerals|Cement", - "petro": "Chemicals|High Value Chemicals", - "ammonia": "Chemicals|Ammonia", - "BCA": "BC", - "OCA": "OC", - "CO2_industry": "CO2", -} - -#: Replacements applied to the final results. -NAME_MAP1 = { - "CO2_industry": "CO2", - "R11_AFR|R11_AFR": "R11_AFR", - "R11_CPA|R11_CPA": "R11_CPA", - "R11_MEA|R11_MEA": "R11_MEA", - "R11_FSU|R11_FSU": "R11_FSU", - "R11_PAS|R11_PAS": "R11_PAS", - "R11_SAS|R11_SAS": "R11_SAS", - "R11_LAM|R11_LAM": "R11_LAM", - "R11_NAM|R11_NAM": "R11_NAM", - "R11_PAO|R11_PAO": "R11_PAO", - "R11_EEU|R11_EEU": "R11_EEU", - "R11_WEU|R11_WEU": "R11_WEU", - "R11_AFR": "R11_AFR", - "R11_CPA": "R11_CPA", - "R11_MEA": "R11_MEA", - "R11_FSU": "R11_FSU", - "R11_PAS": "R11_PAS", - "R11_SAS": "R11_SAS", - "R11_LAM": "R11_LAM", - "R11_NAM": "R11_NAM", - "R11_PAO": "R11_PAO", - "R11_EEU": "R11_EEU", - "R11_WEU": "R11_WEU", - "R12_AFR|R12_AFR": "R12_AFR", - "R12_RCPA|R12_RCPA": "R12_RCPA", - "R12_MEA|R12_MEA": "R12_MEA", - "R12_FSU|R12_FSU": "R12_FSU", - "R12_PAS|R12_PAS": "R12_PAS", - "R12_SAS|R12_SAS": "R12_SAS", - "R12_LAM|R12_LAM": "R12_LAM", - "R12_NAM|R12_NAM": "R12_NAM", - "R12_PAO|R12_PAO": "R12_PAO", - "R12_EEU|R12_EEU": "R12_EEU", - "R12_WEU|R12_WEU": "R12_WEU", - "R12_CHN|R12_CHN": "R12_CHN", - "R12_AFR": "R12_AFR", - "R12_RCPA": "R12_RCPA", - "R12_MEA": "R12_MEA", - "R12_FSU": "R12_FSU", - "R12_PAS": "R12_PAS", - "R12_SAS": "R12_SAS", - "R12_LAM": "R12_LAM", - "R12_NAM": "R12_NAM", - "R12_PAO": "R12_PAO", - "R12_EEU": "R12_EEU", - "R12_WEU": "R12_WEU", - "R12_CHN": "R12_CHN", - "World": "R12_GLB", - "model": "Model", - "scenario": "Scenario", - "variable": "Variable", - "region": "Region", - "unit": "Unit", - "BCA": "BC", - "OCA": "OC", - "Final Energy|Non-Energy Use|Chemicals|Ammonia": ( - "Final Energy|Industry|Chemicals|Ammonia" - ), - "Final Energy|Non-Energy Use|Chemicals|Ammonia|Gases": ( - "Final Energy|Industry|Chemicals|Ammonia|Gases" - ), - "Final Energy|Non-Energy Use|Chemicals|Ammonia|Liquids": ( - "Final Energy|Industry|Chemicals|Ammonia|Liquids" - ), - "Final Energy|Non-Energy Use|Chemicals|Ammonia|Liquids|Oil": ( - "Final Energy|Industry|Chemicals|Ammonia|Liquids|Oil" - ), - "Final Energy|Non-Energy Use|Chemicals|Ammonia|Solids": ( - "Final Energy|Industry|Chemicals|Ammonia|Solids" - ), -} - - -def plot_production_al(df: pyam.IamDataFrame, ax, r: str) -> None: - df = df.copy() - - df.filter( - variable=[ - "out|useful_material|aluminum|import_aluminum|*", - "out|final_material|aluminum|prebake_aluminum|*", - "out|final_material|aluminum|soderberg_aluminum|*", - "out|new_scrap|aluminum|*", - ], - inplace=True, - ) - - if r == "World": - df.filter( - variable=[ - "out|final_material|aluminum|prebake_aluminum|*", - "out|final_material|aluminum|soderberg_aluminum|*", - "out|new_scrap|aluminum|*", - ], - inplace=True, - ) - - df.plot.stack(ax=ax) - ax.legend( - [ - "Prebake", - "Soderberg", - "Newscrap", - "Oldscrap_min", - "Oldscrap_av", - "Oldscrap_max", - "import", - ], - bbox_to_anchor=(-0.4, 1), - loc="upper left", - ) - ax.set_title(f"Aluminium Production_{r}") - ax.set_xlabel("Year") - ax.set_ylabel("Mt") - - -def plot_production_steel(df: pyam.IamDataFrame, ax, r: str) -> None: - df = df.copy() - df.filter( - variable=[ - "out|final_material|steel|*", - "out|useful_material|steel|import_steel|*", - ], - inplace=True, - ) - - if r == "World": - df.filter(variable=["out|final_material|steel|*"], inplace=True) - - df.plot.stack(ax=ax) - ax.legend( - ["Bof steel", "Eaf steel M1", "Eaf steel M2", "Import"], - bbox_to_anchor=(-0.4, 1), - loc="upper left", - ) - ax.set_title(f"Steel Production_{r}") - ax.set_ylabel("Mt") - - -def plot_petro(df: pyam.IamDataFrame, ax, r: str) -> None: - df.plot.stack(ax=ax) - ax.legend( - ["atm_gasoil", "naphtha", "vacuum_gasoil", "bioethanol", "ethane", "propane"], - bbox_to_anchor=(-0.4, 1), - loc="upper left", - ) - ax.set_title(f"HVC feedstock {r}") - ax.set_xlabel("Years") - ax.set_ylabel("GWa") - - -def plot_production_cement(df: pyam.IamDataFrame, ax, r: str) -> None: - df.plot.stack(ax=ax) - ax.legend( - ["Ballmill Grinding", "Vertical Mill Grinding"], - bbox_to_anchor=(-0.6, 1), - loc="upper left", - ) - ax.set_title(f"Final Cement Production_{r}") - ax.set_xlabel("Year") - ax.set_ylabel("Mt") - - -def plot_production_cement_clinker(df: pyam.IamDataFrame, ax, r: str) -> None: - df.plot.stack(ax=ax) - ax.legend( - ["Dry Clinker", "Wet Clinker"], bbox_to_anchor=(-0.5, 1), loc="upper left" - ) - ax.set_title(f"Clinker Cement Production_{r}") - ax.set_xlabel("Year") - ax.set_ylabel("Mt") - - -def plot_emi_aggregates(df: pyam.IamDataFrame, pp, e: str) -> None: - for r in df.region: - fig, ax = plt.subplots(1, 1, figsize=(10, 10)) - - df.filter(region=r).plot.stack(ax=ax) - ax.set_title(f"Emissions_{r}_{e}") - ax.set_ylabel("Mt") - ax.legend(bbox_to_anchor=(0.3, 1)) - - plt.close() - pp.savefig(fig) - - -def report( - scenario: message_ix.Scenario, - message_df: pd.DataFrame, - years: List[int], - nodes: List[str], - config: dict, -) -> None: - """Produces the material related variables. - - .. todo:: Expand docstring. - """ - # In order to avoid confusion in the second reporting stage there should - # no existing timeseries uploaded in the scenairo. Clear these except the - # residential and commercial ones since they should be always included. - - # Activate this part to keep the residential and commercial variables - # when the model is run with the buildigns linkage. - # df_rem = df_rem[~(df_rem["variable"].str.contains("Residential") | \ - # df_rem["variable"].str.contains("Commercial"))] - - # Temporary to add the transport variables - # transport_path = os.path.join('C:\\', 'Users','unlu','Documents', - # 'MyDocuments_IIASA','Material_Flow', 'jupyter_notebooks', '2022-06-02_0.xlsx') - # df_transport = pd.read_excel(transport_path) - # scenario.check_out(timeseries_only=True) - # scenario.add_timeseries(df_transport) - # scenario.commit('Added transport timeseries') - - # Path for materials reporting output - directory = config["output_path"].expanduser().joinpath("materials") - directory.mkdir(exist_ok=True) - - # Replace region labels like R12_AFR|R12_AFR with simply R12_AFR - # TODO locate the cause of this upstream and fix - df = message_df.rename({"region": {f"{n}|{n}": n for n in nodes}}) - - # Dump output of message_ix built-in reporting to an Excel file - name = directory.joinpath("message_ix_reporting.xlsx") - df.to_excel(name) - log.info(f"'message::default' report written to {name}") - log.info(f"{len(df)} rows") - log.info(f"{len(df.variable)} unique variable names") - - # Obtain a pyam dataframe - # FIXME(PNK) this re-reads the file above. This seems unnecessary, and can be slow. - df = pyam.IamDataFrame(pd.read_excel(name).fillna(dict(Unit=""))) - - # Subsequent code expects that the nodes set contains "World", but not "_GLB" - # NB cannot use message_ix_models.util.nodes_ex_world(), which excludes both - nodes = list(filter(lambda n: not n.endswith("_GLB"), nodes)) - - # Filter variables necessary for materials reporting - df.filter( - region=nodes, - year=years, - variable=[ - "out|new_scrap|aluminum|*", - "out|final_material|aluminum|prebake_aluminum|M1", - "out|final_material|aluminum|secondary_aluminum|M1", - "out|final_material|aluminum|soderberg_aluminum|M1", - "out|new_scrap|steel|*", - "in|new_scrap|steel|*", - "out|final_material|steel|*", - "out|useful_material|steel|import_steel|*", - "out|secondary_material|NH3|*", - "in|useful_material|steel|export_steel|*", - "out|useful_material|aluminum|import_aluminum|*", - "in|useful_material|aluminum|export_aluminum|*", - "out|final_material|*|import_petro|*", - "in|final_material|*|export_petro|*", - "out|secondary|lightoil|loil_imp|*", - "out|secondary|fueloil|foil_imp|*", - "out|tertiary_material|clinker_cement|*", - "out|demand|cement|*", - "out|final_material|BTX|*", - "out|final_material|ethylene|*", - "out|final_material|propylene|*", - "out|secondary|fueloil|agg_ref|*", - "out|secondary|lightoil|agg_ref|*", - "out|useful|i_therm|solar_i|M1", - "in|final|*", - "in|secondary|coal|coal_NH3|M1", - "in|secondary|electr|NH3_to_N_fertil|M1", - "in|secondary|electr|coal_NH3|M1", - "in|secondary|electr|electr_NH3|M1", - "in|secondary|electr|gas_NH3|M1", - "in|secondary|fueloil|fueloil_NH3|M1", - "in|secondary|gas|gas_NH3|M1", - "in|secondary|coal|coal_NH3_ccs|M1", - "in|secondary|electr|coal_NH3_ccs|M1", - "in|secondary|electr|gas_NH3_ccs|M1", - "in|secondary|fueloil|fueloil_NH3_ccs|M1", - "in|secondary|gas|gas_NH3_ccs|M1", - "in|primary|biomass|biomass_NH3|M1", - "in|seconday|electr|biomass_NH3|M1", - "in|primary|biomass|biomass_NH3_ccs|M1", - "in|secondary|electr|biomass_NH3_ccs|M1", - "in|desulfurized|*|steam_cracker_petro|*", - "in|secondary_material|*|steam_cracker_petro|*", - "in|product|aluminum|scrap_recovery|M1", - "in|dummy_end_of_life|aluminum|scrap_recovery_aluminum|M1", - "in|dummy_end_of_life|steel|scrap_recovery_steel|M1", - "out|dummy_end_of_life|aluminum|total_EOL_aluminum|M1", - "out|dummy_end_of_life|steel|total_EOL_steel|M1", - "out|dummy_end_of_life|cement|total_EOL_cement|M1", - "in|product|steel|scrap_recovery_steel|M1", - "out|end_of_life|*", - "emis|CO2_industry|*", - "emis|CF4|*", - "emis|SO2|*", - "emis|NOx|*", - "emis|CH4|*", - "emis|N2O|*", - "emis|BCA|*", - "emis|CO|*", - "emis|NH3|*", - "emis|NOx|*", - "emis|OCA|*", - "emis|CO2|*", - ], - inplace=True, - ) - log.info(f"{df = }\n{len(df.variable)} unique variable names") - - # Compute global totals - df.aggregate_region(df.variable, region="World", method=sum, append=True) - - df.to_excel(directory.joinpath("check.xlsx")) - log.info(f"Necessary variables are filtered; {len(df)} in total") - - # Obtain the model and scenario name - model_name = scenario.model - scenario_name = scenario.scenario - - # Create an empty pyam dataframe to store the new variables - - empty_template_path = directory.joinpath("empty_template.xlsx") - workbook = xlsxwriter.Workbook(empty_template_path) - worksheet = workbook.add_worksheet() - worksheet.write("A1", "Model") - worksheet.write("B1", "Scenario") - worksheet.write("C1", "Region") - worksheet.write("D1", "Variable") - worksheet.write("E1", "Unit") - columns = "F1 G1 H1 I1 J1 K1 L1 M1 N1 O1 P1 Q1 R1".split() - - for yr, col in zip(years, columns): - worksheet.write(col, yr) - workbook.close() - - df_final = pyam.IamDataFrame(empty_template_path) - print("Empty template for new variables created") - - # Create a pdf file with figures - path = directory.joinpath("Material_global_graphs.pdf") - pp = PdfPages(path) - # pp = PdfPages("Material_global_graphs.pdf") - - # Reporting and Plotting - - print("Production plots and variables are being generated") - for r in nodes: - # PRODUCTION - PLOTS - # Needs to be checked again to see whether the graphs are correct - - fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(20, 10)) - fig.tight_layout(pad=10.0) - - # ALUMINUM - df_al = df.filter( - region=r, year=years, variable=["out|*|aluminum|*", "in|*|aluminum|*"] - ) - df_al.convert_unit("", to="Mt/yr", factor=1, inplace=True) - - plot_production_al(df_al.copy(), ax1, r) - - # STEEL - - df_steel = df.copy() - df_steel.filter(region=r, year=years, inplace=True) - df_steel.filter(variable=["out|*|steel|*", "in|*|steel|*"], inplace=True) - df_steel.convert_unit("", to="Mt/yr", factor=1, inplace=True) - - plot_production_steel(df_steel.copy(), ax2, r) - - # PETRO - - df_petro = df.copy() - df_petro.filter(region=r, year=years, inplace=True) - df_petro.filter( - variable=[ - "in|final|ethanol|ethanol_to_ethylene_petro|M1", - "in|desulfurized|*|steam_cracker_petro|*", - "in|secondary_material|*|steam_cracker_petro|*", - ], - inplace=True, - ) - df_petro.convert_unit("", to="Mt/yr", factor=1, inplace=True) - - if r == "World": - df_petro.filter( - variable=[ - "in|final|ethanol|ethanol_to_ethylene_petro|M1", - "in|desulfurized|*|steam_cracker_petro|*", - "in|secondary_material|*|steam_cracker_petro|*", - ], - inplace=True, - ) - - plot_petro(df_petro.copy(), ax3, r) - - plt.close() - pp.savefig(fig) - - # PRODUCTION - IAMC Variables - - # ALUMINUM - - # Primary Production - primary_al_vars = [ - "out|final_material|aluminum|prebake_aluminum|M1", - "out|final_material|aluminum|soderberg_aluminum|M1", - ] - - # Secondary Production - secondary_al_vars = ["out|final_material|aluminum|secondary_aluminum|M1"] - - # Collected Scrap - collected_scrap_al_vars = [ - "out|new_scrap|aluminum|manuf_aluminum|M1", - "in|dummy_end_of_life|aluminum|scrap_recovery_aluminum|M1", - ] - - # Total Available Scrap: - # New scrap + The end of life products (exegenous assumption) - # + from power and buildings sector - - total_scrap_al_vars = [ - "out|new_scrap|aluminum|manuf_aluminum|M1", - "out|dummy_end_of_life|aluminum|total_EOL_aluminum|M1", - ] - - new_scrap_al_vars = ["out|new_scrap|aluminum|manuf_aluminum|M1"] - old_scrap_al_vars = ["out|dummy_end_of_life|aluminum|total_EOL_aluminum|M1"] - - df_al.aggregate( - "Production|Primary|Non-Ferrous Metals|Aluminium", - components=primary_al_vars, - append=True, - ) - df_al.aggregate( - "Production|Secondary|Non-Ferrous Metals|Aluminium", - components=secondary_al_vars, - append=True, - ) - - df_al.aggregate( - "Production|Non-Ferrous Metals|Aluminium", - components=secondary_al_vars + primary_al_vars, - append=True, - ) - - df_al.aggregate( - "Collected Scrap|Non-Ferrous Metals|Aluminium", - components=collected_scrap_al_vars, - append=True, - ) - - df_al.aggregate( - "Collected Scrap|Non-Ferrous Metals", - components=collected_scrap_al_vars, - append=True, - ) - - df_al.aggregate( - "Total Scrap|Non-Ferrous Metals|Aluminium", - components=total_scrap_al_vars, - append=True, - ) - - df_al.aggregate( - "Total Scrap|Non-Ferrous Metals", - components=total_scrap_al_vars, - append=True, - ) - - df_al.aggregate( - "Total Scrap|Non-Ferrous Metals|Aluminium|New Scrap", - components=new_scrap_al_vars, - append=True, - ) - - df_al.aggregate( - "Total Scrap|Non-Ferrous Metals|Aluminium|Old Scrap", - components=old_scrap_al_vars, - append=True, - ) - - df_al.aggregate( - "Production|Non-Ferrous Metals", - components=primary_al_vars + secondary_al_vars, - append=True, - ) - - df_al.filter( - variable=[ - "Production|Primary|Non-Ferrous Metals|Aluminium", - "Production|Secondary|Non-Ferrous Metals|Aluminium", - "Production|Non-Ferrous Metals|Aluminium", - "Production|Non-Ferrous Metals", - "Collected Scrap|Non-Ferrous Metals|Aluminium", - "Collected Scrap|Non-Ferrous Metals", - "Total Scrap|Non-Ferrous Metals", - "Total Scrap|Non-Ferrous Metals|Aluminium", - "Total Scrap|Non-Ferrous Metals|Aluminium|New Scrap", - "Total Scrap|Non-Ferrous Metals|Aluminium|Old Scrap", - ], - inplace=True, - ) - - # STEEL - - # bof_steel also uses a certain share of scrap but this is neglegcted in - # the reporting. - - # eaf_steel has three modes M1,M2,M3: Only M2 has scrap input. - - primary_steel_vars = [ - "out|final_material|steel|bof_steel|M1", - "out|final_material|steel|eaf_steel|M1", - "out|final_material|steel|eaf_steel|M3", - ] - - secondary_steel_vars = [ - "out|final_material|steel|eaf_steel|M2", - "in|new_scrap|steel|bof_steel|M1", - ] - - collected_scrap_steel_vars = [ - "in|dummy_end_of_life|steel|scrap_recovery_steel|M1" - ] - total_scrap_steel_vars = ["out|dummy_end_of_life|steel|total_EOL_steel|M1"] - - new_scrap_steel_vars = ["out|new_scrap|steel|manuf_steel|M1"] - old_scrap_steel_vars = ["out|dummy_end_of_life|steel|total_EOL_steel|M1"] - - df_steel.aggregate( - "Production|Primary|Steel (before sub.)", - components=primary_steel_vars, - append=True, - ) - - df_steel.subtract( - "Production|Primary|Steel (before sub.)", - "in|new_scrap|steel|bof_steel|M1", - "Production|Primary|Steel", - append=True, - ) - - df_steel.aggregate( - "Production|Secondary|Steel", - components=secondary_steel_vars, - append=True, - ) - - df_steel.aggregate( - "Production|Steel", - components=["Production|Primary|Steel", "Production|Secondary|Steel"], - append=True, - ) - - df_steel.aggregate( - "Collected Scrap|Steel", - components=collected_scrap_steel_vars, - append=True, - ) - df_steel.aggregate( - "Total Scrap|Steel", components=total_scrap_steel_vars, append=True - ) - - df_steel.aggregate( - "Total Scrap|Steel|Old Scrap", components=old_scrap_steel_vars, append=True - ) - - df_steel.aggregate( - "Total Scrap|Steel|New Scrap", components=new_scrap_steel_vars, append=True - ) - - df_steel.filter( - variable=[ - "Production|Primary|Steel", - "Production|Secondary|Steel", - "Production|Steel", - "Collected Scrap|Steel", - "Total Scrap|Steel", - "Total Scrap|Steel|Old Scrap", - "Total Scrap|Steel|New Scrap", - ], - inplace=True, - ) - - # CHEMICALS - - df_chemicals = df.copy() - df_chemicals.filter(region=r, year=years, inplace=True) - df_chemicals.filter( - variable=[ - "out|secondary_material|NH3|*", - "out|final_material|ethylene|*", - "out|final_material|propylene|*", - "out|final_material|BTX|*", - ], - inplace=True, - ) - df_chemicals.convert_unit("", to="Mt/yr", factor=1, inplace=True) - - # AMMONIA - - primary_ammonia_vars = [ - "out|secondary_material|NH3|gas_NH3|M1", - "out|secondary_material|NH3|coal_NH3|M1", - "out|secondary_material|NH3|biomass_NH3|M1", - "out|secondary_material|NH3|fueloil_NH3|M1", - "out|secondary_material|NH3|electr_NH3|M1", - ] - - df_chemicals.aggregate( - "Production|Primary|Chemicals|Ammonia", - components=primary_ammonia_vars, - append=True, - ) - - df_chemicals.aggregate( - "Production|Chemicals|Ammonia", - components=primary_ammonia_vars, - append=True, - ) - - # High Value Chemicals - - intermediate_petro_vars = [ - "out|final_material|ethylene|ethanol_to_ethylene_petro|M1", - "out|final_material|ethylene|steam_cracker_petro|atm_gasoil", - "out|final_material|ethylene|steam_cracker_petro|naphtha", - "out|final_material|ethylene|steam_cracker_petro|vacuum_gasoil", - "out|final_material|ethylene|steam_cracker_petro|ethane", - "out|final_material|ethylene|steam_cracker_petro|propane", - "out|final_material|propylene|steam_cracker_petro|atm_gasoil", - "out|final_material|propylene|steam_cracker_petro|naphtha", - "out|final_material|propylene|steam_cracker_petro|vacuum_gasoil", - "out|final_material|propylene|steam_cracker_petro|propane", - "out|final_material|BTX|steam_cracker_petro|atm_gasoil", - "out|final_material|BTX|steam_cracker_petro|naphtha", - "out|final_material|BTX|steam_cracker_petro|vacuum_gasoil", - ] - - df_chemicals.aggregate( - "Production|Primary|Chemicals|High Value Chemicals", - components=intermediate_petro_vars, - append=True, - ) - - df_chemicals.aggregate( - "Production|Chemicals|High Value Chemicals", - components=intermediate_petro_vars, - append=True, - ) - - # Totals - - chemicals_vars = intermediate_petro_vars + primary_ammonia_vars - df_chemicals.aggregate( - "Production|Primary|Chemicals", - components=chemicals_vars, - append=True, - ) - - df_chemicals.aggregate( - "Production|Chemicals", - components=chemicals_vars, - append=True, - ) - - df_chemicals.filter( - variable=[ - "Production|Primary|Chemicals|High Value Chemicals", - "Production|Chemicals|High Value Chemicals", - "Production|Primary|Chemicals", - "Production|Chemicals", - "Production|Primary|Chemicals|Ammonia", - "Production|Chemicals|Ammonia", - ], - inplace=True, - ) - - # Add to final data_frame - df_final.append(df_al, inplace=True) - df_final.append(df_steel, inplace=True) - df_final.append(df_chemicals, inplace=True) - - # CEMENT - for r in nodes: - # PRODUCTION - PLOT - - fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 10)) - fig.tight_layout(pad=15.0) - - # Clinker cement - - df_cement_clinker = df.copy() - df_cement_clinker.filter(region=r, year=years, inplace=True) - df_cement_clinker.filter( - variable=["out|tertiary_material|clinker_cement|*"], inplace=True - ) - - plot_production_cement_clinker(df_cement_clinker, ax1, r) - - # Final product cement - - df_cement = df.copy() - df_cement.filter(region=r, year=years, inplace=True) - df_cement.filter(variable=["out|demand|cement|*"], inplace=True) - - plot_production_cement(df_cement, ax2, r) - - plt.close() - pp.savefig(fig) - - # PRODUCTION - IAMC format - - primary_cement_vars = [ - "out|demand|cement|grinding_ballmill_cement|M1", - "out|demand|cement|grinding_vertmill_cement|M1", - ] - - total_scrap_cement_vars = ["out|dummy_end_of_life|cement|total_EOL_cement|M1"] - - df_cement.convert_unit("", to="Mt/yr", factor=1, inplace=True) - df_cement.aggregate( - "Production|Primary|Cement", - components=primary_cement_vars, - append=True, - ) - - df_cement.aggregate( - "Production|Non-Metallic Minerals", - components=primary_cement_vars, - append=True, - ) - - df_cement.aggregate( - "Production|Cement", - components=primary_cement_vars, - append=True, - ) - - df_cement.aggregate( - "Total Scrap|Non-Metallic Minerals", - components=total_scrap_cement_vars, - append=True, - ) - - df_cement.aggregate( - "Total Scrap|Non-Metallic Minerals|Cement", - components=total_scrap_cement_vars, - append=True, - ) - df_cement.filter( - variable=[ - "Production|Primary|Cement", - "Production|Cement", - "Total Scrap|Non-Metallic Minerals" - "Total Scrap|Non-Metallic Minerals|Cement", - ], - inplace=True, - ) - df_final.append(df_cement, inplace=True) - - # FINAL ENERGY BY FUELS (Only Non-Energy Use) - # Only inclides High Value Chemicals - # Ammonia is not seperately included as the model input valus are combined for - # feedstock and energy. - - # The ammonia related variables are not included in the main filter but left in the - # remaining code to be reference for the future changes. - - print("Final Energy by fuels only non-energy use is being printed.") - commodities = ["gas", "liquids", "solids", "all"] - - for c, r in product(commodities, nodes): - df_final_energy = df.copy() - - # GWa to EJ/yr - df_final_energy.convert_unit("", to="GWa", factor=1, inplace=True) - df_final_energy.convert_unit("GWa", to="EJ/yr", factor=0.03154, inplace=True) - df_final_energy.filter(region=r, year=years, inplace=True) - df_final_energy.filter( - variable=["in|final|*|cokeoven_steel|*"], keep=False, inplace=True - ) - df_final_energy.filter( - variable=[ - "in|final|atm_gasoil|*", - "in|final|vacuum_gasoil|*", - "in|final|naphtha|*", - "in|final|*|coal_fs|*", - "in|final|*|methanol_fs|*", - "in|final|*|ethanol_fs|*", - "in|final|ethanol|ethanol_to_ethylene_petro|*", - "in|final|*|foil_fs|*", - "in|final|gas|gas_processing_petro|*", - "in|final|*|loil_fs|*", - "in|final|*|gas_fs|*", - ], - inplace=True, - ) - - if c == "gas": - df_final_energy.filter( - variable=[ - "in|final|gas|*", - "in|secondary|gas|gas_NH3|M1", - "in|secondary|gas|gas_NH3_ccs|M1", - ], - inplace=True, - ) - # Do not include gasoil and naphtha feedstock - if c == "liquids": - df_final_energy.filter( - variable=[ - "in|final|ethanol|ethanol_to_ethylene_petro|*", - "in|final|*|foil_fs|*", - "in|final|*|loil_fs|*", - "in|final|*|methanol_fs|*", - "in|final|*|ethanol_fs|*", - "in|final|atm_gasoil|*", - "in|final|vacuum_gasoil|*", - "in|final|naphtha|*", - "in|secondary|fueloil|fueloil_NH3|M1", - "in|secondary|fueloil|fueloil_NH3_ccs|M1", - ], - inplace=True, - ) - if c == "solids": - df_final_energy.filter( - variable=[ - "in|final|biomass|*", - "in|final|coal|*", - "in|secondary|coal|coal_NH3|M1", - "in|secondary|coal|coal_NH3_ccs|M1", - "in|primary|biomass|biomass_NH3_ccs|M1", - "in|primary|biomass|biomass_NH3|M1", - ], - inplace=True, - ) - - all_flows = df_final_energy.timeseries().reset_index() - splitted_vars = [v.split("|") for v in all_flows.variable] - aux1_df = pd.DataFrame( - splitted_vars, - columns=["flow_type", "level", "commodity", "technology", "mode"], - ) - aux2_df = pd.concat( - [all_flows.reset_index(drop=True), aux1_df.reset_index(drop=True)], - axis=1, - ) - - # Include only the related industry sector variables - # Aluminum, cement and steel do not have feedstock use - - var_sectors = [v for v in aux2_df["variable"].values] - - aux2_df = aux2_df[aux2_df["variable"].isin(var_sectors)] - - df_final_energy.filter(variable=var_sectors, inplace=True) - - # Aggregate - - if c == "all": - df_final_energy.aggregate( - "Final Energy|Non-Energy Use", - components=var_sectors, - append=True, - ) - df_final_energy.filter( - variable=["Final Energy|Non-Energy Use"], inplace=True - ) - df_final_energy.convert_unit( - "GWa", to="EJ/yr", factor=0.03154, inplace=True - ) - df_final.append(df_final_energy, inplace=True) - elif c == "gas": - - # Can not distinguish by type Gases (natural gas, biomass, synthetic fossil, - # efuel) (coal_gas), from biomass (gas_bio), natural gas (gas_bal): All go - # into secondary level. - # Can not be distinguished in the final level. - df_final_energy.aggregate( - "Final Energy|Non-Energy Use|Gases", - components=var_sectors, - append=True, - ) - - df_final_energy.filter( - variable=["Final Energy|Non-Energy Use|Gases"], - inplace=True, - ) - df_final_energy.convert_unit( - "GWa", to="EJ/yr", factor=0.03154, inplace=True - ) - df_final.append(df_final_energy, inplace=True) - - elif c == "liquids": - - # All liquids - df_final_energy.aggregate( - "Final Energy|Non-Energy Use|Liquids", - components=var_sectors, - append=True, - ) - - # Only bios - - filter_vars = [ - v - for v in aux2_df["variable"].values - if (("ethanol" in v) & ("methanol" not in v)) - ] - df_final_energy.aggregate( - "Final Energy|Non-Energy Use|Liquids|Biomass", - components=filter_vars, - append=True, - ) - # Fossils - - filter_vars = [ - v - for v in aux2_df["variable"].values - if ( - ("lightoil" in v) - | ("fueloil" in v) - | ("atm_gasoil" in v) - | ("vacuum_gasoil" in v) - | ("naphtha" in v) - ) - ] - - df_final_energy.aggregate( - "Final Energy|Non-Energy Use|Liquids|Oil", - components=filter_vars, - append=True, - ) - - # Other - - filter_vars = [v for v in aux2_df["variable"].values if (("methanol" in v))] - - df_final_energy.aggregate( - "Final Energy|Non-Energy Use|Liquids|Coal", - components=filter_vars, - append=True, - ) - - df_final_energy.filter( - variable=[ - "Final Energy|Non-Energy Use|Liquids", - "Final Energy|Non-Energy Use|Liquids|Oil", - "Final Energy|Non-Energy Use|Liquids|Biomass", - "Final Energy|Non-Energy Use|Liquids|Coal", - ], - inplace=True, - ) - df_final_energy.convert_unit( - "GWa", to="EJ/yr", factor=0.03154, inplace=True - ) - df_final.append(df_final_energy, inplace=True) - elif c == "solids": - - # All - df_final_energy.aggregate( - "Final Energy|Non-Energy Use|Solids", - components=var_sectors, - append=True, - ) - # Bio - filter_vars = [v for v in aux2_df["variable"].values if ("biomass" in v)] - df_final_energy.aggregate( - "Final Energy|Non-Energy Use|Solids|Biomass", - components=filter_vars, - append=True, - ) - - # Fossil - filter_vars = [v for v in aux2_df["variable"].values if ("coal" in v)] - df_final_energy.aggregate( - "Final Energy|Non-Energy Use|Solids|Coal", - components=filter_vars, - append=True, - ) - df_final_energy.filter( - variable=[ - "Final Energy|Non-Energy Use|Solids", - "Final Energy|Non-Energy Use|Solids|Biomass", - "Final Energy|Non-Energy Use|Solids|Coal", - ], - inplace=True, - ) - df_final_energy.convert_unit( - "GWa", to="EJ/yr", factor=0.03154, inplace=True - ) - df_final.append(df_final_energy, inplace=True) - - # FINAL ENERGY BY FUELS (Excluding Non-Energy Use) - # For ammonia only electricity use is included since only this has seperate - # input values in the model. - - print("Final Energy by fuels excluding non-energy use is being printed.") - commodities = [ - "electr", - "gas", - "hydrogen", - "liquids", - "solids", - "heat", - "other", - "all", - ] - for c, r in product(commodities, nodes): - df_final_energy = df.copy() - df_final_energy.convert_unit("", to="GWa", factor=1, inplace=True) - df_final_energy.filter(region=r, year=years, inplace=True) - df_final_energy.filter( - variable=[ - "in|final|*|cokeoven_steel|*", - "in|final|co_gas|*", - "in|final|bf_gas|*", - ], - keep=False, - inplace=True, - ) - - if c == "electr": - df_final_energy.filter( - variable=[ - "in|final|electr|*", - "in|secondary|electr|NH3_to_N_fertil|M1", - "in|secondary|electr|coal_NH3|M1", - "in|secondary|electr|electr_NH3|M1", - "in|secondary|electr|fueloil_NH3|M1", - "in|secondary|electr|gas_NH3|M1", - "in|secondary|electr|coal_NH3_ccs|M1", - "in|secondary|electr|fueloil_NH3_ccs|M1", - "in|secondary|electr|gas_NH3_ccs|M1", - "in|secondary|electr|biomass_NH3_ccs|M1", - "in|secondary|electr|biomass_NH3|M1", - ], - inplace=True, - ) - elif c == "gas": - df_final_energy.filter(variable=["in|final|gas|*"], inplace=True) - elif c == "liquids": - # Do not include gasoil and naphtha feedstock - df_final_energy.filter( - variable=[ - "in|final|ethanol|*", - "in|final|fueloil|*", - "in|final|lightoil|*", - "in|final|methanol|*", - ], - inplace=True, - ) - elif c == "solids": - df_final_energy.filter( - variable=[ - "in|final|biomass|*", - "in|final|coal|*", - "in|final|coke_iron|*", - ], - inplace=True, - ) - elif c == "hydrogen": - df_final_energy.filter(variable=["in|final|hydrogen|*"], inplace=True) - elif c == "heat": - df_final_energy.filter(variable=["in|final|d_heat|*"], inplace=True) - elif c == "other": - df_final_energy.filter(variable=["out|useful|i_therm|*"], inplace=True) - elif c == "all": - df_final_energy.filter( - variable=[ - "in|final|*", - "in|secondary|electr|NH3_to_N_fertil|M1", - "in|secondary|electr|coal_NH3|M1", - "in|secondary|electr|electr_NH3|M1", - "in|secondary|electr|fueloil_NH3|M1", - "in|secondary|electr|gas_NH3|M1", - "in|secondary|electr|coal_NH3_ccs|M1", - "in|secondary|electr|fueloil_NH3_ccs|M1", - "in|secondary|electr|gas_NH3_ccs|M1", - "in|secondary|electr|biomass_NH3_ccs|M1", - "in|secondary|electr|biomass_NH3|M1", - "out|useful|i_therm|solar_i|M1", - ], - inplace=True, - ) - - all_flows = df_final_energy.timeseries().reset_index() - splitted_vars = [v.split("|") for v in all_flows.variable] - aux1_df = pd.DataFrame( - splitted_vars, - columns=["flow_type", "level", "commodity", "technology", "mode"], - ) - aux2_df = pd.concat( - [all_flows.reset_index(drop=True), aux1_df.reset_index(drop=True)], - axis=1, - ) - - # Include only the related industry sector variables and state some exceptions - var_sectors = sorted( - filter( - # 4th element (technology) ends with one of the following - lambda v: re.search( - "_(cement|steel|aluminum|petro|i|I|NH3)$", v.split("|")[3] - ) - # Exclude specific technologies - and not re.search("(ethanol_to_ethylene|gas_processing)_petro", v) - # Exclude inputs of 3 specific commodities to steam_cracker_petro - and not re.search( - r"^in.final.((atm|vacuum)_gasoil|naphtha).steam_cracker_petro.\1", v - ), - aux2_df["variable"].unique(), - ) - ) - aux2_df = aux2_df[aux2_df["variable"].isin(var_sectors)] - - df_final_energy.filter(variable=var_sectors, inplace=True) - - # Aggregate - - if c == "all": - df_final_energy.aggregate( - "Final Energy|Industry excl Non-Energy Use", - components=var_sectors, - append=True, - ) - df_final_energy.filter( - variable=["Final Energy|Industry excl Non-Energy Use"], inplace=True - ) - df_final_energy.convert_unit( - "GWa", to="EJ/yr", factor=0.03154, inplace=True - ) - df_final.append(df_final_energy, inplace=True) - elif c == "electr": - - df_final_energy.aggregate( - "Final Energy|Industry excl Non-Energy Use|Electricity", - components=var_sectors, - append=True, - ) - df_final_energy.filter( - variable=["Final Energy|Industry excl Non-Energy Use|Electricity"], - inplace=True, - ) - df_final_energy.convert_unit( - "GWa", to="EJ/yr", factor=0.03154, inplace=True - ) - df_final.append(df_final_energy, inplace=True) - elif c == "gas": - - # Can not distinguish by type Gases (natural gas, biomass, synthetic - # fossil, efuel) (coal_gas), from biomass (gas_bio), natural gas - # (gas_bal): All go into secondary level - # Can not be distinguished in the final level. - - df_final_energy.aggregate( - "Final Energy|Industry excl Non-Energy Use|Gases", - components=var_sectors, - append=True, - ) - - df_final_energy.filter( - variable=["Final Energy|Industry excl Non-Energy Use|Gases"], - inplace=True, - ) - df_final_energy.convert_unit( - "GWa", to="EJ/yr", factor=0.03154, inplace=True - ) - df_final.append(df_final_energy, inplace=True) - - elif c == "hydrogen": - df_final_energy.aggregate( - "Final Energy|Industry excl Non-Energy Use|Hydrogen", - components=var_sectors, - append=True, - ) - df_final_energy.filter( - variable=["Final Energy|Industry excl Non-Energy Use|Hydrogen"], - inplace=True, - ) - df_final_energy.convert_unit( - "GWa", to="EJ/yr", factor=0.03154, inplace=True - ) - df_final.append(df_final_energy, inplace=True) - - elif c == "liquids": - - # All liquids - df_final_energy.aggregate( - "Final Energy|Industry excl Non-Energy Use|Liquids", - components=var_sectors, - append=True, - ) - - # Only bios (ethanol, methanol ?) - - filter_vars = [ - v - for v in aux2_df["variable"].values - if (("ethanol" in v) & ("methanol" not in v)) - ] - df_final_energy.aggregate( - "Final Energy|Industry excl Non-Energy Use|Liquids|Biomass", - components=filter_vars, - append=True, - ) - - # Fossils - - filter_vars = [ - v - for v in aux2_df["variable"].values - if (("fueloil" in v) | ("lightoil" in v)) - ] - - df_final_energy.aggregate( - "Final Energy|Industry excl Non-Energy Use|Liquids|Oil", - components=filter_vars, - append=True, - ) - - # Other - - filter_vars = [v for v in aux2_df["variable"].values if (("methanol" in v))] - - df_final_energy.aggregate( - "Final Energy|Industry excl Non-Energy Use|Liquids|Coal", - components=filter_vars, - append=True, - ) - - df_final_energy.filter( - variable=[ - "Final Energy|Industry excl Non-Energy Use|Liquids", - "Final Energy|Industry excl Non-Energy Use|Liquids|Oil", - "Final Energy|Industry excl Non-Energy Use|Liquids|Biomass", - "Final Energy|Industry excl Non-Energy Use|Liquids|Coal", - ], - inplace=True, - ) - df_final_energy.convert_unit( - "GWa", to="EJ/yr", factor=0.03154, inplace=True - ) - df_final.append(df_final_energy, inplace=True) - elif c == "solids": - - # All - df_final_energy.aggregate( - "Final Energy|Industry excl Non-Energy Use|Solids", - components=var_sectors, - append=True, - ) - - # Bio - filter_vars = [v for v in aux2_df["variable"].values if ("biomass" in v)] - df_final_energy.aggregate( - "Final Energy|Industry excl Non-Energy Use|Solids|Biomass", - components=filter_vars, - append=True, - ) - - # Fossil - filter_vars = [ - v for v in aux2_df["variable"].values if ("biomass" not in v) - ] - df_final_energy.aggregate( - "Final Energy|Industry excl Non-Energy Use|Solids|Coal", - components=filter_vars, - append=True, - ) - df_final_energy.filter( - variable=[ - "Final Energy|Industry excl Non-Energy Use|Solids", - "Final Energy|Industry excl Non-Energy Use|Solids|Biomass", - "Final Energy|Industry excl Non-Energy Use|Solids|Coal", - ], - inplace=True, - ) - df_final_energy.convert_unit( - "GWa", to="EJ/yr", factor=0.03154, inplace=True - ) - df_final.append(df_final_energy, inplace=True) - elif c == "heat": - df_final_energy.aggregate( - "Final Energy|Industry excl Non-Energy Use|Heat", - components=var_sectors, - append=True, - ) - df_final_energy.filter( - variable=["Final Energy|Industry excl Non-Energy Use|Heat"], - inplace=True, - ) - df_final_energy.convert_unit( - "GWa", to="EJ/yr", factor=0.03154, inplace=True - ) - df_final.append(df_final_energy, inplace=True) - elif c == "other": - df_final_energy.aggregate( - "Final Energy|Industry excl Non-Energy Use|Other", - components=var_sectors, - append=True, - ) - df_final_energy.filter( - variable=["Final Energy|Industry excl Non-Energy Use|Other"], - inplace=True, - ) - df_final_energy.convert_unit( - "GWa", to="EJ/yr", factor=0.03154, inplace=True - ) - df_final.append(df_final_energy, inplace=True) - - # FINAL ENERGY BY FUELS (Including Non-Energy Use) - print("Final Energy by fuels including non-energy use is being printed.") - commodities = [ - "electr", - "gas", - "hydrogen", - "liquids", - "solids", - "heat", - "all", - "other", - ] - for c, r in product(commodities, nodes): - df_final_energy = df.copy() - df_final_energy.convert_unit("", to="GWa", factor=1, inplace=True) - df_final_energy.filter(region=r, year=years, inplace=True) - df_final_energy.filter( - variable=[ - "in|final|*|cokeoven_steel|*", - "in|final|bf_gas|*", - "in|final|co_gas|*", - ], - keep=False, - inplace=True, - ) - - if c == "other": - df_final_energy.filter( - variable=["out|useful|i_therm|solar_i|M1"], inplace=True - ) - elif c == "electr": - df_final_energy.filter( - variable=[ - "in|final|electr|*", - "in|secondary|electr|NH3_to_N_fertil|M1", - "in|secondary|electr|coal_NH3|M1", - "in|secondary|electr|electr_NH3|M1", - "in|secondary|electr|fueloil_NH3|M1", - "in|secondary|electr|gas_NH3|M1", - "in|secondary|electr|coal_NH3_ccs|M1", - "in|secondary|electr|fueloil_NH3_ccs|M1", - "in|secondary|electr|gas_NH3_ccs|M1", - "in|secondary|electr|biomass_NH3_ccs|M1", - "in|secondary|electr|biomass_NH3|M1", - ], - inplace=True, - ) - - elif c == "gas": - df_final_energy.filter( - variable=[ - "in|final|gas|*", - "in|secondary|gas|gas_NH3|M1", - "in|secondary|gas|gas_NH3_ccs|M1", - ], - inplace=True, - ) - - elif c == "liquids": - # Include gasoil and naphtha feedstock - df_final_energy.filter( - variable=[ - "in|final|ethanol|*", - "in|final|fueloil|*", - "in|final|lightoil|*", - "in|final|methanol|*", - "in|final|vacuum_gasoil|*", - "in|final|naphtha|*", - "in|final|atm_gasoil|*", - "in|secondary|fueloil|fueloil_NH3|M1", - "in|secondary|fueloil|fueloil_NH3_ccs|M1", - ], - inplace=True, - ) - elif c == "solids": - df_final_energy.filter( - variable=[ - "in|final|biomass|*", - "in|final|coal|*", - "in|final|coke_iron|*", - "in|secondary|coal|coal_NH3|M1", - "in|secondary|coal|coal_NH3_ccs|M1", - "in|primary|biomass|biomass_NH3_ccs|M1", - "in|primary|biomass|biomass_NH3|M1", - ], - inplace=True, - ) - - elif c == "hydrogen": - df_final_energy.filter(variable=["in|final|hydrogen|*"], inplace=True) - elif c == "heat": - df_final_energy.filter(variable=["in|final|d_heat|*"], inplace=True) - elif c == "all": - df_final_energy.filter( - variable=[ - "in|final|*", - "in|secondary|coal|coal_NH3|M1", - "in|secondary|electr|NH3_to_N_fertil|M1", - "in|secondary|electr|coal_NH3|M1", - "in|secondary|electr|electr_NH3|M1", - "in|secondary|electr|gas_NH3|M1", - "in|secondary|fueloil|fueloil_NH3|M1", - "in|secondary|gas|gas_NH3|M1", - "in|secondary|coal|coal_NH3_ccs|M1", - "in|secondary|electr|coal_NH3_ccs|M1", - "in|secondary|electr|gas_NH3_ccs|M1", - "in|secondary|fueloil|fueloil_NH3_ccs|M1", - "in|secondary|gas|gas_NH3_ccs|M1", - "in|primary|biomass|biomass_NH3|M1", - "in|seconday|electr|biomass_NH3|M1", - "in|primary|biomass|biomass_NH3_ccs|M1", - "in|secondary|electr|biomass_NH3_ccs|M1", - "out|useful|i_therm|solar_i|M1", - ], - inplace=True, - ) - - all_flows = df_final_energy.timeseries().reset_index() - splitted_vars = [v.split("|") for v in all_flows.variable] - aux1_df = pd.DataFrame( - splitted_vars, - columns=["flow_type", "level", "commodity", "technology", "mode"], - ) - aux2_df = pd.concat( - [all_flows.reset_index(drop=True), aux1_df.reset_index(drop=True)], - axis=1, - ) - - # Include only the related industry sector variables - var_sectors = sorted( - filter( - lambda v: re.search( - "_(aluminum|cement|fs|i|I|NH3|petro|steel)", v.split("|")[3] - ), - aux2_df["variable"].unique(), - ) - ) - aux2_df = aux2_df[aux2_df["variable"].isin(var_sectors)] - - df_final_energy.filter(variable=var_sectors, inplace=True) - - # Aggregate - - if c == "other": - df_final_energy.aggregate( - "Final Energy|Industry|Other", - components=var_sectors, - append=True, - ) - df_final_energy.filter( - variable=["Final Energy|Industry|Other"], inplace=True - ) - df_final_energy.convert_unit( - "GWa", to="EJ/yr", factor=0.03154, inplace=True - ) - df_final.append(df_final_energy, inplace=True) - elif c == "all": - df_final_energy.aggregate( - "Final Energy|Industry", - components=var_sectors, - append=True, - ) - df_final_energy.filter(variable=["Final Energy|Industry"], inplace=True) - df_final_energy.convert_unit( - "GWa", to="EJ/yr", factor=0.03154, inplace=True - ) - df_final.append(df_final_energy, inplace=True) - elif c == "electr": - df_final_energy.aggregate( - "Final Energy|Industry|Electricity", - components=var_sectors, - append=True, - ) - df_final_energy.filter( - variable=["Final Energy|Industry|Electricity"], - inplace=True, - ) - df_final_energy.convert_unit( - "GWa", to="EJ/yr", factor=0.03154, inplace=True - ) - df_final.append(df_final_energy, inplace=True) - elif c == "gas": - - # Can not distinguish by type Gases (natural gas, biomass, synthetic - # fossil, efuel) (coal_gas), from biomass (gas_bio), natural gas - # (gas_bal): All go into secondary level - # Can not be distinguished in the final level. - - df_final_energy.aggregate( - "Final Energy|Industry|Gases", - components=var_sectors, - append=True, - ) - - df_final_energy.filter( - variable=["Final Energy|Industry|Gases"], - inplace=True, - ) - df_final_energy.convert_unit( - "GWa", to="EJ/yr", factor=0.03154, inplace=True - ) - df_final.append(df_final_energy, inplace=True) - - elif c == "hydrogen": - df_final_energy.aggregate( - "Final Energy|Industry|Hydrogen", - components=var_sectors, - append=True, - ) - df_final_energy.filter( - variable=["Final Energy|Industry|Hydrogen"], - inplace=True, - ) - df_final_energy.convert_unit( - "GWa", to="EJ/yr", factor=0.03154, inplace=True - ) - df_final.append(df_final_energy, inplace=True) - - elif c == "liquids": - - # All liquids - df_final_energy.aggregate( - "Final Energy|Industry|Liquids", - components=var_sectors, - append=True, - ) - # Only bios (ethanol) - - filter_vars = [ - v - for v in aux2_df["variable"].values - if (("ethanol" in v) & ("methanol" not in v)) - ] - df_final_energy.aggregate( - "Final Energy|Industry|Liquids|Biomass", - components=filter_vars, - append=True, - ) - - # Oils - - filter_vars = [ - v - for v in aux2_df["variable"].values - if ( - ("naphtha" in v) - | ("atm_gasoil" in v) - | ("vacuum_gasoil" in v) - | ("fueloil" in v) - | ("lightoil" in v) - ) - ] - - df_final_energy.aggregate( - "Final Energy|Industry|Liquids|Oil", - components=filter_vars, - append=True, - ) - - # Methanol - - filter_vars = [v for v in aux2_df["variable"].values if (("methanol" in v))] - - df_final_energy.aggregate( - "Final Energy|Industry|Liquids|Coal", - components=filter_vars, - append=True, - ) - - df_final_energy.filter( - variable=[ - "Final Energy|Industry|Liquids", - "Final Energy|Industry|Liquids|Oil", - "Final Energy|Industry|Liquids|Biomass", - "Final Energy|Industry|Liquids|Coal", - ], - inplace=True, - ) - df_final_energy.convert_unit( - "GWa", to="EJ/yr", factor=0.03154, inplace=True - ) - df_final.append(df_final_energy, inplace=True) - elif c == "solids": - - # All - df_final_energy.aggregate( - "Final Energy|Industry|Solids", - components=var_sectors, - append=True, - ) - - # Bio - filter_vars = [v for v in aux2_df["variable"].values if ("biomass" in v)] - - df_final_energy.aggregate( - "Final Energy|Industry|Solids|Biomass", - components=filter_vars, - append=True, - ) - - # Fossil - filter_vars = [ - v for v in aux2_df["variable"].values if ("biomass" not in v) - ] - - df_final_energy.aggregate( - "Final Energy|Industry|Solids|Coal", - components=filter_vars, - append=True, - ) - df_final_energy.filter( - variable=[ - "Final Energy|Industry|Solids", - "Final Energy|Industry|Solids|Biomass", - "Final Energy|Industry|Solids|Coal", - ], - inplace=True, - ) - df_final_energy.convert_unit( - "GWa", to="EJ/yr", factor=0.03154, inplace=True - ) - df_final.append(df_final_energy, inplace=True) - elif c == "heat": - df_final_energy.aggregate( - "Final Energy|Industry|Heat", - components=var_sectors, - append=True, - ) - df_final_energy.filter( - variable=["Final Energy|Industry|Heat"], - inplace=True, - ) - df_final_energy.convert_unit( - "GWa", to="EJ/yr", factor=0.03154, inplace=True - ) - df_final.append(df_final_energy, inplace=True) - - # FINAL ENERGY BY SECTOR AND FUEL - # Feedstock not included - - sectors = [ - "aluminum", - "steel", - "cement", - "petro", - "Non-Ferrous Metals", - "Non-Metallic Minerals", - "Chemicals", - "Other Sector", - ] - print("Final Energy by sector and fuel is being printed") - for r, s in product(nodes, sectors): - df_final_energy = df.copy() - df_final_energy.convert_unit("", to="GWa", factor=1, inplace=True) - exclude = [ - "in|final|atm_gasoil|steam_cracker_petro|*", - "in|final|ethanol|ethanol_to_ethylene_petro|M1", - "in|final|gas|gas_processing_petro|M1", - "in|final|naphtha|steam_cracker_petro|*", - "in|final|vacuum_gasoil|steam_cracker_petro|*", - "in|final|*|cokeoven_steel|*", - "in|final|bf_gas|*", - "in|final|co_gas|*", - ] - - df_final_energy.filter(region=r, year=years, inplace=True) - df_final_energy.filter( - variable=["in|final|*", "out|useful|i_therm|solar_i|M1"], inplace=True - ) - df_final_energy.filter(variable=exclude, keep=False, inplace=True) - - # Decompose the pyam table into pandas data frame - - all_flows = df_final_energy.timeseries().reset_index() - - # Split the strings in the identified variables for further processing - splitted_vars = [v.split("|") for v in all_flows.variable] - - # Create auxilary dataframes for processing - aux1_df = pd.DataFrame( - splitted_vars, - columns=["flow_type", "level", "commodity", "technology", "mode"], - ) - aux2_df = pd.concat( - [all_flows.reset_index(drop=True), aux1_df.reset_index(drop=True)], - axis=1, - ) - - # To be able to report the higher level sectors. - if s == "Non-Ferrous Metals": - tec = [t for t in aux2_df["technology"].values if "aluminum" in t] - aux2_df = aux2_df[aux2_df["technology"].isin(tec)] - elif s == "Non-Metallic Minerals": - tec = [t for t in aux2_df["technology"].values if "cement" in t] - aux2_df = aux2_df[aux2_df["technology"].isin(tec)] - elif s == "Chemicals": - tec = [ - t - for t in aux2_df["technology"].values - if (("petro" in t) | ("NH3" in t)) - ] - aux2_df = aux2_df[aux2_df["technology"].isin(tec)] - elif s == "Other Sector": - tec = list( - filter(lambda t: re.search("_[iI]$", t), aux2_df["technology"].values) - ) - aux2_df = aux2_df[aux2_df["technology"].isin(tec)] - else: - # Filter the technologies only for the certain industry sector - tec = [t for t in aux2_df["technology"].values if s in t] - aux2_df = aux2_df[aux2_df["technology"].isin(tec)] - - s = NAME_MAP.get(s, s) - - # Lists to keep commodity, aggregate and variable names. - - aggregate_list = [] - var_list = [] - - # For the categories below filter the required variable names, - # create a new aggregate name - - commodity_list = [ - "electr", - "gas", - "hydrogen", - "liquids", - "liquid_bio", - "liquid_fossil", - "liquid_other", - "solids", - "solids_bio", - "solids_fossil", - "heat", - "all", - ] - - for c in commodity_list: - if c == "electr": - var = np.unique( - aux2_df.loc[aux2_df["commodity"] == "electr", "variable"].values - ).tolist() - aggregate_name = ( - f"Final Energy|Industry excl Non-Energy Use|{s}|Electricity" - ) - elif c == "gas": - var = np.unique( - aux2_df.loc[aux2_df["commodity"] == "gas", "variable"].values - ).tolist() - aggregate_name = f"Final Energy|Industry excl Non-Energy Use|{s}|Gases" - elif c == "hydrogen": - var = np.unique( - aux2_df.loc[aux2_df["commodity"] == "hydrogen", "variable"].values - ).tolist() - aggregate_name = ( - f"Final Energy|Industry excl Non-Energy Use|{s}|Hydrogen" - ) - elif c == "liquids": - var = sorted( - aux2_df.loc[ - aux2_df["commodity"].isin( - ["ethanol", "fueloil", "lightoil", "methanol"] - ), - "variable", - ].unique() - ) - aggregate_name = ( - f"Final Energy|Industry excl Non-Energy Use|{s}|Liquids" - ) - elif c == "liquid_bio": - var = np.unique( - aux2_df.loc[ - ((aux2_df["commodity"] == "ethanol")), - "variable", - ].values - ).tolist() - aggregate_name = ( - f"Final Energy|Industry excl Non-Energy Use|{s}|Liquids|Biomass" - ) - elif c == "liquid_fossil": - var = np.unique( - aux2_df.loc[ - ( - (aux2_df["commodity"] == "fueloil") - | (aux2_df["commodity"] == "lightoil") - ), - "variable", - ].values - ).tolist() - aggregate_name = ( - f"Final Energy|Industry excl Non-Energy Use|{s}|Liquids|Oil" - ) - elif c == "liquid_other": - var = np.unique( - aux2_df.loc[ - ((aux2_df["commodity"] == "methanol")), - "variable", - ].values - ).tolist() - aggregate_name = ( - f"Final Energy|Industry excl Non-Energy Use|{s}|Liquids|Coal" - ) - elif c == "solids": - var = np.unique( - aux2_df.loc[ - ( - (aux2_df["commodity"] == "coal") - | (aux2_df["commodity"] == "biomass") - | (aux2_df["commodity"] == "coke_iron") - ), - "variable", - ].values - ).tolist() - aggregate_name = f"Final Energy|Industry excl Non-Energy Use|{s}|Solids" - elif c == "solids_bio": - var = np.unique( - aux2_df.loc[(aux2_df["commodity"] == "biomass"), "variable"].values - ).tolist() - aggregate_name = ( - f"Final Energy|Industry excl Non-Energy Use|{s}|Solids|Biomass" - ) - elif c == "solids_fossil": - var = np.unique( - aux2_df.loc[ - ( - (aux2_df["commodity"] == "coal") - | (aux2_df["commodity"] == "coke_iron") - ), - "variable", - ].values - ).tolist() - aggregate_name = ( - f"Final Energy|Industry excl Non-Energy Use|{s}|Solids|Coals" - ) - elif c == "heat": - var = np.unique( - aux2_df.loc[(aux2_df["commodity"] == "d_heat"), "variable"].values - ).tolist() - aggregate_name = f"Final Energy|Industry excl Non-Energy Use|{s}|Heat" - elif c == "all": - var = aux2_df["variable"].tolist() - aggregate_name = f"Final Energy|Industry excl Non-Energy Use|{s}" - - aggregate_list.append(aggregate_name) - var_list.append(var) - - # Obtain the iamc format dataframe again - - aux2_df.drop( - ["flow_type", "level", "commodity", "technology", "mode"], - axis=1, - inplace=True, - ) - df_final_energy = pyam.IamDataFrame(data=aux2_df) - - # Aggregate the commodities in iamc object - for i, c in enumerate(commodity_list): - if not var_list[i]: - # log.debug(f"Nothing to aggregate for '{aggregate_list[i]}'") - continue - df_final_energy.aggregate( - aggregate_list[i], components=var_list[i], append=True - ) - - df_final_energy.filter(variable=aggregate_list, inplace=True) - df_final_energy.convert_unit("GWa", to="EJ/yr", factor=0.03154, inplace=True) - df_final.append(df_final_energy, inplace=True) - - # FINAL ENERGY NON-ENERGY USE BY SECTOR AND FUEL - # Only in chemcials sector there is non-energy use - # not in aluminum, steel, cement - - # For high value chemicals non-energy use is reported. - - # For ammonia, there is no seperation for non-energy vs. energy. Everything - # is included here and the name of the variable is later changed. - - sectors = ["petro", "ammonia"] - print("Final Energy non-energy use by sector and fuel is being printed") - for r, s in product(nodes, sectors): - df_final_energy = df.copy() - df_final_energy.convert_unit("", to="GWa", factor=1, inplace=True) - include = [ - "in|final|atm_gasoil|steam_cracker_petro|*", - "in|final|ethanol|ethanol_to_ethylene_petro|M1", - "in|final|gas|gas_processing_petro|M1", - "in|final|naphtha|steam_cracker_petro|*", - "in|final|vacuum_gasoil|steam_cracker_petro|*", - "in|secondary|coal|coal_NH3|M1", - "in|secondary|coal|coal_NH3_ccs|M1", - "in|secondary|fueloil|fueloil_NH3|M1", - "in|secondary|fueloil|fueloil_NH3_ccs|M1", - "in|secondary|gas|gas_NH3|M1", - "in|secondary|gas|gas_NH3_ccs|M1", - "in|primary|biomass|biomass_NH3|M1", - "in|primary|biomass|biomass_NH3_ccs|M1", - "in|secondary|electr|NH3_to_N_fertil|M1", - "in|secondary|electr|coal_NH3|M1", - "in|secondary|electr|electr_NH3|M1", - "in|secondary|electr|gas_NH3|M1", - "in|secondary|electr|coal_NH3|M1", - "in|secondary|electr|coal_NH3_ccs|M1", - "in|secondary|electr|gas_NH3_ccs|M1", - "in|seconday|electr|biomass_NH3|M1", - "in|secondary|electr|biomass_NH3_ccs|M1", - "in|secondary|electr|fueloil_NH3|M1", - "in|secondary|electr|fueloil_NH3_ccs|M1", - ] - - df_final_energy.filter(region=r, year=years, inplace=True) - df_final_energy.filter(variable=include, inplace=True) - - # Decompose the pyam table into pandas data frame - - all_flows = df_final_energy.timeseries().reset_index() - - # Split the strings in the identified variables for further processing - splitted_vars = [v.split("|") for v in all_flows.variable] - - # Create auxilary dataframes for processing - aux1_df = pd.DataFrame( - splitted_vars, - columns=["flow_type", "level", "commodity", "technology", "mode"], - ) - aux2_df = pd.concat( - [all_flows.reset_index(drop=True), aux1_df.reset_index(drop=True)], - axis=1, - ) - - # Filter the technologies only for the certain industry sector - if s == "petro": - tec = [t for t in aux2_df["technology"].values if (s in t)] - elif s == "ammonia": - tec = [t for t in aux2_df["technology"].values if ("NH3" in t)] - - aux2_df = aux2_df[aux2_df["technology"].isin(tec)] - - s = NAME_MAP.get(s, s) - - # Lists to keep commodity, aggregate and variable names. - - aggregate_list = [] - commodity_list = [] - var_list = [] - - # For the categoris below filter the required variable names, - # create a new aggregate name - - commodity_list = [ - "gas", - "liquids", - "liquid_bio", - "liquid_oil", - "all", - "solids", - ] - - for c in commodity_list: - if c == "gas": - var = np.unique( - aux2_df.loc[aux2_df["commodity"] == "gas", "variable"].values - ).tolist() - aggregate_name = f"Final Energy|Non-Energy Use|{s}|Gases" - - elif c == "liquids": - var = np.unique( - aux2_df.loc[ - ( - (aux2_df["commodity"] == "naphtha") - | (aux2_df["commodity"] == "atm_gasoil") - | (aux2_df["commodity"] == "vacuum_gasoil") - | (aux2_df["commodity"] == "ethanol") - | (aux2_df["commodity"] == "fueloil") - ), - "variable", - ].values - ).tolist() - - aggregate_name = f"Final Energy|Non-Energy Use|{s}|Liquids" - elif c == "liquid_bio": - var = np.unique( - aux2_df.loc[ - ((aux2_df["commodity"] == "ethanol")), - "variable", - ].values - ).tolist() - aggregate_name = f"Final Energy|Non-Energy Use|{s}|Liquids|Biomass" - elif c == "liquid_oil": - var = np.unique( - aux2_df.loc[ - ( - (aux2_df["commodity"] == "atm_gasoil") - | (aux2_df["commodity"] == "naphtha") - | (aux2_df["commodity"] == "vacuum_gasoil") - | (aux2_df["commodity"] == "fueloil") - ), - "variable", - ].values - ).tolist() - - aggregate_name = f"Final Energy|Non-Energy Use|{s}|Liquids|Oil" - - elif c == "solids": - var = np.unique( - aux2_df.loc[ - ( - (aux2_df["commodity"] == "coal") - | (aux2_df["commodity"] == "biomass") - ), - "variable", - ].values - ).tolist() - aggregate_name = f"Final Energy|Non-Energy Use|{s}|Solids" - elif c == "all": - var = aux2_df["variable"].tolist() - aggregate_name = f"Final Energy|Non-Energy Use|{s}" - - aggregate_list.append(aggregate_name) - var_list.append(var) - - # Obtain the iamc format dataframe again - - aux2_df.drop( - ["flow_type", "level", "commodity", "technology", "mode"], - axis=1, - inplace=True, - ) - df_final_energy = pyam.IamDataFrame(data=aux2_df) - - # Aggregate the commodities in iamc object - - i = 0 - for c in commodity_list: - if var_list[i]: - df_final_energy.aggregate( - aggregate_list[i], components=var_list[i], append=True - ) - - i = i + 1 - - df_final_energy.filter(variable=aggregate_list, inplace=True) - df_final_energy.convert_unit("GWa", to="EJ/yr", factor=0.03154, inplace=True) - df_final.append(df_final_energy, inplace=True) - - # EMISSIONS - # If ammonia is used as feedstock the emissions are accounted under 'CO2_industry', - # so as 'demand'. If used as fuel, under 'CO2_transformation'. - # The CCS technologies deduct negative emissions from the overall CO2. - # If CCS technologies are used, - - sectors = [ - "all", - "aluminum", - "ammonia", - "cement", - "Chemicals", - "Other Sector", - "petro", - "steel", - ] - emission_type = [ - "BCA", - "CF4", - "CH4", - "CO", - "CO2_industry", - "CO2", - "N2O", - "NH3", - "NOx", - "OCA", - ] - - print("Emissions are being printed.") - for typ, e in product(["demand", "process"], emission_type): - # Filter on region and years - df_emi = df.filter(year=years) - - # Identify variables to filter - # CCS technologies for ammonia have both CO2 and CO2_industry at the same time - if e == "CO2_industry": - emi_filter = [ - "emis|CO2|biomass_NH3_ccs|*", - "emis|CO2|gas_NH3_ccs|*", - "emis|CO2|coal_NH3_ccs|*", - "emis|CO2|fueloil_NH3_ccs|*", - "emis|CO2_industry|biomass_NH3_ccs|*", - "emis|CO2_industry|gas_NH3_ccs|*", - "emis|CO2_industry|coal_NH3_ccs|*", - "emis|CO2_industry|fueloil_NH3_ccs|*", - "emis|CO2_industry|biomass_NH3|*", - "emis|CO2_industry|gas_NH3|*", - "emis|CO2_industry|coal_NH3|*", - "emis|CO2_industry|fueloil_NH3|*", - "emis|CO2_industry|electr_NH3|*", - "emis|CO2_industry|*", - ] - else: - emi_filter = [f"emis|{e}|*"] - # When e=CO2, these were already handled above - exclude = [ - "emis|CO2|biomass_NH3_ccs|*", - "emis|CO2|gas_NH3_ccs|*", - "emis|CO2|coal_NH3_ccs|*", - "emis|CO2|fueloil_NH3_ccs|*", - ] - df_emi.filter(variable=exclude, keep=False, inplace=True) - - df_emi.filter(variable=emi_filter, inplace=True) - - # Check units - log.debug(f"Units for {typ = }, {e = }: {df_emi.unit}") - assert ("",) == tuple( - df_emi.unit - ), f"Unexpected units for {typ = }, {e = }: {df_emi.unit}" - - # Convert units - if e in {"CO2", "CO2_industry"}: - # The model represents the in Mt (Carbon) / year - df_emi.convert_unit("", to="Mt CO2/yr", factor=44 / 12, inplace=True) - elif e in {"N2O", "CF4"}: - # The model represents these in kt (species) / year - unit = f"kt {e}/yr" - df_emi.convert_unit("", to=unit, factor=1, inplace=True) - else: - e = NAME_MAP.get(e, e) - # The model represents these in kt (species) / year, but we report in Mt - unit = f"Mt {e}/yr" - df_emi.convert_unit("", to=unit, factor=0.001, inplace=True) - - all_emissions = df_emi.timeseries().reset_index() - - # Mapping from aggregate variable name to list of variables to be aggregated - aggregates = dict() - - # Expression for _i/_I technologies, used below in 2 places - expr_i = ( - "(biomass|coal|elec|m?eth|[fl]oil|gas|h2|heat|hp_el|hp_gas)_i" - "|(sp_(coal|el|eth|liq|meth)|h2_fc)_I" - ) - # Expression for _trp and _tmp technologies - expr_tmptrp = re.compile("m?eth_ic_trp|(coal|elec|[fl]oil|gas|m?eth)_tmp") - - # Collect the same emission type for each sector - # NB this loop does not modify `df_emi` or `all_emissions`; it only uses their - # contents to populate `aggregates` - for s in sectors: - # Determine a variable name for the aggregate - aggregate_name = None - # Mapped sector name fragment - _s = "" if s == "all" else f"|{NAME_MAP.get(s, s)}" - if typ == "demand" and e != "CO2": - # Maybe change "CO2_industry" to "CO2" - _e = NAME_MAP.get(e, e) - aggregate_name = f"Emissions|{_e}|Energy|Demand|Industry{_s}" - elif typ == "process" and e != "CO2_industry": - aggregate_name = f"Emissions|{e}|Industrial Processes{_s}" - - if aggregate_name is None: - log.debug(f"No aggregate name for {typ = }, {e = }, {s = }; skip") - continue - - # Recover dimensions that were concatenated into the variable name - # NB only "technology" and "variable" are used - aux_df = pd.concat( - [ - all_emissions, - all_emissions.variable.str.split("|", expand=True).set_axis( - ["emission", "type", "technology", "mode"], axis=1 - ), - ], - axis=1, - ) - # Unique list of all technologies to be filtered - all_t = sorted(aux_df["technology"].unique()) - - # Filter the technologies only for the sector - if typ == "process" and s == "all" and e != "CO2_industry": - tec = filter( - lambda t: re.search("aluminum|cement|steel|petro", t) - and ("furnace" not in t), - all_t, - ) - elif typ == "process" and s != "all": - tec = filter( - lambda t: s in t and not re.search("furnace|NH3", t), all_t - ) - elif typ == "demand" and s == "Chemicals": - tec = filter( - lambda t: "NH3" in t or re.search("furnace_.*_petro", t), all_t - ) - elif typ == "demand" and s == "Other Sector" and e != "CO2": - tec = filter( - lambda t: re.search(expr_i, t) and not expr_tmptrp.search(t), all_t - ) - elif typ == "demand" and s == "all" and e != "CO2": - tec = filter( - lambda t: ( - re.search("aluminum|cement|steel|petro", t) and "furnace" in t - ) - or re.search( - expr_i + "|eaf_steel|NH3|" - "DUMMY_(limestone_supply_(cement|steel)|(coal|gas)_supply)", - t, - ) - and not expr_tmptrp.search(t), - all_t, - ) - elif typ == "demand" and s not in {"all", "Other Sector", "Chemicals"}: - if s == "steel": - # Furnaces are not used as heat source for iron & steel. Dummy - # supply technologies help accounting the emissions from - # cokeoven_steel, bf_steel, dri_steel, eaf_steel, and sinter_steel. - tec = filter( - lambda t: re.search( - "DUMMY_((coal|gas)_supply|limestone_supply_steel)", t - ), - all_t, - ) - elif s == "cement": - tec = filter( - lambda t: re.search(f"furnace_.*{s}", t) - or ("DUMMY_limestone_supply_cement" in t), - all_t, - ) - elif s == "ammonia": - tec = filter(lambda t: "NH3" in t, all_t) - else: - tec = filter(lambda t: re.search(f"furnace_.*{s}", t), all_t) - else: - log.warning(f"No technology filters for {typ = }, {e = }, {s = }") - - aux_df = aux_df[aux_df["technology"].isin(list(tec))] - - # If there are no emission types for that sector skip - if aux_df.empty: - continue - - # Add elements to lists for aggregation over emission type for each sector - aggregates[aggregate_name] = sorted(aux_df["variable"].unique()) - - # Aggregate over emission type for each sector if there are elements to - # aggregate - log.debug(f"Compute emissions aggregates: {aggregates}") - for variable, components in aggregates.items(): - df_emi.aggregate(variable, components, append=True) - - if len(aggregates): - df_emi.filter(variable=aggregates.keys(), inplace=True) - df_final.append(df_emi, inplace=True) - - plot_emi_aggregates(df_emi, pp, e) - - # PLOTS - # - # # HVC Demand: See if this is correct .... - # - # for r in nodes: - # - # fig, ax1 = plt.subplots(1, 1, figsize=(10, 10)) - # - # if r != "China*": - # - # df_petro = df.copy() - # df_petro.filter(region=r, year=years, inplace=True) - # df_petro.filter( - # variable=[ - # "out|final_material|BTX|*", - # "out|final_material|ethylene|*", - # "out|final_material|propylene|*", - # ], - # inplace=True, - # ) - # - # # BTX production - # BTX_vars = [ - # "out|final_material|BTX|steam_cracker_petro|atm_gasoil", - # "out|final_material|BTX|steam_cracker_petro|naphtha", - # "out|final_material|BTX|steam_cracker_petro|vacuum_gasoil", - # ] - # df_petro.aggregate("BTX production", components=BTX_vars, append=True) - # - # # Propylene production - # - # propylene_vars = [ - # # "out|final_material|propylene|catalytic_cracking_ref|atm_gasoil", - # # "out|final_material|propylene|catalytic_cracking_ref|vacuum_gasoil", - # "out|final_material|propylene|steam_cracker_petro|atm_gasoil", - # "out|final_material|propylene|steam_cracker_petro|naphtha", - # "out|final_material|propylene|steam_cracker_petro|propane", - # "out|final_material|propylene|steam_cracker_petro|vacuum_gasoil", - # ] - # - # df_petro.aggregate( - # "Propylene production", components=propylene_vars, append=True - # ) - # - # ethylene_vars = [ - # "out|final_material|ethylene|ethanol_to_ethylene_petro|M1", - # "out|final_material|ethylene|steam_cracker_petro|atm_gasoil", - # "out|final_material|ethylene|steam_cracker_petro|ethane", - # "out|final_material|ethylene|steam_cracker_petro|naphtha", - # "out|final_material|ethylene|steam_cracker_petro|propane", - # "out|final_material|ethylene|steam_cracker_petro|vacuum_gasoil", - # ] - # - # df_petro.aggregate( - # "Ethylene production", components=ethylene_vars, append=True - # ) - # - # if r != "World": - # - # df_petro.filter( - # variable=[ - # "BTX production", - # "Propylene production", - # "Ethylene production", - # "out|final_material|BTX|import_petro|*", - # "out|final_material|propylene|import_petro|*", - # "out|final_material|ethylene|import_petro|*", - # ], - # inplace=True, - # ) - # else: - # df_petro.filter( - # variable=[ - # "BTX production", - # "Propylene production", - # "Ethylene production", - # ], - # inplace=True, - # ) - # - # df_petro.plot.stack(ax=ax1) - # ax1.legend( - # [ - # "BTX production", - # "Ethylene production", - # "Propylene production", - # "import_BTX", - # "import_ethylene", - # "import_propylene", - # ] - # ) - # ax1.set_title("HVC Production_" + r) - # ax1.set_xlabel("Years") - # ax1.set_ylabel("Mt") - # - # plt.close() - # pp.savefig(fig) - - # # Refinery Products - already commented out - # - # for r in nodes: - # - # fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 8)) - # # fig.tight_layout(pad=15.0) - # - # if r != "China*": - # - # # Fuel oil - # - # df_ref_fueloil = df.copy() - # df_ref_fueloil.filter(region=r, year=years, inplace=True) - # - # if r == "World": - # df_ref_fueloil.filter( - # variable=["out|secondary|fueloil|agg_ref|*"], inplace=True - # ) - # - # else: - # df_ref_fueloil.filter( - # variable=[ - # "out|secondary|fueloil|agg_ref|*", - # "out|secondary|fueloil|foil_imp|*", - # ], - # inplace=True, - # ) - # - # df_ref_fueloil.stack_plot(ax=ax1) - # - # ax1.legend( - # [ - # "Atm gas oil", - # "Atm resiude", - # "Heavy fuel oil", - # "Petroleum coke", - # "Vacuum residue", - # "import", - # ], - # bbox_to_anchor=(0.3, 1), - # ) - # ax1.set_title("Fuel oil mix_" + r) - # ax1.set_xlabel("Year") - # ax1.set_ylabel("GWa") - # - # # Light oil - # - # df_ref_lightoil = df.copy() - # df_ref_lightoil.filter(region=r, year=years, inplace=True) - # - # if r == "World": - # df_ref_lightoil.filter( - # variable=["out|secondary|lightoil|agg_ref|*"], inplace=True - # ) - # - # else: - # df_ref_lightoil.filter( - # variable=[ - # "out|secondary|lightoil|agg_ref|*", - # "out|secondary|lightoil|loil_imp|*", - # ], - # inplace=True, - # ) - # - # # ,"out|secondary|lightoil|loil_imp|*" - # df_ref_lightoil.stack_plot(ax=ax2) - # # df_final.append(df_ref_lightoil, inplace = True) - # ax2.legend( - # [ - # "Diesel", - # "Ethane", - # "Gasoline", - # "Kerosene", - # "Light fuel oil", - # "Naphtha", - # "Refinery gas", - # "import", - # ], - # bbox_to_anchor=(1, 1), - # ) - # ax2.set_title("Light oil mix_" + r) - # ax2.set_xlabel("Year") - # ax2.set_ylabel("GWa") - # - # plt.close() - # pp.savefig(fig) - # - # # Oil production World - Already commented out - # - # fig, ax1 = plt.subplots(1, 1, figsize=(8, 8)) - # - # df_all_oil = df.copy() - # df_all_oil.filter(region="World", year=years, inplace=True) - # df_all_oil.filter( - # variable=[ - # "out|secondary|fueloil|agg_ref|*", - # "out|secondary|lightoil|agg_ref|*", - # ], - # inplace=True, - # ) - # df_all_oil.stack_plot(ax=ax1) - # - # ax1.legend( - # [ - # "Atmg_gasoil", - # "atm_residue", - # "heavy_foil", - # "pet_coke", - # "vaccum_residue", - # "diesel", - # "ethane", - # "gasoline", - # "kerosne", - # "light_foil", - # "naphtha", - # "refinery_gas_a", - # "refinery_gas_b", - # ], - # bbox_to_anchor=(1, 1), - # ) - # ax1.set_title("Oil production" + r) - # ax1.set_xlabel("Year") - # ax1.set_ylabel("GWa") - # - # plt.close() - # pp.savefig(fig) - - # Final Energy by all fuels: See if this plot is correct.. - - # Select the sectors - # sectors = ["aluminum", "steel", "cement", "petro"] - # - # for r in nodes: - # - # # For each region create a figure - # - # fig, axs = plt.subplots(nrows=2, ncols=2) - # fig.set_size_inches(20, 20) - # fig.subplots_adjust(wspace=0.2) - # fig.subplots_adjust(hspace=0.5) - # fig.tight_layout(pad=20.0) - # - # if r != "China*": - # - # # Specify the position of each sector in the graph - # - # cnt = 1 - # - # for s in sectors: - # - # if cnt == 1: - # x_cor = 0 - # y_cor = 0 - # - # if cnt == 2: - # x_cor = 0 - # y_cor = 1 - # - # if cnt == 3: - # x_cor = 1 - # y_cor = 0 - # - # if cnt == 4: - # x_cor = 1 - # y_cor = 1 - # - # df_final_energy = df.copy() - # df_final_energy.convert_unit("", to="GWa", factor=1, inplace=True) - # df_final_energy.filter(region=r, year=years, inplace=True) - # df_final_energy.filter(variable=["in|final|*"], inplace=True) - # df_final_energy.filter( - # variable=["in|final|*|cokeoven_steel|*"], keep=False, inplace=True - # ) - # - # if s == "petro": - # # Exclude the feedstock ethanol and natural gas - # df_final_energy.filter( - # variable=[ - # "in|final|ethanol|ethanol_to_ethylene_petro|M1", - # "in|final|gas|gas_processing_petro|M1", - # "in|final|atm_gasoil|steam_cracker_petro|atm_gasoil", - # "in|final|vacuum_gasoil|steam_cracker_petro|vacuum_gasoil", - # "in|final|naphtha|steam_cracker_petro|naphtha", - # ], - # keep=False, - # inplace=True, - # ) - # - # all_flows = df_final_energy.timeseries().reset_index() - # - # # Split the strings in the identified variables for further processing - # splitted_vars = [v.split("|") for v in all_flows.variable] - # - # # Create auxilary dataframes for processing - # aux1_df = pd.DataFrame( - # splitted_vars, - # columns=["flow_type", "level", "commodity", "technology", "mode"], - # ) - # aux2_df = pd.concat( - # [ - # all_flows.reset_index(drop=True), - # aux1_df.reset_index(drop=True), - # ], - # axis=1, - # ) - # - # # Filter the technologies only for the certain material - # tec = [t for t in aux2_df["technology"].values if s in t] - # aux2_df = aux2_df[aux2_df["technology"].isin(tec)] - # - # # Lists to keep commodity, aggregate and variable. - # - # aggregate_list = [] - # commodity_list = [] - # var_list = [] - # - # # For each commodity collect the variable name, create an aggregate - # # name - # s = NAME_MAP.get(s, s) - # for c in np.unique(aux2_df["commodity"].values): - # var = np.unique( - # aux2_df.loc[aux2_df["commodity"] == c, "variable"].values - # ).tolist() - # aggregate_name = "Final Energy|" + s + "|" + c - # - # aggregate_list.append(aggregate_name) - # commodity_list.append(c) - # var_list.append(var) - # - # # Obtain the iamc format dataframe again - # - # aux2_df.drop( - # ["flow_type", "level", "commodity", "technology", "mode"], - # axis=1, - # inplace=True, - # ) - # df_final_energy = pyam.IamDataFrame(data=aux2_df) - # - # # Aggregate the commodities in iamc object - # - # i = 0 - # for c in commodity_list: - # df_final_energy.aggregate( - # aggregate_list[i], components=var_list[i], append=True - # ) - # i = i + 1 - # - # df_final_energy.convert_unit( - # "GWa", to="EJ/yr", factor=0.03154, inplace=True - # ) - # df_final_energy.filter(variable=aggregate_list).plot.stack( - # ax=axs[x_cor, y_cor] - # ) - # axs[x_cor, y_cor].set_ylabel("EJ/yr") - # axs[x_cor, y_cor].set_title("Final Energy_" + s + "_" + r) - # cnt = cnt + 1 - # - # plt.close() - # pp.savefig(fig) - - # # Scrap Release: Buildings, Other and Power Sector - # # TODO: Make the code better - # # NEEDS TO BE CHECKED IF IT IS WORKING........ - # print('Scrap generated by sector') - # materials = ["aluminum","steel","cement"] - # - # for r in nodes: - # print(r) - # - # for m in materials: - # print(m) - # - # df_scrap_by_sector = df.copy() - # df_scrap_by_sector.filter(region=r, year=years, inplace=True) - # - # if m != 'cement': - # - # filt_buildings = 'out|end_of_life|' + m + '|demolition_build|M1' - # print(filt_buildings) - # filt_other = 'out|end_of_life|' + m + '|other_EOL_' + m + '|M1' - # print(filt_other) - # filt_power = ['out|end_of_life|' + m + '|other_EOL_' + m + '|M1', - # 'out|end_of_life|' + m + '|demolition_build|M1', - # 'in|end_of_life|' + m + '|total_EOL_' + m + '|M1'] - # print(filt_power) - # - # m = NAME_MAP.get(m, m) - # var_name_buildings = 'Total Scrap|Residential and Commercial|' + m - # var_name_other = 'Total Scrap|Other|' + m - # var_name_power = 'Total Scrap|Power Sector|' + m - # - # df_scrap_by_sector.aggregate(var_name_other,\ - # components=[filt_other],append=True) - # - # df_scrap_by_sector.aggregate(var_name_buildings,\ - # components=[filt_buildings],append=True) - # - # df_scrap_by_sector.subtract('in|end_of_life|' + m + '|total_EOL_'/ - # + m + '|M1', ['out|end_of_life|' + m + '|demolition_build|M1', - # 'out|end_of_life|' + m + '|other_EOL_' + m + '|M1'], - # var_name_power,axis='variable', append = True) - # - # df_scrap_by_sector.filter(variable=[var_name_buildings, - # var_name_other, var_name_power], - # inplace=True) - # - # df_scrap_by_sector["unit"] = "Mt/yr" - # df_final.append(df_scrap_by_sector, inplace=True) - # - # else: - # filt_buildings = 'out|end_of_life|' + m + '|demolition_build|M1' - # print(filt_buildings) - # filt_power = ['out|end_of_life|' + m + '|other_EOL_' + m + '|M1', - # 'out|end_of_life|' + m + '|demolition_build|M1', - # 'in|end_of_life|' + m + '|total_EOL_' + m + '|M1'] - # print(filt_power) - # m = NAME_MAP.get(m, m) - # var_name_buildings = 'Total Scrap|Residential and Commercial|' + m - # print(var_name_buildings) - # var_name_power = 'Total Scrap|Power Sector|' + m - # df_scrap_by_sector.aggregate(var_name_buildings,\ - # components=[filt_buildings],append=True) - # - # df_scrap_by_sector.subtract('in|end_of_life|' + m + '|total_EOL_'/ - # + m + '|M1', 'out|end_of_life|' + m + '|demolition_build|M1', - # var_name_power, axis = 'variable', append = True) - # - # df_scrap_by_sector.filter(variable=[var_name_buildings, - # var_name_power],inplace=True) - # - # df_scrap_by_sector["unit"] = "Mt/yr" - # df_final.append(df_scrap_by_sector, inplace=True) - - # PRICE - # - # df_final = df_final.timeseries().reset_index() - # - # commodity_type = [ - # "Non-Ferrous Metals|Aluminium", - # "Non-Ferrous Metals|Aluminium|New Scrap", - # "Non-Ferrous Metals|Aluminium|Old Scrap", - # "Non-Ferrous Metals|Bauxite", - # "Non-Ferrous Metals|Alumina", - # "Steel|Iron Ore", - # "Steel|Pig Iron", - # "Steel|New Scrap", - # "Steel|Old Scrap", - # "Steel", - # "Non-Metallic Minerals|Cement", - # "Non-Metallic Minerals|Limestone", - # "Non-Metallic Minerals|Clinker Cement", - # "Chemicals|High Value Chemicals", - # ] - # - # for c in commodity_type: - # prices = scenario.var("PRICE_COMMODITY") - # # Used for calculation of average prices for scraps - # output = scenario.par( - # "output", - # filters={ - # "technology": ["scrap_recovery_aluminum", "scrap_recovery_steel"], - # }, - # ) - # # Differs per sector what to report so more flexible with conditions. - # # Store the relevant variables in prices_all - # - # # ALUMINUM - # if c == "Non-Ferrous Metals|Bauxite": - # continue - # if c == "Non-Ferrous Metals|Alumina": - # prices_all = prices[ - # (prices["level"] == "secondary_material") - # & (prices["commodity"] == "aluminum") - # ] - # if c == "Non-Ferrous Metals|Aluminium": - # prices_all = prices[ - # (prices["level"] == "final_material") - # & (prices["commodity"] == "aluminum") - # ] - # if c == "Non-Ferrous Metals|Aluminium|New Scrap": - # prices_all = prices[ - # (prices["level"] == "new_scrap") & (prices["commodity"] == "aluminum") - # ] - # - # # IRON AND STEEL - # if c == "Steel|Iron Ore": - # prices_all = prices[(prices["commodity"] == "ore_iron")] - # if c == "Steel|Pig Iron": - # prices_all = prices[(prices["commodity"] == "pig_iron")] - # if c == "Steel": - # prices_all = prices[ - # (prices["commodity"] == "steel") - # & (prices["level"] == "final_material") - # ] - # if c == "Steel|New Scrap": - # prices_all = prices[ - # (prices["commodity"] == "steel") & (prices["level"] == "new_scrap") - # ] - # # OLD SCRAP (For aluminum and steel) - # - # if (c == "Steel|Old Scrap") | (c == "Non-Ferrous Metals|Aluminium|Old Scrap"): - # - # prices = prices[ - # ( - # (prices["level"] == "old_scrap_1") - # | (prices["level"] == "old_scrap_2") - # | (prices["level"] == "old_scrap_3") - # ) - # ] - # - # if c == "Non-Ferrous Metals|Aluminium|Old Scrap": - # output = output[output["technology"] == "scrap_recovery_aluminum"] - # prices = prices[(prices["commodity"] == "aluminum")] - # if c == "Steel|Old Scrap": - # output = output[output["technology"] == "scrap_recovery_steel"] - # prices = prices[(prices["commodity"] == "steel")] - # - # prices.loc[prices["level"] == "old_scrap_1", "weight"] = output.loc[ - # output["level"] == "old_scrap_1", "value" - # ].values[0] - # - # prices.loc[prices["level"] == "old_scrap_2", "weight"] = output.loc[ - # output["level"] == "old_scrap_2", "value" - # ].values[0] - # - # prices.loc[prices["level"] == "old_scrap_3", "weight"] = output.loc[ - # output["level"] == "old_scrap_3", "value" - # ].values[0] - # - # prices_all = pd.DataFrame(columns=["node", "commodity", "year"]) - # prices_new = pd.DataFrame(columns=["node", "commodity", "year"]) - # - # for reg in output["node_loc"].unique(): - # for yr in output["year_act"].unique(): - # prices_temp = ( - # prices.groupby(["node", "year"]).get_group((reg, yr)) - # ) - # rate = prices_temp["weight"].values.tolist() - # amount = prices_temp["lvl"].values.tolist() - # weighted_avg = np.average(amount, weights=rate) - # prices_new = pd.DataFrame( - # { - # "node": reg, - # "year": yr, - # "commodity": c, - # "lvl": weighted_avg, - # }, - # index=[0], - # ) - # prices_all = pd.concat([prices_all, prices_new]) - # # CEMENT - # - # if c == "Non-Metallic Minerals|Limestone": - # prices_all = prices[(prices["commodity"] == "limestone_cement")] - # if c == "Non-Metallic Minerals|Clinker Cement": - # prices_all = prices[(prices["commodity"] == "clinker_cement")] - # if c == "Non-Metallic Minerals|Cement": - # prices_all = prices[ - # (prices["commodity"] == "cement") & (prices["level"] == "demand") - # ] - # # Petro-Chemicals - # - # if c == "Chemicals|High Value Chemicals": - # prices = prices[ - # (prices["commodity"] == "ethylene") - # | (prices["commodity"] == "propylene") - # | (prices["commodity"] == "BTX") - # ] - # prices_all = prices.groupby(by=["year", "node"]).mean().reset_index() - # - # # Convert all to IAMC format. - # for r in prices_all["node"].unique(): - # if (r == "R11_GLB") | (r == "R12_GLB"): - # continue - # df_price = pd.DataFrame( - # { - # "model": model_name, - # "scenario": scenario_name, - # "unit": "2010USD/Mt", - # }, - # index=[0], - # ) - # - # for y in prices_all["year"].unique(): - # df_price["region"] = r - # df_price["variable"] = "Price|" + c - # x = prices_all.loc[ - # ((prices_all["node"] == r) & (prices_all["year"] == y)), "lvl" - # ] - # if not x.empty: - # value = x.values[0] * 1.10774 - # else: - # value = 0 - # df_price[y] = value - # - # df.price = df_price.columns.astype(str) - # - # df_final = pd.concat([df_final, df_price]) - - # Material Demand - comment out if no power sector - # Power Sector - - # input_cap_new = scenario.par("input_cap_new") - # input_cap_new.drop( - # ["node_origin", "level", "time_origin", "unit"], axis=1, inplace=True - # ) - # input_cap_new.rename( - # columns={ - # "value": "material_intensity", - # "node_loc": "region", - # "year_vtg": "year", - # }, - # inplace=True, - # ) - # - # cap_new = scenario.var("CAP_NEW") - # cap_new.drop(["mrg"], axis=1, inplace=True) - # cap_new.rename( - # columns={ - # "lvl": "installed_capacity", - # "node_loc": "region", - # "year_vtg": "year" - # }, - # inplace=True, - # ) - # - # merged_df = pd.merge(cap_new, input_cap_new) - # merged_df["Material Need"] = ( - # merged_df["installed_capacity"] * merged_df["material_intensity"] - # ) - # merged_df = merged_df[merged_df["year"] >= min(years)] - # - # final_material_needs = ( - # merged_df.groupby(["commodity", "region", "year"]) - # .sum() - # .drop(["installed_capacity", "material_intensity"], axis=1) - # ) - # - # final_material_needs = final_material_needs.reset_index(["year"]) - # final_material_needs = pd.pivot_table( - # final_material_needs, - # values="Material Need", - # columns="year", - # index=["commodity", "region"], - # ).reset_index(["commodity", "region"]) - # - # final_material_needs_global = ( - # final_material_needs.groupby("commodity").sum().reset_index() - # ) - # final_material_needs_global["region"] = "World" - # - # material_needs_all = pd.concat( - # [final_material_needs, final_material_needs_global], ignore_index=True - # ) - # - # material_needs_all["scenario"] = scenario_name - # material_needs_all["model"] = model_name - # material_needs_all["unit"] = "Mt/yr" - # material_needs_all["commodity"] = material_needs_all.apply( - # lambda x: NAME_MAP.get(x["commodity"], x["commodity"]), axis=1 - # ) - # material_needs_all = material_needs_all.assign( - # variable=lambda x: "Material Demand|Power Sector|" + x["commodity"] - # ) - # - # material_needs_all = material_needs_all.drop(["commodity"], axis=1) - # df.price = df_price.columns.astype(str) - # - # print("This is the final_material_needs") - # print(material_needs_all) - # - # df_final = pd.concat([df_final, material_needs_all]) - - # Plotting is complete - pp.close() - - # - Convert from pyam.IamDataFrame to pandas.DataFrame. - # - Apply the replacements in NAME_MAP1. - df = df_final.as_pandas().replace(NAME_MAP1) - - # Store - path_new = directory.joinpath(f"New_Reporting_{model_name}_{scenario_name}.xlsx") - df.to_excel(path_new, sheet_name="data") - log.info(f"Wrote output to {path_new}") - - scenario.check_out(timeseries_only=True) - log.info(f"Store timeseries on scenario:\n\n{df.head()}") - scenario.add_timeseries(df) - - # NB(PNK) Appears to be old code that handled buildings reporting output - # df_resid = pd.read_csv(path_resid) - # df_resid["Model"] = model_name - # df_resid["Scenario"] = scenario_name - # df_comm = pd.read_csv(path_comm) - # df_comm["Model"] = model_name - # df_comm["Scenario"] = scenario_name - # scenario.add_timeseries(df_resid) - # scenario.add_timeseries(df_comm) - - log.info("…finished.") - scenario.commit("material.report.report()") - - -def callback(rep: message_ix.Reporter, context: Context) -> None: - """:meth:`.prepare_reporter` callback for MESSAGEix-Materials. - - - "materials all": invokes :func:`report`. - """ - rep.add( - "materials all", - report, - "scenario", - "message::default", - "y::model", - "n", - "config", - ) diff --git a/message_ix_models/model/material/report/reporting.py b/message_ix_models/model/material/report/reporting.py new file mode 100644 index 0000000000..0fa722099d --- /dev/null +++ b/message_ix_models/model/material/report/reporting.py @@ -0,0 +1,3908 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Mar 8 12:58:21 2021 +This code produces the follwoing outputs: +message_ix_reporting.xlsx: message_ix level reporting +check.xlsx: can be used for checking the filtered variables +New_Reporting_Model_Scenario.xlsx: Reporting including the material variables +Merged_Model_Scenario.xlsx: Includes all IAMC variables +Material_global_grpahs.pdf + +@author: unlu +""" + +# NOTE: Works with pyam-iamc version 0.9.0 +# Problems with the most recent versions: +# 0.11.0 --> Filtering with asterisk * does not work, dataframes are empty. +# Problems with emission and region filtering. +# https://github.com/IAMconsortium/pyam/issues/517 +# 0.10.0 --> Plotting gives the follwoing error: +# ValueError: Can not plot data that does not extend for the entire year range. +# There are various tech.s that only have data starting from 2025 or 2030. +# foil_imp AFR, frunace_h2_aluminum, h2_i... + +# PACKAGES + +from ixmp import Platform +from message_ix import Scenario +from message_ix.reporting import Reporter +from ixmp.reporting import configure +from message_ix_models import ScenarioInfo +from message_data.tools.post_processing.iamc_report_hackathon import report as reporting +from message_data.model.material.util import read_config + +import pandas as pd +import numpy as np +import pyam +import xlsxwriter +import os +import openpyxl + +import plotly.graph_objects as go +import matplotlib + +matplotlib.use("Agg") +from matplotlib import pyplot as plt + +from pyam.plotting import OUTSIDE_LEGEND +from matplotlib.backends.backend_pdf import PdfPages + +def print_full(x): + pd.set_option("display.max_rows", len(x)) + print(x) + pd.reset_option("display.max_rows") + + +def change_names(s): + + """Change the sector names according to IMAC format.""" + + if s == "aluminum": + s = "Non-Ferrous Metals|Aluminium" + elif s == "steel": + s = "Steel" + elif s == "cement": + s = "Non-Metallic Minerals|Cement" + elif s == "petro": + s = "Chemicals|High Value Chemicals" + elif s == 'ammonia': + s = "Chemicals|Ammonia" + elif s == 'methanol': + s = "Chemicals|Methanol" + elif s == "BCA": + s = "BC" + elif s == "OCA": + s = "OC" + elif s == "CO2_industry": + s == "CO2" + else: + s == s + return s + + +def fix_excel(path_temp, path_new): + + """ + Fix the names of the regions or variables to be compatible + with IAMC format. This is done in the final reported excel file + (path_temp) and written to a new excel file (path_new). + """ + # read Excel file and sheet by name + workbook = openpyxl.load_workbook(path_temp) + sheet = workbook["data"] + + new_workbook = openpyxl.Workbook() + new_sheet = new_workbook['Sheet'] + new_sheet.title = 'data' + new_sheet = new_workbook.active + + replacement = { + "CO2_industry": "CO2", + "R11_AFR|R11_AFR": "R11_AFR", + "R11_CPA|R11_CPA": "R11_CPA", + "R11_MEA|R11_MEA": "R11_MEA", + "R11_FSU|R11_FSU": "R11_FSU", + "R11_PAS|R11_PAS": "R11_PAS", + "R11_SAS|R11_SAS": "R11_SAS", + "R11_LAM|R11_LAM": "R11_LAM", + "R11_NAM|R11_NAM": "R11_NAM", + "R11_PAO|R11_PAO": "R11_PAO", + "R11_EEU|R11_EEU": "R11_EEU", + "R11_WEU|R11_WEU": "R11_WEU", + "R11_AFR": "R11_AFR", + "R11_CPA": "R11_CPA", + "R11_MEA": "R11_MEA", + "R11_FSU": "R11_FSU", + "R11_PAS": "R11_PAS", + "R11_SAS": "R11_SAS", + "R11_LAM": "R11_LAM", + "R11_NAM": "R11_NAM", + "R11_PAO": "R11_PAO", + "R11_EEU": "R11_EEU", + "R11_WEU": "R11_WEU", + "R12_AFR|R12_AFR": "R12_AFR", + "R12_RCPA|R12_RCPA": "R12_RCPA", + "R12_MEA|R12_MEA": "R12_MEA", + "R12_FSU|R12_FSU": "R12_FSU", + "R12_PAS|R12_PAS": "R12_PAS", + "R12_SAS|R12_SAS": "R12_SAS", + "R12_LAM|R12_LAM": "R12_LAM", + "R12_NAM|R12_NAM": "R12_NAM", + "R12_PAO|R12_PAO": "R12_PAO", + "R12_EEU|R12_EEU": "R12_EEU", + "R12_WEU|R12_WEU": "R12_WEU", + "R12_CHN|R12_CHN": "R12_CHN", + "R12_AFR": "R12_AFR", + "R12_RCPA": "R12_RCPA", + "R12_MEA": "R12_MEA", + "R12_FSU": "R12_FSU", + "R12_PAS": "R12_PAS", + "R12_SAS": "R12_SAS", + "R12_LAM": "R12_LAM", + "R12_NAM": "R12_NAM", + "R12_PAO": "R12_PAO", + "R12_EEU": "R12_EEU", + "R12_WEU": "R12_WEU", + "R12_CHN": "R12_CHN", + "World": "R12_GLB", + "model": "Model", + "scenario": "Scenario", + "variable": "Variable", + "region": "Region", + "unit": "Unit", + "BCA": "BC", + "OCA": "OC", + } + # Iterate over the rows and replace + for i in range(1, ((sheet.max_row) + 1)): + data = [sheet.cell(row=i, column=col).value for col in range(1, ((sheet.max_column) + 1))] + for index, value in enumerate(data): + col_no = index + 1 + if value in replacement.keys(): + new_sheet.cell(row=i, column=col_no).value = replacement.get(value) + else: + new_sheet.cell(row=i, column=col_no).value = value + + new_workbook.save(path_new) + +def report(context,scenario): + + # Obtain scenario information and directory + + s_info = ScenarioInfo(scenario) + + # In order to avoid confusion in the second reporting stage there should + # no existing timeseries uploaded in the scenairo. Clear these except the + # residential and commercial ones since they should be always included. + + # Activate this part to keep the residential and commercial variables + # when the model is run with the buildigns linkage. + # df_rem = df_rem[~(df_rem["variable"].str.contains("Residential") | \ + # df_rem["variable"].str.contains("Commercial"))] + + years = s_info.Y + nodes = [] + for n in s_info.N: + n = n + "*" + nodes.append(n) + + if "R11_GLB*" in nodes: + nodes.remove("R11_GLB*") + elif "R12_GLB*" in nodes: + nodes.remove("R12_GLB*") + + # Path for materials reporting output + directory = context.get_local_path("report", "materials") + directory.mkdir(exist_ok=True) + + # Generate message_ix level reporting and dump to an excel file. + + rep = Reporter.from_scenario(scenario) + configure(units={"replace": {"-": ""}}) + df = rep.get("message::default") + name = os.path.join(directory, f"message_ix_reporting_{scenario.scenario}.xlsx") + df.to_excel(name) + print("message_ix level reporting generated") + + # Obtain a pyam dataframe / filter / global aggregation + + path = os.path.join(directory, f"message_ix_reporting_{scenario.scenario}.xlsx") + report = pd.read_excel(path) + report.Unit.fillna("", inplace=True) + df = pyam.IamDataFrame(report) + df.filter(region=nodes, year=years, inplace=True) + df.filter( + variable=[ + "out|new_scrap|aluminum|*", + "out|final_material|aluminum|prebake_aluminum|M1", + "out|final_material|aluminum|secondary_aluminum|M1", + "out|final_material|aluminum|soderberg_aluminum|M1", + "out|new_scrap|steel|*", + "in|new_scrap|steel|*", + "out|final_material|steel|*", + "out|useful_material|steel|import_steel|*", + "out|secondary_material|NH3|*", + "out|primary|methanol|*", + "out|primary_material|methanol|*", + "out|final|methanol|*", + "out|final_material|methanol|*", + "in|useful_material|steel|export_steel|*", + "out|useful_material|aluminum|import_aluminum|*", + "in|useful_material|aluminum|export_aluminum|*", + "out|final_material|*|import_petro|*", + "in|final_material|*|export_petro|*", + "out|secondary|lightoil|loil_imp|*", + "out|secondary|fueloil|foil_imp|*", + "out|tertiary_material|clinker_cement|*", + "out|product|cement|*", + "out|final_material|BTX|*", + "out|final_material|ethylene|*", + "out|final_material|propylene|*", + "out|secondary|fueloil|agg_ref|*", + "out|secondary|lightoil|agg_ref|*", + 'out|useful|i_therm|solar_i|M1', + 'out|useful_steel|lt_heat|solar_steel|*', + 'out|useful_aluminum|lt_heat|solar_aluminum|*', + 'out|useful_cement|lt_heat|solar_cement|*', + 'out|useful_petro|lt_heat|solar_petro|*', + 'out|useful_resins|lt_heat|solar_resins|*', + "in|final|*", + "in|secondary|coal|coal_NH3|M1", + "in|secondary|electr|NH3_to_N_fertil|M1", + 'in|secondary|electr|coal_NH3|M1', + 'in|secondary|electr|electr_NH3|M1', + 'in|secondary|electr|gas_NH3|M1', + 'in|secondary|fueloil|fueloil_NH3|M1', + 'in|secondary|gas|gas_NH3|M1', + "in|secondary|coal|coal_NH3_ccs|M1", + 'in|secondary|electr|coal_NH3_ccs|M1', + 'in|secondary|electr|gas_NH3_ccs|M1', + 'in|secondary|fueloil|fueloil_NH3_ccs|M1', + 'in|secondary|gas|gas_NH3_ccs|M1', + "in|primary|biomass|biomass_NH3|M1", + "in|seconday|electr|biomass_NH3|M1", + "in|primary|biomass|biomass_NH3_ccs|M1", + "in|secondary|electr|biomass_NH3|M1", + "in|secondary|electr|biomass_NH3_ccs|M1", + "in|secondary|electr|fueloil_NH3|M1", + "in|secondary|electr|fueloil_NH3_ccs|M1", + "in|secondary|coal|meth_coal|feedstock", + 'in|secondary|coal|meth_coal_ccs|feedstock', + 'in|secondary|gas|meth_ng_ccs|feedstock', + "in|secondary|gas|meth_ng|feedstock", + 'in|secondary|electr|meth_ng|feedstock', + 'in|secondary|electr|meth_ng_ccs|feedstock', + "in|secondary|electr|meth_coal|feedstock", + 'in|secondary|electr|meth_coal_ccs|feedstock', + 'in|primary|biomass|meth_bio|feedstock', + 'in|primary|biomass|meth_bio_ccs|feedstock', + 'in|secondary|hydrogen|meth_h2|feedstock', + 'in|secondary|electr|meth_h2|feedstock', + "in|desulfurized|*|steam_cracker_petro|*", + "in|secondary_material|*|steam_cracker_petro|*", + "in|dummy_end_of_life_1|aluminum|scrap_recovery_aluminum_1|M1", + "in|dummy_end_of_life_2|aluminum|scrap_recovery_aluminum_2|M1", + "in|dummy_end_of_life_3|aluminum|scrap_recovery_aluminum_3|M1", + "in|dummy_end_of_life|steel|scrap_recovery_steel|M1", + "out|dummy_end_of_life_1|aluminum|total_EOL_aluminum|M1", + "out|dummy_end_of_life_2|aluminum|total_EOL_aluminum|M1", + "out|dummy_end_of_life_3|aluminum|total_EOL_aluminum|M1", + "out|dummy_end_of_life|steel|total_EOL_steel|M1", + "out|dummy_end_of_life|cement|total_EOL_cement|M1", + "in|product|steel|scrap_recovery_steel|M1", + "in|final_material|methanol|MTO_petro|M1", + "in|final_material|methanol|CH2O_synth|M1", + "out|end_of_life|*", + "in|end_of_life|*", + "emis|CO2_industry|*", + "emis|CF4|*", + "emis|SO2|*", + "emis|NOx|*", + "emis|CH4|*", + "emis|N2O|*", + "emis|BCA|*", + "emis|CO|*", + "emis|NH3|*", + "emis|NOx|*", + "emis|OCA|*", + "emis|CO2|*", + ], + inplace=True, + ) + + # Methanol input conversion from material to energy unit + + df.divide("in|final_material|methanol|MTO_petro|M1", (1/0.6976), + "in|final_material|methanol|MTO_petro|energy", append=True, ignore_units=True) + + df.divide("in|final_material|methanol|CH2O_synth|M1", (1/0.6976), + "in|final_material|methanol|CH2O_synth|energy", append=True, ignore_units=True) + + # Convert methanol at primary_material from energy to material unit + # In the model this is kept as energy units to easily seperate two modes: fuel and feedstock + df.divide("out|primary_material|methanol|meth_coal|feedstock", 0.6976, + "out|primary_material|methanol|meth_coal|feedstockMt", append=True, ignore_units=True) + + df.divide("out|primary_material|methanol|meth_coal_ccs|feedstock", 0.6976, + "out|primary_material|methanol|meth_coal_ccs|feedstockMt", append=True, ignore_units=True) + + df.divide("out|primary_material|methanol|meth_ng|feedstock", 0.6976, + "out|primary_material|methanol|meth_ng|feedstockMt", append=True, ignore_units=True) + + df.divide("out|primary_material|methanol|meth_ng_ccs|feedstock", 0.6976, + "out|primary_material|methanol|meth_ng_ccs|feedstockMt", append=True, ignore_units=True) + + df.divide("out|primary_material|methanol|meth_bio|feedstock", 0.6976, + "out|primary_material|methanol|meth_bio|feedstockMt", append=True, ignore_units=True) + + df.divide("out|primary_material|methanol|meth_bio_ccs|feedstock", 0.6976, + "out|primary_material|methanol|meth_bio_ccs|feedstockMt", append=True, ignore_units=True) + + df.divide("out|primary_material|methanol|meth_h2|feedstock", 0.6976, + "out|primary_material|methanol|meth_h2|feedstockMt", append=True, ignore_units=True) + + # Convert methanol at primary from energy to material unit + # In the model this is kept as energy units to easily seperate two modes: fuel and feedstock + df.divide("out|primary|methanol|meth_coal|fuel", 0.6976, + "out|primary|methanol|meth_coal|fuelMt", append=True, ignore_units=True) + + df.divide("out|primary|methanol|meth_coal_ccs|fuel", 0.6976, + "out|primary|methanol|meth_coal_ccs|fuelMt", append=True, ignore_units=True) + + df.divide("out|primary|methanol|meth_ng|fuel", 0.6976, + "out|primary|methanol|meth_ng|fuelMt", append=True, ignore_units=True) + + df.divide("out|primary|methanol|meth_ng_ccs|fuel", 0.6976, + "out|primary|methanol|meth_ng_ccs|fuelMt", append=True, ignore_units=True) + + df.divide("out|primary|methanol|meth_bio|fuel", 0.6976, + "out|primary|methanol|meth_bio|fuelMt", append=True, ignore_units=True) + + df.divide("out|primary|methanol|meth_bio_ccs|fuel", 0.6976, + "out|primary|methanol|meth_bio_ccs|fuelMt", append=True, ignore_units=True) + + df.divide("out|primary|methanol|meth_h2|fuel", 0.6976, + "out|primary|methanol|meth_h2|fuelMt", append=True, ignore_units=True) + + df.convert_unit('unknown', to='', factor=1, inplace = True) + + variables = df.variable + df.aggregate_region(variables, region="World", method=sum, append=True) + + name = os.path.join(directory, "check.xlsx") + df.to_excel(name) + print("Necessary variables are filtered") + + # Obtain the model and scenario name + model_name = df.model[0] + scenario_name = df.scenario[0] + + # Create an empty pyam dataframe to store the new variables + + workbook = xlsxwriter.Workbook("empty_template.xlsx") + worksheet = workbook.add_worksheet() + worksheet.write("A1", "Model") + worksheet.write("B1", "Scenario") + worksheet.write("C1", "Region") + worksheet.write("D1", "Variable") + worksheet.write("E1", "Unit") + columns = [ + "F1", + "G1", + "H1", + "I1", + "J1", + "K1", + "L1", + "M1", + "N1", + "O1", + "P1", + "Q1", + "R1", + ] + + for yr, col in zip(years, columns): + worksheet.write(col, yr) + workbook.close() + + df_final = pyam.IamDataFrame("empty_template.xlsx") + print("Empty template for new variables created") + + # Create a pdf file with figures + path = os.path.join(directory, "Material_global_graphs.pdf") + pp = PdfPages(path) + # pp = PdfPages("Material_global_graphs.pdf") + + # Reporting and Plotting + + print("Production plots and variables are being generated") + for r in nodes: + ## PRODUCTION - PLOTS + ## Needs to be checked again to see whether the graphs are correct + + fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(20, 10)) + fig.tight_layout(pad=10.0) + + # ALUMINUM + df_al = df.copy() + df_al.filter(region=r, year=years, inplace=True) + df_al.filter(variable=["out|*|aluminum|*", "in|*|aluminum|*"], inplace=True) + df_al.convert_unit('', to='Mt/yr', factor=1, inplace = True) + df_al_graph = df_al.copy() + + df_al_graph.filter( + variable=[ + "out|useful_material|aluminum|import_aluminum|*", + "out|final_material|aluminum|prebake_aluminum|*", + "out|final_material|aluminum|soderberg_aluminum|*", + "out|new_scrap|aluminum|*", + ], + inplace=True, + ) + + if r == "World": + df_al_graph.filter( + variable=[ + "out|final_material|aluminum|prebake_aluminum|*", + "out|final_material|aluminum|soderberg_aluminum|*", + "out|new_scrap|aluminum|*", + ], + inplace=True, + ) + + df_al_graph.plot.stack(ax=ax1) + ax1.legend( + [ + "Prebake", + "Soderberg", + "Newscrap", + "Oldscrap_min", + "Oldscrap_av", + "Oldscrap_max", + "import", + ], + bbox_to_anchor=(-0.4, 1), + loc="upper left", + ) + ax1.set_title("Aluminium Production_" + r) + ax1.set_xlabel("Year") + ax1.set_ylabel("Mt") + + # STEEL + + df_steel = df.copy() + df_steel.filter(region=r, year=years, inplace=True) + df_steel.filter(variable=["out|*|steel|*", "in|*|steel|*"], inplace=True) + df_steel.convert_unit('', to='Mt/yr', factor=1, inplace = True) + + df_steel_graph = df_steel.copy() + df_steel_graph.filter( + variable=[ + "out|final_material|steel|*", + "out|useful_material|steel|import_steel|*", + ], + inplace=True, + ) + + if r == "World": + df_steel.filter(variable=["out|*|steel|*", "in|*|steel|*"], inplace=True) + df_steel_graph.filter( + variable=["out|final_material|steel|*",], inplace=True, + ) + + df_steel_graph.plot.stack(ax=ax2) + ax2.legend( + ["Bof steel", "Eaf steel M1", "Eaf steel M2", "Import"], + bbox_to_anchor=(-0.4, 1), + loc="upper left", + ) + ax2.set_title("Steel Production_" + r) + ax2.set_ylabel("Mt") + + # PETRO + + df_petro = df.copy() + df_petro.filter(region=r, year=years, inplace=True) + df_petro.filter( + variable=[ + "in|final|ethanol|ethanol_to_ethylene_petro|M1", + "in|desulfurized|*|steam_cracker_petro|*", + "in|secondary_material|*|steam_cracker_petro|*", + ], + inplace=True, + ) + df_petro.convert_unit('', to='Mt/yr', factor=1, inplace = True) + + if r == "World": + + df_petro.filter( + variable=[ + "in|final|ethanol|ethanol_to_ethylene_petro|M1", + "in|desulfurized|*|steam_cracker_petro|*", + "in|secondary_material|*|steam_cracker_petro|*", + ], + inplace=True, + ) + + df_petro.plot.stack(ax=ax3) + ax3.legend( + [ + "atm_gasoil", + "naphtha", + "vacuum_gasoil", + "bioethanol", + "ethane", + "propane", + ], + bbox_to_anchor=(-0.4, 1), + loc="upper left", + ) + ax3.set_title("HVC feedstock" + r) + ax3.set_xlabel("Years") + ax3.set_ylabel("GWa") + + plt.close() + pp.savefig(fig) + + # PRODUCTION - IAMC Variables + + # ALUMINUM + + # Primary Production + primary_al_vars = [ + "out|final_material|aluminum|prebake_aluminum|M1", + "out|final_material|aluminum|soderberg_aluminum|M1", + ] + + # Secondary Production + secondary_al_vars = ["out|final_material|aluminum|secondary_aluminum|M1"] + + # Collected Scrap + collected_scrap_al_vars = [ + "out|new_scrap|aluminum|manuf_aluminum|M1", + "in|dummy_end_of_life_1|aluminum|scrap_recovery_aluminum_1|M1", + "in|dummy_end_of_life_2|aluminum|scrap_recovery_aluminum_2|M1", + "in|dummy_end_of_life_3|aluminum|scrap_recovery_aluminum_3|M1" + ] + + # Total Available Scrap: + # New scrap + The end of life products (exegenous assumption) + # + from power and buildings sector + + new_scrap_al_vars = ["out|new_scrap|aluminum|manuf_aluminum|M1"] + old_scrap_al_vars = ["out|dummy_end_of_life_1|aluminum|total_EOL_aluminum|M1", + "out|dummy_end_of_life_2|aluminum|total_EOL_aluminum|M1", + "out|dummy_end_of_life_3|aluminum|total_EOL_aluminum|M1"] + + df_al.aggregate( + "Production|Primary|Non-Ferrous Metals|Aluminium", + components=primary_al_vars, + append=True, + ) + df_al.aggregate( + "Production|Secondary|Non-Ferrous Metals|Aluminium", + components=secondary_al_vars, + append=True, + ) + + df_al.aggregate( + "Production|Non-Ferrous Metals|Aluminium", + components=secondary_al_vars + primary_al_vars, + append=True, + ) + + df_al.aggregate( + "Collected Scrap|Non-Ferrous Metals|Aluminium", + components=collected_scrap_al_vars, + append=True, + ) + + df_al.aggregate( + "Collected Scrap|Non-Ferrous Metals", + components=collected_scrap_al_vars, + append=True, + ) + + df_al.aggregate( + "Total Scrap|Non-Ferrous Metals|Aluminium", + components=new_scrap_al_vars+old_scrap_al_vars, + append=True, + ) + + df_al.aggregate( + "Total Scrap|Non-Ferrous Metals", + components=new_scrap_al_vars+old_scrap_al_vars, + append=True, + ) + + df_al.aggregate( + "Total Scrap|Non-Ferrous Metals|Aluminium|New Scrap", + components=new_scrap_al_vars, + append=True, + ) + + df_al.aggregate( + "Total Scrap|Non-Ferrous Metals|Aluminium|Old Scrap", + components=old_scrap_al_vars, + append=True, + ) + + df_al.aggregate( + "Production|Non-Ferrous Metals", + components=primary_al_vars + secondary_al_vars, + append=True, + ) + + df_al.filter( + variable=[ + "Production|Primary|Non-Ferrous Metals|Aluminium", + "Production|Secondary|Non-Ferrous Metals|Aluminium", + "Production|Non-Ferrous Metals|Aluminium", + "Production|Non-Ferrous Metals", + "Collected Scrap|Non-Ferrous Metals|Aluminium", + "Collected Scrap|Non-Ferrous Metals", + "Total Scrap|Non-Ferrous Metals", + "Total Scrap|Non-Ferrous Metals|Aluminium", + "Total Scrap|Non-Ferrous Metals|Aluminium|New Scrap", + "Total Scrap|Non-Ferrous Metals|Aluminium|Old Scrap", + ], + inplace=True, + ) + + # STEEL + + primary_steel_vars = ["out|final_material|steel|bof_steel|M1", + "out|final_material|steel|eaf_steel|M1", + "out|final_material|steel|eaf_steel|M3" + ] + + secondary_steel_vars = [ + "out|final_material|steel|eaf_steel|M2", + "in|new_scrap|steel|bof_steel|M1" + ] + + collected_scrap_steel_vars = [ + "in|dummy_end_of_life|steel|scrap_recovery_steel|M1" + ] + total_scrap_steel_vars = ["out|dummy_end_of_life|steel|total_EOL_steel|M1"] + + new_scrap_steel_vars = ["out|new_scrap|steel|manuf_steel|M1"] + old_scrap_steel_vars = ["out|dummy_end_of_life|steel|total_EOL_steel|M1"] + + df_steel.aggregate( + "Production|Primary|Steel (before sub.)", components=primary_steel_vars, append=True, + ) + + df_steel.subtract("Production|Primary|Steel (before sub.)", + "in|new_scrap|steel|bof_steel|M1","Production|Primary|Steel", append = True) + + df_steel.aggregate( + "Production|Secondary|Steel", components=secondary_steel_vars, append=True, + ) + + df_steel.aggregate( + "Production|Steel", + components=["Production|Primary|Steel", "Production|Secondary|Steel"], + append=True, + ) + + df_steel.aggregate( + "Collected Scrap|Steel", components=collected_scrap_steel_vars, append=True, + ) + df_steel.aggregate( + "Total Scrap|Steel", components=total_scrap_steel_vars, append=True + ) + + df_steel.aggregate( + "Total Scrap|Steel|Old Scrap", components=old_scrap_steel_vars, append=True + ) + + #df_steel.aggregate( + # "Total Scrap|Steel|New Scrap", components=new_scrap_steel_vars, append=True + #) + + df_steel.filter( + variable=[ + "Production|Primary|Steel", + "Production|Secondary|Steel", + "Production|Steel", + "Collected Scrap|Steel", + "Total Scrap|Steel", + "Total Scrap|Steel|Old Scrap", + "Total Scrap|Steel|New Scrap", + ], + inplace=True, + ) + + # CHEMICALS + + df_chemicals = df.copy() + df_chemicals.filter(region=r, year=years, inplace=True) + df_chemicals.filter(variable=['out|secondary_material|NH3|*', + "out|final_material|ethylene|*", + "out|final_material|propylene|*", + "out|final_material|BTX|*", + 'out|primary_material|methanol|*|feedstockMt', + 'out|primary|methanol|*|fuelMt' + ],inplace=True) + df_chemicals.convert_unit('', to='Mt/yr', factor=1, inplace = True) + df_chemicals.convert_unit('GWa', to='Mt/yr', factor=(1/0.6976), inplace=True) + + # Methanol + + # In Mt units + primary_methanol_chemical_vars = ["out|primary_material|methanol|meth_coal|feedstockMt", + "out|primary_material|methanol|meth_coal_ccs|feedstockMt", + "out|primary_material|methanol|meth_ng|feedstockMt", + "out|primary_material|methanol|meth_ng_ccs|feedstockMt", + "out|primary_material|methanol|meth_bio|feedstockMt", + "out|primary_material|methanol|meth_bio_ccs|feedstockMt", + "out|primary_material|methanol|meth_h2|feedstockMt", + ] + methanol_fuel_vars = ["out|primary|methanol|meth_coal|fuelMt", + "out|primary|methanol|meth_coal_ccs|fuelMt", + "out|primary|methanol|meth_ng|fuelMt", + "out|primary|methanol|meth_ng_ccs|fuelMt", + "out|primary|methanol|meth_bio|fuelMt", + "out|primary|methanol|meth_bio_ccs|fuelMt", + "out|primary|methanol|meth_h2|fuelMt", + ] + + methanol_total_vars = primary_methanol_chemical_vars + methanol_fuel_vars + + df_chemicals.aggregate( + "Production|Methanol", + components=methanol_total_vars, + append=True, + ) + + df_chemicals.aggregate( + "Production|Chemicals|Methanol", + components=primary_methanol_chemical_vars, + append=True, + ) + + df_chemicals.aggregate( + "Production|Primary|Chemicals|Methanol", + components=primary_methanol_chemical_vars, + append=True, + ) + + df_chemicals.aggregate( + "Production|Fuel|Methanol", + components=methanol_fuel_vars, + append=True, + ) + + # add entries for each methanol technology + meth_tec_list = [i.replace("fuel", "M1") for i in methanol_fuel_vars] + df_meth_individual = df_chemicals.filter(variable=meth_tec_list) + df_meth_individual.convert_unit('Mt/yr', to='Mt/yr', factor=(1/0.6976), inplace=True) + var_name = "Production|Methanol|" + for i in df_meth_individual["variable"]: + df_meth_individual.rename({"variable": {i: i.replace("out|primary|methanol|", var_name).replace("|M1", "")}}, + inplace=True) + + # AMMONIA + + primary_ammonia_vars = [ + "out|secondary_material|NH3|gas_NH3|M1", + "out|secondary_material|NH3|gas_NH3_ccs|M1", + "out|secondary_material|NH3|coal_NH3|M1", + "out|secondary_material|NH3|coal_NH3_ccs|M1", + "out|secondary_material|NH3|biomass_NH3|M1", + "out|secondary_material|NH3|biomass_NH3_ccs|M1", + "out|secondary_material|NH3|fueloil_NH3|M1", + "out|secondary_material|NH3|fueloil_NH3_ccs|M1", + "out|secondary_material|NH3|electr_NH3|M1" + ] + + df_chemicals.aggregate( + "Production|Primary|Chemicals|Ammonia", + components=primary_ammonia_vars, + append=True, + ) + + df_chemicals.aggregate( + "Production|Chemicals|Ammonia", + components=primary_ammonia_vars, + append=True, + ) + + # High Value Chemicals + + intermediate_petro_vars = [ + "out|final_material|ethylene|ethanol_to_ethylene_petro|M1", + "out|final_material|ethylene|steam_cracker_petro|atm_gasoil", + "out|final_material|ethylene|steam_cracker_petro|naphtha", + "out|final_material|ethylene|steam_cracker_petro|vacuum_gasoil", + "out|final_material|ethylene|steam_cracker_petro|ethane", + "out|final_material|ethylene|steam_cracker_petro|propane", + "out|final_material|propylene|steam_cracker_petro|atm_gasoil", + "out|final_material|propylene|steam_cracker_petro|naphtha", + "out|final_material|propylene|steam_cracker_petro|vacuum_gasoil", + "out|final_material|propylene|steam_cracker_petro|propane", + "out|final_material|BTX|steam_cracker_petro|atm_gasoil", + "out|final_material|BTX|steam_cracker_petro|naphtha", + "out|final_material|BTX|steam_cracker_petro|vacuum_gasoil", + "out|final_material|ethylene|MTO_petro|M1", + "out|final_material|propylene|MTO_petro|M1", + ] + + df_chemicals.aggregate( + "Production|Primary|Chemicals|High Value Chemicals", + components=intermediate_petro_vars, + append=True, + ) + + df_chemicals.aggregate( + "Production|Chemicals|High Value Chemicals", + components=intermediate_petro_vars, + append=True, + ) + + # Totals + + chemicals_vars = intermediate_petro_vars + primary_ammonia_vars + primary_methanol_chemical_vars + df_chemicals.aggregate( + "Production|Primary|Chemicals", + components=chemicals_vars, + append=True, + ) + + df_chemicals.aggregate( + "Production|Chemicals", + components=chemicals_vars, + append=True, + ) + + df_chemicals.filter( + variable=[ + "Production|Primary|Chemicals|High Value Chemicals", + "Production|Chemicals|High Value Chemicals", + "Production|Primary|Chemicals", + "Production|Chemicals", + "Production|Primary|Chemicals|Ammonia", + "Production|Chemicals|Ammonia", + "Production|Primary|Chemicals|Methanol", + "Production|Chemicals|Methanol", + "Production|Fuel|Methanol", + 'Production|Methanol' + ], + inplace=True, + ) + + df_chemicals.append(df_meth_individual, inplace=True) + + # Add to final data_frame + df_final.append(df_al, inplace=True) + df_final.append(df_steel, inplace=True) + df_final.append(df_chemicals, inplace=True) + + # CEMENT + + for r in nodes: + + # PRODUCTION - PLOT + + fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 10)) + fig.tight_layout(pad=15.0) + + # Clinker cement + + df_cement_clinker = df.copy() + df_cement_clinker.filter(region=r, year=years, inplace=True) + df_cement_clinker.filter( + variable=["out|tertiary_material|clinker_cement|*"], inplace=True + ) + df_cement_clinker.plot.stack(ax=ax1) + ax1.legend( + ["Dry Clinker", "Wet Clinker"], bbox_to_anchor=(-0.5, 1), loc="upper left" + ) + ax1.set_title("Clinker Cement Production_" + r) + ax1.set_xlabel("Year") + ax1.set_ylabel("Mt") + + # Final prodcut cement + + df_cement = df.copy() + df_cement.filter(region=r, year=years, inplace=True) + df_cement.filter(variable=["out|product|cement|*", + "out|tertiary_material|clinker_cement|*" + ], inplace=True) + # df_cement.plot.stack(ax=ax2) + # ax2.legend( + # ["Ballmill Grinding", "Vertical Mill Grinding"], + # bbox_to_anchor=(-0.6, 1), + # loc="upper left", + # ) + # ax2.set_title("Final Cement Production_" + r) + # ax2.set_xlabel("Year") + # ax2.set_ylabel("Mt") + + plt.close() + pp.savefig(fig) + + # PRODUCTION - IAMC format + + primary_cement_vars = [ + "out|product|cement|grinding_ballmill_cement|M1", + "out|product|cement|grinding_vertmill_cement|M1", + ] + + clinker_vars = [ + "out|tertiary_material|clinker_cement|clinker_dry_cement|M1", + "out|tertiary_material|clinker_cement|clinker_wet_cement|M1" + ] + + total_scrap_cement_vars = ["out|dummy_end_of_life|cement|total_EOL_cement|M1"] + + df_cement.convert_unit('', to='Mt/yr', factor=1, inplace = True) + + df_cement.aggregate( + "Production|Non-Metallic Minerals|Clinker", components=clinker_vars, append=True, + ) + + df_cement.aggregate( + "Production|Primary|Non-Metallic Minerals|Cement", components=primary_cement_vars, append=True, + ) + + df_cement.aggregate( + "Production|Non-Metallic Minerals", + components=primary_cement_vars, + append=True, + ) + + df_cement.aggregate( + "Production|Non-Metallic Minerals|Cement", components=primary_cement_vars, append=True, + ) + + df_cement.aggregate( + "Total Scrap|Non-Metallic Minerals", + components=total_scrap_cement_vars, + append=True, + ) + + df_cement.aggregate( + "Total Scrap|Non-Metallic Minerals|Cement", + components=total_scrap_cement_vars, + append=True, + ) + df_cement.filter( + variable=[ + "Production|Primary|Non-Metallic Minerals|Cement", + "Production|Non-Metallic Minerals|Cement", + "Total Scrap|Non-Metallic Minerals" + "Total Scrap|Non-Metallic Minerals|Cement", + "Production|Non-Metallic Minerals|Clinker", + ], + inplace=True, + ) + df_final.append(df_cement, inplace=True) + + # --------------------------------------------------------------------------------------- + # FINAL ENERGY BY FUELS (Only Non-Energy Use) + # HVC production, ammonia production and methanol production. + + print("Final Energy by fuels only non-energy use is being printed.") + commodities = ["gas", "liquids", "solids",'hydrogen','methanol',"all",'electr_gas'] + + for c in commodities: + for r in nodes: + df_final_energy = df.copy() + + # GWa to EJ/yr + df_final_energy.convert_unit("", to="GWa", factor=1, inplace=True) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True) + df_final_energy.filter(region=r, year=years, inplace=True) + df_final_energy.filter( + variable=["in|final|*|cokeoven_steel|*"], keep=False, inplace=True + ) + df_final_energy.filter( + variable=[ + "in|final|atm_gasoil|*", + "in|final|vacuum_gasoil|*", + "in|final|naphtha|*", + "in|final|*|coal_fs|*", + "in|final|*|methanol_fs|*", + "in|final|*|ethanol_fs|*", + "in|final|ethanol|ethanol_to_ethylene_petro|*", + "in|final|*|foil_fs|*", + "in|final|gas|gas_processing_petro|*", + "in|final|*|loil_fs|*", + "in|final|*|gas_fs|*", + 'in|secondary|gas|gas_NH3|M1', + 'in|secondary|gas|gas_NH3_ccs|M1', + 'in|secondary|fueloil|fueloil_NH3|M1', + 'in|secondary|fueloil|fueloil_NH3_ccs|M1', + 'in|secondary|electr|electr_NH3|M1', + "in|secondary|coal|coal_NH3|M1", + "in|secondary|coal|coal_NH3_ccs|M1", + 'in|primary|biomass|biomass_NH3_ccs|M1', + 'in|primary|biomass|biomass_NH3|M1', + "in|final_material|methanol|MTO_petro|energy", + "in|final_material|methanol|CH2O_synth|energy", + 'in|secondary|coal|meth_coal|feedstock', + 'in|secondary|coal|meth_coal_ccs|feedstock', + 'in|secondary|gas|meth_ng|feedstock', + 'in|secondary|gas|meth_ng_ccs|feedstock', + 'in|primary|biomass|meth_bio|feedstock', + 'in|primary|biomass|meth_bio_ccs|feedstock', + 'in|secondary|hydrogen|meth_h2|feedstock', + ], + inplace=True, + ) + + if c == 'all': + df_final_energy.filter(variable=["in|final_material|methanol|MTO_petro|energy", + "in|final_material|methanol|CH2O_synth|energy",], + keep=False,inplace=True) + if c == 'electr_gas': + df_final_energy.filter(variable=['in|secondary|electr|electr_NH3|M1',], + inplace=True) + if c == "gas": + df_final_energy.filter(variable=["in|final|gas|*", + 'in|secondary|gas|gas_NH3|M1', + 'in|secondary|gas|gas_NH3_ccs|M1', + 'in|secondary|gas|meth_ng|feedstock', + 'in|secondary|gas|meth_ng_ccs|feedstock', + 'in|secondary|electr|electr_NH3|M1'], + inplace=True) + df_final_energy.filter(variable=["in|final|gas|gas_processing_petro|*"], + keep=False, inplace=True) + if c == "liquids": + df_final_energy.filter( + variable=[ + "in|final|ethanol|ethanol_to_ethylene_petro|*", + "in|final|*|foil_fs|*", + "in|final|*|loil_fs|*", + "in|final|*|methanol_fs|*", + "in|final|*|ethanol_fs|*", + "in|final|atm_gasoil|*", + "in|final|vacuum_gasoil|*", + "in|final|naphtha|*", + 'in|secondary|fueloil|fueloil_NH3|M1', + 'in|secondary|fueloil|fueloil_NH3_ccs|M1', + "in|final|gas|gas_processing_petro|*" + ], + inplace=True, + ) + if c == "solids": + df_final_energy.filter( + variable=["in|final|biomass|*", "in|final|coal|*", + "in|secondary|coal|coal_NH3|M1", + "in|secondary|coal|coal_NH3_ccs|M1", + 'in|primary|biomass|biomass_NH3_ccs|M1', + 'in|primary|biomass|biomass_NH3|M1', + 'in|secondary|coal|meth_coal|feedstock', + 'in|secondary|coal|meth_coal_ccs|feedstock', + 'in|primary|biomass|meth_bio|feedstock', + 'in|primary|biomass|meth_bio_ccs|feedstock', + ], inplace=True) + if c == "hydrogen": + df_final_energy.filter( + variable=[ + 'in|secondary|hydrogen|meth_h2|feedstock', + ], inplace=True) + if c == "methanol": + df_final_energy.filter( + variable=[ + "in|final_material|methanol|MTO_petro|energy", + "in|final_material|methanol|CH2O_synth|energy", + ], inplace=True) + + all_flows = df_final_energy.timeseries().reset_index() + splitted_vars = [v.split("|") for v in all_flows.variable] + aux1_df = pd.DataFrame( + splitted_vars, + columns=["flow_type", "level", "commodity", "technology", "mode"], + ) + aux2_df = pd.concat( + [all_flows.reset_index(drop=True), aux1_df.reset_index(drop=True)], + axis=1, + ) + + # Include only the related industry sector variables + # Aluminum, cement and steel do not have feedstock use + + var_sectors = [v for v in aux2_df["variable"].values] + + aux2_df = aux2_df[aux2_df["variable"].isin(var_sectors)] + + df_final_energy.filter(variable=var_sectors, inplace=True) + + # Aggregate + + if c == "all": + df_final_energy.aggregate( + "Final Energy|Non-Energy Use", components=var_sectors, append=True, + ) + df_final_energy.filter( + variable=["Final Energy|Non-Energy Use"], inplace=True + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + + if c == "methanol": + + df_final_energy.aggregate( + "Final Energy|Non-Energy Use|Other", + components=var_sectors, + append=True, + ) + + df_final_energy.filter( + variable=["Final Energy|Non-Energy Use|Other"], inplace=True, + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + + if c == "hydrogen": + + df_final_energy.aggregate( + "Final Energy|Non-Energy Use|Hydrogen", + components=var_sectors, + append=True, + ) + + df_final_energy.filter( + variable=["Final Energy|Non-Energy Use|Hydrogen"], inplace=True, + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + + if c == "electr_gas": + + df_final_energy.aggregate( + "Final Energy|Non-Energy Use|Gases|Electricity", + components=var_sectors, + append=True, + ) + + df_final_energy.filter( + variable=["Final Energy|Non-Energy Use|Gases|Electricity"], inplace=True, + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + + if c == "gas": + + # Can not distinguish by type Gases (natural gas, biomass, synthetic fossil, efuel) + # (coal_gas), from biomass (gas_bio), natural gas (gas_bal): All go into secondary level + # Can not be distinguished in the final level. + df_final_energy.aggregate( + "Final Energy|Non-Energy Use|Gases", + components=var_sectors, + append=True, + ) + + df_final_energy.filter( + variable=["Final Energy|Non-Energy Use|Gases"], inplace=True, + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + + if c == "liquids": + + # All liquids + df_final_energy.aggregate( + "Final Energy|Non-Energy Use|Liquids", + components=var_sectors, + append=True, + ) + + # Only bios + + filter_vars = [ + v for v in aux2_df["variable"].values if (("ethanol" in v) + & ("methanol" not in v)) + ] + df_final_energy.aggregate( + "Final Energy|Non-Energy Use|Liquids|Biomass", + components=filter_vars, + append=True, + ) + # Fossils + + filter_vars = [ + v + for v in aux2_df["variable"].values + if ( + ("lightoil" in v) + | ("fueloil" in v) + | ("atm_gasoil" in v) + | ("vacuum_gasoil" in v) + | ("naphtha" in v) + ) + ] + + df_final_energy.aggregate( + "Final Energy|Non-Energy Use|Liquids|Oil", + components=filter_vars, + append=True, + ) + + # Natural Gas Liquids (Ethane/Propane) + + filter_vars = [ + v + for v in aux2_df["variable"].values + if ( + ("gas_proc" in v) + ) + ] + + df_final_energy.aggregate( + "Final Energy|Non-Energy Use|Liquids|Gas", + components=filter_vars, + append=True, + ) + + df_final_energy.filter( + variable=[ + "Final Energy|Non-Energy Use|Liquids", + "Final Energy|Non-Energy Use|Liquids|Oil", + "Final Energy|Non-Energy Use|Liquids|Biomass", + "Final Energy|Non-Energy Use|Liquids|Gas" + ], + inplace=True, + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + if c == "solids": + + # All + df_final_energy.aggregate( + "Final Energy|Non-Energy Use|Solids", + components=var_sectors, + append=True, + ) + # Bio + filter_vars = [ + v for v in aux2_df["variable"].values if ("biomass" in v) + ] + if filter_vars: + df_final_energy.aggregate( + "Final Energy|Non-Energy Use|Solids|Biomass", + components=filter_vars, + append=True, + ) + + # Fossil + filter_vars = [v for v in aux2_df["variable"].values if ("coal" in v)] + df_final_energy.aggregate( + "Final Energy|Non-Energy Use|Solids|Coal", + components=filter_vars, + append=True, + ) + df_final_energy.filter( + variable=[ + "Final Energy|Non-Energy Use|Solids", + "Final Energy|Non-Energy Use|Solids|Biomass", + "Final Energy|Non-Energy Use|Solids|Coal", + ], + inplace=True, + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + + # FINAL ENERGY BY FUELS (Excluding Non-Energy Use) + # For ammonia and methanol only electricity use is included since only this + # has seperate input values in the model. + + print("Final Energy by fuels excluding non-energy use is being printed.") + commodities = ["electr", "gas", "hydrogen", "liquids", "solids", "heat", 'solar', "all"] + for c in commodities: + + for r in nodes: + + df_final_energy = df.copy() + df_final_energy.convert_unit("", to="GWa", factor=1, inplace=True) + df_final_energy.filter(region=r, year=years, inplace=True) + df_final_energy.filter( + variable=["in|final|*|cokeoven_steel|*", + "in|final|co_gas|*", + "in|final|bf_gas|*"], keep=False, inplace=True + ) + + if c == "electr": + df_final_energy.filter(variable=["in|final|electr|*", + 'in|secondary|electr|NH3_to_N_fertil|M1', + 'in|secondary|electr|coal_NH3|M1', + 'in|secondary|electr|fueloil_NH3|M1', + 'in|secondary|electr|gas_NH3|M1', + 'in|secondary|electr|coal_NH3_ccs|M1', + 'in|secondary|electr|fueloil_NH3_ccs|M1', + 'in|secondary|electr|gas_NH3_ccs|M1', + 'in|secondary|electr|biomass_NH3_ccs|M1', + 'in|secondary|electr|biomass_NH3|M1', + 'in|secondary|electr|meth_ng|feedstock', + 'in|secondary|electr|meth_ng_ccs|feedstock', + 'in|secondary|electr|meth_coal|feedstock', + 'in|secondary|electr|meth_coal_ccs|feedstock', + 'in|secondary|electr|meth_h2|feedstock' + ], inplace=True) + if c == "gas": + df_final_energy.filter(variable=["in|final|gas|*"], inplace=True) + # Do not include gasoil and naphtha feedstock + if c == "liquids": + df_final_energy.filter( + variable=[ + "in|final|ethanol|*", + "in|final|fueloil|*", + "in|final|lightoil|*", + "in|final|methanol|*", + ], + inplace=True, + ) + if c == "solids": + df_final_energy.filter( + variable=[ + "in|final|biomass|*", + "in|final|coal|*", + "in|final|coke_iron|*", + ], + inplace=True, + ) + if c == "hydrogen": + df_final_energy.filter(variable=["in|final|hydrogen|*"], inplace=True) + if c == "heat": + df_final_energy.filter(variable=["in|final|d_heat|*"], inplace=True) + if c == 'solar': + df_final_energy.filter(variable=["out|useful|i_therm|solar_i|*", + 'out|useful_aluminum|lt_heat|solar_aluminum|*', + 'out|useful_steel|lt_heat|solar_steel|*', + 'out|useful_cement|lt_heat|solar_cement|*', + 'out|useful_petro|lt_heat|solar_petro|*', + 'out|useful_resins|lt_heat|solar_resins|*', + ], inplace=True) + if c == "all": + df_final_energy.filter(variable=["in|final|*", + 'in|secondary|electr|NH3_to_N_fertil|M1', + 'in|secondary|electr|coal_NH3|M1', + 'in|secondary|electr|fueloil_NH3|M1', + 'in|secondary|electr|gas_NH3|M1', + 'in|secondary|electr|coal_NH3_ccs|M1', + 'in|secondary|electr|fueloil_NH3_ccs|M1', + 'in|secondary|electr|gas_NH3_ccs|M1', + 'in|secondary|electr|biomass_NH3_ccs|M1', + 'in|secondary|electr|biomass_NH3|M1', + 'out|useful|i_therm|solar_i|M1', + 'out|useful_aluminum|lt_heat|solar_aluminum|*', + 'out|useful_steel|lt_heat|solar_steel|*', + 'out|useful_cement|lt_heat|solar_cement|*', + 'out|useful_petro|lt_heat|solar_petro|*', + 'out|useful_resins|lt_heat|solar_resins|*', + 'in|secondary|electr|meth_ng_ccs|feedstock', + 'in|secondary|electr|meth_ng|feedstock', + 'in|secondary|electr|meth_coal|feedstock', + 'in|secondary|electr|meth_coal_ccs|feedstock', + 'in|secondary|electr|meth_h2|feedstock' + ], inplace=True) + + all_flows = df_final_energy.timeseries().reset_index() + splitted_vars = [v.split("|") for v in all_flows.variable] + aux1_df = pd.DataFrame( + splitted_vars, + columns=["flow_type", "level", "commodity", "technology", "mode"], + ) + aux2_df = pd.concat( + [all_flows.reset_index(drop=True), aux1_df.reset_index(drop=True)], + axis=1, + ) + + # Include only the related industry sector variables and state some + # exceptions + var_sectors = [ + v for v in aux2_df["variable"].values + if (( + (v.split('|')[3].endswith("cement")) + | (v.split('|')[3].endswith("steel")) + | (v.split('|')[3].endswith("aluminum")) + | (v.split('|')[3].endswith("petro")) + | (v.split('|')[3].endswith("resins")) + | (v.split('|')[3].endswith("_i")) + | (v.split('|')[3].endswith("_I")) + | (('NH3') in v) + | (v.split('|')[3].startswith("meth")) + | (v.split('|')[3].startswith("CH2O")) + + ) + & ( + ("ethanol_to_ethylene_petro" not in v) + & ("gas_processing_petro" not in v) + & ( + "in|final|atm_gasoil|steam_cracker_petro|atm_gasoil" + not in v + ) + & ( + "in|final|vacuum_gasoil|steam_cracker_petro|vacuum_gasoil" + not in v + ) + & ("in|final|naphtha|steam_cracker_petro|naphtha" not in v) + )) + ] + aux2_df = aux2_df[aux2_df["variable"].isin(var_sectors)] + + df_final_energy.filter(variable=var_sectors, inplace=True) + + # Aggregate + + if c == "all": + df_final_energy.aggregate( + "Final Energy|Industry excl Non-Energy Use", + components=var_sectors, + append=True, + ) + df_final_energy.filter( + variable=["Final Energy|Industry excl Non-Energy Use"], inplace=True + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + if c == "electr": + df_final_energy.aggregate( + "Final Energy|Industry excl Non-Energy Use|Electricity", + components=var_sectors, + append=True, + ) + df_final_energy.filter( + variable=["Final Energy|Industry excl Non-Energy Use|Electricity"], + inplace=True, + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + if c == "gas": + + # Can not distinguish by type Gases (natural gas, biomass, synthetic fossil, efuel) + # (coal_gas), from biomass (gas_bio), natural gas (gas_bal): All go into secondary level + # Can not be distinguished in the final level. + + df_final_energy.aggregate( + "Final Energy|Industry excl Non-Energy Use|Gases", + components=var_sectors, + append=True, + ) + df_final_energy.filter( + variable=["Final Energy|Industry excl Non-Energy Use|Gases"], + inplace=True, + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + + if c == "hydrogen": + df_final_energy.aggregate( + "Final Energy|Industry excl Non-Energy Use|Hydrogen", + components=var_sectors, + append=True, + ) + df_final_energy.filter( + variable=["Final Energy|Industry excl Non-Energy Use|Hydrogen"], + inplace=True, + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + + if c == "liquids": + + # All liquids + df_final_energy.aggregate( + "Final Energy|Industry excl Non-Energy Use|Liquids", + components=var_sectors, + append=True, + ) + + # Only bios (ethanol, methanol ?) + + filter_vars = [ + v for v in aux2_df["variable"].values if (("ethanol" in v) + & ('methanol' not in v)) + ] + df_final_energy.aggregate( + "Final Energy|Industry excl Non-Energy Use|Liquids|Biomass", + components=filter_vars, + append=True, + ) + + # Fossils + + filter_vars = [ + v for v in aux2_df["variable"].values if (("fueloil" in v) + | ("lightoil" in v)) + ] + + df_final_energy.aggregate( + "Final Energy|Industry excl Non-Energy Use|Liquids|Oil", + components=filter_vars, + append=True, + ) + + # Other + + filter_vars = [ + v for v in aux2_df["variable"].values if (("methanol" in v)) + ] + + df_final_energy.aggregate( + "Final Energy|Industry excl Non-Energy Use|Liquids|Coal", + components=filter_vars, + append=True, + ) + + df_final_energy.filter( + variable=[ + "Final Energy|Industry excl Non-Energy Use|Liquids", + "Final Energy|Industry excl Non-Energy Use|Liquids|Oil", + "Final Energy|Industry excl Non-Energy Use|Liquids|Biomass", + "Final Energy|Industry excl Non-Energy Use|Liquids|Coal", + ], + inplace=True, + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + if c == "solids": + + # All + df_final_energy.aggregate( + "Final Energy|Industry excl Non-Energy Use|Solids", + components=var_sectors, + append=True, + ) + + # Bio + filter_vars = [ + v for v in aux2_df["variable"].values if ("biomass" in v) + ] + df_final_energy.aggregate( + "Final Energy|Industry excl Non-Energy Use|Solids|Biomass", + components=filter_vars, + append=True, + ) + + # Fossil + filter_vars = [ + v for v in aux2_df["variable"].values if ("biomass" not in v) + ] + df_final_energy.aggregate( + "Final Energy|Industry excl Non-Energy Use|Solids|Coal", + components=filter_vars, + append=True, + ) + df_final_energy.filter( + variable=[ + "Final Energy|Industry excl Non-Energy Use|Solids", + "Final Energy|Industry excl Non-Energy Use|Solids|Biomass", + "Final Energy|Industry excl Non-Energy Use|Solids|Coal", + ], + inplace=True, + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + if c == "heat": + df_final_energy.aggregate( + "Final Energy|Industry excl Non-Energy Use|Heat", + components=var_sectors, + append=True, + ) + df_final_energy.filter( + variable=["Final Energy|Industry excl Non-Energy Use|Heat"], + inplace=True, + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + if c == 'solar': + df_final_energy.aggregate( + "Final Energy|Industry excl Non-Energy Use|Solar", + components=var_sectors, + append=True, + ) + df_final_energy.filter( + variable=["Final Energy|Industry excl Non-Energy Use|Solar"], + inplace=True, + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + + # FINAL ENERGY BY FUELS (Including Non-Energy Use) + print("Final Energy by fuels including non-energy use is being printed.") + commodities = ["electr", "gas", "hydrogen", "liquids", "solids", "heat", "all", 'solar'] + for c in commodities: + + for r in nodes: + + df_final_energy = df.copy() + df_final_energy.convert_unit( + "", to="GWa", factor=1, inplace=True) + df_final_energy.filter(region=r, year=years, inplace=True) + + exclude = [ + "in|final|*|cokeoven_steel|*", + "in|final|bf_gas|*", + "in|final|co_gas|*", + 'in|final|*|meth_fc_trp|*', + 'in|final|*|meth_ic_trp|*', + 'in|final|*|meth_i|*', + 'in|final|*|meth_rc|*', + 'in|final|*|sp_meth_I|*'] + + df_final_energy.filter(variable=exclude, keep=False, inplace=True) + + if c == 'solar': + df_final_energy.filter(variable=["out|useful|i_therm|solar_i|M1", + "out|useful_steel|lt_heat|solar_steel|*", + "out|useful_aluminum|lt_heat|solar_aluminum|*", + "out|useful_cement|lt_heat|solar_cement|*", + "out|useful_petro|lt_heat|solar_petro|*", + "out|useful_resins|lt_heat|solar_resins|*", + ], + inplace = True) + if c == "electr": + df_final_energy.filter(variable=["in|final|electr|*", + 'in|secondary|electr|NH3_to_N_fertil|M1', + 'in|secondary|electr|coal_NH3|M1', + 'in|secondary|electr|electr_NH3|M1', + 'in|secondary|electr|fueloil_NH3|M1', + 'in|secondary|electr|gas_NH3|M1', + 'in|secondary|electr|coal_NH3_ccs|M1', + 'in|secondary|electr|fueloil_NH3_ccs|M1', + 'in|secondary|electr|gas_NH3_ccs|M1', + 'in|secondary|electr|biomass_NH3_ccs|M1', + 'in|secondary|electr|biomass_NH3|M1', + 'in|secondary|electr|meth_ng|feedstock', + 'in|secondary|electr|meth_ng_ccs|feedstock', + 'in|secondary|electr|meth_coal|feedstock', + 'in|secondary|electr|meth_coal_ccs|feedstock', + 'in|secondary|electr|meth_h2|feedstock' + ], inplace=True) + if c == "gas": + df_final_energy.filter(variable=["in|final|gas|*", + 'in|secondary|gas|gas_NH3|M1', + 'in|secondary|gas|gas_NH3_ccs|M1', + 'in|secondary|gas|meth_ng_ccs|feedstock', + "in|secondary|gas|meth_ng|feedstock", + ], + inplace=True) + df_final_energy.filter(variable=["in|final|gas|gas_processing_petro|*"], + keep=False, inplace=True) + # Include gasoil and naphtha feedstock + if c == "liquids": + df_final_energy.filter( + variable=[ + "in|final|ethanol|*", + "in|final|fueloil|*", + "in|final|lightoil|*", + "in|final|methanol|*", + "in|final|vacuum_gasoil|*", + "in|final|naphtha|*", + "in|final|atm_gasoil|*", + 'in|secondary|fueloil|fueloil_NH3|M1', + 'in|secondary|fueloil|fueloil_NH3_ccs|M1', + "in|final|gas|gas_processing_petro|*"], + inplace=True, + ) + if c == "solids": + df_final_energy.filter( + variable=[ + "in|final|biomass|*", + "in|final|coal|*", + "in|final|coke_iron|*", + 'in|secondary|coal|coal_NH3|M1', + 'in|secondary|coal|coal_NH3_ccs|M1', + "in|secondary|coal|meth_coal|feedstock", + 'in|secondary|coal|meth_coal_ccs|feedstock', + 'in|primary|biomass|meth_bio|feedstock', + 'in|primary|biomass|meth_bio_ccs|feedstock', + 'in|primary|biomass|biomass_NH3_ccs|M1', + 'in|primary|biomass|biomass_NH3|M1' + ], + inplace=True, + ) + if c == "hydrogen": + df_final_energy.filter(variable=["in|final|hydrogen|*", + 'in|secondary|hydrogen|meth_h2|feedstock'], inplace=True) + if c == "heat": + df_final_energy.filter(variable=["in|final|d_heat|*"], inplace=True) + if c == "all": + df_final_energy.filter(variable=["in|final|*", + "in|secondary|coal|coal_NH3|M1", + "in|secondary|electr|NH3_to_N_fertil|M1", + 'in|secondary|electr|coal_NH3|M1', + 'in|secondary|electr|electr_NH3|M1', + 'in|secondary|electr|gas_NH3|M1', + 'in|secondary|fueloil|fueloil_NH3|M1', + 'in|secondary|electr|fueloil_NH3|M1', + 'in|secondary|gas|gas_NH3|M1', + "in|secondary|coal|coal_NH3_ccs|M1", + 'in|secondary|electr|coal_NH3_ccs|M1', + 'in|secondary|electr|gas_NH3_ccs|M1', + 'in|secondary|fueloil|fueloil_NH3_ccs|M1', + 'in|secondary|electr|fueloil_NH3_ccs|M1', + 'in|secondary|gas|gas_NH3_ccs|M1', + "in|primary|biomass|biomass_NH3|M1", + "in|secondary|electr|biomass_NH3|M1", + "in|primary|biomass|biomass_NH3_ccs|M1", + "in|secondary|electr|biomass_NH3_ccs|M1", + "out|useful|i_therm|solar_i|M1", + "out|useful_steel|lt_heat|solar_steel|*", + "out|useful_aluminum|lt_heat|solar_aluminum|*", + "out|useful_cement|lt_heat|solar_cement|*", + "out|useful_petro|lt_heat|solar_petro|*", + "out|useful_resins|lt_heat|solar_resins|*", + "in|secondary|coal|meth_coal|feedstock", + 'in|secondary|coal|meth_coal_ccs|feedstock', + "in|secondary|electr|meth_coal|feedstock", + 'in|secondary|electr|meth_coal_ccs|feedstock', + 'in|secondary|electr|meth_ng_ccs|feedstock', + "in|secondary|electr|meth_ng|feedstock", + 'in|secondary|gas|meth_ng_ccs|feedstock', + "in|secondary|gas|meth_ng|feedstock", + 'in|primary|biomass|meth_bio|feedstock', + 'in|primary|biomass|meth_bio_ccs|feedstock', + 'in|secondary|hydrogen|meth_h2|feedstock', + 'in|secondary|electr|meth_h2|feedstock', + ], inplace=True) + + all_flows = df_final_energy.timeseries().reset_index() + splitted_vars = [v.split("|") for v in all_flows.variable] + aux1_df = pd.DataFrame( + splitted_vars, + columns=["flow_type", "level", "commodity", "technology", "mode"], + ) + aux2_df = pd.concat( + [all_flows.reset_index(drop=True), aux1_df.reset_index(drop=True)], + axis=1, + ) + + # Include only the related industry sector variables + + var_sectors = [ + v + for v in aux2_df["variable"].values + if ( + (v.split('|')[3].endswith("cement")) + | (v.split('|')[3].endswith("steel")) + | (v.split('|')[3].endswith("aluminum")) + | (v.split('|')[3].endswith("petro")) + | (v.split('|')[3].endswith("resins")) + | (v.split('|')[3].endswith("_i")) + | (v.split('|')[3].endswith("_I")) + | (('NH3') in v) + | (v.split('|')[3].endswith("_fs")) + | (v.split('|')[3].startswith("meth")) + | (v.split('|')[3].startswith("CH2O")) + ) + ] + aux2_df = aux2_df[aux2_df["variable"].isin(var_sectors)] + + df_final_energy.filter(variable=var_sectors, inplace=True) + + # Aggregate + + if c == 'solar': + df_final_energy.aggregate( + "Final Energy|Industry|Solar", components=var_sectors, append=True, + ) + df_final_energy.filter(variable=["Final Energy|Industry|Solar"], inplace=True) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + if c == "all": + df_final_energy.aggregate( + "Final Energy|Industry", components=var_sectors, append=True, + ) + df_final_energy.filter(variable=["Final Energy|Industry"], inplace=True) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + if c == "electr": + df_final_energy.aggregate( + "Final Energy|Industry|Electricity", + components=var_sectors, + append=True, + ) + df_final_energy.filter( + variable=["Final Energy|Industry|Electricity"], inplace=True, + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + if c == "gas": + # Can not distinguish by type Gases (natural gas, biomass, synthetic fossil, efuel) + # (coal_gas), from biomass (gas_bio), natural gas (gas_bal): All go into secondary level + # Can not be distinguished in the final level. + df_final_energy.aggregate( + "Final Energy|Industry|Gases", components=var_sectors, append=True, + ) + + df_final_energy.filter( + variable=["Final Energy|Industry|Gases"], inplace=True, + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + + if c == "hydrogen": + df_final_energy.aggregate( + "Final Energy|Industry|Hydrogen", + components=var_sectors, + append=True, + ) + df_final_energy.filter( + variable=["Final Energy|Industry|Hydrogen"], inplace=True, + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + + if c == "liquids": + # All liquids + df_final_energy.aggregate( + "Final Energy|Industry|Liquids", + components=var_sectors, + append=True, + ) + # Only bios (ethanol) + filter_vars = [ + v for v in aux2_df["variable"].values if (("ethanol" in v) + & ("methanol" not in v)) + ] + df_final_energy.aggregate( + "Final Energy|Industry|Liquids|Biomass", + components=filter_vars, + append=True, + ) + + # Oils + filter_vars = [ + v + for v in aux2_df["variable"].values + if ( + ("naphtha" in v) + | ("atm_gasoil" in v) + | ("vacuum_gasoil" in v) + | ("fueloil" in v) + | ("lightoil" in v) + ) + ] + df_final_energy.aggregate( + "Final Energy|Industry|Liquids|Oil", + components=filter_vars, + append=True, + ) + + # Methanol + filter_vars = [ + v + for v in aux2_df["variable"].values + if ( + ("methanol" in v) + ) + ] + + df_final_energy.aggregate( + "Final Energy|Industry|Liquids|Coal", + components=filter_vars, + append=True, + ) + + # Natural Gas Liquids (Ethane/Propane) + + filter_vars = [ + v + for v in aux2_df["variable"].values + if ( + ("gas_proc" in v) + ) + ] + + df_final_energy.aggregate( + "Final Energy|Industry|Liquids|Gas", + components=filter_vars, + append=True, + ) + + df_final_energy.filter( + variable=[ + "Final Energy|Industry|Liquids", + "Final Energy|Industry|Liquids|Oil", + "Final Energy|Industry|Liquids|Biomass", + "Final Energy|Industry|Liquids|Coal", + "Final Energy|Industry|Liquids|Gas" + ], + inplace=True, + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + if c == "solids": + # All + df_final_energy.aggregate( + "Final Energy|Industry|Solids", components=var_sectors, append=True, + ) + + # Bio + filter_vars = [ + v for v in aux2_df["variable"].values if ("biomass" in v) + ] + + df_final_energy.aggregate( + "Final Energy|Industry|Solids|Biomass", + components=filter_vars, + append=True, + ) + + # Fossil + filter_vars = [ + v for v in aux2_df["variable"].values if ("coal" in v) + ] + + df_final_energy.aggregate( + "Final Energy|Industry|Solids|Coal", + components=filter_vars, + append=True, + ) + df_final_energy.filter( + variable=[ + "Final Energy|Industry|Solids", + "Final Energy|Industry|Solids|Biomass", + "Final Energy|Industry|Solids|Coal", + ], + inplace=True, + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + if c == "heat": + df_final_energy.aggregate( + "Final Energy|Industry|Heat", components=var_sectors, append=True, + ) + df_final_energy.filter( + variable=["Final Energy|Industry|Heat"], inplace=True, + ) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + + # FINAL ENERGY BY SECTOR AND FUEL + # Feedstock not included + + sectors = [ + "aluminum", + "steel", + "cement", + "petro", + "Non-Ferrous Metals", + "Non-Metallic Minerals", + "Chemicals", + 'Other Sector' + ] + print("Final Energy (excl non-energy use) by sector and fuel is being printed") + for r in nodes: + for s in sectors: + df_final_energy = df.copy() + df_final_energy.convert_unit("", to="GWa", factor=1, inplace=True) + exclude = [ + "in|final|atm_gasoil|steam_cracker_petro|*", + "in|final|ethanol|ethanol_to_ethylene_petro|M1", + "in|final|gas|gas_processing_petro|M1", + "in|final|naphtha|steam_cracker_petro|*", + "in|final|vacuum_gasoil|steam_cracker_petro|*", + "in|final|*|cokeoven_steel|*", + "in|final|bf_gas|*", + "in|final|co_gas|*"] + + df_final_energy.filter(region=r, year=years, inplace=True) + df_final_energy.filter(variable=["in|final|*", + 'out|useful|i_therm|solar_i|M1', + 'out|useful_steel|lt_heat|solar_steel|low_temp', + 'out|useful_aluminum|lt_heat|solar_aluminum|low_temp', + 'out|useful_cement|lt_heat|solar_cement|low_temp', + 'out|useful_petro|lt_heat|solar_petro|low_temp', + 'out|useful_resins|lt_heat|solar_resins|low_temp', + 'in|secondary|electr|NH3_to_N_fertil|M1', + 'in|secondary|electr|coal_NH3|M1', + 'in|secondary|electr|fueloil_NH3|M1', + 'in|secondary|electr|gas_NH3|M1', + 'in|secondary|electr|coal_NH3_ccs|M1', + 'in|secondary|electr|fueloil_NH3_ccs|M1', + 'in|secondary|electr|gas_NH3_ccs|M1', + 'in|secondary|electr|biomass_NH3_ccs|M1', + 'in|secondary|electr|biomass_NH3|M1', + 'in|secondary|electr|meth_ng|feedstock', + 'in|secondary|electr|meth_ng_ccs|feedstock', + 'in|secondary|electr|meth_coal|feedstock', + 'in|secondary|electr|meth_coal_ccs|feedstock', + 'in|secondary|electr|meth_h2|feedstock' + ], inplace=True) + df_final_energy.filter(variable=exclude, keep=False, inplace=True) + + # Decompose the pyam table into pandas data frame + + all_flows = df_final_energy.timeseries().reset_index() + + # Split the strings in the identified variables for further processing + splitted_vars = [v.split("|") for v in all_flows.variable] + + # Create auxilary dataframes for processing + aux1_df = pd.DataFrame( + splitted_vars, + columns=["flow_type", "level", "commodity", "technology", "mode"], + ) + aux2_df = pd.concat( + [all_flows.reset_index(drop=True), aux1_df.reset_index(drop=True)], + axis=1, + ) + + # To be able to report the higher level sectors. + if s == "Non-Ferrous Metals": + tec = [t for t in aux2_df["technology"].values if "aluminum" in t] + solar_tec = ['solar_aluminum'] + aux2_df = aux2_df[aux2_df["technology"].isin(tec)] + elif s == "Non-Metallic Minerals": + tec = [t for t in aux2_df["technology"].values if "cement" in t] + solar_tec = ['solar_cement'] + aux2_df = aux2_df[aux2_df["technology"].isin(tec)] + elif s == "Chemicals": + tec = [t for t in aux2_df["technology"].values if (("petro" in t) + | ('NH3' in t) + | ( t.startswith('meth_') + & (not (t.startswith('meth_i')))) + | ('CH2O'in t) + | ("resins" in t))] + solar_tec = ['solar_petro', 'solar_resins'] + aux2_df = aux2_df[aux2_df["technology"].isin(tec)] + elif s == 'Other Sector': + tec = [t for t in aux2_df["technology"].values if ( + ((t.endswith("_i")) + | (t.endswith('_I'))))] + solar_tec = ['solar_i'] + aux2_df = aux2_df[aux2_df["technology"].isin(tec)] + else: + # Filter the technologies only for the certain industry sector + tec = [t for t in aux2_df["technology"].values if s in t] + solar_tec = ['solar_' + s] + aux2_df = aux2_df[aux2_df["technology"].isin(tec)] + + s = change_names(s) + + # Lists to keep commodity, aggregate and variable names. + + commodity_list = [] + aggregate_list = [] + var_list = [] + + # For the categoris below filter the required variable names, + # create a new aggregate name + + commodity_list = [ + "electr", + "gas", + "hydrogen", + "liquids", + "liquid_bio", + "liquid_fossil", + 'liquid_other', + "solids", + "solids_bio", + "solids_fossil", + "heat", + 'solar', + "all", + ] + + for c in commodity_list: + if c == "electr": + var = np.unique( + aux2_df.loc[aux2_df["commodity"] == "electr", "variable"].values + ).tolist() + + aggregate_name = ( + "Final Energy|Industry excl Non-Energy Use|" + + s + + "|" + + "Electricity" + ) + elif c == "gas": + var = np.unique( + aux2_df.loc[aux2_df["commodity"] == "gas", "variable"].values + ).tolist() + aggregate_name = ( + "Final Energy|Industry excl Non-Energy Use|" + s + "|" + "Gases" + ) + elif c == 'solar': + var = np.unique( + aux2_df.loc[ + aux2_df["technology"].isin(solar_tec), "variable" + ].values + ).tolist() + aggregate_name = ( + "Final Energy|Industry excl Non-Energy Use|" + + s + + "|" + + "Solar" + ) + elif c == "hydrogen": + var = np.unique( + aux2_df.loc[ + aux2_df["commodity"] == "hydrogen", "variable" + ].values + ).tolist() + aggregate_name = ( + "Final Energy|Industry excl Non-Energy Use|" + + s + + "|" + + "Hydrogen" + ) + elif c == "liquids": + var = np.unique( + aux2_df.loc[ + ( + (aux2_df["commodity"] == "fueloil") + | (aux2_df["commodity"] == "lightoil") + | (aux2_df["commodity"] == "methanol") + | (aux2_df["commodity"] == "ethanol") + ), + "variable", + ].values + ).tolist() + aggregate_name = ( + "Final Energy|Industry excl Non-Energy Use|" + + s + + "|" + + "Liquids" + ) + elif c == "liquid_bio": + var = np.unique( + aux2_df.loc[ + ((aux2_df["commodity"] == "ethanol")), "variable", + ].values + ).tolist() + aggregate_name = ( + "Final Energy|Industry excl Non-Energy Use|" + + s + + "|" + + "Liquids|Biomass" + ) + elif c == "liquid_fossil": + var = np.unique( + aux2_df.loc[ + ( + (aux2_df["commodity"] == "fueloil") + | (aux2_df["commodity"] == "lightoil") + ), + "variable", + ].values + ).tolist() + aggregate_name = ( + "Final Energy|Industry excl Non-Energy Use|" + + s + + "|" + + "Liquids|Oil" + ) + elif c == "liquid_other": + var = np.unique( + aux2_df.loc[ + ((aux2_df["commodity"] == "methanol")), + "variable", + ].values + ).tolist() + aggregate_name = ( + "Final Energy|Industry excl Non-Energy Use|" + + s + + "|" + + "Liquids|Coal" + ) + elif c == "solids": + var = np.unique( + aux2_df.loc[ + ( + (aux2_df["commodity"] == "coal") + | (aux2_df["commodity"] == "biomass") + | (aux2_df["commodity"] == "coke_iron") + ), + "variable", + ].values + ).tolist() + aggregate_name = ( + "Final Energy|Industry excl Non-Energy Use|" + + s + + "|" + + "Solids" + ) + elif c == "solids_bio": + var = np.unique( + aux2_df.loc[ + (aux2_df["commodity"] == "biomass"), "variable" + ].values + ).tolist() + aggregate_name = ( + "Final Energy|Industry excl Non-Energy Use|" + + s + + "|" + + "Solids|Biomass" + ) + elif c == "solids_fossil": + var = np.unique( + aux2_df.loc[ + ( + (aux2_df["commodity"] == "coal") + | (aux2_df["commodity"] == "coke_iron") + ), + "variable", + ].values + ).tolist() + aggregate_name = ( + "Final Energy|Industry excl Non-Energy Use|" + + s + + "|" + + "Solids|Coal" + ) + elif c == "heat": + var = np.unique( + aux2_df.loc[ + (aux2_df["commodity"] == "d_heat"), "variable" + ].values + ).tolist() + aggregate_name = ( + "Final Energy|Industry excl Non-Energy Use|" + s + "|" + "Heat" + ) + elif c == "all": + var = aux2_df["variable"].tolist() + aggregate_name = "Final Energy|Industry excl Non-Energy Use|" + s + + aggregate_list.append(aggregate_name) + var_list.append(var) + + # Obtain the iamc format dataframe again + + aux2_df.drop( + ["flow_type", "level", "commodity", "technology", "mode"], + axis=1, + inplace=True, + ) + if aux2_df.size == 0: + continue + df_final_energy = pyam.IamDataFrame(data=aux2_df) + + # Aggregate the commodities in iamc object + + for i, c in enumerate(commodity_list): + if var_list[i]: + df_final_energy.aggregate( + aggregate_list[i], components=var_list[i], append=True + ) + + df_final_energy.filter(variable=aggregate_list, inplace=True) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + + # FINAL ENERGY (NON-ENERGY USE) BY SECTOR AND FUEL + # Only for high value chemcials there is non-energy use reported. + # (not in aluminum, steel, cement). + + sectors = ["petro",'ammonia','methanol','Chemicals|Other Sector'] + print("Final Energy non-energy use by sector and fuel is being printed") + for r in nodes: + for s in sectors: + df_final_energy = df.copy() + df_final_energy.convert_unit("", to="GWa", factor=1, inplace=True) + include = [ + "in|final|atm_gasoil|steam_cracker_petro|*", + "in|final|ethanol|ethanol_to_ethylene_petro|M1", + "in|final|gas|gas_processing_petro|M1", + "in|final|naphtha|steam_cracker_petro|*", + "in|final|vacuum_gasoil|steam_cracker_petro|*", + 'in|secondary|electr|electr_NH3|M1', + 'in|secondary|gas|gas_NH3|M1', + 'in|secondary|gas|gas_NH3_ccs|M1', + 'in|secondary|fueloil|fueloil_NH3|M1', + 'in|secondary|fueloil|fueloil_NH3_ccs|M1', + "in|secondary|coal|coal_NH3|M1", + "in|secondary|coal|coal_NH3_ccs|M1", + 'in|primary|biomass|biomass_NH3_ccs|M1', + 'in|primary|biomass|biomass_NH3|M1', + "in|final_material|methanol|MTO_petro|energy", + "in|final_material|methanol|CH2O_synth|energy", + 'in|secondary|coal|meth_coal|feedstock', + 'in|secondary|coal|meth_coal_ccs|feedstock', + 'in|secondary|gas|meth_ng|feedstock', + 'in|secondary|gas|meth_ng_ccs|feedstock', + 'in|primary|biomass|meth_bio|feedstock', + 'in|primary|biomass|meth_bio_ccs|feedstock', + 'in|secondary|hydrogen|meth_h2|feedstock', + ] + + df_final_energy.filter(region=r, year=years, inplace=True) + df_final_energy.filter(variable=include, inplace=True) + + # Decompose the pyam table into pandas data frame + + all_flows = df_final_energy.timeseries().reset_index() + + # Split the strings in the identified variables for further processing + splitted_vars = [v.split("|") for v in all_flows.variable] + + # Create auxiliary dataframes for processing + aux1_df = pd.DataFrame( + splitted_vars, + columns=["flow_type", "level", "commodity", "technology", "mode"], + ) + aux2_df = pd.concat( + [all_flows.reset_index(drop=True), aux1_df.reset_index(drop=True)], + axis=1, + ) + + # Filter the technologies only for the certain industry sector + if s == 'petro': + tec = [t for t in aux2_df["technology"].values if (s in t)] + if s == 'ammonia': + tec = [t for t in aux2_df["technology"].values if ('NH3' in t)] + if s == 'methanol': + tec = [t for t in aux2_df["technology"].values if ('meth_' in t)] + if s == 'Chemicals|Other Sector': + tec = [t for t in aux2_df["technology"].values if ('CH2O_synth' in t)] + + + aux2_df = aux2_df[aux2_df["technology"].isin(tec)] + + s = change_names(s) + + # Lists to keep commodity, aggregate and variable names. + + aggregate_list = [] + commodity_list = [] + var_list = [] + + # For the categories below filter the required variable names, + # create a new aggregate name + + commodity_list = [ + "gas", + "liquids", + "liquid_bio", + 'liquid_oil', + "liquid_gas", + 'methanol', + "all", + 'solids', + 'solid_coal', + 'solid_bio', + 'electr_gas', + 'hydrogen' + ] + + for c in commodity_list: + if c == "electr_gas": + var = np.unique( + aux2_df.loc[aux2_df["commodity"] == "electr", "variable"].values + ).tolist() + aggregate_name = "Final Energy|Non-Energy Use|" + s + "|" + "Gases|Electricity" + elif c == "gas": + var = np.unique( + aux2_df.loc[(((aux2_df["commodity"] == "gas") | (aux2_df["technology"] == "electr_NH3")) + & (aux2_df["technology"] != "gas_processing_petro")) , "variable"].values + ).tolist() + aggregate_name = "Final Energy|Non-Energy Use|" + s + "|" + "Gases" + elif c == "hydrogen": + var = np.unique( + aux2_df.loc[((aux2_df["commodity"] == "hydrogen") + & (aux2_df["technology"] == "meth_h2")),"variable"].values + ).tolist() + aggregate_name = "Final Energy|Non-Energy Use|" + s + "|" + "Hydrogen" + elif c == "methanol": + var = np.unique( + aux2_df.loc[ + ( + (aux2_df["commodity"] == "methanol") + ), + "variable", + ].values + ).tolist() + aggregate_name = ( + "Final Energy|Non-Energy Use|" + s + "|" + "Other" + ) + elif c == "liquids": + var = np.unique( + aux2_df.loc[ + ( + (aux2_df["commodity"] == "naphtha") + | (aux2_df["commodity"] == "atm_gasoil") + | (aux2_df["commodity"] == "vacuum_gasoil") + | (aux2_df["commodity"] == "ethanol") + | (aux2_df["commodity"] == "fueloil") + | (aux2_df["technology"] == "gas_processing_petro") + ), + "variable", + ].values + ).tolist() + + aggregate_name = ( + "Final Energy|Non-Energy Use|" + s + "|" + "Liquids" + ) + elif c == "liquid_bio": + var = np.unique( + aux2_df.loc[ + ((aux2_df["commodity"] == "ethanol")), "variable", + ].values + ).tolist() + aggregate_name = ( + "Final Energy|Non-Energy Use|" + s + "|" + "Liquids|Biomass" + ) + elif c == "liquid_oil": + var = np.unique( + aux2_df.loc[ + ((aux2_df["commodity"] == "atm_gasoil") | + (aux2_df["commodity"] == "naphtha") | + (aux2_df["commodity"] == "vacuum_gasoil") | + (aux2_df["commodity"] == "fueloil") + ), "variable", + ].values + ).tolist() + + aggregate_name = ( + "Final Energy|Non-Energy Use|" + s + "|" + "Liquids|Oil" + ) + elif c == "liquid_gas": + var = np.unique( + aux2_df.loc[ + ((aux2_df["technology"] == "gas_processing_petro") + ), "variable", + ].values + ).tolist() + + aggregate_name = ( + "Final Energy|Non-Energy Use|" + s + "|" + "Liquids|Gas" + ) + elif c == 'solids': + var = np.unique( + aux2_df.loc[ + ((aux2_df["commodity"] == "coal") | + (aux2_df["commodity"] == "biomass")), "variable", + ].values + ).tolist() + aggregate_name = ( + "Final Energy|Non-Energy Use|" + s + "|" + "Solids" + ) + elif c == 'solid_coal': + var = np.unique( + aux2_df.loc[ + ((aux2_df["commodity"] == "coal") ), "variable", + ].values + ).tolist() + aggregate_name = ( + "Final Energy|Non-Energy Use|" + s + "|" + "Solids|Coal" + ) + elif c == 'solid_bio': + var = np.unique( + aux2_df.loc[ + ( + (aux2_df["commodity"] == "biomass")), "variable", + ].values + ).tolist() + aggregate_name = ( + "Final Energy|Non-Energy Use|" + s + "|" + "Solids|Biomass" + ) + elif c == "all": + var = aux2_df["variable"].tolist() + aggregate_name = "Final Energy|Non-Energy Use|" + s + + aggregate_list.append(aggregate_name) + var_list.append(var) + + # Obtain the iamc format dataframe again + + aux2_df.drop( + ["flow_type", "level", "commodity", "technology", "mode"], + axis=1, + inplace=True, + ) + df_final_energy = pyam.IamDataFrame(data=aux2_df) + + # Aggregate the commodities in iamc object + + for i, c in enumerate(commodity_list): + if var_list[i]: + df_final_energy.aggregate( + aggregate_list[i], components=var_list[i], append=True + ) + + + df_final_energy.filter(variable=aggregate_list, inplace=True) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + + # FINAL ENERGY ALL BY SECTOR AND FUEL + + # For ammonia and methanol, there is no seperation for non-energy vs. energy. + + sectors = ['ammonia', 'methanol', 'aluminum', 'steel', 'cement', 'petro', + "Non-Ferrous Metals", "Non-Metallic Minerals", "Chemicals", 'Chemicals|Other Sector', 'Other Sector'] + + print("Final Energy non-energy and energy use by sector and fuel is being printed") + for r in nodes: + for s in sectors: + df_final_energy = df.copy() + df_final_energy.convert_unit("", to="GWa", factor=1, inplace=True) + + exclude = [ + "in|final|*|cokeoven_steel|*", + "in|final|bf_gas|*", + "in|final|co_gas|*", + 'in|final|*|meth_fc_trp|*', + 'in|final|*|meth_ic_trp|*', + 'in|final|*|meth_rc|*'] + + include = [ + 'in|secondary|coal|coal_NH3|M1', + 'in|secondary|coal|coal_NH3_ccs|M1', + 'in|secondary|electr|coal_NH3|M1', + 'in|secondary|electr|coal_NH3_ccs|M1', + 'in|secondary|fueloil|fueloil_NH3|M1', + 'in|secondary|fueloil|fueloil_NH3_ccs|M1', + 'in|secondary|electr|fueloil_NH3|M1', + 'in|secondary|electr|fueloil_NH3_ccs|M1', + 'in|secondary|gas|gas_NH3|M1', + 'in|secondary|gas|gas_NH3_ccs|M1', + 'in|secondary|electr|gas_NH3|M1', + 'in|secondary|electr|gas_NH3_ccs|M1', + 'in|primary|biomass|biomass_NH3|M1', + 'in|primary|biomass|biomass_NH3_ccs|M1', + "in|secondary|electr|biomass_NH3|M1", + "in|secondary|electr|biomass_NH3_ccs|M1", + "in|secondary|electr|NH3_to_N_fertil|M1", + 'in|secondary|electr|electr_NH3|M1', + "in|secondary|coal|meth_coal|feedstock", + 'in|secondary|coal|meth_coal_ccs|feedstock', + "in|secondary|electr|meth_coal|feedstock", + 'in|secondary|electr|meth_coal_ccs|feedstock', + 'in|secondary|gas|meth_ng_ccs|feedstock', + "in|secondary|gas|meth_ng|feedstock", + "in|secondary|electr|meth_ng|feedstock", + 'in|secondary|electr|meth_ng_ccs|feedstock', + 'in|primary|biomass|meth_bio|feedstock', + 'in|primary|biomass|meth_bio_ccs|feedstock', + 'in|secondary|hydrogen|meth_h2|feedstock', + 'in|secondary|electr|meth_h2|feedstock', + "in|final_material|methanol|MTO_petro|energy", + 'in|final|*', + 'out|useful|i_therm|solar_i|M1', + 'out|useful_steel|lt_heat|solar_steel|*', + 'out|useful_cement|lt_heat|solar_cement|*', + 'out|useful_aluminum|lt_heat|solar_aluminum|*', + 'out|useful_petro|lt_heat|solar_steel|*', + 'out|useful_resins|lt_heat|solar_resins|*' + ] + + df_final_energy.filter(region=r, year=years, inplace=True) + df_final_energy.filter(variable=include, inplace=True) + df_final_energy.filter(variable=exclude, keep=False, inplace=True) + + # Decompose the pyam table into pandas data frame + + all_flows = df_final_energy.timeseries().reset_index() + + # Split the strings in the identified variables for further processing + splitted_vars = [v.split("|") for v in all_flows.variable] + + # Create auxilary dataframes for processing + aux1_df = pd.DataFrame( + splitted_vars, + columns=["flow_type", "level", "commodity", "technology", "mode"], + ) + aux2_df = pd.concat( + [all_flows.reset_index(drop=True), aux1_df.reset_index(drop=True)], + axis=1, + ) + + # Filter the technologies only for the certain industry sector + + if s == "Non-Ferrous Metals": + tec = [t for t in aux2_df["technology"].values if "aluminum" in t] + solar_tec = ['solar_aluminum'] + aux2_df = aux2_df[aux2_df["technology"].isin(tec)] + elif s == "Non-Metallic Minerals": + tec = [t for t in aux2_df["technology"].values if "cement" in t] + solar_tec = ['solar_cement'] + aux2_df = aux2_df[aux2_df["technology"].isin(tec)] + elif s == "Chemicals": + tec = [t for t in aux2_df["technology"].values if + ((("petro" in t) & ("MTO_petro" not in t)) \ + | ( t.startswith('meth_') & (not (t.startswith('meth_i')))) | ('NH3' in t) | \ + ('resins' in t) | ('CH2O_synth' in t))] + solar_tec = ['solar_petro','solar_resins'] + aux2_df = aux2_df[aux2_df["technology"].isin(tec)] + elif s == "Chemicals|Other Sector": + tec = [t for t in aux2_df["technology"].values if (('resins' in t) \ + | ('CH2O_synth' in t) | ('CH2O_to_resin' in t) )] + aux2_df = aux2_df[aux2_df["technology"].isin(tec)] + solar_tec = ['solar_resins'] + elif s == 'Other Sector': + tec = [t for t in aux2_df["technology"].values if ( + ((t.endswith("_i")) + | (t.endswith('_I')) + | (t.endswith('_fs'))))] + solar_tec = ['solar_i'] + aux2_df = aux2_df[aux2_df["technology"].isin(tec)] + elif s == 'ammonia': + tec = [t for t in aux2_df["technology"].values if ('NH3' in t)] + aux2_df = aux2_df[aux2_df["technology"].isin(tec)] + elif s == 'methanol': + tec = [t for t in aux2_df["technology"].values if ( t.startswith('meth_') \ + & (not (t.startswith('meth_i'))))] + aux2_df = aux2_df[aux2_df["technology"].isin(tec)] + else: + # Filter the technologies only for the certain industry sector + tec = [t for t in aux2_df["technology"].values if s in t] + aux2_df = aux2_df[aux2_df["technology"].isin(tec)] + solar_tec = ['solar_' + s] + + s = change_names(s) + + # Lists to keep commodity, aggregate and variable names. + + aggregate_list = [] + commodity_list = [] + var_list = [] + + # For the categoris below filter the required variable names, + # create a new aggregate name + + commodity_list = [ + "electr", + "gas", + "hydrogen", + "liquids", + "liquid_bio", + "liquid_fossil", + "liquid_gas", + 'liquid_other', + "solids", + "solids_bio", + "solids_fossil", + "heat", + 'solar', + "all", + ] + + for c in commodity_list: + if c == 'electr': + var = np.unique( + aux2_df.loc[aux2_df["commodity"] == "electr", "variable"].values + ).tolist() + aggregate_name = "Final Energy|Industry|" + s + "|" + 'Electricity' + elif c == "gas": + var = np.unique( + aux2_df.loc[(aux2_df["commodity"] == "gas") & (aux2_df["technology"] != "gas_processing_petro"), "variable"].values + ).tolist() + aggregate_name = "Final Energy|Industry|" + s + "|" + "Gases" + elif c == "solar": + var = np.unique( + aux2_df.loc[aux2_df["technology"].isin(solar_tec), "variable"].values + ).tolist() + aggregate_name = "Final Energy|Industry|" + s + "|" + "Solar" + elif c == "hydrogen": + var = np.unique( + aux2_df.loc[aux2_df["commodity"] == "hydrogen", "variable"].values + ).tolist() + aggregate_name = "Final Energy|Industry|" + s + "|" + "Hydrogen" + elif c == "liquids": + var = np.unique( + aux2_df.loc[ + ( + (aux2_df["commodity"] == "naphtha") + | (aux2_df["commodity"] == "atm_gasoil") + | (aux2_df["commodity"] == "vacuum_gasoil") + | (aux2_df["commodity"] == "ethanol") + | (aux2_df["commodity"] == "fueloil") + | (aux2_df["commodity"] == "lightoil") + | (aux2_df["commodity"] == "methanol") + | (aux2_df["technology"] == "gas_processing_petro") + ), + "variable", + ].values + ).tolist() + + aggregate_name = ( + "Final Energy|Industry|" + s + "|" + "Liquids" + ) + elif c == "liquid_bio": + var = np.unique( + aux2_df.loc[ + ((aux2_df["commodity"] == "ethanol")), "variable", + ].values + ).tolist() + aggregate_name = ( + "Final Energy|Industry|" + s + "|" + "Liquids|Biomass" + ) + elif c == "liquid_fossil": + var = np.unique( + aux2_df.loc[ + ((aux2_df["commodity"] == "atm_gasoil") | + (aux2_df["commodity"] == "naphtha") | + (aux2_df["commodity"] == "vacuum_gasoil") | + (aux2_df["commodity"] == "fueloil") | + (aux2_df["commodity"] == "lightoil") + ), "variable", + ].values + ).tolist() + + aggregate_name = ( + "Final Energy|Industry|" + s + "|" + "Liquids|Oil" + ) + elif c == "liquid_gas": + var = np.unique( + aux2_df.loc[ + (aux2_df["technology"] == "gas_processing_petro"), "variable", + ].values + ).tolist() + + aggregate_name = ( + "Final Energy|Industry|" + s + "|" + "Liquids|Gas" + ) + elif c == "liquid_other": + var = np.unique( + aux2_df.loc[ + ((aux2_df["commodity"] == "methanol")), + "variable", + ].values + ).tolist() + aggregate_name = ( + "Final Energy|Industry|"+ s+ "|"+ "Liquids|Other" + ) + elif c == 'solids': + var = np.unique( + aux2_df.loc[ + ((aux2_df["commodity"] == "coal") | + (aux2_df["commodity"] == "biomass") | + (aux2_df["commodity"] == "coke_iron") + ), "variable", + ].values + ).tolist() + aggregate_name = ( + "Final Energy|Industry|" + s + "|" + "Solids" + ) + elif c == 'solids_bio': + var = np.unique( + aux2_df.loc[ + ((aux2_df["commodity"] == "biomass")), "variable", + ].values + ).tolist() + aggregate_name = ( + "Final Energy|Industry|" + s + "|" + "Solids|Biomass" + ) + elif c == 'solids_fossil': + var = np.unique( + aux2_df.loc[ + ((aux2_df["commodity"] == "coal") | + (aux2_df["commodity"] == "coke_iron") + ), "variable", + ].values + ).tolist() + aggregate_name = ( + "Final Energy|Industry|" + s + "|" + "Solids|Coal" + ) + elif c == "heat": + var = np.unique( + aux2_df.loc[ + (aux2_df["commodity"] == "d_heat"), "variable" + ].values + ).tolist() + aggregate_name = ( + "Final Energy|Industry|" + s + "|" + "Heat" + ) + elif c == "all": + var = aux2_df["variable"].tolist() + aggregate_name = "Final Energy|Industry|" + s + + aggregate_list.append(aggregate_name) + var_list.append(var) + + # Obtain the iamc format dataframe again + + aux2_df.drop( + ["flow_type", "level", "commodity", "technology", "mode"], + axis=1, + inplace=True, + ) + df_final_energy = pyam.IamDataFrame(data=aux2_df) + + # Aggregate the commodities in iamc object + + i = 0 + for c in commodity_list: + if var_list[i]: + df_final_energy.aggregate( + aggregate_list[i], components=var_list[i], append=True + ) + + i = i + 1 + + df_final_energy.filter(variable=aggregate_list, inplace=True) + df_final_energy.convert_unit( + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) + df_final.append(df_final_energy, inplace=True) + + # EMISSIONS + # If ammonia/methanol is used as feedstock the emissions are accounted under 'CO2_industry', + # so as 'demand'. If used as fuel, under 'CO2_transformation'. + # The CCS technologies deduct negative emissions from the overall CO2. + + sectors = [ + "aluminum", + "steel", + "petro", + "cement", + 'ammonia', + 'methanol', + "all", + "Chemicals", + 'Chemicals|Other', + 'Other Sector' + ] + + # CO2_industry and CO2 are reported by the legacy reporting in order to + # ensure overall consistency for all emissions. In addition remaining industry + # _i technologies do not have emissions factors multiplied with efficiencies. + # This causes the emissions from Other Sector to be reported lower. + emission_type = [ + # 'CO2_industry', + "CH4", + # "CO2", + "NH3", + "NOx", + "CF4", + "N2O", + "BCA", + "CO", + "OCA", + ] + + print("Emissions are being printed.") + for typ in ["demand", "process"]: + for r in nodes: + for e in emission_type: + df_emi = df.copy() + df_emi.filter(region=r, year=years, inplace=True) + + # Exclude M1 but instead include the share that is used for feedstock. + # meth_import and meth_export have emission coefficients as well + # but they are excluded to be consistent with other materials emission + # reporting. + + exclude = ['emis|CO2_industry|meth_coal|M1', + 'emis|CO2_industry|meth_ng|M1', + 'emis|CO2_industry|meth_coal_ccs|M1', + 'emis|CO2_industry|meth_ng_ccs|M1', + 'emis|CO2|meth_coal_ccs|M1', + 'emis|CO2|meth_ng_ccs|M1', + 'emis|CO2|meth_imp|M1', + 'emis|CO2|meth_exp|M1' + ] + + df_emi.filter(variable= exclude, keep=False, inplace=True) + + # Filter the necessary variables + + # CCS technologies have both CO2 (negative) and CO2_transformation + # coefficent. CO2 is included here because it is the amount captured + # from the atmosphere and not related to process emissions. + + # "emis|CO2_industry|biomass_NH3_ccs|*","emis|CO2_industry|gas_NH3_ccs|*", + # "emis|CO2_industry|coal_NH3_ccs|*","emis|CO2_industry|fueloil_NH3_ccs|*", + # "emis|CO2_industry|biomass_NH3|*","emis|CO2_industry|gas_NH3|*", + # "emis|CO2_industry|coal_NH3|*","emis|CO2_industry|fueloil_NH3|*", + # "emis|CO2_industry|electr_NH3|*",'emis|CO2_industry|meth_coal|feedstock', + # 'emis|CO2_industry|meth_ng|feedstock','emis|CO2_industry|meth_ng_ccs|feedstock', + + if e == 'CO2_industry': + emi_filter = ["emis|CO2|biomass_NH3_ccs|*", + "emis|CO2|gas_NH3_ccs|*", + "emis|CO2|coal_NH3_ccs|*", + "emis|CO2|fueloil_NH3_ccs|*", + 'emis|CO2|meth_coal_ccs|feedstock', + 'emis|CO2|meth_ng_ccs|feedstock', + 'emis|CO2_industry|*'] + + df_emi.filter(variable=emi_filter, inplace=True) + else: + emi_filter = ["emis|" + e + "|*"] + + # Below variables are not reported under Industrial Process + # Emissions. Therefore, they are excluded. + exclude = ["emis|CO2|biomass_NH3_ccs|*", + "emis|CO2|gas_NH3_ccs|*", + "emis|CO2|coal_NH3_ccs|*", + "emis|CO2|fueloil_NH3_ccs|*", + "emis|CO2|meth_coal_ccs|*", + "emis|CO2|meth_ng_ccs|*" + ] + + df_emi.filter(variable=exclude, keep=False, inplace=True) + df_emi.filter(variable=emi_filter, inplace=True) + + # Perform some specific unit conversions + if (e == "CO2") | (e == "CO2_industry"): + # From MtC to Mt CO2/yr + df_emi.convert_unit('', to="Mt CO2/yr", factor=44/12, + inplace=True) + elif (e == "N2O") | (e == "CF4"): + unit = "kt " + e + "/yr" + df_emi.convert_unit('', to= unit, factor=1, + inplace=True) + else: + e = change_names(e) + # From kt/yr to Mt/yr + unit = "Mt " + e + "/yr" + df_emi.convert_unit("", to= unit, factor=0.001, inplace=True) + + all_emissions = df_emi.timeseries().reset_index() + + # Split the strings in the identified variables for further processing + splitted_vars = [v.split("|") for v in all_emissions.variable] + # Lists to later keep the variables and names to aggregate + var_list = [] + aggregate_list = [] + + for s in sectors: + # Create auxilary dataframes for processing + aux1_df = pd.DataFrame( + splitted_vars, + columns=["emission", "type", "technology", "mode"], + ) + aux2_df = pd.concat( + [ + all_emissions.reset_index(drop=True), + aux1_df.reset_index(drop=True), + ], + axis=1, + ) + # Distinguish the type of emissions and filter the necessary + # technologies based on that. + + if (typ == "process") & (s == "all") & (e != "CO2_industry"): + tec = [ + t + for t in aux2_df["technology"].values + if ( + ( + ( + ("cement" in t) + | ("steel" in t) + | ("aluminum" in t) + | ("petro" in t) + ) + & ("furnace" not in t) + ) + ) + ] + if (typ == "process") & (s != "all"): + tec = [ + t + for t in aux2_df["technology"].values + if ((s in t) & ("furnace" not in t) & ('NH3' not in t) \ + & (not (t.startswith('meth_')))) + ] + if (typ == "demand") & (s == "Chemicals"): + tec = [ + t + for t in aux2_df["technology"].values + if ( (t.startswith('meth_') & (not (t.startswith('meth_i'))))\ + | ('NH3' in t) | ('MTO' in t) | ('resins' in t) | \ + (('petro' in t) & ('furnace' in t))) + ] + if (typ == "demand") & (s == "Chemicals|Other"): + tec = [ + t + for t in aux2_df["technology"].values + if ('resins' in t) + ] + if (typ == "demand") & (s == "Other Sector") & (e != "CO2"): + tec = [ + t + for t in aux2_df["technology"].values + if ( + (t.startswith("biomass_i")) + | (t.startswith("coal_i")) + | (t.startswith("elec_i")) + | (t.startswith("eth_i")) + | (t.startswith("foil_i")) + | (t.startswith("gas_i")) + | (t.startswith("h2_i")) + | (t.startswith("heat_i")) + | (t.startswith("hp_el_i")) + | (t.startswith("hp_gas_i")) + | (t.startswith("loil_i")) + | (t.startswith("meth_i")) + | (t.startswith("sp_coal_I")) + | (t.startswith("sp_el_I")) + | (t.startswith("sp_eth_I")) + | (t.startswith("sp_liq_I")) + | (t.startswith("sp_meth_I")) + | (t.startswith("h2_fc_I")) + + ) + ] + + if (typ == "demand") & (s == "all") & (e != "CO2"): + tec = [ + t + for t in aux2_df["technology"].values + if ( + ( + ( + ("cement" in t) + | ("steel" in t) + | ("aluminum" in t) + | ("petro" in t) + + ) + & ("furnace" in t) + ) + + | ( + (t.startswith("biomass_i")) + | (t.startswith("coal_i")) + | (t.startswith("elec_i")) + | (t.startswith("eth_i")) + | (t.startswith("foil_i")) + | (t.startswith("gas_i")) + | (t.startswith("h2_i")) + | (t.startswith("heat_i")) + | (t.startswith("hp_el_i")) + | (t.startswith("hp_gas_i")) + | (t.startswith("loil_i")) + | (t.startswith("meth_i")) + | (t.startswith("sp_coal_I")) + | (t.startswith("sp_el_I")) + | (t.startswith("sp_eth_I")) + | (t.startswith("sp_liq_I")) + | (t.startswith("sp_meth_I")) + | (t.startswith("h2_fc_I")) + | ("DUMMY_limestone_supply_cement" in t) + | ("DUMMY_limestone_supply_steel" in t) + | ("eaf_steel" in t) + | ("DUMMY_coal_supply" in t) + | ("DUMMY_gas_supply" in t) + | ("NH3" in t) + | t.startswith('meth_') + | ("MTO" in t) + | ("resins" in t) + ) + ) + ] + + if (typ == "demand") & (s != "all") & (s != "Other Sector")\ + & (s != "Chemicals") & (s != "Chemicals|Other"): + + if s == "steel": + # Furnaces are not used as heat source for iron&steel + # Dummy supply technologies help accounting the emissions + # from cokeoven_steel, bf_steel, dri_steel, eaf_steel, sinter_steel. + + tec = [ + t + for t in aux2_df["technology"].values + if ( + ("DUMMY_coal_supply" in t) + | ("DUMMY_gas_supply" in t) + | ("DUMMY_limestone_supply_steel" in t) + ) + ] + + elif s == "cement": + tec = [ + t + for t in aux2_df["technology"].values + if ( + ((s in t) & ("furnace" in t)) + | ("DUMMY_limestone_supply_cement" in t) + )] + elif s == "ammonia": + tec = [ + t + for t in aux2_df["technology"].values + if ( + ('NH3' in t))] + elif s == "methanol": + tec = [ + t + for t in aux2_df["technology"].values + if ( + t.startswith('meth_') & (not (t.startswith('meth_i'))))] + elif s == 'petro': + tec = [ + t + for t in aux2_df["technology"].values + if ((('petro' in t) & ("furnace" in t)) | ('MTO' in t)) + ] + else: + tec = [ + t + for t in aux2_df["technology"].values + if ((s in t) & ("furnace" in t)) + ] + # Adjust the sector names + s = change_names(s) + + aux2_df = aux2_df[aux2_df["technology"].isin(tec)] + # If there are no emission types for that setor skip + if aux2_df.empty: + continue + + # Add elements to lists for aggregation over emission type + # for each sector + var = aux2_df["variable"].values.tolist() + var_list.append(var) + + # Aggregate names: + if s == "all": + if (typ == "demand") & (e != "CO2"): + if (e != "CO2_industry"): + aggregate_name = ( + "Emissions|" + e + "|Energy|Demand|Industry" + ) + aggregate_list.append(aggregate_name) + else: + aggregate_name = ( + "Emissions|" + "CO2" + "|Energy|Demand|Industry" + ) + aggregate_list.append(aggregate_name) + if ((typ == "process") & (e != "CO2_industry")) : + aggregate_name = "Emissions|" + e + "|Industrial Processes" + aggregate_list.append(aggregate_name) + else: + if ((typ == "demand") & (e != "CO2")): + if (e != "CO2_industry"): + aggregate_name = ( + "Emissions|" + e + "|Energy|Demand|Industry|" + s + ) + aggregate_list.append(aggregate_name) + else: + aggregate_name = ( + "Emissions|" + + "CO2" + + "|Energy|Demand|Industry|" + + s + ) + aggregate_list.append(aggregate_name) + if ((typ == "process") & (e != "CO2_industry")): + aggregate_name = ( + "Emissions|" + e + "|Industrial Processes|" + s + ) + aggregate_list.append(aggregate_name) + + # To plot: Obtain the iamc format dataframe again + + aux2_df = pd.concat( + [ + all_emissions.reset_index(drop=True), + aux1_df.reset_index(drop=True), + ], + axis=1, + ) + aux2_df.drop( + ["emission", "type", "technology", "mode"], axis=1, inplace=True + ) + df_emi = pyam.IamDataFrame(data=aux2_df) + + # Aggregation over emission type for each sector if there are elements to aggregate + + if len(aggregate_list) != 0: + for i in range(len(aggregate_list)): + df_emi.aggregate( + aggregate_list[i], components=var_list[i], append=True + ) + + fig, ax1 = plt.subplots(1, 1, figsize=(10, 10)) + df_emi.filter(variable=aggregate_list, inplace=True) + df_emi.plot.stack(ax=ax1) + + df_final.append(df_emi, inplace=True) + ax1.set_title("Emissions_" + r + "_" + e) + ax1.set_ylabel("Mt") + ax1.legend(bbox_to_anchor=(0.3, 1)) + + plt.close() + pp.savefig(fig) + + # PLOTS + # + # # HVC Demand: See if this is correct .... + # + # for r in nodes: + # + # fig, ax1 = plt.subplots(1, 1, figsize=(10, 10)) + # + # if r != "China*": + # + # df_petro = df.copy() + # df_petro.filter(region=r, year=years, inplace=True) + # df_petro.filter( + # variable=[ + # "out|final_material|BTX|*", + # "out|final_material|ethylene|*", + # "out|final_material|propylene|*", + # ], + # inplace=True, + # ) + # + # # BTX production + # BTX_vars = [ + # "out|final_material|BTX|steam_cracker_petro|atm_gasoil", + # "out|final_material|BTX|steam_cracker_petro|naphtha", + # "out|final_material|BTX|steam_cracker_petro|vacuum_gasoil", + # ] + # df_petro.aggregate("BTX production", components=BTX_vars, append=True) + # + # # Propylene production + # + # propylene_vars = [ # "out|final_material|propylene|catalytic_cracking_ref|atm_gasoil", + # # "out|final_material|propylene|catalytic_cracking_ref|vacuum_gasoil", + # "out|final_material|propylene|steam_cracker_petro|atm_gasoil", + # "out|final_material|propylene|steam_cracker_petro|naphtha", + # "out|final_material|propylene|steam_cracker_petro|propane", + # "out|final_material|propylene|steam_cracker_petro|vacuum_gasoil", + # ] + # + # df_petro.aggregate( + # "Propylene production", components=propylene_vars, append=True + # ) + # + # ethylene_vars = [ + # "out|final_material|ethylene|ethanol_to_ethylene_petro|M1", + # "out|final_material|ethylene|steam_cracker_petro|atm_gasoil", + # "out|final_material|ethylene|steam_cracker_petro|ethane", + # "out|final_material|ethylene|steam_cracker_petro|naphtha", + # "out|final_material|ethylene|steam_cracker_petro|propane", + # "out|final_material|ethylene|steam_cracker_petro|vacuum_gasoil", + # ] + # + # df_petro.aggregate( + # "Ethylene production", components=ethylene_vars, append=True + # ) + # + # if r != "World": + # + # df_petro.filter( + # variable=[ + # "BTX production", + # "Propylene production", + # "Ethylene production", + # "out|final_material|BTX|import_petro|*", + # "out|final_material|propylene|import_petro|*", + # "out|final_material|ethylene|import_petro|*", + # ], + # inplace=True, + # ) + # else: + # df_petro.filter( + # variable=[ + # "BTX production", + # "Propylene production", + # "Ethylene production", + # ], + # inplace=True, + # ) + # + # df_petro.plot.stack(ax=ax1) + # ax1.legend( + # [ + # "BTX production", + # "Ethylene production", + # "Propylene production", + # "import_BTX", + # "import_ethylene", + # "import_propylene", + # ] + # ) + # ax1.set_title("HVC Production_" + r) + # ax1.set_xlabel("Years") + # ax1.set_ylabel("Mt") + # + # plt.close() + # pp.savefig(fig) + + # # Refinery Products - already commented out + # + # for r in nodes: + # + # fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 8)) + # # fig.tight_layout(pad=15.0) + # + # if r != "China*": + # + # # Fuel oil + # + # df_ref_fueloil = df.copy() + # df_ref_fueloil.filter(region=r, year=years, inplace=True) + # + # if r == "World": + # df_ref_fueloil.filter( + # variable=["out|secondary|fueloil|agg_ref|*"], inplace=True + # ) + # + # else: + # df_ref_fueloil.filter( + # variable=[ + # "out|secondary|fueloil|agg_ref|*", + # "out|secondary|fueloil|foil_imp|*", + # ], + # inplace=True, + # ) + # + # df_ref_fueloil.stack_plot(ax=ax1) + # + # ax1.legend( + # [ + # "Atm gas oil", + # "Atm resiude", + # "Heavy fuel oil", + # "Petroleum coke", + # "Vacuum residue", + # "import", + # ], + # bbox_to_anchor=(0.3, 1), + # ) + # ax1.set_title("Fuel oil mix_" + r) + # ax1.set_xlabel("Year") + # ax1.set_ylabel("GWa") + # + # # Light oil + # + # df_ref_lightoil = df.copy() + # df_ref_lightoil.filter(region=r, year=years, inplace=True) + # + # if r == "World": + # df_ref_lightoil.filter( + # variable=["out|secondary|lightoil|agg_ref|*"], inplace=True + # ) + # + # else: + # df_ref_lightoil.filter( + # variable=[ + # "out|secondary|lightoil|agg_ref|*", + # "out|secondary|lightoil|loil_imp|*", + # ], + # inplace=True, + # ) + # + # # ,"out|secondary|lightoil|loil_imp|*" + # df_ref_lightoil.stack_plot(ax=ax2) + # # df_final.append(df_ref_lightoil, inplace = True) + # ax2.legend( + # [ + # "Diesel", + # "Ethane", + # "Gasoline", + # "Kerosene", + # "Light fuel oil", + # "Naphtha", + # "Refinery gas", + # "import", + # ], + # bbox_to_anchor=(1, 1), + # ) + # ax2.set_title("Light oil mix_" + r) + # ax2.set_xlabel("Year") + # ax2.set_ylabel("GWa") + # + # plt.close() + # pp.savefig(fig) + # + # # Oil production World - Already commented out + # + # fig, ax1 = plt.subplots(1, 1, figsize=(8, 8)) + # + # df_all_oil = df.copy() + # df_all_oil.filter(region="World", year=years, inplace=True) + # df_all_oil.filter( + # variable=[ + # "out|secondary|fueloil|agg_ref|*", + # "out|secondary|lightoil|agg_ref|*", + # ], + # inplace=True, + # ) + # df_all_oil.stack_plot(ax=ax1) + # + # ax1.legend( + # [ + # "Atmg_gasoil", + # "atm_residue", + # "heavy_foil", + # "pet_coke", + # "vaccum_residue", + # "diesel", + # "ethane", + # "gasoline", + # "kerosne", + # "light_foil", + # "naphtha", + # "refinery_gas_a", + # "refinery_gas_b", + # ], + # bbox_to_anchor=(1, 1), + # ) + # ax1.set_title("Oil production" + r) + # ax1.set_xlabel("Year") + # ax1.set_ylabel("GWa") + # + # plt.close() + # pp.savefig(fig) + + # Final Energy by all fuels: See if this plot is correct.. + + # Select the sectors + # sectors = ["aluminum", "steel", "cement", "petro"] + # + # for r in nodes: + # + # # For each region create a figure + # + # fig, axs = plt.subplots(nrows=2, ncols=2) + # fig.set_size_inches(20, 20) + # fig.subplots_adjust(wspace=0.2) + # fig.subplots_adjust(hspace=0.5) + # fig.tight_layout(pad=20.0) + # + # if r != "China*": + # + # # Specify the position of each sector in the graph + # + # cnt = 1 + # + # for s in sectors: + # + # if cnt == 1: + # x_cor = 0 + # y_cor = 0 + # + # if cnt == 2: + # x_cor = 0 + # y_cor = 1 + # + # if cnt == 3: + # x_cor = 1 + # y_cor = 0 + # + # if cnt == 4: + # x_cor = 1 + # y_cor = 1 + # + # df_final_energy = df.copy() + # df_final_energy.convert_unit("", to="GWa", factor=1, inplace=True) + # df_final_energy.filter(region=r, year=years, inplace=True) + # df_final_energy.filter(variable=["in|final|*"], inplace=True) + # df_final_energy.filter( + # variable=["in|final|*|cokeoven_steel|*"], keep=False, inplace=True + # ) + # + # if s == "petro": + # # Exclude the feedstock ethanol and natural gas + # df_final_energy.filter( + # variable=[ + # "in|final|ethanol|ethanol_to_ethylene_petro|M1", + # "in|final|gas|gas_processing_petro|M1", + # "in|final|atm_gasoil|steam_cracker_petro|atm_gasoil", + # "in|final|vacuum_gasoil|steam_cracker_petro|vacuum_gasoil", + # "in|final|naphtha|steam_cracker_petro|naphtha", + # ], + # keep=False, + # inplace=True, + # ) + # + # all_flows = df_final_energy.timeseries().reset_index() + # + # # Split the strings in the identified variables for further processing + # splitted_vars = [v.split("|") for v in all_flows.variable] + # + # # Create auxilary dataframes for processing + # aux1_df = pd.DataFrame( + # splitted_vars, + # columns=["flow_type", "level", "commodity", "technology", "mode"], + # ) + # aux2_df = pd.concat( + # [all_flows.reset_index(drop=True), aux1_df.reset_index(drop=True)], + # axis=1, + # ) + # + # # Filter the technologies only for the certain material + # tec = [t for t in aux2_df["technology"].values if s in t] + # aux2_df = aux2_df[aux2_df["technology"].isin(tec)] + # + # # Lists to keep commodity, aggregate and variable. + # + # aggregate_list = [] + # commodity_list = [] + # var_list = [] + # + # # For each commodity collect the variable name, create an aggregate name + # s = change_names(s) + # for c in np.unique(aux2_df["commodity"].values): + # var = np.unique( + # aux2_df.loc[aux2_df["commodity"] == c, "variable"].values + # ).tolist() + # aggregate_name = "Final Energy|" + s + "|" + c + # + # aggregate_list.append(aggregate_name) + # commodity_list.append(c) + # var_list.append(var) + # + # # Obtain the iamc format dataframe again + # + # aux2_df.drop( + # ["flow_type", "level", "commodity", "technology", "mode"], + # axis=1, + # inplace=True, + # ) + # df_final_energy = pyam.IamDataFrame(data=aux2_df) + # + # # Aggregate the commodities in iamc object + # + # i = 0 + # for c in commodity_list: + # df_final_energy.aggregate( + # aggregate_list[i], components=var_list[i], append=True + # ) + # i = i + 1 + # + # df_final_energy.convert_unit( + # "GWa", to="EJ/yr", factor=0.03154, inplace=True + # ) + # df_final_energy.filter(variable=aggregate_list).plot.stack( + # ax=axs[x_cor, y_cor] + # ) + # axs[x_cor, y_cor].set_ylabel("EJ/yr") + # axs[x_cor, y_cor].set_title("Final Energy_" + s + "_" + r) + # cnt = cnt + 1 + # + # plt.close() + # pp.savefig(fig) + + # Scrap Release: (Buildings), Other and Power Sector + # For cement, we dont have any other scrap represented in the model. + # Only scrap is from power sector. + print('Scrap generated by sector') + materials = ["aluminum","steel","cement"] + + for r in nodes: + for m in materials: + df_scrap_by_sector = df.copy() + df_scrap_by_sector.filter(region=r, year=years, inplace=True) + + # filt_buildings = 'out|end_of_life|' + m + '|demolition_build|M1' + # print(filt_buildings) + filt_other = 'out|end_of_life|' + m + '|other_EOL_' + m + '|M1' + filt_total = 'in|end_of_life|' + m + '|total_EOL_' + m + '|M1' + + m = change_names(m) + # var_name_buildings = 'Total Scrap|Residential and Commercial|' + m + var_name_other = 'Total Scrap|Other|' + m + var_name_power = 'Total Scrap|Power Sector|' + m + + if m != "Non-Metallic Minerals|Cement": + df_scrap_by_sector.aggregate(var_name_other,\ + components=[filt_other],append=True) + + # df_scrap_by_sector.aggregate(var_name_buildings,\ + # components=[filt_buildings],append=True) + # 'out|end_of_life|' + m + '|demolition_build|M1', + + df_scrap_by_sector.subtract(filt_total, filt_other,var_name_power, + axis='variable', append = True) + + df_scrap_by_sector.filter(variable=[ + # var_name_buildings, + var_name_other, var_name_power],inplace=True) + elif m == "Non-Metallic Minerals|Cement": + df_scrap_by_sector.aggregate(var_name_power,\ + components=[filt_total],append=True) + + df_scrap_by_sector.filter(variable=[ + # var_name_buildings, + var_name_power],inplace=True) + + df_scrap_by_sector.convert_unit('', to='Mt/yr', factor=1, inplace = True) + df_final.append(df_scrap_by_sector, inplace=True) + + # PRICE + # SCRAP_RECOVERY_ALUMINUM NEEDS TO BE FIXED + + # df_final = df_final.timeseries().reset_index() + # + # commodity_type = [ + # "Non-Ferrous Metals|Aluminium", + # "Non-Ferrous Metals|Aluminium|New Scrap", + # "Non-Ferrous Metals|Aluminium|Old Scrap", + # "Non-Ferrous Metals|Bauxite", + # "Non-Ferrous Metals|Alumina", + # "Steel|Iron Ore", + # "Steel|Pig Iron", + # "Steel|New Scrap", + # "Steel|Old Scrap", + # "Steel", + # "Non-Metallic Minerals|Cement", + # "Non-Metallic Minerals|Limestone", + # "Non-Metallic Minerals|Clinker Cement", + # "Chemicals|High Value Chemicals", + # ] + # + # for c in commodity_type: + # prices = scenario.var("PRICE_COMMODITY") + # # Used for calculation of average prices for scraps + # output = scenario.par( + # "output", + # filters={"technology": ["scrap_recovery_aluminum", "scrap_recovery_steel"]}, + # ) + # # Differs per sector what to report so more flexible with conditions. + # # Store the relevant variables in prices_all + # + # # ALUMINUM + # if c == "Non-Ferrous Metals|Bauxite": + # continue + # if c == "Non-Ferrous Metals|Alumina": + # prices_all = prices[ + # (prices["level"] == "secondary_material") + # & (prices["commodity"] == "aluminum") + # ] + # if c == "Non-Ferrous Metals|Aluminium": + # prices_all = prices[ + # (prices["level"] == "final_material") + # & (prices["commodity"] == "aluminum") + # ] + # if c == "Non-Ferrous Metals|Aluminium|New Scrap": + # prices_all = prices[ + # (prices["level"] == "new_scrap") & (prices["commodity"] == "aluminum") + # ] + # + # # IRON AND STEEL + # if c == "Steel|Iron Ore": + # prices_all = prices[(prices["commodity"] == "ore_iron")] + # if c == "Steel|Pig Iron": + # prices_all = prices[(prices["commodity"] == "pig_iron")] + # if c == "Steel": + # prices_all = prices[ + # (prices["commodity"] == "steel") & (prices["level"] == "final_material") + # ] + # if c == "Steel|New Scrap": + # prices_all = prices[ + # (prices["commodity"] == "steel") & (prices["level"] == "new_scrap") + # ] + # # OLD SCRAP (For aluminum and steel) + # + # if (c == "Steel|Old Scrap") | (c == "Non-Ferrous Metals|Aluminium|Old Scrap"): + # + # prices = prices[ + # ( + # (prices["level"] == "old_scrap_1") + # | (prices["level"] == "old_scrap_2") + # | (prices["level"] == "old_scrap_3") + # ) + # ] + # + # if c == "Non-Ferrous Metals|Aluminium|Old Scrap": + # output = output[output["technology"] == "scrap_recovery_aluminum"] + # prices = prices[(prices["commodity"] == "aluminum")] + # if c == "Steel|Old Scrap": + # output = output[output["technology"] == "scrap_recovery_steel"] + # prices = prices[(prices["commodity"] == "steel")] + # + # prices.loc[prices["level"] == "old_scrap_1", "weight"] = output.loc[ + # output["level"] == "old_scrap_1", "value" + # ].values[0] + # + # prices.loc[prices["level"] == "old_scrap_2", "weight"] = output.loc[ + # output["level"] == "old_scrap_2", "value" + # ].values[0] + # + # prices.loc[prices["level"] == "old_scrap_3", "weight"] = output.loc[ + # output["level"] == "old_scrap_3", "value" + # ].values[0] + # + # prices_all = pd.DataFrame(columns=["node", "commodity", "year"]) + # prices_new = pd.DataFrame(columns=["node", "commodity", "year"]) + # + # for reg in output["node_loc"].unique(): + # #for yr in output["year_act"].unique(): + # for yr in prices["year"].unique(): + # prices_temp = prices.groupby(["node", "year"]).get_group((reg, yr)) + # rate = prices_temp["weight"].values.tolist() + # amount = prices_temp["lvl"].values.tolist() + # weighted_avg = np.average(amount, weights=rate) + # prices_new = pd.DataFrame( + # {"node": reg, "year": yr, "commodity": c, "lvl": weighted_avg,}, + # index=[0], + # ) + # prices_all = pd.concat([prices_all, prices_new]) + # # CEMENT + # + # if c == "Non-Metallic Minerals|Limestone": + # prices_all = prices[(prices["commodity"] == "limestone_cement")] + # if c == "Non-Metallic Minerals|Clinker Cement": + # prices_all = prices[(prices["commodity"] == "clinker_cement")] + # if c == "Non-Metallic Minerals|Cement": + # prices_all = prices[ + # (prices["commodity"] == "cement") & (prices["level"] == "demand") + # ] + # # Petro-Chemicals + # + # if c == "Chemicals|High Value Chemicals": + # prices = prices[ + # (prices["commodity"] == "ethylene") + # | (prices["commodity"] == "propylene") + # | (prices["commodity"] == "BTX") + # ] + # prices_all = prices.groupby(by=["year", "node"]).mean().reset_index() + # + # # Convert all to IAMC format. + # for r in prices_all["node"].unique(): + # if (r == "R11_GLB") | (r == "R12_GLB"): + # continue + # df_price = pd.DataFrame( + # {"model": model_name, "scenario": scenario_name, "unit": "2010USD/Mt",}, + # index=[0], + # ) + # + # for y in prices_all["year"].unique(): + # df_price["region"] = r + # df_price["variable"] = "Price|" + c + # x = prices_all.loc[ + # ((prices_all["node"] == r) & (prices_all["year"] == y)), "lvl" + # ] + # if not x.empty: + # value = x.values[0] * 1.10774 + # else: + # value = 0 + # df_price[y] = value + # + # df.price = df_price.columns.astype(str) + # + # df_final = pd.concat([df_final, df_price]) + + # Material Demand - comment out if no power sector + # Power Sector + + # input_cap_new = scenario.par("input_cap_new") + # input_cap_new.drop( + # ["node_origin", "level", "time_origin", "unit"], axis=1, inplace=True + # ) + # input_cap_new.rename( + # columns={ + # "value": "material_intensity", + # "node_loc": "region", + # "year_vtg": "year", + # }, + # inplace=True, + # ) + # + # cap_new = scenario.var("CAP_NEW") + # cap_new.drop(["mrg"], axis=1, inplace=True) + # cap_new.rename( + # columns={"lvl": "installed_capacity", "node_loc": "region", "year_vtg": "year"}, + # inplace=True, + # ) + # + # merged_df = pd.merge(cap_new, input_cap_new) + # merged_df["Material Need"] = ( + # merged_df["installed_capacity"] * merged_df["material_intensity"] + # ) + # merged_df = merged_df[merged_df["year"] >= min(years)] + # + # final_material_needs = ( + # merged_df.groupby(["commodity", "region", "year"]) + # .sum() + # .drop(["installed_capacity", "material_intensity"], axis=1) + # ) + # + # final_material_needs = final_material_needs.reset_index(["year"]) + # final_material_needs = pd.pivot_table( + # final_material_needs, + # values="Material Need", + # columns="year", + # index=["commodity", "region"], + # ).reset_index(["commodity", "region"]) + # + # final_material_needs_global = ( + # final_material_needs.groupby("commodity").sum().reset_index() + # ) + # final_material_needs_global["region"] = "World" + # + # material_needs_all = pd.concat( + # [final_material_needs, final_material_needs_global], ignore_index=True + # ) + # + # material_needs_all["scenario"] = scenario_name + # material_needs_all["model"] = model_name + # material_needs_all["unit"] = "Mt/yr" + # material_needs_all["commodity"] = material_needs_all.apply( + # lambda x: change_names(x["commodity"]), axis=1 + # ) + # material_needs_all = material_needs_all.assign( + # variable=lambda x: "Material Demand|Power Sector|" + x["commodity"] + # ) + # + # material_needs_all = material_needs_all.drop(["commodity"], axis=1) + # df.price = df_price.columns.astype(str) + # + # print("This is the final_material_needs") + # print(material_needs_all) + # + # df_final = pd.concat([df_final, material_needs_all]) + + # Trade + # .................... + + path_temp = os.path.join(directory, "temp_new_reporting.xlsx") + df_final.to_excel(path_temp, sheet_name="data", index=False) + + excel_name_new = "New_Reporting_" + model_name + "_" + scenario_name + ".xlsx" + path_new = os.path.join(directory, excel_name_new) + + print(path_temp) + print(path_new) + fix_excel(path_temp, path_new) + print("New reporting file generated.") + df_final = pd.read_excel(path_new) + + # df_resid = pd.read_csv(path_resid) + # df_resid["Model"] = model_name + # df_resid["Scenario"] = scenario_name + # df_comm = pd.read_csv(path_comm) + # df_comm["Model"] = model_name + # df_comm["Scenario"] = scenario_name + + scenario.check_out(timeseries_only=True) + print("Starting to upload timeseries") + print(df_final.head()) + scenario.add_timeseries(df_final) + # scenario.add_timeseries(df_resid) + # scenario.add_timeseries(df_comm) + print("Finished uploading timeseries") + scenario.commit("Reporting uploaded as timeseries") + + pp.close() + os.remove(path_temp) + #os.remove(path) diff --git a/message_ix_models/model/material/report/tables.py b/message_ix_models/model/material/report/tables.py deleted file mode 100644 index fb28d54855..0000000000 --- a/message_ix_models/model/material/report/tables.py +++ /dev/null @@ -1,2561 +0,0 @@ -import logging -import pandas as pd - -from message_data.tools.post_processing import default_tables -from message_data.tools.post_processing.default_tables import ( - TECHS, - rc_techs, - _pe_wCCSretro, -) -import message_data.tools.post_processing.pp_utils as pp_utils - -log = logging.getLogger(__name__) - -pp = None -mu = None -run_history = None -urban_perc_data = None -kyoto_hist_data = None -lu_hist_data = None - -# Dictionary where all functions defined in the script are stored. -func_dict = {} # type: dict - - -def return_func_dict(): - """Return functions described in script.""" - - # This is a crude hack: when iamc_report_hackathon.report(), per config, finds and - # calls this function to retrieve the list of functions in the file, we override the - # lists of technologies used by functions defined in .default_tables. - # FIXME(PNK) read these from proper hierarchical technology code lists; store on a - # Context object. - TECHS.update(_TECHS) - - # Invoke similar code associated with MESSAGEix-Buildings - from message_data.model.buildings.report import configure_legacy_reporting - - configure_legacy_reporting(TECHS) - - log.debug(f"Configured legacy reporting for -BM model variants:\n{TECHS = }") - - return func_dict - - -#: Lists of technologies for materials variant. -_TECHS = { - "cement with ccs": ["clinker_dry_ccs_cement", "clinker_wet_ccs_cement"], - "industry with ccs": [ - "biomass_NH3_ccs", - "coal_NH3_ccs", - "fueloil_NH3_ccs", - "gas_NH3_ccs", - ], - "gas extra": [ - "furnace_gas_aluminum", - "furnace_gas_petro", - "furnace_gas_cement", - "furnace_gas_refining", - "hp_gas_aluminum", - "hp_gas_petro", - "hp_gas_refining", - ], - "refining": [ - "atm_distillation_ref", - "catalytic_cracking_ref", - "catalytic_reforming_ref", - "coking_ref", - "dheat_refining", - "fc_h2_refining", - "furnace_biomass_refining", - "furnace_coal_refining", - "furnace_coke_refining", - "furnace_elec_refining", - "furnace_ethanol_refining", - "furnace_foil_refining", - "furnace_gas_refining", - "furnace_h2_refining", - "furnace_loil_refining", - "furnace_methanol_refining", - "hp_elec_refining", - "hp_gas_refining", - "hydro_cracking_ref", - "hydrotreating_ref", - "solar_refining", - "vacuum_distillation_ref", - "visbreaker_ref", - ], - "se solids biomass extra": [ - "furnace_biomass_aluminum", - "furnace_biomass_cement", - "furnace_biomass_petro", - "furnace_biomass_refining", - "furnace_biomass_steel", - ], - "se solids coal extra": [ - "furnace_coal_aluminum", - "furnace_coal_petro", - "furnace_coal_cement", - "furnace_coal_refining", - ], - "synfuels oil": [ - ( - "agg_ref", - "out", - dict(level=["secondary"], commodity=["lightoil", "fueloil"]), - ), - ( - "steam_cracker_petro", - "inp", - dict(level=["final"], commodity=["atm_gasoil", "naphtha", "vacuum_gasoil"]), - ), - ], -} - - -def _register(func): - """Function to register reporting functions. - - Parameters - ---------- - - func : str - Function name - """ - - func_dict[func.__name__] = func - return func - - -# ------------------- -# Reporting functions -# ------------------- - -@_register -def retr_SE_synfuels(units): - """Energy: Secondary Energy synthetic fuels. - Parameters - ---------- - units : str - Units to which variables should be converted. - """ - - vars = {} - - vars["Liquids|Oil"] = ( - pp.out( - "agg_ref", - units, - outfilter={"level": ["secondary"], "commodity": ["lightoil"]}, - ) - + pp.out( - "agg_ref", - units, - outfilter={"level": ["secondary"], "commodity": ["fueloil"]}, - ) - + pp.inp( - "steam_cracker_petro", - units, - inpfilter={ - "level": ["final"], - "commodity": ["atm_gasoil", "vacuum_gasoil", "naphtha"], - }, - ) - ) - - vars["Liquids|Biomass|w/o CCS"] = pp.out( - ["eth_bio", "liq_bio"], - units, - outfilter={"level": ["primary"], "commodity": ["ethanol"]}, - ) - - vars["Liquids|Biomass|w/ CCS"] = pp.out( - ["eth_bio_ccs", "liq_bio_ccs"], - units, - outfilter={"level": ["primary"], "commodity": ["ethanol"]}, - ) - - vars["Liquids|Coal|w/o CCS"] = pp.out( - ["meth_coal", "syn_liq"], - units, - outfilter={"level": ["primary"], "commodity": ["methanol"]}, - ) - - vars["Liquids|Coal|w/ CCS"] = pp.out( - ["meth_coal_ccs", "syn_liq_ccs"], - units, - outfilter={"level": ["primary"], "commodity": ["methanol"]}, - ) - - vars["Liquids|Gas|w/o CCS"] = pp.out( - "meth_ng", units, outfilter={"level": ["primary"], "commodity": ["methanol"]} - ) - - vars["Liquids|Gas|w/ CCS"] = pp.out( - "meth_ng_ccs", - units, - outfilter={"level": ["primary"], "commodity": ["methanol"]}, - ) - - vars["Hydrogen|Coal|w/o CCS"] = pp.out( - "h2_coal", units, outfilter={"level": ["secondary"], "commodity": ["hydrogen"]} - ) - - vars["Hydrogen|Coal|w/ CCS"] = pp.out( - "h2_coal_ccs", - units, - outfilter={"level": ["secondary"], "commodity": ["hydrogen"]}, - ) - - vars["Hydrogen|Gas|w/o CCS"] = pp.out( - "h2_smr", units, outfilter={"level": ["secondary"], "commodity": ["hydrogen"]} - ) - - vars["Hydrogen|Gas|w/ CCS"] = pp.out( - "h2_smr_ccs", - units, - outfilter={"level": ["secondary"], "commodity": ["hydrogen"]}, - ) - - vars["Hydrogen|Biomass|w/o CCS"] = pp.out( - "h2_bio", units, outfilter={"level": ["secondary"], "commodity": ["hydrogen"]} - ) - - vars["Hydrogen|Biomass|w/ CCS"] = pp.out( - "h2_bio_ccs", - units, - outfilter={"level": ["secondary"], "commodity": ["hydrogen"]}, - ) - - vars["Hydrogen|Electricity"] = pp.out( - "h2_elec", units, outfilter={"level": ["secondary"], "commodity": ["hydrogen"]} - ) - - df = pp_utils.make_outputdf(vars, units) - return df -@_register -def retr_CO2_CCS(units_emi, units_ene): - """Carbon sequestration. - - Energy and land-use related carbon seuqestration. - - Parameters - ---------- - - units_emi : str - Units to which emission variables should be converted. - units_ene : str - Units to which energy variables should be converted. - """ - - vars = {} - - # -------------------------------- - # Calculation of helping variables - # -------------------------------- - - # Biogas share calculation - _Biogas = pp.out("gas_bio", units_ene) - - _gas_inp_tecs = [ - "gas_ppl", - "gas_cc", - "gas_cc_ccs", - "gas_ct", - "gas_htfc", - "gas_hpl", - "meth_ng", - "meth_ng_ccs", - "h2_smr", - "h2_smr_ccs", - "gas_t_d", - "gas_t_d_ch4", - ] - - _totgas = pp.inp(_gas_inp_tecs, units_ene, inpfilter={"commodity": ["gas"]}) - - _BGas_share = (_Biogas / _totgas).fillna(0) - - # Calulation of CCS components - - _CCS_coal_elec = -1.0 * pp.emi( - ["c_ppl_co2scr", "coal_adv_ccs", "igcc_ccs", "igcc_co2scr"], - "GWa", - emifilter={"relation": ["CO2_Emission"]}, - emission_units=units_emi, - ) - - _CCS_coal_liq = -1.0 * pp.emi( - ["syn_liq_ccs", "meth_coal_ccs"], - "GWa", - emifilter={"relation": ["CO2_Emission"]}, - emission_units=units_emi, - ) - - _CCS_coal_hydrogen = -1.0 * pp.emi( - "h2_coal_ccs", - "GWa", - emifilter={"relation": ["CO2_Emission"]}, - emission_units=units_emi, - ) - - _CCS_cement = -1.0 * pp.emi( - ["clinker_dry_ccs_cement", "clinker_wet_ccs_cement"], - "GWa", - emifilter={"relation": ["CO2_Emission"]}, - emission_units=units_emi, - ) - - _CCS_ammonia = -1.0 * pp.emi( - ['biomass_NH3_ccs', 'gas_NH3_ccs', 'coal_NH3_ccs', 'fueloil_NH3_ccs'], - "GWa", - emifilter={"relation": ["CO2_Emission"]}, - emission_units=units_emi, - ) - - _CCS_bio_elec = -1.0 * pp.emi( - ["bio_ppl_co2scr", "bio_istig_ccs"], - "GWa", - emifilter={"relation": ["CO2_Emission"]}, - emission_units=units_emi, - ) - - _CCS_bio_liq = -1.0 * pp.emi( - ["eth_bio_ccs", "liq_bio_ccs"], - "GWa", - emifilter={"relation": ["CO2_Emission"]}, - emission_units=units_emi, - ) - - _CCS_bio_hydrogen = -1.0 * pp.emi( - "h2_bio_ccs", - "GWa", - emifilter={"relation": ["CO2_Emission"]}, - emission_units=units_emi, - ) - - _CCS_gas_elec = ( - -1.0 - * pp.emi( - ["g_ppl_co2scr", "gas_cc_ccs"], - "GWa", - emifilter={"relation": ["CO2_Emission"]}, - emission_units=units_emi, - ) - * (1.0 - _BGas_share) - ) - - _CCS_gas_liq = ( - -1.0 - * pp.emi( - "meth_ng_ccs", - "GWa", - emifilter={"relation": ["CO2_Emission"]}, - emission_units=units_emi, - ) - * (1.0 - _BGas_share) - ) - - _CCS_gas_hydrogen = ( - -1.0 - * pp.emi( - "h2_smr_ccs", - "GWa", - emifilter={"relation": ["CO2_Emission"]}, - emission_units=units_emi, - ) - * (1.0 - _BGas_share) - ) - - vars["CCS|Fossil|Energy|Supply|Electricity"] = _CCS_coal_elec + _CCS_gas_elec - - vars["CCS|Fossil|Energy|Supply|Liquids"] = _CCS_coal_liq + _CCS_gas_liq - - vars["CCS|Fossil|Energy|Supply|Hydrogen"] = _CCS_coal_hydrogen + _CCS_gas_hydrogen - - vars["CCS|Biomass|Energy|Supply|Electricity"] = ( - _CCS_bio_elec + _CCS_gas_elec * _BGas_share - ) - - vars["CCS|Biomass|Energy|Supply|Liquids"] = ( - _CCS_bio_liq + _CCS_gas_liq * _BGas_share - ) - - vars["CCS|Biomass|Energy|Supply|Hydrogen"] = ( - _CCS_bio_hydrogen + _CCS_gas_hydrogen * _BGas_share - ) - - vars["CCS|Industrial Processes"] = _CCS_cement + _CCS_ammonia - - vars["Land Use"] = -pp.land_out( - lu_out_filter={ - "level": ["land_use_reporting"], - "commodity": ["Emissions|CO2|AFOLU|Negative"], - } - ) - - vars["Land Use|Afforestation"] = -pp.land_out( - lu_out_filter={ - "level": ["land_use_reporting"], - "commodity": ["Emissions|CO2|AFOLU|Afforestation"], - } - ) - - df = pp_utils.make_outputdf(vars, units_emi) - return df - -@_register -def retr_CO2emi(units_emi, units_ene_mdl): - """Emissions: CO2. - Parameters - ---------- - units_emi : str - Units to which emission variables should be converted. - units_ene_mdl : str - Native units of energy in the model. - """ - - dfs = [] - - vars = {} - - if run_history != "True": - group = ["Region", "Mode", "Vintage"] - else: - group = ["Region"] - - # ---------------------------------------------------------------------------------- - # Step 1. Calculate variables used for distribution of hydrogen and biogas related - # emissions - # ---------------------------------------------------------------------------------- - - # ---------------------------------------------------------------------------------- - # Step 1.1. Calculate total got HYDROGEN distribution - # - # Calculate all non-ccs gas use which is used for the distribution of HYDROGEN - # related emissions which need to be subtracted from various sectors. - # ---------------------------------------------------------------------------------- - - _inp_nonccs_gas_tecs = ( - pp.inp( - [ - "gas_cc", # _Biogas_el, _Hydrogen_el - "gas_ct", # _Biogas_el, _Hydrogen_el - "gas_fs", # _Biogas_fs, _Hydrogen_fs - "gas_hpl", # _Biogas_heat, _Hydrogen_heat - "gas_htfc", # _Biogas_el, _Hydrogen_el - "gas_i", # _Biogas_ind_rest, _Hydrogen_ind_rest - "gas_ppl", # _Biogas_el, _Hydrogen_el - "gas_trp", # _Biogas_trp, _Hydrogen_trp - "hp_gas_i", # _Biogas_ind_rest, _Hydrogen_ind_rest - ] - + TECHS["rc gas"] # _Biogas_res, _Hydrogen_res - + TECHS["gas extra"], # See above - units_ene_mdl, - inpfilter={"commodity": ["gas"]}, - ) - + pp.inp( - ["gas_t_d", "gas_t_d_ch4"], units_ene_mdl, inpfilter={"commodity": ["gas"]} - ) # _Biogas_td, _Hydrogen_td - - pp.out( - ["gas_t_d", "gas_t_d_ch4"], units_ene_mdl, outfilter={"commodity": ["gas"]} - ) # _Biogas_td, _Hydrogen_td - ) - - # Calculate shares for ppl feeding into g_ppl_co2scr (gas_cc and gas_ppl) - _gas_cc_shr = (pp.out("gas_cc") / pp.out(["gas_cc", "gas_ppl"])).fillna(0) - _gas_ppl_shr = (pp.out("gas_ppl") / pp.out(["gas_cc", "gas_ppl"])).fillna(0) - - _inp_nonccs_gas_tecs_wo_CCSRETRO = ( - _inp_nonccs_gas_tecs - - _pe_wCCSretro( - "gas_cc", - "g_ppl_co2scr", - group, - inpfilter={"level": ["secondary"], "commodity": ["gas"]}, - units=units_ene_mdl, - share=_gas_cc_shr, - ) - - _pe_wCCSretro( - "gas_ppl", - "g_ppl_co2scr", - group, - inpfilter={"level": ["secondary"], "commodity": ["gas"]}, - units=units_ene_mdl, - share=_gas_ppl_shr, - ) - - _pe_wCCSretro( - "gas_htfc", - "gfc_co2scr", - group, - inpfilter={"level": ["secondary"], "commodity": ["gas"]}, - units=units_ene_mdl, - ) - ) - - # ---------------------------------------------------------------------------------- - # Step 1.2. Calculate total for BIOGAS distribution - # - # Calculate all gas use which is used to for the distribution of BIOGAS related - # emissions which need to be subtracted from various sectors. - # ---------------------------------------------------------------------------------- - - _inp_all_gas_tecs = _inp_nonccs_gas_tecs + pp.inp( - [ - "gas_cc_ccs", # _Biogas_el, no-hydrogen via hlim - "meth_ng", # _Biogas_liquids_gas_comb, no-hydrogen via ? - "meth_ng_ccs", # _Biogas_liquids_gas_comb, no-hydrogen via ? - "h2_smr", # _Biogas_gases_h2_comb, no-hydrogen via ? - "h2_smr_ccs", # _Biogas_gases_h2_comb, no-hydrogen via hlim - "gas_NH3_ccs" # _Biogas_fs, no-hydrogen needs entry to hlim entry - # equivalent to CO2 entry (-) - ], - units_ene_mdl, - inpfilter={"commodity": ["gas"]}, - ) - - # ---------------------------------------------------------------------------------- - # Step 2. Distribute BIOGAS emissions - # - # - Calculate biogas-emission attribution to sector - # - Based on: sector_specfific_gas_consumption / total_gas_use * total_biogas_use - # - These emissions will later be subtracted from the respective sectors. - # ---------------------------------------------------------------------------------- - - # Retrieve total bio-gas quantity procuded - _Biogas_tot_abs = pp.out("gas_bio") - - # Multiply quantity by emission factor - _Biogas_tot = _Biogas_tot_abs * mu["crbcnt_gas"] * mu["conv_c2co2"] - - # Variable: "Energy|Supply|Electricity" - _Biogas_el = _Biogas_tot * ( - pp.inp( - ["gas_ppl", "gas_ct", "gas_cc", "gas_cc_ccs", "gas_htfc"], - units_ene_mdl, - inpfilter={"commodity": ["gas"]}, - ) - / _inp_all_gas_tecs - ).fillna(0) - - # Variable: "Energy|Supply|Heat" - _Biogas_heat = _Biogas_tot * ( - pp.inp("gas_hpl", units_ene_mdl, inpfilter={"commodity": ["gas"]}) - / _inp_all_gas_tecs - ).fillna(0) - - # Variable: "Energy|Supply|Liquids|Natural Gas|Combustion" - _Biogas_liquids_gas_comb = _Biogas_tot * ( - pp.inp( - ["meth_ng", "meth_ng_ccs"], units_ene_mdl, inpfilter={"commodity": ["gas"]} - ) - / _inp_all_gas_tecs - ).fillna(0) - - # Variable: "Energy|Supply|Gases|Hydrogen|Combustion" - _Biogas_gases_h2_comb = _Biogas_tot * ( - pp.inp( - ["h2_smr", "h2_smr_ccs"], units_ene_mdl, inpfilter={"commodity": ["gas"]} - ) - / _inp_all_gas_tecs - ).fillna(0) - - # Variable: "Energy|Supply|Gases|Transportation|Combustion" - _Biogas_td = _Biogas_tot * ( - ( - pp.inp( - ["gas_t_d", "gas_t_d_ch4"], - units_ene_mdl, - inpfilter={"commodity": ["gas"]}, - ) - - pp.out( - ["gas_t_d", "gas_t_d_ch4"], - units_ene_mdl, - outfilter={"commodity": ["gas"]}, - ) - ) - / _inp_all_gas_tecs - ).fillna(0) - - # Variable: "Energy|Demand|Industry|Other Sector" - _Biogas_ind_rest = _Biogas_tot * ( - pp.inp( - [ - "gas_i", - "hp_gas_i", - ], - units_ene_mdl, - inpfilter={"commodity": ["gas"]}, - ) - / _inp_all_gas_tecs - ).fillna(0) - - # Variable: "Energy|Demand|Industry|Steel" - _Biogas_ind_steel = _Biogas_tot * ( - pp.inp( - ["dri_steel", "eaf_steel", "furnace_gas_steel", "hp_gas_steel"], - units_ene_mdl, - inpfilter={"commodity": ["gas"]}, - ) - / _inp_all_gas_tecs - ).fillna(0) - - # Variable: "Energy|Demand|Industry|Non-Metallic Minerals|Cement" - _Biogas_ind_cement = _Biogas_tot * ( - pp.inp( - [ - "furnace_gas_cement", - "hp_gas_cement", - ], - units_ene_mdl, - inpfilter={"commodity": ["gas"]}, - ) - / _inp_all_gas_tecs - ).fillna(0) - - # Variable: "Energy|Demand|Industry|Non-Ferrous Metals|Aluminium" - _Biogas_ind_alu = _Biogas_tot * ( - pp.inp( - [ - "furnace_gas_aluminum", - "hp_gas_aluminum", - ], - units_ene_mdl, - inpfilter={"commodity": ["gas"]}, - ) - / _inp_all_gas_tecs - ).fillna(0) - - # Variable: "Energy|Demand|Industry|Chemicals|High Value Chemicals" - _Biogas_ind_petro = _Biogas_tot * ( - pp.inp( - [ - "furnace_gas_petro", - "gas_processing_petro", - "hp_gas_petro", - ], - units_ene_mdl, - inpfilter={"commodity": ["gas"]}, - ) - / _inp_all_gas_tecs - ).fillna(0) - - # Variable: "Energy|Demand|Industry|Chemicals|Ammonia" - _Biogas_ind_ammonia = _Biogas_tot * ( - pp.inp( - ["gas_NH3", "gas_NH3_ccs"], - units_ene_mdl, - inpfilter={"commodity": ["gas"]}, - ) - / _inp_all_gas_tecs - ).fillna(0) - - # Variable: "Energy|Demand|Other Sector" - _Biogas_fs = _Biogas_tot * ( - pp.inp( - ["gas_fs"], - units_ene_mdl, - inpfilter={"commodity": ["gas"]}, - ) - / _inp_all_gas_tecs - ).fillna(0) - - # Variable: "Energy|Supply|Liquids|Oil|Combustion" - _Biogas_ref = _Biogas_tot * ( - pp.inp( - ["furnace_gas_refining", "hp_gas_refining"], - units_ene_mdl, - inpfilter={"commodity": ["gas"]}, - ) - / _inp_all_gas_tecs - ).fillna(0) - - # Variable: "Energy|Demand|Residential and Commercial" - _Biogas_res = _Biogas_tot * ( - pp.inp(TECHS["rc gas"], units_ene_mdl, inpfilter={"commodity": ["gas"]}) - / _inp_all_gas_tecs - ).fillna(0) - - # Variable: "Energy|Demand|Transportation|Road Rail and Domestic Shipping" - _Biogas_trp = _Biogas_tot * ( - pp.inp("gas_trp", units_ene_mdl, inpfilter={"commodity": ["gas"]}) - / _inp_all_gas_tecs - ).fillna(0) - - # Finally check to ensure that the sum of all the varaibles onto which biogas - # emissions are distributed on, matches the total. - # FIXME this variable is assigned but never used - check_biogas = _Biogas_tot - ( # noqa: F841 - _Biogas_el - + _Biogas_heat - + _Biogas_liquids_gas_comb - + _Biogas_gases_h2_comb - + _Biogas_td - + _Biogas_ind_rest - + _Biogas_ind_steel - + _Biogas_ind_cement - + _Biogas_ind_alu - + _Biogas_ind_petro - + _Biogas_ind_ammonia - + _Biogas_fs - + _Biogas_ref - + _Biogas_res - + _Biogas_trp - ) - - # ---------------------------------------------------------------------------------- - # Step 3. Distribute HYDROGEN emissions - # - # - Calculate hydrogen-emissions for each sector. - # - Based on: sector_specific_gas_consumption / total_NONCCS_gas_use - # * total hydrogen-emissions - # - All CCS excluded as hydrogen cannot be used in CCS applications, hence also - # division by _inp_nonccs_gas_tecs_wo_CCSRETRO. - # - Emissions that will later be added to the respective sectors, as the - # emission-entry of `h2_mix` is negative, hence the derived values are negative. - # - All FE-technologies listed here, must have an entry into the relation - # `h2mix_direct` - # ---------------------------------------------------------------------------------- - - # Retrieve total hydrogen emissions - # Note, these are negative! - _Hydrogen_tot = pp.emi( - "h2_mix", - units_ene_mdl, - emifilter={"relation": ["CO2_cc"]}, - emission_units=units_emi, - ) - - # Variable: "Energy|Supply|Electricity" - _Hydrogen_el = _Hydrogen_tot * ( - ( - pp.inp( - ["gas_ppl", "gas_ct", "gas_cc", "gas_htfc"], - units_ene_mdl, - inpfilter={"commodity": ["gas"]}, - ) - - _pe_wCCSretro( - "gas_cc", - "g_ppl_co2scr", - group, - inpfilter={"level": ["secondary"], "commodity": ["gas"]}, - units=units_ene_mdl, - share=_gas_cc_shr, - ) - - _pe_wCCSretro( - "gas_ppl", - "g_ppl_co2scr", - group, - inpfilter={"level": ["secondary"], "commodity": ["gas"]}, - units=units_ene_mdl, - share=_gas_ppl_shr, - ) - - _pe_wCCSretro( - "gas_htfc", - "gfc_co2scr", - group, - inpfilter={"level": ["secondary"], "commodity": ["gas"]}, - units=units_ene_mdl, - ) - ) - / _inp_nonccs_gas_tecs_wo_CCSRETRO - ).fillna(0) - - # Variable: "Energy|Supply|Heat" - _Hydrogen_heat = _Hydrogen_tot * ( - pp.inp("gas_hpl", units_ene_mdl, inpfilter={"commodity": ["gas"]}) - / _inp_nonccs_gas_tecs_wo_CCSRETRO - ).fillna(0) - - # Variable: "Energy|Supply|Gases|Transportation|Combustion" - _Hydrogen_td = _Hydrogen_tot * ( - ( - pp.inp( - ["gas_t_d", "gas_t_d_ch4"], - units_ene_mdl, - inpfilter={"commodity": ["gas"]}, - ) - - pp.out( - ["gas_t_d", "gas_t_d_ch4"], - units_ene_mdl, - outfilter={"commodity": ["gas"]}, - ) - ) - / _inp_nonccs_gas_tecs_wo_CCSRETRO - ).fillna(0) - - # Variable: "Energy|Demand|Industry|Other Sector" - _Hydrogen_ind_rest = _Hydrogen_tot * ( - pp.inp( - [ - "gas_i", - "hp_gas_i", - ], - units_ene_mdl, - inpfilter={"commodity": ["gas"]}, - ) - / _inp_nonccs_gas_tecs_wo_CCSRETRO - ).fillna(0) - - # Variable: "Energy|Demand|Industry|Steel" - _Hydrogen_ind_steel = _Hydrogen_tot * ( - pp.inp( - ["dri_steel", "eaf_steel", "furnace_gas_steel", "hp_gas_steel"], - units_ene_mdl, - inpfilter={"commodity": ["gas"]}, - ) - / _inp_nonccs_gas_tecs_wo_CCSRETRO - ).fillna(0) - - # Variable: "Energy|Demand|Industry|Non-Metallic Minerals|Cement" - _Hydrogen_ind_cement = _Hydrogen_tot * ( - pp.inp( - [ - "furnace_gas_cement", - "hp_gas_cement", - ], - units_ene_mdl, - inpfilter={"commodity": ["gas"]}, - ) - / _inp_nonccs_gas_tecs_wo_CCSRETRO - ).fillna(0) - - # Variable: "Energy|Demand|Industry|Non-Ferrous Metals|Aluminium" - _Hydrogen_ind_alu = _Hydrogen_tot * ( - pp.inp( - [ - "furnace_gas_aluminum", - "hp_gas_aluminum", - ], - units_ene_mdl, - inpfilter={"commodity": ["gas"]}, - ) - / _inp_nonccs_gas_tecs_wo_CCSRETRO - ).fillna(0) - - # Variable: "Energy|Demand|Industry|Chemicals|High Value Chemicals" - _Hydrogen_ind_petro = _Hydrogen_tot * ( - pp.inp( - [ - "furnace_gas_petro", - "gas_processing_petro", - "hp_gas_petro", - ], - units_ene_mdl, - inpfilter={"commodity": ["gas"]}, - ) - / _inp_nonccs_gas_tecs_wo_CCSRETRO - ).fillna(0) - - # Variable: "Energy|Demand|Industry|Chemicals|Ammonia" - _Hydrogen_ind_ammonia = _Hydrogen_tot * ( - pp.inp( - ["gas_NH3"], - units_ene_mdl, - inpfilter={"commodity": ["gas"]}, - ) - / _inp_nonccs_gas_tecs_wo_CCSRETRO - ).fillna(0) - - # Variable: "Energy|Demand|Other Sector" - _Hydrogen_fs = _Hydrogen_tot * ( - pp.inp( - ["gas_fs"], - units_ene_mdl, - inpfilter={"commodity": ["gas"]}, - ) - / _inp_nonccs_gas_tecs_wo_CCSRETRO - ).fillna(0) - - # Variable: "Energy|Supply|Liquids|Oil|Combustion" - _Hydrogen_ref = _Hydrogen_tot * ( - pp.inp( - ["furnace_gas_refining", "hp_gas_refining"], - units_ene_mdl, - inpfilter={"commodity": ["gas"]}, - ) - / _inp_nonccs_gas_tecs_wo_CCSRETRO - ).fillna(0) - - # Variable: "Energy|Demand|Residential and Commercial" - _Hydrogen_res = _Hydrogen_tot * ( - pp.inp(TECHS["rc gas"], units_ene_mdl, inpfilter={"commodity": ["gas"]}) - / _inp_nonccs_gas_tecs_wo_CCSRETRO - ).fillna(0) - - # Variable: "Energy|Demand|Transportation|Road Rail and Domestic Shipping" - _Hydrogen_trp = _Hydrogen_tot * ( - pp.inp("gas_trp", units_ene_mdl, inpfilter={"commodity": ["gas"]}) - / _inp_nonccs_gas_tecs_wo_CCSRETRO - ).fillna(0) - - # Finally check to ensure that the sum of all the variables onto which hydrogen - # emissions are distributed on, matches the total. - # FIXME this variable is assigned but never used - check_hydrogen = _Hydrogen_tot - ( # noqa: F841 - _Hydrogen_el - + _Hydrogen_heat - + _Hydrogen_td - + _Hydrogen_ind_rest - + _Hydrogen_ind_steel - + _Hydrogen_ind_cement - + _Hydrogen_ind_alu - + _Hydrogen_ind_petro - + _Hydrogen_ind_ammonia - + _Hydrogen_fs - + _Hydrogen_ref - + _Hydrogen_res - + _Hydrogen_trp - ) - - # ---------------------------------------------------------------------------------- - # Step 4. Calculate total sector emissions - # - # - For all biomass-CCS or gas-CCS based technologies, total emissions with their - # contributions to emission abatement need to be calculated. - # ---------------------------------------------------------------------------------- - - # ---------------------------------------------------------------------------------- - # Step 4.1 Energy Supply - Heat and Electricity - # ---------------------------------------------------------------------------------- - - # Variable: "Energy|Supply|Electricity" - _SE_Elec_gen = pp.emi( - [ - "coal_ppl_u", - "coal_ppl", - "coal_adv", - "coal_adv_ccs", - "igcc", - "igcc_ccs", - "foil_ppl", - "loil_ppl", - "loil_cc", - "oil_ppl", - "gas_ppl", - "gas_cc", - "gas_cc_ccs", - "gas_ct", - "gas_htfc", - "bio_istig", - "g_ppl_co2scr", - "c_ppl_co2scr", - "bio_ppl_co2scr", - "igcc_co2scr", - "gfc_co2scr", - "cfc_co2scr", - "bio_istig_ccs", - ], - units_ene_mdl, - emifilter={"relation": ["CO2_cc"]}, - emission_units=units_emi, - ) - - # Variable: "Energy|Supply|Electricity" - # Used for reallocation of Diff. - _SE_Elec_gen_woBECCS = ( - _SE_Elec_gen - - pp.emi( - ["bio_istig_ccs", "bio_ppl_co2scr"], - units_ene_mdl, - emifilter={"relation": ["CO2_cc"]}, - emission_units=units_emi, - ) - - pp.emi( - ["g_ppl_co2scr", "gas_cc_ccs"], - units_ene_mdl, - emifilter={"relation": ["CO2_cc"]}, - emission_units=units_emi, - ) - * (_Biogas_tot_abs / _inp_all_gas_tecs) - ) - - # Variable: "Energy|Supply|Heat" - _SE_District_heat = pp.emi( - ["coal_hpl", "foil_hpl", "gas_hpl"], - units_ene_mdl, - emifilter={"relation": ["CO2_cc"]}, - emission_units=units_emi, - ) - - # ---------------------------------------------------------------------------------- - # Step 4.2 Final Energy - # ---------------------------------------------------------------------------------- - - # Variable: "Energy|Demand|Industry|Other Sector" - _FE_Industry_rest = pp.emi( - [ - "coal_i", - "foil_i", - "gas_i", - "hp_gas_i", - "loil_i", - "meth_i", - "sp_liq_I", - "sp_meth_I", - ], - units_ene_mdl, - emifilter={"relation": ["CO2_ind"]}, - emission_units=units_emi, - ) - - # Variable: "Energy|Demand|Industry|Steel" - _FE_Industry_steel = pp.emi( - ["DUMMY_coal_supply", "DUMMY_gas_supply", "DUMMY_limestone_supply_steel"], - units_ene_mdl, - emifilter={"relation": ["CO2_ind"]}, - emission_units=units_emi, - ) - - # Variable: "Energy|Demand|Industry|Non-Metallic Minerals|Cement" - _FE_Industry_cement = pp.emi( - [ - "DUMMY_limestone_supply_cement", - "furnace_coal_cement", - "furnace_foil_cement", - "furnace_gas_cement", - "furnace_loil_cement", - "furnace_methanol_cement", - ], - units_ene_mdl, - emifilter={"relation": ["CO2_ind"]}, - emission_units=units_emi, - ) - - # Variable: "Industrial Processes|Non-Metallic Minerals|Cement" - _FE_IndustryProcess_alu = pp.emi( - ["prebake_aluminum", "soderberg_aluminum", "vertical_stud"], - units_ene_mdl, - emifilter={"relation": ["CO2_Emission"]}, - emission_units=units_emi, - ) - - # Variable: "Energy|Demand|Industry|Non-Ferrous Metals|Aluminium" - _FE_Industry_alu = pp.emi( - [ - "furnace_coal_aluminum", - "furnace_foil_aluminum", - "furnace_gas_aluminum", - "furnace_loil_aluminum", - "furnace_methanol_aluminum", - ], - units_ene_mdl, - emifilter={"relation": ["CO2_ind"]}, - emission_units=units_emi, - ) - - # Variable: "Industrial Processes|Non-Ferrous Metals|Aluminium" - _FE_IndustryProcess_cement = pp.emi( - [ - "clinker_dry_ccs_cement", - "clinker_dry_cement", - "clinker_wet_ccs_cement", - "clinker_wet_cement", - ], - units_ene_mdl, - emifilter={"relation": ["CO2_Emission"]}, - emission_units=units_emi, - ) - - # Variable: "Energy|Demand|Industry|Chemicals|High Value Chemicals" - _FE_Industry_petro = pp.emi( - [ - "furnace_coal_petro", - "furnace_coke_petro", - "furnace_foil_petro", - "furnace_gas_petro", - "furnace_loil_petro", - "furnace_methanol_petro", - "gas_processing_petro", - "steam_cracker_petro", - ], - units_ene_mdl, - emifilter={"relation": ["CO2_ind"]}, - emission_units=units_emi, - ) - - # Variable: "Energy|Demand|Industry|Chemicals|Ammonia" - _FE_Industry_ammonia = pp.emi( - [ - "biomass_NH3_ccs", - "biomass_NH3", - "coal_NH3_ccs", - "coal_NH3", - "fueloil_NH3_ccs", - "fueloil_NH3", - "gas_NH3_ccs", - "gas_NH3", - ], - units_ene_mdl, - emifilter={"relation": ["CO2_cc"]}, - emission_units=units_emi, - ) - - # Variable: "Energy|Demand|Industry|Chemicals|Ammonia" - # Used for reallocation of Diff. - this is an exception. Normally the Diff. is only - # reallocated across SE-conversion technologies, which is where the NH3 technologies - # are located despite being assigned to `Demand|Industry` emissions. - _FE_Industry_ammonia_woBECCS = ( - _FE_Industry_ammonia - - pp.emi( - ["biomass_NH3_ccs"], - units_ene_mdl, - emifilter={"relation": ["CO2_cc"]}, - emission_units=units_emi, - ) - - pp.emi( - ["gas_NH3_ccs"], - units_ene_mdl, - emifilter={"relation": ["CO2_cc"]}, - emission_units=units_emi, - ) - * (_Biogas_tot_abs / _inp_all_gas_tecs) - ) - - # Variable: "Energy|Demand|Other Sector" - _FE_Feedstocks = pp.emi( - [ - "coal_fs", - "foil_fs", - "loil_fs", - "gas_fs", - "methanol_fs", - ], - units_ene_mdl, - emifilter={"relation": ["CO2_feedstocks"]}, - emission_units=units_emi, - ) - - # Variable: "Energy|Demand|Residential and Commercial" - _FE_Res_com = pp.emi( - rc_techs("coal foil gas loil meth"), - units_ene_mdl, - emifilter={"relation": ["CO2_r_c"]}, - emission_units=units_emi, - ) - - # Variable: "Energy|Demand|Transportation|Road Rail and Domestic Shipping" - _FE_Transport = pp.emi( - ["gas_trp", "loil_trp", "meth_fc_trp", "meth_ic_trp", "coal_trp", "foil_trp"], - units_ene_mdl, - emifilter={"relation": ["CO2_trp"]}, - emission_units=units_emi, - ) - - _FE_Industry = ( - _FE_Industry_rest - + _FE_Industry_steel - + _FE_IndustryProcess_cement - + _FE_Industry_cement - + _FE_IndustryProcess_alu - + _FE_Industry_alu - + _FE_Industry_petro - + _FE_Industry_ammonia - ) - _FE_total = _FE_Feedstocks + _FE_Res_com + _FE_Industry + _FE_Transport - - # ---------------------------------------------------------------------------------- - # Step 4.3 Energy Supply - Gases - # ---------------------------------------------------------------------------------- - - # Variable: "Energy|Supply|Gases|Extraction|Combustion" - _Other_gases_extr_comb = pp.emi( - [ - "gas_extr_1", - "gas_extr_2", - "gas_extr_3", - "gas_extr_4", - "gas_extr_5", - "gas_extr_6", - "gas_extr_7", - "gas_extr_8", - ], - units_ene_mdl, - emifilter={"relation": ["CO2_cc"]}, - emission_units=units_emi, - ) - - # Variable: "Energy|Supply|Gases|Extraction|Fugitive" - _Other_gases_extr_fug = pp.emi( - "flaring_CO2", - units_ene_mdl, - emifilter={"relation": ["CO2_cc"]}, - emission_units=units_emi, - ) - - # Variable: "Energy|Supply|Gases|Transportation|Combustion" - # Note that this is not included in the total because the diff is only calculated - # from CO2_TCE and doesn't include trade. - _Other_gases_trans_comb_trade = pp.emi( - ["LNG_trd"], - units_ene_mdl, - emifilter={"relation": ["CO2_trade"]}, - emission_units=units_emi, - ) - - # Variable: "Energy|Supply|Gases|Transportation|Combustion" - _Other_gases_trans_comb = pp.emi( - ["gas_t_d", "gas_t_d_ch4"], - units_ene_mdl, - emifilter={"relation": ["CO2_cc"]}, - emission_units=units_emi, - ) - - # Variable: "Energy|Supply|Gases|Coal|Combustion" - _Other_gases_coal_comb = pp.emi( - ["coal_gas"], - units_ene_mdl, - emifilter={"relation": ["CO2_cc"]}, - emission_units=units_emi, - ) - - # _Other_gases_biomass_comb = pp.emi(["gas_bio"], units_ene_mdl, - # emifilter={"relation": ["CO2_cc"]}, - # emission_units=units_emi) - - # Varpible: "Energy|Supply|Gases|Hydrogen|Combustion" - _Other_gases_h2_comb = pp.emi( - ["h2_smr", "h2_coal", "h2_bio", "h2_coal_ccs", "h2_smr_ccs", "h2_bio_ccs"], - units_ene_mdl, - emifilter={"relation": ["CO2_cc"]}, - emission_units=units_emi, - ) - - # Variable: "Energy|Supply|Gases|Hydrogen|Combustion" - _Other_gases_h2_comb_woBECCS = ( - _Other_gases_h2_comb - - pp.emi( - ["h2_bio_ccs"], - units_ene_mdl, - emifilter={"relation": ["CO2_cc"]}, - emission_units=units_emi, - ) - - pp.emi( - ["h2_smr_ccs"], - units_ene_mdl, - emifilter={"relation": ["CO2_cc"]}, - emission_units=units_emi, - ) - * (_Biogas_tot_abs / _inp_all_gas_tecs) - ) - - # Summation of all gases to be added to total - _Other_gases_total = ( - _Other_gases_extr_comb - + _Other_gases_extr_fug - + _Other_gases_trans_comb - + _Other_gases_coal_comb - + _Other_gases_h2_comb - ) - - # Summation of all gases without BECCS used to reallocate the difference between - # top-down and bottom up accounting. - _Other_gases_total_woBECCS = ( - _Other_gases_extr_comb - + _Other_gases_trans_comb - + _Other_gases_coal_comb - + _Other_gases_h2_comb_woBECCS - ) - # Fugitive is not included in the total used to redistribute the difference - # + _Other_gases_extr_fug - - # ---------------------------------------------------------------------------------- - # Step 4.4 Energy Supply - Liquids - # ---------------------------------------------------------------------------------- - - # Variable: "Energy|Supply|Liquids|Extraction|Combustion" - _Other_liquids_extr_comb = pp.emi( - [ - "oil_extr_1", - "oil_extr_2", - "oil_extr_3", - "oil_extr_4", - "oil_extr_1_ch4", - "oil_extr_2_ch4", - "oil_extr_3_ch4", - "oil_extr_4_ch4", - "oil_extr_5", - "oil_extr_6", - "oil_extr_7", - "oil_extr_8", - ], - units_ene_mdl, - emifilter={"relation": ["CO2_cc"]}, - emission_units=units_emi, - ) - - # Variable: "Energy|Supply|Liquids|Transportation|Combustion" - # Note that this is not included in the total because the diff is only calculated - # from CO2_TCE and doesn't include trade. - _Other_liquids_trans_comb_trade = pp.emi( - ["foil_trd", "loil_trd", "oil_trd", "meth_trd", "eth_trd"], - units_ene_mdl, - emifilter={"relation": ["CO2_trade"]}, - emission_units=units_emi, - ) - - # Variable: "Energy|Supply|Liquids|Transportation|Combustion" - _Other_liquids_trans_comb = pp.emi( - ["foil_t_d", "loil_t_d", "meth_t_d", "eth_t_d"], - units_ene_mdl, - emifilter={"relation": ["CO2_cc"]}, - emission_units=units_emi, - ) - - # Variable: "Energy|Supply|Liquids|Oil|Combustion" - _Other_liquids_oil_comb = pp.emi( - [ - "furnace_coke_refining", - "furnace_coal_refining", - "furnace_biomass_refining", - "furnace_gas_refining", - "furnace_loil_refining", - "furnace_foil_refining", - "furnace_methanol_refining", - "atm_distillation_ref", - "vacuum_distillation_ref", - "catalytic_cracking_ref", - "visbreaker_ref", - "coking_ref", - "catalytic_reforming_ref", - "hydro_cracking_ref", - ], - units_ene_mdl, - emifilter={"relation": ["CO2_cc"]}, - emission_units=units_emi, - ) - - # Variable: "Energy|Supply|Liquids|Natural Gas|Combustion" - _Other_liquids_gas_comb = pp.emi( - ["meth_ng", "meth_ng_ccs"], - units_ene_mdl, - emifilter={"relation": ["CO2_cc"]}, - emission_units=units_emi, - ) - - # Variable: "Energy|Supply|Liquids|Natural Gas|Combustion" - # For Diff allocation - _Other_liquids_gas_comb_woBECCS = _Other_liquids_gas_comb - pp.emi( - ["meth_ng_ccs"], - units_ene_mdl, - emifilter={"relation": ["CO2_cc"]}, - emission_units=units_emi, - ) * (_Biogas_tot_abs / _inp_all_gas_tecs) - - # Variable: "Energy|Supply|Liquids|Coal|Combustion" - _Other_liquids_coal_comb = pp.emi( - ["meth_coal", "syn_liq", "meth_coal_ccs", "syn_liq_ccs"], - units_ene_mdl, - emifilter={"relation": ["CO2_cc"]}, - emission_units=units_emi, - ) - - # Variable: "Energy|Supply|Liquids|Biomass|Combustion" - _Other_liquids_biomass_comb = pp.emi( - ["eth_bio", "liq_bio", "eth_bio_ccs", "liq_bio_ccs"], - units_ene_mdl, - emifilter={"relation": ["CO2_cc"]}, - emission_units=units_emi, - ) - - # Variable: "Energy|Supply|Liquids|Biomass|Combustion" - # For Diff allocation - _Other_liquids_biomass_comb_woBECCS = _Other_liquids_biomass_comb - pp.emi( - ["eth_bio_ccs", "liq_bio_ccs"], - units_ene_mdl, - emifilter={"relation": ["CO2_cc"]}, - emission_units=units_emi, - ) - - _Other_liquids_total = ( - _Other_liquids_extr_comb - + _Other_liquids_trans_comb - + _Other_liquids_oil_comb - + _Other_liquids_gas_comb - + _Other_liquids_coal_comb - + _Other_liquids_biomass_comb - ) - - _Other_liquids_total_woBECCS = ( - _Other_liquids_extr_comb - + _Other_liquids_trans_comb - + _Other_liquids_oil_comb - + _Other_liquids_gas_comb_woBECCS - + _Other_liquids_coal_comb - + _Other_liquids_biomass_comb_woBECCS - ) - - # ---------------------------------------------------------------------------------- - # Step 4.5 Energy Supply - Solids - # ---------------------------------------------------------------------------------- - - # Variable: "Energy|Supply|Solids|Extraction|Combustion" - _Other_solids_coal_trans_comb = pp.emi( - "coal_t_d", - units_ene_mdl, - emifilter={"relation": ["CO2_cc"]}, - emission_units=units_emi, - ) - - _Other_solids_total = _Other_solids_coal_trans_comb - - # ---------------------------------------------------------------------------------- - # Step 4.6 Land-use - # ---------------------------------------------------------------------------------- - - # Variable: "AFOLU" - _CO2_GLOBIOM = pp.land_out( - lu_out_filter={ - "level": ["land_use_reporting"], - "commodity": ["Emissions|CO2|AFOLU"], - }, - units=units_emi, - ) - - # ---------------------------------------------------------------------------------- - # Step 5. Calculate Difference in top-down vs bottom-up - # - # TODO What about _FE_Feedstocks_woBECCS? Should they be included in - # _Total_wo_BECCS? - # ---------------------------------------------------------------------------------- - - _Total = ( # noqa: F841 due to modified calculation of _Diff1, below - _SE_Elec_gen - + _SE_District_heat - + _FE_total - + _Other_gases_total - + _Other_liquids_total - + _Other_solids_total - - _Biogas_tot - + _Hydrogen_tot - ) - - # GLOBIOM with the new lu implementation, LU_CO2 no longer writes - # into _CO2_tce1 (CO2_TCE), as these have emission factors only, - # and therefore do not write into CO2_TCE - # + _CO2_GLOBIOM) - - _Total_wo_BECCS = ( - abs(_SE_District_heat - _Biogas_heat + _Hydrogen_heat) - + abs(_SE_Elec_gen_woBECCS - _Biogas_el + _Hydrogen_el) - + abs( - _Other_gases_total_woBECCS - - _Biogas_gases_h2_comb - - _Biogas_td - + _Hydrogen_td - ) - + abs(_Other_liquids_total_woBECCS - _Biogas_liquids_gas_comb) - + abs(_Other_solids_total) - # Exception made for ammonia as it is as SecondaryEnergy level not at final. - + abs( - _FE_Industry_ammonia_woBECCS - _Biogas_ind_ammonia + _Hydrogen_ind_ammonia - ) - ) - - _CO2_tce1 = pp.emi( # noqa: F841 due to modified calculation of _Diff1, below - "CO2_TCE", - units_ene_mdl, - emifilter={"relation": ["TCE_Emission"]}, - emission_units=units_emi, - ) - - # This was temporarily zero, since the difference was still not zero. - # _Diff1 = _CO2_tce1 - _Total - _Diff1 = pp_utils._make_zero() - - if run_history == "True": - df_hist = pd.read_csv(lu_hist_data).set_index("Region") - df_hist = df_hist.rename(columns={i: int(i) for i in df_hist.columns}) - _Diff1 = _Diff1 - df_hist - - # ---------------------------------------------------------------------------------- - # Step 6. Assign values to IAMC Reporting Variables - # ---------------------------------------------------------------------------------- - - # --------------------- - # Agriculture (Table 1) - # --------------------- - - AgricultureWasteBurning = pp_utils._make_zero() - vars["AFOLU|Biomass Burning"] = AgricultureWasteBurning - Agriculture = pp_utils._make_zero() - vars["AFOLU|Agriculture"] = Agriculture - - # --------------------------- - # Grassland Burning (Table 2) - # --------------------------- - - GrasslandBurning = pp_utils._make_zero() - vars["AFOLU|Land|Grassland Burning"] = GrasslandBurning - - # ------------------------ - # Forest Burning (Table 3) - # ------------------------ - - ForestBurning = pp_utils._make_zero() - vars["AFOLU|Land|Forest Burning"] = ForestBurning - - vars["AFOLU"] = _CO2_GLOBIOM - vars["AFOLU|Afforestation"] = pp.land_out( - lu_out_filter={ - "level": ["land_use_reporting"], - "commodity": ["Emissions|CO2|AFOLU|Afforestation"], - }, - units=units_emi, - ) - - vars["AFOLU|Deforestation"] = pp.land_out( - lu_out_filter={ - "level": ["land_use_reporting"], - "commodity": ["Emissions|CO2|AFOLU|Deforestation"], - }, - units=units_emi, - ) - - vars["AFOLU|Forest Management"] = pp.land_out( - lu_out_filter={ - "level": ["land_use_reporting"], - "commodity": ["Emissions|CO2|AFOLU|Forest Management"], - }, - units=units_emi, - ) - - vars["AFOLU|Negative"] = pp.land_out( - lu_out_filter={ - "level": ["land_use_reporting"], - "commodity": ["Emissions|CO2|AFOLU|Negative"], - }, - units=units_emi, - ) - - vars["AFOLU|Other LUC"] = pp.land_out( - lu_out_filter={ - "level": ["land_use_reporting"], - "commodity": ["Emissions|CO2|AFOLU|Other LUC"], - }, - units=units_emi, - ) - - vars["AFOLU|Positive"] = pp.land_out( - lu_out_filter={ - "level": ["land_use_reporting"], - "commodity": ["Emissions|CO2|AFOLU|Positive"], - }, - units=units_emi, - ) - - # vars["AFOLU|Soil Carbon"] = pp.land_out( - # lu_out_filter={"level": ["land_use_reporting"], - # "commodity": ["Emissions|CO2|AFOLU|Soil Carbon"]}, - # units=units_emi) - - # ---------------------------------------------------------------------------------- - # Aircraft - # ---------------------------------------------------------------------------------- - - Aircraft = pp_utils._make_zero() - vars["Energy|Demand|Transportation|Aviation|International"] = Aircraft - - # ---------------------------------------------------------------------------------- - # Energy Supply - Heat and Electricity - # ---------------------------------------------------------------------------------- - - vars["Energy|Supply|Electricity"] = ( - _SE_Elec_gen - - _Biogas_el - + _Hydrogen_el - + _Diff1 - * ( - abs(_SE_Elec_gen_woBECCS - _Biogas_el + _Hydrogen_el) / _Total_wo_BECCS - ).fillna(0) - ) - - vars["Energy|Supply|Heat"] = ( - _SE_District_heat - - _Biogas_heat - + _Hydrogen_heat - + _Diff1 - * ( - abs(_SE_District_heat - _Biogas_heat + _Hydrogen_heat) / _Total_wo_BECCS - ).fillna(0) - ) - - vars["Energy|Supply|Gases|Biomass|Combustion"] = pp_utils._make_zero() - vars["Energy|Supply|Gases|Biomass|Fugitive"] = pp_utils._make_zero() - - vars["Energy|Supply|Gases|Coal|Combustion"] = _Other_gases_coal_comb + _Diff1 * ( - abs(_Other_gases_coal_comb) / _Total_wo_BECCS - ).fillna(0) - - vars["Energy|Supply|Gases|Coal|Fugitive"] = pp_utils._make_zero() - - vars[ - "Energy|Supply|Gases|Extraction|Combustion" - ] = _Other_gases_extr_comb + _Diff1 * ( - abs(_Other_gases_extr_comb) / _Total_wo_BECCS - ).fillna( - 0 - ) - - # _Diff1 is not disctributed across the variable - vars["Energy|Supply|Gases|Extraction|Fugitive"] = _Other_gases_extr_fug - vars["Energy|Supply|Gases|Hydrogen|Combustion"] = ( - _Other_gases_h2_comb - - _Biogas_gases_h2_comb - + _Diff1 - * ( - abs(_Other_gases_h2_comb_woBECCS - _Biogas_gases_h2_comb) / _Total_wo_BECCS - ).fillna(0) - ) - - vars["Energy|Supply|Gases|Hydrogen|Fugitive"] = pp_utils._make_zero() - vars["Energy|Supply|Gases|Natural Gas|Combustion"] = pp_utils._make_zero() - vars["Energy|Supply|Gases|Natural Gas|Fugitive"] = pp_utils._make_zero() - - vars["Energy|Supply|Gases|Transportation|Combustion"] = ( - _Other_gases_trans_comb - - _Biogas_td - + _Hydrogen_td - + _Diff1 - * ( - abs(_Other_gases_trans_comb - _Biogas_td + _Hydrogen_td) / _Total_wo_BECCS - ).fillna(0) - + _Other_gases_trans_comb_trade - ) - - vars["Energy|Supply|Gases|Transportation|Fugitive"] = pp_utils._make_zero() - - vars[ - "Energy|Supply|Liquids|Biomass|Combustion" - ] = _Other_liquids_biomass_comb + _Diff1 * ( - abs(_Other_liquids_biomass_comb_woBECCS) / _Total_wo_BECCS - ).fillna( - 0 - ) - - vars["Energy|Supply|Liquids|Biomass|Fugitive"] = pp_utils._make_zero() - - vars[ - "Energy|Supply|Liquids|Coal|Combustion" - ] = _Other_liquids_coal_comb + _Diff1 * ( - abs(_Other_liquids_coal_comb) / _Total_wo_BECCS - ).fillna( - 0 - ) - - vars["Energy|Supply|Liquids|Coal|Fugitive"] = pp_utils._make_zero() - - vars[ - "Energy|Supply|Liquids|Extraction|Combustion" - ] = _Other_liquids_extr_comb + _Diff1 * ( - abs(_Other_liquids_extr_comb) / _Total_wo_BECCS - ).fillna( - 0 - ) - - vars["Energy|Supply|Liquids|Extraction|Fugitive"] = pp_utils._make_zero() - - vars["Energy|Supply|Liquids|Natural Gas|Combustion"] = ( - _Other_liquids_gas_comb - - _Biogas_liquids_gas_comb - + _Diff1 - * ( - abs(_Other_liquids_gas_comb_woBECCS - _Biogas_liquids_gas_comb) - / _Total_wo_BECCS - ).fillna(0) - ) - - vars["Energy|Supply|Liquids|Natural Gas|Fugitive"] = pp_utils._make_zero() - - vars["Energy|Supply|Liquids|Oil|Combustion"] = ( - _Other_liquids_oil_comb - - _Biogas_ref - + _Hydrogen_ref - + _Diff1 - * ( - abs(_Other_liquids_oil_comb - _Biogas_ref + _Hydrogen_ref) / _Total_wo_BECCS - ).fillna(0) - ) - - vars["Energy|Supply|Liquids|Oil|Fugitive"] = pp_utils._make_zero() - - vars["Energy|Supply|Liquids|Transportation|Combustion"] = ( - _Other_liquids_trans_comb - + _Diff1 * (abs(_Other_liquids_trans_comb) / _Total_wo_BECCS).fillna(0) - + _Other_liquids_trans_comb_trade - ) - - vars["Energy|Supply|Liquids|Transportation|Fugitive"] = pp_utils._make_zero() - vars["Energy|Supply|Other|Combustion"] = pp_utils._make_zero() - vars["Energy|Supply|Other|Fugitive"] = pp_utils._make_zero() - vars["Energy|Supply|Solids|Biomass|Combustion"] = pp_utils._make_zero() - vars["Energy|Supply|Solids|Biomass|Fugitive"] = pp_utils._make_zero() - vars["Energy|Supply|Solids|Coal|Combustion"] = pp_utils._make_zero() - vars["Energy|Supply|Solids|Coal|Fugitive"] = pp_utils._make_zero() - - vars[ - "Energy|Supply|Solids|Extraction|Combustion" - ] = _Other_solids_coal_trans_comb + _Diff1 * ( - abs(_Other_solids_coal_trans_comb) / _Total_wo_BECCS - ).fillna( - 0 - ) - - vars["Energy|Supply|Solids|Extraction|Fugitive"] = pp_utils._make_zero() - vars["Energy|Supply|Solids|Transportation|Combustion"] = pp_utils._make_zero() - vars["Energy|Supply|Solids|Transportation|Fugitive"] = pp_utils._make_zero() - - # ---------------------------------------------------------------------------------- - # Final Energy - Industrial Combustion - # ---------------------------------------------------------------------------------- - - vars["Energy|Demand|Industry|Other Sector"] = ( - _FE_Industry_rest - _Biogas_ind_rest + _Hydrogen_ind_rest - ) - vars["Energy|Demand|Industry|Steel"] = ( - _FE_Industry_steel - _Biogas_ind_steel + _Hydrogen_ind_steel - ) - vars["Energy|Demand|Industry|Non-Metallic Minerals|Cement"] = ( - _FE_Industry_cement - _Biogas_ind_cement + _Hydrogen_ind_cement - ) - vars["Energy|Demand|Industry|Non-Metallic Minerals"] = ( - _FE_Industry_cement - _Biogas_ind_cement + _Hydrogen_ind_cement - ) - vars["Energy|Demand|Industry|Non-Ferrous Metals|Aluminium"] = ( - _FE_Industry_alu - _Biogas_ind_alu + _Hydrogen_ind_alu - ) - vars["Energy|Demand|Industry|Non-Ferrous Metals"] = ( - _FE_Industry_alu - _Biogas_ind_alu + _Hydrogen_ind_alu - ) - vars["Energy|Demand|Industry|Chemicals|High Value Chemicals"] = ( - _FE_Industry_petro - _Biogas_ind_petro + _Hydrogen_ind_petro - ) - vars["Energy|Demand|Industry|Chemicals|Ammonia"] = ( - _FE_Industry_ammonia - - _Biogas_ind_ammonia - + _Hydrogen_ind_ammonia - + _Diff1 - * ( - abs( - _FE_Industry_ammonia_woBECCS - - _Biogas_ind_ammonia - + _Hydrogen_ind_ammonia - ) - / _Total_wo_BECCS - ).fillna(0) - ) - - vars["Energy|Demand|Industry|Chemicals"] = ( - vars["Energy|Demand|Industry|Chemicals|High Value Chemicals"] + - vars["Energy|Demand|Industry|Chemicals|Ammonia"] ) - - # Intermediate total, used below in calculation of "Energy|Combustion" - vars["Energy|Demand|Industry"] = ( - vars["Energy|Demand|Industry|Other Sector"] - + vars["Energy|Demand|Industry|Steel"] - + vars["Energy|Demand|Industry|Non-Metallic Minerals|Cement"] - + vars["Energy|Demand|Industry|Non-Ferrous Metals|Aluminium"] - + vars["Energy|Demand|Industry|Chemicals"] - ) - - # --------------------- - # Industrial Feedstocks - # --------------------- - - vars["Energy|Demand|Other Sector"] = _FE_Feedstocks - _Biogas_fs + _Hydrogen_fs - - # -------------------------------------------- - # Industrial process and product use (Table 8) - # -------------------------------------------- - - vars[ - "Industrial Processes|Non-Ferrous Metals|Aluminium" - ] = _FE_IndustryProcess_alu - vars["Industrial Processes|Non-Metallic Minerals|Cement"] = _FE_IndustryProcess_cement - - # -------------------------------- - # International shipping (Table 9) - # -------------------------------- - - _Bunker1 = pp.act("CO2s_TCE") * mu["conv_c2co2"] - Bunker = _Bunker1 - vars["Energy|Demand|Transportation|Shipping|International"] = Bunker - - # ------------------------------------------------- - # Residential, Commercial, Other - Other (Table 11) - # ------------------------------------------------- - - ResComOth = pp_utils._make_zero() - vars["Energy|Demand|AFOFI"] = ResComOth - - # ------------------------------------------------------------------- - # Residential, Commercial, Other - Residential, Commercial (Table 12) - # ------------------------------------------------------------------- - - Res_Com = _FE_Res_com - _Biogas_res + _Hydrogen_res - vars["Energy|Demand|Residential and Commercial"] = Res_Com - - # ------------------------------ - # Road transportation (Table 13) - # ------------------------------ - - Transport = _FE_Transport - _Biogas_trp + _Hydrogen_trp - vars["Energy|Demand|Transportation|Road Rail and Domestic Shipping"] = Transport - - # ---------------------------------------------- - # Solvents production and application (Table 14) - # ---------------------------------------------- - - Solvents = pp_utils._make_zero() - vars["Product Use|Solvents"] = Solvents - - # ---------------- - # Waste (Table 15) - # ---------------- - - Waste = pp_utils._make_zero() - vars["Waste"] = Waste - - # Special Aggregates which cannot be treated generically - vars["Energy|Supply|Combustion"] = ( - vars["Energy|Supply|Heat"] - + vars["Energy|Supply|Electricity"] - + vars["Energy|Supply|Gases|Biomass|Combustion"] - + vars["Energy|Supply|Gases|Coal|Combustion"] - + vars["Energy|Supply|Gases|Extraction|Combustion"] - + vars["Energy|Supply|Gases|Hydrogen|Combustion"] - + vars["Energy|Supply|Gases|Natural Gas|Combustion"] - + vars["Energy|Supply|Gases|Transportation|Combustion"] - + vars["Energy|Supply|Liquids|Biomass|Combustion"] - + vars["Energy|Supply|Liquids|Coal|Combustion"] - + vars["Energy|Supply|Liquids|Extraction|Combustion"] - + vars["Energy|Supply|Liquids|Natural Gas|Combustion"] - + vars["Energy|Supply|Liquids|Oil|Combustion"] - + vars["Energy|Supply|Liquids|Transportation|Combustion"] - + vars["Energy|Supply|Other|Combustion"] - + vars["Energy|Supply|Solids|Biomass|Combustion"] - + vars["Energy|Supply|Solids|Coal|Combustion"] - + vars["Energy|Supply|Solids|Extraction|Combustion"] - + vars["Energy|Supply|Solids|Transportation|Combustion"] - ) - - vars["Energy|Combustion"] = ( - vars["Energy|Supply|Combustion"] - + vars["Energy|Demand|Transportation|Shipping|International"] - + vars["Energy|Demand|AFOFI"] - + vars["Energy|Demand|Industry"] - + vars["Energy|Demand|Residential and Commercial"] - + vars["Energy|Demand|Transportation|Road Rail and Domestic Shipping"] - + vars["Energy|Demand|Transportation|Aviation|International"] - ) - - vars["Energy|Supply|Fugitive"] = ( - vars["Energy|Supply|Gases|Biomass|Fugitive"] - + vars["Energy|Supply|Gases|Coal|Fugitive"] - + vars["Energy|Supply|Gases|Extraction|Fugitive"] - + vars["Energy|Supply|Gases|Hydrogen|Fugitive"] - + vars["Energy|Supply|Gases|Natural Gas|Fugitive"] - + vars["Energy|Supply|Gases|Transportation|Fugitive"] - + vars["Energy|Supply|Liquids|Biomass|Fugitive"] - + vars["Energy|Supply|Liquids|Coal|Fugitive"] - + vars["Energy|Supply|Liquids|Extraction|Fugitive"] - + vars["Energy|Supply|Liquids|Natural Gas|Fugitive"] - + vars["Energy|Supply|Liquids|Oil|Fugitive"] - + vars["Energy|Supply|Liquids|Transportation|Fugitive"] - + vars["Energy|Supply|Other|Fugitive"] - + vars["Energy|Supply|Solids|Biomass|Fugitive"] - + vars["Energy|Supply|Solids|Coal|Fugitive"] - + vars["Energy|Supply|Solids|Extraction|Fugitive"] - + vars["Energy|Supply|Solids|Transportation|Fugitive"] - ) - - vars["Energy|Fugitive"] = vars["Energy|Supply|Fugitive"] - - dfs.append(pp_utils.make_outputdf(vars, units_emi)) - vars = {} - - # Additional reporting to account for emission differences and accounting issues - vars["Difference|Statistical"] = _Diff1 - vars["Difference|Stock|Coal"] = pp.emi( - ["coal_imp", "coal_exp"], - units_ene_mdl, - emifilter={"relation": ["CO2_Emission"]}, - emission_units=units_emi, - ) + pp.emi( - ["coal_trd"], - units_ene_mdl, - emifilter={"relation": ["CO2_trade"]}, - emission_units=units_emi, - ) - # Coal cannot be used for bunkers - # + pp.emi(["coal_bunker"], units_ene_mdl, - # emifilter={"relation": ["CO2_shipping"]}, - # emission_units=units_emi) - - vars["Difference|Stock|Methanol"] = ( - pp.emi( - ["meth_imp", "meth_exp"], - units_ene_mdl, - emifilter={"relation": ["CO2_Emission"]}, - emission_units=units_emi, - ) - + pp.emi( - ["meth_bunker"], - units_ene_mdl, - emifilter={"relation": ["CO2_shipping"]}, - emission_units=units_emi, - ) - + pp.emi( - ["meth_trd"], - units_ene_mdl, - emifilter={"relation": ["CO2_trade"]}, - emission_units=units_emi, - ) - ) - - vars["Difference|Stock|Fueloil"] = ( - pp.emi( - ["foil_imp", "foil_exp"], - units_ene_mdl, - emifilter={"relation": ["CO2_Emission"]}, - emission_units=units_emi, - ) - + pp.emi( - ["foil_bunker"], - units_ene_mdl, - emifilter={"relation": ["CO2_shipping"]}, - emission_units=units_emi, - ) - + pp.emi( - ["foil_trd"], - units_ene_mdl, - emifilter={"relation": ["CO2_trade"]}, - emission_units=units_emi, - ) - ) - - vars["Difference|Stock|Lightoil"] = ( - pp.emi( - ["loil_imp", "loil_exp"], - units_ene_mdl, - emifilter={"relation": ["CO2_Emission"]}, - emission_units=units_emi, - ) - + pp.emi( - ["loil_bunker"], - units_ene_mdl, - emifilter={"relation": ["CO2_shipping"]}, - emission_units=units_emi, - ) - + pp.emi( - ["loil_trd"], - units_ene_mdl, - emifilter={"relation": ["CO2_trade"]}, - emission_units=units_emi, - ) - ) - - vars["Difference|Stock|Crudeoil"] = pp.emi( - ["oil_imp", "oil_exp"], - units_ene_mdl, - emifilter={"relation": ["CO2_Emission"]}, - emission_units=units_emi, - ) + pp.emi( - ["oil_trd"], - units_ene_mdl, - emifilter={"relation": ["CO2_trade"]}, - emission_units=units_emi, - ) - # + pp.emi(["oil_bunker"], units_ene_mdl, - # emifilter={"relation": ["CO2_shipping"]}, - # emission_units=units_emi) - - vars["Difference|Stock|LNG"] = ( - pp.emi( - ["LNG_imp", "LNG_exp"], - units_ene_mdl, - emifilter={"relation": ["CO2_Emission"]}, - emission_units=units_emi, - ) - + pp.emi( - ["LNG_bunker"], - units_ene_mdl, - emifilter={"relation": ["CO2_shipping"]}, - emission_units=units_emi, - ) - + pp.emi( - ["LNG_trd"], - units_ene_mdl, - emifilter={"relation": ["CO2_trade"]}, - emission_units=units_emi, - ) - ) - - vars["Difference|Stock|Natural Gas"] = pp.emi( - [ - "gas_imp", - "gas_exp_nam", - "gas_exp_weu", - "gas_exp_eeu", - "gas_exp_pao", - "gas_exp_cpa", - "gas_exp_sas", - "gas_exp_afr", - "gas_exp_scs", - "gas_exp_cas", - "gas_exp_ubm", - "gas_exp_rus", - ], - units_ene_mdl, - emifilter={"relation": ["CO2_Emission"]}, - emission_units=units_emi, - ) - - dfs.append(pp_utils.make_outputdf(vars, units_emi, glb=False)) - vars = {} - - vars["Difference|Stock|Activity|Coal"] = ( - pp.inp(["coal_imp"], units_ene_mdl) - - pp.inp(["coal_exp"], units_ene_mdl) - + pp.inp(["coal_trd"], units_ene_mdl) - - pp.out(["coal_trd"], units_ene_mdl) - ) - # + pp.inp(["coal_bunker"], units_ene_mdl) - - vars["Difference|Stock|Activity|Methanol"] = ( - pp.inp(["meth_imp"], units_ene_mdl) - - pp.inp(["meth_exp"], units_ene_mdl) - + pp.inp(["meth_bunker"], units_ene_mdl) - + pp.inp(["meth_trd"], units_ene_mdl) - - pp.out(["meth_trd"], units_ene_mdl) - ) - - vars["Difference|Stock|Activity|Fueloil"] = ( - pp.inp(["foil_imp"], units_ene_mdl) - - pp.inp(["foil_exp"], units_ene_mdl) - + pp.inp(["foil_bunker"], units_ene_mdl) - + pp.inp(["foil_trd"], units_ene_mdl) - - pp.out(["foil_trd"], units_ene_mdl) - ) - - vars["Difference|Stock|Activity|Lightoil"] = ( - pp.inp(["loil_imp"], units_ene_mdl) - - pp.inp(["loil_exp"], units_ene_mdl) - + pp.inp(["loil_bunker"], units_ene_mdl) - + pp.inp(["loil_trd"], units_ene_mdl) - - pp.out(["loil_trd"], units_ene_mdl) - ) - - vars["Difference|Stock|Activity|Crudeoil"] = ( - pp.inp(["oil_imp"], units_ene_mdl) - - pp.inp(["oil_exp"], units_ene_mdl) - + pp.inp(["oil_trd"], units_ene_mdl) - - pp.out(["oil_trd"], units_ene_mdl) - ) - # + pp.inp(["oil_bunker"], units_ene_mdl) - - vars["Difference|Stock|Activity|LNG"] = ( - pp.inp(["LNG_imp"], units_ene_mdl) - - pp.inp(["LNG_exp"], units_ene_mdl) - + pp.inp(["LNG_bunker"], units_ene_mdl) - + pp.inp(["LNG_trd"], units_ene_mdl) - - pp.out(["LNG_trd"], units_ene_mdl) - ) - - vars["Difference|Stock|Activity|Natural Gas"] = pp.inp( - ["gas_imp"], units_ene_mdl - ) - pp.inp( - [ - "gas_exp_nam", - "gas_exp_weu", - "gas_exp_eeu", - "gas_exp_pao", - "gas_exp_cpa", - "gas_exp_sas", - "gas_exp_afr", - "gas_exp_scs", - "gas_exp_cas", - "gas_exp_ubm", - "gas_exp_rus", - ], - units_ene_mdl, - ) - - dfs.append(pp_utils.make_outputdf(vars, units_ene_mdl, glb=False)) - return pd.concat(dfs, sort=True) - -@_register -def retr_SE_solids(units): - """Energy: Secondary Energy solids. - - Parameters - ---------- - - units : str - Units to which variables should be converted. - """ - # Call the function in default_tables() - base = default_tables.retr_SE_solids(units) - - # Extra retrieval requiring special filters - vars = dict( - Coal=pp.inp( - [ - "bf_steel", - "cokeoven_steel", - "furnace_coal_steel", - "sinter_steel", - ], - units, - inpfilter={"commodity": ["coal"], "level": ["final"]}, - ) - ) - df = pp_utils.make_outputdf(vars, units) - - # Combine with `base`; sum at matching indices - return ( - pd.concat([base, df]) - .groupby(["Model", "Scenario", "Variable", "Unit", "Region"], as_index=False) - .sum() - ) - -@_register -def retr_supply_inv(units_energy, - units_emi, - units_ene_mdl): - """Technology results: Investments. - - Investments into technologies. - - Note OFR - 20.04.2017: The following has been checked between Volker - Krey, David McCollum and Oliver Fricko. - - There are fixed factors by which ccs technologies are multiplied which - equate to the share of the powerplant costs which split investments into the - share associated with the standard powerplant and the share associated - with those investments related to CCS. - - For some extraction and synfuel technologies, a certain share of the voms and - foms are attributed to the investments which is based on the GEA-Study - where the derived investment costs were partly attributed to the - voms/foms. - - Parameters - ---------- - units_energy : str - Units to which energy variables should be converted. - units_emi : str - Units to which emission variables should be converted. - units_ene_mdl : str - Native units of energy in the model. - """ - - vars = {} - - # ---------- - # Extraction - # ---------- - - # Note OFR 25.04.2017: All non-extraction costs for Coal, Gas and Oil - # have been moved to "Energy|Other" - - vars["Extraction|Coal"] = pp.investment(["coal_extr_ch4", "coal_extr", - "lignite_extr"], units=units_energy) - - vars["Extraction|Gas|Conventional"] =\ - pp.investment(["gas_extr_1", "gas_extr_2", - "gas_extr_3", "gas_extr_4"], units=units_energy) +\ - pp.act_vom(["gas_extr_1", "gas_extr_2", - "gas_extr_3", "gas_extr_4"], units=units_energy) * .5 - - vars["Extraction|Gas|Unconventional"] =\ - pp.investment(["gas_extr_5", "gas_extr_6", - "gas_extr_7", "gas_extr_8"], units=units_energy) +\ - pp.act_vom(["gas_extr_5", "gas_extr_6", - "gas_extr_7", "gas_extr_8"], units=units_energy) * .5 - - # Note OFR 25.04.2017: Any costs relating to refineries have been - # removed (compared to GEA) as these are reported under "Liquids|Oil" - - vars["Extraction|Oil|Conventional"] =\ - pp.investment(["oil_extr_1", "oil_extr_2", "oil_extr_3", - "oil_extr_1_ch4", "oil_extr_2_ch4", "oil_extr_3_ch4"], - units=units_energy) +\ - pp.act_vom(["oil_extr_1", "oil_extr_2", "oil_extr_3", - "oil_extr_1_ch4", "oil_extr_2_ch4", "oil_extr_3_ch4"], - units=units_energy) * .5 - - vars["Extraction|Oil|Unconventional"] =\ - pp.investment(["oil_extr_4", "oil_extr_4_ch4", "oil_extr_5", - "oil_extr_6", "oil_extr_7", "oil_extr_8"], - units=units_energy) +\ - pp.act_vom(["oil_extr_4", "oil_extr_4_ch4", "oil_extr_5", - "oil_extr_6", "oil_extr_7", "oil_extr_8"], units=units_energy) * .5 - - # As no mode is specified, u5-reproc will account for all 3 modes. - vars["Extraction|Uranium"] =\ - pp.investment(["uran2u5", "Uran_extr", - "u5-reproc", "plutonium_prod"], units=units_energy) +\ - pp.act_vom(["uran2u5", "Uran_extr"], units=units_energy) +\ - pp.act_vom(["u5-reproc"], actfilter={"mode": ["M1"]}, units=units_energy) +\ - pp.tic_fom(["uran2u5", "Uran_extr", "u5-reproc"], units=units_energy) - - # --------------------- - # Electricity - Fossils - # --------------------- - - vars["Electricity|Coal|w/ CCS"] =\ - pp.investment(["c_ppl_co2scr", "cfc_co2scr"], units=units_energy) +\ - pp.investment("coal_adv_ccs", units=units_energy) * 0.25 +\ - pp.investment("igcc_ccs", units=units_energy) * 0.31 - - vars["Electricity|Coal|w/o CCS"] =\ - pp.investment(["coal_ppl", "coal_ppl_u", "coal_adv"], units=units_energy) +\ - pp.investment("coal_adv_ccs", units=units_energy) * 0.75 +\ - pp.investment("igcc", units=units_energy) +\ - pp.investment("igcc_ccs", units=units_energy) * 0.69 - - vars["Electricity|Gas|w/ CCS"] =\ - pp.investment(["g_ppl_co2scr", "gfc_co2scr"], units=units_energy) +\ - pp.investment("gas_cc_ccs", units=units_energy) * 0.53 - - vars["Electricity|Gas|w/o CCS"] =\ - pp.investment(["gas_cc", "gas_ct", "gas_ppl"], units=units_energy) +\ - pp.investment("gas_cc_ccs", units=units_energy) * 0.47 - - vars["Electricity|Oil|w/o CCS"] =\ - pp.investment(["foil_ppl", "loil_ppl", "oil_ppl", "loil_cc"], - units=units_energy) - - # ------------------------ - # Electricity - Renewables - # ------------------------ - - vars["Electricity|Biomass|w/ CCS"] =\ - pp.investment("bio_ppl_co2scr", units=units_energy) +\ - pp.investment("bio_istig_ccs", units=units_energy) * 0.31 - - vars["Electricity|Biomass|w/o CCS"] =\ - pp.investment(["bio_ppl", "bio_istig"], units=units_energy) +\ - pp.investment("bio_istig_ccs", units=units_energy) * 0.69 - - vars["Electricity|Geothermal"] = pp.investment("geo_ppl", units=units_energy) - - vars["Electricity|Hydro"] = pp.investment(["hydro_hc", "hydro_lc"], - units=units_energy) - vars["Electricity|Other"] = pp.investment(["h2_fc_I", "h2_fc_RC"], - units=units_energy) - - _solar_pv_elec = pp.investment(["solar_pv_ppl", "solar_pv_I", - "solar_pv_RC"], units=units_energy) - - _solar_th_elec = pp.investment(["csp_sm1_ppl", "csp_sm3_ppl"], units=units_energy) - - vars["Electricity|Solar|PV"] = _solar_pv_elec - vars["Electricity|Solar|CSP"] = _solar_th_elec - vars["Electricity|Wind|Onshore"] = pp.investment(["wind_ppl"], units=units_energy) - vars["Electricity|Wind|Offshore"] = pp.investment(["wind_ppf"], units=units_energy) - - # ------------------- - # Electricity Nuclear - # ------------------- - - vars["Electricity|Nuclear"] = pp.investment(["nuc_hc", "nuc_lc"], - units=units_energy) - - # -------------------------------------------------- - # Electricity Storage, transmission and distribution - # -------------------------------------------------- - - vars["Electricity|Electricity Storage"] = pp.investment("stor_ppl", - units=units_energy) - - vars["Electricity|Transmission and Distribution"] =\ - pp.investment(["elec_t_d", - "elec_exp", "elec_exp_africa", - "elec_exp_america", "elec_exp_asia", - "elec_exp_eurasia", "elec_exp_eur_afr", - "elec_exp_asia_afr", - "elec_imp", "elec_imp_africa", - "elec_imp_america", "elec_imp_asia", - "elec_imp_eurasia", "elec_imp_eur_afr", - "elec_imp_asia_afr"], units=units_energy) +\ - pp.act_vom(["elec_t_d", - "elec_exp", "elec_exp_africa", - "elec_exp_america", "elec_exp_asia", - "elec_exp_eurasia", "elec_exp_eur_afr", - "elec_exp_asia_afr", - "elec_imp", "elec_imp_africa", - "elec_imp_america", "elec_imp_asia", - "elec_imp_eurasia", "elec_imp_eur_afr", - "elec_imp_asia_afr"], units=units_energy) * .5 +\ - pp.tic_fom(["elec_t_d", - "elec_exp", "elec_exp_africa", - "elec_exp_america", "elec_exp_asia", - "elec_exp_eurasia", "elec_exp_eur_afr", - "elec_exp_asia_afr", - "elec_imp", "elec_imp_africa", - "elec_imp_america", "elec_imp_asia", - "elec_imp_eurasia", "elec_imp_eur_afr", - "elec_imp_asia_afr"], units=units_energy) * .5 - - # ------------------------------------------ - # CO2 Storage, transmission and distribution - # ------------------------------------------ - - _CCS_coal_elec = -1 *\ - pp.emi(["c_ppl_co2scr", "coal_adv_ccs", - "igcc_ccs", "clinker_dry_ccs_cement",'clinker_wet_ccs_cement'], units_ene_mdl, - emifilter={"relation": ["CO2_Emission"]}, - emission_units=units_emi) - - _CCS_coal_synf = -1 *\ - pp.emi(["syn_liq_ccs", "h2_coal_ccs", - "meth_coal_ccs"], units_ene_mdl, - emifilter={"relation": ["CO2_Emission"]}, - emission_units=units_emi) - - _CCS_gas_elec = -1 *\ - pp.emi(["g_ppl_co2scr", "gas_cc_ccs"], units_ene_mdl, - emifilter={"relation": ["CO2_Emission"]}, - emission_units=units_emi) - - _CCS_gas_synf = -1 *\ - pp.emi(["h2_smr_ccs", "meth_ng_ccs"], units_ene_mdl, - emifilter={"relation": ["CO2_Emission"]}, - emission_units=units_emi) - - _CCS_bio_elec = -1 *\ - pp.emi(["bio_ppl_co2scr", "bio_istig_ccs"], units_ene_mdl, - emifilter={"relation": ["CO2_Emission"]}, - emission_units=units_emi) - - _CCS_bio_synf = -1 *\ - pp.emi(["eth_bio_ccs", "liq_bio_ccs", - "h2_bio_ccs"], units_ene_mdl, - emifilter={"relation": ["CO2_Emission"]}, - emission_units=units_emi) - - _Biogas_use_tot = pp.out("gas_bio") - - _Gas_use_tot = pp.inp(["gas_ppl", "gas_cc", "gas_cc_ccs", - "gas_ct", "gas_htfc", "gas_hpl", - "meth_ng", "meth_ng_ccs", "h2_smr", - "h2_smr_ccs", "gas_rc", "hp_gas_rc", - "gas_i", "hp_gas_i", - 'furnace_gas_aluminum', - 'furnace_gas_petro', - 'furnace_gas_cement', - 'furnace_gas_refining', - "hp_gas_i", - 'hp_gas_aluminum', - 'hp_gas_petro', - 'hp_gas_refining', - "gas_trp", - "gas_fs"]) - - _Biogas_share = (_Biogas_use_tot / _Gas_use_tot).fillna(0) - - _CCS_Foss = _CCS_coal_elec +\ - _CCS_coal_synf +\ - _CCS_gas_elec * (1 - _Biogas_share) +\ - _CCS_gas_synf * (1 - _Biogas_share) - - _CCS_Bio = _CCS_bio_elec +\ - _CCS_bio_synf -\ - (_CCS_gas_elec + _CCS_gas_synf) * _Biogas_share - - _CCS_coal_elec_shr = (_CCS_coal_elec / _CCS_Foss).fillna(0) - _CCS_coal_synf_shr = (_CCS_coal_synf / _CCS_Foss).fillna(0) - _CCS_gas_elec_shr = (_CCS_gas_elec / _CCS_Foss).fillna(0) - _CCS_gas_synf_shr = (_CCS_gas_synf / _CCS_Foss).fillna(0) - _CCS_bio_elec_shr = (_CCS_bio_elec / _CCS_Bio).fillna(0) - _CCS_bio_synf_shr = (_CCS_bio_synf / _CCS_Bio).fillna(0) - - CO2_trans_dist_elec =\ - pp.act_vom("co2_tr_dis", units=units_energy) * 0.5 * _CCS_coal_elec_shr +\ - pp.act_vom("co2_tr_dis", units=units_energy) * 0.5 * _CCS_gas_elec_shr +\ - pp.act_vom("bco2_tr_dis", units=units_energy) * 0.5 * _CCS_bio_elec_shr - - CO2_trans_dist_synf =\ - pp.act_vom("co2_tr_dis", units=units_energy) * 0.5 * _CCS_coal_synf_shr +\ - pp.act_vom("co2_tr_dis", units=units_energy) * 0.5 * _CCS_gas_synf_shr +\ - pp.act_vom("bco2_tr_dis", units=units_energy) * 0.5 * _CCS_bio_synf_shr - - vars["CO2 Transport and Storage"] = CO2_trans_dist_elec + CO2_trans_dist_synf - - # ---- - # Heat - # ---- - - vars["Heat"] = pp.investment(["coal_hpl", "foil_hpl", "gas_hpl", - "bio_hpl", "heat_t_d", "po_turbine"], - units=units_energy) - - # ------------------------- - # Synthetic fuel production - # ------------------------- - - # Note OFR 25.04.2017: XXX_synf_ccs has been split into hydrogen and - # liquids. The shares then add up to 1, but the variables are kept - # separate in order to preserve the split between CCS and non-CCS - - _Coal_synf_ccs_liq = pp.investment("meth_coal_ccs", units=units_energy) * 0.02 +\ - pp.investment("syn_liq_ccs", units=units_energy) * 0.01 - - _Gas_synf_ccs_liq = pp.investment("meth_ng_ccs", units=units_energy) * 0.08 - - _Bio_synf_ccs_liq = pp.investment("eth_bio_ccs", units=units_energy) * 0.34 + \ - pp.investment("liq_bio_ccs", units=units_energy) * 0.02 - - _Coal_synf_ccs_h2 = pp.investment("h2_coal_ccs", units=units_energy) * 0.03 - _Gas_synf_ccs_h2 = pp.investment("h2_smr_ccs", units=units_energy) * 0.17 - _Bio_synf_ccs_h2 = pp.investment("h2_bio_ccs", units=units_energy) * 0.02 - - # Note OFR 25.04.2017: "coal_gas" have been moved to "other" - vars["Liquids|Coal and Gas"] =\ - pp.investment(["meth_coal", "syn_liq", "meth_ng"], units=units_energy) +\ - pp.investment("meth_ng_ccs", units=units_energy) * 0.92 +\ - pp.investment("meth_coal_ccs", units=units_energy) * 0.98 +\ - pp.investment("syn_liq_ccs", units=units_energy) * 0.99 +\ - _Coal_synf_ccs_liq +\ - _Gas_synf_ccs_liq - - # Note OFR 25.04.2017: "gas_bio" has been moved to "other" - vars["Liquids|Biomass"] =\ - pp.investment(["eth_bio", "liq_bio"], units=units_energy) +\ - pp.investment("liq_bio_ccs", units=units_energy) * 0.98 +\ - pp.investment("eth_bio_ccs", units=units_energy) * 0.66 + _Bio_synf_ccs_liq - - # Note OFR 25.04.2017: "transport, import and exports costs related to - # liquids are only included in the total" - _Synfuel_other = pp.investment(["meth_exp", "meth_imp", "meth_t_d", - "meth_bal", "eth_exp", "eth_imp", - "eth_t_d", "eth_bal", "SO2_scrub_synf"], - units=units_energy) - - vars["Liquids|Oil"] = pp.investment(['furnace_coke_refining', - 'furnace_coal_refining', - 'furnace_foil_refining', - 'furnace_loil_refining', - 'furnace_ethanol_refining', - 'furnace_biomass_refining', - 'furnace_methanol_refining', - 'furnace_gas_refining', - 'furnace_elec_refining', - 'furnace_h2_refining', - 'hp_gas_refining', - 'hp_elec_refining', - 'fc_h2_refining', - 'solar_refining', - 'dheat_refining', - 'atm_distillation_ref', - 'vacuum_distillation_ref', - 'hydrotreating_ref', - 'catalytic_cracking_ref', - 'visbreaker_ref', - 'coking_ref', - 'catalytic_reforming_ref', - 'hydro_cracking_ref' - ], units=units_energy) +\ - pp.tic_fom(['furnace_coke_refining', - 'furnace_coal_refining', - 'furnace_foil_refining', - 'furnace_loil_refining', - 'furnace_ethanol_refining', - 'furnace_biomass_refining', - 'furnace_methanol_refining', - 'furnace_gas_refining', - 'furnace_elec_refining', - 'furnace_h2_refining', - 'hp_gas_refining', - 'hp_elec_refining', - 'fc_h2_refining', - 'solar_refining', - 'dheat_refining', - 'atm_distillation_ref', - 'vacuum_distillation_ref', - 'hydrotreating_ref', - 'catalytic_cracking_ref', - 'visbreaker_ref', - 'coking_ref', - 'catalytic_reforming_ref', - 'hydro_cracking_ref'], units=units_energy) - - vars["Liquids"] = vars["Liquids|Coal and Gas"] +\ - vars["Liquids|Biomass"] +\ - vars["Liquids|Oil"] +\ - _Synfuel_other - - # -------- - # Hydrogen - # -------- - - vars["Hydrogen|Fossil"] =\ - pp.investment(["h2_coal", "h2_smr"], units=units_energy) +\ - pp.investment("h2_coal_ccs", units=units_energy) * 0.97 +\ - pp.investment("h2_smr_ccs", units=units_energy) * 0.83 +\ - _Coal_synf_ccs_h2 +\ - _Gas_synf_ccs_h2 - - vars["Hydrogen|Renewable"] = pp.investment("h2_bio", units=units_energy) +\ - pp.investment("h2_bio_ccs", units=units_energy) * 0.98 +\ - _Bio_synf_ccs_h2 - - vars["Hydrogen|Other"] = pp.investment(["h2_elec", "h2_liq", "h2_t_d", - "lh2_exp", "lh2_imp", "lh2_bal", - "lh2_regas", "lh2_t_d"], - units=units_energy) +\ - pp.act_vom("h2_mix", units=units_energy) * 0.5 - - # ----- - # Other - # ----- - - # All questionable variables from extraction that are not related directly - # to extraction should be moved to Other - # Note OFR 25.04.2017: Any costs relating to refineries have been - # removed (compared to GEA) as these are reported under "Liquids|Oil" - - vars["Other|Liquids|Oil|Transmission and Distribution"] =\ - pp.investment(["foil_t_d", "loil_t_d"], units=units_energy) - - vars["Other|Liquids|Oil|Other"] =\ - pp.investment(["foil_exp", "loil_exp", "oil_exp", - "oil_imp", "foil_imp", "loil_imp", - "loil_std", "oil_bal", "loil_sto"], units=units_energy) - - vars["Other|Gases|Transmission and Distribution"] =\ - pp.investment(["gas_t_d", "gas_t_d_ch4"], units=units_energy) - - vars["Other|Gases|Production"] =\ - pp.investment(["gas_bio", "coal_gas"], units=units_energy) - - vars["Other|Gases|Other"] =\ - pp.investment(["LNG_bal", "LNG_prod", "LNG_regas", - "LNG_exp", "LNG_imp", "gas_bal", - "gas_std", "gas_sto", "gas_exp_eeu", - "gas_exp_nam", "gas_exp_pao", "gas_exp_weu", - "gas_exp_cpa", "gas_exp_afr", "gas_exp_sas", - "gas_exp_scs", "gas_exp_cas", "gas_exp_ubm", - "gas_exp_rus", "gas_imp"], units=units_energy) - - vars["Other|Solids|Coal|Transmission and Distribution"] =\ - pp.investment(["coal_t_d", "coal_t_d-rc-SO2", "coal_t_d-rc-06p", - "coal_t_d-in-SO2", "coal_t_d-in-06p"], units=units_energy) +\ - pp.act_vom(["coal_t_d-rc-SO2", "coal_t_d-rc-06p", "coal_t_d-in-SO2", - "coal_t_d-in-06p", "coal_t_d"], units=units_energy) * 0.5 - - vars["Other|Solids|Coal|Other"] =\ - pp.investment(["coal_exp", "coal_imp", - "coal_bal", "coal_std"], units=units_energy) - - vars["Other|Solids|Biomass|Transmission and Distribution"] =\ - pp.investment("biomass_t_d", units=units_energy) - - vars["Other|Other"] =\ - pp.investment(["SO2_scrub_ref"], units=units_energy) * 0.5 +\ - pp.investment(["SO2_scrub_ind"], units=units_energy) - - df = pp_utils.make_outputdf(vars, units_energy) - return df From 19564129092e886eb076486b6b77761ac0d84593 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 21 Nov 2023 17:37:38 +0100 Subject: [PATCH 639/774] Resolve import issues with get_local_path() --- message_ix_models/model/material/data_ammonia_new.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/message_ix_models/model/material/data_ammonia_new.py b/message_ix_models/model/material/data_ammonia_new.py index ade24a556d..9caa1d08c3 100644 --- a/message_ix_models/model/material/data_ammonia_new.py +++ b/message_ix_models/model/material/data_ammonia_new.py @@ -9,8 +9,7 @@ CONVERSION_FACTOR_NH3_N = 17 / 14 context = read_config() default_gdp_elasticity = pd.read_excel( - context.get_local_path( - "data", + private_data_path( "material", "methanol", "methanol_sensitivity_pars.xlsx", From b70328ed0a48ccf5fce8b7b6b94d541eb3d08423 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Mon, 27 Nov 2023 00:03:07 +0100 Subject: [PATCH 640/774] Ensure preliminary compatibility with new ssp2 base model --- message_ix_models/model/material/__init__.py | 38 ++++++++++++++++--- .../model/material/data_aluminum.py | 3 +- .../model/material/data_methanol_new.py | 11 +++++- .../model/material/data_petro.py | 22 +++++------ .../model/material/data_steel.py | 8 ++-- 5 files changed, 61 insertions(+), 21 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 65125b0693..0acd9913d4 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -16,6 +16,7 @@ from .data_util import modify_demand_and_hist_activity, add_emission_accounting from .data_util import add_coal_lowerbound_2020, add_macro_COVID, add_cement_bounds_2020 from .data_util import add_elec_lowerbound_2020, add_ccs_technologies, read_config +import pandas as pd log = logging.getLogger(__name__) @@ -27,6 +28,20 @@ def build(scenario): # Apply to the base scenario spec = get_spec() + if "water_supply" not in list(scenario.set("level")): + scenario.check_out() + # add missing water tecs + scenario.add_set("technology", "extract__freshwater_supply") + scenario.add_set("level", "water_supply") + scenario.add_set("commodity", "freshwater_supply") + + water_dict = pd.read_excel( + "C:/Users\maczek\PycharmProjects\message_data\message_data\model\material\petrochemical model fixes notebooks/water_tec_pars.xlsx", + sheet_name=None) + for par in water_dict.keys(): + scenario.add_par(par, water_dict[par]) + scenario.commit("add missing water tecs") + apply_spec(scenario, spec, add_data_2) spec = None apply_spec(scenario, spec, add_data_1) # dry_run=True @@ -39,7 +54,10 @@ def build(scenario): # Coal calibration 2020 add_ccs_technologies(scenario) modify_demand_and_hist_activity(scenario) - add_emission_accounting(scenario) + try: + add_emission_accounting(scenario) + except: + scenario.commit("no changes") add_coal_lowerbound_2020(scenario) add_cement_bounds_2020(scenario) @@ -179,11 +197,21 @@ def build_scen(context, datafile, tag, mode, scenario_name): print("WARNING: this code is not tested with this base scenario!") # Clone and set up - scenario = build( - context.get_scenario().clone( - model="MESSAGEix-Materials", scenario=output_scenario_name + "_" + tag + + if context.model == "SSP_dev_SSP2_v0.1": + scenario = build( + context.get_scenario().clone( + model=context.scenario_info["model"], + scenario=context.scenario_info["scenario"] + "_" + tag, + keep_solution=False + ) + ) + else: + scenario = build( + context.get_scenario().clone( + model="MESSAGEix-Materials", scenario=output_scenario_name + "_" + tag + ) ) - ) # Set the latest version as default scenario.set_as_default() diff --git a/message_ix_models/model/material/data_aluminum.py b/message_ix_models/model/material/data_aluminum.py index c12bd655d7..10ec6c200c 100644 --- a/message_ix_models/model/material/data_aluminum.py +++ b/message_ix_models/model/material/data_aluminum.py @@ -382,7 +382,8 @@ def gen_data_aluminum(scenario, dry_run=False): # This relation should start from 2020... if r == "minimum_recycling_aluminum": modelyears_copy = modelyears[:] - modelyears_copy.remove(2020) + if 2020 in modelyears_copy: + modelyears_copy.remove(2020) common_rel = dict( year_rel=modelyears_copy, diff --git a/message_ix_models/model/material/data_methanol_new.py b/message_ix_models/model/material/data_methanol_new.py index 62040fb3eb..ea0f01f36c 100644 --- a/message_ix_models/model/material/data_methanol_new.py +++ b/message_ix_models/model/material/data_methanol_new.py @@ -1,5 +1,6 @@ import message_ix_models.util import pandas as pd +import yaml from message_ix import make_df from message_ix_models.util import broadcast, same_node @@ -11,7 +12,7 @@ def gen_data_methanol_new(scenario): df_pars = pd.read_excel( - context.get_local_path( + message_ix_models.util.private_data_path( "material", "methanol", "methanol_sensitivity_pars.xlsx" ), sheet_name="Sheet1", @@ -38,6 +39,14 @@ def gen_data_methanol_new(scenario): for i in pars_dict.keys(): pars_dict[i] = broadcast_reduced_df(pars_dict[i], i) + if scenario.model == "SSP_dev_SSP2_v0.1": + file_path = "C:/Users\maczek\PycharmProjects\message_data\message_data\model\material\petrochemical model fixes notebooks\missing_rels.yaml" + + with open(file_path, 'r') as file: + missing_rels = yaml.safe_load(file) + df = pars_dict["relation_activity"] + pars_dict["relation_activity"] = df[~df["relation"].isin(missing_rels)] + return pars_dict diff --git a/message_ix_models/model/material/data_petro.py b/message_ix_models/model/material/data_petro.py index b61003efbe..d6359641cb 100644 --- a/message_ix_models/model/material/data_petro.py +++ b/message_ix_models/model/material/data_petro.py @@ -46,6 +46,7 @@ def gen_mock_demand_petro(scenario, gdp_elasticity_2020, gdp_elasticity_2030): context = read_config() s_info = ScenarioInfo(scenario) modelyears = s_info.Y #s_info.Y is only for modeling years + fy = scenario.firstmodelyear nodes = s_info.N def get_demand_t1_with_income_elasticity( @@ -55,16 +56,15 @@ def get_demand_t1_with_income_elasticity( elasticity * demand_t0 * ((income_t1 - income_t0) / income_t0) ) + demand_t0 - df_gdp = pd.read_excel( - context.get_local_path("material", "methanol", "methanol demand.xlsx"), - sheet_name="GDP_baseline", - ) - - df = df_gdp[(~df_gdp["Region"].isna()) & (df_gdp["Region"] != "World")] - df = df.dropna(axis=1) + df_gdp = scenario.par("bound_activity_lo", filters={"technology": "GDP"}) + df = df_gdp.pivot(columns="year_act", values="value", index="node_loc").reset_index() + df["node_loc"] = df["node_loc"].str.replace("R12_", "") + df = df.rename({"node_loc": "Region"}, axis=1) df_demand = df.copy(deep=True) - df_demand = df_demand.drop([2010, 2015, 2020], axis=1) + num_cols = [i for i in df_demand.columns if type(i) == int] + hist_yrs = [i for i in num_cols if i <= fy] + df_demand = df_demand.drop(hist_yrs, axis=1) # 2018 production # Use as 2020 @@ -90,7 +90,7 @@ def get_demand_t1_with_income_elasticity( dem_2020 = np.array([2, 75, 30, 4, 11, 42, 60, 32, 30, 29, 35]) dem_2020 = pd.Series(dem_2020) - df_demand[2020] = dem_2020 + df_demand[fy] = dem_2020 for i in range(len(modelyears) - 1): income_year1 = modelyears[i] @@ -107,7 +107,7 @@ def get_demand_t1_with_income_elasticity( df_demand[income_year2] = dem_2020 df_melt = df_demand.melt( - id_vars=["Region"], value_vars=df_demand.columns[5:], var_name="year" + id_vars=["Region"], value_vars=df_demand.columns, var_name="year" ) return message_ix.make_df( @@ -364,7 +364,7 @@ def gen_data_petro_chemicals(scenario, dry_run=False): context = read_config() df_pars = pd.read_excel( - context.get_local_path( + private_data_path( "material", "methanol", "methanol_sensitivity_pars.xlsx" ), sheet_name="Sheet1", diff --git a/message_ix_models/model/material/data_steel.py b/message_ix_models/model/material/data_steel.py index 110c3190f3..194e27dd6e 100644 --- a/message_ix_models/model/material/data_steel.py +++ b/message_ix_models/model/material/data_steel.py @@ -436,10 +436,12 @@ def gen_data_steel(scenario, dry_run=False): continue if r == 'minimum_recycling_steel': # Do not implement the minimum recycling rate for the year 2020 - model_years_rel.remove(2020) + if 2020 in model_years_rel: + model_years_rel.remove(2020) if r == 'max_regional_recycling_steel': # Do not implement the minimum recycling rate for the year 2020 - model_years_rel.remove(2020) + if 2020 in model_years_rel: + model_years_rel.remove(2020) params = set( data_steel_rel.loc[ @@ -562,7 +564,7 @@ def derive_steel_demand(scenario, dry_run=False): # GDP is only in MER in scenario. # To get PPP GDP, it is read externally from the R side df = r.derive_steel_demand( - pop, base_demand, str(context.get_local_path("material")) + pop, base_demand, str(private_data_path("material")) ) df.year = df.year.astype(int) From 74df7f9976bd0d823095b25e37016e78f8a90a65 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Mon, 27 Nov 2023 00:03:42 +0100 Subject: [PATCH 641/774] Add missing vintages for mto fix_cost --- .../data/material/methanol/methanol_techno_economic.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/methanol/methanol_techno_economic.xlsx b/message_ix_models/data/material/methanol/methanol_techno_economic.xlsx index 54bb4a9871..276eae242a 100644 --- a/message_ix_models/data/material/methanol/methanol_techno_economic.xlsx +++ b/message_ix_models/data/material/methanol/methanol_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:80d5afca0619923f9dabed695969ee27e8cfe78589604779928d0d4020a01b78 -size 619049 +oid sha256:9572f5407d54532ee12c96a647e9a496c681bd5a3c434a93b47e68c17b3afb78 +size 619097 From b88e302e8f872ace5c384df9cdfdc9c846769fba Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 28 Nov 2023 09:55:36 +0100 Subject: [PATCH 642/774] Add cli command for ssp cost updates --- message_ix_models/model/material/__init__.py | 37 ++++++++++++++++++++ 1 file changed, 37 insertions(+) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 0acd9913d4..1474c0b24e 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -515,3 +515,40 @@ def add_data_2(scenario, dry_run=False): add_par_data(scenario, func(scenario), dry_run=dry_run) log.info("done") + +@cli.command("modify_cost") +@click.option("--ssp", default="SSP2", help="Suffix to the scenario name") +@click.option("--scen_name", default='SSP_supply_cost_test_baseline') +@click.pass_obj +def modify_costs_with_tool(context, scen_name, ssp): + import message_ix + from message_ix_models.tools.costs.config import Config + from message_ix_models.tools.costs.projections import create_cost_projections + mp = ixmp.Platform("ixmp_dev") + base = message_ix.Scenario(mp, "MESSAGEix-Materials", scenario=scen_name) + scen = base.clone(model=base.model, scenario=base.scenario.replace("baseline", ssp)) + + tec_set = list(scen.set("technology")) + + cfg = Config(module="materials", ref_region="R12_NAM", method="convergence", format="message", scenario=ssp) + + out_materials = create_cost_projections( + node=cfg.node, + ref_region=cfg.ref_region, + base_year=cfg.base_year, + module=cfg.module, + method=cfg.method, + scenario_version=cfg.scenario_version, + scenario=cfg.scenario, + convergence_year=cfg.convergence_year, + fom_rate=cfg.fom_rate, + format=cfg.format, + ) + scen.check_out() + fix_cost = out_materials.fix_cost.drop_duplicates().drop(["scenario_version", "scenario"], axis=1) + scen.add_par("fix_cost", fix_cost[fix_cost["technology"].isin(tec_set)]) + inv_cost = out_materials.inv_cost.drop_duplicates().drop(["scenario_version", "scenario"], axis=1) + scen.add_par("inv_cost", inv_cost[inv_cost["technology"].isin(tec_set)]) + + scen.commit(f"update cost assumption to: {ssp}") + scen.solve(model="MESSAGE", solve_options={"barcrossalg":2, "scaind":1}) \ No newline at end of file From b15a39991e10d948ac46ebe9d59f38d7fbe01d2b Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 28 Nov 2023 09:56:23 +0100 Subject: [PATCH 643/774] Modify r script for debugging --- .../model/material/material_demand/init_modularized.R | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/message_ix_models/model/material/material_demand/init_modularized.R b/message_ix_models/model/material/material_demand/init_modularized.R index 537fe471a1..8c51db72d1 100644 --- a/message_ix_models/model/material/material_demand/init_modularized.R +++ b/message_ix_models/model/material/material_demand/init_modularized.R @@ -49,6 +49,8 @@ derive_steel_demand <- function(df_pop, df_demand, datapath) { drop_na() %>% filter(cons.pcap > 0) + write.csv(df_steel_consumption, file = "output_file.csv", row.names = TRUE) + nlnit.s = nls(cons.pcap ~ a * exp(b/gdp.pcap) * (1-m)^del.t, data=df_steel_consumption, start=list(a=600, b=-10000, m=0)) df_in = df_pop %>% left_join(df_demand %>% select(-year)) %>% # df_demand is only for 2020 @@ -63,7 +65,7 @@ derive_steel_demand <- function(df_pop, df_demand, datapath) { # Add 2110 spaceholder demand = demand %>% rbind(demand %>% filter(year==2100) %>% mutate(year = 2110)) - + print(nlnit.s) return(demand %>% select(node=region, year, value=demand.tot) %>% arrange(year, node)) # Mt } From 8451febffe6903fdf0863accbb9f825c51641ec1 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Wed, 29 Nov 2023 11:08:26 +0100 Subject: [PATCH 644/774] Add cli command for ssp supply cost runs --- message_ix_models/model/material/__init__.py | 40 +++++++++++++++++++- 1 file changed, 39 insertions(+), 1 deletion(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 1474c0b24e..3fc599900f 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -551,4 +551,42 @@ def modify_costs_with_tool(context, scen_name, ssp): scen.add_par("inv_cost", inv_cost[inv_cost["technology"].isin(tec_set)]) scen.commit(f"update cost assumption to: {ssp}") - scen.solve(model="MESSAGE", solve_options={"barcrossalg":2, "scaind":1}) \ No newline at end of file + scen.solve(model="MESSAGE", solve_options={"barcrossalg":2, "scaind":1}) + + +@cli.command("modify_cost") +@click.option("--ssp", default="SSP2", help="Suffix to the scenario name") +@click.option("--scen_name", default='SSP_supply_cost_test_baseline_macro') +@click.pass_obj +def modify_costs_with_tool(context, scen_name, ssp): + import message_ix + from message_ix_models.tools.costs.config import Config + from message_ix_models.tools.costs.projections import create_cost_projections + mp = ixmp.Platform("ixmp_dev") + base = message_ix.Scenario(mp, "MESSAGEix-Materials", scenario=scen_name) + scen = base.clone(model=base.model, scenario=base.scenario.replace("baseline", ssp)) + + tec_set = list(scen.set("technology")) + + cfg = Config(module="materials", ref_region="R12_NAM", method="convergence", format="message", scenario=ssp) + + out_materials = create_cost_projections( + node=cfg.node, + ref_region=cfg.ref_region, + base_year=cfg.base_year, + module=cfg.module, + method=cfg.method, + scenario_version=cfg.scenario_version, + scenario=cfg.scenario, + convergence_year=cfg.convergence_year, + fom_rate=cfg.fom_rate, + format=cfg.format, + ) + scen.check_out() + fix_cost = out_materials.fix_cost.drop_duplicates().drop(["scenario_version", "scenario"], axis=1) + scen.add_par("fix_cost", fix_cost[fix_cost["technology"].isin(tec_set)]) + inv_cost = out_materials.inv_cost.drop_duplicates().drop(["scenario_version", "scenario"], axis=1) + scen.add_par("inv_cost", inv_cost[inv_cost["technology"].isin(tec_set)]) + + scen.commit(f"update cost assumption to: {ssp}") + scen.solve(model="MESSAGE-MACRO", solve_options={"scaind": -1}) \ No newline at end of file From c070d230b025f88e0e92d09f34379ff5e6db4e42 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Wed, 29 Nov 2023 17:03:19 +0100 Subject: [PATCH 645/774] Add refactoring of r code for demand projections --- .../ammonia/FAOSTAT_data_ammonia_trade.csv | 3 + .../fert_techno_economic_iea_data.xlsx | 3 + .../ammonia/~$fert_techno_economic.xlsx | 3 + .../~$fert_techno_economic_iea_data.xlsx | 3 + .../data/material/high_demand_output.xlsx | 3 + .../methanol_sensitivity_pars_high.xlsx | 3 + .../methanol_techno_economic_high_demand.xlsx | 3 + ...nol production statistics (version 1).xlsx | 3 + .../reduced/location factor collection.xlsx | 3 + .../reduced/meth_bio_techno_economic_new.xlsx | 3 + .../meth_bio_techno_economic_new_ccs.xlsx | 3 + .../methanol/reduced/meth_coal_additions.xlsx | 3 + .../reduced/meth_coal_additions_fs.xlsx | 3 + .../reduced/meth_h2_techno_economic_fs.xlsx | 3 + .../reduced/meth_ng_techno_economic.xlsx | 3 + .../reduced/meth_ng_techno_economic_fs.xlsx | 3 + .../methanol/reduced/meth_t_d_fuel.xlsx | 3 + .../meth_trade_techno_economic_fs.xlsx | 3 + .../methanol/reduced/methanol demand.xlsx | 3 + .../methanol/reduced/methanol_all_pars.xlsx | 3 + .../reduced/methanol_all_pars_new2.xlsx | 3 + .../reduced/methanol_sensitivity_pars.xlsx | 3 + ...methanol_sensitivity_pars_high_demand.xlsx | 3 + .../data/material/methanol/scenario_list.xlsx | 3 + .../material/steel_cement/demand_cement.yaml | 36 + .../material/steel_cement/demand_steel.yaml | 36 + .../material/~$meth_h2_techno_economic.xlsx | 3 + .../~$n-fertilizer_techno-economic_new.xlsx | 3 + .../~$petrochemicals_techno_economic.xlsx | 3 + .../model/material/CO2 diff files/_Diff.xlsx | 3 + .../CO2 diff files/_Diff_baseline_low.xlsx | 3 + .../_Diff_w_ENGAGE_fix_and_meth_exp_rel.xlsx | 3 + .../material/TE-tool stuff/fix_om_ratios.csv | 138 + .../technology_materials_first_year.csv | 138 + .../data analysis ipynbs/gdx_pandas.ipynb | 3026 ++++++++ .../data analysis ipynbs/gdx_pandas_3.ipynb | 2059 ++++++ .../model/material/debug_moduls.py | 118 + message_ix_models/model/material/df_py.csv | 169 + .../model/material/diogo_carbon_price.ipynb | 105 + .../MESSAGE-magpie test.ipynb | 2590 +++++++ .../magpie notebooks/data_scp_final.ipynb | 744 ++ .../material/magpie notebooks/magpie.ipynb | 6434 +++++++++++++++++ .../plot_LU_carbon_price.ipynb | 1088 +++ .../read_magpie_raw_files.ipynb | 1785 +++++ .../refactor_emi_drivers_func.ipynb | 508 ++ .../magpie notebooks/tax_emission_setup.ipynb | 182 + .../model/material/magpie_LU_readin.py | 100 + .../refactor_mat_demand_calc.py | 675 ++ .../modify_materials_for_ssp_update.ipynb | 1256 ++++ .../model/material/output_file.csv | 1065 +++ .../add_new_meth_modes.ipynb | 112 + .../methanol_modifications.ipynb | 890 +++ .../missing_rels.yaml | 17 + .../split_h2_elec.ipynb | 208 + .../water_tec_pars.xlsx | 3 + .../model/material/report/materials.yaml | 21 + .../model/material/tax_emission_1000f.csv | 14 + .../model/material/tax_emission_600f.csv | 14 + .../model/material/test_SSP_costs.ipynb | 704 ++ .../model/material/test_db.ipynb | 3677 ++++++++++ .../fetch_run_id_from_scenario.ipynb | 183 + .../format reporting output.ipynb | 1306 ++++ 62 files changed, 29491 insertions(+) create mode 100644 message_ix_models/data/material/ammonia/FAOSTAT_data_ammonia_trade.csv create mode 100644 message_ix_models/data/material/ammonia/fert_techno_economic_iea_data.xlsx create mode 100644 message_ix_models/data/material/ammonia/~$fert_techno_economic.xlsx create mode 100644 message_ix_models/data/material/ammonia/~$fert_techno_economic_iea_data.xlsx create mode 100644 message_ix_models/data/material/high_demand_output.xlsx create mode 100644 message_ix_models/data/material/methanol/methanol_sensitivity_pars_high.xlsx create mode 100644 message_ix_models/data/material/methanol/methanol_techno_economic_high_demand.xlsx create mode 100644 message_ix_models/data/material/methanol/reduced/Methanol production statistics (version 1).xlsx create mode 100644 message_ix_models/data/material/methanol/reduced/location factor collection.xlsx create mode 100644 message_ix_models/data/material/methanol/reduced/meth_bio_techno_economic_new.xlsx create mode 100644 message_ix_models/data/material/methanol/reduced/meth_bio_techno_economic_new_ccs.xlsx create mode 100644 message_ix_models/data/material/methanol/reduced/meth_coal_additions.xlsx create mode 100644 message_ix_models/data/material/methanol/reduced/meth_coal_additions_fs.xlsx create mode 100644 message_ix_models/data/material/methanol/reduced/meth_h2_techno_economic_fs.xlsx create mode 100644 message_ix_models/data/material/methanol/reduced/meth_ng_techno_economic.xlsx create mode 100644 message_ix_models/data/material/methanol/reduced/meth_ng_techno_economic_fs.xlsx create mode 100644 message_ix_models/data/material/methanol/reduced/meth_t_d_fuel.xlsx create mode 100644 message_ix_models/data/material/methanol/reduced/meth_trade_techno_economic_fs.xlsx create mode 100644 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message_ix_models/model/material/CO2 diff files/_Diff.xlsx create mode 100644 message_ix_models/model/material/CO2 diff files/_Diff_baseline_low.xlsx create mode 100644 message_ix_models/model/material/CO2 diff files/_Diff_w_ENGAGE_fix_and_meth_exp_rel.xlsx create mode 100644 message_ix_models/model/material/TE-tool stuff/fix_om_ratios.csv create mode 100644 message_ix_models/model/material/TE-tool stuff/technology_materials_first_year.csv create mode 100644 message_ix_models/model/material/data analysis ipynbs/gdx_pandas.ipynb create mode 100644 message_ix_models/model/material/data analysis ipynbs/gdx_pandas_3.ipynb create mode 100644 message_ix_models/model/material/debug_moduls.py create mode 100644 message_ix_models/model/material/df_py.csv create mode 100644 message_ix_models/model/material/diogo_carbon_price.ipynb create mode 100644 message_ix_models/model/material/magpie notebooks/MESSAGE-magpie test.ipynb create mode 100644 message_ix_models/model/material/magpie notebooks/data_scp_final.ipynb create mode 100644 message_ix_models/model/material/magpie notebooks/magpie.ipynb create mode 100644 message_ix_models/model/material/magpie notebooks/plot_LU_carbon_price.ipynb create mode 100644 message_ix_models/model/material/magpie notebooks/read_magpie_raw_files.ipynb create mode 100644 message_ix_models/model/material/magpie notebooks/refactor_emi_drivers_func.ipynb create mode 100644 message_ix_models/model/material/magpie notebooks/tax_emission_setup.ipynb create mode 100644 message_ix_models/model/material/magpie_LU_readin.py create mode 100644 message_ix_models/model/material/material_demand/refactor_mat_demand_calc.py create mode 100644 message_ix_models/model/material/modify_materials_for_ssp_update.ipynb create mode 100644 message_ix_models/model/material/output_file.csv create mode 100644 message_ix_models/model/material/petrochemical model fixes notebooks/add_new_meth_modes.ipynb create mode 100644 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a/message_ix_models/data/material/methanol/scenario_list.xlsx b/message_ix_models/data/material/methanol/scenario_list.xlsx new file mode 100644 index 0000000000..6ccda6532f --- /dev/null +++ b/message_ix_models/data/material/methanol/scenario_list.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:72f6b1c0c82e33cb1dd491bc3f9327cb1f0851440b86097e62b3cc08f1acb371 +size 21484 diff --git a/message_ix_models/data/material/steel_cement/demand_cement.yaml b/message_ix_models/data/material/steel_cement/demand_cement.yaml new file mode 100644 index 0000000000..f3a389bb6b --- /dev/null +++ b/message_ix_models/data/material/steel_cement/demand_cement.yaml @@ -0,0 +1,36 @@ +- R12_AFR: + year: 2020 + value: 209.10000 +- R12_RCPA: + year: 2020 + value: 237.88000 +- R12_EEU: + year: 2020 + value: 131.20000 +- R12_FSU: + year: 2020 + value: 106.60000 +- R12_LAM: + year: 2020 + value: 94.30000 +- R12_MEA: + year: 2020 + value: 143.80000 +- R12_NAM: + year: 2020 + value: 183.30000 +- R12_PAO: + year: 2020 + value: 66.00000 +- R12_PAS: + year: 2020 + value: 141.30000 +- R12_SAS: + year: 2020 + value: 571.40000 +- R12_WEU: + year: 2020 + value: 131.20000 +- R12_CHN: + year: 2020 + value: 2140.92000 \ No newline at end of file diff --git a/message_ix_models/data/material/steel_cement/demand_steel.yaml b/message_ix_models/data/material/steel_cement/demand_steel.yaml new file mode 100644 index 0000000000..7d437a8ada --- /dev/null +++ b/message_ix_models/data/material/steel_cement/demand_steel.yaml @@ -0,0 +1,36 @@ +- R12_AFR: + year: 2020 + value: 20.04000 +- R12_RCPA: + year: 2020 + value: 12.08000 +- R12_EEU: + year: 2020 + value: 56.55000 +- R12_FSU: + year: 2020 + value: 56.50000 +- R12_LAM: + year: 2020 + value: 64.94000 +- R12_MEA: + year: 2020 + value: 54.26000 +- R12_NAM: + year: 2020 + value: 97.76000 +- R12_PAO: + year: 2020 + value: 91.30000 +- R12_PAS: + year: 2020 + value: 65.20000 +- R12_SAS: + year: 2020 + value: 164.28000 +- R12_WEU: + year: 2020 + value: 131.95000 +- R12_CHN: + year: 2020 + value: 980.10000 \ No newline at end of file diff --git a/message_ix_models/data/material/~$meth_h2_techno_economic.xlsx b/message_ix_models/data/material/~$meth_h2_techno_economic.xlsx new file mode 100644 index 0000000000..38141ad6b1 --- /dev/null +++ b/message_ix_models/data/material/~$meth_h2_techno_economic.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c7a304b4faadb0fb2717ca8841246c6b4dd1ca23fc05137e7fe4e254365763b6 +size 165 diff --git a/message_ix_models/data/material/~$n-fertilizer_techno-economic_new.xlsx b/message_ix_models/data/material/~$n-fertilizer_techno-economic_new.xlsx new file mode 100644 index 0000000000..38141ad6b1 --- /dev/null +++ b/message_ix_models/data/material/~$n-fertilizer_techno-economic_new.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c7a304b4faadb0fb2717ca8841246c6b4dd1ca23fc05137e7fe4e254365763b6 +size 165 diff --git a/message_ix_models/data/material/~$petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/~$petrochemicals_techno_economic.xlsx new file mode 100644 index 0000000000..38141ad6b1 --- /dev/null +++ b/message_ix_models/data/material/~$petrochemicals_techno_economic.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c7a304b4faadb0fb2717ca8841246c6b4dd1ca23fc05137e7fe4e254365763b6 +size 165 diff --git a/message_ix_models/model/material/CO2 diff files/_Diff.xlsx b/message_ix_models/model/material/CO2 diff files/_Diff.xlsx new file mode 100644 index 0000000000..6a561e582a --- /dev/null +++ b/message_ix_models/model/material/CO2 diff files/_Diff.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7caf17c64fbdb8ffb6269b45683453e4b88868392e2bedd6ecf40426220cdafd +size 7993 diff --git a/message_ix_models/model/material/CO2 diff files/_Diff_baseline_low.xlsx b/message_ix_models/model/material/CO2 diff files/_Diff_baseline_low.xlsx new file mode 100644 index 0000000000..98077cb408 --- /dev/null +++ b/message_ix_models/model/material/CO2 diff files/_Diff_baseline_low.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e39139dd7a851f74e0880f838b79cb6ae233ff705f22ec449e5d248af02c0027 +size 12165 diff --git a/message_ix_models/model/material/CO2 diff files/_Diff_w_ENGAGE_fix_and_meth_exp_rel.xlsx b/message_ix_models/model/material/CO2 diff files/_Diff_w_ENGAGE_fix_and_meth_exp_rel.xlsx new file mode 100644 index 0000000000..9df06333c9 --- /dev/null +++ b/message_ix_models/model/material/CO2 diff files/_Diff_w_ENGAGE_fix_and_meth_exp_rel.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7233ead5fd50ab42bc466013c2506f9336d742f09c413195ce04f5dbf17e9cef +size 7970 diff --git a/message_ix_models/model/material/TE-tool stuff/fix_om_ratios.csv b/message_ix_models/model/material/TE-tool stuff/fix_om_ratios.csv new file mode 100644 index 0000000000..d5b2c75c8e --- /dev/null +++ b/message_ix_models/model/material/TE-tool stuff/fix_om_ratios.csv @@ -0,0 +1,138 @@ +tec,fix_ratio +manuf_steel,0 +fc_h2_refining,0 +fc_h2_petro,0 +fc_h2_cement,0 +fc_h2_aluminum,0 +export_steel,0 +export_petro,0 +export_aluminum,0 +export_NH3,0 +export_NFert,0 +dri_steel,0 +fc_h2_resins,0 +fc_h2_steel,0 +meth_exp,0 +raw_meal_prep_cement,0.001 +cokeoven_steel,0.001 +clinker_wet_cement,0.001 +clinker_dry_cement,0.001 +grinding_vertmill_cement,0.001 +grinding_ballmill_cement,0.001 +catalytic_reforming_ref,0.003 +vacuum_distillation_ref,0.02 +visbreaker_ref,0.021 +hydro_cracking_ref,0.021 +coking_ref,0.021 +atm_distillation_ref,0.021 +catalytic_cracking_ref,0.022 +meth_ng,0.022 +meth_ng_ccs,0.023 +ethanol_to_ethylene_petro,0.025 +steam_cracker_petro,0.025 +meth_coal,0.034 +meth_bio,0.034 +coal_NH3,0.036 +gas_NH3,0.036 +biomass_NH3,0.036 +fueloil_NH3,0.036 +meth_bio_ccs,0.037 +MTO_petro,0.037 +fueloil_NH3_ccs,0.038 +gas_NH3_ccs,0.038 +coal_NH3_ccs,0.038 +biomass_NH3_ccs,0.038 +electr_NH3,0.04 +NH3_to_N_fertil,0.04 +meth_coal_ccs,0.042 +solar_steel,0.055 +solar_resins,0.055 +solar_refining,0.055 +solar_petro,0.055 +solar_cement,0.055 +solar_aluminum,0.055 +bof_steel,0.089 +finishing_steel,0.1 +bf_steel,0.1 +sinter_steel,0.1 +pellet_steel,0.1 +meth_h2,0.109 +prebake_aluminum,0.118 +hp_gas_petro,0.138 +hp_gas_aluminum,0.138 +hp_gas_cement,0.138 +hp_gas_refining,0.138 +hp_gas_resins,0.138 +hp_gas_steel,0.138 +hp_elec_cement,0.152 +hp_elec_steel,0.152 +hp_elec_petro,0.152 +hp_elec_refining,0.152 +hp_elec_resins,0.152 +soderberg_aluminum,0.157 +furnace_ethanol_refining,0.165 +furnace_methanol_refining,0.165 +furnace_methanol_petro,0.165 +furnace_methanol_cement,0.165 +furnace_methanol_aluminum,0.165 +furnace_ethanol_petro,0.165 +furnace_methanol_steel,0.165 +furnace_ethanol_resins,0.165 +furnace_ethanol_steel,0.165 +furnace_methanol_resins,0.165 +furnace_ethanol_cement,0.165 +furnace_ethanol_aluminum,0.165 +clinker_dry_ccs_cement,0.167 +clinker_wet_ccs_cement,0.167 +furnace_gas_aluminum,0.209 +furnace_gas_resins,0.209 +furnace_gas_steel,0.209 +furnace_gas_petro,0.209 +furnace_h2_steel,0.209 +furnace_h2_resins,0.209 +furnace_h2_refining,0.209 +furnace_h2_petro,0.209 +furnace_gas_refining,0.209 +furnace_h2_aluminum,0.209 +furnace_gas_cement,0.209 +furnace_h2_cement,0.209 +furnace_loil_petro,0.218 +furnace_loil_steel,0.218 +furnace_loil_aluminum,0.218 +furnace_loil_resins,0.218 +furnace_loil_refining,0.218 +furnace_loil_cement,0.218 +furnace_biomass_steel,0.267 +furnace_biomass_resins,0.267 +furnace_biomass_refining,0.267 +furnace_biomass_cement,0.267 +furnace_biomass_aluminum,0.267 +furnace_biomass_petro,0.267 +furnace_elec_resins,0.271 +furnace_elec_aluminum,0.271 +furnace_elec_cement,0.271 +furnace_elec_petro,0.271 +dheat_steel,0.271 +dheat_aluminum,0.271 +furnace_elec_refining,0.271 +dheat_petro,0.271 +furnace_elec_steel,0.271 +dheat_resins,0.271 +dheat_refining,0.271 +dheat_cement,0.271 +furnace_foil_steel,0.316 +furnace_foil_resins,0.316 +furnace_foil_refining,0.316 +furnace_foil_cement,0.316 +furnace_foil_petro,0.316 +furnace_foil_aluminum,0.316 +furnace_coal_refining,0.398 +furnace_coal_steel,0.398 +furnace_coal_cement,0.398 +furnace_coal_aluminum,0.398 +furnace_coke_petro,0.398 +furnace_coke_refining,0.398 +furnace_coal_resins,0.398 +furnace_coal_petro,0.398 +eaf_steel,0.496 +hp_elec_aluminum,0.152 diff --git a/message_ix_models/model/material/TE-tool stuff/technology_materials_first_year.csv b/message_ix_models/model/material/TE-tool stuff/technology_materials_first_year.csv new file mode 100644 index 0000000000..62c7a34ff6 --- /dev/null +++ b/message_ix_models/model/material/TE-tool stuff/technology_materials_first_year.csv @@ -0,0 +1,138 @@ +message_technology,first_year_original +MTO_petro,2020 +NH3_to_N_fertil,1995 +atm_distillation_ref,1980 +bf_steel,1990 +biomass_NH3,1995 +biomass_NH3_ccs,1995 +bof_steel,1990 +catalytic_cracking_ref,1980 +catalytic_reforming_ref,1980 +clinker_dry_ccs_cement,1980 +clinker_dry_cement,1980 +clinker_wet_ccs_cement,1980 +clinker_wet_cement,1980 +coal_NH3,1995 +coal_NH3_ccs,1995 +cokeoven_steel,1990 +coking_ref,1980 +dheat_aluminum,2030 +dheat_cement,2030 +dheat_petro,2030 +dheat_refining,2030 +dheat_resins,2030 +dheat_steel,2030 +eaf_steel,1990 +electr_NH3,1995 +ethanol_to_ethylene_petro,1980 +finishing_steel,1990 +fueloil_NH3,1995 +fueloil_NH3_ccs,1995 +furnace_biomass_aluminum,1980 +furnace_biomass_cement,1980 +furnace_biomass_petro,1980 +furnace_biomass_refining,1980 +furnace_biomass_resins,1980 +furnace_biomass_steel,1980 +furnace_coal_aluminum,1980 +furnace_coal_cement,1980 +furnace_coal_petro,1980 +furnace_coal_refining,1980 +furnace_coal_resins,1980 +furnace_coal_steel,1980 +furnace_coke_petro,1980 +furnace_coke_refining,1980 +furnace_elec_aluminum,1980 +furnace_elec_cement,1980 +furnace_elec_petro,1980 +furnace_elec_refining,1980 +furnace_elec_resins,1980 +furnace_elec_steel,1980 +furnace_ethanol_aluminum,1980 +furnace_ethanol_cement,1980 +furnace_ethanol_petro,1980 +furnace_ethanol_refining,1980 +furnace_ethanol_resins,1980 +furnace_ethanol_steel,1980 +furnace_foil_aluminum,1980 +furnace_foil_cement,1980 +furnace_foil_petro,1980 +furnace_foil_refining,1980 +furnace_foil_resins,1980 +furnace_foil_steel,1980 +furnace_gas_aluminum,1980 +furnace_gas_cement,1980 +furnace_gas_petro,1980 +furnace_gas_refining,1980 +furnace_gas_resins,1980 +furnace_gas_steel,1980 +furnace_h2_aluminum,2030 +furnace_h2_cement,2030 +furnace_h2_petro,2030 +furnace_h2_refining,2030 +furnace_h2_resins,2030 +furnace_h2_steel,2030 +furnace_loil_aluminum,1980 +furnace_loil_cement,1980 +furnace_loil_petro,1980 +furnace_loil_refining,1980 +furnace_loil_resins,1980 +furnace_loil_steel,1980 +furnace_methanol_aluminum,2020 +furnace_methanol_cement,2020 +furnace_methanol_petro,2020 +furnace_methanol_refining,2020 +furnace_methanol_resins,2020 +furnace_methanol_steel,2020 +gas_NH3,1995 +gas_NH3_ccs,1995 +grinding_ballmill_cement,1980 +grinding_vertmill_cement,1980 +hp_elec_aluminum,1980 +hp_elec_cement,1980 +hp_elec_petro,1980 +hp_elec_refining,1980 +hp_elec_resins,1980 +hp_elec_steel,1980 +hp_gas_aluminum,1980 +hp_gas_cement,1980 +hp_gas_petro,1980 +hp_gas_refining,1980 +hp_gas_resins,1980 +hp_gas_steel,1980 +hydro_cracking_ref,1980 +meth_bio,2020 +meth_bio_ccs,2030 +meth_coal,2000 +meth_coal_ccs,2030 +meth_h2,2020 +meth_ng,2000 +meth_ng_ccs,2030 +pellet_steel,1990 +prebake_aluminum,1985 +raw_meal_prep_cement,1980 +sinter_steel,1990 +soderberg_aluminum,1985 +solar_aluminum,2030 +solar_cement,2030 +solar_petro,2030 +solar_refining,2030 +solar_resins,2030 +solar_steel,2030 +steam_cracker_petro,1980 +vacuum_distillation_ref,1980 +visbreaker_ref,1980 +dri_steel,1990 +export_NFert,2005 +export_NH3,2005 +export_aluminum,1985 +export_petro,1980 +export_steel,1990 +fc_h2_aluminum,2030 +fc_h2_cement,2030 +fc_h2_petro,2030 +fc_h2_refining,2030 +fc_h2_resins,2030 +fc_h2_steel,2030 +manuf_steel,1990 +meth_exp,2020 diff --git a/message_ix_models/model/material/data analysis ipynbs/gdx_pandas.ipynb b/message_ix_models/model/material/data analysis ipynbs/gdx_pandas.ipynb new file mode 100644 index 0000000000..c2e06924fd --- /dev/null +++ b/message_ix_models/model/material/data analysis ipynbs/gdx_pandas.ipynb @@ -0,0 +1,3026 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import gdxpds\n", + "import pandas as pd\n", + "co2_c_factor = 44/12" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "outputs": [], + "source": [ + "#gdx_file = 'C:/Users\\maczek\\PycharmProjects\\message_ix\\message_ix\\model\\output\\MsgOutput_MESSAGEix-Materials_2.0deg_petro_thesis_2_macro.gdx'\n", + "#gdx_file = 'C:/Users\\maczek\\PycharmProjects\\message_ix\\message_ix\\model\\output\\MsgOutput_MESSAGEix-Materials_2.0deg_petro_thesis_2_macro_w_ENGAGE_fixes_test_loil_exp_fix.gdx'\n", + "#gdx_file = 'C:/Users\\maczek\\PycharmProjects\\message_ix\\message_ix\\model\\output\\MsgOutput_ENGAGE_SSP2_v4.1.8.3_T4.5_r3.1_baseline_magpie.gdx'\n", + "#gdx_file = 'C:/Users\\maczek\\PycharmProjects\\message_ix\\message_ix\\model\\output\\MsgOutput_MESSAGEix-Materials_NoPolicy_petro_thesis_2_w_ENGAGE_fixes_test_build_2_macro.gdx'\n", + "#gdx_file = 'C:/Users\\maczek\\PycharmProjects\\message_ix\\message_ix\\model\\output/thesis final/MsgOutput_MESSAGEix-Materials_NoPolicy_petro_thesis_2_final_macro.gdx'\n", + "gdx_file = 'C:/Users\\maczek\\PycharmProjects\\message_ix\\message_ix\\model\\output/MsgOutput_MESSAGEix-GLOBIOM_1.1-R12-MAGPIE-MP00BD1BI00_baseline.gdx'\n", + "#gdx_file = 'C:/Users\\maczek\\PycharmProjects\\message_ix\\message_ix\\model\\output/MsgOutput_MESSAGEix-GLOBIOM_1.1-R12-MAGPIE-MP00BD1BI00_NPi2020-con-prim-dir-ncr_master.gdx'\n", + "#dataframes = gdxpds.to_dataframes(gdx_file)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [], + "source": [ + "mps = [\"00\", \"30\", \"50\", \"76\"]\n", + "bis = [\"00\", \"70\", \"74\", \"78\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 28, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MP00BD1BI00 []\n", + "MP00BD0BI70 [0.03030621]\n", + "MP00BD0BI74 []\n", + "MP00BD0BI78 []\n", + "MP30BD1BI00 [0.08847191]\n", + "MP30BD0BI70 []\n", + "MP30BD0BI74 [0.02933949]\n", + "MP50BD1BI00 []\n", + "MP50BD0BI70 [0.0890051]\n", + "MP50BD0BI74 [0.01935185]\n", + "MP50BD0BI78 []\n", + "MP76BD1BI00 [0.12013137]\n", + "MP76BD0BI70 [0.0857669]\n", + "MP76BD0BI74 [0.02513131]\n", + "MP76BD0BI78 [0.04660288]\n" + ] + } + ], + "source": [ + "import os\n", + "\n", + "for mp in mps:\n", + " for bi in bis:\n", + " if bi == \"00\":\n", + " bd = 1\n", + " else:\n", + " bd = 0\n", + " model = f\"MP{mp}BD{bd}BI{bi}\"\n", + " gdx_name = f\"C:/Users\\maczek\\PycharmProjects\\message_ix\\message_ix\\model\\output/MsgOutput_MESSAGEix-GLOBIOM_1.1-R12-MAGPIE-MP{mp}BD{bd}BI{bi}_NPi2020-con-prim-dir-ncr.gdx\"\n", + " gdx_name = f\"C:/Users\\maczek\\PycharmProjects\\message_ix\\message_ix\\model\\output/MsgOutput_MESSAGEix-GLOBIOM_1.1-R12-MAGPIE-MP{mp}BD{bd}BI{bi}_EN_NPi2020_650.gdx\"\n", + " #print(os.path.exists(gdx_name))\n", + " if not os.path.exists(gdx_name):\n", + " continue\n", + " #print(\"test\")\n", + " df_act = gdxpds.to_dataframe(gdx_name, \"ACT\")[\"ACT\"]\n", + " #print(df_act.columns)\n", + " print(model, df_act[(df_act[\"Level\"]!=0) & (df_act[\"tec\"]==\"bio_backstop\")][\"Level\"].values)\n", + " #break\n", + " #break" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 16, + "outputs": [], + "source": [ + "#df_all = gdxpds.to_dataframes(gdx_file)\n", + "df_all = gdxpds.to_dataframe(gdx_file, \"land_output\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [], + "source": [ + "df_gro = gdxpds.to_dataframe(gdx_file, \"growth_activity_lo\")[\"growth_activity_lo\"]\n", + "df_soft = gdxpds.to_dataframe(gdx_file, \"soft_activity_lo\")[\"soft_activity_lo\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 13, + "outputs": [], + "source": [ + "df_var = gdxpds.to_dataframe(gdx_file, \"ACTIVITY_CONSTRAINT_LO\")[\"ACTIVITY_CONSTRAINT_LO\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 20, + "outputs": [], + "source": [ + "df_var.columns = [\"node\", \"tec\", \"year_all\", \"time\", \"Level\", \"Marginal\", \"Lower\", \"Upper\", \"Scale\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 21, + "outputs": [ + { + "data": { + "text/plain": " node tec year_all time Level Marginal Lower \\\n0 R12_AFR LNG_exp 2020 year 64.134989 0.0 29.753100 \n1 R12_AFR LNG_exp 2025 year 32.884912 0.0 -9.048762 \n2 R12_AFR LNG_exp 2030 year 31.082772 0.0 -9.048762 \n3 R12_AFR LNG_exp 2035 year 12.542668 0.0 -9.048762 \n4 R12_AFR LNG_exp 2040 year 32.041154 0.0 -9.048762 \n... ... ... ... ... ... ... ... \n13732 R12_CHN biomass_exp 2070 year 83.981427 0.0 -16.050522 \n13733 R12_CHN biomass_exp 2080 year 81.488368 0.0 -16.050522 \n13734 R12_CHN biomass_exp 2090 year 80.277312 0.0 -16.050522 \n13735 R12_CHN biomass_exp 2100 year 102.884565 0.0 -16.050522 \n13736 R12_CHN biomass_exp 2110 year 65.048786 0.0 -16.050522 \n\n Upper Scale \n0 3.000000e+300 1.0 \n1 3.000000e+300 1.0 \n2 3.000000e+300 1.0 \n3 3.000000e+300 1.0 \n4 3.000000e+300 1.0 \n... ... ... \n13732 3.000000e+300 1.0 \n13733 3.000000e+300 1.0 \n13734 3.000000e+300 1.0 \n13735 3.000000e+300 1.0 \n13736 3.000000e+300 1.0 \n\n[13737 rows x 9 columns]", + "text/html": "
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nodetecyear_alltimeLevelMarginalLowerUpperScale
0R12_AFRLNG_exp2020year64.1349890.029.7531003.000000e+3001.0
1R12_AFRLNG_exp2025year32.8849120.0-9.0487623.000000e+3001.0
2R12_AFRLNG_exp2030year31.0827720.0-9.0487623.000000e+3001.0
3R12_AFRLNG_exp2035year12.5426680.0-9.0487623.000000e+3001.0
4R12_AFRLNG_exp2040year32.0411540.0-9.0487623.000000e+3001.0
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13732R12_CHNbiomass_exp2070year83.9814270.0-16.0505223.000000e+3001.0
13733R12_CHNbiomass_exp2080year81.4883680.0-16.0505223.000000e+3001.0
13734R12_CHNbiomass_exp2090year80.2773120.0-16.0505223.000000e+3001.0
13735R12_CHNbiomass_exp2100year102.8845650.0-16.0505223.000000e+3001.0
13736R12_CHNbiomass_exp2110year65.0487860.0-16.0505223.000000e+3001.0
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13737 rows × 9 columns

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" + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_var" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 34, + "outputs": [ + { + "data": { + "text/plain": "['wind_ppf',\n 'biomass_imp',\n 'biomass_exp',\n 'LH2_bunker',\n 'LNG_bunker',\n 'eth_bunker',\n 'foil_bunker',\n 'loil_bunker',\n 'meth_bunker']" + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[i for i in list(df_gro.tec.unique()) if i not in list(df_soft.tec.unique())]\n", + "#[i for i in list(df_soft.tec.unique()) if i not in list(df_gro.tec.unique())]\n", + "[i for i in list(df_soft.tec.unique()) if i not in list(df_var.tec.unique())]\n", + "#[i for i in list(df_var.tec.unique()) if i not in list(df_soft.tec.unique())]\n", + "#[i for i in list(df_var.tec.unique()) if ((i not in list(df_gro.tec.unique())) & (i not in list(df_soft.tec.unique())))]\n", + "[i for i in list(df_var.tec.unique()) if (i not in list(df_soft.tec.unique()))]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 17, + "outputs": [], + "source": [ + "df_all = df_all[\"land_output\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 18, + "outputs": [ + { + "data": { + "text/plain": " node land_scenario year_all commodity level time \\\n2201157 R12_CHN BIO05GHG000 2020 bioenergy land_use year \n2201158 R12_CHN BIO05GHG000 2020 bioenergy land_use_reporting year \n2203425 R12_CHN BIO05GHG010 2020 bioenergy land_use year \n2203426 R12_CHN BIO05GHG010 2020 bioenergy land_use_reporting year \n2205735 R12_CHN BIO05GHG020 2020 bioenergy land_use year \n... ... ... ... ... ... ... \n2388263 R12_CHN BIO45GHG4000 2020 bioenergy land_use_reporting year \n2390583 R12_CHN BIO45GHG600 2020 bioenergy land_use year \n2390584 R12_CHN BIO45GHG600 2020 bioenergy land_use_reporting year \n2392903 R12_CHN BIO45GHG990 2020 bioenergy land_use year \n2392904 R12_CHN BIO45GHG990 2020 bioenergy land_use_reporting year \n\n Value \n2201157 238.5000 \n2201158 238.5029 \n2203425 238.5000 \n2203426 238.5029 \n2205735 238.5000 \n... ... \n2388263 238.5029 \n2390583 238.5000 \n2390584 238.5029 \n2392903 238.5000 \n2392904 238.5029 \n\n[168 rows x 7 columns]", + "text/html": "
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nodeland_scenarioyear_allcommodityleveltimeValue
2201157R12_CHNBIO05GHG0002020bioenergyland_useyear238.5000
2201158R12_CHNBIO05GHG0002020bioenergyland_use_reportingyear238.5029
2203425R12_CHNBIO05GHG0102020bioenergyland_useyear238.5000
2203426R12_CHNBIO05GHG0102020bioenergyland_use_reportingyear238.5029
2205735R12_CHNBIO05GHG0202020bioenergyland_useyear238.5000
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2388263R12_CHNBIO45GHG40002020bioenergyland_use_reportingyear238.5029
2390583R12_CHNBIO45GHG6002020bioenergyland_useyear238.5000
2390584R12_CHNBIO45GHG6002020bioenergyland_use_reportingyear238.5029
2392903R12_CHNBIO45GHG9902020bioenergyland_useyear238.5000
2392904R12_CHNBIO45GHG9902020bioenergyland_use_reportingyear238.5029
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168 rows × 7 columns

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" + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_all[(df_all[\"commodity\"]==\"bioenergy\") & ( df_all[\"node\"]==\"R12_CHN\") & (df_all[\"year_all\"]==\"2020\")]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 33, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\maczek\\AppData\\Local\\Temp\\ipykernel_24400\\878313158.py:1: FutureWarning: reindexing with a non-unique Index is deprecated and will raise in a future version.\n", + " v[v.node.isin([\"R12_CHN\"])]\n" + ] + }, + { + "ename": "ValueError", + "evalue": "cannot reindex on an axis with duplicate labels", + "output_type": "error", + "traceback": [ + "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[1;31mValueError\u001B[0m Traceback (most recent call last)", + "Input \u001B[1;32mIn [33]\u001B[0m, in \u001B[0;36m\u001B[1;34m()\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[43mv\u001B[49m\u001B[43m[\u001B[49m\u001B[43mv\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mnode\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43misin\u001B[49m\u001B[43m(\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mR12_CHN\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m)\u001B[49m\u001B[43m]\u001B[49m\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3492\u001B[0m, in \u001B[0;36mDataFrame.__getitem__\u001B[1;34m(self, key)\u001B[0m\n\u001B[0;32m 3490\u001B[0m \u001B[38;5;66;03m# Do we have a (boolean) DataFrame?\u001B[39;00m\n\u001B[0;32m 3491\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(key, DataFrame):\n\u001B[1;32m-> 3492\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mwhere\u001B[49m\u001B[43m(\u001B[49m\u001B[43mkey\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 3494\u001B[0m \u001B[38;5;66;03m# Do we have a (boolean) 1d indexer?\u001B[39;00m\n\u001B[0;32m 3495\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m com\u001B[38;5;241m.\u001B[39mis_bool_indexer(key):\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\util\\_decorators.py:311\u001B[0m, in \u001B[0;36mdeprecate_nonkeyword_arguments..decorate..wrapper\u001B[1;34m(*args, **kwargs)\u001B[0m\n\u001B[0;32m 305\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(args) \u001B[38;5;241m>\u001B[39m num_allow_args:\n\u001B[0;32m 306\u001B[0m warnings\u001B[38;5;241m.\u001B[39mwarn(\n\u001B[0;32m 307\u001B[0m msg\u001B[38;5;241m.\u001B[39mformat(arguments\u001B[38;5;241m=\u001B[39marguments),\n\u001B[0;32m 308\u001B[0m \u001B[38;5;167;01mFutureWarning\u001B[39;00m,\n\u001B[0;32m 309\u001B[0m stacklevel\u001B[38;5;241m=\u001B[39mstacklevel,\n\u001B[0;32m 310\u001B[0m )\n\u001B[1;32m--> 311\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfunc\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:10955\u001B[0m, in \u001B[0;36mDataFrame.where\u001B[1;34m(self, cond, other, inplace, axis, level, errors, try_cast)\u001B[0m\n\u001B[0;32m 10942\u001B[0m \u001B[38;5;129m@deprecate_nonkeyword_arguments\u001B[39m(\n\u001B[0;32m 10943\u001B[0m version\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, allowed_args\u001B[38;5;241m=\u001B[39m[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mself\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mcond\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mother\u001B[39m\u001B[38;5;124m\"\u001B[39m]\n\u001B[0;32m 10944\u001B[0m )\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 10953\u001B[0m try_cast\u001B[38;5;241m=\u001B[39mlib\u001B[38;5;241m.\u001B[39mno_default,\n\u001B[0;32m 10954\u001B[0m ):\n\u001B[1;32m> 10955\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43msuper\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mwhere\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcond\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mother\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43minplace\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43maxis\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mlevel\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43merrors\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtry_cast\u001B[49m\u001B[43m)\u001B[49m\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:9308\u001B[0m, in \u001B[0;36mNDFrame.where\u001B[1;34m(self, cond, other, inplace, axis, level, errors, try_cast)\u001B[0m\n\u001B[0;32m 9300\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m try_cast \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m lib\u001B[38;5;241m.\u001B[39mno_default:\n\u001B[0;32m 9301\u001B[0m warnings\u001B[38;5;241m.\u001B[39mwarn(\n\u001B[0;32m 9302\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mtry_cast keyword is deprecated and will be removed in a \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m 9303\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mfuture version.\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[0;32m 9304\u001B[0m \u001B[38;5;167;01mFutureWarning\u001B[39;00m,\n\u001B[0;32m 9305\u001B[0m stacklevel\u001B[38;5;241m=\u001B[39mfind_stack_level(),\n\u001B[0;32m 9306\u001B[0m )\n\u001B[1;32m-> 9308\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_where\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcond\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mother\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43minplace\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43maxis\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mlevel\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43merrors\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43merrors\u001B[49m\u001B[43m)\u001B[49m\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:9075\u001B[0m, in \u001B[0;36mNDFrame._where\u001B[1;34m(self, cond, other, inplace, axis, level, errors)\u001B[0m\n\u001B[0;32m 9072\u001B[0m cond \u001B[38;5;241m=\u001B[39m cond\u001B[38;5;241m.\u001B[39mastype(\u001B[38;5;28mbool\u001B[39m)\n\u001B[0;32m 9074\u001B[0m cond \u001B[38;5;241m=\u001B[39m \u001B[38;5;241m-\u001B[39mcond \u001B[38;5;28;01mif\u001B[39;00m inplace \u001B[38;5;28;01melse\u001B[39;00m cond\n\u001B[1;32m-> 9075\u001B[0m cond \u001B[38;5;241m=\u001B[39m \u001B[43mcond\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mreindex\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_info_axis\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43maxis\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_info_axis_number\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcopy\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m)\u001B[49m\n\u001B[0;32m 9077\u001B[0m \u001B[38;5;66;03m# try to align with other\u001B[39;00m\n\u001B[0;32m 9078\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(other, NDFrame):\n\u001B[0;32m 9079\u001B[0m \n\u001B[0;32m 9080\u001B[0m \u001B[38;5;66;03m# align with me\u001B[39;00m\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\util\\_decorators.py:324\u001B[0m, in \u001B[0;36mrewrite_axis_style_signature..decorate..wrapper\u001B[1;34m(*args, **kwargs)\u001B[0m\n\u001B[0;32m 322\u001B[0m \u001B[38;5;129m@wraps\u001B[39m(func)\n\u001B[0;32m 323\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mwrapper\u001B[39m(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m Callable[\u001B[38;5;241m.\u001B[39m\u001B[38;5;241m.\u001B[39m\u001B[38;5;241m.\u001B[39m, Any]:\n\u001B[1;32m--> 324\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfunc\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:4804\u001B[0m, in \u001B[0;36mDataFrame.reindex\u001B[1;34m(self, *args, **kwargs)\u001B[0m\n\u001B[0;32m 4802\u001B[0m kwargs\u001B[38;5;241m.\u001B[39mpop(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124maxis\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;28;01mNone\u001B[39;00m)\n\u001B[0;32m 4803\u001B[0m kwargs\u001B[38;5;241m.\u001B[39mpop(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mlabels\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;28;01mNone\u001B[39;00m)\n\u001B[1;32m-> 4804\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43msuper\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mreindex\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:4966\u001B[0m, in \u001B[0;36mNDFrame.reindex\u001B[1;34m(self, *args, **kwargs)\u001B[0m\n\u001B[0;32m 4963\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_reindex_multi(axes, copy, fill_value)\n\u001B[0;32m 4965\u001B[0m \u001B[38;5;66;03m# perform the reindex on the axes\u001B[39;00m\n\u001B[1;32m-> 4966\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_reindex_axes\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m 4967\u001B[0m \u001B[43m \u001B[49m\u001B[43maxes\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mlevel\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mlimit\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtolerance\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmethod\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfill_value\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcopy\u001B[49m\n\u001B[0;32m 4968\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241m.\u001B[39m__finalize__(\u001B[38;5;28mself\u001B[39m, method\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mreindex\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:4617\u001B[0m, in \u001B[0;36mDataFrame._reindex_axes\u001B[1;34m(self, axes, level, limit, tolerance, method, fill_value, copy)\u001B[0m\n\u001B[0;32m 4615\u001B[0m columns \u001B[38;5;241m=\u001B[39m axes[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mcolumns\u001B[39m\u001B[38;5;124m\"\u001B[39m]\n\u001B[0;32m 4616\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m columns \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[1;32m-> 4617\u001B[0m frame \u001B[38;5;241m=\u001B[39m \u001B[43mframe\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_reindex_columns\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m 4618\u001B[0m \u001B[43m \u001B[49m\u001B[43mcolumns\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmethod\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcopy\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mlevel\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfill_value\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mlimit\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtolerance\u001B[49m\n\u001B[0;32m 4619\u001B[0m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 4621\u001B[0m index \u001B[38;5;241m=\u001B[39m axes[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mindex\u001B[39m\u001B[38;5;124m\"\u001B[39m]\n\u001B[0;32m 4622\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m index \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:4662\u001B[0m, in \u001B[0;36mDataFrame._reindex_columns\u001B[1;34m(self, new_columns, method, copy, level, fill_value, limit, tolerance)\u001B[0m\n\u001B[0;32m 4649\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m_reindex_columns\u001B[39m(\n\u001B[0;32m 4650\u001B[0m \u001B[38;5;28mself\u001B[39m,\n\u001B[0;32m 4651\u001B[0m new_columns,\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 4657\u001B[0m tolerance\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[0;32m 4658\u001B[0m ):\n\u001B[0;32m 4659\u001B[0m new_columns, indexer \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcolumns\u001B[38;5;241m.\u001B[39mreindex(\n\u001B[0;32m 4660\u001B[0m new_columns, method\u001B[38;5;241m=\u001B[39mmethod, level\u001B[38;5;241m=\u001B[39mlevel, limit\u001B[38;5;241m=\u001B[39mlimit, tolerance\u001B[38;5;241m=\u001B[39mtolerance\n\u001B[0;32m 4661\u001B[0m )\n\u001B[1;32m-> 4662\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_reindex_with_indexers\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m 4663\u001B[0m \u001B[43m \u001B[49m\u001B[43m{\u001B[49m\u001B[38;5;241;43m1\u001B[39;49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[43m[\u001B[49m\u001B[43mnew_columns\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mindexer\u001B[49m\u001B[43m]\u001B[49m\u001B[43m}\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 4664\u001B[0m \u001B[43m \u001B[49m\u001B[43mcopy\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mcopy\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 4665\u001B[0m \u001B[43m \u001B[49m\u001B[43mfill_value\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mfill_value\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 4666\u001B[0m \u001B[43m \u001B[49m\u001B[43mallow_dups\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[0;32m 4667\u001B[0m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:5032\u001B[0m, in \u001B[0;36mNDFrame._reindex_with_indexers\u001B[1;34m(self, reindexers, fill_value, copy, allow_dups)\u001B[0m\n\u001B[0;32m 5029\u001B[0m indexer \u001B[38;5;241m=\u001B[39m ensure_platform_int(indexer)\n\u001B[0;32m 5031\u001B[0m \u001B[38;5;66;03m# TODO: speed up on homogeneous DataFrame objects (see _reindex_multi)\u001B[39;00m\n\u001B[1;32m-> 5032\u001B[0m new_data \u001B[38;5;241m=\u001B[39m \u001B[43mnew_data\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mreindex_indexer\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m 5033\u001B[0m \u001B[43m \u001B[49m\u001B[43mindex\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 5034\u001B[0m \u001B[43m \u001B[49m\u001B[43mindexer\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 5035\u001B[0m \u001B[43m \u001B[49m\u001B[43maxis\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mbaxis\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 5036\u001B[0m \u001B[43m \u001B[49m\u001B[43mfill_value\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mfill_value\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 5037\u001B[0m \u001B[43m \u001B[49m\u001B[43mallow_dups\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mallow_dups\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 5038\u001B[0m \u001B[43m \u001B[49m\u001B[43mcopy\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mcopy\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 5039\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 5040\u001B[0m \u001B[38;5;66;03m# If we've made a copy once, no need to make another one\u001B[39;00m\n\u001B[0;32m 5041\u001B[0m copy \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mFalse\u001B[39;00m\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\managers.py:679\u001B[0m, in \u001B[0;36mBaseBlockManager.reindex_indexer\u001B[1;34m(self, new_axis, indexer, axis, fill_value, allow_dups, copy, consolidate, only_slice, use_na_proxy)\u001B[0m\n\u001B[0;32m 677\u001B[0m \u001B[38;5;66;03m# some axes don't allow reindexing with dups\u001B[39;00m\n\u001B[0;32m 678\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m allow_dups:\n\u001B[1;32m--> 679\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43maxes\u001B[49m\u001B[43m[\u001B[49m\u001B[43maxis\u001B[49m\u001B[43m]\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_validate_can_reindex\u001B[49m\u001B[43m(\u001B[49m\u001B[43mindexer\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 681\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m axis \u001B[38;5;241m>\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mndim:\n\u001B[0;32m 682\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mIndexError\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mRequested axis not found in manager\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py:4107\u001B[0m, in \u001B[0;36mIndex._validate_can_reindex\u001B[1;34m(self, indexer)\u001B[0m\n\u001B[0;32m 4105\u001B[0m \u001B[38;5;66;03m# trying to reindex on an axis with duplicates\u001B[39;00m\n\u001B[0;32m 4106\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_index_as_unique \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(indexer):\n\u001B[1;32m-> 4107\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mcannot reindex on an axis with duplicate labels\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n", + "\u001B[1;31mValueError\u001B[0m: cannot reindex on an axis with duplicate labels" + ] + } + ], + "source": [ + "v[v.node.isin([\"R12_CHN\"])]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 57, + "outputs": [], + "source": [ + "bio_dict = {}\n", + "for k,v in df_all.items():\n", + " if \"tec\" in v.columns:\n", + " if len(v[v.tec.str.contains(\"bio\")][\"tec\"].unique()):\n", + " #print(v[v.tec.str.contains(\"bio\")][\"tec\"].unique())\n", + " if \"node\" in v.columns:\n", + " v_temp = v.loc[:,~v.columns.duplicated()].copy()\n", + " bio_dict[k] = v_temp[(v_temp.tec.str.contains(\"bio\")) & (v_temp.node ==\"R12_CHN\")]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 69, + "outputs": [ + { + "data": { + "text/plain": "{'is_bound_new_capacity_lo': array(['bio_ppl', 'eth_bio'], dtype=object),\n 'is_dynamic_activity_lo': array(['bio_ppl', 'biomass_i', 'biomass_rc', 'biomass_imp', 'biomass_exp'],\n dtype=object),\n 'bound_new_capacity_lo': array(['bio_ppl', 'eth_bio'], dtype=object),\n 'bound_activity_lo': array(['bio_hpl', 'bio_ppl', 'biomass_i', 'eth_bio'], dtype=object),\n 'initial_activity_lo': array(['bio_ppl', 'biomass_i', 'biomass_rc', 'biomass_imp', 'biomass_exp'],\n dtype=object),\n 'growth_activity_lo': array(['bio_ppl', 'biomass_i', 'biomass_rc', 'biomass_imp', 'biomass_exp'],\n dtype=object),\n 'soft_activity_lo': array(['bio_extr_mpen', 'bio_hpl', 'bio_istig', 'bio_istig_ccs',\n 'bio_ppl', 'bio_ppl_co2scr', 'biomass_i', 'biomass_rc',\n 'biomass_t_d', 'eth_bio', 'eth_bio_ccs', 'gas_bio', 'h2_bio',\n 'h2_bio_ccs', 'landfill_mechbio', 'liq_bio', 'liq_bio_ccs'],\n dtype=object),\n 'abs_cost_activity_soft_lo': array(['bio_extr_mpen'], dtype=object),\n 'level_cost_activity_soft_lo': array(['bio_hpl', 'bio_istig', 'bio_istig_ccs', 'bio_ppl',\n 'bio_ppl_co2scr', 'biomass_i', 'biomass_rc', 'biomass_t_d',\n 'eth_bio', 'eth_bio_ccs', 'gas_bio', 'h2_bio', 'h2_bio_ccs',\n 'landfill_mechbio', 'liq_bio', 'liq_bio_ccs'], dtype=object)}" + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lo_dict = {k:v for k,v in bio_dict.items() if ((\"lo\" in k))}\n", + "{k:i.tec.unique() for k,i in lo_dict.items()}" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 42, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "is_bound_new_capacity_lo ['bio_ppl' 'eth_bio' 'bio_istig']\n", + "is_dynamic_activity_lo ['biomass_i' 'biomass_rc' 'biomass_imp' 'biomass_exp' 'bio_ppl']\n", + "bound_new_capacity_lo ['bio_ppl' 'eth_bio']\n", + "bound_activity_lo ['bio_ppl' 'biomass_i' 'eth_bio' 'bio_hpl' 'biomass_rc' 'biomass_t_d'\n", + " 'biomass_nc' 'bio_istig' 'gas_bio']\n", + "initial_activity_lo ['biomass_i' 'biomass_rc' 'biomass_imp' 'biomass_exp' 'bio_ppl']\n", + "growth_activity_lo ['biomass_i' 'biomass_rc' 'biomass_imp' 'biomass_exp' 'bio_ppl']\n", + "soft_activity_lo ['bio_extr_mpen' 'bio_hpl' 'bio_istig' 'bio_istig_ccs' 'bio_ppl'\n", + " 'bio_ppl_co2scr' 'biomass_i' 'biomass_rc' 'biomass_t_d' 'eth_bio'\n", + " 'eth_bio_ccs' 'gas_bio' 'h2_bio' 'h2_bio_ccs' 'landfill_mechbio'\n", + " 'liq_bio' 'liq_bio_ccs']\n", + "abs_cost_activity_soft_lo ['bio_extr_mpen']\n", + "level_cost_activity_soft_lo ['bio_hpl' 'bio_istig' 'bio_istig_ccs' 'bio_ppl' 'bio_ppl_co2scr'\n", + " 'biomass_i' 'biomass_rc' 'biomass_t_d' 'eth_bio' 'eth_bio_ccs' 'gas_bio'\n", + " 'h2_bio' 'h2_bio_ccs' 'landfill_mechbio' 'liq_bio' 'liq_bio_ccs']\n" + ] + } + ], + "source": [ + "for k,v in lo_dict.items():\n", + " print(k,v)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 12, + "outputs": [ + { + "data": { + "text/plain": "", + "application/javascript": "if (typeof IPython !== 'undefined') { IPython.OutputArea.prototype._should_scroll = function(lines){ return false; }}" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import ixmp\n", + "import message_ix\n", + "\n", + "mp = ixmp.Platform(\"ixmp_dev\")\n", + "df = mp.scenario_list()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 16, + "outputs": [ + { + "data": { + "text/plain": " model scenario scheme \\\n1249 ENGAGE-MAGPIE_SSP2 1000f MESSAGE \n1250 ENGAGE-MAGPIE_SSP2 EN_NPi2020_1000 MESSAGE \n1251 ENGAGE-MAGPIE_SSP2 EN_NPi2020_1000_step1 MESSAGE \n1252 ENGAGE-MAGPIE_SSP2 EN_NPi2020_1000_step2 MESSAGE \n1253 ENGAGE-MAGPIE_SSP2 EN_NPi2020_1000f MESSAGE \n... ... ... ... \n1319 ENGAGE-MAGPIE_SSP2 npi_low_dem_scen2 MESSAGE \n1320 ENGAGE-MAGPIE_SSP2 orgCode_1000f MESSAGE \n1321 ENGAGE-MAGPIE_SSP2 orgCode_1000f_slackLandUp MESSAGE \n1322 ENGAGE-MAGPIE_SSP2 orgCode_baseline MESSAGE \n1323 ENGAGE-MAGPIE_SSP2 test to confirm MACRO converges MESSAGE \n\n is_default is_locked cre_user cre_date \\\n1249 1 0 steinhauser 2023-05-05 13:33:33.000000 \n1250 1 0 steinhauser 2023-07-24 17:05:34.000000 \n1251 1 0 steinhauser 2023-07-24 16:20:13.000000 \n1252 1 0 steinhauser 2023-07-24 16:47:04.000000 \n1253 1 0 steinhauser 2023-07-24 19:49:23.000000 \n... ... ... ... ... \n1319 1 0 steinhauser 2023-07-12 11:09:57.000000 \n1320 1 1 steinhauser 2023-05-13 19:37:52.000000 \n1321 1 0 steinhauser 2023-05-16 09:18:19.000000 \n1322 1 0 steinhauser 2023-05-15 13:04:26.000000 \n1323 1 0 fricko 2023-07-06 07:34:01.000000 \n\n upd_user upd_date lock_user \\\n1249 steinhauser 2023-05-12 17:07:36.000000 None \n1250 steinhauser 2023-07-24 17:12:10.000000 None \n1251 steinhauser 2023-07-24 16:32:50.000000 None \n1252 steinhauser 2023-07-24 16:50:29.000000 None \n1253 steinhauser 2023-07-24 20:05:26.000000 None \n... ... ... ... \n1319 steinhauser 2023-07-12 11:19:35.000000 None \n1320 None None steinhauser \n1321 None None None \n1322 steinhauser 2023-05-15 13:43:43.000000 None \n1323 fricko 2023-07-06 07:36:41.000000 None \n\n lock_date \\\n1249 None \n1250 None \n1251 None \n1252 None \n1253 None \n... ... \n1319 None \n1320 2023-05-13 20:47:53.000000 \n1321 None \n1322 None \n1323 None \n\n annotation version \n1249 clone Scenario from 'ENGAGE-MAGPIE_SSP2|baseli... 1 \n1250 clone Scenario from 'ENGAGE-MAGPIE_SSP2|EN_NPi... 1 \n1251 clone Scenario from 'ENGAGE-MAGPIE_SSP2|NPi202... 1 \n1252 clone Scenario from 'ENGAGE-MAGPIE_SSP2|EN_NPi... 1 \n1253 clone Scenario from 'ENGAGE-MAGPIE_SSP2|NPi202... 1 \n... ... ... \n1319 clone Scenario from 'ENGAGE-MAGPIE_SSP2|NPi202... 1 \n1320 clone Scenario from 'ENGAGE-MAGPIE_SSP2|orgCod... 1 \n1321 clone Scenario from 'ENGAGE-MAGPIE_SSP2|orgCod... 1 \n1322 clone Scenario from 'JST_test|ENGAGE_baseline_... 2 \n1323 clone Scenario from 'ENGAGE-MAGPIE_SSP2|baseli... 2 \n\n[75 rows x 13 columns]", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
modelscenarioschemeis_defaultis_lockedcre_usercre_dateupd_userupd_datelock_userlock_dateannotationversion
1249ENGAGE-MAGPIE_SSP21000fMESSAGE10steinhauser2023-05-05 13:33:33.000000steinhauser2023-05-12 17:07:36.000000NoneNoneclone Scenario from 'ENGAGE-MAGPIE_SSP2|baseli...1
1250ENGAGE-MAGPIE_SSP2EN_NPi2020_1000MESSAGE10steinhauser2023-07-24 17:05:34.000000steinhauser2023-07-24 17:12:10.000000NoneNoneclone Scenario from 'ENGAGE-MAGPIE_SSP2|EN_NPi...1
1251ENGAGE-MAGPIE_SSP2EN_NPi2020_1000_step1MESSAGE10steinhauser2023-07-24 16:20:13.000000steinhauser2023-07-24 16:32:50.000000NoneNoneclone Scenario from 'ENGAGE-MAGPIE_SSP2|NPi202...1
1252ENGAGE-MAGPIE_SSP2EN_NPi2020_1000_step2MESSAGE10steinhauser2023-07-24 16:47:04.000000steinhauser2023-07-24 16:50:29.000000NoneNoneclone Scenario from 'ENGAGE-MAGPIE_SSP2|EN_NPi...1
1253ENGAGE-MAGPIE_SSP2EN_NPi2020_1000fMESSAGE10steinhauser2023-07-24 19:49:23.000000steinhauser2023-07-24 20:05:26.000000NoneNoneclone Scenario from 'ENGAGE-MAGPIE_SSP2|NPi202...1
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1319ENGAGE-MAGPIE_SSP2npi_low_dem_scen2MESSAGE10steinhauser2023-07-12 11:09:57.000000steinhauser2023-07-12 11:19:35.000000NoneNoneclone Scenario from 'ENGAGE-MAGPIE_SSP2|NPi202...1
1320ENGAGE-MAGPIE_SSP2orgCode_1000fMESSAGE11steinhauser2023-05-13 19:37:52.000000NoneNonesteinhauser2023-05-13 20:47:53.000000clone Scenario from 'ENGAGE-MAGPIE_SSP2|orgCod...1
1321ENGAGE-MAGPIE_SSP2orgCode_1000f_slackLandUpMESSAGE10steinhauser2023-05-16 09:18:19.000000NoneNoneNoneNoneclone Scenario from 'ENGAGE-MAGPIE_SSP2|orgCod...1
1322ENGAGE-MAGPIE_SSP2orgCode_baselineMESSAGE10steinhauser2023-05-15 13:04:26.000000steinhauser2023-05-15 13:43:43.000000NoneNoneclone Scenario from 'JST_test|ENGAGE_baseline_...2
1323ENGAGE-MAGPIE_SSP2test to confirm MACRO convergesMESSAGE10fricko2023-07-06 07:34:01.000000fricko2023-07-06 07:36:41.000000NoneNoneclone Scenario from 'ENGAGE-MAGPIE_SSP2|baseli...2
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75 rows × 13 columns

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" + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[df[\"model\"].str.contains(\"MAGPIE\")][\"model\"].unique()\n", + "df[df[\"model\"]==\"ENGAGE-MAGPIE_SSP2\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 17, + "outputs": [], + "source": [ + "scen = message_ix.Scenario(mp, \"ENGAGE-MAGPIE_SSP2\", \"baseline\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 19, + "outputs": [], + "source": [ + "df_lu_old = scen.par(\"land_use\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 22, + "outputs": [ + { + "data": { + "text/plain": " value\nnode land_scenario year \nR11_AFR BIO00GHG000 1990 0.7953\n 1995 0.7953\n 2000 0.7951\n 2005 0.7975\n 2010 0.8051\n... ...\nR11_WEU BIO45GHG600 2070 0.7248\n 2080 0.7245\n 2090 0.7221\n 2100 0.7225\n 2110 0.7225\n\n[18480 rows x 1 columns]", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
value
nodeland_scenarioyear
R11_AFRBIO00GHG00019900.7953
19950.7953
20000.7951
20050.7975
20100.8051
............
R11_WEUBIO45GHG60020700.7248
20800.7245
20900.7221
21000.7225
21100.7225
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18480 rows × 1 columns

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" + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_lu_old.groupby([\"node\", \"land_scenario\", \"year\"]).sum()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 10, + "outputs": [ + { + "data": { + "text/plain": "Index(['node', 'land_scenario', 'year_all', 'land_type', 'Value'], dtype='object')" + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_lu_act = gdxpds.to_dataframe(gdx_file, \"land_use\")[\"land_use\"]\n", + "df_lu_act.columns" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 11, + "outputs": [ + { + "data": { + "text/plain": " Value\nnode land_scenario year_all \nR12_AFR BIO00GHG000 1990 0.7945\n 1995 0.7945\n 2000 0.7943\n 2005 0.7941\n 2010 0.8016\n... ...\nR12_WEU BIO45GHG990 2070 0.7251\n 2080 0.7249\n 2090 0.7231\n 2100 0.7216\n 2110 0.7216\n\n[20160 rows x 1 columns]", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Value
nodeland_scenarioyear_all
R12_AFRBIO00GHG00019900.7945
19950.7945
20000.7943
20050.7941
20100.8016
............
R12_WEUBIO45GHG99020700.7251
20800.7249
20900.7231
21000.7216
21100.7216
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20160 rows × 1 columns

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" + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_lu_act.groupby([\"node\", \"land_scenario\", \"year_all\"]).sum()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 57, + "outputs": [], + "source": [ + "df_lu_act = gdxpds.to_dataframe(gdx_file, \"LAND\")\n", + "df_lu_out = gdxpds.to_dataframe(gdx_file, \"land_output\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 58, + "outputs": [], + "source": [ + "df_lu_out = df_lu_out[\"land_output\"]\n", + "df_lu_act = df_lu_act[\"LAND\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 59, + "outputs": [], + "source": [ + "df_lu_act = df_lu_act[df_lu_act.columns[:-4]]\n", + "df_lu_act = df_lu_act[df_lu_act[\"Level\"] != 0]\n", + "df_lu_out = df_lu_out.set_index([\"node\", \"land_scenario\", \"year_all\"])\n", + "df_lu_act = df_lu_act.set_index([\"node\", \"land_scenario\", \"year_all\"])\n", + "df_lu = df_lu_act.join(df_lu_out)\n", + "df_lu[\"value\"] = df_lu[\"Level\"] * df_lu[\"Value\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 68, + "outputs": [], + "source": [ + "df_matrix = pd.read_csv(\"C:/Users\\maczek\\PycharmProjects\\message_data\\data\\globiom/magpie_input_MP00BD1BI00.csv\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 70, + "outputs": [], + "source": [ + "data_swapped = {\n", + " 'SubSaharanAfrica': 'R12_AFR',\n", + " 'PlannedAsiaChina': 'R12_RCPA',\n", + " 'ChinaReg': 'R12_CHN',\n", + " 'CentralEastEurope': 'R12_EEU',\n", + " 'FormerSovietUnion': 'R12_FSU',\n", + " 'LatinAmericaCarib': 'R12_LAM',\n", + " 'MidEastNorthAfrica': 'R12_MEA',\n", + " 'NorthAmerica': 'R12_NAM',\n", + " 'PacificOECD': 'R12_PAO',\n", + " 'OtherPacificAsia': 'R12_PAS',\n", + " 'SouthAsia': 'R12_SAS',\n", + " 'WesternEurope': 'R12_WEU'\n", + "}\n" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 77, + "outputs": [], + "source": [ + "df_matrix = df_matrix.drop([\"SSPscen\", \"SDGscen\"], axis=1)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 80, + "outputs": [], + "source": [ + "df_matrix[\"land_scenario\"] = df_matrix[\"BIOscen\"] + df_matrix[\"GHGscen\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 82, + "outputs": [], + "source": [ + "df_matrix = df_matrix.drop([\"BIOscen\", \"GHGscen\"], axis=1)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 87, + "outputs": [], + "source": [ + "df_matrix = df_matrix.melt(id_vars=[\"Region\", \"Variable\", \"Unit\", \"land_scenario\"], var_name=\"year_all\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 88, + "outputs": [], + "source": [ + "df_matrix = df_matrix.replace({'Region': data_swapped})" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 93, + "outputs": [], + "source": [ + "df_matrix = df_matrix.rename({\"Region\":\"node\", \"Variable\":\"commodity\"}, axis=1)\n", + "df_matrix = df_matrix.drop(\"Unit\", axis=1)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 97, + "outputs": [], + "source": [ + "df_matrix = df_matrix.set_index([\"node\", \"land_scenario\", \"year_all\"])" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 112, + "outputs": [], + "source": [ + "df_lu = df_lu_act.join(df_matrix)#.swaplevel(0,2).loc[\"2035\"]#.groupby([\"node\", \"year_all\"]).sum()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 114, + "outputs": [], + "source": [ + "df_lu[\"val_eff\"] = df_lu[\"Level\"] * df_lu[\"value\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 137, + "outputs": [], + "source": [ + "indic = \"Biodiversity|BII\"" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "legend1 = []\n", + "import matplotlib.pyplot as plt\n", + "fig,ax = plt.subplots(figsize=(15,10))\n", + "for i, reg in df_lu.index.get_level_values(0).unique()[:6]:\n", + " df_temp = df_lu.loc[reg]\n", + " df_temp[(df_temp[\"commodity\"]==indic)][\"val_eff\"].groupby([\"year_all\"]).sum().plot(x=\"year_all\")\n", + " legend.append(reg)\n", + "ax.legend(legend, loc=2, ncol=3)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "is_executing": true + } + } + }, + { + "cell_type": "code", + "execution_count": 1, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'df_lu' is not defined", + "output_type": "error", + "traceback": [ + "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[1;31mNameError\u001B[0m Traceback (most recent call last)", + "Input \u001B[1;32mIn [1]\u001B[0m, in \u001B[0;36m\u001B[1;34m()\u001B[0m\n\u001B[0;32m 2\u001B[0m \u001B[38;5;28;01mimport\u001B[39;00m \u001B[38;5;21;01mmatplotlib\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mpyplot\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m \u001B[38;5;21;01mplt\u001B[39;00m\n\u001B[0;32m 3\u001B[0m fig,ax \u001B[38;5;241m=\u001B[39m plt\u001B[38;5;241m.\u001B[39msubplots(figsize\u001B[38;5;241m=\u001B[39m(\u001B[38;5;241m15\u001B[39m,\u001B[38;5;241m10\u001B[39m))\n\u001B[1;32m----> 4\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m i, reg \u001B[38;5;129;01min\u001B[39;00m \u001B[43mdf_lu\u001B[49m\u001B[38;5;241m.\u001B[39mindex\u001B[38;5;241m.\u001B[39mget_level_values(\u001B[38;5;241m0\u001B[39m)\u001B[38;5;241m.\u001B[39munique()[:\u001B[38;5;241m6\u001B[39m]:\n\u001B[0;32m 5\u001B[0m df_temp \u001B[38;5;241m=\u001B[39m df_lu\u001B[38;5;241m.\u001B[39mloc[reg]\n\u001B[0;32m 6\u001B[0m df_temp[(df_temp[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mcommodity\u001B[39m\u001B[38;5;124m\"\u001B[39m]\u001B[38;5;241m==\u001B[39mindic)][\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mval_eff\u001B[39m\u001B[38;5;124m\"\u001B[39m]\u001B[38;5;241m.\u001B[39mgroupby([\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124myear_all\u001B[39m\u001B[38;5;124m\"\u001B[39m])\u001B[38;5;241m.\u001B[39msum()\u001B[38;5;241m.\u001B[39mplot(x\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124myear_all\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n", + "\u001B[1;31mNameError\u001B[0m: name 'df_lu' is not defined" + ] + }, + { + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "legend1 = []\n", + "import matplotlib.pyplot as plt\n", + "fig,ax = plt.subplots(figsize=(15,10))\n", + "for i, reg in df_lu.index.get_level_values(0).unique()[6:]:\n", + " df_temp = df_lu.loc[reg]\n", + " df_temp[(df_temp[\"commodity\"]==indic)][\"val_eff\"].groupby([\"year_all\"]).sum().plot(x=\"year_all\")\n", + " legend.append(reg)\n", + "ax.legend(legend, loc=2, ncol=3)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 121, + "outputs": [ + { + "data": { + "text/plain": "node land_scenario year_all\nR12_AFR BIO00GHG000 2035 0.167425\n 2040 0.295972\n 2045 0.394696\n 2050 0.471256\n 2055 0.528590\n ... \nR12_WEU BIO00GHG990 2070 0.036868\n 2080 0.022109\n 2090 0.013248\n 2100 0.007938\n BIO05GHG050 2020 0.759600\nName: val_eff, Length: 305, dtype: float64" + }, + "execution_count": 121, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_lu[(df_lu[\"commodity\"]==\"Biodiversity|BII\")][\"val_eff\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 90, + "outputs": [ + { + "data": { + "text/plain": " Level commodity \\\nnode land_scenario year_all \nR12_AFR BIO00GHG000 2035 0.226219 Population \n 2035 0.226219 TCE \n 2035 0.226219 LU_CO2 \n 2035 0.226219 BCA_LandUseChangeEM \n 2035 0.226219 BCA_SavanBurnEM \n... ... ... \nR12_WEU BIO05GHG050 2020 1.000000 Costs|TC \n 2020 1.000000 Emissions|CO2|AFOLU|Agriculture \n 2020 1.000000 Emissions|CO2|AFOLU|Soil Carbon \n 2020 1.000000 Emissions|GHG|AFOLU \n 2020 1.000000 Landuse intensity indicator Tau \n\n level time Value \\\nnode land_scenario year_all \nR12_AFR BIO00GHG000 2035 land_use_reporting year 1605.5862 \n 2035 land_use_reporting year 863.1115 \n 2035 land_use_reporting year 469.4045 \n 2035 land_use_reporting year 58.1282 \n 2035 land_use_reporting year 715.0514 \n... ... ... ... \nR12_WEU BIO05GHG050 2020 land_use_reporting year 5418.4543 \n 2020 land_use_reporting year -5.1668 \n 2020 land_use_reporting year -40.2557 \n 2020 land_use_reporting year 480.2134 \n 2020 land_use_reporting year 2.0366 \n\n value \nnode land_scenario year_all \nR12_AFR BIO00GHG000 2035 363.214205 \n 2035 195.252274 \n 2035 106.188246 \n 2035 13.149707 \n 2035 161.758257 \n... ... \nR12_WEU BIO05GHG050 2020 5418.454300 \n 2020 -5.166800 \n 2020 -40.255700 \n 2020 480.213400 \n 2020 2.036600 \n\n[39628 rows x 6 columns]", + "text/html": "
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LevelcommodityleveltimeValuevalue
nodeland_scenarioyear_all
R12_AFRBIO00GHG00020350.226219Populationland_use_reportingyear1605.5862363.214205
20350.226219TCEland_use_reportingyear863.1115195.252274
20350.226219LU_CO2land_use_reportingyear469.4045106.188246
20350.226219BCA_LandUseChangeEMland_use_reportingyear58.128213.149707
20350.226219BCA_SavanBurnEMland_use_reportingyear715.0514161.758257
...........................
R12_WEUBIO05GHG05020201.000000Costs|TCland_use_reportingyear5418.45435418.454300
20201.000000Emissions|CO2|AFOLU|Agricultureland_use_reportingyear-5.1668-5.166800
20201.000000Emissions|CO2|AFOLU|Soil Carbonland_use_reportingyear-40.2557-40.255700
20201.000000Emissions|GHG|AFOLUland_use_reportingyear480.2134480.213400
20201.000000Landuse intensity indicator Tauland_use_reportingyear2.03662.036600
\n

39628 rows × 6 columns

\n
" + }, + "execution_count": 90, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_matrix[df_matrix[\"commodity\"]==\"Biodiversity|BII\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 66, + "outputs": [ + { + "data": { + "text/plain": "Empty DataFrame\nColumns: [Level, commodity, level, time, Value, value]\nIndex: []", + "text/html": "
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LevelcommodityleveltimeValuevalue
land_scenarioyear_all
\n
" + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#df_afr = df_lu.loc[\"R12_AFR\"]\n", + "df_afr[(df_afr[\"commodity\"]==\"Biodiversity|BII\")]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 50, + "outputs": [ + { + "data": { + "text/plain": "node year_all\nR12_AFR 2020 494.475077\n 2025 475.040000\n 2030 458.300000\n 2035 441.650000\n 2040 425.000000\n ... \nR12_WEU 2070 82.237854\n 2080 75.834860\n 2090 70.045717\n 2100 64.567990\n 2110 59.274784\nName: value, Length: 168, dtype: float64" + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_lu[(df_lu[\"commodity\"]==\"bioenergy\") & (df_lu[\"level\"]==\"land_use\")][\"value\"].groupby([\"node\", \"year_all\"]).sum()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [], + "source": [ + "df = gdxpds.to_dataframe(gdx_file, \"inv_cost\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [ + { + "ename": "KeyError", + "evalue": "'inv_cost'", + "output_type": "error", + "traceback": [ + "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[1;31mKeyError\u001B[0m Traceback (most recent call last)", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3621\u001B[0m, in \u001B[0;36mIndex.get_loc\u001B[1;34m(self, key, method, tolerance)\u001B[0m\n\u001B[0;32m 3620\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m-> 3621\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_engine\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_loc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcasted_key\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 3622\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m err:\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx:136\u001B[0m, in \u001B[0;36mpandas._libs.index.IndexEngine.get_loc\u001B[1;34m()\u001B[0m\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx:163\u001B[0m, in \u001B[0;36mpandas._libs.index.IndexEngine.get_loc\u001B[1;34m()\u001B[0m\n", + "File \u001B[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5198\u001B[0m, in \u001B[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001B[1;34m()\u001B[0m\n", + "File \u001B[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5206\u001B[0m, in \u001B[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001B[1;34m()\u001B[0m\n", + "\u001B[1;31mKeyError\u001B[0m: 'inv_cost'", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001B[1;31mKeyError\u001B[0m Traceback (most recent call last)", + "Input \u001B[1;32mIn [5]\u001B[0m, in \u001B[0;36m\u001B[1;34m()\u001B[0m\n\u001B[1;32m----> 1\u001B[0m df\u001B[38;5;241m=\u001B[39m\u001B[43mdf\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43minv_cost\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3505\u001B[0m, in \u001B[0;36mDataFrame.__getitem__\u001B[1;34m(self, key)\u001B[0m\n\u001B[0;32m 3503\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcolumns\u001B[38;5;241m.\u001B[39mnlevels \u001B[38;5;241m>\u001B[39m \u001B[38;5;241m1\u001B[39m:\n\u001B[0;32m 3504\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_getitem_multilevel(key)\n\u001B[1;32m-> 3505\u001B[0m indexer \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcolumns\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_loc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mkey\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 3506\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m is_integer(indexer):\n\u001B[0;32m 3507\u001B[0m indexer \u001B[38;5;241m=\u001B[39m [indexer]\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3623\u001B[0m, in \u001B[0;36mIndex.get_loc\u001B[1;34m(self, key, method, tolerance)\u001B[0m\n\u001B[0;32m 3621\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_engine\u001B[38;5;241m.\u001B[39mget_loc(casted_key)\n\u001B[0;32m 3622\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m err:\n\u001B[1;32m-> 3623\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m(key) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01merr\u001B[39;00m\n\u001B[0;32m 3624\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m:\n\u001B[0;32m 3625\u001B[0m \u001B[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001B[39;00m\n\u001B[0;32m 3626\u001B[0m \u001B[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001B[39;00m\n\u001B[0;32m 3627\u001B[0m \u001B[38;5;66;03m# the TypeError.\u001B[39;00m\n\u001B[0;32m 3628\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_check_indexing_error(key)\n", + "\u001B[1;31mKeyError\u001B[0m: 'inv_cost'" + ] + } + ], + "source": [ + "df=df[\"inv_cost\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 14, + "outputs": [ + { + "data": { + "text/plain": " node tec year_all Value\n0 R12_AFR LNG_exp 1995 235.0\n1 R12_AFR LNG_exp 2000 235.0\n2 R12_AFR LNG_exp 2005 235.0\n3 R12_AFR LNG_exp 2010 235.0\n4 R12_AFR LNG_exp 2015 235.0\n... ... ... ... ...\n66344 R12_CHN meth_exp 2070 235.0\n66345 R12_CHN meth_exp 2080 235.0\n66346 R12_CHN meth_exp 2090 235.0\n66347 R12_CHN meth_exp 2100 235.0\n66348 R12_CHN meth_exp 2110 235.0\n\n[66349 rows x 4 columns]", + "text/html": "
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nodetecyear_allValue
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66344R12_CHNmeth_exp2070235.0
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66349 rows × 4 columns

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" + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [], + "source": [ + "df.year_all = df.year_all.astype(\"int\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 17, + "outputs": [], + "source": [ + "df_nam = df[df[\"node\"]==\"R12_NAM\"].set_index([\"tec\", \"year_all\"])" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 26, + "outputs": [], + "source": [ + "df_merge = df.set_index([\"tec\", \"year_all\"]).merge(df_nam, left_index=True, right_index=True)\n", + "df_merge[\"ratio\"]=df_merge[\"Value_x\"] / df_merge[\"Value_y\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 33, + "outputs": [], + "source": [ + "df_merge = df_merge.reset_index()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 38, + "outputs": [ + { + "data": { + "text/plain": "(2018.0, 2055.0)" + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "tec_name = \"meth_bio\"\n", + "\n", + "import matplotlib.pyplot as plt\n", + "fig, ax = plt.subplots(figsize=(10,5))\n", + "legend = []\n", + "for reg in df_merge.node_x.unique():\n", + " df_merge[(df_merge[\"tec\"]==tec_name) & (df_merge[\"node_x\"]==reg) & (df_merge[\"year_all\"] > 2015)].plot(x=\"year_all\", y=\"ratio\",ax=ax)\n", + " legend.append(reg)\n", + "ax.legend(legend, ncol=3)\n", + "ax.set_title(f\"inv_cost - {tec_name}\", fontsize=16)\n", + "ax.set_xticks([i for i in range(2020, 2110, 10)])\n", + "ax.set_xlim(2018, 2055)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 12, + "outputs": [ + { + "data": { + "text/plain": "(2018.0, 2060.0)" + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "tec_name = \"meth_bio\"\n", + "\n", + "import matplotlib.pyplot as plt\n", + "fig, ax = plt.subplots(figsize=(10,5))\n", + "legend = []\n", + "for reg in df.node.unique():\n", + " df[(df[\"tec\"]==tec_name) & (df[\"node\"]==reg) & (df[\"year_all\"] > 2015)].plot(x=\"year_all\", y=\"Value\",ax=ax)\n", + " legend.append(reg)\n", + "ax.legend(legend, ncol=3)\n", + "ax.set_title(f\"inv_cost - {tec_name}\", fontsize=16)\n", + "ax.set_xticks([i for i in range(2020, 2110, 10)])\n", + "ax.set_xlim(2018, 2060)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [], + "source": [ + "hist_act = dataframes[\"historical_activity\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 15, + "outputs": [], + "source": [ + "#h\n", + "hist_act = hist_act[hist_act[\"year_all\"]==\"2015\"]\n", + "hist_act_rcpa = hist_act[hist_act[\"node\"]==\"R12_RCPA\"]\n", + "hist_act_chn = hist_act[hist_act[\"node\"]==\"R12_CHN\"]\n", + "hist_act_merge = hist_act_rcpa.merge(hist_act_chn, left_on=\"tec\", right_on=\"tec\")\n", + "hist_act_merge[\"ratio\"] = hist_act_merge[\"Value_x\"] / hist_act_merge[\"Value_y\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 21, + "outputs": [ + { + "data": { + "text/plain": " node_x tec year_all_x mode_x time_x Value_x \\\n35 R12_RCPA elec_trp 2015 M1 year 5.000000 \n54 R12_RCPA gas_extr_mpen 2015 M1 year 0.100000 \n81 R12_RCPA oil_exp 2015 M1 year 1.000000 \n85 R12_RCPA oil_extr_mpen 2015 M1 year 0.100000 \n156 R12_RCPA eaf_steel 2015 M2 year 0.893333 \n165 R12_RCPA catalytic_cracking_ref 2015 atm_gasoil year 5.953922 \n173 R12_RCPA import_petro 2015 M1 year 0.120969 \n174 R12_RCPA export_petro 2015 M1 year 0.004573 \n175 R12_RCPA gas_NH3 2015 M1 year 0.165746 \n176 R12_RCPA coal_NH3 2015 M1 year 0.834254 \n177 R12_RCPA export_NFert 2015 M1 year 0.041194 \n180 R12_RCPA meth_exp 2015 feedstock year 0.000272 \n181 R12_RCPA meth_exp 2015 feedstock year 0.000272 \n182 R12_RCPA meth_exp 2015 fuel year 0.000272 \n183 R12_RCPA meth_exp 2015 fuel year 0.000272 \n184 R12_RCPA meth_imp 2015 feedstock year 0.177914 \n185 R12_RCPA meth_imp 2015 feedstock year 0.177914 \n186 R12_RCPA meth_imp 2015 fuel year 0.177914 \n187 R12_RCPA meth_imp 2015 fuel year 0.177914 \n\n node_y year_all_y mode_y time_y Value_y ratio \n35 R12_CHN 2015 M1 year 54.270254 0.092132 \n54 R12_CHN 2015 M1 year 153.154140 0.000653 \n81 R12_CHN 2015 M1 year 51.318611 0.019486 \n85 R12_CHN 2015 M1 year 250.804477 0.000399 \n156 R12_CHN 2015 M1 year 72.360000 0.012346 \n165 R12_CHN 2015 vacuum_gasoil year 112.350506 0.052994 \n173 R12_CHN 2015 M1 year 18.270686 0.006621 \n174 R12_CHN 2015 M1 year 0.221271 0.020667 \n175 R12_CHN 2015 M1 year 9.662983 0.017153 \n176 R12_CHN 2015 M1 year 48.637017 0.017153 \n177 R12_CHN 2015 M1 year 20.080371 0.002051 \n180 R12_CHN 2015 feedstock year 0.096487 0.002823 \n181 R12_CHN 2015 fuel year 0.096487 0.002823 \n182 R12_CHN 2015 feedstock year 0.096487 0.002823 \n183 R12_CHN 2015 fuel year 0.096487 0.002823 \n184 R12_CHN 2015 feedstock year 3.405133 0.052249 \n185 R12_CHN 2015 fuel year 3.405133 0.052249 \n186 R12_CHN 2015 feedstock year 3.405133 0.052249 \n187 R12_CHN 2015 fuel year 3.405133 0.052249 ", + "text/html": "
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node_xtecyear_all_xmode_xtime_xValue_xnode_yyear_all_ymode_ytime_yValue_yratio
35R12_RCPAelec_trp2015M1year5.000000R12_CHN2015M1year54.2702540.092132
54R12_RCPAgas_extr_mpen2015M1year0.100000R12_CHN2015M1year153.1541400.000653
81R12_RCPAoil_exp2015M1year1.000000R12_CHN2015M1year51.3186110.019486
85R12_RCPAoil_extr_mpen2015M1year0.100000R12_CHN2015M1year250.8044770.000399
156R12_RCPAeaf_steel2015M2year0.893333R12_CHN2015M1year72.3600000.012346
165R12_RCPAcatalytic_cracking_ref2015atm_gasoilyear5.953922R12_CHN2015vacuum_gasoilyear112.3505060.052994
173R12_RCPAimport_petro2015M1year0.120969R12_CHN2015M1year18.2706860.006621
174R12_RCPAexport_petro2015M1year0.004573R12_CHN2015M1year0.2212710.020667
175R12_RCPAgas_NH32015M1year0.165746R12_CHN2015M1year9.6629830.017153
176R12_RCPAcoal_NH32015M1year0.834254R12_CHN2015M1year48.6370170.017153
177R12_RCPAexport_NFert2015M1year0.041194R12_CHN2015M1year20.0803710.002051
180R12_RCPAmeth_exp2015feedstockyear0.000272R12_CHN2015feedstockyear0.0964870.002823
181R12_RCPAmeth_exp2015feedstockyear0.000272R12_CHN2015fuelyear0.0964870.002823
182R12_RCPAmeth_exp2015fuelyear0.000272R12_CHN2015feedstockyear0.0964870.002823
183R12_RCPAmeth_exp2015fuelyear0.000272R12_CHN2015fuelyear0.0964870.002823
184R12_RCPAmeth_imp2015feedstockyear0.177914R12_CHN2015feedstockyear3.4051330.052249
185R12_RCPAmeth_imp2015feedstockyear0.177914R12_CHN2015fuelyear3.4051330.052249
186R12_RCPAmeth_imp2015fuelyear0.177914R12_CHN2015feedstockyear3.4051330.052249
187R12_RCPAmeth_imp2015fuelyear0.177914R12_CHN2015fuelyear3.4051330.052249
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" + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hist_act_merge[hist_act_merge[\"ratio\"]<0.111]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 38, + "outputs": [ + { + "data": { + "text/plain": " node commodity level year_all time Level Marginal \\\n0 R12_AFR NH3 final_material 2020 year 2.500000 0.0 \n1 R12_AFR NH3 final_material 2025 year 2.735064 0.0 \n2 R12_AFR NH3 final_material 2030 year 2.906112 0.0 \n3 R12_AFR NH3 final_material 2035 year 3.156448 0.0 \n4 R12_AFR NH3 final_material 2040 year 3.342780 0.0 \n... ... ... ... ... ... ... ... \n2060 R12_CHN fcoh_resin final_material 2060 year 0.282172 0.0 \n2061 R12_CHN fcoh_resin final_material 2070 year 0.215595 0.0 \n2062 R12_CHN fcoh_resin final_material 2080 year 0.094929 0.0 \n2063 R12_CHN fcoh_resin final_material 2090 year 0.030637 0.0 \n2064 R12_CHN fcoh_resin final_material 2100 year 0.011176 0.0 \n\n Lower Upper Scale \n0 4.000000e+300 3.000000e+300 1.0 \n1 4.000000e+300 3.000000e+300 1.0 \n2 4.000000e+300 3.000000e+300 1.0 \n3 4.000000e+300 3.000000e+300 1.0 \n4 4.000000e+300 3.000000e+300 1.0 \n... ... ... ... \n2060 4.000000e+300 3.000000e+300 1.0 \n2061 4.000000e+300 3.000000e+300 1.0 \n2062 4.000000e+300 3.000000e+300 1.0 \n2063 4.000000e+300 3.000000e+300 1.0 \n2064 4.000000e+300 3.000000e+300 1.0 \n\n[2065 rows x 10 columns]", + "text/html": "
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nodecommoditylevelyear_alltimeLevelMarginalLowerUpperScale
0R12_AFRNH3final_material2020year2.5000000.04.000000e+3003.000000e+3001.0
1R12_AFRNH3final_material2025year2.7350640.04.000000e+3003.000000e+3001.0
2R12_AFRNH3final_material2030year2.9061120.04.000000e+3003.000000e+3001.0
3R12_AFRNH3final_material2035year3.1564480.04.000000e+3003.000000e+3001.0
4R12_AFRNH3final_material2040year3.3427800.04.000000e+3003.000000e+3001.0
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2060R12_CHNfcoh_resinfinal_material2060year0.2821720.04.000000e+3003.000000e+3001.0
2061R12_CHNfcoh_resinfinal_material2070year0.2155950.04.000000e+3003.000000e+3001.0
2062R12_CHNfcoh_resinfinal_material2080year0.0949290.04.000000e+3003.000000e+3001.0
2063R12_CHNfcoh_resinfinal_material2090year0.0306370.04.000000e+3003.000000e+3001.0
2064R12_CHNfcoh_resinfinal_material2100year0.0111760.04.000000e+3003.000000e+3001.0
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2065 rows × 10 columns

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" + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hist_act = dataframes[\"DEMAND\"]\n", + "hist_act" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 70, + "outputs": [], + "source": [ + "#h\n", + "hist_act = hist_act[hist_act[\"year_all\"]==\"2020\"]\n", + "#hist_act = hist_act.set_index([\"commodity\", \"level\"])\n", + "hist_act_rcpa = hist_act[hist_act[\"node\"]==\"R12_RCPA\"]\n", + "hist_act_chn = hist_act[hist_act[\"node\"]==\"R12_CHN\"]\n", + "\n", + "hist_act_merge = hist_act_rcpa.join(hist_act_chn[\"Level\"], rsuffix=\"_x\")\n", + "hist_act_merge[\"ratio\"] = hist_act_merge[\"Level\"] / hist_act_merge[\"Level_x\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 71, + "outputs": [ + { + "data": { + "text/plain": " node year_all time Level Marginal \\\ncommodity level \nNH3 final_material R12_RCPA 2020 year 2.500000 0.0 \nmethanol final_material R12_RCPA 2020 year 0.204400 0.0 \ni_spec useful R12_RCPA 2020 year 16.366848 0.0 \ni_therm useful R12_RCPA 2020 year 25.932783 0.0 \nnon-comm useful R12_RCPA 2020 year 2.142900 0.0 \nrc_spec useful R12_RCPA 2020 year 9.493912 0.0 \nrc_therm useful R12_RCPA 2020 year 34.041000 0.0 \ntransport useful R12_RCPA 2020 year 55.257000 0.0 \nsteel demand R12_RCPA 2020 year 12.083479 0.0 \ncement demand R12_RCPA 2020 year 237.872109 0.0 \naluminum demand R12_RCPA 2020 year 1.999989 0.0 \nHVC demand R12_RCPA 2020 year 0.440000 0.0 \nfcoh_resin final_material R12_RCPA 2020 year 0.058829 0.0 \n\n Lower Upper Scale Level_x \\\ncommodity level \nNH3 final_material 4.000000e+300 3.000000e+300 1.0 18.000000 \nmethanol final_material 4.000000e+300 3.000000e+300 1.0 24.732400 \ni_spec useful 4.000000e+300 3.000000e+300 1.0 147.301632 \ni_therm useful 4.000000e+300 3.000000e+300 1.0 233.395048 \nnon-comm useful 4.000000e+300 3.000000e+300 1.0 19.286100 \nrc_spec useful 4.000000e+300 3.000000e+300 1.0 85.445212 \nrc_therm useful 4.000000e+300 3.000000e+300 1.0 306.369000 \ntransport useful 4.000000e+300 3.000000e+300 1.0 497.313000 \nsteel demand 4.000000e+300 3.000000e+300 1.0 980.023775 \ncement demand 4.000000e+300 3.000000e+300 1.0 2140.848982 \naluminum demand 4.000000e+300 3.000000e+300 1.0 25.998916 \nHVC demand 4.000000e+300 3.000000e+300 1.0 50.500000 \nfcoh_resin final_material 4.000000e+300 3.000000e+300 1.0 0.724910 \n\n ratio \ncommodity level \nNH3 final_material 0.138889 \nmethanol final_material 0.008264 \ni_spec useful 0.111111 \ni_therm useful 0.111111 \nnon-comm useful 0.111111 \nrc_spec useful 0.111111 \nrc_therm useful 0.111111 \ntransport useful 0.111111 \nsteel demand 0.012330 \ncement demand 0.111111 \naluminum demand 0.076926 \nHVC demand 0.008713 \nfcoh_resin final_material 0.081154 ", + "text/html": "
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nodeyear_alltimeLevelMarginalLowerUpperScaleLevel_xratio
commoditylevel
NH3final_materialR12_RCPA2020year2.5000000.04.000000e+3003.000000e+3001.018.0000000.138889
methanolfinal_materialR12_RCPA2020year0.2044000.04.000000e+3003.000000e+3001.024.7324000.008264
i_specusefulR12_RCPA2020year16.3668480.04.000000e+3003.000000e+3001.0147.3016320.111111
i_thermusefulR12_RCPA2020year25.9327830.04.000000e+3003.000000e+3001.0233.3950480.111111
non-commusefulR12_RCPA2020year2.1429000.04.000000e+3003.000000e+3001.019.2861000.111111
rc_specusefulR12_RCPA2020year9.4939120.04.000000e+3003.000000e+3001.085.4452120.111111
rc_thermusefulR12_RCPA2020year34.0410000.04.000000e+3003.000000e+3001.0306.3690000.111111
transportusefulR12_RCPA2020year55.2570000.04.000000e+3003.000000e+3001.0497.3130000.111111
steeldemandR12_RCPA2020year12.0834790.04.000000e+3003.000000e+3001.0980.0237750.012330
cementdemandR12_RCPA2020year237.8721090.04.000000e+3003.000000e+3001.02140.8489820.111111
aluminumdemandR12_RCPA2020year1.9999890.04.000000e+3003.000000e+3001.025.9989160.076926
HVCdemandR12_RCPA2020year0.4400000.04.000000e+3003.000000e+3001.050.5000000.008713
fcoh_resinfinal_materialR12_RCPA2020year0.0588290.04.000000e+3003.000000e+3001.00.7249100.081154
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" + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hist_act_merge" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "hist_act_merge[hist_act_merge[\"ratio\"]<0.111]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 18, + "outputs": [ + { + "data": { + "text/plain": " tec ratio\n85 oil_extr_mpen 0.000399\n54 gas_extr_mpen 0.000653\n177 export_NFert 0.002051\n183 meth_exp 0.002823\n182 meth_exp 0.002823\n.. ... ...\n166 catalytic_cracking_ref 0.232963\n142 extract__freshwater_supply 0.250000\n179 import_NH3 0.331096\n155 eaf_steel 1.000000\n178 import_NFert 49.573951\n\n[188 rows x 2 columns]", + "text/html": "
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tecratio
85oil_extr_mpen0.000399
54gas_extr_mpen0.000653
177export_NFert0.002051
183meth_exp0.002823
182meth_exp0.002823
.........
166catalytic_cracking_ref0.232963
142extract__freshwater_supply0.250000
179import_NH30.331096
155eaf_steel1.000000
178import_NFert49.573951
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188 rows × 2 columns

\n
" + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hist_act_merge[[\"tec\", \"ratio\"]].sort_values(\"ratio\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [], + "source": [ + "act = dataframes[\"ACT\"].drop([\"time\", \"Marginal\", \"Lower\", \"Upper\", \"Scale\"], axis=1)\n", + "act = act[act[\"Level\"] != 0]\n", + "act = act.rename({\"vintage\":\"year_vtg\", \"year_all\":\"year_act\", \"node\":\"node_loc\"}, axis=1)\n", + "act = act.set_index([\"node_loc\", \"year_act\", \"tec\", \"mode\", \"year_vtg\"])" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [], + "source": [ + "inp = dataframes[\"input\"].drop(\"time\", axis=1)\n", + "node_cols = inp[\"node\"]\n", + "node_cols.columns = [\"node_loc\", \"node_origin\"]\n", + "inp[\"node_loc\"] = node_cols[\"node_loc\"]\n", + "inp[\"node_origin\"] = node_cols[\"node_origin\"]\n", + "inp = inp.rename({\"vintage\":\"year_vtg\", \"year_all\":\"year_act\"}, axis=1)\n", + "inp = inp.drop(\"node\", axis=1)\n", + "inp = inp.set_index([\"node_loc\", \"year_act\", \"tec\", \"mode\", \"year_vtg\"])" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [], + "source": [ + "act_in = act.join(inp)\n", + "act_in = act_in.dropna()\n", + "act_in[\"input\"] = act_in[\"Level\"] * act_in[\"Value\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [], + "source": [ + "out = dataframes[\"output\"].drop(\"time\", axis=1)\n", + "node_cols = out[\"node\"]\n", + "node_cols.columns = [\"node_loc\", \"node_dest\"]\n", + "out[\"node_loc\"] = node_cols[\"node_loc\"]\n", + "out[\"node_origin\"] = node_cols[\"node_dest\"]\n", + "out = out.rename({\"vintage\":\"year_vtg\", \"year_all\":\"year_act\"}, axis=1)\n", + "out = out.drop(\"node\", axis=1)\n", + "out = out.set_index([\"node_loc\", \"year_act\", \"year_vtg\", \"tec\", \"mode\"])" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [], + "source": [ + "act_out = act.join(out)\n", + "act_out = act_out.dropna()\n", + "act_out[\"output\"] = act_out[\"Level\"] * act_out[\"Value\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 8, + "outputs": [], + "source": [ + "rel = dataframes[\"relation_activity\"]\n", + "node_cols = rel[\"node\"]\n", + "node_cols.columns = [\"node_loc\", \"node_dest\"]\n", + "rel[\"node_loc\"] = node_cols[\"node_loc\"]\n", + "rel[\"node_origin\"] = node_cols[\"node_dest\"]\n", + "\n", + "year_cols = rel[\"year_all\"]\n", + "year_cols.columns = [\"year_act\", \"year_rel\"]\n", + "rel[\"year_act\"] = year_cols[\"year_act\"]\n", + "rel[\"year_rel\"] = year_cols[\"year_rel\"]\n", + "\n", + "rel = rel.drop(\"node\", axis=1)\n", + "rel = rel.drop(\"year_all\", axis=1)\n", + "rel = rel.set_index([\"node_loc\", \"year_act\", \"tec\", \"mode\"])" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 9, + "outputs": [], + "source": [ + "rel_co2emi = rel[rel[\"relation\"]==\"CO2_Emission\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 10, + "outputs": [], + "source": [ + "emi_top_down = rel_co2emi.join(act).dropna()\n", + "emi_top_down[\"emi\"] = emi_top_down[\"Value\"] * emi_top_down[\"Level\"] #<" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 11, + "outputs": [], + "source": [ + "emi_bott_up_names = [\"CO2_ind\", \"CO2_r_c\", \"CO2_trp\", \"CO2_trade\", \"CO2_shipping\", \"CO2_cc\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 12, + "outputs": [], + "source": [ + "emi_bott_up = rel[rel[\"relation\"].isin(emi_bott_up_names).values]\n", + "emi_bott_up = emi_bott_up.join(act).dropna()\n", + "emi_bott_up[\"emi\"] = emi_bott_up[\"Value\"] * emi_bott_up[\"Level\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 38, + "outputs": [ + { + "data": { + "text/plain": " Level\ntec \nbiomass_i 167.172537\ncoal_i 646.478318\nelec_i 87.922704\nfoil_i 100.367546\ngas_i 569.083035\nheat_i 176.669585\nhp_gas_i 5.982707\nloil_i 96.592091\nsolar_i 96.415719", + "text/html": "
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Level
tec
biomass_i167.172537
coal_i646.478318
elec_i87.922704
foil_i100.367546
gas_i569.083035
heat_i176.669585
hp_gas_i5.982707
loil_i96.592091
solar_i96.415719
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2035R12_AFRM1203513.711018
2040R12_AFRM1204014.759977
..................
meth_imp2035R12_CHNfeedstock203524.245616
2040R12_CHNfeedstock204031.090559
2045R12_CHNfeedstock204525.668182
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66620 rows × 1 columns

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" + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "act.index = act.index.swaplevel(0, 2)\n", + "act" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Level\n", + "year_act \n", + "2020 3215.410027\n", + "2025 3639.927256\n", + "2030 4032.417858\n", + "2035 4384.430552\n", + "2040 4582.811404\n", + "2045 4625.292203\n", + "2050 4473.551896\n", + "2055 4183.626996\n", + "2060 3755.588088\n", + "2070 2300.091380\n", + "2080 1324.577029\n", + "2090 857.266707\n", + "2100 796.318673\n", + "2110 1468.300602\n", + " Level\n", + "year_act \n", + "2020 39.794360\n", + "2025 68.371773\n", + "2030 116.419351\n", + "2035 192.174476\n", + "2040 317.179604\n", + "2045 476.168020\n", + "2050 703.454829\n", + "2055 1018.044083\n", + "2060 1447.998813\n", + "2070 2065.563856\n", + "2080 2384.014642\n", + "2090 3410.512507\n", + "2100 4269.243252\n", + "2110 5530.804486\n" + ] + }, + { + "data": { + "text/plain": "" + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "fig, ax = plt.subplots()\n", + "legend = []\n", + "for i in [\"loil_trp\", \"foil_trp\"]:\n", + " print(act.loc[i].groupby([\"year_act\"]).sum())\n", + " act.loc[i].groupby([\"year_act\"]).sum().reset_index().plot(ax=ax, x=\"year_act\", y=\"Level\")\n", + " legend.append(i)\n", + "ax.legend(legend)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## identify missing relations" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 16, + "outputs": [], + "source": [ + "rel[\"count\"] = 1\n", + "tecs = list(rel[rel.index.get_level_values(2).str.startswith(\"meth_\")].index.get_level_values(2).unique())\n", + "\n", + "meth_rel = rel.swaplevel(0,2).loc[tecs]\n", + "counts = meth_rel[~meth_rel.index.get_level_values(1).isin([\"1990\", \"1995\", \"2000\", \"2005\", \"2010\", \"2015\"])].groupby([\"tec\",\"year_act\", \"node_loc\", \"relation\"]).sum()[\"count\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 17, + "outputs": [], + "source": [ + "fuel = meth_rel.swaplevel(0,3).loc[\"fuel\"]\n", + "fuel[\"df\"] = \"fuel\"\n", + "\n", + "m1 = meth_rel.swaplevel(0,3).loc[\"M1\"]\n", + "m1[\"df\"] = \"M1\"\n", + "\n", + "w_labels = pd.concat([fuel, m1]).reset_index()\n", + "wo_labels = pd.concat([fuel.drop(\"df\", axis=1), m1.drop(\"df\", axis=1)]).reset_index()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 18, + "outputs": [ + { + "data": { + "text/plain": " year_act node_loc tec relation Value node_origin year_rel \\\n0 1990 R12_GLB meth_trd CO2_trade 0.005545 R12_GLB 1990 \n1 1995 R12_GLB meth_trd CO2_trade 0.005545 R12_GLB 1995 \n2 2000 R12_GLB meth_trd CO2_trade 0.005545 R12_GLB 2000 \n3 2005 R12_GLB meth_trd CO2_trade 0.005545 R12_GLB 2005 \n4 2010 R12_GLB meth_trd CO2_trade 0.005545 R12_GLB 2010 \n... ... ... ... ... ... ... ... \n13679 2070 R12_CHN meth_t_d FE_liquids 1.000000 R12_CHN 2070 \n13680 2080 R12_CHN meth_t_d FE_liquids 1.000000 R12_CHN 2080 \n13681 2090 R12_CHN meth_t_d FE_liquids 1.000000 R12_CHN 2090 \n13682 2100 R12_CHN meth_t_d FE_liquids 1.000000 R12_CHN 2100 \n13683 2110 R12_CHN meth_t_d FE_liquids 1.000000 R12_CHN 2110 \n\n count df \n0 1 fuel \n1 1 fuel \n2 1 fuel \n3 1 fuel \n4 1 fuel \n... ... ... \n13679 1 fuel \n13680 1 fuel \n13681 1 fuel \n13682 1 fuel \n13683 1 fuel \n\n[13684 rows x 9 columns]", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
year_actnode_loctecrelationValuenode_originyear_relcountdf
01990R12_GLBmeth_trdCO2_trade0.005545R12_GLB19901fuel
11995R12_GLBmeth_trdCO2_trade0.005545R12_GLB19951fuel
22000R12_GLBmeth_trdCO2_trade0.005545R12_GLB20001fuel
32005R12_GLBmeth_trdCO2_trade0.005545R12_GLB20051fuel
42010R12_GLBmeth_trdCO2_trade0.005545R12_GLB20101fuel
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136792070R12_CHNmeth_t_dFE_liquids1.000000R12_CHN20701fuel
136802080R12_CHNmeth_t_dFE_liquids1.000000R12_CHN20801fuel
136812090R12_CHNmeth_t_dFE_liquids1.000000R12_CHN20901fuel
136822100R12_CHNmeth_t_dFE_liquids1.000000R12_CHN21001fuel
136832110R12_CHNmeth_t_dFE_liquids1.000000R12_CHN21101fuel
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13684 rows × 9 columns

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" + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "diff = wo_labels.drop_duplicates(keep=False).merge(w_labels[\"df\"], left_index=True, right_index=True)\n", + "diff[diff[\"df\"]==\"fuel\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## co2 emissions analysis" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 19, + "outputs": [], + "source": [ + "def get_bot_up_emi_by_fuel(df, node, year):\n", + " #df = df.loc[node, year]\n", + "\n", + " df_oil = df[df.index.get_level_values(0).str.contains(\"oil\") |\n", + " (df.index.get_level_values(0).str.contains(\"liq\")) |\n", + " (df.index.get_level_values(0).str.contains(\"treat\")) |\n", + " (df.index.get_level_values(0).str.contains(\"coki\")) |\n", + " (df.index.get_level_values(0).str.contains(\"cata\"))\n", + " ].sort_values(\"emi\")\n", + " df_oil = df_oil[df_oil.index.get_level_values(0) != \"liq_bio_ccs\"]\n", + "\n", + " df_gas = df[(\n", + " (df.index.get_level_values(0).str.contains(\"gas\")) |\n", + " (df.index.get_level_values(0).str.contains(\"meth_ng\")) |\n", + " (df.index.get_level_values(0).str.contains(\"smr\")) |\n", + " (df.index.get_level_values(0) == \"h2_mix\") |\n", + " (df.index.get_level_values(0).str.contains(\"flar\"))\n", + " )].sort_values(\"emi\")\n", + "\n", + " df_coal = df[(df.index.get_level_values(0).str.contains(\"coal\")) | (df.index.get_level_values(0).str.contains(\"igcc\"))].sort_values(\"emi\")\n", + "\n", + " return df_gas, df_oil, df_coal" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 20, + "outputs": [], + "source": [ + "def get_top_dn_emi_by_fuel(df, node, year):\n", + " #df = df.loc[node, year]\n", + "\n", + " top_gas = df[((df.index.get_level_values(0).str.contains(\"gas\")) |\n", + " (df.index.get_level_values(0).str.contains(\"LNG\")) |\n", + " (df.index.get_level_values(1).str.contains(\"ane\")) |\n", + " (df.index.get_level_values(0).str.contains(\"h2_smr\")) |\n", + " (df.index.get_level_values(0).str.contains(\"flar\")))]#.sum()\n", + "\n", + " top_oil = df[(df.index.get_level_values(0).str.contains(\"oil\"))\n", + " |(df.index.get_level_values(1).str.contains(\"gasoil\"))].sort_values(\"emi\")#.sum()\n", + " #top_oil = top_oil[top_oil.index.get_level_values(0) != \"liq_bio_ccs\"]\n", + " top_coal = df[(df.index.get_level_values(0).str.contains(\"coal\")) |\n", + " (df.index.get_level_values(0).str.contains(\"lign\"))].sort_values(\"emi\")#.sum()\n", + "\n", + " return top_gas, top_oil, top_coal" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 21, + "outputs": [], + "source": [ + "elec_list = [\n", + " \"coal_ppl_u\",\n", + " \"coal_ppl\",\n", + " \"coal_adv\",\n", + " \"coal_adv_ccs\",\n", + " \"igcc\",\n", + " \"igcc_ccs\",\n", + " \"foil_ppl\",\n", + " \"loil_ppl\",\n", + " \"loil_cc\",\n", + " \"oil_ppl\",\n", + " \"gas_ppl\",\n", + " \"gas_cc\",\n", + " \"gas_cc_ccs\",\n", + " \"gas_ct\",\n", + " \"gas_htfc\",\n", + " \"bio_istig\",\n", + " \"g_ppl_co2scr\",\n", + " \"c_ppl_co2scr\",\n", + " \"bio_ppl_co2scr\",\n", + " \"igcc_co2scr\",\n", + " \"gfc_co2scr\",\n", + " \"cfc_co2scr\",\n", + " \"bio_istig_ccs\",\n", + " ]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 22, + "outputs": [], + "source": [ + "region = \"R12_LAM\"\n", + "year = \"2020\"" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## get global emissions for specific year" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 23, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\maczek\\AppData\\Local\\Temp\\ipykernel_17352\\2128802406.py:7: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " emi_bot_up_new = emi_bot_up_new.append(emi_top_down_new[emi_top_down_new.index.get_level_values(0) == \"meth_bio_ccs\"])\n" + ] + } + ], + "source": [ + "emi_top_down_new = emi_top_down.reset_index().set_index([\"tec\", \"mode\"])\n", + "emi_bot_up_new = emi_bott_up.reset_index().set_index([\"tec\", \"mode\"])\n", + "\n", + "emi_top_down_new = emi_top_down_new[emi_top_down_new[\"year_act\"]==year]\n", + "emi_bot_up_new = emi_bot_up_new[emi_bot_up_new[\"year_act\"]==year]\n", + "\n", + "emi_bot_up_new = emi_bot_up_new.append(emi_top_down_new[emi_top_down_new.index.get_level_values(0) == \"meth_bio_ccs\"])\n", + "emi_top_down_new = emi_top_down_new.drop(\"CO2_TCE\")\n", + "emi_bot_up_new = emi_bot_up_new.drop([\"CO2t_TCE\", \"CO2s_TCE\"])\n", + "\n", + "emi_bot_up_new = pd.concat([emi_bot_up_new, emi_top_down_new[emi_top_down_new.index.get_level_values(0).str.contains(\"alu\")]])\n", + "emi_bot_up_new = pd.concat([emi_bot_up_new, emi_top_down_new[emi_top_down_new.index.get_level_values(0).str.contains(\"clink\")]])\n", + "\n", + "top_new_gas, top_new_oil, top_new_coal = get_top_dn_emi_by_fuel(emi_top_down_new[emi_top_down_new[\"year_act\"]==year], \"test\", \"test\")\n", + "bot_new_gas, bot_new_oil, bot_new_coal = get_bot_up_emi_by_fuel(emi_bot_up_new[emi_bot_up_new[\"year_act\"]==year], \"test\", \"test\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 24, + "outputs": [], + "source": [ + "oil_tecs = ['hydrotreating_ref', 'catalytic_reforming_ref', 'oil_extr_1_ch4',\n", + " 'furnace_foil_petro', 'loil_t_d', 'oil_extr_2_ch4', 'foil_hpl',\n", + " 'foil_t_d', 'furnace_foil_refining', 'oil_extr_2', 'oil_extr_1',\n", + " 'oil_extr_3', 'furnace_foil_aluminum', 'loil_rc', 'oil_extr_3_ch4',\n", + " 'furnace_foil_cement', 'oil_extr_4_ch4', 'foil_trd', 'oil_trd',\n", + "\n", + " 'fueloil_NH3', 'fueloil_NH3_ccs', 'loil_i', 'foil_i', 'foil_trp', 'loil_trp', 'loil_bunker',\n", + " 'foil_bunker', 'oil_ppl', 'loil_ppl', 'loil_cc', 'foil_rc', 'foil_ppl',\n", + " 'catalytic_cracking_ref', 'loil_trd', 'oil_extr_4', \"coking_ref\", 'LNG_bunker', 'sp_liq_I']\n", + "bot_new_oil = emi_bot_up_new[(emi_bot_up_new.index.get_level_values(0).isin(oil_tecs))]\n", + "\n", + "coal_tecs = ['furnace_coal_aluminum', 'coal_hpl', 'furnace_coal_refining',\n", + " 'furnace_coal_cement', 'DUMMY_coal_supply', 'h2_coal_ccs',\n", + " 'coal_NH3_ccs',\n", + "\n", + " 'coal_ppl', 'coal_ppl_u', 'meth_coal', 'meth_coal_ccs', 'coal_rc', 'coal_NH3', 'coal_adv', 'coal_i', 'coal_gas'\n", + " ]\n", + "bot_new_coal = emi_bot_up_new[(emi_bot_up_new.index.get_level_values(0).isin(coal_tecs))]\n", + "\n", + "gas = ['h2_mix', 'gas_t_d', 'gas_hpl', 'furnace_gas_petro', 'gas_extr_2',\n", + " 'gas_extr_1', 'gas_t_d_ch4', 'gas_NH3_ccs', 'furnace_gas_refining',\n", + " 'gas_extr_3', 'DUMMY_gas_supply', 'gas_cc', 'gas_extr_4', 'gas_extr_5',\n", + " 'gas_extr_6', 'gas_i', 'gas_trp',\n", + "\n", + " 'gas_NH3','gas_cc_ccs', 'hp_gas_rc', 'hp_gas_i', 'gas_rc', 'gas_ct',\n", + " 'gas_ppl', 'meth_ng_ccs', 'h2_smr_ccs', 'meth_ng', 'LNG_trd', 'h2_smr',\n", + "]\n", + "bot_new_gas = emi_bot_up_new[(emi_bot_up_new.index.get_level_values(0).isin(gas))]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 25, + "outputs": [], + "source": [ + "top_new_trd = emi_top_down_new[(emi_top_down_new.index.get_level_values(0).str.contains(\"exp\")) | (emi_top_down_new.index.get_level_values(0).str.contains(\"imp\"))]\n", + "bot_new_trd = emi_bot_up_new[(emi_bot_up_new.index.get_level_values(0).str.contains(\"exp\")) | (emi_bot_up_new.index.get_level_values(0).str.contains(\"imp\"))]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## residual carbon emissions" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 26, + "outputs": [], + "source": [ + "bot_new_rest = emi_bot_up_new[~(emi_bot_up_new.index.get_level_values(0).isin(bot_new_coal.index.get_level_values(0))) &\n", + " ~(emi_bot_up_new.index.get_level_values(0).isin(bot_new_oil.index.get_level_values(0))) &\n", + " ~(emi_bot_up_new.index.get_level_values(0).isin(bot_new_gas.index.get_level_values(0)))\n", + "].sort_values(\"emi\")\n", + "\n", + "top_new_rest = emi_top_down_new[~(emi_top_down_new.index.get_level_values(0).isin(top_new_oil.index.get_level_values(0))) &\n", + " ~(emi_top_down_new.index.get_level_values(0).isin(top_new_gas.index.get_level_values(0))) &\n", + " ~(emi_top_down_new.index.get_level_values(0).isin(top_new_coal.index.get_level_values(0))) &\n", + " ~(emi_top_down_new.index.get_level_values(0).isin(top_new_trd.index.get_level_values(0)))\n", + "].sort_values(\"emi\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 27, + "outputs": [ + { + "data": { + "text/plain": "211.23699782794165" + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bot_new_rest.sum()[\"emi\"] * (44/12) - top_new_rest.sum()[\"emi\"] * (44/12)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 28, + "outputs": [ + { + "data": { + "text/plain": "423.78991342878" + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "emi_bot_up_new.sum().emi * (44/12) - emi_top_down_new.sum().emi * (44/12)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 29, + "outputs": [ + { + "data": { + "text/plain": "-12.097574083883933" + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "top_new_rest[top_new_rest[\"emi\"]<0].loc[\"meth_t_d\"].sum()[\"emi\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 30, + "outputs": [ + { + "data": { + "text/plain": "-44.35777164090775" + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "top_new_rest.loc[\"meth_t_d\"].sum().emi * co2_c_factor\n", + "#top_new_rest.loc[\"clinker_dry_cement\"].sum().emi * co2_c_factor" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 31, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index([], dtype='object', name='tec')\n", + "Index(['meth_t_d'], dtype='object', name='tec')\n", + "Index(['CH2O_synth', 'soderberg_aluminum', 'eth_t_d', 'flaring_CO2',\n", + " 'prebake_aluminum', 'meth_trd', 'furnace_methanol_cement',\n", + " 'DUMMY_limestone_supply_steel', 'MTO_petro', 'h2_coal',\n", + " 'clinker_wet_cement', 'clinker_dry_cement', 'igcc'],\n", + " dtype='object', name='tec')\n", + "Index(['soderberg_aluminum', 'prebake_aluminum', 'clinker_wet_cement',\n", + " 'clinker_dry_cement'],\n", + " dtype='object', name='tec')\n" + ] + } + ], + "source": [ + "print(bot_new_rest[bot_new_rest[\"emi\"]<0].drop_duplicates().index.get_level_values(0).unique())\n", + "print(top_new_rest[top_new_rest[\"emi\"]<0].drop_duplicates().index.get_level_values(0).unique())\n", + "print(bot_new_rest[bot_new_rest[\"emi\"]>0].drop_duplicates().index.get_level_values(0).unique())\n", + "print(top_new_rest[top_new_rest[\"emi\"]>0].drop_duplicates().index.get_level_values(0).unique())" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 32, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "negative residual emission bottom-up: 0.0\n", + "negative residual emission top-down: -44.35777164090775\n", + "positive residual emission bottom-up: 2137.403984064366\n", + "positive residual emission top-down: 1970.5247578773321\n" + ] + } + ], + "source": [ + "print(\"negative residual emission bottom-up: \", bot_new_rest[bot_new_rest[\"emi\"]<0].drop_duplicates().sum()[\"emi\"] * (44/12))\n", + "print(\"negative residual emission top-down: \", top_new_rest[top_new_rest[\"emi\"]<0].sum()[\"emi\"] * (44/12))\n", + "print(\"positive residual emission bottom-up: \", bot_new_rest[bot_new_rest[\"emi\"]>0].sum()[\"emi\"] * (44/12))\n", + "print(\"positive residual emission top-down: \", top_new_rest[top_new_rest[\"emi\"]>0].sum()[\"emi\"] * (44/12))" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## check trade carbon balances" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 33, + "outputs": [ + { + "data": { + "text/plain": " node_loc year_act year_vtg relation Value node_origin \\\ntec mode \nloil_exp M1 R12_AFR 2020 2015 CO2_Emission -0.631 R12_AFR \nloil_imp M1 R12_AFR 2020 2020 CO2_Emission 0.631 R12_AFR \n M1 R12_CHN 2020 2020 CO2_Emission 0.631 R12_CHN \nloil_exp M1 R12_FSU 2020 2000 CO2_Emission -0.631 R12_FSU \n M1 R12_FSU 2020 2005 CO2_Emission -0.631 R12_FSU \n M1 R12_FSU 2020 2015 CO2_Emission -0.631 R12_FSU \n M1 R12_FSU 2020 2020 CO2_Emission -0.631 R12_FSU \n M1 R12_LAM 2020 2015 CO2_Emission -0.631 R12_LAM \n M1 R12_MEA 2020 2000 CO2_Emission -0.631 R12_MEA \n M1 R12_MEA 2020 2005 CO2_Emission -0.631 R12_MEA \n M1 R12_MEA 2020 2010 CO2_Emission -0.631 R12_MEA \n M1 R12_MEA 2020 2020 CO2_Emission -0.631 R12_MEA \n M1 R12_NAM 2020 2000 CO2_Emission -0.631 R12_NAM \n M1 R12_NAM 2020 2015 CO2_Emission -0.631 R12_NAM \nloil_imp M1 R12_NAM 2020 2020 CO2_Emission 0.631 R12_NAM \n M1 R12_PAO 2020 2020 CO2_Emission 0.631 R12_PAO \n M1 R12_RCPA 2020 2020 CO2_Emission 0.631 R12_RCPA \nloil_exp M1 R12_SAS 2020 2015 CO2_Emission -0.631 R12_SAS \nloil_imp M1 R12_SAS 2020 2020 CO2_Emission 0.631 R12_SAS \n M1 R12_WEU 2020 2020 CO2_Emission 0.631 R12_WEU \n\n year_rel Level emi \ntec mode \nloil_exp M1 2020 0.399258 -0.251931 \nloil_imp M1 2020 47.653442 30.069322 \n M1 2020 39.268683 24.778539 \nloil_exp M1 2020 10.142300 -6.399791 \n M1 2020 40.339789 -25.454407 \n M1 2020 13.855435 -8.742779 \n M1 2020 39.340764 -24.824022 \n M1 2020 40.069433 -25.283812 \n M1 2020 51.848149 -32.716182 \n M1 2020 23.071599 -14.558179 \n M1 2020 51.197000 -32.305307 \n M1 2020 127.024324 -80.152349 \n M1 2020 10.000000 -6.310000 \n M1 2020 54.575404 -34.437080 \nloil_imp M1 2020 302.049754 190.593395 \n M1 2020 9.866751 6.225920 \n M1 2020 5.307703 3.349160 \nloil_exp M1 2020 15.521467 -9.794046 \nloil_imp M1 2020 33.309633 21.018378 \n M1 2020 23.414262 14.774399 ", + "text/html": "
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node_locyear_actyear_vtgrelationValuenode_originyear_relLevelemi
tecmode
loil_expM1R12_AFR20202015CO2_Emission-0.631R12_AFR20200.399258-0.251931
loil_impM1R12_AFR20202020CO2_Emission0.631R12_AFR202047.65344230.069322
M1R12_CHN20202020CO2_Emission0.631R12_CHN202039.26868324.778539
loil_expM1R12_FSU20202000CO2_Emission-0.631R12_FSU202010.142300-6.399791
M1R12_FSU20202005CO2_Emission-0.631R12_FSU202040.339789-25.454407
M1R12_FSU20202015CO2_Emission-0.631R12_FSU202013.855435-8.742779
M1R12_FSU20202020CO2_Emission-0.631R12_FSU202039.340764-24.824022
M1R12_LAM20202015CO2_Emission-0.631R12_LAM202040.069433-25.283812
M1R12_MEA20202000CO2_Emission-0.631R12_MEA202051.848149-32.716182
M1R12_MEA20202005CO2_Emission-0.631R12_MEA202023.071599-14.558179
M1R12_MEA20202010CO2_Emission-0.631R12_MEA202051.197000-32.305307
M1R12_MEA20202020CO2_Emission-0.631R12_MEA2020127.024324-80.152349
M1R12_NAM20202000CO2_Emission-0.631R12_NAM202010.000000-6.310000
M1R12_NAM20202015CO2_Emission-0.631R12_NAM202054.575404-34.437080
loil_impM1R12_NAM20202020CO2_Emission0.631R12_NAM2020302.049754190.593395
M1R12_PAO20202020CO2_Emission0.631R12_PAO20209.8667516.225920
M1R12_RCPA20202020CO2_Emission0.631R12_RCPA20205.3077033.349160
loil_expM1R12_SAS20202015CO2_Emission-0.631R12_SAS202015.521467-9.794046
loil_impM1R12_SAS20202020CO2_Emission0.631R12_SAS202033.30963321.018378
M1R12_WEU20202020CO2_Emission0.631R12_WEU202023.41426214.774399
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" + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "emi_top_down_new[emi_top_down_new.index.get_level_values(0).str.contains(\"loil\")]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 34, + "outputs": [ + { + "data": { + "text/plain": "\"['gas_exp_chn', 'gas_exp_cpa', 'gas_exp_eeu', 'gas_exp_sas', 'gas_exp_weu', 'gas_imp', 'loil_exp']\"" + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "trd_tecs = sorted(list(emi_top_down_new.loc[(emi_top_down_new.index.get_level_values(0).str.contains(\"exp\")) | (emi_top_down_new.index.get_level_values(0).str.contains(\"imp\")) | (emi_top_down_new.index.get_level_values(0).str.contains(\"LNG\"))].index.get_level_values(0).unique()))#.sum()\n", + "comm_trd = [*trd_tecs[6:13]]#, trd_tecs[0]]\n", + "str(comm_trd)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 35, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total top-down: 10106.387931650237\n" + ] + } + ], + "source": [ + "print(\"total top-down: \", emi_top_down_new.sum()[\"emi\"])" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 36, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "top-down trade balance: -309.90262543642564\n" + ] + } + ], + "source": [ + "print(\"top-down trade balance: \", emi_top_down_new.loc[(emi_top_down_new.index.get_level_values(0).isin(comm_trd))].sum()[\"emi\"])" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 37, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " top-down bottom-up\n", + "coal: 15310.756509423185 15367.135755810177\n", + "oil: 12224.570955760117 12550.335173360832\n", + "gas: 7622.870492500809 7425.670749577608\n", + "total: 35158.19795768411 35343.141678748616\n" + ] + } + ], + "source": [ + "print(\" top-down bottom-up\")\n", + "print(\"coal: \", top_new_coal.sum()[\"emi\"] * co2_c_factor,\" \", bot_new_coal.sum()[\"emi\"] * co2_c_factor)\n", + "print(\"oil: \", top_new_oil.sum()[\"emi\"] * co2_c_factor, \" \", bot_new_oil.sum()[\"emi\"] * co2_c_factor)\n", + "print(\"gas: \", top_new_gas.sum()[\"emi\"] * co2_c_factor,\" \", bot_new_gas.sum()[\"emi\"] * co2_c_factor)\n", + "print(\"total: \", (top_new_coal.sum()[\"emi\"]+top_new_oil.sum()[\"emi\"]+top_new_gas.sum()[\"emi\"]) * co2_c_factor,\" \", (bot_new_coal.sum()[\"emi\"]+bot_new_oil.sum()[\"emi\"]+bot_new_gas.sum()[\"emi\"]) * co2_c_factor)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## carbon IO gas" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 38, + "outputs": [], + "source": [ + "emi_factor_gas = 0.482" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 39, + "outputs": [ + { + "data": { + "text/plain": "node_loc year_act tec mode year_vtg\nR12_LAM 2020 gas_extr_mpen M1 2020 139.81507\nName: output, dtype: float64" + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "act_out.loc[region, year, [\"gas_extr_mpen\"]][\"output\"] * emi_factor_gas" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 40, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\maczek\\AppData\\Local\\Temp\\ipykernel_17352\\2951075765.py:1: FutureWarning: The behavior of indexing on a MultiIndex with a nested sequence of labels is deprecated and will change in a future version. `series.loc[label, sequence]` will raise if any members of 'sequence' or not present in the index's second level. To retain the old behavior, use `series.index.isin(sequence, level=1)`\n", + " inp_atm_dist = act_in.loc[region, year, [\"gas_bal\", \"gas_exp_nam\", \"LNG_prod\", \"gas_exp_cpa\", \"gas_exp_chn\", \"gas_exp_eeu\", \"gas_exp_sas\",\"gas_exp_weu\"]]\n" + ] + }, + { + "data": { + "text/plain": " Level commodity level \\\nnode_loc year_act tec mode year_vtg \nR12_LAM 2020 gas_bal M1 2020 252.783802 gas primary \n LNG_prod M1 2020 37.305296 gas primary \n\n Value node_origin input \nnode_loc year_act tec mode year_vtg \nR12_LAM 2020 gas_bal M1 2020 1.0 R12_LAM 252.783802 \n LNG_prod M1 2020 1.0 R12_LAM 37.305296 ", + "text/html": "
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LevelcommoditylevelValuenode_origininput
node_locyear_acttecmodeyear_vtg
R12_LAM2020gas_balM12020252.783802gasprimary1.0R12_LAM252.783802
LNG_prodM1202037.305296gasprimary1.0R12_LAM37.305296
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" + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "inp_atm_dist = act_in.loc[region, year, [\"gas_bal\", \"gas_exp_nam\", \"LNG_prod\", \"gas_exp_cpa\", \"gas_exp_chn\", \"gas_exp_eeu\", \"gas_exp_sas\",\"gas_exp_weu\"]]\n", + "inp_atm_dist#.sum()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 41, + "outputs": [ + { + "data": { + "text/plain": "node_loc year_act tec mode year_vtg\nR12_LAM 2020 gas_bal M1 2020 121.841792\n LNG_prod M1 2020 17.981153\nName: input, dtype: float64" + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#in_gas_proc = act_in.loc[region, year, \"gas_processing_petro\"]\n", + "(inp_atm_dist[\"input\"] * emi_factor_gas)#.sum()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 42, + "outputs": [ + { + "data": { + "text/plain": "tec\nfurnace_gas_refining 22.566977\ngas_NH3 2.610735\ngas_cc 38.983500\ngas_ppl 2.953142\ngas_t_d 47.083041\nh2_smr 3.473940\nmeth_ng 4.170458\nName: input, dtype: float64" + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(act_in.loc[region, year, act_in[\"level\"]==\"secondary\", act_in[\"commodity\"]==\"gas\"][\"input\"]\n", + " * emi_factor_gas).groupby(\"tec\").sum()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 43, + "outputs": [ + { + "data": { + "text/plain": "tec\ndri_steel 0.428896\neaf_steel 0.019407\nfurnace_gas_petro 2.164642\ngas_i 18.358918\ngas_processing_petro 2.809953\ngas_rc 15.804027\nhp_gas_aluminum 0.014086\nhp_gas_rc 1.386845\nName: input, dtype: float64" + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(act_in.loc[region, year, act_in[\"level\"]==\"final\", act_in[\"commodity\"]==\"gas\"][\"input\"] * emi_factor_gas).groupby(\"tec\").sum()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 44, + "outputs": [ + { + "data": { + "text/plain": "2.932242552118641" + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(act_out.loc[region, year, act_out[\"commodity\"].isin([\"ethane\", \"propane\"])][\"output\"] * 0.81).sum()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 45, + "outputs": [ + { + "data": { + "text/plain": "2.0096798752590517" + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(act_out.loc[region, year, act_out.index.get_level_values(3).isin([\"ethane\", \"propane\"])][\"output\"] * 0.5).sum()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 46, + "outputs": [ + { + "data": { + "text/plain": "1.2016059460355528" + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "act_out.loc[region, year, \"steam_cracker_petro\", act_out[\"commodity\"]==\"gas\"].sum()[\"output\"] * emi_factor_gas" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## carbon IO oil" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 47, + "outputs": [ + { + "ename": "KeyError", + "evalue": "('R12_LAM', '2020', 'oil_imp')", + "output_type": "error", + "traceback": [ + "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[1;31mKeyError\u001B[0m Traceback (most recent call last)", + "Input \u001B[1;32mIn [47]\u001B[0m, in \u001B[0;36m\u001B[1;34m()\u001B[0m\n\u001B[0;32m 2\u001B[0m out_oil_extr \u001B[38;5;241m=\u001B[39m act_out\u001B[38;5;241m.\u001B[39mloc[region, year, extr_tec]\n\u001B[0;32m 3\u001B[0m out_oil_exp \u001B[38;5;241m=\u001B[39m act_out\u001B[38;5;241m.\u001B[39mloc[region, year, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124moil_exp\u001B[39m\u001B[38;5;124m\"\u001B[39m]\n\u001B[1;32m----> 4\u001B[0m in_oil_imp \u001B[38;5;241m=\u001B[39m \u001B[43mact_in\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mloc\u001B[49m\u001B[43m[\u001B[49m\u001B[43mregion\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43myear\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43moil_imp\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\n\u001B[0;32m 6\u001B[0m carbon_in_extr \u001B[38;5;241m=\u001B[39m (out_oil_extr\u001B[38;5;241m.\u001B[39msum()[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124moutput\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m-\u001B[39m out_oil_exp\u001B[38;5;241m.\u001B[39msum()[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124moutput\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m+\u001B[39m in_oil_imp\u001B[38;5;241m.\u001B[39msum()[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124minput\u001B[39m\u001B[38;5;124m\"\u001B[39m]) \u001B[38;5;241m*\u001B[39m \u001B[38;5;241m0.631\u001B[39m\n\u001B[0;32m 7\u001B[0m carbon_in_extr\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:961\u001B[0m, in \u001B[0;36m_LocationIndexer.__getitem__\u001B[1;34m(self, key)\u001B[0m\n\u001B[0;32m 959\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_is_scalar_access(key):\n\u001B[0;32m 960\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mobj\u001B[38;5;241m.\u001B[39m_get_value(\u001B[38;5;241m*\u001B[39mkey, takeable\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_takeable)\n\u001B[1;32m--> 961\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_getitem_tuple\u001B[49m\u001B[43m(\u001B[49m\u001B[43mkey\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 962\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m 963\u001B[0m \u001B[38;5;66;03m# we by definition only have the 0th axis\u001B[39;00m\n\u001B[0;32m 964\u001B[0m axis \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39maxis \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;241m0\u001B[39m\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1140\u001B[0m, in \u001B[0;36m_LocIndexer._getitem_tuple\u001B[1;34m(self, tup)\u001B[0m\n\u001B[0;32m 1138\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m suppress(IndexingError):\n\u001B[0;32m 1139\u001B[0m tup \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_expand_ellipsis(tup)\n\u001B[1;32m-> 1140\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_getitem_lowerdim\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtup\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 1142\u001B[0m \u001B[38;5;66;03m# no multi-index, so validate all of the indexers\u001B[39;00m\n\u001B[0;32m 1143\u001B[0m tup \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_validate_tuple_indexer(tup)\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:859\u001B[0m, in \u001B[0;36m_LocationIndexer._getitem_lowerdim\u001B[1;34m(self, tup)\u001B[0m\n\u001B[0;32m 849\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m (\n\u001B[0;32m 850\u001B[0m \u001B[38;5;28misinstance\u001B[39m(ax0, MultiIndex)\n\u001B[0;32m 851\u001B[0m \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mname \u001B[38;5;241m!=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124miloc\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 856\u001B[0m \u001B[38;5;66;03m# is equivalent.\u001B[39;00m\n\u001B[0;32m 857\u001B[0m \u001B[38;5;66;03m# (see the other place where we call _handle_lowerdim_multi_index_axis0)\u001B[39;00m\n\u001B[0;32m 858\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m suppress(IndexingError):\n\u001B[1;32m--> 859\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_handle_lowerdim_multi_index_axis0\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtup\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 861\u001B[0m tup \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_validate_key_length(tup)\n\u001B[0;32m 863\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m i, key \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28menumerate\u001B[39m(tup):\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1166\u001B[0m, in \u001B[0;36m_LocIndexer._handle_lowerdim_multi_index_axis0\u001B[1;34m(self, tup)\u001B[0m\n\u001B[0;32m 1162\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m ek:\n\u001B[0;32m 1163\u001B[0m \u001B[38;5;66;03m# raise KeyError if number of indexers match\u001B[39;00m\n\u001B[0;32m 1164\u001B[0m \u001B[38;5;66;03m# else IndexingError will be raised\u001B[39;00m\n\u001B[0;32m 1165\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mndim \u001B[38;5;241m<\u001B[39m \u001B[38;5;28mlen\u001B[39m(tup) \u001B[38;5;241m<\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mobj\u001B[38;5;241m.\u001B[39mindex\u001B[38;5;241m.\u001B[39mnlevels:\n\u001B[1;32m-> 1166\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m ek\n\u001B[0;32m 1167\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m IndexingError(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mNo label returned\u001B[39m\u001B[38;5;124m\"\u001B[39m) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01mek\u001B[39;00m\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1160\u001B[0m, in \u001B[0;36m_LocIndexer._handle_lowerdim_multi_index_axis0\u001B[1;34m(self, tup)\u001B[0m\n\u001B[0;32m 1157\u001B[0m axis \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39maxis \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;241m0\u001B[39m\n\u001B[0;32m 1158\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m 1159\u001B[0m \u001B[38;5;66;03m# fast path for series or for tup devoid of slices\u001B[39;00m\n\u001B[1;32m-> 1160\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_get_label\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtup\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43maxis\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43maxis\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 1162\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m ek:\n\u001B[0;32m 1163\u001B[0m \u001B[38;5;66;03m# raise KeyError if number of indexers match\u001B[39;00m\n\u001B[0;32m 1164\u001B[0m \u001B[38;5;66;03m# else IndexingError will be raised\u001B[39;00m\n\u001B[0;32m 1165\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mndim \u001B[38;5;241m<\u001B[39m \u001B[38;5;28mlen\u001B[39m(tup) \u001B[38;5;241m<\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mobj\u001B[38;5;241m.\u001B[39mindex\u001B[38;5;241m.\u001B[39mnlevels:\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1153\u001B[0m, in \u001B[0;36m_LocIndexer._get_label\u001B[1;34m(self, label, axis)\u001B[0m\n\u001B[0;32m 1151\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m_get_label\u001B[39m(\u001B[38;5;28mself\u001B[39m, label, axis: \u001B[38;5;28mint\u001B[39m):\n\u001B[0;32m 1152\u001B[0m \u001B[38;5;66;03m# GH#5667 this will fail if the label is not present in the axis.\u001B[39;00m\n\u001B[1;32m-> 1153\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mobj\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mxs\u001B[49m\u001B[43m(\u001B[49m\u001B[43mlabel\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43maxis\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43maxis\u001B[49m\u001B[43m)\u001B[49m\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:3857\u001B[0m, in \u001B[0;36mNDFrame.xs\u001B[1;34m(self, key, axis, level, drop_level)\u001B[0m\n\u001B[0;32m 3854\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_consolidate_inplace()\n\u001B[0;32m 3856\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(index, MultiIndex):\n\u001B[1;32m-> 3857\u001B[0m loc, new_index \u001B[38;5;241m=\u001B[39m \u001B[43mindex\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_get_loc_level\u001B[49m\u001B[43m(\u001B[49m\u001B[43mkey\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mlevel\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m0\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[0;32m 3858\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m drop_level:\n\u001B[0;32m 3859\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m lib\u001B[38;5;241m.\u001B[39mis_integer(loc):\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\multi.py:3052\u001B[0m, in \u001B[0;36mMultiIndex._get_loc_level\u001B[1;34m(self, key, level)\u001B[0m\n\u001B[0;32m 3049\u001B[0m \u001B[38;5;28;01mpass\u001B[39;00m\n\u001B[0;32m 3051\u001B[0m \u001B[38;5;66;03m# partial selection\u001B[39;00m\n\u001B[1;32m-> 3052\u001B[0m indexer \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_loc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mkey\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 3053\u001B[0m ilevels \u001B[38;5;241m=\u001B[39m [i \u001B[38;5;28;01mfor\u001B[39;00m i \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mrange\u001B[39m(\u001B[38;5;28mlen\u001B[39m(key)) \u001B[38;5;28;01mif\u001B[39;00m key[i] \u001B[38;5;241m!=\u001B[39m \u001B[38;5;28mslice\u001B[39m(\u001B[38;5;28;01mNone\u001B[39;00m, \u001B[38;5;28;01mNone\u001B[39;00m)]\n\u001B[0;32m 3054\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(ilevels) \u001B[38;5;241m==\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mnlevels:\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\multi.py:2905\u001B[0m, in \u001B[0;36mMultiIndex.get_loc\u001B[1;34m(self, key, method)\u001B[0m\n\u001B[0;32m 2902\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m(key) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01merr\u001B[39;00m\n\u001B[0;32m 2904\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m start \u001B[38;5;241m==\u001B[39m stop:\n\u001B[1;32m-> 2905\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m(key)\n\u001B[0;32m 2907\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m follow_key:\n\u001B[0;32m 2908\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mslice\u001B[39m(start, stop)\n", + "\u001B[1;31mKeyError\u001B[0m: ('R12_LAM', '2020', 'oil_imp')" + ] + } + ], + "source": [ + "extr_tec = \"oil_extr_mpen\"\n", + "out_oil_extr = act_out.loc[region, year, extr_tec]\n", + "out_oil_exp = act_out.loc[region, year, \"oil_exp\"]\n", + "in_oil_imp = act_in.loc[region, year, \"oil_imp\"]\n", + "\n", + "carbon_in_extr = (out_oil_extr.sum()[\"output\"] - out_oil_exp.sum()[\"output\"] + in_oil_imp.sum()[\"input\"]) * 0.631\n", + "carbon_in_extr" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 48, + "outputs": [ + { + "data": { + "text/plain": "319.2508439033615" + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "inp_atm_dist = act_in.loc[region, year, \"atm_distillation_ref\"]\n", + "carbon_in_ref = inp_atm_dist[inp_atm_dist[\"commodity\"] == \"crudeoil\"].sum()[\"input\"] * 0.631\n", + "carbon_in_ref" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 49, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10.089172440637014\n" + ] + } + ], + "source": [ + "out_agg_ref = act_out.loc[region, year, \"agg_ref\"]\n", + "carbon_out_agg_ref = out_agg_ref.loc[out_agg_ref[\"commodity\"].isin([\"fueloil\"])][\"output\"].sum() * 0.665 \\\n", + "+ out_agg_ref[~out_agg_ref[\"commodity\"].isin([\"fueloil\"])].sum()[\"output\"] * 0.631\n", + "\n", + "print(carbon_in_ref - carbon_out_agg_ref)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 50, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\maczek\\AppData\\Local\\Temp\\ipykernel_17352\\3261112422.py:1: PerformanceWarning: indexing past lexsort depth may impact performance.\n", + " inp_atm_dist = act_in.swaplevel(0,2).loc[\"atm_distillation_ref\", year]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "252.94682658537567\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\maczek\\AppData\\Local\\Temp\\ipykernel_17352\\3261112422.py:4: PerformanceWarning: indexing past lexsort depth may impact performance.\n", + " out_agg_ref = act_out.swaplevel(0,2).loc[\"agg_ref\", year]\n" + ] + } + ], + "source": [ + "inp_atm_dist = act_in.swaplevel(0,2).loc[\"atm_distillation_ref\", year]\n", + "carbon_in_ref = inp_atm_dist[inp_atm_dist[\"commodity\"] == \"crudeoil\"].sum()[\"input\"] * 0.631\n", + "\n", + "out_agg_ref = act_out.swaplevel(0,2).loc[\"agg_ref\", year]\n", + "carbon_out_agg_ref = out_agg_ref.loc[out_agg_ref[\"commodity\"].isin([\"fueloil\"])][\"output\"].sum() * 0.665 + out_agg_ref[~out_agg_ref[\"commodity\"].isin([\"fueloil\"])].sum()[\"output\"] * 0.631\n", + "\n", + "print(carbon_in_ref - carbon_out_agg_ref)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 51, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\maczek\\AppData\\Local\\Temp\\ipykernel_17352\\2343031665.py:1: PerformanceWarning: indexing past lexsort depth may impact performance.\n", + " sc_in = act_in.swaplevel(0,2).loc[\"steam_cracker_petro\", year]\n" + ] + }, + { + "data": { + "text/plain": "301.4771887846489" + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sc_in = act_in.swaplevel(0,2).loc[\"steam_cracker_petro\", year]\n", + "sc_in[~sc_in.index.get_level_values(1).isin([\"ethane\", \"propane\"])].sum()[\"input\"] * 0.631" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## carbon IO coal" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 52, + "outputs": [ + { + "data": { + "text/plain": " Level commodity level Value \\\ntec mode year_vtg \nLNG_prod M1 2020 37.305296 gas primary 1.000000 \ndri_steel M1 2020 2.696444 gas final 0.330000 \n 2020 2.696444 gas dummy_emission 0.330000 \neaf_steel M2 2005 0.265603 gas final 0.002000 \n 2005 0.265603 gas dummy_emission 0.002000 \n... ... ... ... ... \nmeth_ng fuel 2015 2.359075 gas secondary 1.666667 \n 2015 2.359075 gas secondary 1.666667 \n 2015 2.359075 gas secondary 1.666667 \n 2015 2.359075 gas secondary 1.666667 \n 2015 2.359075 gas secondary 1.666667 \n\n node_origin input emi_factor \ntec mode year_vtg \nLNG_prod M1 2020 R12_LAM 37.305296 NaN \ndri_steel M1 2020 R12_LAM 0.889827 NaN \n 2020 R12_LAM 0.889827 NaN \neaf_steel M2 2005 R12_LAM 0.000531 NaN \n 2005 R12_LAM 0.000531 NaN \n... ... ... ... \nmeth_ng fuel 2015 R12_LAM 3.931792 0.253333 \n 2015 R12_LAM 3.931792 0.253333 \n 2015 R12_LAM 3.931792 0.253333 \n 2015 R12_LAM 3.931792 0.253333 \n 2015 R12_LAM 3.931792 0.253333 \n\n[2135 rows x 7 columns]", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
LevelcommoditylevelValuenode_origininputemi_factor
tecmodeyear_vtg
LNG_prodM1202037.305296gasprimary1.000000R12_LAM37.305296NaN
dri_steelM120202.696444gasfinal0.330000R12_LAM0.889827NaN
20202.696444gasdummy_emission0.330000R12_LAM0.889827NaN
eaf_steelM220050.265603gasfinal0.002000R12_LAM0.000531NaN
20050.265603gasdummy_emission0.002000R12_LAM0.000531NaN
..............................
meth_ngfuel20152.359075gassecondary1.666667R12_LAM3.9317920.253333
20152.359075gassecondary1.666667R12_LAM3.9317920.253333
20152.359075gassecondary1.666667R12_LAM3.9317920.253333
20152.359075gassecondary1.666667R12_LAM3.9317920.253333
20152.359075gassecondary1.666667R12_LAM3.9317920.253333
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2135 rows × 7 columns

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" + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "act_in_yr_reg = act_in.loc[region, year]\n", + "comm = \"gas\"\n", + "bott_dict = {\n", + " \"coal\":bot_new_coal,\n", + " \"gas\":bot_new_gas\n", + "}\n", + "bott_yr_reg_coal_merge = bott_dict[comm].copy(deep=True)\n", + "x = act_in_yr_reg[(act_in_yr_reg.index.get_level_values(0).str.contains(comm)) & (act_in_yr_reg[\"commodity\"]==comm) & (act_in_yr_reg[\"level\"]==\"final\")].join(bott_yr_reg_coal_merge.rename({\"Value\": \"emi_factor\"}, axis=1)[\"emi_factor\"])\n", + "x = act_in_yr_reg[(act_in_yr_reg[\"commodity\"]==comm)].join(bott_yr_reg_coal_merge.rename({\"Value\": \"emi_factor\"}, axis=1)[\"emi_factor\"])\n", + "x" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 53, + "outputs": [ + { + "data": { + "text/plain": " Level commodity level \\\ntec mode node_loc year_act year_vtg \nLNG_prod M1 R12_AFR 2020 2020 60.182113 gas primary \n 2025 2025 69.629561 gas primary \n 2030 2030 94.292866 gas primary \n 2035 2035 101.502785 gas primary \n 2040 2040 100.225163 gas primary \n... ... ... ... \nmeth_ng fuel R12_WEU 2025 2015 0.331997 gas secondary \n 2015 0.331997 gas secondary \n 2015 0.331997 gas secondary \n 2015 0.331997 gas secondary \n 2015 0.331997 gas secondary \n\n Value node_origin input \\\ntec mode node_loc year_act year_vtg \nLNG_prod M1 R12_AFR 2020 2020 1.000000 R12_AFR 60.182113 \n 2025 2025 1.000000 R12_AFR 69.629561 \n 2030 2030 1.000000 R12_AFR 94.292866 \n 2035 2035 1.000000 R12_AFR 101.502785 \n 2040 2040 1.000000 R12_AFR 100.225163 \n... ... ... ... \nmeth_ng fuel R12_WEU 2025 2015 1.666667 R12_WEU 0.553329 \n 2015 1.666667 R12_WEU 0.553329 \n 2015 1.666667 R12_WEU 0.553329 \n 2015 1.666667 R12_WEU 0.553329 \n 2015 1.666667 R12_WEU 0.553329 \n\n emi_factor \ntec mode node_loc year_act year_vtg \nLNG_prod M1 R12_AFR 2020 2020 NaN \n 2025 2025 NaN \n 2030 2030 NaN \n 2035 2035 NaN \n 2040 2040 NaN \n... ... \nmeth_ng fuel R12_WEU 2025 2015 0.253333 \n 2015 0.253333 \n 2015 0.253333 \n 2015 0.253333 \n 2015 0.253333 \n\n[204796 rows x 7 columns]", + "text/html": "
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LevelcommoditylevelValuenode_origininputemi_factor
tecmodenode_locyear_actyear_vtg
LNG_prodM1R12_AFR2020202060.182113gasprimary1.000000R12_AFR60.182113NaN
2025202569.629561gasprimary1.000000R12_AFR69.629561NaN
2030203094.292866gasprimary1.000000R12_AFR94.292866NaN
20352035101.502785gasprimary1.000000R12_AFR101.502785NaN
20402040100.225163gasprimary1.000000R12_AFR100.225163NaN
....................................
meth_ngfuelR12_WEU202520150.331997gassecondary1.666667R12_WEU0.5533290.253333
20150.331997gassecondary1.666667R12_WEU0.5533290.253333
20150.331997gassecondary1.666667R12_WEU0.5533290.253333
20150.331997gassecondary1.666667R12_WEU0.5533290.253333
20150.331997gassecondary1.666667R12_WEU0.5533290.253333
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204796 rows × 7 columns

\n
" + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "act_in_yr_reg = act_in#.swaplevel(0,1).loc[year]\n", + "comm = \"gas\"\n", + "bott_dict = {\n", + " \"coal\":bot_new_coal,\n", + " \"gas\":bot_new_gas\n", + "}\n", + "bott_yr_reg_coal_merge = bott_dict[comm].copy(deep=True)\n", + "x = act_in_yr_reg[(act_in_yr_reg.index.get_level_values(0).str.contains(comm)) & (act_in_yr_reg[\"commodity\"]==comm) & (act_in_yr_reg[\"level\"]==\"final\")].join(bott_yr_reg_coal_merge.rename({\"Value\": \"emi_factor\"}, axis=1)[\"emi_factor\"])\n", + "x = act_in_yr_reg[(act_in_yr_reg[\"commodity\"]==comm)].join(bott_yr_reg_coal_merge.rename({\"Value\": \"emi_factor\"}, axis=1)[\"emi_factor\"])\n", + "x" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 54, + "outputs": [ + { + "data": { + "text/plain": " node_loc year_act year_vtg relation Value \\\ntec mode \nDUMMY_gas_supply M1 R12_AFR 2020 2020 CO2_ind 0.482000 \nfurnace_gas_petro high_temp R12_AFR 2020 2020 CO2_ind 0.523913 \nfurnace_gas_refining high_temp R12_AFR 2020 2020 CO2_cc 0.800120 \ngas_NH3 M1 R12_AFR 2020 2005 CO2_cc 0.534804 \n M1 R12_AFR 2020 2010 CO2_cc 0.534804 \n... ... ... ... ... ... \ngas_t_d M1 R12_WEU 2020 2005 CO2_cc 0.034737 \n M1 R12_WEU 2020 2010 CO2_cc 0.034737 \nhp_gas_rc M1 R12_WEU 2020 2020 CO2_r_c 0.321333 \nmeth_ng feedstock R12_WEU 2020 2015 CO2_cc 0.253333 \n fuel R12_WEU 2020 2015 CO2_cc 0.253333 \n\n node_origin year_rel Level emi \ntec mode \nDUMMY_gas_supply M1 R12_AFR 2020 1.660615 0.800417 \nfurnace_gas_petro high_temp R12_AFR 2020 1.179212 0.617805 \nfurnace_gas_refining high_temp R12_AFR 2020 4.492885 3.594847 \ngas_NH3 M1 R12_AFR 2020 1.065932 0.570065 \n M1 R12_AFR 2020 2.041205 1.091645 \n... ... ... ... ... \ngas_t_d M1 R12_WEU 2020 64.785421 2.250441 \n M1 R12_WEU 2020 15.089721 0.524169 \nhp_gas_rc M1 R12_WEU 2020 33.288225 10.696616 \nmeth_ng feedstock R12_WEU 2020 1.287200 0.326091 \n fuel R12_WEU 2020 1.468320 0.371975 \n\n[520 rows x 9 columns]", + "text/html": "
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node_locyear_actyear_vtgrelationValuenode_originyear_relLevelemi
tecmode
DUMMY_gas_supplyM1R12_AFR20202020CO2_ind0.482000R12_AFR20201.6606150.800417
furnace_gas_petrohigh_tempR12_AFR20202020CO2_ind0.523913R12_AFR20201.1792120.617805
furnace_gas_refininghigh_tempR12_AFR20202020CO2_cc0.800120R12_AFR20204.4928853.594847
gas_NH3M1R12_AFR20202005CO2_cc0.534804R12_AFR20201.0659320.570065
M1R12_AFR20202010CO2_cc0.534804R12_AFR20202.0412051.091645
.................................
gas_t_dM1R12_WEU20202005CO2_cc0.034737R12_WEU202064.7854212.250441
M1R12_WEU20202010CO2_cc0.034737R12_WEU202015.0897210.524169
hp_gas_rcM1R12_WEU20202020CO2_r_c0.321333R12_WEU202033.28822510.696616
meth_ngfeedstockR12_WEU20202015CO2_cc0.253333R12_WEU20201.2872000.326091
fuelR12_WEU20202015CO2_cc0.253333R12_WEU20201.4683200.371975
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520 rows × 9 columns

\n
" + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bot_new_gas.loc[\"gas_i\"]\n", + "bott_yr_reg_coal_merge" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 55, + "outputs": [ + { + "data": { + "text/plain": "mode node_loc year_act year_vtg\nM1 R12_AFR 2020 2005 0.482000\n 2005 0.482000\n 2005 0.482000\n 2005 0.482000\n 2005 0.482000\n ... \n R12_WEU 2060 2040 0.637941\n 2040 0.637941\n 2040 0.637941\n 2040 0.516429\n 2040 0.516429\nLength: 4557, dtype: float64" + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(x[\"emi_factor\"] / x[\"Value\"]).dropna().loc[\"gas_i\"]#.reset_index()#.drop([\"year_vtg\", \"node_loc\", \"year_act\"], axis=1).drop_duplicates()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 56, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\maczek\\AppData\\Local\\Temp\\ipykernel_17352\\3091354.py:1: PerformanceWarning: indexing past lexsort depth may impact performance.\n", + " x.loc[\"gas_i\", \"M1\", \"R12_CHN\"]\n" + ] + }, + { + "data": { + "text/plain": " Level commodity level Value node_origin input \\\nyear_act year_vtg \n2020 2005 1.228475 gas final 1.408451 R12_CHN 1.730247 \n 2005 1.228475 gas final 1.408451 R12_CHN 1.730247 \n 2005 1.228475 gas final 1.408451 R12_CHN 1.730247 \n 2005 1.228475 gas final 1.408451 R12_CHN 1.730247 \n 2005 1.228475 gas final 1.408451 R12_CHN 1.730247 \n... ... ... ... ... ... ... \n 2015 3.930682 gas final 1.408451 R12_CHN 5.536172 \n 2015 3.930682 gas final 1.408451 R12_CHN 5.536172 \n 2015 3.930682 gas final 1.408451 R12_CHN 5.536172 \n 2015 3.930682 gas final 1.408451 R12_CHN 5.536172 \n 2015 3.930682 gas final 1.408451 R12_CHN 5.536172 \n\n emi_factor \nyear_act year_vtg \n2020 2005 0.678873 \n 2005 0.678873 \n 2005 0.678873 \n 2005 0.678873 \n 2005 0.678873 \n... ... \n 2015 0.708824 \n 2015 0.708824 \n 2015 0.708824 \n 2015 0.573810 \n 2015 0.573810 \n\n[62 rows x 7 columns]", + "text/html": "
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LevelcommoditylevelValuenode_origininputemi_factor
year_actyear_vtg
202020051.228475gasfinal1.408451R12_CHN1.7302470.678873
20051.228475gasfinal1.408451R12_CHN1.7302470.678873
20051.228475gasfinal1.408451R12_CHN1.7302470.678873
20051.228475gasfinal1.408451R12_CHN1.7302470.678873
20051.228475gasfinal1.408451R12_CHN1.7302470.678873
........................
20153.930682gasfinal1.408451R12_CHN5.5361720.708824
20153.930682gasfinal1.408451R12_CHN5.5361720.708824
20153.930682gasfinal1.408451R12_CHN5.5361720.708824
20153.930682gasfinal1.408451R12_CHN5.5361720.573810
20153.930682gasfinal1.408451R12_CHN5.5361720.573810
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62 rows × 7 columns

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" + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x.loc[\"gas_i\", \"M1\", \"R12_CHN\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## check *_t_d tecs emission factors" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 92, + "outputs": [], + "source": [ + "df_gas_t_d = act_in[act_in.index.get_level_values(2).str.contains(\"coal\")].join(emi_bott_up.rename({\"Value\": \"emi_factor\"}, axis=1)[\"emi_factor\"])" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 58, + "outputs": [], + "source": [ + "df_gas_t_d = act_in.join(emi_bott_up.rename({\"Value\": \"emi_factor\"}, axis=1)[\"emi_factor\"]).dropna()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 93, + "outputs": [], + "source": [ + "ef_dict = {\n", + " 'biomass':0,\n", + " 'coal':0.814,\n", + " 'electr':0,\n", + " 'ethanol':0,\n", + " 'fueloil':0.665,\n", + " 'gas':0.482,\n", + " 'gas_1':0.482,\n", + " 'gas_2':0.482,\n", + " 'gas_3':0.482,\n", + " 'gas_4':0.482,\n", + " 'gas_5':0.482,\n", + " 'gas_6':0.482,\n", + " 'gas_afr':0.482,\n", + " 'gas_chn':0.482,\n", + " 'gas_cpa':0.482,\n", + " 'gas_weu':0.482,\n", + " 'gas_eeu':0.482,\n", + " 'gas_sas':0.482,\n", + " 'LNG':0.482,\n", + " 'd_heat':0,\n", + " 'lightoil':0.631,\n", + " 'methanol':0.549,\n", + " 'hydrogen':0,\n", + " 'freshwater_supply':0,\n", + " \"crudeoil\":0.63,\n", + " \"crude_1\":0.665,\n", + " \"crude_2\":0.665,\n", + " \"crude_3\":0.665,\n", + " \"crude_4\":0.665,\n", + " \"crude_5\":0.665,\n", + " \"crude_6\":0.665,\n", + " \"crude_7\":0.665,\n", + " \"ht_heat\":0,\n", + " \"lh2\":0,\n", + "}\n", + "\n", + "def get_ef(df):\n", + " df[\"ef\"] = ef_dict[df[\"commodity\"]]\n", + " return df\n", + "\n", + "df_gas_t_d = df_gas_t_d.apply(lambda x: get_ef(x), axis=1)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 67, + "outputs": [], + "source": [ + "df_gas_t_d = df_gas_t_d.join(act_out.rename({\"commodity\":\"comm_out\"}, axis=1)[\"comm_out\"])\n", + "df_gas_t_d = df_gas_t_d[df_gas_t_d[\"commodity\"]==df_gas_t_d[\"comm_out\"]]\n", + "#df_gas_t_d = df_gas_t_d[~df_gas_t_d.index.get_level_values(2).str.contains(\"_extr\")]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 94, + "outputs": [], + "source": [ + "# for IO tecs\n", + "ef_false = (df_gas_t_d[\"emi_factor\"] / (df_gas_t_d[\"Value\"] - 1)).dropna()\n", + "# for input only tecs\n", + "ef_false = (df_gas_t_d[\"emi_factor\"] / df_gas_t_d[\"Value\"]).dropna()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 95, + "outputs": [], + "source": [ + "tec_comm_map = df_gas_t_d.reset_index()[[\"tec\", \"commodity\"]].drop_duplicates().set_index(\"tec\").to_dict()[\"commodity\"]\n", + "tec_list = [i for i in list(tec_comm_map.keys())]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 99, + "outputs": [ + { + "data": { + "text/plain": " 0\nyear_act node_loc mode year_vtg \n2045 R12_PAS M1 2010 0.823070\n2050 R12_NAM M1 2010 0.816775\n2035 R12_CHN M1 2000 0.816429\n R12_RCPA M1 2000 0.816429\n2025 R12_LAM M1 1980 0.814000\n... ...\n2045 R12_SAS M1 2020 0.814000\n 2025 0.814000\n 2030 0.814000\n 2035 0.814000\n2035 R12_PAS M1 2015 0.814000\n\n[476 rows x 1 columns]", + "text/html": "
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" + }, + "execution_count": 99, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.DataFrame(ef_false).swaplevel(0,2).loc[\"coal_ppl\"].sort_values(0, ascending=False)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 98, + "outputs": [ + { + "data": { + "text/plain": "year_act node_loc mode year_vtg\n2020 R12_AFR M1 1960 NaN\n 1965 NaN\n 1970 NaN\n 1975 NaN\n 1980 NaN\n ..\n2090 R12_AFR M1 2080 NaN\n2100 R12_AFR M1 2080 NaN\n 2100 NaN\n2110 R12_AFR M1 2100 NaN\n 2110 NaN\nLength: 632, dtype: float64" + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_gas_t_d.swaplevel(0,2).loc[\"coal_ppl_u\"][\"emi_factor\"] / df_gas_t_d.swaplevel(0,2).loc[\"coal_ppl_u\"][\"Value\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 96, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "coal_adv\n", + "coal_bal\n", + "coal_exp\n", + "coal_extr\n", + "coal_extr_ch4\n", + "coal_i\n", + "coal_ppl\n", + "coal_ppl_u\n", + "coal_rc\n", + "coal_t_d\n", + "furnace_coal_aluminum\n", + "furnace_coal_cement\n", + "furnace_coal_refining\n", + "furnace_coal_petro\n", + "coal_hpl\n", + "furnace_coal_resins\n", + "coal_imp\n", + "meth_coal\n", + "coal_NH3\n", + "coal_trd\n", + "sp_coal_I\n", + "coal_gas\n", + "h2_coal\n" + ] + }, + { + "data": { + "text/plain": "
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+ }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import math\n", + "import matplotlib.pyplot as plt\n", + "fig, axs = plt.subplots(int(math.sqrt(len(tec_list))), int(math.sqrt(len(tec_list))), figsize=(40,20), facecolor=\"w\")\n", + "ax_it = iter(fig.axes)\n", + "for k in tec_list:\n", + " print(k)\n", + " try:\n", + " tec = k\n", + " y = ef_false.swaplevel(0,2).loc[tec].droplevel([3,2]).reset_index()\n", + " ax = next((ax_it))\n", + " y.plot.scatter(x=\"year_act\", y=0, ax=ax)\n", + " ax.plot([ef_dict[tec_comm_map[tec]]]*len(y.year_act.unique()), color=\"red\")\n", + " ax.set_ylabel(\"Mt C / GWa\")\n", + " ax.set_title(tec)\n", + " ax.set_xlabel(\"\")\n", + " except:\n", + " pass" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 64, + "outputs": [], + "source": [ + "fig.savefig(\"test.png\", facecolor=\"w\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "ef_false.swaplevel(0,2).loc[\"hp_gas_i\"].droplevel([3,2]).reset_index()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## aggregated emissions comparison" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df_gas_t_d[\"loss\"] = (df_gas_t_d[\"Value\"] - 1) * df_gas_t_d[\"Level\"]\n", + "t_d_emi = (df_gas_t_d[\"emi_factor\"] * df_gas_t_d[\"Level\"]).groupby([\"year_act\"]).sum() * co2_c_factor" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df_gas_t_d[\"emi_factor_corr\"] = (df_gas_t_d[\"Value\"] - 1) * df_gas_t_d[\"ef\"]\n", + "t_d_emi_corr =(df_gas_t_d[\"emi_factor_corr\"] * df_gas_t_d[\"Level\"]).groupby([\"year_act\"]).sum() * co2_c_factor" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "fig,ax = plt.subplots(figsize=(10,5))\n", + "t_d_emi.plot(ax=ax)\n", + "t_d_emi_corr.plot(ax=ax)\n", + "ax.set_ylabel(\"Mt CO2\")\n", + "ax.set_title(\"CO2 Emissions from t_d technologies\")\n", + "ax.legend([\"current CO2_cc\", \"corrected CO2_cc\"])" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df_gas_t_d.swaplevel(0,2).loc[\"gas_t_d\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "act_in_yr_reg = act_in.loc[region, year]\n", + "comm = \"coal\"\n", + "bott_dict = {\n", + " \"coal\":top_yr_reg_gas,\n", + " \"gas\":bott_yr_reg_gas\n", + "}\n", + "bott_yr_reg_coal_merge = bott_dict[comm].copy(deep=True)\n", + "x = act_in_yr_reg[(act_in_yr_reg.index.get_level_values(0).str.contains(comm)) & (act_in_yr_reg[\"commodity\"]==comm) & (act_in_yr_reg[\"level\"]==\"final\")].join(bott_yr_reg_coal_merge.rename({\"Value\": \"emi_factor\"}, axis=1)[\"emi_factor\"])\n", + "x = act_in_yr_reg[(act_in_yr_reg[\"commodity\"]==comm)].join(bott_yr_reg_coal_merge.rename({\"Value\": \"emi_factor\"}, axis=1)[\"emi_factor\"])\n", + "x#.sum()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "x#(x[\"emi_factor\"]/ x[\"Value\"]).dropna()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## oil emissions" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df_bot_oil = get_bot_up_emi_by_fuel(emi_bott_up, region, year)[1]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df_bot_oil[df_bot_oil.index.get_level_values(0).str.contains(\"extr\")].sum()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "emi_bot_up_refining = df_bot_oil.loc[df_bot_oil.index.get_level_values(0).str.endswith(\"_ref\")]#.sum()\n", + "emi_bot_up_extr = df_bot_oil.loc[df_bot_oil.index.get_level_values(0).str.contains(\"extr\")]\n", + "emi_bot_up_final = df_bot_oil.loc[(df_bot_oil.index.difference(emi_bot_up_refining.index)) & (df_bot_oil.index.difference(emi_bot_up_extr.index))]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "emi_bot_up_refining.sum()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "emi_bot_up_final.sort_values(\"emi\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "print(\"extraction emissions: \",emi_bot_up_extr.sum()[\"emi\"])\n", + "print(\"refining emissions: \",emi_bot_up_refining.sum()[\"emi\"])\n", + "print(\"combustion emissions: \",emi_bot_up_final.sum()[\"emi\"])" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "inp_hyd_crack = act_in.loc[region, year, \"hydro_cracking_ref\"]\n", + "inp_hyd_crack[inp_hyd_crack[\"commodity\"] == \"hydrogen\"].sum()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "inp_steam_crack = act_in.loc[region, year, \"steam_cracker_petro\"]\n", + "inp_steam_crack[~inp_steam_crack[\"commodity\"].isin([\"ethane\",\"propane\", \"electr\", \"ht_heat\"])].sum()[\"input\"] * 0.64" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df_top_oil = get_top_dn_emi_by_fuel(emi_top_down, region, year)[1]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df_top_oil.loc[df_top_oil.index.get_level_values(0).str.startswith(\"oil\")].sum()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df_top_oil.loc[~df_top_oil.index.get_level_values(0).str.endswith(\"ref\")]#.sum()#.sort_index()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "carbon_out_foil_loil_exp = df_top_oil[df_top_oil.index.get_level_values(0).str.contains(\"loil\")].sum()[\"emi\"] + df_top_oil[df_top_oil.index.get_level_values(0).str.contains(\"foil\")].sum()[\"emi\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "act_out.swaplevel(0,2).loc[\"MTO_petro\"].loc[\"2025\", \"R12_CHN\"].sum()#.groupby(\"year_act\").sum()" + ], + "metadata": { + "collapsed": false + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/message_ix_models/model/material/data analysis ipynbs/gdx_pandas_3.ipynb b/message_ix_models/model/material/data analysis ipynbs/gdx_pandas_3.ipynb new file mode 100644 index 0000000000..d6fbd04921 --- /dev/null +++ b/message_ix_models/model/material/data analysis ipynbs/gdx_pandas_3.ipynb @@ -0,0 +1,2059 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import gdxpds\n", + "import pandas as pd\n", + "co2_c_factor = 44/12" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [], + "source": [ + "#gdx_file = 'C:/Users\\maczek\\PycharmProjects\\message_ix\\message_ix\\model\\output\\MsgOutput_MESSAGEix-Materials_2.0deg_petro_thesis_2_macro.gdx'\n", + "#gdx_file = 'C:/Users\\maczek\\PycharmProjects\\message_ix\\message_ix\\model\\output\\MsgOutput_MESSAGEix-Materials_2.0deg_petro_thesis_2_macro_w_ENGAGE_fixes_test_loil_exp_fix.gdx'\n", + "gdx_file = 'C:/Users\\maczek\\PycharmProjects\\message_ix\\message_ix\\model\\output\\MsgOutput_ENGAGE_SSP2_v4.1.8.3_T4.5_r3.1_baseline_magpie.gdx'\n", + "#gdx_file = 'C:/Users\\maczek\\PycharmProjects\\message_ix\\message_ix\\model\\output\\MsgOutput_MESSAGEix-Materials_NoPolicy_petro_thesis_2_w_ENGAGE_fixes_test_build_2_macro.gdx'\n", + "#gdx_file = 'C:/Users\\maczek\\PycharmProjects\\message_ix\\message_ix\\model\\output/thesis final/MsgOutput_MESSAGEix-Materials_NoPolicy_petro_thesis_2_final_macro.gdx'\n", + "dataframes = gdxpds.to_dataframes(gdx_file)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [], + "source": [ + "hist_act = dataframes[\"historical_activity\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 15, + "outputs": [], + "source": [ + "#h\n", + "hist_act = hist_act[hist_act[\"year_all\"]==\"2015\"]\n", + "hist_act_rcpa = hist_act[hist_act[\"node\"]==\"R12_RCPA\"]\n", + "hist_act_chn = hist_act[hist_act[\"node\"]==\"R12_CHN\"]\n", + "hist_act_merge = hist_act_rcpa.merge(hist_act_chn, left_on=\"tec\", right_on=\"tec\")\n", + "hist_act_merge[\"ratio\"] = hist_act_merge[\"Value_x\"] / hist_act_merge[\"Value_y\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 21, + "outputs": [ + { + "data": { + "text/plain": " node_x tec year_all_x mode_x time_x Value_x \\\n35 R12_RCPA elec_trp 2015 M1 year 5.000000 \n54 R12_RCPA gas_extr_mpen 2015 M1 year 0.100000 \n81 R12_RCPA oil_exp 2015 M1 year 1.000000 \n85 R12_RCPA oil_extr_mpen 2015 M1 year 0.100000 \n156 R12_RCPA eaf_steel 2015 M2 year 0.893333 \n165 R12_RCPA catalytic_cracking_ref 2015 atm_gasoil year 5.953922 \n173 R12_RCPA import_petro 2015 M1 year 0.120969 \n174 R12_RCPA export_petro 2015 M1 year 0.004573 \n175 R12_RCPA gas_NH3 2015 M1 year 0.165746 \n176 R12_RCPA coal_NH3 2015 M1 year 0.834254 \n177 R12_RCPA export_NFert 2015 M1 year 0.041194 \n180 R12_RCPA meth_exp 2015 feedstock year 0.000272 \n181 R12_RCPA meth_exp 2015 feedstock year 0.000272 \n182 R12_RCPA meth_exp 2015 fuel year 0.000272 \n183 R12_RCPA meth_exp 2015 fuel year 0.000272 \n184 R12_RCPA meth_imp 2015 feedstock year 0.177914 \n185 R12_RCPA meth_imp 2015 feedstock year 0.177914 \n186 R12_RCPA meth_imp 2015 fuel year 0.177914 \n187 R12_RCPA meth_imp 2015 fuel year 0.177914 \n\n node_y year_all_y mode_y time_y Value_y ratio \n35 R12_CHN 2015 M1 year 54.270254 0.092132 \n54 R12_CHN 2015 M1 year 153.154140 0.000653 \n81 R12_CHN 2015 M1 year 51.318611 0.019486 \n85 R12_CHN 2015 M1 year 250.804477 0.000399 \n156 R12_CHN 2015 M1 year 72.360000 0.012346 \n165 R12_CHN 2015 vacuum_gasoil year 112.350506 0.052994 \n173 R12_CHN 2015 M1 year 18.270686 0.006621 \n174 R12_CHN 2015 M1 year 0.221271 0.020667 \n175 R12_CHN 2015 M1 year 9.662983 0.017153 \n176 R12_CHN 2015 M1 year 48.637017 0.017153 \n177 R12_CHN 2015 M1 year 20.080371 0.002051 \n180 R12_CHN 2015 feedstock year 0.096487 0.002823 \n181 R12_CHN 2015 fuel year 0.096487 0.002823 \n182 R12_CHN 2015 feedstock year 0.096487 0.002823 \n183 R12_CHN 2015 fuel year 0.096487 0.002823 \n184 R12_CHN 2015 feedstock year 3.405133 0.052249 \n185 R12_CHN 2015 fuel year 3.405133 0.052249 \n186 R12_CHN 2015 feedstock year 3.405133 0.052249 \n187 R12_CHN 2015 fuel year 3.405133 0.052249 ", + "text/html": "
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node_xtecyear_all_xmode_xtime_xValue_xnode_yyear_all_ymode_ytime_yValue_yratio
35R12_RCPAelec_trp2015M1year5.000000R12_CHN2015M1year54.2702540.092132
54R12_RCPAgas_extr_mpen2015M1year0.100000R12_CHN2015M1year153.1541400.000653
81R12_RCPAoil_exp2015M1year1.000000R12_CHN2015M1year51.3186110.019486
85R12_RCPAoil_extr_mpen2015M1year0.100000R12_CHN2015M1year250.8044770.000399
156R12_RCPAeaf_steel2015M2year0.893333R12_CHN2015M1year72.3600000.012346
165R12_RCPAcatalytic_cracking_ref2015atm_gasoilyear5.953922R12_CHN2015vacuum_gasoilyear112.3505060.052994
173R12_RCPAimport_petro2015M1year0.120969R12_CHN2015M1year18.2706860.006621
174R12_RCPAexport_petro2015M1year0.004573R12_CHN2015M1year0.2212710.020667
175R12_RCPAgas_NH32015M1year0.165746R12_CHN2015M1year9.6629830.017153
176R12_RCPAcoal_NH32015M1year0.834254R12_CHN2015M1year48.6370170.017153
177R12_RCPAexport_NFert2015M1year0.041194R12_CHN2015M1year20.0803710.002051
180R12_RCPAmeth_exp2015feedstockyear0.000272R12_CHN2015feedstockyear0.0964870.002823
181R12_RCPAmeth_exp2015feedstockyear0.000272R12_CHN2015fuelyear0.0964870.002823
182R12_RCPAmeth_exp2015fuelyear0.000272R12_CHN2015feedstockyear0.0964870.002823
183R12_RCPAmeth_exp2015fuelyear0.000272R12_CHN2015fuelyear0.0964870.002823
184R12_RCPAmeth_imp2015feedstockyear0.177914R12_CHN2015feedstockyear3.4051330.052249
185R12_RCPAmeth_imp2015feedstockyear0.177914R12_CHN2015fuelyear3.4051330.052249
186R12_RCPAmeth_imp2015fuelyear0.177914R12_CHN2015feedstockyear3.4051330.052249
187R12_RCPAmeth_imp2015fuelyear0.177914R12_CHN2015fuelyear3.4051330.052249
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" + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hist_act_merge[hist_act_merge[\"ratio\"]<0.111]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 38, + "outputs": [ + { + "data": { + "text/plain": " node commodity level year_all time Level Marginal \\\n0 R12_AFR NH3 final_material 2020 year 2.500000 0.0 \n1 R12_AFR NH3 final_material 2025 year 2.735064 0.0 \n2 R12_AFR NH3 final_material 2030 year 2.906112 0.0 \n3 R12_AFR NH3 final_material 2035 year 3.156448 0.0 \n4 R12_AFR NH3 final_material 2040 year 3.342780 0.0 \n... ... ... ... ... ... ... ... \n2060 R12_CHN fcoh_resin final_material 2060 year 0.282172 0.0 \n2061 R12_CHN fcoh_resin final_material 2070 year 0.215595 0.0 \n2062 R12_CHN fcoh_resin final_material 2080 year 0.094929 0.0 \n2063 R12_CHN fcoh_resin final_material 2090 year 0.030637 0.0 \n2064 R12_CHN fcoh_resin final_material 2100 year 0.011176 0.0 \n\n Lower Upper Scale \n0 4.000000e+300 3.000000e+300 1.0 \n1 4.000000e+300 3.000000e+300 1.0 \n2 4.000000e+300 3.000000e+300 1.0 \n3 4.000000e+300 3.000000e+300 1.0 \n4 4.000000e+300 3.000000e+300 1.0 \n... ... ... ... \n2060 4.000000e+300 3.000000e+300 1.0 \n2061 4.000000e+300 3.000000e+300 1.0 \n2062 4.000000e+300 3.000000e+300 1.0 \n2063 4.000000e+300 3.000000e+300 1.0 \n2064 4.000000e+300 3.000000e+300 1.0 \n\n[2065 rows x 10 columns]", + "text/html": "
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nodecommoditylevelyear_alltimeLevelMarginalLowerUpperScale
0R12_AFRNH3final_material2020year2.5000000.04.000000e+3003.000000e+3001.0
1R12_AFRNH3final_material2025year2.7350640.04.000000e+3003.000000e+3001.0
2R12_AFRNH3final_material2030year2.9061120.04.000000e+3003.000000e+3001.0
3R12_AFRNH3final_material2035year3.1564480.04.000000e+3003.000000e+3001.0
4R12_AFRNH3final_material2040year3.3427800.04.000000e+3003.000000e+3001.0
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2060R12_CHNfcoh_resinfinal_material2060year0.2821720.04.000000e+3003.000000e+3001.0
2061R12_CHNfcoh_resinfinal_material2070year0.2155950.04.000000e+3003.000000e+3001.0
2062R12_CHNfcoh_resinfinal_material2080year0.0949290.04.000000e+3003.000000e+3001.0
2063R12_CHNfcoh_resinfinal_material2090year0.0306370.04.000000e+3003.000000e+3001.0
2064R12_CHNfcoh_resinfinal_material2100year0.0111760.04.000000e+3003.000000e+3001.0
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" + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hist_act = dataframes[\"DEMAND\"]\n", + "hist_act" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 70, + "outputs": [], + "source": [ + "#h\n", + "hist_act = hist_act[hist_act[\"year_all\"]==\"2020\"]\n", + "#hist_act = hist_act.set_index([\"commodity\", \"level\"])\n", + "hist_act_rcpa = hist_act[hist_act[\"node\"]==\"R12_RCPA\"]\n", + "hist_act_chn = hist_act[hist_act[\"node\"]==\"R12_CHN\"]\n", + "\n", + "hist_act_merge = hist_act_rcpa.join(hist_act_chn[\"Level\"], rsuffix=\"_x\")\n", + "hist_act_merge[\"ratio\"] = hist_act_merge[\"Level\"] / hist_act_merge[\"Level_x\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 71, + "outputs": [ + { + "data": { + "text/plain": " node year_all time Level Marginal \\\ncommodity level \nNH3 final_material R12_RCPA 2020 year 2.500000 0.0 \nmethanol final_material R12_RCPA 2020 year 0.204400 0.0 \ni_spec useful R12_RCPA 2020 year 16.366848 0.0 \ni_therm useful R12_RCPA 2020 year 25.932783 0.0 \nnon-comm useful R12_RCPA 2020 year 2.142900 0.0 \nrc_spec useful R12_RCPA 2020 year 9.493912 0.0 \nrc_therm useful R12_RCPA 2020 year 34.041000 0.0 \ntransport useful R12_RCPA 2020 year 55.257000 0.0 \nsteel demand R12_RCPA 2020 year 12.083479 0.0 \ncement demand R12_RCPA 2020 year 237.872109 0.0 \naluminum demand R12_RCPA 2020 year 1.999989 0.0 \nHVC demand R12_RCPA 2020 year 0.440000 0.0 \nfcoh_resin final_material R12_RCPA 2020 year 0.058829 0.0 \n\n Lower Upper Scale Level_x \\\ncommodity level \nNH3 final_material 4.000000e+300 3.000000e+300 1.0 18.000000 \nmethanol final_material 4.000000e+300 3.000000e+300 1.0 24.732400 \ni_spec useful 4.000000e+300 3.000000e+300 1.0 147.301632 \ni_therm useful 4.000000e+300 3.000000e+300 1.0 233.395048 \nnon-comm useful 4.000000e+300 3.000000e+300 1.0 19.286100 \nrc_spec useful 4.000000e+300 3.000000e+300 1.0 85.445212 \nrc_therm useful 4.000000e+300 3.000000e+300 1.0 306.369000 \ntransport useful 4.000000e+300 3.000000e+300 1.0 497.313000 \nsteel demand 4.000000e+300 3.000000e+300 1.0 980.023775 \ncement demand 4.000000e+300 3.000000e+300 1.0 2140.848982 \naluminum demand 4.000000e+300 3.000000e+300 1.0 25.998916 \nHVC demand 4.000000e+300 3.000000e+300 1.0 50.500000 \nfcoh_resin final_material 4.000000e+300 3.000000e+300 1.0 0.724910 \n\n ratio \ncommodity level \nNH3 final_material 0.138889 \nmethanol final_material 0.008264 \ni_spec useful 0.111111 \ni_therm useful 0.111111 \nnon-comm useful 0.111111 \nrc_spec useful 0.111111 \nrc_therm useful 0.111111 \ntransport useful 0.111111 \nsteel demand 0.012330 \ncement demand 0.111111 \naluminum demand 0.076926 \nHVC demand 0.008713 \nfcoh_resin final_material 0.081154 ", + "text/html": "
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nodeyear_alltimeLevelMarginalLowerUpperScaleLevel_xratio
commoditylevel
NH3final_materialR12_RCPA2020year2.5000000.04.000000e+3003.000000e+3001.018.0000000.138889
methanolfinal_materialR12_RCPA2020year0.2044000.04.000000e+3003.000000e+3001.024.7324000.008264
i_specusefulR12_RCPA2020year16.3668480.04.000000e+3003.000000e+3001.0147.3016320.111111
i_thermusefulR12_RCPA2020year25.9327830.04.000000e+3003.000000e+3001.0233.3950480.111111
non-commusefulR12_RCPA2020year2.1429000.04.000000e+3003.000000e+3001.019.2861000.111111
rc_specusefulR12_RCPA2020year9.4939120.04.000000e+3003.000000e+3001.085.4452120.111111
rc_thermusefulR12_RCPA2020year34.0410000.04.000000e+3003.000000e+3001.0306.3690000.111111
transportusefulR12_RCPA2020year55.2570000.04.000000e+3003.000000e+3001.0497.3130000.111111
steeldemandR12_RCPA2020year12.0834790.04.000000e+3003.000000e+3001.0980.0237750.012330
cementdemandR12_RCPA2020year237.8721090.04.000000e+3003.000000e+3001.02140.8489820.111111
aluminumdemandR12_RCPA2020year1.9999890.04.000000e+3003.000000e+3001.025.9989160.076926
HVCdemandR12_RCPA2020year0.4400000.04.000000e+3003.000000e+3001.050.5000000.008713
fcoh_resinfinal_materialR12_RCPA2020year0.0588290.04.000000e+3003.000000e+3001.00.7249100.081154
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tecratio
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" + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hist_act_merge[[\"tec\", \"ratio\"]].sort_values(\"ratio\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [], + "source": [ + "act = dataframes[\"ACT\"].drop([\"time\", \"Marginal\", \"Lower\", \"Upper\", \"Scale\"], axis=1)\n", + "act = act[act[\"Level\"] != 0]\n", + "act = act.rename({\"vintage\":\"year_vtg\", \"year_all\":\"year_act\", \"node\":\"node_loc\"}, axis=1)\n", + "act = act.set_index([\"node_loc\", \"year_act\", \"tec\", \"mode\", \"year_vtg\"])" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [], + "source": [ + "inp = dataframes[\"input\"].drop(\"time\", axis=1)\n", + "node_cols = inp[\"node\"]\n", + "node_cols.columns = [\"node_loc\", \"node_origin\"]\n", + "inp[\"node_loc\"] = node_cols[\"node_loc\"]\n", + "inp[\"node_origin\"] = node_cols[\"node_origin\"]\n", + "inp = inp.rename({\"vintage\":\"year_vtg\", \"year_all\":\"year_act\"}, axis=1)\n", + "inp = inp.drop(\"node\", axis=1)\n", + "inp = inp.set_index([\"node_loc\", \"year_act\", \"tec\", \"mode\", \"year_vtg\"])" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [], + "source": [ + "act_in = act.join(inp)\n", + "act_in = act_in.dropna()\n", + "act_in[\"input\"] = act_in[\"Level\"] * act_in[\"Value\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [], + "source": [ + "out = dataframes[\"output\"].drop(\"time\", axis=1)\n", + "node_cols = out[\"node\"]\n", + "node_cols.columns = [\"node_loc\", \"node_dest\"]\n", + "out[\"node_loc\"] = node_cols[\"node_loc\"]\n", + "out[\"node_origin\"] = node_cols[\"node_dest\"]\n", + "out = out.rename({\"vintage\":\"year_vtg\", \"year_all\":\"year_act\"}, axis=1)\n", + "out = out.drop(\"node\", axis=1)\n", + "out = out.set_index([\"node_loc\", \"year_act\", \"year_vtg\", \"tec\", \"mode\"])" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [], + "source": [ + "act_out = act.join(out)\n", + "act_out = act_out.dropna()\n", + "act_out[\"output\"] = act_out[\"Level\"] * act_out[\"Value\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 8, + "outputs": [], + "source": [ + "rel = dataframes[\"relation_activity\"]\n", + "node_cols = rel[\"node\"]\n", + "node_cols.columns = [\"node_loc\", \"node_dest\"]\n", + "rel[\"node_loc\"] = node_cols[\"node_loc\"]\n", + "rel[\"node_origin\"] = node_cols[\"node_dest\"]\n", + "\n", + "year_cols = rel[\"year_all\"]\n", + "year_cols.columns = [\"year_act\", \"year_rel\"]\n", + "rel[\"year_act\"] = year_cols[\"year_act\"]\n", + "rel[\"year_rel\"] = year_cols[\"year_rel\"]\n", + "\n", + "rel = rel.drop(\"node\", axis=1)\n", + "rel = rel.drop(\"year_all\", axis=1)\n", + "rel = rel.set_index([\"node_loc\", \"year_act\", \"tec\", \"mode\"])" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 9, + "outputs": [], + "source": [ + "rel_co2emi = rel[rel[\"relation\"]==\"CO2_Emission\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 10, + "outputs": [], + "source": [ + "emi_top_down = rel_co2emi.join(act).dropna()\n", + "emi_top_down[\"emi\"] = emi_top_down[\"Value\"] * emi_top_down[\"Level\"] #<" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 11, + "outputs": [], + "source": [ + "emi_bott_up_names = [\"CO2_ind\", \"CO2_r_c\", \"CO2_trp\", \"CO2_trade\", \"CO2_shipping\", \"CO2_cc\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 12, + "outputs": [], + "source": [ + "emi_bott_up = rel[rel[\"relation\"].isin(emi_bott_up_names).values]\n", + "emi_bott_up = emi_bott_up.join(act).dropna()\n", + "emi_bott_up[\"emi\"] = emi_bott_up[\"Value\"] * emi_bott_up[\"Level\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 9, + "outputs": [ + { + "data": { + "text/plain": " Level\ntec year_act node_loc mode year_vtg \nCF4_TCE 2020 R11_AFR M1 2020 0.000214\n 2025 R11_AFR M1 2025 0.000244\n 2030 R11_AFR M1 2030 0.000259\n 2035 R11_AFR M1 2035 0.000283\n 2040 R11_AFR M1 2040 0.000288\n... ...\nbiomass_exp 2045 R11_WEU M1 2025 0.603323\n 2050 R11_WEU M1 2025 0.603323\n 2100 R11_WEU M1 2100 2.542521\n 2110 R11_WEU M1 2100 2.542521\n 2110 20.742661\n\n[39186 rows x 1 columns]", + "text/html": "
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biomass_exp2045R11_WEUM120250.603323
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39186 rows × 1 columns

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" + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "act.index = act.index.swaplevel(0, 2)\n", + "act" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 31, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Level\n", + "year_act \n", + "2020 3245.894213\n", + "2025 3695.781712\n", + "2030 4134.097917\n", + "2035 4584.361981\n", + "2040 4981.643773\n", + "2045 5219.056006\n", + "2050 5334.279704\n", + "2055 5294.281771\n", + "2060 5022.616845\n", + "2070 4145.596276\n", + "2080 3708.114331\n", + "2090 4277.202731\n", + "2100 5114.284017\n", + "2110 7212.136752\n", + " Level\n", + "year_act \n", + "2020 16.657540\n", + "2025 19.424365\n", + "2030 18.510678\n", + "2035 2.421083\n", + "2040 0.085181\n", + "2045 0.125159\n", + "2050 0.179682\n" + ] + }, + { + "data": { + "text/plain": "" + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "fig, ax = plt.subplots()\n", + "legend = []\n", + "for i in [\"loil_trp\", \"foil_trp\"]:\n", + " print(act.loc[i].groupby([\"year_act\"]).sum())\n", + " act.loc[i].groupby([\"year_act\"]).sum().reset_index().plot(ax=ax, x=\"year_act\", y=\"Level\")\n", + " legend.append(i)\n", + "ax.legend(legend)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 38, + "outputs": [ + { + "data": { + "text/plain": " Level\ntec \nbiomass_i 167.172537\ncoal_i 646.478318\nelec_i 87.922704\nfoil_i 100.367546\ngas_i 569.083035\nheat_i 176.669585\nhp_gas_i 5.982707\nloil_i 96.592091\nsolar_i 96.415719", + "text/html": "
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Level
tec
biomass_i167.172537
coal_i646.478318
elec_i87.922704
foil_i100.367546
gas_i569.083035
heat_i176.669585
hp_gas_i5.982707
loil_i96.592091
solar_i96.415719
\n
" + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#act.index = act.index.swaplevel(0,2)\n", + "#act.index = act.index.swaplevel(1,2)\n", + "#act2020 = act.loc[\"2020\"]\n", + "act2020[act2020.index.get_level_values(0).str.startswith(\"furnace\")].groupby(\"tec\").sum()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## identify missing relations" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 16, + "outputs": [], + "source": [ + "rel[\"count\"] = 1\n", + "tecs = list(rel[rel.index.get_level_values(2).str.startswith(\"meth_\")].index.get_level_values(2).unique())\n", + "\n", + "meth_rel = rel.swaplevel(0,2).loc[tecs]\n", + "counts = meth_rel[~meth_rel.index.get_level_values(1).isin([\"1990\", \"1995\", \"2000\", \"2005\", \"2010\", \"2015\"])].groupby([\"tec\",\"year_act\", \"node_loc\", \"relation\"]).sum()[\"count\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 17, + "outputs": [], + "source": [ + "fuel = meth_rel.swaplevel(0,3).loc[\"fuel\"]\n", + "fuel[\"df\"] = \"fuel\"\n", + "\n", + "m1 = meth_rel.swaplevel(0,3).loc[\"M1\"]\n", + "m1[\"df\"] = \"M1\"\n", + "\n", + "w_labels = pd.concat([fuel, m1]).reset_index()\n", + "wo_labels = pd.concat([fuel.drop(\"df\", axis=1), m1.drop(\"df\", axis=1)]).reset_index()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 18, + "outputs": [ + { + "data": { + "text/plain": " year_act node_loc tec relation Value node_origin year_rel \\\n0 1990 R12_GLB meth_trd CO2_trade 0.005545 R12_GLB 1990 \n1 1995 R12_GLB meth_trd CO2_trade 0.005545 R12_GLB 1995 \n2 2000 R12_GLB meth_trd CO2_trade 0.005545 R12_GLB 2000 \n3 2005 R12_GLB meth_trd CO2_trade 0.005545 R12_GLB 2005 \n4 2010 R12_GLB meth_trd CO2_trade 0.005545 R12_GLB 2010 \n... ... ... ... ... ... ... ... \n13679 2070 R12_CHN meth_t_d FE_liquids 1.000000 R12_CHN 2070 \n13680 2080 R12_CHN meth_t_d FE_liquids 1.000000 R12_CHN 2080 \n13681 2090 R12_CHN meth_t_d FE_liquids 1.000000 R12_CHN 2090 \n13682 2100 R12_CHN meth_t_d FE_liquids 1.000000 R12_CHN 2100 \n13683 2110 R12_CHN meth_t_d FE_liquids 1.000000 R12_CHN 2110 \n\n count df \n0 1 fuel \n1 1 fuel \n2 1 fuel \n3 1 fuel \n4 1 fuel \n... ... ... \n13679 1 fuel \n13680 1 fuel \n13681 1 fuel \n13682 1 fuel \n13683 1 fuel \n\n[13684 rows x 9 columns]", + "text/html": "
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year_actnode_loctecrelationValuenode_originyear_relcountdf
01990R12_GLBmeth_trdCO2_trade0.005545R12_GLB19901fuel
11995R12_GLBmeth_trdCO2_trade0.005545R12_GLB19951fuel
22000R12_GLBmeth_trdCO2_trade0.005545R12_GLB20001fuel
32005R12_GLBmeth_trdCO2_trade0.005545R12_GLB20051fuel
42010R12_GLBmeth_trdCO2_trade0.005545R12_GLB20101fuel
..............................
136792070R12_CHNmeth_t_dFE_liquids1.000000R12_CHN20701fuel
136802080R12_CHNmeth_t_dFE_liquids1.000000R12_CHN20801fuel
136812090R12_CHNmeth_t_dFE_liquids1.000000R12_CHN20901fuel
136822100R12_CHNmeth_t_dFE_liquids1.000000R12_CHN21001fuel
136832110R12_CHNmeth_t_dFE_liquids1.000000R12_CHN21101fuel
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13684 rows × 9 columns

\n
" + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "diff = wo_labels.drop_duplicates(keep=False).merge(w_labels[\"df\"], left_index=True, right_index=True)\n", + "diff[diff[\"df\"]==\"fuel\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## co2 emissions analysis" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 19, + "outputs": [], + "source": [ + "def get_bot_up_emi_by_fuel(df, node, year):\n", + " #df = df.loc[node, year]\n", + "\n", + " df_oil = df[df.index.get_level_values(0).str.contains(\"oil\") |\n", + " (df.index.get_level_values(0).str.contains(\"liq\")) |\n", + " (df.index.get_level_values(0).str.contains(\"treat\")) |\n", + " (df.index.get_level_values(0).str.contains(\"coki\")) |\n", + " (df.index.get_level_values(0).str.contains(\"cata\"))\n", + " ].sort_values(\"emi\")\n", + " df_oil = df_oil[df_oil.index.get_level_values(0) != \"liq_bio_ccs\"]\n", + "\n", + " df_gas = df[(\n", + " (df.index.get_level_values(0).str.contains(\"gas\")) |\n", + " (df.index.get_level_values(0).str.contains(\"meth_ng\")) |\n", + " (df.index.get_level_values(0).str.contains(\"smr\")) |\n", + " (df.index.get_level_values(0) == \"h2_mix\") |\n", + " (df.index.get_level_values(0).str.contains(\"flar\"))\n", + " )].sort_values(\"emi\")\n", + "\n", + " df_coal = df[(df.index.get_level_values(0).str.contains(\"coal\")) | (df.index.get_level_values(0).str.contains(\"igcc\"))].sort_values(\"emi\")\n", + "\n", + " return df_gas, df_oil, df_coal" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 20, + "outputs": [], + "source": [ + "def get_top_dn_emi_by_fuel(df, node, year):\n", + " #df = df.loc[node, year]\n", + "\n", + " top_gas = df[((df.index.get_level_values(0).str.contains(\"gas\")) |\n", + " (df.index.get_level_values(0).str.contains(\"LNG\")) |\n", + " (df.index.get_level_values(1).str.contains(\"ane\")) |\n", + " (df.index.get_level_values(0).str.contains(\"h2_smr\")) |\n", + " (df.index.get_level_values(0).str.contains(\"flar\")))]#.sum()\n", + "\n", + " top_oil = df[(df.index.get_level_values(0).str.contains(\"oil\"))\n", + " |(df.index.get_level_values(1).str.contains(\"gasoil\"))].sort_values(\"emi\")#.sum()\n", + " #top_oil = top_oil[top_oil.index.get_level_values(0) != \"liq_bio_ccs\"]\n", + " top_coal = df[(df.index.get_level_values(0).str.contains(\"coal\")) |\n", + " (df.index.get_level_values(0).str.contains(\"lign\"))].sort_values(\"emi\")#.sum()\n", + "\n", + " return top_gas, top_oil, top_coal" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 21, + "outputs": [], + "source": [ + "elec_list = [\n", + " \"coal_ppl_u\",\n", + " \"coal_ppl\",\n", + " \"coal_adv\",\n", + " \"coal_adv_ccs\",\n", + " \"igcc\",\n", + " \"igcc_ccs\",\n", + " \"foil_ppl\",\n", + " \"loil_ppl\",\n", + " \"loil_cc\",\n", + " \"oil_ppl\",\n", + " \"gas_ppl\",\n", + " \"gas_cc\",\n", + " \"gas_cc_ccs\",\n", + " \"gas_ct\",\n", + " \"gas_htfc\",\n", + " \"bio_istig\",\n", + " \"g_ppl_co2scr\",\n", + " \"c_ppl_co2scr\",\n", + " \"bio_ppl_co2scr\",\n", + " \"igcc_co2scr\",\n", + " \"gfc_co2scr\",\n", + " \"cfc_co2scr\",\n", + " \"bio_istig_ccs\",\n", + " ]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 22, + "outputs": [], + "source": [ + "region = \"R12_LAM\"\n", + "year = \"2020\"" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## get global emissions for specific year" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 23, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\maczek\\AppData\\Local\\Temp\\ipykernel_17352\\2128802406.py:7: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " emi_bot_up_new = emi_bot_up_new.append(emi_top_down_new[emi_top_down_new.index.get_level_values(0) == \"meth_bio_ccs\"])\n" + ] + } + ], + "source": [ + "emi_top_down_new = emi_top_down.reset_index().set_index([\"tec\", \"mode\"])\n", + "emi_bot_up_new = emi_bott_up.reset_index().set_index([\"tec\", \"mode\"])\n", + "\n", + "emi_top_down_new = emi_top_down_new[emi_top_down_new[\"year_act\"]==year]\n", + "emi_bot_up_new = emi_bot_up_new[emi_bot_up_new[\"year_act\"]==year]\n", + "\n", + "emi_bot_up_new = emi_bot_up_new.append(emi_top_down_new[emi_top_down_new.index.get_level_values(0) == \"meth_bio_ccs\"])\n", + "emi_top_down_new = emi_top_down_new.drop(\"CO2_TCE\")\n", + "emi_bot_up_new = emi_bot_up_new.drop([\"CO2t_TCE\", \"CO2s_TCE\"])\n", + "\n", + "emi_bot_up_new = pd.concat([emi_bot_up_new, emi_top_down_new[emi_top_down_new.index.get_level_values(0).str.contains(\"alu\")]])\n", + "emi_bot_up_new = pd.concat([emi_bot_up_new, emi_top_down_new[emi_top_down_new.index.get_level_values(0).str.contains(\"clink\")]])\n", + "\n", + "top_new_gas, top_new_oil, top_new_coal = get_top_dn_emi_by_fuel(emi_top_down_new[emi_top_down_new[\"year_act\"]==year], \"test\", \"test\")\n", + "bot_new_gas, bot_new_oil, bot_new_coal = get_bot_up_emi_by_fuel(emi_bot_up_new[emi_bot_up_new[\"year_act\"]==year], \"test\", \"test\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 24, + "outputs": [], + "source": [ + "oil_tecs = ['hydrotreating_ref', 'catalytic_reforming_ref', 'oil_extr_1_ch4',\n", + " 'furnace_foil_petro', 'loil_t_d', 'oil_extr_2_ch4', 'foil_hpl',\n", + " 'foil_t_d', 'furnace_foil_refining', 'oil_extr_2', 'oil_extr_1',\n", + " 'oil_extr_3', 'furnace_foil_aluminum', 'loil_rc', 'oil_extr_3_ch4',\n", + " 'furnace_foil_cement', 'oil_extr_4_ch4', 'foil_trd', 'oil_trd',\n", + "\n", + " 'fueloil_NH3', 'fueloil_NH3_ccs', 'loil_i', 'foil_i', 'foil_trp', 'loil_trp', 'loil_bunker',\n", + " 'foil_bunker', 'oil_ppl', 'loil_ppl', 'loil_cc', 'foil_rc', 'foil_ppl',\n", + " 'catalytic_cracking_ref', 'loil_trd', 'oil_extr_4', \"coking_ref\", 'LNG_bunker', 'sp_liq_I']\n", + "bot_new_oil = emi_bot_up_new[(emi_bot_up_new.index.get_level_values(0).isin(oil_tecs))]\n", + "\n", + "coal_tecs = ['furnace_coal_aluminum', 'coal_hpl', 'furnace_coal_refining',\n", + " 'furnace_coal_cement', 'DUMMY_coal_supply', 'h2_coal_ccs',\n", + " 'coal_NH3_ccs',\n", + "\n", + " 'coal_ppl', 'coal_ppl_u', 'meth_coal', 'meth_coal_ccs', 'coal_rc', 'coal_NH3', 'coal_adv', 'coal_i', 'coal_gas'\n", + " ]\n", + "bot_new_coal = emi_bot_up_new[(emi_bot_up_new.index.get_level_values(0).isin(coal_tecs))]\n", + "\n", + "gas = ['h2_mix', 'gas_t_d', 'gas_hpl', 'furnace_gas_petro', 'gas_extr_2',\n", + " 'gas_extr_1', 'gas_t_d_ch4', 'gas_NH3_ccs', 'furnace_gas_refining',\n", + " 'gas_extr_3', 'DUMMY_gas_supply', 'gas_cc', 'gas_extr_4', 'gas_extr_5',\n", + " 'gas_extr_6', 'gas_i', 'gas_trp',\n", + "\n", + " 'gas_NH3','gas_cc_ccs', 'hp_gas_rc', 'hp_gas_i', 'gas_rc', 'gas_ct',\n", + " 'gas_ppl', 'meth_ng_ccs', 'h2_smr_ccs', 'meth_ng', 'LNG_trd', 'h2_smr',\n", + "]\n", + "bot_new_gas = emi_bot_up_new[(emi_bot_up_new.index.get_level_values(0).isin(gas))]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 25, + "outputs": [], + "source": [ + "top_new_trd = emi_top_down_new[(emi_top_down_new.index.get_level_values(0).str.contains(\"exp\")) | (emi_top_down_new.index.get_level_values(0).str.contains(\"imp\"))]\n", + "bot_new_trd = emi_bot_up_new[(emi_bot_up_new.index.get_level_values(0).str.contains(\"exp\")) | (emi_bot_up_new.index.get_level_values(0).str.contains(\"imp\"))]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## residual carbon emissions" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 26, + "outputs": [], + "source": [ + "bot_new_rest = emi_bot_up_new[~(emi_bot_up_new.index.get_level_values(0).isin(bot_new_coal.index.get_level_values(0))) &\n", + " ~(emi_bot_up_new.index.get_level_values(0).isin(bot_new_oil.index.get_level_values(0))) &\n", + " ~(emi_bot_up_new.index.get_level_values(0).isin(bot_new_gas.index.get_level_values(0)))\n", + "].sort_values(\"emi\")\n", + "\n", + "top_new_rest = emi_top_down_new[~(emi_top_down_new.index.get_level_values(0).isin(top_new_oil.index.get_level_values(0))) &\n", + " ~(emi_top_down_new.index.get_level_values(0).isin(top_new_gas.index.get_level_values(0))) &\n", + " ~(emi_top_down_new.index.get_level_values(0).isin(top_new_coal.index.get_level_values(0))) &\n", + " ~(emi_top_down_new.index.get_level_values(0).isin(top_new_trd.index.get_level_values(0)))\n", + "].sort_values(\"emi\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 27, + "outputs": [ + { + "data": { + "text/plain": "211.23699782794165" + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bot_new_rest.sum()[\"emi\"] * (44/12) - top_new_rest.sum()[\"emi\"] * (44/12)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 28, + "outputs": [ + { + "data": { + "text/plain": "423.78991342878" + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "emi_bot_up_new.sum().emi * (44/12) - emi_top_down_new.sum().emi * (44/12)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 29, + "outputs": [ + { + "data": { + "text/plain": "-12.097574083883933" + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "top_new_rest[top_new_rest[\"emi\"]<0].loc[\"meth_t_d\"].sum()[\"emi\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 30, + "outputs": [ + { + "data": { + "text/plain": "-44.35777164090775" + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "top_new_rest.loc[\"meth_t_d\"].sum().emi * co2_c_factor\n", + "#top_new_rest.loc[\"clinker_dry_cement\"].sum().emi * co2_c_factor" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 31, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index([], dtype='object', name='tec')\n", + "Index(['meth_t_d'], dtype='object', name='tec')\n", + "Index(['CH2O_synth', 'soderberg_aluminum', 'eth_t_d', 'flaring_CO2',\n", + " 'prebake_aluminum', 'meth_trd', 'furnace_methanol_cement',\n", + " 'DUMMY_limestone_supply_steel', 'MTO_petro', 'h2_coal',\n", + " 'clinker_wet_cement', 'clinker_dry_cement', 'igcc'],\n", + " dtype='object', name='tec')\n", + "Index(['soderberg_aluminum', 'prebake_aluminum', 'clinker_wet_cement',\n", + " 'clinker_dry_cement'],\n", + " dtype='object', name='tec')\n" + ] + } + ], + "source": [ + "print(bot_new_rest[bot_new_rest[\"emi\"]<0].drop_duplicates().index.get_level_values(0).unique())\n", + "print(top_new_rest[top_new_rest[\"emi\"]<0].drop_duplicates().index.get_level_values(0).unique())\n", + "print(bot_new_rest[bot_new_rest[\"emi\"]>0].drop_duplicates().index.get_level_values(0).unique())\n", + "print(top_new_rest[top_new_rest[\"emi\"]>0].drop_duplicates().index.get_level_values(0).unique())" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 32, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "negative residual emission bottom-up: 0.0\n", + "negative residual emission top-down: -44.35777164090775\n", + "positive residual emission bottom-up: 2137.403984064366\n", + "positive residual emission top-down: 1970.5247578773321\n" + ] + } + ], + "source": [ + "print(\"negative residual emission bottom-up: \", bot_new_rest[bot_new_rest[\"emi\"]<0].drop_duplicates().sum()[\"emi\"] * (44/12))\n", + "print(\"negative residual emission top-down: \", top_new_rest[top_new_rest[\"emi\"]<0].sum()[\"emi\"] * (44/12))\n", + "print(\"positive residual emission bottom-up: \", bot_new_rest[bot_new_rest[\"emi\"]>0].sum()[\"emi\"] * (44/12))\n", + "print(\"positive residual emission top-down: \", top_new_rest[top_new_rest[\"emi\"]>0].sum()[\"emi\"] * (44/12))" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## check trade carbon balances" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 33, + "outputs": [ + { + "data": { + "text/plain": " node_loc year_act year_vtg relation Value node_origin \\\ntec mode \nloil_exp M1 R12_AFR 2020 2015 CO2_Emission -0.631 R12_AFR \nloil_imp M1 R12_AFR 2020 2020 CO2_Emission 0.631 R12_AFR \n M1 R12_CHN 2020 2020 CO2_Emission 0.631 R12_CHN \nloil_exp M1 R12_FSU 2020 2000 CO2_Emission -0.631 R12_FSU \n M1 R12_FSU 2020 2005 CO2_Emission -0.631 R12_FSU \n M1 R12_FSU 2020 2015 CO2_Emission -0.631 R12_FSU \n M1 R12_FSU 2020 2020 CO2_Emission -0.631 R12_FSU \n M1 R12_LAM 2020 2015 CO2_Emission -0.631 R12_LAM \n M1 R12_MEA 2020 2000 CO2_Emission -0.631 R12_MEA \n M1 R12_MEA 2020 2005 CO2_Emission -0.631 R12_MEA \n M1 R12_MEA 2020 2010 CO2_Emission -0.631 R12_MEA \n M1 R12_MEA 2020 2020 CO2_Emission -0.631 R12_MEA \n M1 R12_NAM 2020 2000 CO2_Emission -0.631 R12_NAM \n M1 R12_NAM 2020 2015 CO2_Emission -0.631 R12_NAM \nloil_imp M1 R12_NAM 2020 2020 CO2_Emission 0.631 R12_NAM \n M1 R12_PAO 2020 2020 CO2_Emission 0.631 R12_PAO \n M1 R12_RCPA 2020 2020 CO2_Emission 0.631 R12_RCPA \nloil_exp M1 R12_SAS 2020 2015 CO2_Emission -0.631 R12_SAS \nloil_imp M1 R12_SAS 2020 2020 CO2_Emission 0.631 R12_SAS \n M1 R12_WEU 2020 2020 CO2_Emission 0.631 R12_WEU \n\n year_rel Level emi \ntec mode \nloil_exp M1 2020 0.399258 -0.251931 \nloil_imp M1 2020 47.653442 30.069322 \n M1 2020 39.268683 24.778539 \nloil_exp M1 2020 10.142300 -6.399791 \n M1 2020 40.339789 -25.454407 \n M1 2020 13.855435 -8.742779 \n M1 2020 39.340764 -24.824022 \n M1 2020 40.069433 -25.283812 \n M1 2020 51.848149 -32.716182 \n M1 2020 23.071599 -14.558179 \n M1 2020 51.197000 -32.305307 \n M1 2020 127.024324 -80.152349 \n M1 2020 10.000000 -6.310000 \n M1 2020 54.575404 -34.437080 \nloil_imp M1 2020 302.049754 190.593395 \n M1 2020 9.866751 6.225920 \n M1 2020 5.307703 3.349160 \nloil_exp M1 2020 15.521467 -9.794046 \nloil_imp M1 2020 33.309633 21.018378 \n M1 2020 23.414262 14.774399 ", + "text/html": "
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node_locyear_actyear_vtgrelationValuenode_originyear_relLevelemi
tecmode
loil_expM1R12_AFR20202015CO2_Emission-0.631R12_AFR20200.399258-0.251931
loil_impM1R12_AFR20202020CO2_Emission0.631R12_AFR202047.65344230.069322
M1R12_CHN20202020CO2_Emission0.631R12_CHN202039.26868324.778539
loil_expM1R12_FSU20202000CO2_Emission-0.631R12_FSU202010.142300-6.399791
M1R12_FSU20202005CO2_Emission-0.631R12_FSU202040.339789-25.454407
M1R12_FSU20202015CO2_Emission-0.631R12_FSU202013.855435-8.742779
M1R12_FSU20202020CO2_Emission-0.631R12_FSU202039.340764-24.824022
M1R12_LAM20202015CO2_Emission-0.631R12_LAM202040.069433-25.283812
M1R12_MEA20202000CO2_Emission-0.631R12_MEA202051.848149-32.716182
M1R12_MEA20202005CO2_Emission-0.631R12_MEA202023.071599-14.558179
M1R12_MEA20202010CO2_Emission-0.631R12_MEA202051.197000-32.305307
M1R12_MEA20202020CO2_Emission-0.631R12_MEA2020127.024324-80.152349
M1R12_NAM20202000CO2_Emission-0.631R12_NAM202010.000000-6.310000
M1R12_NAM20202015CO2_Emission-0.631R12_NAM202054.575404-34.437080
loil_impM1R12_NAM20202020CO2_Emission0.631R12_NAM2020302.049754190.593395
M1R12_PAO20202020CO2_Emission0.631R12_PAO20209.8667516.225920
M1R12_RCPA20202020CO2_Emission0.631R12_RCPA20205.3077033.349160
loil_expM1R12_SAS20202015CO2_Emission-0.631R12_SAS202015.521467-9.794046
loil_impM1R12_SAS20202020CO2_Emission0.631R12_SAS202033.30963321.018378
M1R12_WEU20202020CO2_Emission0.631R12_WEU202023.41426214.774399
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" + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "emi_top_down_new[emi_top_down_new.index.get_level_values(0).str.contains(\"loil\")]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 34, + "outputs": [ + { + "data": { + "text/plain": "\"['gas_exp_chn', 'gas_exp_cpa', 'gas_exp_eeu', 'gas_exp_sas', 'gas_exp_weu', 'gas_imp', 'loil_exp']\"" + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "trd_tecs = sorted(list(emi_top_down_new.loc[(emi_top_down_new.index.get_level_values(0).str.contains(\"exp\")) | (emi_top_down_new.index.get_level_values(0).str.contains(\"imp\")) | (emi_top_down_new.index.get_level_values(0).str.contains(\"LNG\"))].index.get_level_values(0).unique()))#.sum()\n", + "comm_trd = [*trd_tecs[6:13]]#, trd_tecs[0]]\n", + "str(comm_trd)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 35, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total top-down: 10106.387931650237\n" + ] + } + ], + "source": [ + "print(\"total top-down: \", emi_top_down_new.sum()[\"emi\"])" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 36, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "top-down trade balance: -309.90262543642564\n" + ] + } + ], + "source": [ + "print(\"top-down trade balance: \", emi_top_down_new.loc[(emi_top_down_new.index.get_level_values(0).isin(comm_trd))].sum()[\"emi\"])" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 37, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " top-down bottom-up\n", + "coal: 15310.756509423185 15367.135755810177\n", + "oil: 12224.570955760117 12550.335173360832\n", + "gas: 7622.870492500809 7425.670749577608\n", + "total: 35158.19795768411 35343.141678748616\n" + ] + } + ], + "source": [ + "print(\" top-down bottom-up\")\n", + "print(\"coal: \", top_new_coal.sum()[\"emi\"] * co2_c_factor,\" \", bot_new_coal.sum()[\"emi\"] * co2_c_factor)\n", + "print(\"oil: \", top_new_oil.sum()[\"emi\"] * co2_c_factor, \" \", bot_new_oil.sum()[\"emi\"] * co2_c_factor)\n", + "print(\"gas: \", top_new_gas.sum()[\"emi\"] * co2_c_factor,\" \", bot_new_gas.sum()[\"emi\"] * co2_c_factor)\n", + "print(\"total: \", (top_new_coal.sum()[\"emi\"]+top_new_oil.sum()[\"emi\"]+top_new_gas.sum()[\"emi\"]) * co2_c_factor,\" \", (bot_new_coal.sum()[\"emi\"]+bot_new_oil.sum()[\"emi\"]+bot_new_gas.sum()[\"emi\"]) * co2_c_factor)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## carbon IO gas" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 38, + "outputs": [], + "source": [ + "emi_factor_gas = 0.482" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 39, + "outputs": [ + { + "data": { + "text/plain": "node_loc year_act tec mode year_vtg\nR12_LAM 2020 gas_extr_mpen M1 2020 139.81507\nName: output, dtype: float64" + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "act_out.loc[region, year, [\"gas_extr_mpen\"]][\"output\"] * emi_factor_gas" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 40, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\maczek\\AppData\\Local\\Temp\\ipykernel_17352\\2951075765.py:1: FutureWarning: The behavior of indexing on a MultiIndex with a nested sequence of labels is deprecated and will change in a future version. `series.loc[label, sequence]` will raise if any members of 'sequence' or not present in the index's second level. To retain the old behavior, use `series.index.isin(sequence, level=1)`\n", + " inp_atm_dist = act_in.loc[region, year, [\"gas_bal\", \"gas_exp_nam\", \"LNG_prod\", \"gas_exp_cpa\", \"gas_exp_chn\", \"gas_exp_eeu\", \"gas_exp_sas\",\"gas_exp_weu\"]]\n" + ] + }, + { + "data": { + "text/plain": " Level commodity level \\\nnode_loc year_act tec mode year_vtg \nR12_LAM 2020 gas_bal M1 2020 252.783802 gas primary \n LNG_prod M1 2020 37.305296 gas primary \n\n Value node_origin input \nnode_loc year_act tec mode year_vtg \nR12_LAM 2020 gas_bal M1 2020 1.0 R12_LAM 252.783802 \n LNG_prod M1 2020 1.0 R12_LAM 37.305296 ", + "text/html": "
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LevelcommoditylevelValuenode_origininput
node_locyear_acttecmodeyear_vtg
R12_LAM2020gas_balM12020252.783802gasprimary1.0R12_LAM252.783802
LNG_prodM1202037.305296gasprimary1.0R12_LAM37.305296
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" + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "inp_atm_dist = act_in.loc[region, year, [\"gas_bal\", \"gas_exp_nam\", \"LNG_prod\", \"gas_exp_cpa\", \"gas_exp_chn\", \"gas_exp_eeu\", \"gas_exp_sas\",\"gas_exp_weu\"]]\n", + "inp_atm_dist#.sum()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 41, + "outputs": [ + { + "data": { + "text/plain": "node_loc year_act tec mode year_vtg\nR12_LAM 2020 gas_bal M1 2020 121.841792\n LNG_prod M1 2020 17.981153\nName: input, dtype: float64" + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#in_gas_proc = act_in.loc[region, year, \"gas_processing_petro\"]\n", + "(inp_atm_dist[\"input\"] * emi_factor_gas)#.sum()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 42, + "outputs": [ + { + "data": { + "text/plain": "tec\nfurnace_gas_refining 22.566977\ngas_NH3 2.610735\ngas_cc 38.983500\ngas_ppl 2.953142\ngas_t_d 47.083041\nh2_smr 3.473940\nmeth_ng 4.170458\nName: input, dtype: float64" + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(act_in.loc[region, year, act_in[\"level\"]==\"secondary\", act_in[\"commodity\"]==\"gas\"][\"input\"]\n", + " * emi_factor_gas).groupby(\"tec\").sum()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 43, + "outputs": [ + { + "data": { + "text/plain": "tec\ndri_steel 0.428896\neaf_steel 0.019407\nfurnace_gas_petro 2.164642\ngas_i 18.358918\ngas_processing_petro 2.809953\ngas_rc 15.804027\nhp_gas_aluminum 0.014086\nhp_gas_rc 1.386845\nName: input, dtype: float64" + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(act_in.loc[region, year, act_in[\"level\"]==\"final\", act_in[\"commodity\"]==\"gas\"][\"input\"] * emi_factor_gas).groupby(\"tec\").sum()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 44, + "outputs": [ + { + "data": { + "text/plain": "2.932242552118641" + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(act_out.loc[region, year, act_out[\"commodity\"].isin([\"ethane\", \"propane\"])][\"output\"] * 0.81).sum()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 45, + "outputs": [ + { + "data": { + "text/plain": "2.0096798752590517" + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(act_out.loc[region, year, act_out.index.get_level_values(3).isin([\"ethane\", \"propane\"])][\"output\"] * 0.5).sum()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 46, + "outputs": [ + { + "data": { + "text/plain": "1.2016059460355528" + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "act_out.loc[region, year, \"steam_cracker_petro\", act_out[\"commodity\"]==\"gas\"].sum()[\"output\"] * emi_factor_gas" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## carbon IO oil" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 47, + "outputs": [ + { + "ename": "KeyError", + "evalue": "('R12_LAM', '2020', 'oil_imp')", + "output_type": "error", + "traceback": [ + "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[1;31mKeyError\u001B[0m Traceback (most recent call last)", + "Input \u001B[1;32mIn [47]\u001B[0m, in \u001B[0;36m\u001B[1;34m()\u001B[0m\n\u001B[0;32m 2\u001B[0m out_oil_extr \u001B[38;5;241m=\u001B[39m act_out\u001B[38;5;241m.\u001B[39mloc[region, year, extr_tec]\n\u001B[0;32m 3\u001B[0m out_oil_exp \u001B[38;5;241m=\u001B[39m act_out\u001B[38;5;241m.\u001B[39mloc[region, year, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124moil_exp\u001B[39m\u001B[38;5;124m\"\u001B[39m]\n\u001B[1;32m----> 4\u001B[0m in_oil_imp \u001B[38;5;241m=\u001B[39m \u001B[43mact_in\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mloc\u001B[49m\u001B[43m[\u001B[49m\u001B[43mregion\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43myear\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43moil_imp\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\n\u001B[0;32m 6\u001B[0m carbon_in_extr \u001B[38;5;241m=\u001B[39m (out_oil_extr\u001B[38;5;241m.\u001B[39msum()[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124moutput\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m-\u001B[39m out_oil_exp\u001B[38;5;241m.\u001B[39msum()[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124moutput\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m+\u001B[39m in_oil_imp\u001B[38;5;241m.\u001B[39msum()[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124minput\u001B[39m\u001B[38;5;124m\"\u001B[39m]) \u001B[38;5;241m*\u001B[39m \u001B[38;5;241m0.631\u001B[39m\n\u001B[0;32m 7\u001B[0m carbon_in_extr\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:961\u001B[0m, in \u001B[0;36m_LocationIndexer.__getitem__\u001B[1;34m(self, key)\u001B[0m\n\u001B[0;32m 959\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_is_scalar_access(key):\n\u001B[0;32m 960\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mobj\u001B[38;5;241m.\u001B[39m_get_value(\u001B[38;5;241m*\u001B[39mkey, takeable\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_takeable)\n\u001B[1;32m--> 961\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_getitem_tuple\u001B[49m\u001B[43m(\u001B[49m\u001B[43mkey\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 962\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m 963\u001B[0m \u001B[38;5;66;03m# we by definition only have the 0th axis\u001B[39;00m\n\u001B[0;32m 964\u001B[0m axis \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39maxis \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;241m0\u001B[39m\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1140\u001B[0m, in \u001B[0;36m_LocIndexer._getitem_tuple\u001B[1;34m(self, tup)\u001B[0m\n\u001B[0;32m 1138\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m suppress(IndexingError):\n\u001B[0;32m 1139\u001B[0m tup \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_expand_ellipsis(tup)\n\u001B[1;32m-> 1140\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_getitem_lowerdim\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtup\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 1142\u001B[0m \u001B[38;5;66;03m# no multi-index, so validate all of the indexers\u001B[39;00m\n\u001B[0;32m 1143\u001B[0m tup \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_validate_tuple_indexer(tup)\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:859\u001B[0m, in \u001B[0;36m_LocationIndexer._getitem_lowerdim\u001B[1;34m(self, tup)\u001B[0m\n\u001B[0;32m 849\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m (\n\u001B[0;32m 850\u001B[0m \u001B[38;5;28misinstance\u001B[39m(ax0, MultiIndex)\n\u001B[0;32m 851\u001B[0m \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mname \u001B[38;5;241m!=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124miloc\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 856\u001B[0m \u001B[38;5;66;03m# is equivalent.\u001B[39;00m\n\u001B[0;32m 857\u001B[0m \u001B[38;5;66;03m# (see the other place where we call _handle_lowerdim_multi_index_axis0)\u001B[39;00m\n\u001B[0;32m 858\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m suppress(IndexingError):\n\u001B[1;32m--> 859\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_handle_lowerdim_multi_index_axis0\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtup\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 861\u001B[0m tup \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_validate_key_length(tup)\n\u001B[0;32m 863\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m i, key \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28menumerate\u001B[39m(tup):\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1166\u001B[0m, in \u001B[0;36m_LocIndexer._handle_lowerdim_multi_index_axis0\u001B[1;34m(self, tup)\u001B[0m\n\u001B[0;32m 1162\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m ek:\n\u001B[0;32m 1163\u001B[0m \u001B[38;5;66;03m# raise KeyError if number of indexers match\u001B[39;00m\n\u001B[0;32m 1164\u001B[0m \u001B[38;5;66;03m# else IndexingError will be raised\u001B[39;00m\n\u001B[0;32m 1165\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mndim \u001B[38;5;241m<\u001B[39m \u001B[38;5;28mlen\u001B[39m(tup) \u001B[38;5;241m<\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mobj\u001B[38;5;241m.\u001B[39mindex\u001B[38;5;241m.\u001B[39mnlevels:\n\u001B[1;32m-> 1166\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m ek\n\u001B[0;32m 1167\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m IndexingError(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mNo label returned\u001B[39m\u001B[38;5;124m\"\u001B[39m) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01mek\u001B[39;00m\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1160\u001B[0m, in \u001B[0;36m_LocIndexer._handle_lowerdim_multi_index_axis0\u001B[1;34m(self, tup)\u001B[0m\n\u001B[0;32m 1157\u001B[0m axis \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39maxis \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;241m0\u001B[39m\n\u001B[0;32m 1158\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m 1159\u001B[0m \u001B[38;5;66;03m# fast path for series or for tup devoid of slices\u001B[39;00m\n\u001B[1;32m-> 1160\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_get_label\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtup\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43maxis\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43maxis\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 1162\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m ek:\n\u001B[0;32m 1163\u001B[0m \u001B[38;5;66;03m# raise KeyError if number of indexers match\u001B[39;00m\n\u001B[0;32m 1164\u001B[0m \u001B[38;5;66;03m# else IndexingError will be raised\u001B[39;00m\n\u001B[0;32m 1165\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mndim \u001B[38;5;241m<\u001B[39m \u001B[38;5;28mlen\u001B[39m(tup) \u001B[38;5;241m<\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mobj\u001B[38;5;241m.\u001B[39mindex\u001B[38;5;241m.\u001B[39mnlevels:\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1153\u001B[0m, in \u001B[0;36m_LocIndexer._get_label\u001B[1;34m(self, label, axis)\u001B[0m\n\u001B[0;32m 1151\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m_get_label\u001B[39m(\u001B[38;5;28mself\u001B[39m, label, axis: \u001B[38;5;28mint\u001B[39m):\n\u001B[0;32m 1152\u001B[0m \u001B[38;5;66;03m# GH#5667 this will fail if the label is not present in the axis.\u001B[39;00m\n\u001B[1;32m-> 1153\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mobj\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mxs\u001B[49m\u001B[43m(\u001B[49m\u001B[43mlabel\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43maxis\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43maxis\u001B[49m\u001B[43m)\u001B[49m\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:3857\u001B[0m, in \u001B[0;36mNDFrame.xs\u001B[1;34m(self, key, axis, level, drop_level)\u001B[0m\n\u001B[0;32m 3854\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_consolidate_inplace()\n\u001B[0;32m 3856\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(index, MultiIndex):\n\u001B[1;32m-> 3857\u001B[0m loc, new_index \u001B[38;5;241m=\u001B[39m \u001B[43mindex\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_get_loc_level\u001B[49m\u001B[43m(\u001B[49m\u001B[43mkey\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mlevel\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m0\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[0;32m 3858\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m drop_level:\n\u001B[0;32m 3859\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m lib\u001B[38;5;241m.\u001B[39mis_integer(loc):\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\multi.py:3052\u001B[0m, in \u001B[0;36mMultiIndex._get_loc_level\u001B[1;34m(self, key, level)\u001B[0m\n\u001B[0;32m 3049\u001B[0m \u001B[38;5;28;01mpass\u001B[39;00m\n\u001B[0;32m 3051\u001B[0m \u001B[38;5;66;03m# partial selection\u001B[39;00m\n\u001B[1;32m-> 3052\u001B[0m indexer \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_loc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mkey\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 3053\u001B[0m ilevels \u001B[38;5;241m=\u001B[39m [i \u001B[38;5;28;01mfor\u001B[39;00m i \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mrange\u001B[39m(\u001B[38;5;28mlen\u001B[39m(key)) \u001B[38;5;28;01mif\u001B[39;00m key[i] \u001B[38;5;241m!=\u001B[39m \u001B[38;5;28mslice\u001B[39m(\u001B[38;5;28;01mNone\u001B[39;00m, \u001B[38;5;28;01mNone\u001B[39;00m)]\n\u001B[0;32m 3054\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(ilevels) \u001B[38;5;241m==\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mnlevels:\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\multi.py:2905\u001B[0m, in \u001B[0;36mMultiIndex.get_loc\u001B[1;34m(self, key, method)\u001B[0m\n\u001B[0;32m 2902\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m(key) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01merr\u001B[39;00m\n\u001B[0;32m 2904\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m start \u001B[38;5;241m==\u001B[39m stop:\n\u001B[1;32m-> 2905\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m(key)\n\u001B[0;32m 2907\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m follow_key:\n\u001B[0;32m 2908\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mslice\u001B[39m(start, stop)\n", + "\u001B[1;31mKeyError\u001B[0m: ('R12_LAM', '2020', 'oil_imp')" + ] + } + ], + "source": [ + "extr_tec = \"oil_extr_mpen\"\n", + "out_oil_extr = act_out.loc[region, year, extr_tec]\n", + "out_oil_exp = act_out.loc[region, year, \"oil_exp\"]\n", + "in_oil_imp = act_in.loc[region, year, \"oil_imp\"]\n", + "\n", + "carbon_in_extr = (out_oil_extr.sum()[\"output\"] - out_oil_exp.sum()[\"output\"] + in_oil_imp.sum()[\"input\"]) * 0.631\n", + "carbon_in_extr" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 48, + "outputs": [ + { + "data": { + "text/plain": "319.2508439033615" + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "inp_atm_dist = act_in.loc[region, year, \"atm_distillation_ref\"]\n", + "carbon_in_ref = inp_atm_dist[inp_atm_dist[\"commodity\"] == \"crudeoil\"].sum()[\"input\"] * 0.631\n", + "carbon_in_ref" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 49, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10.089172440637014\n" + ] + } + ], + "source": [ + "out_agg_ref = act_out.loc[region, year, \"agg_ref\"]\n", + "carbon_out_agg_ref = out_agg_ref.loc[out_agg_ref[\"commodity\"].isin([\"fueloil\"])][\"output\"].sum() * 0.665 \\\n", + "+ out_agg_ref[~out_agg_ref[\"commodity\"].isin([\"fueloil\"])].sum()[\"output\"] * 0.631\n", + "\n", + "print(carbon_in_ref - carbon_out_agg_ref)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 50, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\maczek\\AppData\\Local\\Temp\\ipykernel_17352\\3261112422.py:1: PerformanceWarning: indexing past lexsort depth may impact performance.\n", + " inp_atm_dist = act_in.swaplevel(0,2).loc[\"atm_distillation_ref\", year]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "252.94682658537567\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\maczek\\AppData\\Local\\Temp\\ipykernel_17352\\3261112422.py:4: PerformanceWarning: indexing past lexsort depth may impact performance.\n", + " out_agg_ref = act_out.swaplevel(0,2).loc[\"agg_ref\", year]\n" + ] + } + ], + "source": [ + "inp_atm_dist = act_in.swaplevel(0,2).loc[\"atm_distillation_ref\", year]\n", + "carbon_in_ref = inp_atm_dist[inp_atm_dist[\"commodity\"] == \"crudeoil\"].sum()[\"input\"] * 0.631\n", + "\n", + "out_agg_ref = act_out.swaplevel(0,2).loc[\"agg_ref\", year]\n", + "carbon_out_agg_ref = out_agg_ref.loc[out_agg_ref[\"commodity\"].isin([\"fueloil\"])][\"output\"].sum() * 0.665 + out_agg_ref[~out_agg_ref[\"commodity\"].isin([\"fueloil\"])].sum()[\"output\"] * 0.631\n", + "\n", + "print(carbon_in_ref - carbon_out_agg_ref)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 51, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\maczek\\AppData\\Local\\Temp\\ipykernel_17352\\2343031665.py:1: PerformanceWarning: indexing past lexsort depth may impact performance.\n", + " sc_in = act_in.swaplevel(0,2).loc[\"steam_cracker_petro\", year]\n" + ] + }, + { + "data": { + "text/plain": "301.4771887846489" + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sc_in = act_in.swaplevel(0,2).loc[\"steam_cracker_petro\", year]\n", + "sc_in[~sc_in.index.get_level_values(1).isin([\"ethane\", \"propane\"])].sum()[\"input\"] * 0.631" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## carbon IO coal" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 52, + "outputs": [ + { + "data": { + "text/plain": " Level commodity level Value \\\ntec mode year_vtg \nLNG_prod M1 2020 37.305296 gas primary 1.000000 \ndri_steel M1 2020 2.696444 gas final 0.330000 \n 2020 2.696444 gas dummy_emission 0.330000 \neaf_steel M2 2005 0.265603 gas final 0.002000 \n 2005 0.265603 gas dummy_emission 0.002000 \n... ... ... ... ... \nmeth_ng fuel 2015 2.359075 gas secondary 1.666667 \n 2015 2.359075 gas secondary 1.666667 \n 2015 2.359075 gas secondary 1.666667 \n 2015 2.359075 gas secondary 1.666667 \n 2015 2.359075 gas secondary 1.666667 \n\n node_origin input emi_factor \ntec mode year_vtg \nLNG_prod M1 2020 R12_LAM 37.305296 NaN \ndri_steel M1 2020 R12_LAM 0.889827 NaN \n 2020 R12_LAM 0.889827 NaN \neaf_steel M2 2005 R12_LAM 0.000531 NaN \n 2005 R12_LAM 0.000531 NaN \n... ... ... ... \nmeth_ng fuel 2015 R12_LAM 3.931792 0.253333 \n 2015 R12_LAM 3.931792 0.253333 \n 2015 R12_LAM 3.931792 0.253333 \n 2015 R12_LAM 3.931792 0.253333 \n 2015 R12_LAM 3.931792 0.253333 \n\n[2135 rows x 7 columns]", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
LevelcommoditylevelValuenode_origininputemi_factor
tecmodeyear_vtg
LNG_prodM1202037.305296gasprimary1.000000R12_LAM37.305296NaN
dri_steelM120202.696444gasfinal0.330000R12_LAM0.889827NaN
20202.696444gasdummy_emission0.330000R12_LAM0.889827NaN
eaf_steelM220050.265603gasfinal0.002000R12_LAM0.000531NaN
20050.265603gasdummy_emission0.002000R12_LAM0.000531NaN
..............................
meth_ngfuel20152.359075gassecondary1.666667R12_LAM3.9317920.253333
20152.359075gassecondary1.666667R12_LAM3.9317920.253333
20152.359075gassecondary1.666667R12_LAM3.9317920.253333
20152.359075gassecondary1.666667R12_LAM3.9317920.253333
20152.359075gassecondary1.666667R12_LAM3.9317920.253333
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2135 rows × 7 columns

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" + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "act_in_yr_reg = act_in.loc[region, year]\n", + "comm = \"gas\"\n", + "bott_dict = {\n", + " \"coal\":bot_new_coal,\n", + " \"gas\":bot_new_gas\n", + "}\n", + "bott_yr_reg_coal_merge = bott_dict[comm].copy(deep=True)\n", + "x = act_in_yr_reg[(act_in_yr_reg.index.get_level_values(0).str.contains(comm)) & (act_in_yr_reg[\"commodity\"]==comm) & (act_in_yr_reg[\"level\"]==\"final\")].join(bott_yr_reg_coal_merge.rename({\"Value\": \"emi_factor\"}, axis=1)[\"emi_factor\"])\n", + "x = act_in_yr_reg[(act_in_yr_reg[\"commodity\"]==comm)].join(bott_yr_reg_coal_merge.rename({\"Value\": \"emi_factor\"}, axis=1)[\"emi_factor\"])\n", + "x" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 53, + "outputs": [ + { + "data": { + "text/plain": " Level commodity level \\\ntec mode node_loc year_act year_vtg \nLNG_prod M1 R12_AFR 2020 2020 60.182113 gas primary \n 2025 2025 69.629561 gas primary \n 2030 2030 94.292866 gas primary \n 2035 2035 101.502785 gas primary \n 2040 2040 100.225163 gas primary \n... ... ... ... \nmeth_ng fuel R12_WEU 2025 2015 0.331997 gas secondary \n 2015 0.331997 gas secondary \n 2015 0.331997 gas secondary \n 2015 0.331997 gas secondary \n 2015 0.331997 gas secondary \n\n Value node_origin input \\\ntec mode node_loc year_act year_vtg \nLNG_prod M1 R12_AFR 2020 2020 1.000000 R12_AFR 60.182113 \n 2025 2025 1.000000 R12_AFR 69.629561 \n 2030 2030 1.000000 R12_AFR 94.292866 \n 2035 2035 1.000000 R12_AFR 101.502785 \n 2040 2040 1.000000 R12_AFR 100.225163 \n... ... ... ... \nmeth_ng fuel R12_WEU 2025 2015 1.666667 R12_WEU 0.553329 \n 2015 1.666667 R12_WEU 0.553329 \n 2015 1.666667 R12_WEU 0.553329 \n 2015 1.666667 R12_WEU 0.553329 \n 2015 1.666667 R12_WEU 0.553329 \n\n emi_factor \ntec mode node_loc year_act year_vtg \nLNG_prod M1 R12_AFR 2020 2020 NaN \n 2025 2025 NaN \n 2030 2030 NaN \n 2035 2035 NaN \n 2040 2040 NaN \n... ... \nmeth_ng fuel R12_WEU 2025 2015 0.253333 \n 2015 0.253333 \n 2015 0.253333 \n 2015 0.253333 \n 2015 0.253333 \n\n[204796 rows x 7 columns]", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
LevelcommoditylevelValuenode_origininputemi_factor
tecmodenode_locyear_actyear_vtg
LNG_prodM1R12_AFR2020202060.182113gasprimary1.000000R12_AFR60.182113NaN
2025202569.629561gasprimary1.000000R12_AFR69.629561NaN
2030203094.292866gasprimary1.000000R12_AFR94.292866NaN
20352035101.502785gasprimary1.000000R12_AFR101.502785NaN
20402040100.225163gasprimary1.000000R12_AFR100.225163NaN
....................................
meth_ngfuelR12_WEU202520150.331997gassecondary1.666667R12_WEU0.5533290.253333
20150.331997gassecondary1.666667R12_WEU0.5533290.253333
20150.331997gassecondary1.666667R12_WEU0.5533290.253333
20150.331997gassecondary1.666667R12_WEU0.5533290.253333
20150.331997gassecondary1.666667R12_WEU0.5533290.253333
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204796 rows × 7 columns

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" + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "act_in_yr_reg = act_in#.swaplevel(0,1).loc[year]\n", + "comm = \"gas\"\n", + "bott_dict = {\n", + " \"coal\":bot_new_coal,\n", + " \"gas\":bot_new_gas\n", + "}\n", + "bott_yr_reg_coal_merge = bott_dict[comm].copy(deep=True)\n", + "x = act_in_yr_reg[(act_in_yr_reg.index.get_level_values(0).str.contains(comm)) & (act_in_yr_reg[\"commodity\"]==comm) & (act_in_yr_reg[\"level\"]==\"final\")].join(bott_yr_reg_coal_merge.rename({\"Value\": \"emi_factor\"}, axis=1)[\"emi_factor\"])\n", + "x = act_in_yr_reg[(act_in_yr_reg[\"commodity\"]==comm)].join(bott_yr_reg_coal_merge.rename({\"Value\": \"emi_factor\"}, axis=1)[\"emi_factor\"])\n", + "x" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 54, + "outputs": [ + { + "data": { + "text/plain": " node_loc year_act year_vtg relation Value \\\ntec mode \nDUMMY_gas_supply M1 R12_AFR 2020 2020 CO2_ind 0.482000 \nfurnace_gas_petro high_temp R12_AFR 2020 2020 CO2_ind 0.523913 \nfurnace_gas_refining high_temp R12_AFR 2020 2020 CO2_cc 0.800120 \ngas_NH3 M1 R12_AFR 2020 2005 CO2_cc 0.534804 \n M1 R12_AFR 2020 2010 CO2_cc 0.534804 \n... ... ... ... ... ... \ngas_t_d M1 R12_WEU 2020 2005 CO2_cc 0.034737 \n M1 R12_WEU 2020 2010 CO2_cc 0.034737 \nhp_gas_rc M1 R12_WEU 2020 2020 CO2_r_c 0.321333 \nmeth_ng feedstock R12_WEU 2020 2015 CO2_cc 0.253333 \n fuel R12_WEU 2020 2015 CO2_cc 0.253333 \n\n node_origin year_rel Level emi \ntec mode \nDUMMY_gas_supply M1 R12_AFR 2020 1.660615 0.800417 \nfurnace_gas_petro high_temp R12_AFR 2020 1.179212 0.617805 \nfurnace_gas_refining high_temp R12_AFR 2020 4.492885 3.594847 \ngas_NH3 M1 R12_AFR 2020 1.065932 0.570065 \n M1 R12_AFR 2020 2.041205 1.091645 \n... ... ... ... ... \ngas_t_d M1 R12_WEU 2020 64.785421 2.250441 \n M1 R12_WEU 2020 15.089721 0.524169 \nhp_gas_rc M1 R12_WEU 2020 33.288225 10.696616 \nmeth_ng feedstock R12_WEU 2020 1.287200 0.326091 \n fuel R12_WEU 2020 1.468320 0.371975 \n\n[520 rows x 9 columns]", + "text/html": "
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tecmode
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furnace_gas_petrohigh_tempR12_AFR20202020CO2_ind0.523913R12_AFR20201.1792120.617805
furnace_gas_refininghigh_tempR12_AFR20202020CO2_cc0.800120R12_AFR20204.4928853.594847
gas_NH3M1R12_AFR20202005CO2_cc0.534804R12_AFR20201.0659320.570065
M1R12_AFR20202010CO2_cc0.534804R12_AFR20202.0412051.091645
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gas_t_dM1R12_WEU20202005CO2_cc0.034737R12_WEU202064.7854212.250441
M1R12_WEU20202010CO2_cc0.034737R12_WEU202015.0897210.524169
hp_gas_rcM1R12_WEU20202020CO2_r_c0.321333R12_WEU202033.28822510.696616
meth_ngfeedstockR12_WEU20202015CO2_cc0.253333R12_WEU20201.2872000.326091
fuelR12_WEU20202015CO2_cc0.253333R12_WEU20201.4683200.371975
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520 rows × 9 columns

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" + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bot_new_gas.loc[\"gas_i\"]\n", + "bott_yr_reg_coal_merge" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 55, + "outputs": [ + { + "data": { + "text/plain": "mode node_loc year_act year_vtg\nM1 R12_AFR 2020 2005 0.482000\n 2005 0.482000\n 2005 0.482000\n 2005 0.482000\n 2005 0.482000\n ... \n R12_WEU 2060 2040 0.637941\n 2040 0.637941\n 2040 0.637941\n 2040 0.516429\n 2040 0.516429\nLength: 4557, dtype: float64" + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(x[\"emi_factor\"] / x[\"Value\"]).dropna().loc[\"gas_i\"]#.reset_index()#.drop([\"year_vtg\", \"node_loc\", \"year_act\"], axis=1).drop_duplicates()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 56, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\maczek\\AppData\\Local\\Temp\\ipykernel_17352\\3091354.py:1: PerformanceWarning: indexing past lexsort depth may impact performance.\n", + " x.loc[\"gas_i\", \"M1\", \"R12_CHN\"]\n" + ] + }, + { + "data": { + "text/plain": " Level commodity level Value node_origin input \\\nyear_act year_vtg \n2020 2005 1.228475 gas final 1.408451 R12_CHN 1.730247 \n 2005 1.228475 gas final 1.408451 R12_CHN 1.730247 \n 2005 1.228475 gas final 1.408451 R12_CHN 1.730247 \n 2005 1.228475 gas final 1.408451 R12_CHN 1.730247 \n 2005 1.228475 gas final 1.408451 R12_CHN 1.730247 \n... ... ... ... ... ... ... \n 2015 3.930682 gas final 1.408451 R12_CHN 5.536172 \n 2015 3.930682 gas final 1.408451 R12_CHN 5.536172 \n 2015 3.930682 gas final 1.408451 R12_CHN 5.536172 \n 2015 3.930682 gas final 1.408451 R12_CHN 5.536172 \n 2015 3.930682 gas final 1.408451 R12_CHN 5.536172 \n\n emi_factor \nyear_act year_vtg \n2020 2005 0.678873 \n 2005 0.678873 \n 2005 0.678873 \n 2005 0.678873 \n 2005 0.678873 \n... ... \n 2015 0.708824 \n 2015 0.708824 \n 2015 0.708824 \n 2015 0.573810 \n 2015 0.573810 \n\n[62 rows x 7 columns]", + "text/html": "
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LevelcommoditylevelValuenode_origininputemi_factor
year_actyear_vtg
202020051.228475gasfinal1.408451R12_CHN1.7302470.678873
20051.228475gasfinal1.408451R12_CHN1.7302470.678873
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20051.228475gasfinal1.408451R12_CHN1.7302470.678873
20051.228475gasfinal1.408451R12_CHN1.7302470.678873
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20153.930682gasfinal1.408451R12_CHN5.5361720.708824
20153.930682gasfinal1.408451R12_CHN5.5361720.708824
20153.930682gasfinal1.408451R12_CHN5.5361720.708824
20153.930682gasfinal1.408451R12_CHN5.5361720.573810
20153.930682gasfinal1.408451R12_CHN5.5361720.573810
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62 rows × 7 columns

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" + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x.loc[\"gas_i\", \"M1\", \"R12_CHN\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## check *_t_d tecs emission factors" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 92, + "outputs": [], + "source": [ + "df_gas_t_d = act_in[act_in.index.get_level_values(2).str.contains(\"coal\")].join(emi_bott_up.rename({\"Value\": \"emi_factor\"}, axis=1)[\"emi_factor\"])" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 58, + "outputs": [], + "source": [ + "df_gas_t_d = act_in.join(emi_bott_up.rename({\"Value\": \"emi_factor\"}, axis=1)[\"emi_factor\"]).dropna()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 93, + "outputs": [], + "source": [ + "ef_dict = {\n", + " 'biomass':0,\n", + " 'coal':0.814,\n", + " 'electr':0,\n", + " 'ethanol':0,\n", + " 'fueloil':0.665,\n", + " 'gas':0.482,\n", + " 'gas_1':0.482,\n", + " 'gas_2':0.482,\n", + " 'gas_3':0.482,\n", + " 'gas_4':0.482,\n", + " 'gas_5':0.482,\n", + " 'gas_6':0.482,\n", + " 'gas_afr':0.482,\n", + " 'gas_chn':0.482,\n", + " 'gas_cpa':0.482,\n", + " 'gas_weu':0.482,\n", + " 'gas_eeu':0.482,\n", + " 'gas_sas':0.482,\n", + " 'LNG':0.482,\n", + " 'd_heat':0,\n", + " 'lightoil':0.631,\n", + " 'methanol':0.549,\n", + " 'hydrogen':0,\n", + " 'freshwater_supply':0,\n", + " \"crudeoil\":0.63,\n", + " \"crude_1\":0.665,\n", + " \"crude_2\":0.665,\n", + " \"crude_3\":0.665,\n", + " \"crude_4\":0.665,\n", + " \"crude_5\":0.665,\n", + " \"crude_6\":0.665,\n", + " \"crude_7\":0.665,\n", + " \"ht_heat\":0,\n", + " \"lh2\":0,\n", + "}\n", + "\n", + "def get_ef(df):\n", + " df[\"ef\"] = ef_dict[df[\"commodity\"]]\n", + " return df\n", + "\n", + "df_gas_t_d = df_gas_t_d.apply(lambda x: get_ef(x), axis=1)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 67, + "outputs": [], + "source": [ + "df_gas_t_d = df_gas_t_d.join(act_out.rename({\"commodity\":\"comm_out\"}, axis=1)[\"comm_out\"])\n", + "df_gas_t_d = df_gas_t_d[df_gas_t_d[\"commodity\"]==df_gas_t_d[\"comm_out\"]]\n", + "#df_gas_t_d = df_gas_t_d[~df_gas_t_d.index.get_level_values(2).str.contains(\"_extr\")]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 94, + "outputs": [], + "source": [ + "# for IO tecs\n", + "ef_false = (df_gas_t_d[\"emi_factor\"] / (df_gas_t_d[\"Value\"] - 1)).dropna()\n", + "# for input only tecs\n", + "ef_false = (df_gas_t_d[\"emi_factor\"] / df_gas_t_d[\"Value\"]).dropna()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 95, + "outputs": [], + "source": [ + "tec_comm_map = df_gas_t_d.reset_index()[[\"tec\", \"commodity\"]].drop_duplicates().set_index(\"tec\").to_dict()[\"commodity\"]\n", + "tec_list = [i for i in list(tec_comm_map.keys())]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 99, + "outputs": [ + { + "data": { + "text/plain": " 0\nyear_act node_loc mode year_vtg \n2045 R12_PAS M1 2010 0.823070\n2050 R12_NAM M1 2010 0.816775\n2035 R12_CHN M1 2000 0.816429\n R12_RCPA M1 2000 0.816429\n2025 R12_LAM M1 1980 0.814000\n... ...\n2045 R12_SAS M1 2020 0.814000\n 2025 0.814000\n 2030 0.814000\n 2035 0.814000\n2035 R12_PAS M1 2015 0.814000\n\n[476 rows x 1 columns]", + "text/html": "
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" + }, + "execution_count": 99, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.DataFrame(ef_false).swaplevel(0,2).loc[\"coal_ppl\"].sort_values(0, ascending=False)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 98, + "outputs": [ + { + "data": { + "text/plain": "year_act node_loc mode year_vtg\n2020 R12_AFR M1 1960 NaN\n 1965 NaN\n 1970 NaN\n 1975 NaN\n 1980 NaN\n ..\n2090 R12_AFR M1 2080 NaN\n2100 R12_AFR M1 2080 NaN\n 2100 NaN\n2110 R12_AFR M1 2100 NaN\n 2110 NaN\nLength: 632, dtype: float64" + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_gas_t_d.swaplevel(0,2).loc[\"coal_ppl_u\"][\"emi_factor\"] / df_gas_t_d.swaplevel(0,2).loc[\"coal_ppl_u\"][\"Value\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 96, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "coal_adv\n", + "coal_bal\n", + "coal_exp\n", + "coal_extr\n", + "coal_extr_ch4\n", + "coal_i\n", + "coal_ppl\n", + "coal_ppl_u\n", + "coal_rc\n", + "coal_t_d\n", + "furnace_coal_aluminum\n", + "furnace_coal_cement\n", + "furnace_coal_refining\n", + "furnace_coal_petro\n", + "coal_hpl\n", + "furnace_coal_resins\n", + "coal_imp\n", + "meth_coal\n", + "coal_NH3\n", + "coal_trd\n", + "sp_coal_I\n", + "coal_gas\n", + "h2_coal\n" + ] + }, + { + "data": { + "text/plain": "
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+ }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import math\n", + "import matplotlib.pyplot as plt\n", + "fig, axs = plt.subplots(int(math.sqrt(len(tec_list))), int(math.sqrt(len(tec_list))), figsize=(40,20), facecolor=\"w\")\n", + "ax_it = iter(fig.axes)\n", + "for k in tec_list:\n", + " print(k)\n", + " try:\n", + " tec = k\n", + " y = ef_false.swaplevel(0,2).loc[tec].droplevel([3,2]).reset_index()\n", + " ax = next((ax_it))\n", + " y.plot.scatter(x=\"year_act\", y=0, ax=ax)\n", + " ax.plot([ef_dict[tec_comm_map[tec]]]*len(y.year_act.unique()), color=\"red\")\n", + " ax.set_ylabel(\"Mt C / GWa\")\n", + " ax.set_title(tec)\n", + " ax.set_xlabel(\"\")\n", + " except:\n", + " pass" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 64, + "outputs": [], + "source": [ + "fig.savefig(\"test.png\", facecolor=\"w\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "ef_false.swaplevel(0,2).loc[\"hp_gas_i\"].droplevel([3,2]).reset_index()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## aggregated emissions comparison" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df_gas_t_d[\"loss\"] = (df_gas_t_d[\"Value\"] - 1) * df_gas_t_d[\"Level\"]\n", + "t_d_emi = (df_gas_t_d[\"emi_factor\"] * df_gas_t_d[\"Level\"]).groupby([\"year_act\"]).sum() * co2_c_factor" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df_gas_t_d[\"emi_factor_corr\"] = (df_gas_t_d[\"Value\"] - 1) * df_gas_t_d[\"ef\"]\n", + "t_d_emi_corr =(df_gas_t_d[\"emi_factor_corr\"] * df_gas_t_d[\"Level\"]).groupby([\"year_act\"]).sum() * co2_c_factor" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "fig,ax = plt.subplots(figsize=(10,5))\n", + "t_d_emi.plot(ax=ax)\n", + "t_d_emi_corr.plot(ax=ax)\n", + "ax.set_ylabel(\"Mt CO2\")\n", + "ax.set_title(\"CO2 Emissions from t_d technologies\")\n", + "ax.legend([\"current CO2_cc\", \"corrected CO2_cc\"])" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df_gas_t_d.swaplevel(0,2).loc[\"gas_t_d\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "act_in_yr_reg = act_in.loc[region, year]\n", + "comm = \"coal\"\n", + "bott_dict = {\n", + " \"coal\":top_yr_reg_gas,\n", + " \"gas\":bott_yr_reg_gas\n", + "}\n", + "bott_yr_reg_coal_merge = bott_dict[comm].copy(deep=True)\n", + "x = act_in_yr_reg[(act_in_yr_reg.index.get_level_values(0).str.contains(comm)) & (act_in_yr_reg[\"commodity\"]==comm) & (act_in_yr_reg[\"level\"]==\"final\")].join(bott_yr_reg_coal_merge.rename({\"Value\": \"emi_factor\"}, axis=1)[\"emi_factor\"])\n", + "x = act_in_yr_reg[(act_in_yr_reg[\"commodity\"]==comm)].join(bott_yr_reg_coal_merge.rename({\"Value\": \"emi_factor\"}, axis=1)[\"emi_factor\"])\n", + "x#.sum()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "x#(x[\"emi_factor\"]/ x[\"Value\"]).dropna()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## oil emissions" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df_bot_oil = get_bot_up_emi_by_fuel(emi_bott_up, region, year)[1]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df_bot_oil[df_bot_oil.index.get_level_values(0).str.contains(\"extr\")].sum()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "emi_bot_up_refining = df_bot_oil.loc[df_bot_oil.index.get_level_values(0).str.endswith(\"_ref\")]#.sum()\n", + "emi_bot_up_extr = df_bot_oil.loc[df_bot_oil.index.get_level_values(0).str.contains(\"extr\")]\n", + "emi_bot_up_final = df_bot_oil.loc[(df_bot_oil.index.difference(emi_bot_up_refining.index)) & (df_bot_oil.index.difference(emi_bot_up_extr.index))]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "emi_bot_up_refining.sum()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "emi_bot_up_final.sort_values(\"emi\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "print(\"extraction emissions: \",emi_bot_up_extr.sum()[\"emi\"])\n", + "print(\"refining emissions: \",emi_bot_up_refining.sum()[\"emi\"])\n", + "print(\"combustion emissions: \",emi_bot_up_final.sum()[\"emi\"])" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "inp_hyd_crack = act_in.loc[region, year, \"hydro_cracking_ref\"]\n", + "inp_hyd_crack[inp_hyd_crack[\"commodity\"] == \"hydrogen\"].sum()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "inp_steam_crack = act_in.loc[region, year, \"steam_cracker_petro\"]\n", + "inp_steam_crack[~inp_steam_crack[\"commodity\"].isin([\"ethane\",\"propane\", \"electr\", \"ht_heat\"])].sum()[\"input\"] * 0.64" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df_top_oil = get_top_dn_emi_by_fuel(emi_top_down, region, year)[1]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df_top_oil.loc[df_top_oil.index.get_level_values(0).str.startswith(\"oil\")].sum()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df_top_oil.loc[~df_top_oil.index.get_level_values(0).str.endswith(\"ref\")]#.sum()#.sort_index()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "carbon_out_foil_loil_exp = df_top_oil[df_top_oil.index.get_level_values(0).str.contains(\"loil\")].sum()[\"emi\"] + df_top_oil[df_top_oil.index.get_level_values(0).str.contains(\"foil\")].sum()[\"emi\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "act_out.swaplevel(0,2).loc[\"MTO_petro\"].loc[\"2025\", \"R12_CHN\"].sum()#.groupby(\"year_act\").sum()" + ], + "metadata": { + "collapsed": false + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/message_ix_models/model/material/debug_moduls.py b/message_ix_models/model/material/debug_moduls.py new file mode 100644 index 0000000000..41223e32da --- /dev/null +++ b/message_ix_models/model/material/debug_moduls.py @@ -0,0 +1,118 @@ +import ixmp +import message_ix +import cx_Oracle +from message_ix_models.util import private_data_path + +#from message_data.model.material import data_petro, data_ammonia_new, data_methanol_new +from message_data.tools.post_processing.iamc_report_hackathon import report as reporting +from message_data.tools.utilities import add_globiom + +# def main(): + +# clone = scen.clone("test", "test") +# #petro_dict = data_ammonia_new.gen_all_NH3_fert(clone) +# petro_dict = data_methanol_new.gen_data_methanol_new(clone) +# print("test") +# #petro_dict = data_cement.gen_data_cement(scen) +magpie_scens = [ + "MP00BD0BI78", + "MP00BD0BI74", + "MP00BD0BI70", + "MP00BD1BI00", + #"MP30BD0BI78", + "MP30BD0BI74", + "MP30BD0BI70", + "MP30BD1BI00", + "MP50BD0BI78", + "MP50BD0BI74", + "MP50BD0BI70", + "MP50BD1BI00", + "MP76BD0BI70", + "MP76BD0BI74", + "MP76BD0BI78", + "MP76BD1BI00" +] + +if __name__ == '__main__': + #main() + # import warnings + # + # + from message_ix_models.cli import main + # + # mp = ixmp.Platform("ixmp_dev") + # scen = message_ix.Scenario(mp, "SHAPE_SSP2_v4.1.8", "baseline") + # reporting( + # mp, + # scen, + # # NB(PNK) this is not an error; .iamc_report_hackathon.report() expects a + # # string containing "True" or "False" instead of an actual bool. + # "False", + # scen.model, + # scen.scenario+"_test", + # merge_hist=True, + # merge_ts=True, + # run_config="materials_run_config.yaml", + # ) + + # import os + # path_dirs = os.environ.get('PATH').split(os.pathsep) + # + # import sys + # new_path = "C:/Users/maczek/instantclient_21_10" + # sys.path.append(new_path) + # cx_Oracle.init_oracle_client(lib_dir=r"C:/Users/maczek/instantclient_21_10") + # #print(path_dirs) + # # main(["--url", "ixmp://ixmp_dev/MESSAGEix-Materials/NoPolicy_petro_thesis_2", + # # "--local-data", "./data", "material", "debug_module", "--material", "steel"]) + # # main(["--url", "ixmp://ixmp_dev/SHAPE_SSP2_v4.1.8/baseline#1", + # # "--local-data", "./data" , "material", "build", "--tag", "petro_thesis_2"]) + # # main(["--local-data", "./data", "material", "solve" "--scenario_name", + # # "NoPolicy_petro_thesis_2" "--add_calibration", True]) + # # main(["--local-data", "C:/Users\maczek\PycharmProjects\message_data", "navigate_industry", + # # "--run-reporting-only", "run", "SSP2", "--output-model=MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00", + # # "--scenarios", "ENf", "--run_reporting" ,"True"]) + # # exit() + # main(["--local-data", "C:/Users\maczek\PycharmProjects\message_data", "navigate_industry", + # "--run-reporting-only", "run", "SSP2", f'--output-model=MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD0BI70', + # "--scenarios", "EN-step3", "--run_reporting", "True"]) + # for magpie_scen in magpie_scens[5:]: + # print() + # print() + # try: + # main(["--local-data", "C:/Users\maczek\PycharmProjects\message_data", "navigate_industry", + # "run", "SSP2", f'--output-model=MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-{magpie_scen}', + # "--scenarios", "run_cdlinks_setup", ]) + # except SystemExit as e: + # pass + # # if e.code != 0: + # # pass#raise + # # else: + # # pass + # mp = ixmp.Platform("ixmp_dev") + # scen = message_ix.Scenario(mp, "MESSAGEix-GLOBIOM 1.1-R12-MAGPIE", "baseline_w/o_LU") + # sc_clone = scen.clone("MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00", "bugfix_debug") + # add_globiom( + # mp, + # sc_clone, + # "SSP2", + # private_data_path(), + # 2015, + # globiom_scenario="noSDG_rcpref", + # #regenerate_input_data=True, + # #regenerate_input_data_only=True, + # #globiom_scenario="no", + # #allow_empty_drivers=True, + # #add_reporting_data=True, + # #config_setup="MAGPIE_MP00BD1BI00", + # config_setup="MAGPIE_MP00BI00_OldForSplit", + # config_file="magpie_config.yaml" + # ) + # main(["--local-data", "C:/Users\maczek\PycharmProjects\message_data", "magpie_scp", + # "--run-reporting-only", "run", "SSP2", f'--output-model=MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD0BI70', + # "--scenarios", "EN-step3", "--run_reporting", "True"]) + # main(["--url", "ixmp://ixmp_dev/MESSAGEix-Materials/NoPolicy_petro_thesis_2_macro", "report"]) + #, f'MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD0BI70', + # "--scenario", "baseline"]) + main(["--url", "ixmp://ixmp_dev/SSP_dev_SSP2_v0.1/baseline", + "--local-data", "./data" , "material", "build", "--tag", " v0.1_materials_no_pow_sect"]) diff --git a/message_ix_models/model/material/df_py.csv b/message_ix_models/model/material/df_py.csv new file mode 100644 index 0000000000..e8c1944659 --- /dev/null +++ b/message_ix_models/model/material/df_py.csv @@ -0,0 +1,169 @@ +,region,year,demand_tot +0,R12_AFR,2020,20.039797280293683 +1,R12_AFR,2030,52.60984886888077 +2,R12_AFR,2040,103.76875006935163 +3,R12_AFR,2050,170.54616780143323 +4,R12_AFR,2060,236.5908328255232 +5,R12_AFR,2070,284.5276902295362 +6,R12_AFR,2080,307.5960349221543 +7,R12_AFR,2090,308.6898767675193 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0000000000..f43b2f2069 --- /dev/null +++ b/message_ix_models/model/material/diogo_carbon_price.ipynb @@ -0,0 +1,105 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 32, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "This Scenario has a solution, use `Scenario.remove_solution()` or `Scenario.clone(..., keep_solution=False)`\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "New carbon prices added\n", + "New scenario name is 2degrees_1_5C\n" + ] + }, + { + "data": { + "text/plain": " node type_emission type_tec type_year value unit\n0 World TCE all 2025 102.143928 USD/tCO2\n1 World TCE all 2030 130.364412 USD/tCO2\n2 World TCE all 2035 166.381695 USD/tCO2\n3 World TCE all 2040 212.349890 USD/tCO2\n4 World TCE all 2045 271.018249 USD/tCO2\n5 World TCE all 2050 345.895595 USD/tCO2\n6 World TCE all 2055 441.460170 USD/tCO2\n7 World TCE all 2060 563.427476 USD/tCO2\n8 World TCE all 2070 917.763988 USD/tCO2\n9 World TCE all 2080 1494.940829 USD/tCO2\n10 World TCE all 2090 2435.101083 USD/tCO2\n11 World TCE all 2100 3966.523070 USD/tCO2\n12 World TCE all 2110 6461.048116 USD/tCO2", + "text/html": "
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5WorldTCEall2050345.895595USD/tCO2
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\n
" + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "import ixmp\n", + "import message_ix\n", + "\n", + "climate_target = \"2C\"\n", + "climate_target = \"1_5C\"\n", + "\n", + "baseline_name = \"put name of shipping baseline scenario here\"\n", + "\n", + "if climate_target == \"2°C\":\n", + " tax_emission_new = pd.read_csv(\"tax_emission_1000f.csv\") # 2°C carbon prices\n", + "if climate_target == \"1.5°C\": \n", + " tax_emission_new = pd.read_csv(\"tax_emission_600f.csv\") # 1.5°C carbon prices\n", + "\n", + "scenario = message_ix.Scenario(mp, 'MESSAGEix-Materials', \"NoPolicy_petro_thesis_2_final\")\n", + "\n", + "output_scenario_name = output_scenario_name + '_' + climate_target # choose whatever name you want for the carbon price run\n", + "\n", + "scen_cprice = scenario.clone('MESSAGEix-Materials', output_scenario_name,\n", + " keep_solution=False, shift_first_model_year=2025)\n", + "\n", + "scen_cprice.check_out()\n", + "tax_emission_new.columns = scen_cprice.par(\"tax_emission\").columns\n", + "tax_emission_new[\"unit\"] = \"USD/tCO2\"\n", + "scen_cprice.add_par(\"tax_emission\", tax_emission_new)\n", + "scen_cprice.commit('2 degree prices are added')\n", + "print('New carbon prices added')\n", + "print('New scenario name is ' + output_scenario_name)\n", + "scen_cprice.par(\"tax_emission\")\n", + "#scen_cprice.set_as_default()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-29T15:59:11.705882400Z", + "start_time": "2023-11-29T15:58:46.810437100Z" + } + }, + "id": "906a600a96493f6e" + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false + }, + "id": "86789658e0575882" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/message_ix_models/model/material/magpie notebooks/MESSAGE-magpie test.ipynb b/message_ix_models/model/material/magpie notebooks/MESSAGE-magpie test.ipynb new file mode 100644 index 0000000000..eabd4da1f3 --- /dev/null +++ b/message_ix_models/model/material/magpie notebooks/MESSAGE-magpie test.ipynb @@ -0,0 +1,2590 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# MAGPIE setup baseline" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 1, + "outputs": [ + { + "data": { + "text/plain": "", + "application/javascript": "if (typeof IPython !== 'undefined') { IPython.OutputArea.prototype._should_scroll = function(lines){ return false; }}" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "" + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import ixmp\n", + "import message_ix\n", + "from message_data.tools.utilities import calibrate_UE_gr_to_demand, add_globiom, transfer_demands\n", + "from message_ix_models.util import private_data_path\n", + "import message_data\n", + "import importlib\n", + "importlib.reload(message_data.tools.utilities)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-03T12:03:36.363202200Z", + "start_time": "2023-10-03T12:03:20.633202800Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'data_scp'", + "output_type": "error", + "traceback": [ + "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[1;31mModuleNotFoundError\u001B[0m Traceback (most recent call last)", + "Input \u001B[1;32mIn [2]\u001B[0m, in \u001B[0;36m\u001B[1;34m()\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[38;5;28;01mimport\u001B[39;00m \u001B[38;5;21;01mdata_scp\u001B[39;00m\n\u001B[0;32m 2\u001B[0m \u001B[38;5;28;01mimport\u001B[39;00m \u001B[38;5;21;01mimportlib\u001B[39;00m\n\u001B[0;32m 3\u001B[0m importlib\u001B[38;5;241m.\u001B[39mreload(data_scp)\n", + "\u001B[1;31mModuleNotFoundError\u001B[0m: No module named 'data_scp'" + ] + } + ], + "source": [ + "import data_scp\n", + "import importlib\n", + "importlib.reload(data_scp)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-03T12:03:36.942308700Z", + "start_time": "2023-10-03T12:03:36.336284800Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'scen' is not defined", + "output_type": "error", + "traceback": [ + "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[1;31mNameError\u001B[0m Traceback (most recent call last)", + "Input \u001B[1;32mIn [2]\u001B[0m, in \u001B[0;36m\u001B[1;34m()\u001B[0m\n\u001B[1;32m----> 1\u001B[0m df \u001B[38;5;241m=\u001B[39m \u001B[43mscen\u001B[49m\u001B[38;5;241m.\u001B[39mtimeseries()\n\u001B[0;32m 2\u001B[0m df\u001B[38;5;241m.\u001B[39myear\u001B[38;5;241m.\u001B[39munique()\n", + "\u001B[1;31mNameError\u001B[0m: name 'scen' is not defined" + ] + } + ], + "source": [ + "df = scen.timeseries()\n", + "df.year.unique()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [], + "source": [ + "mp = ixmp.Platform(\"ixmp_dev\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-03T12:04:20.124790500Z", + "start_time": "2023-10-03T12:04:13.917186600Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [], + "source": [ + "df = mp.scenario_list()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [ + { + "data": { + "text/plain": "array(['ENGAGE-MAGPIE_SSP2', 'ENGAGE-MAGPIE_SSP2_v4.1.8.3_T4.5_r3.1',\n 'ENGAGE_SSP2_MAGPIE_SCP', 'ENGAGE_SSP2_PtX_FL', 'ENGAGE_SSP2_test',\n 'ENGAGE_SSP2_test_FL', 'ENGAGE_SSP2_v1', 'ENGAGE_SSP2_v2',\n 'ENGAGE_SSP2_v2.1', 'ENGAGE_SSP2_v2_test', 'ENGAGE_SSP2_v4',\n 'ENGAGE_SSP2_v4.1', 'ENGAGE_SSP2_v4.1.1', 'ENGAGE_SSP2_v4.1.2',\n 'ENGAGE_SSP2_v4.1.2_NAV_test', 'ENGAGE_SSP2_v4.1.2_R12',\n 'ENGAGE_SSP2_v4.1.2_sens', 'ENGAGE_SSP2_v4.1.3',\n 'ENGAGE_SSP2_v4.1.4', 'ENGAGE_SSP2_v4.1.4_BZ',\n 'ENGAGE_SSP2_v4.1.4_DAC_PtX_FL', 'ENGAGE_SSP2_v4.1.4_FG',\n 'ENGAGE_SSP2_v4.1.4_R12_CHN', 'ENGAGE_SSP2_v4.1.4_R12_CHN_FG',\n 'ENGAGE_SSP2_v4.1.4_R12_CHN_FG_test', 'ENGAGE_SSP2_v4.1.4_R12_NOR',\n 'ENGAGE_SSP2_v4.1.4_sens', 'ENGAGE_SSP2_v4.1.5',\n 'ENGAGE_SSP2_v4.1.6', 'ENGAGE_SSP2_v4.1.7',\n 'ENGAGE_SSP2_v4.1.7 +transport', 'ENGAGE_SSP2_v4.1.7_DR10p',\n 'ENGAGE_SSP2_v4.1.7_DR1p', 'ENGAGE_SSP2_v4.1.7_DR_10p',\n 'ENGAGE_SSP2_v4.1.7_DR_1p', 'ENGAGE_SSP2_v4.1.7_DR_2p',\n 'ENGAGE_SSP2_v4.1.7_DR_3p', 'ENGAGE_SSP2_v4.1.7_DR_4p',\n 'ENGAGE_SSP2_v4.1.7_GLOBIOMupd', 'ENGAGE_SSP2_v4.1.7_R12',\n 'ENGAGE_SSP2_v4.1.7_R12_CHN', 'ENGAGE_SSP2_v4.1.7_R12_CHN_FG',\n 'ENGAGE_SSP2_v4.1.7_R12_CHN_FG_test', 'ENGAGE_SSP2_v4.1.7_T4.5',\n 'ENGAGE_SSP2_v4.1.7_T4.5_r1', 'ENGAGE_SSP2_v4.1.7_T4.5_r2',\n 'ENGAGE_SSP2_v4.1.7_T4.5_r3', 'ENGAGE_SSP2_v4.1.7_T4.5_r3.1',\n 'ENGAGE_SSP2_v4.1.7_T4.5_r3.1_test',\n 'ENGAGE_SSP2_v4.1.7_T4.5_r3.1_test1',\n 'ENGAGE_SSP2_v4.1.7_T4.5_r3.2', 'ENGAGE_SSP2_v4.1.7_WAT',\n 'ENGAGE_SSP2_v4.1.7_ar5_gwp100', 'ENGAGE_SSP2_v4.1.7_clean',\n 'ENGAGE_SSP2_v4.1.7_clean_FG', 'ENGAGE_SSP2_v4.1.7_clean_R4',\n 'ENGAGE_SSP2_v4.1.7_clean_R4_t12', 'ENGAGE_SSP2_v4.1.7_clean_t12',\n 'ENGAGE_SSP2_v4.1.7_t12', 'ENGAGE_SSP2_v4.1.7_test',\n 'ENGAGE_SSP2_v4.1.7_wat', 'ENGAGE_SSP2_v4.1.7_wat_wat',\n 'ENGAGE_SSP2_v4.1.8', 'ENGAGE_SSP2_v4.1.8.1',\n 'ENGAGE_SSP2_v4.1.8.1_test', 'ENGAGE_SSP2_v4.1.8.2',\n 'ENGAGE_SSP2_v4.1.8.2_test', 'ENGAGE_SSP2_v4.1.8.3',\n 'ENGAGE_SSP2_v4.1.8.3.1_T3.4_r3.1',\n 'ENGAGE_SSP2_v4.1.8.3.1_T3.4v2_r3.1',\n 'ENGAGE_SSP2_v4.1.8.3.1_T4.5_r3.1',\n 'ENGAGE_SSP2_v4.1.8.3.1_T4.5_r3.1_fe_h2_reporting',\n 'ENGAGE_SSP2_v4.1.8.3.1_T4.5_r3.1_test',\n 'ENGAGE_SSP2_v4.1.8.3.1_T4.5v2_r3.1',\n 'ENGAGE_SSP2_v4.1.8.3.1_test_T4.5_r3.1',\n 'ENGAGE_SSP2_v4.1.8.3_T4.5_r3.1', 'ENGAGE_SSP2_v4.1.8.3_test',\n 'ENGAGE_SSP2_v4.1.8.4', 'ENGAGE_SSP2_v4.1.8.5',\n 'ENGAGE_SSP2_v4.1.8_JT', 'ENGAGE_SSP2_v4.1.8_T4.5_test',\n 'ENGAGE_SSP2_v4.1.8_T4.5_test_test', 'ENGAGE_SSP2_v4.1.8_t12',\n 'ENGAGE_SSP2_v4_test', 'ENGAGE_V417_FG', 'ENGAGE_Yoga',\n 'ENGAGE_baseline_jsk', 'ENGAGE_experimental',\n 'ENGAGE_test_hydro_v4.1.7', 'ENGAGE_test_hydro_v4.1.8',\n 'ENGAGE_v4.1.7_SSP2_DAC_PtX_FL'], dtype=object)" + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[df[\"model\"].str.startswith(\"ENGAGE\")].model.unique()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 11, + "outputs": [], + "source": [ + "scen = message_ix.Scenario(mp, \"MESSAGEix-Materials\", \"baseline_DEFAULT_0108\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 13, + "outputs": [], + "source": [ + "df = scen.set(\"balance_equality\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 16, + "outputs": [], + "source": [ + "df_eth = df[df[\"commodity\"]==\"methanol\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 18, + "outputs": [], + "source": [ + "df_eth[\"commodity\"] = \"ethanol\"" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 22, + "outputs": [], + "source": [ + "df_eth = df_eth[~df_eth.level.str.contains(\"fs\")]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 23, + "outputs": [ + { + "data": { + "text/plain": " commodity level\n151 ethanol export\n153 ethanol import\n155 ethanol final", + "text/html": "
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151ethanolexport
153ethanolimport
155ethanolfinal
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" + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_eth" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 35, + "outputs": [ + { + "data": { + "text/plain": " commodity level\n0 BIO01GHG000 LU\n1 BIO02GHG000 LU\n2 BIO03GHG000 LU\n3 BIO04GHG000 LU\n4 BIO05GHG000 LU\n.. ... ...\n130 BIO60GHG400 LU\n131 BIO60GHG600 LU\n132 ethanol export\n133 ethanol import\n134 ethanol final\n\n[135 rows x 2 columns]", + "text/html": "
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commoditylevel
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" + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scen_new.set(\"balance_equality\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 38, + "outputs": [], + "source": [ + "scen_new.check_out()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 39, + "outputs": [], + "source": [ + "scen_new.add_set(\"balance_equality\", (\"ethanol\", \"primary\"))\n", + "scen_new.add_set(\"balance_equality\", (\"ethanol\", \"secondary\"))" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 41, + "outputs": [], + "source": [ + "scen_new.commit(\"add further ethanol levels to bal_eq\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 43, + "outputs": [], + "source": [ + "scen_new.remove_solution()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 25, + "outputs": [], + "source": [ + "scen_new = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP50BD1BI00\", \"EN_NPi2020_500f\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [ + { + "data": { + "text/plain": "True" + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scen_new = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\", \"cumu_cost_test_step7_1000f\")\n", + "scen_new.has_solution()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-03T12:04:22.625833700Z", + "start_time": "2023-10-03T12:04:20.127783400Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [], + "source": [ + "df = scen_new.timeseries()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-03T12:04:50.109013400Z", + "start_time": "2023-10-03T12:04:34.399154600Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 8, + "outputs": [ + { + "data": { + "text/plain": " region variable unit year value \\\n132 CPA Emissions|CO2 Mt CO2/yr 1990 0.000000 \n133 CPA Emissions|CO2 Mt CO2/yr 1995 0.000000 \n134 CPA Emissions|CO2 Mt CO2/yr 2000 0.000000 \n135 CPA Emissions|CO2 Mt CO2/yr 2005 0.000000 \n136 CPA Emissions|CO2 Mt CO2/yr 2010 0.000000 \n... ... ... ... ... ... \n201441 GLB region (R12) Emissions|CO2 Mt CO2/yr 2070 2569.568282 \n201442 GLB region (R12) Emissions|CO2 Mt CO2/yr 2080 -8925.499016 \n201443 GLB region (R12) Emissions|CO2 Mt CO2/yr 2090 -19558.089012 \n201444 GLB region (R12) Emissions|CO2 Mt CO2/yr 2100 -20221.539211 \n201445 GLB region (R12) Emissions|CO2 Mt CO2/yr 2110 -20562.001412 \n\n model \\\n132 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00 \n133 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00 \n134 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00 \n135 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00 \n136 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00 \n... ... \n201441 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00 \n201442 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00 \n201443 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00 \n201444 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00 \n201445 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00 \n\n scenario \n132 cumu_cost_test_step7_1000f \n133 cumu_cost_test_step7_1000f \n134 cumu_cost_test_step7_1000f \n135 cumu_cost_test_step7_1000f \n136 cumu_cost_test_step7_1000f \n... ... \n201441 cumu_cost_test_step7_1000f \n201442 cumu_cost_test_step7_1000f \n201443 cumu_cost_test_step7_1000f \n201444 cumu_cost_test_step7_1000f \n201445 cumu_cost_test_step7_1000f \n\n[266 rows x 7 columns]", + "text/html": "
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regionvariableunityearvaluemodelscenario
132CPAEmissions|CO2Mt CO2/yr19900.000000MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00cumu_cost_test_step7_1000f
133CPAEmissions|CO2Mt CO2/yr19950.000000MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00cumu_cost_test_step7_1000f
134CPAEmissions|CO2Mt CO2/yr20000.000000MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00cumu_cost_test_step7_1000f
135CPAEmissions|CO2Mt CO2/yr20050.000000MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00cumu_cost_test_step7_1000f
136CPAEmissions|CO2Mt CO2/yr20100.000000MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00cumu_cost_test_step7_1000f
........................
201441GLB region (R12)Emissions|CO2Mt CO2/yr20702569.568282MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00cumu_cost_test_step7_1000f
201442GLB region (R12)Emissions|CO2Mt CO2/yr2080-8925.499016MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00cumu_cost_test_step7_1000f
201443GLB region (R12)Emissions|CO2Mt CO2/yr2090-19558.089012MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00cumu_cost_test_step7_1000f
201444GLB region (R12)Emissions|CO2Mt CO2/yr2100-20221.539211MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00cumu_cost_test_step7_1000f
201445GLB region (R12)Emissions|CO2Mt CO2/yr2110-20562.001412MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00cumu_cost_test_step7_1000f
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266 rows × 7 columns

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" + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[df[\"variable\"]==\"Emissions|CO2\"]" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-03T12:06:58.382788Z", + "start_time": "2023-10-03T12:06:58.229677600Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 28, + "outputs": [], + "source": [ + "scen_new.remove_solution()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 31, + "outputs": [], + "source": [ + "scen_new.check_out()\n", + "scen_new.add_set(\"balance_equality\", df_eth)\n", + "scen_new.commit(\"add equality to ethanol supply\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 44, + "outputs": [], + "source": [ + "scen_new.solve(\"MESSAGE-MACRO\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 8, + "outputs": [ + { + "data": { + "text/plain": " model scenario scheme \\\n4390 ENGAGE_SSP2_v4.1.8.5 EN_NPi2020_1000 MESSAGE \n4391 ENGAGE_SSP2_v4.1.8.5 EN_NPi2020_1000_LU_fbk MESSAGE \n4392 ENGAGE_SSP2_v4.1.8.5 EN_NPi2020_1000_step1 MESSAGE \n4393 ENGAGE_SSP2_v4.1.8.5 EN_NPi2020_1000_step2 MESSAGE \n4394 ENGAGE_SSP2_v4.1.8.5 EN_NPi2020_1000f MESSAGE \n... ... ... ... \n4483 ENGAGE_SSP2_v4.1.8.5 baseline_with_lu_bkp MESSAGE \n4484 ENGAGE_SSP2_v4.1.8.5 indci_low_dem_scen MESSAGE \n4485 ENGAGE_SSP2_v4.1.8.5 npi_low_dem_scen MESSAGE \n4486 ENGAGE_SSP2_v4.1.8.5 npi_low_dem_scen2 MESSAGE \n4487 ENGAGE_SSP2_v4.1.8.5 test to confirm MACRO converges MESSAGE \n\n is_default is_locked cre_user cre_date upd_user \\\n4390 1 0 fricko 2023-07-12 10:58:00.000000 fricko \n4391 1 0 fricko 2023-05-10 13:48:13.000000 None \n4392 1 0 fricko 2023-07-12 10:13:53.000000 fricko \n4393 1 0 fricko 2023-07-12 10:37:15.000000 fricko \n4394 1 0 fricko 2023-07-12 13:13:05.000000 fricko \n... ... ... ... ... ... \n4483 1 0 fricko 2023-07-07 13:14:40.000000 None \n4484 1 0 fricko 2023-07-07 22:19:40.000000 fricko \n4485 1 0 fricko 2023-07-11 17:35:34.000000 fricko \n4486 1 0 fricko 2023-07-11 18:08:18.000000 fricko \n4487 1 0 fricko 2023-07-10 16:15:17.000000 fricko \n\n upd_date lock_user lock_date \\\n4390 2023-07-12 11:02:41.000000 None None \n4391 None None None \n4392 2023-07-12 10:21:09.000000 None None \n4393 2023-07-12 10:44:02.000000 None None \n4394 2023-07-12 13:28:49.000000 None None \n... ... ... ... \n4483 None None None \n4484 2023-07-07 22:27:53.000000 None None \n4485 2023-07-11 17:51:18.000000 None None \n4486 2023-07-11 18:20:51.000000 None None \n4487 2023-07-10 16:18:01.000000 None None \n\n annotation version \n4390 clone Scenario from 'ENGAGE_SSP2_v4.1.8.5|EN_N... 3 \n4391 clone Scenario from 'ENGAGE_SSP2_v4.1.8.5|EN_N... 3 \n4392 clone Scenario from 'ENGAGE_SSP2_v4.1.8.5|NPi2... 3 \n4393 clone Scenario from 'ENGAGE_SSP2_v4.1.8.5|EN_N... 6 \n4394 clone Scenario from 'ENGAGE_SSP2_v4.1.8.5|NPi2... 2 \n... ... ... \n4483 clone Scenario from 'ENGAGE_SSP2_v4.1.8.5|base... 2 \n4484 clone Scenario from 'ENGAGE_SSP2_v4.1.8.5|INDC... 1 \n4485 clone Scenario from 'ENGAGE_SSP2_v4.1.8.5|NPi2... 7 \n4486 clone Scenario from 'ENGAGE_SSP2_v4.1.8.5|NPi2... 3 \n4487 clone Scenario from 'ENGAGE_SSP2_v4.1.8.5|base... 8 \n\n[98 rows x 13 columns]", + "text/html": "
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modelscenarioschemeis_defaultis_lockedcre_usercre_dateupd_userupd_datelock_userlock_dateannotationversion
4390ENGAGE_SSP2_v4.1.8.5EN_NPi2020_1000MESSAGE10fricko2023-07-12 10:58:00.000000fricko2023-07-12 11:02:41.000000NoneNoneclone Scenario from 'ENGAGE_SSP2_v4.1.8.5|EN_N...3
4391ENGAGE_SSP2_v4.1.8.5EN_NPi2020_1000_LU_fbkMESSAGE10fricko2023-05-10 13:48:13.000000NoneNoneNoneNoneclone Scenario from 'ENGAGE_SSP2_v4.1.8.5|EN_N...3
4392ENGAGE_SSP2_v4.1.8.5EN_NPi2020_1000_step1MESSAGE10fricko2023-07-12 10:13:53.000000fricko2023-07-12 10:21:09.000000NoneNoneclone Scenario from 'ENGAGE_SSP2_v4.1.8.5|NPi2...3
4393ENGAGE_SSP2_v4.1.8.5EN_NPi2020_1000_step2MESSAGE10fricko2023-07-12 10:37:15.000000fricko2023-07-12 10:44:02.000000NoneNoneclone Scenario from 'ENGAGE_SSP2_v4.1.8.5|EN_N...6
4394ENGAGE_SSP2_v4.1.8.5EN_NPi2020_1000fMESSAGE10fricko2023-07-12 13:13:05.000000fricko2023-07-12 13:28:49.000000NoneNoneclone Scenario from 'ENGAGE_SSP2_v4.1.8.5|NPi2...2
..........................................
4483ENGAGE_SSP2_v4.1.8.5baseline_with_lu_bkpMESSAGE10fricko2023-07-07 13:14:40.000000NoneNoneNoneNoneclone Scenario from 'ENGAGE_SSP2_v4.1.8.5|base...2
4484ENGAGE_SSP2_v4.1.8.5indci_low_dem_scenMESSAGE10fricko2023-07-07 22:19:40.000000fricko2023-07-07 22:27:53.000000NoneNoneclone Scenario from 'ENGAGE_SSP2_v4.1.8.5|INDC...1
4485ENGAGE_SSP2_v4.1.8.5npi_low_dem_scenMESSAGE10fricko2023-07-11 17:35:34.000000fricko2023-07-11 17:51:18.000000NoneNoneclone Scenario from 'ENGAGE_SSP2_v4.1.8.5|NPi2...7
4486ENGAGE_SSP2_v4.1.8.5npi_low_dem_scen2MESSAGE10fricko2023-07-11 18:08:18.000000fricko2023-07-11 18:20:51.000000NoneNoneclone Scenario from 'ENGAGE_SSP2_v4.1.8.5|NPi2...3
4487ENGAGE_SSP2_v4.1.8.5test to confirm MACRO convergesMESSAGE10fricko2023-07-10 16:15:17.000000fricko2023-07-10 16:18:01.000000NoneNoneclone Scenario from 'ENGAGE_SSP2_v4.1.8.5|base...8
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" + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[df[\"model\"].str.startswith(\"ENGAGE_SSP2_v4.1.8.5\") & (==\"EN_NPi2020_1000\")]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [], + "source": [ + "#scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD0BI70\", \"baseline\")\n", + "scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\", \"600f_MP00BD0BI78_test_calib_macro\")\n", + "scen_base = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\", \"1000f_MP00BD0BI78_test_calib_macro\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 9, + "outputs": [], + "source": [ + "scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12\", \"baseline_DEFULT_check\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 10, + "outputs": [ + { + "data": { + "text/plain": " node land_scenario year commodity level time \\\n0 R12_CHN BIO00GHG000 2020 bioenergy land_use year \n1 R12_CHN BIO00GHG010 2020 bioenergy land_use year \n2 R12_CHN BIO00GHG020 2020 bioenergy land_use year \n3 R12_CHN BIO00GHG050 2020 bioenergy land_use year \n4 R12_CHN BIO00GHG100 2020 bioenergy land_use year \n.. ... ... ... ... ... ... \n163 R12_CHN BIO60GHG200 2020 bioenergy land_use_reporting year \n164 R12_CHN BIO60GHG2000 2020 bioenergy land_use_reporting year \n165 R12_CHN BIO60GHG3000 2020 bioenergy land_use_reporting year \n166 R12_CHN BIO60GHG400 2020 bioenergy land_use_reporting year \n167 R12_CHN BIO60GHG600 2020 bioenergy land_use_reporting year \n\n value unit \n0 161.6800 GWa \n1 162.6100 GWa \n2 162.4100 GWa \n3 162.3600 GWa \n4 161.6800 GWa \n.. ... ... \n163 260.2013 GWa \n164 260.3617 GWa \n165 260.3803 GWa \n166 260.0390 GWa \n167 259.9724 GWa \n\n[168 rows x 8 columns]", + "text/html": "
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nodeland_scenarioyearcommodityleveltimevalueunit
0R12_CHNBIO00GHG0002020bioenergyland_useyear161.6800GWa
1R12_CHNBIO00GHG0102020bioenergyland_useyear162.6100GWa
2R12_CHNBIO00GHG0202020bioenergyland_useyear162.4100GWa
3R12_CHNBIO00GHG0502020bioenergyland_useyear162.3600GWa
4R12_CHNBIO00GHG1002020bioenergyland_useyear161.6800GWa
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163R12_CHNBIO60GHG2002020bioenergyland_use_reportingyear260.2013GWa
164R12_CHNBIO60GHG20002020bioenergyland_use_reportingyear260.3617GWa
165R12_CHNBIO60GHG30002020bioenergyland_use_reportingyear260.3803GWa
166R12_CHNBIO60GHG4002020bioenergyland_use_reportingyear260.0390GWa
167R12_CHNBIO60GHG6002020bioenergyland_use_reportingyear259.9724GWa
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168 rows × 8 columns

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" + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scen.par(\"land_output\", filters={\"commodity\":\"bioenergy\", \"node\":\"R12_CHN\", \"year\":\"2020\"})" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [], + "source": [ + "scen_base = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP30BD0BI78\", \"NPi2020-con-prim-dir-ncr\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 9, + "outputs": [ + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[1;31mKeyboardInterrupt\u001B[0m Traceback (most recent call last)", + "Input \u001B[1;32mIn [9]\u001B[0m, in \u001B[0;36m\u001B[1;34m()\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[43mscen_base\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msolve\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmodel\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mMESSAGE-MACRO\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\n", + "File \u001B[1;32m~\\PycharmProjects\\message_ix\\message_ix\\core.py:677\u001B[0m, in \u001B[0;36mScenario.solve\u001B[1;34m(self, model, solve_options, **kwargs)\u001B[0m\n\u001B[0;32m 657\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21msolve\u001B[39m(\u001B[38;5;28mself\u001B[39m, model\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mMESSAGE\u001B[39m\u001B[38;5;124m\"\u001B[39m, solve_options\u001B[38;5;241m=\u001B[39m{}, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[0;32m 658\u001B[0m \u001B[38;5;124;03m\"\"\"Solve MESSAGE or MESSAGE-MACRO for the Scenario.\u001B[39;00m\n\u001B[0;32m 659\u001B[0m \n\u001B[0;32m 660\u001B[0m \u001B[38;5;124;03m By default, :meth:`ixmp.Scenario.solve` is called with 'MESSAGE' as the\u001B[39;00m\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 675\u001B[0m \u001B[38;5;124;03m :class:`.GAMSModel`.\u001B[39;00m\n\u001B[0;32m 676\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[1;32m--> 677\u001B[0m \u001B[38;5;28;43msuper\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msolve\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmodel\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mmodel\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43msolve_options\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43msolve_options\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\ixmp\\core\\scenario.py:835\u001B[0m, in \u001B[0;36mScenario.solve\u001B[1;34m(self, model, callback, cb_kwargs, **model_options)\u001B[0m\n\u001B[0;32m 833\u001B[0m \u001B[38;5;66;03m# Iterate until convergence\u001B[39;00m\n\u001B[0;32m 834\u001B[0m \u001B[38;5;28;01mwhile\u001B[39;00m \u001B[38;5;28;01mTrue\u001B[39;00m:\n\u001B[1;32m--> 835\u001B[0m \u001B[43mmodel_obj\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrun\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[0;32m 837\u001B[0m \u001B[38;5;66;03m# Store an iteration number to help the callback\u001B[39;00m\n\u001B[0;32m 838\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28mhasattr\u001B[39m(\u001B[38;5;28mself\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124miteration\u001B[39m\u001B[38;5;124m\"\u001B[39m):\n", + "File \u001B[1;32m~\\PycharmProjects\\message_ix\\message_ix\\models.py:373\u001B[0m, in \u001B[0;36mGAMSModel.run\u001B[1;34m(self, scenario)\u001B[0m\n\u001B[0;32m 370\u001B[0m lines2 \u001B[38;5;241m=\u001B[39m (\u001B[38;5;124m\"\u001B[39m\u001B[38;5;132;01m{}\u001B[39;00m\u001B[38;5;124m = \u001B[39m\u001B[38;5;132;01m{}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;241m.\u001B[39mformat(\u001B[38;5;241m*\u001B[39mkv) \u001B[38;5;28;01mfor\u001B[39;00m kv \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcplex_opts\u001B[38;5;241m.\u001B[39mitems())\n\u001B[0;32m 371\u001B[0m optfile2\u001B[38;5;241m.\u001B[39mwrite_text(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;130;01m\\n\u001B[39;00m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;241m.\u001B[39mjoin(lines2))\n\u001B[1;32m--> 373\u001B[0m result \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43msuper\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrun\u001B[49m\u001B[43m(\u001B[49m\u001B[43mscenario\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 375\u001B[0m \u001B[38;5;66;03m# In previous versions, the `cplex.opt` file(s) were removed at this point\u001B[39;00m\n\u001B[0;32m 376\u001B[0m \u001B[38;5;66;03m# in the workflow. This has been removed due to issues when running\u001B[39;00m\n\u001B[0;32m 377\u001B[0m \u001B[38;5;66;03m# scenarios asynchronously.\u001B[39;00m\n\u001B[0;32m 379\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m result\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\ixmp\\model\\gams.py:293\u001B[0m, in \u001B[0;36mGAMSModel.run\u001B[1;34m(self, scenario)\u001B[0m\n\u001B[0;32m 286\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mNotImplementedError\u001B[39;00m(\n\u001B[0;32m 287\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mGAMSModel requires a Backend that can write to GDX files, e.g. \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m 288\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mJDBCBackend\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m 289\u001B[0m )\n\u001B[0;32m 291\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m 292\u001B[0m \u001B[38;5;66;03m# Invoke GAMS\u001B[39;00m\n\u001B[1;32m--> 293\u001B[0m \u001B[43mcheck_call\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcommand\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mshell\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mos\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mname\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m==\u001B[39;49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mnt\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcwd\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcwd\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 294\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m CalledProcessError \u001B[38;5;28;01mas\u001B[39;00m exc:\n\u001B[0;32m 295\u001B[0m \u001B[38;5;66;03m# Do not remove self.temp_dir; the user may want to inspect the GDX file\u001B[39;00m\n\u001B[0;32m 296\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mformat_exception(exc, model_file) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;28mNone\u001B[39m\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\subprocess.py:359\u001B[0m, in \u001B[0;36mcheck_call\u001B[1;34m(*popenargs, **kwargs)\u001B[0m\n\u001B[0;32m 349\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mcheck_call\u001B[39m(\u001B[38;5;241m*\u001B[39mpopenargs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[0;32m 350\u001B[0m \u001B[38;5;124;03m\"\"\"Run command with arguments. Wait for command to complete. If\u001B[39;00m\n\u001B[0;32m 351\u001B[0m \u001B[38;5;124;03m the exit code was zero then return, otherwise raise\u001B[39;00m\n\u001B[0;32m 352\u001B[0m \u001B[38;5;124;03m CalledProcessError. The CalledProcessError object will have the\u001B[39;00m\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 357\u001B[0m \u001B[38;5;124;03m check_call([\"ls\", \"-l\"])\u001B[39;00m\n\u001B[0;32m 358\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[1;32m--> 359\u001B[0m retcode \u001B[38;5;241m=\u001B[39m \u001B[43mcall\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mpopenargs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 360\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m retcode:\n\u001B[0;32m 361\u001B[0m cmd \u001B[38;5;241m=\u001B[39m kwargs\u001B[38;5;241m.\u001B[39mget(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124margs\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\subprocess.py:342\u001B[0m, in \u001B[0;36mcall\u001B[1;34m(timeout, *popenargs, **kwargs)\u001B[0m\n\u001B[0;32m 340\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m Popen(\u001B[38;5;241m*\u001B[39mpopenargs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs) \u001B[38;5;28;01mas\u001B[39;00m p:\n\u001B[0;32m 341\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m--> 342\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mp\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mwait\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtimeout\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mtimeout\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 343\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m: \u001B[38;5;66;03m# Including KeyboardInterrupt, wait handled that.\u001B[39;00m\n\u001B[0;32m 344\u001B[0m p\u001B[38;5;241m.\u001B[39mkill()\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\subprocess.py:1083\u001B[0m, in \u001B[0;36mPopen.wait\u001B[1;34m(self, timeout)\u001B[0m\n\u001B[0;32m 1081\u001B[0m endtime \u001B[38;5;241m=\u001B[39m _time() \u001B[38;5;241m+\u001B[39m timeout\n\u001B[0;32m 1082\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m-> 1083\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_wait\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtimeout\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mtimeout\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 1084\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyboardInterrupt\u001B[39;00m:\n\u001B[0;32m 1085\u001B[0m \u001B[38;5;66;03m# https://bugs.python.org/issue25942\u001B[39;00m\n\u001B[0;32m 1086\u001B[0m \u001B[38;5;66;03m# The first keyboard interrupt waits briefly for the child to\u001B[39;00m\n\u001B[0;32m 1087\u001B[0m \u001B[38;5;66;03m# exit under the common assumption that it also received the ^C\u001B[39;00m\n\u001B[0;32m 1088\u001B[0m \u001B[38;5;66;03m# generated SIGINT and will exit rapidly.\u001B[39;00m\n\u001B[0;32m 1089\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m timeout \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\subprocess.py:1361\u001B[0m, in \u001B[0;36mPopen._wait\u001B[1;34m(self, timeout)\u001B[0m\n\u001B[0;32m 1358\u001B[0m timeout_millis \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mint\u001B[39m(timeout \u001B[38;5;241m*\u001B[39m \u001B[38;5;241m1000\u001B[39m)\n\u001B[0;32m 1359\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mreturncode \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m 1360\u001B[0m \u001B[38;5;66;03m# API note: Returns immediately if timeout_millis == 0.\u001B[39;00m\n\u001B[1;32m-> 1361\u001B[0m result \u001B[38;5;241m=\u001B[39m \u001B[43m_winapi\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mWaitForSingleObject\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_handle\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 1362\u001B[0m \u001B[43m \u001B[49m\u001B[43mtimeout_millis\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 1363\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m result \u001B[38;5;241m==\u001B[39m _winapi\u001B[38;5;241m.\u001B[39mWAIT_TIMEOUT:\n\u001B[0;32m 1364\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m TimeoutExpired(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39margs, timeout)\n", + "\u001B[1;31mKeyboardInterrupt\u001B[0m: " + ] + } + ], + "source": [ + "scen_base.solve(model=\"MESSAGE-MACRO\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 12, + "outputs": [ + { + "data": { + "text/plain": "array([1990, 1995, 2000, 2005, 2010, 2015], dtype=int64)" + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = scen_base.timeseries()\n", + "df.year.unique()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 14, + "outputs": [ + { + "data": { + "text/plain": " node type_emission type_tec year lvl mrg\n0 World TCE_CO2 all 2030 508.879384 0.0\n1 World TCE_CO2 all 2035 713.809308 0.0\n2 World TCE_CO2 all 2040 907.404817 0.0\n3 World TCE_CO2 all 2045 1151.119609 0.0\n4 World TCE_CO2 all 2050 1561.134700 0.0\n5 World TCE_CO2 all 2055 1653.599833 0.0\n6 World TCE_CO2 all 2060 2351.461667 0.0\n7 World TCE_CO2 all 2070 2019.232897 0.0\n8 World TCE_CO2 all 2080 1192.303190 0.0\n9 World TCE_CO2 all 2090 1041.983798 0.0\n10 World TCE_CO2 all 2100 1241.847644 0.0\n11 World TCE_CO2 all 2110 2058.269348 0.0", + "text/html": "
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nodetype_emissiontype_tecyearlvlmrg
0WorldTCE_CO2all2030508.8793840.0
1WorldTCE_CO2all2035713.8093080.0
2WorldTCE_CO2all2040907.4048170.0
3WorldTCE_CO2all20451151.1196090.0
4WorldTCE_CO2all20501561.1347000.0
5WorldTCE_CO2all20551653.5998330.0
6WorldTCE_CO2all20602351.4616670.0
7WorldTCE_CO2all20702019.2328970.0
8WorldTCE_CO2all20801192.3031900.0
9WorldTCE_CO2all20901041.9837980.0
10WorldTCE_CO2all21001241.8476440.0
11WorldTCE_CO2all21102058.2693480.0
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" + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-M-R12-NAVIGATE\", \"NAV_Ind_Comb_800\")\n", + "scen.var(\"PRICE_EMISSION\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [], + "source": [ + "transfer_demands(scen_base, scen)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 8, + "outputs": [], + "source": [ + "scen.solve(model=\"MESSAGE-MACRO\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [], + "source": [ + "df = mp.scenario_list()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 8, + "outputs": [ + { + "data": { + "text/plain": " model scenario scheme \\\n6923 MESSAGEix-GLOBIOM 1.1-R12 base_GLOBIOM_noSDG_noConst MESSAGE \n6922 MESSAGEix-GLOBIOM 1.1-R12 base_GLOBIOM_noSDG MESSAGE \n6929 MESSAGEix-GLOBIOM 1.1-R12 baseline_DEFULT_check MESSAGE \n6925 MESSAGEix-GLOBIOM 1.1-R12 baseline_DEFAULT MESSAGE \n6926 MESSAGEix-GLOBIOM 1.1-R12 baseline_DEFAULT_2507 MESSAGE \n6924 MESSAGEix-GLOBIOM 1.1-R12 base_GLOBIOM_update MESSAGE \n6927 MESSAGEix-GLOBIOM 1.1-R12 baseline_DEFAULT_clone MESSAGE \n6921 MESSAGEix-GLOBIOM 1.1-R12 MACRO_test MESSAGE \n6933 MESSAGEix-GLOBIOM 1.1-R12 test to confirm MACRO converges MESSAGE \n6932 MESSAGEix-GLOBIOM 1.1-R12 baseline_prep_macro_macro MESSAGE \n6930 MESSAGEix-GLOBIOM 1.1-R12 baseline_prep_lu MESSAGE \n6928 MESSAGEix-GLOBIOM 1.1-R12 baseline_DEFAULT_gdp_update MESSAGE \n6931 MESSAGEix-GLOBIOM 1.1-R12 baseline_prep_macro MESSAGE \n\n is_default is_locked cre_user cre_date upd_user \\\n6923 1 0 min 2023-08-11 15:17:05.000000 None \n6922 1 0 min 2023-08-01 10:58:46.000000 None \n6929 1 0 unlu 2023-07-31 14:17:24.000000 unlu \n6925 1 0 min 2023-07-27 10:24:37.000000 None \n6926 1 0 unlu 2023-07-25 17:28:24.000000 None \n6924 1 0 min 2023-04-18 14:25:06.000000 None \n6927 1 0 unlu 2023-02-01 09:55:44.000000 None \n6921 1 0 min 2022-05-02 16:57:38.000000 None \n6933 1 0 min 2022-04-30 20:34:54.000000 min \n6932 1 0 min 2022-04-30 20:29:07.000000 min \n6930 1 0 min 2022-04-30 17:29:17.000000 min \n6928 1 0 min 2022-03-03 00:24:43.000000 min \n6931 1 0 min 2022-03-02 14:21:00.000000 None \n\n upd_date lock_user lock_date \\\n6923 None None None \n6922 None None None \n6929 2023-07-31 14:26:45.000000 None None \n6925 None None None \n6926 None None None \n6924 None None None \n6927 None None None \n6921 None None None \n6933 2022-04-30 20:40:13.000000 None None \n6932 2022-04-30 20:34:39.000000 None None \n6930 2022-04-30 18:18:13.000000 None None \n6928 2022-03-03 00:33:38.000000 None None \n6931 None None None \n\n annotation version \n6923 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6922 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 6 \n6929 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6925 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 21 \n6926 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6924 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 3 \n6927 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6921 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6933 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 6 \n6932 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 17 \n6930 clone Scenario from 'MESSAGEix-GLOBIOM_R12_CHN... 17 \n6928 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 25 \n6931 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 ", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
modelscenarioschemeis_defaultis_lockedcre_usercre_dateupd_userupd_datelock_userlock_dateannotationversion
6923MESSAGEix-GLOBIOM 1.1-R12base_GLOBIOM_noSDG_noConstMESSAGE10min2023-08-11 15:17:05.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6922MESSAGEix-GLOBIOM 1.1-R12base_GLOBIOM_noSDGMESSAGE10min2023-08-01 10:58:46.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...6
6929MESSAGEix-GLOBIOM 1.1-R12baseline_DEFULT_checkMESSAGE10unlu2023-07-31 14:17:24.000000unlu2023-07-31 14:26:45.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6925MESSAGEix-GLOBIOM 1.1-R12baseline_DEFAULTMESSAGE10min2023-07-27 10:24:37.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...21
6926MESSAGEix-GLOBIOM 1.1-R12baseline_DEFAULT_2507MESSAGE10unlu2023-07-25 17:28:24.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6924MESSAGEix-GLOBIOM 1.1-R12base_GLOBIOM_updateMESSAGE10min2023-04-18 14:25:06.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...3
6927MESSAGEix-GLOBIOM 1.1-R12baseline_DEFAULT_cloneMESSAGE10unlu2023-02-01 09:55:44.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6921MESSAGEix-GLOBIOM 1.1-R12MACRO_testMESSAGE10min2022-05-02 16:57:38.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6933MESSAGEix-GLOBIOM 1.1-R12test to confirm MACRO convergesMESSAGE10min2022-04-30 20:34:54.000000min2022-04-30 20:40:13.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...6
6932MESSAGEix-GLOBIOM 1.1-R12baseline_prep_macro_macroMESSAGE10min2022-04-30 20:29:07.000000min2022-04-30 20:34:39.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...17
6930MESSAGEix-GLOBIOM 1.1-R12baseline_prep_luMESSAGE10min2022-04-30 17:29:17.000000min2022-04-30 18:18:13.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM_R12_CHN...17
6928MESSAGEix-GLOBIOM 1.1-R12baseline_DEFAULT_gdp_updateMESSAGE10min2022-03-03 00:24:43.000000min2022-03-03 00:33:38.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...25
6931MESSAGEix-GLOBIOM 1.1-R12baseline_prep_macroMESSAGE10min2022-03-02 14:21:00.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
\n
" + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[(df[\"model\"]==(\"MESSAGEix-GLOBIOM 1.1-R12\"))].sort_values(\"cre_date\", ascending=False)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "from message_ix_models import workflow" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [ + { + "data": { + "text/plain": " model \\\n6950 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6951 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6952 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6953 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6954 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6955 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6956 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6957 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6958 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6959 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6960 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6961 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6962 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-M \n6971 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD0BI70 \n6977 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD0BI74 \n6987 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD0BI78 \n6998 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00 \n7005 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP30BD0BI70 \n7011 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP30BD0BI74 \n7013 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP30BD0BI78 \n7020 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP30BD1BI00 \n7028 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP50BD0BI70 \n7036 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP50BD0BI74 \n7044 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP50BD0BI78 \n7052 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP50BD1BI00 \n7060 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP76BD0BI70 \n7068 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP76BD0BI74 \n7075 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP76BD0BI78 \n7083 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP76BD1BI00 \n7084 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE_MP00BD1BI00 \n\n scenario scheme is_default is_locked \\\n6950 baseline MESSAGE 1 0 \n6951 baseline_MP00BD0BI78 MESSAGE 1 0 \n6952 baseline_MP00BD0BI78_test_calib MESSAGE 1 0 \n6953 baseline_MP00BD0BI78_test_calib_macro MESSAGE 1 0 \n6954 baseline_MP00BD1BI00 MESSAGE 1 0 \n6955 baseline_MP76BD0BI70 MESSAGE 1 0 \n6956 baseline_MP76BD0BI78 MESSAGE 1 0 \n6957 baseline_MP76BD0BI78test_scp MESSAGE 1 0 \n6958 baseline_MP76BD1BI00 MESSAGE 1 0 \n6959 baseline_MP76BD1BI00test_scp MESSAGE 1 0 \n6960 baseline_cum_LU_emis MESSAGE 1 0 \n6961 baseline_w/o_LU MESSAGE 1 0 \n6962 baseline MESSAGE 1 0 \n6971 baseline MESSAGE 1 0 \n6977 baseline MESSAGE 1 0 \n6987 baseline MESSAGE 1 0 \n6998 baseline MESSAGE 1 0 \n7005 baseline MESSAGE 1 0 \n7011 baseline MESSAGE 1 0 \n7013 baseline MESSAGE 1 0 \n7020 baseline MESSAGE 1 0 \n7028 baseline MESSAGE 1 0 \n7036 baseline MESSAGE 1 0 \n7044 baseline MESSAGE 1 0 \n7052 baseline MESSAGE 1 0 \n7060 baseline MESSAGE 1 0 \n7068 baseline MESSAGE 1 0 \n7075 baseline MESSAGE 1 0 \n7083 baseline MESSAGE 1 0 \n7084 baseline MESSAGE 1 0 \n\n cre_user cre_date upd_user \\\n6950 maczek 2023-08-03 11:49:57.000000 maczek \n6951 maczek 2023-08-08 13:39:45.000000 maczek \n6952 maczek 2023-08-09 09:30:09.000000 maczek \n6953 maczek 2023-08-09 09:37:24.000000 maczek \n6954 maczek 2023-08-08 10:17:35.000000 maczek \n6955 maczek 2023-08-09 09:10:13.000000 None \n6956 maczek 2023-08-09 09:56:43.000000 None \n6957 maczek 2023-08-09 15:00:57.000000 maczek \n6958 maczek 2023-08-09 15:20:37.000000 None \n6959 maczek 2023-08-09 16:57:44.000000 maczek \n6960 maczek 2023-08-06 20:02:21.000000 maczek \n6961 maczek 2023-08-08 09:50:56.000000 None \n6962 maczek 2023-08-24 09:43:03.000000 None \n6971 maczek 2023-08-23 13:40:54.000000 maczek \n6977 maczek 2023-08-23 13:30:41.000000 maczek \n6987 maczek 2023-08-22 23:50:24.000000 maczek \n6998 maczek 2023-08-23 14:39:04.000000 maczek \n7005 maczek 2023-08-23 16:23:21.000000 maczek \n7011 maczek 2023-08-23 16:12:44.000000 maczek \n7013 maczek 2023-08-23 16:00:04.000000 maczek \n7020 maczek 2023-08-23 17:53:09.000000 maczek \n7028 maczek 2023-08-23 22:52:13.000000 maczek \n7036 maczek 2023-08-23 22:42:02.000000 maczek \n7044 maczek 2023-08-23 18:03:02.000000 maczek \n7052 maczek 2023-08-24 00:05:58.000000 maczek \n7060 maczek 2023-08-24 00:16:04.000000 maczek \n7068 maczek 2023-08-24 08:22:27.000000 maczek \n7075 maczek 2023-08-24 08:32:00.000000 maczek \n7083 maczek 2023-08-24 09:44:01.000000 maczek \n7084 maczek 2023-08-14 13:07:05.000000 None \n\n upd_date lock_user lock_date \\\n6950 2023-08-04 16:29:36.000000 None None \n6951 2023-08-08 15:37:34.000000 None None \n6952 2023-08-09 09:35:09.000000 None None \n6953 2023-08-09 09:47:10.000000 None None \n6954 2023-08-08 11:52:04.000000 None None \n6955 None None None \n6956 None None None \n6957 2023-08-09 15:59:13.000000 None None \n6958 None None None \n6959 2023-08-09 17:26:01.000000 None None \n6960 2023-08-06 20:05:37.000000 None None \n6961 None None None \n6962 None None None \n6971 2023-08-28 11:25:05.000000 None None \n6977 2023-08-28 11:17:38.000000 None None \n6987 2023-08-28 11:09:29.000000 None None \n6998 2023-08-28 11:48:42.000000 None None \n7005 2023-08-28 12:12:04.000000 None None \n7011 2023-08-28 12:03:48.000000 None None \n7013 2023-08-28 11:55:59.000000 None None \n7020 2023-08-28 12:20:17.000000 None None \n7028 2023-08-28 12:43:31.000000 None None \n7036 2023-08-28 12:35:35.000000 None None \n7044 2023-08-28 12:27:41.000000 None None \n7052 2023-08-28 13:10:43.000000 None None \n7060 2023-08-28 13:18:38.000000 None None \n7068 2023-08-28 13:26:36.000000 None None \n7075 2023-08-28 13:34:56.000000 None None \n7083 2023-08-28 13:44:05.000000 None None \n7084 None None None \n\n annotation version \n6950 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 2 \n6951 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6952 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6953 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6954 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6955 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6956 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6957 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6958 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6959 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6960 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6961 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6962 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6971 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6977 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6987 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 2 \n6998 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 4 \n7005 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n7011 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n7013 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 2 \n7020 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 2 \n7028 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n7036 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n7044 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n7052 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n7060 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n7068 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n7075 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n7083 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n7084 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 ", + "text/html": "
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modelscenarioschemeis_defaultis_lockedcre_usercre_dateupd_userupd_datelock_userlock_dateannotationversion
6950MESSAGEix-GLOBIOM 1.1-R12-MAGPIEbaselineMESSAGE10maczek2023-08-03 11:49:57.000000maczek2023-08-04 16:29:36.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...2
6951MESSAGEix-GLOBIOM 1.1-R12-MAGPIEbaseline_MP00BD0BI78MESSAGE10maczek2023-08-08 13:39:45.000000maczek2023-08-08 15:37:34.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6952MESSAGEix-GLOBIOM 1.1-R12-MAGPIEbaseline_MP00BD0BI78_test_calibMESSAGE10maczek2023-08-09 09:30:09.000000maczek2023-08-09 09:35:09.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6953MESSAGEix-GLOBIOM 1.1-R12-MAGPIEbaseline_MP00BD0BI78_test_calib_macroMESSAGE10maczek2023-08-09 09:37:24.000000maczek2023-08-09 09:47:10.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6954MESSAGEix-GLOBIOM 1.1-R12-MAGPIEbaseline_MP00BD1BI00MESSAGE10maczek2023-08-08 10:17:35.000000maczek2023-08-08 11:52:04.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6955MESSAGEix-GLOBIOM 1.1-R12-MAGPIEbaseline_MP76BD0BI70MESSAGE10maczek2023-08-09 09:10:13.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6956MESSAGEix-GLOBIOM 1.1-R12-MAGPIEbaseline_MP76BD0BI78MESSAGE10maczek2023-08-09 09:56:43.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6957MESSAGEix-GLOBIOM 1.1-R12-MAGPIEbaseline_MP76BD0BI78test_scpMESSAGE10maczek2023-08-09 15:00:57.000000maczek2023-08-09 15:59:13.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6958MESSAGEix-GLOBIOM 1.1-R12-MAGPIEbaseline_MP76BD1BI00MESSAGE10maczek2023-08-09 15:20:37.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6959MESSAGEix-GLOBIOM 1.1-R12-MAGPIEbaseline_MP76BD1BI00test_scpMESSAGE10maczek2023-08-09 16:57:44.000000maczek2023-08-09 17:26:01.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6960MESSAGEix-GLOBIOM 1.1-R12-MAGPIEbaseline_cum_LU_emisMESSAGE10maczek2023-08-06 20:02:21.000000maczek2023-08-06 20:05:37.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6961MESSAGEix-GLOBIOM 1.1-R12-MAGPIEbaseline_w/o_LUMESSAGE10maczek2023-08-08 09:50:56.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6962MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MbaselineMESSAGE10maczek2023-08-24 09:43:03.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6971MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD0BI70baselineMESSAGE10maczek2023-08-23 13:40:54.000000maczek2023-08-28 11:25:05.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6977MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD0BI74baselineMESSAGE10maczek2023-08-23 13:30:41.000000maczek2023-08-28 11:17:38.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6987MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD0BI78baselineMESSAGE10maczek2023-08-22 23:50:24.000000maczek2023-08-28 11:09:29.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...2
6998MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00baselineMESSAGE10maczek2023-08-23 14:39:04.000000maczek2023-08-28 11:48:42.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...4
7005MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP30BD0BI70baselineMESSAGE10maczek2023-08-23 16:23:21.000000maczek2023-08-28 12:12:04.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
7011MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP30BD0BI74baselineMESSAGE10maczek2023-08-23 16:12:44.000000maczek2023-08-28 12:03:48.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
7013MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP30BD0BI78baselineMESSAGE10maczek2023-08-23 16:00:04.000000maczek2023-08-28 11:55:59.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...2
7020MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP30BD1BI00baselineMESSAGE10maczek2023-08-23 17:53:09.000000maczek2023-08-28 12:20:17.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...2
7028MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP50BD0BI70baselineMESSAGE10maczek2023-08-23 22:52:13.000000maczek2023-08-28 12:43:31.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
7036MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP50BD0BI74baselineMESSAGE10maczek2023-08-23 22:42:02.000000maczek2023-08-28 12:35:35.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
7044MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP50BD0BI78baselineMESSAGE10maczek2023-08-23 18:03:02.000000maczek2023-08-28 12:27:41.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
7052MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP50BD1BI00baselineMESSAGE10maczek2023-08-24 00:05:58.000000maczek2023-08-28 13:10:43.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
7060MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP76BD0BI70baselineMESSAGE10maczek2023-08-24 00:16:04.000000maczek2023-08-28 13:18:38.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
7068MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP76BD0BI74baselineMESSAGE10maczek2023-08-24 08:22:27.000000maczek2023-08-28 13:26:36.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
7075MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP76BD0BI78baselineMESSAGE10maczek2023-08-24 08:32:00.000000maczek2023-08-28 13:34:56.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
7083MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP76BD1BI00baselineMESSAGE10maczek2023-08-24 09:44:01.000000maczek2023-08-28 13:44:05.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
7084MESSAGEix-GLOBIOM 1.1-R12-MAGPIE_MP00BD1BI00baselineMESSAGE10maczek2023-08-14 13:07:05.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
\n
" + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[(df[\"model\"].str.startswith(\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\")) & (df[\"scenario\"].str.contains(\"baseline\"))].sort_values(\"model\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [], + "source": [ + "#scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\", \"baseline\")\n", + "#scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12\", \"baseline_DEFAULT\")\n", + "#scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\", \"baseline\")\n", + "#scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\", \"NPi2020-con-prim-dir-ncr\")\n", + "#scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\", \"npi_low_dem_scen\")\n", + "#scen = message_ix.Scenario(mp, \"ENGAGE_SSP2_v4.1.8.1\", \"baseline\")\n", + "scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\", \"baseline_w/o_LU\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-11T08:01:14.451774300Z", + "start_time": "2023-09-11T08:01:11.092726Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [], + "source": [ + "def build_magpie_baseline_from_r12_base(scen_base, magpie_scen):\n", + " scen = scen_base.clone(scen_base.model+\"-\"+magpie_scen, \"baseline\")\n", + " if scen.has_solution():\n", + " scen.remove_solution()\n", + " add_globiom(\n", + " mp,\n", + " scen,\n", + " \"SSP2\",\n", + " private_data_path(),\n", + " 2015,\n", + " globiom_scenario=\"noSDG_rcpref\",\n", + " regenerate_input_data=True,\n", + " #regenerate_input_data_only=True,\n", + " #allow_empty_drivers=True,\n", + " add_reporting_data=True,\n", + " config_setup=\"MAGPIE_\"+magpie_scen\n", + " )\n", + " scen.set_as_default()\n", + " return scen" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [], + "source": [ + "magpie_scens = [\n", + " \"MP00BD0BI78\",\n", + " \"MP00BD0BI74\",\n", + " \"MP00BD0BI70\",\n", + " \"MP00BD1BI00\",\n", + " \"MP30BD0BI78\",\n", + " \"MP30BD0BI74\",\n", + " \"MP30BD0BI70\",\n", + " \"MP30BD1BI00\",\n", + " \"MP50BD0BI78\",\n", + " \"MP50BD0BI74\",\n", + " \"MP50BD0BI70\",\n", + " \"MP50BD1BI00\",\n", + " \"MP76BD0BI70\",\n", + " \"MP76BD0BI74\",\n", + " \"MP76BD0BI78\",\n", + " \"MP76BD1BI00\"\n", + "]\n", + "magpie_scen = magpie_scens[0]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [], + "source": [ + "mp = ixmp.Platform(\"ixmp_dev\")\n", + "#scen_base = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\", \"baseline_w/o_LU\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "read-in of MP00BD0BI74 matrix started\n", + "Starting to process data input from csv file.\n", + "Agricultural Demand\n", + "Agricultural Demand|Energy\n", + "Agricultural Demand|Energy|Crops\n", + "Agricultural Demand|Energy|Crops|1st generation\n", + "Agricultural Demand|Energy|Crops|2nd generation\n", + "Agricultural Demand|Non-Energy\n", + "Agricultural Demand|Non-Energy|Crops\n", + "Agricultural Demand|Non-Energy|Crops|Feed\n", + "Agricultural Demand|Non-Energy|Crops|Food\n", + "Agricultural Demand|Non-Energy|Crops|Other\n", + "Agricultural Demand|Non-Energy|Crops|Other|Waste\n", + "Agricultural Demand|Non-Energy|Livestock\n", + "Agricultural Demand|Non-Energy|Livestock|Food\n", + "Agricultural Demand|Non-Energy|Livestock|Other\n", + "Agricultural Demand|Non-Energy|Livestock|Other|Waste\n", + "Agricultural Production\n", + "Agricultural Production|Energy\n", + "Agricultural Production|Energy|Crops\n", + "Agricultural Production|Energy|Crops|1st generation\n", + "Agricultural Production|Energy|Crops|2nd generation\n", + "Agricultural Production|Non-Energy\n", + "Agricultural Production|Non-Energy|Crops\n", + "Agricultural Production|Non-Energy|Crops|Cereals\n", + "Agricultural Production|Non-Energy|Livestock\n", + "BCA_LandUseChangeEM\n", + "BCA_SavanBurnEM\n", + "Biodiversity|BII\n", + "Biodiversity|BII|Biodiversity hotspots\n", + "CH4_LandUseChangeEM\n", + "CH4_SavanBurnEM\n", + "CO_LandUseChangeEM\n", + "CO_SavanBurnEM\n", + "Costs|TC\n", + "CrpLnd\n", + "Emissions|CH4|AFOLU\n", + "Emissions|CH4|AFOLU|Agriculture\n", + "Emissions|CH4|AFOLU|Agriculture|Livestock\n", + "Emissions|CH4|AFOLU|Agriculture|Livestock|Enteric Fermentation\n", + "Emissions|CH4|AFOLU|Agriculture|Livestock|Manure Management\n", + "Emissions|CH4|AFOLU|Agriculture|Rice\n", + "Emissions|CH4|AFOLU|Biomass Burning\n", + "Emissions|CH4|AFOLU|Land\n", + "Emissions|CH4|AFOLU|Land|Grassland Burning\n", + "Emissions|CO2|AFOLU\n", + "Emissions|CO2|AFOLU|Afforestation\n", + "Emissions|CO2|AFOLU|Agriculture\n", + "Emissions|CO2|AFOLU|Deforestation\n", + "Emissions|CO2|AFOLU|Forest Management\n", + "Emissions|CO2|AFOLU|Negative\n", + "Emissions|CO2|AFOLU|Other LUC\n", + "Emissions|CO2|AFOLU|Positive\n", + "Emissions|CO2|AFOLU|Soil Carbon\n", + "Emissions|GHG|AFOLU\n", + "Emissions|N2O|AFOLU\n", + "Emissions|N2O|AFOLU|Agriculture\n", + "Emissions|N2O|AFOLU|Agriculture|Livestock\n", + "Emissions|N2O|AFOLU|Agriculture|Livestock|Manure Management\n", + "Emissions|N2O|AFOLU|Agriculture|Managed Soils\n", + "Emissions|N2O|AFOLU|Biomass Burning\n", + "Emissions|N2O|AFOLU|Land\n", + "Emissions|N2O|AFOLU|Land|Grassland Burning\n", + "Emissions|N2O|AFOLU|Land|Grassland Pastures\n", + "EneCrpLnd\n", + "EneCrpLnd_gr\n", + "Fertilizer Use|Nitrogen\n", + "Fertilizer Use|Phosphorus\n", + "Fertilizer|Nitrogen|Intensity\n", + "Fertilizer|Phosphorus|Intensity\n", + "Food Demand\n", + "Food Demand|Crops\n", + "Food Demand|Livestock\n", + "Food Demand|Microbial protein\n", + "Forestry Demand|Roundwood\n", + "Forestry Demand|Roundwood|Industrial Roundwood\n", + "Forestry Demand|Roundwood|Wood Fuel\n", + "Forestry Production|Forest Residues\n", + "Forestry Production|Roundwood\n", + "Forestry Production|Roundwood|Industrial Roundwood\n", + "Forestry Production|Roundwood|Wood Fuel\n", + "GrassLnd\n", + "LU_CH4\n", + "LU_CH4_Agri\n", + "LU_CH4_BioBurn\n", + "LU_CO2\n", + "LU_GHG\n", + "LU_N2O\n", + "Land Cover\n", + "Land Cover|Cropland\n", + "Land Cover|Cropland|Cereals\n", + "Land Cover|Cropland|Energy Crops\n", + "Land Cover|Cropland|Irrigated\n", + "Land Cover|Cropland|Non-Energy Crops\n", + "Land Cover|Cropland|Oilcrops\n", + "Land Cover|Cropland|Sugarcrops\n", + "Land Cover|Forest\n", + "Land Cover|Forest|Afforestation and Reforestation\n", + "Land Cover|Forest|Forest old\n", + "Land Cover|Forest|Forestry\n", + "Land Cover|Forest|Managed\n", + "Land Cover|Forest|Natural Forest\n", + "Land Cover|Other Land\n", + "Land Cover|Pasture\n", + "Land Cover|Protected\n", + "Landuse intensity indicator Tau\n", + "NH3_LandUseChangeEM\n", + "NH3_ManureEM\n", + "NH3_RiceEM\n", + "NH3_SavanBurnEM\n", + "NH3_SoilEM\n", + "NOx_LandUseChangeEM\n", + "NOx_SavanBurnEM\n", + "NOx_SoilEM\n", + "NewForLnd\n", + "OCA_LandUseChangeEM\n", + "OCA_SavanBurnEM\n", + "OldForLnd\n", + "OtherLnd\n", + "Population\n", + "Population|Risk of Hunger\n", + "Price|Agriculture|Non-Energy Crops and Livestock|Index\n", + "Price|Agriculture|Non-Energy Crops|Index\n", + "Price|Carbon|CH4\n", + "Price|Carbon|CO2\n", + "Price|Carbon|N2O\n", + "Price|Water\n", + "Primary Energy|Biomass\n", + "Primary Energy|Biomass|1st Generation\n", + "Primary Energy|Biomass|1st Generation|Biodiesel\n", + "Primary Energy|Biomass|1st Generation|Bioethanol\n", + "Primary Energy|Biomass|Energy Crops\n", + "Primary Energy|Biomass|Fuelwood\n", + "Primary Energy|Biomass|Other\n", + "Primary Energy|Biomass|Residues\n", + "Primary Energy|Biomass|Residues|Forest industry\n", + "Primary Energy|Biomass|Residues|Logging\n", + "Primary Energy|Biomass|Roundwood harvest\n", + "SO2_LandUseChangeEM\n", + "SO2_SavanBurnEM\n", + "TCE\n", + "TotalLnd\n", + "VOC_LandUseChangeEM\n", + "VOC_SavanBurnEM\n", + "Water|Withdrawal|Irrigation\n", + "Water|Withdrawal|Irrigation|Cereals\n", + "Water|Withdrawal|Irrigation|Oilcrops\n", + "Water|Withdrawal|Irrigation|Sugarcrops\n", + "Yield|Cereals\n", + "Yield|Energy Crops\n", + "Yield|Non-Energy Crops\n", + "Yield|Oilcrops\n", + "Yield|Sugarcrops\n", + "bioenergy\n", + "total_cost\n", + "Price|Primary Energy|Biomass\n", + "read-in of MP00BD0BI70 matrix started\n", + "Starting to process data input from csv file.\n", + "Agricultural Demand\n", + "Agricultural Demand|Energy\n", + "Agricultural Demand|Energy|Crops\n", + "Agricultural Demand|Energy|Crops|1st generation\n", + "Agricultural Demand|Energy|Crops|2nd generation\n", + "Agricultural Demand|Non-Energy\n", + "Agricultural Demand|Non-Energy|Crops\n", + "Agricultural Demand|Non-Energy|Crops|Feed\n", + "Agricultural Demand|Non-Energy|Crops|Food\n", + "Agricultural Demand|Non-Energy|Crops|Other\n", + "Agricultural Demand|Non-Energy|Crops|Other|Waste\n", + "Agricultural Demand|Non-Energy|Livestock\n", + "Agricultural Demand|Non-Energy|Livestock|Food\n", + "Agricultural Demand|Non-Energy|Livestock|Other\n", + "Agricultural Demand|Non-Energy|Livestock|Other|Waste\n", + "Agricultural Production\n", + "Agricultural Production|Energy\n", + "Agricultural Production|Energy|Crops\n", + "Agricultural Production|Energy|Crops|1st generation\n", + "Agricultural Production|Energy|Crops|2nd generation\n", + "Agricultural Production|Non-Energy\n", + "Agricultural Production|Non-Energy|Crops\n", + "Agricultural Production|Non-Energy|Crops|Cereals\n", + "Agricultural Production|Non-Energy|Livestock\n", + "BCA_LandUseChangeEM\n", + "BCA_SavanBurnEM\n", + "Biodiversity|BII\n", + "Biodiversity|BII|Biodiversity hotspots\n", + "CH4_LandUseChangeEM\n", + "CH4_SavanBurnEM\n", + "CO_LandUseChangeEM\n", + "CO_SavanBurnEM\n", + "Costs|TC\n", + "CrpLnd\n", + "Emissions|CH4|AFOLU\n", + "Emissions|CH4|AFOLU|Agriculture\n", + "Emissions|CH4|AFOLU|Agriculture|Livestock\n", + "Emissions|CH4|AFOLU|Agriculture|Livestock|Enteric Fermentation\n", + "Emissions|CH4|AFOLU|Agriculture|Livestock|Manure Management\n", + "Emissions|CH4|AFOLU|Agriculture|Rice\n", + "Emissions|CH4|AFOLU|Biomass Burning\n", + "Emissions|CH4|AFOLU|Land\n", + "Emissions|CH4|AFOLU|Land|Grassland Burning\n", + "Emissions|CO2|AFOLU\n", + "Emissions|CO2|AFOLU|Afforestation\n", + "Emissions|CO2|AFOLU|Agriculture\n", + "Emissions|CO2|AFOLU|Deforestation\n", + "Emissions|CO2|AFOLU|Forest Management\n", + "Emissions|CO2|AFOLU|Negative\n", + "Emissions|CO2|AFOLU|Other LUC\n", + "Emissions|CO2|AFOLU|Positive\n", + "Emissions|CO2|AFOLU|Soil Carbon\n", + "Emissions|GHG|AFOLU\n", + "Emissions|N2O|AFOLU\n", + "Emissions|N2O|AFOLU|Agriculture\n", + "Emissions|N2O|AFOLU|Agriculture|Livestock\n", + "Emissions|N2O|AFOLU|Agriculture|Livestock|Manure Management\n", + "Emissions|N2O|AFOLU|Agriculture|Managed Soils\n", + "Emissions|N2O|AFOLU|Biomass Burning\n", + "Emissions|N2O|AFOLU|Land\n", + "Emissions|N2O|AFOLU|Land|Grassland Burning\n", + "Emissions|N2O|AFOLU|Land|Grassland Pastures\n", + "EneCrpLnd\n", + "EneCrpLnd_gr\n", + "Fertilizer Use|Nitrogen\n", + "Fertilizer Use|Phosphorus\n", + "Fertilizer|Nitrogen|Intensity\n", + "Fertilizer|Phosphorus|Intensity\n", + "Food Demand\n", + "Food Demand|Crops\n", + "Food Demand|Livestock\n", + "Food Demand|Microbial protein\n", + "Forestry Demand|Roundwood\n", + "Forestry Demand|Roundwood|Industrial Roundwood\n", + "Forestry Demand|Roundwood|Wood Fuel\n", + "Forestry Production|Forest Residues\n", + "Forestry Production|Roundwood\n", + "Forestry Production|Roundwood|Industrial Roundwood\n", + "Forestry Production|Roundwood|Wood Fuel\n", + "GrassLnd\n", + "LU_CH4\n", + "LU_CH4_Agri\n", + "LU_CH4_BioBurn\n", + "LU_CO2\n", + "LU_GHG\n", + "LU_N2O\n", + "Land Cover\n", + "Land Cover|Cropland\n", + "Land Cover|Cropland|Cereals\n", + "Land Cover|Cropland|Energy Crops\n", + "Land Cover|Cropland|Irrigated\n", + "Land Cover|Cropland|Non-Energy Crops\n", + "Land Cover|Cropland|Oilcrops\n", + "Land Cover|Cropland|Sugarcrops\n", + "Land Cover|Forest\n", + "Land Cover|Forest|Afforestation and Reforestation\n", + "Land Cover|Forest|Forest old\n", + "Land Cover|Forest|Forestry\n", + "Land Cover|Forest|Managed\n", + "Land Cover|Forest|Natural Forest\n", + "Land Cover|Other Land\n", + "Land Cover|Pasture\n", + "Land Cover|Protected\n", + "Landuse intensity indicator Tau\n", + "NH3_LandUseChangeEM\n", + "NH3_ManureEM\n", + "NH3_RiceEM\n", + "NH3_SavanBurnEM\n", + "NH3_SoilEM\n", + "NOx_LandUseChangeEM\n", + "NOx_SavanBurnEM\n", + "NOx_SoilEM\n", + "NewForLnd\n", + "OCA_LandUseChangeEM\n", + "OCA_SavanBurnEM\n", + "OldForLnd\n", + "OtherLnd\n", + "Population\n", + "Population|Risk of Hunger\n", + "Price|Agriculture|Non-Energy Crops and Livestock|Index\n", + "Price|Agriculture|Non-Energy Crops|Index\n", + "Price|Carbon|CH4\n", + "Price|Carbon|CO2\n", + "Price|Carbon|N2O\n", + "Price|Water\n", + "Primary Energy|Biomass\n", + "Primary Energy|Biomass|1st Generation\n", + "Primary Energy|Biomass|1st Generation|Biodiesel\n", + "Primary Energy|Biomass|1st Generation|Bioethanol\n", + "Primary Energy|Biomass|Energy Crops\n", + "Primary Energy|Biomass|Fuelwood\n", + "Primary Energy|Biomass|Other\n", + "Primary Energy|Biomass|Residues\n", + "Primary Energy|Biomass|Residues|Forest industry\n", + "Primary Energy|Biomass|Residues|Logging\n", + "Primary Energy|Biomass|Roundwood harvest\n", + "SO2_LandUseChangeEM\n", + "SO2_SavanBurnEM\n", + "TCE\n", + "TotalLnd\n", + "VOC_LandUseChangeEM\n", + "VOC_SavanBurnEM\n", + "Water|Withdrawal|Irrigation\n", + "Water|Withdrawal|Irrigation|Cereals\n", + "Water|Withdrawal|Irrigation|Oilcrops\n", + "Water|Withdrawal|Irrigation|Sugarcrops\n", + "Yield|Cereals\n", + "Yield|Energy Crops\n", + "Yield|Non-Energy Crops\n", + "Yield|Oilcrops\n", + "Yield|Sugarcrops\n", + "bioenergy\n", + "total_cost\n", + "Price|Primary Energy|Biomass\n", + "read-in of MP00BD1BI00 matrix started\n", + "Starting to process data input from csv file.\n" + ] + } + ], + "source": [ + "for magpie_scen in magpie_scens[1:]:\n", + " print(f\"read-in of {magpie_scen} matrix started\")\n", + " scen = build_magpie_baseline_from_r12_base(scen_base, magpie_scen)\n", + " if \"MP00\" not in magpie_scen:\n", + " print(f\"adding SCP model to {magpie_scen}\")\n", + " scp_dict = data_scp.gen_data_scp(scen)\n", + " scen.check_out()\n", + " for k,v in scp_dict.items():\n", + " scen.add_par(k, v)\n", + " scen.commit(\"add SCP tecs\")\n", + " scen.set_as_default()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "is_executing": true + } + } + }, + { + "cell_type": "code", + "execution_count": 9, + "outputs": [ + { + "data": { + "text/plain": "'MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD0BI78'" + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scen.model" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 37, + "outputs": [], + "source": [ + "#scen.remove_solution()\n", + "scen.solve(model=\"MESSAGE-MACRO\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 28, + "outputs": [], + "source": [ + "if \"MP00\" not in magpie_scen:\n", + " scp_dict = data_scp.gen_data_scp(scen)\n", + " scen.check_out()\n", + " for k,v in scp_dict.items():\n", + " scen.add_par(k, v)\n", + " scen.commit()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scen.check_out()\n", + "scen.add_set(\"balance_equality\", (\"i_feed\", \"useful\"))\n", + "scen.commit(\"add ifeed to balance eqÄ\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scen.solve(model=\"MESSAGE-MACRO\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 16, + "outputs": [], + "source": [ + "df_ts = scen.timeseries()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 17, + "outputs": [ + { + "data": { + "text/plain": "array([], dtype=object)" + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_ts[\"year\"].unique()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 18, + "outputs": [ + { + "data": { + "text/plain": " model scenario \\\n6969 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD0BI70 NPi2020-con-prim-dir-ncr \n6970 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD0BI70 NPi2030-con-prim-dir-ncr \n6976 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD0BI74 NPi2020-con-prim-dir-ncr \n6985 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD0BI78 NPi2020-con-prim-dir-ncr \n6986 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD0BI78 NPi2030-con-prim-dir-ncr \n6997 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00 NPi2020-con-prim-dir-ncr \n7004 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP30BD0BI70 NPi2020-con-prim-dir-ncr \n7010 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP30BD0BI74 NPi2020-con-prim-dir-ncr \n7012 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP30BD0BI78 NPi2020-con-prim-dir-ncr \n7019 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP30BD1BI00 NPi2020-con-prim-dir-ncr \n7027 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP50BD0BI70 NPi2020-con-prim-dir-ncr \n7035 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP50BD0BI74 NPi2020-con-prim-dir-ncr \n7043 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP50BD0BI78 NPi2020-con-prim-dir-ncr \n7051 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP50BD1BI00 NPi2020-con-prim-dir-ncr \n7059 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP76BD0BI70 NPi2020-con-prim-dir-ncr \n7067 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP76BD0BI74 NPi2020-con-prim-dir-ncr \n7074 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP76BD0BI78 NPi2020-con-prim-dir-ncr \n7082 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP76BD1BI00 NPi2020-con-prim-dir-ncr \n\n scheme is_default is_locked cre_user cre_date \\\n6969 MESSAGE 1 0 maczek 2023-08-25 10:07:00.000000 \n6970 MESSAGE 1 0 maczek 2023-08-24 22:55:18.000000 \n6976 MESSAGE 1 0 maczek 2023-08-24 23:51:00.000000 \n6985 MESSAGE 1 0 maczek 2023-08-24 23:14:26.000000 \n6986 MESSAGE 1 0 maczek 2023-08-22 13:43:19.000000 \n6997 MESSAGE 1 0 maczek 2023-08-25 10:36:08.000000 \n7004 MESSAGE 1 0 maczek 2023-08-25 13:38:00.000000 \n7010 MESSAGE 1 0 maczek 2023-08-25 11:22:25.000000 \n7012 MESSAGE 1 0 maczek 2023-08-28 17:00:51.000000 \n7019 MESSAGE 1 0 maczek 2023-08-25 14:38:55.000000 \n7027 MESSAGE 1 0 maczek 2023-08-25 15:49:51.000000 \n7035 MESSAGE 1 0 maczek 2023-08-25 15:27:05.000000 \n7043 MESSAGE 1 0 maczek 2023-08-25 15:04:01.000000 \n7051 MESSAGE 1 0 maczek 2023-08-25 16:17:51.000000 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2023-08-25 17:50:34.000000 None None \n7082 maczek 2023-08-25 18:12:37.000000 None None \n\n annotation version \n6969 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 4 \n6970 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6976 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6985 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 5 \n6986 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6997 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 23 \n7004 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n7010 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n7012 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n7019 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n7027 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n7035 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n7043 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n7051 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n7059 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n7067 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n7074 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n7082 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 ", + "text/html": "
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modelscenarioschemeis_defaultis_lockedcre_usercre_dateupd_userupd_datelock_userlock_dateannotationversion
6969MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD0BI70NPi2020-con-prim-dir-ncrMESSAGE10maczek2023-08-25 10:07:00.000000maczek2023-08-25 10:29:11.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...4
6970MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD0BI70NPi2030-con-prim-dir-ncrMESSAGE10maczek2023-08-24 22:55:18.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6976MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD0BI74NPi2020-con-prim-dir-ncrMESSAGE10maczek2023-08-24 23:51:00.000000maczek2023-08-25 00:11:14.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6985MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD0BI78NPi2020-con-prim-dir-ncrMESSAGE10maczek2023-08-24 23:14:26.000000maczek2023-08-24 23:34:49.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...5
6986MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD0BI78NPi2030-con-prim-dir-ncrMESSAGE10maczek2023-08-22 13:43:19.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6997MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00NPi2020-con-prim-dir-ncrMESSAGE10maczek2023-08-25 10:36:08.000000maczek2023-08-25 11:05:51.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...23
7004MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP30BD0BI70NPi2020-con-prim-dir-ncrMESSAGE10maczek2023-08-25 13:38:00.000000maczek2023-08-25 14:06:06.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
7010MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP30BD0BI74NPi2020-con-prim-dir-ncrMESSAGE10maczek2023-08-25 11:22:25.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
7012MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP30BD0BI78NPi2020-con-prim-dir-ncrMESSAGE10maczek2023-08-28 17:00:51.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
7019MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP30BD1BI00NPi2020-con-prim-dir-ncrMESSAGE10maczek2023-08-25 14:38:55.000000maczek2023-08-25 15:03:05.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
7027MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP50BD0BI70NPi2020-con-prim-dir-ncrMESSAGE10maczek2023-08-25 15:49:51.000000maczek2023-08-25 16:16:58.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
7035MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP50BD0BI74NPi2020-con-prim-dir-ncrMESSAGE10maczek2023-08-25 15:27:05.000000maczek2023-08-25 15:49:10.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
7043MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP50BD0BI78NPi2020-con-prim-dir-ncrMESSAGE10maczek2023-08-25 15:04:01.000000maczek2023-08-25 15:26:11.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
7051MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP50BD1BI00NPi2020-con-prim-dir-ncrMESSAGE10maczek2023-08-25 16:17:51.000000maczek2023-08-25 16:40:26.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
7059MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP76BD0BI70NPi2020-con-prim-dir-ncrMESSAGE10maczek2023-08-25 16:41:07.000000maczek2023-08-25 17:05:11.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
7067MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP76BD0BI74NPi2020-con-prim-dir-ncrMESSAGE10maczek2023-08-25 17:06:02.000000maczek2023-08-25 17:28:15.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
7074MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP76BD0BI78NPi2020-con-prim-dir-ncrMESSAGE10maczek2023-08-25 17:29:13.000000maczek2023-08-25 17:50:34.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
7082MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP76BD1BI00NPi2020-con-prim-dir-ncrMESSAGE10maczek2023-08-25 17:51:18.000000maczek2023-08-25 18:12:37.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
\n
" + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[(df[\"model\"].str.startswith(\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\")) & (df[\"scenario\"].str.startswith(\"NP\")) & (df[\"model\"].str.contains(\"MP\"))]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\", \"baseline_MP00BD1BI00\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scen_new = scen.clone(scen.model+\"-\"+scen.scenario.split(\"_\")[1], scen.scenario.split(\"_\")[0])" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "import pyperclip\n", + "pyperclip.copy(scen_new.model)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scen_no_lu = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\", \"baseline_w/o_LU\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\", \"baseline_MP00BD1BI00\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "mp.scenario_list().sort_values(\"cre_date\", ascending=False)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "sc_clone = scen.clone(\"MESSAGEix-GLOBIOM 1.1-M-R12-NAVIGATE_test\",\"NPi2020-con-prim-dir-ncr_test\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "sc_clone.remove_solution()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "sc_clone.solve()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df = mp.scenario_list()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df = scen.par(\"land_output\", filters={\"commodity\":\"bioenergy\"})" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df_act = scen.var(\"ACT\", filters={\"node_loc\":\"R12_AFR\", \"year_act\":2020})\n", + "df_act[df_act[\"technology\"].str.contains(\"biomass\")].sort_values(\"lvl\", ascending=False).head(12)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df.sort_values(\"cre_date\", ascending=False).head(20).loc[6764][\"annotation\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df[df[\"model\"]==\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "sc_base = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\", \"baseline\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "sc_base.set(\"technology\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scen = sc_base.clone(\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\", \"baseline_w/o_LU\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "#df[(df[\"year\"]==2020) & (df[\"land_scenario\"]==\"BIO00GHG000\")]\n", + "#df[(df[\"year\"]==2020) & (df[\"node\"]==\"R12_CHN\")]\n", + "df[(df[\"year\"]==2020) & (df[\"node\"]==\"R12_AFR\")]# & (df[\"land_scenario\"]==\"BIO00GHG2000\")]\n", + "#scen.var(\"LAND\", filters={\"node\":\"R12_CHN\", \"year\":2020})\n", + "#scen.var(\"LAND\", filters={\"node\":\"R12_AFR\", \"year\":2020}).sort_values(\"lvl\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df[(df[\"model\"]==\"MESSAGEix-GLOBIOM 1.1-M-R12-NAVIGATE\") & (df[\"scenario\"].str.contains(\"baseline\"))]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-M-R12-NAVIGATE\",\"NPi2020-con-prim-dir-ncr\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-M-R12-NAVIGATE-MAGPIE\",\"NPi2020-con-prim-dir-ncr_MP00BD1BI00\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scen.scenario" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scen.solve(solve_options={\"scaind\":-1})" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "### solve new MAGPIE base (without materials) on LU cum message_ix branch" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\", \"baseline\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "sc_clone = scen.clone(\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\", \"baseline_cum_LU_emis\", keep_solution=False)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "sc_clone.solve()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scen.has_solution()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df_scen_list = mp.scenario_list()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "### get old R11 scenario of Jan" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "sc_base_old = message_ix.Scenario(mp, 'ENGAGE-MAGPIE_SSP2', 'baseline')\n", + "sc_base_old_clone = sc_base_old.clone(\"MAGPIE_SSP2_clone\", \"baseline_old_clone\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "sc_base_old_clone.remove_solution()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "sc_base_old_clone.scenario" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "sc_base_old_clone.solve()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "### get biomass trade parametrization from NGFS scenario" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "sc_ngfs = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-M-R12 (NGFS)\", \"baseline_DEFAULT\")\n", + "#sc_ngfs = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12\", \"baseline_DEFAULT\", version=2)\n", + "sc_ngfs.par(\"input\", filters={\"technology\":\"biomass_imp\"})" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "import copy\n", + "paramList_tec = [x for x in sc_ngfs.par_list() if (\"technology\" in sc_ngfs.idx_sets(x))]\n", + "par_dict_meth = {}\n", + "for p in paramList_tec:\n", + " df = sc_ngfs.par(p, filters={\"technology\": [\"biomass_imp\", \"biomass_exp\", \"biomass_trd\"]})\n", + " if df.index.size:\n", + " par_dict_meth[p] = df" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "par_dict_meth.keys()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "par_dict_meth[\"input\"]#.relation.unique()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "### list all MAGPIE models" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df_scen_list[(df_scen_list[\"model\"].str.contains(\"MAGPIE\")) & (df_scen_list[\"scenario\"].str.contains(\"NP\"))]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scen = message_ix.Scenario(mp, model=\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\", scenario=\"baseline\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "### R12 base (missing biomass trade!!)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "model_name = \"MESSAGEix-GLOBIOM 1.1-R12\"\n", + "scenario_name = \"baseline_DEFAULT\"\n", + "version = 21" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scen_ref = message_ix.Scenario(mp, model=model_name, scenario=scenario_name, version=21)\n", + "scen = scen_ref.clone(\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\", \"baseline\", keep_solution=False)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "#scen.check_out()\n", + "#scen.add_set(\"technology\", \"biomass_exp\")\n", + "#scen.add_set(\"technology\", \"biomass_trd\")\n", + "for k,v in par_dict_meth.items():\n", + " scen.add_par(k, v)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scen.commit(\"add biomass trade to R12 MAGPIE base model\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scen.par(\"land_output\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "### R12-M base" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "model_name = \"MESSAGEix-GLOBIOM 1.1-M-R12-NAVIGATE\"\n", + "#scenario_name = \"baseline\"\n", + "scenario_name = \"NPi2020-con-prim-dir-ncr\"" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df_scen_list[(df_scen_list[\"model\"].str.contains(\"NAVIGATE\")) & (df_scen_list[\"scenario\"].str.contains(\"NP\"))]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "#scen_ref = message_ix.Scenario(mp, model=model_name, scenario=scenario_name)\n", + "magpie_scen_name = \"MESSAGEix-GLOBIOM 1.1-M-R12-NAVIGATE-MAGPIE\"\n", + "scen = scen_ref.clone(magpie_scen_name, scenario_name, keep_solution=False)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "### R11 base" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "model_name = \"ENGAGE_SSP2_v4.1.8.3_T4.5_r3.1\"\n", + "scenario_name = \"baseline_magpie\"" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df = mp.scenario_list()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## remove land use matrix from scenario" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scen_no_lu = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-M-R12-NAVIGATE-MAGPIE\", \"NPi2020-con-prim-dir-ncr_no_LU\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scen_no_lu.scenario" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "#scen_no_lu = scen.clone(scen.model, scenario_name+\"_no_LU\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "import importlib\n", + "importlib.reload(message_data.tools.utilities)\n", + "if scen.has_solution():\n", + " scen.remove_solution()\n", + "add_globiom(\n", + " mp,\n", + " scen,\n", + " \"SSP2\",\n", + " private_data_path(),\n", + " 2015,\n", + " globiom_scenario=\"magpie\",\n", + " #regenerate_input_data_only=True,\n", + " clean=True,\n", + " clean_only=True,\n", + " #globiom_scenario=\"no\",\n", + " #allow_empty_drivers=True,\n", + " add_reporting_data=True,\n", + " config_setup=\"MAGPIE\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "#magpie_scen = \"MP00BD1BI00\"\n", + "magpie_scen = \"MP00BD0BI78\"\n", + "magpie_scen = \"MP76BD0BI78\"\n", + "magpie_scen = \"MP76BD1BI00\"" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scen_no_lu = scen" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scenario_name = \"baseline\"" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scen = scen_no_lu.clone(scen_no_lu.model, scenario_name+\"_\"+magpie_scen)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scen.set_as_default()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df = mp.scenario_list()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df[df[\"model\"]==\"MESSAGEix-GLOBIOM 1.1-R12\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## add MAGPIE matrix to scenario" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "add_globiom(\n", + " mp,\n", + " scen,\n", + " \"SSP2\",\n", + " private_data_path(),\n", + " 2015,\n", + " globiom_scenario=\"noSDG_rcpref\",\n", + " regenerate_input_data=True,\n", + " #regenerate_input_data_only=True,\n", + " #allow_empty_drivers=True,\n", + " add_reporting_data=True,\n", + " config_setup=\"MAGPIE_\"+magpie_scen)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scen.solve()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scen.set_as_default()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scen.solve()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "#scen.remove_solution()\n", + "scen.solve(\"MESSAGE-MACRO\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "### add biomass backstop to calibrate read-in of new matrix" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scen.check_out()\n", + "\n", + "# Add a new technology\n", + "scen.add_set(\"technology\", \"bio_backstop\")\n", + "\n", + "# Retrieve technology for which will be used to create the backstop\n", + "filters = {\"technology\": \"elec_rc\"} #, \"node_loc\": \"R11_NAM\"}\n", + "\n", + "#for node in [\"R11_NAM\", \"R11_CPA\"]:\n", + "for par in [\"output\", \"var_cost\"]:\n", + " df = scen.par(par, filters=filters)\n", + " df.technology = \"bio_backstop\"\n", + " #df.node_loc = node\n", + "\n", + " # Make change to output\n", + " if par == \"output\":\n", + " #df.node_dest = node\n", + " df.commodity = \"biomass\"\n", + " df.level = \"primary\"\n", + "\n", + " elif par == \"var_cost\":\n", + " df.value = 100000\n", + "\n", + " #print(df)\n", + " scen.add_par(par, df)\n", + "\n", + "scen.commit(\"Add biomass dummy\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scen.check_out()\n", + "scen.remove_set(\"technology\", \"bio_backstop\")\n", + "scen.commit(\"remove backstop\")\n", + "scen.par(\"output\", filters={\"technology\":\"bio_backstop\"})" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## create budget scenario" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "mode = \"clone\"\n", + "#mode = \"load\"\n", + "\n", + "#budget = \"1000f\"\n", + "budget = \"600f\"" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scen.scenario.replace(\"baseline\",budget)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "if mode == \"clone\":\n", + " scen_budget = scen.clone(model=scen.model, scenario=scen.scenario.replace(\"baseline\",budget), shift_first_model_year=2025)\n", + "else:\n", + " scen_budget = message_ix.Scenario(mp, model=\"ENGAGE-MAGPIE_SSP2_v4.1.8.3_T4.5_r3.1\", scenario=budget)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "if mode == \"clone\":\n", + " scen_budget = scen.clone(model=\"ENGAGE-MAGPIE_SSP2_v4.1.8.3_T4.5_r3.1\", scenario=budget, keep_solution=False,\n", + " shift_first_model_year=2025)\n", + "else:\n", + " scen_budget = message_ix.Scenario(mp, model=\"ENGAGE-MAGPIE_SSP2_v4.1.8.3_T4.5_r3.1\", scenario=budget)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "extra = scen_budget.set(\"cat_year\", {\"type_year\": \"cumulative\", \"year\": [2020, 2015, 2010]})\n", + "scen_budget.check_out()\n", + "scen_budget.remove_set(\"cat_year\", extra)\n", + "scen_budget.commit(\"remove cumulative years from cat_year set\")\n", + "scen_budget.set(\"cat_year\", {\"type_year\": \"cumulative\"})" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "emission_dict = {\n", + " \"node\": \"World\",\n", + " \"type_emission\": \"TCE\",\n", + " \"type_tec\": \"all\",\n", + " \"type_year\": \"cumulative\",\n", + " \"unit\": \"???\",\n", + " }\n", + "if budget == \"1000f\":\n", + " value = 4046\n", + "if budget == \"600f\":\n", + " value = 2605\n", + "\n", + "df = message_ix.make_df(\n", + " \"bound_emission\", value=value, **emission_dict\n", + ")\n", + "scen_budget.check_out()\n", + "scen_budget.add_par(\"bound_emission\", df)\n", + "scen_budget.commit(\"add emission bound\")\n", + "scen_budget.par(\"bound_emission\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scen_budget.set_as_default()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scen_budget.solve()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 14, + "outputs": [ + { + "data": { + "text/plain": "'600f_MP00BD0BI78'" + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## run reporting" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [], + "source": [ + "import ixmp\n", + "import ixmp as ix\n", + "import message_ix\n", + "\n", + "model_name=\"MESSAGEix-Materials\"\n", + "scenario_name=\"NoPolicy_petro_thesis_2_final_macro\"\n", + "#scenario_name=\"NoPolicy_petro_thesis_2_macro_w_ENGAGE_fixes\"\n", + "\n", + "mp = ix.Platform(\"ixmp_dev\")\n", + "scen = message_ix.Scenario(mp, model=model_name, scenario=scenario_name)#, version=37)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-22T09:43:44.817648400Z", + "start_time": "2023-11-22T09:43:43.081087200Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "processing Table: Resource|Extraction\n", + "processing Table: Resource|Cumulative Extraction\n", + "processing Table: Primary Energy\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "KeyboardInterrupt\n", + "\n" + ] + } + ], + "source": [ + "import message_data.tools.post_processing.iamc_report_hackathon\n", + "import importlib\n", + "importlib.reload(message_data.tools.post_processing.iamc_report_hackathon)\n", + "message_data.tools.post_processing.iamc_report_hackathon.report(\n", + " mp,\n", + " scen,\n", + " # NB(PNK) this is not an error; .iamc_report_hackathon.report() expects a\n", + " # string containing \"True\" or \"False\" instead of an actual bool.\n", + " \"False\",\n", + " scen.model,\n", + " scen.scenario.replace(\"_test_calib_macro\", \"\"),\n", + " merge_hist=True,\n", + " merge_ts=True,\n", + " run_config=\"materials_run_config.yaml\",\n", + ")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-22T09:43:57.385985Z", + "start_time": "2023-11-22T09:43:45.456907800Z" + } + } + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## check if historic TS is missing" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df = scen.timeseries(iamc=True)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "(\n", + "df[df.variable == \"Emissions|Kyoto Gases\"]\n", + " .drop(\"variable\", axis=1)\n", + " .set_index(\"region\")\n", + " )" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scen.set_as_default()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## add missing historic TS to SCP baseline scenario" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 18, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "This Scenario has a solution, use `Scenario.remove_solution()` or `Scenario.clone(..., keep_solution=False)`\n" + ] + } + ], + "source": [ + "scen_ref = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-NGFS\", \"baseline\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 19, + "outputs": [], + "source": [ + "df_ref = scen_ref.timeseries(iamc=True)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "(\n", + "df_ref[df_ref.variable == \"Emissions|Kyoto Gases\"]\n", + " .drop(\"variable\", axis=1)\n", + " .set_index(\"region\")\n", + " )" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 23, + "outputs": [], + "source": [ + "#scen.remove_solution()\n", + "scen.check_out()\n", + "scen.add_timeseries(df_ref)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 24, + "outputs": [], + "source": [ + "scen.commit(\"add historic TS\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 25, + "outputs": [], + "source": [ + "scen.set_as_default()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## add missing biomass slack for NPi2020 scenario" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df = scen.par(\"land_output\", filters={\"node\":\"R12_CHN\", \"commodity\":\"bioenergy\", \"year\":2020})" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df[\"value\"] = df[\"value\"] + 11.3741" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scen.check_out()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scen.add_par(\"land_output\", df)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scen.commit(\"add R12_CHN biomass slack 2020\")" + ], + "metadata": { + "collapsed": false + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/message_ix_models/model/material/magpie notebooks/data_scp_final.ipynb b/message_ix_models/model/material/magpie notebooks/data_scp_final.ipynb new file mode 100644 index 0000000000..e8857bb102 --- /dev/null +++ b/message_ix_models/model/material/magpie notebooks/data_scp_final.ipynb @@ -0,0 +1,744 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true, + "ExecuteTime": { + "end_time": "2023-07-20T15:27:10.206336800Z", + "start_time": "2023-07-20T15:27:03.454843500Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": "", + "application/javascript": "if (typeof IPython !== 'undefined') { IPython.OutputArea.prototype._should_scroll = function(lines){ return false; }}" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Unable to determine R home: [WinError 2] The system cannot find the file specified\n" + ] + } + ], + "source": [ + "import ixmp as ix\n", + "import message_ix\n", + "import pandas as pd\n", + "\n", + "from message_ix_models.util import broadcast, private_data_path\n", + "from message_data.model.material.data_methanol_new import broadcast_reduced_df" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [], + "source": [ + "mp = ix.Platform(\"ixmp_dev\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## add SCP tech and demand" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [], + "source": [ + "model_name = \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\"\n", + "scen_name = \"baseline_MP76BD1BI00\"\n", + "\n", + "scenario = message_ix.Scenario(mp, model_name, scen_name)\n", + "\n", + "sc_clone = scenario.clone(model_name, scen_name+\"test_scp\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-07-20T15:29:34.441051600Z", + "start_time": "2023-07-20T15:27:10.212887200Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 11, + "outputs": [], + "source": [ + "all_years = scenario.vintage_and_active_years()\n", + "years = all_years[all_years[\"year_act\"]==all_years[\"year_vtg\"]]\n", + "first_year = 2025 # when should scp be available?? 2025? 2030?\n", + "years = years[years[\"year_act\"] >= first_year]" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-07-20T12:39:50.996074800Z", + "start_time": "2023-07-20T12:39:50.974724Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [], + "source": [ + "region_setup = \"R12\"" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 13, + "outputs": [], + "source": [ + "scp_demand = pd.read_excel(private_data_path(\"scp\", \"population_beef_demand.xlsx\"),\n", + " sheet_name=f\"demand_{region_setup}\")\n", + "scp_demand[\"value\"] = scp_demand[\"value\"] / 1000\n", + "scp_demand = scp_demand[scp_demand[\"year\"]>=first_year]\n", + "\n", + "input = pd.read_excel(private_data_path(\"scp\", \"scp_techno_economic.xlsx\"),\n", + " sheet_name=f\"input_{region_setup}\")\n", + "output = pd.read_excel(private_data_path(\"scp\", \"scp_techno_economic.xlsx\"),\n", + " sheet_name=f\"output_{region_setup}\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-07-20T12:39:52.665801700Z", + "start_time": "2023-07-20T12:39:52.590786200Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 14, + "outputs": [], + "source": [ + "input = input.pipe(broadcast, year_vtg=years[\"year_vtg\"])\n", + "input[\"year_act\"] = input[\"year_vtg\"]\n", + "output = output.pipe(broadcast, year_vtg=years[\"year_vtg\"])\n", + "output[\"year_act\"] = output[\"year_vtg\"]" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-07-20T12:39:54.146585400Z", + "start_time": "2023-07-20T12:39:54.122675300Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 15, + "outputs": [], + "source": [ + "input = input[input[\"commodity\"]!=\"NH3\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 16, + "outputs": [], + "source": [ + "df_ccu_rel_act = broadcast_reduced_df(pd.read_excel(private_data_path(\"scp\", \"scp_techno_economic.xlsx\"),\n", + " sheet_name=f\"relation_activity_{region_setup}\"), \"relation_activity\")\n", + "\n", + "df_ccu_rel_lo = broadcast_reduced_df(pd.read_excel(private_data_path(\"scp\", \"scp_techno_economic.xlsx\"),\n", + " sheet_name=f\"relation_lower_{region_setup}\"), \"relation_lower\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-07-20T12:39:55.547689700Z", + "start_time": "2023-07-20T12:39:55.465648600Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 18, + "outputs": [], + "source": [ + "df = sc_clone.par(\"land_output\", {\"commodity\": \"Food Demand|Microbial protein\"})\n", + "df[\"level\"] = \"final_material\"" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 19, + "outputs": [], + "source": [ + "#sc_clone.check_out()\n", + "\n", + "sc_clone.add_set(\"technology\", \"syn_micr_beef\")\n", + "sc_clone.add_set(\"commodity\", \"Food Demand|Microbial protein\")\n", + "sc_clone.add_set(\"mode\", \"hydrogen\")\n", + "\n", + "if region_setup == \"R11\":\n", + " sc_clone.add_set(\"level\", \"demand\")\n", + " sc_clone.add_set(\"relation\", \"CO2_PtX_trans_disp_split\") # add relation to mimic CO2 input requirements for SCP\n", + " input = input[input[\"commodity\"]!=\"NH3\"] # remove NH3 input since it is not in R11 base model\n", + "\n", + "sc_clone.add_par(\"input\", input)\n", + "sc_clone.add_set(\"level\", \"final_material\")\n", + "sc_clone.add_par(\"output\", output)\n", + "sc_clone.add_par(\"land_input\", df)\n", + "sc_clone.add_set(\"relation\", \"CO2_PtX_trans_disp_split\")\n", + "sc_clone.add_par(\"relation_activity\", df_ccu_rel_act)\n", + "sc_clone.add_par(\"relation_lower\", df_ccu_rel_lo)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-07-20T12:40:30.081008500Z", + "start_time": "2023-07-20T12:40:05.693199Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 20, + "outputs": [], + "source": [ + "sc_clone.commit(\"add scp model\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 23, + "outputs": [], + "source": [ + "#sc_clone.remove_solution()\n", + "sc_clone.solve(model=\"MESSAGE-MACRO\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-07-20T15:33:12.158785600Z", + "start_time": "2023-07-20T15:29:42.459097500Z" + } + } + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## build 1000f/600f scenario" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 24, + "outputs": [], + "source": [ + "scen = sc_clone" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 25, + "outputs": [], + "source": [ + "mode = \"clone\"\n", + "#mode = \"load\"\n", + "\n", + "#budget = \"1000f\"\n", + "budget = \"600f\"" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 26, + "outputs": [ + { + "data": { + "text/plain": "'600f_MP76BD1BI00test_scp'" + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scen.scenario.replace(\"baseline\",budget)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 27, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Override keep_solution=True for shift_first_model_year\n" + ] + } + ], + "source": [ + "if mode == \"clone\":\n", + " scen_budget = scen.clone(model=scen.model, scenario=scen.scenario.replace(\"baseline\",budget), shift_first_model_year=2025)\n", + "else:\n", + " scen_budget = message_ix.Scenario(mp, model=\"ENGAGE-MAGPIE_SSP2_v4.1.8.3_T4.5_r3.1\", scenario=budget)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "if mode == \"clone\":\n", + " scen_budget = scen.clone(model=\"ENGAGE-MAGPIE_SSP2_v4.1.8.3_T4.5_r3.1\", scenario=budget, keep_solution=False,\n", + " shift_first_model_year=2025)\n", + "else:\n", + " scen_budget = message_ix.Scenario(mp, model=\"ENGAGE-MAGPIE_SSP2_v4.1.8.3_T4.5_r3.1\", scenario=budget)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 28, + "outputs": [ + { + "data": { + "text/plain": " type_year year\n0 cumulative 2025\n1 cumulative 2030\n2 cumulative 2035\n3 cumulative 2040\n4 cumulative 2045\n5 cumulative 2050\n6 cumulative 2055\n7 cumulative 2060\n8 cumulative 2070\n9 cumulative 2080\n10 cumulative 2090\n11 cumulative 2100\n12 cumulative 2110", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
type_yearyear
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\n
" + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "extra = scen_budget.set(\"cat_year\", {\"type_year\": \"cumulative\", \"year\": [2020, 2015, 2010]})\n", + "scen_budget.check_out()\n", + "scen_budget.remove_set(\"cat_year\", extra)\n", + "scen_budget.commit(\"remove cumulative years from cat_year set\")\n", + "scen_budget.set(\"cat_year\", {\"type_year\": \"cumulative\"})" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 29, + "outputs": [ + { + "data": { + "text/plain": " node type_emission type_tec type_year value unit\n0 World TCE all cumulative 2605.0 ???", + "text/html": "
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nodetype_emissiontype_tectype_yearvalueunit
0WorldTCEallcumulative2605.0???
\n
" + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "emission_dict = {\n", + " \"node\": \"World\",\n", + " \"type_emission\": \"TCE\",\n", + " \"type_tec\": \"all\",\n", + " \"type_year\": \"cumulative\",\n", + " \"unit\": \"???\",\n", + " }\n", + "if budget == \"1000f\":\n", + " value = 4046\n", + "if budget == \"600f\":\n", + " value = 2605\n", + "\n", + "df = message_ix.make_df(\n", + " \"bound_emission\", value=value, **emission_dict\n", + ")\n", + "scen_budget.check_out()\n", + "scen_budget.add_par(\"bound_emission\", df)\n", + "scen_budget.commit(\"add emission bound\")\n", + "scen_budget.par(\"bound_emission\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 31, + "outputs": [], + "source": [ + "scen_budget.solve(model=\"MESSAGE-MACRO\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## reporting" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [], + "source": [ + "df = mp.scenario_list()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 12, + "outputs": [], + "source": [ + "import pyperclip" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 21, + "outputs": [], + "source": [ + "scen_list = str(df[df[\"model\"]==\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\"].sort_values(\"cre_date\")[\"scenario\"].tolist())" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 22, + "outputs": [], + "source": [ + "pyperclip.copy(scen_list)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [], + "source": [ + "model_name = \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\"" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [], + "source": [ + "rep_list = ['baseline_MP76BD0BI78test_scp', '600f_MP76BD0BI78test_scp', 'baseline_MP76BD1BI00test_scp', '600f_MP76BD1BI00test_scp']\n", + "# 'baseline_MP76BD0BI70' still to build scp and 600f" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [ + { + "data": { + "text/plain": "True" + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scen = message_ix.Scenario(mp, model_name, \"1000f_MP00BD0BI78_test_calib_macro\")\n", + "scen.has_solution()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 10, + "outputs": [], + "source": [ + "from message_data.tools.post_processing.iamc_report_hackathon import report as reporting" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "baseline_MP76BD0BI78\n", + "600f_MP76BD0BI78\n", + "baseline_MP76BD1BI00\n", + "600f_MP76BD1BI00\n" + ] + } + ], + "source": [ + "for scen_name in rep_list:\n", + " print(scen_name.replace(\"test_scp\", \"\"))" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [ + { + "data": { + "text/plain": "['baseline_MP76BD1BI00test_scp', '600f_MP76BD1BI00test_scp']" + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rep_list[2:]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 8, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'rep_list' is not defined", + "output_type": "error", + "traceback": [ + "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[1;31mNameError\u001B[0m Traceback (most recent call last)", + "Input \u001B[1;32mIn [8]\u001B[0m, in \u001B[0;36m\u001B[1;34m()\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m scen_name \u001B[38;5;129;01min\u001B[39;00m \u001B[43mrep_list\u001B[49m[\u001B[38;5;241m2\u001B[39m:]:\n\u001B[0;32m 2\u001B[0m scen \u001B[38;5;241m=\u001B[39m message_ix\u001B[38;5;241m.\u001B[39mScenario(mp, model_name, scen_name)\n\u001B[0;32m 3\u001B[0m reporting(\n\u001B[0;32m 4\u001B[0m mp,\n\u001B[0;32m 5\u001B[0m scen,\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 13\u001B[0m \u001B[38;5;66;03m#run_config=\"materials_run_config.yaml\",\u001B[39;00m\n\u001B[0;32m 14\u001B[0m )\n", + "\u001B[1;31mNameError\u001B[0m: name 'rep_list' is not defined" + ] + } + ], + "source": [ + "for scen_name in rep_list[2:]:\n", + " scen = message_ix.Scenario(mp, model_name, scen_name)\n", + " reporting(\n", + " mp,\n", + " scen,\n", + " # NB(PNK) this is not an error; .iamc_report_hackathon.report() expects a\n", + " # string containing \"True\" or \"False\" instead of an actual bool.\n", + " \"False\",\n", + " scen.model,\n", + " scen_name.replace(\"_test_calib_macro\", \"\"),\n", + " merge_hist=True,\n", + " merge_ts=True,\n", + " #run_config=\"materials_run_config.yaml\",\n", + " )" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 12, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "processing Table: Resource|Extraction\n", + "processing Table: Resource|Cumulative Extraction\n", + "processing Table: Primary Energy\n", + "processing Table: Primary Energy (substitution method)\n", + "processing Table: Final Energy\n", + "processing Table: Secondary Energy|Electricity\n", + "processing Table: Secondary Energy|Heat\n", + "processing Table: Secondary Energy\n", + "processing Table: Secondary Energy|Gases\n", + "processing Table: Secondary Energy|Solids\n", + "processing Table: Emissions|CO2\n", + "processing Table: Carbon Sequestration\n", + "processing Table: Emissions|BC\n", + "processing Table: Emissions|OC\n", + "processing Table: Emissions|CO\n", + "processing Table: Emissions|N2O\n", + "processing Table: Emissions|CH4\n", + "processing Table: Emissions|NH3\n", + "processing Table: Emissions|Sulfur\n", + "processing Table: Emissions|NOx\n", + "processing Table: Emissions|VOC\n", + "processing Table: Emissions|HFC\n", + "processing Table: Emissions\n", + "processing Table: Emissions\n", + "processing Table: Agricultural Demand\n", + "processing Table: Agricultural Production\n", + "processing Table: Fertilizer Use\n", + "processing Table: Fertilizer\n", + "processing Table: Food Waste\n", + "processing Table: Food Demand\n", + "processing Table: Forestry Demand\n", + "processing Table: Forestry Production\n", + "processing Table: Land Cover\n", + "processing Table: Yield\n", + "processing Table: Capacity\n", + "processing Table: Capacity Additions\n", + "processing Table: Cumulative Capacity\n", + "processing Table: Capital Cost\n", + "processing Table: OM Cost|Fixed\n", + "processing Table: OM Cost|Variable\n", + "processing Table: Lifetime\n", + "processing Table: Efficiency\n", + "processing Table: Population\n", + "processing Table: Price\n", + "processing Table: Useful Energy\n", + "processing Table: Useful Energy\n", + "processing Table: Trade\n", + "processing Table: Investment|Energy Supply\n", + "processing Table: Water Consumption\n", + "processing Table: Water Withdrawal\n", + "processing Table: GDP\n", + "processing Table: Cost\n", + "processing Table: GLOBIOM Feedback\n", + "Starting to upload timeseries\n", + " region variable unit 2025 \\\n", + "0 R12_AFR Resource|Extraction EJ/yr 17.710206 \n", + "1 R12_AFR Resource|Extraction|Coal EJ/yr 2.964129 \n", + "2 R12_AFR Resource|Extraction|Gas EJ/yr 6.834491 \n", + "3 R12_AFR Resource|Extraction|Gas|Conventional EJ/yr 6.834491 \n", + "4 R12_AFR Resource|Extraction|Gas|Unconventional EJ/yr 0.000000 \n", + "\n", + " 2030 2035 2040 2045 2050 2055 \\\n", + "0 14.190102 13.207377 12.677562 12.923691 13.127013 15.616553 \n", + "1 0.686702 0.192814 0.000340 0.000170 0.001140 0.000000 \n", + "2 7.515499 8.521001 9.343876 10.490576 11.391426 14.423792 \n", + "3 7.515499 8.521001 9.343876 10.490576 11.391426 14.423792 \n", + "4 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "\n", + " 2060 2070 2080 2090 2100 2110 \n", + "0 15.670154 14.318625 8.187958 4.633826 2.532917 1.246648 \n", + "1 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "2 14.897350 14.123627 8.187064 4.632625 2.530546 1.243462 \n", + "3 14.897350 14.123627 8.187064 4.632625 2.530546 1.243462 \n", + "4 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "Finished uploading timeseries\n" + ] + } + ], + "source": [ + "reporting(\n", + " mp,\n", + " scen,\n", + " # NB(PNK) this is not an error; .iamc_report_hackathon.report() expects a\n", + " # string containing \"True\" or \"False\" instead of an actual bool.\n", + " \"False\",\n", + " scen.model,\n", + " scen.scenario.replace(\"_test_calib_macro\", \"\"),\n", + " merge_hist=True,\n", + " merge_ts=True,\n", + " #run_config=\"materials_run_config.yaml\",\n", + ")" + ], + "metadata": { + "collapsed": false + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/message_ix_models/model/material/magpie notebooks/magpie.ipynb b/message_ix_models/model/material/magpie notebooks/magpie.ipynb new file mode 100644 index 0000000000..69ce0f5087 --- /dev/null +++ b/message_ix_models/model/material/magpie notebooks/magpie.ipynb @@ -0,0 +1,6434 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "## MAGPIE setup baseline" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 1, + "outputs": [ + { + "data": { + "text/plain": "", + "application/javascript": "if (typeof IPython !== 'undefined') { IPython.OutputArea.prototype._should_scroll = function(lines){ return false; }}" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import ixmp\n", + "import message_ix\n", + "from message_ix_models.util import private_data_path\n", + "import message_data\n", + "import importlib\n", + "#importlib.reload(message_data.tools.utilities)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-16T14:05:34.002478400Z", + "start_time": "2023-10-16T14:05:25.844644400Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [], + "source": [ + "import message_data.tools.utilities" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-16T14:05:34.449804800Z", + "start_time": "2023-10-16T14:05:33.971237200Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [], + "source": [ + "mp = ixmp.Platform(\"ixmp_dev\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-16T14:05:39.928527300Z", + "start_time": "2023-10-16T14:05:34.449804800Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [], + "source": [ + "scen = message_ix.Scenario(mp, \"MESSAGEix-Materials\", \"NoPolicy_no_SDGs_petro_thesis_1_final_premise\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-09T07:43:02.244569500Z", + "start_time": "2023-10-09T07:42:59.922975200Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 22, + "outputs": [], + "source": [ + "sc_clone = scen.clone(scen.model, scen.scenario+\"_updated\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-09T07:49:04.893243700Z", + "start_time": "2023-10-09T07:48:41.128939100Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 25, + "outputs": [], + "source": [ + "sc_clone.set_as_default()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-09T07:49:47.134774700Z", + "start_time": "2023-10-09T07:49:47.068038200Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 24, + "outputs": [ + { + "data": { + "text/plain": "'MESSAGEix-Materials'" + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sc_clone.model" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-09T07:49:15.315295200Z", + "start_time": "2023-10-09T07:49:15.199304500Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 17, + "outputs": [ + { + "data": { + "text/plain": " model scenario \\\n7935 MESSAGEix-Materials NoPolicy_no_SDGs_petro_thesis_1_final_premise \n\n scheme is_default is_locked cre_user cre_date \\\n7935 MESSAGE 1 0 maczek 2023-07-20 11:57:30.000000 \n\n upd_user upd_date lock_user lock_date \\\n7935 None None None None \n\n annotation version \n7935 clone Scenario from 'MESSAGEix-Materials|NoPol... 2 ", + "text/html": "
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modelscenarioschemeis_defaultis_lockedcre_usercre_dateupd_userupd_datelock_userlock_dateannotationversion
7935MESSAGEix-MaterialsNoPolicy_no_SDGs_petro_thesis_1_final_premiseMESSAGE10maczek2023-07-20 11:57:30.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...2
\n
" + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model_pattern = \"MESSAGEix-Material\"\n", + "scen_pattern = \"NoPolicy_no_SDGs_petro_thesis_1_final_premise\"\n", + "df = mp.scenario_list()\n", + "df[(df[\"model\"].str.contains(model_pattern)) & (df[\"scenario\"].str.startswith(scen_pattern))]" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-09T07:46:39.014607300Z", + "start_time": "2023-10-09T07:46:37.682873200Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 19, + "outputs": [], + "source": [ + "def get_scen_list(mp, scen_pattern, model_pattern):\n", + " df = mp.scenario_list()\n", + " print(df[(df[\"model\"].str.contains(model_pattern)) & (df[\"scenario\"].str.startswith(scen_pattern))])\n", + " return df[(df[\"model\"].str.contains(model_pattern)) & (df[\"scenario\"].str.startswith(scen_pattern))]" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-09T07:47:19.226034900Z", + "start_time": "2023-10-09T07:47:19.125116900Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 20, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Empty DataFrame\n", + "Columns: [model, scenario, scheme, is_default, is_locked, cre_user, cre_date, upd_user, upd_date, lock_user, lock_date, annotation, version]\n", + "Index: []\n" + ] + }, + { + "data": { + "text/plain": "Empty DataFrame\nColumns: [model, scenario, scheme, is_default, is_locked, cre_user, cre_date, upd_user, upd_date, lock_user, lock_date, annotation, version]\nIndex: []", + "text/html": "
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modelscenarioschemeis_defaultis_lockedcre_usercre_dateupd_userupd_datelock_userlock_dateannotationversion
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" + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "get_scen_list(mp, model_pattern, scen_pattern)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-09T07:47:21.515731100Z", + "start_time": "2023-10-09T07:47:20.537571400Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 19, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'StringMethods' object has no attribute 'startwith'", + "output_type": "error", + "traceback": [ + "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[1;31mAttributeError\u001B[0m Traceback (most recent call last)", + "Input \u001B[1;32mIn [19]\u001B[0m, in \u001B[0;36m\u001B[1;34m()\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[43mget_scen_list\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmp\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mMESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mnew\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\n", + "Input \u001B[1;32mIn [18]\u001B[0m, in \u001B[0;36mget_scen_list\u001B[1;34m(mp, scen_pattern, model_pattern)\u001B[0m\n\u001B[0;32m 1\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mget_scen_list\u001B[39m(mp, scen_pattern, model_pattern):\n\u001B[0;32m 2\u001B[0m df \u001B[38;5;241m=\u001B[39m mp\u001B[38;5;241m.\u001B[39mscenario_list()\n\u001B[1;32m----> 3\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m df[(df[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mmodel\u001B[39m\u001B[38;5;124m\"\u001B[39m]\u001B[38;5;241m.\u001B[39mstr\u001B[38;5;241m.\u001B[39mstartswith(model_pattern)) \u001B[38;5;241m&\u001B[39m (\u001B[43mdf\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mscenario\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mstr\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mstartwith\u001B[49m(scen_pattern))]\n", + "\u001B[1;31mAttributeError\u001B[0m: 'StringMethods' object has no attribute 'startwith'" + ] + } + ], + "source": [ + "get_scen_list(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\", \"new\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-19T16:44:44.127087500Z", + "start_time": "2023-09-19T16:44:37.699794100Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 12, + "outputs": [ + { + "data": { + "text/plain": " model \\\n7101 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00 \n7103 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00 \n7104 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00 \n7105 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00 \n\n scenario scheme is_default is_locked \\\n7101 bugfix_cost-correction_new-matrix MESSAGE 1 0 \n7103 new-mapping_no-smoothing MESSAGE 1 0 \n7104 new-mapping_no-smoothing_1000f MESSAGE 1 0 \n7105 new-mapping_with-smoothing MESSAGE 1 0 \n\n cre_user cre_date upd_user \\\n7101 maczek 2023-09-18 16:26:29.000000 None \n7103 maczek 2023-09-19 14:37:44.000000 steinhauser \n7104 steinhauser 2023-09-19 15:03:24.000000 None \n7105 maczek 2023-09-19 14:51:21.000000 None \n\n upd_date lock_user lock_date \\\n7101 None None None \n7103 2023-09-19 15:46:39.000000 None None \n7104 None None None \n7105 None None None \n\n annotation version \n7101 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n7103 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 2 \n7104 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n7105 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 ", + "text/html": "
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modelscenarioschemeis_defaultis_lockedcre_usercre_dateupd_userupd_datelock_userlock_dateannotationversion
7101MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00bugfix_cost-correction_new-matrixMESSAGE10maczek2023-09-18 16:26:29.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
7103MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00new-mapping_no-smoothingMESSAGE10maczek2023-09-19 14:37:44.000000steinhauser2023-09-19 15:46:39.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...2
7104MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00new-mapping_no-smoothing_1000fMESSAGE10steinhauser2023-09-19 15:03:24.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
7105MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00new-mapping_with-smoothingMESSAGE10maczek2023-09-19 14:51:21.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
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" + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = mp.scenario_list()\n", + "df[(df[\"model\"].str.startswith(\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\")) & (df[\"scenario\"].str.contains(\"new\"))]" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-19T16:18:24.427634800Z", + "start_time": "2023-09-19T16:18:23.037601600Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [], + "source": [ + "#scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD0BI78\", \"EN_NPi2020_1000f\")\n", + "models = [\"MESSAGEix-MAgPIE_Forest-test\", ]\n", + "scenarios = [\"primary_constraint_1000f_landScenLo_002\"]\n", + "#scen = message_ix.Scenario(mp, models[0], scenarios[0])" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-14T15:35:05.007149100Z", + "start_time": "2023-09-14T15:35:02.841426700Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [], + "source": [ + "#scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\", \"EN_NPi2020_1000f\")\n", + "scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\", \"baseline_w/o_LU\")\n", + "#scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD0BI78\", \"test to confirm MACRO converges\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-16T14:15:07.752450300Z", + "start_time": "2023-10-16T14:15:04.240307100Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [], + "source": [ + "scen.remove_solution()\n", + "scen.solve()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-02T09:32:34.261763900Z", + "start_time": "2023-10-02T09:29:05.034233600Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [], + "source": [ + "#scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\", \"new-mapping_no-smoothing_1000f\")\n", + "#scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\", \"new-mapping_with-smoothing\")\n", + "#scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\", \"new-mapping_with-smoothing_primary-forest-constraint_tax_100\")\n", + "scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\", \"new-mapping_with-smoothing_primary-forest-constraint_tax_1000\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-20T20:22:20.385118700Z", + "start_time": "2023-09-20T20:22:17.697414900Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [], + "source": [ + "#sc_clone = scen.clone(\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\", \"new-mapping_with-smoothing_primary-forest-constraint_tax_100\")\n", + "sc_clone = scen.clone(\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\", \"new-mapping_with-smoothing_primary-forest-constraint_tax_1000\", keep_solution=False)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-20T19:12:30.273833500Z", + "start_time": "2023-09-20T19:12:11.848257400Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [], + "source": [ + "#sc_clone = scen.clone(\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\", \"exp_matrix_test\")\n", + "sc_clone = scen.clone(\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\", \"exp2110_matrix_test\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-16T14:18:16.956351400Z", + "start_time": "2023-10-16T14:17:58.254478800Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 19, + "outputs": [], + "source": [ + "sc_clone.remove_solution()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-20T18:22:13.578298800Z", + "start_time": "2023-09-20T18:22:11.750390500Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [], + "source": [ + "scen.solve(model=\"MESSAGE-MACRO\", solve_options={\"scaind\":-1})" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-20T20:47:05.194037100Z", + "start_time": "2023-09-20T20:22:24.447338300Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 23, + "outputs": [ + { + "data": { + "text/plain": "True" + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sc_clone.has_solution()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-20T18:53:55.190420400Z", + "start_time": "2023-09-20T18:53:55.018939700Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 10, + "outputs": [], + "source": [ + "sc_clone.check_out()\n", + "sc_clone.add_par(\"tax_emission\", df)\n", + "sc_clone.commit(\"add emi tax\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-20T19:12:51.645775700Z", + "start_time": "2023-09-20T19:12:51.058281800Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [], + "source": [ + "scen2 = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP30BD0BI70\", \"EN_NPi2020_1150\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-20T18:01:46.858015600Z", + "start_time": "2023-09-20T18:01:45.373668100Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 11, + "outputs": [ + { + "data": { + "text/plain": " node type_emission type_tec type_year value unit\n0 World TCE all 2020 3310.036812 USD/tC\n1 World TCE all 2025 3310.036812 USD/tC\n2 World TCE all 2030 3310.036812 USD/tC\n3 World TCE all 2035 3310.036812 USD/tC\n4 World TCE all 2040 3310.036812 USD/tC\n5 World TCE all 2045 3310.036812 USD/tC\n6 World TCE all 2050 3310.036812 USD/tC\n7 World TCE all 2055 3310.036812 USD/tC\n8 World TCE all 2060 3310.036812 USD/tC\n9 World TCE all 2070 3310.036812 USD/tC\n10 World TCE all 2080 3310.036812 USD/tC\n11 World TCE all 2090 3310.036812 USD/tC\n12 World TCE all 2100 3310.036812 USD/tC\n13 World TCE all 2110 3310.036812 USD/tC", + "text/html": "
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nodetype_emissiontype_tectype_yearvalueunit
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\n
" + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = sc_clone.par(\"tax_emission\")\n", + "df" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-20T19:12:53.660686600Z", + "start_time": "2023-09-20T19:12:53.550006800Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 8, + "outputs": [], + "source": [ + "#df[\"type_emission\"] = \"TCE\"\n", + "df[\"value\"] = (1000 * 3.310036812)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-20T19:12:37.910946600Z", + "start_time": "2023-09-20T19:12:37.770609200Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 9, + "outputs": [ + { + "data": { + "text/plain": " node type_emission type_tec type_year value unit\n0 World TCE all 2020 3310.036812 USD/tC\n1 World TCE all 2025 3310.036812 USD/tC\n2 World TCE all 2030 3310.036812 USD/tC\n3 World TCE all 2035 3310.036812 USD/tC\n4 World TCE all 2040 3310.036812 USD/tC\n5 World TCE all 2045 3310.036812 USD/tC\n6 World TCE all 2050 3310.036812 USD/tC\n7 World TCE all 2055 3310.036812 USD/tC\n8 World TCE all 2060 3310.036812 USD/tC\n9 World TCE all 2070 3310.036812 USD/tC\n10 World TCE all 2080 3310.036812 USD/tC\n11 World TCE all 2090 3310.036812 USD/tC\n12 World TCE all 2100 3310.036812 USD/tC\n13 World TCE all 2110 3310.036812 USD/tC", + "text/html": "
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nodetype_emissiontype_tectype_yearvalueunit
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\n
" + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-20T19:12:39.535922500Z", + "start_time": "2023-09-20T19:12:39.422454400Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [ + { + "data": { + "text/plain": "True" + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scen.has_solution()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-20T12:09:33.535380800Z", + "start_time": "2023-09-20T12:09:33.462699Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 8, + "outputs": [], + "source": [ + "scen.remove_solution()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-20T11:59:15.670992300Z", + "start_time": "2023-09-20T11:58:32.559401200Z" + } + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scen.solve(\"MESSAGE-MACRO\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "start_time": "2023-09-20T12:10:04.268266300Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [], + "source": [ + "#sc_clone3 = scen.clone(\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00_uncorrected\", \"baseline\")\n", + "#sc_clone = scen.clone(\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00_incOldFor+PrimFor\", \"baseline\")\n", + "#sc_clone = scen.clone(\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00_OldForSplit\", \"baseline\")\n", + "#sc_clone = scen.clone(\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\", \"bugfix\")\n", + "#sc_clone2 = scen.clone(\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\", \"bugfix_cost-correction\")\n", + "#sc_clone2 = scen.clone(\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\", \"bugfix_cost-correction_new-matrix\")\n", + "#sc_clone2 = scen.clone(\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\", \"new-mapping_no-smoothing\")\n", + "sc_clone2 = scen.clone(\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\", \"new-mapping_with-smoothing\")\n", + "#sc_clone = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00_corrected\", \"baseline\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-19T12:51:40.341511700Z", + "start_time": "2023-09-19T12:51:21.425252Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [], + "source": [ + "#scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00_uncorrected\", \"baseline\")\n", + "#scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\", \"bugfix_1000f\")\n", + "scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\", \"bugfix_cost-correction_new-matrix\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-19T09:24:37.660479Z", + "start_time": "2023-09-19T09:24:34.009163800Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 10, + "outputs": [ + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[1;31mKeyboardInterrupt\u001B[0m Traceback (most recent call last)", + "Input \u001B[1;32mIn [10]\u001B[0m, in \u001B[0;36m\u001B[1;34m()\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[43mscen\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msolve\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmodel\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mMESSAGE-MACRO\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\n", + "File \u001B[1;32m~\\PycharmProjects\\message_ix\\message_ix\\core.py:677\u001B[0m, in \u001B[0;36mScenario.solve\u001B[1;34m(self, model, solve_options, **kwargs)\u001B[0m\n\u001B[0;32m 657\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21msolve\u001B[39m(\u001B[38;5;28mself\u001B[39m, model\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mMESSAGE\u001B[39m\u001B[38;5;124m\"\u001B[39m, solve_options\u001B[38;5;241m=\u001B[39m{}, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[0;32m 658\u001B[0m \u001B[38;5;124;03m\"\"\"Solve MESSAGE or MESSAGE-MACRO for the Scenario.\u001B[39;00m\n\u001B[0;32m 659\u001B[0m \n\u001B[0;32m 660\u001B[0m \u001B[38;5;124;03m By default, :meth:`ixmp.Scenario.solve` is called with 'MESSAGE' as the\u001B[39;00m\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 675\u001B[0m \u001B[38;5;124;03m :class:`.GAMSModel`.\u001B[39;00m\n\u001B[0;32m 676\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[1;32m--> 677\u001B[0m \u001B[38;5;28;43msuper\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msolve\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmodel\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mmodel\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43msolve_options\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43msolve_options\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\ixmp\\core\\scenario.py:835\u001B[0m, in \u001B[0;36mScenario.solve\u001B[1;34m(self, model, callback, cb_kwargs, **model_options)\u001B[0m\n\u001B[0;32m 833\u001B[0m \u001B[38;5;66;03m# Iterate until convergence\u001B[39;00m\n\u001B[0;32m 834\u001B[0m \u001B[38;5;28;01mwhile\u001B[39;00m \u001B[38;5;28;01mTrue\u001B[39;00m:\n\u001B[1;32m--> 835\u001B[0m \u001B[43mmodel_obj\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrun\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[0;32m 837\u001B[0m \u001B[38;5;66;03m# Store an iteration number to help the callback\u001B[39;00m\n\u001B[0;32m 838\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28mhasattr\u001B[39m(\u001B[38;5;28mself\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124miteration\u001B[39m\u001B[38;5;124m\"\u001B[39m):\n", + "File \u001B[1;32m~\\PycharmProjects\\message_ix\\message_ix\\models.py:373\u001B[0m, in \u001B[0;36mGAMSModel.run\u001B[1;34m(self, scenario)\u001B[0m\n\u001B[0;32m 370\u001B[0m lines2 \u001B[38;5;241m=\u001B[39m (\u001B[38;5;124m\"\u001B[39m\u001B[38;5;132;01m{}\u001B[39;00m\u001B[38;5;124m = \u001B[39m\u001B[38;5;132;01m{}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;241m.\u001B[39mformat(\u001B[38;5;241m*\u001B[39mkv) \u001B[38;5;28;01mfor\u001B[39;00m kv \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcplex_opts\u001B[38;5;241m.\u001B[39mitems())\n\u001B[0;32m 371\u001B[0m optfile2\u001B[38;5;241m.\u001B[39mwrite_text(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;130;01m\\n\u001B[39;00m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;241m.\u001B[39mjoin(lines2))\n\u001B[1;32m--> 373\u001B[0m result \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43msuper\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrun\u001B[49m\u001B[43m(\u001B[49m\u001B[43mscenario\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 375\u001B[0m \u001B[38;5;66;03m# In previous versions, the `cplex.opt` file(s) were removed at this point\u001B[39;00m\n\u001B[0;32m 376\u001B[0m \u001B[38;5;66;03m# in the workflow. This has been removed due to issues when running\u001B[39;00m\n\u001B[0;32m 377\u001B[0m \u001B[38;5;66;03m# scenarios asynchronously.\u001B[39;00m\n\u001B[0;32m 379\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m result\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\ixmp\\model\\gams.py:293\u001B[0m, in \u001B[0;36mGAMSModel.run\u001B[1;34m(self, scenario)\u001B[0m\n\u001B[0;32m 286\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mNotImplementedError\u001B[39;00m(\n\u001B[0;32m 287\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mGAMSModel requires a Backend that can write to GDX files, e.g. \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m 288\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mJDBCBackend\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m 289\u001B[0m )\n\u001B[0;32m 291\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m 292\u001B[0m \u001B[38;5;66;03m# Invoke GAMS\u001B[39;00m\n\u001B[1;32m--> 293\u001B[0m \u001B[43mcheck_call\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcommand\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mshell\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mos\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mname\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m==\u001B[39;49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mnt\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcwd\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcwd\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 294\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m CalledProcessError \u001B[38;5;28;01mas\u001B[39;00m exc:\n\u001B[0;32m 295\u001B[0m \u001B[38;5;66;03m# Do not remove self.temp_dir; the user may want to inspect the GDX file\u001B[39;00m\n\u001B[0;32m 296\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mformat_exception(exc, model_file) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;28mNone\u001B[39m\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\subprocess.py:359\u001B[0m, in \u001B[0;36mcheck_call\u001B[1;34m(*popenargs, **kwargs)\u001B[0m\n\u001B[0;32m 349\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mcheck_call\u001B[39m(\u001B[38;5;241m*\u001B[39mpopenargs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[0;32m 350\u001B[0m \u001B[38;5;124;03m\"\"\"Run command with arguments. Wait for command to complete. If\u001B[39;00m\n\u001B[0;32m 351\u001B[0m \u001B[38;5;124;03m the exit code was zero then return, otherwise raise\u001B[39;00m\n\u001B[0;32m 352\u001B[0m \u001B[38;5;124;03m CalledProcessError. The CalledProcessError object will have the\u001B[39;00m\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 357\u001B[0m \u001B[38;5;124;03m check_call([\"ls\", \"-l\"])\u001B[39;00m\n\u001B[0;32m 358\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[1;32m--> 359\u001B[0m retcode \u001B[38;5;241m=\u001B[39m \u001B[43mcall\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mpopenargs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 360\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m retcode:\n\u001B[0;32m 361\u001B[0m cmd \u001B[38;5;241m=\u001B[39m kwargs\u001B[38;5;241m.\u001B[39mget(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124margs\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\subprocess.py:342\u001B[0m, in \u001B[0;36mcall\u001B[1;34m(timeout, *popenargs, **kwargs)\u001B[0m\n\u001B[0;32m 340\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m Popen(\u001B[38;5;241m*\u001B[39mpopenargs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs) \u001B[38;5;28;01mas\u001B[39;00m p:\n\u001B[0;32m 341\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m--> 342\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mp\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mwait\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtimeout\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mtimeout\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 343\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m: \u001B[38;5;66;03m# Including KeyboardInterrupt, wait handled that.\u001B[39;00m\n\u001B[0;32m 344\u001B[0m p\u001B[38;5;241m.\u001B[39mkill()\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\subprocess.py:1083\u001B[0m, in \u001B[0;36mPopen.wait\u001B[1;34m(self, timeout)\u001B[0m\n\u001B[0;32m 1081\u001B[0m endtime \u001B[38;5;241m=\u001B[39m _time() \u001B[38;5;241m+\u001B[39m timeout\n\u001B[0;32m 1082\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m-> 1083\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_wait\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtimeout\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mtimeout\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 1084\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyboardInterrupt\u001B[39;00m:\n\u001B[0;32m 1085\u001B[0m \u001B[38;5;66;03m# https://bugs.python.org/issue25942\u001B[39;00m\n\u001B[0;32m 1086\u001B[0m \u001B[38;5;66;03m# The first keyboard interrupt waits briefly for the child to\u001B[39;00m\n\u001B[0;32m 1087\u001B[0m \u001B[38;5;66;03m# exit under the common assumption that it also received the ^C\u001B[39;00m\n\u001B[0;32m 1088\u001B[0m \u001B[38;5;66;03m# generated SIGINT and will exit rapidly.\u001B[39;00m\n\u001B[0;32m 1089\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m timeout \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\subprocess.py:1361\u001B[0m, in \u001B[0;36mPopen._wait\u001B[1;34m(self, timeout)\u001B[0m\n\u001B[0;32m 1358\u001B[0m timeout_millis \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mint\u001B[39m(timeout \u001B[38;5;241m*\u001B[39m \u001B[38;5;241m1000\u001B[39m)\n\u001B[0;32m 1359\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mreturncode \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m 1360\u001B[0m \u001B[38;5;66;03m# API note: Returns immediately if timeout_millis == 0.\u001B[39;00m\n\u001B[1;32m-> 1361\u001B[0m result \u001B[38;5;241m=\u001B[39m \u001B[43m_winapi\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mWaitForSingleObject\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_handle\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 1362\u001B[0m \u001B[43m \u001B[49m\u001B[43mtimeout_millis\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 1363\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m result \u001B[38;5;241m==\u001B[39m _winapi\u001B[38;5;241m.\u001B[39mWAIT_TIMEOUT:\n\u001B[0;32m 1364\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m TimeoutExpired(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39margs, timeout)\n", + "\u001B[1;31mKeyboardInterrupt\u001B[0m: " + ] + } + ], + "source": [ + "scen.solve(model=\"MESSAGE-MACRO\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-20T20:21:54.259873900Z", + "start_time": "2023-09-20T19:52:18.976484100Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [], + "source": [ + "scen.check_out()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-20T19:52:01.382482400Z", + "start_time": "2023-09-20T19:52:01.242075700Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 8, + "outputs": [], + "source": [ + "scen.add_set(\"balance_equality\", (\"ethanol\", \"import\"))\n", + "scen.add_set(\"balance_equality\", (\"ethanol\", \"export\"))\n", + "scen.add_set(\"balance_equality\", (\"ethanol\", \"primary\"))\n", + "scen.add_set(\"balance_equality\", (\"ethanol\", \"secondary\"))\n", + "scen.add_set(\"balance_equality\", (\"ethanol\", \"final\"))" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-20T19:52:03.914047Z", + "start_time": "2023-09-20T19:52:03.835957300Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 9, + "outputs": [], + "source": [ + "scen.commit(\"add ethanol to bal eq\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + 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" + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_cost = scen3.par(\"land_cost\")\n", + "df_cost[df_cost[\"year\"] == 2030]\n", + "df_cost_split = df_cost.join(df_cost[\"land_scenario\"].str.split(\"GHG\", expand=True)).drop(\"land_scenario\", axis=1)\n", + "df_cost_split = df_cost_split.rename({0: \"BIOscen\", 1: \"GHGscen\"}, axis=1)\n", + "df_cost_split[\"year\"] = df_cost_split[\"year\"].astype(int)\n", + "df_cost_split[\"GHGscen\"] = df_cost_split[\"GHGscen\"].astype(int)\n", + "df_cost_split = df_cost_split.set_index([\"node\", \"year\", \"BIOscen\", \"GHGscen\"]).sort_index().drop(\"unit\",\n", + " axis=1) #.diff()\n", + "df_cost_split[(df_cost_split.diff().value < 0) & (df_cost_split.index.get_level_values(3) != 0)]" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-18T10:59:09.311612200Z", + "start_time": "2023-09-18T10:59:08.765307200Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 9, + "outputs": [], + "source": [ + "df_cost = sc_clone2.par(\"land_cost\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-18T10:44:16.060673400Z", + "start_time": "2023-09-18T10:44:15.623115400Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [], + "source": [ + "df_emi = scen3.par(\"land_emission\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-18T12:42:55.460232900Z", + "start_time": "2023-09-18T12:42:46.257002900Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 8, + "outputs": [], + "source": [ + "df_emi = df_emi[df_emi[\"emission\"]==\"TCE\"]" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-18T12:42:55.585650600Z", + "start_time": "2023-09-18T12:42:55.475924Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 9, + "outputs": [], + "source": [ + "df_emi_split = df_emi.join(df_emi[\"land_scenario\"].str.split(\"GHG\", expand=True)).drop(\"land_scenario\", axis=1)\n", + "df_emi_split = df_emi_split.rename({0: \"BIOscen\", 1: \"GHGscen\"}, axis=1)\n", + "df_emi_split[\"year\"] = df_emi_split[\"year\"].astype(int)\n", + "df_emi_split[\"GHGscen\"] = df_emi_split[\"GHGscen\"].astype(int)\n", + "df_emi_split = df_emi_split[df_emi_split[\"year\"] > 2020]" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-18T12:43:04.335485900Z", + "start_time": "2023-09-18T12:43:04.069623400Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 10, + "outputs": [], + "source": [ + "df_emi_split = df_emi_split.set_index([\"node\", \"year\", \"BIOscen\"]).join(df_emi_split.groupby([\"node\", \"year\", \"BIOscen\"]).min()[\"value\"], rsuffix=\"_min\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-18T12:43:07.788537500Z", + "start_time": "2023-09-18T12:43:07.554254700Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 11, + "outputs": [ + { + "data": { + "text/plain": " emission value unit GHGscen value_min\nnode year BIOscen \nR12_AFR 2025 BIO00 TCE 618.938 Mt C/yr 0 339.305\n BIO00 TCE 491.613 Mt C/yr 10 339.305\n BIO00 TCE 469.864 Mt C/yr 20 339.305\n BIO00 TCE 429.854 Mt C/yr 50 339.305\n BIO00 TCE 388.521 Mt C/yr 100 339.305\n... ... ... ... ... ...\nR12_WEU 2110 BIO45 TCE 91.815 Mt C/yr 3000 89.084\n BIO45 TCE 144.318 Mt C/yr 400 89.084\n BIO45 TCE 89.084 Mt C/yr 4000 89.084\n BIO45 TCE 131.225 Mt C/yr 600 89.084\n BIO45 TCE 106.620 Mt C/yr 990 89.084\n\n[13104 rows x 5 columns]", + "text/html": "
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emissionvalueunitGHGscenvalue_min
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BIO00TCE469.864Mt C/yr20339.305
BIO00TCE429.854Mt C/yr50339.305
BIO00TCE388.521Mt C/yr100339.305
........................
R12_WEU2110BIO45TCE91.815Mt C/yr300089.084
BIO45TCE144.318Mt C/yr40089.084
BIO45TCE89.084Mt C/yr400089.084
BIO45TCE131.225Mt C/yr60089.084
BIO45TCE106.620Mt C/yr99089.084
\n

13104 rows × 5 columns

\n
" + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_emi_split" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-18T12:43:08.507091900Z", + "start_time": "2023-09-18T12:43:08.351074300Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 12, + "outputs": [], + "source": [ + "df_emi_split_final = df_emi_split.join(df_emi_split[df_emi_split[\"value\"] == df_emi_split[\"value_min\"]][[\"GHGscen\"]], rsuffix=\"_min\")#.groupby([\"node\", \"year\", \"BIOscen\"], as_index=False).min()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-18T12:43:22.583840200Z", + "start_time": "2023-09-18T12:43:22.366484800Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 13, + "outputs": [ + { + "data": { + "text/plain": " emission value unit GHGscen value_min \\\nnode year BIOscen \nR12_AFR 2025 BIO00 TCE 618.938 Mt C/yr 0 339.305 \n BIO00 TCE 491.613 Mt C/yr 10 339.305 \n BIO00 TCE 469.864 Mt C/yr 20 339.305 \n BIO00 TCE 429.854 Mt C/yr 50 339.305 \n BIO00 TCE 388.521 Mt C/yr 100 339.305 \n... ... ... ... ... ... \nR12_WEU 2110 BIO45 TCE 91.815 Mt C/yr 3000 89.084 \n BIO45 TCE 144.318 Mt C/yr 400 89.084 \n BIO45 TCE 89.084 Mt C/yr 4000 89.084 \n BIO45 TCE 131.225 Mt C/yr 600 89.084 \n BIO45 TCE 106.620 Mt C/yr 990 89.084 \n\n GHGscen_min \nnode year BIOscen \nR12_AFR 2025 BIO00 4000 \n BIO00 4000 \n BIO00 4000 \n BIO00 4000 \n BIO00 4000 \n... ... \nR12_WEU 2110 BIO45 4000 \n BIO45 4000 \n BIO45 4000 \n BIO45 4000 \n BIO45 4000 \n\n[13104 rows x 6 columns]", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
emissionvalueunitGHGscenvalue_minGHGscen_min
nodeyearBIOscen
R12_AFR2025BIO00TCE618.938Mt C/yr0339.3054000
BIO00TCE491.613Mt C/yr10339.3054000
BIO00TCE469.864Mt C/yr20339.3054000
BIO00TCE429.854Mt C/yr50339.3054000
BIO00TCE388.521Mt C/yr100339.3054000
...........................
R12_WEU2110BIO45TCE91.815Mt C/yr300089.0844000
BIO45TCE144.318Mt C/yr40089.0844000
BIO45TCE89.084Mt C/yr400089.0844000
BIO45TCE131.225Mt C/yr60089.0844000
BIO45TCE106.620Mt C/yr99089.0844000
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13104 rows × 6 columns

\n
" + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_emi_split_final" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-18T12:43:23.577548300Z", + "start_time": "2023-09-18T12:43:23.426726100Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 98, + "outputs": [], + "source": [ + "def set_val(df):\n", + " #print(df)\n", + " if df[\"GHG\"] > df.GHG_min:\n", + " df.value = df.value_min\n", + " return df" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-18T13:37:08.494865900Z", + "start_time": "2023-09-18T13:37:08.368149700Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 16, + "outputs": [ + { + "data": { + "text/plain": " emission value unit GHGscen value_min\nnode year BIOscen \nR12_AFR 2025 BIO00 TCE 618.938 Mt C/yr 0 339.305\n BIO00 TCE 491.613 Mt C/yr 10 339.305\n BIO00 TCE 469.864 Mt C/yr 20 339.305\n BIO00 TCE 429.854 Mt C/yr 50 339.305\n BIO00 TCE 388.521 Mt C/yr 100 339.305\n... ... ... ... ... ...\nR12_WEU 2110 BIO45 TCE 91.815 Mt C/yr 3000 89.084\n BIO45 TCE 144.318 Mt C/yr 400 89.084\n BIO45 TCE 89.084 Mt C/yr 4000 89.084\n BIO45 TCE 131.225 Mt C/yr 600 89.084\n BIO45 TCE 106.620 Mt C/yr 990 89.084\n\n[13104 rows x 5 columns]", + "text/html": "
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emissionvalueunitGHGscenvalue_min
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BIO00TCE388.521Mt C/yr100339.305
........................
R12_WEU2110BIO45TCE91.815Mt C/yr300089.084
BIO45TCE144.318Mt C/yr40089.084
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BIO45TCE131.225Mt C/yr60089.084
BIO45TCE106.620Mt C/yr99089.084
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13104 rows × 5 columns

\n
" + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_emi_split" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-18T12:43:59.600890200Z", + "start_time": "2023-09-18T12:43:59.397789600Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 21, + "outputs": [], + "source": [ + "import pandas as pd" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-18T13:08:23.743522200Z", + "start_time": "2023-09-18T13:08:23.558846300Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 22, + "outputs": [], + "source": [ + "df_ghg = pd.read_csv(\"C:/Users\\maczek\\Desktop/ghg.csv\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-18T13:10:04.508971400Z", + "start_time": "2023-09-18T13:10:04.332044300Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 40, + "outputs": [], + "source": [ + "df_ghg_long = df_ghg.melt(id_vars=[\"Region\", \"Scenario\", \"BIO\", \"GHG\", \"Variable\", \"Unit\"], var_name=\"Year\")#.groupby([\"Region\", \"Year\", \"BIO\"]))" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-18T13:14:32.821827700Z", + "start_time": "2023-09-18T13:14:32.649846Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 63, + "outputs": [], + "source": [ + "df_ghg_long[\"Year\"] = df_ghg_long[\"Year\"].astype(int)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-18T13:25:04.369234Z", + "start_time": "2023-09-18T13:25:04.181566500Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 89, + "outputs": [], + "source": [ + "df_ghg_long = df_ghg_long[df_ghg_long[\"Year\"]>2020]" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-18T13:34:26.369940Z", + "start_time": "2023-09-18T13:34:26.244644200Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 90, + "outputs": [], + "source": [ + "df_ghg_long_min = df_ghg_long.groupby([\"Region\", \"Year\", \"BIO\"]).min()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-18T13:34:27.392222600Z", + "start_time": "2023-09-18T13:34:27.104163700Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 92, + "outputs": [ + { + "data": { + "text/plain": " Scenario GHG Variable Unit value \\\nRegion Year BIO \nR12_AFR 2025 BIO00 BIO00GHG000 0 LU_GHG Mt CO2/yr 2138.487660 \n BIO00 BIO00GHG010 10 LU_GHG Mt CO2/yr 2063.784906 \n BIO00 BIO00GHG020 20 LU_GHG Mt CO2/yr 1822.973364 \n BIO00 BIO00GHG050 50 LU_GHG Mt CO2/yr 1599.195430 \n BIO00 BIO00GHG100 100 LU_GHG Mt CO2/yr 1621.774631 \n... ... ... ... ... ... \nR12_WEU 2110 BIO45 BIO45GHG600 600 LU_GHG Mt CO2/yr 506.625325 \n BIO45 BIO45GHG990 990 LU_GHG Mt CO2/yr 419.190754 \n BIO45 BIO45GHG2000 2000 LU_GHG Mt CO2/yr 369.390249 \n BIO45 BIO45GHG3000 3000 LU_GHG Mt CO2/yr 361.430013 \n BIO45 BIO45GHG4000 4000 LU_GHG Mt CO2/yr 355.936202 \n\n value_min \nRegion Year BIO \nR12_AFR 2025 BIO00 1270.916475 \n BIO00 1270.916475 \n BIO00 1270.916475 \n BIO00 1270.916475 \n BIO00 1270.916475 \n... ... \nR12_WEU 2110 BIO45 355.936202 \n BIO45 355.936202 \n BIO45 355.936202 \n BIO45 355.936202 \n BIO45 355.936202 \n\n[13104 rows x 6 columns]", + "text/html": "
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ScenarioGHGVariableUnitvaluevalue_min
RegionYearBIO
R12_AFR2025BIO00BIO00GHG0000LU_GHGMt CO2/yr2138.4876601270.916475
BIO00BIO00GHG01010LU_GHGMt CO2/yr2063.7849061270.916475
BIO00BIO00GHG02020LU_GHGMt CO2/yr1822.9733641270.916475
BIO00BIO00GHG05050LU_GHGMt CO2/yr1599.1954301270.916475
BIO00BIO00GHG100100LU_GHGMt CO2/yr1621.7746311270.916475
...........................
R12_WEU2110BIO45BIO45GHG600600LU_GHGMt CO2/yr506.625325355.936202
BIO45BIO45GHG990990LU_GHGMt CO2/yr419.190754355.936202
BIO45BIO45GHG20002000LU_GHGMt CO2/yr369.390249355.936202
BIO45BIO45GHG30003000LU_GHGMt CO2/yr361.430013355.936202
BIO45BIO45GHG40004000LU_GHGMt CO2/yr355.936202355.936202
\n

13104 rows × 6 columns

\n
" + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_ghg_long_join = (df_ghg_long.set_index([\"Region\", \"Year\", \"BIO\"]).join(df_ghg_long_min[[\"value\"]], rsuffix=\"_min\"))\n", + "df_ghg_long_join" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-18T13:34:36.698101200Z", + "start_time": "2023-09-18T13:34:36.447726100Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 93, + "outputs": [ + { + "data": { + "text/plain": " Scenario GHG Variable Unit value \\\nRegion Year BIO \nR12_AFR 2025 BIO00 BIO00GHG990 990 LU_GHG Mt CO2/yr 1270.916475 \n BIO05 BIO05GHG990 990 LU_GHG Mt CO2/yr 1270.946775 \n BIO07 BIO07GHG990 990 LU_GHG Mt CO2/yr 1270.946775 \n BIO10 BIO10GHG990 990 LU_GHG Mt CO2/yr 1270.946775 \n BIO15 BIO15GHG990 990 LU_GHG Mt CO2/yr 1271.956851 \n... ... ... ... ... ... \nR12_WEU 2110 BIO07 BIO07GHG2000 2000 LU_GHG Mt CO2/yr 360.259355 \n BIO10 BIO10GHG2000 2000 LU_GHG Mt CO2/yr 368.318550 \n BIO15 BIO15GHG3000 3000 LU_GHG Mt CO2/yr 388.080595 \n BIO25 BIO25GHG3000 3000 LU_GHG Mt CO2/yr 364.388988 \n BIO45 BIO45GHG4000 4000 LU_GHG Mt CO2/yr 355.936202 \n\n value_min \nRegion Year BIO \nR12_AFR 2025 BIO00 1270.916475 \n BIO05 1270.946775 \n BIO07 1270.946775 \n BIO10 1270.946775 \n BIO15 1271.956851 \n... ... \nR12_WEU 2110 BIO07 360.259355 \n BIO10 368.318550 \n BIO15 388.080595 \n BIO25 364.388988 \n BIO45 355.936202 \n\n[1092 rows x 6 columns]", + "text/html": "
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ScenarioGHGVariableUnitvaluevalue_min
RegionYearBIO
R12_AFR2025BIO00BIO00GHG990990LU_GHGMt CO2/yr1270.9164751270.916475
BIO05BIO05GHG990990LU_GHGMt CO2/yr1270.9467751270.946775
BIO07BIO07GHG990990LU_GHGMt CO2/yr1270.9467751270.946775
BIO10BIO10GHG990990LU_GHGMt CO2/yr1270.9467751270.946775
BIO15BIO15GHG990990LU_GHGMt CO2/yr1271.9568511271.956851
...........................
R12_WEU2110BIO07BIO07GHG20002000LU_GHGMt CO2/yr360.259355360.259355
BIO10BIO10GHG20002000LU_GHGMt CO2/yr368.318550368.318550
BIO15BIO15GHG30003000LU_GHGMt CO2/yr388.080595388.080595
BIO25BIO25GHG30003000LU_GHGMt CO2/yr364.388988364.388988
BIO45BIO45GHG40004000LU_GHGMt CO2/yr355.936202355.936202
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1092 rows × 6 columns

\n
" + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_ghg_long_join[df_ghg_long_join[\"value\"]==df_ghg_long_join[\"value_min\"]]" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-18T13:34:51.104101600Z", + "start_time": "2023-09-18T13:34:50.932343700Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 95, + "outputs": [], + "source": [ + "df_ghg_long_join = df_ghg_long_join.join(df_ghg_long_join[df_ghg_long_join[\"value\"] == df_ghg_long_join[\"value_min\"]][\"GHG\"], rsuffix=\"_min\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-18T13:36:13.979560800Z", + "start_time": "2023-09-18T13:36:13.792048700Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 100, + "outputs": [], + "source": [ + "df_ghg_long_join_final = df_ghg_long_join.apply(lambda x: set_val(x), axis=1)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-18T13:37:57.749108900Z", + "start_time": "2023-09-18T13:37:56.432295600Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 117, + "outputs": [], + "source": [ + "df_ghg_final = df_ghg_long_join_final.drop([\"value_min\", \"GHG_min\"], axis=1).reset_index().pivot(columns=\"Year\", values=\"value\", index=[\"Region\",\"Scenario\", \"Variable\", \"Unit\", \"BIO\", \"GHG\"])" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-18T13:46:44.808123400Z", + "start_time": "2023-09-18T13:46:44.620360800Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 120, + "outputs": [ + { + "data": { + "text/plain": "Year 2025 2030 \\\nRegion Scenario Variable Unit BIO GHG \nR12_AFR BIO00GHG000 LU_GHG Mt CO2/yr BIO00 0 2138.487660 2618.828003 \n BIO00GHG010 LU_GHG Mt CO2/yr BIO00 10 2063.784906 2285.350912 \n BIO00GHG020 LU_GHG Mt CO2/yr BIO00 20 1822.973364 1905.371082 \n BIO00GHG050 LU_GHG Mt CO2/yr BIO00 50 1599.195430 1579.643292 \n BIO00GHG100 LU_GHG Mt CO2/yr BIO00 100 1621.774631 1545.687581 \n... ... ... \nR12_WEU BIO45GHG3000 LU_GHG Mt CO2/yr BIO45 3000 644.372113 632.714191 \n BIO45GHG400 LU_GHG Mt CO2/yr BIO45 400 732.111950 678.303025 \n BIO45GHG4000 LU_GHG Mt CO2/yr BIO45 4000 644.372113 631.093216 \n BIO45GHG600 LU_GHG Mt CO2/yr BIO45 600 727.080232 672.949900 \n BIO45GHG990 LU_GHG Mt CO2/yr BIO45 990 644.372113 653.500355 \n\nYear 2035 2040 \\\nRegion Scenario Variable Unit BIO GHG \nR12_AFR BIO00GHG000 LU_GHG Mt CO2/yr BIO00 0 3046.831786 3348.098027 \n BIO00GHG010 LU_GHG Mt CO2/yr BIO00 10 2373.001104 2362.038757 \n BIO00GHG020 LU_GHG Mt CO2/yr BIO00 20 2003.213618 2122.678213 \n BIO00GHG050 LU_GHG Mt CO2/yr BIO00 50 1678.182227 1856.242320 \n BIO00GHG100 LU_GHG Mt CO2/yr BIO00 100 1575.732706 1695.510497 \n... ... ... \nR12_WEU BIO45GHG3000 LU_GHG Mt CO2/yr BIO45 3000 590.803423 599.932465 \n BIO45GHG400 LU_GHG Mt CO2/yr BIO45 400 636.289335 682.739098 \n BIO45GHG4000 LU_GHG Mt CO2/yr BIO45 4000 590.803423 570.956727 \n BIO45GHG600 LU_GHG Mt CO2/yr BIO45 600 626.724454 680.439542 \n BIO45GHG990 LU_GHG Mt CO2/yr BIO45 990 639.014404 680.333981 \n\nYear 2045 2050 \\\nRegion Scenario Variable Unit BIO GHG \nR12_AFR BIO00GHG000 LU_GHG Mt CO2/yr BIO00 0 3651.476518 4182.934047 \n BIO00GHG010 LU_GHG Mt CO2/yr BIO00 10 2403.273844 2557.756949 \n BIO00GHG020 LU_GHG Mt CO2/yr BIO00 20 2230.066139 2385.444408 \n BIO00GHG050 LU_GHG Mt CO2/yr BIO00 50 2029.538087 2205.718056 \n BIO00GHG100 LU_GHG Mt CO2/yr BIO00 100 1857.875817 2051.872649 \n... ... ... \nR12_WEU BIO45GHG3000 LU_GHG Mt CO2/yr BIO45 3000 593.256927 551.721501 \n BIO45GHG400 LU_GHG Mt CO2/yr BIO45 400 725.368357 700.713245 \n BIO45GHG4000 LU_GHG Mt CO2/yr BIO45 4000 515.454258 497.398756 \n BIO45GHG600 LU_GHG Mt CO2/yr BIO45 600 708.291402 708.254727 \n BIO45GHG990 LU_GHG Mt CO2/yr BIO45 990 692.023218 674.601341 \n\nYear 2055 2060 \\\nRegion Scenario Variable Unit BIO GHG \nR12_AFR BIO00GHG000 LU_GHG Mt CO2/yr BIO00 0 4920.254792 5741.654839 \n BIO00GHG010 LU_GHG Mt CO2/yr BIO00 10 2844.690571 3302.594530 \n BIO00GHG020 LU_GHG Mt CO2/yr BIO00 20 2639.371534 2977.705404 \n BIO00GHG050 LU_GHG Mt CO2/yr BIO00 50 2378.937662 2594.796580 \n BIO00GHG100 LU_GHG Mt CO2/yr BIO00 100 2241.188245 2467.624768 \n... ... ... \nR12_WEU BIO45GHG3000 LU_GHG Mt CO2/yr BIO45 3000 527.806798 504.809004 \n BIO45GHG400 LU_GHG Mt CO2/yr BIO45 400 660.701527 617.525820 \n BIO45GHG4000 LU_GHG Mt CO2/yr BIO45 4000 481.081028 469.672034 \n BIO45GHG600 LU_GHG Mt CO2/yr BIO45 600 670.617239 626.624804 \n BIO45GHG990 LU_GHG Mt CO2/yr BIO45 990 637.176056 594.923460 \n\nYear 2070 2080 \\\nRegion Scenario Variable Unit BIO GHG \nR12_AFR BIO00GHG000 LU_GHG Mt CO2/yr BIO00 0 6357.920924 6103.922402 \n BIO00GHG010 LU_GHG Mt CO2/yr BIO00 10 3862.491747 4119.423536 \n BIO00GHG020 LU_GHG Mt CO2/yr BIO00 20 3308.837646 3314.616552 \n BIO00GHG050 LU_GHG Mt CO2/yr BIO00 50 2856.081743 2933.449334 \n BIO00GHG100 LU_GHG Mt CO2/yr BIO00 100 2703.323406 2711.529005 \n... ... ... \nR12_WEU BIO45GHG3000 LU_GHG Mt CO2/yr BIO45 3000 467.423790 402.684836 \n BIO45GHG400 LU_GHG Mt CO2/yr BIO45 400 594.705974 576.482651 \n BIO45GHG4000 LU_GHG Mt CO2/yr BIO45 4000 431.316345 399.294218 \n BIO45GHG600 LU_GHG Mt CO2/yr BIO45 600 589.199300 541.679505 \n BIO45GHG990 LU_GHG Mt CO2/yr BIO45 990 561.125293 510.976352 \n\nYear 2090 2100 \\\nRegion Scenario Variable Unit BIO GHG \nR12_AFR BIO00GHG000 LU_GHG Mt CO2/yr BIO00 0 5280.287635 4726.518905 \n BIO00GHG010 LU_GHG Mt CO2/yr BIO00 10 4024.376079 3969.405081 \n BIO00GHG020 LU_GHG Mt CO2/yr BIO00 20 3085.746555 2958.811283 \n BIO00GHG050 LU_GHG Mt CO2/yr BIO00 50 2805.581073 2710.875387 \n BIO00GHG100 LU_GHG Mt CO2/yr BIO00 100 2533.542612 2481.916204 \n... ... ... \nR12_WEU BIO45GHG3000 LU_GHG Mt CO2/yr BIO45 3000 369.753056 361.430013 \n BIO45GHG400 LU_GHG Mt CO2/yr BIO45 400 535.765561 537.274005 \n BIO45GHG4000 LU_GHG Mt CO2/yr BIO45 4000 368.741419 355.936202 \n BIO45GHG600 LU_GHG Mt CO2/yr BIO45 600 506.046646 506.625325 \n BIO45GHG990 LU_GHG Mt CO2/yr BIO45 990 456.903431 419.190754 \n\nYear 2110 \nRegion Scenario Variable Unit BIO GHG \nR12_AFR BIO00GHG000 LU_GHG Mt CO2/yr BIO00 0 4726.518905 \n BIO00GHG010 LU_GHG Mt CO2/yr BIO00 10 3969.405081 \n BIO00GHG020 LU_GHG Mt CO2/yr BIO00 20 2958.811283 \n BIO00GHG050 LU_GHG Mt CO2/yr BIO00 50 2710.875387 \n BIO00GHG100 LU_GHG Mt CO2/yr BIO00 100 2481.916204 \n... ... \nR12_WEU BIO45GHG3000 LU_GHG Mt CO2/yr BIO45 3000 361.430013 \n BIO45GHG400 LU_GHG Mt CO2/yr BIO45 400 537.274005 \n BIO45GHG4000 LU_GHG Mt CO2/yr BIO45 4000 355.936202 \n BIO45GHG600 LU_GHG Mt CO2/yr BIO45 600 506.625325 \n BIO45GHG990 LU_GHG Mt CO2/yr BIO45 990 419.190754 \n\n[1008 rows x 13 columns]", + "text/html": "
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Year2025203020352040204520502055206020702080209021002110
RegionScenarioVariableUnitBIOGHG
R12_AFRBIO00GHG000LU_GHGMt CO2/yrBIO0002138.4876602618.8280033046.8317863348.0980273651.4765184182.9340474920.2547925741.6548396357.9209246103.9224025280.2876354726.5189054726.518905
BIO00GHG010LU_GHGMt CO2/yrBIO00102063.7849062285.3509122373.0011042362.0387572403.2738442557.7569492844.6905713302.5945303862.4917474119.4235364024.3760793969.4050813969.405081
BIO00GHG020LU_GHGMt CO2/yrBIO00201822.9733641905.3710822003.2136182122.6782132230.0661392385.4444082639.3715342977.7054043308.8376463314.6165523085.7465552958.8112832958.811283
BIO00GHG050LU_GHGMt CO2/yrBIO00501599.1954301579.6432921678.1822271856.2423202029.5380872205.7180562378.9376622594.7965802856.0817432933.4493342805.5810732710.8753872710.875387
BIO00GHG100LU_GHGMt CO2/yrBIO001001621.7746311545.6875811575.7327061695.5104971857.8758172051.8726492241.1882452467.6247682703.3234062711.5290052533.5426122481.9162042481.916204
.........................................................
R12_WEUBIO45GHG3000LU_GHGMt CO2/yrBIO453000644.372113632.714191590.803423599.932465593.256927551.721501527.806798504.809004467.423790402.684836369.753056361.430013361.430013
BIO45GHG400LU_GHGMt CO2/yrBIO45400732.111950678.303025636.289335682.739098725.368357700.713245660.701527617.525820594.705974576.482651535.765561537.274005537.274005
BIO45GHG4000LU_GHGMt CO2/yrBIO454000644.372113631.093216590.803423570.956727515.454258497.398756481.081028469.672034431.316345399.294218368.741419355.936202355.936202
BIO45GHG600LU_GHGMt CO2/yrBIO45600727.080232672.949900626.724454680.439542708.291402708.254727670.617239626.624804589.199300541.679505506.046646506.625325506.625325
BIO45GHG990LU_GHGMt CO2/yrBIO45990644.372113653.500355639.014404680.333981692.023218674.601341637.176056594.923460561.125293510.976352456.903431419.190754419.190754
\n

1008 rows × 13 columns

\n
" + }, + "execution_count": 120, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_ghg_long = df_ghg.melt(id_vars=[\"Region\", \"Scenario\", \"BIO\", \"GHG\", \"Variable\", \"Unit\"],\n", + " var_name=\"Year\")\n", + "df_ghg_long[\"Year\"] = df_ghg_long[\"Year\"].astype(int)\n", + "df_ghg_long = df_ghg_long[df_ghg_long[\"Year\"] > 2020]\n", + "df_ghg_long_min = df_ghg_long.groupby([\"Region\", \"Year\", \"BIO\"]).min()\n", + "df_ghg_long_join = (df_ghg_long.set_index([\"Region\", \"Year\", \"BIO\"]).join(df_ghg_long_min[[\"value\"]], rsuffix=\"_min\"))\n", + "\n", + "df_ghg_long_join = df_ghg_long_join.join(df_ghg_long_join[df_ghg_long_join[\"value\"] == df_ghg_long_join[\"value_min\"]][\"GHG\"], rsuffix=\"_min\")\n", + "df_ghg_long_join_final = df_ghg_long_join.apply(lambda x: set_val(x), axis=1)\n", + "df_ghg_final = df_ghg_long_join_final.drop([\"value_min\", \"GHG_min\"], axis=1).reset_index().pivot(columns=\"Year\", values=\"value\", index=[\"Region\",\"Scenario\", \"Variable\", \"Unit\", \"BIO\", \"GHG\"])\n", + "df_ghg_final" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-18T13:50:52.230367300Z", + "start_time": "2023-09-18T13:50:50.480351700Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 129, + "outputs": [], + "source": [ + "df_ghg_new = df_ghg.set_index([\"Region\",\"Scenario\", \"Variable\", \"Unit\", \"BIO\", \"GHG\"])[[\"1990\", \"1995\", \"2000\", \"2005\", \"2010\", \"2015\", \"2020\"]].join(df_ghg_final)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-18T13:57:40.402198Z", + "start_time": "2023-09-18T13:57:40.167794200Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 114, + "outputs": [ + { + "data": { + "text/plain": " 1990 1995 2000 2005 \\\nRegion Scenario Variable Unit BIO GHG \nR12_AFR BIO00GHG000 LU_GHG Mt CO2/yr BIO00 0 NaN NaN NaN NaN \n BIO00GHG010 LU_GHG Mt CO2/yr BIO00 10 0.0 0.0 0.0 0.0 \n BIO00GHG020 LU_GHG Mt CO2/yr BIO00 20 0.0 0.0 0.0 0.0 \n BIO00GHG050 LU_GHG Mt CO2/yr BIO00 50 0.0 0.0 0.0 0.0 \n BIO00GHG100 LU_GHG Mt CO2/yr BIO00 100 0.0 0.0 0.0 0.0 \n... ... ... ... ... \nR12_WEU BIO45GHG600 LU_GHG Mt CO2/yr BIO45 600 0.0 0.0 0.0 0.0 \n BIO45GHG990 LU_GHG Mt CO2/yr BIO45 990 0.0 0.0 0.0 0.0 \n BIO45GHG2000 LU_GHG Mt CO2/yr BIO45 2000 0.0 0.0 0.0 0.0 \n BIO45GHG3000 LU_GHG Mt CO2/yr BIO45 3000 0.0 0.0 0.0 0.0 \n BIO45GHG4000 LU_GHG Mt CO2/yr BIO45 4000 0.0 0.0 0.0 0.0 \n\n 2010 2015 2020 \\\nRegion Scenario Variable Unit BIO GHG \nR12_AFR BIO00GHG000 LU_GHG Mt CO2/yr BIO00 0 NaN NaN NaN \n BIO00GHG010 LU_GHG Mt CO2/yr BIO00 10 0.0 0.0 0.0 \n BIO00GHG020 LU_GHG Mt CO2/yr BIO00 20 0.0 0.0 0.0 \n BIO00GHG050 LU_GHG Mt CO2/yr BIO00 50 0.0 0.0 0.0 \n BIO00GHG100 LU_GHG Mt CO2/yr BIO00 100 0.0 0.0 0.0 \n... ... ... ... \nR12_WEU BIO45GHG600 LU_GHG Mt CO2/yr BIO45 600 0.0 0.0 0.0 \n BIO45GHG990 LU_GHG Mt CO2/yr BIO45 990 0.0 0.0 0.0 \n BIO45GHG2000 LU_GHG Mt CO2/yr BIO45 2000 0.0 0.0 0.0 \n BIO45GHG3000 LU_GHG Mt CO2/yr BIO45 3000 0.0 0.0 0.0 \n BIO45GHG4000 LU_GHG Mt CO2/yr BIO45 4000 0.0 0.0 0.0 \n\n 2025 2030 \\\nRegion Scenario Variable Unit BIO GHG \nR12_AFR BIO00GHG000 LU_GHG Mt CO2/yr BIO00 0 NaN NaN \n BIO00GHG010 LU_GHG Mt CO2/yr BIO00 10 -74.702754 -333.477090 \n BIO00GHG020 LU_GHG Mt CO2/yr BIO00 20 -240.811542 -379.979831 \n BIO00GHG050 LU_GHG Mt CO2/yr BIO00 50 -223.777934 -325.727790 \n BIO00GHG100 LU_GHG Mt CO2/yr BIO00 100 22.579200 -33.955711 \n... ... ... \nR12_WEU BIO45GHG600 LU_GHG Mt CO2/yr BIO45 600 -5.031718 -5.353125 \n BIO45GHG990 LU_GHG Mt CO2/yr BIO45 990 -82.708119 -19.449545 \n BIO45GHG2000 LU_GHG Mt CO2/yr BIO45 2000 83.945207 12.145148 \n BIO45GHG3000 LU_GHG Mt CO2/yr BIO45 3000 -3.327431 -32.931312 \n BIO45GHG4000 LU_GHG Mt CO2/yr BIO45 4000 1.869618 -1.620975 \n\n 2035 2040 \\\nRegion Scenario Variable Unit BIO GHG \nR12_AFR BIO00GHG000 LU_GHG Mt CO2/yr BIO00 0 NaN NaN \n BIO00GHG010 LU_GHG Mt CO2/yr BIO00 10 -673.830682 -986.059271 \n BIO00GHG020 LU_GHG Mt CO2/yr BIO00 20 -369.787486 -239.360543 \n BIO00GHG050 LU_GHG Mt CO2/yr BIO00 50 -325.031391 -266.435894 \n BIO00GHG100 LU_GHG Mt CO2/yr BIO00 100 -102.449521 -160.731823 \n... ... ... \nR12_WEU BIO45GHG600 LU_GHG Mt CO2/yr BIO45 600 -9.564881 -2.299557 \n BIO45GHG990 LU_GHG Mt CO2/yr BIO45 990 12.289950 -0.105561 \n BIO45GHG2000 LU_GHG Mt CO2/yr BIO45 2000 5.249088 -45.788472 \n BIO45GHG3000 LU_GHG Mt CO2/yr BIO45 3000 -53.460068 -34.613044 \n BIO45GHG4000 LU_GHG Mt CO2/yr BIO45 4000 3.235089 -28.975738 \n\n 2045 2050 \\\nRegion Scenario Variable Unit BIO GHG \nR12_AFR BIO00GHG000 LU_GHG Mt CO2/yr BIO00 0 NaN NaN \n BIO00GHG010 LU_GHG Mt CO2/yr BIO00 10 -1248.202674 -1625.177098 \n BIO00GHG020 LU_GHG Mt CO2/yr BIO00 20 -173.207706 -172.312541 \n BIO00GHG050 LU_GHG Mt CO2/yr BIO00 50 -200.528052 -179.726352 \n BIO00GHG100 LU_GHG Mt CO2/yr BIO00 100 -171.662270 -153.845408 \n... ... ... \nR12_WEU BIO45GHG600 LU_GHG Mt CO2/yr BIO45 600 -17.076956 7.541481 \n BIO45GHG990 LU_GHG Mt CO2/yr BIO45 990 -16.268184 -33.653386 \n BIO45GHG2000 LU_GHG Mt CO2/yr BIO45 2000 -71.048467 -78.928898 \n BIO45GHG3000 LU_GHG Mt CO2/yr BIO45 3000 -27.717824 -43.950942 \n BIO45GHG4000 LU_GHG Mt CO2/yr BIO45 4000 -77.802669 -54.322745 \n\n 2055 2060 \\\nRegion Scenario Variable Unit BIO GHG \nR12_AFR BIO00GHG000 LU_GHG Mt CO2/yr BIO00 0 NaN NaN \n BIO00GHG010 LU_GHG Mt CO2/yr BIO00 10 -2075.564221 -2439.060308 \n BIO00GHG020 LU_GHG Mt CO2/yr BIO00 20 -205.319036 -324.889127 \n BIO00GHG050 LU_GHG Mt CO2/yr BIO00 50 -260.433872 -382.908823 \n BIO00GHG100 LU_GHG Mt CO2/yr BIO00 100 -137.749417 -127.171812 \n... ... ... \nR12_WEU BIO45GHG600 LU_GHG Mt CO2/yr BIO45 600 9.915712 9.098984 \n BIO45GHG990 LU_GHG Mt CO2/yr BIO45 990 -33.441183 -31.701343 \n BIO45GHG2000 LU_GHG Mt CO2/yr BIO45 2000 -82.656784 -85.508885 \n BIO45GHG3000 LU_GHG Mt CO2/yr BIO45 3000 -26.712474 -4.605572 \n BIO45GHG4000 LU_GHG Mt CO2/yr BIO45 4000 -46.725770 -35.136969 \n\n 2070 2080 \\\nRegion Scenario Variable Unit BIO GHG \nR12_AFR BIO00GHG000 LU_GHG Mt CO2/yr BIO00 0 NaN NaN \n BIO00GHG010 LU_GHG Mt CO2/yr BIO00 10 -2495.429177 -1984.498866 \n BIO00GHG020 LU_GHG Mt CO2/yr BIO00 20 -553.654101 -804.806984 \n BIO00GHG050 LU_GHG Mt CO2/yr BIO00 50 -452.755903 -381.167218 \n BIO00GHG100 LU_GHG Mt CO2/yr BIO00 100 -152.758337 -221.920329 \n... ... ... \nR12_WEU BIO45GHG600 LU_GHG Mt CO2/yr BIO45 600 -5.506675 -34.803147 \n BIO45GHG990 LU_GHG Mt CO2/yr BIO45 990 -28.074007 -30.703152 \n BIO45GHG2000 LU_GHG Mt CO2/yr BIO45 2000 -99.702109 -102.103303 \n BIO45GHG3000 LU_GHG Mt CO2/yr BIO45 3000 6.000606 -6.188214 \n BIO45GHG4000 LU_GHG Mt CO2/yr BIO45 4000 -36.107445 -3.390618 \n\n 2090 2100 \\\nRegion Scenario Variable Unit BIO GHG \nR12_AFR BIO00GHG000 LU_GHG Mt CO2/yr BIO00 0 NaN NaN \n BIO00GHG010 LU_GHG Mt CO2/yr BIO00 10 -1255.911556 -757.113824 \n BIO00GHG020 LU_GHG Mt CO2/yr BIO00 20 -938.629524 -1010.593798 \n BIO00GHG050 LU_GHG Mt CO2/yr BIO00 50 -280.165482 -247.935896 \n BIO00GHG100 LU_GHG Mt CO2/yr BIO00 100 -272.038461 -228.959183 \n... ... ... \nR12_WEU BIO45GHG600 LU_GHG Mt CO2/yr BIO45 600 -29.718914 -30.648680 \n BIO45GHG990 LU_GHG Mt CO2/yr BIO45 990 -49.143216 -87.434571 \n BIO45GHG2000 LU_GHG Mt CO2/yr BIO45 2000 -80.323076 -49.800505 \n BIO45GHG3000 LU_GHG Mt CO2/yr BIO45 3000 -6.827298 -7.960236 \n BIO45GHG4000 LU_GHG Mt CO2/yr BIO45 4000 -1.011637 -5.493810 \n\n 2110 \nRegion Scenario Variable Unit BIO GHG \nR12_AFR BIO00GHG000 LU_GHG Mt CO2/yr BIO00 0 NaN \n BIO00GHG010 LU_GHG Mt CO2/yr BIO00 10 -757.113824 \n BIO00GHG020 LU_GHG Mt CO2/yr BIO00 20 -1010.593798 \n BIO00GHG050 LU_GHG Mt CO2/yr BIO00 50 -247.935896 \n BIO00GHG100 LU_GHG Mt CO2/yr BIO00 100 -228.959183 \n... ... \nR12_WEU BIO45GHG600 LU_GHG Mt CO2/yr BIO45 600 -30.648680 \n BIO45GHG990 LU_GHG Mt CO2/yr BIO45 990 -87.434571 \n BIO45GHG2000 LU_GHG Mt CO2/yr BIO45 2000 -49.800505 \n BIO45GHG3000 LU_GHG Mt CO2/yr BIO45 3000 -7.960236 \n BIO45GHG4000 LU_GHG Mt CO2/yr BIO45 4000 -5.493810 \n\n[1008 rows x 20 columns]", + "text/html": "
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19901995200020052010201520202025203020352040204520502055206020702080209021002110
RegionScenarioVariableUnitBIOGHG
R12_AFRBIO00GHG000LU_GHGMt CO2/yrBIO000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
BIO00GHG010LU_GHGMt CO2/yrBIO00100.00.00.00.00.00.00.0-74.702754-333.477090-673.830682-986.059271-1248.202674-1625.177098-2075.564221-2439.060308-2495.429177-1984.498866-1255.911556-757.113824-757.113824
BIO00GHG020LU_GHGMt CO2/yrBIO00200.00.00.00.00.00.00.0-240.811542-379.979831-369.787486-239.360543-173.207706-172.312541-205.319036-324.889127-553.654101-804.806984-938.629524-1010.593798-1010.593798
BIO00GHG050LU_GHGMt CO2/yrBIO00500.00.00.00.00.00.00.0-223.777934-325.727790-325.031391-266.435894-200.528052-179.726352-260.433872-382.908823-452.755903-381.167218-280.165482-247.935896-247.935896
BIO00GHG100LU_GHGMt CO2/yrBIO001000.00.00.00.00.00.00.022.579200-33.955711-102.449521-160.731823-171.662270-153.845408-137.749417-127.171812-152.758337-221.920329-272.038461-228.959183-228.959183
..............................................................................
R12_WEUBIO45GHG600LU_GHGMt CO2/yrBIO456000.00.00.00.00.00.00.0-5.031718-5.353125-9.564881-2.299557-17.0769567.5414819.9157129.098984-5.506675-34.803147-29.718914-30.648680-30.648680
BIO45GHG990LU_GHGMt CO2/yrBIO459900.00.00.00.00.00.00.0-82.708119-19.44954512.289950-0.105561-16.268184-33.653386-33.441183-31.701343-28.074007-30.703152-49.143216-87.434571-87.434571
BIO45GHG2000LU_GHGMt CO2/yrBIO4520000.00.00.00.00.00.00.083.94520712.1451485.249088-45.788472-71.048467-78.928898-82.656784-85.508885-99.702109-102.103303-80.323076-49.800505-49.800505
BIO45GHG3000LU_GHGMt CO2/yrBIO4530000.00.00.00.00.00.00.0-3.327431-32.931312-53.460068-34.613044-27.717824-43.950942-26.712474-4.6055726.000606-6.188214-6.827298-7.960236-7.960236
BIO45GHG4000LU_GHGMt CO2/yrBIO4540000.00.00.00.00.00.00.01.869618-1.6209753.235089-28.975738-77.802669-54.322745-46.725770-35.136969-36.107445-3.390618-1.011637-5.493810-5.493810
\n

1008 rows × 20 columns

\n
" + }, + "execution_count": 114, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_ghg.set_index([\"Region\",\"Scenario\", \"Variable\", \"Unit\", \"BIO\", \"GHG\"]).diff()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-18T13:44:41.870578200Z", + "start_time": "2023-09-18T13:44:41.667382800Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 103, + "outputs": [ + { + "ename": "KeyError", + "evalue": "'GHG'", + "output_type": "error", + "traceback": [ + "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[1;31mKeyError\u001B[0m Traceback (most recent call last)", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3621\u001B[0m, in \u001B[0;36mIndex.get_loc\u001B[1;34m(self, key, method, tolerance)\u001B[0m\n\u001B[0;32m 3620\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m-> 3621\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_engine\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_loc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcasted_key\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 3622\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m err:\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx:136\u001B[0m, in \u001B[0;36mpandas._libs.index.IndexEngine.get_loc\u001B[1;34m()\u001B[0m\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx:163\u001B[0m, in \u001B[0;36mpandas._libs.index.IndexEngine.get_loc\u001B[1;34m()\u001B[0m\n", + "File \u001B[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5198\u001B[0m, in \u001B[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001B[1;34m()\u001B[0m\n", + "File \u001B[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5206\u001B[0m, in \u001B[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001B[1;34m()\u001B[0m\n", + "\u001B[1;31mKeyError\u001B[0m: 'GHG'", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001B[1;31mKeyError\u001B[0m Traceback (most recent call last)", + "Input \u001B[1;32mIn [103]\u001B[0m, in \u001B[0;36m\u001B[1;34m()\u001B[0m\n\u001B[1;32m----> 1\u001B[0m df_emi_split_final_min \u001B[38;5;241m=\u001B[39m \u001B[43mdf_emi_split_final\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mapply\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43;01mlambda\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mx\u001B[49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[43mset_val\u001B[49m\u001B[43m(\u001B[49m\u001B[43mx\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43maxis\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m1\u001B[39;49m\u001B[43m)\u001B[49m\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:8839\u001B[0m, in \u001B[0;36mDataFrame.apply\u001B[1;34m(self, func, axis, raw, result_type, args, **kwargs)\u001B[0m\n\u001B[0;32m 8828\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01mpandas\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mcore\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mapply\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m frame_apply\n\u001B[0;32m 8830\u001B[0m op \u001B[38;5;241m=\u001B[39m frame_apply(\n\u001B[0;32m 8831\u001B[0m \u001B[38;5;28mself\u001B[39m,\n\u001B[0;32m 8832\u001B[0m func\u001B[38;5;241m=\u001B[39mfunc,\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 8837\u001B[0m kwargs\u001B[38;5;241m=\u001B[39mkwargs,\n\u001B[0;32m 8838\u001B[0m )\n\u001B[1;32m-> 8839\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mop\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mapply\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241m.\u001B[39m__finalize__(\u001B[38;5;28mself\u001B[39m, method\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mapply\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\apply.py:727\u001B[0m, in \u001B[0;36mFrameApply.apply\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m 724\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mraw:\n\u001B[0;32m 725\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mapply_raw()\n\u001B[1;32m--> 727\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mapply_standard\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\apply.py:851\u001B[0m, in \u001B[0;36mFrameApply.apply_standard\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m 850\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mapply_standard\u001B[39m(\u001B[38;5;28mself\u001B[39m):\n\u001B[1;32m--> 851\u001B[0m results, res_index \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mapply_series_generator\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 853\u001B[0m \u001B[38;5;66;03m# wrap results\u001B[39;00m\n\u001B[0;32m 854\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mwrap_results(results, res_index)\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\apply.py:867\u001B[0m, in \u001B[0;36mFrameApply.apply_series_generator\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m 864\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m option_context(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mmode.chained_assignment\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;28;01mNone\u001B[39;00m):\n\u001B[0;32m 865\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m i, v \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28menumerate\u001B[39m(series_gen):\n\u001B[0;32m 866\u001B[0m \u001B[38;5;66;03m# ignore SettingWithCopy here in case the user mutates\u001B[39;00m\n\u001B[1;32m--> 867\u001B[0m results[i] \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mf\u001B[49m\u001B[43m(\u001B[49m\u001B[43mv\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 868\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(results[i], ABCSeries):\n\u001B[0;32m 869\u001B[0m \u001B[38;5;66;03m# If we have a view on v, we need to make a copy because\u001B[39;00m\n\u001B[0;32m 870\u001B[0m \u001B[38;5;66;03m# series_generator will swap out the underlying data\u001B[39;00m\n\u001B[0;32m 871\u001B[0m results[i] \u001B[38;5;241m=\u001B[39m results[i]\u001B[38;5;241m.\u001B[39mcopy(deep\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m)\n", + "Input \u001B[1;32mIn [103]\u001B[0m, in \u001B[0;36m\u001B[1;34m(x)\u001B[0m\n\u001B[1;32m----> 1\u001B[0m df_emi_split_final_min \u001B[38;5;241m=\u001B[39m df_emi_split_final\u001B[38;5;241m.\u001B[39mapply(\u001B[38;5;28;01mlambda\u001B[39;00m x: \u001B[43mset_val\u001B[49m\u001B[43m(\u001B[49m\u001B[43mx\u001B[49m\u001B[43m)\u001B[49m, axis\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m1\u001B[39m)\n", + "Input \u001B[1;32mIn [98]\u001B[0m, in \u001B[0;36mset_val\u001B[1;34m(df)\u001B[0m\n\u001B[0;32m 1\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mset_val\u001B[39m(df):\n\u001B[0;32m 2\u001B[0m \u001B[38;5;66;03m#print(df)\u001B[39;00m\n\u001B[1;32m----> 3\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[43mdf\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mGHG\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m \u001B[38;5;241m>\u001B[39m df\u001B[38;5;241m.\u001B[39mGHG_min:\n\u001B[0;32m 4\u001B[0m df\u001B[38;5;241m.\u001B[39mvalue \u001B[38;5;241m=\u001B[39m df\u001B[38;5;241m.\u001B[39mvalue_min\n\u001B[0;32m 5\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m df\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\series.py:958\u001B[0m, in \u001B[0;36mSeries.__getitem__\u001B[1;34m(self, key)\u001B[0m\n\u001B[0;32m 955\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_values[key]\n\u001B[0;32m 957\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m key_is_scalar:\n\u001B[1;32m--> 958\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_get_value\u001B[49m\u001B[43m(\u001B[49m\u001B[43mkey\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 960\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m is_hashable(key):\n\u001B[0;32m 961\u001B[0m \u001B[38;5;66;03m# Otherwise index.get_value will raise InvalidIndexError\u001B[39;00m\n\u001B[0;32m 962\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m 963\u001B[0m \u001B[38;5;66;03m# For labels that don't resolve as scalars like tuples and frozensets\u001B[39;00m\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\series.py:1069\u001B[0m, in \u001B[0;36mSeries._get_value\u001B[1;34m(self, label, takeable)\u001B[0m\n\u001B[0;32m 1066\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_values[label]\n\u001B[0;32m 1068\u001B[0m \u001B[38;5;66;03m# Similar to Index.get_value, but we do not fall back to positional\u001B[39;00m\n\u001B[1;32m-> 1069\u001B[0m loc \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mindex\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_loc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mlabel\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 1070\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mindex\u001B[38;5;241m.\u001B[39m_get_values_for_loc(\u001B[38;5;28mself\u001B[39m, loc, label)\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3623\u001B[0m, in \u001B[0;36mIndex.get_loc\u001B[1;34m(self, key, method, tolerance)\u001B[0m\n\u001B[0;32m 3621\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_engine\u001B[38;5;241m.\u001B[39mget_loc(casted_key)\n\u001B[0;32m 3622\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m err:\n\u001B[1;32m-> 3623\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m(key) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01merr\u001B[39;00m\n\u001B[0;32m 3624\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m:\n\u001B[0;32m 3625\u001B[0m \u001B[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001B[39;00m\n\u001B[0;32m 3626\u001B[0m \u001B[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001B[39;00m\n\u001B[0;32m 3627\u001B[0m \u001B[38;5;66;03m# the TypeError.\u001B[39;00m\n\u001B[0;32m 3628\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_check_indexing_error(key)\n", + "\u001B[1;31mKeyError\u001B[0m: 'GHG'" + ] + } + ], + "source": [ + "df_emi_split_final_min = df_emi_split_final.apply(lambda x: set_val(x), axis=1)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-18T13:38:26.714015100Z", + "start_time": "2023-09-18T13:38:26.416722600Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 20, + "outputs": [ + { + "data": { + "text/plain": " emission value unit GHGscen value_min \\\nnode year BIOscen \nR12_CHN 2030 BIO00 TCE 370.205 Mt C/yr 0 276.073 \n BIO00 TCE 359.754 Mt C/yr 10 276.073 \n BIO00 TCE 359.133 Mt C/yr 20 276.073 \n BIO00 TCE 356.055 Mt C/yr 50 276.073 \n BIO00 TCE 352.996 Mt C/yr 100 276.073 \n... ... ... ... ... ... \nR12_WEU 2110 BIO25 TCE 92.979 Mt C/yr 3000 92.979 \n BIO25 TCE 146.292 Mt C/yr 400 92.979 \n BIO25 TCE 92.979 Mt C/yr 4000 92.979 \n BIO25 TCE 137.590 Mt C/yr 600 92.979 \n BIO25 TCE 114.979 Mt C/yr 990 92.979 \n\n GHGscen_min \nnode year BIOscen \nR12_CHN 2030 BIO00 3000 \n BIO00 3000 \n BIO00 3000 \n BIO00 3000 \n BIO00 3000 \n... ... \nR12_WEU 2110 BIO25 3000 \n BIO25 3000 \n BIO25 3000 \n BIO25 3000 \n BIO25 3000 \n\n[2832 rows x 6 columns]", + "text/html": "
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emissionvalueunitGHGscenvalue_minGHGscen_min
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R12_CHN2030BIO00TCE370.205Mt C/yr0276.0733000
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BIO00TCE356.055Mt C/yr50276.0733000
BIO00TCE352.996Mt C/yr100276.0733000
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R12_WEU2110BIO25TCE92.979Mt C/yr300092.9793000
BIO25TCE146.292Mt C/yr40092.9793000
BIO25TCE92.979Mt C/yr400092.9793000
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" + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_emi_split_final_min[df_emi_split_final_min[\"GHGscen_min\"]!=4000]" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-18T12:45:21.805484800Z", + "start_time": "2023-09-18T12:45:21.616944900Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 67, + "outputs": [], + "source": [ + "df_emi_split = df_emi_split.reset_index().set_index([\"node\", \"year\", \"BIOscen\", \"GHGscen\"]).sort_index()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-18T12:08:41.007587100Z", + "start_time": "2023-09-18T12:08:40.886337600Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 58, + "outputs": [ + { + "data": { + "text/plain": " node land_scenario year value unit\n8064 R12_AFR BIO00GHG000 2030 131.20 USD\n8065 R12_AFR BIO00GHG010 2030 1111.61 USD\n8066 R12_AFR BIO00GHG020 2030 1426.91 USD\n8067 R12_AFR BIO00GHG050 2030 2918.26 USD\n8068 R12_AFR BIO00GHG100 2030 4789.62 USD\n... ... ... ... ... ...\n9067 R12_WEU BIO45GHG3000 2030 39074.74 USD\n9068 R12_WEU BIO45GHG400 2030 28098.57 USD\n9069 R12_WEU BIO45GHG4000 2030 37804.04 USD\n9070 R12_WEU BIO45GHG600 2030 28762.85 USD\n9071 R12_WEU BIO45GHG990 2030 34236.04 USD\n\n[1008 rows x 5 columns]", + "text/html": "
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" + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_cost[df_cost[\"year\"] == 2030]" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-18T12:04:53.176338500Z", + "start_time": "2023-09-18T12:04:52.910680700Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 19, + "outputs": [], + "source": [ + "df_cost_split = df_cost.join(df_cost[\"land_scenario\"].str.split(\"GHG\", expand=True)).drop(\"land_scenario\", axis=1)\n", + "df_cost_split = df_cost_split.rename({0: \"BIOscen\", 1: \"GHGscen\"}, axis=1)\n", + "df_cost_split[\"year\"] = df_cost_split[\"year\"].astype(int)\n", + "df_cost_split[\"GHGscen\"] = df_cost_split[\"GHGscen\"].astype(int)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-18T10:48:13.331126500Z", + "start_time": "2023-09-18T10:48:13.154426500Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 25, + "outputs": [], + "source": [ + "df_cost_split = df_cost_split.set_index([\"node\", \"year\", \"BIOscen\", \"GHGscen\"]).sort_index().drop(\"unit\", axis=1)#.diff()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-18T10:49:27.788734900Z", + "start_time": "2023-09-18T10:49:27.623550500Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 29, + "outputs": [ + { + "data": { + "text/plain": " value\nnode year BIOscen GHGscen \nR12_AFR 2030 BIO00 400 -1327.67\n 2000 -22741.68\n BIO05 400 6732.67\n 2000 -14678.76\n BIO07 400 10035.83\n... ...\nR12_WEU 2110 BIO15 100 190049.36\n 4000 442089.01\n BIO25 50 297005.83\n 4000 551895.39\n BIO45 50 541116.47\n\n[1122 rows x 1 columns]", + "text/html": "
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value
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...............
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1122 rows × 1 columns

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" + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_cost_split[(df_cost_split.diff().value < 0) & (df_cost_split.index.get_level_values(3) != 0)]" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-18T10:50:54.654657800Z", + "start_time": "2023-09-18T10:50:54.514383Z" + } + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "sc_clone.solve(model=\"MESSAGE-MACRO\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "start_time": "2023-09-12T11:59:37.621512400Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [], + "source": [ + "import message_data.tools.utilities" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-13T15:36:09.534732900Z", + "start_time": "2023-09-13T15:36:09.519035Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 10, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "processing Table: Resource|Extraction\n", + "processing Table: Resource|Cumulative Extraction\n", + "processing Table: Primary Energy\n", + "processing Table: Primary Energy (substitution method)\n", + "processing Table: Final Energy\n", + "processing Table: Secondary Energy|Electricity\n", + "processing Table: Secondary Energy|Heat\n", + "processing Table: Secondary Energy\n", + "processing Table: Secondary Energy|Gases\n", + "processing Table: Secondary Energy|Solids\n", + "processing Table: Emissions|CO2\n", + "processing Table: Carbon Sequestration\n", + "processing Table: Emissions|BC\n", + "processing Table: Emissions|OC\n", + "processing Table: Emissions|CO\n", + "processing Table: Emissions|N2O\n", + "processing Table: Emissions|CH4\n", + "processing Table: Emissions|NH3\n", + "processing Table: Emissions|Sulfur\n", + "processing Table: Emissions|NOx\n", + "processing Table: Emissions|VOC\n", + "processing Table: Emissions|HFC\n", + "processing Table: Emissions\n", + "processing Table: Emissions\n", + "processing Table: Agricultural Demand\n", + "processing Table: Agricultural Production\n", + "processing Table: Fertilizer Use\n", + "processing Table: Fertilizer\n", + "processing Table: Food Waste\n", + "processing Table: Food Demand\n", + "processing Table: Forestry Demand\n", + "processing Table: Forestry Production\n", + "processing Table: Land Cover\n", + "processing Table: Yield\n", + "processing Table: Capacity\n", + "processing Table: Capacity Additions\n", + "processing Table: Cumulative Capacity\n", + "processing Table: Capital Cost\n", + "processing Table: OM Cost|Fixed\n", + "processing Table: OM Cost|Variable\n", + "processing Table: Lifetime\n", + "processing Table: Efficiency\n", + "processing Table: Population\n", + "processing Table: Price\n", + "processing Table: Useful Energy\n", + "processing Table: Useful Energy\n", + "processing Table: Trade\n", + "processing Table: Investment|Energy Supply\n", + "processing Table: Water Consumption\n", + "processing Table: Water Withdrawal\n", + "processing Table: GDP\n", + "processing Table: Cost\n", + "processing Table: GLOBIOM Feedback\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "Starting to upload timeseries\n", + " region variable unit 2020 \\\n", + "0 R12_AFR Resource|Extraction EJ/yr 22.425189 \n", + "1 R12_AFR Resource|Extraction|Coal EJ/yr 7.762483 \n", + "2 R12_AFR Resource|Extraction|Gas EJ/yr 4.243681 \n", + "3 R12_AFR Resource|Extraction|Gas|Conventional EJ/yr 4.243681 \n", + "4 R12_AFR Resource|Extraction|Gas|Unconventional EJ/yr 0.000000 \n", + "\n", + " 2025 2030 2035 2040 2045 2050 \\\n", + "0 25.994892 29.432686 32.148193 36.073644 39.695341 42.961276 \n", + "1 9.690720 11.680136 13.889250 16.118193 18.702148 21.697660 \n", + "2 6.834491 8.212897 9.515340 9.853557 11.543875 11.805179 \n", + "3 6.834491 8.212897 9.515340 9.853557 11.543875 11.805179 \n", + "4 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "\n", + " 2055 2060 2070 2080 2090 2100 2110 \n", + "0 47.483945 56.304720 67.500799 75.436121 83.513448 72.080130 38.622405 \n", + "1 25.170280 29.195998 39.273137 45.796136 52.383412 49.979985 17.393034 \n", + "2 13.330084 15.774029 18.048166 22.433829 20.245962 14.901028 10.967157 \n", + "3 13.330084 15.774029 18.048166 11.505394 1.328787 0.977987 0.719799 \n", + "4 0.000000 0.000000 0.000000 10.928435 18.917174 13.923040 10.247358 \n", + "Finished uploading timeseries\n" + ] + } + ], + "source": [ + "# report baseline\n", + "from message_data.tools.post_processing.iamc_report_hackathon import report as reporting\n", + "reporting(\n", + " mp,\n", + " scen,\n", + " # NB(PNK) this is not an error; .iamc_report_hackathon.report() expects a\n", + " # string containing \"True\" or \"False\" instead of an actual bool.\n", + " \"False\",\n", + " scen.model,\n", + " scen.scenario,\n", + " merge_hist=True,\n", + " merge_ts=True,\n", + " #run_config=\"materials_run_config.yaml\",\n", + ")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "start_time": "2023-09-20T11:41:19.658236900Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 11, + "outputs": [ + { + "data": { + "text/plain": "" + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import importlib\n", + "importlib.reload(message_data.tools.utilities)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-13T15:21:15.534349300Z", + "start_time": "2023-09-13T15:21:15.487434500Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting to process data input from csv file.\n", + "Agricultural Demand\n", + "Agricultural Demand|Energy\n", + "Agricultural Demand|Energy|Crops\n", + "Agricultural Demand|Energy|Crops|1st generation\n", + "Agricultural Demand|Energy|Crops|2nd generation\n", + "Agricultural Demand|Non-Energy\n", + "Agricultural Demand|Non-Energy|Crops\n", + "Agricultural Demand|Non-Energy|Crops|Feed\n", + "Agricultural Demand|Non-Energy|Crops|Food\n", + "Agricultural Demand|Non-Energy|Crops|Other\n", + "Agricultural Demand|Non-Energy|Crops|Other|Waste\n", + "Agricultural Demand|Non-Energy|Livestock\n", + "Agricultural Demand|Non-Energy|Livestock|Food\n", + "Agricultural Demand|Non-Energy|Livestock|Other\n", + "Agricultural Demand|Non-Energy|Livestock|Other|Waste\n", + "Agricultural Production\n", + "Agricultural Production|Energy\n", + "Agricultural Production|Energy|Crops\n", + "Agricultural Production|Energy|Crops|1st generation\n", + "Agricultural Production|Energy|Crops|2nd generation\n", + "Agricultural Production|Non-Energy\n", + "Agricultural Production|Non-Energy|Crops\n", + "Agricultural Production|Non-Energy|Crops|Cereals\n", + "Agricultural Production|Non-Energy|Livestock\n", + "BCA_LandUseChangeEM\n", + "BCA_SavanBurnEM\n", + "Biodiversity|BII\n", + "Biodiversity|BII|Biodiversity hotspots\n", + "CH4_LandUseChangeEM\n", + "CH4_SavanBurnEM\n", + "CO_LandUseChangeEM\n", + "CO_SavanBurnEM\n", + "Costs|TC\n", + "CrpLnd\n", + "Emissions|CH4|AFOLU\n", + "Emissions|CH4|AFOLU|Agriculture\n", + "Emissions|CH4|AFOLU|Agriculture|Livestock\n", + "Emissions|CH4|AFOLU|Agriculture|Livestock|Enteric Fermentation\n", + "Emissions|CH4|AFOLU|Agriculture|Livestock|Manure Management\n", + "Emissions|CH4|AFOLU|Agriculture|Rice\n", + "Emissions|CH4|AFOLU|Biomass Burning\n", + "Emissions|CH4|AFOLU|Land\n", + "Emissions|CH4|AFOLU|Land|Grassland Burning\n", + "Emissions|CO2|AFOLU\n", + "Emissions|CO2|AFOLU|Afforestation\n", + "Emissions|CO2|AFOLU|Agriculture\n", + "Emissions|CO2|AFOLU|Deforestation\n", + "Emissions|CO2|AFOLU|Forest Management\n", + "Emissions|CO2|AFOLU|Negative\n", + "Emissions|CO2|AFOLU|Other LUC\n", + "Emissions|CO2|AFOLU|Positive\n", + "Emissions|CO2|AFOLU|Soil Carbon\n", + "Emissions|GHG|AFOLU\n", + "Emissions|N2O|AFOLU\n", + "Emissions|N2O|AFOLU|Agriculture\n", + "Emissions|N2O|AFOLU|Agriculture|Livestock\n", + "Emissions|N2O|AFOLU|Agriculture|Livestock|Manure Management\n", + "Emissions|N2O|AFOLU|Agriculture|Managed Soils\n", + "Emissions|N2O|AFOLU|Biomass Burning\n", + "Emissions|N2O|AFOLU|Land\n", + "Emissions|N2O|AFOLU|Land|Grassland Burning\n", + "Emissions|N2O|AFOLU|Land|Grassland Pastures\n", + "Fertilizer Use|Nitrogen\n", + "Fertilizer Use|Phosphorus\n", + "Fertilizer|Nitrogen|Intensity\n", + "Fertilizer|Phosphorus|Intensity\n", + "Food Demand\n", + "Food Demand|Crops\n", + "Food Demand|Livestock\n", + "Food Demand|Microbial protein\n", + "Forestry Demand|Roundwood\n", + "Forestry Demand|Roundwood|Industrial Roundwood\n", + "Forestry Demand|Roundwood|Wood Fuel\n", + "Forestry Production|Forest Residues\n", + "Forestry Production|Roundwood\n", + "Forestry Production|Roundwood|Industrial Roundwood\n", + "Forestry Production|Roundwood|Wood Fuel\n", + "GrassLnd\n", + "LU_CH4\n", + "LU_CH4_Agri\n", + "LU_CH4_BioBurn\n", + "LU_CO2\n", + "LU_GHG\n", + "LU_N2O\n", + "Land Cover\n", + "Land Cover|Cropland\n", + "Land Cover|Cropland|Cereals\n", + "Land Cover|Cropland|Energy Crops\n", + "Land Cover|Cropland|Irrigated\n", + "Land Cover|Cropland|Non-Energy Crops\n", + "Land Cover|Cropland|Oilcrops\n", + "Land Cover|Cropland|Sugarcrops\n", + "Land Cover|Forest\n", + "Land Cover|Forest|Afforestation and Reforestation\n", + "Land Cover|Forest|Forest old\n", + "Land Cover|Forest|Forest old|Other forest\n", + "Land Cover|Forest|Forest old|Primary forest\n", + "Land Cover|Forest|Forestry\n", + "Land Cover|Forest|Managed\n", + "Land Cover|Forest|Natural Forest\n", + "Land Cover|Other Land\n", + "Land Cover|Pasture\n", + "Land Cover|Protected\n", + "Landuse intensity indicator Tau\n", + "NH3_LandUseChangeEM\n", + "NH3_ManureEM\n", + "NH3_RiceEM\n", + "NH3_SavanBurnEM\n", + "NH3_SoilEM\n", + "NOx_LandUseChangeEM\n", + "NOx_SavanBurnEM\n", + "NOx_SoilEM\n", + "NewForLnd\n", + "OCA_LandUseChangeEM\n", + "OCA_SavanBurnEM\n", + "OldForLnd_Prim\n", + "OldForLnd_SndPlant\n", + "OtherLnd\n", + "Population\n", + "Population|Risk of Hunger\n", + "Price|Agriculture|Non-Energy Crops and Livestock|Index\n", + "Price|Agriculture|Non-Energy Crops|Index\n", + "Price|Carbon|CH4\n", + "Price|Carbon|CO2\n", + "Price|Carbon|N2O\n", + "Price|Water\n", + "Primary Energy|Biomass\n", + "Primary Energy|Biomass|1st Generation\n", + "Primary Energy|Biomass|1st Generation|Biodiesel\n", + "Primary Energy|Biomass|1st Generation|Bioethanol\n", + "Primary Energy|Biomass|Energy Crops\n", + "Primary Energy|Biomass|Fuelwood\n", + "Primary Energy|Biomass|Other\n", + "Primary Energy|Biomass|Residues\n", + "Primary Energy|Biomass|Residues|Forest industry\n", + "Primary Energy|Biomass|Residues|Logging\n", + "Primary Energy|Biomass|Roundwood harvest\n", + "SO2_LandUseChangeEM\n", + "SO2_SavanBurnEM\n", + "TCE\n", + "TotalLnd\n", + "VOC_LandUseChangeEM\n", + "VOC_SavanBurnEM\n", + "Water|Withdrawal|Irrigation\n", + "Water|Withdrawal|Irrigation|Cereals\n", + "Water|Withdrawal|Irrigation|Oilcrops\n", + "Water|Withdrawal|Irrigation|Sugarcrops\n", + "Yield|Cereals\n", + "Yield|Energy Crops\n", + "Yield|Non-Energy Crops\n", + "Yield|Oilcrops\n", + "Yield|Sugarcrops\n", + "bioenergy\n", + "total_cost\n", + "Price|Primary Energy|Biomass\n" + ] + }, + { + "data": { + "text/plain": "" + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "message_data.tools.utilities.add_globiom(\n", + " mp,\n", + " sc_clone,\n", + " \"SSP2\",\n", + " private_data_path(),\n", + " 2015,\n", + " globiom_scenario=\"noSDG_rcpref\",\n", + " regenerate_input_data=True,\n", + " #regenerate_input_data_only=True,\n", + " #allow_empty_drivers=True,\n", + " add_reporting_data=True,\n", + " config_file=\"magpie_config.yaml\",\n", + " config_setup=\"MAGPIE_MP00BI00_exp2110\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-16T14:31:09.875880100Z", + "start_time": "2023-10-16T14:20:00.060555200Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 14, + "outputs": [ + { + "data": { + "text/plain": " node land_scenario year emission value unit\n532896 R12_AFR BIO00GHG000 2100 TCE 1153.565 Mt C/yr\n532987 R12_CHN BIO00GHG000 2100 TCE 211.385 Mt C/yr\n533078 R12_EEU BIO00GHG000 2100 TCE 51.079 Mt C/yr\n533169 R12_FSU BIO00GHG000 2100 TCE 108.907 Mt C/yr\n533260 R12_LAM BIO00GHG000 2100 TCE 275.042 Mt C/yr\n533351 R12_MEA BIO00GHG000 2100 TCE 76.391 Mt C/yr\n533442 R12_NAM BIO00GHG000 2100 TCE 124.316 Mt C/yr\n533533 R12_PAO BIO00GHG000 2100 TCE 72.617 Mt C/yr\n533624 R12_PAS BIO00GHG000 2100 TCE 324.909 Mt C/yr\n533715 R12_RCPA BIO00GHG000 2100 TCE 30.069 Mt C/yr\n533806 R12_SAS BIO00GHG000 2100 TCE 582.181 Mt C/yr\n533897 R12_WEU BIO00GHG000 2100 TCE 173.137 Mt C/yr\n561288 R12_AFR BIO00GHG000 2110 TCE 986.762 Mt C/yr\n561379 R12_CHN BIO00GHG000 2110 TCE 204.090 Mt C/yr\n561470 R12_EEU BIO00GHG000 2110 TCE 51.117 Mt C/yr\n561561 R12_FSU BIO00GHG000 2110 TCE 107.176 Mt C/yr\n561652 R12_LAM BIO00GHG000 2110 TCE 214.274 Mt C/yr\n561743 R12_MEA BIO00GHG000 2110 TCE 78.029 Mt C/yr\n561834 R12_NAM BIO00GHG000 2110 TCE 124.822 Mt C/yr\n561925 R12_PAO BIO00GHG000 2110 TCE 75.186 Mt C/yr\n562016 R12_PAS BIO00GHG000 2110 TCE 307.080 Mt C/yr\n562107 R12_RCPA BIO00GHG000 2110 TCE 29.367 Mt C/yr\n562198 R12_SAS BIO00GHG000 2110 TCE 569.786 Mt C/yr\n562289 R12_WEU BIO00GHG000 2110 TCE 176.483 Mt C/yr", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
nodeland_scenarioyearemissionvalueunit
532896R12_AFRBIO00GHG0002100TCE1153.565Mt C/yr
532987R12_CHNBIO00GHG0002100TCE211.385Mt C/yr
533078R12_EEUBIO00GHG0002100TCE51.079Mt C/yr
533169R12_FSUBIO00GHG0002100TCE108.907Mt C/yr
533260R12_LAMBIO00GHG0002100TCE275.042Mt C/yr
533351R12_MEABIO00GHG0002100TCE76.391Mt C/yr
533442R12_NAMBIO00GHG0002100TCE124.316Mt C/yr
533533R12_PAOBIO00GHG0002100TCE72.617Mt C/yr
533624R12_PASBIO00GHG0002100TCE324.909Mt C/yr
533715R12_RCPABIO00GHG0002100TCE30.069Mt C/yr
533806R12_SASBIO00GHG0002100TCE582.181Mt C/yr
533897R12_WEUBIO00GHG0002100TCE173.137Mt C/yr
561288R12_AFRBIO00GHG0002110TCE986.762Mt C/yr
561379R12_CHNBIO00GHG0002110TCE204.090Mt C/yr
561470R12_EEUBIO00GHG0002110TCE51.117Mt C/yr
561561R12_FSUBIO00GHG0002110TCE107.176Mt C/yr
561652R12_LAMBIO00GHG0002110TCE214.274Mt C/yr
561743R12_MEABIO00GHG0002110TCE78.029Mt C/yr
561834R12_NAMBIO00GHG0002110TCE124.822Mt C/yr
561925R12_PAOBIO00GHG0002110TCE75.186Mt C/yr
562016R12_PASBIO00GHG0002110TCE307.080Mt C/yr
562107R12_RCPABIO00GHG0002110TCE29.367Mt C/yr
562198R12_SASBIO00GHG0002110TCE569.786Mt C/yr
562289R12_WEUBIO00GHG0002110TCE176.483Mt C/yr
\n
" + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sc_clone.par(\"land_emission\", filters={\"year\":[2100,2110], \"emission\":\"TCE\", \"land_scenario\":\"BIO00GHG000\"})" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-16T14:51:01.363158200Z", + "start_time": "2023-10-16T14:51:01.162574400Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [], + "source": [ + "sc_clone.set_as_default()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-16T14:47:48.101224600Z", + "start_time": "2023-10-16T14:47:48.022080100Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 9, + "outputs": [ + { + "data": { + "text/plain": "'MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00'" + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sc_clone.model" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-16T14:48:27.832391Z", + "start_time": "2023-10-16T14:48:27.768777Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 10, + "outputs": [ + { + "data": { + "text/plain": "'exp2110_matrix_test'" + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sc_clone.scenario" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-16T14:48:43.500081500Z", + "start_time": "2023-10-16T14:48:43.437548100Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting to process data input from csv file.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp[tmp.columns[i]] = (\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Agricultural Demand\n", + "Agricultural Demand|Energy\n", + "Agricultural Demand|Energy|Crops\n", + "Agricultural Demand|Energy|Crops|1st generation\n", + "Agricultural Demand|Energy|Crops|2nd generation\n", + "Agricultural Demand|Non-Energy\n", + "Agricultural Demand|Non-Energy|Crops\n", + "Agricultural Demand|Non-Energy|Crops|Feed\n", + "Agricultural Demand|Non-Energy|Crops|Food\n", + "Agricultural Demand|Non-Energy|Crops|Other\n", + "Agricultural Demand|Non-Energy|Crops|Other|Waste\n", + "Agricultural Demand|Non-Energy|Livestock\n", + "Agricultural Demand|Non-Energy|Livestock|Food\n", + "Agricultural Demand|Non-Energy|Livestock|Other\n", + "Agricultural Demand|Non-Energy|Livestock|Other|Waste\n", + "Agricultural Production\n", + "Agricultural Production|Energy\n", + "Agricultural Production|Energy|Crops\n", + "Agricultural Production|Energy|Crops|1st generation\n", + "Agricultural Production|Energy|Crops|2nd generation\n", + "Agricultural Production|Non-Energy\n", + "Agricultural Production|Non-Energy|Crops\n", + "Agricultural Production|Non-Energy|Crops|Cereals\n", + "Agricultural Production|Non-Energy|Livestock\n", + "BCA_LandUseChangeEM\n", + "BCA_SavanBurnEM\n", + "Biodiversity|BII\n", + "Biodiversity|BII|Biodiversity hotspots\n", + "CH4_LandUseChangeEM\n", + "CH4_SavanBurnEM\n", + "CO_LandUseChangeEM\n", + "CO_SavanBurnEM\n", + "Costs|TC\n", + "CrpLnd\n", + "Emissions|CH4|AFOLU\n", + "Emissions|CH4|AFOLU|Agriculture\n", + "Emissions|CH4|AFOLU|Agriculture|Livestock\n", + "Emissions|CH4|AFOLU|Agriculture|Livestock|Enteric Fermentation\n", + "Emissions|CH4|AFOLU|Agriculture|Livestock|Manure Management\n", + "Emissions|CH4|AFOLU|Agriculture|Rice\n", + "Emissions|CH4|AFOLU|Biomass Burning\n", + "Emissions|CH4|AFOLU|Land\n", + "Emissions|CH4|AFOLU|Land|Grassland Burning\n", + "Emissions|CO2|AFOLU\n", + "Emissions|CO2|AFOLU|Afforestation\n", + "Emissions|CO2|AFOLU|Agriculture\n", + "Emissions|CO2|AFOLU|Deforestation\n", + "Emissions|CO2|AFOLU|Forest Management\n", + "Emissions|CO2|AFOLU|Negative\n", + "Emissions|CO2|AFOLU|Other LUC\n", + "Emissions|CO2|AFOLU|Positive\n", + "Emissions|CO2|AFOLU|Soil Carbon\n", + "Emissions|GHG|AFOLU\n", + "Emissions|N2O|AFOLU\n", + "Emissions|N2O|AFOLU|Agriculture\n", + "Emissions|N2O|AFOLU|Agriculture|Livestock\n", + "Emissions|N2O|AFOLU|Agriculture|Livestock|Manure Management\n", + "Emissions|N2O|AFOLU|Agriculture|Managed Soils\n", + "Emissions|N2O|AFOLU|Biomass Burning\n", + "Emissions|N2O|AFOLU|Land\n", + "Emissions|N2O|AFOLU|Land|Grassland Burning\n", + "Emissions|N2O|AFOLU|Land|Grassland Pastures\n", + "Fertilizer Use|Nitrogen\n", + "Fertilizer Use|Phosphorus\n", + "Fertilizer|Nitrogen|Intensity\n", + "Fertilizer|Phosphorus|Intensity\n", + "Food Demand\n", + "Food Demand|Crops\n", + "Food Demand|Livestock\n", + "Food Demand|Microbial protein\n", + "Forestry Demand|Roundwood\n", + "Forestry Demand|Roundwood|Industrial Roundwood\n", + "Forestry Demand|Roundwood|Wood Fuel\n", + "Forestry Production|Forest Residues\n", + "Forestry Production|Roundwood\n", + "Forestry Production|Roundwood|Industrial Roundwood\n", + "Forestry Production|Roundwood|Wood Fuel\n", + "GrassLnd\n", + "LU_CH4\n", + "LU_CH4_Agri\n", + "LU_CH4_BioBurn\n", + "LU_CO2\n", + "LU_GHG\n", + "LU_N2O\n", + "Land Cover\n", + "Land Cover|Cropland\n", + "Land Cover|Cropland|Cereals\n", + "Land Cover|Cropland|Energy Crops\n", + "Land Cover|Cropland|Irrigated\n", + "Land Cover|Cropland|Non-Energy Crops\n", + "Land Cover|Cropland|Oilcrops\n", + "Land Cover|Cropland|Sugarcrops\n", + "Land Cover|Forest\n", + "Land Cover|Forest|Afforestation and Reforestation\n", + "Land Cover|Forest|Forest old\n", + "Land Cover|Forest|Forest old|Other forest\n", + "Land Cover|Forest|Forest old|Primary forest\n", + "Land Cover|Forest|Forestry\n", + "Land Cover|Forest|Managed\n", + "Land Cover|Forest|Natural Forest\n", + "Land Cover|Other Land\n", + "Land Cover|Pasture\n", + "Land Cover|Protected\n", + "Landuse intensity indicator Tau\n", + "NH3_LandUseChangeEM\n", + "NH3_ManureEM\n", + "NH3_RiceEM\n", + "NH3_SavanBurnEM\n", + "NH3_SoilEM\n", + "NOx_LandUseChangeEM\n", + "NOx_SavanBurnEM\n", + "NOx_SoilEM\n", + "NewForLnd\n", + "OCA_LandUseChangeEM\n", + "OCA_SavanBurnEM\n", + "OldForLnd_Prim\n", + "OldForLnd_SndPlant\n", + "OtherLnd\n", + "Population\n", + "Population|Risk of Hunger\n", + "Price|Agriculture|Non-Energy Crops and Livestock|Index\n", + "Price|Agriculture|Non-Energy Crops|Index\n", + "Price|Carbon|CH4\n", + "Price|Carbon|CO2\n", + "Price|Carbon|N2O\n", + "Price|Water\n", + "Primary Energy|Biomass\n", + "Primary Energy|Biomass|1st Generation\n", + "Primary Energy|Biomass|1st Generation|Biodiesel\n", + "Primary Energy|Biomass|1st Generation|Bioethanol\n", + "Primary Energy|Biomass|Energy Crops\n", + "Primary Energy|Biomass|Fuelwood\n", + "Primary Energy|Biomass|Other\n", + "Primary Energy|Biomass|Residues\n", + "Primary Energy|Biomass|Residues|Forest industry\n", + "Primary Energy|Biomass|Residues|Logging\n", + "Primary Energy|Biomass|Roundwood harvest\n", + "SO2_LandUseChangeEM\n", + "SO2_SavanBurnEM\n", + "TCE\n", + "TotalLnd\n", + "VOC_LandUseChangeEM\n", + "VOC_SavanBurnEM\n", + "Water|Withdrawal|Irrigation\n", + "Water|Withdrawal|Irrigation|Cereals\n", + "Water|Withdrawal|Irrigation|Oilcrops\n", + "Water|Withdrawal|Irrigation|Sugarcrops\n", + "Yield|Cereals\n", + "Yield|Energy Crops\n", + "Yield|Non-Energy Crops\n", + "Yield|Oilcrops\n", + "Yield|Sugarcrops\n", + "bioenergy\n", + "total_cost\n", + "Price|Primary Energy|Biomass\n" + ] + }, + { + "data": { + "text/plain": "" + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "message_data.tools.utilities.add_globiom(\n", + " mp,\n", + " sc_clone2,\n", + " \"SSP2\",\n", + " private_data_path(),\n", + " 2015,\n", + " globiom_scenario=\"noSDG_rcpref\",\n", + " regenerate_input_data=True,\n", + " #regenerate_input_data_only=True,\n", + " #allow_empty_drivers=True,\n", + " add_reporting_data=True,\n", + " config_file=\"magpie_config.yaml\",\n", + " config_setup=\"MAGPIE_MP00BI00_OldForSplit\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-19T13:02:56.778310400Z", + "start_time": "2023-09-19T12:51:40.341511700Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00 new-mapping_with-smoothing\n" + ] + } + ], + "source": [ + "sc_clone2.set_as_default()\n", + "print(sc_clone2.model, sc_clone2.scenario)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-19T13:02:56.809581600Z", + "start_time": "2023-09-19T13:02:56.778310400Z" + } + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scen.remove_solution()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "extra = scen.set(\"cat_year\", {\"type_year\": \"cumulative\", \"year\": [2015, 2010]})\n", + "scen.check_out()\n", + "scen.remove_set(\"cat_year\", extra)\n", + "scen.commit(\"remove cumulative years from cat_year set\")\n", + "scen.set(\"cat_year\", {\"type_year\": \"cumulative\"})" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 8, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "KeyboardInterrupt\n", + "\n", + "KeyboardInterrupt\n", + "\n" + ] + } + ], + "source": [ + "scen.solve(\"MESSAGE-MACRO\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-11T14:50:37.754227400Z", + "start_time": "2023-09-11T14:50:27.403030200Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [], + "source": [ + "df = mp.scenario_list()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-12T11:43:23.281436900Z", + "start_time": "2023-09-12T11:43:21.916538800Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [ + { + "data": { + "text/plain": " model scenario scheme \\\n7072 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00_c... baseline MESSAGE \n7073 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00_c... baseline MESSAGE \n\n is_default is_locked cre_user cre_date upd_user \\\n7072 1 0 maczek 2023-09-11 16:34:23.000000 None \n7073 1 0 maczek 2023-09-11 13:19:28.000000 None \n\n upd_date lock_user lock_date \\\n7072 None None None \n7073 None None None \n\n annotation version \n7072 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 2 \n7073 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 ", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
modelscenarioschemeis_defaultis_lockedcre_usercre_dateupd_userupd_datelock_userlock_dateannotationversion
7072MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00_c...baselineMESSAGE10maczek2023-09-11 16:34:23.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...2
7073MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00_c...baselineMESSAGE10maczek2023-09-11 13:19:28.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
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" + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[df[\"model\"].str.contains(\"corr\")]" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-12T11:44:04.887557400Z", + "start_time": "2023-09-12T11:44:04.816399100Z" + } + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df.scheme.unique()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scen.scheme" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df[df[\"scenario\"].str.contains(\"_macro\")]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "model_name = \"ENGAGE_SSP2_v4.1.8.3_T4.5_r3.1\"\n", + "scenario_name = \"baseline_magpie\"" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scen = message_ix.Scenario(mp, model=model_name, scenario=scenario_name)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "import importlib\n", + "importlib.reload(message_data.tools.utilities)\n", + "if scen.has_solution():\n", + " scen.remove_solution()\n", + "add_globiom(\n", + " mp,\n", + " scen,\n", + " \"SSP2\",\n", + " private_data_path(),\n", + " 2015,\n", + " #globiom_scenario=\"magpie\",\n", + " #regenerate_input_data=True,\n", + " #regenerate_input_data_only=True,\n", + " clean=True,\n", + " clean_only=True,\n", + " #globiom_scenario=\"no\",\n", + " #allow_empty_drivers=True,\n", + " add_reporting_data=True,\n", + " config_setup=\"MAGPIE\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "add_globiom(\n", + " mp,\n", + " scen,\n", + " \"SSP2\",\n", + " private_data_path(),\n", + " 2015,\n", + " globiom_scenario=\"noSDG_rcpref\",\n", + " regenerate_input_data=True,\n", + " #regenerate_input_data_only=True,\n", + " #globiom_scenario=\"no\",\n", + " #allow_empty_drivers=True,\n", + " add_reporting_data=True,\n", + " config_setup=\"MAGPIE\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "mps = [\"50\"]\n", + "bis = [\"00\", \"70\", \"74\", \"78\"]\n", + "bis = [\"00\", \"70\", \"74\", \"78\"]\n", + "for mp in mps:\n", + " for bi in bis:\n", + " if bi == \"00\":\n", + " bd = 1\n", + " else:\n", + " bd = 0\n", + " model = f\"MP{mp}BD{bd}BI{bi}\"\n", + " print(model)\n", + " scen = message_ix.Scenario(mp_obj, f\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-{model}\", \"baseline\")\n", + " if scen.has_solution():\n", + " scen.remove_solution()\n", + " if \"bio_backstop\" in scen.set(\"technology\").tolist():\n", + " scen.check_out()\n", + " scen.remove_set(\"technology\", \"bio_backstop\")\n", + " scen.commit(\"remove backstop\")\n", + " #print(scen.par(\"output\", filters={\"technology\":\"bio_backstop\"}))\n", + " del scen" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scen.check_out()\n", + "scen.remove_set(\"technology\", \"bio_backstop\")\n", + "scen.commit(\"remove backstop\")\n", + "scen.par(\"output\", filters={\"technology\":\"bio_backstop\"})" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scen.remove_solution()\n", + "scen.solve(\"MESSAGE-MACRO\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## SCP setup" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "base = message_ix.Scenario(mp, \"ENGAGE_SSP2_v4.1.8.3.1_T4.5_r3.1\", \"baseline\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "#base_clone = base.clone(\"ENGAGE_SSP2_MAGPIE_SCP\", \"baseline_MP00\")\n", + "base_clone = base.clone(\"ENGAGE_SSP2_MAGPIE_SCP\", \"baseline_MP80\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "import importlib\n", + "importlib.reload(message_data.tools.utilities)\n", + "if base_clone.has_solution():\n", + " base_clone.remove_solution()\n", + "add_globiom(\n", + " mp,\n", + " base_clone,\n", + " \"SSP2\",\n", + " private_data_path(),\n", + " 2015,\n", + " #globiom_scenario=\"magpie\",\n", + " #regenerate_input_data=True,\n", + " #regenerate_input_data_only=True,\n", + " clean=True,\n", + " clean_only=True,\n", + " #globiom_scenario=\"no\",\n", + " #allow_empty_drivers=True,\n", + " add_reporting_data=True,\n", + " config_setup=\"MAGPIE\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "add_globiom(\n", + " mp,\n", + " base_clone,\n", + " \"SSP2\",\n", + " private_data_path(),\n", + " 2015,\n", + " globiom_scenario=\"noSDG_rcpref\",\n", + " regenerate_input_data=True,\n", + " #regenerate_input_data_only=True,\n", + " #globiom_scenario=\"no\",\n", + " #allow_empty_drivers=True,\n", + " add_reporting_data=True,\n", + " config_setup=\"MAGPIE\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "base_clone.set_as_default()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df = mp.scenario_list()\n", + "df[df[\"model\"].str.contains(\"SCP\")]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## create budget scenario" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 34, + "outputs": [], + "source": [ + "mode = \"clone\"\n", + "#mode = \"load\"\n", + "\n", + "budget = \"1000f\"\n", + "#budget = \"600f\"" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-13T07:27:28.476248600Z", + "start_time": "2023-09-13T07:27:28.422805300Z" + } + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "if mode == \"clone\":\n", + " scen_budget = scen.clone(model=\"ENGAGE-MAGPIE_SSP2_v4.1.8.3_T4.5_r3.1\", scenario=budget, keep_solution=False,\n", + " shift_first_model_year=2025)\n", + "else:\n", + " scen_budget = message_ix.Scenario(mp, model=\"ENGAGE-MAGPIE_SSP2_v4.1.8.3_T4.5_r3.1\", scenario=budget)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "extra = scen_budget.set(\"cat_year\", {\"type_year\": \"cumulative\", \"year\": [2020, 2015, 2010]})\n", + "scen_budget.check_out()\n", + "scen_budget.remove_set(\"cat_year\", extra)\n", + "scen_budget.commit(\"remove cumulative years from cat_year set\")\n", + "scen_budget.set(\"cat_year\", {\"type_year\": \"cumulative\"})" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "emission_dict = {\n", + " \"node\": \"World\",\n", + " \"type_emission\": \"TCE\",\n", + " \"type_tec\": \"all\",\n", + " \"type_year\": \"cumulative\",\n", + " \"unit\": \"???\",\n", + " }\n", + "if budget == \"1000f\":\n", + " value = 4046\n", + "if budget == \"600f\":\n", + " value = 2605\n", + "\n", + "df = message_ix.make_df(\n", + " \"bound_emission\", value=value, **emission_dict\n", + ")\n", + "scen_budget.check_out()\n", + "scen_budget.add_par(\"bound_emission\", df)\n", + "scen_budget.commit(\"add emission bound\")\n", + "scen_budget.par(\"bound_emission\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scen_budget.remove_solution()\n", + "scen_budget.solve(model=\"MESSAGE-MACRO\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scen_budget.set_as_default()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## run reporting" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 10, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "processing Table: Resource|Extraction\n", + "processing Table: Resource|Cumulative Extraction\n", + "processing Table: Primary Energy\n", + "processing Table: Primary Energy (substitution method)\n", + "processing Table: Final Energy\n", + "processing Table: Secondary Energy|Electricity\n", + "processing Table: Secondary Energy|Heat\n", + "processing Table: Secondary Energy\n", + "processing Table: Secondary Energy|Gases\n", + "processing Table: Secondary Energy|Solids\n", + "processing Table: Emissions|CO2\n", + "processing Table: Carbon Sequestration\n", + "processing Table: Emissions|BC\n", + "processing Table: Emissions|OC\n", + "processing Table: Emissions|CO\n", + "processing Table: Emissions|N2O\n", + "processing Table: Emissions|CH4\n", + "processing Table: Emissions|NH3\n", + "processing Table: Emissions|Sulfur\n", + "processing Table: Emissions|NOx\n", + "processing Table: Emissions|VOC\n", + "processing Table: Emissions|HFC\n", + "processing Table: Emissions\n", + "processing Table: Emissions\n", + "processing Table: Agricultural Demand\n", + "processing Table: Agricultural Production\n", + "processing Table: Fertilizer Use\n", + "processing Table: Fertilizer\n", + "processing Table: Food Waste\n", + "processing Table: Food Demand\n", + "processing Table: Forestry Demand\n", + "processing Table: Forestry Production\n", + "processing Table: Land Cover\n", + "processing Table: Yield\n", + "processing Table: Capacity\n", + "processing Table: Capacity Additions\n", + "processing Table: Cumulative Capacity\n", + "processing Table: Capital Cost\n", + "processing Table: OM Cost|Fixed\n", + "processing Table: OM Cost|Variable\n", + "processing Table: Lifetime\n", + "processing Table: Efficiency\n", + "processing Table: Population\n", + "processing Table: Price\n", + "processing Table: Useful Energy\n", + "processing Table: Useful Energy\n", + "processing Table: Trade\n", + "processing Table: Investment|Energy Supply\n", + "processing Table: Water Consumption\n", + "processing Table: Water Withdrawal\n", + "processing Table: GDP\n", + "processing Table: Cost\n", + "processing Table: GLOBIOM Feedback\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "Starting to upload timeseries\n", + " region variable unit 2025 \\\n", + "0 R12_AFR Resource|Extraction EJ/yr 23.180454 \n", + "1 R12_AFR Resource|Extraction|Coal EJ/yr 7.266278 \n", + "2 R12_AFR Resource|Extraction|Gas EJ/yr 6.834491 \n", + "3 R12_AFR Resource|Extraction|Gas|Conventional EJ/yr 6.834491 \n", + "4 R12_AFR Resource|Extraction|Gas|Unconventional EJ/yr 0.000000 \n", + "\n", + " 2030 2035 2040 2045 2050 2055 \\\n", + "0 20.273657 19.952535 18.933601 20.577608 20.405360 22.329736 \n", + "1 2.423100 1.583713 0.867861 0.184598 0.230745 0.253654 \n", + "2 9.028904 10.612705 12.200300 14.689182 15.898805 18.912043 \n", + "3 9.028904 10.612705 12.200300 14.689182 15.898805 18.912043 \n", + "4 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "\n", + " 2060 2070 2080 2090 2100 2110 \n", + "0 21.929689 22.723112 21.102234 12.027721 6.912506 3.818970 \n", + "1 0.310799 0.160775 0.070950 0.017169 0.030747 0.000000 \n", + "2 18.931974 21.239637 20.509762 11.973180 6.868753 3.817505 \n", + "3 18.931974 18.531374 1.567102 1.153387 0.848893 0.624785 \n", + "4 0.000000 2.708263 18.942660 10.819793 6.019860 3.192720 \n", + "Finished uploading timeseries\n" + ] + } + ], + "source": [ + "# report budget scenario\n", + "from message_data.tools.post_processing.iamc_report_hackathon import report as reporting\n", + "reporting(\n", + " mp,\n", + " scen_budget,\n", + " # NB(PNK) this is not an error; .iamc_report_hackathon.report() expects a\n", + " # string containing \"True\" or \"False\" instead of an actual bool.\n", + " \"False\",\n", + " scen_budget.model,\n", + " scen_budget.scenario,\n", + " merge_hist=True,\n", + " merge_ts=True,\n", + " #run_config=\"materials_run_config.yaml\",\n", + ")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-20T14:53:33.158659800Z", + "start_time": "2023-09-20T14:38:43.624078500Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 13, + "outputs": [ + { + "data": { + "text/plain": "'new-mapping_with-smoothing_primary-forest-constraint_1000f'" + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scen_budget.scenario" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-20T14:55:38.002761800Z", + "start_time": "2023-09-20T14:55:37.940314800Z" + } + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scen = message_ix.Scenario(mp, \"MESSAGE-SCP_R11\", \"baseline_MP00\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "# report baseline\n", + "from message_data.tools.post_processing.iamc_report_hackathon import report as reporting\n", + "reporting(\n", + " mp,\n", + " scen,\n", + " # NB(PNK) this is not an error; .iamc_report_hackathon.report() expects a\n", + " # string containing \"True\" or \"False\" instead of an actual bool.\n", + " \"False\",\n", + " scen.model,\n", + " scen.scenario,\n", + " merge_hist=True,\n", + " merge_ts=True,\n", + " #run_config=\"materials_run_config.yaml\",\n", + ")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scen.set(\"is_test\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df = mp.scenario_list()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df[df[\"model\"].str.contains(\"SCP\")]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "base = message_ix.Scenario(mp, \"ENGAGE_SSP2_MAGPIE_SCP\", \"baseline_MP00\")\n", + "base_solved = base.clone(\"ENGAGE_SSP2_MAGPIE_SCP\", \"baseline_MP00_solved\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "base_solved.solve()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "# report baseline\n", + "from message_data.tools.post_processing.iamc_report_hackathon import report as reporting\n", + "reporting(\n", + " mp,\n", + " base_solved,\n", + " # NB(PNK) this is not an error; .iamc_report_hackathon.report() expects a\n", + " # string containing \"True\" or \"False\" instead of an actual bool.\n", + " \"False\",\n", + " base_solved.model,\n", + " base_solved.scenario,\n", + " merge_hist=True,\n", + " merge_ts=True,\n", + " #run_config=\"materials_run_config.yaml\",\n", + ")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "#scen.read_excel(\"sceniario.xlsx\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "#scen.to_excel" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "import pandas as pd" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df_mag = pd.read_csv(\"C:/Users\\maczek\\PycharmProjects\\message_data\\data\\globiom/magpie_input_MP76BD1BI00.csv\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df_glo = pd.read_csv(\"C:/Users\\maczek\\PycharmProjects\\message_data\\data\\globiom/SSP2_globiom_input_noSDG_rcpref.csv\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df_mag[\"GHGscen\"]\n", + "df_mag[\"BIOscen\"]\n", + "df_mag[\"GHGscen\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df_mag[df_mag.Variable.str.startswith(\"Land Cover\")]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df_glo[df_glo.Variable.str.startswith(\"Land Cover\") & (df_glo.Variable.str.count(\"\\\\|\")==1)][\"Variable\"].unique()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "vars = [#\"Land Cover\",\n", + "\"Land Cover|Cropland|Non-Energy Crops\",\n", + "\"Land Cover|Other Land\",\n", + "\"Land Cover|Pasture\",\n", + "\"Land Cover|Cropland|Energy Crops\",\n", + "\"Land Cover|Forest|Forest old\",\n", + "\"Land Cover|Forest|Afforestation and Reforestation\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df_mag[df_mag.Variable.isin(vars)].groupby([\"Region\", \"GHGscen\", \"BIOscen\"]).sum()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df_mag[df_mag.Variable.isin([\"Land Cover\"])].groupby([\"Region\", \"GHGscen\", \"BIOscen\"]).sum()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df_glo[df_glo.Variable.isin([\"Land Cover\"])].groupby([\"Region\", \"Scenario\"]).sum()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df_glo[df_glo.Variable.isin([vars])].groupby([\"Region\", \"Scenario\"]).sum()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "### modify emulator constraints" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [], + "source": [ + "scen = message_ix.Scenario(mp,\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00_uncorrected\", \"baseline\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-12T15:27:28.137030200Z", + "start_time": "2023-09-12T15:27:24.630401300Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 11, + "outputs": [], + "source": [ + "df1 = scen.par(\"growth_land_up\", filters={\"land_type\":\"OldForLnd\",\"node\":\"R12_EEU\", \"year\":[\"2050\"]})\n", + "df2 = scen.par(\"growth_land_up\", filters={\"land_type\":\"OldForLnd\",\"node\":\"R12_EEU\", \"year\":[\"2090\"]})\n", + "\n", + "df3 = scen.par(\"growth_land_up\", filters={\"land_type\":\"OldForLnd\",\"node\":\"R12_NAM\", \"year\":[\"2060\"]})\n", + "df4 = scen.par(\"growth_land_up\", filters={\"land_type\":\"OldForLnd\",\"node\":\"R12_NAM\", \"year\":[\"2090\"]})\n", + "\n", + "df5 = scen.par(\"growth_land_up\", filters={\"land_type\":\"OldForLnd\",\"node\":\"R12_PAS\", \"year\":[\"2060\"]})\n", + "df6 = scen.par(\"growth_land_up\", filters={\"land_type\":\"OldForLnd\",\"node\":\"R12_PAS\", \"year\":[\"2070\"]})\n", + "df7 = scen.par(\"growth_land_up\", filters={\"land_type\":\"OldForLnd\",\"node\":\"R12_PAS\", \"year\":[\"2080\"]})\n", + "df8 = scen.par(\"growth_land_up\", filters={\"land_type\":\"OldForLnd\",\"node\":\"R12_PAS\", \"year\":[\"2090\"]})" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-12T14:26:50.088412100Z", + "start_time": "2023-09-12T14:26:50.057167200Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 13, + "outputs": [], + "source": [ + "df1[\"value\"] = 0.0019\n", + "df2[\"value\"] = 0.0039\n", + "\n", + "df3[\"value\"] = 0.0034\n", + "df4[\"value\"] = 0.0023\n", + "\n", + "df5[\"value\"] = 0.0021\n", + "df6[\"value\"] = 0.0297\n", + "df7[\"value\"] = 0.0088\n", + "df8[\"value\"] = 0.0143" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-12T14:30:36.093022600Z", + "start_time": "2023-09-12T14:30:36.061779800Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 16, + "outputs": [], + "source": [ + "scen.check_out()\n", + "scen.add_par(\"growth_land_up\", df1)\n", + "scen.add_par(\"growth_land_up\", df2)\n", + "\n", + "scen.add_par(\"growth_land_up\", df3)\n", + "scen.add_par(\"growth_land_up\", df4)\n", + "\n", + "scen.add_par(\"growth_land_up\", df5)\n", + "scen.add_par(\"growth_land_up\", df6)\n", + "scen.add_par(\"growth_land_up\", df7)\n", + "scen.add_par(\"growth_land_up\", df8)\n", + "scen.commit(\"fix forrest constraint\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-12T14:32:05.221078500Z", + "start_time": "2023-09-12T14:32:04.065505800Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 17, + "outputs": [], + "source": [ + "df1 = scen.par(\"growth_land_up\", filters={\"land_type\":\"CrpLnd\", \"node\":['R12_WEU', 'R12_PAO', 'R12_AFR', 'R12_MEA', \n", + " 'R12_LAM', 'R12_RCPA', 'R12_SAS']})\n", + "df2 = scen.par(\"growth_land_up\", filters={\"land_type\":\"CrpLnd\", \"node\":\"R12_WEU\", \"year\":[\"2055\"]})" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-12T14:38:34.032636400Z", + "start_time": "2023-09-12T14:38:33.871687200Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 20, + "outputs": [], + "source": [ + "df2[\"value\"] = 0.026" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-12T14:39:03.935959500Z", + "start_time": "2023-09-12T14:39:03.898154200Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 21, + "outputs": [], + "source": [ + "df1[\"value\"] = 0.0206" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-12T14:39:26.166895500Z", + "start_time": "2023-09-12T14:39:26.104375900Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 22, + "outputs": [], + "source": [ + "scen.check_out()\n", + "scen.add_par(\"growth_land_up\", df1)\n", + "scen.add_par(\"growth_land_up\", df2)\n", + "scen.commit(\"fix cropland constraint\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-12T14:39:44.977825400Z", + "start_time": "2023-09-12T14:39:43.958900Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 31, + "outputs": [], + "source": [ + "scen.solve(model=\"MESSAGE-MACRO\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-12T15:47:21.294914600Z", + "start_time": "2023-09-12T15:41:40.318234400Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 20, + "outputs": [], + "source": [ + "df3 = scen.par(\"growth_land_up\", filters={\"land_type\":\"OldForLnd\",\"node\":\"R12_EEU\", \"year\":[\"2050\"]})\n", + "df4 = scen.par(\"growth_land_up\", filters={\"land_type\":\"OldForLnd\",\"node\":\"R12_NAM\", \"year\":[\"2060\"]})" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-12T15:37:19.744066Z", + "start_time": "2023-09-12T15:37:19.628247400Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 15, + "outputs": [], + "source": [ + "df2 = scen.par(\"growth_land_up\", filters={\"land_type\":\"OldForLnd\",\"node\":\"R12_EEU\", \"year\":[\"2055\"]})" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-12T15:31:25.020306Z", + "start_time": "2023-09-12T15:31:24.938051600Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 13, + "outputs": [], + "source": [ + "df3[\"year\"] = 2055" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-12T15:31:11.709356Z", + "start_time": "2023-09-12T15:31:11.668956600Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 17, + "outputs": [], + "source": [ + "df2[\"year\"]= 2050" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-12T15:31:37.731761Z", + "start_time": "2023-09-12T15:31:37.647046400Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 18, + "outputs": [], + "source": [ + "scen.check_out()\n", + "scen.add_par(\"growth_land_up\", df2)\n", + "scen.add_par(\"growth_land_up\", df3)\n", + "scen.commit(\"fix cropland constraint\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-12T15:31:48.421502200Z", + "start_time": "2023-09-12T15:31:47.366177100Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 22, + "outputs": [], + "source": [ + "df4 = scen.par(\"growth_land_up\", filters={\"land_type\":\"OldForLnd\",\"node\":\"R12_NAM\"})" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-12T15:37:44.159751400Z", + "start_time": "2023-09-12T15:37:44.041331200Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 30, + "outputs": [], + "source": [ + "scen.check_out()\n", + "scen.add_par(\"growth_land_up\", df4.set_index([\"node\", \"year\", \"land_type\"]).shift().reset_index().drop(0))\n", + "scen.commit(\"fix forrest again\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-12T15:41:29.477690900Z", + "start_time": "2023-09-12T15:41:28.143364100Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 33, + "outputs": [ + { + "data": { + "text/plain": "'MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00_uncorrected'" + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scen.model" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-13T07:26:49.127432200Z", + "start_time": "2023-09-13T07:26:49.064948100Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [], + "source": [ + "mode = \"clone\"\n", + "#mode = \"load\"\n", + "\n", + "budget = \"1000f\"\n", + "#budget = \"600f\"" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-20T12:10:56.172808300Z", + "start_time": "2023-09-20T12:10:56.110318400Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [], + "source": [ + "if mode == \"clone\":\n", + " scen_budget = scen.clone(model=scen.model, scenario=scen.scenario+\"_\"+budget, \n", + " keep_solution=False, shift_first_model_year=2025)\n", + "else:\n", + " scen_budget = message_ix.Scenario(mp, model=\"ENGAGE-MAGPIE_SSP2_v4.1.8.3_T4.5_r3.1\", scenario=budget)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-20T12:11:22.993257Z", + "start_time": "2023-09-20T12:10:57.074124400Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [ + { + "data": { + "text/plain": " type_year year\n0 cumulative 2025\n1 cumulative 2030\n2 cumulative 2035\n3 cumulative 2040\n4 cumulative 2045\n5 cumulative 2050\n6 cumulative 2055\n7 cumulative 2060\n8 cumulative 2070\n9 cumulative 2080\n10 cumulative 2090\n11 cumulative 2100\n12 cumulative 2110", + "text/html": "
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type_yearyear
0cumulative2025
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" + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "extra = scen_budget.set(\"cat_year\", {\"type_year\": \"cumulative\", \"year\": [2020, 2015, 2010]})\n", + "scen_budget.check_out()\n", + "scen_budget.remove_set(\"cat_year\", extra)\n", + "scen_budget.commit(\"remove cumulative years from cat_year set\")\n", + "scen_budget.set(\"cat_year\", {\"type_year\": \"cumulative\"})" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-20T12:11:24.095437500Z", + "start_time": "2023-09-20T12:11:22.993257Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 8, + "outputs": [ + { + "data": { + "text/plain": " node type_emission type_tec type_year value unit\n0 World TCE all cumulative 4046.0 ???", + "text/html": "
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nodetype_emissiontype_tectype_yearvalueunit
0WorldTCEallcumulative4046.0???
\n
" + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "emission_dict = {\n", + " \"node\": \"World\",\n", + " \"type_emission\": \"TCE\",\n", + " \"type_tec\": \"all\",\n", + " \"type_year\": \"cumulative\",\n", + " \"unit\": \"???\",\n", + " }\n", + "if budget == \"1000f\":\n", + " value = 4046\n", + "if budget == \"600f\":\n", + " value = 2605\n", + "\n", + "df = message_ix.make_df(\n", + " \"bound_emission\", value=value, **emission_dict\n", + ")\n", + "scen_budget.check_out()\n", + "scen_budget.add_par(\"bound_emission\", df)\n", + "scen_budget.commit(\"add emission bound\")\n", + "scen_budget.par(\"bound_emission\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-20T12:11:25.569954500Z", + "start_time": "2023-09-20T12:11:24.100427Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 9, + "outputs": [], + "source": [ + "#scen_budget.remove_solution()\n", + "scen_budget.solve(model=\"MESSAGE-MACRO\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-20T12:38:01.821675600Z", + "start_time": "2023-09-20T12:11:25.553999600Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 14, + "outputs": [], + "source": [ + "scen_budget.set_as_default()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-20T14:55:52.252811800Z", + "start_time": "2023-09-20T14:55:52.168235Z" + } + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "sc" + ], + "metadata": { + "collapsed": false + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/message_ix_models/model/material/magpie notebooks/plot_LU_carbon_price.ipynb b/message_ix_models/model/material/magpie notebooks/plot_LU_carbon_price.ipynb new file mode 100644 index 0000000000..77b6de01fe --- /dev/null +++ b/message_ix_models/model/material/magpie notebooks/plot_LU_carbon_price.ipynb @@ -0,0 +1,1088 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "outputs": [ + { + "data": { + "text/plain": "", + "application/javascript": "if (typeof IPython !== 'undefined') { IPython.OutputArea.prototype._should_scroll = function(lines){ return false; }}" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import message_ix\n", + "import ixmp\n", + "import matplotlib.pyplot as plt\n", + "mp = ixmp.Platform(\"ixmp_dev\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-27T11:17:32.616052700Z", + "start_time": "2023-09-27T11:17:12.853249500Z" + } + }, + "id": "3e36b5b631702f36" + }, + { + "cell_type": "code", + "execution_count": 10, + "outputs": [], + "source": [ + "# insert model and scenario names here\n", + "models = [\"ENGAGE_SSP2_v4.1.8.3.1_T4.5_r3.1\", \n", + " #\"MESSAGEix-MAgPIE_Forest-test\", \n", + " #\"MESSAGEix-MAgPIE_Forest-test\", \n", + " #\"MESSAGEix-MAgPIE_Forest-test\"\n", + " ]\n", + "scenarios = [\"EN_NPi2020_1000f\", \n", + " #\"primary_constraint_1000f\", \n", + " #\"primary_constraint_1000f_landScenLo_002\", \n", + " #\"forest_split-primary_constraint_1000f\"\n", + " ]\n" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-20T17:54:56.009268900Z", + "start_time": "2023-09-20T17:54:55.966256800Z" + } + }, + "id": "7f9af46eb7b85b94" + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [], + "source": [ + "#models = [\"MESSAGEix-MAgPIE_Forest-test\"]#, \"MESSAGEix-MAgPIE_Forest-test\"]\n", + "#scenarios = [\"forest_split-primary_constraint_1000f\"]#\"forest_split-primary_constraint_1000f_landScenLo_0.02\", \"forest_split-primary_constraint_1000f_landScenLo_0.03\"]\n", + "#models = [\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\"]\n", + "#scenarios = [\"EN_NPi2020_1000f\"]\n", + "models = [\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\"#, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\", \n", + " #\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\"\n", + " ]\n", + "scenarios = [#\"new-mapping_no-smoothing_1000f\", \"new-mapping_with-smoothing_1000f\", \n", + " \"new-mapping_with-smoothing_primary-forest-constraint_tax_100\", \n", + " #\"1000f_MP00BD1BI00\",\n", + "]\n", + "models = [\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\"]\n", + "scenarios = [\"cumu_cost_test_step5_1000f\"]" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-27T11:17:50.354024700Z", + "start_time": "2023-09-27T11:17:50.275913100Z" + } + }, + "id": "616851e3869badf4" + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [], + "source": [ + "def get_globiom_prices(scen):\n", + " df_land = scen.par(\"land_output\", filters={\"commodity\":\"Price|Carbon|CO2\"})\n", + " df_land = df_land.drop([\"level\", \"time\", \"commodity\", \"unit\"], axis=1)\n", + " return df_land.set_index([\"node\", \"land_scenario\", \"year\"])" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-27T11:17:51.163084300Z", + "start_time": "2023-09-27T11:17:51.131715900Z" + } + }, + "id": "47e8a5295c7add02" + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [], + "source": [ + "def get_globiom_mix(scen):\n", + " df_land = scen.var(\"LAND\")\n", + " return df_land[df_land[\"lvl\"]!=0].drop(\"mrg\", axis=1).set_index([\"node\", \"land_scenario\", \"year\"])" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-27T11:17:51.647940900Z", + "start_time": "2023-09-27T11:17:51.616699800Z" + } + }, + "id": "c074eb254294a384" + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [], + "source": [ + "def calc_prices(scen):\n", + " df_prc = get_globiom_prices(scen)\n", + " df_mix = get_globiom_mix(scen)\n", + " df_prc = df_mix.join(df_prc)\n", + " df_prc[\"cost\"] = df_prc[\"lvl\"] * df_prc[\"value\"]\n", + " df_prc_sum = df_prc.groupby([\"node\", \"year\"]).sum()\n", + " return df_prc_sum" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-27T11:17:52.059027400Z", + "start_time": "2023-09-27T11:17:52.023117200Z" + } + }, + "id": "8a0665c807be6bde" + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [], + "source": [ + "def plot_globiom_prices(df_glob, df_msg, ax):\n", + " legend = []\n", + " for reg in df_glob.index.get_level_values(0).unique():\n", + " df_glob.loc[reg].plot(y=\"cost\", ax=ax)\n", + " legend.append(reg)\n", + " ax.set_xticks([i for i in range(2020,2111, 10)])\n", + "\n", + " df_msg[\"lvl\"] = df_msg[\"lvl\"] / 3.310036812\n", + " df_msg.plot(x=\"year\", y=\"lvl\", ax= ax, linestyle=\"--\", color=\"black\", legend=False)\n", + " legend.append(\"PRICE_EMISSION\")\n", + " ax.legend(legend)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-27T11:17:52.539309100Z", + "start_time": "2023-09-27T11:17:52.508064Z" + } + }, + "id": "ddc5345118d19091" + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00 cumu_cost_test_step5_1000f\n" + ] + }, + { + "data": { + "text/plain": "
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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "for m,s in zip(models, scenarios):\n", + " print(m, s)\n", + " scen = message_ix.Scenario(mp, m, s)\n", + " df_glob = calc_prices(scen)\n", + " df_tce_price = scen.var(\"PRICE_EMISSION\")\n", + " fig, ax = plt.subplots(figsize=(10.33, 6))\n", + " plot_globiom_prices(df_glob, df_tce_price, ax)\n", + " ax.set_ylim((0,4000))\n", + " del scen\n", + " #ax.set_title(f\"{m} - {s}\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-27T11:18:10.842871100Z", + "start_time": "2023-09-27T11:17:52.992065400Z" + } + }, + "id": "2573f378ce63adde" + }, + { + "cell_type": "code", + "execution_count": 12, + "outputs": [], + "source": [ + "fig.savefig(\"carbon_prices_plot_globiom.svg\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-20T17:56:33.108389900Z", + "start_time": "2023-09-20T17:56:32.997671300Z" + } + }, + "id": "fee3a0df28f3a6f8" + }, + { + "cell_type": "code", + "execution_count": 10, + "outputs": [ + { + "data": { + "text/plain": "False" + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scen.has_solution()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-20T14:55:14.455973500Z", + "start_time": "2023-09-20T14:55:14.331058Z" + } + }, + "id": "187c5c060fc88813" + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [], + "source": [ + "import gdxpds\n", + "import matplotlib.pyplot as plt" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-02T12:11:46.417648500Z", + "start_time": "2023-10-02T12:11:40.952642400Z" + } + }, + "id": "736eb42c45d5161e" + }, + { + "cell_type": "code", + "execution_count": 1, + "outputs": [], + "source": [ + "#file = \"MsgOutput_MESSAGEix-GLOBIOM_1.1-R12-MAGPIE-MP00BD1BI00_new-mapping_with-smoothing_1000f\"\n", + "#file = \"MsgOutput_MESSAGEix-MAgPIE_Forest-test_primary_constraint_1000f_landScenLo_002\"\n", + "file = \"MsgOutput_MESSAGEix-GLOBIOM_1.1-R12-MAGPIE-MP00BD1BI00_new-mapping_with-smoothing_primary-forest-constraint_tax_1000\"\n", + "#file = \"MsgOutput_MESSAGEix-GLOBIOM_1.1-R12-MAGPIE-MP00BD1BI00_new-mapping_with-smoothing_primary-forest-constraint_1000f\"\n", + "file = \"MsgOutput_MESSAGEix-GLOBIOM_1.1-R12-MAGPIE-MP00BD1BI00_cumu_cost_test_step7_1000f\"" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-02T12:11:36.429920600Z", + "start_time": "2023-10-02T12:11:36.407785100Z" + } + }, + "id": "3eb6618656496c4d" + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [], + "source": [ + "dir = \"C:/Users\\maczek\\PycharmProjects\\message_ix\\message_ix\\model\\output/\"\n", + "dir = \"C:/Users\\maczek\\Downloads/\"" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-02T12:11:36.462738300Z", + "start_time": "2023-10-02T12:11:36.423385700Z" + } + }, + "id": "ad637f993ac4f5ba" + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [], + "source": [ + "df_cum = gdxpds.to_dataframe(f\"{dir}{file}.gdx\", \"EMISS_LU\")[\"EMISS_LU\"]" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-02T12:11:50.340321700Z", + "start_time": "2023-10-02T12:11:46.417648500Z" + } + }, + "id": "474e5cadab13f594" + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [], + "source": [ + "df_cum = df_cum[df_cum[\"emission\"]==\"LU_CO2\"]" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-02T12:11:50.716852400Z", + "start_time": "2023-10-02T12:11:50.342314700Z" + } + }, + "id": "65795a510c265309" + }, + { + "cell_type": "code", + "execution_count": 8, + "outputs": [], + "source": [ + "df_cum = df_cum[df_cum.columns[:-4]]" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-02T12:11:51.055284400Z", + "start_time": "2023-10-02T12:11:50.731406300Z" + } + }, + "id": "e44534a69a00974b" + }, + { + "cell_type": "code", + "execution_count": 9, + "outputs": [], + "source": [ + "df_cum = df_cum[(df_cum[\"type_tec\"]==\"all\") & (df_cum[\"node\"]!=\"World\")]" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-02T12:11:51.126810700Z", + "start_time": "2023-10-02T12:11:51.055284400Z" + } + }, + "id": "49f8a4894a1d4e46" + }, + { + "cell_type": "code", + "execution_count": 10, + "outputs": [], + "source": [ + "# df_emi = gdxpds.to_dataframe(\"C:/Users\\maczek\\PycharmProjects\\message_ix\\message_ix\\model\\output/MsgOutput_MESSAGEix-GLOBIOM_1.1-R12-MAGPIE-MP00BD1BI00_new-mapping_with-smoothing_1000f.gdx\", \"land_emission\")[\"land_emission\"]\n", + "# df_land = gdxpds.to_dataframe(\"C:/Users\\maczek\\PycharmProjects\\message_ix\\message_ix\\model\\output/MsgOutput_MESSAGEix-GLOBIOM_1.1-R12-MAGPIE-MP00BD1BI00_new-mapping_with-smoothing_1000f.gdx\", \"LAND\")[\"LAND\"]\n", + "df_emi = gdxpds.to_dataframe(f\"{dir}{file}.gdx\", \"land_emission\")[\"land_emission\"]\n", + "df_land = gdxpds.to_dataframe(f\"{dir}{file}.gdx\", \"LAND\")[\"LAND\"]" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-02T12:11:56.668307100Z", + "start_time": "2023-10-02T12:11:51.131796700Z" + } + }, + "id": "108b3eaef65d5342" + }, + { + "cell_type": "code", + "execution_count": 11, + "outputs": [], + "source": [ + "df_emi = df_emi[df_emi[\"emission\"] == \"LU_CO2\"]\n", + "df_emi = df_emi.drop(\"emission\", axis=1)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-02T12:11:56.757553800Z", + "start_time": "2023-10-02T12:11:56.668307100Z" + } + }, + "id": "316e1dc219e8fcd5" + }, + { + "cell_type": "code", + "execution_count": 12, + "outputs": [], + "source": [ + "df_land = df_land[df_land.columns[:-4]]\n", + "df_land = df_land[df_land[\"Level\"]!=0]\n", + "df_land = df_land.set_index([\"node\", \"year_all\", \"land_scenario\"])" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-02T12:11:56.906444800Z", + "start_time": "2023-10-02T12:11:56.757553800Z" + } + }, + "id": "f48da74d29f29cff" + }, + { + "cell_type": "code", + "execution_count": 13, + "outputs": [], + "source": [ + "df_emi = df_emi.set_index([\"node\", \"year_all\", \"land_scenario\"])" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-02T12:11:56.937522100Z", + "start_time": "2023-10-02T12:11:56.906444800Z" + } + }, + "id": "e3c0040b092ec5b9" + }, + { + "cell_type": "code", + "execution_count": 14, + "outputs": [], + "source": [ + "df_join = df_land.join(df_emi)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-02T12:11:56.984340900Z", + "start_time": "2023-10-02T12:11:56.937522100Z" + } + }, + "id": "498d645b7df704f2" + }, + { + "cell_type": "code", + "execution_count": 140, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "BIO00GHG200 234.0\n", + "BIO00GHG400 210.0\n", + "BIO15GHG600 203.0\n", + "mix: 234.0\n" + ] + }, + { + "data": { + "text/plain": "
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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(16,9))\n", + "df_plot = df_emi.loc[\"R12_WEU\"]\n", + "legend=[]\n", + "for i in df_plot.index.get_level_values(1).unique():\n", + " #print(i)\n", + " if i in [\"BIO15GHG600\", \"BIO00GHG400\", \"BIO00GHG200\"]:\n", + " df_plot.index = df_plot.index.set_levels(df_plot.index.levels[0].astype(int), level=0)\n", + " df_scen = df_plot.swaplevel(0,1).loc[i]\n", + " df_scen.cumsum().plot(ax=ax)\n", + " print(i, df_scen.loc[:2060].sum()[0].round())\n", + " legend.append(i)\n", + "ax.set_xlim((2025,2110))\n", + "\n", + "df_mix = df_join.loc[\"R12_WEU\"]\n", + "df_mix.index = df_mix.index.astype(int)\n", + "df_mix.cumsum().plot(ax=ax, legend=False, y=\"value\", color=\"black\")\n", + "print(\"mix: \", df_mix.loc[:2060, \"value\"].sum().round())\n", + "legend.append(\"reported CO2 emissions\")\n", + "\n", + "ax.legend(legend, fontsize=14)\n", + "ax.set_title(\"CO2 Emissions WEU\", fontsize=20)\n", + "fig.savefig(\"CO2_Emissions_WEU_cum.svg\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-02T13:35:38.478618700Z", + "start_time": "2023-10-02T13:35:38.078003100Z" + } + }, + "id": "a3defc0b51072d05" + }, + { + "cell_type": "code", + "execution_count": 129, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "BIO00GHG200 234.0\n", + "BIO00GHG400 210.0\n", + "BIO00GHG990 156.0\n", + "BIO15GHG600 203.0\n", + "BIO15GHG990 181.0\n", + "mix: 234.0\n" + ] + }, + { + "data": { + "text/plain": "Text(0.5, 1.0, 'CO2 Emissions WEU')" + }, + "execution_count": 129, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(16,9))\n", + "df_plot = df_emi.loc[\"R12_WEU\"]\n", + "legend=[]\n", + "for i in df_plot.index.get_level_values(1).unique():\n", + " #print(i)\n", + " if i in [\"BIO15GHG600\", \"BIO00GHG400\", \"BIO00GHG200\", \"BIO15GHG990\", \"BIO00GHG990\"]:\n", + " df_plot.index = df_plot.index.set_levels(df_plot.index.levels[0].astype(int), level=0)\n", + " df_scen = df_plot.swaplevel(0,1).loc[i]\n", + " df_scen.plot(ax=ax)\n", + " print(i, df_scen.loc[:2060].sum()[0].round())\n", + " legend.append(i)\n", + "ax.set_xlim((2025,2110))\n", + "\n", + "df_mix = df_join.loc[\"R12_WEU\"]\n", + "df_mix.index = df_mix.index.astype(int)\n", + "df_mix.plot(ax=ax, legend=False, y=\"value\", color=\"black\")\n", + "print(\"mix: \", df_mix.loc[:2060, \"value\"].sum().round())\n", + "legend.append(\"reported CO2 emissions\")\n", + "\n", + "ax.legend(legend, fontsize=14)\n", + "ax.set_title(\"CO2 Emissions WEU\", fontsize=20)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-02T13:24:12.925982Z", + "start_time": "2023-10-02T13:24:12.687657700Z" + } + }, + "id": "b93080974064f3d6" + }, + { + "cell_type": "code", + "execution_count": 138, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "BIO00GHG200 234.0\n", + "BIO00GHG400 210.0\n", + "BIO15GHG600 203.0\n", + "mix: 234.0\n" + ] + }, + { + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(16,9))\n", + "df_plot = df_emi.loc[\"R12_WEU\"]\n", + "legend=[]\n", + "for i in df_plot.index.get_level_values(1).unique():\n", + " #print(i)\n", + " if i in [\"BIO15GHG600\", \"BIO00GHG400\", \"BIO00GHG200\"]:\n", + " df_plot.index = df_plot.index.set_levels(df_plot.index.levels[0].astype(int), level=0)\n", + " df_scen = df_plot.swaplevel(0,1).loc[i]\n", + " df_scen.plot(ax=ax)\n", + " print(i, df_scen.loc[:2060].sum()[0].round())\n", + " legend.append(i)\n", + "ax.set_xlim((2025,2110))\n", + "\n", + "df_mix = df_join.loc[\"R12_WEU\"]\n", + "df_mix.index = df_mix.index.astype(int)\n", + "df_mix.plot(ax=ax, legend=False, y=\"value\", color=\"black\")\n", + "print(\"mix: \", df_mix.loc[:2060, \"value\"].sum().round())\n", + "legend.append(\"reported CO2 emissions\")\n", + "\n", + "ax.legend(legend, fontsize=14)\n", + "ax.set_title(\"CO2 Emissions WEU\", fontsize=20)\n", + "fig.savefig(\"CO2_Emissions_WEU.svg\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-02T13:35:13.039124500Z", + "start_time": "2023-10-02T13:35:12.623213800Z" + } + }, + "id": "d32c1c4001ba1275" + }, + { + "cell_type": "code", + "execution_count": 99, + "outputs": [ + { + "data": { + "text/plain": " Value\nnode year_all land_scenario \nR12_AFR 2025 BIO05GHG000 313.340\n 2030 BIO05GHG000 405.057\n 2035 BIO05GHG000 467.444\n 2040 BIO05GHG000 466.584\n 2045 BIO05GHG000 438.874\n... ...\nR12_CHN 2070 BIO45GHG990 31.567\n 2080 BIO45GHG990 19.381\n 2090 BIO45GHG990 5.939\n 2100 BIO45GHG990 -3.808\n 2110 BIO45GHG990 -3.808\n\n[13104 rows x 1 columns]", + "text/html": "
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Value
nodeyear_allland_scenario
R12_AFR2025BIO05GHG000313.340
2030BIO05GHG000405.057
2035BIO05GHG000467.444
2040BIO05GHG000466.584
2045BIO05GHG000438.874
............
R12_CHN2070BIO45GHG99031.567
2080BIO45GHG99019.381
2090BIO45GHG9905.939
2100BIO45GHG990-3.808
2110BIO45GHG990-3.808
\n

13104 rows × 1 columns

\n
" + }, + "execution_count": 99, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_emi = df_emi[~df_emi.index.get_level_values(1).isin([\"2000\", \"2005\", \"2010\", \"2015\", \"2020\"])]\n", + "df_emi" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-02T12:40:11.755879500Z", + "start_time": "2023-10-02T12:40:11.628813800Z" + } + }, + "id": "549dbfa61e6d5953" + }, + { + "cell_type": "code", + "execution_count": 15, + "outputs": [], + "source": [ + "df_join[\"value\"] = df_join[\"Value\"] * df_join[\"Level\"]" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-02T12:11:57.006473800Z", + "start_time": "2023-10-02T12:11:56.984340900Z" + } + }, + "id": "904a5ffdcc47f767" + }, + { + "cell_type": "code", + "execution_count": 16, + "outputs": [], + "source": [ + "df_join = df_join.groupby([\"node\", \"year_all\"]).sum()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-02T12:11:57.053365400Z", + "start_time": "2023-10-02T12:11:57.006473800Z" + } + }, + "id": "ded9a528ca861c11" + }, + { + "cell_type": "code", + "execution_count": 17, + "outputs": [ + { + "data": { + "text/plain": "" + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
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hKpQzJiX/69iS7Aya2jp5fXPFMa4U6T/ihiXScP6fSfFUs/2RW5yOIyIiMmB0zruVFGrJe+vvTkcRERE/UkFSRMTPPB7LsnUlnDhuOBkJkUc8R/tJioiIHFn9gXbe3lrFpbPTCA3+/M+VBaMTyEyIZJnatsoAMmnBOawZ9U2y971Nzst/cTqOiIjIgDDj9CWUmFSi1z/odBTphW0V+7jt6VzuenY9VftanY4jIv2ACpIiIn62ancdJfUtLMnOOOo5h+4neccz6+lwaz9JERERgBfXl9Hhtl+6jxpjuGJuOisL6yiu08M8MnDMu+5/2RYylUnrfkZZ4Tan44iIiPR7rqAgyifdwMTO7eSvfcfpONJNRbUHuOvZ9Sz+/cd8tKOGN7dUcvZ9H/q6iVmn44mIg1SQFBHxs2U5pcSEB3Pu1BTvgaZqaCj50nkH95NcX9zAb97cHuCUIiIi/Y+1lqU5JcxMj2NiSsyXXr9sbjrGwPJ1X76vivRXwSGhDLvuCTzGcODp6+lob3M6koiISL83bfGt7COK5g9/73QU6aKKxhZ+8sImzrzvQ97aUsW3Tx3LJ/ecwet3ncKE5Bi+vyyPb/49h2qtlhQZslSQFBHxo32tHazYVMEls1IJDwkCa+Gpy+FPC2D3x186/4IZI7lu4Sge/KiQd7SfpIiIDHGby/aRX7mfy4/SZSA1PoKTxyexfF0pbo+erpaBIyVzPDvn/4IJnTvIefyHTscRERHp96Ji4tky8lJm7v+Iij16iLs/q2tq439e3cqpv/mA5etKuG7hKD685zTuOW8ScZEhjE6M4rlvLeI/LpjMxztrOft3H/Hi+jKtlhQZglSQFBHxo1fyymnr9HzeZq5kNVTkgXF5C5MFX2418u8XTGZqaizfX5ZHWUNLgBOLiIj0H8vWlRAW7OLimalHPeeKuemUN7by2a7aACYT6b25i29izbALWFD2dzZ/+orTcURERPq90YvvxmLY8/oDTkeRI9jX2sF9b23nlF+/z6Of7uaSmam89/3T+O+LpzIiJvwL5wa5DN84eQwr7jqZsUlRfPe5Ddzyj3VU79dqSZGhRAVJERE/Wrq2hEkpMUxPi/MeWPMghMXBtz+FxPHwzNWQ/9oXrjm4n6TbY7n96VztJykiIkNSa4ebF9eXcd60FOIiQo563tlTkomLCGFpTmkA04n4x7Sb/0JpUCoj3r6ThtpKp+OIiIj0aykZ48iLPZUplf/kwP4Gp+OIT0u7m79+uIuT/+99fv9eAadNHMFbd5/Kb66YSUZC5DGvHZsUzbJbT+DfFk/iwx01nPO7j3hpg1ZLigwVKkiKiPhJfuU+8kobuSI7A2MM7K+ErS/B7Gtg2Ci44RVImQHPXQebln/h2qzEKH512XTtJykiIkPWW1ur2Nfa+XmXgaMIDwniK7NSeXNLJQ3N7QFKJ+IfkdFxtF/yEPG2kd2P3oT16EE0ERGRY4k+9U5iaWbTq392OsqQ197p4R8rizjlN+/zq9fzmZ0Zz6t3nMSfrpnDuBHRXR4nyGW45ZSxrLjzZLKGR3HXsxu49cl11OzXPtsig50KkiIifrIsp5SQIMOls9O8B9Y9Dp5OmPcN7/cRw+D6FyFzETz/DVj/5Beuv3BGqvaTFBGRIWtZTglp8REsGjP8uOdekZ1Be6eHl/PKA5BMxL/GzTyR3Al3Mbv5M9Ysv9fpOCIiIv3axOwzyA+eTPqOJ3B3djodZ0hyeyzPryvljN9+wH++tIXRw6NYdusiHr9pPtMOdgg7ms52cB/5/27jRkTz/LdP4MfnT+L97TWc87sPeSWvXKslRQYxFSRFRPygvdPDP9eXcfaUZBKiQr2/cOU8CuPOhuFjPz8xLAauWQZjT4eXboM1D31hHO0nKSIiQ1FZQwufFNRy+dx0XC5z3POnpcUxeWQsy9S2VQao+Vf9BxvDs5m55dcUbctxOo6IiEi/dmDOt0i3lWx6f6nTUYYUay1vbK7g3Ps/4vvL8oiPDOGJr8/nuW8tZF5WwpEvcndCaQ58dC88cTH8KgPum+x9j+wIhckgl+HWU8fy2h0nkTk8ijueWc93nsqltkmrJUUGIxUkRUT84L38KuoPtHPFwTZz+a9AUxXMv+XLJ4dGwtXPwsQLYMUP4NPPN2fXfpIiIjIUPb+uFGvh8rnpXb5mSXY6m8oa2Vq+rw+TifQNV1AQqTc+zgETiV12M60tB5yOJCIi0m/NPPsaKkkiZO1fnI4yJFhr+WhHDRf/8VNufTIXay1/uWYOr9x+EqdOSPJuU3SQxwOVm2Hln+HpK+H/suDhM+G9n0NzHcy9CRJGw6t3w18WQf4KOMIKyPHJMTx/6yLuOW8i726r5pzffcRrGysC90OLSECoICki4gdLc0pJiQ3nlPFJ3gNrHoJhWTDurCNfEBwGS56AaZfB2z+FD371r1/IDt1P8l7tJykiIoOcx2NZtq6EE8cNJyMhssvXfWVWGqFBLpatK+nDdCJ9JzElg7JT72O0p4i8R+5wOo6IiEi/FRwSStG4a5javpFdGz9zOs6gllNUz1UPruL6R9dQf6Cde6+YyZvfPYXzp4/0FiKthbpd3hWPy26Ee8fDX0+EN38CtTth+uVw+WPww13w7U/h/F/B19+EK58E64Fnr4bHL4CydV+aOzjIxXdOG8erd55E+rAIbns6l9ueyqVOqyVFBo1gpwOIiAx0Vfta+WB7Nd8+bSxBLgMVG6F4JZzzv+A6xnMfQSHw1YcgOAI++CW0H4Cz/x8Yw4UzUllVWMffPipk/ugEzpycHLgfSEREJIBW7a6jpL6FH5wzsVvXDYsK5ewpyby4voyfnD+Z0GA9aykDz4zTL2fVtrdYWP0cG945m1lnXe10JBERkX5p8gV30Hz/X6h/937GzjjB6TiDzpbyRn771g7ey68mMTqM/3fJVK6cl0FYcBA0lsHujz7/2OfbNiEmFcafDaNPgayTIT7jyIMbA5MvggnnwbrHvQ/lP3QGTP0qnPlT7wrKQ0xIjuGFb5/A3z4q5P53drCqsI7/+co0zp8+sm//I4hIn1NBUkSkl5avK8Vj4Yq5vl+81vqKjLOvOf7FriC4+A8QEgGf/R46WuD8X4PLxX9cMIXcPQ18f1ker915MmnxEX37g4hjjDGPAhcC1dbaab5jCcBzQBZQBCyx1u51KqOISF9ZllNKTHgw505N6fa1l2en89qmCt7dVqU3KGTAmv31+9n1m7WM+uSH1ExZRFJqltORRERE+p24YYmsHnERs6v/SW35HhJTRzkdaVDYVdPEfW/v4LWNFcRFhPCj8yZxw6xoIstWwhsPeAuQdQXekyMSYPTJMPp7MPpUGD7WW2zsqqAQmP9NmHGl9z2wz/4I217xHjvlhxD5+b6UwUEubjt9HGdNTuYHy/L49lO5XDQzlZ9dPJWEqFA//1cQkUDRY8QiIr1grWVZTgkLRieQlRgFzfWwcRnMWAIRw7o2iMsFi38DJ9zpLWa+cgd43N79JK+ZQ6fbcof2kxzsHgfOO+zYj4F3rbXjgXd934uIDCr7WjtYsamCS2alEh4S1O3rTxmfREpsOEtz1LZVBq6w8EiClzxGmG2n8okb8bjdTkcSERHpl9LOvZtgPBSseMDpKANeWUML9yzP4+z7PmR1/h7um1XJmuz3+Pa2G4i8fwIsuwE2LoWEsd4OYLd+4m3DuuTvMO9mSBzXvWLkocJj4Yz/gDtzYeZVsPqv8MAs+OR+6Gj9wqkTU2J44Tsn8P2zJ/DG5grO+d2HvLG5stc/v4g4QwVJEZFeWFu0l6K6ZpZk+1ZHbngKOlu8T3d1hzHedq2n/hjWPwkv3ALuDkYnRvHLr04nV/tJDmrW2o+A+sMOXwI84fv6CeArgcwkIhIIr+SV09bp+bzLQDcFuQyXzU3jwx01VDa2Hv8CkX5q1MRZbJ7xE6a3rWfNU//tdBwREZF+KX3cNPKiFjGxdBmtzU1OxxmQava38T8v5fLje/9EVt7v+DDhF6wNupmv5n+PsNxHIXKYt1h489vwoyK4ZimccDukTD/2tkQ9EZsKl/wRbv0UMhfAO/8Ff8yGvGfB8/lD+SFBLu44czwv334SybHh3PrkOu56dj17D7T7N4+I9DkVJEVEemFpTgnRYcGcPz0FPG5Y+zBknuD9Ra27jIHTfwJn/Qw2L/duDt7ZxkUzU7l2YSZ/+6iQd7dV+f1nkH4r2VpbAeD7POJIJxljbjHG5BhjcmpqagIaUESkt5bllDIxOYYZ6XE9HuOKuRl4LDyfW+rHZCKBN+/Su8iNOoW5u/7EjtwPnY4jIiLSL4WeeDvD2MfG1x9yOsqA0tHexgeP/ZRd957BD3PP4R/BP+fbwS+TMSwCc9LdcP3L8OM9cMMr3vapGfO9LVYDIXkKXLPMmyEyAf75LXjwVCj84AunTR4Zy4u3ncjdZ03gtY0VnP27j3hri1ZLigwkKkiKiPRQU1snr22s4KKZI4kMDYaCd2BvUfdXRx7upO/C+b+B/Ffh2a9BezP/ccEUpoyM5fvL8ihraPFHfBkkrLUPWmuzrbXZSUlJTscREemyHVX72VDSwBXZ6ZietnsCshKjmJ+VwPJ1pVhr/ZhQJLCMy8XYmx+lzgwj8pVbaNqnraNFREQON2XR+ewKGsOILY9iPdrapiua9u1l233nc9qeB0gPb6Fl1o3wtaWYHxXBN96BM/8TxpwKIRHOBh1zKnzzA/jqQ9DSAH+/BJ68DKq2/OuUkCAXd53lXS05IiaMW/6xjruf20BDs1ZLigwEKkiKiPTQaxvLaelwc8XBdq1rHoToFJh8Ue8HX3ALXPxHKHgXnl5CuKdZ+0kOPVXGmJEAvs/VDucREfGrZTklBLsMl85O6/VYV2Sns7v2ADl7VMCRgS0uIYn68/7ESE8V2x651ek4IiIi/Y5xuaif/g2yPMVs/vhFp+P0e7WVJVQ+cCZTWtazZsbPSf9JLvGX3gsTzvXu5djfuFwwYwncvhbO/jmUroW/nAgv3gb7yv912pRU72rJu84czyt55Zzzu494Z6u6ion0dypIioj00HNrSxg3IprZGfFQt8u7QjL76/5raTHnOrjsYdjzGfzjUkZHdWg/yaHlZeAG39c3AC85mEVExK863B5eyC3jrMnJDI8O6/V4i6ePJCo0iKVrS/yQTsRZUxaex5rMm5nX+AY5rz7odBwRERnAjDFBxpj1xphXfd//xhiTb4zZaIz5pzEm/ijXFRljNhljNhhjcgIaugtmnHcTtcRjV/7Z6Sj9WknBJtr/diapnaVsOfVvzP/qnU5H6rqQcDjxTrhzAyy6DTYthd/PgXf/H7TuAyA02MXdZ0/gxdtOJCEqlG/8PYfvLd1AY3OHs9lF5KhUkBQR6YGC6v3kFjew5GCbubUPgysE5t7o34mmXw5LnoDyDfDERVw0LoxrFnj3k3wvX09+DRbGmGeAlcBEY0ypMeZm4FfA2caYncDZvu9FRAaF9/KrqTvQzhXZ6X4ZLyosmAtnpPLapgoOtHX6ZUwRJ827/pfkh0xh4tqfUr473+k4IiIycN0FbDvk+7eBadbaGcAO4CfHuPZ0a+0sa212XwbsibDwSHaOuooZrWvZk5/rdJx+aUfuB0Q/uZgI20LpJUuZecYSpyP1TGQCnPu/3hWTky6Aj38Lv58Nax4Ct7fwOC0tjpdvP4k7zxjHSxvKOef+D3k/X02mRPojFSRFRHpgWU6pr81cOrQ1wfqnYMolEJPs/8kmXwRXPwu1O+DxC/jPUxOYPDKW7y3No1z7SQ4K1tqrrbUjrbUh1tp0a+0j1to6a+2Z1trxvs/1TucUEfGXZTmlJMWEceoE/+19e0V2Os3tbl7bVOG3MUWcEhwSSuw1j2ONYf9TN9DR3uZ0JBERGWCMMenABcDDB49Za9+y1h58emsV4J+nwxww8YI7abMhVL59v9NR+p28954l/aUlNJsImq5dwYQ5pzkdqfeGZcHlj8A334cRk2HFD+BPC2Dry2AtocEuvnfORF78zonER4Ty9SfWUlDd5HRqETmMCpIiIt3U4fbwfG4ZZ0waQVJMmLdtRFsjzL+l7yYdfxZcsxwaigl/8iL+dnEyHZ0e7nhmvfaTFBGRAaV6fyvvb6/msjnpBAf578+RuaOGMSYximU5atsqg0Nq1kR2zvs5Ezvzyfn7j52OIyIiA8/9wD3A0d40+Drw+lFes8Bbxph1xpijvtlhjLnFGJNjjMmpqanpVdjuShiRRl7CucyofZ2G2sqAzt2frX3hAaZ++G3Kg9MJ+9a7ZIyb7nQk/0qbAze8Al9b6t0yael18Oi5ULwagOnpcTx0fTbWwqrCOofDisjhVJAUEemmD7bXUNvUxpLsDLDW2yYiZQZkzO/biUefDNe/CAdqyXzxMh44bxjr9uzl3re0n6SIiAwc/8wtw+2xfmvXepAxhiuyM1hbtJfCGj0NLYPD3Au+wZr4xSwoeYwtn61wOo6IiAwQxpgLgWpr7bqjvP7vQCfw1FGGONFaOwc4H7jNGHPKkU6y1j5orc221mYnJfmv80VXjTjnbiJMO/mv/j7gc/c31uNh5WM/Yt7Gn7I1Yg4pd71LYkqG07H6hjEw4Vy49VO46Pewdw88eg48dx3U7SIjIYLE6FByi/c6nVREDqOCpIhINy3NKSEpJozTJibBnk+heqt3daQxfT95xny44WVob+KslTdw10zL3z7UfpIiIjIwWGtZmlNC9qhhjE2K9vv4l81JI8hlWL6u1O9jizhl6s1/ocw1ksS3bqexTr/ziYhIl5wIXGyMKQKeBc4wxjwJYIy5AbgQuMZaa490sbW23Pe5Gvgn0MdPYPdM1uRsNobPZWzR07S3tTodxzHuzk7W/OlGFu35K2vjzmXy91YQHTvM6Vh9LygY5t4Ad+bCaf8GBe/Cn+ZjXr+Hk9MMuXtUkBTpb1SQFBHphur9rbyXX81X56R528yteRAihsH0ywMXInUW3PgaeNx8t+Quzk+q036SIiIyIOQWN7Cr5oC3y0AfGBEbzqkTkng+t5TOAdbSvLOjnbX3X0VB3idOR5F+JiomntZLHmSYbaDw0a9jPQPr/7dFRCTwrLU/sdamW2uzgKuA96y11xpjzgN+BFxsrW0+0rXGmChjTMzBr4FzgM0Bit59C75DEnvZ+ObjTidxRMuB/Wy87yIW1L3EytQbyL7rWUJCw5yOFVihUXDaj+CuDTDnBlj7CL8su4kDdeXUNWkfbpH+RAVJEZFu+FebubkZ0FgG216F2ddBSERggyRPgZtexwSH8cf2/2Ri5w7tJykiIv3espwSIkKCWDxjZJ/NsSQ7nap9bXy8s7bP5ugLOc/8nHkNr9NYmu90FOmHxs86mdzxdzD7wCesef53TscREZGB649ADPC2MWaDMeavAMaYVGPMwd7gycAnxpg8YA3wmrX2DWfiHt+0Uy5ljyuduLyHhtxDOw21lez53dnMPLCS1ZN+zKJbfo9xDeG3+6NHwIX3wQ2vEN65j5Ncm1hf3OB0KhE5xBD+F0pEpHsObTM3bkQ0rHsMrAfm3exMoMRxcNPrBEXE8WToLzDFK7WfpIiI9FvN7Z28urGCC2aMJDosuM/mOWNSMglRoSzNKemzOfxtT34us3f9hdyok5lz/tedjiP91Pyrf8rG8LnM2Pwr9mw74pZgIiIiX2Kt/cBae6Hv63HW2gxr7Szfx62+4+XW2sW+rwuttTN9H1Ottf/rZP7jcQUFUTn5Jsa7C9i25i2n4wRMxZ7tNP75TEZ3FLBh0f0suOonTkfqPzIXYsNimBe0Q/tIivQzKkiKiHTRF9rMdbbBusdhwnkwLMu5UMNGwdffICQulacjfs3mj1/i/fxq5/KIiIgcxeubKmlq6+yzdq0HhQa7uHR2Gu9sq6L+QHufzuUP7s5OWpffSosJI/O6vwztp9rlmFxBQaTe+DgtJhz3sq/T2nLA6UgiIiL9wozF36KBaNo+/oPTUQJi16ZVBD92LsM8e9l1/pPMOe9GpyP1L64gTPp8TgjdpYKkSD+jv3ZFRLpoWU4JkaG+NnNbX4IDNTD/m07HgthUuGkFwYljeCz0Xl547hHtJykiIv3O0pwSsoZHMi9rWJ/PdUV2Oh1uy4vry/p8rt5a++zPmdi5nYLs/yIxpW+LtTLwJaZkUnLKbxjjKWLDo3c5HUdERKRfiIiKYVva5cxs+pSywm1Ox+lTmz95meTlX8GDi/orX2bKwvOcjtQ/ZS5klHsPhSXlA25veZHBTAVJEZEuaG7v5JW8ci6Y7mszt+ZBGD4OxpzudDSv6BG4bnwNz4gp3Of5DU899nvtJykiIv1GUe0BVu+u54rsDIwxfT7fpJRYZqTHsTSnBGttn8/XU3u2b2D2zj+xPvJE5i7+htNxZICYecZVrEq6goU1y8h771mn44iIiPQL4y64GzcuSt4YvHst57z2EBPevpGaoBHwjbfJmpztdKT+K2MBBstk93byK/c7nUZEfFSQFBHpghWbKjnQ7mbJvAwoy4XStTDvm9Cf2qpFJhD+9VfYN3wm32v4JW88/YDTiURERABYvq4Ul4HL5qQHbM4rsjPIr9zP5rJ9AZuzO9ydnbQsu5VWE0rG9X9Vq1bplllff4BdQaPJ+OiH1JbvcTqOiIiI45JSs8iLO51pVS+zv7He6Th+t+qpn5G99gcUhE0h4Y73SE4f63Sk/i09G2uCmOvaznq1bRXpN/RXr4hIFyzNKWF0YhTZo4bB2ochJApmXe10rC8Lj2P4ra+yO2YOFxT8jLUv/dnpRCIiMsS5PZbnc0s5ZUISKXHhAZv34pmphAW7WLauJGBzdsfaZ/+XSZ3b2DnnpySmZDodRwaY8Igogi9/hAjbSsUTN+Jxu52OJCIi4ri4M75LtGlhy6t/dDqK33jcblb95VYW7ryP3OhTGHP3m8QNS3Q6Vv8XGgUp01kUUkBucYPTaUTEJ9jpACIi/d3u2gOs2V3PPedNxDTXw6blMPtaCI9zOtqRhUaRftsrbL1/MXNy/43c0AjmnH+T06lERGSI+qSglorGVv7zwikBnTcuIoRzp6bw4voy/m3xZMJDggI6/7EU79jArJ1/YH3UCcy98Ban48gANWryXFZP+xELtvycDb+9gLbwEVhjABfWuLAYMN6vvccO/d54P/vOO/i198Ng+fx771hffB3jIigmiVMvvC4gbZhFRES6Yvysk9n62jQyC/6Bu/PfCAoe2G99t7U2s/lP17Bw/3usTrqc7G/9bcD/TIFkMhcyo/JxfrSnxukoIuKjf8FERI5jWU4JQS7D5XPSYf1fwN0G87/pdKxjCo+IYswdL7PjgfOYvur75IVFMPOMq5yOJSIiQ9DSnBKGRYZw5uQRAZ97SXYGL+eV89bWKi6emRrw+Y/E3dlJ87Jv02ZCybhWrVqld+Zf9j1WV21hbO17mOateMuLni98/tfX1uItNXoIMv7ZW7VgzFTGTZ3rl7FERET8oTX7VlJX3k7uO08y57wbnY7TY/sa6ij586XMbc9j1Zg7WXDtz/R7Y3dlLCBs9V+J2ptPbdMpJEaHOZ1IZMhTQVJE5Bg63R6ezy3ltAlJjIgOgbWPQNbJMGKy09GOKzI6jrTbXqXoj+cy6cPb2RwSwbSTL3E6loiIDCF7D7Tz9pYqrlmYSVhwD1colm+A6BEQ2/2C4gljh5MWH8GynJJ+U5Bc+9wvWNixlZw5vyI7dZTTcWSAMy4XC25/rGcXWwvWc5wP7znW48Z6PFjrweNxU7JtLWPe/jqNJVtBBUkREelHZp55NeWrfk7Eur/BAC1I1pQXsf/hS5jgLmHtnF+y8JLvOB1pYMpcCEC2azvrixs4e0qyw4FERI9ViIgcw8c7a6na18YV2Rmw4w1oLIH5A6e1Wmz8cJJufZWyoDTGvPNN8le/5XQkEREZQl7aUEa728MVczN6NkDrPnjsfHj4bNhf2e3LXS7D5XPT+aSglrKGlp5l8KOSgk3M2vF7NkQuYu6F33I6jgx1xoArCIJCIDgMQiK8+y2FxXi3JogYBpEJEJWIiUnGFTeSoPg0QhIySZp6CgBt1Tsd/iFERES+KCg4mOLx1zG5Yys7cj90Ok637cnPxf3gWSS7K8k/42HmqRjZc7Gp2LgM5rl2kFu81+k0IoIKkiIix7Q0p4ThUaGcMWkErHkQYtNg4mKnY3VLfGIKsbe8Sq0rkbQV17Nz/UdORxIRkSFiaU4p09JimZIa27MBtr4IHc3QVAVPXwntB7o9xOVz07EWnl9X2rMMfuLu7KTpuVtoN6GkqVWrDHAx8Uk0EI2rocjpKCIiIl8y9YLbaLIR7Pvg905H6Zb8NW8T9+xFhNBOxVefZ/qpX3U60oBnMheyIKSA3KJ6p6OICCpIiogcVV1TG+9sq+LS2WmE7t0JhR9A9tchaOB1u05MySDs5lfY74ol6aWr2b1ltdORRERkkNtc1sjWin0sye7h6kiADc/A8HFw5ZNQuRGW3wwed7eGyEiI5ISxw1m2rgSPxz/75vXE2qW/YnLHVrbP+neSUrMcyyHiL9XBqUQ2FTsdQ0RE5Eti4hLYnHIJMxvfp7pst9NxumT9W0+S9drV7DextF7/JuNmnuR0pMEhYwHDPXXUlhXQ6fY4nUZkyFNBUkTkKP65vowOt/W2a137MASFwpwbnI7VY8npY+H6l2knlJhlSyjemed0JBERGcSWryslNNjV870b63dD8Wcw82qYeB6c93+w43V489+6PdSS7AxK6ltYtbuuZ1l6qbRgMzO3P0BexAKyL/62IxlE/K0pMoPE9jKnY4iIiBxR5vnfw4WHXa/d53SU41q97F5mfHo7xSGjifr2O6SNmex0pMHDt4/kNPc28iv3OxxGRFSQFBE5AmstS3NKmJkRz8R4Cxuehqlfhegkp6P1SuroSbR+7Z8YLGFPXUp50XanI4mIyCDU2uHmn+vLOHdqCvGRoT0bJO9ZwMDMq7zfL7gFFn4HVv8VVv21W0OdNy2FmPBglucEvm2rx+1m33PfosMEM/Lav6lVqwwa7XFZJNsaWlud359VRETkcKlZE8mLPpkp5c/T3NTodJwjsh4PKx++mwVbfs7myPmkf/cdEkakOR1rcBkxBU9oDNmuHazbo30kRZymv4ZFRI5gY2kjO6qauDI7AzY+B+1NMP8Wp2P5ReaEWTRevoxwWuGJi6gqK3Q6koiIDDLvbKuisaWDJdnpPRvA44G8Z2D0KRB3yBjn/A9MvADe+DHkr+jycOEhQVw0M5UVmyvY19rRs0w9tGbp/zGlYzP5M/+NEWmjAzq3SF8KSRxLkLFU7tnhdBQREZEjijjlduI4wKYVDzod5Us62ttY+/trWFT6KGviFzP1e68SGR3ndKzBxxWEyZjHwuCd5BarICniNBUkRUSOYGlOCeEhLi6ckQJrHoTUOZA+1+lYfjNm2gKqLn6GWM8+Wh+5iNqqwK8YERGRwWtpTimpceGcMDaxZwMUr4SGPTDra1887gqCyx6C1Fnw/M1Qvr7LQy7JzqC1w8OreRU9y9QDpbu2MCP/fvIi5jPvktsCNq9IIESnTgBgb2m+w0lERESObNK8s9kZPJ6R+Y/hcXdvH/K+dGB/A1t/dyHzG1awMuMbzLvzKYJDethVRI7LZCxkLMVs36P3vkScpoKkiMhhWtrdvLyhnMXTRhJb8RnU7hg0qyMPNWHOqZQsfoIkdw37/nYBjXVVTkcSEZFBoLyhhY931nD53HSCXKZng+Q9DaHRMPmiL78WGgVXPweRifD0ldBQ0qUhZ6bHMSE5mqU5XTu/tw62au00QYy89kG1apVBZ8Qo7/5WLVUFDicRERE5MuNy0Tjzm2R6ytj04fNOx8HjdpPz8l/Z/9u5TGtey+qp/8mim3+r3xP7WuYCXFhGNG6iZn+b02lEhjT9aycicpg3t1Syv62TK7IzYM1DEDkcpl7qdKw+MXnBuew68yHS3GVU/eVC9jfWOx1JREQGuBdyS7EWLp+b0bMB2pthy0sw5RJv8fFIYpLhmqXQ0QpPL4HW4+8LZIxhSXYGG0oa2Fm1v2fZumHtsl8zpX0T+TN/olatMijFJ6bSTBhm726no4iIiBzVzHNvpJoEXKv/7GiO/NVvUfDLhWTn/oimoDi2n/8sC674gaOZhoy0uVjjYq5rh9q2ijhMBUkRkcMszSkhMyGSBcOaYPsKmHMDhIQ7HavPTD/lErad8kdGd+yi5I8X9dvN3kVEpP/zeCxLc0pZNGY4mcMjezZI/qvQvh9mXn3s80ZMhiv/7u1ksPQGcB9/b8ivzE4j2GVYtq5v2zWVFW5j+rbfsTF8HvMuub1P5xJxinG5qAxKJWJ/sdNRREREjiokNIxdo7/G9Lb17N6yOuDzl+/OZ929lzDp9SsY1lnDmpn/w5h/W8uUhecFPMuQFRaDHTGNeSpIijhOBUkRkUMU1zXz2a46rpibjiv3Me/B7K87GyoAZp15FRsX/JaJ7VvY9YdLaG054HQkEREZgNYU1VNc38ySeek9H2TD0xCXCaNOPP65Y06DC++Hwvfhte+Btcc8PTE6jDMmjeCF3FI63J6eZzwGj9tNw7O34MbFiGv+qhZcMqg1RqST0Kb9mEREpH+bcuGdtNhQat55IGBz7m+sZ+Xf7mD44ycxZf9nrMz4JlE/yGP+pXfgCgoKWA7xco1ayGzXLvKK6pyOIjKk6a9jEZFDLF9XgjFw+axEWPcETFwM8T1sOTfAzF18E7mz/4fpbevJ/8NldLSrr76IiHTP0pwSYsKCOW/qyJ4N0FgGhR/AzKugq4W8OdfByd+H3L/DJ7877ulLsjOobWrng+01Pct4HGuX38vU9o1sm/FjUjLG9ckcIv1Fe0wWKZ5KOjqOv0JZRETEKXHDk9mYuJiZ9W9RV9W3D9J0drSzetm9dPxuJosq/s7G+DPY981VLLr5XiKj4/p0bjmGjAVE0EpHeV6fPZgoIsfX5YKkMSbDGPO+MWabMWaLMeYu3/EEY8zbxpidvs/DDrnmJ8aYAmPMdmPMuX3xA4iI+IvbY1m+rpRTxicxsuR1aKmH+bc4HSug5n3ldlZP+XdmNa9k4++X4O7sdDqSiIgMEPtbO1ixqYILZ6YSEdrDp743PgdYb0GyO07/D5h2Gbz7M9j8/DFPPW1iEkkxYSzNKelZxmMo353P9K2/ZWP4XOZdeqffx5cvM8Y8aoypNsZsPuTYTGPMSmPMJmPMK8aYWCczDmZBiWMJNW6qSgudjiIiInJMI8+9mzDTwY7Xft9nc2z68AVKfzmXBVt+TmVIJjsveYV5dy8lOX1sn80pXZS5EIBpnu1sq9jncBiRoas7KyQ7ge9baycDC4HbjDFTgB8D71prxwPv+r7H99pVwFTgPODPxhitRxeRfuvTglrKG1tZMjcdVv8NEifC6FOcjhVwC5bcw6pxdzO36QNy/3ANHrfb6UgiIjIAvLaxgtYOD0uye9iu1VrIewYyFsLwbr5p43LBJX/2XvvPb0PxqqOeGhzk4quz03gvv5rq/a09y3oEHrebvc/cggcXI772N7VqDZzH8f69eaiHgR9ba6cD/wR+GOhQQ0VUincVcH1JvsNJREREji1zwizyIuYzvvg52lqb/Tr2nm3ryPvVWUx//yZCbBu5Cx9g8k8+ZvzsofeeUr8Vl05nTBrZru3k7tE+kiJO6fJfydbaCmttru/r/cA2IA24BHjCd9oTwFd8X18CPGutbbPW7gYKgPl+yi0i4ndLc0qIjwzh7PgSqNgA878JxjgdyxELr/1vVmZ+i3mNb7D2z1/HetTOQkREjm1pTgnjR0QzKyO+ZwOU5ULtDph1dc+uDwmHq56GuDR45mqo23XUU6/ITsftsby4vqxncx3BmufvY2p7Hlun30NK5ni/jSvHZq39CKg/7PBE4CPf128DlwU01BAyPHMyAM2VOx1OIiIicnyuRbeTSAN5rz/il/H21lSw+o83kfbsWYxu3cKqcXeT+KMNzDnvRj2c1g8Fj1rI/KCd5BY3OB1FZMjq0b+MxpgsYDawGki21laAt2gJjPCdlgYc2gep1Hfs8LFuMcbkGGNyamr6Zh8XEZHjaWhu560tVXxlVhqh6x6B0Jjut4sbZBbe+CtWjryWBXUvsvrB21WUFBGRoyqo3k9ucQNLsjMwPX2YJ+9pCA6HqZf2PEjUcLhmuffrp66A5sPrVF7jRsQwJzOeZTmlWGt7Pp9PedF2pm+5l01hs5n31e/2ejzptc3Axb6vrwCOuiG4/h7tncTU0bTZEDx1atkqIiL937STLmK3axSJmx7u1Xscba3NrHryvwj60xzm1rzIuqSv0PmdHBZe+9+EhUf6MbH4VcZCkqmjrGiH00lkgKirKmXt765kf+OR/66U7ut2QdIYEw08D3zXWnushstHeifiS3/tW2sftNZmW2uzk5KSuhtHRMQvXtpQTrvbw9VTwmHLP2HW1yAsxulYjjIuFwu/+QdWJ17GwsqnWPXYPU5HEhGRfmpZTinBLsNXZn/p+cOu6WyDTcth0gUQHte7MMPHeldKNpbAs9d4xz6CJdkZ7KxuYkNJQ6+msx4P9c/cgsWQqFat/cXX8W4xsg6IAdqPdqL+Hu0dV1AQVUHJhO/f43QUERGR4zIuFzVTv84YTxFbVr7W7eutx0PuG49T+3+zWFhwP7sjplF21TssuP0xEkb08PdgCZzMBQCk7d/o160bZPAq/PR55jW+wc5V3f/3Qo6sW38tG2NC8BYjn7LWvuA7XGWMGel7fSRQ7TteyhefRE0HynsXV0Skbzy3toRpabFMLH8B3O0w7xtOR+oXjMvFvG8/xJr4xSwqeYhV//ip05FERKSf6XB7eD63jNMnjSApJqxng+x4A1obvA8E+cOoRfCVv0DxZ/DSbd79KQ9zwYyRhIe4WJpT2qup1jx/H9PaNrBl2g8ZOWpir8YS/7DW5ltrz7HWzgWeAY7ev1d6bW94OnGtvfvfkYiISKDMWPxN6oml89M/deu6nes/YtsvT2bOqrvoMGFsOv0xZv7obUZNnttHScXvRkzFHRzFXNd2cvc0OJ1GBgB31TYA2krzHE4yeHS5IGm8vZceAbZZa+875KWXgRt8X98AvHTI8auMMWHGmNHAeGBN7yOLiPjX5rJGtlbs46o5I2HtozDmdEia4HSsfsMVFMTc2//BupgzWLjrAVY/9yunI4mISD/ywfYaapvaWJJ91K6Yx7fhGYgZ6b0H+8v0y+GM/4RNy+D9X3zp5ZjwEBZPH8kreeW0tLt7NEXFnu1M2/wbNoXNZv5ld/c2sfiJMWaE77ML+A/gr84mGtzaYjJJcVfgcau9v4iI9H/hEVFsz1jCjAOrKCnYdNzzq0p3sfZ3Sxj/0kUkd5SweupPSf/JOqaf+tUApBW/CgrGZGQzL2gn64v3Op1GBoDIRu8+6eF1Wx1OMnh0Z4XkicB1wBnGmA2+j8XAr4CzjTE7gbN932Ot3QIsBbYCbwC3WWt79pe+iEgfWpZTQmiwi0sjN8L+cph/i9OR+p2g4GBm3PEs6yNPYMG2X7Lmn793OpKIiPQTy3JKSIwO47SJPWx32VQDBW/DjCXgCvJvuJO/D7Ovg49+Deuf+tLLS7IzaGrr5I0tFd0e2no81D79LQCGX61WrU4xxjwDrAQmGmNKjTE3A1cbY3YA+Xi79DzmZMZBL2EsUaaNmqoSp5OIiMhhjDFBxpj1xphXfd8nGGPeNsbs9H0edpTrzjPGbDfGFBhjfhzY1H1v/AV30UkQ5W/cd9RzmpsaWfnI94l9aCEzGt5jZer1hN69gQVXfJ/gkNAAphV/cmUuYqIpZmtRmdNRZABIafXuk57cUuBwksGjy381W2s/sdYaa+0Ma+0s38cKa22dtfZMa+143+f6Q675X2vtWGvtRGvt633zI4iI9Fxrh5sXN5Rz3tQUovIehbhMmHCu07H6pZDQMKbc+Twbw7OZu+GnrHvtYacjiYiIw2r2t/FefjWXzUkjJKiHBblNy8DTCTP91K71UMbAhb+DMafBK3dC4YdfeHnB6ARGDY9k6drut5tc+8L9TG9bz+apPyA1S61anWKtvdpaO9JaG2KtTbfWPmKtfcBaO8H38WNrj9CzV/wmMmUcALV7tjmcREREjuAu4NB/oH8MvGutHQ+86/v+C4wxQcCfgPOBKXgf9JkSgKwBk5iSSV78WUyveY3G+povvOZxu1n74h9puncWi0oeZmvsidTd9CmLbvkDMXEJDiUWv8lcQBAegirW0d6p7g5ydI17axlBPXuJIdVWsb+x/vgXyXHpMV4RGdLe3lpFY0sHN45rhqKPYd7N/l+dMYiEhUcy/o4X2R42jRlr7mHD2087HUlERBz04voyOj2WK7LTez5I3tOQOhtGTPJfsEMFhcCSv8PwcfDcdVCd/6+XjDFcPiedlYV1FNc1d3nIyuKdTNn0azaHzWLeZd/ri9QiA0ZCuvd/u00VOxxOIiIihzLGpAMXAIc+TXwJ8ITv6yeArxzh0vlAgbW20FrbDjzru25QSTjrbiJNG9te+8O/jm35bAWFv5jHvA3/TkNwIvmLlzP3+y/q4bPBJH0eFhezbD5bK/Y5nUb6sYoduQDsGH4mAKX5a52MM2ioICkiQ9rSnBLS4iOYXbkcgsNhzvVOR+r3IqJiyLz9FQpDxjHlkzvY9OELTkcSEREHWGtZmlPC7Mx4xo2I6dkglZuhclPfrI48VHgcXLMMgsPg6SugqfpfL102Nx1jYPm6rrWbtB4P1U9/CxceEq76G64gPcgkQ9uIjHF0WhfuukKno4iIyBfdD9wDHLoMLNlaWwHg+zziCNelAYf+YlTqO/YlxphbjDE5xpicmpqaI53Sb42dvpAtoTPJ2vUUe7ZvYP1vLmDqW1cT7W4kJ/s3jPvJKibNP9vpmOJvYTF0Jk1hrtlB7h7tIylH11js3WM2dr73b9V9ReudjDNoqCApIkNW6d5mPimo5ZqZcZiNz8K0yyFS7Te6Ijp2GCnfeY2S4EzGvfcttq5UV24RkaFmQ0kDO6ubWJKd0fNB8p4BVwhMv9x/wY4mPhO+9qx3z8qnr4R274rI1PgITh6fxPJ1pbg9x+/sufafv2dG6zo2Tfk+qaP7aFWnyAASHBpOtSuJ0MY9TkcREREfY8yFQLW1dl1PLj/CsSP+kmStfdBam22tzU5K6uF+4g7qmP9tUqhl1DOnMqEph1VZtxF/Tx7ZF96ih84GsZCsRcwJKmDDnlqno0h/Vr2VZhvGxOyzaCAaU7XZ6USDggqSIjJkPb/Ou4H118I+gY5mmP9NhxMNLHEJSQz71qtUByUz6o0b2Z7zntORBh1jzN3GmC3GmM3GmGeMMeFOZxIROWjZulLCQ1xcOGNkzwZwd8LGpd69mwP1QFDaXLj8EShfDy98EzxuAJZkp1Pe2Mpnu479pkRlSQGTN/6KLaEzmHf5DwKRWGRAqA9LI6ala6uMRUQkIE4ELjbGFOFtuXqGMeZJoMoYMxLA97n6CNeWAoc+cZYOlPdtXGfMOH0J6yNPYE3CRbR8ey0Lb/wF4ZHRTseSvpa5kCha2bcnz+kk0o9F7SugNGQUrqAgykLHEr9vu9ORBgUVJEVkSPJ4LMvWlXDSmATiNz8B6fMhdZbTsQachBFpRH7jVfa64hn56rXs2viZ05EGDWNMGnAnkG2tnQYEAVc5m0pExKul3c0rG8pZPH0kMeEhPRtk17twoBpm9XG71sNNugDO/QXkvwpv/xSAsyYnExcRwtKc0qNeZj0eqp/6FkF4iL/qQT01L3KI5uhRpHSWY+3xVxmLiEjfs9b+xFqbbq3Nwvt35HvW2muBl4EbfKfdALx0hMvXAuONMaONMaG+618OQOyAcwUFMfue15l/55MkpmQ6HUcCJWMBAJkHNlK1r9XhMNJfjWzbTUP0OAD2x08io6MId2enw6kGPhUkRWRIWlVYR+neFr6dWQz1u2D+LU5HGrCSUrMIuvFlmokg4YUr2ZOf63SkwSQYiDDGBAORDNKnUkVk4HljSwX72zp71651w9MQORzGObA3z8Jve+/9K/8Iax4iPCSIr8xK5c0tlTQ2dxzxkpwX/8CM1hw2Tb6btDGTAxxYpH+zw0YTZw6wt7bK6SgiInJsvwLONsbsBM72fY8xJtUYswLAWtsJ3A68CWwDllprtziUV8T/4tJpj0wh26V9JOXI9tZUMJxGPEneLTpcI6cTYdopK1Tb1t5SQVJEhqSlOSXEhgezoOZ5iBoBUy5xOtKANnLURDqufRE3LmpW/K/TcQYFa20ZcC9QDFQAjdbatw4/zxhzizEmxxiTU1NTE+iYIjJELV1byqjhkSwY3cNWqy17YfsKmH4FBIf6N1xXGAPn/QomnAev3wM73uKK7AzaOz28nFf2pdOrSncxKe+XbAmdzrwr7gl8XpF+LjzZ+/R41Z58h5OIiMjhrLUfWGsv9H1dZ60901o73ve53ne83Fq7+JBrVlhrJ1hrx1pr9Ue+DC7GEJS1yFuQLFZBUr6sfOd6ACLTpgGQMGYOADUFPdmWVw6lgqSIDDmNLR28vrmSGydDUMFbMPdGZ94MHWQyxk2n9drXmHbrE05HGRSMMcOAS4DRQCoQZYy59vDzrLUPWmuzrbXZSUlJgY4pIkNQcV0zKwvruHxOOsaYng2y+QVwt8PMq/0brjtcQXDZI5A8DZbfxDTXHqaMjP1S21br8VD55MFWrX9Tq1aRIxiW7n16vKlCe+uIiIhI/xeUuZBUU8ee3TudjiL9UFPJRgCSx80CIGPibDpsEO1lmxxMNTioICkiQ84reeW0dXq4LvhdMC7IvsnpSING+rhp2gDef84Cdltra6y1HcALwAkOZxIRYfm6EoyBy+am93yQDU/DiCkwcqb/gvVEWDR8bSmEx8PTS7hhWgibyhrZWr7vX6esfelPzGxdy8ZJ3yVtzFTnsor0Y8mjJgLQUbPL4SQiIiIiXZDp3UcyqiqH9k6Pw2Gk36nexj4iGZE6GoCw8EhKg9KJqN/mcLCBTwVJERlyluaUMDM5hMSdz8HkiyA21elIIkdSDCw0xkQa7xKkM/Hu3yEi4hi3x7J8XSknj08iNT6iZ4PU7oSyHO/qyJ6usPSn2JFwzVJoa+Ky/O8xLKiNZetKAKgu282kvF+wNWQa85f8yOGgIv1XWEQ01QwnuHGP01FEREREji95Op1Bkcy0+Wwpb3Q6jfQzMfsKKA/Jwrg+L5/VRY8npaXAwVSDgwqSIjKkbKvYx8bSRn6YthnT2gDzb3E6ksgRWWtXA8uBXGAT3nv2g46GEpEh77NdtZQ3trIku5erI40LZizxX7DeSp4KS54guDafJ+P+wiu5xbR1dFL+5LcIsZ3EXqlWrSLHUxeaSkxzsdMxRERERI4vKBh36hzfPpINTqeRfsR6PKR2FNEYM+4LxzsTp5BCLY11VQ4lGxxUkBSRIWVZTimhQYaFtc9794wapQ6Y0n9Za//LWjvJWjvNWnudtbbN6UwiMrQtzSklPjKEs6ck92wAjxs2Pgdjz4SYFP+G661xZ8KF9zG1eQ3f7XiIZx/6NbNaVpM38S7Sx01zOp1Iv9cUlUliR7nTMURERES6JGz0CUx2FbOlSL+/yOfqKkuIpwmbNOkLxyMzZwFQmp/jQKrBQwVJERky2jrdvLihjG+NriG4ejPM/2b/aBUnIiIyADQ0t/PmlkoumZlKWHAPVwvu/gj2lcGsq/0bzl/m3ojnhO9ybfC7XF11L9tCpjL/yp84nUpkQPAMG00SDezbt9fpKCIiIiLHl7mAIDx07FnjdBLpRyoKNgAQnTH9C8dTJ80DYP+e9YGONKioICkiQ8aynFLqD7RzfdBbEB4H069wOpKIiMiAsXxdKe2dHq7Izuj5IHnPQFgcTLzAf8H8zHXWf7E98Ww6CCFarVpFuiw0aSwA1UX5DicRERER6YL0eVgMo5s3U9nY6nQa6ScOlG4EIGXc7C8cT0zJpI44XNVbnIg1aKggKSJDQnunh798sIsz0jwklrwBs6+D0CinY4mIiAwInW4Pj31axPysBKalxfVskLb9sO0VmHYphIT7N6A/uVyMvfU5Or67hYxx049/vogAEJc2EYDGsh0OJxERERHpgvA4WhMmke3aTm6xOjyIl6smn73EMnxE2pdeKw8bS8L+7Q6kGjxUkBSRIWH5ulLKGlr4z5FrMB43ZH/d6UgiIiIDxptbqihraOHmk0f3fJCtL0FHM8z8mv+C9ZHg4CDihyU6HUNkQBkxajIAbdUFDicRERER6ZrQ0YuY4yogt6jW6SjST8Tu30V56CiM68ulswPDJpPRWUxnR7sDyQYHFSRFZNBr7/Twp/cLmJseTVbRUhh/Ngwf63QsERGRAeORTwoZNTySsyYn93yQDc9AwljImO+/YCLSb0THJbCXWIIaipyOIiIiItIlQaMWEW1aqCvc4HQU6Qesx0NaRxFNseOP+Hpw6nTCTAelBRsDnGzwUEFSRAa9F3K9qyN/NrEI01QJ877hdCQREZEBI7d4L7nFDdx0QhZBLtOzQfYWwZ5PYObVYHo4hoj0e9UhqUQeKHY6hoiIiEjXZCwAIK42l7ZOt8NhxGlVZYXEmBYYMfmIrw8fOweA2oJ1gYw1qKggKSKDWofbwx/fL2BmehxTK16AuAwYd5bTsURERAaMRz7ZTUx4MFdkZ/R8kLznvJ9nXumfUCLSLzVFZpDYXup0DBEREZGuic+kNXwEs8hnc9k+p9OIw6oK1gMQkzH9iK+nj59Fuw2mo1wrJHtKBUkRGdReyC2ldG8LP14Qiin8AObcAK4gp2OJiIgMCCX1zby+qYKvzc8kKiy4Z4NYC3nPQNbJEJ/p34Ai0q90xmWRbOtobWl2OoqIiIjI8RkDmQvJdu1gffFep9OIw1rKNgOQOn72EV8PCQ2jJDiTqL35gYw1qKggKSKD1sHVkTPS41i49xUwQTD7WqdjiYiIDBhPfFaEMYYbTsjq+SDFq2Dvbpj1Nb/lEpH+KThpLC5jqdiz3ekoIiIiIl0SPuYE0k0tuwp3OB1FHBZUk08t8cQnphz1nProCaS2FgQw1eCigqSIDFr/XF9GSX0Ld506CrPhKZh4PsSOdDqWiIjIgLC/tYPn1pawePpIUuMjej5Q3tMQEgWTL/ZfOBHpl2JGTgCgoVQFSRERERkgfPtIuorXOBxEnBZ/YBcVYaOPeY57xFQSaaCuStsU9IQKkiIyKHW6Pfzp/QKmpcVyBquhuQ6yb3I6loiIyICxNKeU/W2d3HzSsf8gO6aOFtjyIky5GMKi/ZZNRPqn5KzJALRW6alxERERGSBSptPhCmdc22bKG1qcTiMO8bjdpHUUcyBu/DHPix41C4Dy7esCkGrwUUFSRAalFzeUs6eumbvOnIBZ9zjEj4IxZzgdS0REZEBweyyPfbqbeVnDmJUR3/OB8l+Dtn0w82q/ZROR/itu+EiaiIC9hU5HEREREemaoBDakmcx17WDXO0jOWRV7NlBpGnDlTzlmOelTZwHwIHi9YGINeioICkig06n28Mf3tvJ1NRYzkpqhKKPYe4N4NI/eSIiIl3x1pZKSve29G51JMCGpyEuA7JO9k8wEenfjKEqKJXIpmKnk4iIiIh0WcTYE5li9rCpsNzpKOKQmsINAMRmTj/mecOSRlJNAkE1WwKQavDRu/MiMui85FsdeeeZ4zG5T4ArGGZd63QsERGRAeORT3aTkRDB2VNSej7IvgoofB9mXKmHgkSGkMaIdBLaypyOISIiItJlQaMWEWw8NO9e7XQUcUhL2SYA0ibMOe65FeFjGb5/R19HGpT0zoCIDCqdbg9/fL+AySNjOWdCHGx4CiZdADHJTkcTEREZEDaUNJCzZy83nTCaIJfp+UAbnwPrUbtWkSGmIzaLFE8VHR3tTkcRERER6Zr0eVgMw+vX09rhdjqNOCCkbjuVJBITl3Dcc5sTJpPuLqG9rTUAyQYXFSRFZFB5ZWM5u2sPcNeZ4zDbXoGWvTD3JqdjiYiIDBiPfLKbmLBglszL6Pkg1kLeM5A+HxLH+S+ciPR7rsQxhBg3VSW7nI4iIiIi0jUR8TTFjmc229lS3uh0GnHAsAO7qA7v2pYlIWkzCDVuSnZs6NtQg5AKkiIyaLg9lj+8V8CklBjOmZICOY/BsNEw+lSno4mIiAwIZQ0trNhUwVXzM4gOC+75QOXroSYfZml1pMhQEz1yAgB1xdscTiIiIiLSdcFZC5nt2sn6ojqno0iAdXa0k95ZSnP8hC6dnzR2LgD1u9b1ZaxBSQVJERk0Xt1YTmHNAe46czyu2u1Q/BnMvVH7VomIiHTRE58VAXDDCVm9GyjvGQgKg6lf7XUmERlYkjInAdBSXeBwEhEREZGuixh7IrGmhcqC9U5HkQAr372VMNOBK3lyl85PGzuNVhuCu2JTHycbfPQuvYgMCm6P5YF3dzIxOYZzp6bAusfBFQKzrnE6moiIyIDQ1NbJM2uKOX9aCunDIns+UGc7bFoOkxZDRLzf8onIwDB85ChabQi2rtDpKCIiIiJdl7EAgJCyNVhrHQ4jgVS3Ow+AYVkzu3R+cEgoJSFZRDeoI0h3qSApIoPCwdWRd545Hpe7FfKehskXQXSS09FEREQGhGU5Jexv7eTmk7q2b8ZR7XwTWuph5tf8E0xEBhTjCqIqKIXw/XucjiIiIiLSdcOyaA5NZELHVsoaWpxOIwHUWrYFgLTxXStIAuyNmUBaWyHW4+mrWIOSCpIiMuAd3DtyQnI0509LgS0vQmsjZN/kdDQREZEBwe2xPPZpEXNHDWN25rDeDbbhGYhOhrFn+CeciAw4e8MziG8tczqGiIiISNcZQ0fqPOaaHeQWNzidRgIotH47ZSaZyOi4Ll/jGTGNYeyjtrK4D5MNPipIisiAt2JTBQXVTdxxxnhcLgPrHoPh4yDrZKejiYiIDAhvb62iuL6596sjD9R6V0hOvwKCgv0TTkQGnLbYUaS4K/C49cS4iIgTjDHhxpg1xpg8Y8wWY8zPfMefM8Zs8H0UGWM2HOX6ImPMJt95OQENL+Kg6PEnkemqYUfBTqejSAANby6kJmJMt66JzZoNQPn2tX0RadBSQVJEBjSPx/KH93YybkQ0i6ePhKqtULIa5t4ExjgdT0REZEB49JPdpA+L4Jwpyb0baNNy8HTCLLVrFRnKTMJoIkw71RVq2yoi4pA24Axr7UxgFnCeMWahtfZKa+0sa+0s4HnghWOMcbrv3Oy+jyvSPwSNWgSAu2ilw0kkUNrbWklzl9ESP6Fb16VNmgdAc/GGPkg1eKkgKSID2uubK9lR1cSdZ44n6ODqyKAwvREqIiLSRXklDawpquemE0cTHNTLPw/ynoaUGZA81T/hRGRAikzxvqFTW7zN4SQiIkOT9WryfRvi+7AHXzfGGGAJ8IwD8UT6r5Ez6HCFMaJhA60dbqfTSACUF24mxLgJSZnSrevihiVSQRKhtVv7KNngpIKkiAxYHo/lgXd3MDYpigumj4T2Zsh7DqZcApEJTscTEREZEB75ZDfRYcEsyU7v3UBVW6EiTw8FiQgJ6RMBOFChdmciIk4xxgT5WrJWA29ba1cf8vLJQJW19mj/UFvgLWPMOmPMLX0cVaT/CAph//CZzDbb2VTW6HQaCYC6wg0ADMua0e1rKyPHM/yAft/tDhUkRWTAemPLYasjt7wAbY2QfZPT0URERAaE8oYWVmyq4Kp5GcSEh/RusLynwRXs3T9SRIa05MzxdNgg3HWFTkcRERmyrLVuX2vWdGC+MWbaIS9fzbFXR55orZ0DnA/cZow55UgnGWNuMcbkGGNyampq/BVdxFHhY05gqtnDxsJyp6NIAHRUbMVtDWnjZ3b72raEyaS7y2htOdAHyQYnFSRFZEDyeCy/f3cnY5KiuHBGqvdgzmOQOBEyFzkbTkREZIB4YmURHmu54YSs3g3k7oSNS2H8ORCV6JdsIjJwBQWHUOUaQei+IqejiIgMedbaBuAD4DwAY0ww8FXguWNcU+77XA38E5h/lPMetNZmW2uzk5KS/BtcxCGRY08kxLhpKFjldBQJgLC92yl3jSQ8Iqrb14amTSfYeCjZntsHyQYnFSRFZEB6a2sl+ZX7ueOMcd7VkRUboSzHuzrSGKfjiYiI9HsH2jp5ZnUx508bSUZCZO8GK3wfmqpg5tX+CSciA97esDTiWkqdjiEiMiQZY5KMMfG+ryOAs4B838tnAfnW2iP+I22MiTLGxBz8GjgH2NznoUX6i4x5AERU5mCtPc7JMtAlNhdSEzm2R9cmjc8GoKFQBcmuUkFSRAYc796RBYxOjOKig6sj1z0GweEw8ypnw4mIiAwQy9eVsq+1k6+fNLr3g214GiKGwYRzez+WiAwKLTGjSO4sx3o8TkcRERmKRgLvG2M2Amvx7iH5qu+1qzisXasxJtUYs8L3bTLwiTEmD1gDvGatfSNAuUWcFzGMhqgxTO7YSuneFqfTSB9qbTlAqqeC9mETenR92ugpNNsw3BWb/Jxs8Ap2OoCISHe9va2KbRX7uG/JTIKDXNDWBBuXwdRLvW+GioiIyDG5PZbHPt3N7Mx45o7q5b2zpQHyX4M510NwmF/yicjAZ4dlEVvTTF1tJcNHpDodR0RkSLHWbgRmH+W1G49wrBxY7Pu6EOj+Zmoig4gnYwFztr3EB3vqet9NRvqtsp15jDWWkNSpPbreFRRESchoYhrzj3+yAFohKSIDjLWWB97ZSdbwSC6e6XtjY/NyaN8Pc29yNpyIiMgA8e62KorqmvnGSWN6P9iWf4K7DWapXauIfC4ieTwA1cXbHE4iIiIi0j1xE04mzjRTtmO901GkD+0tygNg+OieP4PREDeRjPbCAd8VpKG2krqqvt9uQQVJERlQ3t5axdaKfdx+xnjv6kiAnMdgxBTIOOIe6yIiInKYhz/ZTVp8BOdOTe79YHnPQNIkSJ3T+7FEZNCIT58EQFP5ToeTiIiIiHRP0KiFANji1Q4nkb7UUbmVdhtE6phpPR8keRqxHKCqdJf/gjkgf8WfGPbnaeytqejTeVSQFJEBw1rLA+/uZNTwSL4yy7c6snw9VGzwro40xtF8IiIiA8Gm0kbW7K7nphOzPn+4p6dqC6BkNcy8WvdhEfmC5FET8FhDR83AfnNGREREhqCEMRwIGUbqvjxaO9xOp5E+EtGwk/KgNELDwns8RlyWtzt25Y4cf8VyRFTZJxQHZTAsaWSfzqOCpIgMGO9uq2ZL+T5uO33cF1dHBkfAjCXOhhMRERkgHvmkkKjQIJbMy+j9YHnPgHHBjCt7P5aIDCph4VFUm+EENxY5HUVERESke4zhwIhs5pjtbCxtdDqN9JERLYXURY3t1Rjpk7IBaCnZ4IdEzmhrbWZc62Yqhy/o87lUkBSRAeHg6siMhAgunZ3mPdi6DzYth2mXQUS8o/lEREQGgsrGVl7dWMGV8zKJDQ/p3WAeD2x8DsacDrF9+xSliAxMdWFpxDaXOB1DREREpNuixp3IKFc1W3eo/fxg1NzUSKqtoj1hYq/GiY4dRqlJIaxuq5+SBV7BuveJMO2ETzyzz+dSQVJEBoT3t1ezqayRO04fT8jB1ZGblkHHAci+ydlwIiIiA8QTK4vwWMtNJ2b1frCij6GxBGZ9rfdjicig1ByVSVJnudMxRERERLotatxJALTs+tThJNIXSndsACA8dUqvx6qOHE/SgYJej+OUfVvfptO6GDvv3D6fSwVJEen3rLXc/45vdeSctIMHYd1jkDwd0uY6G1BERGQAaG7v5OnVxZw7NYWMhMjeD5j3DITFwqQLej+WiAxK7vjRDKeRxoZ6p6OIiIiIdM/ImXSYUGJr1mGtdTqN+FnDno0AJI6Z1eux2oZPIc1TQXPTwGzvm1C1kl0hE4iJS+jzuVSQFJF+74PtNWwsbeS208Z9vjqyLBcqN0H2jWCMo/lEREQGgufXldLY0sE3Th7d+8HammDryzD1KxAS0fvxRGRQChvh3ZOnumibw0lEREREuik4lPr4aUx1b6OkvsXpNOJnnsottNkQUkdP7fVY4ekzcBlLcX6OH5IF1r6GOsZ27KA+5YSAzKeCpIj0a9Za7n93J2nxEXx1TvrnL6x7FEKiYPoS58KJBIAxJt4Ys9wYk2+M2WaMWeR0JhEZeDwey6OfFjEzI545mcN6P+C2l71t02eqXauIHF1cmndPnsbyHQ4nEREREek+16iFTDVF5BWqBf1gE9lYQGlwBkHBwb0eK3nCPAAad2/o9ViBVrj2TYKNh9jJZwVkPhUkRaRf+3BHDXklDdx2+jhCg33/ZLU2wuYXYPplEB7rbECRvvcA8Ia1dhIwE9ASAxHptvfyq9lde4BvnDQa44/OAhuehmGjIXNh78cSkUErJWsSAB01A3dPHRERERm6EiaeTIhxU7N9pdNRxM+SWwvZGzXGL2ONzBzPfhsBVZv9Ml4gte54jxYbyri5pwdkvi4XJI0xjxpjqo0xmw859t/GmDJjzAbfx+JDXvuJMabAGLPdGNP3u2GKyKBjreUB3+rIy+cesjpy41LoaIa5NzkXTiQAjDGxwCnAIwDW2nZrbYOjoURkQHr4k0JS48I5f1pK7wdrKIaij2Hm1WqbLiLHFBkzjDricO0tcjqKiIiISLcFjfI+gBlUvsbhJOJP+xrqSKaOjuGT/DKecbkoDR1DbGO+X8YLpJS61eyMmE5YeGRA5uvOCsnHgfOOcPx31tpZvo8VAMaYKcBVwFTfNX82xgT1NqyIDC0f76xlfXED3zl97OerI62FnMdg5CxIm+NoPpEAGAPUAI8ZY9YbYx42xkQ5HUpEBpbNZY2sKqznxhOzCA7yQ4OUvOe8n2de1fuxRHrpKA/OzjLGrPI9NJtjjJnvZMahriYkjajmYqdjiIiIiHRfZAK1EVlkNG2iub3T6TTiJ+U71wMQkTbNb2Pui5tIRvtuPG6338bsa7Xle8jyFNOcemLA5uzyOxLW2o+A+i6efgnwrLW2zVq7GygA9EegiHSZtZb739lBalw4V8zN+PyF0rVQvQWytTpShoRgYA7wF2vtbOAA8ONDTzDG3OJ7szWnpqbGiYwi0s89+sluIkODuHJeZu8HsxbynoFRJ8GwUb0fT6T3HufLD87+GviZtXYW8FPf9+KQpsgMktrLnI4hIiIi0iNtI+cxx+xgY8lep6OIn+zbsxGAEWNn+21MkzKdaNNCxZ6Bs3d6Uc7rACTODFyDU3/sIXm7MWaj78nUYb5jaUDJIeeU+o59id5IFZEj+aSgltziBr596N6R4F0dGRoD0y53LpxI4JQCpdba1b7vl+MtUP6LtfZBa222tTY7KSkp4AFFpH+r2tfKKxvLWZKdQVxESO8HLFkD9btg1tW9H0vED47y4KwFDm40HgeUBzSUfEFnXBbJ1NFyoMnpKCIiIiLdFjfxZOLNAXbnr3c6iviJp3obzTaMlMzxfhszfoz37bqqnTl+G7OveQo/pIFoxkxbFLA5e1uQ/AswFpgFVAC/9R0/0mYy9kgD6I1UETmctZYH3tnJyLhwlmQfsndky17Y8gLMuALCop0LKBIg1tpKoMQYM9F36Exgq4ORRGSA+fvKIjo9lq+fONo/A+Y9DSGRMOUS/4wn0je+C/zGGFMC3Av8xNk4Q1vIiHEAVO7Z7nASERERke6LHncSAO2FKx1OIv4S3biDspBMXEH+22UwY+Jc3NbQVprntzH7kvV4yGxYQ2HUHL/+dzieXhUkrbVV1lq3tdYDPMTnbVlLgUN6LJKOnkoVkS76bFcdOXv28u3TxhIWfMg/iHnPQWcrzFW7VhlS7gCeMsZsxPsA0C+cjSMiA0VLu5unVhdzzpRkMof7YYP6jhbY/E+YfBGExfR+PJG+823gbmttBnA38MjRTlTHnr4Xm+p98nxvmQqSIiIiMgAljGF/UDwJ9blYe8Q1VzLApLQV0RA9zq9jRkTFUBaUSnjdwFhHUFq4hRRq6Rh1SkDn7VVB0hgz8pBvLwU2+75+GbjKGBNmjBkNjAfW9GYuERkaDq6OTI4NY0l2xqEvQM6jkDYXRs5wLqBIgFlrN/g6Ccyw1n7FWqtNC0SkS57PLaWhuYNvnDzGPwNu+Se0NcJMtWuVfu8G4AXf18v4/MHZL1HHnr6XPGoKAG1VBQ4nEREREekBY2gYPptp7m0U1zc7nUZ6qaG2kkQacCdOPP7J3VQTOZ7kloHxO2957hsApM45L6DzdrkgaYx5BlgJTDTGlBpjbgZ+bYzZ5Fu1cTrep0+x1m4BluJtK/cGcJu11u339CIy6KwsrGNNUT3fOW0c4SGHrI4sXgm127U6UkREpAs8Hsujn+xmRnoc2aOGHf+C4w7oho9/C8nTYMxpvR9PpG+VA6f6vj4D2OlgliEvNmEE+4jC7C10OoqIiIhIj4SOPoHRriq27NCvlQNd+U7vXqCRadP9PnZH4lRSbRX7Gw/f4r7/CdnzEZUkkj5makDn7XJB0lp7tbV2pLU2xFqbbq19xFp7nbV2um/VxsXW2opDzv9fa+1Ya+1Ea+3rfRNfRAab+32rI6+cl/HFF3Ieg7BYmPZVZ4KJiIgMIB/sqKaw9gA3nzQaY460vXs3bfkn1BXAKT8Af4wn4idHeXD2m8BvjTF5eFud3+JkxiHPGKqDRxLZVOJ0EhEREZEeSZzibWvZsP1Th5NIb+0v3gjAiHGz/D52RKa3q19p/lq/j+1PHrebMQdyKY6fj3H1qolqtwUHdDYRkWNYuauONbvr+a+LpnxxdWRzPWx9CeZcD6FRzgUUEREZIB7+eDcj48JZPH3k8U8+Ho8HProXkibB5Et6P56IH1lrj9ZDeG5Ag8gxNUZkkNy0zekYIiIiIj0SlDabdkIIr+zfhSbpgpp89tsIktP8tLXJIVImzIOPYF/Relhwrt/H95fCzSsZRxOuMace/2Q/C2z5U0TkGB54dwdJMWFcPT/ziy9seBrcbZCtdq0iIiLHs7V8H5/tquOGE7IICfLDr/v5r0DNNjj5BxDgpydFZHDojB1Fiqea9rY2p6OIiIiIdF9wGDUxU8hq3sSBtk6n00gvRO/bSVlIVp+sDByROpoGojFVm/0+tj/V5r0JQFb2+QGfW+8oiEi/sLqwjlWF9dx66tgvro60FtY9DunzITmwPa1FREQGokc+2U1kaBBXz8s8/snHYy189BtIGKu26SLSY67EcQQbD1WlBU5HEREREemRzrT5TDO72VRU6XQU6SHr8ZDaXsS+2HF9Mr5xuSgLHUv8vu19Mr6/RJZ/SpErk8TUUQGfWwVJEekXHnh3J4nRYVyz4LA3T4s+gbqdWh0pIiLSBdX7Wnk5r4wr5qYTFxnS+wF3vAGVm+Dk74Mr6Pjni4gcQUzqeADqS/IdTiIiIiLSM8Mnn0KocVO+daXTUaSH6qrLGMZ+PImT+myO/fGTSO/Yg7uzf66kbWttZnzLJiqHL3BkfhUkRcRxa4vq+WxXHbeeOuaLqyMB1j0G4XEw9VJnwomIiAwg/1i1h06P5aYTR/d+MGvhw19D/CiYsaT344nIkJWY6X3Tp7lyp8NJRERERHometyJAHj2qCA5UFXuzAUgOmN6n83hGjmdSNNG+e4tfTZHbxSse58I0074hDMcmV8FSRFx3APvHFwdedgy8QO1sPVlmHk1hEQ4E05ERGSAaO1w8+SqPZw1OZmsxKjeD1jwLpTnwsnfgyA/rLYUkSFreHImLTYU6gudjiIiIiLSM1HDqQ7NZETDBqy1TqeRHmgq9e7tmDJ+dp/NkTDGO3a1r/jZ3+zb9g6d1sWYeec6Mr8KkiLiqJyiej4pqOVbp4whIvSw1ZEbngJPB8xVu1YREZHjeSG3jL3NHXzjJD+tjvzo1xCbDjO/1vvxRGRIMy4XlUEjCd9f7HQUERERkR7bP2Iu0+12imqbnI4iPeCq2UYD0Qwfkd5nc6RPmE2nddFeltdnc/TGsMqV7AqZQGz8cEfmV0FSRBz1wLs7GR4VyjULD9s70uOBdY9D5iIY0Xd9vUVERAYDj8fyyCeFTEuLZf7ohN4PuPsjKFkNJ30XgkN7P56IDHmNEekMayt1OoaIiIhIj0WOO4FhpomdW/rn6jc5ttj9uygPHY1x9V1ZLDwiitKgdCLqt/XZHD21v7GecR3bqU9e5FgGFSRFxDHr9uzl45213HLKGCJDg7/4YtFH3pZOWh0pIiJyXB/urGFXzQG+cdIYjDG9H/Cj30B0Csy+rvdjiYgArbFZjHRX4na7nY4iIjLoGWPCjTFrjDF5xpgtxpif+Y7/tzGmzBizwfex+CjXn2eM2W6MKTDG/Diw6UX6rxFTTgPgQMGnzgaRbrMeD2ntu9kfM7bP56qNnkBKS0Gfz9Ndu9a+SbDxEDvlbMcyqCApIo554N2dJESFct2iUV9+MecxiBgGUy4JfDAREZEB5pGPd5MSG87i6SN7P9iez6DoYzjxLggJ7/14IiKAK2EMYaaD6vIip6OIiAwFbcAZ1tqZwCzgPGPMQt9rv7PWzvJ9rDj8QmNMEPAn4HxgCnC1MWZKgHKL9GtBSePZ54ojqnqd01Gkm6rLdxNjWmBE3/9z1pk4hRRqaayr6vO5uqN1+7u02FDGzT3dsQwqSIqII9YX7+WjHTV88+QjrI5sqob8V717VumNUBERkWPaVrGPTwpquf6EUYQG++HX+w9/DVFJMPfG3o8lIuITlTIegLo9/a99lYjIYGO9Dm5yF+L7sF28fD5QYK0ttNa2A88CelpcBMAYquNmMr51M01tnU6nkW6oKtgAQHTG9D6fKzJzFgCl+Tl9Pld3pNStpiB8GmHhkY5lUEFSRBzxj1V7iAkP5vojrY5c/yR4OiFb7VpFRESO59FPdhMREsTX5mce/+TjKc2Bwvdh0e0Q6twfKSIy+AzP9O4Lf6Byp8NJRESGBmNMkDFmA1ANvG2tXe176XZjzEZjzKPGmGFHuDQNKDnk+1LfsSPNcYsxJscYk1NTU+PP+CL9lslcwGhXJVt37nI6inRDc+kmAFLHz+7zuVInzQNg/571fT5XV9VWFpPlKeZA2kmO5lBBUkQCrr3Tw9tbqzh3agpRYYetjvR4IPcJyDoZEsc7E1BERGSAqN7fyksbyrl8bjrxkaG9H/DDX0NEAsz7Ru/HEhE5RFLaWNptEO66QqejiIgMCdZat7V2FpAOzDfGTAP+AozF28a1AvjtES490obkR1xdaa190Fqbba3NTkpK8ktukf4ueeppANRs/cjZINItrtrt1BLPsCQ/bHNyHIkpmdQRh6t6S5/P1VVFa7wduofPOMfRHCpIikjAfbqrlv2tnSyenvLlFwvfh71FahMnIiLSBU+uKqbD4+GmE7N6P1j5etj5Jiz6DoRF9348EZFDBAUHU+VKJmxfkdNRRESGFGttA/ABcJ61tspXqPQAD+Ftz3q4UiDjkO/TgfK+zikyUESPzqadYFyla5yOIt0Q31RAZVhWwOYrDxtLwv7tAZvveDyFH9JIFGOmLXI0hwqSIhJwr2+qICYsmBPHJX75xXWPQeRwmHxR4IOJiIgMIK0dbp5ctYczJyUzJskPBcSP7oXwOJh/S+/HEhE5gr3h6cS1ljkdQ0Rk0DPGJBlj4n1fRwBnAfnGmEOXBl0KbD7C5WuB8caY0caYUOAq4OU+jiwycISEUx45iZH78rC2q1uzipM8bjfpHXtoig1cN74DwyaT0VlMZ0d7wOY8GuvxkNmwhl1RcwkKDj7+BX1IBUkRCagOt4e3tlZx9pRkwoKDvvji/krIXwGzroHgMGcCioiIDBAvri+j/kA7N580uveDVW6G/Fdhwa3eoqSISB9oic4kpbMc6/E4HUVEZLAbCbxvjNmIt8D4trX2VeDXxphNvuOnA3cDGGNSjTErAKy1ncDtwJvANmCptbb/9B0U6QdakrOZbHdRVFnndBTpgsrinUSaNsyIyQGbMzh1OmGmg9KCjQGb82hKC7eQQi0do052OooKkiISWCt31dHQ3MH504/Qr3v9P8C61a5VRETkOKy1PPzJbqamxrJwTELvB/z4XgiN8RYkRUT6SsIYok0LtTVaJSki0pestRuttbOttTOstdOstf/Pd/w6a+103/GLrbUVvuPl1trFh1y/wlo7wVo71lr7v079HCL9VezEkwgznRRt/szpKNIF1YUbAIgbNSNgcw4fOweA2oJ1AZvzaMpz3wAgdfa5DidRQVJEAuz1zRVEhQZx8vjD2rV63LDuCRh9Kgwf60w4ERGRAeLDHTUUVDdx80mjMcb0brCa7bDlRZj/TYj0Q3FTROQoIpLHAVC7p//spyMiIiLSXSOnngpA6y4VJAeCltJNAIwcPztgc6aPn0W7Daaj3PkVkiHFH1FJIuljpzsdRQVJEQmcTreHN7dUcebkZMJDDmvXWvAuNJZA9k3OhBMRERkgPB7L79/dyYiYMC6ckdr7AT+6F0IiYNFtvR9LROQYEjImAbC/fIfDSURERER6zhUzgorgNOJrc52OIl0QUredKoYTNyzx+Cf7a87QMEqCM4na6+yDeB63mzFNuZTEz8O4nC8HOp9ARIaM1bvrqT/QzuIjtWtd9xhEJcHECwIfTEREZAB57LMicosbuOe8SYQG9/LX+bpdsHk5zLsZogL3x5mIDE0jMibgsQZ37S6no4iIiIj0Sn3CbCZ0bGV/S7vTUeQ44g8UUhU+OuDz1kePZ2RrQcDnPVTh5pXE04QZc6qjOQ5SQVJEAmbFpgoiQ4M4bWLSF19oLIMdb8DsayE41JlwIiIiA8Du2gP85s18zpg0gsvmpPV+wI/vg6BQWHRH78cSETmO0PAIqlyJBDcWOR1FREREpFeCsxYx3Oxnx9YNTkeRY3B3dpLeWUxz3PjAzz1iKknspb7auf3Taze+BUBW9uLjnBkYKkiKSEC4PZY3t1Ry+qQRX27Xuv4fYD0w5wZnwomIiAwAHo/lnuV5hAS5+MWl03u/d+TeIsh7BubeCDHJ/ogoInJcdaHpxLaUOB1DREREpFdSp58OwN78jxxOIsdSXrSNcNOBK2VKwOeOzpwFQFl+TsDnPiiy7BOKXBkkpo5yLMOhVJAUkYBYW1RPbVM7i6cd1q7V3Qm5f4exZ0BC4JfOi4iIDBSPf1bE2qK9/NdFU0mJC+/9gJ/8DlxBcOJdvR9LRKSLmqMySOosdzqGiIiISK/EpE1mn4khtHyt01HkGGp2bQAgftTMgM+dNmk+AAeK1wd8boC21mbGt2yicvhCR+Y/EhUkRSQgVmyqIDzExemTDmvXWvA27CuDuTc5E0xERGQA2F17gF/7s1VrYymsfwpmXwexqb0fT0Ski+ywMSSwn8b6WqejiIiIiPScy0Vp9HQyDmzE47FOp5GjaCvfDEDa+MAXJIcljaSaBIJqtgR8boCC3A+IMO2ETzjDkfmPRAVJEelzHo/l9c2VnD5xBJGhwV98MecxiE6Giec7E05ERKSf83urVoBPHwAsnPTd3o8lItINocnjAKgu3uZwEhEREZHe6Uidx2jKKSpRO/r+KrR+O+VmBFEx8Y7MXxE+luH7dzgy976tb+O2hjHzznVk/iNRQVJE+ty64r3U7G/j/OmHtWttKPGukJx9HQSFOBNORESknzvYqvWnF07xT6vW/ZWw7gmYeTXEZ/Z+PBGRbhiWNgGAxjJn3pgRERER8ZeESacAUL7pA0dzyNElHCikOnyMY/M3J0wm3V1Ce1trwOceVrmSgpCJxMYPD/jcR6OCpIj0uRWbKggNdnHGpBFffCH372AtzL3BmWAiIiL9XJGvVevpE5O4fG66fwb99Pfg6YSTv+ef8UREuiF51GQAOqoLHE4iIiIi0jtpU06ggyDce1Y6HUWOoKO9jTR3KS3xExzLEJI2g1DjpmTHhoDOu7+xnnEd26lPXhTQeY9HBUkR6VMej+WNzZWcOiGJ6LBD2rW6O2H9P2DcWVqdISIicgTeVq0bCQly8cuvzvBPq9amGsh5FGYsgQTnnhIVkaErIjqWWuJxNRY5HUVERESkV1xhkewJncDw+vVOR5EjKN+1mVDjJmTkFMcyJI2dC0D9rnUBnXfX2jcJNh5ippwZ0HmPRwVJEelT60saqGhs5YLD27XueAP2V0D2Tc4EExER6eeeWFnEmqJ6/7VqBVj5R+hshZO/75/xRER6oCYkjZgDxU7HEBEREem1xqQ5jO8sYF9Tk9NR5DC1uzcCED9qhmMZ0sZOo9WG4K7YFNB5W3e8R4sNZdycMwI67/GoICkifer1TRWEBrk4Y/Lh7VqfgJiRML7/bKorIiLSXxTVHuD/3vBzq9bmeljzEEz7KiSO98+YIiI90BSVSWJHudMxRERERHotYuyJhJkOCjd+6nQUOUx7xWbc1pA+fqZjGYJDQikJySK6YVtA502uXU1B+DTCI6ICOu/xqCApIn3GWsvrmys5eXwiseEhn7/QWAYF78CsayAo+OgDiIiIDEF90qoVYNWfoeMAnPwD/4wnItJD7vgsRlBP84F9TkcRERER6ZXMmacBsH/HJ84GkS8Jq99BuWsk4ZHRjubYGzOBtLZCrMcTkPlqK4sZ7dlDU9pJAZmvO1SQFJE+s7G0kbKGFs4/vF3rhqfBemD2tc4EExER6ccOtmr9T3+2am1pgNV/g8kXQ7Jz+2eIiACEJI0FoLIo3+EkIiIiIr0TPTyNMtdIIqtynI4ih0ls2UVt5BinY+AZMY1h7KO2MjBbFhStfR2AxBnnBGS+7lBBUkT6zIpNFYQEGc6enPz5QY8H1v8dRp8KCaOdCyciItIPHWzVetrEJK7wV6tWgDUPQts+OOWH/htTRKSH4lInAtBQtsPhJCIiIiK9Vxk3k6yWzXjcgVkBJ8fX2nKAVHcFrcMmOB2F2KzZAFRsD0zR2rPrAxqJYsy0RQGZrztUkBSRPmGtZcXmCk4cl0hc5CHtWnd/CA3FMOd658KJiIj0Qx6P5Z7nD7Zqne6/Vq2t+2Dln2DC+TByhn/GFBHphaSsyQC0VRU4nERERESk92z6Aoazj+KCTU5HEZ+ygk0EGw+hI6c6HYW0idkAHCjJ6/O5rMdDZsNadkXNISi4/22VpoKkiPSJLeX7KKlvYfG0w9q15v4dwuNh0oWO5BIREemv/r6yiDW7va1aR8ZF+G/gtQ9DawOcqtWRItI/xA1LooFoXA27nY4iIiIi0msjpp4KQNWWDx1OIgftLfIW/xJGz3Q4CcQlJFFJEiE1W/p8rrLCraRQQ8eoU/p8rp5QQVJE+sSKTRUEuQxnTzmkXWtzPeS/CjOvghA/7YklIiIyCOypO8D/vbHd/61a2w/Ayj/CuLMgba7/xhUR6aXq4FQimwKzj46IiIhIX0ofP4t6Yonb9jQdHe1OxxGgo2ILHTaItLHTnY4CQGXkOBIP9P12BWXr3wAgdfa5fT5XT6ggKSJ+Z61lxaYKThg7nGFRoZ+/sPE5cLfD7OucCyciItLPeDyWHy7fSLDL+LdVK0DOY9BcB6fc478xRUT8YH9kBsPby5yOISIiItJrrqAgCrP/k0kd28h59HtOxxEgvGEnZUGphIb1j0UxLQmTSXeX0dpyoE/nCdnzIVUMJ72fFGIPp4KkiPjdtor9FNU1c/6h7Vqt9bZrTZsLKdOcCycywBhjgowx640xrzqdRUT6xj9W7embVq0dLfDpAzD6FMhc4L9xRUT8oCM2i2RPNW1tLU5HEREREem17AtvYfXwS1hU8Q82vPus03GGvKSWQuojxzgd41/C0mYQbDyUbM/tszk8bjdjmnIpjp+PcfXP0l//TCUiA9rrmytwGTh36iHtWsvWQfVWrY4U6b67gG1OhxCRvrGn7gC/ej2fUyckcUW2H1u1gvdBoAPVcOqP/DuuiIgfBCeOIchYqop3Oh1FRERExC9mfuMv7AoaTdbH36eypMDpOENWy4H9pHqqaEuY5HSUf0kanw1AQ2HfFSQLN68knibMmFP7bI7eUkFSRPzKWstrmypYOGY4w6PDPn8h9+8QEgnTLnMunMgAY4xJBy4AHnY6i4j4n8djucfXqvVXl/m5VWtnG3xyP2SeAFkn+W9cERE/iU6dAMDeku0OJxERERHxj/CIKEKv/gfB1k3DE9fS0d7mdKQhqXTnBlzGEpY6xeko/5I2egrNNgx3xaY+m6N241sAjMo+v8/m6C0VJEXEr3ZWN1FYc4Dzpx/SrrWtCTY/D1O/CuGxzoUTGXjuB+4BPEc7wRhzizEmxxiTU1NTE7BgItJ7/1i1h9V90aoVYMNTsL8cTv2hf8cVEfGTpFHeJ9ZbqrR6QERERAaPjHHT2b7gf5nUuY2cR7/rdJwh6f+zd99xVV/3H8df57K3IMhGUMGJ4tbsPUzSjDZ7J21GkyZp2jRt2t+vu81o2qZJmzTN3tssNXvYJG5FceAGkSmIICLznt8f3PRHrCYK9/K9F97Px+M+gO/9fs/3fXINH+493+85u0pWApA4rMDZIN24goIoC8khpqHYZ+eILP+cElcmSWnZPjtHb2lAUkS8as6qSsy+07WumQ1tTTDpMueCiQQYY8zpQI21dtnX7WetfdhaO8VaOyUpKamP0olIb22ra/bdVK2d7fDvv0D6FBh2rHfbFhHxkoSkDJptGOzc4nQUEREREa+aPOtqFiWew8yq51jxwfNOxxlwOqvW0maDScvxnzskAXbFjSSzbQvWfcD7DnqstaWZ3L2rqB483ette5MGJEXEq+atrmRadgJDYsL/f+PypyAxDzKnORdMJPAcDnzLGFMCvAAcZ4x5xtlIIuINbrfltldWEuwy/PEcL0/VCrDyBWjY1rV2pLfbFhHxEuNyURmcRnhTqdNRRERERLxuwtUPsCloOMM++xGVpZqivi9F7NpAWXAmwSGhTkf5quRxxLKH6u2bvd70puWfEGHaCM3174uSNSApIl6zqWY3G6qbmNV9utaaYti+uOvuSH0oKnLQrLU/s9ZmWGuzgQuAj6y1lzgcS0S84JlFXVO1/uL00aQN8vJUrZ0d8O97IbUAck/0btsiIl7WGJ5BfEu50zFEREREvC48IoqIi5/GZd00Pn0pba0tTkcaMJJbSqiPGu50jP8Slz0RgKoNS73e9u61H9BpDcOn+e/6kaABSRHxonlFVQCcMi7l/zeueBpcITD+AodSiYiI+I9tdc38cW4xR+Ulcd6UTO+fYPWrUL8VjrpNFwKJiN9rjc0m1V1FZ0eH01FERPoVY0y4MWaxMWalMWaNMebXnu33GGOKjTGrjDGzjTGDDnB8iTGmyBhTaIzx/ifnIgNE+rCxbJx5JyM71rP80ZudjjMg7G7YSQo76EgY6XSU/5IxagoAe8sKvd72oKov2BSSR+ygwV5v25s0ICkiXjOnqJIpQ+NJjvVM19rRCiufh1GzIFpr24n0lLX2E2vt6U7nEJHecbstP3l1JUEuw52+mKrV3Qnz74EhY2HkLO+2LSLiA0GDcwg1HVSXax1JEREvawWOs9ZOAAqAU4wxM4D3gXHW2vHABuBnX9PGsdbaAmvtFJ+nFenHJp1yBYuSvsOMmhdY8Z5W4vG18o0rAAhPH+dwkv8WHRvPdpNCWN1ar7a7u2EnI9rXszN5plfb9QUNSIqIV2zZ0URx1W5O7T5d6/q50FwHEy9zLpiIiIifeGZRKQu37OQXp/lgqlaAta9D3UY4+jZw6c98EfF/ESm5AOwsK3Y4iYhI/2K7NHl+DPE8rLX2PWvtl7elLwQyHAkoMsAUXH0/G4NzGf7FT6jYqr97fKmxtAiApOETHE6yfzWRuSTt2eTVNjcvfY9g4yZmzAlebdcX9EmFiHjFvNVd07We2n261uVPQWwGDPfvxXRFRER8bVtdM3fOK+bI3ETOn+qDqVrdbpj/J0gcCaPP9H77IiI+kJQ1GoA9lRsdTiIi0v8YY4KMMYVADfC+tXbRPrtcBcw7wOEWeM8Ys8wYc83XnOMaY8xSY8zSHTt2eCW3SH8UFh5J1MXPAJamZy+ltXWv05H6LXf1WvbaUFKHjnI6yn61Dh5NuruS5qYGr7XZsv5DWmwIIyYd57U2fUUDkiLiFfNWVzIxa9D/3/Gxaxts/hgmXgKuIGfDiYiIOOjLqVpdxnDnt8d7f6pWgPVzoGYtHPVj3R0pIgEjKS2HNhuMe6embBUR8TZrbae1toCuuyCnGWP+M3+hMebnQAfw7AEOP9xaOwk4FbjBGHPUAc7xsLV2irV2SlKSluoR+TppOaPYNPNu8jo2sOKRHzgdp9+KatjI9uAsXEH++Xl0eMYEXMZSVrzMa20m1y5iY/g4wiOivNamr+jTChHptW11zawub2TWuG7Tta7w/E078WJnQomIiPiJZ7tN1Zrui6larYVP74aEYTD2HO+3LyLiI67gYCqDUghrLHU6iohIv2Wt3QV8ApwCYIy5HDgduNhaaw9wTIXnaw0wG5jWF1lF+rtJJ1/KwiHnM2PHyyx/5wmn4/RLKa1b2RU9wukYB5Sc27Us766tK7zSXm3VNnLcpTSlH+mV9nxNA5Ii0mtzV1cCcMqX07W6O2HFM11TtQ7KcjCZiIiIs8p2NvNHX07VCrDhXahaBUf+GIKCfXMOEREf2RWWQdze7U7HEBHpV4wxScaYQZ7vI4ATgGJjzCnA7cC3rLXNBzg2yhgT8+X3wEnA6j4JLjIATLr6b2wIziN3wU8p37LW6Tj9SkNdNUnU05k40ukoB5Q6NI/dNgKqvfNrtWRJ18zbifkneqU9XzvoAUljzGPGmBpjzOpu2xKMMe8bYzZ6vsZ3e+5nxphNxpj1xpiTvR1cRPzHvKJKJmTEkZkQ2bVh88fQuB0mXeZsMBEREQe53ZbbXvHxVK3Wwvy7uy4AGn+e99sXEfGxvTFZpHRWYN1up6OIiPQnqcDHxphVwBK61pB8G3gAiAHeN8YUGmMeAjDGpBlj5nqOTQY+M8asBBYDc6y17/R9F0T6p9CwcKIveQZrXDQ/eykte/d7bYD0QPnGrrsOIzLyHU5yYMblYnvoMGIbir3SnnvzJzQSxbD8w7zSnq8dyh2ST+C5tb+bnwIfWmtzgQ89P2OMGQNcAIz1HPMPY4x/TtorIr2yvb6ZldsbODW/+3StT0HkYBg5y7lgIiIiDvtyqtaf+2qqVoDNH0H5MjjiVggK8c05RER8yCQMI8q0UletuyRFRLzFWrvKWjvRWjveWjvOWvsbz/YR1tpMa22B53GdZ3uFtXaW5/st1toJnsdYa+3vneyLSH+Ulj2SLYf/idzOTRQ+eoPTcfqN3duKAEgeXuBskG/QGDeSzLatuDs7e9WOdbvJ2rWETVGTCAoOjNmSDnpA0lo7H9i5z+YzgSc93z8JnNVt+wvW2lZr7VZgE5prXKRfemd1FQCnfjlda9MOKJ4LEy6E4DAHk4mIiDin+1StF/hqqtYv146MTYeCi3xzDhERH4tMyQWgZpt3rhIXERERCQQFJ17EwuQLmVH7GsvmPup0nP6hZi1NNoLkjOFOJ/laJiWfaLOXytINvWqnfMtaUthBe1ZgrB8JvV9DMtlaWwng+TrEsz0dKOu233bPtv9ijLnGGLPUGLN0x44dvYwjIn1tTlElY9NiGTo4qmvDqhfA3Q4TL3U2mIiIiEPcbstPXlnl26laAUr+DWUL4Ygf6iIgEQlY8Rlda/w0VfTuAxkRERGRQDP56vtYHzyKkYt+TtmmIqfjBLzoxk2UhwzFuHo77OVbg4ZNAqB649JetVO+oms27bRJ+05s6r989crs71MXu78drbUPW2unWGunJCUl+SiOiPhCxa69rNi2i1lfTtdqLSx/CjKmwZBRzoYTERFxyLOLt7FgS51vp2qFrrsjo1N0EZAMaMaYx4wxNcaY1d22vehZF6vQGFNijCl0MKJ8g+SsXDqsi87aLU5HEREREelTIaFhxF32DB0miLbnL6Nl7x6nIwW01LYSGmL8++5IgMyRk+m0htbtK3vVTkjpfKoZTMZw/10zc1+9HZCsNsakAni+1ni2bwe6z02VAVT08lwi4mf+a7rWssVQuwEmXeZgKhEREeeU7Wzmj3PX+XaqVoDSBV13SB5+E4SE++48Iv7vCeArlwRba8//cm0s4FXgNQdyyUEKCQ2nxpVESGOJ01FERERE+lxKVi6lR93L8M4trHzk+07HCVh11dtJoBF30mino3yjiKgYyoPSCK9b2+M23J2d5DQtZ1vcVL+/I7S73iZ9E7jc8/3lwBvdtl9gjAkzxuQAucDiXp5LRPzMvNWVjEqJYVhSdNeG5U9BaDSMPdvZYCIiIg7os6la2/fCu3dAZCJMvtI35xAJENba+cDO/T1nuv4nPA94vk9DySGrC00ndm/ZN+8oIiIi0g9NOO4CFqRewvS611n69sNOxwlIlZtWABCVMc7hJAdnR2QuyXs39fj4LasXEs9uzPBjvBeqDxz0gKQx5nlgATDSGLPdGHM1cCdwojFmI3Ci52estWuAl4C1wDvADdbaTm+HFxHnVDe2sLS0/v+na21phDWvwbhzICza2XAiIiIOeM4zVesds3w4VavbDbOvhYoVcMZ9EBrpm/OI9A9HAtXW2o0H2sEYc40xZqkxZumOHTv6MJp0tzc6iyEdmlRJREREBq4pV/6Z4pAxjF7yP2zbUOh0nIDTtK1rDc7UEZMcTnJw2hPHkmar2d2w32srv1Ft0XsADJ1yqjdj+dxBD0haay+01qZaa0OstRnW2kettXXW2uOttbmerzu77f97a+1wa+1Ia+0838QXEae8s7oKa2FWvme61tWvQnszTLr86w8UERHph76cqvWIEYlcOM2HU7V++GtY+wac9DsYfbrvziPSP1zIN9wdaa192Fo7xVo7JSkpqY9iyb5sfA6DaKKhrtrpKCIiIiKOCAkNI/7yZ2gzIXS8cDktzU1ORwoopraYBqIYnOLD9+NeFJE1HoDtxUt6dHzk9s8odWWSlJbtxVS+FziTy4qIX5lbVEnukGhGDInp2rD8KRgyBtInOxtMRESkj1lruf3VVRhjuPPb+b6bqnXZE/D5X2Hqd2HmDb45h0g/YYwJBs4BXnQ6i3yzsORcAKpKix1OIiIiIuKc5IzhbDv6Lwxzl7DqkeucjhNQYhs3Uh6SEzDrKabkTQWgsWTFIR/b1trCiL1FVA2e7u1YPhcYr46I+JUdu1tZXLLz/6drrVoNFcth0mXgqw9hRURE/NSzi7bxxeauqVoz4n00heqmD+HtW2HEiXDKXaq3It/sBKDYWrvd6SDyzQal5wGwu2KDw0lEREREnDXh2HNZkHYZ03a+xdI3H3I6TkCwbjfp7aXsjh3hdJSDNiQthwaioHrNIR+7afnHRJpWQnOP9UEy39KApIgcsnfXfDldq2dAcsXTEBQK4893NpiIiEgfK63b4/upWqvXwkuXw5DRcO7jEBTsm/OIBCBjzPPAAmCkMWa7MeZqz1MX8A3TtYr/SMkeBUD7jk0OJxERERFx3tQr72VtyDjGLPtfStcXOh3H7+2oLCWWPV3vmQOEcbnYHjqcQY3rD/nYxjXv02kNw6ae4oNkvqUBSRE5ZHOLKhmWFEVecjS0t8DKF2DU6RCZ4HQ0ERGRPtPW4eYHz68gyGW46zvjfTNV6+4qePZcCIuGi16CsBjvn0MkgFlrL7TWplprQ6y1GdbaRz3br7DW6pLyABEeGUMNCQTtKnU6ioiIiIjjgkNCSbziGVpMGO4XL2Pvnt1OR/Jr1Zu6pj2Nzsh3OMmh2T1oFJntJXR2dBzScYOqF7ApJI+4+EQfJfMdDUiKyCGpa2pl4ZY6Zo1L7frgtfhtaNnVNV2riIjIAPKn99azansDd39nPOmDIrx/grY98Nz5sLceLnoR4tK9fw4RET9RG5pOTPM2p2OIiIiI+IUh6TmUH3sfQzu3UfSva5yO49f2bF8NQGpugbNBDpErNZ9I00rF1oOftnV3w05GtBWzM3mmD5P5jgYkReSQvLe2Gnf36VqXPwWDsiDnaGeDiYiI9KGP19fw8PwtXDIji1PGpXr/BO5OePV7ULUKvvMYpE7w/jlERPzInsgsktrLnY4hIiIi4jfyjz6HRRlXMG3XXJa8/nen4/gt14511BFHwpDAuog3YdgkAGo2Lj/oYzYvfY9g4yZm9PG+iuVTGpAUkUMyt6iS7MGRjE6NgZ1bYeunMPFScOnXiYiIDAw1jS38+KWVjEqJ4RenjfHNSd77H1g/B065C0YG3roQIiKHqnNQNonsYs/uBqejiIiIiPiNqVfczZrQfMau+DWl65Y5HccvxTVtpio02+kYhywjbyId1kVb+cqDPqZl/Ue02BBGTNaApIj0c/V72vhicx2n5numa13xDBgXFFzkdDQREZE+4XZbfvhSIXvaOrj/womEhwR5/ySL/wUL/w4zvg/TNTWPiAwMoUNGAFBVUuxwEhERERH/ERwSSvIVz7LXhGNfvpzmJl281Z11u8loL6UpLtfpKIcsPCKK7UEZROxcd9DHJNcuZGP4OMIjonyYzHc0ICkiB+39tdV0ui2zxqVCZwcUPgsjToC4DKejiYiI9IkHP93M55vq+NUZY8lNjvH+CTa8C/N+AiNnwUm/8377IiJ+KjY9D4CGivUOJxERERHxL4lpQ6k47n6yOrezRutJfkVV2UaiTAsMGe10lB6pjc4jZe+mg9u3qowcdylNaYf7OJXv9KsBSXdnJwse/TELn/ml01FE+qW5qyvJiI9gXHosbP4QdlfCpMucjiUiItInlpXW8+f3N3Da+FTOn5rp/RNUroKXr4SUfPj2I+Dywd2XIiJ+Knlo14dIrTUH94GMiIiIyECSf9SZLMq6mqkN77B49t+cjuM3ajavACAua7zDSXqmI3EMKdTSUFf9jfuWLJ0HQOL4k30dy2f61YCkMYbQ+o1M23gfhR8873QckX6lobmdzzfVctqX07UufwqikiBP61qJiEj/17C3nZueX0FqXDh/PCe/qxZ69QTl8Nx5EBEPF70EoYE5/YqISE/FDBpMPbG46rc6HUVERETEL027/C5WhxWQX/hbtq5d4nQcv7B3+xoAUvMmOZykZyKzCgDYXrz0G/d1b/qYRqIYln+Yj1P5Tv8akHS5GHP9M2wOGUHuv29h65pFTkcS6Tc+WFdNe6fl1PxU2F0N6+fBhAshKMTpaCIiIj5lreVnr62iurGF+y+cSGy4l2tf62547nxobYKLX4KYFO+2LyISIGqCU4nas83pGCIiIiJ+KSg4mJQrn2aPicT1yhXs2b3L6UiOC6orpoYE4uITnY7SI2mjpgKwu3TF1+5n3W4ydy1hc9REgoKD+yKaT/SrAUmAiKgYBl31CntMJOEvX0xd9XanI4n0C3OLKkmLC2dCRhysfA5sp6ZrFRGRAeH5xWXMLariRyeNZGJWvHcb7+yAV66CmrVw3hOQPNa77YuIBJDdkVkMbi13OoaIiIiI30pMyaLqxAfI6Cxn3b++i3W7nY7kqPimzVSF5zgdo8cSU7KoIw5XzZqv3a+iZB2p7KAt66g+SuYb/W5AEiApLZuGM59kkG2g5pFzaW1pdjqSSEBrbGnn3xtrOTU/FQOw/GnIOgwSc52OJiIi4lMbqnfz67fWcGRuItceNcy7jVsL79wOG9+D0+6FESd4t30RkQDTEZfNEFur9/AiIiIiX2Pc4WeweOg1TGl8nyWz73M6jmM6OzpI7yijOS7P6Si9UhE2nITd6792n+3LutaPTJ0YuOtHQj8dkATInXgUa6ffzej2tax68MoBf6WASG98tK6Gtk43s/JTofQL2LlZd0eKiEi/t7etkxufW05MeDD3njcBl8vL60Yu/AcseQQOuwmmXOndtkVEAlBw0jCCjKWqdIPTUURERET82rTL/kBR2ETyV/1hwM4SWVlSTIRpIyh5tNNRemVP/GgyO7bR0d52wH1CSudTQwKZI8b3YTLv67cDkgCTZ13JgqxrmNrwDoue/ZXTcUQC1tyiSlJiw5mYOQiWPwVhsTDmTKdjiYiI+NRv56xlQ3UTfz6vgCEx4d5tfN3b8O7PYfS34IRfe7dtEZEAFZPWdXV7/fZih5OIiIiI+Leg4GDivv1Xwmhnw5t/cjqOI3ZsKQQgNivf2SC9FJQ6jjDTTvmmov0+7+7sJKdpOaVx0zCuwB7SC+z0B2HGFXexLOZYpm36G4XvP+d0HJGA09TawScbdnDKuBRcrQ2w9nXI/w6ERjodTURExGfmFlXy3KJtXHv0MI7KS/Ju4+XL4dXvQvpkOOdhCPA3FCIi3pKU1XV1e0v1ZoeTiIiIiPi/rLwCCqOPZGz5i+xu2Ol0nD7XUrEagPS8iQ4n6Z3BwycDsGPzsv0+v2X1QuLZDcOO7stYPtHvP/0wLhdjr3+GzSEjyP3sh2xds8jpSCIB5aPiGto6PNO1rn4FOlo0XauIiPRrZTubuf3VVUzIHMSPTxrp3cZ3bYPnL4DoJLjweQiJ8G77IiIBLD4xlSYbAfVbnI4iIiIiEhBiTriNWJpZ8+ZfnY7S50LqNlBJEtGx8U5H6ZWM3Am02SDay/d/h2Rt0XsAZE+d1ZexfKLfD0gChEdGM+iqV9hjIgl/+eIBO6eySE/MK6okKSaMyUPju6ZrTcmH1AKnY4mIiPhEe6ebm19YARbuv2AiIUFe/HO5pQGePQ/aW+DiVyB6iPfaFhHpB4zLRVVwGhFN25yOIiIiIhIQciceRVHYREZsfpKWvXucjtOnBu/ZTE3EMKdj9FpoWDhlwVlE1a/b7/OR2z+n1JVJUlp23wbzgQExIAmQlJZNw5lPMsg2UPPIubS2NDsdScTvNbd18PH6Gk4dl0JQ1UqoXAkTLwNjnI4mIiLiE395fwPLt+3iD+fkkzXYi9OTd7bDS5dD3UY4/2lI8vKdlyIi/URjRAbxLbqIWESkN4wx4caYxcaYlcaYNcaYX3u2Jxhj3jfGbPR83e9tRcaYU4wx640xm4wxP+3b9CJyqMyRt5LILla+/ZDTUfpMe1sr6Z3baR6U63QUr9gZnUdqy6b/2t7W2sKIvauoSpjmQCrvGzADktB1tcDa6Xczun0tqx68Aut2Ox1JxK99XLyDlnY3p45LhRVPQ1AYjD/X6VgiIiI+8fmmWh78dDPnT8nkjAlp3mvYWphzK2z5GM64r1+s+yAi4ittsUNJcVfT0d7udBQRr+hob2P5u0+z5g9Hsv3Xo1j50UtOR5KBoRU4zlo7ASgATjHGzAB+Cnxorc0FPvT8/BXGmCDg78CpwBjgQmPMmL4KLiKHbuxhp7MhOI/0tQ/T0d7mdJw+UbFlDaGmg+CUsU5H8YrOIWNJop6dNeVf2b5p+cdEmlZC845zKJl3DagBSYDJs65kQda1TG14l0XP/NLpOCJ+be7qSgZHhTItIwJWvQxjzoSIwJ6TW0REZH9qm1q55cVChidF88tvefnzls//2jXt+ZE/homXeLdtEZF+xjV4OKGmk5pyrSMpga2hrpqFT/8vO34/hkkLbmRwWyWdJpgJ87/Hor9fTUtzk9MRpR+zXb78RxbieVjgTOBJz/YngbP2c/g0YJO1dou1tg14wXOciPgp43LRNPUmMmwVK997yuk4faJu60oA4rPHO5zEO6KzCgAoL176le0Naz+g0xqGTT3FgVTeN+AGJAFmXHEny2KOZdrm+yl8/zmn44j4pb1tnXxcXMPJ41IIKn4LWhtg0mVOxxIREfE6t9vyo5dW0rC3nQcumkhkaLD3Gl8zGz74FYz7Dhz3C++1KyLST0Wndk27Vbdt/2voiPi7knVLWXT/ZYT+bRwzNt/HztAUVsz8G4k/X0vybYtYOOQ8pu94hco/HcbWNYucjiv9mDEmyBhTCNQA71trFwHJ1tpKAM/X/S1qng6Udft5u2ebiPixghMuotSVwaDlDwyImSFbK9fitoaM3AKno3hF+qiuKVn3bFvxle3xVV+wOSSXuPhEJ2J53YAckDQuF2Ovf4bNISPI/eyHbFmtPwBF9vXphh00t3VyWn5q110d8TmQfYTTsURERLzu0c+28umGHfzPaaMZlRLrvYbLFsNr10LmDDjz71qDWUTkICRmjQaguWqjw0lEDl5nRweFHzzP6j8eTfaLxzOhdi5FCSey+dvvMvaOz5h48uUEh4QSHhHFjO//i1VHP0qMu4G0l05j4fO/HxAfHEvfs9Z2WmsLgAxgmjFm3EEeur8/Wu1+dzTmGmPMUmPM0h07dvQwqYh4gysoiOr86xjeuZWiT19zOo7Phe0spsKVQnhktNNRvCI+KZUaEgjaseY/25oa6xnetp66ITMdTOZdA3JAEiA8MppBV73CHhNJ5CsXU1e93elIIn5l3upK4iNDmB67E0o/g0mX6oNUkT5mjMk0xnxsjFlnjFljjLnZ6Uwi/c3Ksl3c/W4xJ49N5pIZQ73X8M6t8PyFEJsGFzwHIeHea1tEpB9LTB1Kqw3BvVNTtor/a9xVx8Jnf0PV78dQ8Nl1DGndxoKcG9h7YxHTbn6O4fkz9nvc+GO/g7n+c9ZFTmbG+rspuvskaqvK9ruvSG9Za3cBnwCnANXGmFQAz9ea/RyyHcjs9nMGUHGAth+21k6x1k5JSkryZmwR6YGCWd+jmsEEL/ir01F8bnDzVnZE5Dgdw6sqw4czePeG//y8eem7hJhOYsac4GAq7xqwA5IASWnZNJz1FHG2gR2PnEtrS7PTkUT8Qkt7Jx+uq+HksSkEr3wWTBBMuMjpWCIDUQfwI2vtaGAGcIMxxsuL24kMXLtb2vnB8ytIig7jrm+Px3jrwpu99fDceWA74eJXIGqwd9oVERkAXEFBVAalEN5Y6nQUkQMqXV/IogeuJPgvo5mx8V4agwezbNpfib+jmJmX/4H4pNRvbGNwcgYTbpvHotE/I29vIa6HDmPlRy/0QXoZCIwxScaYQZ7vI4ATgGLgTeByz26XA2/s5/AlQK4xJscYEwpc4DlORPxcaFg4W/OuYkxbEcWL33c6js+0tjST3llOS/xIp6N4VXPCaDI6y2hrbQFgb/FHtNgQRkw+3uFk3jOgByQBcguOZO30uxnVvpZVD16haTJEgH9vrKWptYNZYxNh5fOQdzLEfvMbKhHxLmttpbV2uef73cA6tHaHiFdYa/nF66vZXt/MfRdOZFBkqHca7miDFy+F+pKuOyMTR3inXRGRAaQ+PJO4lnKnY4h8hbuzk5Ufv8yqO49n6PNHM3HHm6wZdCwbz3qb0T9fwORZVxISGnZIbRqXi+nn/5SqC95hlyuBCfOvZdEDV9LS3OSjXsgAkgp8bIxZRdcA4/vW2reBO4ETjTEbgRM9P2OMSTPGzAWw1nYANwLv0vUe9CVr7Zr9nENE/ND4b/2AemJo+eRep6P4TMXmIoKNm5DU/nXNfkj6eEJNJ2UbCgFIrl3ExvBxhEdEORvMi4KdDuAPJs+6kgVV65i57Z8sfOaXzLjst05HEnHUvKJK4iJCOMy9HJqqYeKlTkcSGfCMMdnARGDRPtuvAa4ByMrK6vtgIgHqlWXbeaOwgh+dmMfU7ATvNGotvHUzlPwbzvkXDD3MO+2KiAwwrTFZpO5ZinW7Ma4Bfx21OKypsZ7Vcx4kfcPTTLAV7CCeBUOvI2/WD5ianOGVc2SPnkLLbV+w8IlbmVH9AiV/Wor7nEcYNm66V9qXgcdau4qu94/7bq8D/utWG2ttBTCr289zgbm+zCgivhEZHcfKrIuYue2fbF27hJwxU52O5HV1W1eSAwzOmeB0FK9KGj4ZlsDOzcuIS0onx13CgrTTnY7lVfrL3mPGFXeyLOZYpm2+n8L3n3M6johjWjs6eX9dNSeNSSa48GmIToHck5yOJTKgGWOigVeBW6y1jd2f05odIoduU00T//vGGmYMS+D7x3rxDsb5f4KVz8Exd8D487zXrojIAGMGDyPCtFFbtc3pKDKAbd+0moX/+B7cO5oZ6++iOSiGpVPuIe5nxcy88i4Ge2kw8kvhEVHMuP6frDrmMaLdjaS/fBoLn/sd7s5Or55HRET6vzFn/ohmG0bdO3c5HcUn2ivX0mFdpA3PdzqKV6UPH0eLDaGzsoiSpfMAGJzfvz6X14Ckh3G5GHv9M2wKySXvs1vYsnrRNx8k0g99vqmW3S0dnDXcwMb3oOAiCNLN1CJOMcaE0DUY+ay19jWn84gEupb2Tn7w/AoiQoO474KJBLm8tG7kqpfh49/BhAvh6J94p00RkQEqMjkXgB2l6xxOIgONdbspmj+blXedRNrTRzCp+lWK4w5n/emzGfmLxUw5/RpCw8J9mmH8Md/G9f0vWBc1hRkb7mH1PSdpcF5ERA5J3OBkVqWcQ0HDh1SUrHc6jteF16+nPCidsPBIp6N4VXBIKGXBQ4naVYx78yc0EsXw8Yc7HcurNCDZTXhkNPFXvUyTiSLylYuprSpzOpJIn5tbVEVMeDDTG98F64aJlzgdSWTAMsYY4FFgnbX2z07nEekP7pxXzLrKRv507niSY730gWLpF/DG9yH7SDjjb2C8NMgpIjJADc4cBUBT1UaHk4iv7agoofCD51n92ZtsWvk5FSXr2d2wE+t292mO5qYGFr10N9t+N578j64gY28xi7KupvHaFUy59VVGTjmuT/MkDElnwo/nsmjMz8nbuxLXQ4dT+OELfZpBREQCW84Zt+HGUPb2nU5H8bqkvVupi8xxOoZP1Mfkkd66mYxdS9gcNZGg4P51o1D/6o0XJKVls/Gsp8iYfQ6lj55HzI8+7Hcj7SIH0tbh5r01VZw0Konglc90fbA6eLjTsUQGssOBS4EiY0yhZ9sdnvU8ROQQvbemiie+KOGqw3M4blRy7xvcsQEKn4GlT8CgoXDeUxAc2vt2RUQGuCGZI2i3QXTWbnE6inhZc1MDGxe/y97i90mpXUC2u4z9LTrQYV3sNtE0mWj2BkXTEhxHW0gsnWFxuMMHYSIG4YqMJyQqgbCYwUTEJhA1aAgxgxIJj4g66LVHK7YWs+2dvzKm+k2ms4eNQSNYMvGPjD/5CmY6/FmQcbmYft5PKF13PB2vXE3Bv69l0bp3mXDV/YRHRjuaTURE/F9yxnAWJ5zChB1vUVe93etTjTulpbmJNHcVZQlnOB3FJ9zJ40jYNRdsI2VZVzsdx+s0ILkfuQVHsqzibiYvvoUlD17BlJtfOOg/ZkUC2YItdTS2dHBh8jYoLoFjf+50JJEBzVr7GaBbrUS8oGLXXn7y6irGpcdy+6kje95QSwOsfg0Kn4XtS8AEda21fOqdEJngvcAiIgNYcEgo5a4kQhpLnI4ivdTZ0cHmVZ9Rt+odYis+I7d1LRNMJy02hI3h+SzMOIdBI4+ks72N1qaddDTV4d5bj927C1fLLoLbGghpbySiYxcJrWXE7N5NjG3GZewBz9lmg2k00exxxbA3KIaW4FjaQ2PpDI3DRsRjIgYRFBZN8KZ3mLBnAUMwrIw9mqgjb2DklOP97vOfoaMn03rbFyx8/FZmVD9PyZ+W0Hn2IwzPn+F0NBER8XMpp/6E0GfnsuLNe5j5vfucjuMV2zcWMsJYwlLHOB3FJ2KGTgTPLLupE09xNowPaEDyACbPupIFVeuYue2fLHjml8y87LdORxLxuXlFlUSHBTOx9lUIj4PR/fNKExERGVg6Ot3c8kIh7R1u7r9wEmHBQYfWgNsNJfNhxbOw7i3o2AtJo+DE38L48yHGC3dbiojIV9SFZRDXrGVUAlHF1mLKls4hpOQThu9ZRh57ANgcNIxlaRcSPfokcqccT34P7/Jzd3bS0FhPU/0OmhtraWncSVtTLR176nE312P21uNqbSC4rZGw9gZi2mqI3LuZGNtEtNn7n3bqiWFRxuUMO/UmJmf498xAYeGRzLj+IYo+PYnUj39I7CunsXDVzUy74Oe4gg7x7xoRERkwsvIKWB5zJGPLX2J3wy+JiQv8i2jrS1YBMDhngsNJfCNj1FR4D2pIIHPEeKfjeJ0GJL/GjCvuZNlfNjJ98/0Uvj+aghMvcjqSiM+0d7p5d00VZ+SFE1T8Nky+HEIinI4lIiLSa/d/tInFJTv583kTyEmMOvgD60ug8DkofB4atkFYHBRcCAWXQPokrRUpIuJDe6OzyK5di3W7/e6ONfmqhvpatiyeQ9uGj0jfuZAMW0UaUM1g1g86GteIY8mZOovhyRl4Y9jPFRREXHwicfGJh3xsR3sbu3fV0dRQS1L6MGZGHMLfBX4g/+hzqB8zkzWPX8WMjfey6p5PSbvsMRLThjodTURE/FTMCT8h9vXTWfjGX5jRD2666qhaQ5sNJm3YWKej+ERcQhJbXUOpiZ/IkH74N7AGJL+GcbkYe/3TbPrzseR9dgtbUoczbNx0p2OJ+MSiLTupb27n0qiV0NkKEy91OpKIiEivLdxSx/0fbeScSemcM+kg1sxoa4Z1b8KKZ6Dk34CBYcfACb+EUafpYh0RkT5i43OIrW1m184aBiWmOB1Humlva2Xj8o9pWP0uCVWfM6J9AxONZY8NZ2PURLZnXk7qpFlk5Y4n2c8+SAsOCSU+KZX4pFSno/RYfFIqg348h0Wv/pnxq++i5eEjKDzibgpOuNDpaCIi4odyC46kaN4kRmx5ipa9PyU8wC7G2Vfkro2UB2WQExrmdBSfSbrlU9L6af80IPkNwiOjib/qZZoePpbIVy6mNvFjElMynY4l4nVzV1cSGepiVMVrkFoAqf3vlnARERlY6ve0ccsLhQwdHMVvzxx34B2thbLFUPgMrJ4NbbshPgeO/QVMuAAG6W8/EZG+Fp6cCxuhunSdBiQdZt1utm0opHLFPMK3zSe3uZAxpoVOa9gUMpIlmVcRO+4kcicdS0E//fDM3xiXi+nn/pjS/OPoePlqCj67jkXr3mH8VQ8QERXjdDwREfEz5sgfkvjBpSx6+0Gmn/tjp+P0ypC9WyiPGU+O00F8KDo23ukIPqMByYOQlJbNxrOeImP2OZQ+eh7Rt34Q8FcSiHTX6ba8u7qKK7LrcW1bC6f92elIIiIivWKt5bZXVlK3p5XZlx9OVNh+/uxtrISVz3dNy1q3EUKiYOxZUHAxDD1MU7KKiDgoPiMPgN0VG2DysQ6nGXjqqrezdfEc7OaPydq1mKHUMRTYblJZnXgKIXknMHzaqYzswbSp4j1DR02i9bbPWfj4rcyofp7Se5fRcfYjDM+f4XQ0ERHxI2MPO50Nn+SRsfZhOtpvIjgk1OlIPdLUWE8qOygZnOd0FOkhDUgepNyCI1leeTeTFt3CkoeuYMrNL2odC+k3Fm2to25PGxcEfQLBEZD/HacjiYiI9MqTX5Twwboa/vf0MYxLj/v/JzpaYf1cWPEsbP4QrBuyDoMjboExZ0FYtFORRUSkm+Sho3BbQ/uOzU5HGTBK1i2l6tPHSNqxgOGdWxgMNBDF5ujJlAw9hswpp5GRM4qDmABd+lBYeCQzrn+IovmnkvLRzcS9choLV93MtAt+jisoyOl4IiLiB4zLRdPUm8hbcCNL33uSKad9z+lIPVK+YQUjgfC0r5kBSfyaBiQPwaRTr2RBVTEzSx9iwTO/ZGY/WARWBGBeURXxIW1kls/pujMkPO4bjxEREfFXq8sb+MPcYo4fNYQrD8/umpK1ciUUPgtFL8PeeohNhyNuhYKLYPBwpyOLiMg+wiOiqDKDCW4ocTrKgFD4/nOM/Oxm0uhkY9hYFmbeQML4kxk+/nAmBeujo0CQf9SZ1I+exprHr2LGxntZdc8npF32OIlpQ52OJiIifqDghIsoXXQn8cv/jj316oC82apxWxEAScMKnA0iPaa/Kg/RjMv/yLK/bGD65vtZ8d5IJp50idORRHqlbGczb62q4IepazE1TTDpMqcjiYiI9Nie1g5uen4F8VEh/Om0dMzCB7sGIqtXQ1AYjD69a0rWYceAS3cNiIj4s7rQNGKay5yO0e8tevFOpqy9k80huSR891XGpmQ5HUl6KD4plUE/nsOiV//C+NV30vLw4RQefjcFJ17kdDQREXGYKyiI6vzrmLbyF6z89FUmHHuu05EOWWf1OvbaUFKzRzkdRXoo8IbBHWZcLsZe/zSbQnIZ+fmtbC5a6HQkkR7bsbuVSx5dhLVwnutjGDwCsmY6HUtERKTHfv3GSobVz2du8j+Jf3A8vPszCAqF0+6FH6+H7zwGI47XYKSISADYE5VFUnu50zH6LXdnJwsf+j7T1/2RoqgZZNzyAYkajAx4xuVi+rk/ouai96kLGkLB59ez6s4TKJr/BtbtdjqeiIg4qGDW96gikZAv/up0lB6JbNhAeXAmQZq9IWBpQLIHwiOjib/qZZpMFFGvXkJtla7YlMDT2NLO5Y8tprqxhefOiieiaknX3ZHGOB1NRETkkG1du4TP/3Edt605m0dC7mXwzhUw/Vq4fgFc8zFM/S5ExDsdU0REDkFn/DAG00BTY73TUfqdlr17WPHXbzOj6lkWJZ5D/q1vExmtpTv6k6EjC8j6yRcsyLmRtJaN5H90GVt/N5Elr/+dttYWp+OJiIgDQsPCKcm7kjHtqyle9J7TcQ5ZSstW6qNHOB1DekEDkj2UlJZN49lPE2cbqH3kXFr27nE6kshBa2nv5HtPLmVD9W4evGQyY6vfAFcwTLjQ6WgiIiIHbVdtFQtfuJONv5tCzksnMK36Japix9N53rNw6zo4+feQPMbpmCIi0kPhScMAqC5Z53CS/qWhrpotfz6Jybs/ZuHwm5n2/Ud1p0E/FRoWzszLf0/MT9exeMJvMVimFt5Bwx9HseDJO2ioq3Y6ooiI9LHx3/oB9cTQ8sm9Tkc5JA31tQxhJ52DRzodRXpBA5K9MGLCEaybcTejOtax+sHL2LN7l9ORRL5RR6ebm55fwaKtO/nTuRM4NqYclj0FeadA9BCn44mIiHytjvY2Cj98geX3nEHk/WOZUfxHXLaThXm3sfuGIvJ/9DZBY06HoBCno4qISC/Fpnd94NRQscHhJP1HxdZiGv5+LCPailk69U/MuPQ3GJc+GurvwsIjmXb2TWT/opBVxzxGZfgwZm79OyF/y2fRA1dRvmWN0xFFRKSPREbHUZx1EQV7F7J1zSKn4xy0yg3LAYjIGOtwEukNXQLXS5NOvZIFVcXMLH2Ipj+NYlHiySQecz3D82c4HU3kv1hr+fns1by3tppfnjGGswaXwZPnQvigrrtIRERE/NTWNYuonv84I6rnUcAudhLL8pTvkHTElQzPn8FwpwOKiIjXJWePBqC1ZrPDSfqHjSvmE//GpUTTzqZTnmHKzFOdjiR9zLhcjD/m23DMt9m6ZhG17/+FiTteJ/jJ11gefQSRR93EyKknaJBaRKSfG3Pmj2j+2xPUvXs3OWNfdTrOQWnYtgqAIcMnOpxEekMDkl4w88q7KF5yIrs/+xcTaucQ/urrrH9jJA1jLyX/pCuIiIpxOqIIAHe9s54Xl5bxg+NGcGVqKTx9IcSmwWVvQFyG0/FERES+on5HJes/eJzETa8wonMzGTaI1dEz2V5wMWOP/jYzQsOcjigiIj4UHRtPHXEE1W9xOkrAK/zwBfLm30SDiWXPBbMZM2qS05HEYTljp5Mz9gVqK0rZOOfPjCl/mbh557L+/ZE0TbqeCSdeTHBIqNMxRUTEB+IGJ7Mw5RymVL1IxdZi0nJGOR3pm1WvZY8NJyUz1+kk0gu65MlLRk09gak/fJHWm9awMO/HhLv3MG3lL2i/ZyQL//5dStctczqiDHD/mr+Fhz7dzEXTs7h16FZ49jyIz4Yr52kwUkRE/EZ7WyuFHzzP8ntOJ+qBscxYfxcAC0f+hN03FDHxtjkUnHgRIRqMFBEZEHaEpBO5p8zpGAFt0ct/In/+dZQHZxJy3UcM1WCkdJOYNpSZ37uPkB+vY9HonxHV2cDkxbdQ84exLHzudzQ11jsdUUREfGDYt27HjaFszl1OR/lGaz6fw/gdb7E5Ypzu4g9wukPSy+IGJzPjov/Bun/OmoXvsHfBI0yqeY3QF19mbcg4mvMvZdyJlxIeEeV0VBlAXlm2nd/PXces/BR+m7sJ8+J3ISUfLnkNIhOcjiciIsKW1Yuo+ffj5FbPpYAG6ohjecp5DDnySkaMm84IpwOKiIgjdkdmkNmw3OkYAcnd2cmiR29hZsVTrIyczojvv0RUzCCnY4mfioyOY/r5P6Wz48cs//B5IpY+yIwN99D457+zMOVsck7/EckZmiRfRKS/GJKew+KEU5iw4y1qq8pITMl0OtJ+rV0wj5z3rqQ6KJn0K590Oo70kgYkfcS4XIw9bBYcNoudNeVsePefZGx5kTHLb6d++e8oTPkW6SdcT+aIfKejSj/3wdpqbn91FYePGMx9o4sJevVGyJgGF78E4XFOxxMRkQGsa0rWx0jc9Gq3KVkPo2zixYw96hxNySoiInTE5TBk1/u07N2jC3sPQWtLM0X/uJSZjR+waPCZTL7uEU2/KQclKDiYSSdfCidfyobln7D7o78wtfI53P96gaVxxzLo+FsZMeFwp2OKiIgXpM66ndBn5rLxzXtIvOZvTsf5L2sXvkP2O5ezIyiJ6GvmMThZs/wFOg1I9oGEIenMuPQ3uDt/SdHnb9O+6BGmVD5P8DPPsjqsgNYJV5B//IWEhoU7HVX6mcVbd3LDc8sZmxbLo2NXEfLmbTDsGLjgOQjVm3kREel77W2trPn0VWzhs4xtWsAM08nGoBEsHHk7I0+4kklJqU5HFBERPxKaNAzXNkt16XpNNXqQGnbuYPtDZzOlrYgFOTcy49Lfanoz6ZG8ScfApGOoKFnPtrn3kl/9BlGzP2DNnAl0zPg++UefiysoyOmYIiLSQ5m5E1gecyTjyl+icdcviR002OlI/1G86D2Gzruc2qBEor4712/v4JRD45UBSWNMCbAb6AQ6rLVTjDEJwItANlACnGetHdATz7uCgsg/6kw46syuRcPfe5CckldIWXwLtYt/xca0Mxl64vcDYxFZ8XtrKxq5+sklpMdH8PzYJYS/+yvIOwXOfRJCNPgtIuIk63bT3t5GW+te2lqaaWvdS0drC+1te+loa6GzdS8dbXvpbG/F3f7l1xZseyu2owXb3gKdbdDRgulsw3i+utxtuDpbCXK3EeRuJcjdTrBtw2U7aQmKpjUkjo7QODrDB0FEAkGR8QRHJxAaM5iI2CSiBiUSG59ERGSM1z+43Fy0kB3/fpy8mrkU0Egtg1iech7JR11J7tjpaFl6ERHZn+jUPADqt2tA8mBUlq6n7clvk9tZwdIpdzPzjGudjiT9QFr2SNK+/zAN9X9g4dt/I2fzMyTPv5bSz35L1eirmHDatYRHRjsdU0REeiDmhJ8Q8/rpLHjzr8y87LdOxwGgeOmHZMy9jJ2uBCK+O5fEtKFORxIv8eYdksdaa2u7/fxT4ENr7Z3GmJ96fr7di+cLaIlpQ0m84k46O37HyvmvYZc+xrTypzBPPMWqiCl0TrqC/GPP05Qq0iPb6pq5/PHFRIUE8frYz4j69B4Yezac8y8ICnE6nojIgNOydw9F7z7OoNVPkNleQigdhBpLb6t8mw2mjRDaTAjthNBuQukwXV87XaF0uEJpc0VhcRHa0UTC3hKi9+wm1jYRajoO2G6rDaHRRLPHFUNzUCytIbG0hw7CHRYHEQmYLwcyoxOJiBtMZFwSMfFJRMcM+spA5s6acjZ88DhJm19leOcWMm0Qq6MPZ9ukixl75NmaklVERL5RcvYYAPZWb3I4if/btPIzBs2+mCja2HjS00w5/DSnI0k/ExefyIxLf0N7289Z+u4TDCr8J9PX/Iada/7KiszzyTv9Fk2nJyISYHILjqRo3iRytzxFy96fOj5F/obln5D+1sXscg0i7LtzSUrLdjSPeJcvp2w9EzjG8/2TwCdoQPK/BAUHM+G48+C486gq28TW9x5keNlrDPniBmq++B82Z55DzknXk5I5wumoEiBqdrdwyaOLaO/o5N0JHxO78EEouBi+dT+4NJWKiEhfqinfyua59zGy/FWm0kiJK5PC1PMgOLzrERKGCQ7DBIfjCgnDFRKOKyScoNBwgkLCCQ6LIDg0guDQMELCIjyPSMLCwgkNiyA0KKhHg5rW7aa5eTeN9TvYs6uWvY07aNtdR0fTTjqb62HvToJadhHc1kBYewNxLRVENa8nxjYRaVoP2G6HddFoomlyxdDqiiS7fUvXlKzBuSwa/TNGHn8FkxJTev4fVEREBpy4hCE0EomrfovTUfzayo9fJveTG2g0sTSd/ypjR09xOpL0YyGhYUw541rsad9jzYJ5tH/2N2aW/YvWfzzB4oSTST75R7qjWUQkgJijbiXx/UtY9NY/mH7ebY7l2LhiPilvXkijK5aQq+cwJD3HsSziG94akLTAe8YYC/zTWvswkGytrQSw1lYaY4Z46Vz9VkrmCFKuvpeO9j+y4uOXCFr+BNO3PYp95FFWRM3ENeVKxh11DkHBWvpT9q+xpZ3LH1tC7e69fDJuHgmFT8HU78Kp94DWDBER6RPW7Wb90g/Z8++/M75xPtNxszJqJhUzr2Ps4WeQ7Qe/j43LRWR0HJHRcXCIFz217N1DU30tTQ07aG6opbWxlvY9O3Hv2YndW4+rZRfBrbsIbW9k2eDzSD76anLHTPVRT0REpL8zLhfVQWmEN5U5HcVvLX7lz0wq+i0lwTnEXf0a2bqTQPqIcbkYe/hpcPhplK4vpOrde5lQN4/wF95mXchYdiVOJjxnOpnjj9baXyIifmzszNPY8HEeGev+RUf7zY7M2rhp5Wckv3EBTSaGoCvnkJwxvM8ziO95a2TrcGtthWfQ8X1jTPHBHmiMuQa4BiArK8tLcQJbcEgoE0+6BE66hIqtxZS+/w/yKl5n8PzvUTn/Dkqyv0PuSddr7mT5ipb2Tr775FK21DTw6cjXGLLuVTjsB3Dib8EYp+OJiPR7rS3NrHrncQYVPcaozk00EsmylPPIPPlmJg4b7XQ8rwmPiCI8Ikp/h4iISJ9pjMwkuemgP2YYMKzbzcJHb2Vm+eOsipjKsO+/THRsvNOxZIAaOrKAoSOfZmdNOSvevo/BFR8zpeJZQiqfgi+gwiRTEZNPR+pkBo86guyx0wnR9P0+Y4zJBJ4CUgA38LC19j5jzIvASM9ug4Bd1tqC/RxfAuwGOoEOa61uuxbpx4zLxZ5pN5P3xQ0sffcJppx+TZ+ef9PKz0mafR57iMRc8RYpWbl9en7pO14ZkLTWVni+1hhjZgPTgGpjTKrn7shUoOYAxz4MPAwwZcoU6408/UlazijSrvkbba13s+zD5wlb+QQzSx6k/Z8Pszz6cNqzjyE6fTTJw8YxeEjGV9ZtkoGjo9PNjc+tYEVJDZ8Me46ULe/AMT+Do2/XYKSIiI/tqChh89y/kbv9FabSQKkrg0Vjfs64U69hRswgp+OJiIgEvLbYoSQ3zqejvc2RK/b9UVtrC6v+fgkzG99ncfzpTLz+MQ3uiF9IGJLOzKvuBqCluYnNRV+wa+MXhFYuI6txOUMaP4D1d9HyegibQvNoGFxAWM50MvKP1jph3tUB/Mhau9wYEwMsM8a8b609/8sdjDH3Ag1f08ax1tpaXwcVEf8w4fgLKV14JwnL/46d9d0+G2fYXLSQxNnnsZdI7OVvkzZ05DcfJAGr1wOSxpgowGWt3e35/iTgN8CbwOXAnZ6vb/T2XANZaFg4k2ddCbOupGzjSso/eJCR1W8Tv2Y+rOnaZ7eNoCokg4bIobTHjyB0SC6DMseQNjyfiKgYZzsgPmOt5WevFfHvdWV8lPk46eWfdt0VefhNTkcTEem3rNvN+uUf0/Tp35nQ+AnTcLMqcjoVM69j3BFnMlQXCImIiHhNUOJwQso7KS/bTHo/mnWgpxrqa9n+4DlMaVvJguzrmXHZH3Rxsvil8MhoRk0/CaafBHT9DV1VvoXyok9pL11MfF0hkypfJLTqWVgAVSRRHjOO9tTJxOcdTva4GYSFRzrci8DkWUbry6W0dhtj1gHpwFoAY4wBzgOOcyykiPgVV1AQNeOvY2rhz1n5yStMOO48n59zy+pFJLz6HVoIp/Oyt0jPGeXzc4qzvHGHZDIwu6uOEQw8Z619xxizBHjJGHM1sA041wvnEiAzdwKZuQ/h7uykcvsmakvWsKeiGFO3icjdW8loXElK4wdQCizpOqaKRGrDMtgTMww7eASRqaNIHDqW5MwRWpMywN05r5i3l23i3ZR/krljEZx2b9e6kSIi4nWtLc0UvfcksaseY1THBnbbCJYlf4eMk26iYMQ4p+OJiAw4xpjHgNOBGmvtuG7bfwDcSNcdInOstT9xKKJ4QUxKLqyEnWXrBvyAZFXZJloeP5u8znKWTPojM8/8vtORRA6acblIyRxBSuYI4Gqg6+/r4tUL2LX+c0Iql5G+u4iU3R/Dhj/R+lYIxSEj2DW4gJCh08kYf5TWFOsBY0w2MBFY1G3zkUC1tXbjAQ6zwHvGGAv80zPD3f7a1lJcIv3IhFO/S1XhXwhZcB/4eEBy69olxL/yHdoJofOyNwf833gDRa9Hoqy1W4AJ+9leBxzf2/blwFxBQaQOHUnqfm5j3rtnN5Vb11K/bS1t1cWE1G8mtrmUrNp3iK1thvVd+7XaELYFpVEfMZTWuByCh+QRkzGa1GHjiUtI6uMeyaH656ebeXb+auYN/htDG1bDWQ9CwUVOxxIR6Xdqq7axcc7fyC17mSnsYpsrnUWjf8bYU69lhtZqEhFx0hPAA3StkwWAMeZY4ExgvLW21RgzxKFs4iWJQ7s+oGquOtDn5gPD5lVfEPPaxQy2e1l/wuNMPfJMpyOJ9FpYeCSjphwPU/7/I8Sa8q1sL/qUtpLFxNWtYGLVK4RVPw+LoYYEtkePoy1lMoPyDic7/zDCI6Ic7IF/M8ZEA68Ct1hrG7s9dSHw/Ncceri1tsJTQ983xhRba+fvu5OW4hLpX0LDwikZeRUz1t9N8aL3uu5y94HSdcuIe+kc2gmm9ZI3yRw21ifnEf+jW+P6qYioGIaNmw7jpn9lu3W7qa0pp6ZkDU3l63Dv2Eh44xaSmjeR0vQ5IRWdUNi1bz2xVIVksDsqG3fCCMJSRpKQNYbUnDGEhoX3fafkK15aWsY/5i1lTty9ZO3djPn2ozDuHKdjiYj0KxuWf0LjJ/czvuFjZppOVkZMo2L6dYw78iyygoKcjiciMuBZa+d77vzo7nrgTmttq2efmj4PJl6VmJLFXhuK3bnV6SiOWfXJqwz/+Ps0mShqz3+LcWOmOh1JxGeGpOcwJD0HuALoWjN1w5pF7Fz/GcEVy0hrKiJt03zY9Bfa5gSxIWQ4OxMKCB46jdwZ3yJucLKj+f2FMSaErsHIZ621r3XbHgycA0w+0LHW2grP1xpjzGxgGvBfA5Ii0v+MP+NG6tc/SMsn9/5nym1vKl1fSNSL5+DGRctFs8kake/1c4j/0oDkAGNcLhJTMklMyQRO+cpz7W2tlJUWU1e6lpaq9bh2biK6qYRhu74gcddc2AJ8AXtsOEWxh2HGnsnoI7+t9Skd8P7aav702r95K+ZuMjsqMOc/CyNP+eYDRUTkG7W1trDqvSeJWfkoIzvW02QjWD7kHNJPvpkJ+kNZRCQQ5AFHGmN+D7QAP7bWLtnfjppqLjAYl4uqoFTCdpc6HcURS167j4krf0Vp8FBirppNTnqO05FE+lRoWDh5k46GSUf/Z1tt1TbKVs2nZesi4upWMKH6dSJqXqJ4SI4GJPnPGpGPAuustX/e5+kTgGJr7fYDHBsFuDxrT0YBJwG/8WlgEfEbkdFxrBx6MTNLH2LrmkXkjJ3+zQcdpG0bCol6vmuGhz0XvsHQvAKvtS2BQQOS8h8hoWGe9Sn/awZeGnfVUbVlNY3b19JZ8gW5Oz8hYeFHNC/4CctjZmDHnMmoI79NVMygvg8+wCzaUsdvnnuf2RF/II16zEUvwvBjnY4lIhLwaqvK2Dj3fnK3vcgUdlFm0lg48nbGzrqOGXEJTscTEZGDFwzEAzOAqcBLxphh1tr/mkpOU80Fjl3hGcS3lDkdo09Zt5uFj9/GzLJHWBUxmZzrXyFGf5OIAF13TiemXAJcAnRdZL9p3RJyRk5yNpj/OBy4FCgyxhR6tt1hrZ0LXMA+07UaY9KAR6y1s4BkYHbXmCbBwHPW2nf6KriIOG/Mt25lz9+eoO7du8kZ+6pX2izbVETEc2dhsDSdP5uhIwu80q4EFg1IykGJHTSY2P9cjXY9He1trF78LntWvMbw2o9IXPwpLYtuZ0X0dDpHfYuRR52rN0o+sKaigV89OYeXQn5LctBezMWvwdCZTscSEQloG1fMZ9cn9zNh10fMNB2sCp9K+fRryT/qHDI1LauISCDaDrzmGYBcbIxxA4nADmdjSW+0xg4lZc9i3J2duAZAfW5rbWHlPy5jZsO7LB40i4nff4KQ0DCnY4n4rZDQMEZMOMLpGH7DWvsZYA7w3BX72VYBzPJ8vwX477sVRGTAiBuczMKUs5lS9SIVW4tJyxnVq/a2b1pN2DPfIohOGs+fTfboA84YLf2cBiSlR4JDQhl3+Blw+Bl0dnSwdukHNC57hWE7PmTIss9oW3oHhVFTaR/5LfKOOo+4+ESnIwe80ro9/O+js3nS/JrEMIvr0jchXVf+iYj0RGtLM0UfPEN04WOM6ljHHhvOiqQzST3pJsZryhARkUD3OnAc8IkxJg8IBWodTSS9ZhKGEV7VTnVlCckZw52O4zPW7WbD8k/oeO9XTG1byYKsa5lxxZ0Yl8vpaCIiIjKADPvW7bgffomyOXeRduPjPW6nfMs6Qp75FiF0sOvcV8gZPcWLKSXQaEBSei0oOJgxM06BGafg7uykeNlH7Fr2CtnVH5Cy4me0Lf8FKyMn05p3BiOPOl9z+fdATWMLv/zXi/yr85fERoThuuINSB7rdCwRkYBTur6Qyo8eYmT1HKbQyHaTwsK82xgz63qmDxrsdDwRETlExpjngWOARGPMduCXwGPAY8aY1UAbcPn+pmuVwBKZkgdroXbb+n45INnUWM+adx4hcf2zjOzcSrMNY3HB75h59g+cjiYiIiID0JD0HBYnnMqEHW9RW1VGYkrmIbdRsbWYoKfOIIxW6r79KsO9uB6lBCYNSIpXuYKCGDXtRJh2YteVnYXz2bn4JbKq3ydt5f/QXvgrVkUU0JJ7BrlHXUB8UqrTkf1ew952fvfwM9y393+JiI4l+Mq3IXGE07FERALG3j27Wf3+U0SveZbR7WtIs0EURR9G2ZQrGHfU2WQMgGnfRET6K2vthQd46pI+DSI+NzhzJAB7KjfgmVWwX9i08jPqPv0n+XXvMt20sjkoh0VjfsGYk69mmpZBEREREQelzvoJoc/MYeOb95B4zd8O6diKkvWYJ08ngmZqz3mF4fkzfJRSAokGJMVnjMtF3qRjYNIxWLebjas+p3bxS2RWvkdG0a/oWPUbVoePZ8/w0xl+1AU9usqiv2tp7+TP/3qcP+z+X4JiEgm9eg7ED3U6lohIQNi86gtq5/+L0bXvMJVmtptUFgy7idyTrmGSao6IiEhAGZIxjDYbRNrqh1jQWEnqjHMZOnJSQE5l2tzUwOp3Hyd+3TPkdmwk3YZSFH8CcUd8j7xJxzA8APskIiIi/U9m7gSWxxzFuPKXaNz1S2IPcmapqm0b4cnTiaKZmrNfYsT4w3ycVAKFBiSlTxiXi9yCI8ktOBLrdrN5zWJqFr1IesW7jFv7OzrX/J41Yfk0DT+NYUdeQFJattORHdfR6eYfjzzMT+v+l47YDCK+Nwdi05yOJSLi15oa61nz7mMkrH+e3I6NZNgQiuKOJnz6FYydeRoZ+oBPREQkIAWHhLK44FfErXmWmaUPQelDbDepbE8+jkGTzyFv0rG4/HzWg61rFlHz8UOM2TGPaWYvJa4sFo68ndGnXMO0+ESn44mIiIj8l9gTf0LM7NNY8OZfmXnZb79x/6qyTXQ+fhoxtonqM18kd8IRfZBSAoUGJKXPGZeL4fkzGJ4/A+t2s7V4GVULXiS1/F3Grvsj7rV3si50DA05s8g+8gJSMgfe9KRut+WpJ/7BDVW/ZE/scBKunQPRSU7HEhHxS9btZsPyT2j4/BHG7fyA6aa16wO+vNsYddJ3mZKY4nREERER8YJpZ98EZ99EbUUpmz97iYgt85hc+QIhc56lds4gNg8+mohx32LUYacTGhbudFwAWpqbKHrvSWLWPMOo9rWk2RCK4o4h6vDvMWrqiWTrYikRERHxYyMmHMGqeZPJ3fIULc23Ex4ZfcB9a8q30vHYacS5G6k880XyJh7Vh0klEGhAUhxlXC5yxkwlZ8xU4E+UFi+nYsGLJJe9w4wN98CGe1gfPIr67FMYNOooho6eRkRUjNOxfcpayxvP/I3Ltv2K2pjRpHx/DkTEOx1LRMTvNOzcwbp3/0XyxhcZ6S6h2YaxOv54Yg+/mpGTj9MHfCIiIv1UYtpQEs+7DbiNhvpaNn72Kq71c8ivfYfIT99g9ycRFMXOhNGnM/KIc4iO7fv3U6XrC6n88B+MrnmbqeyhzKSxMPdWRp18rS6WEhERkYDiOvKHJL5/CYvefpDp59223312VJTQ+sgs4t0NlJ/xHCMnHd3HKSUQGGut0xn+Y8qUKXbp0qVOxxA/UbZxJdu/eJGkbe8wonMzAJ3WUBaUQW30SDqG5BOdPYnMMTOJS+g/dw9+/MK9HL3ut2yLmcDQG9/EhMc5HUmkzxljlllrpzidw2nGmFOA+4Ag4BFr7Z1ft/9AqKPW7WbdondpXvAo4xo+Idy0szFoBDtHXciYk64iJi7B6YgiIo5THe2ZgVBH+7uWvXtYv+AtWoveZET9v0mgkTYbzLrISbSOmMWww79Dog/XkW5taabow2eJWPkkY9uKaLNBFMUeRdiMqxk787SAXO9SZCBSHe0Z1VGR/su63Wz4wwxiOncx5I7VBIeEfuX52opSmh85lcGddZSd9gyjpp3oUFLxB19XR3WHpPitzNwJZOZOAP5A9fbNVKxdQEtZIRF1q8lsXEFy4wewCfgAKswQqiPzaEkcR0TWRNJGTScpdajfvuFra22hqrSYnWXFtFRtwOzcTFRTCYmt5RzLDtZFTWXkD97AhEU5HVVEHGKMCQL+DpwIbAeWGGPetNau9eV5dzfsJDpmkN/9/qyr3s7G9x4mfcvLjLEV7LYRrEw8jcFHXUPuhMOdjiciIiJ+IDwiignHXQDHXUBnRwdrl35A44rZZNV8RFrRr3Cv+nXX8iBDTyLzsPNJHzbaK+fdvmk12z/4ByOr3mIKjVSYZBYMu4m8k69lcnKGV84hIiIi4hTjctE87SZGfnEDS995nClnXPuf52qrytjzyCySOmspnfU0ozUYKV9DA5ISEJIzhpOcMRy45D/bdtaUU168mD0lywnZUcSQpvWkl3yOq9TCv6GOOMrDc9mTMIbQjAKG5E0jPWcMrqCgPsnc2dFB1baN1G1bS3PleszOzUTsLiWhtYwUdw1Zxk2WZ99GoqgKTmd77ARKksYy6dyf4QqL6JOcIuK3pgGbrLVbAIwxLwBnAj4bkLRuN0F/HsVeYEdQEo0hSeyNSKUzJo2gQRmED84iNnkog9OG9cmdiO7OTtZ89gbtSx5n3O7PmWE6KQ4Zw+Ix32fciZcxPVp3kIuIiMj+BQUHM2bGKTDjFKzbzeY1i6lZ8ipDyt9nxqa/wKa/sNWVTVX6CSRNOYfh+TMP6YKs9rZWij56npAVT5DfuoIU66Io+jC2T7uasUecSVofve8UERER6QsTjr+Q0oV3MnjFP7CnfQ/jclFXvZ2mh09lSOcOSk59ijHTT3Y6pvg5DUhKwEoYkk7CkLPhqLP/s62psZ7t65awa8tSXNVFDN5dzKjyZwmteAoWQ5ONYFvocBoHjcaVNoHBI6aSNXIiIaFhPcrg7uykpnwLtaVr2VO5AVu3mfDGrV2Djp1VpJtO0j377rHhVAanUx09mrK4WQQnjiAmfSTJ2WMZNDiZWD+7G0lEHJcOlHX7eTswfd+djDHXANcAZGVl7fv0IXG73azKuwEaygltriSqpZrkXQtJrK/HVfbVKd532wjqgpJoDB1CS0QKnTHpBMVnEDE4k9jkHJLSc4js4YBhTflWNr/3EEO3vUa+rWEX0SxP/g4px17DqNGaOUlEREQOjXG5GJ4/g+H5MwCo2FrMti9eIrb0PaZte5SgskeonJ1E6ZBjiSk4m5FTT/ivqci+VFm6npL3HiS3fDaT2EUViSwYeh0jTr6eiWnZfdgrERERkb7jCgqiZvx1TC38OSs/eYnMcUey+5+nMqSzhq0nP8HYGac4HVECgNaQlH6vtaWZsvUr2LlpCbZyJXENxWS1bSbStALQZoMpDc6mPnYUNmU8ccMmkzV66n8+SLduN3VVZdSUrqWpopjO2k2EN5YwaG8ZqZ0VhJv2/5yrxYZQGZROfUQmrbE5BCUOJzp9FEOyxzB4SIbfTYEo4q+0ZgcYY84FTrbWftfz86XANGvtDw50jK/qaHtbK3VV29hVuZWm2lI6dpZhGssJba4iurWa+I4dJLLrv45rIIo6VxK7w4bQEpmKOyaN4EGZRCRmEpeSQ1JaDuGR0QB0tLex+tNXYfmT5O9ZSJCxrA4roCX/YsYdfzHhEZrCWkTkYKmO9ozejw5MddXb2fzZK4Rumsfo5mWEmXbqiWXjoCMIHfctRh12BsEhoaz+5BXMssfI37sUCxRFTsdMvZJxR32HoGBd6y3Sn6iO9ozqqEj/197WSt0fxtAYnECIu42Uzgo2n/QY4w4/w+lo4ke0hqQMaGHhkYyYcDh0W2Oss6ODbVtWU7NhMR3bC4mqX0tu/afE178N66DzbUNpUDodJpSUjgoSTQuJnmPbbBCVQanUh2dSHXMYJnEEUal5JA4dw5C0HHKCgshxpqsi0r9sBzK7/ZwBVDgRJCQ0jJSsXFKycg+4T2tLM7UVpTRUl9C8o5T2+jJcuysIa64kurWGrL3FxNc1/tdx9cSyMyiRmM5dFLCTWgaxOP1SMo+7lnEjxvmyWyIiIiIMTs5g8LdvAW6hqbGeNZ+/jnvd24za9Qmxn82l+d83U28iKaCeHcSzOPMqsk+6noKv+btIREREpD8KCQ2jZORVzFh/Ny02hE0nPKrBSDkkGpCUASkoOJisvAKy8gr+s8263VSVb6Fy3UJaygoJr1uDy91OUcJkzODhRKTkkTh0LMmZIxgaHMxQ5+KLyMCwBMg1xuQA5cAFwEXORjqwsPBI0oeNJn3Y6APu09LcxI6KrTRUl7B3xzY6dnUNWoY3V9JoUtk+4ULyjz2PmT2cRltERESkN6Jj45l06pVw6pW0tbZQtHAuzaveIKSljrLx55N/7HnM0N8pIiIiMoBN+NZNLHl4FRFTLmLckWc6HUcCjAYkRTyMy0VK5ghSMkcAlzgdR0QGOGtthzHmRuBdIAh4zFq7xuFYvRIeGU3miHwyR+Q7HUVERETka4WGhZN/9Dlw9DlORxERERHxGxFRMUz94YtOx5AApQFJERERP2WtnQvMdTqHiIiIiIiIiIiISG+4nA4gIiIiIiIiIiIiIiIiIv2XBiRFRERERERERERERERExGc0ICkiIiIiIiIiIiIiIiIiPqMBSRERERERERERERERERHxGQ1IioiIiIiIiIiIiIiIiIjPaEBSRERERERERERERERERHxGA5IiIiIiIiIiIiIiIiIi4jMakBQRERERERERERERERERn9GApIiIiIiIiIiIiIiIiIj4jAYkRURERERERERERERERMRnjLXW6Qz/YYzZAZR6oalEoNYL7QQC9bX/GSj9BPW1v/JGX4daa5O8EWYgUR3tkYHS14HST1Bf+yv19dCojvaA6ughGyj9BPW1v1Jf+x9v9VN1tAe8VEcHyr9VUF/7K/W1/xko/YQ+qKN+NSDpLcaYpdbaKU7n6Avqa/8zUPoJ6mt/NZD62l8NpNdwoPR1oPQT1Nf+Sn2VQDJQXsOB0k9QX/sr9bX/GSj97M8G0muovvZP6mv/M1D6CX3TV03ZKiIiIiIiIiIiIiIiIiI+owFJEREREREREREREREREfGZ/jog+bDTAfqQ+tr/DJR+gvraXw2kvvZXA+k1HCh9HSj9BPW1v1JfJZAMlNdwoPQT1Nf+Sn3tfwZKP/uzgfQaqq/9k/ra/wyUfkIf9LVfriEpIiIiIiIiIiIiIiIiIv6hv94hKSIiIiIiIiIiIiIiIiJ+QAOSIiIiIiIiIiIiIiIiIuIzATEgaYzJNMZ8bIxZZ4xZY4y52bM9wRjzvjFmo+drvGf7icaYZcaYIs/X47q19YkxZr0xptDzGOJUv/anB32d1q0vK40xZ3drq1/1tdtxWcaYJmPMj7tt89u+9uA1zTbG7O3Wl4e6teW3/YQe9fXibn0pNMa4jTEFnucme/4f3mSM+ZsxxjjYtf/Sg76GGmMe9/RppTHmmG5tBWpfz/X87DbGTOm2/2DP/k3GmAf2acuv+9pf9eDfq+oo/t3XQ+1nt+MCqoaC6qhnu+qo6uiXz/l1X/urHvx7Dcg62oN+BmQNBdVRz3bVUdXRL9vy275+TT9VQwNED/6tBmQNBdVRz3bVUdXRAs9zfvs7twf9DMgaCn5aR621fv8AUoFJnu9jgA3AGOBu4Kee7T8F7vJ8PxFI83w/Dijv1tYnwBSn++TFvkYCwd2Oren2c7/qa7fjXgVeBn4cCK9rD17TbGD1Adry23725jX1bM8HtnT7eTEwEzDAPOBUp/vXy9f1BuBxz/dDgGWAK8D7OhoYue+/SyAKOAK4Dnhgn7b8uq/99dGDf6+qo37e157+viXAamgPX9NsVEf9/vdtD15X1dEA6Gt/ffTg32tA1tEe9DMga2hP+trtONXRftTXfY5VHfXDvn5NP1VDA+TRg3+rAVlDe9hX1dF+2FdUR7/82W9/5/bgNQ3IGvoNfXWsjgbEHZLW2kpr7XLP97uBdUA6cCbwpGe3J4GzPPussNZWeLavAcKNMWF9GrqHetDXZmtth2d7OGD7NHAvHGpfAYwxZwFb6HpdA0JP+hmoetnXC4HnAYwxqUCstXaB7fqN99QBjnFMD/o6BvjQs38NsAuYEsh9tdaus9au38/+e6y1nwEt3bcHQl/7K9XR/ldHB0oNBdVRVEfP8nyvOhoAfe2vBkodHSg1FFRHUR09az+Hq476YV9VQwPfQKmhoDqK6uhZjgT0ooFSRwdKDQX/rKMBMSDZnTEmm66rZRYBydbaSuj6j0vXCPW+vg2ssNa2dtv2uOc24v/xt9touzvYvhpjphtj1gBFwHXdihn0o74aY6KA24FfH6AZv+/rIfz7zTHGrDDGfGqMOXKfZvy+n9Cj/1fPx1O46CoC27s9t92zzS8dZF9XAmcaY4KNMTnAZCCTwO7roQqovvZXqqP9r44OlBoKqqOgOqo6Gjh97a8GSh0dKDUUVEdBddRDdbSL3/ZVNTTwDZQaCqqjoDqqOhoYv3MHSg0F/6mjATUgaYyJpuvW7lustY0Hsf9Y4C7g2m6bL7bW5gNHeh6X+iJrbx1KX621i6y1Y4GpwM+MMeGep/pbX38N/MVa27Sf5/y+r4fQz0ogy1o7EbgVeM4YE+t5zu/7CT36f3U60GytXf3lpv3s5pdXih1CXx+j65f1UuCvwBdAB/2zrwdsYj/b/LKv/ZXq6P4Fch0dKDUUVEe/YX/V0a8K9L4esIn9bPPLvvZXA6WODpQaCqqjB6A6GkC/bwdKHVUNDXwDpYaC6ugBqI526W99/XL/gKyjA6WGgn/V0YAZkDTGhND1H+1Za+1rns3Vput20S9vG63ptn8GMBu4zFq7+cvt1tpyz9fdwHPAtL7pwcE71L5+yVq7DthD1xzr/bGv04G7jTElwC3AHcaYG8H/+3oo/bTWtlpr6zzfLwM2A3men/26n9Djf78X8P9X0UDXL/mMbj9nABX4mUN8XTustT+01hZYa88EBgEbCey+HqqA6Gt/pTra/+roQKmhoDqK6qjqaJeA6Gt/NVDq6ECpoaA6iupod6qj/8/v+qoaGvgGSg0F1VFUR1VHu/j979yBUkPB/+poQAxIGmMM8Ciwzlr7525PvQlc7vn+cuANz/6DgDnAz6y1n3drJ9gYk+j5PgQ4HViNH+lBX3OMMcGe74fStRhpSX/sq7X2SGtttrU2m66rEf5grX3A3/vag9c0yRgT5Pl+GJALbPH3fsKh99VzjAs4F3jhy22267b43caYGZ42L+t+jD/owesaabqmqMAYcyLQYa1dG+B9PSSB0Nf+SnUU6Gd1dKDUUFAd9VAdVR0NiNe1vxoodXSg1FBQHfVQHUV11N/7qhoa+AZKDQXVUQ/VUdVRv/+dO1BqKPhpHbXW+v0DOIKuW0BXAYWexyxgMF0Lim70fE3w7P8Luq4qKez2GAJEAcs87awB7gOCnO5fL/t6qacvhcBy4CzP9n7X132O/RXw40Doaw9e0297+rHS85qeEQj97OlrChwDLNxPW1PoKsybgQcA43T/evm6ZgPr6Vo8+ANgaD/o69l0XSHTClQD73Y7pgTYCTR59hkTCH3tr48e/HtVHfXzvh5qP/c59lcESA3t4WuqOmr9//dtD17XbFRH/b6v/fXRg3+vAVlHe9DPgKyhPenrPsf+CtXRftFXzzHHoDrqt339mn6qhgbIowf/VgOyhvawr6qj/bCvqI5+ud1vf+f24DXNJgBr6Df01bE6ajyNiYiIiIiIiIiIiIiIiIh4XUBM2SoiIiIiIiIiIiIiIiIigUkDkiIiIiIiIiIiIiIiIiLiMxqQFBERERERERERERERERGf0YCkiIiIiIiIiIiIiIiIiPiMBiRFRERERERERERERERExGc0ICkiIiIiIiIiIiIiIiIiPqMBSRERERERERERERERERHxGQ1IioiIiIiIiIiIiIiIiIjPaEBSRERERERERERERERERHxGA5IiIiIiIiIiIiIiIiIi4jMakBQRERERERERERERERERn9GApIiIiIiIiIiIiIiIiIj4jAYkRURERERERERERERERMRnNCApIiIiIiIiIiIiIiIiIj6jAUkRERERERERERERERER8RkNSIqIiIiIiIiIiIiIiIiIz2hAUkRERERERERERERERER8RgOSIiIiIiIiIiIiIiIiIuIzGpAUEREREREREREREREREZ/RgKSIiIiIiIiIiIiIiIiI+IwGJEVERERERERERERERETEZzQgKSIiIiIiIiIiIiIiIiI+owFJEREREREREREREREREfEZDUiKiIiIiIiIiIiIiIiIiM9oQFJEREREREREREREREREfEYDkiIiIiIiIiIiIiIiIiLiMxqQFBERERERERERERERERGf0YCkiIiIiIiIiIiIiIiIiPiMBiRFDpIxpsQYs9cY02SMqTLGPGGMifY8d6wx5mNjTIMxpmSf44YYY543xlR4nv/cGDP9IM53hTHms4PYxxpjzttn+zGe7a/ts32CZ/snB9tvERERb3CojlpjzJ/32X6WZ/sTnp+zPT837fM4f5/jfuXZb1pv/1uIiIgcCgdr6G37bN9ujDnmAPvqPamIiPgth2ppp+d8jcaYQmPM6d2eN8aYLcaYtfs51hhjbjPGbPRk3maMudMYE+aF/xQijtKApMihOcNaGw0UABOBn3m27wEeA27bzzHRwBJgMpAAPAnM+bLo9dLlwE7P133tAA4zxgzeZ/8NXjiviIhIT/R1Hd0MnG+MCe627TL2XwsHWWujuz1e/PIJY4wBLuXANVdERMTX+rqG7gRuN8bEfsN+ek8qIiKBoq9r6QLP+QYBjwIvGWMSPM8dBQwBhhljpu5z3N+Aa+h67xoDnAocB7x0EOcU8WsakBTpAWttFfAuXQUMa+1ia+3TwJb97LvFWvtna22ltbbTWvswEAqM7E0GY8xQ4Gi6CtTJxpjkfXZpA14HLvDsHwScBzzbm/OKiIj0Vh/W0SqgCDgZwPPm7zDgzUOMfCSQBtwMXGCMCT3E40VERLyiD2voOmAB8MMD7aD3pCIiEoj6+nNda62brgHPCGCYZ/PlwBvAXLpd1GOMyQW+D1xsrV1gre2w1q4Bvg2cYow57pA7LOJHNCAp0gPGmAy6rk7Z1INjC+gqXId87D4uA5Zaa1+l683ixfvZ5ynPftD1YewaoKKX5xUREemVPq6j3WvhBXS96Ws9xNNeDrwFfHnX5Olfs6+IiIjP9HEN/R/gh93u5tiX3pOKiEjA6evPdT0z9nwXaAI2GmMige/QdYHOs3z1otfjge3W2sXd27DWlgELgRMPNbOIP9GApMihed0YsxsoA2qAXx7KwZ7pbp4Gfm2tbehllsuA5zzfP8d+psix1n4BJBhjRnr2f6qX5xQREekNJ+robOAYY0wcX18La40xu7o9RnvOGQmcCzxnrW0HXkHTtoqISN/r8xpqrS0E3gNuP8Auek8qIiKBpK9r6QxjzC66Zu65EDjbc9w5dF0k+x7wNhAMnOY5JhGoPEB7lZ7nRQKWBiRFDs1Z1toY4BhgFIdQBIwxEXTdXbHQWvvH3oQwxhwO5AAveDY9B+R7rtLZ19PAjcCxdH0oKyIi4pQ+r6PW2r3AHOAXQKK19vMD7JporR3U7bHOs/1soIOuqXSg6wrWU40xSQebQURExAucei/6v8D1xpiUfdrUe1IREQk0fV1LF3reWyZaa2dYaz/wbL8ceMkzHWsr8Br/f1FPLZB6gPZSPc+LBCwNSIr0gLX2U+AJ4E8Hs78xJoyutTPKgWu9EOFywACFxpgqYJFn+2X72fdpuuYen2utbfbCuUVERHrFgTr6FPAjumriobociAa2eWruy0AIXVe4ioiI9Km+rqHW2mK6Pii9Y5+n9J5UREQCkpOf63qmiz0OuMQYU+Wpod8BZhljEoGPgExjzLR9jssEZgAf9ub8Ik4LdjqASAD7K1DiuQJ0FV3zh4cAxhgTDrittW3GmBC6pnfbC1zmWcj4YH3Z1r7OA66h646PL30b+F9jzE+672it3WqMOZr9LMwsIiLioL/i+zr6pU/pWmtjxaEcZIxJp2sNj1M9Gb90C10fxP6tB1lERER666/0XQ0F+LXnPIauk4Sj96QiIhLY/krf1tIvXQpsoGvWgO6+AC601t5vjHkIeNYYcymwhK67OR8HPuh2l6VIQNIdkiI9ZK3dQdcdF/8DHEVXYZoLZHm+f8+z62HA6cBJwC5jTJPnceRBnOYwT1vdH9/xfH3KWlv15QN4FAgCTtlP1s+stRU97qyIiIiX9VEd/fJc1lr7obV259fs1r3tJmPMrXS9WSy01r63T839GzDeGDPuELstIiLSa31ZQz3n20rXXY5Rnk1nofekIiISwPq6lnZzOfCP7vXTU0Mf4v+nbb0ReAR4BmgC3gE+oevCH5GAZqy1TmcQERERERERERERERERkX5Kd0iKiIiIiIiIiIiIiIiIiM9oQFLEQcaYh/aZHu7Lx0NOZxMREfF3qqMiIiI9oxoqIiLSO6qlIodOU7aKiIiIiIiIiIiIiIiIiM8EOx2gu8TERJudne10DBERcdiyZctqrbVJTucINKqjIiICqqM9pToqIiKgOtpTqqMiIgJfX0f9akAyOzubpUuXOh1DREQcZowpdTpDIFIdFRERUB3tKdVREREB1dGeUh0VERH4+jqqNSRFRERERERERERERERExGc0ICkiIiIiIiIiIiIiIiIiPqMBSRERERERERERERERERHxGQ1IioiIiIiIiIiIiIiIiIjPaEBSRERERERERERERERERHxGA5IiIiIiIiIiIiIiIiIi4jMHPSBpjMk0xnxsjFlnjFljjLnZs/1XxphyY0yh5zGr2zE/M8ZsMsasN8ac7IsOiIiIiIiIiIiIiIiIiIj/Cj6EfTuAH1lrlxtjYoBlxpj3Pc/9xVr7p+47G2PGABcAY4E04ANjTJ61ttMbwUVERERERERERERERETE/x30HZLW2kpr7XLP97uBdUD61xxyJvCCtbbVWrsV2ARM601YEREREREREREREREREQksPVpD0hiTDUwEFnk23WiMWWWMecwYE+/Zlg6UdTtsO/sZwDTGXGOMWWqMWbpjx46exPmKzo4OdtVW9bodEREREREREREREREREem9Qx6QNMZEA68Ct1hrG4EHgeFAAVAJ3Pvlrvs53P7XBmsfttZOsdZOSUpKOtQ4X23L7WbTnYex9fGre9WOiIiIiASehc//kfJf5/HBW8/R3ul2Oo6IiIh8gzmrKhn/q3e57ullvLO6itYOrfIjIiIi0NHpZtX2XTz22VZueHY50//wAec9tEB/KwS4Q1lDEmNMCF2Dkc9aa18DsNZWd3v+X8Dbnh+3A5ndDs8AKnqV9pvyuVzsHDKDaeVPUVGynrTskb48nYiIiIj4ibbWFkasf4gE20D6suuZveptImf9jpMKcjBmf9fJiYiIiJNWlzfwo5cLSRsUwdLSet5ZU0VseDCz8lM5a2I607ITcLlUw0VERAaCxpZ2VmzbxbKSnSwpqaewbBd727sGH9MHRZCfPogP1lXz27fX8ruz8h1OKz110AOSpuuTnEeBddbaP3fbnmqtrfT8eDaw2vP9m8Bzxpg/A2lALrDYK6m/xrBZN2P/9TTb5v2FtOsf8vXpRERERMQPrHrvSaawi6Ij/0Fc9WLO3vgEm2av4Mfzf8r53zqDaTkJTkcUERERjx27W/neU0tJiAzlxWtmEh8Zwueb63hjRTlvrqzghSVlpMWFc0ZBGmcVpDM6NdbpyCIiIuJF5bv2srRkJ0tL6llaWk9xVSPWgsvAmLRYzp+ayeSh8UzJjic1LgKAP8xdx8PztzBlaAJnTfyv1QElABzKHZKHA5cCRcaYQs+2O4ALjTEFdE3HWgJcC2CtXWOMeQlYC3QAN1hrfX4/bXLGcJbFHs2Y6jfYs/tOomIG+fqUIiIiIuKwmJWPss2kMfbYC3AFXUznxjNJe+Va7qr/IX9+5HP+lXsVPzl1LLnJMU5HFRERGdBaOzq57pll1De38cp1h5EUEwbA0XlJHJ2XxO/aOnh/bTVvFFbw6L+38s9PtzAyOYazJqbzrYI00gdFONwDERERORQdnW6Kq3Z3DUCW1rOstJ7KhhYAokKDmJgVz83H5zJlaAIFWYOIDtv/sNVPTh5J4bZd/Oy1IsakxZKn9/cB56AHJK21n7H/dSHnfs0xvwd+34NcvRJ11I3EzvmYRXP/yfTzb+/r04uIiIhIH1q/9CNGdqxn4cifkhUUBEBQ7nFE3ryIjrd+yE/WvciyrSu5+q/XcdiUyfzwxDySY8MdTi0iIjLwWGv5xezVLCut5+8XTWJcetx/7RMZGsyZBemcWZBOXVMrc4sqmb2inLveKeaud4qZlpPA2RPTmTUulbjIEAd6ISIiIl+nqbWDwm27WFKyk2Wl9azYVs+etq571VLjwpk8NJ6p2QlMHhrPqJQYgoNcB9VucJCLBy6ayKy/fcZ1zyzjzRuPOODgpfinfvlqjZx8HBvezSN1/ZO4O3+My/PBlIiIiIj0P7vn/50mG8HYWdd+9YnIBILPewJWvcTEOT/i/aA7+J8Vl3F04dFcfcQwrj16OLHh+iBTRESkrzz+eQkvL9vOTceN4LTxqd+4/+DoMC6dmc2lM7PZVtfMG4XlzC4s52evFfHLN9ZwzMgkzpqYznGjhhAeos9+REREnFDZsLdr6lXPHZDrKhtxWzAGRqXEcs6kDKZkxzMlO6HXMx0MiQ3n/gsncvEjC7n9lVU8cNFEulYblEDQLwckjctF4/iryVt+O6vmz2b8sd9xOpKIiMh/GGPCgflAGF21+BVr7S/32ecY4A1gq2fTa9ba3/RhTJGAUFtRyviGj1k+5NvMiNvPOpHGwITzcQ2dSdjs67i79CEuil7LFR9fzHOLtvGD43K5ZMZQQoMP7opMERER6Zl/b9zB7+as5aQxydxyQt4hH581OJIfHJ/LjceNYHV5I68Xdq03+d7aamLCgzl1XApnFaQzfdhgglz6YFKkt4wxJcBuoBPosNZOcTaRiPiLjdW7WbiljqWl9Swtqad8114AIkKCmJg1iBuPHcHk7AQmZg3yyUXAM4cP5raTR3HXO8VM/jyeq47I8fo5xDf65YAkwPiTr2DH8jth0UOgAUkREfEvrcBx1tomY0wI8JkxZp61duE++/3bWnu6A/lEAsbGefczHTfpJ9/89TsOyoLL34Iv7qfgo9+xJKGYeyNv4Tdvt/P4F1u57eRRnJ6fiksfYIqIiHjd1to93PjcCvKSY/jL+QW9qrfGGPIz4sjPiOOOWaP5YnMtr6+oYG5RFS8t3U5KbDjfKkjjzII0xqTG6q4Jkd451lpb63QIEXHentYO3lxZwfOLt7FqewMAQ2LCmJqdwNVH5DAlO57RqbGEHOT0q7113dHDWFZazx/mrmNCZhyTh+7nAmXxO/12QDI0LJxNQ89nZulDlK4vZOjIAqcjiYiIAGCttUCT58cQz8M6l0gkMLW2NJNb9jKrIqdRMGLcNx/gCoIjboHhxxLy2jX8dMcdXDL+Mm6oOZObnl/Bv+Zv4WenjuKwEYk+zy4iIjJQNLa0872nluIy8K/LphDlxbWeglyGI3OTODI3id+1jeODddW8UVjOY59t5eH5W8gdEs1ZE9M5syCNjPhIr51XRERkoFhd3sDzi7fxRmEFTa0d5CVH88szxnDC6GQy4iMcu/DHGMO9503gjPs/44ZnV/D2TUeQGB3mSBY5eP16bqq8WT+gzQZT9f59TkcRERH5CmNMkDGmEKgB3rfWLtrPbjONMSuNMfOMMWMP0M41xpilxpilO3bs8GVkEb9T9N6TJLIL14zrDu3A1AlwzScw/ToyNjzF68F38OjJoezc08ZFjyzi8scWs66y0SeZRUREBpJOt+WWFwopqd3DPy6eTGaC7wYFI0KDOGNCGo9cPpUlPz+B3501jriIEO55dz1H3PUx5z70Bc8sLKV+T5vPMoj0MxZ4zxizzBhzzf520PtRkf5pT2sHLyzexpkPfMbp93/GK8u2c/LYFF69fibv3nIUVx6eQ2ZCpOOzEMRFhPCPiyexs7mNm19YQadb1/r7O9N1k4Z/mDJlil26dKlX21zy1wsYW/8R7besJS5eV7uLiAQCY8yygbI+hTFmEDAb+IG1dnW37bGA2zOt6yzgPmtt7te15Ys6KuKvrNvNpj9MI9S9l8yfF+Hq6bQwmz+C178Pe2ppP/oOnuB0HvikhMaWds6emM6PThpJ+qAI74YX8bGBVEe9SXVUxPvunFfMQ59u5rdnjePSGUMdyVC2s5k3V1Ywe0U5m2qayAuq5PKUEibNPJ7RU451JJP4N9XRLsaYNGtthTFmCPA+Xe9Z5x9of9VRkcC3pqLrbsjXV3TdDZk7JJqLpmdxzsQM4iK9vxakt7y4ZBu3v1rED44bwY9OGul0nAHv6+pov52y9UsJx91E5GvzWDjnAWZc8iun44iIiHyFtXaXMeYT4BRgdbftjd2+n2uM+YcxJlHrd4h0Wb/8Y0Z1bGTR6DsY2ps1KoYfB9d/AW/fQsjHv+Z7We9z/jUP8PcVbTz+RQlvr6rkysOy+f4xI/z6DZiIiIi/eX1FOQ99upmLp2c5NhgJkBkXyg05VXy/7T3amENY41aog463/s6CNd9n+iW/wRUU5Fg+EX9lra3wfK0xxswGpgEHHJAUkcDU3NbBWysreG5xGSvLdhEa7OL0/FQump7F5KHxjt8FeTDOn5rFstJ67v9oE5Oy4jl21BCnI8kB9OspWwGGjz+MtaH5ZG1+ls6ODqfjiIiIYIxJ8twZiTEmAjgBKN5nnxTj+avPGDONrppd18dRRfxW06d/Z7eNYOysa3vfWGQCnPsknPUQVBUR+/jR/Cx9JR//6GhOH5/Kw//ewlH3fMzD8zfT0t7Z+/OJiIj0cyvLdvGTV1cxPSeBX56x35UHfKulAVa/Cq9+D+4ZDk+chln0T8KScuDUe9hz9WesjDmKmVsfYPU9J1O/o7LvM4r4MWNMlDEm5svvgZPodgGtiAS+tRWN/M/rq5n++w+5/dUi9rR28L+nj2HxHcfz5/MLmJKdEBCDkV/6zZnjGJMayy0vFlK2s9npOHIA/f4OSYDWyd8jbcFNLP/weSadfKnTcURERFKBJ40xQXQNNL5krX3bGHMdgLX2IeA7wPXGmA5gL3CB9ad51kUctKOihAmNn7As+VxmxAzyTqPGQMGFMPQwmH0tzL6W9DHz+PPpf+G7RwzjrneK+cPcYp78opRbT8zjrInpBLkC582ZSH9ljHkMOB2osdaO82z7FfA94MvFrO6w1s51JqHIwFPd2MI1Ty9lSEwY/7h4EqHBfXQt/M6tsOEdWD8PSj8HdwdEJMDIUyHvlK5ZEcJjAYgCJt06m0Wv/ImJa+5i198PY92pDzF6+sl9k1XE/yUDsz2DEcHAc9bad5yNJCK91dzWwdurKnlu0TYKPXdDnua5G3JKgNwNeSDhIUE8eMkkTr//M254bjkvXzeTsGDNgOBv+v0akgCdHR3U/G4UO0NTGHvHZ15vX0REvEtrdvSM1uyQgWLhI7cyrewxKi77nIzhPrjrwt0Jn98HH/8BohLhrH/A8OP4YlMtf5xXTFF5A6NSYvjpqaM4Oi8poN+0Sf80kOqoMeYooAl4ap8BySZr7Z8OpS3VUZHea2nv5PyHF7KxejevXn8Yo1NjfXcydyeUL4P1c2H9O7BjXdf2xJEw8hTIOxUyp4Hr6z+M3LTyc8Jfv5oUdzVLhmkKVxlYddSbVEdF/FdxVSPPLdrG7OXl7G7tYHhSFBdNH8o5E9OJjwp1Op5XvbemimueXsbF07P4/dn5TscZkAb0GpIAQcHBlI64mBmb/srmVV8wfPxhTkcSERERkR5obWkmb/vLrIqcQYEvBiOh64PLI2/tupPitWvg6bNh+vUcdsIveeOGw3lrVQV/em89Vzy+hMNHDOanp4wmPyPON1lE5GtZa+cbY7KdziEiYK3ljteKWFm2i4cumeybwcjWJtjycdddkBveheZaMEFdMxxM+kPXnZCDhx9SkyMmHM7u7C9Y+a8rmbn1AVbds4jMq58mPinV+/lFRET6yN62Tt5eVcFzi7exYlvX3ZCzxqVw4bQspuUE1nSsh+KksSlce/Qw/vnpFqZkx3P2xAynI0k3A2JAEmD0rBtpvu9B6j66XwOSIiIiIgFq1TuPM5VGymde5/uTpRXAtZ/C+7+ERQ/Clo9xnfMvziwYzynjUnh24Tbu/2gjZzzwGY9fMZVjRw3xfSYROVg3GmMuA5YCP7LW1u9vJ2PMNcA1AFlZWX0YT6T/eeTfW3ltRTm3npjHKeNSvNdwQzlsmNd1F+TW+dDZCmFxkHti13SsI46HiPhenSImLsEzheu9FKy5iwZN4fp/7N13fFRV+sfxz5n03iGBFBIIoYQEQgcrKsUGduy9rXUt+9NVt6jruu7quvbeC/aGICqCjZoEEkINJKSQBNJ7n/P7Y4IiRAgkmTPJPO/XK6+ZuXPuvd8gOOW55zlCCCH6qK0ltbyzOo+P1+2itqmNuDAf7j1lJGelRPa72ZC/586ZCazPr+LujzcwMsKfEeG92LFBHBanaNm61+onL2Vs2ZfU35BB8IDBvXYeIYQQ3SMtco6MtMgR/Z22Wtn+j4m462ai781EWey0JhXA9m/h0xugoRxm3AvTbgKLCzVNrcz67w+MjPDnlcsm2i+PEAfhbK+jHTMkF+7TsnUgUAZo4AEgQmt9xaGOI6+jQhy5ZVv3cOVra5mTGMFTF4zr3qwLqxWK1/+6HmRJpm17UCwknGxrxxo9FVzceiT7/qSFq3C219GeIq+jQpjT1NresTZkHun5Vbi7WJidGM4Fk6OZ3I9nQx7MntomTnniJ3w9XPn8xun4efbO+wZxIKdv2bpX+Mw/4vHup6R/+QRTL/+X6ThCCCGEEOIwbE1dyoj27awadS8x9ixGAgw7Ef6wEr64Bb79K2R/DWc8h39gNHPHDubFH3MorW0mzM/DvrmEEAfQWu/ee18p9SKw0GAcIfq97XvquPmddYwI9+ff5yQd2ZeerY222Y9bF9lasdYWg7JA5CQ48W+2QmTocLDDF6q/tHB96YpfWrhGXvG6XNguhBDC4ZTWNvP0su18nF5ITVMbcaE+3HPySM4aH0mwk8yG/D0D/Dx56vxxXPDSav70YSbPXJjilIVZR2Pnb3LMikkYS6bnBIblvUdLc5PpOEIIIYQQ4jDU//g0NXgzZs41ZgJ4B8O5b8C8Z6E4E56dDhnvcea4QbRbNZ9nFJnJJYT4DaXUvgu/nQFkmcoiRH9X3dDKNW+k4u5q4cVLJ+DtfpjXvVfkwoIL4V+x8M65sOFDiJxoe629IxuuXAJH/RHCEuxSjNzLLyCYlD9+zOpR95LQuJ62Z45i8+oldju/EEIIcSgtbVaueiOVt1fncWzCAN69egpLbz+Wq4+Jc/pi5F6T40L406wEFmeV8PJPuabjCJysIAmgJ19HGJVkfv266ShCCCGEEKKLdhfuILnmezYOnIuPX4C5IErB2Avg+p9gwCj45BqG/3gzEwe588m6QnO5hHBSSql3gZVAglKqUCl1JfCIUmqDUioTOB74o9GQQvRTbe1WblqwjoLKBp67eDyDA70O7wAVOfDaKZD7I4y7CC76CP6UA+e9aXut9QntneBdpCwWJp97JwVnfkaL8iB+0XxWvn4P1vZ2o7mEEEIIgEe/3kpGQRX/mz+OJ88fx9ShITIDsBPXHBPHzFEDeXjxFlJ3VpiO4/ScriA55pgzKVCD8M94yXQUIYQQQgjRRTmLn8SCJnrWraaj2AQNgcsXwQl/gY2fcHfgUrJ21bBtd63pZEI4Fa31+VrrCK21m9Y6Umv9stb6Yq31GK11ktb6dK11semcQvRHDy/ewg/bSnlgbiIThwQf3s7lO+C1U6G1AS5bCKf8x9Ye3dXxWp8PS55OwK0ryPA/lqm5T5H171lU7NllOpYQQggntmzrHp7/IYcLJ0dz8piIQ+/gxJRS/PucZAYHeXHDO+mU1TWbjuTUnK4gaXFxoWjEpQxv28aW1KWm4wghhBBCiENoaqxnxK6PyPCZyuC4Eabj/MriAkffDrHHklz2Ja4Wzcfp8gWlEEKI/u/DtEJe+imXy6YNYf6k6MPb+ZdiZCNc+gVEJPVOyB7UWQvXTau+Mh1LCCGEE9pd08Tt72cwItyP+04dZTpOnxDg5cazF46nqqGVm99dR7tVm47ktJyuIAmQePJ11OBN/fdPmY4ihBBCCCEOIfOrVwiiBrdp15uO0rmUS3Cpyee6qEI+XbdLPtwIIYTo19LzK/nzxxuYPiyEe08ZeXg7l++wtWltb7YVI8PH9E7IXrC3hWvhWZ/TojwYvvh8Vr7+Z2nhKoQQwm7arZpbF6ynsaWdpy4Yh6ebi+lIfcaoQf48MC+RFTvKeeybrabjOC2nLEj6+AWyaeBckmq+Z88uWcxUCCGEEMJRaauV4KxXybVEM3raqabjdG7EqeAZyPlu31NS08SqnHLTiYQQQoheUVzdyLVvphER6MlT56fg6nIYXyuVbYdXT4b2lo5iZGLvBe1FQ5Om7dPC9Wlp4SqEEMJunl62nZU55fx97miGDfAzHafPOXdCFOdNiOLpZTtYunm36ThOySkLkgDRs2/FgpUdix43HUUIIYQQQvyOLWu/YVj7DvaMvAxlcdC3rm6ekHQeg4q/JdKjkY/SC00nEkIIIXpcU2s717yRRmNLOy9eMoEgH/eu71yWbZsZaW2DSxfCwNG9F9QOfmnhOvo+aeEqhBDCLtbkVvD4t9uYO3YQ54yPNB2nz/r73NGMivDnj++tp6CiwXQcp+Og3+r0vkGxI8j0mcaIXR/R1FBnOo4QQgghhOhE449PU40PY+ZcZTrKwaVcjGpv4U+DMvkqq4SGljbTiYQQQogeo7XmTx9mklVUzePnjWX4wMOYlVG6zbZmpLXNNjNyYP9Y70pZLEw+5w4Kz/qcZuUpLVyFEEL0msr6Fm5ZsI7oYG/+ccYYlFKmI/VZnm4uPHfReDRw/dtpNLXK67Y9OW1BEsB9+g0EUUvm4pdMRxFCCCGEEPspKdhOUu2PbAqfh7dvgOk4Bxc+BgaN44SmJTS0tLFkY4npREIIIUSPefb7HXyeUcSdsxI4cdTAru9Yug1ePxV0O1y2sN8UI/c1NGkagbf+LC1chRBC9AqtNXd8kEFZXTNPXZCCr4er6Uh9XnSIN4+dO5asXTX8/YtNpuM4FacuSI6aOoccyxDCNr6KtlpNxxFCCCGEEPvIXfwECk3M7FtMR+macRfjU7mFEwKK+DhdvogUQgjRP3y7aTf/XrKV05MHcf2xQ7u+Y+lWW5tWrW1tWgeM7L2QhkkLVyGEEL3l1Z93snTLHu6eM5LEwQ5+oW4fctKogVx37FDeXZPPR2my7Iq9OHVBUlkslCdeQax1JxtXfmk6jhBCCCGE6NDUUMeIoo/J8JnOoCEJpuN0zZizwdWLmwNX8PP2MnbXNJlOJIQQQnTLtt213LJgHYmDAnjk7KSut4jbs8VWjATbzMgBI3ovpIOQFq5CCCF62obCav65eDMnjhzI5dOHmI7T79wxczhT4oK559MNbCmpMR3HKTh1QRJgzJyrqMSf1p+fMR1FCCGEEEJ02PDVywRRi/v0601H6TrPABg1lzGV3+Kum/lsvcySFEII0XdV1rdw1eupeLm78sIl4/F0c+najns229q0Kgtc9iWE9ZELi3rIgS1cZ0oLVyGEEIettqmVG99NJ9TXg38fzkVBostcXSw8cf44/D3duP6tdGqaWk1H6vecviDp6eXDlsFnkVy/kl05m03HEUIIIYRwetpqJXjjq+RYhjB66smm4xyelIuxtNRyfVgWH6XtQmttOpEQQghx2Nrardz4bjol1U08f/F4IgK8urbj7k3wWkcx8tKFEDa8d4M6qN+2cM2QFq5CCCEOi9aaez7JoqCigf/NH0eQj7vpSP3WAD9PnroghfyKBv70QaZ8hu9lsgIqMOyUW2l//g0KljzO4OufNx1HCCGEEMKpbV69hFHtuawe8zfiLH3s+rmY6RAcx3y9nP8Wj2dTcQ2jB8k6H0IIIfqWB7/czM/by/nPOcmMjwnq2k67N8Hrp4HF1damNTS+d0M6uL0tXHckTMP9kytJWDyfVdtvZcpFfzMdTQghhIP7ILWQzzOKuP2k4UyKDTYd57dam6AoHZprwdpm+2lvBWv7r4+tbfs8bt3v8d592n5n/N59OjnesBPhqFt7/FeaFBvM/81O4KFFW3j5p1yuOjqux88hbKQgCYQNGkKq/3GMLvmMupqH8fXv4pttIYQQQgjR45p+eoYqfEmafZXpKIdPKRh3MQOX/p14lxI+Sd8lBUkhhBB9yoI1+by2YidXHRXL2eMju7bT7o22YqSLu21mZOiw3g3ZhwxNmkbdkBVkPncBU7b/l9KiywgbNMR0LCGEEA4qe3ctf/k8i2lDQ/jD8Q7wempth+IMyFlu+8lfBe3NR348i+s+Py773Hfb73HH8y5utvtNNfDtX2FwCsQe01O/3S+uPjqOtLxK/rl4C0mRgY5XCO4npCDZwe/YG/FbuJTVi55j8vy7TccRQgghhHBKJfnZJNf9yJpBFzHVx890nCMz9gL47kFuC13DfetjuGvOCFxd+thMTyGEEE5p7c4K7vssi2OGh3HXnBFd26kky1aMdPW0zYwMGdq7IfsgX/8gAk/5G3wwk9yVnxJ21q2mIwkhhHBATa3t3PjOOnzcXXn8vLG4WAysG6k1VORAzjJbATL3R2iqsj03YDRMvApijwbfAfsVD/ctMLp1UnDseHyka2G2NsIzU2DhbXD9z+Dq0VO/MQBKKf59TjKnP/kTN76TzsKbj2KAn2ePnkNIQfIXCRNmsPWrBAZtfQNr+5+wuHRxsXYhhBBCCNFjchc/QRgwZPbNpqMcOb9wiJ/J8flLqaw7hZ+2l3FcwgDTqYQQQoiD2lXVyHVvphEV5M2T54/r2sU0JRvg9dOlGNkFQ0ZOpIRQ3HK+AW41HUcIIYQDun/hJrburuW1yycywN+OxbC6PZDzfUcB8nuoLrBtD4iCkadC3PG2WYm+Bj/XunnByY/C22fBz0/AsXf2+Cn8Pd149qLxnPHMz9z87jreunKyXFzcw7r8p6mUilJKLVNKbVZKbVRK3dKxPVgp9Y1SKrvjNmiffe5WSm1XSm1VSs3qjV+gJ9WOvYooXcSG7z8yHUUIIYQQwuk01tcysvhjMnyPJiJmuOk43ZNyCZ5NpZziuYGP03eZTiOEEEIclNWquemddFrarbx46QQCvNwOvVNxpm1mpJuXFCO7QFks5IUcxfC6VJqbGkzHEUII4WC+zCzmndX5XHtsXO9f0NpcC9uWwFd3wzPT4D/x8PFVsGUhDBoHpzwGN6XDrRtg7tMw5myzxci94k+EUfPgh39D+Y5eOcXICH8enDeGVTkVPPrNtl45hzM7nPJuG3C71nokMAW4QSk1CrgLWKq1jgeWdjym47n5wGhgNvCMUsqhpx0mz7yUPQSj1jxnOooQQgghhNPZ8NVLBFKHx1F/m3tNQgABAABJREFUMB2l++Jngu9ArvdfwdebSqhtajWdSAghhPhd76UWkJ5fxd9OG83QMN9D71CcAW+cDm4+Uow8DB6j5uCjmti25hvTUYQQQjiQgooG7vook7FRgdwxM6HnT9DeCnkrYfnD8Mps+NcQeOdcWPsy+IbBiX+Dq5fBn3LgvDdh4pW21/Yjba/am2b/07Zm9aI7be1le8HZ4yM5f1IUzy7fwTebdvfKOZxVlwuSWutirXV6x/1aYDMwGJgLvN4x7HVgXsf9ucACrXWz1joX2A5M6qHcvcLN3YMdQ84jqSmNvC3ppuMIIYQQQjgNbbUStvE1dlhiGTXZ4RtrHJqLKySfz4jalfi1lrM4q8R0IiGEEKJT5XXNPLx4C5NjgzkzZfChdyhab2vTurcYGRzX6xn7i+GT59Cs3ajP+tJ0FCGEEA6ipc3Kje+uAwVPnj8Ot55oEao17N4IK5+Bt8+1FSBfnW0rSLY1w7Sb4ZLP4K482+1Rf4TBKbY1Hh2d/yCYcQ/sWAqbPu210/z1tNEkDvbntvfXU1bX3GvncTZH9LdbKTUEGAesBgZqrYvBVrQE9s7dHQwU7LNbYce2/Y91jVIqVSmVWlpaeiRxelTCyTfRrN0o+eZ/pqMIIYQQQjiNTSsXE2vdSXni5ShLP1mjYdzFKN3O1f6r+ETatgohhHBQ/1y8hfrmNh6cl4g61EyIovXwxlzw8OsoRsbaJWN/4e0bwFavsQwu/dF0FCGEEA7i0a+3klFQxb/OSiIq2PvID1RVAOvego+ugv8Mh2enwZK7oWIHJJ0H575pmwF5zTI48a8Qd5yt7XpfNPFqCE+CxXdBU02vnMLTzYW/njaa2qY21uVX9co5nJHr4e6glPIFPgJu1VrXHOTNamdPHDCHVmv9AvACwIQJE3pnju1hCB4wmDVBJzGmbDHVFaUEBIeZjiSEEEII0e+1rHiGSvxImn2l6Sg9J3QYRE/jnNLveShnNruqGhkc2Ec/8AkhhOiX1uRW8GFaIdcfN5T4gX4HH1y0rqMYGQCXfQFBQ+ySsb9pHHICSVsepmD7BqKGjTEdRwghhEHLtu7h+R9yuHByNCePiTj8A5TvgJVPQ85yW+ERwCfMVmyMOw5ij4XAqB5M7CBcXOHUx+GlE2DZP2DOv3rlNMM73hvllNYBA3vlHM7msC4/V0q5YStGvq21/rhj826lVETH8xHAno7thcC+f9sjgaLuxbWP0BNvxVs1s/nLJ01HEUIIIYTo94p2biWp7me2DDoTT+8urFvVl6RcQlBjPpPUFj5dJ7MkhRBCOI7Wdiv3frqBwYFe3Dwj/uCDd6XvU4xcKMXIboiafAYAu9Z8ZjiJEEIIk3bXNHH7+xmMCPfjvlNHHf4BitbDyydBxrsQMgxm/ROuXwl3ZMNZL8G4i/pnMXKvyPG2tS7XvGD7s+gFAV5uhPq6k1Na3yvHd0ZdLkgq21TIl4HNWuvH9nnqc+DSjvuXAp/ts32+UspDKRULxANruh+598UlTmajexJDdrxDW2uL6ThCCCGEEP1a/lf/Q6OInXOz6Sg9b9Rc8PDnhoAVfJxeiNbGG4IIIYQQALzyUy7bdtfxt9NH4+V+kDWjdqXBG/PAc28xMsZuGfujQbEjyLNE4ZO31HQUIYQQhrRbNbcuWE9jSztPXTAOT7fDXLsxbwW8fhq4ecN1P8GF78PUP8DAUXCo9uv9yYz7wDsUFv4RrO29coq4UF9yyup65djO6HBmSE4HLgZmKKXWd/ycDDwMnKSUygZO6niM1noj8D6wCfgKuEFr3Tt/K3pBy4RrCaeUzKXvmI4ihBBCCNFvNdRVM6rkUzL8jiY8apjpOD3P3RsSz2J6y8/sKS1lw65q04mEEEIIdlU18vi32Zw4ciAnjTpIC7LCNHjjDPAKhMu+lGJkDykecAwJTZnU11aZjiKEEMKAp5dtZ2VOOX+fO5phAw7RMn1/2d/Am2eAXzhcsQRChvZOyL7AKxBmPQRF6ZD6Sq+cIjbUh9wymSHZU7pckNRa/6S1VlrrJK312I6fRVrrcq31CVrr+I7bin32+YfWeqjWOkFrvbh3foXekTRjPkVqIJ5pL5qOIoQQQgjRb21Y/BL+1ON11A2mo/SelItxtTZxhttKPk6Xtq1CCCHM+/vnGwH42+kHaRFXmApvzvu1GBkYbZdszsBvzCm4qza2rVxoOooQQgg7W51TzuPfbmPe2EGcMz7y8HbO+gjenQ9hCXD5YggY3Dsh+5IxZ9vWylx6P9Tu7vHDx4X5UFbXQnVja48f2xkd1hqSzsTF1ZX8YRcyqjWL7Rk/m44jhBBCCNHvaKuVgZtfY7vLUEZOOsl0nN4zKAUGJnKl9098nlFEa7vVdCIhhBBObOnm3Xy9aTc3nxBPZJB354MK1tpmX3gHw+WL+vcaVAYMn3gitdqL1s196tp9IYQQ3VRZ38ItC9YTHezNg2eMQR1Oe9W01+DDKyFyElz6BfiE9lrOPkUpOOUxaGuCJX/u8cPHhfkCkFMqbVt7ghQkD2LkyTfQoD2o/O4J01GEEEIIIfqdjSsWMsSaT0Xi5ShLP35bqhSMu5iY5q0MbMjm+62lphP1KU8v284P2+TPTAghekJjSzt//Xwj8QN8ufKo2M4HFazpKEaG2GZGBhzm7A1xSG7uHmT7TWRI5Qq0VS5UEkIIZ6C15o4PMqiob+GpC1Lw9XDt+s4//w++uAWGnQgXfWRb11n8KnQYHHUbZH0IO77r0UPHhfkAkFMqbVt7Qj/+5qf7AoJC2RB2CslV31JWUmA6jhBCiH5CKeWplFqjlMpQSm1USv29kzFKKfWEUmq7UipTKZViIqsQval1xbNU4k/S7CtMR+l9SeeiXdy51PMHPl5XaDpNn1Hf3MZj32zj3TX5pqMIIUS/8NSybAorG3lgXiLurp18JZS/Gt48E3zDpBjZy9qGzmQAFezYsNJ0FCGEEHbwys87WbplD3efPILEwV0sKGoN3/4dvvkLjD4T5r8D7r/T3cDZHfVHCI6DL++A1qYeO2xUkDcuFiXrSPYQKUgeQsTMW3BXbWQvetJ0FCGEEP1HMzBDa50MjAVmK6Wm7DdmDhDf8XMN8KxdEwrRy4pyt5Bcv5Itg8/C08vHdJze5x2MGnEqcy0/8cPmQqobZP2JrsgorKLdqskrbzAdRQgh+rzte2p54YcczkwZzJS4kAMHFKyBt/YtRsq6VL0pbuo8AErTPzcbRAghRK/bUFjNw4s3c+LIgVw2bUjXdrJa4cvb4afHYPxlcNZL4OremzH7NjdPOOVRqNgBPz/eY4d1d7UQHexNTpm0bO0JUpA8hOjhY8nwnEh8/nu0NPdcZV0IIYTz0jZ738m4dfzo/YbNBd7oGLsKCFRKRdgzpxC9Kf+rx7GiiDv5ZtNR7CflErzaazneuoYvNxSbTtMnrMuvAiC/ogGt9//fpBBCiK7SWnPvp1l4u7vy55NHHjiguQ7evxR8OoqR/oPsH9LJhIZHsc11OEG7vjcdRQghRC+qbWrlxnfTCfX14N9nJ3Vt3cj2VvjkGkh9GabfAqc+DhaXXs/a5w2dAYlnwY+PQvmOHjtsXKiPtGztIVKQ7AI15XpCqSJzyaumowghhOgnlFIuSqn1wB7gG6316v2GDAb27Rde2LFt/+Nco5RKVUqllpbKGmuib6ivrWLU7s9Y738sAwfHmY5jP7HHogOjudzrRz6Rtq1dkpZXCUBdcxsV9S2G0wghRN/16fpdrMqp4E+zEwj19ThwwPf/gtoiOPMFKUbaUfmg4xjeuoXKUrlQSQgh+iOtNfd8kkVhZSNPnD+OIJ8uzHBsbYT3LoYNH8AJf4WT7oeuFDGFzayHwNUTvrzN1vK2B8SF+ZBbVo/VKhfJdpcUJLtgzDFnkGeJJCDzZVlsXAghRI/QWrdrrccCkcAkpVTifkM6e7d5wDsfrfULWusJWusJYWFhvZBUiJ6XtfhF/GnA5+gbTEexL4sFNfYiUtozKM7bSr60IT0orTXp+ZUM8LN9cZ5XIX9eQghxJKobW/nHl5tJjgrk/InRBw7YswVWPQPjLoKoSfYP6MRCx52GRWl2rPzUdBQhhBC94IPUQj7PKOKPJ8YzcUjwoXdoroW3z4FtX9najx59W++H7G/8wuGEv0DOcsj6qEcOGRvqS3OblaLqxh45njOTgmQXKIuFkhGXEt+WzdbUpabjCCGE6Ee01lXAcmD2fk8VAlH7PI4EiuyTSojeo61Wwre8TrbLMEZMOMF0HPsbewEaxbku3/PJul2m0zi0nLJ6qhpamTvWNlNHCrhCCHFk/rNkKxX1LfxjXiIWy37XvGkNi+4Adx848e9mAjqxoUnTKSMQlf216ShCCCF6WPbuWv7yeRbThoZw/XHDDr1DfTm8fhrkrYAzX4SJV/V+yP5qwhUwaBws+TM0VnX7cHFhPgDStrUHSEGyi8acfC01+FD/w1OmowghhOjjlFJhSqnAjvtewInAlv2GfQ5comymANVaa+nlJPq8rJ++IMZaQOWYK1AWJ3wrGhiFGjqDCz1+5NP0PFkX8SDSO9q1zh1r61adJwVJ0Qml1CtKqT1Kqax9tgUrpb5RSmV33AaZzCiESRkFVby1Oo9Lpg4hcXDAgQOyPoKdP9pmEviE2j+gk7O4uJAbOJX42tW0tUprciGE6C+aWtu58Z11+Li78vh5Y3HZ/4Kg/dUUwWsnw+5NMP9tSDrHPkH7K4sLnPpfqC+F7x7s9uF+LUjWdftYzs4JvwU6Mt6+AWwKn0dy7Q+UFGw3HUcIIUTfFgEsU0plAmuxrSG5UCl1nVLquo4xi4AcYDvwIvAHM1GF6Fntq56lnACSZl1uOoo5KZcQ0l5GdNUa0vOrTKdxWOn5lfh7ujIqwp9wf0/yKuRqVNGp1ziwy8BdwFKtdTywtOOxEE6n3aq599Mswnw9uH3m8AMHNNXAknsgYiyMd+LXZcNcEmbhTz3b0r4zHUUIIUQPuX/hJrburuWx88YywN/z4IMrcuCVWVBdCBd9BAlz7BOyvxs0DiZeDWtfgl1p3TpUmK8Hvh6u5JbJZ9LukoLkYYiZcwsKTe7i/5mOIoQQog/TWmdqrcdprZO01ola6/s7tj+ntX6u477WWt+gtR6qtR6jtU41m1qI7tuVs5Gk+lVsizwbTy9v03HMSTgZ7RXCBW7L+Di90HQah5WeV0VKTBAWiyI6xFtatopOaa1/ACr22zwXeL3j/uvAPHtmEsJRvL06jw27qrnv1FH4ebodOGD5w1C3G055zDaTQBgxbOrptGoXqjO+NB1FCCFED/gys5h3Vudz7bFxHDs87OCDd2+CV2bb1o689HOIPdo+IZ3FjHvAdyAs/CNY24/4MEop4sJ8yJGCZLdJQfIwRMQkkOF7FCOLPqaxvtZ0HCGEEEKIPqVgyRO0Y2HonJtNRzHL1R2VPJ8TVDo/Z2yhue3IPxj1VzVNrWzbU0tKtK3TZkywN3kVUpAUXTZwb5vzjtsBhvMIYXd7apv491dbOWpYKKcmRRw4YPdGWP0cjL8UIsfbP6D4hX9gCNs8Egnf/b3pKEIIIbqpoKKBuz7KZFx0IHfMTDj44MJUeHUOKAtcvhgGy+txj/MMgNn/hOIM20zJbogL9ZE1JHuAFCQPk+dRNxBIHRsWv2g6ihBCCCFEn1FfW8Xoks/I8D+OAYOHmI5jXsrFuNLGCa3LWLZlj+k0Dmd9fhVaw/iYjoJkiDeltc00tLQZTib6G6XUNUqpVKVUamlpqek4QvSYf3y5meY2K/fPHY1S+61bpTV8eYftS7oT/momoPiN2ugZxFrzKMnPNh1FCCHEEWpps3Lju+tAwRPzx+HmcpDSS85yeP108AqEK76CASPtFdP5jD4Dhp4ASx+AmuIjPkxcmC+7qhppbJELirtDCpKHaeTkWexwiWPAplfRVqvpOEIIIYQQfULWoufxU434HnOD6SiOYcBI9OCJXOj2PR+lSdvW/aXlVWJRkBwVCEBUsK3Fb77MkhRds1spFQHQcfu7VX+t9Qta6wla6wlhYYdoqSVEH/Hz9jI+W1/EdccNJS7M98ABme9B/go48W/gHWz3fOJAERPnAZC36lOjOYQQQhy5R7/eSkZBFY+clfTL55dObV4Ib58DQTFwxRIIGmK3jE5JKTjlP9DeAkvuPuLDxIb6ALCzXGZJdocUJA+TslgoT7yCIdZ8Nv78hek4QgghhBAOz9reTvjWN9jmOpyE8cebjuMwVMrFxFFIVfbPVNS3mI7jUNLzKxk+0A9fD1cAYkJsH/7yZB1J0TWfA5d23L8U+MxgFiHsqrmtnfs+zSImxJs/HDf0wAGNVfD1vTB4Aoy72O75ROei45PYpQbikfut6ShCCCGOQGltMy//lMu5EyKZM6aTVul7ZSyA9y+B8CS47EvwC7dfSGcWHAfH3AEbP4HsI3utjQuzfSaVtq3dIwXJI5A0+woq8Kdt5bOmowghhBBCOLyNP31GjLWQ6jFXoCzy9vMXiWdidfXmLJaxMLPIdBqHYbVq1udX/dKuFWxrSIJtTRYh9qWUehdYCSQopQqVUlcCDwMnKaWygZM6HgvhFF78IYecsnr+fvpoPN1cDhyw7CGoL4NTHgV5TXYYymKhMOQoEhrSaWqoMx1HCCHEYfowrZA2q+aaYzq5GGiv1c/DJ9fCkOlwyWfSpcDept8CIcNg0e3Q2njYu++dIZlTKq/T3SHvPo+Ap5cP2yLPIal+FbtyNpqOI4QQQgjh0KyrnqOMQJJmXXrowc7Eww9L4pmc7rqaRWnbTadxGNl76qhtbiMl+teCZKC3G36erjJDUhxAa32+1jpCa+2mtY7UWr+stS7XWp+gtY7vuK0wnVMIe8gvb+DJ77Zz8phwjksYcOCA4gxY+yJMvBIGjbV7PnFwXqNPxku1sHX1YtNRhBBCHAatNe+tzWfSkGCGDeikVbrW8P0jsPhPkHAKXPABeHQyTvQuVw845TGo3Ak/PnbYu3u7uzIowJOcMpkh2R1SkDxCw06+hTYsFHz1uOkoQgghhBAOq2D7BpIbV5MddQ4engdZR8NZpVyMN41EFS9hh1xpCdjWjwR+M0NSKUVMiDd5MkNSCCE6pbXmr59n4WpR/OXU0QcOsFrhyzvAKxhm3Gv/gOKQhk+ZQ4P2oGnjItNRhBBCHIZVORXsLG9g/qSoA5/U2tYqfdk/IGk+nPsGuHnaP6SwiTsWks6Dn/4LpdsOe/fYMB8pSHaTFCSPUOigGDIDjidx9xfUVssFt0IIIYQQndn19RO0aBfi59xkOopjippMW3A8812W8em6XabTOIT0/EqCfdyJCfltATsm2If8cvnwJ4QQnVmysYRlW0v540nDCQ/o5IvO9W9D4Ro46X7wCjrweWGcp5cP23xSiCr/CW21mo4jhBCiixaszcff05WT91870toOn98IK5+CSdfAvGfBxdVMSPGrmQ+Cuzd8eZutYHwY4kJ9ySmtQx/mfuJXUpDsBv/jbsZXNbJxkawlKYQQQgixv7qaShJ3f0FmwPGEDooxHccxKYXr+EsYb8kmLXUVVqt8sEnPqyQlOgil1G+2R4d4U1jZSFu7fEkrhBD7qm9u4+9fbGJkhD+XTRty4ICGCvj2rxA1BZLPt3s+0XXNsScySO8hf+s601GEAEAp5aKUWqeUWmg6ixCOqKqhhcVZJZwxbvBv125ua4YPLoN1b8Exf4I5j8jazY7CdwCc+DfY+SNkvn9Yu8aF+VDb1EZZXUvvZHMC8q+gG4anHMsW15FEbnsTa3u76ThCCCGEEA5l46Ln8FWN+B8nsyMPKvl8rMqV4xqWsHanc3feqKhvIaesnpSYwAOeiwn2ps2qKa5usn8wIYRwYP9bmk1xdRMPzkvE1aWTr3m+ewAaK+GU/8iXoQ4uZso8AIrTPjcbRIhf3QJsNh1CCEf1cfouWtqsnDcx+teNLfXw7nzY/DnM/AfMuAf2u9hSGJZyGQyeAF/fY3uP1EVxYba1P3OlbesRk3ei3VQ/7ioidTGZyz8wHUUIIYQQwmFY29sZtPUNtromMDzlONNxHJtvGNb4WZzl8iOfpe40ncaodfkd60dGH9hOMLqjhWteuawjKYQQe20pqeHln3KZPzHqN2vv/mJXOqS+CpOuhfAx9g8oDkt41DByLEPwy//OdBQhUEpFAqcAL5nOIoQj0lqzYG0+yZEBjBrkb9vYWAVvngE5y+H0J2HajSYjit9jscCp/4WGclh6f5d3iwv1ASCntK63kvV7UpDspqSTLmYPwbisec50FCGEEEIIh5H1wydE6SJqk680HaVPcJ1wGSGqhsaNC2lqdd7OG+n5lbhaFEmRgQc8FxNi+/CXVyFXowohBIDVqrn3kywCvNz4v9kjOhnQDl/ebmtNdvzd9g8ojsju8GMY3ryRmqpy01GEeBz4E/C7/fKVUtcopVKVUqmlpaV2CyaEI0jPr2Lb7jrmT+qYHVm3B1471XYx0NmvQsolZgOKg4tIgsnX2y7cKljbpV0GBXrh7mohR2ZIHjEpSHaTm7sHO2LPZ0zzOnI3de0vrhBCCCFEf6dXP0cZgSTNvNR0lL5h2Ak0e4dzunUp32zabTqNMWl5lYwa5I+Xu8sBz4X7e+LuYiFfZkgKIQQAH6YVkppXyV1zRhDk437ggPQ3oCgdZj4IngH2DyiOSGDyqbipdrJXSNtWYY5S6lRgj9Y67WDjtNYvaK0naK0nhIWF2SmdEI7hvbX5eLu7cFryILBa4c0zoXw7XLAARs8zHU90xfF3g18ELPwjtLcdcriLRREb4iMzJLtBCpI9YOQpN9Oo3Sn9+jHTUYQQQgghjNuVs5nkprVkR52Du4en6Th9g8UF95SLOMZlA8vXrjedxoi2disZBdWkdNKuFWwf/iKDvaRlqxBCAJX1Lfxz8WYmDgni7JTIAwfUl8PSv0PMdBhzjv0DiiMWn3I81fhg3fqV6SjCuU0HTldK7QQWADOUUm+ZjSSE46htauWLjGJOTx6Er4crbFsMuzfA6U/AsBNNxxNd5eEHc/5l+2+35vku7RIb6iMzJLtBCpI9IDA0nMywUxlb+TVlJfmm4wghhBB9Rk1VOWv+dwGbHjpK2lL1I/nfPkO7VsTNut50lD5FpVyEC1YG7/yY0tpm03HsbktJLY2t7aR0tgZah5hgb/IqpCAphBD/+moLNU1tPDAvEYtFHThg6d+gqQZO/g+oTp4XDsvVzZ1svynEVa/E2u68bdyFWVrru7XWkVrrIcB84Dut9UWGYwnhMD7PKKKxtf3Xdq0rnoTAaBh9ptlg4vCNPA3iZ8Kyh6B61yGHx4X5kF/eQGv773azFgchBckeMnj27bjSTvYXMktSCCGE6IoN339M4+MTSalYTHzzJnY+dw5trS2mY4luamluYnjRZ2T6TGVg5FDTcfqW4FgaBk/nbMtyPl9faDqN3aXnVwKQEh34u2NiQnzIL69Ha22nVEII4XjS8ipYsLaAK4+KZUS4/4EDCtba2rVOuR4GjrJ/QNF98TMJoZrtGT+aTiKEEKITC9YUMCLcj+TIAChMhfyVMOUP4OJqOpo4XErByf8Gaxt8ddchh8eF+dJm1RTIhbJHRAqSPSRyWCIZPtMYuesDGuqqTccRQgghHFZ9bRWrn7yUMcsup8niTc7cT1mX9BeSmtJIe/5a0/FEN21Y+i4hVGOZIGtHHgnvyZcTbSklZ+0i01HsLi2vkoH+HgwO9PrdMdHB3tS3tFNeLxcvCCGcU1u7lXs+yWJQgCe3nBB/4ABrOyy63bYe0nGH/lJNOKZh0+bRrhXl674wHUUItNbLtdanms4hhKPI2lXNhl3VzJ8YhVLKNjvSMwDGySTiPitoCBz7J9j8OWxbctChcWE+AOSUStvWIyEFyR7kdeytBFLHhi+fMx1FCCGEcEgbVyyi+rFJTCz7jFXhFzLwztUMTzmWSWfdyqrwC5lc9jGr3n3IdEzRDR4Zr1FCGInHnG06St808lSaXf2YVLmIbbtrTaexq/T8SlKig2wf6n9HTIg3gKwjKYRwWq+t2MmWklr+ctpofDw6mYWR+goUZ8Csf9jWRRJ9UmBoONnuIwktWm46ihBCiP0sWJuPh6uFM8ZFQkWurYg1/nJ53e3rpt4EoQmw6A5o+f3Pm3GhtoJkrqwjeUSkINmDEiaeyFbXBAZveZX2tjbTcYQQQgiH0dRQx6pnrmHkkguwoth68ntMue4ZPL18fhkz6eonWec9nYlbHiFj2QcG04ojVbg9i8Tm9eTGnIWLq7SqOSJuXlgTz2G2ZS1frtlsOo3d7KltoqCikfEHWT8Sfi1I5lfIhz8hhPMprm7kv99sY8aIAcwaPfDAAXWl8N0DEHuMrGHVD1QOPpb49u2UleSbjiKEEKJDQ0sbn60r4uQxEQR4u8GqZ0G5wGTp9tTnubrDqf+Fqnz48T+/OyzQ251gH3dyyursGK7/6HJBUin1ilJqj1Iqa59tf1NK7VJKre/4OXmf5+5WSm1XSm1VSs3q6eCOSFks1KVcT6QuJvO7BabjCCGEEA5hS+pSSv89kSl73mNt2JkE376GkZMPfGtgcXEh4Q/vkusax9DlN5G7aa2BtKI7CpY+S5u2MGzW9aaj9Gleky/DQ7XSvv492q3OsVZiel4VAOOiD16QjAzyRimZISmEcE4PLNxEm1Xzt9NGdz6b/Nu/2q7oP/lR23pIok8bkHI6ADkrPzUbRAghxC++zCymtrmN+ROjoKEC1r0FY84B/0Gmo4meMGQ6jL0Qfn4C9mz53WFxoT7skJatR+RwZki+BszuZPt/tdZjO34WASilRgHzgdEd+zyjlHLpbti+IPmkCylSA/Bc+4zpKEIIIYRRzU0NrHzhJuK/OAs33UrWCW8w+cZX8PYN+N19vH0D8Lv8AxqVJ57vn0/57kI7Jhbd0dLcRELx52zwmUrYoCGm4/RtEclUB47i5NZvWLWjzHQau1iXX4m7i4XEwf4HHefp5kK4vyf5UpAUQjiZ5Vv3sGhDCTfNGEZ0x2zx38hfBevfhqk3QNhw+wcUPS4ucQp7CMZ1+zemowghhOjw3toC4sJ8mBQbDGmvQmu97bVX9B8n3Q/uPvDlbaA7v0A4LsxH1pA8Ql0uSGqtfwAqujh8LrBAa92stc4FtgOTjiBfn+Pq5k5+/KWMbN3ItvTlpuMIIYQQRmzP+ImiR6YwtegN0oJPxuePa0g8em6X9h0YOZSquW8QqKspffFsmhrlTV5fsOHbtwimBpeJV5iO0i94T76cUZY8Vq/4znQUu0jLqyRxsD8eroe+hjE62Ju8CilICiGcR1NrO3/5bCNxYT5cfUzcgQPa2+DL28E/Eo79k/0Dil6hLBZ2Bk9neN1aWpqbTMcRQginl727ltS8SuZPjEK1t8DqF2DoDAhPNB1N9CSfUFtRMu9nyHi30yGxob6U1TVT09Rq53B9X0+sIXmjUiqzo6Xr3h5Lg4GCfcYUdmw7gFLqGqVUqlIqtbS0tAfimJd46g3U4E3td/81HUUIIYSwq9aWZla+cicxH5+Or7WGjGNeZNIt7+AXEHxYx4kfdwybp/6bEW2byXr2ErTV2kuJRU/xzHiDIjWAxGPOMB2lX3Abey6typ2InA9oaOnfa5O3tFnJ3FV9yPUj94oJ8ZaWrUIIp/LMsu3kVzTw4NzEzi/cWPsi7M6C2Q/ZrugX/YbbiNn4qka2rf3adBQhhHB6C9YW4OaiODMlEjZ8CHUlMO0m07FEbxh3MURNhq/vtbXm3U9cmO39Vq7Mkjxs3S1IPgsMBcYCxcCjHds7W6yg0/mtWusXtNYTtNYTwsLCuhnHMfj6B7Ex4kzG1n5P0c6tpuMIIYQQdrFzcyp5/5rK1PwXyAg4HvebVpM849wjPl7K7MtYGXsDE2q+ZdXrd/dgUtHTCrIzGN2SQX7MOVhcnKJLf+/zCqQ6dg6n8DPfZuSaTtOrNhZV09JmJeUQ60fuFRPiQ1ldM/XN/btQK4QQADmldTz3fQ7zxg5i2rDQAwfUlsCyh2DoCTDydPsHFL1q+NRTadGu1G1YZDqKEEI4tea2dj5OL+SkUQMJ9XGHlU/BwESIO950NNEbLBY49b/QWAXf/u2Ap4d2FCRzyursm6sf6FZBUmu9W2vdrrW2Ai/ya1vWQiBqn6GRQFF3ztXXxJ1yG1Ys5C969NCDhRBCiD6sva2NlW/cx6AFswhuLyV96lNMuO0jAkIGdvvYUy5+kLUBs5ia9xxpX77UA2lFb9j17bO0aheGzbzWdJR+Jfioq/BXDRStfN90lF6Vnl8FQEoXZ0hGB9vWTsuXtq1CiH5Oa81fPtuIh5uFP58ysvNBX98HbU1w8r9BdXZtuOjLfPwC2eKVzKDSH0xHEUIIp7Zk424qG1qZPzEadiyFPZtg6o3y2tufDRxtWx80/XUo3fabp6KDfbAomSF5JLpVkFRKRezz8Awgq+P+58B8pZSHUioWiAfWdOdcfc3AyKFkBMxgzO7PqK4sMx1HCCGE6BUF2RlkP3wUU3OeIMt3GvxhFSmzLu6x4yuLhaTrX2Oz22hGr7mLranOsZ5eX9LUWE/C7oVs8J1G6KAY03H6FUvs0VR5RjK27At21/TftaPS8yoZHOjFQH/PLo2PCbEVJKVtqxCiv/sis5iftpdx56wEBvh18v/InT/Bhvdh+i0QMtT+AYVdNMScQLR1F7tyNpqOIoQQTmvBmnwig7w4algorHgS/CIg8SzTsURvG3+Z7bZg9W82u7taiAr2ZkeZFCQPV5cLkkqpd4GVQIJSqlApdSXwiFJqg1IqEzge+COA1noj8D6wCfgKuEFr3d7j6R1c0Im34aOa2PzF/0xHEUIIIXqUtb2dVe88SOhbJxLRlk/qhH8z7vbPCB7Q6ZLR3eLh6U34NR9RbgkmdOFlFOdJO3RHkrX0bYKoxW3SFaaj9D9KYR17EVMsm1m2YqXpNL0mPb+yy+tHwr4zJOXDnxCi/6ppauWBhZtIigzgwsmdXPDT3gpf3gEB0XDUbfYPKOwmcuJcAApWf2Y4iRBCOKe88npW7CjnvAlRWHZvgJzlMPlacHU3HU30tqBYcPeDkswDnooL9SFHZkgeti4XJLXW52utI7TWblrrSK31y1rri7XWY7TWSVrr07XWxfuM/4fWeqjWOkFrvbh34ju2oUnTyPIYS1zOW7Q099+r2oUQQjiXotwtbP7XcUzZ9m+2eY+j5ZoVTDj1GpSlu0tT/76gsAjazluAG600vX4udTWVvXYucXi8Mt9glxrI6KPmmo7SLwVPu5R2LLDubbTudEn2Pq2oqpHi6iZSogO7vE+gtzv+nq4yQ1II0a899vU2yuqaeXBeIi6WTtrBrX4OSjfDnH+Bu7f9Awq7iRyWSIEahHfeUtNRhBDCKb23tgCLgnMmRMHKp8HdF8ZfbjqWsAeLBcIToTjjgKfiwnzJLavDau1/n9N7U+99cygAaJ98AwOoIHPJq6ajCCGEEN2irVZWf/AoAa8dS0xzNmuSHyDpzq8IGzTELuePGZFC3oxniGrPZ8ez59He1maX84rfl7d1PaNbNpA/5BwsLi6m4/RP/oMoDjuK45u+YdOuCtNpelx6vu3igvExwYe1X0yIj6whKYTotzYUVvPGyp1cNDmGpMjAAwfUFMHyhyF+FiTMsXs+YX+7BhxDQmMGDXXVpqMIIYRTaW238kFaITNGDCCccsj6EMZdDF6BpqMJe4lIhpIssP62AWhsqA9NrVZK+vHyKr1BCpK9bMyxZ5FniSIo4wW01Wo6jhBCCHFEdhfuYMMjJzF54/3keI6k7sofmXTGzb06K7IzY445g7TRfya5cTVrX7zBrucWByr+7llatQvxs64zHaVfCzrqSgaqKrKWf2g6So9Ly6vE083CiAi/w9ovOsRbCpJCiH6prd3KXR9nEurrwR2zEjoftOQeW8vWOf8C1cnsSdHv+CaejIdqZdvKL01HEUIIp/Ldlj2U1jYzf2I0rHketBWmXG86lrCn8CRorYeKnN9sjgvzAZC2rYdJCpK9zOLiwu7RVzK0PYeNKxaajiOEEEIcFm21svbTp/F66SiGNW5g9cg/M/pPSwmPjjeWafK5d7JqwLlM2b2A1R/8x1gOZ9fUWM+I3QvZ4HcUoeFRpuP0az6Jp1DtEsTAHR/Q1t6/LnBLz68iOTIQN5fD+1gSE+zNrsrGfvfnIYQQr/68k41FNfzt9NEEeLkdOCBnOWz8GI6+DYJj7Z5PmDF80izqtBfNm51yRSQhhDBmwZp8Bvp7cNwQD0h9FUbNg6BO1nYW/VdEku12v7atQ8N8Acgpq7N3oj5NCpJ2kHTyNZQTQPvPT5qOIoQQQnRZWUk+6/9zChPX/5ldbrFUXPwdk8/7P4dozTnxmmfJ8JrE+Kx/sOGHz0zHcUpZ37xBIHW4T77SdJT+z8WNimFncpQ1jTUbNptO02OaWtvZuKualJigw943JsSbNqumqEra4wgh+o+CigYe+2YbJ44cwJzE8AMHtLXAojshaAhMv8Xu+YQ57h6ebPOdwJCKn6X7lhBC2ElRVSPfbyvlnPFRuGa8Dc01MO1G07GEvYWNABf3AwqSA/w88HF3kRmSh0kKknbg6eXDtuj5JDeuIW9Luuk4QgghDFNKRSmllimlNiulNiqlDvhGSSl1nFKqWim1vuPnL/bMmLboZVyem8ao+rWsir+N4f/3A5HDEu0Z4aBcXF2Ju+49ClyiiPnuOvK2rjcdyen4bHiTQhXOqGmnmo7iFAYffw2uykrpT6+bjtJjNuyqps2qSYk+/IJkdLCtPU5ehXz4E0L0D1pr7v00C4uC++cmojprxbrqaSjbBnMeATcv+4cURrUPPYmBlJOzcY3pKEII4RTeTy3AquG88RGw6lmImQ6Dx5uOJezNxQ0GjIKSzN9sVkoRG+ZDTpl8Jj0cUpC0k4RTb6FJu7F7yaOmowghhDCvDbhdaz0SmALcoJQa1cm4H7XWYzt+7rdHsMrSYtL+M5fxa26j1DWCkvO/ZsqFf8XF1dUepz8sfgHBeFzyAa244bLgPCpLi01Hchp5m9MY2bqRwthzHWLGrDNwDx/BTp8kkko/p7axxXScHpGWVwlASnTgYe8bE+INQF65rCMphOgfvsgs5vttpdw+M4FBgZ0UG6sK4PtHIOEUGD7L/gGFcbFT5gFQmva52SBCCOEE2q2a99cWcHR8KFHFX0N1AUyV2ZFOKyLJNkNS699sjgv1JadUWrYeDilI2knwgMFkhJ5McsUSykoKTMcRQghhkNa6WGud3nG/FtgMDDabylaMbH96CmNqf2TVkBuI+7+fiRmRYjrWQQ0akkDpKa8QZi2n+IWzaW6S4oQ9FC97jhbtQvysa01HcS4pFxOriln7wyLTSXpEel4lQ0K8CfH1OOx9w/09cXe1kF8h/+aFEH1fVUML93+xkaTIAC6dNqTzQUv+bPsSbPY/7ZpNOI7QQTFsdxlKwK5lpqMIIUS/92N2KUXVTcyfEAUrnoSQYTB8tulYwpSIZGishOrC32yOC/NhV1UjTa3thoL1PVKQtKOIWbfhoVrJ/vJx01GEEEI4CKXUEGAcsLqTp6cqpTKUUouVUqN/Z/9rlFKpSqnU0tLSbmUJCotg+5ALKTh7EVMuewhXN/duHc9eRkw8kQ0TH2JUaxaZz14u6+r0sqaGOkbt+ZIN/scQMjDSdBynEnPUBdTjhcv6t0xH6TatNen5lUe0fiSAxaKICvIir1za4wgh+r6HF2+hsqGVf545BhdLJ61at38Lmz+HY26HoBj7BxQOoyziOIa3bKaqrMR0FCGE6NcWrCkgxMedmb47oHi9bXakRUopTis82Xa7X9vWuDBftIad8rm0y+RfkR1FDx/Leu+pjCh4n6YGmcorhBDOTinlC3wE3Kq1rtnv6XQgRmudDDwJfNrZMbTWL2itJ2itJ4SFhXU705TLHmLomCndPo69TTj1GlZGXc3E6q9Y9dZfTcfp1zZ8/Tr+1OMx5UrTUZyO8vAlN3w2Ext+oGj3btNxuqWgopGyupYjWj9yr5gQH2nZKoTo81bllLNgbQFXHRXL6EEBBw5oa4ZFd0LwUJh2s/0DCocSNPZUXJRm+8rPTEcRQoh+q7S2mW837+as8ZG4rX4KvEMheb7pWMKkgaNBWWxtW/cRF+oDQG6pFCS7SgqSduZ21M0EUUPGl8+ZjiKEEMIgpZQbtmLk21rrj/d/Xmtdo7Wu67i/CHBTSoXaOWafMuXyR0jzm8HUnCdYt+R103H6Lb+Nb1GgBjF66immozilsGOuxls1s+3b10xH6Za0/AoAxh/hDEmA6GBv8isa0Put4yGESTJLXxyO5rZ2/vzJBqKCvbjlxPjOB/38BFTkwMn/BtfDb3Et+pf4ccdSgT9kf206ihBC9FsfphXSZtVcOLQZtn0Fk64Gt07WdxbOw90bQodD8W9nSMZ2FCRzyqQg2VVSkLSzUVNmk+0yjEGbXsbaLr2FhRDCGSmlFPAysFlr/djvjAnvGIdSahK21+xy+6Xse5TFwug/vMVW1xGMWHEH2et/NB2p38ndtJYRrZvYNfQ8lLSrMWLgyGnkuw4hfMcHfboQl55Xha+HK8MH+h3xMWJCvGloaaesrqUHk4n+RCm1Uym1QSm1XimV2tvnK99dSOn9Q1n95CVkLvtQ1jUWh/TMsh3klNbz4LwxeLu7HjigMg9+/A+MmgvDTrB/QOFwLC4u7AiYyrCaVbS3tZmOI4QQ/Y7WmvfW5jNpSDAxW18BV0+YeJXpWMIRhCcd0LLVx8OVcH9PdpRKN8yukm+S7ExZLFSPu5YoXUTmsvdNxxFCCGHGdOBiYEbHl6TrlVInK6WuU0pd1zHmbCBLKZUBPAHM1325+mAnnl4+hFz1IdUqgIBPL2HPrlzTkfqVPd89S4t2JWHWtaajOC+lKIs/lxHWbLI3dLb0bN+QllfJ2KjAztdK66KYEG8A8ivkalRxUMdrrcdqrSf09oma6qsp9E1kTNlXJH1/Ja3/jCPt0XmkfvkitdUVvX160cds31PLM8u3M3fsII4d/jtt97+6C5QLzHrIvuGEQ7MkzCKQOrLTvjMdRQgh+p2VOeXsLG/g0iQvyFgAyeeDjzSrEkBEEtTsgvqy32yOC/MhV2ZIdpkUJA1InnkpJYTituZp01GEEEIYoLX+SWuttNZJHV+SjtVaL9JaP6e1fq5jzFNa69Fa62St9RSt9QrTufuK0PAoGs95Gx/dQM0rZ9FQV206Ur/QWF/LyLLFZPofS1BYhOk4Ti3+pCtp0S5U/PiS6ShHpL65jS0lNaREB3brONHBtvY4so6kcBSD40aTcscXWO7KJeOY59kUfAJDatOZsPYOPB4bRubDJ7L6g/9QVpRnOqowzGrV3P3xBrzdXbnv1FGdD9r6FWxdBMf+CQIi7RtQOLRhU+fSpi1UZiw0HUUIIfqdBWsK8Pd0ZVbDQmhvgak3mI4kHEVEsu12v3UkY0N9yCmt79MdjOxJCpIGuLl7sDP+Eka3bJB2ckIIIUQviB09me3H/o/Ythy2PnuBtEnvARu+fg1/GvCaKu1qTPMLDifL7ygSSpfQ2tJsOs5hyyiowqohpRvrRwJEBXuhlBQkxUFp4GulVJpS6prOBiilrlFKpSqlUktLS3vkpJ5ePiTPmM+kW94m8N4ctsz5gPSI+QQ3FzB54wOEvpDE1gcns/KN+8jftr5Hzin6lvdSC1i7s5J7ThlJqG8n60I218FX/wehCTDlD/YPKBxaQFAo2zxGM7Dke9NRhBCiX6msb+GrrBLOTQ7BNe1lSJgDob+zxrNwPuFjbLf7tW2NC/OlurGVinpZSqQrpCBpyOhTb6JWe1G99L+mowghhBD9UvKM+axNuINx9T+x+qVbTcfp8wI2vkWeJZJRU2abjiIAl7HnE0QNG3/42HSUw5aeXwnAuKjuFSQ9XF2I8Pckv0IKkuJ3TddapwBzgBuUUsfsP0Br/YLWeoLWekJY2O+0zewGF1dXRkyeyZTrnmHwfZvZed5SVg65HgvtTM15guh3jiXv/tGsfOEmtqZ+JxfQOIE9tU08tGgzU+KCOWd8JzMfre3w0ZVQVQCnPgau7vYPKRxeTeTxxFl3srtwh+koQgjRb3yybhct7Vau9FsFjRUw7SbTkYQj8QqCwOgDZkjGhdk69+RI29YukYKkIX4BwWwMn8fYmmWU5GebjiOEEEL0S5Pn/5nVIfOYWvwGaz55wnScPisnazUJbVsoHnoeyiJvHx3BqGPOpBJ/rOvfNR3lsKXlVRI/wJcAb7duHys6xJu8cvngJzqntS7quN0DfAJMMplHWSwMGTmBqZc9TPy9qZRcmcbqkXdT4xbGhF1vk7DwDMofGMrqJy8lc/lHtDQ3mYwresn9X2yiuc3KQ2eMQalO1tH9+j7Y9hXM+RcMOcr+AUWfED5xLgA7V35qNogQQvQTWmsWrM1nXKQfEZtegcHjIXqq6VjC0UQkQ/F+MyRDbQXJ3FL5XNoV8o2SQUNOuR2AnV8+ajiJEEII0T8pi4WUa19gg8c4xq7/GxtXLDIdqU8qXf4czdqNkbOvNR1FdHBz92Bb2ExG166gurLMdJwus1o16wqqSInu3uzIvWKCfWSGpOiUUspHKeW39z4wE8gym+q3wqOGMfm8uxhz93IabtlKasq/KPAdw5iyxSQtv4Lmh4aQ+uiZpC16mdrqCtNxRQ/4bstuFmYWc9Pxw4gL8z1wQOorsOppmHwdTLra/gFFnxGTkEIxYbjnfms6ihBC9Avp+VVs213HH6N3QMUOmHojdHbhkHBu4cm2vx9NNb9sigzyxt3Fwo6yOoPB+g4pSBoUHh3Pev/jGV3yqXzAFEIIIXqJm7sH0dd9SLFLBIO/vprC7Q71fbTDa6irZlTpYjYEHEdAyEDTccQ+gqddgodqZevSN0xH6bKcsnqqGloZ3831I/eKDvGmrK6Fuua2Hjme6FcGAj8ppTKANcCXWuuvDGf6XQHBYUw4/TpS7vgCy125rD/6eTYHzyCudi3j19yGx2PxZDx8Iqs/eJSyknzTccURqG9u475PNxI/wJdrjx164IAdy+DLO2DYSTDzH/YPKPoUZbGQH3o0CfVpNDXKjAwhhOiuBWvy8XZ3YfqeBba2nCNPNx1JOKKIJNvt7l+/V3KxKGJCvMmRGZJdIgVJwwJO+CN+qpGNC580HUUIIYTotwKCQnG96H00Cus751JdUWo6Up+RteRV/FQjPtNlpoajGZZ8NPmWwfhu+8h0lC7bu35kSkxgjxwvJsQbgPxymSUpfktrnaO1Tu74Ga217jMVHk8vH8aeMJ9Jt7xDwL25bJ7zPunh5xDanM/kjfcT/GwSWx6cwqo37qMgO+PQBxQO4b/fbGNXVSP/PHMM7q77fRVTuhXevxTCEuDsV8DF1UxI0ad4jpqDt2pm22qHvdZCCCH6hNqmVhZmFnNDfCUuhatgyg3yWiw6F5Fsu92/bWuYDzmlMkOyK6QgaVj82KPZ6D6GIdlv0trSbDqOEEII0W8NjhtN8awXCW8voeD5s+V1t4sCN79NniWKERNPMh1F7EdZLBRFz2NUSxZFuVtMx+mS9LxKArzciAvtpFXhEYgJtq3XkV8hV6OK/snF1ZWRk2cx5frnGHTfFnLP/ZbVQ67FRbcyJecJot4+hrz7E1n5ws1sS/8ebbWajiw6saGwmld+zuXCydFMGBL82yfry+Gdc8HVAy54Dzz9zYQUfU7ClJNp0m40bpQlCYQQojs+W19EY2s7F7Z/Dp4BMO4i05GEo/ILB58BUPLbgmRsqC/5FQ20tct78UORgqQDaJ10A+GUkvH166ajCCGEEP3aqKlzyBh3P4nN60l/7ir54vYQdmSuYHjbNoqHzUdZ5G2jI4o5/lIA8pe/ajhJ16TnVzIuOhCLpWfWY4numCGZJzMkhRNQFguxoyYy9fJ/EX9fGiVXpLJ6xF3UuoUwcdebDP/8dErvH8rqpy5nww+fyYU3DqKt3cpdH2cS4uvBn2aP2O/JZnjvQqgphvPftbWIE6KLPL192eo9jsiyH+U9rRBCdMOCtfkcF1ZPQN5XMOEK8OiZiydFPxWRBMW/7VISF+ZDa7umsLLRUKi+Q75ZcgBJx59LvmUwAeuelzeRQgghRC+bOO9GVg66lMkVn5P6qbRMP5iy75+nSbsxcva1pqOI3xERk8Am9zEMzv/c4d9HVje2sm13HeOje2b9SIAALzcCvd3Iq5CCpHA+4dHxTJ5/N4l3f0/9zVtYO+6f7PIZRVLpQsZ8dwkND8Wy9r/nsG7J6zTUVZuO67ReW7GTjUU1/P300QR4uf36hNbw+c2QvxLOeBYiJ5gLKfqspiEnMljvJj8789CDhRBCHCBrVzVZu2r4v6DvUMoFJslnX3EIEclQusV2YVmHoWG2zj05ZdK29VCkIOkALC4uFI+8gvj27WxaJb3/hRBCiN42+cr/stF9DAmZD1NatNN0HIdUX1vF6LIlbAicQUBwmOk44iDqE84mSheRve5701EOan1BFQApMT1XkASIDvaWNSSF0wsIGcjEuX9g3J1fwp9yWDftabYFHE189QrGrbwZ9e9hrHtkDms+eZKqshLTcZ1GQUUDj369jRNHDmBOYvhvn/zxP5C5AI6/BxLPMhNQ9HnRU+YBUJz6mdkgQgjRRy1Ym88A1wZGFH8GY84B/wjTkYSjC08Caxvs2fTLpr1LkuSUylIihyIFSQeRfMp1VOJP609PmI4ihBBC9HsWFxf8z30Wd91K4VvXO/zMMhM2LnkFX9WI3/SrTUcRhzDihItp0m5UrnzDdJSDSsurxKIgOSqwR48bHexNvsyQFOIXXj5+jJt5ERP/+B6+9+SSddJbZA44nYiGbUzKuBffJ0ey8aFjWPXuPyjJzzYdt9/SWnPfZ1koBX+fm4hS+7SqzvoYvnsQks6DY+40F1L0eRExCey0ROOb/53pKEII0ec0tLTx2boi/hKxGtXaANNuNB1J9AURSbbbfdq2Bvm4E+jtRk6ZFCQPRQqSDsLT25ctUecytmEl+dvWm44jhBBC9HtRw8awPv5GxjWsIG3xy6bjOJygzW+Ta4khYcIJpqOIQ/ALDGGj/1EML/uGluYm03F+17r8ShLC/fH1cO3R48aEeLOrqpHWdrmwQIj9ubq5kzj9NCbf8DID/5JN9ryFrI28FJ+2SqZsfYTwVyaw/YEUVr76f+zcnCoX6PSghZnFLN9ayh0zExgc6PXrE4Vp8On1EDUFTn8SVM+sqSucV/HAY0ho2kBtdYXpKEII0ad8mVlMc3MjM+s+haEzYOBo05FEXxAUCx4BUPzbdulxoT7klErL1kORgqQDGX7qH2nWbhQvecx0FCGEEMIpTJx/L9tchzN07f1U7NllOo7DyF7/I/Ht29kz/HyURd4u9gWu4+YTRC2bfvjIdJROtVs16/KrGB8T2OPHjgn2od2qKapq7PFjC9GfKIuF+LFHM/Xqxxnylw0UXPQTq+Jupk25MzXvOYa8dwK7HhjFquf+wJa132Jtbzcduc+qbmjl719sJCkygEunDfn1iaoCeHc++A6E+W+Dq4exjKL/CEg6FTfVTvbKz01HEUKIPmXB2gKuCkzFvbEUpt1kOo7oK5SC8DFQsl9BMsxXWrZ2gXzD5EBCBkaSETyL5LJF8qWoEEIIYQcurq64n/UsPrqe3DelPctelT88T6N2Z8QsadfaV4w66gwq8MeascB0lE5l76mlrrmNlOieXT8SIDrEG4A8WUdSiMMSNWwMUy55gBH3rqLsmkxWj76PCo/BpBQvYMSXZ1HxQByrn7yEzOUfOfTsa0f0z8WbqWxo5aEzxuBi6ZgB2VwL75wHbU1wwfvgE2o2pOg3hk84gRq8aduyxHQUIYToM7btriUtr4KrXBbBwESIO950JNGXRCRBSRZYf72ALzbUhz21zdQ1txkM5vikIOlgBs66HU/VytaF/zMdRQghhHAKQ0ZOIG3I1Yyv/Y7137xjOo5xdTWVJJZ/TVbQCQQEyZelfYWbuwfZA2aRWLuC6spS03EOkJ5XBcD4mJ4vSMbsLUjKOpJCHLHQQTFMPucOku5aStMfs0md8G/yfZMZU/YVScuvoPmhIaQ+eiZpi16mrqbSdFyHtjqn3Dbj4qhYEgcH2DZa2+HDK6F0C5zzGgwYYTSj6F9c3dzJ9p1EXNUKmdkshBBdtGBNATNcMwmu3wFTb5QW6uLwhCdBWyOU/boe+9AwHwByZZbkQUlB0sHEjEghw2sSw/MX0NQof3mFEEIIe5hw4f3kWIYw+Oc/U11ZZjqOURuXvIy3asbvKJkd2deETL0Ed9XG1qVvmI5ygLS8SkJ83IkO9u7xYw/088Td1UJ+ubx3FqIn+AeGMOHUa0i543Msd+WScczzbA6eQVztWsavuQ23R+PJ+NdM1nzyJK0tzabjOpTmtnbu/mQDkUFe3HJi/K9PfH0vZC+Bkx+BYbI2s+h51mEzCaWKHZk/m44ihBAOr7mtnY/XFXKn37fgFwGJZ5mOJPqaiGTb7T5tW+PCfAHIKZN1JA9GCpIOyGX6TYRQTeaiF0xHEUIIIZyCm7sH7ac/RZCuZuvrzrt2hLZaCdnyDjmWISSkSMuavmZo8lHkWaLw2+p460iuy68kJSYI1QtXHlssiuhgb2nZKkQv8PTyIXnGfCbd8g4B9+ayafZ7rAs/i7CmXCZl3MuuhyewaeVi0zEdxjPLdpBTWs+D8xLxdne1bVz7Mqx6BiZfDxOvMhtQ9FtDp83DqhVl674wHUUIIRzeko27GdSYzcjGNJh8Lbi6m44k+prQ4eDqCcUZv2yKCfFGKdghMyQPSgqSDmj0tFPZ4RLHwI0vS7sNIYQQwk7ixx7N2sEXMalqERt++MR0HCOy1//IsPYdlCZcgLLI28S+RlksFMWczsjWjRTlbjYd5xcV9S3klNX3yvqRe8UEe5MvLVuF6FUurq6MmjKbKdc/T8R9W1k//Vk8rI2MWjKf1MfOpqwk33REo7bvqeXZ5TuYO3YQxyUM6Ni4FBbdCfGzYNY/zAYU/VrwgMFkuw0npGi56ShCCOHwFqzJ52bvJWh3Xxh/uek4oi9ycYUBo35TkPRwdSEyyIvcMilIHkyXv2lSSr2ilNqjlMraZ1uwUuobpVR2x23QPs/drZTarpTaqpSa1dPB+zNlsVCZfA0x1gI2fO94V7gLIYQQ/dW4ix8m3zKY0O/upL62ynQcu6v68XkatAcjZ8kMjr4q9njbB+r8Za8aTvKrdfm29eZ6Y/3IvaJDbAVJrXWvnUMI8StlsTD2pAsIvHMdKwdfTlL1MjyencSqdx+irbXFdDy7s1o1f/44Cy93F+47dZRt454t8MFlEDYCzn4ZLC5GM4r+r2Lw8QxrzaZ8d6HpKEII4bDyyuvJ2bGNk6w/oVIuAa9A05FEXxWRbGvZus9n0LhQX3JKpWXrwRzOpe+vAbP323YXsFRrHQ8s7XiMUmoUMB8Y3bHPM0opefd9GJJnX8EegnFZ/bTpKEIIIYTT8PTyoWHW4wzUZWS9fpvpOHZVU1VOYsW3ZAWfiH9giOk44giFR8ez0T2JyILP0Var6TiAbf1IV4siKTKg184RE+xNQ0s7pXWynp0Q9uTl48fUqx9n90XfsdNzJFO2/ou8hyezJXWp6Wh29V5qAWt2VnDPySMJ9fWA+jJ451xbK68L3gMPP9MRhRMISzkNi9LkrPzUdBQhhHBYC9YWcLnrEixomHyd6TiiL4tIgqZqqMr7ZVNcmA+5ZfVyoexBdLkgqbX+AajYb/Nc4PWO+68D8/bZvkBr3ay1zgW2A5O6F9W5uLl7kBN3EYnN69mRucJ0HCGEEMJpjJg8kzUDzmZy2UdsXr3EdBy72bzkJbxVM4FHX2s6iuimhhFnE6mL2Za+zHQUANLzKxk9yB9Pt967PjEmxAeAfFlHUggjouKTSfy/paRNehy/9ipGLDyTNf+7gMrSYtPRet2e2ib+uWgzU+KCOWdCJLQ1w4ILoW43nL8AAqNMRxROYuiYaZQShMv2r01HEUIIh9TabmXR2m1c7PYdatQ8CIoxHUn0ZeHJttvizF82xYX50tDSTklNk6FQjq+7iwMN1FoXA3TcdiyUwGCgYJ9xhR3bDqCUukYplaqUSi0tLe1mnP5l5Gm3UK89qfz2MdNRhBBCCKcy5pL/UKQG4PvVrTQ19P92G9pqJWzrO2x3GUr82KNNxxHdNOKEi2nSblStest0FNrarWQUVDOuF9ePBFvLVoA8KUgKYYyyWBh/8uV435bOqvALGVfxFerpCaz+4FGs7e2m4/Wa+7/YRFOrlX+cMQYF8PlNULAKzngOIsebjieciLJYyA2cSnztGlpbpGOAEELsb+nmPZzYtARv3QDTbjIdR/R1A0eBcrG1be0QF2q7UDa3VNaR/D3dLUj+HtXJtk7nqWqtX9BaT9BaTwgLC+ulOH1TQFAoGwaeTnL1d+wu3GE6jhBCCOE0fPwCKT/+P0TpIta9+X+m4/S6benLibPupDzhfJSlt94eCnvxCwhmo//RJJR9TUuz2Sszt5TU0tjaTkovrh8JEBnkhVKQVyEFSSFM8/UPYsp1z7Br/tfsco9l8sb72f7PKWSv/9F0tB63bMseFmYWc+OMYQwN84Uf/g2Z78GMe2H0GabjCSfkNnI2fqqRbWu/NR1FCCEczvtrcrnK7St09DQYnGI6jujr3LwgLAGKM37ZFBdmK0juKJOC5O/p7jdOu5VSEQAdt3s6thcC+/YliQSKunkupxQ953YsWMn5UmZJCiGEEPY05pi5rAk6lUlFb5O97gfTcXpV9U8v0qA9GDXrStNRRA9xHXc+gdSx8fsPjeZIy6sEYHwvFyQ9XF0YFOBFfrl88BPCUQwZOYFRd/1Aasq/CG7bw9BPTmP1U5dTXdE/OiPVN7dx76dZxA/w5bpjh0LWR7DsH5A0H46+w3Q84aTip55Oi3ahdsOXpqMIIYRDKapqxGfHQiIoQ02/2XQc0V+EJ/2mZWu4vyfe7i7klPb/TltHqrsFyc+BSzvuXwp8ts/2+UopD6VULBAPrOnmuZzSoNgRZPgdw+jij6mrqTQdRwghhHAqIy59gnIVhOsXNxmfadZbqivLGFP5LVkhM/ELCDYdR/SQ0UfPo5wAyFhgNEd6fiUD/T0YFODZ6+eKDvaWGZJCOBhlsTDh9OtwvzWdNQPOZkLpJ7Q/kcLaT59CW62m43XLf7/Zxq6qRh46cwzuJenw6R8geiqc/gSozppGCdH7fP2D2OY5hog9/ftiOtH7lFKeSqk1SqkMpdRGpdTfTWcSojveX5vPNS4LaQ0aCvGzTMcR/UVEMtSVQJ1tnp5SithQH3KkZevv6nJBUin1LrASSFBKFSqlrgQeBk5SSmUDJ3U8Rmu9EXgf2AR8Bdygte6/i0b0Mp/jbsWfBrIWPm06ihBCCOFU/ANDKDrqIWKtO0l7+z7TcXrFliUv4qVaCDrmWtNRRA9ydXMne8AsRtetNDobKS2vkvExQSg7fDkfE+JNvqwhKYRD8g8MYcoNL7HzrEXscR3MxPX3sOWfR5GTtdp0tCOyobCaV37O5YLJ0UwMrIN354NfOJz3Nrh6mI4nnFxd9AnEWAsoyt1iOoro25qBGVrrZGAsMFspNcVsJCGOTLtVk73mK8ZYduI2/SaQZUpET4lIst3uM0syNtSHXGnZ+ru6/K9Pa32+1jpCa+2mtY7UWr+stS7XWp+gtY7vuK3YZ/w/tNZDtdYJWuvFvRPfOSRMmMFmt9FEZ79OW2uL6ThCCCGEUxl74vmk+p/I+LyXyd201nScHqWtVgZse5dsl2HEjz3adBzRw0KnX4q7amPL0teNnH9PTROFlY2kRPduu9a9okO8Ka9voa65zS7nE0IcvqFJ0xh+98+sGfN3wlvzif5gNqueuZba6opD7+wg2tqt3P1JJiG+Hvzf8YPhnfOgrQUueB98QkzHE4LBk+YCULDmU7NBRJ+mbfb2HHTr+NEGIwlxxH7ILuWMpk9o9giG5Pmm44j+JHyM7bZ4/S+b4sJ8KaxsoLlN5ud1Ri4H6COaJv6BQXoPGd+8bTqKEEII4XSGXvwUdcqH1o//0K8uDtqaupRYax6VIy80HUX0gqFjprHTEoX/to+MnD8937bcQEovrx+5V0ywDwB5so6kEA7N4uLCpLNuRd2YSlroaUza/R5N/00hdeGLfaKN62srdpK1q4a/n5JAwMJroHQrnPs6hCWYjiYEAFHxyRSqCDxzvzUdRfRxSikXpdR6YA/wjda6b05rF05v+U8/cqLLOlwmXwNuXqbjiP7EMwCCYqHk1xmSQ8N8sGrIk+49nZKCZB+RNGM+hSoC3/Rn+8SHNCGEEKI/CQqLIHfiXxneto3U9/5hOk6Pqf35Jeq1J6NnXWE6iugFymKhOGYuI1s3UZSzye7nT8+vwt3FwuhB/nY5X0yIN4C0bRWijwgMDWfyTW+wfe6nVLmEMCH1Djb+63jytq4zHe13FVQ08OjX2zhhxADmFD0J27+BU/4DQ483HU2I3ygMO5qExvU01teajiL6MK11u9Z6LBAJTFJKJe4/Ril1jVIqVSmVWlpqbpkAIX7PntomRu18k1blgevkq03HEf1RRNIBLVsBWUfyd0hBso9wcXVl14jLSWjbyta1cpWbEEIIYW8pc65gnfc0xmY/TUF2huk43VZdUcqYqqVkhc7Cxy/QdBzRS2JnXI5VK/KXv2r3c6flVTImMgAPVxe7nC+6oyCZVyEFSSH6kuEpxxF392pWj7qH6OZsIt45gZXP30RDXbXpaL+htea+z7JQCh6NXYta8zxMuQEmyEU9wvH4jJ6Dp2pl66ovTUcR/YDWugpYDszu5LkXtNYTtNYTwsLC7B1NiEP6cmUG8yw/0TDqXPAJNR1H9EfhSVCZC022966/FCTL6g62l9OSgmQfknTqH6jCl8Yf/mc6ihBCCOF0lMVC5EXP0qLcqX3/D1jb+/Z6AJuXvICnaiXkmGtNRxG9KDxqGJs9kogq+NyuXTaa29rZsKualOhAu53T39ONIG83aY0jRB/k4urK5HP/ROv1a8gImsnU4jeo+U8K6UvecJgOQQszi1m+tZTHxpcTuPxeGD4bZj5gOpYQnRo+eTYN2oPmTYtNRxF9lFIqTCkV2HHfCzgR2GI0lBCHSWuNZe1LuKk2Ao6/1XQc0V9FjLXdlmwAwM/TjQF+HjJD8ndIQbIP8fLxY/Pgc0iu+5mC7RtMxxFCCCGcTtigIWxJvotRrVms/fA/puMcMW21Ep79LttchzMsebrpOKKX1Y88h8G6hK1p39ntnBuLamhpszLeTutH7hUd4kN+hXzwE6KvChkYycRbF7B5zvs0WHxJWXkTmY/MpHB7ltFc1Q2t/P2LjZwSXsWsTXfBgJFw1ktgsc8McCEOl4enN1t9xhNT/pPDFPVFnxMBLFNKZQJrsa0hudBwJiEOy+pthZzWsoiS8OMhdJjpOKK/ikiy3e7TtjUuzIecUpkh2RkpSPYx8afdRhsuFH31mOkoQgghjpBSKkoptUwptVkptVEpdUsnY5RS6gml1HalVKZSKsVEVnGgiXNvJNNzPImbHqM4b6vpOEdky9pvGGItoGrkhaajCDsYdcJFNGp3qle9abdzpudVApASbd+CZEywt8yQFKIfGDl5FtF3r2XV8DsY2phF2JvHsfLl22lqMPPFzsNfbcbSUMZ/2/6JcvOCC94DDz8jWYToqta4kwinjJ2b15qOIvogrXWm1nqc1jpJa52otb7fdCYhDlf+0pcJVnWEzLzDdBTRn/kOAN9wKP51aZ/YUF9yy+RC2c64mg4gDk9oeDRrgmaSVLqQqrISAkPDTUcSQghx+NqA27XW6UopPyBNKfWN1nrTPmPmAPEdP5OBZztuhWHKYiHs/GfhlWMoffd6wv/0LcrSt67xqv/5RWq1F4mzLjcdRdiBr38Qqf5HM6L8G1qaGnH39Or1c6bnVxIZ5MUAf89eP9e+ooO9WZhZREubFXfXvvXvUgjxW65u7ky54D5Kiy5my7u3MbXgJXb9+wtyQ4+n2cWHJhdfmi0+NLv60GzpeOziTYuLL80uPrQqj0O+Piu1z31Up881t7Xz8ZodLA19GvemUrh8EQRE9vSvK0SPGzL1DMj6OyWpnxM7Wj5GCCGcS2VtI5N2v0uhzygiY6eZjiP6u4hkKPl1huTQMB8qG1qprG8hyMfdYDDHIwXJPihs5m14vb+I9QsfZ+plD5uOI4QQ4jBprYuB4o77tUqpzcBgYN+C5FzgDa21BlYppQKVUhEd+wrDImISWD3qj0ze/E/WfPY0k864yXSkLqsqK2FM9XLWh53GZN8A03GEnbinnE/A90tZ98OHjJt5ca+eS2tNWl4lk2NDevU8nYkO8caqoaiqkSGhPnY/vxCi54UNGkLY7R+T9dPneC77G+NLP8GL5kPu14oL9XhRjxe1eFOPN3V4UYd3x8/e7V7U6V+37ftcHd604cJLga8RWbcBznkdBo+3w28tRPcNGBzLDpc4AgqXm44ihBB2l/bNO5yoSiic9tffXoEkRG+ISILt30JrI7h5ERdm+yyaU1bHeJ9gw+EcixQk+6DYURPJ9JxA/M53aW76Cx6e3qYjCSGEOEJKqSHAOGD1fk8NBgr2eVzYse03BUml1DXANQDR0dG9llMcaOLZd7Lp4S8YkfFPyiafTuigGNORumTL1y8yRbUSdtx1pqMIOxp11FzKvr8DMhZALxcki6qb2F3TbPf1I8HWshUgr6JBCpJC9DOJR50OR51ue9DeCs210Fxju22q2eex7b5bUw2BzbUE/mZMDTQX/vrY2mo73sG+p3Rxh6YWmHEfjJ7X27+mED1qT/ixTCx8neqKUgKCw0zHEUIIu9BaE77xRXZbBhI59VzTcYQzCE8C3Q67N0HkeOJCfQHYUVrP+BgpSO5LCpJ9lJp2I6HfXcbaRS8y8cwDlh4TQgjRByilfIGPgFu11jX7P93JLvqADVq/ALwAMGHChAOeF73H4uKC37nP4P7WCex463pC7ljo8K1btdVKxPYFbHUdQUKitO5yJq5u7mwfOJuUkg+oLt9NQMjAXjuXqfUjAWJCbEXI/PJ6QL54FaLfcnED72Dbz5HSGtqaf1vI/E1hc58iZmA0TLyq5/ILYSdByafguutVsld8woRTrzEdRwgh7GLjysUktm8mbdRdDHSR8oewg4hk221JBkSOJzLICzcXJetIdsKxvzUTvyvxqLlkuwwjKvNJmhrlL7YQQvQ1Sik3bMXIt7XWH3cypBCI2udxJFBkj2yi66Lik1k/7A+Ma/iZ9K9eNR3nkDavXkKMtZDq0ReajiIMCJ12Ce6qnS1L3+jV86TlVeLl5sKICL9ePU9nBvh54OFqIa+8we7nFo5HKTVbKbVVKbVdKXWX6TzCwSgFbp7gGwYhQ2HQOIg7FkaeCmMvgMnXwrF3wswHYNLV0u5N9EnxKcdTiT9sW2I6ihBC2EVjfS0B39xOCWGMPPl603GEswiMBs9AKLatI+nqYiE62Juc0jqzuRyQFCT7KGWx0Dzjb4RTyvqPHjEdRwghxGFQSingZWCz1vqx3xn2OXCJspkCVMv6kY5pwvx7yXaNJ3bN36gsdez/RA0rX6IGb8bMvNx0FGHA0DFT2WmJJiD7o149z7r8SpIiA3Bzsf9HDYtFER3sTV6FFCSdnVLKBXgamAOMAs5XSo0ym0oIIezLxdWV7QFTia9ZSVtri+k4QgjR6zJev50oXUTZCY/i7RtoOo5wFkpB+BgozvhlU1yYLzmlMpFsf1KQ7MMSp59GhtckRm1/kery3abjCCGE6LrpwMXADKXU+o6fk5VS1yml9i7stwjIAbYDLwJ/MJRVHIKrmzuuZzyNr65nx5s3mo7zuypLi0mqXs7m0Dl4+dh/5powT1kslAyZx4jWzezakdUr52hqbWdjUY2R9SP3ignxJr+PzpAsqGhAa+m+3UMmAdu11jla6xZgATDXcCYhhLA7S8IcAqhnW9p3pqMIIUSv2rRyMZN2v8/q0DNJPFre9gk7i0iGPZugvQ2AuDAf8sobaLfK57t9SUGyj/M/7SF8dAOb3/+r6ShCCCG6SGv9k9Zaaa2TtNZjO34Waa2f01o/1zFGa61v0FoP1VqP0Vqnms4tfl/s6MmkxVzJhJpvWb90gek4ndq65HncVRsDZkjbGmcWO+MyrFpRsPy1Xjl+ZmE1bVZtZP3IvaKDfcjvg4W9ivoWjn5kGS/9mGs6Sn8xGCjY53Fhx7bfUEpdo5RKVUqllpaW2i2cEELYy/Dpc2nVLtSs/9x0FCGE6DUNddUEfH0LxZYBJF76X9NxhDOKSIa2JijbBkBcqA8t7VZ2VTYaDuZYpCDZx8WOmkha0BxSSj6gKHeL6ThCCCGE0xp/4QPkWoYw6Me7qa4sMx3nN7TVyuAd77HFbRSxoyaajiMMGhg5lE2eyUQWfoG2Wnv8+Gl5lQCkGJ4h2djaTmlts7EMRyJ1ZwUAY6MDzQbpPzpb8O+AKrXW+gWt9QSt9YSwsDA7xBJCCPvyCwhmq+cYIvZ8bzqKEEL0mszX/shgvZvqmf/Dxy/QdBzhjMKTbLcdbVvjwnwB2FEm60juSwqS/cCQcx6iHQtFn9xjOooQQgjhtNw9PGk77UlCdCVb37jFdJzf2LRyMVG6iNrRF5mOIhxA48hziNQlbE1d2uPHTs+vJDbUh2Af9x4/dldFh3gD9Ll1JFPzKnF3sTBmcIDpKP1FIRC1z+NIoMhQFiGEMKou5iRirIUUbu+dlu1CCGFS1k+fM6XsI1YNOJdRU+eYjiOcVWg8uHpBSSZgmyEJyDqS+5GCZD8wYHAs6yMvZELNt2Sv+8F0HCGEEMJpxY87hjWDLmRS5UKyfvzMdJxfNK16iWp8GDPzUtNRhAMYOeNCGrU7Navf7NHjaq1Jz6s02q4VICa4oyDZx9aRXLuzgqTIADzdXExH6S/WAvFKqVillDswH5B+hUIIpxQ15QwACtd8YjiJEEL0rLqaSkK+vY0CNYjkSx8zHUc4M4sLhCdCsa0gGezjjr+nK7kyQ/I3pCDZTySe+xcq8adl8T290n5LCCGEEF0z7uJ/UaAGEfzdndTXVhnNoq1WdmxYxZia79k84BQ8vX2N5hGOwdc/iI0Bx5BQ/i3NTT1XtMuvaKC8voWUmMAeO+aRiAzyxqIgv7zvXIna2NLOhsJqJsYGm47Sb2it24AbgSXAZuB9rfVGs6mEEMKMwXGjybNE4Zv3rekoQgjRoza+dgsDdRn1c57Ay8fPdBzh7MKTbDMkrVaUUsSF+coMyf1IQbKf8AsIZtuIPzC6JZPM5R+ajiOEEEI4LU9vX+pmP064dQ8b3rjD7uevq6lk3ddvsfrJSyi5fzhDP5qFFQsRJ9xg9yzCcbmnnE8A9Wz6vufeN+5dP3K8wfUjAdxdLUQEePWplq3rC6pos2omDjH7Z9ffaK0Xaa2Ha62Haq3/YTqPEEKYVDTwWBKaNlBTVW46ihBC9IgN33/M5IrPWBNxASMmnWQ6jhAQkQTNNVC1E4C4MB8pSO5HCpL9yLgz/kihiiDgpwdob2szHUcIIYRwWiMnz2Jt2JlM2vMhW1Z/3avn2jsLcuUb97HxoaPxeHQo41bcQGLZEkq841k96l6qrlpFTMLYXs0h+pZR00+njEDIWNBjx0zPr8TXw5X4AeavTI4J8e5TLVtTd1agFIyPlhmSQgghekdA8mm4qXayV0j3aiFE31dTVc6AZXeQZ4lk7CWPmI4jhE1Esu22o23r0DBfSmqaqG+WWs1eUpDsR9w9PNkz+S6GWPNJ++wp03GEEEIIp5Z46WPsVqF4L7mVpsaevSKuunw3aV++xNrH51N2fxxDP5rF1Jwn8GyvJXXQhWw86R3c/pzHuD8tZvK5dxIeNaxHzy/6Plc3d7YPnMPo+lVUlZX0yDHT8qoYFx2Ii0X1yPG6IybEm/w+NENyzc4KEgb6EeDtZjqKEEKIfmr4+BlU4Yt16yLTUYQQotu2vHYjobqC5lOflqVJhOMYMAosrlCcAUBsqA8AuWUyS3IvKUj2M+NmXsJW1xEM2fA/GuqqTccRQgghnJaPXyBlxz9CtHUX6968q1vHam9rY2vqd6x85U62PjgZ3ycSGL/2doZX/UC+31jWJD9I6TUZDL1vPVOvfZLR00/B3cOzh34T0V+FTb8Ed9XO1u9e7/ax6prb2FpSw7hox2g5Gh3sQ0V9C7VNraajHFJbu5X0vEomSLtWIYQQvcjVzZ3t/lMYVr1SumoJIfq0jO/eY1LVItYMvoThKceZjiPEr1w9IGyEbR1JbC1bAXKkIPkLKUj2M8piwXrS/QyggowP/2k6jhBCCOHUxhx7JmsCT2birrfYnvHTYe1bVpLP2k+fJvXRM6l5cAgJC89gct6LKKysib6SLad+jO+9+Yy//VMmnXETYYOG9M4vIfqtuMQp5FpiCNz2cbePlVFQhVWbXz9yr5gQb4A+0bZ1S0kt9S3tTBwi7VqFEEL0LpUwhyBqyU77znQUIYQ4ItUVpUT8cBe5lhhSLnnYdBwhDhSRbJshqTVDQnxQCnJK60ynchiupgOInjdy8izWfT+dMbmvUb77RkIGRpqOJIQQQjithEufpPJ/E1Cf30jryNW4uXt0Oq61pZnstO+o3rCYsN0/Max9B6FAOQFsD5iGij+RoZNPY3hYhH1/AdFvKYuF3UPmMiXnCQq3byBy2JgjPlZ6XiUAY6MCeyhd90QH2wqS+RUNJA4OMJzm4NburACQgqQQQoheN2zaPFrX/InK9V/A5Jmm4wghxGHb9vofGKerqJn7Jh6e3qbjCHGg8CRY/zbUluDpH8HgQC9ySmWG5F4yQ7KfCpn3EJ40s/2Dv5iOIoQQQji1gKBQCqc9yND2XFLf/u3rckl+Nqs/eJR1/z6F5n/EMGrJfCbuepMWF29Wxt7A9jMWEXRfLhP/+D4TTr2GIClGih4Wd8IVWLWi4PvXunWctPxKhg/0JcDLMdZA7EszJFN3VjI40ItBgV6mowghhOjnAoJC2eaRSMTu5aajCCHEYVv39VtMrP6atdFXMCz5KNNxhOhcRJLttqNta2yoj6whuQ+ZIdlPRQ8fy+rQ00kp/ZSC7FuIik82HUkIIYRwWuNmXkTaho8Yv/NF1n4aTXvxBsJLf2KItYBwoIRQNoachFvCTIZNPplRgSGmIwsnMWBwLBs8xxJT+AXa+m+U5fCvV7RaNevyq5iTGN4LCY+Mn6cbwT7u5Fc49gc/rTVrdlYwfaj8mxdCCGEftTEnMjr7UYpytzAodoTpOEII0SWVZSVErbiHHS5xjL/oH6bjCPH7wjs6DxVnwvBZDA3z5YPUArTWKKXMZnMAMkOyHxt6zoO04krpp/eajiKEEEI4vdiLn6Je+TBx/Z9JKfmAWrcwVsXfRt553zHwL9lMvvlNUmZdjL8UI4WdNY08h0F6N1vXfnNE++eU1VPd2EpKtGOsH7lXdLC3w8+QzK9ooLS2mQnSrlUIIYSdDJ58BgD5q7q/hrQQQthLzuvX469rYd6zuHt4mo4jxO/z8IPgoVC8HoC4MB/qW9rZU9tsNpeDkIJkPxYaHkVGzGWk1P/AlrXfmo4jhBBCOLXgAYOpOvdjMo59ibY7cxhz9zKmXPhXYkaOP6JZaUL0lFEnXEiD9qBm9VtHtP/e9SNTYhyrIBkT4vgFybU7bX92sn6kEEIIe4kaNoZ8y2B8dh7ZhUhCCGFv6YtfZXztd6QNuZqhY6aYjiPEoUUk/dKyNS7UF4AdpXUmEzkM+farn0s658+UEYj++j601Wo6jhBCCOHUYkdNJPn4c/D2DTAdRYhf+PgFsingGEZULKW56fBbnKbnVxLg5UZcqE8vpDtyMcHeFFc30tLmuO+B1+ZWEODlRvwAX9NRhBBCOJGisGNIaMqgrqbSdBQhhDio8t2FxK7+C9kuw5hw4f2m4wjRNRHJUJUPjZXEhtk+J8s6kjZSkOznfPwCyUm8hZGtm1j3zdum4wghhBBCCAfkMf4C/Kln4/IPDnvftLxKUqIDsVgcaz2M6BAfrBoKKx13luTavAomxAQ53J+dEEKI/s0v+XTcVTvbVnxmOooQQvwubbWS98b1+OgG3M5+Hjd3D9ORhOia8CTbbXEmEf6eeLpZyCmVgiRIQdIppMy9kTxLFGGrHqK1RXoVCyGEEEKI3xo1/XRKCUJlvndY+1U3tpK9p87h1o8E2xqSYFun0RGV1zWTU1ov60cKIYSwu4SJJ1KDD+2bF5uOIoQQvytt8cuk1P9A2tA/MGTkBNNxhOi6iGTbbUkmFosiNtSXHGnZCvRQQVIptVMptUEptV4pldqxLVgp9Y1SKrvj1vG+pXASrm7uVE67hyhdRPqn/zMdRwghhBBCOBgXV1d2DJxNYv1qqkqLu7zfunxbq7fxDrZ+JNjWkATHLUjuXT9yUqzj/dkJIYTo31zd3NnmN4Wh1Stob2szHUcIIQ5QVpLPsLV/Y6trAhPPv890HCEOj08o+A+G4r3rSPpIy9YOPTlD8nit9Vit9d7LFe4Clmqt44GlHY+FIckzzmOT+xiGbXpK1ggQQgghhBAHCDvqMtxUO1u/e73L+6TnV2FRkBwV2HvBjtAAPw883SzklTtmQTJ1ZwXurhYSB8uaskIIIQxImE0wNWSvW246iRAAbFyxiG0PTmTlq//HjswVaKvjrgMuepe2Wil44xo8dTOe57yAq5u76UhCHL7wJCjOACAuzIeCykZa2uT/a73ZsnUusPfbjNeBeb14LnEIymLBdfaDhFBN1vsPmI4jhBBCCCEczNAxU8i1DCEw++Mu75OeV8mIcH98PFx7MdmRUUoRHeztsAXJtTsrGBsViIeri+koQgghnFD8tDNo0xYq139hOooQAFjbWtEoJu98nqEfz6H0/qGsfvISMr5bQFODtDp0JqlfPMe4hpWsH34TMQljTccR4shEJEF5NrQ0EBfmQ7tVk18hsyR7qiCpga+VUmlKqWs6tg3UWhcDdNwO6GxHpdQ1SqlUpVRqaWlpD8URnRmechxpfseTVPAWpUU7TccRQgghhBAOZnfcPBLatlKQnXHIse1WzfqCKlJiAns/2BGKDvZxyA99DS1tZBXVMHGItGsVQghhRkBwGFs9RhNevNx0FCEAGHPMXBLuXUPF9RtYk/wAhT6jSSxbQvIP16L/Fcv6f81i9QePsmdXrumoohft2ZVLwroH2ew2ionn3WM6jhBHLiIZtBV2byQu1BeAHaWO99nU3nqqIDlda50CzAFuUEod09UdtdYvaK0naK0nhIWF9VAc8XvCz/gHrrSR++G9pqMIIYQQQggHM3TG5bRrxa7vD922ddvuWuqa2xxy/ci9YkK8ya9oQGttOspvrM+vot2qmTAk2HQUIYQQTqw2+gRirTspzttqOooQvwgNj2LSGTeTcudCXO/OZcPxr5I54HQGNOUweeP9DHhxLNsfSGHly7ezLf17rO3tpiOLHqKtVorfvBo33YrveS/g4up4XViE6LLwJNtt8Xpiw3wAZB1JeqggqbUu6rjdA3wCTAJ2K6UiADpu9/TEuUT3DI4bTfrAsxhfvpC8zWmm4wghhBBCCAcSNmgImzzHEb3rC7T14F/upOfb1iVPiXbsgmRTq5U9tc2mo/zGmp0VKIVDF3OFEEL0f4MmnQFA/qpPDCcRonMent6MOfZMJt/wMhH3bWXneUtZGXczrRYPJuW/zPDPT6figTjW/O8C1n39FvW1VaYji25I/fRJkpvWkjHij0QNG2M6jhDdExAJXkFQkom/pxuhvh7klEr76W4XJJVSPkopv733gZlAFvA5cGnHsEuBz7p7LtEzEs65nwY8qfxCpr0LIYQQQojfahp1DoP0Hras/eag49LyKgn1dSc62NtOyQ7f3myOto5k6k7b2pv+nm6mowghhHBi0cPHUqAG4ZV78Nd8IRyBslgYMnICUy95gJH3rKTmxs2kpjxMnu9YRlZ8x7gVN+D6n2FkPnwiq997WGb+9jEl+dmMyPgnG93HMOnc/zMdR4juU8rWtrU4E4C4MB9ypGVrj8yQHAj8pJTKANYAX2qtvwIeBk5SSmUDJ3U8Fg4gKCyCrKFXM7ZhJRtXLDIdRwghhBBCOJBRMy6gQXtQu/qtg45bl1/FuOgglFJ2Snb4YkJsrXHyyh3ng19bu5X0/EpZP1IIIYRD2DXgGEY0rpeZZaLPCQqLYMLp1zP+js/wvCePrJPeYl34WQQ3FzJ58z+JeHUSufcns/KFm9my5hva29pMRxa/Q1utlL59DS5YCZz/IhYXF9ORhOgZ4UmwZxO0tzI0zIccadlKtxsxa61zgOROtpcDJ3T3+KJ3jDv7/9j9yNu4f/cXrJNnyf/ohRBCCCEEAD5+gawNPJYRFUtpaqzH08vngDHldc3kltVz3sQoAwm7bnCgFxYF+RWOM0NyU3ENDS3tTJT1I4UQQjgA36RTcd+9gKwVX5Ay62LTcYQ4Im7uHiROPw2mnwZAQXYGu1Z/gl/+UibuehPXotepXOTP9oCpWBLmMHz6XPwC5L2Yo1jz0WNMbk5n9eh7mRw30nQcIXpORDK0t0DpFmJDfaiob6GqoYVAb3fTyYzpkTUkRd/j6e1L/tjbiG/LZt1Xr5qOI4QQQgghHIjn+Avwp4FNy9/v9Pl1+VWAY68fCeDuamFQoJdDtWxdu9O29qYUJIUQQjiChIkzqcGbts3SQUv0H1HxyUy56G+M/vOP1N+yjdSJ/2GH/ySGV//E+DW34vnYMLL+eSyr3nmAXTkbTcd1akW5WxiT9QgbPMYx6ezbTccRomdFdMzjK84kLtQXwOlnSUpB0omlnHodOZYhRKQ+QnOT43xJI4QQQgghzBo17TT2EIza0HlBMi2/EleLIikywM7JDl9MiDd5DjRDcm1uBVHBXoQHeJqOIoQQQuDm7kG232TiqlZgbW83HUeIHhcQHMaEU65mwm0f4XPPTjbNfo/UQefj11rOlG3/YfAb09j+QAqr3/sX1ZVlpuM6FWt7O5XvXoMVC6EXPI+ySKlC9DPBQ8HNB4oziAuzdR5y9nUk5V+5E3NxdaXumL8wSO9m3cePmo4jhBBCCCEchIurKznhc0isX01ladEBz6fnVTJ6kD+ebo7f9j862Id8B1lDUmtNal4FE2NkdqQQQgjHoeNnEUoV2zN+NB1FiF7l6ubOqCmzmXrt08T8JYtdl6xgVfztKKxM3vwQHo+PIPWxs8j6+Qsp0NvB2g8eYXRLBpuS/o+ImATTcYToeRYLhCdCSSZRwd64WhS5ZXWmUxklBUknl3TcWWzwSGHEtufkKiAhhBBCCPGLgUdfiptqZ9vS13+zvbXdSkZhFSkxjt2uda+YEG8qG1qpaWo1HYWd5Q2U1bUwQdq1CiGEcCDDpp1Bu1aUp39mOooQdjU4bjRTLvwLcfekkz1vIRmhpzC8ZgWJ31xE8YMjWfnaXezZlWs6Zr9UuD2LMZv/S6bnRCaecYvpOEL0nohkKNmAm4LoYG+ZIWk6gDDP6+QH8df1bHr/76ajCCGEEEIIBxE7ejI7XGIJ2v7xb7ZvKa6lqdXq8OtH7hUT7A1AvgOsI7k2twKASbF9489OCCGEcwgMDWer+2gGFC83HUUII5TFQvzYo5l80+u43bmN/2fvPsOjOu63j39n1RvqQhJCBRAdRBEgjHvvNraJcVxT3GLHNd2pfuIkTuK4xw6Ja+KGu+PeOwJE700FgQBVhHrbeV5onb+MBQi1o13dn+vaS7un3sMKjbS/c2bypv2JvQFDmV34ELELprLqzpNY8c4TNDc1Oh3VJ7jb2qh57ipajR9DL9FQreLjEidDcy1U5jMiPkwFSacDiPNGZc1hWdRJTCt5ht3btzgdR0REREQGiLKMcxndupntm1f+b9myovai2nQvuUMyNba9IFk0EAqShZVEhwYwMj7c6SgiIiJfs2/48Yxsy2d38Vano4g4KiQsguyzr2XCLz5jxyVfsCTlcpIatjJ10Q3U/jGT3IeuoWjjcqdjerUlz97BuJZ1bJpyG0NTRjodR6RvJU1u/7p7FSPiwymoqKPNbZ3N5CAVJAWAlPN+D0Dxi7c5nERExPcZYx41xpQaY9YeYP2xxphqY8xKz+PX/Z1RRARg1PHfoc0adn76f8O2Lt++l8QhwSRHhTiYrOvSYsMAKKp0/krUvKIqpqfFYIxxOoqIiMjXJM2cC0DRopcOsaXI4JEyaiKzr7yXmNs2serof1AUNpnpuxeS9uxxbPx9DktevIfafVVOx/Qq2zevZMrm+1gZOpvss3/gdByRvhc/DlwBsGs1GXFhNLe6Kdnb4HQqx6ggKQAkpY1hefKFTN/7LttWf+l0HBERX/c4cOohtvnMWjvF87i9HzKJiHxDXHIa60Kmkb7zv7jb2gBYVlTlNXdHAoQH+RMbFuj4kK2lNY0UlNdpuFYRERmQUkdPYacZSnDBe05HERlw/AMCyTp+PlN//AbV164id9RNhLhrmLnmN7juGsOSey5i4+J3sW6301EHtLbWVhoWXk2TCSRFQ7XKYOEfCAnjYNcqRsS1XyybX+78xbJO0f96+Z/x3/od+0wYdW/+0ukoIiI+zVr7KVDpdA4Rka5oHv8tkmwZm5a8y559jezc28DU1CinYx2W1NhQx4dsXVbYfvV8dnqMozlEREQ6Y1wuiuOPYWz9Cuprq52OIzJgxSUOJ+eS35H6yzVsPP0F1kafwMSqDxj71jy2/34Suf/+NeW7i52OOSAtfeZ2xrRuZMv03xCXnOZ0HJH+kzQZdq/+v4JkWa3DgZyjgqT8T2R0HBszr2Zy4zLWfKIhOkREHDbbGLPKGPOWMWbCgTYyxlxljMkzxuSVlZX1Zz4RGSTGH38R9TaImiX/YXlRe1HNm+6QBEiLCWV7pbMFyaWFVQQHuJiYHOloDhERkQMJm3gmQaaFzYvecDqKyIBnXC7GzjyJmTc9g/vWTSyZ9DsaXBHkbLuXyIeyWPHn01n5wbO0tjQ7HXVAKNqwjKlb/87ysKOYfsaVTscR6V9JU6C+gjhbTkSwP/llukNSBICp5/+IEpNA6Ke3/29YLhER6XfLgTRrbRZwP/DKgTa01i6w1mZba7Pj4+P7K5+IDCKh4ZGsizqWsVUfsnRrCYH+LiZ4WVEtNTaMkuoGmlqd+/12aWElU4ZHEeivP8FERGRgGjPrFGpsCM3rVZAUORzhQ6KZef5NjP1lLkXzP2JZ0nxS69cy5bOrqbxjDIsW3MCOrWudjumY1pZmml+8mnoTQtplD2uoVhl8EicDYHa13yVZoCFbRdoFBYdSMv3HjGwrYNl/H3Y6jojIoGSt3WetrfU8fxMIMMbEORxLRAax4OkXM4R6ype/xuRhkV5XVEuLCcVa2FHV4Mj5a5taWVdSzQwN19ovjDG/NcbsNMas9DxOdzqTiIg3CAwKZkvETDKqvtBF6iLdlDZ2GjnX/J0hv9jC8tkPsCskk5k7nyTlP3NY94cjWfrq32moq3E6Zr/Ke+q3ZLZuIX/m7cQOTXE6jkj/GzoBMO3DtsaHa8hWkY6mnfY9tvhnMnzl32isH7z/OUREnGKMSTTGGM/zmbT31xXOphKRwWz8EWdQSgxn8wnTvGy4VoC02FAAtjs0j+SK7VW4LSpI9q+7rbVTPI83nQ4jIuIt2kadQjxVbFv9hdNRRLxaQGAQ0065lKyfvkvFVSvITb+OyJYyZqz4Oa1/zmTxfZey9rNXfX5I1/y1i5lW8DDLIo5j+unfcTqOiDOCwiF2FHjukCypbqS+udXpVI7wdzqADDwuPz+ajvsNme9dQu4Ld5Jz2f9zOpKIiE8xxjwDHAvEGWN2AL8BAgCstQ8DFwDXGmNagQZgvrXWOhRXRAQ/f3+2JZ3OMSXPMH7X7+G9kTBkWPsjchgMSYGwOGi/lmLASf2qIOnQPJJLC6twGZiaGuXI+UVERLpq1JzzcK+4jfLlr5E59Win44j4hIRhGSRc8Qfcbf+Pdblv07D4MSZVvEPoB69R9cEQtsQcQ0jWeYydfQYBgUFOx+2xspJC8j9fSEj+24xtWEmNCWfEZQ85HUvEWUlZULyYERPCASgor/O6qVB6gwqS0qmJc85i1eezGJ//L/aWX0dUXKLTkUREfIa19qJDrH8AeKCf4oiIdMmIU2+gYOEGRtWshdx3oW2/q7n9gmBIcociZYdi5ZBkiEyBkGhHipbx4UGEBPhR5NAdknmFlYxLGkJEcIAj5x+krjfGXAbkAbdaa6s628gYcxVwFUBqamo/xhMRGZii45PYGDiO+JIPnY4i4nNcfn5MmHMGzDmDhroaVnz+Em1rX2VCxXuEffRf9n4UzuaoownOOp+xR5xJYFCw05G7rGjTSkpynyem+D3GtG4iHig2ySxLvoiU469ieHyS0xFFnJU0Gda+wKjw9r+jVZAU2U/k2X8g7NkTWbrw1+T8YIHTcURERETEQUPTxjD0x54PJ91uqK+AfTugeifs2wnVO9q/7iuBokVQUwLu/YahCQjtULRM2e+5p4AZNKTXi5bGGFJjQtleWderx+2KljY3K7bv5cIZw/v93L7MGPM+0NlVk7cBDwH/D7Cer3cB3+3sONbaBcACgOzsbI1GICICVKUcz+yCByjdWUDCsAyn44gDjDHDgSdp72vdwAJr7b3OpvItIWERTD3lcjjlchrra1nx+Su0rX2Z8VUfEf7Jm+z7JIxVkUcRmDWXsUecTVBwqNORv8bd1saWFZ9Quexlhu35gDT3TtKAzf6jyU2/jqRZ55M6ZirDXZoxTgSAxMkApLdsBSC/rP//Nh0IVJCUA0ofl82SmDOYtucFdubfzLAR45yOJCIiIiIDgcsF4fHtj+SpnW/jboPaUk+Rcuc3C5f5H0PNLrDur+8XGL7fXZYpMGkexI7sUeTU2FAKy/v/j751JftoaGnT/JG9zFp7Yle2M8b8E3i9j+OIiPiUxBnnQsEDFHz5EgnzbnU6jjijlfYRBpYbYyKAZcaY96y1650O5ouCQ8OZevIlcPIlNDXWs/KL12hZ/RJjqj9lyKdvs+/Tm1kdeSQBE+cy9shzCA4JcyRnc1MjGxe9QcOa1xhR8QljqKLF+rExeDKLR1xO+pwLGJ3Ss9/ZRXxWUhYAQeVrGRY1ifyyWocDOUMFSTmojHl30PaPd9n98i8YduvLTscREREREW/h8oMhSe0Psjvfpq0Vand7ipU72u+u/Op59U7Ysw5q90DxErj0pR7FSYsJ5dPNZbjdFper/4aNzSusBGBGenS/nXOwM8YkWWt3eV7OBdY6mUdExNukj51OiUkgKP9dQAXJwcjTj+7yPK8xxmwAhgEqSPaxoOBQppwwH06YT3NTI6u+/C9Nq15izN5PiPziXWo/v4W8yDm4JpzL+KPmEhwa3qd5aqor2fzFy7DhdUbvW8Rk00C9DWJjxCyKRp9B5pHnMykmvk8ziPiE0BiIHA67VpERl0OBAxfLDgQqSMpBxSens2j4Jcze8Sibl3/C6GnHOB1JRERERHyFn3/7HZCRKcCszrf54P/B53+Dmt0Q0f15zdNiQ2lqdVNa00RiZP/Nx7OkoJK02FAShnjPHEA+4M/GmCm0D9laCFztaBoRES9jXC6K444mq/RVGupqCAmLcDqSOMgYkw5MBRZ3sk5zMfehwKBgso6bB8fNo6W5idVfvk7jqpfIrPqE6EXvU//lrSwbcgRm/DmMO/qCXvu/Wr57O9s+e57gbW8xrmEF000rlQxhQ8zxBE06mzGzz2KaQ3dpini1xMmwazUjUsN4eflOrLWYXp6uZKBTQVIOaeK8X1F59wu0vH0bdsqnGI39LSIiIiL9JWs+fPZXWPMCHHF9tw+TGtv+oUlRRV2/FSStteQVVXHcmIR+OZ+0s9Ze6nQGERFvFzrxDII/eoGVuW+0360lg5IxJhx4EbjJWrtv//Wai7n/BAQGMfnY8+HY82lpbmJN7tvUr3qRzIqPiFn8EfW5P2V5xCzs+HMZe9T5hEVEHdbxi7euYeei54kqepfRLRuJM5adZijLE+cROW0uo6efwEx/lRJEeiQpCza9yejJhpqmVspqm0iIGFwXruqniBxSRGQMi8ddx6wNf2TlRwv1i6iIiIiI9J+4TEieBquf7VFBMi0mFICiynpmjYjtrXQHlV9eR2VdMzMzNFyriIh4l9GzTqXuw2Ca1r8J+hxoUDLGBNBejHzKWtuzsfOlVwUEBjHp6HPg6HNobWlm7ZJ3qFvxEiPLPyRuyac0Lv4pK8Jn0TbuHMYePY/wId/8XdS63WxZ+RkVy14iadcHpLuLGQ5s9RvJ4vSrSZx5HunjZjBMN6aI9J6kyYBlon8xAPlldSpIinRm2tybKd74BFFf/J7Wo8/DPyDQ6UgiIiIiMlhkzYe3fgJ71sPQ8d06xLDoEPxchu0V9b0c7sCWFrTPH5mdHtNv5xQREekNQcGhrAufQUbFZ1i3W6NlDTKmfQzBR4AN1tq/OZ1HDsw/IJCJc86COWfR1trK+qXvUbPseUaUf0h83uc0Lf0FK8Jm0Db2bEbOPpcdG5ZQv/oVMso/ZjSVtFoXG4Mnk5v+bdLnzGNUaiajnG6UiK9KnAxAWvNWIIOC8jpy+uli2YFCBUnpkoDAIMpzfsbURTew5LUHmXn+zU5HEhEREZHBYuL58PbP2++SPOn2bh0iwM9FclQwRZX9WJAsrCI2LJARcZpjR0REvE/rqFNIWPUZW9d8yaisI52OI/1rDnApsMYYs9Kz7BfW2jediySH4ufvz/jZp8Hs03C3tbEx7wP2LnueEaXvk7D8S1j+M6KBBhvIxvCZFGWeTuaR5zMxrvvztIvIYRiSDKFxRFVvIMh/JPlltU4n6ncqSEqXTTnpUjYufYiRa+5m98yzSByu62VEREREpB+ExcGoE2H183DCb6Gbd2mkxYSxvaKud7MdxNLCSrLTo2m/yUBERMS7jDxiLu6Vv6Js2WsqSA4y1trPAf0C48Vcfn6MnXUyzDq5vTi54mOq1rxL8LBJjJ1zDlPDIpyOKDL4GANJkzG7V5ERdyH5Zf33t+lAofEWpMuMy0Xw+Q8QZJupe3wedTV7nY4kIiIiIoNF1oVQUwKFn3X7EKmxof12h+SefY1sr6xnhoZrFRERLxU7NIUtAWOIK/nQ6Sgi0gMuPz/GZp/A7O/cydSTLyFExUgR5yROhtKNZMYFkl+ugqTIQaWPy2bbsfeT3lrA5ocuwt3W5nQkERERERkMxpwOQUNg9XPdPkRaTCh761uobmjpxWCdyyusAjR/pIiIeLeqlBPIbN1CeUmR01FERES8X1IWuFvIDt3D9sp6WtrcTifqVypIymHLOm4eS8f+mKn1X7L4Xzc4HUdEREREBoOAEBh/Nqx/FZq7d5djWmwoANsr+v4uyaWFlYQE+DEheUifn0tERKSvJGSfA0D+opccTiIiIuIDkrIAmGgKaXNbtvfTCD4DhQqS0i2zLvw5i2PPZfau/7Dk5fucjiMiIiIig8Hk+dBcC5ve7NbuqTFhABRV9v3QOEsLK5maGkWAn/7kEhER75Uxfga7iSdg27tORxEREfF+0RkQGEFq81aAQTePpP46lm4xLhfTrl7AmqCpTFn5W9Z92b0PhUREREREuixtDkQOh1XPdmv3VM8dkkV9fIdkTWMLG3bt0/yRIiLi9YzLRVHcUYypW0Zjw+D60FRERKTXuVyQOInofRsByC+rdThQ/1JBUrotIDCI1GteYLdfIsPevZIdW9c6HUlEREREfJnLBZPmwbYPobb0sHcPD/InLjywz4dsXb59L26LCpIiIuITQiacQahpYlOuLkYXERHpsaTJ+JeuJT7Uj4LywXWxT58XJI0xpxpjNhljthpjftbX55P+FRkdh+vbCwFoe/pCqqvKHU4kIiIiIj5t8oVg22Dti93aPTUmtM+HbM0rrMTPZZiaGtWn5xEREekPo3NOo94G0bj2daejiIiIeL/EydBSzxEx1RqytTcZY/yAB4HTgPHARcaY8X15Tul/KaMmsvPkf5LUtovtD8+jtaXZ6UgiIiIi4qsSxkJSVreHbU2LDevzOySXFFQyIXkIYUH+fXoeERGR/hAcEsamsGzSKj7Hut1OxxEREfFuSVkAzAreSX65hmztTTOBrdbafGttM/AscE4fn1McMOGI01k55bdMalrOsoev1C+oIiIiItJ3Js+HXSuhbNNh75oaE8qufY00tbb1fi6gudXNyuK9ZKdpuFYREfEdraNOIZFy8tctcTqKiIiId4sfA35BjDf5lNc2U93Q4nSiftPXBclhQHGH1zs8y8QHzZx7A7mJFzOr4hUWP/cnp+OIiIiIiK+adAEYv27dJZkWG4q1UFzZ0AfBYG1JNU2tbmakR/fJ8UVERJyQccRcAErzXnY4iYiIiJfzC4CEcQxv2gowqOaR7OuCpOlkmf3aBsZcZYzJM8bklZWV9XEc6Wszvn8fK0KPYMbGP7P6oxecjiMiIiIivig8AUYeD2ueh8McmSMtNhSA7X00j+TSgkoAstN1h6SIiPiOuMRUNvuPJmbnR05HERER8X5JWURVbwAs+WWDZ9jWvi5I7gCGd3idApR03MBau8Bam22tzY6Pj+/jONLX/Pz9GX3tMxT6pzPi4+sp3JDndCQRERER8UVZ86G6GIq+OKzdUmPCACjqo3kklxZWkREXRnxE0OHvrGkPRERkAKsYdjyZLZsp31186I1FRETkwJIm49e0l+GuCvLLdIdkb1kKZBpjMowxgcB84LU+Pqc4LCwiirArXqDRBBG48CIqS3c6HUlEREREfM2Y0yEwHFYf3rCtceGBhAb69UlB0u22LCuqJDutG8O1NtfBA9mwRqOMiIjIwJQw/RxcxpK/SMO2ioiI9EhiFgDHRJRoyNbeYq1tBa4H3gE2AAuttev68pwyMCQOH0Xl2U8S465izz8voKmxb65AFxEREZFBKjAUxp0N61+Dlq7PB2mMITUmlOLK3v/9dFtZLVX1LczI6MZwrcseh8ptEJXa67lERER6w4iJOewhloCt7zgdRURExLsNnQDGxYzgnWzTkK29x1r7prV2tLV2pLX2jr4+nwwco6cdw7pZdzKuZT2rH7ocqyGoRERERKQ3ZV0ITftg01uHtVtqTChFfVCQXFpYBcCMw50/srUJvrwf0o+C4TN7PZeIiEhvMC4XhbFHMaZ2qS48FxER6YnAUIgbzTjyKayow+22TifqF31ekJTBbfrp32NR2jXMqH6XxU/+0uk4IiIiIuJL0o+CiGRY/dxh7ZYWG8r2yvpe/6Mvr7CSuPAg0mNDD2/HlU9DzS446tZezSMiItLbgiecQahpYlPu4V0MJCIiIvtJnExK4xYaW9yUVHd91B9vpoKk9Lmcy/9I3pATySl8kOVvP+50HBERERHxFS4/mDwPtr4PdeVd3i01NozmVjd7ahp7Nc6SwkpmpEdjjOn6Tm2t8MU9kDwNRhzbq3lERER625ic02mwgTSsfd3pKCIiIt4tKYvQplJiqR4080iqICl9zrhcTLz2STb6j2Pcoh+zZcWnTkcSEREREV8xeT64W2Hti13eJS2m/Q7GooreG25uV3UDO6oayD7c4VrXvQRVhXD0j+BwCpkiIiIOCA4NZ2NYNqnln2lqHhERkZ5ImgzABFch+WUqSIr0muCQMOK+/zx7TSSRr15O6c4CpyOJiIiIiC8YOh4SJ8GqZ7u8S5pnSNXtvViQ/Gr+yJmHU5B0u+Gzv0H8OBh9Wq9lERER6UstI04iiTIKNyx1OoqIiIj3SpwEwNSAYvLLah0O0z9UkJR+E5c4nMZ5TxFm69n36PnU11Y7HUlEREREfMHkC6FkOZRv6dLmyVEh+LkMRZW9dxVqXmEloYF+jEuK6PpOm96Esg3tc0e69KeZiIh4h4wjzgNgd96rDicRERHxYiHREJVKdlAx+RqyVaT3ZUyYxdZj7iWjNZ9ND30bd1ub05FERERExNtNmgfGBauf69LmAX4uhkWF9OqQrUsLq5iWGo2/Xxf/xLIWPrsLotNhwtxeyyEiItLX4pPT2eI3iujiD52OIiIi4t2Sshhr8wfNkK3+TgeQwSfr+Pnk7t5Czua/kvvITeRcdb/TkWSQqKvZy4aPnsXd1oxfYCh+weH4B4XiHxxGYHA4QaHhBAWHExgaTmhYBP4BgU5HFhERka6ISIQRx7YXJI/9RZfuNkyLDWV7Ze8UJKsbWti4ex83nTC66zvlf9R+V+dZ94Kf/iwTERHvUj7seGYV/ZPK0p3EJAxzOo6IiIh3SswifsN/2VdbQWNLG8EBfk4n6lP6y1ccMWv+bSx+YDM5JU+y9JUxzDj3eqcjiQ8r372dLf/9G+N3LiSbrl9t0mz9aDRBNBJMswlqf7hCaPULotUvhDa/YNr8QrD+IbgDQiAgDBMYggkIxRUUhiswDP/gEPyCwkgZM4OouMQ+bKWIiMggN3k+vHwVFOdC2hGH3Dw1JpTXV+/qlVMv316FtTAjPbrrO316F0QkQ9ZFvZJBRESkP8VPPwfX9gVs+/JlYvSZjoiISPckTQZgLNspKK9jXNIQhwP1LRUkxRHG5WLaNf9i7V0nk7Xi16xPHMX4nFOdjiU+pmjTSva8/ReyKt9hFq2sDD+SkKN/SFRiOs0NdTQ31NLSWEdrYx2tTbW0NdXjbq7HNtdjm+ugpQHT2oCrpR5XWyN+rQ34tzXg724kuLWGIHcDgbaJIJoItk0Em5YDZil/L4rii19jeGZWP/4LyEBljHkUOBMotdZO7GS9Ae4FTgfqgSustcv7N6WIiJcZdya8Hgarnu1SQTItNpTqhhaq61uIDA3o0anzCivxdxmmpEZ1bYftuVD0OZzyR/AP6tG5RUREnDBy0hGUvhyD35Z3ABUkRUREuiWp/bPiia4CFSRF+lJAYBDDr36BPfcfTdLb32dnwnsMGzHO6Vji5azbzcal79H4yT1Mrf+SoTaAlXFnkHzaj5g2alKfnruttZXGhloa62tpqq+lubGW5oY6GvbuYvinPybgqfPY/d23SUzN7NMc4hUeBx4AnjzA+tOATM9jFvCQ56uIiBxIYBiMOwvWvQKn/RkCgg+6eWpMGABFlXVMDo3q0amXFlQxYVgkoYFd/PPqs7sgNBamX96j84qIiDjFuFwUxBzJxIp3aW5qJDDo4P2uiIiIdCIiERuWwIR9ReSX1Tqdps8denIVkT4UGRMPFy/EhZvW/8xj394KpyOJl2prbWX524+z+Q85jHvrW6TXr2HR8O9T94OVzPrhEwzv42IkgJ+/P2ERUcQOTSE5Yyzp47IZPe0Yso6fT9V5zxFKPa2PnUX57u19nkUGNmvtp0DlQTY5B3jStssFoowxSf2TTkTEi2VdCE3VsPntQ26aFhsKQFFFz+aRbGptY+WOvcxI6+JwrbtWw5Z3Iefa9iKqiIiIlwoafzphppFNi99yOoqIiIjXMkmTyfIvIr+s61ONeSsVJMVxw0dNYsdJ/yS5rYTCh+fR2tLsdCTxIg11NSxe+Gd23TGRabk3Et62l8XjfkHwjzcw+3t3ETs0xemIAIycfAQlZ/ybGHclNQvOorpij9ORZGAbBhR3eL3Ds+wbjDFXGWPyjDF5ZWVl/RJORGTAyjgGwhNh9XOH3DQ1pr0gub2yZwXJtTuraW51MyMjpms7fHYXBA2BGVf26LwiIiJOGzP7TBptAHVr3nA6ioiIiPdKymKELWZ7WZXTSfqcCpIyIEyYcwYrJv+KyY3LWPaPa5yOI16gsnQnix75EY1/Gc+s9XdQ7xfB8ln3kHjbemZd+FNCwiKcjvgNY2ecSP6J/yS5bSd7HjqT2n2+38lIt5lOltnONrTWLrDWZltrs+Pj4/s4lojIAOfyg0kXtN+BWHfwkTfCgvyJCw+iqKJnV6EuKWjvz7O7codk+RZY/yrM+D6ERPXovALGmHnGmHXGGLcxJnu/dT83xmw1xmwyxpziVEYREV8WEhbBptBppJZ/inW7nY4jIiLinRIn44cb//INWNvpx38+QwVJGTBmnn8zuUMvYlb5iyx+7k9Ox5EBasfWtSy+/3JCH8xidvE/KQqdwPpTniXzF4uZdtp38PMf2FPjTjzqHDYcdT8jWrZS9MDZNNb7/tjg0i07gOEdXqcAJQ5lERHxLlnzwd0K61465KZpsaE9HrI1r7CSEfFhxIYHHXrjz+8G/2DI+UGPzin/sxY4D/i040JjzHhgPjABOBX4uzHGr//jiYj4vsYRJ5Ns97B90wqno4iIiHinpMkApLdso6LOt0ePVEFSBpQZVz7AytDZTF9/J6s/ftHpODKAbMz7gOV/OYvkfx/J1PLXWR1zMkXzP2LKT95m/OzTMC7v+XE25cSLWDnjTsY1rWHT/XNpbmp0OpIMPK8Bl5l2OUC1tXaX06FERLxC4iRImNClYVvTYkJ7NGSr223JK6piZnoXhmvdu7090/TLIVx3tPcGa+0Ga+2mTladAzxrrW2y1hYAW4GZ/ZtORGRwyDjifABKlr7ibBARERFvFZ1Ba0AEE02Bz88j6T2f4Mug4OfvT+a1z7LdL5WMj66jaMMypyOJg9xtbax872k23HEEY18/j1F1y1g87DL2Xb2cmTc+TdrYaU5H7LbsM68ib9KvyWpYwtr7v0Vba6vTkaQfGWOeARYBY4wxO4wx3zPGXGOM+WrM6jeBfNo/QP0noFtpREQOR9aFsGMpVGw76GapsaHs3tdIY0tbt06zpbSW6oYWsrtSkPziPsDAET/s1rnksGguZhGRfpIwLIOtfiOJKv7A6SgiIiLeyRhaEyYywVVEfplvj6angqQMOGERUYRe8QJNJoiAhRdRWbrT6UjSzxob6ljy4t0U3zGZKV9cS1RLKbmjf4zfreuZfdV9xCWnOR2xV8y84BZyM29hWu0nLHvgUtxt3fswVLyPtfYia22StTbAWptirX3EWvuwtfZhz3prrb3OWjvSWjvJWpvndGYREa8yaR5gDnmXZFpsKNbCjqru3SW5tLAS4NB3SNbsgeVPtg8nG5nSrXMNVsaY940xazt5nHOw3TpZprmYRUT6SFnycYxuXs/e8t1ORxEREfFKgSlTGGu2U1hW7XSUPqWCpAxIiamZVJz5GLHuSvb8cx5NjT2b20e8Q3XFHhY9/jNq7xzPzDW/pcUEkZf9F+JvW0/Ot39JWESU0xF7Xc7Fv2HR8CuZufdNljx8NdbtdjqSiIiI9xuSDBlHtxckbad1KABSY8IAuj2P5NLCShIighgeE3LwDXMfBHcLHHlzt84zmFlrT7TWTuzk8epBdtNczCIi/Sh26tn4GcuWLw49f7OIiIh8kyt5CiGmmfqSzmak8B0qSMqANSb7eNbO/BPjWtax+qHvqFDjw0oKN5H74PcJuG8SswsfoiQ4k7UnPMnI2/LIPvMq/AMCnY7Yp3K+82dyh15ETtnzLH70R07HERER8Q1Z86GqEIqXHHCTtNhQoPsFybzCKmakx2BMZzfkeTRUwdJHYMJciB3ZrfPIYXsNmG+MCTLGZACZwIG/EUREpEdGZR1JOVG4trzjdBQRERHvlDgZgOCKtQ4H6VsqSMqANv2M77Mo9SpmVL/Nxj/OYfkHz+NuU2HSV2xZ+RnL7ppLwmM5TC99iXVRx1Iw710m/+x9Jh51DsY1OH5EGZeLWVf/nSXRZ5Kz4xFy//1rpyOJiIh4v3FngX8IrH72gJvEhgUSFujH9srDL0ju3NvAzr0NzEiPPviGixdAcy0cecthn0MOzhgz1xizA5gNvGGMeQfAWrsOWAisB94GrrPWamx8EZE+4vLzIz96DqNrFtPc1Oh0HBEREe8TN5oWE8TQuk20+HD9Y3B82i9eLeeKO1ky4ZfEtJYy7bPvs+UPM8h980laWvWZgrep3VfFqg+fJffvV1J4+yQyXzmTMfsWkZd0EZVXLmXGzQvJmDDL6ZiOMC4X0697gmURx5Oz7V4WL/yL05FERES8W1AEjDsT1r4ErU2dbmKMITU2jKKKusM+fJ5n/sjsg80f2VQLix+C0adB4sTDPoccnLX2Zc9czEHW2qHW2lM6rLvDMxfzGGvtW07mFBEZDALGnU6EaWDz0nedjiIiIuJ9/PzZFzma8RSyo6rB6TR9RgVJGfCMy8XMeT8m9ufrWDHldiJsLTlLfkjhHdP45OV/0NjU7HREOYDWlmY2Ln2fRY/+hPV3zCHorpFkfXo1U/a8TE1AHLmjf4z75nXkXPN3hqZoCDM/f38m//BZVobOZsa6O8h77SGnI4mIiHi3yfOhcS9sOfCHo2kxod26Q3JpYSXhQf6MSxpy4I2WPdY+ZOvRGpJdRER825gjzqLJBlC7+nWno4iIiHgl99BJjHcVkl9a43SUPuPvdACRrvIPDGbquTfiPvMHrHvvMSLz7uOYVT+hYNW9bB17NTPPuorIsBCnYw5q1u2meOtqdi1/i8DtnzKqbgVjTQNua9jmP5K85IuJGH8io7JPZFJImNNxB6SAwCDG/vBF1t9zOlOW/YIVwRFMPfkSp2OJiIh4pxHHQlgCrHq2fQjXTqTFhvLhplLcbovLdZC5IPeztKCKaWnR+B1on5ZG+PJ+yDgGUrK7EV5ERMR7hIZHsipkCilln2Dd7kEzBYuIiEhvCUubRuimpykr3gLjE52O0ydUkBSv4/IPYMJpV2FP/i5bPnmGoEV/46SNv6Z4w4N8MeK7ZJ/zAxKiDnKluvSqij07KFj6Ju6tH5G6dwmplJMKlJgENsSeiN+oExgx41Qy45PIdDqslwgOCSPj+lfZdu8pTPjiRtYEhzHp6LlOxxIREfE+fv4waR4sWQD1lRD6zeFVh8eE0tzqZve+RpKjunZxW3V9C5v21HDm5KQDb7TyKajdA+f9s7vpRUREvErjiJNJWX8HRVtWkzZmitNxREREvEpo2jQAWktWAkc5mqWvqCApXsv4+ZN5/KVw7MVsz30R+8lfOL3gj5TcvYDXUi4j6+wfkjY01umYPqehroYtS9+lfsN7xJfnMrKtgFigmjC2hU2nKP0YUqafxrARE0h2OqwXC4uIIvEHr1P84EmM/OBqNgSFMm7WKYfeUURERL5u8rcg90FY9zLM+N43VqfFhgJQVFHf5YLksu3t80fOyDjA/JFtLfDFPZAyAzKO7lZsERERb5OWMxfW38GuJS+pICkiInK4EibQhovQirVOJ+kzKkiK93O5SD1iHsy+gN3L36T5gz9y9s67Kf37Y7yc8G3Gnnkj49J88xbn/tDW2sq21V9QufodIko+J7NpHZNNK83Wny1BE1iUeh2xk05h5OQ5TPPXj5TeFBkTT+tVr1P+j5NJefNytga/wKisI52OJSIi4l2SsiB+LKxe2HlBMqZ9GPntlXXMHtm1i9mWFFQR4GfISonqfIO1L8Le7XDan8F0fRhYERERb5aYmsk2vwyGFH8I3O50HBEREe8SEExpUBoJdZudTtJnVD0Q32EMidPPgGmnU7X+Q+rf+QNzy/5OxaP/5oXoC8g4/Wamj05zOqVX2Jm/jh15bxJQ9Ckj65YxmjoAtvllsDxxHqHjTiJzxslMCItwOKnvix2aQst3XqP2kVOJfXk+RYGvkjZuutOxREREvIcxMPlC+OB3UFkAMRlfW50cFYy/y1BUUd/lQ+YVVjJxWCQhgX7fXOl2w2d/g6ETYfSpPU0vIiLiVUoTj2XGjieorthDZOxQp+OIiIh4lX1RExi9+3NqGluICA5wOk6v0wzT4nuMIXrCCaTf8gG1F79JTcwkLtj7GKOeymHhX67l01WbsNY6nXJA2Vu+m2VvPsaS+y6h5HejGfbkEcxa/3uG1a1jU9Qx5GX/hfJr1jLyVyvJufZhJh97PiEqRvabxOGjaLvkFdrwI+S5C9iZv8HpSCIiIt5l8rcA036X5H78/VwMiw6hqLJrBcnGljZW76hmZvoBhmvd+DqUb4Ijb9bdkSIiMujETD0bf+Nmy5evOB1FRETE67gTJ5Fg9lK8vcDpKH1Cd0iKTwvPnEP4jW/RWLSM6jf+H98qfZral15i4ZtnMOT4mzgpeyL+foOvLm/dbgrWL6U071Widn5EZvMGphtLjQ1ha9hUitO+S9LUUxk+ajJDXYPv32cgShk1kYJvvUD0wnNp/fc5lH7/HRKGZRx6RxEREYHIFEg/ElY/C8f85BuFwtSYULZ38Q7J1TuqaW5zk91ZQdJa+OyvEDMCJsztjeQiIiJeJXPqMVS8Holr4+tw1tVOxxEREfEqEenTYBXszV8Go0c7HafXqdIgg0Jw2nRSf/AKLVd9TtWw45jX9BLHvnkCL/3xMl74eAmNLW1OR+xzDXU1rHr/GRbffxmlt49ixAsnk1P4IH7uZpakfo+Np79AyC+3M/UnbzHrwp+ROnoKRsXIASVj/AzKznmGIe59NDxyFpWlO52OJCIi4j2y5kNlPuzI+8aqtNhQiirqunSYpYWVAGSnRX9z5bYPYNeq9rsjXZ0M5yoiIuLjXH5+bEk8k2l1n5L7n986HUdERMSrxGfOAMBdstLZIH1Ed0jKoBKQPInhVz2Lu3QzlW/8gfOLXqX1o7d47ZMTaJj1Q849NochPjQ2c0nhJooXv0xwwfuMaVhJlmmhzgazOTybohEnMWL2XDKT08h0Oqh0WebUo1nf9DgZb13Kzn+chd/17xIZHed0LBERkYFv3Nnwxq3td0kOn/G1VWkxYexrbGVvfTNRoYEHPUxeYSWZCeFEh3Wy3ad3wZBhMHl+byYXERHxKtnfu4fl9+4gZ+vd5D4TSM5Fv3A6koiIiFcICo9mm99IJm3/N1tXncWorCOdjtSrenT7kzHmt8aYncaYlZ7H6R3W/dwYs9UYs8kYc0rPo4r0HlfCaIZ953FcNyxn7+h5zLUfcNGic3j/j/NY8Mp7lNU0OR2xW1pbmlm/6C0W/eM6Cm+fRPLjM5m14Y/ENu1g5dC5rDn+cfx/XsDUH7/BzPNvIi45zenI0g3jc05ly7EPkdpayK6/n0V9bbXTkURERAa+4CEw5nRY+yK0Nn9tVWpsKABFhxi2tc1tySuq6ny41qIvYfuXcMQN4H/woqaIiIgv8w8IZNINz7MidA45m+5k8fN3OR1JRETEawRf/BQNhJLw8rfYsuJTp+P0qt64Q/Jua+1fOy4wxowH5gMTgGTgfWPMaGut74+LKV7FxGQw9OKHofqXVLz7F85c/zR+Kz7mjeVzKBx/DUcecSRpMaHEhAVi9ptraKCoKtvFtkWvYLa8S2bNYsZTR4v1Y1PwJHJTL2DYzHMZnplFitNBpVdNPu4CljfVkrXoJtbffw6ZN71BcEiY07FEREQGtqz5sO4l2Po+jP3ftZSkfVWQrKwna3jUAXffvKeGmsZWZqR3MlzrZ3dBaBxMu6y3U4uIiHidgMAgxt/wAqvuPZdZ625niX8gM+f+0OlYIiIiA96wEeMoueINap84k6GvXshm+wyjpx3rdKxe0VdDtp4DPGutbQIKjDFbgZnAoj46n0jPRKYQO+9eqPkFez/4G6esfoKADZeyev0IFtk4ylzxNIYkYYck4x89nPCEdOKGDmN4bDjDY0IJD+q/0Y+t203+uiWULXuNqJ0fkdm8gWxjqSCSTVFH4zf2VDJnn83EyE6u3BefMu3UK1jaWMuMlbex4v4LmHjTKwQEBjkdS0REZOAaeXx70XD1s18rSKbGtBcktx9iHsmv5o+csf8dkiUr2oucJ/waAkN7N7OIiIiXCgoOZcwNL7P63rPIXvkr8vwDyT7raqdjiYiIDHjJ6WPYdcXr1D5+Fkmvzmej+ynGZp/gdKwe640qyvXGmMuAPOBWa20VMAzI7bDNDs+ybzDGXAVcBZCamtoLcUR6IGIoUefeCSf9hLrPHmR4/hdk1JYQ2rCCgMZmaARKgU3QZP3ZY6NZSxzlrjgaQpJoC0/GLzqFkLg0opNHkJQwlGExoQT5+/UoVkNdDZsWvU7ThrdIr/ickVQwEtjiN4olqd8ndmr7eNIz/Hp2HvE+M869nsWNNcza+CfyHvg2025ciEvfByIiIp3zC4BJF0DeY9CwF0KiAAgN9Cc+IuiQQ7YuLawicUgwKdEhX1/x2d8gKBJmfL9vcouIiHip4JAwMn/4KhvuPYMpeT9jmV8g00//jtOxpAuMMY8CZwKl1tqJTucRERlsktLGsPu7b1L96Bmk/PdiNrr/zdiZJzkdq0cOWZA0xrwPJHay6jbgIeD/Adbz9S7gu0BnY1vazo5vrV0ALADIzs7udBuRfhcWS9ipv+Z/A2BaC/WVsG8Hdm8x9eXbqSstwq+qmLSanYyp30JEw5f4N7RBGbC5fbc6G0SxjaXCL5664KG0hCVjolIIik0lMjGDhJQRDI2Nxc/1zf8yJYWbKF78MiEF7zOmYSVTTAt1NphN4TPYPvIkMnLOJTM5jcz++jeRAWvW/J+z6IkaZhc8yOK/f5eZ1z2GcfVoimARERHfNflCWPwwrH8Fpl/xv8VpMaEUVR64IGmtZWlBJTMyYr4+lH/ZJtjwXzjqVgiO7LvcIiIiXiokLIL0H77GlvtOY/LiW1nhH8DUky9xOpYc2uPAA8CTDucQERm0EoePovT7b1P1r9MZ/sYlbLBPMm7WKU7H6rZDFiSttSd25UDGmH8Cr3te7gCGd1idApQcdjqRgcIYCIuFsFhMUhZhwDdm63O3Qe0e3Ht3UL2ngJo9RTRVbId9O0ms28WQpqVENuzFVW5h6//ttteGUe4XT03gUJpCE8E/iMTyRaS7i0kGik0yK4aeR9jEMxg98ySmBWsYMPmm2Zf/gUULapld8gS5C8KYddUDKkqKiIh0JnkqxI2GVc99rSCZGhvKl1srDrjbjqoGdu9r/Ob8kZ/fDQEhkPODPgosIiLi/cIioki5/g3y7z+VCV/cwCr/QLKO/5bTseQgrLWfGmPSnc4hIjLYJQzLoOzKt6j41+mkvXkp691PMH72aU7H6pYeDdlqjEmy1u7yvJwLrPU8fw142hjzNyAZyASW9ORcIgOeyw+GJOMakkx06kyiO9umtZnmqp1UlORTvaeAxvLtuKt3EFBbQmTjHmIq1xFqG9gcPInc1G8xbOY5DM/M+lp1X+RAcr5/D4v/XkPO7qdY9EQEs79zp9ORREREBh5jYPK34MPfQ1URRKcBkBYTxkv7dtLY0kZwwDeHP88rap8/Mjutw/yRVYWweiHMuqb94jURERE5oIjIGBKve5PtD5zM2E9+wBr/ACYdPdfpWNIDmopLRKR/xCenU/79tyj71+mkv30569yPMWHOGU7HOmw9nUPyz8aYKbQPx1oIXA1grV1njFkIrAdageustW09PJeI9/MPJDA+g6T4DJIOsIl1u5moO9ukG4zLxYxr/8XS++qYXfQwuU9HkPPtXzodS0REZOCZ5ClIrl4Ix/wYgLTY9lEoiivryRwa8Y1dlhZWERHsz5jEDuu+uK/9orQjru+X2CIiIt4uMjoOe+2b7Pz7KYz64CrW+Qcx4YjTnY4l3aSpuERE+k9cchpc9TalC05jxLtXsNb9LyYedY7TsQ5Lj6oe1tpLrbWTrLWTrbVnd7hbEmvtHdbakdbaMdbat3oeVWRw0DCb0hMuPz+mXv8flocdRc7mv7D4/sup3VfldCwREZGBJToN0ubA6ufa5wqnfchWgKKKzueRXFpQyfS06P+b+7tmN6z4D0z5NgxJ7pfYIiIiviAqLpHIq99gj99QMt65go2L33U6koiIiFeISxxOxNVvs9sviVHvf481n77sdKTDosqHiIiP8Q8IZOINL5A79CJmlL/Kvrtnsu6LN5yOJSIiMrBMvhAqtkDJcgDSYjwFycpvFiSr6prZUlrLjPQOw7UuegDcLTDnxn6JKyIi4ktih6YQ/v03qHDFkvLmZWxe/rHTkURERLxC7NAUoq59hxK/YYz+4EpWf/yi05G6TAVJEREfFBgUTM61D7P59IW04c+E977N4ge/R31ttdPRREREBobx54BfEKx6DoCYsEDCg/zZXlH3jU2XFbWPNvC/gmR9JSx9FCZeADEj+i2yiIiIL4lLTiPwe69T7RpC4mvfZuuqz52OJB0YY54BFgFjjDE7jDHfczqTiIi0i45PIuYH77DDfzhjPrqaVR8973SkLlFBUkTEh42ddTJxP1pCbsKFzCh9kcq7ZrI+922nY4mIiDgvJArGnAZrX4S2FowxpMaEdnqH5NLCSgL9XExOiWxfsPgf0FIHR97cv5lFRER8zNCUkbiu+C/1hBL38oUUrFvsdCTxsNZeZK1NstYGWGtTrLWPOJ1JRET+T1RcInE/eJti/1TGfXwNKz941ulIh6SCpIiIjwsJiyDnBwvYeOozGCxj35pP7kNX01BX43Q0ERERZ2XNh/py2PoBAGmxoWzvZA7JpYWVTE6JJDjAD5pqYPHDMPZMGDq+vxOLiIj4nKS0MbRd9l+aCSTy+Qso2rDM6UgiIiJeITJ2KPHXvUNRQAbjP/0BK9972ulIB6WCpIjIIDF+9mlE37qUpfFzydnzLOV/ncnGpe87HUtERMQ5I0+AkBhY3X4laWpsKMVV9bS57f82aWxpY83OarK/Gq4171Fo3AtH3eJAYBEREd80bMQ4mi5+BTcuwp47j+Itq5yOJCIi4hUiY+JJuO5tCgNGMv7z61n+zr+djnRAKkiKiAwioeGRzLr+Mdae+G/8bQuZr19A7sM/oLHhm/NliYiI+Dz/QJh4Pmx6CxqrSYsJo6XNsntf4/82WVm8l5Y2y4z0aGhpgC8fgBHHwbDpDgYXERHxPcMzs6i78CVcuAl66lx25m9wOpKIiIhXiIyOI/H6tykIyGTSlzey/O3HnY7UKRUkRUQGoYlHnk3ELUvJizubnN1PsecvM9m8/GOnY4mIiPS/rPnQ2gjrXyMtNhSAoor/u1Anr7ASgOy0GFjxH6grhaNudSSqiIiIr0sbN529F7xAIM24njyLXUWbnI4kIiLiFYZExZL8w7fYFjiGyYtuZtmbA2/qXxUkRUQGqfAh0cz64ZOsOe4xgtyNjHz1XBYtuIGmxm/OnSUiIuKzhk2HmJGw+jlSY9oLkh3nkVxSWMWYoRFEBgFf3AvDZ0H6kQ6FFRER8X0jJs6ifO5zhFGH+/GzKN1Z4HQkERERrxARGUPKD99kS+A4shb/iLw3/ul0pK9RQVJEZJCbdMx5hN60hGUxpzO75Al2/XkWW1Z+5nQsERGR/mFM+12ShZ+RRBn+LkNRZXtBss1tWV5URXZ6NKxeCNXF7XdHGuNwaBEREd82KutIdp35FJHufTT963TKd293OpKIiIhXCB8STeoNb7I5aAJTl/yYvNcedjrS/6ggKSIiDImKZeaNT7PqmH8R6q4l4+Wzyf3XLTQ3NR56Z+kWY8ypxphNxpitxpifdbL+WGNMtTFmpefxaydyiogMCpPmAeC/7gVSokP+d4fkxt37qG1qZWZ6JHx+NwydBJknO5lURERk0BiTfTzFpz9BrLuC2gWnU1m60+lIIiIiXiEsIor0G95gY9Bkpi77GUtf/bvTkQAVJEVEpIOs4+YRdMMSVkSdRM6ORyj+cw7b1uQ6HcvnGGP8gAeB04DxwEXGmPGdbPqZtXaK53F7v4YUERlMYjJgeA6sah+2taiyfQ7JpQXt80ce2bIIKrbAUbfo7kgREZF+NG7WKRSc/BhD2/ZQ9Y8zqK7Y43QkERERrxAaHsmIG99gffAUpi//BUtevt/pSCpIiojI10XGxDPj5oWsnPMQkW1VpL5wOose+yktzU1OR/MlM4Gt1tp8a20z8CxwjsOZREQGt6wLoXwTs0OKKaqox1rL0qIqhkUGE7v8fojNhPH6US0iItLfJsw5g60nLCCldQelfz+d6qpypyOJiIh4hZCwCDJvfJ21IdPIXvkrlr50r6N5VJAUEZFOTTnp2/hfv5hVkccxu+hhiu6cTcH6pU7H8hXDgOIOr3d4lu1vtjFmlTHmLWPMhM4OZIy5yhiTZ4zJKysr64usIiKDw4S54BfIkQ0fUtPYyt76FvIKK7ksfjPsXgNH3gwuP6dTioiIDEqTjp7LxmP+TlprAbsfPIOa6kqnI4mIiHiF4NBwRt/4X9aGZDNj9a9Z/PxdjmVRQVJERA4oKi6R7FteZMXs+4hpK2PYc6ew6Ilf0NrS7HQ0b9fZeH92v9fLgTRrbRZwP/BKZwey1i6w1mZba7Pj4+N7N6WIyGASEg2jT2F06Tv40cbnW8vZs6+RubXPQuRwmPwtpxPKfowx84wx64wxbmNMdofl6caYhg7zMD/sZE4REekdWcd/i3Vz7mNEyxZ2PHAmdTV7nY4kIiLiFYJDwhh946usCpnJrHW3s3jhnx3JoYKkiIgc0tRTLocf5LIm4ihmFzxI/p1zKNqwzOlY3mwHMLzD6xSgpOMG1tp91tpaz/M3gQBjTFz/RRQRGYQmzyeoqZyjXGt4YdkOZpmNJOxdCXNuBL8Ap9PJN60FzgM+7WTdtg7zMF/Tz7lERKSPTD35ElbPuovRzespvP9sGupqnI4kIiLiFYJDwhh746usDJ3NrPV3sPi5P/V7BhUkRUSkS2IShjH9R6+ybObfiG/dReKzp5D771/T1trqdDRvtBTINMZkGGMCgfnAax03MMYkGmOM5/lM2vvsin5PKiIymGSejA2JZq7f53y2pYwbgl7DhiXA1EucTiadsNZusNZucjqHiIj0r+mnf4cV0//EuKbVbLn/HBob6pyOJCIi4hWCgkMZf+MrrAg9glkb/kjuM3f06/lVkBQRkcMy/fTv0XbNItaHzyJn271s/dMctm9e6XQsr2KtbQWuB94BNgALrbXrjDHXGGO+uovjAmCtMWYVcB8w31q7/7CuIiLSm/wDMRPO4xS/PGaZdcxhFWb2dRAQ4nQyOXwZxpgVxphPjDFHHWgjzcUsIuKdss++hmVZv2Ny4zI23TeX5qZGpyOJiIh4hcCgYCbc+DLLw44iZ9OfyX3q9n47twqSIiJy2OIShzPl1v+SN/3PJLYWk/DUieQ+dbvuljwM1to3rbWjrbUjrbV3eJY9bK192PP8AWvtBGttlrU2x1r7pbOJRUQGickXEkwzDwbcS5N/BGR/1+lEg5ox5n1jzNpOHuccZLddQKq1dipwC/C0MWZIZxtqLmYREe8147wbWTz+l2Q1LGbdfefT0tzkdCQRERGvEBgUzKQbX2R5+DHkbLmL3P/8pl/Oq4KkiIh0i3G5yD7ralquWsTGsGxyttzF5juPZsfWtU5HExER6b7hMykPGEaMqaViwncguNM6lvQTa+2J1tqJnTxePcg+TdbaCs/zZcA2YHR/ZRYRkf4z61s/JnfMT5ha9zmr77+Q1pZmpyOJiIh4hYDAICbf+ALLIo4jZ+s9LHryV31+ThUkRUSkR+KS08j60ZssnfIHhrUUsPu1/rmiRkREpE8YQ1HGPPYSTuwJNzidRrrBGBNvjPHzPB8BZAL5zqYSEZG+knPRbeSOvJHpNR+xYdEbTscRERHxGv4BgWTdsJC8IScyZdvD7N6+pW/P16dHFxGRQcG4XMw49zpKZ5zOiIBAp+OIiIj0yLT5v6a56UcEhUQ4HUUOwhgzF7gfiAfeMMastNaeAhwN3G6MaQXagGustZUORhURkT6Wc+ntbF11CpOy5jgdRURExKv4BwQy9YbnKNq4jBGpmX17rj49uoiIDCoJwzKcjiAiItJjxuWnYqQXsNa+DLzcyfIXgRf7P5GIiDhplIqRIiIi3eLn78+IibP6/DwaslVERERERERERERERERE+owKkiIiIiIiIiIiIiIiIiLSZ1SQFBEREREREREREREREZE+o4KkiIiIiIiIiIiIiIiIiPQZFSRFREREREREREREREREpM+oICkiIiIiIiIiIiIiIiIifUYFSRERERERERERERERERHpMypIioiIiIiIiIiIiIiIiEifUUFSRERERERERERERERERPqMCpIiIiIiIiIiIiIiIiIi0meMtdbpDP9jjCkDinrhUHFAeS8cxxuorb5nsLQT1FZf1RttTbPWxvdGmMFE/Wi3DJa2DpZ2gtrqq9TWw6N+tBvUjx62wdJOUFt9ldrqe3qrnepHu6GX+tHB8r0KaquvUlt9z2BpJ/RDPzqgCpK9xRiTZ63NdjpHf1Bbfc9gaSeorb5qMLXVVw2m93CwtHWwtBPUVl+ltoo3GSzv4WBpJ6itvkpt9T2DpZ2+bDC9h2qrb1Jbfc9gaSf0T1s1ZKuIiIiIiIiIiIiIiIiI9BkVJEVERERERERERERERESkz/hqQXKB0wH6kdrqewZLO0Ft9VWDqa2+ajC9h4OlrYOlnaC2+iq1VbzJYHkPB0s7QW31VWqr7xks7fRlg+k9VFt9k9rqewZLO6Ef2uqTc0iKiIiIiIiIiIiIiIiIyMDgq3dIioiIiIiIiIiIiIiIiMgAoIKkiIiIiIiIiIiIiIiIiPQZryhIGmOGG2M+MsZsMMasM8bc6FkeY4x5zxizxfM12rP8JGPMMmPMGs/X4zsc62NjzCZjzErPI8GpdnWmG22d2aEtq4wxczscy6fa2mG/VGNMrTHmRx2WDdi2duM9TTfGNHRoy8MdjjVg2wndauvFHdqy0hjjNsZM8ayb7vk/vNUYc58xxjjYtG/oRlsDjTGPedq0yhhzbIdjeWtb53leu40x2R22j/VsX2uMeWC/Yw3otvqqbny/qh9lYLf1cNvZYT+v6kNB/ahnufpR9aNfrRvQbfVV3fh+9cp+tBvt9Mo+FNSPeparH1U/+tWxBmxbD9JO9aFeohvfq17Zh4L6Uc9y9aPqR6d41g3Yn7ndaKdX9qEwQPtRa+2AfwBJwDTP8whgMzAe+DPwM8/ynwF3ep5PBZI9zycCOzsc62Mg2+k29WJbQwH/DvuWdnjtU23tsN+LwPPAj7zhfe3Ge5oOrD3AsQZsO3vynnqWTwLyO7xeAswGDPAWcJrT7evh+3od8JjneQKwDHB5eVvHAWP2/74EwoAjgWuAB/Y71oBuq68+uvH9qn50gLe1uz9v8bI+tJvvaTrqRwf8z9tuvK/qR72grb766Mb3q1f2o91op1f2od1pa4f91I/6UFv321f96ABs60HaqT7USx7d+F71yj60m21VP+qDbUX96FevB+zP3G68p17Zhx6irY71o15xh6S1dpe1drnneQ2wARgGnAM84dnsCeBczzYrrLUlnuXrgGBjTFC/hu6mbrS13lrb6lkeDNh+DdwDh9tWAGPMuUA+7e+rV+hOO71VD9t6EfAMgDEmCRhirV1k23/iPXmAfRzTjbaOBz7wbF8K7AWyvbmt1toN1tpNnWxfZ639HGjsuNwb2uqr1I/6Xj86WPpQUD+K+tFzPc/Vj3pBW33VYOlHB0sfCupHUT96bie7qx8dgG1VH+r9BksfCupHUT96riMBe9Fg6UcHSx8KA7Mf9YqCZEfGmHTar5ZZDAy11u6C9n9c2ivU+zsfWGGtbeqw7DHPbcS/Gmi30XbU1bYaY2YZY9YBa4BrOnRm4ENtNcaEAT8FfneAwwz4th7G92+GMWaFMeYTY8xR+x1mwLcTuvV/9UI8HRftncCODut2eJYNSF1s6yrgHGOMvzEmA5gODMe723q4vKqtvkr9qO/1o4OlDwX1o6B+VP2o97TVVw2WfnSw9KGgfhTUj3qoH203YNuqPtT7DZY+FNSPgvpR9aPe8TN3sPShMHD6Ua8qSBpjwmm/tfsma+2+Lmw/AbgTuLrD4outtZOAozyPS/sia08dTluttYuttROAGcDPjTHBnlW+1tbfAXdba2s7WTfg23oY7dwFpFprpwK3AE8bY4Z41g34dkK3/q/OAuqttWu/WtTJZgPySrHDaOujtP+wzgPuAb4EWvHNth7wEJ0sG5Bt9VXqRzvnzf3oYOlDQf3oIbZXP/p13t7WAx6ik2UDsq2+arD0o4OlDwX1owegftSLft4Oln5Ufaj3Gyx9KKgfPQD1o+18ra1fbe+V/ehg6UNhYPWjXlOQNMYE0P6P9pS19iXP4j2m/XbRr24bLe2wfQrwMnCZtXbbV8uttTs9X2uAp4GZ/dOCrjvctn7FWrsBqKN9jHVfbOss4M/GmELgJuAXxpjrYeC39XDaaa1tstZWeJ4vA7YBoz2vB3Q7odvfv/P5v6tooP2HfEqH1ylACQPMYb6vrdbam621U6y15wBRwBa8u62Hyyva6qvUj/pePzpY+lBQP4r6UfWj7byirb5qsPSjg6UPBfWjqB/tSP3o/xlwbVUf6v0GSx8K6kdRP6p+tN2A/5k7WPpQGHj9qFcUJI0xBngE2GCt/VuHVa8Bl3ueXw686tk+CngD+Lm19osOx/E3xsR5ngcAZwJrGUC60dYMY4y/53ka7ZORFvpiW621R1lr06216bRfjfAHa+0DA72t3XhP440xfp7nI4BMIH+gtxMOv62efVzAPODZr5bZ9tvia4wxOZ5jXtZxn4GgG+9rqGkfogJjzElAq7V2vZe39bB4Q1t9lfpRwMf60cHSh4L6UQ/1o+pHveJ99VWDpR8dLH0oqB/1UD+K+tGB3lb1od5vsPShoH7UQ/2o+tEB/zN3sPShMED7UWvtgH8AR9J+C+hqYKXncToQS/uEols8X2M82/+S9qtKVnZ4JABhwDLPcdYB9wJ+Trevh2291NOWlcBy4FzPcp9r6377/hb4kTe0tRvv6fmedqzyvKdneUM7u/ueAscCuZ0cK5v2jnkb8ABgnG5fD9/XdGAT7ZMHvw+k+UBb59J+hUwTsAd4p8M+hUAlUOvZZrw3tNVXH934flU/OsDberjt3G/f3+IlfWg331P1o3bg/7ztxvuajvrRAd9WX3104/vVK/vRbrTTK/vQ7rR1v31/i/pRn2irZ59jUT86YNt6kHaqD/WSRze+V72yD+1mW9WP+mBbUT/61fIB+zO3G+9pOl7Yhx6irY71o8ZzMBERERERERERERERERGRXucVQ7aKiIiIiIiIiIiIiIiIiHdSQVJERERERERERERERERE+owKkiIiIiIiIiIiIiIiIiLSZ1SQFBEREREREREREREREZE+o4KkiIiIiIiIiIiIiIiIiPQZFSRFREREREREREREREREpM+oICkiIiIiIiIiIiIiIiIifUYFSRERERERERERERERERHpMypIioiIiIiIiIiIiIiIiEifUUFSRERERERERERERERERPqMCpIiIiIiIiIiIiIiIiIi0mdUkBQRERERERERERERERGRPqOCpIiIiIiIiIiIiIiIiIj0GRUkRURERERERERERERERKTPqCApIiIiIiIiIiIiIiIiIn1GBUkRERERERERERERERER6TMqSIqIiIiIiIiIiIiIiIhIn1FBUkRERERERERERERERET6jAqSIiIiIiIiIiIiIiIiItJnVJAUERERERERERERERERkT6jgqSIiIiIiIiIiIiIiIiI9BkVJEVERERERERERERERESkz6ggKSIiIiIiIiIiIiIiIiJ9RgVJEREREREREREREREREekzKkiKiIiIiIiIiIiIiIiISJ9RQVJERERERERERERERERE+owKkiIiIiIiIiIiIiIiIiLSZ1SQFBEREREREREREREREZE+o4KkyGEyxhQaYxqMMbXGmN3GmMeNMeGedccZYz4yxlQbYwr32y/BGPOMMabEs/4LY8ysLpzvCmNMm+d8+4wxK40xZ3ZYb4wx+caY9Z3sO8EY864xpsoYs9cYs8wYc3ov/DOIiIgcNgf60EBjzF3GmB2ecxYYY+7uZLvfGmOsMWZmd/YXERHpK/3Zdxpj/D3nmdlh2cX795GeZRs9z39rjGnx7PfVY69nXbpnX//9zvO4Meb3Pf23ERER6Yr+/jvUs2+SMeYRY8wuY0yNMWajMeZ3xpgwz3prjBm13z6/Ncb8p8Nra4xZY4xxdVj2e2PM493/1xBxlgqSIt1zlrU2HJgCTAV+7lleBzwK/LiTfcKBpcB0IAZ4Anjjqw7wEBZ5zhcFPAIsNMbEeNYdDSQAI4wxM/bb77/Ae8BQzzY3APu6cD4REZG+0p996M+BbGAmEAEcB6zouIExxgCXApXA5Ye7v4iISD/ol77TWtsKLAKO6bD4aGBjJ8s+7fD6OWtteIdHVNebJiIi0i/67e9Qz2e2i4AQYLa1NgI4ifbPdUceZu5kYP5h7iMyYKkgKdID1trdwDu0d2ZYa5dYa/8N5Heybb619m/W2l3W2jZr7QIgEBhzGOdz095JhgAjPIsvB14F3qTDB6nGmDggA/intbbZ8/jCWvt5N5oqIiLSq/qpD50BvGytLbHtCq21T+63zVG0/5F3IzDfGBN4mPuLiIj0i37qOz+lveD4laOAOztZ1rEgKSIi4hX6qS+9BagBLrHWFnqOVWytvdFau/owI/8Z+N3+ow2IeCsVJEV6wBiTApwGbO3GvlNo78S6vK+n8/k+UAtsMcaEAhcAT3keHT9IrfAc+z/GmHONMUMPN6OIiEhf6ac+NBe4xRjzA2PMJM/dkPu7nPYRBZ7zvD6zw7qu7C8iItIv+qnv/BSYY4xxeS5yDQMWAjM7LBuLCpIiIuKF+qkvPRF4yXNjSU+9RPtod1f0wrFEHKeCpEj3vGKMqQGKgVLgN4ezszFmCPBv4HfW2uou7JLjmYdjN3ARMNez33lAE/Au8DrgD5wBYK21tA8tVwjcBewyxnxqjMk8nKwiIiK9rD/70D/SflfHxUAesNMY03E0gVBgHvC0tbYFeIGvD9t60P1FRET6SX/2nYuBUGAS7XdCfm6trQcKOiwrstZu77DPt4wxezs8PjqcfCIiIv2gP/vSWGBXFw67vGP/Cfysk20s8Cvg18aYoMPJLDIQqSAp0j3nesb/Ppb2q0PjurqjMSaE9jsxcq21f+zibrnW2ihrbZy1Nsda+75n+eXAQmttq7W2ifarZv73Qam1doe19npr7UggjfZx0TXUnIiIOKnf+lDPsDoPWmvn0D5fxx3Ao8aYcZ5N5gKttA97Du2jDZxmjInv4v4iIiL9oT/7zkZgCe1DtB4NfOZZ9XmHZfvfHbnQ8/fqV4/jPMtbPV8D9ts+AGjpahtERER6QX9+llsBJHVhu2kd+0/gT51tZK19E9gOXNXFyCIDlgqSIj1grf0EeBz4a1e291zJ8gqwE7i6J+f2DDFwPHCJMWa3MWY37cO3nu4ZRmf/rMXAg8DEnpxXRESkN/R3H2qtbbDWPghUAeM9iy8HwoHtnn70edo/JL2oi/uLiIj0m37sO7+aR/Io/q8g+VmHZV0drnUX7YXH9P2WZwBFh5FHRESkV/RTX/o+MNcY05u1l18Ct9E+ioGI11JBUqTn7gFOMsZM8cypEUz7h5nGGBP81ZyOxpgA2oeCawAu64VxxC8FNtM+kfIUz2M0sAO4yBgTbYz5nTFmVIe5Pr5L+3xYIiIiA8E99GEfaoy5yRhzrDEmxBjj7xluNQJYYYwZBpxA+5yRUzyPLNqHaL38UPv31j+AiIjIYbqHvv/781Pap/8YDqz3LPuc9rtKptDFgqS1tg14EbjDGBNrjAkwxlxE+4U9bx1GHhERkd50D33bl/4NGAI8YYxJ8xxrmDHmb8aYyd0JbK39GFjD16cYEfE6KkiK9JC1toz2YVB/RfsVow20D/2W6nn+rmfTI2j/0PNkYK8xptbzOKqbp74c+Lu1dnfHB/CwZ10z7Veivk/75MdraZ9v8opunk9ERKRX9UMf2kD7PMq7gXLgOuB8a20+7Rf2rLTWvrtfP3ofMNkYM/EQ+4uIiPS7fvr780sgElhsrbWe81YAZUCptXbLfttf2OH4Xz0SPOt+AFQCq2mfs+t64Axr7Z7utF9ERKSn+rovtdZWevZtARZ75q78AKgGtvYg+i+BmB7sL+I44/ndUkRERERERERERERERESk1+kOSRERERERERERERERERHpMypIigwAxpiHOxniptYY87DT2URERAYy9aEiIiKHR32niIhIz6gvFekeDdkqIiIiIiIiIiIiIiIiIn3Gv6sbGmMepX0S11Jr7cT91v0I+AsQb60t9yz7OfA9oA24wVr7zqHOERcXZ9PT07ueXkREfNKyZcvKrbXxTufwNupHRUQE1I92l/pREREB9aPdpX5URETg4P1olwuSwOPAA8CTHRcaY4YDJwHbOywbD8wHJgDJwPvGmNHW2raDnSA9PZ28vLzDiCQiIr7IGFPkdAZvpH5URERA/Wh3qR8VERFQP9pd6kdFRAQO3o92eQ5Ja+2nQGUnq+4GfgJ0HPv1HOBZa22TtbYA2ArM7Oq5RERERERERERERERERMQ3dLkg2RljzNnATmvtqv1WDQOKO7ze4VnW2TGuMsbkGWPyysrKehJHRERERERERERERERERAaYbhckjTGhwG3Arztb3cky28kyrLULrLXZ1trs+HgNzy4iIoOPMabQGLPGGLPSGJPnWRZjjHnPGLPF8zXa6ZwiIiIiIiIiIiIi3dGTOyRHAhnAKmNMIZACLDfGJNJ+R+TwDtumACU9OJeIiIivO85aO8Vam+15/TPgA2ttJvCB57WIiIiIiIiIiIiI1+l2QdJau8Zam2CtTbfWptNehJxmrd0NvAbMN8YEGWMygExgSa8kFhERGRzOAZ7wPH8CONe5KCIiIiIiIiIiIiLd1+WCpDHmGWARMMYYs8MY870DbWutXQcsBNYDbwPXWWvbehpWRETER1ngXWPMMmPMVZ5lQ621uwA8XxM621FzMYuIiIiIiIiIiMhA59/VDa21Fx1iffp+r+8A7uheLBERkUFljrW2xBiTALxnjNnY1R2ttQuABQDZ2dmdztcsIiIiIiIiIiIi4qSezCEpIiIivcBaW+L5Wgq8DMwE9hhjkgA8X0udSygiIiIiIiIiIiLSfSpIioiIOMgYE2aMifjqOXAysJb2+Zgv92x2OfCqMwlFREQGLmNMsDFmiTFmlTFmnTHmd/ut/5Exxhpj4pzKKCIiIiIiIocxZKuIiIj0iaHAy8YYaO+Xn7bWvm2MWQos9MzZvB2Y52BGERGRgaoJON5aW2uMCQA+N8a8Za3NNcYMB06ivR8VERERERERB/ncHZKL/nUzi564zekYIiIiXWKtzbfWZnkeEzxzMGOtrbDWnmCtzfR8rezzLG43eX87nyUv30drS3Nfn05ERKTHbLtaz8sAz+OrOZXvBn7S4bWICACLn7uTNZ+85HQMEemgaNNKVv3pRMp3FzsdRURE+ojPFSSDKzcysuBp3G1tTkcRERHxKvv2VhBZX8TMVb9i1x+yyHt9gfpTEREZ8IwxfsaYlbTPt/yetXaxMeZsYKe1dtUh9r3KGJNnjMkrKyvrj7gi4rCSgo3M2vAHxnz4fRUlRQaQvTs3MrphFa0PH0fBusVOxxERkT7gcwXJtgnnkUAlG5e863QUERERrxIZE8+oXyxhxREP0moCyc77MUV3TGX5O//Gut1OxxMREemUtbbNWjsFSAFmGmMmA7cBv+7CvgustdnW2uz4+Pg+TioiA0HRB//AbQ27/JIZ+eE1bFz6vtORRATIOn4+O859ERdtJCw8m1UfPut0JBER6WU+V5Acf8y3qLdB1OSp0xIRETlcxuVi6smXkHbbcpbNuAs/Wpm26Hq23ZHNqg8XqjApIiIDlrV2L/AxcA6QAawyxhTSXqhcboxJdCyciAwIba2tjNzxCmtCsgm76i0qXTEkv3EZ+Wt1N5bIQJA59Wi48kN2+Q9j4ifXkPvU7fobVETEh/hcQTI0PJINQ44gs+JDzX8lIiLSTS4/P6af8X2Sf76SpVPuINRdS9anV7LpD0ew9vPXnI4nIiICgDEm3hgT5XkeApwIrLDWJlhr06216cAOYJq1drdzSUVkIFj76UskUEnblEuISxyO6/JXaSSIIS98i53565yOJyJAwrAMkm/+iFXhR5Kz5S6WPnAZzU2NTscSEZFe4HMFSQAz8Xxi2MeGRW84HUVERMSr+QcEMuPc64n72WoWT/gV0a2lTHz/Utb94Wg2LnnP6XgiIiJJwEfGmNXAUtrnkHzd4UwiMkC1LXuSSoYw8bj5ACSnj6HhwhfwpxXz77mUlRQ6G1BEgPYbTqbc8iqLhl3BzMr/suWuk6iu2ON0LBER6SGfLEiOP/o8am0IDcsXOh1FRETEJwQGBTNr3o+I/Okacsf8hKHNRYx98wJW/elEtqz8zOl4IiIySFlrV1trp1prJ1trJ1prb+9km3RrbbkT+URk4CjfXcyk2i/ZPPQMAoOC/7c8bdx0Ss9+ikh3NXX/OktFD5EBwuXnx+wr7yVv2p/IbFpPzQPHsH3zSqdjiYhID/hkQTI4JIwNUUczdu8nNDXWOx1HRETEZwSHhJFz0W2E/mg1uSNuIK1xA5mvnMmKv5xBwfqlTscTEREREenU1vf/RYBpI+m4q76xbvS0Yyk46Z8kt5Ww+6GzqavZ2/8BRaRT2WdfS/4ZzxJq64h6+nTWfPqq05FERKSbfLIgCRA4ZR5DqGPD5+qkREREeltoeCQ5l/0//G5ew6LUqxlVu4y0504i767zKN6yyul4IiIiIiL/Y91ukvOfZ2PAeNLGTut0m4lHns26OfcyqmUT+Q/M1QXuIgPI2Jkn0XjF+1S44hj3wRUsXvgXpyOJiEg3+GxBcvycs9lLOK2rX3A6ioiIiM+KiIxh9nf/jPuGVSwedhnj931O8n+OYck9F1FSuMnpeCIiIiIibFr6Pqnunewbf9FBt5t68iUsm/L/mNS0nHX3X0hba2s/JRSRQ0lOH0PcjR+zLnQGs9b/ntwHv09rS7PTsURE5DD4bEEyIDCITTHHM776MxrqapyOIyIi4tMiY4cy+6r7aPjBcpYmXkhW1XvEPTabxfdfTunOAqfjiYiIiMggVrPoUepsMBNOvOyQ286c+0NyR/+IaXWfsuzBy7Budz8kFJGuiIiMYeKtb5I7dD45Zc+z/q7T2be3wulYIiLSRT5bkAQIm/4tQk0T6z/RXZIiIiL9IXZoCjnX/oPqq5ayIv4cppb/l8gFM8h96Goq9uxwOp6IiIiIDDI11ZVMqPqQdbEnERYR1aV9cr79K3JTvsfMqjdYvOB6FSVFBhA/f39yrv0HSyb+hnENy6m87xhKCjY6HUtERLrApwuS42adRjlRmHUvOR1FRERkUEkYlsGs6x+j/DuLWBV9EjN2P0fI36exaMENVFfscTqeiIiIiAwS6999jFDTROQR3z2s/WZ9968sjjuPnN1PkfvvX/VROhHprpkX3MKmkx4n2l1JyBMnsWHxO05HEhGRQ/DpgqSfvz/b4k9kfO0iaqornY4jIiIy6CSnj2HmTc9QcsknrI88ilk7n8R1XxaLHv2x+mYRERER6XPRm56lwJXG6GnHHtZ+xuVixrX/Im/IicwueIDFz/+1bwKKSLdNPPJs9l38FrUmnJFvfpulrzzodCQRETkIny5IAkTOmE+waWHTJ885HUVERGTQGp6ZRfYtL1J04XtsCc9m9vYFtN09iUVP/or62mqn44mIiIiIDypYt5jRrZvZM2oexnX4H4G5/PzIuv5pVoXMYsba37PsjX/1QUoR6YnhmVkM+eGnbA6eyIyVv2DRgh/ibmtzOpaIiHTC5wuSo6cfz27iCdjwstNRREREBr2M8TOY9uPX2XLu6xSFjGd2/n00/HUSS1+61+loIiIiIuJj9nzyL5qtP2NO+n63jxEQGMTo619kY+AEJi/5Cas/eqEXE4pIb4iMiWfMre+yOPYcZpc8yaq/na0LX0VEBiCfL0i6/PwoTDyZ8fV5mrNKRERkgMicchRZP32Pjae/QGlACjNW/5qNS993OpaIiIiI+IimxnrGlr7JmogjiY5P6tGxQsIiSLnuNbb7p5H58bVsXPxuL6UUcZYx5lFjTKkxZm2HZTHGmPeMMVs8X6OdzNhVAYFBzLzucXJH/5jJtV+w6+5j2bNjm9OxRESkA58vSALEzf42AaaNTR8/7XQUERER6WDszJNIveFNSokh4O0f09rS7HQkEREREfEBaz94mihqCZx5Ra8cb0hULFFX/ZdyVxzJb13OtjW5vXJcEYc9Dpy637KfAR9YazOBDzyvvYJxucj59i9Ze+w/Gdq6C79/Hc/m5Z84HUtERDwGRUFy5KQjKDbJhG5+xekoIiIisp+wiCiKZ/6akW35LHvxLqfjiIiIiIgPCFz9H3YRz4Q5Z/faMWOHpuB/xSs0EELkixdSvHVNrx1bxAnW2k+Byv0WnwM84Xn+BHBuf2bqDVnHzaN8/us0m0BSXz2fZW8+4nQkERFhkBQkjcvFjmGnMq5xFeW7i52OIyIiIvuZdurlrAmayriN96mvFhEREZEeKSnYyKSmFRSmnofLz69Xj52UNobGi17CDzf+/5lL6c6CXj2+yAAw1Fq7C8DzNeFAGxpjrjLG5Blj8srKyvotYFekj8sm+NqPKQjMZPqSW1j02E+xbrfTsUREBrVBUZAESJpzMX7Gsu2Tp5yOIiIiIvsxLheR599DsG2i4JlbnI4jIiIiIl5s+wcLcFtDxolX9snx08ZMofzcp4mwtdQ/chZ7y3f3yXlEBjpr7QJrbba1Njs+Pt7pON8QkzCMEbd+wNLIU5hd9DDL7plHY0Od07FERAatQVOQTB+XTYErjSFbX3M6ioiIiHQidfQUlqVcyozqd1m/6C2n44iIiIiIF2prbSVjxyusDckmMTWzz86TOeUoik5+hKS23ZQ+fBa1+6r67Fwi/WyPMSYJwPO11OE8PRIUHEr2jc+yKON6sve9T9Fdx1G+e7vTsUREBiV/pwP0p92ppzO78CF2F28lcfgop+OIiIjIfrIuup3df32DkPd+Ssv04wkIDHI6koiIiIh4kbWfvUwWFezM+lWfn2vCnDNYWX8fEz+/jo0Pnsuom94kOCSsz88r0sdeAy4H/uT5+qqzcXrOuFzMvvwOlr8zlrFf3sq+h48j/4KnGTFxltPRvIZ1u6mrraa2uoL66goa9lXQXFtJS10V7voqaNiLaarGr6magJZ9BLbWENJWQ5i7ljb8qPaPoy44gebQRIhIwj9qGKFxqUQOTSU2KV0/O0UGiUFVkBx+5CVQ+BCFn/yHxEt+63QcERER2U9oeCSbjvgtU7+8jtzn/0TOxb9xOpKIiIiIeJG2vCepYggTj7+oX8435aRvs7R+LzNW/JwV989j0s2v4B8Q2C/nFukpY8wzwLFAnDFmB/Ab2guRC40x3wO2A/OcS9i7pp1yKVsTMxjy8qUkPn8WK3ffy5QT++dnxUDQ1tpKbXUFtdXl1O+rpHFfJc21lbTVtxcVbeNeXE3V+DfvI6BlH8GtNYS01RJuawm3dYSbNsIPcvx9hFJrwql3RdDkF05lSDqlgUMw7hZCGkuJq99GbM0Swkobv7HvXsKpdMVRExhPU8hQ2iKS8YtMJigmhYj49qLlkOh4jGvQDPgo4pMGVUEyZdREtvhnElv4OvBbp+OIiIhIJ6ac+G1WrXiCSZv/TunOy0gYluF0JBERERHxAhV7djCp9guWJX6LnKDgfjvvjHN+QG59FTmb/szSBy9j+g+fwuXn12/nF+kua+2BqnEn9GuQfjQq60jK4j9i56MXMPmza8ndvZFZ3/6NTxS6rNtNVfkuyrZvomb3VlrLC3FVFxJav5OY5hIS3OVEGjeRB9i/xfpRY8KoM+E0+IXT6D+EmpAUSgKH4A6KxIRE4QqNxj80isDwGIKHxBISEUt4VBzhQ6IZ4u/PkC7krKmupLKkgH1l22mo2EFb9U5cNbsIathDeFMpKY2bia2q/sZ+jTaAclcs+wLiqQ8eSmvoUBiSTGD0MELjU4kamkZcYqouCpFeY61l+fa9TEuNwhjjdByfMKgKkgAV6WeSs/VuduavY9iICU7HERERkf0Yl4u4effg/8QxFD97Cwm3vux0JBERERHxAlve+xc5po2kY6/s93PnXHQbix7dy+ztC8hd8ANmXf2QTxQ4RHxRfHI64Td/xMqHLiZn6z1U3P4Ye/1iqAuIoyk4jtbQBEzEUAIikwmJSWJIXAoxicMJDT9QKa//NNTVULp9M1UlW2gs3QZVRQTVFhPVWMLQtl3EmCZiOmxfThTl/kmUREymKCIFExaPX2gUAWExBEbEEBIRTWhkHBFRcYSERhDjcn1t/74QERlDRGQMjJt+wG2amxqp2F1E9Z7t1JVvp6VqJ+wrIaB+D6GNe0iuWUNc9ccE7m792n5uaygzUZQFphB01l8ZOSmnj1sjvmxhXjE/fXENr1w3hynDo5yO4xMGXUEy/ZhLYOvdFH/6FMNG/MHpOCIiItKJYSMmsCj1CmYX/5O1n73KxKPOcTqSiIiIiAxg1u0mKf95NvqPY+xBPuTuSzlX3EnuQ1Xk7HmWRU9GM/uKPzmSQ0QOLSQsgik3v8zil+7B7FpBYGM5Yc3lDN2bT0zVXgJM2zf2qbPBVLpiqPGPoSEojpaQeGz4UPyGJBIcnUxY7DCiElKIjkvq9l3Sba2tlJUUUFG8mbrSbbRVFBCwbzvh9TuJa91FHHtJA9I829fbIPb4JbI3eBh7wmdCdDrBCSOJSh5FwvBM4sIjiev+P5NjAoOCSUobQ1LamANuY91uqir2ULmrgNry7TRW7MS9rwS/mhIy9i4i7IWzWV5yF9NOubQfk4uvsNbyyOcFAGzavU8FyV4y6AqSicNHsSFgPEO3vwGoICkiIjJQTb3ot+z8y2tEfPQLmmeeQmA/DrslIiIiIt5lU94HjHXvYOnE2x3LYFwuZl7zD5beV83swodY/Fw0sy78qWN5ROTgXH5+zJp36zeWu9vaqKosZW/pdmorSmisLKFt326o3UNAQxnBTWXE128humYxEWUN39i/1booN1Hs84umLrD9rsu2sKG4IoYSGJVESHQyfgFB7Nu1jZbyfEx1ESF1O4hpKiHBXUqiaSPRc6w2a9jjiqcqIIn86DlsjUzHPy6diMRRxA0fTUx8MhmD9G5s43IRHZ9EdHwScMTX1pWXFLHj0QuYtuh6Fu1aR85lf9Bd63JYPt9azuY9tQDkl9U5nMZ3DLqCJMC+kWczbuOfKNyQR/q4bKfjiIiISCeCQ8MpP+p2sj69mkXP3cHsy/6f05FEREREZIDa9+Wj1Nlgxp90uaM5XH5+TLnuP6y85xxmrP8jef+NIvusqx3NJCKHx+Xn16HQdXANdTVU7tlBTfkO6it30rJ3F+6aPfjV7SGoqf2uy+SGzcRU7sXP2E6PUUUEZf5J7AkfS3HESfjFZBCSMJKYlNEkpIwgOTCI5N5upI+LS04j/JaPyHv4cmYXPsSyuzcx4dp/Exwa7nQ08RKPfF5AXHgQQ4L92aaCZK8ZlAXJkcdeTNuGO9n9xdMqSIqIiAxgWcfPZ0XeE2Rt+we7t19GYmqm05FEREREZICpqa5kYtUHrI05iZkRUU7HISAwiLHXv8CGe04lK+/nrAqLJOv4+U7HEpE+EBIWwbAR42DEuINu19baSnn5LqpLi6mrKKGtpZEhSaNISB1NdGQM0f2UdzAJDg1n+k3Ps+jfv2JW/oNs+9uxRH7neRKGZTgdTQa4raU1fLypjFtOGs36kn1sLq1xOpLPGJT3KcclprIhOIthO9/Cut1OxxEREZGDSLzwHgyWkududjqKiIiIiAxAG957nFDTxJA533U6yv8Eh4aTet1rFPmnM+aT61mf+7bTkUTEQX7+/sQlDmfk5COYfNwFTD35EkZOyiEiMsbpaD7NuFzMvvwOVh/1EMktxZh/Hsfm5R87HUsGuMe+KCTQ38XFs1IZER/G9op6WtpUR+oNg7IgCVA/+lyG2xK2rfnS6SgiIiJyEElpY1iVcSXT6j5j9UcvOB1HRERERAaYyI3PUuhKZcy045yO8jURkTHEXP1fSv0SGP7WFWxd9YXTkUREBqUpJ15E6YWv02ICSHv1AvL++w+nI8kAVVXXzIvLdzB3yjBiw4MYER9Oq9uyvbLe6Wg+YdAWJMcc+21arB9luc84HUVEREQOYer8X1Jskon59DYaGzR2v4iIiIi0K1i/lDGtm9g9ch7GNfA+5opJGEbgd16jzoQR+fLFtLY0Ox1JRGRQyhg/g+BrP2Zb0Fiyl/2ERQtuwN3W5nQsGWCeXrKdxhY33z2yfWjfEfFhAORrHsleMfB+U+snkbFDWR86nYxd72jYVhERkQEuKDiUvcf9gRS7mxXP3u50HBEREREZIPZ8/E+arR9jTr7S6SgHlDh8FDum/4R4qihcv8TpOCIig1ZMwjBG3fo+S2LOYnbJE6y660xq91U5HUsGiJY2N08uKuSozDjGJEYAMDIuHID8slono/mMLhckjTGPGmNKjTFrOyz7izFmozFmtTHmZWNMVId1PzfGbDXGbDLGnNLLuXtF89i5JFLGpmUfOh1FREREDmHS0XNZHn4MUwsfoaRgo9NxRERERMRhTY31jC19g7URRxIdn+R0nINKyToegIoNnzmcRERkcAsMCmbG9U+SO+anTK5bRNk9x1JSuMnpWDIAvLlmF3v2NfHdORn/WxYZGkBsWKDukOwlh3OH5OPAqfstew+YaK2dDGwGfg5gjBkPzAcmePb5uzHGr8dpe9nYY+fTZAPYu+RZp6OIiIhIFwybfzduXJQ+f5PTUURERETEYWs/fIYoagmYcYXTUQ5paMpISonBf6fukBQRcZpxuci56BesP+ExYt1lhDx+IusXveV0LHGQtZZHPi9gRHwYx4yO/9q6EfFh5JfrDsne0OWCpLX2U6Byv2XvWmtbPS9zgRTP83OAZ621TdbaAmArMLMX8vaqiMgY1oXnMKrsPdpaWw+9g4iIiDhqaMpIVo+6hin1i1j5vuaBFhERERnMAlf9h93EM+HIc5yOckjG5aI4fDLDatY4HUVERDwmHT2X6ovfotaEM+rti1ny4t1ORxKHLCuqYvWOar4zJwOXy3xt3Yi4cN0h2Ut6cw7J7wJfXUYwDCjusG6HZ9k3GGOuMsbkGWPyysrKejFO19gJ5xPHXjYs1hUQIiIi3mD6hbdR6BpOwhe/pqGuxuk4IiIiIuKAksJNTGhcQcHwc3H5DbhBuTrVkjyDRMrYs2Ob01FERMRjeGYWQ274jI0hU5m55rfk/v1KWluanY4l/eyRzwuIDAng/GnfLGONiA+joq6Z6voWB5L5ll4pSBpjbgNagae+WtTJZrazfa21C6y12dba7Pj4+M426VPjj7mAehtE3bKF/X5uEREROXwBgUHUnXAnybaUVc/8xuk4IiIiIuKAog8WAJB+4pUOJ+m62HFHA7Bj1UcOJxERkY4io+MYf+tb5CZcSE7pQjb89RSqK/v/5ilxRnFlPe+s2823Z6USGuj/jfUj4sMB2KZhW3usxwVJY8zlwJnAxdbar4qOO4DhHTZLAUp6eq6+EBIWwfrIoxhd+REtzU1OxxEREZEumDDnDPKGnMi04ico3qphr0REREQGk7bWVjKKX2FtyDSS0sY4HafL0ifMot4G0VKY63QUERHZj39AIDk/WMCSSb9jTOMq9t1/NMVbVjkdS/rBE18W4jKGy2andbp+RHwYgIZt7QU9KkgaY04Ffgqcba2t77DqNWC+MSbIGJMBZAIDdtZu/0nnE00NG774r9NRREREpIvS5/+NJgKoeuEmrNvtdBwRERER6SfrPnuFRMppmXyp01EOS0BgEAVBY4itXOF0FBEROYCZ59/E1tOeJtzWEvnUaaz55CWnI0kfqm1q5bmlxZw+KYmkyJBOt0mNCcXfZcgv0x2SPdXlgqQx5hlgETDGGLPDGPM94AEgAnjPGLPSGPMwgLV2HbAQWA+8DVxnrW3r9fS9ZNxR57KPUJpWPu90FBEREemiuOQ01o25nsmNeax490mn44iIiIhIP2ld9gRVRDDx+AudjnLY9sVPJ6M1n/raaqejiIjIAYzPOZWGK96nwhXP+A+/S+7Tv9eF0D5q4dJiappa+e6RGQfcJsDPRWpMqO6Q7AVdLkhaay+y1iZZawOstSnW2kestaOstcOttVM8j2s6bH+HtXaktXaMtfatvonfO4KCQ9kYdSxj935CY4O+qURERLxF9ryfsM0vg2G5t+tDHRGRQcgYE2yMWWKMWWWMWWeM+Z1n+V+MMRuNMauNMS8bY6IcjioivaSydCcTa75g09AzCAoOdTrOYQsdeQT+xk3+qk+djiIiIgeRnD6GhJs+ZnXYbHI2/4Wl919Kc1Oj07GkF7W5LY9/Wcj0tGimDI866LYj4sPI1xySPdbjOSR9RcjUeUSYBjZ89rLTUUREZBAyxvgZY1YYY173vI4xxrxnjNni+RrtdMaByD8gkJaT/8xQKlj11C+djiMiIv2vCTjeWpsFTAFONcbkAO8BE621k4HNwM+diygivWnze/8i0LSReOyVTkfplvQpxwFQs/lzh5OIiMihhA2JJuvW18kddgUzq15n610nUlm60+lY0kve37CH7ZX1fO8gd0d+ZUR8OIUV9bS5bT8k810qSHqMO+JMqhiCe80LTkcREZHB6UZgQ4fXPwM+sNZmAh94Xksnxs46maVRp5Fd8hRFG5c7HUdERPqRbffVpcoBnoe11r5rrW31LM8FUhwJKCK9yrrdJG17nk3+Y0kfl+10nG6JjImn0DWc0D3LnI4iIiJd4PLzI+fKe8mb/mdGNG2k8aFjKVi32OlY0gse+byAYVEhnDx+6CG3HREXRnOrm51VDf2QzHepIOnhHxDI5tjjGbfvSw35JiIi/coYkwKcAfyrw+JzgCc8z58Azu3nWF5lxEV/pcEEU/PyzZrXQURkkPGMMrASKAXes9bu/wnRd4FOpxExxlxljMkzxuSVlZX1cVIR6alNyz4kzV1M9bj5TkfpkdKoKWQ0rsfd1uZ0FBER6aLss65m+zkvEmBbSFh4Nive/Y/TkaQH1u6sZklBJVcckY6/36HLZCPiwwHYpmFbe0QFyQ7Cp19IqGliwyfPOx1FREQGl3uAnwAdK2lDrbW7ADxfExzI5TVih6awYfxNTGxaybK3HnE6joiI9CNrbZu1dgrtd0HONMZM/GqdMeY2oBV46gD7LrDWZltrs+Pj4/slr4h0374vHqHeBjH+pCucjtIjJjWHIdSxffMKp6OIiMhhGD3tGLjyI0oChpP1xfUseuIXuijaSz36eQFhgX5cOHN4l7YfER8GQH5ZXV/G8nkqSHYwdubJlBKDa91LTkcREZFBwhhzJlBqre3WmE26s+P/ZJ93C1v8RpG29A5qqiudjiMiIv3MWrsX+Bg4FcAYczlwJnCxtVaTvYh4udp9VUys+oC10ScQPsS7p1dPnHgMAHvWfuJwEhEROVzxwzIYfsvHrBhyHLMLHmTZPfNorNddc96kdF8j/11dwrzs4QwJDujSPrFhgQwJ9ie/TO91T6gg2YGfvz/5CScxoW4x+/ZWOB1HREQGhznA2caYQuBZ4HhjzH+APcaYJADP19LOdtadHf/Hz98fe8ZdxNq9rHv6507HERGRfmCMiTfGRHmehwAnAhuNMacCPwXOttbWOxhRRHrJ+vceJ9Q0MWTO95yO0mMpIyZQyRBcOzQHmYiINwoODWfazS+Sm34d2fveZ/vfjqOspNDpWNJF/84totVt+c6c9C7vY4xhRHy47pDsIRUk9xM9cz6BppVNHz/jdBQRERkErLU/t9amWGvTgfnAh9baS4DXgMs9m10OvOpQRK8yetqxLI09i+zdCzXJvIjI4JAEfGSMWQ0spX0OydeBB4AI4D1jzEpjzMNOhhSRnovc8CxFruGMmX6801F6zLhcFIVOJGnfaqejiIhINxmXi5wr/sCKIx4gpaUIu+A4tq3+0ulYcgiNLW08tXg7J44bSlps2GHtOyI+jHzNIdkjKkjuZ/S0YykxCQRtfMXpKCIiMrj9CTjJGLMFOMnzWrpgzLf/Qo0Jo+GVWzSXg4iIj7PWrrbWTrXWTrbWTrTW3u5ZPspaO9xaO8XzuMbprCLSfYUb8hjTupFdI+dhXL7xUVZT0gxS7C7Kdxc7HUVERHpg6smXsnvefwEIfOk77C7XFDID2SsrdlJZ18z3jsw47H1HxoezZ18TtU2tfZBscPCN3+J6kXG5KEo6lfENy6gq2+V0HBERGUSstR9ba8/0PK+w1p5grc30fNVvtF0UFZfIlkm3Mr5lLXmvPeR0HBERERHpod0f/5Nm68fok77vdJReEzXmKACKV2seSRERbzdi4iwaz3qI4ewm91+30NjS5nQk6YS1lke/KGB80hBmZcQc9v4j4trvqCzQsK3dpoJkJxJmX4y/cbP546ecjiIiIiLdkH3uDWzyH8uIlXdSXVXudBwRERER6aamxnrG7HmDtRFziEkY5nScXpM+6QiarT9N+RreT0TEF6ROP5XiEfM5u+EVHv7PM1hrnY4k+/lsSzmb99TyvSMzMMYc9v4j4sMBNGxrD6gg2YkRE2ay3TWM8C2vOR1FREREusHl54ffWf+fvfuOr6q+/zj++t6bPUlISMgAEgIJEAgjISCKA9wD995WrXVUa22t3bW2ttqqtbZ1j7r3QlBEFFlhr0BYYYUASQiEDDLv9/dHoj+tqIwk34z38/HI4ybn3nvOKw/bkNzPPef7N3rYvRS88DPXOSIiIiJyiFZ+8gpRVOI36orvf3AnEhQcSmHAQHrsWuI6RUREWknyBfdTFRTHaRvv4YkZq13nyP94avZGYsMDOS2r9yE9v2/PEIyBDTpD8pBpILkfxuNhW+IpDKpbTlnxZtc5IiIicgjSso5kYexZZJe+yfpls1zniIiIiMgh8F/+X3YQw5CjznSd0ur29BxBav1aavfphU0RkS4hMJzwcx8hzVNMwyd/ZkZBiesiabG+pJJP15Ry2Zi+BPp5D2kfQf5ekqKCKSzVGZKHSgPJb5Ew7mI8xrL+s+ddp4iIiMghyrjkPvaYCJreux1fk9ZwEBEREelMtm9eQ+a+xWxMPhOvn5/rnFYXmHoEAaaRTctnu04REZFWYgZMpDHrEq73e5//vPQ660sqXScJ8NTsTQT4ebgkt89h7Sc1JoxCnSF5yDSQ/BZ9M0aywZtCjw3vuU4RERGRQxQZFUPh8J+T3ljAwrcfdp0jIiIiIgdh08ePA9B3wnWOS9pGn6xjANizVlfzEBHpSvxO+hOE9uIe829++MxcKmoaXCd1a7ur63lzcRFnDU+kZ1jgYe0rNTaUjWXV+HxaI/RQaCD5HUr6nEpG42qKN61xnSIiIiKHKPuMG1jtP4QBK+5nT9kO1zkiIiLSiRTMn0beP68i79W/UjB/GtWVe1wndRtNjY2kbH2b/KARJPRLd53TJnrGJVFkehO4fYHrFBERaU3BPfCe8RBpbGFS5Uvc9NJiGpt8rqu6rRfnb6G2wcfVR6Yc9r5SY8PY19DEjr21rVDW/Wgg+R36jr8MgC0zddlWERGRzsp4PASd+QDhtpo1L97hOkdEREQ6iV07i4j54AfklL5F7qp7yPjgXILv78fW3w9i8f2nM/eZO1k6/WV2Fm3A+vQiY2vLn/UO8ZRSn3Wp65Q2tT0ii741K/W/IRGRrib9JBh6Pjf6vUvZ+kX8eUqB66Juqb7Rx3NzN3HUgBjS48MPe3/9Y0IBdNnWQ9T1LsDfihJSMljjl07M5snA3a5zRERE5BClDMllXvz5jN7xCmsXf8rAkce4ThIREZEOzNfURNHTV5Juq9l8/ocEh0ezY80C9hUtI7Asn17Va0mqmgmbgM9hD2EUBaZR1SMDb+9hRPcfRfLA4QQEBrn+VjqtxoXPsptwMo+7yHVKm/IljSa6YipbC/NJThvqOkdERFrTyX/BU/gpTwU9zVGzEkmPD+f87GTXVd3KlJXb2bm3jnvPHtYq+0uNDQOgsKyKIwfEtMo+uxMNJL/H7pTTGLPub2xdt4zkAVmuc0REROQQDbn4z+x64EPM5NtpGpaH10+/BomIiMj+zX/lT4ypXUDe4LvIHZILQHyfAcDFXz6msqKcooIF7N20BLNjBT0q1zJ8xxsE7XwZlkK99bLBry/lYQNpisskrO9wkjNGE9kzzs031YmUl2wjs3IWi+POZUxQiOucNhWXeTTkw/YVn2ogKSLS1YREw6l/o/erl/GnXjP41VsB9I8NZVTfaNdl3YK1lidnbSQ1NpSjB8a2yj7jIgIJDfDqDMlDpFfivkfqMZfhW/t3ima9qIGkiIhIJxYeGc2a7LvIXngHeW/+ndzzf+Y6SURERDqg9ctmM3LNAywJPYLR53375d7DI6MZlHsi5J745bbGhno2b1hJ6fqFNGxbQejuVaRUzCOmYiqsBabBDmLYETKAfdGDCEzKoldaNgkpg/B4ve3w3XUOa6c9yRjTRNwx17pOaXN9Bo5gL6GwNQ+42XWOiIi0tsFnwOAzOW/NC0yOGMH1/13MuzeNI6FHsOuyLm/h5t0sL6rgj2dm4vGYVtmnMYbU2DA2lFa1yv66Gw0kv0evxBRWBWaSsHUy1vdnjEfLboqIiHRWo075AfnLn2fQqgcpL7mE6F6JrpNERESkA6mpqiDgnWvZYyJJufrpg34NwM8/gL4ZI+mbMfJr28t2bGX7mgVUb16CX9kqYqrWkLk1D78iH8yDahvE1oBUKiIzIC6T6LQc0rKO7JavQVifj/gNr7LGL530wTmuc9qcx+tlY9Bgeu1Z6jpF5GuMMScBDwFe4Alr7b2Ok0Q6r1Pux2ycyaPhTzF6551c/99FvHr9WIID9GaktvTUrI1EBvtz9sjWfe0nNTaUhZt2t+o+u4vu95vtIahMO4O+vq1sWr3AdYqIiIgcBuPxEHb2gwTZWta/+FPXOSIiItLBrHzyBpKaiik5/h/0iIlvtf3GxCcz9OizGXP53WT/5A36/WYljT/fyrpJ7zF/6O9ZGXsqPjwMKZ1Cbv4fGPDO6Sx4+FKaGhtbraGzWLN4Bv18W6nIuNB1Srupic9u/p7LS12niABgjPECjwAnA4OBi4wxg91WiXRiYbFwyn0ElSzl9awlrCyu4GdvLMda67qsy9paXsOH+Tu4OLcPIQGte15eakwYxRX7qG1oatX9dgcaSB6AtKMvptF62DHnRdcpIiIicpj6ZoxkUcIljN7zAWsXf+Y6R0RERDqIRR88yejdk8lLuoLMcae3+fGCQsIYMGI8o8+5ldybnmLwL2cT8pttbLt8HnMTLmf07sks+ccFNDbUt3lLR7J39pPU2EAGn3CV65R2Ez5gHACbls5wXCLypdHAemttobW2HngZmOS4SaRzyzwH0k9l4KqH+ONRQby3rJh/fbrBdVWX9cycTXiM4fKxfVt936mxoVgLG8u0juTB0kDyAPSMS2JV8Aj6FE/B+nyuc0REROQwDb3oD1TbIPbM/I/rFBEREekAijetYcD8X7HGL4PsK/7qrMPj9ZKYOoix1z3M3NRbyN77McsfPJv6ulpnTe2pau9uMss/ZmXUBMIiolzntJvUrPE0Wg81G+a4ThH5QiKw9StfF7Vs+xpjzHXGmIXGmIWlpTrDV+Q7GQOn/R38Arl4+1+ZNCye+z9aw8erdrou63Iqaxt4ZcFWThnam96Rrb9WZ2psKACFpRpIHiwNJA9QbfqZJNqdrFv6uesUEREROUxhEVHkRx3HkN2fUFNV4TpHREREHGpsqGfvC1dirCX8kmfxDwh0nQTA2MvvZl76zxhZ/TmrHzyd2n1d/0WvVdOeJcTUEXHE1a5T2lVIWCQb/VKJKF3kOkXkC2Y/275xbUlr7WPW2mxrbXZsbGw7ZIl0cuHxcNK9mK3zuL9fHpkJkfz45SWs3VnpuqxLeW1hEVV1jVxzZEqb7D8l5ouBZFWb7L8r00DyAKUffRH11kt53kuuU0RERKQVhI6+jFBTy6pPdEl2ERGR7mzBc78go2EVa3L+QEJKhuucrxlz0S/JG/IbhtYsYP2Dp3T5N1JFrH6ZzZ4k0rMnuE5pd7uiR5BaV0BDfZ3rFBFoPiMy+StfJwHFjlpEupasiyDtePxn3M2TZ8QQHODHD55dyO7q7nWJ9rbS5LM8PWcj2X2jyEru0SbHCAnwIyEyiEJdsvWgaSB5gCKjY1kVOpqUnR/ha9JipSIiIp3doNwTKTa9CMx/xXWKiIiIOLJq7hRGb3mSBT1OJvu061zn7FfuebezaOSfGFS7jM0PnUxlRbnrpDaxefUiMhpXsz31PIyn+71c5Z8ylmBTz6b8PNcpIgALgAHGmBRjTABwIfCu4yaRrsEYOP1BMF56zfgpj146gh0Vtdz00mIam7Rc3OH6ePVOtpbv4+o2OjvyC6mxYTpD8hB0v9/wDkPjoLOIYxdrFn7sOkVEREQOk8frZXPSGQypXcqOretd54iIiEg7q9i1k54f3kixpzeDr+nY60rnTPoRS3MfIK2+gO0Pn0jFrq633tT2Tx+nwXoZcPwPXKc4kTTsGAB2rZ7pNkQEsNY2AjcBHwKrgVettfluq0S6kMgkOPGPsOlzRpW9wz1nZTJ7/S7+OHm167JO78lZG0nsEcwJg+Pa9DipsaEUllZj7TeuZi3fQQPJgzDomAvYZwPYu+Bl1ykiIiLSCvocezUeY9k0/SnXKSIiItKOrM/HhqeuIcruoXbSY4SG93Cd9L1GnXIV+Uc9Qr+GQsr+dSLlJdtcJ7Wa+rpa0ndOZkXYOHrGJbnOcSIuqT87iMW/eIHrFBEArLUfWGsHWmv7W2vvcd0j0uWMvAJSjoaPfsN5aXDNkSk8M2cTL8/f4rqs01q5rYL5G8u5alw//LxtO/pKjQmlsq6R0ipdav1gaCB5EELDe7A6fCxpZdNpbNA1nUVERDq7xNQhrPLPJHHzW1ifLo0iIiLSXcx/4++MrP6cxQNuYcDwo1znHLDhEy9izYQnSGjcxt7/nEhZ8WbXSa1i5ScvEcVevNmXu05xalv4UJKrluv3UhGR7sAYOONhsD547xZ+cVI6Rw2I4dfvrGTBpq55efa29tSsjYQGeDk/J/n7H3yYUmPDACgs1TqSB0MDyYNkhp5DTypYPXeK6xQRERFpBVUZ55Fsi1mzeIbrFBEREWkHm1cvYtjKv7A8aBSjL/q165yDNnT8WRSe+CyxTaXUPn4iO7asc5102PyWPc9OepJ51FmuU5xqTBxNL8rZsbXz/zcVEZEDENUXjv89bPgEvxUv8c+LRpIUFcIP/7uIbXv2ua7rVEr21vLe8mLOy04mIsi/zY+XGhsKaCB5sDSQPEiDjjqHKhvMviWvuk4RERGRVjBo4uXsswFUzH3OdYqIiIi0sdp91fheu5oaE0zClc/g8XpdJx2SIUecwtbTXiDCVmCfOplthZ13zakdW9aRuW8RG5POxOvn5zrHqZ6DxgOwbcVnjktERKTdZF8DfcfB1LuIbCzl8cuzqW/0ce2zC6mpb3Rd12k8N3czjT7LVeP6tcvxEiKDCfL3UFha1S7H6yo0kDxIQSFhFEQeSfruGdTX1brOERERkcMUHhlNfuR4Bu2aRu0+vbNNRESkK1v25M2k+DZRdPTfiInv4zrnsGTkTKTkrNcIohb/505h85qlrpMOycaPHwOgz8TrHZe4129wDjU2kKZNc12niIhIe/F4mi/d2lQP799GWmwo/7hoBKt37OWO15ZjrXVd2OHVNjTxQt5mJg6Ko2/P0HY5psdj6NczlMIyvY50MDSQPAT+w88lkmpWz37bdYqIiIi0gsDsS4mgmvwZL7tOERERkTay9OOXyC17g3lxF5J17Hmuc1pFWtaR7Dn/LTz4CHvpDDbm57lOOihNjY2kbHmL/KDhJPRLd53jnJ9/AIVBg+i5e5nrFBERaU89+8Nxv4K1U2HFaxyb0Ys7T8pg8ort/POT9a7rOry3lmxjd00D1xyZ0q7H7R8bpjMkD5IGkodg0LgzqSCUhqWvu04RERGRVjD4iNMpIRq/FRpIioiIdEWlxZvoO+sO1nv7M+KqB1zntKqUwTnUXPwOTXiJeu1s1i393HXSAdm+eQ2LH76YeEqpH3ap65wOoyp2FCmNhVTt3e06RURE2tOYGyBpNEz5GVSVcN34VM4akcjfpq3lo/wdrus6LGstT83ayODeEeSmRLfrsVNjQ9m6ex/1jb52PW5npoHkIQgIDGJN1LEMqvic2hpNwEVERDo7r58fGxJOY0jNQsqKN7vOERERkVbU1NjIzmevINDWE3DBUwQGhbhOanV9Bg6n4fIPqDEhxL91HgXzp7lO+lalxZvI++dV9HxqLFl7pjMv7iKyTrjcdVaHEZI2Dq+xbFw603WKiIi0J48XJj0C9TUw+XaMMfz57KFkJUVy2ytLKdix13Vhh/T5ujLWlVRxzZEpGGPa9dipsaE0+SxbynXZ1gOlgeQhChl5PqGmllUzdZakiIhIV5Bw9DX4GR/rP3nKdYqIiIi0ovkv/JbMuqXkZ/2SPgOHu85pM4mpg/BcPYU9nij6TL6E/NmTXSd9za6dRcz79w8JfzSbkaXvsCTmNMp/kMeYG/6Dn3+A67wOo9/wY/BZQ9X62a5TRESkvcUOhGPuhNXvQv7bBPl7efSybEID/bj2uYWUV9e7Luxwnpy1kdjwQE7L6t3ux06NCQNgQ6kGkgdKA8lDNGjsqZTRA1a+4TpFREREWkHf9OGs8UsnrvAtrE+X2xAREekK1iz8hOzCf7Mo7Biyz7zZdU6bi09OI/jaqZR4e9H/oytY/qn71ywqdu1k7uM/JvhfI8nZ8TIrekyg5IrZ5N78HPHJaa7zOpyIHj3Z7O1LaMlC1ykiIuLCEbdA7+Ew+Xao3kV8ZBCPXjaKnXvr+NELi2ho0usVX1hfUslna0u5bExfAv287X781NhQAAo1kDxgBzyQNMY8ZYwpMcas/Mq2aGPMNGPMupbbqK/c9wtjzHpjzBpjzImtHe6a18+PDTETGFQ5T9f1FxER6SL2DDyXFN9mNqyY4zpFREREDlNlRTnhk39ImYkm7ZonMZ7u8Z7smIS+RPzwQ7b5JZMx4zqWfPS8k47KinLmPvUzPP/IYuy2Z1gdMY5tl3xKzm2vkJg6yElTZ1ESNZyUfatoamx0nSIiIu3N6wdn/gtqK5rXkwRG9Ini3rOHMq+wnLvfX+U4sON4avYmAvw8XJLbx8nxw4P8iQ0PpLBUy/odqIP5bfwZ4KT/2XYnMN1aOwCY3vI1xpjBwIXAkJbn/MsY0/4j6jYWkXMBwaaegs9edZ0iIiIirSBjwhXUWz/KZj3jOkVEREQO05onr6WXr5SKU/5NZFSM65x2Fd0rkZgbP2KTfyqZs29h0QdPttuxa6oqmPfcr2l6YChjtzzK+rBRbDzvI0bd/laXvmRua/L0ySXc7GNzwSLXKSIi4kLcEBh/B6x8HQqaL8F+9sgkrhufynNzN/NC3mbHge7trq7nzcVFnD0ikZ5hgc46UmNCKSzTGZIH6oAHktbamUD5/2yeBDzb8vmzwJlf2f6ytbbOWrsRWA+MPrzUjic9eyI76Ynf6rdcp4iIiEgriOwZx8rwcaSXTqW+rtZ1joiIiByiBe/8i+y9H7Og33VkjD7edY4TkdGx9L75Q9YFDGJ43u0sePuRNj1e7b5q5r34R/bdP5Qxhf9gc9Ag1k16jxF3TCZlSG6bHrurSRh6LAClq2Y6LhEREWeO+gnEDYX3fwL7mq/Q+POTMjh6YCy/fSefvMJdjgPdenH+FmobfFw1LsVpR2psmM6QPAiHe72SOGvtdoCW214t2xOBrV95XFHLtm8wxlxnjFlojFlYWlp6mDnty+P1sjHuBAZXz6eivHO1i4iIyP55R15MFJXkf/a66xQRERE5BEXrVzJ48e9ZFTCU0Zfd4zrHqfDIaPr9+ANWBQ0nZ+ld5L12f6sfo76ulrzX7mfvXzIZs/Y+dgT0o+Dk18i682MGjBjf6sfrDhL6pVNGD7xFea5TRETEFa8/TPonVJfCh79s3uQx/OOiEfTpGcINLyxma3mN40g36ht9PDd3E0cNiCE9PrztDrR3O7z3Y6jd+60P6R8byu6aBnZX17ddRxfSVgsomP1ss/t7oLX2MWtttrU2OzY2to1y2k7PMRcTYJpY++mLrlNERESkFQw56mzK6AFL9W+7iIhIZ1NfV8u+l6+k0XiJvuwZvH5+rpOcCwmLZMCtk1kWnEtu/t3Me/HuVtlvY0M9C97+J2X3DiM3/27K/eNZOfG/DLlrJhm5J7TKMbor4/GwJXQoCZXLXaeIiIhLCcPhyNtg6QuwbhoAkcH+PH55Ng1NPq59biHVdd1vveEPVmxn5946rj6yjc+OnP4HWPQMbJj+rQ9JjQ0FoLBMZ0keiMMdSO40xvQGaLktadleBCR/5XFJQPFhHqtDSss6kiITT9Cat12niIiISCvw8w9gfdzJZFbPo7xkm+scEREROQiLnrmdAY3r2DD2L8Qnp7nO6TCCgkMZdOu7LA4dz5i19zP32bsOeV++piYWTn6c7X/KImfpL6nxhLHs6CdI/8VsMo88oxWru7f6hBwS7E7KirVOmIhIt3b0zyA242tn6vWPDeOfF49k7c5Kfvzy0m41lLTW8uSsjaTGhnL0gDY8wW3nKlj2UvPnRQu/9WGpMWEAbCjVOpIH4nAHku8CV7R8fgXwzle2X2iMCTTGpAADgPmHeawOyXg8bE04mcG1S1g1d4rrHBEREWkFvY66Cn/TxNrpz7hOERGR72CMCTLGzDfGLDPG5Btjft+yPdoYM80Ys67lNsp1q7S9FTPfYuz258nrOYmRJ17mOqfDCQgMYtitb7AwYiJjNz7C3Cduw/p8B/x86/Ox5KPn2XzPCLIX/JRG48+SIx6h/y8XknXseRhPW12Eq3vqkX4kAFuWz3BcIiIiTvkFwqR/QeV2mPbrLzcfPTCW35w2mOkFOznxwZnMWlfmMLL9LNy8mxXbKrh6XAoez/4u1NlKPvkjBIZDr8FQtOBbH5YUFYy/11CogeQBOeDfFo0xLwFzgXRjTJEx5hrgXuB4Y8w64PiWr7HW5gOvAquAqcCN1tqm1o7vKAacfjtF3kT6T72MJR897zpHREREDlNqZi7rvf2JWf+G6xQREfludcBx1tosYDhwkjFmDHAnMN1aOwCY3vK1dGHlJdvo/cmtbPIkM+zqR1zndFh+/gGMuOUV5kedytiip8h79EffO5S0Ph/LZrzG+ntyGDHnRvxsAwtz7qfvL5cw4oRLNYhsI6lDx1Fn/anfOM91ioiIuJY0Csbe1Hz50MJPv9x85bgUXrt+LAFeD5c+mcedbyxnb22Ds8z28OTnG4kM9ueckUltd5Ct82HNZBh3C/Q/DoqXQuP+14j083ro2zOUwlJdsvVAHPBvjdbai6y1va21/tbaJGvtk9baXdbaCdbaAS235V95/D3W2v7W2nRrbZc+dTAmPpmIGz5mk38qw2bfxPw3HnSdJCIiIhe2W1QAALbRSURBVIeprP/ZpDVtYOOqb38nnIiIuGWbffHXv3/LhwUmAc+2bH8WOLP966S9WJ+PrU9fSbitxp7zJMGh4a6TOjSvnx/ZN/2XvJhzGLPzJeb/6xp8Tft/D/nK2e+x5s/jyPrsB4T69rJg+D30vmsZ2adei8frbefy7iUgMIjCgIFE7VrsOkVERDqCY++Cnmnw7s1Q9//Dr+x+0Xzw46P44dH9eXXhVo7/+2dMX73TYWjb2Vpew0erdnBxbh+CA9ro9xBr4ePfQWgvyL0BknKgqQ52rvjWp6TGhFJYpjMkD4TextZKomJ7k3zrNPKDRzF6xW+Z++xdB3XpExEREelYBk68igbrZefMp12niIjIdzDGeI0xS4ESYJq1Ng+Is9ZuB2i57fUtz73OGLPQGLOwtLS03ZqldeW9/Cey9s1n6aDbSRmS6zqnU/B4vYz+0RPMi7+E3LI3WfjwpTQ1/v/6UwXzp5H/p/FkTruU6IYd5A35NTF3riDnzJvw8w9wWN697IkZRUrDemprdNaFiEi35x8Mkx6BPVth+u+/dleQv5c7T87grR+No0dwANc8u5BbX17C7ur9n9XXWT0zZxMeY7hibL+2O8j66bB5dvPanYFhzQNJ+O51JGPD2LyrmsYmzYO+jwaSrSgkLJKM2yZ/uR5D3n+u/9Z3GYqIiEjHFt0rkZWhuaTtmExjQ9f6JV5EpCux1jZZa4cDScBoY0zmQTz3MWtttrU2OzY2ts0ape1sWD6HkWseYGnIWEaf/3PXOZ2K8XjIve6fzE2+ltF7PmDpQ+exZuEnLLt3IhkfnEtc/Wbmpf+MiJ+vJPe8nxIQGOQ6udsJ7n8EAaaJwuWzXKeIiEhH0GcM5F4P8x+DzXO+cXdWcg/eu/lIfjxhAO8v387xD3zGByu2OwhtfZW1DbyyYCunDutNfGQb/U7i88H030GPvjDyiuZtkYkQnvCd60imxobS0GQp2r2vbbq6EA0kW1lAYBAjf/wq8+IuZEzJqyx+6Hzq62pdZ4mIiMghsFkXE8Me8j9/23WKiIh8D2vtHuBT4CRgpzGmN0DLbYm7MmkrNVUV+L19LXtMBH2velprGR4C4/Ew9pr7mZtyE6MqPyH9/bPoW7uaeam3EPLT5Yy56JcEBYe6zuy2+mYdA0DFms/dhoiISMcx4TfNA7N3boT6mm/cHeDn4bbjB/LezUfSOzKYH72wmBueX0RJZeedUTT5LI9+VkhVXSNXj0tpuwPlvwk7VsBxvwK/r1wRIin7OweS/WObf1cqLNMVDb6PfltvAx6vl9zr/83c1FvI3vsxBQ+cSnXlHtdZIiIicpAyjzmP3YTTuPgF1ykiIrIfxphYY0yPls+DgYlAAfAu0PK2Zq4A3nESKG1q5VM3kty0jZKJDxEV29t1Tqc29op7WDD8Huam3IT3thWMufxuQsIiXWd1e1GxvdniSSR45yLXKSIi0lEEhMKkf0J5Icy451sfNqh3BG/96Ah+flIG0wtKOP7vM3lzcRHW2naMPTxVdY08NWsjx9w/g3/OWM+x6bFkJfdom4M11sMnd0NcJmSe+/X7knJg9yao2v8SD6kxYQAUlmodye+jgWQbMR4PYy+/mwXD/sCQfYvY9tDx7C7tGqdHi4iIdBcBgUGsjT2RzMrZVJRrbTERkQ6oNzDDGLMcWEDzGpLvA/cCxxtj1gHHt3wtXcjiKU8zuvw98hIvJ/PIM1zndAk5Z97E2CvuITwy2nWKfMWOiCz61qzE+rQulYiItEgZD9lXw7x/wdZvP3PPz+vhhmP688EtR5HWK4yfvLqMq59ZwPaKjn1p0e0V+/jzlNWM/fN0/vD+KuLCg/jPpaN44oqctjvokueah44TfgP/e9WNL9aR3Lb/dSSjQgOICvFngwaS30sDyTaWc/aPWXHkv+jTsJHKf09g++Y1rpNERETkIESPu5JA00DBx8+4ThERkf9hrV1urR1hrR1mrc201v6hZfsua+0Ea+2Altty163SenZsWUda3i9Z45dO9pX3uc4RaVvJuURRydb1y12XiIhIR3L8HyAiEd75EdR/9yAsrVcYr14/lt+ePph5heWc8PeZvJi3pcOdLblyWwW3vryEo/4yg8dnFjJ+QCxv/egIXr/hCE7KjMfrMW1z4Ppq+Oyv0OcIGHDCN+/vnQUev+9ZRzKMwlJdsvX7aCDZDoYffzGFJz9PD98evE+fxKbV+5+ki4hI92OMCTLGzDfGLDPG5Btjft+yPdoYM80Ys67lNsp1a3eVNmwcmzx96LH2ddcpIiIi3V5jQz27/3sFHusj7OJn8Q8IdJ0k0qbihowHYOfKzxyXiIhIhxIY3nzp1rJ1MPl2+J7hotdjuGpcCh/eOp7MxEjuemsFlzyRx5Zd31yHsj35fJZPCnZy0WPzOO3hWUxbtZPLx/bjszuO5ZFLRjKiTzu8HJb3H6jaCRN/C2Y/Q8+AkOZLuX7XQDImlMIynSH5fTSQbCeDx5zErvPeAiDqlTMomD/NcZGIiHQQdcBx1tosYDhwkjFmDHAnMN1aOwCY3vK1OGA8HnaknEV6YwFb1i51nSMiItKtLXjuLgY15FOQ/XsSUwe5zhFpc8kDsthDGHbLPNcpIiLS0aQeA8fcCctegsXPHdBT+vQM4cVrc/nTWUNZXlTBiQ/O5OnZG/H52vdsydqGJl7M28LxD3zG1c8sZGNZNb84OYM5v5jAb04fTHJ0SPuE1JTDrIdg4MnQZ8y3Py4pB7YtBl/Tfu9OjQ2jtLKOytqGNgrtGjSQbEcpQ3JpvHIqe00kfSdfzNLpL7tOEhERx2yzL67p4N/yYYFJwLMt258Fzmz/OvlC2oRraLKGbZ897TpFRESk21o1byqjtzzBgsgTyT79etc5Iu3C4/WyKTiTuL26ZKuIiOzH+Dsg9Vj44A7YfmD/VhhjuDi3Dx/dNp7c1Gh+/94qzn90Lhva4ZKjZVV1PDBtLePu/YS73lpBkL+XBy8Yzuc/P5brj+5PZLB/mzd8zewHoW4vTPj1dz8uKQfqq6C0YL93p8aGAlCodSS/kwaS7SyhXzqhN3xMkX9fMmfewPy3HnadJCIijhljvMaYpUAJMM1amwfEWWu3A7Tc9vqW515njFlojFlYWlrabs3dTUxCX1YGZ5O67T2aGhtd54iIiHQ7NVUVRE29ie2eeAZd86jrHJF2tS9+FH19Rewp2+E6RUREOhqPF855AkJ6wquXQ23FAT81oUcwT1+Zw9/Pz2JdSRUnP/Q5//50A41NvlbPXF9SyS/eXM4R937CQ9PXMTy5By9dO4b3bz6SM0ck4u91MKraWwx5j8KwCyBuyHc/Nim7+fZbLtva/4uBZJnWkfwuGkg6EN0rkd63TGN1UBajl/2Kef/9jeskEWlDW9YupXBlnusM6cCstU3W2uFAEjDaGJN5EM99zFqbba3Njo2NbbNGgcahFxLHLlbNmew6RUREpNtZ9sKv6E0pVSf9g7AILa0t3UvkwKMA2LTsU7chIiLSMYXGwHlPw54t8M6N37ue5FcZYzh7ZBLTfjKeY9Nj+cvUAs761xwKduw97CxrLXPWl3HV0/OZ+PeZvLl4G+eMTOLjnxzNk1fmMLZ/T8z+1mxsL5/9pfkSrMf+4vsfG50KwdHfOpDsEx2K12N0huT30EDSkbCIKNJ/MpVF4ccyZsNDzPv3D/E17f/6wyLSeVmfj6aXLyf49UuwvtZ/d5F0LdbaPcCnwEnATmNMb4CW2xJ3ZQIw5LiL2EsIdQv/6zpFRESkW9lcsJjs4heY3+MUMnJPcJ0j0u5Shh1Jg/Wyb8Ns1ykiItJR9RkDx/8eVr8H8/590E/vFR7Efy4dxSMXj6R4zz5Of3gWD368lvrGg389s77Rx1tLijj1H7O4+Ik8lhdVcOvEAcy58zj+fPZQ0nqFHfQ+W13Zelj8X8i+GqL6ff/jjWm+bGvRwv3eHeDnITkqWAPJ76GBpEMBgUGMuPUN8mLPZczOl1j0jwtpqK9znSUirWj98tmk+DbTm1IKV85znSMdkDEm1hjTo+XzYGAiUAC8C1zR8rArgHecBMqXgoJDWd3zeIZUfEZlRbnrHBERkW7B+nxUvnUb+0wQ/S+6z3WOiBPBoeFs9O9PZOli1ykiItKRjb0JMk6Dab+GrfMP+unGGE4d1ptpPzmaU4f25sGP13HGP2exoujALgNbUdPAvz/dwPi/zuC2V5ZR3+Tj3rOHMvvO47h14kB6hgUedFObmfFH8AuC8T898Ock5TSvIblvz37vTo0Na5d1ODszDSQd83i9jL7hceb2u4Gcio9Y9cBp1FQd+HWeRaRjK5/9DPXWD581lCx8y3WOdEy9gRnGmOXAAprXkHwfuBc43hizDji+5WtxLHLM5QSbegqm6yxJERGR9rB4ylNk1i1l9eBb6RmX5DpHxJnyniNJrV9DfV2t6xQREemojIFJj0BkErx2JVTvOqTdRIcG8OCFI3ji8mx219Rz5r9mc++UAmob9n+Fx63lNfzu3XzG3judv0wtIDU2lKevzOGjW8dz4eg+BPl7D+ObagPFSyD/LRh7I4T1OvDnfbGOZPH+3yCUGhPKpl3V+HwHfsnc7kYDyQ7AeDyMvfJe5mf+lsyaBWx98AQtVC7SBdTX1TKw9ENWho9jbcAgYrdNd50kHZC1drm1doS1dpi1NtNa+4eW7bustROstQNabnVKXgeQPuo4tpoEQgtec50iIiLS5VXt3U3ygntY500j++yfuM4RcSqg3xiCTAMbV8xxnSIiIh1ZcA8471moLoM3r4XDWEJq4uA4PrrtaM4dmcR/PtvAKf/4nEWb///lqcVbdvOjFxZx9H0zeH7eZk4cEs/7Nx/Ji9eO4diMXng8DteH/C7T/9C8HuQRNx3c8xJHAuZbL9uaGhtGbYOP4op9h9/YRWkg2YGMPvcnLDviYfo1bKDiXxPZsXW96yQROQz5n71OFJV4R17MnuSJpDVtYMeWda6zROQwGI+Hor6TGFy/guKNBa5zREREurSVL/yCGLsbe+rf8Pr5uc4RcSo561gAdq+Z5bhEREQ6vIThcPK9sGE6fP63w9pVZLA/fzl3GP+9ZjR1DT7O/c9cfv76cs7+12zO/tccZq0r47rx/Zn18+N44ILhZCZGts730FYKP4MNn8BRt0PQQbYGRUJsBhQt2O/dqbGhzYfQOpLfSgPJDmbkiZex/sTniG4qwzx5ApsLtD6ASKe19EXK6MGQo84mcey5AGye87rjKBE5XCnHXYPPGjbPeNJ1ioiISJe1cdUCsne8woKepzNw5DGuc0Sci03oR7HpRUDx/l8EFRER+ZpRV8HQ8+HTPzUP4Q7TUQNi+ei28Vw+pi+vLNxKaVUdvz19MHN/MYE7T84gPjKoFaLbmLUw/fcQkQg5Pzi0fSRlNw8k7Tcvy/r/A0mtI/ltNJDsgIYccQol57yJlyYiXz6dgoW6zKNIZ1Neso3M6nmsjzsZP/8AkgdksdmTROjGqa7TROQwxfcZwKqgLPpufQdf0/7XTxAREZFDZ30+9r19G5UmlPSL73OdI9JhFIcPo0/1cuxhXH5PRES6CWPgtAeg5wB44xrYu/2wdxka6MfvJ2Wy5NfH8+lPj+WqcSmEBnaiq1isfg+2LYJjfgH+hzhATR4N+3ZDeeE37ooNCyQ80I/CMp0h+W00kOyg+g87grrLp1Jlwujz3kUsm6G1qkQ6k7XTn8HfNNHrqKu+3FYcdxzptSuo2F3msExEWsO+wReQYHdSMP8j1ykiIiJdzqL3H2Vw/QrWDb2dHjHxrnNEOoympFxi2MP2zWtdp4iISGcQGAbnPwf11c1DyabGVtltVGgA3o66PuS3aWqET+6GmHTIuujQ95OU03y7n8u2GmNIjQ3VJVu/gwaSHVhi6iCCrv+YYr8kBn96PQvf/bfrJBE5QDHr32C9tz+pmblfbosaOQl/08S6WW84LBOR1jBkwiVU2yCq8p5znSIiItKlVOwuo9/ie1njl072mbe4zhHpUGIGjQegeMUMxyXSFRhjzjPG5BtjfMaY7P+57xfGmPXGmDXGmBNdNYpIK+iVAac9CJtnNw/kuqtlL0HZWpjwa/AexlmdMekQGPEd60iG6ZKt30EDyQ4uJj6ZuFs+Zk3QULIX38m8F37vOklEvsfGVQtIa9pAWf+zv7Z94MhjKaMHZs0HjspEpLWEhEWSH3Ucg3fPoKaqwnWOiIhIl1Hw4s+JshV4T/87Hq/XdY5Ih9JvUDZVNpimzfNcp0jXsBI4G5j51Y3GmMHAhcAQ4CTgX8YY/UAW6cyyLoBRV8LsB2FNN1xOqqEWPv0zJI6CjNMOb18eDySOhK3z93t3akwoxRW11NS3ztmoXY0Gkp1AeGQ0A26bwuKw8YxZ93fmPnqj1gsQ6cB2znyaButl4MSrvrbd4/VSGHUk6ZV51NXWOKoTkdYSOvoywsw+Vn3yousUERGRLmHD8jlkl7zBwtizSMs60nWOSIfj9fNjY9AgYncvdZ0iXYC1drW1ds1+7poEvGytrbPWbgTWA6Pbt05EWt1Jf4H4YfDW9bB7s+ua9rXgCdi7DSb+rnltzcOVlAM785svhfs/UmPDANiodST3SwPJTiIwKISsW98ir+eZjN3+PAv+cQmNDfWus0TkfzQ21JO2YzIrQ3OJ7pX4jfsDh55OmNnHmnlTHNSJSGsalHsixaYXgfmvuE4RERHp9HxNTTS8+xMqTDgZF//VdY5Ih1UVl02/ps1UVpS7TpGuKxHY+pWvi1q2fYMx5jpjzEJjzMLS0tJ2iRORQ+QfBOc/C9bCa1dAY53rovZRWwGf/w36Hwcp41tnn0k5YJugeOk37kqNDQXQOpLfQgPJTsTr58foG59mbp/rGL3nA1b+/XT2VVe6zhKRr8if9Q4x7IGsC/d7f/rY06mxgexb8W77holIq/N4vWxOOoMhtUvZsXW96xwREZFObdE7/ySjcTUbhv+cyOhY1zkiHVZY2hF4jGXT0k9dp0gnYIz52Bizcj8fk77rafvZZvf3QGvtY9babGttdmysfnaLdHjRqXDmI1C8BD78peua9jHnn7CvHCb8pvX2mdiy7O5+1pFMiQnFGA0kv40Gkp2M8XgYe/V95A3+JcNq8ii7fzT5sye7zhKRFo2Lnmc34Qw55oL93h8UHEpB2GhSds3E19TUznUi0tr6HHs1HmPZ+MlTrlNEREQ6rYpdO0lbfj+r/YeQfcaPXOeIdGgpw4+hyRqq1s92nSKdgLV2orU2cz8f73zH04qA5K98nQQUt22piLSbQafD2JtgweOw8g3XNW2rqgTmPgJDzoKEEa2339CezcPd/Qwkg/y9JEQGU1hW1XrH60I0kOykcs//GasmPouHJoZMu5j5D15Exa6drrNEurWK8lIyK2ezNuYEAgKDvvVxTQNOphflrF82qx3rRKQtJKYOYZV/Jkmb3tL6ziIiIoeo4MWfEW6rCJz0AMajlylEvktYRBSb/FIIK1nkOkW6rneBC40xgcaYFGAAMN9xk4i0pom/g+RcePcWKFvnuqbtzLwfGmvh2F+1/r6TcpoHkvabJ5CnxobqDMlvod/0O7HMoyYR/dNFzE24nBG7P6Tp4WwWvveoXhAVcaRg+rMEmgaix135nY8bcOQ5NFoPuxa91T5hItKmqjLOI9kWs2bxDNcpIiIinc66JTPJKXuHhXHnkZqZ6zpHpFMoixpOau1qGhvqXadIJ2aMOcsYUwSMBSYbYz4EsNbmA68Cq4CpwI3WWl3iSaQr8frDuU+DXyC8ejnU17guan27N8HCp2DkZRCT1vr7T8qBqp1QUfSNu/rHhlFYWoXdz7Cyu9NAspMLDg1n7HUPs/W8KZT69SZ70c9Y8deJbCtc7TpNpNuJXPM6mzzJpGUd+Z2P6xETz5rAIcRv/6SdykSkLQ2aeDn7bAAVc59znSIiItKpNDU2YiffTrmJZPDFf3adI9JpePuOIdTUsnn1Qtcp0olZa9+y1iZZawOttXHW2hO/ct891tr+1tp0a+0Ul50i0kYiE+Hsx6FkNUy+fb9n+nVqM/4MHi8c/fO22X/St68jmRobSnV9EyWVdW1z7E5MA8kuIjUzl7Q755A36Bek7ltF9LPjmffcr2mo1//oRdrD1nXLyGhczY5+Zx3QZaYq+51Iim8z2wrz26FORNpSeGQ0+ZHjGbRrGrX7dEkOERGRA7XwrQcZ2LiWTaPuIqJHT9c5Ip1GwtBjAChbPdNtiIiIdG5pE+Don8GyF2HJf13XtJ6d+bD8Fci9HiIS2uYYcZngFwRF33xzUGpMGAAbSrWO5P/SQLIL8fr5kXvBndRcN5eCsNGMKfwHW+8dzdrFn7pOE+nyij59iiZr6D/xmgN6fJ+x5wGwde7rbZklIu0kMPtSIqgmf8bLrlNEREQ6hd2l28nI/zv5AcMYdeq1rnNEOpXefQZQQjTeIi3rJyIih+non0PqMfDBHbBjheua1jH9bgiKgHG3tt0xvP6QMOJbz5AEtI7kfmgg2QX1SkxhxB2TWXLEI4T59pL2zpnkPXINVXt3u04T6ZJ8TU2kbHuf/OBsYhP6HdBzElIy2OjpR8TmaW0bJyLtYvARp1NCNH4rNJAUERE5EOtevJ0QW0vYWQ8e0BVGROT/GY+HorChJFYud50iIiKdnccLZz8BwVHN60nWVrguOjxb5sHaKc3DyJDotj1WUjZsXwaNX79KZXxEEMH+Xg0k90O/9XdhI064lODbFrGg1znklLxB9d9HseSj511niXQ5q+a8Tzxl1A+94KCetyPhONLrVrK7dHsblYlIe/H6+bEh4TSG1CykbMcW1zkiIiIdWsHC6YzePZlFvS+k76BRrnNEOqX6hBx6U0rJto2uU0REpLMLi4Vzn4bdm+GdmzrvepLWwse/g7B4yP1h2x8vKQea6mDHyq9t9ngMKTGhFJbpkq3/SwPJLi48MprcG59k3elvUu0JZ8ScG1ly36n6hVWkFe1b+Dx7CSHz2IsO6nkxo87CayzrZ7/RRmUi0p4Sjr4GP+Nj/cdPuU4RERHpsJoaG/GbcgclRDP0kj+5zhHptKIzxgOwddmnbkNERKRr6DsWJv4WVr8Lef9xXXNo1n0EW+Y2r4sZENL2x0vKab79lsu26gzJb9JAsptIzz6O5DvnMy/1FjKq5hPy2FjyXrmXpsZG12kinVrV3t0M2fMZBdETCAoJO6jnpmUd2XyJx3VT2qhORNpT3/ThrPFLJ67wTazP5zpHRESkQ1r4+n2kNW1g6+jfEBrew3WOSKeVkjmGfTaAhk1zXKeIiEhXccQtkH4KfPQr2PrNIVuH5vPBx7+HqBQYeXn7HDMiASISv2UgGUbR7hrqGpvap6WT0ECyG/EPCGTM5XdTfsVMCoMHk7v6z6y/9wgKV+a5ThPptFZNf54QU0dE7sH/Q2c8Hjb2HE961QJqa3QKv0hXsGfguaT4NrNhhV4YEhER+V9lO7YyqOAfrAgcyciTrnCdI9Kp+QcEUhiYTs/yJa5TRESkqzAGzvxX86DttSuhptx10YFb+TqU5MNxvwKvf/sdNyl7vwPJ/rGh+Cxs3lXTfi2dgAaS3VBi6iCG/uxjFo78C7GN20l+7WTmPnazBiIihyB09SsUmd6k50w8pOeHDJtEiKmjYO77rVwmIi5kTLiCeutH2axnXKeIiIh0OBtfup0gW0fkOQ9gPHo5QuRw7Y0ZSUrDBmqqKlyniIhIVxEcBec/B9Ul8OZ1zWcednSN9fDJHyF+KAw5u32PnZQDezZDVcnXNqfGNF9Jr7BUM5ev0l8A3ZTxeMg+44d4blrAkqgTGVv8HLvuG8mKmW+5ThPpNIo3FjCkfgVb+0w65BdU0secQqUNpn7lu61cJyIuRPaMY2X4ONJLP6S+rtZ1joiISIexat5Ucio+ZFHSZfQZONx1jkiXEJJ2BH7Gx8Zls1yniIhIV5IwAk76M6yfBrP+5rrm+y1+tnkoOOF30N5vevtyHcmFX9ucEhsKwAatI/k1rfJfxxhzmzEm3xiz0hjzkjEmyBgTbYyZZoxZ13Ib1RrHktbVIyae0be+xMrjn8eHl6GfXMnCv59Leck212kiHd7mGU8B0O+4qw95HwGBQayNGEP/3bO0pqtIF+EdeTFR7CX/s9ddp4iIiHQIDfV1BH/0M3YQS9ZFf3CdI9Jl9Ms6FoDKdbMdl4iISJeTfQ1kngsz/gQbZ7qu+XZ1VfDZX6HvkZA2of2P3zsLPH7fuGxrWKAfcRGBFGog+TWHPZA0xiQCtwDZ1tpMwAtcCNwJTLfWDgCmt3wtHVTmuNOJ/dlC5iVdw7CKT/D8azTz33oY2xlOyRZxwPp8JG99h/yALHr3TT+8faWfQk8qWLd4RivViYhLQ446mzJ6wNIXXaeIiIh0CIte+wspvs1sP+J3hIRFus4R6TIie8ax2ZNM8M5vrl0lIiJyWIyB0x+Cnmnw+jVQucN10f7l/bv58rITf9vc3N78g5svFbufdSRTY8IoLNMlW7+qtc5f9QOCjTF+QAhQDEwCnm25/1ngzFY6lrSRoOBQxvzg72y/cBrb/fswetmvWHXvMWxdt8x1mkiHU7BgGkl2BzWDzz/sfQ0YdzYN1svuJW8ffpiIOOfnH8D6uJMZUj2P3aXbXeeIiIg4VVq8icy1/2JZ8GiGT7zYdY5Il7MzMot++/LxNTW5ThERka4mMKx5Pcn6Knj9amjqYFd3qymH2f+A9FMhebS7jqQc2LYYfF//tzg1NpTC0mqstY7COp7DHkhaa7cB9wNbgO1AhbX2IyDOWru95THbgV77e74x5jpjzEJjzMLS0tLDzZFW0HfQKNLvnEXekF+TXL+OXs9PYO7TP9daWCJfUTnvOWpsIIMnXHrY+4qMiqEgaBiJO3WGpEhX0euoqwgwTaz5+GnXKSIiXZoxJtkYM8MYs7plGZEft2wfboyZZ4xZ2vL3Zru8QrHko+dpbKhvj0N1Gptf+gn+NBJz3oOHvO66iHyHPrlEUs3WtUtdl4iISFfUaxCc9gBsng0z/ui65utm/R3qKmHCr912JOVAQzWUrP7a5tTYMCr2NVBerb8PvtAal2yNovlsyBQgAQg1xhzwK/TW2sestdnW2uzY2NjDzZFW4vF6yT3vp9RfN4+V4eMYu/k/FP8lm4K8j1yniTi3r7qSweXTye9xLKHhPVplnzWpJ9HHt43Na5a2yv5ExK3UzFzWe/sTs17rSIqItLFG4HZr7SBgDHCjMWYw8Ffg99ba4cBvWr5uUwXzpzFizo0seOq2tj5Up7Fy1rtkV05ncZ8rSUwd4jpHpEvqnXk0ADvzP3NcIiIiXVbWhTDyCpj1AKyZ6rqmWcU2yHsMsi5qHpq6lJTdfPs/l21NjQ0FoLBM60h+oTXenjgR2GitLbXWNgBvAkcAO40xvQFabkta4VjSzmIS+jLqp++wbPyjBPlqyZhyHgsevJCCBR9rfUnptvI/eZEws4/g0Yd/duQX+h5xLgDFeW+02j5FxK2y/meT1rSBjau0po+ISFux1m631i5u+bwSWA0kAhaIaHlYJM3LirSpjNHHk9fzTMZuf57FH/63rQ/X4dXX1RL+yV0UmzhGXPQ71zkiXVZS/6HsJgJTNN91ioiIdGUn/7V5rcS3rofdm13XwGf3AhaO/YXrEohKgZCeULTwa5v7x4QBUFiqdSS/0BoDyS3AGGNMiDHGABNo/iPwXeCKlsdcAbzTCscSR7KOu5Dw2xcyL+4iMndPJ2PyOWy7ezBzn7qDbYX5rvNE2lVA/itsJ5bBY05ptX3GJ6ex3tufqC3TWm2fIuLWwIlX0WC97Jypy7aKiLQHY0w/YASQB9wK3GeM2UrzEiP7faWitZcQGX7tv1nrN5CBc+5g67plh72/zmzxK/fQ17eV0qP+QFBImOsckS7LeDxsCskkvqJ7/8wREZE25h/UvJ6k9cFrV0L1LnctpWthyfOQ8wPo0cddxxeMab5s6/+cIZkYFUyAn4fCUp0h+YXWWEMyD3gdWAysaNnnY8C9wPHGmHXA8S1fSycWGt6DMTf8h8afrGF+1h/ZExBH7ubHSXzuCAruGUveq/dRsWun60yRNlWybSND9i1mU9LpeLzeVt13aeJEBjYUULZja6vuV0TciO6VyMrQXNJ2TNZ6YiIibcwYEwa8Adxqrd0L3ADcZq1NBm4Dntzf81p7CZHAoBAiLn+RBuNH40uXUVNVcdj77Ix2bF3PsA2PsiTkCLKOu9B1jkiXVxefTbItprxkm+sUERHpyqJTYdIjULwY7kuFB4bCK5fCzPth/cftN6Sc8UfwD4Gjbm+f4x2IpGwoWwP79ny5yesx9OsZwgYNJL/UKivKW2t/a63NsNZmWmsvs9bWWWt3WWsnWGsHtNyWt8axxL3wyGhGn3Uzmb/4jJIfLGRu6i0ENVWRu+qPBP9jMEvuO5XFH/6Xutoa16kirW7D9KfwGkvyMde0+r575ZyNx1gKZ2vNOZGuwmZdTAx7yP/8bdcpIiJdljHGn+Zh5AvW2jdbNl9B83IiAK8Bo9urJ77PAIqOe5i+TVtY9djV3XKpi+JXbsNgiTv/QdcpIt1CZPqRAGxeOsNxiYiIdHmDz4AfTIeJv4ekUbBjJXxyNzx/TsuQMhNevgRm3gfrPoaqw78KyddsWwSr3oEjbobQmNbd9+FIymm+3bboa5tTY8IoLNMlW7/g5zpAOrf45DTiL78b6/s961fMpWzOc6TtnErM3JuomPtzlvY8nsjcS0nPnoDxtMr8W8QZ6/ORsOktVvsPZlBaZqvvP3XIaLa/EUvA+qk0v5FfRDq7zGPOY/fcX9C4+AU47nzXOSIiXU7LsiFPAquttX//yl3FwNHAp8BxwLr27Bo6/izmbpjH2M3/Ie+1+8i94OfteXinVnz2JiOrZjI35QbG9kt3nSPSLaQMO5L6D/yoK5wDXOo6R0REurqk7OaPL+zbA9uXwfalULy0+fOC9////ohE6D0cEob//21Yr0M79se/b16vceyNhxjfRhJGAqZ5Hcm0CV9uTo0N5ePVO2lo8uHv1XxEA0lpFcbjIS1rHGlZ42hsqGf5rHepX/ISw8o+IPiDtymaEs/WpNPpc+xVJKYOcZ0rckjWLZ3JQN9W5g/+bZvs33g8bO51LCN2vkV15R5Cw3u0yXFEpP0EBAaxJOYEhpe+S0V5KZHRh385QBER+ZpxwGXACmPM0pZtdwHXAg8ZY/yAWuC69g7LvfxPLL1/KSNW/YWChdlkZE/4/id1cnW1NfT49JdsNQmMvPA3rnNEuo2g4FAK/AcQuWuJ6xQREemOgntA6tHNH1+orYDty78ypFwKayb///3hvb85pAyP/+7jbJgBGz+Dk+6FwPDW/R4OV1AE9Br0jXUkU2PDaPRZtpbXkBqrddU1kJRW5+cfwLBjz4Vjz6Vq724WfPICwatfI3fLE3iee5wCv0FUDDybjAlXENkzznWuyAHbPedZaq0/GROvaLNjhGWdQeC0V1k15x1GnNh2xxGR9hN95FUEvv0GS6c/S+55P3WdIyLSpVhrZwHmW+4e1Z4t/8vj9ZJy7fOUPTyOqPevpbzP50T3SnSZ1OYWv/wHxtpiVhz7NMlBIa5zRLqVPT2HM2LH69TV1hCo//+JiIhrQZGQclTzxxdq98KOFV8fUq6dCtjm+8Pivz6g7J3VPLg0BqyFj38HkcmQfXX7fi8HKikbVr3b3Gqa/0RJjQ0FoLC0WgNJNJCUNhYWEUXOmTfBmTexs2gDhZ88Q+9Nb5Ox6h7q8+9lSegYyLqAwUefp1+YpUOrq60ho+xD8iOOYlSPnm12nIzRJ1IxLZTGVZNBA0mRLiFt2Dg2vduHHmteAzSQFBHpTiKjYyk982mS3pzE+icvJvKO6Xj9uuaf4cWb1jB845MsDh/PyKPPdp0j0u0EpI4jcOdLFKyYQ0bORNc5IiIi3xQUAf3GNX98oa6yeR3Krw4p130EtmUd9tBezcPJkJjm+878N/gFtnv6AUnKgcXPwa4NEJMGQP+Y5iFk8zqSOjlLF62VdhOX1J+xl99N318tY/1ZH7A4/jySa/IZMfcWau9NI+/hKyiYPw3r87lOFfmG/E9fJZJq/Ede0qbH8fMPYF3EEQyomE1jQ32bHktE2ofxeNiRchbpjQVsWbvUdY6IiLSztKxxLB/+GzLrljL/6dtd57SZna/eisWQcMEDrlNEuqU+WccAsGfN525DREREDkZgOPQdC2NugLMfhRvz4BdFcPWHcPJfIW0iVBTB8pchbigMu8B18bdLymm+/cplWyND/OkZGkBhabWjqI6la741Uzq0A11vMvnoK0lKy3SdKwKAZ/nLlBDNkKPObPtjDTqVHnnTyF/wMUOOOKXNjycibS9twjU0rf8H2z57mj4DH3KdIyIi7Wz0Wbcwf+t8xm57hqXTchl+/MWuk1rVsk9eZkTNHOb1v4UxyWmuc0S6pZj4ZIpMPIHbF7pOEREROTwBodBnTPPHF+prwHjA43XX9X1i0iEwonkgOfyiLzenxoZqINlCZ0iKU1+sN5n9kzdoun0tC4bfw56AeHK3PEHS8+Mo+OMY8l79K3vKdrhOlW6sbMdWMqvz2ND7lHa5xNbAcWdSb/2oXPZOmx9LRNpHTEJfVgZnk7rtPZoaG13niIiIA8OufZR13jRSZ9/OtsJ81zmtpramitjPf8NmTzIjL/il6xyRbm17xDD6VK/QladERKTrCQgB/yDXFd/N44HEkV87QxIgNSas5ZKtooGkdBhfrDeZ+YvPKL12EfNSbyHQV0PuqnsIfXgwG+4ewfwHL2LeS39i1bypVFaUu06WbmL99KfxMz4SxrfPgslhEVGsDhlJcskM/SEp0oU0Dr2QOHaxas5k1ykiIuJAUHAooZe9iMVQ+8Il7KuudJ3UKpa89DsS7E4qJ/yZgMAO/iKRSBfnS8qlJxVsK1zlOkVERKR7SsqBnflQ//9nRKbGhlJWVU/FvgaHYR2DBpLSIcUl9WfM5XfT71dL2XD2FBYmXUa1fxRpe2YxZs1fGDz1AsIfSKHo9xksvv905j1zF8s+eZWy4s2u06ULii18k3V+A+g7aFS7HbM29SQS7U42rV7w/Q8WkU5hyHEXsZcQ6hY86zpFREQcSeiXzqajHyKlcRMrH/tBp3/z2bbCfEZueYaF4RPIHHe66xyRbq/XkKMB2L7yM8clIiIi3VRSDtgmKF765abU2DAACkt1lqTWkJQOzXg89B92BP2HHQGA9fko3bGF4oJ51GxZSmBpPnHVa0ismgmbgJlQRg+Kg9Kojh5MQNJweg3IJjE1E4+3A19fWjqsDSvm0b9pI3kZd7brcfuPOxfy/8CO+W+SMiS3XY8tIm0jKDiUpXFnkL3jVbauX0Fy2lDXSSIi4kDWsecxtzCPsVsfJ++NB8g973bXSYfE+nyUvfpjIvGj70V/d50jIkDf9JHsJQTflnnAja5zREREup/E7ObbogXQbxzQfIYkQGFpNSP6RLkq6xA0kJROxXg8xCb0IzahH3Dhl9v37tlF0er57N24CG/JSqIrCxi07QX8i5+D+VBjA9kS0J+KyAxM7yyi+o+iT8YoAoNCnH0v0jmUznqaZOslfeJV7XrcmIS+rPFLJ2bb9HY9roi0rbSzfknDv99k57u/J/knr7vOERERR3Kv/AvL71vKiJV/Ym3/bAaOPNp10kFb+vGLjKhdwLyBtzMmoZ/rHBEBPF4vm4KG0GvPMtcpIiIi3VNoT4hO/do6kn2iQ/DzGK0jiQaS0kVE9OjJ4LEnw9iTv9xWV1vD+jVL2L1hIb7iZURUFJBZ+gGhZW/CCmiwXjZ6kykLT6cpbijhfUeSNDiXyKgYh9+JdCQN9XUM2DmFlWFHMDImvt2PX548kbEbH6Fk20Z6Jaa0+/GlfRhjkoHngHjABzxmrX3IGBMNvAL0o/kc8POttbtddUrriInvw7ze5zF6+4tsLlhM34yRrpNERMQBj9dLnx88z65/HknEu9ewp88sejj4ffNQ1VRV0HvO79jo6Uf2+e17JRER+W7VcaPI3PQoFbvL9PqGiIiIC0k5UPgpWAvG4O/10Cc6hMLS6u99alengaR0WYFBIaRljYOscV9u8zU1sXXjKkrWLqS+aAkh5atIqcgjpuJDWAtMg2ITx86QAdTGZBKcPJz4gdnEJfXHeLTkaneTP/NNhlPB1uEXOzl+wuhzYOMjbJz9Gr3O/5mTBmkXjcDt1trFxphwYJExZhpwJTDdWnuvMeZO4E7g5w47pZWkn/Nr9v3zDcre/z19M95xnSMiIo70iImndNJT9H37LAqeuITwn36I16/j/4m+cdUCeP0a+toyCk74J37+Aa6TROQrwgeMw7P5P2xaOoOsY89znSMiItL9JOXA8legogh6JAPNl23VQFIDSelmPF4vyWlDW9bt+v9LcJbt2ELx6vlUb1lMYOlKYqvXkbx5FmwGZkEFoRQF9KeyRwae3sOITh1JcvoIXfK1i/MteZFyIhhy9DlOjt8nfQRFpjfBhR8CGkh2Vdba7cD2ls8rjTGrgURgEnBMy8OeBT5FA8kuISq2N3OTLmTstmfYmJ+ndWJFRLqxASPGk7f+LnLz/8DcZ3/O2Gv+5jrpW1mfj7xX7mVEwd+pNKGsOOZxssac5DpLRP5HStZ4Gqd5qNkwBzSQFBERaX9JX6wjOf8rA8kwZq4ro8ln8XqMwzi3NJAUofkSejHxfYBzv9xWtXc3RasXULFpCexcSdTeArJ2vk1wyauw7P8v+borbACNvYYQ2mckiRnZRPdKdPeNSKup2LWTzKo5LI47mzEBgU4ajMdDUdxxjNz+Mnv37CKiR08nHdJ+jDH9gBFAHhDXMqzEWrvdGNPrW55zHXAdQJ8+fdqpVA7X4LPvovIfr7Dngz/AkMmuc0RExKHR59zGgm0LGLv1CZZ9kkvWcee7TvqGsh1b2fbMVYypXcCykFySrnyKrLgk11kish+h4T1Y75dCRMki1ykiIiLdU1wm+AVB0ULIbD7RJTUmlPpGH8V79pEc3X1PctJAUuRbhEVEkZF7AuSe8OW2psZGthSupHT9IuqLlhGyezV99i6i195psB74BEqJYntQGtXRg/BPHEZs/1EkpQ3rFJdfkv9X8PEz5JpGYo+86vsf3IYiR0wiYMcLrJj9FqNO/YHTFmlbxpgw4A3gVmvtXmMO7N1S1trHgMcAsrOzbdsVSmuK7BnH3L6XMXbLY6xfNou0rCNdJ4mIiCPG42HodU+w4W9H0m/mrRSnDCMhJcN11peWffIyyTPvIN3uI2/wXYw+7w4tZyHSwe2KHsHQkvdobKjXZZVFRETam9cfEkZA0YIvN6XGhgGwobRKA0kROTBePz/6DBxOn4HDv7Z9d+l2thXMp2rLUrwl+URXrSVj2wsEFD8HC2CfDWCrfz/2hA/Exg8lot8IkjJyCI+MdvONyPeKWvc6hZ5+9B92hNOOgaMmUD4lAlvwAWgg2WUZY/xpHka+YK19s2XzTmNM75azI3sDJe4KpS0MOftOKh58gaqpd0PWh65zRETEoaCQMIIueQHz3ERqnr+Y2p9+TlBwqNOm2poqlj11C7llb1Do6cfe854kd1C20yYROTDefmMIKX2ddfl5DBh+lOscERGR7icpG/IehcY68AskNbb5d/vC0mqOSXfc5pAGkiKtICq2N1Gxk2he8q1ZfV0tG9YupXzDIpq2LydsTwEDdn9G1O73YTUwBYpNHDtDBlDbczBBSVnEDcyhd58BesexY5sLFjOwcS3zBvyEVMctXj8/1vc4kow9n9JQX4e/o8vHStsxzadCPgmsttb+/St3vQtcAdzbcvuOgzxpQxE9ejKv35WM2fQIaxZ+Qnr2ca6TRETEocTUISw96gGGf3498x+7jtE/fsFZS+HKPDxv/oBc3xbmxV3IiKseIDCo+76TW6SzSRp2LCyAXatnaiApIiLiQlIOzHkYdqyApGx6hgYQEeRHYVmV6zKnNJAUaSMBgUH0HzqG/kPHfLnN+nyUbN/M9jV57NuyHP+yfGJr1pG0eTaeLRbmQBk9KDvlCTJGH++wvnsr/uxpEq2HtAlXu04BwH/wqUTM+YAV86YydPyk73+CdDbjgMuAFcaYpS3b7qJ5EPmqMeYaYAtwnps8aUuZZ9/B7r8/R93H94AGkiIi3d7wCRcyt3AeY7c9zYI3HyLn7B+36/F9TU3Mf+XPjFjzIJUmlBXHPs2Yo89u1wYROXzxyWnsIAb/4gXf/2ARERFpfUk5zbdFCyApG2MMqbFhFJZWu+1yTANJkXZkPB56JabQKzEFuPDL7TVVFWwtWMSejUtIWPUEyZMvpcD+t3kNS2lXTY2N9N/+PvkhOWTFJ7vOASBj3CT2zb6NmuXvgAaSXY61dhbwbQtGTmjPFml/YRFRzOt/NWM2PERB3kf6ud9J1VRVEBIW6TpDRLqI0Vfdz4r7ljJs2d2s759NWta4djlu2Y4tFD9zFWNqF7Ik9Aj6XvkEQ3sltsuxRaT1bQsfSlLlctcZ0oEZY+4DTgfqgQ3AVdbaPS33/QK4BmgCbrHWao0JEZGDEZEAEYkt60jeAEBqbChz1u9y2+WYrgsp0gGEhEWSnn0cuefdTuAPPqDcE03yB5exOk+/77W3VbPfpRflNA69yHXKl4JDwykIzaZv2WdYn891joi0sqyz76CMHjRN/6PrFDlIJds2Mv+hiwm8ry/zXtR/PxFpHV4/PxKveYE9JoKQt6+iory0zY+5dPrLeP8zjgH7lpM3+FcM/+lkojWMFOnUGhJGE8cudmxd7zpFOq5pQKa1dhiwFvgFgDFmMM3voh8CnAT8yxjjdVYpItJZJWW3DCSb9Y8NY8feWqrrGh1GuaWBpEgH0ysxhaBrp1DuiaavhpLtrm7RC1QQypBjz3ed8jUNA04mnjI2rJjjOkVEWllwaDjrB/6AIfXLWDn7Pdc5cgD27tnF3Md/TPhjoxle/iFF3kRGrHmQzasXuU4TkS4iulcie057ghhfGZsevxhfU1ObHGdfdSV5/7yK4Z9fT7k3hpKLPiT3/Du0pr1IF9BzUPPakUXLZzgukY7KWvuRtfaLV8XnAUktn08CXrbW1llrNwLrgdEuGkVEOrWkHNizBSp3ApAaEwrAxrLue9lW/ZUh0gHFJvQj6Nop7PL0pO8Hl7Fq3lTXSd1CZUU5QypmUtDzeIKCQ13nfE3auHNosobShW+5ThGRNjD8rJ9QQjTeT/+sM6E7sPq6Wua9dA9ND2Yxdtsz5EeOp+yqOYReN5UaE0z969fRUF/nOlNEuoj07ONYMvjnZO2bT95zd7X6/jesmEfJ38aSW/Ym8+IuIumO2fTNGNnqxxERN1KG5FJjA2naNM91inQOVwNTWj5PBLZ+5b6ilm0iInIwvlhHcttCAFJjwwDYUFrlqsg5DSRFOqjYhH4EXzuVMm8M/aZczqq5U77/SXJYVn/8HMGmnh5jr3Cd8g3RvRJZGzCYXsXTXaeISBsICg5l46AfMqghn5Wfv+06R/6H9flY9MGTlN47jDFr/sq2wP6sO/N9sn/yBgn90omJT2bjmLsZ0LSehc//ynWuiHQho8+7g4URx5O76VFWfPZmq+zT19TEvBf+QPLrpxLiq2LFcc8x5ob/EBgU0ir7F5GOwc8/gI2BGfQsX+I6RRwyxnxsjFm5n49JX3nML4FG4IUvNu1nV/Zb9n+dMWahMWZhaWnbX2JcRKRT6Z0FHv8vL9vat2cIxkBhqc6QFJEOKCahLyHXTaXUG0u/qVdoKNnGwgteZYsnkYEjj3Gdsl8VfY+nf9NGijetcZ0iIm1g+KSb2UEMAZ/fq7MkO5D8OR+w7k+5jJr/E+pNMMuPfpIhP5/BgOFHfe1xI0+6koURE8ne/CTrln7uqFZEuhrj8TD4uifZ7O1D0oxb2LFl3WHtr6x4MyvvO54x6/5Gfuho/G6cy9Dxk77/iSLSKe3tNYp+jRuprtzjOkUcsdZOtNZm7ufjHQBjzBXAacAl1tovho5FQPJXdpMEFH/L/h+z1mZba7NjY2Pb8lsREel8/IMhfigUNZ8hGeTvJSkqmEJdslVEOqqY+D6EXjeFUm8v+k29gvw5H7hO6pK2FeYzqCGfbX3P7LBr5iSOOReALXNfd1wiIm0hMCiELZk3kt64huWfvuo6p9vbtHohS/9yIkM+uojIxl0sGH4Pfe5axLBjz/3WfycGXPkfdptI/N+9gdp93fcPDBFpXSFhkfhd/AJ+tpG9z11MXW3NIe1n6bQX8T42jgH7VpA35DcM/+lkomJ7t3KtiHQkof3H4Wd8bFw203WKdEDGmJOAnwNnWGu/+o/Lu8CFxphAY0wKMACY76JRRKTTS8qBbYuhqXnJ3tSYMAp1yVYR6ci+OpRM+fBK8mdPdp3U5WyZ8RQ+a0idcI3rlG+VnDaUTZ5kwjd+6DpFRNrIiDNupNjEETr7rzpL0pGSbRuZ/9DFJL88kdR9y5mbeguRP1tOzpk34fXz+87nRkbHsuOY++nn28rSZ25vp2IR6Q6S04ay7oj7GNi4lqWP33BQz91XXUnew1cwfPYN7PL2ouTiaeSed3uHfROeiLSefiOOpdF6qFytpT9kv/4JhAPTjDFLjTH/AbDW5gOvAquAqcCN1tomd5kiIp1YUg40VEPpagBSY0PZWFbN/5+U3r3oLxCRTiImPpnQ66ZQ4u1F6kdXsnL2e66TugxfUxN9i94lP2gEcUn9Xed8p+3xx5Fet4KKXTtdp4hIG/APCKRo2M2kNW1g6ccvus7pViorypn7+K2EPzaa4eUfsiD+Anw3LWHs5XcTFBJ2wPsZdsw55PU8k9E7Xtal1kWkVY088TLm9r6U3F1vs+DtRw7oOeuXzabkb2PIKXuHub0vpc/P5tA3fXjbhopIhxHRoydrAwYTu0OXk5dvstamWWuTrbXDWz5++JX77rHW9rfWpltr9UutiMihSspuvm1ZRzI1Noya+iZ27K11GOWOBpIinUhMfDJh101hpzeO/h9dzcpZ77pO6hJW531Igi2hbsgFrlO+V/TIM/EzPtbNftN1ioi0kZGnXc9Wk0CPeffha9IbkdtafV0t8176E40PZDF229PkR46n7MrZjLnhUXrExB/SPjOvfIjtnl70+OjHVO3d3crFItKd5Vz9APkBw8hc8jsKV+Z96+N8TU3Me/639HnzdIJ9Naw6/jnGXv8IAYFB7VgrIh1BRdIxpDVtoLR4k+sUERGR7ieqH4TEfLmOZP+YUAAKS7vnMi8aSIp0MjHxyYRfP5Wd3nj6T7uGlZ+/4zqp06ue/1+qbDBDJlziOuV7DRhxNKVE4VmrtURFuio//wB2jryVFN8mlnz4nOucLsv6fCz64ElK7s1izJq/sC0wlXVnvk/2T94gISXjsPYdGt6DvSc9TLyvhPxnbmmlYhGR5n8j4q5+kUoTRsAbl1Oxu+wbjykt3kT+XycwZv2D5IeNJeCmuWQeeYaDWhHpCOJGNf//f9M8vXYgIiLS7oxpvmzrV86QBLrtOpIaSIp0Qj3jkgi/fgo7vL1J+1hDycNRU1XBkN2fsCrqWIJDw13nfC+P10th9FGkV86nrrbm+58gIp3SiJOvYZMnmZiFf6OpsdF1TpeTP+cD1v0pl1Hzf0KDCWL50U8y5OczGDD8qFY7xqDcE5nf+2Jyy99l2YzXWm2/IiIx8cmUn/IYcb5SCh+/7Gtn0y/56Hn8HxtH/9pVzB/6O4bf/t4hn+0tIl1DyuAcdtIT74ZprlNERES6p6RsKFsL+3YTFxFIaICXDTpDUkQ6k55xSUT+cArbvYmkfXwNK2ZqKHko8qe/QKipJWzMFa5TDlhQ5umEmloK5k52nSIibcTr58eu7Nvp69vKkg+ecJ3TZWxavZClfzmRIR9dRGTjLuZn/ZE+dy1i2LHnYjyt/2vx8CvuY5OnDwmf3aG1f0WkVWWMPp5FGbczomYOec//hpqqCvL+cRkj5txIqTeeXZdOY/Q5t7XJzzYR6VyMx8Om6HEMrFpIfV33XK9KRETEqaSc5tttizDGkBIbSmGZBpIi0slE90ok8ocfsN2byIDp17Bi5luukzqd4FWvsM3EMWj0Ca5TDlj62FOptkHUrtAaoiJd2YgTL6fQ04+4JQ/R2FDvOqdTKy3exPyHLiH55Yn0r1nG3NRbiPzZckafdTNeP782O25QcCiNk/5DD7uXdc/c0GbHEZHuKfeCX7Ao/FhGFz7Cnr/lkLPrPeYmXE7fn80meUCW6zwR6UACMk4kzOxj7UKdJSkiItLuEkcC5st1JFNjwnTJVhHpnKJ7JdLjhqkUexMZOP1aVnz2puukTmPH1vUMrl3GluRJnerd40HBoawJH03/8plfu0SXiHQtHq+XijF3kGyLWfz+o65zOqXKinLmPn4rYY/mMLx8CgvizqfxpiWMvfxugkLC2qUhLWscC/tdS3bldBZ98HS7HFNEugfj8ZBx3TNs8fbBzzaw6oT/Mva6hwkIDHKdJiIdzMCxp1FvvVStmOI6RUREpPsJDIdeg7+yjmQo2/bso7ah+72u23legReRbxUV25uoG6ZS5JfEwE+uY/mnb7hO6hQ2TX8Kj7H0PfYa1ykHzTfwFGLYw7oln7lOEZE2NHzixaz39idp+cM01Ne5zuk06utqyXv5zzQ+kMXYbU+TH3EUpVfMZsyPHiMqtne79+Rcejdr/QaSOv/XlO3Y0u7HF5GuKzS8B73vmEvknflkjjvddY6IdFCh4T1YEzSM+JKZrlNERES6p6Ts5jMkfT5SY8OwFjbt6n6XbdVAUqSLiIrtTc+WoWT6jOtZPuN110kdmvX5SNz8FqsChpKQkuE656ANGHc2jdZD+eK3XaeISBsyHg/V435Ggt3JkncfcZ3T4Vmfj0UfPE3JvcPJLbiX4oAU1k16j+zb3yQxdZCzLj//AALPe5xgW0vRs9difT5nLSLS9QQFhxIYFOI6Q0Q6uOo+x9HPt5XiTWtcp4iIiHQ/STlQuwfKN5AaEwpAYakGkiLSifWIiW8ZSiaT/ukPNZT8DmsWfUKyLaZq0PmuUw5JZM841gQOpfeOT1yniEgbG3bM+azxS6fPykeoq61xndNh+ZqaWPjQhYyafysNJoBlRz/B4Ds/ZcCI8a7TAOibPpyl6T9m+L55LHz7Ydc5IiIi0s0kjp4EwNa8t92GiIiIdEdJOc23RQtIjf1iINn91pHUQFKki2keSk5hq18fMj69nmUzXnOd1CFVzHuOGhvI4AmXuU45ZJX9TqCfbytb169wnSIibch4PNSP/wXxlLH0nX+4zumQrM/H/EdvIKfiQ+YlXUOfuxaTdex5HW594NEX3EV+wDAGLfuzzk4QERGRdpXUfyhFJp6gTdNdp4iIiHQ/MQMhMAKKFhAS4EfvyCCdIXmojDE9jDGvG2MKjDGrjTFjjTHRxphpxph1LbdRrXEsEfl+PWLiif3RFLb49WXQpz9k2Sevuk7qUGr3VTNo18esihxPWETn/dHU54jzANg2T2fCinR1mUdOYrX/EFJWP0ptTfd7B933yXvuV4wpeYV5vc4n9+r78fr5uU7aL4/XS9TFTwCw+8Uf4GvqfgvYi7Q2Y0yyMWZGy9+h+caYH3/lvpuNMWtatv/VZaeIiGvG42FbzJEMrFmi3ydFRETam8cDiaOgaAEAqbGhbCjTQPJQPQRMtdZmAFnAauBOYLq1dgAwveVrEWknkT3j/n8o+dkNLJ3+suukDiN/xstEUE1gduc9OxIgoV86G7wpRG6e5jpFRNqY8XjwHfNLelHO0rcfcJ3ToeS9dj9jNj3CwojjGX39fzrcWZH/K6FfOquG3cmQ+uXMf+XPrnNEuoJG4HZr7SBgDHCjMWawMeZYYBIwzFo7BLjfZaSISEcQPPhkgk09a/Omuk4RERHpfpJyYGc+1FeTGhNGYWkV1lrXVe3qsF+xMcZEAOOBJwGstfXW2j00//H3bMvDngXOPNxjicjBiewZR+yNH7LZrx+DZ/5IQ8kWfiteZic9GXzEqa5TDltJwgQG1q+ivGSb6xQRaWNDxp3KysDhDFj7ODVVFa5zOoTFU54mZ+UfWRacS9ZNL+Dxel0nHZCcs25hWXAuw9c8yOY1S13niHRq1trt1trFLZ9X0vzm2ETgBuBea21dy30l7ipFRDqGgbknsc8GsG/VFNcpIiIi3U9SDlgfFC8hNTaUytpGyqrqXVe1q9Z4C3kqUAo8bYxZYox5whgTCsRZa7dD8x+JQK/9PdkYc50xZqExZmFpaWkr5IjIV0VGx9Lrxqls9k9tHkp+/JLrJKfKijczpGYhhQmnddhL+h2M2Oyz8BrL+tlvuE4RkXbgd9wv6UkFy9/6m+sU51bMfIfMebezJmAQA296A/+AQNdJB8x4PCRe9ji1JpC6166lsaF7/QEi0laMMf2AEUAeMBA4yhiTZ4z5zBiT8y3P0d+jItJtBIWEsTZkBElln2N9Ptc5IiIi3UtSdvNt0QJSY8MAKCztXpdRb42BpB8wEvi3tXYEUM1BXJ7VWvuYtTbbWpsdGxvbCjki8r++NpT8/EaWTnvRdZIz6z95Cj/jI/GYq12ntIr+Q49gBzH4r9M7XEW6g4zcE1gelE36hqeo2rvbdY4zaxd/Sv/p17LNm0zCj94jODTcddJBi0noy4bRf2Bg41oWPP9r1zkinZ4xJgx4A7jVWruX5r9To2i+jOsdwKvGGPO/z9PfoyLS3dSmTCTR7mTr+uWuU0RERLqXkGiI7g9FC0mNCQWgsJutI9kaA8kioMham9fy9es0Dyh3GmN6A7Tc6hI5Ig5FRsXQ68apbPLvz+BZN7Hko+ddJ7U76/MRV/gWa/wy6DNwuOucVmE8HjbHjCejeiH7qitd54hIOwg64ddEUcnKN+9zneLE5oLFxL57KXs8PQj/wbtERsW4Tjpko065hkXhx5G96XHWL5vtOkek0zLG+NM8jHzBWvtmy+Yi4E3bbD7gAzrvDwwRkVaSPHoSAMUL3nVcIiIi0g0l5UDRAhIjgwj08+gMyYNlrd0BbDXGpLdsmgCsAt4FrmjZdgXwzuEeS0QOT2RUDHE3TmGTf3+GzL6l2w0l1y7+lBTfZvYMPNd1SqsKGXYGwaaegtn6MSvSHQwceQxLg8cwaNMz7N2zy3VOu9qxZR1BL59LE17sZW8Rk9DXddJhS7vyP+wxEXjfuYG62hrXOSKdTstZj08Cq621f//KXW8Dx7U8ZiAQAJS1e6CISAeT0C+dTZ5kQrd84jpFRESk+0nKhqqdeCqLSIkJpbBUZ0geipuBF4wxy4HhwJ+Ae4HjjTHrgONbvhYRxyKjYoi/aSob/dPInH0Liz/8r+ukdlP52T+ptMEMOekHrlNaVXruyewlhIZVk12niEg7CTvpN0RSTf4bf3ad0m7KS7ZR/8wkgtlHxTmvkJg6xHVSq4jsGUfx+L+S4tvM4mfvcJ0j0hmNAy4DjjPGLG35OAV4Ckg1xqwEXgausNZal6EiIh3Fjl7jSa9dTnXlHtcpIiIi3UtSy9L2RQtIjQ3VJVsPhbV2acu6G8OstWdaa3dba3dZaydYawe03Ja3xrFE5PBF9OhJ75umUOg/gKFzfsySD591ndTmSos3kbX3U/LjJxEWEeU6p1UFBAaxNnwsaXtm0dTY6DpHRNpBWtY4loQeSeaW56nYtdN1Tpur2rubXY9NoldTCUUnPU3/oWNcJ7WqrOPOZ3706eQWv0BB3keuc0Q6FWvtLGutaflbdHjLxwfW2npr7aXW2kxr7UhrrU4FEhFpETb0ZAJME2vnvu86RUREpHuJGwJ+wS3rSIaxpbyG+kaf66p201pnSIpIJxPRoycJNzcPJTPn3MayGa+5TmpT6z94CC8+kk+81XVKmzAZpxDNXtYumu46RUTaSY9TfkMotax680+uU9pUXW0Nmx45k5SGDRSM/yeDx5zkOqlNDL7yYXZ4ehE29WadrSAiIiJtKj3nBKpsMA0FU12niIiIdC9ef0gY8eUZkk0+y5by7rN8iwaSIt1YeGQ0CTdPodjbm8jP/4D1dc13Y9Tuqya96HWWhY4lMXWQ65w2MfDIs6m3XiqWaB1Jke4iZUguSyKOIavoJcpLtrnOaRNNjY3kP3wBmXVLWTLijwyfcKHrpDYTFhHFnhMeIsG3k5XP/Nh1joiIiHRh/gGBrA3LoV/57C77OoCIiEiHlZQN25fRP9ofgMLSKsdB7UcDSZFuLjwympKsG+nn28LyT193ndMmlk99imj24j/2h65T2kx4ZDQFwcNJ2vmJ/qAU6UZiTv0NgdSz9s17XKe0OuvzsfBfVzGyeibzBtxOzpk3uk5qc4PHnsz8+AvJ3fU2Kz5703WOiIiIdGFN/SfSi3I2rlrgOkVERKR7ScqBpnrSfBsButU6khpIigjDT76GnfTEb97DrlNanfX5iF75NJs8fRgy7nTXOW1qX8qJJNntbFmzxHWKiLSTvhkjWRI5kaztr1G2Y4vrnFY178nbyC1/l7mJVzLmkt+4zmk3w6/8G5s9ycTNuJ2K8lLXOSIiItJF9RszCYCdi951XCIiItLNJOUAEFqyhJiwQJ0hKSLdi39AIBvTLmdI/XLWLZnpOqdVrZ7/EWlNG9g56EqMp2v/yOs37lwAiue/4bhERNpT3Bm/xZ9G1r/5R9cprWbeC39g7LZnyIs+gzHXPOA6p10FBYdSf/q/iLZ7WPfMDa5zREREpIuKTejHem9/Ios+dZ0iIiLSvUT0hoikL9eR3FCqMyRFpJsZcvotVNpg9n7yd9cprap21iNUEMqwU65zndLm4pL6s85vANFbP3adIiLtKDltKIujTmLEzjcp2bbRdc5hW/D2I4xZ9zcWh44n+0dPd/k3k+zPgBHjWdDnGrL3TmPx1Gdc54iIiEgXVRo/noH1q3RVBhERkfaWlA1bF9A/NlRnSIpI9xMeGU1+77MZvvdTijcWuM5pFTu2rGNY5SxW9T6L4NBw1zntoixxAumNaygr3uw6RUTaUdKk3+LBx8a3/uA65bAs/fglRiz5FSsDhzPk5lfw+vm5TnIm+7J7WOdNI2XeryjbsdV1joiIiHRBUcNPw8/4WD/3HdcpIiIi3UtSDlRsYUj4PnbXNLC7ut51UbvQQFJEvpR6+h348LD1g/tcp7SKjVP+gcGScvKtrlPaTfzoswHYMPt1xyUi0p4SUjJY3PNURpS+w44t61znHJJV86aS8fnNbPTvT78b3yYwKMR1klP+AYEEnPsYIbaWrc9dh/X5XCeJiIhIFzNgxDHsIQzf2o9cp4iIiHQvLetIDqX5NZzCsu5xlqQGkiLypV6JKSztMZGhJe9RsWun65zDsq+6kkHb32RZ2FHE9xngOqfd9BuUQ7GJI6hwqusUEWlnfc/6LWDY8vbvXacctA0r5pE85UpKvL2Iuf49wiKiXCd1CH0HjWLJgJsYUTOHBe884jpHREREuhivnx/rI8bQv2IuvqYm1zkiIiLdR+9h4PGn775VAN1mHUkNJEXka2JOuJ0QU8eq9x5wnXJYVkx5nB5UEXTUja5T2pXxeNgSewyDahZTtXe36xwRaUfxyWksiZ3EiF0fsK1wteucA7atMJ/INy6gxoQQcOU7RMX2dp3UoeRc+CtW+WcyaOk9nfbsVxEREenA0o4nmr2sXzbLdYmIiEj34R8M8UOJ3LUUf6+hUANJEemOUobksjwohwGbXqJ2X+f8QWh9PmJXPcMGbyqDRp/gOqfdhQ8/kwDTyNrZb7tOEZF2lnr2b2jCQ/G7neMsybLizZj/noWXJmovfL1bndF+oLx+fkRe9ARefJS98AOdvSAiIiKtqv/YSfisYdfS912niIiIdC9JOZjiJfSLCqSwVJdsFZFuynPkLcSwh+WTH3Wdckjy57xPim8zuzKvxni634+59JyJzeuArJ7sOkVE2llsQj+Wxp/DyN1T2bpumeuc71RRXkrlE2fQw7eH0jOep2/GSNdJHVZi6iBWZP6czLqlzH/1L65zREREpAuJiu3NWv8MehZ/6jpFRESke0nKgYYajoospbCsc54YdLC63yv1IvK9hhxxGuu9/Ylf9USnPBOjYc6/KSeCYSdd7TrFCT//ANZFjmPA3jk01Ne5zhGRdpZ21q+oI4Cd7/3Bdcq32lddSfG/zyCxaSsbJjzOwJHHuE7q8EafcxvLgnLIKnigww+bRUREpHPZnXg0AxvXUrZjq+sUERGR7iMpG4DR/hvYvKuaxiaf46C2p4GkiHyD8XjYM+KH9PFtY/knr7jOOSjbCleTVT2XNUnnEhQc6jrHGb8hZxBJNfkz33SdIiLtLCY+mWUJ5zGyYjqbVy9ynfMNDfV1rP3nOaTXr2blmL8xdPwk10mdgvF4SLj8CeqNPzWvXEtjQ73rJBEREekiYkeeDsDGee86LhEREelGovpBSAzpjWtoaLIU7d7nuqjNaSApIvs1/MQr2U4sgfP/6TrloGz98CGa8JB2yo9dpziVecx57KQnfgv+4zpFRBzIOPtX7COQXZM71lqSvqYmlv3zErL25bEg81eMPPkq10mdSmxCP9Zl/470xjUsfOF3rnNERESki0jNHEsZPfBsmOY6RUREpPswBpJyiN+7AoDCsq6/jqQGkiKyX37+AWweeCWDGvIpWDjddc4Bqa7cw+Cd77As4hhiE/q5znHKPyCQwpSLyKxbysZVC1zniEg7i4rtzfLkixlZ9RmFK/Nc5wBgfT7mP/pDsvdOY26/G8g976eukzqlUaf8gMVhRzNy43/YsGKe6xwRERHpAjxeL4U9jmBA5XxdhUFERKQ9JWUTvLeQSKooLO3660hqICki32ro6Texl1BqZjzoOuWArPzgUSKoIezoG12ndAiDTr2ZfTaA0mkPuk6R72GMecoYU2KMWfmVbdHGmGnGmHUtt1EuG6XzGXz2XewlhIoPOsZZkvOeu4sxJa8yr9cFjLn8T65zOi3j8ZByxaPsNeF437qWfdWVrpNERESkC/BLP4EIqlm76BPXKSIiIt1HUg4A44I3sUEDSRHpzkLDe5CfcC7Dqz6naP3K73+CQ76mJnqveZa1fgPJyJ7gOqdD6BETz/KYk8kq/5Dykm2uc+S7PQOc9D/b7gSmW2sHANNbvhY5YJHRseT3uYwRNbNZt/Rzpy15r93P2E3/ZmHE8Yy+/t8Yj34FPRxRsb3ZfuwD9GnaysrHr3WdIyIiIl1A2tgzaLBeKpZPdp0iIiLSfSSOBAxHB2+isFSXbBWRbm7AabfTiJdtU+5znfKdVn7+Nn1829g77BrXKR1K/MQfE2gaWPPBw65T5DtYa2cC5f+zeRLwbMvnzwJntmeTdA1Dzv45FYSyb+rv2Fm0gX3VlVifr10bFn3wNDkr/8iy4FyybnoBj9fbrsfvqoYefTZ5fa4hZ88U5r/1D9c5IiIi0slF9OjJ2sAhxO2Y6TpFRESk+wgMh16DyfKsp7Cs658h6ec6QEQ6tpiEvsyPPpGsssmUl2wjulei66T9svP+TRk9GHbila5TOpS+g0axPCibtE0vU1/3OwICg1wnyYGLs9ZuB7DWbjfG9Nrfg4wx1wHXAfTp06cd86QziOjRk7kpVzF24z/hiZEA1Fl/9powqj3h1HgjqPOPoCGgB77ASAiOxoRE4RcWTUBYDMGRPQmJjCU8Kpaw8B4HfWbjiplvMTTvdtYEDGbgTW/gHxDYFt9mtzX6ir+w8r6FDFv6BwoH5JKames6SURERDqxyuRjGbLhIXYWbSAuqb/rHGljxpi7aX4jrA8oAa601ha33PcL4BqgCbjFWvuhs1ARka4uKZt+y9+krGoflbUNhAf5uy5qMxpIisj3ijvxpwS9PJkl7z/I2Ks73pmSW9ctI6t2AXP7XM9YDdy+KfcGYj+7hoUfPkP2GT90XSOtzFr7GPAYQHZ2tnWcIx1Q7qV/YNnM4dSVF+GrLsfu24O3bjd+dRUENlQQUbed0Jq1RNhKQkzdt+6n0XrYa8Ko8oRT42keZNYHRNIU2AMbFIUnJApvaDQBYT0JioyhtqKMtE9/RJE3mYQfvUtwaHg7ftfdg9fPj/irnqfqP0fi/8aVVPWZQ1iElpsVERGRQxOffQZseIjN894h7tyfuM6RtneftfbXAMaYW4DfAD80xgwGLgSGAAnAx8aYgdbaJnepIiJdWFIOQYufJdVsp7C0mqzkHq6L2owGkiLyvfpmjGRp8BgytrzMvurfdLgXlYs/fIg468eAU252ndIhZY4/i82f/5bI5U9gT7tOa7d1HjuNMb1bzo7sTfM7VkUOmsfrJevY8w7osXW1NVTuKaN6dyk1e8uo27uL+qqylkHmbjy1e/Cr20NAQwWh9WXE1m4k3FdJuNm33/1t88QR8YN3iYyKac1vSb4iJj6Z/BP+TcaHF7P08asZedsb+jkvIiIih6Rv+kh2EIt/4ceABpJdnbV271e+DAW+eIPrJOBla20dsNEYsx4YDcxt50QRke4hKQeAEZ71FJZVaSApIhIw/laiPryQvMn/Jvf8n7nO+dLePbvILJ3Msh4TyIlPdp3TIXm8XnZkXEnuqj9SsOBjMnJPcJ0kB+Zd4Arg3pbbd9zmSHcQGBRCYHwfYuIP7vK/DfV1VO4po2pPKTUVZdRVltFQXUG/nFOI0c/mNjfkiFOYu+5HjN34CHmv39+h/p0WERGRzsN4PGzuOY6hZVOoq60hMCjEdZK0MWPMPcDlQAVwbMvmRGDeVx5W1LJtf8/XEiIiIocrZiA2MJyRTespLO3a60jq7dMickAG5Z7IWr+BJK5+iqbGRtc5X1r1wb8INbX0OFZnR36Xoadcx15Cqfn8Ydcpsh/GmJdofrdpujGmyBhzDc2DyOONMeuA41u+FumQ/AMCie6VSJ+Bw8nImUjWcReSffr1Gka2o9xL72ZZ8GhG5P+FdUtmus4RERGRTipw8MmEmDrWzp/mOkVagTHmY2PMyv18TAKw1v7SWpsMvADc9MXT9rOr/S4PYq19zFqbba3Njo2NbZtvQkSkq/N4MInZ5Phv0EBSRASa3ylZOfJHJNntLJ/+guscAJoaG0le919W+w9mwPCjXOd0aCFhkeT3Pousys/ZvnmN6xz5H9bai6y1va21/tbaJGvtk9baXdbaCdbaAS235a47RaTj8ni99L3mv5SbHoS+ew0V5aWuk0RERKQTGph7MnXWn+r8D1ynSCuw1k601mbu5+N/r8DzInBOy+dFwFffWZgEFLdHr4hIt5WUQ3/fFraVdO2/5TWQFJEDNvyEy9hm4ghZ8C+sz+c6hxWfvkai3UnNiB+4TukUUk6+FYth85SHXKeIiEgb6BETz57THiPWt4vCJy7vEP9Wi4iISOcSEhbJmuAsEko/d50ibcwYM+ArX54BFLR8/i5woTEm0BiTAgwA5rd3n4hIt5KUgwcfEeUr8Pn2e1J6l6CBpIgcMK+fH0UZV5PeWEDBAveXb/EueJSd9GTYxEtdp3QK8X0GsCx8PIN3vE115R7XOSIi0gYysiewKP02RtTMIe+lu13niIiISCdU0/c4+vi2sa0w33WKtK17Wy7fuhw4AfgxgLU2H3gVWAVMBW601ja5yxQR6QaSsgEY4ltHccU+xzFtRwNJETkoQ0+9gT2EUfvZg047Nq1eyNC6JRSmXIh/QKDTls4k9OibiaCalR886jpFRETaSO6Fv2RJ6JGMWvsQBVr/SURERA5SUs4kALbm/e9VPaUrsdae03L51mHW2tOttdu+ct891tr+1tp0a+0Ul50iIt1CSDT7IlIY4VnXpdeR1EBSRA5KSFgkBUkXkFU9ly1rlzrr2PnxP6i1/mSccrOzhs4ofdRxrPUbSELBM/ia9AZHEZGuyHg8pP7gWUo8MUR9cD27S7e7ThIREZFOJCktk60mgZDNn7hOERER6T6SchjhWU9hSaXrkjajgaSIHLQBp/+EevzYMfV+J8ev2LWTYWVTWB59AlGxvZ00dFbG42Hv8GtJtsWs+OwN1zkiItJGIqNi2HfmU/Swe9n65GV6E4qIiIgclG2xR5G+byn7qrvui6IiIiIdSVC/XGJNBeXFG1yntBkNJEXkoPWMS2JZzClk7ZpK2Y6t7X781R88QrCpJ2bCj9v92F1B1glXUEI0nrx/u04REZE2lJZ1JEsz72RY7QLm//fXrnNERESkEwnNPJlA08DavMmuU0RERLoFk5wDQMCORY5L2o4GkiJySBJOuh1/Gln3/t/b9biNDfX02/Ai+QHDSM3MbddjdxX+AYEU9ruIoXWL2bR6oescERFpQ6PP+QkLIyaSs/FfrJz9nuscERER6SQGjj6RGhtI7aoPXaeIiIh0D72GUG8Cia1Y7rqkzWggKSKHJHlAFstCj2BQ0avUVFW023FXfPIS8ZRSn319ux2zK0o/9WZqrT87pz3kOkVERNqQ8XgYdO2TFHkTiZ92E2U7trhOEhERkU4gMCiENaGjSN41C+vzuc4RERHp+rx+lIYPYWDDGmrqG13XtAkNJEXkkAUfcxs9qGL5e4+02zEDFz1OsenFsOMubLdjdkVRsb1Z3vMksnZNYU/ZDtc5IiLShkLDe+A79xlCbQ07n7qEpsau+YeNiIiItK76lAkk2BK2rFniOkVERKRb2Bc/gsFmE5t2lrtOaRMaSIrIIcsYfTwF/oPps/YZGhvq2/x4G5bPYXD9Crb0vwSvn1+bH6+r63X8rQSZBlZP/ofrFBERaWMpg3NYOeK3DKlfzvxn7nCdIyIiIp1A3zFnArB90btuQ0RERLqJwL65BJpGdq3vmstsaSApIoelJvtHJNidLJv23zY/1q5PHqbGBjLolBvb/FjdQb9B2awIHEn/jS/RUF/nOkdERNpYzpk3Mb/HKYwteorlM153nSPSKowxycaYGcaY1caYfGPMj//n/p8aY6wxJsZVo4hIZxWfnMZGTz/Ct8xwnSIiItItxGSMA8C3ZYHjkrahgaSIHJasCRex1SQQsfjfbbquRHnJNrJ2T2NFzMlERse22XG6G1/uDfT6v/buO7yqKuvj+G/f9EBILySUEBIINZTQHCkiMAoqQlDAhjKiKIrSdCxYBgcVFAREFAsjCghSbIgFfXEsSBFE6YQSegkQkpCenPcPEiZEWkJubsr38zz3ITl3n3PWuveSRVhn76MT2vDN+44OBQBQBpoNmandtnDV/uFRHd4X7+hwgNKQI2mUZVmNJLWXNMwY01g606yU1F0SN08FgBI6HNxJDTI3KuVU5Vw6DgCA8sTDv5YOmBDV2rukUk4gKbWGpDHGyRiz3hjzRf73fsaYb40xO/L/9C2tcwEoP5ycnXWw8T8UlbNDm1cus9t5tn05TW4mWyHdH7n0YFy2Zp3jzjSUf3/brg1lAED54FHNS04D3perla2k2XdWyl9wULVYlnXIsqx1+V+nSNoiKSz/6cmSHpNkOSg8AKjwvJv3lIvJVfyvnzs6FAAAqoSjHcaqfu5u/TbnGUeHUupKc4bkIzrzy1+Bf0r6zrKsKEnf5X8PoBKK6TVUJ1RDOT9Nscvxs7MyVX/PfP3h3lp1o1vZ5RxVlc3JSQej71aDnO3a9tv3jg4HAFAG6jRooa1t/63o7M367b0Rjg4HKDXGmHBJLSWtMsbcJOmAZVkbLrHPfcaYtcaYtceOHSuLMAGgQolq3VXJ8lT2lq8cHQoAAFVCyx53aK3XtWq1523t3rTK0eGUqlJpSBpjaknqJemdQpt7SypYA/B9STeXxrkAlD/untW1rc5AxaSvUsKW30r9+Bu+na0gnZDV9r5SPzakpj3vV7I8dfqHaY4OBQBQRlr3ulerAvqq/eE5Wv/Nh44OB7hixpjqkhZJelRnlnF9StIlLym2LGumZVmxlmXFBgZyWwAAKMrF1U07qrdVRNIvrKoDAEAZqX/XdKWY6spd/EClWtmotGZIvqYzS+EU/pdJsGVZh6Qzy+hICjrfjlyRClQO0TeOULrlqiNfv1Lqx66+/l3tNzXVrPMtpX5sSNW8fLQ5pI9iUv7L/cQAoAppce907XCKVP1fxujAri2X3gEop4wxLjrTjJxjWdZiSfUl1ZO0wRizR1ItSeuMMSGOixIAKq68yO4KUJJ2/rnS0aEAAFAl+AbW1N4O4xSZu1O/zX3e0eGUmituSBpjbpB01LKsEk2L4opUoHLwDaypPwJvUIuTX+vYwT2ldtzt635QdM4W7W9wp2xOTqV2XJyr7vWPyMjS7i/ts+wuAKD8cXP3VLU75kiS0ubcocyMNAdHBBSfMcZIelfSFsuyJkmSZVl/WpYVZFlWuGVZ4ZL2S2plWdZhB4YKABVWvfa9JUnH1nMfSQAAykrLvw/Sb9W7qNXuN7V78xpHh1MqSmOG5N8k3ZR/5elHkroaYz6UdMQYU1OS8v88WgrnAlCO1eo5Wk7KU/wXr5baMZNXTFOq5aEmPR8otWPir2rWbagNXh3V+NAipaWecnQ4AIAyElovWjuvmqio3Hj9/s4wR4cDlMTfJN2pM7+H/p7/6OnooACgMgkIqa0dzlHyPfCDo0MBAKBKqXfXGzptqiln8QPKyc5ydDhX7IobkpZlPWFZVq38K08HSPresqw7JH0maVD+sEGSPr3ScwEo38IimmiDV0c1ObhQqcknr/h4iYf3qvmp77Ux6AZ5efuVQoS4GM+OD8tbp/XnlzMdHQoAoAy17HGHfg0eqHaJi/Xb0ncuvQNQjliW9ZNlWcayrOaWZbXIf3xZZEy4ZVmJjooRACqDxJqdFZW1RUmJTDYHAKCs+AWFaVfbfykqZ4fWVIKlW0vrHpLn85Kk7saYHZK6538PoJKr3nWUaihNGz+fdsXH2rF0qpyVp1p/f/TKA8MlNWzTTTucoxSy9T/Ky811dDgAgDLU+h9TtNWlsaJXP6V9OzY4OhwAAFDO+MX0kpOxFL+S+QYAAJSl1j3v0brqndR615tK2FKiOyeWG6XakLQsa4VlWTfkf33csqxrLcuKyv/zRGmeC0D51KBVF212babwHe8rOyuzxMfJzEhT1L6P9adnW9WKbFqKEeJCjM2mU83vVd28/dr43yWODgcAUIZcXN3kO+hDZRkX5cy7SxlpqY4OCQAAlCNRLTvrhGpIO75xdCgAAFQ5de+codPGQ5mLhlbopVvtOUMSQBWV1XaYQpSoDV//p8TH+OPr/yhASbJ14N6RZan53+/WMflKq2Y4OhQAQBkLrlVf+zq/prq5Cfrj7fscHQ4AAChHbE5O2undXvWTVyk3J8fR4QAAUKX4B9fSrjbPqUHOdq2dN87R4ZQYDUkApa75NbcqwVZLvr+/KSsvr9j7W3l58vnjXSXYaqvp1b3tECEuxNXNXfF1+6t5xtoKvwQAAKD4ml/TT6tq36O2J5dqzSevOzocAABQjpgGPeSrFO34/QdHhwIAQJXT6vrBWleto1runKGErescHU6J0JAEUOpsTk460mSI6ufu0safPi/2/tvWfqeo3Hgdjh4kY+PHVFlr2Gu4Mi0XHV4+xdGhAAAcoO3dE7XJNUZN1z+v3ZvXODocAABQTkS1v0m5llHS70sdHQoAAFWOsdlU584ZSjduylg4tEKuWMD/9AOwi5he9ylRPrJ+KX5T6/SP05UsTzXryXJxjuAXFKYNfj3UPHGZTh0/4uhwAABlzMnZWcGDP9Rp4ymnhYN0OiXJ0SEBAIBywNs/WNtdGyng0ApHhwIAQJUUEFJb8bHPqmHONq35qOIt3UpDEoBduLl7Kj78djXP+E27Nq667P2O7N+pmOQftDm4tzyre9sxQlxMYLdH5GGytHkpy/UBQFUUEFJHh7u/rrDcg9ry9j9KtAQ7AACofE6FXaPI3J1KPJjg6FAAAKiSWve8V+s9/6aWO6YrYdvvjg6nWGhIArCbRjeNUJrlpuPfvnLZ++xaNlVGlupcP8KOkeFS6jVpp41uLRSxa46yszIdHQ4AwAGa/u1Gra43VLHJy7V60SRHhwMAAMqBwNY3SZJ2rfrUwZEAAFA1GZtNte96UxnGVekfV6ylW2lIArAbb79A/RHcWy2SvtPhffGXHJ+RlqroA4v0R7WrFBresAwixMXktH1AwTquP779wNGhAAAcpN2d/9Yf7m3UcuOLit/wk6PDAQAADhbRpK2Oyk/OO791dCgAAFRZASF1tKPVM4rO2aI18//t6HAuGw1JAHZVp+doGVnas/TSMyv+/Opd+SpFrn8bVgaR4VKad7lF+01NVV//tqNDAQA4iM3JSbX/8YFOGm95fDJYyUnHHR0SAABwIGOzaY/vVYpKWcNqOgAAOFDrG+7Tes+r1GL7NO3bscHR4VwWGpIA7Co0vKE21OiipocWX/Q/Ma28PPlvfE+7bOFq3OH6MowQF2JzctKBhoPUMGertq79ztHhAAAcxDewpk72fEtBeYnaOfNOnTpxzNEhAQAAB3KOvk5eJl3b+T0RAACHMTabat/xprKMq1IXVIylW2lIArA7726jVN2ka/PnUy44ZvOvXykib48Sm9wjY+NHU3nRpOdQpVgeOv3D644OBQDgQNFtu+u3Bo+qZdrPqj4lSlv/3UErZz2uHev/q7zcXEeHBwAAylCDDjcoy3JSyh9fODoUAACqtIDQutrW8mk1yt6sNQtedHQ4l8T/+gOwu8iYq7XRrYUidn6grMyM847J+nm6TspLza+/t4yjw8VUr+GrTSE3KyZ5hY7s3+nocAAADtT+9me07YYlWlN7sJysbHVIeFNRn96ok+Pqac3kW7T2i5lKSjzs6DABAICdVa/hq+3uzRRy9EdHhwIAQJUXe+NQ/e7RXjHbpmp//J+ODueiaEgCKBN57R9WkE5ow7J3//LcwT3b1Dz1Z20N7St3z+oOiA4XU+e6R2VkadeXrzk6FACAgzWM7ar2905S1NNrdfyBTVrb6iXtrtFGkadWKnbtGNWYFq1tL7TVyvfGaPu6FRViyRgAAFB8qXW6Kjxvrw4lbHN0KAAAVGnGZlPYnW8p27goZf795XoVIxqSAMpEs859tdtWVwF/zJSVl3fOc3u/miJLRvWuH+6g6HAxofWitaH61Wp0cLHST6c4OhwAQDnhH1xLsTc9oNiRi1Tj6T3adsMSrao7RJLULuFtNfist5JfqKe1k+K09rMZOnH0gIMjBgAApaVmbG9J0t5Vnzk4EgAAEBgarm0tnlSj7E1as+AlR4dzQTQkAZQJY7Mpsfl9qpe3R3/+sPjs9rTUU2p8+BNt8OqkkNqRDowQF+Pe8SH5KFV/fDnT0aEAAMohJ2dnNYztqg6DJ6rh06t16qEtWtt6gnbWaKeI5NWKXfdP+Uxvou0vtNGv74zU1jXLmT0JAEAFViequQ6aYLnt+c7RoQAAAEmxNz2oDR5t1WzrFB3YtcnR4ZwXDUkAZSbm+nt1VH6y/Trt7LY/v5ypGjqtah2HOTAyXEqjtj0U71RfIVtm/WWGKwAARfkG1lTsjfcrduRC+Yzdox29P9eq8PuVJye12feeopfGKeWFuvrt1T5a88l0JR7e5+iQAQBAMRibTfv8/6YGp9cpI/20o8MBAKDKMzabat4xUznGSUkflc+lW2lIAigzrm7u2hVxh5pm/q74DT/JystTyNb3tcMpUg3bdHN0eLgIY7PpZPN7VTdvnzb++ImjwwEAVCA2JydFteykDve8rOinf1Xq8G36rc2r2uF9teqmrFOb359UwJtNFT+ulVa+/ai2rvpGOdlZjg4bAABcgnvj6+VpMrV91VeODgUAAEgKCqunrTFPqEnWn1qzcKKjw/kLGpIAylTjmx5VquWhpOWTtPGnT1U3b5+Smg2WsfHjqLxr/ve7lSgf5a18w9GhAAAqMG//YLXuda/ajJgvv7G7FN9nqVaGP6Bsm7va7H9f0ctuUdq/w7XulZu0eslUJR5McHTIAADgPBq276kMy0Vpm5Y5OhQAAJCvTe+H9Id7GzXbPEkHdm1xdDjnoAMAoEzV8PHXxpCb1SL5/+T23/E6Lm81v+4eR4eFy+Dm7qkddforJmONErb97uhwAACVgM3JSZExV6vD3S+p0VO/6PQj2/Vb29e01aezaqf+obYbxipgZnNteLmH9m7/3dHhAgCAQtw9q2ubZ0vVSvzJ0aEAAIB8xmZT0O1vKk82JX10X7laupWGJIAyF37DaFkyapCzXdtr3yI3d09Hh4TL1KDXcGVZzjr8zWRHhwIAqIS8/QLVuuc9avvoPAU8s0s7477WytpDFJH2h2rO6apfZ9yvUycTHR0mAADIlxF+rWpZh7Qv/k9HhwIAAPKF1I7U5uaPq0nWH1qz6FVHh3MWDUkAZS6kdqR+97lWWZazoq4f7uhwUAz+wbX0u28PNUtcplMnjjk6HABAJWZsNtVv1l4d/vGKMh9Yo/V+16vt4fnKm9JSqz5+Rbk5OY4OEQCAKq9Wm96SpAOrP3VoHNlZmfrty3fL1SyQisIYM9oYYxljAgpte8IYE2+M2WaM+bsj4wMAlEybPo/oT7dWarbpFR3cs83R4UiiIQnAQaIHv6UD/b9SQGhdR4eCYvK/9hF5mkxtWTrN0aEAAKqIgJDaavvIHO3qu1SHXOuq3aZx2vNirDb9vNTRoQEAUKWFRTRSgq2WqiV855DzZ6Sf1qoFE5U4volarx6pjT9+4pA4KipjTG1J3SXtLbStsaQBkppIuk7SG8YYJ8dECAAoKWOzKfD2mcqTTSfm3ScrL8/RIdGQBOAYXt5+qte4jaPDQAnUb9Zem1xjFL5zjnKysxwdTqVmjLku/4rUeGPMPx0dDwA4WmTM39Ton//Vb21fk2duqpp8e5vWTbxBB3ZtcXRoAABUWYcCO6phxh86nZJUZudMP52iX+eOU/LLTdVu8wtKdvbThk5vqVmnPmUWQyUxWdJjkqxC23pL+siyrEzLsnZLipfU1hHBAQCuTEidKG1qOkZNM3/X6kWTHB0ODUkAQPFltxmqECVqw7dzHB1KpZV/Bep0SddLaixpYP6VqgBQpRmbTa173iPfx37XyrpDFZ26WgHvd9TKmcOVmnzS0eEBAFDlVG/WU64mRzt+/dLu50o5dUIr339K6RMbq/32V3TMtZY2XjtbDZ78VTFdB8jY+K/Oy2WMuUnSAcuyNhR5KkzSvkLf78/fdr5j3GeMWWuMWXvsGLd1AYDyqG3cCP3p1lJNN07UoQTHLt1KlQYAFFuza27VfhOiautnOjqUyqytpHjLsnZZlpUl6SOduVIVACDJ3bO6OtzzslLvW6U/fK5Rh4PvK31SS6355HXuHwUAQBlq0KaHTlvuytz6ld3Ocer4Ea18d7SsyU3VYffr2u/eUFuuX6AmT/6oph1704i8AGPMcmPMxvM8ekt6StIz59vtPNus82yTZVkzLcuKtSwrNjAwsDRDBwCUEmOzyX/gW5KkxLn3O3TpVqo1AKDYnJydtb/BXYrO3qzt61Y4OpzK6rKuSuWKVABVXVBYPbUZ8bG23rBYJ5yD1Ob3pxT/YnttXbPc0aEBAFAluLq5a3v1WNU9/nOp/yfn8SP7tfKth+U8tbk67HtbOz1baEfvz9X8n8vVqN3fS/VclZFlWd0sy2pa9CFpl6R6kjYYY/ZIqiVpnTEmRGd+96xd6DC1JB0s69gBAKUnNLyhNjYZrWaZ67Vm8WsOi4OGJACgRJr2elAploeSV0xzdCiV1WVdlcoVqQBwRnTstYp6YqXWtnpJPjmJil4ap7WT4nRk/05Hh3ZJyUnH9duXs7Rmcn9tWfW1o8MBAKDYsiO6KUSJ2rNlTakc7+iB3fr1jSHyfKOl2h38QFtrXKXdt3yjlo99qaiWnUrlHFWZZVl/WpYVZFlWuGVZ4TrThGxlWdZhSZ9JGmCMcTPG1JMUJWm1A8MFAJSCNnEjtdGthRr/OUGH9+5wSAw0JAEAJVK9hq82Bd+kmFP/p6MHdjs6nMqIq1IBoJhsTk6KvekBVRu1XitrDVbTUz/I6+0OWvneY0o/neLo8M6y8vKUsHWdfv3wWW0a31Eek6PUevWjanjqR6Ueind0eAAAFFt4+5slSYd/++KKjnNw91atmnaXfGbGKvbIQm30vVb7b1+h1qOWqF6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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(3,4, figsize=(32,18))\n", + "ax_it = iter(fig.axes)\n", + "for reg in df_cum.node.unique():\n", + " if reg == \"R12_GLB\":\n", + " continue \n", + " ax = next(ax_it)\n", + " df_tmp = df_cum[df_cum[\"node\"]==reg]\n", + " df_tmp.plot(x=\"year_all\", y=\"Level\", ax=ax, legend=False)\n", + " df_tmp2 = df_join.loc[reg]\n", + " df_tmp2.plot(ax=ax, y=\"value\", legend=False)\n", + " ax.set_title(reg)\n", + " ax.set_xlabel(\"\")\n", + "fig.legend([\"LU_CO2 from EMISS equation\", \"LU_CO2 from land_use_reporting\"], fontsize=16, loc=\"lower center\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-02T12:11:58.939921100Z", + "start_time": "2023-10-02T12:11:57.037716900Z" + } + }, + "id": "3a4f18e634f69e86" + }, + { + "cell_type": "code", + "execution_count": 139, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(16,9))\n", + "ax_it = iter(fig.axes)\n", + "for reg in df_cum.node.unique():\n", + " if reg != \"R12_WEU\":\n", + " continue \n", + " ax = next(ax_it)\n", + " df_tmp = df_cum[df_cum[\"node\"]==reg]\n", + " df_tmp.plot(x=\"year_all\", y=\"Level\", ax=ax, legend=False)\n", + " df_tmp2 = df_join.loc[reg]\n", + " df_tmp2.plot(ax=ax, y=\"value\", legend=False)\n", + " ax.set_title(\"CO2 Emissions WEU\", fontsize=20)\n", + " ax.set_xlabel(\"\")\n", + "fig.legend([\"LU_CO2 from EMISS equation\", \"LU_CO2 from land_use_reporting\"], fontsize=16, loc=\"lower center\")\n", + "fig.savefig(\"CO2_Emissions_WEU_diff.svg\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-02T13:35:28.003253Z", + "start_time": "2023-10-02T13:35:27.787273900Z" + } + }, + "id": "c3c35acb7d0953f6" + }, + { + "cell_type": "code", + "execution_count": 23, + "outputs": [ + { + "data": { + "text/plain": "" + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(12,7))\n", + "df_cum.groupby(\"year_all\").sum().plot(ax=ax, legend=False)\n", + "df_join.groupby(\"year_all\").sum().plot(y=\"value\", ax=ax, legend=False)\n", + "ax.set_xlabel(\"\")\n", + "#ax.set_ylim((-2000, 1500))\n", + "ax.set_title(\"World\", fontsize=18)\n", + "ax.legend([\"LU_CO2 from EMISS equation\", \"LU_CO2 from land_use_reporting\"], fontsize=14, loc=\"lower center\")\n", + "ax.axhline(-650, color=\"black\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-27T11:22:24.291734500Z", + "start_time": "2023-09-27T11:22:24.071522800Z" + } + }, + "id": "f8d4759cdcc20916" + }, + { + "cell_type": "code", + "execution_count": 30, + "outputs": [ + { + "data": { + "text/plain": "" + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "for i in df_emi.index.get_level_values(2).unique():\n", + " if \"4000\" not in i:\n", + " continue\n", + " df_emi.swaplevel(0,2).loc[i].groupby(\"year_all\").sum().plot(ax=ax)\n", + "ax.axhline(-650)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-22T13:14:10.745588300Z", + "start_time": "2023-09-22T13:14:10.542162300Z" + } + }, + "id": "6fd9ebb44395518a" + }, + { + "cell_type": "code", + "execution_count": 16, + "outputs": [], + "source": [ + "fig.savefig(\"emi_accounting_comparison_tax_1000.svg\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-20T20:50:18.277635300Z", + "start_time": "2023-09-20T20:50:18.011832700Z" + } + }, + "id": "e15122db2fa07444" + }, + { + "cell_type": "code", + "execution_count": 48, + "outputs": [ + { + "ename": "TypeError", + "evalue": "'<' not supported between instances of 'str' and 'int'", + "output_type": "error", + "traceback": [ + "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[1;31mTypeError\u001B[0m Traceback (most recent call last)", + "Input \u001B[1;32mIn [48]\u001B[0m, in \u001B[0;36m\u001B[1;34m()\u001B[0m\n\u001B[1;32m----> 1\u001B[0m df_join[\u001B[43mdf_join\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mindex\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_level_values\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m1\u001B[39;49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m<\u001B[39;49m\u001B[38;5;241;43m2055\u001B[39;49m]\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\ops\\common.py:70\u001B[0m, in \u001B[0;36m_unpack_zerodim_and_defer..new_method\u001B[1;34m(self, other)\u001B[0m\n\u001B[0;32m 66\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mNotImplemented\u001B[39m\n\u001B[0;32m 68\u001B[0m other \u001B[38;5;241m=\u001B[39m item_from_zerodim(other)\n\u001B[1;32m---> 70\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mmethod\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mother\u001B[49m\u001B[43m)\u001B[49m\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\arraylike.py:48\u001B[0m, in \u001B[0;36mOpsMixin.__lt__\u001B[1;34m(self, other)\u001B[0m\n\u001B[0;32m 46\u001B[0m \u001B[38;5;129m@unpack_zerodim_and_defer\u001B[39m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m__lt__\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m 47\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m__lt__\u001B[39m(\u001B[38;5;28mself\u001B[39m, other):\n\u001B[1;32m---> 48\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_cmp_method\u001B[49m\u001B[43m(\u001B[49m\u001B[43mother\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43moperator\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mlt\u001B[49m\u001B[43m)\u001B[49m\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py:6690\u001B[0m, in \u001B[0;36mIndex._cmp_method\u001B[1;34m(self, other, op)\u001B[0m\n\u001B[0;32m 6687\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m is_object_dtype(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mdtype) \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(\u001B[38;5;28mself\u001B[39m, ABCMultiIndex):\n\u001B[0;32m 6688\u001B[0m \u001B[38;5;66;03m# don't pass MultiIndex\u001B[39;00m\n\u001B[0;32m 6689\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m np\u001B[38;5;241m.\u001B[39merrstate(\u001B[38;5;28mall\u001B[39m\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mignore\u001B[39m\u001B[38;5;124m\"\u001B[39m):\n\u001B[1;32m-> 6690\u001B[0m result \u001B[38;5;241m=\u001B[39m \u001B[43mops\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcomp_method_OBJECT_ARRAY\u001B[49m\u001B[43m(\u001B[49m\u001B[43mop\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_values\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mother\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 6692\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m 6693\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m np\u001B[38;5;241m.\u001B[39merrstate(\u001B[38;5;28mall\u001B[39m\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mignore\u001B[39m\u001B[38;5;124m\"\u001B[39m):\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\ops\\array_ops.py:73\u001B[0m, in \u001B[0;36mcomp_method_OBJECT_ARRAY\u001B[1;34m(op, x, y)\u001B[0m\n\u001B[0;32m 71\u001B[0m result \u001B[38;5;241m=\u001B[39m libops\u001B[38;5;241m.\u001B[39mvec_compare(x\u001B[38;5;241m.\u001B[39mravel(), y\u001B[38;5;241m.\u001B[39mravel(), op)\n\u001B[0;32m 72\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m---> 73\u001B[0m result \u001B[38;5;241m=\u001B[39m \u001B[43mlibops\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mscalar_compare\u001B[49m\u001B[43m(\u001B[49m\u001B[43mx\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mravel\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43my\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mop\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 74\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m result\u001B[38;5;241m.\u001B[39mreshape(x\u001B[38;5;241m.\u001B[39mshape)\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\_libs\\ops.pyx:107\u001B[0m, in \u001B[0;36mpandas._libs.ops.scalar_compare\u001B[1;34m()\u001B[0m\n", + "\u001B[1;31mTypeError\u001B[0m: '<' not supported between instances of 'str' and 'int'" + ] + } + ], + "source": [ + "df_join[df_join.index.get_level_values(1)<2055]" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-20T13:36:58.865218500Z", + "start_time": "2023-09-20T13:36:57.609076600Z" + } + }, + "id": "487778a8578950ac" + }, + { + "cell_type": "code", + "execution_count": 64, + "outputs": [ + { + "data": { + "text/plain": " Level\nnode \nR12_AFR 1360.246539\nR12_CHN -234.583538\nR12_EEU 94.750907\nR12_FSU -36.503831\nR12_GLB 0.000000\nR12_LAM -461.315355\nR12_MEA 43.783812\nR12_NAM -191.983143\nR12_PAO -43.486300\nR12_PAS 827.924115\nR12_RCPA 45.160749\nR12_SAS 165.671837\nR12_WEU 259.030739", + 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R12_WEU259.030739
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" + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_cum.groupby(\"node\").sum()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-19T20:43:34.345832800Z", + "start_time": "2023-09-19T20:43:34.220548200Z" + } + }, + "id": "c1c60ffe2084680e" + }, + { + "cell_type": "code", + "execution_count": 17, + "outputs": [], + "source": [ + "df_land2 = df_land.reset_index()\n", + "histo = df_land2.join(df_land2[\"land_scenario\"].str.split(\"GHG\", expand=True)).drop(\"land_scenario\", axis=1).groupby(1).sum()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-20T20:50:25.949573200Z", + "start_time": "2023-09-20T20:50:25.902343Z" + } + }, + "id": "41486c4325eefb24" + }, + { + "cell_type": "code", + "execution_count": 18, + "outputs": [], + "source": [ + "histo.index = histo.index.astype(int)#.astype(int)#.plot.bar()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-20T20:50:26.199374100Z", + "start_time": "2023-09-20T20:50:26.152619900Z" + } + }, + "id": "912bd4b1cf1d1f6a" + }, + { + "cell_type": "code", + "execution_count": 19, + "outputs": [ + { + "data": { + "text/plain": "Text(0.5, 0, 'GHG category')" + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/png": 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+ }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(11,7))\n", + "(histo/168).sort_index().plot.bar(ax=ax, legend=False)\n", + "ax.set_xlabel(\"GHG category\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-20T20:50:26.715403200Z", + "start_time": "2023-09-20T20:50:26.530818800Z" + } + }, + "id": "df3db1c98d46d40e" + }, + { + "cell_type": "code", + "execution_count": 20, + "outputs": [], + "source": [ + "fig.savefig(\"histo_tax_1000.svg\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-20T20:50:47.621212Z", + "start_time": "2023-09-20T20:50:47.512023100Z" + } + }, + "id": "fd85a693a9cd1b69" + }, + { + "cell_type": "code", + "execution_count": 28, + "outputs": [ + { + "data": { + "text/plain": " Level\nnode year_all land_scenario \nR12_AFR 2020 BIO05GHG200 1.000000\n 2100 BIO00GHG000 0.001690\n 2110 BIO00GHG000 0.002765\n 2040 BIO00GHG100 0.226219\n 2045 BIO00GHG100 0.401263\n... ...\nR12_CHN 2020 BIO07GHG100 1.000000\n 2080 BIO07GHG3000 0.401263\n 2090 BIO07GHG3000 0.240251\n 2100 BIO07GHG3000 0.143847\n 2110 BIO07GHG3000 0.086127\n\n[314 rows x 1 columns]", + "text/html": "
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" + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_land" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-20T19:00:02.956541Z", + "start_time": "2023-09-20T19:00:02.847331400Z" + } + }, + "id": "d887d1506a4622df" + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false + }, + "id": "6343055268f44a34" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/message_ix_models/model/material/magpie notebooks/read_magpie_raw_files.ipynb b/message_ix_models/model/material/magpie notebooks/read_magpie_raw_files.ipynb new file mode 100644 index 0000000000..67696f2987 --- /dev/null +++ b/message_ix_models/model/material/magpie notebooks/read_magpie_raw_files.ipynb @@ -0,0 +1,1785 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "initial_id", + "metadata": { + "collapsed": true, + "ExecuteTime": { + "end_time": "2023-10-05T08:35:26.746929400Z", + "start_time": "2023-10-05T08:35:25.398719100Z" + } + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import os" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [], + "source": [ + "dir = \"C:/Users\\maczek\\Desktop/MP00BI00/MP00BI00/raw-exp\"\n", + "dir = \"C:/Users\\maczek\\Desktop/MP00BI00/MP00BI00/raw-linear\"" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T08:35:26.747927200Z", + "start_time": "2023-10-05T08:35:26.632018700Z" + } + }, + "id": "a75aaa89554ad632" + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [], + "source": [ + "f_list = os.listdir(dir)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T08:35:26.748925Z", + "start_time": "2023-10-05T08:35:26.647118400Z" + } + }, + "id": "c626ebcfa537a454" + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [], + "source": [ + "df = pd.DataFrame()\n", + "for scen_name in f_list[:2]:\n", + " # if \"BIO00\" in scen_name:\n", + " # continue\n", + " # if \"BIO45\" in scen_name:\n", + " # continue\n", + " # if \"BIO15\" in scen_name:\n", + " # continue\n", + " # if \"BIO25\" in scen_name:\n", + " # continue\n", + " # if \"GHG400raw\" in scen_name:\n", + " # df_tmp = pd.read_csv(dir+\"/\"+scen_name, sep=\";\")\n", + " # df_tmp = df_tmp[df_tmp[\"Region\"]==\"World\"]\n", + " # df = pd.concat([df, df_tmp])\n", + " # del df_tmp\n", + " # if \"GHG600raw\" in scen_name:\n", + " df_tmp = pd.read_csv(dir+\"/\"+scen_name, sep=\";\")\n", + " #df_tmp = df_tmp[df_tmp[\"Region\"]==\"World\"]\n", + " df = pd.concat([df, df_tmp])\n", + " del df_tmp\n", + " #if \"BIO00\" in scen_name:\n", + " # df_tmp = pd.read_csv(dir+\"/\"+scen_name, sep=\";\")\n", + " # df_tmp = df_tmp[df_tmp[\"Region\"]==\"World\"]\n", + " # df = pd.concat([df, df_tmp])\n", + " # del df_tmp" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T08:35:26.864063500Z", + "start_time": "2023-10-05T08:35:26.658212300Z" + } + }, + "id": "293986cfd209e770" + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [], + "source": [ + "df = df.reset_index(drop=True)\n", + "split_cols = df.reset_index()[\"Scenario\"].str.split(\"GHG\", expand=True)#.drop(\"Scenario\", axis=1)\n", + "split_cols[0] = split_cols[0].str.replace(\"SSP2\", \"\")\n", + "split_cols[1] = \"GHG\" + split_cols[1]\n", + "df[[\"BIO\", \"GHG\"]] = split_cols" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T08:35:26.986242Z", + "start_time": "2023-10-05T08:35:26.828612800Z" + } + }, + "id": "3ba1d6cb86af3036" + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [], + "source": [ + "df = df.drop([\"Unnamed: 23\", \"Scenario\", \"Model\"], axis=1)\n", + "df = df.set_index([\"Variable\", \"Region\", \"BIO\", \"GHG\", \"Unit\"])\n", + "df.columns = [int(i) for i in df.columns]" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T08:35:30.501889700Z", + "start_time": "2023-10-05T08:35:29.196964500Z" + } + }, + "id": "a686e64adaf8d750" + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [], + "source": [ + "df = df.melt(ignore_index=False)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T08:35:30.590539800Z", + "start_time": "2023-10-05T08:35:30.396805600Z" + } + }, + "id": "d8ab94cc9465baa5" + }, + { + "cell_type": "code", + "execution_count": 24, + "outputs": [], + "source": [ + "from bokeh.models import CDSView, ColumnDataSource, IndexFilter\n", + "\n", + "source = ColumnDataSource(df.loc[\"Biodiversity|BII\"])" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T08:53:41.407389500Z", + "start_time": "2023-10-05T08:53:40.301937400Z" + } + }, + "id": "d030e89d85a02396" + }, + { + "cell_type": "code", + "execution_count": 26, + "outputs": [ + { + "data": { + "text/plain": "{'Region_BIO_GHG_Unit': array([('AFR', 'BIO00', 'GHG000', 'unitless'),\n ('CHA', 'BIO00', 'GHG000', 'unitless'),\n ('CPA', 'BIO00', 'GHG000', 'unitless'),\n ('EEU', 'BIO00', 'GHG000', 'unitless'),\n ('FSU', 'BIO00', 'GHG000', 'unitless'),\n ('LAM', 'BIO00', 'GHG000', 'unitless'),\n ('MEA', 'BIO00', 'GHG000', 'unitless'),\n ('NAM', 'BIO00', 'GHG000', 'unitless'),\n ('PAO', 'BIO00', 'GHG000', 'unitless'),\n ('PAS', 'BIO00', 'GHG000', 'unitless'),\n ('SAS', 'BIO00', 'GHG000', 'unitless'),\n ('WEU', 'BIO00', 'GHG000', 'unitless'),\n ('World', 'BIO00', 'GHG000', 'unitless'),\n ('AFR', 'BIO00', 'GHG010', 'unitless'),\n ('CHA', 'BIO00', 'GHG010', 'unitless'),\n ('CPA', 'BIO00', 'GHG010', 'unitless'),\n ('EEU', 'BIO00', 'GHG010', 'unitless'),\n ('FSU', 'BIO00', 'GHG010', 'unitless'),\n ('LAM', 'BIO00', 'GHG010', 'unitless'),\n ('MEA', 'BIO00', 'GHG010', 'unitless'),\n ('NAM', 'BIO00', 'GHG010', 'unitless'),\n ('PAO', 'BIO00', 'GHG010', 'unitless'),\n ('PAS', 'BIO00', 'GHG010', 'unitless'),\n ('SAS', 'BIO00', 'GHG010', 'unitless'),\n ('WEU', 'BIO00', 'GHG010', 'unitless'),\n ('World', 'BIO00', 'GHG010', 'unitless'),\n ('AFR', 'BIO00', 'GHG000', 'unitless'),\n ('CHA', 'BIO00', 'GHG000', 'unitless'),\n ('CPA', 'BIO00', 'GHG000', 'unitless'),\n ('EEU', 'BIO00', 'GHG000', 'unitless'),\n ('FSU', 'BIO00', 'GHG000', 'unitless'),\n ('LAM', 'BIO00', 'GHG000', 'unitless'),\n ('MEA', 'BIO00', 'GHG000', 'unitless'),\n ('NAM', 'BIO00', 'GHG000', 'unitless'),\n ('PAO', 'BIO00', 'GHG000', 'unitless'),\n ('PAS', 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\u001B[43msource\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mdata\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mBIO\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mGHG\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m)\u001B[49m\n", + "\u001B[1;31mTypeError\u001B[0m: unhashable type: 'list'" + ] + } + ], + "source": [ + "source.data.get([\"BIO\", \"GHG\"])" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T08:47:53.905643300Z", + "start_time": "2023-10-05T08:47:52.734166100Z" + } + }, + "id": "b230b30f89d3fc65" + }, + { + "cell_type": "code", + "execution_count": 28, + "outputs": [ + { + "data": { + "text/plain": "[False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n 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False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True]" + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"Region_BIO_GHG_Unit\"\n", + "bools = [True if ((y_val[1] == \"BIO00\") & (y_val[2] == \"GHG010\")) else False for y_val in source.data[\"Region_BIO_GHG_Unit\"]]\n", + "bools" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T08:55:33.326373900Z", + "start_time": "2023-10-05T08:55:32.140865300Z" + } + }, + "id": "89154b81107d7375" + }, + { + "cell_type": "code", + "execution_count": 22, + "outputs": [ + { + "data": { + "text/plain": "[False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True]" + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from bokeh.models import BooleanFilter\n", + "\n", + "bools = [True if ((y_val[0] == \"BIO00\") & (y_val[1] == \"GHG010\")) else False for y_val in zip(source.data['BIO'], source.data['GHG'])]\n", + "view = CDSView(filter=BooleanFilter(bools))\n", + "\n", + "#p2.circle(x=\"x\", y=\"y\", size=10, hover_color=\"red\", source=source, view=view)\n", + "bools" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T08:49:45.882385900Z", + "start_time": "2023-10-05T08:49:44.648191600Z" + } + }, + "id": "d02de070eea817a2" + }, + { + "cell_type": "code", + "execution_count": 66, + "outputs": [ + { + "data": { + "text/plain": "4" + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import math\n", + "regs = 15\n", + "rows = math.ceil(math.sqrt(regs))\n", + "if (rows * (rows-1)) >= (regs):\n", + " cols = rows - 1\n", + "else:\n", + " cols = rows\n", + "cols" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-04T21:27:13.662548400Z", + "start_time": "2023-10-04T21:27:12.428171400Z" + } + }, + "id": "daf81ed2700bf664" + }, + { + "cell_type": "code", + "execution_count": 10, + "outputs": [ + { + "ename": "ValueError", + "evalue": "invalid literal for int() with base 10: 'Unnamed: 23'", + "output_type": "error", + "traceback": [ + "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[1;31mValueError\u001B[0m Traceback (most recent call last)", + "Input \u001B[1;32mIn [10]\u001B[0m, in \u001B[0;36m\u001B[1;34m()\u001B[0m\n\u001B[0;32m 1\u001B[0m df \u001B[38;5;241m=\u001B[39m df\u001B[38;5;241m.\u001B[39mset_index([\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mModel\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mScenario\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mRegion\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mVariable\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mUnit\u001B[39m\u001B[38;5;124m\"\u001B[39m])\n\u001B[0;32m 2\u001B[0m df \u001B[38;5;241m=\u001B[39m df\u001B[38;5;241m.\u001B[39mdrop(df\u001B[38;5;241m.\u001B[39mcolumns[\u001B[38;5;241m-\u001B[39m\u001B[38;5;241m1\u001B[39m], axis\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m1\u001B[39m)\n\u001B[1;32m----> 3\u001B[0m df\u001B[38;5;241m.\u001B[39mcolumns \u001B[38;5;241m=\u001B[39m [\u001B[38;5;28mint\u001B[39m(i) \u001B[38;5;28;01mfor\u001B[39;00m i \u001B[38;5;129;01min\u001B[39;00m df\u001B[38;5;241m.\u001B[39mcolumns]\n", + "Input \u001B[1;32mIn [10]\u001B[0m, in \u001B[0;36m\u001B[1;34m(.0)\u001B[0m\n\u001B[0;32m 1\u001B[0m df \u001B[38;5;241m=\u001B[39m df\u001B[38;5;241m.\u001B[39mset_index([\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mModel\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mScenario\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mRegion\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mVariable\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mUnit\u001B[39m\u001B[38;5;124m\"\u001B[39m])\n\u001B[0;32m 2\u001B[0m df \u001B[38;5;241m=\u001B[39m df\u001B[38;5;241m.\u001B[39mdrop(df\u001B[38;5;241m.\u001B[39mcolumns[\u001B[38;5;241m-\u001B[39m\u001B[38;5;241m1\u001B[39m], axis\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m1\u001B[39m)\n\u001B[1;32m----> 3\u001B[0m df\u001B[38;5;241m.\u001B[39mcolumns \u001B[38;5;241m=\u001B[39m [\u001B[38;5;28;43mint\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mi\u001B[49m\u001B[43m)\u001B[49m \u001B[38;5;28;01mfor\u001B[39;00m i \u001B[38;5;129;01min\u001B[39;00m df\u001B[38;5;241m.\u001B[39mcolumns]\n", + "\u001B[1;31mValueError\u001B[0m: invalid literal for int() with base 10: 'Unnamed: 23'" + ] + } + ], + "source": [ + "df = df.set_index([\"Model\", \"Scenario\", \"Region\", \"Variable\", \"Unit\"])\n", + "df = df.drop(df.columns[-1], axis=1)\n", + "df.columns = [int(i) for i in df.columns]" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-04T12:09:31.501717400Z", + "start_time": "2023-10-04T12:09:31.410396100Z" + } + }, + "id": "ca445c3673ae5909" + }, + { + "cell_type": "code", + "execution_count": 44, + "outputs": [ + { + "data": { + "text/plain": " variable value\nVariable Region BIO GHG Unit \nBiodiversity|BII AFR BIO00 GHG000 unitless 1995 0.753832\n CHA BIO00 GHG000 unitless 1995 0.750001\n CPA BIO00 GHG000 unitless 1995 0.832715\n EEU BIO00 GHG000 unitless 1995 0.673826\n FSU BIO00 GHG000 unitless 1995 0.849367\n... ... ...\nZERO PAO BIO00 GHG010 NaN 2100 0.000000\n PAS BIO00 GHG010 NaN 2100 0.000000\n SAS BIO00 GHG010 NaN 2100 0.000000\n WEU BIO00 GHG010 NaN 2100 0.000000\n World BIO00 GHG010 NaN 2100 0.000000\n\n[593892 rows x 2 columns]", + "text/html": "
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variablevalue
VariableRegionBIOGHGUnit
Biodiversity|BIIAFRBIO00GHG000unitless19950.753832
CHABIO00GHG000unitless19950.750001
CPABIO00GHG000unitless19950.832715
EEUBIO00GHG000unitless19950.673826
FSUBIO00GHG000unitless19950.849367
.....................
ZEROPAOBIO00GHG010NaN21000.000000
PASBIO00GHG010NaN21000.000000
SASBIO00GHG010NaN21000.000000
WEUBIO00GHG010NaN21000.000000
WorldBIO00GHG010NaN21000.000000
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593892 rows × 2 columns

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" + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-04T15:19:36.302301400Z", + "start_time": "2023-10-04T15:19:36.244917800Z" + } + }, + "id": "511f547533222d48" + }, + { + "cell_type": "code", + "execution_count": 27, + "outputs": [], + "source": [ + "afolu_co2 = \"Emissions|CO2|Land|+|Land-use Change\"\n", + "for_cov = \"Resources|Land Cover|+|Forest\"\n", + "cro_cov = \"Resources|Land Cover|+|Cropland\"\n", + "#afolu_co2 = \"Emissions|CO2|Land\"\n", + "luc_co2 = \"Emissions|CO2|Land|Cumulative|Land-use Change|+|Gross LUC\"\n", + "luc_co2 = \"Emissions|CO2|Land|Land-use Change|+|Gross LUC\"\n", + "luc_co2_def = \"Emissions|CO2|Land|Land-use Change|+|Gross LUC\"\n", + "luc_co2_all = \"Emissions|CO2|Land|Land-use Change|Gross LUC\"" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-03T12:23:15.398752700Z", + "start_time": "2023-10-03T12:23:15.383133200Z" + } + }, + "id": "54e85d03d17f6bc3" + }, + { + "cell_type": "code", + "execution_count": 75, + "outputs": [ + { + "data": { + "text/plain": "{'Emissions|CO2|Land|Cumulative|Land-use Change|+|Regrowth',\n 'Emissions|CO2|Land|Cumulative|Land-use Change|Regrowth|CO2-price AR',\n 'Emissions|CO2|Land|Cumulative|Land-use Change|Regrowth|NPI_NDC AR',\n 'Emissions|CO2|Land|Cumulative|Land-use Change|Regrowth|Other Land',\n 'Emissions|CO2|Land|Cumulative|Land-use Change|Regrowth|Secondary Forest',\n 'Emissions|CO2|Land|Cumulative|Land-use Change|Regrowth|Timber Plantations',\n 'Emissions|CO2|Land|Land-use Change|+|Regrowth',\n 'Emissions|CO2|Land|Land-use Change|Regrowth|CO2-price AR',\n 'Emissions|CO2|Land|Land-use Change|Regrowth|NPI_NDC AR',\n 'Emissions|CO2|Land|Land-use Change|Regrowth|Other Land',\n 'Emissions|CO2|Land|Land-use Change|Regrowth|Secondary Forest',\n 'Emissions|CO2|Land|Land-use Change|Regrowth|Timber Plantations'}" + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "set([i for i in df.index.get_level_values(3) if \"Regr\" in i])" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-03T13:46:16.752142400Z", + "start_time": "2023-10-03T13:46:16.215256700Z" + } + }, + "id": "4a4e98b9a599d263" + }, + { + "cell_type": "code", + "execution_count": 63, + "outputs": [], + "source": [ + "from matplotlib.backends.backend_pdf import PdfPages" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-03T13:25:45.312912600Z", + "start_time": "2023-10-03T13:25:44.547475600Z" + } + }, + "id": "9a18e5541143276a" + }, + { + "cell_type": "code", + "execution_count": 69, + "outputs": [], + "source": [ + "import time" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-03T13:33:37.364362800Z", + "start_time": "2023-10-03T13:33:36.829228100Z" + } + }, + "id": "924d048bedc50e0a" + }, + { + "cell_type": "code", + "execution_count": 82, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "187.0278775691986\n", + "373.2970609664917\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\maczek\\AppData\\Local\\Temp\\ipykernel_28236\\1713529792.py:13: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n", + " fig, axs = plt.subplots(4, 3, figsize=(25,18))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "560.124425649643\n", + "749.7232000827789\n" + ] + }, + { + "data": { + "text/plain": "
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\n" 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IiBxszb5mU38f3Rs4o2H5pcAaa+0G4DOgszGm5yGsVeSopoBa5MhyGfCztfa38PmPN6zPWmtDGn4i/rDtGdbaQOqf8nYE/tguIiIif18+EGGM8fiLdSY1uV6HWGtDmjYaYwYCrYHPGxZ9CnQ1xvQ4FAWLiIgcpfbnmt3CWjusoaMY/P5mM9baLGAuepgscsgooBY5QhhjfIFzgSHGmBxjTA7wf0B3Y0z35u7HWjuX+vGrnz0khYqIiBzdFgJV/N7D6kBcBhhgVcP1fnHDcr0+LCIicvAc0DXbGDMASALGNrk37wdcsI+wW0QOkP6PJXLkOANwAF2BmibLJ7H/N6wvAunGmB7W2lUHozgREREBa22xMeZB4FVjTB3wM1ALjACGAhV/tb0xxof6B9JXA983aTobeNAYc9c+XkUWERGRZvgb1+zLgOnseR/uS/1kiScBUw9Z0SJHKfWgFjlyXAa8Z63dYa3N+e0HeIX6iZOa/UDJWpsLfAg8cGhKFREROXpZa58HbgPuB3KpH7fyRuDbZmx+BlAJfPiH6/27gDtw4qGoWURE5Gi0v9fsJg+SX256nbbWpgEfoWE+RA4JY611dQ0iIiIiIiIiIiIichRSD2oRERERERERERERcQkF1CIiIiIiIiIiIiLiEgqoRURERERERERERMQlFFCLiIiIiIiIiIiIiEt4uLoAgIiICJuYmOjqMkRE5B9s+fLledbaSFfX8b9O12wRETmUdL0+OHS9FhGRQ+lgX6+PiIA6MTGRZcuWuboMERH5BzPGbHd1Df8EumaLiMihpOv1waHrtYiIHEoH+3qtIT5ERERERERERERExCUUUIuIiIiIiIiIiIiISyigFhERERERERERERGXUEAtIiIiIiIiIiIiIi6hgFpEREREREREREREXEIBtYiIiIiIiIiIiIi4hAJqERH5x7NOp6tLEBEREREREZG98HB1ASIiIodS0a4cpj7/pKvLEBERkb/gcFSwecvjri5DRERE9iF1xdKDvk8F1CIi8o+VuXEDk599TD2oRUREjmAlJWtYvfpGqmsyXV2KiIiI/IWUX2Yz7fUXD/p+NcSHiIj8I6X8MpsvHr0Xn4AALnz8OVeXIyIiIn9grYNt2yawZOnZlBXuYtMPLV1dkoiIiPyJldOm8sMrzxHXodNB37cCahER+Uex1vLrpI/54ZXniG2fzAWPPUdoTJyryxIREZEmKit38svck0nf/hJF2wKYt7A13a9+0NVliYiIyB9Ya1nwxafMeu9N2vY5lrPGjjvox2jWEB/GmFuAfwMGeNta+6IxJgyYCCQC6cC51trChvXHAlcCDuBma+1PB71yERGRP6itqean115k08Jf6DJ0JCOuuh53D09XlyUiIiINrLWkbf6AbdufxDqcbFoWRU3nYTz4r/vw9fB1dXkuZ4xJB0qpv5eus9b2+at7bxERkUPJOp3M/uBtVk6bSuchIxh1zU24ubsf9OPsM6A2xnShPpw+BqgBphljvm9YNtNa+5Qx5h7gHuBuY0wn4HygMxALzDDGtLfWOg569SIiIg3KiwqZ/OxjZG/dzKALL6fvaWdjjHF1WSIiItKgsiKXBXP+BT4bKM/1ZUFOPBdd9STdY3q6urQjzVBrbV6T3+9hL/ferilNRESOFo66On56/UVS5s+h9+gzGHLxvzBuh2Ywjub0oE4GFllrKwCMMXOBM4HTgeMb1vkAmEP9RfJ04HNrbTWQZozZSn24vfCgVi4iItIgd0c634wfR2VJCafdNpakYwbs0a5npCIiIq61fsn77Nw9Hg/vGlJSQnDrcgGPn38Lnu5606kZ/uzeW0RE5JCora7iuxfHk7piKcedfynHnDGmsQNYSem6g3685gTU64DHjTHhQCVwMrAMiLLWZgNYa7ONMS0a1o8DFjXZPqNh2R6MMVcDVwMkJCQc8AmIiMjRLW3lMr6bMB4vH1/OHzeeqDbt9mgvL9/K2nU3uqg6ERGRo1vR7gzmz7gOn6gN1NZ5Mie/A1deOIGk0KQ91tu1axezZs1yUZVHFAv8bIyxwJvW2rf483tvERGRg66qvIxvn36EzE0pjLjqBrqPPKmxLS9/DuvW3XTQj7nPgNpam2KMGQ9MB8qA1UDdX2yyt/ep7V72+xbwFkCfPn3+q11ERGRfVvw4lTkfvE1kq9accfcDBIZF7NGelzebdetvxc3N20UVioiIHJ0cdbUs+eEN8qvfwDe6ik1Z/vh3uZ7Hu/0bd7ffx64sLCxk9uzZrFmzBm9vXa+BgdbarIYQeroxZmNzN1QnMBER+bvKiwr56okHyc/YySm33EWH/oMa2zIzP2fT5gcJ8O9IfX/mg6dZkyRaa98F3gUwxjxBfa/oXcaYmIYnuDHA7obVM4CWTTaPB7IOXskiInK0czoczP7gLVb99D1t+xzLyTfdjpfP7xMrWWvZsfMdtm4dT0BAMt27vcleXuYRERGRQyB9zUoWzbqH8E7bcPM0/FKazL9Oe42EoN9D09LSUubNm8fy5ctxc3Nj4MCBDBw4kHvvvdeFlbuetTar4d/dxphvqB8u88/uvf+4rTqBiYjIASvevYsvH7+fssICzrzrARJ79Abq769T014gPf1VwsMG06XLy0DgQT12swJqY0yLhgtkAnAW0B9oDVwGPNXw7+SG1acAnxpjnqd+ksQkYMlBrVpERI5a1RUVfDdhPOmrltPn1LMYdOFluDXpieVwVLNx033k5HxDixYn0yl5PO7ufi6s+PAwxrQEPgSiASfwlrV2QpP2O4BngMjfJl4yxowFrgQcwM3W2p8Oe+EiIvKPUVqQx5xPJlAXOJnIbuWkl3gT1P52Hky+AjdTP6lSZWUlCxYsYNGiRdTV1dGrVy+GDBlCUFCQi6t3PWOMP+BmrS1t+DwKeIT6e+y93XuLiIgcFHk70vnqiQepq6lhzP2PEds+GQCns4aUjWPJyfmW2JhzaZv0IC+tfv2gH79ZATXwVcMY1LXADdbaQmPMU8AkY8yVwA5gDIC1dr0xZhKwgfqhQG6wmp1KREQOguLdu/hm/DgKszMZefVNdBt+wh7t1dW7WbP2ekpKVtK69a20TryxcSKHo0AdcLu1doUxJhBYboyZbq3d0BBej6T+eg2AMaYTcD7QmfoHyjOMMe11zRYRkf3lqKtjxY9TWLPoVeIG7MTLy8Gquo5cOOIdYgJiAKipqWHJkiXMnz+fqqoqunTpwtChQwkPD3dx9UeUKOCbhr9dPIBPrbXTjDFL2cu9t4iIyMGQtXkj3zz1MO5eXpz38FNEJCQCUFdXypq111NYuIA2rf8P/xbncvWM61i+a/lBr6G5Q3wM2suyfGD4n6z/OPD43ytNRETkd1mbU5j87OM46mo5a+w4WnXtsUd7Scka1qy9jtraYrp2eZUWLU50TaEu0jB50m8TKJUaY1KoH9dkA/ACcBd79rg6HfjcWlsNpBljtlL/GvHCw1q4iIj8T9u5fg0z338Z31YrSBxexK5qT4Jb3sVtHa7EGIPD4WDFihXMnTuXsrIykpKSGD58ONHR0a4u/YhjrU0Fuu9l+Z/ee4uIiPwd6WtWMuXZx/EPCeWc+x8luEX99bmqKpvVq6+kvGIbnZKfIcO05IrvzqWiroInBz3JqZx6UOtobg9qERERl9n461ymvf4igWERnHH3k4THtdyjPWfXVFJS7sbLM5w+vb8gMDC5sa28tpzxS8Yf7pJdyhiTCPQEFhtjTgMyrbWr/9CbPA5Y1OT3DDRQt4iINFNZYQHzPv4P6Rt/JGFkFj6BNaSaJM4Y/D6R/tE4nU7Wrl3L7NmzKSwsJCEhgTFjxtCqVStXly4iIiLApoXz+eHlZwmPi+fs+x7FPyQUgNKyjaxefSV1dWV07/Yuk7M389KKR2gZ2JK3R71NUmjSQa9FAbWIiByxrLUs+upzFnzxCXEdO3Pa7ffiFxTcpN1JaurzpG9/neDgPnTr+ipeXhGN7enF6dwy+xa2l2x3RfkuYYwJAL4CbqV+2I/7qB/D8r9W3cuyvU6oZIy5GrgaICEhYW+riIjIUcLpcLBy2nf8+uVHhCVnkHRmLmXWHd+42/h3xxuw1rJp0yZmzZrFrl27iIqK4sILLyQpKelPh91yOi2L0/KZvGDHXttFRETk4FozcxrT336V2PbJnHn3g/j4BwBQUPAra9Zej4dHAB27vcu4lR8ye+dsRrUaxSMDH8Hf0/+Q1KOAWkREjkh1NTX8/OZLpMyfQ+chwxnx7xvx8PT8vb2ujPUbbicvbwaxMefSocM43Ny8GtvnZczjnnn34O7mzlsj36If/VxxGoeVMcaT+nD6E2vt18aYrtRPavxb7+l4YIUx5hjqe0w37YoeD2Ttbb/W2reAtwD69Omz1xBbRET++TI2rmfmu69TUrCZuJOzCAmvZLdbK0b1/YBw/5akp6czc+ZMdu7cSVhYGGeffTadO3fGzc3tv/ZlrWVdZgnfLt3J1NVZ7K6qxccF5yQiInI0sdayZPKXzP/sA1r36M2pt43F07v+Cpyd/TUpG8fi59eGwFb38a/ZD5Jdls3dfe/mouSLDun8TgqoRUTkiFNRUszkZx4ja3MKx51/KcecMWaPi2Fl5U5Wr7maiopttE96kPj4SxvbndbJ22ve5tVVr9IxrCMvDn2R2IBYV53KYWPqv4B3gRRr7fMA1tq1QIsm66QDfay1ecaYKcCnxpjnqZ8kMQlYctgLFxGRI155USG/fPo+6+fOIKRrBW1H7gQ3g1fMtZzf8Q5ycnL4+JuP2bp1K4GBgZxyyin07NkTd3f3/9rX1t1lTFmZyeRlGWwvrcID6IcHN4WFcsKABKKOrlG5REREDhtrLfM+eY9lU7+m48AhnHj9/+Hu4YG1lvT0V0lNe4HQ0AFs9zuJG2fcSrB3MO+d+B49WvQ45LUpoBYRkSNKfsYOvhk/jvLCQk79v3tof+xxe7QXFi5m7bobsNZBj+7vERY2sLGtvLac++ffz4wdMxjdZjQP9X8IXw/fw30KrjIQuARYa4xZ1bDsXmvtD3tb2Vq73hgzifpJFOuAG6y1jsNSqYiI/E9wOh2s/vkHfp34MQ5bRviZmbRsUUqpWzSDe7+PrQnjyy+/ZP369fj4+DBy5EiOOeYYPJu88QSQVVTJlNVZTFmWwYbcMgzQE3fO9w5gdO84Yo6NxXhWUzZ7jkvOU0RE5J/O6XAw/e1XWDd7Oj1OGM2wy6/BuLnhdNayadODZGVPIrLFaXxV5MfXa56kX0w/xg8aT7hv+GGpTwG1iIgcMdJXr2DqC0/h6e3NeQ8/RXS79nu0Z2R+yubN4/D1bUX3bm/i59e6sW17yXZumXULaSVp3NHnDi7tVN+r2lrLihUrDvepHHbW2vnsfVzppusk/uH3x4HHD2FZIiLyPypr80Zmvvs6u9O34dPNSXyfzQR6WHxbXEjv+P9j3uz5rFy5Eg8PDwYPHsyAAQPw8fl9kI6C8hq+X5vNlOUZLN1ZBEAybtzk5sPoDi1IPDYO95A6ymbPIvuenylfvAQcek4qIiJysNXV1PD9S8+wdelCjj37AgaMuRBjDHV1ZaxbdxP5BfMIi7mER7euY2PBJq7udjXXd78ed7f/fhPqUFFALSIiR4RVP//ArPfeICI+gTPufpCgiMaRKXA6a9my5XEyMj8iPHwIXTpPwMMjsLG96XjTb458k2NjjgWgurqab7/9lpSUlMN+PiIiIv+LKkqK+eXTD1g3+2c8Q3xxOyOL9i2KqXUPoVOHCWxYW8HL37yKtZa+ffsyePBgAgLqJ1Yqq67j5/U5TFmVxfwtedRZSyJuXIU3J0WH0OHYODwjaymfP5ucR8ZTuWIFWItXq1aE/+tfBI4aBd26uvgbEBER+eeoqaxg8rOPsWPdGoZefjW9TjoNgOrq3axefRVl5Rsh8hJuWPkzxhheHf4qg+MHH/Y6FVCLiIhLOZ0O5n74Lit+nEKbXn0ZffOdePn6NbbX1haydt1NFBYuJCHhKtq1vQtj6p/kWmt5Z+07vLzyZTqEdeDFoS8SFxAHQG5uLhMnTiQ/P5+RI0cybtw4l5yfiIjI/wKn08HamT8z/7MPqK6qpK6fN5HJq4jzduITMorakjP56P1fqKmpoVu3bhx//PGEhoZSVetg2rocpqzOZOaG3VQ7nEQZN861npzg50e3PjF4xzioWDqH3GfHU7VuHQDe7dsTccMNBI4aiXdS0iGdeElERORoVFFSzNdPPszu9G2cdOPtdBo0FICy8i2sXn0lNTWFbPM9kZdWfEWn8E48f/zzjffTh5sCahERcZmaygq+f+kZUlcspffo0xl88b9wa/IaUVn5FtasuZqqqhw6JT9NTMzZjW0VtRXc/+v9TN8+nZNan8S4AeMax5tev349kydPxsPDg0svvZTWrVv/17FFRESkXs7Wzcx493V2pW7Bt3U0uT02MySyGDc3Xzw9r+HXGZVUVCygY8eODBs2jLDwCBam5jNlxmqmrcuhtLqOUDc3RjvdGeHmS99OLfCOcVC1fi6Fr4+nessWAHy6diXy9tsIGjkSr8RE1560iIjIP1hJXi5fPf4AJbm7Of2O+2jbux9QP6fTmrXXgvHgu+oO/Lx9Nue2P5e7jrkLb3dvl9WrgFpERFyiJG83345/hLyMHYy46nq6jzx5j/a8vFmsW/9/uLv70LvXpwQH92xs21Gyg1tm30Jqceoe4007HA5mzpzJggULiI+PZ8yYMQQHBx/uUxMREfmfUFlWyvzPPmDNzJ/wCQqiYEQAUbFzGeHrxLp1JmXtMeTn59O6dWuGDRvGbmcAry3KYurq1eSX1+DvZhjs9GAEfhwbE4xfrJOa1PkUf/Qjtdt3gDH49u5F1L1jCRwxAs/YWFefsoiIyD9eQVYGXz72ANUV5Zx97yPEd+oCQM6uqWzYcBfGM4IJOZBZtZ0njnuCU9ue6uKKFVCLiIgLZG/dxLdPP4qjtpazxo4jsdvv4bO1lu073mLbtmcIDOxEt65v4uMT09g+P3M+d827Czfjxhsj3qB/bH8AysrK+PLLL0lPT6dv376ccMIJeHjoMiciIvJH1ulk3ZwZzPv0farLy/Dv14ElsTM5PawUPzd3crKHsGVLHLGxLRg4eiAr89254NMtZBRW4mUMA9w8GIEvA/18CYyF2szFlH09hZLsbHB3x79fP8Kv+BeBw4fhERnp6tMVERE5auxK3cpXTzyIcXPj3IeeJKp1W6y17NjxFlu3PU2VZwKPpRUQEZDAp6OfJyk0ydUlAwqoRUTkMNu0cD7TXn0e/9BQzn3wCcLjExrbHI4qNm68l5xdk2nRYjSdksfj7l4/bIe1lnfXvctLK16ifWh7Xhz6IvGB8QDs3LmTSZMmUVlZyRlnnEGPHj32OOby7QWH7fxERESOZLvStjHz3dfI3rKJ8HZt2dDFEuU/lYsC6qiqimTlumOp8eqITe7Gl1m1bP4qHXcDfT29uQwfBhkvQqPAkbuKsmlfkpu3G+Plhf/AgQTedBOBw4biHhLi6tMUERE56uxcv4Zvn3kUn4BAzrnvUUJj4rDWwabNj5CZ+TGZxPBCai5DW43ikQGPEOAV4OqSGymgFhGRw8Jay+JvJvHrxI+I7dCJ0++4D7+g34ffqK7exZo111JSuoY2bW4jsdX1jRMm7THedOJJPDzgYfw8/bDWsmzZMn788UeCgoK48soriYn5vbd1ncPJ89M389qcbYf9fEVERI4kVeVl/DrxY1b//AM+gYH4ndabb9wnc3FoFaEedWxI682ynBPI9mzJ5pxayCmku683/2d8GGY9iPAxOGs2UD53IgX5ORhfXwIGDyZw1EgChhyPe4C/q09RRETkqLV16SK+mzCekKgYzr73EQLDI3A4Klm3/lby8mawtCqUSXll3Nb3bi5OvviIm5xYAbWIiBxydbW1TH/rZTbMm0Xycccz6pqb8fDyamwvKVnDmjXXUucopVvX14mMHNXYtrNkJzfPvpnU4lRu7307l3W+DGMMNTU1fP/996xevZqkpCTOOussfH19G7fbXVrFzZ+tZFFqAef1acnTh/WMRUREjgzWWjbMm8W8T96jsqSEVkP683XUClo6vuWKQCdLM/szf8dQdlSFY4H2/m5c5+nL8Fp3Yhxg67ZRsegrinJTcQsMJGDo8QSNGoX/ccfh5uPj6tMTERE56q2fO5Of3phAVJt2nHXPw/gGBlFTk8fqNVdTXLKGyUW+bKgL5t0Tn6Vni5773qELKKAWEZFDqqKkmCnPPUHmxvUMOPcijj3r/D2e1ubkTCFl4914ebWgT48vCQjo0Nj2a+av3DnvTgyG10e8zoDYAQAUFBQwceJEdu3axfHHH8/gwYNxc3Nr3G7htnxu+mwlZdW1PDumO2f3jFNALSIiR53c7WnM/M/rZG7cQHS79tSemcxbOd/Rq6gdKYXn80VeJxzWg3g/Ty4P8GZYmaV1hRu2ZidVq7+nJHM17iHBBI4YTuCoe/Dv1w/T5AGziIiIuNby779lzofvkNClO6ffeT9ePr5UVKSxctUVlFdl8V6uJ/4hxzJp8HjCfcMPyjErKysPyn6aUkAtIiKHTH7mTr4d/wilBXmMvvlOOg4c0thmrZNtqc+xffsbhIQcQ9cur+DlFd7QZvnPuv/w0sqXaBfSjheHvkjLwJYAbN68ma+//hqACy+8kPbt2zfu0+m0vD53G8/9vInECH8+vuoY2jjc2DVhxWE8axEREdeqrqhgwRefsHLaVLz9A+hw/lm8mLcaxwofsipuZ5PDm2DPGs4KCWZUEXSscIO6fKo3zqB0+yI8QgMIHDmSwFG34te7F0aTDouIiBxRrLX8OvFjFn8zkaR+Azj5pjvx8PSkqHg5K1dfRXltBW/s9mBUh2u5occNuLu5/+1jVldXs3jxYhYsWHAQzmBP+ktDREQOie1rVzH1hSdx9/Dk3AefJLZ9x8a2urpS1m+4nby8mcTFXkD79g/i5lbfI6uitoIHFzzIT+k/cWLiiYwbMA4/Tz+cTidz585l7ty5REdHc+655xIWFta4z8LyGm6btIrZm3I5pVsMT57ZFZbksPvn7bj5ex728xcRETncrLVs/HUucz96l7LiIrz6n8L37j5sXhpAnXMUfh7l9InYxOiyJAaWt8Atr5LatPlUbP8V92B3AkeNImjUv/Hp1g3T5M2kw6GuxsG6eZmH9ZgiIiL/i5xOB7P+8warp/9I12GjGPHvG3Bzc2f37p9Yu/4W8mudfFQUyl2DnmFw/OC/fbza2lqWLVvG/PnzKS8v36OT2MHSrIDaGPN/wFWABdYCVwB+wEQgEUgHzrXWFjasPxa4EnAAN1trfzrYhYuIyJFrzcxpzHz3dcJi4znjrgcJbhHV2FZZuYPVa66moiKV9u0fJj7u9wkadpbu5JbZt7CtaBv/1/v/uKLzFRhjqKio4JtvvmHLli10796d0aNH49XkFeOVOwq58dOV5JZW8+jpnTm/YzRFH6VQnVqMb5dwQs9KgvsO+9cgIiJy2OTt3M7Md99gxbZsMuL6syEmgcJsg4epo3vEOvrHLqV/SWtapp6KI2sN1Tsm4R5QSeCoEQQ98greyckumTDJ4XCycUE2S79Pp7yo+rAfX0RE5H+Jo66WH195nk0Lf6Hv6ecw6IL6OZrSd7zL1q1PsL3ajbl1XXj9pJeJD4z/e8dyOFi5ciXz5s2jpKSE1q1bM2zYMFq2bMlFF110kM6o3j4DamNMHHAz0MlaW2mMmQScD3QCZlprnzLG3APcA9xtjOnU0N4ZiAVmGGPaW2sdB7VyERE54jidDuZ98j7Lv/uGxB69OeWWu/H282tsLyhcyNq1NwKWHt3fIyxsYGPbgswF3DnvTgBeH/46A+Lqx5vOzs5m4sSJlJSUMHr0aPr06dN4A22t5b1f03nyxxSignz48rr+tMuvZfeEleC0hJ6ThF/vqCNuhmIREZGDpaaygq8//YJvlm1ns39XCuOOw804ia0tYFjsSoZ1nIZfdTCxC87AfdV26vzfIWjEYAIffhLvtm1dVrd1WrYs38WSKWkU51YS3SaIkVd04sY3XVbSEcMY4w4sAzKttacYYx4G/g3kNqxyr7X2B1fVJyIirlFbVcWU558gffUKBl90BX1POxtrnaxOeYC87M9YU+FORdj5vN3vPrzdvQ/4OE6nk7Vr1zJnzhwKCwuJj4/njDPOoE2bNgfxbPbU3CE+PABfY0wt9T2ns4CxwPEN7R8Ac4C7gdOBz6211UCaMWYrcAyw8OCVLSIiR5qaqkp+ePlZti1bTI8TTmHoZf/Gzf33ca4yMj5h85ZH8PVNpHu3t/DzawU0hMzr32PCigm0DWnLhOMn0DKofrzpVatW8d133+Hr68sVV1xBy5YtG/dXWlXL3V+t4Ye1OYxIbsEzp3XB/ryDgpW78UoIJOy8DniE+wLgKCo6fF+EiIjIYZK2PZNLXplBhnsEJjicNoF1dKlMp51/On06/YJPYC6B69sSsaI1wce1IPD6i/Bqci11BWst6WvzWTw5lfzMMsLjAhh9fTdadQ3XA+Xf3QKkAEFNlr1grX3WRfWIiIiLVZaV8s34ceRs2cyoa26m67BROBzV/LryX9SWLOLXMm96dHqC09qdccDHsNaSkpLC7Nmzyc3NJTo6mgsvvJCkpKRDfo3eZ0Btrc00xjwL7AAqgZ+ttT8bY6KstdkN62QbY1o0bBIHLGqyi4yGZSIi8g9Vmp/HN08/Qt72dIZdcQ09Tzy1sc3prGXzlkfIzPyU8PChdOn8Ah4egUD9eNMPLXiIaenTGNVqFI8OfBQ/Tz/q6ur46aefWLp0KYmJiZxzzjkEBAQ07nNDVgnXf7KcnYWVjD2pI5e2iqDo7XU4CqsIHJ5A0LAEjHv9BbT4++/JGffI4f1CREREDrG8giIufHUOuW4hnBdQQWD1dupqS2kbn05sm0W41bnTKv8CWp57E543Re17h4dB5qZCFk3eRk5qCcGRvoy8shNJvaMwbgqmf2OMiQdGA48Dt7m4HBEROQKUFeTz1RMPUpidyan/dw9J/QZQU1PAjMVn4V27k7mVLbjouPfpENbhgPZvrWXr1q3MmjWL7OxsIiIiGDNmDMnJybgdpjkpmjPERyj1vaJbA0XAF8aYi/9qk70ss3vZ79XA1QAJCQnNqVVERI5Au1K38s3Tj1BbVcmZdz9I6559GttqagpYu+5GiooW0yrhatq2vYP6t1brx5u+dfatbCncwq29buVfXf6FMYbi4mK++OILMjIyGDBgAMOHD8e9oSe2tZZJy3by4OT1BPt68umVx9BxWxn5b67BPdSHyGu7492qvrORo7SUnEcfpWTKVHy6d4OlSw7/lyMiInIIVFRVc/6z35NjgjjHIxvfuiwCPWpJTpqHe3QWoYH96dztBby9I11dKgC70ktYPHkbO1MK8Q/x5viLOtBxQAzu7od3Isb/ES8CdwGBf1h+ozHmUuqH/rj9t/mfRETkn60oJ5svH7+fiuJizrznYVp17UFuSQoLlp2Hty1nuVsf7hr5LgFeAfve2V6kp6cza9YsduzYQUhICGeccQZdu3ZtvAf/I+t0kvv883/nlPaqOUN8jADSrLW5AMaYr4EBwC5jTExD7+kYYHfD+hlA0/fG4qkfEmQP1tq3gLcA+vTp818BtoiIHPm2LF7AD688h19wMOc88gwRCYmNbWVlm1i95hpqanbRqdNzxESf0di2IGsBd827C6d18vqI1xkYVz8WdVpaGl9++SW1tbWMGTOGzp07N25TWePg/m/X8dWKDAa2C+f5kcmY79Io3VmKX68WhJzWFjef+staxdKlZN59N3W7dhNx441EXHsNTJp0WL4TERGRQ8nhcHLJ09+w1RnCaPccAjyyiA3dQLvuW4Bq2rV7iPi4S46I4TIKsspZPDWV1JW5+Ph7MvCcdnQZHIeH195veo92xphTgN3W2uXGmOObNL0OPEp9x69HgeeAf+1le3UCExH5B8ndnsZXTzyIw+FgzIOPE9OuA2szppK28XbccFAUfgV39rjvgK75GRkZzJo1i9TUVAIDAxk9ejQ9e/bEw+PPo2JnRQVZd99N5qLNf+e09qo5AfUO4FhjjB/1Q3wMp/6pbTlwGfBUw7+TG9afAnxqjHme+kkSkwB1WxMR+Qex1rJ0ylf88un7xCR14PQ77sc/JLSxPTd3Ous33I67uz+9en1OcFD3xu0+WP8BL6x4gTbBbZgwdAIJQQlYa1mwYAEzZswgPDycyy+/nMjI33t9bcst4/qPV7B5dyk3D2vHVSFBlL67DtzdCLuwI37d6te1NTXkvvIq+W+/jWfLliR+8jG+PXoc1u9GRETkULHWct2EySyvCOQEU0Ck13bad15Ai/A0/PyS6dzpeQIC2ru6TEryKlnyXRqbF+fg4e1O31Na02N4S7x8mzsF0lFrIHCaMeZkwAcIMsZ8bK1tfIPZGPM28N3eNlYnMBGRf47MjRv4Zvw4PH18OP+BJwiPb8nUtU/gsetdaq0HCR1epG/Cqfve0R/k5OQwe/ZsNm3ahJ+fH6NGjaJv3754enr+5Xa1u3aTcd117MjzIaXvnbDs5wM9tb1qzhjUi40xXwIrgDpgJfUXvQBgkjHmSupD7DEN6683xkwCNjSsf4O11nFQqxYREZdx1NUy/e1XWT9nBh0GDOaE627B06t+hmBrLdu3v8621OcJDOxCt25v4OMdDUBlXSUPLXiIH9N+ZGSrkTw28DH8PP2orq5m8uTJbNiwgeTkZM444wy8vX+fcXjq6izu+WoN3p7uvHdBL7quLqRk1la82wQTel4HPILr161OTSXrjjup2rCB4HPOJnrsWNz8/Q//FyQiInKIPPjedH7e7cXxlBHjvZWOneYTGb6dhJZX0rbt7bi5ee97J4dQeXE1y39IZ/38LIybofuIBHqdkIBvgJdL6/pfYa0dC4wFaOhBfYe19uLf3lxuWO1MYJ1rKhQRkcMhbeUypjz/JIHh4Zxz32N4hwXzxrwLaVu7mEICGXTMRGKC92+86by8PObMmcO6devw9vZm6NChHHvssXvce/+Zqg0b2HHd9aQG9GVbp5OIS/jjKFR/X7MeYVtrHwIe+sPiaup7U+9t/cepn9RBRET+QSpLS5jy/BNkbFjHsWdfwIAxFza+TuRwVJGy8R527ZpKVNRpJHd8End3HwAySjO4dfatbC7czC29buHKLldijCE3N5eJEyeSn5/PyJEjGTBgQOP+quscPPZdCh8t2k7vVqE8178NXt9tp7K8luCTWhMwKA7jZrDWUvT55+wa/zRuPj7EvfwSQSNHuuw7EhERORRe/XYhH22u5VhbSSufDbRLWkRkxHZat76FNq1vdmltVeW1rPx5B2tm7cTpsCQPjKHPya0JCHVtYP4P8rQxpgf1Q3ykA9e4tBoRETlkUn6dy7RXnyeiZSJn3zuOPFPMpOln0tMrh1LPRE4/9hu8vYKavb+ioiLmzp3LqlWr8PDw4LjjjmPAgAH4+fk1a/vSWbPYeec9bGx/IdnB3ejcI4IOZTUHenp/Su9YiYhIsxRkZfLt0+Moyd3NyTfeTvKgoY1tVdU5rFlzDaWl62nb5k5atbqmMWhemLWQO+fdidM6eXX4qwyKHwTAhg0b+Pbbb/Hw8ODSSy+ldevWjfvbWVDBDZ+uYE1GMVcNbM011pvqz7fg1sKXiMs64xVXPwFEXV4eWffdR/ncefgfdxwxTzyOZ4sWh/FbEREROfS+mLuOZxfm0Y06OnpvoHXrVcTEbKVl/OW0TrzJZXXVVNWxZlYGK6fvoKaqjvZ9o+h7SmtCWjTvplf+nLV2DjCn4fMlLi1GREQOi1U/fc/M994gvmNnzrjrARbkLmDlulvp6VuFCTqe03q9iZtb86Lc0tJSfvnlF5YvXw7AMcccw6BBgwgIaN5kitZaCt7/gIwXXmN931so9Ixh0OAYwrcUYQ/BNBcKqEVEZJ92rl/DlOeewLi5MeaBJ4jr2Kmxrbh4FWvWXovDUUG3bm8SGVH/co21lg83fMjzy5/fY7xph8PBzJkzWbBgAXFxcZx77rkEBwc37m/Ghl3cNmkVFnjt1M70XlpAdU4+/v1jCD6pNW4NEyuVzppN9v334ywvJ+q++wi9+KIjYkIoERGRg2nu2u3c80Mqba2lt88G4lquJT5hHTHRZ5OUdGATI/1djlon637JZPmP6VSW1pLYLYJ+p7UhIr55N70iIiLyO2sti77+nAWTPqFN72M46ebbeXPdK3jtfofuvk4i4v5Nt/Z3N+uaX1FRwa+//srixYtxOBz07NmTIUOG7HHPvc96amvJefQxMr+bx9oBD1DtGcBJg2LxWpWLe4QvEZd1+u9xNv4mBdQiIvKX1s7+mRlvv0pIdCxn3v0QIVHRjW3ZOd+yceNYvL2i6dnjw8aJmSrrKnl4wcP8kPYDI1uN5NGBj+Lv6U9ZWRlffvkl6enp9OnThxNPPLFxluA6h5Nnft7Em3NT6RwbxDMd4wj6MQOHjwfhl3fGt2MYUD9z8K7xT1M0cSLeHTsS98zTeCcl/eU55KQWH6Jv58hhjGkJfAhEA07gLWvtBGPMM8CpQA2wDbjCWlvUsM1Y4ErAAdxsrf3JFbWLiMjerdmRx9WfrCLaGoZ4byIyJoXWbVcQGXkCHTs+gTFuh7Uep8PJxkU5LP0+jbKCauI6hHDs6W2JbtP8m14RERH5nXU6mfPhO6z4cQqdBg2l92UXcefcq+nHYqJ8DO07jqdl7Dn73E9VVRWLFi1i4cKFVFdX07VrV44//njCw8P3qx5HSQmZt97KjpRC1h97L14BPpzWLQLnit14tw8l/MKOuPkc/DhZAbWIiOyVdTr55bMPWDrlK1p168kpt96Nj399zyhrHWzd9gw7drxNSEg/unV9FU/PUAAyyzK5dfatbCrYxM09b+aqrldhjCEjI4NJkyZRUVHBGWecQY8ePRqPtaukips+XcmS9AIu6BnHDaXuMCsTn45hhJ6ThHvD5EqVa9eSdced1OzYQfhVVxJx8824ef35xEuVpTUs+GorGxflHLov6shRB9xurV1hjAkElhtjpgPTgbHW2jpjzHjqJ1+62xjTCTgf6AzEAjOMMe01sbGIyJFhe14ZF78+H3+nG6O90wiM3EhSh8WEhQ6kS+cXmv2K78FgnZZtK3NZPCWVol0VtGgVyLBLkonvGKq3l0RERA6Qo66On9+YwIZfZtPr5NMJGtWTW34+h3ODcgj08KZX97cJCxvwl/uoqalh6dKlzJ8/n8rKSpKTkxk6dCgtDmDoy5qdO9l5zbWk1iawpceNREYHMCjKl7p1eQQMjCX45DYY90Nz3VdALSIi/6W2qoofXnmOrUsX0n3kSQy9/Brcf+vpXFfKuvX/R37+bOLiLqJ90gO4uXkCsDh7MXfMvQOH08Erw19hcPxgrLUsXbqUH3/8kaCgIK688kpiYmIajzV/Sx63fL6SihoHTw9sx3GrCrA1ToLPaIt/vxiMMViHg/y33yb3lVfxiIgg4b338D+235/Wb52WlAXZLPh6K7VVDnqd0ArePLTfmatZa7OB7IbPpcaYFCDOWvtzk9UWAb89fj8d+NxaWw2kGWO2AscACw9j2SIishd5ZdWcO2EmDqc7F3ll4R22nk5dfiUoqDtdu76Om9vhmXzQWsuO9QUsmryNvJ1lhMb4c9K1XWndPULBtIiIyN9QW1PNdy+OJ3X5EgacezGbOlTx47zLuTyiGl+vcPr0/JCAgA5/un1dXR3Lly/nl19+oaysjHbt2jFs2DBiY2MPqJ6KFSvYccPNbI45kZ0JA2jfMZQutQ7qtpcQcmY7AvrF7Hsnf4MCahER2UP2lk388MqzFO/axdDL/k3Pk05rvAmtqEhn9ZprqKxMp0P7R4iPvwiov4H9aMNHPLf8OVoHtWbCsAm0CmpFbW0t3333HatXr6Zdu3acddZZjbMFO52Wl2dt5cWZm2kXEcCbbcKJ/nU37rH+hJ3fEc+GCZZqMjLIuutuKlesIOjkk4l+6EHc/2L8rLyMMuZ+upGc1BJik0IYfEF7wmOPrjExjTGJQE9g8R+a/gVMbPgcR31g/ZuMhmV729/VwNUACQkJB7NUERH5g7LqOs59cTqFNW7826MAt9A1dO0+nwD/tvTo/g4eHv6HpY6srUUs+nYb2VuLCYrwYcTlySQdE42bm4JpERGRv6O6opxvn36UjI3rGXjZFUz0nU/pxmlcFVmLv18SvXq+h4939F63dTgcrF69mrlz51JcXEyrVq0YM2YMrVq1OuB6iqdOZecDj7K+29Xk+bXhmH5RxO0swWkh4sou+LQNOeB9N5cCahERAepfL1r09UQWfzORgNBwzrn/MRK6dGtsLyj4lbXrbgIMPXq8T1hof6B+vOlxC8fxfer3jEgYwWPHPYa/pz+FhYVMnDiRnJwchgwZwpAhQ3Bzqx8rM7+smlsnruKXLXmc1qEF/5cHnusKCRwST9DIVhgPN6y1FH87mV2PPQbGEPvM0wSfeuqf1l9TVcfS79NZPXMn3n4eDL8smQ7HRh91PbyMMQHAV8Ct1tqSJsvvo34YkE9+W7SXze3e9mmtfQt4C6BPnz57XUdERP6+6joHl74yi/RSuNa9Ahu8gu495uLrG0WPHh/g6RlyyGvI3VHKosmp7Fifj1+QF4PPb0+n42Jx9zi8412LiIj8E1UUF/HVEw+RtzOdnldcyKPl79K5Jo0LwmoIDR1It66v4uER+F/bOZ1O1q9fz5w5c8jPzyc2NpZTTz2Vtm3bHvA9r7WWvJdfYed/JrLumLsp9whl5HEx+K3Pxy3Mh/DLOuMZ4ft3T7lZFFCLiAj5mTv58ZXn2ZW6hU6DhjLsX9fi7VffQ8taS0bmR2zZ8hh+fm3p3u1NfH3re9FmlWVx6+xb2ViwkZt63sRVXa/CzbixZcsWvvrqKwAuvPBC2rdv33is5dsLuOGTlRRU1PBgpzhGppTiEeRF6FVdG5/MOoqKyH54HKXTpuHXpw+x45/CM26vnXsBSF2Vyy8TN1NWWE2ngTH0P7MdPgGeh+jbOnIZYzypD6c/sdZ+3WT5ZcApwHBr7W8BcwbQssnm8UDW4apVRET25HBarv/PfFbk1nKNWy3OwCX06jkHb+8gevb4CG/vyEN6/MKccpZMTWPr8t14+3nQ/8y2dB0aj6eX+yE9roiIyNGiePcuvnriAUrz84m6aCRjc5/jvLAauvvUEBN9Nh07Pt44fOZvrLVs2rSJ2bNns2vXLlq0aMH5559Phw4d/lZnLGd1Ndlj72Xn/BTW9r8f4+PLqb0iYU0e3kkhhF+YjJvv4YuNFVCLiBzFrLWs+uk75n38Hh7e3pxy6z106H9cY7vTWcOmzQ+TlTWRiIgRdO70HB4e9cNlLMlewh1z76DOWdc43rTT6WTO3DnMmTOHqKgozjvvPMLCwhqP9e78NJ76cSMxQT680yKCNhtK8e0WQegZ7XDzq78Qly9YQNY9Y6krKCDyttsIv/JfGPe93xyX5Ffyy8QtpK/JIzzOn1FXdiamXcih/dKOUKb+r5N3gRRr7fNNlp8I3A0MsdZWNNlkCvCpMeZ56idJTAKWHMaSRUSkgbWW+yYtY2ZqGZdiMQEL6d5rNt7eHvTs+QG+vvGH7NilBVUs/T6NjQtzcPd0o8/JifQY0RJvv6PvQa+IiMihkp+xgy8ff4DaqipKTk/ko/w3uDXWhxi3clon3kzr1jfvEThba0lNTWXWrFlkZmYSFhbG2WefTefOnRvfTD5QdXl5ZNxwI2nZXmzsfTsh4b4c39Ifx/p8/PvHEHJK20M2GeKfUUAtInKUKivIZ9rrL7J9zUoSu/fihGtvISAsvLG9piaftWtvoKh4KYmtrqNNm9swpn7ojY9TPua5Zc/RKqgVE4ZOIDE4kcrKSr7++mu2bNlCt27dOOWUU/Dy8gKguLKWu75czU/rdzEiPoQ7cyGgspaQc9vj17MFxhic1dXkvvAiBe+/j1ebNiS+/hq+nTvvtXaHw8nqGTtZ+n0aAAPOake34fG4ux/Vrx8PBC4B1hpjVjUsuxd4CfAGpjf8wbPIWnuttXa9MWYSsIH6oT9usNY6Dn/ZIiLy4k8pfL5qN2dh8PebT5ees/D1rqNnj48J8E86JMesKKlh+bR01s3LBKDb8fH0OrEVfkFeh+R4IiIiR6v1c2cy893Xcff2YulQB9uqpvFwQgC+zkKSOz5FbOyYPdbfsWMHM2fOZPv27QQFBXHaaafRvXt33P+k49b+qN6yhR3XXMtmv76kdxpFYusgermDY3sJIae3JaD/gU2y+HcpoBYROQptWjifGW+/Ql1tLcOvvJ7uI0/a42ltScka1q67kZqaPDp3eoHo6NMAqKqrYtzCcXyX+h3DWg7jiUFP4O/pT05ODhMnTqS4uJjRo0fTp0+fxv2tyyzm+k9WkFVUyf/FhnNWRg3erYIJO68DHmE+9fvdtJmsO++kevNmQi+8gBZ33omb797HusraUsTczzZRkFVO6+4RDDqvPYEN+zmaWWvns/dxpX/4i20eBx4/ZEWJiMg+fbwwjQlz0hiBO7HeC0jqMRtf33K6d3+PoKBu+97BfqqurGPV9B2smrkTR42DjgNi6Du6ta6lIiIiB1lNZQUz3n2dlF9mE9Amnknt1hHoU8O4aF/cbSVdu79DePjgxvWzsrKYNWsWW7duxd/fn5NOOonevXvj4XFw4tuyX+az47Y72dDuAnYFd6FnjwgScytwOiwRV3TBJyn0oBznQCigFhE5ilSVlzHrP2+QMn8O0W2TOOnG2wmL/f21YaezhrT0V9i+/Q28vCLp3evzxpvj7LJsbpl9CxsLNnJDjxu4utvVuBk3Vq9ezdSpU/H19eWKK66gZcv6YY2ttXy6ZAfjpm4gzNuDV/2C6ZxTS9DIRAKPb4lxN1ink4IPPyT3uedxCwqi5ZtvEDBkyF5rryyrYeHX20hZkE1AmDcnX9eV1t0P7XicIiIih9KPa7N54Nv19MOdLp5LSOgxk4CAfLp2fYPQ0GMP6rFqaxysnZ3Bip+2U11RR7veLTjm1NaERvsf1OOIiIgI7ErdyncTxlO8axcexyXxasAMhrWI5/SATDzc/OnR/UMCAzsBsHv3bmbPnk1KSgq+vr6MGDGCY445pvGN5IOh4NNP2fH0y6zrdRPFXtEM7R9F0OZC3EJ9CL+sE56RfgftWAdCAbWIyFFix7rV/PjaC5QXFtD/nAvpd+a5uDd5EltamsKGlDspK0shJvoskpIewNMzCIClOUu5fc7t1DpreXnYywxpOYS6ujp+/OlHli5dSmJiIueccw4BAfXjU5dX13HfN2v5dlUWA8ICuLcAwsO9CLu0A94J9fus3bWL7LFjKV+wkIChQ4l57FE8wsP/q27rtGxclM2Cr7ZRU1lHz1EJ9B3dGk/v5r/eVOdw/p2vTkRE5KBblJrPzZ8sIxkPhrivIaL7TIKDd9G50/NERgw/aMdx1DnZMD+LZT+kU1FSQ6su4fQ7rQ2RCYEH7RgiIiJSz1rLih+mMO+T9/ANCiJrdAt+cs7g6sSudHYux9enNT26/wcfn1gKCgqYM2cOa9euxdPTkyFDhtC/f398fA7eW03W4WDX+PHs+GoW6469j1rPAE7pE4l7SgHebYMJvyi5cT4oV1JALSLyD1dXU8P8zz9g+feTCY2J5YJHnyGmXYfGdqezlu3b3yAt/RU8PUPo1vVNIiNHAA29oDd+yjNLnyEhKIEJQyfQOrg1JSUlTJo0iYyMDAYMGMDw4cMbx8PasquU6z5ZwbbdZVwdFMjFBRDQJ5qQU9vi1hAql0z7ieyHHsLW1BA9bhwh547Z6wzE+ZllzP1sE9lbi4lpG8yQCzsQHhfQ7HO31vLT+l089WPK3/kKRUREDqoNWSX8660FxFp3zjJb8es6nfDwDDq0f6RxWK2/y+m0bFmSw5Lv0ijJqyKmXTAnXN2F2KN0MmEREZFDraKkmJ9ef5HUFUuJ7taFL9uuI7M2gyfbd8S3cjEhIcfStetrVFTA9OlTWblyJW5ubvTv35+BAwfi739w32pylJWTdfvtpK/LZ/0xd+Mf7MOJrYNwphTg3y+akNPaYo6QeZwUUIuI/IPtStvGj688R37GDrqPGs2Qi67As8nT2LKyzWxIuZPS0nVEtTiFDh0extOzftypqroqHl30KFO2TWFoy6E8cdwTBHgFkJ6ezhdffEFtbS1jxoyhc5OJDL9dmcnYr9fi62Z40d2fvnWehF6chG+XCAAcZWXsevwJir/5Bp+uXYl9ejzerVv/V9211Q6W/ZDGquk78fR1Z+glHUnuH4Nxa/5Mwmszinn0+w0sSSsgqUXzQ20REZFDaUd+BRe9Mgd/686VNhvT5UeiotJo0/o24uMv+tv7t9aStjqPxVNSKcgqJ6JlAKfc1J2ETmF7fRgsIiIif9/O9Wv44eVnqSwtIfb0Ibzo/JJ4D3fGtwzBWbmSxFbX06LFlcycuZClS5diraV3794MHjyYwMCD/1ZTbVYWO667nq2VLdna9Vri4gLp5+eOI72EkFPb4D8g9oj6u0ABtYjIP5DT6WDplK9ZMOkTfAMDOeueh2nds09ju7UOdux4h22pL+LhEUCXLq8Q1eKkxvaM0gxun3s7G/I3cH2P67mm2zUYDAsWLGD69OmEhYVx+eWXExlZPwZ0Va2DcVM38NmSHfT08+ahCk/i2oURNqY97sHeAFSsWEHWXXdTm5VFxPXXEXHddRjP/36VKG1NHr98vpnSgiqSB8TQ/6y2+AY0f+ytnOIqnvlpE1+vzCDMz4vHzujC+X1b4nn7gX6bIiIiB0deWTXnT5iJw+HObbaIuk5TSIjbRMv4q0hMvP5v739nSgGLvt3G7u2lhET5ccK/u9C2Z+R+PeAVERGR5nM6HCz86jMWfT2R0OgYKk9P4ondH3JWVDRDfLJwd/rRtdt/SEkxfP75q9TV1dG9e3eGDBlCaOihmZSwcu1atl93IxujTiSzTT+6JIeSVFqNs7iOiMs749Mh7JAc9+9QQC0i8g9TtCuHH199nqxNG0jqN4CR/74R38CgxvaKijTWb7iTkpKVREaeQMcOj+DlVd/D2WmdfLbxMyasmIC7ceeloS8xNGEo1dXVTJ48mQ0bNpCcnMzpp5/eOC7W9vxyrv9kBeuzSrjYy4erqrwIH92GgIGxGDeDra0l97XXyH/zLTxjY2n18Uf49er1X3WXFlTxy8TNpK3OIyzWnzNv70VsUkizz7uipo4356by1rxUHE7L1YPbcMPQdgT5uH48LRERkdKqWi58fjr51W7cb6uo6TCFxIR1REWNISnpnr/ViykntZhFk1PJ3FRIQJg3wy7tSId+0bgdIa/tioiI/BOV5O3mh5efJXPjBpIGDebHNltZkjuRsa1iiHKmEhzUj9aJjzN16jzS0tLo2LEjI0aMICIi4tDVNO0ntt83jnWdr6LAL5Hj+kQSnl6MCfIm8upueLZw7WSIf2afAbUxpgMwscmiNsCDwIcNyxOBdOBca21hwzZjgSsBB3Cztfang1q1iIj8F2st62ZPZ/YHb2OM4aQbbiN50NDGG15rnezM+IBt257Bzc2Hzp1eICrq1Mb29OJ0HlrwECt2r2Bg3EAeOvYhYgJiyM3NZeLEieTn5zNy5EgGDBjQuM20dTnc+eVqTJ2Tp/Dl+NBgws7viFdM/dhZ1WlpZN11N1Vr1xJ85plE3Xcv7gF7DrfhcDhZMzODJd+ngdPS/8y2dB/eEneP5t1UO52Wr1dm8sxPG9lVUs3objHcc2JHWoYdmRdeERE5+lTXObhiwgy2lsN91kFV0mTatF5GaOgoOnd6/IDD6ZL8SuZP2kLa6jx8Az057twkugyKw91TwbSIiMihtGXJAn5+4yUcDgc9rriQZ8o/xBRn82RiAO51abROvBlrT+bdd7+kurqa0047jZ49ex6yYTWsteS/9Tbb3/iYtX3uoNIrlJN6R+K1uRCv1sGEX5yMu/+R23lrnwG1tXYT0APAGOMOZALfAPcAM621Txlj7mn4/W5jTCfgfKAzEAvMMMa0t9Y6Ds0piIhIRXERP7/1CtuWLaJlp66ceP3/ERTZ4vf2iu2kpNxNUfFSwsOHktzxcby9owBwOB18uOFDXl31Kl7uXjw28DFOa3saxhg2bNjAt99+i4eHB5dccglt2rQBoNbhZPyPG3lnfhrJHh48UudH0oB4gk9KxHi6Y62laNIX7HrqKYyXF3EvvkjQiSf8V93Z24qZ++lG8jPLSewazqDz2hMU4dvs816Ums9j329gXWYJ3eODefXCXvRJPPJeVxIRkaOXw2m54bVZLCt0cod1w9H6G9q0WUBAwLH06D6B+lus/eN0WtbOyWDR5FQA+p3Whm7D4vHy0QuyIiIih1JdTQ1zPnqX1T9/T1SbdgSceQz3bH6eQYFujA6rwcvNh+Tu77N2TS1z535CREQEl156KVFRUYesJltTQ/ZDD5M+ex3r+t2Ll78fpyYFw+ZC/PtGE3J6W0wzO4D9mTqHk/T8ctZnlZCSXXqQKv/d/v4FMxzYZq3dbow5HTi+YfkHwBzgbuB04HNrbTWQZozZChwDLDwoFYuIyB62LV/Mz2++THV5GUMuuZLeJ5+Ocau/+FjrJDPzU7ZuGw+4kdxxPDExZzc+td1auJUHFzzI2ry1DG05lAeOfYBIv0gcDgezZs3i119/JS4ujnPPPZfg4GAAsosrufGTlSzfUchZxoubfQKIPrcjPu3rx8+qKygg+/4HKJs1C/8B/Yl58kk8/3AxriqrZeE3W9nwazYBod6cdG1XWnePaPbT5PS8cp78MYWf1u8iNtiHCef34NRusbhpjE0RETmCWGsZ++4cZmTVcI3THe+Eb0lMmoePT2f69H4HN7fmz7Hwm/ysMmZ/tJFdaSUkdA5jyIUdCApv/sNdOfI1dAxbBmRaa08xxoTxJ28vi4jI4ZOfsZPvJ4wnd0c6PU8+jeUdCpm48QmuiQmgjXsuoSEDaNXqEaZOmUt6ejrdu3dn9OjReHnt//W+ueoKC8m86Wa2ZniyqcctREb5MTDYC+f2EoJHtyHguP2fDLG0qpaNOaVsyCohJbv+Z2NOKdV1TgA83Q/+fff+BtTnA581fI6y1mYDWGuzjTG/ddWLAxY12SajYZmIiBxENVWVzPnwHdbO/InIhETOuf8xIhMSG9urqrLYkHI3hYULCAsbRHLHJ/DxiQWg1lnLf9b+hzfWvEGAZwBPD36aExNPxBhDTk4OU6ZMISsriz59+nDiiSfi4VF/uZi3OZdbP1tJVVUd4/BldHI0oWe1w71hEsOyuXPJuu9+nMXFtLjnbsIuvbQxLIf6G/VNi3L49autVFfU0WNkAn1HJza7x1dxRS0vz9rCBwvT8XR3445R7bnyuDb4eu1/7zMREZFD7ZnPfmXStgrOsx5ERk8locMsPD0T6HfMR7i771+o7Khzsnzadpb/mI6XjwcjruhE+2OiDtmrwuJStwApwG+TiOz17WVXFScicrSx1rJuznRmvfcmnl7ejLjtdl4q+piM1EU83NIbX5tPm9a34nCcwH8ahvQ4/fTT6dmz5yGtqzotjR3XXstGzz7s6DicDm2D6FRdhy2uJvyyzvh2/Ou3i621ZBRWNoTQpWzILiYlu5QdBRWN64T6edIpNohLjm1Fp9ggkmOCaBsZgPcTB/dcmh1QG2O8gNOAsftadS/L7F72dzVwNUBCQkJzyxARESBzUwo/vvocxbt30ff0cxgw5iI8POvHk7LWkp39BZu3PA446dDhUeJiL2i8gd1YsJEHfn2AjQUbOTHxRO455h7CfcOpra1l7ty5/Prrr/j6+nLOOefQpUsXoP715AkzNvPyrK20Nu485h5At1OT8D8mGmMMzspKdj/zDIWffoZ3+/bEvvsuPh3a71FzQXY5cz/dRNaWIqLbBDHkwo5ExO85HvWfqXU4+XTxDl6csZmiylrO7d2S20e1p0WQzz63tdbyc37Jfny7IiIif9+7U5bw2ppiTrAetA/6nvguM3B3j2BA/4l4egbtewdN5KQWM/vjjRRklZPUN4rjxiThF3ToemOJ6xhj4oHRwOPAbQ2L/+ztZREROcSqK8qZ/varbFowj4Qu3WhzwancseoBktx2cWd0Hd6eQSQnv8LqVZX88sunREZGctlll9GiRYt97/xvKF+8hPRb72B94nnkBifTr1s40VllmEBPIq/qime0/x7rV9U62Lq7jA1ZJWzIrv/ZmF1CSVUdAMZA63B/usYHc17flnSKqQ+jo4K8D8vD8P3pQX0SsMJau6vh913GmJiG3tMxwO6G5RlAyybbxQNZf9yZtfYt4C2APn36/FeALSIi/81RV8vCLz9jybdfEhgRyXkPPUl8cpfG9qrqHDZuvJf8/LmEhPSjU/J4fH3r/5Nc46jhzTVv8p+1/yHYO5gXj3+R4a2GA5Cens7UqVPJz8+nR48ejBo1Cj+/+kkG88qqufnTlSxIzedkPLk7JpzYC5LxjKxvr1y/nqw776ImNZWwyy4j8rb/w83bu7Gm2hoHy35IZ9X0HXh6u3P8RR3oNDAW04zhOKy1zNq4m8d/SCE1t5wBbcO5f3QnOsU278Z+bWkFD27NZGFRefO+YBERkYPgm1mrefzX3RyLB8d6TyO69wzc3PwZ0P8LvLwimr2fmqo6Fk9JZc3sDAJCvBl9fTcSuzV/+/81TqduC4EXgbuAwCbL/uztZREROYSyt27i+wlPU5KXy8DzLiG7iyc3LbqBC8IddPKuICxsEAktH2bKlNls376dnj17ctJJJx3SIT0Air76irTHX2Btjxsp827BqF4R+G4rxisxiPCLkynEkrIlt8kQHaVszS3D0XCd9fNyp2N0IKd2j23sFd0xOhA/L9fNZbE/R76A34f3AJgCXAY81fDv5CbLPzXGPE/9JIlJwJK/X6qIyNEtP2MHP7z8HLvTt9H5+BEMvexqvBtCZGstOTnfsnnLIzidNbRPepD4+Eswpn54jbW5a3lwwYNsLdrKaW1P466+dxHsHUxVVRXTp09n+fLlhISEcMkll9C2bdvGYy5JK+CGj5ZTXFHDPfhwwdC2BI1IwLi7YR0O8t/9D7kvvYRHWBgJ/3kX/wED9qg5fW0e8z7fTGl+FR2PjWbA2e3wDWzexTolu4THv09h/tY82kT4886lfRie3KJZT29zqmt5MjWbSTkFhHq681T7eK5o7hctIiLyN8xZspG7ftpBsvHkRDuTFsfOxN3dnX7HfI6vb/NHPtyxPp85n2yitKCKLkPi6H9GW7x8/7cnQXQ4LbtKqsgorCSjsILMwsr6z0X1n7OKqlxdoksZY04Bdltrlxtjjj+A7fWWsojIQWCdTpZ99w3zP/8Q/9AwznnwcT4snsz8ZZ9xV4wh0FTTts3t1NaO4N13J1FbW8uZZ55J9+7dD3lduS+8QNrnP7O27z3gE8CINkH4bitmW7Q3H3hUsW7CPHaXVjduExPsQ6eYIEZ2imoMo1uF+e33/E3WWrKqa1lVWsHqkop9b7CfmvUXjjHGDxgJXNNk8VPAJGPMlcAOYAyAtXa9MWYSsAGoA26w1joOatUiIkcR63SyctpU5n36Pl4+vpx2+70kHfN7EFxdk8fGjfeRlzeD4OBedEp+Gj+/1gBU1VXx2qrX+GDDB0T6RvLq8FcZHD8YgJSUFH744QfKysro378/Q4cObXzSa63ljTnbePbnTcRYN94JDKXfBZ3xblM/UWJtZiZZd99DxbJlBJ5wAjHjHsY9JKSxprLCKuZP2sK2lbmERvtxxm09iWuYRHFfdpdW8fzPm5m0bCeBPp48dGonLj62FZ7u+551uMLh5I2du3llx27qnJbrWrbgllYtCPb0UEAtIiKH3Ir1qVz/5Wbi3DwZU/MLEcNn4uFZS8+enxAUlNSsfVSV1TL/iy1sWpxDSJQfZ97Ri9h2IYe28IOk1uEkp7hJAF1Uucfn7KIq6v7QSzoy0Ju4EF+6xAVzQpdo7nVR7UeIgcBpxpiTAR8gyBjzMX/+9vIe9JayiMjfV15UyLTXXiB99QqSjhlA78suYuzS+/GrWMrt0Q58vCLplPw8K1eWMX/+p7Ro0YIxY8YQGRl5SOsqKSpl++13UrC5hPW9bsfp7UEXX/DJLuNVqvlydyntTCCDkiJJjgmsD6Ojgwj1P7De3Hk1dawqrWBVSQWrSytYVVpBbk39cCAeh2DEj2YF1NbaCiD8D8vygeF/sv7j1I+ZJSIif0Npfh7TXnuBHetW06ZXX0ZdczP+Ib8Hvbt2fc+mzQ/hcJTTrt1YElpeQf3E77Bi1woeXPAg20u2c3bS2dze53YCvQIpLS3lxx9/ZMOGDURFRXH++ecTF/d7j66tu8u4d9JqlmQUMRQPHu7akpZntcetoddW8dSp5Ix7BKwl5qknCT799MZezU6HkzWzM1gyNQ2n03LsGW3oMSIBd499h8tVtQ7enZ/Ga7O3Ul3n5IqBrblpWDtC/PZ9QXVay9e7CnkiNZus6lpGRwbzQNtYEn2997mtiIjIwbAlNYMr319DgLsXV1SsIGzkdLy9y+nY4Q0iI3rtc3trLVuX7eaXSZupLq+j90mt6HNyIh6eR85EwDV1TrKLK/+7B3RhZX0AXVxJ0/zZGIgK9CEu1JdeCaHEd/clLsSP+FBf4kJ9iQvxxecP53c0B9TW2rE0zPnU0IP6DmvtxcaYZ9j728siInIQpa9ZyY+vPEd1RTkjrroeZ7cYrpp9GSf47aJraC3h4UOIj3+QyZNnsWPHDnr16sVJJ52EZ8N8UAeDtZbMokpSsktJyS5hQ1YJWdt28K9pr+EV1Jm0zufi8LaMCPDE18KW/i24pHcM41oE4NWM++69KalzsKYhjF7VEEZnVNUC9RMNtvPz5viwQHoE+tEz0I9OAb7s31TP+/a//Y6YiMg/WMqvc5n57ms46uoY+e8b6Tr8hMYguKamgE2bH2L37h8ICuxGp07P4O/fDoCK2gomrJjAZxs/IzYglrdGvkX/2P5Ya1mxYgU///wztbW1DBs2jIEDB+LuXn9jWFXr4LVZW3ltzjZ8rOVuDz8uPbsT/j2jAHAUF5PzyKOUfP89vj17EvvM03jFxzfWm5NazJxPN5GfUUarLuEMOq89wZH7vmxZa5myOounp20is6iSUZ2iGHtyMq0j/Pe5LcCSojIe2prFytIKugX68mqnVvQPad7kiyIiIgdDdnYul762GIeHLzeXpxA68gd8fAtJSBhPy5ZD97l9WWEVcz/dRPrafFq0CuS0W5KbPZHwwVRV6yCrqGnoXPH758JKdpVWYZsE0G4GYoLrg+Z+rcOIC/UlPtSX+FA/4kJ8iQnxwdvjyAnY/4ft9e1lERE5OBx1dSyY9DFLpnxFWGw8Z9/3KHOqlvLR7Mu4KryGEHcHbdveRXXVEN59ZyIOh4OzzjqLbt26/a3jVtc52LKrjA3ZJY1hdMofJi4cSCH3znqLnXGjyWzRly5JQbQtqsbd35PwyzrTOqZ5982/qXQ4WV9W2dg7elVpBVsrfh8SJMHHi15B/vwrzo8egX50C/Ql4DBcyxVQi4gcYSrLSpn57utsWjCPmKQOnHTj7YRGxza25+b+TMrG+6mrK6Ftm9tJSLgaN7f6/5wvzl7MQwseIrMskws7XsgtvW7Bz9OP/Px8vvvuO9LS0mjVqhWnnnoqERG/T7K0YGse936xmvTiKkbhyR3JMbQ9sz3uQfU9kMsXLyHrnnuoy80l8tZbCL/qKoxH/TGrymtZ9O021s/Pwj/YmxOv6UKbHpHNGit6+fZCHv1uA6t2FtE5Nohnx3Snf9vwfW4HsL2ymsdTs5myu4hoL09eSk7gnKhQ3PZ2XKu3XEVE5NAoLCzm4mdnUeAZwB3l2wkbNhU//xzCwu6hQ/uz/nJb67Ss/yWTBd9swzosA85uR/dh8bg1Y1irA1FZ4yCzqIKdTULnpkNx5DYZsxLA3c0QG+JDXIgvxyVFEBfyewAdH+pLdLBPs4bgkv1nrZ0DzGn4/KdvL4uIyN9TvDuH7196huwtm+g6/AQGXHwZz6x8ltycSdwcWYe3dwu6dHqRZcuKWLDgc6KiohgzZswe99PNUVFTx4rtRWzILm7sHb11d1nj0Fe+nu50jKmfuDA5JohOsUG0TFlO9gNvsrbjlRT5JTCseziB20vwSggk/JJOuO9jfqdap2Vj+Z5h9MbyKhwNt8dRXh70CPLj7KhQegT60T3IjzBP10TFCqhFRI4g6WtW8tPrL1JRXMTAcy/mmDPG4NbQw7m2tpjNmx8hZ9e3BAR0omfPDwkM6AhAaU0pzy9/ni83f0lCYALvn/g+vaN643A4+PXXX5k9ezbu7u6ccsop9OrVCze3+pvJ/LJqHp28nm/XZhOHYUJgCCeck4xPhzAAnDU15E6YQMF/3sOrVSsSP/sU365dgfqez5uX7OLXL7dQVVZL92EtOebU1nj57PvSsrOggvHTNvLdmmxaBHrzzDndOKtXPO7NmKihtM7BhO27eDsjFzfg9sQork9ogb/7nzzVTZ0Lsx7d535FRET2V3lZOZc+OpV0r1D+r3IXEYO+JCB4J95eV9Gr57//ctvCnHJmf7yR7K3FxHcM5fiLOhAc6fe36imrrmsMnX8bdqPpUBz55TV7rO/pbogLqR9uY1iHFnv2gA71JSrQGw8F0CIi8g+1aeF8pr/1MtZaTrn1boK7JXHj7KvoZlcxONRBePhQ4uPu5+uvZ5CRkUGfPn044YQT9ntIj6XpBdzy2UqyiusnA44J9iE5JogRyVEkxwSRHBNIq3D/xvthay0FH3xA2ssfsLbXbdR6hzA6ORSP7SX49WxB6FlJGM89r89Oa9laUb1HGL2+rJLqhgA8xMOdHoF+jEwIbgijfYnx3v/xqYsrapm2Pnu/t9sXBdQiIkeA2ppqfvn0fVb+OJWw2HjOuPMBotq0a2zPy5vNxo33UVObT+vEm0lMvA43t/qLyS8ZvzBu4ThyK3O5vPPlXN/jenw9fMnOzmby5Mnk5OTQoUMHRo8eTVBQEFB/wZu0bCdPTN1AeY2Dy/Dm+kGtiRyZiJtXfdBbvXUrmXfeRXVKCiHnnUfU3Xfh5ld/41yYU87czzaRuamIqNZBnHpTDyITAvd5nqVVtbw2Zxvvzk/DzcDNw5O4ZnAb/L33fTlyWMun2fmMT80hr7aOMdGhjG0dQ6zPn1xUM5fDzEcgdQ4Exe19HRERkQNUW1nFVfdPZK1PFNdXFxHT9zOCItJx1J3JccPG/ul2DoeTlT/vYNn36Xh4uTHs0o507B/TrDeP/mh9VjHv/pLG5t2lZBRWUlRRu0e7l4cb8Q0B9KjY4Ibwuf4nLsSPFoHeuDXj4bCIiMg/SW11FbM/eJu1M38ipl0HRt9yJ5udO3l42pmcFZhHmAe0azeWyorjeOedz3E6nZxzzjl06dJlv47jcFpenb2VF2dsJiHMj/9c3oeeLUP/cuJCW1tLzuOPs+2n1azvew/+gX6MjPGDnaUEnZBI4PH1w2xur9wzjF5bWkmZwwmAn7sb3QJ8uSIugh6BfvQI8qOVj9cB/a0B9b2/Z6TsZsqqLOZu3k2t4+C/oayAWkTExXalbuWHl5+lICuDnieeyqALL8PT2weAurpSNm95nOzsL/D3T6Jb97cICqy/KBZXF/P00qeZsm0KbYPb8vzxz9Mtshu1tbVMnz6dBQsW4Ofnx5gxY+jUqVPjxWjr7lLGTlzN0sxiuuHOvTFR9D4vGc/o+rGrbE0N+e9/QN6rr+Lm70/8a68ROKx+/My6GgfLp21nxU/b8fR2Z8iFHeh8XCxmHze3dQ4nE5ft5PmfN5NfXsNZveK484QOxAQ3b2qFuQWlPLw1k5TyKvoF+/Nxuzb0CPqTXma5m+p7TKdMBb9wOOEJ6HMl3H6wp3EQEZGjlaOmhhvHvsdCnwQura0gsdP7hMSmUl4+hFNGP/2n2+3eXsKsjzaSn1FG216RDDqvPf7B+z+h7+Zdpbw4YzM/rM0h0MeD3q1C6dEypHECwt8mIYzwVwAtIiLSVN6OdL6b8DT5GTvoe/o5DBhzERO3TGTxxie4IqQab+8ounZ6iaVL81m48HOio6MZM2YM4eHNG4ryN7tKqrjl85UsSi3gjB6xPHZmVwL20THLUVJC5q3/x6Z0d7Z0v5H4aD96e7qRW17NjjGJpIR6sGpNKqtLKyiodQDgZQydA3w5JzqMHoG+9AjyI8nPB/cDDKN/U1PnZN7mXKaszmL6hl1U1jqIDvLhsv6JnNYjlu7j/9bu/4sCahERF3E6HCyZ/CULv/wUv6Bgzr7vURK79WxsLyj4lQ0pd1NdvYtWra6lTeubcXOrv4mduWMmjy16jMKqQv7d9d9c2/1avNy9SEtLY+rUqRQUFNCzZ09GjRqFr299MFtV6+DVmVt4fW4qvhbu8fDnwlPbE9g3pjFgLl+0iJxHHqUmNZWAEcOJefhhPBrG1tq+Pp95n22iJK+K9v2iGHh2En5B+34laN7mXB7/PoVNu0o5JjGM965Iplt8SLO+oy3lVYzblsWM/BISfLx4p3MioyOD9/7kt3A7zB0Pqz8DT384fiwcez34BDXrWCIiIs3hdDi4567X+MkniTPqaujY8i3C26ZSXNyL0Se/3jiMVlO1NQ6WTE1j9Ywd+AZ5cdI1XWnTM3K/j52aW8aEmVuYsjoLfy8Pbh7WjisHtSHYd/9eNRYRETnaWGtZM2Macz54Gy8/P86+9xGiOiczbsHdhJd+x+khDkLCjich7kG++upnMjMz6du3L6NGjdrvIT1mbdzFHV+sobLGwTPndOOc3vH77L1cs3Mn26+9nqV+/Vg2aDC2nT/zvC33B7mz29tAST5uJdDR34cTIoIbe0Yn+/vgtZe/PQ6Ew2lZnJrPlNVZ/Lguh+LKWkL9PDmzVxyndY/lmMSwQ/bgWwG1iIgLFOZk8eOrz5O9eSMd+g9i+FXX4xtQP0RGXV05W7eNJzPzE/z82tCn9ySCg+uD64KqAp5c/CTT0qfRIbQDrw1/jeTwZCorK5kyfQorVqwgNDSUSy+9lDZt2jQeb/6WPO77YjXbS6o4AU/u7BJH6zOScA+oD5hrd+9m9/inKfn+ezxbtiT+jdcJPP54AMqLqvll0ha2rdhNSJQfp9/ag/iOYfs8xy27Snn8hxTmbMolIcyPNy7uxQmdo5v1WlFBbR3PpeXwQVYevm5uPNA2lqviI/De24W3bDfMexaW/QeMW30ofdxt4L9/T7hFRET2xVrL43c8xxc+nRnmrKNn6KtEdUujqLA9o0a9i7f3f/eGzthYwOyPN1KSV0Wn42IZcFZbvP3270Z3Z0EFE2Zu4esVGXh7uHPN4LZcM7jNX74iLCIiIvWqysr4+a2X2LJ4Aa269eSkG26jyKOCO386iyGeWwj1NbRrdx/lZcfy7rufYa1lzJgxdO7ceb+OU1Pn5OlpG3lnfhrJMUG8fEFP2rUI+NP1y+scrCmrZGnKFhYuWcnaf99HXrBPY3tCNQyMCaJnWAA9Av3oEuiH30GeG8Jay+qMYqasyuK7NVnsLq3G38udUZ2jOa17LMclRRyWCZEVUIuIHEbWWtbO+ok5H7yDm7s7J998J8kDhzS2FxYuISXlbiqrdpLQ8kratLkNd3cfrLX8lP4TTyx+gtLaUm7scSP/6vovPN082bBhAz/88APl5eUMGDCA448/Hi+v+hvWvLJqHvt2Pd+uyyYeN14KDGHUuZ3wSQqtr6eujsJPPiH3pZextbVE3HAD4f++CjcfH5wOJ2vnZrJ4SirOOku/01rTc2Qr3D3/+uJUUF7DC9M38+mSHfh5uXPfyclcOqAV3h5/MolhEzVOJ+9l5vF8+i5K6xxcHBvOna2jifTay418ZREseBkWvQ51VdDzYhhyNwQfneNNG2NaAh8C0YATeMtaO8EYEwZMBBKBdOBca21hwzZjgSsBB3CztfYnF5QuIvI/wVrLi3c8wXtePejrdDLQ6zXijk2jqDCBwYPfIzBwzzd2qsprWfD1VlJ+zSY40pfT/68n8R1C9+uYWUWVvDxrK18s24m7m+GKga25dkhbIgP3f1gQERGRo1HW5hS+f+kZygryGXTh5fQ99SwWZC9g8rIbOS2gFHfPCHp2fZ3Fi3axePEkYmJiGDNmDGFh++6U1VR6Xjk3fbaStZnFXNa/FWNPTsbH8/d74FqnZV1Z5R7jRm8pr6J+1GhDcNuuxBZazqm0dNtZSc+EUBLPbo/x3Pd99IHYvKuUKauymLomi+35FXi5u3F8h0hO6xHL8I5R+HodmuP+GQXUIiKHSXlRIT+/+RKpK5aS0KU7J1x3K0ER9a/3OhyVbNv2LDszPsDXtyW9en1GaEhfAHIrcnls0WPM2jmLLuFdeGTgIySFJlFSUsJXP3zFxo0biY6O5sILLyQ2NhYAp/P3SRArah1cbry5fkhbIoe3apztt2LFCnLGPUL1pk34DxpE9P334dWqFQA5acXM+2wzuTtKSegUxuAL2hMc+SdjPjeornPwwYJ0Xp61lYoaBxf3S+CWEe0Ja0bvLmstP+WV8Mi2LFIrqxkaFshD7WLp6L+XcaNrKmDJWzD/Bagqgs5nwdD7IKLdf697dKkDbrfWrjDGBALLjTHTgcuBmdbap4wx9wD3AHcbYzoB5wOdgVhghjGmvbXW4aL6RUSOWNZa3h77CK979KEDlpFub9JqaColJVH07PkGkZHRe6y/bcVu5n2+mcqyWnqOSuCYU1rjsR83ertLqnh19lY+W7ITi+XCfgncMLQdUUE++95YREREcDodLJ38Fb9O+pigiEjOH/c00e3a88HaVynZ+RInBDrwDxlE25aP8OUX08jKyqJfv36MHDkSD4/9i0snr8rk3q/X4uHuxpuX9OaEznv+XbCmtILrN2xna0U1AOGeHvQI9GVY2mbCv59Nbcgowtz8GNbSH5NbRtDIVgQOa7nn28fWgqMWHDUNP3v7/Nu/1Xtt31lSx5TtXkzd6cPGUm/csAwMK+WGjrs5IXwXwaYS0mpg6x/3t5fPB5kCahGRw2DL0oVMf/NlaqoqGXrZv+l54qmYhuEqiotXsH7DnVRWphMfdwnt2t2Fu7sf1lqmpk5l/JLxVNVVcVvv27ik0yW44cby5cv5+eefcTgcjBgxgv79++PuXn/ju2VXKWMnrWZZZjHdcefeuGh6nZeMZ4v6gLkuP5/dzz5H8Tff4BETQ9xLEwgcORJjDBUlNSz8ZisbF+bgF+zFCf/uQttekX85LIe1lmnrcnjyx43sKKhgWMcW3HtyR9q1CGzWd7OutIKHtmbxa1EZSX7efNKtDcPD9zJutKMWVnwIc5+GshxoNxKGPwAx3ffzf41/JmttNpDd8LnUGJMCxAGnA8c3rPYBMAe4u2H559baaiDNGLMVOAZYeHgrFxE58n380Dgm2D5EA6fWvU/rkzZTVhZK2zYv0KZNh8b1yourmffZZlJX5RLRMoBTbuxOZELzrodQ/+bTG3O28dGi7TicljF94rlxWBJxIZroV0REpLnKCgv48ZXn2LFuNR36D2Lk1Tfi8DQ8PvdfdKz5hVhfQ2LbsVSV9eeddz4B4Nxzz6VTp077dZyKmjoemryeL5Zn0DcxlBfP77nHNdtpLa/t2M34tBwiqOaVsjn0q95ObEUuOZO3szm3PRuTLybWVNLPzwF5dYSGfITf6qWw4g/BsPPAQuFcG8T3jmOZ7BjISpsEQG+ziXEeCznZfRGRVRWQ6QU5nuDu1fDzJ5+9A39fxqIDqufPKKAWETmEqisqmP3BW6yfM4MWiW05+abbCY9PAMDhqCYt7UW273gHH+9oevb4kLCwgQDklOcwbuE45mfOp2eLnowbMI7Wwa3Jz89n6tSppKenk5iYyKmnnto4m3BVrYNXZmzhjXnb8LOGe738ueC0jgT0jsIYg3U4KJo0id0vvIizooLwf19FxHXX4ebnh8PhZN2cDJZMTaWu1knPUQn0OTkRL5+/vkysySjise9SWJJeQMfoQD668hgGJTVv0qdd1bU8lZbN59kFhHq680RSHJfERuD5x0kXnE5Y9xXMfhwK06DlsTDmPWg1YD//1zh6GGMSgZ7AYiCqIbzGWpttjGnRsFoce/5VkdGwbG/7uxq4GiAhIeEQVS0icmT68vGHeaG6D34YxtRMot1Ja6mqDCAs9GG6d+8H1D+sTfk1m1+/2oqj1smxZ7Shx8gE3Js5ZmNRRQ1vzkvlgwXpVNU6OLNnPLcMTyIh/K/fXhIREZE9pa1cxo+vvUBtVRWjrrmZLkNHsqNkOx/OvYh+Xjk4vUPp0/VtFi3KZsmSL4iNjWXMmDGEhu7fMFwbskq48bMVpOWVc/Owdtw8POn/2bvL6KiurwHjzx2JJxN394SE4O5SHIpLoU7d3YWWulFB21IqtFAoXooVd00gCXF3t8n4fT9MXmpo/zXa81srK5krudbmcvY5Z29UP3vvl+oM3JdeyL76Zka1pvPWiUdxc3TFZHKg8HsdabbXkB8/kjjnGmJU7kgKA54R32PjogblgJ8Fhi8jcKyy+cXyBpOKLfky67P0HCjWY5Eh1suOxxI8GJPoTZBHP1DeDQo1/N4Ci9O++n37XYAIUAuCIPxJitPPsPmjd2mqrqL7+Cn0nDQdpcqaS7mxMYW09MdoacnC338qUZFPolI5I8syq7JW8faxt7HIFp7o9gTTYqaBDHv37mXXrl2oVCrGjBlDp06dzo1s3pdVzVMrkyls0jEcNY8mBRI6Ngqlo/V4radPU/7iHHRnzuDQvTu+zz2LbUSE9TzP1rJ3ZRa1pS0Ex7vTZ0oUbr6OF722soZW3vwhg+9OluDpZMOrExKZ0iUI5WVU9G01W1hUVMn7hZUYLTK3B3nxYIgPGvWvXkmyDJk/wI6XoDIVfBJhxrcQNRQuo9Dif5UkSU7AauABWZYbLzL6/Xwr5PNtKMvyYmAxQJcuXc67jSAIwr/Rhjfn8G5DZ0ySxCzjZqKHHcFosgUeoG/fEQDUV2rZ9dVZSjLq8Y9yZeDMWFx9Li+w3Kgz8snePD7Zl0eLwcSY9v7cPySKCK8LF1QSBEEQBOG3zCYje7/+nOMb1+AZHMro+x/DIzCY3fkbSUt/lF62BpTO3WkX+hrffruZsrIyevTowZAhQ64opYcsy3xxqICXN6Xjaq/mq1u70yvC8xfbbKys55GMIgxmI+/kzWd66Qaka15G7zaA/DvvJcVjKJWeHekZrcG7So0qwAnPWfEoNSN+9/W3GsxsT69gfXIpuzOqMJgthHg4cPfASMYm+RPlc/kzuv4OIkAtCILwBzMZjRz49iuOrl+NxtuHqS+8RkCsdaqQxWIgL/9DCgoWYmPjRYekT/HwsBZJLGoq4sUDL3K4/DDdfLvxQq8XCHIOorS0lPXr11NeXk5cXBwjR47E2dn6cqlu1vPSmjOsSy0nEAUfuroxdEo7bMM1AJjr66l87z3qV6xE6emB/1tv4TJqJJIk0VSrY/+qbHJOVOLiaceIOxIJS/K8aDqPFr2JRXtyWbwnB4sMdw2I4M4BETjbnaeI4a/Issyaynrm5pRSojcyykvDM+H+hDmcp9BT/j7YMQeKDoN7OEz8xJpr+vf27v5HSJKkxhqc/kqW5e/aFldIkuTXNnraD6hsW14MBP1s90Cg9K87W0EQhH+2rR/O5YPKDlQrFMyWDxA3+EcsKGiov5mpU69Dtsic3FHIkQ15KJUSA66LIb63P9JldNa26E18diCfxXtyaWg1MrydLw8OjSbG95/deBQEQRCEf6K68lI2zXuTitwskq4ZRf9ZN6NUq/ns2NM4164g3AZ8Qh6C1v58/PFXSJLEtGnTiI2NvaLj1GsNPLYqha1pFQyM8eKtyUl4OP3Unm0xmXk2u4TlZbV0MFcx//j9hGs84PY9NKdXkDvrNpKjb6LJIZBrYl2xL2/BPtETt8nRKH5HUUKDycK+7CrWnypla1oFWoMZb2dbZvUMYWySP+0DNRdt3/+TiAC1IAjCH6i6MJ/vP3ybqoI8Egddw4Drb8XG3jqKqqkpnbT0R2luTsfXdzzRUc+hVrtgkS18ffZr5p2Yh0JS8GyPZ5kUPQmT0cTWrVs5ePAgjo6Ov8iJZbHIrDhaxKsbrUUQb5JsuWtQJJ4Dg5FUCmSLhYY1a6l86y3MjY24Xz8Lz3vvRenkhMlo5tS2Qo5vLkAGuo0Jo+PQ4IsWb7JYZFadKOatLRlUNukZk+TP48NjCHS7vBFixxpaeC67hBONWhKd7PkgLoRebucZHVZ6yhqYztkBzn4w+j3oOLMtx5VwMZL1Xx6fAOmyLL/zs1XrgRuA19q+r/vZ8uWSJL2DtUhiFHDkrztjQRCEf66dS95kYV48OSols5WpxHdfg6SWKSmexnUz7qC2VMvOL85SVdhEaHtP+k+PwcntPB2uv9JqMPPFoXwW7s6ltsXA4FhvHhwaTUKA5i+4KkEQBEH490nfv5vtSz5EUigY+9BTRHXvRaO+gc93XkuMnEmryomkpI85drCCY8dWERgYyKRJk3B1db2i4xzNr+X+r09S1aznmVFx3Nw7DMXPOqVPNmq5O62AvFYd91du4pGz76LudTeWHg9TOe9DCtfu4nTHB5DsXBgd6oyivAXnwcG4DA6+rM7t/2exyBzOq2V9cimbz5RRrzWisVczroM/Y5L86R7mcVkzm/9pRIBaEAThD2AyGDi+aS0HVy3H1tGJcY8+S2QXa15Ki8VIQcFC8vI/RK12pX3iIry8hgCQ35DP8wee50TlCXoH9Ob5Hs/j5+RHbm4uGzZsoK6ujk6dOjF06FDs7a3FFrIqmnjim1McL2ukA0qeDvGj4+Q4VJ7W9bqzZyl/cQ6tJ09i37Ejvs8/h11sLLIsk5dSzb6VmTRW64jo6EWvSZG4eFy88NLBnBpe3pRGamkjHYNdWTCzM51DLi8/V5HOwMs5payrrMfHRsV7sUFM8XVH8ete3KpM2PkypK0DezcY+hJ0mw1qURTqCvQGZgGnJUk61bbsKayB6ZWSJN0CFAKTAWRZTpUkaSWQBpiAu2VZNv/lZy0IgvAPs/fLd/ksNYwTdjbcbFtMQuIyVA5mcrLHMnXKvZz8oYSTWwuxc1RdVjFhsNaJ+PpIIfN35VDVpKdvlCcPDY2mY/CV5bsUBEEQBMHKqNOxY+lCUndtxz8mnlH3PoKLlzdZVcfZe+Im4tQttNon0j1iHmu+3Ux5eTm9evVi8ODBKJWXP1rZbJGZvzObd7dnEuTuwOo7e9E+0PWn9bLMR4WVvJFXho+sY3XKk/QyV8D1a2ht8aBk6nXkGEPJ7fI4nhpbempsoE6H+/QYHJK8L3zgn5FlmZTiBtYnl7IxpZSKRj0ONkqGxvswNsmfvlFe2Kiu7tnGIkAtCILwP5BlmawjB9jz5ac0VFYQ2bUnQ2ffjYPGFYDm5kzS0h+lqekMPt6jiYl5AbXaDZPFxBdpX/DRqY+wUdrwcu+XGRsxFp1Ox9q1azl16hTu7u7ccMMNhIWFAdbG7QfbMlm0NxcHWeJpW0dmXBuHQwdvJEnC3NxM1fvvU/fVcpQuLvjNnYtm/LVICgX1FVr2rsyiMLUGN18Hxt7fgaA494teW151C69+n87WtAoCXO15f3pHxrT3u6wpQs0mM+8XVLCouAoF8FCoD3cHeeOo+tU/BOqLYPdrcGo5qOyh32PQ6x6wEyPJrpQsy/s4f15pgMEX2GcuMPdPOylBEISrzIFv57PqsC+7He2Z4dhAUuQH2LoayDh7Df27zWbz+xnUV2iJ7elL70lR2DlefIaPwWRh5bEiPtqZTVmDjh7h7sy/rhNdQy/+DhYEQRAE4cIq83PZOO8N6spK6DFhKj0nzUChVLLj7Hwai97FX2XBzm82XsaRfPLJVyiVSmbMmEF0dPQVHaeiUccD35ziYG4N4zr48/K1Cb9Ib1miM3BPegEH61sY25zCG8lP4hpzDfLwldR8uYrCj7/mbPyN1DmG0j5KQ3ijHkmW8bw9CZugS6f1yq5sYv2pUtYnl5Jfo0WtlOgf7c0zo/wZHOeNg82/J6z777kSQRCEv1hFXg67li2hOP0MHoHBTHz6JULbdwRAls0UFn5MTu57qFROJCR8iI+3teBBdl02z+5/ljM1ZxgYNJBnezyLp70naWlpfP/992i1Wvr06UP//v1Rq60vv71ZVTy9MpnCJj0jUPNIpyDCRkeicFAjyzINGzdR8fprmKtrcJ06Be8HHkDp6opBZ+L45jxO7ShEqVLQe1IkiQMDUSov3LvaoDUyb0cWnx/Mx06t5LHhMdzcOww79aV7mc2yzDdltbyWV0aVwcQkHzeeDPcjwM7mlxu2VMPet+Hox9bP3e+APg+Bk9fveBKCIAiC8L87svFTtu50ZJ2LC+OcDXQNfA1Hbx1n0/vhbzuZPZ8W4+xhx9j7OhAUf/EAs8ls4bsTJbz/YxbFda10DnHj7clJ9Ir0vOh+giAIgiBcmCzLnNqykd1ffoqdkzOTn5lLcEJ7TGYDKw5cj2frISySHZFx80k/3si2498RFBTEpEmT0GiubBDUzoxKHl6ZTKvBzJuT2jOpc+AvBmutq6zjsYxiTCYj87LnMaVqB9KYtzG49qRk9n3klduR1fVpVLa2jIh1xaagCZW/Ix43tEOluXBasOI6LRuSy1ifXEp6WSMKCXpGeHDngAiGt/ND4/DvTH95WQFqSZJcgY+BBEAGbgYygBVAKJAPTJFlua5t+yeBWwAzcJ8sy1v+4PMWBEH427TU17Hvm885s2s79k7ODLn1LhIHDUPRNk1Iq80jNe1RGhtP4uV1DbExL2Fj44nRYuTT05+yMGUhTmon3uj3BsNDh9PU1MQ333xDRkYGfn5+zJw5Ez8/PwCqmqxFENenlROEgo/c3BgyLQHbEBcA9Dk5lM95Ce3hw9glJBA0fz72iYnWkd1HK9i/OpuWej0xPXzpOT4Cx4u8CI1mC18eKmDejiwaW41M7RrEQ0Nj8HK+dE5NgL21TTyfXUJai45uGkeWJYbRycXxlxvpGuHgh3DwIzBqocMM6P8EuAad/5cKgiAIwl/g2Lbl7F9v4DP3IAY7QR+fObgEtZCR0Qu5bBjltRaSBgfRfWw4atsLd9iaLTLrk0uYtz2L/Bot7QM1vHxtAv2jL50GRBAEQRCEC2ttbmLLgnnkHDtEWMcuDL/rQRxcNFQ35bD1yDR8pFoqlKH0i17EhnXbqKiooHfv3gwaNOiKUnoYTBbe3HKWJXvziPV15sMZnYj0/ql+UrPJzFNZxawsr6OTsZz5Jx4k1CsY+Y691G0/StFbszgbPomq2ASiQ5yJx4Jc2IRz/0BchoYgnScVR1WTnu9PW4PSxwvqAOgY7MrzY+IZ1d4Pb2e7//0G/sNd7gjqecAPsixPkiTJBnDAmtdyhyzLr0mS9ATwBPC4JEnxwDSgHdaiS9slSYoWeS0FQbja/X+e6cNrv8VsNNJ51LX0mDAVO0fry0qWLRQVLyMn500UCjvaxb+Lj88YJEkivSad5w48x9naswwPHc4T3Z7AzdaN48ePs23bNsxmM0OHDqVHjx4olUosFplvjhTy6sZ0dCYzNytsuWtINB79A5GUCixaLdULFlCz9DMUDg74vvA8rpMnIymVVBc3s3dFJqVZ9XgFOzNsdgJ+ERfuLZZlmR3plbzyfTq51S30ifTkmdFxxPq6XNZ9ydHqeDG7lK01jQTZ2bC4XShjvH5VLdjYah0tvfcdaK2F+HEw8BnwurIpVoIgCILwRzuxexXJyyv5wCeKbg4KrvF8AffwRnKyu9CcM4Agl1AGPRaHT9iF34sWi8zmM+W8uz2T7Mpm4vxcWHJ9F4bEeYvAtCAIgiD8j4rTz7Dpg7fQ1tcz4Ppb6TRyHJIkkVywgoKsZ3HDTLPbROIVM1i2dAUqlYrrrruOqKioKzpOQU0L9359kpTiBmb1COHpUXG/mEl8vKGFu9IKKNLpebB8LQ9lzUfd/zFMsbMoe+pF8k9Xc7bj41hUDgxt545DUSMKdzvcb2+Pbegv2+SNOiM/nClnQ3Ip+7OrscgQ6+vMo8NiGJvkT5C7wx9y764WlwxQS5LkAvQDbgSQZdkAGCRJGgcMaNtsGbALeBwYB3wjy7IeyJMkKRvoBhz8g89dEAThLyHLMpmH9rPnq6U0VlUQ0aUH/WfehJtfwLlt6huOk5X1Ko2NJ/HwGEhc7FxsbX0wmA0sSlnEp6c/RWOr4b0B7zE4ZDDV1dV89vVnFBYWEhYWxpgxY3B3t04XzmwrgniirJGOKHk6LIAOk2NRudshyzKNW7dS8eprmMrK0Iwfj/cjD6Py8EDXYuTIhhzO7C7G1kFN/xkxxPfx/0Vl4V9LLW1g7qZ0DuTUEOHlyKc3dmFgzOU1puuMJt7JL2dpSTV2CgVPh/sxO9ALu5+nDzEb4eSXsPsNaCqFiMEw+Fnw7/j7H4ggCIIg/EFSDm0g6+NMXgtIItZOwTiPN/CMrqagoD21uf0ZNGAgnYeHorxA4SFZltmWVsE72zI5W95EpLcT86/rxPB2vhd9/wqCIAiCcGkWi5nD363k4Kqv0fj4MOPlt/AJj0SWzWw9cT9S3Wa0FjVhka9TflrB2pNrCQ4OZuLEiVec0mPdqRKeXnMGhQQLZ3ZmeILvuXVmWWZeQQVv55XjJ2tZc+pxuqua4ebNNGU2UjRxOhleQyhNnEywrz2dHFTIhY04dvdFMzIcRdvsK53RzI70StYnl7DzbBUGs4Ugd3vuHBDB2KQAYnwvnZf63+pyRlCHA1XAUkmSkoDjwP2AjyzLZQCyLJdJkvT/pScDgEM/27+4bZkgCMJVpyI3m53LFlNyNg3P4FAmPfMyIYkdzq3XavPJznmTqqofsLHxJj7uTXx9xyNJEqerTvPs/mfJachhbMRYHuv6GE4qJ/bs2cPu3btRq9WMGzeODh06IEkSOqOZeVsyWLI/D0dZ4hk7J6ZPiMch0RNJkjAUFFD+8lxa9u7FNiaGgLffwqFTJ2SLTNq+Ug6uzUHfYqRdvwC6jw2/aOGmykYdb2/NZOXxIlzt1cwZ147p3YJRXyQ39f8zWmQ+K6nm7fxyGk1mZvp78GiYL142PzuexQKp38HOV6A2BwK7wcQlENrnf3kcgiAIgvCHST2xlfx5h5kT0hN/tYKp7vPxjSukpDiW6qIB3Hz3dXgHnb9xK8syuzKreHdbJinFDYR5OjJvWgdGt/dHKQLTwu8kSZIdsAewxdpWXyXL8vOSJL0AzMbaLgd4Spbl7/+esxQEQfhrNFSW88P89yhOP0Nc34EMueVObOwdaGktYduRaTibS8kye9E/ZhE7N+2nsrKSvn37MmDAgCtK6aE1mHhhfSorjxXTOcSNedM6EOj20+jlwlY996YXcrihhfGNx3kt5Tk07cdj7vssFe98QMHWk6Qn3YdOqWFAghua0hYkGwUeN7XDLsY6CK2iUccHP2ax5kQJLQYzXs62XNcjmLFJ/nQIchWzrbi8ALUK6ATcK8vyYUmS5mFN53Eh57ur8m82kqTbgNsAgoODL+M0BEEQ/jrNtTXs++YLUvfswN7ZhaGz7yFh0FAUCuuLzmisIy/vQ4pLvkKhUBMW9gAhwbegVDqgM+mYf2o+y9KW4WnvyUeDP6JfYD9KSkpYvn45FRUVxMfHM2LECJydrT2kuzIqefbbFIqa9YxEzaNdQwgZFYHCToVFp6N6ycfULFmCpFbj89STuM2YgaRSUZ7XwN5vMqksaMIvUkPfqdF4XaQasM5o5uO9uczflYPRbOHWPmHcMzDqsgotyLLMtppGXswuJadVT383Z16I9CfOyf7nG0HWNvhxDpSfBu92MP0biB4O4qUrCIIg/EOkn95F8SubeSFyKA5KmOn2BYEJaVSUR1BZNph7Hp6Ng+Nvp9bKssyBnBre3prBicJ6gtzteXNSe8Z3DEB1GZ28gnAJemCQLMvNkiSpgX2SJG1uW/euLMtv/Y3nJgiC8JeQLRaSt//Ani8/RVJIDL/rQdr1HwxAQflmzqQ+iI1sJNuuD11d7+O7LzeiVquZOXMmkZGRV3Ss9LJG7ll+gtzqFu4ZGMkDQ6J+8T5fU1HHYxlFyGYjH2W+zcT6AzBpIdrWAIqn3kCmugMFnR7Cx92WazzsoLgZ+yQv3MZFoHBQU681sGB3Dp/tz8ciy4zrEMCEjgF0D/cQHdq/cjkB6mKgWJblw22fV2ENUFdIkuTXNnraD6j82fY/r3YVCJT++pfKsrwYWAzQpUuX3wSwBUEQ/g5Gg57jG9dyZO23mE0muoweT48JU7F1sBb7M5v1FBcvI79gPiZTC/7+kwkPewBbW+skkhMVJ3juwHMUNBYwMWoiD3d5GFts2bJlC4cOHcLJyYlp06YRGxsLQGWTjpe+O8OG9AqCUTDf04Mh09phE2gNMjfv3k35y3MxFhXhMmoU3o89htrHG22jgYNr0zl7oAwHjQ1DboonupvPBXteLRaZDSmlvL75LKUNOoa38+WJEbGEejqed/tfS21u5YXsEvbWNRPlYMuX7cMZ7O78y+MVHIQdL0LhQXALhQlLIGEiKC6/91oQBEEQ/mypyTuoeOFb5kSPxSTJXO+wgfAOh6mtCqaoaBCzZ9943uD0kbxa3t6aweG8Wvw0drwyPpFJnQOxuUD6D0G4UrIsy0Bz20d125doKwuC8J/RUFnB1kXzKDyTQkj7jlxz+724eHpjsZg4kvYMzRXfUm9SYO/3ED65AWw4tYGQkBAmTpyIi8vl1VACa4fzl4cKeGlTOhp7NV/d0p1ekZ7n1jeazDyVWcyqijq6Gor58OTDhPjHIU/aQ9Xn31Hw1TzSE2+hydaHnrGueFe3omgx4jojFof2XrToTSz9MYtFe3Jp1pu4tkMADw6JJtjjv5VX+kpcMkAty3K5JElFkiTFyLKcAQwG0tq+bgBea/u+rm2X9cBySZLewVokMQo48mecvCAIwh9FlmUyDu5lz1dLaaquIrJrT/rNvAk3X/+29RYqKjaSk/sWOl0JHh4DiIx4HCcna5G/Km0V85PnszpzNf5O/iweupie/j3Jyclhw4YN1NfX06VLF4YMGYKdnR0Wi8zXhwt4bdNZdCYztyjtuOuaaNz7BCIpJYwlJZS/+irN23dgEx5O8GdLcezRA7PZQvKOIo5syMVktNBxaDBdRoViY3fhP+fH8mt5aVM6yUX1JAS48M7UDvQI97is+1KpN/J6XhnLy2pxVSmZGxXA9f6eqH/e21uWAjvmQPY2cPKFUW9Dx+tBZfP7H4ggCIIg/Am2LZ2Dy5pKXosbR7VkYaZyLwndt9FY70dmdn9uuGHmuZoQ/+9kYR3vbMtkb1Y1Xs62vDAmnmndgn9RNEkQ/iiSJCmxptWMBD5qm8U8ArhHkqTrgWPAw7Is151nXzFLWRCEq5Isy6Rs/4HdX36KJMHQ2+4hcdAwaypMXRl7jl+PUp9Lit6Z7mHvcurHNKqqTtGvXz/69+9/RSk9GrRGHludzJbUCgbEePHW5CQ8nWzPrT/aVgixRKfnkZKVPJC3FNWQ59B7DKF49oNkN/mR2+UJNE42jAlwRFHegl2sO24TojA5KPlsfx4f7symutnAkDgfHhkWTazv5QfP/6suZwQ1wL3AV5Ik2QC5wE2AAlgpSdItQCEwGUCW5VRJklZiDWCbgLtlWTb/4WcuCILwBynPzmTn5x9TmpGGV0gYw+98kOCE9ufW19UdITv7VRqbUnByiqdjh1dxd+8NQLOhmaWpS/ki7QuMZiMz4mZwX8f7wAhr1qwhOTkZDw8PbrrpJkJCQgDIKLcWQTxZ3kgnlDwVGUCHyXGoNLbIBgPVH39G9YIFIEl4PfwQHjfcgGRjQ3FGHXtXZFJb2kJQvDt9p0Th5nvhEdBFtVpe++Esm1LK8HGx5e3JSYzvGHBZRZtazRaWFFcxr6ACg0XmtkAvHgz1wVX9s9dGTQ78+LI117SdKwx5EbrdBjaiV1gQBEH4Z9G1NrH9kRsx6obwdHwXarAwVTpNj/6raG3x4vSZ/kyePJXAwMBz+5wpaeCdbZn8eLYSd0cbnh4Zx8weIdjbiMC08Odpazt3kCTJFVgjSVICsAB4Ceto6peAt4Gbz7OvmKUsCMJVp7Gqki2L3qfw9CmCEzsw7Pb7cPGyzlCuqNrBydP3YrHoOWSJZaDHE+xcvRtbW1tmzZpFRETEFR3rWH4t939ziopGHU+PjOOWPmHn2scmi8y7BeW8m19BoKWJdcmP09VORr51G7XbTlFw3+2kx8ykzjuMThEuBDcboE6P64QobDt7s/ZUKe9uz6S4rpXuYe4smhVL5xC3P/x+/VtdVoBaluVTQJfzrBp8ge3nAnN//2kJgiD8+Zpqq9n39eek7fkRB40rQ2+7l4SBQ87lmW5pySU753Wqq7dja+vbVgDxWiRJgcFs4NvMb1mUvIg6fR3DQodxb8d7CXYOJjU1lc2bN9Pa2krfvn3p168farWaVoO1COLHB6xFEJ+1d2b6pHgc2lmnErUcPEj5nJcw5OXhPHQoPk8+gdrfn6ZaHftXnSHnRCXOHnaMuCORsCTPC6bzaNQZ+WhnNkv35aNUSDwwJIrb+oXjYHPpP/kmi8yK8lreyi+nTG9khKeGZyP8CXf4qUeZhhLY/Tqc/BJUttD3Eeh1L9i7/s/PRBAEQRD+aMVnj5P12Bz2R97EN05qvGW4xSabLn0+waBzIfn0QIYOHU1cXBwAZ8sbeXdbJltSK9DYq3l0WAw39grF0fZyx/YIwv9OluV6SZJ2AcN/nntakqQlwMa/7cQEQRD+ILIsc/rHLez+4hNkGYbcejfthwxHkiSMxkZOpT9NY/X3VBokap2n0K6iCzsO7CAsLIwJEyacq+d0OcwWmYW7c3hnWyYBrvasvrMXSUGu59YXtOq5O62AY41aJtUf5NUzc3DucgPGhDspffpFcnJlsjo/ga2tLSPDXFCXt6AOc8FtUjQ/ljfw9vt7yaxoJiHAhbnjE+kXdeH2unB+4l9ZgiD85xj1Oo5tXMORdauQzWa6jptE92unYOtgHflrMNSQl/cBJaXLUSjsiAh/mKCgm1Aq7bHIFjblbuTDkx9S0lxCN99uPNj5QRI8E2hoaODrr78mMzMTf39/Zs2aha+vLwC7zlbyzLcpFLe0FUHsEUrIiAgUtkqMFZVUvv46jd9/jzo4mKDFi3Dq1w+T0cyx7/M5vjkfGeg2JoyOQ4NRXWDklsls4ZujRby7LZOaFgMTOwXy6LAYfDV2l7wnsiyzqaqB1/LKyNbq6eziwEdxIfRyc/ppo5Ya2PcOHFkCsgW63gr9HgEn7//5mQiCIAjCn+HY1x9QvbqAt+NuJ0uS6WZQMS7gDF7xizAZ7Tl1egidO/ejZ8+e5FQ18972LDamlOJko+KBIVHc3CcMF7tLFxL+r5JlGQtgkmVMsoxZpu273LaMn/38s/WWC6//L5MkyQswtgWn7YEhwOv/X/upbbPxwJm/7SQFQRD+AI3VlWxd9AEFKScJTmjPNbffj8bbB4CKym0kpz2KZG5iX4sDCf5PojpUy+nq0wwYMIB+/fqhUFx+/YfKRh0PrDjFgZwaxiT588r4BJzb3u2yLLOqoo4nM4tRmA0sOPsG41tOwfQvacjQUTj1JtIDx1EVm0h8sCPRRgtytRbNqDBO+9rxxjcnOFVUT7inIx/N6MSIBN/LmrEs/JYIUAuC8J8hyzJnD+xh71ef0VRTRVT3XvS77mZcfaxBZLNZR1HRUvILFmKxtOLvP42wsPuwtfFElmX2l+znvRPvcbb2LDFuMSwcspBe/r3QarVs2bKFI0eOoFAoGDZsGN27d0ehUFDZpGPO6tNsPFtpLYLo5cmQ6e2w8XdCNpmo+ewzqj/4ENloxPOee/CYfSsKW1vyU6rZuzKTxmodER296DUpEhcP+wte2+7MKuZuSiOzopluYe58NiqexEDNZd2XfXVNzM0p42STligHW5YmhDLcU/NTj6++CQ7OhwMfgLEF2k+DAU+AW8j//EwEQRAE4c9g1uvZ/cTtnGrtz8LIgdgBk7UW+nZaiENQCkatPSmp1xAe1pHYLn14aOUp1p4swU6t5K4BEczuG46rwz+7lkKL2czJRi0nGrU0mcy/CAD/FCQ+f4D4UusvGWD+2XrhD+UHLGvLQ60AVsqyvFGSpC8kSeqANcVHPnD733eKgiAIv58sy5zZuY1dny9BtsgMvuUukoYMR1IoMBhqOJ76GNq6XVQYJDJVvRjkeTP7f9iPnZ0dN9xwA2FhYVd0vJ0ZlTyyMpkWg4k3JrZncpfAc+3cBqOJJzKLWVNZTw9dPh+ceoygsK6Yr91C+VsfkXuogLMJD4HagWFRrtiVt6AKcKKkny9PHitk76Zq/DR2vD4xkYmdAlEpRdHk/4UIUAuC8J9QlpXBzs+XUJZ5Fu/QCEbc8xBB8YmAtQBiefk6cnLfRq8vw9NzCJERj+HoaM1nlVqdyrvH3+Vw+WECnAJ4te+rjAwbidFgZPfu3Rw4cACj0UhSUhIDBgzA1dUVi0XmqwP5vPZ9OjqThVuUdtw9Iga3XgFICgnt8eOUvzgHfWYmjv374fv009gEB1NfoWXft8kUnKnBzdeBsfd3ICjO/YLXlVXRxMub0tmdWUWIhwMLZ3ZmWDufy5pOdLpJy9ycMnbVNRFgq+bd2CCm+Lqj/P99jTo49gnsfRu0NRA3BgY+A96x//sDEQRBEIQ/SX1uBgcencOysKkcdlWSKCsZqjhD5KDPsHVppbU2kdNZHXBx9SVZFcMz7+5FrZS4tW84t/cLx+NnhZL+SaoMRo40tHCkvoXDDS2cbtaeG3FsI0koJQmVBKpzP0so2z6rLrJeLUnYKxTWZYpL7///637v+kv9/vYXvw3/arIspwAdz7N81t9wOoIgCH+opppqti7+gPxTxwmKT2TYnfej8fZFlmVKytZy5uwzYGllb4sznUOeJiq5ld25uwkPD2fChAk4OTld+iBtDCYLb23NYPGeXGJ9nVkxoweR3j+lBDlU38zdaQWU6w08UfQV9xZ9jXL4K7Tooyi87g7SXfpSljiScF972qsk5MoWqnv4sKi5ic1fn8DNQc0zo6y1KUTR5D+GCFALgvCv1lRTzd6vl5G+dycOGleuueM+2vUffC7PdG3tAbKzX6OpORVn5wTaxb+Fm1sPAAobC3n/5Ptsyd+Cq60rj3d9nCkxU5AsEocPHWbv3r1otVri4uIYOHAg3t7WVBdnyxt58utTnKxoojNKno4JJGliHEoXG0w1NVS++RYNa9ei8vcj8MMPcBo8GKPezME1OZzaUYhSpaDXxEjaDwxEqTp/L2xNs553t2fy9ZEiHGyUPDMqjlk9Q7BVXfrlmKfV83peGWsr63FTKXkhwp8bAzyx+/8eX7MRTi2H3W9AYzGED4DBz0FA5//9gQiCIAjCnyhrzRfs/TaXeZEzaJVgrA56RyzAI+4MJr0tFSVTyMyxRbZ3ZXmxH/riCmb1COGuARF4u1w6JdZfRZZlclv1HG4LSB9paCG3VQ+AnUKio4sD9wb70E3jSBeNIy6X8f4XBEEQhL+DLMuc2bWNXcs+xmIxM+jmO+gwdCSSQoFeX8HR0w+ibzxMiV5Bsf0QBrhO5vDGw0iSxKhRo+jcufMVpfQorNFy79cnSC5uYGaPYJ4ZFX8uiGy0yLyTX868ggqCzQ1sSH6cTs72WG7aRsWydeSuWUl64u0Y1K4MitHgXKGlyt2GL4Ps+e5wFvZqJfcPjuLWvmHn0oQIfwwRoBYE4V/JqNdxdP13HF2/Glm20O3ayXS/djI29tY8080tWWRnv05NzU7sbP1pF/8uPj6jkSQF1a3VLEpexKrMVaiVam5vfzs3trsRe6U9ycnJ7Nq1i8bGRsLDwxk8eDABAQEA1iKIP1iLIDoh8ZyjM9Mnt8M+1gPZbKZ2+XKq3puHpbUVj9tuw/OO25Hs7ck+Vsn+1dm01OuJ6eFLz/EROGrOP3pLbzLz2f58PvwxG63RzMzuwdw/JBp3x0tPQ67UG3k7v5yvympQSwoeCPHhrmDvnxq1FjOcWQ27XoXaXGtA+tr5EN7/j3kogiAIgvAnkQ0G9j73KCtaurMpuAvhKBhmyqBdn0+wd9eirYslNaszRpMNmapgjjZ4MLlrMPcMjMTf9cIptP4qBouFM02t1oB021eN0QSAu1pJN40jM/096K5xJNHZHpsraKgLgiAIwt+lqbaabYs+IO/UcQLjExh2xwO4+lhHTRcULycj8yUssoE9Wjd6BT2L6UgF+0r3ER0dzahRo9BoLi9t5f9bn1zKU9+dRiHBgus6MSLR79y6PK2eu9IKONmkZVrtHl5Ofw2nXneh8x5L0W1PkGGOo6DTQwS42dLFRU1dRTML/dWsrKiBBokbe4Vx98CIf+xMq6udCFALgvCvIlssnN2/mz1fL6O5ppronn3pN+PGcwUX9Poq8vLmUVK6AqXSgciIxwgMvBGl0pYWYwvLUpfxWepnGMwGJkVP4vb2t+Nh50FaWho7d+6kpqaGgIAAxo8ffy7/ld5kZsXhQj7clkWlzsgo1DzWO5ygYWEobJS0nj5N+QsvoktNxaFnD3yffRbb8HCqi5vZu+IkpVn1eAY5MWx2An4R538By7LM5jPlvLo5naLaVgbFevPUyDgivS89zanRZGZ+YSWLiqowyhZm+nvyUIgP3rbq///lkL4Bdr4CVengkwDTv4Ho4SAqDwuCIAj/cK1FBax5/E0+ChxGqYOCESjp4b4Y/47JWIw2FOWPJr/QDWfvIFaUemDn6MzKOzrRMdjtbzvnJpOZY22B6MMNLZxsbKHVYs3XEWZvwxAPF7prHOnm6kiEve1lpe4SBEEQhH8KWZZJ3b2DXcuWYDabGHjj7XQcNgpJoaC1tZgjKfdiakkhX6eg1mkcfW2HcnT9Uezs7Jg4cSIJCQlX9O7TGky8uD6NFceK6BTsyvvTOxLo5nDuXFaU1/J0Vgkqs57Faa8w1piNPONbqrelkvfkI6TH3kizvS99Il2wq9ayVKfla7UOXZmFSZ0DuX9INAH/gA7tfzMRoBYE4V+jNPMsu5YtoSw7A5/wSEbd+wiBcQkAmM1aCgs/oaBwMRaLgcDAmYSF3ouNjTtGs5EV6ctZlLKIWl0t14Rcw70d7yXEJYTs7GxW71hNeXk5Xl5eTJs2jZiYGCRJwmi2sOpoMe9vzaBMa6A9Sl729WLgtHaofR0x19dTNvc96leuROXpScA7b+M8YgR6rYk9KzI5s6sYGwcV/WfEEN/H/4LVfpOL6nl5UxpH8+uI9XXmi1u60TfK65L3Q2e2sLSkmvcLKqgzmRnv7cpjYX6EObT1+MoyZO+AH1+CslPgEQWTlkL8tSBGZgmCIAhXgfyN3/HJmkKWB43AXYLb5BISOnyIg3cLzfURnEnvjK2tB6aQdnyQYaZnuCcfzOiI5188+qlUZzgXjD7a0EJacysWQClBgpM9s/w96aZxpJvG8acOZEEQBEG4CjXX1rBtyYfknjhKQGw7ht/5AK6+fsiyhZyCT8jJeQOjxcRenRf9/J5BfyCPwzWHSUpKYtiwYTg4OFzR8c6WN3LP8pPkVDVz98AIHhgSjbotfWW90cSjGcVsqKqnZ2sOHyY/TkDMYAzt51LyzCtkVrmS2/FRPJxs6eVpy7rqer5QmajXmxmR4MvD10T/Ine18OcRAWpBEK56jdVV7F3+GWf378bRzZ3hdz1IfN+BSAoFsmymrGwNubnvoDdU4OU1jMiIR3FwCMMiW9ict5n3T7xPcXMxXXy68OGgD0n0SqSwsJDPvvuMgoICXF1dGT9+PImJiSgUCswWmTXHi5j3QwZFzXriUPCEhztDR0djH+sOskz96tVUvvU25sZG3K+/Hs9770Hh4Ej6/jIOrs1B32KkXb8Auo8Nx87x/A3R0vpW3tySwZqTJXg62fDqhESmdAlCeYFA9v8zWWRWVtTyVl45pXojA92deSrcj0Tnn73o8/fBjy9D4UFwDYZrF0DiFFCK14IgCILwzycbDOyY8wrvahNJ9Yqir0JigNNXBHc5jMWiIi9nMMUl/sQndeSbYg0nMlq4vX8Ej14Tg0r553bCWmSZjBbduVQdhxuaKdYZAXBQKuji4sBDob501zjSycUBR5E/WhAEQfgXkGWZtD0/snPZYsxGEwNvmE3H4WOQFAq02jwOJd+F3JpJVqsSvWYaPbRdObLhGBqNhpkzZxIZGXnFx/vqcCEvbUzDxV7NFzd3p0+U57n1++uauDe9kEq9gacLlnFX+VoUo9+iPlMif+Z9pEZMoz4inM4hTpxqbOa6hhYqsNA3zJNHh8XQPtD1D75DwsWISIQgCFcto07HkfWrObbhO5BlekyYStdxk7Cxs069qanZS3bOazQ3n8XFJYmEhPdxde0CwMHSg7x7/F3Sa9OJcoti/uD59AnoQ0VFBcuXLyczMxMnJydGjhxJp06dUKlUWCwyG06V8M73Z8lr1BGNgrdd3RgxOhr7dh5IkoQuPZ3yOS/RevIk9p074/vcc9jFRFOe18Deb45RWdCEX4SGvtOi8Qo6f09si97Eot05LN6bi0WGuwZEcOeAiEsWYZBlmc3VDbyaW0aWVk9HZwfejwumj9vPjlN8zBqYzt0Jzn4w6m3oeD2oLp3DWhAEQRD+CfRFRXz0/BKWeHVDqZaYra4nIeZNnPybaKoPJjW9G25uIXQb3o9nt5WhM+p+k4fyj6QzW0hu0v5ihHSDyQyAt42K7honbg+0puto52iP6hIdzYIgCIJwtWmuq7WOmj5+BP+YeIbfeT9ufgFYLCYycj6gsOAj9BYz+/UBDPB8krT96ZxqPEWPHj0YOHAgtrZXNrOpQWvkie9S2HymnH7RXrwzJenc7CiDxcKbeeV8WFhJmKmWjclP0MHTG9PUDRS9uYjstBaykh7B3s4WRy8VjzXUUIiFJH8X3h0ZR69Iz0scXfgziAC1IAhXHdliIX3fLvYu/4zmulpievWj34wbcfHyBqC5OYOs7Feprd2LnV0QCe3m4e09CkmSSKtJ473j73Gw7CB+jn680ucVRoaNpL6untWrV3PmzBns7OwYPHgw3bt3x8bGBlmW+eFMGe9sTCezvpUwFLzqomHMqGgcE72QFBLGsjKqFy2ifuW3KF1d8Xv1VTTXjqO1yciOz9M5e6AMB40NQ26KJ7qbz3nzaZktMqtPFPPWlgwqm/SMSfLn8eEx53JnXcyBumbm5pZyvFFLpIMtnySEMtJT89Nxyk/Dj3MhczM4eMA1c6HrLaAWebQEQRCEq0f2ps28sKmMfd7dSQTGaTYS3GkboCQnuw+VlZEMGDCQM0Yf7lmXSZinI4tm9bysmg2Xq9Zo4lhbMPpIfQvJTVoMsjV/dJSDLWO8XOnm6kh3jSPBdjZ/bv5oiwUaisDQArIZLCbrsnM/t32XzW0/X+yz6adl8qW2Pd++ll+tN4FsuYzz+NW+giAIwlVDlmXS9+1i59JFmAwGBlx/Kx1HjEGhUNLcnMGh5DuR9AWktapQu99Cp7pIDm0+jLe3N1OmTCEwMPCKj3m8oI77vj5JRaOOp0bGcmuf8HPpMnO0Ou5KKyC5qZXrqncwJ+NdHAc8QpMhkYIbHibNZwSVse1R+trwlb6FjGYtES52LBobzzXtfEXNh7+RCFALgnBVKclIZ9eyxZTnZOEbEcXoB54gIDYeAL2+gtzc9ygtW4VK5URU5FMEBs5EobClqKmID05+wOa8zWhsNTza5VGmxk5F36Ln+03fc/LkSZRKJX369KF3797Y29sjyzI70it4e2M6aTUtBKFgjrML40dE49TB2xqYLi2levFi6ld/B4Db9Ol43XcvODmT8mMxRzbkYjJY6Dg0mC6jQrGxO/+f3QM51by8MZ20skY6BLmyYGZnOodcunjTmSYtc3PL2FnbhJ+tmndigpji6/7T6KzqLGvxw9TvwFYDg56B7neArcijJQiCIFw9ZIOBlS9/xJut4dS7+DBD3UzXyDdxCaqjsd6fsxk9CAxsz423Dmfu9kI2n8lkZKIvb0xKwsn29zd5ZFmmsC1/9JGGFg7Xt5Cp1QGgliSSnO25NdCL7q6OdHFxxMPmT2xe6ZuhMs3a6VxxBsrPWD8bmv+8Y/6aQmX9kpRtPyt+9rnt69y6tu+S4leflaCyBYXD+fflxF93PYIgCMLv1lJfx7YlH5Fz7BD+0XEMu/MB3P0DsFgMnMl8g7LiT2gxyxwyhNNf8yApe05Ta8hg4MCB9O7dG5Xqyt6ZFovMgt05vLMtE39XO1bd2YsOQa6A9X29vKyWZ7KKsTPr+CTtJUZRgWX6KsqWbSZ7x0LOxt9Hmb0dx13MJOsa8VMpeXNYPBN6h14yjabw5xMBakEQrgqNVZXsWf4ZGQf24OTmzoi7HyKuzwAkhQKTqYXCwiUUFH6MLJsICrqRsNC7UatdqWmtYXHKO6zMXIlKUjE7cTY3JdyE0qRk947dHDlyBIvFQufOnenXrx/Ozs7IssyezEre3pBOclUzfkg84+jM5OHRuHTyQVJKGEtKqF60mPo1a5AA10kT8Zw9G7W/P8UZdexdcZTa0haC4t3pOyUKN1/H815XblUzr24+y7a0CgJc7Xl/ekfGtPe7ZM9tfque13PLWFNZj6tKyXMR/twU4In9/+fVrMuH3W9A8tegsoe+j0Cve8D+0kFv4eolSdKnwGigUpblhLZlHYCFgB1gAu6SZflI27ongVsAM3CfLMtb/o7zFgRBuJjmgiKefu071mmiCVZI3O66l+CkVUhKiezsbjQ2dGT06BHYeYdww5cnyKtu4amRsczuG37FI6FMFpn0llbr6Oi2EdLlBmv+aBeVgi4ujkz0caObqyMdnB1+eu/+kWTZOiq6/ExbILotIF2bB1hHamOrAZ920GEGeMeDveuvAsPnCxT/LBD8e4LKf1kB5c/+ouMIgiAIv4csy5zdv5sfly7CpNfTf9YtdBo5FoVCSUNDModT7kZpLOOUVo3G4zbiszw5fPQIQUFBjBkzBm9v7ys+ZmWTjodWJLMvu5rR7f14ZUIiLm0pMGuNJh7NKGJTVQN9WjL4IOUp/JKuRev9DIV3zSXNrhspSbdzSmPhlKzHzSTxZGIgN0xuh92f2bEsXBHxJARB+Ecz6Fo5um4VxzasAaDHxOl0GzsRtZ0dFouJ0pKV5Oa9i8FQjbf3SCLCH8HBIQStUcvHyQv47Mxn6M16xkeN586kO9EoNRw6eIgDBw6g1+tp3749AwcOxM3NGrg9nFvDm+vTOFbeiDcST9g7MXVYFK5dfZGUCgzFJdQsWmQNTEsSbpMn4TF7Nmo/P5pqdfy45AzZxytx9rBjxB2JhCV5nrdxXK818P6ObD4/mI+tSsGjw2K4pU8YduqLF0qqMhh5J7+CL0qrUUsS9wV7c3ewNxp125/zxjLY8yac+NzauOxxF/R+AJy8/tDnIvxjfQZ8CHz+s2VvAC/KsrxZkqSRbZ8HSJIUD0wD2gH+wHZJkqJlWcyvFgThn+Po2m08uruWfE0koxR6BkW+h2toCY0N3mRk9KRdu4FcP2sIO7PrefSjA9iplXx5a3d6RVxe/sgWs5mTjVoO11sD0scaW2gxWwAIsFXTy82Jbhpruo4YRzsUf/TUX2OrdRR0RepPAemKM6Br+Gkb93BrMLr9NPBNAJ8Ea4FjMQ1ZEARB+Iu11Nex/eOPyD56CL+oGIbf9SDu/oGYzTpOpb1AddlymswSxy1x9HK8g+Qfk5GkYkaOHEmXLl1Q/I7Ozt2ZVTy88hTNehOvT0xkSpegc23svbVN3JteQI3BwLN5n3BnzVakaz+gclsWOS+9zIGEm9np4kCqjR4HGe7QuHDH9Um4Brj80bdG+B+JALUgCP9IssVC2t6d7P16GS11tcT27k/fGTfg4umNLMtUV+8kO+d1Wlqy0Gg60T5xARpNJ4wWI9+c/YYFyQuo1dUyJHgI93a6l2DHYI4dO8aePXvQarXExsYyaNCgc723JwrreGtdGgdK6vFA4mE7R64bEolbD38klQJDURHVixbRsHYdkkKB29SpeMy+FbWvLw1VWk58kc7ZQ+VIColuY8LoODQYlc1vg81Gs4UvDhYwb0cWTTojU7sG8dDQGLycL14UotFkZkFhJYuKq9BbLFzn58HDob742LYVTmyphn3vwtGPrTkcO90A/R4BF/8//NkI/1yyLO+RJCn014uB//8XmAYobft5HPCNLMt6IE+SpGygG3DwrzhXQRCEizHrDbzzyucs1vnibOvMwy6nie7wKQq1hdyczhgMfZkxYxz+AYG8uSWDRXty6RjsyvzrOuGnuXh9hYJWPZ+WVHO4voXTzVrMMkhAnKMdk33d6a5xpKvGkUC7P7CAsCxDU1lbEPr0T8HommxrnmYAtaM1EJ0w0RqE9k20jo62/ePyZwuCIAjC7yHLMhkH9rBj6SKMulb6zbyZzqPGoVAoqa07zNGUe1GZazjaaouv+z2Epqo4VnKMqKgoRo0ahaur6xUfs9Vg5t3tmSzek0uMjzNfz+5BlI81VaXBYuG13HIWFFUSYazm85QnaO8Xjn7EcoqffZPDhmi+7XkPKbZmlJiZLtlwx4AIgoaEISlFB+8/kQhQC4Lwj1N8NpVdy5ZQkZuNX2QMYx96Ev/oOACamtLIyn6VuroD2NsHk5jwEV5ewwD4If8HPjjxAYVNhXT26cz7g94nwT2BlJQUPtj1AQ0NDYSFhTF48OBzxRhOFzfw1oY0dhfU4orEvTYOXD84Es9eAUjqtsD0woXWwLRSidu0adbAtI8PtWUtHF+aStaRChRKBe36+NNxWAjO7na/uSZZltmeXsmr36eTW91C70gPnhkVT5zfxXtudWYLn5VU835hBbVGM+O8XXk8zI9wh7aAdms9HPwQDi0AoxaSpkP/x8At9A97HsJV7wFgiyRJbwEKoFfb8gDg0M+2K25bJgiC8Lcqyszn3vm7OWXnRy+FzPjwBbhHpNHU6EF2Wl+6dRtHr169qNeZmfXJEQ7m1jCrRwjPjI7DVnXhmUh6i4X5hZXMK6jAIkMnFwfuCfahm8aRLi4OP81G+l+Z9FCV8VOe6P8PSLfW/rSNJtg6Gjr+2p9GRbuF/YVpNARBEATh8mgb6tn+8XyyjhzALzKGYXc+gEdgECZTM8dTn6Oxah31JonTcke6qmaRvD0ZOzs7JkyYQGJi4hWn25Jlma1pFczZkEZJfSvXdQ/m2dHx52YbZ7VYCyGebm7l+sotvJDzEfZDnqU2057Td7zExx1u4JCjHWbJzBjU3OLpSuyMeGz8RYfvP5kIUAuC8I/RUFnBnuWfkXlwL07uHoy852Fie/dHUijQ6UrJyX2H8vK1qFQaoqOeJSBgBgqFDYfLDvPu8XdJrUkl0jWSjwZ/RB//PqSnp7NgxQKqq6vx9/dn7NixREREAHC2vJG31qexPbcGZ+AOtT03DYjAq28gChslhoICqhcuomH9eiSVCrfrZuBxy62ofbypKmri+OLT5JysQqVW0H5wEB2HBOPoev5R0KmlDczdlM6BnBrCvRz59MYuDIzxvuiL2izLfFtey5t55ZTojfR3c+apCD+SnB2sG+ib4fBCOPC+dRpwu/Ew4Cnwiv6jH4tw9bsTeFCW5dWSJE0BPgGGYB0w+Gvy+X6BJEm3AbcBBAcH/1nnKQiCwKrl23jxZAtGW0/udCgmqdN7KO2M5OcnoVSM5qabxuHh4cHJwjru+uoEtS0G3p6cxMTOgRf9vXtqm3gys5icVj2jvTTMiQzA/48YId1c9csR0eVnoDrDOpsJQGUH3nEQO8o6ItonwTpK2t71fz+2IAiCIPzJMg7uZccnCzC0auk740a6jB6PQqmksnoXJ888iMLcyCGtA+FuD+J7SsvJ6pO0b9+eYcOG4eh4/jpMF5Nf3cILG1LZlVFFrK8zK2/vSbcwd8AauP6itIbns0uwN7fyWeochqubMI5fQeZby1ioj2Zzr9vRKWCI0oZbzTbE9g9GMzQESSU6gP/pRIBaEIS/naFVy5F1qzi2cQ2SpKDnpBl0HTMBtZ0dJlMTBXmLKCz6FFmWCQ6+ldCQu1CrXThbe5b3jr/H/tL9+Dr68nLvlxkVNor8vHyWLFlCWVkZnp6eTJkyhbi4OCRJIruymXc3pvF9ZhUOwC0qe27pH4ZvvyAUtioM+fnWwPSGDUgqFe4zr8P9lltQe3tTntfA8fkp5KdUY2OnpPOwEJIGB2HvfP4GbmWjjre2ZvDt8WI09mpeHNuOGd2DUV+koJIsy2ypbuSV3DIytTo6ODvwXmwwfd2tU5kwtsKxT2HvO6CthugRMOhpa6NXEM7vBuD+tp+/BT5u+7kYCPrZdoH8lP7jF2RZXgwsBujSpct5g9iCIAj/i8amVp54Yw3fGzXEKm24IWwF3lH7aGl2Jff0QAYOmkX79u0B+PJQAS9uSMVXY8fqO3uREKC54O8t1xt5PruEdZX1hNnb8HX7cAZ6/I68k2YjVGf9smhhRSo0V/y0jbO/dTR09LCfRkW7R4BSNLkEQRCEq4u2sYEdH88n8/B+fCOiGH7Xg3gEBmM0NnD49ONoa7dRY5TIlHqRaLmW09tPo9FouO6664iKirri47UazCzYlc3C3bnYqBQ8OzqeG3qGoGprO9cYTDycUcgP1Y30b05l3unn8O16HTW6bix5bjVfhYykSaWks0rNPSYbYjWOuE+Jxjb0wv9GEP5ZxL+WBEH425hNRlJ3/8iBlV/SUl9HXN+B9J1+A84enlgsRoqLvyI37z2Mxlp8fMYSEf4w9vaBFDcV8+GhV9iUuwkXGxce6fII02KnUVlayZdffEl+fj4ajYZrr72W9u3bo1AoyK9u4b1N6axPr8AWmKW0Y3afUAIGhqCwU6HPy6Nm4UIaNmxEsrHBfdYsPG65GZWXF6VZdRz7+iRF6XXYOqroNiaM9gMDsXVQn/e6dEYzS/bksmB3DkazhVt6h3HvoCg0F9j+/x2sb2ZuTinHGrVE2NuypF0oo7001pHWJgOc/AL2vAVNpRA+AAY+A0Fd//gHI/zblAL9gV3AICCrbfl6YLkkSe9gLZIYBRz5O05QEIT/tkPHs3jw61NUKF2Ybl9P345vYOPUQmFhAvbqadx2+zgcHBzQGc08veYMq08UMyDGi/emdsDV4fydxCaLzNKSal7PK8Moyzwa6svdwd7YXaST+Bxt7c/Sc7QFpKsywKy3rlfagFcMRAz+KRDtkwCOHn/gXREEQRCEv0fmoX1s/2QBBm0LfabfQNcxE1AolZRWfM/ptCeQLC0cbHUm2uUhXI5Vc7rxNN27d2fQoEHY2l68ttL5bEur4MUNqRTXtXJtB3+eGhmHt8tPaTN3txVCrDcYeDFnIbMb92Me/SGffpnKQkMLlREDCTUrmGvvRKdWcOzui2ZkOArbC6f9Ev55LitALUlSPtAEmAGTLMtdJElyB1YAoUA+MEWW5bq27Z8Ebmnb/j5Zlrf84WcuCMJVy6Br5fSOrRzftJammir8omIY98gz+EXFIMsyVVXbyc55A602B1fXbkRFPomLS3tqdbW8f+R1vsn4BqWk5JaEW7g58WZa61r5buV3ZGRk4OjoyIgRI+jcuTMqlYriOi3zvj/Ld6fLUAFTFbbc1jOUkMEhKBzU6HPzqF64gMaNm6yB6RtuwOOWm1F6eFCUXsuxZccpy27A3llNzwkRJPQLwMbu/H86LRaZ9cmlvPHDWUobdAxr58MTI+II87z41Ka05lbm5pSxo7YRXxs1b8UEMc3XHZVCAosZklfArtegvgCCusOExRDW9094MsLVTpKkr4EBgKckScXA88BsYJ4kSSpAR1uqDlmWUyVJWgmkASbgblmWzX/LiQuC8J9kNFt489PtfJxtwEflwDNB2wmJW49W60JayrUMG347MXHWUVhFtVpu/+I4aWWN3D84ivsHR6FQnD9V1rGGFh7PLCK1WcdAd2dejQ4k1P48DWaLGWpzfxoR/f8B6caSn7Zx9LIGn7vfBj6J1oC0ZzQoL97pLAiCIAhXG21jAzs+XUjmwb34hEcy/K5X8AwKwWCo4ciph9A37KPCIFGsHkq0tj8ZxzLw8vLilltuISgo6NIH+JXCGi0vbkhlx9lKon2c+Oa2HvQI/6mzt9lk5o28chYXVxFlqGB5ylO0C2nPZtd3efPjMvIc4vBSSzxu68hogwKlyga3m6Kwj3H/I2+L8Be5khHUA2VZrv7Z5yeAHbIsvyZJ0hNtnx+XJCkemAa0wzoia7skSdGi0SsIgraxgVNbNnLyh43ompsIjEtg6Oy7Ce3QGUmSaGxMISv7NerrD+PgEE77xIV4eg6h1dTKouRFLE1dSquplfGR47kz6U7UejXbNmzj9OnT2NraMmjQILp3746trS3lDTre33yGlcklSDJMUNhwW9cQwq8JQ+moRp+bS/X8BTR+/z2SrS3uN92Ix803o3R3Jz+lmmNLjlFZ0ISjqy19p0YR39sflc35e2BNZgubTpexcHcu6WWNJAS48M7UDr94uZ5PQaueN/LK+a6iDheVkqfD/bgl0AsHpQIsFjizBna9CtWZ4JcEo96GyCFwhUUmhP8OWZanX2BV5wtsPxeY++edkSAIwvnllNVz74c7SDPbMdRGx+hOb+OkqaSkOA5F6yzuuHsCNjbWIPCujEru/+YUsizz6Y1dGBTrc97fWWs0MTenlK/KavG3VfNJQigjPTW/rfmQuRX2vGFN0WHUWpdJSuuo6JDe1hzRvgnWgLTz+Y8lCIIgCP8mmYf3s/3j+ehbWugz7Xq6jp2IpFBQWLqK9IznwaJjv9aVBMdHUB4tJEufxYABA+jTpw8q1ZUlZ9AZzSzcncP8XTmoFRLPjIrjhl6h51JhWmSZ1RV1vJxTSoXBxI3lm3iu4FNSkuZy7X4FySYjbrYeTNKpudPLBdsmI/ZJXriNi0BxiVnLwj/X/5LiYxzWUVoAy7BOHX68bfk3sizrgTxJkrKBbsDB/+FYgiBcxRqrKzm2cQ2nf9yKSa8noksPuo2biH90HACtrSXk5L5FRcV61Gp3YqJfxN9/Kmbg28xvWZC8gOrWagYFDeL+TvfjpfRi987dnDhxAoVCQe/evenduzcODg5UNen5aEMKy48XY5ZlRks23NkliKhh4SidbdDn5FD+/4Fpe3s8br4J95tvRuHqRu7JKo7NP0pNSTMunnYMuC6G2B5+KNXnnw6sNZhYebSIj/flUVzXSoSXI29PTmJ8x4ALjuoCqDIYeS+/gs9La1BKcHewN/cEe+OqVoEsQ8YP8OPL1qJLXrEw5QuIGyMC04IgCMJVT5Zlvtx+hrnb8lBLttznd4T2iV+i1zuSdvxa+va8nQ59rAV/LRaZD3dm8+72TGJ9XVg4sxMhHr+dlWSRZb4pq+Xl3FIaTWbuCvLm4VAfHFW/6lg2aGHbs3D0Y/CIgs43WkdH+yZY37eqK5+WLAiCIAhXs9amRnZ8upCMA3vwDotg8rNz8QoORacv53DyvZiaT1CiV1BjO5bwuiTSjmcQGBjI2LFj8fb2vuLj/Xi2ghfWp1FYq2Vskj9Pj4rD52fpPE41ankmq5hjjVo66otYmjoXW/sI7rT9kN27LbgYDQw1qpnm4kCMLSjMMq4zYnFo7/VH3hbhb3C5AWoZ2CpJkgwsaiuW5CPLchmALMtlkiT9/3+ZAcChn+1b3LbsFyRJuo22acbBwcG/8/QFQfgnqy4q4Oj61ZzdvxuAuD4D6Dp2Ih6B1v/n9foKCos+pbj4c0AiJOROQkNuR6l0YlvBNj44+QH5jfl08u7EuwPeJdopmv3797P88HIsFgudOnWiX79+uLi4UNti4L21qXx+pACjRWaYpOauDoHEjYhAqbFFn5VF+fMLaNz8Awp7ezxuvRX3m29C4aIh62gFx384TF25FlcfBwbfGEdUVx+UF8hTWdtiYNmBfD4/mE+d1kinYFeeGx3PkDifiwamm0xmFhRVsrCoCr3Fwgw/Dx4K9cHP1sYamM7dZQ1MFx8FtzCYsAQSJoJC5M4SBEEQrn41zXoeXryLXZUmOqjNTOswDy+PfMpKomkpmMnMW6/FzdcagG5oNfLQilPsOFvJ+I4BvDI+EfvzzGRKbW7l8YwijjVq6a5x5LXoQOKc7H978LIUWH0rVGdAz3tg0LOgtvvtdoLwN5IkyQ7YA9hibauvkmX5+Yul1xQEQfi9so4cYPvH89E1N9N7yky6jpuEQqkkt/BzsrJfwWIxckDnRaLtA5QdzKGEEkaMGEHXrl1RKC6jpsPPFNVqeXFDGtvTK4j0dmL57O70ivA8t77KYOTV3DK+LqvF09LKe1nz6F2XxZvOT7K+0B5Ho5bBeiXdzC4MDnLCrtGAXYwbbhOjUbqcvx6FcHW53AB1b1mWS9uC0NskSTp7kW3PF52Rf7PAGuReDNClS5ffrBcE4epVkpHOkXXfknv8CCpbWzoMG03nUeNw8bT2YzU3Z1JY9Anl5euQZRO+vuOICH8YOzt/jpYf5d3j73K6+jQRmgg+GPQBPb17cvjwYebtn4derycxMZGBAwfi7u5OQ6uRNzemsfRgAa1mC0NRc1f7ABJGRqBytUOXmUnZCwto+mGLNTA9ezbuN92I5KTh7KEyTmxJp7Fah0eAI9fc2o6ITt4XDDIX1Wr5eG8uK44VoTNaGBLnze39I+gaevEcV3qLhWUl1bxXUEGt0cwYL1ceD/cl0qGtYVx4GH58CfL3gksgjHkfOswQ+S0FQRCEf42daWU8/MURGi1KZnilMSBpCSaTLRnHJhDiOp2pjyahbitmlF7WyB1fHqekrpU549oxq0fIb9J0NJnMvJFXxifF1bipVcyLDWaKr9tv03lYLHDwA9jxEjh6wqy1EDHwL7pqQbhiemCQLMvNkiSpgX2SJG0GJnCe9Jp/54kKgnD1am1q5Melizi7fzdeoeFMevolvELCaG0t4uCxu5C1aeTrFbTaTiWoLJS0krNERkYyevRoXF1dr+hYOqOZxXty+WhnNkqFxFMjY7mxVxg2KmuA22iR+bSkirfyytGZTdxRupY7cr/kS/dHGNI0E+rMXFNVSIxdFIkaW+LVCiSdGc2ESBy7+v72vS9ctS4rQC3Lcmnb90pJktZgTdlRIUmSX9voaT+gsm3zYuDn2dEDgdI/8JwFQfgHkmWZvJPHOLJuFSVnU7FzdqHX5OvoMGwU9s4uyLJMbe0BCos+pqZmNwqFHQH+0wgKugkHhxAyajN4b9+d7CvZh4+DD3N6zWFkyEhOnTzF+9+8T0tLCzExMQwaNAgfHx+adEbe23yWT/bl0WS2MBAVd8cH0mFUJCoPe3QZmZQ/P5+mLVtQODricfttuN9wAzi6kLa/lJNb02iu0+Md4kyfyVGEJnoiXSAwnVrawKLduWw6XYYEXNsxgNv6hRPt43zRe2Juy531Rl4ZxTojfd2ceCrcn44uDtYNSk9ZR0xnbwNHbxj+unW6sRjRJQiCIPxLtBrMzF19nC+TqwiWLNzZ/lPC/c5QVR5J1YlZ9B/Vn8QBgecamGtOFvPkd6fR2KtZcXsPOof8shNYlmXWVdbzfHYJlQYTs/w9eDLcDzf1eZo1DSWw9g7I22NNlTXmfXAQhZOEfy5ZlmWgue2juu1L5sLpNQVBEK5I9tFDbFvyIbrmJnpNvo5u105GoVSQkbeAgrx3MVrMHNIH0I47qNiXjd62jvHjx9O+ffsrDgbvzKjkhfWpFNRoGd3ej6dHxeGn+WmW067aRp7NKiFLq2dg0xleTHudZGkIY7RvUdliS7eaYnpb3HCwi6FvoCNOTQZsApxwnxyNyuM8s6WEq9olA9SSJDkCClmWm9p+vgaYA6wHbgBea/u+rm2X9cBySZLewVokMQo48iecuyAI/wAWs5mMA3s4sn411YX5OHt4MfDG20gceA1qOzssFhPl5espLPqYpqZU1Gp3wsMeICDgOmxs3CltLuXlvU+xMXcjzjbOPNz5YaZETyEzLZP5H82noaGB0NBQpk2bRlBQEFqDiQXbMlm4O5cGk5k+qLg72p8uY6JQezmgO3uW8hfm07RtGwonJzzuvAOPG27AbOdEyp4STm1PpbXRgF+khoGzYgmKcz/vi1aWZQ7m1LBgdw57s6pxtFFyc+9Qbu4T9ouX6vnIssy2mkZeyS3jbIuO9k72vJ0UTH/3toB2ZTrsfAXS14OdKwx5AbrdBja/zaspCIIgCFerMyUN3PfpAXJbLAx1yWV85/lIskTOiYmoqkYx6Y4k/CJdATCYLMzdlMaygwV0C3Pnwxkd8Xb+ZYdttlbHk5nF7K1rpr2TPUsTw+jkcoF3Z+oa2PAAmI0w9kPoOFPUchCuCpIkKYHjQCTwkSzLhyVJulB6zV/vK9JoCoJwXq3NTez8bDHpe3fiFRrOxKfm4B0aTnNzNgeT70ShzyWzVYVkfyM+eW6crc4kMTGR4cOH4+h4Ze3UolotL21MY2taBRFejnx1a3d6R/6UziO/Vc/z2SVsqW4kzFTL52lv4JCv4AEeIs3Wm0CdluuMKvyVUcT42xODjKQ1ohkZhlOfgAsOLBOubpczgtoHWNMWwFEBy2VZ/kGSpKPASkmSbgEKgckAsiynSpK0EkgDTMDdsiyb/5SzFwThb2PU6zizazvHNqyhsaoCj8Bght/1ILG9+6NUqTCZmiksWkpR4afo9KU4OIQRG/Myvr7jUSrtSK1O5fO019mavxWlQslNCTdxU7ubKM0t5dPFn1JdXY2fnx9jxowhIiICvcnCxzuzmb8zm1qDme4ouTvCn55jo1H7OKJLT6diznyatm1H4eSE51134X7D9ZjUDpzYWUzyjynoW0wExbnRZXY7/KPczntdZovMD2fKWbQnh5TiBjydbHh0WAwzu4eguYyKwIfrm5mbW8aRhhbC7G1Y1C6EMV6uKCQJanJg9+uQshJsnKD/E9DzLrDT/NGPRxAEQRD+NmaLzOJdWby9NRNnTNwd+y2dgg9RWx1KxcHZBARGc81T7XDUWIsSVjTquOurExwvqOPWPmE8PiIW9c/qQGjNFt4vqOCjwkrslRKvRAVwQ4AnyvMFnPVN8P1jkLwcAjpb6zl4RPxVly4I/7O2tnMHSZJcsbbDE65gX5FGUxCE38g5fphtSz6itbGBnpOm0338FCSFRGrW25QULURntnDUEE6c4UYyj2bh4mJgxowZREdHX9Fx9CYzS/bk8uHObCQkHh8eyy19fkrn0WIy835hJQsKK1BZTDyZ9Rn99x7iPZup7PKOx8ksM6JFTQ+lJx0jXXBvMUKzEbW/I+5TY1D7iAFd/2aXDFDLspwLJJ1neQ0w+AL7zAXm/s9nJwjCP46uuZlTWzZyYvN6Wpsa8Y+OY9BNtxHesSuSQoFeX0FeweeUlCzHZGrEVdOV6Ojn8fQchEWW2VW0i8/TPudE5Qkc1Y5Mi53GDe1uoKW8hZWfr6S0tBQPDw8mT55MfHw8BrOFz/fl89H2TCr1Jjqj5I1QP/qNi8HGzxFdWhrlc+bTvGMHCmdnPO++G/frZ2FQ2nN0RxGndxZj0JkJbe9J5xEh+IadPxisM5pZdbyYJXtzKajREurhwCvjE5nQKQA79cWLFFpkmV21TSwuqmJXXRM+NireiA5kup8HaoUEDcWw+w04+SUobaD3fdD7ATHNWBAEQfjXKalv5cEvjnCkpJkODmVc3+UjHJV6SjIm05g8hA5DQug5PuJcIeLDuTXcvfwkWoOJD2d0ZHR7/1/8vq3VDTydVUKRzsAkHzeei/DH2/YCHcZFR+C72VBfCP0eg/6PiXoOwlVLluV6SZJ2AcO5cHpNQRCEC9I1N7Nz2WLS9vyIV3Ao4x9/Hp+wCBqb0jh06g6UxhLSWtXY29yGW6aSzIYsunXrxuDBg7G1tb2iY+3OrOKF9ankVbcwMtGXZ0bF4+9qnXksyzJrKuuZk11CucHEtYW7mbXma1bbj2Fy0PNYJAU9dCpu8tIQY69GVaWFSi22UW44dvXBPt4DSXllRRmFq8/lFkkUBOE/rqmmmuOb1pKyYwtGXSvhnbrSddwkAmPbAdDcnEFh4SeUV6xHls14ew0jOPhWNJoOtBhb+Cp9OV+lf0VxczEBTgE82uVRJkRNoK6iji2rtpCXl4eLiwtjx44lKSkJCxLfHCrg/S2ZlOmMJKJkTpAPg8bFYBPoTOuZVIpemk/zjz+icHHB8957cJ81C51sy6FthZzZU4LJaCGioxedR4TiFXT+fNH1WgNfHirgswP5VDcbSArU8MR1nbimnS/KS0wdajGbWVVex8fFVWRp9fjYqHg63I9bAr1wUCqguRL2vg3HPrXu0PVW6PsQOPv+oc9GEARBEP4J1p0q4elvT2E0mZgRvoVBkVuoa/Cn+MQzmJq8GHZrLFFdfABrY/WTfXm8uvksIe4OLJ/d/Re1HYp0Bp7LKmFzdQNRDras7hBBb7cL1H4wm2DPm9YvTQDctBmCe/wVlywIfyhJkrwAY1tw2h4YArzOhdNrCoIgnFfuiaNsW/wBLQ319Jg4jR4TpiIpLJw6O4eqks/RWmROGuKIaJpC7tlcPD09ufnmm684PVBJfSsvb0xj85lywj0d+fzmbvSL9jq3/nSTlqezSjjS0EJ8TT6PrlpNlqIjd0Y9Q6NSor2s4kFvN6KbzchNRpQKBQ6DgnHs4oPKTdRm+i8RAWpBEC6qpqSIYxu+I23PTmTZQmyvfnQdOxGvkLBLFj4sbS5lydG3WJ21mmZjMx28OvBQl4fo59+PrIwsvvn8G4qLi3FwcGDYsGF06dIFhVLFmmPFzPvhLEVaA3EoeNLfi2vGxWAXoqH19BmKXv6I5l27UGg0eN53L+6zZqE1qtm/qYC0/WVYzBaiuvnQeVgo7v7nnwZUWt/KJ/vy+PpIIVqDmf7RXtzRP4Ie4efPSf1zJToDS0uq+bK0hnqTmfbO9nwUF8wYb1dsFArQ1sL+eXBkMZj00PE66PcouIpcgIIgCMK/T0OrkWfXpLA+pZxgmxpu67YQb/saisqG03pgAhovR0Y8nnjundyiN/H46hQ2ppRxTbwPb01JwsXOOtLZYLGwqKiKd/IrAHgm3I/bgrys79fzqc2F726D4qPQfhqMfEOkzhKuZn7AsrY81ApgpSzLGyVJOsh50msKgiD8Wkt9HXuXLyN193Y8g0K49rHn8AmPpK7+BIdT7kJtqiK51RaN6jYcUw3k6/Lp378/ffv2RaW6/BChwWTh4325fLAjGxmZR4fFcGvfMGxV1tnH1QYTr+WW8lVpDa6tzczefhiHWk/mB86gRGUhVKniZSdnOjWYodaAXbwHjl19sY10FTmm/6NEgFoQhPMqy87gyNpVZB87hEqlpv2Q4XQZPR6Ntw8Wi/FXhQ89CA97kMDA61Cr3ThVeYovjrzPjsIdAFwTcg2z4mcR7hDO8ePH+XDVhzQ1NeHm5sbw4cPp2LEjarUNm5JLeWdTOnnNeqJQ8LaPJ6PGxWAX7krr6dMU3f4Rzbt3o9Ro8HrgftxmzqRJq2D32gIyDpaDBLE9fOk4LARXb4fzXldGeROL9uSw/lQpMjCmvR+3948gzs/lkvfkREMLi4qr2FhVjyzDCC8NtwV60U3jaA1q6xrh0Hw4+JE1B2biJBjwpMh9KQiCIPxrHcqt4YGvjlLZbGR4wH4mxK+mqcmDiswX0J7xIbyjF4Ovj8PG3trsyK1q5o4vj5Nd2czjw2O5o3/4uY7h/XVNPJFZTJZWz0hPDXOiAgi0szn/gWUZTi2HzY+BpIRJn0LCxL/qsgXhTyHLcgrQ8TzLL5heUxAEAUDb2MCxDd9xcstGzEYj3cdPpcfEaUgKE8dSn6Cu/FuazRJppk4E1AynKK+IgIAAxo4di4+PzxUda19WNc+tP0NuVQvD2/nyzOg4At2s7W+jRebT7ELeLqyiBYmByQXE59pxyCaBVD8zGgU8Ltkz0qzC1sYGx5G+OHTyRul0gfe98J8hAtSCIJwjyzIFKSc5sm4VRakp2Do60mP8FDoOH4ODxtVa+LDwU4qKlrYVPgwnNmYuvr7jkSUl2wu380XaF6RUpeCsdub6dtczI3YGNMHhA4dZm7IWs9lMeHg4o0ePJioqCkmS2HK6jHc2niWzsZUwFLzm6c7YcbE4RLnRmpxM4W2P0bJnb1tg+gHcZl5HQ6PEj9/mk3WkAoVSQbt+AXS8Jhhn999OA5JlmSN5tSzak8uPZyuxVyuZ2SOEW/uGnXuRXojRIrOpqp4lxVUcb9TirFQwO9CLmwM8CbZvy8tVnWUdLX3qazA0QexoGPg0+MT/GY9JEARBEP52BpOFd7ZmsGhPDm7KZh7tvJRItzzSquLRpD+EtspMzwkRdBwafC4AvSW1nIdXJmOjUvD5zd3pE+UJQKXeyJycUlZV1BFsZ8MXiWEM9bzIKGhtLWx8ANLWQUgfGL8QXIP+gqsWBEEQhH+W1uYmjm9cw4nNGzDqdcT17k+PidNx9w+guvYAx07fh9pcx/FWe7ykO1ElN1AulzN8+HC6deuG4kIzlM6jrKGVlzems+l0GaEeDnx2U1cGxHgD1jb39sMneK6imTwXDZHlOgakGCk12PGls4wJM9Ox4QaFPV5J3jh29cEmxOWSs5eF/w4RoBYEAYvFTOah/Rxdt5rK/Byc3NzpP+sW2g8eho29A3p9BdnZb1BSuhyTqcla+DDmBTw9BtJkbOaL9K9ZfnY5ZS1lBDsH82S3JxkbPpai3CJ++PYH8vPzUalUdOjQge7du+Pt7W0tSni0mE93ZnO2vpVAFMxxd2Xi2DgcYtzQJSdTOPtxWvbuRenqitdDD+E2Ywa1dRa2Lc8j52QVKrWCpMFBdBgajKPmt0UcLBaZrWkVLNqTw8nCetwdbXhwSDTX9wzBzfHiPbR1RhNfltawtKSaUr2RMHsb5kYFMNXXHSeVEixmyPgBjiyCnB+txQ/bTYAed4D/bwa+CIIgCMK/RnZlE3d/foSMah3dPFK4IekLaNFwomIKrkeHYLFRMPb+RAJjrcWAzRaZt7dmMH9XDkmBGubP7EyAqz1mWWZZSTWv5ZWhM8s8GOLDvSE+1joOF5K7G9bcAS2VMOQF6HUfKC5ezFgQBEEQ/m10Lc2c+H4dxzetw9CqJaZnX3pOmoFHYBAmUxOHku+lpeZ7GkwSOeY+uJf2pKi0nIiICEaPHo2bm9tlH8tgsvDp/jze35GF2SLz8NBoZvcLx06txNzQwJkNm3leZ8uhiDBcFU5M3tOIf30F6+0dKVPI9EXF/b7uxPYMxCHJC4WdCEUKvyX+qxCE/zCTwUDq7h0c2/Ad9RVluPkHcs0d9xHXZyAqtZrm5gyy035W+NB7OMFBt6DRdKCosYjXj77Bmqw1aE1auvh04cluT9LVoyspySl8vOBj6uvr0Wg0DB06lI4dO+Lg4EBRrZZX1pxhxfFiGkxmQlHwrKuGKWNicYr3oPXkKYpufYyW/ftRurnh9fBDuM+YQWWFiR+WZZN/ugYbOyWdh4eQNDgI+/NMBdKbzKw5UcLiPbnkVrcQ5G7PS+PaMalzEPY2F2/EZrXoWFJcxbfltbRaZPq4OvFadCBDPFxQSBK01sGRL+Hox1CXD85+MPAZ6HwDOHn/SU9KEARBEP5+sizz+YF8Xt6YigoDt7f7mq5+yWSWhaNqvQ2nE654hDkxbHbCuRlNtS0G7vv6JPuyq5neLZjnx8Rjp1ZyorGFJzKKSWlupZ+bE69EBxLpcJFiSCY9/PgSHPgAPKJg+nbRISwIgiD85+i1Wk5uXs+xTWvQt7QQ1a0XPSfPwCs4FFm2UFi6mtSMOagszRxpdcbPfDvmlGoabBoYP3487du3v6JRyweyq3l23RlyqloYGu/Dc6PjCXSzR5eczOlvvud9lxA2d41DQmJ4Sh1jSvP5UunLBlsHwmUFi+L9GTgkHBt/pz/xrgj/BiJALQj/QXptC6e2fs+J79ehbajHNzKasTNvIrJLD5Ak6uoOUFj4MTW1e1Ao7AkImEZQ4E3Y2wdzovIEnx+/n51FO1EqlIwIHcHM+Jl4Wbw4fPgw85LnYTQaCQkJ4ZprriEmJgZJUrAnq4plO06xq7AOBdAXFdNCfOk/OAy7KDdaT56k6JbHaTlwAKW7O96PPoLr1KmUlxrZsCST4rN12Dqq6D42jMQBgdg6qH9zXY06I18dKuTT/XlUNelp5+/CB9M7MiLBF9VFRmPJssyu2iYWF1exs7YJW4XEBB83Zgd6Ee9kb92oItWaxiNlJRi1ENwLBj8PcWNA+dtzEQRBEIR/k6omPfd9fpCDRS1EOxdwe6eP0ejt2VCSQOey+2nKN5PQL4A+k6NQqq3v3OSieu766gRVzXremNieKV2DqDeaeC6jiC9Ka/C2UbEwPoRx3q4XbyxXnoXvboXy09DlZrhmLthcPEWXIAiCIPybGHStnNqyiaPrV6NrbiKiS3d6TpqBT1gEsixTWb2Tk+nPoTKWUmWQKJeH4pLfjsLqChISEhg+fDhOTpcfJC5v0PHypjQ2ppQR7O7Apzd2oX+gI7Vr17Fv4ynWhXdmfZ9hNDoq6VVUx53ZxWwwePOo5I0z8GznEK4fE4PaTrSVhcsjAtSC8B/SXFfLic3rSd76PYZWLaFJneg6dhJB7RKRZRMVlRspLPyYpuZfFj5E4cSWgi18kfYYaTVpaGw13Jp4K1NjptJQ0sDh7w+Tk5ODUqkkMTGR7t274+fnR0OrkaV78vhiXz4FLXrckLhebc+MzkFEDAhCqbFFe/QohTcvQHvwEEoPD7wffRTXaVMpydexe2EGZdkN2LvY0GtCJO36+WNznulA5Q06lu7P46vDhTTrTfSJ9OTdKR3oHelx0Qav1mxhdUUti4uqyNLq8bJR8ViYL7P8PfCyUYPZZM1veXgxFOwDlR0kToZut4Ff+z/zUQmCIAjCP0ZKcT2zFu6l2SQzJXIjQ0N+pKWoA2tVQXRKG49Wa2HwDXHE9vQ7t883Rwp5bl0qXs62rL6jFwkBLqwoq2VOTil1RhOzA714NMwXZ9VFZjbJsnXG0tZnwMYJpn8DMSP+gisWBEEQhH8Go15H8tbvObJ+Na2NDYR16EyvydfhGxkNQENDMkdTn0TSZdBgkkgzRRHWOg3t6XxULkamT59OTEzM5R/PbGHp/jzmbc/CZJF5cEg0N3q2UvrZMjZm6DkV3o1N4ydR6K0mslHLG4cayWpQ8QDu6CUT13cK4sFRcWjOM6BMEC5GBKgF4T+grryUY+u/I3X3dixmC9E9etN13CR8wiIwmZopKlr6y8KHsa/g63MtzSYdy85+y9fpX1PZWkmYJoxnezzLNYHXkHEmgxWfrKC2thYnJycGDhxI586dcXJy4mx5I/O+OsHa1HJ0Fpl2KJnj4caYgeG4dvDGotPSsP476r9ZgT4zE6WnJ96PP47rlCkUZLXw4/tpVBY04eRmS9+p0cT39kN1ntQc2ZVNLN6Ty5qTJZgtMiMT/bijfwQJARcprASU6Q0sLa7mi9Ia6kxmEp3s+SAumLHertgqFNBSDYeXwdFPobEYNMEwdA50nAUO7n/WYxIEQRCEf5w1x4t49NtTOKmbebbnfEIlM7uyOuDgMIx2hyNRu6kY81giXkHOAOiMZp5fl8qKY0X0jfJk3rSOVMhmxp/M5lBDC11cHFiRFE6C8yVGQDdXwrq7IWsrRA6FcR+Bs89fcMWCIAiC8PczGQyk7PiBI2u/paW+jpD2Hek1eQb+0XEAaLV5HEl9CnPTEVrMcFIXQLB5JrbpNeTp8+natSuDBw/Gzu4i6bN+5UBONc+vSyWrspnhERoekIoo++wr1sqhVHp0Zc9IO45H2OFssvDEmVa8S1p5R2WhED0Dor14ZnQ8kd4ilYfw+4gAtSD8i1XkZnNk/WqyDu1HoVKSMHAoXUZPwNXXD52+nOzs1ykp/dpa+NC127nCh/mNBbx69E3WZa9DZ9bR068nL/R6gTj7OI4eOcr81fMxGAwEBAQwceJE4uLikCUFP5wuY9mPJzhW2YQNMESyYUasD92GhGET6IwuLY3yF+fTsGkTslaLXXw8vi/NwXnkKPLSm9j2zhlqSppx8bRj4MxYYnr4olT9NjXH8YJaFu7OZVtaBbYqBdO6BjO7bzjBHhdv7J5obGFJURUbquqxyDDCS8PsQC+6axytI61LT1pHS59ZDWY9hA+AkW9A9HBRgEkQBEH4T7FYZOauT+aTQyWEORVzf5eFeJa15z1THaNMs9EeUhOU4M6Qm+Kxc7SOkiqu03LXVydIKW7gnoGR3DYogncLKlhcXIWLUsk7MUFM83O31nS4mIwfrMFpQzOMeBO6zYYryJcpCIIgCFcrk9HImZ3bOLxmBc21NQTGJzD6/scJjE8AQK+v4ET6izTXbMUoyxxvdcdTdx02WVoKDaXExcXRv39/fH19L/uYFY065m5KZ31yKT3kWp5oLaN+q5Kdru0wOfcjJ07NpjgntAqYXGhkZHYxn9koOIiaCDdHlo6OZ2CMqMck/G9EgFoQ/mVkWaYoNYUj61ZRkHISG3sHuo6dQKeR43B0daO5OYO0tHcor9jwU+HD4FtxcW7PkfIjvPDjvewp3oNaoWZ0+Giui7sOdb2aQ3sOsSNzBwqFgnbt2tG9e3cCAwOpbNTx4ZYsvjpSSJXehB8Sd9s7Mq13KP49A5AUJho3/0DJQ9+gS0lBsrPDZdRI3KZNRx0TR9axSr5/I4X6Ci1uvg4MuTGOqK4+KH6VM9pikfnxbCWL9uRwNL8Ojb2a+wZFckOvUDycbC94P0wWmU3V9SwpquJYoxYnpYJbAry4OdCTEHtbMBng9CprfuniI6B2hE6zrGk8vC5/KpQgCIIg/Fs06Yzc/ul+DhS20MvnKNe3W4EpqzcfOOkYU/IYzRUmuo0Jo8uIUCSFNXC8N6uK+74+ickss3hWZwxedvQ/mkGZ3sh1fu48Fe6Ph80lmh4GrTWdx7FPwCcRJi4B77i/4IoFQRAE4e9lNplI3b2dQ9+toKm6Cv+YeEbc/TDBCdbUkkZjI6ez3qS6fAWybOZkixOOrVNQ5khUGOqJj4+nf//++Phc/mwjo9nCsgP5zP8hlZ5FqbyjN1NnF8lZ+y7YexixTXTmqzB7su0lutYYue/sQXaqHLnL7IujRcVzo6OZ1TME9UXqPQnC5RIBakH4l7BYzOQcPcyRdd9SnpOFo6sbfWfcSNLQEdjYO1BXd4CsUz8vfDid4KCbUNr48n3e93yxaw6ZdZm427lzZ9KdjA8fT0lmCdu/3k5VVRUODg7069ePLl264OzszNH8Wl5dcpgfcqoxA91R8lSAF0MHR+AY644hP4+qeW/SsHYdlsZGbCIi8HnqKTTXjqO+ScGxfaVkLN2PXmvCI8CJYbMTCO/ohULxyxFSBpOFdadKWLwnl6zKZgJc7XludDxTuwbhaHvhP2H1RhNfldXyaXEVJXojofY2vBwVwFRfd2u+y6Zy2LkUji+F5gpwj4Dhr0GHGWB38RQhgiAIgvBvVVDTwswFuyluNjMtah1D/I9wJrMjJc5BDDk1AKMEo+9OIiTBA7B2IC/YncPbWzOI8nbm6SmJLKiuZWdqOe2c7FjSLpQuGsdLH7j0FHw3G6ozode9MOhZUF24A1oQBEEQ/g0sZjNpe3dyaPXXNFRW4BcZwzW33UtI+45IkoTZrCcjfwGFBYtRo+eM1g6paRLmPEdqDEbatWtH//798fa+shHMh3Nr+GjpDhLSz/Kw0oN61y6UOSiIcDHiEubCfDfY7qPCv9XIvOTNNOubecQ4gKYWmeu6h/Dg0GjcHW3+pLsi/BeJALUgXOVMRiPpe3dydP1q6spKcPX1Y+jse4jvNwiFSqKycjOFZ6yFD21sPAkPf4jAgBk0mWU+y1jBirMrqNHVEOkayZxec+jt0Zvk48ks+2EZOp0OX19fxo0bR0JCAiZZwdpjRXy26xgZja04AROVtlzXMZB2A0NQOitp2r6dwtdWoD1yBNRqXIYOxW36NFTtO5JzvIqdCzMpz21EoZKI6OBFfN8AAqJdf1PMsEln5JsjRXyyL4/yRh2xvs68N7UDo9r7XbSHNlur4+PialaU1dJqsdDb1YlXogMZ4uGCEqDoiHW0dNpasJghaih0ux0iBoFC9PwKgiAI/10Hsqu5delBLLKRhzp9TKJtA18VBJCkuJbQg564BNkz/LZENF72ADTqjDy8MpltaRWMSvInsKsPs7ILUUsSL0cFcKO/JyrFJVJzWMxw4AP48WVw9ILr11lTbAmCIAjCv5jFYubs/j0cXLWc+vIyfMIjGXTzHYR16IIkSciymdyi5WRkv4WN3Exeq5qWhlHo8rwxGU0kJMTQr1+/Kw5MV9Q08u2bK3HIqqWPJgajZx8sko4+IbY4Ke1Y5mphWZgSSZJ5KG8dSWWneFO6kexGFX0iPXh2dDwxvs5/0l0R/stEgFoQrlKGVi3J23/gxKa1NNfV4h0WwegHniCqe08sFi2lpV9QWLQUvb4MB4cI4mJfxcdnHHmNRcw9Oo8NORswWAz0CejDrLhZ+Bv9OXz4MAvOLgAgNjaWHj16EBwcTEGNllfXpvPtqRKazBYiUPCERsPEAWF4dPbFVFlO7bKF1K9ejbmmBnVAAF4PPYTrxAnUt9pwbG8pGcsPYGg14erjQO9JkcT08MXe6bc9rpVNOj7bn88Xhwpo0pnoEe7OaxMT6R/t9Zsg9v+TZZk9dc0sLqpiR20jNpLEBB83Zgd50c7JHow6OLUcjiyCsmSw1ViD0l1vAY+IP/U5CYIgCMI/nSzLLDuQy4sb0vG0reehrh/i2+TGwkpvhlbei64UEvoH0HtSJCq1tSZDRnkTd3x5nKJaLdNGRrFLbWJ1USXXervyQmQAvrbqSx+4oRjW3AH5eyFuLIyZJ4oRC4IgCP9qssVCxqF9HPx2ObWlxXgFhzLukWeI6NK9LTAtU1rxPSln56A2V1GhV1JVOxB9Xihms5mEhDj69euHl5fXFR23ISOX7R9spqnWHsklHJ1nCDGuRqL8nZEqbdhhq+T9eBWlNhJjqvdzU9YKlqhuY35TF0I9HFhyfTxD4rwv2CYXhP+VCFALwlWmqaaa5G2bObV1I/qWFoITkhh214OEJHZAb6ggN/etnxU+7E5szBzc3ftzoOwgL/54HwdKD2CntGNc5DimR02nuaiZwxsOs7V8K3Z2dvTq1YuuXbvi4qJhZ3olz3ywn72lDSiBAZKa6ZG+9BkSjk2gIy1791J87xxa9uwFScJpwADcpk/DpnN3sk9Ws3NJHpX5jShVCiI6e9Gujz9+kb8dLQ2QV93C4j25rD5RjNFsYXg7X27vH0GHINcL3otWs4XVFXUsKa4io0WHl42KR0N9uT7AAy8bNdQXwfZP4PgyaK0FrzgY9Q60nwq2orqwIAiCIBhMFp5adYJVpyqI02Rzd+clmEsi+EYfycCMsVhkiWGz44js/NMIrQ3JpTy2KgU7Fxvaj4vgM62WCBtbViZF0M/9MkdVnVkNGx+0jqAe9xF0uE4UQhQEQRD+tWSLhayjBzn47XL+j737jo/iOhc+/juzvaj3ioRoAkQvtulgbNx7S+IU18RxnHpjJzc3N+Umb3Jzk5t6kzh2YifGvTdwwcZgY9N7EQh11KWVVtq+M+f9YxYQHWyKwOf7YT4ze6bsnF2ho3nmzHM6GurIKCzmim8+wNApFyAST/J2dq1kzbbvY43W0h3TaOiYQrimHEOXVFSMZObMmWRmZh73exrRKDXPLWXDW9W0a8Xo1nK8nh4uKBLkiGQMf5TdcZ3fzElhpUWnPNzAL9b9gY+MC/l83/24bFa+f6k57pPDajlVH42iACpArShnBcPQqd24jk1vL6Z67WokkqFTzmfKldeTO2SYOfDh9n+jtfUVpDT2DXzocA/n1epXeez969jds5ssVxb3jb+PS/MvpXJTJS/8/QWCwSBZWVlcfvnljBkzhmAcnlpRy78+WE1jKEoGgjscbm4+r5jSGUUYwW56nnsc39PPEG9uxpqVReZXvkLqDdfTHfOy5v0mdj73IbGwTlqeh+k3DGX4ebk4PYf2pJJSsr6hm78tq2bx1hZsFo3rJhRy18zBlGYeOV9lSyTGP/Z08K+mDrpiOqO9Ln5fXsxV2ak4hDB7Yq16EHa8Zu4w/FKYejeUzFAXv8o5TQjxd+ByoE1KObpf+deAe4E48JqU8ruJ8u8BtwM6cJ+U8o3Tf9aKopwpnX0RbnvofTa2hJlf+B43DH+J7bVDCPRexZidpaQP8nDRHaP3pfSI6Qa/WLSDhz6oIW9cFq25DtaFwzxQmstXirNxHE+qrLAfFn0XNj4BBZPMgRDTB5/imiqKoijKmSGlZPfaVax4ZiHttdWk5Rdy2X3/xrDzp6NpZtC3t7eSVVsfgOAmgnFBdUcFgepxSF1QUTH6hAPT/p11bHzsfarqLQSd2Vgsg0i3BBhVnEpKrw36IDjMxYNz0lkYDpBs9PKznQ9i7XJxf/jrdEfh5slFfPui4WR61XgQyumhAtSKMoD1+brY8u5bbH7nDfztbbhTUpl85bVUzFtASnYOPt8K1m/4OV1dyxMDH36G4qLbCODiXzue5OnKr+GL+ChPL+fn039Oha2CtavX8vCLD2MYBsOGDeO8886jtLSUrU09fP+JTbyyo42IlIzFwldyMrh87mCSRmUSWr2S5n//A71LlkA8jueC88n53gM4zp9B1YYu3v1HI+31vVhsGkMmZjNqej65ZSmH7S3d3hvhxfV7eGZtAztb+0hyWvnKrDK+OK2E7CTnET+PDf4gf2ts56U2H7qEBZkp3FmYxfmpHkQsCOsegVV/g7Zt4EqDC+4z03ikFp/Cb0lRBpRHgD8C/9xbIISYA1wFjJFSRoQQ2YnykcDNwCggH3hbCDFMSqmf9rNWFOW0297s5/N/XU5XWOe2kU8yLWsrr9UMZWjjvVjb7IydW8T515RhsZlB57beMPc+vp6PfH145xVQa4EL05L42dACBrmO8+K1fqU5EGJPA8y6H2b+G1iOIxWIoiiKopxlpJTUbljLB08vpLV6F6k5eVzy1W8xYvqsfYHpUGgPq7f9gGj3MsKGoKpjKH3VU9DjFsaMGcPMmTPJyMg44nvEe/vo3rST1s0NdNR24+vS6Y65CdozkCKPFEs7+fY+KpLTcca9WIQVx9x8Xipx8KuWdrpDfXy++WVm1a3lt/JLVPbamVqazg+vGMmo/JTT9VEpCnACAWohhAVYA+yRUl4uhEgHngJKgFrgRimlL7Gt6pGlKB+TNAzqNm9g09uL2b12JYauUzx6LDM/extDJk9FaAZtbYupXP0QfX3bsNszKRv8bQoKPkN1byv/tfYvLKpZRNyIM6toFp8b/jlcnS5WvrOStXvW4nA4mDJlCpMnTyYpJY1FG/fwrefeY0NnACdwsWbns2PymTCvFGGL0PPiS9Tc/xTR2losKSmk33orqTfeQI+WwdrlTez8wSriEZ2MAg8zbhrGsCk5h+0tHdMN3t3RxjNrG3l3RxtxQzK2KJWfXTOaK8fmk+Q8/AVq3JAs6ujhb43trOoJ4LVo3FaQxe2FmeYFcVc1vPkwrP8XhHsgd4z5qPDo68DmOsXflqIMLFLKZUKIkoOKvwL8QkoZSWzTlii/CngyUV4jhKgCpgAfnq7zVRTlzFi8pZn7Fq7BroV5YMqfKRVBXqyezKidn8FqsTL3y+UMHrc/t+WOFj9f+NcamgucRIdl4XLYeGRoIRdnJh9fLko9Dsv+G5b9ClKK4EuLoXjqKayhoiiKopwZUkrqNm9gxdOP0byrkuSsHC7+8tcZOXMumsUMTEejXayv/Bk9bS9hSElVZzE91ecTjzoYO3YsM2bMOCAwbQQC9O2oom1jLR3VnXS1R+kOOei1ZRK3eYAkIAmX0YPbESLF4iPFnc4wSz5SA/eIdDyTc1mfZeM/du9hS2M35/du5avb/sFT4jN8xT+JwjQXf/5sOQtG56o808oZcSI9qL8ObAeSE68fAJZIKX8hhHgg8fp+1SNLUT6eQLePLUvfZvM7b9DT2oIrKZkJl17FmHkXk5ZXQF9gF7tr/puWlheIxXz7Bj7Myr6CD5pX8tN3vs2qllW4rC6uH3Y91w26jpbKFt5/8n16e3tJT0/nkksuYdy4cfjCkn8s3c0Ta1bTGdMpROPr3iRunFlC7pQ8Itu30P7rn+BftAgZjeIaP578e76CfeY8dm/0sfTxJjoaarDaNIZMzmHU9HxySg9/kVrZ0sszaxp4ccMeOvqiZHod3Da9lBsmFjI058h5KnticRY2d/FwYzt7IjEGOe38dEgBN+elk6QJqH4HVj4Iu94EzQIjr4Ipd0HRVJXGQ1EONAyYIYT4GRAGviOlXA0UAB/1264xUXYIIcRdwF0AxcXqiQRFOVtJKfnd2zv57ZIqCl1tfGPyH7H5vSyrvYkRtWPJLE3mojtGkZyx/wbvezvbufvFjfSOSQOHha8VZ/ONkhw8luPMRdm5G56/C/asgbG3wCX/Dc7kY++nKIqiKGeZhq2b+ODphezZsZWkjCzm33kvo2bPw2I1O2PpepAtVb+lZc8/0WSMGl8unVUXEIt4GTduHNMmTcLd2UXn4vep29VKV3OI7j4rfks6IVcWCDMQbZFRUrwBBqXFyBwkSS/Npa7NILTDSnkMbAjCyTaSpxXgmZhDixX+fXcTL27spiDWxe8q/8zuYAX3+L+J1Wrh3y4ewu3TS3HaVJ5p5cw5rgC1EKIQuAz4GfCtRPFVwOzE8qPAUuB+VI8sRTlu0jCo37qJTW8vpmr1Rxh6nMKRo5l2062JwRJitLa+zu61T9HTsw4hbGRlXkh+/o04kybwSvWrLFx5I7X+WnLcOXxz4jeZkTyDreu28vSbT6PrOmVlZVxxxRWUlZWxqsbHfX9fx9t1XRjA+Vi5pSSXC+cPxpZto/e1V6m7+SkiO3ageTykXn8dKTfeSI8jj7XLm9j1w9XEowYZhV5m3TKMoVNycbgO/TXSE4zx8qYmnl3TwMbGHqyaYF55NjdMLGLW8CxsliPnqNwdDPNQYwdPtXQR1A3OT/Xws6GFzM9MxhLphTV/g9V/g84q8GTDrO/CxC9Bct4p/KYU5axmBdKA84DJwNNCiMHA4e7kyMMdQEr5IPAgwKRJkw67jaIoA1soqnPfwlW8VdnFxIzN3DHuERqaCgjt+Bo5vmTGzy9m6tWDsfRroxeurOPfl1cRnZBButPKwrFljE1yH98bSgkbFsLr3wWLFa7/B4y+9hTVTlEURVHOnMYdW1nx9EIatm7Cm5bO3Nu+TMXci7HazMC0YcTYVfd3qmv/gF2GaO5Op233BYQCaZQIF7mtEHliF4uebCfgyUe3moFokHiSg2QkG2QUQPaIHLLHDiIly4PeG6V7Qxt7VjZhWdLJUATtFkHniBRGzhqEqySZiCH5Q0Mbv6trQepxvlH7GPnNvfwq9gU6woLrJxby3YuHk5185DSbinK6HG8P6t8C38X8H7JXjpSyGUBK2bw3pyXH2SNL9cZSPs2C/h62vreEzUsW42tuwulNYvyCy6iYt4D0/EJ6ezeza/ePaG19FV3vw+0uY8iQ75GXezWtkRD/3Pksz+78Hv6on4rMCn45/ZcUhYtYs2oNC+sWYrPZGD9+PFOnTsWdnMbzK+u55+mlVPWFSQJusjn57ORiRswuJtZUg+9fv8P/8isYwSCO8nJyf/xjHHMvpmpLL0ufaaJzTxNWh4Vhk3MYOaOA7EFJh/SW1g3JB1UdPLO2kTe2thCNG4zITeIHl5Vz9fiCow6uIKXkfV8fDza281anH7sQXJ2Typ2FWVQkuaF9J7z+U3NApWgfFE6Ga/8GI68Gq/3UflmKcvZrBJ6XUkpglRDCADIT5UX9tisEms7A+SmKcoo1dYf4/F+WU9Ud5erSxVxW+harqkeTtuUevHY7875aTknF/sGXDEPyy8U7+FN1C/qEdIZ7XCwcO5gC53G2ucEueOXrsP1lc4Dia/4CKYWnqHaKoiiKcmY07dzBimcWUrdpPe6UVOZ84U4qLlyAzW5e++qhILWbH2Z354NYHEGiHelsr5+JvzcbT28qaZFh9OlOqgBbaoxUT4y8XDtZQzLJqSgkoygFm2N/r+Z4Z4jQlk6antqJbAoA4EdnfZqN8lnFTJ5SgKZpSClZ3NHDf+7aQ30kxmWd73NV5Zv8lVvZ2utm4qA0/n7FSMYUpp6BT01RDu+YAWohxOVAm5RyrRBi9nEc87h6ZKneWMqnjZSSxu1b2PT2Ynat/AA9HqdgxEjOu+4Whk2dhhQhWlpfomr10/T1bUfTnORkX0Z+/o3Y3OUsaVjCj975N9a0rkETGvOK53Hz4JuJ1kdZ/fJqVvWsIjU1lYsuuojx48fT1Kfzh7ereH7LGvp0g2Fo/CAjjWvmDiZ1eDK9b79Jw10/JLRhA8LhIPnSS0m96Ua6vSWse7+Jqp+sJx4zyCpOYvZnhzN0cg5256G/Muo6Azy7tpHn1jbS1BMmxWXj5slF3DCxiNEFR89N6Y/rvNzWzUON7ewIhMmwWfl2SQ5fyM8k26bBzjdg1YNQ/S5Y7GZe6Sl3QcGEU/lVKcq55kVgLrBUCDEMsAMdwMvA40KI32Cm5BoKrDpTJ6koyqmxtq6LL/1tBWE9ztfGPMKY1N2s3nw1qbsuJndIChfdPgpv2v6eU6Gozjef3sAr0SD6qDTmpifx4KgSvNbjfOy3eim88GUIdMD8n8D5XwPtyE9OKYqynxCiCHOg41zAAB6UUv5OCPEj4E6gPbHp96WUr5+Zs1QUpWX3LlY8s5Ca9WtwJSUz4+bPUz54OEZ9PS2/+ysd1V10OurRp27BmtNJJJJJ9c7z8HUV4AzlUhzLJSfDS9bgdLJHFZJZnIwn1XHItbOUklhLgNCWDkJbO4k1m0HpSnSWEkMMS+OGi4byxcL9Axqu8wf4RXUzy3x9DA818udtD/JW/BLu672b/BQnv7+lnCvG5Kk808qAczw9qKcBVwohLgWcQLIQ4jGgVQiRl+g9nQfsHXRJ9chSlH5Cfb1se28Jm95eTFdTIw6PhzHzL2HMvAVkFBbT3b2Syl0P0Na+CMOIkpQ0muHDf0pO9uVs9VXz220vsrj2XgKxAMVJxdw3/j6mpUxj96bdvPWvt4jFYpSUlLBgwQKGDB3Gu9vbuP0va1jR6scKzBU2PjOygPMvHAyRdrqfepTd97yA3tODvbSUnO89gGP+ZVTtCPHey010Na3D5rAw/LxcRk7PJ3vQoXkig9E4r29u4ek1Dayq6UIImDE0i+9fVs6F5TlHzV0VNQze6ezl2dYu3ur0EzEkIz1OfjuiiKuz03BGumHNn2H1Q9BdB8kFMPc/YOIXwZN5xOMqigJCiCcw029lCiEagf8E/g78XQixBYgCX0j0pt4qhHga2AbEga+q8SIU5dzy1Op6vv/cJpJtvfzgvD+QYUTY9uG3SOkYxMQFg5hyRSlav5Qe7b0RvvSvNaxL1zAKkritIIOfDCnEqh3HRWw8Akt+Ah/+ETKHwWeegryxp7B2inJOigPfllKuE0IkAWuFEG8l1v2vlPJ/zuC5KcqnXsvOHaxY+A9qdmzFbrUxzJ2Lt8tN69/WU+1ppc+bD7lJZM94E0/udvRgErt3XEB722AGF47kivnTKR6ai/Uo18tSSmJ7+syg9JZO4h0hJNDg0niJMCssOrMnF3LHjFIGZXj27bOiu4/f1bWyzNdHqh7kh1V/p7crm/sDd6NpGt+8cAh3zRyMy67yTCsDkzCvUY9zY7MH9XeklJcLIX4FdPYbJDFdSvldIcQo4HHMvNP5wBJg6NEueidNmiTXrFnzCaqhKAOLlJKmyu1sfHsROz96Hz0WI2/ocMbOv5Rh503DwE9z8/M0NT9NKFSH1ZpEbs7V5OffQNiSzcu7X+bFqhep9dfisrq4aNBFXF58OdY2Kxs3bqShoQGr1UpFRQVTp07FnpTOE8urWfhhPU2RGFkIrnG7uWVaCcWTcwisWIbvyScJfvQRWK0kzb+Q1BtvoidjONs+aGL32nb0uEF2STKjZuQzZGL2Ib2lpZSsqfPxzJoGXtvUTCCqU5Lh5oZJRVw7oYC8FNcRPg1z39U9AZ5t9fFKWze+uE6GzcrV2alcl5vG+CQ3onUrrPorbHoG4iEYNM3sLT3icjN3paJ8QkKItVLKSWf6PM52qs1WlIEvrhv89JUtPPpRA0O8dXxt8p8JdaXR+dH9OG3JzL9tJMUjMw7YZ1drL597bA2NQzwYyXZ+OrSAOwqzju8N27bDc3dC62aYfAfM/ynYjzNXtaIcRLXX+wkhXgL+iNlprO9EAtSqvVaUj8+IRonW1BDZVUW4qoo9O6rY1NFCpyWEkBZclhEY3ulgMQPEmjBIz23DO+oFnOnriMRsNNaPoaVlBBPGT2HGjBmkpKQc8f2kIYnW+ff1lNa7I6BBIMfN65EQj3X50d1WPn9+CV84fxAZifSZUkre7vTz+7pWVvuDZOm93Fb9BFltffw+fj1tYQtXjcvn/gUjyE898vW6onwcJ7u9/iRRn19gDrR0O1AP3AAgpVQ9spRPrXCgj23L3mXT24vobKzH7nIzes5FjL1wARlFRXR1LWdb5dfp7HwXKXVSU6dSWnofaenzWN78Eb9f9X98sOcDdKkzIXsCXxr5JYbJYWzfvJ233n0LXdfJysriwgsvZPz48VR1RfnFa7t4vWodUSkZj4WvF2Zz2fwy7N4Q3c8+y+4fPYve3oEtP5+sb3wDx4Ir2V0VY9kbTfha1mN3WiiflsfI6flkFSUdUqfmnhDPr9vDs2sbqekI4LZbuKwijxsmFTG5JO2ojwbtCoR5vtXHc60+6sNRXJrgkqxUrs1JY1ZaEjY9DJWLzN7SdR+A1QVjbjQD07mjT+VXpSiKoijnpJ5gjLv+sYKVDX3MyFnJrRVPUF8zkcjaOykYlsb820bhST1wXIgPqjq44/kN+MekYXVa+dvoEi7KPPKF9D5Smqm43voh2L1wy1MwfMEpqpmifLoIIUqA8cBKzAD1vUKIzwNrMHtZ+w6zjxrnSVE+htCmTfQtXUpwZzWdDd10+zX63Hl0e1PwWfYQ12vAYsfiPI+klPFk5qeRNSSTzOJkknPDVHX8NyHfm+iGoK5uNE1Noxk/7gJuunH6EQPTUjeIVPfsC0obfTGwCuxDUqkcksRva9vY0NxCQaqLe68o58ZJRXgcZghPl5LX2nv4XW0z29oDZHR0MX3PdvqCbv4QvxQDwdjCFP58xSgmDko7nR+lonxsJ9SD+lRRd3eVs5kej1O7cR3blr/L7jUfocdi5JYNZcyFlzDigpnEZTtNzc/Q3PwckUgLdnsmebnXkZ9/Aw3hKC9Wvchr1a/hi/jIdmVz5ZArmZkyk/bd7WzatIm+vj5cLhcVFRWMHTuWtMwcXl+/h0eXVrO5O4gLWGB18LnxBYyZVUxk6xp8Tz5F33vvgZR4Z80i5aYb8eeOYduKFnavb8OIS3IHJzNyegFDJmYfMPACQCSu89a2Vp5Z08jyXe0YEqaUpnPDxEIurcjb1zAeTns0xout3Tzb2sXG3hAaMDMtiety07gkMwWvMKD6Pdj8DOx4DaK9kFoMk++E8Z8Dd/qp/cKUTy3VI+vkUG22ogxcu9v7+Pyfl9Ec1Lll6AvMKX6f6g03Eauew+RLS5h0WSnaQek6nl7dwHeX7SQ6Np0Mp42FYwczJuk4ej/3tsJLX4Wqt2DoRXDVn8Cbfez9FOUYVHsNQggv8B7wMynl80KIHMzxIyTwUyBPSnnb0Y6h2mtFOTqp6/QuWULLo0+ysyuTjsyxBN05SKFh6D6M8EfEozvQLDaKK+YwfsFV5A/Nw+m1ARCP97Jh53/T2fwUGgYtzUNpaBjLmDHTmTljJsnJh6bKlDGd8K5uMyi9vQsZiiPsGs4R6TAslZf8AR78qI4Wf5gRuUl8eVYZl43Jw5ZIx9UbifPHzQ08sa0ZX0cImy+METOP7bFKxg/KYMKgdCaXpjOtLPOQNl9RTqaB1INaUT61pJS0Vlexbfk77PhgGSF/D66kZCrmXszo2ReSVVJEe/tbbNl+F12+DwCNjIyZDBv2Q2zeiSyue4sXltzP9q7t2DQbc4rmcFnhZbg73Gxas4mXml5C0zSGDh3K2LFjKRsylNW13fxqcQ1v7t5I0JAUo/Ht1BRumF1KRomNnpdepO6mp4nt2YMlM5OMu+7Eeek17K4VLFvaRHfrRhxuK6NmFDBqej4ZBd5D6rS1yc8zaxp4cUMTPaEYeSlO7pk9hOsnFlKS6Tni5xHQdRa39/Bsq49lvl50CWO8Ln48JJ+rs9PIsVmg4SNY/CxsexGCneBMgVFXQ8X1UDIDNJULS1EURVE+rqWVbXzlkZUgonx74t8Y4m6kZul3scaGc+nXR1E44sAbwIYh+Z83K/l9VTP6+HSGe1w8PnYw+U77sd+scpEZnI4G4NL/MdN6qMGWFOWkEELYgOeAhVLK5wGklK391v8NePUMnZ6inPX0vgA9nP8nKwABAABJREFUzz9P88LnqLaMZE/hjehJNoqGp1KaY9BWvYSGrSuwWK1MuuIaJl95He7k/b2gDSPCtuo/01j3IDYRoat9EHX14xg1ag5XXTnrkMC0EYkT3uEjtLWD8I4uZNRAuKy4ytNxjc7En+vioVX1PPbqJnrDcc4fnMEvrx/DzKGZNPeEWbylhVW1XSzZ3UFTe8C8TQXkWnuY7qhn4vgRTJw6m2F5KVhUQFo5i6kAtaKcAH9HG9uXL2XbsnfoamrEYrNRNnEqI2fOoWTsRELh3TQ1P0XlBy8Qi/lwOgsYXPoNsnOuYYOvjl9te5El9d8jZsQYkT6C+yfdT7lezq5tu3h/2fsYhkFubi4XX3wxFRUVNPTqPLe8lpeffIe2aBwPMNdi58rh2cycV4LeXInvyf+h6u0lEIvhPu88sr7zHfxFE1j/YRvVv6vG0CV5Q1KYdEk5ZROysR40KEJnX4QXNzTxzJoGdrT0YrdqXDwqlxsmFjJtSOYRG7m4IVnu6+W5Vh+vd/QQ1A0KnTbuLc7h2pw0hrsd0LIJ3vsDbHke/I1mCo/hl0DFDTBkHlgdhz22oiiKoijHR0rJX9+r4peLK8m2+/jW1N/jClupeesn5JWUMv+2UbiTDww6h2M6335mIy+GA+ij0piXnsRfR5XgtR7jZnE0AG/8O6z9B+RWwLUPQfaIU1g7Rfl0EWbuvIeB7VLK3/Qrz5NSNideXgNsORPnpyhns1hTE12PLaTlxTeoTT+fppJ7kMLCkMk5jJjqpXLFq6x79S2EpjF+wRVMuep6PKn702NIqVPd8ASVVb/CQR+93bnU1U5g6IgL+epX5pKUtD9dphGMEdreRWhLB+FdPohLNK8N9/hsXKMzcQxOoaYryK+WV/Pcwj3EDYOLRuYyd0Q2/nCMp1bXc/+zm2jxh80DagIjxUZ2fpSbQ2/wxZQWMufcA0NvUTeIlXOGSvGhKMcQCQbZtfIDti1/l4Ztm0FKCkaMZOTMuQw7bzpY/LS2vkJL6yv09W1HCBtZWfPJz7+JPkshL+1+mZd3v0xrsJUURwqXlV7G7JTZdNd0s3nzZoLBIB6PhzFjxjB27FiEO5UXPqzj+TWN7OwNYwGmYuWKgjQunl5CUr6gd/Hr+J56mmh1NVpKCqlXX4398uupabKx9f0m/O0hHB4rI6aauaXT8w/s/RzXDd7b2c7Taxp4Z0cbMV0ypjCFGyYVceWYfFLctsN+FlJKNvWFeK7FxwttPtqjcVKsFq7MTuW6nDSmpHjQOnfDlmdh87PQuQs0Kwy5EEZfbwanHd7DHltRTjX1yPDJodpsRRk4wjGd7z6xhpe3dTA6ZSf3THyQQNNQ2lffw5TLRjBxwSDEQTeaO/si3P7YWlalCowcF7cXZPLjIQVYj9XrqnkTPHsbdFbBBV+DuT9QN5qVU+LT3F4LIaYDy4HNgJEo/j5wCzAOs+9kLXB3v4D1Yan2WlFMoc2b6frHI7QuXUV94YU051+AFBaGn5dL2Xg7O95/le3vv4sQgop5FzPl6htISs/ct7+UkqbWxWzc/iMcsoPe3nRqasczaPAC5s+evy8wrfdGCW3tJLSlg0h1NxhgSXXgGpWBa3Qm9kHJCE2wvt7HX9+rZvHWFqyaYGiOF4sQ7GzrIxo3/9vnpTrxZrqosUsCyTam6Vv5RsOjTMvMQMz4Ngy64Ex8lIpygJPdXqsAtaIchqHr1G1az7bl71K1+iPi0QipuXmMnDGX8hlz8KQ7aWtbREvrK3R3rwIkycnjyc29kuS0ubzbvIYXq15kbetaNKFxQf4FXFZwGSldKWzeuJm2tjYsFgvDhw9n7Nix5BaV8NamFp5bUcdHbb1IoByNy9KSufK8IvJGJhP4YCn+114nsGIF6DqusWNJuelm/IPPY/vKdmo2dmAYkvyhqYyakc/g8VlYbQf2hKpq6+WZNY08v34P7b0RMjx2rhlfwA2Tihiee+gAiXvVhSK8kBjscFcwgl0I5mcmc11OGvMyknH0NsPW582gdPMGQEDJdBh9HYy8SuWVVgaET/MF78mk2mxFGRja/GG++H/L2NYdY0HhUq4rf4H2LZcS2nMdl9w5hvyhhw6KtLu9j1sfW0PdYDdGsp2fDi3gjsKsY7/Zlufgxa+CKw2u+QsMnnUKaqQoJtVenxyqvVY+zaSu0/vOO3Q98iidW+uoL7uM5qzJCE0wYlo+JaMkW5a+zM6P3sdqs1Mx7yImXX4tyZkHtokdvpWs2vgADqOeUCiJmtpxZBct4LK5l+P1eon7woS2dBLa2kG0zg8SrJkuXKPNoLStwIsQglhc57GVdTyyoo66ziBCmOMMA9gtGqMLkpk4KI2y/GQ2aDrPdHcTMCQLOldwX92/mFA0FKZ/C/LHnf4PU1GOQAWoFeUUkVLSVlvN9uXvsP399wj2dOP0JjH8/BmMnDmX7MGD6Ox8h5bWl+nsfA8pY7jdZeTmXElm9qVs7mlhUc0i3qx9k2A8yKDkQVxVehWjjFHUbK+hqqoKKSUFBQWMGzeO4eXlrKsP8OyyGt6u6yIsJXkILnG5uXp8ASOm5BDZsoqeV1+j7913kZEItvx8ki+/HOvsBVS3utj+QRP+jjBOj40R5+cycno+abkH9pb2h2O8urGZp9c0sKGhG4smmDM8mxsnFTJnRPa+ARcO5ovFeaWtm+dafazsCQBwXoqH63PTuTwrhdSY38wnvflZqFsBSMgfb/aUHn0tJOef4m9MUU6MuuA9OVSbrShn3ubGHr7wl+X44wZfKn+CKXnraProDlJT53HRl0bjSjo0j/RH1Z3c9vwGekanYnNaeXB0CRdlphzm6P0YBrz7X7D811B8Ptz4L/AeR0BbUT4B1V6fHKq9Vj6NjECA7udfoOuf/6S7I0JD+TU0J1egWTVGTssnf2iEzUteoHrdauwuF+MuuoyJl12NOyX1gOP09lXywfp/wxbbSiTipK6hgqScS7h63nU4QpoZlN7SQWxPHwC2PM++ntLWHDe+YIz19T5W1/p4a1sL1e2Bvamj8TqsnF+WwdTSdMYXpzG6IJm2uM6f6tt4oqmDmGFwVfu73NfwBOVDpsC0b0DWsNP6OSrK8VABakU5yXq7Ovblle5srEezWBk8YTIjZ82lZNw4/P41tLa+TFv7m+h6Hw57Djk5l5OdcwVVwSiLaxfzZt2bdIW78Ng8zC+ez+zk2YTqQ2zZsoVwOExSUhJjx45lzJgxtEbtPPteDa/uaKUzppMEzLM6uKo8h/OnFRFv3o7/tdfofeNNjN5etPR0rPOvJjhqJl3xVFpqeuhs7ENKKBieZvaWHpuFxbY/0GwYkg+rO3lmTQOLt7YQjhkMy/Fyw8Qirh5fQFbS4R/JDesGb3f6ea7Vx9udfmJSMtTt4IbcdK7JSaNIRGDH62YKj93vgBGHzGFmTunR10FG2en50hTlY1AXvCeHarMV5cx6aX0j33lqPU5LiG9O/D/ybD72fPBNJsyZw/j5xYek9AB4fl0j315aSWRMOhkOK4+PK6MiyX30Nwr74fm7YOcimPAFczBE63EMoKgon5Bqr08O1V4rnyaxlhZ8jz2G7+ln8OseGsfeTJO9DKvNwsgZ+WQX97DxzRdo2LoJpzeJCZdeyfiLr8DpPTD9ZCi0h/fX348IfYiu26hvHIk19SKuHnsN2u4Qoa2dxFuDANiLkszUHeXp1Bhx1tV1s7bOx/p6H9UdgQOOm+qyccXYPG6fPphBGW5EIm90VTDMH+raeK6lCyF1bmxZxL1Nz1E68kIznVZq8en5ABXlYzjZ7bUaJFH5VIqGQ+xauYJty96hfusmkJK8YSOYd/s9DDtvGjFqaW15mY9WfoNotAOrNYns7EvIybmSZsPLy7VvsXjjt2gJtOCwOJhVOIs5GXPwdHnYsn4LyzuXY7VaKS8vZ9y4cdhTc3nxw3p+/pdNVAcjWIELhI0rSjK5aMYgbPFmel9/jYa/vE60o4tA5hDC076IP7uc9m4bwdYotAawOcLklCYz6bJShk3OITXnwIvLhq4gz6xt5Lm1jezpDpHktHL9xEJumFjEmMKUfQ1hf4aUfNQd4LnWLl5p78YfN8i2W7mtMJPrc9IY7dQQVW/DK89C5WKIhyClCM7/qtlbOrdCDcygKIqiKKeYYUh++eIm/rqqkSJXG9+a8nus/hSa1/+Ey++aTe7gQ3tDSyn57du7+PXOJuLj0in3uHh83GDyHMcINHdVwxO3QMcuMzA9+Q7V1iuKoigDTmjLVroeeQT/4sX0uvJonHwfzUYeNoeF8bPySc1qY8Piv7LquUo8qWnM+txtjJl/CXan64DjRKNdrNjwQ6L+NxBAY/MIJPO5LH0+YmeQwKqdIMBRmoJcMIgdKVY2dPSxrmoPG97ZSm8kDkCq20aq24bDqhGJG0wuSeOeOUOYPSzrgGvxLb1BflfXxqvtPpxGnC82vcRX2l6jYOzVcOUb4M0+jZ+iogwMKkCtfGoYhk795o1sW/4uu1atIB6JkJKdw3nX3szIGbNxpERpaXmZDVt/TShUixB2MjPnkJtzFT3WYt6oW8Li935Onb8Oq2ZlWv40vjL0K6T2pLJr+y7WNJs9FIqLi5k2bRqFpUNZsqWD+5+rY03XDgDGYOF72Wlcfv4g0tNC9L25iD3f+S86/Hb8aUPoG30v3SID3RAQhuSgg8IRKeQOTiFvSArp+V60g3pGhaI6i7Y088yaRj6s7kQImD4kk+8uGM7Fo3JxHpSHeq/tfSGea/XxQquPPZEYHovGpVkpXJ+TzvQUF5ba5bDkWdj+CkR6wJ0J4z9r9pYunALa4VODKIqiKIpycvVF4nzlz8tY3hJicvom7pjwD/rqxhIPf4vPPDAJp+fQwY0jcZ3vPreZZ4N96CNTmZeexIOjSvBYD/93wT7VS+HpL5gB6VtfUPmmFUVRlAFF6jp9S5fS9Y9HCK5ZQ2/2cBrn/4jmUBp2h5WJs/LxJDewftHvaK+vJTkrm3m338Po2RditR94gzYUamTF+h+jB99DEwbtbaXQeREz/VOx9wp0rYvWQV52lKax2YizoambnW80ICVoAoblJHHluHyK0t1sbeph8ZYWekIxFozK5a6ZgxlffOB4EGt6Avy2toW3u3pJMsJ8rfFZ7uxcQtakz8L174Ir9TR+kooysKgUH8oZJaWkt7ODWCSMEY+jJyYjHuu3HEff9zrWr8x83X8/PRY7dH/dnHc01BHwdeHweBh+3gzKZ84hsySLtvbXaGl5id7ezYAgLe08cnOuIuoaxVsNH7CodhG7fLvQhMaU3CnMy55Htj+bqh1VNDU1AVBQUMCoUaMYOnwE6/dEeG55De82+ohKKETj0iQ3V08qpLTUTvNb79H4wQ7aAy78KYMJunMA0CyCrOIkcstSyBtsBqU9qYdPxSGlZF19N8+ubeCVjc30ReIUp7u5YWIh104spCDVddj9miNRXmjt5rnWLrb2hbEImJ2WzPW5aVyUkYSneZ2ZU3rrCxBoA3sSlF8BFddB6WywqHtaytlLPTJ8cqg2W1FOr/rOILf+/l3qI5JrSxazYMgi2jdfyfCK+5gwr+SwT0f5AlHueGwtH6aAkePi9oJMfjK0AMvRekFLCSv/Cm98H7KGw82PQ3rpKayZohyeaq9PDtVeK+caIxik+wUzv3Ssrp6+wVOor7iRlh4XDreVijn52O1VrF/0Ar7mPaTlFzL16hsYMW0WFuuB17HdPZtZsf4/sembQAra20pxNVzIEN9EaiwWdmTY2Wox2NQdxBeKAZDstDK+OI2Jg9KYUJzG2KIUqtsD/OW93Sze2oLNonHdhELumjmY0sz940JJKVnu6+O3tS2s6AmQHu/jzoYnua13JSlTb4cJnwf7MdJuKcoApHJQK2ctPR6js7GBtprdtNVV01ZTTXtdDdFQ8JMdWAgsVmtismGxWtESc3PZnHvTMhgxbSbFY0bS1fMOrS0v0+X7EDBIShpFbs5VaElTeadpHYtrF7O5YzMA47PHMy97HgWBAmp31tLY2AhAXl4eo0aNYsjwcja3RHn9owbeqe2kRzdIRXCh3cGVI3MZlOWgfs12WnZ10SXTidvMxsph1cktSyV/ZDa5ZSlkFydhtR+5V5OUksrWXhZtbuGVTU1Utwdw2SxcWpHHDZMKmVKSfkjvaoDeuM5r7eZgh+/7+pDAhGQ31+WkcWV2Klm+nWZQesuz0F0PFgcMuxgqroehF4Ht8MFuRTnbqAvek0O12Ypy+ny4s507//ERMeLcXfEoozO207rpDi664W5ySpIPu09tR4DPLVxDdYkLmWznp0MLuKPwGAMbxiPw2rdh/b9g+GVw7V/BkXQKaqQox6ba65NDtdfKuSLW2orvsYX4nn4avaeH4MSLqSu9nJYODVeSjYrZuWBsY/3iF+ntaCerZDBTr76RoVPPR9P2X19LKWlue4e1m3+OW6slHrfR1jwcveZydocHscopqIpE0RMhsiHZXiYUp+4LSJdlmU8zSylZurOdv763m4+qu0h2Wrn1/EF84YISspOc+97PkJI3O/z8rq6Z9b1hcmM+vlK3kM9FtuKZ9lWouFGN7aCc1VSAWjkrRIIB2mtraKvdTVti3tnYgKGbuZlsDidZg0rJKhlMVvEg7G7P/iCz5TABZpttX/C5f9DZYrUhNA0hzIZCSh0p40gZwzBiSBlPzGMEAjtpaXmZjs4lGEYUl7OYnNwrcaXO4v32KhbVLGJt61okkvL0cubnzmdQaBANuxpoaGgAIDc3l1GjRlEyZATrmyK8vrKB9xp9hAyJF5iu2ZmZmUSJ20ldTQe+HpCYqTC80Q6yc60UnT+UwillpGS7DtvrqT8pJVub/Cza0syizS1UdwQQAqaUpHPdhEIuHZOH13For+aYIXm3yxzs8I2OHsKGpMRl57qcNK7LSWdwuMkMSG9+Dtq3g7DA4NlmUHrEZeA8NI+lopzt1AXvyaHabEU5Pf7x5g5++k4VaVY/35z8RzJEkGDz97j81mtxuA9N6QGwpraLLz67nq7RqdidVv42uoT5mcdo0/va4KlboeEjmPlvMPv7Ko2Xckap9vrkUO21cjaTUhLasAHfvx7D/+abSMMgPPcWqjNn0toSx51sp2J2NvHwRtYvfolgTzf5w8qZeu2NlI6bdMB1tmHE2V3/NDt2/h63tZ1oxEV13SQa6y9lnUymUupoAqaUpjO5JJ0Jg9IYX5RKqvvA4HFMN3h1UxN/fa+aHS295KU4uX16KTdPKT7gmlyXkpfbuvldbTM7glGKI218re6f3Egjjulfh/IrQTtGui1FOQuoQRKVAUVKSV9XJ2211bTV7t4XlO5pa923jTsllezSMkrHTTQD0oMG404XBIO76QvsJBh8H0MPE5cxpBHDkHFkPI4RjSaCzfuDzPuXDy6LI2X0mOdrs6WTn38TyenzWeVr55/Vi/mo6VF0qVOaUspdI+6iLFxGS3ULdWvr2MMecnJymDt3LvmlQ1nbGOXh1Q2seH01USlJQ3CRxc556R5SgpLaljC+riB+3Ueyv5ZSvZW80XmUXjmN5PFzjhmQ3vuZbmjoZvGWFl7f0kxDVwiLJjh/cAa3TS/lolE5B9yZ7b/fOn+QZ1t9vNTmoyumk26zcEteBtfnpDFB+BHbXoS3n4U9iT9Wi84zBz8aeTV4j9G7SlEURVGUUy6mGzzw0Aqeq+lhqKeB+6b8EenLJD3zIRbcNfGIf0u8tGEP33inksi4dDIcNp4YN5jRScd4ZLhpAzz5WQh2wg2PwKhrTnp9FEVRFOV4GZEI/tcX4XvsMcJbtyK8XiLX3MMu21jamsJ4IxbOuyqHUM9qVj77R8KBPoorxnHeNTdSOLLigDZS10NsqvwzTY2P4rT2EQllsmzX56nsGM8WLBjA6HwP/zGhkCvG5h32GhsgEInz5OoGHl5eTVNPmGE5Xn59w1iuGJuP3br/hm7UMHimxccf65qpCccZFmrgj7WPcrUziHXuN2HIhWrAYUU5ChWgVo6bYej4mvYkgtHm1F5bTajXv2+btLx8csqGUTH3YrJLBpNelIVhbSPQt5O+QCWBvsW07t5JvLJn3z42WzpWixehWdGEDaFZEcKGJmxomh0h3Im5FSGsaJrNXK/ZzNfChti3bEVodnMurAjNPI4QVrCmsrEvyP/VvMnylV8jZsQo8Bbw+SGfZ1hsGB01HdRuqKWDDrKyspg9ezZZxUNZ3RDmT2v2sOb1dehADoKrrXameJw4fBEaugVdnSFi/jpKOzaQGW0gf0YFqXdehnvSJMRx9EIyDMnaeh+LNreweEszTT1hbBbBtCGZfG3OUC4cmUO65/CP/1QHIzzX2sVzrT5qQ1GcmuDizBSuy0ljjkvHVvkqfPgM1L4P0oDcCrjwxzD6Wkgt/sQ/F4qiKIqinBydfRFu+807bAwazMpexefGLqSnfjzT5vyZvJLMw+4jpeRP71bxi+17iI9Lo9zj4vFxg8lzHOOx4S3PwYtfBXcG3P4G5I09BTVSFEVRlGOLtbbie+IJup9+Br2rC8uQ4fTd9f/Y1ZONrzVMUgacf3Uu/rYP+eDJxcTCIcomTWXqNTeSN2T4AceKRrtYtfmX9HW9giZibNwzmVV1c9keziWCIN9j5yuTi7hmQgFDso+czqqjL8KjK2r554d19IRiTClN57+uGc3sYdkHpNYM6gaPN3fyf7UtNMV0xvTt4u+1j7IgzYl22bdg0AWn7HNTlHOJClArh6XHY7TX1tBaU7UvGN1RX0c8GgHAYrWSWVzCkMnnkVUymMyifNxZOtF4PX19lfQFXmdPYBfVm1r2HdNqTcLjGUZa5nzCWjrtup36cJz6YBexWAxDGkgpMaSBgbkskf3K4xhED1O+f3tDGkjkYcubA82E4iGyXFncWHoj5fFyuuu6qX2rljVyDRkZGcyaNYuUgjJW1Uf47ao9bFi8AYBiND5jdTJBk8juGM1Rjdb2MKk9uxnSvZXifEnmjJG4J30O19ixCPuxc0nFdYNVtV0s3tLC4i0ttPVGsFs1Zg7N4jsXD2deeQ4prsM/wtsRjfNSm4/nWn2s8wcRwPQ0L98YlMNlKTaSdr8Jbz0Hu94CIwbpg2HGd8wUHlnDD3tMRVEURVHOnG11Pr7wl/fpknDrkBeZVbKUnj3XcMVN/++IKT2icYMHXtjEU3196CNTuTA9ib+OKsFjPcqjw4YB7/4Mlv+P+STVTf8Cb/YpqpWiKIqiHJ6UktC6dXQ99hi9b74FhoF19gI6Rl/FzhqN8M4YmUVWLrg2m876ZSx/7G30eJxh509n6jU3klVccsDxAoF6Vmz4MUZwOQ19BbxbcxMb28fSJ20kWzSuGZXDdTNKmFicdtixm/aq7Qjwt+XVPLu2kahucNHIHO6eVcaE4rQDtvPHdR7Z08Ff61vojEvO69nMr+v+yey8fMT1P1M3fhXlBKkAtQJAb1cHzTt30LSrkuadO2itqUKPmaPVOjweskvKGDt/AVmDSkgusGP1+AmGqggEttPX9xI7W+qgxcxnrml23O4h2DyjiXin0xa3UhOOUOlvo665ns7w9n3vqwmNXHcuDqsDDTOXtCY0BIm5EGho+5YPKBdHKefQ8snpkynXywk0BKhZUsN6uZ709HSmT5+OK7eMlbUhfvFhM5V+c3DEYWjcodkYE40Q6ZO0IWg2DNJ7axmT5Kd0bBbpF0zBNepLxxWQBvOx3Q93d7JoSwtvbm2hMxDFadOYMzybBaNzmTsimyTn4S9C60MR3ujws7ijh496+tAljPI6+WFZPtdkeMhrXA4rnoUdr0MsAEl5MPVuGH0d5I9XjxMpiqIoygD12vJqvvXaVqxalO+M+xtlKTVY9H/n2lu/dMSUHj3BGHcsXMsHyRKjxMsdBZn8eGgBlqO195FeeP4uqHwdJnweLv21GqBJURRFOa2McBj/a6/T9dhjRLZvR0tORrvpbuoyz2P3tj6MLTolFWkUDY9Qv+Vdlj7yAQAjZ85lylXXk5ZXcMDxfN0bWbH+R/T0NbGyeRIfNP6QjmgaNmBWRhLXzSpl7oR8HEe7eQtsauzmr+9Vs2hLM1ZN47qJBdwxYzBlWd4DtuuMxnmosZ2HG1rxGzCnaxVfb3ic80pGwWf/DzKHntTPS1E+LVSA+lMoHo3SWrOb5l07zKB0VSV9nR0AWGw2ckqHMPbiy8gpy8aTHcewthIM7KIv8CKtgWpa6vfmetawOwvRbbkEk8poigl2B8Ns7W6jsb4eQ9aiSQ1n3Em2NZtCWyEzLTNJdaTiMlxYo1bioTihjhCGYZzyegeDQTYbm0lLS+OCCy5AZJayqi7MT1a0UBfYigBGY+GrUqO8t5e+uJtOzcEewyBba+e8wUHK5o4gZdwchPX4/+tE4jofVHWwaHMLb25rpScUw2O3MLc8h0tG5zJ7eBZu+6HHk1KyuS/E4o4e3ujoYWtfGIBhbidfLcrmmqwUyjvXwob/g+0vQ8gHrjQYcwOMvt58lEgNvqAoiqIoA5aUkv95ZBV/quwg1+7jW1N+j1ePUzb4X5QNm3LE/eo7g3xu4WqqBrmQyXZ+PrSA2wqPMZZEVzU88Rno2AmX/Aqm3KluXiuKoiinTaypCd8TT9L9zDPo3d3Yhw5B/9rPqQwXsWeXH2tHgPLzc0hKa6Tyg0fZ8V4ldpebcRdfzsTLriI5c//TPlJKmlqXsHzd/7KlPZMPm+axu6cUgHEWK/eOyePqS4aSmuY68vnoBmvrfCytbGdpZRs7WnpJclq5e1YZX7qghOzkA3NSt0Ri/Lm+jX/uaSdsSC7tWMZ9e55h7Ihp8MV/QWrRqfngFOVTQgWoz3FSSvztbWYwelclTbt20FZTjaHHAUjOyqFwxChyhw4ipUiiuTvo69tMj/9hmsNtUG8eR7NlErNm47OPoSkKu3qD7OzsRY+AU+/GqYfxGl4ytAzGyyLOi18AEdCj+iHn1Ct6MTwGXq+X5KRk8nLz0E7DaPEut5tochEf1Yb5wQettIYrsQATsHBNzGCYv5cuLY0e4WAPGgXZMcadl8bgeaOxHyHVxpGEYzrv7Wxn8ZYW3t7WSm8kTpLTyvzyHC6pyGPG0EyctkODxzFD8mF3H4s6enizo4c9kRgaMCXFw3+W5bMg3UNpxwbY8U948XnobQabB0Zcagaly+aqnlCKoiiKchYIhuLc+5t3eKc3RkXyLu6Z/Bdi/mxmzX8WT/KRU26sq/fxxWfX0zEyBbvTykMVpVyYkXz0N6teCs980Vy+9XkYPPtkVUNRFEVRjkhKSXD1anyPLaT37bcBcM2dT+ek61lXbaF7cwhPaoSJl+QQD29i69J/0NfVSWpuHnO+eDejZ8/D7to/4K9hxNlS9SQvrnqbda0j2NRxN4a0UECce5K9XDunjLIpBQjL4W/ANveE9gWkP6jqpC8Sx6oJJpWk8R+Xj+TGSYWHPNFcF4rwp/o2nmzqQJeSa1rf4mutLzG8YgFc+gJ4j3GDWFGU43LMALUQwgksAxyJ7Z+VUv6nECIdeAooAWqBG6WUvsQ+3wNuB3TgPinlG6fk7JVDxCJhWndX0bRrx76gdKDbB4DV7iC3bCgTLr+SzMFuXJkBwrFd+P1L6A5U0d1mpuiIael0xVNo7i2nyWely+/EEvPijDtx6k5cuotsaeHgSyeLxYLL48XmScHi9GBxeJE2J9ic6JqduGYnJi2EDUFrKMau3ij+zih9jWZe6Y/v+Hr/NPZF6I5XYQemYuUL4ThlgSCtlgx68dDkdFEyIonpM8soGpGOxXZ8QXMpJd3BGDWdAarbAyytbOOdHW0EozqpbhuXVORyyeg8LhiScdjHinrjOks6/bzR0cOSLj/+uIFLE8xKT+LfSnO50AOZde/C6jdg15sQ7gbNBkPnQ8XPYNgCsHtO4PNSFEVRFOVMatjj54t/WsZuAy7Nf49rRz1PNHgel139DyyWI99ofn1zM/e+vZ3QmDSyHDaeGF/GKO+Re4chJaz6Gyx+ADKHwS2Pm+NSKIqiKMopZIRC9Lz6Kr7HFhKprMSSkoL783fRWDCL7ev9RD6Kkj0oialXptBZ/wErn1lKPBaluGIc8++8l9JxExH9OrFFY0Eef+8R3tjazob2UYTiN5BsCXEpklvK8pk4fzDOkpRDziMaN1hT28V7O9tZWtlOZWsvAHkpTq4Ym8esYdlMG5Jx2DSblYEwf6hr5YXWLixS5+bm1/hqxxsMmnAdXLMInIe+n6IoH9/x9KCOAHOllH1CCBvwvhBiEXAtsERK+QshxAPAA8D9QoiRwM3AKCAfeFsIMUxKeWhXWuUTkVLS3dpM865KmnftoGnnDtrrapCJdBmpuXkMqhhL9tAckvLj4Giip3cjvb0v0xKMQD1EDTsd4STa/EX09mQS6y7AFkklIi30SCchaSOmWYnanIRtLoTDjaE5iAkLEcMMNofiEIga9EXiRAIHp+oIJ6b9XBK8QuBB4EXgBrTjDDJ/ElOxMCUUoTiqs0ekEDCSaE5NZfDEXMomZJNXloJmOXxQWkqJLxijpiNAXWeA2o4AtZ1B6joD1HQE8Ifj+7bN8Ni5enwBl4zO5bzBGdgOc8zmSJQ3Osyg9Pu+PmJSkmGzcllWKgsyU5gh23FXLYK1i6D+QzDi4EqH4ZeYAemyueA8Rm8pRVEURVEGnI8+rOfulzYSxOCuEU8ypXAVSZ47mDL3gSPmm5ZS8tdl1fzX1gbiY9MY6XHx+Lgych1HecIrHoXXvw3r/gnDL4VrHwRH0imqlaIoiqJAbM8efE88Qfczz6L39OAYPhzHd3/Obr2MqvWdGHVdlI7NIKugk9oNL/Leoxux2uyMnDmX8ZdcQWbRoAOOt7Wxnv978zU+qEumOzIIh5ZLhaOLK3Bx+ZRxpEwrwJpx4I3aPd0hlla2sbSynRVVHQSiOjaLYHJJOt+fOILZw7MZmu09Ypu7sTfI72tbeK3Dj8uIcMeeF/lK9zJyp34ebngb7O7D7qcoyidzzAC1NLu29iVe2hKTBK4CZifKHwWWAvcnyp+UUkaAGiFEFTAF+PBknvinTTwWw9e8h86GOjob62mrraZ5VyWhXj8ANqeLvCFDmXz1FaSVWCCpja7ABiKBx+g2euluB90Q+EJeenrzCHfnEvTn0hNKoVu66cZFn81Ln/Dik066IwedQOK1SwOvkHiI4TWipEpJgSHwoOEVNnNC4IHEXODSDZzxGDZDxyYlMSGICEFIaoSwENQFxiEB6kN7VB/SfBwjpn3w6mAcwjKZ1iwXwydkMXhcNtmDkhCJEXyllHT2RajtDFDbETTn/YLQvf2C0JqAgjQXJRkerhpXQEmmh5IMN4MyPJRmerAcNCqwlJIdgTCLO3pY3NHDxt4QAINdDu4ozGRBupdJPVuw7HoS3n7DzA8JkFUO599rBqYLJ6uc0oqiHJUQ4u/A5UCblHL0Qeu+A/wKyJJSdiTK1BNPinKaSCl57LH1/GhrE15LiO+N/z8KvM0MH/YniooXHHG/mG7wg5c285i/F708lQvTk/jr6BI8lqP8TdDXDk/fat7knvEdmPPvcBrSqSmKoiifPlJKgitX0fXYv+h7510QAu/cefTNuomNdQ6aVvVgc/gon5aBzb6LHcufYPu7zXjTM5h+yxcYM+9iXEn7O1/1hKI88cH7PLm6ltqeHDRRwPCkGq60dXItoyibOQXP5Fw0lxnOisR1Vtf4zKD0znaq2szwVUGqi6vHFzB7eDYXlGXgcRw9/PVRdx+/q23mXV+AFD3ANxuf5Y7gejLOvxMqfqxSaSrKKXZcOaiFEBZgLTAE+JOUcqUQIkdK2QwgpWwWQuzN+FAAfNRv98ZE2cHHvAu4C6C4uPjj1+Aco8dj+Jr20NFYT2djPZ0N5tzX0rSvZ7QQGml5+ZROmIirRBBLaSZoVCEii4mIHloDQAACIQ99/ix6e0fQ1p1PU18u3RYvAWsyfXjpjNrpiewPBLtikmIkk/Q4RTqUYCVf2Ei22EkSGm7AauwPvEYNSVhC2JBEEvN9rw0IS0mPIYmBmd5Dg5hmxrqjSKKaJJyYWywCq0XDZhHYLJo5aQKbVcOmaVj3lZtzq5aY7y3X9u5vbnO4HtlOr42SMRnIFBt1nUGWd/qp3dmcCEQHqOsI0hs5MAhdmOZmUIaba8YXJILPZhC6KM2N3Xr0C724IVnVE+CNRFC6LmwOLjkx2c2/D87j4iTB0MZliI1vQNVb5iCHmg1KpsGk22HYxZBe+rF/lhRF+VR6BPgj8M/+hUKIImA++0YWAPXEk6KcPrFwjP/432U82RNmkLOVb079HQ5dY9r01/B6hxxxP384xt2Pr+M9j4ExyMudBZn8aGgBlqMNbti80RwMMdgJ1/8dRl93CmqkKIqifNoZwSA9L7+Cb+FCIrt2YUlNJeW2u2gdeiFr1vjxvx3Cmy4ZNz+FkG81m99aQjQUIm/YCKbdfCtDp1yAxWqGpAxD8n7VHh5e+j4rap3EDBv5HoNLcz5kZl82U92TyZk9BFdFJsKi0dAVZOmGRrOX9O5OQjEdu0Vj6uB0bp5cxOzhWZRleYlJSUc0zq5IlLbeAB3ROG3RGG3ROO3ROO37lmP06gYZsR7+veFJvqhXkTTtq1D+P6qTmKKcJscVoE5crI4TQqQCLwghRh9l88P9xXxId1gp5YPAgwCTJk36JAmIz0p6PIavuYnOxno6GurpbKyjs+HAQLTFBqnFybhL3XgmZBD3RNDtfRiiFwer0RxL0DUDLQKOmAN/Tya13eXUdRfTGiugT0+jJ+6iPWajT+4PprolDJKCC4SFwcJCCRqlWMiWZmA3giSsmYHmUNzAH43TYuj0GgZ+DLoFdFotBK1WdItAdwgMq8CwmHNh05A2Dawawq6h2QQ2qwWbRWDdG2zWNFxWQZKmYdEE4ZhOMKoTiMTNeTROIBInEIkSjMYJRHWi8YPThxyZ3arhsVtw2614HOY81mJQt7KSvn5BaIsmKExzMSjDw8TitH09oAdluCk8jiD0wQK6zntdvSzq6GFJp5+umI5DE0xPTeLeQdlcpPnIqXkT3j0odcewBWZAumyeSt2hKMrHJqVcJoQoOcyq/wW+C7zUr0w98aQop0FXUy93/mkZa3WYmraZ2yY+jIgPYs7Fz2GzHTl/ZaMvyK0L11BZ5EQmu/h/wwr5UkHm0d9s6wvwwlfAnQG3LYb8cSe3MoqiKMqnmhGJEFi+HP+ixfS9+y5GMIhjZDnJ//Fzam3lLPuojUhVK9klSZSN02jdvYSPnlmNpmkMO286Ey69krwhw/cdr6EryOMfbuLpNfV0hjy4rBamZG1gPEGm+CYwsvCzJE0pwMhzs6rWx3uv7+DdnW1U94SQDguZGS7GnJdPXrYHd7IdX1xnUTTOP2v20F4Zwxc/fL+LZKtGtgUyY92M7qsn27eT4YFqrrP7cc+8D4bMg6PdDFYU5aQ7rgD1XlLKbiHEUmAB0CqEyEv0ns4D2hKbNQJF/XYrBJpOxsmejfR43EzN0diQCELX0dW6m0BfI5o7jtUrsXkM7KkC79Q4Kc4INnsYuy2C1RI/9Hi6IBT10tWbxfa6ybT0FNMeyKcznky7tBEU+wOqXqAUC6PRKEVjkNQokBpu3SBsGAT0GH49So9msNphIZzqRuSnkJmbRGGel9KCZNKTHft6LFs0ccQ8TadDTDcIRnUzYB05aB7VCUYOnAcicQLROMGIGfDWhGDSoLREOg4PJZkeClJdJxyE3ktKSV04ypqeAGv9Qdb2BNgaCKFLSLVauDAjmQXpXuYEd+CpegaW90/dMUKl7lAU5bQQQlwJ7JFSbjzod/hxPfGkKMrHt21lI7e/sIFWJNcXv8ElIxbhts9n6gV/QNOOnD96U2M3tz6znvbyZOxOKw9XlDIv4yg3sA0Dlv4clv0Kis6Dm/4F3oOHs1YUZaBLPPH0TyAXMIAHpZS/E0KkA08BJUAtcKOU0nemzlP5dDGiUQLvv28Gpd95ByMQwJKaSvLllxM9/1J27PGw+4N2kE2UjEkjOb2e3Wue58P1tbiSkpl69Y2Mu+hSvOkZAISiOou3NPHPDzawfo+GwGBEeiPzs3czoTeHkdpMCs8bwaoUjX9v7mbNxl20ro6j2zVwaMjxKSBSATPQ1IQBfb24QxrZdivZdhtDPQ4uSPOSbbeSlSjLsmpkdWwlq+YNnNteh/YdZgUzh5mdxcq/DkWTz8yHrCjKsQPUQogsIJYITruAC4FfAi8DXwB+kZjv7ZX1MvC4EOI3mI8MDwVWnYJzHzD0eIy+ri46W3fR0rqd5tYaunvbCMT7iIkYhk0graBbNfRsQTzTStTIJ6Lbiep2IrqNSMhJyO8iEnMSiTuJ6k5iuoOobidm2IhJKzGpEeXQfM0pCEqkRoUU5MclWbpOih4nrsdpQ7LHprEjzUtlrofcHC9FmR6K0t0Up7uZnO7Ge4xcTAOFzaKR4tJIcR1lQKBTKBDXWd8bZJ0/uC8o3RkzbyJ4LBrjk9x8rTiH6W6Y2r4C2/bFKnWHoihnlBDCDfw7cNHhVh+m7LBPNKm0XIpyYqQheeaRtfxwZwuIGF8b9SgVuZspzPsWw8u/etR939jawj1vbSNYkUaWw8oT44cwyus68g6RXnj+bqh8DcbfCpf9GqyOk1wjRVFOkzjwbSnlOiFEErBWCPEW8EVgiZTyF0KIB4AHMMd/UpSTztANQr4gnctX4Xt/Nd2bdhI1bMS9GcgZX8fILiJm9xLoidHzUgC7M8yI85OQ8U3s+OAhwr1+sopLuOjL9zFi2ixsdgdSStbX+3hyVRUvb2giFLeS6eri8kHrGSX6KOucittzG88X2PhlLEpz2x6MoHndb3EKMtxOij0Ohqa4yXPZyLLb9gWjs+xWsmxWPNbDdPoK90DVEti5GHa9mbg2t8KgC2DCF8xr84yy0/wJK4pyOMcTmcwDHk3kodaAp6WUrwohPgSeFkLcjpnT8gYAKeVWIcTTwDbMBvarZ2s+SyklQX8Pe2pqqdxRRX3LHtpDPXTLGH2aRlDYCEoHfYaLvrgbfzSJYDwfMy5/fAQSO+BEmJM0lz1I7BJsUpojUxoGmpRo0jBD1FJiGAZdFo1qm53WFDv2DDf2dDfudDfpaW5KM9zMS3eT5XWgaerxlBMhpaQ6FGFNT5C1/gBr/QG294XZm2RkqNvBhRnJTEpxM9HrYnjvTizVL8HGdw9M3TH0Yhi+QKXuUBTlTCkDSoG9vacLgXVCiCmcwBNPn/a0XIpyIoLdIb7/2+W8GI6Ra+vmaxP/jyxXFxWjHiInb84R95NS8vD7Nfx4cz2xMWmUe1w8Ma6MXMdRbsx31cATt5hPaF3y3zDlLvVIsqKcxRJjPO0d56lXCLEd8+mmq4DZic0eBZaiAtTKcTJ0g3AgTqg3ak59MUK9sX3L4d4owb3rukNEIpL9/RgmQ1miV7EAp9WGK2rD5dDILPRSPDJOT8sKNi5agWEYlE2cyoRLrqRoVAVCCNp6w7zw4W6eXLWLmk4duxZlYs4GxifvpNifS3tkHq+kZbFxuEBP00AzEIaFIkPjAq+XmwZlcl5W8ok9yd252wxIVy469Np82MVm+g7nkVNsKYpyZhwzQC2l3ASMP0x5JzDvCPv8DPjZJz67U8QwDNqq69m2sZLdjW3s6QrQEYnh1+IELIKQZiMo7ASkg0DcRVB3Ay7MMSL3c1pCJNuCJFnC5FnCDHeGSDI0XHEbjpgDe8yBVVrQJMSlICY1IkBQaAStGgGblajNgm63IO0a0mbBatdwWC04bRpOmwWHVcNu0zDsFmJWQdQmiFgEWAXZbgejvA5uTnKQYrfitmi4LZbEXMOtmXN1nXJsvXGd9X4zGL2mJ8g6f2Bfvqoki8aEZA/fKElhYrKHCclu0oItsPsd2PIuVC+FUJd5oJzRKnWHoigDhpRyM7DvOX8hRC0wSUrZIYT41D3xpCinWuWGZr785FpqEJyfvpEvjH8ETU/igmmv4/UOPuJ+cd3gP1/ZyiPdPejlqcxPT+YvowfhsRzl74iaZfD050FKuPV5GDz75FdIUZQzJjGmxHhgJZCTCF6TSLF52Bw+58ITT5FgkI4GMzVme30tnQ119LS3cYSHvM4K0pAYemIyJFIeVJfDVE0edt2h+x3xU5HmjU/z/Y58bkKYoWghDTDiCMNAIBE2K5rdhma3ITSBlkj3aYQhEIZABxi6Tl9XJ3aXi3EXX874BVeQmpNLTDd4c1srT6+u493KdgwpKEup4XPDV5NphbrIhbzouIjaEU4Mu9nO5UiN85PdXFucycyMZJyWE0jDqceh4SMzIL3zDejcZZZnlcMFXzPHe1LX5ooy4J0duR1OgJSSll11rPpwC9vrW2kIRGjXLXQLK0GrjZCwEZR2QoYzsUd6YgIMcIgwKTY/yfZeCq1dpGhRkoVBioRkXcMVt6LFbYSkix6nnZjXgzWpkJS0bLLTM8hMcuK2W3DaLPsCzOby/sCzw2rBogkCcZ3WaJzWaIzWSIz2vcvRGG2ROLWJ5a6YjtkZvZ844A+D/9ifiQa4DgpaHzAdUGbBrWm4LAK3xYJNiP2N1t5JCPY2F+Y60W+dOdfYvx/99tu7ndZvP02AXQicFg2HpuHUBM7E3KFpOE5y7mtDSqqCEdb4A6zrCbLGH6AyEGbvfeJhHieXZKUwKdnDhBQ3w9xOtGgAat+Hle9A9bv7c0l7c8y7sIPnmBeGSTkn7TwVRVFOlBDiCcweVplCiEbgP6WUDx9u23PpiSdFOdOklDz1z7X8aHszUsS5a/jTTC1ehdcxj4lTf43VmnTEffsicb7yxDqWuHSMQV7uLszih0PysRzpbx8pYfVDsOh+yBwKtzwB6UcOfiuKcvYRQniB54BvSCn9x3stdDY98aTHY3TtaaSjvpaOhrp9k7+9bd82NqeLzKJiCoaXox3tht0AIKUkHjWIhuJEQnGiiSkSihOPGfu2E3uvr/dePAOIAx9+2XvdzN7r6X4rhehXvn9h3/Z7N9IEWGwaFquGxaZhtWlYbBYsVoHFKsDvw2huItbchIxGEVYbtrw8bAUFWLOzEccZIM4uHcLo2fOwu9xUtvTyx1e38dy6enxBnWS7n3mD1pKSGaFem8iL9i/T4TFTViUhmJfk4Yr8NOZkJpNlP8E0niEf7Hrb7Cld9ZaZykOzQekMmHKneY2eVnJix1QU5Yw66wLUhmHQWFnLqg83s72xnT2hMB1Co8vqwi+c9OlOwnEb5m/nXHMni0Q4BTg0LA6JxW7gtcew23UcNonLYuCwGNg0ad4VNLxIMpA2B3GnC5mUgtubQrrbSbbHTrrDhsuq4dI0M5BqEfuW7ZrAF9NpSwSXWyNx2gKhxOs4bZFEADoaJ6Abh9TPLgRZdis5DhuDXHYmp3jIsdvIcezPsZTjsJJssRAyJEFdJ2gYBPWDpkRZ6DBl/aeuWPSQ/fQB+KfM3qC1Y9/c/Nz7B7L3rndZ+m+3dxuN7nicdX4zh3RPond0itXChGQ3V2SlMjHFzfgkNyk2Kxg6NG2Ate/A7nehYRUYMbC69uerKpsD2SPVo7SKogwYUspbjrG+5KDXA/qJJ0U5G4R6I/zbr9/l1bBOobOdr038M6m2EKNG/IXc/PlH3be5J8TnFq5le4EDkl38YlghXyzIPPIO8Si8/h1Y9ygMuwSufVClEFOUc4wQwoYZnF4opXw+UdwqhMhL9J7OA9qOfISBRRoGPW2tZgC6XzDa17wHQzevyTSLhfScHPILcxgzppTMZI1Md5xk0Y0ItEC48vScrM1tdjjy5u6fe3MSyzlgdaDrBj1tIbpbgnS1BPC1BOhuCeJrCRKL7L/Pb3dZSS92k5bnIS3XTVquOU/OcKKdSO/gk0TG4wRXr8a/aDG9b76J3t2N5nbjnTeP5EsW4Jk2Dc1x4uMX9IRiPLqmgcdX1lHTEUQTOkOzahg6LMSelCG8br8FXbNgAyZ63Xw1J5XZ6UmM8DhPrBOalNCxywxI71wM9R+B1MGdCSMuN3tJl80Bx5FvCCuKMrANuAC1YRhUb9nF8g/XsbWzgyYsdGoOeqSTQNxBKGonHrNgnnoeYD7WIi0WpMuCdFmRTgs4NdzOKMn2EKnWEF4ZROg6MazEpUBKiRQgNSu6sNOtuQlb7IQ1JyGLk7Bm339SEQmRbuj8+PXyWjRy7DayHVbGJrnJTiT1z3HYyEkk9s9x2EizWo77F7UHONlfoZSSqJT7AtZxKc3PN/H4kGT/a2Pv60RAW+6dEvsYB+2H3L+Nsfe4gCEhKg3CuiRsGIQNg4gh981Dev/XBuHEcliXRBJBd19MT+zbbzvdrMteGjDC4+TK7FQmJruZmOyhzO1A2/t5++pg0/Nm6o7q9yDcbZbnjoHzv2o2eEXngc2JoiiKoihK5eZW7ly4knoszM3/gJtGPoPXfgFTzv8DNtvRA8db9vRw6zPraB2RjMNp5eGKUuZmHGWfvnZ4+lYzn+aMb8OcH4B2+oMciqKcOsK8EHwY2C6l/E2/VS8DXwB+kZi/dAZO76iklAR7uumoT/SGrq+ho66azj2NxKLRfduleO1kJguGlOhk2vxkilbSZCsWISGMORrG3hExXGlmYNiVBuLk/r6TSGJxSSimE4rqhGNxrPFmkuOr8OrdxA0HPr2Q7ngBXfFCfPFCuvRi/PEcJPt7c2u2MDijxFMFIY+dXq+XgMeNbt/bLToMXWHo6jKfWTuRc5QS3ZDEDUlcl8QNIzE/aFk3DtzGMPdDgj0cwBXw4wr6seg6hsggPOUuQp4UIm4vUghYocOKZSf8GRoSfIEoEkjx9JI/PEZLXjEbHbMAGGa18OX8DGanJTE5xXNiaTsAYiGo+2D/IIdd1WZ5TgVM/6aZVjN/gmoLFeUcMSAC1Dtau6j41fOEI1biYQsyDpCcmEBq7As8W9MkLkcMrz1Cii1MqhYgzdqLR8bwCkma001+ejYj8oZQmjuaFGfK4QO+hmEGIPvaINCWmDeY8742jL52IkEf4WAPoXAvYQRhzUFIcxKyOAhrdkIWF2FnBmFXBmFnOiFHKhF7MmkiTjZRckSUHC1GFjE8UkJUQEwzG9cDJktiLg6zLjFph9nGYjcfY7FYE3P7EZYTrzWruazZEsc79HMRQuAQZo/ktCM9ZSMlxMNmg3Hw/HBlh50HIRaGeAj0GDiSwZ1u/vHhSu+3nAbJ6WaZI+mEeysbUhI2zEC2XRMH5nIM+6FyidlDevc70LXbLE/KN+/Cls2B0lngzTqh91QURVEU5dwmpeTxf63hJ9ta0LQY91Y8TEXqLirKf0t+wWXH3H/J9lbufnMbgdGpZDmsPDV+COVe15F3aN4ET37GTPp53cNQcf1JrI2iKAPINOBWYLMQYkOi7PuYgemnhRC3A/XADWfi5AzdYNMHq1i1bg1V4Qgtmp24gL1dl0D2SzMhIbsEkV0CQu5L9bh/QR64DAjR71Fe0e94EZAIzOzI2v5lsb8skTk5Ud5vGwRGv/XmPvvXGzYNaTPXJd74KJ+ABDr6LR8sAPggijkd7ghSYOj7J10XGLq2fzneb1nf9wEdk2Yx0CwSzSKxWAwzZIAk5IEejxdIBKMPOd9Pxl5kx1+QRWtyPsnxIFOExvXDipiV9THSdkgJbdth9xIzKF23AvQIWBxQOtPsNDb0YkgtOvaxFEU56wyIAHUsbiEQcWB36HiTwyRZI6RqITJEkCxHlIJUB8XZBZQXl1OWOxib1X7sgx6LpplBUHc6MOLQ1ZjDIrqANCnNnEaB9n4B7XbzdaAN+vZA13pzXbAT9ChIY/80IIn9wWqL9cBgd/9lwzCDyf2Dy/Hwx39bi91Mk2FzgtUJNpd5HuHtZh6pyFGSamvW/QFsV1oiiJ0OrtT9ywcFuTVXGm6bC7dFSwyesNoMRu9+BxpXm48F2dxQMt3MVTV4DmQNV2k7FEVRFEU5rFAgyjf++y3eiEBpUiP3jH+ILNcQpk1fgc2Wesz9H11Ryw821hKrSGWkx8UT48rIcRzlIn7rC/DiPebfN7cthvxxJ60uiqIMLFLK9zlyRHLeqX7/vt5eli1azPqGBuoMC22aCx9O/HEnwaidaMSKjAug9FSfyrkv0U8Na6K/mgWwC4QLhBWs+9YJhBVIbCOsIKzCfJ3YFwsIcWZCO9mxHmb3dvPZgvOZMWzciY8dFeg0x3jae43e22yWZ42AyXdA2VwzxabdffJPXlGUAWVABKhH5SWx7gfH7m1yxghhBkFdqeZgNCdKygMD1nsnQ+/3+gjbSMMMoh683oibkx4zJyN29GUjbgbO9y3HzNd7l43Eaz1+4P6aZX8g2ebav/xx58caOVePQagbQl0Q7DKD1kda7q4380SHuo4eNLe6zMB1pA8iPYAwL+6mf8MMSBdNAeuJ59tSFEVRFOXTZfvWFu7810oasXLxoCVcWfwGY0b/jKLiq4+5r25IfvLaVh7q7EYfkcpF6Un8eXTJgU939WcYsPT/wbL/hqKpcNNj4M0+uRVSFOVTpbZyO28tf4/tvRGacNCJkx7DSSDqIBy1ooc1kG5g+L59pFWAS8PmMvCmxkiyREjTQuRaIozIdJOZlYuwuUHTEGakFIGG2eVL7JvEcfYEPhwBWMyYLHHdwNcXp8sfpCsQo8MfosMfobMvgjQkQkqEhEyvnfwkJwUpTvKSHeSluMlNceB1HOV61CJOPMB6vKLhxBPc7bh8O7F1bIW2bWYHrRhg90DaKMgqh5xRZqcp+8dPLSmsdoT11Ke+yMrKxWo9gbCSHjPHd9r9jtlTumkDIMGZaj7BXDbPnKcUnqIzVhRloBoQAWrtXM8ZJMT+W5vK0VlsZkqNE02rEQslAtdHCmz7zGOXzoLBsxM95xVFURRFUY7Pww+t4JdVHdisUb5e8RAjU2DO3GXY7RnH3DcYjXPPkxt40xnHGOTl7sIsfjgkH8uRAiGRXnjhy7DjVRj/ObjsN+pmuqIoJ6SqrYNpv32c3riDcNRGLGxBRgH2p0eQAA4Nq1PiSIrhTQ+TrIXIIEwmUTJTMvHkjMYuzCeYjcRgRIY0UykaQFvfqa9LMBJnd3uAqrY+Wvz7OybZLILSTA9DCnOYn+WlLNvL0OwkBmd5cNrOgmtvKaFzNzSsTEyrYPPrsBkzpWfOaPMGZdFUs1NVavHZ+aRvV7WZsmP3u1CzDKK9ZnykcDLM+b7ZSzp//LE7symKck4bEAFqRfnEbC5IKTAnRVEURVGUkyQUivHl/3qF93QHQ1LquL18IZNG38vQIZ87rv1b/WFuXbiGzfl2RLKTXw4r5AsFmUfewVcLT9wC7ZWw4Jcw9e6zMyChKMoZFYrZaGxLQXOC3aGTlhYiyRomzQiRHA9COMaWQBadkUz0CASxEsRKG26q9h6kAdhSd8BxhQBNmP2hheAT9Yw+Xg6rxuAsDxcMyWBItpchWV6GZHspTndjPdGB9wYSISBziDmN/6xZFvJB41ozYN24CjY+Aav/Zq7z5piB6r1B67yxA/PmZaTXDETvfscMTPtqzPLUYnMMhSHzoGSG+YS6oihKggpQK4qiKIqiKMphbFxfz5efWUWz4eLS0rdYkLWViy99FYf9KAHmfrY1+bn1mXU0DU/C6bTy94pS5mQkH3mHmmXw9BfMdG6fe858zFlRFOVjKHDrLL5nKm6PG00IM6gsDgww7y8TaP3mWuKmmHZQ+SlLf6Hs50qDoReaE5hpQdu27e9h3bAStr9irrPYzZ7He4PWhVMgKef0n7NhQPOG/XmkG1aaqURtHiidAefdY/aSzihTN1wVRTkiFaBWFEVRFEVRlIP86n9f4W9tBjarztdGPcTckXMYN+5/jxmg2dMdYtHmZl7f0sLqSJj46DSyHFaeGj+Ecq/r8DtJCasfgkX3Q8YQuOUJ80JeURTlY0pPS2dY4fHdTFMGMM0CuRXmNPkOs6y31exdvTdovfJBWPEHc13qIDNYnTMStFMc7pEGNG8yBzkMdppluWPggq+ZuaTVWE+KopwAFaBWFEVRFEVRlAR/T5C7/vtZPtIzGJpaw+cKF3PDtQ/hduUfcZ+6zgCLtrSwaHMz67sD6PlutMEeYnY3o9xOnhhXRrbDdvid41FY9F1Y+w8YtgCu/Rs4j9LLWlEURfl0S8qB8ivMCSAeMQPFe3NZ17wHm58+PefiyYYh8820HYNnq8F8FUX52FSAWlEURVEURVGAxa+8z49X1dOsZ3DJoLe5fpCDuZe8dthe01VtfSze0szrm1vY2tmHnufGPthL1OXFAszOSOb63DQWZKbgONKA4IEOeOpWqF8B078Fc3+gBolSFEVRTozVAUWTzYl7zadyogESw2CeWnavStuhKMpJoQLUiqIoiqIoyqdaPBbjP3/+IM9E8rFbLHy57CnuvOp+MrJH7dtGSkllay+vb25h8ZZmKjsCGNlOPIOTiY5JQgKjk9xcn5vG1dlpZNqP8Wd2y2Z44jMQaIPrHjYHjlIURVGUT0oIcHjP9FkoiqKcEBWgVhRFURRFUT61dm7cyg9efIdVocEMTdnNF9Mr+cwdjyKEQErJ1iY/r29uZtGWFqo7AsgMB2nDUpHjUogBSU47X8pJ47rcNIa4ncf3pltfhBe/As5U+NIiKJhwCmuoKIqiKIqiKAObClAriqIoiqIonzqGrvO3P/6BR3yptIRLuCjvA74xYSojpn2N9Q3dLN7Swuubm2n0hSDZRnZ5Oo6JafiRhKwaN2alcX1uGlNSPGjH+3izYcB7v4D3fgmFU+Cmx8xcooqiKIqiKIryKaYC1IqiKIqiKMopIw2DWDRCLBQhFokQjUSIhcPEolFi4QjxSIR4NEosEiYWiRCLRc38mafynKTByzs38FJoCg5LlLsylzF77r08U9vH4l++Q3NPGIvbQuGoTNIzs2gydJoEzMtI4vqcdOZnJOO0HCGv9JFE+uCFu2HHqzDuc3D5b8y8oYqiKIqiKIryKacC1IqiKIqifPqEfBDqBmmYwVBpgNQT84Mm4zBlB2wrCcZDbPO1smxzkJomK626nTactBtudHmCgcyB7ICOwkcLIst92x7at/gw+4mD153qAZeshOIzKEuq4yq9h39FLuavC7dgc1goq8jCkeOkMh6jEpjgdXBvThpXZaeRcay80kfiqzXzTbdvhwW/gKlfVoNKKYqiKIqiKErCMf/KFkIUAf8EcgEDeFBK+TshRDrwFFAC1AI3Sil9iX2+B9wO6MB9Uso3TsnZK4qiKIqiHEmkD7qqobMKunZD596pCkJdB2wqgaiwEdHsRLS9cztRzU5YsxMVNgKanUaLm2o9gyZZQpfIxm+48ccc9IYdBEJ57A2sahYdtzeK0xvFYjEAiUAm1u5dPtxrM0Arjlp26D7igKBv/23Yt+/B6/qXH3u7Q8v2v6Uw49Gy35EkIIU57TstkdjHLN+3/WG21aTEInW0owbBP5kMI8aOlmz+lDaO0SPTyM9zsykeZYMhKbYKvlGQw/W5aZQdb17pI6lZDk9/3ryp8dlnYci8k1MBRVEURVEURTlHHE83kDjwbSnlOiFEErBWCPEW8EVgiZTyF0KIB4AHgPuFECOBm4FRQD7wthBimJRSPzVVUBRFURTl08qIhgm0VtHTvAtfRy09/haCwS5C0T6CUtJjS6LbmoTPmkybfTRt6bPoyEmhx+IlbLGjCw1ds2BYLEd4A4noiyF642i9MURvzJzHjH2bSKcFI8mGzLMhvBoWr0BzCnRNENOsxIQZ9jWkead/7yQBA3HIsoFAqt61AAgpsRhgkYnJkP2WwXKk9fvK+q3fu31iuSfZTu5cN016lA9icVKMODfkpnNdjplXWnyS7yAWhratUPWOmXM6vQxueQIyyk7eh6MoiqIoiqIo54hjBqillM1Ac2K5VwixHSgArgJmJzZ7FFgK3J8of1JKGQFqhBBVwBTgw5N98oqiKIqinJ1iukE4phOOGfgDATp72unqacPn76QnEKDbEHTHBd1Y6DMEESNGFIOYgKimEbXaCFsdBG1uem1JRCyl4CoF12HeTBrYYnFsehynHsdp6GTGY7hjHTjjPhwiiMMSRgiDSEQQCtvoC7jo7k2iO5CMTKTosGhxspy95HgCFNiiDEtyMaViOMPHjsfjcGPXxPEPlnccDCnRJRgk5lJiAHqiXCbKzdeyX99nMxgukYn53jJzCwOQie3NZfM9Ev/2rd8bON+7XvY7Vv9jG/EoMuKHsB8j4keGe/fPo73ISB8y3IuM9iEjfRjxsNnnWwgzGJ9YlsKCtHvRHSmEk/IIegsIerIJujIJ2ryEDElQN8zJMAj1W95bfqL9rW2REBdmJHN9bhoXZiTj0D5GOpZoEFq3QPNGaNpgztu3gxE31w+7BK79KzhTTvzYiqIoiqIoivIpcEKJ9IQQJcB4YCWQkwheI6VsFkJkJzYrAD7qt1tjokxRFEVRlLNcdVs7N/7xz+gCdE1D1wSGJtA1DUNoxBNzXdPQhWX/XFjM3spYiQsLurCiYyGOFV3YEke3g8jbH2TUDpzbZAwnIVwEcRkhPNFOMqKNOAnhJpRYt3++d7ITOSgFhskXS6Wht4DG3gJ29hbQE90fQEy1hCiyhpnlDTA6L5+JowYzemwONqftkOOcKpoQaAJOfT7mkyHz+DeNRyHYCcEOCCSmYL95bx3UvAldNezLI2J1QtZwyB4J2eWQPcqcJ+fvy+UspSRiyAMC1qF+ywe/TrFauCQrhXTbCfw5HOmDls1mELp5QyIYvcPMRQ7gzoT8cTDsIsgbB3ljIbVY5ZtWFEVRFEVRlKM47r/IhRBe4DngG1JK/1EeezzcikOuCoUQdwF3ARQXFx/vaSiKoiiKcgYFYnZWNX6SdltiZg8ze5daEtPxigJRrPSQBCR9gvMg8f4GxRbJFIeb4bnJjB6azsQJeWRleD7xsZUjsNohOc+cjiYagPZKaNsObdvMqXopbHxi/zaOFDNQnTMSkT0SZ3Y5zuyRpLvTP/l5hv3Qsqlfz+gN0LGLfX/WenPMIPSIy82gdN5YSC5QwWhFURRFURRFOUHHFaAWQtgwg9MLpZTPJ4pbhRB5id7TeUBborwRKOq3eyHQdPAxpZQPAg8CTJo06dSNgKMoiqIoykmTZY/yzcGteG1OvHYvqW4vLqsLi21/z2LZb5y8A+5bC8xUDjKxLAFhppoQgBSamWZCs4Azmf1dqE8+iSQn28uw0lTs1hMJkSunjd0DBRPMqb9gl9lruXVrIni9HbY8B+G/79/Gm5voaT1yXwCbrBHmMQ8n1J3oFZ3oGd20wRxYc6+kfDMAPfq6/T2jjxVgVxRFURRFURTluBwzQC3MrtIPA9ullL/pt+pl4AvALxLzl/qVPy6E+A3mIIlDgVUn86QVRVEURTkzcrOy+Ppdt53p01A+zdzpMOgCc9pLSuhtTvS0TgStW7fCmr9DPJTYSEDaoETQeiTY3dC8yQxI+2r3HyulyAxAj71lf89obzaKoiinmhDi78DlQJuUcnSi7EfAnUB7YrPvSylfPzNnqCiKoiinxvH0oJ4G3ApsFkJsSJR9HzMw/bQQ4nagHrgBQEq5VQjxNLAN8/ndr0op9ZN94oqiKIqiHOgIF7a/Aq7AzI6xG/iSlLI7se57wO2ADtwnpXzjTJy3onxiQpj5qJPzYciF+8sN3Qw+908T0rYddr4BUofUQWYAesLnzXneOPCcQD5tRVGUk+sR4I/APw8q/18p5f+c/tNRFEVRlNPjmAFqKeX7HHl0nnlH2OdnwM8+wXkpiqIoinLiHuHQC9u3gO9JKeNCiF8C3wPuF0KMBG4GRmE+8fS2EGKYuqmsnFM0C2SUmVP55fvL4xGIhcCVesZOTVEU5WBSymVCiJIzfR6KoiiKcrqduuSOiqIoiqKcVlLKZUDXQWVvSinjiZcfYY4NAXAV8KSUMiKlrAGqgCmn7WQV5UyyOlRwWlGUs8m9QohNQoi/CyHSjrSREOIuIcQaIcSa9vb2I22mKIqiKAOOClAriqIoyqfHbcCixHIB0NBvXWOi7BDqgldRFEVRzpg/A2XAOKAZ+PWRNpRSPiilnCSlnJSVlXWaTk9RFEVRPjkVoFYURVGUTwEhxL9jjg2xcG/RYTaTh9tXXfAqiqIoypkhpWyVUupSSgP4G+ppJ0VRFOUcpALUiqIoinKOE0J8AXPwxM9KKfcGoRuBon6bFQJNp/vcFEVRFEU5MiFEXr+X1wBbztS5KIqiKMqpcsxBEhVFURRFOXsJIRYA9wOzpJTBfqteBh4XQvwGc5DEocCqM3CKiqIoiqIAQogngNlAphCiEfhPYLYQYhzmU061wN1n6vwURVEU5VRRAWpFURRFOUcc4cL2e4ADeEsIAfCRlPLLUsqtQoingW2YqT++KqXUz8yZK4qiKIoipbzlMMUPn/YTURRFUZTTTAWoFUVRFOUccaIXtlLKnwE/O3VnpCiKoiiKoiiKoihHp3JQK4qiKIqiKIqiKIqiKIqiKGeE2D9W0hk8CSF6gcozfR4nUSbQcaZP4iRRdRmYzqW6wLlVH1WXgWu4lDLpTJ/E2e4ca7PPpZ/xc6kucG7VR9VlYDqX6gLnVn1Ue30SqPZ6QDuX6qPqMjCdS3WBc6s+51JdTmp7PVBSfFRKKSed6ZM4WYQQa86V+qi6DEznUl3g3KqPqsvAJYRYc6bP4RxxzrTZ59LP+LlUFzi36qPqMjCdS3WBc6s+qr0+aVR7PUCdS/VRdRmYzqW6wLlVn3OtLifzeCrFh6IoiqIoiqIoiqIoiqIoinJGqAC1oiiKoiiKoiiKoiiKoiiKckYMlAD1g2f6BE6yc6k+qi4D07lUFzi36qPqMnCda/U5U86lz1HVZeA6l+qj6jIwnUt1gXOrPudSXc6kc+lzPJfqAudWfVRdBqZzqS5wbtVH1eUIBsQgiYqiKIqiKIqiKIqiKIqiKMqnz0DpQa0oiqIoiqIoiqIoiqIoiqJ8yqgAtaIoiqIoiqIoiqIoiqIoinJGnJIAtRCiSAjxrhBiuxBiqxDi64nydCHEW0KIXYl5WqJ8vhBirRBic2I+t9+xJibKq4QQvxdCiFNxzie5PlOEEBsS00YhxDUDpT4nWpd++xULIfqEEN85W+sihCgRQoT6fTd/OVvrklg3RgjxYWL7zUII50Coy8epjxDis/2+lw1CCEMIMW4g1Odj1MUmhHg0cc7bhRDf63ess60udiHEPxLnvFEIMXug1OUY9bkh8doQQkw6aJ/vJc65Ughx8UCqz5nyMX4uBmyb/THqotrrAVofodrsAVkXodrrgVyfAdtmH6Uuqr0+AR/jZ0K11wO0Pv32G3Bt9sf4blR7PQDrIgZwe/0x6zNg2+yPURfVXh+JlPKkT0AeMCGxnATsBEYC/w08kCh/APhlYnk8kJ9YHg3s6XesVcD5gAAWAZecinM+yfVxA9Z++7b1e31G63Oidem333PAM8B3Bsp38zG+lxJgyxGOdbbVxQpsAsYmXmcAloFQl0/yc5YorwCqz+Lv5jPAk4llN1ALlJyldfkq8I/EcjawFtAGQl2OUZ9yYDiwFJjUb/uRwEbAAZQCuwfS/5szNX2Mn4sB22Z/jLqo9nqA1gfVZg/Iuhy0r2qvB1Z9BmybfZS6qPb61P5MqPZ6gNan334Drs3+GN9NCaq9HnB1OWjfAdVef8zvZsC22R+jLqq9PtL7n6ZKvgTMByqBvH4VrzzMtgLoTFQwD9jRb90twF9P5xd0EupTCrRi/rIbcPU5nroAVwO/An5EovE8G+vCERrPs7QulwKPnQ11Od6fs37b/hz42UCtz3F8N7cAryT+z2dg/lJPP0vr8ifgc/22XwJMGYh16V+ffq+XcmAD+j3ge/1ev4HZaA7I+pzpz/E4/78O6Db7BOui2usBVB9Umz0g63LQtqq9Hlj1OWvabFR7fVp+Jg7aVrXXA6w+nCVt9nH87ilBtdcDri4HbTug2+vj/G7Omjb7OOqi2usjTKc8B7UQogTz7u1KIEdK2QyQmGcfZpfrgPVSyghQADT2W9eYKDtjjrc+QoipQoitwGbgy1LKOAOsPsdTFyGEB7gf+PFBu591dUkoFUKsF0K8J4SYkSg7G+syDJBCiDeEEOuEEN9NlA+ousDH+h1wE/BEYnlA1ec46/IsEACagXrgf6SUXZydddkIXCWEsAohSoGJQBEDrC5wSH2OpABo6Pd673kPuPqcKedSm63a630GVF1AtdkDtc1W7fXAbK/h3GqzVXt9cqj2emC213ButdmqvVbt9elwLrXZqr3+ZO219YTP8gQIIbyYj618Q0rpP1bKESHEKOCXwEV7iw6zmTypJ3kCTqQ+UsqVwCghRDnwqBBiEQOoPidQlx8D/yul7Dtom7OxLs1AsZSyUwgxEXgx8TN3NtbFCkwHJgNBYIkQYi3gP8y2Z8X/mcT2U4GglHLL3qLDbDbQv5spgA7kA2nAciHE25yddfk75uM8a4A6YAUQZwDVBQ6tz9E2PUyZPEr5p8q51Gar9npgtteg2mwGaJut2uuB2V7DudVmq/b65FDt9cBsr+HcarNVe63a69PhXGqzVXu9z8dur09ZgFoIYcOs0EIp5fP/n737jo+qSv84/jnT0zskEJIQeu8IAiIi9t7WjgXrirq7upa14Kqr+1vXvhawrKhY1i7YsBdAkF5CKEloSSAJyaRMn3t+f8wkBBIkQBrwvF+vce7ce+695wbkZr5z5jnh1duVUmla6yKlVBqh2lG17dOBD4HLtdYbw6u3Aun1DpsOFLZUn3/P/l5PLa11jlKqhlDdr3ZxPft5LUcB5yml/g+IBwyllCe8/yF1LeERA97w8mKl1EZCn5Iein8uW4EftNal4X0/A4YCb9AOriXcpwP5f+ZCdn26C4fmn83FwBdaaz+wQyn1CzAc+IlD7FrCI1P+VG/fecB6oJx2cC3hPjV2PXuzldCn07Vq+90u/p61pcPpni336/Z5vwa5Z7fXe7bcr9vn/RoOr3u23K+bh9yv2+f9Gg6ve7bcr+V+3RoOp3u23K/rHNT9ukVKfKjQRwUvAzla68frbfoEmBxenkyonglKqXhgDqHaJb/UNg4Pg69SSo0KH/Py2n1a0wFcT1ellCW8nEmomHhBe7ie/b0WrfU4rXWW1joLeBL4h9b62UPxWpRSKUopc3g5G+hBaLKAQ+5aCNX2GaiUigz/XRsPrGkP1wIHdD0opUzA+cDbtevaw/UcwLVsBo5TIVHAKEL1lw65awn//YoKL08CAlrrQ+Hv2d58AlyolLKr0NepegAL28v1tJXD6Z4t9+v2eb8GuWfTTu/Zcr9un/drOLzu2XK/bh5yv26f9+twnw6be7bcr+V+3RoOp3u23K+b8X6tW6aQ9lhCw7dXAMvCj1MIFTP/htCnA98AieH29xCqJ7Os3qNDeNtwYBWh2SCfBVRL9LmZr+cyYHW43RLgrHrHatPr2d9r2WPfaew+w/AhdS2Eaq+tJlTzZwlw+qF6LeF9Lg1fzyrg/9rLtRzE9RwLLGjkWIfUnw0QTWg27tXAGuD2Q/hasghN7pADfA1ktpdr2cf1nE3oU1svoUl0vqy3z9/Cfc6l3kzC7eF62upxAH8v2u09+wCuRe7X7fR6kHt2e76WY5H7dXu8niza6T37d65F7tct+3dC7tft9Hr22Hca7eiefQB/NnK/br/Xcizt8H59gH/P2u09+wCuJQu5Xzf6UOEdhRBCCCGEEEIIIYQQQohW1SIlPoQQQgghhBBCCCGEEEKIfZGAWgghhBBCCCGEEEIIIUSbkIBaCCGEEEIIIYQQQgghRJuQgFoIIYQQQgghhBBCCCFEm5CAWgghhBBCCCGEEEIIIUSbkIBaCCGEEEIIIYQQQgghRJuQgFoIIYQQQgghhBBCCCFEm5CAWgghhBBCCCGEEEIIIUSbkIBaCCGEEEIIIYQQQgghRJuQgFoIIYQQQgghhBBCCCFEm5CAWgghhBBCCCGEEEIIIUSbkIBaiHZEKVWglHIrparrPZ5VSl2hlArusb5aKdUpvJ9WSnXf41jTlFJvtM2VCCGEEIe3vdyzM5RS/1ZKbQ2/zldKPVFvH7lfCyGEEK0kfK8+fi/blFIqTym1ppFt34fv2YP2WP9ReP2xLdNjIY5cElAL0f6crrWOrve4Kbx+/h7ro7XWhW3aUyGEEOLItts9G7gSGA6MBGKACcDStuygEEIIIRp1DNAByFZKjWhk+zrg8toXSqkkYBRQ0jrdE+LIIgG1EEIIIYQQzWME8KHWulCHFGitZ7Z1p4QQQgjRwGTgY+Cz8PKe3gT+oJQyh19fBHwI+Fqne0IcWSSgFkIIIYQQonksAP6slLpRKTVAKaXaukNCCCGE2J1SKhI4j1AI/SZwoVLKtkezQmANcEL49eWAfOgsRAuxtHUHhBANfKSUCtR7fTvgB0YppSrqrS/TWndr1Z4JIYQQor769+zvgXOBcuAS4AmgTCl1l9b6tTbqnxBCCCEaOgfwAl8BZkLZ2KmERkjXNxO4XCmVB8RrrefLZ89CtAwZQS1E+3OW1jq+3mNGeP2CPdbXD6eDgHWP41gJBdtCCCGEaBn179lnaa2DWuv/aK3HAPHAw8ArSqk+4fZyvxZCCCHa3mTgXa11QGvtBT6g8TIfHwDHAVOB11uxf0IccSSgFuLwsBnI2mNdV2BT63dFCCGEEFprt9b6P4RGVPcNr5b7tRBCCNGGlFLphELnS5VSxUqpYkLlPk5RSiXXb6u1dgGfAzcgAbUQLUoCaiEOD+8A9yil0pVSJqXU8cDpwHtt3C8hhBDiiKGUulUpdaxSKkIpZVFKTQZigKXhJnK/FkIIIVqXVSnlqH0AVwLrgF7A4PCjJ7CV0ESIe7obGK+1LmiV3gpxhJIa1EK0P58qpYL1Xs8lNLvwaKVU9R5tJ2itFwF/Dz9+BhKAjcAlWutVrdFhIYQQQgDgBv4NdAc0oTfA52qt88Lb5X4thBBCtK7P9ni9EXhKa11cf6VS6gVCZT6eqb9ea11IaMJEIUQLUlrrtu6DEEIIIYQQQgghhBBCiCOQlPgQQgghhBBCCCGEEEII0SYkoBZCCCGEEEIIIYQQQgjRJiSgFkIIIYQQQgghhBBCCNEmJKAWQgghDgNKqS5Kqe+UUjlKqdVKqVvC6wcrpRYopZYppX5TSo2st89dSqkNSqlcpdSJbdd7IYQQQgghhBBHKpkkUQghhDgMKKXSgDSt9RKlVAywGDgLeBJ4Qmv9uVLqFOCvWutjlVJ9gbeAkUAn4Gugp9Y62CYXIIQQQgghhBDiiGRp6w4AJCcn66ysrLbuhhBCiMPY4sWLS7XWKW3dj5aitS4CisLLVUqpHKAzoIHYcLM4oDC8fCbwttbaC+QrpTYQCqvn/9555J4thBCiJR3u9+vWIvdrIYQQLam579ftIqDOysrit99+a+tuCCGEOIwppTa1dR9ai1IqCxgC/ArcCnyplHqMUGmvo8PNOgML6u22Nbzud8k9WwghREs6ku7XLUnu10IIIVpSc9+vpQa1EEIIcRhRSkUD7wO3aq0rgRuAP2mtuwB/Al6ubdrI7o3W/VJKXRuuX/1bSUlJS3RbCCGEEEIIIcQRSgJqIYQQ4jChlLISCqff1Fp/EF49Gahd/h+hMh4QGjHdpd7u6ewq/7EbrfV0rfVwrfXwlBT51rUQQgghhBBCiOYjAbUQQghxGFBKKUKjo3O01o/X21QIjA8vHwesDy9/AlyolLIrpboCPYCFrdVfIYQQQgghhBAC2kkNaiGEEEIctDHAZcBKpdSy8Lq7gWuAp5RSFsADXAugtV6tlHoXWAMEgD9qrYOt3mshhBBCCCGEEEc0CaiFEEKIw4DW+mcarysNMGwv+zwMPNxinRJCCCGEEEIIIfZBSnwIIYQQQgghhBBCCCGEaBMygloIIZpAaw1ao7VGawPDMMAILWut0bXLRu1ro65taFu97bvtZ+w6thE6bm3b3z2+3vM4v39+6vU79HrPduHl+udHYY+MxBYZiT0iEltkVOh1RCT28LLFZidU+lgIIYQQ4uCs/uGbtu6CEEIIIfZhR0Fesx/zoAJqpVQBUAUEgYDWerhSahqhepcl4WZ3a60/O5jzCCEOPwG/ny2rlpO3dBHuqqpwQBrcLczdM7SlQdhbG7DuEdI2ErbWbd+tff1wdo9zG/UC3XCALBoymc3hwHpXcP17gfaebe2RUdgiIjCZzW19KUIIIYRoQwXLFvPVi0+3dTeEEEIIsRfb8zYw//232Pjbr81+7OYYQT1Ba126x7ontNaPNcOxhRCHEZ/bRf6yxaxfOJ/8pYvwud1Y7Q6iE5NQSqFMptCzUiiTGWUKLysTmELPpnAbk9kCylS3n8lkgnDb0Lrwssm0x7FNoW17vm60bXi5ftvwOpSq60vtdsLHCq0P98e0+367nk27XV/DdqZGr1/Vnrfuevd2bXv8HPZx/j2Pj1JobeBzu/G5XHhdNXjdrrpln9uFdy/LVWWllLlq8Lrd+Fw1GMF9z7tntTuaHnJHRmKPqLccbm+x2mQ0txBCCHEI2p63gU+eeJSk9Iy27ooQQggh9lC8cT3z35tF3pJFOKKiOfqCS+DdOc16DinxIYRoUa5KJxsX/8qGhfPZtHIZQb+fiJhYeo4aR4+Ro8noPwiLzdbW3RSNUMqMIyoaR1T0AR9Da03A58XndodCblcNPpcbr7vesqsGn7sGrysUaNcG4VWlJXXLfq9nn+cymc17BNrh8Dq8LIQQQoj2x7ljOx/+8wEc0dGcc+c0Jv/r2bbukhBCCCGAog25zH/vLfKX/oYjOoYxf7iMISedHn5/fXGznutgA2oNfKWU0sCLWuvp4fU3KaUuB34D/qK1Lt9zR6XUtcC1ABkZ8km5EIeTytIdbFi0gA0L57M1ZzVaG8QkpzDo+JPpMfJoOvXug8kkJR2OBEoprHYHVruDqPiEAz6OEQzuPoLb5Qq/rgmN3g4ve2pqcLlqqKpxs8MTxFnmozLgp9qobsarEkIIIURzcFdX8cEj9xPw+7jo3oeJTkxq6y4JIYQQR7zCdWuZ//5bFCxbjCMmlrEXXs6Qk07DFtFyA78ONqAeo7UuVEp1AOYqpdYCzwMPEgqvHwT+DVy1547hMHs6wPDhw6W4qxCHuLKtW9iwaD7rF85je94GAJLSMzjq7PPpPmI0Hbp2k/IL7VSo7rYfw/CjdSC0rAOgDZSyoJQFk8mKUjZMJgtKtc6HC8FgkJ2VNWwqKWHbznKKyp2UVLoor/ZS4fJT5TWo8WrcfoUnYMJrROI1YvFiIUC9Psp3hYQQQoh2J+Dz8dH/PYhzRzHn3fOQlPcQQggh2ti23BzmvzeLTSuWEhETy7iLr2DwCae0aDBd66DetmutC8PPO5RSHwIjtdY/1m5XSs0AZh9cF4UQ7ZHWmuKN69iwcD7rFy2gvHArAGndezHu4ivoPmI0iZ06t3EvW0doMkU/WvvCz34MIxT0ah2oWxdarg2A/WgjEH6uDYZ3tTV0YNf2cFutAxjaF14f2CNUDqCN+scNoH+nbf0+ab3vGtG7M4UDa2v42YJJWVH11qHMaEwYmAgYJir9VpxuG5UeG06PjSqvlWqvnWqvnRqfHZffgdtvxx2w4w448ATtuIN2DPYMw01ABBCBCQOHyUcEASJVkHjlI9riJw4TMZiI12YStJVEw0qCL8BpzfPHLYQQQoiDpA2Dz559jMLcNZx2652k9+nf1l0SQgghjlhbc1Yx/7232LxqORGxcRxzyZUMOuEUbI6IVuvDAQfUSqkowKS1rgovnwD8XSmVprUuCjc7G1jVDP0UQrQDRjDI1pxVrF84nw2/LaC6rBRlMtGl7wCGnnQ63UYcRUxicrOfNzTCN1gXABuGLxy4ekOvtS8Uzhq+cEBb284bbhdabxiNtKsNlffSJnQuX+j1nu3qngPNfs27U7uFwfVHNVPvde0DZUYpG5giMVnMmOqtB3MoPFZmFBa0MoXWKwtgQmNCq1CboNZ4Ay58ARe+oBu3z4vTY+B0a2o8ihq3GZfPisdnw+W34/Xb8QTsePwReIIRuAOhhyfoQLP30fMOs4dIq4soq4toRyUdrcVEW11EWkLrQo+aessuoiwubGYf+zUo/4mD/XMQQgghxMHSWvP9zJdY/+s8jr18Cr1Gj23rLgkhhBBHpC1rVjL/vbfYsnoFkXHxjL/0KgZNOgWrw9HqfTmYEdQdgQ/DX9m3ALO01l8opV5XSg0mVOKjALjuYDsphGg7fp+XTSuWsWHhfDYuWYinqhKL1UbW4KF0/8NlZA8bSUR0zP4d0+/EWbkUp3MplZXLCfidu4fDdYHwrhA49E9K8wkFvLZw8GsLjwC2YTJZw+ttmEw2zKZIlBGLwowRNOH3BKmsMVFa7aDUFUm5O4JybyTl/gjKAxF4DAvoUGqq9/rQGIBRt6zRQBBd99oIH6B2n11aryKS1mYMoxMBw05AWwn+zi1DobERwK6C2E0BHGaDJItBVISXKIuHOLMmQRskBg2SfAbJXk2SFxI9JhyGHWWKBXtnlNmEVgG0KYBWQQzDjQ6Wo6kBiwciDFQUmOMsmOLtmJMjUInRmGIi0TYT6EC9UeS+utHphuEHbm21n50QQgghGrd4zkcs+fwThp16JsNOPautuyOEEEIccbasXsG892axdc0qouITOPbyKQw8/iSs9tYPpmsdcECttc4DBjWy/rKD6pEQos15XTXkLVnE+oXzKFi2BL/Xgz0qiuyhI+kxYjRZg4Y2+RM1rQ1crjycziU4nUupcC7B5doQ3moiOroXdltKOBy2YVI2lGlXaGwyhV+rUHiswm3q1tdvVxs2m2zhdo3sr6z4/Rqvy0t1aSU7S0spLyvDWVFJVZWL0mof5R5wBhROw0SlYaYCE1XaQjUWarR19/rGYTYCRCofNva3XMYuKvwwHfARmpdSEGE2iLL7iLYFiIuwEh9hIzHKTkKklUSzJkH7SQ54iKmswlbuwlTuxqgKYrg1OmAFFYHJHo+KSERZbLsdX+sgWGpQJi/K5sMUVYE5zoYlORprWgq2jBSsnZIx2Wx76eH+urWZjiOEEEKIA7F23o/88PrL9Bw1lvGXXt1g+/6XHRNCCCFEU2it2bxqOQvef5utOauISkhkwuRrGHD8SVht9rbunkwdJYQIqakoZ8OiBWxYNJ/Nq1ZgBANExSfQ95gJdB95NF36DsBs2fc/GYFANZWVy0OBdHiUdCBQCYDFEkdc3BBSU88gLnYIsbGDsFii9ruvhmHg8XjweDy43e66ZY/HQ011DRUV26hyOnFVu/B6PHg9PmoCmsqgmUrDRI224cKKS9tw6V3PbhJgj1IUJgyiTAGiLX46Wn3E29wk2QxSbdDZAl3M0CnoJcbrxlpTjcnvD08GqcBkCqW8itA6Vfs6vIwFDCsaC2BBawtKW9CGBbQFbZh3PRvmumf0PiJsZaDMBlg0ymygzBplCb02WTRYQFl06GHVKCuhhwWURYX7qtCBAIHSMvw7SgmUVWM4fRg1BobPHAqeIxJREYmYIhNQ1kQgNL5bOcBk1yi8KKsfU4TCFKOxJJmxdozDmp6ENS0Bc4wNZZKJM4UQQogjwZY1K/niP4/TuXc/Tv7jn1Gm3X+f0VqzJueONuqdEEIIcXjSWrNp5TLmv/cWhblriE5IZMIV1zFw4olYmm0w2MGTgFqII1jF9mI2LJzH+kULKFyXA1oTn5rG0FPOoMfI0aR179XgzUN9Wmvc7k04nUtxVoZGSFdX5xIqXgFRUT3okHIScXHDiIsbQmRkV5TadTyfz0dpaSkul6vRwLl22e12U+2qxu1y4/N6CQR2ja4JaoW7Xthco224tQ2PtuHWidRoK9VYCDQyLjmSAPHKR6rykUIlHYM1pHlrSPVUkVS1kwTnDqIrSqCqCu3z/e7P0g14IyNRdjtoDTpUtgPDqHuN1uh6yxhGqE291+gmlPBQZpQ1EmyRKGu9hy0SZY3aY1tUeH3t4/cnOdBBH9rnQvtdYARQjhRMjm5gAlNC6AGAKYDJrjFFm7EkOLB0iMGaGo8lOQpznB1zrA1lbi9jwYUQQgjRlkq3bOLjxx4irmMaZ95+T6NviPPyHqe4+MM26F3bUErFAy8B/Ql9xn8VkAu8A2QRKpd5gda6PNz+LuBqIAjcrLX+stU7LYQQ4pChtWbT8iXMe/8titatJTopmeOuup4BE05oV8F0LQmohTiCaK0p3VwQmuRw0XxKNuUDkJKVzdHnXUz3kaNJ7pIZHgHcUDDoprJyZXhk9BKcziX4/TsBMJujiYsdTNesPxIXN5To6IF4vSacTic7dzrJzy/G6czF6XRSWVmJ0+nE5XLtta9KK4JY8RkR+LQDv47BpZOo1haqsFKlTaHSG40Ez1ZtkOR3k+StortrJ0lVJSS5nSR6Kkn2OElyO0nyVGIzdk1uaIqMxBQTgykmGnN0DKaYGMypXTFFD8QcG4MpOrwtJrRsjokOta9djo5GmRuW/jgQu4XYewm2Q+sA9G4h+J77asMIl60OB+JBA+0JEvQYaE8AwxNEe4IYniCG10B7DbQnDsNngAGWxCjMKdFY4h2h4DnejiXOhrI2z7W2hGDQwF3po6bCR43Ti8vppcb5+x8wCCGEEKJlVO8s44NHpmGx2jj3rgcanbtk67ZZFGx6jk5pFwCPtn4n28ZTwBda6/OUUjYgErgb+EZr/ahS6k7gTuAOpVRf4EKgH9AJ+Fop1VNLTRQhhBB70FpTsGwx8997i6INucQkpTDx6hvpP2ESFqu1rbu3VxJQC3GY04ZB4fpc1i+cx4ZF83FuLwal6NyrD8dePoXuI0YR1yG14X5a4/UWUeFcHBoh7VxCdXUOWodCXYcjk6ioUSi64fN1obo6hrwNlTjLnVRULqKq5rtQWFqPGUWEtmAP2rAbMVhJw6sjqcaKU5twYqIcKNNQioGvkaA8zu8m2VNBmruC/jU7SfI4STLcdFB+UqwGHW0QH+3AEhONKSkGU1Y05pjsPULlcNAcE9vs4XJzqC2xsdu6NupLe2IEDdxVfmqcXmoqQqFzjdOLq95yjdOHu8rXYC7JvXzmIoQQQogW5HW5+OCR+/HUVHPhA/8kNqVDgzYlJXPJzb2fpKQJ9Or1IEdCQK2UigWOAa4A0Fr7AJ9S6kzg2HCz14DvgTuAM4G3tdZeIF8ptQEYCcxv1Y4LIYRot7TW5C/9jfnvv0XxhnXEJKdw/JQ/0u/Y49t1MF1LAmohDkPBgJ8tq1awftF8Nv72KzUV5ZjMFjIHDGLkmefRbdhRRMUn7LaPYXipqlqD07mE8vLFodHRgZLQRm0j4OmEq3IY5eWJlFTE4fbV/gNXAVSgNEThwGZEgI7DYXTErR24sFKlLTgNTanWlCgob6RsiN0Ikmy4SNZe+pp8dLAadHCYSI000zHGToe4CFKTY3DEpoTD5RjM0eFwuQm1sUX7ZRgad5WvLnR27RlAO0PbXI0EzyiIjLERFW8nOt5Oh8xYouJCryPj7HXLETE2/vhCm1xeq1FKdQFmAqmE6uxM11o/Fd42FbgJCABztNZ/Da+XrwsLIYRoEcGAn08e/wdl27Zw9h330yEru0Ebp3MJq1bfSmzsAAb0fxqT6Yj5nS4bKAFeVUoNAhYDtwAdtdZFAFrrIqVUbaLfGVhQb/+t4XVCCCGOcFpr8pYsZP57b7M9bz2xKR2ZdO1N9Bs/EbOl/QfTtY6Y3wCEONz5PR7yly9mw8L55C1ZhNdVg9XuoNugo+g2cCSds/pgdgXwl1bi/CqX/MoCKsjB49hAMGoL5philCn0LUGPO4rKqhQqK7OoqkyhpiYBa9COWUcRNCLwBa14gmZcQROVRqjUxk6ThVKLDVcjbyxiTQapkYoOkVYGxdpJjY8kLTmGtI7xpCVFkxYbQWyEZa+lRcShqTZ4dtWObq4fQDtrA2kv7kpfw9LbCiJibKGAOc5OSpdoIuPtRNULnaPi7ETEWDFJretaAeAvWuslSqkYYLFSai7QkdDIq4Faa2/tm135urAQQoiWorXmqxeeZvPKZZx045/IGjS0QRuXK5/lK67Fbu/IoIHTMZsj26CnbcYCDAWmaq1/VUo9Raicx9409ktyg4lLlFLXAtcCZGRkNEc/hRBCtFNaazb+9ivz33+LHfkbieuYygnX30zfccdhbqFBfD7fTkrLvqW05OtmP7YE1EIcQrShCVZ68eYVEyitxLOjnJ1bi6gsdeJzB7CaHHQ0p9Ex+Vx8JnCZg9Ts9LHkxzX8uuQrVPwW7LGFRMftwJFcEz6miarKZErzhrFzZ2ecFSlU+SKp0macykylyUalKYJKUyQB6pXBMIPJAsnRNtLiI+kTayctLoKOsQ7S4hy7PUfY2k/5DHHwtKFxV+8qteGq3DX6uaZiV81nV6UPbTSc9DEixhoe3WwnuUt0XegcGWcPB882ImJtmCV43i/hEVe1o66qlFI5hEZXXQM8Gv5aMFrrHeFd5OvCQgghWsQv77zBmp++Y8wFl9Jv/MQG273eEpYuuxJQDB70KjZbcut3sm1tBbZqrX8Nv36PUEC9XSmVFh49nQbsqNe+S73904HCPQ+qtZ4OTAcYPnx4E2beFkIIcajRhsGG3xYw//23KSnII75jGifecCt9xh7bIsG0y7WJ0tKvKSn9moqK3wADu71hmdiDJQG1EO2MDhoEy734y9z48rfj21SKf0c1Ro1GGxEETFChXFQrN9XKS43yUOXwUx3hokbtxGMKYrF4iI0tITa2BEd0BYY1QLU/hgpvPCWVPdm+bTQ73PFUBOKpCcbiDUSg6w/MsIHVrEiLc5AaF8HAPUPnuNBzSrQdi4SIrUZrjTY02giNTtaG3uMZDMNAG6EQWWuNEdTh/ahbNgyNDmqM8PFC66m3HFof8Bvhchu76ju7wiU3jEaCZ0e0NRQ2x9tI6hxNZHj0c6jcRmg5MtaG2SJ/Z1qaUioLGAL8CvwLGKeUehjwALdprRchXxcWQgjRApbP/ZxfP3yHARNP5Khz/tBgeyBQzfIVV+PzlTJs6CwiIzPboJdtS2tdrJTaopTqpbXOBSYCa8KPyYQKcU8GPg7v8gkwSyn1OKFvPfUAFrZ+z4UQQrQVbRisXziPBe+/TcnmAhLSOnHSjX+iz9hjMTXjnFpaG1RVraKkZC4lpV9TU7MOgOjo3mRl3UhK8vHExPQHmvd9vQTUQrQBwxckuNNDoMyNv8yNf2s5/kInQacf7TcDJjSaGryUUs4Oo4RSUzXlNg9uswFKoTX4MFNtKLwRNQQcfvxmE37s1PgTKa/Mprwknhp/VIPzR9vNpMVF0DfOQeoeoXMohI4gIdJ62JbcqA1ug36DgN8gGDB2LfsNgoFg3XLj2+st+w0CgV3LwaARCn/rB8QGe4TFu0Lh2rC4LjiuFzbXD6BDgXPb/LwcUVai4kMBc2JaZIPQOSpeguf2RCkVDbwP3Kq1rlRKWYAEYBQwAnhXKZVNE78uHD6mfGVYCCHEPm1c/CvfvPw82UNHcPzVNzb4XdIw/KxcdRPV1WsZOOBFYmMHtlFP24WpwJtKKRuQB1xJ6N3+u0qpq4HNwPkAWuvVSql3CQXYAeCPUpJLCCGODNowWPfrLyx4/21Kt2wiIa0zJ9/0F3offUyzBdOG4aO8fAElpV9TWvI1Xt92wER8/Ah69LiHlOTjiYjoss/jHAwJqJuR1pqt5W7c/tb5XcFsUpiVwmxSWMy7lvd8WEwmTIrDNmxsrwyXn0CZh8BON4HS0LO/uIpAqRvthQAaF1CDptJfTbG/lCKq2W4JUmZTVJkseJUFP2b85ih8wSi8QfBrRcBsxR2IxG+EC977gcrQYpzdQ8cYE91So0lPTKFTQvRu5TZS4xxE29vH//ra0HjdAfze4D6C32BdULy3YHivQbIv2Oh+jcdwTWcyK8xWExarCbPFVLdsMptQJoXJRPhZoSzh5/AjtFxv+27rVej/V3PtskKZw9tU6LzKpFBKhZZVuJ05tH3Xcr3jNXK+ffXHZApdX2SMDbNVgudDhVLKSiicflNr/UF49VbgA621BhYqpQwgmSZ+XRjkK8NCCCH2rWhDLrOf/D86dO3Gabfc0eBNs9aatWv/xs6dP9Gn9yMkJ09oo562D1rrZcDwRjY1rIkSav8w8HBL9kkIIUT7YRhB1s3/mQUfvEPZ1s0kdkrnlKm30evocZhMBx9MBwJVlJZ+R0np15SV/UAwWI3JFEFS0jGkJB9PcvIErNaEZriSpmkfKdUhbsOOKj5dXsSclUVs2FHd1t3Zq90C7XAAtduzCgfdpt3DbpNSdQG3SRF+HQ7LVCiIC72uv31Xe0U4rFO7jrXb/mrv+++rDY2ec/dz7OpH84X0WmvwGwTdAbQ7gOEOYLgCBN1+3NU+qmp8VAcNaggF0C40NUaAGiNItdLUKBO++v+gWAFrMqHMKMwA0DjMXiKsPiIj3MRYanCYPURY3cTbq0iNtZGemEJmhyy6pfUjIyUbu7Xt6j0bhsZTHfoZeKr8uKv9uKt8uKv9eMLP7mof7vA2T7W/0RrFTVEboDYWEputJiw2E44oy642FhNmq3mv7c2Wesvh9habuWG7cFuTST7wEe2LCv0D9zKQo7V+vN6mj4DjgO+VUj0BG1CKfF1YCCFEMykvLuTDRx8gKiGBc+68H6vD0aBNXv4TFBW/T9eut9Cp0wVt0EshhBCi/TOMILnzfmLBB++wc9sWktIzOPXm2+k5euxBB9MeTyElpd9QWvI15RUL0DqA1ZpExw6nkJIyiYSEozGbG97D6ytxlfDa6tcOqh+NkYD6AOWX1jB7eSGzVxSRu70KpWBkViKXnt6XlJjf/8NsDhpN0Nj9ETBCNWUD4dqygT237dEmaBgE9e5t9jye1hpDgxF+Dr3WGEZoXdDQ+MPn27W9kfbhddTbZuhQyYKgodE0sn+4pIGmdp+Gbdqq5MG+mLQmKhjAYfiwaT9mcxBl0VgsBqkWPxEWD5FWF1HWSqJtTqLtlcRGVRITWUWk1R0KoS0e7OYAEY5UHBHpOBydiXB0wRHRlQhHF2Ji+mGxRLfodQSDRihwrqoNlkPhsqde8Oyu8tW18bj8ex2ZbI+0EBFjIyLaSlxKBKnZcUREW3FEW7E5LI2HxI0Fz1YzZovCJLWvhdjTGOAyYKVSall43d3AK8ArSqlVgA+YHB5NLV8XFkIIcdBclU4++Mf9aODcux4gMi6+QZut22ZRUPAfOqVdQNesqa3eRyGEEKK9M4JB1s77kQUfvEN54VaS0jM47dY76HnUGJTpwPIPrTU1NesoKfmKktKvqapaBUBkZFcyulxFcsrxxMUORql9B9/bqrfx6qpX+XD9hwR04ID683skoN4Pm8tczF5ZyOzlRawpCtVTGJGVwLTT+3Jy/1QSfRr/1mq0z4Dw1+TDQ4BRJggP6w2N4t3LuvrrG1uHUgSrnAQKC1F2G8oRgcnhQEU4MNkdqOaesTN8CaGhyLue21O5kN1D8XBd3z1C7No2tQxPgGCll2CFl6DTR7DSS8Dpw3B6CTq9BKt8oXZoghgECBLQHoK+SnyenfjdFQT81QQCNQQCLgLagy/Sii/WgTcR/PE+LJF+HA4XDkc1Dns1DnsVNocbk6leR7TCak0mMiqTCEd3HBHhENrRmYiIdOz2VEwmW7P9rAL+YKMB824jnMNhtKfaj9e1l390VKgucUS0lYgYG4lpUTh62sKvrURE23CEnyNiQiG0WQJlIVqU1vpnGq8rDXDpXvaRrwsLIYQ4YH6vhw//+QDVO8s4/75/kJDWcK7dktJvyM29n6SkY+nV68HffR/x9JKnW7K7QgghRLtjBIPk/Pw9v374DuVFhSRnZHH6n+6kx8ijDyiYNowATudvlJR+TUnJ13g8WwBFXOxgunX7KynJxxMV1a3Jx8t35vPSypeYkzcHpRRndT+Lq/pfRcbk5p2bSALqfdha7uKzlUXMXlHEiq1OAIZkxHPPyb2ZlBRDQqkX34ZKfN8sY/vewrwDpNH4CeJRfjz4cCtf3bJPNf+nFQdC1x8uqxpZF16/+5pwC/X76xoch/Bx1J5HC/9XNbKufr+0hqBCGwqNQcDkxzB70BYvhtmNNnnQZi862ouK86MsfkzmIGazH7M5gNkcwGQO7HptCi3bzQEi69o0HHzod1kx/DFEmLoRF9WDpLQBxMR2w+HojMORhslkb7BPU/m9wUZHMofW7QqcPeGyGn5v44MjTSaFIxwuO6JtpGTE1IXLoVHOtrrgOSLGij3KKiUuhBBCCCGOYEYwyOyn/o/tGzdwxl/uplPP3g3aOJ1LWbXqZmJj+jOg/zOYTHt/+/na6teYsXJGS3ZZCCGEaDeMYJA1P33Hrx++Q0VxESmZXTnjz3fTfcSo/Q6mg0EXZWU/UVI6l9LS7wgEKjCZbCQkHE1W5vUkJ0/Ebk/Zr2Ou3bmWGStmMHfTXOxmOxf1vojJ/SaTGpW6X8dpKgmoG1HkdDNnRaim9NLNFQAMSIvl9qEZTLDYSC524/uqCIKFVAKW5AgcfZKwZ8Viy4hBWc2gdai2rhGqUaGDGm0YeH0+XDU1uNxuatwuXO4aXB43LrcLl8dNjauamionbq8blw5iKDCZ/ERGOomMchIVWUFkVAV2e02b/ozav4bhqUkZoYDZFAqTlanp9UGCARNGwBx69psxAiYMvwlfwIThs2L47Rh+heFXWEzxxCX0pEPnwXTucTQdsnocUJ0gnztA1U4PVTs9VO/0ULXTG1ou91C904u7ykfAbzS6r8midguY41LiQssxtrpRz7XPjmgr9khLuxoVL4QQQggh2i+tNd+88jx5ixcy8eob6T5iVIM2Llc+y1dci93egUGDZmA2R+71eB9v+JjHfnuMSZmTWMWqluy6EEII0aaCgQBrfvqWXz98F+f2YjpkdeOM2/5G92FH7Vcw7fWVUhquJ72z/BcMw4vFEkdy8gRSkieRmDgOiyVqv/u3bMcyZqycwY9bfyTaGs3VA67m0j6XkhSRtN/H2h8SUIftqPTw2cpQKL2ooByA3nERTO2UxHiXJrUoAEUVYFaQHkP02M7YM2OxZcYSsBhUVlZS5HRSua2QmpoaampqcLlcDZaDwcZHsJq1xhpwEWktIyK6gpjESiKjq4iIqcYR7alrZxgmgiqZiMjuqGaYtTNU4NnY9QgaoMPP9dftsawMDUYwFMAbwfB63fBY9V8f4GR4e6VUvceer1VdSZRdDzMmSwIWeyyWyATs0R2wx3fAGt8Rqz0OkykSiyUSszkSszkq9GyKxGyJQJksqNrQOzz5YvhFKNhVda+a9A+KYWhcTi9VZR6qwoHznmG0z737KHmTWRGdYCcm0UFajzgiY2x1AfOuwDk0ytnqMEvgLIQQQgghWsTCj/7Hiq+/YOSZ5zH4hFMabPf6Slm67EoABg96FZstuUGbWt9u/pb7593PqLRRPDruUZ7giRbrtxBCCNFWggE/q3/4loUfvYtzx3Y6dO3GmbffS7dhI5uc37hc+XX1pJ3OpYDG4ehM504XkZxyPPFxwzGZrPvdN601C4sXMn3FdBYWLyTeHs9Ng2/ioj4XEWuL3e/jHYgjOqAurfby+apiZi/bxsKCcjTQzWblGksEEwImMpxmDH8QX2cLzt52XNFBXBYflVWbqdxZiTPfSWVlJR6Pp8GxrVYrUVFRREZGEhMTQ2pqKhEWC6qslMr8Nfir1mGy7kQlerAkeXEkerHF+EO1pgHDUPgCsVgjepLSYQSpScOIju5JRERGk4qXt0c6EMDweNEeN4bHg/Z4MNwetM8LJhPKYql7YLagrBaU2QwWC8pqRZnNu7ZbLAdcJL41+DyBcNjsrRc6e+rWVVd4QyPs67FHWYhJdBCTFEGnHglEJ4bC6NpHRKxNymoIIYQQQog2tebHb/n57Zn0GXssYy+8vMH2QKCG5cuvxucrZejQN4mMzNrrsRYVL+L2H26nb1JfnprwFDZz8829IoQQQrQXOwu3MfuJRyjZXEDH7B5MuOI6soeO2GcwrbVBZeVySkrmUlL6NS7XRgBiovvRtestpCQfT3R07wMeoKi15setPzJ95XRWlKwgJSKF24bfxvk9zyfSuvdvPrWEIy6gLq/x8dlvW/l06TYWFldiAJ3RnEGAAaYa4uw+3I4gy5SHn32h8htsIfQIi4yMJDY2loSEBDIzM4mLiyM2NrbuOSoqCqvFQvGyBeT88haVhWvQ9grciX6syX7ix/rqgmhtgNcdQcDSmciovnTNOJak+IFERmQd0Kce7ZmyWDBHWyB6/79i0F55qv2UbqumbGt13XNlqbvB5IImkyKq3ujnmAQHMUkOohMdxCQ4iE60Y3Mccf87CiGEEEKIQ0jBiqV8+cJTZPQfxIk33NJgwIhh+Fm1eipVVWsYNPBF4mIH7fVYa8rWMPXbqaTHpPPcxOda/Y2wEEII0RrWLfiZL194CpPFyhl/uZvuI0b/bqAcDHopL58Xrif9LT5fCUpZiI8fSXr6paQkH4/D0emg+hQ0gszdPJeXVrxEbnkunaI6ce+oezmz+5nYzQc+T9rBaBeJWO6OMiY89XqLn8dZ46C8MgKNIsLuo3NaFR0SK4mM8FKh4CcArVFao7QRejbsoWc0ygivx43CDf5iVBlQBio8IZ9Nexnp+5HY+Aps8V5M/SAmdFh8VVZ8nhiC3q6kpo+mR9Yk4mJ6HNQkeaJ1GEGDiu1uSrdVUba1htKt1ZRtq6amwlvXJiLGSlLnaHqM6Fg36jk60UFMop3IOLuMfhZCCCGEEIesHQV5fPr4P0js3IUz/nI3Zsvug2m01qzNvYeysh/o3ethkpOP2+uxCpwF3PD1DcTaYnlx0ovEO+IBMIzG51cRQgghDjXBgJ8f33iVJZ9/QlqPXpx2653EJjc+UaHfX0Fp6XeUlH7Nzp0/Egy6MJujSEoaT0ryJJKSxmO1xh10n/yGnzl5c3h55csUVBaQFZvFQ2Me4pTsU7C28SDZdhFQ+/wW8osSW/w8RoQZo2sEwdQIPDFWypVifQucZ5W/N9cVvkRVUQe8lZn4KntRVZOJx2QnaDehIszkRFv5NdZHTPwGEpIj6NAhkrSOUaQmReKwHpolPA4Xnmo/pVurKNtWU/e8s7CGYCD0C7PJrEhIjaJzr3iSO8eQlB5FcnoMkbHylUQhhBBCCHH4qSzdwYePTsMWGcU5d07DHtnwG5H5+U9RVPQeXbOm0rnzhXs9VnFNMdfOvRaA6ZOmkxqVCoQC7tmzZ7fMBQghhBCtqLK0hNlPPkrR+lyGnnwGx1x6ZYMPdt3uraFR0iVfU+FchNZBbLYOpKaeRUry8SQkjGq2Aa3eoJeP1n/EK6teobCmkF4JvXhs/GMcn3E85uaY364ZHFRArZQqAKqAIBDQWg9XSiUC7wBZQAFwgda6/PeO0zc1mh/uGnMwXdm3bUuwzDoX3e0O9OipaB2aH1CHRz7XVgOuXV+7ThP6ZUnXa1O3rZFjzC6rYtqmwXzR42XOqrLgK/Pgr/CgK32YqgOYPEHM5X7sJT5MuKgBaoCt4eP60LjM4LcqtMOMJcqMLdpGdJyNuEQHScmRpHaMomvnGOIjJRA9GMGgQcV2F2Xh0dClW2so21pFjdNX1yYi1kZyejQDJqST3DmKpPQYElIjMVvab/1rIYQQQgghmounupoPHpmG3+vlwgf+SUxSwwkPt217i/yCZ0hLO5+uXW/Z67EqPBVcP/d6Kn2VvHLiK2TFZQGh91tfffUVS5YsaanLEEIIIVpFwbLFzHn23wT9fk679U56jR4LhO511dVr6upJV1fnABAV1YPMjGtJTplEbMwAlGq+vMnld/G/df/jtdWvUeIuYVDKIP426m+M6zzugOtWt5TmGEE9QWtdWu/1ncA3WutHlVJ3hl/f8XsHMJvNxMbFN0NXfkfccdDvZFj0Hzj6WohqmRHb18fFUmTAi1tKGDMgnSs7d2u0ndaaKqePwu3VbN9eQ1mpG+dOD9rpxVHlx+oKoFwGNmcQiw4Fps7wIw/4GY3JYsJuN2OxmDBbTZitZixWE+bwa0v4YQ4/drULr7eYdy2H99utvdWEOozKUnhq/KEwOlwvemdRDUYg9OGCyaxISIsivXciSenRJHeOJik9WkZFCyGEEEKII1bA7+fjxx6ioriQc+/+O8kZWQ3alJZ+y9rc+0hKGk/vXg/u9Q2vy+/ixm9uZEvVFl6Y9AJ9k/rWbfvhhx+YP38+I0eObKlLEUIIIVqUYQSZ/97bLPjgbZLTMzj9z3eR2CkdALd7G6tX34KzcimgiIsbRvfud5GSfPzvTiZ8oCp9lczKmcUbOW/g9Do5Ku0oHh33KCNS9z0xY1tpiRIfZwLHhpdfA75nHwF1q5l4Lzx/NPz8OJzwUIud5r5unchzebln/VayI+yMT4xp0EYpRWy8ndh4O717Jf3u8XyeADtL3RRvr2H7DhflZW6WbSyjeKebbnFRjMxIwGRA0G8QCBgE/aGHyx0gGDAI+HetCwQMgr4gWv/uKQ9rkbE2ktKjGVQbRqdHE99RRkULIYQQQghRSxsGn//ncbbmrOLUm2+nS7+BDdo4nctYuWoqMTF96d/vmb1O8u4L+rj1u1tZXbaaJ459ghGpI+q2zZ8/n++//55BgwYxYeKkFrseIYQQoqW4Kp3MefpfbF65jH7jJzLx6huw2h0AlJTMZU3OX9HaoFfPB+jQ4WRstt/PAQ9UmbuM19e8ztu5b1Pjr+HY9GOZMnAKg1L2Pmlxe3GwAbUGvlJKaeBFrfV0oKPWughAa12klOpwsJ1sNh36wKCL4NfpcNQNENe5RU5jVorn+2ZyxpL1XLM6n9lDe9IzynHAx7M5LKSmx5CavivoPt/QvPDDRh6fu46vdvh45qKhDO4S3+RjGsFwcB0OtOtC7HqBdsBvoI3DJ8m2OswkdZJR0UIIIYQQQuzLD2++yrr5P3HMpVfRe8z4BttdrnyWr7gGu60Dgwa9jMXSsC41QNAIctdPdzG/aD4PjnmQ4zJ2TZ64ePFivvzyS/r06cOJp5zGDbOWttj1CCGEEC1h29o1zH7qn7irKjnhupvpP2ESSikMw8eGDf9ky9b/EhPTn/79niYyMrNF+lBcU8x/V/+X99e9jzfo5YSsE7hmwDX0SuzVIudrCQcbUI/RWheGQ+i5Sqm1Td1RKXUtcC1ARkbGQXZjPxx7J6z8H3z/CJz5bIudJtpiZubAbE5evI7LVuTx2bCeJNmab8C62aT444TujMpO5Oa3lnHe8/O4/cReXDMuG1MTynKYzCZsZhkxLIQQQgghhNjdks8+ZvHsDxly0ukMP+3sBtu9vlKWLbsKgMGDX8Fua1iXGkJlDR/69SG+2vQVtw2/jbO6n1W3bdWqVXz66ad0796dM846m5vfWc73uSUtcj1CCCFEc9Nas3jOR/w067/EJnfg4of+TYesbADc7i2sWnUzlVUrSE+/nB7d72y2CQ/r21K5hZdXvczHGz8GDadmn8rVA66ma1zXZj9XSzuohFJrXRh+3gF8CIwEtiul0gDCzzv2su90rfVwrfXwlJSUg+nG/onPgBFTYNmbULKuRU+V7rDxWv+uFPv8XL0qH69hNPs5hmUm8tnN45jUtyOPfL6WK/67iJIqb7OfRwghhBBCCHH4W/frL3w38yW6jxjNsZOnNKhVGQjUsHz5FLy+HQwaOIPIyL2/CX5m6TO8t+49pgyYwuR+k3edY906PvjgAzIyMjjn3PP4y3srmbtmO38/s1+LXZcQQgjRXLyuGj759z/44fWXyR46gkseeaIunN6x40sWLjodlzufAf2fo1fP+5s9nN5QvoE7f7qT0z46jU83fsq5Pc5lzjlzeGjsQ4dkOA0HEVArpaKUUjG1y8AJwCrgE6D2t4/JwMcH28lmN+4vYI2Cb//e4qcaGhfFU70zWOCs4fbcLegWKP4cF2nluUuG8vDZ/fk1r4yTn/qJn9bL6AMhhBBCCCFE021du5rPnnmMTj16c8rNt2EymXfbbhgBVq2+maqq1fTv/zRxcYP3eqzXVr/GjJUzOLfHudw85Oa69fn5+bzzzjt07NiRP1x4EXd+lMNnK4u559Q+XD46q4WuTAghhGgeOwryeOPOW9m4+FfGX3Y1Z/zlbziiojEML7nrprFy1Y1ERnRl5IhP6dDhxGY99+qy1dz63a2c/cnZfLv5Wy7vezlfnPsF94y6h07RnZr1XK3tYGpOdAQ+DH+ibgFmaa2/UEotAt5VSl0NbAbOP/huNrOoZDh6Knz/D9i6GNKHtejpzuqYwEaXl38VFNMj0sHUzI7Nfg6lFJcclcnwzERumrWEy19ZyPXju/HnST2xSikPIYQQQgghxO8o27aFj//vQWKTO3DWX+/Fatt9tJfWmtzceykr+57evR4iJXniXo/10YaPeOy3x5iUOYl7R91bNwp769atvPXWWyQmJnLxJZdw/5x1fLK8kL+e1Isp47Jb9PqEEEKIg7Xyu6/49uUXcERHc8H9j5DeO/TNH5drU/gD3FV06XIl3bv9FZOp+eY/W7x9MTNWzuCXbb8QY4vh+kHXc0nvS4h3xDfbOdraAQfUWus8oME0kFrrMmDvv620F6NvhIXT4ev7YfKnoPZdt/lg/DmrIxtcHh7OKyI70s6pKfEtcp5eqTF8ctNY/j57Nc9/v5EFeWU8feEQuiRGtsj5hBBCCCGEEIe2mopyPnjkfkwWC+fe/QARMbEN2uTnP01h0btkZf2Rzp0v2uuxvt38LdPmTWNU2igeHfco5vAo7OLiYt544w2ioqK49NJL+cdX+by3eCu3Ht+DG4/t3mLXJoQQQhwsv9fDNy+/wOofviZjwGBOnXobkXHxAGzf8Rk5OXehlImBA14gJWVSs5xTa838wvlMXzmdxdsXk+hI5Jaht3BhrwuJtkU3yzkOlNECJYybb9a+Q409Bsb/FT7/K2z8Frq3bKaulOKJ3hls8fi4ac0m0ofaGBTTMqFxhM3MI+cMZEz3ZO56fyWnPP0Tj54zkFMHprXI+YQQQrQ9pVQXYCaQChjAdK31U/W23wb8C0jRWpeG190FXA0EgZu11l+2eseFEEK0KZ/bxQePTMNdWckfpj1KXIfUBm22Fb5DfsHTpKWeS3bXP+31WIuKF3H7D7fTN6kvT014Cps5NHqstLSU119/HavVymWXXcbj32/hrYWbufHYbtwysUeLXZsQQghxsHYWbuPTJx6hdMsmRp17EaPPuxCTyUww6GX9hn+wbdsbxMYOpn+/p4mI6HzQ5zO0wXdbvmPGihmsLltNx8iO3DnyTs7pcQ4RlohmuKIDEwwGKSgoYO3ateTk5DT78Y/cgBpg2BUw/1n45gHIngCmli2F4TCbeHVAV05evI7JK/L5fHgP0uzNN+R/T6cN7MSg9HimvrWUP85aws8bMrjvtL5E2Mz73lkIIcShJgD8RWu9JDxHxGKl1Fyt9ZpweD2JUOktAJRSfYELgX5AJ+BrpVRPrXWwLTovhBCi9QUDAT594lFKNudz9l/vo2N2w5HMpaXfkZt7L0mJx9C798MNJk2staZsDVO/nUp6TDrPTXyOSGtoME5FRQUzZ85Ea81ll13G8/OLeW3+Jq4Z15XbT+y11+MJIYQQbS13/s989eJTmCxWzrlzGl0Hh0oEu1z5rFx1M9XVa8jocjXdut120CU9AkaALwu+5KWVL7GhYgNdYrowbfQ0zuh2BlaztTkuZ7/5/X42btxITk4Oubm5eDwerFYr3bs3/zefjuyA2mKHCffAh9fCmg+h/7ktfsoUm5XXB2Rz2pL1TF6Rz4dDuxNlbrnAuEtiJP+7fjT//modL/ywkcWbdvLMRUPplRrTYucUQgjR+rTWRUBReLlKKZUDdAbWAE8Af2X3iYvPBN7WWnuBfKXUBmAkML9VOy6EEKJNaK2ZO+NZCpYv4YTrbqbrkOEN2lRWrmDlqqlER/ehf/9nMZkaf4Nc4Czghq9vINYWy4uTXqyriVldXc3MmTPxer1MnjyZ/y7ZyYyf8rni6CzuPqWPhNNCCCHapWDAz49vvMqSzz8hrUcvTrv1TmKTUwAo3v4pa9f+DaWsDBo4g+Tk4w7qXP6gn082fsLLq15mS9UWusd359Fxj3Ji1olYTK0f23o8HtatW0dOTg4bNmzA7/fjcDjo1asXvXv3plu3bthsNi688MJmPe+RHVADDDgPfnkKvn0I+pwBrfCpRJ/oCF7om8nklflMXbOZl/pnYWrBX86sZhN3ntybo7sl8ed3l3HGsz9z3+l9uXhkhvxSKIQQhyGlVBYwBPhVKXUGsE1rvXyPf/M7Awvqvd4aXtfY8a4FrgXIyMhoiS4LIYRoZfP+N4vV33/N6PMuYsBxJzTY7nIVsGz51dhsyQwa9DIWS1SjxymuKebaudcCMH3SdFKjUsP7u5g5cyZVVVVcdtllvLummue+38hFIzO4//S+8j5ECCFEu1RZWsLsJx+laH0uQ08+g2MuvRKzxUow6GHd+gcpLHybuLih9O/3FA5HpwM+jyfg4f317/Pf1f+luKaYfkn9eHLCk0zoMgGTatkKD3uqrq4mNzeXnJwc8vLyMAyD6OhoBg0aRJ8+fcjKysLcgoNrQQJqMJlh4n3w1h9g6esw/KpWOe2k5Dge6N6Zezds45G8Iv7W7cD/UjfVMT1T+PyWY/jzu8v424er+GVDKY+cM5C4iLb5qoAQQojmp5SKBt4HbiVU9uNvQMPkARpLBnRjx9RaTwemAwwfPrzRNkIIIQ4dK775kgXvv0X/CZMYfd7FDbb7fKUsW34loBky+FXstuRGj1PhqeD6uddT6avklRNfISsuCwCv18ubb75JWVkZF198MbPzfDz59XrOG5bOw2f1l3BaCCFEu1SwbDFznv03Qb+f0269k16jxwJQU5PHqtVTqa5eS2bGtWRn/3mv3yral2pfNe/kvsPMNTPZ6dnJ0A5DmTZ6Gkd3OrpV748VFRXk5OSQk5PD5s2hSpAJCQmMGjWKPn360LlzZ0wtXAq5PgmoAXqeCBmj4ft/wsALwdYykxfuaUp6MutdHp7ZvINukXYuTEtq8XOmxNh57cqRTP8pj8e+zGX5lp94+qIhDMtMaPFzCyGEaFlKKSuhcPpNrfUHSqkBQFegdvR0OrBEKTWS0IjpLvV2TwcKW7nLQgghWlne0kV8/dJ/yBo8jOOn/LHBm+Fg0MXy5dfg9e5g6JA3iIzs2uhxXH4XN35zI1uqtvDCpBfom9QXCNWrfOuttygsLOSCCy7gu0LF/32xljMHd+Kf5w7EZJJwWgghRPtiGEHmv/c2Cz54m+T0DE7/810kdkoHoLj4Y9bm3oPJZGfQwJdITp5wQOdwep28kfMGb+a8SZWvijGdxnDNwGsY1nFYc17K7yopKakLpYuKigDo2LEj48ePp0+fPnTs2LHNPkSWgBpAKTh+GrxyIvz6Aoz7cyudVvFwj3QK3F5uz91KZoSd0fHRLX5ek0lx/fhuHNU1kalvLeWCF+fz50k9uWF8N/mFUQghDlEq9JvEy0CO1vpxAK31SqBDvTYFwHCtdalS6hNgllLqcUKTJPYAFrZ6x4UQQrSa4o3r+fSJR0nJ7Mrpf7oTs2X3t4OGEWDlqqlUVq1i4IDniYsb0uhxfEEft353K6vLVvPEsU8wInUEAMFgkHfffZeCggLOPvtsFlVE8NCc1ZwyIJV/nz8Is7zXEEII0c64nBXMeeYxNq9cRr/xE5l49Q1Y7Q6CQTfr1v2dwqJ3iYsbTv9+T+JwpO338bXWfFHwBY/8+gjl3nImZkzkmgHX0C+5XwtcTcNzFxYW1oXSZWVlAKSnpzNp0iR69+5NUlLLD5ZtCgmoa2WMgp4nw89PwrArIDKxVU5rNSlm9MvitCXruWplPp8N60nXSHurnHtIRgKf3TKOuz5Yyb++zGX+xjIe/8MgOsQ4WuX8QgghmtUY4DJgpVJqWXjd3VrrzxprrLVerZR6l9AkigHgj1rrYKv0VAghRKur2F7Mh/98gMjYeM65cxo2R8Ru27XW5ObeS1nZ9/Tq9SApKcc3epygEeSun+5iftF8HhzzIMdlhCaHMgyDDz74gPXr13PqqaeyxpvAfR+v5Pg+HXnqwiFYzPv+mrDhk9uQEEKI1rNt7RpmP/VP3FWVnHDdzfSfMAmlFDU1G1i5aio1NevIyryBrl1vxXQAExZur9nOQwse4vut3zMgeQAzTphBr8ReLXAluwSDQTZv3szatWvJycmhsrISpRRdu3blqKOOonfv3sTGxrZoHw6EBNT1TbwXnh8DvzwJk/7eaqeNs1p4fWA2pyxex2Ur85g9tAfx1tb5o4l1WHn2oiGM657MtE9Xc/KTP/HvCwZxbK8O+95ZCCFEu6G1/pnG60rXb5O1x+uHgYdbsFtCCCHaAVelkw8euQ8jGOTcux8gKr5heb/8gmcpLHqXrMwbSe/csC41hELsh359iK82fcVtw2/jrO5n1a2fPXs2q1ev5vjjj2eTKY2731vOsb1S+M8lQ7A2IZwOVvsofW3NQV2nEEII0RRaaxbP+YifZv2X2OQOXPzQv+mQlQ1AUdEHrM29D7M5gsGDXiUp6ZgDOv7769/n37/9m4AR4Pbht3NJn0swm1pmosFAIEBeXh45OTnk5ubicrmwWCx069aN4447jp49exIZ2TrljA+UBNT1dewHA/8Av74II6+DuM6tduqsCDsv9+/KBcs2cu3qAt4c2A1rK30FTinFhSMzGJaZwE2zlnLFq4u49phsbjuhFzZL684cKoQQQgghhGg+fq+Hj/71IFWlpZx3z0N1NTXrKyx8l/z8J0lLPYfs7L2XO3xm6TO8t+49pgyYwuR+k4HQm/Avv/ySJUuWMG7cOMqiu/LXd5YxplsyL1w6DLtl32/G/SUuSl9djVHlO/ALFUIIIZrA66rhi+eeZMOi+XQfMZqTbrwVe2QUwaCL3NxpFBW/T3z8UfTv9wR2e8f9Pv6Wyi1Mmz+NhcULGZk6kmmjp9Eltsu+d9zf6/B6Wb9+PTk5Oaxfvx6fz4fdbqdnz5707t2b7t27Y7e3ToWG5iAB9Z4m3A2r3ocf/glnPN2qpx4dH83/9UrnT2u38Lf1W/lnz/RWLU7eo2MMH980hgdnr2H6j3n8mlfGMxcNJSOpfX/KIoQQQgghhGjIMIJ89sxjFK3P5Yw/3UXn3n0btCkt/Y61ufeQmDiO3r3/sdf3H6+tfo0ZK2dwbo9zuXnIzXXrf/jhBxYsWMDIkSPxdujDn99axoisRGZcPhyHdd/htLfASdnMNaAUydcMgIcO/HqFEEKI37OjII9PH38EZ8l2xl92NcNOPQulFNXV61i1+mZqajaQlXUTXbOm7ndJj6AR5I2cN3h26bNYTBamjZ7GOT3OadZcr6amhnXr1pGTk8PGjRsJBoNERkbSv39/+vTpQ9euXbFYDs2o99DsdUtKyIQRV8PCGXD0VEju0aqnvygtiY0uL89u3kHPKAdT0lNa9fwOq5mHzx7A2O7J3PH+Cs567hdmXD6cYZkNvwYohBBCCCGEaJ+01nz33+lsWLSACVdcR4+jjm7QprJyBStXTSU6ujcD+j+LyWRt9FgfbfiIx357jEmZk7h31L11b7bnzZvH999/z+DBg7FkDuHGN5cyuEs8r1wxggjbvsNp18oSdr6TiyXeQfKV/bAkRexzn8NFeOLiKiAIBLTWw5VSicA7QBZQAFygtS4Pt78LuDrc/mat9Zdt0G0hhDhkrfz2K7555XkiomO44P5HSO/dLzyJ4HvkrrsfszmKIYNfIzFxzH4fe335eu6fdz8rS1dybPqx3DPqHjpG7f/o68Y4nc66etKbNm1Ca01cXBwjRoygT58+dOnSBZPp0K9+IAF1Y8bdBkvfgG8fhAtmtvrp785OY6PLy33rt5EVYef4pNYvXn7ygDR6p8VyxasLuXjGAp66cAgn9U9t9X4IIYQQQggh9t+iT95n2ZdzGH76OQw9+fQG212uTSxbfjU2WxKDBr6MxRLd6HG+3fwt0+ZNY1TaKB4d92hd/czFixfz1Vdf0bdvX2J7jeL6N5bSr1Msr145gij777/N1FpT/fM2nJ/lY8uIJenyvpijGg/HD3MTtNal9V7fCXyjtX5UKXVn+PUdSqm+wIVAP6AT8LVSqqdMbiyEEPvm93r45uUXWP3D12QMGMypU28jMi6eQKCG3HX3UVz8EQnxo+jX7wns9v2bj80f9PPSypeYvnI6sbZY/nXMvzgx68SDHjVdWlpaF0pv27YNgJSUFMaOHUufPn1IS0tr1YoLrUEC6sZEp8Dom+CHR2HbYug8rFVPb1KKZ/tmcNaSDVy/uoBPh/agT3TrjybomhzFBzcczZSZv3HDm4u599S+XDW2a6v3QwghhBBCCNF0OT9/z0+z/kuvo4/hmIuvaLDd5ytj2fIrAc3gQa9itzf+rc1FxYu4/Yfb6ZvUl6cmPIXNbANg5cqVfPrpp3Tv3p3UQeOZ8voSenSMZuZVRxHr+P2gWRsa5+w8qucVEtE/icQ/9EI1oRTIEeJM4Njw8mvA98Ad4fVva629QL5SagMwEpjfBn0UQohDxs7CbXz6xCOUbtnEqHMvYvR5F2IymamuzmXlqqm4XHl07XoLXbP+iFL7dy9aWbKS++bdx4aKDZyafSp3jLiDBMeBVR/QWlNcXExOTg45OTmUlJQA0KlTJyZOnEjv3r1JSWndCgutTQLqvRn9R1g0A75+ACZ/0uqnjzKbmTmwKyf/tp7LVubx+bCepNhaf1RBUrSdWVNGces7S/n77DVsLXfzt1P7YG6lCRyFEEIIIYQQTbd51Qq+eO5J0vv256Qb/4Ta42u/waCL5SuuwestZuiQN4iKym70OGvK1jD126mkx6Tz3MTniLSG5qXJzc3lww8/JDMzk64jJ3H160vITo7i9auPIi7y99+vGL4gO9/OxbOmjOixnYk7pSvqyH1foYGvlFIaeFFrPR3oqLUuAtBaFymlaofydQYW1Nt3a3jdbpRS1wLXAmRkZLRk34UQot3Lnf8zX734FCaLlXPunEbXwcPQWrOt8B3WrXsAiyWWIUNeJzFh9H4d1x1w85+l/+H1nNdJiUjhPxP/wzHpx+x3/wzDYMuWLXUjpSsqKlBKkZmZyfDhw+nduzdxcXH7fdxDlQTUe+OIhWNuhy/uhI3fQbcJrd6FNLuN1wZ25awl67liZT7vD+6Ow9z6dWUibGaeu2QYD81Zwyu/5FNY4ebJCwc3adITIYQQQgghROso2VzAx489REJaJ8687R4s1t0DY8MIsGrVLVRWrmTggP8QFze00eMUOAu44esbiLXF8uKkF4l3xAOQn5/Pu+++S2pqKr3GnMyUN5aSnhDJG1OOIjHK9rt9C1b7KHttDb6tVcSdnk3MmAb56pFmjNa6MBxCz1VKrf2dto2l+LrBilDIPR1g+PDhDbYLIcSRIBjw8+Mbr7Lk809I69GL0269k9jkFAKBatbm3sv27Z+QmDCGvv0ex25L3q9jLyxayP3z7mdr9VYu6HkBfxr2J6JtjZfIakwgEKCgoICcnBzWrl1LTU0NZrOZ7OxsjjnmGHr16kVUVNT+XvJhQQLq3zP8Kpj/H/h6GmQfC21Q32VQTCTP9MlkyuoC/rR2M8/1zWyTOjNmk+L+0/uRnhDJQ3PWcPGMBcy4fDhJ0fZW74sQ4vCntcYbMHD5gtR4A9T4AtR4A1R7g7i8Aaq9AVy+YPg5QI131/KebWr3F0IIIQ5nVWWlfPDI/dgcDs656wEcUbu/YdZak7vuPkrLvqVXz7+TknJCo8cprinm2rnXAjB90nRSo0Lz0GzZsoVZs2aRmJjIoAlnMOWN5XSMdTBrylEk7+M9gb/ERemrqwlW+ki6tA8R/fYvEDgcaa0Lw887lFIfEirZsV0plRYePZ0G7Ag33wp0qbd7OlDYqh0WQohDQGVpCbOffJSi9bkMPfkMjrn0SswWK1VVOaxaPRWXaxPZXf9EVtYN+1XSo8pXxeOLH+e9de+REZPBKye+wojUEU3e3+fz8d1337FkyRK8Xi9Wq5WePXvSp08funfvjsPhOJDLPaxIQP17LHaYcDd8dAOs+Qj6nd0m3TitQzx3u9P4R14R3SMd/KVr201WePXYrnSKc3DrO8s49/l5/PfKkWQlH5mf7gghdjEMjdtfGwYHw2Hy3gPjPQPm2jb1twWMpg38MZsUUTYz0XYLUXYLkXYL0XYzCVGRRNstRIa33d3CPwMhhBCirXhdNXzwyP343C4ufOD/iE1uWKeyoOBZCgvfITPzBtLTL2n0OBWeCq6bex2VvkpeOfEVsuKyACguLubNN98kOjqaESecw5Q3lxMfZWXWNUfRIfb331R7N1VS9tpqUJBy7QDsGa0/AXx7o5SKAkxa66rw8gnA34FPgMnAo+Hnj8O7fALMUko9TmiSxB7AwlbvuBBCtGMFyxYz59l/E/T7Oe3WO+k1eixaa7Zum8X69Q9itSQwdMgbJCQctV/H/X7L9zw4/0FKPaVc2e9Kbhh8AxGWps8Tl5+fzyeffEJ5eTkDBgygf//+ZGdnY7UekZMD75UE1Psy8A/wy9PwzYPQ+zQwt81foKkZHVjv8vCvgmK6Rdo5q+OBFV5vDicPSKNDrIMpry3inOfnMePy4QzLbLv+CCEOjD9oUO0JBcJV4edqr3/XsqfeqGXf74xa9gZw+YPoJn6R1G4x7QqTw+FxXISVzvEOIm2W8DZz3XJtm9rgOcpuIcq2a3+7xdSkb5ZIQC2EEOJwFPD7+fixh9lZuI1z7ppGSmbDSc0LC98jL/9JUlPPplv2Xxo9jsvv4sZvbmRr1VZemPQCfZP6AlBaWsrrr7+O1Wpl9Mnnct1bK4lxWJk1ZRRpcb//Bt21spSd76zFEu8g+cp+WJJaf+L3dqoj8GH49xcLMEtr/YVSahHwrlLqamAzcD6A1nq1UupdYA0QAP6otQ62TdeFEKJ9MYwg8997mwUfvE1yegan//kuEjulEwhUkbP2b+zYMYfExHH06/tvbLakJh93p2cnjy58lM/zP6dHQg+ePu5p+iX3a/L+Xq+XuXPn8ttvv5GQkMAVV1xBVlbWAVzhkUEC6n0xmWHiffD2RbD0DRh+ZZt0QynFY726sNnt49a1m8lw2Bga13Yjl4dlJvDBjWO48tWFXDxjAU9dOJiT+qe1WX+EOJL4AkZdgFxVGyjXBs3e2mV/eHtgt+27wmg/Hr+xz3MpRTgMNu8WCneMcRCZHA6MbbvC4z1D5ahw2Bzaz0KUzYylDWrpCyGEEIcjv8/Ll889yZbVKzjlpr+QOWBwgzalZd+zNvduEhPG0qf3Pxr9UNcX9HHLd7ewumw1Txz7RN3XlisqKpg5cyZaa4459XxueDcHm8XErGuOokti5F77pbWm+udCnJ/lYcuIJenyvpij9j3QZ+frbzT94g9hWus8YFAj68uAiXvZ52Hg4RbumhBCHFJczgrmPPMYm1cuo9/4iUy8+gasdgdVVatZuWoqHs9WumXfRmbmdSjVtPehWms+y/+MRxc+SrW/mj8O/iNX978a634MWM3Ly+Pjjz/G6XQyatQojjvuOGy235+r4VCgtWZruZslm8ub/dgSUDdFr5Ohy1Hwwz9DI6pte/9lrCXZTSZe6d+VUxav48pV+cwf1ZfINgx6uiZH8f4NRzNl5m/c8OYS7jm1L1ePbThiQwgR4vEH64LlxkYt1x+5XD9Irq4XMld5A/gC+w6WzSZFjCMUFkfbLcQ4LCRH2+iaHEW0w0JMeH20Y9f2GIe1bl1MOFyOsJoxmVq/7r0QQgghfl/e0kV8+8oLOHdsZ9zFV9BnXMNJ3SsrV7Bq1VSio3ozYMB/MJkavjkOGkHu/OlOFhQt4MExD3JcxnEAVFVVMXPmTLxeLxPPvJA/vr8OUMy6ZhSZSXsfKKMNjXN2HtXzConon0TiH3qhmjC5eukLL1Ly5JNNvn4hhBBHtm1r1zD7yUdxV1dxwnU303/CJAC2bn2DdesfxmZLZOiQWcTHD2/yMYtrinlowUP8sPUHBiYP5IGjH6B7Qvcm7+/xeJg7dy6LFy8mKSmJq666ioyMjP2+tvbC4w+yapuTJZvLWbKpgsWbyymp8rbIuQ46oFahquK/Adu01qcppaYB1wAl4SZ3a60/O9jztCml4Php8OrJsHA6jL21zbqSZLPwVJ8Mzlq6gXeLd3JF57adYCQp2s5b14zilreX8uDsNWwtd3HPqX0xS6AlDiOGoanxBepC5CqPn0pPKFSuDL+uqvdcGyTXH7Vc7QngC+47WLbUBssOC9F2KzF2Cx1jHHRLsewWHodCZeuu14762604rE0reyGEEEKIQ0tVWSnf/Xc66xfOI7FTOuff+w8y+g9s0M7t3syy5VOwWhMYNOglLJboBm201jz060PM3TSX24bfxlndzwLA5XLx+uuvU1VVxcQz/8AtH+cRMDRvXzuKbikNj1PL8AXZ+U4untVlRI/tTNwpXVH7eF+gtabkiScpmz6d2NNOg9y1+/cDEUIIcUTRWrN4zkf8+OarxKV05OKHptEhKztU0iPnLnaUfE5S0rH07fMvbLbEJh3T0AbvrXuPxxc/jqEN/jrir1zc+2LMpqZPpLhhwwY++eQTqqqqOProo5kwYcIhV2e6yOlmyaYKlmwuZ/GmclYXOvEHQ7U8M5MiGds9maEZ8QzJSGDAP5v33M0xgvoWIAeoP9vFE1rrx5rh2O1H5tHQ4wT4+XEYNhki2q7m8lFxUQyJieTFLTu4rFMS5jYOoRxWM89dMoyH5qzh1V8KKKrw8OSFg3E0YaSEEC1Na43LFwyHyf5wuBzYLVCudO8eMO9qG3qu9gb2WV+5frAcY7cS47DQKd5Rb5SyNTxKedeo5tq29UcxN7WeshBCCCGOLMFAgKWff8K8/81CGwZjL7yc4aefjdnS8M2vz1fG0mVXonWQwYNexW7v0Ogxn1n6DO+te48pA6Ywud9kIFQz880336SsrIyJZ5zPX2ZvxuUL8tY1o+jZMWbv/av2UfbaGnxbq4g7PZuYMZ33eU3aMNj+yKOUv/468eefT+q0++Hfh9fbSCGEEM3H66rhi+eeZMOi+XQfMZqTbrwVe2RU+BtDt+DxbqN7tzvIyJjS5JIemys3M23+NBYVL+KotKO4f/T9dInp0uQ+eTwevvzyS5YuXUpycjJXXXUVXbo0ff+24gsYrCmqZPGmcpZsLmfppnIKnR4gNG/UoPR4rhrblWEZCQzJSCAlxt6i/TmogFoplQ6cSqgW1p+bpUft2cT74YWx8MtToRHVbUQpxfUZKVy3ehNflTo5OSW+zfpSy2xS3H96P9ITInlozhoumrGAly4fTlJ0y/4FFoc3rTVuf7DeqOV6I5jdDUcuV9YLlutGM3sDBI3fT5dry2HE1AuX0xMiiXVYiI3YFSzHOKy7Pcc6rMSGX8uIZSGEEEK0lG1r1/D1y89RurmA7KEjOO7K64jrkNpo22DQzfIV1+L1FjFk8Eyioro12u611a8xY+UMzu1xLjcPuRkAv9/PW2+9RWFhIRNPO5e7viqi0uNn1pRR9O0U2+hxAPylbkpfXUXQ6SPpkj5E9N/3tzx1MEjRfffhfP8DEidPpsOdd8jvUkIIIfZqR0Eenz7+CM6S7Yy/7GqGnXoWAFu2/Jf1Gx7FZktm6NC3iI8b1qTjBYwAb6x5g2eXPYvNZOOBox/g7O5n79e9aN26dXz66adUV1czduxYxo8f325HTe+o8rBkUwVLw6OjV25z4g2XD+0cH8GwrESuyYhnaEYCfdJisVlat6TwwY6gfhL4K7DnR+k3KaUuJ1T64y9a6+avnt0WUvvDgPNhwQsw8jqIbbtJAU9NjqeLo4gXtpS0i4C61tVju9I53sEtby/jnOfn8d8rR9I1ue0mcxS701pjaAgYBkFDEzA0wWD42dC7rzc0geBe1huaoGHU277H+gb771ofCDZsV1tjuf4I5trlwD7CZZOirtxFbZjcOd5BjCNmt2A51mHd4/WuoDnSZpY3ROKQp5TqAswEUgEDmK61fkop9S/gdMAHbASu1FpXhPe5C7gaCAI3a62/bIu+CyGEaJyr0slPs15j1XdfEZ2UzBl/uZvuI0bv9fcWwwiwavUtVFYuZ8CA/+y17uZHGz7isd8eY1LmJO4ddS9KKQKBAO+++y4FBQVMOPlM7vu+lNIqL69POYoB6XF77aN3UyVlr60GBSnXDsCesfcgu5b2+ym84w4qP/uc5BtvIHnqVPldTAghRKO01qz6bi7fvPI8EdExXHD/I6T37offX0nO2jsoKfmK5OSJ9O3zT6zWplU7WFe+jvt/uZ9VZauY0GUC94y6hw6RjX/bqDFut5svvviC5cuXk5KSwoUXXkjnzvv+5lBrCQQN1hZX1Y2OXrK5nC073QDYzCb6d47lslGZDMtMYGhmAh1jHW3c44MIqJVSpwE7tNaLlVLH1tv0PPAgoMPP/wauamT/a4FrgUOrYPiEu2H1h6EJE09/ss26YTEprk1P4d4N21jirGFoXPsJgU/qn8asaxxMeW0R5zz3Cy9NHsGwzLYriXKo8/iDFJTVsHFHDXkl1eSV1rBlpwtf0NhHgKwJBBuub2smBRaTCbNJYTEpzGZFlM1SFx53jHXQvYOlXqC8K1iOdViJjdh9JHOUhMtC1AoQ+lB4iVIqBlislJoLzAXu0loHlFL/BO4C7lBK9QUuBPoBnYCvlVI9tdbBtroAIYQQIdowWPX91/w467/4XDUMP/0cRp93ETZHxN730ZrcdfdTWvoNPXtOo0PKiY22+3bzt0ybN41RaaN4dNyjmE1mDMPgww8/ZP369YybdAoPz6ukqMLDzKtHMjRj77/Hu1aWsvOdXCxxNpKv7I8lee/9q2V4vWy79U9Uf/cdHW77C0lTpuz7ByKEEOKI5Pd6+OblF1j9w9dkDBjMqVNvIzIuHmflclatuhmvt5ge3e+mS5ermpQL+II+ZqycwUsrXiLWHsu/xv+LEzNP3K9MITc3l08//ZSamhrGjRvH+PHjsViao4LygdtZ42NJvTB6+RYnbn/obV3HWDtDMxKYPDqLIRkJ9O8ci93S/kryHsxPcAxwhlLqFMABxCql3tBaX1rbQCk1A5jd2M5a6+nAdIDhw4e3fWrWVIldYfiVsOhlGH0TJDd9Ns/mdlFaIv8qKOL5LSXMaEcBNcCwzAQ+uHEMV766kItnLODJPwzm5AFtN+K8vdNas6PKy8Yd1WwsDQXRG0tCz9sq3LvVP+4U5yAjKZIYhw2zyVQX8lpMalfoW7u+Xgjc6Pq67Y2tNzWy/x7rTQrL7x7btNu+ZqUwyQSaQrQIrXURUBRerlJK5QCdtdZf1Wu2ADgvvHwm8LbW2gvkK6U2ACOB+a3YbSGEEHso2VzA1y89R2HuGjr37svxV99IckbWPvcrKPgPhYVvk5l5PV3SL2u0zaLiRdz+w+30TerLUxOewma2obXm008/ZfXq1YweP5HHfvOyqczFq1eMYETW3ieXqvp5G845edi6xJA0uR/mqH1/pdlwudh6003UzJtPx/vuJfHii/e5jxBCiCOPEQyybsHPzH/vLXYWbWPUuRcx+rwLUcrE5s2vsGHj/2G3d2DY0HeIixvcpGOuKFnB/fPuZ0PFBk7LPo07RtxBvCO+yX1yuVx8/vnnrFy5kg4dOnDxxRfTqVOnA7vAgxA0NOu2V9VNZLh0cwX5pTVAaDBr306x/GFEF4ZmJjA0I57O8RGHxKC+Aw6otdZ3ERqFRXgE9W1a60uVUmnhN8kAZwOrDraT7c4xt8PSN+G7h+D8/7ZZN6ItZi7vlMxzm3ewye0lM6J91XvumhzF+zcczZSZv3HjrCXcc2pfrh7bta271abcviD5pTXklVaTV1LDxpLQc15JNTW+XYMWI21mslOiGJqRwHnD0umWEk12ShRdk6OItLXtJ3NCiPZPKZUFDAF+3WPTVcA74eXOhALrWlvD6xo73qH5rSchhDiE+Dxu5v1vFks++xh7VDQnXn8L/cZPRJn2XQOysOg98vKfILXjWXTLvq3RNmvK1jD126mkx6Tz3MTniLRGorWum9hp+OhxPLtKsXFHFTMmD+fo7o3XkdaGxjknj+pfConol0Tihb1QTZgcPVhVxZbrrse9bBlpjzxC/NlnNTjuwtn5+zyOEEKIw1fA52P1D9+w6NP3cW4vJrFTOufeOY2swcPw+ytYk3MHpaVfk5x8PH37/B9W695LUNVy+V08u+xZ3ljzBh0iO/Cfif/hmPRj9qtfOTk5zJ49G7fbzfjx4xk3blyrjZp2uvws2RKaxHDJ5gqWbamg2hsAIDnaxpCMBC4Y3oVhmQkM6BxHhK39jY5uipb4af6fUmowoRIfBcB1LXCOthXdAUbfCD/+C8bcAp2GtFlXrk5P5oUtO5ixtYSHeqS3WT/2JinazlvXjOKWt5fy4Ow1bC13cc+pfTEfxqNotdYUV3p2C6Brn7dVuOvaKQWd4iLIToni/OFd6JYSRXY4iE6NdRwSn3AJIdofpVQ08D5wq9a6st76vxEqA/Jm7apGdm/0G02H7LeehBDiEKC1ZsPC+Xz72nSqy0oZcNwJjLv4CiJi9l3LGaCs7AfWrr2bxIQx9OnzSKO/QxY4C7jh6xuItcXy4qQX60aMff/99yxYsIBBw0by8gYHa4udvHDpMMb3TGn0XIYvyM53cvGsLiN6bGfiTumKasLv9YHycrZMuQZPbi6dH/83sSedtNt2nyfAN//NIW9ZSZOuWQghxOHF63KxfO5nLPnsY2oqyknt1oPxl15F9+GjUCYTTufSUEkPXwk9etxDl/QrmpSZ/Fr0K9PmTWNr9Vb+0OsP3Dr0VqJt0U3uV01NDZ9//jmrVq0iNTWVSy+9lLS0lqsOYBiajSXVdaOjl2yuYMOOaiBULrV3aixnD+nM0MzQZIYZiZGHTXbULAG11vp74PvwcuPfJzvcHD01VObj6wfg8o/arBtpdhtndUhgVtFObstKJd7a/kbXOqxmnrtkGA/PyeGVX/IprHDz5B+GHLKf6tRy+4Lkle4qxZFXsmtktKveaOgom5nslGhGZCXwh5QuZKdE0S0lmq7JUTiaMNpECCGaSillJRROv6m1/qDe+snAacBEreuKBm0FutTbPR0obK2+CiGEgIrtxXz76gvkL/2NlIwsTrvlDjr36tOkfQ3Dz5Ytr5KX/xRRUb0YMOA/mEy2Bu2Ka4q5du61AEyfNJ3UqFQA5s2bxw8//ECfAYN5c2s8K7ZV8J+LhzKxT8dGzxes9lE2cw2+LVXEnZZNzNimTQYVKClh81VX49u0ifRnnyHm2GN3215Z5uaz51ays7CaMed1hxebdFghhBCHAZezgiWff8KyL+fgddWQOXAIp0y9jS79BqKUQmuDTZtnsHHjY9jtaQwf9i6xsQP3edxKXyWP//Y4769/n8zYTF498VWGpzY+cfDerF69mjlz5uDxeJgwYQJjx47FbG7eDKfK42f5FmfdZIZLN5dT6QmNjo6PtDI0I4GzBndiaGYCg9LjibK3v8yvuRy+V9bSHHEw7i/w1d8g73vIPrbNunJDRgfe217O64VlTM1s/BfKtmY2Ke47vS/pCRE8OGcNF81YwMuTh5MU3b7KkuzJMEKjoeuX4qgNpAudnrp2SkF6QgTZydGMyEokOyWabuEgukOM/bD5REsI0X6p0D80LwM5WuvH660/CbgDGK+1dtXb5RNgllLqcUKTJPYAFrZil4UQ4ogV8Pv57dMP+PWDd1BmM8dePoUhJ52OqYlvfJ3OJaxdew/VNbkkJ0+kd+9/YLHENGhX4angurnXUemr5JUTXyErLguAxYsX89VXX9Gjd18+KktlyeYynr5oCCf1T230fP5SN6WvriLo9JF0SR8i+jde/qPBfoWFbLrySgIlpXSZ/iJRo0bttr1wQwVfvLiSYEBz2k2DyOiX1KTjCiGEOLQ5d2znt9kfsOrbuQQCfnqOPJoRZ55HarcedW2qa9azYf0/KNv5IykpJ9Kn96NYrfv+dtF3m7/joQUPUeop5cr+V3LjoBtxWBxN7lt1dTWfffYZa9asIS0tjcmTJ9Ox48FnbVpr8ktrWLK5IjSZ4aZycrdXoXUoU+rZIYZTB6YxNCOBoZkJZCdHHVFZkgTUB2PEFFjwfGgU9TXjQ3+j2kC/6AiOSYjm5a2lXNclBVsT6tS1lavGdqVTvINb3l7GOc/P479XjqRrcttP8FjjDZBf2rAkR35pTd3MpwDRdgvdUqI4KjuJ7OQounUIleTISpLR0EKINjcGuAxYqZRaFl53N/A0YAfmhn/BWaC1vl5rvVop9S6whlDpjz9qrYMNDyuEEKI5bV61nK9ffp7ywq30PGoMx06+hpikJga+ficbN/6LbYVvY7d3ZOCA50lJOaHRti6/ixu/uZGtVVt5YdIL9E3qC8DKlSv59NNPyczuzhc1GSzIL+PxCwZx2sDGJ3rybqqk7LXVoCDl2gHYM5pWesRXUMCmq67CqKom4+WXiByye1nENb8U8sOsXGKSHJx640ASUtv+PYEQQoiWVbq5gIUfv8faeT+ilIm+xxzHiDPOIbHTrpK1LlcB+fnPULz9Y8zmKHr2vJ/0zpftM6wtc5fx6MJH+aLgC3om9OTp456mX3K/JvdNa82qVav47LPP8Pl8TJw4kaOPPrpZRk0v2VzOvR+tYnVhqAJjjN3C4Ix4TuqfytCMBAZnxBPr2Pdkw4czCagPhtUBE+6Cj/8IOZ9A3zPbrCvXd+nAxSvy+GhHBRek7n227fbgpP5pzLrGwTUzf+Oc537hpcnDGZbZ8n02DE2h0737SOhwSY6iPUZDd0mIJDslilHZSXUlObqlRJEio6GFEO2U1vpnGq8r/dnv7PMw8HCLdUoIIUSdmopyvp/5Emt/+YG4jqmcc+c0ug5p2teNtdZs3/4J69Y/jN9fTpcuV5Ld9RYslsbraPqCPm757hZWl63miWOfYETqCAByc3P58MMPSc/I5MdgD35aX8r/nTuQs4c0PpeNa2UpO9/JxRJnI/nK/liSI5rUX+/69Wy66ioIBMl87b84+vat22YEDX55fwMrvt1Klz4JnDClP46oI/tNuRBCHO625eaw8KN3yVuyCKvdwdCTz2DYaWcRk7jrA1qPp5D8/GcoKn4fpaxkZlxDRsY12Gy/nxdprZmdN5t/LvonLr+LmwbfxFUDrsJqavq9paqqijlz5rB27Vo6d+7MmWeeSYcOHQ74emuV1/j4vy/X8tbCLaTGOnjwzH4clZ1E95RoTIfx3GwHQgLqgzXwQvjlafjmQeh1Kpjb5kc6ITGGXlEOXti8g/M7JrT7EHVYZgIf3HA0V7y6kD+8uID4yIb18ppbtdePx2/UvY6xW8juEM3oeiF0dko0mUmRMhpaCCGEEEI0C8MIsnzu5/zy9usEfF5GnXshI886H6utaaXuXK58cnPvZ2f5L8TGDmLI4FeJidn7iLCgEeTOn+5kQdECHhzzIMdlHAdAXl4e7777LikdU/nV1I/v1pbw4Fn9uWBEl0aPU/XzNpxz8rB1iSFpcj/MTQyR3atWs2XKFJTVSsbrM7F37163zVPj56uXV7NlzU4GHdeFo8/thsncfr/9KYQQ4sBprSlYtphfP/of29auxhETy9HnX8LgE0/dbSJgr3cHBZueZ9u2twHo3PkSsjJvxG5vfMLe+oprivn7/L/z07afGJgykL8f/Xe6xXfbrz6uXLmSzz//HJ/Px/HHH8/o0aMPetS0YWjeW7yVRz7PodIT4Npjsrl5Yg+iD+Ma0gdLfjIHy2yBiffBO5fAsjdh2OQ26YZSiuu7pPCntVv4qbyaYxIb1qBrb7KSo3j/hqOZ/mMeVd5Ai58v0mqma10QHUVKtIyGFkIIIYQQLad443q+fuk5tuetJ6P/ICZefSOJnZo2uaBheCnYNJ1Nm55DKRu9ej5A584XodTe3zRrrXno14eYu2kutw2/jbO6nwXAli1beOutt4hPSGRF5BDmrt7Bfaf15bJRmQ2PYWicc/Ko/qWQiH5JJF7YC9XEwRuuJUvYcu11mGNjyfjvq9gyMuq2lRfX8NnzK6ksdTPhst70HdN4SREhhBCHNiMYZN2Cn1n48XuUbMonOimZCZOvYcBxJ2J17KoF7fPtZNPm6Wzd+jpa+0lLO4+uWTfhcOz7/mBog//l/o8nljyBoQ3uGHEHF/W+CLOp6cFyZWUls2fPZt26daSnp3PmmWeSkrLvUHxfcooqufejVfy2qZwRWQk8eFZ/eqc2rTzWkUwC6ubQ+1RIHwHfPwoDLwBr07761tzO6ZjAI3lFPL9lxyERUAMkRdu565SmzVQuhBBCCCHEocBTU80v77zOsq8+IyounlNvvp1eRx/T5MERO8vnk5t7Hy5XHh06nErPHvdgt+/7q8ZPL32a99a9x5QBU5jcLzRwpri4mDfffJPIqGjWJYzk85U7uPPk3lw1tmuD/bU/SNnbuXhWlxE9phNxp2ajmvgV5Jp589jyx5uwduxIxquvYE1Lq9u2eXUZX760GrNFceatQ+jUI75JxxRCCHHoCPh8rP7hGxZ9+j7O7cUkdkrnxBtupc/Y8Zgtu76F4/dXsnnLy2zZ8irBoIvU1DPpmjWVyMisJp1nU+Um7p93P4u3L2ZU2ijuH30/6TGNl6pqjNaa5cuX88UXXxAIBDjhhBMYNWoUpoOcz63aG+DJuet4dV4BcRFW/nXeQM4dmn5YlPLQhiZQ5sZfVBN6FNc0+zkkoG4OSsHx0+C/p8LC6TDmljbpht1k4qrOyTyaX0xOtZs+0W0TlAshhBBCCHEk0lqz9pcf+H7mS7grKxly4mmM+cOl2CObNgGgz1fG+g2PUFz8IRGODAYPepWkpGOatO9rq1/jpZUvcW6Pc7l5yM0AlJaW8vrrr2O12tjU4Wg+XbGDP0/qyfXjG379OVjto2zmGnxbqog7LZuYsU0b6Q1Q9e13bLv1VmyZmWS8+gqW5FBNUa01K77dyi/vrSexUzSn3DiA2CR5jyKEEIcTr8vF8rmfseSzj6mpKCe1Ww/GX3oV3YePQtULfQOBGrZuncmmzTMIBJx06HAKXbveTHRUjyadJ2AEmLlmJs8tew6bycbfj/47Z3U/a7++Ge90Opk9ezbr168nIyODM844g+Tkpk1UvDdaaz5fVczfP13D9ioPF47I4I6TerVKKduWYLj8+ItDQbQvHEYHtrvQtSVzTWBJiWz280pA3VyyxkL34+Gnx2HoZIiIb5NuXN45mac27eDFLSU82Sdj3zsIIYQQQgghDlrZti18+8rzbF61gtRuPTjnzml0zO6+7x0BrQ0Ki/7Hhg3/JBh0kZV5I1lZf8Rsdux7Z+CjDR/x2G+PMSlzEveOuhelFBUVFcycORPD0GxPH8dHy3cw9bju3DyxYRDgL3VT+uoqgk4fSZf0IaJ/09+sV372Gdv+egeO3r3pMmM6loQEAIJ+gx/eyiVnXhFdByVz/JV9sTnk7acQQhwuXM4Klnz+Ccu+nIPXVUPGgMGcMvU2uvQbuFtoHAx62LZtFgWbnsfv30ly0nFkZ9/6u/Mp7Cl3Zy73zbuPNWVrOK7Lcfxt1N/oENn0SQy11ixdupQvv/ySYDDISSedxMiRIw961HRBaQ33fbKaH9eV0DctlucuHcrQjISDOmZr0cF6o6LDgbS/qIag01vXxhRlwZoWTdRRaVjTorCmRmHtGImymODPzdsf+Q2hOU28H148Br64C85+vk26kGi1cGFaIm8UlnFXdhod7TIjthBCCCGEEC3F7/Py6wfvsuiT97Ha7Uy8+kYGHn8ipibWwayuXsfa3HtwOhcTHz+S3r0eJCqqacE2wLebv2XavGmMShvFo+MexWwyU1VVxcyZM/F6fTizJ/Le0h1cd0w2f57Us8H+3k2VlL22GoCUawZgz2x6ncyK9z+g6N57iRg6hC4vvIA5OhoAV6WPL15cSdFGJ8NPyWLkaV2bXCpECCFE++bcsZ3fZn/Aqm/nEgj46TFyNCPPPJ/Ubrt/AGoYPgqL3qOg4D94vcUkJowhO/tPxMUNafK5fEEfL654kVdWvkKsPZbHxj/GCZkn7Neo6YqKCj799FM2btxIZmYmZ555JomJiU3evzEef5Dnv9/I8z9sxG42Me30vlw6KhNLO53413D5Q6Oh64fR210QqB0VrbCkRGDvGhsKotOisaZGYYqxttrcbRJQN6e0gXDM7fDj/0G3CaF61G3g2vQU/rutlFe2lXJXdtq+dxBCCCGEEELst7yli/j2lRdw7thOn3ETGH/pVUTFN23kVDDoJr/gWTZvfgmLJYY+vf9JWtq5TXojGDSC/FL4C//L/R8/bvuRfkn9eGrCU9jMNlwuF6+//jqVlVW4ekzi7aU7uHJMFnee3LvBsd2rSil7OxdLnI2kK/tjTW56+Y2db7zJ9oceImrMGNKffQZTRGjf0q1VzHluBe4qPydM6UeP4R2bfEwhhBDtV+nmAhZ+8j5rf/kBpUz0PeY4RpxxDomddq//bBgBird/RH7+M3g8W4mLG0a/vv8mIWFUk88VNIJ8WfAlzy9/noLKAs7odga3D7+deEd8k4+htWbx4sV89dVXaK055ZRTGD58+EGPmv4+dwf3f7KaTWUuzhjUiXtO7UOH2KZ946ml6aAmUOoKh9Au/EXV+ItrCDp9dW1MUVasaVFEjwqPik6LwtohPCq6DUlA3dzG3wEFP8HsP0HnYZDUsL5bS+saaefk5Dhe21bKzZkdiDI3fRZTIYQQQgghxO+rLC3h+9dmsH7hPBI7pXP+vf8go//AJu9fWvodueum4fFsJS3tPLp3uwObbd+juUpcJXy44UPeW/ceRTVFJDmSuKr/VVzR7woirZF4vV7eeOMNysrK8PU6gVlLSrjkqAzuO61vg3C66udtOOfkYesSQ9LlfTFHN71WZun0GZQ8/jjREyfS+YnHMdlC++YtLWHuq6uxR1o557ahdNiP0dhCCCHap225OSz8+H/kLV6I1e5g6MlnMOzUs4hJ2r0clNYG23fMIT//KVyufGJi+tO7199JTGz6JMEBI8AXBV8wfcV08p35dI/vznMTn2Nc+rj96nN5eTmffPIJ+fn5dO3alTPOOIOEhIMrvVHkdPPg7DV8trKY7JQo3pxyFGO6H1z96oMRrPHvVprDX1yDf3sNBHSogUlh7RCBPTs+VJojHEabY9pnbWwJqJub2QLnzIAXxsL7V8NVX4Gl9f/wb8jowGelTt4u2snV6Smtfn4hhBBCCCEON8FAgKWff8K8/81Ca83YCy9n+OlnY7Y0raye17uddeseZEfJ50RGdmfokFkkJBz1u/sY2uDXol/537r/8d3m7wjoAEelHcVtw29jQpcJWM2hc/v9fmbNmkVRURFG7xN4fUkpFwxP58Ez++8WDGhD45yTR/UvhTj6JZF0YS+UtWkDWrTWlDz1FGUvvEjsaafR6ZF/oKxWtNb89lkBCz/Np2PXWE6+fgBRcfYmHVMIIUT7o7WmYNliFn78HltzVuGIieXo8y9h8ImnEhET26Btaelc8vKepLoml6iongwc8DzJyZP2K5j+LP8zpq+YzqbKTXSP785j40NzK5hU00f2GoZRN2paKcVpp53GsGHDDqpMhT9o8Nq8Ap6Yu46AobnthJ5cc0w2dkvrDAatGxW9R4mOYGW9UdHR4VHRR3cKhdGp7WNU9P6QgLolxHeBM5+Fdy6Fb/8OJzzU6l0YERfFsNhIpm8p4YrOyZhbqWaMEEIIIYQQh6Nta9fw9cvPUbq5gOyhIzjuyuuI65DapH21DrJ16xtszHscrf1kZ/+ZzIxrMJn2PpCl3FPOxxs+5n/r/sfmqs3E2eO4pM8lnNfzPLLisnZrGwgEePfdd9m0aROqz/G8tnQnZw/pzCPnDMRUr/az9gfZ+XYu7tVlRI/pRNyp2U2uDa21Zvsjj1A+83Xizz+P1GnTUGYzfl+Qb1/LYcPiHfQ6KpVjL+2FpYmBtxBCiPbFCAZZt+BnFn78HiWb8olOSmbC5GsYcNyJWB27l7HQWrNz509szHucqqqVREZ2pV+/J+nY4VRUE0Nlv+FnTt4cZqyYweaqzfRK6MUTxz7BcRnH7VcwDbBz504++eQTCgoK+H/2zjs+jups29dsV9mVtOq9d/eCMWAbg43Bxpge4E1CAqEnpHfedPKmfwlpQEIPJfRiDJjebAPutmTJ6r2X3dX2nTnfH7tqtmxLtmxL9ly/TGZ25sw5ZyWjM3Of59xPTk4Ol1xyCdHR0ROq40C21vdy14t7qWh3cF5RAj+/pJR0a/gx1Xk4ZKf/ACF6AH+nazgqWiuhjw/HmBs9bM+RNHWjoieCKlAfL4rXwoIbYdNfIftcyF9xwrtwa3oCN5XV83q3jTXx0Se8fRUVFRUVFRUVFZXpjstu48MnHmHvuxsxx8ZzyXd+TN6CM8cdjWW376Gi8i4cjr1YrUsoLPg54eGZY5YVQrC9cztPVz7Nmw1v4lf8zEuYx21zbmNl5kqM2oOjkhVF4YUXXqCqqgqp8Dwe2mFjzcxkfn/lLLQjxGd5wEfPo+X4mhxEXZyD+ZzUcf8MhCzT/rOf0f/Ms1iv/yIJP/gBkiTh6PXw2r176GpysPiyXOZekHHCkimpqKioqEweAZ+PsvffZusrz9Pf0YY1JY1Vt32D4nOWjblKqK/vU2pr/0S/7TNMplSKi35LUtKlaDTjkxn9ip9Xal7hX7v/RfNAM8XWYv68/M8sT18+YWFaURQ+++wz3nrrLTQaDWvXrmXevHnHNB71DHj5zWsVPLOtmdToMO7/wnxWliRO2hgnZIVAlxt/u3M4eWGbE8UxIirarEefFEHkWanokyMwJEegiwubVlHRE0EVqI8nq+6Gxi3w4q1w68dgPrEJQlbHR5FhMvDPxk5VoFZRUVFRUVFRUVGZAEJR2PveW3zwxMP4XE4WrL2cxVdei8E0vkSCgYCDmtr/R3PzYxgMscwo/QsJCWvGfLm1++y8UvMKz1Q+Q42tBrPezFUFV3FVwVXkxeSN3T8h6Orq4sMPP6SsrAxtwTIe2OXggpJE/nzNHHTa4RfYQLeb7of2ErD5sF5XTPjM8XtmCr+f1h/8EPurrxJ7263E33knkiTRXmtjw717CPhk1tw2i6xZJ8+HU0VFRUXl6PC6XOx6cwPbN7yEs7+PpNx8Lvn8j4ITsWMkE7TZdlJb+//o7fsIoyGRwoJfkJJy1WFXBI3EL/t5qeYl/r3n37QMtFAaW8r3z/g+y9KWHZX429PTw0svvURjYyN5eXmsXbuWqKioCdcziKIInvqsid++XoHTG+C2c3P52nl5hBuOTT4VQuDe24NnX09QjO50gTwiKjohHFP+AV7RE8gNcSqgCtTHE30YXPkg3H8uvHAzfP4FOMZsoRNBK0ncnB7PXVUtbLU5WRAVccLaVlFRUVFRUVFRUZmudDXU8da//0Hr/n2kFpWy4sbbiMvIGte9QeH4Dfbv/wVeXyepqf9Dbs630esP9uzc072HZ/Y/w+t1r+ORPcyMm8kvzvoFq7JWEa4/eAmx1+ulrq6OqqoqqqursdlsAGjyzuaB3S7OK0rgb9fNQz9CnPY22Ol5tAwExN80E+MEEhcqXi8t3/o2A2+/Tfy3v0XcTTcBULGljXf/U0FktJF135hDbErkuOtUUVFRUTn5uGz9bH/tZXa+8Spel5OMmXO46KvfJmPG7DGFYoejnNq6P9Pd/TZ6vZX8vB+Rmvo/aLWmMWo/GJ/s48XqF/n3nn/T5mxjZtxMfrToRyxJXXJUwrSiKHzyySe8/fbbaLVa1q1bx5w5c44pwnlvi427XtzLzqZ+FmVb+dWlM8hPNB91fYN4G+3Y1tfia3QEvaJTIoksiMEQEqN18WFI2lMzKnoiqAL18SahCC76Dbzyddj0Fzjnmye0+WuTrPy+rp1/NnXyQFT2CW1bRUVFRUVFRUVFZTrh87jZ9MwTbN/wEqaISFbd9g1Kl50/7hdet7uZyv0/o6fnXSIjS5g5659EWWaPKuP0O3m19lWe2f8MFb0VhOnCuDj3Yq4quIqS2JJRZQejpKurq6mqqqKxsRFZljEYDOTk5HDOOedQ4YniJxuqWJIfxz/+Zx6GEUt/3Xu76XmqEm2Ugbgvz0AfN77obwDF5aL5q1/DuWkTif97F9b/+R8URbDlhRp2vNlIamE0F940E1Pk+BJEqqioqKicfGydHWxd/zx733mTQMBP/hmLOeOSK0nKKxizvNNZTW3dX+js3IBOZyE359ukpV2PTje+AEiv7OX5qud5YM8DdLg6mBU/i58s/glnp5x91GJyd3c3L730Ek1NTRQUFHDxxRdjsYx/8vVAHB4/f3pzP49sqscaYeBPV8/msrmpx2znEejzYHu9HveuLjRmPTFX5BM+P3HcuR+mKnJ/P97q6kmvVxWoTwTzroead+GdX0HWEkhbcMKajtBpuT4llr81dlLv9pIVpmbTVlFRUVFRUVFRURmJEILqTzfzziP3M9DTzczzV7Hk2usJM4/vhVdR/DQ2PUhd3T1Ikob8vB+TlvbFUV6cFb0VPFP5DOtr1+MKuCiIKeCuRXexJmcNkYbhCGSfzzcUJV1VVTUUJR0fH8+iRYuITc2i3m3i45pe/rixmy5HJ4tzYrn/CwswjUhO6PioBdurtRjSzcR+sWRCS4XlgQGabrkV944dJP/610Rffhk+d4CND5TRsLeHGctSOefqfLRqxJeKiorKtKC7sZ5PX36Oio/fR5I0lCxdzsJLrsCakjZmebe7kdq6e2hvfwmtNoysrK+SkX7jQauBDoUn4OG5qud4cM+DdLo7mZswl1+c/QsWJy8+auFXURQ2b97Mu+++i06n47LLLmPWrFlHXZ8Qgld2t/Gr9eV0DXj5/KJMvnNBIVHhxzbxqngDON5rxvFhCwDm89IxL0tDY5xeEqzscOCtqsZbXYW3uhpvVXAvd3Ufl/am109nuiJJsPYv0LIdnv0y3PoRmI7eE2ei3JAWzz+buri/qYtfF4z9x0dFRUVFZXojSVI68CiQBCjA/UKIv0iSZAX+C2QB9cDVQoi+0D0/BG4EZOBOIcQbJ6HrKioqKieV/o523nnoXup2bCU+M5u13/g+KQXF47/fto2KirtwOvcTH7eSgoKfYDKlAOAOuHmj/g2eqXyG3d27MWqNrMpaxdWFVzMrLvhSfWCUdENDA7Iso9frycnJ4cyzz2HAGM/2Ng9/qeii7J0qAGLC9ZyTH8+S/DgumZ0yJE4LRWB7tZaBj1sxlcZi/VwhGoP2kP0/kEBfH0033YynooLUP/4By0UX0d/pYsM/dmPrdLPs2gJmLFPfKVRUVFSmAy2V+/j0pWeo3fYpeqOJeRetZf6ayzDHjp03wONppa7+77S1PYskacnIuIHMjFswGKzjas8T8PDM/md4aO9DdLm7mJcwj7uX3M2ipEXHFJHc1dXFiy++SEtLC4WFhVx88cWYzUdvv1HTNcBPXtrLx9U9zEyN4t/XL2BWWvRR1wfB8de5tR37xgaUAT/hcxOwrMpCFz21A0XlASe+muqQCF09JEYHOjqGykjh4Rhzc4k8ZwnGvDyM+XmwbNmk9kMVqE8UYdFw5QPw4IXwyjeC3tQnKMN1klHPZYnRPNnWy3ezk4jRq792FRUVlVOQAPBtIcR2SZLMwDZJkt4EvgS8LYT4jSRJPwB+AHxfkqQS4BqgFEgB3pIkqUAIIZ+k/quoqKicUAJ+P1tffo5PXngaSavl3C9+hbkXrkWjHZ+Y6/f3U13zO1pb/4vRmMysmfcRH78CgJr+Gp7Z/wwv17yMw+cgOyqb7y/8Pmtz1xJljMLn87F///4hUbq/vx8IRkkvXLiQsIRMapwG3qrpZfP6Hly+TnQaiXkZMXznggKW5MczIzUK7QHLhIVfpvepStxlPUSenULUmpwJLSUOdHXReMON+BoaSPvrPZiXL6e5opfX798LEqz9+hzSCmPGXZ/KoZEkSQtsBVqEEBerE8oqKiqThRCC+p3b+PSlZ2netxdTpJnFV17H3AsvPuTKIK+3i/qGf9LS8iQgSE29lqzM2zEaE8bVpsvvGhKmezw9LExayG+X/paFSQuP6bvIssymTZt47733MBgMXHHFFcyYMeOoxW6PX+bv71Zz3/u1GPUafrmulOsWZR40nk643qo+bK/W4m93YciyEH19KYb0Y/evnkwUtxtvTW0oEjoYDe2rqsbf2jpURjIaMeTmEHHmIgx5eSExOh99SsqYSTMnE1WpPJGknwHn/Rje/gXkLod5XzxhTd+ansDT7X081trDnZmJJ6xdFRUVFZUTgxCiDWgLHTskSdoHpALrgHNDxR4B3gO+Hzr/lBDCC9RJklQNnAFsPrE9V1FRUTmxCCFo3LuLtx+8l77WZgrOPIdzr/8KZuvY0WRj3d/e/iJV1b8mELCRkfEVsrPuRJH0bKjdwNP7n2ZbxzZ0Gh0rM1dydcHVzEuYR29vL+Xby8eMkp5zxmJ6tHFsbXHx5K4umvtqAciMDefyeakszY9ncW4sZtOhlx3LTj89j5Tha3IQdXEO5nNSJ/Rz8be10filL+Pv6iL9vnuJWLyYPe818+HTVUQnhrPm9plExR+cuFHlqPk6sA8YVIt+gDqhrKKicgwIIajd/hkfP/0fuupriYyN49wv3sSs81ehN42dzNDv76Oh4V80NT+KED6Sk64gK+urhIWNbwxx+V38t/K/PFz2ML2eXhYlL+IPs/7AgqRjs7bt6+tj37597Ny5k87OToqLi1mzZg2RkUeflPedig5++nIZTb1uLp+byg9XFxNvPrboZn+nC9urtXgq+9BaTVj/p5iwGbHH7F99LCheL77a2uGI6JA1h7+5GYQAQNLrMeTkEDZ3LtFXX40xPyhG69PSkMY5UT/ZHLNAPZGZXxXg7G9C7fuw4XuQvgjiC09IsyWRYZwbY+bfzV3ckh6P8TjPfKioqKionDwkScoC5gKfAIkh8RohRJskSYNhEKnAlhG3NYfOqaioqExrhBC47TZsXR3YuzqxdQb39u7O4OeuDgJeL9GJyVz+w5+TPWf+uOt2ueqoqPxf+vo2Y7HMoajwV/SJCO7ZeR8vVr9In7ePdHM635z/TVZnrMbebqdqWxX3VN0zFCUdFxfHgoULkaPSqHLoeLaml517epCVbiKNOhbnxnLLslyW5seRGTu+JFSBbjfdD+0lYPNhva6Y8JnjE9sH8TU00PjlG5AdDjL+/W+Ms2fz3hOVlH3QQtbMWFbeUIohTI1tmiwkSUoD1gB3A98KnVYnlFVUVI6atqpKPnj8IZr37SU6KZlVt36d4iXnotWNPbEZCDhobHyQxqYHkWUnSYmXkJ39NcLDs8fVnsvv4smKJ3mk7BH6vH0sTl7MbXNuY27C3KP+Dr29vZSXl1NeXk5rKKo3KSmJq666itLS0qOut6Xfzc9fLmNjeQd5CZE8edOZLM6NPer6IDgpbH+rAecnbUh6LVGrs4k8KwVJd+K0NuHz4a2rH46Grq7Gu78KX1MTKEqwkE6HMTsL04xSoi5dhzEvH2N+PoaMdCTd1BrXJ6M345r5nYR2Tg00Grj8fvjn2fDsDfCVt0E/9kzWZHNrRjzX7KrlhY4+rkk+tv8YVVRUVFSmJpIkRQLPAd8QQtgPM3s/1gVxiDpvBm4GyMjImIxuqqioqBw1Qghctv4hsdne1Ym9qwNbVyf2zg7s3V0EfN5R95giIrHEJxKTnErW7LlYU9IpXrocvWF8kVOy7KWh4V7qG+5FqzWSl/8z9ssJfHvz/2Nz22a0kpbl6ctZnbSayP5IqndU868X/jUUJZ2dnU3h3DNpFdFsb3Hxr0+6sbkbkCSYmRrFbctyWVoQz9yMaPQTTDzobbTT80gZCIi/aSbGzPElsBq6v7qaxi/fgPD7yXj4IcjM5+U/76S1qp95qzJYtC4XzTEufVY5iD8D3wNGrv8+5glldbxWUTn96G1t4aOnHqHqk02ER0Vz/g23MfP8VWgPIT7Ksoum5sdoaLifQKCf+PgLycn+OpGRBeNqb8A3wJMVT/Jo+aP0e/s5O/Vsbp11K3MS5hxV/3t6eigvL6esrIz29nYAUlJSWLFiBSUlJVit4/O+Hgu/rPDAR3X85a1g7obvX1jEjedkYzgGEVkEFAY2tWJ/pxHhk4lYlIzl/IwJJSKecJt+P77GxmAk9KBHdHU1vvp6kEOLabRaDBkZGAsLsaxZMxQRbcjMRDIcv75NJsckUE9w5ldlEHMSXHYvPH4lbLwL1vzhhDS7LMZMcYSJe5u6+FyS9aQuOVBRUVFRmXwkSdITFKcfF0I8HzrdIUlScuhlNxnoDJ1vBtJH3J4GtDIGQoj7gfsBFixYMKaIraKiojJZCEXBaesfFp1DAnRQkO7E0dVJwO8bdY/JbCEqPoHYtAyy5y7AEp9IVEIClvhELHEJGMOP3pqit3cTFZX/i9tdjyV2JZ8FsvjZ5gfpdneTEpbCV5K+Qpo3jebdzXz8/scAxMbGMnveAgYiUqi0aXiwppfq3T1AD4kWIytLEllaEM85eXFYIyb24qj4ZHz1djzVfXir+vG3OdHGmoj78gz0cWETqstdVkbTjV9B0uvJfOxRBiKS2fCbrTj7faz4cgmFi5ImVJ/KkZEk6WKgUwixTZKkc8dzyxjnxhyL1fFaReX0wdnfx+Znn2TPO2+g1elZfOW1LLj4MgxhY493suylpfUJ6uv/id/fQ2zsueTkfBOLeca42nP4HDy+73EeK38Mu8/OktQl3Dr7VmbFz5pw37u6uoYipTtCifjS0tK44IILKC4uJibm2HMdfFLbw10v7qWqc4ALShL5ydoS0mKO/llACIF7bze21+qRez2YiqxErc5GnzB51ldCloNC9GA09KAgXV8Pfn+wkCShz0jHmJePecUKjPn5GPPzMGRnozlBQrSiCDrr7ZNe77FGUP+Z8c/8juK0n93NXwmLvwqb/wY550Lxxce9SUmSuDU9ga9XNPJer4PlsROLrlBRUVFRmbpIwVnHB4B9Qog/jbj0MnA98JvQ/qUR55+QJOlPBD0t84FPT1yPVVRUTleEouDs7wuJzx0HREIHrTjkwRexEGFmC5b4ROLTM8mZt5Co+KD4HNwnHPKF/Fjw+bqpqvo/2jteBH0CH4kFPL9rM+G+XZxtOJsMfwb2Jjt9gT4cOgdZWdkkFy+kVbGwpXmAv27qwyc3Y9RpOCPbyucWpLO0IJ6CxMgJBYoIWeBrceCt7g9uDXaQBWgljJkWLBdkErEoGW3Eof2px8K1fQdNN9+MxmIm86GHaLFF8ObftqE3arn023NJyo6a4E9MZZycDVwiSdJqwARYJEn6D5MwoayionLq43O72Lr+Bba+8gIBv49Z51/I4iuvJSJ6bFFXUXy0tj1Lff3f8XrbiYlZTE7ON4mOGp+9ld1n5/Hyx3ls32M4fA7OTTuXW2ffSmnc+C03hBB0dXVRVlZGeXk5XV1dAKSnp7Nq1SpKSkqIipqcMad7wMuvN+zj+e0tpMWE8cD1Czi/+NjysPmaHPS/Wouv3o4+KZyYG2dgyj96EV0oCv7m5mGP6JAY7autRfiGJ+D1aWkY8/KIPHdZMBo6Lw9jTg6asIlNRk8GTpuXpvJeGsp6aCrvxesKTHobkhBHN7EamvldLYS4PTTz+52QB3W/ECJ6RLk+IcRhf3MLFiwQW7duPap+TGsCPnhgJfTVw20fQ1TacW/Spygs3FxOUUQY/52Te9zbU1FRUZkqSJK0TQhxbNk6pjCSJJ0DfAjsAUKmY/yIoA/100AG0AhcJYToDd3zY+AGIEDQEuS1I7Vz2o7ZKioq40ZRZJx9faOtN0bsHd1dyIHRLzZhlqgh0dkSn0BUaD+4GUwn7mVMCIXW1v9SVf1bArKTza4oPm62kObLIcObgeIM/omNjY0lISOXfmMSFf3wUU0vXY6gtUhBYiRL8+NZWhDPGdlWTPrxJxwSQhDocuOt6cdT1Y+3th/hCS7h1SdHYMyPxpQXgyHLgsZwdImMnFu20HT7Hejj40l/6EH27Pax5aVa4tPNrL5tJpExJ8aCcCxO9fF6JAe8R/8e6BlhlWkVQnxPkqRS4AmCvtMpwNtA/pGSJKrjtYrKqYUcCLDn7TfY/NyTuGz95C86i3OuuR5rytgpZISQaW9/idq6e/B4moiyzCUn51tYrWeNqz2b18Zj5Y/x+L7HGfAPcF76edw6+1aKY4vHdb8Qgo6OjqFI6e7ubgAyMzMpKSmhuLgYi2XygiZlRfDEp438/vUK3H6ZW5bmcsfyPMKOcpwECPR7sb9Rj2tHJ5pIfXBCeEES0lHYXgX6+uh95BGcH36Et6YG4fEMXdMlJ2PMywtGQ+flBe05cnLQRIwvD8XxQJYVOmptNJT10ljWQ3fTAADhFgMZpVYySmMpWJg0qeP1sURQT3TmV+VAdAa48kG4byk8dxNc/wpoj69JuUGj4ca0eH5d20b5gJuSyBM/86KioqKiMvkIIT5i7GXAAOcf4p67Cdp0jZu97f2c85fHyfW1k1VTjsEeWt4lSUPrjQUSDEUHBvdi5GcpVCZ0PNzt4GdJkggz6Ag36AgzaNFpRtQnSUiD5UNlBz8MH44uM3hBGtEvSQKtTofOYERvNKE3mdAbTeiMRvTG0DmjacSxMXTtwLImtDqdapulclqhKDIDvb1D0c+D1htDn7u7UOTRAnR4VDSW+AQSsvPIP+OsEdHPiVji4tGbTp4gOhK7Yx/b9n4TxV1FizOCPbVnEmbL5kxFg06nIz0zGzkmgyZ/JG80D7B3sx1oIzpczzl5cSwtiGdpfjxJURP7PrLdh6emH29VH97qfmR7MIJKG2MkfFY8xtxojLlRk+Jx6Xj3XVq+/g0MmZmk3Pcv3n+9m/2fdpC/IIHlXyxGfwwv8yrHxG+ApyVJupHQhDKAEKJMkqSngXKCE8p3HEmcVlFROXUQQlD1ycd89NSj9LW1klpUyrrv3EVKQdEhyit0dr5Gbd1fcLlqMJtLKSx8gFjrsnE9r/Z7+nm0/FGeqHgCp9/JiowV3DL7FoqsY7d3YF/b2tqGROne3l4kSSIzM5NFixZRVFSE2Ww+Yj0TZU+zjbte3MOuZhtn5cbyy0tnkBsfedT1KV4Zx/tNOD5oAQTm5emYz01DY5y4Xhfo66P3oYfp+89/UNxuws84g5jPXY0hLw9Tfj6GvDy0kUff18lkoM9DY1kwSrp5Xy8+j4ykkUjOjeLMS3PIKI0lLjXyqAT68XDUEdSjKhnHzO/h7j/tZ3d3/RdeuBnO/SGc+4Pj3lyfP8D8zeVcHB/FPcWZx709FRUVlanA6RSRdTwxpeaLxC/+GUkEDTB1kYLYSCfZooec/nYifFoQAoRAECrE4GeC10LnxYjjoTIC/LKMzeVHCT2jWEw6YsINxITriQnXodNqQvUEEaH2QrUMHx+iDATbkQMB/F4PAa8Xv9czdHxgZOeRkDSag0TtQfFaZzSi0Zy4bN4qKscTr8uFvbsTR3cXijxaH4uIjglGO8clYElIHBUNbYmLR2+cGgL0oWi3NfDB1u8Rwzb8AT31tQvo7MjBHBVFXEYRnbo4ynsFn9T14fLJ6DQS8zJiWFoQx5L8eGakRqGdwAub4g3grbXhre7HU91PoMMFgCZcFxSj86Ix5UWji53cYBL7a6/R8t3vYSoqwvrHv7PxiQY6GxwsuiSH+RdlTonJNnW8nhxO+3dsFZVTgObyvXzw+EO0VVcSm5bBkuu+RM68hWP+rRZC0NPzLjW1f2JgYB8REfnkZH+T+PgLxvW3vc/TxyNlj/BkxZO4A25WZq7kltm3UBBz+OSJQghaW1uHROm+vj4kSSI7O5uSkhKKioqIPE4CrM3t548bK3lsSwNxkUbuWlPMJbNTjnosE4rAta0D28Z6FIefsDnxRK3KQncUq4oOFKYtF11E3O23YczLO6q+HQ9kv0JbTf9QlHRvqxOAyBgjGaWxZJRaSSuyYgwbW5if7PH6eITrjjnzq3IYZn8Oat+F938LWUsg6+zj2lyMXse1SVYebe3hRzkpJBkn5lenoqKionL6MiM5ike/kMYD725ipxxJq9tCZ1cknXIkn5CJZIIoi4csfT/LzHDL1dcQHj7x5Wkev8zOpn4+qe1lS20PLzT24fUrSHYoTrKwKMfKmTmxLMq2Eh0+uQlB5ECAgM+L3+PBP7j3ekcJ2cPCdujcgWVDx+4BByjKkRtVUZkG6EwmkvMKKVy8BEtcQlCETkjEHBeP3mA82d2bML29vbyz/R2amzaQl7iVWJOTtvZc2gbOQ5+wCEd0JG81Omj+zA10kWEN5/J5qSzNj2dxbixm0/ifoYWs4GtyBC07qvvxNTlAEaDTYMy2EDEvAWNeDPrkiOMWmdT//Au03XUXYXPnYrzrD7zwj/14PTIX3TKTnLnxx6VNFRUVFZWJ093UwIdPPEzt9s+ItMZywa13UrrsfDSasVe49PZuoqb2T9jtOwgLy6C05E8kJl6MJB15RUyPu4dHyh/hqYqn8AQ8rMpaxS2zbiEv5tBCqqIotLS0DInSNpsNjUZDdnY2S5YsobCwkIjjaE8hhODFnS3c/eo+ep0+rl+cxbcuKMAygXH5QDzVfdhercPf5sSQYSbqCyUYMyZuQRLo66P34Ufoe+yxKSlM27vdNJb10FDWS3NlHwGvjEYrkZIfTdGZyWTMsGJNjjgpE9aTEkF9rKizu4B3IGj1EfDArR9BuPW4Ntfg9rJ4yz7uyEjgx7kpx7UtFRUVlamAGpE1OYw1Zvd0d/LP559ns8dEk8+C3W6EwfweeokIi480k50FehdfvWQNySnpB1d8BLwBmV1NNrbU9rCltodtDX14AwqSBIWJZs7MiR0SrGMiTkwGaxUVlZNHIBDA5/MdtHm93oPOub1unG4nLo8Lt9eN1+vF4XCg8dnIzd2KNa6Jip5Sat3X0uhMZ2ezDVkRRBp1LM6NZWl+0LojM3b8L9tCCAIdrpAg3Ye3zobwKSCBPs2MKRQlbcy0IOmP/yqL3scfp+OXvyLirLNw3/BT3v1vLeFmA6tvn0Vc2tRYWjyIOl5PDuo7torK9MPR082mZx6n7L230ZtMnHHpVcy7aO0hVyHZbDuoqf0jfX2bMRqTyM76GsnJV6DRHFmo7XZ38/Deh3l6/9N4ZS8XZl3ILbNuISc6Z8zyiqLQ3Nw8JErb7XY0Gg25ubmUlJRQWFhIePjkJyw+kOpOB3e9uJcttb3MTo/m7ktnMCP16BMs+rtc2DbU4dnXizbGSNRF2YTNjJuwQCv399Pz8MP0PfYfFJcLy0UXEnfbbRjz84+6b5NBwCfTWtVPQ1kPjWW99IdWbJljTWSWxpIxI5bUgmgMponHL0/2eK0K1FOJ1p3w7xWQfwFc8/gI/87jw4176/iob4Dti0uI0KlecyoqKqc26gvv5DCeMdvjdvPIc0/xVm+A2kA0fQPhKMFnIYQGTBaZpDAHM3R2bjhzPvPnnzHhfngDMrubbWyp6eGTul62NvTi8QcjlYuSzENi9RnZVmIjp19kp4rKqYQsy0cUkQ933eP14PF6hj4H/AGEMvIdRiBJSmgTw3uNgkZSCGh8yJIfRSMT0AZQpAC6CDfow9jXW0Rl/0wcXi2SBDNTo1iaH8+S/DjmZcag145fPA70e/FW9+GpDkZJKwN+AHRxYUOWHcacKDThJ3b1Yve//kXXH/9ExHnn07LiTrZvbCY5L4qLbplJmHnqTeip4/XkoL5jq6hMHzzOAT576Vm2b3gZIRTmrFrDoss+R5h57Aheh2MftbV/orvnHfT6WLKybiM15Tq02iM/83a5unio7CGeqXwGn+JjTfYabpp1E9lR2QeVVRSFxsZGysvL2bdvHw6HA61WS15eHiUlJRQUFBAWdmLymrl8Af76TjX/+qCWCKOO719YxDUL09Ec5aoj2enH8XYjA1vakPQaLOelE3lW6oQnjaeaMC2EwNbpDgnSPbTs70f2K2j1GlILoskoiSVzRixRCWHHHCWtCtSnOpv/AW/8EFb/Ac646bg2tc3mZM32Kn6Vn8pX0tRlfSoqKqc26gvv5HC0Y/arr73Is1VtVCjRdDsj8Tk0SAQ9o/VmQWyEkwKdjStyE7n0onUTrt8XUNjd3M+W2pBgXd+H2x/0yC1MNA9ZgpyRbSVOFaxVphFCCISQUZQAilBQ5ACyHEBWZGQlgD8Q3AcCMrKQkeUAAVlBVmQURSYgBzdFKEOfZUUJlVWQFQVFUUL1CRRx4HHwuiIEATmAzx/AH/DjD8j45QB+WQ71QSGgyKG2FQKKQA5tigAFCYEU2oOCBiGCnxUkFCEh0KAccE4ZPCc0wQ3N8PEBmzy01476rAgtshg7GCPBrGNpQRJLC+I5Jy8O6wRWYCguP95a25AgHeh2A6CJ1A8L0nnR6KJPjv+2EIKue+6h55/3Erb6Espzr6NuTw8lZyez9NpCtLqp6Y+vjteTg/qOraIy9Qn4/eza+Cpbnv8vngEHxeecy9mf+wJRCYljlnc6a6mt+zOdna+i01nIzLiJtLTr0emOvMKn09XJg3sf5Nn9zxJQAqzJWcPNs24m0zI6L5ksyzQ2NlJWVsa+fftwOp3odLpRorTpBCc2frO8g5+9XEZLv5sr56fxw4uKjjoARQQUBja3Yn+7CeENEHFGEpaVmRNOQiz399PzyCP0PfoYitOJOSRMmwoO79l9PPB7ZVoq+4ZEaXu3B4DoxHAySqzBKOn8aHSTnARZFahPdYSAJz4Hte/BTW9D0szj2tzabVV0+PxsPrMY7RRIiqKioqJyvFBfeCeHyRqzd2zbwkObt7NHttDmNuO265BCVs2SQRAl2dEw7N0clLRGMMaQdWCZMdLHjL5+yGHv4GejsYuOr5wkjfWsNVhy7OewQ/XtoJ/DBBjXvUd4FDiW9k9HBBIMirMiJNAKDQJGfw6JssPnhsVaERJph69N31VvGklGIyloQ3tJCkY4j94EGkmg1QzuQSsJdBoJnQa0Wgm9RkKr0aDXajFoNOi0GrQaCZ1GGt5rNaHPGnQaTXCvDR7rNBoiw8JZWjSTwiTzuCOIhF/B22DHWxNMbOhvdoAAyaDBmDMisWFi+ElPNiiEoPM3v6X3kUfQX/55PjOtoK/NydlX5TNredpJ79/hUMfryUF9x1ZRmboIRaHi4/f56L//wd7VQeasuSy57kskZueOWd7tbqau/q+0tT2PVmsiPe1LZGTchF5/ZI/kdmc7D+59kOf2P4csZC7JvYSbZt5EumXYck+WZerr64cipV0uFzqdjoKCAkpKSsjPz8doPLEBHn5Z4b3KLv6zpYH393dRmGjml5fO4Izso7PDFULgKeuh/7U65B4PxoIYotdko0+cmFf2QcL0hRcSd/uJFaaFEPS2OWkMJTdsre5HCQh0Bg1pRdagKF0aS1T88Y1unw5JElWOBUmCS/8B/zwbnr0Bbn4PDMfPXP7WjHhu3FvPhi4baxOij1s7KioqKioqI5k7/0zmzj9z6HN7cwP/eOVVPvOHs49UBoSVUk8VBuEfKiMgFHU9hrAihv4v9FHiUOLvyJLSAScOIRcf8v6hY2nsfgX7LB1Qy+GFoZGxA2JEUXHQ/w+WGfzJHNy3iXCk+8QBe5XxEZSVRdByAgHSAZ8BSVKA0dfH+owU2qOE9qFjSSAOOC9C50EgQvUjCRAjPgMgIyQQKEP3DNYlQsdCCl5TEMgaPwH8+DUBFElGQaDX6gk3hBFuiCDCEE6kMRKLIRKLIQKLMZIokxmL0YzZENwsBgtmg5lIfSRmgxmDdurZTAAIReBvcw7bdtTZIaCABgzpFsznZWDKj8aQZkaaQtHIQpZp/9nP6X/mGQJX384m1xyEx8var80hveT45rlRUVFRUTk89bu288ETD9NVX0tCVi4rb/4qWbPmjlnW6+2kvv4ftLQ+hSRJpKd/iazMWzAY4o7YTttAGw/sfYDnq55HCMG6vHV8ZeZXSDOnAUFRura2lvLycioqKnC73ej1+lGitMFw4sfn8lY7z25r5qWdLfQ4fcRFGvjR6iK+fHb2hGy3RuJrdtD/ai2+Oju6hHDivlyKqXBi46Fss9H7yCP0PvoYysBAUJi+7TZMhSdGmPa5AzRXDEdJD/R5AbCmRDDr3DQyZsSSkhuN9gTktTheqAL1VCQiDq74FzxyCbz2fVj3t+PW1IVxUWSFGfhnUycXx0dN6WgKFRUVFZVTl6S0TH5x2+0A7Hj1l1xlOI+e8KW8sLCEOL0Ov1fGM+DHPeDH7fDhcfpxO/yhc77g3uEPnffhdQUO2ZbBpMUUqScs2khUYjjmxDDMiWFERunQyi5kux3FPkDA0Y/P1oPP1kvA1o/ssKE47DDgQBpwonE60TpdaF0eDE4vGkUZsz2/BrriTdjSIulOjqArORxbYhR+cwRG2RDcAnqMsn7E3nCI4+G9STagESf+IdSp99AV3k9XhI2uiP6h4wGD+4gR2KcjkiShkTTBPZqhY4ngeY2kGToeLKthxPHQde2osgfWicSo+g9dz5HbPPDa4GeD1jAsMuuDInOEIQL9OJIxTRcCPe4hyw5vTT9K6G+JLjGcyEVJwcSG2VFojiKZ0IlA+P20/vBH2Nevx3bND9nRmY4lXs+a22cRnXj8k1epqKioqIxNR10NHzz+EI17dmKJT2T1V79N0dnLkDQHP8v5/X3UN9xHc/NjCBEgJfkqsrLuwGRKPmI7LQMtPLDnAV6ofgGAy/Iu4yszv0JKZAqBQID9+/cPidIejweDwUBhYSElJSXk5eWh15/4Mb17wMtLO1t5blsz5W129FqJFcWJXDk/jaUF8UctTAdsXuxv1OPa3okmQk/0pXlELExC0o7/gfUgYXrVKuJuv/24C9NCCHpaBmjYG0xu2F5jQ1EEepOW9CIrC1YHo6TN1pNjI3Y8mJpPViqQvRSWfBs+/APkLocZVxyXZrSSxM1p8fyoqoXPbE7OiJ5aWbxVVFRUVE4/5i6/g/889HmuLfoZn9tZw3Nz84gx6TCYdFjixrdUTZEVPM5AULx2BIVtz4AvKHAP+PE4fNh7PNRt68TvkYfuCzPrsaZEYE2KxpqSSkxBBNaUCMKO4EsnhEB4PCFx247scCDb7QR6+3h1/Wbkmirm1vdQvK1h6B6N2YwxPz+4FeRjKizAWFCANmp8mciFEBBQUDwywieDCJ0L+keEPg8fD58b/RkhDig3oowysoxAtvmI6HQR0+Eiu9OFaB2eCJBMOvSJ4egTwtElhKNPDO61UQZ1AlxlyiI7/XhrgoK0p7ofuTfo26i1GDAVxwZ9pHOj0VqmZpT3SBSfj5ZvfQv72+/SctXdVLVHk1ESwwVfKcV4ghMzqqioqKgEsXW289FTj1Hx8fuYIs2c+8WbmH3BanRjCMGBgIPGxgnXnr8AAQAASURBVAdpbHoQWXaSlLSO7Kw7CQ/PHKPm0TQ5mvj3nn/zcvXLSJLEFflXcOOMG4kzxlFTU8OW8i1UVlbi9XoxGo0UFhZSWlpKTk7OSRGlfQGFdyo6eW57M+9WdBJQBLPSovjFulLWzkohZgI5IQ5E8co4Pmhm4INmhCIwL0vDvDx9QpPLQWH6UXofffSECdMep5+mfUHbjsbyXlw2HwBx6ZHMWZlBRqmVpNwotEcp2E91VA/qqYwcgIdXQ+c+uPVDiMk6Ls04ZZn5m8o5KzqSB2cenLlVRUVF5VRA9bScHE7YmP3ZA7y/6Qm+MOsPlJgjeGZOLmbd5HvvCiEY6PPS2+akt9VJX5szeNzmPFi4To4gJjkCa3JQtLYmRxBmPvLDs8cvc/2Dn7K9sY9HripmjtyLt6oK7/79ePbvx7u/CsVuHyqvS0zEWFAQFK0LCjDm52PIzUVzgr3/joQQAmXAj7/DRaDThb9zeK8MDFuzSAYtupBwrU8IHzrWRhuRjjLzusrphRACFIGQg5MyQg4dy6HjgAKyQIQ+Ezon5ND5gBg+DpVRBoLCtL/NCYBk0mLMicaUH0psGHfs2e1PJIrbTfNXv0b/J9vZf/HddPQbmX1+Omddnotmmr3IquP15KC+Y6uonFzcDjtbnv8vuza+iiRpmLdmHQsvuQJTxMFBgbLsprn5Meob7iMQ6Cc+fhU52d8gMvLIYmiDvYF/7f4X62vXo5W0XFlwJV8o/AKONgfl5eVUVlbi8/kwmUwUFRVRUlJCTk4OOt2Jj1cVQlA2wsKjz+Un3mzk8rmpXDE/jYJE87HVrwhc2zuwvdGA4vARNiuOqAuz0U0gyli224eFaYcD8wUXEHfH7ZgKC4+pb4fqb1eTYyhKuqPOhhBgDNeRXhyMkM4otRIRNbXeAQZRkySebvQ3wr3nQGw+3PA6aI/PzNZvatv4S0MHHy8qJid8av7jV1FRUTkW1BfeyeGEjdmKDPefy0ZdJjfkfYcFlggen51DhPbEJIgbKVz3hcTrQwnXMUnDgvWgiB1+QLSlze3nqns30drv4elbFlOSYhnVVqCjA+/+/aNEa19NDcIfEnq1WgyZmaOF64IC9GlpYy4NPdnITv+waN0R3Ps7XSh231AZSa9BFx82SrTWJYSjs4ZNaOnldOTgKHaAwUh2ho3IlZDn+JHKjrjnoKj5YPHDnBcIRYCiBAVfRQm2qyjB87Ic3AsBkgbJYASFA8TiEeJvQAwdjz43LCaPEpdH1DN87mBhedLRShgzLUHLjrxoDKnmafvvTh4YoOnWW+ne10r50h/i9OlYdl0hJWennOyuHRXqeD05qO/YKionB7/Xw/YNL/PpS8/i93goPXcFZ119HWbrwb7RiuKjpfW/1Nf/HZ+vi1jrUnJyvoXFMvOI7dTaavnX7n+xoW4DBo2Bqwqv4urMq6ncUcm2bdvw+XyEhYVRXFxMSUkJWVlZJ0WUBuh0eHhpRyvPbW+mot2BQadhZUnQwmNJXhy6SZhI9dT0Y3u1Fn+rE0O6maiLczBmHjmJ5CAHCdMrVxL31TsmXZh2O3w0lg9HSXtCQR0JmeaQIB1LYpZ5WkwuqwL16Uj5S/D0F+Hsb8DKnx+XJjq9fhZsLue6lFh+U5B2XNpQUVFROZmoL7yTwwkdsxu3wIOreHHJH7lds4AlMWYemZmN6SQ+sAkhcPZ7RwnWg5HXvhHCtSkyFHGdFI7OoAUJXN4AL+1qRRFw6dwUzCY9wQBNafB/wf1g1KaiINtsyL09BHp6kHt6kHu6kW02pNDzm6TXobXGoouOAimUjlE6+NkuWONgQr6xU02O/iyGyo0qM4Z+Jx1YZkQdo85JAgktesmEHhM6aeQ2PDkuhpTXU4dRaTKnUVTuUSMJ0EhI2uCGXouk0yLpJCStBnSa4Wva0LFOA9rg9eD50LFuRBltqIxuxPFged2IMrpx1jOFI/iF34/scAQtg+x2ZLsDxW5DtjuQHSErIbsDxWHHU76PNqeZ8tm3oAszcNEtM0nOiz7ZX+GoUcfryUF9x1ZRObEoskzZ+2+z6ZnHGejtIWf+GSy59nri0g+251CUAO3tL1JXfw8eTwvRUQvJyf02MdELj9hOdV819+++n9frX8ekM/G5ws9xWdpl7Nu2j+3bt6MoCjNmzGDOnDlkZWWhPUHBHQfiDci8va+TZ7c18/7+LmRFMCc9mivnp7F2VgpRk2Q95e9yYXutHk95D9poI1EXZhE2O37cq6Bku53eRx+j95FHhoXpO27HVFQ0Kf0DcNq8VG/rpHprJ+11NhDBd4WMkmCUdHqx9aDglunAZI/Xqgf1dKBkHcz/Mnz8Z8hZBrnnTXoTCUY9lyfG8N+2Hr6XnYRVr/7TUFFRUVE5yWScCbOv5dJNP8RzzYd8o9nBLeX1/Ls0G/1JEpYkSSIyxkRkjImM0tih80PC9QFWITXbu5ADSijoVTBDaPH6ZSo/bEM/GK056PscKjMkzQ4d6IDE4GYluB2If4xz0wgdfiK1EmYNRGglpn7MyPgRgEbxExawEybbCJft6IR3eDJi8J+yRHCSIbRnaD9iAkMjDb9waUaWGaxLCp0fcZ9GCkY+a0KNaKXhshKg0YTKhO7VaEJ90ATbC51DAgIBZJcDMWBHtttCgmk/sq0P2daHYusHITMmkoTWYkETHYU2KhptVNTwFh2NJioKrXn4nCZ0XmuxIJ2kl+tjQSgKitM5WmB22JFt9oME5uC5kQK0A+FyjV0vIGtN+MOikaPjkc1x2JPOozpsPtakSFbfNhNL7Pi8+lVUVFRUjh0hBLXbP+XDJx6hp7mR5LxC1nztu6SVzBijrEJn5wZq6/6Cy1WL2TyTosJfYbUuOaKgWtlbyf277+fNhjcJ04Vxw4wbuCT5EvZ8tof/bPgPAHPnzuXss8/Gah3rYfH4I4Rgd7ONZ7c18/KuVmxuP0kWEzcvzeGKeWnkJUxezjPF5cf+diMDm9uQdBosq7Iwn5OCpB/fM8PBwvSKoMd0cfGk9M894KN2RxdVWzto2d8PAmJTIznj4mwySmNJyDBP6Ynyk4GqQk4XVv06GEn2/C1w28cQmTDpTdySHs9T7b082tLNN7KSJr1+FRUVFRWVCbPi57BvPdd8+hPc5/6DH+5v5qv7GvhHSSbaKRSJOkq4Lok9bNlPanv4wgOfMjMtise/sgjTER6kB+0gRgnYg+K1AMHI6yPuGfk5dO+I3YhrIxIqcsD9g22MqPygcqM+D990YDsH1isO/A4HlJ0wU+efwxBCge4mB80VfeyrCMNpiwEdmGNNpBfFkFZkJbUwZlpGzYyFCASCYmy/DdnWj2yzodhswZUA/f2h88OffQ0NyP39ozzYx0JjsRwkaAf3I8TsqJDwHR0dPG+xIB3jUmZlZOLTQTE5JDgrDsew2DwkMIeuOxwoDkfQNuVQSBKS2QxRsQQs8cjmRAIZRfjDogjozfj04fg1Yfgw4lV0eP0avD7wegTKGJYnOXPiOf/6YgwTSACloqKionJstO6v4IPHH6KlooyY5BTWfuuH5J9x1kFisxCCnp53qan9EwMD+4iIyGfmzH8QH3fBEYXpfT37uG/3fbzd+DaR+khumnUTF8VdxM5PdvLY+sfQarUsXLiQs846i6hxJtqebDrsHl7Y0cKz25qp7hzAqNOwqjSJK+encXZeHNpJFGJFQGFgSxv2txsRngARC5OwrMxEO468MACyw0Hvo4/S+8ijKHb7pArTXneAup1BUbppXx9CEUQnhrNgdRb58xOxpkQccxunMqrFx3Sioxz+tRwyz4b/eTYYzTLJXLurhr0Dbj47s+SkLqFWUVFRmWzUJcOTw0kZszf9DTb+GK79L38Pm8cva1q5JsnKn4rS0UwhkXoibNjTxh1PbGdlcSL//Pz8SX1wV5maCCHoa3fRXNFHc0UvLfv78bkDAMSmRpBWaCWtKIaUgujTTmQUshwUfW2jBexRgrYtJHqHxG+l34Zst4+eMTkATWTkaFE7eljM1oRHoAwMhKKZHSOE6GGxWfh8h6wbQAoLQ2s2o7GY0ViiwByD3xxLICwGv8lMwGDGrw3HK5nwY8Ara4NCsxc8bhnPgH9MsRmCke+mCB2mCD2mSD1hkYbg50gDpkg9pgg9YZGha2YDljjTtErqeDjU8XpyUN+xVVSOH72tLXz01CNUfbKJ8KhoFl95HTPPuwDtGBOjvb2bqKn9E3b7DsLCMsjJ/gaJiRcjSYcPUCjrLuPeXffyXvN7mPVmPl/yeVZEr2D7lu1UVFRgMBhYuHAhixcvJjJy8iKTx4vHL/NmeQfPbmvmw6ouFAELMmO4Yn4aa2YlYzFNbv40IQSe8l5sr9UR6HZjzIsmak0OhuTxib4HCtORK84n/vbbMZWUHFO//F6Z+t3dVG3toKGsByUgMFtN5C9MIG9BInFpkafM+Hwgqgf16c5nD8Cr34KVv4Sz75z06j/odXD1rhr+VJTOdcmHjwBTUVFRmU6cDi+8kiQ9CFwMdAohZoTOzQHuBUxAALhdCPFp6NoPgRsBGbhTCPHGkdo4KWO27Id/ng2yF27/hN839/HH+g6+nBrHr/NTp+1D38Mf1/GzV8r5/JkZ/HLdjGn7PVSODkURdDU6aK7opbmij7YaG7JfQdJIJGZZSCuKIa0ohqTsKLR6NWhgLISihCKZQ4K2zTZC1B6O4A70hwTtEWI3igJabdB2xGJGa4kKic0WNGYLwhyFPyyGgNGMXx+BTxuOHyNeYcAna/H4wOuS8Tj9uAf8hxebJYZE5dHismGEAK0fVcYYpjttl/6eDuP1iUB9x1ZRmXyc/X1sfvZJdr/9Ojq9gQVrL2fB2sswmA62VrLZdlBT+0f6+jZjNCaRnfU1kpOvQKM5vHC7u2s39+66lw9bPsRisPDFki+yzLyMrZu2Ul1djdFo5Mwzz2TRokWEh4cfr686JkIIdjT18+y2Zl7Z1YrDEyAlysTl89K4Yn4a2XHHJ0LY1zKA7dVavLU2dPFhRK3JwVQYM65nZ9nhoPexx+h9+JFJE6YDfpnGvb1Ube2gfnc3Ab9CRJSBvPmJ5C1MIDHLclo816se1Kc7C26A2nfhnV/CvC9AWMykVr8kJpKSCBP3NnZxbZL1tPiPSkVFReUU4mHgb8CjI879Dvi5EOI1SZJWhz6fK0lSCXANUAqkAG9JklQgxKEMZE8iWj2s/h08ug42/5XvLPkOTlnh3qYuwrUa7spJnpbj1ZfOzqbN7uG+92tJjgrjjuV5J7tLKicQTUiITsyyMP/CLAJ+mfYaWzDCurKPba/Vs3VDPTq9huT8aNIKg4J1XLoZzWkqXB6IpNEMRUiTkXHE8kIIHL0eelsH6G914PEIPM4AngEfHmdQZHYP+PH0+VG6DhSbZcCFJLkwjohetsSFkZBlCX6OCEU3DwrOqtg8IZr7XPzs5fKT3Q0VFRWVg/C5XWxd/wJbX3kBOeBn1oqLWHzFNUREH6zHOBz7qK39E90976DXx5KffxepKdeh1RrHqHmYHZ07uHfXvWxq3US0MZo7597J2aaz+XTTpzxV/xTh4eGcf/75LFy4EJPJdLy+6pi02dw8v72F57Y1U9vtxKTXsHpGMlfMT2NxTuykP5cIReDvcOFrsuOt7se9pxtNmI7odblEnJEUTHZ8BOSBgWDE9KAwff75xN9x9MK0HFBo2tdL9dZOand14ffImCL1FC1OJn9hAsm50afVWB+QD2NjdpSoAvV0Q5LgrK/Dvleg6k2YdfUkVy9xW0YCX9vXyDu9Ds6PtUxq/SoqKioqxw8hxAeSJGUdeBoY/GMeBbSGjtcBTwkhvECdJEnVwBnA5hPR1wmTc24wafAHf0SadQ0/zU3DLSv8vbGTCK2Gb03T3AnfX1VEh83D79+oJMFs5KoF6Se7SyonCZ1eS1qRlbSiYGIjrztA6/4+mir6aK7oY/MLNQAYw3VDYnVakZWohLBpOUFzPBkUovvaXPS2OultG6C3zUVfmxO/d3gOTpI4pNhsHBHlrIrNxxdfQOHfH9Vyz9tVSFPRTF5FReW0RQ4E2P3262x57ilctn4KzjyHc675AjHJqQeVdTprqa37M52dr6LTWcjN+TZpadej0x0+qnhr+1bu3XUvn7R/gtVk5ZvzvslC7UI+2fQJTzc/TWRkJKtWrWL+/PkYDCcuZ4XbJ7OxvJ1ntzXzUXU3QsAZ2VZuXZbLRTOTME+ihYfs8OFrdOBrsgf3zQMIX3C81oTriFySimV5BpqwI0uY8sAAfY89Rs/Dj6DYbESedx5xd9xOWGnphPulKIKW/X1Uf9ZBzc4uvM4AxnAdefMSyFuQQFphDJrTwBrX45epbHdQ1mpnb6uNslY7FW2Hzx9yNKgC9XQkdT5EJkHF+kkXqAHWJURzd00b9zZ1qgK1iorKtEYIgUtRcAQmf4Z3GvEN4A1Jkv4AaICzQudTgS0jyjWHzk1dLrgb9m+EjT9GuvpR/q8gDbei8Lu6dsI1Gm7NmPwEwscbjUbid1fOpnvAxw+e30O82ci5hdPve6hMPsYwHdmz48meHQ+A0+Ydiq5uruilZkcXAJExxlGCdUT04SO0TiWGI6KdQTG6bSB43O4aJUSHWwzEJEdQdFYy1uQIrMkRRCeGExapV8Xmk8yW2h7+98W9VHUOcEFJIj+9pJS0X53sXqmoqJzuCCHYv+VjPnrqEfrb20grmcGl3/tfkvMKDyrrdjdTV/9X2tqeR6s1kZV5OxkZN6HXH1pLEULwafun3LvrXrZ2bCUuLI7vzP8Os8QsPvn4E55rf46oqCjWrFnDnDlz0Osn18/5cP3a1tDHs9uaWb+7jQFvgLSYMO48L58r5qWREXvsliLCr+BrHcDX5MDXGBSk5X5v8KJGQp8SQfj8BAwZFozpZrSx48uxIA8M0Pef/9Dz0MPDwvTttxM2Y2LCtFAEbbU2qrd2Ur29E7fdh86oJWd2HHkLEskotp7S1msOj5/yVjt7W+2Utdoob7VT1TmArARXlplNOkpTLHz+zEx+MsltqwL1dESjgaLVsOu/4PeAfnKXdxg0Gm5Mi+Pu2jb2OlzMMJ9YXyMVFRWVQfyKwB6QccgytoCMIxDc20ccOwLKqGuOgIxdDpaxB2QCJz/VwsnmNuCbQojnJEm6GngAWAFjhqmN+dOSJOlm4GaAjHEsoz9uRKfDkm/Du7+C2vfQ5JzLnwozcMuCn9W0EqbVcH1q3Mnr31Fi0Gn45+fn8bn7tnD749t56uYzmZUWfbK7pTLFiIgyUrgoicJFSQghsHW5hxIu1u/poWJLOwAxSeGhSOwYUguiMYafmJfa48mxCNHW5AhMkdP/Z3Cq0T3g5dcb9vH89hZSo8N44PoFnF+ceLK7paKiokJT+R4+ePwh2qv3E5uWwWXf/ynZcxccJJJ6vZ3U1/+DltankCSJ9PQvkZV5CwbDoZ9FhRBsbt3MvbvvZUfnDhLCEvj+gu9T7Ctmy/tbeKnrJaxWK+vWrWPWrFlotYdPpDhZNPe5eGF7C89tb6a+x0W4QcvqmclcMS+NRdnWo7bwEEIg93pCYrQDb5MDf+sAhHI2aKONGNLNGM5OwZBhwZASgaSf2Hc+SJhevpy4O+6YkDAtRDAvSNVnHVRv62Sgz4tWryFrRix5CxLJnBmL3nBifhcnki6Hl7JQRPTgvqHHNXQ93mxkRoqFFcWJlKZYmJEaRVrM8Mq9yRao1SSJ05Xqt+A/V8B1T0PBqkmv3uYPMG9zObnhRv5dmkVG2OkTjaOiMlGcAZlOX4AOnx+3rKDXSBg1GvSShEET3IaOJc2oc5pTeFm2IgROWRkSikdtsoLdP1pIPnhTcCtHjnyO1Gqw6LRDm1mrJUqvxazVEKXTYg6d/1Ja/GmRdClk8bF+RJJEGxAthBBS8GnCJoSwhBIkIoT4v1C5N4CfCSEOa/Fx0sdsvwf+sQi0RrjtY9Dq8SkKN+6t580eO/cUZ3B1kvXk9e8Y6LR7uPyfm/D4ZZ677SwyY49PohmVUw+hCLqbB0IR1r20VvUT8ClIEsRnmIcE6+TcKHRT+AVrIkK0NSWCmEEROiUkREeoQvRUR1EET37WyO9er8TlC3DTkhy+dl4+YSP+XapJEieHkz5eq6hMM7ob6/nwyUeo3f4ZkbFxnH3V/1Cy7Dw0mtHjpt/fR33DfTQ3P4YQAVKSryIr6w5MpuRD1i2E4KOWj7h3973s7tpNYngiN5bcSK47l80fb6avr4+EhASWLFlCaWkpGs3xj9B1+QK8vjdo4bGppgeAxTmxXDk/jQtnJBFhnHg8q+IJBMXokCDta7KjOAMASHoN+jQzxgxzUJTOMKO1HL3OFBSmH6f3oYeQj0KYFkLQ2+qk6rMOqrZ2YO/2oNFKZJRYyVuQSPbsOAymUyOmVwhBc597lBBd1mqjw+4dKpNhDac0xRLcUqMoTbGQYD58MOxkj9eqQD1dCfjg97lQeilc8tfj0sRrXf3cua8RjSTx/4rSWR0ffVzaUVGZiggh6PXLdPr8dPj8QQHa6w99DtDp9Q+J0s5jSBCgk4KrFgyShF4jYZAGxWsNRs3B5w4SvMe8NySQjzw3dDyifOicXhNqSxp9TgIGQgLyISOVB49lGZs/uLcHlKEI5yP9ZIwaKSgoh4Tk4H604Dy0aQePh6+bdVq04xT5T5cX3jEE6n3AbUKI9yRJOh/4nRBiviRJpcATBH2nU4C3gfwjJUmcEmN25evw5OeClh9nfRUAj6zwhT21fNw3wH2lWaxNiD65fTxKaroGuOKfm4gO0/PcbWcRG6lOEKtMHDmg0FFno6mij5aKPjrq7CiKQKvTkJQbFbIDiSEhw3xSvBOFInD0eUL+0E762pyHFaKtySExWhWipzVlrTZ+/MJedjb1syjbyt2XzSAvwXxQudNlvD7eTInxWkVlGtDVUMdnLz9HxccfYAgL44xLr2LuRWvRG0Y/gwUCDhobH6Sx6UFk2UlS4jqys+8kPDzzkHULIXi/+X3u3XUvZT1lpESkcEPJDaTb09myaQt2u53k5GSWLl1KYWHhcRemFUXwaX0vz21rZsOeNpw+mQxrOFfOT+OyuamkW8e/en5kIkNfY1CQDnS5htZj6hLCMKRbMIQEaX1iBJL22IOz5AEnff/5z7Awfe65QWF65oxx3d/f4aJqawdVWzvpa3MiSZBWFEPegkRy5sRP+2cMWRHUdQ+wt2WkGG3H5vYDoJEgLyGSGSlRlKRYKA3to8Im/r1VgVplmGdvgLoP4NuVoDk+0TANbi83l9Wzy+HmhtQ4fpKbguk0MIFXOXXxK4KuQZHZ5w+JzoEhIbrDGzzu8gXwj/H3MUKrIdGgJ8GgI9GoJ9GgJ37EcbhWg09R8AuBTwluQ8dCCX5WBD4xfM2rKPiHjgfLK6PvHXn/AecG6/MqJ/bvuQaGopNHicYhwXlUVPMI8TlqRLkT+ffkdHjhlSTpSeBcIA7oAH4KVAJ/IWjr5QFuF0JsC5X/MXADEAC+IYR47UhtTJkx+/GroWETfG0rmIMJEp2yzLW7atlud/LQjGxWxkWd5E4eHdsa+rjuX1soSjLz5M1nEm44NaI3VE4ePk+A1qr+kH91Hz3NAwAYTFpSCoJidXqRlZjk8ElNuHgoIbq33UVAFaJPGxweP396cz+PbKonJtzAj9cUc9nc1EP+WzsdxusTwZQZr1VUpiBCCFoqyvj0pWep27EVvSmM2Ssv4ox1VxJmHu0dLctumpsfo77hPgKBfuLjV5GT/Q0iIwsOWb8iFN5tfJf7dt/Hvt59pEamcmPxjST1JrFl8xacTifp6eksXbqUvLy8457suKnXxXPbm3luezNNvW4ijTrWzEzmygVpLMiMGZ/H8xESGQajokOCdJp5XAkNJ4I84KTv8cfpffDBoDC9bBlxX72DsJkzj3ivvdtN9bZOqrZ20N00ABKk5EWTNz+B3HkJhFtOXPLJycQbkNnfPkBZq21E8kIHbn/w92LQaShOMlOSEjVk0VGUZMY0QRuVQ6EK1CrD7H0uKFLf8AZknHncmvEpCnfXtHFfcxczI8O4rzSLnHA1oktlauEMyHSEIpo7fX46vcHjDp+frhHHvf6xg0Nj9bqg6GzQk2AM7hONIfHZoB8SpSN0U3tptCzAKw4QvIcEcWWUOO4TAr+iDInifkXgDZ3zKQIBROq0WLRjRzRHaDXTyqJEfeGdHKbMmN1TA/84E2ZcAZfdO3TaHpC5amc1FU4P/5mZwxLrwdF504GNZe3c+p9tLCuI519fXIBOnRxWmURcdh8t+/uGPKzt3R4AwqMMoxIumq3jy3MybiE6yjDsDT3CokMVok9NhBC8uqeNX64vp9Ph5bozMvjeqiKijuCLro7Xk8OUGa9VVKYQQlGo2f4Zn770DG37KwizRDHvwrXMXrWGsMjRz4yK4qOl9b/U1/8dn6+LWOtScnK+hcVyaEFUEQpvNbzFfbvvY3/ffjLMGdxQdAPWTiuffvIpbrebnJwcli5dSmZm5nEVpge8ATbsaeO5bc18UteLJMHZuXFcOT+NVaVJo6yVDmQ8iQwHBemJJDI8Go5WmB7o81KzPShKd9TZAUjMtpA3P4G8+QlExkxuLrfjzYA3wL42O3tbhqOiqzocBAaTFxp1FKdYmBESo0tTLeTGR6I/Du8QQghavH7Sw4xTQ6CWJMkEfAAYCUZlPSuE+KkkST8DbgK6QkV/JITYcLi61MHzKPHY4Xc5sOgWWHX3cW9uY7eNr+9rxCcEvy9M5/LEmOPepsrpjSIEfSNsNgajm8drs6GXJBIMOhJGiM4JBj2JoeN4g55Eg454gx79USZ+UJk+qC+8k8OUGrPf/gV8+MeDJmp7/QEu31FNg9vHf2fncEZ05Ens5NHz+CcN/PiFvXxuQTq/uWLmcY+uUTl9sXcPJ1xsruzD7QguA42KDxsSq1MLozGF61UhWmVc1Hc7+cnLZXywv4vSFAu/unQGczPG9+6gjteTw5Qar1VUTjJywE/Fxx/w2cvP0dPciCU+kQVrL2PGuSvQG0cLlYoSoL39Rerq78HjaSE6aiE5ud8mJnrhoetXZDY2bOT+3fdT3V9NliWLGwpuILItkq2fbcXr9VJQUMCSJUtIT08/bt/T7ZP5qLqb1/a08dredtx+mey4iCELj5TosIPuGVciwyHf6KNLZDheAn19ePdX4a2sxFNZgbdyP96qKoTXS8SypcTfcQdhs2Yd+vs7fNTs6KLqsw5aq/tBQFx6ZEiUTiQq/uDvPxXpGfAOidCDNh31PU4G5du4SAOlg0J0ShQzUi2kx4QfdTLLQyGEoM3rp9LpCW4uz9CxU1boOG/ulBGoJSBCCDEgSZIe+Aj4OnAhMCCE+MN461IHz2PgP1cEo8ju3AEn4MW1xePjtvIGPrU5uS7Zyq/y0whXo7pUJoAQAntApscv0+MP0DPCXqMrJDZP1GZjUIQetNkY/Byj106rCF+V44v6wjs5TKkx2+eEvy2EcCvc/P4ou6sun59Lt1fT6fPz7Nw8ZpvH76k3lfjTxkrueaeaO8/P51srD72UVEVlshhMGjQoWLdU9eP3yCCBTq8h4BueED5QiB606FCF6NMXj1/mvvdr+ft71Ri0Gr59QQFfODNzQqtA1PF6cphS47WKyknC53Gz5+2NbHv1RRw9XcRnZLFw3ZUULl6CRjtaZBVCobNzA7V1f8HlqsVsnkFuzrexWpccMkggoAR4vf517t99P3W2OnKjcvly/pcxNhnZtm0bfr+fkpISlixZQnLyoZMoHgvdA17e2dfJxvIOPqruwuNXMJt0XDwrhSvnpzEvI3pU/w+byNCgQZ8aSmSYYcaQbkF7HOwvhN+Pr74eT+X+UWJ0oKNjqIzWasVUVIgxvwDL6osImz17zLq8Lj+1O7uo2tpJc0UfQhHEJIWTtyCR/AUJxCRN3aTjQghabR7KWmzsbbVTHhKj22yeoTJpMWFBe46UKEpTg4J0gtk4uZZsQtDhC4TEZ/eQCL3f5cEeGH7ui9PrKIwwDW1fSouf1PH6qE1hRFDZHgh91Ie2k+8XcrpRtAbWfxM690FiyXFvLtVk4Pk5efy+vp17GjrYZndxX2kmRRHTYyZKZfJRhKA/INPjCwQF55DoPPK4e8S5Xr88pugMo202CiKMQxHPQ5Yb08BmQ0VF5QRiiIALfgXPfhm2PQwLbxy6FG/Q8/ScXNbtqOKanTU8PzeP4sjpN1Z9c2UBbTYP97xdRZLFxHWLMk52l1ROcSRJIjY1ktjUSGafn44sK3TWO2iu6MXj9A8J0qoQrXIgH1Z18ZOXyqjrdnLxrGT+9+ISEi3Tawm1iorKqYHLbmPH6+vZ+cZ6PAMO0opnsPKmO8iaM/8gYU8Ima6uN6mr/xsDA/uIiMhn5sx/EB93wWGF6VdrX+Vfe/5Fg72B/Jh87p5/N5p6DTue34GiKMycOZNzzjmHhISESf9+1Z0DvFnewVv7Otje2IcQkBodxjULM1hRnMgZ2VYMOk0wkWH74RMZmopiJz2R4UgCPT0hEXpQjK7EV12N8AdXa6HXY8zJIeLMRRgLCjEWFmIqLEAbF3fIn7/PE6B+dzdVWztpLOtBkQWWOBNzL8ggf0EisakRU3LlYUu/m631vZSHoqP3ttrodw0nL8yJj2RRtnUoOrokxUJ0+ORNEAgh6BoUokdEQ1c6PdgCwyvhrHothREmLk+0BsXo8KAgHXtAXpwvTVrPghyTB7UkSVpgG5AH/F0I8f2QxceXADuwFfi2EKJvjHtvBm4GyMjImN/Q0HDU/TitcXTAHwth+Y9h2XdPaNPv9dr5ankjTlnm7oI0rk2yTsk/AioTQxaC3kOIyz3+g4XovkBgcPXPQZi1GmINOmL1oe0QxwmqzYbKCUCNyJocplxElhDwyFro2Atf2x6Mph5Bg9vLuu3VyAhenJtHbvj0E0v8ssJNj27lg/1d3P+FBawoSTzZXVJRUVEZosPu4Vev7uOVXa1kxYbzi3UzWFoQf9T1qeP15DDlxmsVlROAvauTretfYM87Gwn4vOQuOJMz1l1BSkHxQWUDASdtbc/Q2PQwHk8TYWEZ5GR/g8TEiwlKXQfjV/ysr1nP/bvvp3mgmSJrEddnXk+gNsDu3bsBmDNnDueccw5Wq3XMOo4GWRFsa+jjrX0dvFXeQW23E4AZqRZWFiexsiSR4uSgh7a/1Yl7bze+BvvBiQwzLCGrjslPZCh8Pry1tQeJ0XJ391AZXXx8UIAuCgrRxoJCjNlZSIYji7ABn0zD3h6qtnbQsKeHgF8hItpI3oIE8hckkpBpnnJ6lBCCvS123gz93srbgl7YBq2GwiRzyKLDQmlqFMVJlsP6gk+Ubl+AipHR0KF93wghOkYXFKILRkRFF0aYiDccIgBBkaGrAlq2Qcs2pEvumRoWH6MqkaRo4AXgawS9p7sJzsn8EkgWQtxwuPvVwfMY+fdKkH1wy/snvOlOr5/byxv4qH+AyxNj+F1BGpFqdOuUwqco9I6w0zhUlPPgcZ9fPuRSiGid9hBCc/B8nEEfPDbosOp1GDWq/YvK1EF94Z0cpuSY3bkP/nk2zPsirP3zQZernB4u3VGNUSPx4tw8MsKmX6Jfly/AtfdvobLDwRM3ncm8cXq5qqioqBwvZEXw6OZ6/rhxPz5Z4fZzc7l1WS6mY/Am3TfgpsQcro7Xk8CUHK9VVI4T3Y31fPbyc+z7+H0kSaL4nOUsvORyYtMOXnnm8bTR3PwYLa1PEgjYiYqaR0b6jcTHrzy0MC37ebHmRf69+9+0OlspjS3lCxlfwFPlYe/evWi1WubNm8fZZ59NVFTUpHwnly/AB/u7eWtfB+9UdNLr9KHXSizOjWNlcQIrShJJjgquDpQdPlw7O3Ft68Df7hqVyNAYEqUnK5GhEIJAZxfe/ZWjxGhvbS0EBq1CDBjz8g4QowvQTVC0dzt8tNXYqNnRSd3ObvxemTCznrx5CeQtSCQ5NwppigW5eQMyW2p7ebO8nbfKO2m3e5AkWJAZw4riRM7Jj6Mg0TxpyQt7/YFRkdCDW48/MFTGotNQFBE2LEKHDwrRukP/mxACbM0hMXortGyH1p3gD06OYIpC+mHT1BOoASRJ+ingHOk9LUlSFrBeCDHjcPeqg+cx8tGf4a2fwjfLICrthDcvC8E9DR38vq6drDAj95VmMnOaen1OB4QQtIf8mQ8tOAcF6W6/f5Rn0EgkIEavJU6vJ9agHTOyOW7EcYxep0Y4q0xrVIF6cpiyY/brP4Qt/4Sb34WUuQddLhtwc8WOaqJ0Wl6cl0eycfL99I433QNervjnJuxuP8/edha58dMz+aOKisr0Z1dTPz9+cQ97W+wsyY/jF+tmkB139D6fQggebOnmFzWtNJ47Rx2vJ4EpO16rqEwizRVlfPbSs9Ru/wy90cTM81cxf82lWOIOXsXhcJTR2PggHZ3rEUIhIeFCMtJvICrq4OfGQbyylxeqXuCBvQ/Q7mxnVtwsrku9joHKAfbt24der2fhwoUsXrwYs9l8zN+n0+7h7YpO3izv4KPqbnwBBYtJx3lFCawsSWJpQRxmUzC6VQQU3Pt6cG3rxLO/FxQwpJsJn59A+Kx4NOHHbsOleL14q6vxVlTi3T8sRst9wyYJuuRkTAUFo8RoQ2Ymkm5i0dmyrNDb4qS91hbc6uzYu9wAGMN15MyNJ39BIqkF0WimWB60fpePdyuDv7f3K7tw+mTC9FqW5MexsiSR84oSiI08tgCZ/rGEaJeHLt+wEB2p1YyKhB7ckgz6I09OuPugdQc0bxuKkMbZGbymNUDSLEidP7xZc5C02qkhUEuSFA/4hRD9kiSFARuB3wLbhBBtoTLfBBYJIa45XF3q4HmMdFfD3+bDRb+HRTeftG5s7h/g9vIGenwBfpaXwpdTD+0ZpDI+PLJCpctD2YCbMoebsgE35U73mKKzTgKr/sh2GoPHMXotWvX3o3IaoQrUk8OUHbM9NvjrfIjJhhvegDFWcGy3O7lqZw0pRj0vzM0nzjB5yxpPFA09Ti7/xybCDFqev/0sEszTz7JERUVl+mJz+fn9xgoe/6SR+EgjP1lbwpqZycf0zN/jC/DNikY29tg532rhiTm5p/x4LUmSCfgAMBLMC/WsEOKnkiRZgf8CWUA9cPWgXaYkST8EbgRk4E4hxBuHa2PKjtcqKseIUBRqd2zl05eepbWyHJPZwrwL1zJn1RrCzJbRZYVCT8/7NDb+m77+LWi1EaSkXE162vWEhaUfsg1PwMNzVc/x4J4H6XR3Mid+DtcmXUtveS/V1dUYjUYWLVrEmWeeSXj40QfnCSGoCvlJv1newc6mfiCYGG9lSSIrSxJZmGUdirYVQuBvHsC5rQPXri6EO4DWYiB8XgLh8xLRJxxdX4QQBNrb8VRWjhKjffX1IActISSTCWNBAabCAowFITG6oADtUUaMO21eOurstNfa6Kiz01lvJ+AP6hzhFgNJOVEkZltIyrGQmB2FVje1ROnGHhcby9t5a18Hn9X3ISuCeLORFcUJrCxJ5KzcuKNaUWQPyCNE6GGLjo4RQnSEVkNB+MFCdIpxHEI0QMAL7XtDkdEhMbqnevh6XMEIMXoeJM4A3cEC+2S/Xx+LQD0LeATQAhrgaSHELyRJegyYQ9Diox64ZVCwPhTq4DkJ/O0MMCfB9S+f1G70+ALcua+Rt3vtrImP4k+F6UTpp58AcDLo8vkpG3Cz1+Gm3BkUpatdniF/53CthpIIE6WRYRRFhpFkGC04R+m06oSAisphUAXqyWFKj9k7n4AXb4NL/wlzrhuzyOb+Aa7bVUNOuJHn5uQRPQ3HqN3N/Vxz/xay4yJ46uYzhyJpVFRUVI4XQghe3NnC3a/uo9fp4/qzsvjWyoJj/vvzUZ+Dr5Y30usP8L+5KXwlLQ6NRnPKj9dS8KE9QggxIEmSHvgI+DpwOdArhPiNJEk/AGJCeZ5KgCeBM4AU4C2gQAghH6KJqT1eq6gcBXIgQOWmD/j0pWfpaW7EHBfPgosvZ+bylehNoyfsZdlDe/sLNDY9hMtVg9GYRHra9aSkXINebzlEC+AOuHmm8hkeKnuIbnc38xPmc3X81XTu7aS+vp6wsDAWL17MGWecgcl0dEECAVlha0PfUJLDhh4XALPTolhZksiKkkQKE0f7Kcs2L84dQQuPQJcbdBrCZsQSMT8RY270hGwuFJcLb3X1sBhdWYln/34Uu32ojD4tbShZ4aAYrU9PR9IenYWTHFDobhqgvc5GRyg62tHjAUCjlYhLN5OUYyEpO4rEHAtm6+TYkUwmiiLY1dw/9Hvb3zEAQGGimRUlwQj3WalRaMb5uxgIyOx3eqhweagcGI6IbvP6h8qEaTQURBhDAvSwRUeqUY9mvD8fRYHeGmgeIUa37wEl1E5kIqQuCArRqfODK1HDosdV9ZQRqCcTdfCcBN76OXz8F/hu9UEJok40ihDc19TF3bWtJBn13FeSxfyoo1/yd6oRUAQ1bi/lA272DrgpHwhGRneOmBFLNeopiQxjRmTY0D4zzDD+P0IqKioHoQrUk8OUHrMVBR5cBX118LVtYBo7ouO9Xjtf3F1HUYSJx2fnHDoRyBTm3cpOvvLIVhbnxPLglxZimGJRJSoqKqcO1Z0O7npxL1tqe5mTHs2vLp3BjNRj81j1K4I/1LdzT0MHueFG7i3JZEbIIvB0G68lSQonKFDfBjwKnCuEaJMkKRl4TwhRGIqeRgjxf6F73gB+JoTYfKh6p/R4raIyAfweD3ve3cjW9S/g6O4iLj2TheuupHDxErQH2Ej4fN00N/+H5pbH8ft7MZtLyUj/CgkJF6HRHPp5z+l38nTl0zxc9jC9nl4WJS7ispjLaNvTRnNzM5GRkZx11lksWLAAwzgS+h3IgDfAh/u7eLO8g3cqO+l3+TFoNZyVF8vKkkTOL0okKWq04K34ZDzlPTi3deCt7gcBhiwLEfMSCZsVh8Z0+CALIQT+lpahZIWDYrSvsTHoLwxowsOD/tCFBZgKh72itZHHZiM30OcNRUbbaK+109XoQA6tAo+MMYYio6NIzI4iPiMS3THkLjieePwyH1cHfcDf2tdJl8OLViNxRpaVFSWJrCxOJCP2yFHrshDssrt4p9fBdruTSqeHlhFCtEkjkT9GRHS66Sg0IEfH6Mjolh3gtQWvGSKDAvSgGJ26ACwpcJQ602SP19MvbEhlbIouho/+BFUbYfZhHVWOOxpJ4raMBBZFRXBLeQPrdlTxo5wUbk2PP+0EVntAPkiIrnR68CjBAUEvSRRGmFhutVAaaaIkMozSyDBipmFEn4qKispJR6OB1b+H+8+F934LF/56zGLnWi08NDObr+ytZ+32Kp6anUvWNEucuLwwgd9cPpPvPrub7z+3mz9dPXvKRZqoqKhMb9w+mb++U8W/PqwlTK/l7stmcO3CjHFHhx2KBreX28ob2G53cV2ylV/mpxJxlFF50xkpmI1tG5AH/F0I8YkkSYmDq49DInVCqHgqsGXE7c2hcyoqpyz27k52bdzA7nc24nHYSS0qYcWNt5M9d8FBzzwDziqaGh+kveNFFMVPXNz5ZKTfQHT0GYd8PpIVmU/aP+Hlmpd5u+FtPLKHs5LPYq15Lc27mvlsy2dERUWxevVq5s6di14/sYCGDrtnKNp2U3UPPlkhOlzPeYVBC4glBfFEGke/9wsh8DXYcW3rxLW7C+GV0UYbMZ+XQcS8BHSxYYdt09fcgv21DQy89z7eigoUZyihnSShz0jHVFiE5ZK1Q2K0PjUVaQxbvIkg+xW6mhwh72g7HXU2Bvq8AGh1GuIzzMw4N5Wk7CiScixExkxte7qeAS9vV3TyVnkHH1Z14/bLRBp1LCuMZ2VxIucWxhMdfuRJik6vn3d7Hbzba+f9Xgd9ARkJKI4wsSg6cihRYWGEiYwww9HZr3oHoG3niOjo7WBvDl6TtJBYCjMuD4rRaQuC1h2aqTveqirYqULKXDAnQ8X6ky5QDzIvKoK3FhTwrcomflHTykd9Du4pzpyWnp9HQhaCereXypA1R/mAh70Dbpo8vqEyVr2W0sgwvpQaR2lIiM4LN2I4xgFBRUVFRWUEKXNg/pfgk3th3hcgoXjMYufHWnhuTi6f31PLxduqeGJ2DrOmWYLfqxak02H38IeN+7FGGPjhRUXopljSGBUVlenJOxUd/OSlMpr73Fw+L5UfrS4m7hgTPAG80NHHdyub0EhwX2km6xJiJqG305OQPcccSZKigRckSZpxmOJjKRcHLYWWJOlm4GaAjIyMyeimisoJRQhBU9ludry+npqtnwCQu+AMFlx8OalFJQeV7evbRGPTA/T0vI9GYyI5+Uoy0m8gPDz7kG3U9Nfwcs3LrK9dT6erE7PBzNrstSxgAXU769jatRWr1cq6deuYOXMmunEm+xNCUNnh4M2yoCi9qzkYtZoZG84XF2eyoiSRBZkxYz6rBfo8uLZ34treQaDHg2TQEDYjjvD5iRizow5r4eHv6MDx+uvYNmzAs2s3AKYZM4i69NLhyOi8PDQRx76qXQgxHB1da6e9zkZXkwMlEPxzZLaaSM4NRkYn5UQRlxaJVj/1n01ruoI+4G+Vd7CtsQ8hIDnKxJXz01hZksiiHCtG3eGFXb8i2Gp38m6PnXd7HewZCCZ4jDfoWBln4TyrhaVWM9ajDUaU/dC5b0R09HboqgARyk8WkwUZiyD1jqAgnTQTDNPr3ebUUwpPVzQaKFoT9N/0u0F/+Jm1E0WUXse/S7N4uLWHn1a1sOKzSv5RkslZMce2ZORkIQtBo9s3KmtqpdNNtcuLNxQVLQG54UbmWcL5QkrskEVHokGnRrepqKionAjO/wmUvwgbvgvXv3LIZWvzoiJ4eV4+1+yq4bId1Tw0I5ul1mPPwH4iuWN5Hh12Lw98VMd7lZ38aHUx5xUlqOONiorKUdHa7+bnr5TxRlkHeQmRPHXzmZyZE3vM9ToDMj+qauG/7b0stETwj9JM0k0HRKD5nLDxrmNua7ohhOiXJOk94EKgQ5Kk5BEWH52hYs3AyIxuaUDrGHXdD9wPQYuP49pxFZVJxOdxU/7Bu+x8Yz09zY2YzBYWXnI5s1euxhKfMKqsovjo6HiFxqYHGRiowGCIIyf7m6SmXofBMLbdaZ+nj9fqXuPlmpcp6ylDK2k5J/kcbk65GUO3gaqPq/jU9Snx8fFcccUVlJSUoB3Hyg6/rPBZXS9v7guK0k29QVFyTno0311VyAUlieQlRI75XKZ4Zdx7u3Ft68BbGxSzjTlRmM/LIGxGHBrjodsP9PTg2LgR+6sbcG3bBkJgLC4m/tvfwnLRRRjS0o7Y9/EQ8Ml0NjqGEhm219pw2YJBeDq9hvhMM7OXpwftOnIsRERNjxWJsiLY3tjHW6HklLXdwUjz0hQLd56Xz8qSREpTLEd8nm7x+IaipD/odeCQFXQSLLBE8OOcZJZbzZREhk3cSUAI6G8IRUZvDwrSbbsgEPz3RZg1KEKXrAv5Rs+DiGMfq082qgf1qUTNO/DYZXDtU1B40cnuzUHsdbi4uayBereX72Qn8fXMxKNbxnACUISgyTNCiA5tVa5hew4IekWP9gkKoyDCeFouU1RRmeqcbp6Wx4tpM2Z/9m949dtw1cNQetlhi7Z7/Vy7q4Zql5d7ijO4LHF6RfQJIdhY3sFvXqugrtvJWbmx/HhNMaUpx+YRq6KicvrglxUe+riOP79VhSIEd56fz1fOyZkUf/tdDhe3hd4BvpGVyLcyk9AdGA3Ysg2evxl6apB+bjvlx2tJkuIBf0icDgM2Ar8FlgE9I5IkWoUQ35MkqRR4guEkiW8D+WqSRJXpTm9rC7s2vsre997C53aRkJ3L3AvXUnTWUnQHeD37/f20tDxJU/Oj+HydREQUkJF+I0lJa9FoDhZG/bKfD1o+4OXql/mg5QMCSoDi6GLOiziP6P5o6qvr8Xg8GAwGCgoKmDlzJvn5+WiOsMLZ4fHzfshP+t2KTuyeAAadhiV5cawoSeT84gQSzGPbWAhF4K2z4drWgXtvN8KnoI01ETEvkfB5CegOY38h9/fjeOst7Btew7llCygKhtxcLKsvwnLRaow5h44aHw9CCBw9nlFWHd1NAygh/cMSZxryjU7KsRCbFol2Gq3cc/kCfLA/6Cf9TkUnvU4feq3EmTkhH/DiRFKjDx/o6VUUPul38k5vMEq60hlM9Jhq1HNerIXlVjPnxJixHCHa+uDO9Y7wjA5trp7gNZ0JkmePTmQYk3XUvtGTiZokUeXQBHzw+zwoWQvr/n6yezMmAwGZ7+9v5rmOPs6JjuTvJZkkGk9ecipFCJo9B0ZEe6hyenErylC5lJAQXRASoovCg8eRE/3Do6KictJQBerJYdqM2YoM9y8LPvB99TMwHH5Zo80f4Po9dWyxOflFXgo3pycctvxUxC8rPL6lgb+8XUW/288V89L4zgWFByXdUVFRURnJ1vpefvzCXio7HKwoTuCna0tJtx77suDBxOm/rm0j3qDjb8VjrKKUA/DR/4P3fwORSXDZP5Fylp3y47UkSbOARwAtoAGeFkL8QpKkWOBpIANoBK4SQvSG7vkxcAMQAL4hhHjtcG1Mm/Fa5bRDKAp1O7ex44311O/chkaro+DMs5l74VqS8wsPilp1ueppanqY1rZnURQ3VusSMtJvxGo956CyQgjKesp4ueZlXqt7jX5vP/GGeC6IvIDEgUTa6trw+XyYTCYKCwspKSkhJyfniP7Srf1u3t7XwcbyDrbU9uCXBdYIA+cVJbCiOJGlBXGEH8bKNNDtxrm9A9f2TuR+L5JRS/jseMLnJWDIPHSkrjwwwMA772B/dQMDmzaB348+I2NYlC7IP+pVc36vTGeDfVR0tNsRTNynM2hIzLKQmBNFUraFxOwowi0TTw55sum0e3i7opM3yzv4qLobX0DBYtIFf28liSwtiMdiOvzvvt7t5Z2QbcdHfQO4FQWDJLE4OpLlVjPLYy0UhBvH/3vwu6Ft92gxuq8udFGC+CJImx9KYjgfEkpAOzUTuqsCtcrhee4rwUjq71RNWfNzIQRPtffyo/3NhGu13FOcwaKoY/dDOhL9AfmgiOj9Lg8ueViITjIcGBEdFKInPAOmoqIy5VAF6slhWo3ZjVvgwVWw5NtB248j4JEV7tjXwKtdNu7ISOCunORpaZVhc/v5+7vVPPxxPVqNxE1Lc7hlaQ4RRtXZTUVFZZhep4/fvLaPp7c2kxJl4meXlHJBadKk1N3l83Pnvkbe7XWwOi6KPxalH5wEvLcWnr8Fmj+FGVfCmj9CWLQ6Xk8S02q8Vjkt8AwMsPe9N9m58VVsHe1ExliZtfIiZp1/IRHRo1evKUqAnp53aGl5kp7eD5EkPUlJl5CRfgORkYUH1d3h7GB97XpernmZWlstYYRxXth5ZLgz6Gvuw+/3ExYWRnFxMcXFxWRnZx/WW1oIQXmbfSjJ4d4WOwDZcRGsLElkZUki8zJi0B7GG1rxBHDt7sK1rRNfgx0kMObHEDEvgbDSWCT92BqD4nYz8N572De8xsD77yN8PnTJyVguugjL6tWYSksm/HwqhMDW6aajLhgd3V5no6fFiQhFR0cnhpOYbQlFSFuITYlAM42iowcRQrC/Y4C3QpMJu5r6AUiLCRv6vS3MsqI/zHdzyQqb+gd4t8fOO7126txBS5OsMAPnWYNR0mfFRI5/1bwcgNbtUP021LwNrTtACQSvWdKGo6JT5wdz6Rinj92gKlCrHJ6yF+CZL8GXX4PMs052bw5LpdPDLWX1VISWRZxIEgy60UJ0KINq1NEa1quoqEx5TocXXkmSHgQuBjqFEDNGnP8a8FWCUVevCiG+Fzr/Q+BGQAbuFEK8caQ2pt2Y/fwtUPY83L4FYnOPWFwWgh/tb+aR1h6uSorhT4UZ6A/z8jGVaep18dvXK1i/u414s5FvryzgqgXph32ZUlFROfVRFMEz25r4v9cqGPAEuHFJNl8/P/+w0X8T4Z0eO3fua2RAlvl5XipfTIkdLaYIATsfh9e+D5I2KEzPumro8ukwXp8Ipt14rXLK0tVQx4431rPvw/cI+LykFpUy98KLyVu4GO0BIrHH00pr69O0tj2D19uO0ZhESvLVpKZeh9EYP6qsy+/inaZ3eLn6Zba0bUGraFmsW0yuNxdXuws5IBMREUFxcTElJSVkZmYe1lfaF1D4tK6XN8vbeWtfJy39biQJ5mXEsKI4KG7mJRw+l5ZQBN7qfpzbOnCX9UBAQZcQRvi8RCLmJqA9hEez4vPh/PBD7K9uwPHeewiXC218HJZVF2JZvZqwObORjmA9Muq7eAJ01tuHrDra6+x4BoLR0XqTlsSsYTE6KTsKU+TUjNAdDwFZ4dP6Xt4q7+StfR009roAmJ0ezcriBFaWJFGQOLYPOARF7SqXl3d77bzT42CLbQCvIgjTSJwdY2a51cx5VgvZ4RPw17Y1DwvSte+BxwaSJugVnb0U0hYEBWnz5EwKnywme7xW1bhTjbwVoDVAxatTXqAujDCxYX4Bz7b34hwRxXy8iNBqhiw6DorgUFFRUTk1eBj4G/Do4AlJkpYD64BZQgivJEkJofMlwDVAKUE/y7ckSSo4nJ/ltGTlz4Nj4us/hP95+ojFtZLEbwrSSDLq+W1dO92+AP8uzSJiGq6kSbeG87fr5nHDOX38an05P3h+Dw9vqudHq4tZWhB/5ApUVFROOfa12bnrxb1sa+jjjCwrv7x0BoVJkxOt5VUUfl3bxn1NXRRFmHi2NJeiiAP8PJ09sP7rsO8VyFoCl/4TotPHrlBFRWXaIgcCVH+2hZ1vrKd53150BiPF5yxjzqqLScjKGVVWCJmeng9oaX2K7u53AEFs7FIKC35GbOxyNJrhd3dFKGzr2MbLNS+zsX4jfq+fUrmUawLXEOgKoCgKmGHB/AWUlJSQnp5+SE9pWRHsa7PzSV0vn9T2sLm2B4cngEmv4Zy8eL5+fj7LixKINx9ZmPR3unBt68C5oxPF7kMK0xGxIJGI+Yno08YWR4Xfj3PLlqAo/fbbKA4H2uhootauxXLRRYQvXIA0gdxWA31e6nZ1UbOji9aq/qHo6JikcLJmxZEUipCOSY5AM82DFQZ9wN8q7+Ddyi5sbj8GnYZz8uK4dVku5xcnkGg5tMWdIyDzYZ+Dd3sdvNNjp8UbFO8Lwk18KTWO86wWFkVFYBpvFLnfDQ0fQ/U7QVG6qyJ43pwCxWsh93zIORfCx07iqRJEVelONYzm4D/8fa/ABb+aEsbphyNcq+GLqXEnuxsqKioqpwRCiA8kSco64PRtwG+EEN5Qmc7Q+XXAU6HzdZIkVRNMvrT5RPX3hGBOgnN/ABt/DJWvQ+GFR7xFkiS+mZVEgkHPdyubuGJnDf+ZlUPcJEUXnmjmZcTw3G1nsWFPO795fR9ffPBTlhXE86PVxZMmTKmoqExtnN4Af35rPw9+XE9UmJ7fXzmLK+enTZqNUY3Lw21lDewecPPl1Dh+kptC2IEv9tVvwYu3B3MDrPwlLP4qTCAiUEVFZerj7O9jz9tvsOut1xjo7cESn8jSz9/AjOUrCYsc/czh9XbS2vYMra3/xeNpwWCIIyvzFlJSriEsLG1U2UZ7Iy/XvMz62vV027rJ8mRxYeBCNP0ahCIIiwqjZFEJJSUlpKamjilK+2WF3c02Pq3r5dO6HrbW9+HwBq0WMqzhrJ6RzIqSRM7JiyPMcGRhWHH5ce3qwrm9E3+TAzRgKrASvjaBsOJYpDGSzApZxvXZZ9g3vIZj40bk/n40ZjPmFSuwrF5NxJmLkI7ghz0SW5eLmh1d1O7ooqMuaEMSkxTO3AsySM2PJiHLgili+kZHj2QsH/CYcP1QdPuS/LhD2tkJISh3engnZNvxmc1JQECkVsPSGDPfyDJzrtVCummcPttCQFdlUIyufgsaNkHAA1pjMFB07ueDonRC8ZTX5KYS0/NNS+XwFK2Bqo3QUQZJM45cXkVFRUXlVKYAWCJJ0t2AB/iOEOIzIBXYMqJcc+jcqceiW2D7o/D694OTuPrxJQ38n5RYYvU6bi2v55LtVTw5O4fMsAks75tCSJLEmlnJrChJ4NFNDfz1nSou+ssHfG5hOt9cWXDIbPMqKirTGyEEb5S18/NXymmzebj2jHS+t6qImIjJSXYlhOC/7b38qKoFoyTx8IxsLoyPGl3I74Y3fwKf3g/xxfD55yBp5qS0r6KicvIRQtBevZ8dr79C5eaPUOQAmbPmsuIrt5M9dwGaEbmxhFDo69tMc8sTdHe/hRABYmLOIi/vh8THnY9GM/y3yeV38Ub9Gzxf9TwVbRWkulKZ65+L0WEEAVarlZKzgqJ0cvLBeUM8fpmdTf18UtvLp/U9bG/ox+0PLhTMjY9g7ZwUFmVbOSPbSnLUAas9DvVdZQXP/j5c2ztxl/eALNAnhRO1JpvwOQlozQf/bRWKgnvnTuyvbsC+8Q3krm6k8HDMy5djWbOaiHPOQWMY399kIQS9bU5qdwQjpXuaBwCIzzCzaF0OOXPisSYf//xexxtFETT3uSlvs7G3xc67lZ2UtQ77gH/57Owj+oD3+QO83xuMkn63106nLzgZMSMyjNvSE1hutbAwKmL8Vn7uvqBdR/Xbwbxv9pbg+bgCWHBDUJDOPAsMx55k+HRFFahPRQpXwyvfCC5pVgVqFRUVldMdHRADnAksBJ6WJCkHGOtpbMzEFJIk3QzcDJCRkXGcunkc0eph9e/g0XWw+a+w9LvjvvXC+Cienp3LF/fUsXZ7FU/OzqU0cnwvMVMRo07LTUtzuHJ+Gve8U8Vjmxt4eWcrty7L5StLcsYVMaSiojI9aOxx8dOX9/JuZRdFSWb+dt085mfGHPnGcWIPyHyvsokXO/s5KzqSvxVnkHJg9FnrTnj+ZuiuhDNvh/N/Ou5JQhUVlalNwOejcvOH7Hh9PR21VRjCwpi98iLmrFqDNWV0BLTP10Nb23O0tD6J292IXh9DevqXSE25hvDw7KFyQgj2dO/h+arnebP6TWJtsRR4Csh1BvOIxMfHUzwn6CmdmJg4SpR2egNsa+jj07pePqnrYVeTDZ+sIElQlGThcwvTWZRtZWG2lbjIiQUc+NqcuLZ14NrZiTLgRxOhI/LMZMLnJaJPiThIHBdC4Nlbhn3DBuyvvUagvR3JaCRy2TIsqy8ictkyNGHjFMWFoLPBQe2OLmp3dtHf4QIJknOjOPvKPHLmxGOJm77Pph6/TGW7g/I2O/uGNgcDoeh2TcgH/AcXFbGi+NA+4IoQ7HS4eLcnKEhvt7tQgGidlmXWoJf0cquFROM4I8oVGVq2D0dJt2wDoYAxCnKWwbLvBUVp1aZq0lCTJJ6qPLAK/C649cOT3RMVFRWVKcHpknQpZPGxfjBJoiRJrxO0+Hgv9LmGoFj9FQAhxP+Fzr8B/EwIcViLj2k9Zj/9Rdi/Eb762YQfJiucbq7bVYsjIPPwzGzOjjk1rDHqup385rV9vFHWQZLFxHdWFXL53NRp702oonI64w3I/OuDWv76TjU6jcQ3VxbwpbOy0I3XS3McbLM5ubW8gVavj+9mJfG1zES0IwUaRYZN98A7d0NEHFz6D8g9b1x1ny7j9fFmWo/XKlOazvpa9r73Jvs+fA/PgANrajpzV11MydLlGMKGo0eFEPT3f0pL65N0dr6BED6io88gNeVa4uNXodUOi8R9nj7W167n+f3PY2u3kevMJcWZgqRIxMfHM2PGDIqLi0lISBi6x+bys7WhN+ghXdfL3hYbsiLQaiRmpEYFo6OzrCzMshIVPnGbC3nAh2tnF65tHfjbnKCVMBVZiZifiKkwBumAv6lCCLz792Pf8Br2DRvwNzWBXk/k2WdjWbOayOXnoY0cX3Szogjaa/qH7DsG+rxoNBKphdHkzE0ge3YcEYdIuDiV6XR4KG8NCtCDgnRt1wAhu2wijTqKk82UJFsoTrZQkmKhINGMST92AEWXz897oSjp93rt9PplJGCOOZzlsWbOt1qYYwkfPT4dDltLSJAeTG7YD0iQOi8oRuetCCY31KqxvqAmSVQZL0Vr4M3/hf5GiJ6G0W4qKioqKpPFi8B5wHuSJBUABqAbeBl4QpKkPxFMkpgPfHqyOnlCuODuoEC98cdw9aNHLj+CoogwXpmXz7W7arl2Vy1/L8lkbUL08ennCSQ7LoL7vrCAT2p7uHvDPr7zzC4e+riOH68p5qxcNUeEisp0Y1N1N3e9tJfaLierZybxvxeXjHvp+niQheBvDZ38rr6NFKOBl+bmsyDqAMGlrwFeuBUaN0HJOrj4z2piKBWVaY5nYIB9H7/H3nffpLOuBq1OR97Cxcw8bxUZM2ePiiD2+/tpa3+BlpYncblq0OkspKZeS2rqtURG5A+VU4TCJ22f8HzV82yu2UyqPZUSVwl6nx6jycjMeTOZO3cuKSkpSJJE94CX1/a0DQnSFe12hACDVsPs9ChuW5bLGdlW5mXGEHkIL+IjIQIKnopenNs68FT2gSLQp0YSvTaHsDkJaMfwc/bW1gZF6ddew1dTA1otEYsWEXfrLZhXrEAbFTVGSwcjBxRaKvuo2dFF3a4u3A4/Wp2G9BIri9blkDUzbtr4SQdkhdpuJ/va7JS32ofE6O4B31CZ1OgwSlIsrJ6ZTEmyhZJkC2kxYYcNkggogm12ZzC5Ya+d3Q43AHF6HedZLZwfa2FpjJnY8eaN8XuCyQ1r3gmK0l37gufNyVB0MeSdBznL1THsBKEK1KcqgwJ1xQY489aT3RsVFRUVlROAJElPAucCcZIkNQM/BR4EHpQkaS/gA64XweVTZZIkPQ2UAwHgDiGEfHJ6foKITocl34Z3fxWMisg5d0K3p5oMvDQvj+v31HFzWT13+1K5IS3+uHT1RLMoJ5YXbz+bV3a38rvXK7nuX5+wojiBH1xUfMillCoqKlOHLoeXu18t58WdrWRYw3noywtZXphw5BsnQJvXx1fLG/m4f4B1CdH8vjAdi25EVJsQsPu/sOG7weNL74XZ16gJolRUpilCUWjcu5u9771J1aebkP1+4jOzWf6lWyhecu6opIdCCOz2HbS0PElH56soiheLZS7Fxb8lMWENWu3wRFm7s50Xq1/k5f0vI3VI5DnzWOFeAUBubi5z5swhv6CQpn4vHzf1s+OTvXxa10NNlxMAk17D/MwYvnF+AYtyrMxJjz5khO0Rv6MQKA4fvlYnnspe3Lu6UFwBNGY9keekEDEvEX3SwVHPvubmoUhpb0UFSBLhCxZg/fz/YL7gAnSxseNq3++TaSrrpWZnJ/W7e/C5A+iNWrJmxpIzN4GMUisG09SW7ewePxVtDspbbUOR0ZUdDnwBBQhOIBQkRbK8MIGSlGBkdHGSZdxR7W1eH+/2OHi7186HfQ7sAQWtBAssEfwwO5nlsWZmRIahGc9YIwR07w+K0dVvBcXpgAe0hqB/9JzrIO98SChRx66TgGrxcSrz9zODS+q+tP5k90RFRUXlpKMuGZ4cpv2Y7ffAPxYFs2zf9nHQn3qCuGWFW8vreaPbzjcyE/l+dtJB3oPTGY9f5sGP6/jHuzW4/TLXnZHBN1bkEztBv0YVFZVhhBD4ZYGsCAKKQkAWBJTgZ///Z+/Ow+O66vuPv8/smtGM9n21ZMv7GttxEoc4e0gCCYGEhL1QKFsLFMraHwXatGwtpaUtpCyFskNCoAlZTZzEcRbbidd4lS3ZsnaNllk06z2/P+5IGjmyLdtaRtL39TzzzJ07d67OkWUdzWfO/Z6kkdo/8lzytO24oUme4XFnIMp3n24kGjf44FV1fPjq+Rcc1pzJY939fOLgCQaTmn9sqODu0vzRv/fCfnj4r2H/76D6MnjT9yCv5oK+lozXE2PGj9di2vR3drD/6SfZ//RmBro6cXmyWbRxE8uuvp6SefWjjk0kArS3/55Trb8gGDyI1eqhtPQ2KsrvwetdMnxcPBnn6Zanuf/w/Rw6foiqgSpqwjVYDAt5+XnULFxJMq+KIz1xdp/sY++p/uEaxF6njUtq87h0XgHr5+WzvCIHh+38SxbppCbRHSbeFiLWGiLeFiTeGsIIxc0DbIqsJQW4LynBNT8PZR39t128vZ2BRx5l4JFHiOzZA0DWypX4brkZ7403YS8Z34eC0cEEzXu7OfZKF837e0jEDJweG/NWFlG/qojKxXnYJvh3+ETQemjhwoHhmdEH2gc46R8cPibf4zBnQ5f7UqU6cqgr8mA/zxJTffEED3X1c3+Hnxf6QmigzGnnmlQd6SvzssmxjzO4H+wzJ6Y0boajf4KBFnN/wQKzZMf8a6HmClnc8AJIiQ8xfotuga3fMv9glEsShBBCCHNxrpu+Br94K7zwn3DFx877FFlWCz9YOo/PHD7JvzZ30BGL842GKmyzpG6zy27lw5vmc9faKr795BF+/tIJHnzlFB++ej7v2FCN1zUzLi8VM4fWOi2g1SSSBvHk6CA3kTRS95r48P6R+6HwN55Me27o+LQAeOR5M+AdCY3NrzFWUJweBg+1MXm2kDnV1mTq6ycMY7i+5mS5Yn4BX7ltGfVFE3vFw2DS4CuNrfzoVDfLs7P4r6U1zHeftsjhsS3wuw9BqBOu/SJc8XGwZF64IoQ4s3gsytHtL7DvT49zYt9uUIqa5au48m3vYf7aDdgcIwugGkYUv38bnZ2P0Nn1CMlkGG/2UhYt/AdKSt6AzTbye+hY/zF+d+R3PHbwMXw9PupD9WyIXk2fxYe/aB591lyO9MToeCoEHMRuVSwu8/Gm1RWsrMplVVUudYWe814bw4gmiLeFzFtriFhbkHh7GFKzerEq7KUeXIvzcZR5sJdnYy/3YDmtNEiiu5uBxx5j4I+PMLhzJwDOJYsp/tQn8d70ehyVFeNqz2AgxvE9Zih98qAfI6Fx5zhYdFkZdauLKF+Qi3UC1wm4WJF4kiMdQV5tS82KToXRgYj5oYFSZpm4lZW53L2umiXlZomOYq/zgidtRJIGT/QM8EBHL5t7BohpzXy3k0/VlnJzUQ6LPK7xndtIQusr5izpxs3Qsj21uKHPXNzwdZ8yQ2kphZtxZAb1bHbqZfjvq83L61bdM92tEUKIaSUzsibGrBmzf/E2OPIYvOv3ULvxgk6htebrx9v5VnMHNxT4+O7SWtwZ9OZiohztDPBPfzzI5oOdAFTmZbGwxEtDqde8L/FSV+SZ8BmbYnokkgb+cIzuQIyeUJSeYIzuYJTuYIxAJJ4W0hqjwt/4UDCbFg6b4a9xWqg8clzcSL02ObXvRywKbBYLNqvCalHYrRasFoXNorBZFTbL6MdWiwW7xTx26HmbZezXWlPPmcelXpv6Oub+kdemb9vTX3uWr2tNO/fQY6fNQtFFhAJncigU4YP7mzgQivAXlUV8vr4MpyXtd1w8Apu/Ai/8BxQ2wB33Qfnqi/66Ml5PjFkzXotJo7Wm83gje596goPPbSEaCuErKmHZputYetW1+IpGZgQnEiF6/M/Q1fko3T1bSCaDWK3ZlBTfTEXFPXi9y4d/B4XjYR5vfpwHDz1I+/Eu8vtXkByspNvIpt+WS0/MxtBvfTPkzBkOoxeX+c7r7wmtNcZAjFhbiHhrMBVIB0n0RIaPsbhtZgCdCqIdZR5sRVmvWeRwSLKvj4EnnmDgj38k/OJLYBg45tfju/lmfK9/Pc5588bVtmBvhGO7zEUOW4/0oTX4Cl3UrSqifk0xJbU+VAZMbugORlMLF47Uim7sCpFMfbLqdljNshypGdGLy7wsLPXiHm+d57NIas1zvUHu7+jlj119BJIGxQ4bbyrO447SPFZkZ41vbBtoHQmkj22BwV5AmWPS/KHFDdfK4oYTTGZQi/ErXw3ecjj4kATUQgghRLo3/Rd8/zr41Tvh/X+C/PG92UinlOIzdWWUOO187nALd+06yk9W1JE/3ksOZ4j5xV5+8J517Gjy88KxHg51BDncHuCZI13DwaLVoqgpcA8H1gtLzfvaAje2WRjazyRaa0KxJD2pkLk7aIbO5uMo3SFzeyiI7g3HxzyP3arwuuzYhwLatJDUZjXD1qEw1e2wjQpybVYzyE0PgIf2WS0K+1BYm3ZOe+qcQ+HtyLlSr7WkPXfauUftS71mKCS2WyznPRNvrtFa87+tPXzx6CmyrVZ+vqKOawp8ow9q3wcPvB86X4V174frvyKXRwsxQ4QH+jm41VzwsOtEEza7gwWXXs6yq6+naslyVOqDqHi8n+7uzXR2PYbf/yyGEcVuz6ek5BaKi24kL+8yLBZzZrXWmn1d+/jhKw+x7dVTWPrnEYvehN/wYGCeL89tY011/nAYvaIyh1y344ztPJ1OGiS6BkeH0W1BjFBi+BhrgQtHmQf3mhLs5WYgbfU5zhlyJnp7CT79NAOPPELouW2QSGCvqabgLz6A7/Wvx9XQMK429nWGOfZKF8d2ddFxfMDsd5mHS15fS93qIgors6etLFzS0BzvDvLq0IzoVCDdFYgOH1Oe42JxmY8bl5ayOLVwYXW+e0LHTa01e4KDPNDey4OdvXTEEmRbLdxSlMubS/K4Ii8b67m+R/GIuRDv0c3mAoedr5r7s0th4c1Qn1rc0DO+WuAiM8yud1BiNKXMMh+v/BRiYfmjUQghhBjiyoF7fgnfvxZ+cTe87wlw+c79ujG8p6KQQruND7/azG0vH+EXK+updI3/DddMsbY2n7W1IyXD4kmDpu4QhzoCHG4PcKgjwMH2AI/ub2foAj2H1UJ9cTYLS7JHzbiuyD37Ku3i7BJJg95wfCRsDqWHz6NnPfeEokTixpjn8bpsFGU7Kch2UF+UzaV1+RR4nBRmOyjMdlKQeq4w24nPZZtVtdbF2HrjCT516CQPd/WzKc/Lvy2uptiZVtbHMMwZ05u/Aq5cePtvYcH109ZeIcT4GEaS5t2vsO+pJzi640WMZIKSugVc+74Ps+iK1+HymGU5otEuurqfoKvrcXp7n0frBE5nKeXld1NcdCO5uWtRypzh7A/F2Np4nN/te5mXm/wEAwUk9XJgOTaSVGcrrqkr5HVLq1lVnUdF7jhnw2LWi453hIidDBA7GTDD6I4QJFJ/YNiGSnQU4EiV57CXerCMc0HBhN9PePsOwi+9RHj7dqKHD5unLS8j/93vwnfzzbiWLDlne7XW+FtDNL5izpTuORUEoLjGy4bb66hbVUTeGIssTrZgNMHBtBnRr7aaCxcO/T1gtyrmF3t53YIic2Z0qkTH+XxgcL6aBqM80NHLAx29HA1HsSvFtQVe7ijJ5/oCH1lnm9CgNXQfSdWRfhKanoPEoLm4YfVl5oek9ddCyVJZ3HAGk4B6tlt8K2z/bzj2lBlWCyGEEMJUUA93/QT+901w//vMwPoC66beWpxLvt3Gu/ce4w0vH+HnK+pYnJ117hfOYHarhQUlXhaUeGHFyP7BWJLGriCH2gMc7jCD65eO+3lwV+vwMR6HlQUlXhpKsodnXC8s8U5KmYKZIhxL0B2I0Z1WVuM1s55TQXRvOMZYVfpsFkVBtoMCz0joXJDtoCDbmQqcHRR6nBR6HeR7HDhtUpZFjHi+L8hHXm2mK5bgi/XlfLCqCEv6/8f+FvjdB6HpWVh0K7zh2+aC7EKIjNXb3sr+LZvZ//STBP09uLw+Vt14C8s2XUdRjXn12ODgKU6c/A2dnY/S378T0GRl1VBd9T6Kim/E512BUopoIsnTRzr4w97DPN/YR5t/6MMrJ3kql3pLP/X5Nq5dOZ+bLltJtmd8E+S01iR7o8NhdKwlQPxUEJ0KU4dKdGRfXo6jzAyjbYXu1yxieDaJ7m7C27cT3r6d0EsvETvaCIDKysK9ehW+mz+GZ8MGXCtXjiuU7mwKcGxXJ40vd9HfNQgKyupz2HjnAuatKsRXMPV/A3YHozyyr52H97Ty4nH/8N8JuW47S8p8vOPSmlSpDh/zi7MvaKHJ89UVi/OHzj4e6Ohl50AYgMtyPXywqphbinLIO9tVh8FOOP4MHH8aGp+C/pPm/oL5sOZdZtmO2ivAMfUfAIjJccE1qJVSLuAZwIkZdP9Wa/13Sql84FdALdAE3KW17j3buaQ+1iRKxuEb9eYfkbf/53S3Rgghpo3UtJwYs3LM3vFDeOgTcNlH4cZ7L+pUrwYHuWd3IxFD8+Pl89iQO7ELls1kA5E4RzqCZmidCq8PdwToDsaGj8l126kvysZln/w3TRZllpiwp0pNOKwWHDZLap8Fu03hHN427x02Cw6rGj7GfGwZPo8j7bihfaFoMjWbOT14NsPmruBIeY3BeHLMdnqdtuFZzOlhc2EqiC4c3ucgJ8s+ZwN+ceEShuZfmtv516YOarIc/NeSWlb5TguW9v4WHv5rSCbg9V+F1e+ctFlqMl5PjFk5Xs9gyUSCcH8fob5etDH2VS0Tyd/awr4tT9Dy6j6UslC7ag3Lrr6e+kvWY7XZCYWO0dX1KJ1djxEI7AMgO3sRRUU3Ulx0Ix5PA1prtjUf58E9B3nx2ACnutwYhg2FQa61l2pLmDIVpMKTZMOaNaxatYqioqJzts0Ix4m1BEcF0kYwVV7KpnCUZ+Oo8g7frPnjXBwvTbyz0wykXzJD6dixYwBY3G6y1qzBvW4d7vXryFq6FOU496xhw9C0He2j8ZUuju/qItgbxWJRVCzKo351EfNWFuH2Tf3Vc/5QjEf3tfPw3laeb+zB0FBX5OHmZWWsqTFrepf6zv/7dzFCiSSPdvdzf0cvT/cGSGpY4nFxR0kebyrJo+JMVxkO9kHzc6lQ+pmRsh1OH8x7nVlLuv5ayKuZsr6Is8ukGtRR4BqtdVApZQe2KqUeAe4ANmutv6qU+izwWeAzE9BWcSGsdmi4CQ49Yv5BKUXhhRBCiNHWvhc6D8Dz34GiheasjAu0JDuLhy5p4J7djdy9u5H/WlLD64tyJ66tM5jPZeeSmjwuqckbtb87GDXD6vYAhzqCHO8OEj1DSYqJlNR6eJG/WMIgljSIJw3iST38OJaY+HZYLYoCz0ioXFfoGX5ckO0YLrlRkO2kwOOQxSfFpDoZifGRV5t5qT/EXaV5/OOCSrLTZ9YP9sEf/wb2/hoq15kLIebXTVt7hcg02jAYDAwQ7PUT7O0h6O8h6PcTGnrca26H+vsY89KXSZRbUsbGu9/FkquuITuvgGDwAE0n/p2urscJhY4A4POtYn79ZygqugHlKGHbib18Z8sOdhx7mtbOHBIJ84P2bGWjwdJDuX2AUusAFSUF1FbXsmDBAurr67Faxx6rdMIg1hokfjIwHEonugfNJxXYirJwLczHUZWNo8qHvdR9xsULzybe3j4SSL/0ErHmZgAsHg9Zay8h94434V63zizbYbef42ymZMKg5WAvx17p5PiebgYDcax2C9VL8tlwWx01ywtxecZ3ronUF47x2P52HtrTxrbGHpKGprbAzYc3zefWlWUsLPFO+YfVcUOzxT/AAx29PNo9wKBhUOG08+GqYu4oyRv7qsJYCE48PxJIt+0GbYAtC2ougxV3mcF06UrJseaIC/5X1ubU62DqoT1108BtwKbU/h8DW5CAenotugX2/ApOvgC1G6e7NUIIIUTmufGfzNp2D/015NeblwxeoCqXg9+vXsA79x7jffua+PS8Uv6ypuTcC77MUYWpGcGX12deqQCtNQnDDLHjCW2G1kmDeMIMs4dC7PiYQbf52OO0UeBxUuQ1ZzznZNml/rbICH/o7ONTh05gaPjPJTXcUTL6wyOOP2uW9Ai0wdVfgI1/LSGBmDO01kTDITNwToXMY273+TGSr70Cxp2Tiycvn+y8fErm1ePJKyA7Lx9Pbh5W2+T/P3J5vZTUzWcgsIv2zvvoPPg4kchJwEJu7joaFnyRuHsZu/1t/M+eRnYd/zXd3UVEY8VALg6yqbAMUGE7Tq07wrLacqqqFlBVVUV5eTmOMWYda0OT6BkcmRmdqh1NakFli9eBo8qL+5ISc3Z0Zfa4a0afLt7aSihVPzq8fQfxEydSX8OL+5JLyH3rW81AevEi1Hl8v+PRJCde7eHYK1007ekmFklid1mpXV5I3aoiqpfm47jANl+M/sE4j+9v5+G9bWw90k3C0FTnu/mL19Vxy4oylpT5pjyU1lqzYyDM/R29/KGzF388SZ7Nyp2ledxRksf6HM/oMlGJKLTsGAmkW7aDEQeL3fwA9HWfNgPpyrVgc05pX0RmuKj/Wcqsjr8TmA/8h9b6RaVUida6DUBr3aaUKp6AdoqLUX8tWJ1w8GEJqIUQQoixWG1w54/g+9fBr98J7/8T5NVe8OkKHDZ+s6qeTx48yVePt/OUP8C/L66mOkv+4J5JlFLDJUCYfeteijkqlEzyd0da+WlbD6u9br67tIaa9N9NiSg8dS8892+QPw/e97gZGAgxSyQTCUJ9fgI9QzOeuwn0dI8OoHv9JGLR17zW6fGQnVeAJy+fqvLlZOePBM/uPB+eXC8urxtl1WgjhmHEMYwYWpv3hhEDomhtAAZaG2gMGL7XI4+1Aei0Y/Vpr9FnfG1/pIWj254gFutEKTs5uRsg7/UcjGbxbHMrB54+QLgvTChajsF8FAbFKkS5rYWlhRYunV9OddUKqqqqyMvLe034qQ1NMhAjfipIrGUokA6iIwkAlMOKozKb7I0VOKu82Ku82HIu7G8grTXxU6eGZ0eHt28nfuoUAJacHNxr15L3tnvwrF+Pc+FC1Blmc4/FMMxFDtuP9XPygJ8T+3pIxA1cHjv1a4qpW11E1aJ8rFNQeux0A5E4T77awcN72njmSBfxpKYyL4v3XTmPW5eXs6xi6kNpgMOhyPBihyciMVwWxY2FOby5JI9N+V4cltT3KpkwZ0Uff9oMpE+8YC5sqCxQthIu+4gZSFdvkDrSArjIgFprnQRWKaVygd8ppZaN97VKqQ8AHwCorq6+mGaIc3FmQ/3VcPAhuPEfZVVTIYQQYixZeXDPr+D718DP7zZDGZfvgk/nsVr5ryU1XFfg47OHW7h2+yG+2lDJm0vzJ7DRQggxfvuDg3xwfxNHw1H+srqYT88rw54+o7/zADzwfmjfC5e8B26413wvIcQMEY9FU6FzD8GebgL+nlT43E2wt4twqINYtAebK4HNlcSWZd7bPeDMteErtZPvtGF1WrHZLVhsYLEplFWjLAZad6GNVgxths8JI0avjuMPJyAMnJru74BJWVzEXQs5ziKeao1xYmc2iZCLQKSciK4HwKcGWezoY3WZi9ctLGPBvGVUVFQMz47WSU2yL0L0SB8J/yCJ7giJnkES/giJnggMlcGygL3Eg3tlIY5Ks260rdiNuoCrhYxolNixY0SPHCF65CjRI0eIHDxIor0dAGtuLu5168h/97txr1+Hs6EBZRl/eBwJxek4PkD7sX7aj/XT0TRAPGLOgPfkOll8eRl1a4opn5+D5QJKjVysYDTB5gMdPLSnjacPdRFLGpTnuHjP5bXcsqKclZU50xJKt0Vj/K7DXOxwX3AQC/C6PC+fmlfKzYU5Zmkow4CuAyMzpJueg2i/eYLiJXDJu81AuuZy829uIU4zIdcmaK37lFJbgJuADqVUWWr2dBnQeYbX3AfcB+YCDhPRDnEWi26Bw49Cxz4oXT7drRFCCCEyU+F8uPPH8NM3w/1/Dvf8AiwXXgNYKcVbSvNZl+PhLw+c4CMHTvBkzwBfbagk52wrlwshxATSWvODU9185WgreXYrv15Zz5X53pEDDANeug+e+CI4vXDPL2Hh66evwUKMIRoOE/T3EPB3E+juItDbSqj/FOFwO5FwJ7G4H01wVPBscyWx5RtklSfJdiTOeO6IthHTFsIoDK1IokhqSMQVRkyRGHoMJLUiYSjiWpHQTuI6i7ihSGhF3LCQ0BbiGhJJC3HSj7WQSJoVLzTA8L1CD20bCo0l9UCZz6GGt8370c8P37CgUsckY3kkQgsYiFbSZ5j/1x0kqM2KckmF5urFZVyyqJY8by7JvqgZPJ+KENpzgv6eCMmeQRK9UTBGohplt2DNd2EryMLVkIetIAt7qRt7eTYWx/n9raTjcWLNzaOC6OiRI8ROnDB/HwHY7Thra3GvWUPWJebChs7588cdSGtD09seHg6j24/109seNvtiURRUeFh0aSkldTmU1uXgK5zaxQSHhGMJNh/o5OE9bTx1qJNowqDU5+IdG2q4ZUUZq6typ6U0WH88wcNd5mKH2/qCaGCV183fz6/gtuJcih028B+DXQ+mQulnIdxtvjhvHiy9HequgtorIVsKK4hzu+B3RkqpIiCeCqezgOuArwF/AN4NfDV1//uJaKi4SA2vNy+lOPCQBNRCCCHE2dRfDa//GvzxU/Dkl+CGv7/oU9ZkOXlg1Xz+/UQH32xq56X+EP++uIbL82RmohBicnXHEnz8oPnh2PUFPr61qJpCR9rbwIE2ePBDcOwpc3H1N/67hAliWmmt6WtvpenAZjpbnyEcacQggMUeGxU8W/I0ljzIxrwNSRowmLQQSEK/oeiOZtPr9xGI5BKI+ghHcxmM+YglcognfMQTbmKGi6RhRQMahdZqZJuhANncB0OhcWZTaGrcSa4pd7CpupArCoqxBwyS/giJ/YPEnmmkdSCaCrtTr3FasRVmYa/IJmtFEbZUIG0rdGHxOs47wNXJJPGWFqJHUyH0YTOIjjY1QTxuHmSx4KiuxrlgAb6bb8a5YD7OBQtw1NSMe0FDgNhgwpwdfbyf9kZzdnQ0bH4o4fLYKa3z0XBpKWV1ORTX+rA7p28R4sFYkqcOmaH05oMdROIGRV4n96yv5pYVZVxSnTctoXQkabA5tdjhkz0DRA1NXZaTT9aWckdJHnXxbjj+JOxIzZIeaDFf6C2D+deZM6TnXQm5UiVBnL+LmbpTBvw4VYfaAvxaa/2QUup54NdKqfcBJ4A7J6Cd4mJlF0HVBrMO9dWfm+7WCCGEEJlt/fuh6yBs+zcoWgSr337Rp7RZFJ+oLeWqfC8febWZN+86ykeri/mbeaUj9fqEEGICPesP8NEDzfQlkty7oIL3VhSODphe/T3838cgHoFbvwWX/JmUAxRTTmtN96lGmg/9ka6O54gmj5BVkJoJXQ7uhGIwZmEwYSEQt+AP+ejuzaE37iMUyyEaLyAWzyUWzyEa9xBNuIgaDiJJGxFtgzHDZI2LBC6VwGM1KLRrXLYESiksgMUCFpR5rxQWReqmRh5bFNbUvUUprGroeAvWoddZzP3W1DFDx6q0mxlEjr5XSqHMhBxljNwrsww1ami/AWg9vG3ea3yGlfWGi6y+GMbROBwNMkiQQcDisWMrcOGsy8FWYAbQ1tS9xW27oFnEWmsS7e3DM6Gjh4+YoXRjIzoSGT7OXlGBc8ECsjdtGgmi6+qwOM+vPrXWmv7OQdoa+2k/3k/HsX56WkNm2K6goNzD/EuKKU3Njs4pzpqW2dHpIvEkWw518fDeNjYf6CAcS1KY7eDOS6q4ZUUZ62rzsU5DKG1ozba+IA909PJQVx8DCYMih413lRdwh8/Cqs4XUPuegf97BvyN5ouy8s0get5fw7yroKBexg5x0S44oNZa7wFWj7G/B7j2YholJsmiW+DxL0Bv00Ut/CSEEELMCTd9FbqPwEMfN//wrt4wIadd4/Pw5NqFfPHoKf79RCdP+wP859Ia5rtdE3J+IYSIG5qvHW/jP050Mt/t5Ocr61manTVyQGQAHv0s7PoZlK+GO/4bChdMX4PFnGIYBh3NOzl+5CG6e15A2U6RlTeIsoC12EJXdwmHmxfSGq6nN1xNf9THoGEjbNiIYjvj7GUnCdyWJB6LQb4NvK4kuXZNvsNKvsNGodNBsctJcZaLoqwssu0uHBY7auh8WqO1eW8+ZrgWhx7aHqrFkXaMHq7Pkf78mfZrdMJAx82bETcgnsRIPdYxA51IjtR3Pl8WUHYrFpcVa74N26JsbIVZIzOhC1xYXOcfAxmDgyT9fhJ+v3nf4yfp7yHWfMIMpI8exQgGh4+3FRfjXLCAvLvvHg6infX1WDwXthheLJKgszlA+3AgPUAkZM7AdrptlMzzUb/GDKRLan04sjKjjFo0keSZw908vKeVJ17tIBRLku9xcPvqCm5dXsaldQXTEkprrdkXHOSBjl4e7OyjLRrHY7Vwc76bNyeb2di2Gduep80SsQAOL9ReAeveZ86SLl5qfoojxATKjP+1YmosutkMqA/+ES778HS3RgghhMhsVjvc9WP472vhl2+H9/8J8mom5NQem5V/XlTNtQU+PnXoJNdvP8SX5lfwrvKCaZ/hI4SY2ZoGo3xofzOvBMK8s7yAL8+vwJ2+2Ffz8/C7D0B/C7zu03DVp83fd0JMkmRikOajT3Kk8f8YDO3D6e4hYbfRHi6mNVbGyY7VtB6qoCNSSG8iG4ORn1cXCQpUghIMfCpODnHyUORhpRArhdpKMXaKsePCZs4oNjCLRZ9RBIgQBaJnPCaVLCtGZoYO3SnFUFlolErdSC0KqMzry1Hmy9Jfnwoild1i3mwWLFk2lNeBzZF67LDC0HOpfcphHXmN3Zral/Z4+DkLapwL+xnR6KjAeTh07h0Kn1PP9fSQ6O1Fh8Njnseam4uzoYGcN74RZ8MCM4iePx9rTs642nHG9hmajuMDNO3t5uSrfrpPBoY/E8gr8zBvVeHw7Oi8kgtbkHGyxBIGW4928dDuNp54tYNANEGu284bVpZz64pyNtTlY5uGBRgBmgej/K6jl/s7ejkSjmJXcI0rxt8Zu7mh8be4T70E2gCbC6ouhWv+nzlDunw1WCU+FJNLfsLmkvw685Ougw9LQC2EEEKMR1YevO1XZkj9i3vgfY+ZC4hNkJuLclnj8/CxAyf4zOEWNvcM8C+n14cVQohxur/dz2cOt2BViv9eWssbinNHnkzE4OmvwtZvmfVB/+xRqL502toqZq9wuI3dr/6C1pNP0x8ZoF9l0zFYTHuomNbAPbSFSggkRmbSKjS5xChWCVYaA9REQtQH/DR0tZDb14Ix2As6aR48PHN5aIaxOTM5gUFwaOVBQJ/2PBgjKxOmv17rtOP0yPMXw2ZDORwoux3lsKPsdix2B8phB7sdZbOjbLbUzWoeb7OjrFaU3WY+tqaeH+ux1Zp2jrTX26wom3k8hnHW0NkIhcZsurLbsebnYy3Ix5ZfgKO2Blt+Adb8fGwF+VjzUvf55rbF456wD9YjoTgnX/XTtK+bE/v8REJxLBZFaX0Ol9xcOzw72uXJvA/U4kmD545289CeNh7f385AJIHPZeOmZaXcurKcy+sLsE9TKN0TS/CHrj4eaO9l+4D5774BP+/vfoZbj/6E/GgPWGxQsRau/JQ5Q7pyHdjlyj4xteTdz1yz6BZ49psQ6gFPwXS3RgghxARSSv0QuBXo1FovO+25TwHfAIq01t2pfZ8D3gckgb/SWj82xU2eGQoXwJ0/gp/dCQ98AN76swm9rLHUaecXK+v4fksX9x5r4+rtB/nX1OxqIYQYj2AiyWcPt/Dbjl4uzfHwH0tqqHQ5Rg7oOgwPvB/adsHqd5gljCbwwzYxdyWNOLuPbeb57U/T3hegx3DSFculLVRCR/g9xI2Rn0MncQpUnPk6QXmim6pIhOpgiLJYHOWwkvC4weNF5xWhS2vpXXYFPXYX2Ow4sqw4XDacqXtHlhWny4rDZcWRlbbfZcViVWnB82hn2A1oSCbRsRg6Hh++N2IxSN2n79exuHkfj41sv+aYMe5jMXQyiU4kIJHACMfQiYT5OJlAx81t85g4xBPDx+tEYmRhwfGy2bDl5Q0HzFmVK7Dm56VC5zxsBQWjQmdLdvaUXcmltaa3PUzT3m6a9/bQ1tiPNjSubDs1ywuoWVZA9ZJ8nO7MC6QBEkmD54/18PCeNh7d305fOI7XaeP6pSW8YUU5V8wvxGGbnlA6lEzyePcA97f72eIfIIFiUbyDL7Q+zO1tj1IV7YSyFXDJ28wZ0tUbZEwQ004C6rlm0S3wzNfh8KMTsuCTEELMCGd+NzLb/A/wHeAn6TuVUlXA9ZiLFw/tWwLcDSwFyoEnlVINWg9NURKjzL8WbvoneOTTsPnLcP2XJ/T0FqX4QFUxV+Z5+dCrzbx9zzHeW1HI/6svJ2uaZtwIIWaGlwdCfOTVZpoHY3yytoRP1JRis4zU1GXHD+CxvwV7Ftz1v7DkjdPbYDFjaK3p7+rhmR3b2H2inaaYQRcO+rSTQNJJJGbDOhgjGPUAGwFQGOTaghSqOJfoQYqNOMVJG3mxLGyJbBJ69JjW6zJvACTBElbYYhasdvNmsydRKkkskiQ6mCARPfefKXZnKrR221Jhdmo7y4YzKxVuu83Q25llx5Ha58334LjI0hRTIT3g1sO3JCTiw+E2SmHLz8fi82VU6bBk3ODUkV6a9vbQvLebgW5zAcWCymzW3FhN7fJCimt9qQUjM08iafDScT//t6eNx/a34w/F8DisXL+khFtXlHNlQyFOm3Va2tYWjbG5J8DmtjaeHogSxkpFtIu/6HiCN3c+wRK3w5wdfek/Q80V4M6flnYKcSYSUM81ZSvBV2mW+ZCAWgiRqYwkxEIQC0I0aN7Hgua+aBBigbTt4GnHhSAaOO344Lm/5iygtX5GKVU7xlPfAj4N/D5t323AL7XWUeC4UuoosB54ftIbOlOt/wB0HoDn/hWKFsGqeyb8SyzOzuLRSxr4x2Nt3NfSxdbeIP+5pJplXveEfy0hxMyUMDTbB0I82TPA5p4BDoYiVDjtPLB6Phtys0cODHTAHz4KRx6H+mvh9v8Eb+n0NVxkFK01Lc3HeeL5bbza3UebYaMHFwPaRTDhIBK3E49aMKKgtBWoGHmtAosTcFpI5ntZHTvG0r4wtaqAuoIifEULcOT5sPs82BxWrDYLtuHAOT18tmCzW4cfW22WcwaTRtIww+pwgthgguhg6j71OBZJvOa5wUCM/s7w8HNG8gwTFxTkl3koqfVRXOujpNZHfoUHa4Z9UKysVpTVCk7ndDdlXEJ9UZr39Zj1pA/2kogmsdktVC7KY/UNNdQsK8Cbn7nlJJKGZnuTn4f2tPLovna6gzHcDivXLS7hlhVlXNVQhMs+9aF0UmteHgjzZKefJzs62B83472KSDt39rzAbYP72VBcgmX1VTDvr+X3v8h4ElDPNUqZs6hf/gnEwuCQN7xCiAmQjJ8hJB4KmQNjB87R1LGxQNp2EOJjL8QyJrsbHNng8IAz21xl2lME+fNS+7PN/fy/Set+JlNKvRE4pbXefdoMmgrghbTHLaS/+xx9jg8AHwCorq6epJbOAErBzd+AnqPwf39lru0wCfVbXVYLX1lQwTUFXv7qwAlu3nmEz9aV8cGqIiwZNAtKCDF1umMJ/uQf4MmeAbb4BxhIGNgUbMjJ5kv15dxdlk+uPe2t3cGH4Q9/aY6rr/8GrH//yEJtYk4IBgP803f/k+ZIki7lpA8XA4aTcMJBNGYnEbVATAN5qZtJW8DqArsjQY4vSrY1Si6D5CbDFCYjlNrtLKxuYN6C+QzabHz+8G52eNewovcQ77397klf/M1iteDyWC64DrHWmmTcGAm2BxPEwuZ9X0eYjuMDHN/TzYFtbQDY7BaKqr3DgXXJPB/eAldGzUrONNrQdJ4IDJfu6DoRACA738miS0upWV5A5cI8bI7pmWk8Hoah2Xmil4d2t/LHfe10BaJk2a1cs7iYW5eXsWlhMVnT0H5/PMFTPQNsbm/nqd4wvdiw6iTr+vfxt307uC4rwcLaNahL32X+nSrEDCIB9Vy0+FZ46XvQuBkWv2G6WyOEmA6GAfHU7OJoIBUgp+6H9g2HxmealZw2izl55jXQR1MjgbHDk9r2gq8ibX/2acd4Rx8/6jgPWMb7x+HcC6iVUm7gC8ANYz09xr4xpxRpre8D7gNYu3btnKmXMiarHe76Cfz3NfCrt8P7/2QuNjYJNuX7eGrdIj516CRfaWzlTz0D/NviasrT68oKIWYlQ2v2Bgd5stsMpXcFwmig2GHjlqJcrivw8bo8L97TLyWPBuGxz5mTUUpXwJu/D0ULp6UPYnod7zf4XlPN6J12hdVp4HQkyHXH8Foi5BCh0AhTQpKFxaVcsmod8+qrcbvGN6P1NxUFvPOpJ/h+3iLafvoT7n3T3ZT4Mnc2rFIKm8OKzWHFkzP2DGStNYGeCB3HB+hoGqCzaYB9z5xi9+aTALiy7SOzrOf5KKnx4crOzDrJUyUWSXDygN8s3bGvh8GBGEpBaV0OG26vo3Z5IfnlnowO9g1D88rJPh7a08of97bRMRDFabNwzaJibllRxjWLinFP8SLWWmv2Bwd5sruPzW1t7IxYMJSiMObnev+LXBs5yqbiYnKWXg21X5cJiGJGk4B6Lqq+HFy55swKCaiFmDm0hvjgSFAcHRgjUB4rZD5t39Drx7NKubKOzEoenqGcDe7C0SGx0/vaWczpxw+Fyna3zOCaWvXAPGBo9nQl8LJSaj3mjOmqtGMrgdYpb+FM5M6Ht/0Kvn8d/OIeeO9jqVn6E6/AYeOHy2r5eZufvz1yimu2H+IbC6t4Q3HupHw9IcT0CSSSPO0P8GTPAH/yD9AZS6CA1T43fzOvlOsKfCzLzjrzlRQnt8PvPgD+47DxE7Dp82CTD7QyUWptiJ8ApYAB3Ke1/rZSKh/4FVALNAF3aa17U685r4WNnfYEl5adoEjHqHXZuHLNWtauWYt1guvjFvpyeeCG1/OuJx7ioepV9P36Z3zsqjdy5YKiCf06U0kpha8wC19hFgvWlQCQTBr4T4WGA+uOpgGa9/cM/zntK8oyZ1inQuvCyuyMniE8Efo6wzTvNUt3tB7pw0hqnG4b1UvyqVleSPXSfLKyM/t3UCSe5IVjPWw51MXj+9tp7Y/gsFnY1FDELSvKuHZxCdnOqY3Ngokkz/QG2NzeweaeAdq1+eHHqoHDfKJ3O9c5w6ysXoZlzZvNhbzlvZWYJZTOgIWj1q5dq3fs2DHdzZhbfvdBOPQI/E0jWOVzCiEmjdaQiJ4hUB5vyJx2nDbO/TWVxQyMnb6RGcrO1L3Dm3o8nn3ZYHPNmj96lFI7tdZrp7sdky1Vg/ohrfWyMZ5rAtZqrbuVUkuBn2PWnS4HNgMLzrVIoozZaY48CT+/ExbebC48Zpncy5qPhaN8+NVmdgXCvLU0n3sXVJA9TQvxCCEuntaao+EoT/aYs6Rf7A+S0JBjs7Ip38t1BT6uzvdReK4Ze8kEPPMN8+argDd9F2qvmJpOTIK5MF4rpcqAMq31y0opL7ATuB14D+DXWn9VKfVZIE9r/ZnUwsa/YGTMfhI468LGUz1ex6KDfOCJ3/GoZwnrGneyqfoKPn7dQqwZutjdRIhFEnQ1B+hoGplpHew1ryq0WBQFldmj6lnnlbpRM/j7kYgnaT3cR/M+c5Z0f9cgAHmlbmqXF1KzvIDS+pyMq9l9upbeME8d6mLLwU6ea+wmEjdw2S1snF/ILSvKuG5xCV7X1M2I11pzbDDKk939bG49xfNhiCsL3kSQTf7tXBs+yLWFORTNfx3UXWW+VxMiA0z0eC3J5Fy16BbY/Qs4sc1cyVUIMX5aQ6gb+k+mbi3Ql9oOdb82eDbi4zipGpmFnB4Ue0vSAuTskSD5NfvSwmh71qwJlcX5UUr9AtgEFCqlWoC/01r/YKxjtdb7lVK/Bl4FEsBHzhVOi9MsuA5u/Ed49LPw1D/AtV+c1C9X53byf2sW8C9N7Xy7uYMX+oL8x5Ia1uZ4JvXrCiEmTiRpsK0vOBxKn4jEAFjkcfHBqmKuK/Cx1ufBNp4Qq6cRDj9q/k3fvhdW3A03fx1cOZPcC3GxtNZtQFtqO6CUOoC5DsRtmOM4wI+BLcBnmAELGzucWXz/xjv42OO/5f76S0g2vcD27/fxb/esotibuSU/LobDZaNiYR4VC0dqeIf6oqMC68MvtbPvmVMA2F1Wimt8lNb5KKvPpWSe74JraU+Vge5Bmvf1cGJ/Dy0He0nEDax2CxUNeay4poqaZQXkFGVNdzPPKp402NHUy5ZDnTx1qJPDHebi6dX5bu5eV82mhUVsqCuY0oUOI0mD5/uCbO7o5MkuP02GOdN8YaiJ9/tf4jrbAOsqF2C/6noo+bi8txNzggTUc1X9NebMyIMPS0AtxOkSMRhoMYPn9PB5KIzub4FEZPRr7B7IrTIX58utGUegfNo+u2fSZ1+K2U9rfc85nq897fG9wL2T2aZZ79IPQucBePafoWgRrLhrUr+c3aL4TF0Zm/K9fORAM7e9coSP15TwiZrS8QVaQogp1xKJsTkVSG/tDTBoaLIsio15Xj5SXcy1BT4qx1NbPhGDE8/D4cfMYNrfaO4vWgxv+REsu2NyOyImRerKp9XAi0BJKrxGa92mlCpOHTauhY2ne1Fjm93Fv994F57Hf8FPalexqmUfr/92kH+/exWXzy+c8vZMB0+uk7pVRdStMkucaEPT2xE2y4Kkalq//NgJtNEMQH65h9L6HMrqciitzyGnKGta6zQnEwZtR0dmSfe2mwuX+wpdLL6inJplBVQ05GZ8+ZLOQIQth7rYcqiTZw93E4gmsFsV6+flc9faKq5eVExd4dTWxD4VibG5u58nW1t4NmQwiJWsZIQr+l7mg4F9XJPvobp+A1z7GcjKO/cJhZhlJKCeqxweM6Q++DDc9FX5RE7MHVpDpC8teG557UzoYAevqc+cXQI5lVCyDBpuMhdFy6mEnCrzPitP/h8JMRcpBTd/05zJ+PuPmiumV07+lemX5mbzp3WL+PzhFv65qYOn/AG+3lDJMq8sjiPEdEsYmu0DoeFQ+mDI/FC72uXgnrICrivwcVluNlnjuQw+2AVHnzAD6aN/MkuBWZ0w70rY8CFYcAPk1Zz7PCIjKaWygfuBj2utB84Slo1rYeNMWNTYYnPwtRvehufx/+W/KtdwieMQb//Bi3z82gY+es38WV3yYyzKosgv85Bf5mHRZWWAWRqks2mA9mP9tDX2c3RHJ68+ay4DkuW1U1qXQ1l9LqX1ORRXe7HaJ3cSS7A3MhxItxzsJR5NYrEpKhbksmSjGUrnlrgzeoHDpKHZ09LHUwc7eepQF3tP9QNQ4nNyy4oyrl5UzBXzC6e0nnTC0OwYCLG5o4snO7o4kDQ/iKwabOdu/wtcp7q4vLyWrMuuhbIPymQlMedJQD2XLboFDv0R2vdA2crpbo0QEyOZgGB7Wvh84rSZ0C3mm7t0VmcqbK6E+deZM6HTw+ecSrCNvcq3EEJgc8BdP4HvX2MumviBp8zfG5PMZ7PynSU1XFfg4wtHTnHDjsO8t7KQT88rwye1qYWYUt2xBE/5zUB6iz9AfyKJTcGlOdn8XX051xX4mO92njvg0dos1zE0S/rUTkCDt8ycId1wk1mD1CGlfWY6pZQdM5z+mdb6gdTuDqVUWWr2dBnQmdo/oxY2VjY7X7zxXXge+xHfLF7H+kuP8S+bD7G9yc+/3r2Kwuy5/Xe1w2WjclE+lYvyAXOWtb8tRFtj/3BofXx3NwBWm4XiGi+lqRnWZfU5ZHkvbuFBI2nQfmxgOJTuOWWWvMjOc9KwvsScJb0wD4crs+OivnCMZ45089TBTp4+3IU/FMOiYE11Hn9z40KuXljM4jLvlAbr3bEET/WYtaS3DETpw47NSHBp/z6+OLCL63LsLJi3DvW6j0L2zF1IVIjJIIskzmWhbvjmAnjd38DVn5/u1ggxPtHgSOA8FD73pZXeGDgFp5fRzco3w6LhWc9D4XOVGUa7C+UT6zlgLiy6NBVkzD6LzgPw/eshvxbe+9iUBkh98QT/dKyNn7T2UOiw8Xf15by5JC+jZzsJMZMZWrM3ODg8S/qVgTAaKHLYuDbfx3UFPq7K9+Idz4dFsTAcf9oMpA8/DoFWQEHFGjOQbrgRSlfMmSu15sJ4rcxfzj/GXBDx42n7vwH0pC2SmK+1/vSFLGycEeO1YfBfj/03X3ZdymWDJ9n/gpVcp4N/u2c1G+oKprdtGS48EKO9sZ+2xj7aj/XT2RzASJrZTU5xFmX1OcMzrcez+GKoP8qJ/X6a9/Vw8oCf2GACi0VRNj+H6mUF1CwrIL9saktenC+tNQfaAjx1qJOnDnby8oleDA15bjubFhazaWERVzUUkeu+uAD/fAyPBZ3dPNnWzisxO1opimI9XNvzItcZrbyurBzfgmugYi1YMzv0F+J8TPR4LQH1XPejW8xyBx96brpbIgQYBoS6zhA+px4P9o5+jbKaK9afPus5NxVA+yrMGs9izpsLb3ingozZ53D4cfjFW82rlO78yZR/+LU7EOazh1p4JRBmQ46Hf2qoZHF2Zi9eJMRMEUgkedofYLN/gM09A3TGEihgldfNdQU+riv0sTw7C8t4Ap6+E6lZ0o/B8WcgGTUXO66/xgylF1wP2cXnPs8sNBfGa6XURuBZYC9gpHZ/HrMO9a+BauAEcKfW2p96zReA92IubPxxrfUjZ/saGTNeGwY/fvw+PutYz/pEB/17XZzoDvPJGxbyoavqscyxkh8XKhFP0tkcSIXW5kzrSNBciN3pto3MsK7LoXieD6vNQmfTyCzprhPmFaTuHAc1S81AunJxPs6szA5MByJxnm/sMRc4PNhF+4BZMml5RQ5XLyxi06JiVlbmTmnpmOGxoPUUm3tDdOJAaYPVgQNc2/8K17kNlteuxNJwPfjKp6xdQkw1CajFxHr+P+Gxz8Ff7YL8edPdGjEbaQ3RAXPGfrjHvIW6IZx6HOpJW5DwlPkGLZ3DO0b4nFb/2VsKFrmUXZzbXHjDOxVkzB6Hbd+Bx79gXqF0zd9O+Zc3tObnbX7ubWxlIJnk/ZVFfKq2lGwp+yHEedFaczQc5ckeM5B+sT9EXGt8NgubUrOkr873UuSwn/tkRhJatqdmST8Gna+a+/PrRmZJV19ulgya42S8nhgZNV5rzW8e/x4fs69jtdFDVVcBj+5u56qGIr711lXke+Tn/nxprenvHDRnWKdC66EFDS0Whc1pJTaYQCkorRuZJV1YmZ3Rs6Qj8SQvN/fyXGM3zx3tYU9LH4YGr9PGlQ2F5kzphiKKfa4pa5PWmiPhKJs7e3iy7RQvRmwklIWceIBNvS9xXayZTcWFFC24Cqovk9/jYs6QgFpMrN4m+PZKuOFeuPyj090aMRMkEyNB83DIPFb47B/Zb8THPpfNZZbX8JWPlN54zeKDuVPaPTF7yRveiSFj9jhoDX/4KLzyU3jzD2D5W6alGf54gn9sbONnbT2UOOx8aX45txXnZvQbUyGmWyRpsK0vOBxKN0diACzyuLi2wAyl1/k82MYzW2+wF45uNgPpo0+Yjy02M8BouMm8Fc6f5B7NPDJeT4yMG6+15qEnv8uHLGtZpPt5i2ce//zwYfLdDr7zttWsrc2f7hbOeJFgnPbjZlgdCcSoXJRP1ZJ8XJ5xfIg2TZKGZt+pfp5r7Gbb0R62N/mJJgysFsWqqlyuqC/g8vmFXFKTh308C8tOkMGhsaD1FJt7Bjihzbrpi4ONXNe3g2tdMdZWL8LWcD3k1U5Zu4TIJBJQi4n3XxvB6YX3nvUqMTFbxUJjBMyp8DmUCpqHt3vMkjBn4soFdwF4Cs3g2Z2ftj20v2BkWxb4EVNI3vBODBmzxykRg5/cZi5wduVfw7K3TFsQ9XJ/iM8ebmFPcJCNudn8Y0MlDZ6pm3kkRKZricTYnAqkn+0NMmgYZFkUV+R5ua7Ax7UFPqpc45gRpzV0Hx6ZJX3iBXNdDHcBLLjBnCVdfw24cia/UzOYjNcTIyPHa63Z/Kfv8T69mmpC/H39cr7423209A7yNzcu5ANX1knJj1lOa01jV4htjd08d7Sb5xt7GIgkAFhU6uXy+kKumF/A+nn5eF1TG6yfjMTY3NXLk6dO8lwYBpWNrOQgV/a+zHWRw1xTmEfl/I1QewXYpXyaEBJQi4n31D/BM1+HTx6WlWRnOsMwA+TXhMxjhc+pfYnBsc9lsaeFyvlmyHx6wOwuGNmflQfWzP10Xgh5wzsxZMw+D6FuuP99cOxpQEPZSlj2ZvOWUzmlTUlqzf+29vBPx9oIJw3+oqqIT9SW4LFK2Y/zobUmbBj0x5P0J8zbQGJkO31/fyJBf8Jcv8ypLDgsCodF4bRYcFoUDosFp1Kj9juUwmlN3586VqW/7sz77UrN6RnyWmsMIG5oklqT0JqENn/+46nHSQ0JremKxXnKH2BzzwAHQmZN0yqXw6wlXeDj8txsssYzWy8RhaatqXrSj0Jfs7m/ZLkZSDfcZC52KOXIxk3G64mRseO11jy35b95Z3IZxcT40SVr+Pc/HuGPe9u5ZlEx/3znSvKk5Mes0t4f4bmj3cOzpIfqSFfkZrFxfiGXzy/g8vpCirzOKW1XRzTO870BnutoZVtvkEZtfnhfM3iK6/wvcZ0jwGWV83E1XA8F8+fMQrVCjJcE1GLite2B710Jb/wOrHnndLdGnEksDD1HoOsQ9Bw1FxM8fYbzoB+0MfbrHdljBMsFo0Pm9PDZ6ZNBWMwq8oZ3YsiYfQEGWmH/72Dvb6H1ZXNf9WVmUL3k9in9cLgrFufexjZ+2e6nwmnny/MruKUoZ86FmglDcyISGxUkjxk6jwqczf3xc/zt7LFayLVZ8dms5KTqfse0JmZoooZBzNDEtLkdNcz95zrn+ThjoH1a6D0UkpvHpu839zksr91vYSTsHQp6zdBXE08FwYm0IHh0UJzap8e57wwh87n2nQ+bgktzsodLdyxwO8f3fyHQDkceN0PpxqcgHjLLltVtMkPpBTdM+YdQs4mM1xMj08frnc/8N2+LLCLbovn1pavZureHex8+QGG2g++8fQ1rqvOmu4niAvWH4zx/rIdtjd1sPdrNsa4QAHluO5fPL+SK1Czp6nz3lP790RmNs603wLb2Vrb1BTmaCqS9iSAb+vewcbCRa/Pc1NdvQNVdBc7sKWubEDORBNRi4mkN/7oCSpbC23453a0RkX7oOgxdB6H7kBlIdx0yV3sn9f9VWSArf+yyGWOFz+4CsMvl3GJukze8E0PG7IvU0wj7HoB9vzV/zysr1F1llgBZfOuUXfr/Ul+Qzx1pYX8wwtX5Xu5dUEmde2pnLk01rTX7goP8tr2XBzp76YolxjzOrhQ5qXA5x24d3vbZrCPBs91Kjs02clxqn89qHV9t4tMYqQD79OA6ahijwm1zf+r+rPvN7aH95nlT24Ymcob90dTXT07A2wObAptSwzerUmfdZ1UKu0VhTTvGqsxZ4ePZZ1Ngs5zr64089lgtXJqbjW88i4caBrTtGpkl3bbL3O+rHJklPe9KueR7gsh4PTFmwni9b+sPeWtoHharg1+vW048YPCRn79MW1+Ez75+Ee/bOG/OfYA6E0XiSXY09aZmSHez91Q/hoYsu5X18/KHZ0kvLvVNaQmXzmicbX0BtrWf4vneIEe0+Tt6KJC+fPAYl3sdLKtciHXeRnPRWvl5E2LcMiagVkpVAT8BSgEDuE9r/W2l1JeA9wNdqUM/r7X+49nONRMGz1nvkc/Cjh/Cp4/JJ4VTJdSdCp8PmvdDYXSgbeQYqxMKG6CoAYoWQdFCKFxoDp6yOrAQ50Xe8E4MGbMniNbQ+ao5q3rf/WZZAKvDnHm57M1m4OVwT2oTEobmf1q7+dqxNqKG5sPVxfxVTQnuKVyEaCq0R+Pc39HLb9r9HAxFsCvFDYXmjNkCu80Mne1Wcm02fDYrWZa5XSYDzJnJZwquh8Jh+1hBcCogtsDM/x5GA3BsS6qe9OMQ6gQUVK0fCaWLl0iYMQlkvJ4YM2W8Pvz8j7mrv5yo3cMv1yyh1uXh07/dzWP7O7h+SQnffMtKctxSRjCTROJJdp/sY3uTn22NPexo7iWWMLClFja8fH4hG+cXsqoqF4dt6v6m6IrF2dYbZFtbC9v6RgLp7ESIDf27uTxyfDiQts3bCHnz5He4EBchkwLqMqBMa/2yUsoL7ARuB+4Cglrrb473XDNl8JzVmrbC/9wCd/0vLHnjdLdm9tDaDJy7Do7Mih4Ko8M9I8c5slNB9KLRYXRujdQsFGKCyBveiSFj9iTQ2lxMce9vYf8DEOwAuwcW3WzOrK6/ZlI/lOyMxvlKYyu/7eil0mXnH+ZXcmOhb0YHjKFkkke6+vlNey/P9gYwgLU+N3eW5vPG4lzy7LbpbqLIRP5jZhh9+FFofg6SMXDmwPxrzUB6/nXmFWpiUsl4PTFm0njd/OL/8paeQvqcefx05QLWF+Tzo+ea+KdHDlDsdfGtt65iRWUOLru8L5oO/YNxdjb7eel4Lzua/Oxp6SeWNMtKLir1csX8oYUNC8h2Tt342hWL83xvkG1tJ3muLzQqkL60f89wIL18OJCulUBaiAmUMQH1a06k1O+B7wBXIAH1zJNMwDcXmDO37vjedLdmZhrsg1M7oGN/WomOwxAdGDnGlTsSPqeH0b4KGSyFmGTyhndiyJg9yYykGYzt/S28+ntz4VtXrvnh8bK3QO3GSfvg8vm+IJ893MKhUITrCnzcu6CCmqyZU/YjqTXbeoP8psPPQ139hJMGVS4HbynJ487S/FlfwkRcgGQcTr6YmiX9mPl3G5iTBhpuhAU3QvUGWQR6isl4PTFm2njduuMX3NXh4ZSrlB8vn8friovYdbKPj/zsZU71mYu6e102irKdFGY7KfI6Kcx2pG2n7lP7neMp3yPG1DEQ4aXjfrY3+XnpuJ9DHQG0NssoLavIYf28fNbV5rO2Jm9KF7TsjiXMRQ3bTrKtL8ThVCDtSYTNQDp6nMu9TlZUNpiBdG6NvMcWYhJlZECtlKoFngGWAX8NvAcYAHYAn9Ra957t9TNt8Jy1HvwwHHwI/qZR/hA/F62h9ziceNF8Y3PyReg8wHCN6OySkXIcRQtHAmlPkQySQkwTecM7MWTMnkKJGDT+ySwBcvBhcyG27FJY+iazDEjl2gkfU+KG5gctXXyjqZ2E1vxldQkfqS4mK4PLfhwKRfhtu5/7O3ppjcbxWi28sTiXO0vzWZ/jwSLj7tykNcSC5hVr4R4I9Yxsh3vM2dKNT0G0Hyx288Ofhpug4QazlJqYNjJeT4yZOF53vfIb3tpi4ainhu8vruSGslL6w3Ee299OZyBCdzBGVyBKVzBKdzBKVyBKIDL2egI+ly0VVpvBtRlsO4bD7KH9BXM8zNZa09QTZvtxPy+lAukT/jBg1pBeU5PLutp81tfms6o6F7djamZIa61picbZ0RfgpfZTbOsLckibpc/cyTCX9u/l8shxrvA6WDE8Q7pmStomhDBlXECtlMoGngbu1Vo/oJQqAboxk7q/xywD8t4xXvcB4AMA1dXVlzQ3N19UO8QEOPgw/PJt8K7fm6uQixGJKLTtNoPoEy/AyZdStQgBpw8q15kzbKrWQ9lKyJJVp4XINPKGd2LMxDe8s0IsbM7y3Hc/HHncLD2QW2MG1cvebC50PIFBbHs0zpeOnuLBzj5qXA7ubajkugLfhJ3/YnXHEjzY2cuv2/3sCQxiVXB1vo87S/O4oSAnowN1cYES0dEB81ih8+m3ZGzscykreMugfpMZStdtAqd3KnsjzkLG64kxU8fr3t0P8LamKHu9DXynoYzbK8vPenwknqQ7GKU7GKN7KLwOpALsYJTuQGwkzI6OHWbnZNkpzHZQkAqx8z0OCjxmeF3gcZLvcQzvz3U7sE7hQn8TLWloDrQN8NJxPztSZTu6g1EA8tx21tWas6PXzctnabkP+xSNpzHDYF9wkB09frZ3trIjrGnDBZwWSPucLK9owC6BtBDTLqMCaqWUHXgIeExr/S9jPF8LPKS1Xna288zUwXPWiYXh63Ww5p1w8zemuzXTK9Q9MjP6xIvQ+gokzYGbvFqo2gDVl0LVpebMaKkTLUTGmwtveJVSPwRuBTqHxl6l1DeANwAxoBH4M611X+q5zwHvA5LAX2mtHzvX15AxOwNE+uHAQ2ZYfWwL6KQZVhfUm/d5Neb90La74ILD62f9AT5/pIUj4Sg3Ffp4T0UhZU4HpQ5zQcGprFMdSRo80TPAb9r9/Mk/QELD8uws7izN400leRQ5puDqL61TN8O8kbadvl8bqeMN828Em8tc+NgiwTlglrIZ7INw9zhDZz/EAmc+nyvX/Dn3FJr37vzU/dDttP2uHLmiLYPNhfF6Kszk8Tqw7w+882gvL/qW88/1RbytpmpCzjsUZncFosOzsbvTZmP3BGP0hKL0hGL0heNjnsOiIM/toGAoyM52UpAKtPOzHaltx3C4nZNlxzKNgXb6goYvNfXycnMvwVRQX5GbNVyuY/28POqLsqdsXO+JJdjZH+SljlZ29PaxK+4goszZ2VWDbawb2Mda7WddjpvFZfOx1V4ugbQQGSZjAmpl/ub6MeDXWn88bX+Z1rottf0J4FKt9d1nO9dMHjxnnV++3QxjP7F/7vzhbhhmzcGTaeU6eo6az1nsUL7KDKKHbt6SaW2uEOLCzIU3vEqp1wFB4CdpAfUNwJ+01gml1NcAtNafUUotAX4BrAfKgSeBBq118mxfQ8bsDBPsglcfhKZnobcZ+pph8LTKanYP5FanBddp23k1Zlh3FjHD4L6TXfxzUweDhjG8P8uiKHbYKXXaKXHaKXUM3dvM+9S+7Iu4dFprzfb+EL/p6OX3nb0MJAxKHXbeXJrHW0ryWJyddX4nHOwzy5nte8C8MmpU0KzPEDTr0aHzxbDYzbDa5ky7ucDqOMP+tG1b2jGj9p/neawTfHm21hANjATJY4XOYb/54f/Q48Fehsuinc7uGQmThwPn00PntP1ZeRPfJzGt5sJ4PRVm+ngdPvAI73u1hafy1/GZyhw2FheTb7eSZ7eRa7NOevmmRNLAH47hD8VSwXUMf9AMr3tCMXqC0VHP9Q+OHWhbLcoMtFOhdZ7HgcNqQSmwKIUlda/Sti2K1OPUPos6v+OVwh+Osf346AUNG0qyU2G0GUqX557nGHqBDK05HI6ww9/H9vZT7AjGaMQs12E34iwPHmZd6AjrHDHWFhRTWr0CKtaCK3Ou3BJCvNZEj9cX89fcFcA7gb1KqV2pfZ8H7lFKrcL8q7MJ+IuL+Bpiqi26xXzj1rYLyldPd2smRywMrS+PlOo4+aK5CBVAVr5ZqmP1O8xZ0uWrwD41A7cQQlwsrfUzqauX0vc9nvbwBeAtqe3bgF9qraPAcaXUUcyw+vmpaKuYINlFsP795m1IZAD6Tphhdd+JkeC67wQ0PffamaiunLFnXufWQG4VDoeHj9aUcE9ZAUfCEdqjcTpi8dR9gvZonP2BQZ6MDRBOvjbEdVstqfDalhZijwTbJannPNaRILtpMMpv2v38tr2X5kiMLIuFW4pyuLM0n4152VjPJ5iIBuHQI+aM88bNI+VRFt1sBrrKMnJDmR/QK0vaffpzaY8VZ3lOjew3EpCImF83ETFLVQzfIuYVWkPbiag5Qz4RPW1/6rXG2AHIeVGWMwTdQ+H2WYJuIzF26HymdlnsI0GypwBKl502s3koeE6b4Sx/dwkhAPfi1/M/lsf50J6tfI2NfK2lf/g5C5Brt5Jvt6VuZnCdb7eRZ7OS77BRkLadb7eRY7Oe19hhs1oo9roo9rrGdXw8adAbitEdTIXaIXNGdvp2TyjGgdYBEobG0BqtzfDWvJkfyhpD+4z05xnz+LO2P7Wg4XuuqJ3yBQ1DiSSvBMJs72hje083O6M2+pX5tQtiAdYN7OOeRDvrsh2sKK0ha9l6KLpHrjISYo674IBaa70V80/z0/3xwpsjpl3DTeYbl4MPz56AOtQNzc+ZgfSJF6B9j/kGC8xFDBe/IVU/+lIomD93Zo4LIeai9wK/Sm1XYAbWQ1pS+17jtHUjJrN9YiK4fGYQWDpGhTWtzdmrvU0jIXZvKrzuOgRHnjCD0HSeIsitoSC3moKyFVB7pfkB7hizVoOJJO1D4XUqwO6IxmmPmY9fCYRp744TGeOdtddqodRpx2FR7A9GUMDGvGw+Oa+UWwpz8JzPTOz4oFmre9/9cPhxSAyCrwLWfwCW3QHla2bmeG8YqeB6rKB7KABPC7TPFHSfKTAfOjbSN8Y5IubfiENhcl4tVKwZI3AuHJnt7PTOzO+zECIjOBfewPdtTvY99690B3rwKye9dh9+ey5+bxV+TwV+ZyEnbF52KSf+hCZ2hivEFZBnt5JnS4XajrTtVNidZ7eSYzPvc1PbWRY1rrIXdquFYp+LYt/4Au2JMCrQ1qMDbbtVTcnij0OLGW7v7Wd720l2DITZb7gxlAWlDRaG2nlj8CBrrSHW5ecyr2YZquoe8woZIYRII9fDidHc+VBzhRlQX/O3092aCxPshKatZijd9Bx0HTD321xQcQlc/lepch3rzf4KIcQcoJT6ApAAfja0a4zDxnxXp7W+D7gPzEuGJ6WBYmoolQoP881w8XRam+PocHDdPLJ9aifsf8A8zpEN1ZfBvCuhdiOUrgSrjWyblfk2K/PdZ36DrrVmIJGk/bTwuj213R9P8oW6Mu4oyaPCdR6zvRIxaPyTGUof+iPEgma4vvod5kKSVZfO/NlZFgtYsmSWsRBizrDUX8WK+qvMD+h6j0PHPujYD+3b4eSPzDEqRWflES5dQ0/JanoLFuHPnY/fXU6vttATS9CbSOKPJ/DHErREYuyJD+KPJ4ieZTqy06LIsVnJtQ0F2GZ4nWuzpe6t5KbKjgxt59jM42yTXHtaKYVVgXXMP+kmR388wZ5AmF3dHezq7mJnRNGuzDHJk4iwJnCQj0VOsN6tWFNSSc7CS6D0DWCdgnUihBAzmgTU4rUW3QqPfgZ6Gs0FlzLdQFsqjE6F0t2Hzf12j7mQ4Yo7oWajOSPcNjWXNQkhRCZRSr0bc/HEa/XI4hMtQPqqQ5VA61S3TWQYpcy1Frwl5ge5pwt2mfWum7aa9088Ye53+tIC6yuhdPkZFxBWSpFjt5Fjt7HQc5EzzZIJaHrGDKUP/J9ZHiMrzwykl91hjv9Sn1gIIWY+i8V8b1pQD0tuG9kf6YeOV6FjH6pjH572fXh2/ifV8bD5vLJAwQLzqqKSpVCyHCqXgq8clEJrTdgw8MeT9MUT9MWT9CWS9CXStuOJ1H2S1mic/cFB+hNJgmOUtUrns1nMGdk2Kzn2kZC7NsvJAreTBo+LKpdj0utpX6hQMsm+wCC7urvY3d3BrsEkx/AMP1876OfywEHW0ce6XC+LKhqwrX895Ix5QZ4QQpyV/MUuXmvRzWZAffBhuOKvprs1r9V/KhVIP2vOkPY3mvsdXqi5DFa93ZzNVbZSPqkVQsx5SqmbgM8AV2mtw2lP/QH4uVLqXzAXSVwAvDQNTRQzSXaRGfwuu8N8HOgYHVgfeczc78oxr8iq3WgG1iXLJm72smHAiefNUPrV35uL8jm8sPhWWHoH1G2SD6SFEGKucOWY7wFrLhvZNzTbun2vOdu6Yx+c3G6OG0Oy8qBkGapkGZ7SZXhKllKVVwuenHGPV3FD05dI0J8Kr08Ps9ND7v54koPRQXriCfzxkfWosyyKereLBW4nCzwuFrhdLPA4qcty4pjCq36ihsGrwQi7e7rZ1dXGrlCcw9os1QFQHhlgVeAgbzV6WOW2saKolLz5K6H0WrmqRwgxISSgFq+VWw2lKzInoB5a1KlpKzRvNetmAjhzoOZyWPtn5pvg0hUyS0oIMacppX4BbAIKlVItwN8BnwOcwBOpGoovaK0/qLXer5T6NfAqZumPj2itk2OfWYgz8JbA8reYNzCvahoKq5ueNUttALhyU2F1KrAuXnJ+gbXWZomRfffD/t9BoA3sbnPtjGVvhvnXgX3q6n4KIYTIYOmzrZfePrI/0p8KrPenwut9sPN/zHUKhiiLGV5n5Zv37vy07dH77Vn5FGXlUeTOhyz3uGvu98YTHAlFOBKOcjgc4Ugowo6BML/r7Bs+xqpgXpbTDKzTw2u38/zWZBhDwtAcCUd4xe9nV2cru4MRXjXcxJV53vzYIKsCB7k50c4ql4VVRcUUz1sGpe8x17kQQohJIGmeGNuiW2HLP5l1KLOLp+7ram0G0EP1o5u2Qv8J87mhN7fr/wJqr0jNxpr8hR+EEGKm0FrfM8buH5zl+HuBeyevRWLO8ZWZpbVW3Gk+7j81OrA++JC5PyvfHMtrUyVBiha9NrDW2gwQ9t1v1r7uOwFWByy4wZzB3XATODwIIYQQ4+JKTXCquXxkn5EE/3Ho2GuOWYN+czHhcOp+4BS07zO346Ezn9vqPC3Qzj0t3B7ZznN4WO/wsN7jhjw32IvA5iCUTNIYjg6H10fCEQ6HIjzR008irUx2hdNOQ9ps6wVuFw0eF/n218Y7htYcH4yyq7ef3R0t7AqE2Zt0MajMK429iSgrA4f5i1grK12aVfkFVNYvRpXfJes1CSGmlATUYmyLboEt/wjPfdusKaksp93UGPtOf/5cx6SOS0Th5EsjdaQHTpltcBeYM6Mv/6h5f76zrYQQQggxvXIqYOVbzRtA38lUYL3VrB194P/M/e6CtHIgS+HYFjOY7jkKFhvUXQ2bPm+WIXPlTFt3hBBCzDIWKxTON2/nEo+YQfVg72lB9mmh9mCvuZ5Ty3ZznxEfRztseOweVjjcrLC7weE211RyuInbsznuKueIo4Qj9iIO63yORH284HczyMiErQKLwQKnosFlJZskewYC7Ek4GVBOALKScZYHj/OOyElWOZKsystjXvVCLBVvmNpJaUIIMQYJqMXYSpaai0k8/x3zNhU8xeZsqqGalUWLxn2ZlBBCCCFmgNwqWHWPeQPobR6ZYX38WbOmNJgfYtduhMv/Eha/UWZxCSGEmH52F9jLzKuFxktriIVGh9qxEMTC5ozsUfdh87l4eGR/ZAB7oJ2G2G4a0vcDBooWZwlHPDUccddw2G3e/8FdQ8iaxeJQK7eHj7PKHmdVro+G8vnYKq8Db5m8zxZCZBwJqMXYlII/fwL6W0AbqZtO3Yxz3DRwruPSnldWc0HDwgUyUAohhBBzSV6NeVv99pEyXx37oHK9Wd9aCCGEmMmUAme2ecutmphzag3xQSzxMNWxENXxMNemBd061oVhsWEt3wC5b5X32EKIGUECanFmWXnmTQghhBBisikF+fPMmxBCCCHGppRZAsThBk/ha58GZKUmIcRMIwV9hRBCCCGEEEIIIYQQQkwLCaiFEEIIIYQQQgghhBBCTAsJqIUQQgghhBBCCCGEEEJMCwmohRBCCCGEEEIIIYQQQkwLCaiFEEIIIYQQQgghhBBCTAsJqIUQQgghhBBCCCGEEEJMCwmohRBCCCGEEEIIIYQQQkwLpbWe7jaglAoAh6a7HROoEOie7kZMEOlLZppNfYHZ1R/pS+ZaqLX2TncjZrpZNmbPpp/x2dQXmF39kb5kptnUF5hd/ZHxegLIeJ3RZlN/pC+ZaTb1BWZXf2ZTXyZ0vLZN1Iku0iGt9drpbsREUUrtmC39kb5kptnUF5hd/ZG+ZC6l1I7pbsMsMWvG7Nn0Mz6b+gKzqz/Sl8w0m/oCs6s/Ml5PGBmvM9Rs6o/0JTPNpr7A7OrPbOvLRJ5PSnwIIYQQQgghhBBCCCGEmBYSUAshhBBCCCGEEEIIIYSYFpkSUN833Q2YYLOpP9KXzDSb+gKzqz/Sl8w12/ozXWbT91H6krlmU3+kL5lpNvUFZld/ZlNfptNs+j7Opr7A7OqP9CUzzaa+wOzqj/TlDDJikUQhhBBCCCGEEEIIIYQQc0+mzKAWQgghhBBCCCGEEEIIMcdIQC2EEEIIIYQQQgghhBBiWkxKQK2UqlJKPaWUOqCU2q+U+lhqf75S6gml1JHUfV5q//VKqZ1Kqb2p+2vSznVJav9RpdS/KaXUZLR5gvuzXim1K3XbrZR6U6b053z7kva6aqVUUCn1qZnaF6VUrVJqMO3f5rsztS+p51YopZ5PHb9XKeXKhL5cSH+UUm9P+3fZpZQylFKrMqE/F9AXu1Lqx6k2H1BKfS7tXDOtLw6l1I9Sbd6tlNqUKX05R3/uTD02lFJrT3vN51JtPqSUujGT+jNdLuDnImPH7Avoi4zXGdofJWN2RvZFyXidyf3J2DH7LH2R8fo8XMDPhIzXGdqftNdl3Jh9Af82Ml5nYF9UBo/XF9ifjB2zL6AvMl6fidZ6wm9AGbAmte0FDgNLgK8Dn03t/yzwtdT2aqA8tb0MOJV2rpeAywAFPAK8fjLaPMH9cQO2tNd2pj2e1v6cb1/SXnc/8BvgU5nyb3MB/y61wL4znGum9cUG7AFWph4XANZM6MvF/Jyl9i8Hjs3gf5u3Ab9MbbuBJqB2hvblI8CPUtvFwE7Akgl9OUd/FgMLgS3A2rTjlwC7AScwD2jMpP8303W7gJ+LjB2zL6AvMl5naH+QMTsj+3Laa2W8zqz+ZOyYfZa+yHg9uT8TMl5naH/SXpdxY/YF/NvUIuN1xvXltNdm1Hh9gf82GTtmX0BfZLw+09efok7+HrgeOASUpXX80BjHKqAn1cEy4GDac/cA35vKf6AJ6M88oAPzl13G9Wc8fQFuB74BfInU4DkT+8IZBs8Z2pebgZ/OhL6M9+cs7dh/BO7N1P6M49/mHuD/Uv/nCzB/qefP0L78B/COtOM3A+szsS/p/Ul7vIXRA+jngM+lPX4Mc9DMyP5M9/dxnP9fM3rMPs++yHidQf1BxuyM7Mtpx8p4nVn9mTFjNjJeT8nPxGnHynidYf1hhozZ4/jdU4uM1xnXl9OOzejxepz/NjNmzB5HX2S8PsNt0mtQK6VqMT+9fREo0Vq3AaTui8d4yZuBV7TWUaACaEl7riW1b9qMtz9KqUuVUvuBvcAHtdYJMqw/4+mLUsoDfAb48mkvn3F9SZmnlHpFKfW0UurK1L6Z2JcGQCulHlNKvayU+nRqf0b1BS7od8BbgV+ktjOqP+Psy2+BENAGnAC+qbX2MzP7shu4TSllU0rNAy4BqsiwvsBr+nMmFcDJtMdD7c64/kyX2TRmy3g9LKP6AjJmZ+qYLeN1Zo7XMLvGbBmvJ4aM15k5XsPsGrNlvJbxeirMpjFbxuuLG69t593K86CUysa8bOXjWuuBc5UcUUotBb4G3DC0a4zD9IQ28jycT3+01i8CS5VSi4EfK6UeIYP6cx59+TLwLa118LRjZmJf2oBqrXWPUuoS4MHUz9xM7IsN2AisA8LAZqXUTmBgjGNnxP+Z1PGXAmGt9b6hXWMclun/NuuBJFAO5AHPKqWeZGb25YeYl/PsAJqBbUCCDOoLvLY/Zzt0jH36LPvnlNk0Zst4nZnjNciYTYaO2TJeZ+Z4DbNrzJbxemLIeJ2Z4zXMrjFbxmsZr6fCbBqzZbwedsHj9aQF1EopO2aHfqa1fiC1u0MpVaa1blNKlWHWjho6vhL4HfAurXVjancLUJl22kqgdbLafDbn258hWusDSqkQZt2vjOjPefblUuAtSqmvA7mAoZSKpF4/o/qSmjEQTW3vVEo1Yn5KOhP/XVqAp7XW3anX/hFYA/yUDOhLqk0X8n/mbkY+3YWZ+W/zNuBRrXUc6FRKPQesBZ5lhvUlNTPlE2mv3QYcAXrJgL6k2jRWf86kBfPT6SFD7c6In7PpNJvGbBmvM3O8BhmzM3XMlvE6M8drmF1jtozXE0PG68wcr2F2jdkyXst4PRVm05gt4/WwixqvJ6XEhzI/KvgBcEBr/S9pT/0BeHdq+92Y9UxQSuUCD2PWLnlu6ODUNPiAUmpD6pzvGnrNVLqA/sxTStlS2zWYxcSbMqE/59sXrfWVWutarXUt8K/AP2qtvzMT+6KUKlJKWVPbdcACzMUCZlxfMGv7rFBKuVM/a1cBr2ZCX+CC+oNSygLcCfxyaF8m9OcC+nICuEaZPMAGzPpLM64vqZ8vT2r7eiChtZ4JP2dn8gfgbqWUU5mXUy0AXsqU/kyX2TRmy3idmeM1yJhNho7ZMl5n5ngNs2vMlvF6Ysh4nZnjdapNs2bMlvFaxuupMJvGbBmvJ3C81pNTSHsj5vTtPcCu1O1mzGLmmzE/HdgM5KeO/1vMejK70m7FqefWAvswV4P8DqAmo80T3J93AvtTx70M3J52rmntz/n25bTXfonRKwzPqL5g1l7bj1nz52XgDTO1L6nXvCPVn33A1zOlLxfRn03AC2Oca0b92wDZmKtx7wdeBf5mBvelFnNxhwPAk0BNpvTlHP15E+antlHMRXQeS3vNF1JtPkTaSsKZ0J/pul3Az0XGjtkX0BcZrzO0P8iYncl92YSM15nYn1oydMw+S19kvJ7cnwkZrzO0P6e99ktk0Jh9Af82Ml5nbl82kYHj9QX+nGXsmH0BfalFxusxbyr1QiGEEEIIIYQQQgghhBBiSk1KiQ8hhBBCCCGEEEIIIYQQ4lwkoBZCCCGEEEIIIYQQQggxLSSgFkIIIYQQQgghhBBCCDEtJKAWQgghhBBCCCGEEEIIMS0koBZCCCGEEEIIIYQQQggxLSSgFkIIIYQQQgghhBBCCDEtJKAWQgghhBBCCCGEEEIIMS0koBZCCCGEEEIIIYQQQggxLSSgFkIIIYQQQgghhBBCCDEtJKAWQgghhBBCCCGEEEIIMS0koBZCCCGEEEIIIYQQQggxLSSgFiJDKKWalFIxpVThaft3KaW0UqpWKfU/qWOCabfdpx3vSe3/49T2QAghhJjdUmN1h1LKk7bvz5VSW9IeK6XUMaXUq2O8fktqTF952v4HU/s3TWLzhRBCiDklNW4Ppt4fdyilfqSUyk49957U2HvXGK9bopT6g1KqXykVUEo9pZS6fOp7IMTcIQG1EJnlOHDP0AOl1HIg67Rjvq61zk67rTzt+bcAUeAGpVTZ5DZXCCGEmHNswMfO8vzrgGKgTim1boznDwPvGnqglCoANgBdE9lIIYQQQgDwBq11NrAGWAf8bWr/uwF/6n6YUqoeeA7YC8wDyoHfAY8rpS6bqkYLMddIQC1EZvlf0t60Yg6WPznPc7wb+C6wB3j7BLVLCCGEEKZvAJ9SSuWe4fl3A78H/shpb3pTfga8VSllTT2+B/ONb2yC2ymEEEKIFK31KeARYJlSqga4CvgAcKNSqiTt0C8Bz2utv6C19mutA1rrf8N8r/61qW63EHOFBNRCZJYXAJ9SanHqjetbgZ+O98VKqWpgE+ab358xOuwWQgghxMXbAWwBPnX6E0opN+aVTEPj8N1KKcdph7UCrwI3pB6/i/P/MFoIIYQQ50EpVQXcDLyCOfbu0FrfDxxg9MSu64HfjHGKXwNXpMZ6IcQEk4BaiMwzNIv6euAgcOq05z+llOpLu/047bl3AXu01q8CvwCWKqVWT0mrhRBCiLnji8BfKqWKTtt/B2aZrceBhzDLgdwyxut/ArxLKbUQyNVaPz+ZjRVCCCHmsAeVUn3AVuBp4B8x3zf/PPX8zxl9xVMh0DbGedowM7S8SWupEHOYBNRCZJ7/Bd4GvIexZ1R9U2udm3ZLH0zfhTljC611K+YAPNblxUIIIYS4QFrrfZgB9GdPe+rdwK+11gmtdRR4gLHH4QeAa4C/xBz3hRBCCDE5bk+9b67RWn8Ysxb1POCXqed/DixXSq1KPe4GxlrLqQwwgN5Jbq8Qc5IE1EJkGK11M+ZiiTdjvoEdl9SqwguAzyml2pVS7cClwD1KKdukNFYIIYSYu/4OeD9QAaCUqsQMnd+RNg6/BbhZKVWY/kKtdRizDuaHkIBaCCGEmErvBhSwKzVWv5jaP1Qe80ngzjFedxdmberw5DdRiLlHAmohMtP7gGu01qHzeM27gSeAJcCq1G0Z4AZeP8HtE0IIIeY0rfVR4FfAX6V2vRM4DCxkZBxuAFowF0I83eeBq7TWTZPcVCGEEEIASikXZtD8AUbG6lWYVzS9PTWx68vA5Uqpe5VS+Uopr1LqLzED7M9MR7uFmAskoBYiA2mtG7XWO87w9KeVUsG0W3faQPvvWuv2tNtxzJlZUuZDCCGEmHhfATyp7XcD/3naONwOfJcxxmGtdavWeusUtlUIIYSY624HBoGfnDZW/wCwAjdprY8AG4GVQBNm7ek3AzdqrZ+bllYLMQcorfV0t0EIIYQQQgghhBBCCCHEHCQzqIUQQgghhBBCCCGEEEJMCwmohRBCCCGEEEIIIYQQQkwLCaiFEEIIIYQQQgghhBBCTAsJqIUQQgghhBBCCCGEEEJMCwmohRBCCCGEEEIIIYQQQkwL23Q3AKCwsFDX1tZOdzOEEELMYjt37uzWWhdNdztmOhmzhRBCTCYZryeGjNdCCCEm00SP1xkRUNfW1rJjx47pboYQQohZTCnVPN1tmA1kzBZCCDGZZLyeGDJeCyGEmEwTPV5LiQ8hhBBCCCGEEEIIIYQQ00ICaiGEEEIIIYQQQgghhBDTQgJqIYQQYhZQSlUppZ5SSh1QSu1XSn0stX+VUuoFpdQupdQOpdT6tNd8Til1VCl1SCl14/S1XgghhBBCCCHEXJURNaiFEEIIcdESwCe11i8rpbzATqXUE8DXgS9rrR9RSt2cerxJKbUEuBtYCpQDTyqlGrTWyenqgBBCCCGEEEKIuUdmUAshhBCzgNa6TWv9cmo7ABwAKgAN+FKH5QCtqe3bgF9qraNa6+PAUWA9QgghhBBCCCHEFJIZ1EIIIcQso5SqBVYDLwIfBx5TSn0T84Ppy1OHVQAvpL2sJbVvrPN9APgAQHV19aS0WQghhBBCCCHE3CQBtRBCiFlJJzWJ3iCDJ44x0PHKdDdnyiilsoH7gY9rrQeUUv8AfEJrfb9S6i7gB8B1gBrj5Xqsc2qt7wPuA1i7du2YxwghhBDnQ2uDZHKQZDJMMhlK3Yenu1lCCCGEOIdEIjTh55SAWgghRMbTWqOjSYxQnGQoTjIQJdHZR6Krn4Q/iDEQIRmKk0iGibhbieS0Ec09RcTXRNzdAc65kakqpeyY4fTPtNYPpHa/G/hYavs3wPdT2y1AVdrLKxkp/yGEEEIMM4zYcIA8dEsMhcqJUNr+UOq50aHz6c9LGC2EEELMLFob9Pa9SHvbA3R2PTrh55eAWgghxJTTcYNkKI4RimOEU/dD4XP/IMneEMmBiLk/BiQtnL5sQtIWIuJrJpJ9jEhpI9GcE8S9PcPP2yI+sgYryYtcQihWC3xyKrs45ZRSCnN29AGt9b+kPdUKXAVsAa4BjqT2/wH4uVLqXzAXSVwAvDRlDRZCCDHhtDYwjIgZEA8Hx6MD5MSosHhw9POJsQNmrePjboPF4sBq9WC1ukfd7Pa84W3bqOdT2zY3VosbeN3kfYOEEEIIcV7C4Sba2h+gvf1BIpFTWK3ZlBTfCuyb0K8jAbUQQoiLog2dFjInMMLxkfA5FMcIJ8zHwRjJQBRjMAGJM5xLG+hYCB0LoqNB8z4WIOkIEi/3EyvtJ1bcSzS3m5irb/h1TnspuTmX4PUtw+ddRn+ijmcbk2zp6GRbYzfRxJyYQX0F8E5gr1JqV2rf54H3A99WStmACKla0lrr/UqpXwOvYv6LfERrnZzyVgshRIbR2kDrOIaRQOuEua0TaOO121onUselts9wnKFT5zLSj0vb1nG0kbatk6nH8dTr0rZPf2wkSBrh4XIZZ6jWNAZlBsMWNxaLC4slC4tyoZQLu8rFYXOCzYnSDtAOMOypmw2dtGEkbeikFSNhw4hbMBIWknGFThjEEwki8TjJRMK8xeMYyQSJeBwjkSCZ6CEZ7yCZOH3/Gf5AEEIIIcSUiccH6Ox8mLb2B+jvfxmwkJ9/BfV1nyLXeyVdzS3AVyf0a0pALYQQ4jV0wiDeFkoFyyOznJNpofPw7OfBxBnfC2sdh3gYIzqADvWhY4HR4XNyEIvbjjUnC1uBF1tRDpZyL9HiKNGcXgbtrQQTjUSip4bP6XJVkuPdgM+7DK93GV7vUgyVw4vH/Tywp5OnD3dxvHsPANlOG8acyKZBa72VsetKA1xyhtfcC9w7aY0SQogMo3WSky0/oaXlpxjJwVTImxwJoo0EYExRaxRK2bFYbChl3izKjhp+nP6cHYuyYbE4UMqNRdkBK4MDAYI9vSSjLoyEFSNuIRm3YMQtJOJgxBSJiCYRw7yPauIRg8SgJhkzeO2wEU/dAhfUI6vNhtVux2Kzm9vD90P7bdhsdhxZbqxec5817ViLzQY//OXFfVuFEEIIcd4MI4G/dyttbQ/Q3f0EhhHD7a6noviDxP31dOzsYN/hzXSd+D7amPi/lSSgFkIIAZh1nuOtIcI7Owjv6sQInzaLSWmwJsGIQiKEERnACPaS7O9Ch/teM+vZ4rZjKy7EVlKMo7gY26ISbCXzsJeUYCsuxlZSQtJtEAy9SiCwD39gP4HAC0QiLebXi0GWpRpfzkoqvG/H612Kz7sUuz0PgOaeEA8d6mLLoaM8f6yHSNzAYVVU5rkpynbSFYwSTxrcsryMO9dWsfFrU/wNFUIIkVGCwUMcOPg5BgZ2k5u7nqysGizKlgqEzQBYKRvKMnrbDI7NY5RlrO3XhsljH5c6l2Vo23LuRp9Ga03bkYMc2LqFQ9ueZTAwgNPjxVdQNBIK2817WyoMtuSM7LPa7MPbFmvafvvpgbIZFr92/0ioPPp583xmtamL9L4PXfw5hBBCCDEuweDhVAmP3xOLdWJRXqzRSwg0FnB4dzfh/qeBp7G7siib38CaN9zOduth+PXDE9oOCaiFEGKOSwZjhF/pIryzg3h7CCyg6CC6/3ESXSdSgXMQElEAlMuFraQYe3EJ9uJishaVYC9ZhG0oeC4uwVZchMXhGPV1YrEeAoF9dAX2EQjsI3BgH5HoyJp8WVnV+HwrqKx4W2pm9DLs9pzh5yPxJNuO+9lyaD9PH+riWLe5cnB1fhYb5hXQOxhj36kBjnWHWFOdyyeub+CmxcUEToQ4+bJ/Cr6TQgghMpFhRDne9J80N38Xm83H0iXfoqTkDRMTpk6RnlMnObh1Cweee5r+jnZsdgd1ay9l8cZNzFu1BqvNPt1NFEIIIcQMEYv5ae/4P06d/BXhyCG0thDtKqJ9dxX9TW600UdemYd5q9ZStmAR5Q2LKKiqZmvrc/zDC/9AW6htwtskAbUQQsxBOmEQOegntLODyKFeMDS2QhsYewg8+mNUMoL3pptw3rImFTgXYy8xZz1bvN5zvqmPxroJdO8lENjHQCqQjkbbh5/PyqolJ2cNlb53mWF09lLsdt9rztPcE2LLoS62HOocniXttFnYUFfA9UtK6A5GeepQF1sOd1HsdfL+K+dxXWUB1rYIJ57v4df/e4xkwsBqP/9ZakIIIWa+vr4dHDj4ecLhRkpLbmfBgi/gcORPd7PGJejv4eC2ZziwdQudxxtRykL18pVc9uZ7mL/uMpxu93Q3UQghhBAzRDQ8wLGDv6Kj6w8krAdRFoNwt5PewyUEThZTXLmURasWUXbXIsrmLyTLO/L+vCvcxd8882keb36c+px6fnzTj7lk7CqSF0wCaiGEmCPGKuFh8TpwNdgZ3PEgvQ8+iHK7yb/nreS/5z3YS4rHdd5otDMVRO83Z0aPCqMVbvc8cnPX4fUuS9WNXorN5h3zXJF4kheP+9lyqHPULOmaAjdvXVvFJTV5tA9EePCVVp4+3IXDauHqhkKuzPNR3JPg1JYeXuw3P83NL/ewbFMF1UvyKZ+fy4e+c9HfQiGEEDNEIhGgsfGfaTn1U1zOMlat/CEFBVdNd7POKRoOceTFbRzYuoUT+/eA1pTUzWfTu/6chZe/juy8mRGuCyGEEGL6aK3p72in9fAB2k48QzD+LK7ik9iyksTjVgZPVJJluZzq6o1seKc5O9pisb7mPIY2+O3h3/KvO/+VaDLKR1d9lPcuey9268RfuSUBtRBCzHKvKeFhVWQtKcCS1cPA779L//++gCUnh8KPfpS8t78NW17eGc8VjXaYM6IH9g3Pjo7FOlPPKtzuOvJyLx0u0eH1Lj5jGD1krFnSDpuFy+oKeOdlNVy5oJAT/jC/2dHCJ3+zm3hSs7DAw3tqS6jtSRJ6IUC/DhBx26hanE/Vknyql+STneeawO+iEEKImaK7+ykOHvpbotEOKivfRX3dJ7HZPNPdrDNKxOMc37WDg89uofHll0jG4+SUlLLhjreyeOMm8ssrp7uJQgghhMhg8WiE9sYjtB4+SNuRg3Se3Iur5CT5Df1klUfxGBassUUUeW6lbu2duH3n/sD7aO9Rvvz8l9nVtYv1pev5fxv+H7U5tZPWBwmohRBiFhqrhIe9ykvOG+swevfj/9G3iezbh624mOLPfIa8u+7E4hn95j0SbScwsHe4REcgsJ9YrCv1rAWPp578/MtHwujsxdhs2eds27lmSW9aWMyGugJO9YX5zc4W3vbfL9IZiOKzW7nS7aGuO0leo4GyBPDN87H01nlULymgqMaLxTJz6okKIYSYWLFYN4cP/z0dnQ/h8Sxg+bLvkJOzerqbNSZtGLQc3M+BrVs48sJzREJBsnw5LL/mRhZv3ETZgoUzqka2EEIIIaaG1pr+zg7aDh+g9chBWg8fpKv5OKgEObVBSpZHqFvjRymN27mYyuq7KS19w6j1nc4mmoxy3577+OG+H+Kxe/j7K/6e2+pvm/S/SySgFkKIWWLsEh52sq+swL0in/BLT9F5798Sa2zEXl1N6Ve+TM7tt49azDAcbqaz82E6Oh4iGDqU2jsURm9MlehYhte7BKt1/LUvT/SEeepQ5xlnSW9aWMy8Qg8DkTj/t7uVu7/3PLtP9WMBGpSdy0IO6uMWcvPtVK0toXpJPpUL83C6X3tpUSQSYWBggP7+/lE3IYQQs5PWmvb2Bzl85B9IJkPMm/cxams+iMXiOPeLp1hX83EObN3CweeeIdDThd3pYv66DSzeuInq5auw2uTtmRBCCCFGjJ4dfYi2IwcJ9/cBYHe5qFqdT+WVLsg6gEEYp7OU0tIPUlZ6Bx5P3Xl9rRfbXuTvX/h7mgeaeUPdG/jUuk+R7xo92zoYDPLcc89NVPeGyV9AQggxw41ZwmNpAe5LSnBUZtH/uwdofucPSLS24Vy4kPJ//ia+G29Epd4ERyJtdHb+kY6OhxgI7AEgJ2cNC+Z/npyc1WRnLzqvMBrGP0s6y2HFMDTPHe3mq7/fz1ON3cQMTWFSsSlmY4V20LAwn6rFZtkOb6GTYDBIf38/h462vyaE7u/vJxqNjmqLxWLB6z17mREhhBAz0+BgCwcP/S1+/7Pk+FazaPE/ke1ZMN3NGmWgu5MDW5/m4NYtdJ9sRlks1K5cw5Vvezfz127A7pKSVEPiRny6myCEEEJMmzPNjtaGAUBeWQW1K9dQ0lCMo/A4A5EtDA6+AhYXxUU3UVZ2B3l5G1DqtfWkz6Y30ss3d3yTPzT+gSpvFfddfx+XlV826pjBwUG2bdvGCy+8QCKRmLA+D5GAWgghZqAzlfDIvb0e94oidDJC789/wckf/5ik30/W6tWUfvGLZF91FUopYrFuOlsepaPjIfr6twPg9S5lfv1nKC6+haysivNu04meMFsOd/LUwbPPkh5ysLmPn2xu5NFjXfgTSZwGLI4r1ufYWFJtx12owBFjIHCY7cf62bxrgEAggNZ61NfNysoiJyeHvLw8amtrsWRZGLQN0qt6aYud4lTPMfZ2tVzcN1wIIURG0TrJyZafcOzYvwDQsOCLVFa+47zfkE2WwWCAw89v5cDWLZw6uB+AsoZFXPPeD7Lwsitx+8Z3me1MpLUmnAjTH+2nP9rPQGxg1H1/rJ+B6AADsQEGogP0x0aOC8VD0918IYQQYsrEoxE6Go8Oh9GjZ0dnUTZ/Aetvu5PyhkUU11UTGHyOtvYH6O19AXo1ubmXUlvzIYqLbxpXuc3Taa156NhDfGP7NwjEAvz58j/nL1b8BS7byIfn0WiUF154gW3bthGNRlm2bBmbNm3iS1/60gR9F0wSUAshxAxxthIenjXF2Es8JHp66P7ud+j9+c8xgkE8V15J4QfeT9batSQSA7S1/ZaOjofw924DDNzu+dTN+zglJbfids87r/aczyxpgOhgjB0vNPLgS8083R7ipGEBramyDHKJs5dyaxfKiBMHdp8AToDVaiUnJ4ecnBzq6uqGtz1eDwEGaB1o5lTHUdrad3PwRAeDvX04w+CJWPEM2lB40GW1hCvWAU9O6L+HEEKI6REMHuLAwc8zMLCLgoKrWLTwH3C5yqe7WcRjUY7tfIkDW7dw/JWdGMkE+eWVXHHXO1i0cRO5JaXT3cTzEk/GR4XJY4XMp98Phc4JfeaZVXaLnRxnDj6HjxxnDqXuUhryGvA5fPicPj7Mh6ewl0IIIcTUOH12dNuRQ3Q1H8dIJgHIKyunduUayhsWUbZgEYXVNSil6Ot7iba2X7Nj96Mkk2GyXNXMm/cxykpvJyur6oLbc2LgBF954Su82PYiK4pW8HeX/R0NeQ3Dz8diMbZv387WrVsZHBxk4cKFXH311ZSWTs7fMxJQCyFEhjtbCQ/X/DyUVRE/dYr2v/8Wfb/9LToWw3vjjRS8/8+xL6yhu3szR/Z8gB7/s2gdJyurmtqaD1JSciseT8N5LXZwplnSG+bl87Y1xawoceAhysDAAH3HT3L/i710dfk5OmBwKOmjKZlPAiteFWONrZtlnjBVBdnk5OaSk1ODz+czQ2ifD7tF0d/XzvGWg7S2HaPxSCtBfw/J/hC2UJKsqBWLNtvuS92iWXl01C2isXohJ4pqOJHlw1AKJ/qs/RJCCJH5DCNKU9N/0dT8XWw2L0uXfIuSkjdM62KChpHkxL49HNy6hSMvbSM2OIgnL5/VN93K4o2bKJ5XP63t01oTjAdHguXTZjSfHjKnPz+YGDzrub0O73DInOPIocxTRo4jB5/TR44jZziE9jl9I8c5c3BZXWf9nkhALYQQYraIhsOc2LeL47t20rTrZQI9XcDI7Oh1b3wLZQsWUrZg4airq8LhJo43/Rvt7b8jEjmF1ZpNSfGtlJbdQW7O2ov62yKejPM/+/+H7+35HnaLnS9c+gXuWngXFmUBIJFIsHPnTp599lmCwSD19fVcc801VFSc/1XW50MCaiGEyEDnKuFhSS0OGG1spOe+/6b/4YcByLntjeT+2TsJ+po42vFfdG/9E4YRxeksparyXZSU3IrXu3zcA9rQLOmnXm1l++EWevv68agYFW6DtxYpcm1xVHyQQOsALSeTpBfSCBkuGuPFHNUVDODAoTQbih3csbaUDbU5qHiMcJ+f/q5O2tqbaD2+g4PdncT6BiAYw2KMbkvSorF6FHavh6yyXPKKSikonUeodCGHXHnsTCheCUaJa41dKS7xuXlzXjYb87ys8bmRCp9CCDFz9fXv5MCBzxMOH6W05HYWLPgCDkf+uV84CbTWdB5v5MDWpzi47VlCvX4cWW4WXHoFDVdcSemiRWgFCSOBP+InqZMkjARJI0lCJ8zt1L6hW1InRz1/+jHD5xhjX8JIEIgFzGA51k8gGhgOmwOxAEmdPGNfHBbHcHDsc/gozy5nsWPxcMh8etg8dGy2PRurJTPKqcwmSqmFwK/SdtUBXwR+ktpfCzQBd2mte6e6fUIIIc5Oa033iSaO79rJ8V07aD10ACOZxJHlpmb5KtbfbpbrKKyuwXLaOJpIBOjoeJi29gfo798JKPLzN1Jf9ymKiq7Has266Pbt6tzFl5//Mkf7jnJ9zfV8dv1nKXYXA5BMJtm1axdPP/00AwMD1NTUcOedd1JTU3PRX3c8JKAWQogMMZ4SHkMG9+6l5777CDy5GeV0kvP2u7C8eQk98W0cPnUXyRMh7PYCysvuoqTkVnJy1qBSn4ieTSQSoaWlhZf2HWbXoSai4SBuojhVknUADvM4lVB4E158nhycnmIKnBUM+qG/U9OobRywWzlhs6BRLMqKcUdWO3WDxwgfP0XTy30cj4++9NdQmrArSciVIO6z4qj24isspqikkqry+cyvWsa80gaUxcqewCBbewM80hvkpf4gg4May2CUFV43f1FVxMa8bNblePBY5Y2zEELMdIlEkMbGb9Jy6qe4nGWsXPkDCgs2XfD5tNZsa93GI8cfIZqMnjHwHSsQtg8kKG7WlDVbyA5aMJSmtSRG0yVhThadImo5AK/8AF6ZuP6Ph0VZyLZnjwqRK7IrRs1cPtN9eo1JMf201oeAVQDKLKh+Cvgd8Flgs9b6q0qpz6Yef2a62imEEGJENByiee8ujr+yk6bdOwn6ewAoqpnH2lvfxLxVaylrWITV9toIVuskfv9W2toeoKv7CQwjits9n/r6T1Naehsu58SU0wjEAnz75W/z60O/pthdzL9d/W9cXX01AIZhsG/fPrZs2YLf76eiooLbbruNurq610xsMwzNs0e7+Z/njk9Iu9JJQC2EENNszBIeSwpwrx0p4QGpRYdefIme+75HaNvzKF82zk/ezOBagyMDvyPR8j/YbD5Kim+hpORWcnMvxWI58695rTV+v5+TJ08O3zo7OwEwNCSVm9ycHCqKC1hQVUxhfh4edxaxvggdh9tpO9JC94F2BiL9tNssvJpdwqGsSmIWB974AGt7D7M4eBBfMkDUpdnvjBFyJQhVJhnMSuLKzaWguJzysnnUlTVQl1dPra+WPFfecBsNrTkUirClN8g/vHqC5/uCDCTMqdWLPC7eXl7Axlwvl+V6yLHLkCaE+P/s3XWcXNX5+PHPHVmZnVl316xkk427J8RICO5QCoUa1Om3QmlpgZYKpeVHcZcCCZBA3F2IrSUrWXf38Zl7fn9MPJsQT6Dn/WJfu3Pl3HMnvObOfe45zyN9k7S1baC45DHs9mZiY+8lJfln51UACDzXvC31W3gp7yUK2goI8A4g0DsQnaJDq9Gi0+hO+NtL8fL8timYaqz4HerFq9mTLsoVY8Q+OgzSw4j09SZOoz+6n1Y5tS2tokV/ZJsjyxWdZ90Z9jt+m+PXH93v8Laas3gALX0tTQfKhRDViqIsAKYcXv42sBEZoJYkSboihBC0VldSuX8PVXn7aCj1jJL2NviRMGgIiUOHk5QzHGNwyGnb6OsrpbHpU5qaluBwtKDTBRAVdQvRUTed04zns+nr2pq1/HnXn2m3tXNX5l08PPRh/PR+qKpKcXExGzZsoLW1lYiICO644w4GDDg1DajZ7uKTfXW8tb2KilYzoUbvi9K/433l3byiKG8A84AWIUT24WXBnGaKkaIovwYeANzAj4QQqy56ryVJkr7m+k3hEWskcEEKhpwwFF8dqtuN2+3EbXfSt2kzbW+9ieXgAdTBRsSTmZjDanC5P0PT5UuA33gC/KZg8BmKomqwtak0tJQgVBWhqqhuN06nk9aODlo6Omjt6KStsxu70wmAXqvFz2Cgx+VLc6+L1PBAFgwIwdHTSU/dASp2rye/ox2Xw3z0HMxaX0qM6RwMz6ZTH4hWuIhQSgnQ70fnX05HrJvNRh2REfEkBiWT7J9EUoDnJ8E/AS+t16nvixBUWuxs7eplS2cf2zr7aD882jrJ14sF4UGMDzQyPshImJf+q99oey9U74CqzRfnH06SJEm65ByOdkoP/Ynm5i/w80tjUPbzBAQMO6+2hBBsqN3AS3kvUdRRRIwxht+P/T0LUhag1/Z/HXHabJTt3kHR1o1U5e9HqCqh8Ylk3jmFjPGT8A8Nv5DTk6SzdTvw38N/RwghGgGEEI2Kosj/CSVJki4jm7mP6vxcKnM9QWlzZwcA4YkpjLzuJhKHDCc6LQPNGWbxOp2dNDV/QWPjp/T2FqAoWkJCphAVeSOhoVPRaC5u0LfJ3MRTO59iY91GMoIzeH7a8wwMHYgQgtLSUtavX09TUxOhoaHccsstZGZmotGc+NC7pt3C2zuq+Hh3Lb12F4NjA/jnbTnMHRSFz+8uanfPagT1W8D/w5P36oh+pxgpipKF50I6EIgG1iqKMkCIMyQ+kyRJ+gaw9HTz5eKFNJWXorrdh4PCKkJ1o6oqqur52ygCidamEKNPwUvjg81tptZWQpXlID11rajbVVTVDeL4on4C31AbQWN6CLzThZepDdXVTs8hI51lMfTUGhGueuD9wz8eqk6P29fo+TH4ofoY4PAoK8VuQ2vtw9vah9bSh8Zhww3EHP6hCXbng0brg8CIojGBJhlnqI4CkzeFukC6XfGAFo1vNWFhuxmYaCMtOI6kgHlHA9ERhoivfPrbYHOwtauPrZ29bOvso97uCZpHeeuZGmxiYpCJ8UFGYn1ODWifwmGB2p1QuQWqtkD9PhBu6CcYLkmSJF1dhBA0NS3mUNlTuFx9JCX9mMSE757XDZsqVNbVrOPlvJcp6SwhzhTHH8f9kXkp89BrTg1Mu10uqgv2U7RlI2V7duKy2zGFhDFi/o1kTphCWHzihZ+gJJ0lRVG8gOuAX5/jfg8BDwHEx8dfgp5JkiT9bxCqSktVhae4Yd5eGkqLEaqKt58fCYOHkTRkOIk5wzAGnbkexpEUHg2Ni2htXYsQDozGLNLSHiMyYj5eXqEXve9u1c0HxR/w/P7nAfj58J9zd9bd6DQ6KioqWL9+PXV1dQQFBXHDDTcwaNCgEwLTQgi2l7fz5rYq1hU3o1UU5gyK4r5xiQyLD7xkxZ+/MkAthNisKEriSYtPN8VoAfChEMIOVCqKUgaMAnZcpP5KkiRdVZw2G3uXL2H354tw2uzEZGTh5euNRqNB0WjQaLXo8SbEHkGwNRxflx8qKr1+3TT7N2IxmtFofUnSjDy6vaIKHKUl2Kp3okloQ5fjRAl0gtCgFxl4qyPw1uYQlWpEM8BzHBSFbrOF9u4eWju7aO3swmy1AqDVaggPDiEyLJTI8HCiIiMwGAxoNBpazE6eWVHC3toexqeF8X9z0ymtP8jBRd3o+/zp8u2gLrCYUr9mGlU9jt4c3C4f/HQuZgzUcPOwaCYkTcbodfZTrtscLrZ1eYLRWzv7qLDaAQjWaxkfaOJHQUYmBBlJ9vX+6ouf0wZ1uz3B6Motnr9VJ2h0EDMcJvwUkiZC3Gh43HDe/86SJEnSpWW11lNc8ls6Orbg7z+UzIynMRoHnHM7btXNmuo1vJz/MmVdZST4J/DUhKeYmzQX3Ulpr4QQNB4qpmjrRkp2bMXa042Pn5GsCVPJnDCFmIwszzVWki6/OcA+IUTz4dfNiqJEHR49HQW09LeTEOIV4BWAESNGiP62kSRJkvpn7eulOn8/Vbl7qczdi6W7C4CI5FRGX38LiUNGEJU64IyjpI+wWKppbFxEY9On2O1N6PVBxMbeRVTkTZhMmZfsHIrai3hixxMcaD/AhJgJPDbmMWKMMdTU1LB+/Xqqqqrw9/dn/vz5DBkyBO1x52J1uPlsfz1vba+ktLmPYD8vfjgllbvHJBAZcOlrVpxvws7TTTGKAXYet13d4WWSJEnfKKrbTeHGNWxf+AHmzg5SRoxh4h3fIiQ2Djh9Cg+/4REYcsLQGPoZvdXXR/Oi/9Bw6APcGX14TxYgFIKCxxIZMZ+wsJno9YEAWCwW6urqDueOrqK+vh7n4XQdJpOJxORk4uLiiIuLIyIiAp3u1Jvyz/bX8/slZahC4YHrgrH7fMmvlr7P6AM3IjQ+1EzJp1sfR1HVGGraVPRahdlZkdw8IpaJqaHotGd3097jcrOzyxOM3trZy0GzzdNPrYaxgUbuiwlhQpCJDD8fNF8VkHY7PaOiKzd70nbUfgkum2dkeNQQGPsDSJwE8WPA+/zylEqSJEmXjxBuauveoaLiWQAGpD1ObOzdeOrDnT2X6mJl1UpeyX+Fyu5KkgOS+cvEvzA7cTZazYltdTbWc3Dzeoq2baK7uQmd3ovk4aPInDCFxCHD0enPIoXUVUgIcdofVVUv2fqL3bYEwB0cS+8B8DnwLeAvh38vuRKdkiRJ+iYRqkpzZbknbUfuPhoPlSCEio/RRMLgoUdHSfsFBn11Y4DbbaGlZSUNjYvo6toFaAgJmciAtN9dkhQex7M4Lfwn9z+8V/QeAd4B/G3S35iVOIvGxkbeW/weZWVl+Pn5MXv2bIYPH47+uO86dZ0W3t1RzYe7a+m2OsmK8udvNw9mfk40Pvpz+z52IS52Ran+Igv9fsuQ048kSfo6EkJQvmcXWz54i46GOqIGZDD/J78iODmdPy49wNZ3ShF2N6rDjRDCMwLYS4vGWwvmTsSmTthUfEKbbrcDt60Ht3AgtNGg+wVKmR5tlS8arQ+gQVVV3O4duFxu3G5P2pAjNNpgtJowNFoNGo0WjVWBEhDFvcBBBAdP6L8QYHa4cLrdaDRuUNz8e5WKTmSiVQeTZwK9tw5LwUDcqmBQjIknrovlupxogvy+OlWGxa2yu9vM1s5etnb2kddrQQV8NAqjAvz4TUQUEwKNDDYZ0Gm+KiDtgqY8z+joys1QsxOch/NgRwyCEQ94RkjHjwXfwLP7R5QkSZKuCn19pRQV/5qenlxCQiaTkf4kPj7R59SGS3WxrGIZrxa8SnVPNamBqfxt8t+4Jv6aEwLTNnMfpTu2UrhpLY2lxSiKhrjswYy58XbSRo3D23D1z7Jxu910dHTQ1tZGa2srra2ttLW10d7ejtPplMHdbwhFUQzANcB3j1v8F+BjRVEeAGqAW65E3yRJkr7urL09VOXvp2r/Hqry93tGSSsKkcmpjL7xNpKGDCcyNQ2N5uwCs0IIenryaGhcSHPzUtzuPnx940lJ/jmRkTfg4xN1aU8I2Fy3mad2PkWDuYGb0m7ip8N/iq3LxkcffURxcTG+vr7MmDGDUaNG4eXldbTfX1Z28Oa2KlYfbAJgdnYk941LYmRi0CVL43Em5xugPt0Uozog7rjtYoGG/hqQ048kSfq6qS8pYvP7b9JQcpCg6Fiu+/lvSB05lrpOKze9sI2i5l4mosNXUdAGGNCF+KA1eaMonPD4TkFBVW1Yeiqx9VbgMljBBDqHHh9DAoagVGw2MJvNmM19mM1m3G5PKn+dVotfgB9GoxGj0YifnwGNRotyQvvH/a0cOyZARXsHe+pLEYYW9DpPoDfSN5Ko7hRcHRoCwnyJywxBo1Uw+eiYOyiKzCj/M74vDlVlf4+FrZ19bOnsZV+PBYcQ6BQY7u/HTxIjmBBoYniAAe+vmiqtqtBceDhlx2ao3g72Hs+6sAwYcickTYLECWA4c74vSZIk6eqkqnaqql6kqvoldDojA7OeJSLiunO6GXKqTpaWL+WV/Feo66sjPSidf075J9Pip6E5XG9Bdbupzt/PgU3rKNuzE7fTSUhsPBPvvI+siVMxBodcqlO8IE6n82gQ+vjf7e3tJzyg9vf3JywsjPj4eLy9PWmxTv7RaDT9Lj+b9Vdq3yeeeOIKvvtXnhDCAoSctKwdmH5leiRJkvT1JVSVpopDVOXuozJ3D01lhzyjpE3+JA4eStLQESTmDMPgH3BO7dodbTQ1LaaxcRFm8yE0Gl8iwucQFXULgYEjL0uAt83axjNfPsPKqpUkByTz1uy3SNAmsOaLNRQWFuLt7c2UKVMYM2YMPj6eFB02p5vPcxt4c3sVRY09BBr0PDQphXvGJhAT6HvJ+3wm5xugPt0Uo8+BDxRFeRZPkcQ04MsL7aQkSdKV1NFQx5YP3qZs9w78AoOY8Z0fMmjaTDRaLVsOtfLI+/tw2d38VWNg9uw0/EZE9JvCw+nsoqV1FY1VC+m25kK4QNeg4GceCCm30eIIpLa2lqbStUdHQYWGhhKXEXc0XUdISMgplXW/Sre9m6Xlq3h13ye0aYvwShCk+GdyU/p8JgdNZ9fbDbTU9zBqfhIj5iZ+5cXULQQFvVZPUcOuPnZ2mbGqKgowyOTLd2LDmBBkZHSAH366r3jyLAS0Fh8uargZqraCtdOzLjgFsm+ExImeH1PEOZ23JEmSdPXp6t5LUdFvsFjKiIxYQFrab/HyOvtAscPtYEn5El7Lf40GcwNZIVn8e+S/mRI35ej1q622mgOb1lG0dSPmzg58jCYGTZvJwMkziEhOvSKjgvpjtVpPCUS3trbS1dV1dBtFUQgODiY0NJT09HRCQ0MJCwsjNDQUb+9LN1VYkiRJkr6uLD3dVOftO1zgcB/W3h5QFKJSBjDmpttJGjKciJTUsx4lfYSqumjv2ERjw0La2jcghIsA/6FkZDxNRPhcdDrTJTqjk/ohVD459An/3PtPbC4bPxjyA26KvYkdW3ewNHcpOp2OCRMmMG7cOAyHZ4g1dlt5b2c1//2ylg6zg/QIE3++cRDXD4nB1+vypfE4k68MUCuK8l88BRFDFUWpA37PaaYYCSEOKIryMXAQcAE/FEK4L1HfJUmSLqm+zg52LPqAgvWr0Xl5M+7Wuxhx7Q3ofXwQQvDSpnL+urKYRKHhL8ZAhnxrEF5xJ16UXK4+WtvW0ty8lI72zQjcKO1aHBXhdCqjaCSGXosZGkvR6/XExMQwYcIE4uLiiI2NPXpBOVdWl5VNdZtYXrGczXVbcAsXqiOUwYE384dpd5MekkxzZQ/Ln8vHYXMz57uDSB4a1m9bQghKLLajOaS3d/XR4/KM4Er38+HOqGAmBBkZG2gkUP8VlxUhoL3cE4yu3OIJSJsPT8IJjIf0az0pOxInQoAsYSBJkvRN4XL1UV7xd+rq3sPbO5KcnNcJDZly1vvb3XY+O/QZrxe+TpO5icGhg/ntmN8yMWYiiqJg7e2heNsmDmxaR3NFGYpGQ9LQEWRPnkHSsJFXLK+0EIK+vr5T0nK0trbS19d3dDutVktoaCgxMTEMGTLkaBA6JCTklDoSkiRJkiQdo6pumsvLqMzdQ2XuXprKD4EQ+PoHkDhkOElDhpMweOg5j5I+wmwuP1zw8DMcjlb0+hDi4r5NdNTN+PmlXuSzObOKrgqe2PEE+1r2MSJiBL8Y9Auqcqt4aclLKIrC6NGjmTBhAkajESEEe6s9aTxWFDahCsGMzAi+PT6RsckhV80D+yO+8tuOEOKO06zqd4qREOIp4KkL6ZQkSdKV5LBa2P3Fp+xZ+hmqy8WQmdcy5sbbMAQEAmC2u3h0YR7LC5uYio7fJ4QTd/dAtCZPPie320Zb+waam5fS1rYBIeyoVl86a+Ko68mgxxoKKAQEBJAQF3dCMUPtWVQEPh2X6mJX4y6WVy5nbfVaLC4LvpogbO1jCHSP4Z83XMvYlFAASnY2suG9EvwCvbjuR0MIiTmxoGCfy83ilq6jeaTbnC4AEny8mB8WyMQgE+MCjYR7n8UNf2f14aKGWzxB6d7DmZ9MUZAy1ROMTpoIQYnnfe6SJEnS1autbQPFJb/Dbm8iNvYeUpJ/jk53doVsbS4bnxz6hDcK3qDF2sKQsCE8MfYJxkaPRXW7Kd/7JQc2rqVi325Ut4uwhCSm3PsgmRMmH71uXw6qqtLd3X3KaOi2tjZsNtvR7by8vAgLCyM1NfWE0dBBQUHnPENKkiRJkv5XWbq7qDoySjp/P7beHhRFQ2TaAMbdcidJOcM9s6bO89rqcvXR0rKchsaFdHfvQ1G0hIRMJTrqZkJCpqDRXN4H33a3nVfzX+X1wtcx6Az8btjv8K/357M3P0NVVYYNG8bEiRMJCAjA7nLz6b463txWRUF9NyYfHfePT+TesYnEBV+9NTfk43hJkqTD3C4n+WtXsuOTD7H2dJM+diITbr+XwMhjhQ2q2sw8+PZuylvN/ABvHhybSOC8ZBSthvb2HVRWvUN392bAhsvpS0tLIq2tifR1hRDm7UPmoEHEp6QQFxeHv/+ZczufDSEE+W35LKtYxqqqVXTYOjDpTYyNmEZBaQrlNRHcPDye38/PwuSjR1UFOz4tI3dtLTHpgcx6MBtf44mFD3d09fGjohpqbQ4ivHRMCTYxPsjI+EAj8b5nMZ24p+FYUcOqzdBV41nuF3YsGJ04CUJSjiXJliRJkr5xHI52Sg89SXPz5xgMqYwY/jEBAcPOal+L08LC0oW8Wfgm7bZ2RkSM4OmJTzMqchSt1ZVsfPtVirZtwtrTjSEgkKGzryVr0nTCE5Mv6TkdKVR4ciD6SKHCIwwGA2FhYWRnZx8NRIeFhWEyma66EUuSJEmSdLVTVTdNZaVU5u6lcv9emivLQAgMAYEkDx1xdJS0r+n877GFEHR376WhcSEtLctxuy0YDCmkpvwfkZE34O3d/4zjS213027+uOOPVPVUcW3ctUxwTiDv8zxcLhc5OTlMnjyZoKAgWnpsPLumlA92VdPW5yAlzI8/XZ/NjUNj8PO++sO/V38PJUmSLjEhBKU7t7L1v+/Q1dxI3MDBTLrzPiJTB5yw3friZn78wX4Up8o/NH7MvDkTv2ERdHXtofDAU9jt+TidXrS1xdPdGIdXiSDM7mTohKGkfec2vC9CQPqI8q5yllUsY3nlcur76vHSeDE5bjJzEudSURPHP1ZXYPTW8dLdg5idHQmA3eJk9esHqDnQwaApsYy/JRWt9tgTZZtb5S+Vjbxc20qCrxefDkllbKDfV99I97UcK2pYuQU6yj3LfYMgYTyMfcQTlA7LkAFpSZKk/wFCCJqal3Do0JO4XH0kJf6IxMTvodF89UNOi9PChyUf8vaBt+mwdTA6cjR/y/kbWT6pFG3dyLubHqG1pgqtTkfy8FEMnDydxJzhaC9yGgyHw0F7e/spaTk6OjpOKFQYEBBAaGgoiYmJJwSizzdFlyRJkiRJHk6bjbLdOyjft5vq/P3Y+npRFA1RAzIYf8tdJA0dQXhi8nmPkj7Cbm+msfEzGhoXYrVWodX6ERE+j+joW/D3H3rFHix32br4x95/sLhsMXG+cfwy+JfU7apjj30P2dnZTJkyhdDQUHJru3hi1X6WFTTidAumZYTz7fGJTEgN/Vo9FJcBakmS/qfVHshn8/tv0lR+iND4RG781R9IHDL8hA9yVRU8v76M59aWkoqGZ0xBZN83CKvxELu+/Al9fV/icPjQ1jCGsM0woLCU4DAtoQ8+SMC8a1G8vM7Qg7PXZG5iReUKllcup7ijGI2iYXTkaL6X8z2mx0+nx6Lj0YV5bC8vY3pGOH+5aTBhJk8woLPJzPIXC+hpszLlrnQGTjwxv3N+r4WHD9ZQarFxb3QIv0+JPn2BQ0uHJ3f0kaB0a7Fnubc/JIyDkQ94RkpHZIOcrixJkvQ/xWqtp6TkMdo7NuPvP5TMjKcxGgd85X59jj7+W/xf3jn4Dl32LsZFj+PBzAfwr3Nw4O3lbMndi1BVIlPSmH7/90kfPwlf44UXI7Jarf2m5ThdocKMjIyjaTlkoUJJkiRJuriEELRUllOwfhVFWzfhsFrwCwwiZfhokoYOJ2HQUHyMZ5cm7ExU1UFb2wYaGhfS3r4JUAkMHEVS4g8ID5+DVnvlHjQLIVhasZS/7/k7fdY+7jLchVKpUG4tJz09nalTpxIcGs6Kwkbe+ngb+2u6MHrruHtMAveOTSQp1O+K9f1CyAC1JEn/k1prqtjywVtU7t+DKSSM2T/4KZkTp5xSybfH5uRnH+aytriFWeh5LCkc0/WCgvqH6SrZgtPpTWP9SMILQhi6aiOGjHRCn3oK04zpKBeQT/qIbns3q6tXs7xiOXub9yIQDAodxP+N/D9mJ80m1DcUIQSLc+t5fMkB3KrgLzcO4raRcUeD7NWF7ax+rRCtXsOCnw4lOjXwaPsuVfDvmmaerWoiVK/ng8HJTAs5aaS3rRuqtx8uargZmgoBAXo/iB8DObdD0iSIzAGtvKxIkiT9LxLCTV3du5RX/AOAAWmPExt7N4py5mthj6OH94ve572D79Hj6GFi9ARuD7wWR34tu/77LDZzH8agYEbMv5GBk6YTEht3Qf00m80UFBRQUlIiCxVKkiRJ0lXC1tdH0baNFKxfTWtVBTq9FwPGjCd72kxiMwZe8CjpI/r6SmhoXERT02Kczg68vSJITPguUVE3YTAkXZRjXIjanlr+tPNP7GrYxXh1PPFt8dgsNlJSUpg2bRreAaH8d1cN7+4spKXXTlKoH3+Yn8XNI+Iwfg3SeJzJ17v3kiRJ56inrZXtH7/Pgc3r8DYYmHTXtxkyex56r1NHQJW19PLgW3uo6bDwY7y5daJKa8x/KD2wDpfLm7raoQS05TD04yX4aioIf/x3BN566wVfPK0uK5tqN7GsYhlbG7biUl0k+ify/SHf59qka4n3jz+6bafZwW8XF7C8oIkRCUH849YcEkI8T0yFEOxfU8OOz8oJjTUy9/uDMQX7HDs/i41HDtawv9fCDeGBPD0gliD94ctCTwPsetkzQroxF4QKWm+IHw1Tf+tJ2RE9DHQXZ3S4JEmS9PXV11dKUfFv6OnZT0jwJNLTn8TXN+aM+3Tbu3mv6D3eP/g+vc5epgdPYHrvQNqWF7K74SV0ei9SR41l4KRpxA8ecsoD5HPhdDopLS0lLy+PsrIyVFUlPDz8hEKFYWFhBAYGykKFkiRJknSZCCGoKyqkYP1qDu3chsvpICwxmen3f5+MCZPx8bvwkdIATmcPzS1LaWxYSE9vPoqiJzR0OtFRNxMcPBGN5sqHRp2qk7cPvM3LuS+T0JfALb234LK4iEiIYNq0afTqAvnn9io+z8vH4VKZNCCMZ25OZHJaGBrN1yeNx5lc+X8FSZKky8DW18euxR+zf+UXAIyYdwOjrr/ltNODVxY28rOP8vB2qfzdz0LilM8odm7A3epFbW0OOus4ctZsw6/4A/yvvZaIX/0furDzL5rgVJ3sbNjJ8srlrKtZh9VlJdw3nLsy7mJu8lwygzNPyR+1oaSF/1uUT6fFwS9np/PdSSloD1+cXA43G94rpvTLZlKGhTP9W5novT0396oQvFHfxpPlDfhqNLw8MIEF4UHHGi5fD598B2w9EDsCJj3qSdkROxL0PkiSJEkSgKraqap6iarqF9HpjAzMepaIiOvOmO+w09bJuwff5YPiD7DZLMx1jSS9IYD2FWWUilqi07O45qGHSR87EW/D+U9RFUJQV1dHXl4ehYWF2Gw2TCYTY8eOZfDgwURERJx325IkSZIknT9zVycHNq2jcMNqOhsb8PI1MHDKDAZNm0lEcupFOYYQKp2dO2lsXERL60pU1Y7RL520tMeIjLgOL6+Qi3KciyGvNY8ntj+Brc7G7L7ZaK1aImIimHzDVA5ZDfxiRRW7qwoxeGm5fWQc945NJDX84gTvryYyQC1J0jeay+Fg/6qlfPnZx9gsZrImTmX8rXfjHxbe7/ZuVfDsmhJe2FBOhsbNT7KXoY9aR5tdT13tICx9YxhW30XA4tfxio8n8rXXME4Yf159E0KQ15rHsoplrK5eTYetA5OXiblJc5mbNJfhEcPR9jNizOJw8dSyIt7fVcOACCNvfnskA6MDjq7v67Sz4qV8Wqp7GX1dMsPnJBwNFtTZHPy4qIZtXX1MD/bn2Yw4Irz1nh1VFbb8HTY87SloeP8qCE07r3OTJEmSvtm6u/dRVPwbzOZDREYsIC3tt2e82Wu3tvP2wbf5sOhD/FsF13YPwL/SjsvWjD1UZfSNt5I1aRpBkdEX1K/Ozk7y8/PJy8ujo6MDnU5HZmYmOTk5JCcnyxHSkiRJknQFqKqbqrx9FKxbTcW+L1HdbmIyBjL6htsYMGY8eu+LMxDKZmugsfETGho/wWarRaczERV1M9FRN2MyDbqqigb2Ofp4bu9zbN2/lUHdg/Cz+xEREcGI2ZPZ3eHFfYuqaei2ERfsy2PXZnLLiDgCfPVXutuXjAxQS5L0jaSqboq2bGTbx+/R29ZK4pDhTLzjW4QnJp92ny6Lgx//dz+bDrUxPbCcW4b9B51WobZ2IK2tQxnhF0Hk+++A2UzI979H6He/i8bn3C+kZZ1lLK9czvLK5dT31eOt9WZy7GTmJs9lYsxEvLSnT5uxr6aTn32US3WHhQcnJvHzmen46I8FsZsqulnxUgFOu5s53xtE8hDPqG4hBB83dfLYoTpU4O/pcdwVFXzsAm3pgE8fhLK1MPg2mPdP8Pp6Flf4X6UoShzwDhAJqMArQoh/HV73CPAw4AKWCSF+eXj5r4EHADfwIyHEqivRd0mSvj5crj7KK/5BXd27eHtHkjP4NUJDp552+zZrG28Wvsmy/YuIq/XipqZYdD0O9N52BowZT9ak6cRlZV9QeiybzcbBgwfJy8ujuroagMTERCZOnEhWVtYJhQyFEBQ19rKrsh2nW0UIUIVndpEQAlVweNmx1+qR5QjPOvXYcjiy/si+4uj+x9rt//exvzl8rH7aVY/tIzipP8e1dbKT778VlDOsO/3OJ687074n3/SfaV/l5LVXT7xAkiRJuoi6W5op3LiGwo1r6Wtvw9c/gGFzF5A99RpCYi6srsQRbredtrY1NDQuoqNjKyAIChpHSvLPCAubiVZ79c0CXlu1lpfWvURscyxjHGMIDgkmZeJENjdpeG5hNXaXyvjUEJ5YkM20jPCjM6WvBq4OG5a8lovergxQS5L0jSKEoCpvH1vef5PWmioiklOZ/f2fEJ+dc8b9ihp7eOitnTT22Lk3YyGTY7+ksTmdmupMBiUOZuz+5ah7P8IwYgSRT/wB75SUc+pXY18jK6pWsKxiGaWdpWgUDWOixvCDIT9gWtw0jF5nnqLjdKv8e90hXthQRlSALx98ZwxjU04cqVa8o5EN7xdjDPTmuh8PISTG02arw8mjJbWsbOthTIAf/8qMJ8H3uJzbdXth4begrxmufRZG3H/qHaj0deACfi6E2KcoignYqyjKGiACWAAMFkLYFUUJB1AUJQu4HRgIRANrFUUZIIRwX6H+S5J0lWtr20Bxye+w25uIjb2HlOSfo9P1f/1qNjfz5v7X2LtlJYk13szv8DwwjRuYwcDJ00kbPQ4vH9/z7ouqqlRUVJCbm0txcTEul4vg4GCmTZvG4MGDCQwMPLqtEIKS5l6W5TeyLL+RijbzWR1DUUCjeEKpGkU5+lqjeIKxx7/2rD+y7Mhyz2vlpNdH2+C415pjx1KOa/PoPhrQKJoT2ji+f8dftk+OVx//Upy08uTQ9vGrT113aiD89McUp11/pm3PcAhJkiTpa8LldFK+ZycF61dTXZALQFLOMKZ96yGSh49Eq7s4o4B7ew/Q0LiQpqbPcbm68fGOJinxEaKibsTX9+IEvy+2JnMTf135V5ylTrLt2fia/AjMmcjGesGzSxvw0Wu4cVgs941LJD2y/3SkV4JqcWIpaMOyvwVHVc8lOYYMUEuS9I3RXFHG5vffoKYwn4DwCK790aOkj534laOyPtl9gN8sLsdH28ejw98k0GVk55fXkRCbzY26JsTTf0YxGol6+mkCbrj+rKcFddm6WF29mmUVy9jXsg+AwaGD+dWoXzErcRahvqFn1U5ZSy8/+SiXwvoebhoWy++vy8Lf59hFXXWrbP+snLy1tcSkBzH7wWx8jJ71y1u7eLSkjl6Xm9+nRPNQXBjaI/0XAna/Bit/DaYoT0qPmGFn1Sfp6iOEaAQaD//dqyhKERADPAj8RQhhP7zuyOPuBcCHh5dXKopSBowCdlz2zkuSdFXr7s6lqvpF2trWYjCkMnz4RwQGDO9324aeet5a+Rz1u/YS1+jDGHcAfmFh5Nw6k6yJ0wgIv7Dcz83NzeTl5ZGfn09fXx8+Pj4MGTKEnJwcYmNjT7hGH2ruZWl+I8sKGilr6UOjwJjkEB6YmMT0jAiMPrrjgsuegPEJr+XD2ivi4+9d6R5IkiRJ56O9roaC9as4sHkDtt4eTKFhjL3pDrKnzsA/tP8Um+fK6eykqelzGhoX0dd3EI3Gi7DQmURF30Jw0DgU5epJ5SWEoNXaSlV3FeVt5ZRXltNc1EyINQSXtxfuAeP4olalbks7MYG+/HpOBreNjCPQcPoZ1ZeTcKpYizuw5LZgK+4At0AX5ov/rAQMOeHwzMU9ngxQS5L0tdfV3MTWD9+hZPtmfE3+TL3vIXKumfOVT2bNlmYe/3QpnxRGkhpQzd1R22koHYIrIJHrMqLwfeE/OOvrCbjxRsIf/QW6oKAztgdgcVrYWLuR5ZXL2Va/DZdwkRSQxMNDHmZu0lzi/M/+Sa6qCt7aXsUzK4sxeGl56e5hzM6OOmEbm9nJ6tcPUHuwg8FTYxl/cyoarYYel5vfHqpjYVMng4y+PD80hQy/40aq2fvgix9D4SJImwU3vASG4LPum3R1UxQlERgK7AL+BkxUFOUpwAb8QgixG0/weudxu9UdXiZJkoQQgo6OLVRVv0RX1y50ugCSk35KQsKDaDTep2xfXL6fzxb/B0dBLUarjiQvEynjxjBqxgKi008t9Hsu+vr6KCgoIC8vj6amJjQaDWlpaeTk5DBgwAB0umO3NGUtfZ6R0gUNlDb3oSgwOimYb43LZvbASMJMp/ZdkiRJkqTz47BZKd2xlfz1q2gsLUaj1ZE6YjSDps0kfvAQNP3UVDpXQrjp6NhGQ+NCWlvXIoQDk2kgAwb8gciI+ej1gRd+IhfA7DRT3VNNVXcVVT2en+qOavpa+vA3+xNmDSPIEeSZOaWJoiJsCLubFKz5ZkYnBfPYtZnMyIxAp73ywXWhChxV3Vj2t2IpaEXY3GhMeoxjozEMDUcf7XfJHuDLALUkSV9blp5udn76IXmrV6DRahl9w22MvO4mvA2GM+7ncLSTV/waj6/0pqgjjUnBBxjgaqW5eQhTR48k5vPPMT///1BSUkh49x0MI0eesT1VqOxt3stnhz5jbc1arC4r4YZw7s66m7lJc8kIzjjnD/GGLiuPLspjW1k70zLC+ctNgwg3nZg7q6PRzPL/5NPbYWPqPRlkjfcUltrS0ctPimtocjj5aUIEP02MwOv4UeStJfDRPdB+CKb9Dib8zDN3WPpGUBTFCHwC/EQI0aMoig4IAsYAI4GPFUVJpv+Mn/1OrlYU5SHgIYD4+PhL0m9Jkq4OQrhpaVlJdfXL9PYdwNs7krTU3xIdfRs63Ym1CewWMzvWf86utYvRNZrRIdAmhDBu1i2MmDgHvdf5B4OdTiclJSXk5eVRVlaGEILo6GjmzJlDdnY2fn7H+lLReiQo3UhxUy+KAiMTg/njgoHMzo485fopSZIkSdL5E0LQXH6IgvWrKd6+CYfVSnB0LJPvvp+sSdMwBARelOPYHW3U171HQ+Mi7PZGdLpAYmLuOFzwMOuiHONsuVQXjX2NVPZUHg1EHwlKt1hb0KgaQuwhhNvCiXZEk2HNQBEKaMAYGkSvMZm8LiP7Gyx4ORSuHxLNt8YlMjA64LKex+k4m81Y9rdg2d+Ku9uO4qXBNzsUw5BwvFMCUbSXflaZDFBLkvS147Tb2LtsCbs/X4TTZid72jWMu/lOjMEhZ97P2Ul1zWtsPbCOf++7ix67P9N9i0mwWhk+cho5LS30/vKXWFwuwn7yY0Luvx/F6/TTa5rMTSwpW8LissXU9dVh1BuZmzSXa5OvZXjEcDTnMb1ICMGS3AZ+t6QQtyr4842DuH1k3CkB7qqCNla/fgCdXsP1Px1KVGogFrfK0xUNvFbXRoqvN18MS2OY/0mFDgsWwec/Ar0v3PMZJE855z5KVy9FUfR4gtPvCyE+Pby4DvhUeJKHfqkoigqEHl5+/JD+WKChv3aFEK8ArwCMGDFCZgiVpG8gVbXT2Pgp1TWvYrVWYzAkkZnxFyIjrzthxLSquqnOz+XLtZ9Tu28filtgNjrxm5DKLTc8THJs5nn3QQhBbW0teXl5FBYWYrfbMZlMjBs3jpycHMLDj00Prmozs6zAk1P6YKMnF+LIxCD+MD+LOYOiiPCXQWlJkiRJupisfb0UbdlI4fpVtNZUofPyJn3sRAZNm3nBs6WO12c+RG3NGzQ2LUYIJ8HBE0hL+w1hodP7ncV1sQgh6LR3Hg08V/ZUUt1dTVVPFTW9NbhU19FtA/QBpGvTGWcfh2+vL85OJ8ItUBSF6OhokoYl4TRFsaXexTv5TfTUuIgLFjw6K507RsUT7Hfl03i4u+1Y8lqx7G/B2WgGDfikBREwJxGfrBA0Xhc++v1cyAC1JElfG6rbTeHGNWxf+AHmzg5SRoxh4h3fIiT2zGkznM4eampfp7b2LTbXZPFu0Q/wEU5m60sYExXB1MHjcfz9H3QXFuI3fjyRv38cr9OMErW77Wyo2cBnZZ+xo2EHAsGoyFH8YMgPmJEwA1/d+Rd86jQ7eGxxIcsKGhmeEMSzt+aQEHJigFkIwf7VNexYXE5orJG53x+MKdiHfT1mHjlYQ7nVzndiQ/lNcjSG46cIueyw+jH48hWIGwO3vAn+0efdV+nqo3i+Eb4OFAkhnj1u1WJgGrBRUZQBgBfQBnwOfKAoyrN4iiSmAV9e1k5LknTFuVy91Nf/l5raN3E4WjCZBjEo+z+Ehc1AUY7dmHQ01FG4YQ0Fm9di6+rGrndTE2cnefx4Hp7+COF+559bsqOjg/z8fPLy8ujs7ESv15OZmUlOTg5JSUloDs/yqWm3eILSBQ0U1nuC0sPiA/ndvCzmDookKuD8r8GSJEmSJJ1KqCq1BwspWL+KQ19ux+10EpGcxozv/JCM8ZPwNvh9dSNncxwh6OzaSU3Na7S3b0Sj8SY6+mbi4+7HYEi6KMc4wu62U91TfUpajqruKnocxwoA6jQ64k3xJPonMjlmMuGucPTdesxNZhorG3E6nQCERIWQOCqRpKQk/EOjWFnUxrN76jjYWIW3TsOc7EhuHRnHmKQQNJorW99CtbmwFrZjyW3BXt4FAvRxJgLnJ+ObE4bWeObAuepwYN66lZ4VKy9632SAWpKkq54QgvI9u9jy37fpqK8lakAG83/yK2Iyzjytx+Xqpbb2LWpqX8fmsPBpxcOsqkghUtPD9T7NXH/NNQRuXEHn/X9FGxxM9D/+jv/cuf0++S1qL+Kzss9YVrGMHkcPkX6RfDfnu1yXch1xpguvELyxpIVfLsqnw+zg0VnpfG9yCtqTLl4uh5v17xZzaHczqSPCmXZvJkKn8ExFI/+uaSbSS8/CnBQmBp9U7berFhZ+C+r3wtiHYcYfQHtxKidLV5XxwD1AgaIouYeX/QZ4A3hDUZRCwAF86/Bo6gOKonwMHARcwA+FEO7L321Jkq4Eh6ON2tq3qat/D5erh+Cg8SRk/Z2goHFHr4Nul4vyPTvJW7OcmsJ8hAJ1YRZqRjgZP3EBT+bcd9YFf09ms9k4cOAAeXl51NTUAJCUlMTkyZPJzMzE29szQqq2w8LyAk/6jvy6bgCGxAXy2LWZzBkURUygDEpLkiRJ0sXW19nBgY1rKdywhq7mRrz9/Bg0bRaDps0kPDH5oh1HVZ20tKygpvY1ensPoNcHk5T0E2Jj7sTL68wzpM/YrlBpNjefEHyu7vGMhm7oa0Acl9kw3DecxIBEZifOJsE/gcSARBKMCWjMGmqra6msrKR6ZzVljjLP9uHhDBs2jMTERBISEvDx8WVbeRvP76lj1YGtOFwqg2IC+NP12VyXE02A75W99xZuFVtpJ5b9LVgPdoBLRRvig2lavCevdOiZv0sJh4O+bdvoXbmS3nXrUfv60ARc/NQkMkAtSdJVraG0iM3vv0l98UGComO57ue/IXXk2DNOH3K5zNTVvUt1zau4XF0I71k8s2s6Fb0+5Ghb+FFECMNSMmlAdoNiAAEAAElEQVT79U/pbGkh8PbbCP/pT9H6+5/QTpeti2WVy/js0GeUdJbgpfFievx0rk+7ntGRo9FehIIPFoeLp5cX8d7OGtLCjbxx30iyY079sO/rtLH8xQJaa3sZc30yw2YlUGKx8UheDQV9Vm6NDOLJtFj8dSf1qWwtfPIguJ1w67uQdd0F91m6OgkhttJ/XmmAu0+zz1PAU5esU5IkXXWs1jpqal6jofFjVNVBWNgsEhO+i7//4KPb9LS1UrB+FQXrVmHu6sThp6FgQCf1iW5uHHoHj2fdQ7DPuRfWdbvdVFRUkJubS0lJCS6Xi5CQEKZNm8bgwYMJDAwEoL7LyvJdFSwtaCSvtguAnNgAfjM3gznZUcQFn7nWhCRJkiRJ5051u6nM3UPB+tVU7NuNUFXisgYx7pY7SR097oJqS5zM5eqloeFjamrfxG5vxGBIJiP9KSIjr0erPfs0Xb2O3lNGQR8ZHW1z245uZ9AZSPBPYHDYYBakLDgaiE70T8SgNyCEoKWlhcrKSqoKqthetR2bzbN/SEgIgwcPJikpiYSEBIxGIwB1nRZe2V7Hwj111HdZCfDVc+eoeG4dEUdWtH+//b1chBA4ano9Qen8VlSLC41Bh9/ICAxDw/GKM50xpiIcDsw7d9KzYiW9a9ei9vai8ffHNHMm/nNm4zd6NHhf3HQrMkAtSdJVqaOhji0fvE3Z7h34BQYx4zs/ZNC0mWi0pw8Ku91W6us/oKr6JZzODoKDJrO3ejrP7jJgF1ru0nTzyNAhONe+QtN/NuKdnk7sv57Dd8iQY22obnY07uCzQ5+xoXYDTtVJVkgWvx39W+YkzSHA++I9Kdxf08nPPs6jqt3MdyYk8YtZ6fjoTz2/xvJuVrxcgMvhZu73BxM/KISXalv5S2UjRq2WN7ITmRsWeOJOqhs2PQOb/grhWXDbuxCSctH6LkmSJH299PWVUF39Cs0tXwAaIiOvJyH+Ifz8PKOghKpSnb+f3DUrqNi7y3OjFukmf3g7JIVwe9ZDXJ96/XldB5uamsjLyyM/Px+z2Yyvry9Dhw4lJyeHmJgYFEWhsdvKJ1srWZbfwL6aLgAGxQTwqzkZXDtIBqUlSZIk6VLpamqkcOMaCjeuxdzZgSEgkBHzb2TQ1GsIioq5qMey2RqorXub+voPcbv7CAwcTUb6HwkJmYJymhpOTtVJXW/dCaOgK7srqe6ppt3WfnQ7jaIhxhhDon8io6JGkejvCUAnBiQS5ht2QkBWCEF7ezsHcg94gtJVVVgsFgCCgoLIzMwkKSmJxMRE/I8byGZzuvkir4GP99SytawNgAmpofxqTgbXZEX0ez9/OTlbLVhyPXml3R020GnwzQrGMDQcnwFBKNrT18kSTifmnbvoWbmC3rXrULu70ZhMmKZP9wSlx449Y42uCyUD1JIkXVX6OjvYsegDCtavRuflzbhb72LEtTeg9zn9U1S3205Dw4dUVb+Iw9FKUNB44DqeX97Dxl5//BU3f9X7MCrUQsdTDwAQ/uijBN97D4reM92mpqeGxWWLWVK+hBZLC4HegdyWfhvXp15PenD6RT1Hp1vl+XWHeGFjOREmb97/zmjGpfQ/RfrgtgY2/bcEY5AP1/9kKL1BOm7aX8bObjOzQ/35W3ocYV4nTRkyt8GnD0L5esi5E679B3jJG3tJkqT/RV3de6mueom29vVotQbiYu8jLu7b+PhEAWDp6ebAxrXkrV1Jd3Mjqq+Wg8k9FMf1kJM6ml9n/prx0ePPedZQb28vBQUF5OXl0dzcjEajYcCAAeTk5JCWloZOp6O5x8Zb26tYlt/InupOAAZG+/PL2elcOyjqlDoMkiRJkiRdHC6Hg7LdOyhYv4qawnwURUPS0OFkP/B9koeORKu7uOHC3t6D1NS8TnPLUkAQFjabhPjvnDCD62S7m3bz7J5nKe4oxiWOFSgM9gkmwT+BSbGTPOk4/BNI8k8izhSH/jSpLIUQdHR0UFVVRWVlJZWVlfT19QHg7+9PWloaiYmePNJHZnQd72BDDx/vqWVxbj1dFicxgb78eHoaNw+PJTboyt5ru/scnmKHua04a3tBAe+UQPynxeObHYLG5/T/lsLlwrxrlyd9x+o1uLu70fj5YZoxHdPs2fiNH4/mEgaljycD1JIkXRUcVgu7v/iUPUs/Q3W5yLlmLmNvuh1DQOBp91FVBw2Ni6iqegG7vYnAwNFERvyO9RubWFTVxyF3BDkInjGG4JX7Bu0FOzFOnUrkY79FHxODxWlhddkyFpctZm/zXjSKhvHR4/nVqF8xOXYyXtqL/0Fc1tLLTz/Ko6C+mxuHxfCH6wbi73PqRVR1q2z7pIz89XXEZgQx8zsD+bSnl8d316MB/pURz62RQadOy6n9Ehbe5wlSz/83DLsXLlI1ZUmSJOnrwTMqaCPV1S/T1b0bvT6IpKSfEBd7D3p9IEII6kuKyFuznNKdW3E7nfSGa9g3pJW2WIXr0q/n9+m3kxiQeE7HdTqdlJSUkJubS3l5OUIIoqOjmTNnDtnZ2fj5+dHSY+P9L+tYlt/I7uoOhICMSBO/mDmAuYOiSA4zXpo3RTojoQpQBQjh+dt9+Lfg6GtUgRCHfx95fXibo9sf2eaE9QKhcto2TjmmJEmSdMm01VRRsH41B7dswNbXi39YBONvvZuBU2ZgCjm/uhKn4wkKb6a65jU6O7ej1RqIjb2HuNj78PWNPe1+7dZ2nt37LJ+Xf06MMYZvZ3/7aCA60T/xrGdzdXd3Hx0dXVlZSXe3p5aFn58fSUlJR0dIBwcH95vuotvq5PPcej7eU0dBfTdeWg0zB0Zw28g4xqeEXtGCh6rDje1gO5b9LdgOdYIK+ig/AuYmYcgJQxtw+vQbwuXCsnu3J33H6tW4u7rQGAwYj4yUHj8ezUVO33E2ZIBakqQryu1ykr92JTs++RBrTzcDxk5kwu33EBQZfdp9VNVJU9NnVFb9P2y2egIChpGY8ARfftnNjrxcNrsH0Ow2cA9efEfpwfLhj3AHBxDz/L8xTp9Ofls+n21/hZWVK7G4LMSb4vnxsB8zP3k+EX4Rl+Q8VVXw9o4q/rKiGIOXlhfvGsacQVH9bmszO1n1aiF1xZ0MnhZL6rwEHiyrY217DxMCjTyXGU+sz0nBcyFg18uw+rfgHwMPrIboIZfkXCRJkqSrk6q6aGlZTnXNy/T1FePtHcWAtN8RHX0rWq0Bh81K3sYV5K1ZTmt1JXhpqYy1khfbRnBsHHdm/JT5KfPx05/9yGUhBDU1NeTl5XHgwAHsdjv+/v6MHz+enJwcwsLCaO2182l+I0vz8/myyhOUTo8w8dMZnqB0avjlCUoL17EiQfbyLk+AVAFQPP8duc88er+pHPv7+O1Oes1xmx9t5Oh2ynH7c/zOx36d3JbSz36HNzx+9xO2UQVCeH5/ZbDYzQmvORJkliRJkr6RHFYLxdu3ULh+NY1lJWh1OlJHjmXQtFnEZw9G0Zw+7cP5UFU7TU1fUFP7OmZzKd5eEaSm/JLo6DvQ60+fm1kVKotKF/Hcvuewuqw8OOhBHhz8IL66syuI3NvbezQYXVVVRUdHBwC+vr4kJiYyfvx4kpKSCA0NPW3+ZVUV7Kxo5+M9tawobMLuUsmM8ucP87O4fmgMgYbLM5q4P8ItsJd3efJKH2hDOFS0Ad6YJsViGBKOPvL039+E241l9x5P+o7Va3B3dKAYDJimTvUEpSdMQHOGWeuXgwxQS5J0RRydUrxmOd0tzcQNHMykO+8jMnXAafdRVRfNzZ9TWfU8VmsN/qbBpKb8gYMHFVat3E6908AWdQguNzyFD2OrNmDJ/4zge+6B79zBwqa1LF6ygKqeKnx1vsxKnMUNqTcwNHzoGQsEXKjGbiu/WJjHtrJ2pqaH8cxNgwn37//Dv6PBzLIX8+nrsDH1ngzK0gxM21uKVVV5Mi2G+2NC0ZzcV3svfP4IHPgM0ufC9f8B36BLdj6SJEnS1cXtttHY+AnVNa9is9ViMKSSlflXIiKuQ6PR01ZTRe6aFRRtWY/DasUR4sXe7A4qYyxMSJzM3zLvYHTk6HO6FnZ0dJCXl0deXh5dXV3o9XqysrLIyckhMTGRTouTlQeaWLZ4Jzsr2lEFpIYb+fH0NK4dFEVahOkSviPHCCFw1PZi2XdckSA/PT5ZIWi8tUcDs0IcF6EVR3c+6fVx2x0f0D1lO+H583TtHFl2JC582uOJEzY/7TYC0GlQNAqKRgGNgqLh8G/Pa05Yd/IyzrjdsXWcoY1jbZ25jbM45jP9/1tKkiRJZ08IQeOhEgrWr6Zk+2acdhshsfFMufdBMidOweB/8WorHeF0dlFf/19q697G4WjFaMwgK/NvRETMQ6M5c2C3qL2IP+38EwVtBYyMHMljox8jOTD5jPuYzWaqq6uPpuxoa/PkhPb29iYxMZGRI0eSlJREeHg4mq8Iwjd0Wflkbx0L99ZR02HB5KPj1hFx3DYyjoHR/pc0XnAmQgic9X1Y9rdgyW9F7XWi+GgxDAnHMCQMr8QAz7Wzv33dbix799K7ciU9q9fgbmtD8fXFNHUKptmzMU6ciMb37IL/l4MMUEuSdNkcuUjmrVlOyY4tuJ1OYjOzmXb/90gaMuK0H/pCuGluXkZl1fNYLBWYjAMZlP0yjY1hvP/+Orq7e+gMG8yqem+i0fCUW0vkzhchXKHl+Ud5gT1sXbEAt3AzNHwo92ffz8zEmec0Qux8z/fzvAZ+t7gQlyp4+oZB3DEq7rTnWZnfxpo3DqDz0jLtJzn8y9nLpweqGGIy8HxmPGl+/QS1W4rgo3ugoxxm/AHG/Rgu8hNwSZIk6erkcvVSV/c+tXVv4nC04e8/hAFpvyU0dDpul5uS7dvIW7Oc+uKDoNPQEivYHdWII8KXmwbcwr/SbyXGePbFj6xWKwcOHCAvL4/a2loAkpKSmDp1KhkZGZhdCqsONPH4+t3sqGjHrQqSw/x4eFoa8wZHMeAyBaUBXO1Wz83c/hZc7YeLBA0M8RQJSgs8Y5EgSZIkSfq6Ut1uclcvp2DdStpqq9F7+5A+bhKDps0kKi39kgRardYaamrfpKFhIapqJTh4IvHxfyc4aPxXHq/X0csLuS/w3+L/EugdyNMTnmZe8rx+97NarVRXVx8dJd3c3AyAXq8nISGBoUOHkpiYSFRU1FcGpAEcLpW1Rc18tLuWLYdaUQWMSwnh5zMHMGtg5BUteOjqsGHJPfw9ptUKWgWfjGD8hobjkx6Mou///ISqYt23j54VK+lZvQp3axuKjw/GyZPxnzMb46RJaAxXZ30qGaCWJOmSc9psFG3bRN7q5bRUlePl60v21JkMuWYOofGJp91PCJWW1lVUVv4Ls/kQfn4DGDToP9ismSxZsor6+q2ERkTRGDiK1aXdjEfLb3q60Oa9Tu61sfwnqZz2un8Q5hvGfQPvY0HqApICki7LOXdZHPx2cSHL8hsZFh/Is7cOITG0/4C4EIK9K6vZ9XkFYXEmjHcmcVt9I21OJ79MiuRH8RHo+nsqmvcRLP0JeBnh3s8haeKlPSlJkiTpqmC3t1Jb+yZ19e/jdvcRHDyRxITvERg4mp7WZrb89x0KN6zB2tONCPShYKCFA1HtJEem80jGY8xJmoOP7uymcVqtVkpLSykqKuLQoUO43W5CQ0OZPn06gwcPRuh9WX2gmb+/l8v2ck9QOjHEwPcnp3Dt4CgyIk2XbdSR2+zEWtCKZV8LjprDRYKSAzBN/eoiQZIkSZL0TfDl4oVs+/g9IlMHcM1DD5MxbhJevpcmINndnUtNzWu0tK5CUbRERswnLv4BTMaMr9xXCMHKqpX8bfffaLO2cWv6rTwy9JFT8ktXVVVRWlpKVVUVjY2NCCHQ6XTExcUxbdo0EhMTiYmJQas9+2BySVMvH+32FDzsMDuICvDhh1NTuWV4HPEhVy54q1qcWArasOxvwVHVA4BXoj+BE2IwDApFYzhNAUhVxZqb68kpvWoVrpYWFG9vjJMmeYLSkyej8bv6C0/Lb2mSJF0y7fW15K9ZwYFN67BbzITGJzLjOz8gc8KUM14khRC0ta2lovJf9PUVYTCkkD3w33h7j2PduvUUFLyB0Whk7Ix5/HuPhYPV3TyAN3fW5ZPnXMxzd3TQG9DGlMgp3JB2A+Oix6HTXL6Pu02lrfxyUR7tfQ4enZXOdyclozvNSC2nw836d4oo29NC/Khwtoz1553KOgYYfHhncBKDTf28Ty47rPwV7HkDEsbDzW+AKfISn5UkSZJ0pVks1dTUvkZj4yJU1UV4+BwSE76Ln18Glfv3smHNE1Tm7gXAHO/D9vQWmsOcXJN4DT/NvJOcsJyzChb39vZSXFxMUVERVVVVqKqKyWRi+PDh5OTk4BcUxpqDzbz+aQlbD7XhUgUJIQa+OymZawdHkRV1+abCCqeKtbgdy/5WbCUd4BboIgwEzEnEd0g4ujMUCZIkSZKkb5LuliZ2ffYxA8ZMYP5Pf3VJjiGESlvbOqprXqO7ew86nYmE+AeJjbsXH++zuyet6q7iqV1PsbNxJ1khWfx72r/JDs0+YRuz2czy5cs5cOAAGo2G2NhYJk2aRFJSErGxseh053Z/32Nz8kVeAx/vqSOvtgu9VuGarAhuHRHHxLQwtFeo4GG/32PCffGflYAhJxxdcP8DCoSqYs3L86TvWLkKV3MzipcXfpMm4j97DsYpU9Aar/6g9PFkgFqSpIvK7XJRvncXeauXUVOYj0arY8CY8eTMnEtMetYZb1iFELS3b6Si8jl6ewvx9U0gK+sfBAXOZPv2nWzf/gIAkyZNQonM4Kcf5eO0ufgLvmSUf84fMtfSN2IAD6Y9yLXJ1xLsE3y5ThsAi8PFn5cX8+7OatLCjbz+rZFkx5w+t1dvh40VLxXQWttLwI0JPGWyU93UwffiwvhVUhQ+/QW1O6th4begYT+M+xFM/z1o5Ue5JEnSN1lv70Gqq1+muWU5iqIjKupGEuIfRDgCKFy7hvx1/6CntQWN0YfKTJXdEfUYgoO4ZcB93DLgFsIMYV95jPb29qNB6bq6OgCCg4MZO3YsmZmZmILDWFvUyu/WNLDlUB5OtyAu2JfvTExm3uCoy5qfUagCR1WPZ+prfivC5kZj8sI4LhrD0HC8oi9P0UVJkiRJuloIIVj/5ssoWi1TvvWdi96+222lsekzampex2qtwscnhrS0x4iOugWd7uyuuzaXjdcKXuONwjfw1nrz61G/5rb029Bqjo1+FkJQUFDAihUrsNvtTJ06lbFjx+Llde7FCYUQ7Krs4OM9tSwvaMTmVEmPMPG7eVlcPySaEOOVeYgtVIG9sttT7LCw7fD3GD3GsZ7vMfpov36/UwkhsOXne9J3rFqFq7ERRa/Hb+JE/H/xc4xTp6I1fn2/A8mohiRJF0VvRxsF61ZRsG4VfZ0dmELDmHD7vWRPvQa/wDMX7BNC0NG5jYqK5+jp2Y+PTyyZGc8QHn4dhYUH+eD9F+nt7SU7O5vp06fz6u4qXnxnHwlC4SmnhgrLKyx/JJNfDfyYrJAzB8EvNiEEBxp6WLinliV5DXRZnDwwIYlHZ6WfMWdVY1kXK14uwOZWqb43gfdsvcTgxadDUxkbeJqLSulq+PRBT0Gk296HzHmX6KwkSZKkK00IQVfXbqprXqK9fRNarZGE+O8QG3sfrRUtrH/lIw59uQPV7cId58/OEd2UhVaTEzGE32f8kGsSrkGv7X8q6JH2m5ubKSoqoqioiJaWFgAiIyOP5pQODQ3jy6pOnt1Wy/LCAmxOlZhAX+4fn8S1g6MYFBNwWa+5zhbL0bzS7i47ipcG3+xQDEPD8U4JPG2RIEn6OlEUJRB4DcjGUwLzfmAW8CDQeniz3wghll+RDkqSdFUq37OLin27mXz3/ZiCQy9auw5HG3V171FX/z5OZwf+psGkDPw3YWGz0JzDLOWt9Vt5etfT1PbWMjdpLo+OfJRQ3xP72dPTw9KlSyktLSUmJoYFCxYQHh5+zn1u7rGxaG8dC/fUUtVuweit44ahsdw2Mo6c2Ev73UWoAuF0IxwqwuFGOFVUx7HXjmrPA3Z3twPFS4tvdsgZv8cIIbAVFnrSd6xcibOhAfR6jOPH4/+TH2OcNg2t6fLV+LiUZIBakqTzJoSg9kA+uauXUbZ7J0IIknKGMePBh0kaOhyN5qvzQHV27qSi4jm6unfj7R1FRvqTREXdRG1tI2+88RYNDQ2ei9ONC8hzlHDdf/5LbW8CU4SOn9u6aJ9p55ZZb591Ls2LpbXXzpLcehbtraO4qRcvnYZZAyP51tgERiSeeeT2wW0NbPqghN44X5ZN8qfE1stdUcE8kRqDUdfPe6a6YcPTsOXvEDkIbn0Hgs9c0ViSJEn6evJMnV1PdfVLdPfsR68PJiX554QF30DJtj18+OITtNfVoPX1pjXDi00h1dj8G5mbPJcnM+4gKyTrtG2rqkpdXd3RoHRXVxcA8fHxzJo1i4yMDIKCgqjvsvLR3joW7T3oqWTvreOmYbHcNDyWoXGBlzUo7e51YMlrxbK/BWd9nyevdFoQAbMS8RkYgsbryhUwkqRL5F/ASiHEzYqieAEGPAHqfwoh/n5luyZJ0tXIabOx4e1XCI1LYOic6y5Km2ZzBTW1r9PU9Cmq6iA0dAbxcQ8QGDjynL4HNJubeWb3M6ypXkOifyKvznyVMVFjTthGCMG+fftYvXo1brebmTNnMmbMmLMqdHiE062yrqiFj/fUsrGkBVXAqKRgHpmWxtxBUfge/r4g3ALV6ToaMFYPB5HF8b8dKuqRIHN/weaj2524r+pwg0ucuaMa8EkLImBOEj5Z/X+PEUJgO3CQ3pUr6FmxEmd9Peh0+I0fR+gjj2CaPg2tv/9ZvzdfFzJALUnSObOZ+zi4aR25a1bQ2VCHj9HE8GuvJ2fGHAIjo75y/yMjpqsq/x9d3bvx8gpnwIA/EBN9K93dFj75ZAkHDhzAZDIx6ppR7NXs5d61P8JWcjNd2ni+p3hzR4CN2CduQutz+tFhF5vDpbK+uIVFe+vYWNKCSxUMiQvkyeuzmT84moDTFC04wu1W2baojLyNdRycGMTSaA2BQuXdQUlcE3qaVCB9rfDJ/VC5GYbeA3P/BnrfS3B2kiRJ0pWkqk6am5dSXfMyZvMhfHxiSR/wBFrbUApWrWPJtodx2e1oowMpGOEiN6SGcP9I7k9/mBvTbiTIp//ZSi6Xi6qqKoqKiiguLsZsNqPRaEhOTmbixImkp6djNBqxOd2sOdjMx5+WsrWsDSFgfOqVqWSvOtzYDrZj2d+C7VAnqKCPMRIwLxlDThha07lP85WkrwNFUfyBScB9AEIIB+C4nA+FJEn6+tn52Uf0tLZw2xPPoD3H3MzHOzJ7q6b2Ndra1qHReBEZeSPxcffj55dyTm25VBcfFH3AC7kv4BZuHh7yMN/O/jZe2hOv4R0dHXzxxRdUVlaSmJjI/PnzCQkJOdZOlx13l+2EYLF6XNC4vMvKZ3UdLG3ppsPlJlSn5d5Af+b5+hJn0yA2NNG5qp6Ow/vj/ooA8skUUPRaFC8NipcWRe/5rdFr0Ph7e5YfWa/Xojlpu6PbH16vDfZB63dq3EAIgb2oyJO+Y+VKnLW1nqD02LGE/uAHnqB0YOC59f1rRgaoJUk6a80VZeStWU7R1k24HHai0tKZ88OfMWDMBHRnkRPqSI7pyqr/R09PLt7ekQxIe5zo6NtwuRTWr9/Cjh07UBSFoKwgNus381bZWySWZ9JteQCt1pt/4Mu0aXH4X5N22UZwHWjoZtHeOpbkNtBhdhBu8uaBiUncPCyWtIizm05j63Oy8tVCCuq7WXtDKCV6lXmh/jwzII4Qr9N8FNfshIX3gbUTFrwAQ+++eCclSZIkXRXcbisNDR9TU/MaNnsDfn4DSB/wDF1l/mx+cRWNZR+i0euxpplYH1xHs381oyNH84/MR5kcO7nfIsAOh4OysjKKioooLS3Fbrej1+tJS0sjMzOTtLQ0fHx8jqap+nhdIUtyG+i2OokJ9OVH09K4eXgsccGXr5K9UAX28q7D+RjbEQ432kBvTJPiMAwNQx/x9Sr0I0nnKRlPGo83FUXJAfYCPz687mFFUe4F9gA/F0J0nryzoigPAQ+BZ2aEJEnffO31tez54jMGTp5ObMbA82pDVV20tq6kuuY1ensL0OuDSUr8EbGxd+Hlde7pQnJbcnly55OUdJYwIWYCvxn9G+JMcScdU2XXrl2sX78eRVGYN28ew4YNOzpqWrW76VlTTd+2ek+yo+NYEKzHyVKcFOJGC0zQeTHfZGSswQe9t+5YgPhwkFhzfNC4v6Dy8UHo45aj01yyuIMQAntJyeGg9Aqc1TWg1eI3Zgyh330I4/Tp6ILOnC71SnA7VWqLOy56uzJALUnSGbkcDkp2bCFv9XIay0rQeXmTOWEyOdfMJSI59azaEEKltW0NVVUv0Nt7AB+fGNLT/0R01E2AntzcXNatW4fZbMYR7mCD7wb6rH2MdmUwe++tLPIdSopWw5+9/Bh0Xw4+KYGX9JwB2vvsLMltYOHeOooae/DSargmK4KbR8QyMTUUXX8FDE/XVn0fy17MZ2OwYP21gXjrFP4zIIEbwk8zTVoI2PkfWPM4BMTBA2sgavBFPDtJkiTpSnM6u6mre5faurdxOjsICBhOTNiPqdzZyZKXF2Ez9+EVFkjdKF82B5Sg9fXhupTruCPjDlICTx3FZLFYKC0tpaioiPLyclwuF76+vmRmZpKZmUlycjJ6vWfETofZwQdbK49d43Qa5mRHcuuIOMYmh6C5jLmcHY1mLPubseS2ovY4ULy1+A4OxW9YOF6JATKvtPS/RgcMAx4RQuxSFOVfwK+A/wf8CU+Y5k/AP/Dkpj6BEOIV4BWAESNGnOMwQUmSvm6EEKx/40X0Pt5Muuvb57y/y9VHQ+NCamvfxGarx9c3kfT0PxEVeQNa7bnP2u2ydfHcvuf45NAnhBvCeXbKs8yIn3HKPW9raytLliyhrq6OtLQ05s2bR0DAsRnF1qJ2upaU4+6y4zcqEt/sUNAr7G8zs6i4iRWlrVicblLC/Pj1iDhuHBZLmOnKFDw8V0II7KWH6Fm5gt4VK3FUVYFGg9+Y0YQ88ACma665KoPSLoebmoMdlO9voSqvDYfNfdGPIQPUkiT1q6upkby1KyjcuBZbbw9B0bFMve8hsiZNw8fv7CrDCuGmpWUFlVUvYDaX4uubQGbGM0RGLkCj0VNbW8viLxbT3tJOt283e6P3ogQo3Jh4A9PzfPjbZidrwzKYiZ7fRgYTc182usBLl2va6VbZWNLKwj21rC/2pPAYHBvAHxcM5LqcaAIN5z6luCK3lU//W8SS4QYOhemYGmzi2Yw4orxP05atB5b8EIo+h4x5cP1/wOc06T8kSZKkrx27vZmamtepb/gQt9tMSPAUtJYJFK8oZ1PBWyhaLUpaGNvDWik1VpMQkMBP03/JgtQFmLxOnLXT09NDcXExRUVFVFVVIYTA39+fYcOGkZmZSXx8PFqtJzWHWxVsKGlh4Z5a1hxsxukW5MQG8Kfrs7nuLNJUXUzubjuW3MN5pZvMoFHwSQ/CMC8c38wQFP3ZPwSWpG+YOqBOCLHr8OtFwK+EEM1HNlAU5VVg6ZXonCRJV5fi7ZupKcxn+gM/wBAQeNb72exN1NW+Q33DB7hcvQQEjGBA2u8IDZ2Oopz7NVgVKkvKlvDPvf+kx9HDt7K+xfeHfB8//Ymzn9xuN9u2bWPTpk14eXlxww03MHjw4KMBbHePna4vKrAWtKELNxD2vcF0h3jz0b56Pt5TS0WrGT8vLfNzorl1ZBzD4i9vXYwjhNOJarV6fiwWxNG/rajW07y2eJZZ8/NxlJeDRoNh1CiC77sP0zUz0B2X1uRq4XS4qSlsp3xfC1UF7Tjtbrz9dKQMCydlWDgPv3xxjycD1JIkHaWqbir37yF39XKqcveiaDSkjRxLzsy5xA0cfNYf/qrqorn5C6qqX8RiKcdgSGVg1rOEh1+LRqOjz9zHe5+/TmNJI1atlQPhB0gekMzv037P6L4I9v75X/wocDyNoaH8CG++NSyO4BtSUS5R/sviph4W7qlj8f562s0OQo3e3D8hiZuGxZIeeX4VcYUQ7FlRxeu59ayaYQIvDc+kxnBvdMjp38fmA/DRPdBZBTOfhLEPg8w5KEmS9I1gsVRSXf0KjU2LATfBgdfQV5nMro/z6Ov8BO+gAHpHhbHalE+fdyUTYyfy04wnGBc9Ds1xN4ttbW1Hg9L19fUAhISEMH78eDIzM4mOjj7hOlPZZmbhnlo+3VdPU4+NYD8v7h2byC0jYsmIvHwFdlS7C2uhJ6+0vbwLBHjFmwhckILv4LB+8zFK0v8aIUSToii1iqKkCyFKgOnAQUVRooQQjYc3uwEovHK9lCTpamC3mNn0zmtEJKcxeMass9qnt6+YmprXaG5eihBuwsNnEx/3AAEBQ867H4c6D/HkzifZ17KPIWFDeGzMY6QHp5+yXWNjI0uWLKGpqYmsrCzmzp2L0egZ+CZUgXlXI90rqxBuFf+ZCZQlGfnzzgqW5TfiUgUjEoL43s0pXDsoCj/vM4cyhRAIh+Ocg8envj687ITXVnA6z+k9Ury80Pj6ohgMeMXHE3z3XZ6R0qHnnj7lUnPYXFQXtlO+r5XqwjZcDhUfo560URGkDg0nOj0Q7TnMJj8XMkAtSRLmrk4KN6whb+0KettaMQYFM/bmOxk0fSam4LP/0FRVB01Ni6mqfhGrtQajXzrZ2c8THjYLRdHSbm3nvbXv0ZLbgtatpSGkgeFjh/No1qOECAOt/+8FPlq9h38MuwMvrRf/VPyYdt0A/MZEXfQnox1mB5/n1rNoXx2F9T3otQozMiO4eXgskwaEob+AD12n3c2S9w7ygs5C0VgjI0wGns9KIMlwhmlHuR/A0p95RkvftxQSxp338SVJkqSrR09PAdXVL9PSuhKNxguT91Qa95jI3VGEEDX4psZQMriPnb75GL1N3JB2O7el30a8vyd/rBCCxsZGioqKKCoqorW1FYDo6GimTZtGZmYmYWFhJxzTbHexvKCRhXvq+LKqA40CU9PD+cN1A5mWEY6X7vKMUBZuga2sE8u+FmwH2xFOFW2wD6Zp8RiGhqMPlUV/+yOEAJcLcfyP03niMqcL4Tq8zO1GGxiEPioSja98T78BHgHeVxTFC6gAvg38W1GUIXhSfFQB371ivZMk6aqw/eP3MXd3cf0vH0ejOf1ALiEEHZ3bqKl5jY6OLWi1BmJi7iQ+7tv4+saddr+vYnFaeDHvRd49+C4mLxN/HPdHFqQuOOGhOoDT6WTz5s1s3boVPz8/br31VrKyso6tbzLT+ekhHDW9KMn+7Mzw5938GvJWd2Py1nF7pOB6moi3HUJdvZGeJVa6rDZUqxVhtZw2mIyqntP5KL6+aI78GHxRfA1ofH3Rh0ccfu2L5vCyE14bPPv0+9pgQOPjg3IBhSsvB4fVRVVBmycofaAdt1PF19+LjDFRpAwPJzo1AM0lCkof74LeJUVRfgw8CCjAq0KI5xRFCQY+AhLxXDxv7a+AgyRJV5YQgvriA+StWUHpzm2obhfx2TlMvfdBkoePOqfqv6pqp6FhEdXVL2GzN2AyDWTwoBcJDZ2BomgoaC3gwz0f0pPXQ4gtBNWkMmraKB7PeRydRkfv+g2UPvkkbwUM5sOR3yJT0fK0r4mse7LxTrp46S1cbpVNpa0s2lvH2iLP9ObsGH/+MD+L64bEEOx37ik8TtbbYeNvHxbwQaIWm483v02O4gfx4WhPF2B32mDFL2Hf25A4EW56HUwRF9wPSZIk6coSwk3hgZ/Q0rIcrdaIt3MaZevttFfV4G00IkbEsiqwkEZdFamBqfwu83GuTboWg96AqqpUV1cfDUp3d3ejKArx8fHMnj2bjIwMAk+q5C6EYG91Jx/vqWVpfiMWh5vkMD/+b3YGNw6LIcL/0qXIOrkfzvo+LPtbsOS1ovY50Rh0GIZHYBgajle86YpMxz2e6nBgO3AAYbcfDfQKl+tYAPjkZc4jgeEzLHOeFFB2Oc97GS7XeZ+bNiAAXVQU+shIdFGR6COj0EdFoouMRB8VhT4iAuUsCltLV44QIhcYcdLie65AVyRJukq1VFWwf+VScmbMITIlrd9tVNVBc/NSampfp6+vGC+vcFKSf0FMzB3o9YHnfWwhBOtr1vOX3X+hydzEjWk38pNhPyHI59S8yTU1NXz++ee0tbUxZMgQZs2ahe/hB6mqw03v+hp6N9fT4a2wMsuPj2taaKuoIznMj8eGmBjz8f9De7AAgHaN5vAo5OMCxYeDxdqQkJOCy2cRTDYca0Px8UHR/G+lF7NbnFTme4LSNQfbUV0CvwAvsiZEkzosjMiUwMtakwQuIECtKEo2nuD0KMABrFQUZdnhZeuEEH9RFOVXeIo6/N/F6KwkSRfOYbVwcMtG8lYvo622Gm+DH0NmzmXwNXMIiTm3J6hut42Ghg+prnkVu70Jf/+hpKf/kZCQKThUB5+Xf8GHBz5EqVBI7U4lRB/CmBljmDluJhqNBndXF3WP/56GDVv4+6QH+dIUz7Xo+VVMGNH3ZKENuDiFDkqbe1m0t45P99XT1mcn5PD05puHx5IZdXGmN1v7HBQfbON3RXXsyfQiVavnlWHJZBnPMJKpoxI+vhea8mHCz2Dqb0F7dT9dlSRJks5OTc3rtLQsx902nIMr7DgsDZgSY2mbFsZKr30IrcK0+Gk8lXEHIyJG4Ha7qayspKioiJKSEsxmM1qtluTkZCZPnkx6ejp+fn6nHKe5x8Yn++pYtKeOirbDuRkHR3PryFiGxQddtmCwq8OGJbcFy/4WXK1W0Cr4ZgZjGBqBT3oQymUatX06QlWxfLmb7qVf0LtqNWpv7/k1pCiekVB6PYpOd8IPeh2KTn/qMi8vNAaDZ5leB7qTtjubZfrD7fW3TKPF3dGOs7EJZ1MjrsYmnI2NWPbvR+3uPuUUtKGh6CMjDweuo078OyoSXVjYVT/aS5Ik6X+VUFXWvv4ffEwmJtx+7ynrVdVJbe2b1Na+hd3RjJ9fGpmZzxAZMR+N5sLur+t66/jzl39mc91m0oLS+OukvzI0fOgp2zkcDtatW8euXbsICAjg7rvvJjU19eh6W2knnYvLyO/oY3GwlrVdfTgPdjEtI5y7swJI++gV+l5dhi46iojn/olx6lQUL68r/oD7685mdlKZ10r5vlZqizpQ3QJjkDeDJsWSMjycyCT/K1qc+kK+eWQCO4UQFgBFUTbhyYe1AJhyeJu3gY3IALUkXXFtNVXkrlnBwc3rcdqshCemMPO7PyJj3CT0Puc2qsrlMlPf8AE1Na/hcLQRGDiKrMy/EhQ0jgZzA8/te45PSz/F0GlgeOdwvJxeDBoyiDkz52AwGACw5uZS97Ofccim5ekFv6fJpeMX+HDnyDiCFqRe8I1sl8XBF3kNLNpbR15dNzqNwrSMcG4eHsvUjPALSuEBng/3hkNdVJV0sL6thx1+KqXRXrji9DwUEsRvs+PwPtNT2JIV8Nnh2Zl3fATpsy+oP5IkSdLVo898iPKKf9JTHUDtRjf67GS2h5ZRqNlGkHcQ3x7wALem30qQLoiysjI+2foJpaWlOBwOvLy8SEtLIzMzk7S0NLy9T72ZdLhU1hc38/GeOjaWtKAKGJUUzA+mpjInO/IrczNeLKrVhaWgFcu+FhxVPQB4JfoTODEGQ3YomstYeLE/QgjsRUV0f7GUnuXLcTU3ozEYMF0zA+OMGWgDAjzBXv1xAWWdDuWk4DPHb6O9NPUwLhXVYsHZ5AlYu5qaTghi2ysqMW/bjmqxnLiTVosuLOz0o7AjI9GGhPzPjTaTJEm6GhRuXEtjaTGzf/BTfA7ncD5eecU/qKl5leCg8WRm/png4EkXHNh1up28deAtXsl/BUVR+MWIX3Bn5p3oNade5ysqKvj888/p6upi5MiRzJgx4+h3GXefg5bPy1me38AnOhcHcWEy67h7bAJ3j4gm4ItFtH3vJcwuF6E/+D4hDz4oU1ddIGuvg4rcVsr3t1Jf3ImqCkwhPgyeFkfKsDAiEq5sUPp4F/LttRB4SlGUEMAKzAX2ABFHCjgIIRoVRQm/8G5KknQ+3C4nh3ZtJ3f1cuqLD6DV68kYN4mca+YSmTrgnC9ULlcvdXXvUlP7Bk5nJ8FB40kc+DwBgSPY2bCT/+b+iM11m/Fz+jHVPBV9h56IiAjmzZtHXJxndLYQgo4336Ll2WfZlTGeZ9Kvw8+t8LzGl4kL0jGOjjrv83W5VbaUtbFoTx1rDjbjcKtkRJr43bwsrh8STYjx/J8Y2y1OGsq6qS/ppOZQJ7uddg7GeVES44UtXI+/ULjOZOSBtAiGBZ76ReEotws2PAlb/wlROXDrOxCUeN79kiRJkq4uquri4MFHcTugZnsUiyc10qYrZWDIQJ7KfIqJ4ROpKqti/ZL1lJeX43a7MRgMZGdnk5GRQXJyMrrTjF4tburh4911LM6tp8PsINLfh+9PSeHm4XEkhZ46uvpSEC4VW0knlv3NWIs6wC3QhfniPzMBw5BwdMGXJ5XImThqa+lZupTupctwlJeDTodx4kQC/u+XGKdO/Z+62dUYDHgnJ+OdnNzveiEEam8vzsYmXE2NJ47CbmrCdvAgfevWIxyOE/ZT9Hp0ERGHg9j9pxTRBgbK0W6SJEkXkbW3h80fvEVMxkCyJk07ZX1n55fU1LxGdPTtZGY8dVGO+WXjlzy560kquyu5JuEafjnyl0T6RZ7aN6uVNWvWsG/fPoKDg7nvvvtITEwEPEUQq7fW8c7qUha77HQgSA7044/j07lxWCzs3EbzfT+ntboa44zpRPzf/+EVd/75sf/XWXo8QemyvS00lHYiBPiH+TLkmjhShoUTdhWkW+vPeQeohRBFiqI8A6wB+oA84KwTpimK8hDwEEB8fPz5dkOSpH70tLWQv3YlBetXY+nuIiAikkl330/2lBn4ms49pYXT2U1t7VvU1r2Fy9VDSMgUkhIfRuObypKyJXy08U9U9VQR4hXCnfo7cdQ40Gq0TJ01lVGjRqE9PNrI3dVFw69/Q++GDXw693u85pVKplvDX/z8ybg3G++E80u3UdbSy8K9dXy2r56WXjtBBj13jo7nlhGxDIw+vxzWDquLhrIu6ku7qC/ppKWul+pQHQcTvCke6YVZ54VRUZgfFsgNkUFMDDKh/6onj73N8MkDULUFht8Hs58B/ZW/kZckSZIunurql+jtLaB6YwxbYzsYkjyG2xJuQ2lVKNpSxL+r/40QgoCAAEaOHElGRgbx8fFoTjMatdvi5PO8ehburSO/rhu9VmFmViS3jIhlYloY2ssw6kWoAkdND5bcVqz5ragWFxo/PcbRURiGhaOPMV7xGx1Xezs9K1bSs3Qp1txcAHxHDCfyD3/ANGsmuqBTc2NKoCgKWn9/tP7+kD6g322EELg7O08YhX0smN2Ede9eelpaTsmdrfj6oo+IODFwHRV1XBA7Cq3x8jxYkSRJ+ibY8t+3sZv7mP7A90+57rpcvRws+gW+vnGkpf7mgo/VZm3j73v+zrKKZcQYY/jP9P8wMXZiv9uWlJSwdOlS+vr6GD9+PFOmTEGv94yu3p3XyOufF7PWbMEFTE4K5v6pqUxMDcVVW0Pzjx6mb9MmvJKSiHvtNYwTxl9w3/8XmbvslO9vpXxfCw1lXSAgMMLAsNkJpAwLJzT2yn9X+yoXNP9PCPE68DqAoihPA3VAs6IoUYdHT0cBLafZ9xXgFYARI0aIC+mHJEmeXFTV+fvJXbOcir27AUgePpIh18wlYfDQ85qG6XC0U1P7JnV17+J29xEWeg2JiT+kye3Nvw5+yNKKpVhdVgaHDeZXyb+ifX87HR0dDBw4kFmzZuHvfyzgfCSlR197Ny/e+RdWW3TMRM8fsmOIWJCG1nRuBXu6rc6jKTxya7vQahSmpntSeEzLCMfrHFOEOGwuGss9I6TrS7torelFVQUN4ToqB5rIHR1Cp0bgq1GYHRrAgvAgpgSb8DnbVCFV22DR/WDrhutfgiF3nFP/JEmSpKtfb28RlVXP010VRFmHNwmTRjHg0ABWbloJQFhYGBMnTiQjI4OoqKjT3iioqmBbeRsL99Sx8kATDpdKZpQ/v5+fxYKLVNT3qwghcDZZsOZ6ih26u+woeg0+WSEYhobjkxaIchkqup+JajbTu24d3UuXYt62HdxuvNPTCfv5zwi49lr00dFXtH/fFIqioAsORhccDAMH9ruNcLtxtbX3Owrb2dSIeds2XK2tIE687dOYTKeMvpYkSZJO1VBaTMH61Qy/9nrC4hNPWV9a+idstkZGDP8Ine78H/65VTcLSxfy733/xuq28tDgh3hw0IP46E4dWGU2m1mxYgWFhYWEh4dz++23ExMTg8Ol8tmeWl5ffYjCHisG4PbUcO6/LpPkcCOq2Uzbc8/R8eabKF5ehP/ylwTffZcs4nuOejtsVOxvpXx/C43l3SAgKMqPkXMTSRkWTnC031UflD7eBQWoFUUJF0K0KIoSD9wIjAWSgG8Bfzn8e8kF91KSpNOy9vZQuHEt+WtW0NXciCEgkFHX38zg6bPxDzu/DDt2eys1Na9SV/8BqmojPHwOsfHfZVdHHc9u/wd7m/firfVmTtIcFsQuoPLLSg58eYDg4OBTCiAcn9KjO3kofxj/S0osbn7gZ+SR2wbhOyD4rPvlVgVby9pYtLeOVYdv2NMjTDx2bSYLhsQQZjr7FB5Oh5umsm7qSjtpKO2kpcoTkFY0Co6B/hQviGCnr5smtxtvjcKMEH+uCw9kRog/fueSf1II2P5vWPuEJ5XHPZ9CRP83d5IkSdLXl6o6OFj0C9wOPVWbQmkZFUz8ISNqpMr06dPJzMwkNDT0jG3UdlhYuLeOT/bWUd9lJcBXzx0j47hlRBzZMec3I+hcudqtWPJaseS24mqxgAZ80oLwn5mA78AQNJcpv/XpCIeDvm3b6PliKb3r1yNsNnTRUYTcfz/+8+fhM6D/UcDSpaVotegjwtFHhOObk9PvNsLpxNXScjgn9nGjsBsbcTY1YisoxN3ZeZl7LkmSdPVTVTfrXn8RY2AQ424+daBTS+sqGps+ITHxhwQEDDvv4xxoO8Cfdv6JA+0HGB05mt+O+S1JAUmnbCeEoLCwkBUrVmCz2ZgyZQoTJkygw+rin2tK+WB7Fa1WJ3Fo+GVMKHfdOYiAEANCCLqXLqPlb3/D1dxMwIIFhP38Z+jDZWbgs9XTZj06Urq50lP/IyTGyKh5SaQM9QSlLwe3evHHGV/oN8xPDuegdgI/FEJ0KoryF+BjRVEeAGqAWy60k5IknUgIQVNZKXlrllO8fTNup5OYjIGMv+1u0kaPQ6s7v6JENlsj1TWv0NDwEarqJDLiOvwjb2Np7V4WrfwxLdYWYowx/Gz4z1iQvIDS/FJWv78at9vNlClTGD9+/NGpPHAspUffpi3Uzvkxv9ZHY3G6eW5oAtfdlHXWhRDLW/v4ZG8dn+6rp6nHRqDBc8N+8/A4smP8z+qpoMvhpqmi+2jKjuaqHlS3JyAdnmAiaHYM+ZE61rttVNkc6BQHUwL9eSw8kFmhAZh051EUydoFS34IxUshawFc9//A5/zSmEiSJElXt8rK5+nrK6ZqXSwH41UG9A0mICKA73znO6fNKQ1gdbhZeaCRhXvq2F7ejqLAxLQwfj03gxmZEfjoL31RPnevA0teK9a8Vhy1vcDhYofXp+CbHYrWeGVHNAlVxbp/P91ffEHvylW4u7rQBgQQcP0CAubPx3fo+c0Uky4vRa9HHxODPibmtNuoNhv8D+UIlyRJOht5q5fTUlXOvJ/8Ci9fwwnr7PZWiosfw2TKJinxkfNqv8fRw/P7nuejko8I8Q3hmYnPMCdpTr/32T09PSxbtoySkhKio6NZsGABjQ5vfrGogGX5jThVwVh0/MYYwKxbsjCkewak2YqLaX7yKSx79uCTlUXMP/+JYdjQ8+rv/5ruVgvl+zxB6ZZqz/e00DgjY65PJmVoOIERhq9o4eKxOFws3FPH61srL3rbF5ri45QENEKIdmD6hbQrSdLpNVeWs+aV52muKEPv40v2lGvImTm332k+Z8tqraO6+iUaGj8BVCIjr8fsN4m3Kjeydu93cQkX42PG83j640yImUBDfQMfv/MxTU1NpKamMnfuXIKDTxwJfSSlh1BD2HnDP/iz20moXss79wxn4IAzjyAD6LE5WZbfyMI9teyr6UKjwJT0cB6fn8X0zHC8vyJg7HK6aa7sOZqyo6myG9UlUBQIizeRMz0Od4qRnX4qz3f0UGK2oDHDhCAjP0qIYE5YAEH6C/iIbMyHj++F7lqY9WcY8334Gk2vkSRJks5eT08+VdUv014ZTG2bD9ljZ9Pb1MeNN97Yb3BaCEFeXTcf76nli9wGeu0u4oMN/PyaAdw0PJbowEsfoFOtLqyFbVjyWrGXd4EAfZQfAXMS8c0JQxd45Wsk2EpL6fliKT3LluFsaEDx8cE0bRr+8+dhHD9eTgX+BtL4XPn/7yRJkq4m5q5Otn74LgmDhzJgzIn5mYUQFBX/GrfbzMCsf6DRnNtANSEEyyuX87fdf6PT3sntGbfzyNBHMHmZ+t12//79rFq1CrfbzdTpM2g3JPDdRWXk1nbhp9NwvcaLG4WerMnxmKbFo/HS4u7qovXf/6bzw4/QBgQQ+ccnCLzpJpRzmZX8P6ir2ULZvhbK97XQVtsHQHiCibE3pJAyLIyAsMsXlAZo7bXzzo4q3t1ZTZfFydD4wIt+jCs7R0+SpHNSumsbK154Fh8/I9Mf+AFZE6ec8gT1XFgsVVRVv0hT02JAQ3jE9ZSQyAuHVlPauRyTl4k7Mu/gtvTbSPBPwGKxsGzpMvbt24fJZOLWW28lMzPzhCerQgg63nqb1hdex3vYXbwRnMK7bgcjIky8/OBoQoynT8PhVgU7yttZuLeWlYVN2F0qaeFGfj0ngxuGxhDuf/qbFrdLpbnqSEC6k6aKHtxOFRQIizMxeEosMelBqHEGlnf38mpLF/kdTdABYwL8+POAWOaFBRDmdX6jz0+w711Y/gvwDYb7lkP86AtvU5IkSboqud12Dhx8FJfTi4aNoXhPGk5XfTezZs0iIiLihG1be+0s3l/Px3tqOdTSh49ew9xBUdw6Io5RicFoLnHBQ9XhxlbcgSW3FVtJB7gF2hAfTFPjMAwJRx9+eW92+uNsaKB72TJ6vliKvbQUtFr8xo0j7Cc/xjR9Oho/WVRPkiRJ+t+x6d3XcTsdTPv2904Z0dzQ8CHt7RsYkPY7/PxST9NC/yq7K3lq51PsatrFwJCBvDDjBQaG9J+KsrOzky+++IKKigpCYpLoixjCLza30tqbT1KQgUdDg7imzUVgvD9BN6ahj/RDuN10fvgRrc89h7unh6A77yTskYfRBlyelGVfRx0NZsr3e4LS7fVmACKT/Rl/cyrJQ8LwD738M4zKWnp5bUsln+6vx+lWuSYzgocmJTMiMRjlhxf3WDJALUlfA0IIdn7yIdsXvk9UWjoLfvEYfoHnX43ebC6jquo/NDV/gUajxz9sPpv7fFmUu45e51LSg9L5/djfMzdpLga9AVVV2b9/P2vWrMFqtTJ27FimTJmCt/eJwWZ3VxcNv3kMe40WzfQ/8LjGxRbh4LbhsfzphkGnLVxY2WY+nMKjjoZuG/4+Om4ZEcstw+MYHBvQ79Qit1ulpaqX+tJO6ks6aSrvxnU4IB0aayR7Ygwx6YFEpwXSqYUvWrp4uqWDvfvrABhqMvBEajTzwwKJ9rlII7CcVlj2C8h9D5Imw02vgzHs4rQtSZIkXZUqKp/FYimjcnUsLYmB+LX4kpQUy+jRnoeTTrfKxpJWFu6pZX1xCy5VMCw+kL/cOIhrB0dh8rkID0bPQLhVbGVdWHNbsR5oRzjcaExeGMdEeYLSV0FVd1dnJ72rVtO99Ause/YC4DtkCBGPPYb/nNnoQkKuaP8kSZIk6UqoPZBP0daNjLnxNoKjT0yPZLFUUXroKYKDxhMbe+9Zt2lz2Xi14FXeLHwTH60Pj41+jJsH3IxWc+qIZlVV+fLLL1m3bh2tqoH2sAm8X+XAWV7H1AFh3JLmR3ZeB1odBFyfit+oKBSNgmXfPpqefBL7wSIMI0cS8dhj+KTLGhEnE0LQ0WD2jJTe20JnkwUUiEoJYMItaSQPDcMUfPlnFgkh2FXZwaubK1hX3IK3TsMtw2N5YEISyWHGS3ZcGaCWpKuc025j5Yv/onTHFrImTeOaBx9Gd55TWnv7iqmqeoGWlhVoND4ogdewuN3K+r2r0Ck6rkm4hjsy72BI2JCjN6tNTU0sW7aM2tpa4uLimDdv3ikjwgCseXk0Pv5vtLGz6MyO4NdediqcLn4/P4v7xiX2e/Pb2G3l0YX5bC1rQ6PApAFh/ObazH5zbqpulZaaXupLOmko7aKhvBuX3Q1ASIwfWROiiRkQRPSAQHz89LQ5XCxr7WJxSRU7u8wIINvoy2+To7guPJAE37MvqHhG9l4oWwclK+DQarB2wKRHYcqvoZ+LvCRJkvTN0dW1h5qa12ksD6StzY/YMRPo6e7l+uuvBxSeX3eIt3dU09ZnJ9TozQMTkrhlRCyp4adOnb2YhCpwVPd48koXtKKaXSg+Ogw5YfjmhOGdHIByiUdrfxXVaqVvwwa6v1hK39at4HTilZxM2I9/hP+8eXjFxV3R/kmSJEnSleR2OVn7+osEhEcw6oZbT1inqi4OHvwFGo2ezMxnUJSzq8OwuW4zT+96mvq+euYlz+PnI35OqG//6TdbW1v5bMnnbK02U6HLps6qw+hyc9foBG6PDyFwfT2u0g58B4USOD8Frb8XzpYWWv7+d3o+/wJdZCQxz/4D05z+c1n/rxJC0FbbR/m+Fsr3t9LVbEFRIDotkEFTYkkeEoZf4EWKVZwjl1tl5YEmXt1cQV5dN8F+XvxkRhr3jEk440z4i0UGqCXpKtbb3saSvz9Jc2U5k+76NiPm33heH+49PQVUVb1Aa9saNFo/ug1jebehkbLqLYT7hvODIT/g5rSbCTMcG+1rt9vZuHEjO3fuxMfHhwULFpCTk4PmpCJEQgjaX3+P3k0teGXeR56Pm8eEDZcCb317FJMG9D+CeHt5G498sB+b082js9K5aVgskQHHng6qqqCttpe6kk7qS7poLOvCeTggHRTlR+aYSGLSg4hOC8TX5AnYdztdfNbWzZJDXWzp6sUtIM3gzc8TI1kQHkia30V6+thd5wlIl6yAqi3gdoBvEKTNgqF3Q9Ip6fklSZKkbxi328LBol9itXvRuimciCmzqK9r5eabb8Zk8ue3iwv575c1TMsI545R8UxJD0OvvXSF/IQQOBvNnqB0bivubjuKXoNPZjCGIeH4DAg66+LEl6yPLhfmHTvpWfoFvWvWolos6MLDCb77bgLmz8P7pLRhkiRJkvS/au+yJXTU13L9Lx9H73VicLC65mW6e/YzMOuf+PhEfWVbTeYmnvnyGdbWrCUpIInXZ77OqKhR/W7rdrtZvn4rb2z+/+zddXhTZ//H8feJVJI2dVdaaEtxt23YsDFgwjZmMGXC3OW37dkzl2euwIQhE9gYDBgOw91pS4G21D1p0ric3x/pgI4iG0V3v66Lq83JOXdODpDkfHKf7zeXfa4ILHI0LYK0/OfyJK7OjMazohjzD/uRg30JG5eJf+swZIeDmq++ovrTz5CdTsLuvYfw8eNRaM596bBzSZZlzAYH+jIzteVm9GVminL0GKusSBLEpYfQYWACKR0j0OjOXV8Ns93FT1uK+GpNPsV6Ky3Ctbx6VVtGd4lvslm3R5ZZWmNs9v0QAbUgnKfK9u9jzruv4rDZuOrJF0jt0vQbyInU1W0nv+ATampWIim1FCjb8m1xEQbXDrpGdeXdzk8yIHEA6qOaKciyTFZWFgsXLsRkMtG5c2cuv/xyNE28ubj0BspengLK1qhjE1jS0o/X86tICNEwaVxXUpu4/EOWZSatzuOthftIDtPw5a29aBkZgMcjU1V4pGRH6X4DDps3kA6O0pDWI5q4tGDi0kIavXjXu9z8UqHn1wo9K2pNOGWZJD8fJiREclVUCK21fqd/sivLULajIZReAOW7vctDU6HHPZA2DBJ6gFK8pAqCIPxbHDj4NlbrIQqXJCCnpFBaYqBdu3ZkZrbhudm7+WFzERP6p/LE4PQzGrq6qq1YdlZh2VGJq8oKCgm/tBCChibjlxmGwvfcXs0jyzK2Xbuo+20ext9/x11TgyIwkMArhhF05Qg03bqKRkmCIAiCcBRjdSXrf/6e1K49j8kBjMbd5Od/RFTklURHjzzhOE6PkxnZM/h0x6fIsszDnR9mXOY41Mqmy4st236A9+ZtI9vsj4dYLkkN4a7LWnJpy3Dse2owfLoTj9lJwKVx6C5PQuGrpH71aipeex1HQQEB/fsT9ewz+CQmNtuxuBB4PDKmGlujIFpfbkFfZj6caQD4alREJevoMiSJFh3CD0+0O1cqjTa+XVfAtA2HMNpcdE0K4YUrM7m8dRTKJq60c3g8/FKh57PCKnIttmbfH5GmCMJ5KHv1ChZ9+REBIaGMfv4VwhOT/9b2ev0mCgo+oVa/FlmhZaszgZnF1SiUZYxIuZoxGWNoFdLqmO1qamr4/fffOXDgAFFRUVx//fUkHOcSW+PSrRjm5qHQdMajMTM5I4Qp24q5tFU4n9zYmSDNsW96ZruLp2btYv7uMoa1jebta9tTtK2KBT/nUbrfgN3iAiAowp+WXaOIS/cG0tqgxt8YW90eltUY+bVSz9IaIzaPTKyvmjvjwxkVGULHQP/TDwOcNshfBbm/w76FYCoFSeENogf9F9KvgPBjj6EgnCuSJCUA3wHRgAeYKMvyh0fd/wTwDhAhy3J1w7JngTsBN/CQLMuLzvqOC8IFqLZ2HcXFU8k/oKO+KhifhJYEyjJDhw07HE4/0L8ljw9OOyPhtNtox7KzGsvOSpzF3s7uPi10BF8Sh3/bcJTaM1vX+lTY8/IxzvuNunnzcRYWIvn4ENCvH7oRVxLQty+Kf1iuTBAEQRAudiunTAYZBtw2vtFyt9vG3qzH8fEJJz395ROOsb1yO69seIX9+v30je/LM92fIT4w/pj1HC4P83YW88niPeTVyaglP67MCOaR4Z1IiQjAVWujdkoW9lw96vgAwm9vi09cAI7CQirefIv65cvxSU4mYeKXBFx2WbMeh/ON2+2hrtLaEECbqS2zoC/3htFup+fwehqdDyExGtJ7RBMSo/X+idag0fmcF1eK5VaYmLQqjzk7SnF6PAxtE81dl6bQJanpPmf1LjfTSmuYWFxFqd1JptaPzzKTuLaZ90sE1IJwHpE9Htb8OJVNv84kPrMtIx59Fo3u1LrcyrKMXr+O/IJPMBg24ZQ0rKwPYonBQWxgII92u5uRqSMJ9Dm27qXT6WTt2rWsXr0apVLJ0KFD6datG8omZjS5THaqPl6K26hDUvnj6enH8zU+rN5WzO19knn+itaomriE+WBVPfdO3crBqnqeGZbBuC4JLP86i4LdNejC/UjpGEFceghxacEEhBxbisPh8bCy1sScSgMLq+swuz2Eq1XcFBPGqMhgugVpUZzui725GnIXeWdJH1wBTjOotdByoDeQbjUYtKJRk3DecgGPy7K8TZKkQGCrJElLZFnOagivBwGFf64sSVImMAZoA8QCSyVJSpNl2d3U4IIgeLlcJvZkPUmdTYXxjxjC+wygsLKWW8eO5eUF+/lpSzEPDWjJo4OaN5z2WJxY99Rg2VGJPb8OZFDHBRB0RQv820egOkf1Co/mrKjEuGABxt9+w5aVBQoF2p49CL/nHgIHD0IZeGZrbwuCIAjChS5/+xb2b1rHJWPGoouIbHTfwYPvYLEcpGPHKajVwU1ub3FaeGvzW/yy/xeitdF80P8DBiQMOOYzSZXJzoyNhUxZl0etxYVOsnF1kopnx/QnMkSH7PZg+qMI49JCkCSCrkwhoHcsss1K5QcfUPv1N0gqFZFPPE7o2LFIF9EXzy6HG32F5fBM6Noy76zoukorHo98eL3AUD9CYjTEpYcQGn0kiPY7DyYK/JUsy6zPq2HSqjxW7KvCT61gTPcE7ujTguRwbZPbVDmcTC6u5tuSaupcbvoEB/BuegL9QwPPSNAuAmpBOE84rBYWfPIeB7dsoN3AIQy8416UqpO/sMmyTE3NSvILPsFo3IEVPxYafNhQL9E7/hI+7jqGnjE9URynccKBAwdYsGABtbW1tG3blsGDB6PT6Y59HI+MaWUedYsKQNaCKxvrPVdy77xcimotvHlNO8Z0b/pSnkV7y3n8p534qBRMvbMHrdQ+zHx9C+Y6O5eNSaNt37gmX+BcHpm1hnp+rdSzoKqOOpebEJWSqyNDuCoqmF7BAShP54VRlqE61xtI71sIRRsBGQJjocMYbyidfAmoz37nXEH4u2RZLgPKGn43SZKUDcQBWcD7wFPAnKM2GQX8IMuyHciXJOkA0B1Yf1Z3XBAuMDn7X8NuL6d4WSK+yRkUVtbSs1cvvthqYubWYh4a2IpHL2/VLB/cPQ43tuxaLDsqseXqwS2jCvcncEAimo4RqCPOfW1Ht9GIackS6n6bh2XjRpBl/Nq2JfKZp9FdcQXqyMiTDyIIgiAIAk6HnWXffEFobDxdR1zd6L7a2rUUFX9LfPxYwkIvaXJ7t8fN06ueZlXJKm5vczv3drgXjbrxZ4WdRQa+XVfAvF2lON0ycQoDI4PqmXBtf9LT0gBwFJnQ/7wfZ7kZv9ahBI9qiTLIB9Pvv1Px9ju4ysvRjRxB5ONPoI66cN/nHVbXkZIcZZbDvxtrbNCQQ0sSBEVqCInW0KJjBKHRGkJitARHafDxO/8jVZfbw/zdZUxanceeEiPhAT48PiiNW3omEaJt+kuFAqudzwor+bG8FodH5oqIICYkRtJZd1SQ7fE0ue3pOP+PpiD8C9RVVvDrO69QU1RI/9vuodPQK096YivLHqqrl3Iw/2PM9VkYPWoWGtTkukIZ1epanku/ntiA2ONubzQaWbRoEXv37iU0NJRbb72V1NTUJte1Fxqp/X43br0Hd00+AT20ZPW9igdmbEelVDDtzh70SDl2ZrHbI/Pekn18uuIgHeKD+PTmzhh21fLzzN1odD5c80QXolo0DsM9sswGg5k5lXrmVdVR43QRoFQwLCKIqyJDuCwkEHUT9ZBOmdsFRRuO1JOuzfMuj+kA/Z6B9GEQ3d77TiQIFyhJkpKBTsBGSZJGAiWyLO/8y+tKHLDhqNvFDcuaGm88MB4g8V9WU04QjlZT8wcVZTPZfzAQR2UInrZRRATqWGKI5OftxTw8sBWPDko7rceQ3R5s+w1Yd1RizapBdnhQ6HwI6BXrDaXjAs755aEeu536P/7A+Ns86v/4A9nhQJ2YSPh996G78kp8U1qc0/0TBEEQhAvR5jmzqKso57oXXms0Wc3prCMr+yk0mlRapj513O3f2fIOK4tX8nyP5xmTMebwcofLw+97yvh2XQHbCw34qxW08dXTwlXIwO7tuPzya/Hz88Njc1G3qADzhjKUgT6E3dIa/7bh2PblUvLQa1g2bcK3dWvi3vsfms6dz+ixaE5Wk+NISY4ys3dGdLkFs8F+eB2FSiIkSkNkko70njGERGsIjdESHKlBqT63Tab/iXq7ix83F/H1mnxKDFZSIrS8cU07ru4U12TjQ4CdJgufFlYyr9KASpK4PjqU+xIjSNUcNWHP7YK9s2H1u82+zyKgFoRzrDh7D3P/9zoet5trnv0PyR1O/EIvyzKVVb+z7+D7OK151LgULDb6YPXvwA2dbuKj5CH4Ko9/ma/b7WbTpk2sWLECj8dD//796dOnDyrVsS8HbrOTuoX5WDaX47HV4SpZStTztzPTHMQr326hZUQAk8d1JSH02BlcerODh37Yzur91YzplsDzQzJY/+N+9m+uILFNGINuz8QvQH34OW0zWphTaWBupYFyhxN/hYLB4Tquigymf6gOvybKhpwymxEOLvOG0rmLwGYApQ+0uAx6TYC0oRB0bD0uQbgQSZIUAPwMPIK37MfzwOCmVm1imdzEMmRZnghMBOjatWuT6wjCxc7prGPbnkeptSqxrozFv0svqs02CqI7sWB7KY9c3opHLv9n4bTskXEU1GHZWYV1dzUeiwvJX4WmYyT+HSLwbRGEdDpfzjYD2e3Gsnkzdb/9hmnxEjwmE8qwMIJvuIGgEVfi167dOQ/OBUEQBOFCpS8vZdOcWWT06Uti2w6N7tuX+xIORzVdu3yOUunf5PbTs6czPXs6YzPHHg6n/yzjMW3jIapMdpJC/bm2hQff0q1EBwczcuSNJCcnI8sy1j3V6OcexGNyoO0ZQ9CQZGS7mfJXX0P//fcoAwKI/s9/CL5u9HnZ3FiWZcwGu3cm9OEa0d4g2lbvPLyeyldJaLSG+PQQQmI0hERrCY3Rogv3Q3E6mcN5orzO2/hw+sZDmGwuurcI5eWRbRiQEYmiic+SsiyzSl/PJ4UVrNbXE6hUcH9iJHfHRxDle9QV/W4n7PoRVv/PO8kvonWz77sIqAXhHNq9fDFLJ39GUGQUVz31AqGxJw9Jt+17E0PpZCqcEstNGqKjruSRzjfTNrztSbctLCxk/vz5VFRU0LJlS6644gpCQ0OPWU/2yJi3lFO3IB+P1Ynz4FLU0SZivvwP/11ZzPebsri8dRQfjOlIgO+xLyN7Suq4Z+pWqkx23rymHYMTwvjt3W0YKiz0GJlCl6FJSAoJi9vDJ4UVzCzXU2Rz4KuQGBCqY1RkMIPCdWhP543PUOgt27FvARSsAY8T/EO9ZTvSh0Fqf/AVtTCFi4skSWq84fR0WZZ/kSSpHdAC+HP2dDywTZKk7nhnTB/dBTUeKD3LuywIF4zNex5HdtVRvjIVdXwaFUYzeRF9WJNdw2OD0nho4N9rnCvLMs5SM5YdlVh3VeGucyCpFfhlhqHpGIFfqxAk1bk/UXLV1mKY9TOGH37AWVqKQqMhcNAgdCNGoO3ZA6mJL7gFQRAEQTh1siyz/OsvUKpU9L31zkb3lVf8RkXFb6S0eBSdrn2T268oXMFbm95iYOJAHu/6OLuKDXy7toB5u8pwuD30TYtgUE8V5dtXYC6vp1efXvTr1w8fHx9cBjuGOQewZdeijtESfmsm6lgNhp9/pur9D3DX1REy5gYiHnoIZXDwWTgaJ+bxyJhqrH8Jor3NCp22I610fDUqQmO0pHQIP9yoMDRGS0Cw7zn/0v9MyCk3MmlVPnN3luD2yAxrF8Pdl6bQMSG4yfVdHpl5VQY+K6xkV72VKB8V/5cSw9i4cHSqo3IYlx22T4M1H0Bdofdq8xumQfpweKB5v6gQnygF4RzwuN2smv41W+fPIal9J658+Gn8AgJOuI0sy/y862101ZPZ5/BHE/cw7/QdTYhf051Wj2axWFi6dCnbtm1Dp9Nx/fXX07p16yZnOjmKTejnHMRZZMJjPIR1xzQi7r0J+ZobuG36Njbm13J/v1SeGJze5DdwM7cU8fyvewjX+jDz3l74l9mY+eYW1D4KRjzckYQMbyC+Vm/i8X1FFFgd9A8N5MkW0QwND2r8Yvh3eDxQtr0hlP4dKnZ7l4e1gp73eYPphO6gOP++7RWE5iB5/0N/BWTLsvwegCzLu4HIo9YpALrKslwtSdJcYIYkSe/hbZLYCth01ndcEC4ABaW/YtWvIPdQKI6yQKzpYexQZrKr2MHjg9J48G+E084qC9adVVh2VOGqtoJSwi8thKBhEfhlhqHwOffvU7IsY9u5k9oZMzD9vhDZ6UTTvTsRjz9G4MCBKPxEbwZBEARBaC77N62jYOc2+o+7m4CQIxPIbPZy9u17EZ2uE0lJ9za57d6avTy9+mnahLXhjUvf4Os1Bbw6Pxutj5KbeiQyukMk+7asYvfq3URERDBmzA3Ex8d7e0ytKcG4+BDIMkHDWhBwSSzWXTspeeQ1bHv34t+1C9H/93/4ZWScrUPRiOyRKcqupaLAeLhZoaHCgtt5pP6xRudDSIyWjB7RjYJo/0D1RX9llyzLrD1Qw8TVeazKrcJfreTmHknc0acFiWFN9ymxuj38UF7LF4WVHLI5aKnx5b30BK6NDsFXcdTECIcFtk2BtR+CqQziu8Hw/0GrQWesHKoIqAXhLLNbzMz78G0Kdmyl07AR9Lv1LhQnmSlssBl4dd3T9HItw6L054ZL5hMZmHTSx/J4POzYsYMlS5Zgt9vp3bs3ffv2xdf32BIgHouTusWHMG8sA4UL647pSI58Er94n8LIFtz12VoqjHY+uKEjV3U6tkyt3eXmv79lMX1jIb1Tw/jgug5kLyhkzaoSYloGMeSutmiDfal3uXnlYClTSmtI9vfhl44t6R1y4nD+uJxWyF91pMlhfTlICkjsBYNfhbRhEN7yn40tCBeePsCtwG5JknY0LHtOluUFTa0sy/JeSZJ+wttE0QVMkGXZ3dS6gvBvZrVVsTf7WQw2JfalUZDZiVX2Fhxw+vHkkHQm9D/5+4y7zo5llzeUdpbUgwS+LYIIuCwOTdtwFJrzo9u7x2rFOH8+tTNmYM/KRqHVEnzddYTcdCO+LcX7qSAIgiA0N4fNyoopk4hIakHHIVceXi7LHrKznsbjcdIm810UimPju9L6Uh5Y9gAhviF8PPBj5m6v4tX52QxrG81b17aj8GAuC378BpvNRt++fbn00ktRqVQ4SurR/7IfZ0k9fukhBI9qCW4TZc89R92cOagiI4l99110w684JyGv7JHJ21HF5vkF1JTUAxAY6kdIjJb4jBBCY7SERGsJidbgpz0/PkOdTU63h/m7ypi4Ko+sMiPhAb48OSSdm3skEqxpuvGh3uni25JqJhdXU+N00Vmn4aWWsQwND0Jx9N+x3QSbv4L1n4C5CpIugau/gBZ9z3ifLhFQC8JZpC8r4de3X8FQUcagux+g/eVDT7rN5vLNPLP6aa7RFhPkp6Bb1x8IOoVwury8nPnz51NUVERiYiLDhw8nKirqmPVkj4xlWyV1v+fjsTiR7bnUL/mUgMt6E/v6L6wosfHwZ2vR+Kr4cXxPOiUeO2O7rM7KfdO2saPIwD19U7i3SyJLP99D5SETnQYl0uOqFJRKBStrjTyeU0Sp3ck9CRE83SIGzd+t81Rf6a0jnbsQDi4HpwV8AqDlQO8s6VaDQXNs2RJBuNjJsryGputKH71O8l9uvwa8dgZ3SxAueAs3j0WDg/oNHXHHxPO7JZF8T/BJw2lZlrHsqMKyuRx7fh3IoI4PIGh4Cpr24SiDjt8v4mxzFBSg//4HDLNn4zEa8W3VkuiXXkQ3YiTKAO3JBxAEQRAE4R9ZP+t76muqGfHI040mrhUXT6VWv4b09FfQaJKP2c7kMDFh2QTsLjuTr5jMxv1Onv1lN33TInh1eCoL5vxCTk4OsbGxjBo1iqioKDx2N4aFedSvLUGhVRN6YwZ+GTr002dQ/emnyA4HYXffTfi996DQnv33f9kjc3B7FVsW5FNTYiY4SsPlt7UmpVMkat9zf4XZuWayOflhUxFfr82nrM5Gy8gA3r62PaM6xeJ7nCvRS2wOJhZVMbWsBovbw8BQHQ8kRdIzSNv4ywerATZNhA2fgVUPqQPgsichqffZeXKIgFoQzppDu3cw7/03QaFg9P+9SkJmuxOu7/K4+Hzn50zaNYnRERoy/NxkpL9KkO7EtabtdjsrV65kw4YN+Pv7M2rUKDp27Nh0OY8yM4ZfD+A4ZEQVrsS68QscBbuIevIJgm+9lS9X5fP2ohzaxgYxcWwXYoKObciw/mAND36/DavDzec3d6a1rObnN7ciyzDs3nakdIygzuniP/uL+b6sllYaX37r3IquQaf4hifLULWvYZb071C8GZBBFw8db/bWk06+BFTnz4m+IAiCcHFYkfUOOmcuhZWp6AtVLIrvQr47jKeGpnN/v+OH0y69Df3P+7EfMKCK8Ec3MBH/jpGow5tubHQuyG439X/8gX76DMxr14JKhW7wIEJuvBH/rl0v+stiBUEQBOFcqy46xLYFc2jbfzCxaUeazpnNBzhw8C3CwvoRF3vjMds5PU4eX/k4BXUFfD7oc4oqAnnkxy10SQrh2UvDmfTlF7hcLgYNGkTPnj1RKpVYc2ox/HoAt8GOtkc0QUOSsWzbRP7Vr+PIyyOgb1+inn0Gn+Tks3gEvP4MpjfPz6e2tCGYvj2TVt2imiwr+m9TVmflm7UFfL+xEJPdRc+UUF6/uh190yKOe3xyzFY+K6zklwo9MnB1ZAgTEiNpHfCXz6LmGm8ovWki2I3eq9AvexLiu5z5J/YXIqAWhLNgx6L5LP/2S0Jj47nqqRcJjoo+4fol9SU8veppdlbt5LaUXnR0riAqaiSxsWOOu40sy2RlZbFw4UJMJhNdunRh4MCBaDTH1h7y2FwYlxyifn0pCn8VPjFV1Ex+GXVEOMnTpyFltuXxmbuYvb2EK9vH8M7oDvj/pSamLMt8tSafN37PISlMw4w7e1CzsYr5Cw8RnhDA0PFtCYrQsLi6jqf2FVPldPJQYiSPJUfjd7JZ024nFK73BtL7fgd9vnd5TEfo96w3lI5ud8YvMREEQRD+vfJqdmAq/RKLS4N+YSBL4oeT7w7j8ctTjxtOy7KMeXM5dfPzQYbgq1qi7RF9XoW9rpoaDLN+Rv/jD7hKy1BFRRH+0IMEjx6NOjLy5AMIgiAIgnDaZFlm2Vef4+Ov4dKbxh1e7vE42Zv1OEqlhtYZbx7zGUKWZV7b8Brry9bzSp9XUNrTuHfaRtKiAnmpfxQ///Q9wcHB3HDDDYSHh+M22qn5LRfr7mpUkRoi7m2PpDJR8tSj1C9dhjopkfgvPiewX7+zfAS8DQ8Pbqtky4ICakvNhERrGHRHJi27imAaIKvUyOTVeczdWYoMXNEuhrsvbUH7+ODjbrPRUM8nhZUsqTHir1BwW1w49yREkuD3l9IfpgpY/zFs/tp7VXrmSLj0CYhpuhHn2SACakE4g9wuFyu+ncjOJQtI6dyNKx58Et8mAuOjLSxYyH/X/RcZmbd6P09gxQeoNC3ISH/1uCe4NTU1LFiwgIMHDxIdHc31119PQkLCMevJsox1RxWGBXl46p1oOoZiXv0VddN/J2DAAGJff40ahR/jJ25gR5GBxwel8cCAlsc8rtnu4umfdzFvVxlD2kTxypDWrJu2j5JcA5mXxHLpDa0wIvNA1iFmVehprfVjSvsWdAg8wXO31cGBpd5Aev9i722lL6T0hT4PQdpQ0MWe/KALgiAIwmmyOC0s33o78UoZ084+zIvsSp47jPE9Innw8qYbBbkMDbOm9xvwTQ0i5No0VKHnRzNB7/v/DvQzvse0sKHpYc+eRD39DIED+iOp/331GwVBEAThXMpevYLi7D0MGv8AGl3Q4eX5BZ9gMu2hXdvP8PWNOGa7r/Z8xc/7f2Z8+/G09O/PjRM3EBvsz6uDYpgz6weCgoIYN24cWo2W+g2l1P1egOz2oBuShLZbGLVff0XN5K9ApSLisccIvW0cCp+m6xafKR6PzMGtlWxeUIC+zBtMD76zDaldIv/1wbQsy6zeX82k1Xms3l+NxkfJ2F7J3N4nmYTQpvMUjyyzpMbIJ4cq2Ww0E6pW8mRyNLfHhxOq/kvsW1fibXy4bQq4HdB2NFz6OESem0aYRxMBtSCcIVaTkd/ef5OivbvoNmo0l4y5FYXi+HWTLE4Lb21+i1/2/0L7iPa8ecnrVB18njqXiU4dp6BSHVsSw+l0smbNGtasWYNKpWLYsGF07doVZRNNF50VZvS/HsSRX4c6PoDA3j5UvPowzooKIp95mtBx49hbauTu79ZisDj54pbODG0bc8w4eVX13DttKwcq63l6aAYjYkKY/+52HFYXA29rTUbPGOZVGngmtxiDy8UTydE8lBSJj6KJWdNOG+ycAVlzoGANeFygCYeMK72zpFP6g+8/bKAoCIIgCP+ALMtMXncnbVRGzJZ+fF3Wnjy/GEanKnju6m5Nrm/ZUoFhXh7IMsFXpaLtHoN0HpxgeSwW6ubPRz/je+zZ2SgCAgi+4QZCbhyDb2rqud49QRAEQfhXspnr+WPa18S0TKdd/8GHl9fVbaeg4DNioq8hMnLIMdstzF/Ih9s+ZFiLYQyLvY3rJ25A56/mraFxLPjlx8PhtG+9RNV3O3EUmvBtGUzwqFSsW1eTN/ItXKVl6IYPJ/LJJ1BHn/jK7ubm8cgc2FrBlvkF6MsthMRoGXxXG1I7i2Da4fLw285SJq3OI6fcRGSgL08PzeCm7okEHaeZtsPj4ecKPZ8VVrLfYifBz4fXWsVxY0zYsb2+9AWw5n3YPh2QocMYuOQxCDt/Pg+KgFoQzoCa4iJ+ffu/mGqqGDbhMTIvG3DC9XNqc3jyjyc5ZDzE3e3u5r6O91FU8Cl6/Xpat36LgID0Y7Y5cOAA8+fPR6/X07ZtW4YMGUJgYOAx63nsbozLDlG/phTJV0nwVanY9y2l+KF3UUdEkDx9Gv4dOjB/VxmPz9xBqMaHWff1ok1s0DFjLd5bzuM/7USllJhye3e0+RbmfrCDoEgNIx/uiCfCl7v25DOvqo72Af781DGVzL/WOAJvCY8d0+GPt8FYAuFp0OsBb5PD+K5wgiBfEARBEM6kmVkTSXFuwiTF8enyrhz0S6R/cC1v3n5sDUhXnd07azpXj29KECGjz49Z0/b8fAw//IDhl9l4TCZ809KI/s9/CBpx5TlpeiQIgiAIwhFrf5yK1Wjkmmf+g9QwkcvttrA363H8/GJIS3vxmG22V27n+TXP0zmyM/dlPs9NkzahkODtKxJYMmcmOp2OcePGodhnoeKX/Sj8lYRcn4YyoI7SJydg2bAB34wM4t5+G03Xrmf1+Xo8Mge2VLBlgTeYDo31BtMtO0eeF1/on0tGm5PvNxbyzdoCyo020qICeGd0e0Z2PH7jw3qXm6mlNUwsrqLM7qRNgB+fZSYxMiIY1V+PZ/UBWP0/2PWjN2fpPBb6PAwhSWfh2f09IqAWhGaWv30L8z58G5WPD9e/9EajZgd/Jcsy07On897W9wjxDWHS4En0iOlBTe0a8gs+ISb6WmJjRjfaxmazMXfuXLKysggLC2Ps2LGkpKQ0ObZ1dzV18/JwGx1oukYR0CeMitdeon7pssMlPSRdEO8vyeXDZfvpkhTCF7d0ISKwccNBt0fm/SW5fLLiAO3igvjw2vZk/1rArl3VtOwSSb9b0plvrOf/NuVT7/LwfEoM9yVEHvvi6PHAnp9h5etQmwfx3eCqz71lPARBEAThHNtesZ2qQ/+jhVrNt+vuYr9PGN3VRbx5+9WoVEc+NjeaNe2RCR6VirbHuZ01Lbtc1K9ciX7G95jXrWtoejiYkJtuxL9Ll/OqDrYgCMcnSVIwMBloC8jAHcA+4EcgGSgArpdlWX9u9lAQhNNRkXeAnYt/p+OQ4USlHOlpsf/AG1ithXTuNB2VqvHEs0JjIQ8tf4iYgBhe7PYOt3+zjXq7iw9HJvPHvJkEBgZy2223wU4j+nl5+LYKJnh4HDVff4F++gwUAQFEvfgCIddfj6Q6ezGgx+1h/xZvjWlDhTeYHnJ3W1I7Rfzrg+kSg5Vv1uTzw+Yi6u0u+rQM481rvY0Pj/eZrcrhZHJxNd+WVFPnctMnOID/pSfQPzTw2G0qsmD1u7B3trd0ao97oPeD53XZVBFQC0Iz2rZgDiu/+4rwpGSuevL/0IUfv9lQra2WF9a+wKriVfSL78d/+/yXEL8Q7PYK9u59FK22FenpLzfaRpZlZs+ezf79+xkwYAC9e/dudML8J2eVBcPcg9j3G1DHaAm9uTUe4yEKb7mhUUkPq9PN4zO28fueckZ3iee1q9se8y2d3uzg4R93sCq3ihu6JvBgp0SWf7oHs8HOpTe0IqJXFHfnFrG4xkgXnYb3MxJJ0/5l9pgsw74FsPxVqMyCqLZw4w/eutLihFkQBEE4D1Rbq5m24R4Ga+GrPc+zwxJKd1UhjwzrQFRU1OH13HV29L/sx7ZPj0+LIEJHt0IV1sTVQmeJq7oaw6xZ6H/8CVdZGaroaCIefojg0aNRRRxbu1IQhPPeh8BCWZZHS5LkA2iA54Blsiy/KUnSM8AzwNPncicFQfj7ZI+HpV99hr9OR58bbjm8vLp6BSUlM0hMvJuQkB6NtjHYDNy/7H4A3ur9MROm5VBhtPO/EclsWPgzAQEBjBs3Ds+GWkzLCvFrG4bSN5v8a+7FrdcTfP31RDzyMKqQkLP2PD1uD/s3V7Dl90MYKiyExWkZOr4tKR1FML2npI5Jq/OYt6sMgBHtY7jr0hTaxh17Bfuf8i12Pi+q5MfyWhwemSsigpiQGElnXRNXxZXugFXvQM488AmA3g9BrwkQcP43whYBtSA0k6xVy1kxZRItu/XiigceR+13/Et815eu57k1z2G0G3m2+7PcmHEjkiTh8bjYs+dhPB4b7dp+glLZ+IR37dq17Nu3jyFDhtCrV69jxvU43JiWF2FaXYykVhA8MhVNj2gM06dT8c47qCLCD5f0KDVYuWvKFnLKjTx/RWvuurTFMd+67Smp495pW6k02nn96ra0t6v47f3taAJ9uOqxTqzUuHlpcw4Oj8x/UmO5OyEC5dFjyDLkrfAG0yVbITQVRn8NmVdDUzWpBUEQBOEccHlc/OePBxnsb2Di3ofZWh5KD2UBQ1M19OzZE2iYNb21EsO8g+CWCR6RgrZX7Dk50ZJlGev27einz8C4eDE4nWh69STquWcJ7N//rM6OEgSh+UiSpAMuA24DkGXZATgkSRoF9GtYbQqwEhFQC8IFZ/fyxZQfyOWKBx7HV+MNFx2OWrJzniFAm05qyqON1ne4HTy84mHK6sv4uP9EXvi5lLwqM28OT2LH0l+84fTYcXhWVlG/rhRN50isO6Zi/OVn/Dt3JnryJPwyM8/a8/O4PeRu9pbyqKu0EhYXwNB72pLS4d8bTMuyzN5SI8tzKlmWU8nOIgMBviru6JPMbX1aEBd8/EkOO00WPjlUyfwqAypJ4vroUO5LjCBV00TWVLTZG0zvXwS+QdD3aehxL2hCz+Cza17i06sgNIPinL0s/vIjEtq058pHnkKparqIvdPj5JPtn/DNnm9IDkrmi8u/ID30SH3pvPz3MdRtpk3m+2i1jYvV5+XlsWzZMtq0aXP4ZPlPsixjy6rB8FseboMdTedIgoa1ANlG6SMPY1qy9HBJD2VwMFsP6bln6lbsTjdfjetG/4xjv02btbWY52fvJlTrw/d3dKd2VTl/bKwgMTOU1je34rHiclYUmugZpOW9jERSNI3LglC4EZa/AgWrQRcPIz+BDjeCUrzsCIIgCOeXD7d+QGvXdr7OvZMdlcn09OTSPtDCNdfcgkKhwG20o//lALacWnySdYRel3ZOZk17LBbqfpuH/vvvsefkoAgIIGTMGG/TwybKfQmCcMFJAaqAbyRJ6gBsBR4GomRZLgOQZblMkqTzfyqcIAiNWIx1rJ7xLQmZ7ci4pB/gPY/P2fd/OJ1GOnacgkJx5JxalmVeWPsC2yq38Vqft/hsoYsdRQZeGZrEvj/moNVqGXfrONyLy7Fsq0TbKxrzis8wLVxI+P33Ef7gg2etvJfH7WHfxgq2/l5AXZWV8IQAht3TjhYdwv+VwbTZ7mLNgWpW5FSyYl8lFUY7kgTt44N57ooMxnRPROfXdGYkyzKr9PV8UljBan09gUoF9ydGcnd8BFG+TWxTsMYbTOetBP9QGPACdL8b/I4/I/t8JZIiQThNhvIy5rz7GrqIKEY+9txxw+kiYxFPrXqKPTV7GJ02mqe6PYW/6sjJbXX1Cg4d+oLY2DFER49stG1dXR2zZs0iLCyMkSNHNnqjcdVYMcw9iG2fHlWUhoh72uPbIgjr7t2UPPoYzvJyIp9+mtDbxiFJErO2FvPcL7uJCfbj+7t70CqqcX0rh8vDf+ftZdqGQnqlhPHawHQ2TMtFX26m28hkstoHMmTvQWTgjbR4xsWGoTj6ja9sl3fG9P5FoI2EYW9Dl9tA9ZcAWxAEQRDOA4sLFpNb8A3lhbexs6otfWy7aBVs58oR16DT6TBvrcDwWx64PQRdmUJA77M/a9qel4/++++pmz0bT309vunpRL/8MkFXDhdNDwXh4qICOgMPyrK8UZKkD/GW8zglkiSNB8YDJCYmnpk9FAThH1k1/RscNisD77zv8Pl8eflsqqoW0TL1aQIDMhqt/+mOT1mQv4AHOjzE/PWRrDlQwXMDEyhc9xsajYbbbh2H87cSbFk1BPaLpe6XtzCvWk3k008TdvttZ+U5ud0ecjeWs+X3Qxj/DKbvbQim/2WlPA/VmFmW7Q2kN+bV4nB7CPRVcWlaOP3TI+mXHnlMr6+juTwy86oMfFpYye56K1E+Kl5IjWVsbBiBf22WKMtwcDmsehcK13lzl0GvQNc7wDfgDD/TM0cE1IJwGmzmema/9TLIMlc//SJ+AU2/GMzLm8erG15FISn4X9//MTh5cONxbKXszXqCgIDWpLV6odF9LpeLmTNn4nK5uOGGG/D19b6oyU43xpXFmP4oQlIoCBqeQkDvGFBI1E6dRsXbb3tLekybin/Hjrg9Mm//ns2Xq/LonRrGpzd1JkTr0+ixyuts3Dd9K9sLDdxzWQpXhwWz5KNdqH0UdL6/LW+5jKzdX8JlIQG8m55Aov9RL7BVubDiNcj6FfyCYeBL3kL8PuLEWRAEQTg/5RnyeG/dC6hLx7K7ui1DyCEmyEbrjNa0SU6nZkrW4VnTIaPTUIefvVnTssuFacUK9DNmYFm/AdRqb9PDm2/Cv1Onf92Jn3B8siwDiH8TF4dioFiW5Y0Nt2fhDagrJEmKaZg9HQNUNrWxLMsTgYkAXbt2lc/GDguCcHIlOVnsXbmUbiOvJSze++WR1VrMvtyXCQ7uTmLinY3W//XAr3y560uubnkNubndWLS3hIcvjaNmy3w0Gg1jb7oVxy9F2A8Y0A2Oo/brF7Fu3Ub0K/8l5Lrrzvjzcbs97NtQztbfCzBW2whPCOCK+9qR3P7fE0w7XB62FNSyPKeS5fsqyasyA5AaoWVc7yT6Z0TSLTkUtfLEpU2tbg8/lNfyRWElh2wOWmp8eS89gWujQ/D9a1lUWYbchd4Z0yVbQRcHw96BzreC+tz1Q2kuIqAWhH/I7XLx2/tvYqgoZ/T/vUJITNwx65idZl7b8Bq/5f1G58jOvHnpm8QExDRax+NxsHvPQ8iyi3ZtP0apbFxPaPHixRQXF3PdddcR0dDsyJpTi2HuQdy1Nvw7RBA8vAVKnS9uo5Gy5/8P05IlBPTvT+wbr6MMDsZkc/LwDztYnlPJrT2TeHFE5jEvlBvyanhgxjYsDjef3NARXU49y+ZmE5UaRPmoWG4qK0clwXvpCdwYE3rkjUd/CP54C3Z+Dyp/uOxJ6PUA+Ac338EWBEEQhGZmdpp5ePmjuEtGk1OTye0t6pAKKvHzD+byFr0pf38bsvPsz5p2VVdjmDnT2/SwvNzb9PCRh71ND8PDz8o+XMxcTjeVh0zoy8z4+Kvw06q9fwK8P1U+igvm5LrY5mBScRUzSmswuT2oJFBLEipJQq1o+HnUbfWftxVHLf/rbYV0SuOc+pg0+RjHe3yff+Gl4EeTZblckqQiSZLSZVneBwwEshr+jAPebPg55xzupiAIf4PH7WbZV58RGBZBz2vHACDLbrKynwQgs/U7SNKRGbIbyzby8rqX6RHdE7l6ND9vK+SuHtHYdy/E39+fsTfcjHNmIY4iE0HD46j+4Cls+/YR97930V1xxRl9Ln8NpiMSA7ni/jSS24VdMO+dp6PKZGflPu8s6VW51dTbXfgoFfRICeXWnkkMyIgkKezUJujpnS6+LalmcnE1NU4XnXUaXmoZy9DwoMZXqAN4PJA91ztjumI3BCfClR9Ax5suqivVRUAtCP+ALMss/+YLCnfvYMi9D5OQ2e6YdfZW7+WpVU9RXF/MfR3uY3z78agUx/6XO3jwXYzG7bRt+zEaTYtG9+3atYtNmzbRs2dP2rRpA0D9hjIMvx5AFeFP+F3t8GsZDIB19x5KHn30mJIeh2rM3DVlC3nVZl65qi239kw65rl8tSafN37PISlUw9fXdWLfL/kcOmQiYnAcU5IkNpdUMDBUxzvp8cT6Ncy6NpV7XyC3fguSAnreD5c8Clpx8iwIgiCc32RZ5rlVL1G571Iq6lrzSK96rCvXYw8KY1BwD6yzD+GTpCNkdCvUEZqzsj/Wbdu8TQ+XLAGnE23vXkT/3/ME9Osnmh6eBovRQdlBA+UH6yg7WEdVoQmP+/gTS5UqBX5a1eHA2k+rxveo3w+H2Zoj6/hq1SjOYrC622Thi6Iqfq3UAzAiIpgUjS8uj4xTbvjjkXE1/O6SaXzb8+dyGavHg8t55Paf2x6+/ZdxhLPiQWC6JEk+QB5wO6AAfpIk6U6gEDjzUyQFQWgW2xfOo6qwgJGPPYePn3eWa2HR1xgMm2jd+i38/eMPr3vQcJBHVzxKclAyLeWH+GJ9ITd0jECZswQ/fz9uve5mHD8cwlllJejKGCrfeAhnUTHxn3xMYL9+Z+w5uF0ectaXsXXhIUw1NiKTArn0+jSSLvJg2uPxNjhcllPBipxKdhbXARCl8+XK9jEMyIikT8twtL6n/jmtxObgy6IqppXVYHF7GBiq44GkSHoGaY89lm4X7P0FVv8PqnIgrCVc9Tm0uw6UTZeWvZCJT7uC8A9sWzCXXUsX0m3UaNr2H9ToPo/sYcreKXy07SPC/MP4avBXdI3u2uQ4VVVLKCz6ivj4W4mKbPxtZ0VFBb/99huJiYkMGuR9DPshI4bfDuKbFkL42EwklQJZltFPm+4t6REeTtLU79B06gTAuoPV3D99G7IMU+/oTu+WjcNji8PFU7N2MW9XGYMzo3i0bQJrv8zCJcvUjEvmbYcJf4uCj1snMjoqxPuCaamFtR/AxongcUKnW72zpoOOnUEuCIIgCOejKXtnsGR9DJb6DO7vvpOgnRYMQWG0JYmIYl+ChicR0CfujM+a9pjNR5oe7tuHIjCQkBvHEDLmRnxTWpx8AKER2SNTW2am7GAd5XneQNpYZQW8wXNkUiAdBiYQnRJEeHwATocbu9mJrd6Fzez0/ql3Hvnd7KS2zIzN7MRuduHxHD+h9dWo8G0UYquODbVPY7a2LMusrDXxWVElq/X1aJUK7oqL4K6ECBL8fE4+QDOQ/wy7mwyvj9x2/CUEbxR4N7qPYwJzp0fmibPybM5fsizvAJo6eRh4lndFEITTVF9bw7qZ02jRsQstu/cCwFSfw8GD7xERMZiY6GsPr1ttrWbCsgn4qny5RPsSHy8t5MrMMILzl+Pn58stV43BMaMAj8lB8PBIyl+8D3dtLQmTJqLt3v2M7P/hYPr3Q5hqvcH0ZWPSSGp78QbT9XYXa/ZXsTynkhX7qqgyeRscdkwI5vFBafTPiKRNrO6Unr/Z7Wa/2U6O2UqO2UZOvY01BhMycHVkCBMSI2kd0ERpDrcTdv4Aa96D2jyIzIRrv4I2V4NCeez6FwkRUAvC33Rw6yZWTp1Mq+69uXTM2Eb3VVureW71c6wvW8/AxIG83Ptlgnyb7p5qtRaRlf0kgYHtaNXy2Ub32Ww2fvzxR3x9fbnuuutQKpW4TQ5qpmWjDPIlbEw6kkpx3JIeANM3HuKlOXtJDtcyeWxXksMbX2qSX23m3qlb2V9p4slBaXQ2Klg+cS+O9EAW9Apgt9XIFeFBvJkWT6SvGmxG2PA5rP8E7CZofz30ewZCU5rv4AqCIAjCGVZhruCt+XlY6ztze7u5XO53OYtc2YTgT++o9kRc3/qMz5q25+Whn/E9db/+6m16mJFB9H9fJujKK1FozvyM7YuF0+6mosBI+UFDQyhtxGF1AeAfqCY6JYi2l8YR0zKIiIRAlOoT14E8EVmWcdgaAu2/Btn1TmzmIyG31eRAX+4Ntp0293HHPJXZ2kqtipXYmWY2kmt3EOWj4vmUGMbGhhGkPruncpIkoZZAzZkNJf7tAbUgCBePlVO/wu1yMeD2e5EkCY/HTlbW46jVOjLSXz0cclpdVh5a/hA11hpujPuYjxaV0L9lMHGlf3jD6RE34JxxCI/DQ9CQYMqevgvZ4SBxyrf4tzv2au7T5XZ5yF5XxtaFBdTX2olqoaPvTekktgm9KIPp/Gqzt5Z0TgWb8mtxumUC/VRclhbBgPRI+qVHEBZw/FIaDo+Hgxa7N4Q227yBdL2NQpuDP7/a9lVItNL4ceeJvlx22WH7NFjzAdQVQkwHuGEapA+Hv9ajvgiJgFoQ/obKgjzmf/g2US1SGTbhMaSjXiRK6ksY+/tY6ux1vNDzBa5Lu+64L94ej53dex4EoF3bj1EojrzYybLMnDlz0Ov13HbbbQQGBiK7PNRMz0a2uQi/oyMKjfq4JT2cbg+vzMviu/WH6J8ewYc3dkLn1/jyj6VZFTz64w5USonJN3TCuKKczbkG9g+N4NdgDzqXm4ltkhkREYTkssHaz2HN+2CthYwrof/zEJV5Bo6wIAiCIJxZzy6ZhFXfmaHJS7klsTd/LCrDqZS5svsgYoZ1OGOzpmWXC9Py5ehnfI9lQ0PTwyFDCLnpJvw7dbwoT/iaW73e5g2iG8p1VBfXIzfMag6N1dKyayQxKUFEpwYRFOHfrMdUkiR8/VX4+qvQ/Y1mmW6X53Bw/Xdma1uUsC3Vl02t/DBpFETUuRiZY6NtkQN/31p+O2omtq9WhZ9G7Z3FrfHe9tV4y5Acua1Cpb54Z10JgiCcbw7t2sG+davoNfomgqO9fajy8j6gvj6HDu0n4+MTBnivwH5u9XPsqd7DzQn/45PFNfRM0tGqei2+PmpuGjIa54xDoJTQDfCn9LE7kXx8SJz6HX5pac26z26nh+x1pWxdeIh6vTeY7ndzBomZF1cw7XB52JRf2zBLupL8am+Dw5aRAdzepwUDMiLpkhRyTN8utyxTYPUG0fv+DKPrbeRZbYfLYCklSPH3pX2ghuujQ8kI8CND60eSny+q433GdFhg2xRY+yGYyiC+Gwz/H7QaBBfRcT8ZEVALwimq19fy69uv4KvVctWTL6D2O9LMsMZawz1L7sHqsjJ12FRah7U+4Vj7D7yBybSb9u0+x98/odF969atIzs7m8GDB5OU5K0XbZifh6PASOiYdNTRGmqnTmuypIfB4mDCjG2sPVDD+MtSeHpoBsqjXgTdHpkPluby8fIDtI3T8WqfVmz//gCH1DJLbojkoOzi6ohgXmkVT7jCA1u+gj/egfpySB0IA/4P4jo31yEVBEEQhLNqbfEGVm+PJtSvlltjDBxa6EehOp9ebTuTOrzjGXlMZ2UlhlmzMPz4E66KClQxMUQ88gjB141GFRZ2Rh7zYuBxe6gpaSjXcdBAWV4d9bV2AFQ+CqKSdXQekkhMajBRLXT4ac/PWoxKlQJtkC/aoFNrYlRiczCxqIrpZTXUuz109/PjJt8AOvorsetc2No4sR8VbFuMDmrLzNjNThwnmK0NoFQrDofY3vC6IcBuKE/iq1EdCbVFuC0IgvCPuZxOln39OcFRMXQfNRoAvX4ThwonERs7hvDw/ofXfW/LeywtXMrVsc8zeZmTtjFa2tatx89HxU0Dr8X1YxEKfxXa7jKlj96FMjiYxG++xicxsdn21+30kLW2lG2LvMF0dIqO/rdmkND64gmmK002VuZ4S3esOdDQ4FCloFdKGLf1TmZARiQJod6r2GRZptjuZJ/BRk699XAgvd9iw3ZUua8kPx8yAvwYFhFEhtYbRKdofPE91dnOdhNs/sp7lbq5CpIugau/gBZ9/1XB9J9EQC0Ip8DpsDPnnVew1hsZ8/LbBIQeOaGsd9Rz39L7KDeXM2nwpJOG0xWVCygunkpiwp1ERAxudF9+fj5Lly4lMzOTXr28NarMWyswry8j4JI4/NuHU/HKK+hnfE9Av37EvvnG4ZIeByrruWvKZkoNNt4Z3Z7rujYOvg0WBw//sIM/cqu4rnMc1wUGserrbDZ2CWBlsppwNXyb1oKhYQGw6ydY+QYYDkFiLxj9NST3aYYjKQiCIAjnhtPt5Im583Dae3Fz++8I3D6aZcq9hGj8GXTNlc3+eLLLRfl/X8Hwyy/gcqHt04foF18goG9f0fSwCXari4q8OsryvDOkK/KNOO3ewFUb7EtMahDRlwcRkxpEWHwASuXFdanrHpOFz4uqmFOpRwZGRgRzX2Ik7QNPveSLx+3BYXVjszixW1zYD//0ztpuvMxJvcFOTYkZu+Vvhtvao0PsI7+LcFsQBMFr67zZ6MtKuObZl1H5+OBymcjKfgJ//wRatXzu8Ho/5vzIlKwp9A+/i59WBdEi1Jduli34q5WMufQqXDOLUYX6omljpvTRh1HHx5P49Veoo6KaZT9dTjfZa73ND80GO9EpQQy4tTXxrUMu+GDa45HZXVJ3eJb0roYGh9E6P0Z0iGVARiS9U0OxSJBTb2Oh2UxOZfXhMLre7Tk8VoyvmgytH31CwhuCaH9aaX3RKv/h+5vVAJsmwobPwKr3Tga87AlI6t0Mz/zCJT4dC8JJyB4PCz99n/K8A4x6/HmiWqQevs/hdvDIikfI1efy0YCP6BTZ6YRjWSz5ZGc/i07XidTUJxvdZzQamTVrFqGhoYwaNQpJknCU1KOffQDflCB0l8dT+swzGOf+RuiddxD5xBOH3zRW7qvkwRnb8VUr+H58D7okhTYae09JHfdN30p5nY1XrmhN6J56ft1RyMIrQyj1hRuiQ3k5JZrgAwvgx9ehep+33tHw/0HLy/+V394JgiAIF5dPt8ygsrgzHSJ2M7imDYtcWchKD7fccSeKZq7rJ3s8lP3fC9T9+ishN91IyK234ttCND38kyzLGKtthxsZlh80UFNqBtn7kSMsPoCMntFEtwwiJjWYgBDfC/5EuSl/Nj78vKiSVQ2ND09Ym/IkFEoFfgEK/AL+/mzyRuG2uXGQbWsIuRuF23ob1cXe2yeqsw2g+jPc1qobz9puKtw+T2fCC4IgnKq6ygo2/PIjrXr0pkXHLgDk7n8Vm62Mrl1+RKXy9oZaVbyK1ze9TkfdCJZvyiAyQElv53Y0aokxPUbimV2KOkaLb2I5JY8/hV9aGgmTJ6EKDT3Rw58Sl9NN1ppSti08hLnOQUxqEAPHtSY+48IOpk02J2v2V7Msp5KV+6qorvc2OOyUEMwDQ9JISAjC6iexz2Lnc7OJhzdXUus88h4WqlaSofX3lubQ+pHe8Cf4dHs+uJ1gqQVLDez52RtO242QfgVc+gTEdznNZ35xOK2jLEnSo8BdgAzsBm4HNMCPQDJQAFwvy7L+tPZSEM6htT9NJ3fDGi675Q5adut5eLnb4+aZ1c+wsXwjr13yGpfFX3bCcdxuG7v3PIQkqWjX9iMUiiMfwF0uFzNnzsThcDBu3Dh8fX1xm53UTM1CqVURfG0KJY89Sv3y5UQ89hjh4+8GvCc2X63J5/UF2aRH65g8ritxwY3rIv68tZjnZu8mROPD16M6sH/eISbHKth4eRDRfmpmpMUzQL8Jvr0FynZCeDpc/x20HimCaUEQBOGiUG4uZ+LyShTouCNuK/v3DaFeVUy/Hj0JCw9v1seSZZnKd96l7tdfCX/wASImTGjW8S9EbpeHqiIT5UfVj7YYHQCo/ZREpwSR2jmS6JQgolro8PG7uOfQODwefq008HlhJdlm2+HGh7fGhp3+SfA/dLrhtt3qagi2jxNuHzWD21R76uG2IAjChWbFlIlIkkS/sd5z9sqqRZSVzSI5eQJBQd5ymTm1OTz5x5Mk+nZjz+5+BPhI9PXsItAHru80HHl+OT7JOlRB+yl75kX8O3cm4YvPUQYGnta+uRxu9q4pZfuihmC6ZRADb88kPv3CDKZlWSav2syKnEqW51SyuaAWB6AJ9aVl2zAyIjXY/BQctDlYZzdDkbfWdIBSQYbWjyvCg0lvKM2REeBHuFp18uPgcnh7c1lqjoTOlpqGZbVHlh1eRw/2uqMGkCBzpDeYjml/xo7NhegffwKSJCkOeAjIlGXZKknST8AYIBNYJsvym5IkPQM8AzzdLHsrCGdZ1qrlbJz9I+0GDKbrlVcfXi7LMq9tfI0lh5bwRNcnGJk68qRj5e5/hfr6LDq0n4yfX2yj+5YsWUJRURGjR48mMjIS2SNT+0MObpODsLFplD72AJaNG4l68QVCb7oJALvLzQu/7uGnLcUMbRPNezd0QONz5L+0w+Vtljh1wyF6tgjhkZQ45szP47fuWqq1CsbGhvGCupDAuY9D4XoIToSrvoD214NCXIopCIIgXDwem/81lroOXNtyHrF5Q5jvKSJc60/focOa/bFqJk+m9ptvCLn5ZsLvv7/Zx78Q2Oqdh2dHlx00UHnIhNvpvVRWF+5HfOuQhmaGwYTGalGcocaU5xujy83U0homF1dRZneSrvXjg4wEro4KOfV6lechhVKBf4AP/gF/f9Z3U+H2A1+egZ0UBEE4Cw5u3cjBLRu57Obb0YVHYLdXkZPzfwQGtqFF8oOA90vzCUsn4CfHUnHweiRkBiqzCVJ5GN16KNLiKvwyQsG9hYr/vIn20kuJ/+hDFP6n3qD3r1wON3tXl7Jt8SEsdQ5iWwVz+R1tiEsLvuCCabvLzab8WpbkVLD4UA0lLjeeADXaCD8UKbE4FGAHNgN+Thtpaj8uCQkgXeNHRoA/GVo/4nzV3ufttDUEyeVQXPuXoLmm6bDZYTr+zvkEgCYU/ENBEwahKd6fmjDwD/H+jG4P4S3P1uG6oJzuV/QqwF+SJCfemdOlwLNAv4b7pwArEQG1cAEqztnL4i8/IqFNewbeeV+jF+5Pd3zKzNyZ3NH2Dsa1GXfSscrL51Ba+gNJSfc2aogAsHv3bjZu3EiPHj1o27YtAMbFBdj3GwgaFkf5Cw9i27uX2HfeJmjECACq6+3cN20rmwv0PDSwFY8MbNXo5K68zsb907eyrdDA3b2SaF8Lr+WVsaVvIPF+amZF2Llkw0NwcDkERHtLeXQaC6q/f2IhCIIgCOezFYfWsmVPArHaMq7z+LHMWoJS8jB2/L3NflJmmDWLqv+9h274cKKef+6CO+n7J2RZxlBh8ZbqaKgfrS+3AKBQSIQnBtL2sjhvDenUoFNuFngxKbE5mFRcxbRSb+PDS4IDeDc9gQGhgf+KfyMncjrhtiAIwvnEabex/JuJhMUn0vmKUciyTHbOs7jdZjIz/4dCocbsNPPAsgcwWkFddh8Wu4fhfrmEqJxcm3o5ylV6/DtE4K5cQs3nnxE4ZAhx77yN5PPPXiMPB9OLDmExeoPpwXe0IS49pJmf/ZnjlmW2VJn49UAl6yrryLc7cGpUyBoVtAkCQAnEaHxI94EMlYMM6slw15Bkr0RproGqvwbNDSG003z8B/bVHQmVNeEQntYQNId6Q2hN6FG3w7y3Vf++zzjN6R8H1LIsl0iS9C5QCFiBxbIsL5YkKUqW5bKGdcokSYpsantJksYD4wESm7H7qCA0B0N5GXPefQ1dRBQjH3sOperI5Y7Ts6fz5a4vubrl1TzS+ZGTjmU2HyRn3/8RHNSNlBaPNrqvsrKSuXPnkpCQwODB3oaJlt3VmFYW498uiKp3H8ZxqJD4jz8mcIA32N5bWsf477ZSXW/n4xs7MaJD49nYG/NqmDBjOxaHi/eHZbJ7RxVPpiip0/pxR6iC5/a9g3bxbO8L6aBXoPvdoP7n38YKgiAIwvnK4XbwzJylOBxduS19Gvv398Sm1DNi0BB0DU2Gm4txyRLKXnwJ7SWXEPvG60gX8IzYE3E53VQeMh0u1VF+sA6b2QmAr1ZFTEoQ6T2jiUkNIjJJh8rn33tV1h6ThS+Kqvj1NBofCoIgCBeGjbNnYqyq4PqX3kCpUlFS8gM1NStIa/UCAdpWuDwunvjjCXJrigmufonKejdXaA4SoXZwdVw/1BtNaLtHY8ueiWHqVIKuuYaY/778jxorOx1u9q4qYdviQqxGB3HpwQy+qw1xaRdGMO2RZVbVGPkop5TNNjNOpfcYSDoF0Q47Ga4i2pnKyDTnkV6XQ6p+Lz7OE8xs9g06EigHREFk5rFB89Fhs3+ImLx3DpxOiY8QYBTQAjAAMyVJuuVUt5dleSIwEaBr167yP90PQWhuNnM9s996GWSZq59+Eb+AgMP3LchbwJub3qR/Qn9e7PXiSWe9uN1Wdu+ZgELhR5u2H6BQHPkvZ7PZ+PHHH/Hx8eG6665DqVTirDCjn5mLKsoX/ZSncVdXkjBxItqePQCYs6OEp3/eRbC/DzPv7UX7+ODD48myzNdrC3h9QTaJoRpe7ZXKJwcr2dLel0QJptT9Qo9VH3kvO+n3HPS8D/x0zXvwBEEQBOE88t766VSVdaR3zEZal3ZmGXpaRITSpc8lzfo45o2bKH38CfzbtSP+ow//8Uyn85HD6qL0gIHSXAOlBwxUFZrwuL0f3YOjNCR3CPfOjk4JIiRKg/QvKddxPLIs84fexGeFzdP4UBAEQTj/1ZYWs3nuz2Re2p+EzHZYLAXsP/AaoSF9iI8fiyzLvLnpTVYXbSJM/xKlBplh2kPEqK2MCr8M351WAi6Lw7z6K4yzfyF03Fgin376H33Znb+zihXT9zUE0yF0v7sNsa0ujGA6u97K1KIqZpbVYpLA32Xlquo/uMSwjQxzPq0sh9D4+B+ZsawJg5gUSO3adNCsCfWGzUrRgPdCcDolPi4H8mVZrgKQJOkXoDdQIUlSTMPs6Rigshn2UxDOCrfLxW/vv4mhopzR//cKITFxh+9bW7KW59c8T5eoLrx92duoFCf/77Nv30uYzQfo2PFb/HyjDy+XZZk5c+ZQW1vLuHHj0Ol0eGwuaqZmg0LGNP8VZJOexG+/wb99e1xuD28v2sfEVXl0Sw7hs5u7EBF45PIRi8PFMz/vZu7OUgZnRJIeoOEhWy2mZB/utmTz3PbH8EeGPg9Bn0e8L9SCIAiCcBErNZUydWU9fsoAxgWW8UeBAl/Jw41339Osj2Pdu5fi++9HnZhAwpdfoNBc2LNjjw6kS3L1VBWakGVQqCSiknR0GJhwuFyHKMtwhMPjYU5D48Os86TxoSAIgnDmybLMsq+/QO3ry2W33IHH4yIr6wkkSUXr1m8hSQqm7J3CD9mzCNE/R1GVkiEBRSSo6xkVdAn+OQ50gxIwzv0A06JFhE+YQPgDE/52CSiP28OGOXlsX1xIeEIAQ+9uS2yr4DPzpJtRqc3B7EoD04uqyXM4UHjcXFK7jZsqF3Cpo4zQS8YjJb/iDZz9gkEp3lMvVqfzN1sI9JQkSYO3xMdAYAtgBsYBbzb8nHO6OykIZ4Msyyz/5gsKd+9gyL0Pk5DZ7vB9u6p28ejKR0kNTuXjAR/jp/I76XilpbMoK/+ZFskPEhbaeKbW+vXryc7OZtCgQSQnJ3ubIv64D1eNFdu2z8FeR+LU7/BLS8NgcfDg99tZvb+aW3sm8cKVmfiojnyTWlBt5p6pW8mtNPHAJSmstliZG+Yk3upgxo5n6Fq/F7rcBpc9AYHRCIIgCMK/wUNzv8Nc346xreZQVJSCS7Jy45gb8PFpvvqAjoICiu4ejyJIR+LkySibuWzI2WC3uig7YKAk10DpXwLp6BZBdLkimbi0EKJb/LvLdRzPxdr4UBAEQTg1+9avpnD3DgbccS/a4BDyCz6lzridNpnv4+cXw5JDS3h38/voDA9TVKnl8sAyWqjrGOHfC02+i6DhSei/exXz6tVEPvM0Ybfd9rf3wWyws2jyHsoO1NHm0lguub4VKvX5+55tdLmZX2VgVrmedYZ6ZCChrpj/VMxmdPUSNFHt0Ax6GFpeDuK99F/jdGpQb5QkaRawDXAB2/GW7AgAfpIk6U68IfZ1zbGjgnCmbVswl11LF9Jt1Gja9h90eHmeIY/7l91PmF8YXwz6gkCfwJOOVV+/j325LxES0osWLR5sdF9BQQFLliyhdevW9O7dGwDTiiJs2bXYc34BdxVJ06fhk5hIdpmR8VO3UFFn561r23FDt8b12pdlV/DIjztQKiTu6N+SL9xmLGES4/L/4L/Fr+HbfjT0/Q5CkprhCAmCIAjChWFR3mp2ZSfTQldAn/pQ1st2OrRqSWrrNs32GM6KSgrvvAtkmcTJX6GOvjC+BBaBdPMosTmYXFzF1LPZ+FCWvc2dag5C7UGozfP+ri8At+PMPKYgCIJwXHaLhZXfTSayRSodBg3DaNxNfv5HREVeSXT0SHZV7eKZVc/hr7+T0opILtVWkKau5Up1DwJKJYJHJVL9yXNYt24j5tVXCB49+m/vQ3FOLYu/2ovT7uby2zNJ73F+fh5xeDysqDXxc4WeRVV12GUZrdXKqLJVPFn1Hcn2ctxtrsVn5DyIaX+ud1c4B05rbrwsyy8BL/1lsR3vbGpBuGAc3LqJlVMn07JbLy4dM/bw8nJzOeOXjEclqZg4aCLh/uEnHcvlMrN7zwOoVIG0yXwfSTpycmc0Gpk5cyahoaGMGjUKSZKw7qvFuKQAZ8lmcO0nafp01FGRzNtVypMzd6HzV/HDPT3pnHikbpTbI/Ph0lw+Wn6A9Hgd/ik6PlVbiTXb+W7ny/ROiYL71kBEWvMeKEEQBEE4zzncDl74dTVOVzvuiF3IxrwkAtUSo2465VYpJ+Wuq6Porrtw6/UkTpmCb0qLZhu7udmtLsr2e8t1lO43HBNId20IpKNEIH1K9tZb+bywslHjw3sTI+nQnI0PLbVHQuhGYXQe2OuOrCcpITgRQluA+sIuLdPYunO9A4IgCKdk/azpmA16Rj3+PLLsZG/WE/j4hJOe/jLFpmIeWPYgnqqrqalMoYeminY+VQyXuqGrURF8TQJVrz+KLTeXuPf+h27YsL/12LJHZuvCAjb9lk9wlIarHu1MaKz2DD3Tf0aWZbYYLcwqr2VupQG9y42fRyax9CAPlH/P9ealuNRBKHvchaL73Sh1Med6l4VzSBRvEf71KgvymP/h20Qmp3DFA48fbkRgsBkYv2Q8ZqeZb4Z+Q4Iu4aRjybJMzr7/w2IpoHOnqfj6Rhy+z+12M3PmTBwOB+PGjcPPzw9XjZWa7/bgrisG2xaSpk5FCgrmzd9z+OKPg3RJCuHzmzsTFuBLsd5Csd5KUa2F33aV8UduFb26RrIlUIFF6eG67D28HbAQ/3FvQUyHM3a8BEEQBOF89tof06isbMfl8WupL41HxsMtd45H0UyXiHqsVoruux9HQQEJE7/Ev13bZhm3udgtTsoO1FGSq6ck10B1kQikT5csy6zS1/NZYSV/6E1olAruiIvgrvhwEv3/YckYSy3U5jcOoWsagmib4ch6kgKCEiAsFdpfD6Ep3t9DU73htOoirAN+44xzvQeCIAgnVXUon22//0b7AUOIaZVObu4rWCwH6NhxChaPxIRlE9CX9cJU2YGO/rV0UldwhacLwRY/gq+OofzF+3EWF5Pw6ScE9O37tx7bVu9kyTdZFO6toVW3KPrdnI6P3/kT7x2w2Pi5XM8vFXoO2Rz4ABEmO70PLOcF4zckU4FDl4x0xbuoO94EPudXsC6cG+fPv2BBOAfq9bX8+vYr+Gq1XP3Ui6j9vLWlLU4L9y+7nxJTCV8M+oKM0IxTGq+09AcqKuaSkvIYISE9G923ZMkSioqKuPbaa4mMjMTjcFPx8TpkqxPZuhb/Dz9mTaWTN6esJqfcRGqEFrVS4tov1lFmsOHyyIfH8tNItOvjz8oANbE1Dj4qW8gVoy6HxFub7+AIgiAIwgWm0FjMz2ucBPmaGKYystsRSb8eXYiKjTv5xqdAdjopfuQRrNu3E/f++2h79WqWcU/H8QJppUpBdIpOBNKnwemR+bVS/88bH1oNDcFznjd4PjqMtuqPWlFqCKFToO21RwLo0BRvmTZV89VNFwRBEE6f7PGwdPJn+GkDuOTGsdTWrqWo+Fvi48eiC+rBvUvvJTc/HmvFpWT6GejpU8pwVydCZS1BIyMoe3o8br2ehEkT0Xbv/rceuzyvjkWT9mAxOeh7YxptLos7c6Wl/oYqh5NfKwzMqqhlp8mKAkiVVGTklTG68AdulxahxYozvhdc8j4+aUNBIT6XCEeIgFr413Labcx55xWs9UbGvPw2AaFh3uVuJ4+ufJS9NXt5r997dIvudkrj6fWb2Jf7X0JDLyU56b5G9+3Zs4cNGzaQktmRAk8Ya1YcoPv8HUQqI8g+9DuPpQ2m/qONjbaps7rQ+avpmBDCiPb+xAX7Yw9UscdYxFyTxHaFD8P3VPFaFyXR1z4H58GbkiAI544kSQnAd0A04AEmyrL8oSRJ7wAjAAdwELhdlmVDwzbPAncCbuAhWZYXnYt9F4Tm8uAv06m3tOW+lnPYXRRNZIA//YaPaJaxZY+H0uefx/zHKqJffhnd0CHNMu7fZbc4KW0IpEtzDVQVmeDoQHp4C+LSgr2B9HncIOl8Zjqq8WGp3Umaxo/3MxK4pqnGhzZj49nPf/6sPeitF32YBEHx3tA586ojIXRYKoQkixBaEAThArL3j2WU5mYz5N6HUfnJZO16Co0mldSUJ3lp/X9Yk+PBXnEFLX1NXOJTxBX2ToT7BKG7XEfpI3cgOxwkfvvt37oKS5Zldq0oZt3PB9AG+3Ltk12ITNKdwWd5cma3m4VVdcyq0LNKb8ItQ4a/L33sSuSt67nD9jPDlJuQFBK0uRp6TUAd1/mc7rNw/hIBtfCvJHs8LPz0fcrzDjDq8eeJapEKgEf28Pya51lXuo6Xe7/MwMRTK6duNuexc/e9KNSxVCieZfLqAor1For0Vmqqq2hr2oJe1vLdNiWerdt4t+IgUdGdya/czNxR19FLVvBHbjX+Pkr+OyqTwZkx+DfMcsoxW/m1wsDHFXoKKh2o3GpalrsZV6fnpjv74atRn7HjJAjCBcUFPC7L8jZJkgKBrZIkLQGWAM/KsuySJOkt4FngaUmSMoExQBsgFlgqSVKaLMvuc/UEBOF0zN23kqz9qWSG5hBQq8Upy4y9976Tb3gKZFmm8q23MM79jYhHHibkhuubZdxTccJAOlVHNxFIN5tSm4NJxVVMK63B5PbQJziAd9ITGKAFSZ8PWX8cNSO6IYg2VzUeRBfnDaFbjzgyC/rPEFrtf06elyAIgtB8rPUmVk3/hti01rTpO5C92Y/jcFTTtcvnTN77HT9vz8dRdhNJPmb6+xQw3NaBiMBQAi9RU/Lg7Ug+PiRNm4pvq1an/JgOq4vlU3M4uK2S5HZhDLwtEz/tuckBXB6ZVXpvs8Pfq+uwuD3E+6q5KjCQ2pwagrJmc5fyd7oq9uHyD0TZ9QHocY/3S1pBOAERUAv/Smt/mk7uxrVcdssdtOzmLcUhyzJvbnqT3wt+55HOj3BNq2uO2c7l9lCst5JfbSav2kxeVT1ltWUMi3kBpeTh9U3jqLYeACDQV0ViiA8d7Vmo1Wo69RrGNZGhJH43A//IrqCopOeXD7Fx+UE+WXGAjgnBfHlrF6J0fhRa7cw5VM3sCj1ZZhsK4FJbHmP2b8O9fyAdO4Yy4IFu58WlPIIgnB9kWS4Dyhp+N0mSlA3EybK8+KjVNgB/tgcfBfwgy7IdyJck6QDQHVh/FndbEJqF3W3ntbmbkeVWXBOSRXFxPKOGDSEgsHlmFtV8OZHaKd8RMvZWwu65p1nGPB6b2UnZAQMlud7GhtXF9Y0C6e5XegPpyGQRSDeXvfVWPi8o5dcqEzIyI6QK7jOtp0P+Rlh6EMyVjTcIjPGGz+nDjsyCDm0IoX0upmaFgiAIwl+t+X4KNnM9l991PxVV870lPls8yqrqQj5cswRH6W3EqG1c7pPHcEt7oiMi0XRyUTLhTpShoSR+8zU+CSfvb/WnmpJ6fv9yN8ZqG72uTqXToETvjOSzSJZldpqs/FxRy+wKA9VOF8EqJVdFBBNe52Tj+gOEV8/nGfVCEtQVuHQJ0PtNVJ1uAd/As7qvwoVLBNTCv07WquVsnP0jbfsPpuuVVx9ePnHXRL7P+Z6xmWO5MukmNubVHBVEm8mvrqew1oLTfaQWdLhW5tHOHxHka6RIeoeXr+5CUpiGhBANOn8VM2fOJDvbzK233kqLhARKnnkZj6cXksaB9olhjJ+2jRX7qrihawIPXZHOb7VGft1fyBajBYCuOg2vhTsZueJBPFUwu/ZVIpN19BvbToTTgiAclyRJyUAnYONf7roD+LHh9zi8gfWfihuWNTXeeGA8QGJiYnPuqiA0ixeXTKWiJpORiSsoK48mJSqcTr16N8vY+h9/ouqDD9CNHEHUM880+/vvcQNptYLolCARSJ8J5hrk7dNYZbDwuSKVlf5paNwW7iibx13FP5NoL4eAaG/wnDa4cQgd2kI0cxIEQfiXKjuwj13LFtF52EgCo/3ZuPFFdLpOVPt149k5b2AvuZ0wpYPBvgcYYWlHbHwsvi0NlDz0COr4eBK//gp1VNQpP172ujJWfb8PH38VVz3akdhWIWfw2R3rkNXOLxV6fq7Qc8Bix0eSGBSuY2BgAIdyqln501quds5nmno5gWoznrhu0OcdVOnDQSniRuHvEf9ihH+V4py9LP7yIxLatKfXrXezt9RIXrWZBQW/sNbwJQHOHnw3ry2f/rz88DY+KgUtwrS0igxkcJtoUsK1pERoSQ7TUJL/OFVVebRr+ymDIxvXoly/fj1ZWVlcfvnlJMfGUvzAw3joiTJUi/GWNtz61SaKai2M6p9MfqQv3TZm4wEytX48nxLDqIggErd/CfNepl7blpnWl9EE+zLs3nYo1X+pfygIgtBAkqQA4GfgEVmWjUctfx5vGZDpfy5qYnO5iWXIsjwRmAjQtWvXJtcRhHPloP4QCzaoiPSvoqWzHotLw413N88sZ+OixZS//DLavpcR+9prSH+tP/wP2MxOSvcbKM01ULL/eIF0CFHJOvF+fwbkZS1n4ZZ5/BzSi70BrYh0GXnetolbtVaCO3eDy8d4y3L4BpzrXRUEQRDOIx6Pm6WTP0MbHEKv0TeSnTUBj8dJUMIj3DL/bcyF49BJHob5HGSUuR1xqYn4RBVR+tjT+KWnkzB5EqqQUwuYXQ43q37IJXtdGXHpwQy6ow3aoLPTq6DW6WJupYGfy/VsNpoB6BWs5b6ESBLsMHNDIT/uWcDtivk8qdyAUi1D65HQawKKhL/X8FEQjiYCauFfodJoY/aqnRh//B92Hx0f0ZunXvGG0KrA3fjFzUBtzyRVdTupnYNoEa4lJSKAFuFaYoP9UTZxCc3+A29SVbWQVi2fI/Iv4fShQ4dYvHgxGRkZ9OzQgaK77sbtao1Pcgs2XhrJczO24FFKOLqG86OPkySbxENJUVwVFUyG1h8stTB7LOxfhDPtGubnjcfpcjDy0fb4B/qclWMmCMKFR5IkNd5werosy78ctXwccCUwUJblPwPmYuDo6wvjgdKzta+C0FwenvUDJltbbkn5FX1xLGNvvhG1+vTfK80bNlD6xBP4d+hA/AcfIKn/Wa3HEwXSMakikD7TPLLMNqOFhRXVLCoqYL8iFBLG0s4X3m/xZ+PDy871bgqCIAjnuV1LFlKZf5DhDz1Jlf4XavVrSEh5lvuWfkbNwTFokRjus5+rrW1IyGyBQr2H0qf+g3+XziR8/jnKwFMrdWGosLBw0h5qiuvpMiyJ7iNSUJzhkh5Wt4clNUZ+rqhlWY0RlwzpDRPnRoQHsXN/Dd/+kkVo6QruUS+ku3ovHrUWRZd7vPWlQ5LP6P4J/w4ioBYuegaLg5s/W0n3PdPQejxkdbiOnkkJtAjX4vbJ5ZuDP5EZ1p7JQybhrzq15jXFJTMoLJxEfNytJCTc0eg+k8nEzJkzCQkJ4cp+/Si67Xbs9ki29e3LZJ1M3qoDeHRqdN0juTEpgquigukUqDlyyXDRJph5O5grkYe+w9IdvagpqeKK+9sTFidm8wiC0DTJ+yLyFZAty/J7Ry0fCjwN9JVl2XLUJnOBGZIkvYe3SWIrYNNZ3GVBOG0/7l5GTn46XSN34q4OoWNGS1qkZ5z2uNbdeyi+fwI+yckkfPE5Cv+/39zOWGNl+XfZlOQaGgXSPUa0IDYthKgkEUifKVa3h9V6Ewur61hcbaTa6UIlu+hVt59xAQoG9xpNYjPVJxcEQRAufmaDnjU/fEdi2w7Ed4hj85b7CAm9jOe3baIgewQ+Hh+G+e7nWltrkjq1xGNaR8Vrb6O99FLiP/rwlD9HHNxWybLvslEoJYZPaE9yu/Az9pzcssx6Qz2zyvXMrzJgcnuI9lFzd3wEo6NDiZIlfthcxNhpW7jUspQPfRaS6FOKRxcHPV9F0Xks+AWdsf0T/n1EQC1c1BwuDxO+WUOnrJmEeUxc93+vkpDZDoC9NXu5Y+HLJAcl8dnln55yOF1dvYJ9+14iLKw/rVr9X6NalG63m5kzZ2Kz2bhh2DAWvvQqi3sOZWVmR6w5dSjzbSS1DOGlUW3oG65DeXQdS48H1n8Cy172doC/YxEbtwSRt+MQl1zX6oy+OQmCcFHoA9wK7JYkaUfDsueAjwBfYEnD69UGWZbvlWV5ryRJPwFZeEt/TJBl2X32d1sQ/hmL08L78/eilOLo5XMQyZzEiDE3n/a49rx8isaPRxkcTMLkySiD/v7JV+HeGhZ/vRfZLXtnSKeLQPpMq3I4WVpjZFF1HX/UmrB6ZAKVCgZ4yhiaM4kB9nyCRrwLqf3P9a4KgiAIF5hV077Gabcz4I67ycp+GKVSw9SKELZt74jSrWWY737G2DNo0SsDZ/4Caj7/nMChQ4l7+y0kn5Nf1eV2eVj/y0F2Li8iMlnHkLvboAv7+1+On4qseiuzyvXMrtRTZncSoFQwPCKY0VEh9A4JILfcxDcLc1mzI4sbWch8n2UEqk3IMZ2g1ysoMkeB8p9dVSYIJyICauGiJcsyL8xYS9L6bwjzmBjx6DOHw+mCugLuX3o/wb7BfHH5FwT5ntrJp8mUxZ69DxMY0Jq2bT5EoWj8X2jJ0qVsra1D7j2Q4QeqqBxzF35GB/6bq1Hb3Tw/IpM7eicf22DJUgu/3ge5C731m0Z+zL5dNrb+nkXmJbG0HxDfLMdEEISLlyzLa2i6rvSCE2zzGvDaGdspQTiDnlvwHeWGVK5JXIKlPI57778bxWnWiHaWl1N4150gSQ2NjCL/1vayR2bL7wVsmpdPWKyWoePbERylOa19Eo7vgMXGompvKL25zowMxPqqGRMTxhBfG72XPYxP4RpoczUMXwKa0HO9y81GlmWsTjdGqwuTzYlHBoUEkiQhSaCQJBQNP6Xj/PxzHQkJSXHsNhJ/uS0adAuC8C9UlLWbrNUr6HH19RjsszGZ9pCjGsHctW2QnMEM9TnIzfZ0Uvu3xbJpBoZpUwm69hpi/vtfJOXJmxubam0snryH8jwj7frH0+falihVzfuFdonNwS8Ven6p0JNttqGSYECojv+0jGVwWBA+ksSy7ApunrUHff4O7lH/zuvqdShlF1LacOg1ASmxF4j3AeEMEgG1cNH6dO5GfBd9RpDk4LrnXiaxbQcAKswV3LPkHmRZ5stBXxKlPbUuujZbGTt33oVKpaNDh0moVN4O7maXm7WGen7al8cqtxZjl/6oXC66l+znikIlc0uM+Pmr+ebubvRMCTt24KLNMPM2qK+AYe9A97spzzeyfGo2cWnBXHZjmjghEARBEISj5FTls3SrjoSAYiJNEr169SAiOua0xnQbDBTedReeOiOJ303BJzn5b21vMztZ8nUWhXtrSO8RTd+b01H7nPzEVDh17j/rSVfXsai6jgMWOwBtA/x5LDmKIeFBtNP6Ie36EWY96T2RvnoitL/+vDuplmUZm9OD0ebEaHU2/HQdddvV5PK6o+5zec5uz9rDATd/Dbq9PzkmGG8IwI8KxI/epukw/fz6exIE4d/N7XKx7KvP0UVE0vrydHbuvhWTTw/eX5qB7IhksE8e45wtSRvWAdPvn1M3ezah48YS+fTTp9RYuXBvDUu+zsLt8jD4rja06npq2cSpqHO6mF9Vx6wKPesN9chAV52GN9LiGRkRTJiPCqPNyfR1BUxZl09K3UYe811Id9+dyGoNUsfboOd9EJbabPskCCciAmrhovTL0s3U/vgeAQqZG196ndhW6QDU2eu4d+m9GOwGvh76NclByac0nstlYueuu3C5zXTp/CN5Th3LKypZUWNkU50Zhyyjcrtp6XJw9y/T6Je7l6WDn+HLKiNtQjRMvKcnccF/uURHlr0lPZb+x1vS487FENcZY42VBZ/vIjDEj6Hj26FUisuBBUEQBOFPsizz+E8zMTtac0P0SkLMLek7bPhpjemxWCi6516chwpJmDQJ/zZt/tb2lYeMLPxyD2ajnb43pdPm0lgRtDUTy1H1pJf8WU9agt7BAdweF87g8CAS/Boun7bUwqz7IOtXSOwNV38BIUlnbN9sTnejALnOeuJw2WhzYTpqucPtOeH4vioFOn81Qf5qdH4qQrQ+JIVp0fmr0Pmp0fmr0fmpCfRToVRIeGQZj+z9PyLLHL7tkWXkw/c1vn2ybbzLjt1G5s9l4PEcWQ5/jnFkXI8HZI4e93iP610mCIJwvtj2+1xqigsZ8cST7Nv/LE7CeWZ5T9y2eAb65HOnK4X0UZ0xzHgb0+LFhD/wAOET7j/pZwCPR2bz/Hy2LCggNEbL0PFtCYnWNss+7zFZ+OBQBUtqjNg9Mqn+vjzZIpprokJI9vcFIK+qno/WFTB3ax6D3KuY4beIBJ9C5IAY6P4SUpfbLqqrjoQLgwiohYvOH6s3k/PVm6hUPtzyyuvEJrcAwOqy8sCyBzhkPMTnl39Om7BTO/n0eJys2/UEq83hFIf8h0d2uSl37AMgQ+vHjaEajKtXkGY2MHDmLOTQSD4c/DTLq+q5MkLHuw/1xk/9lxlUllr49X7I/R1aj4CRn4B/MA6biwWf7cLtkhk+oT1+AaK2kyAIgiAcbcq238kuyqB39FbUlVHc8sR9pzWe7HBQ/PAjWHfvJu7DD9D27PG3ts9aU8qqH3LxD1RzzeNdiGohmu+driqHkyUN9aRXHVVPemCYjiHhQQwIDSRI/ZfTmIPLvZ+tzNUw8CXo8zAoTjyD3e5yH3fWsjdsPvGMZofrxAGzj9IbMP8ZKAf5q0kM1aDzUx0OlxuHzapGofMxnx//JSaPO9d7IAiCAKaaatbPnEFKl+54dEupLyrm+c3jsVlSuUxVwL2eFrQe3Zmaz17CvGYNUc8+Q+i4k7+AWYwOlny9l+IcPRk9o7nspua54qrC7uTN/DJ+KKslSKXk1tgwro0KpWOgP5IkIcsyq3Kr+GZtPjv3HWScegmrfJYRqDBARDvo9SVSm2tAdfKa2YJwJoiAWriobFm3gQ2fvonTJ4Bx/32D2OQEAJweJ0/88QQ7q3bybt936RFz4pNPtyyzw2hheY2R30v3ku0YjywpCTIpuSxES/+wQPqFBKJzOZg8eTKhdXVcOmcuda0785+2N1BQZeax0CAeeLgXCtVf3myKNsOs28FUDsPehu7jQZLweGSWfLWX2jILIx7o0GzfoAqCIAjCxaLebmbi70Vo1UG0c9cw7Iqb0QYE/uPxZI+H0mefw7x6NdGv/BfdoEGnvK3L4WbVD7lkrysjoXUIg+5sg3+AOKn7p/abbSyqrmNRtZEtRm896ThfNTfGhDEkPIhewVp8mrhc2mm34Fr8H/y3fok1uCV7Lv+cEv806jYUYbB4g2aD1XG4PMafy+qsTuwnCZjVSqlh9rKawIYAOS7E/3DYfLxw+c/l/9aAWTg9kiQVACbADbhkWe4qSdJ/gLuBqobVnpNl+bg9JgRBOH0rp0xC9njofG1bcgue4n+7bqO6rjU9VUU8JCWROboTlW8/hXX7dmJee5Xga6896ZilBwwsnrQHm8VF/1szaN075rSvuLK4PXxRVMknhZU4PTL3JETwSFIUwQ1f5FocLn7ZVsK36wqQq/YxwW8Rk/xXoZIdkDIEej8AyZeed6WwhH8fEVALF42dq1ay4rP3MPqEMuaFV0huCKdlWebldS+zqngVL/R8gcHJg5vcvtzuZEWtkRW1JlbVmjC43EjIpMi13BZk5NqWQ+gYqEGl8L5wO51Ovp0yHZNeT/8lS3EPHsVD/j3xGB18oNFxxX3dG4fTsgzrP4WlLzWU9FgEcV0O371h9kEKdtdw2Zg0EjLF5TSCIAiC8FdPzf6a0voUro1fTIKiNR269/zHY8myTMXrb2CcP5+Ixx4j5LrrTnnbuiorCyfuprqonq5XJNPtyhYoFOLE7u9wyzJb6swsqjaysLqOPKu3nnRLXx+u0wWSjgqdQ8ZYYmPtARMLjgqb66ze2cxRllze4GPSFcV86xrMG+U3YZ9rAXYcfpwAXxVBDWUygvzVpEYEEKxRNyqd4Z3lrG4Ino+Ezr4qhSjVIpwr/WVZrv7LsvdlWX73nOyNIPzL5O/YSu7GtfQecw0FJW8yJfs69lV2orOqlCdUCWRe24GKlx7Ctn8/ce/9D93QoSccT5ZldiwtYv3sgwSG+XHtAx2ISPjnX7CDtzTS7Ao9r+WVUWp3MjwiiBdSYw+X8SgxWPlufQE/bCykjWMHb2gX0813C7LSD6nDzdDzfohIO619EITmJAJq4aKwY+lClk76lAq/KIY//gLtWiUcvu+LXV8w5+Ac7u1wL9enX394ud3jYZPBzPJaIytrTWSbbQBE+qgYEh5EZ+UBgoofpWVkP9q0eR9JOjJrx+Px8POMGZSUltJn3Tqi7xjPnbWJyAYbX0haOt/WAWXgUbOoLLUwZwLsWwAZV8KoT8E/+PDd2etK2b6kkHZ942jXL/7MHShBEARBuEDtKN3Hyj3RtAzKI642gBueu/u0xqv+/HP006YRettthN191ylvV7CrmqXfZgEwfEJ7ktuFn9Z+XCxkWcbicGOwOqmzND1rucbqYJ/HRaFaptJfwqVSgEdGUWtHVWlDWWWj2Oam+C9j+6oUBGuOhMzxQb6MVM9nmH0ydpWOJe0+IySxH1803B/8Z/jsr0YtenkIgiAIf4PL4WD5118QEhOLf8pGpm/swZriS2irrOBZn1gyr2lH2VP34CwpIeHTTwi47LITjme3OFk2JZv8ndWkdIpgwNjW+PqfXhS30VDPSwdK2WGy0D7Qn08zk+gVHADAnpI6Plt5gOV7S7hSWst87WLiyUP2iYBLnkfqeidow07r8QXhTBABtXDB2zRnFqtnfEuhfwLd73qM/u2PNMOZe3Aun+34jJGpI7m/w/3kW+wsb5glvVZfj9XjQS1J9AjS8n8pMQwI09Fa60dd3Va275hAYFA7Wrd+u1E4DbBo+nRy8vPpmJ1Dp+df5O49EuV6Ix/K/rQfnYFv4lH1J4u3wMzbwVQGQ9+CHvc0unymdL+eldP3kZAZyiXXtzrjx0sQBEEQLjSyLPPCDwuwu1O5zD+Xa0c/iEr1z/o0OCsrqZk8Gf13UwkaNYrIp548pVmyHo/M5nnehkbhCQEMu6cdunD/k253oZNlmaJaK1sLa8mvtlBncTTMZD5SKqOuIYB2NdHhTvZR4I7wQ47yxx3qC0oFSrdMpNVDC5dMmkJFZJCGoBifwwH0n2F0cEPI3KhURl0xzL4XKldDxpWoRnzEIHGiLVxcZGCxJEky8KUsyxMblj8gSdJYYAvwuCzL+r9uKEnSeGA8QGJi4tnaX0G4qGye+zOGijIGPz6Qadv2MC9vJK0U1bzkH02bkemUPnwHboOBxMmT0HTrdsKxqgpNLJy4m/paO31Gt6TDwITTujLnkNXOqwfL+K3KQLSPmo9aJzI6KgSFJLGnpI4Plu5nXfYh7vFbzFvapQQ6qyGoNfT6BKnddaD2+8ePLQhnmgiohQuWLMus/n4Km+fMIlfbkoRRd3DLJUcC3o1lG3lp7Uv0iO5Bl5aP0nNDNodsDgCS/X0YExNK/9BA+gQHoD2qFIfFUsCu3ffi6xtL+3ZfoFT6Nnrc1V98ycbyMlpVVDL4jdd5bE0tO4oreUXy59Ib2qDtFPnnDsKGz2DJi6CLPaakB0BdlYXfv9iDLtyfIXe1QSFm+QiCIAjCMT5f8zO7K9PoF72ezmE9SGr19y9JdRw6RM1XX1M3ezay203wdaOJfvFFpCbqGv+V1eRg8Vfehkat+8Rw2Q1pqJqhodH5yO5ys7fUyNYCPVsP6dlaqKfK5C2/IUkQ6KsiWONzOEiODfZvPGvZT4XVV0GW7GSbzU62zX64nvTQ8CCGhAfR8zj1pE9q9yyY9xjIbu/VaB1vFjUzhYtRH1mWSyVJigSWSJKUA3wOvII3vH4F+B9wx183bAizJwJ07dr12G+MBEE4IUN5GRt//YmMyzoya992ftx3A8mKWl7TRZI5OJmSCbcjO50kfvst/u3aHnccWZbJWlPK6h/34x+o5qrHOxOTGvSP98vocvNBQQWTi6tQShJPJEdzX2IEWqXycDC9NLucq/y2szFoKoH2CkgcCL0mQOoA8V4pXBBEQC1ckDweN8smf86uZQvZE5iJ8pLRPDui3eH7D+gP8OiKR0kOSuaajq8yPruYTK0/r7eKo3+ojhYa3ybHdTr17Nh5JwAdO3yFj8+RWtCy08nWN95guctFnMPB9W+8zpsrilmYXckDkh/X3tIB/zYNM3isem8n+eOU9ACwW13M/3QXMjLDJ7THV/PPZoIJgiAIwsWszlbPd8vrCfWz0sUGV9x809/a3paVRc3kyRgXLkJSqQi69hrC7rgDn1OcXVieX8eiiXuwmpz0vzWDzD6x/+RpnLeq6+1sO9QQRh/Ss6ukDkdD88DEUA2XtAync1IIXRJDSIsKQNXEl+l/1pNeWF3HlGojeXpvoN0+wJ8nkqMZEq6jTYD/P581ZjXA/MdhzyyI7w7XfAmhKf/0KQvCeU2W5dKGn5WSJM0GusuyvOrP+yVJmgTMO1f7JwgXK1mWWf7tlyiUSnaH1fDdjutIUNbyRkgEbfrGUHLf7Ug+PiRNm4pvy5bHHcdpd/PHjH3s21hOQmYog27PxD/wnzVRdnlkppXV8HZ+GbVON9dHh/BsSgwxvj5HBdMVtParZUXsD7SoXQPBbWH4VEjs8U8PhSCcEyKgFi44bpeTBZ+8R+761ewM60J12kBm3tgZZUNzoipLFfcvux9flS8P9fqAu3MqaaXxY1anluhUx5/t5Hbb2bnrXuz2Ujp1nIpGk3z4Ppdez56nnmZRRDghvr7c+uKLTFtXwlcbD3GtwocJt3fCv1VDmF28FWbe1lDS403oce8x31h63B4WT9pDXaWVkQ93JDhS09yHSRAEQRAuCk9Pm0i5NZ0bopdz45gnUJzCzFtZlrFs3kzNpMmYV69GodUSducdhNx6K+rIyFN6XFmW2fNHCWtm7kcb7Mu1T3UhIvH0Ghqdax6PzP7KerYe0rPlUC3bDukpqLEA4KNU0DZOx7heSXRJCqFzYgiRuuNfCmx2u1lVa2JhtZElNXXUOt2oJYk+wQHcnRDB4DAdcX7/7IS8kfzV3pIepjLo/zxc8hgoxSmMcHGSJEkLKGRZNjX8Phj4ryRJMbIslzWsdjWw55ztpCBcpA5sXk/+9i3Iw9oxaWdPYn1qeSM4inbdQyi+5w6UoaEkfvM1PgkJxx1DX25m4cQ91JaZ6T6iBV2GJf/jJsrLa4z850ApuRYbPYO0vNwqjg6BGnYX1/HCsp0sza4k1A9mpK+hV8nXSCYlDH7Nmz+I90nhAiT+1QoXFKfNxtz3Xqdg5zZ2x17GvojO/Hp7N7S+3n/KFqeFCcsmYLAbeHPA1zyy34ROpWR6+5QThtOy7CE7+ynq6rbQts1HBAd3PXyfff9+ch9+hMVtMlFrNIx94AFW7qzmlaW59FGoeeWOjvjrKiF7HZRshXUfgy4G7lgE8V2afLy1sw5QmFVL/1syiEsPad6DJAiCIAgXiXUHt7EiL5m2ITlcmdqP8OiYE64vezzUr1xJzZcTse7ciTIsjIhHHyXkxjEodboTbns0p8PNyuk55G6sIKltGJffnomf9sK70qne7mJnkaEhkNazvVCPyeYCIEzrQ5ekEG7snkiXpBDaxgU1rvXchEq7kyU1RhZW17Fab8LmkdGpFFweFsTgMB0DwnQn/Lz1t7jssPxV7+eq0BS4c8lxP1cJwkUkCpjdcLWBCpghy/JCSZKmSpLUEW+JjwLgnnO2h4JwEXLabKz4dhL16S2ZntudKL8aXgmJpn2mL8X3j0edmEDiV1+hjoo67hj7N1ewYloOSrWCkQ92JCEz9LjrnkiO2crLB0pZUWsi2d+Hr9smMyw8iD0lRu76ZTNLsyvR+al4r5uRUaX/Q3loP7Qe6Z0cFxT3Tw+BIJxzIqAWLhi2+npmv/UyZfv3kdvqCjZKKfw0thsxQd4GRS6PiydXPck+/T7euOwjXi5SYXE7mNu5FbEnmcGTl/ceFZXzSE19iqio4YeXm5avoPDpp1l5SR8cgYHcPrA1BxfP5ZFtYaQBnwZ9hGbaGryfFRu0HgEjPwb/poPnPatK2LWimA4DE8i85OK6TFgQBEEQmossy7zx01pk4hmsquPSESOOv67TiXHB/7N333FSVWcDx39n+sz23pdlYekdBBERe4u9G2NJjKZpmt2Yrm+MGo2J0cReYzf23hBUVECQ3rf3XqbPPe8fd4ClKeAuM7s8389nuHfu3Hv3PLPLPnufOfec12m5/34C69ZjLygg53e/JfW007C49mxCoPYGL2/eu4yWWrP307TjSlB72ftpX9JaU93mY3GlOVTHwvI2Vtd3YmjzRq6ROUmcODGfqcVpTB2SxpAMz06H3IhoTbU/yCZfgI3eQHRpPt/kM8eTLnTZ+V5+BsdkpHBgaiL2vn5/GlbCC5dCwzKY+n045iZwJPTt1xAiDmmtNwITd7L9/Bg0R4hBr7u1hbUL5rNi7vts9GpejMwm1dnBDdmJTCnU1Pzy57hGjqTo/vuwpe38+j4SMvj4uXUsm1tD3rAUjv7hOBLTdj6k6NdpCoa4dVM9j9e2kGiz8Idh+fygMJM1tV1c8uhC3l3VSIrbzg1zMrig+z4cy56DtBI47zkoO+pbvhNCxJ4UqMWA0NPexvM3/ZbW2mrqp57J263p/Pt7kxhfaE40oLXm5s9v5qPqj7hm+g3c31bARm8PT04sZXSi+2vPXVP7NOUV95CfdxZDko6G9e+imzfQ8uxbNL62is8PP4iW5GTONl4k8PonXBK6nFQ0D+Y/RWpBLqRfAxnDzEf6sB3Gmu6tenUrHz21liHjMjjo9F2PWyWEEELs72559j6WdZRyTPYCLr706p3uY/h8tD//Aq0PPkiothZnWRn5t95C8nHHoWx7/mfuxiVNvPfwSixWCydePpHiMRnfNox+EwwbrKjtYFFF25aidEOnOfZzgsPK5OI0Lju8jKlD0phUlEqKe2sPcENragIhNnkDbPSZj03RYnSFL0hQb/3g3W2xUOpxMDrRxZm5aRydmcKYBNfejyf9dQwDPv8PvPN7cCbBuU/ByOP6/usIIYTY7xiGpicYprmlnRVfLGT1l19SVVFFUNmxZhXwQcFsPHYvVxU2cWDCEOquvgrP1KkU/vserImJOz1nZ7OPt+5bTmNFF5OOLOLAU4dh3clcDV/HHzG4v7qJOysa8BoGFxVkckVJLrWNPfz0sUVbCtNXHjmMiz1zcc+9CUJeOOQqmH0F2L++3iHEQCEFahH3Ohrree7G39LT3kb4iB/wzFor1x8/imPG5m7Z55EVj/D0mqe5cMxFfKIP5JP2Nu4aXczBaduNFWkY0FULLRugdQMtrfNZY51Leo+dkc/fjwrfjRGGus9T6az0sOawyVRkF3H0UEVG+g2c9bGboNI88YMZ5JedsEdxmD2ylpOW6+Hoi8fu9VhUQgghxGBX197IMys85HkauHjiHBISt83nkY4O2v77X1offYxIWxvuyZPJ+e0NJM6Zg9qNMaq3Z0QMFry0kS/friR7SBLHXDqO5Iz4uuBr7QlumchwcUUbS6vbCUQnMyxMczOzNMMcO3pIGiNzkrBaFPXBEBu9AV5p62RjTbQ3tC9AhS+A39hahHZZFCVuJ2UeF0dnplDqdjLU7aTU4yTHYeufYvT2OmvNCaY3fgAjjjXvRkvcvfHChRBCDF7+UIROX4iuQJhuf5ieQJiugLnsDoTp8m9d7/ZHl9HXu/xhuv0huv1hvNGcudVEyN16w0KKtYNfjviIw7wzafjD70k4ZDaFd96Jxb3zvwfKlzXz7kMr0YbmuB+Np3Ry1h7FpbXmlaYObtxQS6U/yJEZyfx+WD7+tgDXPPnl1sL00SP4fmk7CW//GGq/hKGHwHduh8yyPX0rhYhrUqAWca25qoLnb/ot4WCQjDN/zm8+7uLc6UVcMnvrzO1vlb/F3xb9jaOHHI0//RxeqGziuqF5nJEbHfNJa/jiflj4ELRuhLAPgG6PlWWTUkkI2Bjvn4RlxkhCZFH9j1fxV1XS+KNLWNrRwbRp0xidP42Ln1lCNREePm8qo8sy9ygOf0+IV/+1FGVRfOenE3C45b+eEEIIsSs33PcQLYFx/Dh3IdOP+MGW7aGGRlofeYT2p57C8HpJmHMImZdcgmfatK8529fzdgZ5+/7l1KxtZ+whBcw+swyrfc+L3H3JMDQbmrpZ2KsgvbG5BwC7VTE2P4XzDxzClOJUhhQk02VlSy/oD1tb2VRdxyZfEJ+x9WLcoRRD3A5KPU4OS0+iNFqAHup2kue0Y9kXRehdWfEivPILiAThhDvMYT1i2R4hhBBxYcHGFn7w8Bd4g5Gv3c+mIMFqIUFZ8KDwaPAYmgwDElB4sOEgQkh58dtb0YmVpCTXk5HcQEpCK26bn2Srh4lrT6Dp7ltJOvZYCm75K8qx41ChRsTgs1c2sfjNCjKLEjn20nGkZHn2KK7FnT38YX0tn3f0MDrBxdMTh5HmjXDzs8t4b/XWwvSFU9JJ+uRmeOR+SMiC0x+AcadLjhSDklTJRNyqW7+GF/7yB6x2O6N+eB0/fq2GWcMz+NPJ47b05FnSuITr513PpKxJjBl2Fb9ZX8/5+Rn8fEi0x03IB6/+CpY+CUUz4ICLIWMYgeR0ljTcjE1ZmDjtOWyufHxLllB1+eXoHi/hG//Mh8uXM3z4cA7JnMxVz3zFYiLcfup4Dh6X+zWt3lEkYvDWfcvpavVz8i8nk5wZXz2yhBBCiHjy3JvP8GHrKKalreSKn10HQLCigpb7H6DjxRfRkQjJxx1HxiU/xDVq1Lf6WnXr23nzvuUEvWGOuGg0ow78+kkY+4s3GGZJVTuLyttYVGkWpDujkxmmJTgYPzSNA6flk5zhIuy2UhkIMdcb4KH2ZnpaGrecx6ZgiMvJUI+Tg9OSGOpxRntDOyhwObDG2wWtvxPeuAaW/hfyp8Bp90GmDIEmhBACOv0hfv3Ul2QoCz/PSMITNHD5I3hCGg8qWngGDwqHBovdjiXRRogAnd3NNDVX0mR00J7qxchtwJNaT2pqPQkJHQCEtR2LezSl+eeTnT6b7n88T/vjD5Jyxunk/fGPKOuOk/72dAR4+/4V1K5rZ8zsfGafVYbtGyYY7q3GH+T/NtbxfEMbmXYbt40sYmzEwl0vr962MD1zCEnrXoL7roeeJph+CRx+A7hS+urtFSLuSIFaxKWKZUt46dYb8aSmcuBPr+d7z6ynON3D3d+dij06plNFZwWXv385uQm5nDrpZi5bU8+RGcn8pazQLGC3V8HT50HdUjj0enOMJouFcLiHpYvPJWz0MHXKU7hc+bS/+CL1v/0dttxcXLffzgvvvktWVhbHFs7ijhdX8hYhfnX4cE6bUbxHcWitmff0OqpXt3HEhaPJH57aD++WEELsO6sa2ph2xzOA2XlDoVEAvdfR0dfMbQAWZS4377O5Tqa0jr6mAI3FXGDBQKGxbl7XGgsahYF1y7rGoqMPog/D3G6NvmbFQGmizw0s2myLzdA4dBirNqJtiTZ4c9t6NXLzy1sem/fdvKPqdcxmcVYHHEje70jFac3i5wdOILJuPQ333UfXW2+jbDZSTj+NjB/8AEfxnuXj7Wmt+er9aj55fj1JGS5OvHwSmYU7H1+yr2mtqe3wb+kZvbCilVV1XYStCu2xkpOXSN70XIqS7PTYFbXBEG9HDNBeaPZiVVDkcjDU7WR6SsKWInSpx0mh04FtoAwhVvEp/O9S6KiGOdeYf6dZ7d98nBBCiP3Cbx9ZRENngHvsSUxKTsSa5MCa6MCSaMeaFF0mOlAJVmor17Dqs3ms/uIz/HZNQolB4rQuUlMbKU1sRSmIRCwYbakkVY8ivT4TW5XCaO/E6HiG2vZ7MXp6SL/wQrKvvWanQ1vVrGnj7QdWEPTt+YfaPeEId1U2ck9VIxr4eXE2h9lc3P/2Bm7oXZg+qISk7nJ45gzYNBfyJ8N5z5hLIQY5KVCLuLPu80947c5bSMsr4Khf/5bz/rsaBTx40QGkeMwLlzZ/Gz9996cAXD7zn/xkbQvjktz8Z8wQ88Js0zx49kKIhODcp2HksQAYRpjlK35BV/cqJk64l0TPKBpuuZXWBx/Ec+CBpNz4Zx569lnsdjsnFc/h+Tc38hBBzppSyM+PGrHHsSz7sJoVH9Uw5ZhiRs2MTa8sIYToS+GIhea2BPOJjj52tt57KcQeOjtzESWPVbFp/nwsCQlkXPwD0s4/H3v21jGJDUMT9IUJ+sIEvGECvjBBb5iAL2Q+32ZbmIA3tHVfb5hQIMLQiZkcceFonJ7+K4yGIgYraztZVNHGp1VtLGzspBkD7bFhSbTjGp5AZGwSgei1cBVQg6bABqUuBwemJ1HqdmwZE7rY5cQ+UIrQOxMOwtybYf4dkFoMP3gLiqbHulVCCCHihA5FeO7Rpby0qYWLEzzMnOOGUDORjk4iLR0EN3UQae+gsameTR0tlNsNbEM0Sfleik5oISmpBaU0OqLQ1U488xx4Vho4KhTKEsCa0olKBpKTsWdlYxk+HGtyCq5xY0k5+eQditPa0Cx+u4LPXtpISraHk34xiYyC3ftQO6I1T9e3cvPGOhqDYU7JTuV0TyJPzd3EvasbSfXYueqYkVwwcwhJ1jDM+yt8fCfY3HD8bTDtB2DZ/R7aQgxkUqAWcWX5B+/w9n/+Se7wMk648ndc+sxKatp8PHHJDIZkmAURf9jP5e9fTn1PPTce9gBXbvCS6bDx+PhSEqwWWHAPvPUbyBgO5/x3y62iWmvWrvszLS0fMHLEn0hzTKXqxz+hZ9480s47j/Qrfs0jTzyB1+vljGFH8cX8Rm7Fz6xhGdx0+vg9niCoYkUL859Zx9CJmRx48rA+f6+EECIWxuensPBP39nt/UPBIKFQkFDITyQUIhgMEPT7CIVCREIBgsEA4VCYSChEOBIi4A8S7KwlXPk5oe5mwo4k/NkTCaSWElJWIoYmZBgEDYOIYRDWENEQ1hAyDCJoIloRBgytiaCIaE1EgwGEURhKETE0XquDNpuHNrubdrubUK8LAJsRITXoIzXoJdXvJS3YQ2rAXHdHQt9cnI92t1a61ya9TTdsUJatz5UCLNtt7y8as0N7r8mCtGF+RW32QDeXvfcxt7G5B3yv4zf3kt/ce3779c1vUO80unVdb3mu0KDA441Q+GYbK5JysZ55E6p4GOtDiuBj1QS85QR8IYLeMEH/149HiQKn24bDbcPpseF020jOdEfX7WQUJjDqwDxUPxR7ewJhXl1Wxz0b69kQDhF2WdEJNsi1Qm7qlv3ynfZtxoLevBziduDci8ke417TWnjhEqhbApO/B8feDM6kbzxMCCHE4GN4vYTq6gjV1hKqqSVUW0uwroumyARudDgY6e/mlJevo/qJEGD+xdDpdlCZk07rpGRcY4Ik5bQzNqUZi8VAG4qejkS8VSXkOydSmnEozjE5WA9KwZqcjDUlBeV279F1vb8nxLsPr6RiWQtl07I59HujcLh2r4w2v62LP6yvZXm3j6nJHq7LyuSdT6q4ZPWKbQvTLjusewdevxLaymH8WXD0jZCUsxfvqhADlxSoRdxY9NqLfPjo/QyZMJmTfn09v3l1LQs2tvL3sydxQIk54aGhDX4z/zcsbVrKHw6+nRurnYR1mP9OKCXbEob/XQZfPQWjToBT7gFX8pbzV1U9SE3N4xQX/5Cs0EGUn30Owaoqcv/4R1LOPINnn32WmpoavlN8CPVLvPzW6mdYVhL3nL91WJHd1Vrbw9v3LSe9IJEjvz+mXy5+hRBiILA7HNgdDmAPh0/QP4FNH8FHt0L5X6A1A2b+DA64ZJvf7X1Fa019MMQGb4CN3gAbfNGlN8Bif4BIr97g6XYrpW4nwzwuhkWHVxjmcVLiduLeg3yhtSYQNvCHIvhD0WU4gi9oPvzBCH5/CJ8/gt8fxh+IEAhECAYiBEMRQoEIoWCEUMig2xuius1LMGgOY+KyQkGig/xEB7mJdrI8DpJtFnQkghExMMIRjLBhPiIRdESb64aBEdEYEcPcZmi0YS4NQ6M1GBq0wdb1LQ8Vfa4wMJcatWVpbB3g5WvfFx+wLjoMsaPbhnNTD44tBWYXTnfiludOj32bAvTW7TYcLts+zb+GoVmwsYXnFlfzcm0rPcOT0Fl2Egw7pQ47Y1M8jE1NMHtDe5wMce3Zz8uAtnnC6rd/C3Y3nP04jD4x1q0SQgjRT7TWRNraCNXWEaqtMYvQtbWE6+q2FKMj7e3bHGMbMhPnhO/yV3sQn47w58JmCv9wA51OC1/VLaBNr8KT0UpqWg0Z1kq0hjZfEhX+YWTmzeLAEeeTm1TSZzE0lHfy1r3L6ekIcMg5Ixg3p2C3itsbvH7+tKGWt5o7KXDauS4nkyWf13Hd6nU7FqY7auCla2HVy5BRBhe8DKVz+iwGIQYSKVCLmNNa88kzj7PghacZMWMWx11+Jfd+XMGzi6r5+RFlnDK5YMu+dyy6g7cr3ubnU67k4fYSqgNenpk4jLJQIzx2HtQvg8N+A7OvhGjPo3C4m+qaJ9iw4Vayso4lt3YW5VecjbLZGPLQg3gOOIB33nmHVatWcXDmJOxrrVzjDJDgtPPQRQeQ7Nqz23593UFeu3spVoeV7/x0wm5/wiqEEKIXpcw/0EvnQOUC+Og2eO9P5m2PM35sPjzpffjlFHlOB3lOBwenbdujM2RoKv2BLcXrjT5z/aO2Lp6ub916DswescM9Lko9ZtF6uMfJQamJOHbSG1YphctuxbUHk+t8HcPQVLR6+aq6na+qO/iqup03ajrxtZg9jROdNsYVJDOhMJUJhSlMKEilKH3PehL1BW1oDK13LIBHzHWtweGyYnfZsAyAD3g3NHXzwuJq/re4hupQGMakEpyYToHdxl9HFXFk5n4+oVFXA7z0M1j/Dgw7Ak65G5L2bMJpIYQQ8SfS3U1g9eotxedQTe3WHtF1dWifb5v9lceDPT8Pe14+rvHjsefnY8/Pw5adh2+1Df+qLl7NsLCgJcAfTxrP9INO5P1X/0bQ9gCeEQE8QFdPEhs78lCpo5k48kzm5B+CvY/nL9Bas3xuDfOfW0dCspPTrppKTsk3d45oC4W5vbyeh2qacVosfD89ldolTdyxqnxLYfrCg0pIdNogEoZP7oIP/g90BA7/LRx0OdicfRqLEAOJVM5ETEXCIT545H6Wvv0a4w8/miMv+RlvrmjkljfXcNLEfH51ZNmWfZ9a/RQPr3iYM0eczQIOYWFnB/eOLWFG22J49qLoeNNPbRlv2h+op7rqEWpqnyQc7iIjfQ55iydR/def4Cwro/Bf/8JRWMCiRYv4+OOPGZc4jKLqNH6RHKbLb/DMRTPIT3XvYTwGb/5nOT3tQU65YjJJ6a6+fLuEEGL/VHwgfO85qP3SLFTP/St8+i844Icw8zJIzOrXL2+3qGhv6R1/p/eEI1sK1ht79bp+vqGVzrA5BMZQt4PrSvM5MSulX4vBFotiaGYCQzMTOHmS+eFuxNCsb+zmq+p2ltV0sLS6g4c/LicYMduW6rEzviDFLFhHC9e5ya5+baeyKKwoGMBDKrZ7g7zyVR3PL6pmSVU7yqbImZZDJMWK22rh2pJcfliYudMPJvYrq1+Dly+HYA8cdytMvwT28QciQggh+l64rY1Np51OuK5uyzZrejr2/Hycw4eTOHs29oJ87Pn52PLysOfnY01N3eHvi1B9Dy3/XUW4qYvWmTncuXAjs8syOf/AISz97HmC9vsJhRws6xxDwYgjOOSAEylMKuy3uIL+MB8+vpp1CxsZMj6DIy8agyvh6wvgQcPgkZoW/lZeT2c4wjHJiQRXtPHkyhU7FqYBKj+D134NDcuh7Gg4/lZIK+m3mIQYKPa6QK2UGgk83WtTKfA74NHo9hKgHDhLa922900Ug5E2DFZ/Oo+Pn36MjoZ6psw5koOOPZllX6zir09+yZHZidw0I5VQVRUAn9d9zoML/o8Tcw7A5ziN15o7+F26m2M+vYfgvNshbQiccAe4h9C97n1q2p+hqet9NJrMxEMoTDmTwCPzaH7hdpKOOor8m/+CJSGBDRs28Oqrr1LsyGFKcxF/yLGwrinA/RdOY2z+7vd4CvjC1K1rZ8X8WmrXtXPUxWPIHbqf95gSQoi+lj8ZznkCGlbAvL+Zvak/+w9MvQhm/RyS8/d5kxJsVsYneRif5Nlmu9aa5lCYLzp6uGVTPZeuKGdykoffDsvnoLQ9HO7kW7BaFCNzkxiZm8SZ04oACIYN1jZ0bell/VV1B/+eu5GIYY5jkpXkZELB1oL1+MIUMhOlR08oYjB3TRMvfFnNuysbCUYMRuQkceyxw/jIGqI8HOGc3HSuL80j29l/ky4OCIFueOs6WPwo5E6A0++HrJGxbpUQQog+oLWm/vd/INzcTMHtf8M5ajT2vFws7t3v3KW1xvtFA20vb8DispL6/bFc9s4qHDYLt54xkZpNC6lp+T1KWWj2nMNVJ1zXjxGZWmq7efM/y+lo9HLgKaVMOXrI1w4VprXm7ZZO/ri+lo2+AJPdLjwVfj54a83OC9PeVnjnd/DlY5BcYA53NeoE+eBWiCiltf7mvb7pJEpZgRpgBvAzoFVrfbNS6logTWt9zdcdP23aNL1w4cJv3Q4xMJR/9SXznniYxvINpDpclK3eSFaX75sPBJ47/Dj+deYFnP7+G1z27KNbtms0gdGaniMjBEZrlB88n1pIeN+KrWXrL/zMn/2MzJ/9FGWx0NDQwIMPPEhixMnx3sncOyyRp9c38n+njue7M4q/th1Bf5i69R3UrGmjZm0bTZVdaA1Wm4XpJw5lyjFD9u7NEUL0G6XUIq31tFi3Y6CLq5zdvA7m3wFLnzJnOJ/8PZj1S/NDyzgS0Zpn61u5ZVM9tYEQR2Yk85vSPEYn7tldOv3JH4qwsq6TZdUdLK1uZ1l1B+ubutn8Z2JBqpvxBSlMH5rO4aOyKclMiG2D9xGtNStqO3lhcQ0vL62huTtIRoKDkyblM3p0Jg+0t7O0y8e0ZA9/LitkcrLnm0862FV9YU6E2FYOB/8SDr0ebI5Yt0oMIJKv+0Zc5WsxqLS/8D/qrr+erCt+TeYll+zx8UYgTNsL6/EtbcI5PJX0s0dy9+cV/O2dtfzz3MkcUmgwd/6JON1eVm48gTHW7xMJG9984m+palUrdpeNYy4eS8HItK/dd3mXl9+vr+Xj9m6K7Hayq70sX9JIusfOJYeUcsHMXoVpw4AlT5jFaX8HzPwpzLkWnPuuw4IQ/aGv83VfFaiPBn6vtZ6llFoDHKq1rlNK5QEfaq2/tsuEJM/9Q/2Gdcz778NULl9KYmIyZVX15NY0knH++ejhZdz1/npae4L84ogyclPM26jb/O3cs/QerMrKyAOu4drkIo4KtPP3N6/B2l6FMfYEWsdm0+CYi89ah91IJjs0m6zgTGxse5FoLy7GM3kyAF1dXdx/3/2EuvycFJrOOxOz+duiSn5y6DCuOXbUDm0PBSLUbWinZk07NWvbaKzoQhsai1WRMzSZgpFpFIxII7c0GVsfjSUqhOhbcsHbN+IyZ7eVm72pv3wcjAhMPAcO/jVkDo91y7bhixg8UN3EPyob6A4bnJ2XzlUlueS74rN41x0Is6Kmw+xpXdPB0qp2Klu9AJRmJnDoyGwOH5XN9KHpOGyDayiLxk4/Ly2p5fnF1ayu78JhtXDE6GxOn1LIyJJU/lpez3MNbeQ67NwwLI/Tc9L2+VjecScSNic1/ehWs2fYqf+GklmxbpUYgCRf9424zNdiwAtWVbHp5FNwjR1L8cMPoax7du0brOmm9b+rCLf6ST5qCEmHFrGstoPT7v6E48fnceMxQ3j/g+NJSGlh/aenYNQcR2KaE6en/+9MSsl2c8g5I0hI2fVdYw2BEDdvquOpulYSLRaKmoJsWNRAunsnhWkw7/p79ddQtQCKDoQTboecsf0eixD7QrwWqB8EFmut71JKtWutU3u91qa13uHjJ6XUpcClAMXFxVMrKiq+dTtEfGqrr+Xjpx5jzafzcCcmMko5yf74czyjRpN345+xjxrNJY8u5KN1zTz8/QOYXWaOJdoZ7OSC1y+g0dvIlbMf4lcb/Uywh3hm/nnY8FFz+BlU+xcQCDaQkDCCIcU/JCfnBCyWr78NORgM8vD9D9HY2MgJ+gA2HTycX723hhMn5nPn2ZOwWBShYIT6jdEe0mvaaSzvxDA0FosiuySZgpGpFIxMI7c0BbtDCtJCDARywds34vqCt7MWPv4HLHoYIgEYeyrMviLuLgRaQ2HurGjgoepmLAouKczisuJsUuzxPzVIZYuXD9Y08v7qRj7d2EIwbJDgsHJwWSaHjczmsFHZ5CQPzPkX/KEI76xs4PnF1Xy0tglDw6SiVE6fWsiJE/JwOW3cW93E3ysaCBuaHxdl8YshOSTY5O8AWjbAC5dCzUKYcA4cfwu4ZKgzsXckX/eNuM7XYkDS4TAV519AYP16Sl96EXv+7g+tprWmZ0Ed7a9uxJpgJ/3cUTiHpuAPRTj+7/Po6AlyVUoa1syrSC+oYt2Xcygr+hXjZwwhqzgp5h8CeyMG/6lq5J+V5hBfhR0R6hY2kO6w7bwwHeiGuTfDp3eb+fCoP8Gk82B/n5tCDCpxV6BWSjmAWmCs1rphdwvUvUnyHJx62tv49PmnWPbem1htdsYOG0nO6+9h8/nIvOwyMr5/Edpq40+vruThT8q56dRxnDfDvC07FAnx43d/zOLGxdww+z/cUOUmLdLNswvPo6fETW0mRAw/6WmzKC7+Ienps3craRmGwdOPPcmaTes4Wk2G46Zy0cvLmFCQwl8PGUlztCjdUN6JEdYoiyJ7SBIFI9IoGJlK3rBU7E65EBViIJIL3r4xIHJ2d6M5ieIX90Ow2xzf75ArzTGs40ilL8Atm+p5vqGNVJuVX5bkcFFBJs4BcvHiDYb5ZH0LH6xp5IPVjdR2+AEYm5/M4aOyOXRkNpOKUrF+zfiNsaa1ZlFFG88vrubVr+ro8ofJT3Fx6pQCTp1cyPDsRLTWvNXcye/X11DhD3JsZjJ/GF5AiVvG5UZrWPwIvHkdWB3mfCDjTot1q8QAJ/m6bwyIfC0GlOZ77qHpzn+Qf+utpJx4wm4fZ/jCtD23Ft+KFlwj00g7ayQhYOOSJv76/lrm9vRwZredaZPuIrN0BWvWjeLQ029lTOaY/gtmd9uuNf9raOOmjXXUBkLk9Bi0LW4kA8vOC9Naw6pX4M1robMGplwAR/4RPOmxC0KIfhKPBeqTgZ9prY+OPpchPvZzAa+Xha/+j0Wv/o9IOMTYGbMo+XIFkQWf4542lbw//Zm2jDyeW1TF0wurqGr1cfHBQ/ntCWYC0lrzm/m/4ZWNr3DNzL/wj+ahdPvauaP1F9jS21AWGzk5J1Bc9EOSkkbvUdvefP5VFixbyEzLKJJnzeCSD9bg1vDdDieOsEYpyCreXJBOI294Cg5X/PdoE0J8M7ng7RsDKmd7W81JFD+7xxzzb/hRcMhVUDwj1i3bxvIuLzduqOPDti6KXA6uHZrLqTlpWAbQkBFaa9Y0dPH+6kY+XN3Eoso2IoYmPcHBnBFZHDYqm0PKMkn1xHY4k4ihqe/0U93qZcHGVl74spqKFi8eh5Vjx+Vy+pRCZpZmYIkW1df0+PnduhrmtnVR5nFyY1khc9KTYhpD3OhphpcvhzWvw9A5cMo9kFIQ61aJQUDydd8YUPlaxD3fV19Rfu53ST7uOApuu3W3jwtWddHy31VEOoJ4Diui3m1jw+Imqle3sdES5tnEIMfmpnFqyX+xJb9BZWUpeQf/mmOHH9eP0eyez9q7+f36WpZ0eUkOGPiXtpAR0Fx6yDDOnzlk28I0mEPOvX4VrHsbsseaw3kUHxiTtguxL8Rjgfop4C2t9UPR57cCLb0mSUzXWl/9deeQ5Dk4hEMhvnr3DRY8/xS+rk5GHDiLcY5EAvc/hLJYyLjiChaNn8PTi2r4cE0jhoaDhmVw9gFFnDghf8vF4N1L7uaepffwwwk/42XvRNYHLPyG3zGCSgqLLqSw6EJcrrxdtsMwNEbEwAhrjIgmEjEwIprP35rPvDXzGRkppK2jlP84/YSU5vLkdCaMyqRgRBp5Zak43VKQFmIwkgvevjEgc7a/0+xN/eld4G2BktlmoXroIXE1c/rc1i5u3FDLsm4f4xPd/HZYPocM0GJohzfE3HVNfLi6kQ/XNtHaE8SiYOqQNA4bZY5dPTKn72/ZjRiaxi4/1W0+qlq9VLf5qG4zl1VtXura/YQN829fpWBmaQanTSnkuHG5JPS60GwPhbmtvJ6HappJsFq4qiSPiwoyscdxb/B9au3b8NLPzA9+jvw9zPiJ3LYs+ozk674xIPO1iEtGTw8bTzsNHQxR+tKLWJOTv/EYrTXd82voeKMcw2llXYKDtRvNYTOTM13kTszg+tWVpCQ4+NP0RfiC/6KxoZCWIadx2UG/2AdR7VqFL8CNG+p4pakdZ1hjrGonoz3Ejw4ZxgUzh2zz9wIA4QB88g/46DZQVjjsepjxI7D2/7jZQsRSXBWolVIeoAoo1Vp3RLdlAM8AxUAlcKbWuvXrziPJc2DThsHqTz7i46cfo6OxgeJxE5hx8BFE7rkX/1dfYZl5MG8cdQGPbwzQ3B0gJ9nJmVOLOH1SHpalrXQvaYKwgdbgDXrpCDVjLVjGrSPKWGIdwxXBu5lVnoOj7nAIu9FagwZtaLQGtLnU0eVmBgZei49uSw9d1i42OarIiaSTapvK/VYflf4Aj15wAAeOzIrZeyeE2HfkgrdvDOicHewxx6f++B/QXQ+F02HONTD8iLgpVG++lfTmTfVU+YMcmpbEDcPyGJfk+eaD41TE0CytbueD1Y18sKaR5TWdAOSnuDh0VDaHj8zmoOEZeBzf/AGxYWiaugNbi85bitBmIbqm3Ucosu3fttlJTgrT3BSmeShKN5eFaW5G5CTtMF52RGueqG3h5k11tIUinJ+fwdVD88jcjbbtF4JeePsGWPiA2Tvs9Pvibox3MfBJvu4bAzpfi7hS97vf0/7ssxQ//DAJM6Z/4/7eRi8NT6zC3uClLmTwpTeCO8PF8KnZDJuSTVZxEj9/aglvLKvjzuO9OPzX0tmRxeeWA7j5lDtjNt50ZzjC38sbuLeqEcPQqI1dZDb4+fHBuyhMA2ycC69dAS3rYPRJcOzNcjeR2G/EVYG6r0jyHJi01lQsXcxHTz5CU/lGskpKmX3W90iYv4CW++4j7E7gf7PO5kFnGVarhcNHZXP2tEJGBhStc2tw1/XgURDSmrBShO2ddBa9S0/RB9zvPJf31TFcXrWcs9cUorCBUiiAzflqyxONVwVoo4t21U073bTRTQc9RDC27Jpry+SMC7/LDXM38eaKeu45bwrHjtt1T2whxOAy2C94lVJFwKNALmAA92qt71RKpQNPAyVAOXCW1rotesx1wMVABPi51vqtb/o6gyJnh/zw5WPw8Z3QUQX5U8xC9Yhj4qZQHTAMHq5p5u/lDbSHI5yek8Y1pXkUuWI7REZfaOj082F0osX565rpCUZw2CwcWJrB4SOzOGh4Jt2B8Jaic1VrtPjc5qO63UcwbGxzvszEzQXoHYvQBaluXPbdmztiQXs3N6yrYXm3jwNTErixrGBAfzDQp3paoHwevH+jeRF+0OVw+G/BJuNwi7432PP1vjIo8rWIua733qP6Z5eR8cOLyb7yyl3u5+8OsXFpE/Wf1FLU5MWhYIPVgntaDsOm5mwz0eFLS2r4xVNL+NFMF1PdP8XnT+KtpjL+duGDuG3uLees8AV4p6WTT9u7CRjb1q3ULtZhxz/ltt1X7fK1j1u76DAMLDU9ZNX4+cmBJZx/4C4K092N8NZvYNkzkDoEvvM3KDtql++PEIORFKhFXKhfv5Z5Tz5M5fKvSMnOYdbZ51PsSabiuhtQFZuYN2Qqd405iYyCbM6aVsSRuSl0f9qAXtNKptZYlKLHbcMxJZuMgyOsqrqLtqbXsCt4O3Iej9hO47KeBdxw1Hng3Hp7s9frpbGxkcbGRhoaGrasBwKBLfskJyeTnZ295ZGTk0NmZiZ2u50bX13J/fM3ccN3RvPD2aWxeOuEEDEy2C94o/M+5GmtFyulkoBFwCnARUBrr6G30rTW1yilxgBPAtOBfOBdYITWOvJ1X2dQ5exwEJY+CfP+Bu0VkDsB5lwNI78TN8MVdITC3FXZyH3VTRgavl+YyS+H5JBmHxw9eoNhgy/KW3k/2rt6Y1PPDvukJzgoSttadC5Mc1OY7qEozU1Bqge349tNXlzjD/KnDbW81NhOgdPOb4flc3J2asx6cMWFrnqo+BjKP4aKT6Bplbk9ucAca7p0TmzbJwa1wZ6v95VBla9FTISbmth40snYcnMZ+vRTKMe2H5L7e0JsXNLEhkWNVK9uY5hdMcptxXBacZ84jOyp2Tvk0roOH8fc8RElGTZ+NOyXgOK1tcP43SX3kOXJ4ctOL283d/BWSydreszJl90hjTVibLlbuncFS0f/0b226F4vanZ1jGb7Spj2RUiv9nHZ1OJdF6aNCCx8EN77M4S8cPAvYfYVYHfvuK8Qg5wUqEVMtdXVMP+px1i7YD7upGQOPP1ciicfxLIbbyX73ZdpdqfwnylnkHv0EZwyIofECi/eRQ1kBQPYPS14k+oIFrdhG9pBkEp6ejYQDncQ1rDMn0BS9yH8PvuHnEIdvykbT3NT0zYF6e7u7i1tcblcWwrQvQvSbvfOk8Mjn5Tz+5dXcNFBJfz+xDH794WnEPuh/e2CVyn1EnBX9LHD5MXR3tNorf8S3f8t4A9a60+/7ryDMmdHQrDsWfjoVmjdaA5dcMiVMOZksHy74mdfqfUHuWVTPc/Ut5Jks3J5cTYXF2bhtsZHIb2vlDf3sLCijTSPnaJ0DwWp7p1fIPYBX8Tg7spG7qpsQAM/Lc7msuIcPIPsPd0t7ZVmIbp8vrls3WBudySaEzwNOQiGHAz5k8E28Hvxi/i2v+Xr/jIo87XYZ7TWVP3oR3g/+5yhzz+Hc/jwbV5vqe3mpb8vwdcZJCPDydREG+6OIO7xmaSdXobFtWPuNgzNBQ9+zqKKVq6bdBvZibW8sXw8I06+nrWhdN5t6aQlFEYBid1hfNXdWJr8jEn2kOZxYLGARanow1xXvdatFoVS7PC61bLjvhZF9LWt2/JSXJw5rWjXf3fULIbXfg21X5rzmHzndsgs64d3X4iBoa/z9eDofiP6XU97G58+9yTL3n8Lq83OjNPOwRg3h/f+9z4H/OZEcr1tfDz+UFwXX8zllla8m95HfVZBKLkefUAd1Z4GsIS3nM/mTUfbc+i2j2RJWwPLGxyMC47hjtHnUdzVQtaXX/DA3M/MfW02srKyGDZs2DbF6KSkHSdX8ociVLd5aeoKmI9uc1nX7ufZRVUcOTqH354gxWkhxOCmlCoBJgOfATla6zqAaJE6O7pbAbCg12HV0W37H6sdJn0Xxp8FK14wC9XPfR8yR5qTKY47LeaF6nyXg7+PLuZHRVnctLGOGzfW8WBNM1cPzeXM3HSsgySvlWQmUJKZsM02rTUBQxPUmoBhEDQ0QUMT0OZ6wNiz7UFD4zcM3m/tpNof4sSsVH43PH9QDJ+yW7Q2P4ipiPaOLv8YOirN11ypZjF62vdhyCzzrgKrXC4IIcT+pu3JJ+n5aB45N9ywY3G6ppuX/v4lyqI49byR8FEVRk+I1FOHkzA9d5fX2o9+Ws789c2cUvYhy1NG82H3T6mdUkq4Cpy6FXd7CHtlF5ZmP+PyUzhuXBHHjcujKD3Gw235O8we01/cDwlZcNr9MP6MuBkWTojBQnpQD0S1X5qD8aMBFf3FqEBZeq3vbMk3vL7juQKBEAsXrGTh56swIhFGTRiOkV/IyupaRmz6ggJLPb4SN6ExmRjODpSzbWs7DQu2cAZhWyKtETsNXhv1nQ6aOlwoXxKJoUQSwgkoFK2eJF6cfAgp4QBXdNdTkpW5pXd0ckoq7b7wNgXnbQvQ/i3PO/1hdiY9wcGU4jT+ce6k3ZqESQgx+OwvPbKUUonAXOAmrfULSql2rXVqr9fbtNZpSql/AZ9qrR+Pbn8AeF1r/fxOznkpcClAcXHx1IqKin0RSuwYEVj5klmoblwJ6cPMHtXjz4qbYt0nbd38aUMtS7q8jE5w8eOibFLtViyATSms0d5Bm9etCqxKYVMKC1vXN2+3Rve10GtdKWzR1y2YPY0MrfFHC7z+iLHNus/Y/rm57osY5jZDR4/Zup8veo6t+xgEoq9tLiYH+/BvVYdSOCzmo8Tt5DelecxKS/rmAwcyraFpDVTM31qQ7q43X/NkQskssxg9ZBZkj4mb4W3E/mt/ydf9Ta6xxd4KbNjAptNOxzN9OkX3/mebgnNzdRcv/X0JViscf1A+wQW12DLdpH93NI68hJ2ez9CaVzc186sHvsCapumckg9K4Q534W6x01XZhbU9yPTidI4bn8ux43LJS4mDITO0hmXPwVvXQ08TTL8EDvsNuFNj3TIh4oIM8bG/MgxY9xZ8cpd5gdGPwoai3p9EpTeFFZEsVLJBYU4rOdkdhBOhx2Uj4tiapHTIibUnD5c3h4g3gya/Yq3RRUXQiiuUTEIoASu9ep6pIE7VQapuJ1EH6LIk8c/S89GGkzPSU/FvLkZHC9At3QGMnfyYJjisZCU5tz4Snds9d5GV5CQj0YF9f7xdVwixjf3hglcpZQdeBd7SWt8e3bYGGeJj7xgGrH4VProF6pdBWok5zuCEc+JimAOtNa80dfCXjbVs8gX7/etZFUS+xZ+NTovCZbHg2ry0mutuiyX6fPPrFpwWhdNi2VJMdipzfVfbHdFz7my7M3o+R/T23kHPiEDDimgP6WgvaW+L+VpSfrQgHR2yI7NMeoCJuLM/5Ot9Yb/K16LP6GCQTeecQ7iuntKXX8KWlbXltabKLl6680scdgtHj04jvKoVz6QsUk8tw+Lc9k4zb8RgflsXbzV38HZTBx3z6lC+MEUHdTCko5al7evwVp7AzNJMjh2XxzFjc8hOcu3rcHeteR28dgVsmmsOb3XCHeZSCLGFDPGxvwn5zAmcPr3bnDU9uRCOvhEmfhfsLvNTPfQulnzD6+Yy4PVSu2E9VRuX0NzyJUFVgTurB0+Wn1Jb5ZamVAVScDQ7CVZl0RUpRQfSiQQT6QxZ6VBewsrYsq+h0shMtGJPTcCamE7Elk6X4aE5YKGpR9PYGaGlK0x481Rcy32Aj6csHVsKzHkpLiYUpuy0AJ2Z6Oy3MSmFEGIgUmbl7QFg1ebidNTLwIXAzdHlS722/1cpdTvmJIllwOf7rsUDgMUCY06C0SfC2jdh7l/h5cth7i1w8K9g8vfA5oxZ85RSnJSdynGZKaz3+glpTVhrDA2RXuthrYlgbjMfW9fDGiLsuD0SPW7r8eY2qwK3xYLbatl1sdnaa/uWfc0e2aIfREJQ99XWHtIVn0Kgw3wtdQiUHbO1l3RaiRSkhRBC7FLTP+8isHIVhf+6a5vidGNFJy/fuQS308LhhYmEV7WSfNQQkg4v2vLhb30gxDstHbzd3Mm8ti78hsatFImrO7B0hvj+xCcoqunhiWCEq6bcyonnDyM9IfYf+G8j5DMnz/74TrC54fjbYNoPYj7UmxD7A6nwxavuJvjiPnOcI28L5E2C0x+AMSdTHdIs7/KhCaEwJwKIDsyBUpat65h1aF8oTJc/TLc/TKcvRGd7B921m6BrFW4qSEhowZ0ewF4YQhWCNtJp6ymmuS2J9lAS4VAG9GQRtCYQsFsIJtkJWW2ErDbCNgchh52wzUEwaCPkh2BPmFBPGNUTRrWGUVu6WwXRCrTbivbY0AVOVIKNlBQX140u5LjCdFLddiwWuXASQoi9MAs4H1imlFoS3XY9ZmH6GaXUxUAlcCaA1nqFUuoZYCUQBn6mtY7scFZhFvRGHgcjjoX178Hcm81Jcj66zZy9fcoFMZ293W5RjE6Mg1thxb4RDpgTNW0uSFd+BqEe87WMMhh7CpQcbPaSTimMaVOFEEIMHD2ff07L/feTeuaZJB1xxJbtDeVmcTrFZeXgNAeR2m7Szh6JZ1IWy7t9vN3cydstHSzt8gGQbbUyzAeN6zvorO6mC82s/M8YFmnhBZ/ioQvvpTi5OFZh7trat+H1K6G9whzW7egbISkn1q0SYr8hBep407QGPr0Llj4NkQCMOA4Ouox1mVN5o6WT177cuOUX/15TQMEQYMjOX7cCydFHb0ED5Q1j6Qnh8Eaw+SMob4RwTwgjFNh6egVpiQ7SU91kD3WTn+6hKN3N0MxESjPcpDrsJFgtJFmtOC37ye22QgjRz7TW8zF/w+/METvbqLW+Cbip3xo12CgFZUfC8CNg44dmT+o3rjZ72hz0c3NiOcfOx18UYq8FvVD9xdbhOqq/gLDffC17rDnBZ8ksKD5ILqSFEELslUhnJ7XXXou9uIica6/Zsr1+Ywcv3LUEnWXHn2LlARc0HJpLudHOhvkNtIUjKGC43c4Un6JqVQsdTT6CdguzyzJZktCBEWnnmKwFvF7p4Jqzf0UxdmirgEhw6yO8eT1g3hm0zbbg1m1GKLq++Xm41z7hb9gnus0Ibft88z6BTvOD3gtehtI5sftmCLGfkgJ1PNAayueZ40uvewtsLvSk77Js0o95PZTCaw3trNu0BoC0gMZT3U24yQcRcAB2rXAAKQ4bKVYLGUYrGfb1JLsrcSU24UrzYXNH0CiMiAWfL4Uebyo9vhS8PSkEQm5Cho1I2EUk4sKIOAgbdiKGFR2BbkPRZBh002vgSQXZKW6GZiZRMspDSUYCQzMTKMlMoCjNg8MmYz4LIYQYpJSCYYeZj/L55tAfb/8G5t8BB10OB/wQnImxbqUYqAJdZq/ozWNI1yw2L6aVBXInwLSLowXpmeBJj3VrhRB9TClVDnQBESCstZ6mlEoHngZKgHLgLK11267OIcSeqvnzTVSHDIK3/JUP27xsqG5lZXMPa9q9dJyQjO7VqSyjq5khwVYO99Uwqm01k+u/ICvQglNFSLIbJKQaOFWYqyrOorlnNj8Z/SqfrE/hRM+nzHj49L5rtNVhPiy26LrdfFjs2z63OsDmAmfyTvaJHmuxQ/pQmHpRTIdvE2J/JgXqWIqEYMX/4JN/Qv1XRDzZfD7nFt7IOYLX2gPUrPejtB9Pdxh3ZTeFdQHGh2yM0Aqnz4oRBq3DWOyr8WQtx51UiSerGUdKJ96Im65QIi3dWTRU5tHck02bNx2vP4WQ4SKIA5+20YOVHqV6l563UBhkaRiS7GJGaTqlBclbCtFF6R5cdhmHSQghxH6u5GDzUbnA7FH97u/NcQtn/hSmXwqulFi3UMQ7X5s5bvTmgnTdUtCGecGdPwVm/sz8GSuaLj9PQuw/DtNaN/d6fi3wntb6ZqXUtdHn1+z8ULE/8wbDrK7voqrVizcYwReM4AuZS28wQls4TJMRoQWDdgt0WjWG4af7qNMJHX+u+dFIVw2OcIiMThjdE2J8wEKpt45JwX8w3L+KxIiPLu2mQufQaUkmJTGBtOwcMlISsdqdYHXwemcyzy2fyREF82jZ5CR/gpdzsq8259HaXDS2Os11W3RpdW4tLNucWwvQW17rVUy2WGVOBSEGGSlQx4K/AxY9DJ/9h2BXA/OHnMjrR/6e14xs2iIGlrounM0BhtQEmFTjY4wfEg0HyVYHyRaDusR1rMxspssRwGsx6DZcdAfH0Nkyg87aJLxhD3ond3krIAVIVZBqgVKrIs1uJd1pJd1jJyPJSWaKh4zMBLKyUsjISMCR5kJZ5Re/EEII8bWKD4TzX4DqhWah+v0bzQ+gZ/wEDroMnEmxbqGIF91NW4frqPgYGlYA2rz4LpwGs680e0gXHiBDxgghNjsZODS6/gjwIVKg3q9pranv9LOqrpOVtZ2squtiVV0nG1t60DYL2mlBJ9jQHhtGgh0SbObzRAtmZcCKxTDI9bVSUl7JkI4apmSuYZi/isQON580/JyJbihyJKM9G/ANX0co/Vw2pZZC+lAcSVmkOG2MSHbtcPd0XUsl1975CfkJdRS2tlB5gJV7j3sKZbHH5L0SQgwMUqDel9oq4LN/07PkaT5IHMtrZVfzVsJ4vMqKxafJrO/myPJuDmmEHOykWBWpFifJHoO1dh8vJ5SzMJREo78Q2gpRGLitARIsQRKtijxXAlMzkinOSCMjyUVmipPMVDcZaW4yk52keRxYZQJCIYQQov8UToPznoHaL81JFOfeDEuegBPugLKjYt06EQudtVD+8dYe0s1rze12j9kr+rDfmBMaFkw1e5YJIfZ3GnhbKaWB/2it7wVytNZ1AFrrOqVUdkxbKPaZsKGp9wdZ0tDF0sZO1rT2UN7lp8YbxK802mEBhxVbqg2Vk0LQkore7pI/N9LFsEA1w1o3MKxrA6W+Kob5qijy1lP7UQ7+FsXQX87EUTaVau/ZvPFqIjPS7GQZkDAzj9QTD0btZh0hGGzjskefwxsaxrH2L1g0oobHjngCuxSnhRDfQArU+0L1ItoW3MvbTe28nnkIHxzwDEGLDWcgwvhNPg6uNziwVZOpFMlWF5YEhUazVgd4wFPBgoiLpmAaqquYspSNHJi5mtMOOYpZYw7BbpNhNoQQQoi4kz8ZznnCHEv45cvhiTPMGeGP/QskZMa6dWJf6KqHp86DmoXmc2ey2dN+0nkwZBbkTzJvWxZCiG3N0lrXRovQ7yilVu/ugUqpS4FLAYqLi/urfYOOL2LwcXs3DYEQVgVWpbAphVUprIqt67DDdotS2L5pHXPdqhRKQUcoQksobD6C0WUoQkswTJ0/SE1PgOZgmE7DILj91E4uwGUH7Hgw74jOc9nJVgEyumvIaFtLRssKsvyNDPNVU+qrJSE5G9LNns8MmQzpp0PaUFpe/BBv3Z3k/d9NOE47larVrbz7r6+YlWQj2dCkHD+UxNkFqN0cSiMS8XP7/25iUcNhHOxZxuKchdxz7P2kudL6+DsmhBiMpEDdXwyDhlVv8eryBbxqHcbnaZcSybCS7o1wdEWYoxoDzOgwsGkARcQaQrkCVGV5eD7UxEdtQZpDqShfDiPT1nNQ9kJmjxrGaYdfjsUiExAKIYQQA0LxDPjxPJj3N5h3O2x4D469GcafKWMnDmatG+HRU8DbAkf9GYYeArnjzTEzhRDia2ita6PLRqXU/4DpQINSKi/aezoPaNzFsfcC9wJMmzZtZ9MMiagaf5B3Wzp5p6WT+W1d+I0Yv11aYwlpdCCCChkQjODRiiK3g+IEJ2WpHsZlJTI2M5Fsp500HcBWPg/WvQ3r3oWOSvM8WaNg+JEw9AzIHA4pRTv9MNS/ciWN/7ybpKOPJuXUU6hc2cLcfy/j4EQrbgXp543CMz5rD5of4Z2Pr+Thrw6j0NFIc+Kb3HDcHylLK+urd0gIMchJgbqPratt5LH3P2WeK5XV6bno7FMp7olwfnmYwxr8jO6IEIz4sbhCuEsT8IwvpjIzhWeXrOHVrxpprtEoXIxKr2JO0nxGpts447jrSU/Oi3VoQgghhNgbNiccdj2MOcXsTf3CJbDsWfjO7ZBaFOvWib7WsAIeO9WcDPvCl82hO4QQYjcopRIAi9a6K7p+NPAn4GXgQuDm6PKl2LVyH/v0bvj8P+adScMOh9LD9ip3RrRmcaeXd5o7eLelk5U9fgCKXQ7Oy8vgqMxkhntcRLTG0BDWmrDWGJjrEa2J6F2tb/c8esw259Gaxu4AX5S30djqo7HZS8gXhpCBPWwwPC2BMXnJjM5LYUxeCqPzkshIdG4NQGtziKhlz8D6d8x5DCJBsCdA6RyY/SuzMJ36zT3nDb+fmquuxpaWRu4f/0DlylYW/GcZB3usOFw2Mi8ai3NI8m6/t1prVqz6HXd8OhzDsJDpeYMTjvoehxYdumffJCHEfk0K1H1k/aYafv/ROt4vSkEXDGFkZ4RL1gWYUd1Fgd9LakkCmUeX4B5fgsVhZXV9F88uruT5NxfT6HejMBiVXseR6e9R7OjioBkXM2nkr2MdlhBCCCH6Ss4YuPht+PxeeO9PcPeBcMTv4YAfgtwdNThUfW4O52JPgO+/DNmjYt0iIcTAkgP8Lzqkgg34r9b6TaXUF8AzSqmLgUrgzBi2cd/56DZ4/89mcbpyAaz4n7k9o8wsVg87DEoO3uVExB2hMB+0dvFuSyfvt3bSGopgVTA9JYHfDcvnyIxkyjzO3R7CYm9tau7hn++t48UlNThsFiYVpXJsaXa0IJ1MWU4izp0N3Rnohk0fmQXp7XtJT7/UnNuieKb5QfgeaLztbwQ3bKDogfupro6w5P7lzPRYsaW7yPrBOOyZ7j06X3n53Ty6oIE1bQcx1jaPUYeUcvG4i/foHEIIIQXqb6m9s4c/PP0x/xuahVGYwmnrOzmqvpYJB42h5OJJWKKJRmvN6vouXvtwA//7Yh013SpalK7kqCFLGWJrIqdgFscfcjd2myPGUQkhhBCiX1iscOBPYOTx8Oov4Y2rzN7UJ/1TipkD3fr34OnvQVIeXPDibvViE0KI3rTWG4GJO9neAhyx71sUI1rD3L/Ch3+BCWfDyXeb+bNpNWz4ADa8D4sfNXtWW2xQOB2GHY4uPZx1qaN4t62Hd1o6+Lyjh4iGdLuVw9OTOTIjmcPSk0ix75sySGWLl3+8v47/fVmD3aq4+OCh/GjOMDITd1FQ3txLet0737qX9K50z5tH2+OPk37hBTQllLH2geVMc1uxFSaS9f1xWBP2bG6E2tpn+eirJ3l+3VXkqxqSZlTwh5kP9XvRXwgx+EiBei8Fw2HuePBtHinKpXV4NnNqfJzbtJGTf/ndLb+Mtdasquvk9WV1KKDSHQABAABJREFUvLy4kor2YLQovY7DR3/JcFcVyjqUIw77HQUZw2IckRBCCCH2mbQh8L0X4Kun4c1r4T+zYfaVcPCvQD6oHnhW/A+ev8T8kOF7L0BidqxbJIQQA5PW8P6NMO82c1LZk/65dfz+7NHmY+ZPIRyAygUENs7l07pq3qns5N2eeirc5r5jLT4uy07jqIIiJid7sO7DgmlVq5e73l/Pc4ursVkUFx1Uwo/mlJKd5Npx537qJb0z4dZWaq+/HmfZcLoOu4C6R1cx1mXFMTqdrO+OQtl3f54EwwjS2PgmX634Lf9ecjV2HcE59mX+ccSDuGw7iVMIIb6BFKj3wuOPv83diWlsLMtnTFuYK5as5rxfn47LOTPaU7qT176q49WltWxq8aIwGJG6gfNHL2Jc6lr8PVmUjr2Q2RP3jzuzhBBCCLETSsHEc2DYEfDmNfDh/8HKF82L8cJpsW6d2F2LHoZXfgnFB8J3nwZXSqxbJIQQA5PW8O7v4eM7YcqFcMLfdzoEVn0gxHst3bzbU8xcxyl4Cw3cFsXB1m5+2jmfIzc8RUHLMnPn9GG9hgOZDa7dH1t5T1W3efnXB+t5dmE1Fovi/AOH8NNDh5Gd3Ktguw96Se+M1pq63/0Oo72DwJX/oOfZ9Qx1WHAflE/6CaUoy9cX8A0jSGfnV7S1f0Z722e0dyzCMPw8u+os6nw5pBU8yV3H/ZVsj3xAK4TYO1Kg3gMfvf4xt3VY+LwgmzxvhF99vp6LL5xD5mnTaOzy89CnG3huURUbmnpQGJQlb+L80QuZlLWMYFcy1pSDOeGwe/A4E2MdihBCCCHiRWIWnPEgjD8LXvs13H8kzPgxHH4DyN8M8W3+HfDuH6DsaDjzEXB4Yt0iIYQYmLSGt34DC/4F0y6G42/bUpw2tGZpl493WswJDr/q8gFQ4LRzZk4aR2WmMCs1EbfVAswC/TOzCLzhfXNIkCVPwBf3gbJC4QHRgvXh5tjW1m9ZEtGausYmnvjgSz5evpY0evhLmYNjhjpI1ktgfit4W8EXXXbVQ3e9eWw/9JLelY7nn6f73fcI/ODP2OZ3kWO3kHj8UFIPKdxh30gkQmdnKw2Nn9PW9hk+3xIMYx1KBQHo6Umjra2E5Y3jeK95FgmJC7npO99lbObYfmu/EGLwU1rrWLeBadOm6YULF8a6Gbu0ftEK/rK0iTeHpOCOwGmr6rjksOGUjB3OB6sbeWZhNR+sbiCiYWhCJQcVLWBazhII2OkIjOKgg69kRMGkWIchhBD7NaXUIq21dEv9luI9Zw94/k5474/wxf2QUgwn/h2G7z/Djg4YWsM7v4NP/gHjz4RT7gHrno3bKYTYOcnXfWNA5Wut4Y2rzUmEZ/wEjv0LKIXWmsdqW7i1vJ6mYBgLMC0lgaMyzPGkRyW4dm+s43DAnMR2w/uw8QOoXQJocKZA6SFQephZsE4u2FpI9rX1Wu+93Lo94m0BbxtWIrv+2s4U8KSBOx086eDJhOIZ/dJLeleC5eVsPO10OqeeQ0radLBEsB6XSzjHQkdHR/TRSiCwFq3XYNgbiTiDdIUSaQ8k09SVQ1NXDq3eVDoDiXi1C69yEsGKzdrKj09q58oZl++TWIQQ8aOv87UUqL9Ge3UtN7/0Fc+MyCFggeM2tnN+gZ3CAybxzMIqnv2iglZvhCRbN7PyP2N20Sek2bpoaisiZ9iZHH3A97Hs5JYkIYQQ+55c8PaNeM3Zg07Fp/Dy5dCyDiaeC8f8n3lhK2LPiMArv4AvH4MDLoHjbtnpLehCiL0j+bpvDJh8bRjm3UOLHoKDLoej/gxK0RmOcMXqKl5pamdWaiLfzUvnsIxk0vtigsOeFtj0YXTCxQ+gs/qbj7E6zTzsTiPoSGV9t4NlrRbadCLFhYUcOLaM9Mzc6D7RYrQr9dv30N4LPT09NDQ00NHRQXtrKzUvv0KrPYVOdzLNGHRjw4eNiD1EyG4QUBZ6DBcdwSQ6A8mE9Y5ttmk/NksX2tGJ4ewAWxcWWxdHjEnnrmP+jEVJHhRif9PX+VqG+NiJUE8Pd93/Lg+XFdEwJpcD63yc5mvBNWkCt3+6gaUfzcWCwfjMlZw34hNGpaynrSMXh+t4Zh/8C5LcMvagEEIIIb6FITPhx/Pho1vh47+bY1Ue91cYd7o5drWIjXAAnv8hrHoZDrkaDrtevh9CCLG3DANe+bn5gd/Bv4YjfgdK8WWnlx+tKKcmEOSG0jx+WpyNpS9/1yZkmPl03OnRMaHXwcYPwd8O7rRti8ybl3YPjd0B/v3hRp74rIKwoTljSiGXHT6covTYDe9kGAbNzc1UVVWxblMVX5Y3sak9RId249V2fNqOzz0HH3aM0HbvYQgS7D0k27pIiPRQEKyhpGcjhrUbleTDkalJKHCSm51ETmI66a50MlzDSXenk+HKIMOdQZozbfd6sQshxDeQAnUvRiTCs/9+kXvyh7B6whCGtwU5fmUNvrx8btwQIbhuGdnuJs4o+4Tp2YsJexNxph7MIYc8QpI7NdbNF0IIIcRgYnfBEb+FsaeavamfvxiWPQvfuR1SCmLduv1PoBuePs8sYhzzF5j501i3SAghBi4jAi/9DJY+CXOugUOvQwP3VjVy44Y6sh02XppcxrSUhP5th1KQNcJ87EJTV4D/vL2Kxz+rIBTRnDa5gMsPL6M4Y98XpoPBIDU1NSxbX8HCDQ2sru+mIeigVXvo0i4gHwCLxUeCpYOh+ElL7iIhqYpUdyspzk7cAS+2tjCWhgjhnkw8uSVkDR9O8ahpDC0dR5IzSYrOQoh9TgrUUR89+gr/sGUxf8ww0jtCzPq4nMpQiCcDHhyttRyQu5iD8j4nw/DiSJ7JUYe+TZJbbrUVQgghRD/LHQc/fBcW3APv3wj/mgFH/QGm/kCGlthXvK3wxJlQ+yWc8m+YdG6sWySEEANXJAz/+xEsfw4O+w3MuZrWUJhfrKrknZZOjstM4Y5RRaT2xXAe30JLd4B7P9rIo59WEAhHOGVyAT8/vIySzH4umvfS0dHB5ys38vm6OlbUdlLZadBiuPHhABKBROzWDqzuTeQl1zE1p4fpSdXkOdZjsYYAUJ3pNFVpfBtTSfRMJG/oJPImjiJv+EjcScn7LBYhhPg6+32Beu2bH/L3KsXLQwuwNQUomr+Jlh4bi7BTmlLNcaULKHNWk5A0kaMOfYykhOxYN1kIIYQQ+xuLFQ66DEZ9B179Jbx2BSx7Dk78x9f2+hJ9oLMOHjsVWjfC2Y+Z3wMhhBB7JxIyh0pa+SIc+Qc4+Fd81t7NT1ZW0BwMc2NZARcXZMa0B29rTzBamC7HH4pw8qQCLj98OKVZif36df3BEF+sruTT1dUsq25nQ1uQppCDULRso0jAbevA5l5PgqeSEbkhDs0PM9LdgcO/AW10ABDscOGqn0BG53Tq6x2kHTac8SeNJqOoGIvF2q8xCCHE3tpvC9RtS5fy9w/reWJIGn7Dh3tuNaGQFb89wBHF85mUvJr8lGIOm3MzqcmFsW6uEEIIIQSkD4XzX4Ql/4W3rod/z4I5V8OsX4LVHuvWDT4tG+CxU8we1N97DoYeEusWCSHEwBUOwnPfh9WvwtE3Ycz8Gf8sb+CW8jqKXQ5enVrGhKTYjefc7g1y37yNPPxxOd5QhBMn5PPzI8oYnt33hWlvMMzSihY+WVnJ0soW1rf4afBbiWDeGWVFkWjzk+IpJ+KpwJPRwozCDKaneSiwdmLxrcXvrwANRquTUH0e7taDyOo4iKRQEYbWlEd6mPOXY7Da5W4rIUT82+8K1IHaGv75+Kfcn5VFj9+CdUETdgxGZa5mWtpXjEl3ceSh15OWVhrrpgohhBBC7EgpmHweDD8S3rjaHPZj2fNw+A1m714ZN7Jv1C83e04bYbjwFSiYEusWCSHEwBUOwDMXwto34Ni/0jj5Yi5buoGP2ro5NTuVW0YWkWSLTe/e5u4AD39czsOflNMTDHP8+Dx+eUQZZTlJ3+q8oYhBXbufqjYv5c3drK1pYUNDB+uavDT6QGPmawdhUmzd5Lob0O5KwknllBUlMyl7NBMSPWQZafg7KvAFvoBOTSDsQDfl4Wk+hsyOGXi8Q1EovBGD9lCIOl8DFpeXg28+W4rTQogBY78pUDfVV/HXh17jdU8RPZ0eVGs3ae5OZhd9xrQMLyccdjlZOVfHuplCCCGEELsnKQfOegRWvwZv/9acwC93Ahx2PYw4VgrV30blAnjiLHAmwkWvQtbIWLdICCEGrpAfnv4erH8HvvM3Php2Nj9buIbucITbRxZxbl56TIb02NTcw33zNvLcompCEYPjxuXyiyNGMDJ39wrTEUPT2OWnqtVHVauXqjYv1W1eypu6qWztoak7jO61v0KToIKkWnoY5moGVzWBxPUkZoWYkDWBsdaJFBoF2EPZBMML0Z2PorpDtBoWVGsRnpYjSW+fhqdzGBhWuoMdeDub6e6ZhytLkzmpmKIDp+IadRjKKkN5CCEGlkFdoA4GAzz6wH281ONkjS+bYE8RdEFJZg3HpS7je0eeQ2GxTHIjhBBCiAFs1Heg7BhY9izM/Ss8eQ7kT4ZDr4eyo6RQvafWvWsWUlIKzOFUUoti3SIhhBi4gl546ruw8UPCJ/yD29KP5s6lGyjzuHhm4jBGJ7r3eZOWVLXzn7kbeHNFPXaLhdOnFvDD2aUM226Maa01LT3BaPHZLEJXt/mobvNS1eqlpt1HKKK3OSZBhUjAT6IKkGsNkmD3oR3N+B01+J11OC0JDHeNpUSNID0wAWeoE5qWYgssw5P9GtrhJWgDi7cAT90hpLRNxNM2AiNkpdNXS0/LBuhZQMLwTHJnTMIzbTaO0lKUTJoshBjgBl2BWmvNWy8/zSPrWlkWyqW7YygAKsXCiOJ6rpqcyZEH/jCmky4IIYQQQvQpqw0mnQvjz4CvnjYL1f89EwqmwWHXwbAjpFC9O5Y9B//7EWSPge+9AIlZsW6REEIMXMEe+O/ZUD6f2pPu5SdM5rOKBr6bl86NZYV4rPuuqKq15sO1Tfz7ww18tqmVJJeNn8wZxkWzSkjzOPh8Uyvvr2o0i8+9itG+UGSb8yQ5FKn2CB7DxyjVSaItQKIKkGQL4kn0025vpppqOhwd1Dg6KU0qY1JwFrktMzEqnRjhVjxZa0nIeZ2EIauxudoAsAYySWiaRkLLGDytYwj7rDT7q6nrKMfhXUR+aRbFh87AM/0H2AsLpZ4hhBh0Bk2BevWShfz97fl8rvJpbUsCIwk8FvKKOjjE3c1153yPVI8r1s0UQgghhOg/VjtM/h6MPwuW/hc+ug0ePx2KZsCh10HpoVKo3pUvHoDXroAhB8G5T4IrJdYtEkKIgSvQZQ6VVLWAt094gl96iwkYPu4eM4TTctL2WTNCEYNXltbyn7kbWdPQRV6Kixu+M5qTJuWzqLyNv7y+mvdWNdDpDwOQ5LKRm2gn3WlQnB3BHupC9bTgCHWTqALYlYHT7SSSEKHF1sKGyAbKrU147V0UelKYkl7CgXoEqV4HuruTcE8jNverOErbsI3tAGV+HWs4CU/LKDwbxuJpHUOgw0JToJZqfzWRnqfIzU2m7NDDSZ91Avbc3H32fgkhRKx8qwK1UioVuB8YB2jgB8Aa4GmgBCgHztJat32br7Mr7a1N3Hz/o3xky6WuLRUdKkPbFem5PqbbG7j29LMYmp3ZH19aCCGEECJ+2Rww9SKY+F348jGY9zd47BQoPsgco3ro7Fi3MH5obb4/7//ZHLv7zIfBvu9vORdCiEHD3wlPnEGwZgk3HfM//tOVyrhEB/eOLaHU49wnTegOhHnq80oenL+J2g4/I3IS+eNJY3FYLby3upFb31pDIGyQ4rYxLc9Bic2Hu7sGb3szultDN9jtdhLTE4lkBuhyVNOs19FjqSbRHibdqhjrTuBQhw0PQSxGAKiPPoAkwGPHFsrAHkzH3jECW306tkA6rrahhOstNATq2Biuo8X3NOmJDoZPmsaRJ/2YpOIh++Q9EkKIePJte1DfCbyptT5DKeUAPMD1wHta65uVUtcC1wLXfMuvs0UkHObOf9/N66EUNnVmEPGNQlsgITPMJEcdvzzycKaPGtFXX04IIYQQYuCyOeCAi81e1YsfNQuxj5wAJbPNHtUls2LdwtjSGt6+AT69y+x1fsrdZi90IYQQe8fXDo+fRkVbIz867BWW+Jz8oCCT3w3Lx7UPhvRo7PLzyCflPPZpBZ3+MJMKU5gzMouNTT388ZUVGBqyE2wclGOQFajF1VWDtT5CUmIzCfYWsrO7cNi7sTm7cbgCODxhHO7IDl/HCNnQXgPV4sLmy8MZzMITzMUVzMbmT8fuT0OHXPjC7XQHWugONNMTbKUjVE2TsQSDCIVFJYw5/CjKZh+GO3H3JmYUQojBaq8L1EqpZOAQ4CIArXUQCCqlTgYOje72CPAhfVCgfu6ZJ3is1stqbw6BzmFowJGuGZtRz4VjhnL64Yd/2y8hhBBCCDE42Zww/RKYfD4sehjm3w4PHw9D58Bhv4HiGbFu4b4XCcMrv4Alj8P0H8GxN4NMMiWEEHvP2wqPncrLkQyumH4bChsPjCvmO1mp/f6lNzZ1c9+8TTy3qIpQRDM8O5HMRM2S6g6WVHdQmGjhoJRO0n1V5NvrSLY2kZxRR3JJM+7U7m1+/QeDFoIBByqYgurIwNaUjcOXhaM7A7c3h4RgHtbI1jtt/PgJqB4C4Taae+rpaV+Jt6MWf7AdZbNiTU/HlpGOtSCd9PSRTBh/FsMPmClFaSGE6OXb9KAuBZqAh5RSE4FFwC+AHK11HYDWuk4plb23X+DLzz/htvlLWBrOpastFaVTsSTBsMIWTs6wcvk535XJAYQQQgghdpfdBQf+GKZeCAsfhPl3wINHm5MoHnY9FE6LdQv7n9bga4OXL4fVr8Kca+HQa2VsbiGE+DZ6WvA/djq/Tz6CR/JOYkqSh3+PGUKxu3+H9Piyso1/f7iBt1Y2YFHgcdgIRcJsaOym0BVkpquGUclrKU6vIDmhkcSkVmwOcxzoYFjR5HPS0F1KmjGe/OAEcn3D8LS5Cbf6Iay3fJ2Q1nRHND02C6EsN57kIAm+DURWfkbky4VYOjpwA4lZmXimTsNz7Ol4pk3FOWIEyjZopv4SQoh+821+U9qAKcDlWuvPlFJ3Yg7nsVuUUpcClwIUFxdv2d7UWMfvHn+Gz3Q+ra0eiAwBlyI/r4PDHW387geX4HTIrZdCCCGEEHvN7oaZPzPHqf7iAfj473D/EVB2tDn0R8GUWLdwz2wuOvc0QXcj9DSayy3rTdsuI0HzuGP/ahbshRBC7L3uJtY9dSk/Kvw5KxNK+WlRNteV5mG39M8Hf4aheWdVPX97ey1rG7pRaECh0GRRy+ys9UzJXkp+egUutxcAbSj8PUkEOkbg9I8ho30cKU3FWMK9uk5bIOK20RIJ0eyN0Bk28KFIKE4gM6mHjLZVJC3/BP8bywgHAnQAjqFDSTrqSLMoPXUK9qIi6UQnhBB74dsUqKuBaq31Z9Hnz2EWqBuUUnnR3tN5QOPODtZa3wvcCzBlyhT923/cyXtGFrVtKRAYjrYpMjK9zLDW8ccLziM7Nf1bNFUIIYQQQuzAkQCzfg7TfgBf3Acf3wn3HQYjjjN7FedPil3bNhedv7bgvHlbExihHc+hrJCQBYlZkJANWaMgMdtcL5gCQw7a93EJIcRg0tXAsy/dxDUlV+OyO3liXClHZCT3y5dq6Q5w+ztreXlJDd3+EFpZcKgQI5M3MSV7KdMKv8Bj9wNg+JJwdg4nuWoUnvZhuLpKsEScaAVhp42A3UKLG3oMTWfQoK0nTIc3jCZESoaD3JwAhZ1rSVj5EZF3V4BhELJasY4ZQ9q55+KeOgXPlCnYMjL6JVYhhNjf7HWBWmtdr5SqUkqN1FqvAY4AVkYfFwI3R5cvfdO5VjR00Vo7HK0gMSPIxPQ6rjliNhPHjd/b5gkhhBD7HaXUg8AJQKPWelx02yTg34ALCAM/1Vp/Hn3tOuBiIAL8XGv9VizaLeKAMxEO/hVMuxg+/w98chfcOwdGnWAWqnO/5d9kWkOw2xyf1NsCvlbwtkWXvbdF13uaokXn8I7nstjMonNCFiTmQM7Y6Hq08Ly5GJ2YA+40GVdaCCH6SU9bNdd98BLPFPyAA10R7pkyjjynY4f9IoYmFDGIGJpwRBMyDHO5eZthEIqYr4UNg3B0/83PNzR288S8ZVR2WTGwkGTr5uCCZUzNWcKojHXYDCv2jiEkVR2Bp2M49vahBAIp9IQ1bYam2jAL0T2RED4NSgVx2jVOu4HTFsFpDZPpCTHEqCN59YfYP1wOgPJ4cE2aiPunP8UzbSruCROweDz7+m0WQoj9gtJaf/NeuzrYvOi9H3AAG4HvAxbgGaAYqATO1Fq3ft15XIXD9dFXXcvFw7M5+Tsn7XV7hBBCiF1RSi3SWg/qAXaVUocA3cCjvQrUbwN3aK3fUEodD1yttT5UKTUGeBKYDuQD7wIjtNY7TlXfy7Rp0/TChQv7NQ4RB/wdsODf8Om/INABo08yh/7IGQNGxHx9+8LyDsXmzQXoFnP7zno4b+ZKBU86uNPN5TaF5uxexedsc18pOgsxaO0P+XpfcBaU6cIf39GvX8PASkQrEgwvLsNHRFswtJWIthDRVgxtIaItaL797+xsdxOTs79iUtZySqxeXJ1DcLeWodvK8HXkEAgGCAa6Cfnawd+OPdSFI9iNPdSNI2Qu7cEuHKFubGFfdFiQbVnT0/FMnWr2jp46DdeokSi7DC8qhBA709f5+luN1q+1XgLsrDFH7Ml5xuWm8vIvfvhtmiKEEELs97TWHymlSrbfDGy+1zYFqI2unww8pbUOAJuUUusxi9Wf7ou2ijjnSoFDr4EZP4IFd8OCe2DVK+BOBV877OTCHjB7N28uMrvTIb3UnHix9zZPOngytq67UsEqE0gJIURfslg0Hnew379OdqSZVN2JVRlYlIF1yyNiPmf77b2fR8zn7Pw1i9JYVQQPETJ7nHgaC0nddDpOuwWHI4TLDc7MThyFXiwOO8rhQNnTsThyUfboc4dj6/qulr3XZfxoIYSICbkaEEIIIQa3XwJvKaVuw7zLafOguwXAgl77VUe3CbGVOxUOux5m/Bi+uB+6G7YrNmeAJ23rNmcyyMW9EELE3NicVBZeeVqsmyGEEELsFilQCyGEEIPbT4Bfaa2fV0qdBTwAHAnsrIq4026xSqlLgUsBiouL+6udIp550mHO1bFuhRBCCCGEEGIQkgH8hBBCiMHtQuCF6PqzmMN4gNljuqjXfoVsHf5jG1rre7XW07TW07KysvqtoUIIIYQQQggh9j9SoBZCCCEGt1pgTnT9cGBddP1l4ByllFMpNRQoAz6PQfuEEEIIIYQQQuzHZIgPIYQQYpBQSj0JHApkKqWqgd8DlwB3KqVsgJ/oUB1a6xVKqWeAlUAY+JnWOhKThgshhBBiC6WUFVgI1GitT1BK/QEznzdFd7lea/16rNonhBBC9DUpUAshhBCDhNb63F28NHUX+98E3NR/LRJCCCHEXvgFsApI7rXtDq31bTFqjxBCCNGvZIgPIYQQQgghhBAiDiilCoHvAPfHui1CCCHEviIFaiGEEEIIIYQQIj78HbgaMLbbfplS6iul1INKqbSdHaiUulQptVAptbCpqWlnuwghhBBxSQrUQgghhBBCCCFEjCmlTgAatdaLtnvpHmAYMAmoA/62s+O11vdqradpradlZWX1a1uFEEKIviRjUAshhBBCCCGEELE3CzhJKXU84AKSlVKPa62/t3kHpdR9wKuxaqAQQgjRH5TWOtZtQCnVBayJdTv6UCbQHOtG9BGJJT4NplhgcMUjscSvkVrrpFg3YqAbZDl7MP2MD6ZYYHDFI7HEp8EUCwyueCRfA0qpQ4ErtdYnKKXytNZ10e2/AmZorc/5huMlX8evwRSPxBKfBlMsMLjiGUyx9Gm+jpce1Gu01tNi3Yi+opRaOFjikVji02CKBQZXPBJL/FJKLYx1GwaJQZOzB9PP+GCKBQZXPBJLfBpMscDgikfy9U7dopSaBGigHPjRbhwj+TpODaZ4JJb4NJhigcEVz2CLpS/PFy8FaiGEEEIIIYQQQgBa6w+BD6Pr58e0MUIIIUQ/k0kShRBCCCGEEEIIIYQQQsREvBSo7411A/rYYIpHYolPgykWGFzxSCzxa7DFEyuD6X2UWOLXYIpHYolPgykWGFzxDKZYYmkwvY+DKRYYXPFILPFpMMUCgyseiWUX4mKSRCGEEEIIIYQQQgghhBD7n3jpQS2EEEIIIYQQQgghhBBiP9MvBWqlVJFS6gOl1Cql1Aql1C+i29OVUu8opdZFl2nR7UcppRYppZZFl4f3OtfU6Pb1Sql/KKVUf7S5j+OZrpRaEn0sVUqdGi/x7GksvY4rVkp1K6WuHKixKKVKlFK+Xt+bfw/UWKKvTVBKfRrdf5lSyhUPsexNPEqp83p9X5YopYzoTOUxj2cvYrErpR6JtnmVUuq6XucaaLE4lFIPRdu8VCl1aLzE8g3xnBl9biilpm13zHXRNq9RSh0TT/HEyl78XMRtzt6LWCRfx2k8SnJ2XMaiJF/Hczxxm7O/JhbJ13tgL34mJF/HaTy9jou7nL0X3xvJ13EYi4rjfL2X8cRtzt6LWCRf74rWus8fQB4wJbqeBKwFxgC3ANdGt18L/DW6PhnIj66PA2p6netzYCaggDeA4/qjzX0cjwew9Tq2sdfzmMazp7H0Ou554Fngynj53uzF96UEWL6Lcw20WGzAV8DE6PMMwBoPsXybn7Po9vHAxgH8vfku8FR03QOUAyUDNJafAQ9F17OBRYAlHmL5hnhGAyMxZ76f1mv/McBSwAkMBTbE0/+bWD324ucibnP2XsQi+TpO40FydlzGst2xkq/jK564zdlfE4vk6/79mZB8Hafx9Dou7nL2XnxvSpB8HXexbHdsXOXrvfzexG3O3otYJF/v6uvvoyBfAo4C1gB5vQJfs5N9FdASDTAPWN3rtXOB/+zLb1AfxDMUaMD8ZRd38exOLMApwK3AH4gmz4EYC7tIngM0luOBxwdCLLv7c9Zr3/8DborXeHbje3Mu8Er0/3wG5i/19AEay7+A7/Xa/z1gejzG0jueXs8/ZNsEeh1wXa/nb2EmzbiMJ9bv427+f43rnL2HsUi+jqN4kJwdl7Fst6/k6/iKZ8DkbCRf75Ofie32lXwdZ/EwQHL2bvzuKUHyddzFst2+cZ2vd/N7M2By9m7EIvl6F49+H4NaKVWC+entZ0CO1roOILrM3skhpwNfaq0DQAFQ3eu16ui2mNndeJRSM5RSK4BlwI+11mHiLJ7diUUplQBcA/xxu8MHXCxRQ5VSXyql5iqlZke3DcRYRgBaKfWWUmqxUurq6Pa4igX26nfA2cCT0fW4imc3Y3kO6AHqgErgNq11KwMzlqXAyUopm1JqKDAVKCLOYoEd4tmVAqCq1/PN7Y67eGJlMOVsyddbxFUsIDk7XnO25Ov4zNcwuHK25Ou+Ifk6PvM1DK6cLfla8vW+MJhytuTrb5evbXvcyj2glErEvG3ll1rrzm8ackQpNRb4K3D05k072U33aSP3wJ7Eo7X+DBirlBoNPKKUeoM4imcPYvkjcIfWunu7fQZiLHVAsda6RSk1FXgx+jM3EGOxAQcDBwBe4D2l1CKgcyf7Doj/M9H9ZwBerfXyzZt2slu8f2+mAxEgH0gD5iml3mVgxvIg5u08C4EK4BMgTBzFAjvG83W77mSb/prt+5XBlLMlX8dnvgbJ2cRpzpZ8HZ/5GgZXzpZ83TckX8dnvobBlbMlX0u+3hcGU86WfL3FXufrfitQK6XsmAE9obV+Ibq5QSmVp7WuU0rlYY4dtXn/QuB/wAVa6w3RzdVAYa/TFgK1/dXmr7On8WymtV6llOrBHPcrLuLZw1hmAGcopW4BUgFDKeWPHj+gYon2GAhE1xcppTZgfko6EL8v1cBcrXVz9NjXgSnA48RBLNE27c3/mXPY+ukuDMzvzXeBN7XWIaBRKfUxMA2YxwCLJdoz5Ve9jv0EWAe0EQexRNu0s3h2pRrz0+nNNrc7Ln7OYmkw5WzJ1/GZr0FydrzmbMnX8ZmvYXDlbMnXfUPydXzmaxhcOVvyteTrfWEw5WzJ11t8q3zdL0N8KPOjggeAVVrr23u99DJwYXT9QszxTFBKpQKvYY5d8vHmnaPd4LuUUgdGz3nB5mP2pb2IZ6hSyhZdH4I5mHh5PMSzp7ForWdrrUu01iXA34H/01rfNRBjUUplKaWs0fVSoAxzsoABFwvm2D4TlFKe6M/aHGBlPMQCexUPSikLcCbw1OZt8RDPXsRSCRyuTAnAgZjjLw24WKI/XwnR9aOAsNZ6IPyc7crLwDlKKacyb6cqAz6Pl3hiZTDlbMnX8ZmvQXI2cZqzJV/HZ76GwZWzJV/3DcnX8Zmvo20aNDlb8rXk631hMOVsydd9mK91/wykfTBm9+2vgCXRx/GYg5m/h/npwHtAenT/GzDHk1nS65EdfW0asBxzNsi7ANUfbe7jeM4HVkT3Wwyc0utcMY1nT2PZ7tg/sO0MwwMqFsyx11ZgjvmzGDhxoMYSPeZ70XiWA7fESyzfIp5DgQU7OdeA+t4AiZizca8AVgJXDeBYSjAnd1gFvAsMiZdYviGeUzE/tQ1gTqLzVq9jfhNt8xp6zSQcD/HE6rEXPxdxm7P3IhbJ13EaD5Kz4zmWQ5F8HY/xlBCnOftrYpF83b8/E5Kv4zSe7Y79A3GUs/fieyP5On5jOZQ4zNd7+XMWtzl7L2IpQfL1Th8qeqAQQgghhBBCCCGEEEIIsU/1yxAfQgghhBBCCCGEEEIIIcQ3kQK1EEIIIYQQQgghhBBCiJiQArUQQgghhBBCCCGEEEKImJACtRBCCCGEEEIIIYQQQoiYkAK1EEIIIYQQQgghhBBCiJiQArUQQgghhBBCCCGEEEKImJACtRBCCCGEEEIIIYQQQoiYkAK1EEIIIYQQQgghhBBCiJiQArUQQgghhBBCCCGEEEKImJACtRBCCCGEEEIIIYQQQoiYkAK1EEIIIYQQQgghhBBCiJiQArUQQgghhBBCCCGEEEKImJACtRBxRilVrpTyKaW6lVINSqmHlFKJ0dcuUkpppdRZOznueqXUpuhx1Uqpp/d964UQQoj9g1LqYKXUJ0qpDqVUq1LqY6XUAb1eT4jm5Nf39FghhBBC7D2l1HXb51+l1LpdbDsneo3dE83bmx9XR/d5WCl143bHlUSPsfV/NELsH6RALUR8OlFrnQhMAQ4AbohuvxBojS63UEpdCJwPHBk9bhrw3r5rrhBCCLH/UEolA68C/wTSgQLgj0Cg125nRJ8frZTK28NjhRBCCLH3PgJmKaWsAEqpXMAOTNlu2/DovgATtdaJvR63xKLhQuyvpEAtRBzTWtcAbwDjlFJDgDnApcAxSqmcXrseALyltd4QPa5ea33vPm+wEEIIsX8YAaC1flJrHdFa+7TWb2utv+q1z4XAv4GvgPP28FghhBBC7L0vMAvSk6LPDwE+ANZst22D1rp2XzdOCLEjKVALEceUUkXA8cCXwAXAQq3188Aqtr3YXQBcoJS6Sik1bfOnwkIIIYToF2uBiFLqEaXUcUqptN4vKqWKgUOBJ6KPC3b3WCGEEEJ8O1rrIPAZZhGa6HIeMH+7bR/teLQQIhakQC1EfHpRKdWOmUDnAv+HeXH73+jr/6XXMB9a68eBy4Fjovs3KqWu3ZcNFkIIIfYXWutO4GBAA/cBTUqpl3vd3XQB8JXWeiXwJDBWKTV5N48VQgghxLc3l63F6NmYBep5222b22v/xUqp9l6PY/ZdU4UQSmsd6zYIIXpRSpUDP9Rav9tr2yzM5Fmota6PDvexCZiitV6y3fF24BTMHlsnaq3f2kdNF0IIIfZLSqlRwOPAOq31uUqptcB9Wutbo6+/j1mw/uU3HbsPmy2EEEIMWkqpw4GnMYfWWqG1zo/OA7EOGAU0A8O11puUUhoo01qv38l57gdatNbX9NpWBqwG7FprYx+EI8SgJz2ohRgYLgQUsEQpVY95uxJse8swAFrrkNb6WcwxL8ftuyYKIYQQ+yet9WrgYcw5Iw4CyoDrlFL10bw9AzhXKWX7umP3XYuFEEKIQe9TIAVzDqePYctdTLXRbbVa6027cZ5KoGS7bUOBKilOC9F3pEAtRJxTSrmAszCT6KRej8uB85RSNqXURUqp7yilkpRSFqXUccBYthayhRBCCNFHlFKjlFJXKKUKo8+LgHMx54S4EHgHGMPWnD0O8ADHfcOxQgghhOgDWmsfsBD4NebQHpvNj27b3fGnnwe+o5Q6WillVUrlAzcAT/Vle4XY30mBWoj4dwrgAx7VWtdvfgAPAFbgWKATuB7z09124BbgJ1rr+TFpsRBCCDG4dWH2iv5MKdWDWVxeDlyB+aHyP3vn7GgPrccwi9dfd6wQQggh+s5cIBuzKL3ZvOi27QvUS5VS3b0efwfQWq/A/CD5L0ArZs/sz4A/9nPbhdivyBjUQgghhBBCCCGEEEIIIWJCelALIYQQQgghhBBCCCGEiAkpUAshhBBCCCGEEEIIIYSICSlQCyGEEEIIIYQQQgghhIgJKVALIYQQQgghhBBCCCGEiAlbrBsAkJmZqUtKSmLdDCGEEIPYokWLmrXWWbFux0AnOVsIIUR/knzdNyRfCyGE6E99na/jokBdUlLCwoULY90MIYQQg5hSqiLWbRgMJGcLIYToT5Kv+4bkayGEEP2pr/O1DPEhhBBCCCGEEEIIIYQQIiakQC2EEEIIIYQQQgghhBAiJqRALYQQQgghhBBCCCGEECImpEAthBBCCCGEEEIIIYQQIiakQC2EEEIIIYQQQgghhBAiJr6xQK2UKlJKfaCUWqWUWqGU+kV0+x+UUjVKqSXRx/G9jrlOKbVeKbVGKXVMfwYghBBCCCGEEEIIIYQQYmCy7cY+YeAKrfVipVQSsEgp9U70tTu01rf13lkpNQY4BxgL5APvKqVGaK0jfdlwIYQQYnf1tLfFuglCCCGEEEIIIYTYiW8sUGut64C66HqXUmoVUPA1h5wMPKW1DgCblFLrgenAp33QXiGEEGKPrJz3Ae/ef3esmyGEEHGvwxeius1LZUsP62qa2VDfTlVrD/VdIVr9YAA5LoMhqXZG5iQxYUgWB4woID8jOdZNF0IIIYQQfUBrja+rk+7WFrrbWsxlawvdba3brPe13elBvYVSqgSYDHwGzAIuU0pdACzE7GXdhlm8XtDrsGq+vqAthBBC9Lmgz8t7D9zDynkfUDBqbKybI4QQMaW1ps0boqbNR3Wbl+rosrypi6qWbuq6gvjC2x5jI0KiCpLm1EzOtGNRFqo6Db6oCzO/rguWdAEbcVvC5Lk1Q9OcjMxLZmJJNgeMKCQ9yR2TWIUQQgghxI5CAf8uC86bC9I9ba1Ewtv9UagUnuQUEtMySMrIJK9sJNz/3z5t224XqJVSicDzwC+11p1KqXuAPwM6uvwb8ANA7eRwvZPzXQpcClBcXLznLRdCCCF2oWHjel698690NDQw84zvcuBpZ3Pun26JdbOEEKLfaK1p7g5S3ealpt23pQBtFqR91LT58Ia2HXHPQYQEFSBRBShRQTISFUVpHoblpjKyKIthhblkZWVht9u3OS4SibChpomF62tZXtXK+qYwVZ0Gc6tDvFfdDl+0A2tJsobIT1CUprsYnZ/C5NIcpgwvIMHl2GfvixBCCCHEYGcYEbzt7TsUm7tbW7fpBR3w9uxwrN3lJjE9g8S0dApGjd2ybi4zSExPJyE1HattuxLypZf3aQy7VaBWStkxi9NPaK1fANBaN/R6/T7g1ejTaqCo1+GFQO3259Ra3wvcCzBt2rQdCthCCCHEntKGwaLXXmTek4/iSU3lrN//H4Wjx8W6WUII8a0ZhqaxK0BN++bez72K0O1mAToQNrY5JsEGybYwbsNHidFDoi1IggqQ7tQMy0mlOC+LnJwSsrOzyc7Oxu3evR7PVquVEcW5jCjO3WZ7KBRmZUU9izfUsaK6jQ3NIaq7I6wrD/JGeQt80oJiBam2MIWJFoZluhlTkMqUYXmMH5qL075HN3cKIYQQQgxqWmuCPi/drS10tZq9m7euby0897S3o/W2fwcqi4WEtHSS0jJIyyugaOyEbYvP0QK00+OJUXTb+sa/ApVSCngAWKW1vr3X9rzo+NQApwLLo+svA/9VSt2OOUliGfB5n7ZaCCGE2E5Pextv3vN3ypcsYvgBMzn6xz/HnZgU62YJIcRuCUcMGroCVLdu7QFd0+ajOlqQrmv3E4xse+GR5rGR6baSYQ9TmO7HFuzE4m0jgQAJKoDbpsjMzCQnJ4fs7MLoMpvk5GTMP/H7lt1uY+LwQiYOL9xmu88fYMmGWpZsamBlTTsbW8NUd0VY3h7gxfWNMLcRC1+S6QhTmGRleFYC44rSmTo8n1GFWVitlj5vqxCxpJR6EDgBaNRaj9vutSuBW4EsrXVzdNt1wMVABPi51vqtfdxkIYQQfSwSDtHT1rZNsbl3EXpzD+hQwL/Dsa6ERBLTM0hISyejaAhJ6RkkpJlF56Todk9KChaLNQaR7Z3d6aYwCzgfWKaUWhLddj1wrlJqEubwHeXAjwC01iuUUs8AK4Ew8DOtdQQhhBCin5R/9SVv3PU3At4ejrj4p0w86rh+Kb4IIcTeCkUM6tr9WwrOW4be2FyA7vATMba9qTAryUlhmpvROQnMLHTh0X7swS50dzPB9gYIByB6p2ZaWhrZQ7LJyZlAdnY2OTk5pKenY7XG/sLE7XIyc+xQZo4dus32zm4vi9bVsKS8gdW1nWxqC7OuLcLiFj/PrK6Fd2qxESHbGaE4xc6InETGF2cwbXg+Jbnp8nteDGQPA3cBj/beqJQqAo4CKnttGwOcA4zF7AD2rlJqhFxjCyHEwLLq47msnv8hXdFez77Ojh32sdps0cJzBlklwyidcgCJaRkkpGeQFC1AJ6SlYXe6YhBB//rGArXWej47H1f69a855ibgpm/RLiGEEOIbRcIhPn76cb54+XkyCos544YbySou2WYfrTUtLR/GpH37UvSi9lEgFzCAe7XWdyqlngZGRndLBdq11pOix0iPLCH6SW27j/vmbWR5TQc1bT7qO/30rj8rBbnJLgpS3UwbkkZBmpvcRAcJKoA92InuaaGtuYaGhga8jV7A/I9tS0gwh+QYNmlLj+isrCycTudetdPQBsFIkEAkgD/sJxgJ4o9sXQYiAQLhAAEjABo8dg8em2ebZYI9AbfNjUXtWU/n5EQPh00u47DJZdtsb2zrZOHaGr6qaGJNfRfl7RGWNoVZ0NgDy3qASpwqTK7LYEiqg5G5SYwfksX0EYXkpsudMyL+aa0/UkqV7OSlO4CrgZd6bTsZeEprHQA2KaXWA9OBT/u9oUIIIfrEui8+5fV/3kZqdi7pBYXkDRuxpQd0UnrGlnV3Uv/c5TYQyEBvQgghBqT2+jpe++et1K9fy4Qjj/1/9u47Tqrq/v/4605vO9t7YQu7dJYOihUL2I2KGo0VNck3xkRTjMZEozHRGGNM+UWJGjXGkthFReyKSIeFXVi2915mp7d7z++PWRYQkCKwlPPMYzIz55Y5sy47d9733M/hlKtv2OlMss9XR1X1vfT1fT5MvTykosBPhBBrFUWJA9YoivK+EOKyrSsoivIwMDD4WI7IkqSDoMsd5P99UsvzK5oQCCbnJTKrKJmcRBs5CVZyEq1kOM2YVR/9vT10dnbS1dVIV2UXtf39Q/sxGAzEJ8eTlp+GPcGONdGKKd6EMAlCaghP1EOv2sva/rWEe7YLlaODofL2t2hot21hLXzA3rvVYN0pvB56bIgF2bsKuLcPum0GGzaLjVOmjOCsGaOHvqQJIWjq7GN1dSsbm3qp6vLSNKCyvF3ls3YPrPMAddh1UTKsgoIkM6MznZSOSGVaSQ6JcYdHfUVJ2h1FUc4HWoUQZV8JJ7KB5ds9bxls29U+bgJuAsjLyztIPZUkSZL2RXvNFt75yx/JLCph/q/vP+JHPwshqGhzH/D9yoBakiRJOuJsXvoJHzzxdxSdjvNu/QUls07YYXk06qW+4W80Nz+NTmemuPgu4Prh6ewhMjgvRPvgY4+iKJuJfYHdBENzSlwKzBncRI7IkqRvSBManrCH/mA/Ta5eXviyhw82RohqMDZ/gJKCOvTaAKpHpbkZWit1rPUbMQVN6ERstLGGhs/ow2Vy4U5w4za5GTAN4DP4YtcwBgZv7V/XEzDqjFj0FswGM2b9zjen2blz+y7WtRgsmPQmLPpt91uXAfij/tgt4scX8RGIBvBHYm2+iG9o2da2geAA7dH2HZape3keTEGJhd7bhddWgxV7up2sHBsjDTYseitRjwNXj5XePhPdbiPdfiMfN+v5oNkFK11ANU59mCybQmGyhTFZCUwbmcHM0XnodLK+tTT8FEWxAb8EztzV4l20iV20IYRYCCwEmDZt2i7XkSRJkg6dga4OXv/DfdgTErjw5786YsNpTROsbepncXkHiys6aOkPHPDXkAG1JEmSdMQIBwN89NTjVHz6AVmjxnLOD3+KMzVtaLkQgo7ON6ipeZBwuIvMzPmMLPopJlMKR3tAvb3By4YnAyu2az4R6BRCVA8+3+sRWZJ0LBBC4I/66Q/24wq5hu77/H30e/tx+Vx4fB58fh+BYIBQKEQ0HIWIlU7feNr8o1AxkGNsY6ylkaQuFVOHCZ1wDL1G1BRFtatEUiIocQompwmz00yCKYECfUEsHNaZsBgsXxsmD4XHg2GyWW/e5/Iaw0UIQUSL7BBYb30ciAR2Drq/Enj7Ij56g700e5p3WEcTGjiI3QCbpsMSyMboH4ESzCISTqXNl8gWj553GnphWS9WZR3FCVHmTsjjkuPHk55gH9afjXRMKwIKgK2jp3OAtYqizCD2+Zy73bo5QNsh76EkSZK0T4JeL68+8Bu0aJRv3f17bPEJw92lfRJRNVbU9bG4op33Kjrp9oQw6XWcUJzCLXOKuezBA/t6MqCWJEmSjgid9bW8/egf6O9oY9bFl3Pcxd9Gt93kXx7PJrZU3cPAwBqccROZOOEfxMdPGr4ODxNFURzAK8CPhRDbX3v1beCF7Vfdxea7HG0lLxmWjjbL6pfx1vK3CAQChENhouEoWkTDoBowasYdbgax7XA5bvB/ABGhY5OaRkU0kzAGSmxdnJpex4gEH1aLwGJSMRgHsFjsxMVlkJiQjc2WisEYj9EQj9GYgMEQj16/f7Wjj1SKomDSmzDpTSSSeED2KUSs7MlQuB32EfT6CQ34iXhCqN4wwhtF8alEvRGa3SrlwRBrIxqV/U42fNbDQ599TLo5yKwR8Vw0czSzR2dj0B8Zob905BNCbASGzrgritIATBNC9CiK8ibwvKIofyJWkqsYWDksHZUkSZL2ihqN8ObD9+PqaOeSu+4jOTt3zxsdBoIRlaXVPSyu6OCDzZ24/BGsRj2njk5l7rgM5oxOI85iRAjBZXve3T6RAbUkSZJ0WBNCsPadN/n8+X9hdcZz6a/uJ3fcxKHlkUg/tXWP0Nr6AkZjAmNG/57MzEtQjpDRhAeSoihGYuH0f4QQr27XbgAuAqZut/pej8iSlwxLR5PKzkpefeFVHGEHZswINPSWCGarijkuisWiYbOoWCxRrGYfVpOG0RBFrwuAEiCsBnm/fhSLambiidopTd3IhUXvkOds3cWrGQiHNXp7NXp7d90fnc6C0ZiA0RCPwZiA0eCM3RvjMRoSvhJoO2PrGhPQ6x3H1CQ6QhNo/giaN4LqCcfuvWFUbwTNM3jvDaP3RrB7I9g1gUBD6FU0fRRNH0IzhlAcKnlpKifodBhrc4mqOr5QBvhA72ZzxMSbVSHeqNqASVnLmGQdZ03M47zpI8lOlKOrpQNHUZQXgFOAFEVRWoC7hRBP7mpdIUSFoij/JVayKwr8QM4XIUmSdPgSQrDksb/QvGkjZ9/8E3LHTtirbUBDiNht6+PYvQDU2DIECI3Yx4DYYd0dtxMIoSLQYutvt93Q48Fl3pDKF3VhPqqK8kWdij8CDjOcWKhwajHMHBHFrG9F0ExXq5c1zU00Nzcd8J+bDKglSZKkw5bfPcDi//cI9etWUzRtJmd+9xZszngAhFBpbXuJ2tqHUVUPOTlXUVjwY4xG5zD3engM1ph+EtgshPjTVxafDlQKIVq2a5MjsqRjjivo4rH//IOxqW2MLGlHrwRRVTdfl/UoihGFOCJRJ580TeWN2mm4QnbGJbRxQcbn5Ci9hFoz6dqcjN8VIjgQIRrSoYb0CFUHCHQmDYNZRT94M1rBHKfH7NBhtILBKtCbA+hNHnTGMOhDoAuCEtl9v9BjMDoxDIbXRmP84ONdBNs7tDnR6UwH4ae774QaC52jbj8Rj4eIZ4CIz0PU7yYa9BAJelFDPqJhL6rqR9OFEPogmj6MZgjGQmdDCGEIoaYE0dJDaLogmj6I0IVBt/ufH4DINmCJTGSmdiZndk8mVB+gJezhA8XDSiVKdY+Nso9aeOCjFlItEU4oTOb8aUUcV5yOxaj/2n1L0tcRQnx7D8vzv/L8fuD+g9knSZIk6cD48uXn2fT5x8y+9DuMOfFUIPbddfPmX9DVvXiH4Dl2DKod8j56IzbKusazpquUit7RRDUjTpOb6ekbmZJWxuikagw6FYJQs2Xn7bMOQmFIGVBLkiRJh6XGjet59+9/Iuj1MOe67zJp7rlDowVdrtVUVd2Lx1tBQsJMRpXcjcMxaph7POxmA1cBGxVFWT/YdqcQ4h3gcnYs7yFHZEnHHFVT+e1z9zE5pZacnM3YTKUYyUJTjESDOiIBiPgFQbdKYCBEwBXC2+PHMxCg0l7CqsRpeAxxZAXbmNP/PjkNnahOJ93ORGzx8cTFxZOekYDNGY8tPgGr04nVGQ+aIBTwE/b7CPn9hPw+wgH/tsc9fjz+weWBwTZ/ACE0FL2G3qxiMGtD4bbBoqI3qegtGia7gtHqw2j1oDc3ojNF0BkioA/xdYOrdTrrULBtMiYMjdw2Dj2O3yHg3jp6W6czoqoBVNX/lVsAVfWhqgGiES/RgJdo0Es05CES8hINeVGjPlTNjyaCaEoQTYmFyUIfQuijO3bQMnjbBSFARPRoUR1qREGLKKhBHZpXhxbRoUV1aBET4EDBjA4zOp0Vvd6KXm9Hb7BjNNjRRBBfZCVqRhnNtrU0ZukhoYhU6xn8n3Ee17cGGajtZl3Yw+dKkI0hPW9scvHapnUYFI1xqUbmTsjlrMn55CfbjqnR7JIkSZIk7VrFpx/y5csvMO6U05l50bYiGDU1D9Le8SoZ6RdiMqeioANFh4KCouiHHsfu9bHjCkW33Xrb3yso6Aa3U3ZYpihfXU8/eGWxQq8PPqnR+KgqyuqmKKqADKeOS6dYOGO0ldKcbAy6caBcAUKhpaWNLVuqqKurJxpViY9PZPToMYwePZaEhEQO9PRFSmyo+PCaNm2aWL169XB3Q5IkSToMqNEoy/73H1a+8TJJmdmc86Ofk5ZfCEAo1EVNzYN0dL6O2ZxB8cg7SEs7Z6+CAUVR1gghph3s/h/t5Ge2dKR64M3fkef9mPSMOkIdo9j8hsJXS7Fb7A6s8bGQ2eKMZ6Mui0WuJDpDekYl6Pnu1CROGp2OLT4Bi8OBTndwRtEKIYiEgkNhdex+a4C9c5gdC71jAXg44CcU8BGNuEEfGgy4tw+3NfSW7dsEButgAG6KougO0CgeTYdONaNTzSiqBSVqhKgREdWjRfVoEYVoREENQySioaoKqlDQ0IPOhF5vw2CwYzA4MJocGM1OjCYnJqsTi9WO0WrFZLFi2v7easNksWIwm/c6MO7vaKFm4//o638fHLUYLFHUsI5QTwYO8/GMyLwAu5JGW1UDHU09rA5FWY1GvbDjFrEUPdmkMbswiXOmFTK7OBWHWY4B+ibk5/WBIT+vJUmSDq2m8jJe+d2vyRk7gYt+cQ96Q+x4oKXlP2yp+jU5OVczquTuQ9qn5j4/71V0sLi8gzVN/QgBhSl25o3PYN74DCZkx+9wzNTZ2UlZWRkbNmzA6/VisVgYP348paWl5OTk7LDugf68lkdPkiRJ0mFjoKuDt//yEO3VW5gw50xOveYmjBYLmhamuflp6hv+hqZFyB/xffLz/w+93jbcXZYk6Qjw0qrnyA8tJjWjlVDbZDa/FeD4S79DVvEYrE5nbMRznBO9wYCmCRZXdPCn96uo6fQyJtPJ/WeUcNqYtEM2SlZRlFjoarFC0v7vJxqJDI7W9hH2D47aDmx9PHgf8BPq3hpu+wgF3ahRN1HVjSa8CPykOtPJTsjFaNCjUy0og8GzFjGgqsRGM6OgYUDoDAiDCcwGdHYDOrsRfZwJU5JtMEwevLdaMW4XMG8/6e2hlJiRw/SMW4FbiUaDNGx+ndbW11FSy9CML1PjfRV3kxNDeAKZk+ZxXeEUzunro3lTLY0dvVREjGyKmninUvBmpQs9gtHxRs6ckMPpU3IYm+mUo6slSZIk6SjX29LEmw//jsTMbM6/7Y6hcLqn9xO2VN1DcvKplBTfdUj6UtPlZXF5O4srOihvdQMwJtPJraeXMG98BsVpO85j4vV62bhxI2VlZXR0dKDT6SguLqa0tJSSkhIMhkMTHcsR1JIkSdJhoXLZZ7y/8G8oisIZN93MqONOBKC393Oqqu/F768jJXkOxcW/xGbL3+f9yxFZB4b8zJaONKvqP6Nh/c9IdPYQbJvNlkV9nHrNjUw5+4Id1hNC8FFlFw8vqWJTu5uRaQ5uO6OEeeMy0OmOzYAx3OrF9VYt4QY3ujQzxmmJmFMdmJIcmBKt6ExH71gXTYvQ3fUZjTUv4Al+CbogakjHQGMc/rZ0kpNPJG/8VKKZDqrqq+mo7KTVZadas9GoxdEvYidQnTrB8RlxnDkpl5OnZJPsMA/zOzv8yc/rA0N+XkuSJB0aPlc/z9/1E9RIhCt++zDO1DQAPN5K1qy5FKt1BFOnvIjBcHAmXBZCUNHmZnF5B4srOqjp8gIwJS+BeeMzmDsugxHJO752JBJhy5YtlJWVUVNTgxCCrKwsSktLGT9+PHb7nvsqR1BLkiRJR5VIMMhHTy+k/OMlZBaP4pxbfk58WjqBQDPV1ffT3fM+VusISic+QUrKqcPdXUmSjiAtfZtpLv8RCXEePC2nUfdOGydcfvUO4bQQgqU1PTy8pIr1zS5GJNt45LJSzi/NRn+MBtOqL4J7SQO+lR3obAYSLhqJfVoGyjH089DpjKRnnEZ6xmloWoT+/mW0Nr+OwfwBSSVVqOFatlS9jGtxHPpoCRMmTGH2zHyqrJ1Ub1qPv0lPdzCdBjWOj9tUFrd5Ud7ZxEizjlNzU5lTmsnU0kyMJjnZoiRJkiQdqSLBIK89eC9+9wCX3/PgUDgdCnVSVrYAgyGO0tJ/HvBwWtME65r7eXdjLJRu6Q+gU2BWYTJXHzeCM8dmkBG/44QeQgiampooKyujoqKCUChEXFwcs2fPZuLEiaSlpR3QPu4rGVBLkiRJw6aroY5Fj/6B/vZWZn7rUo675ApQItTV/ZnGpoUoip6iwp+Rl3cdOp0cdSZJ0t4b8NWxZuVlOCxBuhvPoH1JMzMunM/Mb106tM7K+j7+uGQLK+v7yE6w8uDFE7hoSg5GvW4Yez58hKrhXd6O+/0mRFjFcXwWztNHoLMaEEKgut2DsxQKhq7CHHy+Q7sAELtYtrv2XW+zx3Yh9n8bnQ5DUhKGlBR08fFfW4ZDpzOSnHwyyckno2lh+vqX0dn5DkbLYpJGtSCiXfTXr6f2dTu+dgejCseQMW4s/RmwIlBBb1U3BlcRnZFUWsLx/LNGY2FNF7ZX1jPDbuGUvGROmZRF3pgUdDKwliRJkqQjgqapvP3Xh+iqr+WCn/2S9MKRAKiqn7INNxKNupk65SUs5owD8noRVWNFXR+LK9p5r6KTbk8Ik17HCcUp3DKnmNPHppNkN+20XV9f31Bd6f7+foxGI2PHjqW0tJT8/Hx0usPjuFcG1JIkSdIhJ4Rg3eJFfPbck1jinMy/67fkjptId/cSqmvuJxhsJT3tXEaO/AUWS+Zwd1eSpCOM11vFF19ehFlRqa8+A++njUyedx4nXH41ADVdHn7z1iY+r+4hNc7MvReM47LpuZgNx244GKzux/VWHdEuP+biBBLOK8KYFitTEdi4kY677yG4adMw9/IgMRoxJCfHbikp6FOSMaSkxp6npqBPHnyekowuLo6U5FNIST6FMaN/S1/fF3R1vYPB/D6Jxc2gWfB3+KhZsxpPi40cs42ZY8ajjHayxd6KufsjxnUlovmKaVeTWOmL55PNIdjcRh5wgsPOSQXJHDchA0dRAnq7cbh/OpIkSZIk7cKnzz5J7eoVzLnuuxRNnQmAECrlFbfi8WymdOLjxMWN/UavEYyoLK3uYXFFBx9s7sTlj2A16jl1dCpzx2Vw6ug0nJadjxUCgQAVFRWUlZXR3NwMQEFBASeffDJjxozBbD78Bn/JgFqSJEk6pPzuAd577FHq1qykcMp05n7/xwh9N+vXX0Nf/xc47KOYMvl5EhNnDndXJUk6Ag0MrGfFmishqrCp8jS05Q1MOPUMTr3mRhRFobx1gO88uQIF+OXZY/jOrBFYj+FRq9HeAK636wlu6kWfZCH5qrFYxiahKAqq203XI4/gevElDKmppN56KzqLGRQFUGL3igIKoCixUcg7LNtduzL4cLvn7Ll9h30NLdtd+x72papE+/pQe3qI9vQS7ekh2ttDpLuL4ObNRHt7QVV3+nkpJtOOAXZKMikpOaQl34o/tRuXrYK+7DXYsnwo2NHc+XSWr6fj9TBGTWFOcgoJozLpzHGxXluPs9dPvGcUA6Ec2rR4XvRqPL/Rh3ljI5MwMNth46TCZEaOTsGSH48+0SwnXZQkSZKkYbb2nTdY++6bTD3nAibPO2+ovbrmAXp6PqCk5G5SUubs1769oSifbOlicXkHH1d24QurxFkMnDEmnbnjMzipOHWXx66qqlJbW0tZWRmVlZWoqkpKSgqnnXYaEydOJD4+fr/f76EgA2pJkiTpkGkq38C7f/sjAY+bU6+9iQmnn0JDw99obnkGvd5GScndZGddgU4nP54kSdp3vX1LWV92E6GAgfLyUzGWNTD6uBM546abUXQ61jX1c81TK4mzGHnhxlnkJduGu8vDRgupeD5pxvN5C4pOwTk3n7gTslGMOoQQDLz1Fp0P/gG1r4/Eq75D6i23oHc4hrvbh4zQNNSBAaLd3ai9gwF2Ty/Rnm7UwUA70t5OYONG1L4+0DQAjECaQRAaYyAwPUxwwiYyjhdkTjVCezoDzUZaVq8lHI4yFkjIyEAbEaIyazVqoIfRvgyEv5iOaBKbtXhWeFX+tMFDxoYGZmHkeJuFWYUpJBUlYC6Ix5BmO6Zqg0uSJEnScKtZtZyPn32CkdOP46TvXD/U3tzyb5qbnyIn5xpyc67ep326/GE+2BwLpT+r7iYc1UhxmDh/UjbzxmdwXGEyJsPOpTiEEHR0dFBWVsbGjRvx+XzYbDamTp1KaWkpWVlZR8yJbZkASJIkSQedpqp8+fLzLH/tvyRmZHHBz3+FsG5g+YozCId7ycqcT1HRTzGZkoe7q5IkHaE6u96hvOJWBnw2KspOw7ylkaJJUznr5p+g0+lZ1dDHdf9aRZLdxPM3ziQn8dgMp4UQBMq6GXinHtUdxjY5jfh5+ejjY5d6hurq6bj3XvzLl2OZOJG8hY9jGfvNLk89Eik6HYbERAyJiXtcV6gqan8/0d5eot09qL09Q4F2+JNOPOZq3Okt+Atbcea3MG4y6MqN+KrsdNX6GWizkqVTyBGCBNGCQd9ItwPc8UkMWAtpJ5NWLZ63tXhe90cwlHuYUK5nJgZmmcyMK0jElOXAmGbDkGbDkGqVtawlSZIk6SDoqKni7b88REZRMWf/MHaMCdDT8zFVVfeSknIaJcW/3Kt9dXmCLKno5L2KDr6s7SWqCbLiLVw5M4954zKYlp+02wm73W43GzdupKysjK6uLvR6PSUlJZSWljJy5EgMhiMv7j3yeixJkiQdUdzdXbz9l4doq9rM+FPPYNols6lvuIMB9zqczkmUTvwnTufE4e6mJElHsJbW59my5df0+hLYsv5MTI3NFJSM4rwf/wK9wcCy2h5ueGY1GU4Lz984a6dZzY8V4VYvrjdrCTe6MWY7SLpyDOYRTgC0YJDehQvp/ecTKBYLGffcTcL8+Sh6GXTuiaLXY0hJwZCSAqNG7XY9VQ3R2/0xHa1v0Bv3OZZZLgq1CA53EdFaJ91bVNq9HvqEAh5I7uun2Ps5Dk1HyJlAZ3omFcmFtIgEmlQn67DzWDhE0pYBxm4xUoiOIvQUoWNEvBVrhh1Dqm0wuLZiTLOhs8ma1pIkSZK0Pwa6OnntD/diT0jgwp/9CqM5djzp8WymvOJHxDnGMG7sIyjK7o+dmvv8vFfRwXsVHaxu7EcIKEixc+NJhcwbl8HEnN1P3BwOh6msrKSsrIy6ujqEEOTk5HDOOecwbtw4bLZDN/hiaLLsA2iPAbWiKLnAs0AGoAELhRCPKoryEHAeEAZqgeuEEC5FUfKBzcCWwV0sF0J874D3XJIkSTrsVS1fypLH/4oQGnN/+D30KV+yfsPlGI1JjBnzIJkZF6Eoh8eswZIkHXmEEDQ2/oPauofpCmRQve5UDF3djMjO5MKf3oXBZOKzqm5ufHY1eUk2/nPjTNLijr1wWvWGcb/XiG91Bzq7kcSLi7FNTR8qDeH9fCkd991HpKkJ5/nnkf7zn8fCVumA0uvNpGXMIy1jHqoapLfvU7q63qXH+CFqgh/nrCRGps4lznYCrno9jWXrady4ng73ABDB6G+nJNxIvkGPYknGZc+jWZ9Cm+pkU9TCF4oNMfiZanR5yXd1U7TFSCF6RqKnEB2pdhOmdDuGNBvGVGvsPs2Gzmk6Yi4BliRJkqRDLejz8uoD96BGI1z6699jT4hdYRUKdVK24QYMhjgmli7EYLDvtK0Qgi/revnLh9Usr+sDYEymkx+fVsJZEzIoTnPs9jNY0zQaGxspKytj06ZNhMNh4uPjOfHEEyktLSU5+dBegSwiGv71XXi/aD3g+96bEdRR4CdCiLWKosQBaxRFeR94H7hDCBFVFOVB4A7g9sFtaoUQkw54byVJkqQjQiQU5JNnnmDDh4vJKB7J9CuKae/5NWq7l9zc6ygsuAWDIW64uylJ0hFMCI2amgdoan6Sfq2ELaunYvB4yY2z8a3b78ZosfDh5k6+/9xaitIcPLdgBsmOw2/G8oNJqBreL9txf9CICGs4ZmfjPD0PnSX2FSDS2UXnA7/H8+5iTPn55D39L+yzZg1zr48Ner2FtNS5pKXOjYXVvZ/S2fU27R2v06q9gNGYROHcecz6zg9QPek0lW+kccM6mjeXY/SGQNeDPdWNPT5KfpwdqxZHfCQRr5ZIv7DRr1np16x8rllYrGw7KRPnURnp7aGozkQRBorQU4AOm9kwVCLEmGYdGnmtT7LIGteSJEnSMU2NRnjz4d/h6mjnkl/eS3JOLgDRqI+yshuJRj1MnfISFnPGDtsJIfiytpc/f1DNyoY+0p1mfj5vFOdMyGRE8s5B9vZ6enooKytjw4YNDAwMYDKZGDduHKWlpeTl5aHTHdpBXkGXj87PqhFr3RiCCj1x7gP+GnsMqIUQ7UD74GOPoiibgWwhxJLtVlsOXHLAeydJkiQdcbqbGlj05wfpa21m6iXTseStoLnjLRITj6ek5Nc47MXD3UVJko5wmhalsvIO2jteJWw9gXVLsjAHI2QR5ZI7f4fZZmdxeQc/fGEtYzKdPHv9DBJspuHu9iEVrOrHtaiWaFcAc0kiCecWYkyLXfopolH6n3+B7kcfRUQipP7oFpIWLEBnOrZ+RocLvd5CWtpc0tLmoqqBbWF1+2u0tj6P0ZhM2sh5nHr82Tjsd9BRXUXjxvU0bliHtboGhBsUN9H4boRTw2+JkGQzkWy0YVcdOMKpDEQTcQkr/cJKi2ajXNOIKLGvgooQpPvDFDfqGdlsphATRejIRofeoMOYsq1EyNYR14YUK8ouJmuSJEmSpKOJEIIlj/+V5ooNnHXzT8gdN3GwXaVi0614vJspnbiQuLgxO2zz1WD6N+eP47LpuViMuy//4ff7KS8vp6ysjNbWVhRFoaioiNNPP51Ro0ZhOgTHaREtQpO7iVpXLbWuWjxNPYysSmNybwl6oWOlYyNv5H1Mb5r/gL/2PtWgHizfMRlY8ZVF1wMvbfe8QFGUdYAbuEsI8fku9nUTcBNAXl7evnRDkiRJOgwJIShb8g6f/PsJ7ClGTrglGW/oWdRoFhPG/53U1Lny8mFJkr4xVQ1SXvEjeno+wJ76bd573Y8tqict2M9ld/8ea5yTt8ra+PFL6ynNiefp62fgtBw7dXejvQFci+oIbu5Dn2wh+eqxWMYkDf39DWzYQPs99xDatBn7iSeS8au7MMlj8cOGXm8lLW0eaWnzUFU/Pb2f0NX5Du3tr9Da+h9MphRSU+cxbu7ZzL7sSoI+Py2bNtLVUE9PUz09TY2kNHeAiAADYPQSSuxFdYQJmiOkWMykGWw4ok7MoUzc0XhcwkqfZmODZmWp0CMUFQCTppLnD1LcYmBkh5UizBShJxEdKGBItmJI/UpwnWZFZ5bTHEmSJElHhy9ffoFNn33E8ZdeydgTTx1qr67+HT09H1JScg8pKbF2IQTLanv58wdVrGroJ8Np4d4LxnHptN0H09FolOrqasrKyqiqqkLTNNLS0jjzzDOZMGECcXEH56rjrUF0jauGOldd7H6gjgZ3A5qqcZxnIhf2zWF84BRC+ggNRb2Ep1gZN2IO58YvwGqwonBgv9vv9dGDoigO4BXgx0II93btvyRWBuQ/g03tQJ4QoldRlKnA64qijNt+GwAhxEJgIcC0adMOfHVtSZIk6ZAJeD0seexRatcsY9RcK7b8TfgjKvn5N5M/4nvo9dbh7qIkSUeBaNRD2Ybv4nKtJKfg5/zjv+UkRJNI9HRyxV33YU9I5JU1Lfzs5TKm5Sfx1LXTcRwjYZkWUvF83Izn8xYUvQ7nvHziTsgeGuWqut10PfIIrhdfwpCaSvafHyFurjxxeDjT622kp51NetrZsbC652M6u96hvf1lWlufw2RKjYXZxWdTMOXCobqX4WCA3uYmupsa6Bm8xTc1kOX1EPva5gZHCH9iD6otQNAcId1sJUdnxxFNQhfMxB1xxkqFGKx8ohl5Fw0IAOCMhiiIBihpMzCy20HRZisFGDAPflHVx5sGa1zvGFzrHXKEviRJknTkqPj0Q758+XnGnXw6sy66fKi9ueVZmlueJjf3OnJzrtrnYFoIQWtrK2VlZZSXlxMIBLDb7cycOZPS0lIyMjJ22mZ/RdQIje5GagdiI6K3BtKN7kaiIgqAgkJOXA5j7aO5wTef0Y3ZmL06dAkm4ubkYJ+eTpHl4B9P79UrKIpiJBZO/0cI8ep27dcA5wKnicEpHIUQISA0+HiNoii1QAmw+gD3XZIkSToMtGwq5+2//RG9o5nJC7xouh6Skk6npPiXWK1yVJ4kSQdGONzD+vXX4/VtYfSYh3j41Y9JCCRjc3dx9S9+jTMllRdXNnHHaxs5viiZf149DZvp6A+nhRD413cz8G49mjuMbUoa8fPy0TvNQ8vdixbR+eAfUPv6SLzqO6Tecgt6h2OYey7tC73eRnr6OaSnn0M06qO3NxZWt7X9l5aWfwNgNCZisWRjsWRjteSQWJJN5sQiLNaTsZizCHki9DQ10N3cSE9jPd3NjcTVNlEQVQAVdB5EYhRPfAeqxUvIpGI1WynAgS2cjghl4o7E0a+30mK0US6sxMZaB1CERnrER2E4QInfSHGfk5HVDjIxoBsMrnU2w7bAOtWGMX0wuI43yxMlkiRJ0mGlqXwDSx7/K3njJ3LGTT8Y+pzq6fmYqqr7SEk5nZFFv2BpdQ9//qCK1Y2xYPq+C8Zx6fRczIYdg2khBL29vWzatImysjJ6e3sxGAyMHj2a0tJSCgsL0et3X/5jT7YG0TUDNUPlOWpdtTS5m3YIonPjcilMKOTUvFMpjC9kZMJI8iKZhFf04P+iExHWMBXEE/etLCxjkg/pPBR7PGpXYv8VngQ2CyH+tF37PGKTIp4shPBv154K9AkhVEVRCoFioO6A91ySJEkaVpqqsvzVF1m75N+MOKUfW2YPFlsBJcUPkZx80nB3T5Kko0gg0MK69dcQCnVQOnEhf1/yAc7uJIxeF9ffdjuJGVk8+2UDv36jglNGpfLYd6Z+bY2/o0W4xYPrzVrCTR6MOQ6SvzMGc55zaHmorp6Oe+/Fv3w5lokTyVv4OJaxY4exx9KBYDDYSU8/l/T0c4lGvfT1fYHfX08w2EIw2IrPV0Nv7ydoWugr2zljAfaIbApHZTPOMgmT6SwiXiOezjC9zd30NDXS09SIsy5I7KuihmIOEE3uwBXXRNg6gMMEdpOVkqgTSyQHNZjOQMRBv97GRrOTZcIMKEAAkxYhJ+SmMBxgtNtIyUACRQ3xxLNtNLVi0g1NymgYrJMuSZIkScOlt6WJNx++n8TMLM677U70hlipOI9nE+UVt+BwjMFl/hXzH1/JmsZ+MuMt3HfheC6dljMUTAsh6O/vp6Ghgfr6ehoaGvB4PACMGDGC2bNnM3bsWCwWy277sSsRNUKDuyEWQA98fRBdlFDEnLw5FCUUURRfREF8ARaDZah/oVoX3rfa6N+yEXQKttJUHLOzMWUPzyCGvRlWMhu4CtioKMr6wbY7gb8AZuD9wTMJy4UQ3wNOAu5VFCUKqMD3hBB9B7rjkiRJ0vBx93Txzt8fRLV/zujLXBgMZgoKbic391p0OnkJryRJB47XV8369deiqn4mT36W19euQd0M+rCfa2/6Him5I3ji8zp++/Zmzhibzt+umLzTqJWjjeoJM/BeA/41nejsRhIvKcY2JX1olIsWDNK7cCG9/3wCxWIh4567SZg/H+UbjMyRDk8Gg4O0tLk7tQshiER6CQRbCQZbCQZaCAbbCARbCASa6O//ElX17bCNPtNOZkE2BZZsTIZSogErwX4Fd0eI3sYBbLXtJPu3fpEW6OIjhBKb6XFUE7T0EWfWE6+3YYskYA7nEw6lxoJrg5XPrU4+YGst+CDOaB95wQGKQn7GYKDYnURBaxImZEkwSZIkafj4XP28+sA9GEwmLvrFPVjssbA2GOpgfdmNbO6fyJLyG1nXXLZTMO1yudi8XSA9MDAAgN1uJz8/n/z8fEaOHEliYuIe+xFWwzS4G3aoD13jqqHJ3YQqYtcv6RRdbER0fCGn5Z1GYUJsRHS+M38oiP4qEVHxr+vG80Ur0U4/OoeRuDl5OGZloo8b3u/xewyohRBLYZeVr9/ZzfqvECsHIkmSJB2FqlZ8wfJ3f0PqlCZMjggZ6RcwcuTtmM3pw9213drcu3m4uyBJ0n4YGFjP+rIF6HQmpk55kTVNLdR/UIdO0XPppZeQXTKav39cw0PvbeGcCZn8+fJJGPW64e72QSOiGt4v23B/0ISIaDhOzMY5Jw/ddnUBvZ8vpeO++4g0NeE8/zzSf/5zDCkpw9hraTgoioLJlILJlEK8s3Sn5UIIolFXLMAOxELswOAI7GCwlYGBNUSjbtCDkg0p2ZB2ggWTMR1FTSDitxDoA3d7gMRmL7kDZqJ+A4peQUlW8SfU0WnfSMDRR7xJTwoOrKE0jJF8QqFkXDo7bUYnmxzpvEHs36xOhEgNdx3qH5UkSZIkARAJBXn9D/fidw9w2d0P4ExNi7VHvDy75Df8t+JKalwjyIpX+e2F45lXEk9rcyOL315PfX09LpcLAKvVSn5+PrNnzyY/P5/U1NTdlrLaGkRvXx+6xlVDs6d5pyC6KL6I0/NOpyihiJEJIxnhHLHbIPqrVHcI75ft+Fa0o/mjGDPtJF5Sgq00FcV4eBw7H/2F+SRJkqQDIhIO8ekLD+LV/Y/sk/xYLcWMHftbEhKmDXfXdkkIwerO1Tyx8QmWtS0b7u5IkrSPevuWsnHj9zGZUpg86RnaB4J89O+3UExO5pw4i1GTp/PI+1U8+mE1F07K4o/zSzEcxeF0cEsfrkV1RLsDWEYlEn9uIcbUbeUQIp1ddD7wezzvLsaUn0/e0//CPmvWMPZYOpwpioLRmIjRmIgzbvwu14lE3EOBdax8SNtQiB1VqjCa+knOgOTJW7cwQNRJxGfC3wsjelQmuYyEPQpqBMK2MO74LbTFDeDV95JgNpGlOjEHctFHcgmEknDpZYkPSZIk6dDTNJV3/vpHOupquOCnd5FRVIwQgs+quvjdW++xpedc0hzwf9MTKdL30LryDf62OFYswmKxkJ+fz6xZs8jPzyctLQ2dbsdjUiEEzZ5mynvKdyjN8dUgOi8uj8L4Qs4YcQYjE0ZSlFBEfnw+Zr15v95XuMWDd2kr/g09IASWMck4ZmdhLow/7OZ/kAG1JEmStEedDRV8+eHNOEY0EYeV4pF3k5t7JYpy+F0urgmNT5s/5YnyJ9jQvYEkSxI/mvIjbuTG4e6aJEl7qbPrHSoqbsNuH8mk0n8RDOt55k+/Q7FlMHZ0HiecdhZ/eG8L//iklvlTc3jg4onoD+EkLodStCeAa1Edwco+DClWkq8dh3V00tByEY3S//wLdD/6KCISIfVHt5C0YAE6kyy3JH0zRqMTo9FJXNyYXS6PRn3bBdg7jsC2p7YSDvfssL7QFCI+IyG3gbDXSNijomkGAvo2emydVNt9ePS9h+KtSZIkSdIOPv33U9SsWs6p136Xoqkz+LSqmz+9V0lZq5ski8L5WeuJ743i3yioMpsZMWIE06ZNo6CggPT09J0CaYBOXycrO1ayvH05KztW0uHrALYF0UUJRZyZfyZF8UXfOIjenlAFgU09eJe2EW50o5j1OI7LxHF8Fobkw7eUlgyoJUmSpN3SNJVVH/0GV+glHPlR4i1nMGnG7zEa91w361CLalHerX+Xp8qfosZVQ7Yjm1/O/CUXjrwQi8Fy1AfUiqLkAs8CGYAGLBRCPDq47IfAzUAUeFsI8fPB9juABcTmjLhFCPHecPRdkrYSQqO17UW2bPk18fFTKZ34T9DM/OW3N6PYsklJszP/suu4b9Fmnvqinitn5nHfBePRHYXhtBaK4vmoGc/SVhS9jvizCnDMzkIxbPsCFNiwgfZ77iG0aTP2E08k41d3YcrLG8ZeS8cSg8GOw1GCw1Gyy+WqGiQYbCMYbBkMr9sIBprxehsJBlpQtV5QYiH2SGCmBhGfgU8P4XuQJEmSpLXvvsnad95g/JnnUm7I5LbfvUODB5wGL1ePXUQhXgy6iymYWkB+fj4ZGRnodzGvx0BogJUdK1nRvoIV7StocDcAEG+OZ0bGDG4YfwOT0iZREF+ASX/gBxJo/gi+1Z14l7WhukLokyzEn1uIfVr6DuXgvolwn4vaFz+g8cu6A7K/7cmAWpIkSdql7o4vWbfqVvT2boikMHHyn0nLOm64u7WTYDTI6zWv83TF07R6WxmZMJLfnfA75hXMw6gz7nkHR48o8BMhxFpFUeKANYqivA+kAxcAE4UQIUVR0gAURRkLXA6MA7KADxRFKRFi8BozSToEVDWI272BgYE1uAZWMzCwlmjUTXLyqUwY/1cQRh6572ZUcyZGK3z3xlv51RvlPLe8ietm5/Prc8cedpcnflNCE/jXdTGwuAHNE8Y2JY34eQXondu+yKhuN12PPILrxZcwpKaS/edHiJs796j7WUhHNr3egt1eiN1euMvlmhYiGGwnGGzF522kv6cSj74WqDq0HZUkSZKOSYFAgOUfvs8XS96lffQcXqhJpKe6E4cS5qK8ZuaW/J34uJlMm/oSBsPOgbI/4mdN55qhULqyrxKBwGqwMjV9KpeUXMKMjBmMShqFTjl4Zegi3X68X7ThX9OJiGiYC+NJOK8Iy5ikoQm095fQBN3NHure30DzujZ6wk40fRKKLeHAdH47MqCWJEmSdhCNeli/6he4/IvRFAP20OUcd9696HZxlng4ecIeXtryEs9teo7eYC8TUydy+/TbOTn35IN6AHC4EkK0A+2Djz2KomwGsoEbgQeEEKHBZVtnoLoAeHGwvV5RlBpgBvDlIe+8dMwIh3sHw+g1uFxr8HjKESICgN1eTFraWSQmzCQt7WxAx5MP3I5PJIFB4+bv/4y73tjES6ub+e7Jhfxi3uijLpANN3twvVlLuNmDMTeO5KvGYM5zDi0XQuBetIjOB/+A2tdH4lXfIfWWW9A7HMPYa0naPzqdGZstH5stn6Sk2eQODf5/fji7dUgoivIUcC7QJYQYP9h2H7HPZg3oAq4VQrQNLpNXPEmSJH1DwWCQpqYm6uvraWhooK2tnVYtnvX2U+gJOUixKvxsZhbzpytsKv81Nttopk55DP3gaOewGqasu2wokN7YvZGoiGLUGSlNLeX/Jv0fMzNnMj5l/EEfKCWEIFTjwru0leCWftAr2Cal4ZidhSlr/48LhRAMdAdoqeynqayd1sp+wmosB7AHohQkd1Nw6jjyT5vADxYeqHcTIwNqSZIkaUh7+9ts2ngnQufFXZfLzFMeIbtk8p43PIR6A708t/k5Xqx8EW/Ey/FZx3PDhBuYlj7tqAur9peiKPnAZGAF8BBwoqIo9wNB4KdCiFXEwuvl223WMtgmSQeEEIJAoAGXazWugTUMDKzG768HQFFMOJ0Tycu9noSEacTHT96hdJDQNF569D7aB0CzGbj62uu5591aXlvXyi1zRnLrGSVH1b931RNmYHED/jWd6OKMJM4vwTY5bYdRL6G6ejruvRf/8uVYJk4kb+HjWMaOHcZeS5L0DTwN/I1Yaa6tHhJC/ApAUZRbgF8D35NXPEmSJO2fUChEc3PzdoF0G0IIdDo9fmceHwWK6dIlkOU08fvTR3HxlBy0aCerV1+MwRDP+PGPUdlfz/L25axoX8G6rnUE1SA6RcfYpLFcM+4aZmTOYHLaZKyGQ1PbWQur+Nd34V3aRrTLj85hxHl6HvaZmejj9q9siN8dpmVLHy2b+2mp7MPTFwLAHOonqa+SjMQIhWdNIf38b6GzHrz3KQNqSZIkiVCok7K1P8UTWEbAZcYavpLzrr4To8Uy3F0b0upt5enyp3mt5jXCapgzRpzB9ROuZ1zyuOHu2mFFURQH8ArwYyGEW1EUA5AIzAKmA/9VFKUQ2FW6J3azz5uAmwDyZH1baTc0LYzHUxEr1eGKjZKORGKzmxsMCSQkTCUrcz7xCdNwxo1Hp9v1JDBCCN594q/UNnShJaZy6jmn8aelfby9oZ2fnlnCzXOKD+XbOqhEVMO7rA33h02IqIbj5Bycp+buUCdQCwbpXbiQ3n8+gWKxkHHP3STMn49yCK9qEULQ1ejB5wqRnO3AmWI5qk4QSNKhJoT4bPBk8vZt7u2e2tn2mSyveJIkSdoL4XCY5uZmGhoaqK+vp62tDU3T0Ol0ZGdnc8IJJ9CpT+X5sn7K2704hZe7Ts3m6tMmYjLoiEQ8rFx3NaGIi0/FTH7++sV4wh4ARiaM5KLii5iZOZNpGdNwmpx76M2BpQ6E8H7Zjm9lO5o/ijHTHhvQUJq6w/wkeyMcjNJW7aKlMhZI97b6ADDqoiS6qsjs2ECK0kXWWSeReMmVmPLzD8I72pkMqCVJko5hQmg0Nz9PddXv0LQw/ZvzmXHqg+RPnDbcXRtS01/DU+VP8U79OyiKwvlF53PduOvIj88f7q4ddhRFMRILp/8jhHh1sLkFeFUIIYCViqJoQMpge+52m+cAbbvarxBiIbAQYNq0absMsaVjTyQywMDA2sHR0Wtwu8vQtNiIC6t1BCnJpxCfMI2E+KnYbIUoXym9I4RAjUaJhkJEQkEioRDRcIiKzz6kbM16IpkjKCgt5vnNVpZsaufOs0dz00lFw/FWD4pAZR8Di+qI9gSwjE4i/pwCjKm2Hdbxfr6UjvvuI9LUhPP880j/+c8xpKQcsj72d/ioWtVJ9cpOBroDQ+0mi56U3DhSch2k5MSRmucgMdOOXn/slVeSpANp8Gqnq4EB4NTB5r2+4kmeUJYk6VgSiURoaWkZCqRbWlrQNA1FUcjOzub444+noKCAnJwcvqh38ecPqtnQ0kyiEuS03pXcefMVWIpSeLv+DVa0LSM/8A6FxiD/7DHjMbRzxogzmJkxkxmZM0ixHrrjr+2Fmz14lrYS2NgDQmAZm0zc7GxMBc69Hiygqhqd9W5aNvfRsqWfzjo3mibQGxRS4yOMDpfh2PgBcb5W4k4+iYQFl+E46SQU46Gdz0kG1JIkSccon6+OjWW34QtuxNNuwxq+igtu/Clmm324uwZAWXcZT2x8gk+aP8FqsHLFmCu4euzVZNgzhrtrhyUldoTyJLBZCPGn7Ra9DswBPlEUpQQwAT3Am8DziqL8idglw8XAykPaaemAaK9fw6a1fwMEiqIfvOlQdHoUDLF7nR6dokdRDOh027XpDCi6WJtOMaDoDeh0+sE2Izq9Pnaviy0Lhhrx+TfiD20iorUAAoQOnZaNEp6KLpCJ5kvD7TfQGwoRDVUSCZVtC6HDoR0CaaFpO72fqM1BMK8EW4aTj13FfLKlk3vOG8u1swsO+c/2YIj0BBhYVEewsg9DipXk68ZhHZW04zqdXXQ+8Hs87y7GlJ9P3tP/wj5r1iHpn7c/RPXqTqpXddLd5AEFckYlMvWsESRm2ult8dLT7KW72cOmpW1Ew7H/hjqDQlKmndTcuO3CawemAzRrvCQdC4QQvwR+OVhz+mbgbvbhiid5QlmSpKNZNBqltbV1qGRHc3MzqqqiKAqZmZnMmjWLgoIC8vLyMJvNCCH4eEsXt/5zFRtaBshJtHJ5fDNJ698hfOYIbq75BS3rY8ezV6boKbYG8SdcxCMzfkROXM6wvU+hCgIVPXiXthJu8qCY9TiOz8JxXCaG5D2X2BCaoLfNR0tlHy2V/bRWu4iGVBQFUvPiGD/ZirN+BcYPXkJx92PMyyPhhouJv/BCjOlph+Ad7po8YpQkSTrGaFqYhoaF1Nf/lWhYo7esiBmn38fIaTOHu2sIIfiy7UueKH+CVR2rcJqcfL/0+1wx+goSLAnD3b3D3WzgKmCjoijrB9vuBJ4CnlIUpRwIA9cMjqauUBTlv8AmIAr8QNazPPL43N2sX38tpkQ/EEss9imR0AZv+0AN6fB1WfG1p+DrtOLrsqKpelDc6E1hdJYODGYLOpMZncmEzmhCZ7GjOBLQGwzo9Xp0qAgtQlCLENIihLQwIS1KVFOxafHobEbKdMfxZVU3v/vWBK6YeeSPBNSCUdwfNeP9ohXFoCP+7AIcx2ftcFmmiEbpf/4Fuh99FBGJkPqjW0hasACdaf9qCu6toC9C3bpuqlZ10FrlAgFpI+I4YX4xI6elYY/fVo4loyB+23vSBANd/qHAuqfZQ/2GHjYvax9aJz7VSkpubJR1Sk4suN5+f5Ik7dLzwNvEAuq9vuJJkiTpaNPZ2cmWLVtoaGigqamJaDQKQEZGBjNmzCA/P58RI0Zg2a40pRCCDzd38uiH1WxoGSDVqXDKlBYcW94ltTzKumIXddYepiVO4ztjv8MYXRv9rY+Rl7uA4uI7h+utovkj+FZ14F3WjjoQQp9sIf68QuxT03co/7Yr7p4ALVv6h0ZJBzyxScgT0m2MnpVBdp4ZR/Uy/G/8jdDmzShmM3FzzyTh4kuwTZ+Gohv+q+BkQC1JknQMGRhYT/nGnxEM19FfF4clfCHf+sFtWOMObQ2tr1I1lQ+bPuTJ8ifZ1LuJNGsaP532U+aXzMdmtO15BxJCiKXsepQVwHd2s839wP0HrVPSQSWE4PP3rsGU7Kd84xxcrgwURaAosZg69lgDRaAMPt/+sU6noNeDooBOB3rdtsexbUGnE+gUUBQNRYFw2IHfn4iqCaKoRJNVovHRPXdWBVQVQup2TSqqTkVTQGewYLUYMBvNmK1OloVLWVffxx8unsj8abm73+8RQGgC/9ouBhbXo3kj2KamEz8vf6eJbAIbNtB+zz2ENm3GfuKJZPzqLkwH8RL9aFilYWMvVSs7aKzoRYsK4tOsTD+ngJLp6SSk7/lvr06nkJhhJzHDTvH0dCD2e+lzhelp9tDT4qG72Ut3k5vatV1D21mdJlJzHbGR1jkOUnPjiE+17jAppCQdaxRFKRZCVA8+PR+oHHwsr3iSJOmY1NLSwlNPPYWmaaSnpzN16tShQNpm2/k4RQjBuxUtPLSkgvouFbPFgzVzCYH4NXjrnEwoj8c4MZfbb/o9Y5LHYNAZ6O7+gA0b7yY19UxGjvzFMLxLiHT58S5rw7+mExHRMBfFk3BBEZbRSbs9Ngp4w7RucdE8OEraPViKzRZvIndsErmjk8guiUdXvRHXK0/ieWAJ/aEQ5rFjSP/1r4g/91z0zuHNAL5KBtSSJEnHgGjUR23tw7S0PEvEZ6BzTQmz5t3JqONOHNZ+RdQIi+oW8VT5UzS4G8iLy+Oe4+7hvKLzMOkP7ohBSTrSrfrotxiTt9DUNJ6SkovIy8tD07Qdbqqq7tS2vzdVVTFb9SSlGDEajRgMRjSdgk8N0R/14Ap76A0P0BN20R3oxx31E1UEKgoCPU5LIgnWVJIsqSRYUki0pOI0JmLU2YiqGuGoRlgVfFbbQ0Wbmz9fNokLJu2yzOo3IlSNSLuPaF9wxwXKtgc7lfTbbtlOp4EU2GmDwdVERMP9aQuRZg+mvDgSrhmHKTduh1VVt5uuRx7B9eJLGFJTyf7zI8TNnXtQJiHUVI2WLf1Ur+ykdn03kaCKLd7EhJNzKJmRTmpe3Dd+XUVRcCSacSSayZ+4rV5jKBClt8VDd5OXnmYP3S1eWjY3oWmxMf9Gs56UHMdQeZDU3DiSMu3ojcM/okeSDjRFUV4ATgFSFEVpITZS+mxFUUYRu66lEfgegBBCXvEkSdIxJxAI8PLLLxMXF8eCBQtw7iZMjWgRyrvL+c+aDby3Vo/Hm4hi7MWW9SmTCyMclz2DUd6zKF/yH7LHjeXi23+D3hCrrez2lFNe8WOccRMYN/ZPO82XcjAJIQhVu/AsbSVU1Q8GBdukNByzszFl7lxyMxJWaR+c2LC5so+eFi8IMFr0ZJckMvHUHHJHJ5GYaSPa1c3Aa6/ReferRJqa0MXFkXDxRcRffDHWceMO2XvcVzKgliRJOsr19HzM5s2/JBTupKciEXN4Hpfc+hPsCYnD1id/xM8r1a/wTMUzdPo7GZ00modOfogz8s5Ar9MPW78k6UjRWrsUV/RZvP5UjIaLmDt3LroDeGmeNxSltstLdZeXui4v1V0eNrf34wqEiagaUVVBCIgVFDEByYO33evZqcUzeNvGpNfhtBr427cnc9aEzAPyXrRAlFCTm3CDm3Cjm3CzBxHZx7om34AuzkTipSXYJqXtMApGCIF70SI6H/wDal8fiVd9h9RbbkHvcBzQ1xdC0NngpnplJ9Vrugi4w5isBkZOSaN4RjrZJYnoDsHIZbPVQFZxIlnF2z571IhGX7tvsDyIl54WD5VfthP5JJa96XQKiZn2baOtB+/NVvkVRjqyCSG+vYvmJ79mfXnFkyRJxwwhBG+99RYDAwNcf/31O4TTmtCo6q9iRfsKlretYFmNB0/nCWjBHMwWN3OmtvGdGSXMzPoLdqOd3pZmXvjrT0nMzOL8n9w5FE4Hg22Uld2IyZjIxIkL0ev3XNv5QNDCKv51XXi/aCXaFUAXZ8R5xgjsMzPQO7YN0NJUja5GDy2VfTRv7qejfgAtKtDpFTIK45l5XgE5o5NIGxGHTq9DRCJ4P/uMlntfxvvZZ6Bp2GbMIPWHNxN3xhnotiuBcriSR3eSJElHqXC4h6qq39LZ9RbBfgvty0dx3Lk/YexJcw7KyLy9MRAa4PnK53l+8/O4Qi6mpk/lnuPvYXbW7GHrkyQdaUJBNxs23IxiM9DSPI8bbpi/3+F0vy9MTbeX6k4vNYNBdG2Xl7aBbaOLdTqBydxP1NCCYvOi1wmSLHYSLU6SbfGkWBNItSeR6UjFabZhMugw6XWYDDqM298PPVZ2uY5Bp3zjvwNCCNTeIKHGWBgdanQT7fLHcnQdGDMd2KdnYBrhxJhu2zYaWnx1P1v/b/vGr66w62XiK8uMGXZ0ph1PvIXq6um49178y5djmTiRvIWPYxk7dl/f7tfq7/BRtbKTqlWduLsD6A068ickUzwjnRHjkzEYh/9koN6oIzUvjtS8baPKhSYY6A7Q0+IdCq6bNvVRubxjaB1niiVW1zp3a13rOOwJJvk5IkmSJElHgTVr1rBp0yZOP/10cnJyaHQ3sqJ9BSvaV7CqYxV9wX5U7xhE39kE/amkOuHmuSO5YkYxRv22Y2Kfq59XH7gHvcHIt26/G4s9NgggGvVStuFGVNXP5Kn/xWxOPejvKdobwLuyA/+qDjR/FGOWPTaAYWIqikGHEIK+Nt9QyY62qn7CQRUUSMlxUHpqLjmjE8kcmYDRvO0YLlRfz8Crr+J67XXUnh4Mqakk33ADCRdfhGnEiIP+vg4kGVBLkiQdZYQQdHS8ypaq3xKNeOlYk4I5OodLb78NZ8rB//DdlS5/F89WPMv/qv6HP+rnlJxTWDBhAZPSJg1LfyTpSPbZO9dgSvCwqXwO3/rWgl3W4NueEIJuT4jqrm0hdHWnl9puLz3e8NB6VqOeojQ7U/PjmWL00xxaRY1vKYqpl0lpEzi/6HyOyzqOLHvWYXOlg4hqhFu9Q2F0uNGN5o1NCqNY9JhHOLFNTMWU78SUG7dTUHyoacEgvQsX0vvPJ1AsFjLuuZuE+fNR9AemX97+ENWrO6la2UFPsxdFgexRiUw7awSFk9OOiJHHik4hId1GQrqNkVO3zSTvGwjR0xIrD7J1Usa6dd1Dy61xxp1KhMSn2Q7J6HBJkiRJkg6Mzs5OFi9eTFFREfZiOxe8cQH1A/UApFrTyDecj9Y9mpZehRHJNn54TjEXTsrCoN9xsEYkFOT1h+7D73Zx2d0PEJ8WmytD06KUV9yCz1dN6cQncThGHbT3IlRBsLIP74r2WBkPHVjHJOM4IRtTvhOfK8SWVZ1DobR/IHZc7ky1MnJ6eqyO9KgErI4dS19qgQDu995j4OVX8K9eDXo9jlNOIeHii3GcdCKK4fA/3tuVI7PXkiRJ0i4FAk1srryL/v4v8Hc5aP2ihFnn/YDSM84alpFlTe4mnip/ijdr30QTGvMK5nH9+OspSSw55H2RpKPBuqWPoEvYQEvzWKbPWEBu7rYJBDVN0OoKULNdEF0zWKbDE9w2kWGcxUBxmoM5o9MoTotjZLqDwhQrraFy3q5bxPuN7xPwBch2ZPN/4y7m3MJzGeE8PEZgqN4w4UbPtpIdrR6IxkYs65MtWEoSMY1wYh7hxJBmO6wm3fN+vpSO++4j0tSE8/zzSP/5zzGkpOx5wz0I+iLUreumamUHrdUuEJA2Io4T5hczcloa9njzN+/8YcAeb8Yeb2bEuG2lZMLBKL0tXroHy4P0NHsp+6gZbfB3wmDSkZwdC6u3lgdJzrJjGOYTFZIkSZIk7SwcDvO///0Pi8WCq9jFte9dS6Y9k1/OuIuIdwwvfjnAZ21uRiTb+OP8XQfTAJqm8s5fH6ajtpoLfvJLMoqKgdigjarq++jt/ZTRo35LcvLBmY9JdYfxrerAt7IDdSCEzmki7rQ8TKUptLf6qVjeQfPTm3F1+oHYSfacUYnkjEkiZ1QizpSdy40IIQiWV+B65WXci95G83oxjsgj9Se3EX/BBRjT0nba5kizx4BaUZRc4Fkgg9iEDQuFEI8qipIEvATkAw3ApUKI/sFt7gAWEJuz/RYhxHsHpfeSJEkSEDsT3NzyL+pqH0GNarQsy8CiHc9lv7yVxIysQ96fyr5Kntz4JEsal2BQDFxUfBHXjLuG3LjcPW8sSdIudbWspdv7D3zBFMzmy5g5cyYAH27u5M8fVFPT5SUQ2TZvVorDxMg0BxdMyooF0WkOitMcpMaZh05Y1bpqeav2ee7fsIhOfycOo4OzC87mvKLzmJw2Gd0hnCzmq4QmiPYECDdsGx0d7YnNUI5ewZTtwHFcFuYRTkwjnOjjDs+JVSOdXXQ+8Hs87y7GlJ9P3tP/wj5r1jfaZzSs0rCxl6qVHTSW96KpgoR0GzPOLaB4WjoJ6V8/qv5oYbIYyByZQObIhKE2NarR3+HfYaR11apOyj9rBWIjtBMzbCRn2bE4TJhtBsw2AyarAYvNiGnwudk62G4xHFYnOiRJkiTpaPXuu+/S09NDS0kLK7as4JyC85jpvJF/LmlhU3sz+ck2Hp5fygW7Caa3+uy5p6hZ9SWnXnsTI6dvO+Zqbnma1tbnyMu7kezsXU0FsP+EEIRqB/CtaCdQ0QuawDwygYTzCtHlO9n4WSvrH1hDyB/FYNaTXZzAuBOzyBmdRHKWfbfHGurAAANvLcL18suEKitRLBacc88k4ZJLsE6bdlSVN9ubEdRR4CdCiLWKosQBaxRFeR+4FvhQCPGAoii/AH4B3K4oyljgcmAckAV8oChKiZxpWJIk6eBwe8qprLwTj6cCT3MiLV+kM+u8BUw55wJ0h/gy/DWda3hi4xMsbV2K3Wjn2nHXctXYq0ixfvNRgpJ0LIuE/axZfSM6q462lrNYcMMlKIrCp1XdfO+5NYxItvPtGXmxEDrdwchUB4n2XQe2fcE+3q1/lzdr32RT7yb0ip7ZWbO5fdxPOS5uBno/aE1hvBUtqJ4wmjuM6gmjeiOIqBY7gNYrsXudsvNz/dZ73eByQK/beflO68fuhSqItHoJN7nR/LGR3zq7AVOeE/v0dEwjnJiy41CMwxeefx3V6yPS3ES4qZnQlkr6nnkWEYmQ+qNbSFqwAJ1p/4J0TdVo2dJP1cpO6tZ3Ewmq2OJNTDg1h5Lp6aTmxR1VX1L2l96gi5X6yHHAcbE2IQSe3uC2yRibPXQ2egj5IoQC0Z1qkO9AYVtYbTVgthmHQu2t7WabcXDZV5bbDIdFrW9JkiRJOtxt2LCBdevWUZNYQ43azNlJD/LlCisv9m7a62AaYO27b7Hm7TeYctb5TDnr/KH27u73qa6+n9TUuYws+vkB67fmj+Bb24VvRTvR7gA6mwHH7CzsMzMRDiMbPm5h/b82EfJFyZ+YwqTTcskoikdv2P37EJqGf+VKXC+/gmfJEkQ4jGXcODLu/jXOc85Bv92kkUeTPQbUQoh2oH3wsUdRlM1ANnABcMrgas8AnwC3D7a/KIQIAfWKotQAM4AvD3TnJUmSjmWqGqCu/lGamp5CRMw0fJyNRZnMt3/9E5Jz8g5ZP4QQfNbyGU+WP8m6rnUkWZK4ZfItXDb6Mpymo/PDU5IOtaWLF2ByuqjcfAoXX/I9LBYLqxr6+O6/V1OcFscLN80i3mrcaTuhaqieCEGXlw3169jcWE5fTxcJ0TiuV84hV3cjzrAdKqOggYuKHbZXLAb0TiP6OBPmvDgUox6haqAJhCZAHbzf/rkqIKKhqdFYuyp2sb6GUIndD26Ptu11DWlWLGOTMefHRkcbUqyHTfgqhEDt7yfc2EikuZlwUzPhpkYiTc2Em5tRe3t3WN9+0olk3HUXprx9/7sshKCzwU3Vyk5qVncS8EQwWQ2MnJpGyfR0skoSZY3lvaAoCs4UK84UK0WTd7wEVmiCcEgl5I8Q8kcJ+6OEAtGh56HtnocHn7s6/UPhdjSs7eZVY/QG3Q6jtHcOuLc9N32lzWQ1yP++kiRJ0lGvob2BV954hRajj0b9FLw13+GlkMrkPBM/nzuauePS9xhMA9SuWcEnz/yTommzOPnqBUPtbvdGyituxRk3gXFjH0Y5AFcIhps9eJe34y/rhqiGKS+OxPkl2CamEFEFGz9tZd2SJoK+CCMmJDPj3ALSRnz9d+NIZycDr72G65VXiTQ3o3M6SZg/n4RLLsYyZsw37vPhbp9qUCuKkg9MBlYA6YPhNUKIdkVRth7tZQPLt9usZbBNkiRJOkD6+r6gsvIuAsEmBmrTafkiiennXcmMC+ajP0STIkS1KO81vMeT5U9S3V9Npj2TO2bcwbeKv4XVsHPdLEmS9k/FqsfRHCtpbR3Fccf9gIyMDMpbB7j+X6vISrDyr8sno1/XzYA7hDo42lnzDN77ttWezsNMHlMRCLDpMcVb0cWZ0MeZ0Dtj97rtHuvjjCjbjf4MlJURbmpGMRpik68YDCgGI4rBgGLQD7YZh5ZvvX21ja3LdDt+ORCaACFAgPI1o0oOBaFpRDs6toXPQ0F0E5GmJjSfb9vKioIhIwNTbi5xc07FmJuHKS8PU14uxrw89A7HPr9+X7uP6lWxyQ7dPUH0Bh35E5MpmZ5B3vgkOSr3AFJ0SiwUthogec/rf5Ua1WLBdiBKcLsQe6eQ2x97HvSGcXX5h4JwoX3d8G0wWfSxEdo2A5ahkHvnoNvmNGNPMGNPMGGyGg6bEzqSJEmS9HWWtSxj4dPvUxcspFlLRufTMW98GgtOKGBKXuJe76ezroZFj/6BtIIizvnhT4euJA4G2yjbcCMmUxITJy5Er9//76laWCWwvhvvinYirV4Ukw77lDTsszIxZTmIhFTWf9zK2iWNBL0R8sYlMePcQtILdh9Mi0gEzyefMPDyK3g//xw0DdusWaTecgtxZ5yOzmLZ7/4eafY6xVAUxQG8AvxYCOH+moOeXS3Y6chLUZSbgJsA8vZjRIkkSdKxKBLpp7r697R3vIIIJ1CzOA+rYSyX3X0b6QVFh6QPITXEGzVv8K/yf9HibaEwvpD7T7ifswrOwqjbeQSnJEn7r69zE629f8IfSsJuvZrJkydT0+Xh6qdW4rQaefaKqYj/bMHV5Qedgj7OSNQGnfoetthqaLK24TUHyMssYGrRDCbmT8botMbKaeylUG0tXQ/9Ee8nnxzYN6fTbQutjbGgWxhMeBy5mPUR7IYQOosFncWMYragWMzodrjfeZnOGmtXzGZ0FguKeXAdiwWdeds9RiMiEiHS0jpUjmNr+BxubibS3IyIRLb11WjElJ2NMS8X25QpmEbkYczNxZSXhzEnJ7bPb8jbH6R6VRdVqzroafaiKJAzOpFpZxdQODk1FqBKhx29QYfNacLm3PfSLUIIIiF1KMAOByIEfbGwe2ugvS3sjj139wSG1o+Edl1B0WDUYUsw40gwY483bfc4FmDbE8zY4s0Y5WSRkiRJ0jDxh4Pc9vYzLF1jxhudhtUIN84u5Jrj88lO2LcQ2d3dxWsP/gabM55v3f5rjIOhbjTqoazsBlQ1wORJz2I2p+5XXyOdPnwrOvCt7UQEVQzpNhIuKMI2OQ2dxUAkrLL+gybWvtdIwBMhd2wSM84tIKMwfrf7DNXV43rlZQZefwO1txdDWhrJN91IwkUX7ddVd0eDvTrSVRTFSCyc/o8Q4tXB5k5FUTIHR09nAl2D7S3A9rNg5QBtX92nEGIhsBBg2rRpXz90QJIk6RgnhKCzaxFVVfcSiQzQX5lH8xd2pp1zCcfNvxKD8eAHw96wl/9W/Zd/b/o3PYEeJqRM4KfTf8qpuacO60RqknS0UqMhVi6/DsUKHa3ncP0NF9HSH+DKJ1ag1yn8+5ppmF6vI9wbwHZ1IR/pv+SturdY27UWBYUZmTM4r/A8Th9xOnajfZ9fP9rTQ/df/4br5ZfRWa2k/uQ24k47DRGNQjSK2HqLREHd8bmIRratE9m6bLu2qDr0XAtH6fbbaPYl0RJMJSJif890IopduHFE+7F7erF1dWHzN2L1tKP3u9FCIVD3c4oTnW5wtPa2Q1DFZsOUl4e5qAjHqadgys0bDKLzMGZmoOgPfJgX9EWoXdtF9apOWqtdICAt38kJ84sZOS0Ne/w3D76lw5eiKJgssYkY45L2fXtV1WJhti+K3x3C5wrjGwjhdYXwu0L4BsJ0Nnrwl/UQjexcisRsM2CLN+NIMGGPN+8QZNsSTDgSzFidJvR7cVm1JEmSJO0NdzDC3z9dx7+W1REO5RCnBLm4UOM315yFw7zvJ+ODPi+vPnAP0XCY+b+6H3tCbNS1pkUpL78Fn7+G0tKncDhK9mm/IqoRqOjFu7ydcP0A6BWsE1JwzMzElO9EURSiYZWNHzaz9r1G/O4wOaMTmXFuwQ6TN29P8/lwL3kf18svE1izBgwGHKecTMIll+A44YTYoI1j2B7fvRIbKv0ksFkI8aftFr0JXAM8MHj/xnbtzyuK8idikyQWAysPZKclSZKOJcFgG5VbfkVv7ycQyqDyzTys5kIuu/tWskoOfi2qvmAfz216jhe3vIgn7GFW5iweOPEBZmTMkJcQS9JB9MX738cY10NV5clcMv8H9Ac1rnxiBcGIxos3zMT5XjPBJjdrZjdz75ofEdbCFMYX8qMpP+LcwnPJsGfs1+tqgQB9Tz9N7z+fQAuHSfz2t0n5v+9jSNqPBG03hBD0NHupWhWrreztD2Ew6SiYlkpBaQrhQJT+Tj+uDj/9nX46egKI7SplOBLNJKTbSEyzEJ9sIj5BhzNOwWaMIEJhRCiIFgwiQiFEMIgWDA22hYaWKXoDxtycwXIceeiTkw/J37RIWKVhQw/VqzppLO9FUwUJ6TZmnFtA8bR0EtJtB70P0tFBr9dhdZiwOkxf+3sjhCAciMYCbFcI38DgrT8WYvsGQvR39OMfCKN9teSIAtY4E/b4WGBtGwywHQlmbPGx0dj2eDNWhzE28akkSZIk7UJjr49/fdHA8yvrCUcVrLYBzrB1MznVyI3XLcBo3PdwVo1GeOtPv6e/vY2L7/zN0DxMQgiqqn9Db99njB51P8lJJ+z1PqP9QXwrO/Ct6kDzRtAnWXDOy8c+LR29I3a1VDSismlpG2sWN+IfCJM9KoG5N44nqzhhx/55fQTWrcW/ciX+lasIVFRANIopP5+0n/2U+AsuwJCSss/v+2i1N78Bs4GrgI2KoqwfbLuTWDD9X0VRFgBNwHwAIUSFoij/BTYBUeAHQoj9HN4iSZJ07BJCpaXl39TWPYymafSVF9O0TM/keedz4revwWg+uPWo2r3tPF3xNK9Wv0pIDXH6iNNZMH4B41LGHdTXlSQJqjY8R8T8Ke1txZxw4q0YbPFctvBLer0hnrthJunLOvFX9vHhuDL+2Ps4c/Pnct246xibPHa/Q1ahqgy8/gbdjz5KtKuLuDPOIPW2WzEXFByw9+Xq8lO9qpPqVZ30d/jR6RTyxidz3EVFFExMxWje9SjlaERloCuAq9NPf4ef/k4frg4/lSu7iAS3HWYazXoS0m2x8DojicQMO4mjbMSnWYe1brOmarRU9lO1qpO6dd1EQir2eBMTTs1h1IwMUnId8oSfdNAoijJYs9pIUtbur6YQmiDgjcRC7K1B9tDjMF5XiM4GNwFPZKdtdXolFlhvDa8Hy4t8NdQ2WvTyd12SJOkYIYRgVUM/T3xex/ubOkHR0Met5/hRXk5xFdHd4ebS+d/FuB9XAwsheP+ff6epvIx5/3creeNLh5Y1Nz9Fa+vzjMi7iezsy/e8L00QrOrHt7yd4JY+ACyjk7DPysRSnDh0AlaNaGz6IhZM+1whsooTOPP6cWSPio3aVr1eAmvW4Fu5Ev+q1QQrKmJX+xmNWMePJ3nBAhwnnYh1yhT5WbgLewyohRBL2XVdaYDTdrPN/cD936BfkiRJxzSvdwubK+/E7V6PEhpJxSsKFksW8+/6MXnjJx7U1272NPPPDf/krdq3ADi36FyuG38dhfGFB/V1JUmKcffXUt/2W4LhRJyOBeQXj+bKJ1bQ0OvnmetmUFjhwrOmk/fyVvCo9iw/mfoTrhl3zTc60PV+8QVdf3iI0JYtWCZOJPuRP2GbOvWAvB/fQIia1V1Ureygq9EDCmSNTKD0tFyKJqdhcez5S4nBqCc520Fy9o4TDgoh8A+E6e/wbRde++moHaB6Vee2FRVwJltISLeTmG4jIcOGI9F80L8caKpGc2U/Nas7CXgimKwGRk5Lo2RGBlnFCejkiFPpMKLolKF62ql5cbtdT41q+N3hrwTZ20Zn97X7aK7sJxyI7rStwazHPhhkxyZ1jAXZ2x7LsjaSJElHunBU452N7Ty5tJ6NrQPYzRCXvgx9wjJ+ftxNZLRn8OnGT7nwwgtJ2c8RxCtefYmKTz7guEu+zbiTt0WT3d1LqK75Pamp8ygq+tnX7kP1hvGt6sS3sh21P4TOYSTulFzsMzMwJGwbDKZGNTYva2fNuw14+0Nkjozn9GvHkJGpJ7B2LZ2vr8K/ahXBTZtA02KB9MSJJN90I/bp07FOmoTOJq+Q25Nju8CJJEnSYUZVQzQ0/p3GxsfRKXZ610+keUWYCXPmcvJVN2A+iB9sW4PpN2vfRK/ouXTUpVw3/rr9LhMgSdK+U9UIX35+NYpF0Nl2Dldccz7XP7OaTW1uHr9qKuPbggx82sKHKatY6Pwffz35r5yce/J+v15wSxVdDz2Eb+lSjDk5ZP/pYeLOOusbB7dBX4S69d1UreyktaofBKTmxXH8xSMpnpaGI/HAXAGiKMpQsJUzescSJJGQiqtrW5kQV4eP/k4/bVX9u6zHe7DoDTryJyZTMiODEeOS0RtlPV/pyKY36IhLshCX9PX/jsPBKP7BEiKxMDu8rbyIK0Rn/QA+Vxg1euj+PUqSJEkHj8sf5j8rmnj2ywY63SEKUmwcX9rIhuATlCQX8uBJT6B36Xnms2coLS1l0qRJ+/U6mz7/mC/++xxjTzyV4y65Yqjd7d5AecWtOJ2ljBv7MMou5kkSQhCud+Nd0U6gvAdUgbkwnvizCrCOTUYxbNtGVTUql7Wz+t0GvH0h0vPszJoQIr7hfQJ3rqJ682YQAsVoxFpaSsr3vodtxnSspaXorPs20aMkA2pJkqTDRn//Siq3/BK/vw59aBIb/hvEZI7jW7/4IYWTpx+01212N7Nw40Leqn0Lg87At0d/m+vGX0eaLe2gvaYkSbu24pMfY3B0UF11Ahddcgs/fLGMVQ19PHr5ZI4LKvS9Xccy53pezH+f5057juLE4v16nUhnF91/eZSB115HFxdH2u23k3jlFehMpv3uezSs0rCxl6qVHTRW9KJFBfFpVqafnU/x9HQSM/Z9osZvwmjWk5obR2rujiNBhSbwDo70PBQSM+yYrfKQWzr2bJ0Ack/1sUP+6A4lRXj8EHZSkiRJ+sZqu708tbSeV9a2EIxonDAyhe+fEc/Lzfey0dPAdROu5YeTf0gkGOGxVx4jMTGRs88+e79eq3nTRt77x6PkjpvImd+7ZWhQRTDYRtmGmzCZUpg48XH0+h1PomrBKP61XXhXtBPt9KNY9DhmZWKfmYkxbcfPKVXV2LK8g9WL6vD0h0kyeZjW/zFxny5BEwKXyYR10iRSfvADbNOnYy2diM5ycMtvHgvk0bIkSdIwi0Tc1NQ+SFvbi5iMmfSumUXz6gFGzz6FOdd/D6tj95fZfhNN7iYWbljIorpFQ8H09eOvJ9WWelBeT5Kkr9ew5TX8LKazo4gTT7yd37zXyMdbuvn9RRM43Wym55kKNtqqWVK6nudPe55ES+I+v4bm89H75FP0/utfiGiUpKuvJuV730WfkLBffVYHaytXr+ykbn2strIt3sSEk3MomZFOal7cYVdjT9EpezX6U5Kkg09RFCx2Ixa7cacSPpIkSdLhSwjBstpenlxaz0eVXZgMOi6clMW1x49gWe/LPLru7yRZk3jizCeYmTkTIQT/e/1/+P1+brjhBszmfS/p1NvazBt//C0JGZmcf9ud6A2xMnHRqIf1ZQvQtCCTJ/8bs2lb2ZBwqxffinb867oQEQ1jjoPEi4uxlqaiM+04P0m4p4+K19ZQtj6CT7UQ525kYsPbpPhqsE2ejO2HN2OfPh3LxIno9qP/0teTAbUkSdIw6up6jy1V9xAO92CKnMTaZ/swGgTn3foLSmbt/WzD+6LJ3cTjGx7n7bq3ZTAtSYcJn7eFLXW/JBSNJ95xE89ujvL2xnZ+efYYzkmx0vHEBppN7aw/uY1/nPAYRv2+TSYjolFcr7xK91//itrTg/Pss0i99VZMubn73FehCTrqBqha1Unt2i4Cnghmm4HiaWkUy9rKkiRJkiRJR61QVOWN9W08tbSeyg4PKQ4TPz69mO/MGkGEPu5ceiurO1dzxogzuPu4u4k3xwOwfPlyqqurOeuss8jMzNzn1/UPuHjtgXvQG4xc9It7sDhiJzU1LcLG8h/i99cxqfQpHPZiRETFX9aDb0U74WYPilGHtTQVx6xMTDnbBn9Fe3vxr1qNd8VKaiu8VFsmE7ClEedrZ4apmvwTs7H/5A4sEyZ8o6sMpb0jA2pJkqRhEAp1sqXqHrq7l2C1FNO3ejxNa9oZOX0Wp9/wA+wJ+z4yck8a3Y0s3LBwKJi+YswVXD/+elKs+zcxhSRJB4YQKl98fCWKJUpn03m0Z43mpdWN3DJnJGfn6Wh7Yh0evZfOCxTunHzXPo1IFkLg/fRTuv74R8I1tVinTiX973/DWlq6542/wtXpZ/OydqpXdeLpC2Iw6sgvTaF4WrqsrSxJkiRJknQU6/GG+M/yJv69vIEeb5hR6XH84eKJnD8pC4tRz7v173Lfl/ehCpX7Zt/HBUUXDB2ztra28v777zN69GhmzJixz68dCQV5/Q/34XO5uOzu3xOflg7EjnOrqn5DX9/njBn9e+LUybgW1eFb04kIRDGkWok/txD7lDR0NiPR7m7c73yOb1VsUsNQTR2dadNoKDgbf1IaCdYQx51oZ9Q5l8kR0sNABtSSJEmHkBAarW0vUlPzIEJEsKnns/LxRvQ6N2f94DbGnHjqAb8cfmswvahuESadiSvHXMl14687JoLpUCjE5s2bKSsrG+6uSNJurfzsdvT2Fmqrj6c/5xKe+ryRa4/PZ06hh+4nqzCgR3w7nUvHzt6n/QY3baLzDw/hX74c44g8sv/6F+JOP32//sZsWdHBx89VoqmC3DFJzLygkILSFEwWeSgpSZIkSZJ0tNrS4eGppfW8tr6VcFTj1FGpLDihkNkjk1EUBU/Yw28+/x2L6hYxMXUiD5zwALnObVfoBYNBXn75ZRwOB+eff/4+H4dqmso7f32Y9toqzv/JnWSMLBla1tT8JK1tL5BlvRrjO2PorFkDOgXr+GTsMzPR2UMEVq+m88FV+FeuJFxfD4Bis9M/7UKqR96MJ2giOcvOyefFjm0VeRXgsJHfKiRJkg4Rn6+Wyspf4hpYRZxjGs2fZdCwqpoREycz93s/Ii75wAbGDQMNsRHT9W9j0pm4asxVXDv+2qM+mNY0jfr6esrKyti8eTORSISE/ayveyRRFCUXeBbIADRgoRDiUUVR7gFuBLoHV71TCPHO4DZ3AAsAFbhFCPHeIe/4Ma6lfjGe6Gt0d+XTl3ozj33exCVTc5hcWIPv30HS1GQM1+QyftSovd5npL2d7j//mYE330IfH0/6L39J4mWXouzHpYmaJvjytVrWv99EVnECZ94wDnu8HFEiSZIkSZJ0tNI0wafV3Ty1tJ7Pq3uwGHXMn5rDdbMLGJm2bb6AtZ1ruePzO+jwd/B/pf/HjRNvxKDbFjMKIVi0aBEul4vrrrsOm233k+buzmfP/YuaVV9y6jU3Ujz9uKH29oZF1NQ+QFzvTBxrTyGaEMB+fDJE6giuX4LrmZWEGxsB0DkcWKdOIf7ii+lMmkjZBpX+Dj9JSXbmnlNA0eRUGUwfBmRALUmSdJBpWpjGxsepb/h/6PVWnMo1LP9/GxFaK6ff8AMmnj7vgI6arh+oZ+GGhbxT/84xFUx3dXVRVlbGhg0b8Hg8mM1mJkyYQGlpKXl5edx6663D3cWDLQr8RAixVlGUOGCNoijvDy57RAjxx+1XVhRlLHA5MA7IAj5QFKVECKEe0l4fwwL+Lioqf0JYdbLJfzP/LuvhrPHpZGZ9iOWVRLLDBcRdXUzyqOy92p/q9dK78J/0PfMMCEHyDQtIvukm9HH7N9Fq0Bfh/ScraNrUx4STs5l9aTF6vSzjIUmSJEmSdDQKhFVeXdfCU0vrqe32kRZn5mdzR3HFjDwS7dsGOkS0CI+VPcYTG58gy57FM/OeYVLapJ32t27dOsrLy5kzZw55eXn73J+Vb7zMmrdfZ/K885hy9gUITRCqcdG15lNqUu7A4ikku/4q0K3H99G7uJ4eDKTj4rBNnUrCZZdhmz4d86hR1G3s54u36+lb7SEx086ZN4xj5JQ0GUwfRmRALUmSdBANDKxjc+Ud+HzVJCeeScPHCaxesZKcMeOZ+/0fk5CeccBeq36gnsc3PM679e9i0pm4euzVXDPumqM6mPb5fJSXl7N+/Xra29tRFIWRI0cyd+5cRo0ahdG4bxPJHcmEEO1A++Bjj6Iom4GvSzYvAF4UQoSAekVRaoAZwJcHvbMSQmgs/fgKdOYwn1TewKtdJk4YmQgpTzPi4zGMDRSR8O0S4sbs+W+EiETo/+9/6fn7/0Pt68N5/nmk/ehHGLP3Ltjelb52H+/8YwOe3iCnXDmKcSfu/74kSZIkSZKkw1eXO8izXzbynxWN9PsjjM928shlpZwzIQuTYcfBCY3uRu74/A429mzkgqILuGPmHdiN9p332dXFO++8Q0FBASeccMI+96ns/Xf5/PmnGT37ZE665Fo8n7bgXdZMMNRB48x70ftNJPzZg7vrNnROJ7Zp00i84nJs06djGT0aRa9HCEH9+h5WPriO3lYvCek2zlgwlpFT0+WE3ochGVBLkiQdBNGol9q6h2lp+TdmcwbJph/zxd+WEQl1cMrVNzDlrPNRdAdmJGLdQB2Plz3O4obFmPVmrhl7DdeMu4Zka/IB2f/hJhqNUlVVRVlZGdXV1WiaRkZGBnPnzmXChAk4HI497+QopyhKPjAZWAHMBm5WFOVqYDWxUdb9xMLr5dtt1sLXB9rSAbTmy1+js9bz/oZv8Ub3KCbm2HAnPMyc9TOZ5Z1IwgVFOEr3HE77vvySjvvvJ1xTi23GDNJ+/nOs48d9o741bOhhyVMVGIw6Lrx1MpkjE77R/iRJkiRJkqTDT3nrAE8treetDW1ENcEZY9JZcEIBMwqSdrrCVwjBazWv8cDKBzDqjPzx5D8yN3/uLvcbiUR4+eWXMZlMXHTRRej28Xtv5Ref8sGT/48J4+cwITKRjvu/BPSEBjbQevJjaFqAvOUnkHTDnNgI6ZISFL1+h77Wre9m1dv19DR7iU+zcvp1YymeLoPpw5kMqCVJkg6wnp6PqdzyK0KhDjLSLqP+IwsrvniPjKJi5v3fbSTn5O55J3uhzlXHYxseY3H9YiwGC9eMu4Zrxh6dwbQQgpaWFsrKyigvLycYDOJwOJg1axalpaWkp6cPdxcPG4qiOIBXgB8LIdyKovwDuA8Qg/cPA9cDuzo6E7vZ503ATcB+XZ4n7aij5WNc/hdZ2XA8r3bPITdFT3fCfVzRdDJnuo4j7rQ8HMdlfe0+Iq2tdD74BzxLlmDMzSXn73/DMWfONyoXJIRgzeJGVrxZR2puHGd9bwJxSZb93p8kSZIkSZJ0eFE1wYebO3lyaT0r6vuwmfRcOXME183OZ0TyziOhAVxBF/d8eQ8fNn3IzIyZ/PaE35Jh3/1AisWLF9PV1cV3vvMd4vax1FzdipVUPv0B5+QuwOFLJuoKEO1agSFpgN7zVhO1hCgtfYbkc3aePFwIQePGXlYuqqe7yYMz1cpp146hZHo6Olmm7rAnA2pJkqQDJBzuoarqPjq7FmG3F5NouIfP//oeAY+HEy6/munnX4xuuzO7++urwfR146/jmnHXkGRJOgDv4vDS39/Phg0bKCsro6+vD4PBwJgxYygtLaWgoAD9Afh5Hk0URTESC6f/I4R4FUAI0bnd8n8CiwaftgDbny3JAdp2tV8hxEJgIcC0adN2GWJLeycc6mX9xluoHSjh2frLSIhT6Uu4n2vdZ3Jh16nYZ2bgPH33JwG0UIjeJ5+kd+E/AUj90S0kXX89OvM3m7gwElL56N+bqVndRfH0dE69ajRGk/z3JUmSJEmSdDTwhaK8vKaFp76op7HXT3aClTvPHs1l0/OIt+6+LOKytmXctfQu+kP9/GTqT7h63NXolN2HvRUVFaxZs4bZs2czcuTIve5fpNNH+5vrUaoDTEs+k+hAM6paQdLlJ2I/+adsqf4VnrZaxox+gOTkHcNpIQSN5b2sWlRPV6MHZ4qFOVePYdRMGUwfSWRALUmS9A0JIejoeJ2q6t+iqn7ycm6m9kONLz55kdS8fC664zek5Rd+49epddUOlfI4moPpYDDIpk2bKCsro3Fw5uX8/HxOPPFExowZg8UiR3TuihIbOvsksFkI8aft2jMH61MDfAsoH3z8JvC8oih/IjZJYjGw8hB2+ZgjhOCzj75DRyiBv224CaMljC/lYb5vOp8Lq47HOi6ZhAtG7nIUtBAC70cf0fn7B4i0tBA3bx7pP/8ZxqyvH2m9N9y9Ad59bCM9LV6Ou6iIyWfkHdCJWyVJkqQdKYryFHAu0CWEGD/Y9hBwHhAGaoHrhBCuwWV3AAsAFbhFCPHecPRbkqQjT5srwDPLGnhhZRPuYJTJeQn8bO4o5o3LwPA14W1IDfHo2kf596Z/UxhfyN9P+ztjksd87Wv19/fz5ptvkp2dzZw5c/bYNxHVCFT04vm8iUiLHyGidHq2kCqayVhwMfbpVwDQ2LiQtraXyB/xfbKy5m/bXgiaN/WxclE9nfVu4pItnHrVaEbNypATex+BZEAtSZL0DQSDbVRuuYve3k+Jj59CvO4aPv7zK3j7epn5rUuZdfG3MXzDifpq+mt4fMPjvNfwHhaDhevHX881464h0ZJ4gN7F8FNVlbq6OsrKyqisrCQajZKUlMScOXOYMGECiYlHz3s9iGYDVwEbFUVZP9h2J/BtRVEmESvf0QB8F0AIUaEoyn+BTUAU+IEQQj3EfT6mrF91P91qH39c9VM0g4qW+RfuyLiWU74cg6nASdLlo3c5k3iorp7O3/0O39KlmItHkvf0v7DPmnVA+tRW3c/iheWoUcG5PyhlxPijr0SQJEnSYehp4G/As9u1vQ/cIYSIKoryIHAHcLuiKGOBy4FxxE4of6AoSon8zJYk6eusa+rnyaX1vFveAcC88RksOKGAKXl7/l5V3V/N7Z/fTnV/NZePupzbpt2G1WD92m2i0Sgvv/wyAJdccsnXXukadQXxrejAt6INza8SDfRS6SujNVjJBTf/kJRZPxhat7PrXWpqHyQt7RwKC28DBss/bu5n5aI6OurcOJLMnHLlKEYfl4neIIPpI5UMqCVJkvaDEBqtbS9SU/MgQqgUjvgFtR+H+PT9x0jMyuHb9z1EZvGob/QaNf01PLbhMZY0LMFqsLJgwgKuHnv1URVMd3Z2UlZWxoYNG/B6vVgsFiZNmsSkSZPIzs6Wozj3gRBiKbuuK/3O12xzP3D/QeuUNKS7czlVXa/xh5W3EUSHLfdx/jjup4xbnIQhzUrKNWNRjDseUKteHz3/+H/0PftvdGYz6XfeQeK3v43yDU96bVX+WSufv1iFM9XK2d+fQGLGrusOSvsnFFV5dW0r/f4wSTYTSXYTyQ4TiTYTyXYzcRaDnKhHko5RQojPBic03r5tyXZPlwOXDD6+AHhRCBEC6hVFqQFmAF8eir5KknRkaXMFuOWFdaxu7CfObOD62flcc3w+OYm2PW6rCY3nNz/PI2sewWFy8PfT/s5JOSft1et+9NFHtLa2Mn/+/F0OLhKaIFTdj3d5O8HKPtAE0c6NeFu/4PO0AFgsXP7QH0nMzCYQaKGr6x06u97B49lIvHMyY8f8AVBoqYyNmG6vGcCRaObkK0Yx5ngZTB8sUVUjrGqEIhqhqEYoqsbuI9oBfy0ZUEuSJO0jv7+RzZV34HKtIDHxeOzRy1ny8P/w9vUy9ZwLmH351RhN+18Ptrq/msfKHuP9xvexGqzcMOEGrh57NQmWhAP3JoaR1+tl48aNlJWV0dHRgU6no7i4mNLSUkpKSjAY5EeTdHQJhwf4ZPlt/Gn9zbgjNlJLXuDxWb8j9eUIisNAynXj0Vm2/d4LIXC/9RZdD/2RaHc38RdfRNptt2FIPjCjm9WoxucvVVHxeRsjxidzxoJxmK3y392BIoTg3fIOHni3kqY+/27X0+sUEm0mkuxGkuym7W5mkmxGkhzmoWA7yW4i0W7EbJB1wSXpGHE98NLg42xigfVWLYNtkiRJO1A1wY9eXEdlh4e7zxvL/Gm5OMx7d4zX7e/mV1/8ii/avuCknJO49/h7Sbbu3bFndXU1y5YtY9q0aYwbN27HPvki+Fd34l3RjtoXBBEkVP0R0faVmM86iRUOK6pf5cI7foQnspia1e/gdq8HwBk3kZFFt5Od/W06agOsfGsTbdUu7PEmTrq8hLGzs9Abj+5gWtVELBTeRUA89HiPyzVCke0e78P6Ue3QTT8kv41IkiTtJSFUmpufprbuT+h0RgpH/IqKRZ1sWfYPknPyOP++O77RqOmtwfSSxiXYDLajKpiORCJs2bKFsrIyampqEEKQlZXFWWedxfjx47Hb5chN6egkhOC9JQv4a/nV9ASSKBz3Hv86/SH0z3QiUEi5fjx6p2lo/eCmTXT89n4Ca9dimTCBnL/9FWtp6QHrj98dZvHCjbTXDDBl7ghmXlAoR/EeQOua+rn/7c2sbuxnVHocz1w/gxn5SfT6QvT7IrF7f5heb5h+f5g+37bHlR0e+n1hXIEIYjffBRxmw1fC7O1utq1Btolku4kkh4k4s0FeiSJJRxhFUX5JrPTWf7Y27WK1Xf6VUBTlJuAmgLy83U+4K0nS0envH9ewqqGfP182iQsn7/15rI+aPuLuZXcTjAa5a+ZdXDrq0r0+fnC73bz22mukpaUxd+5cIHb8G27y4Fvejn9jN0QFItxBsOwtNG8NSVdeju3iZ3jtsd9gzq5mzIlOtjRdCUBc3DiKin5OetpZWK15tNe4WPTXalq3uLDFmzjxsmLGnpCFwXhknLR3+cPUdnup7fJR0+2lxxPaKRgODgXGBz4gVhSwGPSYjTrMBh1mgz52b9z22GE2xB5/zTqx5/odlp/x4AH6IQ3aY0C9mwkcXgK2pjAJgEsIMWnwMqXNwJbBZcuFEN87sF2WJEk69LzeKjZX3oHbvZ6U5DnoXPN45/evEPb7OX7+lcy48BL0hv277L6qv2poxLTdaOfGCTceFcG0EIKmpibKysqoqKggFArhdDqZPXs2paWlpKamDncXJemgW7nyIf5SfhItniymT1zHk+c+gO+paqL+KKk3TcCYEqvnF+3vp/vRR3H993/oExLI/O19xF90EYruwI0K6W7y8M4/NhD0RjhjwVhKpmccsH0f61r6/fxh8RbeLGsjxWHi9xdNYP7UnKHJh3JMNnL2sjpTVNUYCETo84W33fxh+ryD94Ntne4gm9vd9PrChKO7vszSqN86Svsr4fVXg21H7D7RbsIoJxWSpGGjKMo1xL57nybE0KmqFiB3u9VygLZdbS+EWAgsBJj2/9k77/go6vSPv2d7tqX3XklIIKFD6CCIWBDFhr13PT39nZ7eeXfqeXd6trNh711RVCxIkxI6hJJCeu9tk+27M78/NgQQUFACAeb9es1rd2a+M/PMZrPznc88388zcuSxS3uTkZE57myu6uCZpSWcmxN12OK0zW3jPxv/w2cln5ERlMG/Jv6LpICkwz6mKIosXLgQt9vNBRdcgFJU0LO+Aeu6BtwNVlBKiB07sG/8HEHrIvjqq/Cb+w+au5ayaf15RExpQxBAqTGTHPNHwsLOQK9PBMDa5eSH93dRsrEJP7OGCRekkjkxCpVm4AnToihR32WntLmHshYrZS09lDb3UN7SQ2uPq6+dRqUgzKRFp95H9FUpCTJofkEYPhLhWLF3372vKoVwwiQrHE4G9Zv8rICDJEkX7XkvCMJ/ga592pdJkpRzlOKTkZGROa6IopuqqpeoqHwelcpEUuzf2fLpbiq3vUFkWjqn33gHwTG/LUOluL2YBdsX9AnTNwy9gSsGX4G/1v8on8Wxpb29vc9XuqOjA7VazeDBg8nOziYhIQHFURTcZGQGCqIk0tLdREVNEdWVZXS1laJR5fNx3XRKOxOZlVHDc/P+TNvrBbhb7IRclYkmxoTk9dL5ySe0PPU03p4eAi+9lNDbb0NpNh/V+Eo2NrHs7UJ0RjXn3TuC0DjTUd3/qUq3w80LK8p4bXUFAnDb1BRumpJ82MNpD4ZKqSDYqCXYeHhWUZIkYXN5DxCzO2wu2qwuOqy+13ari8J6n6DdZXcfcn8mneoAEXuPsO3vp0anVvZNfmolOrWi93XP5JtXyUK3jMwRIQjCLOBPwGRJkvb1B1oEvC8IwpP4iiSmAhuOQ4gyMjIDFIvDzZ0fbiUqQMc/zs06rG12tu7kvlX3UW2p5pqsa7gt5zbUyiNLuFq1ahUVFRWcOWUWqrVdNGwpQXJ6Uei9eOqXYt/8FeqIEAL/eDHOsRoq27+nc9tjgIRX1BKiOY/Bw2/CYEju26foFdmxoo4NX5Xj8YiMnJ3A8FnxqAeAMO1we6lss/qyoZt79grRrT049vFkDtCrSQk1Mj09nJQwI8lhBpJDjcQE6lHKIxcPya/2ng9WwGEPgk+GvxCYdpTjkpGRkTnuWCw7KCy6j56eIsLCzsRZM5avHv4cgKlX3UjO6bNRKI78QlncXsxL+S/xY/WPGNQGbhx6I5cPvvyEFqbtdju7du0iPz+fmpoaAJKSkpgyZQrp6elotb/dk1tG5njj8rpo7Gmkpr6U2ppyWhoa6G7vxNntQHSC4FUioEFQafEz9xAbuwu/ACsv5F9LgzWCs6Jrefaym2l7rxBXlYWgS9LRpQZi27KFxkcewVlQiH70aMIfeADdoLSjGrsoSqz/spwt31cRmeLPrBuGoN/HUkTmt+Hxiny4sYanluymzepi7rBo7j19EFEBv1zhvj8QBAGDVoVBqyI26NcLIIEv/g6bez+7kT1i9r6Z23WdDnbWWWi3unB5j6wYjkoh+IRrzcFEbCV+asU+Ivf+4rafRolO1butSuGb30cQ318gV8o3ezInHIIgfABMAUIEQagFHgLuB7TAkt5st3WSJN0kSdIuQRA+BgrwWX/cKkmS9/hELiMjMxD56xc7aehy8PGN4zDrfllk9opeXt/5Oi9se4Fgv2BeO/01RkWMOuJjVpZXsGL5ClJ1sUR858KqbERptmLL/whXUR6qIQmoH5+FJaKWqs7/QLmIXp+Eu3EYZT91MumiexkyceZ++2wo7WTlB7tpq+shLjOIiRelERB2eH2bo0mnzdUnQJe17BWja9pt7HHcEASIDvAjJczIuORgkkONPjE61HDYSQYy+/N7PagnAk2SJJXssyxREIStgAV4UJKkVQfbUPbHkpGRGah4vU4qKp+luvoV1OpgEiIfZv17m2koeZ/EnBGcdv2tmEPCjni/Re1FvJT/Ekurl2JUG094Ydrr9VJWVkZ+fj5FRUV4vV5CQkI47bTTGDJkCP7+J+Z5yZw6SJJEj7uHJmsTdc0V1NdX0FxfT3drJy6LHdEuIXiVKCUtglKLpNZA30MpE2BCoRIRlG7MAa3ExRcTEFjHpsbhvLH+NrRqLW9eNZxJg86k8/NSHAVtBJyTjDpCou7//g/Loq9QRUQQ/dSTmGbNOurD75x2D0te30XVjjYGT4xi0kVpcoXz34kkSazY3cI/vymkpLmH0QlBvHF1BkNjAo53aEeESqkg1KQl1KSF8F9vL0kSVpcXi92Nw+3F7vbicHtxuEXsLi8Oj7f3VcTh8u7TRtyn7d7tuuxumi175+0uX9sjFcH3oFEq+oTrg4rbqt7l+4jbAX5qxqeEkBllPmGGvsqcPEiSdMlBFr/2C+0fBR7tv4hkZGROVBZureWLbfX8cUYaI+J/2U+svqee+1fdz5bmLcxKmMWDYx884ntRT6eD1tWVfLLpC0yijgnSINTBLXQvXoCrqxLPmdG4bk+hWyhGYjd+zgQSEm4mLHQ2a9/7nl3LlzD58lsYMnWvOG2zuMhbWEpRXiPGQC2zbswiKSe0X6/PoihR12nvy4Iua7FS1itEt1n3t+VICjGQFe3PuTnRJIcZSQk1khhiwG8AZHWfTPxegfoS4IN95huAOEmS2gRBGAF8IQhCpiRJlp9vKPtjycjIDEQ6OzdRWHQfNlsFERHz6CxIY9ELH6PR6znjtj+SMWHKEV8ofy5M35R9E5dlXHZCCtOSJNHY2Eh+fj47duzAarWi1+sZMWIEOTk5REZGDsgbfadHTjSS2Z9Hn7sLW3UPKkmLUqFFUmmRVGpfOgQBKAhAoQFED0qFiEarwmDUExQUTExUPLFxSYSGR+BwbKKqegGdnRsQFEF8W/8PPtkeQE5sAC9cOpyoAD+6vq/EurER0+QonIXfUnfbC0huN8E33UjIDTeg0B/9zJDOJhvfvLAdS4udyfMHkTXp8AvlyBycokYLj35TyKqSVhKC9bx02QhOzwwfkL95RxtBEDBqVb/LuuRw8IrSz8Rs8YB5u9vrE8E9e4XtfUXwPW3tvdu2W1377693W7fXd/sRbtYyLT2c0zLCyE0OkW82ZWRkZGROGKrbbPzli12MTgjilqkpv9j2m/JveGTdI0hI/HPCPzkr6azD7sNIooSzpIOedQ3Yi9pYotqOXelkrj4S27d/pDWhA9c1ZuzRIhKV+OniiAu/gfCw2RiNGQCsfPd1di5fwtjzLmLkWXMBn0hcsKqOdV+W43Z4GX56HCNnJ6LWHr1rscPtpaLVeoAQ/XNbjkC9mpQwIzMGh++TDW0kOtBPHql1jPjNvUxBEFTAecCIPcskSXICzt73mwVBKAPSgE2/M04ZGRmZfsXjsVJW/ji1te+i00UTF/IIa15bRVvtQtLHT2bqVTegNx+ZoFzYVshL+S+xrGYZJrWJm7Nv5tKMS09IYdpisbBjxw7y8/Npbm5GqVSSlpZGdnY2KSkpqFT9K1ocKR6vyI66LtaWtZFX1samqvbjHZLMAKK6qRx3ox9qPxMKQUSjVqDX+xEYEERURDSR0bGERUbhHxCAWn3gMElJ8tLc/B0FBXfT3bMLrTaCoKi/8M8VKWys7OKKcfE8eOZgNCoFPWvq6F5egyYWWp+9HVdFBcYpUwi//z408fH9cn5Vu9r44dVdKJQC5/whh+i0w6zOJ3NQmrsdPPnDbj7eVINJp+YvZw3m8rHxaORs9KOOUrHXrqS/ae1xsryomWVFzSzaVscHG6rRqRWMTw5hWkYY09PDifDX9XscMjIyMjIyvwW3V+SOD7ciCPDUxTmHFFEtLguPrnuUxRWLyQnN4bGJjxFjijmsY3itbmybmujZ0IC3zYHCqKY0sZ26pnpG2NfRFViN8y+AQkKnMxAXdgFhYbMxmbL2E7/Xff4Rm79eSM7pZ5F74WUANFVa+OmDYpqruokeFMiki9MIijT85s+jw+qitKWnLwt6jxhd02FD2seWIybQj5RQI7nJwb5s6F4hOsggW+Adb35P7+80oEiSpNo9CwRBCAXaJUnyCoKQhK+AQ/nvjFFGRkamX2lrX01R0QM4HHVERc6nYUMYi759H1NQCHP/9BBJw4/Mk6uwrZAX819kec1yTGoTt2TfwqWDL8WsObpFz/obl8tFUVER+fn5lJeXI0kSMTExnHnmmWRmZqLvh6zP34ooShQ2WsjrFaTXV7TT4/QAkB5h4uJRcfz9OMcoM3D45PPXQaVhzJgszjhj3mFvJ4pOGhq/oKrqZez2SvT6RDLS/0W1fRI3fLiDbkc3T1+U01c53bq5ic6vypHctbQ99wjquBhiF7yEcfLkfjkvSZLYuqSadQvLCIo2MvvmIZiDj70n8smC3eXl1VXlvLiyDJdH5KrcRO6YnkKAXr6BORkIMWq5YGQsF4yMxenxsqGinaWFzfxY2MTSomYeYCeZUWamZ4QzPT2MIdH+KOQMKhkZGRmZAcKzS0vYVtPJc/OHEX2IGhibGjfx59V/ptnWzK05t3LdkOtQKX5ZBpQkCVd1N9Z1Ddh2tIBHQpNoRjdMoKTwWVr8qxibWI9CKYIylLiocwgLPxOzaehBM7K3fv81az56h4yJU5l21Q04rR7yviyjYHU9erOGmddmkjIy7LCyuffYcuwrRJc1Wylt6aF9H1sOrUpBUqiRoTH+nDc8muRQnwidFGpAp5ZHSg1UflWgPlgBB0mSXgMuZn97D4BJwD8EQfAAXuAmSZLktDUZGZkBidttoaT0nzQ0fIJen0SU6e+sfn4pltat5MyczcRLrkTjd/gibFF7ES9se+GEFKYlSaK7u5v6+nrq6uqor6+npqYGl8uFv78/EydOJDs7m+Dg4OMdKuCLt6zFSl5ZK2vL2lhX3kaHzQ1AYoiBc3KiyE0OZmxSMCG9RSpkgVoGQJRELNU9CCoDM2fOPaxtPB4rdfUfUFP9Ok5XEyZTJllZzxEaMoPX11Tz2LebiQvS8861o0mP8P2/27Y10/HpbjytRTi2vUroXX8g6KorUWj6R9z0uLwse6eIko1NpIwIY9oVGUd1eOSphChKfLGtjse/L6ahy8HpmeHcd0YGiSG/PatHZmCjVSmZmBrKxNRQHjp7MCXNPSwtbGZpYRPPLSvh2aUlhJq0TE8PY1p6GBNSQ9BrBtbIIRkZGRmZU4d15W08t7yUC0fGcNbQqAPWu0U3L257kVd3vEqMKYa3z3iboaFDf3GfotOLbVsz1nUNuBusCFolulEBWIPXUFH1Dj2eBsgEk1NPRMAc4tLmYzbnIAiHHlFWuGo5y15/ieSRY5h5wx0U5jWS93kZTruH7GmxjD4rEY3foa+n3Q43H22sYVtNJ2UtVspbenB69tpyBBk0pIQaOT3TZ8uxxx86OsBPfqh8AvKrPatDFHBAkqSrDrLsM+Cz3x+WjIyMTP/S0rKEouK/4na3ER15DRXLVKz96QOComK4+G//Jjp98GHvq9pSzXNbn+Pbym8xaUzcknMLl2YMbGG6p6eH+vr6/aaenh7A5zUaFhbGkCFDGDJkCHFxcSgUx38oe027jbW9gnReWRvN3U4Aovx1TM8IJzc5mHHJwUT6yxmjModmSd6XCBp//AMUKJW/LOC63R3U1LxFTe3beDxdBAaMJSPj3wQFTcDq8nLbB/ks3tHIrMwIHr9gKKbequm2HS20f1SMt7McT/VCkr/6AnVkZL+dU0+Hk8Uvbqelppsxc5IYMSv+lPBF7g/Wlbfx6DeF7KjrYki0P09flMOYpIHxYE7m2CAIAmnhJtLCTdw8JZl2q4sVxc0sLWzmm+0NfLixBo1KQW5yMNMzwpmWHnbIzDUZGRkZGZmjTZfNzV0fbSMh2MBDZ2cesL7V3sptS29jV9su5qbM5U+j/4RBfeiH7O4mKz3rGrBtaUZyelFGqhDPrqXN8w0dzvVILhGFQcDalkFZcxznnnsPiYlJvxpn2eb1fPvCU8RmDmXs+bfw5dP5NJZbiEzxZ/IlgwiONh5yW4fby9t5lby4oowOm5u4ID3JoQYmpAT3+UMnybYcJx3yo38ZGZlTCperjeLdf6e5+RuMxgz8pZtZ9uT3OK09jD3vIsbMvQjVYWY4tthaeCn/JT4v+Ry1Us31Q67nqqyrBpwwbbfbDxCju7q6+taHhISQnJxMVFQUUVFRREREHNR391jTZHGQV9bWJ0rXdtgBCDFqGJccQm5yMLnJwcQF6WUxTuawWbd0OQhBzDnnoM/fAXA46qmueZ26ug8RRTshIaeREH8T/v7DANjd1M1N726mqs3Gn2enc/3EpL7voH1XG+0fFCPa6nFseomE99/uV3Ha3uNi0TNb6elwMvvmoSQODem3Y53MVLRaeWxxIT8UNBHpr+Opi7KZkx0tZ9/IEGTQcN7wGM4bHoPLI7Kx0mcFsrSoib98sZO/ABmRZqanhzE9I4zsmAD5eyMjIyMj0y9IksT9C7fT0u3k81tyD6jZ4PK6+MPyP1DeVc6TU55kRvyMg+/HI2IvaKMnrwFXRRei2o1neBXdURtpt65AdDpRWMBQ4Ed41Dm0pc5m5bYfmTJlymGJ0zW7tvPVU/8iND6J0KSL+fzxfHRGNdOvymDQmIhD3ru5PCIfbarhf0tLaO52MiktlHtmpjE0JuCIPyuZEw9ZoJaRkTklkCSJpqav2F3yDzweK9ERN1L4tZXyzR8TkZzKzAcfITQ+8bD2ZXFZeGPnG7xb8C4e0cP5aedz49AbCdWH9vNZ/DpOp5OGhob9rDo6Ojr61gcFBREbG8uYMWOIiooiMjISrVZ7HCPeS7vVxbryvYJ0eYsVALNOxbjkYK6fmMS45GBSw4yyIC3zm2jvaUVy6FEo7SQlpx2w3motp6p6AY2NXwIi4eFnEx93I0bj3rZfbqvjvs92YNCqeO+6MYzdJ7vWXtRO2/uF4G3DuuzfxDzzBNrU1H47H7fTyzfPb8fS6uCcO7OJSpWLIR4pnTYXzywt4Z28KrQqBffMTOPaCUn4aWR7FJkD0agUjE8JYXxKCH85K4OyFitLez2rX1hRynPLSwkxapg6yCdWT0wNPSYFH2VkZGRkTg0+2VTL4h2N3HdG+gGirSRJ/HP9P8lvyeeJyU8cVJz2dDqwrm/EurERj9WGI2E3ttO30SmsxStaUbQq0W2SMFaEETXtRgL/cCFtVivvvfwyCQkJTJo06VdjbCzdzcL/PIyfKQSn+0wK17SSNSmaMXOS0OoPngTlFSUWbq3jmaW7qWm3MyohkP9dMkwexXaKIfeYZGRkTnoczkaKi/5Ca9syzKZshI4zWfKf7xC9IlOuuI5hZ5yNQvHrYoTdY+eDog94bcdrWFwWZifO5rac24g1xx6DszgQl8tFY2PjfpnRra2tfev9/f2Jiopi+PDhfdnRfn4DZxiyxeFmY0U7a8vaWFvWRmGDBQC9RsnoxCAuHhVLbnIIGZHmQ1allpE5Ej5c+AqodaSl71+53GLZQWXVS7S0fI9CoSE6+mLiYq/Hzy+6r43LI/LPxYW8ubaSUQmBPDd/OOFmXd96x+4O2t4tQFDZsCz6O6G33ohp2rR+OxevV+S7l3fQXGlh1o1DZHH6CHF6vLyTV8WzS0vocXq4aFQsd81II8yk+/WNZWTwWYGkhPmGGd84OZlOm4uVu1v4sbCZ73Y18snmWjRKBWOTg/u8q2ODBk5xYRkZGRmZE4uylh4eWrSL3ORgbph4YBbzJ7s/4bOSz7huyHWcnnB633JJlHCWdNCzrgF7cTPW4F3YhuZjMW3AK/Wg9Prht1ODZoUKkyOBkOtvwP/+sxE0GtxuN59++ilqtZrzzjvvV20f22qr+fTRvyKJWtzeswgOC2TyJYMIjTMdtL0oSny3q5Enl+ymtLmHrGgzD1+dxeS0UDkh6RREFqhlZGROWiRJor7hY0pK/okkeYgOu5VtnzRSV7SQuCE5zLj+NgLCI351Px7Rw8LShby07SWa7c1MiJ7AncPvJD0o/RicRW8MHg9NTU37idHNzc1IkgSA0WgkKiqKrKwsoqOjiYyMxGg8tK/X8cDu8rKpaq8gvaO2E1HyZaSNjA/knplpjEsOYWiMP2rl8fe8ljn5aCysR6HyZ+65lyFJEh0deVRVvUR7xxpUKhMJ8TcRG3sVGs3+NhkNXXZufW8LW6o7uXZCIvedkb7fd9RR1knr2wUo/ES6Pvoz5hlTCL7xhn47D0mUWPZWIdW72pl6WTpJOcd/9MaJQn2nnffXV/Phxmpae1xMTA3hgTMz+opbysj8VgL0GubkRDMnJxq3V2RTZQfLippYWtjMQ4t28dCiXQwKNzE9w5ddnRMbKD98lZGRkZE5LFwekTs/3IpWreDJC3MOsJLa3LSZx9Y/xoToCdyWcxsAXqsb26YmujfU0M1WumM20TN9C16hG5XShL8lBeWX9ajWdqAblEbITTdgmjEDYZ8aLT/88ANNTU3Mnz8fs/mX+0otNXV88OD9uJ0SxvALmHjBKDJyIxEOcq2TJIkVu1v47w/F7KyzkBJm5MVLhzMr69D2HzInP7JALSMjc1Jit9dQWPRnOjrWEuA/BlftRL579QfUGi2n3/wHMidP/9WLnyiJLKlawnNbn6PSUkl2aDb/mvQvRkWM6tfYvV4vLS0tfUJ0XV0dTU1NiKKvYrGfnx/R0dEMGjSoLzP61zoMxwOnx8u26k7yyn2C9NbqDtxeCZVCIDs2gFunpjAuOZjhcYHo1PJwepn+ZX3RKhSqQPR+XrQ6HQUF99LQ+DkaTQjJyf9HTPR8VKoDszvWlrZy+wdbcbi9PD9/OGcO3d9P2lnRRdubu1CaFFgW/hltShyRjz7ab51rSZJY81kpuzc0MeacJAZPOLByu8z+SJLEmtI23s6r5MfCJiRgenoYV+UmMiFV9uyWOfqolQrG9RbufeDMwZS39LCsqJkfC5tY8FM5L6woI8igYcqgUE7LCGdiakhfkVUZGRkZGZmfs0fIXXD5CCL89x/t1Wht5O4VdxNtiubfk/4NNi9ti4tprVlFd+h6enK24FV2o1QaCAmYgn67FvfLaxBbCvAbOYKQBTdimDDhgL5rQUEBGzduZNy4caSlHWiNtwdJkti5cjc/vvIwotdBxqTbmHbFBHTGg1/X1pe38fj3xWyq6iA2yI//XpDNucOi5Ye2MrJALSMjc3IhSV5qat+mrOy/CIKSyKA72PR+CS1V35A2dgLTrr4RQ8AvD4WXJIm8hjye2fIMBW0FpASk8OzUZ5kSO+Woi06iKNLW1tbnF11fX09jYyMejwcArVZLVFQU48aN6xOjAwICBuSTZY9XZGe9hbVlreSVtbGxsh2HW0QQICvKn2vGJzIuOZhRCUGyJ6fMMWfJVwtBYea0mWfQYy2hofFzoqMvJTXlAZTKA33YRVHipZ/KeOL7YpJCjbx02XBSwvYXsJ3VFlrf2IXCrMa64l8ISi+xzz+Poh+tdLb+UE3+0hqGTI1hxBnx/Xack4Euu5vPNtfy7roqylutBBk03Dg5mfmj42SrBZljSlKokaRQI9dNTKLL7mbl7haWFfqyqz/fUodaKTAmMZhp6WGclhFOXLD8/ZSRkZGR8bG6pJUFP5Vz6Zg4Ts/cf/Svw+PgzuV34vQ6eWPqG+htGio+epfauOfwDutCqdATEnoaIboJ8GUZXe9/grOnB8PkSYTccAP6ESMOesyOjg4WLVpEVFQU06dPP2RsHY1Wlr+zjcqtr4Bk5fSbHyRr8sH3mV/TyRM/FLOqpJVws5ZHzs3iwpGxaFTyyFkZH7JCICMjc9JgtZZSWHgfXZatBAVOorNwKN+9uBRDQABz7nmQlFFjf3UfO1p28MyWZ1jfuJ4oQxSPTniUMxPPRHkYHtW/hiRJtLe372fT0dDQgMvlAkCtVhMZGcnIkSOJiooiOjqawMDAX/X6Ol6IokRRYzd55W3klbWyvrydbqdPWE8LN3LxqDjGJQczNjEY/0MUxJCRORbY3XY8HUoEpYNhw8dSVPwACoWGpMQ7DypOd9nd/PHjfH4sbOKsoZH8+/yhB1ZJr+2m9fWdKExq3MVv4a4uJf6tN1FHRh6wv6NF4dp68haWkToyjIkXpA7IB1UDgYJ6C++sq+SLrfXY3V6GxQXw1EXZnJEVKY/WkDnu+PupOSc7inOyo/B4RbZUd/YVWvzH1wX84+sCUsKMPiuQ9HCGxwWgkm2vZGRkZE5J2q0u7v54GylhRh48c/B+6yRJ4uF1D1PQVsAzU58h1h1B+aevU5v6DH66BJLTHsPsTKXzjffo/OxhJJcL8xmzCL7+enQZGYc8ptfr5bPPPkMURebNm4dKdaBs6HZ62fRtJVt/KMVl+QykTs67/28kDM05oO3upm7++0Mx3+9qIlCv5oHZGVw+Ll7uk8kcgCxQy8jInPCIopvq6lcor/gfSqWecPOdrHsjn66mpQydPotJl12NVm/4xX2Ud5bzv63/48fqHwnSBXHf6Pu4IO0CNErNb4pJkiS6urr2E6Pr6+txOBwAKJVKIiIiyM7OJjo6mqioKEJCQgasGA2+cypvtbK2zCdI55W10WFzA5AQrOes7Chyk4MZmxRMqOlA0U9G5njxyeLXQWMgJsYft7uDxsYviIiYi0ZzYGXwgnoLN7+3mboOOw+dPZirchMOEIJd9T20vLYThV6N4PoJ6+qlRD76CPrhw/vtHCq2t7L83WJiBwcx/arBB/XzO5Vxerx8t7ORt/Oq2FzVgU6tYE52NJePiycr2v94hycjc1BUSgWjE4MYnRjE/bMzqGqzsrSwmaVFTby2qoIFK8sJ0KuZOshXZHHyoFDMshWIjIyMzCmBJEn836fb6bS5efPq0fhp9hd03yt8j0Vli7gl+xYmqEZT+ukC6tNewOQ3mMEhf8Py34+o+OYeEAQCzp1D8LXXoklI+NXjLl++nNraWubNm0dQUNABMVXkt7Lq4910t1lRK79D9DRy9t33HSBOV7ZaefrH3XyZX49Ro+Ku09K4ZkKCbGklc0hkgVpGRuaEpru7gMLC++ju2UVw0EyaNsbx/Y8/EBgZxYUPPUbs4CG/uH2jtZEXtr3Al2VfolPquCX7Fq7IvAKD+pcF7YNRW1tLSUlJn12HzWYDQKFQEB4eTmZmZp9NR1hYGErlwH9qXNNuI6+srddHupUmixOASH8dU9PDyE0OYVxyMNEB/WdpICPzeyndVIhCCOL886+kru59RNFJbOzVB7T7dHMtDyzcQYBezUc3jmVEfNABbdyNVlpf3YFCo0QbU0fTP14l8PLLCTj//H6Lv760k+9f2UlorJFZN2ShlIdC9lHXaef99VV8tLGG1h4XCcF6HjwzgwtGxMojN2ROOOKDDVwzIZFrJiRicbhZtbuVpUVNLC9qZuHWOlQKgVEJQb2FFsNJDDnyvoqMjIyMzInBu+ur+bGwib+cNZjBUfvXG1rXsI4nNj3BtNhpXOV/CSVf/o/GtNfwNw4nsf48aq69FEGnI+jS+QRdfTXqiIhDHGV/SktLWb16NcOHDycrK2u/dV0tNn76sITqXW0ERfkRGrWW2oISZt1yF6mjc/vaNXTZeXZpKZ9sqkGlFLhxUjI3Tkoi0PDbEr9kTh1kgVpGRuaExOt1Uln5HFXVL6NWBxKsvYN1L27CZslj9Jx5jJ13CWrNobN4Ox2dvLrjVT4o+gAJifnp87l+6PUE6Q4UpH4JURTZvXs3a9asoaamBkEQCA0NJS0trU+MDg8PR60+MYQSh9vL2rJWlhU1s3J3CzXtdgCCDRrGJQeTmxxCbnIw8cF62V5ggCEIQizwNhABiMDLkiQ9s8/6e4DHgVBJklp7l90PXAt4gTskSfr+mAfezxTXF6AgAI3KhdnfwI5d7xAUNBGjIbWvjdPj5e9fFfD++mrGJQXzv/nDCDEe+PvhbrbR8uoOUCkwjldSd+tfMeSOI/xP/9dv8bfV9bD4he2YgnScdVs2Gp3cdRNFiTVlrbydV8XSwiYApqWHc8W4eCakhBxQ2V5G5kTErFNz5tBIzhwaiVeU2FbTwY+FzSwtbOKRbwp55JtCkkINTE/3idXD4wJlH08ZGRmZk4TdTd088nUBk9NCuTo3Yb91td213LvyXhLMCfw94k+Uff80TYPeIdA0nqSeS2l48I8Yxo0l6r//RRX4y7WX9qW7u5uFCxcSGhrKrFmz+pZ7XF62fF/Flu+rUagEcs9PonH3Qgp+2sTUK68nc7LPo7q1x8mLK8p4Z10VkiRx6Zg4bp2aQphZd6hDysjsh3yXIyMjc8LR1bWFgsL7sNnKCAk6i8rl/mxYt4SwhGTm3vc3whOTD7mtzW3jnYJ3eHPXm9g8Ns5OOptbcm4hyhh1RDG43W62b9/O2rVraWtrIyAggFmzZpGTk4NOd2JdhOs77SwramZZUTNry1pxuEX0GiW5ycFcMz6R3OQQ0sKNsiA98PEAf5QkaYsgCCZgsyAISyRJKugVr2cA1XsaC4IwGLgYyASigB8FQUiTJMl7PILvL774/G1Q6hiTO4ampq9xuVqJi722b31th41b3tvC9toubpqczD0z0w7q9+putdPyyg4AAs+NoOaGy1BFRhL95JMIB/HmOxpY2ux89ew2VGoFZ9+RjZ/p1M486bK5+XSLr+hhRauVYIOGmyYnM39MHDGBclE5mZMXpUJgRHwQI+KD+NOsdGrabSwraubHwibeWlvFK6sq8FMrGZUYxLikYMYlB5MVZZa9q2VkZGROQBxuL3d8sBWTTsUTF2Tv9+Dd5rbxh+V/wCt6eSbyMapW/4/WtI8JDphOCtdRe9eN6NLTiX72fyiNhz/KRhRFFi5ciNPp5IorrkCj8fU5K3e0suqj3VhaHaSOCif3vGQ2LnqHgp+WMm7efIbPnkOX3c0rP5Xz+poKHG4v5w+P4Y7pqXJBapkjRhaoZWRkThi8Xhtl5U9SU/MmWm0k/tJtrHpmHV53DRPnX8XIs+aiOIRthtvr5tOST1mQv4A2RxvTYqdx+7DbSQlMOaIYbDYbGzduZMOGDVitViIjIzn//PMZPHjwCWHZAeAVJbZWd/SJ0kWN3QDEBvlx8ag4pqaHMSYxSC5ccYIhSVID0ND7vlsQhEIgGigAngL+D/hyn03mAB9KkuQEKgRBKAVGA3nHNPB+xCN6sNY5USgFpkydzabN52AwpBIUNAGAlbtbuPPDrXi9EgsuH3FAZfS+/bTZaX1lO4gSwVemUX/XDUh2O7FvvI4yIKBfYrd3u/jq2Xw8bpG5fxyOOfjUtdHZWdfFu+uq+GJbHQ63yPC4AO68KIczhkSgVcm/UzKnHrFBeq7MTeDK3AR6nB5Wl7T6akOUt/Hv74oAMGlVjE4M8hUrTgpmcKRZHl0gIyMjcwLw7++KKGrs5o2rRu1X10eSJB5a+xC7O3bzVuRzdOx4mbaURYSFnEmK/lZqLr0SVXgYsS8vOCJxGmDNmjWUl5dz9tlnEx4ejqXNzuqPS6jIbyUwQs+cP+QQkx5E3qcfsGXxlww/4xyyz7mA55eXsmBlGRaHhzOHRnLXaWmkhBmP9kcic4ogC9QyMjInBO3taykqegC7o5rQwPMoWgw125cSO3gIM264jcDI6INuJ0oiiysW89zW56jrqWNE+Aienvo0OWE5R3T8jo4O8vLy2Lp1K263m5SUFMaPH09CwoEF1AYiXTY3K0taWFbYxMrdLXTY3CgVAiPjA7n/jHSmZ4SRHCpnSZ8sCIKQAAwD1guCcA5QJ0lS/s/+vtHAun3ma3uXnTR8teoTFFp/ggPVdHWtp6eniIz0xxAEgddXV/DwNwUMCjfx4mUjDunl6ulw0PLKDiS3SMh1Q2h54iEcBQXEPP882tTUg27ze3E5PHz9XD7d7Q7OuTOH4OhTr6Pv9HhZvKOBd/Kq2FLdiU6t4NycaC4bKxc9lJHZF6NWxaysCGZl+R6wtXQ7WVfuqx2RV9bG0qJmAAL0asb0ZljnpoSQGiZf82VkZGQGGsuLmnljTSVX5SYwNT1sv3Wv73yd7yq/42nd35BqP6Q96Xsiw+eREnwnVZdcBmo1ca++iir4wCLgv0R1dTXLli0jMzOT7CE5bPq2ks2LK0GAcXOTyZ4ei1KlYMu3i1j7yXukT5pGRcp07n18Ba09Lqanh3H3zDQyo+T+mczvQxaoZWRkBjQeTzclpY9RX/8Rfn4J6G03sfz1PJRqNTNuuI0hU2ciKA4cwipJEqvqVvHMlmfY3bGb9KB0XjztRcZHjT+iG7K6ujrWrl1LQUEBgiAwZMgQcnNzCQ8PP5qnedSRJInS5h6W9mZJb67qwCtKBOrVTB0UxtT0MCalhp4SRcREpxdXteV4h3HMEATBCHwG/AGf7ccDwMyDNT3IMukQ+7wBuAEgLi7uqMR5LNi2ci2CFMTcuZdQXfNP1OogwsPnUNth47FvC5k2KIzn5g8/oCr6HrxdTlpe2YHo8BJ6/RAsX3+AZfFiQu+6C9O0qf0Ss9cj8t2CHbTU9HDGTUOISgnol+MMVNp6nLy2uoIPN9bQbnWRGGLgL2cNZt6IGPz9Tv7fKxmZ30uoScvZ2VGcne2zLmvosrOuvI21pT7R+vtdPt/2EKOGMUnB5CYHMy4pmMQQgyxYy8jIyBxHWrqd3PtpPukRJu47I32/davrVvPM5mf4u/tO/FlMR8IKYqIuJzniTqovvxLRYiH+nbfRxMYe0THtdjufffYZ/v7+DE+bwEePbqSzyUbysFDGX5CKKchnXblr5VKWv/kyupRs/tk1hLqvixibFMSCy9MZEX/4PtcyMr+ELFDLyMgMWFpbl1FU/BeczmZC/C9k+2fdNJetJGXUWKZfczPGoIM/Hd7avJWnNz/NluYtxJpi+c+k/3B6wukohMPzYpQkiZKSEtauXUtlZSVarZZx48YxduxYzGbzr+/gOOFwe1lX3sbyomaWFjVT2+ErcJgRaeamyUlMSw8nJzYA5Uk+xNfb48JVacFZ0YWzyoK7vsdXMvAUQBAENT5x+j1Jkj4XBGEIkAjsyZ6OAbYIgjAaX8b0vr3YGKD+YPuVJOll4GWAkSNHHlTEHmg0dNYhuIwoFQ4Cg6G4dDmJCXegVGp5btl2BAQePjfr0OK0xeUTp61uQq8bgrN4Iy1PPYV59myCb7i+X2KWRImlbxZQU9jBtCvSSRwa0i/HGYh0O9y8sqqC11aVY3d7mZ7hK3o4Plkueigj83uI9Pdj7rAY5g6LAaCm3UZe2d4M62+2NwAQbtaSmxzS52Ete4fKyMjIHDtEUeKeT/Lpdnh4//qx+1ktVlmq+NPKP3F/53XEhqykK2ot8XE3khh9OzXXX4+zspK4V15GN3jwER1TkiS+/PJLuru7GRw8he9eLMQ/1I+zbs8mPnPvffbu9Wv57sVnaDHH8Yl7NFn+ev59wTDGpwTLDzZljiqyQC0jIzPgcLna2V3yME1NizDoU/G2ncnSV9bhZzJz9l33kTrm4FnQuzt2878t/2NF7QpC/EL4y9i/MDd1LmrF4WXdeTweduzYwdq1a2lpacFsNjNz5kyGDx8+YAsfNnY5+ryk15S2Ynd70akVTEgJ4eYpyUwdFEZUwMnrXStJEt52B84KC87KLlyVFjytPmEelQJNrAnT5Fi0if7w7+Mba38j+P4pXgMKJUl6EkCSpB1A2D5tKoGRkiS1CoKwCHhfEIQn8RVJTAU2HPPA+4mPFr4GKi1ZQ5KoqX0TQdAQHXMpla1WPtlcy+Vj4w/5v+HtcdHy6na8Fich1w5BcjZRf++96DIyiHz0kX7pjEuSxKpPSijZ1My4uclk5B5Z4dYTFYfby7vrqnh+eSkdNjdnZEXwx5lppISZjndoJwySJOGRwCmKOEQJpyjiEiUcooizd955kPmfvx64fv82DlHCrFIwLsDI+AAjOWY9moOMYJIZ2MQG6YkN0nPhqFgkSaKi1donVq8qaWHh1joAYgL9+sTqccnBRPqfvH0JGRkZmePNm2srWbm7hYfPzSItfG8fyOq2ctePd3F77TwSY9dgCd9EUuLdJMTdRN0f/oB98xai//sEhrFjj/iYGzZsoKioCJMtmdYWkdFnJzJsZhyqXnFckiQWfrOCsnefplkTQkH6ebx0xhBOywiThWmZfuFXBWpBEF4HzgKaJUnK6l32N+B6oKW32Z8lSVrcu+5+4FrAC9whSdL3/RC3jIzMSYgkSTQ3L6Z499/weLoJNl7Clg+a6KhfS9bUGUy67Br8jAeKFrXdtbyw7QW+Lv8ao9rIncPvZH76fPTqw8v+cTgcbNq0ifXr19Pd3U1YWBhz584lMzMTlWpgPcfzihL5tZ2+LOnCZgoafNYV0QF+zBsRw7SMMMYlBZ+0BQ4lUcLdYMVV2YWz0oKz0oLY7QJA8FOhjTdjGBWOJsEfTbQRQXVKiSfjgcuBHYIgbOtd1nd9/jmSJO0SBOFjfEUUPcCtkiR5j0mk/YwkSbSVtqFUGDl91ums3/hvIiLmoNWE8MzSbaiVArdMTT7otl6rm9ZXd+DtcBJydRYqf5GKC29F8PMj5vnnUPj1j0iz+dsqdiyvJXt6LMNmnjg2Kr8Vj1fksy21PPNjCfVdDiakhHDv6YPIjg043qEdVcptThqcrgOEYFevAHwoEdl1CNF473ppP0H69w4SEQCdQoFOIaBVKND2vuoUApre90FqBY1ON/+uaATAT6FgjL+B8YE+wXqoSY9KznY/oRAEgaRQI0mhRi4dE+8bQdbc48uwLmtjSWETn2yuBSAxxMDYPYJ1UvB+hbtkZGRkZH47BfUW/vVtEadlhHPZmL19QFES+cuKB7m0aDLRySvoCd1OasoDxMZeTePf/k73kh8J//OfMc+efcTH3L5+N999+z0aZyCD4oYw8aJB+Ifu7eOuKW3lxU+WkZn/Pg5dIONuvI9/j0mVR7XJ9CuHo7y8CTwHvP2z5U9JkvTEvgsEQRgMXAxk4svG+lEQhLST5YZXRkam/3A6myku/istrUswGrJwlg9j6eJN+IdHMO/BR4gfknPANq32Vl7Z/gof7/4YpaDkqqyruDbrWvy1h1egoauri3Xr1rF582ZcLhdJSUnMmTOH5OTkAfVU2OJws2p3K0uLmlhZ3EKb1YVCgBHxgfxpVjrT0sNICz85ix1Jbi+umh6cvYK0q8qC5PRdUpT+WrTJ/mgT/NEmmFGF6RFO4U6TJEmrObiv9L5tEn42/yjwaD+GdVxYsf17lCp/THpobvkcUXQQF3s1JU3dfLGtjhsmJRFmOnBUhGhz0/raDtytDkKuGowmzkDNDTfgaWgg7u23UEdG9ku8u1bVsX5ROWljwhl/fspJ+b+8B0mS+HZnI0/8UEx5i5Xs2ACeuCCb3JSTx86k3Obky+YOvmzupMjqOKxtNILQJwr//FWnEDAolQSp912+973uZ9vsmdf8yvp9j6ESOOzvXbvbQ15nD2s6eljT2cOj5T6LCINyj2BtYnyAkSEmP5Qn8Xf5ZEQQBNLCTaSFm7gyNwFRlChstJBX1sa68ja+zq/ngw3VAKSGGfvE6rFJwQQaNMc5ehkZGZkTD7vLyx0fbiVAr+Y/84budy1+dfMrTF+fRuCgb7AGF5E+6BGioy+h5bnn6fzoI4Kvv56gKy4/ouNZu5ys+rSIjRXfoVCqmTt3Lhmj94rim6s6eOL7YoqLdnNB45f4+Qdy86OPExBy8vTTZAYuvypQS5L0kyAICYe5vznAh5IkOYEKQRBKgdFA3m8PUUZG5mRGkiQaGj6jpPRRRNFJgHY+m96qwtq+hRFnzWX8BZei/pm9Ro+rh7cK3uKtXW/h8rqYmzqXm4beRLjh8AoXNjY2snbtWnbu3IkkSWRlZZGbm0tkP4lPR4okSZS3WllW6LPu2FjZjkeU8PdTM2VQKNPSw5icFkqA/uS7GRRtbpxVvsxoV6UFV203eH2Wx6pwPfqcULQJ/mgSzagCBqbtiszx56dvvwMhgNNnnUFN7R8IChyP0TiIp77cjEGj4qZJB2ZPiw4PLa/vxN1kI+SKwehSAmn85z+xrs0j8tFH0Q8b1i+xlm9tYeX7xcRlBjPtioyT9iGLJEmsLm3l8e+L2V7bRUqYkZcuG8HpmeEnhSBfZXeyqLmTL5s72dnjsxka42/gkdRoMgw6dApFXybywURixQn0GQSpVZwZGsCZoQEAtLjcrO0VrPM6e3i4zGdlb1YpGOtvZHygkdwAI5lGvxPqPGVAoRDIjPInM8qf6yYm4fGK7Kq3kFfextqyNj7dXMvbeVUIAqRHmBnXW3RxdFIQZp1c1FRGRkbm13jkmwJKm3t499oxBO3zoG9l8XKSv1Wgy/wce0A5gwc/QWTEuXR8+BGtzz2H/9y5hN5912EfR/SK7FhZx4ZF5bTpCvDq7Fw6/3JS03zi9K76Lv77w26WFTUTr7FzWed36M0GLvnHY/jL4rTMMeL3jF2/TRCEK4BNwB8lSeoAooF1+7Sp7V0mIyMjcwDd3YWUlj5Ge8caTMZhNG9KY8vKrYTGJTDn7geJSEnbr73T6+Sjoo94ZccrdDo7OT3hdG7LuY0E/4RfPZYkSZSXl7N27VrKyspQq9WMHj2asWPHEhAQ0D8neAQ4PV42VLSztLCZ5cXNVLXZABgUbuL6SUlMSw9jWGwAKuXJZVnh6XTuteuo6MLT5DtvlAKaaCPG8dFoE8xo4s0oDfLNrsyv0+XoQuzWoBTsBIc30NTWTGzGY+ys62LxjkbumJ56QKaf6PTQ+vpO3A1Wgi/LQDcoiM7PPqPj7XcIuvIKAs4/r19irdvdwQ+v7SIswcysG7JQnmT/33vYWt3Bf74rJq+8jegAPx6fN5Tzhsec8AVbax2uXlG6g/xunyg93Kzn7ylRnB0aQJTu5HuIeDBCNWrmhAUyJywQgCbnXsF6TWc3P7T5rKgCVEqff3WvJcggg04WrE8wVEoF2bEBZMcGcNPkZNxeke21nawt9RVdfG99Fa+vqUAhQFa0f1+G9aiEIAzagWWZdrw5hI3mBcDfgAxgtCRJm/ZpL9toysicZPywq5H31ldz46QkJqTuFYHLa0pQf1yFYsj7OM31DMn6H2Fhs7AsWULjP/6BYfIkIv/x98N+wN9Y3sXKD4pprelBn9yDw9rEpEmTSE1LpqylhyeX7Oab7Q2YdSrumRCB5rsXcAsS8x54GP+wiP46fRmZA/itPYUXgYcBqff1v8A1HHxosXSwHQiCcANwA0Bc3MnvtSgjI7MXu72G8vKnaGxahEplxizMZ8NLu3E7Cxh/0eWMOud8lPt4P3tED1+VfcUL+S/QaG1kXOQ47hx+J5khmb96LK/Xy65du1i7di2NjY0YjUamT5/OyJEj8esnL9nDpdniYHmxL0t6dUkrVpcXrUpBbnIw101IZGp6GDGBh+ejfSIgiRKeFpsvO7rCJ0p7O50ACBolmngT+qGhaBLMaGJNKDQnp4+2TP/y8devIaj1JMQHU1P9Onp9MsFBk/jTW5vx91Nz7YTE/dqLLi+tb+zCVdtN8PwM/DKCsW3ZQsPf/o4hN5ewe+/tlzhba7tZ/MJ2zCE6zro1G7X25Pu+727q5onvi/mhoIlgg4aHzh7M/DFxaFUn7rk2OF181dzJouZONll8D9SGmvz4S3IUZ4f6E+cn+/KGa9XMDQ9kbrhPsK53uHyCda9o/W1rFwBBaiW5Ab7s6vGBJtL02pMim/5UQq1UMCI+iBHxQdw+PRWH28vW6k7yyttYV9bG66srWLCyHJVCIDs2oK/o4oj4wJO2VsYR8CYH2mjuBM4DFuzbULbRlJE5+WjscvB/n20nK9rMH2cO6lve2dhC59vr8WS/hsfYytChLxESMhXbxo3U//Ee/IYMIeappxDUv564Y+92kbewjMK1DRgCtIy7JIZvV39CXFwcKdmjufeTfD7bUotOreS2qSlcPjyEb//1FyzdFi78y6OExMb350cgI3MAv0mgliSpac97QRBeAb7una0FYvdpGgPUH2IfLwMvA4wcOfKgIraMjMzJhcvVSkXl89TVfYAgKAnxv4iSH9xUbdtKdPpgZtxwO8HRe39CJEliWc0ynt3yLOVd5WQFZ/Hw+IcZG/nrVYqdTidbtmxh3bp1dHV1ERISwjnnnMPQoUOPW+FDUZTYUdfFsiKfKL2jzneTHumv49xh0UxLDyM3OQS/k0SYlTwirvoeXBUWnJVduKosiDYPAAqjGm2iP5oJ0WgT/VFHGBCUsjAh8/upzq9AKQQw/bTBFJU8TfqgR9ha08XSombuPX0Q/n57O/SS20vbW7twVVkIuiQdv6wQ3PX11N5+B+qoSKKf/C9CP/xedLXY+erZfDR+Ks6+Iwed8eQaHVDTbuPpH0tYuLUWvUbF3TPSuGZCIsYTNIOy2enmq5ZOvmruZH2XFQnINOr4c1Ik54QFkCCL0r9IlE7DvIgg5kUEAVDjcLGmo5s1nT2s7ejh6xbftTBUo/KJ1b1Z1kl+smB9oqFTK31Z08nBMMPnrbqpqt1XdLG8jRdXlvHc8lI0SgXD4gL6Mqxz4gJO6AdXv4WD2WhKklQIB/WDl200ZWROIkRR4u6Pt+F0izxz8TA0vUXdHfUWat9YiiX7Obx6C8NyXiMoKBdH8W5qbrkVdUwMMS+9iEL/ywlMoihRsLqedV+U4XZ4GTYzjpyZMbz97psolEoqTVlMf/InBEHg6vGJ3DwlGbPSyycPP0BnUwPn3ff3A0Yyy8gcgK39qO/yN90pCIIQKUlSQ+/sXHxPewEWAe8LgvAkvqe7qcCG3x2ljIzMCY3H00N19WtU17yGKDoI9p9NzVozW1ZtQWc0Mf2am8mecQaCYu/w9o2NG3l689Nsb91OgjmBp6Y8xfS46b96s9rd3c369evZuHEjTqeT+Ph4Zs+eTWpqKgrFsR8+3+P0sLqkpde6o4XWHieCAMPjArn39EFMSw8jPcJ0UtyEi04PrupunBVdPv/omm4ktwiAKsQPXUYw2kQzmgR/VMG6k+KcZQYWWys2oRQC0Kk9tHZ8jFodSETEXP70Rj7BBg1X5Sb0tZXcIq1vF+As7yLwwkHoh4Yi2u3U3HYbksNB7NtvoewH+x+bxcVXz27D6xWZ84cRmIJOHi/11h4nzy0r5b31VQiCwLUTErl5Ssp+noonCq0uD4tbfJ7SeZ09iMAgg457EyM4JyyAFP3J83c71sTqNFwcGczFkcFIkkS1w9VXcHFNRw9fNncCEKFR9/lXjw80Eq/TyNeNEww/jZKJqaFMTA0FfH2ijRXtvR7WrTyztISnfyxBp1YwMj5or7gt83NkG00ZmZOIl1eVs7asjX+fP4TkUCMAzmoLVe8tpmXYE3h1dkaOeIcA/xG46+qouf56FH5+xL36CqrAwEPuVxIlyra2sOGrcjoabUSnBTDp4kEERRlYuOhrGhsb+cmbRtXWVi4cGcsd01OI9PfD43Lx+b8eoamijHP++ABxWUOP1Uchc6LhdUPJEsh/H4q/O+q7/1WBWhCED4ApQIggCLXAQ8AUQRBy8Nl3VAI3AkiStEsQhI+BAsAD3CoPPZKROXURRSd1dR9QUfk8bnc7gf7TaNsey9IfNqFSaxh73kWMPPs8tHpD3zYFbQU8u+VZ1tSvIVwfzt9z/845yeegUvzyz1VzczN5eXls374dURTJyMggNzeXmJiY/j7NA6hotfZmSTexoaIdt1fCrFMxeVAY09JDmZwWdkIKNj/H2+3qLWbos+twN/SACAigjjJiGB2BJsGMNsEfpenEP1+Zgc/iLz8ChYHxEwfR2vo3EhJuYUOlldWlrTx4ZkafB6okSbR/XIyzpJPAeakYhoX5CrY+8ADOwiJiXnwBbfKBhRR/Ly67h6+fy8fa6WTOXcMIijL8+kYnABaHm1d/KufV1RU4PSIXjIjhjumpRAUcXxulI6XD7eHbli6+bO5kdWc3XgmS/bTcGR/OnPAA0g0n1vmcCAiCQLyflng/LfOjfIJ1ud3ZJ1j/1NHNZ00dAERr1eQG7smwNhF7inh8n0wYtSqmpocxNT0MgC6bm/UVvuzqvLI2Hv+++DhHOGCRbTRlZE4Sttd28sT3xcweEsGFI30jhx0lHdR88h11w/6NW+Ni3MiPMJuH4OnooPq66xHtduLffRd1VNRB9ylJElU721i/qJzWmh4CI/TMuiGLpGGh9Dg9PP7RMqyFmyjwhDM0K4PXT0sjIcTXBxW9Xr5+5j/U7NrOGbf9kZSRY47ZZyFzgiBJ0Lgdtn0AOz4BWyvoQ2D09cC/juqhflWgliTpkoMsfu0X2j8KPPp7gpKRkTmxkSSRpqavKCt/EoejFn/TaHrqM1n1+hZEsYWcmWcyZu6FGAL2PgGutlTzv63/47vK7/DX+nPPyHu4aNBF6FSHzlKTJImqqirWrFlDSUkJKpWK4cOHM27cOIKCgo7FqQLQZXezvrei/U+7WyhvtQKQGmbkmvGJTEsPY0R84Ald4FCSJLxtDpy9YrSr0oKn1VcUDJUCbZwJ05RYtAn+aOJMKHQDZyi/JEm46+qOdxgy/YzT68TZLKJUOAiNLKW+XkV01KXc92Yx4WYtl43d66Nn39aCfUcr5lkJGEb6ir+0LXgZy+JvCf3j3ZimTDnq8XndIotf2kFrbQ+zbx5CRJL/UT/Gscbh9vJ2XiUvrCij0+bmzCGR3D0zrS8b6ETA4vH2itId/NTRjUeCeJ2GW2PDmBMeyGDDKT7aw+uBthJo3AEN+SB6ID4X4seDIeTXtz9CBEEgWa8jWa/jiugQJEmixObsza7uZmmbhU8afYJ1nE7Tl109PsB4yhSlPJnw16uZmRnBzMze3+EeJ+sr2jnz38c5sIGHbKMpI3MSYHV6uPPDbYSatDw2dyiCIGDf2UrtV99RPfzf2FUexo/6FLM5E9Fmo/amm3HX1RH32qvoBh3ccqO2uIP1X5bTWN6FOUTHaVdlkDo6ApdX5NVVFby+fBdTxG0IWn/+dv3FDI7ee/8tiSLfv/g0ZZvWMe2amxg8ceqx+ihkTgS6m2DHxz5hunkXKDWQNgty5kPKaaBUc8wFahkZGZnDRZIk2tpXUlb2BD09hRgMGWi7ryLv7e24HOvJnDSNcfPm4x8W3rdNs62ZBfkL+Lzkc9RKNTcMvYGrMq/CpDEd8jher5eioiLWrFlDfX09er2eKVOmMGrUKAyG/s9ItDo9bKxs78v42VnXhSiBTq1gdGIwV+YmMC09jNigE7fAoeQVcTfafN7RlT4PabHbDYDgp0KbYMYwypchrYk2IqgGhvguSRKexkbsO3fi2LkLx86dOHbuxNvVdbxDk+lnPl/2HgqNicgwgYaGT4gIP5v11Qo2Vnbw8LlZfQW5vN0uOr8qQxNnwjTJN8Kie9kyWp55BvNZZxF83XVHPTZRlFjyRgF1xR2cdlUGCUOOvrB3LPF4RT7ZXMszP5bQaHEwKS2Ue2cOYkjMiSO6b+yy8r+qJla0d+OSJGJ0am6ICWNOeABDjX6npijtskLTLl+WTMN2nyjdXAAeh2+9UgsKJax/yTcfmgEJE/ZO/SRYpxl0pBl0XB0dgihJFFsdff7V37V28WGjzwMx0U/D+ABTny1IuPbk8nY/FQg2apk9JPJ4hzEQkW00ZWROAv7+1S4q26x8cP1Y/PVqrJubqF+ymOoRT2ARPIwa/gGB5kwkt5u6u+7GvmMH0c88jX7UqAP21VjRxfovy6kt6sAQoGXy/EFkjI9EqVSwprSVuz/eRrPFwYXmcgySgptvvILg4H3EaUli+VuvULBqOeMvvIxhp591LD8KmYGK2wHFiyH/AyhdCpIXokfA7Ccg63zQ928SoCxQy8jIHBW6urZSWvY4nZ3r0eniMLgvZ+trJdi61pMyaizjL7p8v0rA7Y52Xt/xOh8Vf4RH9DAvbR43Zt9IiN+hb3BdLhdbt24lLy+Pzs5OgoKCOOuss8jOzkZ9GJWMfyt9VenLWllb1sa2mk48ooRaKTAsLpA7pqeSmxxCdqz/CVnkR7R7cDf04Gqw4q634m7owd1kA68v8UYZoEWXHIAmwR9tohlVqB5BMTDEG09rK/YdO/rEaPuuXXhbW30rlUq0aWmYZs5Al5kFl1x8fIOV6VcK1m5FKQWSO9FIU4udmJirue/tYqID/LiodwilJEl0fFGK6PISOC8NQSHgLCmh/p570Q0eTOQjDx91YVKSJFZ9tJuyLc3knp/CoLEnrvgiihKLdzbw5A+7KW+1MiwugKcuyjmhPGO9ksT/qpr4T0UjIRoVV0eHMCcsgGFm/aklSve0+IToxl4humE7tJXS5xqgC4DIoTDqOogYChFDICTNt75+K1Sugso1sO192PiKb5t9Bev48WAMPephKwSBDKMfGUY/rosJRZQkCnrsff7Vi1o6eLehDYAUvbYvwzo3wEioRhasZQYOh7DRbAf+B4QC3wiCsE2SpNNlG00ZmROfr7fX8/GmWm6bmsLYpGC619TRsPobakY8RZvoISXzBWKChvss5x76Gz0rVxLxt79hnjFjv/201vawflE5ldtb8TOpGT8vhazJ0ajUSiRJ4tVV5fxzcSHJoUZuG+SkbEcH55x3HsHB+/fV1n7yHlu/+4oRZ57LmPMuOpYfhcxAQ5KgdqOvT7frc3B0gSkKxt8B2ZdA6KBjFoosUMvIyPwurNYyysqfoKXlB9TqYEzCJeR/UIuleROxmUOZc88VRKWl97Xvcnbx5q43ea/wPZxeJ2clncVN2TcRa4o95DF6enrYsGEDGzduxG63ExMTw+mnn86gQYP6pfCh2yuyvbaLvLJW8srb2FTZgdMjohBgSEwA109KIjc5mJHxQfhpThxBWpIkvB1OnwDdYMXVK0Z7O5x9bRQGNeooA8bx0WiiDGgSzKgCBkYxME9Hh0+I3rWzL0Pa09joWykIaFOSMU6YgC4rC7+sTLTp6Sh0+8QuC9QnLeWtZSg9ZjRKG52WpQQGjGVdTRDbayv4z7yhfdXR7dtbcexqw/+MBNRhejwdHdTcciuCQU/M88/t/305SmxaXMnOlXUMmxnHsBn94wda0tTNhxtrcHlEPKKEKEq+V0naZ17EK4JXFPFKva+itP8kSXi8P99u76vTI9Jld5MWbuTly0cwY3D4CSXqNjvd3FZYxU8dPcwNC+A/g2IxnYAPFY8IUYTOyr0Z0XsE6e6GvW3843xi9JB5PiE6Yij4x8Ch/raxo33TxD/6iuXUb+sVrFf/TLBO30ewntBvgnWWSU+WSc+NsWF4JYkd3XbW9grWnzd18Ha9T7AeZND1+lcbGRdgJEgt3wbJHD8OYaMJsPAQ7WUbTRmZE5S6Tjv3f76DnNgA7pieguXHKhq2fk3tiGdp9IgYEh5keLRPiG556mm6Pv+ckFtvJfDivcJxR6OVDV9XULqpGY2fijHnJDF0WgyaXltFh9vLfZ9t54tt9czKjOD20WY++eA9cnJyGDp0/6KHm7/5gnWffUjW1BlMvvzaE6ovJ3MU6ayB7R9C/oe+JAWVH2ScDTmXQOJk36i5Y4zcM5ORkflNOBwNVFQ8S33DpyiVfvhrzmfXok5aq7YRnpTCjOvvJH5ITt8Fr9vVzbsF7/J2wdtY3VZmJczippybSPJPOuQxWltbycvLIz8/H4/Hw6BBgxg/fvxRL/oiihIFDRbyynxV5TdUtGN1+RJTMiLNXDY2ntzkYEYlBmHWnRgZWJJHxN1k84nR9VZfdnRDD5KjN+FGAFWIH5pYE+oxkagjDWgijShM6gHRSfF2d+PYVYBj5w7svdnR7travvWahAT0I0eiy8rELysLXUYGimNg7yIzMPls4ZugVDN8tITT2Uha2j/483u7SQwxcN6waAC8PS46vyxFHWvCOCGmb/ikp7GR+HfeRh0RcVRj8ri9rPq4hIJV9aSPi2Dc3KNfdBF8Nz3zX11Pl92NUatCqRBQCoLvVSGgUggo9rwKAipl72vvcrVSgU4t9G2n+Nk2++5LoRAYlRDIOdnRKAfIKIrDZWV7N7cWVGH1enlyUCyXRAYNiN+6o4rHCS1FezOiG3f4Jle3b72g9InGiZN9gnTEEN/kF/jL+/0llGqIHeWbJt69v2BdtcbnW7jxVV/bYyBYKwWBHLOeHLOeW+LC8IgS27ttfRnWHzS083qdb5RNplHHlCAzU4NMjPI3oO2HB94yMjIyMqc2XlHirg+3IYoST1+Yje3bShpKvqJ++AvUuiUa/S/iT+lXAtD+9ju0vfwyARdeSMhttwJgabOz8ZtKivMaUGqUjJgVT86MOHSGvfekdZ12bnxnE7vqLfxxRhrzBpt47713CQ4OZvbs2fvFs2P5D6x4+1VSx+Qy44bbTr6+kMwv4+yBwq8g/32oWAVIvlFv4/8Ag+eAznxcw5MFahkZmSPC7e6ksuolamvfRpIkAvxmU/y9h4bCAgKjYjj77vtJHZ3bd7GzuW28X/Q+b+x8A4vLwvS46dyScwtpgQcv9ABQXV3N2rVrKSoqQqlUkp2dTW5uLiEhR8ffUpIkSpt7WFvm85BeV9FGp83nr5wcamDu8Ghyk0MYmxRMkGHgF13yWt24ewXoPouOZjuIvqHaglqBOtKAPjsUdZQRdaQBdYQBxQDJ/hZtNhyFhftZdbgqK/vWq2Ni0GVlEXjxReiystBlZqI0HdqjXObUwit6sVR2oxT8CAopwCsmsq5uEEWN+TxzcU5fcdLOL8sQnV5C56UiKAUa//U4tnXriHzsMfxyco5qTF0tNr57eSetNT0MPz2eMeck9ssNgMXh5po3NuJwe/n69gmkhcv/Fz/HI0o8XtnIs1VNpOp1fDosmXRPO3xyFdSsh7DBEJUDkdm+KSD+0JnDAwlH114Beo8Y3VIEou9ahtoAEVmQfbFPhI4c6rPgUPfziJiDCdYN+XszrPM/3CtYhwza38PaGHbUw1EpBIb7Gxjub+D2+HBcokh+t501Hd2s6ujh5ZoWnq9uRq9UMCHAyJQgE9OCzST4aY96LDIyMjIypx4vLC9lQ2U7/52XjXlFPfV1C2nIfpUql4I8YTQvjHkIAMvixTQ99himGacR8dBfsVlcbF5cya7V9QiCwNCpsQyfFY/evP+96bryNm59bwsuj8hz56fjqtnOggX5+Pn5ccEFF6DR7G2/e/0alix4jvihw5h9+70ojkOGrMxxQBSharUvaaDgS3BbITABptzn6ycGJhzvCPuQBWoZGZnDwuu1U1PzFlXVC/B4uvE3TKd6lZ6tG0swBocw86Y7yJw0HYXSd6Gze+x8XPwxr+98nXZHO5NiJnFrzq0MDh580P2LokhxcTFr166lpqYGnU7HpEmTGD16NEaj8XfFLkkSNe121vZ6SK8ta6O1x2drER3gx8zB4eQmhzAuOZhw88CwszgYkijhbXfg6hOiey06ulx9bRRmDZpIA7r0YNRRBtSRBlTBfgPGM1p0OnEWFfksOnbsxLFrJ86yct+FE1BFRKDLysT/3DnoMrPQZWWiCvwd2X0yJz3fb/4KpcqfqIgWunt2kpzyDx74uJRB4SbOHhoFgG1HC/YdrZhPT0AdbqBr0SI63nmHoCuvIGDuuUc1nrKtzSx7qxBBIXDmrUP7rSCi2yty63tbKGvp4a1rRsvi9EGoc7i4uaCKDV1WLo0M4uGkCPSbX4Hl/wTRA4POgNZSWPOMbx5A579XrI7M8U1BSXC8smslCSz1+9hz9BYw7Kza28YQ5hOgU0/r9Yseenxj3helGmJG+qYJd/1MsF4D2z+CTa/52h4DwVqjUDDK38AofwN/SACrx8uazh6WtXezvM3CD20WKKkj0U/D1CAzU4J8RRcNSvkmXkZGRkbmyNhc1cHTS0uYMzSSSQVd1Hd9TtOQt6jyaPnUGsE7Zz6DWqHGmpdH3Z/uw2/EcIL+/hh5X1SwY0UtolcifXwko2YnYAzc/x5VkiTezqvi4a8LSAzScEOql63ffYAkSeTm5jJx4kT8/Pz62ldu38riZx8nMnUQc/74AKp+rN8kM0BoK/MVO8z/CLqqQWuGIedD9nyIGzsgEzJkgVpGRuYXEUUPDQ2fUFHxP5yuJsyGcTTujGDbyiL8TGamXHEd2TNmo+p9Ouvyuvhk9ye8uuNVWu2tjIscx63DbiU7NPug+3e73eTn55OXl0dbWxsBAQGcccYZDBs2bL8nvkdKY5ejT5DOK2ujrtMOQJhJy4SUYMYlB5ObHEJskP43H6M/kdxe3I22n4nRVqRe6xEUoArVo03035sVHWlAaRw4Gd+Sy4WjpKQ3K9pn1eEsKQGPTwhSBgfjl5WFaebp6LIy0WVmog47+oKEzMnNuiXLUQiBDMm24PL4s75pLOWtRbx02QgUCgGv1U3nF2Woo42YJsXgKC6m4a8PoR85krB77jlqcXg9Inmfl5G/rIawBDOnX5+JOdjv1zf8DUiSxIMLd7KqpJX/zBvK+JT+EcFPZH5o7eLOwmpcksQLg+M5z1UKr82Hph2QOhNmP743Y8TtgOYCn3C6Z1r/Mnh7/fk1Rp/o2ydcZ/sKBiqPcjda9EJrSa8Ynb83Q9rWtrdNUDJED4cRV0JEti872hR+dOPoTw4QrD37Z1jvJ1in7W8J0g/naVApmRniz8wQfwAqbE6WtVtY3t7dZweiEQTGBBiYEmRmWpCJdINOHhItIyMjI/OLWBxu7vxwK5FmHX/oVtLo/ZDmzPepEwN5uUXitTOeJdgvGEdBAbW33Y4iMY3Gcx/gh39sxu30kjY6nNFnJeIfeuC9qsPt5a9f7uTTTdWcHWUn0l7Brq12hg4dyrRp0wgICNivff3uQr584hGComKY+6eHUPdDzRWZAYK9E3Yt9AnTNetBUEDSVDjtIRg0GzT7f588ooRNFLF590xe3+t+y8SDL+tNMDuayAK1jIzMQZEkiZaW7ykrfwKbrQKDPgtr2QRWfVuESutk3Lz5jDjzXLR634+cW3TzRekXLMhfQJOtiRHhI3hi8hOMCB9x0P07nU42bdrE2rVrsVqtREZGMm/ePDIyMlD+hkylth4n68rbWVvWSl5ZG+WtVgAC9GrGJQVz0+QkxiWHkBxqGHA3lt5uV1829J7ChZ4WO/gcOhC0Sp9Fx4gwNHvE6HADgnoAZMf1Ink8OMvKe4VoXwFDZ1ERkts33Fzh749fVhbGa6/t841WRUQMuL+FzIlFi7UFweqH3q8Zm2MdUTE38cDCKoZE+3N6pk/M6lxUhujwEHrBEERrN7W334HSZCL6qScRjlL2SHe7gx9e3UljuYUhU2MYf34KSlX//X++sKKMjzbVcPu0FC4ceegCs6ciLlHkkbIGXq5tYYjRjwXJgSSt+TtsfhNMkXDhO74CMPv+9qh1PtE3evjeZV63zzJjX9F6y1vgtvnWq/x8Fhr7itahGaA6zIeELtteUXxPdnRTAXh8D1NRanz2I4Nm+/YdMQTCM0F7kmXKK1UQM8I3TfjDXsG6anWvYP0JbHrd1zYkzeeTuEe0Nh1d33iARL2Wa/WhXBsTilMU2dBp7ROsHy6r5+EyiNComRpsYkqQiUmBJgLlYosyMjIyMj/jr1/spKHTwYKwYCy8R2v6Z7QrE3iypolHJv6LzOBMXDU1VNx4GzUJp1MdNxPnjw0kDQtl9NmJBEcdfARxY5eDm97ZRGd9BVf6NyG19xCZmMiMGTOIioo6oH1LVQWf/+tvGAODOP+Bh9H9zpHJMscWSZJwSdIvC8VuN7amImz127C1VWITVNgCZmNLvhubfzw2hQa7W8SWX3PAti5JOqJ4NIKAXqnom442co9KRkbmANo78igrexyLJR8/XRJCy3msW7Qb2M3w2Wcz+twL0Zt92UYe0cPX5V/zUv5L1PXUMTR0KA+Pf5ixkWMPKj46HA42bt72wB4AAMxWSURBVNxIXl4eNpuNpKQkJk6cSEJCwhGJlV12Nxsq9grSRY2+IlBGrYoxiUHMHxPHuORgMiLMKAaIvYUkSnha7QcULhS73X1tlAFa1JEG/LJC+sRoZaBuwFh0AEiiiKuyEsfOndh37MSxcyeOwkIkhwMAhcGALjOTwCsu9xUwzMpCHRMji9EyR52PvnoNQa0jY2g5gqBkffPp1HZU88i5WQiCgH1nK/b8Fswz4lGF+VF762246+uJf/ttVKFHp0Bb1c42fnyjAK9X5PTrs0gZ0b+jABbl1/P498XMyYni7hmH9vI/Fam0O7lxVyX53XaujQ7hr7a1aF/7M9jaYdytPq+9wxV4leq9RQSHXeZbtifDeV/ROv+jvZ7Ke0TlfS1Cwgf7xOg9GdF7/KLbSkDqzTzR+fsytEdes7d4YUiaL4ZTjX0F6/F3+gTrxnyfWF25GnZ8Cpvf8LUNTt3fEuQoC9ZahYKJQSYmBpl4CKh3uFjR0c3ytm4Wt3TxQUM7CmC4Wd+XXZ1t1qOUr3UyMjIypzQLt9byxbZ6rjcaCPZ/n9aEr3Hph/NIcSGXD76Ks5LOwtnUwuq7XqA87Q5cajNxSf6MOSeJsPhDF6nbXNXO/W8vZ5CnnByNlVD/MGbMmENKSspB77M6Gur49NG/oNbquODBRzEEyLaJxwuvJLHb6mCLxUatw/ULWcreA8Ro72FpyCYwTATDRPwECT+VyiciuwT0Si96pYIwrdq3TKHYT2Q+YH6/Zcr9lql+pkkc7R6PLFDLyMj00d29i9Kyx2lvX4VWE4G651w2vV2Fx1FM5pTpjJt3CeYQn/jiFb18V/kdL+W/RKWlksHBg/nzmD8zMXriQS+Qdrud9evXs27dOhwOB6mpqUyaNInY2MPL/rO5PGys7CCvrI28slZ21HUhSqBVKRiVEMS9p0eRmxzMkGj/vqJoxxPR6cXdaN1PjPY0WpHcvYKEUkAdpkeXGthn0aGJNKDQDyxBQpIk3DU1+4vRBQWIVl+GuuDnhy4jg8CLLuwtYJiFJiEe4Th7n0qSRHebg+aqbpqrLDRXdR/XeGSOPpIk0birDq3SD6O5EP/Ac7j/myZGxgcyOS0Ur9VNxxelqKMMmKbE0LZgAT3LlxP+4IPohw/73ccXvSIbvq5g87dVBEcbmXVDFgHh/WsZtKmynXs+yWd0QhD/mTdUfuizD180dXBPcQ1KQeCNOA1nrLrJZxsRPRIu+9wn/P5eFEoIS/dN2Rf5lokidFRAw7a9onXhIl+2NfiGVkr7DIH0j/UJ0Jlz9xYv9I8dkD6AAwKlCqJH+KY+wXr7LwjW4yFhoi/T2hx5VEOJ0mmYHxnM/MhgPKLEtm4by9otrGjv5r+VjTxR2UigSsmkIBNTg0xMDTITrh1Y13QZGRkZmf6lus3Ggwt3kq1SMTP2Y9pjv0cXNJP/27GO0ZHjuCPnTnYtq2DdB9txhMwgIlLFuEuHEpUS8Iv7fXPZdlatWEauohM/o5GZp80hOzsbxSHuubrbWvn00b8giSLz/voY5lDZRvFY0uJys9ViY7PFxuYuK1u7bVi9vv6gAIcUh4M0mp8tU+4/77Gir1mLvmwJ+pYC9JIbfdwo9IPPQp86HT+NFsUJ3KeUBWoZGRlstirKK56iqekrVCp/dK6zyP+wCXtXMWljxpN70WUER/uEZFES+bHqR17Y9gJlXWWkBqby9NSnmRY77aBiic1mY926daxfvx6n08mgQYOYNGkS0dHRvxiT0+Nla3Vnr4d0K9tqOnF7JdRKgWGxgdw+LZVxycEMiwtAqzr+xYskj4htazOO3R24G6x42vax6PBToYk0YBgdsdcvOkyP0I8WAL8Fb08PrtJSnGVlOEtKce4uxr6rALGrCwBBrUabkYH/nHPQZQ1Bl5WJNikJQXX8LyXWTmefEL3n1dHTay+iFAiJkYeznWzklaxGJQQSG7cTUbSR1zqXJksHT180DEEQ6Pq6HNHmIeSaLKxr19Ly7P8wn302gZfO/93HtnY5WfLaLup2d5IxPpJJF6Wh0vTv71BFq5Xr395EdIAfCy4fMSB+9wYCdq/IX0vreKe+jZEmHS9aviH2vcd8HntnPQXDr+rfYoEKBQQn+6as833LJAm6anoF6+2gM/cWLxwC+qD+i+VUQKnaa8cy/o4DBeudn/vsXACCU3qzq4++YK1SCIz0NzDS38D/JUbS7vbwU3t3n2D9ZXMnAIMNOqYGm5kaZGK0vwHNQChcKSMjIyPTL7i9Ire/swnBJXLToMVYYr8nJOJi7t6ZR6hfOLca7+eThzfR2WzHZGtnwllm0i4+eHLXHto7LTz1zkKE1gqilSrGT5zC5Am5h6zVJEkSDSVFfP/Sszh6urnwr48RHCPbwfUnLlFkZ4+dLb1i9GaLjWqHCwClAJkGPy6ICGKEWc8Is4FEP82RJZl4XLD7O5+vdMkPvsLekdkw/koYMg8MJ08tmuOvKsjIyBw3nK5WKiueo67+AwRBhZ5Z7PysC0tjGfFDhzHh4iuISE4FfBe7FTUreH7b8xR3FJPon8jjkx9nZvxMFMKBN1xWq5W8vDw2bNiAy+UiIyODSZMmERl58BtEj1dke11Xb4Z0Gxsr23F6RBQCDIn259oJSeQmBzMyIRC9ZuD8dIkuL9YNjfSsqsXb5UIZoEUTbUSfE+oTo6MMKP21AyrT0dtjxVVWirO01CdEl5XhLC3F09DQ10bQatEkJ2GeORPdkCz8srLQpqQg/I7ClUcLe4+L5qpuWqosNFX6Xq1dvk6AoBAIijSQODSEsAQzYfEmgqOMKNUK+PNxDlzmqLLk6y9AYSAusRaNPpc3llgZ31sA1V7Qhm1rM6bpcSB2UX/PPWhTU4n8+99+9/9iXXEHP7y2C5fdw/QrM0gfd3SzNA9Gu9XF1W9sQBAE3rhqFIGG4/9/OBDYbXVw465KCq0ObjPZ+NOaa1B3lMHQi2Hmw2A8TtlCggABcb4p4+zjE8Opws8Fa9H7y4J1fG+GdcKEoypYB6lVnBseyLnhgUiSRIHVwbI2n1j9ck0Lz1c3o1cqmBBg7BOsE/y0R+34MjIyMjLHn6cW7iK/sZu7Etagif+W2Lib+EfxNgIaYzmn/VrW/FiJWdHNkJ3vM+QPFxE4b+oh9+V0OvlxxSrWrctDEEXUEancedkczKaDJ924HHYKV60gf8liWqoq0OoNnPt/fyU8KaWfzvbURJIkap1uNndZfYK0xcrOHjtO0ZeZFqlVM9ys56roEEaY9Qwx6X+bV7MkQf0W2PYB7PwU7B1gDIexN0P2fJ+F3EnIwFF5ZGRkjhkeTzfV1a9RXfMaouhEr5xE8WIPrRVVRKSkcfpf7iUuKxvw/QivqV/D81ufZ2fbTmJNsfxzwj+ZnTgbpeLADL7u7m7Wrl3Lpk2bcLvdZGVlMXHiRMLDww9oa3V6+GxLLSuKW9hQ0U6P0wNAeoSJS8fEk5sczOikIMy6gTdEVnR46MlroGd1HaLVjSbBTOD5aWhTAwaMGL1XiPYJ0HumA4TopCT0I0eiTUlBm5KMNiXF5xn9G4pVHm2cdg8tfZnRvuzo7jZH3/qAcD3R6YGExZkJSzATEmtE3c+ZrDLHH6vbirdNQXhYKZLURl7LpbT2uFgwYxCizU3HwlLUEQaM40KpvuJyJFEk5n/PotD/dgsOSZTY/H0VGxaV4x+m55w7cwiO7v/MfIfbyw1vb6K+y8EH148hIcTQ78cc6EiSxEeN7dy/uw69QuL9zs+ZtvIZn8XDFYsgafLxDlHmeKFQQtQw35R7+4GC9a6Fe+1XgpJ935XEyZA46ahltwuCQKbRj0yjH7fHh9Pj8bKms4fl7d0sa7PwQ5sFgEQ/DVODfGJ1bqARwwC45srIyMjI/DZWra7ipU3VTAkpJSvtI5IS7+H9zU6S1k0nvCcBZaia0SHFGD59irC7/kDgvPMOuh+v18vWrVtZsnQZTruNOimIWTNncOH4jIO2b62uZNuSbylctQyX3U5ofCKnXXcrGRMmo/HrX+u5UwGrx8u2bhtbLLY+QbrZ5dMsdAqBbJOea6JDGGE2MNysJ0r3O5NILPWw/SOfMN1aDCodpJ8J2ZdA0lTfg/mTmJP77GRkZPZDFJ3U1r1PZeULuN3tGDTjqPhJR/3ORoJj4jjnngdIGbm3uOGGhg08t+05tjZvJcoQxd9z/87ZyWejVhwoGFssFtasWcPmzZvxer0MGTKEiRMnEnqQQmTtVhdvrq3krbWVdNndJIYYmJMTRW5yCGOTggg2DtysIm+Pi5419fTk1SM5vGjTAjFPjUWb6H/cYhKt1r22HKWlOHuzoz31+wjRGg2a5GT0I0b4hOjUlAElRAO4nV5aa7r3s+nobLL1rTeH6AiLN5M1OZqweDOhcSa0fvJlbA+CIMQCbwMRgAi8LEnSM4IgPAzM6V3WDFwlSVJ97zb3A9cCXuAOSZK+Py7BHyEf//AWCrWBhJRKJFUK725WMC09mBHxgbR/XIxodRF85WCa/vkojoICYl54AU18/G8+nqPHzY9vFlC1s43UkWFMuSwdja7/v3uiKHHvp9vZVNXB8/OHMyJetofo8Xi5b3ctnzZ1MF7RxfPrbifC2QRTH/Rl0KoG7vVD5jhwUMF6h0+srvgJtn8Mm14HBJ8FS9IUn2gdNw40R+dhkFGl5PQQf04P8UeSJCrsrj4rkA8a2ni9rhWNIDAmwNAnWKcbdAPmYbeMjIyMzC/TuLWJP369kzBdF/OGLiA88F6WvJdAQK0GyeBi6mXphBZ+S+t/nyTw8ssJvuH6A/YhSRLFxcX8+OOPtLa20iwZqdTm8PhVU8mM2v8+0+N2U7JuNfk/fktdUQFKtZpB4yaSPWM2kamD5OvHb0SUJEptTjZbrL3+0VYKexzsqSSS5KdlUqCJEf4+MXqwwQ+14ih81i4bFH0D+e9D+Qpf7ZLYsXD2MzD4XPAL+P3HOEGQ7+xlZE4BJMlLY+MiyiuexuGoRa/NpmXrcLZtqMMcGsasW+4iY+IUFL0Z0Vubt/Lc1ufY0LiBMH0YD455kPNSz0OtPFCY7uzsZM2aNWzZsgVRFMnOzmbixIkEBwcf0La+084rq8r5cEMNdreXGYPDuXlKMsPjBn5FYU+Xk56farFuaETyiPhlBmOaEosmxnTMYugTovsyoksOLkQnJaEfPgLthftkRMfGDhghGsDrFmmt6/HZdPTadbTXW5F6fbsNAVrC4k0MGhPuE6PjTfgZZVuDX8ED/FGSpC2CIJiAzYIgLAEelyTpLwCCINwB/BW4SRCEwcDFQCYQBfwoCEKaJEne4xT/YVO6sYAgoxedXxOrW+6iy+7m7hlp2IvasW1pxjQtFtvab+n6/HOCb74J07RDD6H8NRoruvj+lZ3YLC4mX5JG5qToY9bx/++SYr7Kr+dPs9I5c2j/W4kMdHZ227hxVxUVdgf3tn7LH3Y9jjJlGsz+AoKSjnd4MicCCiVE5fim3NvA64a6LVCxEspXwroXYe2zoFBD7GifYJ042WchcpA+0JEiCAJJei1J+lCuiwnF4RXZ0GXtE6z/UVbPP8ogQqNmarCJKUEmJgWaCFTLt2wyMjIyAxHrlibu+2QzbYKX+7NfRdl8LSs/T8Ou7qYzu4C/XnM79u8WU//fJzDPPoPw++87oB9ZW1vLkiVLqKqqAq2Jpa4UYuKTefeyEQTtY+vW2dTI9h+/ZefyJdi7LQRERDL5smvInHIafibzsT71E552t6dPiN7SZWNLtxWLxydHm1UKhpsMzIz3Z4S/gWEmPcFH02ZUkqA6D7a9D7u+AFc3+MfBxHsg+2JfbZNTELm3IyNzEiNJEm1tKygre5weazF+2lRsu2eybXkNen8rU6+6kaGnzUKl9t107WzdyXNbn2NN/RqCdcH8adSfuGDQBWiVB2akdXR0sGrVKrZt2wbAsGHDmDBhAoGBB4rNpc09LFhZxhfb6hAlmJMTxc2Tk0kNP3bi7m/F02ane2Ut1s1NIEnoc8IwTYlFHdZ/Q6ZEqxVnefl+GdGuklLc9fV9bfqE6GHDe4XofTKiB0DRwn0RvSLtDba9RQwrLbTV9SB6fWq0zqgmLN5MYnaozzc6zoQhQM6CPFIkSWoAGnrfdwuCUAhES5JUsE8zA33lO5kDfChJkhOoEAShFBgN5B3DsI+YgoadqL0BxMauwiGG8/F2E2dkhTI42EDTW5tRhetRh3ZR/cdHMIwfT+htt/2m40iSxPZltaz9vBRDgJbz7x1BWPyx6/x/tLGa55eXccnoWG6afGqLr5Ik8WZ9G38rqSXQa+PT7feT622EC173ZZbImUIyvxWlGuLG+KbJ/wcuq++GsXylT7Re/k9Y/ihojD7/6j0Z1mGDj8r3TqdUMCnIxKQgX3+o3uFiRW+xxcUtXXzQ0I4CGG7W92VXZ5v1KOXvvIyMjMxxpyevnre/3sYKSeL8lMVoCmZQ0zqC7Uk/0JhYwDtz3sa1fh31DzyIftxYIv/1L4R9iuW2t7ezdOlSdu3ahZ9eT6P/YL5v0nP1+CT+PDsdlVKBKHop37KJ/CWLqczfgiAIJI8YQ/bM2cRnZe+3P5lD4xYlCq12NvcWMtxisVFudwKgADKMOuaEBTK8t5Bhil6L4mhfa11WaN0Nu7/3FTzsqAS1ATLP9Vl4xI/v38LeJwADS8WQkZE5anR1baG09D90dm1Eq4nGUzuDvMW1aHRtjL/wMoafOQeNzg+AovYint/6PCtqVxCgDeDuEXdz0aCL0KsPFGHb2tpYtWoV+fn5KBQKRowYwfjx4wkICDig7fbaTl5YXsb3BY1oVQouHRPPdRMTiQkc+H5Y7kYrlhU12PNbQClgGBWBaVIMqiDdUTtGnxBdWtaXDX2AEK1Wo0lKwm/YMAIuvABNsi8jWhMbO+CEaPD59HY22/qE6OaqblpruvG4fU+jNTolofFmck6LJTTOTFiCCVOQPJT5aCMIQgIwDFjfO/8ocAXQBexJJ44G1u2zWW3vsgHNF1+8h1bvJiikjiVND2JzeblrRhqdX5fj7XERPCeG2tsuRxUaStQTj/+mkQNOu4flbxdStrWFhKEhTL8yA53h2Hnhrypp4c8LdzIpLZR/zMk6pf8/utwe7i6q4ZvWLqZ1beHZgn8QMuwimPoA6ORsIZmjjMYAKaf5JgBbu88KZE+GdUmvC5Ih1OdbvSfDOvC3WwjtS5ROw/yoYOZHBeMRJbZ22/qKLT5R2cjjlY0EqpRMDvJlV08NMhOuHXh1OmRkZGROZiRJontFLflLt/O05GZQYCXZzekkZ87meelhyu0lvD/jfdRFlVTdcSfatFRi/vc/FL3F5q1WKz/99BMbN25EqVSSMXwsz+9S0Gjx8sQFQzh/RAw9He3sWPY9O5b+QHdbC8bAIMadfwlDps/EFBRynD+BgU+D08XmLlufXUd+tw17byHDUI2KEWY9l0QGMdysJ8ekx6A6iiON7Z0+IbqlCFqKfVNrMXRW9zYQfH2IKff7imkfJUuxk4FfVTcEQXgdOAtoliQpq3fZ48DZgAsoA66WJKmz94a4ECju3XydJEk39UfgMjIyB6fHWkJZ2RO0tv6IWhUMbaex8YtGFEIzI886j9Fz5vUNASrtKOWF/BdYUrUEk8bE7cNu59KMSzGoD/yRbGlp4aeffmLnzp0olUpGjx7N+PHjMZv3FwgkSWJtWRsvrChlTWkbJp2KW6ekcNX4BEIGsLf0Hlw13ViW1+AoaEPQKDBOjMY0IQal+bfbS4g2G86y8j5bDlevRYe7rq6vTZ8QnZNDwAXz0PRmRA9UIRp8f2tLq4PmKgste3yjq7txO3wOESqNgtA4E5kTowlLMBEWb8Y/1A/haHh1yRwSQRCMwGfAHyRJsgBIkvQA8ECv5/RtwEPAwf4Q0kGWIQjCDcANAHFxcf0R9mHh8rqw1zpITSnC4vLny4Iw5mRHEtfloXVTE8ZJ0TQ/8SDetjbi338f1UFGdPwaLTXdfP/yTixtDnLPSyFnRuwxFYiLG7u55d0tpIYZeX7+MNS/pfL3ScKWLis37iilweXhr+ULuEksRXHVFz57BhmZY4E+yJfZlHmub76zZq9YXbESdn7mWx6Y4BOqk6b4bjoNv188UCkERvkbGOVv4E9JkbS5PPzU0c3ydgvL27v5orkTgEyjjilBZgYbdMTqNMT6aQjXqI9+5peMjIyMDJIk0bqwlM5thTyobUbtNHNzYgQzZ83l0e3/YEfZNp6d+izRHQJVN96EKjiYuAULUBqNuN1u1q1bx+rVq3G5XAwfPhxnaDoPflNKoF7JJzeOIrCrmq+efIfSTesQvV7ihw5j6lXXkzxiDIoBZNc4kLB7RbZ329hssbHF4suOrne6AdAIAkNMflweFeLLjvY3EKNV//6+vSSBtdUnQrcW7xWiW4qhp3FvO5XOV8Q7ZjQMuxxCB0H0SPAf8DlBx4XDUT3eBJ7DV3hpD0uA+yVJ8giC8G/gfuBPvevKJEnKOZpBysjI/DoORz3lFc/S0PAZSoUfyp6pbP+iDZe9gSFTZzJ23sV9T1sruyp5Mf9Fvq34Fr1az41Db+SKzCswaw7MRmtqauKnn35i165dqNVqxo0bx7hx4zCZ9rfnEEWJHwqaeHFFKfm1XYSatNx/Rjrzx8Rh0g3s7B5JknCWd9G9ogZnSSeCnwrT9DiMuVEojyBrUnS5cBbv9llylJb2WXQcIEQnJuKXnU3AvPN7M6JT0cQNXCEafJ+RtdPVa9Nh6Stk6LT6qhgrVAIh0UYGjYkgLN4nRgdG6FGcwuLa8UAQBDU+cfo9SZI+P0iT94Fv8AnUtUDsPutigPqDbIMkSS8DLwOMHDnyoCL2sWDRuk/R6PyIiKng67o7cItw+8QkOt4uRhXmh7NwEba8dUQ++gh+WZlHtG9Jkihc08BPH+5GZ1Bx7t3DiEoJ6J8TOQTNFgdXv7EBvVbJ61eNOuzfzpXt3XzZ3IFHkpDw9ZklfMVeRPa+37NOROpd9svrRKS+fe1pp1EoMCj3nZR736uUB6wzKhXo92mnUwi/elMgShIvVdbzz8omIh1NLCp9guFjLoaRL/o8hGVkjhcBsTDsMt8kSb4b0fIVPrF610LY8pavXfgQnxVI4mSIzwWt8XcfOlijYm54IHPDAxEliYIeO8vbu1ne3s2CmmY8+/wyawSBGJ3GJ1jrNMT57fNepyFUozqlR2bIyMjI/BZsXU6qXtmBrqea/4Zupqoul8fO0HPu5Km8W/Aui8oWcUvOLUzQZVJ1yXwQBOJeexVFcDDbtm1j2bJlWCwW0tLSmDb9NN7Y3MaCL3YzNlrHzdGdbH7yfjrqa9EZTQw74xyyT5tFYKQsZO7LnmLDWyxWn12HxUpBj73vGhin0zDG3+ArZGjSk2nyQ/t7bDMkCSx1+2dCtxT7hGl7x952GqNPfE6e5nvdMwXEy33XI+BX1RBJkn7qzYzed9kP+8yuA+Yd5bhkZGQOE7e7k8qqF6mtfRtJklC7cin4sgdreyODcicx/sJL+y5std21vJT/El+Vf4VWqeXqrKu5OvNqAnQBB+y3oaGBn376icLCQjQaDRMmTGDcuHEYDPtnV7u9Il9sreOllWWUtViJC9Lz6Nwszh8eg049sH+MJUnCUdxB9/IaXFUWFEY1/mckYBgbiUJ7eGKxJEk4du6k8/PPsXyzGNFiAfYVoofif/55vR7RA1+I3oPN4qKluns/32ibxQWAoBAIijKQlBNKWLyZsHgTwdFGlCpZjD6eCD614TWgUJKkJ/dZnipJUknv7DlAUe/7RcD7giA8ia9IYiqw4RiGfMRsW5FHXHgTXW4D35bEM294DEHrm7FanBhGumh8YAEBF1xAwPnnH9F+3U4vK98vpnh9IzHpgcy4JhP97xg18VuwOj1c89ZGOu1uPr5xHFEBfr+6TZfbw9/K6vmgoZ0AlRKjSoGAgAJQCPjeC75UeQEBQWD/dQACKPZpp9inHYBC8LUTerdxiRJNTjdWr0iP14vVK2L1igdPvT8ICsCgVGDsFbP1+wrdCgVGwUNFZztrXRrObPmJJ1W78b/qfTCFH/FnKiPTrwgChKX7prE3gdcDDdugfLkvw3rDy5D3HChUEDOqN8N6si9zSvX7fl8UgkCWSU+WSc/t8eHYvSK1Dhc1DhfVva81Dhc1dhfftnbR5vbst71Osb+Ava94HeunIUQtC9gyMjIye3Da3Gz7oRphdR0h5ia+SfuU77Zfwbxhei6ZPJX1Det5YtMTTIudxnWJ86m5/Eo8HR3Ev/UW1W43SxYsoKmpiaioKM477zwCwqK4/f0t7N5VwA3aKvTrd7DJ7SIyLZ0zbr2btLETUGnk4vAAbS4PO3psbLHY2NxlY2u3lXa3b7SuQalgmEnPrXHhjDDrGWbWE6r5jYlxohc6q/bPhG4pgtYSXwHDPfgFQmg6DJ7jew1J872ao+SaKEeBo6GSXAN8tM98oiAIWwEL8KAkSasOttFAGS4sI3Oi4vXaqal5k6rqBXg8PWjF0RQv9tBZ10ZizgjG/98VhCf6qr82WhtZsH0BX5R8gUJQcGnGpVyTdQ0hfgcOQa2rq2PlypXs3r0brVbLpEmTGDt2LHr9/r7RNpeHjzbW8MpP5dR3OciINPPsJcOYnRWBaoBnzUqihH1nK93La3A3WFEGaAmYk4xhZDjCYYrq7uZmLF99RefChbhKyxC0WkwzZmA6bTratDQ0cXEnhBAtihJdzTZaa3toremhtbab1pqePjEaAQLD9cRmBBEabyI8wUxIjBGVZmA/fDhFGQ9cDuwQBGFb77I/A9cKgjAIEIEq4CYASZJ2CYLwMVAAeIBbJUnyHvOoD5OarhqUdgPR0YV8UXs1IHBDUijWj0vxyzbS/M/r0Q0ZQvhfHjyi/bY3WPn+lZ20N1gZdVYiI2cnoDjGNjReUeLOD7dSUG/h1StHkhXt/6vb/NDaxf8V19LidnNHXBh3J0SgO06/vZIkYRclrF4vtl7B2uoV6fF4sXq9WB1WrHYLVnsPNqcVq8tBj8OF1ePB6pWwitCMAhsqrEodIgoea/2Wq8aehZDyh+NyTjIyR4xSBTEjfdOke8Fth+p1vZYgK2Dlv2Hlv3wFkeJz92ZYh2f97qJIfkoFqQYdqYaD18mwer3UOtxU2517xeteMTu/29Z3s9+3v30E7Dg/7f4Ctk5DkFopC9gyMjInPW6nl+3La8j/oZpsQSIgsJ6CES/y6rpbSAxW8/DcSdR213LPyntIMCfw6OiHqL/5dpxlZWgff5xPtm6hvLycgIAA5s2bR2ZmJruqW3jwby8S07iNHGcLaq2OjMnTyJ4xm7CEU7sodpPTzfZuG9u77ezosbGj205dr1WHAKQZdMwK8We42cAIs540g+7IiwZ7XNBe3is+7/GJ3g1tJeBx7G1nivSJzznzIbRXhA4Z5LPwkq9//cbvUk8EQXgA303te72LGoA4SZLaBEEYAXwhCELmHg/MfRkow4VlZE40RNFFfcOnVFT8D5erGa0ih6plGlpKLESlZXD6Q1cSMzgLgBZbC6/ueJVPdn+ChMS8tHlcP/R6wvRhB+y3pqaGlStXUlpaik6nY+rUqYwePRo/v/2z+Lpsbt7Kq+TNtZW0W12MTgji0fOGMCUtdMDfrEheEdvWFrpX1uBpsaMK8SNwXhr6YaEIhyHsiE4nPcuX07lwIdZVq0EU8Rs2jIh//B3zGWeg/JntyUDD4/LSVmftE6Fba7tprbPicfpuTBUKgcAoA3GDgwiJNRESayQ01oTGb+AL7TIgSdJqDu4rvfgXtnkUeLTfgjqKfLLoDUIimuiW/FhWOYj5I2PQ/1CDEKyl64N/IKjVxDzzdF8BmsNh94ZGlr9XjFqj4Jzbc4gdHNSPZ3BoHv66gB8Lm3l4TibT0n85U7jD7eEvJXV82tRBhkHHW0MTyTYdh8Kzzh6wNkNPM0JPM/qeJvTWFuhpgp7e1971+3X49yAofYXmjKFgDAdDGBh7J3MUTH8U1EevKK2MzDFH7QfJU30T+IYCV672idXlK+GHJb7l+mCfb/WeDOvAxKN+82tQKhlkUP4/e+cdp0dV7//3zNPbPs/2vtmS3WTTewKhBEJJgEAAsYsKysXey/V6Fa969ao/+9UrKqIIKohBQpGaQgIhPdnU3Wzv/el95vz+mGdbEkjbJJtk3q/X5MycOTPPzO7mOXM+8z2fL1PeQsAOJpUxonVrNE5bNE5LJM5OfxhvcqyAbTfIYwTro61EPEZdwNbR0blwSSYU9m/sYMe/mkgEE1ydbcVoqad1yY95aO/dhJJp/Pm9ixFSjM+u+yyKUPjZ1T/B+7Vv0bt/P/X33cfBN7dgs9lYsWIFCxYswNfVwcP/7yd07tjEXDWOI6+IJTd9jOorr8FiPw/PcecRIQRtsQQ1AU2EHhKke+LabB8JqLBbWOR2MNNlZ6bTxuw0O2mnksgwHtZE597asT7RAw2gjppV5CnRxOfyq7Uye4omTNs843rPOifHaasOkiR9EC154nIhhAAQQsSAWGp9hyRJ9UAVsH0crlVH55ImmQzQ3vFXWlsfJhbrwmKoondLOe17fGSV5LP6y5+jfN5CJEliIDrAQzUP8dfDfyWpJlk9eTX3zbqPAmfBMedtampi48aNNDQ0YLfbWb58OQsXLsRqHTuI6fZH+f2mRh7d0kworrB8ag4fW1bBgtLzI+icCiKhENreTWBDG4o3hinfQcZ7p2KbkXXChH1DFh6+NWvwPfscqs+HMS+PzI9+FPfq27CUlZ2juzg1IoE4fa1BeofF6CDerhAi9TrQbDWQVexi2uX5w2J0Rp4Dg2liR7/rXJooqkL/4T7mzzvAmqY7MMoy70+aUXw+iG8gdqSWkt/9FlPBsd9xxyMSiPP6mnoOvd5J/mQ3N9w7A2f6+Uni+tCmRh5+vYmPXFHGBy4rfdu2z/Z6+WptG4OJJF8ozeUzk3Ixy7Imdu35i/bALQQIVVsYWj+6HL3vqP3HrR/VPurTBOhE6DhXKGmRJUNic2aFVjpyNBF6SIB25oIt44yjRnV0Lihs6VC9SlsAfO3QuHEkwnr/Gq3eUzI24aLz2KCC8cZpNFDttFHtPL61kH9IwI6Mjr7WorG3eIMEFHXs+QzysF3I0dHXxVYzbpP+4ltHR2fioSgqh17vZPtzTQQHY0yqdDNHVQmLGtoW/pQNndews7uKr988lekFaXx545epHazlV8v/F37+JzZ1d1F36yrweVm6dCmXX7aE1j07+fu3/4P2Q/tRkPHlVPP+D7+HGXNnXxIv8lQhaIrE2RsIUxOMDIvSg6kXnwYJquxWlmW4mJUSo6c7bThPVoyO+kdFQo/yiR5sZjj3u2SAjHJNfK5eNWLNkVUJZsfbnl7n3HJaTweSJK1AS4p4tRAiPKo+GxgQQiiSJJWj+Vk2jMuV6uhcosRiPbS2Pkxb+6MoShCbcQbevZNpeqMPd66Nmz75EaYuvRpJlvHFfDy8/2EePfgoMSXGLeW3cP+s+ylOKx5zTiEEjY2NbNiwgebmZhwOB9dffz0LFizAYhkr0jT1hfjNxnqe3NFOUlVZNbuA+6+uoDr/2ISKEw01miT0ZieB19pRgwnMk9LwrJ6MdUr6CR8I3srCw337ahxLliBNkCzKQhX4+iIjEdEpq46QNzbcxpluIavYRcW8bLKLNDHalWm9JB6KdC4O1u1/iXRPkrBJZnP7DD44Iw/Hzl6MGV4GH3qU7M99Dsfll5/wPEpSpWZ9G9uebSIZU5h34yQW3VqG4TxZY7y4v4tvP3uAG6fn8rWbqt+yXV88ydfq2ni6x8tMp42/zq5gutOm+eW9+l3Y+ENN/LK6QZJThtGytjBqXZJOsE9+i0UaaZ9VNTbaebQAbc/UbA50dHROjLsQ5rxHW4TQfC6HxOqDT8OuR7R2OdNH7EBKl4Ll3M/WSjMamJ4SDY6HL5Ec632dir5uicTZNBgkdJSAnWaUKbFajht9XWzVfVd1dHTOPa2HBlj/6GH8vRFyy9K49s4KTOvb8Bt20T73Z/QlpvPYgRu5qiqLe5aW8Yf9f+BfTf/iM7M/Q/Svb/JoNEq8uprZs2ezYNZMmrZu5k+f/xhhn5e4LZ1t6UuovPJa/utdi7GcSiTwBYQiBEfCMWpSNh17A2H2BSMEU32ASZKodli5KdutidEuG9UOG7aTeQ4P9Y+NhB6y5giMyu9uMGvPqQXzYPYoa46McjCen0AUnVPjhKMISZL+AiwDsiRJagO+Cfw7YAFeSgkcW4QQ9wNXAf8lSVISUID7hRADZ+nadXQuakKhBlpafktn11MIkcQqzaNjs5nOfYM40wXL7/k4M5ffgMFoIhAP8MiBR3jkwCOEEiFWlK7g/jn3U+4e62MlhKC+vp4NGzbQ2tqK0+lkxYoVzJs3D/NR0+L3tfv49YZ6nq/pxGiQeefCIu67soKSzIk/BUkJJQi+3kFwcwcimsRS6SHtmmLMZe63FWXVeJzgq+vwrVlDcNMmUBRsc+aQ961vkbZyBYa08yvKJxMKAx2hsX7RbUESUe0NtCRLpOfZKZqSTlaxk6wiJ1lFLqzO00wWcREQTSg09oVo7DtetKfOhcLGf/2L6uLDrKlfhdVo4K7WOLJTYvDP38B53XIy7/voCc/RvK+fTU/U4e0OUzI9gyvuqiQ97/xFTexp9fLpv+5iVpGHn75r7nF9r4UQ/LPHy9fq2ggmVf69LJ+Pl+RgkiUIdMGTH4Gm12DO++GmH+hRIDo6FyqSlBpIV8Gij2ovnzp3a7MjGjfA9odgy6+0KLDC+Vp0dfnVWvLFCTDodpuMzDQZmXkcuyEhBINHRWAPidkNkRjrBwJEVPU4Z9XR0dE5N9Rt7+blPxzAnW3jpo/PojDPRv/v9+Ozb6d91i8wWir43e77cVoUfnTXLF7v3MxPt/+Um+0343+2m02JBIVGI3OuXErb9td5/G8PISGRO2Me62OT2JHM4Zu3Tuf9SyZdNAFCcVWlNhRlbzBCTUCLjN4fjBBRtahlmywxzWnjHXkZzHLamOmyMcVh1Wb+HY9EFHytWuSztylVtmiJCwebITJKVjQ5tP6y/OqRJIXZU8AzSQ+UuMA54W9PCPGe41T//i3aPgk8eaYXpaNzKePz7aS5+UF6+15GlswYY/OpeymGty1EVnEWKz7+IaYuvQqD0UQ4EebRvQ/z8P6H8cf9LC9ZzsfnfJyq9Kox5xRCUFtby8aNG2lvbyctLY2bbrqJuXPnYjKZxrTb2jjAr9bXs6G2F6fFyH1XVXDPFaXkuCa+F6jijxN4rY3Qm52IuIp1WqYmTBe/dbSRZuGxP2Xh8axm4ZGbS+a99+JevRpL+fmx8IiGEvS1BsaI0YOdYdRUp2+yGMgqdjJ1Sf6wGJ1R4MB4kkkeLyZUVdDlj9LQG6KhL0hDb4j6Xq3s8EWGbU10LkwGIgNYokmilgRbu2bzkaJ00trihGsexJyfS8H3vve2D/uDXSE2//0Izfv68eTaufkTsyideWyC2HNJ60CYe/+4nSynhd/dvQDbcZKOdscSfLW2jef7fMx12flJdTFTHanoxfpX4R/3QTwEq3+tJXDR0dG5eJBTQnThfLjy89rAvfXNVIT1BnjtR7DxB2C0waTLtOjqrCpIywdXgebvPkEsdCRJIsNkJMNkPK5fvhCC/sQoD+xIjE+dh+vU0dG5NNm3oY0Nf60lv8LNzZ+YjTQQpe83Nfgz36Rj6q9wOqfybMc3OdzdyUMfWkBEdPO9f32PFf0rsIat4O2lOhLAb1TZ+ODPcHjSWXLHu/AVz+fL/2rBYpZ59MPzWFyeeb5v9bSJKCoHQ0NCdIS9wTCHglHiqUGW0yAzw2njAwVZzHRpYvRkmxXj6OALJakJ0EOC8+jS2wKBzrEfajBrlleeEsifo9lxZE/REhWmFU6YPk5nfNFfL+joTACEUOnrX0dz84P4fNsxGNKQ/Jdz8F9+IoNeSmbM4toP3Enp7HlIkkQkGeHx/Y/x+5rfMxgb5Kqiq/jEnE8wLXPamPOqqsrhw4fZsGEDXV1deDweVq1axezZszEajaPaCV491MOvN9Szo3mQLKeZL904hfcvmYTbNvGjb5MDUQIbWglt7wZVYJ+djWtZMaa3iY5M9vbiW/sMvjVriNXVaRYe112H+/bbcVx27iw8hBAE+qNj/aJbAwQHRyw6HB4LWcVOSmdlkZWy6HBn2U7on32xEYwlaUgJzw29Qer7QjT0hmjqCxFJjCRwcpgNlGc7WVCaTnlWMeXZDsqzHcz4n/N48Tqnzd9efJjCkmaealiJyyxzR1scNbCPZMd+Sv/217dMThoLJ9j2bBM169owmmWWvmMyM5cVYTCe3wdaXyTBPQ9vI55U+Ot9i8l2jY1+FELwRPcg36hrJ6qqfKOigPuKsrWHfFWB9d/XLD2yp8AHn4GcqefpTnR0dM4ZJqsWKVZ+NSwHIl5o3jwSYf3yN8e2l43gzEsJ1vla4tFjyrwJMetCkiSyzEayzEbmpmkCti5Q6+jonG2EEOx4vok3n26kdGYmN350BkpHkN4/7Mdf9DodFb/B7Z6Lz/ZD/vjGPj50eSkVaVF++rufstC/EKvRgLv+AOFYgDYJSmbM4uoP3EvZvMX8+rUmfvzPWmYWuvnNB+ZT4Dm+PdJEJJRU2BeMUBPULDpqAhFqw1GUVMCPx2hgpsvGR4qymZUSo8tsFmQhtATZ3lqobzkqErpZy7sgRiXclWRIK4L0SVCxXBOi0ydpUdDpk7Q+TBehLzl0gVpH5zyiqjG6utbS3PJbwuEjmIw5JLuuYN+/+lESXqZcdiULbrmd3PLJAPhiPp6ofYJHDz5KX6SPy/Iv4xNzP8Hs7NlHnVfl4MGDbNiwgZ6eHjIyMrjtttuYNWsWhlHCa1JReWZvJ79eX8/h7gBF6Ta+fdt07lpQjPUCiMRN9IQJrGslvKcHJAnH/FxcVxdhzDz+Q4AajxNct16z8HjttXNu4aEkVQY6QyP2HKnkhfFIKmOxBJ48B/mTPWQVO8kucpFZ5MSedun4MSYVlbbByHAkdENfaFiU7gmMiPayBMUZdsqzHFxWnjksQldkO8lxWS6a6XOXOkIIOnbXUjQ9yu6Ds7jPZiNNihJY/xsKfvDfWKuqjjlGVQUHN3ew5Z8NREMJpi0tYPGt5RPi/1E8qfLxR3fQ1B/ij/csYnLOWHG9IxrnS4fbeGXAzyK3g59MLabCnpq9olt66OjoDGHzwNSbtQW0xKXeFs2L0985tuw9BPXrIB449jwW93FE7FQU9gSMxr6YkCTpIeAWoEcIMSNVlwH8DSgFmoB3CiEGU/v+HbgXzUbz00KIF87DZevoXBQIVbD570fY82orVYtzufbuauK1gww8dghfxUY6S35HRvpSCsp/zv2/2MbkbDsz1XoeenAHTuw4A33Q3kxCUZlz3QrmrrqdjIIigrEkn/jrbl480M3tcwv53h0zJ/SY2pdIphIXRoYTGB4Jx4ZSC5JlMjLLZePGLDcznVZmmhIUh9qQfEeguwUOjY6EbgElNvYDnLma4Fy8GGZOGitCu4vAMPED4XTOLbpAraNzHkgmA7S3/4XW1oeJxbsxGyYRrL2MIxsGMZnDzL7uNuatvI20bC1ze4OvgUcPPMrT9U8TVaIsyV/Cj67+EfNz5485r6qq7Nu3j40bN9LX10dmZia33347M2bMGCNMRxMKT2xv5TcbG2gbjFCV6+Qn75rNLbMKMJ2nZGGnQrwtQGBdK5ED/UhGGeflhbiuLMTgPtaHUQhBdP8BfGvW4H/mGRSfD2NODpn33IP79tVYysuP8wlnTiycOMYreqAjhJp6/Ww0y2QVOalamJuy6HCRUejAdJyp/hcjg6E4DX1B6ntDwxHRDX0hmvtDJJQRTw6P3UR5loOrqrI1ETrLSUW2g5JM+0WbYERnhB0t2yjOG+DpxhtxGwXviMiEN/0vGe9/N+6bbz6mfXvtIK89Xkd/W5D8yW6ufGcV2SXnPqHY8RBC8B9rath8pJ8f3TWbyyuyxux7rHOAB460kxTwncpCPlyYhWHoRYtu6aGjo/N2OLO1hflv3SYWOFa89ndq06r9KSE72A3iKD/oMdHYeWPF69Gl/sLsVHkY+CXwp1F1XwVeEUJ8X5Kkr6a2vyJJ0jTg3cB0oAB4WZKkKiFGhyPq6OicDIqisu6RQxze0sWsa4tYeudkgq+143+hCd/MdXTl/ZGszGuZNu0X3PPH3fjCca5SdlOzO4hxsBtrbxfp0RiTgjEu+7/f4UiNJRv7Qtz3p+009IX4z1umcc/S0gkVMNMXT1ITCI+JjG6Oxof3F1hMzLSbWe1IMDPZy6xII7neI0ito3ygj37RafVognNONUxZkYp+Lh2x5jBdOJHjOhMDXaDW0TmHxGLdtLY+TFv7YyhKELNUTd+2Ytp2BnGky1z57g8za/kKrE4nQghe73idRw48wqb2TZhlMzeX38z7p73/GI9pRVGoqanhtddeo7+/n+zsbO68806mT5+OPCrqxR9N8MgbzfxhcyN9wTjzSjw8sGo6107NOW6CrolGrNGHf10rsdpBJKsB1zXFOC8vwOA8NjLyGAsPs3nEwuPyy8bdwiPki3Fkew8ddV56WwME+qPD++xpZrKKnZRMyxz2i3bn2C+In/mZEEsqtPSHNRG6LzhGiPaGE8PtTAaJSZkOyrMcXFedS3mWIxUR7STDcf6jXnXOH889/ThpmTH2NVbzcSwYW15DnuQi54tfHNPO3xfh9X/UU7+zB2eGhRs+Mp3J83Mm1MDgf9cd4YkdbXx6eSXvmF80XN8SifHFw61sHAxyucfJj6cWU2pLvWzTLT10dHTGC4sLsl1aYqm3QklCqPdY8XpYxD4M9etPPRrblafVObI1f20dhBAbJUkqPar6NmBZav2PwHrgK6n6vwohYkCjJElHgEXAG+fkYnV0LhKScYUXfrefpr19LFpVxrzrihn822ECh+sYXPo8A/YXycm5ialTfsC3//YGrx0Js9jYTIavFbm/FbnEwbUNCZydfUz68yNYU+L0usM9fPovuzDIEn+6ZxFLJ5/fXCeKEKwbCLDbH6YmqInRHbGRsdckg8JM/LxPaWdmoJYZAzvJ7j8A4f6xJzLZRyw3Ji0da8HhKQGr+xzfmc7Fji5Q6+icA0KhIzS3/I6urqcQQsGYnEnrOon+hhiZRZnc+LGPMnXp1RhNJmJKjH/U/YNHDjzCEe8RMqwZfHzOx3ln1TvJtI1NrpBMJtm7dy+vvfYag4OD5Obm8s53vpOpU6eOEaZ7AzEe2tzIn99oJhBLcnVVNh9bVsHisowJJeAcDyEEsdpB/OtaiTf5kR0m0m4sxXlZPrJ17FeYiMcJrF+Pb81TBDdu1Cw8Zs8m74EHSLtp5bhbeMQjSep39VK7tYv2w4MIAe4cG7llaUy/soCsYhdZRU4cx4nsvlgQQtATiA0nJWzoDdHYp4nQrQNh1FEJCrNdFsqzHKyckU9FypKjPMtJUboN4wUQua9zbgknwqQl2nmu9VoyDElWh1WS7a9S9sRfkFLJXRMxhZ0vNLPrpRYkYNGqMuZeX4Jxgs1E+Ofudn70Yi23zy3kc9dVAqAKwR87+vlOfQcA/1NVxAcKMpGHvpN1Sw8dHZ1zjcGoicpp+VD4Nu2Go7GPI2IHOqH+cCoa+6gAX8mQisI+jpXI6PLSJVcI0QkghOiUJCknVV8IbBnVro23/w3p6OgcRSyS5Llf7aXjiJer3l1F9Zwsun/7Ot22J/Be/QpCUiguuofB7qV87Xu/4slwFcVigCsiNbyZcRDDomy++VSSeFMrRb/7HdapUxFC8OsN9fzwhcNMyXXx27sXUJxxbDLYc0ljOManDzSyLRBFEoLJqpcl4SZm+vYxs38HM4J1eJJBrbHBDO5iTWyuXjVKfC7VSnum5kGpo3OO0AVqHZ2ziNe7neaW39LX9zKyZIHgLOpeiBLqjVM8fRZXf/UOSufMR5Ik+iJ9/G3/33j88OMMRAeoSq/i20u/zU1lN2E2jI0iTSaT7Nq1i02bNuHz+cjPz+fd7343U6ZMGSM4tw6E+c3Geh7f3kZSUVk5M5+PXV3BjMKJ/7ZTqILI/n4C61tJtAcxuM24V5XjWJiHPEp8EkIQPXAA35qnNAsPr/esWngoSZXmff3Ubu2mqaYPJaGSlmVl/spSKhfmkpF/cQpI4XgyJT6nLDlSEdGNfSGCseRwO6tJpizLyYxCN7fNLqA820l5toOyLAcu67n3GQspCk2ROPXh2Ikb60wontz8F6LpErWHJ/M5zLDz/yj68Q8w5eQghKB2azdvrKkn5I1RuTCXy26vwJVhPd+XfQxbGwf40hN7WVyWwffvnIkkSTRFYnz+UCuve4Ncne7iR1OLKbaO+p7XLT10dHQmMicTja0qEOwZG40d6BzljV2rJXmM+c/ddV+4HE8hEsepQ5Kk+4D7AEpKSs7mNenoXDCE/XHW/mI3A+0hrr9nGkXpcOAf36K/6hlUY4Tc3Fvxts9lzaN1BMRGXopNxS4p/HB1Jd/peIJQVOW3r6YR2/MGhT/5CY7FiwjHk3zp73t5dm8nN8/K54fvmIXdfP7kNSEED7f38V91rZiSEX565Ofc0v8aTkeGJjx7SqB4BXj+bSQS2pWv5xjQmVDoArWOzjgjhEpf3ys0tzyIz7cTg+wi2buAwy8ESUaiVC25goWfv2M48eHhgcM8cuARnmt8joSa4Oqiq/nAtA+wKG/RMdHNoVCInTt3snXrVgKBAEVFRdxyyy1Mnjx5TNvDXQF+vf4Ia/d2YpAk7pxfyH1XVVCWNfHFU6GohPf0EljfSrIngjHTSvqdldjn5iAZRzrQZF/fiIVHbW3KwmN5ysLj8nG18BCqoOOIl9qt3dTv7CEWTmJ1mph2eT5Vi/PILUub8JHop0I4nmRjbS+v1/cPR0Z3+kYsSyQJCtw2yrMd3DmvcFiELs92kp9mPefWJYoQtEY1Ebo+HKU+HKMhEqM+HBsznU3nwqJ1x3peji8lyxDlxiO7ybp3Nfb58+lu8rPp8Vq6Gvxkl7i48SPTyZ/sOd+Xi6IK2kcl+Gzs017k7GrxUpRh48EPLMBokHmwtYfvNXRikiV+PLWY9+SNmsmiW3ro6OhcLMiGk4zGDh4bhf2tL5yzy5xgdEuSlJ+Kns4HelL1bUDxqHZFQMfxTiCEeBB4EGDBggXHFbF1dC4l/P0Rnv7ZbkKDMVZ+vBpl8Em21v0epdRHuvMq+nsWsfbxDmJSPbIqqLdOwRtz8McPLeBPrd+iPdDGn/YuIbFxA7nf+E/SVtxI60CYj/5pO4e7A3xlxVTuv7r8vI4F26NxPr/3IBtCgmUD2/nxwNMUrPgyFD8CRt0uUefCQReodXTGiURikO6e52lt/QPhcANGOYdw43yOvBrEYFCYuXw181beijsnF1WobGjdwCMHHuHNrjexGW3cUXkH769+P6Xu0mPO3d7eztatW9m3bx+KolBWVsbq1aspLx/bGe5oHuBX6+p55VAPDrOBe68o494ryshNm3hRhUcjEiqhHd0ENrSiDMYw5dnJeM8UbDOzkVKCp4jHCWzYgO8fa4YtPKyzZ5H3wDdJW7kSg3t8I8P72oLUbu2ibls3wcEYRouB8tlZVC3Ko6g6HcNFZEvRH4zxysEeXjzQxWt1fcSSKg6zgck5TpaUZ6Z8oUeioc91RmohBH2JpCY+h2PUR0bE6OZInLgYGYO5jQYq7BYu9zipsFuosFupsFuYcU6vWOdMqO07TNRhoaG3lC8mIziLAphvvYtX/niAQ290YUszc+3dU5m6JH/4++FcIIRgMJwY9lIftrTpDdHcHyaujCQXc1mNlGc7uWlmPp9ZXkkvCu/f2cQ2f4jrMtP44ZQi8i2jBg26pYeOjs6liMUJlkrIqhxVeckK1E8DHwS+nyr/Oar+MUmSfoyWJLES2HperlBH5wJioCPE0z/fTTKe4Kp7O2jt/DpxQydWdSqBgXfx+oYwitSNUVGYV12JYdoyHvpbDR+9sow9ob/xWvtr/OLI5Rif3UDmx+4n473vZfORPj7x2E5UVfCHDy1k2ZScE1/IWUIIwROt7Xz9SAcJVeF/Wh7m7ukLkFY/pVk26ehcYOh/tTo6Z0AiMUhv70t09zzH4ODrmr80xQzsmUPLm1EcbguX33UXs69bidXpJJwI85dDf+HRg4/S7G8m157L5+Z/jjsr78RtGSuuJpNJDhw4wJtvvkl7ezsmk4m5c+eyaNEicnJGOkIhBOtre/n1+nq2Ng6Qbjfx+euruPuySXjsE/+NqRpTCL3ZSeC1dtRAHHOxC8+qCqzVWlThkIWHd81T+Neu1Sw8srPJvOfDuFevxlJRMa7X4++PULetm9qt3Qx0hJBkiZJpGVx2ewVls7MxWSaWt+2Z0NIf5sUDXby4v5vtzQOoAgo9Nt67uIQbpuWxsDT9nHtDhxSFxnCMI6OioLWI6Cj+5Ij4Z5YkSm0WKu1WbsxyU263MNlmodxuJdNkuKgi2i9Fnn72Qdb1LSTXFOD6pi10ve8TPPfNN1EUlXk3ljB/RSlm29l7hIkmFJr6QzT2hmjoC1HfGxy2t/FFxib4LMmwU57t5NqpOcMzCcqyHGQ6zEiSRFIV/F9rDz+s6cImy/yiuoR35KaP/RsdY+nxfzDnPWft3nR0dHR0zj+SJP0FLSFiliRJbcA30YTpxyVJuhdoAe4CEELslyTpceAAkAQ+IcTRBt86Ojqj6W70s/aXu3Dk1VC55Bk6BuowhAsZ7Hkn+9tNCCJY4hEWz53LNavvpDuUZNUvNjG9II05U1v56qbf8pXWWeQ+sRHPXe8g61Of4nevNfDfzx2kItvJg3cvOK+zk3ujcb687Q2eT7pZ5D/Mz421lL7rv8GReeKDdXQmKLpAraNziiQS3pQo/SyDg28gRBKzsQD8i2l+I8RgY4zMolxu/LfbmXrFMowmE12hLh7b8SB/r/07gXiAmVkz+cFVP+C6Sddhksf68vp8Pnbs2MGOHTsIhUJkZGSwYsUK5syZg9U6EgmtqILnajr59fp6DnT6KXBb+eaqabxrYfF59b86ESKpEm8NEGvwEWvyEW/2I+Iqlgo3rndNwVLh1kSd/n58a9fiW/MUscOHx1p4XHYZknH87jEaTHBkZw+1W7voPOIDIK/czVXvrmLy/Bxsrokv9J8MQgj2d/h5cX8XLx7o5lBXAIDq/DQ+dW0lN0zPZVr+2bcrSaopS45RUdBDUdGdR1lyFFpMVNgt3JmbQYXdQrnNQoXdQpHVjOFkrjMZg+790LHrLN2NzniTUBP0eOO0Bov4XKyNN0vvIPBsK2Wzs7j8zsl4csYn+UxSUenyR0d5q49ERXf4IowKyic3zUJ5lpObZ+VTnuWgIiVCv1WCz7iqss0XYosvxNoeLzXBCDdlufl+VRE5llHf+bqlh46Ojs4liRDird5ELn+L9t8Fvnv2rkhH5+Kh9eAArz7+JAVL/4HFU0vMn0VX4wrq+rJAVbFF/Fx22eVUXr2Sl+sG+NCfdrKlYQCzQeazKz38x5Yv8K6OEuY/ugvntdfi+drX+cITe1mzq50bpuXy43fNwWk5f+Pt5w7t4EutYQKyjf8cfJb7L7sZQ+EHztv16OiMFxNXxdLRmUAkEj56e1+ip+dZBgZfR4gkFnMhxshSWrfF6TowiGwIUDZ3AcvedSNls+cjyTI1vTU8cuARXmx+EYFgecly7p52N7OzZ48RAYUQNDc3s3XrVg4ePIgQgqqqKhYtWkR5eTnyqOQFkbjCml3t/GZjPc39YSqyHfzwHbO4bU4hZuPEs5xQ4wrxFj+xRj+xBh/xVj8kNeXHlGfHPj8X+9wcLCVpiHic4Cuv4B2y8Egmsc6aRd43v0HaTTeNq4VHIq7QtLeP2q3dtOzvR1UE6Xl2Ft9aTtWiXNKybOP2WeeThKKyrXGAFw908+L+Ljp8UWQJFpZm8J+3TOOGablnJdu0EILeeJL6iCY+HwlHaUitN0XiJEapf56UJccV6U4qbCOWHKU2C/ZTieBWEtBzQBOjh5buA6DqPtQXEk+8/mfW+RaQZxnA7J0MLhMLPzQZR4mDI+EokboQ4bhCNKEQjitE4gqRhEI4niQSV4kkkkTiqX0JZXh9uH2qbrQdB4DDbKA828n8SenclV2kWdpkaZY2jhMMQiKKyk5/iDe8IbZ4g+zwh4io2t/4FIeV/5s2idtyPGNf/uiWHjo6Ojo6Ojo648rBbZuprfshRVfWgOKmtfYKmrsnISdUHME+pi5ZRlfebH55qJ9tP9qIEFCe5eDfrirnhhluvvrmh5nbZuLOx1qxzZmD9I1vc9fvtrKv3c/nr6/ik9dMPuf5dobwebv5jzc38ndzJbNivfw8H6Ze+zUtQZCOzkWALlDr6LwFiYSP3r6X6Ol5joGB1xEigcVSiFW9ho5dguZtbUAPhVOncd1H3kfVZVdgc7pIqkleanmZRw48wu7e3ThNTt5X/T7eW/1eCp1js8TE43H27t3L1q1b6enpwWq1ctlll7Fw4ULS09PHtD3SE+DRN1t4ckcb/miS2UVu/v3987lhWu556ySPhxpNEmv2E2/0aYJ0WxBUARKYCpw4lxRgKXNjKUtDtmuRhNGDB+n671/iX/sMyuCgZuHxoQ9qFh6TJ4/ftSkqbYcGqd3WTcOuXhIxBYfbzKxriqhalEdWsfOisIYYSnL44v5uXjnUgy+SwGKUuaoqm89dX8Xy6lwyHOMTFR5KKsMidP0ob+iGcIzAKAHQImuWHFUOKyuGLDnsVsptFjJOx5JDSULf4bFidNc+UGLafqsbCubC5Z/Uyvw58K3ScblnnbPH1vZt/Hyfnd6wE2Wmh/+dI/AHk6g7a5E3JJACCaTk8XM+yRLYzUasJgN2swGbyYDNrK27bSZsqTq72YDVbMBuMpLlMlOe5aQi20G2y3LSf4ehpMJ2f5g3vEHe8AbZ5Q8TFwIJmO608b6CTC7zOFnsdpJ1vBktuqWHjo6Ojo6Ojs64EQ43sevN7xEVL2PLstLSuIDW9krccSf4+ohMvZztajb/u9sP1DEl18VnlleyckY+VblOFKHw8Zc/jrWxi888LmOeVEL3V7/Lx3+7g1hS5bd3L+D6abnn5+aUBOu3PMHnAjn0mMv5vHKAz117Myb7+OZf0tE53+gCtY7OKBIJP319L9Pd8xwDA5sQIoHVUojTeD3dNUZqNjegJNvIKChi6bvupvqKq3Hn5NEf6WfnwF721u/lqbqn6Ah1UOQs4quLvsrqyatxmMZGxQ0MDLB161Z27dpFLBYjNzeXW2+9lRkzZmA2jwiHsaTCC/u7eXRLM282DmAySKyckc/7FpewqCxjQoipajihRUc3+og1+kh0BEEAsoS5yInrqkLMZW4sk9KQrSNfOcn+frxPPIN3zVPEDh1CMplwXrccz+2347j88nGz8BBC0NMU0JId7ugh4o9jthmZvCCHqkV5FFR6JpTAf7ocL8mhx27iuupcbpiey5WVWadt/ZJIWXIcSQnPDZGUR3Q4Rld8JDpZAgqtJipsVt6R59ASFKYsOQpP1pLjeKgK9B8ZK0Z37oVkRNtvdmoC9OL7NDG6YC6kl+nRBBcYtf21fPXZw3Q2pZORHuH65B6a1Wz25VTSX+QZbpdjNDDZYmGK3cI0p405aXYqnFZsZ9F73J9UeNMbZIsvxBveIHsDYZICDBLMdNq5tyiLyzxOFrkdeExv8/9Mt/TQ0dHR0dHR0Rk3YrFuGht/SVv731BVifb2GbS3TsMeK6MnauVVl4cmhwFaYWahxJdunMLKGXnkeiQO9h9k68CbPFx3gJq+GsItTfzsSTsml4Nt93+Tr//tICUZdh68ewGTc5zn5f5C9Rv5r927+GPG1VTKvTxUYWfupPeel2vR0Tnb6AK1ziVPMhmgt/dlenqeo3/gtWFR2mO7mb7DVnZtqCMWasTu9jD7hlvIXTiLLkeInYOHeLTmuxwcOEh3uHv4fPNz5/PlRV9mWdEyDPJIQj1VVamvr2fr1q3U1dUhyzLV1dUsWrSIkpKSMcJKS3+Yx7a28MT2VvpDcSZl2vnqyqm8Y34RWU7LOf35HI0SiA+L0fFGH4musLbDKGEuTsN1bQmWsjTMJWnI5rEJBUU8TmDDBnxP/ZPghg2ahcfMmeR+4z9x33QTBo9n3K7T2x2mdmsXtdu68fVEkI0SpTOzqFqUy6QZmRhNF36yw7dKcvieRSXcMD2XRaUZp5XksDMW59leH68NBjgSitEcjTE6aDU9ZclxVYaTCptmxzFkyWE706SKQsBAw1Fi9B6IB7X9Jjvkz4YFHx6JjM6cDPLEs7fROXm6Ql184e+vUd+UhzstwnPvvZz8wtuh+XXEvr/Ts38j+w1Z7PfMZH/+UvZTypZQGLV3EACnQWa608Y0p43pqWWqw3raf48DiaQmSHs1QXpfMIIKmCSJOS47Hy/O4TKPk4VuB07jSX6X6JYeOjo6Ojo6OjrjQiLho7Hp17S2/hGhJunonMzexiUMhidzBA/NAEaYl+3hzup0yvJ99CUPc2DgaT6z6SDN/mYE2gAnx5bDPPNk3vdPL2YlwRN3fYEHN/Vw7dQcfvruOaRZTW97LWcFbytvvvorPmNdSnP6lfybPcBX51+L7WSfO3V0LkAkIY4/VfZcsmDBArF9+/bzfRk6lxDJZIDevlc0Ubr/NYSIY7Hkk2a7gsH6NA6vryXQ14fBYsE1vRR/hZ1Drm4ODh5iMKYJIhISZe4yqjOrqc7QlikZU3Bbxk61iUQi7N69m23btjEwMIDT6WT+/PnMnz+ftLS0kWtSVF4+2MOjbzbzWl0fBlni+upc3rekhKUVWectyjfpixFv8A2L0sleLWpVMsuYJ6Wl7DrcmItdSMfxwBZCEDt4EO+ap/CvXYvi9WLMzsZ9263jbuER8sU4sl1LdtjTHAAJCqvSqVqUS8XcbCz28/BwMY68VZLDqXkubpiexw3TcplecHpJDtujcZ7t9fJMr4+tvhAAFTYLU51WKmwWyu0j3tAZbxchemo3BN6WsWJ0x26IaYkqMVggf9ZIVHTBXMiqAvn0HswkSdohhFgwPhd/6TLefbYv5uO+hx5jS2sJdnucNaumMHX2tLGNknFoWA/7/g6HnoV4kLCrmMPTP8D+ouXsN+WyPxTlQDBCMGUtIwMVdsuwYD205JiNx/wf6Y0neCMlRm/xBjkYigKaNc38NAdLPA4u9ziZl+Y4NV/0IUZbetz8Y93SQ0dHR+dt0Pvr8UEfY+tcjChKhIaG39LS+ltUNcye5sVsbrqcplgug7IdCajOkCgu9yE7a2gO76Y10Dp8fL4jn+qMaqZlTqM6Uystb+yl64FvkfT5+PWqz/F0MotPXjOZz19fde7H4Iko0c3/yw9aB/h14Z0USVF+NnMKl2dlntvr0NE5Cca7v9YFap1LhmQyQF/fqyn7jo2oahyLJY/0tGvxN3vYv/4wvtYOhATBQjMHcvupyxwkaRQYJSOT0ydrQnRKkK5Kr8Jueuvkct3d3WzdupW9e/eSSCQoLi5m0aJFVFdXYxxlX9HhjfDXba38bVsL3f4Y+W4r71lUwrsWFpObZj0XP5phhBAoA9GUGK3ZdigDmlAjWQ1YSlNidFka5kIn0tsINcn+fnxr1+Jb8xSxw4fPmoVHPJKkYXcvtVu7aDs0iBCQVeykalEelQtycaaf34jzM+WtkhwuKM3ghmm53DAtj5LM00ty2BqN80yPl2d6vezwa5Hw0xxWVuV4uCXbQ6VjHP/+hAB/x1Fi9C6IDGj7ZRPkzRgrRmdPBcP4vVTQB7zjw3j22dFklPt/83+s66zCbE7yyJWZLLlm6dsfFA9D3Quw70mofVHzHfeUwIw7UaffSau7kn2hKPuDkeGlLTpiRZNlMqaira0EFZU3vEGOhDXvcpsss8jt4DKPgyUeJ3PT7FhONzo/Mgi9h+Hwc7D555qlx11/1C09dHR0dE6A3l+PD/oYW+diQlUT1Nb+npbWB2kMpLOp6Qr29M7AL+zIQjBfMjLLGOalogcZtLUDUOgsZFrmNG3JmMbUzKlkWDOGz5ns66Pru98l8Py/UMsq+Fb1HexzFvCju2Zz08z8c3uDQsDh59m78f/4VMGHOOwo5wOZFr45rerkZ+vp6JxjzrlALUnSQ8AtQI8QYkaqLgP4G1AKNAHvFEIMpvb9O3AvoACfFkK8cKKL0DvPk0dVY0QircRiPQhU7YsMFSG0iDGtTgUE2u9WDNcJBAhx1HGj24zep60LoSBEEqEqCJFACAVVJLW6oUVNpuoTI+2H65Op9iPHj63X9p0L4vEeVDWO2ZKHbJtPS6OB1i3dyC1+JKDXHaO+MERnUZJJeZUjkdGZ1Uz2TMZsOHFSOUVROHToEFu3bqW5uRmj0cjMmTNZuHAhBQUFI+1Uwca6Xh7d0sKrh7oRwLKqbN63eBLLpmSfli3D6SCEINkbGWPZofjiAMh2o+YdnVpM+Q6kE7xBFvE4gfXr8a15iuBrr2kWHrNn4bn9dtJWrsTgHp9EDkpSpWV/P7Vbu2nc24eSUEnLslK5MJeqRXlk5F/Y0+bfKsnhlZXZ3DA9l+VTc8g8TauX5kiMtT1apPTugCZKz3TaWJXj4eZsNxX2cRKlA11aNPRoMTrUo+2TDJAzDQrmjIjRudPBeHZfJlzsA15JkoqBPwF5gAo8KIT4mSRJPwRWAXGgHviwEMKbOua89dmKqvDJX/8/nuueiVFK8vNZEjffecupnSTq0yKqa/6uRVgLRXuxMeNObcmsAMCbSHIgGOVAKMK+QIQDwQiHw1HMksQit5PLUhHSM112TKcaKRMegN5DqeUw9BzUymDXSBvd0kNHR0fnpLnY++tzhT7G1jmbRCIRent7iUZTwUySlNIkVCRJIISKJKlH1Sloj6jaMtxmSMOQBAgFgYqEQKAgIfAFmtlyZAN7BirY3jkXXyINWShMinSwzJzGO4yF1GYeYuv8RqpypgyP44+e2TyEEAL/00/T/d/fQw2H8b7jA3wkPpV0t4Pf3r2AqXlpxz3urNF3hMS//p2fJ/L5yaQPkmmU+PH0ySzPPMfXoaNzipwPgfoqIAj8aZRA/QNgQAjxfUmSvgqkCyG+IknSNOAvwCKgAHgZqBInUCD1znMsqhonEmkjEmkiHG4iHGkiEm4iGG4gHusCzm/Uu4qMQAZkhCQhUtvDiyQDo+slhCSP3U61G9o+Guk4dSfiRLYGoaTEoaYkob1xirpsGFWZoEMhXOkiY940pk2eT3VGNaXuUgySgXg8TiQSOeESjUaH18PhMMlkEo/Hw8KFC5k7dy52+0h0a28gxuPbW/nL1hbaBiNkOS28a2ER715YQnHG6UXBngpCFSS6w8QbRyw71KAWWSi7TJoYXa4J0sZs+wkFadA6+OiBA/jWPIX/mWdGLDxW36ZZeFRUjNu1d9Z7Oby1m/odPcTCSaxOE5Xzc6hanEdu2elZW0wUjpfk0G0zsbw6hxum5XFV1eknOWwMx3im18vaHi97g5pFy2yXjVuyPazK8VBqO0NhONR3rBgd6ND2STJkTRkbGZ03A0y2M/vM0+BiH/BKkpQP5AshdkqS5AJ2AKuBIuBVIURSkqT/ATjffbYQgi/85vs82TUHWVH4VpmXu+95/xmdk1AfHHgKap6Elte1uoK5MOMdMP12cBeOaZ5UBZLEySfvDPVD78FjheihFy8AJocWKZ1TrZXZU7V1T8mZ3ZuOjo7OJcTF3l+fK/Qxts6ZEk8qNHa1cKCploPtLbQNeukPC/wxC/6EE2/CRUIZmu0oRuUn10b4kiTGbB/dTkrpGsOlNKJzSAiQtDKmmIkk7RhIUhJpZXKggRlkcF3JEtKiFmzX55N5bcVJjQUT7e10fvMBQps2YZs7lx3v/Bhf2upnRqGbhz608Nzme4oFYOMPqd39DJ+a+u/scVZye7ab/55STPp4WSrq6JxFxru/PuFfvRBioyRJpUdV3wYsS63/EVgPfCVV/1chRAxolCTpCNrA941xut6LBlVNEI22jRKgmwmFGwmGG0jEOhktQseEgb6kTGdcpS9pYDBuIpQwI4SUereoNdfKkW1JSEgqoEqpdQlJgKTKSELW1oWEpEiggixkJCEhqxKSkJGFBKqkfY4AIaSUMK0x+uv/rboCSRy1Z2hTjBWhJa3/QRJD1z6ygIQkxMj1j2mnnUUWI+eU1JHzIaTh8ziiRtJVEx6bBeucAnJKKnG7czSBuS9Cd2s3TZGmYdFZVdW3/P0ZjUZsNtvwkp6eTkFBAVarlUmTJlFVVYWcmhYuhOCNhn4efbOFF/Z1kVQFSydn8rWbqrmuOhfzcXybxwuhCBKdQU2MbvARa/IjIkkADB4L1sp0LOVuzGVujJnWUxJ4k319+NY+g2/NGmK1tUhmM67rluO+/XYcl102bhYe/e3B4WSHwYEYRrNM2exsqhblUjwtA8M5ijY/G5ytJIcA9eHocKT0vpQoPddl5xsVBdyc7WbS6YrSkcGjxOjd4GtJ7ZS0hIWlV4wSo2eC5fxkvb7UEEJ0Ap2p9YAkSQeBQiHEi6OabQHekVo/b332A3/4Ef/onYOUUPlMbht333P/mZ/UkQULP6ItvjbY9w/NBuTF/4AXvw6TLteiqqetBkcmxuO9gBNCE7p7U+Jz7yHoSUVHh/tG2pldmgBddYMmQg8taYV6wk4dHR0dHR2dCY+qCnoDARp72mnt66a1f4CWPh9t3hh9YSODUQf+uHPU+L8AKMBuDOMx+0gz+Sm0d2GWlZQGISEEqVJ7xtLKERlaHVU/ol2k6o46bljaFhKyrFLQ3cykUDNpzmoyHdeyNCMDGcj40FRsU0dsO94KoaoMPvoYPT/5CQC5X/8PHs6az8/XN7BsSjb/+955OCznSBQWAmqeQH3xGzzouZLvzX8Qh8nMg1NKuDXHc26uQUdnAnK6/wNzUwNhhBCdkiTlpOoL0Qa/Q7Sl6i5JVDWpidCpCOhwpIlQqIlAuJ5krAttaotGTMj0JmQGYgaCETeRsJ1EyI0UycYc92BSrUjCiEPIOE4junjCcKLgb4m3VrvPkGRqAYhGYPBwExZL5xih2e12j9m22WxYrdZj6kymE/viDobiPLmzjcfebKGhL4THbuLDS0t5z6ISyrPPjmAnkirx9hFBOt7sR8S0YEhjlg3b9MyRCOn0U7dyUONxguvW41uzRrPwUBRss2eT98A3x9XCIzAQpW5bN7Vbu+hvDyHJEiXTMrhsdQWls7IwWy/MN8pvl+Twk9dWnlGSQ4DaUHQ4UnooyduCNDsPVBRwc46HYuuJbWrGEPVD556xkdGDjSP708ugaAEs+qgmRufPBqs+FW0ikHqxPBd486hd96BZdMF56rN//Pef80j3dIiofDCjgc9++lPj/yHuIlj6aW3pO6IJ1fv+Ds9+Hp77ElRco0VWp+UfK0QP+aIDWNI04XnKylFR0dWQVgAX8IwNHR0dHR0dnYubvkE/DZ2dNHW30jrQT6cvRHcoSW/YQF/EymDMiSJGj6ksmGUPHosPjznAZEcvLkcYZzKEPRrC4Q9iHQxgjCmctQH7WyBJMuXzlzLQvZxCkcZ0s4wxzUzm3dMwZZ94FnKsvp7Or/8nkV27cFxxBVnf/Cbf3NLPE+sbeOeCIr57+0xM5yroqXMvPP9lmnsa+czM77LFPpkbMtP40ZRicizjl3tHR+dCZLxVnuN9Ux1XkpQk6T7gPoCSkgtr6qsQgmTSTyIxQDzeTzzRTyI+tN6HP9RAKNRAMt6NNEqETigy/oiZcNhGJFxILJRGPOIhHskgqThBHvl1mFMLqoKUSCArcUxSHIvBgNVixmqxIBuMGAwGJFlGNhi0ZWhdNiAZDBhS9ZIsa21T24ZRbeRR7WSDMXUOI7JBRhodCXa8wbgQY8W0MW2klJ81w38Zx7PuEGj2HJIsa1HHknRWrBpkWT5GeDYYxjfhgBCCnS2DPLqlhWdqOoknVRZMSudTyyezckY+VtM4f15CIdYSGLbsiLcEEAntb86Ya8c+NyflIZ2GIe30ImaFEET3H8C3Zo1m4eHzYczJIfOee3DfvhpLefm43Es0lODIjh7qtnXTUecFIK88javeXcXk+TnYXKcork4QRic5fOlAN+3eyHCSw6/fXH1GSQ6FEBwaFqV91IY1UXqR28G3JxdyU7abwlMRpeNhaHlD8/JtWA9dNQx/hbtLNM/oeXenoqPngC39tK5b5+wiSZITeBL4rBDCP6r+P9De0z06VHWcw89qn/3Iy3/mVw0VqH7B6vR6vvXFT5/2uU6arMmw7Ctw9Zehe5/mV73vH/DUqKhtq1sTnqtXjbXncOXrQrSOjo6Ojo7OhGFIi4jFe4hFewj6mvH2HaG3v53d3QZqBnI46C+lJ5o96ignBsmGx+LDbfZTbO9nmiuCQ4pjVWLY4hEssTjmuIRDWEg3u8ly55CWVozZZsdis2O2p0qbHYtd2zbb7BjGadbs29HXFuL5Xx+gWoZiGaxV6WS8ZyryCYKWRCJB/+9+R9+vfo1st1PwP9/HcONNfOyxXWyo7eXT107mc9dXnRubyPAAvPodxI4/8GjxXXxz8XeRDCZ+UlnIu/MyLmirSh2d8eJ0v026JUnKT0VP5wNDBoxtQPGodkVAx/FOIIR4EHgQNH+s07yOY8+rCoLeGN7uMD0N3dTtb6G9P0Z3wkgk5ZkMICSBALRZJAIhJ1EMcYQhhmqII+R4qkwg5KS2GBSQkgg5dWxqGoqKhBCytq5aUJRZJJMLSCpGkqqJpGpEUUyoQh6e+pK6CM2SAgBZE2nRRGFJkpElgzZV2KKJtkJCs9qQQMQlbdA8Whse/uc4CsPonalVIUj5Og2hpJajDpUkZFkTl2UJ5NTnypK2LSEhpbalUfVII+3fqp0kpcRp0Nqk1qWhfdppRtoy9pjhfal7kkfVS0ipmdbSsL6gqlEUdQBFCFRVoAhtepOiiuG6pCpQRapu9PqotiN1Q+cRqCrEkgp9wThOi5F3LyzmvYtL3jbJglAFIqEikqpWJhREUmhlqp4x+7V1NZQg1uwn3hoARfMzMeU7cCzKw1LmxlyahsF5ZoJusrcX39Nr8T21hljdkZSFx3WahcfllyGNg7ifiCs07e2jdms3Lfv7URVBep6dxbeWUbkwD3f2ufcpHg/eLsnhZ66rPKMkh0IIDoaG7Du81IVjSMBit4PvVBZyc7abfMtJ/u5VBTp3a2J0/TpofROUOMgmKF4My74KhQs0MdqRdVrXq3NukSTJhCZOPyqE+Meo+g+iJTxeLkaST5zTPvvFnf/iO3vcJAfg2oxGfvrlcyBOj0aSNMuZvJlw3QPQvkPz/supBmeuLkTr6Ojo6OjonDeEUIjH+4nFe4jHeonHe4nFeojFe4nHeoiEO4lGu0kqAyAlUYVEi7+Iff3V7O+fyhHvHFRhwCzHKXe2Mttdi4sEVlXFnAQlqRCRBUaXCU+2h6KicqpLp1GVXYXL7Drft39cVFVw4LV2tq+pZ5FVxgO4lhWTdsOkE+ZKitTso/PrXyd2+DCulSvI+4//YNDi4p7fvsn+Dh//fftM3rv4HARKqgrseBhe/TZdqonPL32UVw0FLPU4+Wl1yanPcNXRuYg5XYH6aeCDwPdT5T9H1T8mSdKP0UyKKoGtZ3qRxyMWSTLQ5qNxdy31tT20+VS6FSM9skyfLOiVBF5SnkaSAcyCo8VXba+WUVaSBDIykmpFFmZtnyQY64o05Gs8StQVUkoNlobUbgxII4uQMCJhQsaIhGFUKafON1wO+SkrY+u1dZEqUwJsKtXg23HaQ+2UuDvqloZz7aLZVacEem1RR/0YjrsvtWg/fS0nr5q69uH90tjj3n4Rx9aJE7RJ/ahkSUuIJZMqpdTvKyVuG1Jt5KPqTJKENVUvS2AcEuuRMEiytm4Ag9HEzHQ3Nzjt2AYlxNomeo4jMA+Lz8ppvpuRwVTowrm0ULPsmJSGbDvzt9dqPE7w1XWahcemTZqFx5w55D3wAGk3rcSQdub2DYqi0npggLpt3TTu6SMRU3C4zcy6poiqRXlkFTsvyDfI/miCF/d38699neOe5FAIwb5ghGd6fazt8dIQiSEDl3mc3FOUzU1ZbnJPZkqYEDDQAA3rNFG6cSNEfdq+3Jmw6D4ovwYmXQZmx2ldq875Q9L+4/weOCiE+PGo+hVoeSKuFkKERx1yzvrsnY07+fzGKLFeEwszW3noS588Gx9z8kiSZk+jo6Ojo6Ojc0kjhCAW68Tn20U80X8OPk8hER/QhOd4D7GUGB2P9zPaAnSIZNxEPGImEbPQG8rmgG8JtcFymqKFRFQt4CVTDjJd7ibHMIDb2o9sVTHajWRkZVOUX0R1STVTcqdgN53erM0zQSgqIq4i4gpqXEHEFERc1daHl1HbMS1QK9wfpa/ZjxpJcrVdxiTLZNxVhX129tt+nhqJ0PuLXzLw8MMYs7Io+t9f4lq+nMa+EB/89ev0BKI8+IEFXDct9+zffMsWeO5LiK69PDXt4/x77ruIIfGdigLuKczSAv90dHSGOaFaIknSX9ASImZJktQGfBNNmH5ckqR7gRbgLgAhxH5Jkh4HDqBNI/6EEOLYkNyTREmqdNW2c+iNg9S1+GiMKLQZodsoMyCM+BUTSVXWbkMGZJAtAmEzoNjMKHYTwm5E2AwIuxHZKLhRfYZV0bXICYlkwkIybiaZsKIkLahJG6piQ6h2ZOHCILmwGO1YLDZsZgsOqxOH1YbZbMFisWAxWbCYzRiNRgyyhGkoHlqo2hexqoKiIBQFoQiUhIqSVFCTKqoiUBOKVioqakJFVUVqWyCUVBuV4TZCFaiqhKoKOnoNBJJOMgcOMKljA/aqCuzz5mObMweDJx1UgVDRQq5VgTiqHL3/uPuE0MRToXXao7IUDK8LdWhlbD1CjIoSF2P2H1M/uv1QnXrUtjjqOkaVY9uNaqO+RZsT9QHH3T9KPT9Om2MOUSWkWAzFJCMZZSSzAdlh0tZNqcU4ujQgGSWtTO3neG3HHGdAMoxPhyaEILpvH741T+F79llUnw9jbi6Z996Le/VqLOVlZ/4ZqqDjiJe6bd3U7+wlGkpgsRupXJhL5cJcCio9yCd4Cz4RicQVXjnUzdo9Haw73Es8qY5bkkMhBHsCEZ7p1SKlmyJxZGBpupP7i7NZme0m23wSonSwRxOiG9ZBwwbwtWr17mKovhXKl0HZ1eB8+4c9nQuCpcAHgBpJknan6r4G/BywAC+lXv5sEULcP9599lvRPNDCR59tINhlozqrmye+OA4JEXV0dHR0dHR0TgNFiRII7MPn34XPpy3xeM+JDxxHBBKqsBNLWIiGTcTCFhKRMhJRO/GEi5jqJJ5wEIo56FI8dKhptKtp+IQmMNsMcSa5w0zJDTFvkoOqwhKK0wrJjKWh9sVI9kVQfDEtQqxNW6K0Ej0b95I8SoCOK4iYNjNYjSmnFpAlASaZpCKIxlXMBgl7nh1Hjp206yZhLnz7PE6hLW/S+Y1vkGhpwXPXXeR86YsY0tLY3erlnoe3IYTgsY8uYV7JWbYpDHTBS9+AvX+jP30qX73hWdbGnMxz2vlFdQkV9lPPBaWjcykgjcz0PX+UV1SId3/ic7QrRvqxMqhaCCTNRGImEvGxNgLCIGmCs82IsGulZJNwWMOkWYI4RRB7MoQ1GcOuqrhkCzm2TAo8xTzmtXDIYqAi3MIDR35L6dVfpnLOlefprs8MVVHZ/nwT259twmaIM6N9Dc6DGwGwzpyJ67rrcF1/3bj5A+tcfCR6evCvXYt3zRriR+qRLJYRC4/LlpyxhYcQgt6WAHXbujmyo4fgYAyjWaZsdjaVC3MpmZaBwXiOklGMI/GkysbaXtbu7eClA92E4wrZLgu3zMpn1ewC5hZ7TjsCXAjBrkA4Zd/hozUaxyDBlR4Xt+R4WJHlJutEUdix4Fgf6e59Wr3VA2VXaYJ0+TLIKL/kLA0kSdohhNDDZs+QBQsWiO3bt59U28HIILc89C/aW9MoyRrg1c++B+M58CrU0dHR0blw0fvr8eFU+uuLFSEE0Wg7Pt9OfP7d+Hy7CAYPIEQSgIRIwxdyMdBjxNdiJOE3p2ZgD1mDyqlZxRKSJA/vk6RUdNzwtlaOWRhbJw3XyyiqLXX88IWCSIKs4je76Dbk0Klm0BKwkFTBYpRZVJbBlZOzuDzbRbmQUfqjJHsjJHrDJPsiqMHEyPlkCYPLfE5yGUpGGcmiBVrJFgOSeWiRkc1HbVtG1iWzYdR+GYwS+1/v5M21TSTjCnOuK2b+ylLMJ/CZBlD8fnp++CO8TzyBqaSE/P/6LxxLFgPw6qFuPvHoLrJcZv744UWUZ7+9yH1GJOPw5q9hww9AifPi5d/hC5bL8SZVvlSWx8eLczBegEFZOjpvxXj31xNCoLbkV4r8D/4USAWpWg1gkzHZFKzWBA5zlDRTiDRjEKccxWUwkGXxUJE1iekFU5mclY3LajopUejp9n6+crABr2zgQ+1PUeA1M3/l/VxWkXlB2gp0Nfh46Q8HCPRFmLXYTVVsJ6FXXia6dy8A5vLyYbHaOmPGBXmPOuOHGosRXLcO75o1hF7bBKqKbe5c3LevJm3lSgyuM/cfG+wKUbetm7rtPXi7w8gGiZLpmVQtzKV0VhYmy/gmijwXKKpgS0M/T+/u4Pl9nfijSTx2Eytn5LNqdj6LyzIxnObDhioEO/1h1vZ6eabHS3ssgVGCq9JHROkM09s8mClJ6Ng5Iki3bgU1AQYLlCwZEaTzZ2s+NJcw+oB3fDjZAW9MibHqN3+ltiWL7MwAGz9+GzbHhekrr6Ojo6Nz7tD76/HhUhSok8kwHQNv0Nm3Gb9/NyJaj0ENavtUmQG/ncBAGuH+LKKD2SQUF6rJhDCaTu85WQxlpiJlvzliEXpsScpWVMLhtJOVk0fJpAqc+WUc9BvYXD/Aa3V99AVjAFRl2Lk808Uii4VZCTAOxEj2R7WZwilkhwljtg1jlg1Tjh1jlk3bzrAineYszvNBR52XjX+tpb89SHF1Ole+q4r0vJOzGwy8+ipdD3yLZF8fGR/+ENmf/CSyTXve/OvWFv7jqX1U57t46EMLyXGdxcjlIy/D81+F/joCU27jP6u/wl8HE0xzWPnFtElMd+rPwDoXHxelQO0pKRW3fumL5JrjVJbksGjaYqYXlmMynh0xJZRU+NbBIzzSG6Io2s3NB19in2UVH72mmuurcy84q4F4NMmmx+s4+HonOZNcXH/PdBzCT+DlVwi8/DLhbdtAUTDm5+NavhzXdddhXzAfSY9iuyQYsfBYg+/Z5zQLj7w83Lfdhnv1bVjKztzCIzAQpW57N3XbuulrDYIEhVXpVC3MpXxuNlbHSVhRTDBUVbCrdZCnd3fwbE0XfcEYDrOBG6bncevsApZOzsJ8mhHgqhBs94VY2+vl2V4fHbEEJkni6gwXq7I93JiVhuetRGkhoK92RJBu2gQxPyBpIvSQIF2yBEz6g9Bo9AHv+HAyA15FVbjz1w+xq7UAd3qUdR+6gszcc+D1p6Ojo6NzwaP31+PDxShQCyEYjA3SEeyg1dtCV+9+ov4DmBKtpMl9pFuCyJKmb4TDDgL+bAKBHPz+bEIhD0NRy5IQmEwyLoeTjMwsMnNySUtLw2q1YjKZMBqNxyzHqzcYDKccABZNKGxvGmTj4R42HurhUF8IgHSjgUVmMwsSMgsTEllDEdYGSROgs2wYs+2aAJ2tbcv2C2+MNZqQL8brTx6hdms3zgwLV9xVSfmc7JP6mSb7+uj67ncJPP8vLFOmkP+d72CbOQPQ/k5+9kodP325jquqsvnV++bhtJwl7WOgEV74Dzj8LGRUsOmaH/OZQDadsQSfLMnhC2V5WOQL52WBjs6pcFEK1Oer83xz0M/ndu2hQXJxc+dG9h9Ix5lRwv1XV3DbnAJMF9BbR4D6nT2s+/MhlKTKFXdVMu2KAiRJIjk4SHD9BgIvv0xo0yZELIbB48F5zTW4rr8Ox+WXI1t1H6SLjUR3D/61T+Nd8xTx+pSFxw034Ll9NfbFi8/YwiMSiFO/s4fabd10HtES7eWUplG1MJfJ83NweCzjcRvnFCEE+zv8rN3bwTN7Omn3RjAbZZZPzWHV7AKunZqD1XR6PzdFCLb6Qqzt8fJsr5fueBKzJHFNpotbsj3ckJmG+61E6UDXiCDdsB4CnVp9etmIIF12FdgzTuvaLhX0Ae/4cKI+WwjBB375K17rKMXuSvDsndMon1J1Dq9QR0dHR+dCRu+vx4eLQaA+0tfFD559lQOtMdRkEpOUwGJIYjYmMJviSLKWOkOIVH6phJlkwkwiZgIVDLIBk8mE2WrFarNhczix2GzIsnyM050QWhCJog6VAkVRSSoqiiJQVYGiCJKqljtKSS2qKkimSmX08alFFQyXSSEYSvZhBGZhYCFGFmFkisuKZViAtg+L0IZ0K9IFFkB3IhRFpWZdG1ufaURJqsy9voT5K0sxmU88zhJC4PvnP+n53vdRw2GyPvFxMu+9F8mkifVJReXrT+3jr9tauWNeIf9z56yzo+vEw7DpJ7D5ZyAbCV/1Fb6bs5rfdwxSbrPwi+oS5rv1pPM6Fze6QD3ORBSV/7djE78KOshOeJnR3MnmhnQKPTY+emUZ71pYgu0kvignCsHBGK/88QBthwYpm53FNR+Yis1pHt6vhsMEX9tE4OWXCa5fjxoIINntOK+4Atf11+NcdvW42DzonB/UWIzgq69qFh6bNmsWHvPmaRYeK1ac8e82Hk3SuLuX2m3dtB4cRKiC9HwHVQtzmLwgF0/Ouc8MPR7U9wZ5encHa/d20NAbwihLXFGZxa2zC7h+Wi4u6+lFJyhC8IY3yDO9Pp7r9dITT2KRJa7NSGNVjofrM9NwHW+mSCwATZtHBOneg1q9LQPKrx4RpdNLT++GL1H0Ae/4cKI++99+8VP+1VmJ2a7wl+tyWLBkyTm8Oh0dHR2dCx29vx4fLlSB2hcJ8/9eeImX9/roDKcjkMmwDGI1jqT4E8IAihEUE5JqRlZMyJKMhDQcfXs61paS0GKsZSEwAAYkhtymDcOlNGZ7aH10WxkwSJK2yNoiGySMsoTJIDMjw8HiSem4853Dthzy2YrwnWC0HR5k419rGewMUTI9kyvfWYkn9+TGkIn2djq/+QChTZuwzZ1L/ne+jaWiYnh/OJ7kk4/t4tVDPXzimgq+eMOU8bc4FQIO/BNe/LqWfH7mXey47D/5dEuY+kiMjxRl8bXyAuwXWLCjjs7poAvUZ4ndLYf53P46DlqLuD7WRrDFw64GHxkOMx++vJS7LyvFfYFMoRGqYM+rrbzxVD1Wu4nlH6ymZHrmse3icUJbtxF4+SUCr7yC0tsHJhOOxYs13+rl12LMzj4Pd6BzKgghiNbU4F2zBv+zz6H6/ZqFx+rb8Kxejbm09IzOn0woNO/rp25bN001/SgJFVeGlcqFuVQuzCWz0HFBepu3DYZZu6eTtXs6ONDpR5JgcVkGq2YXsHJGPhkO84lPchySqiZKr+318lyvj75EEpsscW1mGquyPVyXmYbzaFFaSUDb9hFBun07qEkwWmHS5SOCdO5M0KeInTb6gHd8eLs++4u//DFPdE3FYFL51TyJFatuPsdXp6Ojo6NzoaP31+PDRBhjnyyKovL7TRv5++vNNPrdJIQJj9nHkoJtLMnbTnFMRuorQBrIxxqpwGbJxeZyYXO6sDpdGC2W8RmPyJKW9M8ka6VRRjJKSCYDmFLrQ/Um+Zi2DG0bZCTDhTc+OpsEB6NsfvIIR7b34Mq0cuU7KymdlXVSvzehqgw++hg9P/kJADmf/zzp730P0qhxUX8wxj1/3E5Nm5dv3TaDDyyZNP430XMQnv8yNG6E3JnEV/6A/6eW8YvmbvItJn5WXcIV6Xqwn86lgy5Qn0Xi0SA/f+XP/Mw6nzQR56NZ2ezZ7WPdoR4cZgPvXzKJe68oIyftwrDD6GsL8OLvDzDYGWLWtUVcdnsFxrewJxCqSmT3HgIvv0zg5ZdJtLSAJGHweMBg0L78ZXm4xCAjyYZj6yT5lNtLVivOq6/Gdd1y3WrkFEh0d+N7+ml8a54i3tCAZLXiuv76cbHwUBWVtsOD1G3rpmFXL/Gogs1lYvJ8TZTOK0+7IEXpnkCU5/Z28vSeDna2eAGYU+xh1ewCbp6ZT5779P7+YqrKa4NBnu/18nyfj4GEgk2WuT4rjVuyPSzPdOEY/fsQQnvAGRKkmzdDPKhl9i6YOyJIFy0Ck/5/YrzQB7zjw1v12f/1fz/joc5KJATfqhjk7rvffx6uTkdHR0fnQkfvr8eHiTLGfjte2FfDgy/u4nCfnaBqw2qIMj93N0vydlCViBBodGGwTKN8zlXklleSWVyCyXzh2QheiAghEJEIitdLcnAQZdCLMjioLd5Brc7rRcQTIweNGh4OjRVVIVGvTuawOhWBRKVcR6WhDoOkMuyzMnpceZxzxJuaiR44gOPKK8l/4JuYCgvHXGtzf4gPPrSVTl+Un79nLjdOzxvXnwURL6z/Pmx9ECwuuPbrHJjyHj55uI0DoSjvzsvgvyoLSTtLOdR0dCYqukB9thGCg2/8ic/1WdjtmspKl8S9ucU8vqmZZ/Z2YJRl7pxfxP1XlzMpc+J7CiXjCq+vqadmXRsZBQ5uuHc6mYXOtz1GCEGsto7gq6+Q6OkBRUWoipYxWFEQQgVFBaEiFBVUBXH0vuPUjT2HViJUkl4vSm8fcloa7ltuxn3HnVinT7sgBdCzTaK7h+D69QReeonQ669rFh7z5+O5fTWuFSswON/+d/t2CCHoavBTt62bIzu6iQQSmK0GyudmU7Uwj8IpHuQLcKqSNxznX/u6WLu3gzfq+1EFTM1zsWp2AatmFVCSeXq2JKGkwisDAZ7r9fJyv5+gouIwyFyfqdl3XJORNnZql699rI90qEerz5w8IkiXXgG29DO7YZ23RB/wjg/H67N/8ecH+XF9ESIp+GxhK5+9//7zdHU6Ojo6Ohc6en89PkyoMfYoDnZ08ON/rmd3B/Qm3MgozMg6yJK8HcxUB4m22LE451C54BrK5y3EYp/4Y+4LATUaRfGOiMzHis7elPA8UidiseOfTJIwpKVh8HiQRgeYjdaWhKDPUsIB9zJCpgxyIvVU+zZgS3pHtTnqvEcdP/xxZjOZ93yYtFtvPUYj2Nvm5Z6Ht5FUBb//4ALmTxrHnDyqCrsfhVe+BaE+mP8hktd8nV/1K/ywsQuPycD/m1LMDVnu8ftMHZ0LCF2gPkckm97gN6/9nR/mvxOL0ci3ppRymcnKg6818MSONpKKys2zCrj/6nKmF0z8L6Tm/f288seDxMIJLltdwexriydMsgWhqoS3bsX75D8IvPgiIhbDUlWF5847SFu1CmPGpZv4Tagq0f0HCK5fT3DdOqIHDgBgKioibdUtmoXHpNOfviSEoL89SN22buq29RAYiGIwyZTOzKJqYS4lMzLeMup+IhOKJXnpQDdr93Swsa6XhCIozbRrovTsAqpyT2/qVX88yYv9Pp7v9bFhMEBMFWSYDKzIcrMyy82V6S6sQ6J0xAtNm0YE6f46rd6RPSqx4dXgKT7zG9Y5KfQB7/hwdJ/9+Nq/8dU9TtQI3J3TwH995lPn8ep0dHR0dC509P56fJhIY+y+QIAfr3mJ1xv8NEczEciUpTWzJG8Hc+lG7jBhT19E5cKrKZ09D6PZQrKjg2hdHfEjR0h0dHDWdQshNMFUiJQ4KrTPHF2vqqPqR7UVQgvKOqpuTFtVHSvAnmVEPK5FP3s1IVpEIm/ZVna7MXo8GDweDOnpoxatzjimLh1DWtrbztYNDETZ/EQd9bt6Scu2aXYeM7PG/R7XHe7hE4/uJN1u5o/3LGJyzukHax1D2w54/kvQvgOKF8PKH1DvmcqnD7awwx9mVbaH71cVkWm+NLzDdXSOhy5Qn0sCXdSv+SJfcF3PFs9srkl38oOpJVjiKr/f3MijW1oIxpIsm5LNx5dNZlHZxBZSI4E4rz5yiKa9fRRXp7P8g9NweCbWFCnF78f/3HN4n/wH0ZoaMJlwXXMNnjvvwLF0KZLx4u8A1HCY0JYtBNetI7h+A8neXpBlbHPm4Fy2DNc1yzBPnnxGEea+3jB127qp3dbDYGcISZYors6gamEOZXOyMVsvvJ9zNKGw/nAva/d08MqhbqIJlXy3lVtm5bNqdgEzC92n9TNrj8Z5vk8Tpbf4gigCCi0mbsp2szLLwyK3A6MsgapA61Y48rImSHfsBKGCyQGlS0dE6ZxpY6ex6Zwz9AHv+DC6z173+qvctyFM3C9xW/YRfv75z5znq9PR0dHRudDR++vx4XyPseOJJP+39iVeOtDGoVA2CWEiy9rP4rydLJDbcXQLPLmXU1Y5ixzJRLKxkVhdHbEjR4gfOYIaDg+fS3a7z8i+8KSRpNQCEtKobUnzpz66bqitJB+nToKh9rI89rznApMRoyf9KNFZWzemj6p3u8dtjK0kVHa91MKO55sAmL+ylDnXF5+VgKcntrfy1X/UMCXXxcMfXjh+NqzBXnjlAdj1Z3DmwvX/hTrznTzU0c936zuwyDLfqypidY5Hn/Gtc8mjC9TnGiWB+tI3eLilg+9UfBzJaOXrkwv5YEEmgWiSP29p5qFNjfSH4iyYlM7HllVwzZQc5AkSnXw0Qgj2v9bB5ifqMJoNXPP+qZTPnZiJEKO1tfj+sQbf00+jDAxgzM7GvXo17jtux1JWdr4vb1xJdHZq1h3r1xPe8iYiFkN2OnFccQWua5bhuOoqjOlnZv0Q8sY4sqOH2q1d9DQHAMif7KZqUR4V87KxOU8vKeD5JKGobD7Sx9o9nby4v4tALEmmw8xNMzVResGk9NP6v3gkHOX5Xh/P9vrYHdAejivtFm7K9nBTtptZTpv2QBIPQf2rcPh5qP0XhPtBMkDRghFBunABGC+8n+3FiD7gHR+G+ux9h/bxjqfriQwYuTaniT98/hPn+9J0dHR0dC4CLvX+WpKkzwAfRXPj/a0Q4qeSJGUAfwNKgSbgnUKIwbc7z/kYY6uqyj9ffZ2/79jLnkAWwaQDuzHEgpy9zDe2kdftxxUvoiBuwN3eRby+HjUQGD7ekJWFZfJkbamsxFI5GUtFBQb3xJ+xfKnTVNPHpsfr8PVGKJ+bzdJ3TCYt0zbunyOE4JevHuH/vVTLFZOz+PX75+Gyms78xEoStv0O1v03JEKw5GNw1Zdpw8pnD7awyRvk2gwXP55aQp5lHD5PR+ciQBeozxc1f6flhW/zpcrPsyFtNkvcDn48tYRyu4VIXOHx7a08uLGBdm+EsiwH71tcwl3zi3HbJ+aX12BXiJceOkBvS4DqpflccVflhI2aFfE4gQ0b8D35D4IbN474Lt9xB2krbkR2XHi+ZEJVidbUEFi/nuC69cQOHQLAVFKC65plOJctwz5/PpL5zITNaChB/c4e6rZ3017rBQHZJS4qF+QyeUEOrowLLwGfqgq2Ng2wdk8Hz+/rYiAUx2UxcuOMPG6dXcDlFZkYT9ErWwhBTTDCc70+nuv1URuOAjDbZePmbA8rs9xUOlI/q0CXJkYfek6LlFZiYHVD5Y0w9SaouFbb1plwXOoD3vFiwYIFYu1zz3DDI68T6LWwILeNv3/u3873Zeno6OjoXCRcyv21JEkzgL8Ci4A48C/gY2iC9YAQ4vuSJH0VSBdCfOXtznWuxthCCN7csoc/vrGRHX4PPdFMDFKSWZkHmWdqprSjjbSaIDmdg7gjMSTA4PFgmTwZc2VKiE4J0mcakKNz7vH3RXjt8Tqa9vbhybVz5bsqKZmWeVY+K6mofOPp/Tz2Zgu3zy3kf+6chdk4DjmSfO3wxIegbas2llvxP4isSv7WNcB/1rWjAt+aXMj78jP0qGkdnVHoAvX5pOcg4q/v46/mqTww5fPEZBNfLsvn34qzMUgSCUXluZpOHnmjme3Ng1iMMrfOLuDuy0qZWTTxBCslqbL1mUZ2vtCMO8vG9fdMJ7cs7Xxf1tuS6OnB//TTeJ/8B/HGRiS7nbQVK/DceQe2efMmdIehhkIEX3+d4Lr1BDdsQOnvB4MB+9y5OK9ZhvOaazCXlZ3xPSRiCo17e6nb1kPL/n5UReDJtVO5IIfKhbmk512Agr4Q7G3z8fSeDp7d20mXP4rVJHNddS6rZhdwdVU21lOcOqYIwVZfiOd7fTzX56UtmkAGlnic3JTtZkWWmyKrWfOK6zkIh5/TlvYd2gk8k2DqzTBlJZRcBoaJ+TJKZ4RLecA7nsybN0/Id3+Vvi4HU/O6+ddn7znfl6Sjo6OjcxFxKffXkiTdBdwohPhIavs/gRhwL7BMCNEpSVI+sF4IMeXtzjWeY2whBGokSXtzBzvqDnKgu5P2aJweRaYzZKQ9kAPA5LRG5lobmdzejOuQwKO4sHjyITcXsnK0JScHHE4kSRpyvUhZZ4y44EnavadKMBtlXBbTWXfJk0bZc2iuHalrHKqXR7alo9vJo449at/R9aO3geHcUMN1wxYhHHWMViExqs2wq8jYNgiBqggURaAmVZSkQFVUlKSq1Y8ukwJFGV2qY44buy9VKoJEJEndjh4kWWLhTaXMXl6MYTwE4+MQiSt86i+7ePlgN/dfXcGXb5wyPrPWGzbA3++BRARu/TnMuJOeeJIv1bbyQp+fJW4HP6suYZJtYlmj6uhMBHSB+nwT9cFTH6er4Q2+Ou+H/MtSwVyXnZ9UFzPVMTKF5UCHnz+/2cxTu9oJxxVmF7l5/5JJrJpdcMpC2tmmo26Ql/5wgJA3zqJbSpm3onTCWpQMIYQgsms33n88SeC551HDYcyTJuG+807ct92GKTfnfF8iAIn2dgLr1hNcv57wm28iEgnktDScV16Jc9kynFdegcHjOePPUZIqLQcGqNvaRePePpJxFYfHQuWCHKoW5ZFV7JzQ4v1bcbgrwNo9Hazd20FzfxiTQeLqqmxWzS7guupcHJZTi/qPqSqbBoM81+vlX31++hNJzJLE1RkuVma7uSHTTZbZqE3xanljRJQebNJOUDhfE6Sn3Aw51bqX9AXGpTzgHU9cJeUi872/oCTPy8bPvu98X46Ojo6OzkXGpdxfS5JUDfwTuAyIAK8A24EPCCE8o9oNCiHeNtz4ZMbYkUCY3TV72F5XR1MwTK8i4RUmApgJqSYiSROxhJFkQkaNS6Acew7VbkQtsJLuipLnT5DvtVDgtZDrTWI+TnudCxvZICEbZQypsmhKOpffUYEz/ezNzB0Ixbn3j9vY3erlgVXT+eDlpWd+UiFg80/hlf+CzMnwrj9D9hTW9nj5Sm0rIUXla+X5fLQoG1kf8+noHBddoJ4IqCps/ini1W/zz/L38rVJHyWgSnyuNJdPleRiGiXu+qMJ1uxs55EtzRzpCeK2mXjngiLet3gSpVkTJ5I1Fk6w4S+11G3rJr/CzXUfnkZa1vh7Rp0N1FAI/4sv4XvyScLbt4Ms47jyCjx33InrmmVnbJNxKghFIbJnL8H16wmuW0esrg4Ac1mZJkhfswz73LlIpjOPtlVVQUedl7qtXdTv6iUWTmJ1mKiYn0PVwhzyKzzDb+MvJJr7Q5oovaeTw90BZAkur8hi1ex8VkzPP2XbnFBS4ZWBAM/3enmp309QUXEYZK7LTOOmbDfLM9JwGg0Q9UP9Kyk/6Rcg6gWDBcqvhik3QdUKSMs/Ozetc064lAe844klv1LM+vJ32XDfzdgvQIslHR0dHZ2JzaXeX0uSdC/wCSAIHEATqj98MgK1JEn3AfcB5BQWzr/1i19hUDLjx0JImAkrZqJJI4mEESUhocZBOo4cIADJLCGbBSaTgsWUxGaI45BiOEQEVzJEWiyAOx7ClDkJX2EurSYrhy3pDJqcAMhCpSzUwZRoDxXJKCWyiQJzBsJVQsSSjUACBAJNKxwq1ZQ+IYQWlCQEBONJ6rqD1HYHaO4PoaauOdNhZkqei6pcF1V5TqbkppHhOL2x35AuItSRzxWpCxNCHKdeq1OFgKP2CQFCHXXscKkdg0jdt6r9tIVgTNuhH0DqY7Q26sh1jm5/dN3w+QCDUUY2SGNLo4TBII8RmYdK2TC0Tzr+sQbpnAU9xZIKNW0+tjYN8Pi2Vjp8UX72rjmsnDkO47FU0CGHnoHptyNW/ZwG1cyPGrtY0+NltsvGL6onUeW48OwwdXTOJbpAPZGoXwdP3ksfVv7zyt+yJupgmsPKV8vzWZ6ZhmHUl7cQgi0NA/x5SzMv7O8iqQquqsrmA0smce3UHAwTREg8/GYXG/9yGAFc/Z4pVC3KvaAib+NNTXjXPIVvzRqSPT0Y0tNx37oK9x13YJ0yMgtOKApqJIIaDiPCYW09EkENR1DDIcSY7TBqJKzVDW9HUCPh1PGRUceHIZkEoxH7/Pk4r1mGa9kyzKWl43J/qqLSWe+jcXcfdTu6CfvimCwGyuZkUbkgl+JpGRhO0X95ItDli/LM3g7W7ulgT5sPgAWT0lk1u4CbZuaT7Tq1KVUDiSQv9ml+0hsGA8RUQYbJwIosNyuz3FyZ7sJqkDW/scPPaaJ002ugxMGWoYnRU1ZqHmQW59m4ZZ3zwKU+4B0vbMWTReuuLWRlZZ3vS9HR0dHRuQjR++sRJEn6b6AN+AynaPFhya8U+R/86fC2kEE2SxjMCmaTgs2YwC5rorNLjeBWImQoUfJNMtUFxUyePAVXegYWhxOrw4nZbkOW334msBCCzkiEmo4G9vb1UBOOsVd10mVwDbcpD7cyM9zITCnAbLuRGZnZpOdNhZxpYPOc8GcSjic50OGnpt1HTZuPve0+6nuDw6JsvtvKjEI3swrdzCxyM7PQTaZTt2eY6ARjSXY2D7KtaYA3GwfY0+olltRU+Sm5Lr69egaLyjLO/IO69tH5j0+xW7jYPfM+dqVNZU8ggi+pYJTg86V5xwQd6ujoHB9doJ5oeFvh8buhYycvXPF9vma/ivZYghKrmXsKs3hPfgZu01gbgh5/lL9ua+WxN1vo8kcp9Nh47+IS3rmg+JSFuLOBvy/Cyw8foPOIj6xiJwWTPeRVuMmvcJ/VqTvjiVAUQps34/3HGgKvvAKJBMbsbEQioYnK8fgpnU+yWJDtdmSbDcluQ7bZh7dluw3JbtfqbDasU6fguOIKDGnj4+cdDSVoOdBP095+Wvb3EwsnkY0Sk6ZnUrUoj0kzMzGZJ5ZtzNuhqoKm/hA17T72tvnY1TLIrlYvQsD0gjRunV3AzbPyKUq3n9J5O6Jxnu/z8Xyvjzd8QRQBhRYTK7Pd3JTlYZHbgVECumo0Qfrws9C5Rzs4o0JLcDjlJihaBIaJmTBU58zQB7zjw6xZs8TevXvP92Xo6Ojo6FykXOr9tSRJOUKIHkmSSoAX0ew+vgb0j0qSmCGE+PLbnSeruER84Aufp9BiYnZFGYvnL8KVnoEkn/tglp5YgpqBfmp62tjrC7A3YaJNHgkCKY50MitYy6xEFzPNCjM9brJzKzXROqsKjG8fFR2MaaL13jYv+9o10bqhNzS8v9BjY0ZhGrOKPMws1ETr9NOMtNYZH/qDMbY1aYL0tqYB9nf4UVSBQZaYXpDGwtIMFpVlsLA047Sj4gEGE0n2BMLs9ofZ1VbH7lCcbrOWxNEgQbXDxhyXnTlpdpZ6nJTZz78eo6NzoaAL1BORZAye/wrs+APJsmU8v+Rb/D7kYIsvhE2WuSsvnXuKssZ4VIOWhfblgz38eUszm470YTJIrJyRz/uXTGJhafp5jVxWVUHNujYa9/TS3egnmdDeXjrTLeRVuMkr1wTrzCLnhI/YTQ4O4l/7DNGDB0cEZdsokfk421o7O5LNjmyzIhnOrQDs7Q7TVNNH094+Oo74EKrA5jIxaUYmpbOyKK7OwGyd+CKqEIK2wQh723zsbfdS0+ajpt1HIJoEwGKUmVaQNuwrXZF9atHK9eEoz/VqkdK7AmEAKu0Wbsr2sDLLzWyXDUlJQPPmkUhpXysgQfGiET/prErdT/oS4FIf8I4XF3yfraOjo6MzobnU+2tJkl4DMoEE8HkhxCuSJGUCjwMlQAtwlxBi4O3OM9H764FEkn3+MHv6uqkZ7KcmotLISIBKfqyHWYFaZoaOMFuOsDgvn7TJy7Tk5MYTi4iBaIL9Hf7hKOuaNi9N/eHh/UXpNmYVuVPR1ppwfapWgjonT7s3wrZGLTp6W9MAR3qCgDYenFPsGRaj501Kx3mKeYaGCCkKNYEIu/1hdge0pSkyEphWEW5hjjrAnOqrmJOdy3SnDfsE1zJ0dCYyE0agliRpCvC3UVXlwDcAD/BRoDdV/zUhxHNvd66J3nmeNLv+DM9+AZJRyJ7Kvhkf5veeq1jjTRBVBVd4nHykKJvrs8bafwDU9wZ5dEsLT+xoJRBNMjXPxfuXTGL13MLT/oIeLxRFpb8tSFeDj656H50NPoIDMQCMJpmc0jQtwrpcE66tTr1jP1WGrDua9vbRVNOPt1t7eMosdFA6M4vSWVnklKZN6OSVQgi6/TH2tnlTgrT2IDgYTgBgMkhU56cxs9DNrCI3s4o8VOY4MZ7CQ4EQgn3BiCZK9/k4HIoCMNtl46YsDyuz3ZpXWMQLR16GQ89qZcwPRhtUXJPyk74RnBMjkabOueNSH/COFxdNn62jo6OjMyHR++vx4ULsr32JJPuCEWr8QWr6e9kbjHIkaUJIErJQmBWo5Qp/DUttCouKKnFUXgOZFSd//kiC/e1awMzelEVIy8CIaD0p086sIg+zi9zMLvYwvSANu3niBwVNNIQQ1PcG2dqoRUhvbRyg3RsBwGU1smBSOgvLMlhUmsHMIjcW46kHg8VVlYOh6IgY7Q9zOBQlZdVNgcWkRUabE8zd9mNmNa7FvfjDsPwBfbasjs44MWEE6jEnkSQD0A4sBj4MBIUQPzrZ4y/EzvMtCQ/Agadg7+PQ8gYAA6XLebTyXh6mhPa4QrHVzIcLs3hvfgaeo+w/wvEka/d08Kc3mtnf4cdpMXL73ELev2QSU/Jcx/nA80NwMEpnvW9YtO5rDaKmslV4cu3DliB55W7Sc+0XZLK+s81bWXcUVqVrovTMzAmdqLIvGNMiEtp8mijd7qM3oL24MMgSVbmuYe+3WUVupuS5TuvhQxGCbb4Qz/f6eLbPS1s0gQws8Ti5KdvNiiw3RVYzDDanrDue0yKm1SQ4slN+0jdB+TIwn5ptiM7FhT7gHR8uqj5bR0dHR2fCoffX48PF0l+Hkgq7AmE29w3yek8XO2JGkpKMSU0wN3CQpdEmlrptLCibgbXsylPOH+MNx9nX7mdvu5e9rdq4psOnBcHIEtqYJiVYzy7yMCXPhUmPuh1DUlE50Olnayo6elvTIAMhLXI5y2lhcVkGC0s1UXpqXtop599SheBIODYsRO8OhNkfjBBL6Q/pRgNz0uzMcdmZmypzLKbhnGEk47D6f2HabeN+7zo6lzITVaC+AfimEGKpJEkPcCkL1KMZbIaaJzSxuu8wSdnCCzM+xu9yVvBGwoZNlnhHXgb3FGZR7RwrRAoh2N3q5c9bWli7t4N4UmVRWQYfWDKJG6fnYTZOrE4xEVfobfanRGs/XfU+oiEtctZiN5KXiq7Oq3CTM8l1QdhTnA0uVOsObzg+7Bld0zb2wU2SoCLbyaxUZPTMIg/T8tOwnYEvdlxV2TQY5LleH//q89GXSGKWJK7KcHFTtpsbMt1kmQzQsWtElO7epx2cNUWz7ph6MxTOhxMkc9G5dNAHvOPDRdtn6+jo6OhMCPT+eny4WPvrkKKwzRdiU0cbm/sH2KPYUSUZixpjgf8ASxngiqwM5lQuxJw7/bRs/HoD2qzQPa1e9qTGPkOzQs1GmWn5acwp9gzPCi3Pckzoma7jQSyp0BuI0e2P0RuI0u2P0e2PUtPuY2fzIKG4AkBJhp2FpRmaKF2WQWmm/YTWpRFFpTOWoCMWpz2aoD0Wp2OojCVojcYJK1pstN0gM8tpGyNIl1jNYz9DVWHTj2HddzUP83f9WbN01NHRGVcmqkD9ELBTCPHLlED9IcAPbAe+IIQYPM4x9wH3AZSUlMxvbm4+4+uYsAgBXXs1obrm7xDs4kD6LH4/9RP8wzKFiJC43OPkI0VZ3JDpxnhU5zYYivPEjlb+vKWFloEwWU4Ld84v5PKKLOaVeHBZJ56lhhACX09kJMq6wcdAh5aoQpIlsoqcKcE6jbwyN850C/JF+Cb6QrTuCEQT7Gv3U9OuWXXUtPtoHuXXVpppZ2aRZ1iQnl7oHhcbmlBS4dWBAM/1enm5309AUXEYZK7LTGNllpvlmWm4SELja1qCw8PPQ6ATJFnzopuyUouUPoVpfjqXFhf7gFeSpGLgT0AeoAIPCiF+JknSXcADQDWwSAixfdQx/w7cCyjAp4UQL5zocy7WAa+Ojo6OzsTgYu+vzxWXSn/tTyps6R9kc2s9m/0R9pOGkGRsSoQlwcNcbo6zNL+QWVVLMDoyT+szhvLq7G71asJ1m4997T7CKVHWZTEyo3AoytrNrGIPBW7rec0pdbJEE0PCc5SeQIwef5TuQIwef4yeQJQef4zuQBRvSqAfjUGWqMxxsrA0Y9iyI89tHdMmqQq64gk6oprY3JYqO1IidFsszkBCOebcmSYjhRYTBVYTRVYz051aMsMqh/UYu9QxRLyw5n6ofR5mvANW/eyUo+p1dHROjgknUEuSZAY6gOlCiG5JknKBPkAA3wbyhRD3vN05LpXOEwBVgabXNLH6wNMMqvDYpHfzUOHttEsOCi0mzf6jIJOMo+w/VFWwsa6XR95oZn1tL4oqkCWoztey3GodQzo5LutbfPj5JRpK0N3op6vBR2e9j+4mP8lYqjOSwOY0YU8zpxYLtuF1M3b3yLrVbprQliEXknVHJK5woHPIpkOLDmjoCzH0tVDosaWiosc3eYgQgu54koZwjCPhKK8M+NkwECCqCjJMBm7McnNTlpsr011YY16oe1Hzk65/FeJBMDlg8rVagsPKG+A0HzZ1Li0u9gGvJEn5aH3uTkmSXMAOYDVaf6wCvwG+OCRQS5I0DfgLsAgoAF4GqoQQx44SRnFJ9dk6Ojo6Oueci72/Pldcqv31QCLJGx0tbG5rZHNYcNiYBYArGWRJrIUrHBJLSyqZVjoH+Qy8iBVVcKQnyJ4273AOnoOdfhKKNpDKcpqZXeRhVpGHWcVuZhd5yHCYx+UeT4QQgmhC1QTmIfHZHxsWoHtGCdK+yLHCs8kgke20kJNmJcdlIXdUmZ1mGV732EwMKgrt0aOin2OaIN0eS9AdSwz7Qg/hMsgUWM0UWkwUWs0UWEwUWMwUWk0UWszkW0xYTyd4rasG/vYB8LXCjf8Ni+47rQh6HR2dk2MiCtS3AZ8QQtxwnH2lwDNCiBlvd45LtfMkEdGiQGueIFn3Mi+mL+b3Ze9ns2MqVgnuyMvg3qJspjuPFTNDsSS7WrxsbRpge9MAu1q8RFJvHksz7Swo1d5gnuy0mvOBqqj0t4foafYT8sYI++NjF18cJXl0dwayLI2I124zdtdoEdsySuQ2Y7IaTurelaRKIqaQjCupUiURS5KIja1PxBWSMYVEXE2VynCZiGlLf3toQlp3xJIKhzoDw8kL97b5qO0OkLLuIsdl0R6ghgVpN5nOE2fIfiuGROjGSIzGcIyGSGx4vTESJ6KO/G4LLSZWZrtZmeVmsduJ0dsIh57T/n+0vAFCAWfeSJR02VVgmpgvYnQmLpfagFeSpH8CvxRCvJTaXs9YgfrfAYQQ30ttvwA8IIR44+3Oe8n22To6Ojo654RLrb8+W+j9tUZvJMrmxn1s7u5gc9xGgzkbgPSkn8uVHi73OLiifCZV2YVnPGaOJRUOdgZS9iBa8M+R3uBw8E9xho0ZBW6sJgNJVaCqgqSqoqiCpCq0UhEoQoyqU7U6dVS9ctT+4XON1B8Pk0Eix2UlZ5TInOM6Voh2p4TnrliC7niSnliCrrgmNvfEk3SPWk8cpSdZZYkCS0p0TgnOR5eu08hLdEJ2PwbPfA5s6XDXH6Fk8fh/ho6OzhjGu78eD8XsPWgRWIAWwSWE6Ext3g7sG4fPuDgx2WDGHTDjDozhAW7av4abah7mYH83DxXczt+VFTzWOcASl4WPlOSzImvE/sNhMXJFZRZXVGpvhBOKyr52H9ubBtnaNMArB7v5+442QEtMsLA0fTjKujrfhXEC2GnIBpnsEhfZJcdP/iiEIB5VCPuOI16nBOyQN0ZvS4BIIIE4TkdsNMnD0dcmi0ETnodE6FFCs/oWnfhbYTDJmMwGjBatNFkMGM0GHG4LJdMzKTvP1h0JRaWuOzicvLCmzcehrpE3+hkOMzML3dwwLVez6yhyk5t26oLv0SJ0YyQlRB9HhDZJEpNsZkptFq5Id1Fmt1BmM1Nms1BiNiJ17IQdv9f8pHsPaQflTIcrPgdTb4L8uSCf/79bHZ0LgdQL4rnAm2/TrBDYMmq7LVWno6Ojo6Ojo3NRkG2zsnraAlZP07Y7BnvYfGQXm/sH2UQGz0ZyYH8f2ckGJhtiWG0eLI50LAYjVlnGIkvDpeWobavhOPvdJhak53LF7HwsskQyqVLfGeBQu4997X4OdfpJqgKjLCHLEkZZwjCmlJFlMJsMY+oNY9rJxz/ekColCYvJMCI6p1nIdVlxWo30JZN0x5L0xBMpATrBtniS7liQrvZBemJJehMJlOMMj9ONBnItJnLNJirSLeSaTeRbRsTnAouZTNPJBYiNG8kYPP8V2PEHKL0S3vEHcGafu8/X0dEZN84oglqSJDvQCpQLIXypukeAOWhTipuAfxslWB8X/e3uUaSSKw7uf4a/mKr4Q+HttFrzKZSTfKg4n/cW5ZJpfvt3C6oqaOgLsrV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\n" 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\n" 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sa20cMAgYDNx1NOoVERFpbYwxvwQeBO4D2gBZwGPAxEO81CSgKPgsIiIiTaAB7fZrwb50OvA18LYxxgTfOx+oAU4xxrQ7mnWLtCYKsEWanyuBOcDzHEaH1lq7DZhGIMgWERGRRmSMSQT+DNxkrX3bWlthrfVYaz+w1v76EK7TCTgeuB6YYIxp00Qli4iItFqH0m5baz3AZKAtkBrcPQl4AlgEXHYUSxdpVRRgizQ/VwIvBR+H3KE1xnQATgPWNEFtIiIird0oIAp45wivcyUw31r7FrAcdYpFRESaQoPb7eAUnlcBudbaAmNMFjCOXf3zK5uuTJHWTQG2SDNijBkDdAJet9YuANYCl9Y75FfBebl2GGMK9jj9XWNMGbAJyAPuOSpFi4iItC6pQIG11nuAYy6s117vMMbs2McxVwIvB7dfRtOIiIiINIUGt9sE+tJDgXOC+68EFllrlwGvAH2NMYObsFaRVksBtkjzMgn41Fq7M5zes0P7T2ttUvCRtse551hr4wl8Q9wL2PN9EREROXKFQJoxxnWAY16v114nWWuT6r9pjDkW6Ay8Gtz1MtDfGDOoKQoWERFpxQ6l3c6w1p4YHEwGu+6Oxlq7BZiJvnAWaRIKsEWaCWNMNHAhcLwxZpsxZhvwC2CgMWZgQ69jrZ1JYP7sfzZJoSIiIq3bbKCaXaOzDsckwAALg+393OB+3ZosIiLSuA6r3TbGjAa6A3fV65+PAC45SBguIodB/1OJNB/nAD6gP1Bbb//rHHqH9kFggzFmkLV2YWMUJyIiImCtLTHG3A08aozxAp8CHuAk4ASg8kDnG2OiCHxhfT3wUb23fgLcbYz5zUFucxYREZEGOoJ2exLwGbv3xaMJLOZ4GvBBkxUt0gppBLZI8zEJeM5am2Ot3bbzATxCYGGnBn8hZa3NB6YAf2iaUkVERFova+2/gV8CvwfyCcyZeTPwbgNOPweoAqbs0d4/AziBU5uiZhERkdbqUNvtel82/7d+W22tXQ+8gKYREWl0xlob6hpERERERERERERERPaiEdgiIiIiIiIiIiIiEpYUYIuIiIiIiIiECWPMs8aYPGPMknr7/mGMWWGMWWSMeccYk1TvvbuMMWuMMSuNMRNCUrSIiEgTUoAtIiIiIiIiEj6eZ+/57j8D+llrBwCrgLsAjDF9gIuBvsFzHjPGOI9eqSIiIk1PAbaIiIiIiIhImLDWfgUU7bHvU2utN/hyDtAhuD0ReNVaWxNcQG4NMPyoFSsiInIUuEJdQEOlpaXZ7OzsUJchIiIt1IIFCwqstemhrqO5U3stIiJNSe01ANcArwW3MwkE2jvlBvftxRhzPXA9QGxs7NBevXo1ZY0iItLKNWab3WwC7OzsbObPnx/qMkREpIUyxmwMdQ0tgdprERFpSq29vTbG/A7wAi/t3LWPw+y+zrXWPgk8CTBs2DCr9lpERJpSY7bZzSbAFhEREREREWmtjDGTgDOB8dbanSF1LtCx3mEdgC1HuzYREZGmpDmwRURERERERMKYMeZU4A7gbGttZb233gcuNsZEGmM6A92BeaGoUUREpKloBLaIiIiIiIhImDDGvAKMA9KMMbnAPcBdQCTwmTEGYI619gZr7VJjzOvAMgJTi9xkrfWFpnIREZGmoQBbREREREREJExYay/Zx+5nDnD8vcC9TVeRiIhIaGkKEREREREREREREREJSwqwRURERERERERERCQsKcAWERERERERERERkbCkObBFRA7CWovXU4unuhpvbQ2e6prgczWe2hq8NTV4aqrxBJ+9NTV4auttBx/e2hqcLhfDz7mQzJ69Q/1jiYiIiIiIiIiEPQXYItLs+bze3YPjekFzIGCurguRPTXVdeGzt7Zm99C5XtC8ZyB9qBxOF+7ISNyRkbgiI3FHROKKiqIsP49X7/41A08+jTGXTCIqNq4J/kRERERERERERFoGBdgiErastSz+/FPWLphbN8q5LmiuN/LZ7/Md8rVdkZG4I6OCIXMUrohI3FGRxCQk7Paea89j6kLpqLptd2RUXUjtjgoc53Tt+5/X2uoqvn39Rb6f+gFrvpvDCVf9jB4jj8UYc6R/XCIiIiIiIiIiLY4CbBEJS2VFBXz6xMNs+PF7ktq2IyYhCXdUNDGJybuPbI6MCoxu3jOQrh8uB8Np186A2R0RssA4IiqacVf+lN5jTuDTJ//Lhw/eT5chxzD+mhtJSM8ISU3SehhjOgJTgLaAH3jSWvuQMSYFeA3IBjYAF1pri4Pn3AVcC/iAW62100JQuoiIiIiIiLRSCrBFJKxYa1nx9ZfMeO4JfF4v46+5kYEnn4ZxtKw1Z9t06cZl9/6bHz75gG9ee5Hn/+/nHHvR5Qw+9SwcTmeoy5OWywv8n7X2e2NMPLDAGPMZcBUww1p7vzHmTuBO4A5jTB/gYqAv0B6YbozpYa099NseRERERERERA6DAmwRCRuVpSVMf/pRVs/9lnY9enHaz39BcrvMUJfVZBxOJ0PPOIfuw0cz49nH+XLK0yyb9QWnXH8Lbbp0C3V50gJZa7cCW4PbZcaY5UAmMBEYFzxsMvAlcEdw/6vW2hpgvTFmDTAcmH10KxcREREREZHWSgG2iISFNfPn8tmT/6Wmopyxl17FsLPOxeFoHSORE9IzOOc3d7Nqzjd88fz/eOm3v2TI6Wcx+sLLiYiKDnV50kIZY7KBwcBcoE0w3MZau9UYs3M+m0xgTr3TcoP7RERERERERI4KBdgiElI1lRV88fxTLJ05nfROnTn/938lPSs71GUddcYYeo4aQ6cBg/j6lcks+Og9Vs39lvHX3EjXocNDXZ60MMaYOOAt4HZrbekB5oTf1xt2H9e7HrgeICsrq7HKFBEREREREVGALSKhk7PkRz55/EHKCwsZce6FjDr/Epwud6jLCqmo2DhOuu4meo85gc+eeoR3//5neowcwwlXXU9cckqoy5MWwBjjJhBev2StfTu4e7sxpl1w9HU7IC+4PxfoWO/0DsCWPa9prX0SeBJg2LBhewXcIiIiIiIiIoerZa2KJiLNgqemms+f+x9v/OV3uNwRXPznvzPm4itbfXhdX2avPlzxwEMce9EVrF0wl+d/eSM/fjYV6/eHujRpxkxgqPUzwHJr7b/rvfU+MCm4PQl4r97+i40xkcaYzkB3YN7RqldERERERESkwQG2MeZZY0yeMWZJvX1/NMZsNsYsDD5Or/feXcaYNcaYlcaYCfX2DzXGLA6+97A5wH3LItLybF29khfuuI0fPvmAwaeexRUPPET7Hr1CXVZYcrrcjDzvIib94xHadOnK9Kcf49V77qAgZ0OoS5Pm61jgCuDEPdru+4GTjTGrgZODr7HWLgVeB5YBnwA3WWt9oSldREREREREWqNDmULkeeARYMoe+/9jrf1n/R3GmD7AxUBfoD0w3RjTI9jpfZzAPJlzgKnAqcDHh1W9iDQbPq+H2W++wrx33yQuJZXzf/9XOvUfFOqymoXkdpmc//t7WfbV53z5wjO8cOdtHHP2Txhx3kW4IyJDXZ40I9bar9n3vNYA4/dzzr3AvU1WlIiIiIiIiMgBNDjAttZ+ZYzJbuDhE4FXrbU1wHpjzBpguDFmA5BgrZ0NYIyZApyDAmyRFi1/43o+fvTf5G9cT9/jT+KEq35KZExsqMtqVowx9D1+PJ0HD+OrF59l7juvs3L2LE667iZ9ESAiIiIiIiIiLVZjzIF9szFmUXCKkeTgvkxgU71jcoP7MoPbe+7fJ2PM9caY+caY+fn5+Y1QqogcTX6/j7nvvsGLd/2Cih3FTPz1Hzj157crvD4CMQmJnPrzX3D+7/8KwJt//T0fP/pvKktLQlyZiIiIiIiIiEjjO9IA+3GgKzAI2Ar8K7h/X7cn2wPs3ydr7ZPW2mHW2mHp6elHWKqIHE3FWzfz6j138PUrk+k6bDiT/vko3YaNCHVZLUan/oO48h+PMOLci1jxzUye++WNLJ05A2v3+0+qiIiIiIiIiEizcyhzYO/FWrt957Yx5ingw+DLXKBjvUM7AFuC+zvsY7+ItBDWWn78dCozX3oWp8vF6Tf/H73GjEPrtTY+d0QkYy6+gl7HHsdnTz7CJ4/9h2VfzeCk624iud1+b24REREREREREWk2jmgEtjGmXb2X5wJLgtvvAxcbYyKNMZ2B7sA8a+1WoMwYM9IE0qwrgfeOpAYRCR+lBfm8dd/dzHj2cTr06sukfzxK77EnKLxuYmkdO3Hxnx7gpOt+zvZ1a5n865uZ89ar+LyeUJcmIiIiIiIiInJEGjwC2xjzCjAOSDPG5AL3AOOMMYMITAOyAfgZgLV2qTHmdWAZ4AVustb6gpe6EXgeiCaweKMWcBRp5qy1LJ/1BZ8/9z98Pi8nXfdzBpx0moLro8g4HAw8+XS6DhvJF88/yTevv8iKb7/i5J/eTGavPqEuT0REREQayBjzLHAmkGet7RfclwK8BmQT6HtfaK0tDr53F3At4ANutdZOC0HZIiIiTcY0l/lShw0bZufPnx/qMkRkD5WlJXz25COs+W427Xv24bSf/4Kktu0OfqI0qXXff8f0Zx6jrCCfASedythLryIqNi7UZYU1Y8wCa+2wUNfR3Km9FhGRptQa2mtjzHFAOTClXoD9d6DIWnu/MeZOINlae4cxpg/wCjAcaA9MB3rUG0C2T2qvRUSkqTVmm31Ec2CLSOu2+rvZfPbkI9RWVnDcZVcz9MxzcDicoS5LgC5DjuGqPo/x7esv8v3UD1g7fy4nXHU9PUaO0ch4ERERkTBmrf3KGJO9x+6JBO6IBpgMfAncEdz/qrW2BlhvjFlDIMyefVSKFREROQoUYIvIIauuKOeL559k2Vefk5HdldP+cC9pWdmhLkv2EBEVzbgrf0rvMSfw6ZP/5cMHH6Dz4BmcdO3PSUjPCHV50kLlbt9+8INERETkULUJrimFtXarMWbnL3OZwJx6x+UG94mIiLQYR7SIo4i0PhsXLWTyr29m+ddfMvInF3Ppvf9UeB3m2nTpxmX3/ptxV15H7rIlPPd/NzL/w3fw+w54Z6nIYSn2RnHfow+HugwREZHWYl+31u1znlBjzPXGmPnGmPn5+flNXJaIiEjjUYAtIg3iqa5mxrOP8+a9v8cdGcUlf/kHx154OU6XO9SlSQM4nE6GnnEOV/3rMbL6DmDmC8/w0u9+yfZ1a0JdmrQ0Bp4p7MI3sz4PdSUiIiItyXZjTDuA4HNecH8u0LHecR2ALfu6gLX2SWvtMGvtsPT09CYtVkREpDEpwBaRg9qyajlT7riFhdM+YsjpE7nigYdo161nqMuSw5CQnsE5v7mbs35xJxXFRbz021/yxeSnqK2uCnVp0kIkuGrxVhlunZNHVVVFqMsRERFpKd4HJgW3JwHv1dt/sTEm0hjTGegOzAtBfSIiIk1Gc2CLyH55PR5mv/ES373/NvFpaVx493107Dsg1GXJETLG0GPkGLL6D+LrVybz/dT3WD33W8ZfewNdh44IdXnSzHXKSCcms4yc3Hiu+N+zvHn7LaEuSUREpFkxxrxCYMHGNGNMLnAPcD/wujHmWiAHuADAWrvUGPM6sAzwAjdZazVPnIiItCjG2n1OjxV2hg0bZufPnx/qMkRajbwN6/jk0X+Tn7OBfieczLgrf0pkTEyoy5ImsHnlcj578r8U5ubQY8SxnHDV9cSlpIa6rKPOGLPAWjss1HU0d8OGDbOfTv+Ukc/MprYALs/4nr/+4g+hLktERFoItdeNQ/1rERFpao3ZZmsKERHZjd/nY+47r/PSb39JRckOzvnNH5hww20Kr1uwzJ69ueKBhxhz8ZWs/X4ez/3yRhZ+OhXr94e6NGmmUpJS+PsANzbaycvFg3jj9adDXZKIiIiIiIg0UwqwRaRO0ZbNvHrPb/j61Sl0O2Ykk/75qKaUaCWcLjcjzr2QSf94hLZduzHjmcd45Z7fUJCzIdSlSTN1zsmncEb6Nnw+J/etjWP9hhWhLklERERERESaIQXYIoL1+/nhkw944Y5bKd6ymdNv/TVn3n4HMQmJoS5NjrLkdpmc//t7OfXnv6B46xZeuPM2Zr0yGU9tTahLk2bo0RsmkdWhmuKSeG5/ZxperzfUJYmIiIiIiEgzowBbpJUrLcjjzXv/wOfP/Y8Offox6Z+P0vvY4zHGhLo0CRFjDH2PH8/V/36c3mPGMe/dN5jyq5vZuGhhqEuTZsYYwwdXnIQ708WP27tx53/vCXVJIiIiIiIi0swowBZppay1LJ05g8m/upmtq1dy8k9v5rw7/9gqF++TfYtJSOTUn/+CC/5wLxh4897fM/WRf1FZWhLq0qQZSYpL4KFjYvEnunm7YCSPPfHHUJckIiIiIiIizYgCbJFWqGJHMe/9814+eew/pHfqzJX/eIQBJ52qUdeyT1n9BnLlPx5hxLkXsfLbr3juFzew5MvpWGtDXZo0E6ePHMPZ7QvxOV08ntebr756N9QliYiIiIiISDOhAFuklVk19xsm/+omNvy4gOMvv4YL77mPpDZtQ12WhDl3RCRjLr6CKx54mJT2HZj2+IO88ZffUbxtS6hLk2bi4SsuIrOTj9LKOO6bm0NRkf7uiIiIiIiIyMEpwBZpJaorypn6yL/44N9/Iz4tncv/9iDDzjoPh8MZ6tKkGUnr2ImL//QAJ113E3nr1zLl17fw/dT3sH5/qEuTMOdwOHj3gtG4ukSzorArf3jhv/j190ZEREREREQOosEBtjHmWWNMnjFmSb19/zDGrDDGLDLGvGOMSQruzzbGVBljFgYfT9Q7Z6gxZrExZo0x5mGjOQtEmtyGH79n8q9uYsU3Mxl1/iVc+td/kdaxU6jLkmbKOBwMPPk0Jv3rUTr27c8Xk5/itT/dpdHYclAZCSk8OCwKX0YUU7cdy98f+WWoSxIREREREZEwdygjsJ8HTt1j32dAP2vtAGAVcFe999ZaawcFHzfU2/84cD3QPfjY85oi0kg81dVMf/ox3rrvbiKiY7j0r/9i9AWX4XS5Ql2atADxKWmce8c9TLjxdgpyNgRGY3/8vkZjywGdNWQ0Z2cW4Yt2MaVgDK+8+kCoSxIREREREZEw1uAA21r7FVC0x75PrbXe4Ms5QIcDXcMY0w5IsNbOtoHVv6YA5xxSxSLSIJtXLGPKb27hx+kfM/SMc7j8/gdp27V7qMuSFsYYQ79xJzHpn4/SsU8/vnj+SV7/82/ZsW1rqEuTfdjP3VQpxpjPjDGrg8/J9d67K3jH1EpjzITGquPB839Cuy5Q6Y3mf+sTWLL0y8a6tIiIiIiIiLQwjTkH9jXAx/VedzbG/GCMmWmMGRvclwnk1jsmN7hPRBrRt2+8zKt/vAO/38+Fd9/HuCuvwx0RGeqypAWLT03j3Dv/yIQbbiNvwzom/+Zmvv/4A43GDj/Ps/edT3cCM6y13YEZwdcYY/oAFwN9g+c8ZoxplEnz3U4Hb5w+GHrFs6Eki39On05l5Y7GuLSIiIiIiIi0MI0SYBtjfgd4gZeCu7YCWdbawcAvgZeNMQnAvua7tge47vXGmPnGmPn5+fmNUapIi7dq7jfMfvNleh97PJP+8V869ukf6pKklTDG0O+Ek7nqX4/RoXc/vnj+f7z+l9+yY/u2UJcmQfu6mwqYCEwObk9m151RE4FXrbU11tr1wBpgeGPVkpXShgf6O/B2jOXLraO4/9nfN9alRUREREREpAU54gDbGDMJOBO4LDgtCMHObmFwewGwFuhBYMR1/WlGOgD7XfXLWvuktXaYtXZYenr6kZYq0uKVFxfx2VOP0qZLNybceDsR0TGhLklaofjUNM6784+ccsOt5K1fx+Rf38QPn2g0dhhrY63dChB8zgjuzwQ21Tuu0e+aumjwGE7rUIw/0c2rW8fz6JO3NeblRUREREREpAU4ogDbGHMqcAdwtrW2st7+9J23GRtjuhBYrHFdsGNcZowZaYwxwJXAe0dSg4gEWGv59ImH8FZXc9rN/6eFGiWkjDH0P+EUJv3zUTr06svnz/2PN/7yO43Gbl4afNfUkdwx9dDpZ5LezVDriGTK9oFMnfbo4dQqIiIiIiIiLVSDA2xjzCvAbKCnMSbXGHMt8AgQD3xmjFlojHkiePhxwCJjzI/Am8AN1tqdtyzfCDxN4Fbktew+b7aIHKYfP/uY9QsXcNzlV5Oa2THU5YgAkJCWznl3/YlTfnYr29evZcqvb+aHaR9qNHZ42R5cZHnnYst5wf25QP1/TPZ719SR3DEV43bz4vG98PVLZHtFBs8u2cH6DQsO/acQERERERGRFqnBAba19hJrbTtrrdta28Fa+4y1tpu1tqO1dlDwcUPw2LestX2ttQOttUOstR/Uu858a20/a21Xa+3NO6cdEZHDV7RlMzNfeIZOAwYz6JQzQl2OyG6MMfQ/MTAau33P3nz+7BO88ZffUZKn0dhh4n1gUnB7ErvujHofuNgYE2mM6Uzgbqp5TVFA74yO3N3Vh6d7IvPzB/HI1MnU1lQe/EQRERERERFp8TTHwFHiqalm2VdfsHDah1SXl5Ge3YWM7C6kd+pCRucuJGW0xTgaZU1NaWV8Xi8fP/JPXG43p954u/4eSdhKSEvnJ7/9M4s//5SZLzzN5F/dzHGXXc3Ak0/T39ujJHg31TggzRiTC9wD3A+8HryzKge4AMBau9QY8zqwjMBCzTdZa31NVdt1Q8fyxfYP+HpHDO9sOom2z97Cr298pqk+TkRERERERJoJBdhNrKyogIXTPmLR9E+oLi8jI7srWf0GkrdxPRt+/L7uNnp3VDTpnTqTEQy2M7K7kNohC1dERIh/Agl3c995nW1rV3Pm7XcSl5Ia6nJEDsgYw4DxE8geOJhP//dfZjz7OKvmfsOEG24lMaNtqMtr8ay1l+znrfH7Of5e4N6mq2gXYwyPjT+F43yzKSt38dK28WS88EsmXfHvo/HxIiIiIiIiEqaaTYBdsqM41CUckm1rV7Pgo3dZNedr/H4/3YaNZOjpE8ns3ZfA+pXgra2lMDeHvA3ryNuwjvyN61g6cwYLp30IgMPpJCWzIxmdOu8asZ3dhei4+FD+aBJGtq5ZyZy3X6X32BPoOWpMqMsRabCEtIzgaOxpzHzhmcBo7MuvYeBJp2o0diuWFBXF00OyONdfQMk8Py9vyaLL15MZO2bSwU8WERERERGRFqnZBNhbamHyr26kx8jj6Dl6LCntO4S6pL34fT7WzJ/Dgo/eY8vKZURERzNowpkMPvUsktrsPbLQFRFBmy7daNOlW90+6/ezI28b+RvWkbdhPXkb1pKz5EeWzfqi7pj4tPS6Udrp2V3I6NSFhPSMumBcWgdPdTUfP/Iv4pJTOfHqn4W6HJFDFhiNfSrZA4cw7YmHmfHMY6ye+zWn/Ow2EjPahLo8CZHhHbpye24O/+mTzMol3Xlh4Ux6dF9NmzbdQ12aiIhIyBljfgFcB1hgMXA1EAO8BmQDG4ALrbXNawSYiIjIAZjmsoZiZLvu9u/3TcA7bSNYS3qnzvQcNZaeo8aS1LZdSGurqaxg8eef8sMnH1Can0diRhsGn3o2/U44mciYmEb5jMqSHfVGaq8nb/1airZuhuB/v8jYWDI6dakbqZ2R3YW0rGyF2i3Y9Kcf48fpH3PB7+8lq9+AUJcjckSstSyeMY0vXwjMeXz85Vcz4KTTjuq/YcaYBdbaYUftA1uoYcOG2fnz5x/RNXzWcu60j/lxdRRmcxXXdHmT3179BC53ZCNVKSIizVVrbq+NMZnA10Afa21VcK2KqUAfoMhae78x5k4g2Vp7x4Gu1RjttYiIyIE0ZpvdrALs/n/5C//L3o6pzWbl7K/Zsmo5AG26dKPHyDH0HDX2qI7a27FtK99/8j5LvpiOp7qKDr37MeT0s+k6bAQOh7PJP99TXU3Bpo3BYHst+RvWk5+zAW9tDQDte/Zh3BXX0q57zyavRY6u9T/M5+37/8jQM89l3BXXhrockUZTmp/HtP89TM7ihWT1G8iEG24jIT3jqHx2a+4QN6bG6hBvKy/j+K8W4vu+DFd1Jbd0ep+bfjqlESoUEZHmrDW318EAew4wECgF3gUeBv4LjLPWbjXGtAO+tNYesBOoAFtERJpaqwywY9p3s+nX/ZfbxzzNZT1+TUb2SEoL8lg1+2tWzvmabWtWAdCuW096jBpDj5FjSEhLb/Q6rLXkLlvMgqnvs3bBXBwOJ71Gj2XI6RN3mwokVPx+H8VbtpCzZCFz3n6NypId9Bx9HGMvuVILpLUQlaUlTPn1zUTHJ3DZff/RQp/S4lhrWTT9E2a++CwAx19+DQNOOrXJR2O35g5xY2rMDvH0dSu4fEU5cd9uoW3UNm7rvZ4LfvK3Rrm2iIg0T629vTbG3EZggeUq4FNr7WXGmB3W2qR6xxRba5MPdB0F2CIi0tRaZYCdld3NOi5+iKQ+Hv6d/ldGH/cRUbG7RluX5G1j5eyvWTl7Fnnr1wKBEcg9g2F2XHLKEX2+1+Nh5bdfsWDqe+RvWEd0fAIDTz6NgSefTlxK6hFdu6nUVlXy3ftvMf/Dd7F+H4NPO5sR515IVGxcqEuTw2St5YN//421C+Zx2X3/JiO7S6hLEmkyJXnb+fR/D5Oz5Eey+g9iws9ubdLR2K29Q9xYGrtD/PtvZvDsllgiFhQwos0CfnN8f4YOOa/Rri8iIs1La26vjTHJwFvARcAO4A3gTeCRhgTYxpjrgesBsrKyhm7cuPEoVC0iIq1Vqwywhw0bZqtOvZuyqEhuHPESYzxFHHvq2zgce48+Ld66mZWzv2bV7Fnk52wAY+jQuy89Rx1HjxGjiUlMavDnVpbs4MfPPmbhpx9RWbKD1A5ZDDl9Ir3HjsMd0Tzm4iwrLOCb115g6VefExUXz6ifXMLAk0/D6Wo2a3hK0NKZM/jksf8w9tKrGD7x/FCXI9LkAqOxP2bmC89iHIbjL7+W/uMnNMlo7NbcIW5MjR1g1/j9nPbZdNavj8BuqOCiLu9z5/m/JzmlY6N9hoiINB+tub02xlwAnGqtvTb4+kpgJDAeTSEiIiJhptUG2KdddgcvbI8hekAUD7a9kTbOsxh0/L8OeF5h7iZWzp7FytmzKNq8CWMcdOzbn56jx9LtmFHEJCTu87z8nA18P/U9ln/9JT6Ph86DhzHk9Il06j+o2S6MuH39Wma+8Aybli4iuV0mx112NV2HjWi2P09rU5K3nSm/uZn0Tl248J77jso86yLhIjAa+yFyliyi04DBnPKzW0hIa9zR2K25Q9yYmqJDvK6kmPFzVxD5/Q48JbXc1P0Vbr/qJf07KCLSCrXm9toYMwJ4FjiGwBQizwPzgSygsN4ijinW2t8c6FoKsEVEpKm12gD762/nMOCej6lKjeaSrMVMSHuE7m3vIavPlQc931pL4aaNdWF28dYtGIeDTv0H0XNUIMyOjIlh/cIFLPjoXXKW/IgrIpK+x49n8GlnkZrZMkZ6WWtZ9/13fPXisxRtyaVDn34cf/m1tO3aPdSlyQH4/T5e/9Nvyd+4jiv//shRXaxUJFxYv58fp3/CVy8GR2NfcS39T2y80dituUPcmJqqQ/zmykXcvMFL0jebiDZl/LTXyyT5a4iO7smgfpfQqduxOByORv9cEREJL629vTbG/InAFCJe4AfgOiAOeJ1AkJ0DXGCtLTrQdRRgi4hIU2u1Afb8+fP5+V9fZ2p5LI5jEnjc9xCulMUMGfgSyekN//Ow1pK/cT0rv/2KlbNnUZK3HYfTRWxSMmWF+cSlpDL41LPoP34C0XHxTfhThY7P62Xx55/y7RsvUVVaQu+xJzDm4iubZOFLOXLfvf8WX730HKf+/Bf0PX58qMsRCamSvG1Me+JhNi1t3NHYrb1D3FiaskN881fTeacwnuh52/FaJ1HOKronr6Nn8ho6x2wi2V+F05FFz25n02fAmURERjVJHSIiEjpqrxuHAmwREWlqrTrAziuuZNQDn1OTGcsxSeXcnnwvRHgZcdxHREUd+qhUay3b162pm2Kk15hx9BhxbKuZH7qmsoJ5777BgqnvYTAMOWMiwydeQGRMTKhLk6C8Det46be/pOvQ4Zz1y7s05YsIwdHYn33MVy89FxyNfR39TzzliP7/UIe4cTRlh7jC42X8F19RWu7gnC+2sjUynhVuD1ttYD2MSEcN3YKBdpe4HFK8peBrS2aH4xky+BLiU9KapC4RETl61F43DgXYIiLS1Fp1gA1w8d2vM8cTi//YVK5cuoYTh9xLrLMHx4x7Y5+LOsrBlebn8fWrU1j+9ZdEJyQy+oLLGDB+Ag6n5hcNJW9tLS/97pdUluxg0j8f3e+c7SKtVUneNqY9/hCbli0me+AQTr7+lsO+k0Qd4sbR1B3ixQXbOfPHHKJ9VZy/5lO8cyPINm0oTWjHmkg3S101bLZuANyOWromradn8lq6xa8n1bsDX00KKanDGDzwYtp16oHRtCMiIs2K2uvGoQBbRESaWqsPsJdtLOL0x7/F2yWeMVnwk+VzSBn4JG3jfkLf4X8PcaXN27Y1q/jyhWfYvGIpKZkdOf7ya+g8eJhG/YbIzBefZf4Hb3PunffQZfAxoS5HJCxZv5+Fn03lq5eew+FwMu7K6+h3wsmH/O+WOsSN42h0iFeUlnH3Dwv4yp9Ej4oNnL51MZPX9CarsooLjJdUfxwb3YmsjfDzo6OaTdaFxeAyHrombaBH8hq6J6wj3bcDT0UsUbF96Nf7PLr3GYE7StOOiIiEM7XXjUMBtoiINLWQBNjGmGeBM4E8a22/4L4U4DUgG9gAXGitLQ6+dxdwLeADbrXWTgvuH0pgteRoYCpwm21AEXs2sKfe9SarHFHUHN+WcZt9XO9/AW/nj+mR/Sc6drm8QT+T7Ju1ljXz5zDrpeco3rqFrH4DOf6Ka8nI7hLq0lqVTUsX8fpffsfAk07lpOtuCnU5ImFvx/ZtTHviQXKXLSF70FBOuf4W4lMbPmWEOsSN42h1iK21TNu0kXtWbWSjM5FTdsznmEp4YmUmFbV+hraP43J3Lcm5ZZSWRrIlMorVTg/fO6rZEAy0ncZL58SN9ExeQ/fEdbT1F1JbGolxZtO566n07XM80XHxuKMicbrcTf4zhTNrLX6fF7/Xh8/rxe/z4vN58Xu9+Lw+/D4vXo8H6/fhcLpwOBwYhwOH0xl4Dr4ObDv3eHZgnM5dx+hLcxE5ALXXjUMBtoiINLVQBdjHAeXAlHoB9t+BImvt/caYO4Fka+0dxpg+wCvAcKA9MB3oYa31GWPmAbcBcwgE2A9baz8+2Ofv2cDO+D6Ha19fjK9XPOO7R1HyWTG/7vMY1akrGDL4ZZJShh7CH4Psi8/r4cfPPmb2m69QXVFO3+PGc+zFl2sO0aOgprKCyb+6GVeEmyvuf1gjAkUayPr9LPz0I756+fnAaOxJ19FvXMNGY6tD3DiOdoe4xu/nycXzeTDfj8c4uK5sHt1i+vH0IlhXUEFaXCSXjsjinMw4Cr9aScmqCrz+GLa7YanTwwJTzXrrwI8Dh/GRnbCJHslr6JG0lnYmjwi/B3wGv9eBz2fw+xz4/U6sdWCtC4wbYyJxOKNwuGJwueJwu2OIjEwiKiqRmOhUYmNSiY9NIyo6HndUNO6oSCKionFHRu02VZff58Pn8eD11OLzePB5PXhqa/DWVuCprcDrqcDjrcTnqcDrqcTnrcTrq8Lnrcbvr8Lnq8bvr8bvrwk8bC3W1mBtLRYPFh9YHxZ/YBs/WD8WP5jgaywYPxgbeG0sxti6Z+MAHBZjwDh27g9sm+BsLNYGLmP9BmsN1g/s8Wyt2e2YXduA32AxYHc+HEDwOBxgHRjrANwY3BjjxpgIHCYChyMChyP438MRicsZhdMVjdMVhcsVjcsdi8sdfI6IxR0RizsyFndkHBERsbgjI3FFROCKiNQ0aiJhSu1141CALSIiTS1kU4gYY7KBD+sF2CuBcdbarcaYdsCX1tqewdHXWGv/FjxuGvBHAqO0v7DW9gruvyR4/s8O9tl7NrDWWkbf+S6FkS5Kx3XgT8mpLH5rAReO/DtE+xgx5gMiIw99UUfZW3VFOXPfeZ0fPn4f43Ay7KxzOebsnxARFR3q0lqsjx/5F8u/mcklf/4H7br3DHU5Is3Ojm1bmfbEQ+QuX0LnQUM5uQGjsdUhbhyh6hBvr6zivvmzeM2XQUZtIb91bqRduxOZ/F0+X6zMw2kMp/Vvx1WjO9G/TQI58zaQM3Mt3gJDlCuCbS4/PxgP86lmHQ587Job22m8RDpriXB4iHDWEuGsDbyu9xzhrCXCUUuE07Nrv2PnMYF9bmpx48NpvbisF5c/8GwAn3HgN058Dic+48BrXPiNAy8uPH4XXr8Lj9+Nx+8Obrvw+Opt+/fervW58fhcePwReP0urCUQQgOGYCBNvdeAMf6618Du70EgsK63f+cxBI9xYHEaH06HD6fx4zJeXA4frrp9XlwOL05H4NllvDgdvsB28HXdtsODy+HFXX/b6cVpfDgcfhwOX/Dhx+n0c6QDt60F6zX4fQbrcwSe/Q6sz4n1OwNfWNidoXkEDhOJ0xGN0xGF0xmN0xmFwxmNyxUTeLhjcUfE4XLHEhEZjzsijoiohLpHZHQcDqdLI85FDpHa68ahAFtERJpaOAXYO6y1SfXeL7bWJhtjHgHmWGtfDO5/BviYQIB9v7X2pOD+scAd1tozD/bZ+2pgJ3+6nHs+X4cdkMDw7GiyN7vpsfJHug6/n7joHgw79jUcjsgG/3xyYCV525j18mRWzp5FbFIyoy+8nH4nnITDoRFKjWnl7K/58MH7GXX+JYy+4LJQlyPSbFm/nx+mfcSsV57H6XQx7srr6DvupP2GReoQN45Qd4i/37qJPyxZygJXWwZVrOWvHWJIyxzLlDk5vDF/E2U1XvpnJjJpdDZnDmhHpMtBQW45675YSfHiIiI9bhLcDjY5/azGRwV+KvFRiZ8q/FTawHMNlhpjqcFSawy1GGpx4LEOvBzddtGBHyd+XCbw7LQWJxYXu55dBIJmy86//4FY2tbbJvj+7tvUO4bgo/727q/9gBeDD/ACPpoinLVEGS/xpoZ4aog31cSZGhIc1cS7KoiJrsREebBRtfjd1fjdNfgd1YGHqcbYWhw+Lw7rw+n34bS+wJ+V9eEGXCbwcDssbmNxOcDlsHUPp3PXw+G0OF2Ht56M9YHf58D6HMGR6sHR5sER54FnB1gndSPPjRNwYnAEn50Y4wpsGxfGBJ4dO7cd7sBrhxvjcOEwbhwOFw6nOzha3YVxROB0unE4I3A4I3DWPbtxuiJxOCNwBZ+driicLjdOdzQuZ2TwOu66zw08tDCqNC21140j1O21iIi0fM0hwH4UmL1HgD0VyAH+tkeA/Rtr7Vn7+bzrgesBsrKyhm7cuHG39z0+P0N++yE22k/B8dlM6ZPN/c9/z91RP+Dr/xjtUs6nz6AHGvzzScNsWbWCL194mq2rVpCWlc2JV11Px74DQl1Wi1BeVMjkX99MUpu2XPznf+B0uUJdkkizt2PbVj55/EE2r1hK58HDOPn6m/c5FZI6xI0jHDrEfmt5e9l3/HVzJdvcSZxf8SO/GzKChNRuvP3DZiZ/u4E1eeWkxEZwyfCOXD6yE+0SA3cVVZd72PjjdnJmraN2ey3GbzDW4DKOumDTZQzunSGnMbjYuQ0OY/BhqQFqsFQDVfgpx0cFPsrxU4GHcr+XSrxUWB9giTAOInAQaRxEGQeRBB4ROIjCEIkhCkfdcxQOojFE4sCJA1MvKPZZi88GFiHx2UCo7LPBSUOCr+ueg39e9ff5dnsP/Ni696wJ/N5YN5sHwalDDOCwuJ2WNFcJKTVbiSrKxVtUQk1ZNV5nBF5XND53ZOA5PglfXAK+6Hh8kdF4XVF4nG58OKm14PH4qfX68fj8eAAPFi/UbRdi2Rph2Ozws73Gi7/er7MuY0lye4mjhhhfBXGmOhB2mxriTA1x0ZEkJibu9UhISCA+IZ6o2Ch8+Kj11VLtq6bKU0Wlt5IqbxWVnuBz/deeSqq9ZdR4yvBUl+GpLcdfW4m/tgq81eCtwfg9OH0eXFgiDURgcQf/zgT+3licBhwmMIrdYcDpMMF9BqexOBxggsea4DQujrqpXepP87LzfYsJwRiDumlgdk7/Yg22Lozf+RcnEMoHgvh6YTxOMI5gMB8MxXGCw4WjXkgeCN6DQb0jEKLXhfQOd12wXhfMO9x14bzTFRF43xkZeH+vAL7+lwF7PzCBLxB23+8IfpGgOdyPBrXXjSMc2msREWnZwinADtkUIjv97dUF/G/hNpxD4uncPpb7MrO5/X9zeajHNEqzP6Rntz/TIUujWBubtZZVc77hq5eepTQ/j56jxnL8Fdce0oJpsjtrLW//7R5yly/ligceIqV9h1CXJNJiBEZjf8islyfjdLkYN+mn9D1+/G5BgzrEjSOcOsQVtTX8d+50Hq9Nx2F93MY6bjj2LCKj4vl2bSHPf7uB6cu34zCGCX3bcNXozhyTnbzPAMrn8VNb48VT7aO22oen2kttjS/4OrDfU+2htsqHv8KLt6IGf5UHf5UXW+uDWj/GZzE+i8O/exAO1AucbTAoNoEBuC4HxmXA7cS4nZgIB47gszPCiSPShTPKiSPKiSvKhSvahTvKhSvCgSvCiTvCGdx2YP3g91l8Pj9+785nf3Cfxe/1173n91l8O9/z+vH7/MFj9jh/53lei9/np6yomoJN5QBEx7vJ6pNKx15JtE3x4Crehmfz5uAjl9rNm/Hkbsa7fXsw9QxyOnG3bYs7MxNXZgfc7TNxt+uAK70trtQMTGwSnpxyqpYWUJtThgdLQUok+R1jyUuOINfnZVNRJRsLK8kpqqSy1rfbf8ukCIIBdzXRvnKifRV1AXckXhwOQ3x8PElJSfTv35+BAwcSERHRKH8nPT7PXmF4haeCSm8lFZ6KwLankgpvve3g/n0dV+2r3v+HWXBYi8tviDAO4h3RxDqjiHNGE+eIItYRQbQjghhHBFHGTaTDSaRxEWmcRBhH8MuZwLPDGlzGBsZ+Wz/G+vH7PVjrxW+92J3bfi/W+rDWiyXwjPUF9gXnXa+bi93s/JrEF5h3nfpzr/t3zb2+M5B3sOv1zjnYg/vr5mcPA4G52k1wnva6b3fYPbAPbtcLwwPhd71R9nuG5w5nvRDfGQzrXcH9wedgGB8I83c97/oME/yCgECNO799qrcvsB14Dh7Fzm+pdt8f+EbF7Pz5THAfJrg/eJzZc58J/htr9qhh57Xq1bDb/l2fl5x8jNrrRhBO7bWIiLRM4RRg/wMorLeIY4q19jfGmL7Ay+xaxHEG0D24iON3wC3AXAKjsv9rrZ16sM/eXwNbUlnLMX/6lITYanLHduN/fTux6oft/PDFem4d/CSVacsYMvQlkpL0O05T8NTW8N17b/Lde2+BwzDyvIsZesY5uNzuUJfW7Pww7UM+f/YJxl9zI4MmnBHqckRapOJtW5j2+ENsXrGULkOO4aSf3lQ3GlsBduMIxw7xxsIt/HnBHD5yd6FjTR73pFvOGHwyxuFgU1ElL8zZyGvfbaKkykPvdglcNboTEwdlEuVumuGr1m/x1PrwBANway3uSGdd4OxwmX2G6M1FZWktm5YXsXFJIZuWFVFd4QEDGVnxZPVNJatvKm2y43E4AwGXra3Fs20bntxdoXZd0J2bizc/f/cPcLuJHTWS9Ftvw92xG9VLC6laUkDN+hKw4EyJIrpvKtH90nB3iKOoysPGwsrdQu2cogpyiirZXlqz26WjXYb0aEhy+Yj2lZFRnUtmjJ9hw4YxfPhwEhISjtYfY4N4/V4qvZV1QfehhN8VngoqvLsf5/F7GvS5LoeLWHcssa5YYtwxge3gIzUqlfSYdNKj00mLTiM9JvCcHJmM8xCnnfP7ffi9Pvw+Lz6fD7/Xi8/rxe8L7Kv/2uutxe/14PNW4/PVBh7eWvy+WvzeWvx+Dz5vDX6/F58v8Nrv8wSe/V6svzZwTevB+r3BgD4YxvuDIXwwiLd+f/DZB2bXa/BjbSCUD97XEHi2O0N56hY+rVsM1dhgNrszsN+5vXOh1HoBfb1FU/e1f6/z6107XAL+xnDS+HVqrxtBOLbXIiLSsoQkwDbGvAKMA9KA7cA9wLvA60AWgelBLrDWFgWP/x1wDYEpEG+31n4c3D8MeB6IJjAv9i22AUUcqIG99fFZfLChhMhjYknISOCLkYO46H/fclLhDoYMuw9ifYwYrUUdm1JJ3ja+nPI0a76bQ3K79pww6Xo6D9bvlQ1VuHkTL955Ox369OO8O//YrIMLkXBn/X5++OQDZr0yBafLxQlXXU+f407E4XCoQ9wIwrlD/PWKefxhQz7LIzM5tnodf+nXmz4dewNQVevj3YWB6UVWbCsjKcbNRcM6cu6QTHq1Da/Qsjnx+y35OWXkLC0kZ2kR29eXYC1Exrjo0CuFrL4pZPVJJS55/2uW+Gtq8GzeUhdq127YQMk77+ArKSF+wgTSb72FyK5d8ZXXUr28iKolBVSv2QE+iyM+IhhmpxLZOQnj3L19rar1kVtcP9iuZGNhRd22x2fpGOMj25NDZ1cxA/v1YeTIkWRmZjbxn1xoeHyeumB7Z9BdPxTfMxjfMxQv85RRUFVAWW3ZXtd2GicpUSl1oXZdwB2dTlpM4HnnPrez5Q2EsNZi/f5d4bvPH3z21T18Pi/W58Pn8+3xHNjv9/v3cYwX69t9/74+w/oD51t/MGS3wdd2577Al2nW+vZ+bQMhvPX78Vv/rtfWX/dzBUbWB19bP/gD4b3f+nfOK1O3bf07w/16234/gUmJdl7fBq7FzvNtcMVYyw3/man2uhGEc3stIiItQ8hGYIfSgRrYjQUVjPvHF3SMK2XVmD7c1z2T4yKiOfvhWTyaVIId8Gfi4noybOSrWtSxiW1YuIDPn3+S4q2b6TpsBOOu/ClJbdqGuqyw5vN6eeUPv6YkbxuT/vkocckpoS5JpFUo3rqZaU88xOYVy+gy5BjOu/OP6hA3gnDvEHt9Xl6c8yF/r0hhhyuWK/wb+M3I8aTGJQGBkGnu+iImf7uBT5dtx+e39GwTz9mD2nP2wPZ0TIkJ7Q/QzFVXeMhdURwMtAupKKkFIDUzlqw+qWT1S6Vd10ScrgMPF/WVlVH03PMUPf88/upqEidOJO2mm4joEAiW/dVeqlcEw+yVxViPH0eMi6jegTA7qlsyxn3gzyip8vD297m8MGcj6/IriHVBV0ce3dlG3+w2jBw5kl69euFwtKChrY2k2ltNQVVB3SO/Kp/8yvy67YKqAvIr8ymqLsKyd18kKTKJtOi0/QbcOwPwGLf+f2wtdgbj1u/H5Y5Qe90Iwr29FhGR5k8B9j5c9I8Z/FBQQeywKGoz0ph77EDe/m4T/35vGVMyV5Pf92HaZVxA775/0+jWJubzeljw0XvMeetV/H4fx5z9E4ZPPB93ZFSoSwtL37z+EnPeeoWzfnkXPUYcG+pyRFoV6/fz/ccf8PWrU7j9xbfVIW4EzaVDXLxjO/+a8xnPRfQizlfNrxOrmDR0PG7nrjCyoLyGqYu38t7CLSzYWAzA0E7JTBzUntP7tyMtTl+KHwlrLUVbKtgYHJ29dc0O/D6LK9JJh57JdOqbQlbfVBLSovd7DW9REYVPPkXxyy9jrSX5wgtJu+FnuNLT647x1/qoWVVM1dJCqpYXYqt9mEgnUb1SiO6bSlTPFByR+5/awlrL7LWFvDBnI58u3Y7fWjpFVNDVbqZvimHUyBEMHjyYqCj9nnOovH4vRdVFgVC7Mhh019surCqs2+f1e/c6P8YVUzdFyc5wu/60JTtD78TIRP3+34Joyq/G0VzaaxERab4UYO/Dd+sKueDJOfSOzeOHMYP5VXZb/i+7DVc99x2la4u5u8vHFHX+gJ49/kyHDlrU8WgoKyrgqxefY8U3M4lPS+eEK39Kt+Gj1IGoZ8uqFbx6z2/oPWYcp930y1CXI9JqFW/dTEr7DuoQN4Lm1iFeuXYedy9fy8zY3nSvzeMv3TMZ16XvXsdtKqrkg0VbeO+HLazcXobTYRjTLY2Jg9pzSt+2xEW6QlB9y1Jb7WXzqh11o7NLCwILFCa1iSGrTyDMbt8jCXfE3mGzZ9s2Ch57nB1vvYWJiCDl8stJve5anImJux1nvX5q1u4IhNlLC/FXeMDlIKpHcmCqkd4pOGL2P33FtpJqXpmXwyvzcsgrqyHJ5aUrW+kbXcrooQMYMWIEycnJjfsHI1hrKakp2RVwB0dw72t0d5W3aq/z3Q73biH3XqF3cIR3SlQKLof+Xw53CrAbR3Nrr0VEpPlRgL0fJ/3pE/Iqq4kb7GJ720zmjO6HrfYx4cGvuNIdyaguD1KZvowhQ7So49G0adliPn/ufxTkbCCr/yBOvOpnpHboGOqyQq62uooX7rgVv8/HlX//L5ExsaEuSaRVU4d434wxpwIPAU7gaWvt/Qc6vjl2iK3Py6fz3uGeHXFsiGrHBP9m/jh0FJ2T9j2l04ptpby/cAvvLdzC5h1VRLocnNSnDRMHtuf4nulEuppm8cfWxFpLSV5V3ejsLauK8Xr8OF0O2vdIYsAJHcjun7bXebUbNpD/yKOUfvQRjrg4Uq+9lpQrLscRu3cba/2W2g0lVC0ppGppAb6SWnAYIrsmEt0vjeg+qTjjI/ZZn8fn57Nl23lh9kZmryvEaSzZjiJ6OfM4rm8Wo0aNpGPHjvrSPgQqPBXkV+bvNYJ756junUF3SU3JXuc6jIPkyOS6gDszLpOO8R3Jis8iKyGLDvEdiHTqzotQU3vdOJpjey0iIs2LAuz9eG9+Lre9+SPDYzbx9ZjhXNUhnft6dOSTJVu54cXvealtDL6uv4N4L8NHvkdUVLujVL34fT4WfjqVb994EU91NYNPO5tRP7mEyJjWO3fhZ089wqIZ07jw7vvo2Kd/qMsRafXUId6bMcYJrAJOBnKB74BLrLXL9ndOc+4Q15Tl89TX7/AfVz9qHRH8LLaM/xt2HNH7CaSttXyfU8x7C7fw4aKtFFXUkhDl4vT+7Th7UHtGdE7F6VCA2Ri8tT62rNlBztIi1v+YT2lBNSPP6cKQCZ32GRJXr1xJ/oMPUf7FFzhTU0n72c9IuvgiHBH7DqSttXhyy6laUkDVkgK8hdXggKSJ3YgbceDfF1dvL+OluTm8uWAT5TU+Up1V9DTbOLZjFMeNHkGfPn1wOvWlRrip9dXumpO7/vQlO+ftrswntyyXMs+uBSkNhjaxbciKz6JjfMdAuJ2QVfdac3IfHWqvG0dzbq9FRKR5UIC9Hz6/ZfgfPsblrSKuP6zM7MLXI/uQHR3Jr974kU8W5PJ222o2972b2IQeDB32Kk6NojiqKkt2MOuVKSz58jNiE5M47vJr6D1mXKsbobTu++9454E/Meys8zj+8mtCXU5Ys8GV543RIlnStNQh3psxZhTwR2vthODruwCstX/b3zktoUOct2E+9y5cwGuJI+juK+bRIQMZkHLgBXY9Pj/frCng/YVbmLZ0GxW1PtokRHLWgPZMHJRJv8yEVtfWNRWvx8fnU1aw+rvt9BzZlhMu64VzPwsyVv7wA/n/eZDKefNwtW9H+k03kThxIsa1/2kirLV4t1eyY+p6alYVkzSxK3Gj2h+0rooaL+8t3MLkb9ezcns5kcZHF0cBQxMrOXX0IIYOHUp09P7n85bws3PqkpyyHDaVbQo8l26qe11UXbTb8WnRafsOtxM6khCREKKfouVRe904WkJ7LSIi4U0B9gE88dlq7p+xihNj1jB99GhOb5fOE32zKav2cPrDs+jghbtjF7Ol/8O0a3s+vXvfrw5lCGxds5LPn32CbWtX075nH8ZfcwMZ2V1CXdZRUVlawuRf3URMYhKX3fcfXO79z7XZUvj9HrzeUrzeUjzeUryekn1sl+D1luH1lAT2e0vweALnuFxxpKYeT1raeFJTjsftVidQGp86xHszxpwPnGqtvS74+gpghLX25j2Oux64HiArK2voxo0bj3qtjc7vY+bMZ7itpjMFESn8pm00N/XujbMBvzNU1fqYsWI77y3cwpcr8/D4LJ3TYjl7YHsmDmpPl/S4o/ADtGzWWuZP3cC8D9bTrlsip93Qn+i4/Y+urpw9m7z/PEj14sVEZGeTftutxE+YgHHs/8tR6/VT+NJyqpcXkXR2V+JGHzzE3vl53+cUM2X2RqYu2oLHD+0cpfSNKOTsYZ05dtRIUlNTD+vnlvBSVlvGprJNdY+c0py6kDuvKm+3Y5Mik+rC7D1D7uTIZPVHDoHa68ahAFtERJqaAuwDqKjxMvSeabS35UT38rEguw/ThvVgYHwM8zcUceH/ZvPbThkMc75MYdf36dnjT3TocPlR+AlkT9bvZ8mX05n18vNUl5cz4OTTOPaiy4mOiw91aU3GWsv7/7qX9T/M57L7/kN6p86hLqlBrLX4fJW7Quhg6Oz1BsNmz84AOvD+nq99vsoDXt/hiMDlSsTlSsDtSsDlTsDlSsTtSsTliqemZjsFhV/g8RRhjIukpGNISxtPetp4oqOzjtKfgrR06hDvzRhzATBhjwB7uLX2lv2d09I6xMUrZ3DH4uW8nzqG4e5q/jt0MJ2iG373Vkmlh4+XbOW9hVuYs74Qa6F/ZiITB7XnzAHtaZsY1YTVt3yr529nxuTlxCZGcMbPB5LSfv/rSVhrKZ8xg/yHHqJm9Roie/cm4/bbiD3uuP2Gh9brp/DlFVQvKyTxrC7EH5t5SPUVlNfw+vxNTPlmPdvKaokxtfRw5nNq93gmHDeS7OxsBZctVKWnktzyXDaVbqobvb0z3N5asRXLrj5YnDtur+lIdr5Oj07X35E9qL1uHC2tvRYRkfCjAPsg7n59ES8syOGsmGVMHTWWIRnpvD6oGwD/mLaCR79Yy7vd21Md9xcq05cweMhLJCcd05TlywFUl5fz7RsvsXDaR0TGxTH24ivpd+LJOBwta77IytIS5r37Ogs+eo/jLr+GY84676h+vt/vxecrqwuf9xoJHXy9z21vKdZ6D3h9lyselyshGETH43bvDKQDzy737tu73kts0FQ+1vooLf2R/IIZFBTMoKJiNQCxsd1JSzuJ9LQTSUgYSGDKXpFDpw7x3lrrFCJ7sjs28fYnD3NX6tn4nFH8pUcWl2RmHHKotK2kmg8XBRZ/XLy5BGNgZOdUzhuSyVkD2xPl1r9fh2P7+lI+enwRvlofE37aj6y+Bx7dbH0+Sj/6iPyH/4snN5fooUPJ+MXtxAzb9//+1uun8JUVVC8tJPGMLsSPPbQQGwLT3H2xIo/nvl7LN+uKcWDJchQzKt3LRScMZsCAATgOMBpcWpZaXy255bnkluXWjdreGW5vLt+Mz/rqjo12RdMhvkNgIck9RnC3iWmDs4X9vtwQaq8bR0tsr0VEJLwowD6IrSVVHPu3z+lri4ns6uHr7oN5bWBXjk+Jp9br57zHv2F7URVvpUSzsfOd2IRahg9/X4s6hlj+xvV8/tz/yF2+hDZdunHi1TfQvkevUJd1xCp2FDP/w3f48dOpeGpr6HvceE654ZYmC+h9vkrWrvs3ZWXLgtNwBKbm8PnKD3ieMe5AqOyuNxLalYCr/us93tt5rMsVf9SD48rKjRQUfk5BwQx27JiHtT7c7lTS0k4kPe1EUlLG4HRqMSVpOHWI92aMcRFYxHE8sJnAIo6XWmuX7u+cFtsh9taS+9n93FbVgW+Sh3BaYgT/6NeDtIj9z6V8IOvyy3n/x0CYvb6ggqQYNxcO68hlI7LolLr/UcSyb2VF1Xz02CKKtlQw9sLu9B/X4aDn2Npadrz9NgWPPoY3P5/YsWNJv+02ovv13ftYn5+iV1ZQtaSQxNM7E3/cwa+/PxsLK5jy7Xpe+y6H8lpLkqlkdHI5Pzt1GP379VWQ3cp5/B62lW/ba9T2znm3PX5P3bFuh7su3K4/gjsrPot2ce1wOQ7v36dw19rba2NMEvA00A+wwDXASuA1IBvYAFxorS0+0HVabHstIiJhQwF2A/z0ybl8uTaf86IXMnXkWNqmtmXasB44jGFNXhlnPPw1p2WlcOuOTWwc/EdiE7sxdOhrWtQxxKy1rPj2K7564RnKi4voe/xJjL10ErFJyaEu7ZCVFuTx3ftvs/jzafi9PnqNOZ4R51xAaoemm/KisnI9ixb/nIqK1SQlDsPlDk7DUTclR3xghLR775HRDkdUs71F1eMpobBwJgWFn1NY+CVebxkORwTJyaNJSxtPWtqJREW2DXWZEuZae4d4f4wxpwMPAk7gWWvtvQc6vqV3iP1L3uHJ76ZzX9ZVJLqc/LtvV05OSzzs61lrmb2ukBfnbGTa0u34reX4HulcMbIT43pm4HQ0z3+XQ6G22stnzy5jw6IC+o/rwJgLuuFwHjwM9ldVUfzyKxQ++SS+khLiJ0wg/dZbiOzadbfjrM9P0asrqVpcQOJpnYk//vBDbIBqj48PftzCI9OXs3GHh3hTzbHJFdx0xjH069Or2bbJ0nR8fh95lXn7XVSyyltVd6zLuGgX164u3M5OzKZHcg96JPcgMfLw/80KB629vTbGTAZmWWufNsZEADHAb4Eia+39xpg7gWRr7R0Huk5Lb69FRCT0FGA3wOLcHZz1yDeM8m/H2amW6X1G8nifTpzbJhCETv52A/e8v5RHR3ahy9pP2TL4YTIyTiez/SVER3cgMrIdDkfLX1wvXNVWVTLn7ddY8NF7uCIiGHL6RPoedyJJbcN/lPyObVuZ994bLJ35OQB9jz+RYyaeT3Lbhi3+dLjy8qaxbPlvcDjc9O37IKkpY5r088KV3+9hR8l8CgpmUJA/g6rqHADi4/vVTTUSF9dHwYDspbV3iBtLq+gQF6xm+Xt3clPGJSyL68aV7VK4p3smsc4juxNlW0k1r8zL4ZV5OeSV1dAhOZrLRnTiwmEdSI3TF+wN4fdbZr+zloWf5ZDVJ4VTftqPyOiGjUL1lZVR9Pxkip57Dn91NYkTJ9LmzjtwJu4K+6zPT9FrK6laVEDCqdkkjOvYKDV/tmwb/5i6mDVFHmKp4djUKm476xj69Oyu9koaxFpLQVXBrpHbpbuH3GWesrpj28a2pUdyD3om96wLtbMSsprNiO3W3F4bYxKAH4Eutl5H3hizEhhnrd1qjGkHfGmt7Xmga7WK9lpEREJKAXYDTfzXTNZtL+Oc6AV8OnwUNqUDX4/oRYTDgbWWq577jjnrCpk2tCs78p+ksNs79c52EBXZlqjoDkRFZRId1YGo6A6B56gOREa2xdFMfslrzoq25DLzhWdY98N8sJb2PXrT57gT6DFqbNgt9liYm8Pcd99gxdczcbic9D9xAsecfR4JaRlN+rl+v5e16/5BTs7TJCQMpH+/R4iKatqwvLmw1lJRuYaC/BkUFM6gpOQHwBIZ2bZuEcjk5JE4HAqGpHV3iBtTq+kQ11ZS89GveKAsnsc7XEx2lJtH+3ZmSOKRT//h8fn5bNl2pszewJx1RUQ4HZw5oB2Xj+rE4I5JCjQbYNk3W5j50koS28Rwxs8HkJge3eBzvUVFFD71NEUvvkhk925kPfMMruRdd4JZn6Xo9ZVU/ZhPwoROJJzQOHdWWWv5cuV2/v7hYpYX1BKFhzFpVfzy7OH06dH14BcQ2Y+d4fbK4pWsKl7FquJVrCxayYaSDXiDa5xEOiPpmtS1LtDeGXAnRSWFtvh9aM3ttTFmEPAksAwYCCwAbgM2W2uT6h1XbK094C2sraa9FhGRkFGA3UCfLdvGT6cs4GTfJsis4YOBx/PX7plc1yEdgLzSaiY8+BUdk6J5KjqRym3riZ4YjT+tlKrqTVRX51JdtZmq6lxqarZBvdXCjXESGdlun+F2YAR3Gy0m14hKC/JZ8c1Mln31OYW5OTicLroMGUafsSfSecgxuNyhGy2ft2Edc99+jVXzvsUdEcnAU05n6BnnEJec0uSfXVOTx5Klt7FjxzwyMy+nR/ffKow9gNraAgoKvqSgcAaFhbPw+6twOmNJSRlDetp4UlPHERFx4MW/pOVqzR3ixtSqOsTWwvdT+PbrKdzS8062RaZxe3Zbbu/UFncjTf2xensZL8zZyNvfb6a8xkvf9glcOaoTZw/MJDpCv2ccyOaVxXz85GKMMZx2Q3/ad0s6pPPLZ80i9+ZbiOjUiaznnsWVuqt9sD5L0RsrqVqYT8LJnUgY37jTg327Oo/7P1jIojwPkXg5Nr2GX00cTp9u2Y36OdK61fpqWV+yvi7Q3hluF1YX1h2TEZ1B95Tuu43Wzk7Mxh3CO1Vbc3ttjBkGzAGOtdbONcY8BJQCtzQkwDbGXA9cD5CVlTV048aNR6dwERFplRRgN5Dfbxn71+l4y2qYEDOfb4YMZmtqF+aO7EO8K9Dp+2TJVm548Xt+OaYLF62rwrO9ktRLehHdL22Pa9VSXb01EGpXb6aqalPgOfi6pmY7uwfcLqIi2xMVnRkItaOCz9EdiY3tgdudcMR/Jq2RtZa8DetYPutzln89k8qSHUTFxtFj1Bj6jD2R9j17H7WRaVtWrWDuO6+x7vvviIiOYchpZzH4tLOJSTg68woW7/iOJUtuwesto1eve2nX9pyj8rkthc9XQ3Hxt4GFIPNnUFO7HTAkJg4hPW08aWnjiYnpqpGOrUhr7hA3plYVYO+09UdK3/wZv00/jzfbnMKg+Gge7dOJrjFRjfYR5TVe3v1hMy/M3sjK7WUkRLk4f2hHLh+ZRZf0uEb7nJZmx/ZKPnpsEaWFVZxweS96jTy0qcgqZs9m040/x52ZSdZzz+LO2HVXlfVbit9YReUPeSSclEXCSZ0au3y+W5fP397/ge+3eXDjZXS6l99MHEbfbo3/WSI7FVQVsKp4FauLV9eF2mt3rK1bQNLtcO82Wrt7ciDgTo0+OoMAWnN7bYxpC8yx1mYHX48F7gS6oSlEREQkzCjAPgRTvlnP3R8s41zPanxta3h92AR+0akNd3TZ1YH51Rs/8vb3ubx59XA6zNhC7aZSki/oSezghk/94PfXUF29harqzVRX5QaD7dy67dra/LpjjXGTknIsGemnkZ5+Em530iH/XAJ+n4+Nixey7KvPWfPdHLy1NSS2aUvvMSfQZ+w4kttlNvpnWmvJXb6EOW+/Rs7ihUTFJzD09IkMmnAGUbFHJ0Cw1rJp07OsWfsAUVEdGdD/MeLiDvj7qRyEtZay8qWBqUYKZlBWvhSA6OhOdVONJCYO07RBLVxr7hA3plbbIa7aAe/+nPcLK7ij951Uu2K4p1smk9qnNuoXYdZavttQzJTZG/hkyTa8fsvY7mlcPrIT43tl4GrAooWtTXWFh0+eXMLmlcUMmdCJkRO7YA5hhHzld9+R87MbcKenkzX5edxtdy0KbP2W4jdXUfl9HvHjs0g4KatJvvhcuKGA+95bwLytHlz4GZXu445zhtGv65HPwS3SEB6/hw0lGwKjtYNTkawuWk1eVV7dMalRqbumH0kJjNjunNiZCGdEo9bS2ttrY8ws4Dpr7UpjzB+BnXNXFdZbxDHFWvubA12n1bbXIiJy1CjAPgTVHh/D/vQpGdVejotbwKL+PfkhrQ9zR/YhIzJw61tZtYfTHpqFMfDWtSMxb6+hZl0JSed0I25E4ywa6PNVBwPuHIqLZ5OX9zHV1ZsxxkVK8mgyMk4jPf1k3O4DTlUm+1FbVcnqebNZNusLcpb8CNbSrntP+ow9kZ6jxxIdf2Qj3q21bPzxe+a88xqbVywjJjGJY846jwEnn0ZEVMPn1TxSXm8Zy5bfSX7+J6SnT6BP7wdwucJrLvCWoLp6CwUFX1BQMJ2i4jlYW4vLlUhq6vHBqUaO1597C9TaO8SNpVV3iK2Fb//Ltq/+yy/63s0X8f05MSWe//TKok1k499un1dWzWvzNvHyvBy2llTTPjGKS0dkcdExWaTHazqp+nw+P1+9uopls7bQZXA6J13VB3dkw6dgqfz+BzZdfz3O5GQ6Pf8c7sxdX5Jbv6X4rdVULthO/IkdSTi5U5PdvbNsUyF/fec7Zm/xYrCMTPdz17nD6N+l8b+0F2mI4uriulHaOx9ritdQ668FwGVcZCdm1wXaOx/p0emH/f9Ja2+vg/NgPw1EAOuAqwEH8DqQBeQAF1hriw50nVbdXouIyFERVgG2MaYn8Fq9XV2Au4Ek4KfAzqHHv7XWTg2ecxdwLeADbrXWTjvY5xxJA/v3D5fz2Kx1XO5ZTE2Gl1dGnMFl7dN4oOeuUSvf5xRz2VNzaZ8UxUtXD8f1/nqqVxSReHpn4o/rcFifeyDWWsrKFpOX9zHb8z6munoTxjhJTh5NRvqppKefQkRE08+h3BKVFRWw4uuZLJv1BQU5G3A4XXQePIw+x51AlyHDD2m+bOv3s3bBPOa8/Rrb160mPjWdYyb+hH4nnIw74uiGA+XlK1m85CaqqnLo2vU3ZHW8VtNbHAVebzlFRd9QUDCDgsIv8HiKMMZFUtLwuqlGoqM1Aq4laO0d4saiDjGw8VvsG1fzXNJY/tz158S43fyjZ0fOSE9qko/z+vxMX57HC3M28M2aQtxOw2n92nHFqE4M65SstiLIWsuiz3P55s3VpHWM5/QbBxCX3PC2vGrxYnKuvQ5HXCydnn+eiKxd815bv6X47dVUzt9O/AkdSTil6UJsgJWbC7n3rXl8vcWLBYanw13nDGVQVy3iLKHn9XvJKc3ZtWBkcMT2toptdcckRybXTT+yc8R216SuRDoP/v+k2uvGofZaRESaWlgF2LtdLLBq4WZgBIFvgsuttf/c45g+wCvAcKA9MB3oYa31HejaR9LA5pfVMOq+6fSp8TIk4QfW9+rI9DZD+WpEr93mp5y3voirn5tHenwkL10znKhpOVQtKmjSW0JhZ5i9hLz8T8jLm0pVVU4gzE4aSXrGqWSkT9DCcocpb8M6ls36ghXfzKSiuIjI2Fh6jhxL77HjyOzVd7//Tf1+H6vmfMPcd16nIGcDSW3aMfycC+hz3Ak4XUd/0Zpt295j+Yrf4XLF0a/vwyQnDz/qNQhY66Ok5AcKCj4nv2AGlZVrAIiN7VE31UhCwkCM0S38zZE6xI1DHeKg8jx48xpWb9/ITUP/zSJnOhe1TeGv3TPr1uFoCmvyynlp7kbeXJBLWbWXXm3juWJUJ84ZlElspKZBAtiwuIBPn15KRJST038+gIxODb9Lq3rZMnKuuRYTGUnWc88R2aVz3XvWb9nx7hoq5m0j/vgOJJya3eRfHqzZUsh9b81l5mYffgxD0g13TRzCsG6NcwehSGMqqSlhdfFqVhavrJtfe3Xxaqp91QA4jZNOCZ12m4KkR3IP2sS02e3/JbXXjUPttYiINLVwDrBPAe6x1h4bnI9rXwH2XQDW2r8FX08D/mitnX2gax9pA/uLF7/ng8VbubpmAVVpPl4+diLj01N4ql/2bsct2FjMVc/OIzHGzSvXjiD2i81ULthO3JhMEs/o3OQdEWst5eXL6kZmV1VtABwkJ48gI+N00tNPITIi7WCXkT34/T5yFv/IsllfsHret3hrakhIb0OfsePoPfZEUtoHbr31eb2s+GYmc995neKtm0nJ7MjIcy+k5+jjcDibLnDYf921rF59H7mbXyAp8Rj69XuYyMiGz80uTauyckMwzJ5OScl8rPXhdqeSlnYi6WnjSUk5FqczJtRlSgOpQ9w41CGux++DL+7D8/V/+HefX/NQ2mlkRkXw395ZjExq2nUTKmu9vLdwC1Nmb2T51lLiI138ZGgHrhjVia5a9JHCzeV89OgiqspqOemaPnQ9hHVPqleuIufqq8HpoNNzzxHZrVvde9Zv2fHeGirmbiPuuA4kntb0ITbAui35/O3tuXyR68eLk4FpcNfZQxjZQ0G2hDef38emsk17TUOyuXxz3TEJEQm7TT9yfs/z1V43ArXXIiLS1MI5wH4W+N5a+0gwwL4KKAXmA/9nrS02xjxCYOXkF4PnPAN8bK19cx/Xux64HiArK2voxo0bD7u2VdvLOOU/X3FsdQ3dkxZR2C2VNzLHMnVod4YkxO527KLcHVzxzDxiI5y8fN0IkmZvp/zbLcSOaEvSxG6HtPDPkQiE2SvIy/+YvLyPqaxcBzhISjqGNhmnk54+gcjI9KNSS0tSW13Fmp3zZS/+EWv9tO3Wg079B7H865mU5m8nPbsLI8+7iO7HjMI4QjOatrp6C4uX3EJp6UKysq6ja5df4XAc/dHf0jAeTwmFhTPJL5hOYeFMfL5yHI5IkpNH1wXakZFtQl2mHIAC7MahDvE+rJoGb1/P/Nju3Dzwb2z0ubk5K4Nfd25LRBO3MdZavs8pZsrsjUxdvBWPL7Do45WjsjmxVwbOo/Q7TTiqLK1l6uOL2L6+lJHndGHIhIZP+1GzZg0br74afH6ynnuWqJ67FlO21rLjvbVUzNl61AZA7LRhSx73vz2Hz3Mttbjom2q486xBjO2lqUWkeSmrLWPNjjWsKqq3aGTxaiq9lSy5aona60ag9lpERJpaWAbYxpgIYAvQ11q73RjTBigALPAXoJ219hpjzKPA7D0C7KnW2rcOdP3GaGAvfvRbFm8s5qqab6lKtbx23Hn0TEzirUFd9+pYLN1SwhXPzMPlMLx83QjSfyik7MtNxAzOIPn8Hhjn0e3wWWupqFhVNzI7MHWBISnpGDIyTiMjfYLCscNQXlTIim8C82Xnb1xPu249GfmTi+k8eFhI5wwtKvqGJUtvx++vpU/vB8jIODVktcih8/tr2bHju7qpRqqrNwEQH9+f9LTxpKefQmxsD81LG2YUYDcOdYj3Y0cOvD6Jim3LuWf0Y7zo7ELfuCge7dOJXrFHZzHg/LIaXp2Xw0tzc9hWWk1mUjSXj+zERcd0JCU24qjUEG68Hh+fT1nB6u+203NkW064rBdOd8O+VKhZv56cq67GVlfT8dlniO7bt+49ay073l9LxeytxB3bnsQzuxzVf/M3bN7G39+ew+eboRo3PZMd3HHmQE7o005tjzRbfutnc9lmshKz1F43ArXXIiLS1MI1wJ4I3GStPWUf72UDH1pr+4VqChGAWavzueKZeZxaUU7b1OV4OkXxTPapvDygCyem7j3/4cptZVz29FzA8tJ1I2m3vJjSaRuJ6ptK6iW9MK7QzXNbXr6qbs7siorVgCExcShtMk4jPeNUoiLbhqy25qqytITo+ISQduys9bNh4+OsW/cfYmO70b/fY8TGdglZPXLkdn75tDPMLi1dCFiio7PJCM5xHx/fX4FCGFCA3TjUIT4Abw1M+x189xTTel7NLztcTbkf/tQtk6syj970YF6fn8+WbWfy7A3MWVdEhMvBWQPaM2l0JwZ0SDpqdYQLay3zp25g3gfradctkdN+1p/o+IYF+rWbNrFx0iT85RVkPf0U0QMG7Hbdkg/XUf7NFuJGtyfxrKMbYgNszN3CP9+ZzYzNDiqJoGuSk1+f3p8J/dur3ZFmS+1141B7LSIiTS1cA+xXgWnW2ueCr9tZa7cGt38BjLDWXmyM6Qu8zK5FHGcA3ZtyEcedrLWMf+BLSguruLR2JlXJhvfGnUdcTALTj+mJYx+/yK/JK+fSp+bg8fl58boRZK0vp+SDdUT2SCb18t44Io7+vMh7qqhYQ15eYJqR8oqVACQmDiGz/SW0aXMmDkfrHFXV3Hg8JSxd9n8UFn5BmzZn07vXvZo/uQWqqckjv2A6+XmfULxjDtb6iIxsR0b6BNLTJ5CUNJTAerhytKlD3DjUIW6AxW/C+7eSH92G20c/yYwqNzd2TOcPXdvv83eRprRqexlTZm/g7e83U1nrY1DHJK4c1YkzBrQjsgkXmwxHq+dvZ8bk5cQmRnDGzweS0j724CcBns2b2XjV1fiKiuj41JPEDBlS9561lpKP1lP+9WZiR7Uj6ey97/o7GtbnbOLBd2czY6uLchtJpwQn/3daX84Y2KFVTyMjzZPa68ah9lpERJpa2AXYxpgYYBPQxVpbEtz3AjCIwBQiG4Cf1Qu0fwdcA3iB2621Hx/sMxqrgX1j/iZ+/eYiLiwtIiZjLTGZfv7d7Twe6Z3F+W1T9nnOhoIKLn1qDhW1Pl64djhdt1ZT/PZqIrITSJvUF0eU64jraiwVFevIy/+Ybdvep7JyDRERGXTscCWZmZfgdieFujzZj9KyJSxefBM1Ndvp0f33ZGZeppFRrYDHsyMQZud/SlHRLPz+WtzuVNLTTyYjfQLJySP1BdRRpA5x41CHuIHyV8JrV+ArWMPvT3iB5/yZnJORxEO9s4gMwdoLpdUe3l6Qy5Q5G1mXX0FqbAQXHdORy0Z2IjPp6ExxEg62ry/lo8cX4av1MeGn/cjqm9qg8zzbtpFz1dV48vLo+MTjxA4fXveetZaSqespn7WZ2JHBEDtEofG69Rv57/uzmbHNRamNpn2ck19M6M05QzridobuzkKRQ6H2unGovRYRkaYWdgH20dBYDWyN18eov04nsczP2Z4Z1CQ7mH7iOeyISOSbEb3322ncVFTJJU/NoaTSw+Rrh9Or2EPRa6twt48l/Zp+OGLCa3E9ay1FRbPIyXmGouKvcTiiad/+fDp2uJqYmE6hLk/q2bLldVauuge3O4X+/R4lMXFQqEuSEPB6yyks/JK8/GkUFn6Jz1eJy5VAWtqJZKSfSkrKWJzOqFCX2aKpQ9w41CE+BDXl8N5N2GXv8sjxT3IvPRmdFMdz/bJJdIfmy3FrLd+sKWTy7A3MWL4dgJP7tGHSqGxGdU1tFV+ulhVV89FjiyjaXM74q/rQc0TDpmXz5OWRc801eHI30/GxR4kdPbruPWstJZ9soHxm7lFfFHxfVq9ZyxMfzubz7ZEU2xjaxDq55aReXHBMx1Y38l6aH7XXjUPttYiINDUF2Efov9NX8a/pq7m+MJfazK20zSjlj72v5M/d2nN9x4z9nrdlRxWXPjWH/LIanrt6OP2rLIUvLcedHk3atf1xNnC+xKOtvHwlOTnPsG37+1jrJT39ZLI6Xkti4tBW0RENVz5fNStX/ZGtW98gJXkMffv+m4iIho30kpbN56umqOhr8vOnkV8wA6+3BKczhtTUcaSnn0Ja6jhcrvhQl9niqEPcONQhPkR+H7x3M/z4Mm8d9yC3O4bQNSaSlwZ0ITMqtL9X5BZX8tLcHF6dl0NxpYduGXFcOaoT5w3pQFxk+Nx91hRqq71MfXwRW1bt4ORr+tL9mIYtlO0tLCTn6muo3bCBDo8+QtzYsXXvWWspnbaBsi9ziR3elqRzQhtiW2tZvXo1T340my/zoiiwcaRGO/n5iT24dEQnosNgmjyRfVF73TjUXouISFNTgH2EiitqGXHvdHpWOTjRNw1vsot5J57FEkcynx3Tkw4H6DBuL63mkqfmsHVHNc9MGsYQ66RwyjKciZGkXdcfV1Jko9TYFGpq8sjNnULu5pfxektISBhIVsdrSU+fgMPRsjui4aaqKodFi2+ivHwZ2dk30aXzbZr3WPbJ7/dQvGNuIMzO/5Ta2gKMiSA1ZQzp6RNITx+P250c6jJbBHWIG4c6xIfB74MPboUfXmTW2L9xjXsMcS4nLw/oQu+40E/fUe3x8dGirUyZvYEfc0uIi3Rx3pBMrhzViW4ZLffLNE+Njw8f+ZGta0s45dq+dBu6/0EO9XmLi8m59lpqV68h86GHiD/xhLr3rLWUfrqRsi82ETOsDcnndQ9piL2zpuXLl/P8J3OYmR/NdptAYpSDG8Z15/KRnYiPCq+7DEXUXjcOtdciItLUFGA3gt++tYjX5m3i9m0rKOxcRt/kzfxu8C2kR7h5d0g30iP2/8t6flkNlz09h42FlTx55TBGRkRQ8NxSHNEu0n/aH1dq6DubB+LzVbJ169vkbHqWqqqNREVl0rHDVbRvf4FGdR4F+QUzWLbsVwD07fNv0tJOOMgZIgHW+igp+YG8/Gnk50+junozxjhJShoRXATyFCIjGxawyN7UIW4c6hAfJr8fPrwdvp/MsmPv5tKYCVT4/DzXvzNjksOnbV64aQdTZm/gwx+3Uuvzc2y3VK4Ymc1JvTNwtcA5lGurvXzw8I/kbShlwvX96DIovUHn+UpKyPnp9VQvW0bmv/5FwoRT6t6z1lL62UbKPt9EzNA2JP8k9CE2gN/vZ+nSpbw4bQ6zimLZ4k8kPsLBtWO7ctWx2STFhOedhtL6qL1uHGqvRUSkqSnAbgTr8ssZ/6+ZjKxyMtx+gi/ZxehThnKZtw/dYqJ4a3A3Eg4wB2BheQ2XPzOPtXnlPH75EMbGx1Dw7BJwOki/rh/uNg1buT6UrPVRUPA5OTnPsKPkO5zOODIzL6Zjh0lERbUPdXktjs9Xydp1/2HTpmeJj+tL//6PEh3dMdRlSTNlraWsbAn5+dPIy59GZeU6wJCYOJj09AlkpE/Q369DpA5x41CH+Aj4/TD1/2D+s+SO+jWXJv2E9VW1PNQ7i/PahNedFoXlNbw2fxMvzclh844q2idGcdnITlx0TEfS4sL3brTDUVvl5f2HF5KfU8ZpP+tP9oC0Bp3nKytj0/U/o2rRIto/8ACJZ56x2/ul0zdSOj2HmMEZgRDbFR5fAPh8PhYtWsTr0+fwTXE8Of5kot0OJo3uzHVjO7e4/77S/Ki9bhxqr0VEpKkpwG4kVz8zjzkr8/nV5u/Z1N3HaZHfU3H1U0xasY0hCTG8MrArMQcYTbSjspYrn53H8q2l/PeSIYxPjyf/6cXgt6Rd05+IzLhGrbcplZYuYmPO0+TnfwJARsbpZHW8loSE/iGurGUoKvqWFSt+R1V1DpmZl9G92+9wOtUBlMZhraWicg35eYEwu7x8GQDxcX1JzwiE2bGx3UJcZfhTh7hxqEN8hKyFqb+G755ix8jbuDrjCmaXVPD7Lu24KSsj7Nau8Pr8zFiRxwuzN/L1mgIiXQ6uGp3NjeO6tqgRuzWVHt57cCGFW8o5/cYBdOrbsDUrfOUV5N5wA5Xff0+7++4l6Zxzdnu/9PMcSj/dSERWPKmX98aZED6/G3i9Xn744Qfe/nwuc0oS2OBPIcLl4NIRnbj+uC60SwzvOw6l5VJ73TjUXouISFNTgN1I5qwr5OIn53BKhZNejqn4kyKY1L2Ub09+gBuWbeSElHie79+ZCMf+Q+zSag+Tnp3HotwSHrxoEKdlJpP/9GL81V7Sru5HZKeERq25qVVVbSY3dzKbt7yGz1dOUtJwsjpeS1raiRgTHiODmhOPp5Q1a/7Glq2vEx3did69/kZy8ohQlyUtXFVVDnn5n5Kf9wklpT8AEBPTNTDNSMYE4uP6hl0IFg7UIW4c6hA3Amvhkzth7hPUDL+RW7J+xvv5JVyTmcZfumfiDNP/f9fklfPYl2t454fNxEW6+NlxXbj62M7EtpAFH6srPLz34A8Ub63kjJsG0LF3SoPO81dWsummm6icM5d2f/kzSeefv9v7lYvzKX5jFSbCSerlvYnMTmyK8g+bx+NhwYIFfPDlXOaWJbLOl4bT4eD8YR258fiuZKXGhLpEaWXUXjcOtdciItLUFGA3EmstZzw4i21byvn1+q9Y28+FBY7rkczG467kN6s3MzEjicf6dDpgZ7G8xss1z33H/I1F/POCgZzdJY2Cp5fgK60h9cq+RHVLatS6jwavt4wtW15n06bnqa7ZQkxMZzp2vIZ2bc/F6dSIm4bIz/+MFSvvpra2gE5Z19G58204nVGhLktamZqa7YEwO38aO3bMw1ofUVEd6ubMTkwcoi+ngtQhbhzqEDcSa2Ha72DOo/iPuY4/9/gFT+QWcHpaIo/26UR0GM83vXJbGf/8dCWfLdtOWlwEt5zYnUuGZxERJlNkHImq8lre+88PlORVcebNA8ns2bCpXfzV1eTecisVs2bR5u4/kHLppbu979leQeELy/EWVZN0VhdiR7YLuy8aa2trmTdvHh9/NY/5Fcms8acDhomDM/n5uG50y2g+dx5K86b2unGovRYRkaamALsRvbdwM7e9upDzS2Hc2r9RdNJQ1lUk0yYlnh0nnM1DBRVc0T6Vv/focMCORGWtl+smz2f2ukIeOG8AP+nVhvynF+MtrCL1st5E927Yrabhxu/3kpf/MTk5z1BWthi3O/n/2Tvv8Diqqw+/M9ubeu9ykS033G2M6TXGtNBrIBCSkEAIIaEmgRAIgSQQ0j4IhN57M80Gg2nuvdvqve9q+87M/f7YVbPlhmWrzfs888yde+/M3JVWOnN/c+45ZGdfQk725Vgs+5fIaLgRCjexbdvdNDQswOkcS/HYPxEXN6m/h6WjQzjcQlPTIhoaP6Kl5SuECGM2p5KaegppqaeSkDATWd5zAtuhjj4h7hv0CXEfIgR88jv4+hGYdhX/PeJ2frezhulxDp6eVEiSaWB7Nq8sb+WBD7ewtLSFnEQbN51cxFmTszEMgISFB4PfE+ath1bT3hzgjOsnkzU6Yb/O08Jhqn9xI97PPiP9tltJ+sEPerYHFFpe3kpwS0s0uePZo5BMA0/0DwaDfPvttyz8ajmr/ElsF+komsS8iZn87PhRjMsaXKsPdQYfur3uG3R7raOjo6NzqNEF7D4komocdd8irG0Kd6x9j7D6GatPzcYSmkoQO5VHncR7Bgc/z0vjzpF7T2wYjKhc++xKvtjWyD1nT+CSiVk0PbmBSI2PpIvGYJ80eAVfIQRt7hVUVDxOU9MiJMlERsaZ5OX+EKdzTH8Pb0AghKCu7i22bf8jquqnsPDn5OddO6wFQZ2Bi6K009T0GY2NH9PUvBhNC2A0JpCaciJpaaeRmHjUsIvTrk+I+wZ9QtzHCAGL7oYvH4KpV/DurLv5+ZZKcq1mnp80gnzbwP47FULwxfYmHvxoCxuqPRSlO7n5lDGcPC59wHkYHwg+d4i3/rYaX1uIM38xmYwR+xf2Q4TDVN/8a9o//pi0m39F8jXX9GzXBJ5FFbQvqsCU4yT5snEYEwbm79jv9/P111+z+JsVrAulsFVkEFIlrplbyK9PG4NlL8nQdXQOBt1e9w26vdbR0dHROdToAnYf8+jnO/nTB1u4JmTktK/uQYSb+ecZZjISpmFtz2PZ+BmsTsnmjhGZXJ+fvtdrBSMqP3t+FYu2NPC7+eO4cnouTU9tJFzuIfHc0TimZxySz3A48ftLqah8itra19C0IIkJs8nJvYKU5BOR5YHtDXaoCAZr2LLlDppbviA+bgrFxffrSfN0Bg2qGqClZQkNjR/R1LQIRWnHYHCQnHwcaWmnkZx0LEajo7+HecgZ6hNiSZLOB+4CioGZQogV3dpuA64GVOAGIcRHsfppwFOADVgA/ELs48FBnxAfAoSAz+6FLx6EyZfx7TF/4sqN5ZhkiecnjWCSa+DHINY0wQcb6vjrx1spafIxJS+B35w6liNHDs4VagC+thBv/nUVgfYwZ/5iCumF++d5LBSFmt/cgmfBAlJuuJ7U667brU9gYzMtr2xFMsokXTIW68iEPh593+H1evnyyy/5ctkqVoYz2aykMTbdyd8vnsqYDFd/D09nCDLU7fXhQrfXOjo6OjqHGl3A7mPcgQhH3reQwoDEucLG+IpXiFv/KR/MhvdmpTOrbQ5LCo9kR3ou9xamc3VB5l6vF1Y0bnhxNR9urOPW743l2iMLaH52E6HtbSScORLnnL17cg8WIpFWqmteobrqOYKhGqyWLLKzLyUr6wLM5v1LbDTYEUKjqvp5du58EBCMHHEzOTmXIUm615HO4ETTwrS2fktD40c0Nn5CJNKMLFtISjqatNRTSUk5EZNpYCUY6yuG+oRYkqRiQAMeBW7uELAlSRoHvAjMBLKAhUCREEKVJGkZ8AvgW6IC9iNCiA/2dh99QnyIEAIW3w+f3w9HXMy2k/7KxevLaFNUHh9fwPHJgyNsg6JqvLayiocXbqfOE+SYolR+c+oYJmQPzv8r7S1B3vrbKkJ+hbNunEJq3v4JtkJVqb39dtxvv0Pyj64h9Re/QDL2dAKINPhpfnYTSnOA+HkjcB6VNaC91j0eD4sWLeL9VWV8rY4gIpm47XvFXDmnAHmQh43RGVgMdXt9uNDttY6Ojo7OoUYXsA8Bd7+7kWe+LuN7BjvFzRrjbGWkL/grvswID16cieTLpTT3ciqS0rkjTub66Ufs9XoRVeOmV9by7toabjq5iOuPHUnzC1sIbmom7rQC4o7LPWSf5XCjaQpNzYuoqnyG1rZvkWUL6elnkJtzBS7X+P4e3iHD5yth85bbcLtXkJQ4l7Fj78Vmy+nvYeEOufm04lP8ip8UWwrJ1uTo3paM0+Qc0JNfnYGFECptbStpaPyQxsaPCIXqkCQjiYlHkpp6Cqmpp2Axp/T3MPuM4TIhliRpMT0F7NsAhBB/ih1/RNRTuwz4TAgxNlZ/MXCcEOLHe7u+PiE+xHz+QNQbe+IF1H3vES7dWM4WX5C/jsnloszB480cjKg8+005/1q8gzZ/hNMnZnLTKUWMTB18iQA9zQHe+utqwkGFs2+aQkrO/ovYdXf/gbZXXsE6bhyZ9/4Ra3Fxjz5aUKHllW0ENzVjn5xKwvdHI5sH9kvy0tJSXnl7Ae82JFClJXDUiEQeumgqaXF6ImudvmG42OtDjW6vdXR0dHQONbqAfQhw+yP84uXVLN7ayHi7jWNqBaOTIox6/7dYpXaqbr2Mx8R2NtouptmZxll1a7hr3umkJ+45pIiqCX796lreWF3N9SeM4pcnjKL11e0E1jYSd1IecSflH7LP0194vVupqn6O2to30bQA8fHTyM25gtTUU4dMLGhNi1BR8QSlZX9Hlm0Ujb6DjIzv96swHFbDLKlawrsl7/JF1RdEtEiv/SwGS6egnWRL6iFwd4jcKdbo3m4a+EvSdQ4fQgja29fT0PAhDY0fEgiUAxJWaxY2Wx42Wz722D56nIfROLiEqOEyIe5FwP4n8K0Q4rnY8RPAB0QF7PuFECfF6o8GbhFCzN/b9fUJ8WHgi7/Ap/fAhHNpP+M/XLO5is9b2/lNYQa/zB9csaU9wQiPf1HC41+WElI0zp+Wwy9OGk1mvK2/h3ZAuBsDvPW3VSgRjbN/OYXk7P37/yeEoP2jj6n74x9RW1tJvvpqUn52HbKlK+610ATtn1XiWViOKcNB8uXjMCYNbDE4EomwZMkSHl+8lWXhHGxmAw9eMIXTJux9FaOOzv4wXOz13pCiyz1XANVCiPmSJCUBLwMFRO33BUKI1r1dQ7fXOjo6OjqHGl3APkQIIXju23L++P5mLJLEiR4jkwwSE1f/BUddGak3XM+K08bxy60KHmMCp21YwtgChatOuYoUe+9eiKomuP2N9by8opIfHzOCW04dQ9vr2/GvasB1Qi5xJ+cPqonm/hKJeKitfY2q6mcJBCowm9PIzr6E7OyLB7XHZnv7RjZvvo1270bSUr9HUdHvsVj6JzmnEILVDat5r+Q9Pir7CE/YQ7I1mXkj5jF/xHwyHBk0B5ppCjTRHGymOdDcedxR1xRoojXYimD3/wM2o62H93aH2J1sS+5xnGJLwWoc2BNpnb5FCIHPt42mpkX4fDvwB8oJBCqIRFp69DOZkncTte32aNlkSh5w//uGwoRYkqSFQG/JFu4QQrwd67OYngL2v4BvdhGwFwAVwJ92EbB/I4Q4o5f7XgtcC5CXlzetvLy8rz+azq58+TAs/D2MO5vwOY/xqx11vFrXymWZydxflINxkIVsaPKG+OenO3hhaQVI8IMj87nuuFEkOsz9PbT9pq3ez5t/W4XQBGffNJWkzP3PHaC2tVH/wIO433gDc0EBmff8AfuMGT36BLa00PLSFiRZIunisVhHJ/b1R+hzmpqaePL1D3ipzEKzcHDWhBT+dME07ObhmTNFp28YCvb6YJEk6SZgOhAXE7AfAFqEEPdLknQrkCiEuGVv1xg3rlhs2rT5cAxXR0dHR2eYogvYh5gdDe3c+PIaNlR7mCqZObpNYkrTq6St/wLXscdi+eO9fG9LOQ0hlbPWfIlQd5AxI4OrZlxFjmv3EBKaJvjdOxt47tsKrjqqgN/OK6btzR34V9TjOi6HuFMLBpyQ01cIodHc/DmVVU/T0rIESTKRnjaPnJwriI+f3N/D229UNURp2T+oqHgMkymRMUV/IC3t1H4ZS5m7jPdK3uO9kveo9lZjM9o4Ie8EzhhxBrMyZ2E8wESaiqbQFmrrIXY3BZq6jgPNnXVtobZer+E0OaPCtnV3cXtX8dtsGDxihM6BoSjtBAIVUUHbX0EgJmwHAhUEQ7XQ7UWJweDYxXM7r/PYas3slzjyw2VCrIcQGUJ8/Q/4+E4oPhNx7hPcX9HM38vrOSk5jkfH5+MwDOxQE71R2eLn4YXbeXN1FQ6zkR8dM4Kr5xbisAwOwbO1zsebf1uNBJx90xQSMw4sAa7v66+p/d3viVRVkXDhhaTd/CsMrq6QJJGmQDQudoOf+NMKcR6TPeCfIYUQrFi1hvveXcvqYArpdpl/XT6D6YX94wCgM/gZLvZ6T0iSlAM8DdwL3BQTsLcStdG1kiRlAouFEGP2dp2EvALRVlF26Aeso6OjozNsGXACtiRJZUA7oAKKEGL63pYxxSbLV8f63yCE+Ghf9zjcE+KwovHwwm38Z/FOko1GTmmVmRlcw4gVr2BPjkP620Nc4DfgCQWZt/JTXD43mxM3M/KIkVw98WrGJPV8XhBCcM97m/nfV6VcPjufu88Yh/udnfiW1uE8Opv4eYUDfgJysPj9pVRWPUtt7euoqpc41yRycq4gPX0esmzZ9wX6iba2FWzecit+fymZmecxetTthz2JXUuwhQ9KP+D9kvdZ37QeWZKZnTmb+SPmc2LeiYct3EdEi9ASaOkhcnd4d3cXv5sCTbSH23u9hsvs6jV0ya7id5ItCdMQCTujE30JFAxWxQTt8k6v7ehWhRDhzr6SZMJmy+khattjXtxWay4Gw6H5fzFcJsS9CNjjgRfoSuK4CBgdS+K4HLgeWErUK/sfQogFe7u+LmAfZr75N3x0G4ydD+c9ydP1Hm7bVsUkl51nJxWSah6c/0e31bfzl4+28vGmepIdZn5+wigumZWHxTjwRfmWGh9vPbQKWZY4+1dTSUg7MBut+f00PvIPWp55BmNqKhm//x2uE07oag+ptL62jcD6JmyTUkg8r2jAx8UG8Pl8/Of1hTy5SSWAmR9MS+XOc2diGGSrBXT6n+Fir/eEJEmvAX8CXETt+XxJktqEEAnd+rQKIfa6TMOSNVpUrf2a1FT9ZZKOjo6OzqFhoArY04UQTd3qel3GJEnSOOBFuibKC4EiIYS6t3v014R4WWkLv3x5DbVtAWYHZU4KeZhU8ibOmk347/gtV+QUY0JwRcVa3Nt20GZpY3nKciYWTuTqCVczPX16pzAthOD+D7bw6BclXD23kDvmjcX9bgm+b2pxHpVF/PwRQ17EBlAUL7V1b1JV9Sx+/05MpiSysy4iO/sSrNaBExtRUbzs3PkXqqqfxWrNYezYe0lOmnvY7h9UgiyuXMx7Je/xVfVXKEJhbNJY5o+Yz/cKv0eaPe2wjeW7EFbDPby3e/Pw7hC/vRFvr9dIsCSQYkshx5nDtPRpzMiYwZikMQfsZa4zsBFCJRSqx+8v6yZqV3SK3Kra/fshYbGk9xC1uwvdJlPcdx7HUJ8QS5J0DvAPIBVoA9YIIU6Ntd0B/BBQgBuFEB/E6qcDTwE2onGxrxf7eHDQBex+YOmj8MFvoOh7cMHTfNQW5Ccby0i3mHhx0kgK7QP3JfG+WFXRyoMfbuWbkmayE2z88uQizpmSPeBFz+ZqL2/9bTVGs8w5v5pKXMqBx/QOrF9P7R13Etq2Ddf3TiPjjjswpkTDsAkh8H5RhfvDMkzp9mhc7OTBETd8/ZYd/PrllWwJOCl0qvznB0cyNlcX0HT2n6Fur/eGJEnzgXlCiOskSTqOAxSwu4f8MmeMmnbWbTfxyg0/PTyD19HR0dEZdgwWAbvXZUx7WqoshPhmb/fozwmxJxjhrnc28saqanJUle/5LMxV1pL6xZPUXXQpPznhTFLNJu63K3z94QICgQBlSWWsdq1mfOp4rp5wNcfnHY8syQghuPvdTTz1dRnXHTeSm08pwrOgDO+X1ThmZ5Jw5kikAT4p6yuEELS2fk1l1TM0NS1CkmRSU08lJ+cKEuKn96uY39S8mC1b7iQUqiM35weMGHETRuOBLQP+LmhCY0XdCt4teZdPyj/BF/GRbk/n9BGnM3/EfEYnjj7kY+gPAkpgN7G7+/HOtp2UecoAcJgcTEmbwoyMGUxPn05xcrHuqT2EEUIQibTsImp3eW+Hw009+ptMibuI2nnYbQXYbHmYzal7/b8ynCfEfYkuYPcTy/4LC26G0afABc+y0q9y+foSAJ6bOIKp8Yfehh0qhBB8uaOJBz7cyvpqN6PTnNx95njmjBrYOTUaK9t5+6HVmG1GzvnVVFzfIfGiCIdp/t//aPrXv5HsdtJvvZX4s8/q/F8W3NZK84tbQEDyxWOwjknq649xSFAUhQdeWcyT6/xIwE9mJPKLs+dgGIRhb3QOP8PZXkuS9CfgcqIvnK1AHPAGMIMDDCFizR4tMn/0ME8f5eeYk88/1EPX0dHR0RmGDEQBuxRoJRrg9FEhxGN7egssSdI/gW93SRb1gRDitV6uO6CSQr23roY7XltNIKRyfMDKPHuAEe//gR2zZvDLy37CaIeNZ8dk8fXCT1i3bh3mODOrU1azRWyhIK6AqyZcxfwR8zHJJm5/cwMvLqvgppOLuP6EUbg/LMP7eRWOmRkknD1q2IjYHQQClVRVP0dNzasoihuns5icnMvJSD8Tg+HweRRFIq1s2/5H6urewm4fxbjiPxEfP/WQ33dH6w7eLXmX90vep95fj8Pk4OT8k5k/Yj7T06djkPUJXaO/kRX1K1hRt4IV9SsocUeFGZvR1kPQHp88HpNBF7SHC4riJRCo7AxN0t1zOxisAbTOvrJsw2bL7ea53eXBbbVmYzCYhu2EuC/RBex+ZMWT8N6NMOokuPB5SiISF6/dSUM4wqPjCzgl5fCGv+prhBB8sKGOBz/aSkWLn/vOmcCFM/L6e1h7paHcw9sPr8HqiIrYzsTvlvQ4VFJC7Z2/JbBqFY6jjiLj7rsx52QDoDQHaH5uM5E6H3Gn5OM6LnfQrOhbt7Oanz+3nIqAifHOAH+//EhG5Wf397B0BjjDWcDuzi4e2A8Czd1WPycJIX6zt/PTcvKF/bJ/U5Rbw2sXnk1cir4SQkdHR0enbxmIAnaWEKJGkqQ04BOi8TLf2YOA/S/gm10E7AVCiNf3do+BMiGudQe4+bF3+arZwaiIzLmyhZmr/82GVDt3/uiXTE1w8uIRI6ku2cl7772H2+0mfWw6n1s/Z6N7I2m2NM4bcx4n5Z7MfxZ6eWNVNbfPG8uPjh6B5+Ny2j+rxD4tncRzRw87ERtAVQPU1b9DVdUzeL1bMBoTyMo6n8SEmUC3n4ckIXUeS9EtNlmTuvfbrV7aQz0EAuVs3/FnFMVNfv5PKCy47pDG5m70N7KgdAHvlbzHlpYtGCQDR2UfxfwR8zku9zhsxsGxFLi/aAo0sbJ+ZaegvaNtBwBWg5Uj0o5gRvoMpmdMZ2LKRD1x5DBF08IEg9U9RO3um6aFOvtKkpETT9imT4j7gIFir4ctq56Bd26AEcfBxS/SKIxctq6E9e0B/jI2l0syk/t7hAdNezDCdc+vYsn2Jm44YRS/PLloQAu2daVu3vn7GuxxZs65aSqOhO/2bCE0jdaXXqLxL39FCEHajb8g8bLLkAwGtLBK6+vbCaxtxDY+mcQLipAHSeJLRdX4/Utf8eJ6NzYpwk+nOLj27OMwm3XbrdM7uoAdZRcBOxl4BcgDKoDzhRAtezt/6tRpInjR7/B7jNwU9z4/u+nvGIy6E4iOjo6OTt8x4ATsHheUpLsAL/AjhkgIkV3RFJX//ese/lw7BbMwMi9o4Qz3N2wIl3DPNTdwfKKTp44YiYhE+PTTT1m6dCnx8fGMOHIE73ne49vabwEojCtE9U5g884CfnfyyfxgTgHtiyrwLKzAPiWNxPOLhqWIDVEvq7a25VRVPUNj08fsI0R6n+FyTaC4+M+4nGMPyfUDSoCF5Qt5ryT6PdCExoTkCcwfOZ/TCk4j2Tb4hYX+ojXYGhW0Y17a21q3IRBYDBYmpU7qFLQnpU7CcoiSAOoMHoTQCIXquwna5Ywa9Wt9QtwHDCR7PWxZ/Ty8/TMoPAYufgmfwcLV68tY3NrOvaOzuTpn8HvZRVSN299Yz6srq/j+1Gzu//4kzEa5v4e1R2p3unnnkTW4Ei2cfdNU7HHfXZyN1NZSd9fdeD//HOsRk8i85x6sRUXRuNhf1uD+oARjio3ky8dhSj08SZ77gm+313L98ytpDMI0Ryv3nD+LcWOL+ntYOgMQXcDuG6ZPny7mnfkjng7kkJXVwh+dHk686mf9PSwdHR0dnSHEgBKwJUlyALIQoj1W/gT4A3AivSxjkiRpPPACXUkcFwGjB2oSxz0SaGXLP8/nhrZL2KamMzlk4Aqjl+2tX/DQpT/kDKeZ/5tejEGSqKys5O2336apqYlJkyYx7ZhpfNP0DQvLF7KifgWqUNHCiczOOI7rZ32fwg2JtH9cge2IVJIuGINkGJ4idgehUCOhUC0Agtj3VQjoKCOi9d2+y/vXr2d/WTIRHz8N+RAkCKzx1vDSlpd4ffvreMIeshxZ0bjWI+czIn5En99PB9whdw9Be0vLFgQCk2xiUuokpqdPZ3rGdI5IPUL3dtcB9AlxXzHg7PVwZe1L8NZPIf8ouORlQkYbP95YxodNHu4Ykcn1+en9PcKDRgjBI4t28NDCbcwdlcK/L5tKnHXgeg/WbG/l3X+sxZVs45ybpmBzfXcRWwiB5/0F1N97L6rXS8qPfkTyT36MbDYT3NlGywubEYog6cIx2MYNnpfj/rDCb15cynub20iSfFw9zsAVZ5+Cy+Xq76HpDCB0e903TJ8+XSz89AtOevRjGltMXJ35JhdMu5yxRx3b30PT0dHR0RkiDDQBewTwZuzQCLwghLh3b8uYJEm6A/gh0eQTNwohPtjXfQbkhLhmNcHHT+dB6y94omUCSarEhZIZt2EF/zvlNC7UAjx8wmwkSUJRFJYsWcKSJUswm80cc8wxzJw5k3alnU/KP+Xv37yOm01IkkqqLZUbQj9g+sYRWCYkkXJxMZJh4HoV6fSOEIIV9St4fvPzfFb5GRISJ+adyEVjL2Ja+jRkSf+dHk48YQ+r61ezon4Fy+uWs7llM5rQMMpGJqZMjAra6dOZnDYZu2nweKzp9B36hLhvGJD2eriy7lV481rIOxIueYWIycENm8t5s6GNX+an85vCjAEdemN/eW1lFbe+vo5RaU6evGoGmfED96Vk1ZYW3vvXOhLS7Jz9yylYnQcnuCutrdT/6U943nkX88iRZP7xHuxTpqC0BWl+djORai9xJ+XhOiFvUK3q+2B9Db95ZTX+iMaR1lpumDeZ6dOnI8v6s5OObq/7ig57/Zc7/80/RD7xyX5+Wvo0F9zyH5Jzcvt7eDo6Ojo6Q4ABJWAfLgbshDiWMOmriX/kxo1jafaFOTpiwpbTzFtTirmqppT7LpiPZIpOUBoaGvjoo4/YuXMniYmJnHzyyRQXFxNSNH7w1OesavqGacWV7PSu5LSGOfy44TxKMxoQ56QyO2e2Hst3EBBUgiwoXcDzm59nW+s2EiwJnFd0HheOuZAMR0Z/D08nhjfsZXXDapbXL2dl3Uo2Nm9EFSpGyci4lHGdgvaUtCk4zc7+Hq7OYUCfEPcNA9ZeD1fWvwZvXAs5M+Cy11DNTn69tZIXalv4cU4qd43KGhIi9pLtjfz0uVU4LUaevGoGxZlx/T2kPVK5qYX3/72OxEw7Z904Bavj4L3GvV98Qe1dd6HU1pF46aWk3ngjssVK65s78K9qwFqcRNKFY5CtgyMuNkBDe5Abn1/B12VucuQ2zssLcfE5p5OePvhXD+gcHLq97hs67HVdczsX//cTStssnF/8MePWhrj0vocwWwfuy0AdHR0dncGBLmAPJISILtFd+xJt573KrSsS+XBLPdmKTGqRiW9Hp3Ddss+57cqLMHV74N6xYwcff/wxDQ0N5OXlccopp5CUlsEP/reM1RVtPHzROKzxO2j4fAfHbBrPUud6Hi54gTl5R3Fy/snMyZqje4kOMOp8dby05SVe2/4a7pCbosQiLi2+lHmF87Aarf09PJ194I/4WdOwhuX1y1lRt4INzRtQNAWDZKA4qZjpGVFBe2r6VFxmfSnzUESfEPcNA9ZeD2c2vgmvXQ35c+DS19CMFn63o5rHq5q4PCuZPxflIA8BEXtzrYernlyON6Twn8umcvTogRvru3xDMwv+bx0p2U7OvHEKFtvBC8uq10fjww/T+vzzGDMzyLzrLhxHH43vm1ra3ivBmGQl+fJiTOmOPvgEhwchBE99XcZ972/CKBTmmku5YO54jj32WD3J4zBGt9d9Q3d7/civH+JvtjGYbQq3+P5CRtoZzLv+5iHxglNHR0dHp//QBeyBRtgPj58E7bWIaz/nzRKJO97YgKpopOQ6KRkXx81vv8hPLzwbx5w5naepqsqaNWv49NNP8fl8TJgwgSOPPo6fvbaVTTUeHrtiGseNScP9dSXt75RRmdbEHRmP0BhpwmqwMjd7Lifmn8ixOcfqglo/IYRgVcMqnt/8PJ9WfIpAcELuCVxSfAnT06frD32DmIASYG3jWpbXRQXt9U3riWgRZElmTOIYpmdMZ0b6DKamTyXeEt/fw9XpA/QJcd8woO31cGbdK1FP7DHfgwueQchG/lRSyyMVDZyXnsjDY/MwDqIQE3ui1h3gqieXs6PBy5++P5Hzpw/cZfCl65r48NH1JGU5OPqC0WSNTuyT6/pXr6b2zt8S3rmTuDPPIP2221DbZJqf34wIayRdUIRtQkqf3Otwsa2+netfWMnWeh9jDfWcmNLO2fPnMXr06P4emk4/oNvrvqG7vd5Z1cwtTyxkRcDJCRO+YdLHKzj20p8z5dT5/TxKHR0dHZ3BjC5gD0SadsBjx0HqGLjqAyo9Cj97cgXrGtuxx1tonZrInc/+iwuOnEbKT3+K1C2GXygU4quvvuLrr79GCMGU6TN5bKuZrU1BnrxyBnNGpeBbVkfrm9sxj4yn7BQ/n9QsYlHFIhoDjZhkE7MzZ3NS/kkcn3s8ida+mQDp7JmQGmJByQJe2PICW1q2EGeO49yic7lozEVkObP6e3g6h4CgEmRd47poUsj6FaxtWEtYCyMhUZRY1CloT0ufRoI1ob+Hq/Md0CfEfcOAt9fDmWX/hQU3w6QL4ez/A1nm4bI67i+t4/TUeP4zLh/zEIgx7AlG+OlzK/lqRzO/PKmIG04cNWBfKJeta+Kz57fgd4fJHZfE7LNGkJZ/8OFPtHCY5v97lKb//heD00n67bfjOPokmp/fQqSyHdfxucSdnD+o4mKHFJW/fLSV/y4pJckY5ih5O8dMLOS0007TkzwOM3R73Tfsaq8f+fl9PJw2GSEED4z8B1ULrFx095/JHD2mH0epo6OjozOY0QXsgcqmd+CVy2HmtTDvQVRN8MiHW/nHFzvBJBOemMgfXv4HJ8aZybr3XkxZPYVOt9vNokWLWLduHTa7nY1aDit8iTx99SxmFCThW1lP62vbsBTGk3zleDBJrGtcx8LyhSysWEi1txqDZGB6+nROyDuBCSkTyI/L171D+5B6Xz0vb32Z17a9RmuolVEJo7i0+FJOH3E6NqMeJ244EVJDrG9c30PQDqpBAEYljGJ6+nRmZEQF7WRbcj+PVmd/0CfEfcOgsNfDmS8ehE//GH1W+d4DIEk8VtnA73bUcEKSiycmFGIbAomjw4rGbW+s5/VVVVwwPYd7z5mIaYB+LiWssv7zalZ9WE7QF2HE5FRmnllIctbB518IbttG7W9/S3DtOhzHHkPGnb/Ht8yHb3kdlqJEki8ag2w/+Bjch5OvdjRx0ytraGoPMdVYzWRbM6ecfBLTpk3TkzwOE3R73Tfsaq/Xbq/j0cfeY4EhkyPGbOG8sq8JNMZx2f0PY4/T55M6Ojo6OgeOLmAPZD66A775J5z7BEw8D4C1Fa38+H/LqQtG0PIc3LXkRY7avor0228n/pyzd/MKqqmp4aOPPqK8vByfZGe1lseDV5/KlLxE/KsbaHllK+b8OFKuGo9sicZMFEKwuWVzp5hd6i7tvF6iJZGC+ALy4/LJj8unIC5azovLw2KwHL6fzSBFCMHaxrU8v/l5Pin/BE1oHJd7HJcVX8aMjBkD1qtL5/ASUSNsaN7AirqooL26YTUBJQDAiPgRnYL21PSppNpS9e/NAESfEPcNg8ZeD1eEgI/vjD6rHPMbOOEOAJ6taeI3W6uYk+DkmYmFOIyGfh7owSOE4KGF23lk0XaOHp3Cvy+diss6cMXacEBh7aeVrPmkgnBIpWhGOjPmF5KQdnA5T4Sq0vrcczQ8/HckSSL1VzdhHnk8be+WYIi3kHz5OMyZgycuNkCbP8ztb65nwfo6CuxhZqibGZObxvz588nI0BNmD3V0e9039Gav//7ju/h33ixCQcH9Rz5C/Ytmcoonc86tv0eWB79d0NHR0dE5vOgC9kBGjcDTZ0DtOvjRp5A2FoBAWOWXT6/kw52NYDfyw7YSzlvwCK4TTiDz7rswpvZMNCSEYOvWrXzw4Ue421ppIJ5Lvn8GcyeNwr+2kZaXt2DOjYnYvWSUr/RUstO9k3JPOWWesujeXUZjoLGzj4REpiOzV3E705GJYZg/pITVMB+Wfcjzm59nU/MmXCYX3x/9fS4aexE5rpz+Hp7OACeiRdjUvKlT0F5Vvwq/4gfAZXZRGF9IYVwhBfEF0XJ8IbnOXEyGgSuuDHX0CXHfMGjs9XBGCHjnelj9LJx6Hxz5MwBeq2vhF1sqmOKy8/ykEcSbDj6x4EDgleWV3PbmeorSXTx11QzS4wZ2YuWgN8LqT8pZ92kVmioYe1QmM+YV4Ew8uHGHq6qo+93v8X39NbapU0m5/k48n3oQQYXE84qwHzFwk172hhCC11ZWcdc7G0FozDFXkKPVM2fOHD3J4xBHt9d9Q2/2esn6Chb//QUeT5lIfkEtfx61nq//u5Mjz7uEOedf0k8j1dHR0dEZrOgC9kDHUwuPHg22pKiIbelaAvrcN+Xc+dFmCKqMl+HmxY+QrXnIuOv3xJ122m6XUhSFhUu+4YvPP8eEwsixEzh73ikYy8O0vLgFU7aT1B9OQN7P7PW+iI9yT/luwna5pxxvxNvZzySbyHPlRUXt+IJOYTs/Lp8ka9KQ9h5t8DfwytZXeHXbq7QEWxgRP4JLiy9l/oj52E0H5wWlM3xRNIUtLVtY27iWUncppe5SytxlNAQaOvsYJAO5rlwK4rpE7YL4AgrjCvW42ocBfULcNwwqez2c0VR47SrY9Dac+U+YejkA7ze28ZON5Yx1WHnpiJEkm4eGiP35tkaue24l8TYTT141kzEZAz9mss8dYuUH5WxcUo0kSUw4Npupp+Zjj/vuwqwQAvfbb9Pwp/vR/H6Sr70eIU8nXOnFeUwO8acWIBkG1zNeebOPG19ew+qKNqanaBS1ryEt0cW8efMoKirq7+HpHAJ0e9039GavhRD89ao7eG7MXFrbBb+Z809GVM9g02erOfe2uyk4Ymo/jVZHR0dHZzCiC9iDgZLP4dmzYfw50XAi3QTf1S3tfP/1NYgSDyYNzm/byUVfPEby904l/bd3YkzcPQnj1qpm7v3fGxRoNZiNBubOncuUxLG0v7IDU4aD1KsnHFQMQyEEzcHmHuJ2h7Bd0V6BoimdfV0mV1TMjs8nz5VHuj2dNHsaafY00u3pxFviB5XArQmNtlAbJW0lvLLtFT4p+wRVqBybcyyXFF/C7MzZg+rz6AwuvGEvZZ6yLlE7Vi73lBPRIp39OkIBFcYX9hC4s53ZGOWhITD1N/qEuG8YdPZ6OKOE4MWLoGQxnP8UjDsLgEXNHq7eUEqe1cKrk0eSbhkaK0M21ri56snlBMIqj14+jTmjUvp7SPuFpznAivfL2PJNLQazgSNOyGHKyXlYDuK5T2lqov6++/As+ABL0VhcZ91KaHsYy6gEki4ei8ExuH7niqrxz8928I9Pd5BiN3K8rRxrezXjxo3jtNNOIy7u4BNj6gwcdHvdN+zJXr+/dAc19z/CH4tPJSnNy+Nz32HDS/F429q4/P6/E5cyuFZr6Ojo6Oj0H7qAPVj44i/w6T3wvQdh1rU9mr5sbefib7biWN9GsDVEBoKfL32GmUotmffcg+u443a73I6Gdq76v8WMo5xMrQmXy8XcsTPJ/BosaQ5Srpl4SCYciqZQ66vtErfdZZ3e27W+2t36WwwWUm2pnYJ2h7id5uh2bEs7pKEShBB4wh6aA800BZpoDsb2seOmYBMtgRaaAk20BFtQhQqA0+TknNHncPGYi8mNyz1k49PR2ReqplLjq9lN2C5zl9EcbO7sZ5SN5Lnydvfaji8kzqxP2A8EfULcNwxKez2cCfvg2XOgZjVc8jKMPAGAr1rbuXx9KWlmI69OHkWudWiEY6huC3DVk8sobfLx53Mn8f2pgyckWGudj2XvlbJjRQMWu5HJJ+cx6fgczL2Ekttf2j/9lLq7/4DS2EjChTejhkdjcJmjcbGzDz6J5OFmZXkrv3x5DVWtfk4fYSKldilmk5ETTzyR6dOn60kehwi6ve4b9mSvVU3wl8t+w3sTjqXSI3HV7Be5bOwpLLj/XZKz87jw7vsxGAfXSy4dHR0dnf5BF7AHC5oGL10MOxbBDz+EnJ6/s7fqW/nJpnLG1YZpXtOEWxbMaa/hp189zsjTTyL9ttswOHtOHjbXerj4v9+SZfQxP6mBhrpa0hJSmNacR0FydlTEdh6+SWZEjdAYaKTB30C9v54Gf8Nu5QZ/AyE1tNu5SdakLnG7mwd393KcOa7T+1kIgS/i212MjonQHeXmYDPNgeYe3qsdGGUjydZkkm3JpNhSSLGldB6n2dOYkzUHh2lwJTLSGX64Q+7OVRLdxe1dV0skW5N79drOcmQN+xj3vaFPiPuGQWmvhzuBVnhqPrSUwBVvQ+5MAFa6fVy8bicug4FXJ49ihH1oJH52ByL85NmVfFPSzM2nFPGz40cNqpVWTVXtLH2nlLJ1TdhcJqadVsD4Y7Iwmr7b/3W1vZ2Gv/6VtpdexjxmJtYp1yAUmcRzR+OYktbHoz/0eEMKd7+zkVdXVjE+w8GJjko81TvJzs7mjDPO0JM8DgF0e9037M1ev/TpRqx/uIsbj74Ki1Ph0Vl/IMNyL+8//A+mnHYGJ1z148M7WB0dHR2dQYkuYA8mAq3w6DFRMfvHX4AjuUfzY5UN/G5HDefbnfjeqeBzJYBREly66QPO920l794/4pg9u8c566vcXPL4tyTbTfzxuASWf/U5brebXJHCHOcEin4yB4Nr4HhKdXhD703gbvA30BJs2e1ci8FCmj0NTWg0B5oJqsHd+siSTJI1qYcY3VFOsaX0EKu7C+I6OkMNRVOo9lb36rXdGmrt7GeWzeTF5e0mbBfEFeA0Dz6Pu75CnxD3DYPWXg93vA3wv1PB3wxXLoCMCQCsb/dz4dqdGCWJVyaPZKzD1s8D7RvCisYtr6/jzdXVXDQjl3vOnoDJMLi8c+tK3Hz7dgnVW1txJlqYPq+AsXMyMXzHz+Ffvpza3/6OSE0zznl3AEk4j8oifl4h0iD72QAsWF/LbW+sJ6xo/HBKHNr2LwkGAxx55JEcd9xxepLHQYxur/uGvdnrYETlb5fczNIJx7A2YGbe5M+4YYqD2m9zWLXgbU6/4deMPerYwzxiHR0dHZ3Bhi5gDzZqVsMTp0DB0XDpq7CL5+PdO6r5T2Ujt+ank/tlC/9eW8FOk0ZO0M3PV7zAcacdSdqvbkK2dU0aV5a3cvkTS8lKsPHcD6ezY8Nqlnz+BeFwhHHmPE69+mziMnaPpT2QCavhnt7cvi5xW5ZlUqwpnYJ0si25U6BOsCTo3qTdUBSF2tpaAoFAZ93e/s731PZdzjlUbQaDAZvNht1ux263Y7PZMBr1uM8HQmuwtYfXdofAXdle2RlCByDNltbptd1d4M5wZCBLg0/AOBD0CXHfMKjt9XCnrQKeOBU0JbpyLHkkAFt9QS5Ys4OIELx0xEgmuYZGQmMhBH/9eBv//GwHxxal8q9Lp+K0DD7bUrWlhW/fLqG+1EN8qo2ZZxQyeno6knzgL+y1UIimf/2b5v89iXXKJZiyj8JcGEfypcWHdYVfX1HrDnDzq2v5akczJ45J4cS4eratX0V8fDynn366nuRxkKLb675hX/b6sfdXU3TXr7jq9F8jyRr/nHMzR059ng8fepHG8lIuve8hknP0kIs6Ojo6OntGF7AHIyuehPduhONug+Nu7dGkCcH1myt4vb6VR4rzOLIN/vPUOt7X/LhlwbFVq7mudTUT/3gn9ilTOs/7tqSZK59cRkGyg5eunY1JRFj07ses3rwOk2Rg3NhxjJ82kcLCQl3sG8IoikJ1dTVlZWWUl5dTUVGBoij7PnGQY7FYeojaHcL2no5tNhsmkx6vb1ciaoRKb2WXqO0uo9QTLbeH2zv7WQ1W8uPyu2Jsx0X3LrMLk2yKbobo3iybB+VLJX1C3DcMens93GncCk9+D0yOqIgdnw1AWSDEeWt24I6ovHDESGbED51wWy8sreC3b29gbIaLJ6+cQVqctb+HdMAIIShf38y3b5fQXO0lKcvBrDNHUHhEyndaeRbcvJnaO+5E8biwTvsBBpeVlB9MwJzrOgSjP7RomuCJL0t58KOtxNtN/OaYDOrXLqapqUlP8jhI0e1137Ave+0ORPjnJTdRVjSTT6Qkpo3ZxG/Gf07xyMd57tZfYXPFcel9f8NsHRorc3R0dHR0+h5dwB6MCAFv/RTWvgSXvQajTurRHNY0Ll1XwjdtXp6ZOIKj7HY+fXELz66rZplVwahGuGzLR/zw2FFk3HA9cmzZ45LtjVz99ArGpLt4/keziLOaqF5byqdvfkyFaCAiqVjMZorGjGHs2LGMGjUKi2VoxLAcrkQikU7BuqysjKqqqk7BOj09nYKCAvLz84mPj9/jNfY2mT2cbft7jqIo+P1+/H4/gUCgs9zbcTgc3uM1TSbTXgXv3uqGq+gthKAl2NIjFElHudpbjSa0vZ4vS3KXsL2LwN2577aZDebd+hll4x7bdquPtfV6zh7OM8rGHl7l+oS4bxj09lonunLsqTMgLguu+qAz/FlVMMwFa3ZSF47wzMRC5iYOPjFzT3y2pYGfvbCKRLuZp66awej0wfnZhCbYsaqBZe+W0lbvJy3fxayzRpBbnHTAQrZQFFqefprmp97EOvVHyPZEEr4/GueMzEM0+kPLphoPv3hpNdsbvPzgyDyOjmvhmy+/wGjUkzwONnR73Tfsj73+66tLOeqeG7jigrsJhzX+MvdOZo79GcI9jdfv/R1j5hzNvOtv1kM06ujo6Oj0ii5gD1bCfnj8JGivhZ8sgficHs3tiso5q3dQEgjxxuRRTI6zU7K6kdef38SH+Nlp1Mjz1HFj8zLm/f4XWMeNA2DR5np+8txKJmbH88zVs3BajGj+CK1flLPt642UinoqzM0E1RBGo5GRI0dSXFxMUVERdvvQWAY8lIlEIlRVVfUQrFU1GvIhIyODgoICCgoKyMvL03+fRMXu7qL2rgJ3b3Wh0O5JRjswGo377eXdXfQeyg/yYTVMhaeCck85PsVHRI0Q0bptaoSwFu4sR7QIiqbssa1jC6vhHv2610e0CIK+t1dGydgpfH9zyTf6hLgPGBL2WgfKvoTnzoXUsfCDd8Ea9VBtCEW4YO1OSgMhHh9fwMkpe35ZOtjYUO3mqqeWE4yoPHb5dI4cmbzvkwYomqqxdWkdy98ro70lSNboBGafNYLMUQkHfK1weTm1v78PIU/DmFaMbbyTpIuPQDIOPrE3GFG5/4MtPPV1GWPSXdx9WgHbli+mpKSE7Oxs5s+fT2bm4BTohxO6gN037I+9bvAEefzSXxLMGcsz8aPIz2vg7nF/Zfbsj1mzYAlfvfwsJ/zwJ0w5df5hGrWOjo6OzmBiQAnYkiTlAs8AGYAGPCaE+LskSXcBPwIaY11vF0IsiJ1zG3A1oAI3CCE+2td9hsyEuGkHPHYcpI2NJkky9ownWB+KMH/VdgKqxnvTRlNgs+D3hFn8/BY+2VjPYmeEVgTHVa/mNzNSKf7pD5FMJj7cUMvPXljN9PxEnrpqJjZzdPm+6ovg/aIKz9dV1KmtVGV4KQnX0u5tR5IkCgoKKC4uZuzYsfryyQFCOBzuIVhXV1ejqiqSJO0mWNts+pK9vkBV1b2K3r2J4MHg7glFOzAYDPsUuXfd9GRSe0cIgSrUHiJ5byJ4D5G8l7a9ieS3z75dnxD3AUPGXuvAto/gpUsgd3Z09ZgpanNaIgoXrdnJZl+Qf4/L54y0hP4dZx9S2eLnqqeWU9Hs58HzJ3HW5Oz+HtJBoUY0Nn5Zw4oPygh4wuSNT2b2WSNIzTswD3MhBK2vvkbbm5sx5x+PZPWT/oujMSYOzhfni7c2cPOr6/AEIvz61CJmxHv5+KOPCAT0JI+DAV3A7hv2117f/fQXnPHADVx52f2421XuPOohZuaOZNKE//DWg/dQtnY1F939ZzJHjzkMo9bR0dHRGUwMNAE7E8gUQqySJMkFrATOBi4AvEKIv+zSfxzwIjATyAIWAkVCdMsi1gtDakK88U149UqYfR2c9qfdmnf4g5y5ajtxRgPvTh1NqtmEEIIt39Tx6Stb+cYQZqkxhFEJc6V7HT/7zWU4i0bz9ppqbnx5DXNHpfDfK6ZjNXXFoFXbw7R/XoX321qEpuErNlGR6GZr6Taam5sByM7Opri4mOLiYpKTB6/X0WAjFApRWVlJeXl5p2CtaRqSJJGZmdlDsLZaB19czqGKqqoEg8H9Fr07jvfEruFN7HY7DoejV7Hb4XBgs9n0pc59jD4h7huGlL3WgfWvwevXQNGpcOFzYIiGVfIoKpeuLWGlx8fDxXlckJHUzwPtO9z+CNc+u4KlpS38/oxxXHVUYX8P6aCJhFTWL65i1UflhPwKI6emMvOMESRlHlgs80hDA/X3PY0wTgXCJJyRhevYIw7NoA8xzd4Qt7y+noWb65k7KoV75hexftkSVq2KJnmcN28eY8bogtxARLfXfcP+2uvSJh8v/uCXJDhT+POIo0hMDfLXyb9h0sT/w2mbzXO33ojQNC67/2HscUNnVY6Ojo6OzsEzoATs3S4oSW8D/wSOoncB+zYAIcSfYscfAXcJIb7Z23WH3IT4g1th6X/g/Kdh/Nm7Na90+zhvzQ6KHFbemDwKhzEqRnuaA3z6zGY2bG/h64QImzSN/PZ67ig2c/JPL+a11TX8+rV1nDg2jX9fNhWLsWciNdUTwvNZJb5ldQA4ZmQQmmRjW9VONm/eTG1tLQBpaWmdntkZGRlDOhzC4SYUClFRUdEpWNfU1HQK1tnZ2eTn53cK1nq88qGFpmk9RG+fz7ebZ/eu9XuL6b2rZ/feBO8OL2/9b3nP6BPivmHI2WsdWP4EvH8TTDwfznkMYi/PfIrKD9aX8mWblweKcrgiO6WfB9p3hBSVG15czUcb67nltLH89LiR/T2kPiEUUFizsIK1CytRwipFszKYOb+QuJQDW9HV+vontH/hQ7LEY4yrJP3mC5AH4Ut2IQQvLqvknvc2YTHJ/OmciYyLC/Pee+/R2NhIfn4+s2bNYsyYMRgMgy858VBFt9d9w4HY61//30IufeQmrrv8AardKj+a9irHZe1g9qyPaCqv4aXf/Zrc8ZM459bfIw/CRN46Ojo6OoeGAStgS5JUAHwBTABuAq4EPMAK4FdCiFZJkv4JfCuEeC52zhPAB0KI1/Z27SE3IVbC8NQ8aNgC1y6GlFG7dfm4yc2V60s5LsnF0xNHYJKjwpPQBOs+q+Lrt3ZQatb4zOChUTZzkq+Mu647jcVuI3e+tYGxGS4eOG8Sk3ISdr99W4j2zyrwLa8HGZwzM3Edn0u76mfLli1s3ryZiooKhBAkJCR0embn5OToXp8HSDgcprKyktLSUkpLS6mpqUEIgSzLPQTr3NxcXbDW2Y1IJNLpvd2b4N1bnab1nmBx19Am+xK8bTYbRqPxMH/i/mOoT4glSXoQOAMIAzuBq4QQbbG2XkN7SZI0DXgKsAELgF+IfTw4DDl7rRNlyV9h0R9gxjUw7y8QexkWUDV+tLGMhc0e7h6VxY9z0/p5oH1HRNW46ZW1vLu2hhtPGs0vThw9ZF4CBrxhVn1UwfrFVQhNMO6oLKbPK8CRsP/PIZG6Zuof/hxIRW1aTfLVs3AeOevQDfoQUtLo5caX17Cuys3503K4Y94YNq1dxdKlS3G73cTFxTFjxgymTp2Kw3FgXus6fc9Qt9d7Yy/hO5OAl4ECoAy4QAjRurdrHYi9XlfVxgc/+hUjNPj1jHOxOAX/mnkD+blXM3r07az95AMWPv4vjjzvEuacf8lBfEIdHR0dnaHEgBSwJUlyAp8D9woh3pAkKR1oAgRwD9EwIz+UJOlfwDe7CNgLhBCv93LNa4FrAfLy8qaVl5f3yVgHDG2V8OgxEJcFV38C5t3jCD5f08yvtlZyYUYSD4/N7TFxaqn1seipTVSXe9iaEuKTcASzpnBdHhSdMIffvrORxvYQ1x4zkhtPGt0jpEgHSksQz6cV+FfVIxlkHEdm4jomB4PTjM/nY+vWrWzevJmSkhJUVcXhcDB27FiKi4spKCgYVuLW/tKRdLG0tLQz6aKmaZ2CdUdIkNzcXD2+ok6fI4QgFArtl3f3/sTztlgsexW8rVYrZrMZs9mMxWLpsR9s/x+G+oRYkqRTgE+FEIokSX8GEELcsrfQXpIkLQN+AXxLVMB+RAjxwd7uowvYQxQh4JPfwdePwNE3w4m/7WwKaxrXbSrnvUY3Nxdk8KuC9CEj9Kqa4JbX1/Hayip+cuxIbjltzJD5bAC+thArFpSx6csaJIPExGOzmXpaPjbn/j2fCE3Q/ORXBLcL1JYSTOnVpP/65xgGYV6ViKrx94Xb+ffiHeQm2XnowslMzoln69atLF26lLKyMoxGIxMmTGDWrFl6ssd+ZKjb672xl/CdVwItQoj7JUm6FUgUQtyyt2sdqL3+6d/e57rHb+GWyx5gk0dw2phlXFDwIjOmv4XTWcyH//obm75czLm33kXB5Gnf+TPq6Ojo6AwdBpyALUmSCXgP+EgI8bde2guA94QQE/QQIruwfSE8fx5MvhTO/levXf5SWsdfyuq4MT+dW0f0fFhWVY2VH5SzYkEZAYfEl0olqy1JFCpuvj8lm82qnQWbGhiR6uCBcycxvaD3GJWRpgDtiyrwr2lAMsk452TjOiYb2R6NdRkMBtm+fTubN29m+/btRCIRLBYLo0aNIjMzk/T0dNLT03G5XENqYrc/KIpCTU1Np4d1ZWVlZ9LFzMxMCgsLKSws1D2sdQYsqqrut9jdUa+qe01bAEQ9vnsTtnur258+h3r59nCaEEuSdA5wnhDi0j3ZZaIeXJ8JIcbG6i8GjhNC/Hhv1x6y9lonKmK/+wtY9TSc8keYc31nk6IJfrm1glfrWjkzLYGHxuR2hj8b7Gia4Ldvb+D5pRVcOaeA358xbsg963iaAix/r5StS+swmg0ccVIuk0/Kw2LbvxeRvpU1tL66DS3kQyn/kOQfn0H8qacc4lEfGpaVtvDLl9dQ5wny8+NH8fMTRmEyyNTX17Ns2TLWrVtHJBIhLy+PmTNnUlxcrIcXOcwMJ3u9L7qF7/wnURtdGxO5Fwsh9hrE/UDt9Zfbm1h6/a+Z3N7CNSf9BIzw2JzbSIgfwfRpr6KEIjx/x0343G1cfv/DxKUMnRU5Ojo6OjrfjQElYEvRJ/inib7xvbFbfaYQojZW/iUwSwhxkSRJ44EX6PL0WgSMHlZJHHfl03vhiwfgzH/C1Mt3axZC8OutVTxX28z9RTlc2UuMyYZyDwuf3ERLnQ9fmp93W1uockT7pYkQsqZSZ7Bx6ax87ji9GLu59wlJpMGPZ2E5gfVNSGYDzrnZuOZmI3ebwEQiEUpKSjo9sz0eT2ebzWbrFLM7ttTU1CHlaayqKrW1tZSVlVFaWkpFRQWRSASAjIwMCgoKKCwsJD8/X0+6qDMkEUIQDofx+/2EQiFCoRDhcJhwONxZ3te+e3l/7ZDRaOxTQXzXcEjDaUIsSdK7wMtCiOf2FNqLqIB9vxDipFj90cAtQoj5e7v2kLbXOqCp8PrV0YTUZ/4Dpl7R2SSE4F8VDdxXUstIu4X/TShktGNo2EEhBPe8t5n/fVXKJbPy+ONZE5DloSViQ3R137J3S9i5qhGL3cjUU/OZeFwOJsu+BdpIvY+mp9eitqioraXIlhIy7vgppvT0wzDyvsUTjHDX2xt5Y3U1E7Pj+dsFRzA63QVAIBBg9erVLF++nNbWVlwuF9OnT2fatGk4nc5+HvnwYDjZ672xS/jOCiFEQre2ViFEYi/nfOcVzkIIrrjvLW557k4euPTPfOGVOCKnihvGPcCYoj+Qk3MpLTVVPH/7L0nOzuPCu+/HYDQd5KfU0dHR0RnMDDQBey6wBFhPNA4XwO3AxcBkoiFEyoAfdxO07wB+CCjAjftajgxDfEKsqfDsOVC5FK5ZCBkTd+uiaIKrN5bycZOHJyYUMC81Yfc+YZVv3y5h7aeVxCdbSEp1s3XnRpZ5BGuSRxIwWZGEhguFU6YVctmRBUzMjsfQywQsUufD80k5gY3NSFYjrqOzcc7NQrbsLnwHAgEaGhqor6+nrq6O+vp6GhoaOkVdSZJISkraTdhOSEgYFB5MmqZRX1/fGRKkvLycUCgEQGpqKoWFhZ1hQez23cPA6AxthBComkAToHWWBZoWOxY9jwVgkiXMRjm6GWSMhuEbV14IQSQS2afIvT9CeMd+fzGZTD0E7Z/+9KeDfkIsSdJConExd+UOIcTbsT53ANOB7wshxJ5CewEVwJ92EbB/I4Q4o5f7Du2QXzo9UcLw0sWw81M478ndklF/2drOtRvLCGuCvxfncXovzyyDESEED360lX8v3sm5U3N44LxJvT5DDQUaK9pZ+k4J5RuascWZmf69fMbPzcZg2ru9EprAv6KW1ne2gGJCqV+L69g0kq44D2kQ5lD5cEMtt7+5AW9I4denjOGHcws7f+eaprF9+3aWLVvGzp07MRgMjB8/nlmzZpGdnd3PIx/a6AJ2r+E72/ZHwO7Od5lfv7+ulu0338qRLeVcetatKIrgoZkPkeRqYPasT7BYUtn27Ze8+9D9TD51Pif+8Cff5eP1iqapBDwevC3NeFub8ba04G1twdfajM/dRmpeAYWTp5M5egyyvipCR0dHZ0AwoATsw8WQFrABvI3w6NFgskWTOlrjd+viVzXOX7ODDd4ArxwxklkJvXt5VG9tZfELW2mr92M0yRSMTyBTrqRs7bd8VRdkVcpodiTkICSJeLPM0WPTOWZ0KscUpZIR39NTKlztxbOwnODmFmS7EecxOTjnZCGb9/5QoGkara2t1NfX99haW7tyiVgsFtLS0sjIyOgUtdPS0vo9zIYQgsbGxs6QIOXl5QQCAQCSkpI6Q4IUFBTonjbfAU0T+CMq/pCCN6TgD6v4Qgq+sIIv1FGOtYcV/CGVYETtFIh3FYlVIXYTkTv79BCRo/fuaBOCbuJyrI/W/fyYOB1r1wRd9xVdx32BLNEpZpuNBizdxG1zb2WjjGUvbZ3He+pjkDHLMmZZiorpsoRZiu5NUrQsi6gQgSYQmkAoKiIYRoTCaKEIIhRCC0VAdE8YKaIhBrof97bv6CPt3l8IgbTrOUKARJenduwYIZBi53QcC00QESphRSGiKoTU6D6iqYQVlbCmElEVwqraY4toKlf+6qYhPyGWJOkHwE+AE4UQ/lidHkJE58AJ+6Mv36tXwiUvwaiTejRXB8Ncs6GM1e1+fpaXxm2FmRiHgNgrhOAfn+7gb59sY/6kTB66cDKmIfwSsnZHG9++XULN9jacSRZmnF7I2NkZyPv4zFpYxf3eRrxLm0BICO9GUn92CrbxRYdp5H1HY3uI299czyeb6plZmMRfzz+C3KSeDguNjY0sW7aMtWvXEg6HycnJYebMmYwbN27Q5YMYDAx3Abu38J2SJG3lEIcQgeiz70W/f4W7XrubZy66j5cDJrLTfNwz5fekpZ7ChAl/B+Czp//LqgVvc/oNv2bsUcfu9ZpCCII+L96WZnwtzXhbW7q2lmZ8rdE6X1srYtdE5ZKEIz4Bq9NFS00VQtOwOpzkHzGVEVOmUzB5Gva43efVOjo6OjqHB13AHqqUfwNPnQ5j58EFz0Iv3snNYYUzV22nKaLwztTRjNnD0lwhBPWlHrZ+W8f2FfWE/Aq2ODOjJyWQHt7BpvffpsodZk3aaFZmjKPVHM2oXpTu7BSzZxYmdSZ+DFe24/6knNC2VmSnCdexuThnZyD1khhyb4RCoU5v7e5bh0czQGJi4m7e2na7HU3TEEKgadpu5b217U9Z0zTC4TCVlZWUlZXh8/kASEhI6AwJUlBQQHz88HoAEkIQUjR8MaE5KjgreENRgdnXQ3yOCtD+DiE6Jj73OCccvc7+YjbKOMwGrCYDsiQhy2CQpFhZQpZAliQMcs+6rj67tEtgkCUkSYr2ibXvsU+3drnjONbPEOvbeSxLSJpA0gSyBpKqIasCqWNTosdC1YioGmFVxPbRcljVCGuCiCYIa7E6ES1H66JbJFYXFoKIILYXhIH9/8nuGwNgBkxImHopG5EwA+bOffdy37SZAJnDJ3jl/vmYIT0hliTpNOBvwLFCiMZu9XsM7SVJ0nLgemApUa/sfwghFuztPsPCXutECbTB0/OheSdc/hbkzerRHNI0fre9mqdrmjkqwcn/jc8n1Tw0lpM/+vlO/vTBFk4Zl84/LpmCZYjE++4NIQRVm1v59u2dNJS3k5BuZ+b8QkZNS0Pax0sJxR2k6fElRBrMEPFjTHWTduPZGGyDK7SMEILXVlbxh3c3oQnBnfPHcdGM3N1WEgaDQdasWcOyZctoaWnB6XQybdo0pk+fjsvl6qfRDz2Gs4C9l/CdDwLN3ZI4JgkhfrO3a31Xe/3C0gqa77ydo1vKuPTc3+L1qdw6/mWKMr5k8hFPkZx8NKqi8Mrdt9FYXsrZv/ktIHUK0b2J02ps5W53rE4XzsQkHIlJOJOScSYmR4+TknDG6hzxiZ3e1kGfl/J1ayhdvYLSNSvwu9tAksgcWUThlOkUTplOeuHIQbkaREdHR2ewogvYQ5mv/wEf3wmn/gmOvK7XLhWBEPNXbcckSbw7dTRZ1r3Hl1YjGuUbmtm6tI6y9U1oqiApy4GrwMLyLz9h1JYvSQx7WZ1SxOq8SayPyyGCjMUoM7MwiWOLooL26DQn4Yp2PJ+UE9rRhuwy45qbhbkgHlOGA3k/4iP2hhACt9u9m6jd3Ny837Fx+wqXy9UpVhcWFpKYuNeVdwMSTRM0+UK4/ZEuT+ZdBOgens7dxOcOIbqrr7rfXsayBA6LEYfZiMNi6FG2m42x41h9rM5pMWLvrOvWbjZitxgOuVeb0AQirKKFVERs00JKbK/22HeWO/oHla62WB3qgXxfBaDFPJA1hNCi3swdm6YihBoNMaSqCE0BVYn1U0GL7UXsXE1FExoRNCJCEBKCCIIIxPaCsASKwUhElokYDEQMMhHZgGKQCRsMKHJHXbQ+usWOJQMRWSIiyUQkmbAkEUEiLCTCGtH7aRASENYgLEDpgz9fkwQWWcIsEfUUlyUseyx3eZNbZKJe5hJRb3Upeg1LzMvcIkux60pY5Gj7rCtmDOkJsSRJOwAL0Byr+lYI8ZNYW6+hvSRJmg48BdiIxsW+XuzjH/Owsdc6UbwN8L/TwNcEV73faxi0l2tbuGVbJYkmI4+PL2BavKMfBtr3PPVVKXe9u4njxqTyf5dN63zpP1QRQlC6toml75TQUuMjOdvJrLNGUDAxeZ8h4QKbq2l+ailIqWihZuJOyCR+/sxBEUquO9VtAX796lq+3tnM8WNS+fO5k0iL212M1zSNnTt3smzZMrZv344sy4wbN45Zs2aRk5Mz6D73QGOYC9h7Ct+5FHgFyCMaAux8IUTL3q71Xe11MKJy3u0v8cC797Lw4rv5a9BOQpLKv2c/iGQwMmvWBxgMVtpbmnj2ll8Q8Lh7nG+yWGOCdEyETkyKitNJUbHalZSMIyEJ40HkUBKaRkNZCSWrllO6egW1O7eBENjjEyicPI3CKTPInzQZq0NfTaujo6NzKNEF7KGMEPDyZbDtQ7hywW7eTB1saPdz9uod5FjNvD1lFPGm/VueGPRF2LGyga3f1lJX4gEJ1FQLS9qaKHCv42L/FqQtG1mfWMia0TNYlVlMmRJ9eMiMt3L06BSOHp3KDJMJ+YsawmWxBI4SGJNtmLIcmDKdmLIcmDOdGOK++4NHJBKhsbGR+vp6gsEgsiwjyzKSJPVJeddjg8GA0+kc0JMKIQRt/gg17gC1bUFq3QFq3EFq2wLUtAWpcQeo9wSJ7IeQajd3iMgdAnNvonM3QdlsiArOlm7ndOtjMcqH/GcnhEBEtJ6CckhFC6uIkLK72LzrPhwrB6MitYho+75p9M4gqYAKIgJaGKGEEJEAIuJHBH1oAS8i0I5QAgglCEoIEQnGyoFo/1i9ZDYiWSzIFgtSbOssWy3I5m5liwXJvB/lXa/To2xFtpiRTIfX81HVBGFFI6SohBSNUKRbuZf6cGd9rC0SLfe4hqIRiqiE1d2v19u9DiTKS/mf5w/bCXFfMmzstU4XbZXwv1NBDcMPP4Lkkbt12dDu54cbyqgNRbhndDY/yNq36DkYeHFZBbe/uZ4jRyTz+A+m7zFJ9lBC0wQ7VtSz7N1S3I0B0gvjmH3WCHLGJu31PCEEba9/SfuSRmRbKsgtpFw1G+vowZXkUdMEz3xTxp8+2ILNbOCesyZwxhFZe+zf3NzMsmXLWLNmDaFQiMzMTGbNmsWECRP08CLfkeEsYPclB2Ov/7N4J+KPv+Xolp1cff491HsiXJr3JSeOeYWC/OsYOfJXALgb6qjeurlTrHYmJmG2Hf6cQX6Pm7K1qyhdvYKyNSsJ+rxIskz2mHGd3tkpuflDwi7p6OjoDCR0AXuoE2iDx46NJkn6yRJwpPTabUlLO5esK2F6vJ0XJ43EeoDeqm0NfrYtrWPr0jo8TUEUCbYZFZIKjPwoqQaxeCH+ZctosMazbsJc1oyawXLVhSesIUkwKSeBGZnxpGgQH1SJb1dwtYZJ8ISJQ8KAhOw0YcpyYs50RMXtLCfGZNs+l5wOV9qDEWrdQWraAtR2CNPuqFBdGxOog7uIriaDRHqclax4G5kJVjLjbWQlWEm0m3fzdO4QoO0mA3I//Q6EJtACCiKgoHVuETR/rOzvWR/t1yFAK12+JvtCFiBrSCiAglDDoEZFZBH2I0J+tGBUdNb8HkTIHxOYo6JzVzkUFWUAyW7H4HAgu1zITicGpwPZGSu7nMgOZ6zNgcHpjLU5MMT6y04Xst2mL108TAghUDpF9L2J4tH6eZOy9AlxHzCs7LVOF43b4MnTwGiLxsTuxRO7LaLws00VLGrxcH5GIn8uysU+BOJHv7GqiptfXcu0/ET+d+UMXNahESZlX6iqxtZv6lj+fine1hDZYxKZfdYIMkbsPdya2u6j/m+vobSlIZntmLM0Uq6cgyG+f3OgHCg7G73c9Mpa1la2MX9SJvecNYFEx54dN0KhEGvXrmXZsmU0NTVht9s7w4sMtxB1B4suYPcNB2OvPcEIF976PH/78M9suOS33BxKwOoSPDvnSbzqBmbNfA+HY1Qfj7hv0FSV2u1bKV2zgpLVK2gsKwHAmZzCiMnTKZw6g7wJkzBbbf08Uh0dHZ3Bjy5gDwdq18LjJ0PBUXDpayD3viz1zfpWfrqpnDNSE3h0fD7yd3hrLISgbqebzd/WsnlpHUQEPlmQPjGJU49Kwrz+S9o/+BD/ypWoSJROmsO6ycezwprBhqbevX1lCRJMRpJkmUQN4iOCJCGRiESSQSYlyUZ6upO0bBeZBYk4sp37TAw5WFE1gTeo4AlG8IYUWn1hajpF6qjndIdA3R5SepwrS5DmspKZEBOo461kJtjI6rZPcVoOuxgtRCz0RnfB2d8hSkd6EaI7+kQQwX1EapYFkqyCpABh0GKicyQQFZ4DXrRgO5rPg+ZzIwLeXYTnEChBupIAgmQ2RwVklxNDp8jsjInMzi4BujfR2dEhTjuQBqGnlBDRhJVCFaiqFn2BoAk0NbqJbmUtlrBRVTWE2q2fJqLHHXVaVztEY4lLshTdJAnZ0FGOxgiXZKlzH912qZeidd37yb30lWLxxjv69rWXjD4h7huGnb3W6aJ2HbxwIQTb4Kx/wYTv79ZFE4K/ldXz17I6xjmt/G9CIfm2wSVc9sZ762q48aU1jM+O55mrZhJvHx4iNoASUdn4RQ0rPywj0B4ha3QCR5yYS8GklL0+n/hWrqPp0UXI8UcgyRKOI9OJnzdmUD0PKqrG/32+k4cXbifRYeaBcydx/Ni0vZ4jhKCkpIRly5axdetWJEmiuLiYWbNmkZeXp3uAdiAEtNdB83Zo3gFNO6L75h1Iv1it2+s+4GDt9f0fbCH5wd8zx13Kjeffy/a2MMem7+SH057C6RzD1CkvDIrvc3tLE2Vrot7Z5etXEw4EMBiN5IybSOHkqHd2YmbWoPgsOjo6OgMNXcAeLqx8Gt69AY67DY67dY/d/lPRwN07a/hRTgp/GJV9UMZVjWh882Uln3xQSrJHxYBEfIad8XOyKCw0oH37GZ4PPySwciUApsJCQvkj8aTn4EnOwBOXQps9njaTnRbNSJMvTLM3RFN7iGZvGF+kd/HSDiQZDCRbTaQ4LaQkWklLdZCaZCPFacFhMWKSJUxGGaMsYTLImAwyRoOEObY3ynJX2SBhkuWDEnY7Ehh6ghHagwreoEJ7UKE9GKE91FXurA9FYnWx+lifvSUtTHaYu7ymY6J0ZryVrNg+Pc56SONAC0WLCc17EJ39sXpvGM0XjvYLqoiQ6K4P93JhDSHCoAWjITXCvugW8KAFPIhgezT0RtiHiPgg7EdEfIiwPxrXOYZkMiE7HMh2e1R07iFAx0Rmxz4EaKcT+SBi6O33z1IIlIhGOKAQCaqEgwrhgEI4qBIJRvfhYLRN6yEGC7ReBOMucVnbXXDeRXTucX7Hud2uM1SRpG7CeafoTTdRvKtuVxFdjgns3eu/f/M0fULcBwxLe63TRXs9vHI5VC6Fub+EE37b60v4Rc0efrapHAH8a1w+JyXHHf6x9jEfb6zj5y+sZlSak+eumUXSXrxxhyLhoMLGJTWs+6wSb0uIuFQbR5yQw9gjMzFbe38BLCIRmh5/Ae+3bZgypyKZVOLPHINjWsagWq23odrNr15Zy9b6di6emcsdp4/Dadn3S+/W1laWL1/OqlWrCAaDZGRkMHPmTCZOnIjpMIf96jdC7bsI1DHBunknhL1d/Yw2SB4FySORLnxGt9d9wMHa6wZPkMtue46/L/orjZffxhWhVGQzPDn5ZRT7EorH/pmsrPP6cMSHHlWJUL1lEyWrV1C6egUt1ZUAJKRnUjhlOiOmziBvwhGdiSN1dHR0dPaOLmAPF4SAt34Ka1+Cy16HUSfusevvt1fzaFUjvx2Zxc/y9u75sT+omuB/n+7gow9KKA4byIjISBLkjE1kzOxMcjM1gosX4vt2KZHaWiI1NWjungk6MJkwZWRgysrClJmJKSsLJSOL9uR03M4kWm1xNDaHaKz30tAUoMkTpNkXpkVVaUXgRuxVI90fDLKEUe4mchu6RG6TobsYLmGQJXwhNSY8R8VoZT8C6NrNBlzWaJgOl9WEy2qMbhYTzo6y1YTLEi3H201kxdvIiLf2acInLayiecKonjBKqw+1xYvaHkT1htB8USE6mnBQICISQpVB7FkcF0KLejV3iM/dBGcRE5yJ+DvLIuwHwkgmCdlqjArPHZvdvpdjO7I9tnc4MDgcnaE6pMMgPGua6BKYA11Ccw8hukN87tHe0dbRT0Xsx/dFkiUMBgnJEPVUljsEVIOEbIi+dJG7tUm7HMsGuauu277H+bF6qXufmFe0oZfzd71HZ79ezu9xXuxlWYfnttCiQn7HsdZR1+M4thd0en8LIbr1oUffaBud/bru1fP6Wsc1VIEmuu7Z87503qvndaL9dQG7bxiW9lqnJ0oYPvg1rHwKRp0M5z4OtoTdupUHQly9oYwN3gC/KkjnVwUZ32kl2UBi8dYGfvzsSvKT7Tx3zSzSXLsn+BvqaKpGyZom1i6qoK7Eg9lmZPzcLCYen4MrqfefR7iigto//gsYjyGpEGOykYRzirGOSjisYz8YQorK3z7ZxmNflJCTaOMv5x3BrBHJ+3VuOBxm/fr1LF26lIaGBmw2G1OnTmXGjBkkJCQc2oEfDtQItJb3FKg7BGtvXbeOEiTkQcromFgd21JGgysLYiHY9BVTfUNf2Ovb3lhP0T//wOz2cu46/36WtgQYl9LMH459C3+wgtmzPsZs3nt8/IGMu6GO0tUrKVm9nMoN61AiYezxCYyZczTj5h5P+sjRume2jo6Ozl7QBezhRNgHj58E3nr48RKIz+61myYE120q562GNv5ZnMd5GX3zoFDa5OOW19exfUcrp7lcFAVk/K0hjBYDIyenMnJaGkmZDlzJVoTfT6SmGiUmaEdqaohU13QK3EpDQ1SU74YhNSUmcGdF91lZGFKykCzJRMJ2mhtD1Ne04/OEiChaRzRjFCACqAYJzWZEtRhQrQY0swHVIqOaZDSzjGKUUY1ytL+qoWgaEVVEy7F9tF6gqAKHxdBDiHbGxOe4bgK1MyZEx1lNOCwGjIfIQ1oIgebzoTS2EqlvQ2lsR23xo7pDqD4FLaAhIjKoRgRWJLl3sVcooS4Burv4HPYjtBCSpIBBRTKBZJaQzRKyzYhkN0djPHeKzfYe4rOhFzH6cCUJFEKgRrQeXs1R8bm7EN3h/bwHb+hYHyW8f0G1TRYDJqsBs9WI2WrAbDN2lk22WF3HsdUYa4/V2QyYLNG94TAku9T5bugT4r5h2Nprnd1Z8T9Y8GtIyIeLX4TUMbt1Cagat2yr5JW6Vk5IcvGvcfkk7mdi6oHK1zuauPrpFWTGW3n+R7PIjB++cVTrStysXVTJztWNAIycmsrkE/NIL9zd414IQdsbb9Hy1EeYCr+HbE/COjaB+NNHYko9/EnfvivLy1r41StrqWz1c83cQn51ypj9dlgQQlBWVsayZcvYsmULAGPGjGHWrFkUFBQM7OcHIcDb0E2g3h71om7eDq1loHULk2dPjonTo6NJXzsE68RCMO37pY9ur/uGvrDXpU0+rr3zOR5Z/DDKNbdyti8dVQj+XPg2qXmfkZF+FuPGPdBHI+5fIuEQZWtWsvnLxZSsXIaqKCRmZlN89HEUzz2ehPSM/h6ijo6OzoBDF7CHG03b4bHjIG0cXLUADL2LhCFN45K1JSx1e3l+0kiOTXL1ye01TfD80nLu/2ALCLh5WgH57YKdqxoJB6IPo7JRIj7FRnyanYQ0Gwnp9ljZjiPBjCRJiHCYSENDVNSuqSFSU02kthalm9AtwuEe95adTkyZmRiSkpBtLiR7IpI1HskUh2R0gGwDLAjNjFCNiLAM2u4P95JZxhBnxhBnQY4zR8suC4Y4U2xvRnaZkQxSLC4BILHfEwWhaYhIpGsLR0CJ9KyLbVogiOJ2o7a0o7ZGBWnNr6IFBSJiQKgmkKxIRgeSJR7JuLswLZQwIuRGqD4QQSQ5gmTWkK3RxJkGlxlDvBVDnA3Zae8Sm7sLznb7YROcO9A0QSTUJR5HdvFm3lvojR4ezwE1Fn9578iyhMnWISz3FJJ3FaJ3Fac7j21GTJb+S3qpc/jQJ8R9w7C21zq7U/5NNKRIJAjffwzGztutixCCZ2uauWN7NRkWE/+bUMBE1+ARLHtjRVkLVz65nESHiReumU1u0uD+PAeLpznA+s+q2PRlDeGgSsaIeI44MZcRk1OQd3EEUJqbqbvvz4S2RTCPPR3JaMF5ZBauE/MwOAZHWA1fSOG+BZt5fmkFo9Oc/O2CyUzMObBEjW1tbaxYsYKVK1cSCARIS0tj5syZTJo0CfNhWKG2R0JeaNnZU6DuCPkR8nT1M1ohaWRPgbpDsLYfnKONbq/7hr6y1z97fhVHPnEvM/w1/OfiB3mr1k1Oip8nTl5BVfNrTJr4H5zOcUiSjCQZQDIgSwbAEKszdrUxOBw9gj4v2779ii1fLqZy03oAsoqKKZ57HEVHzsUepydm1dHR0QFdwB6ebHgDXrsKZv8MTrtvj908isrZq7ZTHgzz7MQRzEl09tkQqlr93PbGepZsb2JWYRL3nTkBm1ehrSGAu8FPW32AtgY/7oYAqtLl1Wo0y13CdlpM2E6PHludps6HFKFpqC0tXd7bNV2e3KrbjRbwI/wBtEDXRiSy+0CNNmRrPJI1Ackaj2xNQLJFj2VbIpItAckShyTvexIU/fuIbaLbXghAi5W1mGO51q1tlz7RDxgtG4xRYbqXFxFCCyMRAkME2SyQbTFBOt6CIcmBMSUOU2YCxtREDLbD49ElhEBVNMIBlUiou5CsxsJsdBOZg+ouYTZ6CtGR0D4SOMYwmuVO8Tjq0WzodtwhLht69OkuNnfUGUyD4yFYZ2CgT4j7hmFvr3V2x10FL10KtWvg+Dvg6Js7QwF0Z5XbxzUby2iJKNxflMNFmfsXfmGgsqayjSueWIrTYuSFH82mIMXR30Pqd8JBhc1f17Lu00o8TUFcSVYmnZBD8VFZWGw9Pe+9X3xB7R8fxJA4E3PhsUhWI3En5OGck4VkPHT5QfqSxVsbuOX1dTR7w1x/wmiuO37kAec2iUQibNiwgaVLl1JXV4fVamXKlCnMmDGDpKRDFJpBVaCtvDNpYlSsjonU7TXdOkoQn7uLSB0L+RGX0+vfeV+g2+u+oa/s9foqNzf/4Xke/uIfWH52G6e70/EHVW5I+YzZM5cTDFYe0PWkvYjbsmSE2HHPzYjNlofLWYzTNQ6XsxiLJfOwzAM8TQ1s+eoLNn3xKc1VFcgGAwWTpzHu6OMZMW0mJvPgT1Sso6Oj813RBezhyoLfwLJH4YJnYdyZe+xWF4pw3podlAZC3D4ii+tyU/vMeAsheHVlFfe8t4mIqnHjSUVcNju/R6IaoQm8bSHa6v1dwnajn7Z6P+1NwR6esxa7kfjU7h7bXeVdJzK9jicS6RK0/X5EZzkQFbx7O/YHUP0BRFBBC4GIyFGvZ2EC2YhkMILBgGQwIskGkA1IBgMY5Ggiqo5j2YAkyyAZQJZjfeVoOZpJLtonmimOWHY5JKMBQ7wVY7IDY4oLY5Idg8uMHGdGNvddTGzR4e3ca7zmXYTojvjOIbWn6Bzrsz+JACVZ6hEyY1evZlO3kBs9hebuntCGqLfzIUxcqaOzJ/QJcd+g22udXokE4N0bYd1LUHwGnP0fsOy+UqwprPCTjWV82ebliqxk7hmdjeUQiWCHg401bi5/YhlGWeKFH81iVFrfrI4b7GiaoGxdE2sWVlC7w43JamDcnCwmnZBDXErXC3rN56PxkUdoe2sR1skXY0gswpBkJf57hdgmJA+Kl9Ruf4TfvbOBt9fUMCknnr9dcMR3+h4IIaioqGDZsmVs2rQJIQRFRUXMmjWLESNGHPjPQgjwNe4iUMe2llLQujmJWBN6j0udNAJMhz9Ejm6v+4a+tNeXPb6UM5+/nyNCDbxz5cP8s6SBpGSFBSe1QYYLITQEKkJEN4TaVaepsTYtVq/s1r+rLbbt0ia0MD5/CYFAWeeYjMYEXK5iXM5xOJ3FuFzjsNtHIO+HE9N3QQhBY3kpm79czJYvF+NtbcFsszF61lEUzz2O3PETkXtJaqyjo6MzlNEF7OGKEoYnvwdN2+DaxVFvhz3QrqjctKWSdxvbOC0ljr+PzSO+D2NK1nuC3PHmBhZursdpMXLetBwum53PqLS9e3yrqkZ7U7DTUzu6j4rc7a1BumdttLlMUY/tdDuOeDNmmxGLLeZx263csTeah563rRrRCPojhPxKbNulHIiVfbH6QFefSHD/vZ1NVuNu4nPPeM57ju9sionPRt3bWWeQo0+I+wbdXuvsESHg23/Dx3dCyhi4+IWoALYLiib4c2kt/6hoYLLLzhMTCsi29mPIhINkW307lz6+FE0TPHfNLIozd4//PJxpKPewdlElO1Y0IIRgxORUjjgxl4yR8Z3PFYH166m987corQbss68COR5zQRwJp4/AnDs4XgosWF/LHW+uxx9W+c1pY7lqTsF3Dk/m8XhYsWIFK1aswO/3k5KSwsyZMzniiCOwWHbx9owEdhepO8J/hLolYDeYo3+P3QXqjrAf9qRoiL1+RBUCt6LSFlEZ6bDq9roP6Et7/eX2Ju574CX+uuRfuG66jbOaMmjyRjg/aRV/+eVtYDg8uQ0UxYvXtxVv+2bavZvwtm/G69uKpoUAkCQzTudonM5x3by1x2I09u3/EU1Tqdq0gU1LPmP70q8IBwI4E5MYO/c4iuceR2p+oT5v0tHRGRboAvZwpq0SHj0a4rLhmoV79XoQQvBEdRN37agm22Lm8T6OKSmEYE1lG898U87762oJqxpzR6VwxZH5nFicjuEAH8qViIq7MRAVtju8t2Mid8AT3jX/4250xDveVdjek+Btthl6HluNIEU9l6ORQQRCiGjkj1hZ0wSIqOdQz3679Beix7EmBMT2akTrKUZ3E51Dvp7HamTvCQaNFgNWuxGLPfbZ7CasdiNme8/Ptcf4zlbd21lHpwNdwO4bdHuts092fhYNiyYEnP8kjDyh124LGtu4YXMFZlni0XEFHN1HuT36g5JGL5c+vpRAROXZH8464HjIwwFva4j1i6vYuKSakF8hLd/FESflMnJqGgaDjIhEaH7yKZr+/R9M+UdhmXguKAZsk1OJP60AY8K+k//1Nw3tQW57fT2LtjQwe0QSD553xEHFR1cUhY0bN7J06VJqamqwmAxMzrEzM66ZZN/WqNNLWyU9PETiciClmyd1R1zqhLzoSsNDjCYEHkWlTVFpiSi0RVRaIwqtSnTfFtmlTYnu3Yra+SnqT5ii2+s+oC/ttRCCM//5FVe/8SDjIi2s+Nl/uG19OfZ4eH3scsbNu6VfvPUBNE3B7y/B6+0Stdu9m4hEWjv72Gx5naK2yxX12LZYMvpEZI6EQ5SsXMamJZ9RtmYlmqqSkptP8dHHM/aoY4lLST3oe+jo6OgMVHQBe7iz/RN4/jyYcjmc9c99dl/h9nHtxjKaIwr3jc7hksykPn/j29ge4uXlFTz3bQV1niDZCTYum53PhTNySXIcvNeUEB3J/6IhLkKBWHzlbuXudV3HKqFApDMUBgPt6y7RJazbdxGg7SYsdmMvx6ZOwdowSGJA6ugMBnQBu2/Q7bXOftFSGo2L3bgZTrob5lzfq4fnTn+Qq9aXscMf5LYRmfw8L23Qeq1Vtvi5+L/f4vZHeOqHM5mWn9jfQxqQREIqW76pZe2nlbgbAjgTLUw8Lodxc7OwOkyEy8up/f1d+FeswXHMlciJU0GScR2djeu4HGTL4fH0/K4IIXh1RRV/eG8TAL+dX8wF03P373uthKG1NOpB3bStx74qZGMpk9lIERoGRpmbmJUpGFmQi5xaFBOrR4K5b2KxCyFoV7Wo+BxRaVOi+92Pu8ptMTF6b+4Z8UYDCUYDCSYDSSYjCUYDiSZj57FZgx/kp+n2ug/oa3u9YH0t/3n4FR746v9IuvW3XFibRpk7xJGJFYyKbEQ2qxhtViyueKxxSTgcDpwOJ3HOOBKcCSTFJZHoSMRlcWE1WA/p/3ohBKFwfQ9P7XbvJgKB8s4+JlNiNPRIt7jaBxuCxO9xs+3br9i85DNqtm0GIGfcBIrnHk/R7KOwOvouf5WOjo7OQEAXsHXg0z/CFw/CWf+CKZfts3tzWOFnm8pZ3NrOBRmJ3F+Ui/0QeN4qqsYnm+p5+psyvi1pwWyUOfOILH5wZEG/ext1xITek+AdjoXckCQpGrJakpBkKZp/RpKiyzylqKd3NKx1tH3X/p1tsb28S7vBKHcK1mab8TsvH9XR0elbdAG7b9Dttc5+E/LC29fBprdh4gVw5iO9euj5FJWbtlbydkMb81Liebg4jzjj4IwjWtMW4NLHl1LvCfK/K2cwe8TgTlR5KBGaoHxDM2sWVVK9tRWjxUDx7AwmnZBLfJoN9xtvUv/AA6BZcJ3xKzR/IrLTRNzJ+TimZyAZBvbzVWWLn1+/tpZvS1o4cWwafzp3ImmumBe5vyUW6mNbT6G6pRREtxBxrsxoqI+Uotg2mnZbDiu3VrNi5Uq8Xi9JSUnMnDmT8ePH43A4kHeJKS+EwKdqPTygWyIKbYpKW0yMbu0QoLuXFYW9pUhxGWQSTEYSuwnQiZ3HBhJi4nRSR5vRSLzRgLHbc3GLL8yGajcbatxsrPawocZNebOf8j/P1+11H9DX9lrVBCf9dTE3f/AQRaqHylv+yw+/2Q4CrFaIJ0iq2kaK4iFeCuGSghilnl8iDY2QIUTYEEY1qgiTABMYLAZMFhMmmwmrzYrD4cBhdxDvjCfOFofL7CLdnk6uKxe76WBWNbTj9W6l3bsZb/sm2r2b8Pm2oWlhIBqCxGEvxOEYHd2co3E6irDZ8mKJJveftrpaNn+1mM1LFtNaW43BZGLE1BkUH308hUdMw2gevKGzdHR0dDoYEgK2JEmnAX8HDMDjQoj799ZfnxDvgqbCs2dD5TK4ZhFkTNjnKaoQPFRWz1/L6hjrsPL4hAJG2g/dcsutde08800Zb66uxh9WmZKXwA+OLOB7EzOwDNKJp46OztBFF7D7Bt1e6xwQQsCSv8Cn90LmJLjweUjI7aWb4PGqJu7eWU2e1cK/xuUzJa7vwqIdTho8QS59fCmVrX4eu3w6xxTpy8f3RWNlO+sWVbJteT2aJiiYmMLkE3NJS1Jo+NP9eBYswDJxLvZZV6I0aRjT7SScPgJr0QD2ctdUtNZynvpiK39ermCXFf6YuojTQx+Av6mrn8EMSSN3E6pJHgXWaDx1TQjaFRWPqtGuRMNttIXCbKmoZGNpGfVeH2GjiZDJgmK1EbZYCRlNBAwmfLKMyp7FfodB7vSCTjQZSDBG9x1idEKPclcf0wE6aDR4gmyocbOh2sOGajcbazxUtwU62/OS7EzIjmN8Vjw/P2G0bq/7gENhr19cVsEr//c69339GKm/u4t7SjL5Qg7jRkMNqkjhnv73SZJCOh6SzF4cjjB2u4TDpGHVfKihCFpYgzBIioS0h++pIimEDCHaTe24zW5Up4or2UVGaga5cbnkunLJc+WR68ol3hJ/wJ7dmhbB7y+Jitrezfh82/F5txMM1XT2kWUzdvtIHI7RODvEbcdobLbcfQrbQgjqS3aweclnbPn6C/zuNiRZJjEzm9S8AlLzC0nJKyA1vwBXcuqgXYWko6MzPBn0ArYU/S++DTgZqAKWAxcLITbt6Rx9QtwL3gb4v6OjSwGvXdz5ELsvFrd4uG5TOWFN8NDYPM5ISzikw/QEI7y+sopnvimntMlHitPMxTPzuGRWHpnx/RMLTUdHR2dXdAG7b9Dttc53YuuH8MaPomLdBc9AwVG9dvu2zcu1G8toCCvMjndwbW4qp6bEYxhkE/pmb4jLnljGzgYvD104me9NyNBXZO0HPneIDZ9Xs+HzaoK+CCm5TiafmEuGfysNf/wDSm0d8edfj2SfitoWxlKUSMLphZjS+yZsxnci5IXm7d3CfWzrSqKohhDABgq4RbmOTUoOc1NbuHCKjJqUjceejsecQLsGbkWNitS9bF5V22eUPDMCp9CwaSpWJYw5FMIUCmAI+LFEwliVMJZIJLYPk2QykmyzkOh04nQ6cblcnfuOstPpxGQ6sHAKQghq3MGoSF3tZkONh/XVbhrbO5LsQWGKgwlZ8UzIjmNCdjzjM+OJt3fdR7fXfcOhsNfBiMrRf/6U+xb9nQLJT+qzb7JjbSvNtV62tflZISLsjINqK4RDKrJfwdqugF8honV9iw0Ism2CEZnxFGQkU5BsI8tlIs0uEWdQCAb8uL1u3F43Hq8HT7uHlqYW/G3+zpCRqqzSZmqjzRzd3BY3ml0jOyGbXFeXsJ3jyiHPlUeqPRVZ2v8Vyorixeffic+7HZ9vGz7fdry+7YRCtZ19ZNmCwz4Kh2NUl9d2p7C9+700VaVi/Rqqt22msbyMpopS3A31ne0Wu4OUvHxS8gpj4nYBKbn5mG2D84Wujo7O0GcoCNhHAncJIU6NHd8GIIT4057O0SfEe6D8a3hqPhTPh/Of3u8M4dXBMNduLGOlx8+1OancOTITs3xo4ylrmuDLHU08800Zi7Y0IEsSp45P54ojC5hV2PdxuXV0dHQOBH1C3Dfo9lrnO9O0HV68OBrj97T7YcY1vT7XeBSVF2qaeby6kapghDyrmWtyUrg4MxnXIFrh1eYP84P/LWNtlZv0OAsnj0vn1PEZzCpMxqznuNgrSlhl69I61i6qpLXOjz3ezIQ5aaStewv/C09hTM8k8fI7CVVaECEVx8wM4k7Ox+Ds+yX5YU3DE1Fpb6vF3VxGe1sNbk89Hm8L7QFvVHg2OvAYnXiMLjzWZNotibiNTtplKx5hQEECTWAobce4sx3MMpEJiWgp0ZWSBikaG9plMBBn7NpcRjlabzQQZzAQZ4ruO+s6N3mPz/mqquL1evF6vbS3t++27yh7vV56mzdardZexe2OvUc1UubW2Nzg7/SsbvFFwzHIEoxOczE+O44JWfFMzImnODMO5z7imOv2um84VPb6/z7fycKn3uSeb54g84/3kHDeeZ1tmiZobw7QVONjZb2Hr71+1koRdjggIgQGn0JWU5i01ggmn4I3rFKFSrCb97XZIJOXbKcg2cGIVAcFyQ4KUxwUpTuJtxpobGykrq6Ouro6qmuqqa+vJxKORE+WIGKL4Da7qZPraDG34Da7CRvCWAwWcpw5nV7b3T23M52ZmPYz9rWitOPz7egUtH2xLRSq6+wjy1YcjpExQbuo02vbas3eTdgO+f00VZbTVFFKY3kZjRVlNFWUEQ74O/vEp2eQmlcQ9dTOKyAlr5CEjAzkXRKzauEwSn09kdpalIZGEBqSwRBN4GqQkQxGJIMMnXsDUmzDYABZRjIakeTufWJ72YBk3KWP0YhkMkXLOjo6w5KhIGCfB5wmhLgmdnw5MEsI8fM9naNPiPfCV4/AJ7+NTvZm/3S/TwtrGvfsrOG/VU1Mj7Pz2PgCsqyHJ9ZWZYuf574t56XllbgDEcaku7hiTj5nT87GMcCT7+jo6AxN9Alx36Dba52DIuiGN66FbR9Gk1Wf/lcwWnrtqmiCD5vc/LeqkaVuH06DzMWZSVydk0qBrfdzBhqBsMpHG+v4aGMdi7c2EoiouKxGThybxqnjMzimKFV/LtoLQggqNrWwdlEllZtaMJpkRo42k7b4MUybluI6ZT62OVcQWNeGZDLgOj4X11HZSCaZiCbwqlHvZa+i4lO16LGiddZ3eDy3K1pUiI5E8IT8eMIhPKqgXcgEpH2LWi5JiwrJJjNxJiMuYzeR2SD3EKVbmwM8sWArVU1+zpmew23zxpJqM/e7o4emafj9/l7F7WjZS5vHg9/nRWi7p2mMCBnVYMFkdRAf5yI9OYHc9CQS4+N6iN5W676T9+n2um84VPbaE4xw1H2L+MeX/yDHEGHkBwuQjHv/PxZUVL6scfNpbRvf+nxsQUWTwKQKxjdHmNyokN0SQfYrVAqVClml2ihRq2qEu3lup7ksjM2MozjDxdhMF8WZcRQk2fF7PZ2idm1tLXV1dbS3t3eeZ7QbEU6B1+qlzlBHqVZKq9RKh25ukAxkOjLJdmWTZksjxZbSuaXaU0m2JZNqS8Vpcu7x+xsVtruJ2t6YsB3u8rA2GOzY7SOx2XIxmZIwmxIxmZMwmRIxm5KideYkjMYEfC1uGruL2uWltNZWd75oMsgGEiw24gS4/CEcza046hsxq3tLo3oIMBgwZWVhzs3BlJOLKTcHc24uppxczLk5GOL7N0+Wjo5O36GoGu1BBU8wgicQ3c8dnTroBezzgVN3EbBnCiGu36XftcC1AHl5edPKy8t3u5YO0fiRL10K2z+Cqz6A3JkHdPrbDa3ctKUSiyzxn3EFHJvkOkQD3Z1AWOXdtTU89XUZm2o9uKxGzp+Wy+VH5lOY0o/LPXV0+oiwouEORDq39mAERRWoQqBqXZuiCbTYXhUCVdVQBaiahqpF97v36bpO97Zd+yiaQBPd2zQ0DRSt6x6KumufbtfZ5X7d2yQJDFI0WaksgyxJ0WNZQpbAEEt0Gu1DrF7CEGvvKEuShCF2vix1ndtxbIglRu1e19mn45qx6/V2787zpa7+u/a58eQifULcB+gCts5Bo2nw2b3R2Ng5M+HCZ8GVsddT1nj8PF7VyFsNragCTk2J49qcNI5McPS78Le/BCMqX25v4qONdSzcXE+rP4LFKHP06BROGZ/BScXpJDmGb1IvTQj8qhYVnLsLzbF9fXOAHdtaqK71ETSAwaYguStQjBqRgpH4ZBs+RcNnkvCbJML7eV+bUHBpQeIjHlzhNuIUb2zzRT2gLTZc9njinEnEx6fjSswhzpVCnMlInEHGaTQccIibYETlb59s479LSshNtPPXC45gRkHSgf/QDhGKqrGj0dstXnXUs9ofVgGB0ygYn2pmdKKBHKdEikVglcIEfL4eonckEtnt2kajcbeQJbvuMzMzdXvdBxxKe/3nD7ew5qV3+N23T5L+u9+SeP75SAcQbqZdUfmmzcuS1na+aGpnazAaYsYZUZnUHGZGs2BuqyDPp9IgNLZqKtsNglJJoxyNaqGixK5lADItZvLtFvKdVgpdVkYmOnCZNdpDrXj8Lbi9zbR6mnC3t3UKwCazGWeiEzlOJugI0GRqokqroinURGOgkYi2+/fXarCSbEuOCtu2LmG7Q+juEL2TrEkY5aioH4m4O720Ozy3g6FawuEWFKVtjz8jWTFhCJmQvRJSm4rUGkFqh0jIQChkwq+Y8Ws23MJGIGRAC8uAhN1mJzktg5TcfOzxCVisVixmKxaLBbPZisVswmKyYJRl0ARCVUDTEEpsr6qgqghVA1VBqFq0T/e91tVH83qJVFcTrqokUlGJ2tra83PExWHOycGUm7u7yJ2ZeUDfGx0dnYOjNwHaE4j0cqz0Wu8Lq7tdsy8TL+shRIYKgTZ49BjQFPjxEnAcWFb7Hf4gV28oY5svyM0FGfyyIB35ME74hBCsqmjl6a/LWbC+FkUTHFOUyg+OzOe4MWkY9LiQOv2Iomp4ggruQIQ2f7hTjPYEIrT5Iz0E6rZYfcexv5d/4geDJIExJsAaY+KtUZYwyDIGGYyyjNyxlzqOO/p02yQJo2H36/Tou68+koQgKiqoWvTvuEPsFiKajV4TsU2LJpKNlgWqoLPc43wh0ER0iWmP82PX6+yjddy3q73jeNd7R8t03Uv0PO5w3OlL4zqc0e21Tp+x8S146zqwuOCi5yFn33+edaEIT1U38UxNEy0RlQlOGz/KSeXs9AQsg2gJs6JqrChv5aONdXy8sZ7qtgCyBDMKkjhlfAanjk8nJ3FgxzwVQhCMeTn7Yl7O3g4BuhePZ1+3tg6R2tfNG9q3n16DEmAVYAppmMICWyRCnKeRJDlMUv4oLI0KVq9KvFkhOaWJeKkGp78ep7cGp7cKp7cGh+rHqQRwqT7MBhOkjNolgWIsiaL50P4OlpY0c/Nra6lqDXDt0SP45clFWE2HN0xOSFHZXu9lQ7W7M8ni5loPISX6+7CbDYzLjMWqzopjYk48I1OdmAx7/3sTQhAKhfYZuqS9vZ1QKNTj3Lvvvlu3133AobTXDe1B5t7/Kf/75p8k15RGvXBzsjEXFGDOz49uBQWY8wswZWZEQ1TshcZwhK9aY4J2q5fKYPQVVGo4yNTGNqY2w/QWI3khMxISCoIKNHagsRWV7WiUCpVWqUv7sGuQqsqxTSJVk0lSNTD5UYxeFJOvc48U+/8jJIyaHZPmwKQ5MGp2DJoNWZgRgIqKhooqVBShoAgFleiLHYFASCJalgQGWcYoGzHKBsxCxiwkTJqEURWY1AimcABTqB2j0oBRa8RgdmMw+5BtfiRbANkRQU6UIA6EU0WzKqimIEJSdvv5RTEgaQ7UsImwD4JuBTUkoSkSakRGi8hoStdeqEYMRjsmkwuT2YXZEofZGo/VloDFEY/V4cLqcGJxOrE5XFicTqyO6Ga0WPb48lj1eolUVRGurCRSWUWkqpJwZRWRykoi1dWI7i+2ZBlTZmYPcducGxW7TTk5GBISBs1Lah2dw8GhEKC7I0kQZzURZzNG993Ltt2PXVYjR45MGfQCtpFoEscTgWqiSRwvEUJs3NM5+oR4P6hZA0+cAgVz4dLX4AAnaj5V5ZatVbxW38rxSS7+WZxPsvnwL1tt8AR5cVklzy8tp6E9RG6SjQum5TJvUiYjU52HfTw6QwNNE7R3iNCBcE/R2d8lOu8qSLsDEbyhPT0IRrGbDcTbTLttCfaucpzNRILdjNNixGyQe4rJuwjDHYKxwdDlPdwhKOtJvvoeEROxjQZZnxD3Abq91ulT6jdG42K318L8h2DKZft1WkDVeKO+lceqGtnqC5JqNnJlVgpXZCeTah5c3lxCCDbWePh4Yx0fb6pnS1102fv4rDhOHZ/BKePTGZPuOuSTeCEEHkWlPqzQEI7QEFaoD0WoD0dojJUbI0oPAVrdz2mGVZZwGAw4DTJOo4zTYMAR8152GmRcBgOOWL2zW333Pk5JwhlqxtZei9xehdpSw7ZNKmu2/j977x0nyVXe6z+nqjr35LQzs7vaXbQraZPSKiEhiSQyCCEB9vUFh2vANhicjfH9mXuvuc7ggBPYGLhgggARJUsIA17lLG1S3jS7k3PnCuf3R1XH6dmkCT2z7/PZ+pxTJ/U53b3zdn3rrfesYSLdTrgwQ//x/2JH03/QtuV8Ztz34NFBLPQALZ0/xuqIQXM/tKwN0n5o3wTNa0/7N/VCkso7fOIHB/jKQ0fY0pPkk++8iO39i/PIfc52OTA4w97jM8EGi9M8MzSLHXyQTRGrFK96e79/bOxMLLijiZsqYA+msYfS2INpMsenmRmdIuPlyao81/7JLWKvF4DFttd/cNse7rzvaW67RBMdPk7h0GEKh/1DZ8rxm1U4TGj9OsLnbCC8oVrctrq76v5tO5zNszsQtO+ZnGXc9kWX/twwV0w9wy7b5tL4uawPb8Gb1jgTOZzxHFMFmxfweB6XF/B4wfB40XNLT2SYCtYnImxsjnNOc5T18ShroxZGLs3U7BgzmUnS+WlS+Wlydqq8BgxioSbioWbioWZioWZiRpKwG8LL5slnUjjZLE4+i5cvoAs2quCiHA/T1WgUKAONQisDz7DIh6M4VhTPjIE6eVgsjUaHHFQsSzgxi5VIE06kCMcyhGJpQuEMoUgG00pjWimUmgWVQ+ssns4Cpx5aRHvg2YYvfJdEb+WntoHnmhhEUEYMU0X91IhhmQlMK4FpJX1RPNxEKNJMJNJKONpKOBLDzOZgYgJGx9EjI+jjg7gDAxQGBnDHx6vmYSSTvrhd68G9tp9Qfz9G+Ox9aklYmdhFAbqu6GxX1J2ZAG0oaDoNAbo5avlpkE+ErZIe4bouhUIB27bnTW3b5rLLLlvZAjaAUuqNwF/jP9XzOa31J07UXi6IT5FH/g2+/xF45cfgut897e5aa740OM7Hnj1GZ9jis9s2cGnL8oTysF2Pu/YN88X7D/HgwQkAzl/TxBt39PLGHb2c2y1i9tmG1ppU3pkjOhc9n0uic61XdKbAbN7hRH/uwpZBa4343FzMx8K0xCxaSoJ0uEqols22VgcSU3NhEHstLDiZCbj15+HgT+Hy98PrPgHmqYnQWmt2T6b456Oj/GhihrBS3NTTxi+v62JbMra4814kDo2luWv/EHfuG+axI5NoDed0xLkh2ATy4vVtpyUoOp5mzHYYLtiM5ANhOhCoRwKBeqTgMFqwyXlzDWnEUHSHQ/SELTrDFs2WWSU0J8xAeK4QoCvF6IRpEjrZfLX2vwczAzB9DGaOwfRAkB7zy2cGofaRfiuGbu5ngCt5YvBKjkz2Y3g2fal9XHrTuTQlzid13yja0ySv7qP5lesxYo0Zc/zHT4/we998iol0gQ+/ejO/cv3LsE7i5Xwi0nmH/YN+CJBiKJDnR1O4wWfcGg+xo7+FbX0tbO9vZkd/C+va4gt6I127Hs5oFnsoTWHQF6vtwTTebDnAi9EUItSbJNSbILwm4ae9SbHXC8Bi2+tDY2le9Vc/4VXn9/CKzZ00xyyaIiGaIibNmWliI8eJDA2gjg1gHzlM4dAh7CNH0YXy56/i8bLHdknYPofwxg0l71tPa55O57h/KsUD45M8MDHFKL5w2VGY4komuLJ3LVecs43zDQs9mccdz+GMZ3EmcuTHsxwcTfNcJs8LuDyPxwu4DFP+e9dqmWxpjXFuV5KOpjCtlkfczaBnxvBmxsjPTpLPpci4BVKh8t8Qw3Vpmp2lZXqa5ukZWmZnaTcMWpNJwt3dWN3dWN1dWN3dmF1d5FpjTDYpZmOQ8XJknAxZO0u6kCGTzpHJ5MhlC+SzDnbOxc67ODmNlwddUFAwULaJYYcIu1HCTpSQG/XzbpSQV18Id8N5VGuGWIdLa7dBV1eUnp4EzW0moUgB18viuVkcN+2nTppCfgY7P4NdmMV2UrhOGtfN4Hk5PHJoCqBslHHqT6MWRfCSR3iQ154FXhjlhcC1UK4FBYXKgcp6MGvDdA4j56IKYOTBtMGKNhNp7iTS1k20Zw3Rvj6iff3E1q0jsmYNoUiUUCSCcZInAAThVNBak7O9QGi2mc46zObKYnPZM3q+vEPWPrkAPUdonkd0bopaxC1F3IKIqYkaGgsH13FOKDifTJQupl6d/S7qsZBPTS2bgH26yAXxKaI13PYBeOpr8PZ/ggvffUbDPDmb4Zf3HmIwb/NH5/bxS/2dy/p4zuB0ljv2DHH7nkEeOezHzRIxe/XguB5jqQKD01mGpnMMTucYmgnS6SxjqQJTmQIzOad0YVUPy1BVwrMvPld4RcfD83pIL/UjuULjIQL2wiD2WlgUXAfu/iO4/9Ow4RXwjn+Fpp7TGuL5TI5/GRjja4MTZD2Pq1uTvG9dF6/paD7tGMWNwshsjrv3j3DX/iHufX4M29V0JiO8dms3117Qzaa1zUy6ni9GBwJ12YPaZjjvMG471LOsrZbpC9MRi+5wiO6wFZxX5APB+iX9RtTa37yzUoyuEqmP+3knV93PCEFzX7XHdJUH9VqItfnPuwZMDqV55EsP8vyzNp4Rojsyya53XErLUJ7M4yMYMYvm155D4vI1qJcgDi8WU5kC//M7+/jek8e5cF0rn3znhaf0dOJ01vbjVB+bCcKATPPiWLp0Y78zGWFHfzEMSAs71rbQ13LyzRRPBzdtlwRq37M6hT2coeSmbypC3XFCvQlCgVAd6k1gJud6UIq9nh+l1OuBv8F3EvsXrfWfztd2Kez1x7+7j8/fd+iEbZSCZCQQX8IG6+xZ1mXHWDM7StfUMG2TwzSNHSc6NoyqFEySTZjr1xPduIHYhg2ocBjPcXBsm4NGiMdMxRORMI93ruV4i28vEoUc28ZG2DY6wrbBY5w7MohVKATxnRWG8j2ELauZXLiVI9F2DoWbOBiK8ILSHMElNc86AEytacIjoTxihkfUdDENB0MXUF6OMA5R5RAzNT1tSdZ2tbG+p4O1vd10d3fT3t6OsQBPfHjaI+tkydgZMk6mlKbzaWYzGTKpHKlUlvHRGWZHcxQmwZiNksy2kcy3Y1Ceg2vaeE05Qm3Q1Bmha00r6/p7WNffQ1N7FOMkfys9z8Z1s7huOjgyOG4ap5Ain5uikJ/yxfDCDI6dwnH8w3WzeF4G18uhyaHJ46vTpyeKl+bhKN9b3FFot5z3HAPtGGjPBM8EQigVwVBhFGE/b0QwjSiGGQUMDGWCMlAYoBRKmX7eMIJ8UKaMoJ1vJ5WyUIaBCvoqo9jGxFBmUBeMrczSGIZhBm2DIxjDMEL+WEGdURzbUEEbVT5XwblhlOpQCgUow4+PjiLoU6wL0lIZgPLHm1Pn2wtlGEFdvbEI1uvvTeTnqRprOXFdj1Su6CRXKInKvghd9nSezTkl0Xk25/pp3mE25+KcQKsAX69oipg0RUySEZNk2CARUiWhOWpqogbETY+IcgkbHmEcwsolpG1M18FxHRzHwXZsbKeYr0xdHMfBcU///4llmliGiWWafpgjFZwro5xiYhLUYWJh+Ic2sbRJCAPLM4Nzg52/80YRsIUTUEjDl98Jh++BKz4AN/zxKXsqVTJlO/z6gSPcNT7DW7tb+eR560hayy/yDU3nk9ySIAABAABJREFUuGPvILfvGeThQ76YfV6PL2a/aecazu1euk0ohZNTcDyGZ6oFaT/NldKR2Ry1f+vDlkFvS5Q1zVG6miJVYnNrLFwtUgd18fBLvIgWzmrkgnhhEHstLCpPfhW+++ugXTj3tbDzFjjvjRA6dW/qKdvhS8fH+dyxMY7nbTbEwvyPtV28e017Q/zOORGe1kzYbpUAXcwfyxZ4bjrLYK5ACg11ng6yFHQFYnRPOOQL05GyGN0TDtEVCNQLFjO8kJ5HmD5WPi/USELKgKbeeYTpfj+sR6LrjEN7pIenePiT3+a5kRYKkRZamuDS6zbQMTBL4dAMVleMljduJHp+e0P+rvj+U8f5w2/vJVtw+f03nM97r9qAYSi01gzP5EubKhbTgclsqW9fS5Rt/S1BGBDfs7q7Obpgc9OuxhnLVIUAKQym8WYqvKqToZJAHepNEu5NYHXGUKf4RJvY6/oopUz8MJ2vBQbww3T+jNZ6f732S2Wv845bFnwqvA1nax+HrzifzTnM5svikafB9Fx6MhP0p0bpT43Rlx4r5buyUxgVt+IcZeAqA9cw8ZRioq2VZ859Gftedh5PbjqfQ2vWARCyC2w6coRzDx1k06FDrB8YwHJcPGXiGgaeMtCmiTYsItFmrEgLs5FWZmNtZKNN5ENxclYEbVpgGDhADk3G85hFM4NmOkhz87w/AAYeERyiyiVpQszywwlaBoQMRSjYlyZkKMKmQchUhCyDsGkQtgxClkHEMgmHTCKWSSRsEglbREKmvylj2CQatoiELKJhi2jIIhK2ymUhi5BlMmVPcXRqgMPHjzM0OMHUSJrshIuetoikm2jKdWDpsq7gKRc7kcFsdYm3W7T3NNHb28nG9X109bRiLZKDUFkUT5UEcd8zPINdSGEXUjiFNLadxrHTOHYGp5DCSU/iZGdwCylcJ4Pn5dEU0MoGS0NIoypS1dg/CeqiPf++MFqBxv9f4fn7FpXKgvo5SuCJpMF566pt5AnlxVOSHv05loYt5evZ4ooyrSjYFplCnHQhRtaOkbFjZBw/zbrR6sOLkvOi5HSEnI6SI0JeR6puetfD8mwi2iZcPAjOcQjjEMIhgksI1xedlS9EhwyPsPIwFCjTQCv/hsfJXm/OirXG0ASHwtRgagNTKyxtYKJ84TgQlEOYhLCwMAkTIoRFiBARLEKEiahQKTUJbj6cIf5eVQ6udvC0W0q3fertImALJ8G14e6P+55K66+CWz4PTWtOexhPa/7hyAj/98VBNsUjfHbbBi5ooEduK8XsRw77j9Fu6Un6YvaOXjb3iJi9mGQLbiBMZ6sEad+D2i8bSxXm9IuHTXpbovS2xFjTEvWF6mLa7Je1xUMNedEorF7kgnhhEHstLDpjz8FjX4Q934DZ4xBugq1vhR23wMZrwTi1K07b09w+NsVnj47yyEyGJtPgZ/s6+KX+TtbHTh5vdCHJe76X9GgQsqPkJR0I1JVhPJw6P92TplHyku6JhOiwTLKzBY4OpjhwaJLZ6TxhB64+p43Xb1vDay7ooavpJa7R8yA7AbNDkBqC2WE/VnmlMD09ALmpuX0T3fML0y39kFwD5uKH8kg99gSP/9m/c9DaymzTeiJxk107OukeSeNN5sEyMJtCmE1hjKYwZlMYMxnCaA5jJsOYzUF5MrTkHttDU1k+/LUnePDgBH2tUfpbY7w4mmY8Xf7dtbEzwda+Zrb1NfuhQPqa6Ugu3Hfby9hVoT/soTT2cJrSl9RQhLpjpRAgRe9qs+mlxaUVe10fpdRVwMe11q8Lzj8KoLX+k3rtV4q91lqTLri+F2QgaJceuQ8E73Q6i6nADFmEQxZhy/SFXdMXeYtib2v6BdYd/Cbm0bt5LLqee7tezgOdL+eA2YqHf5NveyLG5S0JXt7WxJWtCVpD1X+L8o7LyEyewek610AzvrPOyEyeKNCMohlFC4o2w6ArEiIZMokYChVsOp5xHSacHBOuzaTnMI2mgMIBXK1wKR9eKV2cvzcGHgYaE+2nKkgBU1WUB6mBxtRgABb4Yhrlw0BjGhrD9DBMDzPkYYY0VkgTMn1RPmyZhEJ+Gg4ZhMNWILiHiEZCxGJh4pEwyWiUeDRGIhqhKRYjEY4TDUUJm+GXfM2otcadnKzaXLIwcJTCsQEKQ0coTAyhTQ8dBlSwYOXrwcU8CrRlYoRDEAmhwv5B2CofIQvCIQiZKMtChywIGWCZYJkoy4SQgbYM/7eMqSBkgqHQpgLTAFOhDcN/XUP558E8tPbQ2gPtlvKlczRoD097gIvWboWgrKnSBbW/6Wjl+1NsXGqnNZ7WOJ5JwTNwKo6Ca+BoA9szcVwD2/PPHS8oC1LbM3CK59os9bdryvxzE0fXtvXLbW1S8Cxs78ROmwqPmJUjbmWJhbJ+amVrznPEQ1niVqYi77eLWVksw8PzDDzPrHMY4Fno4EBbfsgbLwSenyovhHJDGG4IwwtjuGEML4TphTHdiJ96YUxtYZUOE1OHCGkLQ/v/s5Q2QBsordD4nvcYvjc+hgLD99THNFGmQpn+d6XyUKYK/uMaKKui3DKCOgNlggoF30VLYViGf7M5SCsPTMoe/xVe9cm2dhGwhVNk7zfhOx+CSBLe+UVYf+UZDXPfZIr37z9EyvH4i/PWcvOa9gWe6EtneCbHHXsGuX3PEA8fnkBr2NwdiNk7e9kiYvYpo7VmNu8wXEeUrjyfztpz+rbEQnME6crznpYoTRFLxGmh4ZAL4oVB7LWwZHguHL7XD5u2/7uQn/GFzx03w853wpqdp+zZ8th0ms8MjPK90Sm0hjd0tfAL/Z10hCwKWpN3PQpak/M0Bc8j72nyQVrwNDnPo1As0zoon9suX1Hmj+eRdjwmnbmPeSqgM2xVhOsIQndEQmWP6UiIrrBF4gTxO11P89iRSe7cO8Sd+4c4OpFFKbh0fVtpE8hzOir2O3EdSI+WRemqNDhSw/7h1dnkONZWFqJrhenmfj/sh7W0NwhOhLZtxv7lczz/5Ts5uvaVjLZuxTQNLt3cQm9njKip0Gkbd7aAN1vAy9Tf2NlIWBjJQOSuFLybQhX5MCp6+k+LFRyPZ4dn2V/hVX1gcKZqsyZDwSXr23jTjjVsX9vKBb3NJCMLcxNAuxpnPFsWqgdT2ENp3OkKr+pEpVd1EAakO37KXtWng9jr+iilbgZer7X+H8H5fweu0Fp/sKLN+4D3Aaxfv/7Sw4cPL8tclx0nD8/c7t8MfeHHzJpxHj7/53hg/Zt4wFrDE7M5ClqjgK3JKFe0JLmyNcmVLQm6Iyd/stl2PUZn8xXXTdkKgTs4ZnJzQiNGDYNNzVHObYuztSfJBT3NnNeVoD0WRrseeBrtarTjkSs4FAo2mbxN3nbI5h1ytn/kbYe87ZJ3/KPguL4dc10KrufnPQ/b9XC0xvbKh6M1jgY7SJ1SCi7l1BfXwa0Q232BnQqhffHEdl+286rEdAONUlScV+SV38dEo1RQV5GaxXMV6H9B3lSBIK89LM9DVQi4aMrnnkaVdN7AzVn7ZbrYrqLMd5Um2DvT88sCdHF88Df4pKay0vG4WKWAYigPpdDFkB/K8AVupUoev1r5i/Q/OwNHGdgYOMXzqrzyD61wg/NiP+8leOvWUvl5msW8qv6MTVW8weJVtSmeh5VDOPB8LobdCOMSUi4R7RBDEVIGIfz7BiFDE1Iay9CEDI1leJiGh2VoTMP1X8/wsEwXQ7kYhocyHAzDBdNBGf6BaYPhoE0bbdQeTkXqgFH/N8RiUvLIJ0h1tUc+Ws1pU+nF73+dy20rvfjL9XXqPL/uzb/wpAjYwmkwvB++9nMwdRhe93/h8ved9qMKAMN5mw/sP8T9U2ne09fB/z63n2gDxgcEGJnJccfeIX6wZ5CHD/li9rndZc/sLT3Js1ZA1VozmbEZnM4yPJObE86j+AOr3g62nckIa1oic0TpNUVv6uYosfAKfNZKEFj9F8RKqf8DvA3/58QI8PNa6+NB3UeBX8K/Jvl1rfWdQfmlwOeBGHA78GF9kh8OYq+FZcHOwrN3wlNfh+fu8jfz6zrf98recQu0nXNKwxzPFfi3Y2P8v+PjTNURlU+GpSBiGEQMVUrDyiBqKMJBWdhQRIM0YigSplkK6dEViNI94RAdIQtrATfMw8mjZ4c4dPgge59+hiOHD+LODtLNFJuis2yIzNLmTWLlxlG6zsY8sXY/rEdTj3+jYL40HF+4OS8h+YMHGfqjjzO25yBDF7+TgcQ2HFsTjlms39bOhh2dnLOtg0jUxE3ZeLMF3OAo56vLS/GdKzmJV3c+avJCJs/eyTR7h2bZd3yG50ZmsYOxEmGTC3rLXtVb+5qJhgz+4La9PHRwgtdc0MOf3LTjjD3svYxdvaniUBp7KANO8J0wFFZXjHAQ/qMUq/olelWfDqvdXp8pSqlbgNfVCNiXa60/VK+92OuAycPw+Jf8Y/Y4JLrIXvjfeHzLz/AArTw4lebhmTQZ1/8/8LJYhKtak1zVmuCq1iR90TP77rueZjxV9OQOwizO5BicyvHs8CzPjZQ3Ve1pjrBzbSs7+1vYuc5P2xJL93/uVNGe9kV2T4Or0a6HdjzyBZfJ0Qyjx2cZH04xOZ5meiLL7Gwex/VwlcZVHioCOqIgAp7l4VoaR3m+mO562J6m4GocLxDVPX8jYluDpxUe/p9dP/XPi+VVZdRJ69TpkghPIIe/pHenlJvfsp9Km3r1p96vso0RiMBFcbjsYR8IyYEHvhWIymYxrzSW1liB8G8BFppQkFpBWWhO3m8T0sU0KA/OFX5YDIX200DsLzm9V4TP8Mv8mwiG1hieh+VqTNfFcj0s18F0XUzHwXJclOugPBs8B+054AZpTZmf98vwHLRb06Z4nCFaaQics3WofGDp8rmF71Hv34EpvjH+uaqoKz4BUHOuq8r1KbWb24/qpw3qtJszn7rjaF71rhdFwBZOk9y0v7njM7fDjnfCW/7mjC4wHE/zZwcH+bsjI+xsivHZbRs4Z4kfsz1dRmZy/Me+IX7w1CAPBWL2y7oSvGlHL2/a2bfqxOxU3uHQWJpjU5WPtGWrNkYsONUXpoaCnmZfiF7TXClMx0pxqLubI0QaPDaoILwUVvsFsVKqWWs9E+R/Hdiqtf6AUmor8BXgcqAPuBvYorV2lVIPAR8GHsAXsP9Wa33HiV5H7LWw7GQmYP+34alb4ch9ftn6l/vxsrfeCPGTP0WWdl1+OjGLo6kSnyNKETENwkpVi9RBuiybQRbSZa/oOelg2Xs6Ozmnq1YG2VA7Q14Lh/LNDOtWctFOuvs2cO6ml3HupnMxm9dAsgesxhNLFhqtNdPf/CbDf/4X2HmX/Bt/noneSzk64JKdKaAUrHlZCxt2drJhRydta+Lz/obUWqOzDm7Kxp0p4KUKuDMF3FQBb9b36C5M53FmCpj5+jdLptFkwwY6bhFpjdDSGaetO4HVHJ7j1a01fO7eg/z5nc+QjFh84sbtvGFH7/xr9Wq9qv3Dnc6X2hgJyxepKzZVXCyv6tNhtdvrM2W1hhBZMjwXnv8RPPYFePY/fJFq/cvhkvdgn/9W9hTg/qk0D0yleHA6xUxwPbU+Gq4StNdHX3pIC/BDNe4fnObJo9PsOTbNkwNTvDiaLtWva4+xs7+VnWtb2Lm2le39zTRFT3/fq+XE8zSz41kmjqeZGEwzfsxPp4YyuBXXq82dUdp7E7T3JWjvS9Lem6BtTRxrkRynqoT4ote762EX/FAc2vOCUBp+6mk/PIfWfigOrQIb4HlB6A6N1uBp13fC9rxiQA605/fXBK8V5DUaz/OCONZ+ex3cRCm2157v/ur3oTQvXQwJoinldYUHuAZ/bENhFDd8NBWG6Xtm+xs9qvphUpQ/b98xXPuCbDHEiPaCENZFt3P8smBtvve59kOQBfNDe6W64E2qyQf1aJRXr40/lvZ0sFmlP1GlfDtVCmeB8jdIDTaPLIe4CNpV5oubXFJu678fFRtUUrFRZsXrUdwoE1U9hlFuVxqj1M6os2lmsU/FZpzFsVXl/Mpe96UD6tcF8/PfH+aWV2zsWa/8TDfaXEibLQL22YTnwT1/Bf/5CejZBu/6f9C+6YyGumtsmg8dOIKjNf+9r4P3r+uiN9L4FzYjsznuDDyzHzo4gReI2W/c0cvrt6/h3O7kihBpc7bLkYkML46mOTiW5tCYnx4cTzM6m69qGzKVL0g3x+gphfWo9pzuTIaxGtSbXhCWirPpgji4qF2vtf6V2gtcpdSdwMeBQ8CPtdbnB+U/A1yvtX7/icYWey00FJOHYc+tvmf22DNghGDzDX6IkS2vh9DCbV63oGjtOx/UFaWHquNOF2bn9jdCvuhc8o4OjmRPdZroKsUMH0/lufvAMHftG2b382MUHI/2RJhXn9/N67at4ZrNnUQXaUOuRsMZHWXkk59i5gc/QBcKRLZvx33dzzDetpXDz8wwdtTfdLK5K8aGHR1s2NlJ37mtmPMIu1prBiazFZsr+qFAhmf832whYGtLjEvak2xtjrExHqbPtIjZ3ml7dR8KwR8NjXEgnefN69r5n1dvoq0jjra9IPRHhsJgCmc4g7aLXtVgdcVLoT/CgVhtNC2MELfQnE32+nRQSln4mzi+GjiGv4njz2qt99VrL/b6BMwOw5Nf8UOMTLwAkWb/aZ5L3wu9F+Jqzf5UlvunUiVRuxgKqj8S4qpWP+TIVa0JNsUiC/b/aCZns/fYNE8NTLNnwBe1i5u0KgWbOhO+p/baFnaubWFrb8uKfDrWcz1mxnJMHE8zfjzFxGCaieNppoYzeMHfQKWguTMWiNoJOvqTdPQnae2OYch1rSA0DCJgCy+N5++Gb/4P/27WTf8CW244o2EOZ/P82cEhvjMyiYHiHT1t/Or6brYkGvRisIbR2Tz/sW+I258a5MGD4xRDkLXFQ/Q0R4MjEngf++drgrKOZARzIR/rrYPtegxMZjk0lubFSpF6LM3x6WzVDr+dyQgbO+Ns7EywoTPBxo4E69rjrGmJ0h4PYyzyXAVhNXA2XBArpT4BvAeYBl6ptR5VSn0aeEBr/aWgzb8Cd+AL2H+qtX5NUP4K4Pe01m+uM67E1BQaG61h6ClfyN7zDV/8jTT7mz/ufBecc40fG3KxcR3IjAUxpEeCY54Y005ubn8rVid0R604vcaPQ/0S1pPKO/z0mVHu2j/Efx4YYTbvEA+bXLeli9dtW8Mrz++mJbayPP3OBHdqiunvfo+pW28l/9xzqHic5je8Huv1NzHs9XB4zzgDT0/iOh7hqMm6rR2s396O0xPhualsSajePzjDbM5/5Ng0FC/rSrCtr4Vtfc3+Jou9LbTET/5+nopXtztboDCT5/PZDF8gTzuKjxLjcvx42EbcKseoLoYA6Y6jQitH8Dkb7PWZopR6I/DX+GF7P6e1/sR8beX6+hTQGg7f53tl7/+O/3d543VwzW/AputLXoqe1jyTzpUE7funUozZ/v/5nrAViNlJrmxNcF48uqA3hibSBZ4amAoE7Wn2HJsq3RwzDcXm7iQXrm1l57oWdva3ct6aJsLL/BTFmeK6HtPD2UDQrhC2R7KBNzKYlhEI2gk61zbR0Z+gY22SWLLxne0EYTUiArbw0pk8BF/77zC0B67/fbj2d8/4QudINs8/HR3lK4PjZD3NGzpb+OD6bi5tSZy8c4MwOptn93OjHJ/KMjSTY3gmz/BMjuGZHKOz+ZK4XcQ0FF3JCD3NEborhO1y3k+bYyferNDzNIMzuTki9aGxNEcmMjgVL9wUtdjUmSiL1J0JNnUmOaczTvMKe1xMEBqR1XBBrJS6G1hTp+pjWuvvVLT7KBDVWv+RUurvgftrBOzbgSPAn9QI2L+rtX7LieYg9lpoeDwXDv6XL2Yf+C4UUv7mgtvf4YvZa7af3nhaQ24qEKOHK9LhuWXpMao2YyoSaZ7rHZ3smRtzOtJ8RvuYvBQKjscDL45z574h7to/zOhsHstQXPWyDm7YtoYbtvbQ07wynBfOFK01uaeeYvLWW5m5/Q50JkNk87kk3v4Ojlx8LXueyzH6zCSh4TwRBzw0x02PwxEN/TE2bGxhW38L2/paOH9N05J4smvH44nnxvnt7+7lhckMb3lZJ++9eiOXnN/pP0a9glkN9roREHt9mmQnfY/s+//Bv+HYd7EvZJ//ljnX0Vprns/kuX8qxQPTvqA9mLcBaA+ZQcgR/7ggEcVY4L/rwzM5nhqY5qmBKV/UHphiMuO/ftg0uKC3iZ1rW7loXSuXnNPGho75wyGtBFzbY3I4zfhAirFjacaPpRgbSJGdKW80G28J0xl4aXesTdK5NklrT3zeJ2cEQVgYRMAWFgY7C9//TXjy32Hz6+Cmf/Y9ds6QsYLDvw6M8m/HxphyXK5sSfDBc3p4dXvTyjaInmYsVRS08wzN5BgJdpAens37+ZkcU8GPgkoillESs7ubI/Q0R7FMxeGxjC9Uj6fJV8T3ioXMQJwOvKk7Emzq8tP2RGM+xikIq4Wz6YJYKXUO8AOt9XYJISKc1RQy8Owdvpj9/N1+zNPurX6Ika1vA1R9QTo9Wn3uFuaObYYh0Q3Jbl+MnpMW890QXhk3/T1P8/jRKe7aP8Rd+4Y5OObHY71oXWvgmd1FWzyMaSgsQ2FWHJZhYChW7G+ZqUyBfcdnePrFQewf3sWGB37IOaOHKRgW9/btYPeWq1EXXsTOZJK+NDCYZXYwA/jxW8/Z0cnGHZ30bZk/1MhikLNdPvXDZ/ni/YfJ2i6bu5O867J13HTJWtobcBO4U+FssteLidjrM8TOwVNfhXv/BiZehI7NcPWH/Rug8+wVoLXmcK7AfVOpwEs7xUDOv3ZssUyuaEmUBO3tydjCbuBLOYxRUdR+asCPq53K+17ibfEQF69v45L1rVy8vo0L17WSjFgLOoflIDNTYPxYyj8GUowd8722PcfXwAxT0bYmQcfaBJ39TXSs9UORxJvlulsQFgoRsIWFQ2t45F/hjt+Hln5415dP3/OohrTj8uXBcf756CjH8jZbE1F+bX03b+tuW3Bj3EjkbJfR2XzgwV3txT00nWNkNs/QdA7H81jf7gvUtd7UPc0LFyNNWBxsTzPjuMy6LjOOf8w6LjOOV7/McZlxi+cuader53tXv2zeP8/1K0513BONPW/7eWospQgpRcioSZVB2FBYyt/8LFTRrnheVRekYcPAUhBWRtWY4bqvUa6zSm2M+n2C1zqRh8tqvyBWSm3WWj8X5D8EXKe1vlkptQ34d8qbOP4I2Bxs4vgw8CHgQXyv7L/TWt9+otcRey2sWNLjsO9bfszsow/O00hBovMkgnSQRluX3Ft6KdFa89xIirv2DXHnvmH2HJs+pX5WSdCuFLiNcrlZWW9gGlTXG3PrLcOorjMrRfPaMct9rWADqzljm/5mSofG0+w7PsP+4zMcm8qW1tDbEmVbXzNXeBNc+NRPabrnbpidJbR+Pa0330zLjW8j1N1NajLPoT1jHN4zxtGnJ3Ftj1DUZP0F7WzY2ck52zuINS2NiJzKO3z/yeN89eGjPHF0ipCpuGHrGt512TquObdzRYWbW+32eqkQe/0S8Vw/rMg9n/JDVDX1wcs/CJe8FyLJk3Y/mivwQIWgfTDr3whNmgaXtyS4pq2Jq9t8QXsxNgh2Pc3zIykePzLJY0cmeezIFM+P+PH9lYLzepq4eH0bF69v5ZL1bWzqTKyovxPz4boeU8OZQNQue2unp8r7SMWaQmVP7cBru603jnWW7AMhrH601nhu8fBKebciX1vn1dS5J6jzvPL5lW97mQjYwgJz9CH4+nsgOwVv/Vvf8+glYnua20Ym+fThEZ7N5FgbDfGBdd38bG8H8bN0YwUd7EC8Goz/SiTveYG47JVFZrcsOM86XpXYXGpX0SZbG0+mDjFD0WSZNFsmTWaQWgbNlknCNOYVUeuVzvdNUfPU1Bt6/jFOs7xmcK01Lv7/9YLWOJ6moD1sT2Nr/yh4GkfrUptSXbFP0MbWHrYHtvZwFtEsGTBHNPfFboP7r9q6qi+IlVLfBM4DPOAw8AGt9bGg7mPALwIO8BGt9R1B+S7g80AMPy72h/RJfjiIvRZWBRMvwvM/glCsWpiOd4K58r3SFoNjU1kefHGcnO3hav/CxvE0rqdrUg/XA9erqXcr6nVQ79b290rn3pxxg/pgHE/XjuvXn4IZL6EUbOwsx6ve1tfM1t5mOpKRqnZeLsfsXXcx9fVbyTzyCJgmyVdeT9stt5C45hqUaWIXXI49PcnBPWMcfmqM9HQBFKzZ2MyGnZ1s2NFJe19iSRwZnhma5WsPH+W2xweYzNj0t8a4Zddabtm1jv7W2KK//ktFBOyFQez1AqE1vPAjuOev4dBu/4nmy98PV7wf4u2nPMxQ3uaBqRT3BcfzGV9QbbFMrmpNcHWrL2ifvwghR4pMZ22eODoViNp+Wozf3xIL+SFHAlH7ovWtqyqMZS5l+2J2hcf2+PE0brDZrTIUrd0xIvEQoahJKGwSiphYEZNQ2Cjlw8WySE2bisMKm6IHrEI8T+M6Hq7t+Wkpr2vOK+vr9HFOLiK789V5c/u5dQRmfTo/hl4iH/znV4uALSwCqRG49Rfg8D2+0b3hj+d9DOp08LTm7vEZPn1khIem07SHTH6xv4tfXNtJe0guAoVTI+dWejh7JZH5RIJzbVn+FP5Qx02DZrMsOPvis0mzaZKsLDNNmi2jJFRXitUh+UHykvD0PKJ3hSBeqBDC/XLPL68Uxyv6V/WZ08YX3f9p+0a5IF4AxF4LgtDIeJ72BfZ64renq0Tz3pYoidN8jD5/8CBT3/gG07d9G3diAmvNGlpvuonWd9xEqL8f8G8Ajx1NcfAp3zt75PAsAE3tUTbs6GDDzk76t7RhLvLGinnH5Yf7h/naw0e55/kxAF6xuYt3X7aO11zQ07AbvYmAvTCIvV4Ejj7se2Q/8wMIxeHSn4erfg1a1p72UEN5m/umUtwzOcu9kykO53wP7faQyctbk1zd1sQ1rUnOjS/eE7yep3lxLMVjh6d47Mgkjx+Z4tmRWbT2b/Bt7k5y8bo2LjnHDz1ybldyVQmznqeZHskwHsTVnhhMU8g62HkXO+/iFNyKvHfyASswQ0ZZ5I76onalyO2L4hZWxKgSvkORuUdl+WqP6a21L75qz79m1J7vIOi5FWJwXaH4BEJysa5un+KhcYJzr6LeCeo82xePFwKlwDANDFNVHMacvHmCujltjfnravPmCcesHsM82WsbSkKICIuIa8PdH4f7Pw3rroR3fsHfSGiBeHAqxaePjPDD8RlihsHP9bXz/nXdrI2uzBh8wkvH1ZqRgs3xnM3xvM3xfMFPczaD+QKDeZvRgkPhFP5WJUzjJOLyicuaTHNVh7kRToxcEC8MYq8FQRBAFwrM/vgnTN16K+l77wUgcfXVtN5yC02vvB4VLv/2TU/5oUYO7Rln4MAEju1hRYqhRjo4Z3sn8ebF/a18dCLDrY8O8I1HjnJ8Okd7IsxNF/fzrsvWsbmnaVFf+3QRe70wiL1eREYO+DGyn/o6KMOPj331h6FryxkPeTRX4N7JWe6dSnHvZIrjwaaQ3WGLq1uTpZAj50QXN37zTM7myaNTPH6kLGpPZ/25NEUtLlrXWo6nva6Nlvjq8dI+EdrT2IVaYdvDzjs4QWoXPOycW24XiN+V54WacifvniCs5FwMU9UVtst5XxgPRQxfKdUazyMQhX0xWAdPMVWez6kv5nW5zvMIxvMFZl0pMHt1xtPldvXqPQ2U5sKieg0bpsIMGZhW8VCYIdNPgzIrZGAU60MKK8gbFf2sULm/UXVe7FfuP6es2G+VRSsQAVtYfPZ+E77zQYg0wS1fgHOuWtDhD6Sy/MPREW4bngTgxu42fm19NxckG/+xReHUORVxeqhg49b8GYoZir5ImN5IiN5oiO5wiJbAE7qpKFKXROfy+WLEhxPOHuSCeGEQey0IglCNfewYU9/8FlPf+hbO0BBmRwctN76N1nfcTGTTxqq2TsFl4JlJDu0Z5/CeMVKTeVDQs6GZDTs62bCzg47+5KIJVK6n2f3cKF97+Ch3HxjGdjWXrG/l3Zet5007e0/bI30xEHu9MIi9XgKmjsB9n4bHvghODi54M1zzG9B/6UsaVmvNoWwhELNnuWcqxWjBD/XRHwlxdVuyFHJksR3FPE9zcDzNY4cnefzoFI8dnuTZ4dlSuKZNXX4Ypq29zWwNwjB1NUVOPKhQQmvfM9guuPOL3znfA9zOO4FoXlle9hKv9RrX2r+/YigFhvI3WjaUfwT5wIMWZRCk9eqDfkE7o6ZNqU5V1BnVYxpVYwR1wblxwtcOXtNU9YXiogAdqiciB/WmgRIntkVDBGxhaRjeD1/7OZg6DK/7v3D5+xZ8M6KBXIHPHB3lS4PjZFyP13Y088H13VzRevKNL4TlpVacHswXOJa3GczbHM+dujjdH+T7IiH6omH6IiFaLVM2sxSWHLkgXhjEXguCINRHuy7pe+5h8tZbSf34J+C6xHftovWdt9B0ww0Y0Wh1e60ZG0hx6CnfO3vk0AwAyfaIL2bv6KT/vNZF21hsLJXntseO8bVHjvL8SIpE2OQtF/bxzsvWcfG61mX7rSb2emEQe72EpMfgwX+Chz4DuWnYeC1c85uw6foFub7WWvNcJs+9QciR+6dSTNguAOdEw1zT5occubo1SU9k8T2iU3mHp476HtpPHJ3mwGD1RrhdTRG29gb7CgSi9oaO1bFJpCAI1YiALSwduWm47QPwzO2w453wlr+BcHzBX2bCdvi3gTH+9dgoE7bLZc0JPnhON6/taF60TSqE+SmK04M5OxClT12c7o34IvRLEqddB/IzkJvyv4OFDMFzQ/MfnruA9TpI3XnanKheV5TVqQcwQ2BG/BjzZgSsCJhh/ziVMitSMUZlWbicN6wFv+G02pEL4oVB7LUgCMLJcUZHmbrt20x94xvYR45gNDfT8pa30PrOW4ied17dPunpPIf3jnPoqTGOHpjAKXhYYYN1F7SzYWcn52zvINGy8J6NWmseOzLJVx86yvefGiRru2zpSfKuy9bz9ov7aU8sbShAsdcLg9jrZSA/C4/8G9z/95Aagt6LfI/sC94CxsLdiPK05ul0zo+fPZXi/qkUM44fo3lzPFKKoX1Va4Ku8NKE+JjO2OwfnGHf8Wn2D86w//gMz4+kcAJX7XjY5Pw1TWztay55bJ+3ponoIt2gEwRhaWgYAVsp9RfAW4AC8ALwC1rrKaXUBuAA8EzQ9AGt9QeCPpcCnwdiwO3Ah/UpTEIM7DLieXDPX8F/fgJ6tsG7/h+0b1qUl8q4Hl8ZHOcfj44wkLPZEo/yq+u7eHtPGxFjdcUCWi7OVJyOBp7TteJ0byREf9TPtxXF6ZIAPV0WoU/nKKSW5b2pi//8UsVhVuSV/2Ozbr0KnsmqqQc/1rybB6dQkRbAsxdy4oGoHYjdtUJ3reB9wrJ6YwQCvBWFUAysGISi/oY1xbJQzM+vECFdLogXBrHXgiAIp472PDIPPczUrbcye9ddaNsmumMHrbfcTPMb34SZTNTt59gux56Z8mNnPxWEGgG6z2liw07fO7tz3cKHGpnN2Xz/qUG+9vBRnjg6Rdg0eO22Ht61ax3XnNu5JB6UYq8XBrHXy4iThye/6sfJnngBOs71Y2TvfLf/+3qBcbVmz2y2FHLkwek0abcsaF/ZmuSq1iRXtiToW8K9qfKOy3PDqZKgvf/4DPsHZ0jl/XAohoKXdSVLXtrb+lq4oLeJjqSEIBGElUIjCdg3AP+ptXaUUn8GoLX+vUDA/r7WenudPg8BHwYewBew/1ZrfcfJXksMbAPw/N3wjV8CNNz0WdjyukV7KdvTfHdkkk8fGeFAOkfMMLi8JRHE80qysylOSB4xqovtaZ5OZzmULVSJ04M5Pwb1qYjTfWGLPsOhV+Xp9zL0ujO0FaZQ+VMVoGdPMksF0ZY6R2v98lAsEIJrxGDjROLymdTXEaiXUnz1PF/IrhS33UKN0J33f/TOEcGDtlXtT1BWNUZlWZ32vMQndYrithWrFrZDlecnEMCr2lSWx+eO+xK8V+SCeGEQey0IgnBmOJOTzHzve0zdeiv5555HxeM0v/ENtN1yC9GdO+cVo7XWjB9LB6FGxhg+NAMakm0RztnRyYYdHfSe20o4urAh2p4emuFrDx/ltsePMZWx6W+N8c5d67hl11r6WhdvXxux1wuD2OsGwHPhwHdh9ydh6Clo6oOrfg0ufa+/F9UiYXuaJ2cz3D+V4oGpNA9Np5gNBO310TBXtia4KhC1F3tTyFo8TzMwmWX/4HRJ0N5/fIbj07lSmzXN0ZKoXUzXt8clBIkgNCANI2BXDaTU24Gbtdb/bT4BWynVC/xYa31+cP4zwPVa6/efbHwxsA3C5CE/LvbQHrju9+G63/NFwEVCa81PJ2f54dgM906leDrtG66EGQjaweNPO5IxrLPQYLla83wmzxMzGZ6czfDEbIZ9qSz5ih16owr6TJdeZdNHlj4vRZ8zRW9hnP78CL2Z47Rlh1GnLUA3n1x4nk+kDicX9XsjLCBag+fUiNsVgreTBzvjb0xjZ8DOgZMFOzjqlp+gjZ316zznzOZrhMrC9mkK4+oVvyEXxAuA2GtBEISXhtaa3JNPMnnrrczcfgc6myWyeTOtt9xCy1vfgtnaesL+mZkCh/f6cbOP7J/AyfshzKywQbwlQqIlTLw5QrwlXMonWsLEWyLEm8PEkqHT2tAq77jctW+Yrz9ylN3PjaEUXLu5i3ddto7XXNBD2FrY33wiYC8MYq8bCK3hhf+Eez4Fh3b7v003vxa23QibXweRxd0fytWa/alsSdB+YLocQ3tNOFQStK9sTbIlHlmW+PeT6ULZU7sYgmQ0hRtc91qGoqc5Sl9rlN6WGL2tUfpaYvS2ROlr9dP2xNKK8YIwH57W5DxN3vPIeR45V/tpUOZqjav9/5tu0L5URkWd1ng1ZZ4Gp1gHVWN5VNTVGd+paFc7vuOBh54znt+v3nj++UMv39aQAvb3gK9prb8UCNj7gGeBGeAPtda7lVK7gD/VWr8m6PMK4Pe01m+eZ8z3Ae8DWL9+/aWHDx9ekLkKLxE7C9//TXjy3/0NKK74AJz7Gj+swCIzWrC5fyrNfcHjT89l/Mclm0yDK1qTgaCdZFsyhrnKjJPWmiO5Ao/P+EL1k7MZnprNlh7/iivNTj3BRTNPc9HgbjbPPktvfpQ2Z4a578RLEaBbINwkArSwuLh2hbidLQvbdq5CMM/O06aeeH4Cgd3xb4yp/zUjF8QLgFwQC4IgLBxuKsXMD25n6hvfILdnDyocpumGG2i9+WbiV1x+UjHGtT2OPTvJ2ECKzEyBzHSe9HSBzEyB9HQeO+fO6WMYilhzuErU9sVuP59o8cXveHMYs0acPjqR4dZHjnLrowMMTufoSIR5+8X9vOuydWzuWRiPUhGwFwax1w3KwKPw1Fdh/3cgNeyL2VtugK03+k9Ah+uHFVpIvGBTSF/Q9mNoDxd855L2kBmEG0lyZWuCrct43Z2ziyFIpjk8nmFwOsexqSyD01mGpnPYNY8dRyyjQtCOzRW7W6M0R5cmJrjQGGitsSvE5KzrVQjLmpzrlYTlnOeRD9KsW84Xy7OuR851yLsuueDIux7ZojCt8cfRikIdhWYpsLSLicZAY6IxtVc6t7SHiVdVZwSpRTlvEvQJ8pb2MPAwtfbrSud+u394y88vnYCtlLobWFOn6mNa6+8EbT4G7AJu0lprpVQESGqtx4OY198GtgHnAX9SI2D/rtb6LSebqBjYBkNreORz8ONPQGYcIi1wwZth+ztg43VgWksyjZG8zX1TqUDQTvFC1he0WyyTK1t9D+2XtybZmoytuM0gh/I2TwRiddHDetLxLzLCCraZOS7KHuaisYe46NiPODd9GFPhxylfd6Ufp3w+ATrSLAK0IBTxPHByqEhCLogXALHXgiAIi0Pu6aeZ+vqtTH/ve3izs4TOWU/rzTfTeuONWF1dZzSmnXfJzASi9rQvaheFbv+8QGYmT3a2/j4d0USo7MldIXBHmkPsm81wx4tj/OTFMRxPc8n6Vt592XretLOXROTMrxVEwF4YxF43OJ4LRx6Afbf5YUZKYvbrAs/sG5ZEzAZf5DuULXD/tC9oPzCV5kiuAPiOZJe3JLkq8NJulFCfnqcZS+cZnMoxOJ3leDGdzjE4lWVwOsfwTA6vRg5LRix6W6L0tsboa6kWuNe0RIhYJpapMA2FZRiYSmGaCssolinx8l5AtNZkXI9Z12PWcUm5HinHJeW6zJbyHhnXI+u65BybvOv4ArLjVIjSgeCsNTkP8lqR1Yo8Cu8liMlhzybq5Yl6BSJunqiXJ+bliXgFoqW0QNT12/hty/Uxr+Dng74RbRMBTDRWUQRWGqNSHFZFsbmiHI2pNYbysLSuEam1PwbgL7UYJnW+9GRtTmWMcqr+x12N44GtlHov8AHg1VrrzDxtfgL8NnAMCSGyunAdOPgT2PstOPA9f+O+eCdsfRvsuNkXUpdQKB3MF7h/Ks29wY7Lh7K+YW2z/DvFLw9iaJ+XiDaUoD1hO1VhQJ6YyZTucpvA+RG40B3loqk9XDTwI84feYCwdiCUgLW7YP2VsO4KWHuZ71ktCMJpIxfEC4PYa0EQhMXFy+WYvfNOpm79BplHHgHLoumV19N6880krrkGZZ75fhDz4boe2Rm7QuzOB17cFV7dQZlX4/WYVpqn4x57Qg6jeESU4qr2Jl53TgcXrW0l0RopeXVH4ycPXyL2emEQe72C8Fw4cr8vZu//LqRH/PB4m2+AbW8PxOz4kk7pWK7gi9nTaR6YSpWejI4ZBrta4iUv7Yub48TMxnScclyPkdl8tcA9leN4IHAPTmcZSxVOe1xD4YvbgaBtGNUCty94V9QrVSGKV4jjFedmZX/DqBgnqKsS0o3q16sap1xXlGlUIODWyiNFIV5V1NW2LcmdqvJMU8D3as5qTQ4/zXuarPbI4QvJWc8jqyGnPb+d55JzHXKeS0ZrskBWG+hT0G1M7RJ1fWE4ViMSRwOROOZWl5fblkXkiHaJoAmrIEURVpqIUoQNRVgpQsokYhiEDRPMCNqK4JkRdHCU8la4dI4ZrSgvp54ZRZvh0jlG+eZupVRbzGt0Rb5YpyvypR512vlt55RR3VDXvF65Xfn1Ss3rtat53Ru2rWkMAVsp9Xrgk8B1WuvRivIuYEJr7SqlNgG7gR1a6wml1MPAh4AH8Tdx/Dut9e0ney0xsCsAO+dv9Lj3m/DMHf5j+k19sP0m/+i7ZGk3xMM3rEXv7PumUqU7xe0hk5cH8bNfvsSxvFKOGwjVWT+dyZTmBXBuLMRFRpoL0y9w0fB9bDt0O/HcmF/Z1BuI1VfC+iugZ8eSebsLwmpHLogXBrHXgiAIS0f+xYNMfeMbTH/727gTE1i9vbTedBOt77iJUF/fks9Ha00+7fie3NMF0jN+mpkukJrOsW88zb0zs+xxC9gKOl3FjoLF1oJJXCsMU/khS5rDFfG6K/ItEdZsbBF7vQCIvV6heC4cvg/2f9sPM5Ie9cXsLa/zxexzX7vkYjb4oT4fmEoHonaK/akcGjCA/miYjbEwG2IRNsYibAjy58QixBtU3C6Sd1yGpnMcn8oxMpuj4Hi4nsbxdEXq4Xgab065xnEr6nXxvLJd9Xh1x3bLde6cMbyq9sU5nC4aAuVdoS0FloE2/dQvC9LaMrNch6nK5aegrRieS8LN0uSmaXIyNLspkm6GpJMh6WZoctMV+QxhJ4/puGgHtKtxHYXjGBQck7wXIq/D5AkFR5i89vMFLP+cUKmstk2xXeCnLCwgh//szQ0jYD8PRIDxoOgBrfUHlFLvAP434AAu8Eda6+8FfXYBnwdiwB3Ah/QpTEIM7Aojn4Jn/8MXs5/7IXg2tG30Q4xsfwf0bF2WaR3J5n1BeyrFfZMpjuX9xyG7whaXtySIGUZVYPuq4Pm6IkB9VZuawPWU805Vfz8ofs7zSnej1kZDXBQ1uMg+zkWTT7LzyH/QfPwR0C6ggnAgV5Q9rFvXL/lNAEE4WxABe2EQey0IgrD06EKB2f/8MVPf+Abpe+8FIHHNNbTecjNNr3wlKtRYcV1nczbffvQYX3v4CHuHZgkZisu7mrm2rZlN2iQ7a5c8umvDl3zwn18t9noBEHu9CvBcOHwv7Pu2H2YkPeo/oVsUsze/1t+wfBmYtB0enk7zxGyGQ9kCBzN5DmXzpZCYRXojITbEwmwsiduRktidtBb+aZLVgBeE1ci4HmnXI+26FXn/POWUQ22kHI+045bqs56fZlyPtFdOvVN4bVN7JHSBpJcPxOcMSSdFkz1DU2HGF50DEdrPZ0k6QZmbJWYYxE2LmBnCCsfxws044RbccDNOJEhDzbiR5iDfghNuxgk3+57LRe/eOp7ERcr+yBUNqrPzttfzttd1yzmVcU5zTK2rPdwrPeHBr6v1iC9F9uDknvMlNamibq5Hfb2y8tlJ51fRbsfa1sYQsJcSMbArmOwkPP0D2PMNOPhT0B50b/W9srfdBB0vW5ZpFTdFLHpnPzqTxtHFm4fBYziApfzHevwbiiq4MakwCdqU2vtpbZtSXvnhQFryE+ycfZqdg/fQdeTHMHXEn5AVqwgHcqWfj7Uuy3sjCGcjImAvDGKvBUEQlpfCwDGmv/VNpr75LZzhYcyODpLXXkuodw1Wd7d/dHVj9XRjdXQsSsiR0+HpoRm+9vBRbnv8GFMZm/7WGO/ctY5bdq2lrzUWhC8phyvZdFG32OsFQOz1KsN1fDF7/7f9MCOZMV/MPu/1gWf2a5ZNzK5kynY4lC1wKJvnYHAcyhY4mM0zGoTQLNIVtkoe20Vxuyhwt4Ya/ynkkwnNmUoh2fUF5ux8bUupS/Y0PawTyiOOS0I7JHSBuJcn7uZIummS9ixNhRmShSmS+Un/fI4InaHJyZBwM8RMCxVt8XWKaAtEW8v7bNWWxSrqoq0QTso+XGchC3mNLQK2sLSkRn2juvebfhwv8EOLbH+Hb1hb+pd1egtKdgqG98HwXv8Y2gsjB/zQKgDJnupwIGt2gtlY3jGCcDYhAvbCIPZaEAShMdCuS2r3bqa+8Q1yTz6FMz7ub1xciWFgdXZi9fQE4nYXoe5urO6ektgd6unGaGlZ9HB7Odvlh/uH+drDR7nn+TGUgms3d/Huy9bx6gt6CFu+8CH2emEQe72KcR04fE/ZMzsz7ouHW17vbwDZIGJ2LSnHDYTtssBdFLuP56ufxGizTF/MjvsCd0sdb+16Ulc99etUy7wgbvN8QnOt4Jyt/Xt7EuKmQcIwSBgQV5qEckngEPds4l6ehJcj7mZI2Gni9iwJe4ZEfpp4YZJEboJEdpx4foKEmyXuZom7OWJeHqNyNYYVCMzNfnpSIbqiTaQZrPBprUkQRMAWVgfTA/5GFHu/Cccf98vWvxx2vAO23giJzmWd3injeTB5sCxSF9PpI+U2sXZYs92PWd270w8H0rZBwoEIQgMhF8QLg9hrQRCExkQ7Ds74OM7ICM7ICPbwcJAf9dPg3J2entNXhcNl7+2e7kDkLh49JeHbSCQWZK5HJzLc+shRvv7IAEMzOToSYW66pJ93XbaOzT3NYq8XALHXZwklMTvYADI7EYjZr/MdqFrXl49EV8Nen2Zdj8O5PIcyhQrPbV/sPpYrnFL4i4UibhokTIO4EaSmQcI0S/m48kjoouicJ+FmiDtpEnaKhD1DPD9NIj9FIjdBPDdGPDtGLDuGkZ2E/Iz/xPqJiLRArKVGeG6t7/VcK0qH4g37GQurExGwhdXH+Auw91uw9xsw+jQoEzZdB9tvhvPf1DihNPKzMLy/xqt6PxRSfr0yoONc6NleFqzXbPc3XxRDIQgNjQjYC4PYa0EQhJWNl8/jjI6WBG1nZAR7ZARneKTqXGcyc/oayWSFsN1FqKfHD1dS4c1tdXWhwqfmxed6mv96bpSvPXSUuw8M43h6QTeEOpsRe30W4jpwaLcvZj9zB6RHquutKLSsqxa1qwTu7oYMAVHwPHLzhNWodwV+0jKtwc74T1TnJoN0CpWbIpqdxMhPQ24KctP+ka3I56bALZx4wqH4XJH5ZMJzsTzSBIbEBhdWDiJgC6ub4X2+V/beb8LkITDD0LnFf2Ql2uz/0Y4EabQ5yNeeNwWPuTSdWVgOrf3Y1CWv6j1+Onmw3CbSEojU28tp9wUN+TiWIAgnRwTshUHstSAIwupHa42XTpcEbWd42Be5a7y57dFRsO05/c22tiBsSZcvbNcJW2K2t1fF5x6dzXPb4wO8/7pzz0p7rZT6C+AtQAF4AfgFrfVUUPdR4JcAF/h1rfWdJxtP7LVAbgamj/rXvbXH9FE/9EglZrhC4C6m5/hpyzpoWtNY4qrn+R7N2ck6x9Q85cHhzf27VaIUhmMekXm+UBzRFl+vsCJLsHhBaAwW8hq78SPfC2cfPdv841X/E44/5ntmT7zoez/PHPeNUH7WN7gnMixFrFiFuN00V+AuCuNmBMafCwTrfZAvPj6poH0jrNkBF/1sWbBuWSde1YIgCIIgCMJZh1IKM5nETCaJbNo0bzvtebjT09Xe3MPDZaF7ZITcgQO4Y+NzA9aaph+fuyJsyU3d3bx/kdfWwPwQ+KjW2lFK/RnwUeD3lFJbgXcD24A+4G6l1BattbuMcxVWAtFmiAbX3vXIpwKB+yhMHa4Wt5+5A9Kj1e2NELSsrRa3E11+nfaCQ4N2K85rD+2n3nxtdEW+ok0hPVeEzk2fOBxHOAmxNl9ojrVB9/nB+TxHMR60hOEQhGVBBGyhcVEK+i/1j3poDU6+QtCe9tNKgTsfHLmZ6rrUSPV5cWODcNI34DtuLocA6b4AIsklW7YgCIIgCIIgrAaUYWC1tWG1tcH558/brio+d8mbO/DoHh7GPnyE7MOP1I3Pfbagtb6r4vQB4OYg/zbgq1rrPHBQKfU8cDlw/xJPUVhtRJL+tXD3BfXrCxl/X6upI2WBu+jR/dwPITX80l5fGX5oUWXUOVT1eThRFprbNpxYiC6K0bIhoSCsKETAFlYuSkEo6h/J7jMfx/P8GNZ21r9D3IBxvQRBEARBEARhtaIsi1BPD6GeHtixY952Xi4HMQnXB/wi8LUg348vaBcZCMrmoJR6H/A+gPXr1y/m/ISzgXAcurb4Rz3snB+GpJ74bMwnTFe0EQRBqEAEbEEwjODxqeblnokgCIIgCIIgCPNgRKPLPYVFRSl1N7CmTtXHtNbfCdp8DHCALxe71Wlfd6MrrfVngM+AHwP7JU9YEE5EKAotde+lCIIgnDYiYAuCIAiCIAiCIAjCMqO1fs2J6pVS7wXeDLxa61LQ8AFgXUWztcDxxZmhIAiCICwPEitBEARBEARBEARBEBoYpdTrgd8D3qq1zlRUfRd4t1IqopTaCGwGHlqOOQqCIAjCYiECtiAIgiCcRSilflsppZVSnRVlH1VKPa+UekYp9bqK8kuVUnuCur9VSgISCoIgCMIy8WmgCfihUuoJpdQ/AWit9wFfB/YD/wH8mtbaXb5pCoIgCMLCIyFEBEEQBOEsQSm1DngtcKSibCvwbmAb0AfcrZTaElz8/iP+Zk8PALcDrwfuWOp5C4IgCMLZjtb63BPUfQL4xBJORxAEQRCWFPHAFgRBEISzh08Bv0v15k5vA76qtc5rrQ8CzwOXK6V6gWat9f1BnM0vAjcu9YQFQRAEQRAEQRCEsxsRsAVBEAThLEAp9VbgmNb6yZqqfuBoxflAUNYf5GvLBUEQBEEQBEEQBGHJkBAigiAIgrBKUErdDaypU/Ux4A+AG+p1q1OmT1Be73Xfhx9qhPXr15/SXAVBEARBEARBEAThVFgxAvajjz6aUko9s9zzWCA6gbHlnsQCsZrWAqtrPbKWxmQ1rQVW13rOW+4JvFS01q+pV66U2gFsBJ4M9mFcCzymlLoc37N6XUXztcDxoHxtnfJ6r/sZ4DPBa82KvW5YVtN6ZC2NyWpaC6yu9aymtax4e90IrLLra1hd33FZS+OymtYja2lMVtNaYAFt9ooRsIFntNa7lnsSC4FS6hFZS2OymtYja2lMVtNaYHWtRyn1yHLPYbHQWu8BuovnSqlDwC6t9ZhS6rvAvyulPom/ieNm4CGttRuI0VcCDwLvAf7uFF5O7HWDsprWI2tpTFbTWmB1rWe1rWW557BKWDX2Glbfd1zW0pispvXIWhqT1bQWWFibvZIEbEEQBEEQFhit9T6l1NeB/YAD/JrW2g2qfwX4PBAD7ggOQRAEQRAEQRAEQVgyRMAWBEEQhLMMrfWGmvNPAJ+o0+4RYPsSTUsQBEEQBEEQBEEQ5mAs9wROg88s9wQWEFlL47Ka1iNraUxW01pgda1nNa1lOVlN7+NqWgusrvXIWhqT1bQWWF3rkbUItay293E1rUfW0rispvXIWhqT1bQWWMD1KK31Qo0lCIIgCIIgCIIgCIIgCIIgCAvGSvLAFgRBEARBEARBEARBEARBEM4iRMAWBEEQBEEQBEEQBEEQBEEQGpJlE7CVUuuUUj9WSh1QSu1TSn04KG9XSv1QKfVckLYF5a9VSj2qlNoTpK+qGOvSoPx5pdTfKqVUg6/lcqXUE8HxpFLq7St1LRX91iulUkqp326UtZzJepRSG5RS2YrP558aZT1n8tkopXYqpe4P2u9RSkVX4lqUUv+t4jN5QinlKaUuWqFrCSmlvhDM+YBS6qMVY63E/zNhpdS/BfN+Uil1faOs5wRruSU495RSu2r6fDSY7zNKqdc1ylqWkzP4Toi9btD1VPRrOJt9Bp+N2OsGXItqYHt9hutpWJt9BmsRe73KOYPvRMPa6zNcT8Pa7NNdS0U/sdcNtp6gTmx2461F7PXyr2fxbbbWelkOoBe4JMg3Ac8CW4E/B34/KP994M+C/MVAX5DfDhyrGOsh4CpAAXcAb2jwtcQBq6LvSMX5ilpLRb9vArcCv90on8sZfjYbgL3zjLWiPhvAAp4CLgzOOwBzJa6lpu8O4MUV/Ln8LPDVIB8HDgEbGmEtZ7ieXwP+Lch3A48CRiOs5wRruQA4D/gJsKui/VbgSSACbAReaJT/M8t5nMF3Qux1g66nol/D2ewz+Gw2IPa64dZS07eh7PUZfjYNa7PPYC1ir1f5cQbfiYa112e4noa12ae7lop+Yq8bbz1isxtwLYi9boT1LLrNXtKFnuRN+A7wWuAZoLfijXmmTlsFjAdvQC/wdEXdzwD/vILWshEYxv9DuCLXAtwI/AXwcQLj2ohrOZX1MI+BbcT1nMJa3gh8aTWspabt/wU+sVLXEszxe8H/+Q78P/jtjbiWU1zP3wM/V9H+R8Dljbie4loqzn9CtXH9KPDRivM78Q1qw62lEd7HU/z/Kva6wdbDCrHZp/C3ZwNirxtuLTVtG9pen+Jns2Js9imsRez1WXac5v/XhrbXZ7CehrbZp7IWxF436nrEZjfgWhB7vezrqTj/CYtksxsiBrZSagP+HeAHgR6t9SBAkHbX6fIO4HGtdR7oBwYq6gaCsmXhVNeilLpCKbUP2AN8QGvtsALXopRKAL8H/K+a7g21Fjit79lGpdTjSqmfKqVeEZQ11HpOcS1bAK2UulMp9ZhS6neD8pW4lkreBXwlyK/EtXwDSAODwBHgL7XWEzTYWuCU1/Mk8DallKWU2ghcCqyjwdZTs5b56AeOVpwX59xQa1lOxF43pr2G1WWzxV6LvV4KVpPNFnst9rqW1WSvYXXZbLHXjWmvQWw2DWqzxV43pr2GpbfZ1hnNcgFRSiXxH435iNZ65mQhT5RS24A/A24oFtVpphd0kqfI6axFa/0gsE0pdQHwBaXUHazMtfwv4FNa61RNm4ZZC5zWegaB9VrrcaXUpcC3g+9cw6znNNZiAdcAlwEZ4EdKqUeBmTptG30txfZXABmt9d5iUZ1mjb6WywEX6APagN1KqbtpoLXAaa3nc/iPCz0CHAbuAxwaaD21azlR0zpl+gTlZxVirxvTXsPqstlir8VeLwWryWaLvS4h9jpgNdlrWF02W+x1Y9prEJtNg9pssdeNaa9heWz2sgrYSqkQ/oK/rLX+VlA8rJTq1VoPKqV68WNXFduvBW4D3qO1fiEoHgDWVgy7Fji++LOv5nTXUkRrfUAplcaPO7YS13IFcLNS6s+BVsBTSuWC/su+Fji99QReB/kg/6hS6gX8u6wr8bMZAH6qtR4L+t4OXAJ8iZW3liLvpnxnGFbm5/KzwH9orW1gRCl1L7AL2E0DrAVO+/+MA/xGRd/7gOeASRpgPfOsZT4G8O9uFynOuSG+Z8uJ2OvGtNewumy22Gux10vBarLZYq9LiL0OWE32GlaXzRZ73Zj2GsRm06A2W+x1qW9D2etgTstis5cthIjybzf8K3BAa/3JiqrvAu8N8u/Fj6eCUqoV+AF+7JR7i40DV/tZpdSVwZjvKfZZKs5gLRuVUlaQPwc/0PmhlbgWrfUrtNYbtNYbgL8G/q/W+tONsBY4o8+mSyllBvlNwGb8zQyWfT2nuxb82EI7lVLx4Pt2HbB/ha4FpZQB3AJ8tVi2QtdyBHiV8kkAV+LHflr2tcAZ/Z+JB+tAKfVawNFaN/r3bD6+C7xbKRVR/uNam4GHGmEty4nY68a018GcVo3NFnst9nopWE02W+y12OtaVpO9Dua3amy22OvGtNfBnMRmN6DNFnvdmPY6mNPy2Wy9fIG+r8F3D38KeCI43ogfcP1H+HcYfgS0B+3/ED+mzRMVR3dQtwvYi7+b5acB1eBr+e/AvqDdY8CNFWOtqLXU9P041TskL+tazvCzeUfw2TwZfDZvaZT1nMlnA/xcsJ69wJ+v8LVcDzxQZ6wVtRYgib+b+D5gP/A7jbKWM1zPBvwNKA4AdwPnNMp6TrCWt+Pf8c3jb/BzZ0WfjwXzfYaKXZCXey3LeZzBd0LsdYOup6bvx2kgm30Gn43Y68Zdy/U0oL0+w+9Zw9rsM1jLBsRer+rjDL4TDWuvz3A9DWuzT3ctNX0/jtjrhllP0EdsdoOtBbHXjbCeRbfZKugkCIIgCIIgCIIgCIIgCIIgCA3FsoUQEQRBEARBEARBEARBEARBEIQTIQK2IAiCIAiCIAiCIAiCIAiC0JCIgC0IgiAIgiAIgiAIgiAIgiA0JCJgC4IgCIIgCIIgCIIgCIIgCA2JCNiCIAiCIAiCIAiCIAiCIAhCQyICtiAIgiAIgiAIgiAIgiAIgtCQiIAtCIIgCIIgCIIgCIIgCIIgNCQiYAuCIAiCIAiCIAiCIAiCIAgNiQjYgiAIgiAIgiAIgiAIgiAIQkMiArYgCIIgCIIgCIIgCIIgCILQkIiALQiCIAiCIAiCIAiCIAiCIDQkImALwgpCKXVIKZVVSqUqjk8rpX5eKeXWlKeUUn1BP62UOrdmrI8rpb60PCsRBEEQhNXNPDZ7vVLqr5RSA8H5QaXUpyr6iL0WBEEQhCUisNWvmadOKaVeVErtr1P3k8BmX1hT/u2g/PrFmbEgnL2IgC0IK4+3aK2TFccHg/L7a8qTWuvjyzpTQRAEQTi7qbLZwC8Au4DLgSbglcDjyzlBQRAEQRDqci3QDWxSSl1Wp/5Z4D3FE6VUB3AlMLo00xOEswsRsAVBEARBEARhabgMuE1rfVz7HNJaf3G5JyUIgiAIwhzeC3wHuD3I1/Jl4F1KKTM4/xngNqCwNNMThLMLEbAFQRAEQRAEYWl4APhNpdSvKqV2KKXUck9IEARBEIRqlFJx4GZ8kfrLwLuVUuGaZseB/cANwfl7ALkpLQiLhLXcExAE4bT5tlLKqTj/HcAGrlRKTVWUj2utX7akMxMEQRAEoZJKm/0T4B3AJPDfgE8B40qpj2qtv7BM8xMEQRAEYS43AXngLsDE187ehO9hXckXgfcopV4EWrXW98u9aUFYHMQDWxBWHjdqrVsrjs8G5Q/UlFeK1y4QqhknhC98C4IgCIKwOFTa7Bu11q7W+u+11lcDrcAngM8ppS4I2ou9FgRBEITl573A17XWjtY6D3yL+mFEvgW8CvgQ8P+WcH6CcNYhArYgnB0cATbUlG0EDi/9VARBEARB0FpntdZ/j++RvTUoFnstCIIgCMuIUmotvij9c0qpIaXUEH44kTcqpTor22qtM8AdwK8gArYgLCoiYAvC2cHXgD9USq1VShlKqdcAbwG+sczzEgRBEISzBqXUR5RS1yulYkopSyn1XqAJeDxoIvZaEARBEJaWkFIqWjyAXwCeBc4DLgqOLcAA/kaNtfwBcJ3W+tCSzFYQzlIkBrYgrDy+p5RyK85/iL878lVKqVRN21dqrR8G/ndw3AO0AS8A/01rvXcpJiwIgiAIAgBZ4K+AcwGNf4H8Dq31i0G92GtBEARBWFpurzl/AfgbrfVQZaFS6p/ww4j8XWW51vo4/oaOgiAsIkprvdxzEARBEARBEARBEARBEARBEIQ5SAgRQRAEQRAEQRAEQRAEQRAEoSERAVsQBEEQBEEQBEEQBEEQBEFoSETAFgRBEARBEARBEARBEARBEBoSEbAFQRAEQRAEQRAEQRAEQRCEhkQEbEEQBEEQBEEQBEEQBEEQBKEhsZZ7AqdKZ2en3rBhw3JPQxAEQVilPProo2Na667lnsdKR+y1IAiCsJiIvV4YxF4LgiAIi81C2uwVI2Bv2LCBRx55ZLmnIQiCIKxSlFKHl3sOqwGx14IgCMJiIvZ6YRB7LQiCICw2C2mzJYSIIAiCIAiCIAiCIAiCIAiC0JCIgC0IgiAIgiAIgiAIgiAIgiA0JCJgC4IgCIIgCIIgCIIgCIIgCA2JCNiCIAiCIAiCIAiCIAiCIAhCQyICtiAIgiAIgiAIgiAIgiAIgtCQiIAtCIIgCGcJSql1SqkfK6UOKKX2KaU+HJS3K6V+qJR6LkjbKvp8VCn1vFLqGaXU65Zv9oIgCIIgCIIgCMLZiLXcE1gpaK3xPA/HcXBdtyo91bJiahgGsVis6ohGo8RiMSKRCIYh9xUEQRCERcEBfktr/ZhSqgl4VCn1Q+DngR9prf9UKfX7wO8Dv6eU2gq8G9gG9AF3K6W2aK3dZZq/IAiCIKwalFLrgC8CawAP+IzW+m+UUu3A14ANwCHgnVrryaDPR4FfAlzg17XWdwbllwKfB2LA7cCHtdZ6KdcjCIIgCIvFWS1gz87OMjw8zNDQEMcHhxkcnyTkFeYVn5fC/iuliEajJUG7nsg933koFFr0+QmCIAgrF631IDAY5GeVUgeAfuBtwPVBsy8APwF+Lyj/qtY6DxxUSj0PXA7cv7QzFwRBEIRVyULeWP5H4H3AA/gC9uuBO5Z8RYIgCIKwCJwVArbruoyPjzM0NMShgUH2D4zz/EiKkaxiSseY1lFmdRSPXjrCHi9r9ji3Gc5rM+hKmFiWhWVZmKZZldYrO1FdMe+6LtlsllwuRzabLR2158WyycnJUv5EIrplWSVROx6Ps337di666CIRtgVBEIQ5KKU2ABcDDwI9gbiN1npQKdUdNOvHvxAuMhCUCYIgCILwElmoG8tKqUNAs9b6fgCl1BeBGxEBWxAEQVglrDoBO5vNMjQ0xDOHB9l7ZIznhmc4Om0z5UWY8mJkCAMtQAsmml4Ldhqw0VMkUTzd3sqj01keGrMB6G+NcsWmdq7c2MEVm9pZ3x5HKXXSeWityTs5ZtJTTE9NMJueIpWeJpOZobO9j4svuJrm5ubTWpvneRQKhboid+35xMQEP/jBD/jpT3/Ky1/+cnbt2kU4HD6Dd1QQBEFYbSilksA3gY9orWdOYNfqVcy5k6qUeh++1xfr169fqGkKgiAIwlnDS7yxbAf52nJBEARBWBWsWAHb8zzGJyZ58vkBnjo0wnNDMxyeyjOaN5nWUQpYgAm0EcGjF5fLtcVmFWYDJhsw6MfAdDSuPY7rjKLMCOGhJhwzwr71BveF0+yZnuTOp2b51mPHAEiaOXpD46xhmB7vOMnCONgOuuCgbA/D9jBssBywXIWqe+0Pd0Y8vP5mei+4gEt3vZoLz3/5SWNfG4ZRCi/S1tZ2wrZaaw4ePMh//dd/cdddd3HPPfdw5ZVXcvnllxONRs/kLRcEQRBWAUqpEL54/WWt9beC4mGlVG9wkdwLjATlA8C6iu5rgeO1Y2qtPwN8BmDXrl0Sb1MQBEEQToMFuLEsN5wFQRCEVc2KEbAnJib5X5/8DIfTHoNOmBE3xpSTwMUMWkRpMqE3OsOO8AjrojOsi06yJjpEc3gMz8rimnk8M4+2bNKmw3OWi2F5Va9TmEgSG93Ky6avZ+f0FsayQzw/+xBPFYY5GunhWLSXY9E+nrP6gUuIeRnWMEhveITe2Cgd4QyhaBgrEiUUixKOxojEEkRjCWKJJLFYkuHBwxzZ9xTqyATTLz7Mf/7gYe6IeOi1LfSefwG7Lnst27dcjmGatW/DKaOUYtOmTWzatIkjR46we/du/vM//5N7772XK664giuuuIJEInHG4wuCIAgrD+VfEf8rcEBr/cmKqu8C7wX+NEi/U1H+70qpT+LH2twMPLR0MxYEQRCE1c0C3VgeCPK15VXIDWdBEARhpaJWysbEkd7Nuve9f43Coys+Rm98hN7kEL2JYdYkhulNDJMIZQHwbAPPMXAdhecYeK6JG6SeZ+J5Fp5nobWFJoRWYZSKYMU8mhKDhGOjKBOUEyE+tYOmoQuJz+wket56Wq5cS7S/lUPjGR48OMGDL47z4MEJBqdzALQnwly2oY0rgpAjF6xpxjDq30H3PI9nDj3BQw/dxcD+vegjE8Syvhd2PqxhbQt9F2zlsstvYOuWXaiTeGifjOPHj7N7924OHDhAKBTisssu46qrrqKpqekljSsIgrAaUEo9qrXetdzzWEyUUtcAu4E9QPEO7h/gP678dWA9cAS4RWs9EfT5GPCL+BtNfURrfcJ4mrt27dKPPPLI4iwgoJDP4Dl5DDOKYVoYhoEyjFMK8SUIgiCsbFaTvQ5uLH8BmNBaf6Si/C+A8YpNHNu11r+rlNoG/Dv+hsp9wI+AzVprVyn1MPAhfJt+O/B3Wuvb53vtpbDXgiAIwtnNQtrsFSNgRzdu0W1/82U6jFmuHNrP5qHjUIjg2DEcO4ZnJ8BNoNw4FhEsTCzA0hCaJ4zHHAx4wy/voH+DzQt/96tMq30UdkVwIr4wHpnZQHJ0B62Rl9O18xXEt/egLAOtNUcnsjxwcJwHX5zgwYPjDEz6fZqjFpdvbC8J2lt7m7HM+kK053nsP/goDz/8Qwb274WjU8QzfttCWKPWtdJ3wTYuv+wGzttyyRkL2iMjI+zevZu9e/diGAaXXHIJV199Na2trWc0niAIwmpgNV0QLyeLeUF8+Jm7eXbf30JyP4bl/37xXNCugfYU2lVBGpx7RunAM9DaAM/00yCPLh9amyhM0H4YMqUtNAqFUe6DnyoMNAaqWEZFPWbQRvljK79cabNUr1AoY+6TVlprCH6bab+gWIEuPg2uqdjUWVP+KafL5RW/7+r91ptX7K8oL7dR9aprylWpSBkmhmEENxZMlGFgmEap3D8PyovnQbtiXemmREVdZV/TsjBDIcxQGCsUwgyFsELhqvNyWUhubgjCKmI12euFvLGslNoFfB6I4W/e+CF9got9EbAFQRCExWbFCNhKqfOAr1UUbQL+P6AV+GVgNCj/gxPdHQa4dF1Mf+xPfpl/2PIr7E3n6Q5b/PLaLt7T10FLyCJnu8zkbGayNtPBMZN1/HymwHTaZjZdIJW2SWdtMhmHXM4/bNvD0nBFPkSfNnjTr+zknK1tjP71XzP22c9i3bAV8wMvZyL1IDOpJwAPo5AgOX0h7a3X0XfRG4n19FXN99hU1vfODgTtQ+MZAJIRi+39zWzsTLKpM8HGzgQbuxKsa4sTtqoFaa01Tz3/EA8/chfH9u9HDUyRyPgXu3YY1Lo21l6wncsuew2bt1x82oL2+Pg49957L0888QQAF154Iddccw0dHR2nNY4gCMJqYDVdEC8nC31BbNs59j34TwyOfIVw6xiFgsWjhy8nXUgQMfNErBwxM0/EzBM1c4QtB8Pw/EMVUxfD0BiGhwryKqhTykUZHoax9Df0tQdaq0BcLwru1efFPEUhXgdttIH2TCi204FYXxLng37aBE+hS2lFf+2L/uW8URrPf33l9/GCcleBVoCqDqxaI5Z7nof2XLTn4bl+3vO86nLPQ7tuUObVvjULji94h7HCYUwrhBX2xe/KfEn4tkKldqZlYViWL6qbFqZpls5Ny/KfALBMTDNoE5SZxfaWX2+YlX3Mmn5BG8MsCfsiuAvC/Ii9XhhEwBYEQRAWmxUjYFe9kFImcAy4AvgFIKW1/stT7b9r+xb9yM3D6Ks/zH9d+jv8w5ERfjo5S8I0+LneDn55XRdro+EzmpvjeszkHH7vK0+w5vEZejF5y69dyLqt7Ux//wcMfuxjmB3trPv0pzE39zE+tpuRwz9kcvYeHHMatCKe30J7+7WsOf8NNLfsQKlqMXl4JscDQbiRZ4ZmOTiWZiJdKNWbhmJdW4wNgajti9tJNnYl6G2OYhgK13N58vkHefiRHzJ4YD9qYIZkhaBtrGtn3QXbuezyG9i0eQdGHc+uekxNTXHffffx2GOP4bou27dv5xWveAXd3d0n7ywIgrBKkAvihWGhLoinxw/x5AN/Tsb7MVa8wLOj5/KTg69mz8xmsnp+e2/gYeESUkGKR0j5abE8hIsVpFV55RA2bCKGTciwiZoFQjiYpgfKF71VkGrlgnLRhguGn0d5YHgVeT9VhuefK18wx/CqxlLKQxnaF9yVn5pBnal0IMLr0mGqYluCvMZUXkX9S377T4gOvM/9eGsmGhOlrODcQqkQhhEO0giGEcY0IxhGBMuIYpoRLCNGyIphmVFMFcUwQvhhYEMYKgTaQikLheV7xOsgj1US5V0HXMfDc8CzNa7t4jo2jl3AtR1cu4BTKARlNm4xXyi2sf02to1rV5b5ece28RwHz3XxXD9dKkqe6KaFYRplcdssCt1BmWWVzst1NYdR7GfMX1d3XKPmNefrW55frSd9pee8qvDKNwwDZdZ43td43RfPBaEWsdcLgwjYgiAIwmKzUgXsG4A/0lpfrZT6OKcrYO/apR/5o6vh0S/AL9wO57ycvbMZ/vHoKN8emUQBN3a38Svru9mWjJ3RHGdzNu/6u3u58rBLlzJ564cupH9LG9m9+xj44Adxp6bo/cQf0/KmNwGgtcfU0GMM7b+DifRucokXQWks3UpHx3V09b6a9vZrCIVa6r7eVKbAwbF03SNTKF8kRSzD99TuTFQJ3Ovaoxw8/jCPPfojhp5+GmNghqaMvy+nEwI3YaGjJjpqoaIhVCyMEQtjxqJY8RiheIxQIk44kSCaSGKoEJPPTTHyzAie49GzoYfzdp1Hz5oeQmaIiBkhbITLeTNM2AiLl5AgCKsCuSBeGF7qBfGhA3fy7IG/QyWfZrzQxk8OXs+Dw5cy6TRhorkMj9dPDdCVbCbX1EcqB1mtyaLJRUzyTSHyCYtcxCRrKTKORypnk847ZAoO6bxLuuCSd07N69cyNImQRzKkSYRcEiGPuOmSsFxipkPcdImbNlHlEFEulDyQNdqtyHvV+VKZ65VDgmj8MCEaeEk/zyo9zL0g75ZF88A7HcOFkIO2gsO08Uwbz6hIVQHPsHFVAU/5ZQoXU+Ef+CJ6MW+p4ECX86oiT835Av+E8DDQ+GFbNCY6ENXBrBDYfbFdqRAoC0OFUIYvvCsjhKEsDCOMoUKYRhjTCGMZYSwzgqVCmCqEScgPI+P5Xuklj3VN4ElPxaGC7wN4QZnnaD91NZ5b/i5o10Nr33PdPzSe56JdjXZ9z/Vyneunnod2Kuoct7rODc69oCwQ4+fWuUviDX+6VArf9cLR1AtFU7euSlCvaadOJLif4PVM0+9bR6Sfdx4nGbP+nOcT+ueG2pm3jVo9nv1irxcGEbAFQRCExWalCtifAx7TWn86ELB/HpgBHgF+S2s9WafP+4D3Aaxfv/7Sw8/ug3+62n9U9VfuhYi/+eDRXIHPHh3lS4PjZFyP69ua+NX13byiLXnaP9QOjaV519/ey41TFu0YvPXDF9P7shacsTEGPvwRso8+Sscv/zJdH/kwyix7OGtPM7v/BYb3/weT9v2kO/fghdKASUvzxXR2Xk9Hx/Ukk+efdE5aa0Zm87w4WhS0UyVh+8hEBtstf2bNUYuNXcmSoO3ZR5k+/gj20b2EMhmMvIuR97AKGisPljv/a2s0hZBHPmLgtq7BTPSiDIucM864PsiMNUEh7JEP+Uch5JEPe1jxGPFIgkQoQTKU9NNwkrgVJxlOVpXXtklYCRJhvyxsnpkHvSAIwkIgF8QLw5lcENt2jr0P/D3HR76Kk8jx0OCl7D56NUcyfniu83B44/gAVx+4i2RmgPiuS8nt3Yc7MYHZtYam17+T6NYr8XIxCkdncSf8jZVREOpJEF7fRHhtE+H1TVjdcZShcD1NuuCQybukCw7pvC9uZwoOqbxDpuAyk7UZTxcYm80zmsozniowlsozni7genN/P1mGoiMZpjMZKR9NYbpqzjuTEdriYcx5NnkuUgrHoXXVUa/sZG09zyOfz5PJZMhms2Sz2ap87bl7Am/jUChELBYjEo0QjUUJR8NEohFCkRBm2MTVLq7nllOCNChztIOnveDcwdV5NAW0Z/upttHYgI2n/RQcFA5+yFcbCMK/EAjzpdQX55VyfT/xCq/1slc7c7zYzSrv9or2EIxxWl/phsJfRcVR+h0aeDcrVc5XtT3Buaa6XKvgdSidz8kHqS71r6wrxnyvOJ+3Tfko9dE6CJkTxI8v6vDFcSpvElWeexVh5r0gnrwOUo/S/yP/xkQ5Rn25b0UM+uJ7UjnP0ntTeV77HgRJxTpLb51WNee1721x/nXeozpzURiglP+kqFKoUt4o5ZVSoAwMZQDK94KvOffb+WMYpXoFRjG+f7lNqa3hj+GPHwjzqmID3tK5We5XOY5Rvllw5VvfL/Z6ARABWxAEQVhsVpyArZQKA8eBbVrrYaVUDzCG/3Pq/wC9WutfPNEYJQN75AH43OvhkvfAW/+2qs2U7fDF4+N8dmCU0YLDjmSMX13fzVu6WrFO48rjvufH+MC/PMR7czGaMXjrRy6mZ0MzulBg6I8/wdTXv07iumvp/8u/xGxqmtPfmcqReugYYwfuYzbxKJmePeQShwCIRNaQiL8Mje9hg/bK+TllLv6GTMW8h+PCWCbJYKqVwVQbg+l2htLtDKU6GM+1Vs0jZmVpjaRojaZojaRpi6Zpi2VojWRoDadpCWdoCacJk0d7Gtf18ByN63i4tofreNiehWOE8TCh4GBks1Cwg5iY/uHkDVxC2FjklEVWKWaBacNjXBWYNLNkIw7ZsEcu7Pp7XNUhZITmiNzFfCwUI2JGSkfUihI2wkStaFV5xArqzeicsuKxWrxPBEFYWETAXhhO54J4euwgjz/wJ8x493MgtYV7j13J3vHz8DBZg8MN06O89tmfssYZIPmq62l61auIX345RiSCtm1Su3czfdttzP74J+A4RLdvp+XtN5J85etwZwwKR2YoDKQoHJ1FZx0AVNgkvDZJeF1T6TBbIqe1Rs/TTGVtxlL5krg9FojbY7O+wF3Mj6UKFNy5Xq2GgvaEL2bHw2ZZyyrv01hVoKvqdHXbmj6Vv+0q+1S201pXvEZNmQYvEL29CiHcL6sQx3XgOVwhmCvAVB5+YBHPPyrOrZrzyvpyXf1zs+I8jB8yZenQFSFfyikV50aQ1oabqUoD73hl1IxDEFm8Ku+nOijXyvM/HKXLqdJovEC/9ObUgUZX5AnGLY/j1Zzr0nyqxqrppyrP0YEmXjO3umPUlFeMU5LD1TyyeWmfUD1vXa0MT1BXT5Iv1+m6dYYql8035nx9hMXlNa9+Uez1AiACtiAIgrDYrEQB+23Ar2mtb6hTtwH4vtZ6+4nGqDKwP/wjuPev4We/DlteN6dtzvX45vAk/3h0hOczedZGQ7x/bTc/29tOwjq1uNBfvP8Qf37bPt7vJImheNtvXEzXOl+snvzqVxn6408QXreOtX//90Q2baw7hnY9cgcmSD00RPrQQdJde8hufBqvLYURDfneBAReDxQ9GQz8cOGBFwSm7yFBUK5UUGb4Hg7KLPXPuyaDszGOzcQ4NhNhLG0xkTEZz1hMZMJMZEMU3Lnrj5gObbEMbdEsrdEMbdFUhfA9Q0t4ipgaI6QnUbgYJpiGf9Hme0KdGNdWuHkTt2DgFky0G0brMJoInoriGhEcI0LetMhaFhnTYNrSTBoek26eGTtL1smSd/MU3AI5J0fFZfdpUwx/Uk/kNpWJqUyMwMOjmC+mlrLqllfmTWWW2lSOZxrlesuw5qTFvGmYWCo4N/z+ISNUrjMsLGWV8qX+qrq9ZVgi1gvCaSAC9sJwKhfEhw7cwZN7PsVRTB4cvpSHhy4m50ZpwuHa7DRvfv4BtkRGab7+WpKvehXRbVtP+PfMmZhg5vs/YOrbt5HffwAVCpF81at8Mfuaa8A0ccayFI7Olg57MA3BE01mc9gXs9f7gnaovwkjcmq/F06G1pqZnFMlaI+l8sHh53N2tbdzca2VYlnxfL66Ykl122K+XFdVVinCKVXqU1tGRZ+SGFhbFpzbjkvB1eRtP0xLzvYoOEHe8cg7XrnO8SicYiiXejRHLVrjIdpioSC1aEuEaI35ZS0xi7Z4iNaYRVssRCLie4kWf/ueSnqqnu6nW+56LgWngOM52K5dvklQ3OCy4ty/SVCRr62vCFvj6bltqubg1eRr2mtPz2lfe75YKEP58bQrj2LIEFPNPTfK5VXnQaoM5afFNkZFWb00eH18R+OKG0UVN4QIPsNiXXBeqi96aOMFdV7QLrj5AIGzSm2dn/OdWHRx5FLbqr7Fel1u57uDU3FebFPzuhTvIZTXQGkewc2Iirx/f6Gyra7qO7eO0uuVblDU9FOl97SiPmhfvy5Ymy6mml971b+LvV4ARMAWBEEQFpuVKGB/FbhTa/1vwXmv1nowyP8GcIXW+t0nGqPKwDp5+OyrIDUCv/oAJDrq9vG05ofjM/zDkREenE7Tapn8fH8nv7S2k65w6IRz1lrzB7ft5Qf3H+VXdZIQiht/82I6+pIAZB5+mIEPfwRt2/T/1V+SvPbaE47njGdJPzxE+pFhvJRN5NxWWl6/gfDauR7ci0XxInp0NsfITJ7hIB2ZDY6ZHKOzeYZncqQLcx8dDlsGLWEw7TQRN0tHIsQl5/bxjotaaUpowmEX08rjuWkcZ4ZcbpxsapRcZpxCfgK7MI3jzOJ6KTRZMPIoy0YZJ/4OalehvRDas8ArbuAU8g8sNP5mT1qF0FhoZflbdSkTjxCOMvwDA1sZ5BUUMMihySqPrNKkcclpG9fz/MeatVtKi488e3punecFbXRNG6/c5qWI7S+FqBmlM9ZJV7yLzlinn49V5IPy9mh78FioIJzdiIC9MMx3QWwXsjx6z1/x5LH7eTy9hQcHL2Uy30YYl112ijcd3cvLmydpeeU1NL3ylYT6+s7o9XNPP830bbcx/d3v4U5OYnZ20vLWt9L69huJbN5caqdtj8JgisKRQNQemMUdrww9EifUl8RsDmMkwhjJEGYiVE4TIZQlfztfKp6nKbgeedsj77jkalJfBJ+bzuYcJtIFJtIFJjOFUn48XZhXFDcNRVs8RHsiTFs87KeJMO1BvvK8LRGiIxEhFl6YGxmrifkE9OLhui6O4+A4zknzC912oa5rDMPANE3C4TDhcJhIJFJKK/OnmoZCIXEqWEDEXi8MImALgiAIi82KErCVUnHgKLBJaz0dlP0/4CL8++iHgPcXBe35mGNgh/bCZ66H898It3yh0gWpLo9Mp/mHIyPcMTZN2FDc0tPOB9Z3cW48Om+fguPxc//6IIcOTfM/7ASWobjpty6htScOgH3sGEc/+CHyTz9N92/9Ju2/9Esnj29te6QeGGT2x0fwMg6xHZ00v/YcQt3xE/ZbatJ5pyRqjwSi9mggdA/P5Dg6Os3obJ6cNmlSOS6zjrLOmMIwFPF4nEQicUpHKBRC6xyZ1AizU8dITw+RnR0mmxkjlx2nkJ/EsafxdI5iPEyUDYYDhoMyXJTp+htSWR7K8jAsfbKvQ118J5cgXqBWfpiUUuzBYt4/SnkUaKM6j0JTUYaB0n7qxzxU5fGgOg1iSFY+Fl7so0uPeKuiAwpo5fv06Ioj6O9qF9uzcTw/LbgOrvZvTFQ+No4CS4UJmRahYKOqkGkFG3aGq1LDMP35B+53qhiHUanAw698jiquNfAYVH690pT+v6ognmLQqdymeI4q/59Sxfe4XFfspyrHoLbeqBhvbjpnDrp+uzkPIM+ZK1V954xbk6rieoKbB6rUh3JdadmV/YpZVepXfj8r11XhRln1PlKnX/k/jKpwpVQVr1OvrDjPyjkVvTpr51dvblVzKI6rKj5PtcCvUbGWemvuPmejXBAvALX2emL4Wb5315/xZCbCQ6MXMZDqx8DjfJ3iDaNHeENHmu5XXUXimmvqhuWqRWs/1JUVOrGwqAsFUrt3M3XbbaR+8tOqECMtb3oTZmvrnD5u2q7y0naG0rhpu+SpXYuKWphJX8w2EiE/n6zIJ8Ll+ngIZYqAtdhorcnari9sp23G0/lA4LaZDATuyXSBiUyQBgL4fI7FXU2R0ubZxQ21N3UlWNceJ3KKT/UJS4fneQsmjDuOg23b5PN5CoVC3dS27VOal1LqlMXuU2ljmmf3d08E7IVBBGxBEARhsVlRAvZCUdfA3vMpuPvjcNNnYec7T2mcFzI5/vnoKF8bmqDgaV7X2cyvruvm8tZk3fYT6QJv/fQ9xLIe75oNY4VM3v5bl9DSFQPAy2YZ/NjHmLn9Dprf9CZ6//j/YMRiJ52Hl3OY3X2M1O5jaNslfmkPza85B6v19OJwLiee53H7Yy/yF3cf5PBUgYt6QrzrXIMkWdLpdNWRz+frjmFZ1ikJ3eFwGNP0Q3MUvWIqU8Mw8FwXxy5g5/M4hTSFQgonP0OhkMG2Z3ELGWwnjeukcZwsrpPBdbO4bg7Py+J5ebR2/Uc5i2kpPnn5vBijnGJ9KV8Mq+LnNR5KlR8F9WNG1otPSRBvsljmS+Bz6oNDHHhWP1WbTwFl4b7yCV1ValNKKjZ70hV19TaQIhi34snkYPyacYs3DKraFOtX15fxre/eLxfEC0DRXv/4J5/nrj2P83hmA89MnovGYL01xfVTk7yrx2XDDVcRu/giHM8gl7LJpmxyaZtcKjjSQVmqUCovtvEczdZX9HHtu7dgmif3gvZDjHyfqdu+Tf7A3BAjyrLm7au1Rudc3FQBL23jpWzcIPXSdqncDc69tF39VHwRBUbMCgTucJXwbbVHCfcnsbriInIvA56nmcnZJTF7POWnY6kCh8bSpY20x9OFUh9Dwdq2eEnQ9gXuJBu7EvQ2RzEkGPJZged584rb86UnqvO8UwupY5rmaYveyWSS7u5uEonEivcGFwF7YRABWxAEQVhsRMAu4rnwb2+AkafhV++Hlv5THm+0YPO5gTH+7dgYU47LZc0JPrKhh1d3NM9p+/TQDO/4h/u4sDnOq45DOGrx9t++hKZ233tba834Z/+F0U99isgF57Pu058+5Uef3VSB2R8fJfXAIChIXtVH0/XrMBMnDnHSSNiuxxfuO8Rf3/0cBcfjl16xkQ++8lwSkbIg4DjOHFH7RIfrzg1hciJUsAt6rbB9ItG7Ng2Hw+zYsYNzzz234X/Y+xt7BsK61qVNPsubgbqBYFkRBxE9Jz6hrlIvyzEVXc9hOj/FRH6SydwEk7kJJoJ0KjfFRH6Cqdwkk7lJHF32Pir6DUeMMC3RFtoibbRF22gNt9AWbaU10kprpMVPoy00hZr8i/ya+JKl2JClzcuCmwIVym5pPTXxGMvnXnl9ujompP8viBlZEUeyqm1pg7M6YxRjVFa8n+XzcrvK2JNVr1NsF8TJRFfPodRPV45V8bqVc6rqVx5vzpwq4mZWf/blutqxfLwTlhf/p9S1JFWf69yyiobVAntNXW25rlNWWzB3vLKyX2dWoOGaV35LLogXgL51ffqKD/0Ge6Y243ghOsPTXOKkuFZ30t65hryKki+J0zbefN7NCqLJENFEqCqNJUNkUzYH7h1k/bZ2XvfL2wlH5xega8kdOMD0t79dDjHS1UnLW+aGGDlTtKfxsg5ejbBdErhThSoB3MtU7CVhGYTWxAn3JQn1J/10TRx1Em9zYWmYztgcHE9zcCzFwdE0L1aI25mK0GsRyyh5a1d6bW/sTNIWl1ASwvw4jnPaoveJ0nrE43G6urro7u6mu7u7lI/HG+tp0BMhAvbCIAK2IAiCsNiIgF3JxIvwj9fAusvg524D4/TiUaYdl68MTfBPR0cYyNl875LNXNaSmNPurn1DvP9Lj3LLpm42788STYa46bcuIVHhMT37k59w/Ld/BxUOs/Zv/4b4rlP/jJzJHDN3HyHz2DAqbNJ07VqS1/Qv2CZSS8HIbI4/veNpvvXYMdY0R/nYmy7gzTt7T/tCTWtNPp8vidmpVArbtnFdtxRbsTKtV3YmbVKpFJlMhr6+Pq677jq2bNkiF5knQWvNrD3LWGaM0ewoo9lRxrPjjGYq8kH5bGF2Tn9DGXREO+bE5a6M190V76Ip3BQECimHflBUh4CorasKQ1FxXttWEIrIBfHCEOndrM/9pU9wUXSIrZNraEl3YBjKF6ArxOhYspgP+2kgThfbRGIW6gRerPt2H+OnX3mWjv4Eb/7ghSRaTu8JprohRnbs8EOMvPGNdUOMLAba9XDGc9jHUhSOp4I0jc4FwrYBVlelqJ0g1JfEOA3RXlhctNaMzOZ5cbQoaKc4OOYL3EfGMzgV8UlaYqHqkCRdCfpbYzRFQzRFLRIRi3jIFA9u4SXjeV5VCJTp6WlGR0cZGRlhZGSE0dHRqickk8lkXWE7Gp0/3OJyIfZ6YRABWxAEQVhsRMCu5ZHPwfd/A97wF3DF+85o/LTrctUDB9gYi/Dti+t74P79j5/nL+58ht/ZtYHQ7jGSbRFu/M1LiDeHS23yL77IwK/+GoWBAdb84R/S9u53ndY87OE003ceJrd/HCMZovmV60hc0buiNop69PAE/9939rHv+AxXbmrnf711O+etWbrNKs8Ux3F48skn2b17N1NTU6xZs4brrruO8847D+M0b4wIc8k5OcZzvrg9lvUF77HsmJ/PlPPjuXE8fWqP0C4Uc8TtGvG72EapaiG8Xt9SmapfFzEjXNB+ARd2X8iFXReyrWMb8dDK8XpazcgF8cIQftkF+s1/8Cmco2GOpHNklSavYE1LlK19zWztbS6l69vjL0moO7RnjDv/ZR/RhMVbPngR7X1zb0CfCnVDjLz61bS87a0krr4aIxw++SALiNYadzKPfbxa1PZmyx6VZkfUF7X7AlG7P4mZXNp5CifHcT0GJrMlQbsobh8cTXN8Ole3j1KQDPtidjJqkYxUHNHqfCJi0RScJyJWSQRPBvmIZcgNW6EuWmtmZmZKYnalsF0Z27u5uXmOsN3V1UUksnxhD8VeLwwiYAuCIAiLjQjYtWgNX74FDt0DH9gNnWf2CPDnj43x+88O8MUdG7mhs6XOy2h+/atP8P2njvM3r7qAwe8dobkzxo2/eTGxiotGd2aGY7/926T/azet734Xa/7gD1CnefGbPzLDzH8cIv/iNGZbhObXnkP8ou4TeqM1Eq6n+cpDR/jLu55hNufwnqvO4SOv2UJLrPFDo7iuy1NPPcXu3buZmJigp6eHa6+9lgsuuECE7CXA9Vwm85NVovZsYbYcIoQgJm0pwIeuOp9TX9O2HD6k3Lc2X2/cyrAV9erq9a3XNm2n2Tu2l0MzhwAwlcmWti3s7NrJhV0XclHXRaxtWiuCwzIgF8QLQ+fLztFNn/0mu1++k4QLBwZn2D84w/7jfvrcSAo38EhNRiwu6G2qELVb2NyTJHoaITNGj8zy/U8/iWN7vPEDO+g/r+0lzb82xIgRj5O45hqaXv0qktddt2Se2fVwZwu+oF0harsTZRHUaA4Honai5LFttkbk70mDki24HBpPc3wqSyrvkMo7pPMOqZzDbDGfd5jNlfOpnFNqO9/Gk5WYhppfAK8VwWvzYV8EL4rj4RXkTCGcOZ7nMT09PUfYHhsbw3HKIY9aW1vrCtuh0OL/1hd7vTCIgC0IgiAsNiJg12N2CP7hSmjfBL94F5in/2it7WmufegAYcPgPy87D7POBV+24PLOf76fF0dT/Mvrd/DkV56jbU38/2fvrMOjOto+fJ+zHndBQhIIBHe3UuotFeruXqj727ftW/d+dRfaUnfqpS2UUtwlSBISJO6yes58f+zGiEBgYzD3de01c+bMzJldwj47v/Oc5+HUm4djCaj7wSY0jYLnn6fozbewjRpJj//7P4yRka1ajxAC57ZSyn7OxL2nCmNsAKHHJmLtH9FlNqMlVS6e/nULc5dlExlo5o7jUjljRI8u8Wispmls2LCBhQsXUlRURHR0NFOmTGHgwIFSyJYcNKWOUtYVrmNtwVrWFqxlfcF6qj3VAERYI2oFbeml3X7IDbF/GNI7RlS8/h1ToqN4f2ifRucdbo1teZVsyilj055yNu4pZ3NOOVW+GMJGVaFPTFADT+0B3UIIC2j+RnB5oZ15L62lrNDO9Iv603dM3EG/D+FyUbVkCRXz/6Dyjz/wFBSAwUDAyJEEHTmN4OnTMffsedDXOVh0u8cnalfVemx78qtrb7gpNmOth7a1XwSWpNAuczNc0jxCCOxurVbUrnJqVDjd3rprLxF8L0G8vgju7b9/eUfMRpVgS52Hd31BvMb7O9hiJCrYQkywhZhgKzEhFiIDzRj3I9mqpHOj6zolJSVNCts1yScVRSE8PLyRsB0VFYWxhWS5rUXaa/8gBWyJRCKRtDVSwG6ODV/BF5fCtP/A1NsP6Drf5pdw9cYs/i81gbPjI5rsk1vmYMZLi7CaVF6dPpC/391EdM9gTp49DLOt4Y+zsnk/kHPvvRgiIujx0ovYBg5s9ZqELrBvKKT81yw8hXbMCcGEHpeIJTnsQN5ih7Bhdxn//XYDq7JLGdYzjP+dMpAhPcI6eln7ha7rbNy4kYULF1JQUEBkZCRTpkxh0KBBGAxdJ0a5pHOj6RrbS7fXCtrrCtZJL+12Rm6I/cOowf3EpTcewUO9r+W9QUkcF934iaa90XVBdnF1raf2xj1lbMopJ6+8Lj5r9zAb/X1i9qBuIRyZGtNAFHNUufnptfXs2VbKuFOTGXFsL7/9HxG6jmPjRirmz6dy/h84t20DwJKSQtD0IwmePh3rwIEoneTmpu7ScOdWNRC13blV4BGoQSZsg6IIGBKFOVGK2RLv/78qV50HeIWjYb3WE7yeIF4rgNcea1Q63TjcjUOAKQpEBpqJDrb6hG0LMSE+gbtePTrY0qqnLySdA03TKC4ubiRsFxUV1T6NpigKkZGRDWJrx8TEEBERcUC/paW99g9SwJZIJBJJWyMF7Jb44nLY9A1c8Tt0G97q6+hCcPzKrRS6PPwztj/WZjxGVmeXcPYbSxiREMZDo3rz+1sbiU0OYcasYZj2Srxo37CRXbNmoZWUEP/Iw4SeeGKr1wXeRE9VK/Mo/z0bvdyFpW84occmYu4edEDztTe6Lvh69W4e+ymNoion54zuye3HphIR2DViduq6TlpaGgsWLCAvL4/w8HCmTJnCkCFDpJAtaROkl3b7IjfE/mHUqFHi3/PdHDPgScqDe7BwTCqBxgP7jiysdLI5x+ulXROCJKOgEl3ABeMSePjUwQ36a26d+e9vYtuKfAZO6c6Us1NQ28Dz07VzJ5V//EHF/D+oXrkSNA1jTAxB06YRPP1IAsaNa/e42ftCd2k40oqxry/EkVaMcOuowT4xe2g05oQQKWZLDhqnR6Ow0kVBhZP8cgf5FU7yK5wUVDjIL3f6jh0UVrpqQwnVJ9hqbOC9Xb8eHVwndIdYjfImbifH4/FQVFTUILZ2fn4+xcXFtX1UVSUqKqqRsB0eHt7i047SXvsHKWBLJBKJpK2RAnZL2EvglfFgCYGrF4DJ1upr/V1cwZlr03mgdzeuSYhptt9Xq3Zxy2druWBcApf0jOXXtzbQrW84J10/BKO54WbdU1TErtk3Yl+5ksCpUzD3TMAUF4sxNs5bxsVhjIlB3Y+EKMKtUbk4h/K/diLsHmxDowk9uhfGqNa/146g3OHmhd+38e7iHQRZjNx2TF/OG9sLQxfZOOu6ztatW1mwYAE5OTmEhYUxefJkhg4d6tfHIyWSvdkfL+2h0UMZEj1EemkfAHJD7B9GjRolVjx1OstXz2PG8Je5pmc0D/Tp7rf57S6NR3/czIdLs/jimgmM7NUw5rXQBUu+TWfVL9n0GhzJMZcPxGxtu+9mrbSUygULqPjjT6r+/hu9urpTxc1uCt3pE7PXFWDfUgIeHTXETMDgKGxDojH3DJZitqRN0XRBSbXLJ2o7fCJ3Q9E73yd6Oz2NvbqtJrVW0K7z6rb62uqE7shAc5cIW3c44Xa7KSwsbCRsl5aW1vYxGo1NCtuhoaGoqirttZ+QArZEIpFI2hopYO+L7fPhw5kw/gY49pEDut45a9JZW1HN0vEDCGnBc+yxHzfz+sIMHjp1EGMUC7+/t4mEARGccM0QDKaGngPC5SL/ueep/Hshntw89MrKRvMZIiIwxsViion1lnFxdSK3r1QDvF6Wut1DxcJdVC7ajdAEgaNjCZmegCGk47KCt4ateRU88N1GFqcXMSA+hAdPGcjoxKbDtnRGhBBs27aNBQsWsHv3bkJCQpg8eTLDhw+XQrak3ajx0l6Tv4Z1BetYXyi9tA8UuSH2D6NGjRIrfv8Knh/CbVPn8LGSwK+j+jEwyH83WSudHo5+dgEhVhPzZk/C1ISX9YYFu1j4yVaiegZz4vVDCAxte9uoO51UL13a6eNm10d3enBsLqZ6XSGOrcXgERhCzdgGR2MbEuUVs+WNMEkHIYSgwumpFbq9IvdeordP+C53eBqNN6gKUUHmBuFK6ocyia4RvoMsMkllB+N0OpsUtsvLy2v7mEwmoqOjufrqq6W99gNSwJZIJBJJWyMF7P3hh1th+dtw8feQNLnV11tfUc3RK7ZyY69Y7k6Ob7afpguunLOCBVsL+ODyMYTluvjzgzQSh0Rx3FWDMLTwY1irrMSTl4c7NxdPbh7uPG/pycvDnZeHJzcXrZ43Qg1qSAimWK/XtikuFkNkd3QtEU9hIKgQOCaakKN7Ywho+yzgB4sQgp825PLwvE3sKXNw2vDu3H18KjEh1o5e2n4jhCA9PZ2//vqLXbt2ERwczMSJExk5cmS7ZGKXSOqzv17aQ2O8onaPIOmlXYMUsP1Drb2ecyolpTlMGvEWiTYL349IQfXj39pvm/K4cs4K7jwulWuP6N1kn8x1hfz61gZswWZmzBpKeFyg366/L7pa3GwA3VEjZhfg2FoCmsAQZsE2OIqAIdGYegTJ7wtJp8Xh1nyCdsNwJXUit7csqnLS1PYnPMDUKFxJclQgqfHB9I0NlvG5OwiHw9EgtnZBQQEXX3yxtNd+QArYEolEImlrpIC9P7iq4LXJoLnh2n/AGtLqa167cQc/F5bx77gBxFmaFyIrHG5Oe2UxhZVOvr1+IuXrS1j4yVZ6j4jmmMsHHlT8Td3h8InceXjycr1lbm6twO3Oz0MrKARACYjC0v9kjD3GgMeJcBaCoqEYdBQzKFYjaoARQ5AFQ6gNQ1gQxsgQjNFhmOIiMQR3nGdmtcvDK3+m88bCDEwGhRuPSuGSCUldyhtGCEFmZiYLFiwgKyuLwMBAJk6cyKhRozB3slioksOLfXlp14QdGRo9lEFRg7AZu0Y4In8jBWz/UGuvN3wJX1zG56fNY1ZxME/27cFF3aP8eq2rP/DeQP71pqkkRDZtw/J2lPPDy2vRNcEJ1w6hW0qYX9ewv7QUNzv01FMIGN76vB1tie7wYN9UhH1dIY5tPjE73IJtSDQBg6MwdZditqRr4tF0iqpcDb269xK+C3wvl+YNX6IqkBQVSGp8CP3jgkmNCyE1PpjuYTb5/6ADkPbaP0gBWyKRSCRtjRSw95edy+GdY2DoeXDqy62+ZpbdyaSlaZwbH8GT/Vp+5HdHYRWnvPwPMcEWvrpuAumLcvjni+2kjI7lqEsHtGn8PeFy4SkoqBW1nVkluPZYEC4VoakgjKCYwWBBUZoXhIXmAs2BEC4UxYNiFCgmUK0G1AATarAVQ4hP+I7yCt+G8CBUqxHFpPrlB/yOwioemreJ+Wn59I4O5IGTBzI5Jfqg521vduzYwYIFC8jMzCQgIIAJEyYwevRoLPsR41wiaWv29tJeW7CWrPIs4PD20pYbYv9Qa6/dDnimH6L3NE7v8x82VtpZNDaVaLP/nkzJKbNz1DMLGJkYwfuXjm7277SswM68l9ZSXmTnqEsGkDIq1m9rOBA8JSVULVzoDTWyaBGiuprgY44h5rZbMSckdOjamkK314jZBTi2lYIuMERYCRgShW1wNKZugYfFd4Tk8ELXBdnF1aTllrM5p4K03HLScivIKqqu7RNsMZIaXydop8aF0C8umCCLDCXXlkh77R+kgC2RSCSStkYK2K1h/kPw99NwzlxIPbHVw+/Zuov39xSyYEwqfQJaDmvxz/ZCLnpnGUf0jeaNi0ax5tcslnyTQf8J8Uy7ILXDEyIJXaBXOHAXlODJL0UrLsdTUoVWbkevdKFXu9GdGsJFnfCtmlGMNhRjy+9dCB10Nwi31+tb0cEgUAygmBQUi4pqMaJYTag2M2qgBTXQgiHYhiE0EENIIGqAGcViQLUY+DO9iP/9uImsomqOGxjHGSN7YDKqGFXF+zKomAwKRlXFaPC2mQw1dV8/g69NVTCoSodsrrOzs1mwYAHp6enYbDbGjx/PyJEjsVgsqKraYoZ1iaQ9KXGUsK5gXW3Ykaa8tGtjaUcNPCS9tOWG2D80sNc/3gEr32XbdRs4cn0up8SE8dKAXn693rv/ZPLg95t48dzhzBjardl+jko3P766jpz0MibM7MOwo3t2CtFVr66m6L33KHrrbYTbTcT55xN17TUYQkM7emlNole7sW8qonpdIc7tpaALjJFWbEOisQ2OwhQvxWzJoU2l08PWvAo255STViNs51RQ4ayLwZ0QEUB/n6BdUyZEBMiEkn5C2mv/0FYCttvlxF5eRnVZGdXlpYRExRDV07+2XyKRSCRdAylgtwaPC946Espz4LolENQ6b94Cl5txSzZzREQwbw9K2mf/9xfv4P7vNnLtEb2587hUln2fwfIfdjBoSnemnNu3S27qhKbhKSnDk1eMp6AUT3EFWkkVWnk1epUL4dTQXRrCLRAeQFMQQvF5fhtRVDMYLShGK4px/z2QHbqbT3HxgaLh8MPHZkBgBIwKtaUB4T32tRkUMPraavrXjkPs1eZ9GYS3vyp8bcLbz1Bb17ELD7sVF6WKB1URqOiovrEGRWBUauajdl4jYFLAoOiYUDAivMeoGBRQFRUVBVXxvVBRfMcGRUFRvAK5oqoYVAOKqqKqBlSDwdtuMGAwGFANRm/dWK9uMKAoire/oqCq3vm8cyq+DPCq78aAWivGKzV9VQOqqmDwXb/+OVUxoKgKBoNab41KXRxYhbr/JzX/7qqCYlBQDCo0KOvVD5NNoa4LdCHQBb7SW9d0gdirrtX023uMXlf39sXX1zdOB4+msbNiF9tL00kvzSC9NJP8qgJARVUMxAd2p1dwIkHmIAyK999aVVQMiorB4CsVFaPq7W9QFQyqAYOiYFSNGFTv349RqamrGFWD96V4/2YNqopRMWA01NSNGA0G700qxft3alIMGA3eOUyKEYOqgELt/wsFb11RqasrdaX3/4z32GBQD/kNsaIo7wAnAflCiEG+tgjgUyAR2AGcJYQo8Z27G7gc0IDZQohf9nWNBvY6dz28NgmOe4InYk7huaw8Ph/am8kRwX57T5ouOO2Vf9hT6mD+LVMJbSH/g8et8fu7m0lflc/gqd2ZdHbfTiMoufPzKXjhBcq+/ApDSAhR119H+DnnoHTiEFRalRvHpiKq1xXgTC8FHYzRNgJHxxEwMhZDoMwDITk8EEKwu9ReK2hvzq0gLaeczMIqdN9Wy2Yy0C8uuFbQTvWFImnpO0vSNIeSgN2MXX4AuBIo8HW7Rwjxo+9ck3ZZUZSRwHuADfgRuFHsY6O/v/trXdOwV5RTXV5GdVkp1eVl2H1ldb3SXl5OdXkpLru90RypE6cy6ZwLCY2J2/eHIpFIJJJDBilgt5a8TfDGVEg5Bs7+EFopIj+VmcMzO/L4cUQKI0JbTgAlhOCerzfw8bJsnj97GKcM68aSb9JZ9Us2Q4/sycQz+3RJEftgEJqGXlWFXlWFVl6BVlaJVl7tFcAr7eiVDjS7C1HtRne4ES4d4dIQHhAeKBUGclQLmqqiKSoeRcWjGPCg1rXhK1VvXVdVPCh4FG8fDwIN8NS+6o4133FdHTTfcf3zWhPntWbma+NPtFYgV/cuFW9dRUdBNBjTsKz7b6DUa6//l6m00Fa/vak2EPXON27zXr/h/L5DlNqXUq9eN1pBaWL2vVer1L5B4ZtJKPX6KUpdX6XeWKXeKuu114ytf17UHjceU/OqHSNA+EoUxSse4xWOa4TkRnV8wrOvFFC7CZa0DVlPnHTIbIibQ1GUKUAlMKfeRvlJoFgI8biiKHcB4UKIOxVFGQB8DIwBugG/A32FEC1+zTWy169PBd2D/cqFTFuxBRWFP0b3w3oQ+SH2ZsPuMk5+aRHnjEng0dMGt9hX6ILFX21nze87SRwSxTFXDMRk7jzJ2RxbtpD/xBNULf4Xc69exNx+G0HTp3f63w5apQv7xiKqV+XjyioHg4JtcBRBY+IxJ4V0+vVLDi+E984tQhfg0RF71dEFQhOoQSYMIeYD/vt1uDW25lWQllPBZp+n9ubcckqr3bV9uoVa6R9fF4Kkf3wwiZGBGP34HXmocYgJ2E3Z5QeASiHE03v1bdYuK4qyDLgRWIJXwH5BCPFTS9dOTEoUi376AbvPU7q6idJeXoa9soKmMp8qqkpASCgBIaHYQsNq6wGhYdhqyuAQMlcvZ+UP36JrGsOOPZGxp51FQEjnfMpIIpFIJP5FCtgHwj8vwG/3wamvwrDzWjW00qMxdslm+gVa+XJY733+iHV5dC54eylrdpby2dXjGdojlEWfb2PdH7sYcWwvxp2aLDdy7Yiu694NiUcHj4bu0cGtITwawq0jNA3c3nZFVUBVAdWrcfqOFRSveza+46b+/XxNXs9X8Og6Hl3g0b3Hbl+9pt2lCzxC4NZ03Lqo9/IeezSBy1d319Q9Oh5N87V5j12a7q3XvHznNE1H6AIhdHTdV9d10IX3WIja9hrPW+Hz3BX1vHGF8L4n4XtvQoCgpqTuuLZe/+UTe2uF3ToRuL44XCcK06A/9eZQfCKy12vW93HXE+Fr2uv+OUSjegOxXoi9+rVwrDQ1Dw2k9L3PN9V373OqIhrMs3dJU+1Kw/Nqc/32nkep3773HHuvo+lrNT13Q8G+ftn4E1Vq9z/1/82b7Nds2151UX+evf6O9mc99erfPnL1IbMhbglFURKBefU2yluAI4QQOYqixAN/CSH6+by8EEI85uv3C/CAEOLfluZvZK+XvwU/3ApX/slftj6cszaD2xPjuDXJv15YD8/bxFuLMvny2vGM7BWxz/7r/tzJ359tI6ZXCCdeN4SAkM7j6SyEoGrhQvKefApXejoBo0YRc+ed2AYP6uil7Rfu3CqqluVStSoP4dC8Xtlj4gkcGYMqvU0PWYQuEE4N4dEbi8E1dU0gtJp6U22N696y+XrNPE23+dahC4TH1657595fFJOKMdKGMcqKMSrAV9owRtlQA02t/k0vhCC/wukNQeLz1N6cU0F6QSUe351qs1Glb2xQrad2/3hvGRkkc6nAoSVgQ5N2+QGaFrCbtMt4n576UwiR6ms/F69dv7ql61riU8RZ91/ItD2/U5oehPCoWIOCveJzSCgBoaEEhIQ1KL3nvHVrYFDdE5T7oKK4kH8/n8uGP3/HZLUy5pQzGHHCyZgsLYeplEgkEknXRgrYB4KuwfszvI8zX/sPhLUuSdJbuwr4z7bdzB2SzJGRIfvsX1zl4uSXFuHy6Hx3wyRiQyws+HgrGxfuptegSMJiAggINRMYaiYg1OKrW7AEGKW4LTkkEEKA243uciNcToTL1eilu1wIl7uuze1rd9b0957Tq6rQiovxlBSjFdWVwuls8tqKzYYxIgJDRERdGRmBITwCQ2RNWyTGiHAMERGo1uZ/PAvh3Xjrmo5wa2huDV3T0d0eNI/3Boju1rx1j47u0bw3DzwamkdDaN6xdXUN4RG+GyoC3aN5N9we3dvu9var2Wjrbt1Xeo8bra/R10UT3+mKN06OYlDBpHpDrxh9oVdMqrfdd14xes8pBgWM9c4ZFTB4jxWTUtuOQUUxe+eqfxOnqbKlc52hnDp16iG1IW6OJjbKpUKIsHrnS4QQ4YqivAQsEUJ86Gt/G/hJCPFFE3NeBVwFkJCQMDIrK6vupL0UnunnvXl80nNcs3EHPxWW8efoVJID/CfGVDk9HP3sAoKtJubNnoRpP7wXM9YU8OvbGwkMNTNj1jDCYgP8th5/IDweSr/4goIXXkQrLibk5BnE3Hwzpvj4jl7afqG7NOzrCqlaloMruwKMCgGDowkcE4c5UXpld1aEEAiXjm53o1d5vDlSqj1NHNerV7vR7Z4mTdBBY/SGGfPaIV/IMaMvjJhaZ79Qvbaqxbov/JjXnjWs14Qoq62rClq5C0+hHU+Rw1sWOxo8DqVYDLVids3LFGXDGGlt9c0ap0cjPb+qNllkjcBdUFH3Wyc62NJA0O4fH0Lv6CDMxsPLW/swEbAvAcqBFcCtQoiS5uwyXgH7cSHEUb72ycCdQoiTmrhWrb229OgzMu785zmy10LO6fM9PXucTo8e5xAcPLDN3mvRrmwWzn2PjJXLCIqIZMKZ5zPwiOmoaud5EkoikUgk/kMK2AdKyQ54dSJ0Gw4XfefztN0/XLrOpKVpBBtVfhvVD3U/Nl1pueXMfGUxfWKC+Ozq8VgMKou/TidzTQFV5S48zsZPYRuMKgEh5lpBu4HIHVLXZgs2d5q4nZIDR9cFmkdHc/teHh1Pvbrm1vG0dL7+cf3zHh1F8f491b5MNXWlXl1tWDcqTbSpGEwKBqPBV3rbVEPHJMasQQiBqK7GU1zsFbeLitFKfGVxMZ7iIrTiktpSKypCuN1NzqUGBGCIjMQQEY4xon5ZJ3wbI71CuCEiArWDYtKKGi82d81La1h3eUV0b+k7bq6vu/E5vd4xntbbBsWs+hKxGmsTsioWA6p1r2OLAcViRLV6j2vbrEZvaTZ0SEzzQ21D3BytELBfBv7da6P8oxDiy5bmb9Jef3klbP0Zbt1CnjAxaelmhocE8OnQfT/V1Bp+35THFXNWcMdx/bjuiD77NSY3o4wfXlmHEIITrx1CfJ8wv63HX2iVlRS9/gbF778PikLEpZcQecWVGIJaDmvWmXDlVFG1LIfqVfkIp4YxJoDAMXEEjpBe2W2J8Oh1QnNVndCsV7vRqj2Iag9atbueEO0915KHsmI2oAYYfS9Tw9Jm9N0EbaVYXL9ecxNVVUGlU93oEJqOVuLEXWj3Cdu+stCOVupsIOCrAcY6YTuyocitWvZfrCusdLLFJ2hv9sXY3pZXict3U9uoKvSJCfLG1K4nbMcEWzrVZ+dPDjV73YRdjgUK8f5FPQTECyEua84uA9nAY3sJ2HcIIWa0dN1+A/uL6mtfwbCrmsFhW7h2+BtYTG6CgwfRrdvZxMXOwGj0X86K+uzavIGFH75LzvYtRPZIYPJ5l5A8YvQh+zcrkUgkhytdSsBWFGUHUIEvXLAQYlRLCaOaw29ZklfNge9mwbGPwfjrWjX0q7wSrtuUxcv9Ezg9bt+PJwP8sjGXqz9YySnDuvH82cMaGGWXw0N1mYuqMmfDsrzm2EV1mRNntafRvIqqYAs21YncIV6Ru75Ht8VmxGw1YrIaMFkM8gfBQaLrArdTw+3w4HJouB0aLqfHWzr2Kn396tdrxrhdWq3grPshqLFqqBOkjaY6UVrUiOMeUSt2a24dvRWPzbaIgjdZn6me8L23+G1qpr05sbxmrr36mm1GgsItBASbD1jYFEJ4PbmLiupE72KvJ3dD4buuxNP4/x6AGhTk9eQOj8AQGdnIy7u+2G0MD0cxdT1xpqFYXl8M30sId+roTg3h9PhKzVs69jr2nd9fYdwrhhv3Er3bVgw/1DbEzdHuIUQAMhd6n4I67Q0Yejbv7Crgnm27eXVAL06LDffr+7vmg5X8uSWfX2+eQq/I/RN4S/OrmffiWipLnBx92QB6j4jx65r8hXv3bvKfe57yefMwREURPWsWYafPRDEaO3pp+43u0rCvLaBqWS6unRVgVAkYHEXg2DjMvaRXdnMIXSAcHrTqvbyemyrtHvQqb124WghZb1Aai9A2I2qgCUOAEdVWrz2w7lg5zLx99xfh1vEU2/EUOhoI255CO1q5q0FfNdjUQNg21QrdVhTTvsVtj6aTWVhVmyyyJhTJnjJHbZ/wAJM3BEl8MP3jQugWZsNoUDAZFIy+RMomQ02pYDSoGFXF91IxGhTvy9e3M3Go2eu97XJz5/wdQmTUqFFi5mP/4eWdkZi2lhOnlnFetx/ol7Ido6kQVbURG3sS3budTUjIML9/Pwsh2LZsMYs+fp+SnD306D+IKedfSnxKP79eRyKRSCQdR1cUsEcJIQrrtTWZMKqlefwmYAsBH58L6X/A1QshJnW/h+pCcMyKrZR5NBaNTcWynx7cL/2xjad/3doqj7D6eNwa1WUuqsubELvLXFSXO6kqc2GvcDX/6KYCJosBs8WA2WbEZDFgshoxWw21IrfZWtdmsjQ8Z/LVa/p0Bu9vXfN6G+seUet1XN8zuYH3smev826B5tHQ9hrrdnh8gnNjMdrjahy+oSkUhXqfo6FB3Ww1YrQYvEKzqZ7gbKx3XE+ENhr3Ot7rvMGotvrfQugCTfMJ2019bh7RRFvNsWjc3qCvaOKzbqbd11+0QsRXVYXAMAtBERaCwq0E1a+He0tbkMkv3rtCCPSKCjxFRWglJd6yuAStuAiPz6O7QUiT4hLQmhYK1NBQjOHhXrE7MhJzYiKW3smYk3tjTkrqUl6UB4vw6E0K28JR0+bxlg4N4dLQHZ56feuNcWj7HcO0gRhuNaCa6wnctYK3gdAjex1SG+LmaELAfgooqmeTI4QQdyiKMhCYS12yqPlASquTOALoOrw4HEJ7wiXz0ITgxJXb2O10sWhMKqEm/wmwuWUOjnp2AcMTwphz2Zj93nDbK138+Mo6cjPLmXh6H4Yd1bpQY+2Jfd068h5/AvuqVVhSUoi54w6CJk/q6GW1GteeSqqW5VK92ueVHRtA0Jg4AoYf+l7ZQgj0SjeeYgdaqbNpMdru2b/wHApe4XlvIbopD+l6pWJuJqeHxO/oLq2ex7ajTtwusqNX1ns6TAFDqKVWzG4Qczvcus+bB2XVbtJyy2vDj2zOrWBrbgV298GlGFcUGgrbaj3B26Bg8oncTbfV9TcZFAyqiklVavt72+qJ6b72GmHdoKq1onvN3KeP7HlI2esm7HK8ECLHV78ZGCuEOKclu6woynJgFrAUr1f2i0KIH1u67qhRo8TSZcs4+ed5bCqKQN1YRghOpls2kxiwndHTrNjti9C0agID+9Kt21nEx52GyRTm1/eveTys/+NX/v1iLtVlpfQdO5FJ515EeHx3v15HIpFIJO3PoSBgN+nt1dI8fhOwASrz4ZVx3o30Fb+DYf83SX8WlXPuugweTunOFT2i92uMEILZn6xh3ro9vHnhKI4aEHugK28RXdOxV7i94na5C5fd6/VbI8TWiLIuh4a7nuewq965/fXONZpUTDYjJvN+iPj7sTnaVw9dbyy2+uVPV6GBSOwVnOuJ+j4Bun69kehvMTYYYzTJDWFraBBGZW8x3Fd32j1UFjuoLHVSWeKgsthJZamTqhInmqfhjQXVqHiF7VpRu6HAHRRuwRrU+qRL+0LoOnp5eUPv7ia8vD35+bh27mzg3W2Mi8OSnIQ5ubdX2E5KxtI7GUNUlPxbaoFaMbwZb2/hqBO+G4nj9Y+dXjG85xNTDqkNcVMoivIxcAQQBeQB9wPfAJ8BCXgfQz5TCFHs638vcBngAW4SQvy0r2s0a68XPgV/PAyzV0NEMusqqjluxVYu7BbJE/16+uPt1fL+4h3c/91G/u+cYZwybP83wB6Xxm/vbCJjTQFDjuzBxDNSOsUN26YQQlDx62/kP/MM7uxsAidNIuaO27H27dvRS2s1utPrlV25LAf3rkoUk4ptcBSBY+MxJwR32e9BoQu0Mqc3fnKRN36yVuSLp1zkaNI7WrEYar2ga0JytCRCGwKMKFZjh4Rd6uroDgdaaSlaSUlt6amtl6KVlQGgWi0oVpu3tFhrS8VqQbVaUSw1pbVhX6sV1eItFUvz4Tx0h6eBt7an0I67yIGnwI5w1HsSTAVDuBVjZD2Pbd/LEGZp9m9A0wVZRVUUVDjRfAnDPZov0bjmSy6u1SUZ99ab6ONrc2sCTa8/zpc83Jd0XNMbjqvpX9Onyf6aN4l5Tdu+yHripEPGXjdjl48AhuG9bbUDuLqeoN2kXVYUZRTwHmDDGxd7ltjHRr/GXmcX5zF9ZToxu12UplWhCo2j1fWEGh0kJQdyxFE9ycv9gvKKdaiqmejo4+je7WzCwsb69fvZZa9mxbyvWfH912geN4OnH8f4088hMMy/T2pJJBKJpP3oagJ2JlCC1wC/LoR4o7l4my3N41cBG2DTt/DZRTD1Lph2934PE0Jw5pp0NlXZWTpuAMHG/YthZ3dpnPX6v2QUVPLVdRPpF9c28cQOFs2tNwiLUV/cri2dmlccd2p4Wno0FfYrmc/+/AmqqrKX57GC0aSiGht7MdcvjU2Fs6jXR1U7No6z5OAQQmCvcHtF7RKn7+VoUFaVOhvdmDGYVJ/I3YTQHWEhKMyKJbDtEqoKtxvXzp0409NxZWTiykjHmZGJKz0dvbq6tp8aEoIlKQlz794NBG5T9+5dKmRAV0B4dFST4ZDZEHckzdrr8j3w3ECYdDNM/y8A923bxVu7CvlhRAojQv33JIKmC2a+8g+7S+3Mv+UIQlvhzavrgn++2Ma6P3aRNDSKqef1IzDUf8km/Y3uclEydy6Fr7yKXllJ2OmnEz17Fsbo/bvJ3tlw7a70xspeXYBwaZjiAggcE+/1yrZ1vu894dHxlDhqRWqtnljtKXY0fFLEoGCM8AqQxggrhkhfPcziFaxtMjzHgaLb7Q3EaE9JiVeErmmraS+tE6iF3d7sfGpwMIbQUFAUdIcd4XAiHI5mc2nsD4qloahdW1qtvrKeMG6xgsWCagoEQzAQiNCsCI8F3WlEtyug1fuNooIh1FTrtW2KDcIYHYAx2obhIMKvdQRCCHQBbp+ArjUQt71id1J0kLTXfqC+vf5i3WJuKArg1A15rNqtUSYEM6qXExQJZq2aGeefR2J3C3v2fEpu3jd4PBXYbL3o1u1s4uNPx2KO8tu6qkpL+PeLj1k3/2eMZgujZ8xk5EmnYrba/HYNiUQikbQPXU3A7iaE2KMoSgzwG95Hm77bHwG7fpbkhISEkVlZWf5d3FdXw/rP4fLfoMfI/R62urya41du5ZbEWO5Iit/vcbllDma8tIgqp4frp/Xh8klJWPcjzp1EIjlwhC6ornB5xewSJxX1BO6a46pSV6NQJkaTSlCElcAwC8Hhltp6ULiFYF/dEuBfkVsIgScvD1dGBs70DJwZXoHbmZGOVlD7EAuKyYQ5MRFzcnJtKBJLchLmpCRUm/xxf6AcajE1O4oWbzh/dCbkroebNoDBSKVHY/KyNCJNRn4e2RejH0WWjXvKOPmlfzhrVE8emzm41ePXzt/J4i+3o5pUhh/Vk2FHJ2C2dj4BtQZPSQmFr75KydyPUc1mIq+6koiLL+6y3wm600P1Gm+sbPdun1f2kGhvrOye7euVrTs9tV7TniI7WrFPpC5yoJU1TNynmA1eETHSiiHSFwYiwoYxyoohpHkvWYkXIQTCJ0Y38Iau5yWtldaI1HUCtXA4mp1TDQnBEB6GISwMY1g4hvBwDGFh9cowb5ivmrbQ0GZzVwhNQzid6E6voK07HIjaurNO7HZ6j2tLhwPd6UA4nHVlPWG8dr69SuF0Nvu+FEsoalAMalAMSmCstx4YixoUjWIw11uzC+EsQXgqQDgBF+BCUTygelANOhh1FKOOYgLVbEQxmVHMJhSTCcVs9pa1ba08V9vH99rPEIwtIe21f6hvr4UQXPvnD8wTcdy6OI95VSqZQuc09yZijAXYbTbi4wO4+JIbMZkE+fk/sWfPZ5SWLUdRjERFTad7t7OJiJiEovhnf1u8ZzeLPnmfbUsXExAaxoQzz2PQtGMwSCcOiUQi6TJ0KQG7wcUU5QGgEriSjgwhUoO9FF6dAKYAbzxsc8B+D71yww7mF5ezdFx/os3779m1s7iah3/YxC8b8+geZuOeE/pzwuA46QUskXQgui6wl7u84nax12u7pl5Z4qCq1Nu299el0WLwitvhFgJ9ntzB4VYCfW3B4VbMfvIY1MrLa4VtV2adwO3eucsbXxhAUTB16+YVtpOT6wTu3r0xhsvHL/eF3BD7hxbt9abv4LML4bzPoO+xAMzLL+WKjTt4sE83ru7p3+SJj/ywiTf/zuTza8YzOnH/ki/XpzS/miXfpJO+qoCAEDNjZiTRf0I8qqHzesm6duwg/5lnqPjtd4xxccTcfBMhM2b4RTTqKFy7KryxstfkI1w6prhAzAneJ9mELrwCcs0XtPAKMbVtvlIImmlr2Ld+m9B0b3zqyoZet2qgz9M1op5I7SvVQP+HqOqqCCEQ1dWNxOZaMbq0aW/plkRbNTQUY1hYndjcQIwOxRAe3liM7sJil9B1r2BeXyivJ5h7BXJHXR+7A83hQFTp6HYF3WlEuE0IzQrCgsAMihlFaX7vIjxOhLsK4apEOCu9pasS4apqpqwET/P/Zo0wGuuJ23XCtmo2g8mEatpL+K4Rxuu1xd//X2mv/cDe9rrU4eDIhf9idldz3SID3wtYjsap4W6GbP6S7F7JGISLY06dwdgREwCoqkpnz55Pycn9Gre7GKulG/HdzqJb/BlYrfvv6NUSe7amsfCjd9mdtpHw+O5MPvdi+owZL79rJRKJpAvQZQRsRVECAVUIUeGr/wb8D5hOEwmjWpqrTQRsgIy/YM4pMPYaOP6J/R6WXu1gyrI0LuoWxWN9e7T6sovTC/nf95tIy61gTFIE/z1pAIO6h7Z6HolE0j7omk5Vmcsrbhc7aj26K0scVPjK6vLGiVRNVgNB4VaCwy0EhlsIibQS1TOYmF4hBISYm75Ya9blcuHasaPWU9uVnoEzMwNXRmYDjzRDWFijUCTm5GRM3bp1aWHLn0gB2z+0aK89Lni2P/QaD2d/CHhFrgvWZfJvWSV/j0mlu/Xg/1/UUO3ycPSzCwkwG/hh9mTMBxieITejjMVfbicnvYzwuADGz+xD4uDITr15rl6+nLzHn8CxcSPWgQOJuf12rKn9qP3dJ3xqra7XCb7UtdUKvDVtzfUVAqHrtW2qxeINc9SMB+vBoDs8VK8toGp5Llqp05tAQ1G8qTYUpfYYBe+/jUJdG/Xa1Lq+CjQYVzeft58xrCbUh8+TOtKK2ok98dsS4XLhqS9CFxd7xefiEl/86OJG3tLC5Wp6MkXBEBLSUICu5xFtCPN5Rdc/HxLSpcXozoRwa2jVHvSqvZKHVrkbJBL19nGhV3sQjhbCBiqgmEExCq8nt1EHVUNRPYAbcHm9v3UnaA6EZkd47OB2IdxuhGuvskHdhXC50Wv7ukldtlTaaz/QlL3+J3sbZ2yvYEb+YqatTmWhovMTbqbHhXByxjukq2GUh4USEWHm0ktnERzsvZmo604KCn5nz55PKS75B1CJjJxC925nExk5DVU9OJsghCBj1TL+nvs+Rbuyie+bypTzL6VH6sCDmlcikUgkbUtXErCTga99h0ZgrhDiEUVRImkmYVRztJmADfDTnbD0NbjoW0g+Yr+H3bFlJ3Nzivh7TH+SAlofH1PTBZ8u38kzv26huNrFmSN7cNux/YgJtrZ6LolE0vFomk5VqbNRuJL6Qre9vG4zHxRhIbZXCDGJIcT0Cia6VwgWP3lsC13HvSfH562d3kDg1kpKavspFgvmpCSvx3bvGs/t3pgTe6FaOm/c37ZACtj+YZ/2+pd7vTb3ljQI8sZpzrI7OWJZGtMiQnhncJJf1zN/cx6Xv7+C24/tx/XT+hzwPEIIMtcU8u836ZTmVdMtJYwJp/chNjHEj6v1L0LXKZ83j/xnn8OTm9t+FzYaMffogTkpCXNykjeWv+9lCA/v1ML/4YIQAr2ysrEQXVriTT5cUopWXFyX2LCkBL2iotn51NDQOsE5PLyhCN2Ut3RICIpBhtHrSghNoNv3Eryr3ehVdcdatXsvMdwDejN7TQUUqxFDQE3S0r2SlAbWP/YmLFUDTCjeZOnSXvuB5uz1Q0v+5GV7OFdt+5nUjDFsReM9XAwLC+SBfnls+vxLtvfuB4rOxGOO4MiJRzX4Xrfbd7Jnz2fsyfkClysfmy2Bfn0fJDJyykGvWdc0Ni6Yz+LPPqSypJjeo8Yx+dyLiezh32TQEolEIvEPXUbA9idtKmC7quH1KeCuhlkrwbR/8SLznG7GLdnMsVEhvDYw8YAvX+5w8+L8bby3eAcWo4Hrp/Xh0omJMj62RHII4nJ4KMiuID+rgvyscvJ3lFNeWOcpHRYbQEwvr4d2TGII0T2DMJr9+13gKSnxhiPJyPB6bPtibbt37657DF9VMfXo0TAUiS80iSH00HxaRG6I/cM+7XV+GrwyFo55GCbMqm1+MSuPRzJymDM4iWOi/Ps3dt1HK5m/OZ9fbppCYtTBJYvUNJ1Nf+9h+Q+Z2CvcpIyKYewpvQmN7ryxpnWHg/IffkSvqsTnnuzzQvYlMq5xOfa1t9hWr11RG7fp9urap0JcmZm4srIaeOEaQkNrxWzvK9H7PdOzJ4rZf973hxvC7a4LxVHiE57rC9GlJXiK63lOl5ZCMwkJFbMZQ0SELxxHGIZwb90Q4QvP4Ts2RtSJ0tIzWtIUQgiEU2vk5a1Vt+z5LVx6s3MqZpUeD02S9toPNGevXbrOiX/8wW7NyJEbNjOpKJUSXecp4aCHzcxHlw4m7ck72CqCKIiJISjYwMUXX0N0VMPkwbruoajoD7anP0l1dSYxMSfSN+U/WCwHHy7M7XSw6sfvWPbtF7gdDgYdeTSTz7sEW1DwQc8tkUgkEv8hBey2YPt8+HAmnPEuDJq538Mez8jh+aw8fhnVl6HB+x9DuykyC6t45IfN/L45j54RNu49oT/HDpTxsSWSQx1HpdsrZmeVk7fDK2xXl3kFH0VViOgWSGyvYJ+ndggR3QMxtEEMXt1ux7VjR52wnektXTt2NBSgoqK8HpW9k7H0ScGSkoKlb0qXj7MtBWz/sF/2+q2jwFEO1y+tDe/g0nWOWr6Val1jwZhUAv3onZlX7mD6MwsYnhDGnMvG+MWuuuweVv+WzZrfstF1weAjejDqhESsgf4PndGVEZqGOycHV0YGrsxMnJmZuDJ34MrIwFNQUNfRYMDUozuWxCTMycleYbvGazuyc4dr8TdCCPSqqjoP6BohulaYri9Eez2n99s7OiLCl7RwL1G6VrAORwkIOKw+b0nnQ7h1dLsbrda7u6HHd/iM3tJe+4GW7PXW0hKOWbmN8RUbKV4fyuXOCBSPzn+EnUCjgbnXjce26ldWvPchm/sNxGMyMmLCCE48agaGvey3rjvZkfUGWVmvoChmeve+lR7dz/dLssfq8jKWfvUpa379kaiEXpz130exBBzcjWqJRCKR+A8pYLcFugbPDYL4oXDeJ/s9rNyjMW7JJgYHBfDpsN5+WcqibYU8NG8TW/IqGJccwX0nDWBgt0PT41EikTRNZYmzVtTOz6ogf0c5zmoPAAaTSlSPoNrQIzG9QgiPDfB6Q7YBQtNw797tC0VS33M7A728vLafISoKS0ofr6Ddx1empGAICmqTdfkbKWD7h/2y1yvfh+9nw+W/Qc8xtc1LSis5dfV2rk+I4b7e3fy6rjn/7uC/327k+bOHcerw7n6bt7LEybJ5GaQtzsFsMzLiuF4MmdYDo3yKap9olZVeMXuH11vbWeO1vWNHg0R+anCwNxRJYp3ntjEqsjb5W12yt71Kk6ldwlQIXfcl1XOgV9sRDju63eEt92rTHXZv32q7t253oFdVNoglrZWUIJrzjjaZvGJzRIT0jpYctkh77R/2Za/f3bSGu/PgvqKfeX3dMB5QAghx6twiqnGr8OZFoxgXa+Kfe2exzWVhV8+eWGwq5593CQk9ExrNV12dyZYt91Nc8g/BwYNJTX2YkOBBfnkvGauX8+1TDxOfksrp9zyIySJDckokEklnQArYbcWv/4Elr8KtWyEwcr+Hvb4zn/u37+HTob2ZGuGfx5Y8ms7Hy3fy7K9bKLW7OWd0T245uh/RwYdXTFqJROJFCEF5oZ38HRXk+UKPFOysxOP0JlUyWQ3EJNSFHonpFUxwpLVNveiEEHjyC3Bu2+Z9bd+Gc9t2nNu3I6qra/sZu8VjSUnB6hO0LSkpmJOTUa2da3MhN8T+Yb/stbMCnu4Lg06HU15qcOrmtGw+zy3mt1H96B/kv7Acmi44/dXF7CyuZv6tUwkL8G+4iqLdlSz+Kp3sjUUER1gZe0oyfUfHttmNpUMZoet4cnLqCdo+z+2MTDx5ea2bzGBoLGo3JXSbvSUmM5rJhmYKQFPN2DzlKM4qRLVPjK4RnR0ORHW1t6wntu83RiOqzYZqtaIGBNR6QDcrREdEYAgLRw2U3tESibTX/mFf9loIwQUL/+IfdwBvutZww6KePG0LIbZKY7ZeRaEqePzkQZwxoRe75n3JmtfeZMOAIThsNvoN7cfME0/Hslc+FSEEeXnfs237I7hcxfTocSG9k2/GaDz4PXTa4oX88MJTJA0dwSm3/weDUT4RJZFIJB2NFLDbitz18NokOPEZGH3Ffg9zaDoTl24m0mTk51F9Uf24sSizu3lh/jbeX7wDq8nArCP7cMnERCxG6dklkRzu6LqgJKeqQTztwt2V6B7v97o1yOQTtINrk0UGhLR9jFlvAsk9OLduqxO3t23DlZFR51WoqpgTEmoF7RrPbXOvXl4RqQOQG2L/sN/2+pvrYNO3cOsWsNR56Re5PExetpk+AVa+Gd7HrzZ1055yZry0iDNH9uDx04f4bd767EwrZvGX2yncWUl0QjATZvamR2pEm1zrcESvqsK5Ywd6WRnC7fa+XC6E243ucuFxunHZPbgcOi6n7+USuFzg9oDLreLWfC/dgFsYvS9MuDHjUcy1YW0AFKER7CkiVC8iTC0j3FRJqM2F0Wbxic82FKsN1WZFsVpRrTZfm7VWoFZsNlTbXm0d9D0nkRwKSHvtH/bHXhc4XRzx93Li7LncGRzGtT87eCUsnB6lbm7Uq9mu6twyuTezT0zFXVzMirtns70S0lP6YDTD6aefTf9+/RvN63aXk57xDLt3f4TFHENK3/uIiT7uoG/QrZv/M7+98RL9xk/mhNm3oapyzyyRSCQdiRSw2woh4NUJYAmGy39t1dDPcouZvTmb1wb04tRY/8eBTS+o5JEfNvNHWj69IgO454T+HDMgVnrhSCSSBmhunaI9leTvKCfPF3qkJKeqNjdjULilVtSO6eX11LYEtI+QItxuXNnZXi/t+sJ2VhbovoRNJhOWpKQ6YbuvtzR1746i+j/ud33khtg/7Le9zvoX3j0OTnkZhl/Q4NTHOUXcnLaTZ/r15Pxu+/9E1P7w2I+beX1hBp9dPZ4xSW0jLAtdsHV5Hku+Taey2EnCwEgmzOxNZPeuEU6ns+GsdlNe6KCiyEF5kZ3yIgf2Cheuag9OuweX3VdWe/C4m0/+BoACZqsRi82I2WbEEuArbUbMAQ3bDUaV4j1VFGR7QznVhHFSDQqR3YOI7hVc++RLRLdADMa2/Y6SSCRepL32D/trr3/dtZOLthVxbeEv9Ig8kUd+zeKd6Ch6Fji5U9hZoXg4a2A8j50/HIOqsOf7L9n0wsusHTyMipAQEvv14rzTz8fcRKLesrI1pG25j8rKTURGTqVf3wew2RqHH2kNy7//ioUfvsPg6cdy9JU3yP2yRCKRdCBSwG5L/n4W5j8Is9dARNJ+D9OE4KjlW7DrOgvHpGJuI6FlwdYCHp63iW35lUzoHcl/ZwwgNS6kTa4lkUgODdxOjYKdXjG7Jp52WYG99nxojI2YXiHE+kKPRCUEYzK3n8eK7nR6Y2vXiNpbt+Hcvh337t21fRSbrS6udk3ZNwVjTIzfNiZyQ+wf9tteCwEvjYaASLj8l71OCU5bvZ20KgeLxvYnyuy/GL7VLg9HP7sQm9nAj7MnY25D0dHj1lj35y5W/pSF2+EhdUI8Y05KJihchgOrj8vh8YnTDiqK7JQXOigvtFNR7KC80IHL7mnQ32Q1EBBixrK3AF17bMJiM2AO8JU2U20/s8VwQGFdhBBUFDnIz6qoFbQLsuuJ2kaFyG5BxPQKJlqK2hJJmyLttX9ozf76juVL+aDCxGf2X5ivncycxTv4uHss8bureVRx8bNwMqVHOG9cPRaryYCnqIjVd84is8zNpgEDsISaufqSa4mIaHzjWNc97Nr9ARkZzyGEh6TEWSQkXI6qHvhTg4s++YClX3/KqBkzmXL+pVLElkgkkg5CCthtSelOeH4QTPsPTL29VUN/KyzjwvWZPNa3B5d2j2qjBXrjY89dls2zv22l3O7m3DEJ3HJ0XyKD5IZYIpHsH44qNwVZdfG087MqqCr1xnFVVIWI+MAGXtqR3YPaXYjRKqtwpTf01nZs24ZWUFjbRw0JaRCCpOZlDG/9kzByQ+wfWmWvFz0Pv98P1y+H6L4NTm2pcjB9eRozY8N5oX8vv67xz7R8Ln1vObce3ZdZ01P8OndTOCrdrPhpB+v/2oWqKgw7OoHhxyRgth4eyfU8bq1OoC70elB7Paq9dUdlw4SFRpNKcKSVkCgbIZFWgiNthERZa9ssAcZOIUZ4cxM4yM8qpyC7olbUrhHcVaNCVPcgonuFEJPgFbYjugdiMEhRWyI5GKS99g+tsdfVms4xC/6hymXn92527lvfg5/X5/J1Yjcid1TyusHDB55q+kcEMveGCYQHmhFCkPPZXDa/9haLJ47DYzEx8/TTGTpgaJPXcDhy2LrtYQoKfiYgoA+p/R4iPHxMk333hRCCP959jTW//MCkcy5i7GlnHdA8EolEIjk4pIDd1rx7IlTmwQ3LG8RB3Bc1HmPbq50sHdefwDaOU11a7eL537fxwZIsAswGbpyewkXjE9vUm0wikRy6VJU5az20vTG1K3BUeYUlg1ElskcQsb2CfUkiQwiLC0DtgAR1npKSeokjt9d6bevl5bV9DNFRWFNSMPu8tb31FAxBgc3OKzfE/qFV9roiD57tD+Ovh2MeanT60fQ9vJCdz5fDejMx3D9Jkmu4fu4qftuUxy83TSEpqvm/C39SVmBn6bfpbFuRjy3YxOgTkxgwuVuXFjQ1Tcde7sZe4aKqzEl1mYtyn0hdI1BXl7kajFGNCsERVq847ROpQyJtBEd5S1uwqVMI1AdCbcLdrAoKsirIzy6nILuyVtQ2GFUiuwcS0yuEaJ+3dkQ3KWpLJK1B2mv/0Nr99drSck5ctY0Tihfzf1NP4uIvc1mbXcK3vXsQtK2Mr62C5+0VxASY+fSGCSREem1r6XffsfV//2P+tEk4AkMZOHYgZxx7BmozTywXFv7Jlq0P4HDsIj7udPr0uQuzufUhv4Su89Mrz7H57z858rJrGH7sSa2eQyKRSCQHhxSw25qV78H3N8JVf0G34a0bWlbFiau2cUdSHLckxrXJ8vZme34FD/+wmb+2FJAUFci9J/Rnen//PVYvkUgOT2oemc+rF3qkILsCt1MDwGQxEJMYTFxSKLHJocQlhWALbvskkc2t1ZNf0MBbu0bgFva6cCmmbt0axNa2pKRgTk5GtVjkhthPtNpef3we7FoGt2wGQ8N47NWazhHL0jCrCvNH98Pix/Bc+eUOpj+zgCE9Q/nw8rHtajPzMstZ/NV29mwrJSw2gH7j4rDWhLiwNQyJYbYaMFuNBxT24kDRNR17pZvqMhfVFS7s5S6qm3jZy121N7nqo6gKQeEWn9e0T6COsnk9qCNtBIaa2/X9dDRCF5QV2n2CdgUFPo9tl8P7XaooEBBqISjcQlC4laAIC0FhdfXgcCu2EHOH3DCUSDoj0l77hwPZX7+weTOP5jp5If8Tjj3xfs54Yxl5pQ6+6dMD86ZiFoYY+F9pCWazgTlXjWVYgveJuNKvvmbnfffx+/SxlEX0JKBbALMunIXNZmvyOppmJ3PHS2Rnv4XBEERKn7uIjz8dRWnd7wBd0/ju2cdIX7GE42+4lQGTp7VqvEQikUgODilgtzX2Eni6L4y+Ao57rNXDL1ufyYKSCpaOG+DXuJ374s8t+Tw8bxPpBVVMToni0dMG0zMioN2uL5FIDn10XVCaV+310M4sJzeznKJdlei615aERNuISw4hLimUuORQIrsHonagZ6HQddy7d/sE7XrhSDIywO0T3lQVc69e9Pn5J7kh9gOtttdpP8In58I5cyH1xEanfy8q54J1GdyVFMdNfr4x/MGSLO77ZgPPnT2U04b38Ovc+0IIwY71Rfz7dTolOVX77G+yGuqJ2kbMNkOd2G2tL34bGiQprBHDTRYDjmqvp3RTQnRtvcKFvdINTfw8NFoMBASbCAixEBBiJiDEjM1X1n8FhVs69P99V0DogrICO/nZ5ZTkVFNZ7KCy1ElliZPKYkejZJSqqhAQZiY43LqX0O0rw63YgkyH1Y0ByeGLFLD9w4HsrzUhOH3REjY4dOYblmIacS0zX1kMQvBFcndYW8jGaAt35BZiN8Ir54/gqEFe213y2Wfk/Pd+lh4xnMy4Pug2nUvPv5TePXo3e73Kyq2kbbmPsrIVhIaOIrXfQwQF9W22f1N4XC6+fuIBdm7awMm33EOf0eNaNV4ikUgkB44UsNuDTy+A7KU+j7DWidBbqxwcsSyNy3tE8VBK+26I3ZrOh0uyePa3rRhVhVfOH8n43pHtugaJRHJ44XZpFGRVkJtZRl5GObkZZVSXe0MGGM0qMb1CiEsOITYplNikEAJDOz5ev3C7cWVn1yWN3LaNni+9KDfEfqDV9lrzwHMDoNsIOO+TJrtcsSGT34vK+WtMKok2//396Lpg5quLyS6uZv4tUwkPbP8nCIQQeNw6LrsHl92D01e67Boux95t3nan3YPb0bCv5tH3fbEmMJpUAkLN2ILNDUTpwFpx2kJAiAlbsPmwidnd0QghcFZ5qChxUFXipLLEQYWvrCpxUlHipKrE2ejfXDUqdZ7b4fXLOsHbGth1w7NIJDVIAds/HOj+eqfDxfTFq0kt38pXQxLZHjCUM1/9l7gQKx/0jMWzqoDdPYOYnZlDgVFw/4kDuHhyEgDFc+eS97+HSD9iJP9064ERE+OOHseJ4xvfwK5BCJ2cnC/Ztv1xNK2ShIQrSEq8AYOhae/tpnDZq/ni4fvI35HOaXc9QK/Bw1r9viUSiUTSeqSA3R5s+g4+uxAu+Ar6TG/18FvTsvkst4RFY1Pp5cfN9v6SWVjFlXNWkFlYxf0zBnDhuF5ywyKRSNoFIQQVxQ7yMr1idl6m93F5XfPam+BIK3HJXjE7LjmUqB7tnyCyKeSG2D8ckL3+7X5Y/CLcsgmCG3tZ5zhdTF6axqiQQD4emuxXe7Y5p5wZLy5i5ojuPHlG04mlugKaW68TtB0NxW6X3YPbqWEJMNZ5TQebCQg1Y7IY5O+DLogQAnuFm6pSJxXFDq/ndknDsqrUWfu9W4PRpBJYX9RuouwsSTIlkuaQ9to/HMz++qtdOVy3LY8793zGzafdxuIcnUveWc6wHiG8EhONY3ke5X1CuSFtNxlGncvHJ3LvjAGoqkLx+++T99jjVB0zmU+6BxNkDyaobxA3nX0TxhYcx1yuYranP0FOzhdYrT3o1/cBoqL2PySIvbKCzx64i7L8PM687xHiU/od0HuXSCQSyf4jBez2wO2AZ/pC3+Nh5uutHp7jdDF+yWZOig7jpQG92mCB+6bC4ebmT9fw++Z8zhndk/+dMkgmeJRIJB2Cx61RuLOS3IyyWlG7ssQJgMGkEpMQXCtoxyaFEhTe/jf+5IbYPxyQvS7cDi+NhOn3w+Rbmuzy1q4C/rNtN68P7MUpMeF+WGkdj/+UxmsL0vnkqnGMS5ZPLUkODYQuqK5w1Ynaxb6y1FlbrypzIfS9RG6LgeBwC4FhFoIivKJ2TeiSQF/dbJPe+JKOQ9pr/3Cw++trV6zhu3KN74s/YsTpz/D9uhxmfbya4wfG8khQKNVLc3H3j+DWtbtYZfZwdGoML5w3ApvZQNHbb5P/1NNYTjqet1OsmHLMOMOc3HDRDXSP6N7idUtKlpG25T6qq7cTHX0sfVPuw2qN3681V5YU8+n9d+KorOCsBx4nOiHxgN+/RCKRSPaNFLDbi+9mw/ov4PZtYA5s9fCH0/fwcnY+v4/ux8Cg/X/EyZ/ouuC537fy4h/bGdUrnFcvGEl0cMc/vi+RSCSVJQ5yM8prQ48UZFfUPhIfFG4hNinUG087OZTonsEYTG17A05uiP3DAdvrd46HyjyYtdKb1W4vNCE4fsVWcl1uFo3tT4jR4IfVerG7NI5+bgEWo8qPN07G4se5JZLOjK4Lqst8sbfrC92ljtp43FXlrkYx0c1WQ624HVRP6A4Ks2IJNGIJMGKxmTDbDDIeusTvSHvtHw52f13m9nDkohWYqwv5PSqPwLGX89bfGTz8w2YuHpfATdioWpKD1jeM/63L4Q+rm4HdQnjnktHEhFgpfO01Cp7/P0JnzuT7iT3ZtWI3bqObo04+iqMGH9XitXXdRXb222TueBFFMZKcfDM9ul+Iqu775lpZfh6f3H8HQtc5+8EnCI/rdsCfgUQikUhaRgrY7cWORfDeiXD62zD4jFYPL3V7GLtkMyNDApg7tPnkFO3BD+tyuO3ztYQFmHj9wpEM6RHWoeuRSCSSvdHcOoW7Kn0e2mXkZpRTUewAvLFdo3sGE5cUSqxP1A4Kt/j1MXe5IfYPB2yv18yFb66FS36ExIlNdymv5viVW7m0exSP9vVvjok/t+Rz6bvLueXovsyenuLXuSWSroym6VSXubyJJkucVNSEKKkXn9vuy3vQFCaLAUuAL6FogC/BaIAv+ahP6G5wvl7dbDNikAK4ZC+kvfYP/thfLy6p4PTV2zkv7yeeOfIUiB3Iw/M28daiTO46rh/nVChU/rMHrVcIL2zO5/sAN5GhFt69dDSpcSEUvPgShS+/TNjZZ5N59jHM+3oeBo+BiOERzD5pNga15RvKdns2W7Y+QFHRAqKijmLwoBdQ1X07axXt2sknD9yJ2WrlnAefJDgy6qA+B4lEIpE0jRSw2wtdh+cHQ+wAOP/zA5ri5ex8Hkrfw5fDejMxPNjPC2wdG/eUcdWclRRWOnni9CGcOrzlx7MkEomko6kqc9YmhszNLKMgqwKP2+ulHRBqbhBLOyYhGKP5wD1n5YbYPxywvXZVwdP9oP9JcNprzXa7Z+su3t1dyM+j+jI0OOAgVtqYG+au4tdNefx842SSo4P8OrdEciijuXWqypxUljpxVntwVbtx2j04q33JRn2ls9qXmNR33lXtYV9bEaPFgGVv8dtWvy4F8MONQ8leK4ryDnASkC+EGORriwA+BRKBHcBZQogS37m7gcsBDZgthPjF1z4SeA+wAT8CN4p9bPT9tb9+ZPN2Xsyt5N2dr3D8Oc+jG23M/mQ189bl8NxZQ5heKij/NQstNoB3t5fwdagbj0nh5fNGMLVvNAXPPU/RG28Qfv75qDdcxYtzXsRQYqAyrpLbL7id2KDYFq8vhGDXrvfZuu0hIiImM2Twq/uV4DEvYzuf/e9ugsIjOfvBJwgICT3oz0IikUgkDZECdnvy+wPwzwtw6xYIim71cLumM3HpZmLNJn4cmdLhSXGKKp1c99EqlmYWc/WUZO44LhWDKhP1SCSSroGm6RTtqiQ3o9znpV1GeaHPS1tViOoZVBt6JDYplJAo635/7x5KG2J/oijKccD/AQbgLSHE4y31Pyh7/d1sWPcZ3LYVrCFNdin3aIz6dyPHRoXyYn//5pjIr3Aw/ZkFDOoWytwrx3a4zZZIDnWEELidWj1hu0bw7gQCuM2I0WxANSqoqiK/DzoRh5K9VhRlClAJzKknYD8JFAshHlcU5S4gXAhxp6IoA4CPgTFAN+B3oK8QQlMUZRlwI7AEr4D9ghDip5au7a/9tUvXOWnxKnZVV/Gn62diT3oMp0fj4neWsWJHCe9dOobhJR5Kvt6GFmrhi+wKvo7U2ONx8+DJA7lgXC/yn3yK4nffJeLii4m8/TZe//J1CjYXUGYr45TTT+GIPkfscx179nzO5rS7CQsbzdAhb2I07vtG9K5NG/jy0f8S0aMnZ/33USwBrQ8bKpFIJJLmkQJ2e5K3CV4dD8c/BWOvOqAp5uYUcUvaTt4amMhJMWH+Xd8B4NZ0Hpq3iTn/ZjG1bzQvnDucUJupo5clkUgkB0R1uas25EheZhl5O8rxuLxe2rZgU10s7aRQYhJDMFma9tI+lDbE/kJRFAOwFTga2AUsB84VQmxqbsxB2etdK+Ct6XDS8zDq0ma73b5lJ1/mlbB+wkAC/Ryv+qOlWdz79QaeOXMop4/0b5gSiUTiX9pDAAdAAYNRxWBQMJhUVIOKwahgMKqoRtV7zndsMKqovn4GX7+6PipqTT+DisHkm6Ombmii3171hvMrh2WM8UPNXiuKkgjMqydgbwGOEELkKIoSD/wlhOjn875GCPGYr98vwAN4vbT/FEKk+trP9Y2/uqXr+nN/va3KwTFLNzCuZCUfpcahDjyFMrubs1//l92ldr6/YRKxeQ6KPk5Dsxj4aU8VP8YrrKu2c9nEJO45IZXCxx6j5MMPibzyCqJvuYX5y+ez4OcFuBU3cRPimDVt1j5DiuTmfc+mTbcSHDyIYUPfxWTat1d1xurlfPvUw8SnpHL6PQ9islj98plIJBKJpAsJ2Iqi9ATmAHGADrwhhPg/RVEeAK4ECnxd7xFC/NjSXB0mYAO8OgmMFrhy/gEN9+iCacvT0AUsGJOKsZN4PH+8LJv/fruBHuEBvHnRKPrEyMelJRJJ10fXdIr2VJGXUUZupjf8SFm+HQBFVYjsHkhcPS/t0BgbiqIcchtif6AoynjgASHEsb7jBpvnpjgoey0EvDIezAFw5R/NdltWWsnJq7fzYv8EzoyLOLBrNYOuC854bTGZhVXMv/UIIgLNfp1fIpF0HvYlgHtcOrqmo3kEmltH89V1j47m8bV7dO+xpqO5ha9/3Tnv+Xp1zb97L8UnrqvG5kV11eArVQXF4PUo36+6QUFR966r9epeu6oa6o9r4TpNzlevXjPP3nMq3ra693xo2esmBOxSIURYvfMlQohwRVFeApYIIT70tb8N/IRXwH5cCHGUr30ycKcQ4qQmrnUVcBVAQkLCyKysLL+9j/d25nHX9hwe3vEGV5x2N4T1ZGdxNSe9uIj4UCtfXTcBw+4qCt/fhEcI/iyw83eCifmlFRzVP5bnzx5KxeOPUPrJp0Rddy3Rs2eTvSebtz98G1EtKE0s5d4z7yU2sOWQIgUFv7F+w2wCA3szfNh7mM37jm+dtnghP7zwFElDR3DK7f/BYJTOXRKJROIP/Gmz952m9+DwALcKIVYpihIMrFQU5TffueeEEE+38fX9w5Cz4Lf7oCgdIlufjNGoKtyb3I1LNmTycW4RF3brHEkizh2TQEpMENd8uJLTXv6H588ZxvT+Lf8gkEgkks6OalCJ7hlMdM9gBk31ttkrXeRllpPnE7S3LM1lw8LdAFgDTcQmNx2uQkJ3YGe9413A2L077bUhPvCrKQqMuBB+ucf7BFTsgCa7jQ4NpJfVzOe5xX4XsFVV4dGZgznphUU89uNmnjpzqF/nl0gknQdFUTBbjZitbb0lqkMI4RW0a4TuJkTvvQXyGuHb279evV6/2jGa8PVpOJ/HpXuvrQl0XSD0enVf2Vxd6B3/xK6iUCuEH8Y09eZFC+2NG4V4A3gDvDec/bc0uLhHDPPzC3go4RImfX8vqee9Q8+IAF44dziXvLuMu75cz/+dM4yYa4ZQ8M4Gjgy3YNnpJi4lko/T8jj7jSW8deMdhHo8FL7yKhiNJFx3HXfNuovXPnoNdYfK/W/cz3kzz2NKrynNriM6+miGDnmDdeuvYeWq8xg+fA5WS1yLa0+dMAWXvZrf3niJn156lhNm34a6D29viUQikbQvbfprTQiRA+T46hWKomzGuxnuWgw+A377L6z/HI6464CmODYqhNEhgTydmcvpsREEdJLH/UYlRvDdDZO46oMVXDFnBbcd04/rjugt4/xJJJJDCluQmcTBUSQO9t5A1HVBSU6VLzlkOXkZZR28wk7Lfm2K/bohHnIO/HY/rP4Ajmva0VtRFM6Mi+CZHbnscbjoZvWvl3RqXAhXTknm1b/Smdw3mpOHdvPr/BKJ5PBFURRvuBBT59gL7A9C+ARvn+jdqK6J1onjTc2x13yN5qlX58WO/kTanDxFUeLrhRDJ97XvAnrW69cD2ONr79FEe7uiKArPDu7HtMVruS7sZH5a+AyWaXcytW80tx7dl6d/3cqwnmFcNimJmGuHUvjOBibpAnO6nbhB8by2O5/TXv2Xt6+5jXC3h8IXXkQxmYi68kpuvOxGvvvtO1b/u5qvPvqKFZNXMHvibIxq03JGZORkhg17j7Vrr2DlynMYMfwDbLaeTfatYcj043BWV7Pww3cwBwRw9JU3yD2xRCKRdCLaLQa279GohcAg4BbgEqAcWIHXS7ukpfEdGkIE4P0ZULYLZq3yugAcAEtKKzl19XbuSorjpsSW7wK3N3aXxl1frePbNXs4aUg8T54xhABz+3mjSCQSSUdzqD2S7A/aPYRIDZ9dBJl/w61p3hBeTZBldzJ2yWbuTY5nVi//Pz3k8uic/9YS1u0q4/NrxjOkR5jfryGRSCSS1nOo2esmQog8BRTVS+IYIYS4Q1GUgcBc6pI4zgdSfEkclwOzgKV4kzi+2FEhOn8rLOPC9Zlcs/NTHphyEiRORNcFV3+4kj/S8pl7xVjGJkeiVbkpfG8jrp0VrK32UDEqmhd351Fqd/PC2UPp/84zlP/wAzF33UnkJZcAsDFtI59/8Tku3UVxSjEPnPwAcYHN76vLyteyZs2lGAw2hg/7gMDA5H2uf9EnH7D0608ZNWMmU86/VIrYEolEchD402a3y61/RVGCgC+Bm4QQ5cCrQG9gGF4P7WeaGXeVoigrFEVZUVBQ0FSX9mPI2VCcAbtXHvAU48KCOCEqlOey8thcaffj4g4em9nA82cP4+7jU/lhfQ5nvOpNuCGRSCSSw5rlQIqiKEmKopiBc4Dv2vyqwy8EezFs+anZLr1sFsaGBvJZbjFtcTPebFR59YKRRAVZuHLOCvLKHX6/hkQikUgObxRF+Rj4F+inKMouRVEuBx4HjlYUZRveJMqPAwghNgKfAZuAn4HrhRCab6prgbeA7UA63tjYHcLRUaFcHBvCaz3P5u9fn4fqYlRV4ZmzhtIrIoDr564mt8yBIdBE9JWDsfYNZ1iAkaiVhfy3Tw96Rwdx1Yer+OXUawk+9ljyH3+C4g8/AmBg6kBmXTuL0JBQYrfEctu7t7Fg54Jm1xIaMpQRI+ai625WrjqHysot+1z/xLMvYNixJ7Li+69Y9s3n/vpYJBKJRHKQtLmArSiKCa94/ZEQ4isAIUSeEEITQujAm3jvIjdCCPGGEGKUEGJUdHR0Wy+1ZfrPAIMF1n12UNM80a8HIUYD12zKwq7pflqcf1AUhaun9uadS0azs6Sak19cxNKMoo5elkQikUg6CCGEB7gB+AXYDHzm20C3Lb2PhJDu3jAiLXBmXATbqp2srWibG65RQRbeungUFQ4PV32wEodb2/cgiUQikUj2EyHEuUKIeCGESQjRQwjxthCiSAgxXQiR4iuL6/V/RAjRWwjRTwjxU732FUKIQb5zN4j2esy6Ge7vl0iKWTA74SpKvr8dhCDEauK1C0dS7fJw7UcrcXl0VLOBqIsHYBseTX+bgYDFOdyZGM/RA2L53w9pvHHEZVinH0Xeww9T8smnAERGRnLzdTeT3C+ZPoV9+PDjD3nm32dw6+4m1xIclMrIER+jqiZWrjqP8vJ1La5dURSOvORq+k+exqJP5rD6l3l+/3wkEolE0nraVMBWvM/bvA1sFkI8W689vl6304ANbbkOv2ANhX7Hw4YvQWvaOO4P0WYTL/ZPYEuVg/+lt3tosv1iWr8Yvrl+IqEBJs5/aykfLvFfdmqJRCKRdC2EED8KIfr6NsWPtMtFVQMMOw+2z/eG72qGGdGhWFSFz3OLm+1zsPSPD+HZs4axdmcpd325rk28vSUSiUQiOZQIMKi8PKQfhZZIblOGIJa8DkDf2GCeOmMoq7NLeWjeJgAUg0rEmf0InNydZIsB05+7uDomiqumJDNn6U4eGHE+6tQjyX3gAUq//BIAs9nMRedcxPRjptPd3p09f+xh9vezqXJXNbmewMDejBzxCUZjMKtWX0hpacuhUxRV5bhrb6L3qHH88c5rbPr7Tz9+OhKJRCI5ENraA3sicCFwpKIoa3yvE4AnFUVZryjKOmAacHMbr8M/DDkbqgsh/eAM2BERIVzTM5p3dxfyS2HnTBzWOzqIb66fyOSUKP7zzQbu+Xo9Lk/n8hiXSCQSySHMsPMBAWvmNtsl1GTk2KhQvs4vwaW3nY06blActx3Tl2/W7OG1BRltdh2JRCKRSA4VhgQHcE/v7vwQPZWXN6+rDcV54pB4rpqSzAdLsvhipfcmtaIqhJ+YTMjxiXQ3qyi/ZjHDHMijpw1mUXoxN6aeReXko8j5z32UfeeNZKYoCpMnTOaSiy8hTA0jZF0IV39zNYX2wibXY7P1ZOSIj7FYolm95hKKi/9pcf2qwcBJN95BwqAh/PzKc2xfvsSPn45EIpFIWkubCthCiEVCCEUIMUQIMcz3+lEIcaEQYrCv/WQhRE5brsNv9DkKbOGw/uDCiADcnRzP4CAbN6dlk+s8cI/utiTEauKti0dz3RG9mbs0m/PfWkJhpbOjlyWRSCSSw4GIJEia4g0j0oI4fWZsOMVujT+LK9p0OddP68OMod148pc0ft+U16bXkkgkEonkUOCanjGcEhHAo4lX8NdPz4C9FIA7ju3H+ORI7v16PRt21zl0hUztSeiZKUSZVJSfdzC8Et67dDS7Sx1c3+tkdk48lj133U3ZDz/UjklMTOTqy68m1BxKz7SeXPXVVWSXZze5Hqs1nhEjPiHAlsDadVdQUDi/xfUbzWZOue0/xCWnMO/5x8lav+agPxOJRCKRHBjtksTxkMFohoGnweZ54Dy4jbJFVXl1YC/smmDW5iz0TvpIskFVuOO4VF44dzjrd5dx8ouLGvzIkEgkEomkzRh+EZRmw46FzXY5IiKEKJORz9owjAh4Pb2ePH0Ig7qFcuMnq9mS27aCuUQikUgkXR1FUXh2UG/6WeCahGvImncvCIHRoPLiecOJCDRz9QcrKaly1Y4JHhlHxEUDCDYpqD/vIG6nnS+vm4DJaODG+GNYOWEGe+64k/Jffq0dExMTw5WXXUmoJZTUjFSu+foaNhY1nbLDYo5ixIi5BAWmsn79deTl/dBkvxrMtgBOu/sBwuO78+1TD5OXsd0/H45EIpFIWoUUsFvL4LPAY4e0lg3d/tAnwMrDKd35u6SSV3cW+GFxbcfJQ7vxxTUTADjjtcV8t7Zzxu+WSCQSySFE/5PAEgqrP2y2i0lVmBkbzm+F5ZS4PW26HJvZwJsXjSLAYuSKOcsprrfhlkgkEolE0phAg4F3RwxEmGxcZj2C6qVvA95Eya9eMJKCCiezP1mNptc5dAUOiCT6isGYjV4R27ihhG+un0i/uBDui5rMdxPPYtett1Lxxx+1Y6Kjo7ny8isJs4UxPHs4s7+dzeLdi5tck8kUxvDhcwgJGcaGjTexJ+eLFt+DLSiY0+99CGtwMF8/8SDlhfl++GQkEolE0hqkgN1aeo6FsARYd/BhRADOi4/gxOhQHsvYw5ryar/M2VYM6h7Kd7MmMbh7KLM/Xs0TP6c1+KEhkUgkEolfMdlgyJmw6TuwlzTb7cy4cFxC8F1+aZsvKS7UyhsXjiSv3Mm1H66U+SEkEolEItkHiTYLrw7uy6bAPtyWVYbYvRqAYT3DePCUgfy9rZBnf9vSYExA7zBirx8GRhX11x1U/5vLJ1eN44TB8bwaMZJXp1zKjptuoXLBgtoxkZGRXHHZFYQHhDN+93ju+fEevk//vsk1GY3BDB/2LhHhE9i8+U527Wr+ZjlAUHgEM+96AI/LxVePPYCjqvLgPhSJRCKRtAopYLcWVfV6YWf8CRUHHwNTURSe6deTWLOJazftoMqj+WGRbUdUkIWPrhjHeWMTePWvdC5/f7mMiy2RSCSStmP4haA5YX3z3lGDgmykBlr5vI3DiNQuKSGcJ08fwtLMYh74fiOik4YBk0gkEomks3BkVCh39wzjq5jpvPHXx+DwhqU8d0wC54zuyct/pvPrxtwGYwJ6BBN/43BcBhVlfhYFv2Xz4rnDue6I3nwf0o8Hp17Llptvp3JRXULGiIgILr/sciKCIpiSO4Wn5j/Fexvea9JWGwwBDBnyBlFRR7Fl6/1kZb3R4nuI6tmLk2+9l5KcPXz/7KNons6Zy0oikUgORaSAfSAMOQuEDhu+9Mt0YSYjLw3oxQ67i3u37fbLnG2J2ajy6GmDefjUQSxOL+LY5xY2+rEhkUgkEolf6DYM4gbDqjnNdlEUhbPiIlhRXk1GdfvcVD11eHeumepNcvzBkqx2uaZEIpFIJF2ZWX0SOTFQ43/xZ7PopyfBJyo/cPJAhvQI5ZbP1pJe0NCzOSA2kB63jKTCoMKCnez5Lp07jkvlyTOGsCa4J7dOncWK2+6latmy2jHh4eFcdullRIREMC1vGu/+8y5PrXgKXTR+aspgsDB40EvExJzI9vQnyMh4vsUb0wmDhnDsNbPJ3rCOX197Qd7ElkgkknZCCtgHQnQ/iB8K6/0TRgRgfFgQN/WK5ZPcYr7Ja/4x6c7EBeN6MW/WJOJCrVz1wUpu/3wtFQ55F1oikUgkfmb4RZC7DnLWNttlZmw4KrSbFzbA7cf246j+MTz4/Sb+2V7YbteVSCQSiaQroigK/zdiGMmKg6sCjmbXUu/NaavJwKsXjMRsVLnmg5VUORvmtAiIspF02yiKDCosyWH3p1s4c2QP5lw+huKQaG6aeD2/3/0ozm3baseEhYVx6SWXEhkaybT8afy46kfu+vsuXFrj/BWqamLQwOeIjz+DzB0vsn37Yy0K0wOmHMmEs85n099/svjzuX76dCQSiUTSElLAPlCGnA17VkPBVr9NeUtiHCNDArhj606y7V0jLEff2GC+vm4iN0zrw5erdnHc83+zJKOoo5clkUgkkkOJIWeCwQKrPmi2S5zFxNSIYL7IK0FvJ28og6rw/DnD6R0dyHUfrSKzsKpdriuRSCQSSVclyGjgvdEjcButXJZvw75rDQDdw2y8dO5w0gsqueOLdY0E5IAIK31uH8UeRUGszidnzibGJ0by1fWTCIoM484RF/PZ7Y/jzqsL8xkaGsoll1xCZJhXxF6+aTnXzb+OSlfj+NWKYqB/6mP06HEh2TvfZsvW/yKa8NiuYdzMcxg07WiWfPkxG/78zT8fjkQikUiaRQrYB8qg00FR/eqFbVIVXhnQC13ADZuz8XSRBIlmo8ptx/bji2snYDIonPvmEh75YRMOd+eO5y2RSCSSLoItHPrP8Npct6PZbmfGRbDT4WJpWfsJyUEWI29dNBpVgSveX065fBJJIpFIJJIW6R1k4+V+PVgX3Jc7li5C2L3xsCf0ieLO41L5YX0Ob/6d0WhcULiV1NtGkomCvrmYnLfW0zvcxtezptAnJpj7+53Kq3c8i1ZRUTsmJCTEK2KHR3JEwRFkZWZx6S+XUmhv/OSUoqj0TbmfXglXs3v3XDZtvh1d9zTq5+2rcNQV19NryHB+e/Mlstat8c+HI5FIJJImkQL2gRIcB0lTYd1ntbG7/EEvm4Un+/VkWVkVz2cdfJLI9mREQjg/3jiZ88cm8ObfmZz80iI27C7r6GVJJBKJ5FBg+AXehE9p85rtclxUKIEGlc/aMYwIQEJkAK+cP5KsompmzV2N1kVuQEskEolE0lEc06MHt4W7+TxiEu/8/k7tnvqqKcmcMDiOx39KY3ET4blCIm0MuXUkaShoGWXkvLqWSIPKp7OPYGKsmWfjp/Dg3a+hO+ueaA4ODuaSSy4hOjKayfmTqcqp4oIfLyCrvHEOC0VR6N37dpKTbiY39xs2brwJXW8cdgTAYDQy4+a7iejek++efZSC7B3++XAkEolE0ggpYB8MQ86G0izYuWzffVvBzNhwzogN59kduSwrbfx4U2cmwGzk4VMH8/5lYyitdnPaK//w8p/b8WjNP34lkUgkEsk+SZoKYQktJnMMMKjMiA7j+/xSqtvZ7ozvHcmDpwxkwdYCHv9pc7teWyKRSCSSrsgtQ0dxjFLA/QGTWbLU+2Szoig8ecZQkqODuOHj1ewptTcaFxptY9TNI1gnwLO7itxX1mB1aLx749GcFq0zJ2gAN/z3Q1yeuieCg4KCuPjii4mOimZc3jhsJTYu/PFCNhRuaDS/oigkJd1ASp97yS/4iXXrr0PTmg7xaQkIYOZdD2C2Wvnq8QeoKJY5MSQSiaQtkAL2wdD/JDDaYN2nfp/6sb496Gk1c93mLMrcTT+21JmZ2jeaX2+ewrED43jqly2c9fq/MjaoRCKRSA4cVYVhF0DmAijZ0Wy3M+PCqdR0fils/yeAzh/bi4vG9+LNvzP5fMXOdr++RCKRSCRdCVVReGnCEfTSyriiLIY92d5kzUEWI69fOBKXR+faD1c2GZoyLDaAcTcOZ6UGrkIHua+sgRInz95yEleFlfGjEsdF//uSynoJIQMDA7n44ouJjYlleM5wutm7cdkvl7Fo96Im15eQcBn9+j1EUdFfrF13OR5P0/vZ4MgoTrvrAZxVVXz9+IO47NUH/+FIJBKJpAFSwD4YLMGQegJs/Bo8TT9WdKAEGw28OrAXuU43d2zd1WIW5M5KWICZl84bwQvnDmd7fiUn/N/ffLAkq0u+F4lEIpF0AoadByiw+qNmu4wPC6K7xdTuYURquO+kAUzoHcm9X29gZVbHrEEikUgkkq5CiNnEuyMGYDdYuXx9Ok57OQC9o4N4+syhrN1VxoPfb2xybGT3ICbNHsZSl8BV5iL/tbW4c6u5+85zuceYyTKHlTMe/5H88rr8GQEBAVx00UXExcYxYOcABmuDmTV/Ft+lf9fkNXp0P48B/Z+ipGQpa9Zegttd3mS/mMRkTr75Lgp3ZvH980+gebqeE5pEIpF0ZpSuIiaOGjVKrFixoqOX0Zitv8Dcs+DcT6Df8X6f/oWsPB7NyOH51J6cEx/p9/nbi9wyB7d/sZa/txUypW80T50xhNgQa0cvSyKRSGpRFGWlEGJUR6+jq9Pm9vqDmVCQBjetB9XQZJfHM3J4ISuP1RMGEmsxtd1amqG02sUpL/9DldPDtzdMonuYrd3XIJFIDgyHW2NxeiELtxZS4fBgVBVUVcGoKhh8r/r1muO6PioGBQwG1Xus+PoYFFSlqbEqqgpGVW15fsU7h8E3p1FVMRjqze9bw+GAtNf+obPtr3/YuITL862c79jIM8edB4r37/nJn9N45a90Hp85mHPGJDQ5NntjEX+9spbJYWbMJpWoSwdh7h7AFzf9j/usw4gIsjDnuqn0iQmqHeNwOPjwww/ZvXs3hSmF/OX+i5tG3MRlgy5DURr/X8rP/5kNG28iMDCF4cPexWyOanIt6+b/wm9vvMjg6cdy9JU3NDmXRCKRHC7402ZLAftg0dzwTD9ImgJnvuf/6YXgzDXprKmo5vdR/UgOsPj9Gu2FEIIPl2TxyI+bsRgNPHzqIGYM7dbRy5JIJBJAboj9RZvb641fw+eXwPlfQspRTXbZXu1g0tI07u/djWsTYtpuLS2wPb+C015eTM+IAL64djwBZmOHrEMikeyb3aV2/kjL58+0fP7ZXojTo2MzGYgINOPRdTQdNF3Howt0XeDRBZqv7Gw0JX4bVIWYYCtjkyMYnxzJ2KRIQgPa/+aev5D22j90xv314399zfMiiacs2Vw44WQANF1wybvLWJpRzOfXjGdoz7Amx675PZtVX27nyFgbJk0n8qIBmLtb+PWq27greioiOJi3LxvHqMSI2jEOh4OPPvqIXbt24RjgYF71PM7vfz53jL4DVWn8sHpR0ULWrb8WqzWe4cPmYLU2vZdd9Mkcln79GZPOuYixp5118B+MRCKRdFGkgN3Z+OE2WP0B3LYNrCF+n36Pw8X05VtIsJn5fkQKZrVrR37JLKzi5k/XsGZnKScP7cb/ThlIWIC5o5clkUgOc+SG2D+0ub32OOGpFOg/A059udluJ6zcikPT+WNMatutZR/8mZbPZe8v57iBcbx83ojDxjtSIunseDSdVdmltaL1lrwKABIiAjgyNYYjU2MYmxyBxdj0Ux71qRG0deETtjWBJoRP+Ba1r73Fb63+OM3XJoRXKNfqzVevb/PzNRTZNeFdR838bk2QXVzFih0lOD06igID4kMYlxzJuORIxiRFEGrrOoK2tNf+oTPurzVN48JfvuJvSyJfJ1kYlTwEgJIqFye9uAghBN/PmkRkUGOnLiEE89/fzI6luRzTKwi1wkXEOamYuyksvvga7k6eQUFwFC+cO5zjBsXXjnM6ncydO5fs7GxMQ0x8XP4xxyYey6OTHsVsaLxHLS1dwdp1V2AwBDF82BwCA5ObXMuPLz5N2j8LOGHWbfSfdIT/PiSJRCLpQkgBu7Oxczm8fRSc8goMP79NLvFjQSmXbdjB9Qkx3Ne763stezSd1xak8/zv24gMMvPUGUOZ0je6o5clkUgOY+SG2D+0i73+8grYPt9749jQtGfzu7sLuXvrLuaP7sfAoI4L4fHmwgwe+XEzN05P4eaj+3bYOiSSw52SKhcLthbwR1o+C7YWUGZ3Y1QVRidGcGRqDNNSY+gdHXhIP+7v9Gis3VnGv+lFLMkoYmV2CS6foD2wWwjjkiIZ3zuS0UkRhFg7r6At7bV/6Kz769LSPI77dzV2g5Vfxw8lNjgcgA27yzj91cWM7BXOnMvGYDQ0duryuDW+eXY1ZXuqOC4pGJFXRfjMFEwxTtZecBn3Dz2HzUHduH/GAC6ZmFQ7zuVy8fHHH5OZmUnU6CheL3ydMXFjeH7a8wSbgxtdp6JiE6vXXAzA8GHvERw8sIm1uPny0fvI2ZrGGfc+TI8Bg/z1EUkkEkmXQQrYnQ0h4IXhEJYAFzed/MEf3LFlJ3P2FPHZ0N5MiWhsSLsiG3aXcfOna9iWX8lF43tx1/Gp8jFriUTSIcgNsX9oF3u98Rv4/GK4eB4kTW6yS7Hbw9B/NnJZjyge7NO9bdfTAkIIbv9iHV+s3MXL543gxCHx+x4kkUgOGiEEabkVtV7Wq7JL0AVEBpo5op/Xy3py36hOLdS2NQ63xpqdpSzJ8Araq7JLcXl0VAUGdgtlfO9IxiVHMDoxguBO9DlJe+0fOvP+evPWxZyQrTBIL+XL6cdgNnifhvh8xU5u/2IdV09N5u7j+zc5tqrMyeePLsdoUDg6MRh3RhmhJyZhDCliy6VX8vSES1gU1IurpyRz53GptU9HuVwuPvnkEzIyMug1vhcv5L1A77DevHrUq0QHNHa0qq7OZNXqC9G0SoYOeYuwsMZ/ko7KSj7+7+1Ul5ZwzkNPEdm9px8/JYlEIun8SAG7M/Lno7DgSbhlM4S0zea0WtM5dsUWyj0af4xOJfIQEXodbo2nf9nC2/9kkhgZyDNnDWVEQnhHL0sikRxmyA2xf2gXe+2shCeTYdSlcPwTzXa7bH0my8urWD1+IMYODN/h9Gic+8YSNuWU88U1ExjUPbTD1iKRHMrYXd4EjDWi9Z4yBwCDuodwZD+vl/XQHmEynE8zONwaq7O9gva/GUWsyS7FpXkF7cHdQ70hR3pHMjoxgiBLx+1DDhd7rSjKDqAC0ACPEGKUoigRwKdAIrADOEsIUeLrfzdwua//bCHELy3N39n319/+NYerxRAuNhXyxKS6nBf/+WY9Hy7J5pXzR3DC4Kb33Xk7yvn66VXEJQYzKc6GY0MRwdN6ohgzyZ41i7eOuYZvrEmcPLQbT505pDZckNvt5tNPP2X79u30n9ifZ/OfJcwSxmtHv0ZSaFKj6zgce1i95iIcjhyGDH6FyMipjfqU5ecx9z+3YjRbOO/hpwkMk/tciURy+HBICNiKohwH/B9gAN4SQjzeUv/ObmAp3A4vjYRjHoYJs9rsMhsr7Ry/YivTIoN5b1DSIfWY47/pRdz2+VpyyuxcP60Ps45MwWzs2vG+JRJJ1+Fw2RC3Ne1mr+eeA3kb4Kb10Iwt/LmgjEs2ZPLRkGSmR/o/R0VryK9wcMpL/wDw7Q0TiQm2duh6JJJDhV0l1fyZls/8tHz+TS/C6dEJMBuY1CeqNjRIbIj8/3YgONwaq7JKfB7axazeWYJbExhUhUHdQxmfXOehHdiOgvbhYq99AvYoIURhvbYngWIhxOOKotwFhAsh7lQUZQDwMTAG6Ab8DvQVQmjNzd/p99e6xkPfv8rLIZN4Nt7AeamDAXB5dM5+41+25lbwzfUTSYlt+snkLUtz+f3dTQya3I0hVgNVy3MJHB+PqF5J7oMPMG/mbF7RExifHMlrF46sjQPv8Xj47LPP2Lp1K8OmDOO5gufQhc7L019mSPSQRtdxuQpZveZSqqq2MXDgs8TGnNCoT276Nj598C6ieiRw1n8fw2SV30kSieTwoMsL2IqiGICtwNHALmA5cK4QYlNzYzq9gQV4YxrobrhmUZte5s2dBdy3fTeP9e3Bpd2j2vRa7U25w83/vt/EFyt3Mah7CM+dNazZHyUSiUTiTw71DbGiKGcCDwD9gTFCiBX1zjXptaUoykjgPcAG/AjcKPbxw6Hd7PXqD+Hb6+GqBdBtWJNdXLrOsMUbmRIezGsDE9t+Tftgw+4yznztX1Ljg/n4ynFYTftOECeRSBpSk4Bxfloef6blszWvEoBekXUJGMck7V8CRknrsLs0VmV7Be1/04tYu6u0VtAe0sProT0+OZKRvcLbVNA+1O11Dc0I2FuAI4QQOYqixAN/CSH6+ew4QojHfP1+AR4QQvzb3PxdYX/tqcjjvD9+YUlQf74ZmsSIKO/eN7fMwUkv/k2I1cS3N0xsNsTN4q+2s/rXbKae25ceVW4qF+4iYFg07j0/U/zG6yy7/C4eLo0mOSqI9y4bTXyoN2eGx+Ph888/Z8uWLYyZOoaXSl6i0F7I01OfZkqPKY2u43aXs3bdFZSVraZ/6qN063Zmoz7pK5fy7VOPkDxyNCffeg+qKr+jJBLJoY8/bXZHubeOAbYLITKEEC7gE+CUDlqL/xhyNuSuh/zNbXqZK3pEcWREMA9u383mSnubXqu9CbGaePrMobx2wUj2lDo48cVFvPV3BrreNULdSCQSSSdmAzATWFi/0ee1dQ4wEDgOeMV3oxngVeAqIMX3Oq7dVrsv+h4Higpp85rtYlZVTo0J5+fCMso9zTqhtRuDuofyzFlDWZ1dyj1fr6erhHGTSDqa4ioXX6/exayPVzPy4d856/V/efvvTKKCLPznxP7Mv3Uqf912BPfPGMjklGgpXrcRNrOBiX2iuPWYfnxx7QTW3n8MH14+lmumJqMqCm8uzOCid5Yx9MFfmfnKPzz1Sxp/byug2uXp6KV3VQTwq6IoKxVFucrXFiuEyAHwlTG+9u7Aznpjd/naujTG4FheG5hErKuQy9emUeB0ARAXauWl80aQVVzNrZ+tbXavOO7U3vQaFMnfn26jsncoIccmUr2mAEPEUYScchpj3n6cl/q42F1qZ+Yri9mSW+G9rtHIWWedRf/+/Vm2YBk3RdxEUmgSs/6Yxfsb329kv02mEIYPe5+IiIlsTruL7Oy3G62l98ixTLv0KtJXLOXP996UvwEkEomklXSUgH1IGlgGzQTFAOs+a9PLKIrC//VPIMhg4NpNWdg1vU2v1xEcNyiOX26awpSUKB7+YTOnvPwPv2/Kk4ZeIpFIDhAhxGYhxJYmTp0CfCKEcAohMoHtwBifZ1eIEOJfn9f1HODU9lvxPgiMgoQJsLl5ARvgzLgIHLpgXn5p+6xrH5wwOJ4bp6fw1ardvPl3RkcvRyLplAgh2LSnnJf/3M7pry5m1MO/cfOna/k3vZCjB8TyyvkjWPXfo5l75TiumJxM7+igQyqsXlchwGxkUkoUtx+bypfXTmDdA8cw57IxXDUlGQG8tiCDC9/2CtpnvLqYp3/ZwqJthdhdHX9DsYswUQgxAjgeuF5RlMauv3U09R+g0cZJUZSrFEVZoSjKioKCAn+ts02J6DOZd62ZlAojVy5dhtsnVo9LjuSeE/rz66Y8Xl2Q3uRYVVU4+vKBhMbY+OXNjTA4irBTe+PYUoIp6SwCJ04h4Zn7eG+MFV0IznhtMYvTvQ7vBoOBM844g4EDB7L4r8VcFXgV0xOm8/SKp7l94e1Uu6sbXMtgsDF0yBvExJzAtu2Pkp7xXKO96/BjT2LkSaex5pd5rPzhG/9/WBKJRHII01HZN/bbwOL1/CIhIaGt13TwBMVA72mw/nM48j5Q2+7+QLTZxAv9EzhvXQYPp+/hkb492uxaHUV0sIU3LxrFN2t28+xvW7lizgoGdgth9vQUju4fKxPwSCQSiX/oDiypd1xzU9ntq+/d3ogOs9f9T4Kf74KidIjs3WSXYcE2UgIsfJZbzHndIttvbS1w4/QUtuVX8NhPaaTEBDMtNWbfgySSQxy7S+Of7YX8scWbgDHHl4BxcPdQbjgyhSNTYxjSPVT+/uvEBJiNTOkbzZS+0QBUOT2syCrh3/QilmQU8eqCdF76czsmg8KwnmG+GNqRjOgVLkMqNYEQYo+vzFcU5Wu8TzHnKYoSXy+ESL6v+y6gZ73hPYA9Tcz5BvAGeEOItOX6/cmgqVfzzJf/5bqoM/nf+vU8NNQbi/qyiYms2VnKM79uYXD30Nq/vfpYbEZOuHYIXzyxgh9fXc/M20cQYTVS/NlWLEOuxFNagXrfLcx97R2uWVTKJe8s5+mzhnLy0G4YDAZmzpyJqqr8veBvZkydwcARA3lh9Qukl6bz3BHPkRiaWHstVTUzaODzpBmC2LHjJTyecvqm3Iei1OkCU8+/lIqCfBZ8+A4h0TH0HTuxzT8/iUQiORToKA/s/TawQohRQohR0dGNjVGnZMjZULYTdi7Zd9+D5MjIEK7uEc3buwv5rbCsza/XESiKwmnDe/DHrUfw9JlDqXJ6uPqDlZzwwt/8uD5HhhaRSCSSeiiK8ruiKBuaeLUUpqu5m8r7dbMZOtBep57oLTd/32wXRVE4My6CJWVVZNmd7bSwllFVhafPHEr/uBBmf7yabXkVHb0kiaRD2FlczZx/d3DxO8sY+r9fuWLOCr5dvZshPUJ54vTBLLtnOt/PmsQtR/dlWM8wKV53MQItRqb2jeau41P55vqJrL3/GN69dDSXTUrC5dF56c/tnPfWUoY88Ctnvf4vz/62lcXphTjc0kNbUZRARVGCa+rAMXhDgX0HXOzrdjHwra/+HXCOoigWRVGS8Ib9Wta+q25DVAMzj5vNVXnzeLNY5/NdOYDXxj9x+mD6xgYz+5PV7CyubnJ4WGwAx1wxkOI9lcx/fzO2IdFEXjQAd4ET29gbUaN7ot16A3NPTmRYQhizP17NGwvTEUJgMBg47bTTGDp0KAsWLCB8WzivTHuFQnsh5/5wLn9m/9ngWopiIDX1URJ6Xs6uXXPYtPl2dL0ujI6iqhx3wy3Ep/TjpxefYc/Wtg0/KpFIJIcKHZXE0Yg3ieN0YDfeJI7nCSE2NjemKySZAMBVBU+lwJAzYcb/tfnlnLrOCSu3kuN08+foVGItTSewOFTwaDrfr9vDi39sJ6Ogir6xQcw6MoUTBsdjkJsaiURyEBxGSaH+Am6rSeLYXOInYAfwpxAi1dd+Lt7EUVe3NH+72+vXp4DBDFf83myXXQ4Xo//dxG2JcdyaFNd+a9sHu0vtnPLSIkJsJubNmkSAuaMejJNI2gePprMyq4Q/0vL5Iy2fbfneBIyJkQFMkwkYDzsqHG5W7Cjh3wyvh/aG3WXoAsxGleE9w7xJIXtHMqxnWAMP7cPBXiuKkgx87Ts0AnOFEI8oihIJfAYkANnAmUKIYt+Ye4HLAA9wkxDip5au0WX21/Vwp//F2esyWRU6mO9GD2BIcAAAOwqrmPHSIhIiAvjy2gnNevSv+T2bf77YzuiTkhhzUhLOjDIK39+IYoLK3x7BECCI/+BDbv91Bz+sy+GSCYncd9IADKqCrussXLiQv/76i7i4OKbNmMZ/V/+XTUWbuHrI1Vw79FoM9RIzCiHYkfUKGRnPEh11NAMH/h8Gg6X2fHV5GR/fdxvOqirOffhpwuO6te2HJ5FIJB2AP212hwjYAIqinAA8DxiAd4QQj7TUv0sZ2C+vhG2/wG3bwGjZd/+DZGuVg2NXbGFMaBAfD/UmUTnU0XTBD+tzeHH+NrblV9I7OpBZR6Zw0pB4jIaOerBAIpF0ZQ6HDTE0KWAPBObifTS5GzAfSBFCaIqiLAdmAUuBH4EXhRA/tjR/u9vrBU/Bnw/DLWkQEt9stzNWb2eX08W/Y/t3qli5/2wv5IK3l3LO6AQemzm4o5cjkfid4ioXf23xCtYLtxZQ7vBgVBXGJEVwpE+0To4O6uhlSjoB5Q43yzOLWZJRxJKMYjbsKUP4BO0RCWGMT45iXHIE43pHHRb2uq3pUvvrehT8+QzHOQaBLZxfJ4wg0nfz94+0PC57bwUzR3TnmTOHNmnrhRD8MWczaf/mctxVg+g9IgbX7koK31mP8Hio+u1RzIkR9HjrLR77I5O3F2Vy/KA4njt7WK0ovmXLFr766isMBgOnnn4qH+R+wDfbv2FS90k8PvlxQi2hDa65c9cctm59kPDw8QwZ/DpGY2DtuZLcPcz9z21YAwM596GnCQhpOFYikUi6OoeEgN1aupSB3fY7fHQ6nP2RNz5nO/DBnkJu37KL+3t349qEwyeWpq4Lft6Yywvzt5GWW0FSVCDXT+vDqcO6SSFbIpG0ikNdwFYU5TTgRSAaKAXWCCGO9Z1r0mtLUZRRwHuADfgJmCX28cOh3e11/mZ4ZRyc+CyMvrzZbp/mFHNjWjbfj0hhdGhgs/06gsd+2szrCzJ47YIRHDeoeRFeIukKCCHYlFPOnz4v69U7SxECooLMHNEvhumpMUxKiSLYemg/NSg5eMrsXkG7xkN7U045QkDWEycd0va6vehS++v66BprPrmWU2KvYEywlY9HDcboexL3+d+38vzv23jolIFcOD6xyeGaW+frZ1dRtLuS0+8YSVSPYNz51RS+vR6t0kn1n08SMDKZ7s8/x9uLs3jkx82MTAjnzYtGER5oBqCwsJBPPvmEoqIijj76aHZG7OTx5Y8TGxDL/037P/pF9GtwzZycr9mcdifBwYMZNvRtTKaw2nO7t2zm84fuITapD2fc9zAmc9s7wEkkEkl7IQXszo7mgWdTIWE8nP1Bu1xSCMEVG3fwa2E5P4xMqX2c6nBB1wW/bsrjhfnb2JRTTkJEADdM68NpI7pjkkK2RCLZDw51Abu9aHd7LQS8OALCE+HCr5vtVunRGPzPRs6MC+fJfj2b7dcRuDw6p7+6mOzian6+aTLxobaOXpJE0mq25VXw3uId/LFXAsYaL+vBMgGj5CApq3azNLOIYwfFS3vtB7rU/npvKnL55PP/cFPSDVzbLYz7+yUC3j3hlXNWsGBrAZ9ePY6RvSKaHF5V5uTzx1agqgpn3j0KW7AZT4mDwrc34CmqovqfFwk5dgSx99zNj+tzufnTNfSIsPH+pWPoGeHdZzudTr7++mvS0tIYPHgwieMTueOfOyh3lvPf8f9lRu8ZDa5ZUPAb6zfMJiAgkeHD3sdiqXM627pkEd8//wR9x07kpBvvQFHl/lUikRwa+HOPLb8Z2wKDEQadDlt/Bntpu1xSURSe7teTKLORazdmUeU5vJKfqKrCcYPi+GH2JN66aBRhASbu+HId057+i7lLs3F59I5eokQikUjaAkWB1JMgc2GLNjfIaODE6FC+zS/FoXUum2A2qvzfOcNweXRu+XQtmkxQLOkiCCFYvruUCz5fzfS5y/j4/9k76/C2jqwPv1csS2a2EztxEoc5DZR5i9ukzAxpu8Vtt9vddrftt1hm2JQppbQpU8qUpmFOHLATM6NYd74/rizLtuyQbMvOvM+j586dO3fuWLZ1NL85c055LWnDE7h+9hgW/elwPvjDQdx0TD4TZQJGSQSIjzFy7NjoyWMg6UNiMzj7sHO5pOQ9niqtZ2FFHaDNCR88axLZiVaufnU5lY2usLfb4s2ccPV4HE0ePvvfWvw+FUOihdS5EzBmxBJz4HU0fbWe2hde5MQJmbxy2XSqm9zMefJnvtlYCYDZbObMM8/kyCOPZM2aNfz2wW/MO2Qe41LG8Zcf/8K/f/03XtUbfGZq6jFMmvgsLlcxy5afhdO5M3gtf+bBHHbeJWxe/CPfv/5iz71vEolE0o+RHtg9RckymHck/P4xmHJhrz32p7omTl+5lXMyk3hwVE6vPTfaEELw7eYqHllUwMqd9WTFW7j68GGcecBgmRhIIpGERXpgR4Y+sdc7l8Bzx8Cp82DCmV02+662ibNWbWXe2CGcnJbQe+PbTd76bSd/WrCaPx03kmsOH97Xw5FIcPlVytxeStweil0eSlxaucTlZXuLixK3B383MeXteh2DLKa2l9nIIIuJwYHzVJNhv8jdIoks0l5Hhn43vw6D5+t/ckbjYFbHj+OjA0Yz1q7tYNpY3sicJ35mXHYcr18xs8sduZuXlPPl8+sZe2g2h5+rhf1QnT6qX1yHp7AB18pXSL1+NvEnnkhBRRN/eH0FmyqaOGvaYO44aXQwFNLmzZtZsGABOp2OOafNYWHdQl5Z/wpT0qbwwOEPkGJNCT6zoWElK1ddil5nYdLkl7DbRgCB+NwvPM3Kzz/m0PMvZdpJc6IqZ4dEIpHsDTKESH9ACHh8GsRmwsUf9eqj/72tjEeKKnhmbC6npCX26rOjDSEEP26p5pFFBSwtqiMjzsLcw/I4e3pOl9mpJRLJ/omcEEeGPrHXqgoPjobB07sN3eUXgqk/r2dCrJWXJ+T14gB3DyEEf3h9BZ+vK2fB1QcycXBCXw9JMoARQlDj9QcEaU2ULu5QrvL4Ot2XZNCDw0dDrROjRzAzK55TR2cwKj6GZKOBKo+PYpcmeBcHhO9il5dil4f6DjsETYpCtsUYInC3it1aXZbZhFF6bks6IO11ZOh38+tw+H1UvnYev0u7Ap09lc+mjyHVpInKH6wq5fr5K7j4wCHc9fuxXXbxy3tbWf55EYedk8+4wwYBoHr81Ly8DveWBtwb3iPjL2djmzEdt8/Pw4sKeOa7rWTGW7n39AkcNFwTp2tqanjjjTeorq7mmGOOoS69jrsX343daOfBwx9kUtqk4DObmzexYuVFCOFj0sTniYuboD1X9fPRw/+l4NefGX3IERxz+bUYLZYeevMkEomk55ECdn/h2//Ct/+Cm9ZB/KBee6xXFcxeUcDmFheLDhhJrlUmghBC8MvWGh75qoBft9eSGmvmqkPzOG9GLlaTFLIlEomcEEeKPrPXH90Eq96AP20DY9cxpP9vaynP7KxkxYFjg5PcaKLB4eX4R77HZNDx0fWHYDcb+npIkn5KOO/pUIG6xO3B1SFcjVWnMMhiIttsIttiJDuk7Gv28taPRXy6upQYo56LDhzC5YfkkRRIarY7NPn8IeK2t60ceFV0EMx1QIbZ2MmDu/WVbTFi08vvcfsb0l5Hhn45vw5HYxmrXr6Q2aP/yfj4ON6eko85EEP6/z5az3M/buehsyYyZ3L4+biqCj55ajU719Xy+xsmkT1ScwATPpWa19bi2tCAp/ArMu8+B+tIzUt7+Y46bnlrFduqW7hwVi5/Pn4UMSYDbrebhQsXsmHDBsaNG8eYQ8dwy4+3UNZSxm0H3MZZI88KelU7HEWsWHkRXm8dEyf8j8TEGdpzVZXF773Jz2+/TnL2YE6++XaSs6Mrd4dEIpHsLlLA7i/UboNHJ8PRd8PBN/bqo3e6PBz92yaGWE18OGUEJpkIIsjibTU89nUBP22pIcVu4opD8jh/Zi42KRJIJPs1ckIcGfrMXm/5Cl49Fc6eD6NO6LLZxhYnhy/ZxD9GZHP5oNReHODu8+u2Gs6et5jTpgzi/jMm9vVwJFFIq/d0scsTxoNaE6fDeU+nmwztRGlNrA4cLSYSDfpOW9YLKpp49OstfLQPwvXu4lZVSgPe2jvdoeK2Vlfq9uDrMHVJMurbwpJ08OAeZDGREOZnkvRvpL2ODP1yft0V277jg08f4soxd3FWRiIPj8pBURS8fpXzn/2VVcX1LLj6QMZmxYe93eP08c5/l+Js8nLG7dOIS9EWwoUqqH1tFc51TfgqlpD1r/MwZWpx2J0eP/d9vonnf9pObnIMD5wxkWlDkrQdwD/+yFdffUV6ejonnXYS/1nzH74v/p7fD/s9d868E4tB86p2uctZufJinM4djB/3BCkpRwTHVLR6JR8/dh8+t5tjr7qOUQcd1sNvokQikUQeKWD3J549BjwtcM3Pvf7oT6rquXRtIVcNSuXuEdm9/vxoZ2lhLY98VcAPBdUk2UxcdvBQ5kzOJiuha889iWSg4PWruLx+XN7WY6Ds8+P0BM59odfa2joDZbfPT2KMicx4C5nxVjITLGTFW0mNNbCHe+0AAQAASURBVKPvh1u+5YQ4MvSZvfZ54L7hMPpkmP1Et02PXboJgC+mjeyNke0VD3yxice+3sJj50zm5IlZfT0cSS/j8quUur2UBMJwtMWe7s57WhcUbtt7UGt1GWZj0Ctxd+hN4Xp38QtBRavndogH984Qoduptk/SamuNw21uE7YHh3hxp8k43P0Oaa8jQ7+dX3fFDw9w35YdPDDkEv42LItrctIAqGpyc/JjP2I0KHz4h4NJiAn/GVZf4eCd/y7Fnmjm1FunYrJozk1CCGpfW4ZzrRN/w0ay/nYKxsy04H2Lt9Vw6zurKK5zcvnBQ/njsSOxGPUUFBSwYMECFEXh1NNOZVHzIp5c9SSjk0bz0BEPkW3X5uceTy0rV11Kc/MGxoy+j4yM3wf7bqqt5qOH76V003om/e4kDrvgMgzG6Ns9JpFIJF0hBez+xJJ58MktMPcnyBjX64//6+Ziniup5uXxQzk2JfyK8/7O8h11PPZVAd9sqgJgVEYsh49M48hRaUzJScDQRdIPiSSSqKrA5esoEgeE4hDRWBOWNZHZHRCYnR5/u3tdYdq2Cs4ur4rT68ev7t1nv1GvYDHoMRv1mA06als8OL3tY5oadArpcRZN2E6wkhUfWtaE7mSbKeo84uSEODL0qb1ecAVsWQS3FIC+6101zxZXcUdBCd9OH8koW3QuWnr9Kmc8/Qtbq5r59IZDGJQY09dDkvQARU43n1c3tPOeLnZ5qPa2955WgHSTsZ0onR0QZVvrwnlP7w3RKFzvLkIIar3+kNjb7T24i10e6sLE4c6yGEO8t9uE7pyAZ7o+yuzV/o6015Gh386vu0JVUd84l6sMM/ko9TBeGp8XnP+u2FHHWc8sZuawZF64+IAuHS12rq/lw8dWMnRiKsddOQ4lpF3NSz/i3CBQnVUknzcG2/RRwWvNbh//+mQDr/+6g+Fpdh44YyITBydQU1PDm2++SVVVFUcffTTewV7+8uNf0Ol03HvIvRyYfSAAPl8Tq1ZfRX39EkaOvIdB2ecG+/b7fPzw+oss+3ghGcPzOfnGPxOX2iagSyQSSTQjBez+REs1PDASZl0Lx9zT6493qyonLSug2OVh0QEjybZE/+Sjr9hS2czXGyv4ZmMVvxXW4lMFcRYDh+ancuSoNA7LTyXZLuOJS3aNy+unvMFFWYOL8kandgycVza5cXlCBWrN09njU3fdcRh0CliMeu1l0GEx6bEY9FiMOixGPdbANXO7c12gjT7QXhfsI3i93bGt/44LOkIIGpxeSutdlDU4KW1wUVav/cyl9W0/u8ff/uczGXQBz21LUNTOjLeS1XqMtxJnNfSqyC0nxJGhT+31uoXw9kVw0Ucw9JAum1V5vEz+eR1XDU7jzmHR6928o8bBCY/+wOjMWN64cla/3NkgCY9bVXlyRyWPFFXgUkWX3tOtAnWm2djj4eD6s3C9J7T4/IHwJJ1jcBe7vFR4vITOjoyKwiCLkSFWM7lWM0MsJnKtJoZYzeRYTTIGdx8g7XVk6Lfz6+5w1uGYdyyzh93OVvswPpqaz2i7tlD9+q87+Mt7a7juyOH88diud2Ct+monP75dwAEnDmH6ye0TPjd8sZyGT8tQjDHETLSQdM7MdiL395uruG3Baiqb3Fx92DCuP2oEwu/l/fffZ/369YwdO5YpR07h1p9uZUvdFq6fcj2XjbsMRVHw+12sXXsd1TVfMyT3aoYMuQ69vm3uWfDrz3z21MPo9HpO+MMfGTpZ/gtIJJLoRwrY/Y3Xz4Ky1Voyxz6IRb3N4eaYpZsYa7fy7qThGOQEeJc0urz8VFDN1xsr+WZTFdXNbhQFJg5K4IiAd/bYrDh08r3c72h2+yhv0ITZVnG2vLFNoC5vcFLn8Ha6L95qJDPeQmqsGZvJ0Fkcbj036LCaAqJzQIi2dmhnNWoe0BajDpNeF3WezB1RVUFNi0cTuANCd6jAXVbvpKLJ3ckrPMak1wTuBGswTElWB6E7krHr5YQ4MvSpvXY3w715MO0SOP6/3Ta9cPU2Vjc5WXbgmKj2rnx3eTE3v7WKm4/J5/qjRvT1cCQR4Me6Jv68uZgtDjcnpyZw57BMBlv6blfK/iJc7y4eVUt+udPlYYfTQ6HTTaHLQ5HTTZHTQ30HD+5Uk4EhFjO51jZhe4jVTK7FRKqpdxdi9xekvY4M/Xp+3R1lqyl95RyOn/wUZnsyn04dSbLJgBCCPy9Yw5tLd/K/C6Zy7NiMsLcLIfj6lY1s/LmM310xjuFT23s7uwqKqHhwEfr4fPQJPtKuOQh9XNvnZYPTyz0frmfB8mJGZ8bxwBkTGZ0Zy08//cSiRYtIS0tj9umzeXjjw3y6/VOOHHwk/zz4n9hNdlTVy8ZNd1JW9jZWay4j8/9OcnJb7Ou68lI+fPDfVBVtZ+apZzHrjHPR6eQimkQiiV6kgN3fWPMOLLgMLvoQhh7aJ0N4r6KOq9cXcUNuOrfnZfbJGPorqipYV9oYELMrWVVcjxCQGmvm8IB39sEjUoi1yHhk/RkhBI1OH2UdPKZbxerywKvJ3TkpVrLNREbAmzgjILRmxLWdZ8RbiDHJJKHd4VcFlU2uNoG73kVp4Njq2V3d7KajyYqzGNoE7mC4krZ43BnxFizG3ftiLyfEkaHP7fXrZ0PFWrhxDXQjHH1YWc8V6wp5c+IwDkuK7cUB7hlCCG58cyUfrS7jratmMTU3sa+HJNlLqjxe7t5SyjsVdeRaTPw7fxBHJsf12XhChWurUc/F+7lwvbvUe30UuTRhu8ipCduFAaG71N3eeztGryM34LGdGyJsD7GaGWTpea/6gYq015Ghz+11T7LiVZZ//RhzJj/J5IQ43po0DJNOh8vr58xnfmFbVQvv/+EghqXaw97u96osfGg51cXNnHrrVFIHt/+e4Kuro/TPT6HEzkAx6Ug6fzwxY1LatflyfQW3v7uGBqeHG44awdzDhlG4fRvvvPMOAKeddhqLvYt5YOkDDI4dzMNHPMywhGEA1NT+yObNd+FwbCc19Xfkj7gDi0XbMeb1uPn6+adZ+82X5IybyInX30pMfEKE30CJRCKJDFLA7m94HHD/CBg7G07pPrFUT3Lzxh3ML6vljSifrEc71c1uvt9cxdcbK/l+cxWNLh8GncIBQ5I4YpQmaA9LtUuPmyhCCEFti6dNmG7sLEyXNbg6xXJWFEiLNZMRbyUzzhJWpE6LM++2QCrZNzw+lYrGNs/tdgJ34BjO+z3ZZmoLURIQukM9u9PjLBg1T3Y5IY4AfW6vV7wK718LV34LWZO7bObyq0z8eR3HJMfx+Jjc3hvfXtDo8nLCIz8A8MkNhxAnF0z7FaoQvFpawz+3leHwq/whJ43rc9Ox9lGOjY7C9UUHDuEKKVxHBLeqUuzyBAXtHU4PhS5N4N7hdOMM2WmkA7IsRoZYAsJ2UOQ2kWsxEW+UC99dIe11ZOhze93TfHAd7xaXcs3oOzkvM4n7Rw5GURRK6p2c/NiPJNtMvHftQdi72M3X0uDmnf8sBQXO+PMBxMS1/4xUPR5Kb/8Xfkc++vjB2Gakk3DSMJSQeUFdi4c731/LR6vLmDgongfOnEiSwcsbb7xBVVUVRx11FKZhJm79/lZcPhf/OPgfHJN7jNa/6mbHjufYXvgEoDB06HXkDL4EnU4bx9pvvuSr557CYrdz4o23MWjU2J55HyUSiWQfkAJ2f+S9q2HjR1piKaOlT4bg8Ksct3QztV4fXx8wkjSznADvKz6/yvId9XyzqZJvNlaysbwJgMFJVo4YmcYRo9KYlZcsBc4exK8KaprdISE9nAGBun2Ij44xpvU6hYy4Ng/pNoHaGhSqU2PNGGUSz36F0+PvHKIkNHRJfWcv+taFiiV/PUZOiCNAn9vrlhq4fzgcfDMcdWe3Tf+0aSdvl9ex5qCx2A3R/Tm9rKiWM59ZzMkTMnn47K6FeUl0sbbJwZ82F7O80cFBCXb+kz+IEba++R4oheu+RQhBpceneW6H8eDumLgz0aAnNyBsBwXugPd2ptmIbj92lJACdmToc3vd03hd8Nwx/Md+MA9nn8U9w7O4crAWDuTnLdWc/9yvHDcugyfOndKl41FlUSPv3r+ctNxYTrlxMnpDh1wwqkrlQ4/S8ksNpuHHYkixkHTeGEyZtnbtPlpdyp0L19Li8XPrsSM5f3o2H334AevWrWPMmDHMOmYWf/7lz6yuWs2l4y7lusnXYdBpwrrTWUxBwT+oqv6SmJjhjMz/O0lJWvLHysJtfPjQv2morODQcy9m6klzpBOVRCKJKqSA3R/Z+jW8MgfOeEnzxO4jNrY4OX7pZqbF23hj4rCojvvZHympd/JtQMz+aUsNTq8fi1HHgcNSOGJkKkeMSmNQYkxfDzOqEULg8as4PX4cgVeD0xsQpJ0hHtTaq6LRha9D7GSTXtcmTLcTqK3BxIHJdrNMiLaf0uTydorBXdrg4oEzJ8kJcQSICnv94klaEuVrF3fb7LeGFk5eXsAjo3I4KzOplwa39zyyqICHFm3mobMmMmfyoL4ejqQbmn1+7ttezrziKpKMBu4ensWp6Yl9IixI4bp/0OzzsyMgbBc622JuF7rcFLs8+EK+6pgUhRyriVyL5rEd6sGdYzH1mXd/byEF7MgQFfa6p6ndjvq/w7l8zD18FjuRVyfkBUM3PfPdVv796UZuP34UVx02rMsuNv9WzpfPrWfMIVkcfu7IsJ/jdW++RdVTb2OddhmK2U788UOxH5TVrm1lk4u/vLuWRRsqmJabyH2nT6B08yoWLVpEamoqp55xKvO2zOOtzW8xLnkc106+loOyDgr2UV39DZs334PTtYP0tJMYMeIvmM3puB0tfP7UIxQs+ZnhB8zid1ffgMUWPjSKRCKR9DZSwO6PqH54cDRkT4NzXu/TobxeVsPNG3fy56EZ3DgkfPIKyb7j8vr5dXst3wRiZxfVOADITrCSbDeREGMiMcZIYoyJhJBjks3Uri7GpI+6lXQhBC6visPjw+Hx4/S2is2+oPDsDJw7vP52YrSz0z0hdR4/Dq+/UzK/UCxGXTC2cZtA3T7ER5Kt75JhSfovckIcGaLCXi9+Gj67Da5bDsldT0qFEMz6dQODzCbemTy8Fwe4d/j8KufMW8yGsiY+uf4QcpLlgmi0IYTg46oG7txSQrnbywVZyfwlL5OEPggHsaWyiUe/2sKHUrju9/hUQYnbownaHTy4C51umv3td5llmIxaKJIQD+6hVjP5MWZsUb7bZHeQ9joyRIW97g02fUbLmxfz+wNfY4cplY+n5pNvsyCE4NrXl/PZ2nJeuWwGBw1P6bKLXxZuZflnRRx6dj7jDw+/gNz8/fcU33oH1kkXok8ciTk/kaQz8tHHtn3mCiF4d3kJd324Dp9fcPsJo5iV6uPdBQsQQnDaaaexSb+JR5c/SllLGeNTxjN34lwOyT4ERVHw+10UFT1D0Y6nURQTeUNvYNCgC1EUPcs/eZ/vX3uBuJQ0Tr75dtKG5EX8rZRIJJI9RQrY/ZXP/wq/PgO3bIaYvvP0EkJw7YYdLKyo493Jw5mZIFdoexohBNuqW/hmYyVrSxqoc3ipd3ioc3ipc3hocnVODNiKSa9rJ3AnxphItBmDArh2DC1rRyAgEvs6CMiBujACcqiI3LkuRJz2+jsl0+sORYEYox6ryUCMSU+MSY/VpMdqbC0bAtf1Idfb2sZZjZrndJyVOKtBitOSHkFOiCNDVNjr+h3w8Hg4+m44+MZumz6wvZz7C8v5bdYYBlmiX9grrnNw/CM/MDzNzttXzcIwwD0t+xNFTje3by7m69omxtmt3Js/iCnxtl3fGGGkcL1/IYSg1uvXPLY7enC7PJS52+eGyDYbybdZyI+xaEebhREx5j5ZZNlbpL2ODFFhr3uLr/6P4iUvc/ys17FZbHwyNZ8ko4Fmt485T/xEdbObD687uMudskIVfPLUaorW1fK7K8YybHJa2HbOdesonns1upSpmEefii7GSOLp+VhHtZ/7lzU4+dM7q/mhoJqDhifz12OG8N0n71FRUcFRRx3FjFkz+GDbB8xbPY/SllLGJY/j6klXB4Vsh6OQzQX3UFPzHXbbSEaOvIeEhGmUbNrARw//B2dTI0ddejXjjzw24m+lRCKR7AlSwO6vlK6E/x0GJz0E0y7t06E0+/wcs3QTLlWwaNpIkk3950vrQMTnV6l3hojaLR7qA+J2m9jtaSd81zs8eP2R+/816JQQAdkQIi6H1Jn0xBjbC8ztRGdje4E6JtDGbNBJ0VkS9cgJcWSIGnv9zKGgN8Hli7ptVuR0M2PxBv6Sl8n1uem9NLh944NVpVw/fwXXHzmcm48d2dfD2e/xqCpP7ajioaJy9IrCbUMzuDQ7FUMvh6mSwrUkHE6/yg6Xh20OFwUON5tbXGxucVHgcLVLKpluMjAiRNRuFbhTonCOIO11ZIgae90bqH54ZQ5L6xs5deLDHBBv542JwzDqFLZWNTP78Z8YkmLj7bmzusxd5HH5+PDRlVQWNXHcVeMZOiG8x7a3tJSdV12Ft9pD7PF/RnUYsB+YRfzxQ1GMbYvOQgjmL9nJPz9ej6Io/OX4fIw7l7Fu3VqGDh3KwQcfzODcwXy47UPmrZlHSXMJY5PHcvXEqzl00KEAVFV/webN/4fbXUZmxqkMH34bPqeBjx+7nx1rVjL2sKM56rK5GM19k3tBIpFIpIDdXxECnpgBBjNc+R3o+tZrak2TgxOXFXBoUiyvjB8qBcZ+hhCCFo+/g9jdVgbaeTd3FJWDwrNRE6ZNBunFJ9m/kRPiyBA19vq7++Cbf8DNGyEus9ums5cXUOP18f30Uf3GFv7xrVW8t6KYN66cxfSh0R+/e6DyU10Tf95cTIHDzUmp8fzfiGwyzb0rGEvhWrI3qEKw0+UJiNkBYduhiduhIUmSjPp23tqt5XRT3+2Ik/Y6MkSNve4tWqrhmUN5O/kwrsu5iguzkvlv/iAUReGLdeVc+coyzpg6iHtPn9Dl37bb6eODh1dQXdLMCVdPIHdscth2/sZGiq+7Hsdvy0g47x78TSkY0mNIPmcUxoz2O3N21jq45e1V/Lq9liNGpnJWnp81S36kpaWFjIwMDjzwQPJH5/Np4ac8s/oZSppLGJM8hrkT5nL44MNRVSfbC59gx47n0OutDMv7I5mZZ7J4wdssfvcNUgbncvJNt5OUlR3xt1QikUh2Rb8QsBVFuQ84GfAAW4FLhBD1iqIMATYAmwJNFwsh5u6qvwFjYFfOh4Vz4fQXYNypfT0aniuu4q8FJdw1LIu5OeG3QkkkEsn+gJwQR4aosdeVG+DJmXDiA3DA5d02fa20hj9u2smnU/OZHNc/4ko3u32c+OgPeH0qn95wKPExxr4e0n5FlcfLPVtLebu8jhyLiX/lD+LoQGKw3kIK15KeQAhBmdvLZoeLghZ3UNTe1OKi3ucPtovV6zqJ2iNizAyymND1sLAt7XVkiBp73ZvsXAIvHM8/Jv+Dx+2z+OeIbC4blArAA19s4rGvt/DPOeM4b0Zul124Wrx88MhKaktbOPHaCQweHX4RWXg8lN15Jw3vf0DcqVeiWGehun0knJCHbVZmO5FcVQUv/VLIfz/biNmg58ajhjHCWMfK3xZTXV1NXFwcM2fOZMLkCXxZ/CXz1sxjZ9NORieNZu7EuRwx+Agcjm1s2vx36up+ITZ2LCPz76F2u59PHn8A1efld3NvIH/mwZF9PyUSiWQX9BcB+1jgayGET1GU/wIIIW4LCNgfCSHG7Ul/A8bAqn54+mDwueDaJaDv2wmnEILL1hbyRU0DH0wZwZS43o/VKJFIJNGAnBBHhqix10LAY1MhMRcueK/bpo0+PxN+Wsu5mcn8Kz98cqZoZOXOek5/6md+Ny6Dx8+Z3G+8x/szqhC8VlbDP7eW0eJXuTYnjetz04npxVjkUriW9AVCCKq9voCndlsoks0OF1WetlwuVp2OETYz+TEWRtoswbAkuVYT+gh9Rkl73TWKohwHPALogWeFEP/pqm3U2Ove5tdn8H/6Zy45/C2+IpXXJwzjsKRY/Krg0hd/4+et1bx51Sym5CR22YWr2cvCh1bQUOngpD9MJHtk+LZCCKofe5zqJ5/EdtCRWGdeiXtrI5ZRSSSePgK9vf3n9raqZv70zmqWFtVhNeo5aUImB2VCXcEKiooKMZvNTJ06lWnTp/FD9Q88s/oZdjbtZFTSqKBHdlXVJxQU/AuPp4qsrDNJT7iEzx57mrItm5hywikcet7F6A1y0VsikfQO/ULAbvcQRZkDnC6EOG+/F7ABNn0K88+GEx+EAy7r69FQ7/Vx9NJN6FD4clo+8f0oiYtEEs34haDK46PU7aHU5aXM7aXO5yPVZCTLrL0yzSaSjXopPEUBckIcGaLKXn/5N/jlCbh1K1gTum06d10h39c1sfLAsZj6OMTXnvDEN1u47/NN3Hf6BM6YNrivhzOgWdfs5LZNO1na6ODABDv/yR9Evq334opK4VoSrdR5fRSEEbZLQxJImnUKw6zmkMSR2nGo1bTHn7nSXodHURQ9sBk4BigGfgPOEUKsD9c+qux1byIELLiM5o2fcfLhH1IqTHwydQTDYizUOzyc/PiPeH2CD687mNRYc5fdOJs8vPfgCppqXZx83USyhid02bZ+wQLK/n4X5mHDSL76PzT9WI3OYiDpzJFY8tuL30IIVhU38MaSHXywqhSHx09+up3j8+NJaiygcLMWM3vcuHHMmDmDZc5l/G/1/yhqLCI/MV+LkZ11AIWFj1Nc/BIGQxxDh9zM5q8aWfnpR2Tmj+LkG/9MbHL4GN4SiUQSSfqjgP0h8KYQ4tWAgL0Ozbg2AncIIX7YVR8DysAKAc8fB3Xb4fqVYOr77crLGlo4ZUUBx6XEM2/sECmmSSS7wKcKKj2aKF3i9lLm9lDq1s5LXR7K3F7KPV52J8+mWaeQaTaSZTYFhe0si6mdyJ0kRe4eR06II0NU2eudv8FzR8Op82DCmd02/aqmkfNWb+OFcUM4PjWhd8YXAfyq4LxnF7O6uIGPrz+EoSlyJ1Wkafb5ua+wnGeLq0gwGLhreBanpyf22meyFK4l/ZUmn5+CQAiSzSHhSHa6PLR+PTIoMLRV2A6JtT3MasbSxc4Gaa/DoyjKLOAuIcTvAue3Awgh/h2ufVTZ697G3QzzjmSHT8dxU58h0Wji46kjSDAaWF/ayKlP/cTEQQm8evkMjN3ssGlpcLPwwRW0NLj5/fWTyMiL77Jt808/UXL9DejsdjL/8zjNi934KhzYDwokeAyTj6jZ7ePDVaW88dtOVu2sx2TQceSIJEYZa2jetgKfz0teXh4zZs1gk7KJeWvmUdhYyIjEEcydMJeZyYMp2Hw39Q2/ERc3GYt7Nt/8730MRiMnXH8rQyZMjsjbKZFIJF0RNQK2oiiLgIwwl/4qhHg/0OavwDTgVCGEUBTFDNiFEDWKokwFFgJjhRCNYfq/ErgSICcnZ2pRUdFejzXqKPoFXjgOjvobHPLHvh4NAE/sqOT/tpbyn/xBXJwtV2Ql+y9eVVDRKk4HxOgyt5dStydw9FLh9qJ2uM+qU8gym8g0G8m0tAnSmSFCdIJRT7XHR2lIf63PKA2UKzxefB0+mi0hInem2Ui2pa3vbItWl2iQIve+MNAnxF3lpghcux24DPAD1wshPg/UTwVeBKzAJ8ANYhdfHKJqQqyq8OBoGDwdznql26Y+VTD5l3UcEGfj+fFDe2mAkaGswclxD/9AbnIM78w9UCbljRBCCD6tbuCOghJK3V4uzErm9rxMEntpp5oUriUDFYdfZWursB3itV3ocgcX/nVAjtXUKYHkiBgzdqNhQNvrvUVRlNOB44QQlwfOLwBmCCH+EK59VNnrvqBqM8w7gl9zTuD0QdcyK8HO6xOGYdApvLeimJveXMVlBw/lzpPGdNtNS72b9x5YjrPZyyk3TiItt+t8CK5Nm9h55VWozc1kPfgwvvoMWn4pw5hpI+nskRjTu16E3lDWyBtLdvDeihIaXT5ykqzMTFWxVa1BOBpIT09n5qyZ7LTtZN5aTcgenjCcuROuYrylhS1b/ovXW0dKwimsfLOB6qJycsZNZMT0Axl+wEzsiTIhtEQiiTxRI2DvsnNFuQiYCxwlhHB00eZb4BYhRLfWc0Aa2NfP0oTsG1ZCTN8bDFUIzl+9jZ/qm/lkaj5j7da+HpJEEnE8qkp5QJAO9Z7WPKc1UbnS46PjJ2OMXkd2QDDObPWUtrSVM81GEiIkIIcLPVLSYYzhvLutOqVtPBYj2QGxOytE5I7UGAci+4GA3VVuijHAfGA6kAUsAvKFEH5FUZYANwCL0QTsR4UQn3b3nKiz1x/dBKvegD9tA2P3du3vW0p4vriaVQeNJamfhdP6bG0Zc19dztWHD+O240b19XD6PTUeHzdu3MGXNY2MtVu4N38wU+N7x7tdCteS/RW3qrLN0eapvbnFTYHDxVaHG2/InLXiyMkD2l7vLYqinAH8roOAPV0IcV1Im4HrILY3rH0X3rmE+Qfey03GGVyanRLMhXHXB+t48edCHjl7EqdMyu62m6ZaFwsfXI7b4eOUGyeTmhPbZVtveTk7r5qLe8sWMu+5G/PYI6h7ezOq20/CSUOxzcjs9ru6y+vn07VlzF+ykyXbazHoFKZmmhnkLsLWtIP4uFimz5hObUotz218ju0N2xmeMJyrxl3AEN8qSkpex2hMhJpD2PJdA3Wl5aAoZI0YxYjpsxg+/UAS0sP5KEokEsme0y8E7EACiQeBw4QQVSH1qUBtYGKcB/wAjBdC1HbXX9RNiCNBxTp46iA48Do49v/6ejQAVHt8HP3bJmx6HV9My8dm0Pf1kCSS3cYdEKdbQ3l09J4udXvbJRpqxabXaSKv2USmxdgunEerABwXZcKvXwRCmLjaRPiSkBAmpW4v5WG9xHUh4nuIyB0SsiQ+yn7W3mKgC9ihdMhN0W6LsaIonwN3AYXAN0KIUYH6c4DDhRBXddd31NnrLV/Bq6fC2fNh1AndNl3X7OSo3zbx7/xBXNIPdyLd/u5q3vhtJ69dPoMDh/W/8UcLRU43567aRonbw5+HZnL5oFQMup7/TJTCdYQQQkua7vcEXl5QvW3l4NHb1kb1hbQPKRvMYI4Fkz3kaG8718nvyb2BVxUUudo8tW8amrnf2Os9QYYQ2Us+ux0WP8ldx73P084E/ps/iIuyU/D4VM57djFrShp475qDGJ3ZtWc1QGO1k/ceXI7PrTL75skkZ9u7bOtvbqbkhhtp+eknkq+eS9Ilc6l7pwD35joso5NIPK1zgsdwbK1q5q3fdvLOsmJqWjyk2gyMsTSQ2ryFJDNMmTIF1yAXL2x5gW0N2xgWP4y5o04gqekzmppWodfbsVkm4qxKpHhZE2XrKwCF1NyhjJh+ICOmzyJ5cO5+OS+QSCSRob8I2FsAM1ATqFoshJirKMppwD2AD22r8t+FEB/uqr8Ba2Dfmwvr3oPrlkN89yu7vcXPdc2cvnILp6Yn8viY3L4ejkQCgNPfKk63hdsobfWedmnlam9ncTrOoGvzmDa395huFW1jB+hCTdg43S5vu/Al4UTumICgn9XB2zw0Rne0CfqRYD8TsENzUzyOZqNfDVx7DvgUTcD+jxDi6ED9IcBtQoiTwvQXvR5dPg/cNxxGnwSzn9xl8yOXbMSi1/HJ1PxeGFxkcXh8nPTYjzjcfj694RASpfC5x6xqcnDeqm34hODl8UOZntC1ABEp+r1wrargrIOWSnDUgM/VXgTeYwHZ26Hd7lzv0F9vYYzpLGq3HjvVtZ7HhZTtYAq0NVhggNnVnmJ/std7gqIoBrQ8U0cBJWhJHM8VQqwL137Azq/3FL8XXjwRf/l6Ljz2E75tUXlz4jAOToylstHFSY/9iNWk54NrDyY+xthtVw1VDt57YAWqX2X2TVNIyup6547weim7+24a3llA/Cm/J+Pue2hZWk3Dp9tRdAoxk9OwzczElLVrO+TxqSzaUMEbv+3kh4IqEJAf5yfLtZ1cfQPjxo1ByVV4pfgVtjZsJS9+KHOHzWKwvp6G+sW4XMUAmIzpCGcu1Zthx9J6/E49iZlZDA+I2Rl5I1D6UaJriUTS9/QLATvSDFgDW1cEj0+DCWfBKY/39WiCPLC9nPsKy3lkVA5nZfZ9eBPJ/kWZ28PyRgfLGhwsb2xhs8NFrdffqV2CQR8I6dEWIqOj97R9gIrTkcIXGu87ZDGgNCQpZbh4361e62PtVqbG2ZgaH8M4uxVTP/5SOxAmxHuZm+IJ4JcOAvYnwA7g3x0E7D8JIU7ubgxRaa8XXAFbFsEtBaDvPjTIUzsquXtrKT/OGMXwGEsvDTByrC1pYM6TP3HkqDSePn/qgFto6km+rmnk8nWFJBn1zJ8wjBG2nv39CyG4/4tNPPnt1ugTrltF6eYKTZhurgocK0LKldBSpb3UzgvIu4XOAHoT6I3aUWdsK+tN2v9rsGwMXA9pr+/QXteh/W61MXYxBqMmxrubwdMcODZpR3dToK4p5FrgvGOdN2wUxfDvRVjBOyB6hxXKW8uxHURx+4D2Dh8I9rqnUBTlBOBhQA88L4T4Z1dto9Je9xWNpfD0ITTZszlxytNUev18OjWfoTFmlhXVcvb/FnPw8BSeu+gAdLvYkVNf4eC9B5YDMOePU0hIj+myrRCCmqefpuqRR4mZMYNBjz2K6jLQ/EMJjpWVCK+KaUgc9llZWMclo3STULKVnbUO3l66k7eWFlPe6CLOCEOVCoZRwcRhWViGW3ij6g22NGzBoBgYmTSS6clDGRujkKhW4Gxajs+npSfTi0G0lMZSutpBU4kFW0Iaw6fNZMT0Axk0eiw6/cD9nJFIJJFBCtgDjU//DEuegWt+hdTo8PjyC8GZK7eyvNHB59Pyye/hSZxk/8XpV1nd5GBZoyZWL290UOr2AmBUFMbZrYyLtQbiT7fFd840G7HJL029QriklqVuD8UuL6ubHJQEfl9mncJ4u5Wp8TZN1I6LIdsSBULMbrI/TIjD5aYY8CFEANa/D29dCBd9BEMP6bZphdvL5J/XcX1uOn/Oy+ylAUaWed9v45+fbOBfc8Zz7oycvh5Ov2B+WQ23bNrJGJuVVyfkkW7u3stuX/Grgr++t4Y3ftvJGVMHcfsJo3teuFZVcNZqwnNzhSY8N1e2CdTtxOoqEJ0XjtEZwZ4GtlTtaE8DW1pbnS0FDNbwwnA48Xh/WGBR/SHidnNn4dvd1CaMdymUh9SF+72Ew2hrL2pbkyA2E+IyA8estqMttV8J3vuDve4NotJe9yXbvoNXZlM0/iKOT72MZJOBj6aMIN5o4JVfCrnz/XVcf9QIbj5m1/P12rIWFj64HJ1ex5w/TiY+tWsRG6Dhgw8o/esdmIfkMvjppzFmZ6M6vLQsraB5cRn+Whe6WBP2GRnYpmeij9u1vfD5Vb4vqGL+kp18vaESvxBkG1sYRjnTMgxkjkmlxFzCmqY1rKleg9PnBCA9JpVDU4Yw3qYnWVTic2xCCC9gwNeYSvUmaNhhRrjbxOyccRMxmPrPd36JRNJ7SAF7oNFSDY9MhGFHwFmv9vVogpS7vRz12yZSTQY+nZqPdTdWfCWS7hBCsN3pYVljS1CwXt/sxBf4GBpsMTE1LoapcTamxMUw1m7FIv/uop4yt4dlDY7g73V1kwOXqv1SM81GpgR+p9PiYhgfGxO1nyUDfULcTW6KscDrtCVx/AoYEchV8RtwHfArmlf2Y0KIT7p7TlTaa08L3JsHUy+G4/+7y+bnrNrK5hYXv80ag64fCmyqKrjohSX8VljLR9cdwvC0ng+D0V8RQvBgYQX3FZZzeGIsz44b0uM7d9w+Pze9uZJP1pRz3ZHDufmY/L33lFf94Kht7xHdXBFSrgy5Vt2NKJ0O9tSAGN16DK0LvCwJ+4foHK0IEfAKD+PpHeoVHk4od9RAUxk0lXf+O1D0EJsRInBntQndoWK3OTo+Swa6ve4totJe9zU/PAhf3c1Pxz7FWZ4xHJIYyyvj89ArcMvbq1mwvJg/Hz+Kqw7N2+Xndk1JMwsfXIHBrGPOzVOIS+k+kXTL4l8pvu46FIuZwY89hnXSJACEKnBtrqPll1Jcm+pAp2Adl4z9wCxMuXG7ZT8qGl28s6yYN5bsYGedE4vOz1ClmgxdI1k2GJGZhDnRSIOxgUJRyCrHKopbtLAidoOJw1OymGAzkkY1eEq0cfksNBZbaSgy46pKZnD+QYyYcSBDJ03FZO1esJdIJPsPUsAeiHz7H/j233D5VzAoer6PfVPTyDmrt3FBVjL3jRzc18OR9DPqvT5WNDq0cCCNLaxodFDn0yZNNr2OSbExmmAdrwnWqaae9XiT9A4eVWV9s4ulAY/6ZQ0tFLm0mKQGBcbarUyLswU8tWPIsZiiIszBQJ8Qd5WbInDtr8ClaPkpbhRCfBqonwa8CFjR4mJfJ3bxxSFq7fXrZ0PFWrhxzS4FuPcq6rh6fRELJg3joMTYXhpgZKlsdHHcIz+QEWfhvWsPxCzDKXXCpwr+vLmYV8tqOCMjkQdH5mDs4WSNLW4fc19dxg8F1dxx4mguPySvc6NWUbrb8B2BY0sViI5BntA8nFvFaHt6m8d0u7pAWYrS+xeqX/u7aSzVBO3gsQyaSgPHMnA3dr7XHBde5O5lb+6Bbq97i6i1132JqsKb50HBF7x66ufcUmXgykGp3DMiG7fPzy1vr+bDVaVcctAQ7jxxzC7DiVTtbOL9h1ZgshqY88cpxCZ1v6vZvWULO6+8Cm9pKfYjjiDlmquxjh8fvO6rdtK8uIyWpRUIlw9jpg3brExiJqWhM+36/05VBb9sq2H+kh18vrYcb8DhxKioJChOEhQHiYqDZIObYSkWYhIF9cZ6tqnbWONag0NxEKsTzEqIZ5LdQrpSg15tAsDTZKFxh4WW8ngSkw5kxNTDGTZ1OtbY7pNfSiSSgY0UsAci7iZ4ZBKkjYaLPoyqicQ/tpby+I5Knh6Ty+z0xL4ejiRK8amCjS1OloWI1QUONwAKkG+zMDUuhimB0BL5Ngv6KPo7l/QsVR5vUMxe2uhgZZMDh18TXVKMBqbGxwTDjkyKjcHWB2KbnBBHhqi11ytehfevhSu/hazJ3TZ1+FUm/LSWk1ITeHh0/w3BsWh9BZe/vJQrDhnKX08c09fDiSpa/H6uWlfEoppGbsxN57ahGT2+kFbv8HDxC7+xuriee+eM5vTMGtjxM1Ssbx/Sw1HdhShtbh++wxYQotvVtXpKx0fVd0lJP8TdrHlrB0XtjsduvLnt6eHF7Qh5c0t7HRmi1l73Nc56+N9h4PNwx3Ef8GxlCw+MHMx5WcmoquAfH2/g+Z+2c+KETB48c+IuF4grixp5/+GVWOxG5tw8BXuiudv2/qYm6l59ldoXX8Lf0IDtkENIufpqYqa0fXdRPX4cKytp+bkMb3kLisWAbVo69pmZGHbh6R38MT1+Nlc0sam8iQ3ljWwsa2RDWSP1zracBjGKNyhqJ+mcZNhUEhPcNJpq2eLfQrGyE7ulmfF2PZPtFjKUBvSKDyHAUWmhucSOURmByZiC0ZiM2ZyMxRaLOcaG2WbHbLNhjrFhsdkDddp5f4itLVQVVVURgZeqqqiqP+RcK6t+FaH627f1+9vdH2zbZRt/+Gd06kdF+APPEm1tOo6ztY2iU9Dp9Cg6HTq9Hp0+UNbp0el1IeWO10Lu0emCZUWv167rdJ3Lel03zwrtL+TZgXOZOLR/IwXsgcqvz8Cnf4LzF8Dwo/t6NEG8qmDOigI2trhYdMBIhli7N7qS/YNyt5dlIR62q5qcOFVtwp1sNATEak2UnBQXQ6z0/pOE4FMFmxyugKCt/R1tCSx46IDRdktA0NYSRA6zmntcXJIT4sgQtfa6pQbuHw4H3wxH3bnL5jdt3MEHlfUsnTWGRGP3iR+jmTsXruWVxUW8fOl0Ds1P7evhRAVVHi8XrN7O6iYH/8kfxIXZKT3+zMrqGh58/hUGNa/mnPRikutWQyDeKHHZWviGduE7wsSXlqK0JNoI681d3tmz293Q+d523twdxe6Ah7c9Law3t7TXkSFq7XU0ULYanjsG3+BZXDDxfn6ob+aticM5MNGOEIJ5P2zjX59sZGZeEv+7cBpxlu53kZZvb+CDR1Ziizcz++bJ2OJ3PZ/2N7dQN/91al94EX9tLTGzZpJy9dXYpk8PthFC4ClspPmXUpxra0AILPmJ2GZlYclPRNnDXUVCCKqa3Wws04TtjeVNrC+pZ0t1C16/phspCOIVF4mKg0SdkySDi4TYFtzmcoopxB5bSVZsA5MsBlLNThSlvd7kc+nxOfX4nAbt6NLK3tY6lx5UG3pdPCZDvCZ0t4rcIaK32WbDEtN2ruh0+Dwe/F4PPq8Xn9eD36OV/V4PPk+gLnBdu+bB7/XiC2nnD9zb1iZwv9eD3+NFDYjR9AMdrVUY1oTiNkG69VyAJoIHBG1VbSsLNcxCeh8SKma3ieW6oECu1xvQGQzoAy+dwRhSNmAwGAPXjcG60LYGo7FDXfu2bfdr9aFt9QYj+o736w1RscM4GpAC9kDF54HHp2oTlCu/hyhaadrp8nDMb5vIsZr4cMoIzFE0NknP4/SrrAkkWmz1ri7pkGix1YN2ShSFhJD0L+q8vmC4meUNDpY3tdDo0748JRj0bbG0421MjoshLsKLInJCHBmi2l6/eJImtlz76y6brm92cuzSTZyUmsDTY4f0/Nh6CJfXz8mP/Ui908sn1x9Cauz+vQi93eHmnNVbqXB7eXrsEH6XEt8zD2qphh2/QNEvuLf9iL5yDQZUhKJDyRgPOQdCzkzImQWx6T0zBokkWvC0dOHFHRC8G8uguRxUX/v7uvDmVg79o7TXESCq7XU0ENi51XDInznRPptar49Pp+aTG3DmWriihFveXsXwNDsvXTqd9Ljuw4OUbqnnw8dWEZtkYc7Nk7HG7l7SQ9XhoO7Nt6h57jn81dXETJtGyrXXEDNzZrv5lr/RTfOv5bQsKUNt8qJPtmCfmYltajq6mH0L0+jzqxTWtLCxvImNZU1sKGtgfWkDZY2eYBsTfs1bW+ckUXEQZ21GMW/DEr8To9GJyejGbPBiNXiJ0fuINQhsBkGM3o9Z7wv7XCEUVK8Jv9uoidwOHZ4mwgrfqleHUEGoCkIoCFUBFbS9wO3RGwzojSYMJu2lN5owGI0YjCb0psCxtc5kQt96zWhsE4UVXYgXcYhIHOKVHPQwDp538FjuRlxWOrTThd4f4s3ceq3j+b56LQshOnhs+9s8vgPnwbJf7XDuD3qWty+rYcVyrY2/7VkhInrnZ3V+tlBV/D4vqt+P3+dD9Xnx+3z4g0cfasi5GqjzewPn/vB/f/uKTh8ichu7E8Bb6wKCefBvqM0bXdF1/LvSdf5b0bX/W+l8veM9rf2H9K0ooFNAUVAUBaFTtIWwkHqhoC2AKCAUUHRKSFkHgTKKgtDBsJQRUsAesKx6E967Ek57Dsaf3tejacenVfVcsrYwGAdMMjAJTbTYKiaGS7TYKiZGPNGi6oeGnVCzBaq3aEdXPZhjNW8dc6y2yNPuPC7kPA4MMgv2QEAVggKHW0sO2aAliNzU4kKgfRUdEWNhanwM0wILJ/salkYK2JEhqu314qfhs9vgD8sgZfgumz9cWM5/tpf3+xBaG8oamf3ETwxNsfH6FTNJsu2fn5HLG1u4YPV2BIJXxucxNd4WmY6FgPoiKPpFE613/ALVmwFQ9WZW+IexnFEcddxs8iYdodksiUTSHtWvLfyEC1XSwZtbubtR2usIENX2Olr44DpY/jLbzniHE2ozSDMZWTB5WDBvz/ebq7j61WUkxJh46dLpu0yaXLKpjo8eX0V8Wgyzb5qMxb77wrLqclH/9jvUPPssvooKrJMmkXLtNdgOPridkC18Ks51NTT/UoqnsBHFqCNmUhq2WZmYsiKbiLXJ5WVzheapvaGskXXF9RRUNtPsafPeteLBrPgx4cOk+DEHjsFzxYcRLxZzM2ZzAxZTAzHmeizmFkymgPBt9GA2eIkx+rAafJh0YRISd4FAB+hRFD0oBhSdAUXRoygGFMWITjGg6LSjTmdEpxjR6QzoFBN6nRFFZ0RRDOgUY6APJeB9LQL9i+CT2g5hroku2kMX10QX1+jiWsdxhLlPhHl2kMDfUOvfkhCaABk4IjqOQWl3Huw52D7siDtdb9+m7T0UHepEsL79M0VgrO3eicDvRyAQIe+Ddk0NjiN4l1ADv1IVVQhQhdYuIOIjtH5E8Frg9yk6lgmWtVdgLIF6JaQcfI9bfwghUELeECVUp+3wa1NC68KidHOqdNks7E2d2oS5KUz/ocM785KlUsAesKgqPHOIljH82t+iToi7o6CYZ4ureWn80J7zWpL0Kg1eHyuaHCxr6MVEi0Jok5SaAk2grtkCNVu1Y+028Let5mOOg5gkLRaju7H9ta7QmzuI2qGid0fhO64LITy2x5MQSfacJp+fFYGFlWWNDpY3tlDr1f5e7XpdcGFlSuBvNmkPQj9IATsyRLW9rt8JD4+Do++Gg2/cZXOfKvj9igK2O9x8O30U6eb+m2j2x4JqLnvpN/JS7bx++QwS9zMR+4vqBq5aV0iaycj8icPIi9kHT3RVhaoNUPRz0MuaplLtmiUeBs+E3FlsNI7l3E89mM1WXrlsOsPT+mdCUIkkqvC0oJjt0l5HgKi219GC1wXPHwt1hfx47ldcsN1JqsnA/Il5DIvRPK7XljRw8QtL8KmC5y46gKm53S9479xQy8dPrCYxM4ZTbpyMxbZn3y1Uj4eGd9+l+n//w1dahmXcOFKuuQb7EYd32gHrKW2m5ZcyHCsrEV4V05A47LMysY5NQTH0zI5qIQRlDa5gbO3tVS3UO9zUt7hpcHppdPlodPlxeLsPUaFH1YRuxY9J+DApPsz4MSk+TIqXGL0Lq9GJ3ejAZmzBbHBjUHzodH70Oi96na/tqPdi0PnQ633oFB86nYqiqCitR6XtXKeIwLlAp7SeC3SBV/DnDP2ZO41eaasTndt0bN8anEV7/wJF0VW7bvoIPksJ6aO1w47lDr0EL4mQcnttsqN0GU7/3GVdWF1UoLQOeE+ep+xCgxXtr7aeaT/ebjg9Be5vaxmuFG5k3fQuOl8J7VXTtEMWpETr4kHo700JaODh6kLbduiHtusiOBal7f6QMYqO96O0//sKPYoO93cY35VnfyEF7AHN5i/g9TPghPth+hV9PZp2uFWVk5cVsMPlYdEBIxlk2b8mwP2d0ESLywPiX48mWnQ3tQnTwWOgHBoPUW+CpDxIHt75ZUtpH/PT59b6dTVoR3dj4DxwdDd0OO/YJvAKlyCrI0ZbGCG89TxEEO/oAR56brLJmKU9SOiOgaUN2q6B9S1OAmH6yLOamRIXw7R47W96tM2KoYt4gFLAjgxRb6+fOQz0Rrh80W413+JwccxvmzgwIZZXJwzt1+GRvt9cxeUvL2V4qp3Xr5hBQsz+YcNfKa3mtk3FjI+18uqEvD1fiPV5oGxlm2C9Y7G2Mwi0sAY5syD3QO2YNgZ0Or7dVMncV5eRFW/llctnkJ2we0m1JBLJrpH2OjJEvb2OFuoK4ZlDISGX5We+zwXrSxEIXh6fx7TATp6imhYuen4J5Y0unjh3CkeN7j40VNG6Gj55ajUp2XZ+f+NkzNY9z7UhPB4aPviA6qefwVtcjHn0aFKunkvs0Ud3Ch+hOry0LKug+Zcy/LUudLFGbNMzsYxIwJhhQ2fp/VwfPr9Kk8tHo8tLg7Pzq9HpCxy91Ds91Ds8NDi8NLm8NLn9qPsgY+kQ6FtFaUKOCHSobWVFRSc0ua+1TgmnLNOVkCp2cb07AXbX9wavK+HfjD35xtrmIa10qOvsUdt1fee60Pp2zwlT17HPNqlS2aPntBuf0lbu2FfbGELuU7r7+TuOI1DXYZGiY9s27/EO7UT492OgUfTfk6SAPaARAl44QRP6rl+xT1m6e4LtDjfHLN3EaJuVdycPx7iHySEkvUe528vygKdquESLmrfqPiZa9Hm0rdNBcTpErG4qC2moQPxgbdt+UKAeph3jB/eut7MQ4HWEiNwBUTus6N2VUN6o7ZTYFYquvQBuTdTE+tSRkDISUvMhPieqYt73d1r8flY1OoNhcJY2tlDl0WKbWXU6JsVZAwkitb/9tIBXrZwQR4aot9ff3Qff/ANu3qjFVd0Nni2u4o6CEu4fOZjzs5J7eIA9y7ebKrny5WXkZ9h57bKZxO9jXMxoRgjBvdvLeaiogqOS4vjf2Fxsu2PnhNAE602fQuFPULIUfC7tWvIIyJ3VFsM6cUinRcoPVpVy85srGZkRy0uXTifFvn/HHZdIIo2015Eh6u11NLH5c3j9TJh8PoXHPMA5q7dR5vby1Jhcjk9NAKC62c2lL/7G2pIG/jVnPGdPz+m2y+2rq/nsmTWk5cZy8vWTMO2liCy8Xho++piap5/GU1SEecQITcj+3e9Q9O1tnlAFroI6Wn4uxbW5LqieGZItGLPsGDNtGLPsmDJt6OKiN6eRqgqaPT4aHN6gyO3y+fH4BD5VxetX8foFPr8IlFvPA2VV4PWp+FSBx6/i86v4/K1l0aGNiifk3lbCRXjQ6kWY6yGe26Jz291pL0JcrNs/r4v+wo0p5EKrk3Xbr1hBUdrqlMA5Hc5b9WAl4GWsKCHnYe4P+iIrnfvpfB7yzIB7dWh9sNyhrvX5OkUJhGlu7aeLZykd+glebzsP/Zl0He7tst924+rQZlf9Bt7osO9/SB/B31ZXbQLvd9vvTulwva0+9Dz02a3vf/v2bb+Ttv6U9te7eP4pk7KlgD3g2bkEnjsGjrgDDru1r0fTiYUVdcxdX8S1OWnckZcZtcZtf8OtqnxX28T7lfUsrm8Om2ix1bt6jxItqqomRrcTqAPhP+qKQITEIYtJ6SxQJw+HpKFgHGAeaKo/RPDeTdHbUQPVBeCobuvHYNFEkdR8TdROGaEJ3MnDwSCFj31FCMFOlycoZi9rcLC22Yk3YP9a47o/M26onBBHgKi315Ub4MmZcOIDcMDlu3WLKgRnrdrKskYH3xwwMpjAqb/yzcZKrnplGSMzYnn18hnEWweeiO1VBbds2smb5bWcm5nEvfmDu9x9AWif5zt/hQ0faq+GndoCZMaENu/qnFlgT+32ua8sLuJv76/lgCFJPHvRNOIsA++9lUj6GilgR4aot9fRxtf/gO/vg+lXUX3kP7hwXSErGx38M38Ql2SnANDi9nHNa8v5bnMVNx+Tz3VHDu92vrVtRRWfzVtLRl4cJ183CaN57x16hN9P4yefUv3003i2bsWUl0fK3KuIO+EEFENncdzf6MFT2oy3pBlvWTOeshb8Na7gdZ3NiDHLhjHTjinLhjHThiElBkUv5/0SiWT3iaTNlgJ2NDP/XCj8Aa5fCbbo8/i6ZeNOXi2r4ZBEO/cMz2a0fYCJk/0Enyr4qb6ZhZV1fFLVQIPPT6JBz6FJsUEP091OtOisa/Oeri5oE6trt2oey60YY9qL00GROk+LVy3ZNY5aqNqkJfqq3hwob9Ji9AYzNOg0776UfO3V6rWdMgKsCX04+P6Py6+yptkZTA65rLGFFQeNkxPiCBD19loIeGwqJOTAhQt3+7YSl4cjftvIGJuVBZOH71t4pSjg640VXPXKMsZkxvHyZQNLxG72+bliXSHf1DZxy5AM/jgkPbyA4PNA4feaYL3xY2ip0nIoDDsSRp8MI4/fbZsmhOCJb7Zw/xebOWpUGk+cNwWLUeZRkEh6AilgR4aot9fRhhDwxR3wy+Mw4WwcJz3G3I3FfFHTyB9y0vhLXiY6RcHrV7ltwWreXV7CeTNyuOeUcei7WUAtWFrBl8+tIys/gROvnYjRtG+2Q6gqTV98QfWTT+HevBljbg4pV15F/O9PRjF2b+tVlw9veQve0hZN3C5rwVveQjA2n0GHMSMGU5Y9KG4bM2zo9kF4l0gkAxspYO8vVG6Apw6EmdfA7/7Z16PphE8VvFxazb3by2ny+7koK4Vbh2aQuAdJ0yR7hyoEvzW0sLCyng8r66n2+rDrdRyfGs/stEQOTYztOrSL1wm120MSKIbEpnbUtLVT9Jp4GupNnTJCK8dmdtoyLYkQHkdgASFE1G5dTAhNYGlP7yxqp46Uv5t9QE6II0O/sNdf/g1+eQJu3bpHi0Fvlddy/YYd/G1YFtfkpPXc+HqJResruPq1ZYzJiueVy6YPCG/hSreX81dvY12Lk/vyB3Nux5AvHgds/Ro2fACbPtPyJpjsMOJYTbQecYwW7mkPUFXBPz/ZwHM/bmfO5GzuPX0Cxt1ZNJZIJHuFtNeRoV/Y62hDCPjhfs0be+QJ+E57nr9sr+bl0hpOS0/koVGDMel0CCG47/NNPPntVo4dk86j50zudlFz06/lLHpxPYNHJXLCNRMwRGABVKgqzV9/TdWTT+JevwFjdjbJV15JwpzZKKbdz4Eh/Cq+KqcmaJe2aN7apS0IpxaeDwUMydb23tpZdvSx+0eeDYlE0j1SwN6fWHgNrHkbrlsOCYP7ejRhqfX6uG97OS+VVBNv0POnvEwuyEzufquuZI8RQrCqycnCyjo+qKyn1O3FolM4Jjme2ekJHJUU1+ZlrfqhfkeHxIkBsbohxMMXNMEzGO5jRJtgnZirJTqTRAd+nxZrvJPX9mYtNEkr5jhNzO7otZ04BPRycak75IQ4MvQLe73zN3juaDh1Hkw4c7dvE0Jw2dpCFtU08vm0/AGx8+jL9RVc89oyxmXH8/Kl04ntxyL2FoeLc1Zto9rjY964IRydHKddcDVoCbI3fABbFmk7iqyJMPIETbTOOwKMlr16ps+v8ud31/DOsmIuPnAIfztpDDr5/Uci6VGkvY4M/cJeRytL5sEnt8CQQxBnv85jFU7+ta2MQxLtPDduKHGBfAsv/rSduz9az9ScRJ69aFq3yZM3/FzG1y9vIHdcMsdfNR69MTILoUIImr/7juonn8K1ejWGzEySL7+MhNNPR2feu5BoQgj8DR68AS/tVm9tf21ICBK7UYurnWpFF2NEZ9GjWA3oLAZ0Vu2lWPToLAYUs16GJJVIBihSwN6fqN+pbXUefwbMfqKvR9MtG5qd3FFQwk/1zYy2Wfi/EdkcnLhnXkySzmxscfJ+RT0LK+vY7vRgVBSOSIpldnoixybHYQ9NSKX6YeVr8PU/obm8rd4c396DujX8R9KwqEsSKtlDhIDmijYxO9RrOzSJps4Y+BtoFbUDAnfKCDDZ+m78UYScEEeGfmGvVRUeHA2Dp8NZr+zRrdUeH4ct2UiW2cjHU0dgGgAJWD9fV861ry1nwqB4Xr5sBnZz/1vs+q2hhQtXb0OvKLw6IY9JeocWFmTDh7DtW1C9YM+A0SdponXuQfu8SOvy+rl+/gq+WF/BjUeP4IajRsgJuETSC0h7HRn6hb2OZla/Be/NhcwJcN4C3m5SuGnjDvJjLLw2MY9MsyZWf7y6jJveXElucgwvXTqdrISuF7/X/VDCt69tInN4PIefO4qkrMh9RxdC0PLTz1Q/+STO5csxpKZiO+ggLGNGYxkzBvOoUejt+zYvVJ0+TdAua/PW9lU7ER61+xsVUFqF7VZRO/TcGji3GNBZ9SHXAkK4WY8iF48lkqhECtj7G5//FRY/CVf/Ammj+no03SKE4JPqBu7aUspOl4cTU+P5+7Ascvp5wqveptDpZmFFHQsr69nY4kIHHJxoZ3ZaIsenxocP07L1a/jiTqhYC4Omw5QL2jyqbSkyrMT+iKtBE7I7em3XbQcR8kUyPqctBEmowG1L6bux9wFyQhwZ+o29/uhmWDUf/rRtjxPMflbVwMVrt3NTbjq35WX20AB7l8/WlnHt6yuYPDiBFy+d3q9E7E+q6rlmfRFZRoX5ynJyN70DRT9pn3MJuTDm9zD695A9DSK04NDs9nHFS0v5ZVsNd508hosPGhqRfiUSya6R9joy9Bt7Hc1s+gzevkjb6XjBe3zni+WytduJM+h5fWIeo2za94tfttZw5ctLsZkNvHTpdEZmdO3ktWlxGT+8VYDX5Wf8kYOYfuJQTNbI2WQhBI5fl1D7yis4V63CX92WWN6Ym4NlzBgso8doxzGjMSTte34j4VdRXX6E04fqCrycfoTLh9pa5/QhXP6QstZGdfkQbv8un6EYdaDXoRgVFL0OxaBDMShgaC3rtASUoecGrS2t5dB2Rl3nfvQ6TSjXK5rorlNApwSPoXXoFG1RWxeuLlAv5+eSfogQAlQ0RzohNFlBCFAFQrSWA+2EwJholQL2fkVLDTw6CYYcAue83tej2S2cfpVndlbySFElKoJrBqfxh9w0bHqZ4KErSl0ePqisZ2FlPSubtISJ0+NtzE5L4OS0BFJNXXiKVW7QEopsWaRN1I+5G8bMloK1pGt8bqjd1sFre7Mmdvucbe2sSe29tVvL8YMjJgJFE3JCHBn6jb3e8hW8eiqcPR9GnbDHt1+/oYgFFXV8OHkEU+IHxi6GT9aUcd38FUzJSeDFS6Zj6wci9vObNvDXUheTXTt4efl1pHgbIHW05mU9+mTIGB9xe1jb4uHiF5awrrSR+8+YwJzJgyLav0Qi6R5pryNDv7HX0U7hj/D62VpoqgsXstaUyXmrt+FUVV4cl8eBiZpX84ayRi5+YQkOj59nL5zGjLzkLrt0NntY/P421v9YijXWxIGnDmPk9Iwe8TL2Vlbi3rAB14YNuNatx7VhA97i4uB1Q3p6QNQeHfTWNmRm9qr4KvwC4fahuvxBwTsohreK3F4/wquCXyB8auAlwK8ivKrWR6C+fZu2dvS2NKYQEL5bRfAQsVtRUHS0Cd/h6kLF8Hb3tV4nILZ3ENkDdSgBEV0heN5WT9v3p0BZadcupI6OfYT229ZPuLrgOOni/ta+dUqwSccxtNYHxVNB4CW0X6kA1EA92lF0aEfrYTfaBYVaQp/XKt6GqevQTuu3c53WTLQbr1BF2z0dy2rIc9S2+0IF5rafqb2o3FYOXG8nPne4r8Oz9/T/ZPB/D5UC9n7Hd/fBN/+Ay77Utjz3E0pdHv6xrYx3K+rINBu5c1gWc9IS5GpjgGqPj4+q6llYUcfihhYAJsRamZ2WyO/TEhhk6Sb5RXMlfPNPWP6ylnDq0Fth+pVgkN7ukr1EVbUY6R1jbFdtAmdtWzuDFVKGa7G14zIhJkXz1o5JAVsq2JK1ssnWrxZS5IQ4MvQbe+3zwH3DtZASs5/c49sbfX6OWLIRi07HlweMJGaAJO37eHUZ17+xgqm5ibx4yQHEmKJQxK4uQF3zNv+q1vN4yu/4XfWPPFX/ATGjjtNE65QRPfbo0nonFzz3K8V1Tp48bwpHjU7vsWdJJJLwSHsdGfqNve4PlK6AV08DRQ8XvMfOhHzOXbWVIqeHR0fnMDs9EYDiOgcXPb+EnXVOHjlrEseP734XV2VRI9+/sZmK7Y1k5MVz6Nn5pOb0fIhOf0MDrg0bNVF7/XpcG9bj2bZdmysA+vh4LGPHYB49OuitbRqSi9KPHVxEq3Dn08RtWsXtUOHbpwa9TYMCotrBAzVYH9Jnxzq/COkjXF1ANPSL9v3uQR1+EXx+UJAMV9dOQO1GtO0fsmH0Ek7gh05ivBIqzOtCFg1ay7qQxYfQRQmlrdxuoUNpf19bmbZFkNZyu/uVzuPoeF3XOubQcsgYA7sM7NMzo1/AVhTlLuAKoCpQ9RchxCeBa7cDlwF+4HohxOe76m+/N7DuZnh0sjYpu/jjfiUKASypb+aOLSWsbnJyQJyNf+RnMzE2pq+H1Sc0eH18Wt3Awop6fqhvwi8gP8bCnPQETklLJC9mFwK0xwGLn4AfHwafCw64HA67DWL2fXuXRNIlLTVabO3QcCTVm6GpAvzu8PcYLAFRO1kTtoNCd3KI4B0ifptj+/SzTU6II0O/stcLrtB2r9xSsFdJTn+sa+L0lVu5fFAK/xgxcLxwP1xVyg1vrGD60CSevziKROyqTfDtf/Cs/5CbRt7GgvRjuFBfzr/Gj8GQmNPjj99W1cwFzy2h0enl2Yu6956TSCQ9h7TXkaFf2ev+QNVmeGU2eJrh3Lepy5zKJWu2s7ihhbuGZTE3Jw2AuhYPl730Gyt21nPP78dywawh3XYrVMHGxWX88t5WXM1exh6SzYxT8rDYejfpsup04t60Cef69ZrH9voNuDdvRni9ACgxMVhGjWrnqW0eNgzF1I1DlqTfEBS1tZOgwC06iN+oId7MYb2hO4jjInzf4TyURaiYHsbzWdEBtImpBE7DeZm3E4q7abd73ujh2oURq/dT+kUM7ICA3SyEuL9D/RhgPjAdyAIWAflCiG4DG0kDS1u243Pfhvxj+3o0e4wqBG+U1/KvrWXUeH2ck5nE7XmZXYfGGCCoQrCu2cl3tU38UNfML/XNeIQg12Jidnois9MSGGWz7PqDTVVhzVvw1T3QWAKjToKj79Y8YSWSvkII7Yt6SzU4agLHamipClNXo9WHhikJRW9qE7yD4nZqZ8G79WiJj6jgLSfEkaFf2ev178NbF8JFH8HQQ/aqizsKinm2uJq3Jw7jkKSBk7j4/ZUl3PTmSmYMTeb5iw/AaurDEGDVBfDdf2HNOzSbk7l0xlN8r6Rx+9BMrs9N65WJwdqSBi56fgkAL106nXHZ8T3+TIlEEh5pryNDv7LX/YX6HfDybC2R+lmv4Bp6JH/YUMRHVQ1cMSiFu4Zno1cUnB4/181fwaINFVx7xDBuOXbkLm2Z2+FlyUfbWfNtCWargZmz8xh9UBa6PkxeKDwe3Nu2BUOPuDZswL1hA6pDC4epGI0Yc3LQWa0oFjM6kxnFYkExm9CZLShmMzqLGcVk1q6bzSjmwHWLpVO9zmJGaS2bTYG+zChG434vEkok0Up/F7BvBxBC/Dtw/jlwlxDil+76kwYWbbvzEweAyQ5X/dBvY9A2+vw8WFjOs8VVWHU6bh6SwWWDUjD1058nHEVONz/UNfN9XRM/1jVR69XWZ0bZLByeFMspaYlMirXuvqHd/gN88VcoWwVZk+HYf8KQg3rwJ5BIehBPS3tR21GtnbdUdRC8AwK4pzl8PzpjiLidHD6MSTvBO6Hbz005IY4M/cpee1rg3jyYejEc/9+96sLhVzl26SacfpVvpo8izjBwcj0sXFHCzW+tZGZeMs9d1AcidvUW+P5eWPM2GKxUTb+W8+Jms87p5cGROZyV2Ts7j37dVsPlLy0lzmrklcumk5dq75XnSiSS8Eh7HRn6lb3uTzRXajk2KjfCac+ijjmFu7aU8r/iKk5MjeeJ0blY9Dp8fpU731/H/CU7OGPqIP516niMuxGOrLq4mR/e3ExpQT2pObEcenY+GXnRs6gqVBVPURGugKe2p6gI1e1GuNwIt1sru92obhfC7UG4XKgeDwQ8ufcKRQGDtltMaT1v5wnbVg5eD722J9f1ehS9HgwGLWSKQY+iN7TV6fVaMslgXeC6QQ86/e7V6fUout2s0xu0RJTd1GljCnmeIeRnCHlhMKIYA+cyd5kkQvQnAftioBFYCvxRCFGnKMrjwGIhxKuBds8Bnwoh3umuP2lgA6x5BxZcBqfOgwln9vVo9oktDhd/Lyjlq9pGhlnN3D0im6OT4/p6WHtFrdfHj3XN/FDXxPe1TRS5PABkmo0ckmjn0MRYDkmMJd28h97m1QXw5d9g0ycQNwiO/juMO73fLl5IJHuF1xle8A56ete0F7zdjeH7UfRdCN7aS5l+hZwQR4B+Z6/nnwPla+DGNXvt0b+8sYWTlxdwWnoij47OjfAA+5Z3lxfzx7dXcdCwFJ69aBoWYy9MaGq2wvf3weo3tVBEB1xO0bRrOWdzHWVuD/8bO4RjUnpnsv7VhgqueW05gxKtvHr5DDLjrb3yXIlE0jUDXcDem1CciqJMBV4ErMAnwA1iFxP9fmev+xPOenj9LCheAic/AlMu5Jmdlfx9Sykz4m28OH4oiUYDQgge+aqAhxcVcMTIVJ44b8puhe0SQlCwtIKf39lCS4OHUbMymDVnODFx/Tdch/D5EB5PUOAWLheq24NwuzTB2+VGeNyoroDw7XYFhXHV7QokYQzGtdD6DA1BQeDYllUvTOiKcNfbh60Qqh98foTfD34fwuffZZ3w+0Kuh6nz+RCq2lYOtOtTFEUTtY3GdiK3VmdAMRi7rzMaAgL5HtYZW8X0buqMoaJ7a7vWxQBd27F1sSH0XBc4So/9XiOSNnufghoqirIIyAhz6a/AU8D/of2r/x/wAHApgUWxDoQ1roqiXAlcCZCT0/OxDfsFY0+Fnx6Gr/8BY2aDof8aqeExFl6bmMeimkb+XlDC+au3cVRSHHePyGJ4jKWvh9ctTr/Kbw0tfF/XxPd1TaxpciKAWL2OgxLtXDk4lUMTYxkeY967D8eWGvjuP7D0eS1h3lF/g5nXgFFOnCX7IUYrJAzWXruDz93Zizv0vLVctlo7uhp6dvyS6GbUSdoiYdlKbYfLXjAlzsb1Oek8VFTB8SnxHJ+aENEh9iWnThmEKuDWd1ZxxctLmXdhD4rYtdvg+/th1RtaSKGZ18BBN7CeWM5ZtRW3Knhr0nAOiLf1zPM78N6KYm55ezVjs+J48ZLpJNn673cuiUTS73ioi1CcZwNjCYTiVBSlNRTnU2jz5sVoAvZxwKe9O2RJEGsCXPAevHUBfHAdOOu56qDryTAbuW79Dn6/vIDXJw5jsMXEjUfnkxZr4Y6Fazhn3q88f9E0ku3d50RSFIX8AzIYMj6FpZ8UsuqrnWxbUcX0k/MYf3g2un6YWLpVkNTF7J95sjqiJVtsL2oLny8ggKuaUL6rOp8fVH+nOuEPKfu82jWfD+H1aQsJ3dUFz9vqg3UeD6rD0b4uXLvAC5+v795gRWkvaOt0bV71Heu7Ou5O+1BBvdXbPmx9+2P7eN3BYNqd68Nc29P68H3t5jM63BO2PpK/tp7ywG73EEUZAnwkhBgnQ4hEgIJF8NppcPy9MOOqvh5NRPCoKs8VV/NgYTlOVeXyQancPCQjarZi+4VgTZNT87Cua2JJQwtuVWBUFKbGxXBoUiyHJsYyKTYGw77EIfO6YMkz8P0DWtiEqRfD4beDPTViP4tEIumA3wuOGpS4yGVI3p/pd/baUQv3DYODb4aj7tzrbjyqyonLCih1e/l2+sgBl9/h7aU7+dOC1Rw6IpVnLpgaWRG7rlDzuF45H/RGmHYpHHQjxKazuL6ZC9dsw6bXM39iHqNsPb+Qu2JHHY99vYWvN1Zy4LBk/nfhNOzmKElkKZFI9hcP7N0OxQkUAt8IIUYF6s8BDhdCdDtR7Hf2uj/i88B7V8G6dwPfM/7Gz/UtXLJ2O2adwusT8hgXqwm2X66v4A+vLycrwcqLlxxAbvLuL9bWlbfww1sF7FxfS1KWjUPPyid7ZGJP/VQSSUQQQmgieejL621f5w2I3+3Ow9T5feBXNc/31mOrJ3zH+pCjJvAHjmHat7seevT7EWrrokHo+S6OAW/7XbYL3RHQC5ptTzJm08Z+EUIkUwhRFijfBMwQQpytKMpY4HXakjh+BYyQSRz3ACHgpZOhcgPcsBLMAydpVJXHy7+3lTG/rJZko4G/5GVydmYSul7e4iGEoMjl4ftaTbD+qa6ZOp/2JzrGZuGQgGA9M96GLRIiuxCwdgF8dbeW/GPE7+CYeyBt1L73LZFIdouBPiHuLfqlvX7xJC0czbW/7lM3G1uc/G7pZo5MiuP5cUMG3PbEt37TROzDR2oitnlf7V9dEfxwP6x8XQvxM+0SOPgmiNU2931e3cBV6woZZDHxxsRhDLL0rAf0ku21PPZ1AT8UVJMQY+Syg4ZyxaF5vRM2RSKR7DYD3V7vaShONAH7P0KIowP1hwC3CSFO6u45/dJe90dUP3x8Myx7EaZdBifcz0anm/NWbaPe5+e5cUM4PEkLo7msqJbLXlqKw+3ntKmDuPqwYeQk755HshCC7auq+fGtAppqXYyYlsaBpw3HnhjdO5slEsnuITqK2oGX0C52rtcuhKnvEFIntL7Le7rpK8y10HrrqFHREUJkF9yrKMoktGEXAlcBCCHWKYryFrAe8AHX7kq8lnRAUeDou+DZo+CXJ+Hw2/p6RBEj1WTkwVE5XJSdwh2bS7h5005eLK3mtPREDIqCQVHQKwp6hbDnekXBEFLWEyjrWtsGzunch0eo/FrfEvCybmZnII51ltnI71LiOTQplkMS7ZH3qtvxK3z+FyhZCunj4cL3Ie/wyD5DIpHs9yiK8n/AKYAKVAIXCyFKA9ciFlOzXzLqJPjsNi1pYMrwve/GZuW2oZncs7WUtyvqODOjd5IM9hZnHjAYvxDc/u4arn51OU+dP2XvROz6nZpwveI17TvNtEs14TouK9hkflkNt2zayQR7DK9OyCN5N2KC7g1CCH7eWsOjXxXw6/ZaUuwm/nz8KM6fmSu9riUSSY8R4VCcMkRnNKPTw0kPa8nEf3oYXA2MmvM0H00dwXmrtnH+6m08EEhMPDU3iY+vP4Snvt3CW0uLefO3Hfx+YhZXHz6ckRndO60pikLepFQGj0lixedFLP98B9vX1HDACUOYeORg9Mb+F1ZEIpG00RbOo0N9H4ylr+iVECKRQK4Qh+GN82Dbt3DDKi0J2QBDCMF7lfX8Y2sppe59yEq8h8QZdBycEBvwsraTZ93LONa7onYbLLoL1r8PsZlw5J0w8WztS45EIul19gOPrjghRGOgfD0wRggxNxBTcz5tO6MWAflCCL+iKEuAG2iLqfmoEKLbmJr90l7X74SHx8HRd8PBN+5TV34hOG3FFtY1O/lm+qge9xruC17/dQd/eW8NR49O48nzpmIy7OakuKEYfngAlr+inU+9SNtSHZ8dbCKE4PEdlfxzWxlHJMXy7Nghkdnp1AEhBN9uruKxrwpYvqOetFgzVx02jHOn52A1STsskUQzA91eh7I7oTiRIUT6Dz8+pM3/RhwLZ7xEk87MpWu380NdM38emsENuenBeWdlo4tnf9zOq4uLcHj8HDsmnWuPGM7EwQm79aiGKic/vVPA9lXVJKTHcPCZI8gdm9xzP5tEIpGEIZI2WwrY/ZmqTfDkTJgxF477d1+PpsfwC0Gzz49PaGU/Ilj2BV6qIFj2B8r+DmXtOqgh9/lD7lOASbExTNjXONa7wlmnJan69Rkt1udBN8KBfwBT7ySlkkgk4dnPJsS3AzlCiKvlhDjAM4dpn8mXL9rnroqcbo78bROTY2N4a9KwXg+D1Ru8uriIOxau5Zgx6Txx7pTuReyGEvjxQVj+sra1cPL5cMgfOyVmVYXg7i2lPFNcxZy0BB4ZnYNJF1mPMVUVLNpQwePfbGF1cQPZCVbmHj6MM6YOkqFCJJJ+wkC313sTilNRlN+A64Bf0RacHxNCfNLdc/qtve7vLH0BProJcmbBuW/gMcVy88advFNRx4VZyfxrxKB2c9G6Fg8v/lzIiz8X0uD0csiIFK45fDgz85J2y8mqaG0NP7y1mYZKJ0MnpnDwGSOIS+n5fBISiUQCkbXZcm9kfyZ1JEw6D357FmZeDQkDcxuYXlGINw6AP1UhYMUr8OXfwFmvTeCPvCMY61MikUh6GkVR/glcCDQARwSqs9E8rFspDtR5A+WO9eH67f9bkkefBF//AxrLIC5zn7rKtZq5e3g2t2zayfMl1Vw+aOAl4j1/Zi5CCO58fx1/eH05T5w3BaO+g9jcWKYJ18teBKFq31kOvSXs9xWvKrhp4w7eqajj8kEp3DM8O6LCv18VfLq2jMe/3sLG8iZykmL472njmTN50O57kEskEknvsDehOK+mLeTXp4GXJBqZdglY4uDdq+DFkzCd/y6Pjc4h02zksR2VlLu9PDU2F5teW1RNtJm46Zh8rjg0j9cWFzHvh+2cM28xU3ISuPaI4Rw5Kq1bITt3XDKDRs5g5Vc7WPpJIa/f/StTjs1hyu9yMcgdRxKJpB8hPbD7Ow3F8OgUGHcqzHm6r0cj6QpHLXx4PWz4EHIPhuP/Cxnj+npUEokkhIHg0dVdTE0hxPsh7W4HLEKIvyuK8gTwS4ekUJ8AO4B/d0gK9SchxMndjaHf2uvKjfDkDDjxATjg8n3uTgjB+au381N9E4sOGMnwmIGZROmlnwv5+wfrOG5sBo+dO1kTsZvKtW3SS18A1QeTztWE68QhYfto8fu5fG0h39Q2cfvQTK7P7X4yvif4/Cofri7l8a+3sLWqhbxUG384Yji/n5iFoaPgLpFI+gUDwV5HA/3WXg8UChbBm+drYbQuWAgJg3mhpJq/bi5mqNXMn/MyOSk1vpM9dHn9vL2smKe/3UpJvZNRGbFce8RwThifiX4Xu4ib61z8tGALW5ZWEptkYcjEFJKzbCRn20nKsmGyDACnMYlEElXIECKS9nxxB/z8OFz9E6SP7evRSDqy7Tt4by60VMFRd8Ks6yDCW6IlEsm+sz9NiBVFyQU+ljE1QxACHp8G8YPhwoUR6bLC7eXwJRsZYjXz4ZQRPRueqg95/sft3PPRek4bbePetM/RL3se/F6YeI4mXCcN7fLeWq+P81dvY2Wjg/tGDua8rMjE5/T6Vd5bXsKT326hsMbBqIxY/nDkcI4ft+sJvkQiiW72J3vdk/Rbez2QKPoFXj8LzLHad4+UEXxX28SdBSVsdriYGGvlL3lZHJpo7yRke/0qH6ws5clvtQXaoSk2rj5sGLMnZ+9yZ1Hxpjp++2g7lTua8Ln9wfq4FAtJWXaSszVROznLTkK6Fd1+vuCr+lU8Lj8epw8ARaeg5dNTOpRbE+2BTqdo5UBdaxuJZH9DCtiS9jhq4ZFJkHsgnPtGX49G0orPA9/8A356FJKHw2nPQtakvh6VRCLpgoE+IVYUZYQQoiBQvg44TAhxuoypGcKXf4NfnoBbt4A1MSJdvl9Zx1XrirhtaAY3DRm4IaPmf/kTB/xwOXm6crZnnUTDATeQlTeO9LiuEyGXuDycvWorO1wenh6Ty/GpCfs8DrfPz9tLi3kq4Jk2LjuO644cwTGj09FJ4VoiGRAMdHvdW/Rrez2QKFsNr56qLaSfvwCyJuEXggUVddy7vYxil5eDEuz8NS+TKfGdcyapquDzdeU88e0W1pY0khlv4cpD8zj7gF0nJRaqoLHGRU1JM7WlzdSUtFBT0kx9pROhajqRzqCQmGHTRO0suyZsZ9uwJXRt36MNv1/F4/ThdvjaHzvUuZ1ePI4O9U4fXpd/1w/ZHToI3YrSKoZ3FsHbXdO1F8tRFM0fLtBOp1M6lAl7X7tn6RT0egW9SY/BoENv0mEw6jAY9RhMOvQGHQaTdq43diiHOdcbdf3m70HSu0gBW9KZHx6Ar+6BSz6D3Fl9PRpJdQEsuBzKVsLUS+B3/5RJGiWSKGegT4gVRVkAjARUoAiYK4QoCVz7K3ApWkzNG4UQnwbqp9E+puZ1YhdfHPq1vd75Gzx3NJw6DyacGbFu564r5KOqej6Zms+E2JiI9Rs1VG6EV0/F42jgSu8tfOvOD16ymfQMS7MzLNVOXootWPZadVy4rpAmn5+XxudxYKJ9n4bg9PiZv2QHz3y/lYpGN5NzErj+yBEcPjJVTqgkkgHGQLfXvUW/ttcDjeot8MpscDXAuW9qjmmAW1V5pbSGhworqPH6OD4lntvyMhhl65yEUQjB9wXVPPH1FpYU1pJsM3HpwUO5YFYucRbjHg3H5/VTV+6gtiQgagfE7ZZ6d7CNOcZAUiD8iOatbSMp247ZGrkwJEIVeN1+zfvZpQnJHrcPrzNwDNS3eke7HT7tPESEdjt97bzMw6KA2WrAZDVgjjF0KBsxWfWYY4wYLXoURVtrEKpofxQibBmhHVVVQLC9QKiBdt31Ea692rHPDu0Endp3Hpt29PtU/F4Vn0fF51Pxe/zsizwYFLcDgrbBpA9fDgjlbaK5Dr1RH1YYN5jarulDBHYpmvcfpIAt6YynBR6dDIlD4dLPQP4j9w1CwPKX4LPbwWCG3z+uJQaTSCRRj5wQR4Z+ba9VFR4cDYOnw1mvRKzbOq+Pw5dsJMFo4POp+VgG0lbcnUvgtTM0m3f+AkT6OCoa3WytamZbVTNbq1rYWtXM1spmShtcAKjxRjxTUtADM6v8TEqwMSzVzrBUG3mpdlLspt2ekLS4fby6uIh5P2yjutnDjKFJXH/UCA4cliwnNRLJAEXa68jQr+31QKShRBOx63fA7Cdh7KnBOX2Lz8//iqt4ckclLX6V0zMSuWVIBjlWc9iulmyv5YlvtvDd5ipiLQYumjWESw4aQrI9fPvdxdXibeepXVPSQm1pM54QD2V7kjkYfiQpy0ZcsgWvxx8Qm9uEaK/bh6eDEN25ze55Pis6RROZrQbMMcawQnRonXZuDJ4bzXoUuUsLIQSqX+Dzqvg8fk3c9raK3P62slfF5/Xj87SeB655OlzzqYE2/kCf2rW2PiInmus7CuHt6trEb71R18njXG9s8zhv8zzXoTe0eaO3eqFr1/VyV98eIAVsSXh+ew4+vhnOeRNGHtfXo9n/cNTCB9fBxo9g6GEw5xmIy+zrUUkkkt1ETogjQ7+31x/dDKvmw5+2gbGzh9Pe8nVNI+eu3sbVg1P5+/DsiPXbp2z+HN66SLN1F7zXZZLGVhweH28UVnFXSQUxQmFmlZ+K8ha2VTXj9qnBdnEWA8PS7OSl2BmW1iZu5yTZgnE9G11eXv65kOd+3E6dw8shI1L4wxHDmZEXmRjaEokkepH2OjL0e3s9EGmphtdOh9IVMGg6HH0XDDkoeLnW6+OxogqeL6lGFXBRdjI35KaTagrvYb22pIEnv93Cp2vLsRj0nDM9hysOHUpmfOS+3wghaKp1URviqV1b2kxduQPV37XWpNMrmCwGjBY9JosBk0UfLBstekxmA0Zr4GjRYwotd7hPeuL2X1pF86CoHUYo7ySMdyGa+32B86Bw3iqi+9vEdF+bKL8v6HRKJ1E7KHSHEdCDQnmHurBCu0GHTq9Dp1c6vAJ1us7n0bwAIwVsSXj8XnhiOhgsMPdH0HUf80oSQbZ9G0jUWA1H/x1mXisTNUok/Qw5IY4M/d5eb/0aXpkDZ8+HUSdEtOs/bdrJK6U1vDd5ODMT9i1kRp+zcj68fy1kjIfz3gF76i5vWVBeyw0bdzDaZuX1iXnBCbeqCkobnJq3dmUz26qb2VqpeW5XNrVtV9brFHKTYshNjmFZUR2NLh9HjkrjD0cOZ0pOZGKWSySS6Efa68jQ7+31QMXvhZWvwbf/gaYyGH4MHPU3yJwQbFLq8vBgYQXzy2sw63RcNSiVq3PSiDOEn/9vqWzmqW+3snBlCToFTpsyiLmHDWNISs+FuPT7VOorHDTXuzGa9Zg6iNN6o5wrS/qOdiFUvG2hVFoF7lZP8XbXve3F9bajv1Nd6D1tQrpWp6qR12AVBXR6HYpei20eFLYDQndrnaJrO1c6CuEdxXJdd+cKOt3uiexDxqdIAVvSBWsXwDuXwuynYdI5fT2agY/PA1//H/z8GKSM0BI1Zk7s61FJJJK9QE6II0O/t9d+L9w3DEadpG3hjSAtPj9H/rYJAXx9wEjsXUw0o56fHtESXg49DM5+Dcyxu7zlmZ2V/H1LKQcl2Hlx/FBid/Nnb3J52dYahqSqmW1VLWyraiEv1ca1RwxnXHb8vv40EomknyHtdWTo9/Z6oON1wpJ5Wq4rVz2MOw2O+CskDws22epw8d/t5XxQWU+SUc91OelcnJ2CtYtQZTtrHfzv+228uXQnXr9KdoKVoSk2cpNjGJJs014pMQxOisHcX7+jSCRRjuoPCNydPMVV/D5NJFf9ou2ldjj3C1R/oE7t6lwgAnX+1nO1rZ2/w3nofcG60POQ63vKH545SgrYki5QVZh3ODjq4LqlWkxKSc9QtRnevRzKVgUSNf4LTAMwOZdEsp8gJ8SRYUDY63evhIIv4ZYC0EcuGRHAr/XNzF6xhfMyk7l/1OCI9t3jqCp8eSf88jiMnaOFytrF9wwhBP/cVsbjOyo5MTWeJ0bnDqwY4BKJpNeR9joyDAh7vT/grNecpRY/CX4PTL4ADrutXajK1U0O/r2tjG9qm8g0G7llSAZnZSRh6CKsQGWTi7eXFrOpvImimha2V7fQ6PIFrysKZMW3idva0cbQlBgGJcZgMUpxWyLZH2lNCNpe8O5eVM8clhAxmx3ZWZmk79HptFhZr8yBb/4Jx9zT1yMaeAgBy17UEjUarXD26zDqxL4elUQikUgixagTYfWbsONnGHpoRLuekWDnmpw0nthRyXGp8RydHBfR/nsMvxfe/wOsfgMOuAKO/+8uQ5X5VMGtm3cyv6yWC7OS+Xf+IPQyRqVEIpFIJLuPNQGOuhOmXwnf36fNQ1e9ATOugoNvBGsiE2JjmD9xGD/VNfGvbWX8cdNOntxRyW15mZyUGo+ug+1Ni7Vw7RHD29XVtXgorGnRXtWOQNnBx2vKqHd4g+1axe0hKTGaqJ3cJnIPTuodcVsIgcev4vapuL0qbp+WCNCo16HXKRj1SuCow6DTyjJGtkSy7yhKa1gSIHzo/R5FCtgDkWFHwrRLtS2+aWNh4ll9PaKBQ2iixrwjYPZTMlGjRCKRDDSGH63lk9j4ccQFbIA/Dc3gq5pGbt64g2+njyLJGOVfxzwt8PbFUPCFtn350Fu1GWw3OP0qc9cX8nl1I38cks4tQzLk5FEikUgkkr0lNh1OvB9mXQvf/lub6y99AQ6+AWbMBZONgxJj+WiKnc+rG/n39jKuXFfIBLuV2/MyOTwptls7nGgzkWgzMTlMTol6h4fCGgeF1a0CtyZuf7qmjLow4nZuckzQYzs9zoLHFxCbfZrYrInOgXKICB1s4/WHbe8JabOntArZoSK3QRdG8NYr6HU6jLr2dYZAOSHGSJLNRJLNTLLNFCibSLZrRxl6RSLpOWQIkYGK36t5Ye9cAhd/DIMP6OsR9X+2fgMLrw4karwLZl4jEzVKJAMIuSU5MgwYez3/HChbDTeu6ZHP+rVNDo5fVsAJqfE8M3ZIxPuPGI5aeP1MKFkGJz4I0y7Z5S0NXh8XrdnOrw0t/Ct/EJdkp/TCQCUSyf6CtNeRYcDY6/2VinXw1f/B5k/Bnq4tLk+5CAwmAPxC8G5FHfduL2eny8OBCXb+mpfJ1PjIJm5scHjDeG63UFTjoLbF0+V9JoMOs0GH2aAPHHVanbHt3GzQYzZ2aGfUYda3b2cy6FAUBZ9f4FPVtqMqAmWBzx96roava23b7lpbG49fpd7hoc7hxd9FIj672UCizdhO4O4sdLddizHp5QK/ZEATSZstBeyBjKMW5h0BHgdc+S3EZ/f1iPonPg98fU8gUWM+nPZcuyzQEolkYCAnxJFhwNjrde9pXscz5sJx/9mlx/He8EhhBf/eXsZ9IwdxfmZy9E1gGorhlVOhrlBLUjzm97u8pdzt5ZxVW9nicPP4mBxOSevsySWRSCT7grTXkWHA2Ov9nR2LYdHdWtizxCFwxB1awsfA4rtbVXm1tIaHCiuo9vo4LiWO24ZmMtpu7fGhNTi8VLe4MekDwnNAhDbpdei6iM/dH1BVQaPLS02Lh9oWDzXN2rG2xR2sa1/vweMP7zVuNuhIDni/t4nd5qBHd6gAnmwzE2c1RN/3RYmkG6SALdl9KjfAs8dAch5c8plMMrinVG2GBZdB+WqYdhkc+w/5HkokAxQ5IY4MA8ZeCwGf/xUWPwFH/R0OuTnij/CpgtNWbuHXhhYmx8bwx6EZHLWLLb69RuVGePVUcDdpuR6GHrLLW5Y2tHD1+iJqvT5eGDeUQ5Nie2GgEolkf0Pa68gwYOy1RPvOsmWRJmRXrIH0cXDU32DEscEF+Bafn3nFVTyxo5Jmv8pp6YmcmBrPWLuVwRZTdHz3GKAIIWh2+zRRu8VDbbOHWkdHodvddr3Fg8PjD9uXQaeQGCJqJ7YTuDXxOzSkSWKMCX0/XiyQ9H+kgC3ZMzZ9BvPPhrGz4fQXesSLbMAhBCx7AT77i5ao8ZQnYNQJfT0qiUTSg8gJcWQYUPZaVeG9K2HN25odmHx+xB/hUVXeKq/j4aJyil1eJsXG8Mch6RydHNd3k8mdS7SwITojnL+g211HqhAsqmnkyR2VLG5oIcVo4NUJeUyKk4u9EomkZ5D2OjIMKHst0VBVWPcufP0PqNsOObO0RfjcWcEmtV4fjxdV8nxJFa5AGIwEg56xdivj7FbGxWrHETEWDFL47DNcXn9Q7K4JiNtdC+BuGl2+sP0oCiRYjUEP7lDROz3eQla8hawEK1nxVundLekRpIAt2XN+fBgW/V1LvnTYn/p6NNFNS42WqHHTx1qixjlPQ2xGX49KIpH0MHJCHBkGnL32eWD+WbDtO80TeeRxPfIYj6rydnkdDxdVsNPlYWKslT8OyeCY3hayN38Bb12o2b0L3oOkoV2O992KOp7cUcVmh4tss5GrBqdybmYydpnASCKR9CDSXkeGAWevJW34vbD8Zfjuv9BcASN+p3lkZ4wLNnH4VTa2OFnb5GRts/ba0OzEGRC1zTqFUTYL4+0xjI21Mt5uZbTdgk0vbXw04vWr1IV4cGtCd3uv7tDwJnUODx2lQJtJT2aClawEK9kJFjLjrQFxWxO5M+ItWIzy9y/ZM/qFgK0oypvAyMBpAlAvhJikKMoQYAOwKXBtsRBi7q76kwZ2HxEC3psLq9+AM1+GMaf09Yiik63faO+Ts1ZL1DjjapmoUSLZT5AT4sgwIO21uwleOlkLq3HRBzB4eo89yqsK3i6v5aGAkD0h1sotvSVkr5wP718L6WM1z2t7WqcmTT4/r5TWMK+4ijK3lzE2C9fkpHFKWiJG6aklkUh6AWmvI8OAtNeS9ngcsOQZ+PEhcDXC+DPgiL90uTjtUwVbnW7WNjmCovbaJid1Pi2chQIMizFrntpBb+0YUkyGXvyhJJHArwqqm92U1DsprXdSVu+ipN5JWYOT0noXZQ1Oqps7J+FMsZsCwnab53ZWgpXMBAvZCVZS7eZ+Hd9cEnn6hYDd7iGK8gDQIIS4JyBgfySEGLeL29ohDWwE8LrgpZO0jMWXfi4TEYbic8NX98Avj0PKSC1ZlXx/JJL9CjkhjgwD1l43V8Hzx4KzTrOhqSN3fc8+4FUFb1fU8khhBUUuDxPsVv44NINje0rI/ulR+PJOGHoonPUaWOLaXS53e5lXXMXLJdU0+VUOTrBzbU4ah0dLzG6JRLLfIO11ZBiw9lrSGWcd/PQILH4aVC9MvRgOvXW3dhkLIShxe1nX7GRNk5O1zZq4XezyBttkmo0dRG0rOT0YV1sIgYr2XcknBF4hUACjoqBXFIw67SjZN1xeP+UNLkrrnZS2HjuUO8bqNugUMuItAWHbEhC323t0x1lkqJL9iX4lYCvaX+YO4EghRIEUsPuYpgqYdwSgwJXfhPWu2u+oLoB3LpWJGiWS/Rw5IY4MA9pe1xXCc8dqsaEv+wLis3v8kV5V8E5FLY8UVVDo9DDebuXmIekclxIfmS//qgqL/gY/PwZjZsOp/wODOXh5c4uLp3ZW8k55HX4hOCktgWsGp8kY1xKJpM+Q9joyDGh7LQlPUzl8dy8sf0n7LpM9BVJHaa+0wNGWuls5s+q8vqCova7ZyZpmJ1scLvwBeSnOoGOs3Uqe1YxfgE+0ic0+IdqJz37RXowOvd6+juD5rmgvaINBUTAqCobAy6hrKwevBdq1nrcK4a33mXQKCQY9yUYDySYDKYFjslF7WfT7185tIQSNTh+lDe2F7bJ6zYu7tMFJeYMLn9r+92Uz6dsJ21nx1kDoEq0sQ5UMLPqbgH0o8GDrgAMC9jpgM9AI3CGE+GFX/UgDG0FKV8Lzx2kexhd92G6iul8hBKx4BT69DQwWmahRItnPkRPiyDDg7XXZanjhBIgfBJd+CtbEXnmsTxUsqKjjoaJyCp0exoUI2bq9FbL9Xi3nw6r5cMDlcPy9oNMmDEvqm3liZyWfVzdi1SmcnZnM3MGp5Fr30+8MEokkapD2OjIMeHst6ZqarbD4Kc2Bq3IjuBvarlmTIG20ttMsNXBMG71bwrbTr7KxxcXaZkdQ2N7p8rQThcMJyqFisiYkh1xTFPQKwTZd3SMQ+AJCeUcxvL0g3iaC+8KI5n5B23lIG58QuFVBvc+HrwsJzabXBcXsUGE7xdSxTk+yybBfxBP3q4KqJndQ5N6TUCWtIUpaw5OEhi6RoUr6D1EjYCuKsggIt+/kr0KI9wNtngK2CCEeCJybAbsQokZRlKnAQmCsEKIxTP9XAlcC5OTkTC0qKtrrsUo6sO49ePtimHSeJtzub1s4nHXw4Q2w/n0YehjMeQbiMvt6VBKJpA+RE+LIsF9MiLd/D6+eBllT4MKFYLT22qN9quDdyjoeKixnu9PDWLuFm4dkcPyeCtkeh/Y9oOBzOPwvcNifUIHPqxt4YkclSxsdJBn1XJKdwiXZqTK+pUQiiRqkvY4M+4W9luwaIaCpDKo2amJ21ca2ckdhO9RTu/VlTxt4WoIQgZcKBMoIQEHojTT4/NR4fdR4fNrR6w8p+6gOKdd4fHi60NysOoWkLry5g3Uh9Xa9bkCG3tibUCVGvUJ6nCWQcNJKepyFFLuJ1FgzqXYzKYFjvNUohe4+JmoE7F12rigGoASYKoQo7qLNt8AtQohurac0sD3AN/+G7/4Dx/4TDvxDX4+m9yj6GRZcAc3lWjbmWdfJRI0SiUROiCPEfmOv1y3UBOCRx8OZr4C+dwVenyp4r7KOhwor2OZ0M8amCdknpO6GkO2ohdfPguLf4MQHcE25hAUVdTy1s5ItDjc5FhNXDU7l7Myk/cI7SCKR9C+kvY4M+429luwdQmghR6o6iNpVG8AVKmwnhoQhCfHc3h1hWwjwOsDdDJ5mLWm2p7nDeUsX15rB09R27ve0Cc0C2gnPoceOonTH464w2sCWonmk21I7lDucxyQjdHqa/Wo7wbs6UK5uJ4Jr5VqvD6cafhxmndImcIcRvJON7cObxBn0A0Lw3p1QJRWNLrz+zu+bQaeQbDeRYjeTGmsmxW4OKZuk2N0LRNJm9/Rs62hgY6h4rShKKlArhPAripIHjAC29fA4JOE47DaoXK8lbUodCSOO6esR9Sx+H3z3X/jhfkgcApd9qcX9kkgkEolkTxk7G1rug09ugY9vgpMf7VUPJINO4YyMJOakJbIwIGRfvq6QUTYLfxySwYldCdkNJfDqqVC7jYbTX+bl2BnMW7yeSo+PCXYrT4/J5aTUBAzyC7xEIpH0KIqinAHcBYwGpoc6dCmKcjtwGeAHrhdCfB6onwq8CFiBT4AbhBAisMv5ZWAqUAOcJYQo7LUfRjLwUBRth3JcJgw7oq1eCGiugMoNULVJE7SrNmk7vJe90NbOkqAJ2nHZ4HW2F5tbj57mgKC8GxhjwGQHsz1wjAV7BiTbwWQDvTnwPUzp4ggoul20CT2GtkU7CqHt5G6p0l4NxVC6QisLf5hBg2JNItaWSqwtlSHdCt/pYIkHRaHF3+rR3ebpHU7s3uZ0U+P10eIP/x4aFYWkEFG7o/CdYTaSZTaSbTGRGMVit6IoxMcYiY8xMjozLmwbIQQNTi/VzW6qmjxUNbupbnJT3ewO1LmpbvawqbyJ6mZ3t2J3Z6G7TexuPU+IMUbt+zWQ6WkB+2xgfoe6Q4F7FEXxoRnkuUKI2h4ehyQcOh3MeRqe/52WxPDyRZqQPRCpK9S8rouXwKTz4fj/asZPIpFIJJK9ZfoV2iTu+/vAng5H3tHrQzDoFE7PSGJOeiILK+p4qKiCKwJC9k1D0jk5NaFNyK7aBK+cSolq5H8nfMSrtVZaqso4PDGWJ0ancXCiXX4Zl0gkkt5jLXAq8ExopaIoY9Dm0WOBLGCRoij5Qgg/8BRaiM3FaAL2ccCnaGJ3nRBiuKIoZwP/Bc7qrR9Esh+hKBCbob06CduVbYJ2q8BdskwTmE12iEmChJyACB0bIkaHO+9Q1kXxjjBVBVc9tFS3idstVR3Oq6FinVZ21YfvR2cEWyo2Wwo2Wyo54YTuuNbzLDBaAC32eG2IsN1J7A6cr3I5qPH6aPR1FrytOoUss4ksi5Hs0GNA4M42G7EZovd3oCgKCTEmEmJMDE/rvm13YndVUPTetdidYjeTEmsKL3a3hjKRYndE6fEkjpFCbnHqQep3wrwjNMNwxdeaYRlIrH4bPr5ZK5/0EIw/vW/HI5FIohK5JTky7Hf2Wgj48HpY/jKccL8mavchfiH4oLKeBwvLKXC4GWmzcFNuOie7t7B54R95MvNU3ks5HKHA7LRErh6cyrjYmD4ds0QikewJA81edwypGfC+Rgjx78D552ie2oXAN0KIUYH6c4DDhRBXtbYRQvwSCONZDqSKbib7+529lkiiBZ8HHDVdC93typXgc4XvxxTbTTiTDsJ3TFJwEcCjaiFNyt0+SlweSt0eStxeSl1eStweSl1eKjzeTgFV4g16ssxGsswmskOE7iyzkUEWExlmI+YBFpo1VOyuDHhxhxO7q5rc1LSEF7uNeoVkW5vY3Rq2pFMokwEqdvenECKS/kDCYDjrNXjpJC2e5/kLQG/s61HtO+4m+ORWWDUfBs+AU+dBYm5fj0oikUgkAwlFgRMf0iYZn9yqTRjGzumz4egVhTnpifw+LYEPK+t5YHsZc9cXcZe7gfLxj2LVwSVZKVw5OI3BFlOfjVMikUgkXZKN5mHdSnGgzhsod6xvvWcngBDCpyhKA5AMVId2rCjKlWge3OTk5PTE2CUSya4wmNrCs+wKIbQ44J3E7Q7ndYVabhNHdRdhWRSISQZbKiZbCpm2VDLjspicOAQScjWdJDcnmJjcqwrKPV5KXZq4rQndXk3sdnlZ0dRCrbdz2JRUk0Hz2g6I3KFe3dkWI2kmI/p+JNC29+yO7bZtV2J3+3AmHjaWNXUrdifZTNhMBqwmfdvRrMdqNBBj0hNj1hNjNGh1Jr1WZwpca1fWjlajfsDE9pYCtkQjZwac/AgsvBo+ux1OvL+vR7RvFC+DBZdBfREc9mc49NZeT7AlkUgk0YiiKLcA96F5ZlUH6vYo1mZfjDuq0Rvg9OfhlTnw7pXaBGHooX07JGB29Xec/Mvf+NCUx5vDLuKi7KFcPDSXRKO0hxKJRNIbKIqyCMgIc+mvQoj3u7otTJ3opr67e9pXCPE/4H+geWB38XyJRBItKIoWRsVsh6Shu26vqu3jdHclfJethI0fg9/d/n57OiTkYkzMZXBCLoMTczWBOyEHBg1q5+jo8KuUBTy2iwPHUrcmdBc4XHxX19QpPrdBgXRTW1iSLIspKHi3Ct1JxuiNx90deyN2V7UK3CFid22zhxaPD6fHT4vHR73DQ2m9H4fHj8Pjw+Hx4w4TBqY7rEZ9O/E7VBS3mfUBoTtEFDfqiTFrAniriB4qirfWmQy963EvZzCSNiadqyV1/PkxLeHCAZf19Yj2HNUPPz0M3/wLYjPh4k8gd1Zfj0oikUiiAkVRBgPHADtC6vYm1qakI0YrnDMfnj8e5v8/e/cdZ1dV7338s3Y5dXqfZFIhCQQIJSE0EQQBBRUV0AsoKCrXrle9iu1a8WLvj1dsIIgFEEHpoEgvoSeENNIzyfRy6m7r+WOfmTkzmVRmMieT35vXfu2+z1qZkDXne9ZZ6yJ47x3QvGBiyrJ1Kdx1Bax7CLPhMN76xk/x1gkO1IUQ4kCktX79Xty2CZhWtN8CbCkcbxnlePE9mwpDiFQCMs+UEAcaw4BkbbhwyM6vDYJwiJLu9WHHv+710LMuXG98Apb+dfjklMoMJ+UshNqJ6hkcVDWDgwZC7sbG8PULtNb0eT5b8i6bBntwD/XmfqYvw+3tvTgj+sbEDUVz0fjbxespMZuWaISyEh6Pe3cUh91zGncedo/G8wOy7kCoPRRsZxyfTL6w7RZtF84PhOID213pLFnHI13YzzgewR58tGkZaijYLgThCbtoOzK2kbME2GK4138tnGzhzs9C3ZwJ70G2R/q2hD3f1j0Ufn37TT+CeNVEl0oIIUrJD4HPAsW9vs4F/qS1zgNrlVKrgcVKqXVAhdb6MQCl1O+BtyIB9o7Fq8NhuH5zJvzhfLjs7t3rLTNWMl3wrythyW/DmezP+T4c8x75BpIQQuxfbgNuUEr9gPCD5TnAk1prXynVr5Q6HngCuAT4adE9lwKPAecD/5RvTAkhdsowhibknH7c9ud9D/o2jQi4C+vV90Fq6/DrzWjYU7sQaKvqGVRWTaeyagaHVs+E2tqwR3mRQGs6XY/NReNvby704t6cc3iwu59teZeR/Y0rLCMcnqRo/O36iEW9Ha7rCkvS3L+D7h2xTINy06A8NrZD/2qtyXsBGccnnfeGQvKdhOKjbXenHTZ1h6H4WJJ3NGI4w4Tzfg2/PgP+ckk4qWPN7Iku1a4t/wfc9tFwQoRzfw5HXbzdP45CCHEgU0q9BdistX5+xNfy9maszZHPljE1B1ROhXf/FX57Flz/drjsHiirH9/X9L0wtP7XleH8D8e+H079/OSblFkIISYRpdTbCAPoeuB2pdRzWuuztNbLlFJ/AV4CPOAjhW9FAXyIoaG97mToQ+XfANcVPoTuIvxmlRBC7D3TguqZ4TIaNws9Gwuh9rrhIfemJZDrGX59pHww3B5YG9UzqK+aQX31DI6qqBr9ZQLNtp2Mx/1cf2bU8bgBEqZBvW2F4XZkKNyuj9jbHS8zjf1y6JKxpJQiZpvEbJOa5NjMlRNOSzw2JMAW24tVhl+D/tVp8McL4X33Qqxioks1OicD93wxfOPefBSc9xuoO3iiSyWEEBNiZ2NtAl8AzhzttlGO7WqszeEHZUzN4ernwUV/gWvfAjdcAJf+HaJ7/vXA3fLKA3DnFdC+HGadAm+4Chrnj89rCSGEGDNa61uAW3Zw7krgylGOLwEOH+V4DrhgrMsohBA7ZMehfm64jCbXO3rv7a418Mq/wM0Mvz5RNxRwF/Xktqtn0lI5jZaqsh0WxQkCOhyPdtej3fFod9xwv7Dd7ni8ks3zZG+aLtcb9Q1NzFDU2kOB9rDQe0TYXWXtn+N07+8kwBajqz0I3vH7cEKqm98fBtpGiX39YuuLcNP7oGMFnPhxOO3L4Yy+QghxgNrRWJtKqSOAWcBA7+sW4Bml1GL2bqxNsSvTFsM7rg0/CP7zu8NAeyzbqK61cM+X4OV/hL/ov/MPcMg58u0jIYQQQggx8WKV4Xwwo80Jo3U4meRovbe3PAvLb4PAK7pBQcWUYb23i9eR8uZwQsjYrn/X9gJNlzsQdruFkHtou8Px2JJ3eL4/Q6fr4Y+SdttKhT25bYva4rB7lN7eNbaFKb+fjwkJsMWOzT4Fzv4O3P5puP9rcMbXJ7pEIa3hiV/CvV8Oxxt99y1w0GkTXSohhChZWusXgYaB/cL41ou01h1Kqb0Za1PsjrlnwVt+Crd+OFzedvWwyW32Sj4FD/8AHv0ZGFb44e0JHwU7NjZlFkIIIYQQYjwpFQ6xV1YPLYu2Px/44RxnI3tv96yHV/4N/a0M+2KoMqGsESqaoXxgaQpD7/KmoWOxSixD0RC1aYjahKMx7VigNd2uT7u7fY/uwcV1WZHO0e54uKNMPWBAGHIX9e4euT8QetfaFrYhYfeOSIAtdu7Y98O2l+CRH0P9oXDUhRNbnlR7GAKsugfmviEc7zpZN7FlEkKI/dhejrUpdtfRF0NqW/hBcLIBzrpy73pJaw0v3gj3/k/4S/uCd8Lrvxr+Yi6EEEIIIcRkYZhQNS1cZr5m+/NeHno3DfXe7t0E/VvD0LtzDax7KBzCZCQ7MTzQHtgeGXzbYbBtKEVtIXAmufMia63p9Xw6XG9Yr+6OoqC73fF4pTdPh+OSDUYfdbHaMkmYBgnTIGkWbxetjR2fH1yMofMxQ02KIU8kwBa79sZvQ8dK+PvHofZgmHbsxJRj9X1wy4fCf4jO/l4Yrk+C/wmFEGJf01rPHLG/R2Ntij30mv+CVBs8/nMob4STPrFn929+Bu78HGx6EqYcDRdcO/ps8UIIIYQQQkx2VjQc9rb2oB1f42TCTh/9Wwvr1qGQu38rbH46PObltr83Xl0UcBd6cQ8LuZuhrGHYMLtKKapsiyrb4uDEzouvtSbtB+GQJSOGMul0PdK+T8YPBpe2vEvaD8gE4X7a9/H2YNYhA4aF22EIbm4feo8IzoeH5dufTxgG1j7sMS4Bttg10w7Hw/7VafCni+Dyf0Fly67vGyteHu7/Ojz2s7AX+CV/g8bD9t3rCyGEEK+GUnDWtyDdFvagTjbs3jea+reF7d9zf4BkffitoyMvevXDkAghhBBCCDGZRRK7Drm1hlwP9LUWhdwDQXdhu215+G1KHQy/VxnhsCU7C7nLm8IwfETHS6UUZZZJmWUyi+heVc8ZDLOHrzNBsF0Anim+puh8v++z1XGHnc8Gwa5fvEjUUIMh92gB+FiSAFvsnkQNXPgn+PXrwwmpLrsLIrv4DsWe8pzwKyCdq4cv7S9DphOO/QCc+Y3Br3MIIYQQ+w3DgLf+ImzPbv0IJGph7pmjX+s58MT/wb+/E/YKOfGj8NrPQqxi35ZZCCGEEEKIyUqpMGCOV0Pj/B1fF/jhtykHe3RvGR5yd6+FDY9Ctnv7e63YiJB7yujDmER20W17hIhhEDEMquw9rPMuBFqTHdbbuzgA94dCcm8gDB/lfKHXeGYPw/BdkQBb7L6GQ+D838IN74C/fRguuGbPh/AIgvB/9sGAek247lgVjl1U/KlWoi4csmTuG+Gwt8KcM8ayNkIIIcS+ZUXhndfDNefAjZfCpX/ffuKalXfDXZ+HrjUw56yw53bdwRNTXiGEEEIIIQ50hhn2rq5o3vl1bm77kLu/tRB0b4XW52DFneBlt783VhmG3GUNEC0Pl0gZRMsK693YN199mm0oRdIySWLu+uLdMJYDjEiALfbM3DPhjK/DvV8Oe4ad+rnRr8t0DYXTxWF11xpwM0PX2YWvdUw5Co44Pwysa+dA7ezwUzAhhBBiMomWw8U3wW/OhD9cAJfdDfVzww9y7/o8rL43bAcvvkk+uBVCCCGEEGJ/YcegZla47IjW4bxuw8bmbh3qzZ1qCxcnBfn+cB14u/f6ZnQnAXcZRMqH9iPJEdeUj7i2bNgY36VAAmyx5078GLS9BA98C5J14VLcm7pzdfgV6QHKhOqZYTg967WFcYgOhro54VclZCJGIYQQB5KyBnj3X8MQ+/q3wyFvgqd+FX6oe+aVsPhysCITXUohhBBCCCHEWFIK4lXh0nDIrq/XOpwXrjjQzqdG3x92LgVOf9i5tGfD8HvYzRkg7cSe9wIfuT+GJMAWe04peNOPwsD69k8NHS9vDoPpQ99c6EldWKpnjslXGYQQQohJo2Z22Mv6mnPC8a6PeTec9j9QVj/RJRNCCCGEEEKUAqXCnt12LOw8+mppHY6KsKMQfFf7/a3QWbRfPMLCOJMAW+wdOwbvuhnWPQQVU8Ne1WP86YoQQggxqU05Cj7wz3BimJ1NHCOEEEIIIYQQr5ZS4fAhkSTQ+OqfF/ij9/we2P/aha/+NQokwBZ7L1YBh5wz0aUQQggh9l/18ya6BEIIIYQQQgix5wwznIAyVrmDC8YuwDbG7ElCCCGEEEIIIYQQQgghxBh6VQG2UuoCpdQypVSglFo04tznlVKrlVIrlFJnFR1fqJR6sXDuJ0rJDH5CCCGEEEIIIYQQQgghtvdqe2AvBd4OPFh8UCk1H/gP4DDgDcD/U0qZhdO/AC4H5hSWN7zKMgghhBBCCCGEEEIIIYSYhF5VgK21Xq61XjHKqXOBP2mt81rrtcBqYLFSqhmo0Fo/prXWwO+Bt76aMgghhBBCCCGEEEIIIYSYnMZrDOypwMai/U2FY1ML2yOPj0opdblSaolSakl7e/u4FFQIIYQQQgghhBBCCCFEabJ2dYFS6j6gaZRTX9Ra37qj20Y5pndyfFRa66uBqwEWLVq0w+uEEEIIIYQQQgghhBBCTD67DLC11q/fi+duAqYV7bcAWwrHW0Y5LoQQQgghhBBCCCGEEEIMo8KhqF/lQ5R6APiM1npJYf8w4AZgMTAFuB+Yo7X2lVJPAR8DngDuAH6qtb5jN16jHxhtvO39UR3QMdGFGCOTqS4wueojdSlNk6kuMLnqM09rXT7RhdjfSXtd0iZTfaQupWky1QUmV30mU12kvR4Dk6y9hsn1d1zqUromU32kLqVpMtUFxrDN3mUP7J1RSr0N+ClQD9yulHpOa32W1nqZUuovwEuAB3xEa+0XbvsQcA0QB+4sLLtjhdZ60aspb6lQSi2RupSmyVQfqUtpmkx1gclVH6XUkokuwyQh7XWJmkz1kbqUpslUF5hc9ZlsdZnoMkwSk6a9hsn3d1zqUpomU32kLqVpMtUFxrbNflUBttb6FuCWHZy7ErhylONLgMNfzesKIYQQQgghhBBCCCGEmPyMiS6AEEIIIYQQQgghhBBCCDGa/SnAvnqiCzCGpC6lazLVR+pSmiZTXWBy1Wcy1WUiTaY/x8lUF5hc9ZG6lKbJVBeYXPWRuoiRJtuf42Sqj9SldE2m+khdStNkqguMYX3GZBJHIYQQQgghhBBCCCGEEGKs7U89sIUQQgghhBBCCCGEEEIcQCTAFkIIIYQQQgghhBBCCFGSJizAVkpNU0r9Sym1XCm1TCn1icLxGqXUvUqpVYV1deH4GUqpp5VSLxbWpxU9a2Hh+Gql1E+UUqrE67JYKfVcYXleKfW2/bUuRfdNV0qllFKfKZW67E19lFIzlVLZop/P/5VKffbmZ6OUWqCUeqxw/YtKqdj+WBel1MVFP5PnlFKBUuqo/bQutlLq2kKZlyulPl/0rP3x/5mIUup3hXI/r5Q6tVTqs5O6XFDYD5RSi0bc8/lCeVcopc4qlbpMpL34OyHtdYnWp+i+kmuz9+JnI+11CdZFlXB7vZf1Kdk2ey/qIu31JLcXfydKtr3ey/qUbJu9p3Upuk/a6xKrT+GctNmlVxdprye+PuPfZmutJ2QBmoFjCtvlwEpgPvAd4IrC8SuAbxe2jwamFLYPBzYXPetJ4ARAAXcCbyzxuiQAq+jetqL9/aouRffdDNwIfKZUfi57+bOZCSzdwbP2q58NYAEvAEcW9msBc3+sy4h7jwBe2Y9/LhcBfypsJ4B1wMxSqMte1ucjwO8K2w3A04BRCvXZSV0OBeYBDwCLiq6fDzwPRIFZwJpS+X9mIpe9+Dsh7XWJ1qfovpJrs/fiZzMTaa9Lri4j7i2p9novfzYl22bvRV2kvZ7ky178nSjZ9nov61Oybfae1qXoPmmvS68+0maXYF2Q9roU6jPubfY+regu/hBuBc4AVgDNRX8wK0a5VgGdhT+AZuDlonMXAr/cj+oyC9hG+A/hflkX4K3Ad4GvUmhcS7Euu1MfdtDAlmJ9dqMuZwPXT4a6jLj2W8CV+2tdCmX8e+H/+VrCf/BrSrEuu1mfnwPvKrr+fmBxKdZnoC5F+w8wvHH9PPD5ov27CRvUkqtLKfw57ub/r9Jel1h92E/a7N34t2cm0l6XXF1GXFvS7fVu/mz2mzZ7N+oi7fUBtuzh/68l3V7vRX1Kus3enbog7XWp1kfa7BKsC9JeT3h9ivYfYJza7JIYA1spNZPwE+AngEatdStAYd0wyi3nAc9qrfPAVGBT0blNhWMTYnfropQ6Tim1DHgR+KDW2mM/rItSKgl8DvjaiNtLqi6wR3/PZimlnlVK/VspdXLhWEnVZzfrMhfQSqm7lVLPKKU+Wzi+P9al2DuBPxa298e63ASkgVZgA/A9rXUXJVYX2O36PA+cq5SylFKzgIXANEqsPiPqsiNTgY1F+wNlLqm6TCRpr0uzvYbJ1WZLey3t9b4wmdpsaa+lvR5pMrXXMLnabGmvS7O9BmmzKdE2W9rr0myvYd+32dZelXIMKaXKCL8a80mtdd+uhjxRSh0GfBs4c+DQKJfpMS3kbtqTumitnwAOU0odClyrlLqT/bMuXwN+qLVOjbimZOoCe1SfVmC61rpTKbUQ+Fvh71zJ1GcP6mIBrwGOBTLA/Uqpp4G+Ua4t9boMXH8ckNFaLx04NMplpV6XxYAPTAGqgYeUUvdRQnWBParPbwm/LrQEWA88CniUUH1G1mVnl45yTO/k+AFF2uvSbK9hcrXZ0l5Le70vTKY2W9rrQdJeF0ym9homV5st7XVpttcgbTYl2mZLe12a7TVMTJs9oQG2UsomrPAftNZ/LRzeppRq1lq3KqWaCceuGri+BbgFuERrvaZweBPQUvTYFmDL+Jd+uD2tywCt9XKlVJpw3LH9sS7HAecrpb4DVAGBUipXuH/C6wJ7Vp9Cr4N8YftppdQawk9Z98efzSbg31rrjsK9dwDHANez/9VlwH8w9Mkw7J8/l4uAu7TWLtCmlHoEWAQ8RAnUBfb4/xkP+K+iex8FVgHdlEB9dlCXHdlE+On2gIEyl8Tfs4kk7XVpttcwudpsaa+lvd4XJlObLe31IGmvCyZTew2Tq82W9ro022uQNpsSbbOlvR68t6Ta60KZJqTNnrAhRFT4ccNvgOVa6x8UnboNuLSwfSnheCoopaqA2wnHTnlk4OJCV/t+pdTxhWdeMnDPvrIXdZmllLIK2zMIBzpftz/WRWt9stZ6ptZ6JvAj4Fta65+VQl1gr3429Uops7A9G5hDOJnBhNdnT+tCOLbQAqVUovD37RTgpf20LiilDOAC4E8Dx/bTumwATlOhJHA84dhPE14X2Kv/ZxKFeqCUOgPwtNal/vdsR24D/kMpFVXh17XmAE+WQl0mkrTXpdleF8o0adpsaa+lvd4XJlObLe21tNcjTab2ulC+SdNmS3tdmu11oUzSZpdgmy3tdWm214UyTVybrSduoO/XEHYPfwF4rrCcTTjg+v2EnzDcD9QUrv8S4Zg2zxUtDYVzi4ClhLNZ/gxQJV6XdwPLCtc9A7y16Fn7VV1G3PtVhs+QPKF12cufzXmFn83zhZ/Nm0ulPnvzswHeVajPUuA7+3ldTgUeH+VZ+1VdgDLC2cSXAS8B/10qddnL+swknIBiOXAfMKNU6rOTuryN8BPfPOEEP3cX3fPFQnlXUDQL8kTXZSKXvfg7Ie11idZnxL1fpYTa7L342Uh7Xbp1OZUSbK/38u9ZybbZe1GXmUh7PamXvfg7UbLt9V7Wp2Tb7D2ty4h7v4q01yVTn8I90maXWF2Q9roU6jPubbYq3CSEEEIIIYQQQgghhBBClJQJG0JECCGEEEIIIYQQQgghhNgZCbCFEEIIIYQQQgghhBBClCQJsIUQQgghhBBCCCGEEEKUJAmwhRBCCCGEEEIIIYQQQpQkCbCFEEIIIYQQQgghhBBClCQJsIUQQgghhBBCCCGEEEKUJAmwhRBCCCGEEEIIIYQQQpQkCbCFEEIIIYQQQgghhBBClCQJsIUQQgghhBBCCCGEEEKUJAmwhRBCCCGEEEIIIYQQQpQkCbCFEEIIIYQQQgghhBBClCQJsIXYTyil1imlHKVU3YjjzymltFJqplLqmsI1qaLl+RHXJwvH79i3NRBCCCEmv0J7vU0plSw69n6l1ANF+0op9YpS6qVR7n+g0K4fOeL43wrHTx3H4gshhBAHlEK7nS28R96mlPqdUqqscO49hbb3HaPcN18pdZtSqlcp1a+U+pdS6sR9XwMhDgwSYAuxf1kLXDiwo5Q6AoiPuOY7WuuyouXIEefPB/LAmUqp5vEtrhBCCHFAsoBP7OT8a4EGYLZS6thRzq8ELhnYUUrVAscD7WNZSCGEEEIA8GatdRlwDHAs8KXC8UuBrsJ6kFLqIOAR4EVgFjAFuAW4Ryl1wr4qtBAHEgmwhdi/XEfRG1rChvT3e/iMS4H/A14ALh6jcgkhhBBiyHeBzyilqnZw/lLgVuAORrwpLvgD8E6llFnYv5DwjbEzxuUUQgghRIHWejNwJ3C4UmoGcApwOXCWUqqx6NKvAo9prb+ote7SWvdrrX9C+H792/u63EIcCCTAFmL/8jhQoZQ6tPCm9p3A9bt7s1JqOnAq4RvjPzA8DBdCCCHE2FgCPAB8ZuQJpVSC8NtQA23xfyilIiMu2wK8BJxZ2L+EPf/AWgghhBB7QCk1DTgbeJaw7V2itb4ZWM7wzl9nADeO8oi/ACcV2nohxBiSAFuI/c9AL+wzgJeBzSPOf0Yp1VO0XFt07hLgBa31S8AfgcOUUkfvk1ILIYQQB5b/AT6mlKofcfzthEN53QP8g3C4kXNGuf/3wCVKqXlAldb6sfEsrBBCCHEA+5tSqgd4GPg38C3C9843FM7fwPBvTNUBraM8p5UwZ6set5IKcYCSAFuI/c91wEXAexi9N9b3tNZVRUtxQ3sJYW8vtNZbCBvn0b66LIQQQohXQWu9lDCgvmLEqUuBv2itPa11Hvgro7fFfwVOAz5G2PYLIYQQYny8tfDeeYbW+sOEY2HPAv5UOH8DcIRS6qjCfgcw2nxSzUAAdI9zeYU44EiALcR+Rmu9nnAyx7MJ39zulsKMyHOAzyultiqltgLHARcqpaxxKawQQghxYPsK8AFgKoBSqoUwlH5XUVt8PnC2Uqqu+EatdYZwHM4PIQG2EEIIsS9dCijguUJb/UTh+MAQnPcBF4xy3zsIx8bOjH8RhTiwSIAtxP7pfcBpWuv0HtxzKXAvMB84qrAcDiSAN45x+YQQQogDntZ6NfBn4OOFQ+8GVgLzGGqL5wKbCCdqHOkLwCla63XjXFQhhBBCAEqpGGEQfTlDbfVRhN+IurjQ+etrwIlKqSuVUjVKqXKl1McIA+7PTUS5hZjsJMAWYj+ktV6jtV6yg9OfVUqlipaOokb4p1rrrUXLWsJeXTKMiBBCCDE+vg4kC9uXAv9vRFu8Ffg/RmmLtdZbtNYP78OyCiGEEAe6twJZ4Pcj2urfACbwBq31KuA1wJHAOsKxr88DztJaPzIhpRZiklNa64kugxBCCCGEEEIIIYQQQgixHemBLYQQQgghhBBCCCGEEKIkSYAthBBCCCGEEEIIIYQQoiRJgC2EEEIIIYQQQgghhBCiJI17gK2UqlJK3aSUelkptVwpdUJhltZ7lVKrCuvq8S6HEEIIIYQQQgghhBBCiP3LvuiB/WPgLq31IYQztC4HrgDu11rPAe4v7AshhBBCCCGEEEIIIYQQg5TWevwerlQF8DwwWxe9kFJqBXCq1rpVKdUMPKC1nrezZ9XV1emZM2eOW1mFEEIc2J5++ukOrXX9RJdjfyfttRBCiPEk7fXYkPZaCCHEeBvLNtsai4fsxGygHfidUupI4GngE0Cj1roVoBBiN4x2s1LqcuBygOnTp7NkyZJxLq4QQogDlVJq/USXYTKYOXOmtNdCCCHGjbTXY0PaayGEEONtLNvs8R5CxAKOAX6htT4aSLMHw4Vora/WWi/SWi+qr5cP2YUQQgghhBBCHJiUUtOUUv8qzC21TCn1icJxmWNKCCHEpDbeAfYmYJPW+onC/k2Egfa2wtAhFNZt41wOIYQQQgghhBBif+YBn9ZaHwocD3xEKTUfmWNKCCHEJDeuAbbWeiuwUSk1ML716cBLwG3ApYVjlwK3jmc5hBBCCCGEEEKI/ZnWulVr/Uxhux9YDkwFzgWuLVx2LfDWCSmgEEIIMU7GewxsgI8Bf1BKRYBXgPcSBud/UUq9D9gAXLAPyiGEEEIIIYQQQuz3lFIzgaOBJ9jLOaaEEEKI/cW4B9ha6+eARaOcOn28X1sIIYQQQgghhJhMlFJlwM3AJ7XWfUqp3bpPa301cDXAokWL9PiVUAghhBhb4z0GthBCCCGEEEIIIcaAUsomDK//oLX+a+GwzDElhBBiUpMAW4iCbHYDWktHBCGEEEIIIUTpUWFX698Ay7XWPyg6JXNMiQOW254hv74Pd2sarytHkHHRfjDRxRJCjLF9MQa2ECVvy5YbWf7yFUyb9l7mzvnSRBdHCCGEEEIIIUY6CXg38KJS6rnCsS8AVyFzTIkDhNYatzVNdmkH2aUdeG3Z0S+0FEbUREUtjIiJioaLsd3aGrY/2jkVMdjdoXqEEONDAmxxwOtPvcyKlV/BtqvZuPF3xGMtTJv2nokulhBCCCGEEEIM0lo/DOwoRZM5psSkpbXG2dhPdmkn2aUd+F05UBCdVUnZ8VOwamMEeR+d90esveHHMi5+dy7cz/lox9+9AihQUQu7MUFkahl2SxmRqWVY9QmUIcG2EPuCBNjigOZ5/bz44kewrEoWH3srK1b8DytXfZNYbAr19WdOdPGEEEIIIYQQQogDjg40zrq+sKf1sg78XgcMRfTgKspPbSE+vxazLPKqX0O7owXfI495BBkPtzVN+qmt6EfDIUpUxMBuLpNQW4h9QAJsccDSWrP85S+SzW7gmKP/QDTawGGH/ZBnnn0XS5d9kmOO/gOVlUdPdDGFEEIIIYQQQohJT/sB+Vd6C6F1J0HKBUsRm1NNxZl1xA+twUjYY/Z6ylCoqAVRC3N3yxhovPYMzqYU7uYUzuaUhNpC7AMSYIsD1ubNf6Ct7XYOmv3fVFcvBsA04xy54GqWPH0+z79wOYsW3kQiMWOCSyqEEEIIIYQQQkw+2gvIreomu7ST3PJOgoyHihjE5tUQP7yO2CHVGNHSia6UobAbk9iNSVjYCOxhqD21jEiLhNpC7KnS+VdAiH2or+9FVq66ktraU5kx4/Jh5yKRWo468rc8teR8nnv+MhYtvJFIpGaCSiqEEEIIIYQQQkwegeOTW9FNdmkHuZe70HkfFTOJH1pL/PBaYnOrUfbu9YnWvg/GxE6yKKG2EONPAmxxwHHdPl5c+jEikVoOm/89lDK2uyaRmMWRC37Js8+9mxde/E+OPuo6TDM2AaUVQgghhBBCCCH2b0HOI/dyF9kXO8it7Ea7AUbCIn5EHfEj6ogdVIWytn9v7vf3425pxW3dgtfaWtgeWLbgbWvDiMeJzptHbN5conPnETtkHtE5czCSyQmoaUhCbSHGlgTYouT1d3VgR2PEkmWv+llaa5Yv/yz5fCsLj/kjtl29w2urqhYxf/4PWLr0Y7z00mc4/PCfjBp2CyGEEEIIIYQQYrgg45J9qTMcHmRVN/gaozxCYmEj8cPriLQk8Dra8VrX0HdHUTi9tRWvsB2kUsMfalnYTU3Yzc0kjz0Wq6mZoL+P3IqV9N72d4LUHwcvtadPD0PteYcQnTeX2Lx52C0tKGNi3tdLqC3E3pMAW5S09S88x63f+ybVzVO5+Fs/wDB3d2qF0W3c+DvaO+5lzsFfpLLymF1e39jwRvIHf55Vq7/F6tVXMWfOF17V6wshhBBCCCGEEJNVkPfD0Pq5tjC0DkDFNFZVP9rdgN+xit4/tdLR2orX1gZaD7vfrK7Gbm7GnjGdxHHHhdtTmrGbm7Gap2DV1aJ2kAtorXE3byG/cgX5FSvIvRyu+++7f/B1jESC6Ny5ROfNC0PtQw4hOncuZtnwDnOBDljbu5YX2l/ghY4XWNG1Ai/wUEphEA5ZMrBtFDq6GSrcVkqhUIPbA9cPrpWBQg1uGxigwKg2wuVwRXWqjIbeKup7K6nrLaf2iXJsP6y3Z/p0VaXpqkrRXZ2hpzpLqiIHxujPHu01q6PVtJS30FLWQkOiAdN4dVmLEONNAmxRslY+8Qh3/OS7xCuraFu3hiX/uIXF556/18/r7X2W1Wu+TX3dGUyb9t7dvm/atMvI5jayYeNviMVbmNZyyV6XQQghhBBCCCGEmEy0H5Bb2U36iU3kVvZCoND5Hpz1j+FteYagZz0AyraxpjRjN08heeKJg+G01Rwes5ubMOLxvS6HUopIy1QiLVMpP+20weNBNkt+1SpyK1aQX7GS/IoV9N15J8Gf/zx4jTGlmeyMejY32SyrSvNoYjOvlGXQhqLcLufQ2kOJW3ECHRAQgGZwW2uNRhPoAC/wCHSARqO1HtweuR44F+iwp/XA9uA5AgIzQFfrcAk0zblaZmamMCszldmZKczsmsohOgJATuV5Jb6Z1bENrI5tZGVsPRsjrQRq+AcEo7EMi6llU2kpa6GlvCXcLoTbLeUtlEfK9/pnIsRYkQBblKQX7r+b+371c5rnzONtn/sKd//fj3nsxhuYc9yJVDdN2ePnuW43Ly79GNFoM4ce+p09muBBKcXcOV8ml2tl5cpvEIs2U19/xh6XQQghhBBCCCGEmAx0oMmtaKP/nytwNnmgbYJ8Cm/zEvzOF4nNq6fqnGOwW04d7EFt1tRMyPAdRjxOfMEC4gsWAOD4Dis6X2b5iofZ9vyT5FeuoGrjVmasbGXm4zBbw5uBIGpjHjyLikOPIDbvECKzZxGdOROruXlM66G13qMlCAqheaBxOzI4rWmsrSlmbosxpa2Jk7sXoQEshVkfx6yPYdRHMWqjUGUT6ICsztKjetjmbmNzajOb+jexKbWJFztepM/pG1a+ymjlDsPtpmQTtmGP2Z+FEDsiAbYoOU/eehMP3XANM49ayFv+6/PYsRinX/ZBfvepD3Hv1T/jgi9fuUcBtNYBy176DI7TyaKFf8a2K/a4TEqZHH7Yj3jm2YtZuuyTHHPMDVRWHLnHzxFCiFKllFoH9AM+4GmtFymlaoA/AzOBdcA7tNbdE1VGIYQQQggxcbTj0P/Qc6Qf24DXk0BZ5WjPxWt7ASPeTeKoqZS95y3EDvs8yiqNuElrzebUZl7seHFwOJDlnctxAxeAhoYGFhx2PLX1C6iuO4IZydmY61tJLV/OlpUr2Lh1K21bW+nNdOA/+zg6MNGGiYpEwLbDxbLQphmu2f1AeiCIHlNmYRnQVVhW7OByw6S8vJz5FfNZXLaY8spyIokIjuWQNtJ06S62+dvYnNvM8s7l3L/hfrzAG7pfmTQlm4aF2sXrymjlHuU3QuxIafyLIgThP/IP/uF3LPn7X5l34mt540f+C9MKP8krq6nllHddxr2/+hlLH7iXI1535m4/d/36q+nsfIB5c79GRcWCvS6facY5csHVPLXkfJ5//v0cu+hm4vHpe/08IYQoQa/TWncU7V8B3K+1vkopdUVh/3MTUzQhhBBCCLEv6SAIx5B+cAnZpV3ooAmzfAo6qEPnN2JVt1N+8sEkFn8IIxab6OICkHbTLO1YGobVhcC6K9cFQMyMMb92PhfPu4j5iZlMN8qJuBn6ezfRvXY5m1/6N2v9HrRKYdg57CNyRI7JMcXOMdUIg2atwfcsAt8k8Mxw2zOHL4ENQQStI2gVBRVHmQk0MRRRlIqhVBSlylAqhmHGMc0ohmVimhamaWJaNqZlYtp2eMy2MQwDwxgaf3tHy8hr0BD0O/idOYLOPF5HjnRXPxk/S0Y5ZLryZFMZtpq9vOLnyPvOdn+u06xpHFp2KOXl5dhxmyAakDfz9Kk+OnUnW/u28lDHQ7Q5bVCUV5fZZdv12h7YnlI2hYgZ2Sd/L8T+TwJsURIC3+feX/2cpf+6hyPPOJvTLvtPjBGTCBxx2pksf/gB/n3db5h99LEkq6p3+dzu7idY88r3aWg4h6lTL37V5YxE6jjqyN+y5OkLeO75y1i08EZse9flEEKI/dS5wKmF7WuBB5AAWwghhBBiUtJa427cSPrRx0g//gzOBgej5nCsurkYyZlg9BI72KXiDUcSmXLqRBcXL/BY07OGF7Y8x7JNz7Jh2zKyfVuoQlOpYFYkxqJIhKRVQ9RyMawcKr8Es+sxrJTP1uKHJaEsCb5v4uZjBG4U5SRRbjOGVYVtV6O1RxBkMHQOrXMEOo82cmgrB1YejDSYHsryMewAtQejjASeInANfNfAyxs4aaOwHx4PXIPAM9GBjQpsIILSUSCKoaIYKoZhxDFUHNNIYFkJTDuOZUcxbRsrEsGyI1jVEcyGCPWJWhKqnGg+ipkyoNvHa83g9+Vx8cmoPLm4T74KcsmAbMQloxzSbpbezl76+/txnDDojhFjZuE/y7KIJWMYMQPf9snqLH3dfXR0dLDcW06/6idrZfFUOCFmQ6Jh1HC7pbyF2lit9N4Wg9SYf11hnCxatEgvWbJkooshxoHnutzxk++y6slHOf68/+DECy7e4T9SXVs28/vPfpSDFh7Hm//rip0+N+908OSTb8aykhy76G9YVtlOr98TPT1LePa5d1NevoCjj/o9phkds2cLISaGUupprfWiiS7HRFFKrQW6AQ38Umt9tVKqR2tdVXRNt9Z6u0/tlFKXA5cDTJ8+feH69ev3UamFEEIcaA709nqsyPtrMcDr6CD92OOkH3+MzBNPo3U9dstxmI2HoQwLFfNIHNNI+UmzsGr3foLFvaW1JtPbQ2/7NtZseJ6NW58gnVqDCtqJRzKUJV0iCQ8r7jGiD1zhfvDdCJ4bx3Hj5J0ojhvHdWKgy4jH6qmqbKG6fg7x2kPJmZVs68uxtTfH1r4c2/pytPbm6EjlMZTCNg1s0yBiDm3blkHENIhYRcdMheU7qFQPKt2DSnVBqguV6YFsDwZ5DDPAtD3MqMYqt7CSJlYCrJjGjPrhOTOHQRbTyGAaOSwzh2X4WMrDNHyMnUzSqAPCHuGuge8oAs8YDMn9vImXNXEzFl7Wws2YGLqcmNVATWQ21dFmyo1qEn4ZkVwUpQsZjQFGbZTI1DJUY4J8pSaX8Em7Wfr7+4ctqVSKvr4+XNfdrmyGZaCiCtd2yZgZenQPXXSRM3NkzSw5KwdRaKpoGjXcnlI2hbi17/8+ij0zlm229MAWE8rJZrj1e1eyYenzvO7SD3DM2efu9PqaKVM54bwLefhPv2f1U49z8LHHj3qd1j4vLfsUntfLUUf9bkzDa4CqqkXMP/R7LF32cV5a/hkOP+zHqD35eFUIIUrPSVrrLUqpBuBepdTLu3uj1vpq4GoI3xCPVwGFEEIIIcTe01rjtbaSW76c9BNPkHnscfKr12DWH4I96zXEFn4WlI2RMEgsbCZxVAP2lOS49oINAp9UVxd9HW30tbfR276V/u5X6E2vxPE2Y9q9RCvyxKry2GUejc1D9zq5CL5bhWU2ENONeG4ZOSdOf9qks8ujq9vHcaLkiWCU1RCpbEAlqnAjSTJ2hM6Mz7aOHK1rcvRmXWD5sLIlIyZNlTGaKmMsnB724XB9jeMHuAOLp8lkXVyv6FjxNV6A6ydx/DgwFSKEy0j9hWUPmQpsM1wsE2wjwDI0lhGEQbfhYykf0/AwlYuJg6kcIkaKRKyHRHk/SStL0k6TtDNgv0KPtQxHO3S5Hl4a3IyFkS8j4lQT8xooyzRTsWIayZeaMZ1yIhigcljRHOVlAbo6itVYRfSIchKVVZixOL5hkM07pFKp7ULu/v5+qvuraXFbtqufNjWO5bDJ2MQKYwU5M0fOCkPuaCJKTWUNDdUNTK2aSktZOMnk1PKpNCYasQyJPCcT+WmKCZPp6+WWq77KtrVreONHPsX81562W/ctevPbWfHog9z/m//HtMOOIJpIbnfN2nU/p6v7EQ495H8pLztkrIsOQGPjOeTyW1i9+ipWx6Yy5+Cd9wgXQohSprXeUli3KaVuARYD25RSzVrrVqVUM9A2oYUUQgghRMnTOsBxOslmNuE6aSKxckwrgWnEMIwohhHDNKMoFZHhAcaRn0qRX7mS/MqV5FasIL9yFfmVKwn6w5TUbJhL/KhziCyYC76FipnED68jcXQD0VmVKGNsfja+59Lf0UFfRxu97dvoa2+nv6ON/q5NZPMb8NU2IhU5YpUO0SqHaKWDWaWpKdzveopUPkaf0UjcnE1D7dHUxA4nnUrQ1t7PurZO1mztoT3tktE2GR3BtZO4VoJUYNHjhaEzeaATII9SeerKojRVxJhWk+DYmTU0VcZorIjRVBGjqTJKY0WM8pg9Jn8GUPjwINCDobfjB2HIXQi+nYHgO5cns7WNbOtWsq3byLa1k23rINfZTT6dwTMsXNPCMyyCqmp0dQ1BZRVBeSVBWQV+sgzPjuIGuihUL7yeF5ArrNMZj56sS8bxd1hmhSZhOyTtDAkrRcJOk4ynSVoZkvaLJO0nSFpZEsqjHKgILGqCOJV+nOSmMvzVSbrSkE459PVn6MtmyUcdIhUJEhWVJCsqqa+oID6tiVj5XOxEEm1H8JXCCSDnDA+8e/p6SPWnCPxgu7JuU9tYb64nb+bJmTkc08GO2ySSCarKq6ivqqe5ppkZdTOYUT2D+kS9/Puzn5EAW0yI/s4Obrryy/S2beXcz3yRgxYet9v3mpbFmf/5cW740md46IZref37PzzsfFfXI6xd+xOamt5Gc/MFY130YaZPez+57GY2bPgV8VgLLS3vGtfXE0KI8aCUSgKG1rq/sH0m8HXgNuBS4KrC+taJK6UQQgghJprvZ0mnNtDbuYb2jvW0dm+jra+X9nSWXhdSfoSUH6PfLaMvX46nLSKGg226REyHiOESMV1sI9y2DY+I8okYPhFDEzEDoiaFRRGzDGK2STxiE7NtktEIsUgMO5LEspPYkSR2tJxIpALLKl7KsawylBplTIlJRnsezrp1w0Lq/IoVuFu2AKAi5ZgNM4nMXkDZWW/ASDSgvQRBRoOliB9SQ+KoBmKH1KCs3ftWsdaaXDpFpqeHTG836Z5uMr09pHt7yBSWdE8P6d52XL+NaGWeWJVDtDJPtMohNtOl5hBv8HmBhp68wQbXIOvVEovOoj55GHXW4VQ5zaS3dbNmUzdrOzJsy2j69BZ6dYx+HcWjAqgYfFbMMmhKhmH0gkLv6aZCMN1Y2K4vj2Kb+/Yb1Eop7MKwI6P2wC42pxE4YrvDfl8fzvr1OGvX4qxbR37tWpx1y3DWrUNns0OvFY8TmTmTyMwZRGfNCrcLa7O8fNgzHS+gN+vSm3Xozbr0ZMKlN+vSk3XpzQwd787kaM/mWdXr0p/T+Dv53qVtOCTsLEkrTbIyQ7IuTtLOUma4lCufBBliQQ92exY29KOyKeyci5X18bImXtbCMGziFRUkyisor6ikoaKS+NQG7LIyVDSOtmx8ZeJqTdZx6M700pfuJ51O42Qdgv4AVZhRsrPw31KW4imPvJlHRzRWzCKeiFNRXkFNZQ1N1U1Mq51GQ1UDZWVlRKNRCbpLhATYYp/r2rKJm678Mvl0mvO+8HWmzd/+H+ZdaTp4Lsec/Raevv1vHPKaU2g55DAA8vltLF32SZLJgzlk3tfH/R8apRRz536ZXH4LK1Z+jWismfq608f1NYUQYhw0ArcU/s20gBu01ncppZ4C/qKUeh+wARjfTwWFEEIIMSG0Dujv28wrG15i9frVbOnqoDuTo88LSPlWGEwHMfrdJH1OOf1OOXl/OjB9u2dFjTwJI0/ccDDRZLSBh4GnTTxt4g6uLWDP368pgkIIPhCK9xGz2oiZeWJWnpiVI2bmwm3DIWb6xE1NwlIkIwbJiEVZNEpFPElFIklVWSWJZC2xRD3RaFUh/A5DcMPYVdK472it8drbya9YWehZvYLcylU4q1ejfY2RbMConII97RBiC08ifnI92o+BN/RnrDEwEjGs+gSxedXED6/DiIWxkA4CMn29QwF0b09RQF1Y94brfLYbI5rDjntYCQ8r7mMnPOyET7RCEWv0KYu7KDuHKhqjOeObtLqaNk/R1mOjrQZq44fRZB5OpdmI6rPZuj7Nxp487TmDPu3Qp7eSxwYqgUoMBc3lNofVJZnbXMns+nJaquM0V8ZpqohREbcmbeBoVlQQP+II4kcMz1B0EOC1teGsWzcs3M4te4n+u++BYKjHsllbS2TWTOymZqzaGsyaWuzaGhpraplaW4NZW4s1vRqVSOz0z1FrTSrvDYbdg+F31qE349KdTtOVTtGTztKdydOb8dicCuh3DHL+DqJIG5QdEK/JUmZnSFg5klaehOEQI08020ekdxtWPkPEcQuBt4OV87E9D1u7GGiSsTg18Th2PIGZSKJicbQdwTE0WQLyGvKBheMG+HmN052nR/fQSy9rWctjPDZUT0NjRA2i8SjJsiTVFdXUV9VTXVFNMpmkrKxscC1h9/iSSRzFPrXtldXc/L9fAeC8L3ydxlkH7fWz3FyOaz7zESzb5t3f/gmGZfDsc++mr+9FFh/7N5LJg8eq2Lvk+xmefuYi0unVLDzmBioqFuyz1xZCjA2ZFGpsSHsthBBiPEl7PTbKp83Sr/3op/F9XegxnaTfLSPQ2/dYVgQkrQxlZo6E4ZBUPkmtSfoGSc+mLIhQEZiUaZNkYGL5Ntq3CXwDtIFpagzDDxcVoAwfpXwwAlzlhws+eRWQU5q8ghyaPJqc0uS0xiEcASIPuICvFAEKXyk8VBiQY+BqEwcLJ7BwtI0/Sn1GEzGcMPi2csTNgRA8T9TMETVcIoZLzPCImy5xyyNpBSRsTbltUB61KI/axCNJbKuSSLSSWKSKWKyGRKyWSLQSO1KGFYmEix3BMHderiCTIb96NbkVK0m/vJrM6nVk1m7A8yKo8maMsgas6ilEKhqw7XJsFRnsaQrgKJ+McknrPCk/S7+Xoc9J0e+k8D2XwHcxzTymmUPrDIHXjx/0YcUcrLiPlfAGw+lIMiBSDnbCx4y5mJE8yth+CIdAK3JESGuLfl/R6wW0uy5trqLNVUTUNGZaR1DhziSXqqCzz6S136fLtegLYqSJDntedUwxvTrGwY0VzJtSzUH1ZcyqSzKtJrHPe0/vzwLHwd24cUSv7fV4bW34nZ0Emcyo96lYDKumEGgPrAuBt1Vbg1ldE65ra7Gqq1GR3f+wZ6DXd3dfjo6NfXRs7aK9o52uvl56Mhl6tUuf0vQDKW3SH9ikvQgpN4Zm5z9723CJmg6xwgdcUeUSVQ4RXGwc7MDBCnLYgRsuvosdeFiei6kDzABMrTA0GJhgmGjbJDAttBlBmTZK2aMG1UopYrEIZYkw0C4vr6C8soKKisphQfeBFHaPZZs97gG2Umod4VD0PuBprRcppWqAPwMzgXXAO7TW3Tt7jrwh3v9tfOlF/vadrxNNlnH+F79JzZSpr/qZ655/hpu/9T8c//Z30rhoG+vX/4L5879Pc9NbX32B91De6WDJkvPw/SzHLrqZeHzaPi+DEGLvyRvisSHttRBCiPEk7fXYiDbP0Y0f/ClxO0+D6qZZd1OmfZIBlHkGZa5NuRenRkWotiMkE3GiyRhWIoKdiGAnYkSSMezyOJGKBHZ5HDtmY0dMrIiBFTExTAUa3LxPPuvhZD2yPRlyXX3kutPkezPk+nLk03mctIeT88jnNa4Lvm8ANsqIYCkTW1FYFJbSGMpHmQFKFcJw5YHhgwrQSuMqHw+fnPZJqYA0moyCtNZkCJcsmpwR4KgAxwiDcVeFIbhL2FPcCWzy2sbxbVy98/GQI0aepJ0lYWdIWJnB7aSVIW5liRt5YipcojjEcIhqh4jvYfgK37EIXAt8g6hhEzEiRIwIUTNC1IwSNaNYykQR9lvX+DhBhnyQwQmyOHpgnUNTGNdYEfaALtykCLeVobASOuw5HfOw4nkMa/tQWgeKnBsj60ZI+Sb9AfQGAf2BJuNZ5LwIOTcKQRVa12DrCgydRAUJdBDHc2P0Z+N0ZS16/Aj9OkpQFEDGTc2UCptZtQnmTanmkKnVzKoLg+pkVAYM2BeCbBa/qwuvqxu/qxOvs2u7tdfVid/ZhdfVBa476nOMiooRgXcNVk1tYV0UfNfWYlZWoozRg2gdaPzuHG5rGqc1jbslhduaxu/JE6BJGTl6K7L017j0lbn0RfL0K5+MH5DxA7KeJuNqsg5kXMi5BlnPIOta5DyLnB8h70Vwgt0P3C3lETXzRE2HiOGEgXhhsZVLBI+I8rDwsVQQLlD4t8rA0mBqMIJwbQYKfBPbsIiYJlHbJhaNkIjHiMfjWKaFaZqFxRjctqzwuGVamJaFaVpYVuG4ZWFZFpZtD16nDBNlGBiGgSosw7d3fH53ad9H5/ME+Tw6n0fncgR5B53PofN5kosXj1mbva/+RXid1rqjaP8K4H6t9VVKqSsK+5/bR2URE2D1kif4x4+uorKhifO/+A3Ka+vG5LkzjzyG+a89jeVP/45c/XqmTHnnhITXANFIHUcd+VuWPH0Bzz1/GYsW3ohtV01IWYQQQgghhBBiR2ZVW7zhoJd5oO4wVkVnkPLTvH9WCxdOn0qNPYYxgYJI3CISLzxzahnQsEeP8FyffMYjn3bIdfaT7SwE4H0Z8n058ikfNw++q/Ac8F0NngGBhYWiWpvUYWAoC9MwMJWJZZiYhomt1GAwbiswVYBSAb4KBkNwj7CXeI48/fiFnqHhktKQMj3SKiCjAjIaMk6ErBNjqzbJBjbZwMbZRfhtGwMT5WWIW3ls5RDVLjYuERwi2iGiXSLGiHHEB4ZSMVxsAyKmha0i2KaHbYTnbNPBJECFnyeEi1bkPZt+N0o2V06qp5JMrppMrpKsU07GKSPrxsl6URysQs92E0dbOAPhftHaY8c9yi2lqY8rDqmKclBjOfNbajlkag2z6pLUJGUiz4lmxOMYU6diT911B0OtNUEqhd/ZidfVhdc5EGwXrbu6cdatxXv6afzubhit06xpYlZXDw+6awo9umtqsGrD/fi8WspOmImRTKBzPm5rmprWVBhsb03jvpQGb/vnq6iJEbfCpczCSFgYcRtVOKZi4EVdMlaejMqTxiGtHdJ+nlTeIZ3Pk847pPMu6bxPxvHIuJqMMxCO2+GHOp5JzjXJeTY53ybvR7ev6w6Yyh8KxT2HqOMQSbvYRg8GAYbSGCoIv7WCRqkAAz14fGg/QA1cS4BhaNTA/QRF1wbD71UaAx9DhdebKkAZA8/0C+c1yvAxC8fUwDONYPDZhhrxXAJMQ6NU+I2bsbSvemAvKg6wlVIrgFO11q1KqWbgAa31vJ09R3p07b+W/ft+7v6/H9M46yDedsVXSVRUjunzezpW8ORTbyFwEpx+9sPYdnJMn7+nunue4tlnL6Gy8iiOPuoaDGP3/xETQkwc6dE1NqS9FkIIMZ6kvR4bA+31408+yoNP38KjTYfxeNVRRLXP2xuruWx6E0eUJya6mONGa43O53H707h9haU/g5fJ4vbn8FI5vLSLn/EIcgGBE4AHhmlimAaGHa5Ny8SwTAzbwrBMTMtEWRambYXX2DbKsvCUQUor+gLo8zX9rk9P3qXHcelxPXpdj17Xpc91yaDJo8JhUzTkPU3e1zieJu+HAfRe1Bhb6ULPUI2pAlxt4GgTV++6t6VtQMJSxExFTEEUiARgexozr7E9iGpFNFDEtEFZxKK2Ks7UpiQnnz6TKbPHNgMQ+wft+/g9PWHQXRx4d3dtH3x3dhGkUqM+R0WjRUF39WDPbrO6FiNRD0aSwNFoF3BBuwrtFa09VdhWoHf2gUnYVVqZAcoMoLBWZjjkkTIK26aPMvzBb30owwflExCQCVzSgU9ae6R0QBqfft+nx8/T57v0+T7pQJMJFFltkAtM8kRwdBSHKG4QIcBAa2NwrVEEWg0eC7QqnCusUQTaIEAVHSuNYXbWf/tN+1UPbA3co8KR+3+ptb4aaNRatwIUQuw9+whW7Deevv1vPPD7XzP98CM59zNfJBIf21+CgsBh1bovYkUtlv21ngb7Po45+9wxfY09VV11LPPnf4dlyz7JSy99lsMO+yFKlcY/HkIIIYQQQggx4PjFJ7Jo4fEcdt9dXLDkKp6bMp+bgjP5Y1sfx1bEuaylgXPqK4nswVfK9wdKKVQsRjQWI1pfu09es34MnqG1xvEDck5AzvPJOv7Q2g3IuT451yfrhvvZwv7I4znXJxExKY/ZlMeswXVF0fbQ2iJq7biHtdaafMajryNLX0eOvs4s/YX11pd6ueXpp5kyp4qjz5jOjMNrUYb0uD5QKNPEqq3Fqt29/8eCfL4QdHfhd++gh3dnF/lVq/E7O9GOs+eFMmxUJIGyEyg7CZFwPXQsgYokw2N2onC+sJg7/iaF1gG4GQwnQ5mbIelmqHfSaDeHdjJoNwNuBu2k0W4G7abRbhbtZcHPh88o5OuBbeFHTDzbwLUUjgWOpcmbmpwVkDMCMqZH3gwK58C1wBm8dmBR+HYEI5rAiCewY2VYsSRWrJxorBw7VkY0Xk40XkHcShIzE8TNBDErQcxMEDPjRM04USMOKBzXw/N9PM/H9QLcwrbjefh+gOf7uH6A5/l4fnj+k3v+E9qhfRFgn6S13lIIqe9VSr28uzcqpS4HLgeYPn372Y1F6dJa88ifr+eJW/7MnONO5OyP/TeWvfOvTe2NNWu+R1/fsxx+2E/oOegpHv7TdRy06HgqGxrH/LX2RFPjm8nntrB6zXeIxaZy8MGfndDyCCGEEEIIIcRoLNPgnLPOpvfkM/Bv/QtnP3oFK5sP5jct5/Ohviz1tsW7p9ZyyZQ6mqJj/55O7D6lFFHLJGqZVFIaPwulFLGkTSxp0zCjYtg5J+fx0sNbeP7+jdz+/16guinBUWdMZ+7iRix79ybYFAcOIxrFaG7Gbm7e5bVaa4J0Br+rM+y5bRigBgd7D4emKV6Kjw9cWzgernZwfeFaDWFv7pyPzgcE+cI65xPkAnTOD49l/cIxjyDro3MeQdaDnY2mYVAY8sRGRU0Y7TOeUb56oXWAHwQEOiDQPoEO8Ae2g4CAIFzrIPwmSXbg2uGLRhdeMgNkUEWvpQrfBlFKYWJgKwNjYKFoe9i+QhW2P7nLn+TuG/cAW2u9pbBuU0rdAiwGtimlmouGEGnbwb1XA1dD+BWn8S6rGBtB4PPP3/6S5++9gyNOO5PXf+AjGMbYN05t7XezYeNvaGm5hMbGc3j9+4/lmk99mPt+/XPe/vmvTfhYWtOnX042t5n1G35JLN5Cy9SLJrQ8QgghhBBCCLEjlQmbSy68mFfa3sJzN/+eXz92Be11Dfxq+sX80F3AT9Zv45z6Ki6bWsfiyuSEv98SpS8Sszjq9dM54nUtrHm6jWfv3cC/rnuZx299hQWntnD4KVOJJUsjiBf7F6UUZlkSs2xih5DdHVprtOMTZD2CTBho62y4HrZkXHTe3/GDRvk314LRA+8dXM8o1wcEeIGHH3h42sML/MK6sBS2ncDD0w7uwPHAHTznBh66kNIPjlQ9xinuuAbYSqkkYGit+wvbZwJfB24DLgWuKqxvHc9yiH3H91zu/NkPWPHYQxx77vmcfOGl4/KLTTa7geXLP0dF+QLmHHwFABV1Dbzmwkv51zW/5OWHH+DQk1835q+7J5RSzJ3zP+RyW1ix4ivEok3U1Z02oWUSQgghhBBCiJ2Z3VDOf33oIzy08nwe/dtv+OYzV2EmFL8+6H3cxOu4ta2Hw8piXDa1nrc1VpMwJ9fwImLsmabB3MVNzDm2kU0runnu3g08cdsrPH3XOg49aQpHnT6Nirr4RBdTiHGhlEJFLYyoBVUTXZrxobUm7+dJuSlSToq0m6bf7Ycvjt1rjOskjkqp2cAthV0LuEFrfaVSqhb4CzAd2ABcoLXu2tmzZFKo0ufmctz2g2+x7vlnOPmi97D43PPH5XV8P8/Tz1xANruRxcfeRjw+bfBcEPj86X8+S/fWVt77g1+M+YSRe8Pz0jzz7EWk02tYeMwNVFQsmOgiCSFGIZNCjY2xbq99zyPb10umr5d0TzddXVvp7emgprqR+ubpVDU1k6iskl5gQghxgJD2emzsbnvt+QF/fnIdK+75DR8I/kyN2c+N8y7n9y1v4yXHoNIyubC5hvdMrWNmXCavF7uvc3OK5+7dwMqntqEDzUELGzj6jOnbDUMihNh/jWWbPa4B9liSALu0ZVP93PLtr7F11UrOuPyjHHHameP2Wi+v+AqbN1/PgiN+SX3967c737FxPdd97hPMO/Fkzv7op8etHHsin29nydPnEQR5Fi28mXi8ZaKLJIQYQd4Qj43daa/dXI7+7k7aO7fQ3rGFbR3tbOvsobs3TW8qTzrrk3UM8p6FqyPkzRh5I0LeiOCYERxlk/Qy1OY7qcu1Uxd0UV8B1Q0NNE+dTf2U6VQ1NFHZ2ExFfcO4zMEghBBiYkh7PTb29P11b9bl5/e+RP7J3/FR8xbqVA+PHfJerjn4Uu5IKXwNp9dWcNnUOk6tKceQD5bFbkp153nhXxtZ9uBmnJwfTvh45nRmHCYTPgqxv5MAW5SUVHcXN1/5ZbpbN3POxz/LnONOHLfX2rbtHyxd9gmmT38/cw7+/A6ve/TGP/DYTX/k7Z//GrOOWjhu5dkT6fRqljx9AdFoE4uP/TuGsS/mUBVC7C55Qzw2mqe16A//1wdJuQGZQJEJTHLaJBfYZLVNXkfI6QjZIEIuiJLzowTs/KvHUTNP3MqSsDIk7Cwx06E9W0Nbph5duNfEp0r1UuP2UpvtpjbXRl22jfIgTayqkuqmKdQ1TaOqsYnKxiaqGpupbGwiXla+L/5YhBBCjBFpr8fG3r6/fqU9xff+8Rwtq6/nI/bfqSTFlsMu4vpDP8x1vYp2x2NWPMJ7p9bxzqYaKm15zyN2j5P1eOmRcMLHVHd+cMLHeYubMG0ZpkaI/ZEE2KJk9Gxt5aYrv0Smt5dz//tLzDjiqHF7rUxmLU8+dS5lZfM45ugbMIwd96jzXJfrPvdxPCfPpd/7OZFYaYynta3tDpYu/RiHH/ZjGhvfNNHFEUIUkTfEYyPaPEc3X/qjYcciRp6EnSVhZUnYWeJmlriVJ27mSZh54oZDwnSJGy5JwyOhfJIqCBcgoiOYfgQjiGAFUUzfJhtvJxVrY7PSbHbL2ZJuYlP/FDanptCdrxoqj5Gnxuyh0u+hJt1HQ7qDusxW4n4uLFsiQVVjc2EpCrcbmiivqxuXSYiFEELsPWmvx8arfX/98KoOvv/3pzil6y/8p30nMfK4Cy7i9iM/wW+7FU/1pYkbBp+c0cgnZjaOYcnFZOf7AauXtPHcfRvo2JgiURHhiNe1cPhrZcJHIfY3EmCLktC5eSM3fv0L+L7P26/4Cs0Hzxu31/L9HEuWvJ2808biY/9OLNa8y3s2r1jOn77yWY55w5t53XsuH7ey7QmtAx5/4ixMI86xx94qY7YKUULkDfHYqJoxS9d+7/dk4nEqPZfjt/VwwoYM5XlFNGpSXxmjpSFCZaXCtFQ4CbahhmbDVgxuK1U4V3xcgc47ZJ5dQe6lDeishapqIpgSx6nqJ5XcTHusnXXKZ5MfZ0umhs2pKWxKNZP1EoPlLLdS1FjdVNJDudNPXX8vjb1tRPKZwaKYlkXN1GnUTZtB3fSZhfUMymvr5d9vIYSYINJej42xeH/t+QF/emojv71nCf/h3Mx77XuxlEYtvJQXFn6C77d73N3Rxx8WzOb0WhnXWOwZrTWbXg4nfNzwUhdW1GT+ic0cKRM+CrHfkABblITbvv8tNix7ngu//j1qW6bt+oZXYfnyz7Ol9S8cdeRvqa09Zbfvu/+3v+C5e+7gom98j+Y54xew74ktW25k+ctXcNSR11Bbe/JEF0cIUSBviMfGoqMX6Ecvi3CXPZtfHfppngqiKK1p2epyzKosh2/1MTRoBUZVhMZZFcybX0vz7Eqqm5IYezjWobNpM+mHHiT17wdJL3kOZddg1s4gevDRmHUz8RXkjM1k41vYEOtgteGyKbDZnKtgU7qZ1nQjXhD25lEE1EZ7qLW7qTB7KPN6qUqnaOzqxuodCrYj8UQYZhcC7YGAO14ub86FEGK8SXs9Nsby/XVv1uVn/1zFnY8+w0etv3GB8U8M0ya/+IOcVfEOenzNvxYfQo0MJyL2UsemFM/dt4FVT25Da5nwUYhS5nsB2X6HTJ9D48xKCbDFxOprb+PXH3s/i97ydl570XvG9bVaW//KS8v/m5kzPsxBB+3ZpIz5TIZrPvNhYokk77rqR5jWxH/lKAjyPPrYaSTiMznmmD9MdHGEEAXyhnhsLFq0SC/51+1w3dugcxVr33Y918eP4E+tXXS6HjUoDmrzaFjeR02HR7NnEC1Ew9pSVExJMmtuNVMOqqRxZgXJquhu93YO8nkyTz5F6t//JvXvf+Nu3AhAZO5hJBafTmT2AoxYHW57DndbCidoJxNvZU20k5VWnvWYbMol2Zyuoz1bNzi+dszM0RRvpybSSTmdVDi91PakKGvPo3Le4Osnq6oLPbWnUzct7LFd2zIdOxYb4z9lIYQ4cEl7vWNKqTcAPwZM4Nda66t2dO14vL9e25HmW3cs5+XlL/DFxK2c5f+bF6e9nrMP+hLn1Ffxy8NmjunriQNPqjvHC//cxLKHwgkfp86t4qgzZMJHIcab1pp8xiPT65Dpy5PuDQPqcMkXjjtkeh1yaXfwvo/+8nQJsMXEevCGa1hy2195/89+TUVdw7i9ju/neOTR15BMHMzRR1+/VxMfrnn6Sf72na9z0jvexfHn/cc4lHLPbdjwG1at/haLFt5MZeVRE10cIQTyhnisDLbXmS74w/mw5Tl42y9xDj+POzt6uX5LJw91pzAVnFpZxuGeSX5lL5tW96A7HZp9gwZfYRZCbStpMWV2Jc2zw0C7YUY50cSuP4zUWuOsW0f6wbB3duapp9Cui5FIkDzpRJKvPYX4whMhiONuzeC1ZXC3pnG3ZfDdLP3xbayIdrI8mmNNoFifLWNzup6cH4bRioDGRDsNsTaq7A7KdAc1uT4q2sFs88Hzw4IoRVVD02BP7dppM6ifPpOqpimYlvREE0KIPSXt9eiUUiawEjgD2AQ8BVyotX5ptOvH8/31I6s7+PrfX2J6+7/4VeQH/PC0a/m2P5P/mz+DtzZWj8trigOLk/VY9vAWXvhnOOFj3bQyTjp/Di3z5O+XEHvCc/yiINoh05sn3TcURg8cy/Q5BP72+bFpGSQqIyQqIiQroyQqIoP7iYoIs49qkABbTBzXyXP1h97DtPlH8JZPf2FcX2tL600sX/45jj76emqqT9jr5/zjR99m9VOP8e7v/JTaqeM73Mnu8LwUjzz6Wqqrj2PBEb+Y6OIIIZA3xGNlWHud74c/XgjrHoY3/RAWvReAVzJ5/tDaOdgre1oswsXNNbyxspz1W/p5cnUnK1Z0km7N0OQZNPsGNcHQ7PMVDXEWnz2Tecfvej6EAUE6TfqJJ0j9+0FSDz6I19oKQPTQQyl77WspO+W1xBcsANPE73XwtqVxWtO4m1M4m/rxu/P4eKyLtfNCRTerjDxr8xabMlV05ysHX6cy0suU5FbqYtuosNoo9zupSTnY2+IEbR4E4e9dpmVRM6WF2sJQJDVTWqiZOo2qpqaS+LaQEEKUKmmvR6eUOgH4qtb6rML+5wG01v872vXj/f7a8wN++s/VHPngBzghsobzzrqbV/IBDyw+hKaotHNibPh+wKqntvHEba+Q6sozc0EdJ7ztIGqakxNdNCEmjA402ZQ71Dt6IIzuLdrvc0j3OjhZb/sHKIiX2SQqoiQqIyQHQ+noYDCdqIyQqIwSiZk7/basjIEtJtSL/7yHe375E97xlf9l2vwjxu11tNY8teRcgsDhuMV3vqoJs9I93VzzqQ9RO2067/zKVSjD2PVN42zNKz9k3bqfc/xxd5NMHjTRxRHigCdviMfGdu21m4W/XAqr7oYzvgEnfXzwlBME2/XKPqu2kndPqeWUmnLSeY9nNvTw5NpOnlnVSfv6fupcxcGuSbNvMOe4Rl530SHYUXOPyqi1Jr9q1VDv7GeeAd/HqKyk7KSTKDvltSRPPhmrpmbwHj/t4m7qx9mUwimE2kGfA0AXLs/XdvCS3ccrrsfGbJxt2Wp8HZYrYjhMLdtCc7KVmtg2EsY2ar0+Er1x9KYobrvLwMyVyjCoamymZmpLGGoXgu2aKS3Eysr28qcihBCTh7TXo1NKnQ+8QWv9/sL+u4HjtNYfHe36ffH+Ogg0n7v6r1zZ+gFePuJ9nFv/Lo6vKuOGBbNlMmQxpjzX54V/buLpO9fhOgGHvWYKx75pFomKyEQXTYgx4+S8op7Sw3tHF/egzvS76GD7rNeOmiN6SEeH9ZYe6EEdL7cxzLHJzCTAFhNGa811n/0YGrjkOz8d1188enufZcnT5zNv7tdpabn4VT9v6QP3cfcvfsTr3/9hjjzj7DEo4avjOJ088uhraWx8M/MP3eHwdEKIfUTeEI+NUdtrz4FbLodlt8BrPwuv+wKMaD9G65X9ruZa/qO5hsZCT62c6/Pcxh5ue3Yz6x9s5YS8TXlDnDd98Ahqp+x9uOv39ZF+9DFSD4a9s/2ODlCK+IIFVL7tbVS86RzMUcJjvy8fBtqb+gd7agfpsBdDzgh4qcrhxWg3q90UG3KKLdkKsv7QeNgNiTamlW2hKdlKeaSDhNFBpZ8mmo5BZxR3Czh9ZjjjJZCorCoKtqdROzUMt8tr60rig1khhNgXpL0enVLqAuCsEQH2Yq31x4quuRy4HGD69OkL169fP+7lau3NcucPL+cybuPX5/2TL3WYfGduC5dMrRv31xYHnmy/w1O3r2Ppg5uxIgYL3zCDI0+bhhXZs84OQuxrTs6ja0ua7q3pcHzp3hG9p/sc3Ly/3X3KUCTKbRKVRb2jKyJD+0UBdSS274cvlABbTJhNLy3lz1+7gjMu/xgLTj9rXF9r2bJP0d5xP6856VEs69V/BUhrzU3f/BJb16zkPT/4BeU1E/9L04qVX2Xz5j9x4gn/Ihbb/a/CCyHGnrwhHhs7bK8DH/7+CXj2OjjuQ3DWt2CU0DUfBNzV0ct1mzt5uCeFpeCsukre1Rz2yjYKwfc9y7byw+tf4LRek6Rh8LqLDuHQE1/9v6M6CMgtX076wQfpu/Mu8itXouJxKt74RqrOP5/40Uft8MNbrTV+Txhqu5v6B3tq61z4y6ZvatbV+jxv5XjZ7WVdLk9rLkqPW4Ye6IFNQG2sm6bkNpqT22hIbqPS7qTc6CSWD1C9cbx2i3yHgdNvE7gmViRK9ZSpRT22W6idOo2q5inYkeir/jMRQohSIu316EptCJFif39yBcfdfiZ+xTQ+cdo1LOnP8M9j5zEzLm2UGB/dW9M8dssa1j7fQVl1lOPPnc3cxU0y0aOYcEGg6WvP0rEpReeWFJ2bUnRuTtHXkRt2XSRukSwKnwd7Sxf3nq6IECuzMUr477UE2GLC3PaDb7Fx6Qtc/otrsKOxXd+wl/JOB4888hqmTr2IeXP/Z8ye27O1lWv/+6PMWHAU537mSxP+1bVsdhOPPX4a01rew5w54zueuBBi5+QN8djYaXutNdz9BXj8/8HR74I3/wSMHfeIeSWT5/otnfxpayddrj/YK/vC5hoaojabujN86tpnmLk6ywzP5ODFjbzuonlj1rtAa01u6VJ6bryJvn/8gyCTIXLQQVSdfz6V575l2BAjO3xGoPG6ckPDj2zqx92SQjtBeD6i6KmHVTGH1YHD2lyOTekcbTlNp5vA00N1SVgZmpPbBsPt5uQ2qiLtJHU/ZKL4PRGcdoN8XwSn18bN2lTWNw6G2hX1TVQ2NFBR10BFfSPRRGJM/pyEEGJfkvZ6dEopi3ASx9OBzYSTOF6ktV422vX78v211pprfnEV7227imdO+Sn/YR3FIck4txx9MKYMJSLG0eYV3Txy82raN/RTP72ck847mKky0aPYR3Ipl47NhZC6EFZ3bUnjueH7AKWgqjFBzZQy6lqS1E4to7o5SVlVdNJ8a0ACbDEh+jra+fXH3seiN72N11783nF9rbVrf8Yra3/I8cfdSzI5e0yf/dTf/8qD1/+WN//XFcw9/jVj+uy9sWzZp2nvuIeTTnwI266a6OIIccCSN8RjY5fttdbwwFXw76tg/lvh7b8Ca+fjE+aDgDvbw7GyB3plXzmnhUun1uF4Ad+9aznL7t3EiXmbsvoYb/7gAmqnju140UE6Td9dd9Fz401kn3sObJvy159O1fnnkzzhhD0awkMHGq89M2z4Ebc1jS78MotlEGlO4jdGWBfN87KTYVVfmnVdWTb3O3Q4JulgqNeaqXzq4x00l20dDLabktuoj7VjuAFufxS3y8Lps3DTNm7KwknbGFRQUddMRV0DlfUNVAwsdQ1UNDQSS5ZN+Ae9QggxkrTXO6aUOhv4EWACv9VaX7mja/f1++vO/iybv/9aWlQbd7/nYf7rlU6+NLuZj85o3GdlEAcmHWhWPrWNx/+2hlR3ONHjiW8/iOommehRjA3fC+jemqFzRFid7nUGr4mV2dS1lFE7tYzaqWFYXdOcnDRB9Y5IgC0mxEN/vJanbr2Z9//011TUN4zb6wSBy6OPnUoyOYejj7pm7J/v+9zwpU/T39nBe3/wfxM+KVYqtYInnjyb2bP+i1mzRp1jRQixD8gb4rGx2+31oz+Fe74EB58B77wO7PhuPX9NJseXV23mX139/ObwmZxdXwXA/cu38f3rX+B13QZJZXDqRfM49MTmcQlg86tW0XPTzfTeeit+Tw/21KlUnvd2qt7+duympr16pg40Xkc2HEt7cwp3S7jWA2PdmQq7MYE9pQx7SpLOpGZZupeXW7tZ3dbP+u48WzMBPZ5NwFCYXm6naEy0MaVsC1PLttJcFgbc1dEeAJy8hZuO4PWbuP0WTsrGTYdBt3bLSCSnUlHXNBRsF4XcicoqCbiFEPuctNdjYyLeXz/58L0suvcCnppyEVef9N/c29HHXYvmMr9s934HEOLV8Byf5/+5kafvWo/nBBx28hQWv2kW8XKZ6FHsHq016R4nDKo3p+jYlKJrS4ru1gxBYdJEw1LUNCcLQfVQWJ2oiByQvzdLgC32OdfJc/WH30vLIYdx7me+OK6vta3tTpYu/SgLFlxNfd3p4/Iabete4frPf5LDTjmdsz74iXF5jT3x3PPvp6/veU468UFMU36BE2IiyBvisbFH7fWS38E//gtmnAQX/hFiFbt1W8YPuOC51SxLZbnpqINZVBn2oNnSk+XTv3+GaSszzPBMDlrUwGnvOmTcJiwJHIfUfffRc9NNpB99DAyDspNPpuqC8yk75RSUbb+q5+tA43fnhgXa7pbU4ESRKLDqE0SmlmFPKSMyNUlQH2P51g5eXN/Gy5t7WNuZYVO/R3vOIK+HenhElEtdtIf6eAdNya1MKd/MtMqNTC3bhmUEQ2XQ4OYKPbf7LdxUuO2kLAInTjTSTLK8hYraZirqGyivqydRWUWionJwbZiTu2eJEGLfkvZ6bEzU++snf3wRR3fdxRNvvZsPpuM0RCzuXDSXqExGLPaRTJ/DU7evZdlDW2SiR7FDbt6na0s6DKo3D41Vnc94g9eU1UQHg+q6wrqyMY5pyr9nAyTAFvvc0n/dy93/92Mu+PK3mH74gnF9raefuYhcbjMnnvBPlBq/RuShG67hyVtv4vwvfZMZRxw1bq+zO3p6lvD0M+9k7tyvMK3lkgktixAHKnlDPDql1BuAHxN+HfnXWuurdnb9HrfXL94Et/wnNC2Ad90MiV2PKw3Q6Xi8+ZlV9Hgefz9mDgclwnkZXD/g+3ev4Pl7NnBSziZZG+PNH1pAXcv4ftvG2biRnptvpvevt+C1tWHW1VH1trdSdd55RGbOHLPX0Vrj9znDemq7m1P4fUNfUTRrY0SmlGFPLQvXU5IYSZu2viwvrGtn+eYuVm3rY21njs39Ht35oecrAiqNLLV2H/WxLhrj7TSXb6WpYgMV8W7ikRwRa/sZ0N3sQLht4WUtvJyJlzXxshaKMmy7mmi0jli8kURFTSHcriJRWTm0rqwiEk8ckL1ThBC7T9rrsTFR769TnVvgpwtZZs5j6/tu5j9XbOTj0xv4wkFT9nlZxIGte2uaR/+6hnUvdFBWE+X4cw9i7rGNMtHjAUYHmt6OLF2b02FQXQirezuyUIhL7ag52JO6uGd1NPHqOqscCCTAFvuU1prrrvgE2ve55Ls/G9c3lgPDaRx80OeYMePycXsdCHuV//6/PwoaLvnuT8d1UsrdseTpd5DPb+WE4+/HMOQfQiH2NXlDvD0Vfoq4EjgD2EQ4IdSFWuuXdnTPXrXXK+6Ev1wKtQfBu2+B8t0bhmNdNs85T68iaRrcvnAO9ZGhfzv/taKN7173PKd2FYYU+Y+5zH/NlHEPR7XnkXroIXpuupnUAw+A75NYvJiqC86n/IwzMGLj09b4/U7YS3tLCndLGmdzCr9raDZzszJKdHYl0YOriM2pwqwYGkM7nfdY055iTXuKlVv7eXlLD690pNnUk8cv+jWxzPSoVFkq6afW7qMh3klTvI2aRBeRRAor2k/UzhCxHaKWi2GM/jum5xj4hXB7MOjOWXhZE9+JoFQ5EbuGWLyBRKKJeHkNycqqwR7d8coq4mXlROIJIok4xk4mAhVCTD7SXo+NiXx/ve4f32Xmkm9y3ayreOa4c/hzaxe3HTNn8BtVQuxLm1Z082jxRI/nH8zUuTLR42SUS7uF4T/Sg8OAdG5J4w0M2aegqiGxXVhdURuTDzb2kgTYYp/a9PIy/vyVz3HGBz7Kgte/YVxf6+UVX6a19WZec9Ij2Pb4Nxoblr7Ajd/4Ase+5bxxn5hyVzo6/snzL3yA+fO/T3PTWye0LEIciOQN8faUUicAX9Van1XY/zyA1vp/d3TPXrfXr/wb/nghlDfCJbdC1fTduu3Zvgxvf3Y1c5NR/nr0wSSLhqvY2pvj079/hikr0sz0TGYvbOD0d4/fkCIjudva6P3b3+i56SbcjRsxKiqofPObqXrHBcTmzRv31w8yLk5rOuytvamf/JpegrQLgNWYIHZwFdG51URnVWKM8rVZ1w/Y0JVhdVuK1W1hwL2mLcXq9hTp/FAv7IQFDTFNje1QprNE/DS2myZp9FMV6SMRyWDbuXCJ5LDtPFYki2Vnse0cETtPxHJ2GHj7jjEs5PZyJr5jon1F4Ck0FigblI0yoygzimHFMa04ViSJHUlix8qJxiqIxSuJJapJJKspS9aQLKskliwjEt/zIDzQAV7g4QUebuDiBm647bvD94Ohfdd38Pw8bpDH9fN4gUsQuJgKDMAYttaYgEIP7iulw/OEa4UOF6Ux0KCDcM3AWqMIUDoAHaADD7QPgYfWPgQ+unhbewSBhw48Au0VzoXnw6V420czcCwA/MLaQmkLBhc7XAcmaAsdWKBNdGCCb6EDEx0Y4X5gon0T7Rto3yTww+OBpwrHDHSg0UGA1pogGKhXuK21Rgd+YR0UrvUHr9U6/DMKBs8FaB0Mf14QDF5rmAamZWPaNpZtY1o2hmWF+4XjZuH44LpwfmA/vG/4se32t3ueNfTMgdc1Tfl2QoG012NjQt9f+y7t311EOpPl5Xfex5f6+rGV4r5j5w1rx4XYV3SgWfnkVh6/9RVS3XlmHVnHCW+TiR73V74f0DMwqWJRYJ0q+tphLGlT2zIUVNe1lFHdnMSWoWTGlATYYp/6+w+vYv2Lz/Kf/+9a7HHqOQbgun08/MiJNDa+ifmH7vQb6mPqnl/+hKUP3MfFV/6AxtkH77PXHUnrgCeePAeA4xbfIW9ShNjH5A3x9pRS5wNv0Fq/v7D/buA4rfUOZ5x9Ve31xqfgD+dBpCwMsevm7NZt93T08p4X13JabQXXHD4Lq6iHhOcH/OjelSy5az2vydkka6O86YMLqJ9Wvndl3As6CMg8+RQ9N95I/z33oF2X2BFHUHX++VScczbmPppMWAcad2ua/Koecqu6ya/rBU+DqYjOqCA6p4rYwdXYU8t22stEa822vnwh2O5nTXs63G5P0d6f3+76ZMSgNmFREzOoimjKrYCk4RLHIRpkMZ1+yPfhuX0YKj0Udtth2B2G3jksO4cdyWJbOSzTwzADDLX3v8fqAIJCSBr4BkGgCHwDPzAICgsqDIeHrY2hbUNpUIQBcmHbGDwXHh9aoFSa9iBQaK1AG2itihZjlG2jcK0KJwgdcTxcF64BlBFgKA/T8DAMD8Pww8X0MQtrwwx2UcId08FAkG0Ohd5BISAPTPRAcB4Uh+jhorQN2KDtMGTHRhEeM4iE+yqC0jZK2QSeSeAG+J6L57r4rkvge4PbvldYF217nkcwcNzzdl6ZPaHUYMi9XYhu2Zi2NSJMt7YL30cL3K3i0HwwYN/+eaM9Y6JCdWmvx8ZEv792VtxH5I/n8XPzYuZ84Ftcsnwd75lax1VzWyasTEKMnOjx8JOncKxM9FiyAj8g1Z2nZ1tmaPiPzWm6W9MEha8RGqaiujk52Ku6bmoZtS0H7qSK+5oE2GKf6e/s4FcfvYyF57yVU9512bi+1oaNv2PVqm9y7LG3UlF++Li+VrFcKsU1n/4QyaoaLv7WDyZ0sqnW1lt4aflnOHLBr6mre92ElUOIA5G8Id6eUuoC4KwRAfZirfXHRlx3OXA5wPTp0xeuX79+719064tw3dvC2QMv+Rs0HbFbt/1+cwefXbmJdzXX8t15Ldv9QvrQqnb+99rnOKXLoEwZnPLOuRx28vgPKTKS191N39//Qc+NN5JftQoVj1Nx5hlUnHMOyRNOeNUTP+4J7frk1/WRW9VDflU3bmsaACNhET2oajDQtmp2/8PrjOOxtTfH1r4c2/pybO3NF9ZDx9r68/jB8N8/LUPRUB6loSJKXcKiJm5SHYUKOyBpeCRwiOkcXj5LJpPBcR0838P3XbTOE+g8aBdNHvAAB/BQuCgVrg3loVSAYRaFqtstHoYRYBoeyvDDsJbtA14Gttk++N3ROUYGw4Tb2x3fbj3inqDovhHPgKFrFUPlgELIDIUUXUFhNdoSPkoX1qAL4XxgFNZKowvntdLhvqHxCfB9n8AL8D0/fPPoAz4YgYGpTazAwtImNhBRCtMIhoLugZB7WPA9Ytv0Ma3C2gwKS2Hb8Ieep/zwZ688FA6DA1nuIaVMDCOGYUQxjRiGGcMwYpgD68FjA+fjmEZ08B6IDIblBGGwrnVRb3Mv/NBEe+E3CgJPD4bfIwNyb1hoXjhfFJYPXOsVjgWeN7hdHLKPmaJQfViQXnTM2lWv9Z31VC96RvGx5oPnSns9Bkrh/XXv796Bte4Bvn3w9ajjD+OXm9r544LZvK529yZ1FmK8ZPocnvrHWpY9vAU7YrDwjTNZcFoLli29c/elINCke/L0dWTp78zR15mjvzNLX0eO/s4cqZ48uuh3yrLq6LAxqmunllHVlJBJFSeQBNhin3n4T7/nib/dyPt/8isqG3ZvTNK9oXXAY4+fQSRSw6KFN47b6+zIyscf5u8/vIrXv/8jHHnGG/f56w8IApfHHjuNaGwKixb+ecLKIcSBSALs7e3TIUSKdayC358LTgouvgmmLd6t2/73lVZ+vH4bV8xq4pMzt2+z2vpyfPq6Z2hcnmaWZzLz6HrOuORQIvF9M6RIMa01uRdfpOfGm+i76y6C/n7MqirKzzqLirPPJrFoIWoff6Dqpxzyq3sGA+2BiSGt2hjROdXhkCMHVWG8yj8vP9B0pPIjgu7h29v68qTy2/derYzbNFXEqC2LUBGzqYhbVMRsKuM2FfGh/Yq4XXTcIm6HPUW11uFEmL5PUBgqYmAZ7diO7OyDjx2d29lxwzAG1yO3Rzu3P/IDn6yXJetlyXgZMm64pPIp0rk0qWyKTC5DNp8lm8+Sd/LknTyu4+I4Dp7n4bs+vuejPY32NXigAlUIxS3MwMTShW1d/P+PDj+8KArCzREfXCjLw7R8DCvAtHwsK8C0AmxbY5kay9LYVoBlakwjwDIDDBXer3BBO2gcgiBPEOQIk/s9NxSYjwzJo4Vj8e3DdCNaCM7DfcOMDm4PXG8YEZSyQRkQKAJfE/gQ+Brtge8FaF/jezrsde77BIVQfLQQfKehevG5gfMDAftoz9rDnuqf+cvt0l6PgZJ4f921Fu+ni/m7dyz6/F/xw1wf/V7AA4vnUWXv+7ZZiJG6WtM89tfVrHuxk7LqKPXTy0lWRklURrZbx8sjGDJO8h4JAk2mN18IpLOFgDpHX2cYWKe68gTFnR4UlFVFKa+NUV4bo6I2TnltjMr6OLVTy4glZS6xUiMBttgnPMfh6g+/hynz5vPW//7SuL5WZ+eDPPf8ezls/g9panrLuL7WaLTW/PFLnyHd28NlP/olpjVxvzBt3HgtK1d9nYXH/JmqKvndXIh9RQLs7SmlLMJJHE8HNhNO4niR1nrZju4Zs/a6Z0MYYvdvgwtvgNmn7vIWrTUfW76Bm7Z18+NDpvPO5prtrvEDzU/uW8njd67jNVmbZHWUN39oAfXT992QIiMFjkP64Ufou+MO+v/5T3Qmg1VfT/kb30Dl2WcTO/LIfR5aaq3x2rPhUCOresi/0ot2fDAg0lIeBtpzqohMK0eNU6+WVN4rhNnbB9zdGYe+rEdv1qUv55Jxdh4WWoYqhNoWFfFCsB0bGXhbhRC8cC5mYZsGlqmwDAPTUFiG2m5f3qxOLD/wyfm5MBh3M4MBeTqfJpULw/FMLkMmH4bjuXyOvJPHcZwwGHe9wXBce5rAD8AH5Q8F41YQLra2RwTjO6F8lO1h2B6WrbEiAVZEE7HBtiESgUhEEbEgaisiNkSscN82wTQClOEChUU7BDqH7+cIghyBn8MPcgRBHt/PAns/LMsOKoBSdiFQt1BqYDGLjtuF/eHnDWWjjKHrlLIwiu8v3GcUzqOssNu/NgrfMFDgg9aKwA+H+9FBYfj2AI484ePSXo+BUnl/Hdz3DYyHv8d71Dd4z+WX8K4V63hLQzX/b/6MiS6aEIM2vdzFc/dvpL8zR7o3Tz69/QdvylDEy+2hYLsiQqIqGu5XDAXdicrIAdMjWAeadK8zajjd15kj1ZUbHOpjQLIyQnkhmK6ojVFRFx8MrMurY5j2gfFnN1lIgC32iaUP3Mfdv/gR53/pm8w44qhxfa3nn/8Aff0vcNKJD2EYEzO+1CvPPMUt3/4aZ33wExz+ujMmpAwAvp/lkUdfS2XFURx55K8mrBxCHGgkwB6dUups4EeACfxWa33lzq4f0/a6fxtc91boXAPvuBbm7fobMk4QcPELr/BYT4o/LDiIU2pGD6YfWd3Bldc+xymdijIUJ18whyNO3X7okX0tyGRI/fvf9N1xB6l/P4h2HOypU6k4+41UnH020UMOmZAyai/A2dBPbnUYaDub+kGDippEZ1eGvbPnVGPVxyekfK4f0J/z6Mu6g6F2X9YrrHd2LLwn7+19+Gcotgu4TcPAMhSmobBNVThnjLpvGmpwbGylVDiKR2GEj5HHwlCRon1QA8dUuD04OkjhguLnDTwLNXTf8GuGnsXOzhc9n6JzhgLLNIiYCts0sC0Du3i/8IFApLAdni/aNw0iVvhnM9o521T77O9XoANyXm6wt3jaTZPxwh7jqWy4ZPIZMtmhYNxxHPL5fBiKOx6BFxC4AdrTg6G4re3hwbje/U4TgRGABZhg2AaGZWDZFnbEIhIxiUYNYlGTWMQkETWJRy1iEZN4xCRihX/2pqIwjntQGJvdB4Jwsk8VoAn3tXbDiTqDcB3ogQk9C0th8s9g4LphxwuTgGq/6DnFx4snBx24f/d7rL/+9FekvR4DJfP+2knj/nghK1NRfjDzl8w/dQbfXbeNqw+byVsaqia6dEKMyncD0n15Mr0OmV6HdG+edG++sO2Q6cuT7nXI9jujjmAVK7NJVkZIVEaHryuG71slPqGg1ppMnzM8mC7uTd2VI/CG/wHEKyJhMF0bo7w2TkXdUG/qspqoDNMyyUiALcad1prrr/gkvudy6fd+Pq5vFrLZDTz62GnMmvlRZs/+5Li9zq5orbn+85/EyWR47w//b0LHwl679qe8svZHHLf4DsrK5k1YOYQ4kEiAPTbGvL3OdMH150Hr8/D2q+GI83d5S5/n89ZnVrEh5/C3ow/m8PLEqNe19ef47+ufpW5ZitmeyYyj6jjj0vlEJ2BIkdH4/f30338/fXfcQfqRR8H3icyaRcXZZ1NxztlEZ8+esLIFGZf8K73kVnWTW9WD35UDwKyJkTi6geTCxj0aO3ui5Vyf/pxXFHSH4bbnB3iBxg80XqDx/GBw2w807s72fY0bDN/3gqLnFfb9QKMJh33XhBsDv52Hx3S4Lpwv/t192PnCufAZFD2j+PlDz2Jn5ym+Zvvnh8/QhbNDrxfosF6OP9a9gYfYhTDcMhQRa3i4HQbgw/eHnRsMzof2raLrIgPXjniGZYTBevG2ZYwSuBtDwb1lhOF7cQ/94lA87abDYNxJ05/tpz/bTzqbHj6MSm5oGBXPDXuJD4Tig2OL+8ZgEG4H9h4F4juiGRjjPBzzXBfGPsdgcK2UCteGGrYYhjG4NkxjcG0aZrg2TUzDxDRNLNPCMA0s08KyDCxlYlsGpqmwLLBNE0up8JypsMzwz/jIeedIez0GSur99dKb4abLuMJ9P0e85eP83syxMefwr2MPoTEqQwKI/VfgB2T73ULA7ZAZZZ3pC0PwYMT8IACRuDV60F0ZIVkxNISJHRufSXW11mT73cFwOgyoi8aj7srhu8Pb/Hi5HQbTg8N8xCivC/fLamLYJR7Ki7G1XwXYSikTWAJs1lq/SSlVA/wZmAmsA96hte7e1XNKqoE9AGx++SX+9JXP8vr3f5gjzzh7XF9r1er/ZePGazjpxAeJRhvH9bV2WZanHuO2713JGz/6aeafPHGTKLpuD488ejL19Wdy2PzvT1g5hDiQSIA9Nsalvc73ww3/AesfgTf9EBa9d5e3tOYdznl6Fb7W3L5wLi2x0b/d4wean/9zFY/cvpbXZG0S1VHe/MEjaJhRWhNIed3d9N9zL323307mqadAa6KHHhr2zH7j2URapk5s+Tqz5Fb3kF3aQX51D2iIzq4kcWwT8cNqMeTNygFF64FAPwyz3YHFG9r3Rp7zAxxPD9t3fT1s2/GGn3P8AK9o2/WGn3NHe63CazhFzxk5qehYMhSjhuWD4Xihh7o1SuhuFYXqA9uWURSwF3r8o3wCXDQuvnbw/Byum8Pxs3heHs/L4XkuOvAJdBCug4BA++jAB60JAh8dBOGiNToIwk8qAg06CCfJ0kOLCj+xCNdaY2g1uFaAodXQgsLUBoZWmIRf/VaF6UVh6NsEu+trX/uatNdjoKTeX2uN/t3Z9G9axlneD7jqP0/n0tUbeE11OdcdMWvCvx0lxHjTgSabcgd7bg8F3E6hZ/fQvj/Kt8asqBkOWTLK2NzFQXc0aQ37/0lrTS7lDh/eo2NossT+zhzeiIA6lrQHe00PD6rDoT7sqPzOJ4bsbwH2p4BFQEUhwP4O0KW1vkopdQVQrbX+3K6eU1IN7AHgHz/6Nuuef4b//MW12LHx60Hl+1kefuQkampO4ojDfzpur7O7dBBw3ec+ju95XPr9n2MYE/eP78pVV7Jp07WccPy/iMcnNpgQ4kAgAfbYGLf22s3CXy6BVffAmd+EEz+2y1uWp7Kc++wqmiIRbjvm4J1OCPXYmk6+ee2znNyhKEfxmvPnsOB1Ez+kyGjcbW30330XfbffQfb55wGIH3kkFeecTflZb8BubJjQ8nk9OTJPt5F+eht+Vw4VNUkcWU9iUWM4ZnYJ/pmKA1sQhL3lXV8PhuB5Lxjsde8Ugu6BbW9EsD4yJPcGw/VRrguGgvqBbS8oCteD7UP3oecMf63JpHhonKG1LhrGRg9e9/I33yLt9RgouffXW19E//K1/CE4i9uaP8HpZx/EV9Zs4fvzpnHxlNqJLp0QJUFrTT7jhcF2YQiTdE9++H5hKBM3v/3QTKZlkCgE3W7ep68zhzfiumjCGjbudEVROF1eGyMSK41vKor9w34TYCulWoBrgSuBTxUC7BXAqVrrVqVUM/CA1nqXYySUXAM7ifV3dfCrj1zGMW98C6de8v5xfa0tW/7C8pc/zzHH/InqqmPH9bV214rHHuYfP7qKcz7xWQ458bUTVo5crpVHH3sdU6deyLy5X5mwcghxoJAAe2yMa3vtOfDXD8BLf4O3/AyOefcub3mku58Ln3+FYyoS/Pmog4gaO574pSOV57//8CzVL/ZzkGcy/cha3vC+w0v6q47Opk303XknfXfcSX75clCKxOLFVJx9NuVnnoFVXT1hZdOBxlnXS3rJNrIvdqDdAKshTnJhE4ljGjDLJ2bOCyEmA60HhrUZ3hN9R73XvSCAgQ7VaAIdDvlCYR0UhoUZWA8MCTO4X7iueF38HD3i/sHnD3vOwLV6sHN3MDgsjS56flEZAj1Y5oH7v/HWI6S9HgMl+f76H58iePoa3pD7Fm9/w+u5twKe68/wz2PnMSMenejSCbFfcXLesEB7cF0Iuu2oOSyYHgitS2UoPTE57E8B9k3A/wLlwGcKAXaP1rqq6JpurfWo766UUpcDlwNMnz594fr168etrGLII3++jsdv+Qvv+/GvqGpsGrfX0Vrz5FNvAQIWH/uPkumRpYOAaz7zEQzD4JLv/BS1k7BjvL20/HNs2/YPTjrxQSIR6XkgxHiSAHtsjPsb4sCHa94EHSvh489ArHKXt9yyrZsPvbSecxuq+MX8GRg7aW+CQPOLB1bz77+/wmuzNnWzKnj7J47aL3qb5F95hb7b76Dvjjtw1q4FyyJ54glhmH366Zjlo09ouS8EeY/sCx2kl2zDWd8HBsTm1ZBc2EjskBqUJTPKCyF2j7TXY6MkA+xMF/onR/Myszi3/7P8+oPH8761mzisLM7NRx+MWSLvF4XY36TyHi+39vFSax/LW/tY3tpP3gtIRMyixSIeMUnYhf2oRSJiErfDc9tdV7Qfkd/jxA6MZZs9bu/GlFJvAtq01k8rpU7dm2dora8GroawgR270okd8RyH5++7i9nHHDuu4TVAb+/TpFIvcci8K0smvAZQhsHxb38nd/z0e6xe8jhzFp84YWWZMf0DtLbexKZN103oBJdCCFEyDBPe8L9w9anw4PfgzG/s8pa3NVazJe/yjTVbmBK1+crBOx6WyTAUHzltDsfOquXbv3yaU9f28pfvP8MFnzqm5HukRGfPpv5jH6Xuox8h//LL9N1xB32330HrFZ9nayRC8sQTKX/96ZSddhpWTc0+LZsRtUge20Ty2Cbc9gyZJdtIP9NGbnkXRtIOJ35c1IjdlNyn5RJCCFFCEjWo077EoXd8hnOjT/PtW8r56nmH8umVm7h6Yzsfmj6xQ2QJUeq01mzty/HSlr5wKQTW6zoz4fmIQaIuRl1TGbZl4Dg+nfmAIJPD7fTJZ12yWY+c67MnfV0tQxWF2kNhdzxiDQbi8YhJMmoVAvGh8/+fvbOOk6PI2/i3x23dXbIWd09IcAvB3YLLAYfdAQd3B9yLO8Fdg0uAAIFAEiDuno1n3X13fOr9Y2Y3u8nGZ1br+8l8urq6u6pmszs19fSvn5/Zd6z5OqNOjbmVSK5VS3Fc4iWQK7HxwFRFUU4DDECwoigfAaWKosS1shApC+AYJIdJ7qI/sdbVMuyUqQHvq6DgQzSaYGJjA9/X4ZI9biKLvpzB4q8+I2Pk2E4T2M3mDKIiTyS/4AOSk69Do5ELe4lEIiF+CAy5FBa/6k3oGJ5+0EtuToqi0Obg1fxyEgw6rk2MOuD5o9LCeeyOMdw/fQnH5NfzyZPLueju4RjMWj+9icChKAqGvn0x9O1L1J13Yluzhtoff6Rhzm80zJsHqv9iHDaUoBNOIOiEE9AlJnbo+LRRJkJOTSP4pFRsW6tpWl5Cw6IiGv4qRJtowTwiBtOgKFSmrv+zlkgkEomfGX4VrHiPh+s/YUjRAIo3VHBKXDCP7ShmcngQfS3Gzh6hRNIlcLo9bCtrYFNxW7G6usmJUCsIi5bwWBPmnBBizBFUq6HO48EOVLe0sq9NnlZRCNeqCdVoCFarCFapMCsqTIARBb0HtB6BxgVql0BxeXA7XDTZPVgdbpqcbqwOF00ON7VWJyW1Vpocbu8xhxurc19v7gOhVSsYtT7xu1kk12raCOYWvZoQk45Qo5ZQk/cVYtQRYmwua6UQ3gMIeBJHAF8EdrOFyFNAZaskjuFCiH8erI0u+YhTD0MIwcf/ugOn3c60Z14JqGhrt5exYOFEkhKvJDPzXwHr52hYP28Os199nrP++R/6DB/VaeOorV3F8hXnkZlxP8nJV3faOCSSno58JNk/dNh8XV8CLw6DjOPgwo8O6RK3EFy7fhc/V9Ty1oBUTo8KPeg1+VVN/OOFxYwrFViijFz8zxEYu6l3sxAC++bN1P86h/rffsOemwuAPjvbJ2Yfjz4np1Nu2robnTStKqNpeSnOkkbQKBj7R2IeEYO+TyiKqus8qSWRSDoXOV/7hy69vt71F7x3OrOjruamghN467rR3FpUQrxey6zhmeg60eJRIukMaq3OFqF6k88KZGtpA3aPB2HRoA7WERpjRh2io1GnUM0enc+kVpFtMtDXYiDHbCDHbCTHbECjUqhyuqh2uqlyunwvN9W+cnv1+0sfrFEgTKshXKshXKv2bTWEabzlMF99hFZDiEaNCQW1W2BzemhyesXuJrubJocLq9MrdHvrXD5B3Huspd7hahHEmxxu6m1O6u2uA0aNW/SaNoJ2s8gdatK2CN+tRW9vvQ6DVtWlHAO6G93GA7ulk7YCdgTwOZAM5AHnCyGqDtZGl55gewhFWzbxyb//wfFX38SQk08PaF87dr7Izp0vMnbMHEym1ID2daS4XS7eveMGjEHBXPLIs536obVi5SVYrbsZN3YuKlX3FE4kkq6OXBD7hw6dr/94Cn7/P7jyB0ibeEiXWN0ezl+9jfUNVj4f3IdRoZaDXlNWZ+POFxczosCNOUzPJfeOxBzS/ZNJOfLyqP/td+p/m4N1xUoQAm1Cgtdm5PjjMQ0fjqLu2ASWQgicRY00Li+haXU5wupCHaLHNDwa8/AYNBEy8k4i6e3I+do/dPn19RfTELk/ca76BWq0sdx26SBu3JzHHSkx3JMe19mjk0gCghCCgmorG/eKqs6vtiJMaoRFizHcgDHCgNOkoUYlWkRljQIZJgN9m0Vqn2CdZNAdMP/LoeIRgjqXu0XMrtxL6N5H8HZ5y+79SI5qBcI0GsJ8wrZX6G4reIe3iOLeYyEa9X7fi9sjqLc5qWlyUmt1UmN1UtPk8JabfC+rg7rmfWvzuQ6c+xskoNOoCDUeSPTWto3+NuoIMWkJ0mtQyQCM7idg+4MuP8H2AGa9+BQ7Vi7jhtfeR2cI3ALR43GwYOExBAX1Z8jgtwPWjz9Y+9vP/PrGS5x730OkDhneaeOorPyD1Wuuom/OE8THn9dp45BIejJyQewfOnS+dlrhpZFgDIPr53n9sQ+BSoeLM1Zupdrp4vvhmWSYDAe9pqbJwR3TlzBopwNjsI5L7x1JUPjBr+suuCoraZg7l/pf59C4aBHC4UAdFobl2GMJOuEEzOPGojJ07PsVTg/WTZU0Li/FvrUaBOjTQzANj8E4MBKVrmPFdYlE0jWQ87V/6PLr65p8eGkkFQnHMiL3MqaNS6Uq08KXJdX8MCyTYSHSWlHSvbG73GwtbWgjVm8srqNeePBYtIggLZZII0qQlnqdgqvVtSkGnS+i2htNnWMxkG7Ud7mnE4RP9K52ualyuKhyNYvdXpF7j+DdVgh37kenVAGhPsG7WeQO02qI0WlJNep/HMpYAAEAAElEQVRIM+pJM+qJ0mkOOQBRCEGTw91G8K5tJXDXWB3e/VbCeG2TgxqrkybH/u1QVAoEG71Cd1uBux3Ru1X0d4hR26OSYkoBW+J3GqoqefOWqxly8hSOvfK6gPZVWvoD6zf8ncGD3yYyYnJA+zpa3C4nb992PUERkVz08JOdFoUthGDpsql4PHbGjP4ZRek5H2gSSVdBLoj9Q4fP1+u+hK+ugTNfhqGXHfJlu612Tl+xFaNaxaxhmUTrD+633Gh3cderS8jabMNg1nLZfSMJjux5EcHuhkYa//qT+jm/0TB/Pp76ehSjEcuECQSdeAKWSZNQh4R06JhcNXaaVpbSuKIUd6UNxaDGPCwG85g4tNGmDh2LRCLpXOR87R+6xfp6/pMw9xHez5rOf9dG8PpVI/lXZTkGlYpfR2Zjkp62km5CVaNjXwuQqkacJg2eIC3qYC36cAN2gxp7q1/raJ2GHLOBvmYj2RbvNsukx6zpuTfxhRA0uD0tkdx7BG+vuF3ZRuz2nlPudLaJ9DapVaQZdaT6BO2Wl0lHjE7rl4h08N6EaBa8a9tEdjva7Ne2Er1rmpzU2ZwHtDvRqLy+3wadGqNW3aqs8u7r1Bi0e4612de1vzW0rtOq0WtUHRIhLgVsid9Z8PnHLP76U65+/nXCYuMD2tfyFRficJQxdsxv3UKIXT17Fr+98yrn//sRkgcM7rRxlJR+z4YNtzNo4KtERZ3UaeOQSHoqckHsHzp8vhYC3j4JanbDrStAH3TIl66qa+KcVdvINOv5ZkjGIS0GbE4397y5nKR1DRgMGi69ZwRhsT03Ckw4HDQuXUb9b3NomPMbrvJy0GgwjxqJ5fjjCTr+eLSxsR03HiFw7KyjYUkx1vUV4Bbo00Mwj43D2C8CRYoZEkmPR87X/qFbrK+dVnh5FB6tmZOtj9DghP9eNYxpm3ZzTUIkj2R1bBJiieRgeDyCvKqmNlHVG0rqKHa7EUFaPBYN2jA9wqLFrtkjHgapVeSYjfS1GMg27/GqjtBpOvHddB+cHkGBzcFOq52dVju7rHZ2Wh3sstrZbXW0ieg2qhRSfIJ266jtVJOeeL0WdQcELR7I7qS2yYnV6U12afP5f3v3Pdhayt56m3PP/pFIu4ZmQXwvsfzAArmqXUF8f9frtWopYEv8h8vp5M2/XUVsn0zOvue/Ae2rvn4jS5edQWbGv0hOviagffkLl8PBW7ddS3hcAhf897FOG4fH42Lx4hPR6sIYMfwrmUhAIvEzckHsHzplvi5YDm8dDxPvhuP/fViX/lpRy5XrdjI5PIgPBqajOYRIBJfbwwPvrSR8eS1GnZpL7hlBRPzBvbS7O8LjwbZuHfVzfqN+zhwcO3cCYBg4kKDjjyfoxBPQpad32PzkrnfQuLyUxiXFuGvsqIJ0mEfGYB4dh6YHeJRLJJL2kfO1f+g26+tN38Nnl1Ew9iEmzc/izMHxmIZF8lZBBZ8P7sMx4Yd+41oi8SdWh5vc0vqWyOoNxXVsrG2kUa/CY9FCkBZ1iA6nXoXwfTfSKQpZLQK1gRyLkb5mA/F6rVzfBwi3EBTaHOyy7itw77basXn2aKI6RSGlVeR2i8Bt0pOo1x3SOqEzEEJgd3n2CNqO1gK4Z48gvj8BvPX5LfuevQR0Nw7X/tJ47p/dT0yRArbEf2z8cy4/vfQM5/7rYVIHDwtoX5s23UdJ6XdMGL8QrbZjHz8+Glb+OJO577/JhQ8+TmLfAZ02joLCGeTm/pthQz8mLGxMp41DIumJyAWxf+i0+fqr62DjTLh1OYQmH9alHxZV8I/cAi6NC+fp7KRDWkB4PIJHPlmL7q8KjBoVF9w1jNjU7jOv+QP7jh0tYrZt7VoAtCnJWMaPxzR2LObRo1EHBwd8HMIjsOVW0bi4GNuWalDAkBOBZUwc+oxQlC662JBIJEeGnK/9Q7dZXwsBH54FRat5ddAXPPFHOS9cOpSnmmppcnuYOzKbEK2MUpUEFiEES3dWsSq/hg1FdaypqGe3w4HbosVj0aIEa/GYNAjfdw4VkGrU0ddibImm7msxkGrQd1kRtDfiEYJiu9Mnajv2iNtNXoHb6tkj2GoUSDK0tSVJNepIM+lJNui6nP94IHB7RBuRe/8CuE80d7i49fgsKWBL/MfH/7oDu9XKVc+8ghLAPzqns4a/FownNvZM+uY8GrB+AoHTbuOtW68lKiWN8+7/X6eNw+22s3DRMVgsfRk65L1OG4dE0hORC2L/0GnzdW0BTB8BOafBee8c9uWP7yjm+d2l/DMtljtTD80SQwjB899sxPZrMWaVinPuHEpin7DD7rsn4CwtpeH336mfN4+mZcsRTU2gUmEYOADzuHGYx47FOGQIKp0uoONwVdloXFJM4/ISPI0uNBEGzKPjMA2PQW0+uM+5RCLp+sj52j90q/V12WZ4dRzuYVdw1q7zKKyx8vS1I7k8dxdnR4fxUr+Uzh6hpIfz6p87+L/tRXiCtShBWjytkuxFazUMCDJ6EypaDPQ1G8gwGTBKW7NujRCCMoerVdS2T+BusrPDaqfBvUfcVgGJBt0+UdupRj0pBh2GXvy74M85W96q7OUUb82lZPtWjrv6xoCK1wBFxV/i8dhITLg8oP0EAq3ewIgpZ/PHx+9StGUz8Vk5nTIOtVpPUuI0tu94mvr6jQQF9euUcUgkEkmXIyQRxt8G85+AUTdA8ujDuvyetFgK7Q6e3FlCvF7LRXERB71GURTuOKc/b5q0lH2Xx1fPrGLqbYNJyzn4tT0NbUwMYRdfTNjFFyMcDqxr19K4cCGNCxdR+cabVL76GorRiGnkCMxjx2EeNxZ9VpbfH5fVhBsIOTWN4BNTsK6roGFxMbU/7qT2l92YBkViHhOHLilIPqYrkUgk3YnoHBh1Peolr/HyeRdywqcuZvyylb9PTOTZ3aWcGhXC6VGhnT1KSQ/lz63l/F9RKZ5kM8ODzQwMNvkSK3r9quUTAD0TRVGI0WuJ0WsZE9rWKlAIQaXT7bMiaSVwN9mZWVZDjcu9px0gXq9ta0ti8pZTjDrM6p6bkNPfyAjsXs6sF59ix8ql3PDq++iMpoD1I4SbRYtOQK+PYfjwTwPWTyBx2Ky8ecs1xGVkcc69D3baOJzOOhYsnEhkxGQGDHih08YhkfQ0ZESXf+jU+drRCNOHQ3A8XDMHDvPGrMPj4bK1O1hY08CHA9M5NuLQ7S8+mb+DbZ/tIBgVp9wwkOzBUYc7+h6Lu76epqVLaVy4iMZFi3Ds2AGAOiIC89ix3te4sWjj4gLSv6O4kcbFRTStKkc43GgTLFhGx2EcEoVKJxcNEkl3Q87X/qHbra+tNTB9GERm8VbGy/zfj5t57NyBvIuVAruD+aNyiNLJJ20k/iWvsoljv19FY4qZ/6TFcXNqTGcPSdINqHa62ojarX23K52uNufG6DQ+YXuPqJ1k0JFg0BGt06Dq5kEXMgJb4hcaqqvYsngBQ046LaDiNUBl5R9YbXn06XN3QPsJJDqDkRGnn8Vfn35A6Y5txKRndMo4tNpgEhMuYXfeW6Q33YnJJB+Zk0gkEgB0Zjj+P/DtTbD+Sxh0weFdrlLx9oA0zly5lWs37OLboRkMDDq0+fHiSen8YNCy4oNcZr+2DvvV/Rg08tCsSHo66qAgb5LH448HwFlcTOOixTQu8gradT/8AIAuLc0rZo8fh2nUKNRB/knMpYszozs7k5BT02haVUbD4mKqv95KzY87MA+LwTwmDm10YL8HSSRHixACXAKPw42wuxEONx77XuXWdXZ3m3OFW3jDwAAUBUXxbmm1VQBUyoHPA++5e52H4o1Wa3OeytcGrdvYq91W53lPa3vePuPq5gt5yVFgDIXj/wvf38bVI1bya1oij/ywiZdvGMVlW/J4Zlcpj2cldvYoJT2IRruL82eupjHNzBlhwdyUEt3ZQ5J0E8K0GsK0GoYFm/c5VufaE7m9q2mP7/bcqjo+dbQVt7WKQpxeS4JBS4JeR6JB11JOMOhI0GuxaHpPMIaMwO7FLPziYxZ9+QlXP/86YXEJAe1r9eqrqG/YzPhxf6BSdd874/amJt685SoS+w7krH880HnjsJexYOEk4uPPIye78zy5JZKehIzo8g+dPl97PPDmsdBYDrcsB93hC5PFdgdTVmzFKQS/jcw+rIiuuWuKmf/mBsJdKsZensXI8XIxfSCEENi3bKVx0UIaFy1q459tHDgQ83iff/bgwSh+8s8WQuDYVUfD4mKs6yvALdCnh2AeE4exfwRKL/YplPgP4RFe4djejrjcSnBuIzS3PtfhQdhdCLunpQ7PIa7bFFD0alQ6NYre91KrvMnwvP9ayi1tCoEQeOuaj+ETzlvXNbfR5jxfnWdPeZ/zWvd5lMvPpCeOkfO1H+j0+fpI8LjhzeOgoYyCS//glFdX0j8+mPhjEvimrJplY/vJKGyJX/B4BJd8voJ5ESoyTXrmjO2Lvhck6ZN0Lo0uN3k2BwU2B4V2J4WttgU2ByUOJ+695tBQjbpF1I73idqJvm2CQUeMTtupiUNlBLbkqHG7nKz59SfSho4IuHjd1LSTyqo/SEu7vVuL1wB6k4lhp57Joi9nUL57J1EpaZ0zDn00cXHnUFz8JWlpf0evi+yUcUgkEkmXQ6WCUx6Dd0+FhdNh8j2H3UScXseHg9I5dcUW7s7N570BaYfsmXzs4DhMt2iY9fIaFn+Yi93uZsJx8kmZ/aEoCobsLAzZWURMm+b1z16zxhudvWAhFa+9TsUrr6KYTD7/7LGYx45Dn5V5xD7WiqKgTwtBnxaCu95B4/JSGpcUUzVjM6ogHeaRMZhHx6EJ0fv53Uq6A8Lpxl3vxN3gQNjceHwisrC7fKLyfiKe9xKlhdNz8M58KFqVV2TWqVE1b40aVKH6NnVeUVqFoteg6FWo9BoU37633nsuGlWX93nfVxj31dG2bo+w3qruiU4atKTzUanh1CfhnZNI3PAq/znjKv755Vpu7BeF3SN4u6CCe9MDY0cl6V088ftW5gdBkEbNlyOypHgt6RDMGjV9LUb6WoztHnd5BKWOPcJ2gc1BUYvQ7WBpbWMb/20AtQKxOp+o3UrYbhG6DTqCu0kUtxSweylbFv1FU20NQ085I+B9FRR+jKJoSYi/KOB9dQTDTp3KilnfsPjrzzjjjns7bRwpyddSVPQZ+fnvkdGNrVkkEonE76SMg35nwYLnYdjlXk/sw6Sfxch9aXE8uL2IT0uquPgQkjo2MzonCtNdw/n82ZWs+nwbNpubE05LP+wx9EYUnQ7TyJGYRo4k6rbbcNfV0bRsGY0LvBHaZfP/AEBlsaDPykKflYkhOxt9djb6zMzDth1RB+kIPjaJoEmJ2HKraFxcTP3cfOrn5mPICceQHYYuORhtjBlF3bUFQcn+EW4PngYn7noH7gYnnnoH7gYHnnpfXb2j5biwuw/cmAoUncYrKuv3iMcqswGtT0RuE/28twDdznGlEyOjOosWqxD2vPfe91OQHBHJo2HQRbBwOufffBm/9I3hndlbmXR2Bu8WVvC35GiCuokYI+ma/Li+mOk11Sjhej4blkGMvnsH4Ul6DhqV4hWfDft/KrHB5W4Vve2g0Ob0RXQ7WF7byHd2B669oriD1CqfqO21KEk06IhvJXTH6XVou8B3FSlg91JW/fwDYXEJpA4aGtB+XK5Giou/JDr6FPT6npHQymCxMPSUqSz59nMqC/KISEzulHGYTGlER59KQcGHpKbcgEbjH69QiUQi6RGc+BDk/gi/PQxnv3ZETVyfFMXsylr+vbWQ8aEWko2HHpE7MDUM470jef/J5Sjf7cRmdzHl7KwjGkdvRh0c3K5/tm39emxbcqn78SdqPv2s5XxtfLxXzM7OwpCVhT47G11KCormwF95FZWCsW8Exr4RuKpsNC4ppnFlGbZNVd7jOhW6xCB0KcHokoPQJQejNssFbWciPAJPk3OPMF2/R5D2NHiFam+dA0+Tq902FIMadZAOlUWHNt6MwRKGKkiHOkiLyqLzRkG3iX5Wg0bp8tHNEkmP54QHYdP3KL88wGPnvMvxz8zDvbWW2hgNHxZVcnOy9CqWHBlbSuu5eeUOPElmnslMZFjIvh7GEklXxqJRk61Rk202tHvcLQTlDpfXlsQncLcWu1fVN1LlbHszXwFi9dpW0dt7hO7mulCNOuDfj6SA3Qsp3pZL8bZcjp12A0qAH4UpKZ2Jy1VPYuLlAe2noxl22lRW/jiTxV9/xum3/aPTxpGSfD1lZT9SWDiDlJQbOm0cEolE0uUIS4UxN3ujsEddDwnDDrsJlaLwQk4yxy3L5bZNeXw9NOOwMoFnxAdzwwOjeP3RpSizC/jS7uK8i/od9jgke9DGxRF6ztlwztmA13LAVVKCLTcXe+4W7Fu2YN+SS8Mff4Db++Vb0enQZfTBkOWN1DZkZ6HPykIT2b79libcQMipaQSfkoq72o4jrw777jocefXUz88HnzOEJtLYImbrkoPQxpp7ZSStPxFCIGzuVlHRDtz1zpZtm7pGR8v/RWsUrcorQlu0aCKNqNNCUFu0vjodqiAtal9Z0cpHwiXdB0VRngLOABzAduAqIUSN79h9wDWAG7hNCDG7s8bZIQTHwaR/wJwHiRr5J9dMSOe5OVsYenYfXs8v45rESGn5IDlsapocXDBrLbY0M5dHh3FporTplPQ81IpCrF5LrF7LcNq/QdPk9lDUStxuLXSvrW/ip/JaHHvlUzSpVa38t33JJg8QKX4kSAG7F7Lq5x/QGY30n3R8QPsRQlBQ8CFBlv6EBB++cNCVMQWHMOTk01n+/TeMPe8SwuMD6yO+P4KDBxIeNoG8/HdJTJyGWi39OiUSiaSFiXfB6o/h5/vg6p/hCKICko16/peZwB2b83k9v5ybDjOqKzHSwq3/GctLjyyGeSV8ZHNz2bSBhz0OSfsoioI2Lg5tXBxBkye31HscDhzbt2PfsgVb7hbsubk0LlhA7bfftpyjjojwitmZWS1R2/qMDFR6fUvbmnADmnADpiHRvnbdOAsasOd5BW3blmqaVpZ5z9ep0SVZvIJ2SjC6pCAZpe3D43D7bDt89h172Xa0tvXY57lWAJXSEhWtDtajjbd4ReggHSqLV5BujpxWdIGPAJJIOolfgfuEEC5FUZ4A7gPuURSlH3AR0B+IB+YoipIlhDiIH043Z8zNsPID+Olepl01n7f/2oFhVyOl0Wq+LKnm0vhDt/6SSFxuD5d/vZqSFBNDjQYe69s5T1lLJF0Bk1pFhslAhqn9KG6PEFQ6XRTsFb1daPcmm1xXb6XC2f7Tb0eDFLB7GY011eQu/JPBJ56K3mQKaF81NUtpbNxC35zHe+RCYsSUs1n18w8s/fZzTrn5jk4bR0rKDaxafTklJd+QkNAzfMYlEknHoSjKg8B1QLmv6l9CiB99x7p3RJchGI57AL7/O2z8FvqffUTNXBQbzuyKWh7bUczk8KD9JlbZH9GhRu56cBzP/m8xLC7nbfsqrr5+SI+cG7sKKp0OQ9++GPr2JaRVvauqyhulnZuLbcsW7LlbqP70U4Td7rtQhS411StmZ2aiiYxEHRqKOiTUuw0NRZsQgj7d26oQAneVDUdefYuo3d2jtIVbIFyelhdOT5t90bIvYO96hxt3o3MvsdqJcLSjoymgMmtboqK1kSEtInSzrUdrK4+u/nOTSAKNEOKXVruLgfN85TOBT4UQdmCnoijbgFHAog4eYsei0cMpj8OMCwhZ+y7XTDiZZ+dsIWtqGi/nlXFRXDhqOc9KDpF/z97Eskg1kWo1M4ZnoJFzjkSyX1SKQpROS5ROy9Dg9nVFq9tDsd1JHz/2KwXsXsbaOT/jcbsYcvKUgPdVUPAhGk0oMTGBTxTZGZhCQhl0wims+vl7xpx7MaExsZ0yjrCwsQQFDWR33hvEx5+PosikJRKJ5LB5TgjxdOuKHhPRNfRyWPom/PofyDoVtO1HEhwIRVF4KjuJyUtzuXVTHj8Oz0R3mI8mh5j13PPQeJ7+30IiVlXzyvQV3HzrcClidzCa8HA0Y8ZgHjOmpU643Th257XYj9hyt2Bbv4H6n37ebzuK0egTtkNahO3msqFPGIo6HI87CE+jA+vGikOO0hZCQLOA7NxXNMYl9iMme7xistMrLLc5Zx8Ret/2WwvR7dlyHA6KQdMiQmsTgzC0su9QB7Wy8jBrZWJMieTIuRpoTgCQgFfQbqbAV9fzyToZMk+C+U9w1XVn8/ZfGoILrSyPVPNjeS1nRId29ggl3YDPVuTznqMRbbCOL0dmEqaVMplEcrQY1SrSTf51CJB/mb0It8vJmjk/kTpkeMAtL2y2YsorfiEp6WrU6sMXC7oLI884hzW//sjSbz/npBtu65QxKIpCasqNrFv/N8rKZxMTfVqnjEMikfQ4ekZEl0oNJz8KH0yFxa/AxDuPqJkonZZnspOYtn4nz+4q5d70uMNuw6TXcO9/x/PUo4sI3VjH808v5e93jUQlfTo7FUWtRp+ehj49DU45uaXeY7Phrqlp9ardU65tW7Zv2dJSbvbebtOHKQp1eDqa2BzcNenYtsWgKM3/7w686XFUgD9uQntAEXu2igcF79a7L1BalZvPUekFGARKc52q+VzhHZoiQOVBUYGiar7OO2xFhbd9tdjj1CMAG7itAnc5OAHa+CX6yq3qROvjbU7d99w2J/jq217fTlv7O85+xgDe/ydFAZUCiuLLH6OASgWK93sYrc7Ze7/tNb799q5pdc7e+y3XHOCcdvdb1SkqBdRqUFTeskq1p6xW7xlnc726udy6XkFRq0Gl2tNfc7l1veSIURRlDtBeVMz9QoiZvnPuB1zAx82XtXN+O148oCjK9cD1AMnJPcQi4eTH4OVRBK94hWsmTOPZOVtImJLC9LxSpkSFyN9JyQFZnVfN3bn5iDgTr/ZLIcd8eE/ZSSSSjkMK2L2ILUsW0lhdxUk33BrwvgqLPkEID4kJlwa8r87EEh7BwONOYu2c2Yw59yKCIzsn43VU1ImYTGns3v0a0VGnyi9qEonkcLlFUZQrgOXAXUKIanpSRFf6JMg+Df58FoZcCkExR9TMKVEhXBgbzou7SzkhIpgRR5CZXqdVc+8D43jmySWYtzfy1GOLueueMWg0UsTuaqgMBlSxsWhjD/0JKyEEnoaGgwjey3FXNeC26RGEgsoMHhd4nOBxIdx7bT1OcHtfbcsuhNvhO9fRUo/wgBBe9aq18Nv8al0v2UPr707N5dY/M8mh0yxsNwvfewveLfWK9wbBIZ3bqr4HI4Q44UDHFUW5EpgCHC/23G0pAJJanZYIFO2n/TeANwBGjBjRM365IzOg7xRY/TFX3XwPb/+1g8hSO2vCXfxZ3cAx4UGdPUJJF6Ws3sbFv23EmWrm1oQopsSEdfaQJBLJAZACdi9i1c/fExobR9rg4QHtx+OxU1j4KZGRx2E0Jh38gm7OyKnnsXbObJbO/IoTrrmpU8agKGpSkq9n0+b7qKpeQET4hE4Zh0Qi6ZocKKILeBX4H95orf8Bz+B9NLlnRXSd9H/w8miY+38wdfoRN/N/mQksqKnn1k27mTMyG7P68CNm1WoVd98zmheeW45lawNPPLSAW+8ZRbBFJuLt7iiKgjooCHVQECR1/e9A4kAC9yGI4N7iXsfbE4JbfZy0ucfevNPuNe2X23wwtXfuwa4/gpv8ovm9eXw3B/bab69un32PAA5yzV7n7LMvBBzsHE/z/9Ne1wgPwuPZt+xx71vv9rR/fkt9c3/N5VZteDwI0U4b+6sXHoTbs1d7njb1QrRqw9O9XKz8iaIopwD3AJOEEE2tDn0HzFAU5Vm8ll+ZwNJOGGLnMeIa2DiT4O2zuHbiCJ6Zs4WIU5OZnlcqBWxJu9hdbi7+ejXVKSYmWkzclxnf2UOSSCQHQQrYvYSS7Vsp3rKZY6+8zvcYY+AoK/sZp7OSxITLA9pPVyE4MooBk09g/e+zGX32+QSFR3bKOGJjz2THjufZvft1KWBLJJI2HCyiqxlFUd4EfvDt9qyIrog+MOp6r43IqOshduARNROkUfNCTjLnrd7Ow9uKeCL7yERKlUrF7XeO5PXXVhGyppqXHljAebcNJStdRv9IOg6lPdG39fEOHEtXpq0dh/y5dDpvvtnZI+gsXgL0wK++v93FQogbhRAbFEX5HNiI11rkb90uX8XRknYMRGTCsreYdvl5vPXnDiIqHfzpdrOqrmm/ScYkvRMhBLd/t54NcToSNVreG9oHlXyCWSLp8vTsZ7AkLaz6+Xu0BiP9Jx+ShnFU5Bd8iMmURnj4+ID31VUYddZ5eDweln/3daeNQaXSk5R8FdXVC6mrW9tp45BIJN0LRVFamzmfDaz3lb8DLlIURa8oSho9IaJr0j/AGAY/33dUtgDjw4K4PimK94sqmVtZd8TtKIrCjTcNI/nMVAx2wQ9PrWTO77uOuD2JRCKR9FyEEBlCiCQhxBDf68ZWxx4RQvQRQmQLIX7qzHF2CooCI6+FwuUEV23g2onp7Fheilml4qW80s4enaSL8eaiXXyrdWDQqPhqZCZmjT/yT0gkkkAjBexeQFNtDbkL/6D/pOPQmw7fr/NwqKtbS13dKhITLmuVnKjnExIdS79jjmPtnJ9prKnutHEkxF+ERhPMrt2vd9oYJBJJt+NJRVHWKYqyFjgWuANACLEBaI7o+pmeENFlDINj/wW7/oTcH4+qqfvS4sgyGbh9cx7VTtdRtTX11D6c8PdBWLUKmz/fzrtvrPbZAkgkEolEIjkkBl8EWhMsf5tp41MJ0aqJq3HxY3kt25psnT06SRdhwbYKHsovAbOGDwank2LshfZtQkBtAZRtgvItULkdqnd56+pLoKEcmqrAVguORnDawO2SOSEknU5ALUQURTEAf+B91EkDfCmE+K+iKOHAZ0AqsAu4wJcwShIA1s75GbfLxZCTpwS8r4KCj1CrTcTFnRvwvroao8++gI3zf2f5D98w6bKrO2UMGk0QiQmXsWv3qzQ27sBsTu+UcUgkku6DEGK/fk9CiEeARzpwOIFn+FWw9E345QHIOBE0uiNqxqBW8VK/ZE5bsYX7thTwWv/UoxrWoJwoEh4ex/Qnl6CsrOL5Bxdy/T9GYA7qhQsriUQikUgOF2MoDDwP1n5B8In/49qJ6Twzfxva4+J5Ja+MZ3O6aI4OSYeRX9XElQtycSebeSA1lmMigjt7SB2DywElayF/ie+1FOqLj6wtRQUqDShq71al9r0OVNdqf791qlbXtj7nEOs0Bu9ngCF0363WuF+rNEn3ItAe2HbgOCFEg6IoWuAvRVF+As4BfhNCPK4oyr3AvXgTUkj8jNvlYs2vP5IyaCgRCYFNJuRwVFFa9j1xceej0fS+ZBlhsfHkjD+G1b/MYuTUczEFh3TKOJKSrmR33tusW38bAwe8KEVsiUQiaY1aAyc/Ch+fC0vfgHG3HHFTg4JM3JUayxM7SzglspqzjjJ7fUSogfsfnsCzr67EuKGO1x5YyLm3DiU1I/So2pVIJBKJpFcw4hpY+QGs+YRp46/lrT93YKpz8wXV/CMtljj9kd20lnR/mhwuLpi5hoZUM1NCg/hbakxnDylwNJR5RepmsbpoFbjt3mOhKZA6ARJHgSUKPG7fywXCt223zuPbHmLdAdtyg8t+kPNa1++nrv3c8vui1rUvbLfeGsOk+N0NCKiALbypzRt8u1rfSwBnApN99e8D85ACdkDYunQhDdVVnHj9rQHvq6j4CzweB4kJlwW8r67K6LMvZNOC+ayY9S0TL76yQ/sWQrB9+3aWLFlCZeVYsnMWsXjJ6WT0eYDk5Ev2JGqSSCSS3k7mCZBxAsx/EgZfDOaII27q1uQYfq2s494tBYwONR/14lirUXPPrSP56IdcCmcVMPOZlQw9J51jTkw9qnYlEolEIunxxA+BhBGw7G2CR9/ItRPTefqv7biPieWN/HL+m5HQ2SOUdAJCCK7/Zi07E/VkarW8PDit56yNPW4o27hHrM5fCtU7vcfUOogbAqOug6TRkDQKgmI7dbh+xePZI4A7rWCrAWvNwbcNpVCe69231XFAIfxQxO/9bbUmKX77mUBHYKMoihpYAWQALwshliiKEiOEKAYQQhQrihId6HH0Vlb99D2hMXGkDRke0H6EcFNY8BFhoWOwWLIC2ldXJiIxiawxE1g9+wdGnHEORkvgI9Htdjtr1qxh6dKlVFRUYDabGTbsEsrKRqDTfcS27f9hx47vGDnyRSyWHnynWSKRSA6Hkx6BV8fBvMfg9KePuBmNSmF632ROWJbLnZvzmTEo3S+LosumZLMgNZSf31jPuq92ULi9hguuHYRa03vyS0gkEolEctiMvBa+vRF2/sG08eN4688daBs9fFBUyd9TYgjVBlwCkXQxnp63jd8sHoI0Wr4cmYVe1Y2/S1lroGC5V7AuWOotO3wxo5YYr0g98hqvYB03GDQ92IpOpQJUoNZ6I6VN4YffhscN9rpDEL6rD1/8VmkPIHCH7f+YIQR0Zil+t0PAP719CZ+GKIoSCnyjKMqAQ71WUZTrgesBkpOlZ9XhUrpjG0VbNjH5iutQAvwhXVHxOzZ7EZmZDwS0n+7AmHMuZMuiP1n103eMO//SgPVTVVXFsmXLWLlyJXa7nfj4eM4++2z69++PRqMBjic//3SWL38US9Bv/PnXiZjNtzJm9JXodPLxOYlE0suJzoERV8Pyd7yL3eicI26qj8nAfzISuG9LAe8XVTItIdIvQxw/IIbk/1p4/pllsLqK1x9axBV3DccSavBL+xKJRCKR9Dj6nw2z74NlbxF84SSum5jOU4t24Bgfw3uFFdye2oMiUCUHZfbGEp6rqkIJ0/HZsAxi9NrOHtKhIwRUbmsbXV2+GRBez+iYAd4nCZNGQ9JIrz2IFD0PD5XaJyYfgQ2gxwP22kOL+rbVQGM5VG717ddyYAsUBXQW0Fu8YrbOAvqgVmWLd9vmHN/xfY75jqvUh/8euxgddvtRCFGjKMo84BSgVFGUOF/0dRxQtp9r3gDeABgxYoRMeXqYrPr5e7R6AwOOPSHgfRUUfIheH0tk5PEB76urE5WcSsbIsaz88TuGn34WepPZb20LIdi5cydLliwhNzcXlUpFv379GD16NImJiftE/SUlJZOU9Bpbt/7Gjh334XA8zhdf/kxqyt8ZOXKMFLIlEknvZvJ9sPZz+OV+uOyro2pqWnwEs8treWhbEceEBZFu8k/ES1KEmf89OIGHX15GTG4Tb/9nEWfeNIjUvkdueyKRSCQSSY9Fa4Chl8GiV6CumCvHp/LWXzsRVg9vFlRwfVI0JnU3jsCVHDLbyuq5YcUOPIkmns5MZFiI/9blAcHRCIUrvZHVzYK1tcp7zBDqja4ecK53mzDcK05KOg+V6ijF77r2hW5bLdgbvJH1joZW5UaoK9pTtjeAs/HQ+9Sa9ojZesvBBe82gnk74rm6428GBVTAVhQlCnD6xGsjcALwBPAdcCXwuG87M5Dj6I001dawecF8Bhx3sl8F1PZobNxGVfUC0tPvRKWSj2SBNwp727JFrPr5B8accyEA9Y56Xlj5AitKV5Adns3AyIEMjBxIdng2evWBhQ6Hw8HatWtZsmQJ5eXlmEwmjjnmGEaMGEFw8MGzJ2dmHk96+jxWrroPRfmB8oq7eO21kxg5cgojRoxAq+1Gd6IlEonEX5gjYPI9MPtfsPVXyDzxiJtSFIXn+iYxeWkut27azcyhmWhU/omCMek0PPr3MUz/ZiN1vxXz/QtrGDY1lXGn9iAPx16E2+3BZXfj9L1UahXBEQYUP/2+SCQSSa9nxNWwcDqsfJ/gyfdy7YQ0nlq2C8foKD4truTqxKjOHqEkwNRanZw/ax22VBOXRoVxWaJ/no7zG0JAbUGr6OolULLO6+kMEJkNOaf5oqtHQ0SmzzJD0iNQqXzJI0PhaHLAezxeEXsfwbvRV67fT9l3blMFVO/aU+9oAOE5tL7V+rbR3/uLFvcjgVYb44D3fT7YKuBzIcQPiqIsAj5XFOUaIA84P8Dj6HWs/W02bpeLoSdPCXhfBYUfoSg6EuIvDHhf3YWY9AzSh41kxaxvGXbqGfxRtpBHlzxKpa2SkTEjWVq8lFk7ZgGgUWnIDstmQOSAFlE7NSQVlaKiurq6xSbEZrMRGxvLWWedRf/+/Q9bdFarTYwc8QJl5aexYcO99O33FWvW7GLBgiFMmDCR4cOHSyFbIpH0PkZeB8vehtn3Q/rko4omiNPreDwrkZs27ualvFK/PqasUin8/dz+zEoN4ff3N6P6bheF22s5+/pBaPXd/5HArojb5cFhc7UIzS67B6fdhdPh3Xr39wjRTkfzeb6yzY3L4d7nHI9r34cKtXo1EQlmIhKDiEy0EJloITzejM4gAwMkEonksAlP9yZrXvEeTLyLK8en8uZfO3HaBa/kl3F5fCRaedOwx+L2CK74ZjXFyUaGGAw80a8L2NG6HFCy1idY+0Tr+mLvMa3JG1E94Q6vWJ044sj8nCW9D5XKKxbr/ZR7TQhvQsz2or/t9W2jvx31rcq+l63OFyXe6nw/ogjRPZw5RowYIZYvX97Zw+gWuF0u3rr1GsITkjj/gf8LaF8uVz1/LRhPVOSJ9O//TED76m4Ub8tlxv13UT06nJkRq8gJz+HBsQ/SP7I/AKWNpayvWM/airWsr1jP+or1NLmaQECSK4mBTQMxVhlBgT5ZfZg0fhJJSUl+ibaz20vZsPFuqqsX0tSUzZrVgzEYIpg4cSLDhg2TQrakV6IoygohxIjOHkd3p1vO15tnwaeXwGlPezO1HyU3bNjFrPIafhqexcAgkx8G2JYNhbU8P30Fg2sEunADF/x9KKEx/u+nN+Jyutm1tpLcJSXkra/E4zm078kqlYLWoEarV6PRebdtyyq0eg1avarNca1ejdPuprKwkYqCeioLG3FYXd5GFQiJNBKZaCHCJ2pHJFoICjfIyHtJr0bO1/6hW87Xh8PmH+HTi+GCD6HfVKb/tpUnV+fhHBbBy32TOTdWCoQ9lQd+2sRbShMRBi1/je9HWGck7mwo2xNZnb8UilaB2+49Fpq8J7I6aRRE9we1vGEt6YEIgaJS+W3OlgJ2D2TpzC/5c8Z7nHPfQ6QNGR7QvvILPmTLlgcZMfwrQkKGBLSv7oRHePg893NWvPQOobVqYu88mysGX4VWtX9h2Ga3MW/ZPFYvX42txoZL7WJH0A62BW3DqrESbYpmUOSglkjtfhH9sBzFIxlCeMjLf5vt259BpQqmqOgUtuSqCAoKYsKECVLIlvQ65ILYP3TL+VoI+GCq99HN21YdmZddK6qdLiYv3UyIRsMvI7IwBMBrs6LBzv2vLCNzpx2DRsVp1wwgfYh8JPpIEEJQvL2W3MUlbFtRhsPqwhyiI2NkDMERhn1E6X3FaTVqjX/+j4UQ1FfaqChooLKwgYoC76uu3Npyjt6kISJhj6gdmWghPM6MRicj8SW9Azlf+4duOV8fDh43vDDYG4195XfU2ZxMeGIu1jFRJIQZmTsyW94M7IF8uaqAW3cXoQnW8evobHLMxsB36nFD2ca2yRard3qPqXUQN8QrVCeNgsRREBwX+DFJJF0Ef87Z8jZPD6O2rJRFX35CnxFjAi5eCyEoKPiQoKCBBAcPDmhf3Ylt1dt4cNGDrClfw7Gjh2H4oZIhRdFoh7YvBtfW1rJs2TJWrFiB1WolJiaGk6aexMCBA3ErbjZXbWZ9xXrWVaxjXcU65uTNAUBBIT0knQGRAxgU5RW2M8MyDyiSt0ZRVKQkX0dY2Fg2bLiDmJgZZGWez9q1Cfz000/89ddfLRHZGo38qJBIJD0YRYGTH4XXJsL8p+CUR4+quTCthudykrlk7Q4e31nMgxkJfhroHiIteqbfOY6HP1uDsrCSn15bx6CTkhl/Vh9U8rHoQ6KmrIncJSVsWVJCXYUNjV5NnyFRZI+JJSE7rFN+joqiEBxpJDjS2OaGhMPmorKwkcqC+hZRe9PCYlx2t+86CI0xtYrW9lqRmEJ0UqCRSCS9E5Uahk+D3/8HFVsJjszkuglpPLk+n82DFOZU1nFiZEhnj1LiR9YV1HDHpjxEnIlX+6UETry21kDBcq9gXbDUW262SjBHe4XqEVd7I6zjBnsTi0okkqNGRmD3ML558mHy1q/hqmdfJTgyOqB9VVUtYNXqK+jX90ni4s4NaF/dAbvbzptr3+Tt9W9j0Vr4x8h/cEb6GXzxv/upKsznmulvodV5kzUKIcjLy2PJkiVs2rQJgJycHEaPHk1KSsoBF5s1thrWV3oF7fUV61lXvo5qezUAerWenPAcBkYOZELCBMbEjUGtOnhEltvdxNatj1JY9AlBlv4EB9/JggXbyc/PJzg4mIkTJzJ06FApZEt6NDKiyz906/n6u1th9Qy4eQlEZhx1c//MzefDokq+GpLBuLDAZIoXQvD+nztZ8uU2Bjg0RGWGMPXGQRjM8gma9rA1Otm2vJTcJSWU7KgDBZJywsgeHUvakKhu5TktPILacute0dr1NFTZW84xWLRtLEgiEy2ExZr9FjEukXQGcr72D916vj5UGsrg2X5ee7BTHvNGYT85l/qxUQyKsDBzWGZnj1DiJyoa7BzzxXKqUs3ckhDJA1mJ/u3AVue9GbLzTyjfDAhQVBDTv60dSGiK966yRCIB/DtnSwG7B7Ft2WJmPv1/HHPZ1Yw845yA9uV2W1m6bCpudxNjx/yOWq0PaH9dneUly3lo0UPsqtvFlPQp/GPkPwg3eH3V8jes5fOH/8VxV93AwBNOZd26dSxZsoSSkhIMBgPDhw9n5MiRhIaGHlHfQggKGwpborTXV6xnY+VGbG4b0cZopvSZwpl9ziQ9NP2gbZWX/8Kmzf/C7baRmXk/1qYRzJ8/n4KCAkJCQpg4cSJDhgyRQrakRyIXxP6hW8/XDWXw4jBImwgXf3LUzTW63Ry/LBeXEMwdmUOQJnAWD39tLWf6W6sZX6vCGKLjrL8NISrZTwldujlul4fd6yvJXVzCrnUVeNyC8Hgz2WNiyRoZiyWsZ32HsTU6WwTtSl+0dlVRI26XN6u8Sq0QFmveR9g2Buk6eeQSyaEh52v/0K3n68Phy6th2xy4czPoTEz/bStP5Bbi6hvKzKEZjA4NzA1mScfhcHk4fcYy1iXqmBBk5vMRmaj8KSJ7PPDZpbBlNvQ5bo9YnTDMf8nzJJIeihSwJfvgsFl5786b0ZtMXPb4C6gDLDBuzv03hYWfMHTIB4SHjwtoX12ZOkcdzy5/lq+2fkWCJYF/j/k34xPGtzlHCMFnD95DTXkZYcecwqbNm4mOjmb06NEMHDgQnc7/C0aH28H8gvnM3DaTvwr/wi3cDIwcyJl9zuSUtFMI0e//cbnWCR6jok4iJ/sRdu+uZN68eRQWFhIaGtoiZKvV0m9T0nOQC2L/0O3n6z+fhd8egitmQvrko25ueW0jU1du5YLYcJ7vm3z04zsAuysbufu1ZQwvdBOkqDju0hz6juudPotCCEp31pG7pISty0uxN7owBuvIGhlD9phYIhMtvcpaw+P2UFNqpaKwvkXUrihooKnW0XKOOURHhM96pFncDo02ogqAh7tEcjTI+do/dPv5+lDZvRDePRWmvgTDLqfe5mT8U3OpGRvFpOgQPhx08CAfSdfm9u/W8anBSbxey5/j+2H2d8DA3Mdg/uNwyuMw5ib/ti2R9HCkgC3Zh/kfvcPy77/mooeeJCGnX0D7Ki+fw9p1N5CcfC2ZGfcFtK+uihCCX3b/wuNLH6fKVsUV/a7gpsE3YdKa2j1/15qVfPLKi9jjUjnuuOOYOHFihy2cK6wVzNoxi5nbZ7K1eitalZZjk47lzIwzGRc/Do1q35sd3gSP77B9+9PotOH06/cUYWHj2LZtG3PnzqWoqIjQ0FCOOeYYBg8eLIVsSY9ALoj9Q7efr502eHkk6IPhhj+8HppHyWM7inlhdynvDUjjlKjA+m3W25zc/eFKQtbUkeJS03dCPJMuzEKt7R0iZF2FldwlJeQuKaG2zIpaqyJ9SBTZo2NJ6hsmxdi9sNY7WsTsZmG7urgRj8e7PlBrVUTEm9tEakckWNCbpEWNpPOQ87V/6Pbz9aEiBLwyFjQ6uH4+KAov/b6Vx7cV48oMZu7IbPpaOiDRnyQgLN1VxZlrtqEL0vHH2L6kGP38VNWm7+Gzy2DwJXDWK9IeRCI5TKSALWlDed4uPrznNvpPOoGTb7wtoH3Z7eUsWXoaen0sI0d8iUrVsx67PRRKGkt4ZPEjzCuYR9/wvjw47kH6RRz4pkF5eTmvvPQSOqedfzz0PzTajl/4CSHYXLWZmdtn8uOOH6m2VxNpjGRK+hSm9plKZti+HnD19RtYv+EOmpp2kJx8LX3S70RRtGzdupW5c+dSXFxMWFgYxxxzDIMGDZJCtqRbIxfE/qFHzNcbvoEvpsEZL3gTQB0lDo+H01ZspdjuZN6obKJ0gZ0D3B7B0z9vZsPsPEbbtUQkBzHlpoFYwnpmEiF7k5PtK8vZvLiY4m21ACRkhZI9JpY+Q6PRGaXt1eHgdnmoLmncR9i2NThbzgkKN7QVtRMthEQaUWQCUUkHIOdr/9Aj5utDZemb8OPdcO3vkDicepuTcc/Mo2pMJGfHhfNSv5TOHqHkCHB7BONmLGF3goHpWUmcnxDh3w7KNsFbJ0BkFlz1k0zGKJEcAVLAlrQgPB4+/e89VBUXcvVzr2EMCg5cX0KwZs3VVNcsYeTImVjMvSvphdvj5tPcT3lx5YsIBH8b8jcu7XtpuxHMrXG5XLz11ltUV1Wh2bCMU6+9mQHHnthBo24fp9vJH4V/MHPbTP4s+BOXcNEvoh9T+0zltLTTCDOEtZzrdlvZuvURb4LHoP707/ccZnMfhBBs2bKFuXPnUlJSQnh4OMcccwwDBw6UQrakWyIXxP6hR8zXQngfN67cBretBv3R+2NuarBy8vItHBcRxLsD0jrkKZyZqwt5/eP1nNSgxWTUMGhSAkk54cSmh3T7iGy320P+hipyl5Swc00FbpeHsFgTWaNjyRoVQ3BE146ms7k9bLfa2dxgpdrlJlanJV6vJd6gI0qnQd3FIryEEDTVOloSRTaL2jWlTTQvJbR6NREJZqJTgolJCyY6NZiQKGOvsmqRdAxyvvYPPWK+PlRsdfBsX+h3pjeKFnjp9608trsUkWph8Zi+JPs7clcScN5avIsHaivJMhmYP6Gff+ebpip48zhwNML18yAkwX9tSyS9CClgS1pY9/sv/PL6i5x80+0MmHxCQPvKz3+fLVsfJivrQZISLw9oX12NLdVbeGjhQ6ytWMv4+PE8MOYBEoMOLbPxzz//zOLFi7noootY9t6rWOvqOPf+h4lISArwqA+NKlsVP+74ke+2f8emqk1oVBomJ05map+pTEicgFbljRQsL/+VTZvvw+22kpX5APHxF6EoCkIIcnNzmTdvXouQPWnSJAYOHIhK1b0FEknvQi6I/UOPma/zl8LbJ8LJj8LYv/mlyVfyynh4exHP5yRxUZyfo4T2w5r8Gv7xznJGVECCSwUCNFoVcZmhJOaEkZQT7vWD7uKRs0IIasuslOyopXh7LTvXlGOtd2KwaMkcGUP26FiiU4K6nFjq8gh22exsbrCxudHG5kYruY02dljtuPfzFVyt4BO0dcQZtMTptSTotcTpdT6RW0u0TtslRG6Xw01lUWMrX+16yvPqcTm8CSP1Zg0xqV4xO8b3kskiJUeLnK/9Q4+Zrw+VH+6A1TPgzk1gCqfe5mTsc/OpHB3BVYlRPJp1aGs7SdegutHB8G+W0xRv5NcR2QwMbt/K84jwuOHj82DnnzBtFiSP9l/bEkkvQwrYEgCa6mp5944biUhM5sIHHw/ooq2hIZdly88iLGwcgwe91eUWiIHC7rbz+prXeXf9uwTpgvjnqH9yetrph/z+t27dyscff8yoUaM47bTTKN6ayzdPPITL6eTkG/9O9tgJAX4Hh0duVS4zt89k1o5ZVNmqCDeEc1raaZyVcRbZ4dnY7aVs3PgPqqoXEBV5In37PoZW643W9ng8LUJ2aWkpERERTJo0iQEDBkghW9ItkAti/9Cj5uv3pkDldvj7atAcfWSWWwjOXbWN9Q1W5o7KIcnQMUJeaZ2Ne75ay8LN5eSodJwQEUJ4vYfa0iYADGYtCdlhXkG7bxjBkZ0fNeuwuSjbVUfJjjpKdtZSuqMOW6PXwkJn1JDUN4zsMXEk9w9H3QV8rYUQFNidbG6wsrnRRm6jV7De2mTD7vOTVoA0o54cs4Fss4Eci4Ecs5EIrYZSh5Mim4Miu5Niu5Miu4Mi256y1dP2+3qzyB3XLGwbvBHccXqdT+z2ityaTrgx4XF7qCpupHRnHWW76ijdVUdVUWNLpHZwpKFF0I5ODSYqOQitTj65JTl05HztH3rUfH0olKyH18bDSY/AuFsAXxR2YRnqJAsrxvULuMWXxH/c8t1avjS7OT0smLeH9fFv47/8Gxa+6DcrOYmkNyMFbAkAP7/6PJv+nMvlT7xIZFLgfLs8HjvLlp+D3V7G6NE/oddFBqyvrsTS4qU8vPhhdtftZmqfqdw94u421hoHo6GhgVdffRWz2cx1112H1ud7XV9ZwffPPUbx1lyGnXYmx1x6FWpN1/LndHqcLChcwMxtM5lXMA+Xx0VOeA5T+0zl1NRTaKyY2SbBY3j4+JZrPR4PmzdvZt68eZSVlREZGcmkSZPo37+/FLIlXRq5IPYPPWq+3vYbfHQOTJ0Ow67wS5O7rXaOW5bL4CATXw7pg6oDheK1BTVM/30bv24sxaLXcMWQRI4LD6Z2Zz0FudU0VNsBCIowtERnJ2SHYQoOrNAuhKC23ErpjtoWwbqyoKFF8AyLNRGbHkJseggx6cGEx5o7LWJcCEGF09UmorpZsG5we1rOi9drvSK12UBfi5Ecs4EMkwHTEYjtQghqXG6K7U4KbQ6KfSJ3ob1V2ebE6vG0uU6tQEyLyO0VuGO0WsI1aiJUakJQMKPgcHmwOdxYnb6Xw42tpezB6vTttzpnn31f2eHyoNeqMem8L6NOg0mrJkijItQOwU0eTPVutLUuVFa3d6AKaMP1mOJMhCSYCUuyEBlvwWzQ+NrRYNCqOv2miqTrIOdr/9Cj5utD5e2TobEMblkBKhX1NidjXvyDyhHh3JEayz3pcZ09QskhsL6wlpMWbEITaWDFhP7+vfGw9gv4+loYcQ1MedZ/7UokvRQpYEso2Liezx66l5Fnnscxl0wLaF9btz5KXv7bDB70FpGRxwa0r65Arb2WZ5Y/wzfbviHRksh/xv6HsfFjD6sNj8fDjBkz2LVrF9dddx0xMTFtjrtdTuZ/+A6rfv6e+Ox+TLn9nwSFd80bAzW2Gn7c6bUY2VC5AY2iYWLiRM5MHIqxagbWlgSPd6FS7RE5PB4PmzZtYt68eZSXlxMVFcWkSZPo16+fFLIlXRK5IPYPPWq+FgJeP8brf3jLMlD5J0p0RnEld27O56GMeG5IivZLm4fD5pI6Xp67nR/WFqHXqLhkVArXTUzDYPNQsLna+8qtxmF1ARCRYCGxbxiJ2WHEZ4aiMxzdTVen3e2Nrt7pFaxLd9ZirfdGV2sNamJSg/cI1mnBGMydExFX53K3RFTveVmpcrpbzgnXqskxewXq5le22UCI9vB+Rm6PaEc83lcgbn2OzelpU1fnclMjPNTioVERNKrAqlZwaBVcOhVuvQr2FtCFALsHxebe92X3bnVugUmjxqhVY9SpMWjVGLUqjDpvnXffe0yrVmF3uWmyu2lyuGlyurE6XDTaveNscri89Q43Bpcg1q0izqUizq0i1qXCgFekdiAoUXso0XgoVnso0QpcOhUmvaaVOK5uEbhb6rQazHrfMa33mFGn9tZp95xn0nuFdaNOjV4jxfHuhpyv/UOPmq8PlWZx8rKvIeN4wBuF/WhZBaY4M6vH98eikU+EdGWEEBz/4RI2Jhn4Z3IMd/bx402HotXwzskQPxSu+A400vJKIjlapIDdy3G7nHx4z99x2m1Me/oVtIbAZcOtrPqL1auvJCHhMnKyHwpYP10BIQQ/7/qZx5c+Tq29liv7X8mNg2/EqDn8RFCLFi1i9uzZnHbaaYwaNWq/521eMJ9fXp+O1mDg9Nv+SfKAQUfzFgLO1uqtfLf9O37Y8QMV1gqi9MFcGx9KjGszFks/BvR/HrO57SNcHo+HjRs3Mm/ePCoqKoiKimLy5Mn07dtXCtmSLoVcEPuHHjdfb/gGvpgG578H/c/2S5NCCK5ct5P51fXMHpFFjrlzEg5uL2/g1Xnb+WZVIWpF4fwRidw4qQ9J4SY8HkF5Xj0Fm6vI31RNyfZa3C4PKpVCTHowiTnhJOWEEZ0WfEALDyEEdRU2SnbUeiOsd9ZRUdCA8FlihMaYiE3fI1iHxZlRdbIfd7nDyTO7SvmoqAKX72uyWa1qJVL7BGuLgUit5rDET49HsKOikZV51azKq2bl7hp2VjTicHsOfvFeqBR80clqjDqVV0RuFpR94rJRq8bQIjSrUHRq7BoFmxoaVdCgCOqEh2rhocrtocLlwrrX2kAFRPsiuZutSuL1upao7niDjhidFu1h/L8JIXC4PVh9YnaTw02j3Ul1qZXq/Hrqi5qwljThqrSD70fj0auwB2totKioNaqo1EG9x3ut1en2iuQOF01ON4ezvGn+ObaJGveVgw1aQkxaQo1aQoxaQk3ebYhR11IONWkxatVSBO9A5HztH3rcfH0ouOzwbD9IHgMXfQxAvc3J6Ff+pGpoOP/tE89NyR1/Y1ly6HyxIp/bikqIDNKz4pgB6Py1nmwohzePBeHxJm20yN8DicQfSAG7l7N05pf8OeM9zvrnf+gzfP/i6NHidFazeMlpaDTBjBr5LWp15yywO4I6Rx33/XkffxT8Qf+I/jw47kFywnOOqK3i4mLeeustMjIyuOiiiw66oKksyOO7Zx6luriI8Rddzqip56J0cWHX5XGxsGgh323/jt/zfidbZ+XSCDd6lUJy+l1kpVy3z/v2eDxs2LCB+fPnU1FRQXR0NJMnTyYnJ0cK2ZIugVwQ+4ceN1973PDyKNCa4IY/wE8iVbnDyaSlm0nQ65g1PNN/C7AjIL+qidfmb+eL5QV4hOCsoQncPLkP6VGWlnNcDjfF22t9EdpVlOXVgwCtXk18VihJOeEk5oQRHGWkfHc9JTtqW14t0dV6NdGpwXsE67QQDJau4zfa6Hbzen45L+eVYfN4uDQughMjgsmxGEnUa49IoKy3OVmTX8vKvGqfaF1DrdX78wgyaBiWHEZObBBmvdcm40ACdNsIaDVateJ30VQIQZ3L3cqLe18/7iK7k8a9BHcFiNZpSDDo6Gc2MiDIyCCLkRyL8YhsU5pxOz1UFDRQumuPn3aNz7sdvPYyrf20IxMtqNQKdpfHJ4q7fJHfvuhv+56o8CZHq3qHN4q90bHnWKPDTb3NSW2Tk1qrE5dn/2smrVppK2r7BG+v+K0jxKgh1KRrVdcshGvRdAEP9+6GnK/9Q4+brw+VOQ/Cghfg9nUQ4k3c+NLvW3m0qoqwaBMrJ/RHL9cmXZJ6m5MRHy+hNt3Ce/1TOSU61D8Nu53wwZlQuAKu/tkbgS2RSPyCFLB7MbVlpbx3182kDh7KmXc/ELB+hBCsW38zFRVzGTnia4KC+gWsr86mzlHHDb/cwObqzdwx7A4u7Xsp6iN8TNzhcPDGG29gs9m46aabMJvNh3adtYlfXp9O7qI/6TNiNKfcfAcGs+XgF3YBau21zN41m1+2fcFQZTU5Bg9FIpqY1H8wOXUKOnXbR688Hg/r169n/vz5VFZWEhMT0yJky+glSWciF8T+oUfO1ys/gO9ubfPIsT/4sbyGq9fv4uakaP6TEe+3do+Uklobb/yxgxlLd+NweTh9UDx/O7YPObHB+5xra3RSuKWagk1eu5HWomIzIVFGr1DdJ4TY9GDC4y2dHl3dHi6P4NOSKp7aWUypw8XpUSHclx5HhunwnnATQrCzopGVeTVewXp3Nbml9S3RwJnRFoYlhzEsJZRhyWH0ieqaP4+DIYSg3u1pI2w3+3HnWR1sbLBS7fLarKiADJOBQUFGBliMDPRtD9dipTW2Riflu+sp9QnarS1oVBqFqKSgFlE7JjWYkOijT0oqhKDR4aamyUGt1Stq11i9wnaNT+CutTpaynvqnDTYXQdsO0ivIbhVdLd3q2sT8R3qE769dd5jZl3vjfqW87V/6JHz9aFQvRteGAzH3A3HedfT9TYno95YQPXAUJ7NTuKS+IhOHqSkPe7/YQNv6+wMCzYxa3S2/z4DZ90Ny96Es9+AwRf6p02JRAJIAbvXIoTg2ycfJn/DOqY9+wrBkYF7rKWo6HM2bb6PjIx7SUm+LmD9dDatxevnJz/PpKRJR9Xe999/z4oVK7jiiitIT08/rGuFEKz6+Xvmf/g2QZFRTL3zX0SnHl4bnc32mm0s2vg/oqx/0ehR+KYulOyEszgr4yz6R/Rv8yXD7Xa3CNlVVVXExsYyefJksrP9+GVEIjkMevqCWFGU84EHgb7AKCHE8lbH7gOuAdzAbUKI2b764cB7gBH4Efi7OMgXhx45X7sc3sVuRB+Y9oNfm/5nbj4fFFVyd2osd6fF+rXtI6Wiwc7bf+3kg4W7aHS4OalfDLccl8GgxND9XlNfZaNgcxX1lTaiUoKJTQvGGNS1vSOFEPxaWcf/bS9mS5ONEcEm/puRwMiQQ7v53Gh3sSbfJ1bn1bAqr5rqJl90tV7DkORQn2AdxpCkUEKMXSfaPJAIISiwO1lf38TaeivrG7yvYruz5Zxkg46BQUYGWowMDDIx0GIkWn9kPx8hBA3Vdkp31rVEapftrsPl8EaJ602aNoJ2dGpwwBOTtsbp9lBn3SN4e8VvRxsRvK0g7mgRv53u/X/calQKoSatV/w2thW3Q9oRxIMNGsx6DWad1ye8O0d+9/T5uqPokfP1oTLjQihaBbevb/E5fmnuVh6pqyYhwsSS8f1Ry/VIl2JraT3HzV6LK9HM3NHZ/rNfaw5SGHsLnPyIf9qUSCQtSAG7l7J12SK+e/oRJl12NSPOOCdg/TQ17WTJ0jMICRnC0CEfoCjd9wvugfC3eL1x40Y+//xzxo8fz4knnnjE7RTmbuKH5x/HVl/P8dfcxIBjj7ytzqKmdh0r192McBQxv0HPd9UqUkL6cGbGmUxJn0K0ac/NF7fbzbp165g/fz7V1dXExcUxefJksrKypJAt6VB6+oJYUZS+eN1kXwfubhawFUXpB3wCjALigTlAlhDCrSjKUuDvwGK8AvaLQoifDtRPj52vF70Ms/8F18yBpJF+a9YtBHdszuPzkmruTI3hH6mxXeazr6bJwXsLd/HOXzups7mYlBXFrcdlMCI1vLOHdtSsqmvi4e2FLKppJN2o5/4+cZwWGbLfn70Qgt2VTS1WICt215BbUkezq0SfKHOLWD08JYyMbhpdHUjKHU7W11tZ12BlXb2V9Q1N7LQ6Wo5H6zQMtJi8wrYvUjvZoDuivweP20N1SVOLqF26q46qwoaWaPigcAMxacEtwnZUchBafddK3CaEoMnhbonorrE6vEJ4c5S3r+wVxx17Ir+bnNQfJOobQKdRYfElxWzemvUaX1mDRe9Ndtn6WLP4vXfZotd0aDLMnj5fdxQ9dr4+FLb8AjPOh/PehQHedXW9zcmIdxZS2zeEt/qnMsVf9hSSo0YIwVkfLmVJoo6LY8J5rn+KfxrOXwbvnQYp4+HSL0F9dEmqJRLJvkgBuxfisFl5986bMJgtXPbY86g1gflw9XicrFhxAU3W3YweNQuDwY9ZfbsQdY46bvz1RjZVbfKLeF1bW8urr75KeHg4V199NZqj/P9pqq1h1otPkrd+LQOPO4njrroRja5rR7LtjdttZeu2xygs/BiXNp7vG6KZX7YFlaJibPxYzuxzJscmHYtBY/Cd72bt2rXMnz+fmpoa4uPjmTx5MpmZmV1GzJH0bHrLglhRlHm0FbDvAxBCPObbn403UnsXMFcIkeOrvxiYLIS44UDt99j52t4Azw+A5LFw8Sd+bdotBHdtzufTkiruSInhn2ldR8QG76L+o8V5vPXnDiobHYxJD+fW4zIZ1yeiS43zUNhttfPojmJmltUQodVwd1osl8VF7JOAsMHuYl1BbUuyxVV5NVQ2esVWi17DkKRQhiWHMjQljKFJoYSautcc3VWoc7nZ0GBlXX0T6xqsrK+3sqXJRnPgcYhGzQDLHk/tAUEmMkz6I4qMdNrdlOfVt/HTrq+0AaCoFMLjzW2itMPjOz+h6JHicnuos7laIrprrE7qbS6a7C4a7K4Wf/BGh4tG+77lJoebBruLRrvrgN7frVGrFK/QvR+Ru0UU1/lEcb1XODfr9pRbHzPrNKj38/PvLfN1oOmx8/Wh4HHDi0MhJAmumtVSPX3uVh5tqiUzzMT8sX273RzXU5m1tojrcvPQRxpZMaE/4UdhQ9VCXTG8MRm0BrhuLpi6/815iaQr4s85W95i6iYs+vITGiormPL3ewImXgPs3DWduvq1DBgwvceK1/WO+hbx+rnJzx21eO3xePj6669xu92ce+65Ry1eA5hCQjn3/v+x8PMZLPnmM0p3bOeMO+8jNKZrPF5+KKjVRnKyHyYi/Bg2bb6Pc42buW78jcyv9fD9zh/45x//JEgbxClppzC1z1QGRw1m6NChDBo0iDVr1vDHH38wY8YMEhISOO644+jTp09nvyWJpKeSgDfCupkCX53TV967vneit8CoG2D+41C6EWL8lxtCrSg8m5OESoHndpcigHu6kIgdZNBy0+Q+TBuXyidL83j9j+1c+tYShiaHcutxGRybHd1lxro/qpwunt9VyruFFWgUhTtSYrg5OZogjRqb0836gjrWFtSypqCGtQW1bC/fE62bHmlmcnY0w1JCGZ4SRmZ00H6FNcnhEaxRMzbUwtjQPXk/rG4PmxttrKtvYr0vWvv9wgpsPiHVqFLoZ/FGaA8KMjEgyEiO2XDQpGtavZr4zFDiM0Nb6prqHC1idtmuOravLGPjX0UAaHQqgiKMGC1ajEE6TEFaDM1biw5TsLfeaNGhN2lQutDvhEatItysI9ysAw7NEqc9hBA43J79iNwuGuxu39abILPBV99od/vOdVFU42xzndXpPuT+DVpVi7DtFcS9YrhEctSo1DDiapjzXyjbDNE5AEwbm8r0DxaxxaThr+oGJoYHdfJAJU0OF/9auA1PdjD39onzj3jtssNnl4G9Hi7/WorXEkk3QX4D6AaU797JilnfMvC4k0jI7huwfmpqlrNr16vExZ5LTPRpAeunM6l31HPDrze0iNeTkyYfdZt//fUXu3fv5qyzziIiwn8JP1QqNRMuupy4zGx+evkZPrrv75z6t7voM3yU3/roCKKiTiA4eBAbN/6DirznmRR5Atef8Qmrq7Yxc9tMvt/+PV9s+YLU4FSm9pnKGX3OYNiwYW2E7A8//JARI0Zw0kknoetmkegSSUeiKMocoL07XfcLIWbu77J26sQB6tvr93rgeoDk5ORDGGk3ZfQNsHA6LHgeznnDr02rFIWns5NQgOd3l+IRgvvS47qUMGzUqbl6QhqXjknmyxUFvDpvO1e/t5x+ccHcelwGJ/eP7XIRq1a3h7cKypmeV0qDy8OFseGcY7ZQVNrAY2s2sraghtyS+hav4UiLjkGJoUwZFMfgxFCGJIUSZpbzTkdiVKsYGmxiaLCppc7lEWxtsrVEaa9raOLr0mreL6oEQKNAttnAgGYLEouR/hYjFs2BbUFMwTpSB0WSOigS8Aq2tWVWr6C9u47GajvWBidVRQ0U1DuwN7ZvzaGoFAwWLaagZlHbtw3SYdy7LliHztA9EjAqioJeo0avUfvE8KPH7RE0OfZEebcWvttEh7cSwb3iube+pslx8E4kkkNh6GUw9xFY/jac9hTgvWH7t8w4HrPX8eiWQn4ak9PJg5RMn7eNsiQTSVoNVydGHX2DQsCsO6FwOVzwAcT0P/o2JRJJhyAtRLo4wuPhk//+k5riIq567jWMQcEB6cflqmfJ0tNRUDNq1PdoNJaDX9TNaC1ePzvpWY5NPvao28zPz+edd96hf//+nHvuuQFbjNSUlvD9s49Rtms7o8++gHEXXIpK1bW8Gg+GEB7y899j2/an0GpD6d/vacLDx9PgaODX3b8yc/tMVpSuQEFhTNwYpmZM5fjk49EIDb///juLFi0iIiKCc889l/j4+M5+O5IeSG95JFlaiBwlP/8LlrwGt62EsFS/N+8Rgnu2FPBhUSW3JEdzfxcTsVvjdHuYubqIV+ZuY0dFIxnRFq4cl0pKuInoYD1RFj1hJl2niNpuIfiypIpHthdT5nSR4lIIz2ti167algjQIL2GgYkhDEoMZXBiCIOSQokPMXTZn7ekLR4hyLM5WFe/x4JkXb2VCqdXZFaAdKOeAa2SRQ6wGInQHXn8jtvtwdbgxFrvxNrgwFrvwFrXXHZ695u3DU4c1vYFb5VGwWhpJW4Haffab1un1XcPwbuj6C3zdaDp8fP1ofD19bD5R7hrs/dJK7y2WUNnLKEhzcLPw7MY0upmmqRj2VXRyKSvV2DPDOaTQekcG+EHLWTJG/DTP+CYf8BxDxx9exKJ5IBID+xexNrfZvPrG9M55eY76D/p+ID1s2HDnZSW/cDwYZ8REjI0YP10FoEQr202G6+99hoAN954IwaD4ajbPBAuh4Pf332Ndb//QvKAwZx+2z8whYQGtM9AUF+/kfUb7qCpaRvJSdfQp89dqFR6APLr8vlux3d8v/17ChsKMWvNnJx6MudknkNQfRDffPMNjY2NHHvssYwfPx7VQR4XlkgOh96yIG5HwO4PzGBPEsffgExfEsdlwK3AErxJHKcLIX48UPs9fr6uLYQXBsPwaXD60wHpwiME924p4IOiSm5OiubffbquiA3eiMof1xXz0u/byC2tb3NMo1KItOhbBO2oID3RQd5tVJChzb5Be+Q3ZoUQlNTZWJNfy3eFlfwi7DToVSi1DjS5tZjqXfSPD/aK1Ule0Totovt6HEvaRwhBqcPVxlN7bUMTBTZnyzkJeq1P1Da1JIuM12sD8jfmdnr2Ere9wra13kFTvRObb9tc77K3b6+h0aowBGkxNYvbvmjuNnWtIr01uu4V5HC49Jb5OtD0+Pn6UMhfCm+fCFOehxFXtVQ/O3crTzrrmRhm4YuRWZ03vl7OZR8s47c4NRMigvhiWObRN7jzT/jgTMg8ES76BORaUiIJOFLA7iU01dXy7u03EJmcygX/fSxgi9eSku/YsPEO0tJuJz3t1oD00Zk0e15vrNrIM5Oe4bjk4466TSEEX3/9NevXr+fqq68mKSnJDyM9NNbP/ZXf3n4VQ1AQZ9xxL/FZgbOVCRTeBI+PU1j4ERZLPwb0fx6zeY/HtUd4WFG6gpnbZvLL7l+wuqwMjR7K5ZmXU7Wyik0bN5GcnMw555xDaGho570RSY+ipy+IFUU5G5gORAE1wGohxMm+Y/cDVwMu4HYhxE+++hHAe4AR+Am4VRzki0OvmK9n3gLrvoDb14ElOiBdCCG4b2sh7xVWcGNSFP/tE9+lRWwAj0eQV9VEWb2d8no75fW2lnLrbWWjnfZ+i4INGp+gbdhL6G5bF2rSUt3kZK3Pr3ptQQ1rCmopxYMrKxhPpAGdw8NYh5ozo0MZnBRKVkwQWrVcqPZWqp0uNjRYWVtv9flqN7Gtyd7iiRSuVbcRtAcGGUkz6lF18N+c0+FuG8XdjvDd+pjb5Wm3Ha1evU80t1av9v7deQQC71P0Qog2+3vKAuHxVgrYqyy85x7oGN7rhRAg9vTVcl2b/QMd27Pfup2rn5zYo+frg6Eoyt3AU0CUEKLCV3cfcA3gBm4TQsw+WDu9Yr4+GELAaxO95Rv/BN/ffL3NyZDPltKYZGLBmL70MQU2UEmyL79vLuXyZdsQiWb+GJ1DxtH+H9TkeZM2GsPhut/AEOKXcUokkgPTbQRsRVGSgA/w+nF6gDeEEC8oihIOfAak4n1E+QIhRPWB2uqNE+zPrzzPpr/mcsWT04lIDIynqNVayJKlp2GxZDFs6CeoVD3LFj0Q4jXAmjVr+Oabbzj22GOZNOnokkAeCWW7dvDds49SX1HOpMuvYegpZ3R5YaM9yit+Y9Ome3G7m8jMvJ+E+Iv3eR9Nzia+2fYN7294n+LGYjJCMjjDeAYlK0pQFIXTTz+dQYMGddI7kPQkerqA3VH0ivm6Yhu8NAIm3AEn/Ddg3QghuH9rIe8UVnBDYhQPZnR9EftQcLk9VDU6Wgnddsob7JTV2Xxbe8u2vYRvWrXS4lmtKJAYZ8GZEcJuI1hUKm5PieHa5KiDJvWT9G4a3W42NdhY2ypZ5OZGG07f2sisVtHHqCfJqCPZoCPZqPduDTqSDDoMnXxDRAiB0+bex76kqd6BzbdtEb7rHLgcbhSV4v0MUbx/O3vKile3a1Xe99ieesX31g96TAXgO6ZSvIkVfPXNn2UHPOYrKwAqXzvAcVf067XztW99/RaQAwwXQlQoitIP+IQ9T1LNAbKEEAfMmNkr5utDYfm78MPtcM2vkLQn19ATc7fwnLuRU8KCeG94RueNrxdic7qZ9PoCdvcL4trEKP4vK/HoGnQ0wTsnQ/UuuO53iPRDNLdEIjkkupOAHQfECSFWKooSBKwAzgKmAVVCiMcVRbkXCBNC3HOgtnrbBJu/cR2fP3Qfo848j4mXTAtIH0K4WbnyUuobNjF61PcYjT0r8Va9o54b59zIxkr/iteVlZW8/vrrxMbGMm3atE6zsbA1NvDzK8+xffkSssdO5KQbb0NnMHbKWI4Gu72MjZv+SVXVn0RGnkDfnMfQ6fbNBO30OPl558+8u+FdtlZvJVmTzISqCdgr7QwYMIDTTz8do7H7vX9J10EK2P6h18zXn18J23+HO9YHNIpHCMG/txXyVkEF1ydG8VAPEbEPBSEEjQ63V9iut7cRt0OMWvrEB/GHx84HJVUAXJcYxa3J0YRoe9bNeEnH4fB42NJoY63PfmSn1U6+zUG+zYHd03bNFKPTkGzQk+wTuFuEboOOeL0OjbSnCRi9eb5WFOVL4H/ATGCET8BuN5eFEGLRgdrqNfP1wbA3wLN9IfvUNgmaG+wuBn65FFuckZXj+xOnlwl9O4qX5m7lkaoqLJFGlo/rd3TzuhDw1TWw/mu45DPIOtl/A5VIJAfFn3N2QL/hCyGKgWJfuV5RlE1AAnAmMNl32vvAPOCAAnZvwu1yMuetVwiOimHMuRcFrJ/du9+gpnYZ/fo+1XPF64qNPDPZf+K12+3mq6++QqVScc4553SqB7PBbOHMu+5n2fdf89cnH1Cet4upd/6LiMSOszPxB3p9NEMGv0N+wfts2/YkS5aeRr9+TxMRPqHNeVqVljP6nMGU9Cn8VfgX76x/h0+cnzCIQbABdu3exbnnnEtaWlonvROJRNKrmHAHbPwWlr0NE+8MWDeKovC/jAQU4I2CcjwI734vELEVRcGi12CJspAetSe5tN3j4b3CCm7bVUqNy835sWH8My2ORIMUFyRHh06lYkCQiQFBJojbU+8RgjKHizyrnTybw/uyerdLahv4ptRJazMPjQLx+ubI7X0juKN0ml7xNyzxL4qiTAUKhRBr9vr9SQAWt9ov8NW118b1wPUAyck9a/13xOgtMPgiWPEenPwomCMBsOg1XBMfyUuikUc2FvDS0PTOHWcvobDGyrPrChADw/h3RvzR35Re+CKs/wqO+7cUryWSbk6HhagoipIKDMWbCCrGJ24jhChWFKVdA8neOsEu/+FbqgrzOfue/6LVB8Zvq65uLTt2Pk909OnExp4dkD46iwZHQ0DEa4C5c+dSVFTE+eef3yW8lxWVilFnnkdsnyxmvfgkH//rDk664VZyxne8rcnRoCgqkpOuIix0DBs23sHq1Vfuk+Bxz7kKExMnMjFxImvL1/LO+neYu3Uuo8pH8f777zNw5EDOPPlMNBoZgSeRSAJI/BDocxwsfgXG3ATawD0BoigKD2ckoELh9YJyPAIeyewdInZrhBB8V17DI9uLybM5mBwWxAN94rxio0QSQFSKQqxeS6xey6h2jjs9giL7HlHbK3B7xe5fK+sod7janG9UKSQadG0iuJNbRXDLpwh6L4qizMFrv7k39wP/Ak5q77J26tp9zFoI8QbwBngjsI9wmD2PEdfA0jdg1Ucw4faW6tvHpfPWt8v5RtTyf04XofJvM+D8b9YmrBlBZBp0XBofcXSNbZsDcx6EfmfBxLv8MTyJRNKJdMgnsKIoFuArvImh6g51wdUbJ9jashIWf/UpGSPHkj5sZED6cLubWL/hDnS6KHKy/9ejFsANjgZumHMDGys28vTkp/0qXu/YsYO//vqLYcOG0b9/f7+16w+SBwzissef54fnn2TWi09RtGUz4y+8HL2pey3qg4L6MnLEt2zd9hh5+W9TVb2IAf2fw2xu33duUNQgnj/2eXYO28n7a95n17JdsAxWb1rN6WeezpjMMR37BiQSSe9iwp3w/hRY/TGMvDagXSmKwoMZ8aDA6/nleIDHepGIvby2kQe3FbK8rom+ZgOfDk5ncnhwZw9LIgFAq1JIMepJMerbPd7k9pDfStTOsznI94ndy+oaqNsrGWOIRt0iaicZ2kZwJxl0GGVC0h6LEOKE9uoVRRkIpAHN0deJwEpFUUbhjbhu/QhmIlAU4KH2LKJzIHUiLH8Hxt0KKjXgjcK+PDqMN7Hy2MYCnhic2rnj7OEs3FbBD431iPgQHstJQn0033Eqt8OXV0N0PzjrlZYEnRKJpPsSUA9sAEVRtMAPwGwhxLO+ulxgsi/6Og6YJ4TIPlA7vcGjSwjBt08+TP6GdUx79lWCI6MC0s+mzf+iqOhzhg39iLCwniPw7S1eH598vN/abmpq4tVXX0Wn03HDDTeg03XNx5TdLhd/zniXFbNmAqDR6zGHhGIKCcUUEoY51LcNCcUU6q03h3r3tQZjlxJCKip+Z+Ome3C7m+iTfhcJCRehVh9YkC9rKuPdue9Su7IWtUdNfWo9F5xwAWPjx3ap9ybpmvRmT01/0hvm6xaEgLdPhIYyuHUlqAMfFyCE4OHtRbyaX86V8RE8lpWIqgd/vu222nlkRzHfldUQrdNwb1ocF8aFH92iViLpYtQ4XW1sSZojuJv9t217+W9H6zT72JI0i90JvcB/W87XoCjKLvZ4YPcHZrAnieNvQKZM4niYrP8avrwKLvkCsvYEujfYXfT7YQWE6Mg9drC8gRQgnG4PJ778F7n9gjgpOoT3Bx2FZYu9Ht46ARpK4fp5EJbqr2FKJJLDpNt4YCtexehtYFOzeO3jO+BK4HHfdmYgx9Fd2LZsETtWLmPSZVcHTLwuL/+FoqLPSEm+oceJ1822IU9P8q94LYRg5syZNDY2cskll3RZ8RpArdEw+YrrSB82mpLtW2iqraGptobG2hpqy0oo2rIJa32dV3TZC41O7xW0Q0Ixhe4rcrccCwlDZwy82B0ZeRyjR/3Ipk3/ZOu2R9i5azrx8ReQmHAFRmO7tn5Em6K55/R7KBlbwnufvYd6p5oPPvqAl7Je4oqhV3Bi8omofREVEolEctQoijcK+9OLYcPXMOiCDuhS4T994lEpCi/nlSGAx3ugiF3rdPH87lLeLqhArcCdqTH8LSkas0Z+hkt6HqFaDaFaDYPascMRzf7be0Vw51kdLKttZGZZNe5WX+vUPv/tJENba5IYnRaTWoVRrcKo8m5NvrK2hwvePR0hxAZFUT4HNgIu4G8HE68l7ZAzBSwxsPztNgK2Ra/hovBQPsDKM5sKeGBA77E27UjeX7iLLeEa1GqFhzPbX+sdEh4PfHMjVGyFy7+W4rVE0oMIaAS2oigTgD+BddCS2+RfeH2wPweSgTzgfCFE1YHa6ul3iB02K+/eeRNGs4VLH3sedQD8e+32UpYsPR2DIZ4Rw79Epeq6Quzh0Cxeb6jY4BWvU/wnXgMsW7aMWbNmcdJJJzFu3Di/tt0ZeNxurPV1NNZU01RTTWMrkbuptsZb7ys31dW2L3ZrdfuK3K2F71YR3zqj6ajEbiEEtbUryC94n/Ly2QghiIo6kaTEaYSGjtxv20IIFi1ZxK+//ooDB8silqGOUTOt/zTOzDgTgyYw/vKS7ouM6PIPPX2+3gePB14d5xWzb1wAHZTcVwjBozuKmZ5XxmVxETyZ3TNEbKdH8H5RBc/uKqHa6eaC2HDuTY8lTt8zvrNIJP7G5REU2h0+i5J9PbjL9vLfbg+NQouYbVSrMKnaF7rbLyst55paXdv6PKNK5deocDlf+4deN18fCr8/An88BX9fA2EpLdX1Nif9Zq9GY9KQe/xgdB001/cWyuptTHx9AbXDI7glKYoHMo5CwJ73OMx7DE5+DMbe7L9BSiSSI6LbRGALIf6i/aQSAP5VGbs5C7+YQUNlBWfcfk9AxGshPD47Biv9+z0nxetDpKysjNmzZ9OnTx/GjOkZEesqtdprGxIadtBzPR431rq6NqJ2414id11ZKcVbc7HW1SGEZ582vGJ3s6jtFbqbI7pb25mYQ8PaFbsVRSE0dAShoSOw2YooKPyYwsJPKS+fjcXSj6TEK4mJOQO1et9kj+PGjCMjPYOvvv4KbYmWGlHDY3WP8cqaV7i076VcmH0hIfqQo/uBSiSS3o1KBRPugG+uh62zIfvUDulWURT+lR6HSlF4YXcpHgRPZyd1WxFbCMHsijr+t72I7VY7E0ItPJgRLxM0SiQHQdPaf7udr3ZWn/92pdOF1e3B6vHQ5Pa0W7a6BU0e377bQ5PHQ63duc95e1uaHAo6pa3QbVQrrcqt6lV77e9T7p6fcZJuwvAr4c+nYcW7cMKDLdVBBi3nBQfxCTbuW7WLZ4Yfhb2FZB8e/2kz9X2CiNCouT21vRymh8imH7zi9eCLvQm2JRJJj0Km0e0ClO/eycofZzLw+JOJz+obkD7yC96nqupPsrP/h9ncJyB9dDQNjgZumnMTGyo28NSkp/wuXjudTr788kt0Oh1nnXUWql54p12lOnyxuyWau1V0d3O5vryMkm1b9it2q7XaVlYlzUJ3GKGxcaQMGkJQeDwZff5BWuotlJTMJL/gfTZtvodt258gIeFiEhMuRa+PadNmdHQ01117HXPnzmXBggVcGnIpeaF5TF81nbfXvc15Wedxeb/LiTUfxZcliUTSuxlwLsz9P/jzWcg6pcMSBSmKwr1psaiA53aX4hHwbE73E7HX1Dfx4LZCFtU0kmnS88HANE6MCJa5CyQSP2BUq8gy+/epM7cQ2HwCt7VlK9qK4s3H9lPeI4oLqpzOfY7Zj0Akl0iOmpBEyD4NVn4Ik+8DzZ4AmccnZvLDDyuZ4a7lhtomskLkDVZ/sGJ3FV8UV+EZFM4DGfFYjtQqrGwTfHMDxA+FKc/JpI0SSQ9ECtidjPB4+PWtlzGYLUy8ZFpA+mhoyGX79ieJjDyehPiLA9JHR9MsXq+vWM9Tk57ihJR2E3YfFb/++itlZWVccsklBAUF+b39nkZrsftgDu4ejxtbfT2N7YjcLWJ3ZQWlO7bRVFvbInZHJaeSOnQEaYOHEZ99LvHxF1JdvYj8gvfZtesVdu9+nejoU0lKnEZIyJCW/jQaDSeeeCIZGRl88803RK6N5MHRD7LMuIyPN33MjM0zOD3tdK4acBV9QnvGDR6JRNKBqDUw7jb48W7YvQBSJ3RY14qi8M+0WBQFnt1VisArYneHJIeFNgeP7Sjmy9JqwrVqHstK5LK4COnHK5F0cdSKglmjxkzgPOldHoHNs0fobmolek8MWK8SCTDyGtj8A2z8Dgad31Kt16h5aVAqV+4o4PLFW1hy8pDOG2MPwe0R3P/dBjzZIQwwG7gwNvzIGrJWw6eXgNYEF34MWqN/ByqRSLoEUsDuZNbN/YXiLZs55eY7MFr8L5K63XbWb7gdjSaYvjmP9YhopkZnY4t4/eSkJwMiXm/ZsoWlS5cyevRosrKy/N5+b0elUrdYihyK2F2Zn8fO1SvYtXoFK374hmUzv0RnNJI8YDBpQ0aQNuR/ZGb8i4LCDykq+oLS0u8JDh5CUuKVREefikqlBSAtLY2bbrqJWbNmsW7xOjKTMrny5CuZWTSTr7d+zcztM5mcOJmrB17N0Oihgf9BSCSSnsPQy2D+E/DXcx0qYEOziB2HCoWnd5XgQfB8TnKXFbEbXG5eyivjtXxvEspbkqO5LSWGYJmgUSKR+NCoFCwqNZYAiuQSSbukTYbwdG8yx1YCNsDJ6VGM217KQp2L6WvzuXVQUqcMsafwydI81hkEbr2aR440IbUQ3qSNNfkw7QcIOQr/bIlE0qWRAnYn0lRbw58fv0divwH0O+a4gPSxffuTNDZuYcjgd9DpIgLSR0fS6Gzkxl9vZF3FOp6a9BQnppzo9z7q6+v59ttviYmJ4YQT/C+OSw4PlUpNVEoaUSlpjDrzPOxNTeRtWMOuVSvYuXoF25YtBiAiMZnUwcNIGfQaqrDNFBZ9xIaNd7Bt2+MkJFxCQsLF6HQRGI1GzjvvPLKyspg1axbffPANp512Gtefcz2f5X7GjM0zuOKnKxgaPZSrB1zNMYnHoFJ6n32MRCI5TLRGr9/ibw9D8RqIG9zhQ7g7LRYFeGpXCULAC327lojt8gg+KankyZ0llDtcnB0dyn3pcSQb9Qe/WCKRSCSSjkClghHXwC/3Q8l6iB3Q5vC7x2Qz8Pe1PFlQxsUZ0USa5Bx2JFQ3Onhi/lbEiAjOjApldKjlyBpa/xVs+RlOfhSSe0bOKolE0j6KEN3DX6wnZkn++ZXn2PTXfK54cjoRif6/e1tZ+Qer11xFYuIVZGf91+/tdzQdIV57PB4++ugj8vLyuP7664mOjvZ7HxL/IYSgqjCfnau9YnbhpvW4XS40ej3J/QcSP9SIKmQ1dY1LUal0xMRMJSlxGkFBXq/5mpoavv76a/Ly8ujXrx9TpkwBLXyz7Rs+2PABRY1F9Anpw7QB0zg97XS0am0nv2NJIPFnhuTeTE+crw8ZWy08NwAyjofz3+u0YTy3q4QndpZwbkwYL3YREfv3yjoe2l5EbqONUSFmHuwTz7AQc2cPSyKRdEPkfO0fevV8fTCaquDZvjDkEq+f8l68tbmIB4rLGGpV+Om0jr9h3RO4/5t1vGerRx1nZuGYviQadIffiLUaXhrljbq+9jdQySc2JJKuhj/nbBmB3Unkb1zHhvm/Meqs8wMiXjsclWzc9E/M5kwy+tzj9/Y7mmbbkECK1wCLFy9mx44dTJkyRYrX3QBFUYhITCYiMZkRU87GabORt2Etu9Z4Be0dK0sAiMocQ8JIGyWe7yku/pLQ0NEkJV5JVNQJTJs2jQULFjB37lzy8/M5++yzubTvpVyQfQGzd83m3fXv8u8F/+alVS9xeb/LOS/rPMxaKbpIJJJ2MIR4vTMXvACV2yGiczz170iNRYXCYzuLEcCLOcloOslXelODlYe2FTGvup5Uo463+qdyelRIj7A0k0gkEkkPxRTuTdC89nM44SEwBLc5fG1OPO8VVLBK5+arTSWc21cmgz8c1hfW8uGWEtyjovh7SvSRidcAcx6Cpgq47EspXkskvQApYHcCbpeTOW+9QnBUDGPOudD/7butbNp8H05nLUOGvIda7d/M4x1Ns3i9tnxtQMXroqIi5syZQ05ODsOHDw9IH5LAojUY6DN8FH2Gj0IIQU1JUYt39vov1iGUFCL71+MZvJ6ampvRaWJJTpnGmDEX0KdPH7766is++OADxo4dy/HHH8+U9CmcnnY6C4oW8M76d3h6+dO8vvZ1Lsq+iEv6XkKkMbKz37JEIulqjLkZFr8KC56HqdM7bRh/T41BpcAjO4rxCMFLfVM6VMQuszt5YmcxnxRXEaxR81BGPNMSItGrpCWTRCKRSLoBI6+B1R/D2s9g1HX7HP5wTBbjF2/iH5vyODk9EoteSiuHgscj+PfM9Yh+ocToNPwt+QiDxvKWwIp3YewtnWLbJpFIOh75KdsJLP/+G6oK8zn73v+i1ftPXHY6ayko/Ij8/PdwOqvIzHyAIEuO39rvDFaUruCxJY+xrWYbTx7zZMDEa5vNxldffYXZbGbq1KkyMqwHoCgKYXEJhMUlMOzUqTgddgo3rvfajfyyAo9uE1EDq3G4Hmfr1qcJNkzm4gtvYsmyQhYtWsT27ds599xziYmJYULCBCYkTGBd+Tre3fAub617i/c3vM9ZGWcxrf80koJlAheJROLDEu1N6LjifZh8HwTHd9pQbk2JQQH+b0cxHuC6xCi0ioJWpXi3vrJOUdA0bxUFnUo5skRKQJPbw2v5ZbyUV4bTI7g2MYo7UmMI08qvnBKJRCLpRiQMh7ghsOxtGHkt7DUvplkMTIsK4x11DTf9spEPzxjUOePsZnyzqpBlLgcui5n/9InHrD6CyGm3E364HYITvd+1JBJJr0CuJjqY2rISFn/1KZmjxpE+dKRf2rTbS8nLf5fCwk9wuxuIiJhMSvINhIWN8kv7ncHuut08t+I5fsv7jRhTDC8c+wKTkiYFpC+bzcaHH35IdXU1V1xxBSaTKSD9SDoXrU5P6pDhpA4ZzrF4/xZ3rl7J7g2/4NQtQKT9xpr1c1DbYhnVfzLrttfyxhtvcOKJJzJq1ChUKhUDowby7ORn2VW7i/c2vMc3277hy61fcmLKiVw14Cr6R/Tv7LcpkUi6AuNuheXvwqKX4eRHOnUot6TEoCgK/9texHdlNYd8nQrQqfYI2ppWgrd277JKQaeo0CgKGxutFNudnB4VwgPp8aTJ5FYHxuWAukKv/YwhRD4CLZFIJF2JkdfCd7dA3iJIGbfP4YcGpvDt3FrmqBz8vqWM47KkBeWBqLM5eeSXXJThYQwPMnFOTNiRNbRwOpRthIs/Bf0RJn+USCTdDpnEsQOpLili1gtPUVVUwFXPvkpQxNHZDzQ17WR33psUF3+DEC5iYqaQknx9S4K67kiNrYbX1r7GZ5s/Q6fWcc3Aa7i83+UYNcaA9Ge32/nwww8pKiriggsuICene0esS44Ml9NJ3saF7N75Pg7dYjQGO7ZaHaXFfSms7EtUWCznnX8BkTExba4rbyrno00f8Xnu5zQ4GxgdN5qrB1zN2LixMoq/GyKTQvmHnjBf+4WvroPNs+CO9V4vzU5mQ4OVMrsTpxDel0fsW/YIXELg2M+xtnUenB68W1+dQwjCNRpuT41hTKhcUB6QhjLvTY7lb0ND6Z56fQgYQ8AQCsbQQ9yGSfFb0quQ87V/kPP1IeBogmdzIOMEOO+ddk+ZW17Lxet3ElZoZdn5I6WVyAH43w8beb20Eld6ED8Oz2RY8BHkFaraCa+MgcwT4cKP/D9IiUTiV2QSx26G8HhY9fP3/PnJB6g1Gk792x1HJV7X1a1jd94blJX9hEqlJT7+fFKSr8VoTPbjqDsWh9vBjE0zeGPtGzS6Gjk381xuHnJzQD2G7XY7H330EYWFhZx//vlSvO7FaLRa0gdPIn3wJDweJ7u3f06eeAdDyBoSnespKcng7be2Ee40M2jIUNKGDCcqJY0oUxR3DL+DawdeyxdbvuCjjR9xw6830De8L1cPuJoTUk5Ao5IfsxJJr2TCHbDuc1j6Jkzu/GTK/S1G+lsCczNYchgUrYYlr8H6r8Dt8Ioiff8FTitYa8BW03ZbscW7tVaD237gtg9X/DaGectS/JZIJJL20ZlgyKXeubyhzGsTthfHRoUw2WJiXpzgvtkbmT5VWom0x5bSet5ZlY9nfAznxYQdmXgtBMy6C1RaOPVJ/w9SIpF0aWQEdoCpKSlm9msvULBpPWlDR3Di9bcQFH74oqwQgurqReze/TpV1X+hVltITLycpKRp6HXdN5GcEILZu2fz/IrnKWwoZGLCRO4cficZYRkB7ddut/Pxxx+Tn5/PeeedR//+gbV+cHsEi3dU8u2qQraWNRAfaiAxzERCqJHEMCMJYUYSQo0EGbQBHYfk8KitXU1e3ruUlf+EEG6qqhIpzY3DvqERS0gYqYOHkTZkOCkDh2KwWHC4Hfyw4wfeXf8uu+p2kWhJ5Mr+V3JWxlkYNN07mWpvQEZ0+YfuOl8HhBkXQf4SbxS27ggWapKegdsFm7+HJa97H0PXmmHIJTD6BojMPPR29idyH2zrd/E7bE9Zit+STkDO1/5BzteHSMVWeGkEHPdvOObudk8pszsZsWADrkobXwzpw/jMqA4eZNdGCMGlby1hQQioYowsHNOXOL3u8Bta9yV8dY1XvB59g/8HKpFI/I4/52wpYAcI4fGwavYs/vzkPVQqNcdOu57+k44/bFsBITyUl//K7t2vUVe/Fp0uiuSkq0hIuASNJihAo+8YVpet5qnlT7G2fC1ZYVncPeJuxsaPDXi/DoeDjz/+mLy8PM4991wGDBgQkH6EEKwvrOPb1YV8v6aIsno7Fr2GAQnBlNXZKaix4nB52lwTYtR6Be1Qo1fgDjO27CeFmQg2aqQ1RSfg9Zn/kLzdH4FSj7UpDGd5f4oWNmKrs6EoKuIys0nzeWxHpaYxr2A+76x/h7UVawk3hHNJziVclHMRIfqQzn47kv0gF8T+obvN1wElfym8fSKc/BiMvbmzRyPpaJqqYMV7sOwtr891aIp3wT30Mq/w25Eckfhd7S0fjvhtCoeQRAhNhbAU73sOSwFLzD4J0CSSI0XO1/5BzteHwQdnQsU2uH3tfm/avbKrlId3FhO7tZ4FV47FLK1EWpi1tpgbf16Pc2QU96bFcntq7OE3Yq2Gl0ZBSAJc+5u8eSqRdBOkgN3FqSktYfZrz1OwcT2pQ4Zz0vW3HrZliMfjoKRkJrvz3qCpaQdGYzIpydcTG3sOanX3ToiUX5fP8yuf55fdvxBljOLWobcytc9U1B0wCTkcDmbMmMHu3bs555xzGDhwoN/72FXRyMzVRcxcU8iO8ka0aoVjs6M5c0gCx/eNxqD1vk+PR1DRaKew2kpBtZXCGisF1U0UtpStNDncbdq26DUtgvYecdvUEsUdYdZJgTuAuN12Nm58j91572A0VgBmwsyn0JSfwq4V2yjdsRUAY3AIqYOHkTp4GLVxKj7c9Rl/Fv6JUWPkvKzzuKzvZcRb4jv3zUj2QS6I/UN3mq87hHdPh+qdcNtq0BxBtJGk+1G6wRttvfZzcFkh7RgYfRNkndw9F9yHI343VUJNXltfbwCNAUKTfa+UtuJ2aIo3qlt+f5EcInK+9g9yvj4MNn4Hn18OF30COae1e4rLIzhm0UZ21Nm4yqHjsTP9v87sjjQ5XBz3zHxKB4cSEWrgz9F9MapVh9/Q97fDyvfh+nkQN9jfw5RIJAFCCthdFOHxsPrXH/nz4/dQVComX3ktAyafeFiCosvVSFHRZ+Tlv43dXoLF0o/UlBuIijoFVTf30q211/LG2jeYsXkGWpWWqwZcxZX9rsSkNXVI/06nk08++YQdO3Zw9tlnM3iw/ya+8no7P6wt4tvVRazJr0FRYHRaOGcOSeC0AXGEmA7fGkQIQU2T0yduN1HgE7qbxe7C6ibqbK421xi0qpbo7fQoM31jg8mJCyIzOgijrhsumrsoVquVX355GZttFhGR+SiKiujok4kKO5fK7R52r1nJrjUrsdbXgaIQ2yeT4OxUlhq380PTfDyK4ITkE7ii/xUMjpJfwLoKckHsH7rDfN2hbJsDH50LU1+CYZd39mgkgcLjhi0/e/2td/7hFWwHXQijb4SYfp09uo7HafUK2dW7ocb3qm61tdW0PV8f3L6wHZrsLUsLHkkr5HztH+R8fRi4XfD8AIjpD5d9td/Tltc2MmXlVtQ76/lyUj/G9onowEF2TZ6avZkXthbj6h/GG/1TmRodeviN5C2Gd06GsbfAyY/4fYwSiSRwSAG7C1JbVsLsV18gf+M6UgcP48TrbyU48tC9rxyOKgoKPiC/4ANcrlpCQ0eTmnIj4eETu31ErdPt5NPcT3ltzWv/z959h0d2lHnf/1bnbrXUynkkTdBocvbMOBscMY7YgG2wDRhMhmUDYeF5WPZ9YEnLLixLMNEBHHDCNs42DmNPzlkTlHNW51jvH6dH0uQkqVua+3NdfXXr9DmtKqXS+XWdu/BGvNxcfTNfWPQFClzjVxssGo3yyCOPcODAAW666SYWLVp01q/pDUV5eWcHT29p4Z393SQ0zCnJ4sZFpVy/sJTS7LFfLGswFB2ewd0XGDGTO8j+Th/BqDGDWymYmpfBrJJMZhVnMavYuC/PcWIyTeyfr1TasWMHL7/8MAUFOykrP4jWfjIz5zGl/G4KC95HV30TdVs3UrdlI+37atE6gd3tJjw7hxc922m3DbCgYAF3zbmLyysulwUfU0xOiEdHuo/X405r+PUlEA3A59dNzBm44viC/bD5IVh3nxHOZpXD8k/CkruNchri2EIDhwfaR97Hgofv78o/Otw+dO+ZIlc3nGNkvB4dMl6fpjd+AG98Dz6zCoqPP7v6SzsbeKy9l4qdg7zxmQtx2c7d/+/ru/1c+T9vE7mkmMU5GTy1eMbpZxuxiPF/VMQHn1sDdvfYNFYIMSYkwE4jOpFg6ysv8Naf/oAyKS6985PMf+9Vp/yHORRqpaHxt7S2PkoiEaIg/0oqKz+Nx7N4jFs+9rTWvNr4Kv+18b9o8jZxQekF/OPSf6Qmt2Zc2xGNRnn00UfZv38/N954I4sXn/nXNhJL8GZtF09vaeHVXR2EYwnKc5zcuKiUmxaVUV2UPnXJEwlNY2+APe1e9rQPsqfNuG/oDXDo1z7DZqamOJNZJcOhdk1xJh6nLCZ5qgYGBnjqqadobNzHggUBCgt3EAwdxGbLp6z0DsrK7sBuLyDo89KwbTP71rzD/g1rSMTj2KYXs6Gkjc3uRkoyS7lj9h18oPoDZNrS5+foXDLZT4iVUh8E/g2YDSzXWm9Ibq8CdgN7k7uu0Vp/JvncUuCPgBN4HviyPsk/Duk6XqfUjifh8Y/Dhx6AOTemujViNHTvM8qEbPkzRP1Qcb4x23rWdWA+d8OKUaE1+LuOP3t7oAkSI65AUybILE0G2scoUZJZIm8cTTKTfbweLzJen6ZgH/xssVG+4s6nj1v2qDsSY+XqXQS6g3zKmsF3bhib9ZbSXZ8/wod+vZr9+RZCUzJ4edlM5meewZXXb/8nvPbvcPujUHPN6DdUCDGmJMBOEwOdHbz0q5/StHMblQsWc9Wnv0hWfuEpHevz1dLQeB8dHc8CUFx0IxWVn8KdcRqr0aex7V3b+fGGH7OpcxMzsmfwT8v+iYvKLhr3dsRiMR599FH27dvHDTfcwJIlS077NRIJzfr6Xp7e0srz29sYCEbJzbDx/vkl3LS4lCUVORNqlnwgEqO2w8eetkH2tHvZnbwfCEaH9in1OIZD7ZIsZhdnMjU/A8uZ1Cs7ByQSCVavXs1rr72Gy+Xk6qsriCdeoafn7yhlpajw/UyZcjdZWQsA8PX1sv31l9j26ov4enuwetw0T0vwRu5eTG4nN8+4mY/M/gjlmeUp7tm5ZbKfECulZgMJ4NfAPx8RYD+ntT7qDEsptQ74MrAGI8D+mdb6hRN9nnQcr1MuEYefnwf2TKN24wQaM8QIiQQceM0oE7L/VTDbYN6txsKMpYtS3bpzRyIOg63Hn73tbQNGnN+YrJA95TglSiohI19+JyeYyT5ejxcZr8/Aml/Ci1+HO/4CM6867m4PtHTz1dpmrNt6+cv1C1k57dwqJeIPx/jIb9eyfcBP6PxCbivJ5T9nVZz+C/UehF+cD9VXwYcfHP2GCiHGnATYKaYTCba++iJvPfR7UIrL7ryH+ZdffdIQM5GI0d+/jqbm++nufhWTyUlZ2W1UTPkEDsfkWNCtxdfCTzf+lBfqXyDPkccXFn+Bm2bclJLSCLFYjMcee4za2lquu+46li079d8ZrTV72r08vaWFZ7e00joQwmk1c/XcIm5cVMZF1flYJ1GYq7WmYzDM7uRM7b3tRqi9v9NHLGH8jbCZTcwodCfLkCRLkZRkUuC2T6gAfyy1tbXx5JNP0tXVxYoVK7jwwhm0tf+ZtrYniMf9eDxLmFJ+NwUFV2Iy2UnE4xzYuJatr7xAw7bNKJOJ0LRM3szfT3tuiMsrL+fOOXeyqGCRfI3HwblyQqyUeoNTCLCVUiXA37XWs5If3w5cprX+9IleP53G67Sy8X549ktw51Mw/b2pbo04HWEvbHkY1v0aevaDuwjO+yQs/Ri4T23ighhHsTD0Nx179nZ/g7HQ5EjWjOFa20eG3K58cGSB1SUhdxo5V8brsSbj9RmIReAXK4w3xj777nGvuIlrzfs21LKzz8/UHQO8/IWLz5lSIuFYnE/ev4FV9T3kXVnBoErwzorZFNhO8wpfreGhD0DTevjCOsiaHHmJEOcaCbBTaLCrk5d+9VMad2ylYv4irv70l8gqOP7JSyIRprfz73S1PEHX4BqiOoBF25niuYLymn/Ellk1fo0fQ4ORQX677bc8tPshzMrM3XPv5uPzPk6GNTWL7sRiMf7yl7+wd+9e3v/+93Peeeed0nFNvQGe2drKX7e0UNvhw2JSXDKzgBsXlXLlnKKj/vGIJzShaJxAJE4oGieYfByMxAlGYwQjCYLROMFIbOg5h9XMssocFpRnY7OkdwgeiSU40OUzSpC0e4fKkHQMhof2ycuwGWVIkoH27OIsqovcOKzn5uW60WiUV199lbVr11JQUMAtt9xCfn4GrW2P09z8AMFgI1ZrDkVF11NaciuZmXMB6GtrYesrL7DzjVcJ+X2Ql8GWsi52FnUzq2Qed865kysqr8BqkvIuY+VcOSE+ToC9E6gFBoFvaa3fVkotA76vtb4iud/FwNe01tcd4zXvBe4FqKioWNrQ0DAeXZlYYmH46ULImwEfey7VrRGnorcO1v0GNj8I4UEoXQIrPwtzbpKayxNZ2Hv4ApNH3kd8Rx+jzMYVFI4ssHuS91nJ+8wRj7PA4Tn2c7ZMMKX3/30TxbkyXo+1dDm/nnB2PwuPfhTe/xM4757j7rZlMMD7NtZiavBxb14O375+7jg2MjXiCc2XHt7MczvaqHpfFQcSUR5eMJ2Lc8+gPOL2x+GJe+B9P4IV945+Y4UQ40IC7BTQWrPt1Rd586HfA3DpRz/BgiuuOXxWZDwGvQeItW+mp+tVukLb6Lb2EDeDOZYgvzdCYa8mryeEOR43avYVzYPKC4zaiZUXTLiZPNFElL/s/Qu/3PpLBsID3DD9Br6w+AsUZxSnrE3xeJy//OUv7Nmzh2uvvZbly5efcH9/OMpv367nLxubaO4zFg3Kd9sozXaSl2EjNiKkNsLo4ftwLHHG7XRYTSyeksOKabmsmJrH4orsUQl9u5q87Hm3jXAghs1lwe60YHdZsCXvjY+tQx/bHGZMpzmbvM8fOaq29t4OL6Go8fUwKajKz2B2csHImuJMZpdkUZZ97iwauX//fp5++mmCwSCXX345K1euRClNb+8qWtueoLv7FRKJCG73bEpKbqG46AZstjyikTB7332brS//jfYD+1A2C81TYmwsacVWkscds+7glpm3kGXLSnUXJ53JcEKslHoVONYf4G9qrf+a3OcNDg+w7YBba92TrHn9NDAXqAH+44gA+6ta6+tP1IZUj9dp7d2fw8vfhE++BuUT+kdt8tvxBDzxKWPW7ZybjPrWU07tzXAxgWlt1Lntq0/O1u413rwIeyE0aDw+7H5g+DkdP8mLqyPC7iOD75EB+HGes2dJjXUmx3idDmS8PkNawx+uhe5a+NJm4/fzOL62t4kHWnqwrenkL7cvY/nUybu4r9aabz69gz+tbWTeNVVs1FH+a9YUbi85g/IpwT6j9JpnCnzyVVnHQIgJTALscTbY3cnLv/4fGrZtpmLeAq6690t4HDHo2AWdxi3atYNuXU9nroneXBsJk8IaUziilfhdF9GVcT47VDG7ow6cKsFVli6u9K6nqPENaN4wvNp63ozhMLvyAuMSxnG4ZDEajxKIBQjGgsP3UeP+0O2oj2MBNrRvoH6wnhXFK/inZf/E7LzZY97WE4nH4zz++OPs3r2ba665hpUrVx61TySWYGtzP2/XdvHstlbqugNDzynAaTOTYbfgtJpx2cw4rObhxzYzLqsZp83Yduj+0H4umwWnzTT8eORrJPftD0RYX9/L2rpe1h7sZXf7IFobJToWTvGwYmoey6fmsrQyhwz7qZ2kRMNx9m3oYOfbrXTWD2K2mnBl2YgEY4SDscNKQR6L1W4+KuQ2wm/rcbYPb7M5LZjNJuKHFo1sG2R3u5c9bUao3dAz/PV12y3UHAq0k/W1a4ozyXJMzlnFfr+fZ599lj179jB16lRuuukmPB4PANHoAB0dz9LW9gSD3m0oZSE/7z2UlNxKXt6lmExW2g/sY+srz7N71ZvEoxEChVY2lLTRUZ7gxpqb+ejsjzIla0qKezl5nCsnxEcG2Md7HmhBSoiMrrAP/nseVFwAt/851a0Rx7PzaXj8EzBlBdz6O7lsWZyc1hANHB1yHxV4HwrDB479XDxy8s9ldR0n3M48Yvb3Ec/ZMoyZ5CZT8t5ihELKbNwPPR65PT1njJ8r4/VYk/H6LLRsgt+8By76R7ji28fdrT8a44I1uwn0h6jc4+PFL1+C0zY5w9gfv7SXn/99P0svr+QdS4yvVBbxtWklZ/Ziz34ZNj1orBtSsmBU2ymEGF8TJsBWSv0euA7oPFRbUymVCzwKVAH1wIe01n0ne61UDLBaa7a/+CRvPvwndCLOJYtzWehpRXXthvAAYZuiM8/O3uIK9rqL6VBFdFFBh5pFY6KcLm0nbD58gMoIBYlYLEQtRmDnDPXjSfQwNdHMrMguiv21KG+zcakxGJcbespQWeXGAjSuvKFAW3F4sD1yNrjWmnA8fMLwORgd/jimY5wqhcJpceK0OCnJKOEzCz/DJeWXpLxGbzwe54knnmDXrl1cffXVnH/++YARWG9v6WfNwV5WH+hhfX0P4djwz31Bpp1blpTxyYumkee2jXo/dEIT7w8T7fATrusiPhjEWpCNOduFKcNK0KLY0etjbfsgqxr72NE6SDyhsZgU88o8rJiay4ppuSytzMXjPDzo7WnxsfPtVvaubScSjJFT7GLuJWXUrCjGkWEd+vyRcJxwIGoE2gHjFgkl7w9tC8aIBGKEg9HDtkeCMU72Z8JiNw/N9B4ZctudFuJ2RUc8QUskQmMgTL0vyMG+IN7I8M9cWbYzuWBkshTJJFo0UmvN5s2beeGFFzCbzVx33XXMnTv3sJ8zn28vbW1P0Nb+NNFoD1ZrHiXFN1FScgtudw0hn4+db77G1leep6+thYTDzO7SfvZUDHJezSXcNfculhQuSfnv4ER3rpwQH2MGdgHQq7WOK6WmAW8D87XWvUqp9cAXgbUYizj+j9b6+RO9vpwQn8Tf/wPe/D58bg0UpvZNX3EMe/4Gj90FZUvho08YwZ8Q4yUWHhFqjwy5vUcE3id4Lho4+ec5HSbLiJDbYlxBOvT4UPBtOiL4thwnKDeNyuupK/7vOTFejzUZr8/SE5+C3c/AFzYY5+nH8UhbD/+wpwnLjj4+PbWI/3PdnHFs5Pj47dsH+X9/282KC8t50635QFEO/zu74szOTRrXwO+vhvO/AFd/d/QbK4QYVxMpwL4E8AEPjAiwf4hxovx9pdTXgRyt9ddO9lpjPsAmEtC+DTp2QucuBht28vL6ARoGMyjNGGTe1AF68ivZU7iQne4i6sxu2k25dFJERNmHXkYlEmSGg2QFfWQF/XhCAeM+6KcoECUnbsNnilKXZaPNk09HVj5tnlxCNqOWoivQTYZvD3G9FxXfiznahjpy+qw6tGVEYH2MKbYOs2MoaHZZXUOPnRYnLosLp/WIj0c+P2L/I/d1mB1pF5TF43GeeuopduzYweVXXIlryhzWHOxhzcEeNtT3EYwal3V6nFa8oShaw3tqCvn8e6eztHJ0LuXSsQTRDh+h2lYi9d1EO/zEvRods6PU8ExqnYijjncZlNIoh4mQzUyfUrRFYjQEw/RqzQAJ3DlOphRnUqIsRJoDtDf6UBYT05cUMPfiMkpmeMYkgI+G44SHAu0o4WCcSCA6tG04/B4ZikeHth/5Z0aj8SpNl1nTbU7QbTUe96gEh4qyWBSUOexUZtipLnCzbG4Bi6rzKM5Kv5+/U9HT08OTTz5JS0sLhYWFrFy5kvnz52O1Dr8pkUhE6el9i7a2x+nufh2tY2RmzqOk5FaKi67HYvHQuGMrW19+nv0b1qATCdqLouwo7yNrVhV3zbubq6qukjrZZ2iyB9hKqZuB/wEKgH5gi9b6aqXULcC/AzEgDnxba/1s8phlwB8BJ/AC8EV9kn8c5IT4JAK98F9zYc6NcPOvUt0aMVLtS/DIR6BkobHY5gkuCxcibcWjxwi8k8G2TkAibpQ7ScSSj5PbErHk9viIfY7YrhMjjjv0Golj7Js44vViJ/jcp/966t/6JvV4PV5kvD5L/U3w82Uw+wa45TfH3S2hNTdt3s+2Pj+80crj96xkWdXkKSXyxMZm/ukvW1m+pJi1hRYWZ7l4dNF07GdyBUcsAr++xFiL4HNrwO4e/QYLIcbVhAmwYWhxqOdGBNh7MS5BblNKlQBvaK1rTvY6YzbAag17X0D//bvUer0ccJTztmkBm80z6PPk4c3Kpd+VSUIN/wG26TD5iW48IS8ubwynN0Z+IExJIEZpCDwJOxnagVvbcWo7ZkwouwV/jp3BrEzMETOuzj4igW46LL00m/poctuNQNtTQJsnF7/dCMXdPh+FrZ2UDvaz0tTOlfbdzAhtI8NXbzTG6jLqaFZcAJXnQ/l5xiWC55BINMYDD/+F5oN76cmu4bXeHAIRI7CuLsxgWoGb1v4Q21sGsFtM3Lq0nHsumsq0gjMbEGMDfkK7Ggnv7yDa7iXeHyMRsYHJjRrxc5Lwd5Pwd6BMAUwZGkuBE1tFLmaPi2hbF9HWLmJdg8T7/SS8YTQ2lM1t3OyZmByZKGc2ypaBMjmO338TaKcFR5YNW6Ydc4YVU4YVc7Yda6ELS4ETs8eOSlHtaa2TAfihYPuosDs6FIL7/VEavSEa/SGaI1HaYlE6iOMf8f+Py2xiel4G8yqzqSnOZGZxJjVFmeS57cdvRJqIx+Ns27aNNWvW0NHRgcvlYunSpZx33nlkZR0elEQiPbR3PENb2xP4fLtRykZBwRWUlNxCXu7F+Pr62P7aS2x77UX8fX2EXLCzvI++mQ5uXXQHt868FY/dk6KeTkyTPcAeL3JCfApe/Aasu8+onZldkerWCID9r8LDt0PhHLjrr+DMTnWLhBDHIeP16JDxehS89u/w9n/Cp143rtw5jl2+IFeu30tGZ4iyphAvfPniSbHg/au7Ovj0QxtZMDOPPdNd5FgtPLe0mlzrGdbqf/s/ja/p7Y9CzTWj21ghREpM9AC7X2udPeL5Pq11zsleZ9QHWK3hwOvEXv8ej4UL+M8pn6DFM7yAoiMWIj/eQ4m5iVJzM0W0k+2LktPpIbd9Gu5gCfaEFaUVERWlxzZIj2MQn8dKJCuHuKOQsCmH7oCJhp4ADT1+/JHDF3dxWs1M8TgosZjJDkZwBAexxPuJmXoIZkB7dh5dnkJaPXkMOI0AMzPgZ1p9HUVtHcwYaOZ8cz1zshsp4SAmEmiTBVWyyKifXX2lEWxPssVeYvEEO1sHWX2whzUHuqFhPVWqm43RMkJ5M1k5zagh7Q/HeHhdI1ubB8jNsHHX+ZXcubLylILOmNdLZG8DoX1tRJr7ifdFSATNoDJRIxbP04kYOtAF2ovJGceca8dW7sE+owT79CosBQWoU3j3WWtNYmCAaGsr0bY2oi2tRFtbCbV20NTjoEHNIJBZhV3HKfTup2hgPxkxH1F3PkFnLlGHB2yZ2B1uMmxOHMqKecT6kspqwpLvxFLowlrgxFLgMh7nO1Bp/s+T1pqDdf2s3tjGltpe9vf46TYl6DYnCI3I5PPdNqoLjfraM4syqSl2U12UnvW1tdbU19ezZs0a9u7di8lkYu7cuaxcuZKysrKj9vd6d9Ha9jgdHc8QjfZhtxVRXHwTJSW34rBXcGDjWra89Deadm5Dm6CuyE/dtAgXnvc+Pjr3TiqzKlPQy4lHTohHh5wQn4KBFvjpQlj2cbj2R6lujTj4Bvz5w5BfDXc9A67JMzNOiMlIxuvRIeP1KAh74WeLjXWsPv7CCdeu+r/7WvhNcxfW1Z18emE533z/xC4lsvZgD3f9fh3TyzLpXZTLYDzO35bMZKrrDCcV9R6EX5wP1VfBhx8c3cYKIVLmnAmwlVL3AvcCVFRULG1oaBidRtW/Q/j1/+D3gWJ+XnknPW4POf5BLu/ezWz7bso8G8h0tEHChLl/JrpvHl2+MvbGOqm3NdFo9jFocuFxVJOhq0hE8/D6HbT2RwhFh1NDq1kxJcdFZZ6LyrwMqpL3AI29ARp6AjT2+mnsDdDYGzjsWAVkmzRZhHHiw2yNE8m0E8720JufS7fHBYAzHGFeQx3V+3ZT2d7IdH8DU7P7qcxqwZUVJO70EJl2Jc7512OaccWEvAwnntDsbB1gzcGeZA3rPnzhGKC5xt1Mcayd0jnLuPl9V5JhN/PY+iZ+904dTb1BqvJcfPLiadyypPyoBTO01sS7uwls30Noy34ijb3Ewg5QHszOApTVNbxvLATxAZQtgtljwVqaiWNaEfa5VVhyc0a9pEV/R4Cdq1rZs7qNkC9KVr6DOecXMb1SYxnoINbWZoTdLa1EWlsJNLWgOzswJYbfJInbPfSXzIHy2biLp+LJKsSRsBMfiAwv6qjAnOMYDrULnFgLXFgKnZgyrGlZqiMSitG8p4+GHd3s2NFNgy9Ej1njzTTTZ4fWcIRgbPh3qcTjSAbamVQXuqkpzmRGoRuXLT3e2Ont7WXdunVs2rSJSCTClClTWLFiBbNnz8Z8RA39RCJCd/ffaWt7nJ7eN9E6jidrMSUlt1BUdB0DHf1se+UFtr3xMrFgiP7MKHsqvJQsX8ydiz7GsqJlafk9TRdyQjw65IT4FP3187D9cfiHHeAuSHVrzl31q+ChWyF3Ktz9HGTkpbpFQoiTkPF6dMh4PUo2/B6e+wp86EGYc8Nxd/PG4ly0djfxQAzf6y088ZnzR62U5Xjb0TLA7fetId9jx3lxCTsDIR5fNIPzPGd4JbjW8NAHoGk9fGGdLJ4sxCQy0QPs1JUQaVqP/40f8jPvFH5fdStep4siXw8f8L7LBVlPYXINkNBmmmP5vNHjZGu/h2A4HxUrxKkr0JE8fAE7scRwAGSzmKjMHRFQ5xv3VXkZlHgcp7wIndaaTm94ONjuMYLtht4AjT0BevyHr0puJYbdmsDktBLJdOLNdqCdFix2xeyeLhYdrGXB9nXMaK+lMKufrJwg5mxNS9k8umdchXnW+6isnEZZthNTikpLjKS1pmMwTF23n7puP/U9/qHHjT0BInEjkJxWkMHKaXmsnJpLYP9a9uzYxmWXXcacpSt54N0GHlzTwEAwytLKHD518TSunFOE2aRIRCJE9u8nuKuW0O4Wws0D1Dny2V1eya7CHHZ6zNRmmoiP+FoorVHo4Xs0Jg49Tu6TfGzCeNNh6KaMxS6PfqzIMCuqs7KZ6XZRk+FgZoaDGS4Hdg0Ht3Sx8+1WWvb2YTIppi7MZ+7FZZTPyjlpCRAdjxPr7ibc3ELDzv20b91FdM9uslvq8IS8ACRQ9OeVwczFZE+bR37pVMwWD/H+GLHuIHrkmyhOy1CwbS0cDrgtuU6UOfU/M2D83PS2+mnY2UPjjh7a9g8QTyQIOs3oChfBXAtdJs3B3gD7u3xEksG2UlCR60rO2HYPBdxT8zOwW1IzIz0UCrFlyxbWrl1LX18fWVlZLF++nCVLluByuY7aPxzuor3jadransDv34fJ5KCw4Gpj4UfnYvauXsWGF/9Kb0MDMYtmf6mPyPwCbr3wbq6pugarOf1mpqeanBCPDjkhPkXd++Dn58HF/wiX/99Ut+bc1LAaHroFPOXwsb/JGwlCTBAyXo8OGa9HSTwGv7oQ4hH43Fqw2I6761MdfXx2VwP5dX4K+6I8/6WJV0qkrtvPB3/1LlaziZnXVPFSv5dfz63kxsKTXlR/fNsfhyfugff9CFbcO3qNFUKk3EQPsH8E9IxYxDFXa/3Vk73OWQ2wbdvo/ft/8oOBKh6bdi1Bm50qXwu3Rl9gYdaL9Ec8vNS0lI09NQyGCohHcjAiSYPLZh4KqCuS4XRl8r44yzEuAbAvHKOxx5ipXd/tY2d9J/ta+mj3RxlIWEZEqoAyaiInsqyYs6zMjIe5uKmeZbs3U7VvIxbViz07Sr8nk+051eysfC/R2RdQXZJFdZFRfqHUM/oL5Wmt6fFHhkPqZFB9sMtPQ09gaKFFGH5joCo/g2n5GcwpzWLltDyKshwkEgmee+45Nm3axIwlF7EjWsiTm1qIJhJcPaeIj8/LYa63heDuWsL7Ool1BOl0FLCncjo7S/LY5bGwy2PGbzH6545FWRj3szDHgcum0PGYURokHjXuE3HjsY6j4zESiTg6ETW2JxeY0Yl4cnvc2G/kY62HYm+tFP0WN/sypnLQWU5MGf+wKA05gQR5/THKIorFZR4umV/IgsJMMs4yUE0kEjTta6J21QZ6Nm9D7a+lqKOeomD/0D6BnHyYUUPBwqW4K2ZjziwmETIT6woS7QqQ8EaHX9CssOQ5jGD70KztZK1tkyO1s5ojwRhNe3pp2NFDw44eAgPGGz/5U9yUz83FPCWDXivs6/RR2+Flb4eXum4/8YTxd9BsUkzNz6Am+Xsws8jNzOJMKnNdp/xm1NlKJBLU1taydu1a6urqsFgsLFq0iBUrVlBQcHS4orXG692eLDHyLLHYIA57KcUlH6C4+GYGW8NsevFZ9q5+Gx2L05ETonWG4pL33sKH5txGtiN7XPo1EcgJ8eiQE+LT8NhdcOAN+MoOWTBwvDWthwdvhswiI7zOLE51i4QQp0jG69Eh4/Uo2vcK/OlWuPo/4PzPHXc3rTW3bjnAlgE/sddb+czKKr5x7exxbOjZaR8Iccsv3yUYjXP5TTN5sLuPb04r4YuVRWf+osE+4w19zxT45KtgmliBvhDixCZMgK2Uehi4DMgHOoBvA08DjwEVQCPwQa1178le64wG2M49tLzx3/x7/yyen3oRUYuFOf5abjU/RjH7Wd2ylDfaF9PlmwrAlDwL80vzmJafaQTU+UZQXeC2p/Vl96FwhE276nh360H2tHTTHojQj4NOnUkkYQwA2gQ6y4Y900qNUlzS38sle7aT17Kf+EAjOtjJgMfFHk8F67Nm0ZpfiaW6mqnlecwsykwG226Ks04ebPcHIiNmUQeGw+puP95wbGg/i0kxJddFVZ6LqflupuYbX/Op+RmUeJyYj/HGgNaa5577G39bv4e2rDls7dbYleb9ll5u6dhHSbefgLuEvVUz2V1ayM5sCzs9ZjodRvhoSSSYE/OxOEOztKSUxUXlTM9wYBrL728iDtEgxELGCvBdtcR3Pcf+zW38Pfoetjvn0p1lIlBqorcgg0atiY74vSx3WKlxOZmZYWdmhsOYte1y4D6LYLs/EGHzjnoOrt6Mb/tOHA37mdrXTJmvG1Oyvkgsy4Nt1hyyF87FMXMulqJpaFMW8e4g0c4gsa4AsZ4QJIbbasq0GbO2Cw8vR2LOGv9FJLXW9LT4hsLs9oOD6ITG7rJQMSeXinl5VMzJw+wyU9ftZ2+7l9oOL7UdRrjd2Bvg0LfBZjExo8A9FGgfCrjH+gqG9vZ21q5dy7Zt24jH40yfPp2VK1cyffp0TMeorR6Ph+nufoXWtsfp7V0FaLKzl1NScgtZzovY/fa7rH3xKcLdfQRtceoqQky9+EI+cv49TPVMHbN+TBRyQjw65IT4NLRuhvsug8u/bczEFuOjZRM8cCO48uDjz8ulykJMMDJejw4Zr0eR1saboq2bjQWaT7CWQq0/xOXr91IZgtY3m3n8sxewpOIsZi+Pk/5AhA/9ejUtfUHu/vBcftLZw0dL8vhRTfnZZSXPfhk2PQj3vgElC0atvUKI9DBhAuzRdFoDbM8Bal//X749OJ+3KheQMCmWhTdzDU/S3pnL2y1LqR+cDpgoy1V8cMl0blxUztT8M6zZlGYikQj19fVsWL2V7XUNtMZttKhsOsnGGzUPhXIJp5mMDCvVJgsXheNc29ZIRlcj8d4m4gNNxAdb6MjIYW9WKQc9pdRlldJRVEF+RSkzi43Z2vluG409Rkhd12OE1H2B4dm6SkFZtpOpyWC6Ki9j6HFZjhPrKcxq1VoT6+jAu3MX/726luf9OXRrN55YmBt8fSzKzqaprISd2VZ2ekzUuU3o5CBaFfGxxBpiSUEOi0unMTfbg2OcZtIei7c3xK5Vrex6p5XAQAR3JswpO8DsyB9xB3aAMhOtuoT6mluoLbmIWu1krz9ErT/E/kCYyIjf1zK7lZnJEiQ1LsfQ46wzCLbDsTg7WgbYuKeV5g1bCe/aTWlXI9P7W6j0tmPRRukN7crAOXcOrjmzccyZg61mFmZPKfHeCNGuALEuI9iOdgbQoeEZ9cpqMsqPFLiGZmtbC11Y8pwo6/h8P0L+KE27e2nc2UPDzl6Cg8bs7MLKTCrm5VE5L4/CyqyhQDoQibG/0zcUaB8KuNsGQkOv6bKZqS7KpKbInZyxbZQiKcwc3Te9/H4/GzZsYP369fh8PvLy8li5ciULFy7EZjv2ZYqhUBvt7U/T2vY4wWA9ZrOLwoL3UVz8AQYarbz7/OO0bdsOWtNcEMSytJJbr7qXFaUr0/oNu7EkJ8SjQ06IT9OfPggN78JnVhl1mMXYatsK918PjmwjvPaUp7pFQojTdC6P10qpLwJfAGLA3w5dyayU+gZwDxAHvqS1fulkryXj9Shr3wG/ughWfg6u+d4Jd/1/B1r5eWMnpTsGyIlo/pbmpUQCkRgf+e1adrYM8pXb5/Pd7m4uys7kwQXTsJ7NZJ7GNfD7q+H8L8DV3x29Bgsh0oYE2MfT38S6F3/J/xdZwobyaZhIcH5oFZU9e9jdWs3uvmo0ZgqyEty8uJJbl0xlZlHm+HQgRRKJBAcP1LFu1WbqmvYTjIfpSWTQYp1Cpz2bnqAmmgwatQlcGVammyxcGFVcFdAUBTuIDbYS66wnMWAE236LiYOeMva6i2nLyMOkNbk2RYHDRJ7DRJ5dkW1VZFvBbQZTPIaORNHRk93i6LhGJxTEQScUWoPX5OD10kU8XDaLPpOdTHOC6fmZ+Ka42ZdtJpysx5wbC7GYQZZ47CwprWJhURm51tQv1Ke1pnl3H9vfbKZ+WzcaqJyXx9yLy6icm4vJbDLetW/bArufhV3PQM8+4+Dy5TD7eph9PbHsKhpCYWr9IWr9YWoDIfb6Q+wPhAiNmAVdYrcy0+Wgymmj3DF8K7NbKbJbMZ9COJlIaA52+1hf38fG/R20b9lFRuMBZgw0Uz3QyrTBNqyxZF12ux1HTQ2OObNxzJ5jBNvVMyBqMsLsriCxzuR9V4B4f/j4i0gWOofKkozlIpI6oelq8hph9o4e2usGQYMjw0rF3Fwq5+UxZU4uTvfR4fBAMMr+Ti97233JGdvGrds3XKfe47RSU5RJdZGxaOShcDs34/g18U5FLBZj165drFmzhtbWVhwOB0uWLGH58uVkZ2cfu69aMzC4ibbWx+nofJ543IfTUUFJyQdw2y9jy+ur2fb6S+AP43XG6K2xc9m1t3PdvA9gM59deyeac/mEeDTJCfFp6m+CX14IhbPgY8+DOfXj1qTVvgPuvw5sbqNsSE5lqlskhDgD5+p4rZR6D/BN4P1a67BSqlBr3amUmgM8DCwHSoFXgZla6/gJXk7G67Hw1y/A1kfg82shb/pxd/PH41yydg+WuKb9+QY+e8l0vv6+WePY0FMXiSX45AMbWLWvi3/98AJ+MNhHucPGM0uqyTybUpexCPz6Eoj44HNrwO4evUYLIdKGBNhH8rbzwtP38SPLUnYVl2GLBpnXtRHawuztmUFcW/BkRLluQSm3L6thbmnWOTnDMJFIcGBfHeve3kx9y36iOgRaETMX01EynSZrBvWdfoJ9YVTyx8JuU1RaTKyMWrkoaqYaE9a4l0Sgg2hbLYnBdjBZUGYrmK0osw3MNpTVgbLaURY7WGwoi7HdZ7YyaLYxYLIwoCwMmEwMYGJAKbrN0KM0/WgG0Pi1JpjQDC0r6LEQmZpFotCBXcdZEOtliSvB4sJiFpfNpCLDmVbf13Awxp5329jxVgv9HQEcbitzLipl7sWlZOU5T3xw117Y/YwRaLdtNbYVzR8KsymcbUxvB+Ja0xSKDM3UPnTfGIrQHzv8/1aLghK7jXKHlTK7jSkOG2UO4+Nyh41Suw3XcWaodw6G2NDQx/r6XjYd7Ka/dj/T+luYMdDCgkA7Fb3N2EKB5CeyYJ8+HcdsY6a2Y85s7LNmY3ZnkIjEiXUHh2drJwPuIxeRNLksQwtHWgtdWMvc2ErdmJyjH+6EfFEadxthduPOXkK+KCgoqsqiMjk7u2BK5gnLoPT4wsOztTu81LYb997QcNmcfLedmmI380o9zC3zMK80i6q8jNMuQ6K1pqmpibVr17Jr1y4AZs+ezYoVK6ioqDju70E8HqCz62Xa2h6nr281oMjJOZ/iwpvpqXPy1nNPEqprI27StJXFqb7sUm67/NPkOifmCumn61w9IR5tckJ8Brb9BZ78JLznW3Dpv6S6NZNT52744/vBbIeP/w1yp6W6RUKIM3SujtdKqceA+7TWrx6x/RsAWuv/SH78EvBvWuvVJ3o9Ga/HgLcdfrYEZlwOH37whLv+raufe3bUc55fsfOdZp783IUsmpI9Pu08RfGE5suPbOa5bW188+Z5/CruI6Y1zy+dSZnjLCe6vP2f8Nq/w+2PQs01o9NgIUTakQA7Sfu6eOCRB7gvZy4HPIW4OgcobG+ku9tDLGElwxHm8rl5fHzFAhZNyU6rcDPVtNbs21XH2lWbaGw7QJQgaMi0FVA4cxbN5VNY3epjT8sA4Z4QKmwEiyalKbdEOS8OSxJZVGDGlwych28JelScHtPIMBoCCQ5fbHJkexRgNaGtJrAZ99pmAqsJZVG4nHEWuAa5riKHpVNmUpNXdHaXK42h7mYf299spnZtO7FIgqKpWcy/rJwZSwoxn0m5jL4G2POcMTO7aS2gIW/GcJhdumQozD6SLxanORyhORSlJRShORShJRw17kMR2sLR4TcIkvKsFsocViPcPhR2H5rJbbeRazWjlMIXjrGlsZ/19b1sbOhjU0MvWf1dTO9vYXG4g/mBdko6G7EO9hkvrBS2ykpjpvacOdiT4bYlx6j5phOa+EDYWDiyM2DU2D7GIpLmXAe2MjfWUnfyPgPzMWZKnymd0HQ2eGlIzs7ubDBmZzszrVTMNcLsKbNzcWRYT/5aWtMxGB4KtGs7vOxpN8qRROLGV95ttzCnJIt5ZR7mlRn30/IzTnnRyP7+ftavX8/GjRsJhUKUlJSwcuVK5s6di8Vy/LA/GGymrf0p2tqeIBRqwmx2U1T0flymi1j18lpa1mzCHNX0ZUVxLpvBrTd+npriOaf2RZygztUT4tEmJ8Rn6PFPwK6/wj2vQNmSVLdmcumqNcJrZTLKhpxgVpwQIv2dq+O1UmoL8FfgGiAE/LPWer1S6ufAGq31Q8n9fge8oLV+/BivcS9wL0BFRcXShoaG8Wr+ueONH8Ab34OPvwiV5x93N601d2w7yLoBP7nrusk2mXn2ixelTSkRrTX/5687eGhNI/90TQ3PuuPsD4R5evEMFmS6zu7Few/CL86H6qtOGvQLISa2cz7Ajvt7+fH9f+Yv5TNoDXpwtA2gusLE4xac1iDn12TwqfOXsmJqwZgusDZZJBIJ9myrY/07m2nuqCNq8oOGLEc+s2bPpnTFfN4cjPLK/m52Nw8Q7Q2hBqNDs7SPpBXDIfSIQBqbGWWFDBtk2RVZNsi0aDJMCRzxCKZQCO31kRgcwB4J44hGcEbDZNptLFu6lMsvvzxt34SIxxIc3NzF9jebads/gNlqYuZ5Rcy7tIzCyqzR+0TedtjzN2Nmdv3bkIhBVjnMvs4IsyvOP62Vm6MJTXtkONBuDiXD7vDw42Di8IjbaTINzeAud9goS87eLrZaCA1GqG8eZHNjH+vr++jyhskJDbIg0MZFuoeZ3jbyW+swdbYPvZ6lpMSYpT1itralqOiw73XcFyHa6ifS6iPa4iPS6iPeM1yL2uyxjQi0jXtTlm1Ufl6C3giNu3qN2dm7egj7YygFxdM8Ru3suXnkT3Gf1ueKxBLs6/Sys2WQHa0D7GgZYFfbIKHkDHSH1cTskizmlXqYX+ZhblkW1YWZ2CzHD7UjkQhbt25l7dq1dHd343a7Oe+881i6dClu9/EvydM6QX//OtranqCj8wUSiSAu1zQK82+gfreJjS+9ibkrQMSSIDAzi0uvu43Ll9yYtr+LZ+NcPSEebRJgn6Fgn1FKxOqET78FtsmxLkfK9RyAP1wLOm6UaCmYmeoWCSHO0mQer5VSrwLFx3jqm8B3gdeBLwPnAY8C04CfA6uPCLCf11o/caLPJeP1GIn44X+WGgsE3/MqHGPh9UPqAmEuW7+HpXYHm57ez+cum85Xr0mPUiI/eXkvP3t9P5+6dCp7yxy82jPI/fOncmW+5+xeWGt46APQtB6+sE4WUhZikjtnA+y3XnuR7zz4GM/lTmOw2465MwAxcJpCLJgW454LV3J5TQVmCa3PWCKh2bnhABtWb6Glu46Y2Q9AljOPOXPnsOT8RTRbHLzePcDLB7tp7A3idlrIddnIy7BR6LZT4LSSZ7OSazXjiscw+X3gHSTe30ugp5venh56e3uJxYZLK1itVvLy8o665ebm4nKd5Tu8Y8jXF2bn2y3sWtVKYDBCVr6DeZeWM/uCklOaoXtWAr1Q+5IRZh94DWIhcOXDrGth9g1QdZERhJwFrTW90fhQoN0SMsLu5hEfd0djhx1jAortVsrsVnJMZgjE8PaFaG330dbuQwXj5EUCXG7pZ3m8i2l9LWQ2HSTeUM+hFUbNubmHBdqOuXOxTplyWGiaCMaMQHtEqB3rCg7V1za5rUeF2uacs1tcMZHQdNYP0rDDmJ3d1egFwOWxUTk3j4q5Ru1s+xmUOYnFE9R1+9neMsCOZLC9q3UQX9j4+trMJmqKM5lXlsXcZLBdU5x51CyNRCLBwYMHWbNmDfv378dsNjN//nxWrlxJcfGxzodGtCHmo7PzBdranqB/YD1gIjf3InRsEate3EFoZyvmhGKgUFF92aXcct1ncE2ienWT+YR4PMkJ8Vk4+CY8cAMsuweu+0mqWzPx9dYZM69jIaPmdeHsVLdICDEKztXxWin1IvB9rfUbyY8PACuBT4KUEEkrm/8Ef/0c3PI7mH/rCXf9YV0bP6nv4MoBeGdtC0997kIWpriUyO9X1fHvz+3iw8umYFmQy+9auvledRmfKC84+xff/jg8cQ9c+2NY/qmzfz0hRFo7JwPs3IpKnfsvvyLaqVFRjdUcZW52Bx+54gJuWjAX6ylebi9OXSwaZ/uaA2xct5X23gZiVh8AWa5c5s2fy5LzFpKfn084HKanp+eYt3A4PPR6JpOJ3NzcoWB6ZFCdmZk5YWZ0aq1pqe1nxxvNHNzajdaaynl5zL+0nIo5uSeskzxmwj7Y/6oRZte+BBEvKDMUzoHSRcbl6KWLoXAuWEZ3Yb5gPEFLeES4nQy4D33cGo4QO+LPjF2DJZwg5I1AIIYKxZli1lyYGOR8bxvz2uuwHthLeP8BiBrlQ8w5OTgXLMCxcAHOhQtxzp+POevw2e2JcJxou98ItFuS4XZHAJKLXCqHBVtZxlA9bWuZG0ue84y/Z/6BMI07e2nc2UPjrl4iwRgmk6J4umeodnZuacYZ/2wnEpqG3gA7WgaGZmrvaBlkIJj8mpgU1YVuo/xIqVF+ZHZJFhl2I0Dv6upi7dq1bN26lWg0SlVVFStWrKCmpgbTCWaDAAQC9bS1PUFb+5OEw+1YLB5ysq9g+7Yg9X8/iMOnCNsTmGYWM2PJci694CYKsk8ckKe7c/WEeLTJCfFZeumbsPrncMdfYOZVqW7NxNXfaMy8jvjg7meheH6qWySEGCXn6nitlPoMUKq1/r9KqZnAa0AFMAf4M8OLOL4GVMsijimUSMB9l0CwH76w/oSTioLxBJeu24MFiL/Rhsdh4dkvXoT9bBZIPAtPbW7mK49u5eq5RSy5tIJvH2jl0+UFfKe67OxfPNgHPz8PsiuMkmmnceWwEGJiOicDbHtJtS79xH9R5e7ipnk5fPqa63BYR38xN3Fs4UCUre8eYMuGbXQONhGzGTNPrRYb0VjksH09Hs8xZ1N7PB7M5ok7SEVCMfauaWf7my30tfmxZ1iYc0Epcy8pw1NwdjOdR1UsDHVvQeMaaN0MrZuMfxYAzDYommeE2aWLjWA7vwbMY/e7FNeazkj0iIA7+TgYoTEYJnDk36GExhJOkKNgaiLKvEAfCxv3UbXuXbK2bh3azTZ9Os4FyUB74QLs1dWoI+o+62iCaId/KNCOtPiItvs5lKormxlracZQoG0rc2MpcKHMpxc6J+IJ2g8ODtXO7mk23vBx59iHameXz8rB5ji7r7XWmua+IDtbh2dq72gZoNtn/B4qBdML3EOB9txSD9NzbezbtY21a9cyODhIdnY2K1asYPHixTgcjpN8vji9fatpa3ucrq6XSSTCZGTMZMBXxeY3O2B/CGvcREJp/IUWPLOmsWj5e7lw0dXYRvnNkrF2rp4QjzY5IT5LsTDc9x7wd8HnVkNGfqpbNPEMNBvhdagf7nrGeCNXCDFpnKvjtVLKBvweWAREMGpgv5587pvAJ4AY8A9a6xdO9noyXo+xQ1dVXf5tuPgfT7jrK90D3Lm9jtszM3nq8T28d1Yh759fwnlVuUzJdY7bRK/X93TwqQc2smJqLrdfX8O9uxt4X76H38yrwjwabXjmS7D5Ibj3DShZcPavJ4RIe+dkgJ0/pULv2raZwpy8VDflnOfrC7FtVR1bN29nwNeLOe7EiovCwnzKpxZTNiOP4mkeMrLtqW7qqOht9bP9zWb2rmknGo5TWJnJvEvLqV5WiMU2AQJ5raG/IRlmb4aWTdC2FcKDxvMWp/EPROmS4WA7b8YJ67WNtsFYnOZQhKZghM1dXrb0eDnoC9ERjRG2msAx/HXO1DA7FmbhQBcz9u6icvUqCg/sQwHK6cQ5d+7wLO2FC7EWFR31+XQ8QbQzSHRkqN3qQydrUGMxYS3JwFY6YrZ2cQbqBDWoj+TrC9O4ywizm3b3Eg3FMZkVJTM8lM/Kpbwmh4LKTMyjcPXIocUih2dqD7KzdYC2geE64ZV5LuaWZFFoDRPtOEC8p4Esu4nFixezfPly8vJO/rc1Gh2ko/M52tqeZHBwM0qZcbvn4fXnUL/fT8dWL/ZO45/boD1OtCKL8gULuPD865hdviDtr7I4V0+IR5ucEI+Cjp1w32Uw40q47U/HXahXHMNgG/zxWvB3w11PQ9nSVLdICDHKZLweHTJej4M/3wb1q+BLm8F94vIbH9t+kDd7fdwdtvLEu414Q0YZwaIsO+dV5bJ8ai7nVeVSU5Q5Jut8ravr5c7fraWmOJNv3L6QO3bUUZPh4MnFM3CNxtXuDavhD9fABV+Eq/7f2b+eEGJCOCcDbBlg05OvL0z7wQHa6wboODhAZ6OXRHJmqzvXTvE0D8VTPRRNy6JgSibm0wgAUykeT1C3pZsdbzbTUtuP2WJixrJC5l9aTtHUUVyUMVUSCeg9cHio3b4NogHjeVumMWOtdNFwsJ1TlZIQpW0gyJsHu3mpqY9N/T66TKCzrGi3Zag9bmBOLMTMzjam795O1drVTGlpxJxIYCkqGpqh7Vy4EMfcuZicR8+Y1wlNrDs4HGgn62rrUPLqS5PCWuQamqVtLXVjLcnAdApvYsRjCdoPDCQXguylp8WYnW21mymtzqasJofymhzyy92jWoKmyxtmZ+sAO1sHh8Ltpt7g0PM5Nk1mvJ885WdBeQ7XX7SIpXOrTylo9vv3097+V/r61zA4uB2to4DC4ZhOX38GrQfCDG6LowYtaDT9uRrH9BJmLl3JZctvpDCjcNT6OVrkhHh0yHg9St79Obz8Tbj+Z7D07lS3ZmLwdhg1r71tcOdTMGV5qlskhBgDMl6PDhmvx0FXLfxiJSz92EnXtmgKRbhk7W7em5fFb+ZUsbfDy/r6XtbV9bK+vpeOQaM0Z5bDwrKqXJZV5bC8Kpf55Z6zLjeyq3WQD9+3moJMOz/92DLu2FOP3aR4YelMCmyjsK5TLAK/vthY4PLza2WhaiHOIRJgi7QVjyboavbScXDQCLYPDuDrMwZbs8VEQUUmxdOyKJ7moWiqB3dOes3S9g+E2bWqlZ1vteAfiJCZ62DepWXMvrAEp3tilUM4bfEYdNcOlx1p3Qzt2yGeLBHjzBmeoV262Ai2s0rHPdTuGAyx5mAPqw72sKp9gKZEDJ1lQ3lsJNwWEskA2AHMDAeobmtm+q5tTNu6mamtTdh0AvvMmUagvWAhzkULsVVVoY4x41xrTbw3ZCwW2eJP3ntJ+JMLVyqwFLhGLBSZgbXUjekkZUKC3ggttf207O2jeW8f/R3GGwd2l4WymTlDgXZOiWvUZy33ByLsah00FotsHWR7Ux/1I0LtDHOMWYUZrKwpY8GUHOaVeSj1OE7Yjng8yMDgFvr7N9Dfv46Bgc0kEsZrms3F9Pdm0Lk/RmiPmajPRtiawFtiJXd2NUtXXsH5My/DaUl9GR45IR4dMl6PkkQCHrwRmjfCZ96GvOmpblF683XB/dcZta8/+iRUnp/qFgkhxoiM16NDxutx8rd/hg2/N8qCFdSccNef1nfwH3Vt/GpOJdcXZg+V7ThUQvBQmL2uvpeDXX4A7BYTC6dks7wql/Om5rKkIptMx6mHzg09fm755WqsZsUf7l3Bpw800xaO8OySmdRknLjU4Cl768fw+v8Htz8KNdeMzmsKISYECbDFhOLrC9NRN5AMtAfpavQSjxmlGtw5yVna05KztMszMVvHZpZ2NBIn7I8S8kcJ+WMjHkcJ+2MMdAWp39ZNIqGpmJPLvMvKqZyXNyaXaE0YsQh07hqeqd26CTp2waE1YTIKh2tpHwq23eM7s/ZQoL3mYC9r6no4EAyjM61Ycuw4C5z4HSYOLSVqQTMt6Ke6tZFp27cy48BeZjQ14LZZk7W0F+BI1tS25OQc8/NprYkPRg5fKLLFR3xwuBa8Jc+BdSjUNu7NGcf/R9LXF6al1gizW/b04e01Sn84s2yUz0zO0J6VQ1b+2NTA84aibG/q49VNe1lX20pzwMSAdqIxPlduho25yZra80o9zCvLoiL3+OF6IhHF691Jf/86+gc20N+/nlgsWbJGexjodtG3H4KNTsL9NvoyY8SrPFQsWMSFy69lbuF8zClY1EVOiEeHjNejaKAFfnk+5FXDJ14a0/UKJrRAL/zxOug9CB/5C0y9ONUtEkKMIRmvR4eM1+PE3w0/WwwV58NHHjvhruFEgivW72VfIIzHYuaCbDcX5ri5MNvNrIzDJ5R0+8JsqO9lXV0fGxp62dk6SDyhMSmYXZJ1WNmRgsxjTxrrGAxx66/exReK8fCnV/J/2zp5t9/HIwunc1FO5uj0v+cA/PICmHk1fOiB0XlNIcSEIQG2mNAOm6WdDLZ9vSNnabspSpYeKZ6WhTvn8Hd+Y5G4EUAHooR8UUIBI4AOHRFIDz+OEgrEiB+qb3wMZosJZ6aV6UsKmXdJGdlFrjH9Gkxo0aBRn7Vl03Co3bUXSP4tySpPlh5JBtsli8CVO27N6xwMsaauNxlq93Cgy492mnHkOSgoz0R5bHSbNf3x4Z+HioCP6uZ6pu/azozGOqqb6inIzTFKjxwKtmfNQtmOPws/7o0Y5UdaD5Uf8RPvHa5Bbc62G4F2aQbW4gwshS4sec5jLhY52B2keW8fzXv6aKntIzBghOPuXDvlydnZZTU5R/1ujAatNfX19bz17hrW7m2mT7uJe0rpV5nU9YaIxo3vc6bDwtzSLGYVZzGzKJOZRW6qizLxOI8O6rVO4PPX0t+/3gi1+9cTiXQBkIg7GOxy4auz4G91Mdhvozs/gbO6jDnLLuLiuVdRnlk+6v08FjkhHh0yXo+yHU/A45+Ay/4VLvtaqluTfoJ9cP8NxhVEtz8C09+T6hYJIcaYjNejQ8brcbTqv+HVb8Ndf4Vpl51w14FojNd6vazq8/JOn4+GkHEekGe1DIXZF+W4mea0HxZo+8IxNjf2sb7OmKG9pamfUPL8d2p+BudV5XBelRFoV+a5GAzG+NCvV9PUF+DPn1zB/QEvD7f18tNZFXy4ZJTO3bSGB2+G5g3whfWQVTI6ryuEmDAkwBaTjr8/PFRypKNukM6Gw2dp213WoTA6doIg2mRRODKsQze7y4LDbcXhsmLPsAxvH9rH2DYhFmNMZ2GfUUP7UD3t1s1Gje1DcqYOz9AuWwIlC8E+Su/qn8SRgfbBLj8acGXamDY9G09xBmGXhcZYlKZwdOi4ooCPGY11TD9QS3VjHTM7WqgoLsK1aGFylvYirGWlJ5wRnQhEibT6D1soMtYdHMr6MSusBU4sRRlYC11Yi1xYilxYcoeDba01/R0BI8ze20dzbR/hZAkTT6FzKMwum5mDK2t0y9z09vaybt06Nm3aRCQSobisnOKZi/Hb89jV5mVH6yD7OrwEIvHhr1uWnZlFmVQXDofa1UVuskZcyqi1JhhsSAbaxi0YajS+ZnEL3k4n/iYH/jYXbT4zAyU2CubMYtl5V3B+5YVk2camDr2cEI8OGa/HwBOfMoLse16BclmUcEiwHx68yXhT9baHofqKVLdICDEOZLweHTJej6NoCH5+Hjg88Ok34TSuNGwKRXinz8uqPh/v9vtoTZ6vFNusXJTj5oIcNxdlu6lwHj7LOhJLsKN1gPXJsiPr6/sYCBrHFmbacdrMtPWH+P3HzmOjNc5/1LXxlcoivjZtFEPm9b+Dv/0jXPtjWP6p0XtdIcSEIQG2mPTisQTdTb5koD1ALJo4KnS2u5KP3YceW7HYTGNSYkGcgWA/tG0ZsVDkZhhoTD6pIL96eIHI0sVQPB9sYz/zvXMwxNojZmgDuO0WFk7LobzSgy3XQZdJs9Mf5EAgzKG3TDyhADMa6pjRcJCZTXXUePupLi0mI7lIpGP+fMxu9wk/fyISJ9YZINph3IzHfuJ94eGdzAprgRFmHxZs5xl1ontafUOBdsu+fqLJhSZzSzNGBNrZ2F2jsOgKEAqF2LJlC2vXrqWvr4+srCyWL1/OkiVLcDictPQH2dfppbbDR22Hl30dPvZ1eodmfQCUeBxUF2Uys9BtBNzJcNttN0oihMLthwXafn+t8fWKm/B3OvC3ORlsd1EXNREty6Vq0VIumH8FiwoXYTWPTj/lhHh0yHg9BoL98KuLwGwz6mHL4kcQGoSHPgCtW+DDD0lNTSHOITJejw4Zr8fZ9sfhiXvgxv+FxR89o5fQWlMXjPBOvxFov9PnoztqTGyZ4rANzc6+MMdNif3wiS2JhGZfpy8ZZvdyoMvHF99bTSDfzmd3NXBLUQ4/n10xeufSWx6Gpz8L099rlPdKQXlAIUTqSYAthJiY/N1G2HBokciWTeBrN55TZiicPVx+pHQJFM0Fy9gu9NnpDbH24LED7fOqclg8NZf8UjdBp4ld/hDbBgPs9Yc4VPHaGQkzvbGO6qYGqpvqmaNizC4tIitZS9s+YwbKfPJ/2BLhZLCdDLdjHX6iHQHi/SOCbYvCmp8MtpM3c76THl+U1n3GopBt+403fJSCgopMY1HIWTmUTPdgO8nikidtYyJBbW0ta9eupa6uDovFQllZGQUFBYfd3G43WkNzX5DaDi+1nUaovbfdy4EuH+HYcLBdlu1kZtGhUNuYtT2j0I1VeZOLQq6nr28dXt9OIIFOQLDbga/NRU+Pnf0JC7apVcxbegkXTruUaZ5pZ/yPt5wQjw4Zr8dI/SqjzvPSj8H1/53q1qRW2AcP3QItG+CD98Ps61LdIiHEOJLxenTIeD3OtIbfXgEDzfClTaPyZrTWmtpAmFV9Xt7tNwLt/pgxsWW6026UHMlxc0G2mwLb0RM+1vT7+NCWAyzJcvHoounYj7Go/RnZ9hg8eS9MvQTueBSsqV+sXQiRGhJgCyEmj8G2wxeJbN0MgR7jOZPVCLEPzdIuXQSFc2CUZtwey8hAe21dL/s7fYARaC+rymHltDyWTs3F5rGxKxBihzfItn4vO/0hAslFD62xKFWtTVQ31TOzo5V5NgvzyovImz8fx/z5WAoLTzlkHQq2OwJEO/3EkjO3jwq2kzO2LflOfEB7b5jGRi/tdYMk4hqTSVE0NcuYnV2TQ/G0LCzWM58J0d7ezqZNm2hra6Orq4tQaLjet8PhOCrULigoICsri4SGxt5Acqb28Kztg11+Ism65EpBeY6TmYXDofa0fAsF9n2E/Ovp6XoXr287KGPGSbDXhq/dReuAlQZTBp6ZC1i24D1cUH4B+c78U+6TnBCPDhmvx9DL/wfe/ZlR67nmfaluTWoE++Hh26FpLdz6e5h7U6pbJIQYZzJejw4Zr1OgcS38/iq47Btw2ddH/eUTWrPLFzRmZ/f7WN3vw5f8/3pWhmNohvb52W56ojGu27iPPJuFZ5dUk2MdpYWitz8OT34KKi+EOx4blytshRDpSwJsIcTkpTUMNB2+SGTrVggPGM+b7SNC7UXGIpGFs8cs1O70hlg3VHJkONDOsJk5b2ouK6flsXJaHnNKMmmMRNnhDbLdG2Brdx87/GH6k5fLmRIJpnS0MqOpnpr2FuZFQ8xzWskuL8NWWWncqqqw5OScUrsS4RixziDR5EztQyH34cG2CUu+k6jTwkA0QXtfmJZ2P764sXBp8XSPsSjkrBwKKjMxm89s1oXWGp/PR1dX11G3QCAwtJ/NZiM/P/+oYDs7O5uEhobewGGh9r4OHwe7fUMLRyoFFbkuqgszqS50Up7VS4F1D47Qa0QiW1EmY158eNDKQKeTJp+ZDmc+xbMu4vyZl7G0aClOy/FngMgJ8eiQ8XoMxcLwm8vB2wafWw3uwlS3aHwNthozr7v3wQfug3kfSHWLhBApIOP16JDxOkUeuwv2vQJf3DTmixrGEpptvgDvJMuNrB3wEUxoFOAym7CbFM8vnUmVc5SueN3xpFEmpeIC+MhjUvJMCCEBthDiHKM19B4cUVN7C7RthfCg8bzZDsXzjFC7ZJERbBfMGpNQu8sbZm1dzzED7WVVhwLtXOaVebCYFK3hKNuTs7S3tXexPRyjI9kupRNM6WhjZkMdNY0Hmdl4kJkDfWSXFB8Wahv3lZgzT77wZSIcS5YgGVmOJEB8YDjY1mZFxGZmIJqg2xvFG9cELSY80zyUz8qlfFYOeeVuTKazr4Hn9/uPGWz7fL6hfSwWyzGD7ZycHBIoGnr8h4XatR1e6rr9xBLG+GVSUJnnYlqepsTRhkdvJd++gRJPC1ZTjGjATG+3g4agFZ+nkqrZV3PBlIuYlTsL84h6fJP9hFgp9SPgeiACHAA+rrXuTz73DeAeIA58SWv9UnL7UuCPgBN4HviyPsk/DjJej7HO3fDrS2H6e4yZ2OfKug+de4zwOjQAtz0E0y5LdYuEECky2cfr8SLjdYr0HoSfL4eFHzbqYY+jSCLB5sEA7/T72DIY4B8qi1jiGaWQeefT8PgnYMoKo+a1/cTrAgkhzg0SYAshRCIBfXXD5UfathrBdsRrPG9xQNG84ZnapYshvwbMo3R5XFKXNzxihnYP+04QaFuTM5y7IlG2eoNsHQywbdDHlgE/HYdmGWtN5UAvNY0HmbF3NzX1B5jR3IAzEsacm3tUqG2rrMRWUYEp48T/fCZCMaKdgaESJMZjP/GByNA+ccAb03gTmoBJYSvJIGdWDsWLCsgrdY/qAqnBYJDu7u6jgu2BgYGhfcxmM3l5eUcF27m5uSQwUdftP7wUSaeXhp4A8WSwbVZQmhmhyNZCkWs/U7KbKXW3kW/upm/AQlPEQbxgDjUz3s/5ZRdTnlk+qU+IlVJXAa9rrWNKqR8AaK2/ppSaAzwMLAdKgVeBmVrruFJqHfBlYA1GgP0zrfULJ/o8Ml6PgzW/hBe/Dtf9Nyz7eKpbM/Ya18CfP2ysifCRx6FkQapbJIRIIQmwR4eM1yn00jdh9f8aCzMXz091a87e7mfhLx+DsmXw0cfBfvJJN0KIc4ME2EIIcSyJxDFmam+BSHK2r8V5xEztxZA/c1RD7eMF2q5koL28KofqokymF2QwJdeF3WLMAO4IR9nqDbDNG2SrN8BWb4DOiFHj2aQ106IhZvV0UlN/gOk7tjJ1+xYc0eHw2VJYeFioba2sxF5VhbWiApP9+JcFHhlsh1q8RNoDmIKxoX1iWhNAkciyYS9zkzMrB091DpYcB2oUZmmPFA6Hjxls9/X1De2jlDos2D40ezs/P5+EMnGwy3/YbO19nT4aevwkc21MKk6Ro4uyrDZK3W2UONtx6G7+8a63zpkTYqXUzcCtWuuPJGdfo7X+j+RzLwH/BtQDf9daz0puvx24TGv96RO9tozX4yCRgIduhqZ18JlVkDc91S0aO3v+ZszoyiqDO5+EnKpUt0gIkWISYI8OGa9TKNgHP1sMxQvgrr9O7Kup9vzNKItSugQ++gQ4slLdIiFEGpEAWwghTlUiAb0HDg+027YeEWrPP2Km9kwwnfkChyMdL9AGo/RFeY6LaQUZTM3PYFp+BtMK3EzNz6A4y0FnNMY2b4AtI4LtrkOhNlBtUcwN+5nV3cHMugNU7dyG6eAB4r29ww1QCsuxSpJUVmErL0PZbMf+soWMUiTeA/0M7Osn2uHH4o/hGPH/dUJBwmFBZVgxZ9qw5tix5zmwZjswZ9owZ9kwua2YXNazDrqj0egxg+3e3l5GjmM5OTlHzdjOz89Hmywc6PINhdq1HV72tPTQMhhDJxffbPjBdefMCbFS6lngUa31Q0qpnwNrtNYPJZ/7HfACRoD9fa31FcntFwNf01pfd4zXuxe4F6CiomJpQ0PD+HTkXDbYCr843wivP/HSmC5umzIb/gB/+0fj7/Idj0HGqS/KKoSYvCTAHh1yfp1ih66muuMxmHl1qltzZva+AI/eaVwZdedT4PCkukVCiDQjAbYQQpyNRAJ69idLj2wZrqkd9RvPW13DofbQTO3qUQm1B4JR6rv9HOz2Udfl52C3n4Ndfuq6/QSj8aH9nFYzVclQe2p+BtMKMqjKc+H2ODgYjQ7N0t7mDdIdNUJts4Ial4P5DgtzQ35mdXUwrX4/qr6eSH09kYYGEoODw40xm7GWlo4ItZMzuKuqsJaUoCyHz0zXWtPXMEjHli4G9/cT7QxijSWwm8ChFA4TWI4xg0Qr0HYzKsOKJcuGNceBNdtuhNyZNkzJe3OmDWU9vYUkY7EYvb29RwXb3d3dJBKJof08Hs8xg23MNg50+djd3MuHVk6b8CfESqlXgeJjPPVNrfVfk/t8E1gGfEBrrZVS/wusPiLAfh5oBP7jiAD7q1rr60/UBhmvx9HOp4xLdi/9OrznG6luzejRGt74Prz5fai+Cj74R1kISggxRALs0SHjdYrFIvCLFWCywmffHfUyh2Ou9mV49CNQNBfufBqc2alukRAiDY3mmD3B/koKIcQoMJmgYKZxW/hhY1siPhxqt24x7jc9ANFfGc9bM4zZBYcWiSxdDHkzTjvU9jitLJySzcIp2Ydt11rTMRjmYJePg91GoH2wy8fO1gFe3Nk+VNcZIC/DxtRksH1RfgaeXCchl5l2FWeHP8Srg0EejWpwFWKZW0jN8veyMNPFAreTeSrO9I42VGPDUKgdaWgguHEjiUBguEFWK7by8uFge6oRcmdWVpJzwzSUyQiaI6EY/v4wvv4w/r4w/u4goe4g0b4QscEI2h/DHI1jDyVweKPYO4I4TAPYFcesqa1tJkwZViweOxbPiJA7y4bZbU3O6rZhcllQSmGxWCgsLKSwsPCw14nH4/T19R0VbNfX1xOLDZdHyczMHAq0J4NDYfPxKKXuBq4DLh+xGGMzMGXEbuVAa3J7+TG2i3Qx92aofQne+hHMuAKmnJfqFp29eMyYdb3pflj0Ebj+p5NzdrkQQohzm8UGV/5/Rgi86Y9w3idT3aJTt+9Vo92Fs42Z1xJeCyHGQcpmYCulrgF+CpiB32qtv3+i/eUdYiHEuEvEoXvfiJnam6F9O0STQa81A0oWGoH2oZnaeTOMgHwURWIJGnsD1HX7qev2cTA5c7uu20+XNzy0n0lBWY6TqfluCgpcmHNs+Bxm2oizJximL2bM8LYomJ3hZEGmkwWZLhZmupiVYcfS23tYqB2pT4bcjY3o8PDnUXY7toopWCsrseTmYc7KxJSZhSnTjTkzK/lxJuZM4147MghEzEbQ3Rc27ntDRtA9ECYxGIFQDIdSQ7O57SZwmBQOk+KYbxGYFKZkoH2smdymzORzbhvKMvz9SCQS9Pf3HxVsd3V18a1vfWtSz+hKjrs/AS7VWneN2D4X+DPDizi+BlQnF3FcD3wRWIsxK/t/tNbPn+jzyHg9zkID8MuLjDfTPrMK7O5Ut+jMRQLwxD2w93m4+J/hvd+a2HVBhRBjQmZgjw4Zr9OA1vDH90PXXvjS5olRP3r/a/Dw7cZEoLueAVduqlskhEhjE76EiFLKDNQCV2LM8FoP3K613nW8Y2SAFUKkhUQcumsPn6ndvh1iQeN5m9sItUfO1M6dPuqh9iGDIaMkSV23nwPJUiR1yfIk/shwSRK71URZsZvMYhd47AzYFC06jjdZZsOqFLMzHEagnWUE27MzHNhMJnQiQayjIxlq1xvBdkMDkcZG4v39JAYH0dHoiRtqNmN2uzFlZQ0F2+asTExu4x53FhG7h5Ali5ByEUw4CEQtBEKKsF8T9cbQ/ig2hsuV2JMBt8uisCuwHmc4U07LUE1us9uanM1tG5rNbc6yYcqwYHHbJ/UJsVJqP2AHepKb1mitP5N87pvAJ4AY8A9a6xeS25cBfwScGHWxv6hP8o+DjNcp0PAu/OFaWHIn3PA/qW7NmQn0wsO3GQtTXvsjWP6pVLdICJGmJMAeHTJep4mWTfCb98BFX4Er/i3VrTmxA383xuq8arhbwmshxMlNhhIiy4H9WuuDAEqpR4AbgeMG2EIIkRZMZuNyucLZsOgOY1s8Bt17hxeJbN0MG34HsZDxvC1zxEzthVAwy6ipbXWedXOyHFYWlGezoDz7sO1aazq94eRsbSPQruv2c7DOS2NvB/GERgM2p5mMAieuQhedYc3jviAPtSWbrRSz3A4WZbqMYHvuAmqWLyfniDBea40Oh0l4vcS9XuN+0EvCO0jc6zv8ftA7tF+kvsHYf3DwsPIlFiAzeRtJuTKI5pYQzSom7C4g4szDa/MQjrsJ4iIUt5OIWbAqddhsbkckTkYwhrM3hB2wxhOoibH8w6jSWs84wXPfBb57jO0bgHlj2S4xCiovgIv+AVb9F8y8Bma9P9UtOj39TfDQLdBXDx+6H+bcmOoWCSGEEOOjbAks+DCs/gUs+wRkV6S6Rcd28E1j5nXudLjrrxJeCyHGXaoC7DKgacTHzcCKI3dSSt0L3AtQUZGmf8iFEMJsMRYwKZoLiz9ibIvHoGvP8CKRrZth/W+HQ22U8Q9qQQ3kz0ze1xiX4zlzzrpJSimKshwUZTk4f3reYc9F4wmaegNDi0ceTNbbrts7gPaGsTnN6CwrCY+NfbkRdrkDPGA2LuO3ADNddpZ53CzMclHptFFit1Jss5JRUIDlDGtJ61iMhM93eADuO1EQ3kqia+9QAB73+SAeR6OIWjMI23MI2bMJ27MZsOfQac8m7Mgh7MwlZPVgMltxKKNMiV2BY2wmyAsxfi77V+Oy3me+CGXLILMo1S06NR07jfA6EjDqaFZdmOoWCSGEEOPr8v8Lu/4Kr/073PLbVLfmaPWr4M8fhpwqY+Z1Rt5JDxFCiNGWqgD7WAUNj5oPp7W+D7gPjEucxrpRQggxaswWKJ5n3BZ/1NgWjxrlR7r2Hn5/8E2ID9eYJqPwiGA7eZ9ZMir1YK1mE9MK3EwrOLpWri8co+7QrO1uvzGDu8nPfm8Qv8tMIsvKnqwwuwaCYD089bUmIENDJgqPMpNjNpFrMVNosVBks1DssFHqsJFlt+C0mXHZzLisxmObxYI5OxtzdvYZ9UlrjQ4EiPt8RqB93CC8nfjgICFfhIA/QSBkIhizEkjYz+jzCpE2LDb4wG/gvkvhmS/AHY+lf/3o+lXw8B1gy4BPvGC8CSiEEEKcazzlcP7n4e3/hBWfhfKlqW7RsIZ34U8fNCbe3P0MZOSnukVCiHNUqgLsZmDKiI/LgdYUtUUIIcaH2To8U3ukRBz6G6Cr1ihFcuh+++MQHhjez55llB7JrzEC7UPhdk6VUdpkFLjtFuaXe5hf7jlsu9aaLm94aPHIA11ednYF6Y7H8aHxmzQhEwStCq/FRJPdBHYzaAVRIAgMYCxWE0mgwnFUKHkfjmOOJLDHNRkJcGMiQ5nISIbcTmvy3mYxQm+b2QjArWZctuEw3Li348wsxpVXNrzdakadSpCnfjAqX0MhUqZwFlz57/DCV2HD7+G8e1LdouPb+TQ8+SnImQoffQKyp5z0ECGEEGLSuugrsOkBePmb8PEX0uNN6MY18NCtRsB+97PgLkx1i4QQ57BUBdjrgWql1FSgBbgNuCNFbRFCiNQymSF3mnGruWZ4u9bg6zhixvZeOPA6bP3z8H5mO+TNMMqPHCpDkl9jbLM6RqWJSikKsxwUZjlYOe3klw3GExpfOEprIEJTIEJzMEJbOEJ7OEpXNEZPPE5vPE5/IoEfTQwIA4OHviRaY4uBJZrAHEmgQmH0gJ9YIEbUH4VD4Xf81C7OGQ7BR4ThR2wTYlI471NQ+xK89E2Yeonxple6WXufEbJPWQG3Pyx1NIUQQgh7JrznX+G5r8DWR2DhbakNsZvWGSW+skqM8HqilCYTQkxaKQmwtdYxpdQXgJcAM/B7rfXOVLRFCCHSllKQWWzcpl16+HPB/sND7a5ao872zqcZqsikTJBdeew62w4PY8lsUnicNjxOG7NPkndHEgk6IzHaw1HjFokOPx7xsS+eOOrYDJOJfIuZHLOZbGUiUyncWuFMgCMOtqjGHI0TjiQIROMEI3ECkRiBiPG4bSBKMGpsE2JSMJngxv+FX55vzHC+5xXj6o90oDW8/v8Zl0jXvB9u/d2oLGYrhBBCTAqL74L1v4OnPwNvft9Y1HjOjVC6ZHzD7OYN8OAHjBnXdz9rnIsIIUSKpWoGNlrr54HnU/X5hRBiQnNmw5Tlxm2kaBB69h9dZ/vA6xCPDO/nLh4xY3tEwO0uGvfZHjaTiXKHjXKH7YT7+WLxw8LttnCUjhEf749E6QjHiOrDZ2UrK+S5LJTYbRTZrZTYrVTbjPtDHxfZrBR8cyx7KcQ4yiqB638Gj90Jb/4A3vutVLfIWAPg2S/Dlj/B0o/Btf9prBUghBBCCIPZAh9/HnY9A7uehtX/C+/8FDwVMOcGmHMTlC8b2//VWzbCgzcbta7vfg6ySsfucwkhxGmQMwchhJhMrE4onm/cRorHknW29x5eZ3vrIxDxDu/n8BxehuRQuJ1dMWp1ts+U22JmhsXMDNfxy6IktKY3Gqc9HKF95KzuETO5twwG6I7KjGsxyc25ARZ9xJjtPONKqFiRurZE/PDY3bD/FbjsX+HSr6ZHbU8hhBAi3Tg8sORO4xbsgz3Pw66/wtpfw+qfQ1Z5Msy+EcqXG1dejZbWzUZ47cqFjz0HnrLRe20hhDhLEmALIcS5wGyBvOnGjWuHt2sNg62Hh9pdtUYN3c0PDe9ncUBe9dF1trNKwJGdNmGUSSnybRbybRbmnWC/Y5Ut+dS4tVKIcXLN96F+FTx1L3xmlVFfc7z5u+FPH4S2Lcas8KV3j38bhBBCiInImQOLP2Lcgv1Q+6IRZq//Laz5BWSWwOwbYO5NxroSZzPZpG0rPHCTEaDf/ZyxcKMQQqQRCbCFEOJcppQxu8JTBtPfe/hzgd7Dy5B07YXm9bDjicP3M1nAlW9caujKS97nQ0YBZOQNP5dRYDzvyB7d2SJn4FhlSyTAFpOOIws+cB/84X3w4teN2tjjqbfOWABqsAU+/CeYde3JjxFCCCHE0ZzZxsKOC2+D0KAx2WTX07Dxj7Du10YZwNnXG2VGKi84vTC7fTs8cKPxRvfdz0H2lLHpgxBCnAUJsIUQQhybKxcqVhq3kSIB6NkH3fvA2w6BbmOWZaAH/F3Qssl4HB489usqczLoPiLgHgq68w8PvdMg8BZiwqpYCRf9I7z9Y2NRJs8UY1ZV9pThx54pxiyu0axJ3bYVHroVElG465nUljARQgghJhNHFiz4oHELe2Hfy8ZC7pv/ZMzOziiAWdcZM7MrLzrx+N6+A+6/AawZRtmQnMrx6oUQQpwWCbCFEEKcHpsLShYatxOJhYdD7aGAuzsZeHeBv8d43LbVuA8NHPt1lNkI048VcA8F4SO2OXNSXq9biLRy2dfBbIP2bTDQBK3JN5lGUmZjoaZDgfZhIXfyY7v71D7fgb/Do3cas8U++pxRS18IIYQQo8+eCfNuMW4RvxFm7/orbHsUNv4BnLkw+zpjZvbUS8BsHT62Yxc8cIOxhs7HnoWcqlT1QgghTkoCbCGEEGPDYjcCsVNdvTwWMUK1QzO6h8Lu7sO3tW837kP9x34dZTL+WR8KtZMh9/FCb1euBN5icjNb4bKvHb4t4oeBFhhohIFm6G8y7geaoGkN7GyFxBGLnTpzkgF3xYiAu3w45M4oMEoMPf1ZY/HXjz5+6r//QgghhDg7tgyYe7NxiwRg/6tGmL3jSdj0gDGOz3q/EWa7i4wFG802uPtZyJ2W6tYLIcQJSYAthBAiPVhsxqKQWSWntn88esSs7u4jZnx3G7O8O3eD/y1jJfdjUkaILcS5xJZhLMZaMPPYzyfiRomggWSw3d84HHD31UHdWxDxHn6M2Q7xsHG58m1/MmZgCyGEEGL82Vww5wbjFg3BgdeMMHvXM8MLtbuLjZrXedNT21YhhDgFEmALIYSYmMxWyCw2bqciHoNg77HLmPi7gf8a0+YKMaGYzMMLvB6L1kbZn6GAu8l4bHXBRV8Bq2N82yuEEJOcUmoR8CvAAcSAz2mt1yWf+wZwDxAHvqS1filV7RRpyOowZl7Per9R4u/A3+HgG3DePZA/I9WtE0KIUyIBthBCiHOD2QLuQuN2TBJgC3HKlDJmWDuzoXh+qlsjhBDngh8C39Fav6CUujb58WVKqTnAbcBcoBR4VSk1U2sdT2FbRbqy2KHmGuMmhBATiCnVDRBCCCGEEEIIIcQJaSAr+dgDtCYf3wg8orUOa63rgP3A8hS0TwghhBgzMgNbCCGEEEIIIYRIb/8AvKSU+jHGRLQLktvLgDUj9mtObjuKUupe4F6AioqKMWuoEEIIMdokwBZCCCGEEEIIIVJMKfUqcKzFPb4JXA58RWv9hFLqQ8DvgCsAdYz99bFeX2t9H3AfwLJly465jxBCCJGOJMAWQgghhBBCCCFSTGt9xfGeU0o9AHw5+eFfgN8mHzcDU0bsWs5weREhhBBiUpAa2EIIIYQQQgghRHprBS5NPn4vsC/5+BngNqWUXSk1FagG1qWgfUIIIcSYkRnYQgghhBBCCCFEevsU8FOllAUIkaxlrbXeqZR6DNgFxIDPa63jqWumEEIIMfokwBZCCCGEEEIIIdKY1noVsPQ4z30X+O74tkgIIYQYP1JCRAghhBBCCCGEEEIIIURaUlpPjMWHlVJeYG+q2zFK8oHuVDdilEymvsDk6o/0JT1Npr7A5OpPjdY6M9WNmOhkvE5rk6k/0pf0NJn6ApOrP5OpLzJej4JJNl7D5PoZl76kr8nUH+lLeppMfYFRHLMnUgmRvVrrZaluxGhQSm2QvqSnydQf6Ut6mkx9gcnVH6XUhlS3YZKQ8TpNTab+SF/S02TqC0yu/ky2vqS6DZPEpBmvYfL9jEtf0tNk6o/0JT1Npr7A6I7ZUkJECCGEEEIIIYQQQgghRFqSAFsIIYQQQgghhBBCCCFEWppIAfZ9qW7AKJK+pK/J1B/pS3qaTH2BydWfydSXVJpMX8fJ1BeYXP2RvqSnydQXmFz9kb6II022r+Nk6o/0JX1Npv5IX9LTZOoLjGJ/JswijkIIIYQQQgghhBBCCCHOLRNpBrYQQgghhBBCCCGEEEKIc0jKAmyl1BSl1N+VUruVUjuVUl9Obs9VSr2ilNqXvM9Jbr9SKbVRKbU9ef/eEa+1NLl9v1LqZ0opleZ9Wa6U2pK8bVVK3TxR+zLiuAqllE8p9c/p0pcz6Y9SqkopFRzx/flVuvTnTL43SqkFSqnVyf23K6UcE7EvSqmPjPiebFFKJZRSiyZoX6xKqfuTbd6tlPrGiNeaiL8zNqXUH5Lt3qqUuixd+nOCvnww+XFCKbXsiGO+kWzvXqXU1enSl1Q6g58JGa/TtD8jjku7MfsMvjcyXqdhX1Qaj9dn2J+0HbPPoC8yXk9yZ/Azkbbj9Rn2J23H7NPty4jjZLxOs/4kn5MxO/36IuN16vsz9mO21jolN6AEWJJ8nAnUAnOAHwJfT27/OvCD5OPFQGny8TygZcRrrQPOBxTwAvC+NO+LC7CMOLZzxMcTqi8jjnsC+Avwz+nyfTnD700VsOM4rzWhvjeABdgGLEx+nAeYJ2Jfjjh2PnBwAn9f7gAeST52AfVAVTr05Qz783ngD8nHhcBGwJQO/TlBX2YDNcAbwLIR+88BtgJ2YCpwIF1+Z1J5O4OfCRmv07Q/I45LuzH7DL43Vch4nXZ9OeLYtBqvz/B7k7Zj9hn0RcbrSX47g5+JtB2vz7A/aTtmn25fRhwn43X69UfG7DTsCzJep0N/xnzMHteOnuSL8FfgSmAvUDLiC7P3GPsqoCf5BSgB9ox47nbg1xOoL1OBDow/hBOyL8BNwI+AfyM5uKZjX06lPxxngE3H/pxCX64FHpoMfTli3+8B352ofUm28dnk73wexh/83HTsyyn253+Bj47Y/zVgeTr251BfRnz8BocPrt8AvjHi45cwBtS060s6fB1P8fdVxus06w8TZMw+hb89Vch4nXZ9OWLftB6vT/F7M2HG7FPoi4zX59jtNH9f03q8PoP+pPWYfSp9QcbrdO2PjNlp2BdkvE55f0Z8/AZjNGanRQ1spVQVxjvAa4EirXUbQPK+8BiH3AJs1lqHgTKgecRzzcltKXGqfVFKrVBK7QS2A5/RWseYgH1RSmUAXwO+c8ThadUXOK2fs6lKqc1KqTeVUhcnt6VVf06xLzMBrZR6SSm1SSn11eT2idiXkT4MPJx8PBH78jjgB9qARuDHWute0qwvcMr92QrcqJSyKKWmAkuBKaRZf47oy/GUAU0jPj7U5rTqSyrJeJ2e4zVMrjFbxmsZr8fDZBqzZbyW8fpIk2m8hsk1Zst4nZ7jNciYTZqO2TJep+d4DeM/ZlvOqJWjSCnlxrg05h+01oMnK3milJoL/AC46tCmY+ymR7WRp+h0+qK1XgvMVUrNBu5XSr3AxOzLd4D/0lr7jtgnbfoCp9WfNqBCa92jlFoKPJ38mUub/pxGXyzARcB5QAB4TSm1ERg8xr7p3pdD+68AAlrrHYc2HWO3dO/LciAOlAI5wNtKqVdJo77AafXn9xiXC20AGoB3gRhp1J8j+3KiXY+xTZ9g+zlFxuv0HK9hco3ZMl7LeD0eJtOYLeP1EBmvkybTeA2Ta8yW8To9x2uQMZs0HbNlvE7P8RpSM2anNMBWSlkxOvwnrfWTyc0dSqkSrXWbUqoEo3bVof3LgaeAu7TWB5Kbm4HyES9bDrSOfesPd7p9OURrvVsp5ceoOzYR+7ICuFUp9UMgG0gopULJ41PeFzi9/iRnHYSTjzcqpQ5gvMs6Eb83zcCbWuvu5LHPA0uAh5h4fTnkNobfGYaJ+X25A3hRax0FOpVS7wDLgLdJg77Aaf/OxICvjDj2XWAf0Eca9Oc4fTmeZox3tw851Oa0+DlLJRmv03O8hsk1Zst4LeP1eJhMY7aM10NkvE6aTOM1TK4xW8br9ByvQcZs0nTMlvF66Ni0Gq+TbUrJmJ2yEiLKeLvhd8BurfVPRjz1DHB38vHdGPVUUEplA3/DqJ3yzqGdk1PtvUqplcnXvOvQMePlDPoyVSllST6uxCh0Xj8R+6K1vlhrXaW1rgL+G/ie1vrn6dAXOKPvTYFSypx8PA2oxljMIOX9Od2+YNQWWqCUciV/3i4Fdk3QvqCUMgEfBB45tG2C9qUReK8yZAArMWo/pbwvcEa/M65kP1BKXQnEtNbp/nN2PM8Atyml7Mq4XKsaWJcOfUklGa/Tc7xOtmnSjNkyXst4PR4m05gt47WM10eaTON1sn2TZsyW8To9x+tkm2TMTsMxW8br9Byvk21K3ZitU1fo+yKM6eHbgC3J27UYBddfw3iH4TUgN7n/tzBq2mwZcStMPrcM2IGxmuXPAZXmfbkT2JncbxNw04jXmlB9OeLYf+PwFZJT2pcz/N7ckvzebE1+b65Pl/6cyfcG+GiyPzuAH07wvlwGrDnGa02ovgBujNXEdwK7gH9Jl76cYX+qMBag2A28ClSmS39O0JebMd7xDWMs8PPSiGO+mWzvXkasgpzqvqTydgY/EzJep2l/jjj230ijMfsMvjcyXqdvXy4jDcfrM/w5S9sx+wz6UoWM15P6dgY/E2k7Xp9hf9J2zD7dvhxx7L8h43Xa9Cd5jIzZadYXZLxOh/6M+ZitkgcJIYQQQgghhBBCCCGEEGklZSVEhBBCCCGEEEIIIYQQQogTkQBbCCGEEEIIIYQQQgghRFqSAFsIIYQQQgghhBBCCCFEWpIAWwghhBBCCCGEEEIIIURakgBbCCGEEEIIIYQQQgghRFqSAFsIIYQQQgghhBBCCCFEWpIAWwghhBBCCCGEEEIIIURakgBbCCGEEEIIIYQQQgghRFqSAFsIIYQQQgghhBBCCCFEWpIAWwghhBBCCCGEEEIIIURakgBbCCGEEEIIIYQQQgghRFqSAFsIIYQQQgghhBBCCCFEWpIAW4gJRilVr5QKKqV8SqkOpdQflFLu5HMfU0pppdSHjnHcvyql6pLHNSulHh3/1gshhBDnBqXURUqpd5VSA0qpXqXUO0qp80Y8n5Eck58/3WOFEEIIceaUUt84cvxVSu07zrbbkufY/uS4fej21eQ+f1RK/b8jjqtKHmMZ+94IcW6QAFuIiel6rbUbWAKcB3wruf1uoDd5P0QpdTdwJ3BF8rhlwGvj11whhBDi3KGUygKeA/4HyAXKgO8A4RG73Zr8+CqlVMlpHiuEEEKIM/cWcKFSygyglCoGrMCSI7bNSO4LsFBr7R5x+2EqGi7EuUoCbCEmMK11C/ACME8pVQlcCtwLXK2UKhqx63nAS1rrA8nj2rXW9417g4UQQohzw0wArfXDWuu41jqotX5Za71txD53A78CtgEfOc1jhRBCCHHm1mME1ouSH18C/B3Ye8S2A1rr1vFunBDiaBJgCzGBKaWmANcCm4G7gA1a6yeA3Rx+MrwGuEsp9S9KqWWH3lUWQgghxJioBeJKqfuVUu9TSuWMfFIpVQFcBvwpebvrVI8VQgghxNnRWkeAtRghNcn7t4FVR2x76+ijhRCpIAG2EBPT00qpfowB9k3gexgnv39OPv9nRpQR0Vo/BHwRuDq5f6dS6uvj2WAhhBDiXKG1HgQuAjTwG6BLKfXMiKuj7gK2aa13AQ8Dc5VSi0/xWCGEEEKcvTcZDqsvxgiw3z5i25sj9t+klOofcbt6/JoqhFBa61S3QQhxGpRS9cAntdavjth2IcbgWq61bk+WE6kDlmittxxxvBW4CWPG1/Va65fGqelCCCHEOUkpNQt4CNintb5dKVUL/EZr/aPk869jBNr/cLJjx7HZQgghxKSllHov8ChG6a6dWuvS5DoU+4BZQDcwQ2tdp5TSQLXWev8xXue3QI/W+msjtlUDewCr1joxDt0RYtKTGdhCTA53AwrYopRqx7gcCg6/JBkArXVUa/0XjJqb88aviUIIIcS5SWu9B/gjxpoVFwDVwDeUUu3JcXsFcLtSynKiY8evxUIIIcSktxrwYKwh9Q4MXQXVmtzWqrWuO4XXaQSqjtg2FWiS8FqI0SMBthATnFLKAXwIY5BdNOL2ReAjSimLUupjSqn3K6UylVImpdT7gLkMB91CCCGEGCVKqVlKqX9SSpUnP54C3I6xJsXdwCvAHIbH7HmAC3jfSY4VQgghxCjQWgeBDcA/YpQOOWRVctup1r9+Ani/UuoqpZRZKVUKfAt4ZDTbK8S5TgJsISa+m4Ag8IDWuv3QDfgdYAauAQaBf8V4d7gf+CHwWa31qpS0WAghhJjcvBizqtcqpfwY4fMO4J8w3nT+n5FjdnKG14MY4faJjhVCCCHE6HkTKMQIrQ95O7ntyAB7q1LKN+L23wBa650YbzT/B9CLMbN7LfCdMW67EOcUqYEthBBCCCGEEEIIIYQQIi3JDGwhhBBCCCGEEEIIIYQQaUkCbCGEEEIIIYQQQgghhBBpSQJsIYQQQgghhBBCCCGEEGlJAmwhhBBCCCGEEEIIIYQQacmS6gacqvz8fF1VVZXqZgghhJikNm7c2K21Lkh1OyY6Ga+FEEKMJRmvR4eM10IIIcbaaI7ZEybArqqqYsOGDaluhhBCiElKKdWQ6jZMBjJeCyGEGEsyXo8OGa+FEEKMtdEcs6WEiBBCCCGEEEIIIYQQQoi0JAG2EEIIIYQQQgghhBBCiLQkAbYQQgghhBBCCCGEEEKItCQBthBCCCGEEEIIIYQQQoi0JAG2EEIIIYQQQgghhBBCiLR0ygG2Uur3SqlOpdSOEdtylVKvKKX2Je9zRjz3DaXUfqXUXqXU1SO2L1VKbU8+9zOllBq97gghhBBCCCGEEEIIIYSYLE5nBvYfgWuO2PZ14DWtdTXwWvJjlFJzgNuAucljfqGUMieP+SVwL1CdvB35mkIIIYQQQgghhBBCCCHEqQfYWuu3gN4jNt8I3J98fD9w04jtj2itw1rrOmA/sFwpVQJkaa1Xa6018MCIY4QQQgghTigYbOGddy6jdt93SSRiqW6OEEIIIYQQQogxdrY1sIu01m0AyfvC5PYyoGnEfs3JbWXJx0duF0IIIYQ4oUQiyuZNnyYQaKap6fds2vQxotHBVDdLCCGEEEIIIcQYGqtFHI9V11qfYPuxX0Spe5VSG5RSG7q6ukatcUIIIYSYeGr3fJ9geDfta2bQvKqM/v41rH73Ovz+g6lumhBCCCGEEEKIMXK2AXZHsiwIyfvO5PZmYMqI/cqB1uT28mNsPyat9X1a62Va62UFBQVn2VQhhBBCTFRd3X+npf2P9OzO5d2KXIIrb6D9ncUEfO2sWXM93d1vprqJQgghhBBCCCHGwNkG2M8Adycf3w38dcT225RSdqXUVIzFGtcly4x4lVIrlVIKuGvEMUIIIYQYQ0qpKUqpvyuldiuldiqlvpzcnquUekUptS95nzPimG8opfYrpfYqpa5ORbtDoTa2b/0SwR47a8MVvKt28r9t9/P20iwC+28g2KvZsvUeDuz7BcYSG0IIIYQQQgghJotTDrCVUg8Dq4EapVSzUuoe4PvAlUqpfcCVyY/RWu8EHgN2AS8Cn9dax5Mv9VngtxgLOx4AXhilvgghhBDixGLAP2mtZwMrgc8rpeYAXwde01pXA68lPyb53G3AXOAa4BdKKfN4NjiRiLFpw73EYiH21c7m5dxafnTJj/jOBd9hU/82flq0nnDi4wzWZ1Lf9J9sWvdZEonweDZRCCGEEEIIIcQYspzqjlrr24/z1OXH2f+7wHePsX0DMO9UP++I4073ECGEEEKMkLwS6tDiy16l1G6MxZRvBC5L7nY/8AbwteT2R7TWYaBOKbUfWI7xhva4qN3zA4KRXbRumsaDRfv4zKLPcFXVVQAsyF/AP7/5z3xn4EE+UXQLZbvegDmv8Pbr72flxQ9jt0v5MSGEEEIIIYSY6MZqEcdRt7ezlx/+8os8/ubj9A32pbo5QgghxISmlKoCFgNrgaJkuH0o5C5M7lYGNI04rDm57cjXGpNFl7u736K57ff01ubwx8x+Lpn+Xj678LNDz8/ImcHD1z3MB6o/wO96HudvZaUM1F5AJF7PW3+/nJ6uDaPWFiGEEEIIIYQQqXHKM7BTLRqz8IJ/Nnc2/pFfrFpPyG6jtLKUCxZcwNzpc7FYJkxXhBBCiJRSSrmBJ4B/0FoPGstSHHvXY2w76pIorfV9wH0Ay5YtG5VLpsLhTrZu+QKhfhvPDmaQU5PL9y76HiZ1+HvvTouTf7vg31hRsoLvrP4O+7LM/EPPzbjdz7Jp8+1Ulv4rM+d9fDSaJIQQQgghhBAiBSZM6muym6jrruT+yHV8ccF9dHeU07YrwVM7n+IJ0xO4Cl3Mq5nH8rnLKSgo4AQn40IIIcQ5SyllxQiv/6S1fjK5uUMpVaK1blNKlQCdye3NwJQRh5cDrWPdRq3jbFj7KRKJABt3T+VgdZCH3/szXFbXcY9539T3MTdvLv/y1r/w7z0v8rH4+6jp/ztNlv9Hz2sbWfGen2IyjWv5biGEEEIIIYQQo2DClBCZag8TW5hDo3cKP17/T2QXt7Fi0VOUTa+jP7eXrp4uNry5gV/84hd85wff4TcP/4Yt27bg9/tT3XQhhBAiLSjj3d3fAbu11j8Z8dQzwN3Jx3cDfx2x/TallF0pNRWoBtaNdTv37voxodgO6jeX8Ux5Nz+59CdMyZxy0uMqsip48H0P8tHZH+WPg6/yiJqOv72SgHqB1565mpC/Z6ybLoQQQgghhBBilE2YGdgudx6f5Xm+d9719K1X/OCdb/H5+b9mWtkqSh15FJ33BfZYTGzavQlfm4/Ivggte1vQaDLyMpg7cy5zZs5hypQpUm5ECCHEuepC4E5gu1JqS3LbvwLfBx5TSt0DNAIfBNBa71RKPQbsAmLA57XW8bFsYE/3OzS330ffAQ9/dA/wz+d/jeUly0/5eJvZxteWf43lxcv51jvf4ns6xr90L8OTu4G/v/JeFi78DaVTT/31hBBCCCGEEEKkltJ6VEpVjrlly5bpdY/9hNu21LLaPJ/81T2EteYrleupnPowJmuMaM9Mlpz/QzJLprO6ZTVv7X6L+oP1uAfd5IXzMGFCmRVlFWXMq5nH9OnTyc/Pl3IjQgghUEpt1FovS3U7Jrply5bpDRvObPHESKSHt998LyFvhN/ud7Pwovfz7fO/fcbjdLu/na++9VU2d27mU5kLmOVaj05AYcY/sfjST5/RawohhEgtGa9Hx9mM10IIIcSpGM0xe0IF2Bs2bKDjiS/ynsxbSPRbKdw4QCuar+dHmDnvKSLWd0jETJj9l7Dyih+S4ckjoRPs6N7B3w/+nc17NxPvilMYLCQzlgmAI8NBTXUN1TOqmTZtGi7X8etrCiGEmLzkhHh0nOkJsdYJ3n3rAwTCO3hlXTEdS6fz26t+i9VsPWy/Xn+EH7+8l/fUFHLlnKKTvm4sEeMXW37Bb7f/lhWOUm5xtWJx+NE9V/CeD/wPFpvttNsqhBAidc6F8VopNQV4ACgGEsB9WuufKqVygUeBKqAe+JDWui95zDeAe4A48CWt9Usn+hwSYAshhBhr53SAjbedlx/8LHfN/jbFBwYprfWzy5Tg8/YEH/tMGVs3fhXlqiMyaCfbfgfLLv8q1hEnp62+Vt5oeoNV+1fR2thKvj+folAR1oQVZVIsX7Gc91z6HhwOR+o6K4QQYtydCyfE4+FMT4h3b/8hrV2/pnZjEU9VZvDw9Y+Q58w7bJ/aDi/33L+ept4gALcvn8L/uW4OLtvJS4Otbl3NN97+BomIl3/xOHC52/E1VHLxNX8kt6TitNsrhBAiNc6F8Tq5oHKJ1nqTUioT2AjcBHwM6NVaf18p9XUgR2v9NaXUHOBhYDlQCrwKzDxR2S8JsIUQQoy10RyzJ8wijkMyi7lq8ZXc0/wE7dOz6Mq3sDxu4n/DJn70s2Yuu+pFppb8B2arg4D9D7z05AXsWP0Ih4L6Uncpd8y+g19c/wv+dO+f+PCHPwzvgfWV66lz1bFm9Rq+/5Pv8/ybzxOPj2mZTyGEEEIA3d2raem4j966LB7xxPjZFf9zVHj96q4Obv7fdwhHE/zlM+fzmUun88j6Jt7/s1Vsbeo/6ec4v/R8Hr/hcWYVLeWbfQM0eitxVzbw9us3sHvNCSepCSGEEONKa92mtd6UfOwFdgNlwI3A/cnd7scItUluf0RrHdZa1wH7McJsIYQQYlKYeAE2wPJ7+T/+t5gdbGRgRSH77fDe/5+9+w6volgDOPzbU9N7TyAhBAihQ+i996bSEVABEQsoduyKYsF2VRQEFQURFCkKCEjvPfRAKum9l1Pn/pHQQdoJWOa9z3l2z+7s7LdHr5P9dnbGrGKJUDPppdX4B/ajW+89eDo8iM6tmPSS6fy+sDvJ0XsvqcZR60j34O7MaD+DlWNXMnHkRMoiy8hWZbN3015e+uAlvtvyHaWm0rt0oZIkSZL072Yy5XH4wCSMRRq+z1fxQo83CPcIP79fCMHszbFM+H4/NX2cWPlYO5qHePB873AWjW9FucnCvbN38tnGM1isf/1WmZe9F192/5Inmk7lk4JcNuT54ehbTHzaFDb+8DYWs6mqL1eSJEmSboqiKCFAE2AP4CuESIOKJDfgU1ksEEi66LDkym2SJEmS9K/wz0xgqzXY9XmPL4+9hNFixr1XMHtUVgYYVWxQqRn91kYKUgto3Go6HTvtwEHphJ1fPCfiRvHHovvJz0y9skqVmjYBbXiv33u8PuV1fFr7gAXiN8XzzEfP8Pr61zmefZx/ypArkiRJkvR3J4Rg97YHEapi1p1ypVvnsfQK6XV+f7nJwlNLonh37Sn6NQxgycOt8XO9MMRX65qerJ3Sgd4N/Plg3WmGz9lFUu5fP3RWKSrGNxjPN72+Yb9w4PMse9QOKsxe3/Drp2MpzMqssuuVJEmSpJuhKIoT8AswVQhR+FdFr7LtihtXRVEmKoqyX1GU/VlZWbYKU5IkSZKq3D8zgQ1QvSV1arfn9ZhPOW4y0rp/GOs1VoYbVRxWFIZ+uofkIwno7Txp03UezZqsQEMoGr+d7NzRlS3LpmMoLblq1Z72nkzuOZk3nnmDiNYReBm9sOywMPO7mYxYPoLFpxZTaPyrvx8kSZIkSbqek0dnYeQI0Uc9sDZvwWNNHju/L7OwnOFzdvProRSe7lGbT4c3xk6rvqIOVwctnw5vzEfDGnEqrYg+n2zj10PJ133g3MSnCT/3/5kg3068laVQIhxwa7SH378bQsz+PTa/VkmSJEm6GYqiaKlIXi8UQiyr3JxROT72uXGyzz11TQaqXXR4EHBFry0hxBwhRKQQItLb27vqgpckSZIkG/vnTeJ4sZJsxP+a8WCDd9jgWI9RZj3L1scyyqzmR40FD+DrXtWJ6Nzg/CFn41YQfeoNVHb5lKS7EuT7GI07j0WluvKm+PxpSkpY9+c6og5FYVFZOO56nGT3ZLrX6M69te6liU8TFOVqD70lSZKkf4r/wqRQd8KNTgqVnbmHw0dGkZfkyFL7EL65dxGOWkcAjiYXMGHBfgrLTXw4tDG96vvd0LmTckt5aslh9iXk0a+hPzMGNcDVQfuXxwghWHRqEZ8e+IBxrhZqO5aQe9oVH5fJtB/2ACr1tf8+kCRJku68/0J7rVTcXH5HxYSNUy/a/j6Qc9Ekjh5CiGcVRakHLOLCJI5/ArXkJI6SJEnS3WTLNvufncAG2DeP3D9eo2v7X7HXO9Axw8LSnWcZr+hYJEyoEHzRzJ02Q9ucP8RqNXHy8MekZs1DUZsoSapO/SavUrNxp7+MITMzk3Xr1hETEwMOcNDtIPG6eGq41eDeWvfSv2Z/POw8bHzlkiRJ0p3wX7ghvhNu5IbYaCxgy58dMBkNfJvizazRi6jmUtFx7LcjqTy9NApPRz1zx0QSEeBy/jiz2cy+ffvYvWs31atVo3vPHri4uFxSt8Uq+HJLLB+tP42Ps55ZQxvTuualE0JezfGc4zyz5WkiRAK93Y2UZNhhSOjG4Kc/QKP96yS4JEmSdOf8F9prRVHaAduAo4C1cvOLVIyDvQSoDpwFhgghciuPmQ48CJipGHJkzV+dQyawJUmSpKomE9gXs1rg667ssLpxX53XGOrnjuVQDquj0piic2CRsYxCBO/V0NN/UrdLDjUaczm060WKTOuxmlQY0htSLXgwYU364OR+7UR0TEwMf/zxB1lZWTj6OnLS6yT7SvehUWnoUq0L99a+l1b+rVAp/9wRWiRJkv5r/gs3xHfC9W6IhRBs23APRo7wW5QXw0d9Qiv/Vlitgo83nObTjTE0D3Fn9uhmeDnpzx8THR3NujV/kFuQh7fVhVxVMWqNmk5dOtOyZUvUl/WUjkrKZ+pPh0nIKWFih1Cmda+DTvPX7XKxsZg3dr9BcvoqxnqYsZaq8FRepkX/0bf/w0iSJEk2Idtr25AJbEmSJKmqyQT25VIOwtwuzGz7OR9r6vFZeHVWrD7DzjPZPO/mxqK8Qs4qVl5yhwee63vF4QUFpzi05yksmmgAjMUazIV+uDhHElJnINVrt0NRXXrTa7FYOHjwIJs2baK0tJSaETVJ8Uvht9TfKDAUEOgUyOCwwQwKG4Svo6/Nfw9JkiTJtuQNsW1c74b4+KEPSM+bzfGjbnh0e5LR9UZTajTz1E9RrD2eztDIIN4cVB+9piIhnZaWxtrf15CYfBY3qwOtVOHUbdmAtMMJ7Cg9RpI6B29PL/r060uNGjUuOVep0cybv53kx71nqRfgwifDGxPm4/yX8Qsh+DXmV74/+CaPehWSc8KH/mPX4uDievs/jiRJknTbZHttGzKBLUmSJFU1mcC+mt+exHTwewb12MAZk4pVDcN49odDnEwt5FV/L348m8MxlYXJOjPTXumHWnPlmJYlJXGcjV1FRuomTESj0hkBMBbr0ZhD8fLuQM0GQ3Bxu3CDXFZWxrZt29i9ezdqtZrWbVtTFljGr3G/sid9DypFRYfADtxT6x7aB7VHo9LY/LeRJEmSbp+8IbaNv2qvs9L3cvjoSPLT7DkW1J/XOs8gsQzFFAABAABJREFUJb+MCQsOEJ1eyPS+ETzYNgRFUSgqKuLPPzZw+FgUdkJLU2tNIls3x61TdVQOWqxGCwXrEjixK4rd2jMUUUb9+vXp0ePKYUXWHU/n+WVHKTGYealvXUa3Cr7u3BUnck6wZsu9NHQyoUqbSNexz9rsN5IkSZJunWyvbUMmsCVJkqSqJhPYV1OaC59FkujbnK41plPX0Z55daoz/KtdZBUYeCsskB+PJrNbbWEIZt55tTcae901qxNCkJN5kPiTy8jL3w36JNT6ijkwLKXO2GnrE1i9B0GhfdDrvcjNzWX9+vWcPHkSFxcXunXrhmuwK8tjl7M8ZjnZZdn42PswMGwgQ2oPwd/J39Y/kSRJknQb5A2xbVyrvTYZC9m4vh1Wi4HfCxvw4YhFHEkqZtIPBzCYrXw2sikda3tjNBrZuW0HO3bswGKxUM9SjbYNW+LVMwyNq/6Keo0pxWT9fJL9WSc4ok1ErdHQqXOnK4YVySwq55mlR9hyOovOdbx5775GeDtfWd/FPt37GuEF31MQ40aXAcvxDKx227+PJEmSdHtke20bMoEtSZIkVTWZwL6WQz/AikdZ1vsHJpdW46kQX0a5u3Hf7J2YLIJ36wezaHscf2rMdLZa+OzZTjh6ufx1nZUsZiPxJ9aSHPc7xeVR6NxyUOsq59MweuHi3Jyg0D6UllZnw4YdpKWlERgYSK9evfAL9GNr8laWnVnG9pTtqBU1I8JHMLHhRFz18pVkSZKkvwN5Q2wbV2uvhRBsXDsQoT3O+lOBPPPQMjYeL2P6r0cJcnfg67GR1PB04GjUUTasXUeRoYQQizcdajSnWr/6aH0c/vKcwiIo3pFC8vqT7FRFk6Rk4+3tTZ8+fS4ZVkQIwYJdiby9+iROeg3v3deQrnWvPcxXsbGYT1e3o4VzESUnBjDw8Y9u78eRJEmSbptsr21DJrAlSZKkqiYT2NditcI3vSAnhsd7/8Ev2SUsaxKGp0Ew5KtduNprea9pKD/+Fs1yvYkmVitfT4zEM+zme0PnZ6USc/hXMtI2Y1ZO4+BTgkorEAI0VENR1yf6jIqMdGfCwxvTvXt33NzcSC1OZXbUbFbErMBJ68RDDR5iVN1R2GnsbvGXkSRJkmxB3hDbxtXa66i975Nd/CUnTrrTcci3rNyr4uvt8bQL8+LzkU3Jz0xlzfLfSc/PxMvqTHvvJoQPjERf/cYeMp9jzi0nb3kMp2NOs9suhiJr6VWHFTmdUcSUxYc5mVbIqJbVealvBPa6K4cWA1gRvQht4iuUpzgS2fx7qtdvePM/iiRJkmQzsr22DZnAliRJkqqaTGD/lfSj8FUHipuNp7vXAxitgj+b1yEurYhRc/cQ4uXIey1r8uPiYyzRmwgRMO++2gS3qHXLsZmNRs4eP0j8iVXk5e9G65qJg28ZKrVACBVFRZ4UFPgTFNiFNm3G4ODgypm8M3x88GO2Jm/F18GXRxs/yoCaA1Crrn4DLUmSJFUteUNsG5e31xkpezhyfBT5mXYoDd5i1YEAtpzOYlybECa38ePPFWuITorBQehp5RhB8wHtsKvjccUY1cJqpfz4CYq3baXs8GFcevbC9Z7BV5YTgrLDWWSvOs0hUyxHNImotRo6duxIq1atzg8rYjBbmLXuNHO2xhHq7cgnw5rQIOjKt6Kswsr7a7sQqU8ic09Lhj33PSrZVkuSJN01sr22DZnAliRJkqqaTGBfz5rnYc+XHLr/T/onqenp5crX9ULYcjqL8d/tp2mwO2+3CmPh/MMstjPiCszpGEDDPk1uO04hBLkpycQe2kFK3DrKLCdwDCjB0asMRSWwWlXodfWpGTYcH+8eHM6J4cP9H3Is5xhhbmE82exJ2ge2v+7kUpIkSZJtyRti27i4vTYZi1i/tg2KysB+0xg2x7UjMaeUl/vUwTfjJHuPHkQloJGuJu17dsK1aQCK6kL7Z87Lo2T7doq3baNk+w4submgKGh8fDBnZODSrx9+r72G2snxijgsJSYKfo8j/VACexxiSbRkXnVYkZ0x2Ty1JIrsYgNP9ajNwx1qolZd2gYfzTxA7MHhqPL01PD7iAadu1fRrydJkiRdj2yvbUMmsCVJkqSqJhPY11NeAJ81B5cAPuuxmLfi0/mgTjVGB3iy4nAKUxYfpkeELy+3DGXR7EP8qC9HAJ/Wc6HjmPY2jdtQWkLikUPEHd5JauZONEHlePikYe9QDKhwc2mJn38/jpVr+TRqPmeLztLcrzlPNXuK+l71bRqLJEmSdG3yhtg2Lm6vf1/eC73zGVZE92Rr1iBUCjweoSfrxE7KrAZqKwF07tAZv/Y1UTQqhMVC+dGjFG/dRvH27ZQfPQpCoHZ3x7FdO5w6tMexbVvUrq7kzJlD1v8+Q1etGoEffYhdRMRV4yk/k0ferzHE5SexxzGWQlPJFcOK5Jcamf7rMX4/mkaLGh58OLQRQe6Xjrv9xZaR1LHsIXl7bUY++ytaOzn0lyRJ0t0g22vbkAlsSZIkqarJBPaNOLIUlo3H2udDhus6sq+ghHWRdajlaMc3O+J5fdUJhkVWY1rzEBZ9coCftGXkIfhjRATBjWtcv/5bIKxWju7dw7K1a/FVn8XV4ThuoUXoXY2ACleXFpxV/JgTu4eUsgJ6hvRkSpMpVHOpViXxSJIkSRfIG2LbONdeb9/6KuWmH/jpUG825vammrOWrtZoLKYc/IQ7XZu0p2bvRliL8ijevp2Srdso2bEDS0EBqFTYN2yIY/t2OHXogF29eigq1RXnKt23j5RpT2PJy8Pn+edwHznyqm8wWY0Wiv48S962RI7aJXOYeNQaNR07dqRly5ZoNBqEECw7mMKrK4+jKPDWoPoMbBx4vo6s0nQ2be6IY5kKN/Eibe+7v0p/R0mSJOnqZHttGzKBLUmSJFW1v10CW1GUJ4HxgACOAg8ADsBPQAiQAAwVQuRVln8BeAiwAE8IIf643jluuoEVAr7rD+lHyXh4L52PZxKg1/F7s1roVSpmrYvmfxtjeKRTTSY0qsaPs/YyS1PKCDsrM17rfzOXf9O+//570tLSGDGgH7H7d5FwYi06z0Tcahahd6lIZhergvgjN4eoUoW+tYbxcMOH8bT3rNK4JEmS/svkDbFtREZGip+XzuLEqQksODaM3TmtqK0rp7lyHE/0tK/ZnPCazpTt20HJ1m2UnzgBgNrLC6d27XBs3w7HNm3QuLvf0PnMubmkvvACJVu24ty9O/4z3kLtcvXJH42pxeQtO0NOShZ73eJJKEvDy8uLPn36EBoaCkBSbilTfzrMgcQ8BjcJ5P37GqJRVyTPlxx4Ec+Cn0jeHsi9j/2Kk4dslyVJku402V7bhkxgS5IkSVXtb5XAVhQlENgORAghyhRFWQKsBiKAXCHETEVRngfchRDPKYoSAfwItAACgA1AbSGE5a/Oc0sNbFY0zG4LDYexrt3bjDkaz8Qgb96oFYgQghd/PcaPe8/yUt+69Av05Okvd3NIMbPrte642Otu+re4UWfPnmX+/Pn06NGDNm3aIIQg7Uw00bu3knjyD3ReSbjXLELnYsQqFM6UqzhutKdRyDhG138YB63D9U8iSZIk3RR5Q2wbTZs2Ec88r2F+7GjOFITRUJ1GS3U6kXaBhGeeoXzHBqxFRaBWY9+4MU7t2+PUoT368PCr9rK+EcJqJffb78j88EO0vr4EfjgL+0aNrlFWULwzlcJ1CSSKLPY4xlJQXkS9evXo0aMHrq6umC1WPv3zDJ9ujOGF3uE83LEmAEaLkaXrmuJhNiFSHqTPpOdu+XeSJEmSbo1sr21DJrAlSZKkqmbLNltji0oq67FXFMVERc/rVOAFoFPl/u+AzcBzwEBgsRDCAMQrihJDRTJ7l41iucC7DrR+FHZ8TI+m9/NgYBBzkrPo6OFMV08X3hpUn7wSI2/9fhL3IQ3paFGxXQvfLzvIo6Na2Tycc6pXr06NGjXYuXMnzZs3R6vVElA7nIDa4QjreFJPn+LUrq2c3f0Heu8UaoQVUce1GEvOZ/yw4Wt8ffvSo95z2Otlzy9JkiTp7yU7N4FZJz8mr8ydDpo4+pqM1N28HW1OIkYfH5x79sCpXXsc27S+Zk/pm6WoVHg++AAOzZqS8uRTJIwajc9TT+ExbuwVSXFFpeDcLhD7ep7ol8cQEO3GMY80Dp+K5vTp03Ts2JFWrVrxZPfanEwv4sP1p+ke4UuotxM6tY7qtaZhPPsWqXm/kpkwBJ+QUJtcgyRJkiRJkiRJknR1thpCZAowAygD1gkhRimKki+EcLuoTJ4Qwl1RlM+A3UKIHyq3zwPWCCF+vkq9E4GJANWrV2+WmJh488EZS+CzFmDvRvn4TfQ+FEem0cym5nXw0WspN1l44Jt97E3IZaavBwtT80lVmdnxVh+06lvrCXYj4uPj+e677+jduzctW7a8ahmr1ULKqRNE79rG2dPrsPNNxS20EL2LCasAxb4edUPux8e7B1qta5XFKkmS9F8ge3TZhp1/mAh78G2GWNLpc2gHPr52FZMvtm+Pvnbtq45RbUuWggLSXnqZovXrcezYgYCZM685HIkQgrIj2eSviqWgtJB9PknE5SedH1bE0SuA7h9uoY6fMz9NbI1KVRH7wvWt8bJkk7O/NyOmf1Ll1yRJkiRdINtr25A9sCVJkqSqZss2+7YztIqiuFPRq7oGFUOCOCqKMvqvDrnKtqtm0YUQc4QQkUKISG9v71sLUOcIvd6BjGPYHZjHl/VCKLFYeOLkWaxCYKdVM2dMM+r6O/O/tAJ6mbVkCoVVh1Ju7Xw3KCQkhOrVq7N9+3bMZvNVy6hUaqpFNKDbQ5MZ984vtOsxF23Wo8SsrkNWlCfGrNOcOvU8W7Y259ChB0hN+xmTqaBK45YkSZKkvyJUKoYoCn2CI2i4eC7BC77Dc/x47OrUuSOJXrWrK4GffoLvyy9RunMX8YMGU7pv31XLKoqCQyNv/J5qhm/TELqk16aXPhJzuYkFCxawbd1vvNC7DvsS8vhhz4WH6C0bvYtKa0Vx3EX8IXnzL0mSJEmSJEmSVJVs0cW4GxAvhMgSQpiAZUAbIENRFH+AymVmZflkoNpFxwdRMeRI1anbH8K6wcYZ1LHm83pYIJvzivgqKQsAZzstrw+oz1lhxs9iJQQVX645gS16p1+Loih07NiRoqIiDh8+fN3yKpWa6vUb0X3C40x4dxUtu3zB2eheHF8ZTGaUK2lnd3Hy5HNs3dacQ4cfrExm51dZ/JIkSZJ0VQ4q1jbwZ0+UikXvHOHwhrOYDH85zYXNKYqCx6hRhPy0GMVOT+LYcWTPno2wXD0OlYMWj/tq4zWhASFaPwZlN6GFV32OHTuGLvkg7Wt5MXPNKZJySwEI8+lAgS4Cr/q5/PnTh1ivUa8kSZIkSZIkSZJ0+2yRwD4LtFIUxUGp6FrVFTgJrATGVpYZC6yoXF8JDFcURa8oSg2gFrDXBnFcm6JA7/fAYoQ/pjMmwJPeXq68HZdGVFHFzWijIFec7TTEeNkxxKrjdImZHTE5VRpWaGgoQUFBbNu2DctN3Pyq1GpqNo5k0rMf88A7y8kLGcDqbYEcX1GdjMOupF+UzD5wcDRJyd9TbkivwiuRJEmSpAr+SjnxPk6sqV+InZcdO36OYcH0nRxYm4Cx7OpvHFUVu4gIavyyDJfevcn65FPOjh+POSvr2uVruuE7pSnuXUJomOpHc1Utjh49wqDAUhTgxV+Pnn+43bXJJ1gVBbeQaA5vWHOHrkiSJEmSJEmSJOm/57YT2EKIPcDPwEHgaGWdc4CZQHdFUc4A3Su/I4Q4DiwBTgBrgUeFEFXfdcmzJrSbCsd+RonfyqzwanjpNDxyPJESswWNWkWrmp5sFSYiTCo8BHy16UyVhqQoCh06dKCgoICoqKhbqsPZ3oVH73mZN95aSsnQbiyyaFi9xZ/oVaFkHHInI/Egp0+/xo4dbdm7dxAJiV9RWhpv4yuRJEmSpApezh60KDrJ6drefKoqoPOj9fEJdmH38jgWTN/J3lVxlJeY7lg8aidHAj54H/+33qTs0GHiBg2mZOfOa5ZXtCpce4Tg83gTGosa1NYGcXT3Fu5v5Mq2M9ksPZAMgLtTKIpbV9xrF7Bn7RcYSkvu1CVJkiRJkiRJkiT9p9hklkIhxKtCiHAhRH0hxP1CCIMQIkcI0VUIUatymXtR+RlCiJpCiDpCiDvXbandk+AWDKufxkOx8lnd6sSXGXgppmK863ZhXhwpLiNfJRii6NkWl8vJtMIqDalWrVr4+/vfdC/sy3nYefBC2+nMfngJdgOa8VWDdPJrDECTOYn43xuRutebjPgYYmPfY9fubuzc2Z3Y2FkUFh6t0qFSJEmSpP8YRcUHARrK1TpEYDHPbo6m88R6DHkhkoBabuz7PYEF03ey69dYSguNdyYkRcHtvvsIWfITanc3zj40nsyPP0ZcYw4KAJ2/I54j69K2uBYBdl4Yj2+gkb8jb/12gozCcgA6NXwXk1Dj2zCZrT//cEeuRZIkSZIkSZIk6b/GJgnsfwytPfR5H7JPw+7PaevuzJRgX35My2VFZh5tw7xAgbwaLvSwaLEXgrlb46o0pHNjYefl5XHs2LHbrq+aSzXe7/g+LYJascyymS4TJvPg+0vpPPBrnMqnkLimJcnbfcmMzSA+YTb79g9i+/Z2nD79Jnl5e7gTneElSZKkf7faTe/libwNJPgFc9pYzOSFB3EPcqLPIw0Z/nILQup7cnBdIt9P38n2JWcoyTfckbjsatemxpIluN4zmJwvvyJx7DhM6dceYsuuljsevWvSJb8uzjoH6pdFYTBbeWn5MYQQ6HRueAeNxTW4hNionyjIzLgj1yFJkiRJkiRJkvRf8t9KYAPU7gl1+sKW9yA/iWkhfjR1ceCZ6CR0Tlr8XOw4phcUmAT90LEyKpW0grIqDalOnTr4+vqydetWrFarTeocHj6cjNIMNidtRqVSE1S3Pp3HTuCB9xbS6/7v8dY9Q+r6Tpzd7E/WmWLOJi7g4KGRbNnanBMnnyc7eyMWy51JKEiSJEn/MioVT0R2Iqw0EYeGzmyJzeb5Xyre+PEMdKLH+PqMfLUlYc18OLI5mQUv7WTLomgKc6q2vQVQOTgQMGMGAe+9S/nJk8QPGkzR5s3XLO/UPhD3xoF0L6yHk6WUVk45rD+RwW9H0gBoUmsaBuGAf/MM1iz4X5XHL0mSJEn/FHtX/MzKWW+TeOSwfOtXkiRJui3/vQQ2QO+ZIAT88QJalcLsiGCsAh49eZY2YZ5sSs0jXw9DFT3CKvhmR0KVhnNuLOycnBxOnDhhkzo7BnXE39GfxacWX3Eu3xo1aTvsfsbM/JqBk34iyPNlsrb1IX5dIFmnBClnfyXqyAS2bm3G0aOPkZ6xCrO5yCZxSZIkSf8N+pDWvG/cS7rGieYdvfjlYDKz1p0+v9/dz5Gu4yIY9Xorwlv7c2JHKgtf3s3GBSfJzyyt8vhcBwygxi8/o/HzI3nSI2S8+x7CeOWQJoqi4H5PLbx9fehqrE9IeSyB9mZeXXmcnGIDarUdtcOewcmnnOLsraSePlXlsUuSJEnS31322QS2L15A7IE9/DzjJb6dNpmo9asxlZff7dAkSZKkf6D/ZgLbrTp0eBpOroIzGwi21/NqWAD7CksIqOFKXpkJXUMf7M3Q0Wpl0Z6zFJZX7YRTdevWxdvbmy1bttikF7ZGpWFonaHsSd9DbH7sNct5BATSYuB9jHjjE4Y9u4ywkDfJ3zeYuDXBZB7Xk5q0gePHp7J1aySHDj9ASspiDMbs245PkiRJ+vdr3Wkio9NXs1OjoXurID7bFMMPuxMvKePqbU/nUeGMfrM19ToGcnpfBote3c36+cfJTa3aiRH1NWoQ8tNi3EeOIPebb0gYfT/G5OQryql0ajzHRBCk8aKjXX0iLacoKDXy+qqKh841q4/CrPbBr2UmK+a9L3uZSZIkSf9pQgg2fTcXSw0/lE716PbwE2j1ejZ8/QVfPTKWzQu+Jj/j2kN4SZIkSdLl/psJbIA2j4NnGKx+GkzlDPB2Q6NArqMagCRnhSSTYKTKmWKDmcV7z1ZpOCqVivbt25OVlcWpU7bpvXVPrXvQqXT8eOrHGyrv7OFFk579GPLSTEa/uoL6Dd6j7PhI4n4LIyPKhfTEPZyKns727a3Yt38oZ8/Oo6ysan8XSZIk6R/MPZiXfBQ8TXkkelvoFO7DKyuOse74lTetzh52dBhWm/vfak2jbtWJi8rmxzf3sParo2QlVd1bQCq9Hr9XXiHw448xxsURP/geCv9Yd0U5jbsdHiPrUrvQh87u1WigTmFlVCobTmSgKGoa130De1cT9vanOL5zU5XFK0mSJEl/dzH7dxN/Npa6bfcSHvwrP5/5jT4vv8LwN94npHEzDq1dxbwpE1j+/pskHpXDi0iSJEnX999NYGv00OcDyIuHHZ/gqtXQ2s2JbUUl1PF1ZmdWIcWOauoIFc00gvnbEzCabTM+9bXUr18fDw8Ptm7dapNG3MPOg141erEqdhXFxuKbOtbeyZl6Hbsy6JlXGPf2CiLbfII4+wBxqyJI2+9JRsIJzsS8zc5dndm+oyMnT00nI3M1JlPebcctSZIk/Xu4tXuct5IXcMSoolnHIBoEuvLE4kMcPHv19sLRVU/be8MYM6M1zXoFk3QylyUz9vH751FkxBdWWZwuvXpS49dl6EJCSJkyhfQ33sRquHQuCLuabrj2DaVpRhADvdW4K6U89/NhCspM+Hh3Q21fB5/mWWxY9BlmU9W+uSVJkiRJf0dmo5HN38/Du1UJ9vZFaNQWGngcZuqCKWS5G+g35VnGfzaPVoOHknr6FD+/9RLfPf0oUevXyOFFJEmSpGv67yawAWp2hnqDYfuHkBtPTy9XzpQaqF/Lk73xuQS1CiDTLBhWrpBeWM5vR1KrNByVSkWHDh1IT0/n9OnT1z/gBowIH0GpuZSVsStvuQ6tnR21Wrahz+PP8OD7y2jXczb6vEnE/9aU5O2+ZJ7KJznxZ44de5yt25qze3c/YmLeJSd3OxaL/CNEkiTp70JRlPmKomQqinLsom2vKYqSoijK4cpPn4v2vaAoSoyiKNGKovS8pZPqnRjQpAfdc3byYUIab4xohK+LHQ99u4+4rGs/XLV30tFqYE3GvN2GFv1rkBZXwM/v7mflJ4dIPZN/S6Fcj65aNUIW/oDHAw+Qt2gRCSNGYMm/9FxObQJwaupLp7RQ+nnkkVtq4uVfDqIoCk0i3kJvb8GrWiqbV/xQJTFKkiRJ0t/ZgdUrKLIvIyj4FEpqEzyih+HpmUIrIXh22bN8d/w7nNw9aTvsfiZ+/g29Jj+JWqtlw9ef89XksWz+fh4FmXJ4EUmSJOlS/+0ENkDPt0GlgTXP0d3DGQDFzw6D2UqZr55ko5XWGmfCnLXM2RpX5a83NWjQADc3N7Zs2WKTc9X3qk8Drwb8eOpHm9Sn1mgJadSU7hMe46EPfqLnqO8I8nqZgv33EftbLdL2epERk0hCwlwOHx7Lli2NOXBgJAkJX1BQGIUQltuOQZIkSbpl3wK9rrL9IyFE48rPagBFUSKA4UC9ymO+UBRFfSsnVRqP4J2CP1AsRt5NSuPbcc1RKQpjv9lLZtFfP+jUO2hp3rcGY2a0ofU9NclOLubXWQfZ+tPpKmmTFZ0O3+eeJeiLzzGeiSF56pOIi3pTK4qC++BaOAS5Mbo4jCZ2Oaw8ls36I0m4ujbF2b0D3k2yObp2KaWFBTaPT5IkSZL+ropzc9i58heqt0jAatYx+/QYXkhphSa3LmFhB+hYVJ/Zu2bzxKYnKDAUoNHpqNexK6Pf+bhieJGGTTm4egVfPzGB5e+/xdljUXJ4EUmSJAmQCWxwCYBOL8CZPwg++yd1He04o1jRqBT25RVT7q7FahUMM5RxKr2IbWeqdgJDtVpN+/btSU1NJTb22pMv3owR4SNIKExgd9pum9R3jqJS4VezFi0G3sd902fw4Lur6DTga7z1z5O9YwBxa4PJiHImLS6K2LhZ7N9/D5s3NyEqahLJyT9QWhov/yCRJEm6g4QQW4HcGyw+EFgshDAIIeKBGKDFLZ1YpSKo23O8ED+HjXmlRFlNzB/XnOwiIw9+u49ig/m6VejsNDTtEcz9M9rQsHMQRzcls3lRNMJaNe2Ic5cu+L35BqW7d5P+5luXtFeKVoXn6AictQ68YBeAi1LO00sOkl9cRr3aL6HWgF+DTH5d8FGVxCZJkiRJf0fbfvwOu3oW3N3T2R01iX1GNdFW2HZ4AhqVioha+xhQ2J/dSbsZumooR7OOAhUPhwPr1KXf1OeY8Nl8Wg4aSmr0CZa+OZ3vnn6UIxvWyuFFJEmS/uNkAhug5cPgEwFrnqOnhxP7i0qoH+LGjphsarYKItUMnUvt8XHWM3dbXJWH06hRI1xcXGzWC7tHSA887DxueDLHW6XRagmKqE+bIaMY/uqHjH3jd1p1/hJnwzTSN3cnYUMgWSe0pCZuIfr0q+za3Y1tW1tz4sSzpKevxGDIqtL4JEmSpGt6TFGUI5VDjLhXbgsEki4qk1y57QqKokxUFGW/oij7s7Ku8d/y4DY86CZoVHSal06fJdjPic9HNeFkWhGTFx7EZLmxeSa0OjXthtaiaa9gTmxLZeP3J7FWURLbbdAgPCdMIH/JEvIWLLhkn8ZNj+eouvgX2jPN25ECs5rH5qzDwSEUv4D78KqXR3bUTjJTEqokNkmSJEn6O0k7E83xo3sIiThJemYEi3KCaZEbS9uMEywwqbEeG4uLSypeTscYz3gQMGbtGBaeXHjJPa+zpxftht/PxC++pecjU1FrtKyf+xlzJo9jyw/zKcjMuItXKUmSJN0tMoENoNZCp+ehIIkelmQsArxrunE0pQCfCHeSjVbsNPaM8NSw7Uw2x1Or9pVgjUZDu3btSEpKIj4+/rbr06v13FPrHrYkbyG1uGrH8b6Yzs6eGo2b0XH0g4x640tGvvAbTSL/hzbrcVL/bEvSVj+yTpeTnLic4yeeZPuOVuzY0Y3Tp98kO3sTZvPNTTwpSZIk3ZLZQE2gMZAGzKrcrlyl7FUzxUKIOUKISCFEpLe39zVPpO72GrNiPiTPZOHN2FS6hPsyY1B9tp7O4oVlR2/4oa2iKLQaGErzfjU4tSudDd+cwHqDCfCb5f3kVJy7dyPj3fco3rLlkn36Gq64DQila6Yjnd0E2zPVfL18I2GhU1FUWvybZ7Lkq3eqJC5JkiRJ+rsQVisbvp2Db+t81NpyFh8bi8Zi5O176vNCQ0fUFiNz0xriXB5JzbDD5KVFM9VtKu0C2jFz70ye2vwUhcZLJ2rW6HTU79SN0TM/Zvjr71G9YRMO/L6ceU9MYMUHMzh77Ih8m1eSJOk/RCawzwlpD0Dj9O146zTkO2sQAo4XlWLxtsNgMdP7bC6OOjVfb7v9pPL1NGnSBCcnJ7Zu3WqT+obWHgrAkuglNqnvVtg7u1CrZRu6PTSZ+2cs4N4nVlA3/COs8ZNI2tCE1N3eZMVmkpj4HVFHxrNlaxP27L6HuLhPyMnZSnl5qvwjRZIkycaEEBlCCIsQwgrM5cIwIclAtYuKBgG39xTUPZj6jfowKWkxC9Ny2ZlXzPAW1ZnStRY/H0jmw/U3PoGxoii06FeDlgNDObMvg3XzTmCpgiS2olIR8O672IWHk/LUNMovm2TZsaU/DpG+PJ/vgrtG8L+9eRw/mUlI9YfwDCtClR3LsYPbbR6XJEmSJP1dnNi2iRxS8A+KZdvRMRw12zNVl0xYry7UHX8/Y+M3swcLx3eORqOyo2mzKPbs2MXDvg/zdOTTbE7azLBVwziRc+KKuhVFITA8gv6Vw4u0GHQfyaeOs/TNF1nwzGMc+XMtJoMcXkSSJOnfTiawz3HwAJ8IVGd30MPThUPl5Tjaadgek02tlv4kmxQcze4MaxzAqqhUUvPLqjQcrVZLu3btSEhIIDEx8bbr83fyp3O1zvxy5hcMFoMNIrx9zh5eRLTvTK/JTzF2xlL6PbiM0ID3KD/xAEl/hpNxyJ2MhGji4j/lcNQD7NjZni1bG7Fv/z2cOPEsiYlzyM7eRFlZEhV5F0mSJOlmKYrif9HXwcCxyvWVwHBFUfSKotQAagF7b/uE7Z5kWvZqqptyeDY6iXKLlandajEsshr/2xjDwj031+ZF9g6hzb1hxB7M5I85x7CYbN8eqBwcCJr9BSoHB5InPYI5J+f8PkVRcB8Uhnt1V17CiUJhz+u/7EOt7otK7YxP6wxWf/MpwirbKUmSJOnfx1hWysaffiCkeSwZBYEsTW9M87w4xj03jmn743jyRAYP9G5EzYJUPjOrcUkej1qdQL16qaxYsYIeHj34ptc3mKwmRq8ezeJTi6/ZaalieJExTPziG3pOmoKiVrN+zmfMeWQccQf33eErlyRJku4kmcC+WHBbOLuHnh5OFFushNX1ZEdMNmHNfEgyWVFUGu7Jz0EA3+yo+l7YTZs2xdHRkS2XvbJ8q0aEjyDfkM/a+LU2qc+WFEXB3S+Aht160X/KdMa+uYru9/1EoPPbFB0aRtzqmiRt8yP7pBMF6blkZvxJTOy7RB0Zz85dndi8pQF79w7g+PGnSEj4gqysdZSWxmO1Xn9iMEmSpP8KRVF+BHYBdRRFSVYU5SHgPUVRjiqKcgToDDwJIIQ4DiwBTgBrgUeFEJbbDkLvjEOX53nv5NvElBn49GwGiqIwY3B9Otfx5uXlx9hw4ubGt2zSvTrth9UiPiqbNV8dxWy6/TAvp/X1JeiLLzDn5pL86GNYDRceBisaFZ6j69La3p7eOh1HTb58/uMG/P0ewj2wFFdtFuvX/GDzmCRJkiTpbtv96xLswvNxdM5j0eFJqMwm3rm3EV+nlLCwqJCfi4sw9bmHJxM3kCusfH+sFp72XfD02o6bWwmLFy+mlmMtlvZfSkv/lszYM4Nntz5LsfHaw0lqdXrqd+7O/TM/Ydjr7+Ls7cPvn75HbmryHbxySZIk6U6SCeyLBbcBUwntyuOxVymo/BxIyCmlUA06f0dKTEbcovPp19CfH/cmUVhuqtJwdDodbdq0IS4ujqSkpOsfcB0t/FoQ6hpa5ZM52oKiUuEdXINmfQcy+Om3GfvWClr3eBe78oGcWOrNgS/9iV7SGFP8YDz04/H3G4ZW50Fe/l5i42Zx5Ogj7Nrdjc1bGrBnTx+OHnuCuPj/kZG5muLi01itxrt9iZIkSXecEGKEEMJfCKEVQgQJIeYJIe4XQjQQQjQUQgwQQqRdVH6GEKKmEKKOEGKNzQJpNJJOdibuy93G/xIzOFVShkat4vNRTWkQ6MpjPx7k4Nm8m6qyYedqdBpVh8RjOaz+4ggmo+2T2PYN6hMwcyZlhw+T9tLLl/QQU7vo8RwdwRNmO9zVKjYVB7B+gxWtzg+fNhkc+HkJhvKqfXtLkiRJku6k/Ix09u9eS/Xwk2yKHshxowtT7VLJb9KEt9Iz8S4wY1UpzNubRtsxg+kXv4tfMZG1axgajTONGh+ktLSQJUuW4KJ14fOunzOl6RTWJ65n+O/Dic6N/svzK4pCUHg9Bj3zMmqtjpWz3sZYVnqHrl6SJEm6k2QC+2LBbQFwSNpBBw9n4tVWBFT0wm7ux1mzGhQvHgj3othgZtGes1UeUmRkJPb29jYZC1tRFIaHD+d4znGOZh21QXR3js7Onjqt29Fv6nNMnruQQc++TGjDdsTuSGbjpztY9/YRkjeF46W8Tevmu4iMXEbduu9SrdpY7OwCKSo8Snz8Jxw79jh79vZm85b67NrdgyNHJxMb9yHp6SspKjqByVQgx9mWJEmqaioV9JrJa6fex0kYeeZUMlYhcNBpmDeuOb4udoz/bj9xWTc3mW+99oF0GRNO0qk8fv88CpPB9klsl1498Z7yBIWrVpHz1VeX7NMHu1B9UC2etdiRY7VnU7o9memtcPEw4ONXyI/fv2vzeCRJkqR/H0VR5iuKkqkoyrGLtr2mKEqKoiiHKz99Ltr3gqIoMYqiRCuK0vNOxfnngrkEtMokp9yNZYkdaZ4fz+Bpoxl3KBbHMisLD76IV0kJ60tLUHXsw0MFUXhYDLyXaaKa5lkMhjN06VJOYmIia9asQaWoGN9gPPN6zqPMVMbI30fy8+mfr3t/5uLlTb8pz5Kbkswfsz+R93OSJEn/QjKBfTFnX/AMg8Qd9PR0Jd1kxt3Xge2Vw4gkV46rGbDzJG3DPPlmRzxGc9WOaanX62ndujVnzpwhNfX25s4CGFBzAI5ax39EL+xr0eh01GzWkl6Tn2TSnB+4d/qb1G3fieSTx/j9k/eY88jDbPxyBXmn3anm9xiNGs2lTZtNdOp4lBbNV1Iv4kOCq0/E0SGUkpLTJCTM5viJJ9m7rz9btzVl85YG7NrdjYOHRnP8xNPExH5AcvIPZGVtoLDoGEZjthxzW5Ik6XYFt8GrVmdeO/M/9hWW8H1qxbjSXk56vnugBQow9pu9ZBXd3LwNddsE0G1cBKmn81n1v8MYy2w/lJTnpEm49O9P1sefULj2j0v2Obbwo2fLanRFQ5Q5kG1HvLBaA/Bpk0Xm5r1kZsrXmyVJkqTr+hbodZXtHwkhGld+VgMoihIBDAfqVR7zhaIo6qoOMPHIYdJKj+Dpe5bvD1UMHfL2fY156FAquSrB9MO/UFNzgsHZ64jz0bB5XRI1HnuYhw8s4QxWfv3DGz+fQZSV/0KbNoHs37+f/fv3A9DMtxlL+i8h0i+S13e9zgvbX6DU9Nc9q6vXb0T7UeM4vWcH+1ctq+rLlyRJku4wzd0O4G8nuC0cX053D0cUwCfMnZ2Hs3DysMOpmjP5Wfm4xhmZMD6Ucd/uY2VUKvc1C6rSkFq0aMHOnTvZunUrw4cPv626HLWODKg5gJ9P/8y0yGl42nvaKMq7Q63RENKwCSENm9D1oUdIjT7JmT07ObN3F3EH9qJSq6lWryG1WrQhrHkrnN3q4exc75I6rFYDpaUJlJTGYihPw2DIoNyQhsGQTn7eHgzGTIS4NAGiKDr0el/s9H7o7fzQ6/0qv/tXrNv5odd5cwf+dpQkSfrn6v4GQz9rztKQYbwVq6Knlyt+ei0hXo7MG9ecEXN28+C3+1g8sRWO+hv/k6VOSz9UaoX180+w8tPD9H+8EXoHrc3CVhQF/7fexJSUROrzz6MNDMS+Qf3z+9361+TZlCIOJKcTpW1AyLE4GjbciHedfL6f+zrTps+1WSySJEnSv48QYquiKCE3WHwgsFgIYQDiFUWJAVpQMedFlbBaLKz94SuCO8SwMa4bJ8u8ecEhgcVuzdhblM+QqGgGmb9HO2k9/Rc9zNzgwfyelkfbLh3p6jiXDSUZzMWX3inj0Dntxt5+KWFhI1i9ejXe3t4EBwfjae/J7G6zmXtkLl9EfcGJnBPM6jiLWu61rhlXZL/BpMecZtui7/CpUZPgBo2r6ieQJEmS7jDln/J6TWRkpDj3RLZKRf0Ev06ESdvpk6Inp8RIxupEfn+iHcbjBaSsiqOxgxqP0cEMXp8AwNqp7VEUpUrD2rx5M5s3b2bSpEn4+fndVl1x+XEMXDGQJ5o8wYSGE2wU4d+LEIKM2DOc3ruTM3t2kJ+eBopCYJ0IardsQ1iL1rh4+dxgXRaMxhwMhvSKxHZ5OgZDemWiOx1DZbL78nG1FUWNTueNXu9/SaLbzbUZrq5NquKyJUm6DYqiHBBCRN7tOP7pbrq9/vMN4vctpnOrhXT1cmNe/RoXdp3MYMKC/XSo7c3cMZFo1Tf34ljc4Sz+mHsMz0AnBkxpjJ2j7ZLYAObsbBKGDkOYzYQsXYLW1/f8PkuRkYUf7uaVsiJ6+5XRvfqH2DvkcfL7ELq++BKN6rW1aSySJEn/Ff+V9roygf2bEKJ+5ffXgHFAIbAfmCaEyFMU5TNgtxDih8py84A1Qoifr1LnRGAiQPXq1ZslJibeUmwH16wkKnEu9kHpvLb9ZeoVJDHxqcGMS8qgflIRy2OHYxq+ELeIzpQuHEVrjwdwyXXhhUI72tfOYv8zrzCpx4u0VGuZ9ZiWIyfHE+A/jg0b3CgrK2PixIm4ubmdP9/etL08t+05io3FvNjyRQbXGnzN2IzlZSyaPo3SgnxGz/z4hu/5JEmSJNuzZZsthxC5XHCbimVCxTAiiRYzQq9iR0w2NZt6k2qyYrVaKFx9hAkdQonOKGLL6awqD6tly5bodDqbjIUd6hZKS/+WLDm9BLPV9q9W/x0oioJfWG06jBzHgx/PYcz7n9H63uEYSorZ9N1c5j76IAtffJK9K34mLy3lOnWp0et9cHFpiI93T6pVG0tY2HPUq/chzZouok3rTXTqeIL27fbRovkqGjWcS506bxJc/WE83NugUTtQXHKa1NSfiIl5hwMHh1FYdOwvzylJkvSf0e5JamjMPJW9ht+zClibVXB+V9e6vswY3IDN0Vm8uOzoTY9pGdrYm96TGpCTWszyjw5RVmTbCXw1Xl4EzZ6NtbiY5EcmYy298Hqz2lnHsAca007R8GeGPXHZfdHpjHhEFrJy3kdyfE5JkiTpZs0GagKNgTRgVuX2q/WkumojI4SYI4SIFEJEent731IQZUWFbNu0GP/Q03xzeAIqs5kn72nCY/EZeBZZmB83lfwOL+EW0RkAhwYDGZS9kVhfDSdP5FAc0oyQWkHcn3aQrRYTOzd4Ehg4ktS07+g/oC4Wi4XFixdjNF5os1v4t2Bp/6U08m7EKztfYfr26dccUkRnZ8+AadOxmM2snPUOZqNt235JkiTp7pAJ7Mu5VQO36pC4gx5eLgB4hbqyPSYHF097PEJcyDYZMWVq6F/fH18XPXO3xVV5WPb29rRs2ZITJ06QmZl52/WNCB9Bekk6W5K22CC6vzdFUfCuHkKbIaMY+8HnPPDRV7QbMRYhYNuib5k/9WG+e+Yxdi5dSMKRQ5QVFd7SOXQ6D5ydI/Dy6kJQ4Ehq1pxGRMT7NGmygNat1tGxwxHatd2FVuvByZPPY7WaquBqJUmS/mH0ztD1FR45/gF11QZeOJNMkfnC5IsjWlTnia61WHogmY82nLnp6kMaeNF3ckPyM0pZ/tEhSgpubkzt67GrU5uAD2dRfuoUqc89j7BemCNBX92FN/tEoBOwPaslubmhBNTPxim/nFUbvrNpHJIkSdK/mxAiQwhhERWT8cylYpgQgGSg2kVFg4DbnzzpGrYs/o7AyCS2pbQhujiQRx0zmG51ohz4MOp/mGu2IrDb5AsH1OpBv6xtWNUqzgTr2LMyDu+pUxm4dzGhKphxPBkflyewswsiNWUGgwf3IT09nRUrVlzysNfL3ouvun/FI40eYVXsKkb+PpK4/Kvfh3sEBNL7sWlkxJ3hz/mz5UNjSZKkfwGZwL6a4LaQuJNwBz3V7XSoAxzZG59DuclCWDMfEsxaFI0jxr1neKBtDXbE5HAspeD69d6mVq1aodVq2bZt223X1TGoI/6O/v/oyRxvlUdAIC0HDWH0Ox8x4fP5dBozAb2DI7t+WcwvM17mi/EjmfvYg6z4YAa7f1lM/KH9lOTn3fZ5FUVBr/chvM4bFBefJDHxKxtcjSRJ0r9Ao5Fofesx68RbpBtMzIxLu2T3k91qMTQyiE//PMOiPWdvuvrqEZ70e7QhhdllLP/wECX5tk1iO3fqhM+zz1C0fj1Zn3x6yb4a7avxdE0/DhcZSCx5CpXKgmcHAweX/Ey5scymcUiSJEn/Xoqi+F/0dTBw7pXOlcBwRVH0iqLUAGoBe6sihqyzCSTkbMbiWM4vpwfRrPAspzp3Ilpn5ZEj26jtlEKNUZe2g9i70czbCx9DDruCtKRE55PnWgfX1i154sgyMhF8tPg0EXXfo6w8CZRf6NatG8ePH7/ivletUjO58WS+7P4leYY8hv8+nFWxq64aa1hkS1rdM4xjm9Zz9M8/rlpGkiRJ+ueQCeyrCW4LpdkoOWfo6eVCmkZQZhUcPJtHWDMfMkwCi9lI0cZoRrasjpNec0d6YTs6OtK8eXOOHTtGTk7ObdWlUWkYWmcoe9L3EJsfa6MI/3lcvHxo1ncgw19/l0e//pEhL8+gw6gH8K8VTk7yWXYs+YFlM1/jy4fv56tJY/j13dfZuXQhMfv3UJSTfUtP8729e+Dj04f4hM8pLrn53oSSJEn/OioV9H6XpulbeZBE5qdkc7Cg5PxuRVGYMbgBnep489Lyo/x5MuOmTxEU7kH/xxtTkm/g11kHKcott+UV4DF2LG5DhpDz1VfkL19+yb4xDzSmpZ2eeSetlBj74x+UgKOPL/MWzrBpDJIkSdK/g6IoP1IxCWMdRVGSFUV5CHhPUZSjiqIcAToDTwIIIY4DS4ATwFrgUSGE5RpV3zIhBL999wnVGp7mmyNjUZmsdOrThKXCQPvYdB4om0P1h5eA+spJl1XhfRmQtYlMLw1mNw27lsfiNWUKdWJ2MlBjYVFGPknJwVSv9iApKQsJr2ulQYMGbNy4kejo6CvqaxPQhqX9lxLhGcGL21/ktZ2vUWQsuqJc6yEjCWncjD/nf0nq6VO2/kkkSZKkO8gmCWxFUdwURflZUZRTiqKcVBSltaIoHoqirFcU5Uzl0v2i8i8oihKjKEq0oig9bRGDTZ0fB3s7Pb1cMQF42bEjJhsndzt8a7qSbjZjKXbCEYXhzavx25E0kvOuPg6XLbVp0wa1Wm2TXtj31LoHrUrL4lOLbRDZP5+dkxPV6zei+YB76TflWR78+Cse+2YJw16dSacxE6hWvxEFmRns/uUnVrz/JnMmj2P2xNH88s6rbF+8gNN7dlCQmXFDSe06tV9Fo3Hk5MnnqYK/LyVJkv55gttAxCBe2P0k/loV06KTMFkv/PdUq1bx+cim1A905dFFB2/pzaeAWm4MmNKYsiIjv846SGG27XpAK4qC3ysv49CyJekvv0LpgQPn96k0at4b3xyAhTu7o6AhqEECOYcySM65tQm0JEmSpH8vIcQIIYS/EEIrhAgSQswTQtwvhGgghGgohBgghEi7qPwMIURNIUQdIcSaqojpzN6daANPsTurMacKajDCrZAP9HqCcgx8fPYZXB74CZWjx9UPrtOX/lmbEWoVG0I0ZJ0tItXsj1O3roze9BnOKLz4yxGCQ57EwaEmp069QJ8+nfD39+eXX3656hCaPg4+fN3jayY0mMAvZ36h85LOPLf1OXam7sRirbi/UqnU9Hn8aZw9PVn10Ts2eatWkiRJujts1QP7E2CtECIcaAScBJ4H/hRC1AL+rPyOoigRwHCgHtAL+EJRFLWN4rANj1Bw9ofEnbR0dcJFo8ItxIXtMRW9nms28yHepENR6yj64zAPtquBAnyzI6HKQ3NyciIyMpKoqChyc3Nvqy4POw961+jNytiVFBuLbRThv4vewYGgiPo06zuQPo9NY9ysL3j82yWMePN9ujzwMKFNm1OSl8u+lb+w6sN3+Prxh/jioREsfXM6W36Yz6mdW8lLS7lkTFQAnc6L2rVeobDwMElJchxUSZIkALq/jpO5mHfyfudkSTlfJl16w+qo1zB/XHNc7LRMX34Mq/Xm34LxC3Vl4JNNMJaZ+XXWQfIzbffwWdFqCfrkY7QBASQ/9jjG5OTz+4KDXJnWIYy9JsHRmAn4+CRgX92OeV+9b7PzS5IkSVJVMBuNrF89G61PNkui76VxcQq/Nm2AyiKYfeIN1APfxjGo3rUrcPGnuYs93qZ8Drgq2HvZsWdlHF6PP4FTbjJP6Io5Vm5k4doEIiLex2jMIj5+JsOHD0er1bJ48WLKyq586KxRaXii6RMs6beEQWGD2JayjYfXP0yvZb349OCnJBYmYu/kzIBp0ykvKuK3T97FapGdhyRJkv6JbjuBrSiKC9ABmAcghDAKIfKBgcC5zNx3wKDK9YHAYiGEQQgRD8RwYQKKvwdFqegJlrgDrQJdPVwoctVwJDmfglITYU19yLEKjMZSSvamEOBmT7+G/izee5aCsqqfmK9NmzaoVCq2b99+23WNCB9BqbmUlbErbRDZf4PWzo6A2nVp0qs/vR6Zypj3/sfj3y5l1IwP6Tb+UWq3aoehtIRDa1by+yfvMX/qw3z24DB+eu15Ni+Yy9FN64g/tB9KI3BzaU9s3CxKSxPu9mVJkiTdfe4h0PpReh6YSV8nwayEdOJLLx2v2stJzzM96xCVlM+qI7c2R5VPsAsDn2yC2WTl11kHyUsvuf5BN0jt5kbQl7MRFgvJjzyCpfjCA+IHe9WmqZcT8xLqUFQWQGiNA4hyD37fttxm55ckSZIkW9u+4icCGsXw7fGRqEyg6tiIVDuFF6N+xaNRO/ya9b9uHarwPvTL3Ijw1nPUR0V+Rinx2U64DuhP61XvE6nWMGtnPAbqEBw8ibT0ZRiNexk2bBj5+fksXboUyzWSz3U96/JSq5fYNHQTH3T8gDC3MOYdm0e/X/sxZs0YtpsO0/6hCSSfOMbWhd/Y+ueRJEmS7gBb9MAOBbKAbxRFOaQoyteKojgCvudea6pc+lSWDwSSLjo+uXLb30twWyhKg7x4eni5UqqAxUXHrrgcHN30BIS5kWoFq9kNc34pEzqEUmK03NLkUjfLxcWFpk2bcvjwYfLz82+rrvpe9Wng1YDF0Yvl7My3QaPT4RdWm0bde9N94mOMfudjHv9uKfe/+yk9Hn6Cuu27YDGbiFq3hnVffsqyma+x8IWp7Pg8DVO5kT9XDGbBs4/xy9uvsPaLj9m26FsOrl7BqZ1bSTpxlNzUFAylpfKfkSRJ/37tnwInX2YcewutovDs6aQr/tt3b9Mg6gW48N7aaMpNt9aTyruaM4OebIIQ8OuHh8hJsd2bSPoaNQj65GMM8QmkPPUUwmwGQKVSeH9sM4yKwuL9E3B1y8DdJYm96/dSbrDtmNySJEmSZAtFudnEJi/jUHEYJ/Nq09wHdnrpGRh9hg6uCdQc8PyNVRTen35ZWxBqFb9YSnALcmTfb/G4TZoMpnKmkYxBCF5beIgaIY/h5BTByVPT8fNzol+/fsTFxbFhw4a/PIVeradnSE9md5vN+vvW82SzJ8k35PPqzld5OOUVyhp6cuD35Zzcsfn2fxhJkiTpjrpyhoVbq6Mp8LgQYo+iKJ9QOVzINShX2XbVrJyiKBOBiQDVq1e/3ThvTnDbimXiTjrXH45GAZW/PTtisulV34+wZj4cis0nxE5FwcoD1BvTnnZhXnyzI54H24Wg11TtqCjt2rXjwIED7Nixg759+95WXcPDhzN9+3T2pO+hlX8rG0UoqTVafEJC8QkJpUHlNqvFQmF2FiX5eZTm51GSn0dB+QbUvitRwvPIP6MlO/kspfl5V329TaPT4+jmhoObO46u7ji6e+Do5oajm3vFp3Kbg6srao32zl6wJEmSLeidoesr+K14lJfqPcxzeT4szchjqN+FcTVVKoWX+kYwYu5u5m2P59HOYbd0Ks9AJwY/1YTlHx1i+UeHGDi1MV5Bzja5DMfWrfF7+WXSX32VjPfew+/FFwGo6e3Ekz1q8+4fgmZJ7akffpw9+6vz9cLZPPbgkzY5tyRJkiTZysrvPsI+NJ0le56hlimPjY0jqJ1exJP5s6n99JqKt5dvhFcYrXQGvMzF5Ps5kFCmw21vCadjFfyGDoElnzB2yMd8nZjLtug8mkV8wN59A4mOfoUmTf5Heno6u3btwtfXl8aNG1/3dD4OPjxY/0EeqPcAR7OPsiJmBWtYQ9skB1Z+/j7ri3cxuPVoqrlUu70fSJIkSbojbJHATgaShRB7Kr//TEUCO0NRFH8hRJqiKP5A5kXlL24lgoCrvgMshJgDzAGIjIy8s11PveuAgyck7MCtyWhauTpx2F+w/Wg2AKFNvNn602lKywrgeEVoEzuEMmb+XlYeTmVIZNU2hK6urjRu3JiDBw/Svn17XFxcbrmuniE9+WDfB/x48keZwK5iKrUaN18/3Hz9zm8Tog+HDmejVh+h28g12NkFIKxWykuKKalMcl/8OZf4zktLIfnkMcqLr5xxG0Crt0NrV/HR6e3Q2tlXfK/crrOr3KbXVy4rt50/rmKf7txxdnZodXoUla2GzpckSbqGRiNhz1fcv/0pfu7wM6/FpNDFwwUv3YU/W1rX9KRHhC9fbIphSGQQPs52t3Qqdz9HBj/VlBUfH2L5h4cYMKUxPsG33qZeUvewoRjjYsn9bgH60Jq4Dx8GwIQOofx+JI2FZwbzmt9B/J2Pk5pYj7MpZ6keeIcf2EuSJEnSNaREn0TtuZcF0fchLGqS2tTCwSj46MxbhDy+EEV7c22vOrwvfdI3ssivPz9sy+Ct2j4cWJvI8CkTyF/2KyOLD/GHOoLpS4+w4YUuhNaYSmzc+2RkrKJnz75kZmayatUqvLy8CAoKuqFzKopCQ++GNPRuyDPNn2Fdg984+fECchZvZlDSj9QPbMKgsEH0COmBo9bxVn4mSZIk6Q5QbDEkgaIo24DxQohoRVFeA879lz9HCDFTUZTnAQ8hxLOKotQDFlEx7nUAFRM81hJC/OU7wJGRkWL//v23HetN+Wk0pB2BqUeYm5TFyzEp6Lams/OJDgS5O7D8w4O4ns2mlp093pProavmTu9PtmEVgj+mdkC50afRtygvL49PP/2Uli1b0qtXr9uq65ODnzD/2HzW3LOGAKcAG0Uo3aiysiR27+mNu3sLGjWcd1P/7phNJkoL8isS2wWVie68PAylJZgM5ZjKyzGWl1eul2EyGDCVl53fZjYYrn+Si2jOJbXPJb4rk95XJr8v33ZZIv2y42RiXLrbFEU5IISIvNtx/NPZrL1O2AHf9uFUp7fpTjsG+rjxWUTwJUXis0vo/uEWhkQG8c49DW/rdIXZZSz/6BCGEhP9n2iMX6jrbdV3jrBYSJo8mZLtO6j+9VwcW7cG4GRaIf3/t50WHseZELGEbbu6onF24PmnX0Il/3soSZJ0TbK9to3rtdfCauXr9x4kOcDMdydG4BtkR1K4B5/unUvnoQ/gGdb85k+acpBtS6cxpNFHaA/l8EKwP2J9Os371SDk1C/kzJtP3LiveDSvkEfbhzKtdy0OHBxOaWkcrVquwWJxZs6cOZjNZiZOnHjLnbhSok/y0+vPow71YkPTDBKKErHX2NM9uDsDaw4k0i8SlSLbYkmSpGuxWq2UlpZSVFREYWEhhYWFV11/8cUXbdZm26IHNsDjwEJFUXRAHPAAFeNrL1EU5SHgLDAEQAhxXFGUJcAJwAw8er3k9V0T3BZOroKCZHp4efNyTApWHzt2xuQwtLkDYZG+7D6TT5jeSsHKA/g81p2JHUJ5akkUm09n0bmOz/XPcRvc3d1p1KgR+/fvp127djg5Od1yXUNrD2X+sfksiV7C1GZTbRekdEPs7atRs+Y0zpx5i/T05fj7D77hYzVaLS5e3rh4ed/SuYXVisloqEx0l2Eqr0h6X0h+X0h6mwyVyfBLthkwlpdRWpB//phzx98MjU5/zWS4RqdDrVaj0mhRa9SoNBpUas2FbeqKbWqNBpVajVqjrVxqKstetk2tQaW5wW1qdZU/jJIk6SpC2kLEQMJ3vMVj927ho7Q8hvh50NHjwhAfNbwcGdM6hG93xjOmdQh1/W+957SLlz2DpzVl+UeHWPnJYfo93oiAMLfbvgxFrSZw1iwSR4wkecpUQhYvRh9ag7r+LkzuHManfwoii/wJ8znF6ZzGbNu9jY5tOt72eSVJkiTpduxZvxJdzbP8dPhRvDUmEiMCePDYXpq1bXNryWuAgCa0tmbjYS1FG+bGd8dSeaWRD4c3nCXimXHkLf6Jhsnr6enYjq+2xzO4eRD1It5nz95+nDz1Io0afs2IESP4+uuv+emnnxg3bhxa7c0PmxhYpy6dx05g4/wveaHxSBx6R7AidgVr49eyMnYlgU6BDKg5gP41+1PNWQ4xIknSf4vZbKaoqOgvk9NFRUVXTKyrKApOTk44Ozvj6elJSEiITeOySQ/sO+Gu9MBOOwJftYd75kLDoXTae4r41EIGlmn5dEQTSguNfPvcdjprC3HS6wl6txcmq6DDe5uo4eXIjxOrfjiOnJwcPvvsM1q3bk2PHj1uq64pG6dwMPMgG4ZsQK/W2yhC6UYJYeHAweGUlMTRqtUf6HVedzuk23JxYvxcQvtC8vvybRf1EL+kx3jFNrPJiMVswWo2YbFYsFrMWMxmrOaK9ap2Ibl9WeJco67cp7lsn+aipPhF2yq/a3Q6NFotGp0etVaLRqurSNKf237ue+Wyoryuomzld5VaIxPrNiZ7dNmGTdvrvAT4rAXl9e6ja8DjmIVgU4twHNQXekXllxrp+P5mGgS68v1DLW77/xfFeQZWfHyI4rxy+j7aiKA67rd5ERWMySkkDB2KytmJkMWL0bi7YzRb6ffpNgoKUnmr1bvs29oOo70n0558Gmdn24zFLUmS9G8j22vb+Kv22lhWyo/zR7G0tC3RObUpbudPs9x83lFtpMHod27vxKuf5ekCN37274NYl8J7XcPJ+TmRhl2rEZ61gaxPPkWZ/DVDUguoW82Vnya3ITn5O06feZO64e8QEDCUkydP8tNPP9GoUSMGDRp0S22/EIK1X3zEiW2bGPzsK4Q2bU6ZuYyNZzeyImYFu9N2IxBE+kYyMGwgPYJ74KB1uL1rlyRJuouEEBgMhmv2lj63XlJScsWxGo0GFxcXnJ2dcXFxueq6k5MTavWl8wHass22VQ/sfyffeqB3hYTt0HAoPb1c+bS4nG0nsrFaBQ4uOgLruHM20Uw9tT1lx5JxaFiNB9uF8PbqUxxNLqBBkG1eQb4WT09P6tevz759+2jbti2Ojrc+bteIuiPYmLSRPxL+YEDNATaMUroRiqKmbvhM9u7rx+no12jQ4LO7HdJtUVQqdHb26Ozsq/Q8QgisFye1LRas5nPrf73NYjFjNVd8LOfKVG47X6bymIp1E1bzVbadK1d5jLGs9LJzWi45t9lkxGw0wu08QFSU84lujVZbmfy+KPF9LtmtvSgxfi5hfo2yF9dxecK84vu55LnslS7dIe4h0PpR7LZ/yPv1H+KeFDWzEtJ5ueaFoa7cHHRM6VqLN347weboLDqH397bT07uegY91YQVHx/mt8+i6DImnNrN/a5/4HXoggIJ+ux/nB07jpQpU6n+9Vx0Oh0v9K3LA98UsyWjES1rJnIgyYNfVy1jzMixt31OSZIkSboVK75/n1gnJ44l1UUV5oKXRfBi9kIaTJ1/+5WH96Xfqjf4wa8PwaEVvbCntfLj2OYUGjw/DPX3P6CP+onJPoN4NymfpQeSGdJsDJlZ6zh95i3c3dtQt25dOnXqxObNm/Hz86N15fBcN0NRFLpNeJSsswms/uwDRr39Ee5+AfQN7Uvf0L6kl6SzMnYlK2JW8PKOl3l7z9v0rtGbRxs/io9D1b5pLUmSdLOsVislJSXXTU4bjcYrjrW3tz+fhA4ICLhqctre3v6u5wBkD+zrWTQMcmLh8f0cLCihz8EzaI/ksm5oJHX9XTi+LYVtC6Pp4yzQeRnwe64PheUm2ryzkc7hPvxvRJMqDzErK4vPP/+c9u3b07Vr11uuRwjBwBUDcdQ48mO/H20YoXQzEhJmExv3AQ3qf46Pz+2NbS79fZ1LvJuNRiwm4/mkdsV3E2ajAbPJVNH7vHK72WSqKGu8vHzFPrPRUHnsZd8ry1ou2n47FEWFWqe9JNl9RY/xi5PhV+lNfm6b9lzi/JLyFYl27fmEux6NTlvlvc5ljy7bsHl7bSiCT5uCewhPtZ3HTxm5/NGsNvWdL/SCMpqt9Px4KyoF1k7tgFZ9++NWlhUbWfvVMVLP5NO0Z3VaDqyJSnX7//4VrFxJ6rPP4TbkPvzeeAOAoV/t4kxKGu+0e41TfzalwKEGo0ePJiws7LbPJ0mS9G8j22vbuFZ7nZOSzK9/PsIH0WMR9o6UtvDlk4NzGDDlfTT2Nng7yGLGNKsODZstpKbWhWOr4/l2aBNOzo+mTks/Got9ZLz9Dh5T5zA+oYRkOxV/PtMJe1Ume/b2xcW5Pk2afI8QsGTJEqKjoxk1atQtt5kFmen88PxUnDy9GPnmB2jtLp2YUgjB4azDrIhZwarYVWjVWh5r/BjDw4ejUcn+gJIkVT2TyXQ+EX2t5HRxcTFWq/WS41QqFU5OTtfsMX1ueStDMd0o2QP7TgpuA6fXQnEmjV288dSoyfO2Y/uZbOr6uxDaxJstP56moDwft2xnhNmKi52WkS2rM297PM/2rEM1j6p91cjb25uIiAj27NlDmzZtsLe/tR6viqIwInwEb+95m6NZR2ng3cDGkUo3onr18WRmrSH69Ku4u7dCq3W72yFJVUBRFNSVQ43AnX0dUQiBxWy+kAyvTIifT5wbL0uUX5EAv2jb5Yl0oxFjaSmlBflXLWe13PqUB4qiutBT/KJe5Zf2Ltdflgz/i32XJd6lvym9M3R9BVY+xivG/azX1mZadBKrm9VGXflAQ6dR8ULvcCZ+f4DFe89yf+uQ2z6tvZOOAVMas23JGQ7+cZaclBK6P1QPvf3t/enkOmAAhrg4cr78Cl1oTTwfGMdzvcK578s8NpxtT69GCew87sevK5Yx9Yknq/QPSkmSJEm63KqlL7Hc1AGj0FPWxJspx/+k27jnbJO8BlBr0NbqQa/sbfzm3xMPJx0LjqTwQIdAjm5KptEL/dF88y2lm7/m2VrjGVdeyNurT/LBkEbUrjWdk6deIDl5AdWqjWPw4MHMmzePn3/+mZEjR1K9evWbDsfVx4++TzzDLzNfY92c/9Hn8acv6TChKApNfJrQxKcJD9V/iBl7Z/DuvndZEbuCl1u9TEPv25tEWpIkyWAwkJOTQ3Z2Nrm5uVckp8vKyq44RqfTnU9C16hR46rJaUdHx1ueHF4IgRBmrFZD5cd40frl3y+sWy4pc3sd5y4nE9jXE9y2Ypm4A1W9wfT0dmWxwczWmGwmdAjF3klHULg7CfFmmqjtKN5+GudO4YxrE8L87fHM3xHPq/3rVXmYHTp04MSJE+zZs4dOnTrdcj39Q/vz8YGP+fHUjzKBfZeoVFrqhr/Lvv2DOH3mLepFfHC3Q5L+ZRRFqRxrW4ve4daHHboVVovlkt7jlyTBr+hdbsBivKg3+lUT54bzvcqNpaWU5uddUdZsNCAuexot/cM0Hgl75+D258u8OWwjk06n8UNqDmMDL8wV0D3Cl9ahnny4/jQDGgfian/7iV+1RkWnkXXwCnJi2+LT/DxzP30nN8TN9/YeOnk/8QTG+AQy33sPXUgwkZ070yXch7UxPenYfjr+ZidSi+zZunXrbb1ZJUmSJEk349D2tcQ4Kxw7E4Ep3JVO6cmM6twMF78atj1ReF/6rf+UH32607NVEL9tiGPaxJqod6Sxb20KrR6dTNpLLxPeZRwj4nT8cCCZ+5oF0bLGEDKz/iAm9j08PDrg6BjKiBEjmDdvHvPnzyc0NJROnTrddCI7pHEz2g27n+2LF+AfVpumfQZetVw1l2rM7jqb9YnreXfvu4xePZr7at/HlKZTcNVX7dChkiT9s1mtVvLz88nOzj6frM7JySEnJ4eioqJLyjo6OuLi4oyrqwPVqnni7GyHo6MORyc9DvZaHBy0aDTisqRxPlZrJlarAZPZSFa2gYzMi5LJFgNWYbz0e2Xy2WI1ICqT0JZLks+3ew9t27en5RAi12MxwcxgaDIK+rzPH9kFjD0aj9OhXI4/0RG9Rs2JHals/v4UfR0MaF0tBLzaD4CnfjrM2uPp7Hq+K64OVd+DavHixSQkJDB16lTsLnv16WbM2D2DX878woYhG/Cw87BhhNLNiI37kISEz2nUaB5enp3udjiS9I9WMVzLhWS32XjxkCsGzEYjoU2by1eSbaDK2uuEHfBtH0Sn6fR3HkS60cSulhFoLxrW41hKAf0/286E9qG82KeuTU+fcjqPtXOOYbUIeoyvR3A9z9uqz1pWRuLo+zHGxxP84yLiXfzp88k2+lbfwn2+B9i2PRyrmw+TJ0/G29vbRlchSZL0zyeHELGNy9trq8XC3Lmj+Sj5PsqdnPEOc2S25hiRgx62/cmNpRjfr02D1r/QycuHzUtOcU/TIAZpHNm/OoH7nm1C8eSRKDp7aPEMo8sLsHe3Y/WU9mDJZvee3jg4hNKs6WJUKg0Gg4H9+/ezY8cOSktLqVGjBh07diQkJOSGQxJWKys/fJvYA3sZ8vIMqkX8dWeuElMJnx/+nIUnF+Kmd2Na5DT6h/a/62PESpJ0d5WWll6SoD63zM3NxXL+bWSBkxP4+iq4uxtwdCpFp80DJQOTKQOr1YAQV45VfbMURYdKpUOl0qNW6VFUOtQqPaqLP+rK5bmy6sv2q/Tn6zj3UV91n+6K4xRFg0qlslmbLRPYN2LBICjOhMk7KbVYqbP1CNbEYn7pUJdWoZ6UF5v45tnttNbk4mnnQcBr7VA7aDmRWkifT7fxTM86PNq56sexTE1NZc6cOXTp0oUOHTrccj1x+XEMXDGQKU2nML7BeBtGKN0Mq9XA3n0DMZuLaNVyLRqNjV7bkyTpquQNsW1UaXu9ZAycWc/6MTu5P6aAj8OrMdz/0kTyM0ujWHE4lfVPdSDY07ZvGBRml7H6y6PkphTTanBNmnSvfls3qqaMTBKGDEHl6EiNFct5ctlx/jiSzIz2r2DYFUq0UpfgkFAeGPeAvCGWJEmqJNtr27i8vV72/Vt8nW7Hiby60Nybj9PXMuiRV6ougMWjeELTgj98uzAwS7DiYAqbp3Rg9cyDeAc706lWGilPPoXXUx+xOU7HM5QxrXttHu9ai/T0lRw/8SQ1Q58hJGTS+SqNRuP5RHZJSQkhISHnE9k30o4aSktZ+OKTGEpLGD3zY5w9vK57zKncU7y5+02OZB0h0jeSl1q9RE23mrf100iS9PdmNpvJy8u7aqK6tLT0fDmt1oyvL3h4mnB2LkOvz0elysZsTsFiKT5fTlE02NtXw8G+Bnb2QajVDlckiNXXSCZfO9msQ1Fuf16g22XLNlsmsG/E1vdh41vwbDw4eDDycCybUvOYpnHi6R7hAKz632GMcdm01OtxbOmA++BmANw/bw+n0ovY/lxn9Bp1lYe6cOFCkpOTmTp1Knq9/pbrGb9uPImFiay5Z42cnOIuKig4zP4DQwgMHE54nTfvdjiS9K8mb4hto0rb67wE+Kw5ov69dA96kjKLla0tw8+PhQ2QUVhOp/c30zncmy9GNbN5CCaDhT+/O0nswUxqt/Sl86hwNLpbb9+Lt20jacJEvKdOoWTI/XSdtYWOvvu5v+Yadv9Wi3K/UAYOHEiTJlU/KbQkSdI/gWyvbePi9rowL4d3lkznx/j+mMJdeDp/J1MffwpFXYVvEUctZt2WbxnTYCYfVPfnpbn7mdqtFh2FHTt/iWHg1EYYX5iAtagYp14zmF5UwDaLiXVTOxDs6cCxY4+Tlb2BFs1X4ORU55KqjUYjBw4cYMeOHRQXF1O9enU6depEjRo1rpvIzkk+y8IXn8KrejBDX52J5gbmorAKK8vOLOOjAx9RaiplbL2xPNzoYew1tzY3lSRJd58QguLi4qsmqfPy8jiXS1WpTHh4mPHytuLqUo69fSFqTQ4WSxoWS/5FNSrY2QXi4FADB/sQHBxCsHcIwcE+BDu7IFT/0rybLdvsu5+O/yc4Nw722V0A9PVxQ9hrWJeUe75IWDNf0stUmEpzKNmfdn77xA6hZBUZWHEo9Y6E2rFjR8rKyrjd5MGIOiNIL0lnS9IWG0Um3QpX18ZUr/YAKSmLyMvbfbfDkSRJurvcQ6D1oyhRP/KEczGxZQZ+y8q/pIivix2TOtZk9dF09iXkXrWa26HVq+k5oR4tB9Tg9J4Mfp11kOK8W5+gxKl9e5x79SJ79pf4l+QwrHk1Nqc3JR0L4aElWE1FrFu3jpKSEhtehSRJkiRdsOiH6fyc1Auru44+ZUlMenBC1SavAWr1oGP+IZyFiYMmA13Cffh+VyK12vjh6KZn94p4vKdOwZSchNopmccNWnTAyyuOAVCnzutoNC4cP/E0Vuulr9rrdDpat27NlClT6N27N3l5eSxYsID58+cTExPDX3Xi8wyqTq/JU0k7E83m7+be0KWoFBX31b6PVYNX0Se0D/OOzWPQ8kFsTtp8iz+OJEl3islkIj09nePHj7NlyxaWLVvGnDlzmDlzJrNmzeLbb7/l999XcOzYeiyW/YTUOE27dnF07nKQzp3X0LbdYupG/Iy39zJ0+tVotDE4OLjg69uTsLDnadjgS1q1/IPOnY7Tts0WmjT+ljp1XqNatXF4eXbCwSHkX5u8tjXZA/tGmA3wTjVoMQF6ziDTYKLhzuNozxRwfEwbXOy0lJdUDCPSQpuLr50PvtOaovV2RAhBn0+3Y7JYWTe1AypV1b8CvGDBAjIyMpgyZQo6ne6W6jBbzfRe1ptgl2C+7vG1jSOUbobFUsaevX0AaNliNWq1fJIvSVVB9uiyjSpvrw1F8EkjLEEt6FjzVfQqhQ2RdS7pUVVqNNPlgy34uuj5dXLbKmt74w5nseGbE2j1anpPaoBf6K1N4GTKyCCuT1/smzZF98EndPpgM5Fup3io3nccXlKLgur1aNy4MYMGDbLtBUiSJP0DyfbaNs6114f3rWfa5tPE5IUQWNueJW29CKrV8M4E8d0AHnPpzZ9e7fjKz48x8/Yy854GNDCo2bwwmt6TGqB6/0lMZ8/iOvwTFmfm86GhlE+GN2Zg40CystZz5OgkQkIeo2bok9c8jclk4tChQ2zfvp3CwkKCgoLo2LEjYWFh1+yRvXXhN+xb+Qs9J02hfufuN3VZ+9P389but4gtiKVztc680OIF/J38b6oOSZJsx2q1UlhYeH7SxIt7VBcUFACgKFb0+mI8Pc14eJpxcipBrysAJROLJRO4kDvVaj1wcAjBwb7GRT2pa+DgEIxafXuTvf/b2LLNlmn+G6HRQ1BzSNgOgI9eS22djjPe9uyKzaFnPT/sHLVUi/DgTIwVX6Dg90N4jWuHoihM7FCDJ3+KYvPpTLqE+1Z5uB07duSbb77hzz//pHfv3rdUh0alYVidYXxy8BPi8uMIdQu1cZTSjVKr7akb/g4HD40iLu4jatV68W6HJEmSdPfonaHlJNSbZvB4s+lMSbWwIaeQ7l4XkscOOg3P9qrDU0uiWBmVyqAmgVUSSmhjb+59rhmrZx/l1w8P0mlkHeq2CbjperS+vnhPmULG22/jtmsL49rU4KstVrobnKgbaWVnbAqHD0Pjxo1vakIqSZIkSforQggW7F5PbE5HtDUdeCeo5M4lrwHq9qffnl/52a01Vg89Ef4ufL09nj+eaMeh9WfZszKO/lOmkDRmDFiPM9BQnT9c7Hnzt5N0quODt3d3/P3uITFxNnq9L36+A9Forpz/QqvV0qJFC5o2bcrhw4fZtm0bCxcuJCAggI4dO1K7du0rEtntho8hIy6GDfO+wDu4Br6hNz6nVaRfJEsHLOX7E9/zZdSXDFwxkEmNJnF/xP1oVVXcs12S/mPKTRaKDWYATEYjubm55OXmVizzcsnJySU/Pw+TyYSCQKcvwdm5HE8PM9XDyrHTF6BR52KxZgAWFEBBoNY4obMLwd6hFY4OwRUJasdgHOxD0GldAFAU5Dw1d5DsgX2jNr1dMRb2c4lg58KHcem8l5jO6CI1HwyomKH41O40/vz2JL1UmeidXQic2RtFUTBZrHR4bxPBng4sntj6joS7du1adu/eTd++fWnevPkt1ZFbnku3pd24t9a9TG813cYRSjfrVPTLpKQsJrLZUlxdG9/tcCTpX0f26LKNO9Jel+XBR/Ux1elHm4Ap+Oo0rGpa65I/IK1WwcDPd5BdbGDjtE7Y38Y41ddTXmLij7nHSD6VR8MuQbS9NwyV+uZGaRMWCwlDh2HOzMRz2Qo6fr6XcIezPNLkQ04vqUtG9fp4uvswadIkNBrZ/0CSpP8u2V7bRmRkpHh80kBeT2iM2UnPCz6pPPLguDsbREEKZZ80pn77NdwT4EvrEoWnlkTxzQPNCSoSrPv6ON0eiMB+3iuURR3BY9IcjsbmM95SxIgW1ZkxuAEmUyGHDt9PUdEx1GpHfH36EhA4HBfnhtdMLJnNZqKioti2bRv5+fn4+/vTsWNH6tS57I2uwgJ+eH4qKDD6nY9xcLn5N61Si1OZuXcmm5I2EeYWxkutXqKZr+3n6JCkfxOLVZBTYiCr6MInPb+U1LxiMgrKyCoqJ6fERF65lTLz3Y72AkUB5fy6UpEMVyr6bitc2Me5cgogKsqc6+GtUoGdFux1CvY6BQedgoO+cl1f+V2nqljXq3DUq3DQVSwd9RXbtWo1CkplDMpF8SioFFXFNoWKMheVO7+8+NjKcueOu+TYi+qsuJwr6/Jy8JKTON5xcZthwUAY9QvU6sbJ4jI674vGP7GUQ+PaAGAoMzP/mW001ecRqPXGa2Jd7EIrZi6euzWOGatPsvKxtjQMcqvycK1WK4sXL+bMmTOMGjWKsLAbf2J8senbp7MhcQN/DvkTJ52TjaOUbobZXMTuPb3RaJxo0XwFKtWtT9IpSdKV5A2xbdyx9vqP6bB7Nt+O3MXzyWX83Lgm7dydLymyJy6HYXN2M617bR7vWqtKw7FarOz8JZaojUkEhbvTc3x97JxurpdV2dFjJAwdivvo0fzSZijv/xHNi5GfElZix7Zd9gi/cLp06UKHDh2q6CokSZL+/mR7bRtNmjQW2hFPk5HvQVffHOY/MfpcFuXOmtOZSX73s9WjOftbRtD5/U3U8nHm+wdbsOSdfRjLzAwe6kLS0CF4TJiCKaces33VLErP45dH2tC0ujtCCAoKD5KauoSMjN+xWstwcqxDQMAw/PwGotW6XfXUFovlfCI7Ly8PPz+/84lslaoiIZMee4bFrz5LYHg97n3xdVSqW3sgvjlpM+/seYfUklQG1hzIU5FP4WHncYs/mvRPIYQAKyAEwipACLAIhAAqvwtrZRmrqCwvENbL9gsqjxOV26k8Tlx0XGUd585j5aL6roxD0ahQ6TUodmpUdheWKjs1il6DolPZtHexEIIig/mSpHRWUTlpeaWk5ZeQWVhGVrGRvFIzBQbB1TKVWizYK0YcVAacNGU4a0vxsC/Dxb4cnd6ASl2KQjFg4VyqUyhqFJULQu2EUDljVTlgVTliVuwxo8UirJitFqzCikVYMFstWKwWLMJauTy3XrH/3NJ60baKYyvKX1i3YhXWi6K/8FsKoVy2/aLvQoWw6sFqh7DYISqXWPUIqx2IG7i/UIwoqnIUdTlULhVVOYrKAOfWK5eoDBftLz+/H8Vskybh2LhjMoF9xxlLYGZ1aPM4dHsNIQR1Nx+hMLOU/T0bE+BWMS7x718cITcmh04a0FWz4Pt4NwCKyk20eWcjHet489nIpnckZIPBwPz588nPz+ehhx7Cx8fnpus4mnWUkatH8kKLFxhZd2QVRCndjOyczURFPURIyKPUDH3qbocjSf8q8obYNu5Ye12YCh83pLzZA7RwG0sdRzuWNr7yYe2k7w+w9UwWm5/uhI+LXZWHdXJnKpsXRePkpqfP5IZ4Btzcw9/0N98i78cf8Vm0mJ6/ZeCnyWBq5KukrmxCnG8NNMKFyZMn4+Ehb3olSfpvku21bXhVqyacRn2JbzUTWx/qgd7uLo3bum0Wv0dt4qF6b/Fz45ocicrgvbXRrH6iPY55Jn77XxQdhtfGffksirduxfuZ78g5kscYJwNujjpWPd4O7UVvPZnNRaRnrCI19SeKio6hUunw8e5NQMBQ3NxaXjUhZ7FYOHr0KFu3biU3NxdfX186dOhA3bp1UalUHN20jnVffkrTPgNpM2Qkeocrhym5EaWmUr468hULji/AUefI1KZTuafWPed7L0p3l9VowVpoxFJoxFJ0YWktNFR8LzZVJJGvljgWAmE5l3CmMoEsuGoW9p9CBYq+IqH9V4lus1YhF0GO1UqO2UK22Ux2uZn0kjLSi8rIKjKQU2oir8yCyXrlaRSsOCimiqS0ugwnTSkuuhLc7ItwcyjEzT4PN/scPByycNIWolFd2eXaIiDXrJBlVsgyqyqWpoplnkVBcOOZWI1Kg1alRafWoVVpr1jXqrVX3X5uXaPSXPPYGymjqZzQUZz7n6j8VP7PaLJSYrRSYrBSYrBULq2UnvsYBSXGc98FpUZBmVFQaqhcGgUG0/V/B7UK7HUVHzsd2GkFdjqw1wnstGCnE+grt1WsW9HrBHqNtbK8Fa1GMDJihExg3xVfd694Kv3QOgAePRzHL9kFzHL1ZFTz6gBE70lnwzcn6Kkko3f2I+id7ijqiv+zvLP6JHO3xbHlmc5U87gzfyAUFBQwd+5cNBoNEyZMwNHx5hvbEb+NoMRcwoqBK+T4Pn8Dx09MIyPjN5pHLsfZue7dDkeS/jXkDbFt3NH2euXjcGQJXwzbxRvJhaxuWoumrpe2cwnZJXT/aAv3NAni3fvuzLie6XEFrPnyKCaDhW4PRBDa2PuGj7UUFRHbpw9aXz+2TH2X1347yVMN59NIU8KO3/RY6zQnuHowo0ePlm2yJEn/SbK9tg29fy0R9MTH/HZPberWqdq3lP5SVjSls9tTr/0ahgb48GI1P1rP/JPe9f35YEhDln94iLyMUoY+6E/SPQNwH/UAlvLW7AzQ80xiBi/2CWdih5pXrbqo6DgpqUvIyFiB2VyEvX0wAQHD8Pe/F73O64ryFouFY8eOsXXrVnJycvD29qZjx45ERETw57wvOLJhLSgKnoHVCKgdjn/tcAJq1cUjIBBFdeNJ6Ji8GN7a8xYHMg7QyLsRL7d6mToedW75J7weIQQFhgIyyzLJKs0iszST3PJcnLROeNl74WnvWfGx88RB+++bgM5qsGApNGAtulpy+sJ3YbBcebBGQe2iR+2sQ+WkRdGoUFSV4z+olIr1yu/n11UVwyegApQLZZTK7xeOu2i/ooBaqejxWvldUXH1+tTKRcddI47z9V07DmG2Yi23IMrNWMvNFeuGiqWlzEROkZGsonKyS0xklRnJKTeRaTCRaTSSbTGTYxXkA6XXSA7bUZGUdlSX46QpxVlbjJuuGHe7Qtzs8vFwyMHdIQsXhzy0mqtnVMssCoVWhWKrinJ0mLDHqnYEtQsqjTtanSd2Om/0eh+0aju0ai06le58ovni9YuXOpXuqutalfY/8fe12VKR/C4sN1FUbqaocllsqFgvLDef316x7UKZi7dbr5NOVikQP7OfTGDfFetfhV2fw/NnQefA1txChkbF0T5fsHRwEwCMZWbmP7Odho75VFc8cR8ajGPTiuR2ekE57d7dyMDGgXww5NpjctlacnIy3377Lf7+/owZMwat9uZeaV4Zu5Lp26czt8dcWvm3qqIopRtlMuWze09P9HpfIpstQ6WSY6FKki3IG2LbuKPtdXYMfBZJSbunidQPpLmrIwsaXjnp8Fu/nWDejnh+f7w9EQEudyS04jwDa748QmZiES361yCyT8gNt/uFq1eT8tQ0PKa/xL2p/thb8ni25XMUbGjOITsXtMKfe++9lwYNGlTxVUiSJP39yPbaNvQBtcRrr0zjhUmT7nYo8L9IJoROZbdbYw63qcebq06wcE8i25/rgjXLwLL3D9BqUCj+2+ZSuHIVvq8vonhvLq+GaNmVUsD6pzoQ5H7txKvFUkZm5hpSU5eQX7APRdHg5dWFgIBheHq0R1EuHRbEarVy/PhxtmzZQnZ2Nl5eXnRo3x5nrKSfOUXqmVOknTmFoaQEAL2jI/61wgmoFY5/rTr416pz3V7aQghWxa3ig30fUGgsZGTdkTza+FEctTfe4UwIQaGxsCIpXZmczirLOr/MLL2wzWS9gS6XgIPGAU97z4rEtp3nJcntc8nuc/vsNFX/ZttfEVaBpdCAJddQsbys9/S5hLUwXpmYVrQqVM461C461JXLy7+rnXUo9pp/TUJTCEFhmZnsEgPZRQayiysS1FnFBjIKykjPLyWjsJzsEiMF5darJic1WCqS0qryyiE8inHRFeGiL8DNPg93+1w8HHPwdshBrzVePRCTPSqTMxqjK1qDG9pyNzRGFzQGV9RGFzRGV9QGF9RGF1TnhstQKRU9vu00qPSVy4t7hDtoL/yzc9VX/PN01FYk7aUqIYSgxGi5IrF9+fozvcJlAvuuOLMeFt4HY1ZCaEdMVkHopsNoswzEDrvwOtLq2UfIismliyhH4wP+z/c6X8U7a07y1ZY4hjevxluD6qO5yUmebtXx48dZunQpDRo04J577rmp/wgbLAa6L+1OE58mfNLlkyqMUrpRmZlrOXrsUWqGPkNIyN/gj05J+hf4L9wQK4oyH+gHZAoh6ldu8wB+AkKABGCoECKvct8LwEOABXhCCPHH9c5xx9vrJWMhdiOz7tvG+8n5bGxehwgn+0uKFJSa6PTBJur6u7Bw/NVfH64KZqOFTQtPcXpPBjWbetN1bARa/fXHzhRCkDR+AmVRURz54DueWRvPYxG/0Nz1DHt+ckXVogPCpPDYY49hb29/3fokSZL+Tf4L7fWd8Le4vz5nw2usPB3FxLqvsqxxGIFWhU4fbOaRjjV5tlc4v38eRVpsAcMfrUHyoL44DxiMYtebbA8dw9IzaRvmydwxkTfUvpeUxJKatoS0tGWYTLno9f4E+A/B3/8+7O0DLylrtVo5ceIEW7ZsISsrCw8PDxo2bEi9evXw8vQkNy2FtNOVCe3Tp8hOPlsxdMRN9NIuMBTw8cGP+fn0z/g4+PBc8+foHtydYlPxJYnpzNJMssuyK5LSFyWnjdYrk4TOWme8HbzxdvDGx94HLwcvfOx9Kr47+JxPQJeYSsguyyanPKdiWZZz/ntOWcUnuzybAkPBVX9LJ63T+eT2xYntixPdLjqX88MgWLGCAKuwIhDnl9fahxBQbEEpsKAqFKgLrKgLRcWnQKApBuWyISmEFhQnDSpnHRpXPVoXezQuOlQuetTO2vO9qRU79T8mMW22mCk1llJmKKv4mMooM5ZhMBooKisjq7ic3CITuaVmCsosFJQJisoFxUaFEqOKEpOKMpOaUrPmqkNpKAjsFWNFT2l1RVLaWVeMq74AV7vK4Tscc/ByyMFFX3b1GNFjUTuiVPaM1uk80et8cLTzw9khEFeH6jja+aHTeV4xl5ewiPM9v63lZoShclluwVq5XZRfurQaKvdftP2K4VpUCmpnbcU/e5fKhxKXrFd8/yf9u/BPZMs2Wyawb0Z5IbwbDB2egc4vAtBn2wkOlpSxsVEYEf4VsxKf2ZfBunnH6a4kYO8cQuBbHVHpKm5YhRB8uP40/9sYQ48IXz4d0QQ77a1NBHGztm7dysaNG+nUqROdOnW6qWM/PvAx3xz/hrX3rMXfyb9qApRuypGjj5KTs5EWzX/D0fHqr81JknTj/gs3xIqidACKgQUXJbDfA3KFEDMVRXkecBdCPKcoSgTwI9ACCAA2ALWFEFd5v/KCO95epx6GOR3J7/oWkaIj3Txd+LJeyBXFvtuZwKsrjzNvbCRd6/resfCEEBxen8SuX2PwCHCizyMNcPG6ftLZmJhIXP8BOHTrxvhqAygrKeTlVk9h3NeC7fkm7B3q0axZM/r163cHrkKSJOnv47/QXt8Jf4v763OS91Myvx/1269mRKAPb9cOYtL3B9gVl8OuF7pQmlnOTzP20rRHdWqcXELewkX4v/MjxTvzWRHpzvv7ExkWWY1hLarRpJrbDSWjrFYjWdl/kpr6E7m52wHw9GhPQMAwvLy6oFLpLipr5dSpU+zZs4fExEQAfH19qV+/PvXr18fd3R0AQ2kJaTGnLyS1b6KXdlRWFG/uepPovGj0aj0Gi+GKmM8N+eHj4HMhOX35dwcv7DW2fbhtspjILc8luzz7fGL7aknv7LJsioxFN16xAFeLE74mT3xNnvgZPc+vn/voLpuwLlddQIYuh3RtDhkXfbK0eeRqCihVlV8yH56Cgr3GHgetAw4ah6uu22vsK75r7a+63U5lh16lRyd0lJvLMRgNFQlkkwGD0VCxNBkwmoyYTCaMJiNmixmTyYTZbMZsNmOxWLCYLVgtVixmC8IisFqtYKmcjNECWMFqAZNVh9Gqw2jVY7DqMAodZUJLORrKUVMuNJSjpcyqw8TV38bWKGac1GU4akpx1JTipC3BWVeMU2Vy2kVfiIdDHl4O2bjaFaJSrpIXVDmi1rqh1Xqi1/vgaOePvd4Hnc4Lnc4bnc6zcv3KpPSdJiwCS3HlkDAX98a/+HuBsSLRfRlFq6rosX1JgvvyRLcO5Q7l7f5tZAL7bvqqI+idYdxvAMyLy2B6YhoPqx14vUNtAIzlFcOI1HMpoobFDZfuXrh0vXSs4m93xPP6bydoHuLB3DGRuNrf3LAet0IIwfLly4mKirrpV49Ti1Ppvaw3D9Z/kClNp1RhlNKNMhiy2L2nF46OoTRruviKV98kSbo5/5UbYkVRQoDfLkpgRwOdhBBpiqL4A5uFEHUqe18jhHinstwfwGtCiF3/Z++846uo0j/8zO0lN703EiAQEnoHEZEigiiKi4q9d1fXteyu23f9uequZd21d13sgogCggVFeu89lfR+c/udOb8/bipJIIEEEpiHz3zOmTNnzsxNwn3nfOc973us8U+LvX7vMijayd8v/ZYX8ytYNWYAvS3NH6R9ssL0534EYNkDE5slfDoV5Owq55vXd6HRSlx4+0AS+oUd95zSF1+k7N8vcODvL/HLjQ5uS/uWc+JWsmN+Msqk8VTmO7j11ltJTEw8BZ9ARUVFpXtwttjrrqbbzK8BFAWezeCWjL+yMTiDLeMz2ZxTyS9eXsNfZ2dy/bgUlr+5i8NbSrnqwXQKL7+IoPPOQxc/D79W4oVkA19sO4Lbp5AaaWXOsAQuHZbQ7rxTLlc+hYWfUlD4CR5PEXp9BHFxc0iIvxKLJbVZ35qaGnbv3s3OnTvJz88HICEhgczMTDIzMwkJCWnoKxSlQ17awbExLDi0kJyanIAobY5q8JqOMkd12/jUQgjsHj/VTh/lDif51RUcqamkyG7H4XBj8UsE+zUEeTUEezQEuTQEOzWEOCSssgYjAaEZQDYJ/MES/mCQg6XAFiKhBGuQQzSglQIisFfG7/Hj8/rweXx4vV7cXndg87nx+r0NgrLPf5SQLAeEZFGXkFHIgUSMkpDQCi0aoUEjNI112vPMqKDVymg0fjRaP1qNXFf68QMujQaX0OImIEa7FCMuxYhTNuGUzTj8Zhx+Cw6fGdHK9SSUgPBssB+3DDbYMeoCnvmSZECrNaPVmtBqLWg0dXWNGZ0+BIMhCqMhsk6IbrqdflG6K1C8ciC0TLUX2e4JlPVhZ6rrknVWe8HfMtukZNa14cldVw8xoLEaGnLgqQRQBezTydLfwcY3AnGwdUaqfH7Sf9pB72qF1ZeNaOz2yg4KDlQw1VuJLtxA/J9nthhq0bYCfv3xVvpEBfHuzaOJDu76GFJ+v593332XI0eOcOONN5KUlNTuc+//7n62lGxh+dzlGLVn3pdZT6SwcAG79zxEv7Q/kJR04+m+HRWVHs3ZMiFuRcCuEkKENjleKYQIkyTpP8BaIcT7de1vAEuEEJ8ea/zTYq+zfoR3LqZ0xr8Z5R7KZTFhPJue3KLbt3uKueWdjfz54gxuPCe1lYG6lqpiJ1+9uJ2aUhfnXpnGwPOOLTwrXi9Zl8xGURQeueT3HKm089exv0K7dzjf7a8hIuV8rBYrt99+O1qt+hJTRUXl7OBssdddTbeZX9fz1a9ZkH+Eu/r9hi+G9WV0iJXLXlxNldPLt7+eRG25i/l/WkfGhHgGlC6l/KWXiX9mPvYfawn7RT+UgeEs2VnE55vzWXu4AoAxqeFcPjyRGYNisZmO7zAmhEx5+Y8UFHxEWfl3CCETGjqa+PgriY66EK22+Xy9qqqKXbt2sXPnTgoLCwFITk5m4MCBZGRkEBQU1OIa7fHSDo2JRavTodHqAmWTemDTo9Fq647p0egCda1WX9emDfTR6er66RvObRxTi+z34/d6kX0+vF4PNbVuKms9VDpcVDl9VLu8VDv91Lh91Hhk7B4Zu1eh1ge1PoHDL+GQJZyKptXQFO1FQmDU+DBp/Ri0PowaH0atD4MmUDdovBg0nobSqPFi0Howaj2YtF6MWg86jR+NJNBIChqUxrok0GoEWkmg0wi0WtBKAq0GtBqBRhNInChJGhoc9yUAgWgoBQIBCDSSHyFknH4DtT4TNR4jtV4TNV4Ldq+NGm8Qdk9dWbfvVVrXTkxaN8FGByFGF8FGN6EmD6EmH2FmhVCLQrhZEGbREG7VEmrWo9ebA2J0nQitaaib6+p1InVdXaMxqfmyTgAhBMLlbxCzAwJ3c7FbqfYg13rhaJ1bAk1QQMxuiMddH1O9Pja3zYDGcubEVj8eqoB9Otn7FXx4Ndy0FHqNA2D48m0Uu71kzRiBQRd4W3ZgYzHfvL6LqdosLEF9iXtsLLrgll9cPx0o5Y73NhFuNfDeLWNIjWx/0oYTxeFw8Prrr+P1ern11lsbljwdj7WFa7ntm9t4fMLjXNLnki6+S5X2IIRg2/ZbqKxcz9gxSzCb2/9CQkVFpTlny4S4AwL2f4E1RwnYXwshPmtlzNuB2wGSk5NH1C+vPWUIAa9PBUcpj81YzDuFFawdm0GiyXBUN8G1b6xjV0ENKx86nxBL169+OhqPy8/yN3aRs7OczHPjOffKfmh1bXv2ONauJffGm8i55VfcWZ7ADX02M6nXhxz8aCD2CZnU5ChccMEFjB8//hR+ChUVFZXTx9lir7uabjO/rufQ99TOn0fmhCVcnxjF39ISWby9gHvnb+GV60YwPTOWlfP3sXtVAVc+nEnpVZdgHjoE4+C7UGo8xD40smGJf36lk4VbjvD55iMcLnNg1GmYnhnLnOEJTOgb2a48VB5PKYWFn1FQ+BEuVy46XTDRURdiDeqH2ZyM2ZSE2ZyEVhsI11FeXs7OnTvZtWsXJSUlSJJESkoKAwcOZMCAAVgsrXtPt+alXVtVieL3o/j9+P2+wHPOSSKjoUofSpkhgjJDBOWGcFxaM26NEY/GiFdjQEht/1w0QsYoPJhwY5bcBOm8BBt82AxebEYXQaZaTKZKzKYKLAYHVr0Ti86FQevDIxtw+424ZSMe2YjbX1+acDfdr6/7jbhlU7NzPH4jnjaE4JNBIiB0azUiIHZLLcXv+naAWq8Ju7f1+9BqBGFmQZgFwuuE5wirngirgSibiUibmcggK9HBQUTagrEYVafAnoxQBEqt76iQJY31+nAmirNl2BJ0UnMv7ibJJ7WhRgyJNqRjzA96EqqAfTpxVsBTqTD5DzDxIQAe3HCI+bV2Xk2I55J+0QD4PDJvPvwT/cNd9HUHYRlpIfwXI1odcnt+FTe+tQEJePum0QxKDGm1X2dSWlrKG2+8QXBwMDfffDMm0/G9v4UQzP5iNladlQ9mfdDl96jSPtzuAtaum0Fw8GCGDX33rHmTp6LS2ZwtE+IzMoQIwJ7F8NE15F/6DmOrUrg+PpL/69fSw3l3QQ0XvfATt5yTyu9nZZz6+wQURbDui8NsXpZDXN8QLrx9EJZgQ5v9jzzyCDVLlvL4Hf9mZ5mLv497mKD8AXy71k6fiVdxJPcI99xzD6GhoafuQ6ioqKicJs4We93VdJv5dT2yD57uw43Dn2d7UD82jstAUQTnPf0DCaFmPr5zHI5qD+//fg29h0UxTF5N6b+eIf65d7D/4EEbYcIyJArL0Gj00QGxWAjB1rwqFmw5wqJtBVQ5fUTZjFw6NJ45wxMZEBd83NsSQqGyah0FBR9TVvYdslzb7LjBEI3ZnBQQteuEbbfHxuHDdnbtzKWiohKNRkPv3r0ZOHAg6enp7Zp7N0VRZBS/jOz3I/t9AXFblpH9PuSj634/1U4v+8vdHKjwcqjKz+FqmRy7wF8n/eglQYJZIcwIQXoIMvoJMngJMrqwGpxYDbVY9NWY9eWYtKUYKUSrKUVqJU6y4rXg91nx+Mw43QY8XhNerxmv14zPa8FkisBgtGA0mDGZzBhNVkxGMyaTBZPJgtlsxWy2NuybjAa0kgahyAHvcL8P2etF9vvxejw43F7sLg+1Lh+1bh8Oj59atw+X24PH7cbt9uBxe/B4PHg93kDp9eLz+lCQEJKmrpRQ0KBIdd7jWh0avRFJb0CjNwRKnQF0eiSdDrR6NFodIWY9EVY9kUEmIoONRIdYiQmzEhMWRHiwFU0riTpVzm6ETwl4cx8Vm1s5SvgW3kZ3bkmvwdg7BGNaGKZ+YeiizD1W51EF7NPNi+PAFgvXLQBgR6WDaVsPMFnWM39qZkO3Za/t5Mi+CqY48tGFh5LweMswIvUcLq3lujfWU+X08sp1I5mQFtnlH+Pw4cO8//779O7dm3nz5rVr+fH8PfN5Yv0TzJ85n0FR7Y+hrdK15B+Zz759fyA9/f9IiL/ydN+OikqP5GyZELciYD8NlDdJ4hguhHhEkqRMYD6NSRy/BdK6XRLHehQFXhwLWj2/mvQBC0oq2TAugyhDSy/rRz/dzudb8ln+q/NIOQUrn9pi/4Yivnt3L0azjknXppM6uHXb7y8r49DMi8gZNI7bo6ZxVe/DTOv9b4oWjiV3cCRKaWSDLVdRUVE50zlb7HVX063m1/V8fjufVni4t8+vWDw8jZEhVl7/6TB//2oPX9xzDkOSQlmz4BCbv8nhiocGU3njZRiSexH96LM41hfhOVQFAvQJQQExe0gU2pCAl6vXr/D9vhI+35zPd3tL8MmC9Fgblw9PZPbQ+HaF8xRC4PNV4nLn4XLmBEpXHi5XLi5XLh5PEdCor2g0ZvS6WNweG2VlEjXVBjzeEGJjBpKefg79+w/EYGj7BfbxUBRBdrmD3YU17C6oYHdBJXuLnBTVNHp8hptleke4SQ2z0yu4nOSQQqLNRxByNV5vKX5/dSsja9D4Q1A8QfhdZjweEy6fkVqvHofX0ChQ+8wYjRbCwsKabeHh4YSFhREcHNxtQpwpiozH6cRTW4u71o7bURvYamvxNNTtjfu1dtwOB+5aOz6Pu30XkST0RhMGkwm90YT+qNJgqqubzOiNxrrShN5oxNC0zdT8HJ3RiEbTPX6OKl2DEALhkZFrvPhLXbgPVuI5UIW/zAWANsSIMS0UU78wTH1D0ZyGVaQniipgn26+egi2zg/EwdYGYgqlLtmMQRbsm9XoZX1ocwlLX93JZFM2NlMa0Q8MxRBra3PY4ho3N7y5nkOltTxzxVAuHhLf5R9l06ZNfPnll4wePZqZM9sW2Oup9dYy5ZMpTO01lccnPN7l96fSPoRQ2LzlWuz2XYwduwyTMfZ035KKSo/jbJgQS5L0ATAJiASKgT8BC4GPgWQgF5grhKio6/8YcDPgBx4QQiw53jVOq73eOh8W3sXhKz5nQnEEdyVH84c+LW1pSY2bSf/8gYlpUbx8Xeuro04VZfm1rHhrN+VHakkfG8uEK9IwtvJQWvnhRxT9+c88c8vT/GzX8sSYPxNeE833S30MveYuNq/ewpVXXsmAAQNauYqKiorKmcPZYK9PBd1qfl3P7kXUfHYnAyd8xU1J0fylbwJ2t4/xT3zHpPRoXpg3DLfDx/t/WENcnxDGhe2i+K9/I+m1Vwk691zkGi/O7aU4t5Xiy7ODBMbUEMxDo7AMjGwQfSodXhZvL+CzzUfYmleFRoJz06KYMzyBCzJiMRtOTCxUFA8u1xFc7txmwrbblYfTlYuiuBr6CgFerxWNJprg4D5ER2cQZE3FbE5CozHh91fj81fj99Xg81dT47BzoNTPwTKJQ+UGDlfayK0OxSMHBHCNJBNrKSbJVkCS7UjDFmK0AxI6nQ2tNhit1oZGE4QkLPgcJjw1Bhw1Wuw1GqodGqp8Eh5/IK0iBJIrhlhtAXE6OqJBnK7fzGbzSf3KewKy34fH4cDtqMXnduN1u/B53PjcHnx1da/bjb+uDByrL1343J66c+r7e/B7PR26B53B2ET0rhO8TUZ0RlNz8btVwbx+34yuQSwPCONa3dkTi7kn4q9w4z5QiWd/Je5DVQi3DBLoE22Y6gRtQ5IN6RQnp+8IqoB9utn5OXx6E9z6HSQGJr6zV+xkneRjw5gBJAUF3t76vDJvPryKtBgvadUGzP21RN484ZhDV7t83PbORjbkVPDnizO5YXxKV38ali1bxpo1a5gxYwZjxow5bv/H1z7OZwc+Y8XcFYSbwrv8/lTah9OZw7r1MwkPG8/gwa+qhkhFpYOoE+LO4bTaa9kHzw+F0GTuHPMSy8tr2DgugzB9ywQ2//nuAP/8Zj8f3T6WMb0jTv29NkH2K2z8OptNS3OwBBuYfF06yZnN70koCjnzruZgmZPbR9/B7NRyZvX9C/Zl57E5RiHWNBq3280999yDUY2pqKKicgaj2uvOoVvNr+vxOuCp3lw39k12W1LYOC4DSZL4v6/38MaqLH585HwSQs1sWprN2oWHueyBwTjvnYcm2Ebqp58iNQnf4C9z4dxagnNbKf5SF2glTP3DsQyNwpQejqZOpD5UWsuCzUdYsOUIR6pcBBl1zBwUy5zhiYxOCUej6Zw5lRACr68ctysXhzOHoqIdlJbuweXKxWCoxmh01fWDcncY+fYE8ppsJa6ohrEsOjdJtgoSg6pIsNYSb3YQZfKArMXnM+D16PB6tbjdWtxuDS4XyPLR2eYaMaAjxGAj1BZCeGQ4EYnRRMRHERYWRkhISLfxoj6TUBQZv8eDz1Mnbrubit7uBlG8QQT3eOr6uBrP8TQK6E3P6VDMdElCpzegM9RtTesGI1q9Hp3B2Mrx1trq2o86R2s46hydvtn/VZX2IWSBN9+Oe38lngOVePPsIEAyajH2CcXULxRTWhi6iO71UqlbCtiSJGmBjcARIcQsSZLCgY+AFCAbuEIIUVnX97fALYAM/FIIsex443crA2svhn/1g2l/g3N+CcDbuwv4TXEJdweH8scRKQ1dv3ljF3m7y5lSsx9dRBIJT1xwXGHR7ZO574MtLN9dzH2T+/LgtH5dKkYqisJHH33E/v37ufrqq0lLSztm/0NVh7j0i0u5f/j93Dro1i67L5WOk5v7BgcO/h+ZGc8SG6sm2lRR6QjqhLhzOO32eu3LsPRR9lzzDefnG3koJZaHUluuSnH7ZCb/8wcigox8cc85nTZBPRmKs2v49u3dVBY5yZgQzzm/6IvB1Ci+u/fuJevyX/Di7IdYKkXz5OhniFIU1nxmZeh9d7N6yTrGjh3LhRdeeBo/hYqKikrXcjbYa0mS3gRmASVNQn6dufPrpnwwj488Idzf606WjOjHsGALR6pcTHzqe24an8LvZ2Xg88i894c1hMVYOL9PHoW/+Q3Bs2YRNu8qzMOHN5s7CyHwFTgaxGylxotk0GLOjMAyNApj3zAkrRTIT5FVweeb8/l6RyEOr0xCqJk5wxO4bFgCvaOC2nX7iiJweP3UuP3UuHzY68oat6+ubNLu9lHt8lFW7aDC7qTWK+MRWgSN4l6wxkm45CRMchMmuQjXOLHiRZJAq9ViMBjQ6/UYDIaGul6nR+eT0LoFGqdAqpXR+SR0BPqbwq2Yo21EJEcT2TeOoKiuz8GlcmoQQuD3eY8viLvd+L1e/F4Pfp8vUHq9jZvP29Am1+376o7JdaUQbb8UOR4BYbwNwbuJSK43GI4vousDfSSNBo1GW1dqkOo2jUaDpNW2aNNotU32m9dbnN8NBXfF6cN9qBrPgUrc+yuRqwJe/doIE6a0MIxpoRhSbAg9KH4ZRQ7Eyw9sfuRW2hS/jCz7EbKMLB99XG6Iud+s3e9vpW9gLEWRufCuB7qlgP0gMBIIrhOwnwIqmsTUDBNCPCpJUgbwAY0xNVcA/bptTM22eGEERPSFqz8CwOXz0/u7bfSRdKy6YEhDt8NbS1ny8g4mB+di0/Qh4qZ0zP2j2hq1Ab+s8NiCnXy0MY95o5P42+yB7cqWfKJ4PB7eeustKioquOWWW4iJiTlm/1uX3UquPZev53yNTtPSs03l9CCEzMZNV+By5TB82Hx0OhuSpEWSNATeMWmOUT/94o2KyunkbJgQnwpOu732OuG5gZA4ihsGPcG6Kgcbx2UQpGvpPbRwyxEe+Ggrz1wxhDnDWyZ8PB34fTLrv8xi6/JcgsJMTL5hAIn9wxqOF//jSfZ9tIBbZ/yBqSkeLu/7ML7Vk/hBqeacYTewefNmbr/9duLi4k7jp1BRUVHpOs4Gey1J0kSgFni3iYB9Zs+v69nyP6q+eoRB5yzmtqQY/tg3EArslx9s4bu9Jaz57WRsJj07fsjnxw/3M+vuQRi/eIWqBQsQTif65GRCLp1N6OzZ6BMSmg0tFIEnqxrX1lKcO8oQbj8aqx7z4EgsQ6MxJNuQJAmXV+ab3UV8tvkIqw6UoggYlhzK1AEx+GSFGpe/iSDto8blx+6pK90+lONILGa9lmCzjmCTnmCznmCTDptJT5BRi+Kuxab10zfSTN8oC6FWU4NA3bTU6/VotdqAYFnmwptrx5tTgze3Bl+xMxCKWwJdtAVjr2AMyTYMvYLRRfbcZHAq3QchBIrsby56extF74AA3tgm14nkDSK479j9A6J6ExG97rgiH/NrreuQpFZF7eaCeV1dG6i3EMybieRNjmmPEt0lTUuRWA4Iy60K0XUishkbUboEovVJRBkT0WkMKEKmzFNAsSuLImcWld5iBF0ThUOj1aLR6tBoNXWlFo1Ox50vvdO9BGxJkhKBd4DHgQfrBOx9wCQhRKEkSXHAD0KI/nVvhxFCPFF37jLgz0KINce6RrczsIt+CbsWwqNZUBdQf8SiTRSaNWRPGYKh7g2N3yfz5kOrSE2C9CIfhiQNMfdPbtclhBD885t9/Pf7Q1yQEcO/5w3DpO+65TvV1dW89tpraLVabrvtNoKC2n7L/G3OtzzwwwM8d/5zTEme0mX3pNJxah0HWL/+EoTwdvBMqRVhW6oTwJvWNUhooEEY1wCNdQktNNSb9KNuzFb7aUGqG79ZXQOSBqMxlpjoGVitfTv956WiUs/ZMCE+FXQLe73yKfj+cTbfsIqZ2TJ/7BPP3cnRLbopiuCyF3+muMbDdw+dh8XQfV7IFh2uZsXbu6kucTFoUiLjLuuD3qhFrnVweNYsXkmbzueRg/jHqLeJMeay/aMkUu69kr0rswkJCeHWW29F0w29RVRUVFROlrPFXreSdPnMnl/X4yiHf/bl6nM/5KA5iXVjByBJEtvzq7jkPz/z+4sGcOu5vZH9CvP/vBaDWccVvx2FcDmpWb6c6gULca5bB4BlzBhCLruU4GnT0FibJ20WfgX3vkqc20pw7a4Av4I2zIhlaDSWoVHoYwL9S2rcfLG1gM8257O3yA5AkFFHsElHsFmPzdRciA6Ude119aZitc2kQ38SjmmKR8abb8ebW4M3J1AqzkDSRsmkxZAcjDHZhiE5IFprTN3n2UZF5WRRZLlVr3HZ70MoCoosB0pFQdRtiqKgKHKz/YZ6s/5yQ9vR5wvR9tiiWX+57npNxmvzeq3fn1CURjFY11wU1taJwhqtNuApXlfX1vetb5O0mN1WLLVmTDVmDI5A/H9FJ/BFyMhRAiVaQrLVjdUgPrcyVpNjGq0WbbO2RrG6rRdj3S6EiCRJnwJPADbgoToBu0oIEdqkT6UQIkySpP8Aa4UQ79e1vwEsEUJ82sq4twO3AyQnJ4/Iyck56XvtNLZ9BAtuhztXQewgAB5cuY/5iotX+iYxO6kxduXyN3eRs6ucqVXb0Uamk/j4ZCRd+43WWz9n8ZcvdzM6NZzXrh9JiLnrMo4WFBTw5ptvEhsbyw033IBe3/q1/IqfGZ/PIN4azyvTXsGkO37WZpVTh92+C7t9F0IoCBSEkOHoulAQHKsuEEJGICOEACHX7SvN+glRt99WXcgIRN11m9Yb7yew/KjtutdbDgiCgtKJiZ5FTMxFmM3Jp/eHrHLGcbZMiLuabjEhdlXCswOh/0zm9nmUfQ4368dmYGplwrghu4K5L6/hV1P7cf/UY4fQOtX4vDJrFx5i+3f5BEeZmXLDAOL7hlKzfDm7fv1bbrnoT4xN1XFN33uRtk9kaUEp8675G18s+IKZM2cyevTo0/0RVFRUVDqds8VetyJgn/T8uindwl63xduzmK/vx4Px1/PNyH4MtlkAuOKVNRypdLHy4UnotBr2rStixVu7ueDWTNJGNq4g9uYfoXrRF1Qv/AJfbi6SxULw9OmEXHYplpEjW4QDUNx+XLvKcW4rxXOwEhTQx1oDyR+HRqELDcx17W4fZr22S1dGN0UIgVzhxtPUu7rIAXWRG3RRZgy9gjEmB2PoZUMXZUHqBiHRVFRUuhdyrRfPwSrc+ytxH6hCsQecHXXR5kC4kX5hGFNDGnIDdCadabNP+nWcJEn1sbk2SZI0qT2ntNLWqoouhHgVeBUCBvZE77FL6DU+UGb/3CBgX9snhvl7s/ggt7SZgN13RDT71xfjjAsl2G/EsSmPoDG92n2pm85JJdxq4KFPtnHlK2t49+bRRAd3jWAcHx/PnDlz+Pjjj/niiy+4/PLLW32TotPouCHjBp7c8CRTP53KnLQ5XNX/KuKD4rvkvlQ6hs2Wic2Webpvo9PweEooKVlCccliDh3+J4cO/5Ng22BiYmYRHT0Tk0ldKq+iotIEcxiMuBHWvsT9ox7hF5V+Piiq4KaEyBZdR6WEM3NQLC+vPMRVo5OI6SL7eiLoDVrOvaIfvYdG8d27e1jwr80MmZLEmIvPJ3H8KC47uJL/ifO5IGI6MQNWMWDHYHaWr6F37958++23DBgwAJvNdro/hoqKiopK19Lu+fVRDmJdeU8nR/osLlzxOA/HX8+XJVUNAvatE1K5/b1NLNlZxMVD4kkbFcPmZTmsW3SY3sOi0NYJy4bEBKLuvpvIu+7CtXkzVQsWYF+ylOoFC9AnJBBy6aWEXDobQ1ISABqTDuuIGKwjYpDtXlw7ynBuLaFmaTY1S7MxpARjGRqNeUA4kgyy4q9z9gEUEfhpK6LO6ae+Xn8s0Cbq6wp1/QL15uMIhAL+8rqQILk1KLU+ACSDFkOyDdukpIBonWRDY+k6xzYVFZUzB22QoW51SXQg7FCxs07MrqR2XRG1PxeAVsKYGoIpLRRjWhj6OGu3Czd00h7YkiQ9AVwH+AETEAx8DoziTF/i9NwgiBsKV74HBJYi916wHm2IkYNThjT8smWfwpsP/0SvVD3pWZVoQ8yEXzUEU3p4h/4gftxfyp3vbyIiyMC7N48hNdJ6/JNOkFWrVrFixQrOO+88zj///Fb7CCHYWLyR+Xvm813edwBMSpzE1QOuZnTs6G73x65yZuByHaGk5CuKSxZjt+8CIDRkVJ2YfSEGQ0uBSkWlPZwtHl1dTbex1zWF8PxgxPDruTj2Doq8PtaMyUDfimdSbrmTqc+sZPbQeJ6eO6SVwU4/XrefNZ8fYuePRwiLtTBxRiTFd1/LLVMfZUBKMLf2uwPD4XF8ta2Ue37/Gm+/9jbp6enMnTv3dN96j8TncVN86CCxaf3RtbEaTUVF5fRwttjrszaECEBVHjw3kKsmLSDHFMvqMYEwIooimPyvHwgx61l4zzlIkkTWtlK+fmkHkUlBJGdEkJgeRlyfEHRHeRIqLhf2FSuoXrAQx5o1IASWkSMJuewybNOnow1qObf2l7twbi/FuaUUf4nzVH16AHSR5kDc6uRgDL2C0ceo3tUqKiqdj/DJeLJqcNclg/QXB77rNEH6Bu9sU99QtDbDCY3f7UKINAwW8MCuDyHyNFDeJMlEuBDiEUmSMoH5NCaZ+BZI65FJJhbcCQeWw8MHoU6svWjhFjaFSHw/sh8D6t4UA6x4ezfZ28qYXLwQXdRENOYIdLEWgqckY86MbLcx2pZXxU1vb0AC3r5pNIMSuyZjsBCCRYsWsWXLFubMmcPgwYOP2b+wtpCP93/Mp/s/pcpTRZ+QPsxLn8fFfS7Gorcc81wVlRPF6cyiuDggZjscBwAN4WHjiImZRVTUdPR6NaO2Svs5WybEXU23steL7oPtH7P8xg1cd6CC59KTuCouotWuT3y9h1d/OsyX905gYEL3/e7I21PBd+/uwVHlIT2mktUbVvD6oFn8bfR6EoI/pGThREonJzIo/Dx++OEHrr32Wvr2VfMHtJfqkiK2fvM1O7/7Brejlti+/bjkwd9hi1BfjqqodBfOFnvdioB95s+vm/LKRN4PPZeHoufy7aj+ZAaZAXhvTTZ/+GIXn9w5jlEp4Qgh2P5dPoc2l1CcVYOiCLQ6DbF9gknsH07igDCik21omoT98BUWUr3oS6oXLMCbnY1kNhN8wTRCLrsMy+jRLUKMCCHwFTrwZtcEGjTU5eyRAv7vGgk0UkASqGuTNHXHpCbHmrQ1HNc0GaeurzbYgNaqvjxVUVE59cg1Htz7q3AfqMRzsBLFEYixr4+zBsTstDCMKcHtDovcUwTsCOBjIBnIBeYKISrq+j0G3EzAa/sBIcSS443dLQ3s5vdg0b1wz3qI6g/Aq+uy+aOzituiwvnbwMZlWdk7yvjqv9uZPjcW46f/xX3QhTFjFhpzFLooM7bJyVgGRyFpjy9kHyqt5fo31lPl9PLKdSOZkNY1kyq/3897771Hfn4+N9xwQ7uWmXlkD0uyljB/z3z2VOzBprcxu+9s5qXPIzm4Gy9TU+nx1Nbuo7h4McUli3G5cpEkPRHh5xITM4vIyCnodG0nJVVRgbNnQtzVdCt7XX4IXhiBmPArpoVchUtW+HFMOtpWVghVu3yc/88f6B9jY/5tY7r1KiKPy8/Pnxxgz+pCjL4yXg6BuF4RPJBxL6biTL5Z6eCOp9/gs/c+Q1EU7r777jZzWqgEhIncHdvYsuxLDm1aj9ag0G9KFLZ4J3uX2fFVh3PxA4+SlHnsl/kqKiqnhrPBXkuS9AEwCYgEioE/AQs50+fXTVn5NGWrXmDI+EXc1yuG3/QOhAx0eWXG/eNbxqSG88p1zf8MvG4/BQeqyN9XSf7eSsrzawEwmLTE9wsjMT2whdctjRdC4Nq6leqFX1Dz9dcodju6+DhCZs8m9NJLMfRqf9hPFRUVlTMNoQh8BbW4DwTiZ3tzakARSHoNxt4hGNPCMPULQxdl7jlJHE8F3dLAlh+CF4bDRc/AqFsAOFLlYuT3O0gMM7Nh0qCGrrJf4a1HVpE6OJIpN2bgWLeekqeexl9pxDhkDhpjFNpwE7ZJiViHxxz3bUZxjZvr31jP4bJanr1yKLMGd03saafTyeuvv47b7ea2224jLCysXecJIdhWuo35e+azPGc5spCZkDCBqwdczfj48WikU5P4QuXsQwiB3b6jTsz+Co+nCI3GSGTEZKJjLiIy4ny02u4T41al+3A2TIhPBd3OXn98Axz6jkXXr+P2/SW8ktmL2dGt27J6r67Xrh/JtIyYVvt0J7J3lPH9WztY5/WwNEjmj6MP0yv0OVzLLmR7Pw3zJj/IO++8w7nnnsuUKVNO9+12O7xuF7tXfseWZYupOJJLeB8NKefowboPRXGj0RgRikz59jTy1kmcd+0tDJ85u1u/3FBRORtQ7XXn0O3s9dGU7IEXxzJ38pcUGKJYNSa94fv3n8v28d8fDvL9ryeRcoywmi67t0HMzt9bQU2ZGwBLsIGE/o2CdnCEGcXtpva776hasBDHzz+DomAePpyQyy4l+MIL0ao5JVRUVM5yFI8fz+Fq3Psr8Ryowl/mAkAbYsSYFoqpLtxI0/j8qoDdXRACnhkAvc6BX7zR0Dzo/TWUJpjZPj6TaGPjL+7bd/dweEspNz81Aa1eg1AUar76mtJnn0NRIjGPvBJJF4k2xIBtYiLW0bFI+razgFY7fdz67gY25lTyl0syuX5cSpd8zLKyMl5//XVsNhu33HILJlPHxL9SZymf7P+Ej/d9TLm7nF7Bvbiq/1XM7jsbm0F9EFDpOoRQqK7eTHHJYoqLv8bnK0ertRIVOZWYmFmEh09AozmxWE4qZx7qhLhz6Hb2umArvHoe8pQ/c57hQowaiRUj+7cqQvplhQuf/wlZESx7YCKGdi6NO524HT6W/PZj/mYMQ6cT/GPSHzHYo1i12MjlTz3Jzp/3sGPHDu68806io6NP9+12CyqLCti6dDE7f1iBkKpJGqsjLK0CmRK02iBioi8iX5PCwpw1TDfnYfVl4Svrx+4FEv3GTmL6Hb9E38FnIRUVlc5DtdedQ7ez10cjBLwwnHcSL+fRsFl8P6o/A+rCiJTUuJnw5PdcNTqJv84e2O4ha8pczQRtlz2QIDEkylwnZoeT0D8UnaOKmi8XUbVgId5Dh5CMRmzTphFy2aVYx41rEWJERUVF5WzEX+EOhBo5UIn7YBXCLYME+kQbpnpBOzVUFbC7DZ/eDDmr4cE9DXGw7160nc9tCk+lJXB9YlRD15xd5Sx+YRsz7x5M6uDGsB+Kx0Pl+/+j7OWXkSzJWMZfDyIMTZAe27mJWMfGojHqWr282ydz7/wtrNhTzC8n9+VX0/p1iWdQVlYW7733HqmpqVx99dVotW0L623hk318k/MN8/fOZ3vpdiw6Cxf3uZir06+md2jvTr9nFZWmKIqfqqp1FBcvpqR0GX5/NTpdMNFRFxIdcxFhoWPRaFr/f6ZydqBOiDuHbmmv37sMinby0byfuP9AEe8NSmVaZOtxrr/fW8JNb2/gj7MyuHlC6im+0RPDX1nJv+98hn+njmWe7RBTxz0Pq2bwfVANv73jJf7zn/8QFRXFjTfeiOYsnXQLRSF722a2LP2S7O0bCUl1kDRaQhucDyiEho4mPm4udn0/ntr8HOsK1xFtjqbUVczMEJlpwV58nmj2fxJCSHgfLnnoMcJiu2b1m4qKyrFR7XXn0C3t9dF88wdKN33A4LGf8mBKDA+nxjUceuiTbXy1vZA1v51MqKXjDilCCCoKHA1i9pEDVfjcgbDhkUlBJPYPI6F/GOHeApxfLaT6q69RqqsxpKYSdt21hM6ejcbatve3ioqKytmEkAXefHudd3Yl3jw7CEh6cqIqYHcbNrwBXz0Iv9wC4QERdsmOAm7OLWBUpI1FY/o3dJVlhbceXkVUso2L7hmM7ijvan9lJeUvv0zF/A/QRvXHeu5NCI8NjUVH0DkJBI2PR2NuKbD5ZYXfLdjBxxvzmTc6mb9fOhBtF2Qo3rx5M4sWLWLUqFHMnDnzpITyXWW7mL93PkuyluBTfIyNG8vV6VczMXEiWk3HxXEVlY6gKF4qKn6muGQxpaUrkOVa9PpwoqNnEhMzi9CQEUhqmJuzDnVC3Dl0S3ud9RO8MwvfzGcYJ48l1qDny+FprdoxIQTXv7me7fnVrHx40glNik8HlZ9+zi9WlFEZlsA/xvwDk0Zh+2fJjPzT3RiqrCxatIjJkyczceLE032rpxSP08mulSvYumwxLncWMUNchKXVgMaB0RBDXNwc4uIux6+L4KWtL/HB3g+w6q3cO+xe5vabS6GjkM/2f8ae3PlcYitHUbQUrEjBUxLKrF8+TO/ho073R1RROetQ7XXn0C3t9dHkrYc3pnHZlCWUG8L5cUx6w6E9hTXMeP4nHp7en3vOP/lkxYqsUJJjJ39vBfl7Kyk8XI3iF2g0EjG9g0noG0xE9X40i9/Bs2MHGpuN0MsvJ+zaazAkJp709VVUVFTOJBSXH/fBKqyDo1QBu9tQshdeHAOz/wvDrgUCoT0yPl6HlBzE/vMGY2mS8Xj79/n89NF+YlKDmXHnIKwhxhZDenNzKXn2WexLlqJPHYp14s3I1SYko5agcfEETYhHG9R8Qi2E4Oll+3jxh0NMz4zh+auGYTpG+JETZfny5fz8889ceOGFjB079qTHK3eV8/mBz/lo30cUO4tJCErgyv5XMidtDiHG1r3jVFQ6E1l2U16+kuKSxZSVfYeiuDEaY4mJvoiYmFnYbIPUeKdnCeqEuHPolvZaCHh9KjhKeevy5fz2YCGfDu3DhLDWw1jtLaph5vM/ceP4VP54ccYpvtkTQwjBp7c+zMNR53N7PwdjUn6Lfut0fsjz87unX+KLLxayY8cOZs6cyejRo0/37XY55Ufy2LpsMXtWLycosZjYIR70IZVIko7IyCnEx80lPPxcBBILDy7k+c3PU+WpYm6/udw77F7CTM3jpHtlL98d/B+u/H9hxcXBTZE4N0WSMuN85lz3KzQnsDJNRUXlxFDtdefQLe310SgKPJPOm/3u4He2KawcnU5/a2MIp+veWMe+IjurHp3c6WG/fF6ZooPV5O8LCNoluQFvwuAoM31TBFE7vsK3YhHIMkGTJxN+3XVYxow+q+cNQgjk8nJ8R47gzc/Hd6QAuaICSa9HMhiabHo0RmPzNr0ByWhA06xf3WY0IukNaAx60OvP6p+xikpPQ42B3Z0QAp7uA2nT4bKXGponvbWGvSlm3h2UygVHLVM+tLmEFW/vxmTVM/PuwUQltT6Bdm3dSvFTT+PavBnjwLFYJ9yIr1iDpNNgHROHbWIC2uDmAvibq7L46+LdjEkN57GLBpARF4xO23nGXFEUPv74Y/bt28e8efPo169fp4zrV/x8l/sd8/fOZ1PxJkxaExf1voh56fPoH97/+AOoqHQCfr+DsrJvKS75ivLylQjhw2xKJjomIGYHWVuPm6tyZqBOiDuHbmuv9yyGj67BPedNRtv7099q4pOhbXts/fbzHXyyMY+P7xzH8OT2JTA+3bgPHOCKJ5eQF5nEs5PmgzhI3qejcSVcwOV3XsA3Kxezb98+Lr30UoYOHXq6b7fTURSZrC0b2bL0S8pKfyZyQA2hfWqRNH6s1jTi464gNnY2BkMEAFtLtvLE+ifYXb6b4dHD+c3o3zAgYsAxr+H321m/9U5cNWvJzQ+icmkCZTEa0q+bw+zMy9WX7yoqpwDVXncO3dZeH82XD1C0ZwXDRr3Pw6mxPJgS23Doh30l3PjWBv41dwiXj+haL2i3w0f2jjL2ri7kyP4qkCCxTxCJzl1Yl76JqCjD2K8fYdddS8jFF6M5A3MlCCGQKyvxHTkS2PLz8TbUj+ArKEC43c3OkcxmhN8PPl+n3UczYbteEDeZ0VgsaKzWQFm/WRvrksWC1mpFajhubd7HbEbSqSElVVQ6E1XA7m58dC0UbocHtjc0/WPZHp6TXFyREM6/M1NanFKaa+frl7bjdviYelMGfYa1nlhJCIF9+XJK//UM3pwcLOMvwDL2ajzZXpAkrCNjsJ2XhC680UB+sfUID32yDZ8sCDLqGJUSxtjeEYzpHcHA+JMXtL1eL2+99Rbl5eXccsstxMTEnNR4R7OvYh8f7P2Arw5/hVt2MyJmBJMSJxFrjW3YIs2R6NR4xSpdiM9XTWnpcopLFlNZuRohZCyWvsTEzCIm+iKsVjVu+5mGOiHuHLqtvVYUeHEsaPW8OPNz/nq4kK+HpzE8pPX4lVVOLxf/ZxU+v+DL+yYQZWu5Yqo78u2TL3FLZTJ39HczutcjmPdPZu3qICwhsxk5K5ldRT+RnZ3N3LlzycjoGd7lx8NdW8vO779hx4+foY84RESGHUOQG63GSmzsJcTFzyXYNrjhBWSps5RnNz3Ll4e/JNoczYMjH2RmavtDowmhkJX1AlnZ/8brjWT/J6GUK/DzqGrGDZ7KFf2vYFCkunpHRaWrUO1159Bt7fXRHFwB71/O7GnLqdEH8/3oxjAiQgguePZHdFoNX/9ywin73q0udbJ3TRF71xRSW+nBaNHRK6KWyM0LMO1ahTYkhNArriDs6nno4+KOP2A3QQiBUl2NN/9IM5Had+QIvoIjeI8UIJzOZudoQ0LQJyaiT0ho3BITMCQkoI+Pb4gTLhQF4fMhvN7GzeNB8XoR3qbtnoa60tCvrvQ1nqt4vM3GUtxuFKcDxelEOJ0oDieK04nicCA6IJ5LJlNzAfwoIVwymtCYTUhGE5LJiKa+NAXaNCYjkskcKBv2TUjGuj4mU8AjXX1GUDlLUAXs7sbal2Dpb+BXuyAk8OZ39aEyfrFuP7aEIPZMHISmlS8oR7WHJS/voDirhjGX9GbEjF5tfpEJn4/Kjz6m7L//Ra6sxHbJFZgHz8G1J7CUyTIsGtukRPRRFgBK7R7WHC5n3eFy1h4u51CpA4Ago46RKWGMSY1gbO9wBiaEoD8BQbumpobXXnsNn89HSkoK8fHxxMfHExcXh7WTkllUe6pZcGABH+77kCO1R5od00paIs2RjaK2JbaZwB1rjSXcFI5GjWOs0gl4vWWUlCyjuGQxVVUbAIEtKDPgmR19EWazGvfuTECdEHcO3dpeb50PC+/CcdWnjCyNZVSIlXcHt/0yaldBNZe/tJohiaG8f+uYE7KXpxrF5eLaB15lW3ASL0//Fp/4jsovplEcMQJRnER4oonqsJ2UlBcxb9480tLSTvctnzBludlsWbqQ/LwvCe1Tii3JgSRBaMho4uOvIDr6QrRac0N/n+zj/T3v8/K2l/EpPm7IvIHbBt2GRW85oeuXlq5g1+5fg9ByeHkcFTla1gyuYn9MJenh6cztN5eLel+EVa8m+VJR6UxUe905dGt73RS/F57uw+tDf8vvTeNZNSadvpZG562PNuTy6Gc7eOvGUZyf3rpTWFehKIL8vRXsWV1I1tYyZL9CeLiG+KqthK36H3rZhe2CaYRfdx3mYcO6hWgphMBfWIh77168ubn4jhQ0E6oVh6NZf01wcJ0wHY8hIbFBoNYnJKJPiEcbFHSaPkn7EV4vissVELTrN0eTep3wrTgcDW2itX4OB4rHg3C7UTyeE/cq12gCQrnR2HpZJ3TXi+AthPL6PkZT60K52awK5irdBlXA7m4UbodXzoU5r8HgKwBw+2QyXvkRZ2bYMT28/D6Z79/by/71xaSNimHydenoDG3HUZTtdspffY2Kd98FIQi75hb0fafh2lqB8CuYB0cRfH4S+tjm1yuxu1mfVcHaw+WsPVzBwZJaAKwGLSNTwhnTO5yxvSMY1AFBu6SkhJ9++omCggLKy8sb2kNDQxsE7XpR22w2H2OkYyOEwO6zU+QoarYVO4ub1T2yp9l5Oo2OGEsMMZaY5uJ2E7E71BiqfpmrdAi3p4iSkiUUFy+mpmYrAMHBw4iJuYiY6JkYjZ27IkHl1KFOiDuHbm2vZR88PxRCk/jX+W/zdHYR343qT0ZQ2zZqwZZ8fvXRNm4+p+fEw96y+Dvm/OTg6rAKpoz8PywFI1m8vporr/8Pmz7Lx2534knag1u2c+2115KSknK6b7lDHN6ygS0r3sSr30h4v2p0JhmdNpLExCuIi7sciyWlxTk/5f/EUxueIrsmm0mJk3h41MMkByef9L04HIfZvuNOnM5s7AcyOfSdF9s5mXyTfIh91fux6Cxc1Psiruh/Benh6ccfUEVF5bio9rpz6Nb2+mg+vYWCvO0MH/oav02N4/6Uxudtt09m5vM/Uen08tld4+kddXoEVbfDx4ENxexZXUhprh2NViLeVE7kti8Iy9+EOTOD8OuvwzZjBhrDqUkQXS9Wu3btwr1rF+5du3Hv2oVcUdHQR2O1HuVBHY+hyb42OPiU3GtPRPj9AS9yt7tB1BZuN4rbg/C4G9ub7XtQPHWl24VoOOY5aoyjxjwZwVyS2hDITUhmUyD8itlU5zneSpu5rr+57nhrbWZzIJyLqq2oHIUqYHc3FBmeTIXMS+GSfzc0X/XWOn7oZeD+lFh+27vtpUNCCDYvy2HtwsNEpwQz867Wkzs2xVdYSOlzz1O9aBHakBAibr8XbfQYHOtLEF4ZU0YEtnMTMKQEt/olUmr3NAja67LK2V8cELQtBi0jegVCjoztHcHgxPYJ2m63m8LCQgoKChq2ysrKhuPh4eHNBO24uDhMnRgXTAhBpaeSYkedqO1sKXYXO4vxK/5m55m0JmKsMcRaYomxxhCkD8Kqt2LRW7DqrYG6ztK4r7M2HLfoLeg1+k77DCo9D5crl+LirykuWUxt7R5AIjR0NDExs4iOmt4QZ1WlZ6BOiDuHbm2vAda+DEsfper6pYzItzI1IphXWgn11ZQ/L9rF26uzef6qocwemnBq7vMkueOh1/heiuK1aRvxaz/Et+xSltr2c8dNT1H6s4ZtKw9TE7kDDF5uuPEGEhO7/0qSmtISfvjwH4iw5QTFO0FoiQg/n8TkeUSEn4sktXQAyK3J5akNT7EyfyUpwSk8MuoRzk08t1Pvy++3s3v3w5SWLUfUZrD9I5nE/kPpfe0sFhZ8zdLspXhkD4MjBzO3/1ymp0zHrDvxF/sqKmc7qr3uHLq9vW7KrgXwyY3MuuA7PPoglo9qniMpp9zBnBdXYzXq+Pzu8UQGnd6wX2X5dvasLmT/umLcDh9mg0xc2Uai9yzBZhWEXXklYVddiS4qqtOu2Uys3lkvWO9Crp+Ta7UY+/bFlJmJaWAm5owMDCkpaEJCVNGxhyD8/gYxvFXB3OVuFMOPFsxd7gbhvJlg7nYHhHRXXX+XC8XlQng8x7+ho5EkpDpBu17Ubksk11jMzYXwYwnmTYVzk1GNU97DUAXs7sj8K6H8ENzXeI+vrDzEX0rL6J0QzKpxx04KBHB4aynL39qN0azjorsHE5XcenLHprh376b46adxrlmLvlcyUfc+CKZ0alcXIFx+9LFWrOPjsAyNRnMMz+6y2oCgva7OQ3tfsR0As17LyJR6QTucQQmh7c7w7HQ6W4ja1dXVDccjIiJaeGobuvBttCIUyl3lLby368XuEmcJtd5aHH4HilDaNaZBY2gmaDcVuOvF76MFcavOSpAhCJvBhk1vI8gQRJAhSBXDezgOx0GKi7+iuGQxTudhJElLWNh4YmJmERV5AXq96r3Q3VEnxJ1Dt7fXXic8NxASRvK3sc/xYm4Jq8ak08fS9ktVn6xwzWvr2H6kigV3n8OAuO7///nwvhwueGMrM/w5XD7zDYwVvdi80EKpsYoBcy9hUsa1LH9/K4fdq5H0MldfdR19+p+8R3JXIPv9bPr6Y7Ky/01ERikayULvPr8kPv5yDIbwVs9x+py8uv1V3t39LnqNnjuH3Mm1A65Fr+0aWyuEQnb2ixzOeg4diez82IpeE80lv/4d5sRovjz0JR/v/5is6ixsBhuz+8xmbr+59A5V8ymoqHQU1V53Dt3eXjfFY4en+vDy2Cf5s24Ya8cOIMXcXKTeklvJvNfWkh4bzAe3jcV8jLnvqUL2KWTvKGPP6kJyd5UjBISLUmL2LSWqcgfh0ycTft31mAcN7NC4Qgj8BQV1ntW7Wxer09IwZWZgyszEnJmJsX//MzKxpErXIBSl0RPc5QoI3S43wu1CcdWJ3m53QOyuO9YohDcK4i3E8aZtLhfIcofvTdLrW4jjushIzEOHYh42DPOQwWhtx9fSVE4NqoDdHfn5eVj+R3joAAQFYm/tPFLNhYu24E8PZd3YAfQyH/9NcFm+na/+ux13rY8pN2bQd8Tx43gJIXD89BMlTz+N58BBzEOHEvXrh0HEU7umAF+hA8mkwzoyhqCxcegij+/1U14vaNd5ae8tCgjaJr2Gkb3CGds7nDG9IxiS2H5BG8DhcDQTtAsKCrDbA2NLkkRkZGQzUTs2Nha9/tQKu0IIPLIHh8+B0+fE4Xc0qzt9Thy+xjanv/m+w+doaKs/pz2CuFlnJkgfELaDDEHY9LZW60H6IIINwS3qVr1VjfndDRBCUFu7l+KSxRQXL8btzkeSDERETKwTs6eg1Z5YvFWVrkWdEHcO3d5eA6x8Cr5/nNJbVzHqkMJlMWE8m35s8bbE7ubiF1Zh1GlZdO85hFpOzfLfk+GRJz/ls3IDr43ZhAh7n5CSi1jx4yH05RL+1FBuuOfvHNrm4Nv1XwCC80dewjkXZaLpRrG+8/fs4ufFfyS4/04MQX4iwy4hY+Af0evDWu0vhODrrK95ZuMzlLhKuKTPJTww/AGiLB3zcnO73WzduhW3242iKA2bEKLZ/tGbVrcHm+0jhJA4sG0klSURhMUlYA4JRVEU7B47la5K7F47kpAwa81EhEQwZfwUMjMz0aleRSoqx0W1151Dj7DXTfnfFeRXljAy8zke6x3Hfb1ahu1btquIO9/fxLQBMbx07Qi0mu7jWVxb6WHfukL2rC6kusSFTpKJLtlEbN5PxPYOJuL667BNm4Z01Nz3uGK1TlfnWd1+sVoIgd+noNFIaLSS6oGtctoQPl+dON4ohLcUyY9qqxfEm7T58vLxHDgQSNouSRj79m0UtIcNxZCSov6dnyZUAbs7kr8JXp8Mc9+GzMuAQFKHof/6jpKREfytbwK3JbVv8uSs8bLk5e0UHa5h9MWpjJzZvv9swu+nasECyv79Av7SUoJnziTq4YcQHgu1awpx7SgDRWDqH4Z1XDymfmFI7TTqFQ5vkxjajYK2TiMRF2oiMdRCYpiZxLD60kxiuIXYYNNxHxzsdnsLUdtRlzxCkiSio6MJDw/HbDa32CwWS7P9Uy12twchBG7ZjcPnwOVzUeurpdZXS423hlpvy7rda8futVPrrcXua6x7Fe8xryMhEaQPavDoNmlNaCUtOo0OrSZQ6iRdsza9Ro9W0gaOS7pAe2vn1B1vcU7d8frzWrQdNW5rx7VS3Zh1fTWS5owxLkIIamq2UVyymJLir/F4iwFaXebeNZyKn+PR12jcb/5rbLvf0cea//6PdV5b5xx7/Ob32Fg/b+ImdULcCXR7ew3gqoRnB0L/Gfxu0J95t6CMtWMzSDQdW5TelFPJVa+uYXyfSN68cVS3mhi3Rkmlg4lPLGdcxQF+edU+KjTfo5NCyClOpHCxF42sI3PmDPqNmMGHn3yI4pfobRzPtGuGEZN6er3MXfYafvzoeRzaTwjp5UCvSWbwsH8SGjKizXP2lO/hifVPsKVkCxkRGfx29G8ZGj20Q9cVQrBr1y6WLl1KbW1tQ7tGo2lzkySp2b7RWE18wkIMhgryDw8jb38SQeERhMcnotVq0Wg0+PFT6CgkrzYPg9NAsC8Ys9XM2NFjGTFiBEE9ICmWisrpQhWwO4ceYa+bsvldWHQfM6avRNGbWTayf6vd3v45iz9/uZsbx6fwp4szut28QghB4aFq9q4u5MDGYvxeBYu3nNj8n0iUDxM392IMvVMDYvXOnbh3724hVhszMtANGISmd39EXC98fgmP04/X7Q+UrkDpcfnx1pcuPx6nr64uI5SADiRJoDVo0Rs0aPUadHotOoMGnV6DzqBFp9egbaVNZwi06w1HHa+vG7Ro9Rq0OgmhBLQRoYijSpq1He94837Nj0X3spHQP6zb/b5VTh1yrQP3ju04t2zBtWUrrm3bUGpqANCGhgYE7XpRe9BANBbVqexUoArY3RHZB//oBcOugZlPNzTfO38zX4QojI0P5ZNhfTswnML3/9vLvrVF9B0RzeQbBqBv5zIoxemk/PU3KH/9ddDpiLzzTsJvvAHhBsf6QmrXFaHYvWjDTQSNjcM6MgaNpWPCb6XDy/rsCnbkV5Nf6SS/0kV+pYtiu5umf1InInAHRL+aZoJ2TU0NLpcLp9OJorTtzazT6doUt48lfndH4ftoPLKnQcxuU/RuUvfKXvzCj6zI+BU/sgiUR7f5FB+yIjccb+in+BGcnu+HowVunSbgkaaRNEgEvATqSw2a5vt1fVr0b6WvhNRizHov9noh/Xh9JSSQaBi72flN7xUIF+WEixI0Eg33GHjICuzV96XZZ4SGERrGatJe95DWWG+8XuN4NLmPxrFpGJsWY3NU32b9m3ymtmn5t9N6f9HkuNTwNxcY/Vh/f20fk9r9dyua1ccNeUGdEHcC3d5e17PsMVj7Ivl3bGTsXjvXx0fyf/2OHwf6f+tyeGzBTu6b3JdfX9D65Lk78cS7P/LKbjvvWfeTlJhEYfC7OMN2ITThbN2sQ1ofjj40mNFzbuDb9ZsRXi0h5UMYcm5vxszujdF8aj2ChRDs/GEZu7Y8QcTAI2g0OlJ7P0BKyq1oNK3fS6W7khe2vMCn+z8lzBTG/cPv59K+l3Z4VVJFRQVfffUVhw4dIi4ujlmzZhEXF4dG03GPdL/fwZ49j1JSugTJlcm2+X5iUgdw8YO/xRYe2dAvu8zO+zu+Ys2B+fSqSiTaGY1Wq2XQoEGMHTuW2NjYDl9bReVMRxWwO4ceY6/rcZTBP9P478QX+RsDjrnC+e+Ld/P6qix+f9EAbj23+4Zq8rr9HNpcwp7VhRQerAahEFGxG5s9F7/BigiLQQmJQDaH4NeZ8QkdHpeM1+0/9mMyoDdqMVp0GMw6jGYdBkugNJoDbQazDkUW+H0yfp+C36sge+vqPgW/V8bvVfD7ZOS6435ffZvSIIB3J2JSgxk5I4VegyJUIVsFoSh4Dx/GtXVrg6jtPXw4cFCrxdS/f52H9jDMQ4eiT4hX/266AFXA7q68eynUlsDdqxuaPlyfy6935EBvG7snDCRE3/6JoBCCLd/ksmbhIaKTbcy4czBBYe1PSOHNz6f4H/+gdsW3GHr1Iuax3xE0cSJCVnDtKqd2dQHe7BokvQbL0Gis4+IwxJ+cx4/HL1NY5Sa/0kVepbOZuJ1f6aS4pnkygKYCd1J4U4E7UMYcJXALIfD5fA1itsvlanVr7Zh8jPhK9cK3TqdrsWm12pNuP1abVqvttl+UilAaxOzWBO7W6q22KTI+0VwoP/q4X7RyjuJDFjKyIiPq/4lAqQilod6s7ah9CPzdtHas6b4QAgUFBCgoDecALfvX9W3t+vU/t7butf5YfVuz/TbaG9pQ2h2fXaXj7Lxxpzoh7gR6hL0GqCmE5wfD8Ov5VZ/7WVBcyYZxGUQZjv1CUwjBo59t5+ON+bx63QguyOzeAmO1y8eEv3zNgKIDvHn7BVQvsePNyKK0z8fYa3dR5TWTszoEsS+UyIHDyJNMGLVBmPIGYLNZmXBFP/oMjzoldqo8P5eVn/wZY/IaTGFeQqznMnDIE5hMrSfC9it+Pt73Mf/Z+h+cPifz0udx19C7CDZ0zHvc7/fz888/89NPP6HRaJgyZQqjRo06IeG6KUIIcnJf5dChp9FLiez6xAb+EM6762G2esNYsCWfDdkBrzqbSUtoeD618s+M1dqIr4lC9sukpKQwZswY+vfvf9L3o6JypqAK2J1Dj7HXTXlzBjmyjjF9/8Yf+8Rzd3Lr4TYVRXDP/M0s3VXEf68ezsxBrduR7kRVsZM9awrZuyofZ62MwazFaNYHBOh6IdrSKEAf3Wa06Juco+3ycGCyrCB7W4rdTfdln4IsKwEnn7pQJRqNhKQNOMzU1zWShNQknImkoUm97hxNXbtGU1c2HhPAgQ3FbF6ag73CTWRSECNnpNB7aFS7V5yrnB3IVVW4tm0LCNpbt+Havh3hdAKgi4pqFnbElJmJpgtztJ0tqAJ2d+XHp+G7v8MjWWAJJBXKq3Byzis/4x0bxcsZvbg0pvWYjccia1spy9/cjd6kZeZdg4lJ6djErPanVRQ//jje7GyCzj+fmN/+BkNyINant6AWx9pCnFtKED4FQ69ggsbHYc6MROpAbOv24vbJFFa7G4TtvApng7idX+mixN5S4I4PNRMZZCDYrMdm0hNs0tXVdQSb6kpzXbtJ33DMrG8UhtsrfPv9/habLMttth9LFO8I7RW+O7Jfv0y5vuxo/ei27iqyn43UC9xNhfZjCd7HE8SPJ6S3dR1ZHPX3L47eFa3W6z9DW31b+7xtjnuccZrtH+f+Lki5QJ0QdwI9wl7Xs+g+2P4xh+/cwoQdJdyVHM0f+sQf9zS3T+aKV9ZwuNTBF/eeQ5+o7h3u4cVlu3jq+2yePLSIGXf/lppleYRe3gdH8hYOHv4XblcOhdV6qn6Kx1Udhys5jYiwaMJqhlCZ76HXoAgmXtWP4Ijj59A4EXweN2sWvkFJ1RuEpVWjIYKBg/5BVNTkNs/ZULSBJ9Y/wYHKA4yJG8NvRv2GvmHtX+lWT1ZWFl999RVlZWVkZmYyffp0goM7N3xKefmPbN3xa7aX9OHb7RPY40tClnT0jQ7ismEJ9IkK4ts9xSzfU0yV04ek8aEzH2BsuIkBbhmvvYrQ0FDGjBnDsGHDMKlJuFTOclQBu3PoUfa6njX/hWW/44ILf0KnN/H1iH5tdnX7ZK5+bS07C2r44LYxjOjVetLf7kbAWSYg0Kp0DFlW2L+umE1Ls6kucREWa2HEjBTSRkZ3q/weKt0H4ffj2b8f59atgbAjW7fiy8sDAskiTZmZzURtffTxc9SpNEcVsLsrOavhrRlw1XxIv6iheeLT35M9LJSL4sJ4KTPlhIYuP1LLVy9ux1njZcoNA0gb2TJpxbEQXi8V771H2X9fRPj9hN9yM5G3347GHJiMKk4fjk0l1K4tQC53o7HpsY6OI2hMLNrg9nt9nyxun0xBlauZ13ZepYsKhwe720+Ny0eN24/d7cMnH/tvV6eRGsTterG7qeDdUgDXE2LWE2oJbE0F8LZQFKWZwH0ssft4bcfbP1afY4VV6SzqY3y2R+w+UZG8rbpWq0Wv12MwGNDr9c22pm06nU71UFM5YdQJcefQI+x1PeWH4IURMOFX3Bl3A8vLa9g4LoOwdqyWOlLl4uIXVhFuNbDwnnMIMnbf5Hsur8ysp5dTXG7nVfMB+ibPwldQS8yvRqAJ1lBQ+An7Dv4T5GoKiy2Ub+hLpW0wUWGhjE6/hM1fBx7kR1/cmyGTEzt1Enho0zo2/PAnwjIOoTVAYvxNpPX7FVpt62L5jtIdvLHzDb7N/ZZ4azwPj3qYKclTOvyS1eFw8M0337Bt2zbCwsKYOXMmaWlpnfGRGhBCsD2/ms8357NoWz6VThmb3s4IWx4xO3Zx7shMpt95H3pjQJD2ywrrsytYvD2fL7Zn43DpAZk+EQr9tS6Cqg8TapQYOnQoY8aMISIiolPvV0Wlp6Da686hR9nreipz4PnBvDD5bR6XU9k47tj5KyocXua8+DPVLh+f3TWe3t38hbNK56AogkObSti4JJuKAgfBUWZGXNiL/mNi0XaBk57KmYW/tDQgaNeJ2u6dOxHeQD4yfXx8s7Ajpv79WiReVWmOKmB3V/weeCIJRt8G0x9vaP7dgh38z1WLMdHKrgmD0J/g21SX3cuSV3ZQeLCakTNTGD0rtcNLYnzFJZT885/UfPklurg4Yh59BNv06Y2eyorAfaASx+oC3PsrQZIwD4wgaFw8hpTgbuOFK4TA7VOwu33UuAOido3LFxC53b4mYndj/ehjDu+xvacNWg0hFj2hdaJ2iNkQELfr9y2Ghnpo3bEQix6bUXfKf06KorQQuGVZRlGUBpG9admeekf6dvY1TgadTndcobuttvb0Ub3Rz1zUCXHn0CPsdVM+vgEOfcfu2zczefsRHkqJ5aHU9oUFWX2wjGvfWMf0zFhevGZ4t/5uyKtwMvvp5ehra5g/MQbTDiuGXsFE3jwQSZKQZSc7D/2bgrw30SNTlh9L1pFx6L0WLrtsHnt/9pG9vYyIxCAmXdOf2NSQk7ofe3kZKz/6P5SwZVhj3JgNgxgy7F9YrX1a9FWEwqojq3hr51tsLN6ITW/juszruCnzJky6jnkjK4rCli1bWL58OV6vl3POOYeJEyd2ai6M/EonC7cc4fMtRzhc6sCg0zAtI4ZLh0QSJT9DedmXaL0D2fK+n4i4VC556PeExjT/m1MUwcLdW3j6+28oKo1FeAOJwFOCFKI8BSRJFYxM78XYsWNJTU3t1n97KiqdjWqvO4ceZ6/reXkCWeZkxiU/wl/6xnNH0rE9IrPLHMx5aTVBRh2f3z2eyKBT55ylcnoRiiBrexkbv86mNNdOUJiR4dN7MWB8HLp25hdTURFeL+49exrDjmzejL+kBADJbMY8cGCDh7Z56FB0YR2PunAmowrY3Zm3LgJvLdyxsqHp6x2F3PHNbnzDI/h0aB8mhNlOeHjZr7By/j72rC6kz7AoptyYgd7Y8S9f58aNFP39cTx792IZO5bYx36H8SjPI3+5i9q1hTg2FCPcfvSxVqzj4rAMi0ZzBnzh+2WFWo8fu9tPdZ3YXePyUeX0UVVXVru8gf26tmqnlyqXD+cxxG+tRgp4cpv1TQRwAyHmgId3ZJCBpHALvSKsJISaMahvgZshhGgQsuuFbVmW8fl8eL1efD5fs621tvb29da9Se0IkiSdsEBe32Y0GjEajZhMpoZSr9erAsRpRp0Qdw49xl7XU7AVXj0Ppv6Z64Nns77awcZxGQTp2mfnXv3xEP/39V5+MyOdO89rKb52J7ZmlXLlS6tJqS3mnVkT8P9YTuhlfQka0xgb1OkuYcGGO4jwbkcjNBQW9KdwdxKj04eRkH4haxbm4aj2MGhiAmMu7dPhJI+KLLN56SccznqO8PRSNASRPuBPxMVf1uI70Cf7+Drra97e9TYHqw4SY4nhuozr+EW/X2DVWzv8+YuLi1m8eDF5eXn06tWLWbNmERUV1eFxWqPG7WPJjkI+33yEdVkVAIxODWfOsARmDIojxBwQyIUQ5Oa9wcGDT6LXJrD381C8NUam3Hwn6RMmtfgZ+BU/8/d8wHNrP8BTM4Bg37kUVgTGitC6SaScYVESsyYMY8iQIT0iKbWKysmi2uvOocfZ63p++Af88A+mzliFWWfkyxHHXz2zObeSea+uZUBcMB/cNhbzGTCXVWk/Qghyd1ew8atsig5XYwk2MHRaMpnnxmMwdd8VdCrdEyEE/sLCuuSQW3Ft2YJ7717w+wEwpKQ0eGibhw3F2Lcv0lm8SlwVsLsz3/9fIBb2ozlgCsRQrHR4GfbECvyT47kpKZK/pSWe1CWEEGz7No/Vnx0kIjGImXcNxhbe8XiIwu+n8uOPKX3+3yi1tYRfew2R996L1tZcYFe8Mq6tpdSuKcBX6EAyabGOjCVobBy6yK6Jh9nd8fhlql0+qpuI3VVOL9UNAri3TgD3NbY5vdS4/c3G0UgQF2ImOdwS2CIsjfVwC6EWVdTsSoQQ+P3+ExK/29vm9/uPfyMEhPGjRe2mZXvaDAaDGkLlJFAnxJ1Dj7HXTXnvMijayeZb1jFzW+4xE0MdjRCCez/YwpIdhbxz82jOTescQbSr+Pr77dyzNIcJ7gKezhyLnF9LzAMj0B31HPHtwY/ZtPcvDDO7UWQ9BQdTcO8I45zLb6aqLJkdK49gDTZ0KMnjkX17WP3V77Gl7URn8RMdMYcBmX9Ar28ec7rWW8un+z/lvT3vUeIsIS0sjZsyb+LC1AvRazou0Hq9XlauXMmaNWswmUxccMEFDBky5KTtq19W+OlAGZ9tzmf57mI8foXUSCtzhiVw6bAEksItbZ5bUfEzO3fdjyL7KN86iOw1VfQbdy5Tb70bc1BLR4ciRxGPr3ucH/J+IMU8gjHBt7EtW2JjdiUCCJLc9DHUcuGgOOZNG0NoyMl5yKuodGdUe9059Eh7DVC0E14+h+cu+JB/eOLYMj6DOOPxE60t3VnEXf/bxAUZMbx4zQi0aozpsw4hBAX7q9i4JJv8vZWYrHqGTEli0PmJHX4hr6LSFMXlwr1zZ2Ms7S1bkCsDibo1QUGYhwxpFLWHDG6huXVXhNeL7HCgOJwoTgdKfd3haHur6yc7HAinkz6LF6sCdrfl8A/w7my45jNIm9rQfMl/VnGwl5mgSDPrxg7oFFEye0cZ37yxC51By8y7Bp3wcl5/ZSWlzz5H1SefoA0PJ/rXvybk0tkt3hIJIfDm1FC7phDXjjJQBKb0cEIuTEEf23FPqLMRWRGU1XrIrXCSW+4MlE220qOSWNpMulbF7V7hVuJCTejVZBTdHkVRWgjcHo8Hj8eD2+1uVh6vrT3hVQwGQ7vE72Md02rPTq8UdULcOfQYe92UrJ/gnVlw0TPM1Z/HPoeb9WMzMLXzO9bh8XPZiz9Tavew6N4JxxQuuwMvvvgFT+XquDrIwT2+RAwJQUTeOqhFWLJ8ez5///EeRnrzSA2pxucxUbQ+BKMynqHTr2f7927K8moDSR6v7EdwGy+13bW1/PTps9RKnxCc5ECvSWHIsGcICRnSrF+Js4T397zPJ/s+odZXy5jYMdw48EbOiT/nhJ+b9u3bx9dff011dTXDhw9n6tSpWCwn/vsRQrCroIbPNx9h0bYjlNV6CbXouWRIPJcNS2BoUmi779Xlymf7jruord2N3jOWzR/UYLKEceGd95MydESr1/4u9zv+b93/Ueoq5ar0q7gm7U5WH6zli41ZbMizIwsJMz6GREnMHZvGxWPS1ZVeKmccqr3uHHqkvQYQAv49lIMxY5gQczd/T0vg1sT2vTx+6+cs/vLlbm46J4U/XZzZxTeq0p0pOlzNxiXZ5Owox2DWMfj8RIZMTsIUpK5kUjl5hBD4cnMDYUfqkkN69u8PfH9JEsa+fZvE0h6CISWlUzRC4fejOFsKzHKz/Xox+jhCtMOB8Pnad2G9Hq3FgsZqbbElvfBvVcDutnid8I8kGH8fTP1zQ/OTS/fy4qEiPBmh/DC6P+nWzvFcrihw8NWL23BUeTn/unT6j2lf3M7WcO3cRfHf/oZr2zbMQ4YQ8/vfYx40sNW+co0Xx/pC7KsKEF4/1jFxhEzrhcaifuGfDE6vn7wKF7kVTnLKHeTVCds5FU7yK1x45UYBU6uRiA810SvcSlITr+1eERaSwi0Ny5VVzgyEEPh8vjaF7vaK4O3xCNfpdO0WwSMiIoiJicFo7PnxBNUJcefQY+x1U4SA16eCo5RV1/3IL7Zn80S/RG5KiGz3EFllDi75zyqSwy18dtd4TPru+yJICMHDD7/Mp7pkHu1l4+IcidCLexN0TkKLvl7Zyz83/JOSbWuYGFVGSGgJXruRwvVRJKdeQUjcFDZ/UwxCMHpWb4ZMaUzyKIRg90/L2bX1ccLSj6DRGOjd+1ekpN6MJDX+fA5VHeLtXW+z+PBiFKFwQa8LuHHgjWRGnLi4UF1dzZIlS9i7dy9RUVHMmjWLXr16nfB4hdUuFm4p4PPN+RwoqcWg1TA5PZo5wxOY1D/6hEViWXZz+PAz5Oa9iUEXR96PiRTusDN0+kVMvOamhgSPTan11vLClhf4YO8HRJmj+O2Y3zIleQq1Hj+LN2Xz6doDbCv140eLUaMwLjmIueP6cf6AGCwG1cNMpeej2uvOoUfa63qWPQbrX+X8C1YSotezcHj7k/D+bfFu3liVxR9mZXDLhNQuvEmVnkBprp1NS7I5tKUUnVHLoIkJDJmahDWk589tVLoXcm0trm3bGpJDurZtQ7HbAdCGhQW8s4cOxTQgPSBEtyIotxSim4vQwu1u381oNK2IzQEBWnt0exvCdLPN0PYqGDWESHfn9WkgSXDLNw1NPx8s4+r3NuCZFMfvesfxy14xnXY5V62Xpa/spOBAFcMv7MXYS3p3OLljPUJRqP5iESX/+hdyeTmhv/gFUQ/+qs1A9LLDR83yHBzrCtGYdQRP64V1dBySVl2S1dkoiqDY7ianznM7r8LZrF7uaB7POSHUzMiUMEamhDMqJYx+0TY06lK5sx6/398hD/DWjrUWOzw8PJzY2Nhmm81m61EhcNQJcefQo+x1U/Z+BR9ejZjzBhf7BlPk9bFmTEaHEi9/u6eYW97ZyJzhCfxr7smHqOhKvJVV3PCbd1gX2pun46IYWy4Tc//wNkODLctaxkcLP2SIRke/jJ0YtCU4y0yUb+9Fxui7KMlLImdHBZFJQUy7ORNEJSs//SPGpNUYQ3yEWCcxaOgTGI2B0CxCCDaXbOatnW+xMn8lJq2JS/teyvWZ15NkSzrhzyXLMuvWreP7779HCMGkSZMYN27cCa0sqfX4WbqziAVb8ll9qBwhYESvMOYMT+CiQXGEWo6/ZL29VFauY/eeR3C7j0DNaLZ9XEVodBIz7n2QuL79Wz1nR+kO/rLmL+yr3MekpEk8NuYxYq0BR4bqWif/W7GRr7Yd4aDLggcdeg2cmxbJRYMTmDIgulPvX0XlVKLa686hx9prgJw18NaF/GvGAv7pDGfr+ExijO1z3lEUwT3zN7N0VxEvXj2cGYPijn+SyhlPeUEtm5fmcGBDMRqdhowJ8QyblnxCoVpVVNqDUBS8hw41JofcsgVvVlab/dslJFsb+2iPIUBLJtMpm6eoAnZ3Z/mfYM1/4Te5YAgsU3X7ZIb85RtMk+LRW3V8PyodWzsTRLUH2a/w44f72b2qgNQhkUy9KeOkEhLIdjtl//kvFe+/j8ZqJer+XxJ25ZVIutbH9BU5qPryEJ5D1ehiLITO6o0pTc2+eiqxu33NvLe351ezPruiISxJsEnHiF71gnY4gxNDurWHoEr3RVEUPB4PLpeLsrIyioqKGraKioqGfhaLpYWoHRER0W1DlKgT4s6hR9nrpigKvDQONDqWz/2a63Zk8Vx6ElfFRXRomGeX7+f5bw/wt9mZXDcupWvutZMoW7uBeW9tIi8snv8awhgUF0zUHYPbfAmeU53Ds+88S2hFCPHD8kgP24vPX4z9iAXPkRH0GXwXW1c48HpWEDNsKaG9q9GIKAYPeYqIyIkAyIrM93nf89bOt9hetp0wYxjz0udxVfpVhJlO7rkhLy+PxYsXU1xcTFpaGjNnziTsBDPBf745nz8s3InDK5McbuGyYQlcNiyBlMiuC5nm99dy4OATFBR8iEGbxMFlkVRmeRk75yrGXHYF2laewfyKn/d3v8+L214E4L5h9zEvfR46TaCvoijs23+Aj77fxLp8N7lKGA5hQCvBuD6RTM+M4YLMWGKC1Qm6Ss9BtdedQ4+11wCKDP/qz74+szkv7Dr+Ly2Bm9sZRgQCc/OrX1vLroIa5t82hhG9wrvwZlV6ElXFTjYvy2Hf2iKQIH1cHMOn9yIk6uzM/aVyavFXVuI9fBjJaGoUoy1WNBZzj00E2a0EbEmSkoB3gVhAAV4VQjwvSVI48BGQAmQDVwghKuvO+S1wCyADvxRCLDvedXqUgT2wHP73C7h+EfQ+r6H52tfXkS37OdzPytzYcJ4fkNyplxVCsP37fH7+5ADh8UFc/MshJ730xXPwIEWPP45zzVqM/fsT+4ffYxnZ+t+eEAL37nKqvspCrnBjyogg9KJUdBHql/3pQghBXoWLDdkVbMypYEN2JQdLagEwaDUMSgxhZEoYo3qFM6JXGGFW1RtL5eTweDwUFxc3E7WLi4uRZRkArVZLTExMM1G7u4QgUSfEnUOPstdHs/UDWHgnYt4nTKvphUtW+GF0eoe8sBVFcNu7G1m5v5QPbx/LyJTuPSne8+yL3JBlQ7aF84ocRP+L+mA7t+1k0y6fi6deewpRIihLLeKuczIpyX0DBTtVh234asIIz8hH0oBSdRnnzfoTJosVt9/NokOLeHf3u+TU5JAYlMgNmTcwu+9szLqTe05wuVysWLGCTZs2YbPZmDFjBgMGnFi+EbdP5q+LdzN/XS6jU8N5ZHp/RvQKO6Xe9GXlP7Bnz2/x+SrwFQ9n58IaYvv0Z8Y9vyY8vmWYF4AjtUd4fO3j/HTkJwaED+BP4//UIgRLSUkJa9eu49stBzjsDaZQG02ZN/BCcVhyKNMzY5meGUtqF4r0KiqdgWqvO4ceba8BFt0HOxcwcco3RBoMfD6sb4dOL6/1cPlLq6l2+fj87nPU7z6VZtSUu9jyTS57fi5EUQT9RsUwbHoywRFmGjQ0AQ1qmhA0k9YEiCYdGk5p2knQ5BzRss9R5zb0aTpEk/Hqj4lmN1Z/vmjWp/5cRRYIRaDUbUKpaxMg5CZtTcqm5zQtm56rtHGuOMb1AuMqKAqN4zUcb+O6skCp+zxarYRGK6HVadBoNXV1CY1W01A2HNdJaOv2NTpN4Fxd3XFt8+PN+teP1/Q87dHHA+c029dqTjhKQk+nuwnYcUCcEGKzJEk2YBNwKXAjUCGE+IckSb8BwoQQj0qSlAF8AIwG4oEVQD8hhHys6/QoA+uugSd7wcSH4fzfNTS/9MMhnly6l+tvHMyrheW8MTCFi6JCO/3yubvKWfLKDmJSg7nk/mEnHTZCCIF92TcUP/kk/sJCgmfNIvrhh9DHtB4GRfgU7D8fwf5dLkIW2CYkYJuchMaoxl3sDlQ4vGzKqWRjdgUbsivYcaQanxz4HkiLDmoIOTIqJZzEMHO3XgKv0jOQZZny8vJmonZhYSEul6uhT3cIQXK2T4glScoG7AReLvuFECOP9TK6LXqUvT4a2Qf/HgYhiSy75CNu2JHF3UnR/LFvfIeGqXb5uOQ/q3B6Zb66bwLR3di7Vfj9rLzpXu6JnkaMJYgXFQt97h+BPrrtRId+v58X3niBqsIqdsbv5P4L7yDOvYPc3NdB48WsH4rW/is2feklKMKIc9IBPih/iwp3BZkRmdw08CamJk9Fqzm51RhCCHbs2MGyZctwOp2MGTOG888//4RfiOVVOLnrf5vYeaSGuyb14dfT+qE7TcmSfb4q9u3/M8XFX6KXUtn3hRVnuY7zrr2ZIRfMbPW7UQjBNznf8I/1/6DCXcHV6Vdz37D7sOib/y6dTiebNm1i/fr15FX7KDXGU6iN4XBVIEdC/xgb0zNjmD4wloy4YPU5QKXbcbbb686iR9trgP3fwPy5PDXra56zW9l2TiZRho7lAMouczDnpdXYTDo+v2s8EUGn36FCpXvhqPKwZUUuu348gt+rHP8EFSQJJK2ERpICpUZC0hxVNmun+XGthCQFyvq2Fsc1NBtPUCeaywqKXyD7lYZ92d+8VOTG4w2lLFDq6l32c9FIRwnldSJ43X6D8K3TYDBpMZh1DZuxoa7FYGq6r8No0aE3arvt81q3ErBbDChJXwD/qdsmCSEK60TuH4QQ/eu8rxFCPFHXfxnwZyHEmmON2+MM7CvngdEGNy5uaNp5pJpZL6ziX1cM4RXFQb7by/ej0tsdr6sj7FldwHfv7mXspb0ZcWFKp4ypuFyUv/Ya5a+/ATodUXffRfj11yO1EbBdrvFQvTQb5+YSNDY9IdNTsQyPPmvfPHVX3D6ZbXlVbMypZEN2BZuyK7F7ApPYmGBjQNCuCz0yIC4Yrfr7U+kEhBDY7fZmovbRIUjMZnMLUTsyMrLLQpCc7RPiOgF7pBCirEnbU7TyMvpY4/Q4e300616BJY/ATUt5xJ3IuwXlvD0wlQujQjo0zL4iO5f+92cy44OZf9vYE07ydyrw5uez4MZf8bsR1zNMa+C5+GgS7hp6zHwWXq+XN955g8KCQtZEr+Gi0Rdxe+bV+L1F2GyDKHAUMP+7BYgV8Zh8QZQM3sEll57LqNhRnfKAXVZWxldffUVWVhbx8fFcfPHFxMWdeBzTFbuLefDjrQA8c8VQpmZ0Xq6Sk6G45Gv27fsjfr8DZ9ZA9i1zkjJkBNPvvJ+g8NbD29R4a/j35n/z8b6PibHG8NiYx5iUNKlFP1mW2bNnD2vXriU/Px+vPghfTAaHPDa25tegCEgMMzM9M5YLB8YyPDlMfQZQ6Rac7fa6s+jx9trvgad6s2fwrZxvvYz7kqN5rE/HXjgDbMqp5OrX1jIgLpgPbhuL2dA9Q92pnF5cdi8HNpbg98lI1NlCKSDW1tPwfFNfSIGd5n1odlLDKa2e2+T8poePPl+i2T01G+8YfZoLy9QJw5oGcbipkNxQtilEN47XXYXU9lDv6d2ayN2qCO4XyHKTso3+sr9RID+6v+JXGgR0WRbIPgWv24/X5cfj8uN1yQjl2LqtJBEQtE2NQndTkfu4Iri5TgTvgue8bitgS5KUAvwIDARyhRChTY5VCiHCJEn6D7BWCPF+XfsbwBIhxKfHGrvHGdilv4ONbwTiYOsCb3IVRTD878s5Ny2KX14ygGkb9zE+NIj/De7d6f/JhRB888YuDm0uZc7Dw4lN7djE+1h48/IofuIf1H73HYa+fUh+/XX0sbHH6G+natEhvHl29IlBhF7cB2Ov4E67H5XORVYE+4vtdR7aAVG7sDqQzTbIqGNYciijUsIZmRLGsKQw9SFPpVNpTwiS6OjoFiFITKaT93A92yfEbQjY+2jlZfSxxulx9vpovE54biAkjMR91YdcsvkA2W4Py0f2p5e5Y55ZX24r4L4PtnD9uF78dfbALrrhzqH6y8W88++PeGbEVcxEzxPT0wk5/9ihztxuN2+9/RbFJcWsjF5Jcq9k7hxyJwsPLmRZ9jIkJC5KuITBO6dTusdNyuBIplw/AFNQ+17cCyFwuVzY7fZmW2VlJdu3b0en0zF16lRGjBiB5gTjAvplhX9+s5+XVx5iYEIwL149guSItr3PTwceTyl79z1GWdm36JQ+7PrMgPDamHrrPfQfN6HN87aWbOUva/7CwaqDTE2eym9G/4YYa+vCfH5+PmvXrmX37t0IIUjok44noh8bi3z8fLAcr6wQGWRgWkYs0zNjGN8nslu/lFE5sznb7XVn0ePtNcAnN0L2Kh6Y9S0fFlXydP9ErouP7PAwS3cWctf/NnNBRgwvXjNCfVmnoqLSbRBC4PcqTQRt/1F1Ga+7bt/ZpL2DIjgSRwnbnSOCd0sBW5KkIGAl8LgQ4nNJkqraELD/C6w5SsD+WgjxWStj3g7cDpCcnDwiJyenU+71lLD3K/jwarhpKfQa19D8t8W7eWNVFq9cN4L8EC2PHTjCP/olcmNCxw3t8fA4fXz09w1IGrjysdEYzJ0bwsP+ww8U/PohtJER9HrnnWOK2EIROLeVUr0kC6XGi2VoFMEzUtGdZIxulVPDkSpXQ8iRjdmV7Cu2IwToNBKZCSENHtojU8KIVJfeqXQyrYUgKSoqwul0NvQJCwtr4a0dHNyxpe9n+4RYkqQsoJJAZLxXhBCvtmXLjzXOGTEhXvk0fP93uPNnckLSmLZxHykmI4uGp2HqYEiJx7/azWs/ZfHPuUP4xYi2Y0t3BwoefZT/7vfwv/7TuFUy8uj949DHHjsmqMPh4O2336a8spyf4n6iSFeERWdhbr+5XJtxLbHW2IYcHas/P4g5yMAFt2QQkWxpIUw33WpqarDb7Q0vr5piNptJS0tj2rRp2Gy2E/68JXY3983fwrqsCq4ek8wfZ2V02+TGQggKiz5j//6/IYRM5c5+ZP3kYcC5k5l80x2YrEGtnudTfLyz6x1e3vYyOo2OXw77JVf2v7LN8C01NTVs2LCBjRs34nK5iImJYfCI0ZRoo1ixr4zv95bg9MrYjDomD4hmemYs5/WLwqqGiVM5hZzt9rqzOCPs9Y5P4bNb8N24lBurY/i+ooY3BqYw4wTCdL65Kou/Lt7NTeek8KeLM49/goqKikoPoUMiuMuPx3lyInhT8fvyh0d2LwFbkiQ9sBhYJoR4pq6tVa+tsyaEiLMCnkqFyX+AiQ81NLt9Mle8soasUgeL7j2H3xUUs7aqluWj+tPX0vkxMgsPVrHgX5tJGx3DtJs63xC7tm4l95ZbAyL2u++2GRe7HsUjY/8hD/tP+UiShG1SEraJCUjddMKo0jrVTh+bcysbBO2t+VV4/YGYYL0jrYxMCauLpR1OSoSlRy8jUumedEUIkrN9QixJUrwQokCSpGhgOXAfsKg9AnaPfuHcGq5KeHYg9J8Bl7/OsrJqbtiRxfXxETzVP6lDQ/llheveWM/m3Eo+u2s8AxM6b0VUZyPX1nL4sjk8mXIhKyL685ewUK5/aBzScUT7mpoa3nrrLRxOB6mTUzm397koLqWFKF1eWkVJQTl+4UZoWgrTer2e4OBgbDZbi62+PSgoCL3+5EOvrT1czn0fbMHu9vF/lw1izvDu/XKhHre7gN17HqGycg0abxo7PpEwGWO58O4HSB44pM3z8ux5/H3t31ldsJpBkYP407g/0T+87cUUPp+PHTt2sHbtWkpKSrBYLIwcOZLBw0awrcjN0p1FrNhTTKXTh1Gn4dy0KKZnxjB1QIyaEFqlyznb7XVn0ePm163hroGn+8Do23FM/Stztx5id62Lj4b0YUxo6y/2jsVfv9zNmz9n8YdZGdwyIbULblhFRUWlZ9JREbx+u+zXI7qPgC0FlKl3CMTIfKBJ+9NAeZO4meFCiEckScoE5tOYxPFbIO2MSuJYz4vjwBYL1y1o1pxX4eTi/6wiNtjEy7eMZsbWA/QyG/lyeBr6LliutOGrLNZ/mcXUmzLoP6ZtL+kTpaMiNoC/wk3114dx7SxHG2ok5KJUzAMjVaGzh+Lxy+w8Us2G7EByyI05lVQ5fQBEBhkY2SvgnT0qJZyM+GD0pykplsqZz8mEIFEnxI1IkvRnoBa4jbMthEg9yx6DtS/CTUsgeSx/PVjAi3klvJjRizkxx3RCb0F5rYeLX1iFJEl8ed8EwruxwOfaupUD197An2Y+xk6NlVdGpDJ5bsZxz6usrOTNN9/Ebre3OKbRaBqE6CCrjeojPmoKZSKiQhl7UTpRseHYbLZOCQV0PBRF8MqPh3l62V5SIqy8dO0I+seeuBf36UAIhfwj73Pw4JOAjuINKRzZ6GfERZcy4aob0LWRm0QIwddZX/PUhqeo9lRzfcb13DnkzhZJHo8+Jysri7Vr17J//340Gg0DBw5k7NixRMfEsiG7kmW7ili2q4jCajd6rcQ1Y3rxyylp3frvXKVno9rrzuGMsdfv/wLKD8Avt1Luk5m95QClXj8Lh/VlQJC5Q0PJiuCe/21m2e4iXrpmOBcOPPHcCioqKioq3SyEiCRJE4CfgB1AfVrW3wHrgI+BZCAXmCuEqKg75zHgZsAPPCCEWHK86/RIA/vVQ7B1fiAOtrb50srv95Vw89sbmDMskUnnJXPb7hweTInhkdTON5KKIlj4zGbK8mq58vejCInq/NiOzi1byLv1tg6J2ADuQ1VUf3kYX5EDQ2oIoRf3xhDf8bflKt0LRREcKq1tELQ35FSQV+ECwKzXMiw5lJEp4YxOCWdocihB6tJjlS6kvSFIHnjggbN2QixJkhXQCCHsdfXlwF+BKbTyMvpYY/VIe90arip47fxATOw7VuKzxvCLrQfZUeti6Yh+9LN2TGzdllfF3JfXMDo1nHduHt2t42uWvfwyh//7Kg9f8ncqZPj42pEMGBh93PMqKyvZtWsXFoulmfe02WxuFqNaCMG+tUWs/GAfOoOWKTcMIGVQ54dSO5pqp49ff7KNFXuKuWhQHP+4fBA2U+cn0j5VOJ1Z7Nr9MDU1W8DRjx2fCkIjezPj3l8Tk9qnzfOqPdU8u+lZPjvwGfHWeB4b+xgTEyce93rl5eWsX7+eLVu24PV6SUpKYuzYsaSnp6PRaNieX82HG3L5aEMeVoOOu87vw83npHbbsCwqPRdVwO4czhh7vfEtWPwA3PkzxA4kz+1l1qb9aCSJL4enkWjq2Ms0t09m3mtr2V1Qw/zbxjKiV8deWquoqKioNNKtBOxTRY80sDs/h09vglu/g8QRLQ4/s3w///72AE/MGcRqm+Dz4koWDUtjRMix402eCPYKNx/9fT0h0RbmPDwcbRd4wNaL2LrISJLffafdIrZQBI4NRdQsy0Zx+bGOiiX4gl5og1TPnTOJomo3G3MCIUc2ZFewp7AGRYBWIzEgzsbIXoGQI6NSwogO7novPJWzm9ZCkFx55ZVn7YRYkqTeQP1yIR0wXwjxuCRJEbTxMroteqS9bovi3fD6FIgdDDd8SaEMUzfsJ0KvY8nINKxthKNpi4825PLoZzu487w+/GZGehfd9MkjZJncG24kO7eSB8bfjVmr4YuHzyMqtGOebMejssjBstd2UX6klqFTkxh7aR+0XZQYcOeRau763yYKq9w8dtEAbhyfckas+hJCJifnNQ5nPYcGC3k/xlO2T8f4uVczavblaNqIdQ2wqXgTf13zVw5XH2Z6ynQeHfUoUZao417T7XazZcsW1q1bR1VVFSEhIYwePZrhw4djNps5WGLnH0v2smJPCXEhJn59QX/mDEtA041f2qj0LFQBu3M4Y+x1bQk8MwAGXAK/eBMkid21Li7dcoAYg55Fw9MI03fMWaa81sOcl1ZT4/Lx+d3nkBrZ+fPzMw0hBKW1HvIqnORWOMktd5Fb4aS01oOiCAQCIUARgVIIEAgUEThXEYEkLDTUBYoSaBOiyblN9gWN42k1EgatBr2urtRqMOg0GHWN9frS0KRsPCZh1DXv1zCGVoNep8Gs19IrwtKjX36rqJxqVAG7p2Avhn/1g2l/g3N+2eKwrAhufGs967IqePe2sdxbUIBeklgxsj9WXed7qxzcVMKy13Yy/MJejLu0bc+ck8G5eQt5t3VcxAZQnD5qvs2ldk0hkkFD8JReBI2LQ1Kz3J+R2N0+tuRW1SWHrGRLXiVuX2ARR3K4pSHkyKiUMPpEBZ0RQoNK90adEHcOPdJeH4udn8GnN8PoO2DmU/xYYefKbYe4PCaMFwYkd/i76XcLdjB/XS4vXTOcGYO679JkX2Ehh2dfyuEhF/NAxDD6BZv55OHzOt2b1u+TWf3pQXasPEJ0LxsX3JrZqSvFhBB8uCGPPy3aRYTVwH+uHn5GetPZa/eye/dD1NbuwV/Rj91fQGzqIGbc8yChMW2Hj/PJPt7c+Savbn8Vo9bIAyMe4Bf9foFGOv6zl6Io7N+/n7Vr15KdnY1er2fIkCFMmDCB0NBQ1hwq54kle9ieX82AuGB+NzOdc9OOL5CrqBwP1V53DmeUvf7xafju73DpyzB0HgCrK2uZt/0Qg4LMfDy0L5YOOnBllTmY8+LPhJj1fHbXeCLURPW4fXKjQF235TWULly+5lFh40JMRNuMaDUSkiShkUBCQpJAkkAjSQ0l0KRP4zFJko7ab6UNkAX4/ApeWcEnK3j8Cl5/oN609MoCr1+u6yeQj5eUrhUSw8ykx9roH2sjPTaY9FgbqZFWdGqYTBWVFqgCdk/ihREQ0Reu/qjVwxUOL7P+/RMajcQfbxjGDXtyuO4EkkS1l+/f28Pu1YXMvn8oienhXXIN5+Yt5N16K7qoKJLffRd9zPGXHTfFV+KkavFhPPsr0UWZCZnVG3P/rrlXle6DT1bYVVBTJ2gHPLXLHV4ArAYtSeEWkuu3CAtJ4RaSwiwkhpnV5ckqnYI6Ie4ceqy9PhbLHoM1/4HLXoEhV/GvrCKezi7i6f6JXBffsdAXHr/MVa+uZX+RnYX3nENaTPeNv1yzdBlHHniArXOf4Hc+PdN6R/LyraO7xJP20JYSvn9vL4oiOP/adNJGtv8FeFu4vDKPLdzB55uPcG5aJM9fNeyMjsusKF6ysl4gO+dltIRyeHkktUesTLrhNgZNvuCYL1uyq7P5+9q/s65oHUOihvCncX8iLSyt3dcuKipi3bp1bN++HYDRo0dz7rnnYjKZ+XJ7AU8v20d+pYuJ/aL47Yx0BsQFn/TnVTl7OdvttSRJ2YAdkAG/EGKkJEnhwEdACpANXCGEqDzWOGeUvVZkeOdiKNwGd/4E4b0BWFxSxW27spkaEcxbA1PRddB+bcqp5OrX1pIRH8wHt43t1DmH169Q6fRSVuuhvNZLuSNQltV6kRUFq1GH1aALlEZt83rDMS0Wg67TwpIpSsCLOuBB3Vygzq1wUmL3NOtvMWgb52dN5mjJ4RYSQnvGHE1WRIPg3SBy+5UWbT5ZUOvxc6i0lr1FdvYV1XCo1NEggBt0GvpGBZEeayM9zkb/OmE72mZUHbFUzmpUAbsnseiXsGshPJoFbSzj3JJbyRWvrGFC30hSJiTwUn4p7w5K5YLIkE6/HZ9H5pMnNuB1+bnyD6Mxd1GYjpMVsYUQuPdVUr34MP4yF9oIE/ooC7pIM7ooM7pIM/pIM5pgg2oQzlCEEGSVOerCjdibPTx5/EqzvrHBJpLDGx+YkiPMJIUF6lHqQ4NKOznbJ8SdRY+118dC9sN7l0L+BrjlG5TYwVy97TBrqmv5cngag20d8xguqnYz64WfCDbpWXjvOQR346WoBb//PdVfLGHJpU/yb5+HW89J5fcXHz+p44lQU+5i+Ru7KDpcQ8Y5cUy4sh96w4lNfg+X1nLX+5vZX2Ln/ilp3Dc5rVvHHe9Mqqu3snvPQzidWbgL+rJviZbUIWO54Pb7sIa27X0uhODLw1/y9IanqfXWcuPAG7lj8B2YdO0P61VVVcUPP/zA1q1bMRqNTJgwgTFjxiA0Wt5bk8ML3x2kxu3jF8MTefCCfsSFdG5YGpWzg7PdXtcJ2COFEGVN2p4CKprkrAgTQjx6rHHOOHtdlQcvnwMRaXDzUtAGbOvbR8r4zf585sWF80z/pA7PC5bsKOTu+Zu5ICOGF68Z0aYtURRBjdtHWa2X8loP5Y5AWdZEnC6v9VJWV692+VodR6+V0Gk0LbyZj4VZr20hbB8tcgcZdViMWoLq2o16DSU1nhbe1E3nWZIEccGmVh2JksMtRFjP7rm4xy9zqMTBvuIa9hba2VtkZ29RDcU1jUJ/mEXfzFO7f6yNfjE2rGoOKJWzBFXA7kls+wgW3A53roLYQW12e29NNn/4YhcPXJDGoiCFEq+fH0anE2no/C+20lw7nz61keSMCGbeNajLjE6DiB0dTfI773RYxAYQfgXH+iI8WdX4S134y10IXxOjatCgi2gUtXVRFvSRgbrGrBqFMxEhBKX2wMNWXmVjfLV6gbuoxt2sv0mvaRCz6x+2Gkszli74P6bSMznbJ8SdRY+118ejthRePS/wMvr2lZTpgpm2cR8GSeKbkf0I6WB8zXWHy7n69XVMTo/mlWtHdNv4wIrDQdblv0AYE3l5wBV8ipe/zs7k+nEpXXI9WVZY/2UWm5flEBZrZfqtmUQkdCy581fbC3n0s+3otRLPXzWMif3OvrAVsuzi0KF/kpf/NholigNfBeOvjWLa7feSNmrcMc+tdFfyr43/4otDX5AYlMgfxv2B8fHjO3T94uJivv32W/bv309QUBCTJk1i2LBh2D0y//3+IO+szkGjgVsmpHLneX3UeKIqHeJst9dtCNj7gElCiEJJkuKAH4QQ/Y81zhlpr+tzUJ37EEz5Q0Pzk4cLeTanmAd6xfCb3h0P3/XGqiz+tng3lw1LIC0mqE6MDojU9YJ1hcOLv5VwFJIEYRYDEVYDEUEGIoKMRFoDZUSQgQirkcigxn2bUYckSciKwOn14/DI1Hr8OL3+QOmRcdS1Ozz+Jsfkuv71bXJjf48fh9dPa9EyrAYtyRFWksPNDSJ1gxd1mBljF4Q2PdOpcnoDYnZhDfuK7XUe23ac3sBLCUkKhMzsH2MjPa5R2E6JsJ41L9tVzh5UAbsnUZ0Pz2bChU/C2Dvb7CaE4IGPtrJoWwF/u24YvyspZXKEjbcGpnaJwLx1RS4/f3qQiVf1Y9CkxE4fvx7n5s2BxI4nIWI3RSgCucYTELPLGjdfmQu5wl2X+SGAxqpv4bGtizKjCzcj6dX4VGcqbp/MkaomonZ5c68Ch7e5N0NkkLHFA1v9Q1tssKnbikoqnc/ZPiHuLHqsvW4P+ZvgrQsh5Vy45hM22N1ctuUA0yJCeHNgx5MCvrkqi78u3s0tE1L5zYx09N00dqJr5y6y583DcsGD/E4fxxrJz2vXj2TKgJMP89EWebsrWP72brwuP+dekUbGhPjj/ny9foUnluzhrZ+zGZYcyn+vHk58Jyee7GlUVK5hz55HcbsLsR9O5fC3OjLPm86k62/DaDn2yoENRRv465q/kl2TzczUmTwy6hEizBEdun5OTg4rVqwgLy+PiIgIJk+eTEZGBvmVLp5eto9F2wqIsBq4f2oa80Ynd9v/Ayrdi7PdXkuSlAVUEpj5vCKEeFWSpCohRGiTPpVCiBZLLiRJuh24HSA5OXlETk7OKbrrU8jCe2Dr/+DGryDlHCAw1354Xz7vF5bzeFoCtyR2/MXm3xbv5o1VWUBA9G0pQAfqEf/P3nvHx22c+f/vAbB9l72LEtW7LFmSe4t7iW3Zjp3enTg9l3Z3yeXyTXJ3yV0u+aWXi1Od3u04ceK4924Vy1Zv7L1tXyyA+f2B5XJJUYUSKZHUvF+vec1gMMBiuCQfzAcPnifspaJgX2nQMyViI0spSWedvOCdytpURfyUBj2ntBf1icJxJC39KXZ0RPOC9vaOKAd6EvkHC36PxqKqyMj42rURKlT8dcU0RgnY042vr4LaNfC6nx92WNK0uOE7T9ITN3n965bz1dZuvrpkNm+sG99i4WiQjuSv39lC664Bbvnk+nF7N42HESL2z+7AU3V8IvahkJaD1ZceIW5ne5JYPSmcWMErWgL0Ep8rag+FJckVvcSHUILljEVKSX8yOzLpyFB8t/4kbQOpEZ4JXl2jvjQw4rW52WXD28pjbGZxqi+IJ4ppba+PhhfvgL98OO/d9X9NXXxubxufW1DHe+eMP1zWv9/1Mr98tomlNRG+fPNqVtVPfPiwiaD3Rz+i62vfwnPj1/igNGnSJL97zzmsnDV515uMmjzw0200b+tjwdoqLn7zEnzBsf/vtg2k+MCvNrKpaYB3nDeXT129DK9KAg2AZcXYvfuLtLX/DmFVsePPYTxiDld/4KPUL1t52GMzdoYfbf0RP9z6QwJGgI+t+xg3LrrxqJI8DiGlZOfOnTz44IN0d3dTV1fHZZddxvz583mpZYAv/m07z+zrY15FiH+9aglXrqhRYorisJzq9loIUSelbBNCVAH3Ax8C7j4aAbuQGWuvM3H4/gVgmfC+JyDg/hgsR3LrK/u5ryfK91fM5fqqknGfuieeIeQ1CBxjeCuFYjTprM2erjjb26Ps7IixszPG9vYYPfHhMCQVYW9e0HZrNwzJdIgxrlAoAXu6ced7Yff98M973PdFDsPe7jjXf+sJFlZH0M+pYnM8xUNnLGFuYOKfuiWjJr/5r+cIhD3c8sn1GJNoiJMvvkjzu2+bdBH7UDhpa1jUHuW9LTMFHrm6wCjzu6U8gFHuR68IuNulfoRaDM9osrZDW857e6i09A1vj45VVxr0jBmaZE5ZkNpi/5TwtlAcPaf6gniimNb2+mi5+8Ow8Q543S+RS1/NrS8f4L7eQe48fRFnFIfGfbp/vNLBZ+56md6EybsvmM9HLls05RYl0nFouvVWzKYEyTM+wHt9aWyvzp0fOI9Zk+jlLB3JpvubeObP+wiX+rjiXSuomTdSNH9sVzcf+e1mMlmb/715Na8+bfyvh58K9PQ8xPYd/4Zp9tO3bTZNT/o547qbOfeWN2F4Dv9Adt/gPv7z6f/khc4XWFu1ls+e81nml8wf1+c7jsOWLVt4+OGHiUajLFiwgMsuu4yamhoe2tHFf/99B3u64qxvKOVT1yxjXcNhtTfFKYyy18MIIT4HxIF3o0KIDNP6IvzoClh6Ldzy0/waPGU7vG7LXjZHk/xq9XzOL526iZQVpzY98Qw7O2L5hJE7OmLs6oyRzoVT1QTMLQ+5CSOrXWF7WW2E2aVB9QaxYkqhBOzpxsafw90fhA88B5WHvY8A4G9b23n/LzfymvMa+EuJw5JggDtPXzjurMlHQ+Mrvfz1W1tYddEsLnzDka/teEi++CJN774NT3U1c+746QkXscdCSokTz2J1u97adm8aqzeF1ZvG6k0jC8NNCNBLh8TtYYHbKA+gl/nR1JP4Gc9gMuvG3R6Vkbu5L0lLf2pE3DtdE8wqCRSEJXHbDWUhFlWHp5w4pVAL4oliWtvro8XKwE+uhu5dcNvDDBbP44oXdmFKyf3rlxxT/orBVJYv3rOd377QzLyKEF96zWmcOa9sEi7+2Ml2drF/wwZ8q99AY+lpvN+TZlZZkN+/75xJT0TZsW+Q+374ComBDGfdMJ/TL5uDA3zrod1848HdLKoK8703r2NB5eS9UTYTyGb72bHz/9HV9Tdkupodd4UoKlnKNR/8OBVz5h72WCkld+25i6+88BWSVpJbV97Ku097Nz59fE4W2WyW559/nscff5xUKsXKlSu55JJLKCou4fcvtvDV+3fRHctwzaoa/uXKpcytGP9DIcXM5lS210KIEKBJKWO59v3AfwCXAr0FSRzLpJT/crhzzXh7/fhX4cHPw4bvwOlvznf3Zy02bNxDe8bkrrWLWBE+tUNNKaYPtiNp7E3khe0dHa7XdmNfkiFZL+jVWVQdYVlhGJKaCKUh78m9eMUpixKwpxu9e+Fba+HVX4Uzbj2qQ4ZibL3xxqX8OBnjU/Nq+ae5kxNr8onf72bLg81c875VzFs9uYmOpqKIfSjy4nZe0B6u7b40TtIaMV4r8h4kbA+1Nb9KFDjTsR1J+2DqIK/tIYG7N2Hmx2oC5lWEWF5XzLLaCMtqi1heW0RVxKdemz6JnMoL4olkWtvr8TDYAt+/CILl8O4H2WrqXLtxN+cUh/nl6vnox/i3/MTuHj75p5do6U/xlrMb+NerlxKeQpnqYw8+SMuHP0Zkw1fYGAryscQgZ88v5yfvOGPS4xdnklke/vkO9m7qpnxZCX8JmDy5r5ebTp/Ff924UiXlHQednX9lx87PYlsJOjfOonNTmPNf/zbWvfoGhHb477E31ctXXvgKf933VxqKGvjM2Z/hrNqzxn0NqVSKp556iqeffhrHcVi/fj0XXnghwuPnB4/v4/bH9mFaDm8+u4EPX7qIMrX4VuQ4le21EGI+cGdu0wB+JaX8ghCiHPgdMAdoAm6RUvYd7lwz3l47NvxsA7RuhPc+DuUL8rta0ybXbdyNJSV/WbuIhkl421mhOFEkTYtdnXF2dkTZ3h7LCdxR+pPDbw9XRXzDCSOrIyytdcOQqNwTislGCdjTDSnhq8ug4Ty4+UdHdUjWdnjD7c/wcluUtTcs5LFYnHvWLWZ15PAJd44FO+vwh/99gXhfhtd/5kxCJZNrwPMidk0Nc376kyktYh8OJ5l1Be2+FFZPgcDdNyrmNqCFDFfMLvPjmRUhtL4aLaAW2qcSiYxFc3+S/d0JtnfE2N4eZXt7lJb+VH5MWcjrCto1RSyrdcvCqrCK43qCOJUXxBPJtLbX42X/Y/CzG2Dpq+G1P+MX7X18Ymcz/zy3ho/Pqznm0yZNi6/8Yxc/eWo/tUV+vnDTKi5eMnVsZfvnP0/svhcIXvAvPDg/yGf3dfDa9fV86TWnTdpDuJRp05vI0BvP8NSTrXx3YxNJTfIvFy3ktqsWq4d/x0Am08X2Hf9Gb+/DWLEadt0domrOWq5+/8coqjzy79vTbU/zn8/8J82xZq5fcD2fWP8JSv3jD/sRjUZ59NFH2bhxI4ZhcO6553LOOecQNeFrD+zmt883EfIavO/iBbzzvHnqDSaFstcTxClhrwdb4XvnQtk8eOd9YAw/CNuZSLNh427KPAZ3r110TG9PKRRTFSkl3bFM3lN7KHHk7s44pu2GIQl4dNY1lHLWvDLOml/O6tnF+AxlYxUTixKwpyN/eCc0PgUf237EONhDdEbTvPqbjxMKexk4o5wiQ+e+9UsITMJTsv6OBL/74vPUzC/m+g+vmfREhskXXqDptvfgqamh4Y6fYlROruf3icbJ2HlP7REe3D1p7MEMwqsTOquGyPmz0IvVE/9TmcFUlh05MXt7u5uNemdHjIzl3lh4dMGCyjDLa4dF7WW1EcpVNuoJRy2IJ4Zpb6/Hy1Pfhvs+DZd9HnneP/Gh7U38sbOf36xewEVlxxdb88XGfv71jy+xpyvOTafP4jPXLp8Sr4A6qRT7b7kFvfQ8jFnn86vTi/juphb++colfODihUc8XkpJ0rTpS5j0Jkz6Ehl640Ntk9642ze0vzduksraI85RF/FzXcygJO5w4esWs/z8OiViHwNSStrbf8+u3f+FY9u0PlXFwJ4KLnnHe1l+4SVH/JmmrTS3v3Q7P3n5J4S9YT6+/uNcv+D6cSV5HKKnp4eHHnqIbdu2EQwGufDCC1m/fj37e1N86d4dPLC9i9piPx+/Ygk3nT5Lxfg8hVH2emI4Zez1tj/D794K538ULvvciF3PDya4ZfMeloYC/HHNAkJKvFPMcCzb4UBvgm3tMTY29vPMvl52dMQA8Bkap88p4ax55Zw1v4y1c0rVQ2PFcaME7OnI8z+Cez4GH94EZUef9Obpvb286YfPsHZdLU+Ua9w6q4IvLK6flEvc9mQbD/98B+fcuIC1VzZMymcUMtNF7ENhtsaJPdZC6qVu0ATBNVVELpyFp1rFeFS4FN5YbM+L21E6o8PZqKuLfAWCdhHLayPMLQ+pxJHHgVoQTwzT3l6PFyndh9Tb7oI3/5HE3Iu45sXd9JgWD5yxmFrf8QnOGcvm2w/t4XuP7KUk6OE/Nqzk6pU1J12sTe/cyYHXvpHQFf+BXlbB/zZ4uOuldj573XJmlwbpS5j0JDL0xc0CodotPfFM/iHdaLyGRkXIS1nYS1nIR3nIS1muVOT6ykJeltcWgWlz/4+30bytj6Xn1HDRG5ZMakLqmUwq1cK27f/CwMCzZHpr2P23MPNWXcRl7/oAwaLiIx6/p38Pn3/682zu3szy8uV8bN3HjimsCEBraysPPPAA+/fvp6SkhIsvvphVq1bx7P5+/vvv23mpZZBltUX82zVLuWDRqXHvqBiJstcTwyllr+/+kJuX6m13w7wLR+z6R88g79i6n4vKItyxah7eI4RRUihmGgNJk+f29/Hs/j6e3d/LtrYojgSvrrF6djFnzivjrHnlrGsoJTSFwtoppgdKwJ6OdO2A7551UBKJo+F7j+zlS/fu4PSr5/G0Y/Kb1fN5VVnRhF+ilJJ//OBl9m/u4aZ/Xkf1vIn/jNEkn3+epve895QTsQGsvjSxx1tIvtCJzDr4l5URuage39wjLxQVpyZ9CTMvZm9rj7KtLcre7jhZ2/0/7jM0NwN1TVE+tvbS2iKKA5ObXG2moBbEE8O0t9fHgpmAH14GsQ647RF2eau56sVdrAwH+OOahXgmwFN0W1uUf/3jS2xtHeTKFdX854aVVBX5J+Dij52+n/2M7u/9htBFn8SzrpoP97mLn0L8Ho3ykI/y8LAQ7YrSbl95vs9HWdhLyKuPS5x3HMnz9+znhXsOUF4f5ur3rKS4cuLDrZ0KSOnQ3HIHe/d+GcfWaXy4HLN7Nle+95+Yd/qR/zU60uGv+/7Ktzd9m/ZEO+fPOp+PrP0IS8rGnyRcSsnevXt54IEH6OjooLq6mssuu4z58xfw163tfPkfO2npT3Hh4ko+dfVSltVO/j2rYuqg7PXEcErZazMB378QzCS870kIjkyS/Mu2Xj6+s5mbq0v55rI5aOqNHsUpTDSd5YUDfTy7r49n9vfxcusgtiMxNMHKWcWcNb+Ms+eVs35uKZFJTuKtmP4oAXs6IiV8eQEsuhJu/N44D5Xc9vMXeWhXF2VXzSErBA+fuYRSz8Q//Uonsvz2C8+h6Rqv+/QZeE9A8sG8iF1bS8NPf3JKidgAdtwk/nQ7iafbcJIW3oYiIhfV419aNumhXBTTH9Ny2NMVH/bUziXv6CtIGjmrJJD30l5e53pszy4NqtevR6EWxBPDtLfXx0rvXrj9YiibC+/8B3f2pXnftkbeN7uSzy6cNSEfYdkOP3xiP1+9fxd+Q+Pfr13OLevqT5o3tpSS5ve8B6u/Bu+Cy54EXhwAAIwsSURBVAm/ZSkvewURv+GK0mHvCUuseGBrDw/8ZBtSwmVvXzbpSalnMonEXrZt/2ei0S0k22rYe1+ElRdex0VvvhWP/8gPTTJ2hl9v/zU/2PoDYmaM6xZcxwfXfJDacO24r8VxHF555RUeeugh+vv7aWho4LLLLqOqto6fP93Itx7aQzSd5ea19Xz8iiXUFJ/chzqKE4Oy1xPDKWev2zbBDy+HJVfBa39+UFjPrx3o4Ev7OybUbisUM4F4xuLFxn6e3dfLs/v7eKllgKwt0QSsqCvOx9A+c24ZxUElaCtGogTs6cpv3wztL8FHXhr3oYOpLNd/+wkGvdCzupSrK0q4fUXDpCxa2/YMcNf/t5HFZ9Vw2duXT/j5xyL5/PNuOJG6OtcTu6LihHzuVMIxbZLPdxB7vBV7IINRFSByYT3BNVUIlcRPMQ6klHTFMmwrjK3dHmVfdxwn9y8/5NVZWjvsqb0sl5X6RIlNUxG1IJ4YZoS9PlZ23gu/fh2sfiPc8F3+dVcLd7T18tOV87iqcuLertnXHeeTf9zKcwf6uGBRBV+8cRWzy06O17HV08O+G16Df/1HMKpqqfno+pOWpDjak+Le21+muynG2qsaOOu6eWgqrNIx4TgWjU3fZ//+byEtL/vuK0MzF3H1Bz5O3eKlR3WOwcwgP9r6I365/ZcAvGnZm7h11a0U+8b/t2BZFhs3buTRRx8lkUiwdOlSLr30UjyhYr7z8B7ueKoRTYNbz5/Hey9acJBHmLQdZMbGSdtI08bJ2Llty62HtjM2MjPcJzSB8OkIr1s0r+a2fTqaV0cMbXt1NJ+O8GjD4z3aSQ/1M1NR9npiOCXt9ZPfgPv/H1z3TVj3thG7pJT82+5WftLaw+cW1PHeOVMnebJCMZVImTYbm1xB+5n9fWxuHsC0HISApTVFnDWvjLPnl3HmvHLKpkDuFsXJRQnY05Vnvgf3fhI++goUjz+O9Sttg9z03acoX13BvgoP3142h5tryo584DHw3F/28fw9B7j8nctZfGbNpHzGaJSI7SJth9TWHmKPtJDtSKAXeQmfP4vQmTVoJ8AjXjFzSWdtdnW6Yva2tuGkkbG0BbiOKHPLQ66oXZOLr11XRF2x/5RYhKsF8cQwI+z18fDwf8Oj/wOv/v/IrHsn123czYFUhvvXL6EhMHHJVx1H8stnG/mfv+9AAv985RLees5c9JPwZkX8scdo/ecvELr40wTWVFH22iUn7Q0iK2vz+O92s+3xNmYtKeWKW1cQLFKLp2MlFtvGtm2fIJ7YSXR/NY2PlHLGdW/g7Jtej24c3T1Je7ydb2/+Nn/Z+xci3gjvXvVu3rDsDfj0Q/89SCnBkjgFYrJM26QTKZ7b9iLP7dqMZVusqF7E2dWnMZDW+FZTF/8YSFCqadwaDrNB96GbDk7GAuso1zuG5grROYFaSonM2MisjZNx4BCx28dEgPDkRO684D0sems5IVx4tYJ9uc8uFMJHCefC0E75N/SUvZ4YTkl77Tjw8xug5Xl4z2NQsWjEbltK3vPKAf7aPch3ls3hNZO01lYoZhLprM3m5gGe3efG0N7Y1E8669rLxdXhfFLIs+aVUxmZuHthxfRACdjTlfaX4PsXwE0/gNNee0yn+P0LzXziDy9Rfnk9MY/goTOXMts/8Qszx3a466ub6GmN87pPn0lxZWDCP2MsEs89R/N73otnVh0NPz11RWxwF2+ZXf3EHm0hs28Q4dcJn11L+LxZ6BG1GFdMDFJKWvpTIzy1t3dEaexN5scUBzwjPLWX1xaxsCo847JSqwXxxDAj7PXx4DiuF/beh+Ht99BYuYYrXthFg9/L3WsX4Z9gj+DWgRT/9qetPLqrm3UNpXzpNatYWBWZ0M84Gjq++EUSz/bhW7YBz6wwpTcuxFt/4q9jiO1PtfPor3fiDxpcedsqaheo/BLHiuNk2Lf/mzQ23o7Mhth7bykh/2qu/uDHKZ81e+RY0xWaRwjPubqjr5Un9j9OV38HFXoZp5esod5b5wrEB3lB2+RfGRqDFCabjf1s11sRCFbqDawNLGK/pvPNWIyN6QxzfB4+PKeCS2tK0P2GKwz7dIRfR/MVbBfU4gh/n9KRSDPnxW06eXFbZmx37qYzvD8zvD1iX35sQV92HMI4jPT89g6L5CO2Cz3DvRqa30CLeNEjXvSwBxEwpu3DaWWvJ4ZT1l5H2+B750HJbLj1ATBGrqvStsMbXtrL84MJfnHa5OSeUihmMqbl8FLLAM/u7+OZfb282NhP0rQBmF8Z4qx55ZydE7RV6K+ZjxKwpyuODV+aB8uuhRu+e8yn+eQfX+JXL7ehXVTL6cVB/rhm4aQkmoj2pvjtfz1PaU2QGz+xFv0EvYarROyDMZtjxB5rIfVyD+iC0NpqwhfW46k4MQ8WFKce8YzFzo4o24ZE7fYoO9pjpLLuzYeuCRZUhphbHmJ2WZD60gCzS4PUlwWoLw0SnoYZqtWCeGKYEfb6eEn1u/Gwsyl4z2P8I+PnbVv389a6cv53yewjHz9OpJTcuamV//jrNpIZm3+6bBG3XTgfzwkMn+FkMhx47euQdhX+dW9GmoLwOXUUXdFw0t4e6m6Oce/tLxPvTXPuzQs57eKTFy98JjDQ/yLbtn2CVKaJzO7FiJfOZsGS8ygrqsEeNLH6M8jcGz2HQwJpPUNcJLE9DqVF5RRFSkYJymMLzFpOiB7qG0hEeeTRR3jppZfw+/2cf/75nHnmmTy+t5///vsO9nTFWd9QyqeuWca6htLJ/yEdI9KROSG8UPB2BW5XCLcPI5w7B+/PCeWHFcZ1gR72uKJ22IsW9uTF7XxfxO0TvvElV51slL2eGE5pe73jHvjNG+HcD8MV/3nQ7qhlc8PG3RxIm/xpzULWFKnkwArFsZK1HV5uHeTZ/X08u6+XFw70E8u49wsN5UE3hnbOS7u+VP2tzTSUgD2duftDsPHn8Jofwqqbj+kU6azNzf/3FDsNh9jSYv7fgjreP0kxuna/0Ml9P3yFdVc3cPaGBZPyGWORePY5mt+bE7HvuAOjvPyEffZUJtuTIv5YC4mNnWBLAivKiVw0G+/sk+flpjh1cBxJY18yF34kyo6OKE19SZr7Unlhe4jSoIf60iCzc4J2XuAudbcD3qnnva0WxBPDjLHXx0vHy/Cjy6F2NbztL/zH/m6+29zFd5c3cFP15Ahp3bEMn7v7Fe7Z2s7y2iL+9+bTWDnrxHkem01NtHzow2T2NlF0wz8jrXq0Ii8l1y0gsLL8pAhgmWSWB366nQMv9bBwfRUXv3npCUlQPR2RWRtrIIOdK9ZABrs/PdwezOCQpnvR7xiY8yCeRA0VW96GnqyneF4tvsoIerEPLWAMezSPJT57NCSS+w7cxzc2foOWeAtn1ZzFR9d/lBXlK47p2js6OnjggQfYs2cPkUiEiy++mBUrV/HHTe189f5d9MQzXLOqhn+5cilzK0IT/JOburjCeE4UT2ax41mcmDmitmMmTtzEjmVxEiaMpXkbGnrEkxO1C0TuMfq0E2Dflb2eGE55e/2Xj8CLP4G33AULLj5od0cmy7Ubd5GyJX9Zu4j5QRX6QKGYCGxHsq0tyrP7e3lmXx/PH+hjMJUFYFZJgLPml3F2TtCeUxacUg9QFeNHCdjTmWwafvEaaH4G3vAbWHT5MZ2muS/JNd98HHNNGakSL/euX8zy8OR44z70s+1sf7qdGz5yOrOWnDjvlREitvLEHoEdM4k/2Ub8mTZk2sY3v5jwRfX4F5eqf/CKE46Ukr6ESUt/iub+pFv3JUdsm6PihlaEvXlhu1Donl0aoK4kcFLCk6gF8cQwY+z1RLD1D/DHW+Gs95K98n+4efMetsZT3LtuMYtDk/fK5L0vd/CZP79MX8Lktgvn80+XLjphf1NOJkP3175O309/im/l+QTOeAd2v41/aRkl1y/AKDvxr4pKR7Lp/iaeuWsvJdVBrnrPKspqTx0RE9z/007SGilID2SwB9L5thPPjjxI4HrklvrRS3wYJT70Eh96qZ+YsYldHZ8lk+mg66Uqel+ZxWXv+CBLz7toXNeVtbP8btfv+P6W79Of6efquVfzobUfYnbk2N5UOHDgAPfffz+tra1UVFRw6aWXMnveQn74xH5uf2wfpuXw5rMb+PCli1RiqTGQjsRJZnFywvaw0G3ixLK5Oid2J7OuO/0ohFd3PbfDhd7cnnz4kmFPby/Cc2xviSh7PTGc8vbaTMLtF0E6Cu97CkIHO0ztSaa5fuNuwrrOX9cuosrnGeNECoXieHAcyY6OGM/u7+XZfX08d6CPvoQJQE2Rn7Pml3Fmzkt7QWVI6R3TDCVgT3fSUbjjWujeBW+9C+acfUyneWhHJ+/45UbEq2qZF/Hz93WLJzy2JoCZtvj9f79ANmPz+n8/E3/4xBnuxLPP0fye9yAMg5LXvpayt74FT82JSSo5HXAyFolnO4g/0YodNfHUhIhcVE/gtIojxnFUKE4UjiPpSWRo7kvRkhO0W/qT+e3WgRRZe6Qtqor4RoYmKQ3kt2uLA3iNif/9VgviiWFG2euJ4N5/g2e+AzfeTsfSm7js+Z2UeQz+vn4RIX3yROXBZJb/umcbv3+xhfkVIb5082mcMffEJaNKPPUUbZ/8FNbAIKVv+Qx2rAYkFF3WQPj8upNio1p29nPfD18mazpc8palLFpffcKvYbKQtoM9aA4L0v2ux7SVE6ztgcxB4SSER3MF6RIfRqkfvdiHXjokVPvRi72H/Z4sK8auXf9Je8cfycaK2HtvOQ1Lr+CCN7yN4qrx/WzjZpwfv/xjfr7t51jS4vVLXs9tp91GqX/8jhNSSnbs2MEDDzxAb28v9fX1XHbZZQTLavjaA7v57fNNhLwG77t4Ae88b96My+dwopC2xEmMFLXz7UJP77iJkxw7tIzw6weJ2tpYnt4hD6LA7it7PTEoe42bo+qHl8LCy+H1v3Qzmo9i42CC12zey4KgjztPX0jEUP8zFIrJRErJ7q54PuTIs/v76I5lAKgI+9yQI7kY2ouqwmineGLjqY4SsGcC8W74yVWQ6Ia3/w1qVh7Tab7yj518Y2sz2XUVvG92JZ9dOGuCL9SluynGH770Ag0ry7n6vatO6FOv9M5d9H7/+0TvvRc0jeJXv5qyd74T/5LFJ+wapjrSckhu7ib2WAtWVxK9xId/WRlGWQCj3I9R7kcv9Z+Q1zoVivFiO5KuWHqk53aubhlI0jaQxi5I5qUJ92l8fUHM7UKhu7bYj3EM4phaEE8MM85eHy92Fn52A7S+CLfex+O+ebx2y15eU13Kt5bNmXR7+vjubj71p6209KdYM7uEVy2p5FVLqjhtVvGk3/Bb/f10/L/PErv/foLnXEzwnHeR2ZfAUxOk5MZF+BpOfGKseH+Gf/xgKx37opx2ST3nvmbhCcvxcTw4aWvssB5D2zHzIG9YLewp8Jwu8KLOeVRrwYlJ4tfd/QDbd/wbWXOA9hcq6N5axqIzLuSM626iev7CcZ2rK9nFdzd/lzv33EnQCPLOle/kzcvfTMAY/1uGtm2zefNmHnnkEWKxGIsWLeLSSy8lJkJ86d4dPLC9i9piPx+/Ygk3nT5LLYAnEWk52IlR4UtiZoGnd87DO2a6CTzHQAsaaGE3bEnVbauVvZ4AlL3O8dS34b5Pw7Vfg/XvHHPIg71R3rZ1H+uLQnx5yWwWTeJbVAqFYiRSSvb3JEYI2u2DacANW3nmvDLOnl/OeQsrWFQVVh7aUwwlYM8UBprgx1eBY8E774Wy+eM+he1I3vrjZ3nMZ2PVh/jDmgWcVzo58ZA33d/EU3/cw0VvXMLKCydHKD8cZksrfXfcwcAf/oBMpQhdcAHl73wHwbPPVv+kckhHkt7RR/yJVszW+EGLAC3idQXtslwpD6CXuQK3FvKon6NiSmLZDh3RdN5ju7m/wJO7L0l7NE2hKdM1QW2xf0TM7XyIkrIAVRE/+hhChRKwJ4YZaa+Pl3gXfP8i0D1w2yN8tcvkf/d38OUl9bylbvLDYyUyFj95cj/3b+/ipZYBpITykJcLF1fyqiWVXLCoctLCKUgpGfzTn+j4whcRhkHFB/8Ts7UYO2oSOrOG4ivnogVP7CvZtuXw1J/28NJDLdQuKOaKd60kXHryYptKR+LEzJwgnROn+0eG+JDpUaKeLtCLR4b1yLdzQrU4gZ7FptnHzp3/j67uv4MdoPuVIrq2RKidv471197E3DXrxnWPsW9gH1/f+HUebn6YqkAV71/zfjYs3IChjT9+uWmaPPfcczzxxBOk02lOO+00Lr74Ynb0Ofz337fzUssgy2qL+LdrlnLBospxn18xscisXeDNPbZXd/X71yh7PQEoe53DceCXr4HGp+E9j0LlkjGH/bGjj4/tbCbjSK4oL+J9c6o4u1iFM1AoTjRSSpr7UjyTCzny7P5eWvpTgOuhfe6Ccs5d4Aras8tUUsiTjRKwZxLdO10R218E7/wHRMYfHqM3nuGa7zxB22klVBT5efSspRRNwqtN0pH85dtbaN89wC2fOoOyupMTP9IeGKD/N7+h7xe/xO7pwbd8GeXvvJWiq65EGCox0xD5eJd9aazeFFZvGqsvjdWXwu5NY0fNEeOFV8co86PnPLZdkdv14NZLfCokiWLKYloO7YOpkZ7bBUJ3ZzQzYrxHF8wqKfDczoUmueH0erUgngBmrL0+XlpegJ9cDXMvwHnj73jTy408NRDnL2sXcVrkxN1c98YzPL67h0d2dvHY7h76EiZCwOp61zv74iVVrJoE72yzsZHWf/kX0lteomjDjQTPfiuJ57rRQh5Krp1PYHXlCRcBdr/QyUM/34HHq3HFu1ZSf4LyfEhHYrbESO/sJ7O7H7M1DqPCKAm/gVFaKEj7c0K1K05rYS9iinkMSynpH3iG5uaf0tPzIEhBrLmM9hfDBH1LWX/dTSw970J04+gfWGzs3MhXX/wqW7q3ML94Ph9Z+xFeNftVx/S7kkqleOKJJ3j22WeRUrJ+/XrOv+ACHtozyJf/sZOW/hQXLq7kU1cvZVntiX87QHH0qAfOE4Oy1wXEOuB750KkDt79IBhjP9TsNrP8pLWHn7b20Je1WRMJ8r45lby6ogRjiv1PVihOJZr7kjy1t4en9vby1N7efMiR+tIA5y2o4NyF5ZyzoJyqiHp74kQzIwRsIcRVwDcAHfihlPJ/Djd+RhvYlhfhjuugdC684x4IjH8B9WJjPzf/+gXSZ1ZwU3UZ31nRMPHXCSQGM/z2v54jWOTl5k+uxziJcQOdTIbBu++m78c/wdy/H6OulvK3vY3i19yMHj61kjMdCzLrYPUPi9t2X07g7k1h9afBKvjfoIFeMuS17db6UHiSMj+aXz04UExd0lmbtoHUCM/tQqG7J+4+zGn80rVqQTwBzGh7fby88BP460fgwn+m9/xPcvkLO/EIwX3rF1PsOfH/R21H8lLLAI/s7OaRXd2T7p0ts1l6vvd/9Pzf/+Gpq6PqU18ktd0g2xLHt6iE0g0LMSomJyH1oehrT3Dv97cy0Jnk7BsWcPoVkxPWxR7MkN7VT3p3P+ndA8iUBQK89RG884oxytwwH0Ne1NPdrqZSTTS3/Jy2tt9h23HMgWLaXgxh9Tdw+pU3sPryq/EFj+5eTUrJQ00P8fWNX+dA9ABrq9bysfUfY3Xl6mO6tsHBQR555BE2b96Mx+PhvPPOY+36M/nNi+1866HdxDIWN6+t5+NXLKGmWC10pyJKwJ4YlL0exc574devg3M+CFd+4bBDk7bD7zv6+H5zN/tSGWb7vdxWX8kba8sIqRjZCsVJRUrJnq44T+5xBe1n9vUSTbu5GBZVhTlvYQXnLCjn7PnlFAdUYtbJZtoL2EIIHdgFXA60AM8Db5BSbjvUMTPewO59GH71Wqg7Hd5yJ3jHL8D+9Mn9fHpbM/bCIr6/ooENVZPjSXRgaw/3fOclVl1cz4WvO/lxqKXjEH/kUXp//CNSL7yIVlRE6eteR+lb3oynqupkX960RDrSjUnYm8qJ2kPe22ns3tRByXi0kIFRNhyOxCgL5MVuLTL1vMQUikJSpk1Lf5LFNUVqQTwBzHh7fTxICXd/CDb9HF7/K16ofRU3bNrNoqCfd9dXsqG6ZFITOx6JE+Wdndy4ibZ/+ReybW2Uv+c9BNbdSPT+ZqTtUHTxHCIX1Y9I2DbZmGmLh3+xgz0vdDFvdQWXvm0ZvuMMayKzDpkDg65ovasfqzMJuKG8/ItL8S8uxbewBD00sxdOlhWnveNPNDffQSp1AMf007k5QnR/DcvPv461V19PUcXRhe3IOlnu3H0n3938XXrTvVw25zL+ae0/Mbd47jFdW3d3Nw8++CA7duwgFApx0UUXMX/pSr7/+AHueKoRTYMbT5/F9atncda8MhUjewqhBOyJQdnrMbjn4/D8D+HNf4KFlx5xuC0l9/UM8r3mbp4bTFBs6Ly1rpxb6yup8c3s/+8KxXTBdiSvtA3y5J5entrbw/MH+khnHTQBq2YVc86CCs5dUM4Zc8sIqJxhE85MELDPAT4npbwyt/0pACnlfx/qmFPCwG77M/z+7bDgEnj9r8EYn8eTlJIP/mYzdwayBEt93Ll2Easm6bXkx3+3i5ceauHV7z+NuadNfvzOoyW1ZQu9P/oxsfvvRxgGRddfR/k73oFv4fiSCCkOj5O2cqJ2KheiZNh72x7IjEwkZWgYZb7hcCS52NtGmR+j1I/wqNAkiqmBWhBPDKeEvT4esmk3iXPPHrjtYe6Rlfzv/g52JtJEdI3X1JTx1rpylodPrCfyaCbbO9uOx+n8ry8weNdd+E87jZrPfZHkJpPU1h6MygClNy7EN79kYid1GKSUvPRwC0/9YQ/hcj9Xv2clFfVHn1NESonVkyK9q5/Mrn4y+waRWQd0gW9eMf5FpfiXlGJUB0/JeKlSOvT2Pkpzyx309T2OdHT6d0foeaWchqVXsv7aG6mae3S5YJLZJHdsu4OfvvxTMnaG1yx6De9b8z4qAsd2P9rc3MwDDzxAY2MjpaWlXHLJJRTVzuNbD+/lb1vbSZo2NUV+rltdy4Y1s1hRV3RKfodTCWWvJwZlr8cgm4LbXwWpfnjfUxA6+v8rLw4m+F5zF3/rHkQXgpuqS3nv7EqWnWR7rlAoRpKxbDY3DfDk3l6e3tvDpqYBLEfi0QWnzynNhxxZM7sEzwwOoyqlxJJgOg6mlJiOJOM4ZPNtSVbm+hyJKXN9jkMmNyabO8aUQ22JKR1MJ7dfSr67Yu60F7BvBq6SUr4rt/0W4Cwp5QdHjbsNuA1gzpw56xobG0/4tZ5wXrwD/vJhWHkz3PQD0Mb3B5PIWFz1w6fZsyiE9GhcVl7ERxqqWV88sSE17KzD77/0ArHeNGdvmM+KC+rQptAft9nY6CZ8/NOdyHSa8EUXUXbrOwmecYZadEwy0naw+zP5eNtWb7ogREkKaTrDgwXoRV43HMmQ93bOg1sv86MFDfV9KU4YakE8MagF8VEw0Ay3XwShSnjXA0hvmOcGE/y8rZe/dA+QcSTrioK8pa6c66tKCU4B+zpZ3tnRe++l/f99FmlZ1Pzbp/CtvJiBP+/F7s8QXFdN8TXzJt1LWUpJwrTpT5gc2NnHtj/sw0rbeM8qJ7SkhJBPJ+wzCPmMEXVQgmiKkdntelnb/W68RaMigG9RCf4lZfjmF6Mpb54RJBJ7aG65g/a2P+HINImOMF1biikpuoAzrruFOatWH5Xt70n18P0t3+cPu/6AR/fwthVv4+0r3k7IM/57Xiklu3fv5oEHHqCrq4uamhouu+wyamc38NCObv68uY1Hd3WRtSXzK0NsWD2L69fUMa9Chaw7GSh7PTEoe30IOl6GH1wCCy6GN/wGxrkWOZDKcHtzN79u7yPlOFxcFuH9s6s4vzSs1jUKxRQkkbF4/kBfLn52D6+0RZESgl6dM+eV5ZJCVrC8tmjC38bKOA5RyyZmuXXUsonbdl5AHi0GZ5zcdk5ALhSf86XgmJHtg4+ZSDVYAD5N4NUEXqG5tSZ49pwV017AvgW4cpSAfaaU8kOHOuaUMrBPfA0e+Byc8W645svjNpp7umLccPvTJOuCiPkRElJyXkmYjzRUT6jhHOxO8dDPttO2e4DSmiDn3byIhpXlE3LuicLq76f/l7+i/5e/xO7vx79qFeXvfAeRyy9XCR9PAlJKnHh2RDiS4RAlKZxYdsR44dcxygPoxT60oIEW9KAFDfRcrQU9aKFcHTBO6CvnipmHWhBPDKeUvT4e9j0KP78Bll0Ht9yRt/V9WYvfd/Tx87Ze9iQzFBs6t9SU8pa6CpaEpkY8XtuRbG0d5JGdXTy8c6R39vq5pRQHPIR8BiGvQTAn/ga9BmGfTtDrCsAhn04o1/b1ddHz7/9O8tlniVx+GdWf+RzJjVFij7Wi+XWKr5lPcF3VUd+/ZCybgWSW3rhJf9KkLzFcDxV3O0t/wqQvaWJaww9Xgw5cm/TSYOk0GjZP+C3aDAcBLELjTAzOwmAVOgaCJJKXDcl2P+yN6GRC7tzDPoOwv0D49uojRPChdnnYS0V47IRhM5lsdpC2tt/S3PwzMmY72biPrq3FiOQa1l/1WhafcwH6UdyrNUYb+ebGb3Jf432U+ct43+r38ZrFr8Gjjf/Bh+M4bN26lYceeojBwUEqKytZsWIFy5cvxxsu4e8vd3DXplaeO9CHlLC6vpjr18ziutNqqSqaGn+fpwLKXk8Myl4fhme+B/d+Eq75Cpz57mM6RV/W4metPfyotYdu02JlOMB7Z1eyoaoUz0kMSZSwbHYm0+xLZjCEIGLoFBk6YV2jKNcO6RqaEttnDo4NiR6Id0K8K1d3gpUG6YxR5CH6j3KMYx/FOSZ4vyfg5pQrmwel84br0rngC4/rx9WfMHl2f28+5Mje7gQAJUEP58wvdwXthRXUlwWI2ZKYZRO1bbceUZz8PlekdvuG9sdsm4wzfj3WIwQeTeATrkDstrWD2t4xxGSvGOrXDtEeGqPh0wSeXL9P0/Dk9g21R3/+oRLZqhAipwL3fQae+iZc9K9w8b+N+/DmviQf/NVGNrdHWXt+PXuLNLpMi9MjQT4yt5rLy4smxChJKdm/uYcn/7SHaHeKOcvLOPfmhZTXje+fxGTjpFIM/vnP9P7kJ2Qbm/DU11P29rdTctONaMHJCbOiGD+OaY8MSZLz4HaiGeykhZPMjkwuOQrh00cI3aNrfXR/yIPw6cobQgGoBfFEccrZ6+PhyW/C/Z+Bde+As98HlUvyu6SUPD2Q4OdtPdzTPYgpJWcVh3hLXTnXVpbgnwJe2UMUeme/1DpIMmOTyFgkTIujvS/3CMlr9z/O61+6h4Q/zO8vfyfO7FXc0uswJ+XQGtF5fkkEu8RPyKeTsZy8+Nw/JEwnTfoTWeIZ65CfUxzwUB7yUhryUhr0UhbyUBryUhZ0+4b2lfg8dG7sZveDLRRnLOpLfZQjMXJvEcWKPXSU+WgqNmj0C2JZm4RpEUtb7twzNvHczyCRscjah/9BlIe8LK6OsKTGLUPtsG/mP2x3HIuengdoavoJg9EXcCyd3p1FpFoWcNqFb2DVJVfgDRz5Xm1r91a++uJXeaHzBRqKGvjw6R/m8obLj8nGW5bFpk2b2Lp1K01NTQBUVlayfPlyli9fju2LcM9LHfx5Sysvt0bRBJyzoJwNq2dx5coalRRqklH2emJQ9vowSAm/vAUOPA63PQJVy475VGnb4U+d/XyvuYvdyQx1Pg/vqq/kzXXlFE1iwseM47AnmWFnIs2OeIodiTQ7Emma0uYRjxWQF7Qjhk5E14kYI7eLDI1wTvAu0nXCxrAAHs7t9456m1xK14t0KBxBYZ2RbpiC0fvMgvAE+TrfHt43J+DlkrIiZvknJvH0lEdKMOMQ6xwWpAvF6cK+RLcr8o6F0A5TxATs1yfx/AVjMjHo3w/9B9wQQIWEqnKC9ty8uG2VzCNa3EDMW0LUdhgsEJhj9rAIHbNsOlNZWuNpulJZopZNVgMMAUdxLx7UNYoK/n4Ky9Df0dBDpOLc31dY1/DlBGSvJvCIkWLydHu4NBMEbAM3ieOlQCtuEsc3SilfOdQxp5yBlRLu/iBs+gVc9SU4+73jPoVpOXzp3h386In9LK8v4rLL5/PrvkGa0ybLQn7+qaGa66pK0CfgD8C2HLY+0sLz9xwgm7FZcX4dZ143j0BkahkQadvEHnyQvh/9mNSWLejFxZS88Q2UvfnNGOVTy3tccTBSSmTWwUlmcRKuoO0kx66HBG8nYSHThxYz0ERO1B4Stl1v7rxn90HCt9tW3t4zD7UgnhhOOXt9PEgJf/0obLzDXVjUrHJDiK18DZTMzg/rMS1+19HHL9p62ZfKUGrovLamjDfXlbNoinhlj4WUknTWyYu4iYydbydNV+BNZiwSZk7wzlj4D+zhwt9/i7KeNp5aczl3rdvA2pTOG5IaPuCXmPyMDCbuq52lQS/l4SExeqQoXR70UOrzUOYxKDY0IkJDsxxkxsbJ2CNr063zfaaNEzfJdrjJF00JnVkHWRti0YYFVC4eX6LsjGW788/kRG7TcgXujEVnNMPuzhg7OmLs6oyRNO38cbNKAiytibC4JuLW1REWVIbxzlAbFIu9QlPzT+nouBuwiDaFGNhdx/wVr2PdVdcTLjv8vZqUksdbH+drL36NPQN7OK3iND667qOsrzn2f+3RaJTt27ezbds2hsIZVlRU5MXsuAhx90vt/HlzK429Sby6xsVLK9mwZhaXLK3C71EhZCYaZa8nBmWvj0C8C753rit+vfsh8ByfvXWk5KG+GN9r6uLJgThhXePNdeW8u77yuERXy5EcSGfYEU/nROoUOxNp9qUyDD07NQQsDPpZGvKzJOTWC4N+JOS9RmO2U9AeFvPiBWJe3HLynqbpo3hC7dcEfk0bIVpPBoYY9nFaGvJzSVkRl5RHOLM4dJCIPuWxs67gHOsoEKSH6lF92eTBx2sGhKshXDWqLixVbvFOrzBYUkpSjiRpOyRtm6Tj5NoOCXvYszmaShBN9BNLxYhmUsSyJlFbEpU6UeEjaoRI6UeOTR/QxLDYnHtQEzE0hCWJxky6+pO0dCVIJbOQldSEvKytK+ac2WVcMK+MhqLAIb2STyWmvYANIIS4Bvg6oAM/llJ+4XDjT0kDa1vw+7fBjr/CjbfD6tcd02nu39bJJ36/BduR/NdNK8lWB/hmYye7kxnmBbx8aE41N9eUTsg/91Tc5Pm/7Oflx9vweDXWXTOX1RfPRp+CifqSGzfS+6MfE3/oIYTHQ/GG6wmsWYNn1iw89fV4qqsRHuVBMxOQjsRJFQjcibGE74PF78N6e3v1vBd3XtwOFAjhoYPFb+FX3t6TjZRuLC8JOBIkMleDg3TfNsNdRLh9roboIKnyedWCeAI4Je318RLrgFfuhK1/gNbcz27OOa6QveLGfBIpR0qeGojzs7Ze/t49SFZKzikJ8da6Cq6pLMY33RZph8BJpej68lfo/9Wv8C1eTN2Xv4xn1lwG79lPclMXotSHf0U5miXHFp8LROijdgE3BJpXd9/k8ekIn4Hm1/E2FOFfXIos87P1kRY23d+MmbJYuK6KM66dR1ntxC7+HEfSOpBiZ0eMnZ0xt+6Isbc7jpWbi6EJ5lWEXFG7eljcnl0anPDYjCcL0+yhtfXXNDXegeX0kx7w0vtKBVWV17H+1a+jYnbDYY+3HZu7997Ntzd/m65kFxfVX8RH1n6EhaXHl9Q7FouNELOllJSXl+fF7M6sn7u3tPOXl9rojmUI+wyuXFHDhjV1nLugHGMKvTkxnVEC9sSg7PVRsOs++NUtcNb74Or/mbDTbokl+b+mLu7uHkAAG6rchI+rIod+28SRkpa06XpUJ9L5encynQ9DIIC5AS9LQ4FhsTrsZ37AN+FCruk4xAq8VWNDcXztobYrgKcdJx96YCjUgCfvWSrG3jeq/7D7cmurnck0D/XGeKg3yrODCbJSEtI1LiyNcEl55OR6Z0sJ6QFXdB4hTA+J0wV9yd6xz+EvgUjNKEG6CsKj+gKl486jNpFIKUcIy0nbIWU7B4nN+f1OTowesT32uJTtHHXMZr8mCkRn19O5yNAp0iBiJyjKDFCU7iGS7KQo1kJR9ABFA/soyvQRsRIU2XE84DqUFHhuj6h9YRxHsrMzxpN7enh6by/P7u/Lvwm4rLYoFz+7nDPnlRHxn5ra0owQsMfLKWtgs2nXaB54Et7wa1h85TGdpqU/yYd+vYlNTQO86aw5fPrVy3hoIM43GjvZGk8xy+fhfXOqeFNtOYEJuLnua0vw1J/20PhyL0UVfs69aSHzT6+ckuJdZt9++n76UwbvugtpFrxSpet4qquHBe1Zs/DMmoW33q2N6mqEfnK8aux4HKuzE6uzk2xXF1Znl7vd3UW2swurqwtpmu716fqoWkPoRr4WmgbGUK0jND1fC0MHzT1O6jq2YWAZBo7hwTZ0t9Z1LMODo+tYho6jGdiGjqUZOIaOpenDta5jaRq2pmNrGrbu1pamY2vC7RcalhBuf66Wmga6gabraIaBMHSEYaDpBkLX0BAMrdkFIIRw6/z2UFvkQ8qLw44VOVVTgmmD6bhCSMYG00ZmnFxdsJ2x3ASVpo2QBZ8hh8+NAN2rIXUNx9CQhsi1BVJ3i6O7fdJw24421JfbrwmkNtwe2nY08rUjcn24wqwjwWZYtLWl2ze0zxmzL3fckNCbazu584ysXaFYjhKKHUaKxUNC8bC47O4D8ucr3CcLzjNacJYjzjM8j+Oh85LT1YJ4Ajhl7fVE0bcPXv6jK2Z373BfvVxwMay6BZa+GnwRALrNLL9pd2NlN6VNyjw6r6sp4y11FcwPzox4yvFHH6Xt3z6NE4tR9fGPUfqWt5DZN8jAn/di9aVdoXmE6KznRWi3zxixT3gLxhXWXv2o36pJJ7JsebCZLQ82Y5k2i8+sYf2r51JSNbnhyEzL4UBvwvXS7hj21m7qG/a+Cnh0FleHR4QiWVIToTLsm5L3X0eD45h0df2d/ft+QDK9HTuj0bujBL+8iPVXvpX65asOO7eUleKX23/Jj7b+iKSVZMOCDbx/zfupCdUc97XF4/G8mH3gwAGklJSVlbF8+XKWLlvOgaSHu7e08feXO4ilLSrCXq49rY7r19Rx+uySafudTAWUgD0xKHt9lPz9X+HZ/4M3/QEWXT6hp25Om/ywuZtftPeSsB0uKA3z3tlVrAwHcgL1cOiPnYk0CXv4bneWz5Pzpg6wNDzsVT0VEj+fbOKWzRP9cR7qi/Jgb5TWjJtnacK9s7NpSHQVCNOdo7ymC4Rpe4zQLboPIqM9owuF6QJvaWNi7+1StutdXyg0Hywgjy0sH05sTjnjW5FpuGE2QrpGcKho+nA73zfcDuTqUEF/SB/ylHaF6mNy6nAc9zvr2++GIxmq+w+47VTfyPGhyoPEbaukgW3pch5rFTy1r48XGvsxLQddE5xWX8x5Cyo4d2E5a+eUnjJvaCkB+1QjE4M7roOu7fCWO6Hh3GM6TdZ2+Mo/dvL9x/axrLaI77zxdOZVhHi4L8Y3Gjt5djBBhcfgPbMrefusCiITEJOraVsvT/5hD31tCeoWlXDezQupaig67vNOBtI0yXZ2km1pIdvaitnaSrallWyrW6yuLlc5G8Iw8NTW4skJ2t4RQnc9RmWFKwqP5xqyWazubrKdnVhd3a4o3TVKpO7qwkmOfF0oGgzRPncBHfMX0D5rDm2V1SS8PiwEtgAbgQXYQmAJkesfLpbQhmtNYAstJyxr+bYzQzz7ZjK6IxGAJnOFXClsgyv4A5oo2Bag52pNjKx1IXJtgabl+jWBLgS6LhCauy2Ee14h3HOKwnbuswTu8YUPFoY+f8Q+yJ9PKxhH/jwjjxn67dTEqH25zy48tzbquoY+79bZVWpBPAGc0vZ6IpESOl+Bl/8AW/8Ig01g+N0H2atugYWXg8ePIyWP9cf4eVsv9/YMYku4oDTMW+oquKqiaPq9OjsKq7eX9k//O/FHHiF07rnU/vd/Y1Sd/AfiqbjJpn80sfWRFmxbsvScGtZfM5ei8iO/kjqRJDIWu7vi7OyIsrMjzs5Ot+6JZ/JjSoMeV8wu8NZeVB2haBp5AkkpiUY3sX/fD+jtewApHQYbI9i9qzjt/Hez+Kzz0A7jVDCQHuD2rbfzmx2/QRMab172Zm5ddSsRb2RCri+RSOTF7P379yOlpLS0lOXLl7NwyTJ2RnXu3tLGA9u7MC2H2WUBNqyexYY1dSyqnphrOJVQAvbEoOz1UZJNww8ucYXK9z3liokTzGDW4hftffywpZv2zMik9hUeIx/2wxWqAywJ+Sc1fvZMQkp5/N7ZmTjs/gfsvh8GW4a9p9ODYwwWECwf5S19iDAe/uJ8Eu/jmV/MdujLWvRlLfqzNv0F7b6sRb81sq8/a5EaR/JAQzC2sKyNFJkDo0Rlt+hjjh3q8+XWkNOC9OAhxO0DEG0ZGWfcE4LSudglDXTqtbySLuOpviIe7Q7T5JSjG27i83MXVHDugnJWzSqesW9pKQH7VCTRAz++yv1H+fZ7oPa0Yz7VQzs6+fjvtmBaDl+8aRUb1swC4OmBON9s7OThvhjFhs47Z1XwrvpKyr3Hl0DIsR22PdnOc3/ZRyqeZelZNZy1YQHh0unlIeaYJlZbG2aBqJ0Xu9tasbt7RowXXi+eurq857Yrbtehh8PDInXOWzrb5bbtvr6RIjkgPB60qmoG5s2no2Ee7bV1tJZV0RIuosnnpwmd6CgDVOP1UOLRMYRAF2AI9/UqXYiRfdoYfflx5PeN2acdYhygI9GlxHAct5YOeq6tOw4e6aA5NrrjYDgOmu3gcWw0x8GwbTTpoFuWu8+yEZaJNLM4ZgYnk8HOZHAyJo5p5vpMnIyJnR3azrr7subIcWau3zRxslmcjImUrou0RCCFcF9LEgJZ0Ae4+wr3kxujG+DzIjxe8PmG216v2/Z6YWjb6wGPBwNXZNUBISRarj0kqA716wg0KRBCQ5da7hgdgYYmNTQ0NCnQ0BFSg9xZpHRHSjSQbpG546XUcN21c20pcm2R70ce402EJhGaBH107SA0idBlwRhnuK05w+NE7jiR2y+AoZfFCv42RtiuoXbhn4E8+JgjnafiHe9QC+IxEEJcBXwD9xfsh1LKw75De8rb68lASmh+Drb+3g01kuwBXxEsux5WvQbmXgi6QWcmy6/be/lFey8t6SwVHoM31LqxshsC08vmFiKlZOC3v6Xzf76E5vdT85//QdHlE+sFd6wkBjNsvLeRlx9vBQnLz6tj3dVzT/o9Tm88kw9Bsrt9kD2tfTS29WOlM3idLB7boj6oM7/YYF7Ew+yITn1Qp8ov0K0sMmMiMxlkzu4ObTvmcFtmhvZl8vuwbPTSUoyKCozKCvSKCrddUYlRmWuXl7u28RhJp9tparqDluZfIUWCVI+PeNNcFq14F6ddfA0e/6Hj1LbGW/nWpm9xz757KPYVc9uq23j90tfj1SfutfJEIsGOHTvYtm0b+/btQ0pJSUkJy5cvp2HhUrb2Cf68pY0n9/TgSPcV4w1r6rhudR2zSk7sA5DpihKwJwZlr8dB5za4/VUw93y45afgnxyHLNNxuKd7kN6slQ8BUumdPg8bpwNH7Z2dTbqi9St3usK1lXY9bssXjiFMF4jVoQrQj+07sxxJvzUsMg8J0IUi9Ii+rM2AZR0y6qUGlHh0yjwGpYZBqUen1GNQlusLG/ohheVCr+jp7gxxQrBMGGga23O7/wBYqfxQR+gMeqo54FSyPV1Oo6ymy6ilZNZi5i1exekLZ1MS9OD36AS9OgGPPq1DxCkB+1RlsAV+dCXYGXjnP6B8wTGfqn0wxYd+tYkXGvt5/Rmz+dz1K/KvMGyOJvlmYyd/6xkkqGu8pa6c982uosZ3fMYzk7J48e8H2PJQM5omOP2KBk6/fA4e38x4euyk02Tb2kYK2wVit93Xd9AxelkZRnW1601WXUP3rDm0VVXTUlpOSzBMs+7lgCVpSmdGPCXVBcz2e5nr9zE36GOu38u8oI+GgJcGv29CwsCcKkjLyi28Ry/ITaR58LaTySDTw4t6O50lk7LIpB0yGYlpgpmFjKWRtXRMRyfrGJh4yOLFEl6ElICNkBIhbYR0DlFsKGgL6SCcgra0YcT2UFuOqh0E7n4ON47hbU1oaJqGEBqa0N22pqMJHaEbuW0DHYGuaQjDhzD8MFTrvlF9PtD9bt84buqklUFaabAySDuDtDJgpYfrUX0j9tuFfbnz2CYjle5hlu/coRbEoxBC6LhJly8HWnCTLr9BSrntUMcoez3J2Bbsf9QNMbL9L2DG3ARTK26EVTdD/RnYwMN9MX7e1sP9PVEc4MLScN5jaygZTXEuNmCRJ1fn9nmm6E1yZt8+2j7xz6S3bcO/ahXeeXPxNjTgbcjVcxvQIyfHmzXen+aFvzey/Yk2hCZYeeEs1l7VQLDo0MKok0iQbWvD6u1zbUo6PdLWHEJElrkHt4XCsUxnxhaYTROswyQyPho0DeHzofl8iFzRfF6EN9f2+/JtoWtY/f3YPT1YXd3Yg2N5p4FeXIxRVZkTuCtzIndFXuTWKyowKivRi4sP+Tabbadpb7+TfXv+j6zTgpXSGdhbzaza13P65W8kVHLoRJvbe7fztRe/xtPtTzMrPIsPnv5Brpl3DZqY2PunZDI5Qsx2HIfi4mKWL19O7bzFvNgNd29pY1PTAABnzi3j+jV1XLOqlrLQ1EqEPpVQAvbEoOz1OHnhx27i5UAZXPgJWH/rcSd2VJxcDvbOjpOVEHJMLux/nkt6n+aS9D5mLToPlt8Ac84G7ei0i5TtuIJzTnjuzR5CmM7aedF60LIPeT6fJkaI0KUenXKP4baNMfo87n2eNl28m2cyUrohZgrF7Zyw7fTtR0uNjHveI4toklU0ymqaZDWNThXtWi3dnlpS3gr8Xp2g1yCQE7eDXn1U2zhE/1DbIOjVT5hArgTsU5nuXfCTq9yMse/8BxTVHfOpLNvhq/fv4ruP7GVJdYTvvOl0FlYNL/x2JFJ8q7GLOzv7MYTg9bVlfGBO1XF7cQ12p3j6zj3s3dhNqMTHOTfMZ/GZNYgpumCeKJxEgsGWVhpjCVojxTR5AzRmbRpTGfanMrSkzRFPT/2aoCHgY27Ay9yAj7kBH/Ny7Vk+75QVGKYjjiMxkxbpRJZM0iKTzJJOZskkrIJti0zB/kzSIp20sDKHvtEA8Pp1fEEPvpCBL+jB69fdGM5SIm2J40g3yeRQbefiPtsSKd3toX0Hjctvk+8/GQgBXr+Gz6/jC2junP06Pr+GL6APl1yfx6fj9wg8uuvFT1YisxIn6yCzDtKUbp11kGauzsrhdq52hvpMe1wBsIVHQ3gLi47m0ah6j4qBPRohxDnA56SUV+a2PwUgpfzvQx2j7PUJJJuC3fe5Yvauf7gPuEsa3OSPq26B6uW0pU1+1d7HXV39dGayxOwj/7EENG042c1YJZeFvbhQCC8oYV2btNdBpWnS++Mfk3j6GczGRqyOjhH79bIyV8yeMwfv3Aa8DQ14ciK3Hp7YhItjEe1J8fzfDrDzmQ50XbBslY9lNTG03jb3IXdBcQ4h7h6EEMPCsdebbwufF21IOPb7hts+rys2j7ntHRaic9uOx0tHymF/1GJfNMuufpOd/Rn2R7NkhIGj6fg9GouqIvlQJEPxtasih4+v7Zgmdm8vVk8PVncPVk83Vk+PK3B397j9PT1Y3d3IdPrgExgGRnl5XuDWKws8uoe8vMvLiXv3sefAD4mlngUJg/tLiHivZP3l76esrv6Q1/dU61N8bePX2NG3g2Vly/jouo9yTt05R/e9jJNkMsnOnTvZtm0be/fuxXEcioqKWL58OaX1C3mu0+HPW9rZ0xXH0AQXLq5kw5o6LltWTch3fG9DzjSUgD0xKHt9DLS+CA/+B+x7BIrq4VWfhNVvAF39jU5bMnHYdS9su4v4vid4Irych6ov5sHyc2nV3PuGJSE/l5YVcUFpGAdGiND9Vq4eFb7jcCE6wrqWF5nLDIMy76FF6FKPQZnhhuGYNqE2FOMjPZj31h5s28Vg6y58sSZCiWaC6Q60goVuRgTo8dTSodfSptXQQjUHZDX7rCoarTJiWUhlD69PjIXP0AjmhHG/R5tQgTzs9ygB+5SmdaMbE7t4NrzjbxAsO67TPbqrm4/+djMp0+a/bljJa9aNvNE/kMrwnaYuftveh43kpupSPjSnmsWh43vi3LZngCd/v5uuxhhVDRHOu2URdQtLjuucJxMpJQOWTUvazJWsW2dMmnN9faP+mRQZWoE47XpQz8uJ1tVej3piOg6kIzEzdl5kHhagc9uF4nRBfyaRxUwf/p+84dXwBT34cyK0L2jgC7m1P2iMEKj9+f0GvoCBdgK94aXMJVK0pZv08JACeaEwzmGEcbcuPGZYeHfDA5lpe8TPckjYz/clrcMK65om8AYN92c2+mccHG77Q54R276ggcen52/kpOUK2U4uuaaTsZG5BJvSHN4e7h8eO9Rf86G1akE8CiHEzcBVUsp35bbfApwlpfzgoY5R9vokkR6EHfe4Yva+R0DaULU8J2bf7CaZwU3GGrdsorZD1LJHlEHLJnZQ7YzYjlo25hHuHTXIi9rFhk6xp6BdIHi7+4yR24aOfxzxEJ10GrOpCbOxkWxjI2ZjI+YBt7a6ukaM1Ssqch7bQ2VOXuzWQuMTt6VluYmU20YL0+1k29oY7M+yr/ZSOqvWo9sZZrc8TEPfMwSryzDqat0QY7liVFS6Xsx+P8LrHenp7PWCx3NSFq1J02J3Z5ydHbF8OJKdnTG6Y8PxtUuCHhZXu3G1h+oVdcUEvON7u05KiZNIYucEbqtQ4O7uHha+u3uw+vrAPthui2AQubCY/vPSJJd0I7wOiU4/smURy2bdQP3KdXiqKt0QJp7hN4Ec6XDPvnv49qZv05Zo49y6c/nouo+ytGzpsf/wjkAqlRohZtu2TVFREcuWLcNXPZ9nOmz+uqWdtsE0AY/O5cur2bCmjgsWVeI9yoSjMxklYE8Myl4fB/sehQc/7wraFYvhkn93w3qptdv0oEC0zocHCdfA8uvzntZSaGPGzi5EgCsy5zyjywqE5zKPkdsevV9XITkUR89YoUkKa3v4ngzNgOLZyNJ52CVzyUTmkArPIRGaTdRfT1z6SGdtkqZNKmuTMq2Ctj2qbR2i3x63QN74pWuVgH3Ks+9R+OXNULsa3vpn1yP7OOiMpvnwrzfx7P4+bl5Xz39sWEFwVOzr9ozJ95q6+XlbD2lHck1lMbfOqmR2wEuFxzimsBXSkex6roOn79pHYiDDgrWVnHvTQooqpl4MQEdKukwrL1A3FwrVGbedGOXZFtAE9X7vcPF5mR3w5r2qSw1dPUkdA9tySMVMklGTxKBJKmaOFKMPEkyzmElrdPjwEWiGyAnMw4Kzv1AoDRXuG+73Bz3oHnWTcaxIKcmm7eHvLDnWQ4WRDxSGBPAjfqe6GCVqFzxUGCV4Dwvj7ndveA72YlAL4oMRQtwCXDlKwD5TSvmhUeNuA24DmDNnzrrGxsYTfq2KAuLd7qJs6x+g+Rm3b9Y6960tzQChu7Wm58qR+rRc7falhYeY8DAofESFlyieXDEYlDoxBwYcjagUDDqCqKMxIHWi6ESlTorDi5teaVOESbEcKmmKHLcudlIUOWkiTpqIkyEsTcJDtTSJOBlCMkPQMZGmjdmXGVl6Tcx+Ezs+MqyGEdbxlnrxlHnwlnrwlhp4ij3YVoBs2ks2JshGLbL9KbK9UazeQTdbfQF6efkIYdpTV0ciVMtL+wIc2JPGFzBYc/kcTrukHq9/+nrr9SVMdnbE2NUZY0eu3tURI5Zxf6a6JlhaE2HN7BJOn1PKmtklzK8ITdjrqdK2sQcGRnh1j/boNgc76ZvXRvTcFFq5jZkwSGyKUPaIQ01bCuOgON2VUF7C8+Zu7ux7hHZfijNXXMGr17yO5RUrCHkmz3s/nU7nxew9e/Zg2zaRSISly5Zhl87lmXaLv73cwUAyS0nQwzWratmwuo4z5pZN65iYx4Oy1xODWl8fJ1LCjr/Cg/8JPTuh7nS49LOw4OKTfWWKsTgK0fpw4UHils3mWBK/puXFaBWiQ3FScRyItR9a3E4PjBwfrobSeVA2D8rmD7dL57lOsUf5uyylJJ11jihyDwnk733VQiVgK3BjX/7urTD/VfCG34JxfLHyLNvhmw/u5lsP72FhZZjvvGkti8fIjN5jWvywpZsftXSPeBU5qGtUeAzKPQYVXrcu9xpu3+jaY+AvELyzGZtN9zex6b5GHEey+pLZrLt6Lr7AiVvgmY5DeyY7UpgeamdM2tLZg7zOSgw9J0578gJ1oWBd7jnxArWUksFUloRpY9sSW0psZ1SREttxsB2wHAdnqJYSK+e9a406ZqjPcUbtk/KQn2PljrMdiW27oR9IO4iMjcg46GkH3XSLYUo8WQePKfEc4qGeBByPQHo0NJ+G5tPxBAy8AdfjORj2Egp7iBR5iRT5KCnxEYl48Ye9YwqWiqnNkbzqx/SoH6pT1qFCXQOjH2i4gvd1H1yjFsSjUCFEZgADTfDyH2HH3yATcz2zHStXHLfO99m5UtA3CWSEh6gRZtAIEzXCDBgRop4Ig55iokaEAU/RqP1honqIQT3EoBHEEke+N9CkQ9hJu+L2UC0zRJw0ISdDOJsmkEzhT6QIRJN4B5P4+hN4e+P4B5ME0ymC6TTBdAq/mUHDwRO03RKyMXK1J2TjKQ3iqSpDK6p0kzcFy3N1RX67O1bCc4/bHNgWxx/ycPqVc1j1qno84/RUnqpIKWkbTLOtLcqW5gE2Nw+wpXkgL2oX+Q1Wzy7h9JyovXp2yQmJ8WynU3TsvYvdB36IHTiAYwni+8soa1nCnL4IoqcvL3rLTOag41Ne6CiFWFUEbXYdRQuWUL90PfNXnI+/umbC7yvS6TS7du1i27Zt7N69G9u2CYfDLF66jERkDk+3Zbl/WxeprE1tsZ/rV9dx/Zo6ltcWnVL3OErAnhiUvZ4gHBu2/AYe+W8YbIZ5F8Kln4P6dSf7yhTHKVorFNOaVL8rZPfty4naB4bF7VjbyLG+IvdtzSFBu7AumnVcfycqBrZimI0/h7s/6CZves2PJuQf8BO7e/jIbzcTz2T5j+tXcsv6+jFviqOWzTMDcXpMi56sRW9B3Zsdbh/qVeOwrh0kchfZgviOAeK7BinVNM46u44zzqjBq+ukHIeU45C2Za52SDsOKUeSth2S+b7h/SnH3R5qp8bc755zrOus9hojPaj9Xup9rlg92+8lbJx4g5exbDoG07QOpGgbSNM2kKJtIJXbdvuOJe7RsaJLCElByIGwFETQCDuCsBQEpSDkCIIOBGzQOfj3yBaQMcA0BKZHkPUILK/A8mpYXg3bK8joMGjbRLM2cdMmls4Sz1gcTchnjy4I+wzCfoOQ1yDiN3LbHsI+3W37PIT9BhGfQSg3Nuxzx4Z8ufE+A/0U9XSajkhHusk1k2MJ3mN4fictXvfpM9WCeBRCCAM3ieOlQCtuEsc3SilfOdQxyl7PIKTEDbI/StQeEr7H6pNOgQe3NuzVPdqbW+gjx43D6yPpOMQth5htE7cc4oW17RCzbBK2ux3L9SeGxtsOcStX2zb2Ud4Gh3SNsAZhIQlhERry/LaThLMJQtkoYXOAcLqPcLqXcKqLkJVw99spwlaSsJ0kkarjpfhraU6fRsCIs65hCysW9mCU1roe8kV1blzVojrwFx3Hl3fycRzJ3u44m5oH2NTkito7O6J5291QHuT02SV5T+1ltUWTGh4jFtvJKxu/TNx8DKHbJDsjlASuZe3FHyNYVIoTj4/w6I61N9O5+yWSB/ait3YR6U2hFzjdZ3waqepi9Nn1lCxcRuWiVfjmzsUzZw5GZeVxC8qZTGaEmG1ZFuFwmPmLl9EXmMVTrSaP7erBciQLq8JsyInZDeWTH+v9ZKME7IlB2esJxsrACz+Bx74MyR5Yei1c8hmomrxwRIoxUKK1QnFksinobzzYa7tvn+v84mSHx+peKJlzsNd22Tw3984RktkqAVsxkie/Cfd/Bta9A6792oTE3uqKpfnIbzbz1N5ebjx9Fv91w8pjSiAjpSRmOweL26NE7sK+0bGljgWfJvBrGgFNw68Lty5oB3RtzL4an4fZOS/qOr8H3wmOTyWlpC9h0jaQLhCkU7QNpmjNidWFsSeHqAj7mFXip64kQF1JgNpiPxG/ga5p6BpuLQS65hZDE2hDtRAYeq7O7ReAzNhkExZWwiKbyGLG3ZKJZ8nEsqSiJulYFjM1hoeegEDYQ7DIR7DYS7DILaFiX7491O8NGMe0yJNSksraxDMW8bQ1ss6VWNoikRneF8vVCXPk9tEK/kGvnhezh0Tug7YLhfD8toeQT8+3/cobfEqiFsRjI4S4Bvg6oAM/llJ+4XDjlb1WTBeklKQdOUIAzwveOSF8SOhOFAjkccshYQ/vG9o+XMKmQjTpEHQsjCzopk4gaxPJpijOJijNDlJu9VNh91At+6j1p6kK6ZQFI5REyiiKVKEV1w+L3f7iaRVzNZGx2No6yObmATY19bO5eYDOqHtf4zU0VtQVcfrsUtbMcb2160sDE24vTbOf7Zu/SVfvH9B8ScyYF495DmvO/SQVsxYf8jjHNGndu4X9rzxF964tpA7sx2jrobLXomoQjAJx2/F7MebMJjRvAd45DW5C0Tlz8DQ0HJO4nclk2L17N6+88kpezA6FQsxeuIxOTy1PtmR47kA/AGtml7BhTR2vPq2Wqsjx5auZqih7PTEoez1JZGLw9HfhqW9BNuEmeXzVJ10BSDE5ZGJuMmslWisUx49jw2DLIUKTHAAzVjBYuPejpfOgbO7BAnegVAnYijG4/7Pw5Nfhgk/ApZ+ZkFPajuTbD+3hGw/uYm5FiO+8cS3LaifXG0hKSdSy6c3a9JhZXtnZy4sbO0nEs3hsiWGDT0JRyEtpkZeyEj/lZX4qygJUVQapqAgQ8kzdWFQZy857TY8QqAv6MtbImJp+j0ZdSYBZJQHqigM5kdrvbpcEqCn24/ccnUHOmjbJQZPkYIZk1MyXxND2oLudipo4YyzEDa9GsNhHKC9A+0aI0UMCtT/iQT+ByQuPF8t2SJj2wSJ42iKeyeaEcJt4JnuQMB4bNd46CgFDCEZk6B0uw9sBr0FoqN/n9gc8OiGfmxE4WNguONYzjX7uUw21IJ4YlL1WnKpYjiwQth0SBQL4kOd3YmhfTvjuHkjRFc0QtR3iOMQNyBwm74KQknA2nRe7y5woFSJNqSEo8RiU+PyUBUKUhEopiZRTVlxFSbiMIo8xZe+N2gdTeQ/tTU39bG0dJJ1174Uqwt4RsbRPqy8m4vcc4YxHh+NY7N/5a/bvvR0RbMPOCpyBpSxd9VHmLr/0qM5hOzYHogfY2rmZ/Tufo2fXVqymZqr6bGr6YdaARkW/g15wbyCCQbxz5rilIJmoZ04DRtWRxW3TNEeI2dlslmAwSM38ZbRo1TzRnGZbewxNwHkLK7h+dR1XrqyhaIJ+blMBZa8PjRDiKuAbuA+dfyil/J9DjVX2epJJ9MITX4XnfgBIWH8rXPBxCFee7Cub/kgJPbtcsXr3fdD0NNimEq0VislGSkj2up7aY8XdToxMoE6gFPHJRiVgK0YhJfzlw7DxZ3DlF+GcD0zYqZ/e28uHf7OJwVSWz123gjecOfuEeo9KKUkOmgx2JxnoSjHYnWKwK8Vgd5LBrhTZzLD3rNAEkXI/JZUBiquCFFcGKK4KUFwZoKgigD7JmdullPQnszT2JmjqS9Lcl6SxN5lvt0fTI5LSCQGVYd+wQF3gRT0kUJcGPSN+3lJKrKyDmbLIpm3MtBvvN5ty22bawkxZpGLZgwTqbPpgT2MhIBApEKTzYnSBQJ0Tqad80ikpwc66r7zYJthWrp0t6B/VPtL+Qx5n5T5jqJ1F2iaOlcW2TOxsBtvK4lgmMrfPHWNjI7DRsKWGhYYlNSwpyErNLY7AzNVZR2ChYaPh5GobHVtqufPo+X0WOggNTTfyRdcNNMPA0A0MY6h40A0PXsNA93jwejx4PQaGx4vP48HjMfB5vfi8Xrz5tgdRkMQNTRsOASD0XAgALdevDfdpBfvyYwr7CsedXHFFLYgnBmWvFYrjIx43ae1J0tqbpGMgTVcsQ0/SpCedZcC0iGuQ8gpSPo2UV5D2ClI+SB/mYbaQDiVOmlJMSjSHEkOn1Oul1B+gJFhESSBMmddDiaFT4tEpNQxKPTpFJyFBVdZ22NkRY1PzAJubBtjc3M/e7oQ7DwGLqsIjRO3F1ZHjDvHV2foU2zZ/Gcu7FU2XZHprmD37bSxZ+0a83vC4zmXaJjv7dvJy78u83PMy2zq3Em/eT3W/Q00fLEqEmBcLUNFr4e8cRNgF97GBwBjC9hy8DXPHFLdN02TPnj288sor7Nq1i2w2SyAQoKRhGU1U8XhTkqa+FF5DY83sEqoiPirCPiojPirDPioiXirCvnyZzBAuE4my12MjhNBxw35dDrTghv16g5Ry21jjlb0+QQy2wKNfgk2/AE/QXaef88FpHybqhGMmYP/jrmC95343zAFA5VJYdDksvlqJ1grFySYTh/4DI4Rtcd3XlYCtGAPHht+/HbbfDTd8D9a8ccJO3RPP8NHfbubx3T1ct7qOL964csI8YI4HKSWpWJbBriFxOzkscHclMQsEWyEgUu53Re3KoCts50Tuogo/xtF6MdsOrf0pmvqSw6V3uE5kLATkS1XYx+zSALNLA9SXBqmL+KkKeCnzGkR0HWk6I4RnMy9E2+52urDPFarH8o4ejdevEywe6SHtFp8rTucEan/Yc3Ky2Fumm1gg1Q+pPrdO9o1q5/ZnYmMKxtjmyLY8AbG/hQ66x40FpRluW/Pk+sZqG7mxubZmFMSUtUcmT5NOQdvtl46NdGwce7gtRyRbs0FaCMcBaaNJGyFdWXs6YqMh0ZBC4KAjETgi14fAESP7HDSkcPfJMftGjRcaFJ5fCGSuXv6Jf6gF8QSg7LVCMbmYKYtYX5pob5pYb4pYb5pYb5r+3jTd8Qz9WYu0VyPpFaR8gowP7EAWy58l482SNmySXo24x0fUEyTmOXTMZIGkRBOUenRKvD5X3Pa44naJYVDi0SnzGHnhu8xjUO31EJjgt4EGk1m2tAzF0nZDj/Qn3fiMQa/OafXFrJntCtpr55RQVXRsoTPig01seeZ/iNsPYfjd89umAXYQXRTh8VQQCNYQLppNpHQufn81Xm9FrpSjaWMnpoybcbb1bmNrz1Ze6X2Fl3tepj3RjuZIqqIaa7O1rEyVMzfqo6I3i6eth2xrG2SHY1Dmxe05c/DOHRK2G/A2NGBUVWFZ1ggx2zRN/P4Agfql7HcqaI5Db8KkJ24Sz4ydoLU44KEi7M2L3MO1N789FcRuJWCPzXgTLyt7fYLp2Q0P/Zcb5iJQ5npjn/GuI8aPPWWREnr3DgvWB54EOwOeEMy/yBWtF16mQrMoFFMcFUJEcWisDPzyFjjwhCtgr7wJ5l7oCmfHieNIvvfoXv6/+3YypyzIt9+4lpWziifgoicHKSXpeDYnaCcZKBC2B7tTZJIFN+8CQsU+dEPgOBLHkVi2xLYdbFti5/rcfFZyhEAtAA0xRnrCY0P3aHj9Ol6/gTdg4A3k2kPbfj1fe/wGvtwYT36MO36yvc3z2BakB48gQhduD7jbZvzQ59QM98YuUArBMvCGXRF4tBg8Qhj2DI/Jtz05kbmwPUp8PuI5jGFRWvO4HsbThZyoPTIB27BwnrWypDJZMhmTlJkhk8mSNk3SpknGdPvNbJZMNkvWdNtm1iKbNclmLbJWFuk4aDiAgyYdkG7tysc2QkoEDkI6CNyxQ32atBE5yRkp0QrGa9LJ7XP7NNzjR+4b2R7e5zAkZQuZ+8y8tO3kz5ffRqJJh+Wf36wWxBOAstcKxcnFTLsC95CwHetNjxC8U7HsyAN0iVaUQQQT4IvhGANkjUFMr0XaCwmvwYCniAFPMf2+MrfWw0Q13yGvodTQqfV5qPW5OUXctme4z+chchyJsKWUNPYm2dw8HHpkW3uUbC4rZ12xP++hvWZOCatmFR91uDWArJlk2/PfZ6BvG+lUJ1m7D4cYus/EE7DQfc6Yx2kijNdbgT9Qg89XiddbiddbgS8vcrvbHk8ZfZkBXul5ha09W/Pe2oOZQQB8uo9lxUs4Q8xlRbqMhgEv4a4Y2aZmzMZGzJaWg8Xt2bPzntti9mxaAwH2xGLsamzENE0AIpEIJSUlBMLFECjC8YbIan4ywkfC1hhIO/TEM/TEM3THMlNW7FYC9tgIIW4GrpJSviu3/RbgLCnlB8car+z1SaJtEzz4H7D3ISia5cbHXv3GCVmvT3vMpKtj7MmFBuk/4PZXLIZFV7iCdcO5YBza/igUiqmFErAVhycTg79/Erb92Q2wHiyHZdfDihth7vnH/VrNs/vckCL9iSwfvnQhy2qLKA/7KA95KQ97CXqnrvG1HUlvwr0pb+9O0NWaoL8zSaIvjRk1SaZt4qZFxnYY+suQgM+juUn6/B4iAYNIwENxwEtR0E3Op2kCoQmEEAiBW2tujcDdn2vruhhTePYFDDx+/cQJz6NxHMgM5oTm0aLzYbbTg4c+p9DAX+KK0IFSV5QubAdG7SsUrKdorE7FzEUtiCcGZa8ViqlN1rSJ5wXtoZJyt/vSJAfNEeM1TRIKWhT540Q8fUREBxGnkaC1B+kZxPTZRL1h+o0Ifd4yOooW0BaeTYe/mnZPKW0iRI88+N4wrGsHidqjhe4yj37UYevSWZtt7dER8bRb+lMAGJpgaW3EDT2SSxI5rzw0rjfQpJSkooP0t7fR136Age49RAcOkIy2ks50IowUnqCFEbDxhGy8YYnhzyL0sQRggcdTii8naHu9lXi85aSkh45Mkv3xXrYPtvFS/wF6zTQSQcQbYUX5ClZVrGJF6TKWW1VEuuKYjY1kG5tcYbupCbO5eYS47QSD9KxYzkBVNYlAgLjXQ0II4o6DPWod6PP5KC4uzpeSkhJ8oQiOJ4Sp+Ug6Br2JbE7czhyT2F2RC18yJHYPCd/lIS9lIYEhTGwnhWOnsO1Urp0e0Z4167XKXo+BEOIW4MpRAvaZUsoPFYy5DbgNYM6cOesaGxtPyrUqgP2PwQOfh9YXoHwRXPLvsHzDqbcG6t0Lex5wBesDT7gJGI2A62W98DLX07p07sm+SoVCcYwoAVtxdGTTrjF45U7Y+Xc3C3Ko0jWMK26EOeccs5jdG8/w8d9v4ZGd3Qft83s0ykPuTWlZyOuK22GvK3CHhtpuXRbyjssj5lAkMhZdMfcG2i3p4e14hq6oW/fGM4wVfSPiN6iM+JhdGmROWa6UD7dDvqkryo9JNg3xzmGhuTAcx6FE6fSAG8LiUPiLR4rMo0XnEdulbu0rnl4ey4pTGiVgTwzKXisU0xsraxPvyxDrTRPNhSiJ9qbzondiMAMF91JCQCjsUBTKEPYOEqSXgNNB0GwmkG0moA2g6QmiAS/d/jLaS5bQVjSf9lA9bb4KOvRi2vHTYbvv8xTi08QIQXuoXVewXek10A8h+HTHMmxpHmBTLuzIlubBvMhaHPCwenZJLp52CWvqSygNjR0C5Ei4+VoG6G9vZaCjnf6ONgba2+jvaCPa04LUExgBC0/QwhOShCv8BEsNvBFX5EZPYssBpDTHOLuGo4VISoP+rE1HJknUhqgtEEYxVZGFzC5ZweLK9ayoOosiI0y2vQOz8QDZpibMA42uyN3Vid0/gN3fj0ynkUDa7ycZDJIIhUgGgyRDIZLFRSTDYRJ+P6Y+dI8uEcLBo1tEAjpFYQ9FRX7CJUHCJUFCIQ/+gIbQBf0pm+64RW/Coi/h0JuEvqSgP2nQnzYYSHkZSPtJWWP/rINGkmJflCJvzC2+GMXe6Ij2u256VNnrMVAhRKYhUsKOe+Ch/4TuHVC7Bi77LMy/eOYK2dk0ND6RS8B4P/TtdfvLFxZ4WZ+nQqsoFDMEJWArxo+ZdF/FeflPsOsfYKVyWXpzYvbss8YtNEopaR1I0RM36Uu4nhe9uXZv3KQ3YdI71I6bmPbY4mjYZ+TF7NHC91DbsiXdsQxdsXRelHa33TppHhzv19AEFWEfVUXDnh5VkVzimoiPyog/n8wm4J3GyR7MJHS+DG2boX2zW3fvOHQ8aG9kpMh8NKK0v1i91qaY8SgBe2JQ9lqhmNnYlkO8f7QHtyt2x/syJGMmdnbsez6vYRHwpAjogwRkLwGnk4A2SEAbxKvHSAUkg2WV9JfV0lNcS0ewlnZPOe16mHbppT0L5qi1iy6gxnuwyJ0Xuv1eqr0GXk3DdiR7u+NsbnJF7U1NA+zqjOWdG+aWB4dDj8wuYV5liIjPOK7k5VJKEv19eWG7UNwe6GjHMjNDI/EENEpnl1JSV0S4wk+gVHejqPlNEHFMs4e02YVp9oA82NvZkpCSBlKP4PVWUByYTWVkAUF/DZrmz3swW5kYVmoAKzWInYlhm3FsK4ltJ7GdNA4mjjBxNBvHsMEjGW+sPCkBx0BIL0J4MfQAhieE11eE7g1jyRAxs5hBM8xgJsxgOsBgJkB/2kt/0kN/SqcvqdGXhMQoXb/xS9cqez0GQggDN4njpUArbhLHN0opXxlrvLLXUwjHhpd+Bw9/EQabXCF7xQ3uW9TlC0721R0/fftzXtb3u57nVgoMP8y7EBZeDosug7L5J/sqFQrFJKAEbMXxYSZcEfuVP7lGxEpDpM41kituhPozJvyJr5SSeMYaFrbjGXoTJn0Jk554Jid8u+2+hDvGPkSiwqKct7QrSPsL2r4R/SWBk5SccDI5klgdqnRveOrWQElDToQuEKT9JWAcm3eRQjHTUQL2xKDstUJxaiOlJJuxScWypGJmrmRJFrSH+pNRk3Q8y9jLEYeAFssL3AFtAL82iBXxECsrJlpaQn9xOf2REnr8xXQaQdodnTbTJjmG00Sl1zjIe7vW56FU0xjsS9PaHuflXEztrlgmf1zAo1NV5KM64nfrItcBorrI3a6K+Kku8hE+BqFbOg7xgb68oN3f3sZArh7s7MDKDiu3hsdLcXUNpbV1FNfUUlJTmhe5M9oALYPb6IrtJZpqJmv24iNNRIOILtFHXZabpULHwcARRr6WeNxaeJHCgxQe0LxIPAjHQDdBT4OekhhJiRa3kUkHKwFWSiOT0UjbHlKOl4QRIBkMYXlGJn3XbJtgMknQzBC0bYJIgh6dUMBLKBIiVFKEp7QEraQEvaQEvbQEO1JMP36iKUl/wuKm0xcpe30IhBDXAF8HdODHUsovHGqsstdTECsDG38Gm38FbRvdvqoVsPx6V8yuWjY9PLPNJDQ+CXsedIXr3t1uf9n8nGB9Bcw9DzyBk3udCoVi0lECtmLiyMRcMfvlP7ke2rYJxbNdz+yVN0Hd2pNiJB1HEk1nc2K3iaGLvBf1RIQcmRaYSejYOixUt2/OidW5RVmoyhWqhwTr2jVQVDc9bmoUiimIErAnBmWvFQrFeJCOJJ3Mkoq6wnZySOSOm6QGM6T6o6QGk25fEjLZsd8I0zEJaFH8RgLCDqkSL/GSILGSMIPhEP3BEL0+P91Ah2UxaB38plpJLvlkma6jZxyEaeOkbcyURSpmEotm6O9Pk0pmEaOWUEGvTlXER1WRn+oiP9URX4HgPSx+h48yLJ10HGJ9vXlB2/XYdr22BzrbsQviXBteHyU1tZRU11JaW0dJTR2UBmjz9LMjs4/dfZsZSHWTcSAjHbJSYksHx3GwpY0jD107hwsvd9gJSPwZKE16KEsGKc4ECGeD+O0gXhlE04KgB7E9gYPuXf2plCtyJ5KEkgmCiSS+VBLsBBZJrnvkJWWvJwBlr6c4A82w/S+w/W5oegaQbpiNZde7gnbtmqmz7pMSura5gvXeB6HxabAzrpd1w3muYL3o8pnhTa5QKMaFErAVk0N6EHbe63pm73kQnCyUzHG9slfcBLWrp46RnGmYCVesLvSs7tl5aLG67nSI1KrvQ6GYQJSAPTEoe61QKCYTO+uQiue8uKNpUt3dJLt7CoRui1RSkDS9pLIhbA7x5pnXxCyBdIlOothHrNhPNORnwK/Tp0EPNv2Oc1BcbnCjaRTpGmGhEZACry0RWScndrvXFh00MZNZRNYB08lH4Ah5daqL3DcIq4tc7+2qMby7D5d/xXFs4r29BcJ2a857u52Bzg4cezi8iOHzUVpdS6i0DE3Xc8UY1dbQdAPd0BGajm4Ybq3rCF1HaBpC10ATudptowuEpiGFQOgCKYDcOKmB1ITbFhI0gSNwaw2kAKlJbMchnTZJxlOk4ynS/XEy/Qky8RSZlEUm6+CMil/y+c9/XtnrCUDZ62lErBN2/NUVs/c/7r59WzwHll3nitn1Z574vEPJPtj3MOx5yBWtY+1uf+UyWHgpLLgEGs5VXtYKxSnORK6xVVBbxTD+Ylj9OrekBtyEEq/cCU9/B578hvvKz4ob3VK9Uomnx8qRxOpwtStUDz1Zr1ujxGqFQqFQKBQKQPdohEt9hEt9QASoPORYadtk+ztJtTWT6mwj1dNLqj9GcjBFKm6RiglSA2FSTjFJp5i0U4Rk+E0/R0DaI0j4BUmfRsInSPo1Ej6NVECQDGgk/Ro9Po2EV5AMg4x4oWqkaC4kBG1JwAK/JUmZkpaMTXMqTbY3hpO2IeNA1kaaDk7WwefTKS3yUhbxUVnip7o4MOzhHfFRVRSieulKGk5bM+KzHMcm1tNdIG63M9DRRjI6iGPZOI6NY1lubQ+1neE+y8a2LaRzjJ7Xx4HQXLFc03Q0oRPUNAJCB93A8fhwdA+OoZavilOQSDWccatbkn2w82+w7W54/gfwzHfc3FbLrnW9sxvOm5zcRbYFrS8Me1m3bgSkG6Jy/qtyovWlUDxr4j9boVAoUAK24lAESuD0N7kl2ec+8X3lTnji6/D4/wfli1whe/FVrpd2sPzEP/WdikgJiR6ItsBgK0RbYbDFLdFWty/WViBW17gC9fINBWFAak/iBBQKhUKhUChmBkLX8VbU4a2oo3isAUP3bYNNMNCE7N9OuqudVE8fqYEY2ZSJZVpYlsTKeLHTXqwBL5b0YUkvNl4sOVR8ZEWQAV+EAV+IQW+AQa+fqNdD3Osh5tWJe3WSPkHUr5EMGaR8XuBg70ThSIKmJJlx6E9LOjJZ9rSY+NMOPtPBl3HwZBy8psSXdQhKgcej4/Vp+HwGfr9BKOghHJxHUWgx9Wt8eD0ajiWxbQfHcrAtB9uWbp3rty3HHWM5WJYraltWTuTOWli2hZO1cWzLFb/tXNuykbaNlA5S2oAE7Nz9bq5IB1nQzvcftM9GIpHYYLvnkZaDMCUaDtqYPvEKxSlEsAxOf7Nb0lHYfR9s+7MbN/v5H7r5j5ZeA8s2wPyLwPAd+2cNNLti9Z4HYd+jkBkEocGs9fCqT7qC9ay1oJ0iIT4VCsVJRQnYiiMTLIO1b3VLoseNxfXKnfD4V+Cx/3XHaIYrxkaqXW/hSI1bwjUF27XuuaarJ7GUbpiVISF6TJG6zY33VYjuc59EF82CeRdA6Vw3HIsSqxUKhUKhUChOHkJAuNIts9YhcOXkgyRl2wIz5uaOSUfdOpOr04MF2y0j9w/ti0chHUXaFhYe7JwAniJAr1FGt6eMHk8ZPd5Sen1l9PnK6POV0O8N0x8IMVjkp9HjI3EI72NX8HYIZCSBtEMw4xBOpwgPJgl1SYJpB39W4rFBtyWGLdFsiXAkmgPCluBIpAAbtzgCbFdGzrXBFmAjcPBgCw8OYGtg+0aNAxwhR50rdz7hytb5OtdnM9Qvc/3k+yUwMorIN477q1coZgT+Ilh1s1vMpJswcfvdrnf2pl+Ar8h1OFt2HSy8DLzBw5+vMPni3gehZ5fbXzTLfTt44aWut3WgdNKnplAoFKNRArZifIQqYP073BLvhuZnINbhxrwaqvv2uYYv1X/w8brXDZExJHAPidvhUduB0hMndEsJjgVW2p1Dobf0kEg91GfGRx4rdDdxYtEs9+nzsuuguN7dLq53S7B8+or2CoVCoVAoFKc6uuHemx6naCOyaTyZGJ5MFDJRwukolZkYS/NieDQnhO8c3h4cFsvNTJI+R9DrKabXU+IK3p5iejylue0Sev0l9ERK2e0tIWqEx3V9PsfE61j4ZBafk8U3op3FJ81c28QnLfyO6badbG6cid/J4HOy7j5purVt4s/tDzgZfE4mN9bEb2fwSROBzCXGlICDkDLXpqAtQUq1gFUoxsIbdEXm5deDlXE9prf/GXb8Dbb+DjxBV8RevsFNqugvyiVf3J7zsn7g4OSL697uellXLlHrWYVCcdI5LvsvhPgycB1gAnuBd0gpB3L7PgXcivvg/MNSyn/k+tcBP8V1bvgb8E9yumSSVIwkXOkKtocim4Z458ECd6wD4h3Qsxv2P+Z6p4xG94305jYCrsicL7abZDLftkYWe/TYoXb24PGHy64ernbF6MrFbiKKIU/qIXE6XK1emVIoFAqFQqFQHBmP3y3hQ8ftPhxeoEZKasz4KG/wwYLtQcg0QyZGJhGnL2vRYzlEpU5G85ARbkkLg3ThtuYhLTxkhEFGGKSFkes3yOgGGRFiUBS7/RgF43TSGDji+EIJ+qSND7f4pY0fGx+Ou51rD9Ww4bg+S6GY8Rg+WHyFW661oPEJ1yt7KBGk7oU5Z0PPHje8JUDlUjjjXbDwEle8VskXFQrFFON4H2DfD3xKSmkJIb4EfAr4VyHEcuD1wAqgDnhACLFYukHRvgfcBjyDK2BfBfz9OK9DMRXx+KG0wS2Hw0y6gnZe4O4cKXh3bnOfBGtGQdFB84zsM/ygewr2G4cpuf2F43Wv6wk+JFIX1R1fzDCFQqFQKBQKhWIiEQJ8EbcU1R12qA+ozZXJREqJJSHjOKQdmasdMo4cru2R22OOccYYY7t1X8ExCoViHOiGG/Zj/qvgmq9Ay3OumL3vEZh9huthvfBS1zlLoVAopjDHJWBLKe8r2HwGuDnX3gD8RkqZAfYLIfYAZwohDgBFUsqnAYQQPwNuQAnYpzbeIJTNd4tCoVAoFAqFQqGYNggh8AjwaDrjC1pyjJ93Aj5DoZiRaJrreT3n7JN9JQqFQjFuju9dr5G8k2EhehbQXLCvJdc3K9ce3a9QKBQKhUKhUCgUCoVCoVAoFArFCI7ogS2EeACoGWPXp6WUf86N+TRgAb8cOmyM8fIw/Yf67Ntww40wZ86cI12qQqFQKBQKhUKhUCgUCoVCoVAoZhBHFLCllJcdbr8Q4m3AtcClBckYW4DZBcPqgbZcf/0Y/Yf67NuB2wHWr1+vEj0qFAqFQqFQKBQKhUKhUCgUCsUpxHGFEBFCXAX8K3C9lDJZsOtu4PVCCJ8QYh6wCHhOStkOxIQQZwshBPBW4M/Hcw0KhUKhUCgUCoVCoVAoFAqFQqGYmRxXEkfg27gJru939WiekVK+V0r5ihDid8A23NAiH5BS2rlj3gf8FAjgxsxWCRwVCoVCoVAoFAqFQqFQKBQKhUJxEMclYEspFx5m3xeAL4zR/wKw8ng+V6FQKBQKhUKhUCgUCoVCoVAoFDOf4woholAoFAqFQqFQKBQKhUKhUCgUCsVkoQRshUKhUCgUCoVCoVAoFAqFQqFQTEmElPJkX8NRIYSIATtP9nVMEBVAz8m+iAliJs0FZtZ81FymJjNpLjCz5rNEShk52Rcx3VH2ekozk+aj5jI1mUlzgZk1n5k0F2WvJ4AZZq9hZv2Oq7lMXWbSfNRcpiYzaS4wgTb7eJM4nkh2SinXn+yLmAiEEC+ouUxNZtJ81FymJjNpLjCz5iOEeOFkX8MMQdnrKcpMmo+ay9RkJs0FZtZ8ZtpcTvY1zBBmjL2Gmfc7ruYyNZlJ81FzmZrMpLnAxNpsFUJEoVAoFAqFQqFQKBQKhUKhUCgUUxIlYCsUCoVCoVAoFAqFQqFQKBQKhWJKMp0E7NtP9gVMIGouU5eZNB81l6nJTJoLzKz5zKS5nExm0s9xJs0FZtZ81FymJjNpLjCz5qPmohjNTPs5zqT5qLlMXWbSfNRcpiYzaS4wgfOZNkkcFQqFQqFQKBQKhUKhUCgUCoVCcWoxnTywFQqFQqFQKBQKhUKhUCgUCoVCcQpx0gRsIcRsIcTDQojtQohXhBD/lOsvE0LcL4TYnatLc/2XCyFeFEJszdWXFJxrXa5/jxDim0IIMcXncqYQYnOubBFC3Dhd51Jw3BwhRFwI8YmpMpdjmY8QYq4QIlXw/fzfVJnPsXw3QojThBBP58ZvFUL4p+NchBBvKvhONgshHCHEmmk6F48Q4o7cNW8XQnyq4FzT8W/GK4T4Se66twghXjVV5nOYudyS23aEEOtHHfOp3PXuFEJcOVXmcjI5ht8JZa+n6HwKjptyNvsYvhtlr6fgXMQUttfHOJ8pa7OPYS7KXs9wjuF3Ysra62Ocz5S12eOdS8Fxyl5Psfnk9imbPfXmouz1yZ/P5NtsKeVJKUAtsDbXjgC7gOXA/wKfzPV/EvhSrn06UJdrrwRaC871HHAOIIC/A1dP8bkEAaPg2K6C7Wk1l4Lj/gj8HvjEVPlejvG7mQu8fIhzTavvBjCAl4DVue1yQJ+Ocxl17Cpg3zT+Xt4I/CbXDgIHgLlTYS7HOJ8PAD/JtauAFwFtKsznMHNZBiwBHgHWF4xfDmwBfMA8YO9U+Zs5meUYfieUvZ6i8yk4bsrZ7GP4buai7PWUm8uoY6eUvT7G72bK2uxjmIuy1zO8HMPvxJS118c4nylrs8c7l4LjlL2eevNRNnsKzgVlr6fCfCbdZp/QiR7hh/Bn4HJgJ1Bb8IPZOcZYAfTmfgC1wI6CfW8Avj+N5jIP6MT9Rzgt5wLcAHwZ+Bw54zoV53I08+EQBnYqzuco5nIN8IuZMJdRY78IfGG6ziV3jX/J/c2X4/7DL5uKcznK+XwHeHPB+AeBM6fifIbmUrD9CCON66eATxVs/wPXoE65uUyFn+NR/r0qez3F5sM0sdlH8b9nLspeT7m5jBo7pe31UX4308ZmH8VclL0+xco4/16ntL0+hvlMaZt9NHNB2eupOh9ls6fgXFD2+qTPp2D7ESbJZk+JGNhCiLm4T4CfBaqllO0AubpqjENeA2ySUmaAWUBLwb6WXN9J4WjnIoQ4SwjxCrAVeK+U0mIazkUIEQL+Ffj8qMOn1FxgXL9n84QQm4QQjwohLsj1Tan5HOVcFgNSCPEPIcRGIcS/5Pqn41wKeR3w61x7Os7lD0ACaAeagK9IKfuYYnOBo57PFmCDEMIQQswD1gGzmWLzGTWXQzELaC7YHrrmKTWXk4my11PTXsPMstnKXit7fSKYSTZb2Wtlr0czk+w1zCybrez11LTXoGw2U9RmK3s9Ne01nHibbRzTVU4gQogw7qsxH5FSRo8U8kQIsQL4EnDFUNcYw+SEXuRRMp65SCmfBVYIIZYBdwgh/s70nMvnga9JKeOjxkyZucC45tMOzJFS9goh1gF35X7npsx8xjEXAzgfOANIAg8KIV4EomOMnepzGRp/FpCUUr481DXGsKk+lzMBG6gDSoHHhRAPMIXmAuOaz49xXxd6AWgEngIsptB8Rs/lcEPH6JOH6T+lUPZ6atprmFk2W9lrZa9PBDPJZit7nUfZ6xwzyV7DzLLZyl5PTXsNymYzRW22stdT017DybHZJ1XAFkJ4cCf8Synln3LdnUKIWilluxCiFjd21dD4euBO4K1Syr257hagvuC09UDb5F/9SMY7lyGklNuFEAncuGPTcS5nATcLIf4XKAEcIUQ6d/xJnwuMbz45r4NMrv2iEGIv7lPW6fjdtACPSil7csf+DVgL/ILpN5chXs/wk2GYnt/LG4F7pZRZoEsI8SSwHnicKTAXGPffjAV8tODYp4DdQD9TYD6HmMuhaMF9uj3E0DX//+3de3RV5Z038GfnRiIJCRgugoFwDSoaLtYKltHBZbFYL+i02lbraMfBqlTUdr1WxUsdtX1dRXRspV28S2utLa1aClVrqzPT6UgdHBkjckkiJYIgBiRCzI1czvtHEiYG7IRIODvw+ax11oJ9nnPYD7+z97P39zxn71h8zpLJeB3P8TqEw2vMNl4brw+Fw2nMNl7vZbxucziN1yEcXmO28Tqe43UIxuwQ0zHbeL33tbEar9vWKSljdtIuIRK1ft3w/0II6xKJxIIOTy0LIVze9ufLQ+v1VEIURXkhhGdD67VTXm5v3DbVvjqKolPb3vOr7a85VLrRl5FRFKW1/XlEaL3QeUVv7EsikZieSCQKE4lEYQhhYQjh3kQi8XAc+hJCt2ozMIqi1LY/jwohjA2tNzNIen8OtC+h9dpCJ0VRdFTb5+30EMLaXtqXEEVRSgjhCyGEX7Qv66V92RRCmBG16htCODW0Xvsp6X0JoVvbzFFt/QhRFJ0VQmhKJBJx/5x9nGUhhEuiKOoTtf5ca2wIYWUc+pJMxut4jtdt63TYjNnGa+P1oXA4jdnGa+N1Z4fTeN22fofNmG28jud43bZOxuwYjtnG63iO123rlLwxO5G8C31/JrROD38jhPB622NWaL3g+kuh9RuGl0IIA9ra3xZar2nzeofHoLbnTg4hvBla72b5cAghinlfLgshrGlrtyqEcEGH9+pVfen02jvDR++QnNS+dLM2F7XVpqStNufGpT/dqU0I4dK2/rwZQvi/vbwvZ4QQXtnPe/WqvoQQskPr3cTXhBDWhhC+FZe+dLM/haH1BhTrQggvhhBGxKU/f6Uvs0PrN74NofUGPy90eM2tbetbGjrcBTnZfUnmoxufCeN1TPvT6bV3hhiN2d2ojfE6vn05I8RwvO7m5yy2Y3Y3+lIYjNeH9aMbn4nYjtfd7E9sx+wD7Uun194ZjNex6U/ba4zZMetLMF7HoT89PmZHbS8CAAAAAIBYSdolRAAAAAAA4K8RYAMAAAAAEEsCbAAAAAAAYkmADQAAAABALAmwAQAAAACIJQE2AAAAAACxJMAGAAAAACCWBNgAAAAAAMSSABsAAAAAgFgSYAMAAAAAEEsCbAAAAAAAYkmADQAAAABALAmwAQAAAACIJQE2AAAAAACxJMAGAAAAACCWBNgAAAAAAMSSABsAAAAAgFgSYAMAAAAAEEsCbAAAAAAAYkmADQAAAABALAmwAQAAAACIJQE2AAAAAACxJMAGAAAAACCWBNgAAAAAAMSSABsAAAAAgFgSYAMAAAAAEEsCbAAAAAAAYkmADQAAAABALAmwAQAAAACIJQE2AAAAAACxJMAGAAAAACCWBNgAAAAAAMSSABsAAAAAgFgSYAMAAAAAEEsCbAAAAAAAYkmADQAAAABALAmwAQAAAACIJQE2AAAAAACxJMAGAAAAACCWBNgAAAAAAMSSABsAAAAAgFgSYAMAAAAAEEsCbAAAAAAAYkmADQAAAABALAmwAQAAAACIJQE2AAAAAACxJMAGAAAAACCWBNgAAAAAAMSSABsAAAAAgFgSYAMAAAAAEEsCbAAAAAAAYkmADQAAAABALAmwAQAAAACIJQE2AAAAAACxJMAGAAAAACCWBNgAAAAAAMSSABsAAAAAgFgSYAMAAAAAEEsCbAAAAAAAYkmADQAAAABALAmwAQAAAACIJQE2AAAAAACxJMAGAAAAACCWBNgAAAAAAMSSABsAAAAAgFgSYAMAAAAAEEsCbAAAAAAAYkmADQAAAABALAmwAQAAAACIJQE2AAAAAACxJMAGAAAAACCWBNgAAAAAAMSSABsAAAAAgFgSYAMAAAAAEEsCbAAAAAAAYkmADQAAAABALAmwAQAAAACIJQE2AAAAAACxJMAGAAAAACCWBNgAAAAAAMSSABsAAAAAgFgSYAMAAAAAEEsCbAAAAAAAYkmADQAAAABALAmwAQAAAACIJQE2AAAAAACxJMAGAAAAACCWBNgAAAAAAMSSABsAAAAAgFgSYAMAAAAAEEsCbAAAAAAAYkmADQAAAABALAmwAQAAAACIJQE2AAAAAACxJMAGAAAAACCWBNgAAAAAAMSSABsAAAAAgFgSYAMAAAAAEEsCbAAAAAAAYkmADQAAAABALAmwAQAAAACIJQE2AAAAAACxJMAGAAAAACCWBNgAAAAAAMSSABsAAAAAgFgSYAMAAAAAEEsCbAAAAAAAYkmADQAAAABALAmwAQAAAACIpbRkrwDxtGrVqplpaWl3JBKJIcEXHQAAAAD0nJYoirY1NTXdNXny5BeSvTLES5RIJJK9DsTMqlWrZvbp0+fhwsLCPVlZWfUpKSk+JAAAAAD0iJaWlqiuri6zoqIio6Gh4TohNh2ZWcs+0tLS7igsLNzTt2/fOuE1AAAAAD0pJSUl0bdv37rCwsI9aWlpdyR7fYgXATb7SCQSQ7KysuqTvR4AAAAAHDmysrLq2y5nC3sJsNmfFDOvAQAAADiU2vIoeSUf4QMBAAAAAEAsCbABAAAAAIglATZHhEcffbT/zJkzRw8dOvTEzMzMyYWFhROuvfbaYVVVVR/ZBrZv35568cUXj+jfv39xVlbWpGnTpo1buXJlVuf3q62tjebMmXPswIEDT8rMzJw8ceLE8c8//3x253bNzc3h29/+9pBhw4ad2KdPn8lFRUXHP/bYY3k92NUj2vTp08dGUTTlG9/4xtCOy9W193jrrbfSzz777FE5OTkTs7OzJ332s58dXV5entGxTWlpaUYURVP299ixY0dqx7ZdrSnds2TJktyTTz656KijjpqUnZ09acKECcctW7Ysp/35g73t0T3Lly/PmTJlSlFmZubk3NzciRdccMHIzZs3p3Vut2LFiqzp06ePba/njBkzxrz55pt9OrdTr57Vlf1gVVVVyj/+4z8ee8oppxRlZ2dPiqJoym9/+9uc/b2feh06h+o4hAO3YcOG9Msvv7xg4sSJ47OysiZFUTSltLQ0o3M7tUq+ruwDf/Ob3+Scf/75IwsKCiZkZmZOLigomPCVr3xl+JYtW/YZ29SqZzz99NP9Tj311HH5+fnFGRkZkwcPHnzSrFmzRr322muZHds5D+tdkpWbQFcIsDkiLFy4cHBqampi/vz5W55++umyK6+8svLxxx8feMYZZ4xrbm4OIYTQ0tISzj777DH/9m//lvvd7353809/+tMNjY2N0cyZM8dt2LAhveP7XXLJJYVPPvlk/s0337x1yZIl5YMGDWqcPXv2uBUrVnxkpz1v3rxh3//+94d+7Wtfq3zqqafKp0yZUnPllVeOXrJkSe4h7P4R4Uc/+tGA9evX7zNoqmvvUV1dnXLmmWcWbdiwIeuHP/xhxaJFizZWVFT0mTFjxrjdu3fvM15de+2121588cX1HR95eXnNHdt0taYcuPvvvz//K1/5yuji4uLan/3sZxt+8pOfbDj//POrampqUkLomW2PA/e73/0ue/bs2WP79evX/JOf/GTDvffeu2nlypXZM2bMKKqrq4va261evbrPWWedNb66ujr1xz/+8caHH3544zvvvJMxY8aMos6BgHr1nK7uBysrK9OWLFmSn5aWljjttNN2/7X3VK9D41Aeh3Dg1q1bl/nb3/52QG5ubtOUKVM+3F8btUq+ru4DFy1aNLCqqirtm9/85rtPP/102Q033LDtD3/4Q96nP/3p43bt2vWRY0a16hk7duxIKy4urr3//vs3/frXvy67/fbb3ykrK8s6/fTTjysrK8sIwXlYb5Ss3AS6Ikok3KuPjyopKakoLi7ekez1OJi2bt2aNnTo0KaOyx5++OGj586dW/ib3/ym7Lzzzqt+4okn8i677LLRy5YtKzv33HOrQwjh/fffTx01atSJs2fPfv+xxx7bHEIIf/7zn7OmTZt2/MKFCyuuv/7690MIobGxMYwdO3bCqFGj6v/lX/7lrRBC2LJlS9rIkSNPuvbaa7c98MADW9v/3alTp457//3308rKytYeuv+Bw9uOHTtSx48fP+Gee+7ZfPXVV4+cO3fuuw899NDWEEJQ197j7rvvHnTnnXcWlJSUvDlhwoSGEEJYv359xoQJE0687bbb3rnzzjvfC6F1Bvb48eNP/P73v//2jTfe+LH7qq7WlANXWlqaUVxcPOGWW2555/bbb6/cX5uDve3RPdOmTRv3zjvvZGzYsOHN9PTWc4o//vGPR51xxhnH3XfffZtuvvnm7SGEcPHFF4947rnn+m/cuHF1fn5+cwitMxaPP/74E6+44orKRYsWvROCevW0ru4HW1paQkpKa0azdOnSnNmzZ49bvnx52ec///nqju+nXofGoTwOoXuam5tDamrrj7QWLFiQf9NNN41Yv3796qKioj3tbdQq+bq6D9zfud3zzz+fPWvWrKIHHnigYt68ee+HoFaHWklJSZ+JEydOuP3229+566673nMe1vskIzf5OCUlJfnFxcWFPdRVeiEzsDkidN4JhxDCtGnTakIIYfPmzekhhLBs2bLcgQMHNrbvhEMI4eijj24+88wzP/j973+f177smWeeyUtLS0tceeWVVe3L0tPTw+zZs3f+x3/8R7/2GW1Lly7t19jYGF155ZXvd/x3L7nkkvfLy8uz1q9fv8/PFumeuXPnHjt27Ni6OXPm7Oz8nLr2Hs8991xecXFxTfsJSwghjB8/fs+kSZM+fPbZZ/MO9P26WlMO3COPPJIfRVHim9/85vaPa3Owtz265/XXX+87ffr03e3hdQghnH766bV5eXlNy5Yty2tftmrVquxJkybVtIfXIYQwevToxrFjx9Y9//zze9upV8/q6n6wPbz+36jXoXEoj0Ponvbw+q9Rq+Tr6j5wf+d206dPrwkhhC1btuw9FlerQ2vQoEHNIYSQnp6eCMF5WG+UjNwEukqAzRHrxRdfzAkhhBNPPLE+hBBKS0uzxo0bV9e53fHHH1/37rvvZrT/HG3dunVZw4YN25OTk9PSsd0JJ5xQ19jYGK1Zs6ZPCCGsWbMmKyMjI3HCCSc0dGx30kkn1YUQwuuvv+5nMwfBCy+8kP3MM88cvWjRorf397y69h7l5eVZ48eP36dWRUVFdW+99VZm5+V33333sLS0tCk5OTkTZ8yYMabzdde6WlMO3CuvvJI9atSo+sWLFw8oKCiYkJaWNmX48OET7rvvvoHtbQ72tkf3pKamJjIyMvb5uV16enqivLx87zaTkpKSSE9Pb+ncLiMjI7F58+Y+tbW1UQjq1dMOdD/4v1Gvnneoj0PoOWqVfJ9kH/i73/0uJ4QQjj/++Pr2ZWrV85qamkJ9fX20evXqPn//938/Ij8/v/GKK67YGYLzsMNFT+cm0FX73OQAPs63niopKNtWfVQy12HckJza+/+uePMnfZ+NGzemf/e73x06derU3X/zN39TG0IIu3btSisoKNjTue2AAQOaQ2i9UUFubm5LVVVVam5u7j7fTObn5zeF0Ho9sBBCqKqqSsvJyWnuPEtq4MCBzSG0/szmk/bjE1t6bUGoXJvUmoZBx9eGC37QrZo2NDRE11577Yg5c+ZsKy4ubthfmyOtrvNfnl/wVtVbSa3pmP5jau8+7e4DrumuXbtS8/Ly9qnBgAEDmqqrq/eOV5mZmYkvfelL22fOnLl78ODBTWvWrMlcsGDBMX/7t387/k9/+tO6yZMn14cQQldrmixbb7m1oKG8PKm16jN2bO3Qe+854Fq999576du3b8+44447jr3tttu2jB07tmHJkiX9b7nlluFNTU3R/PnzKw/2tpdsLz2+rmDnlg+TWq8Bw7Jrz/zqcQdUr8LCwobXXnutb8dlZWVlGTt27EhPS0vbG2yPHj26/rXXXstuaGiI+vTpkwih9UaB5eXlmYlEImzfvj1txIgRjb2hXi88srBgx+a3k1qr/IIRtTO/Pq/H9oNdFfd67XyqrKBxW01Sa5U+pG/tgL8b12uOQ5Jl6dKlBZWVlUmt1aBBg2ovuOCCT3we8HEOl1qtXfd/Cmo+LEtqrfpmj6s9/rjvHbJ9YFVVVcq3vvWtglGjRtVfeumlVR2Wx7pWIYQwb92mgvU19Umt1/i+mbULjxverW1r4sSJx61Zs+aoEEIYPnx4wwsvvFA2bNiwphCOvPOwdnKT3rGvpPcxA5sjzq5du1LOPffcMWlpaYmf/vSnFe3LE4lEiKJon1lqna8T39Zun/dNJBLRftr9r+9H982fP39IfX19yr333vvux7VR196lKzUYMWJE45NPPrnp8ssv/+Dss8/+8Kabbtrxxz/+cX0UReGuu+46psPruvR+HLhEIhHV1NSkLFy48O2bbrppx3nnnVf9s5/9bNP06dN3P/jgg8e0tLQc9G2P7rnmmmveW716dd9vfOMbQ7ds2ZL23//935lf/vKXR6akpHzkMhTz5s17r7KyMv2yyy4bvnHjxvSysrKML33pS4V1dXWpIbTO0A5BvQ6Fg/n/q149KxnHIfQctYqHA/2/bWxsDBdeeOGoysrKjCeffPIvHS+ZpVY97/HHH9/40ksvrV+0aNHG7Ozs5s997nPjSktLM0JwHtbbHarcBLrKNx502cH4Bi/Zamtro5kzZ47ZvHlznz/84Q+lo0ePbmx/Ljc3t6mqqmqfbaKqqio1hP/5Zrd///7NW7du3efnLu3f+LZ/o9i/f/+m3bt3p3W80VIIrTf6CaH1OlEHuXsHrpszn+OgvLw846GHHjrmgQceqKivr0+pr9/7a8HQ0NCQsmPHjtS8vLzmI62u3Zn5HBf9+vVr/rha5eTk7PPtfUdjxoxpnDJlSnVJScnemaZdrWmydGfmc1zk5eU1vf32233OO++83R2Xn3nmmbv+9Kc/9du0aVP6wd72ku1AZz7Hxde//vWd69evz/zRj3405J//+Z+PiaIonHPOOTv79eu3q6ysbO9PbT/72c/W3HfffZvuueeeYb/61a/yQwhh6tSp1RdeeOGOpUuXHt1+XcveUK/uzHyOi0+yH9yfuNeruzOf4yBZxyHJ0pMzn+PicKlVd2Y+x8WB7gObm5vDRRddNHLFihX9fvnLX5Z/+tOf/shlDeJeqxBC6O7M57ho/+XjjBkzai666KJdI0eOPPGuu+4a8uSTT2460s7D2slNese+kt7HDGyOGA0NDdGsWbNGv/HGG32feeaZ8lNOOeUjBzhFRUX1Ha8H2m7dunVZxxxzzJ7c3NyWEEI47rjj6rZs2ZJRXV39ke1n7dq1Wenp6XuvyXXCCSfU79mzJ1q7du1HdtqrV6/OCiGEiRMn7nPdKLqutLS0T0NDQ3TNNdeMHDhw4MT2Rwgh/PjHPx48cODAiStXrsxS195j7NixdaWlpftc37CsrCxrzJgx9ft7TUeJRCLqOBugqzXlwBUVFe33c94+oyIlJSVxsLc9uu/BBx/cWllZ+fp//ud/rn377bdLli9fvrGioqLPpz71qeqO7W6++ebtlZWVJa+++uqa8vLyN1asWFG2bdu2jJNOOqmm/bIi6tWzPul+sDP16jnJOg6h56hV8h3oPvDSSy8d8dxzzw1YvHjxX84///zqzs+r1aGVn5/fPGLEiIaKiorMEJxf91aHOjeBrhJgc0Robm4Os2fPHvnnP/+5389//vO3zjzzzJrObc4777wPKisr05999tns9mU7d+5Meemll/LOOuusD9qXXXjhhR80NTVFjz32WP/2ZY2NjWHp0qX9P/OZz+zOyspKhBDC7Nmzd6WnpyceffTRAR3/nV/84hdHjx07tm78+PH7XDeKrjv11FNrly9fXtb5EUII559//s7ly5eXnXDCCQ3q2nvMmjXrg5KSkuy1a9fuvYN4aWlpxqpVq/rOmjXrg7/22vLy8oxVq1ZlT5o0ae+23dWacuBmz579QQghLF26NLfj8hdffLHf4MGDG4cPH950sLc9Ppl+/fq1nHLKKXUFBQVNTz31VL+NGzdmXnPNNds7t8vKykqcfPLJ9WPGjGlcuXJl1ooVK3Kuuuqqve3Uq2d9kv3g/qhXz0nWcQg9R62S70D2gVddddWxS5YsyX/wwQc3XnbZZR90fq8Q1OpQ27x5c9pf/vKXzMLCwoYQnF/3RsnITaCrXEKEI8JXv/rV4c8//3z/uXPnvpudnd3y0ksv7b3MQGFh4Z7Ro0c3fvnLX/5gwYIFNV/72tdGfec739l89NFHN3/ve987JpFIhPnz529rbz9t2rS6c845p+rWW28taGxsjEaPHt3wyCOPDNyyZUufxx9/fGN7u2HDhjX9wz/8w3sPP/zwMTk5OS0nn3xy7c9//vP+r7zySs4TTzzx1qH+Pzjc5OfnN3/+85/fZ6ZFCK03EGl/Tl17j3nz5u1YvHjxoAsuuGDM7bffvjWKosR3vvOdYUOGDGm88cYb9wZoV1111bEtLS3R1KlTPxw8eHDTunXrMhcuXDgkiqLEHXfcsfc6pF2tKQfui1/84q6FCxdW33DDDSO2b9+eNmbMmIZf/epX/V9++eV+Dz74YEUIB3/bo3tefvnlrOXLl+eefPLJtSGE8O///u/ZixYtGnL11VdvO+uss/aelGzYsCF94cKFg0477bQPMzMzW1599dW+Dz300JCZM2d+MGfOnJ3t7dSrZ3V1PxhCCL/85S/71dTUpL7xxhtZIYTwr//6r9nbt29P69u3b/MXv/jF3SGoV09K1nEI3ffoo4/2DyGE11577agQWr+EHTRoUNOgQYMazznnnA/VKvm6ug+89dZbhyxevHjwF77whR3jx49v6HhuN2TIkKb2mZ1q1XPOOuus0RMnTqwtLi6uy83NbV6/fn2fH/7wh4NTU1MTN99887YQnIf1RsnITaCrIhe8p7OSkpKK4uLiHclej4Np2LBhJ27dujVjf8/dcMMN7y5YsGBrCCG89957qdddd13B73//+7w9e/ZEEydOrFmwYMHmqVOnfuRnMx9++GE0b968YUuXLj26uro6taioqPbee+/d0vlEpqmpKdxyyy3HPPHEE/k7duxILywsrP/2t7/97hVXXFEV6BFRFE2ZO3fuuw899NDW9mXq2nuUl5dnXHfddQUvv/xyv0QiEaZOnbr7Bz/4weaioqK9MyoWLlx49OLFiwdt2rSpT21tbWpeXl7T1KlTd//TP/3T1uLi4o/8FK2rNeXA7dy5M+X6668/9rnnnuu/e/fu1JEjR9bfeOON266++uq9YefB3vY4cP/1X/+VOWfOnBFlZWVZjY2NKaNGjaqbM2dO5fXXX/9+x3abN29Ou/jii0etW7cuq6amJrWgoKDh0ksv3XHbbbe91/GGWCGoV0/ryn4whI8/thk6dOieLVu2rG7/u3odWofiOITuiaJoyv6Wf+pTn/pw5cqVpSGoVRx0ZR94yimnFL366qvZ+3v9hRde+P7TTz9d0f53teoZt95665ClS5f237RpU5+mpqZo8ODBjdOmTau+44473u1YK+dhvUuycpP9KSkpyS8uLi48KB3jsCDAZh+HY4ANAAAAQPwJsOnMNbABAAAAAIglATYAAAAAALEkwAYAAAAAIJYE2AAAAAAAxJIAGwAAAACAWBJgsz8tLS0tUbJXAgAAAIAjR1se1ZLs9SBeBNjsI4qibXV1dZnJXg8AAAAAjhx1dXWZURRtS/Z6EC8CbPbR1NR0V0VFRUZNTU2WmdgAAAAA9KSWlpaopqYmq6KiIqOpqemuZK8P8RIlEolkrwMxtGrVqplpaWl3JBKJIcEXHQAAAAD0nJYoirY1NTXdNXny5BeSvTLEiwAbAAAAAIBYMrMWAAAAAIBYEmADAAAAABBLAmwAAAAAAGJJgA0AAAAAQCwJsAEAAAAAiCUBNgAAAAAAsfT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\n" 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\n" 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\n" 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\n" 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\n" 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\n" 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\n" 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "start = time.time()\n", + "\n", + "vars = [\"Resources|Land Cover|+|Forest\",\n", + " \"Resources|Land Cover|+|Cropland\",\n", + " \"Emissions|CO2|Land|+|Land-use Change\",\n", + " \"Emissions|CO2|Land|Cumulative|Land-use Change|+|Regrowth\"]\n", + "for var in vars:\n", + " df_var = df[df.index.get_level_values(3)==(var)].melt(ignore_index=False)\n", + " with PdfPages(f'{var.split(\"|\")[-1]}.pdf') as pdf:\n", + " for bio in set([i[0] for i in df.index.get_level_values(1).str.split(\"G\")]):\n", + " #print(bio)\n", + " df_var_bio = df_var[df_var.index.get_level_values(1).str.startswith(bio)]\n", + " fig, axs = plt.subplots(4, 3, figsize=(25,18))\n", + " ax_it = iter(fig.axes)\n", + " for reg in df_var_bio.index.get_level_values(2).unique():\n", + " if reg == \"World\":\n", + " continue\n", + " legend = []\n", + " ax = next(ax_it)\n", + " df_reg = df_var_bio[df_var_bio.index.get_level_values(2)==reg]\n", + " for ghg in set([i[2] for i in df.index.get_level_values(1).str.split(\"G\")]):\n", + " #print(df_reg[df_reg.index.get_level_values(1).str.endswith(ghg)])\n", + " if ghg == \"000\":\n", + " continue\n", + " df_reg[df_reg.index.get_level_values(1).str.endswith(ghg)].plot(x=\"variable\", y=\"value\", ax=ax, legend=False)\n", + " legend.append(ghg)\n", + " #ax.legend(legend)\n", + " ax.set_xlabel(\"\")\n", + " ax.set_title(reg)\n", + " ax.set_xlim((2020,2100))\n", + " #ax.set_ylim((3000,5000))\n", + " \n", + " fig.suptitle(bio.replace(\"SSP2\",\"\"), fontsize=30)\n", + " fig.legend(legend, loc=\"lower center\", fontsize=16, ncol=12)\n", + " #fig.savefig(f\"example_{bio}.svg\")\n", + " pdf.savefig()\n", + " \n", + " end = time.time()\n", + " print(end - start)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-03T14:07:26.337893200Z", + "start_time": "2023-10-03T13:54:31.461068600Z" + } + }, + "id": "18b06ce1d852eeaa" + }, + { + "cell_type": "code", + "execution_count": 241, + "outputs": [ + { + "data": { + "text/plain": "(2020.0, 2100.0)" + }, + "execution_count": 241, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "var = cro_cov\n", + "\n", + "fig, ax = plt.subplots(figsize=(16,9))\n", + "df_var = df[df.index.get_level_values(3)==(var)].melt(ignore_index=False)\n", + "legend = []\n", + "for reg in df_var.index.get_level_values(1).unique():\n", + " df_reg = df_var[df_var.index.get_level_values(1)==reg]\n", + " df_reg.plot(x = \"variable\", y=\"value\", ax = ax)\n", + " legend.append(reg[-6:])\n", + "\n", + "ax.legend(legend)\n", + "ax.set_xlim((2020,2100))" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-28T12:19:47.138608200Z", + "start_time": "2023-09-28T12:19:46.953653Z" + } + }, + "id": "ffb479ab6d5173e" + }, + { + "cell_type": "code", + "execution_count": 240, + "outputs": [ + { + "data": { + "text/plain": "(2020.0, 2100.0)" + }, + "execution_count": 240, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "var = luc_co2\n", + "\n", + "fig, ax = plt.subplots(figsize=(16,9))\n", + "df_var = df[df.index.get_level_values(3)==(var)].melt(ignore_index=False)\n", + "legend = []\n", + "for reg in df_var.index.get_level_values(1).unique():\n", + " df_reg = df_var[df_var.index.get_level_values(1)==reg]\n", + " df_reg.plot(x = \"variable\", y=\"value\", ax = ax)\n", + " legend.append(reg)#[-6:])\n", + "\n", + "ax.legend(legend)\n", + "ax.set_xlim((2020,2100))" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "start_time": "2023-09-28T12:19:09.645799400Z" + } + }, + "id": "7d19d7ab80b97de1" + }, + { + "cell_type": "code", + "execution_count": 368, + "outputs": [], + "source": [ + "import pyam" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-28T14:39:07.445167500Z", + "start_time": "2023-09-28T14:39:07.398032200Z" + } + }, + "id": "2bcc5a675de60409" + }, + { + "cell_type": "code", + "execution_count": 369, + "outputs": [], + "source": [ + "py_df = pyam.IamDataFrame(df)\n", + "#df_lands = df[df.index.get_level_values(3).str.startswith(\"Resources|Land Cover|+|\")]\n", + "#df_lands_tot = df[df.index.get_level_values(3)==(\"Resources|Land Cover\")]" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-28T14:39:10.165945300Z", + "start_time": "2023-09-28T14:39:07.845642500Z" + } + }, + "id": "5dde75e29e69b3f2" + }, + { + "cell_type": "code", + "execution_count": 286, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Resources|Land Cover|+|Cropland\n", + "Resources|Land Cover|+|Forest\n", + "Resources|Land Cover|+|Other Land\n", + "Resources|Land Cover|+|Pastures and Rangelands\n", + "Resources|Land Cover|+|Urban Area\n" + ] + } + ], + "source": [ + "for i in py_df.filter(variable=\"Resources|Land Cover|+|*\").variable:\n", + " print(i)\n", + " py_df.divide(i, \"Resources|Land Cover\", f\"Share|{i}\", append=True)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-28T13:21:46.066795200Z", + "start_time": "2023-09-28T13:21:45.242754400Z" + } + }, + "id": "55e3ebea09dc9b6c" + }, + { + "cell_type": "code", + "execution_count": 309, + "outputs": [ + { + "data": { + "text/plain": "" + }, + "execution_count": 309, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig,ax = plt.subplots(figsize=(16,9))\n", + "py_df.filter(variable=\"Share|Resources|Land Cover|+|*\", year=[i for i in range(2020,2110,5)], scenario=\"SSP2BIO10GHG400\").plot(ax=ax)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-28T13:35:24.153172200Z", + "start_time": "2023-09-28T13:35:23.878086800Z" + } + }, + "id": "314905a7f7f1dc6a" + }, + { + "cell_type": "code", + "execution_count": 313, + "outputs": [ + { + "data": { + "text/plain": "" + }, + "execution_count": 313, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(3,2, figsize=(30,25))\n", + "\n", + "py_df.filter(variable=luc_co2+\"*\", year=[i for i in range(2020,2110,5)], scenario=\"SSP2BIO10GHG400\").plot(ax=ax[0][0])\n", + "py_df.filter(variable=afolu_co2, year=[i for i in range(2020,2110,5)], scenario=\"SSP2BIO10GHG400\").plot(ax=ax[1][1])\n", + "py_df.filter(variable=\"Emissions|CO2|Land|Land-use Change|+|Regrowth\",\n", + " year=[i for i in range(2020,2110,5)], scenario=\"SSP2BIO10GHG400\").plot(ax=ax[0][1])\n", + "py_df.filter(variable=\"Emissions|CO2|Land|Land-use Change|+|Peatland\",\n", + " year=[i for i in range(2020,2110,5)], scenario=\"SSP2BIO10GHG400\").plot(ax=ax[1][0])\n", + "py_df.filter(variable=\"Emissions|CO2|Land|Land-use Change|Gross LUC|+|Deforestation\",\n", + " year=[i for i in range(2020,2110,5)], scenario=\"SSP2BIO10GHG400\").plot(ax=ax[2][1])" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-28T13:40:11.560359900Z", + "start_time": "2023-09-28T13:40:10.103852500Z" + } + }, + "id": "7d9f5b6ecce0f8e8" + }, + { + "cell_type": "code", + "execution_count": 378, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(3,2, figsize=(30,25))\n", + "vars = [\"Emissions|CO2|Land|Land-use Change|+|Regrowth\",\n", + "\"Emissions|CO2|Land|Land-use Change|Regrowth|CO2-price AR\",\n", + "\"Emissions|CO2|Land|Land-use Change|Regrowth|NPI_NDC AR\",\n", + "\"Emissions|CO2|Land|Land-use Change|Regrowth|Other Land\",\n", + "\"Emissions|CO2|Land|Land-use Change|Regrowth|Secondary Forest\",\n", + "\"Emissions|CO2|Land|Land-use Change|Regrowth|Timber Plantations\"]\n", + "ax_it = iter(fig.axes)\n", + "for var in vars:\n", + " ax = next(ax_it)\n", + " py_df.filter(variable=var,\n", + " year=[i for i in range(2020,2110,5)], \n", + " scenario=[\"SSP2BIO10GHG400\", \"SSP2BIO00GHG400\", \"SSP2BIO45GHG400\"]).plot(ax=ax)\n", + " ax.set_title(var)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-28T14:42:52.735931400Z", + "start_time": "2023-09-28T14:42:51.235604300Z" + } + }, + "id": "ac9126c2a5e09ccd" + }, + { + "cell_type": "code", + "execution_count": 384, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "fig, ax = plt.subplots(3,4, figsize=(30,25))\n", + "\n", + "ax_it = iter(fig.axes)\n", + "for var in df[df.index.get_level_values(3).str.startswith(\"Resources|Land Cover|For\")].index.get_level_values(3).unique():\n", + " ax = next(ax_it)\n", + " py_df.filter(variable=var,\n", + " year=[i for i in range(1995,2110,5)], \n", + " scenario=[\"SSP2BIO10GHG400\", \"SSP2BIO00GHG400\", \"SSP2BIO10GHG000\"]).plot(ax=ax) ##.diff({var:\"test\"})\n", + " ax.set_title(var)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-28T14:50:44.149458Z", + "start_time": "2023-09-28T14:50:41.744151500Z" + } + }, + "id": "dae0386acc8f0156" + }, + { + "cell_type": "code", + "execution_count": 381, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Emissions|CO2|Land|Land-use Change|Gross LUC|+|Deforestation\n", + "Emissions|CO2|Land|Land-use Change|Gross LUC|+|Forest Degradation\n", + "Emissions|CO2|Land|Land-use Change|Gross LUC|+|Other land conversion\n" + ] + }, + { + "data": { + "text/plain": "
", + "image/png": 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+ }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "fig, ax = plt.subplots(3,3, figsize=(30,25))\n", + "\n", + "ax_it = iter(fig.axes)\n", + "for var in df[df.index.get_level_values(3).str.startswith(\"Emissions|CO2|Land|Land-use Change|Gross LUC\")].index.get_level_values(3).unique():\n", + " ax = next(ax_it)\n", + " py_df.filter(variable=var,\n", + " #year=[i for i in range(2020,2110,5)], \n", + " scenario=[\"SSP2BIO10GHG400\", \"SSP2BIO00GHG400\", \"SSP2BIO10GHG000\"]).plot(ax=ax) ##.diff({var:\"test\"})\n", + " print(var)\n", + " ax.set_title(var)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-28T14:48:36.476892800Z", + "start_time": "2023-09-28T14:48:35.260721Z" + } + }, + "id": "352a10845b469e43" + }, + { + "cell_type": "code", + "execution_count": 322, + "outputs": [ + { + "data": { + "text/plain": "" + }, + "execution_count": 322, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(3,2, figsize=(30,25))\n", + "\n", + "py_df.filter(variable=\"Resources|Land Cover|+|Forest\", year=[i for i in range(2020,2110,5)], scenario=\"SSP2BIO10GHG400\").plot(ax=ax[0][1])\n", + "py_df.filter(variable=\"Resources|Land Cover|Forest|+|Managed Forest\", year=[i for i in range(2020,2110,5)], scenario=\"SSP2BIO10GHG400\").plot(ax=ax[1][0])\n", + "py_df.filter(variable=\"Resources|Land Cover|Forest|+|Natural Forest\", year=[i for i in range(2020,2110,5)], scenario=\"SSP2BIO10GHG400\").plot(ax=ax[2][1])" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-28T13:47:32.577397500Z", + "start_time": "2023-09-28T13:47:31.575902900Z" + } + }, + "id": "35a72ce4cdc7bf17" + }, + { + "cell_type": "code", + "execution_count": 337, + "outputs": [ + { + "data": { + "text/plain": "" + }, + "execution_count": 337, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/png": 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\n" 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "py_df.filter(variable=\"Resources|Land Cover|+|Forest\", year=[i for i in range(2020,2110,5)], scenario=\"SSP2BIO10GHG400\")\n", + "py_df.filter(variable=\"Resources|Land Cover|Forest|+|Managed Forest\", year=[i for i in range(2020,2110,5)], scenario=\"SSP2BIO10GHG400\")\n", + "py_df.filter(variable=\"Resources|Land Cover|+|Forest\", \n", + " #year=[i for i in range(2020,2110,5)], \n", + " scenario=\"SSP2BIO10GHG400\").diff({\"Resources|Land Cover|+|Forest\":\"test\"}).plot()\n", + "py_df.filter(variable=\"Emissions|CO2|Land|Land-use Change|Gross LUC|+|Deforestation\",\n", + " #year=[i for i in range(2020,2110,5)], \n", + " scenario=\"SSP2BIO10GHG400\").diff({\"Emissions|CO2|Land|Land-use Change|Gross LUC|+|Deforestation\":\"test\"}).plot()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-28T13:59:54.979218500Z", + "start_time": "2023-09-28T13:59:54.625535100Z" + } + }, + "id": "16c24c855d33b6f4" + }, + { + "cell_type": "code", + "execution_count": 486, + "outputs": [ + { + "data": { + "text/plain": "" + }, + "execution_count": 486, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/png": 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\n" 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\n" 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\n" 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\n" 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\n" 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\n" 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\n" 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ghg1 = \"400\"\n", + "ghg2 = \"400\"\n", + "bio1 = \"10\"\n", + "bio2 = \"00\"\n", + "scen = [f\"SSP2BIO{bio1}GHG{ghg1}\", f\"SSP2BIO{bio2}GHG{ghg1}\", f\"SSP2BIO{bio1}GHG{ghg2}\", f\"SSP2BIO{bio2}GHG{ghg2}\"]\n", + "#fig, ax = plt.subplots(3,3, figsize=(30,20))\n", + "py_df.filter(variable=\"Emissions|CO2|Land|+|Land-use Change\", scenario=scen).plot(figsize=(16,9))\n", + "for i in df[df.index.get_level_values(3).str.startswith(\"Demand|Bioenergy|++|2nd generation\")].index.get_level_values(3).unique():\n", + " py_df.filter(variable=i, scenario=scen).plot(figsize=(16,9))\n", + "for i in df[df.index.get_level_values(3).str.startswith(\"Resources|Land Cover|+\")].index.get_level_values(3).unique()[:3]:\n", + " py_df.filter(variable=i, scenario=scen).plot(figsize=(16,9))\n", + "py_df.filter(variable=\"Emissions|CO2|Land|Land-use Change|Gross LUC|+|Deforestation\", scenario=scen).plot(figsize=(16,9))\n", + "py_df.filter(variable=\"Emissions|CO2|Land|Land-use Change|Gross LUC|+|Other land conversion\", scenario=scen).plot(figsize=(16,9))\n", + "py_df.filter(variable=\"Emissions|CO2|Land|Land-use Change|+|Regrowth\", scenario=scen).plot(figsize=(16,9))\n", + "#py_df.filter(variable=\"Productivity|Landuse Intensity Indicator Tau\", scenario=scen).plot()\n", + "#py_df.filter(variable=\"Productivity|Landuse Intensity Indicator Tau\", scenario=scen).diff({\"Productivity|Landuse Intensity Indicator Tau\":\"test\"}).plot()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-29T12:07:37.713681400Z", + "start_time": "2023-09-29T12:07:35.942886400Z" + } + }, + "id": "b6e4c67b9c1a13ab" + }, + { + "cell_type": "code", + "execution_count": 477, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['SSP2BIO45GHG100', 'SSP2BIO00GHG100', 'SSP2BIO45GHG4000', 'SSP2BIO00GHG4000']\n", + "['SSP2BIO45GHG100', 'SSP2BIO00GHG100', 'SSP2BIO45GHG4000', 'SSP2BIO00GHG4000']\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "pyam.plotting - INFO: >=13 labels, not applying legend\n", + "pyam.plotting - INFO: >=13 labels, not applying legend\n", + "pyam.plotting - INFO: >=13 labels, not applying legend\n", + "pyam.plotting - INFO: >=13 labels, not applying legend\n", + "pyam.plotting - INFO: >=13 labels, not applying legend\n", + "pyam.plotting - INFO: >=13 labels, not applying legend\n", + "pyam.plotting - INFO: >=13 labels, not applying legend\n", + "pyam.plotting - INFO: >=13 labels, not applying legend\n", + "pyam.plotting - INFO: >=13 labels, not applying legend\n" + ] + }, + { + "data": { + "text/plain": "" + }, + "execution_count": 477, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
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\n" 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\n" 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\n" 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myjaO9wMuSykX6uPVUuAi2iJZaW1WLGUVPPKvSGcW8X8n/W8/ICzvhtQmcBFo0qofEKm/dHmE5fu7KvBC/o802orUHTti5st/H5pUOR2t09zWMfWJRjduScq/oamu8+wf/fTy56WXAdwokM0KffLhI6XsLqU8XkKRButh8375BzP08m3Uy+slpdxfTDpeUkp3KWU9KWXeQJuGNlnOjwuaqUNZ7gNMkdok1w7oj+YjUpq5mz+aEFRWPtXzsAdaAp+IfGYc+bgX9QFACNFJ3FLvlrRSGpHv76poH/nofP1xLppUDwX6RoG/C3Lbe6H/bcXtJmb5TQAzuPVOlcYhPWxDNO3GXqkJNxH5ruX5dxRVjvzvV5zUzNaKq0NJ7+9tCE2VnNfmhTRi+sC/kVsfkYfIt2IlNLOMQF29m4S2Wpr/A1ZsewvNDO4jIcQVoangr+q3ioyvt1cChccab8ABzY8prw/8rl8vK4cKvPM1C9wv69h6E33iOwJ4Gq1/bhS3TAxfRlvZP6I/g6JMgrzQVqYK9oX82tyb/VEf9wCcSsm7IDfQJl3FEV/CfV/9/k2EZpo3Buinf8TvCCGEj9AchyP1frGIwpOi4t7Dgu97sX2/DOUorW8XLIOdvkhR4jsoNBO9vHeuOPO3/HwCBANbhWZykbdRSVU0k5j83+LXKMIkFkhEm8CU9JwBbugTnvz1ctLL3UZom4PECSGS0frW3T6XgmN4WesBBfqrlPIr/Vs1G+1bkEfBcbKkcbXINpZSBqOtes8AYvV+eadzHVOBcqH/P7eo+pQFIcSr+dpqA9qiVv72K4qS+kCh97gIiuvvZXl+JX13b0MI4SCEmCs086cUtO+im7jdx7RM/UwfBwvOBfPwQptDFSVIljRHLrEMep8JBAbowsdAbgkeVYHhBdqqI7c/k4LlL8s4XrBvQwnfCso4d7nXDiZRaA0A3FwxrAxEokm+/vq1PKrk+zsCeL/AR9pBl7D+DIvQ1J1FaQ9Go7XBeqHZa4agdZg8c6do8qnThGajdzf+G3fCArTyFnTsLo3zaHZ7+Vf8m3DLJOE8t1YSEJrDly1FOK/rEvNetAGz2N0NdMl+ANpK7R0hNc4B+yna0S0IsBKanWoeJdXHEc1htZBgoa8y5al3SzLFyv9Bj0DTuHnl648u+eLf1jfQ+nlx3PZeoPV7E0WbmN0R+gfwKJqg6CulvKjf2qtfa8wtwaOocuR3dC5pK9TS3t+C5WqQr82L6x9L0TSM7dAE0T9AExSBV9BWM931j38y2oS6LGV9BE0F3wNtUldNv54//s3nJYRwQlu1K+j0HY82+W+Qrw+4Sm1zhH8UKeUWKWVPtA/LRbSVQKSUMVLKcVJKPzSz1W9E4R2e4rmlGcmjCto4fdd5F8F2YIgo3pFxJ1BZCNE6/0V9QagtsCPftceBV4H7pG5/fxd8iNZvGkvNcXgUt/eJkojm9ne82L5fEmXs2yWVodh3UGo76eW9c3lCfAaa8JxHxXzhU6WUL0gpa6CN488Lzc8gAs08M/+32FlKmX/1NC+NDDQN6581u1wHVJZSugLfcWfPpbhxuMz10NmB5jtYGgXHnmLH1RLaGCnlEillRz2uRDPrvBPCuTW25VGdWxPFHUAlUcBvtSSklB/ltRXa92Nf/vYrJk46Wh8YXsTtB7n1Ht/pVttleX53kuYLaFq2Nvr7n2eOXNZ3L/83w4Hi54LxaKb+BReZoOQ5clnIM7caBFzQhRHQ2mphgbZylLf78tzWVmUcxwv2bbiDb0Vx3GvBYwXQT2jbnFqjPehs4ABaxzQBU4TmFPMAmi1kHvOAp/XVDyGEcBSa09mfMZ0BTfXak6J39RmDphZtmu83VK+DJ5rt4AChOdfY6GHLOiDeLbv18hZnm10kUvOFOAW8JYSwE0IMQZt05tmsLkarSyd9kv4OmmlLIQ0BgD4ZrE8RE3khhLXQbGyXon3IZuW7Zytu2Svb6GUpss10CbtjUXnog9mvwDt6X+iA9rLlCWRr0NSEQ/X83gTO5Jt4/ymklNFotvKfCSFchOYgXlPc2v53BfCsEMJfaA6qr5SQ3FLgOSFEdX2S+wGaeZ+phDh3wh601bMD+a7t06/FyFsq/KXAdCGEt9AcwN5EE8zLQmnv792wCW1QewetPfJU1856XnFowuebFNZulYQz2rhzA23S9UERYfoKzX/GBs0v4rDUfAtuopdnHvC50Jz00Z93cTbifwtCc0AfqL/H2WjaP7N+b7i45TCZiPaxuc2UUWomDSuA94UQzkIzd3ueMvSFkvIuglloz22+nkde+80SQjTWx6zvgMVCc4w0Cm0TgNXAdinldj3OSLRn2FOWfTvgvLEn72dE6xdpQJLQbKJL3Ja3ACvQNjGor0863ipDHGOBMtjw5/r23byDp4BH9LbtQz7TEqFt5lJLH59T0J6jGTgCpAjN+dlej9tQCNGqmDxeRmubl/TvJkKIJkKIZWWslzOQIKXM0oXQR0qLkI8VwDShOQ77o5kI53Gn9ZgBdNL7p79eDy80f6CSKHZcLa6NhRABQojuQvMNzUJb4ChpQ5ai+tJy4DGhbWUshOY/8ByavwRSystoPqpLheYUn/dOPCTybcN/j3gVeFRoW/c668/jPTT/nzyT7euAp7jd5LUk7vT5lYYzWjsnCc13oSzvcB6rgP75vhnvUMz8Wf9u/ATMEppzvFFoW9/bUvIcuSwsQ1sMnsAtbQdo/W2AEKK3np+d/syL9EO5g3F8E1BHaEcxWAkhRqDNCzeUsbxFck8FD6ntODIKbdIcjybhD5CafV4O2mrCWLQP4gi0iWVe3GNo9tRf6feDKWaXAqGbzpSxTAlSyh0F1NMIIdqirRZ8ra8S5v3W6Xk/LDWbt8loDzsazYwnlmIcvsvAenH7fv5riiiv1Mt7J+ZLeTyEZr6U53MwTGo2p+h1eRpNAIlFewmfKRD/q7yyoU3wp0spN+e7P0K/l4S2QnUDaCFv3x70EtrL7Y/mrJ/J7RLzy3oe6WgT+5/RTJiK4hm0lfBYtMF9gl4P9HoNRbPTTERzhr3XW2yOQTNJuaDnsYpbqst5evnPoDn3b0KbFBT18v6E1p570Jzps9D6VamUsa/vRjMBy2/Pv0+/ll/gfg/N7vMM2o5nJ/RrpVLa+3s3SM1c5lc0zUT+QXQLmpN1ENrqXRZ3oFJH0xqGoa3KXKDoTRKWoH14EtB8s4ozTXkFbTw4JDT1/Hby2eOLYkzJ8tFOFD7H424/nHkY0D5YUXr5u3DrXW4FHNb7zDo0O+PQItKYjOZnFYLWV5ag9dM/k/dt6GNYezTtymEhRCra6mcyWpuCNlH8Ae3DmYZmyraL21fQ30NbXTyarw1LO5fmPNrYk/d7DG0C1FzPfyN30H/1cXA2mpYmWP+3NF4tUIad/Im+fZfv4LNo3+EktD6+Nt+92mj9OQ1NqPlGav4WZj1OU7TxKh7tGRU5YZRSHkDbArk7ECKESAC+RxsTy8IzaAtMqWiT9hWlhM/PO2jO26F6XVahf5/voh5BaJq2SsBpvTz70fr6GyWUoaRxtcg2RrM2+EgvUwzaWP1aCXkU6ktSyi369Z/R+vQmNH+s7/PFm4I2p/oarQ9cQdsKf30Jed0xUjNr743WP6PR+nYzoKMuAKEvCi5F6yNJohTTsjt9fmVgNtp8Ih7tm/B7WSPq846JaONkNNr7V5Lm9UW0vnAUbZycCRhKmiOXsRzRaP2oPfmOD9AXzQah9aE4tDHlJYqf45dpHJdS3kDTfL2ANt97Gegv734DJQBEgfm4ogSEtlqdBNQu5mOu+D9FaH4q30kpi9yWVaH4f0AIIaWUf7VWuGCevwC7pJS//J35FihDV2CGlLLrP1UGBQghJgAPSSkLOg0XFbYaWr+p9leXq7xyt/1W9XfFn+Fem1r95xBCDBCaU5Ij2g4EZ7n3294p/mXoqt++uvrRH231vJAGS6FQKBR/DUIIXyFEB6GZwgagrcyqcVihKMcowaN0BqGpo6LQ1KYPFTTbUvxfItBMNxLRTK0CyXe+g0Lxf0pZtuC+16yl+LNE/i6uAr/8w2X4f8QGzVQ3Fc2U7Tc0v4aykIRmfvP/zFXurt/ebTyFQplaKRQKhUKhUCgUir8epfFQKBQKhUKhUCgUfzlWpQdRKP5/EdpWvXvQdiGxAlZJKd8SQsxA24UtTg/6mtRO5EUIMQ14Am2Hqyn67iMIIVqgqaft0XYgebY0sz0vLy9ZrVq1e1wrhUKh+O9y/PjxeCnlnRz0qVAo/iaU4KFQlEw20F1Kmabvu71PCJG3xfDnUspP8wcWQtRH29a3Adqpn9uFEHX0rQG/BcajbeW3CeiDtrVmsVSrVo1jx47d0wopFArFfxkhxF2fLK9QKP5alKmVQlEC+rkqeedoWOu/krQUg4BlUspsfcvlYKC1EMIXcJFSHtS1HAuAwX9h0RUKhUKhUCjKFUrwUChKQT8J9BTaQYbbpJSH9VuThBBnhBA/CSHc9Wv+3H4g2DX9mj+3HziUd72o/MYLIY4JIY7FxcUVFUShUCgUCoXiX4cSPBSKUpBSmqWUTdFOtG0thGiIZjZVE+1U1WjgMz14UYenyRKuF5Xf91LKllLKlt7eykxZoVAoFArFfwMleCgUZURKmQTsAvpIKa/rAokFmAe01oNdAyrni1YJ7QyYa/rfBa8rFAqFQqFQ/F+gBA+FogSEEN5CCDf9b3ugB3BR99nIYwhwTv97HfCQEMJWCFEd7dDJI1LKaCBVCNFWCCGAMWiHXSkUCoVCoVD8X6B2tVIoSsYXmC+EMKIJ6iuklBuEEAuFEE3RzKWuAk8BSCnPCyFWABcAEzBR39EKYAK3ttPdTCk7WikUCoVCoVD8l1AnlysU5ZiWLVtKtZ2uQqFQlB0hxHEpZct/uhwKhaIwSuOhUCgUinKNtEiS4zOJj0gjPTmbum0rYutg/U8XS6FQKBR3iBI8FAqFQlFuMJssJESlE38tlbiINOIjUom/lkZulvlmmAv7oug3sTEunvb/YEkVCoVCcacowUOhUCgU/wg5mabbBIy4iDQSo9OxmDUTYCtbI17+TtRtUxGvKs54V3YmMy2HLfPOs3rmcfpPaoJ3Fed/uBYKhUKhKCvKx0OhKMcoHw/Ff43kuAwOrgkhLiKVlLjMm9ftna3xruyMV2VnvCo74V3ZGVdve4Sh8BE4CVHpbPjqNJnpufR+sgHVGnn9nVVQlHOUj4dCUX5RgodCUY5Rgofiv8aGr04TeTmJqg088KqkCxlVnHF0tb2jdNKTs9n49RniI1Lp/HAADTv7/0UlVvzbUIKHQlF+UaZWCoVCofhbuBGZRti5G7QeUJ1W/ar/qbQcXW0Z/Hwztv54nt1LLpESn0m7wTWL1JAoFAqFonygBA+FQqFQ/C2c2BqGla2RRl0r3VX8qIgrLF/0CTYNKuNXrz5e9l7UeNALaxdPTm4NJzUhi/serYeVtfEel/zeIaUkNdtEYnoOCek5JGbkkJCeq/0/I4eENO3fvP8npudgZ21k3piWNPR3/aeLr1AoFH8KJXgoFAqF4i8n5UYml4/G0rhrJewc73wr3MtHD7Luy5mQbSLr1DX2ee7kREAS8W45IKFZ1R5wbADHQ05zvdMJPN1c8bb3xtvBG297b7wcvPC298bDzgMrw7379GXlmrmRnlNAkMgvOOSSoN/LEyRMlqJNnK2NAg9HG9wdbPBwtKGerwseDjZsD7zOuAXH+G1SB3yc7e5Z2RUKheLvRgkeCoVCofjLOb09AgE06VH5juJZzGb2LV/I0d9WccMtF9MDdRhq7MLRtavwO2CPS4Ma2HSuS1LjXBIDz+G6vx5W29vzR8MFXDOEFErPIAx42HncLpTYe+Hj4IOXvdfN6572nlgbbglIq49fY8/luJvCRZ5AkZlrLpQHgBDg7mCDu4M1Ho42VPNyoLmj202h4ua/jjZ4ONjg4WSDo40RIQqbio1oVZnh3x1k/ILjLBvfFrtyrNFRKBSKklDO5QpFOUY5lyv+C2Sl5TL/tf3UbO5Dj7H1yxwvIzmJDV98TMT5M8gmviz2PcLqB9ZQw7UGOZkZnNi0jmMb1pCdmUHd9p1p/+BIstIc2PTtWYQBej1VH2PFHOIy44jLjCM+I57YzFjiM+OJy9CuxWXEkZCVgOT2b6FA4G7njpe9FzlZTlyOFjib2uJv1yif4GB9S3BwvF2QcLG3xngP/U1+PxfN04tOMLipH5+PaFqkgKLQUM7lCkX5RWk8FAqFQvGXcmbXNUw5Fpr1qlLmOFFBgaz//COyUlNp98RjTL7+Af1rDKSGaw0AbOwdaDv0IZr07sexdas58ft6Lh3cS8NuPek9bhB/LI5kwxdn6fV4Axo3a1xiXiaLiRuZNzSBJDOO2IzYm3+figznSkIk9m6JGK0C+XbQWnydfP9Ue9wNfRr68kLPOny2LYjaFZyZ2K3W314GhUKh+LMojYdCUY5RGg/Fv53cbDMLXjtAxRou9JvYpNTwUkpObdnArgU/4uzlxcDnX+Pb6IWsDV7LhiEb8Hcqetvc9KREDq9ZwZntm0EIGnTpTcL1usRHWug4rDZN7rszEy/QzKteXHWaATWteTnzJYY6mWhidGFuo8mIuveDvfsdp/lnkFLy7LJTrDsdxXejWtCnYcW/Nf9/C0rjoVCUXwz/dAEUCoVC8d8l8EAUWem5NOtdtdSwuVlZbPryU3b+PJdqTZox6oPZZHlYsfbyWobXGV6s0AHg6OZO98ee4vHZ31OvY1fO7tzE9aCvcHI+wd7lZ9m7PAhLMU7dRbHxTDQvrTpNlxrOzOYTKiXH8IJTXQ7KNFbtfBE+qQULBsHRHyA1pszp/hmEEHw8rDFNKrvx3PJTnI9K/lvyVSgUinuF0ngoFOUYpfFQ/Jsxmy0sfuMQjm62DH25RYlhE6IiWT/rA25ci6DDiFG0HjQMYTDw6t5X2RG2g81DN+NlX/YTyhOiIjmwcjGXDuzBaG2PMDanZsse9H6qOdY2JTtnb7twnQmLjtOssitLPX/E6sJqGD4fWX8Q47eO50zcKX717IL/5Z2QcAUQUKkV1BsA9fqDR40yl/NuiE3JYtDX+xHAWrXTVSGUxkOhKL8ojYdCoVAo/hKCj8WSmpBF894l+3ZcPnKAxa89R3pSIkNfe4c2Qx5EGAwEJQaxKWQTI+uNvCOhA8DDz5/+z77M6JlzqNq4Eaas/Vza/wkLXv2WlBtpxcbbExTHxMUnaODnwqJaf2hCx31vQoPBCCF4u8PbCIORtwxJWCYdhWcOQbfXwZQF296AOc3g2w6w6yOIOQd/weKej4sd88a0JCEjh6cWHiermJ21FAqForyhNB4KRTlGaTwU/1aklCx/7ygWi+ThN1oXeaK4xWxm37IFHF23moo1azPg+Wm4ePncvD9l5xSOxRxj89DNuNr+ucPzooIusv2HH4kLC8Rg5UK7YQ/RakBfjFa39lg5FHKDsT8fobqXE6vaR+CwYQLZFQeRZuxE+oED2Ddris+zz7IqaBVvH3yb6W2mM6LuiFuZJF6FixshcD2EHwIkuFfXNSEDwL8lGO7det/ms9FMWKx2uiqI0ngoFOUXJXgoFOUYJXgo/q2EnbvBhq9O031MPeq1L7wLVHpSIhvnfELE+TM06dmXro+Ow8r61rkZZ+LOMHLTSCY1ncRTTZ66Z+U6ve0gfyz4GXNOFE6eFeg8cgx123XiREQyY348THUH+MEnEMvqOaRfd8SUpmkTrCpWxBQTg9/HM3EZMICntz/NydiT/DrwVyo5F3ESe1rsLSEkdA9YcsGpItTtpwkh1TqC8c4PUizIlzsu89m2IF7qHaB2utJRgodCUX5RgodCUY5Rgofi38qaz06QEp/JqHfbYbS6fZU/8lIgGz7/kKz0dHqOm0j9zt0LxR+3dZxmavXAJhytHe9p2ZLjMlj14SqSY/5AmuNw8vAh5oY1Ta9HUicuFCwWDDbg2LkbTt3uw7FjJ6w8PQh//Akyz5yh2vJlJFVyZchvQ6jnWY8fev2AQZSgychMgstbNSEkeDvkZoCdGwTcrwkhNbuDtf1d1SX/TldzR7egdwO105USPBSK8ovy8VAoFArFPSUmNJmoy0k0ua9yIaEj6PB+Vrz9KlY2tjz87idFCh2How9zKPoQTzZ68p4LHQBONrkM7OKNr3s/rB37khOXiJOIJN4bcpo4ULVvNnW2/Ualr77BbehQrCv4IKys8J/1GUYXF65NnoK32YGXW73M0ZijLLu4rOQM7d2g8YMwYiG8dAVGLNaEjkubYNkj8HENWD4azqzQhJQ74OZOV5VceW75KS5Epdx1uygUCsVfjdJ4KBTlGKXxUPwb2fzdWSKDEhnzQXts7G75UCRGR7Lw1al4Va7CA9Pexs7RqVBcKSWjNo/ievp1Nj6wEVuj7Z8uj7RYyLoQSPq+vaTt2Uvm6dNgNmNxcWdvnbGYHGvg5n2FjIj1pGZB5ZpV6Th2En516hVKK+PEScLGjMGpUyf8v/qSiX9M4vj146wesJrKLnd4Vog5F67u0zQhFzdCWgwYrKFGF6jbXzPLcvIpPR20na4GfrUfg1A7XSmNh0JRflGCh0JRjlGCh+LfRmJMOkvePkyLPlVpO6jmzeumnByWTH+B1BvxjJ45Bxcv7yLj74rYxeSdk5nRbgZD6wy963KYk5JI27+f9D17Sdu/H3N8PAB2DRrg2LkTGU1b88jeVLItgvdqViJkbzSVrI/h1yCDYyeCyUhOokbzVnQYMRqfardvj5uwcBHX338f76lTMY0exAO/PUBt99r83Ofnkk2uSsJigchjELhOE0QSrwICqrTVtCUtHoNSnMfPRSYz7LsD1PN1Yem4tthZl7xt8H8VJXgoFOUXJXgoFCUghLAD9gC2gBWwSkr5lhDCA1gOVAOuAg9KKRP1ONOAJwAzMEVKuUW/3gL4BbAHNgHPylJeQCV4KP5t7FwYSNCR64x5vz0OLjY3r2//4WtOb9vMkFfeokbzVkXGtUgLw9cPJ9uczZpBa7A2lN35WlosZJ0/T9qePaTv2Uvm2bNgsWB0dcWxY0ecOnfCsUMHrLy8iE7OZPh3B0nLNrF0XFvqhfzM+d/2sjt1Ap6VnOn9ZACXDm7l2LrVZKWnUaddJzo8OBIPP82JXEpJ1IsvkbJ5M1V+mMc2n1je2P8Gr7R6hVH1R/25BtQygOvn4eIGuPAbxF6ABxdA/UGlRs3b6WpIM39mPdjk/3KnKyV4KBTlFyV4KBQlILSvtqOUMk0IYQ3sA54FHgASpJQfCSFeBdyllK8IIeoDS4HWgB+wHagjpTQLIY7ocQ+hCR5zpJSbS8pfCR6KfxPpSdksmH6A+u396PJIwM3rFw/sYeMXH9NywAN0GfV4sfE3h27m5T0vM7PTTPrW6FtqfqbERNL37SNt717S9+3HnJAAQmDXsCFOnTrh1LkTdo0aIYy3Vv5jU7N4aO4h4lKzWTyuDY1T9sCKMdBgCGF1P2bLDxewdbCi/6QmOLrB8Q1rOL7xN0w5OXQYMYo2Qx4EwJKRwdURIzDF36DaqpU8d/EDjkQfYdXAVVR1Kf2U9jJjMcM37QCpnRliKF2LMWfHZWZtC+LlPgE80/X/b6crJXgoFOUX5VyuUJSA1Mg7bcxa/0lgEDBfvz4fGKz/PQhYJqXMllKGAsFAayGEL+AipTyoazkW5IujUPwnOL0jAmmWNO1568DAxOhItn3/Jb516tLxoTHFxs215PLVya+o7V6bPtX7FBsu8/x54r78itAHR3C5fQeiXnqZ9L37cOzQAb9PPqb2/n1UX7kC7ymTsW/a9DahIyE9h1E/HCYmJYtfHm9FYxECv46HSi1h8DdUbeTNkBebIy2SXz85Tlx4Dh1GjObJr36kVuu27F++iJjgIAAMDg74z5mDzMkhcupzvNHiNayN1ryx/w3Mlnt4oJ/BCN2nQ3wQnFlepiiTu9diQBM/Ptlyia3nY+5dWf4GsrOvc/36BkJCZv/TRVEoFH8BSvBQKEpBCGEUQpwCYoFtUsrDQAUpZTSA/m+eB6g/EJEv+jX9mr/+d8HrReU3XghxTAhxLC4u7p7WRaH4q8jOyOXc3khqtvDB1VvbGtaUk8P62TMxGK3o/+zLtx3WV5B1wesITw1nSrMpxfpJpB84wNWhw4j/5hsQ4DVxItVWLKf2vr34f/IxrgMGYOXhUWTc5MxcRv94mLAbGfzwaEtauGXA0ofAyRseWnJzO1vvys4MfaUlTh52bPjyNIEHonFwcaX308/i6ObG1u+/xGwyAWBbvTq+H31I1tmzyNnzmNZ6GidjT7IocNGfacrC1BsAvk3hjw/BlF1qcCEEnwxrTGN/V6aW452upJRkZkYQHb2aC4GvcuBgd/btb8+5888SHvETJlPqP11EhUJxj1GCh0JRClJKs5SyKVAJTXvRsITgRRlUyxKuF5Xf91LKllLKlt7eRTvgKhTljfN7o8jNMtO81y0zo10LfiDuagh9nnnuthPJC5Jtzubb09/S2LsxXSp1KTbcjR9+xMrbm9oH9lN9+XK8J03EvnHj27QaRZGWbWLsz0cIup7K3NEtaF/JFpaMgNxMeGRFoZ2jnD3seOClFvjVcWPngkCOrA/Bxt6B7o8/TVxYKMc3rr0Z1qVnTzyffIKkZcvpeNpE18pd+fLkl4Qmh5bSYneAEHDfm5AcDicWlCmKnbWR78e0xMXOmifnHyUutXSB5a9GSkla+mWuRS7h3Pmp7D/QkQMHu3Ih8GXi4rbi6FibWrWm0arlGjp3Oo6VlfM/XWSFQnGPKX75SaFQ3IaUMkkIsQvoA1wXQvhKKaN1M6pYPdg1IP+empWAKP16pSKuKxT/eky5Zk7viKByPXe8q2iTxYsH9nB62yZaDniAmi1alxh/5aWVXM+4zgcdPyjWGTrrUhDpBw7gPXUqVu7uZS5bZo6Zx385yplryXwzsjlda3vC0ochNhBGrgCfwlvmAtjaW9F/chN2LbrI0Y1XOXc1CI9e2VRr0ZKDK5dQp00H3CpqJ7J7T51K5tlzXH/7bV5Z8A0jrp/gjf1vML/PfIxl8MkoEzW7Q9WOsPtjaPoI2JR+vkkFFzt+eLQlw747wFMLj7Hkb97pSkozqWmBJCUdJSnpCElJx8jNTQDAxsYHN7dWuLu1xs2tFY6OtRF3uyOYQqH416DecoWiBIQQ3kIIN/1ve6AHcBFYBzyqB3sU+E3/ex3wkBDCVghRHagNHNHNsVKFEG11h/Ux+eIoFP9qLh2KISMlh2a9NW1HWf06ADJyM5h3dh5tfNvQ2rd4ASVhwXyEnR1uIx4sc7mycs2MX3iMY1cTmD2iqXaq95bX4fIW6Psx1OpRYvywtKscCfiN01W3k3neht0LLvO520ZyhInlX75Dtm72dPNwQTc3sl6cwauNpnI67jQLLywsc1lLRQi47w1Ij4Uj35c5WkN/V2Y92JQT4Um89utZ/soNZSyWbJKSjnH16recOvUYu/c05+jRQVy+/B5pqRfx8uxGvbof0a7tDjp2OECjhnOoVGkUTk4BSuhQKP5PUBoPhaJkfIH5QggjmqC+Qkq5QQhxEFghhHgCCAeGA0gpzwshVgAXABMwUUqZ52k6gVvb6W7WfwrFvxqLRXJyWzjeVZypFOB+R34dAIsCF5GQlcCUZlOKDWOKjydl/QZcHxhSZm1HrtnCpCUn2Hs5nk+GNWZAEz84Mg8OfwttJ0KrJ4vOy2JiV8Qull1cxuGYw1gbrOndsTdVatnDjub4NXLmXP2VND4VwdjP+hDQqQv9avSjRYUW+M/+nLAxj9Lky+3cN6I7X578ks6VOlPDrUaRed0xVdpC7d6wb7Z2roe9W5mi9W3ky3M96vD59iBqV3BmQteapUcqA2ZzBsnJJ0lKOkpi0hFSUk5hsWjCmKNjbSpWHIibayvc3FphZ+d7T/JUKBT/bpTgoVCUgJTyDNCsiOs3gPuKifM+8H4R148BJfmHKBT/OkJPxZEcm0nvcQ0RQtz06xj88psl+nUAJGcn88u5X+hWuRuNvRsXGy5x6TJkTg4eYx4tNkx+TGYLU5edYntgLO8ObsjwlpXh8nbY/DLUuR96vVsoTlxGHKsvr2Zl0EpiM2Lxc/Tj2ebPMqTWEDztPbGYLawOPk7q8fp8+fpvLP3weZqfj2Gd92ZWX15NRceK3F/9fvpOeYz0z77nmaZPctzdgen7p7Pg/gVYGe7R57b7dJjbCQ5+pf1dRqbcV4vguDQ+3nKRmt6O9GpQ8Y6zzs1NJjn5OIlJR0hKOkpq6jmkNAEGnJ3r4+8/Ene3Vri6tsTGpmgnf4VC8f+NEjwUCoVCcVdIKTmxJQxXb3tqNPPm0sG9ZfbrAPjl/C+k5aYxqdmkYsNYsrNJXLoUpy5dsK1RvdQ0LRbJy6vOsPFsNNP71WN026raYXwrx0KFBjD0h5tnYUgpOX79OMsvLWd72HZM0kQHvw680fYNOvl3us0/w2A00H1MPVZ8cJRDq68ybPLrLHh5Ci+lDsFqQGM2hm5k4fmF/Gydy7RmLjSd8wPjZ47k4/jlzD8/nycaPVF6g5YF38bQcCgc/AZajy/kGF8ceTtdhd9IZ+ryU6x6uj31/VxKjJObm0hC4iHdP+MoaWkXAYkQNri4NKZKlXG6oNFcOYIrFIoyoQQPhUKhUNwVkUFJxIal0uWRAJJjo9k6dw6+tQNK9esAiM+MZ3HgYvrW6Esd9zrFhkvZsAFzQgIeY0vXdkgpeX3tOX49GcmLverwZKcakHpd28HK1gkeXg62TqTnprPhygaWXVpGcFIwzjbOPFLvEUYEjKCKS5Vi0/f0d6Jl32ocWR9K7ZY+tB48nEOrlzK0S0/63vc1iVmJbL26la1u6/C4doI6by7G63kPvjz5Jc19mtOsQiHl6d3R9TU4vxb2zoL7PypztLydrgZ9tZ9xC46xdmIHvJ1tiwwbE7OOi5emYzanYzDY4+banBrVn8XNrTUuLk0wGu3uTV0UCsX/FerkcoWiHKNOLleUZ9bPOUXctTQeeasFK995lZS464z+eE6pJlYAHx7+kOWXlrNu8LpiJ/tSSkIHDgSDkepr1xS741Ve2Hc2XODn/VeZ2K0mL/WuCzkZML+/toPVY5u54uDMsovLWB+ynvTcdOp51OPhug/Tp3of7K3sy1Rns8nCyg+PkZmWw4OvNWP5jBcw5+Yy9tOvsba7NRkPP3+I5FFPcdlf8NYwEwhBZ//O9K/Zn66Vu5Y5v2JZNxlOL4PJJ8Ctcunh83H2WjLD5x6ggZ8rS8a1wdbqlmbHbM4iKOhtoqJX4Oragtq1puHs3BCDwfrPlfdvRJ1crlCUX9Q2EgqFQqG4Y+IiUgm/kECT7pXYt+wXYq9eoc/Eks/ryCMqLYoVQSsYUntIiRqG9AMHyL4cjMejj5YqdHy85RI/77/KEx2r82KvALBYYO3T5EaeYGvXZ3n87BwG/zaY1ZdX071ydxb3Xczy/ssZUnvIHQkBRisD3cfUJTM1l8O/hdNr/CRS4q6zf+Xi28JVadCWap/Ool5wNs9dawTA8djjvLznZbou78pre19jf+R+TBZTmfO+jS6vaP/unnnHURtVcuWz4U05HpbItHw7XaWnB3P02BCioldQteoEmjdbgqtrs3+V0KFQKMo3ytRKoVAoFHfMya3hWNsZsbW7yumtG3W/jjZlivvt6W8xYOCpxk+VGC5h/nyMXl649O9XYrivdgbz7a4rjGxThen96iGEIHbrNFZH/cGqWgHEBi/Gz9GPqc2nMqT2EDzs/pzjs09VF5r1qsKJ38Oo1bIJje/rw4mNv1GvQxcq1Kh1M5zzfffhOX487b7/ntNvNGGP+RLvtH+H03Gn2Rq2lfUh6/G086RP9T70q96Phl4NSxSwbsO1krYz1+G50OFZ8Kp9R3Xo19iX4Fh9pysfZwYHnOLipbcwGu1p3Phn4uxasTA6CV9ba7p4OGNrUOuUCoXiz6NMrRSKcowytVKUR1LiM1n0xkEC2thzYdcsPCtVYcSMmaVunQsQmhzK4N8GM6reKF5q9VKx4bKvXCGkX388J03CfdhYssNTyAlLxXQjE/v6nji2qojB3op5e0J4f1MgQ5tX4uOhjTgRd5xlhz5mZ1IgZiHo4N+RhwIeoqN/x3t3mB/aoYkr3j9Kbo6ZB15owOLXJuPo5sHID2ZhyHeSujSbCX/ySa5fOM5LU1yo4OLH4n6LkVKy99peNoZuZHfEbnIsOVRxrkLfGn3pV70f1VyrlV6ItDj4ognU6Q3Df77jOkgpmbTkGJvOXmd403XYVK1LuMP9HE0xkWgy3wznbDTQ28uV/t5udPFwxt5YvoUQZWqlUJRflOChUJRjlOChKI/sWR7Eud1hODqsIy0hrsx+HQBv7n+T36/+zu9Dfy9S82BOyyEnIpWEhRvJjc3G2q8+MscCgMHBCqOrLbnR6QgbAxH+DrwYGkPDRi50ah7OykvLuZJ8BVezhSFGdx4ctIDK9+oMjSKICUlm9SfHadjZH9/qcaz//CM6j3qcVgMeuC2cKSGB0AeGcqh6Lh93TWZi04k83eTpm/dTc1LZHradjaEbORJ9BImkgWcD+tXox/3V78fL3qv4Qux8D/Z8Ak/t1Xa8KgMmi+RMWga7r4eyNeIsgYfckekWctp4U9XHkXZuTrRzc6K1qyNXMrLZEJfE73HJJJrMOBoN9PR0ob+3G909XXAoh0KIEjwUivKLEjwUinKMEjwU5Q1TrplfXtmP0bCXhIiDDH75jTKbWCVkJdBzZU+G1B7C9LbTkWYLuTEZ5ISnkBOeSk54CqYbWQBIixlhSMexbQA2VZyxreKC0dMOIQQ5kWmcWxeEW1gqVghOOAeyyn0rJl8LD4WfpY/RHbsntpX5gL0/w76Vlzm9I4JBzzXl2LqvCTtzikc//Rq3Crefk5F5+jRXR43m60c92V8hiWX9lhHgEVAovdiMWDaHbmZjyEYCEwIxCAMd/TvyQccPcLV1LVyAzCRN61G5DYxcUWQZcywWTqVkcCg5nYNJaRxJTifdrAlzfsTQws6Nw9tTcbAysH5SxyJ3usq1SA4kpbEhLomNcUkk5JqxNxjo4elCfx9Xeni44Gh17zRKfwYleCgU5RcleCgU5RgleCjKG5ePXmfzt2vITd9Ai/5D6Dq67OdTzD01l2PbjlIruya1bPypm1oR11zNsdvgZI1NFRdsqjiTdXInN36YRY21q7CtfbvvQq4ll5l7VrEkcCleNjcYmNSVQcndsM+xxdoqEifb33F4+i2Ed+lnftwLcnPMLHv3CAD9JtZg0SuT8KtTl6GvvVPIXyNx6VIuf/wOL05xpoJXVZb0XYK1sXjH7ZCkEDaEbOCX87/QzKcZ3/X8DuuiHL33fQ7bZ8DjW6BKW7LMFk6kZHAwKY2DSWkcT0kn06J96wMcbAiQp6masYm2ro50bPQetjZenLmWxINzDxa501VBTBbJoeQ01scmsSk+mbgcE3YGQXcPFwb4uNHD0wXnf1AIUYKHQlF+UYKHQlGOUYKHoryxeuZ2wk5+Q8Va1cvs1wGQmJLIh998gEOWI74Wd64bkrFgoap3JVq1bkW95g0xGo1YcnIIvu8+7ALqUuWHebelEZwYzNjN40jOjcdaevF0s1EMD3gAN2lHxnczSIttRK6sisHRGse2vji19cXobPNXNMNtRF5KZO3nJ2nSozKOThfZ+fNc7p/0AvU7dbstnJSS6FdfZXvgej4ZamRCkwk80/SZUtNfd2Udr+97nWF1hvFm2zcLCTQZmalYfdmcGOcqTGn9LSfTMsm2SARQ38nupulUA2MkUZemkJkZTo0aU6lWdQJC3DKV2ngmmolLTvBAc38+G96kTI7uZik5kpzOhtgkNsYlE5OTi61B0NXDmf7ebvTydMHV+u/dx0YJHgpF+UXtaqVQKBSKMpF4PZmw04sw2ljTf+orZRY6IiMjmb9oPk6ZTnSiPh3G9yHX3cCJEyc4evQoqzauwWXvDlq2bEmt69cxx8Xj8cHtBwbmmHN4dueLJGVm4m+ayMoxj+FibwtSwtoJOCYvxeHhH8l2aETavkhSd4STuisChybeOHX0x8bP6a9oEgD8A9xp2Nmf0zsiGPJCR3xr72LX/HlUa9IcB5db5lFCCCrOmEHHhy5x+NJVvhff061yN+p51isx/YE1BxKSFMKP536kpmtNhtR5mCO62dTBpHROpWYwyu8RPgyeTb3YgzSp3YP2bk60cXXEzdoKKSWRkYu5HPw+1lbuNG+2GHf3wifL92vsy+XY2szefpk6FZx5ukvNUutuFOKmYPNubX+OJaezIS6ZDXFJbIlPwVoIung409/bld5errj/zUKIQqEoXyiNh0JRjlEaD0V5YvHr7xETfIjez7xGwy7tyxTn9OnTrF+3HhuTgc6mejSdcB92lVxu3jebzQQFBXH06FFCQkIwWCxUTUyk+8svU6lSpZur7p8e/ZT5F+ZDzOPsnTwRNwddk7HnE83Butvr0OXlm+nmxmWQdiCKjGPXkbkWbKq74tzRH7t6HghDGbesvQNyskwsfecw1jZGuo/xYen056jboQv3T3y+cNjwcM48MpTnR+fiXbEGy/ovL9HkKsVk5mBiCrMOvUpUwiFSfZ4ny64JRgFNnB1o5+ZEe2cbui3uhsHBA8bvAr3dTKZUAgOnERu3GU/PLtSv9wk2Np7F5iWlZNLSk2w6G833o1vSs36Fu2oPi5ScSslgfVwS6+OSuJaVi5WATu6aJqSPlyueNn+NEKI0HgpF+UUJHgpFOUYJHorywtmdW9k6dw5ufl144vPit8HNw2w2s337dg4ePEhF4U6b7BqE981kUMcHi40Tvn0He3/6kbC6dcmxWPD19aV169ZkeGXw9M6nyUlsw6RGrzD5Pt3v49xqWPU4NB4BQ+benGznx5KRS/rR66QdiMKcnI3R0w6n9n44tqyAwfbeTnzDz99g/ZenadGnKqasAxxes5yhr79LtcbNCoVN3fkHa2dN5OPhRp5q/BSTmk0qFMYsJZ9djeGLsOuYJVjLbHzi3seSe52XO81loH+j2x26Ty2BtRPgwYVQfyApKWc4e24K2dlR1KzxIlWqPHmbaVVxZOaYGfH9Qa7EprFqQnvq+bqUGqckpJScTs1kQ1wS62OTCMvKwSigg5sT/b3duN/bFW+be3dIoRI8FIryixI8FIpyjBI8FOWB2KshLHn9BSS+9J7wOvU7+pcYPiMjg1WrVhESEkJDY1Ua51TirWrf8cOYBThaOxYbL2LCM2SeOkXl3zdzLiiII0eOEBcXR64xl3Cn61xOuZ9trwzE2c4aIo7CL/3AvzmM+Q2sCu/ElB9plmSejydtfxQ5YSkIWyOOrSri1N4PKw+7u2qXotixIJBLh2J44IXGbPryNSwWM49+8hXWtoXziJ09m7evzWN/IyOL+y+lgWeDm/du5JiYeCGMXYmpDK3gzsO+HjR3cSQlK5aHNz6MrdGWJf2W3L4lscUM37RDAhEDniI45BNsbbxp2PALXF2b31E9YpKzGPjVPhxtrVg/uSNO90hIk1JyPi2T9XHJrI9NIiQzGwPQzs2J/j5u9PVypYLtnxNClOChUJRflOChUJRjlOCh+KfJzkhn0bSppCemY+0yiic+7YWNXfGT0OvXr7N06VJSU1LpaKhPbXNFpvh+SJumnXil9SvF5xMaSsj9ffF6ZgLeU6YAYLFYePW3V0kMTsQ33R8D4G5rTSNfN+qFfksFdysM43aAY/GmQ0WRE5FK6r5IMs/Gg5TY1/fU/ECquZT95PBiyErPZek7h7F3sqHdYDtWvfcarQYOpfPIxwqFlWYzFyY8zoTGJ3D3rsTKYb9hY7ThZEoGT54LJS7HxAd1KjHS1+O2cp2NO8tjWx6jgWcD5vWah43xlgO96ewSrFZP4HyAE6YG/alfbybW1m53VZfDITd4eN4h+jf244uHmv7ptimIlJKL6Vmsj0tiQ2wyQRlZCKCNqyP9fdx42NcDR+Od746lBA+FovxS/k7+USgUCkW5QErJlm+/IDn2OlZO/ajTqlqJQseFCxf44YcfMOWYGGBoRYDFj52dAgm2DeeRuo+UmFfiwoUIa2vcH3745rUNoRvYnLKZCG9/9pjr4pSRTGJGFnuCo1kQ3ohvT9dh3dy5nNn+O8mxMWWul01lZzwfrkvFV1rh3KUSWSHJxM09Q+xXp0g/GYs0WcqcVkHsHK3p+kgANyLTiA13pmG3XhzbsIbroVcKhRVGIwEzZ/PMIRdCsq7xzaHZLIiMZ9CJywgB65rXZpSfZ6EJfyPvRrzb4V1OxJ7gnYPvkLeAmJx8ksPJ35DiZE1ApA2N68+5a6EDoE0NT57rUYd1p6NYdjTirtMpDiEE9Zzsebm6L3va1GV367q8WK0iySYzH4VEY+Te++IoFIp/FqXxUCjKMUrjofgnOb7xN3YtmEe9zsMJPVuZIS80w6+2e6FwFouFXbt2sWfPHvwr+NE1IQBHiw1uj9fj/n2DaOzVmC/v+xIpJZb0DIRBYHBwuBnfnJTE5W7dcenTB78PPwDgWuo1hq0fRiWHWpzbP5DxqVuwykymdzPBtkRfbggPqjjbk3PlImk34gBwq+BL1cZNqdqoGZUbNsbOsWw7WVlyzGSciCVtfySmuEwMzjY4tfPFsY0vRse7M/vZ+uN5rpyIZdDUevz2yYs4e3rxyHufYShiBT/zzBleXjCS3Q0hscJbdPRrxtf1q+JRyg5Q35z6hm9Pf8vU5lPp4WLmSshn2Nr60tRpJI6/vgh9P4XW4+6q/HmYLZJHfzrC0asJrJ3Y4U/7e5SVuJzcu/b7UBoPhaL8ogQPhaIcowQPxT9F5KVAVrz9KjWat8JiuJ+0hGxGvdsOIQQyJ4fs0FAsKSlkJCay6dw5QlJSqGvjQLvUphilETL+4HrMKWJir1DV4I1Neg7mtDQwmxE2Nng8OgbP8eMxOjsT//084mbNovraNdjVrYvJYuLxLY9zOfEyNdNepsr+dfiYkxn6YBcqHPsQU+8P2RDpyIULF2jQoAGdWrUgKvA8YWdPEnH+LLlZmQhhoGLN2pog0rgZvrXrlrr9r7RIsi4nkrYvkuzLSWBlwLG5D04d/LCuULxvSlFkpuWw9O3DOHvY0bBzJpvmfEyX0U/Qsv+QQmFDM7KZ++UXHHJYgNHOjQ2PbcfeqnS/E4u08OKuqWwP/4PHvbLpXqUn9ep+iLWVs+b/ciMYppwCG4dS0yqJuNRs+s7Zi7OdFesndcTxHjvl32uU4KFQlF+U4KFQlGOU4KH4J8hISWbhq89itLJi0IsfseKDM7QeUJ1W/aqTGxtLxJPjyA4KItXZib0dO5Hm7ESzC1do4j8Cg9GK7Au/IKwyuWyKItNO0Lp2N4zOLhhcnDE6u5B16SIp69ZjdHfH65kJxM/7AduaNaj6888AzD09l69OfcVTtV4ldO4f+OXGMeiF17m+biunYtsCBgzWghyXSBKtg7EzOFPHvT1urm5Y2xvIybhGSmwQiVEXSYoOQUqJta0d/nUbUq1pM6o1bo6Hf6USfRZyr6eTtj+K9BOxYLJgW9sN117VsKnsXOZ2vHzsOlt/OE/bITUIPzWf8PNnGPvpN7j63Nqidkt8MpMDwzBKeGXHLD6rcZzRLj15ecisUtNPTDrKybPP8tm1FGLNNiy8fwl1PetqN8MOws99oMfb0HFqmctcHAeC4xn542GGNPNn1oNN/3R6fyVK8FAoyi9K8FAoyjFK8FD83VgsZn79cAbXAs/x8LufEnbewNGNoYx5vz12mTcIe/xxTHHxZEx8hi3R0RgMBga17oLLIQNIgff4RlhXcOT49eOM/X0sb7Z7k+F1hhfKJ/PceWJnziTj6FEAPCc+g/ekSZyLP8fozaPpVakHDqtycLwRSqcxUwg/ak1cjIV6tVNwbdCU7HQTWRm5XE+4RnDiESTgndMQQ6orptxbPhrSkoXFFIHFFI4lNwxpSQLAYOWMg2tNnL3r4O5bFycPN+wcrbF1sMbO0QpbR2vsHKyxMYC8mEDWsRhkjgXvp5tg41s27YeUkt/nniPs3A36PVOdNTNfxL9ufR54dQZmCR+HRjMnPJbGzvb80KAalYTkxZn3saNKCj+1mkWLhj2LSdfC1bBvCQmZjb19ZXxrvsOTu9/CIAws7bcUL3svLeDi4RBxBKaeATvXItO6Ez7fFsQXOy7zybDGDG9Z+U+n91ehBA+FovyiBA+FohyjBA/F382BlUs4uGoJPcdPolG33ix84yCu3vb0GehG+OOPY8nIIOG1aWw5dYqKFSsyrOcgcpaFgQTvcY1umiQ9v+t5jsQcYduwbdhb2ReZl8ViIaR3H3Kjo8Fkwq5Naz5oE0WYFzwU2JrEC6exajQU+7gaGMwZdHefS43pi8D2dt+NhIQEli1bRlxcHPfddx9tWrUlJ9NMVnou2Rm5ZKWbbv6bfD2GuPALJEVfIu1GMBZzJgAGKx+EsQoG66oYrPwQ4nb/AjsDdHOzwdbRmoqTmmJ0LXn73jzSk7NZ+vZhPHwdqdrgGrvmz6PjhOf4xLUq+5LSGO3nybu1/LEzanu9JIReYujG4dhhxeqxO3FwcrstveyceC6cf4GExH1UqDCAugHvYWXlROCNQB79/VFqu9Xmpz4/YWu0hejTMLczdH4Zur9epvKWhNkiGfXDYU5FJLFuUgdqVyi79ufvRAkeCkX5RQkeCkU5Rgkeir+Tq2dOsvqDN6nfqRt9nnmOqKAk1n5+ki693bD5ZDIIgefXXzF30yYqVarEsPsGkfRzIHC70BGdFs39v97Pow0e5bkWzxWbX8axY4SNGk2F6dPBYiH8i0+xTs/hRu0qnLEWRHv2wM/YHN/qDvRMfxTndkPg/o+KTCsnJ4fffvuN8+fPU79+fQYNGoStbcnCgcViJjbkCmFnT3H1zAmiLl3EYjZhtLbGu2oAXlXq4+4bgI1jRTKScgnbc412dkaMHrb4T21e5gMILx6KZscvgXQcXpPDWz8n7no0Cx+eyozGdXjIt/BWwDs2f8fU2K8ZdqMmbz635qZJWELCAc5feB6TKYU6td/Ez2/EbeZi28O289yu57i/+v3M7DRTu7dyLARthWdPg5N3mcpbErEpWfSdsxcPRxt+m9gRe5s73+72r0YJHgpF+UVtp6tQlIAQorIQ4g8hRKAQ4rwQ4ln9+gwhRKQQ4pT+65svzjQhRLAQ4pIQone+6y2EEGf1e3PEvd4UX6H4E6TeiGfTnE/w9K9MjyeeQQhB4MForK3B8OFkhK0tVRct5OC1a5hMJnq36a4JHQK8xze+zfl66aWlADwU8FCJeSbMn4/R1RW3oQ9wqqs/T48zc7V+JTyDw+ly8RrtI6Jp1dOXwS3/wNlwvcQdmmxsbBg2bBg9e/YkMDCQH3/8kRs3bpSYv8FgpGKtOrQZ8iAj3vqIiT8tZcirb9GkZ19ys1I4t3MFexe/y4Flr5EYuYYmwxwJtLNCJmRxZdYJTDmmMrVtQJuKVGngwb61ISxs2BOb7Ezeuri/SKED4L77n2ZAdl1WewSzd9kspDQTEvIFJ0+NwcrKhVYt1+Dv/1AhH5UeVXvwbPNn2Ry6me/PfK9d7PY6mDJh3+dlKmtp+LjY8fmIplyOTeOtdefuSZoKheL/B6XxUChKQAjhC/hKKU8IIZyB48Bg4EEgTUr5aYHw9YGlQGvAD9gO1JFSmoUQR4BngUPAJmCOlHJzSfkrjYfi78BsMrHi7WnEhV9l1Ief4+FXiZwsEz+/uAefyIM0ythD1Z9/Jsnamm+++YYWDZrS/GIFTegY1xhrn1u7JmWaMumxsgdtfNswq2vxDtI54eFc6d0Hz3HjMEwYzQNrH6DVJQ/8Ak04UpfaUZH4xZ3Cyscb74AYXDs3RoxeVab6XLlyhVWrViGlZOjQodSuXfuu2iU1IZ7ws6cJO3OSq2dOkpORTt9np5F6yIYKUWnE2hqpObkprl4l7xqVbjYz7Wgo1RaFk1XBjtbVL3F6/WqGTX+Pqo2aFhknLSuFQfO7YczI4R3/aqR7XaBixSEE1HkbK6vifUyklLy27zU2hGzg0y6f0rtab/htIpxZCVNOgGulu2qLgny65RJf/RHM5yOaMKTZvUnzXqE0HgpF+UVpPBSKEpBSRkspT+h/pwKBgH8JUQYBy6SU2VLKUCAYaK0LMC5SyoNSk/YXoAkwCsU/zt4lvxAVFEivpybj4adNIs8t2o3JBFUM4VRbtAhrPz927NiBtbU19a943tJ0+Nw+6d4QsoGUnBRG1RtVYp4JCxeBlRVuIx9h+v7p1Dpni1+gCaNtEy569yLojbepumQxVq62RO81Ero0mfRDh8pUn5o1azJ+/HhcXV1ZvHgxe/bs4W4W2Zw9vGjQ5T76Tn6Rxz7/Ds/KVdk4+0PcOuSQG+CBT7aZ4x8dI+RUXLFpBGdk0ff4ZVZlpmHVwxfXyCx8/LviVtGX7fO+Jjcnu8h4TnYuTGszgWgPWHM4hLpV3qZB/U9LFDpAO5RvRvsZNPVuyvR90zkffx66vAJI2P3xHbdBcUztUZvW1Tx4fc05gmPT7lm6CoXiv40SPBSKMiKEqAY0Aw7rlyYJIc4IIX4SQuSdquYP5D/i95p+zV//u+D1ovIZL4Q4JoQ4FhdX/IRGobgXXD58gOMb19K0d3/qtu8MQPL69VzYEYKjOZEmP3yElbc3YWFhXLx4kRaudbFJkniOrIe19+1Ch5SSJYFLqOdRj2Y+zYrN05ySQvLq1bjc34dVCTswb4+mQbAtVrb1uVa3D3t8BI92rYFD8+ZUG2DGr5cNliwL4WMfI+LpCaTt3Yc0lWzm5O7uzhNPPEHDhg3ZuXMnK1asIDu76El+WbB3cmb49PfxrlqNdZ99iLlhKlYB7tSxEpz58Rx7lgdhzr39xPMNsUn0ORZEXE4uy5rUZPKguvgHuHPot3A6jBhP0vVoDq1aWmR+UVGrMCR+Rjes2dxIELE9tMxltTXaMrvbbNzt3JmycwrXrW2h5eNwchHcKHyC+t1gZTQw5+Fm2FkbmbTkBFm55nuSrkKh+G+jBA+FogwIIZyA1cBUKWUK8C1QE2gKRAOf5QUtIros4Xrhi1J+L6VsKaVs6e39551BFYriSIyJ4vdvZ1OxVh26jH5Cu7ZsGcFvfUqya00aDmiMlbs7Ukq2bt2Kk50jAREeuNxXBdtqhbdnPRR9iOCkYEbWG1niGRlJK1dhycggbfB9nJy3j7qhZmydA2g2cRKrUlJ4qksNHGys4NpxRNRxXEc+Q43Nm/B+4XkyTpwgYtw4LnfuQsw775Jx4mSx2gwbGxuGDh1Kr169uHjxIj/88EOpfh8lYefkxLDp7+FTrTrrP/+ItHqp2FRzoYWTFVF7Iln18TGSrmdgskhmBEfy5Pmr1HG0Y1vLADp7OCOEoNuoukiL5PJxGxp07cHR9b8SezXkZh4WSy6XgmZw5Mw7ROf2oWmtediZrPg+ejXm1NQyl9XT3pOv7vuKtNw0Ju+cTGa7iWBlC398cNf1L0hFVztmPdiEizGpvL3+wj1LV6FQ/HdRPh4KRSkIbV/NDcAWKWUho3VdE7JBStlQCDENQEr5oX5vCzADuAr8IaWsq19/GOgqpXyqpLyVj4firyI3J5ul018kNT6O0TPn4OLtQ/y8ecR9NouIrpMIFvV49MMOOLrZcv78eVauXEln2YCGFevgPb4xwlhYsJi8YzJn4s+wbdg2bIw2ReYrTSaCe/Uip1IA612rYh27H0evmjz68UeMX3aaC1Ep7H2lmyZ4/DoeLm6E5wPBzgUAS04O6Xv2kLxhI2l//IHMzsba3x+Xvn1x6d8fu4A6ReYbEhLCypUrsVgsDB06lDp1ig5XFrLS01j9wZvEhl6h/zMv43zYltzkbPalmUkxS052cGWtNzzm78XbtfywMdy+xnd6ZwT7Vlym/YOV2bf4LYwuHoiBzxIcm8i58PNEpjiQlntry+CKjmtIr3KYT5MG0fvZ9+6orLsjdjN552R6VO3BpyZXDPtmwdP7oGKju65/QT7afJHvdl9hzsPNGNjE756le7coHw+FovyiNB4KRQnoO0/9CATmFzp0n408hgB527usAx4SQtgKIaoDtYEjUspoIFUI0VZPcwzw299SCYWiCHb9Mo+4sFDun/wCzl7exH4+m7jPZuF8f1+i3JpQub4njm62mEwmtm/fjofRhdrCD4+HAooUOiJSIth9bTcPBjxYrNABkPL7FiJNfuywaYp17AHsKvjxxGcfcT4ug72X429pO9Ji4dyv0HTkTaEDwGBjg3OPHlSa/Tm19+/Db+ZH2NSowY2ffiJ00CBCBgwkfu735Fy7dlu+NWrU4KmnnsLd3Z0lS5awe/duLBZLweKVCTtHJ4a9/i4Vqtdiwzcfk9YsG6ONkbaeNsQ7G2i0M4EPguCdqr7YGAwkpOdw7GoCK45G8OGmQL4MjyHeDnauDGerXWsyo67y+4pVbL8QCpZUegQ4ML1fPX4e24o9L3Vj2qBp2GUZWJCwjiNnw+6orF0qd+GFli+wLWwbX7s4aAcJ7ny/bJEtZijD4uQLverQoqo701afITQ+/Y7Kp1Ao/r9QGg+FogSEEB2BvcBZIG+W8hrwMJqZlUTTZjylCxcIIV4HHgdMaKZZm/XrLYFfAHtgMzBZlvICKo2H4q/g2sXzLH/rFVoOeIDOj4zl+vsfkLh4MW7Dh5EzYgobvjpL73ENqdXCh0OHDvH777/TO6cpTUZ0wKFJ0eZ/M4/MZNmlZWwduhVvh6LDWLKz2T7qfS661CQnfTXSw54pn/6ErYMjo344zMWYFPa8rGs7dn8Mf7wPk46DV61S62S6cYOU338nZeMmMk+cAMC+aVNc+vfH5f4+WHlqW9fm5OSwfv16zp49S926dRk8eDB2dnZ31Y7ZGRms/vBNYoKD8Ok/gbbnnQizF5x2tMYmKJ0MW8HvLrmE5ObejGNjNFDNy4G6jvbUOZWGbSUHZOp80sLDaDIqlVYdv8XFuWGhvGauf5tFCavotLsF7ce/zcg2VUo0Z8uPlJIZB2fw6+Vf+cinM/0OL4IntkHl1lqA3Ey4EQxxlyDuov67BAkhYO+hhavcBqq0Bd8mmslWAaKSMuk7Zy9+rvb8+kx77Kz/ufM9lMZDoSi/KMFDoSjHKMFDca+xWMwsmvYcWampjP3kK+LffY/ktWvxeOwxfF5+iW0/nif8QgKPzexIjimbObO/wD3TgaENe+P5YECRaabnpnPfyvvoWrkrH3Uq+oA/gJMfL2FfkJHstCVkOJuZOPMHPD0qcvRqAsO/O8jrfesxrnMNMOXA7EZQoQGM/vVmfLNFcvRqAq721lTzdCz28Lqca5GkbNpEyoYNZAcFgdGIY7t2uPTrh3PPHhgcHTl8+DBbtmzB09OThx56CC8vrzK1X0J6DiFxaYTEpXMlLo3gyBt4HpiPd0YMsb7DmGxXnX2YWGa00C3ZgLUFrFt5UrttRWr6OFHJ3QGjQRMYjm8J5dCaULybfkvkvkyqNGjCA6++V6RAkZGbQa9fOlIp2sTZpDcZ2LEOMwY2wNaqbBP8XHMu47c8wZn4s/wYm0BTgxNUaAjxlyDxKjddzoQRPKqDd13wrAWpMRBxSA8DGG3Br5kmjFRpqwkkjlrb7Qi8zhPzjzGmXVXeGVRYePq7UIKHQlF+KduxqwqFQqH4T3B2x1birobQd9ILxL46jdRt2/CaMhmvCRPIyTQRciqe+h18MVob2Lt9D5nZWfRzbIX7oOK1DmuD15Kem17iFroxp8M5dNkDS8ZKsq1z6PvKG3h6VARg9vYgvJxsGNW2qhY4cB2kxcDAOTfjx6dlM3XZKfYFx9+8VtHFjupejlTzcqS6lwPVPB2p4e1I5YoV8Ro/Dq/x48gKCiJloyaERE+bRsyMGTh17Ur9/v3wefhhVq1dy7x583jggQcICChasIpNzeL9jYHsCYojMeOW9sLaKJAOVsj6DzA6dD0VY1YT020KnUNt6dvWD+vOldjxywUiDt8g12zEd6TnTaEjJycBvN7CzqMLSZeeoM3gFA6uXMClg3tv7i6WHwdrBx6v9Qif28xnXPYfzD5iz6WYVL4b1QIflwIam8xEiAvSNBfx2r/WcZf4PDWSR/wq8KyHI0ujovGLM4B/C2g8ArwDdGGjZpEaDVKvQ8ThW79D38IB/fl41IQqbbmvcmvGtajFvINhtK3hSd9GvoXTUSgU/9cojYdCUY5RGg/FvSQzLZWfpj6FV+WqtItKJH33biq8Ng2PMWMAOLcnkt1LLjF8WktsXC18+cUcqpt8ePCpkdhUci4yTYu0MHDtQFxtXVncd3GRYdKTs1k+bQc5aeGkZ2/Aultdpjytnb15JDSBB+ceZHq/ejzZqYYW4cdemo/H5BNgMHD0agKTlpwgKSOXaffXxdPJlqvx6YTGpxN6I52r8em3CQQGAX5u9ppQ4umo/+uAb3wEzru2kPH7Zsw3bmBwdkb07MkOL0+up6TQtWtXOnfujEF3BpdSsvpEJO9uuEBmrplBTfwIqOhMDW9HrggzH0TH4WhlZG6DqrSyM/LrRzOICrrI4K4vYhMqcO1fA6f2fpzYGsbhdaE4e9jSe1xD7D0iOXP2aXJyYvF1/4A/vnejRjMv4q78ROqNOMbO+hZ7p8LtnWXKovfPHagYl8vYrit57/fTNLSO5vXWgirmCN1EKkgT2vKwsgev2ppQ4R1AiJMHowLn4puVzgKzJ47jdkEZTbZuIzcLok7qgsgRTSuScYMcaWS46R1CpD8bO4dRJaAF+DcHm5LPH7mXKI2HQlF+URoPhUKh+D/hwIpFZKel0cLFi/Sla6nw+ut4jL6lpQg8EI2nvyPeVZxZ9cNSsEC3jp2LFToA9kXuIywljI87F304nSnXzMZZR8jOFWSa1mOyETw9+s2b9zVthy0j2+jajrzJbO8PkULw/e4rfLzlEpXd7fn5mdbU93MpMp/kjFxCb6QTGp9GaHwGV+PTuXojnbWnIknNunXmh5WhGZUGt6eyMQff2DC8z5/CJ/kCbvWqsvOPXVy7eJGhjz5KQja89utZdgfF0bKqOzOHNaamtxM5FgvvXoli3rV4Wrs68n2DalS0tQbggWlv8+uHM/jtj88Y3PYFkjeGYOVmS4s+1fCr5cbWH8+z6uOj+DReQ4UGJpo3X4arSxNS+4ZyZH0o7YaMYtcvb7Nn0c/0fnrKrcqlxkBsIHZxl3hC+PKJfxhi0UD2+8SAGTgIuVaOWFeoB7Xuu6W98A4A1yqQb1etGsCnFeryzPaneTX7GrMD12OsP7DY51ss1nZQtZ32A80JPSEEm/BDfHX5NP1OVmTSbgMrDw7G1iDBt7FmlpX3cy3pHFaFQvFfRWk8FIpyjNJ4KO4VcWGhLHzlWRq2aEPl+ctw7t4d/y9m3/QnSIhKZ+k7h+kwrBaefmZ+XD6fZs51GPj8wwhD8SviT217iuDEYH4f9jvWBuvb7kkp2fbzBS4fuU7l0Hlcdkulbv8+9Bs9CYDDITcY8f2h27UdaybAhd9IfuYsL6wLZXvgde5vWJGZwxrjYmddMPtSkVKSkJ6jaUd0YeRqfMbNvzNybh18Z7SYcTLk4CHTcUxJI9bBk75dGjGuU3X83OyJycll/LkwjqakM76SN2/U9MO6QNvkZGWyZubbxFwMYnCTqVilG/Ea1wibyo4EnpvN0TWCtKimVGnoTM+xTbFzssZstrDyw2NkpuZQtd4lTm5ew4NvfkBlH1vY9hYEbb6Vvr07fVyccU+SLG/5OFl+DXhtby7rrsKYdtV4o399rI2lb1i55MIiPjw6k8dyrHj+iWNguLfO4FvOx/DUwuM8Vt/AW/5HNa3ItWNgytQCuFbWndbbav9WaAjGe7MWqjQeCkX5RQkeCkU5RgkeinuBlJIVb08jPiKM7pGJWGVlU2PtGoxubjfDHFgdzOkdEYx5rx1Lv/mROFMSUyZMwrFC4YMC8whJCmHQb4OY3Gwy4xuPL3T/+O9XObQ2hOqh6zle8SSOFjcmfLvwphnRw98fIjgujb0vd9N2QUqPh1n1OVv7GSaEdSYmOYvX+9VjbPtqZd7B6U7bJTY1WxNC4tM5FRLHwROXSDZYk4od5nw7ztsYBbKKE6a6rnxetwqDK7gXm25uVhZrZr5N3KVQBtR9BqOwIr7bUuJzNuHn9wjZkU9wcE0oDs429HyiAX613IgLT2XlR8eo3cqDsOOzMWQlMdp/L9Z29tBuIlTtoGkxHL1YsvdrPgydywepvRgw6TNMZgszf7/IvL2htK7uwTcjm+PlVISfRoG6v7/5CZbHHeWdyv0Z0v3De9auecxYd55fDlxl7ugW9G5QEcy5EHP2lp9I+GFIjdICWztCpRa6INIGKrfStv69C5TgoVCUX9Q5HgqFQvEf59LBvVwLPEcjB1cIC8dv5szbhA6L2cKlwzFUbeRJ8JZjXDPF0aFJ2xKFDoDFgYuxMdgwrM6wQvdCTsVx6LcQKqScw5S9E/tsJ5r3HnBT6DgUcoODITd4ukvNm1uvymO/sDC7E0PPtsJikax4uh2Pdaj+lwgdAEIIKrjY0aqaB2nZJtaejyXBwZMX+zbgpcoRDLM9zdMZh5l4ajXtwk6QG5rKo7HZJQodANZ2dgx59S2869bg96Afyc1MxfmPTgRU/YB6dd+l6X3VGPpSCwxWBtbOOsmxTVfxrORE8x6+BB2+QRObGBJTczls3Q+mnISur0L1TuDkDUIwvMNTVMi248eMbeQmJGBlNPB6v/rMHtGU0xFJDPxyH2evJZda91d6fUtbsxXvhG/geNThe9m0AEzrW5dG/q68tPI0EQkZYLTW/D3aToDhv8DzF2DqORj6IzQbCZlJsPdTWDwUZlaDrJLroFAo/n0owUOhUCj+w+RmZbF70U94enjjtW03nuPG4dim9W1hwi8kkJGSQ0A1J3YHHsLVxon2A7qUmG5ydjLrQ9bTr0Y/POw8brsXfy2NbT9fwMM+i7qnfuBIgCdWVta06v/AzTCfbwvC29mWkW2qAJCWkcmUnVm8YXqc9rW82TilE82rlDzBvxcEXU9l6LcHeG9jIB1rebHtuS6M6lyfJ8aPo1WTemR5GLjSozGdqku6R53il+NxLP18ETLf2RxFYW1rR8fH24PPNXZf+xXrjArYbKuLzNWOA/Kp6sKI11pRq4UPh9eFsP7936l36QncjRFcME+gbsuWHL2QRNyNtMJpG615qv4TXKkg2bD47ZvXBzfzZ/WE9gAM++4Aa09GllxGK1s+bTeDSrm5TN05iYjUiDttvhKxtTLy9SPNkRImLz1JjqnAgY1CgFtlaDQM+n4CT++FVyNgzG/QZ+ZdazwUCkX5RQkeCoVC8R/m8NqVpN2IJ+BcEPaNGuE9eVKhMBcPROPibE340RMkGNLo0bcXVlYl29uvubyGTFMmI+uNvO16RkoOG785jY2NoM6+DzlWU+Ke4kij7r1xdNMEiYNXbnA4NIEJurbjUkwqA2fvYGN2E15qIfjp0Va4OxZ/+vm9INds4csdl+k/Zx9hN9L54qGmzBvTkoqu2ta0pzNymFmhNgdqN6FiegoZ3q689eIgmpgTeDPKifUjnyHj+PEi05bSwpWQWQQGPUvj4a7Y13LlYOwGcq6mkLAqCGnRTJxt7K3o2TWWbpXXEhMJqyNfoUHXaqTlOGHrNQBbB0e2zf0Si8VcKI8h7Z/EP9uBH7P/ICfhxs3rDf1dWTe5I00ruzF1+Sne23ABk7n4E9pd6w7kK2MlLLmZTN4+kdSc1D/TrIWo4unAzGGNORWRxCdbLpYewdYJanSFNoVN9xQKxb8fJXgoFArFf5SkmGiOrV9NFWGDR3oW/p9+grC+3UE7My2H0DPxtHC34qj5Mn4+vjRs0qjEdE0WE0svLqVlhZYEeNw6+8Kca+H3uWfJSs2loWUHNmnxRDaoi0EYaDVQ03ZIKfl8exA+zrY80qYKq49fY9DX+0hJz2Cx+w9MHNobQwnO7PeCc5HJDPpqP59tC6JXgwpse74Lg5r6I4RASsnciFgGn7yMlcHApwN789jYR8nOzmb19q18+8pAfJ1teKNyLw6Nm0LU9OmYEhNvtY0plTNnnuLq1a/x9R1O6zZLGfLquxhq2HE6cTeZp+NI2RoGsYGweDhi4WDqO/zBsIeysPP2Yd9OMz5Vnbl0OJkmvR4hOvgSp7ZsKlQHK4MVTzccT5i35LfFM2675+Vky6In2zC2fTV+2BfK2J+PkpieU3RjCEHV+97h8+uxhKVc5aU9L2GymIoOe5f0beTL6LZVmbc3lB2B18sUx2zOuKdlUCgU5QMleCgUCsV/lF0Lf0RISa1zQVSYPh2bqlULhbl89DpVjYKQ9GAyRDa9+vYu1adid8RuotKjbjswUErJrqWXiL6STIfujjhuWsGO1g44XjNRv3N3XLx8ANhzOZ4joQk82ak6b/12nhdWnqZpBWs2Wb1Iu0497vnuSvnJyjXz8e8XGfT1fuLSspk7ugVfPXLLETs518Tj567yVnAUPT1d2dayDo2dHahatSojR44kPT2dP7Zs4KenOyFcXHjn/he4tmELIX37kbRmLWlpwRw9NoQbCXsIqPM29ep+iMFgi7WNLYNemk5WlRyupJwidVcEaV++rTlX93wXJh3Fs+sQhk9rRf32vsReTcVoZeDySTeqNGrGvmULSEtMKFSfAW3HUiXLkZ9yd5N9I+62e9ZGAzMGNuDjYY05EprAwK/3ERidUnTDVG1P60qdeD05k/2R+/ns2Gf3vO1f71eP+r4uvLDyNFFJmcWGs1iyuRz8EYcP98NkurfaF4VC8c+jBA+FQqH4D3L11HGuHDtEzcg4fHr1wnXI4CLDhe2NpKaDmdM2YQQEBFCtWrUS0zVbzMw9Mxd/J3+6Vu568/qp7RFcPBBNy77VSF0wjVQ78GjWG4vJTOtBmvP5pZhUJi85QWV3e349EcnyYxFM7FaTRZXW4mOTC82KP/n8z3I8LJF+c/byza4rPNDMn+3PddF2WtI5nZpBz2NBbLuRzNu1/PipYTVcrW+Zm/n5+dG7d28uX75MTNBp5o5uSSR2zHpsJqJaDaKnTePKIwOxhCfSrOlCKlUadZsAZ42JQW0diM9ZRXRGCIm5E8jsuxc6TNHOxACsbYx0G12PXk80AAOkJ+ZgZd8NU042h9esKFQno8HIhKbPcM1Tsnrxm4XuAzzYsjLLn2pLjsnCA98cYOOZ6KIbqPt0hiXEMso5gEWBi1hxqXB+fwY7ayNfj2xOrsnClKUnyS3C/Cst/TJHjw0jPHweHh4dEOKvE0IVCsU/gxI8FAqF4j+G2ZTLzp/n4miW1DLaUXHGjCK1GHFXk6mZmsNp2zBMmOnRo0epaf8a/CuBCYFMaTYFo66duHo2ngO/BlOzuTeOOX/gFhjJuYGNSDpxmTrtOuLu609EQgZjfjqMwSC4kZ5DTEoWP49txUsdvbE6txIajwD7e+9MnpFj4p31Fxj23QGyci3Mf7w1nwxvgquDZnImpWR+ZDwDjl/GJCVrm9Xmqco+RbZXq1atqF+/Pjt27KCiMY2PhzXmUHQmn/XsR+IjJqwjBR7v5JD7ywEsmfqqvsUMJxbCly2w2vshg7pXJtLzIknZccSvCCMnqrDzeO1WFXhoemvsnKyJugxuFVtwZvvvpMTFFgrbt/Uoqmc684tlH1lxRZsxNavizvpJHann68zEJSf4ZMtFzJYCW+n7NYP6g3jx4kE6VWzDB4c/4HD0vd3pqrqXIx8ObcyxsERmbQu6eV1KScS1BRw9Oojs7BgaN5pL3brvYTQ63NP8FQrFP48SPBQKheI/xsnN60mMiaJexHWqfPwxRpeiT/uOXX0ZaZXJRcM1mjdvjre3d4npJmcnM+fEHFpUaMH91e8H4EZUGlt/PI9XJSfaD69E0qwviPKxonpAL3KzMmkz5EHi07IZ89MRUjJzSc82Uc3TkY1TOtGtrg+cmA+mLGh9752JDwTH02f2Xn7aH8rotlXZ8lxnutS5Vcc0k5lnLoTxStA1Oro7sa1lAC1dHYtNTwjBwIEDcXd3Z9WqVXSvYcNDDS/xe5ALWwImU/P3bbj278+NuXMJGTCQtOVfwtzOsG4SuPjD41uwemQR/V97hzCfy2RmpxL93XFMSdmF8nLzceCRt9pgbWskI70pUkoOrl5aKJxBGJjYYjLR7rBySdFaDwAfFzuWjm/Lw60r8/UfV3hy/lGSMwvszNXtdYy5GXxs8aC6a3We2/UcV5Ovlt7Qd8DAJn483LoK3+66wq5LsWRnx3H6zBMEBb2Nu3tb2rTehLd36QKwQqH4d6IED4VCofgPkZaYwIHlC/FOSaf+yDE4tCz6HLX0c/G4xmWy3z4Uo5WRrl27lpr2Vye/IiUnhWmtpyGEIDMth03fnMHKxkjfCY3ZNnsyngm52E+ZQOD27dRs2QY7H3/G/nyEa4kZ5Jol9X1dWDquLf5u9mA2wdEfoXpnqFD/nrVBSlYu0349yyM/HMYgYPn4trwzqCFOtrdMpwLTMulzPIjfYpOYVt2XRY1r4GlT+snZdnZ2DB8+nIyMdBYu/IAevt/Ro3YW80/WZHu0wO+jD6kyewYiM5aIt77h2vpEcrvNhie3Q5W2AFhZW9PnxRcJ8QjEnJXLtS8PYskq7NBt72xD7/ENEQZnDNaNOb9rBwlRhbfI7dXyIWpnujCfA2ReL8aUCm172w8faMz7Qxqy93I8g7/eT3BsPj8K7wBo8jBOx37hy1bTsRJWTNo5ieTse3uexlsD6lO3ojNTlx1l854RJCYeok6dGTRp/CO2tiULvwqF4t+NEjwUCoXiP8SeH7/FnJNDMxcfvJ55psgw5uRsElYGccWSRJQlhg4dOuDs7FxiupcSLrEiaAUjAkYQ4BGA2WTh97nnSE/Koe+ERlyK3ku1NceJauqPtU0FstLTaD5wGE8tPM6FqBQsFmhUyZWFT7a5aebEpU2Qcg1aP3XP6r/z4nV6zdrD8qPhjO9cg83PdqZNDc/bwiyLvkHf40GkmMysbFqTZ6tVwHAHhxRaW1+kZq0TxMe7YTa9yVdjBtOyqjvPrzjJiUXTcTz8NNX7JuI9pA1pkTaEPP81CQsXIc23tsW1sramx0vPEup6EUMaXP1iD7IIv4eqDTyp0sADa4c2SGlgx08/FwojhGBSq+e47gZLl71RavlHtqnK0vFtSc0yMfjrA2w9H3PrZpdXQFqodHwBs7vNJiotiud3PU+uueRzS+4Ea0M2L7TbQ2ZONnNPP0yzFmupXGn0X3ZQpEKhKD8owUOhUCj+I0QGnifw6EGqJ6UT8NlniCLO4pAWScLyS5hzTByyDcbJyYl27dqVmK6Ukg8Of4CrjSsTm05ESsmeZUFEXU6i2+i6OPkbOfPBNGxN0PTNWRzbsIYqjZry4dEMDlzRzphoVsWNBY+3xsUu33a+h+eCaxUIuP9P1z0xPYfnlp/i8V+O4WJvxa/PdOC1vvWwt7nloJxhtvDcxXCmXoyguYsj21sG0MG9ZIGrYDtcvfodp8+Mo0YNE/Xq1eDQoSvEXA3i+xr7qGi+zvhz9YloPBXDc6fw+vAXaqxfh32zZlz/4AOuDn+QzLPnbqZnZW1N11eeIszpMjaJ1gTP2YmUslC+HYbVRhgcsHVqSfjZQ4ScCCwUplvzodTNcGOB4TDp0ddKrUurah6sn9yBGt6OjF94nNnbg7BYJLhXhZaPwYmFNLdy5e32b3Mk5gjvHX6vyLLdKSkpZzhydCCG9F+Y2jGWwHg/fjz459NVKBT/DpTgofjPI4QwCCHOlR5Sofj3Ii0Wtn36Aba5Jjo+NRmbSpWKDJe6M5zskGT25Fwn05hMt27dsLW1LTHtTaGbOBF7ginNp+Bq68r5PZFc2BdF8z5VCWhTkXm/vkab4+nIYX0JDw8jIzmJs96t2HwuBoE2yZ3/eGuc8wsdMecgbB+0euJPb6G76Ww0PT/fzfrTUUy5rzbr9QP08hOckUW/40Esi07guaoVWNG0Jj621kUnWAQmUzrnzk/hSsgnVPDpR6uWKxk8cBgeDlasWvIzNgc/56d6x8m1c+fx0K4kC02gsalShcrzvsf/81mY4uK4+uCDxLz7HuZUzcTJaGVN+2ljibIPw/66LZe+21oobw9fRxp28kdYNwdhy6avvycr/XYNhBCCyW1f4IYLLFoxvUx18nW1Z8VT7XiguT+zt1/mqUXHSc3KhU4vgtEGdn3IgJoDGN94PL9e/pUFFxaUub0KIqWZ0Ktfc+z4cMzmTJo1W8iE+5/mwZaV+OqPYPZdjr/rtBUKxb8HJXgo/vNIKS3AaSFElX+6LArFX8WJBT9xIy2ZZhWr4jV0aJFhskOSSdkRznUrQYRjKJ6eXjRt2rTEdDNyM5h1bBb1PeszpNYQMlJyOLDmCpXre9B2YA12hf9B5Z+2YXKyI2Dq6xxd9ytUqMaCEE2YaFfTk58fa4WjbQHty5Hvwcoemo+56zrHpmbx9MLjPLP4BBVd7Vg3qSPP96yDrdXtgsza64n0PhbE9ZxcFjeuwSs1fDGW0awnNzeZsPB5HD7ch9jY36lV61Ua1P8cY8h+bH++j+HpP5GFLb/6vUb1UXP4bnRrrt5IZ+LiEze3jBVC4HL//dTYtBH3kSNJXLqUK337krJpE1JKjFbWNH9tBDdsr+MU5sC5nwofGNhqQHVs7J1wrdie7LTLrP1sE+YCplmdmg6iYYYHi62OkxodUab62Vkb+Wx4E97sX5+dF2MZ8s0BQrOdoO3TcHYVxJxjYtOJ9Krai8+OfcbO8J1lSjc/mZnXOH7iEUJCZuHt3Zs2rTfh4a5p2d4e2JDaPk5MXX6S2JSsO05boVD8u1CCh+L/BV/gvBBihxBiXd7vny6UQnEvSL8ew4GNa/DItdDmo0+KDGNOzyVh2UXM9lbsTY0k15BB9+7dMBpL1jbMPTOX2MxYXmvzGkaDkcPrQjDnWOg8og5JOUms/XEaDcLB77kXuHT6GKk34lhsbA1C0LGWFz8+2gqHgk7bGQlwZgU0Hg4OHndcXyklq49fo+esPey8FMvLfQJY+0wH6vvdvntXtsXCtKBrPH0hjPqO9mxrGUB3z6J3+CpIWtolAi++zr797QkO/gg7+8o0a7aAqjZtEYsegMVDITeTig/O4v7+gwmJusGePXtoV9OTDx9ozL7geN5Ye+428ySjszMVp79OteXLsfapQOTzLxDx5DhywsKwsramwbSBpFon4XzJgTML199WHnsnG1r2rUZWZgOsbB2JvrSVPUuDbktfCMGU9i+T6AQLV7xe5vYUQvB4x+osfLw1N9KyGfjVPvb6PAK2LrDjbQzCwHsd36OBZwNe3fsqgTcKm3oVhZSS6Ji1HD7Sj7S0i9Sv9ykNG3yBtbXrrXrZGPn6keakZZuYuvxU4W1+FQrFfwoleCj+X3gb6A+8A3yW76dQ/OvZ+frL5AjoPm4iVkVsnSulJHFlEOb0XA4m5ZDtFYWnpyf16tUrMd3Q5FAWXFjAoJqDaOLdhPhraQTuj6JhV39cfex5f+8MhmxJgepV8Bg+nD+WL+OAe2sSbD3pUsebHx5teZuPxU1OLgRT5l05lUclZfLYL0d5YeVpavk4sWlKJ57pWgsr4+2fs7DMbAacuMzPkfE8XdmbX5vVwt/OpsS0pTQTF7eNEydHcfhIX2Ji1lCxwkBat9pAi1qf4rF7EXzXCaJPQe8PYeIRqD+I5s2b07hxY3bt2kVISAjDWlRiUrdaLDsawdw9IYXysW/UkGorllNh+nQyT50iZMBA4r/9FoMBar3ckxyrbJzO2HFy2drb4jXuWglXH1cc3NpjMYVx7o/DnN5xu2ajXZN+NM3wYon1SVIir95R27av5cW6SR2p5O7AmKWXOeA/Fi5vhSs7sbeyZ073ObjaujJp5yRiMwqfKZKf3Nxkzp+fyoULL+DkFECb1hvw9R1SpAN57QrOvDOoIQeu3OCrncF3VGaFQvHvQgkeiv8XGgFnpJS78//+6UIpFH+Wqwt+4XLKDer4VaHq/f2KDJO2P4qsiwlc97QnxhJPpjmZDh06YDAU/wmQUjLz6EzsjHZMbTEVKSX7V13Gxt6KVv2qsyl0E7a/bscnSVJ5+pusW7eNY2Yfjru1oFuAN9+PaYGddRFCh8UMR36Aqh2hYsM7quvFmBR6f76HwyEJvDWgPiueakctH6dC4X6PS6bXsSCuZmbzS8PqzKjlj7WheNMqzZzqBw4c7M6Zs0+TkXGVmjVfpmOH/dSr9grOh1fAnOZwdgW0nwRTTkK7Z8BKE2SEEPTr1w8vLy9Wr15Namoqz/esQ//Gvny0+SKbzxbe4lYYjXiMGkmNTZtwuq87cV/MIXTQYHLOn6bqcx0R1gYcjlpxbOWvN+MYrQ20f6Am2dl1sXV0xcp4hH2rLhN65nb/iCmdppHsCD+vLLvWI4/KHg78OqE9/Rv78diFZiTY+CK3vA4WM94O3nzV/StSc1KZsnMKmabMItNITDzE4SP9iI37nRo1nqd5syXY21cuMd/hLSrxQDN/vtgRxEF9QwKFQvHfQwkeiv8XKgJHhRArhBB9RBn3bRRCVBZC/CGECBRCnBdCPKtf9xBCbBNCXNb/dc8XZ5oQIlgIcUkI0Tvf9RZCiLP6vTllLYNCURzZ4eHsXrkYK2HgvrfeLzJMzrVUkjeHQhVnDgUlY6gUi7OzM40bNy4x7V0Ru9gfuZ8JTSbgZe/F1bM3uHYxkdYDqpNMAl/ueJfhB8CxaxeC/OuycPtp9nh2oluAN3NHtyzkZ3GTkwshORza3NmBgRaL5LVfz2JjZWDL1M481qE6xgLCRK5FMiM4krHnQqlqb8O2lgH08XYtJkVISwvi4sXp7NvfgeDgD7Gz86NRw69p324X1fzHYn1sCcxpCvtmQb3+MOko9HqvyBPWbW1tGT58ONnZ2axevRqQfDq8Cc2ruDF1+SlORSQVWQbrCj5U+vxzKs/7HmkyET52LPGfvIPPQ7Wxs3HC5oCJI6tW3gxfo6k3frW9MNq0ISM5DGe362z78Tzx126dgN6qYS9apvuw3PYMSdcKa1xKw97GyJyHmvJ413q8kTYcEXsBTi0GIMAjgI87f8yFGxd4fd/rWOQtPxOLJYfg4JmcODkKg8GWli1WUr3aRAyG0s9HEULw7uCGVPdy5NllJ4lPK3yookKh+PejBA/F/wVSyulAbeBHYCxwWQjxgRCiZilRTcALUsp6QFtgohCiPvAqsENKWRvYof8f/d5DQAOgD/CNECJvBvYtMF4vR239vkJxV0iTiWMvPU+8gy3th4zAwb2wr4Qly8SNpRcxOFlzKD4LK88sEtKu065dO6yK2Go3j2xzNjOPzqSma00ervcwZpOFA6uDca/oQP1Ofrx98G0G7czA1iTIHjeJCV//zgm3prTxFnw/piU2VkV8WiwW2Pk+rH8WqrSDgKK1M8Wx/FgEJ8KTeK1vPap4OhS6H5WVwwMng/kuIo6x/l6sa1abqvaFd+vSzKm2c+LkaA4fuZ/omNVUqNCf1q3W06L5Uny8emE49yt81RK2vAa+TWD8bhj6A7hXK7GMFSpUoF+/fly9epXdu3djZ21k3piW+LjY8uT8Y1xLzCg2rlOnTtRYvw7PCU+TvGkz18Y9iEO1JNxtKyJ3p3LpwF5Am6B3HF4bs6yHraMnmA9hbWdk4zenSU++NVmf0vV1Uh3gx1V3rvXIy+fl3gG4tRzOcUtt0n9/G7I14aZr5a680PIFtoVt46uTXwGQnh7M0WNDCQv/Hj+/EbRpvR4Xl5KF24I42lrx9cjmJGfm8tzyU9r2vgqF4j+FEjwU/zdIzQszRv+ZAHdglRDi4xLiREspT+h/pwKBgD8wCJivB5sPDNb/HgQsk1JmSylDgWCgtRDCF3CRUh7Uy7EgXxyF4o6JnT+f0zmpuLt50Hz4w4XuSylJXBuMOSGLxNoeXI/KwKpyHPb29rRo0aLEtH8+9zORaZG82uZVrA3WnNsdSdL1DNoPrcXOazsIP7GHLqdysRn+EEPWXeO60Y16uREsnNwTa2MRn5XsVFgxGvZ8DE1HwZjfwFj6Knge8WnZfLT5Im2qe/BAc/9C9/+4kUKPY5e4kJ7Jd/Wr8lGdStgVKEdubgrh4T9y4OB9nDn7FBkZIdSs8SId2u+nfr2PcHauD8E74PvO8Os4sHOD0Wu0svo1LXNZmzVrRpMmTdi9ezdXrlzB08mWn8e2Ittk5vFfjpKSVfxBfAY7O3yefZYaa9dgFxBA/OfTsMTvxd+xNsGLdpMcex0An6ou1G3njzS0Ij48hEadsshKy2Xzd2cx5WiHFDar35126b6stD9HQvjlMpc/P0II3hnciD+qTsUxJ57zK9+5eW9M/TEMrT2U/7F33uFRVG0fvmd3s5vee08IgZCEQBJ6770KiKgoICqCYsOCIKIIogKC0qUJooggHem9JpBACum997IpW+f7IwgiHX0/eXn3vq5cGc6cOec5s1l2fnuesjpmNesj3udixGBUqgKaB68goOnnSKW3i8MHoamzJZ8MDqSgsp7SGvUjjWHAgIHHF4PwMPA/gSAIbwiCcAn4EjgDBIuiOAkIA+6ce/T2MbyBlsAFwEkUxXxoECeA4/VubsCfoz1zrre5XT/+a/ud5nlZEIRIQRAii4uLH2yBBv6n0OTmcmXjOurkRnR/7U0kd8hMVXupkLroYow7uXHmdB4OTWRkF6TTunXre9btyFPmsSZmDb28etHWpS31Sg0Re9PxCLDBzl/B4pPzeHu/EYKlFc+ILVGqdDRRJjK3jxdy+R3qYpSlw/e9IHE/9P0ChnwHsnvXDfkr8/YlUKvW8vmwoFuCk3WiyPy0fMZcTcNJbsSBcH+GOt3qBqWsSSYh8WNOn2lPcspcjBXOBAV9R/t2J/D2noRcbgt50fDDENg0HOorYfjqhl2ORt0fys4/GDBgAA4ODmzbto2qqir8HC1Y+VwYacW3ptm9G4pGjfDcsB6XL+ahituLtiieILO2HF78LTqtFoC2Q3wxMm6GwsyBmKPb6PlCAIXpVRz94dqNTFdv9JhJjTGs3v5oux4AUonAGy+M4bxpV3yT13HsYhTQIEqmhb5KM3NLvonfS5GsGW1a78PBodcjz/UHo1t5sHVSGA4WD/d3YsCAgccfg/Aw8L+CPTBcFMU+oihuFUVRAzdqfAy838WCIJgD24A3RVGsulfXO7SJ92i/vVEUV4miGC6KYriDg8P9TDPwP4YoiuTN/pQ0G3Mc3T3xat7ytj6aoloqdqai8LUiKr8WnVaP6FyIkZERbdq0uef4X0d+DcC08GkAXNybjrpOS4cRjVl2aQljNxfiVKTh7a5TKVJBc/IZqrtKcLeetw+WdgJWd4PqfHhuG7SdBA8Z2nQ+rZRtl3OY2MkXP8ebVcaLVBpGRaeyKLOQp51t2Rvmj5+p8fV7pKO45AhRUWO5cKEv+flbcXIaQOtWuwgL+xknx34NcQdl6fDrBFjVBfKvNmSqmhIJzUfBPQLv74dcLmfkyJFoNBq2bduGTqejvZ89c4cFcyq5hFm74u5bBVwQBKyHDsV3317QXEYigl+ZF2e3bALA3MaY0D7e6GlDWW42qpo42g71JTmyiIi9GQAENelExxo3tpleozj9wVLg3nE9MgkhLy5EJugp3/MxZ1JKKC45wqWIQYy1LsfZ1JbvcgopUj1aHQ6NTkNsSSw/JfzE9FPTGbxjMH2290Cr1z6yzQYMGHg8MQgPA08013cOFgNngcI79RFF8Z6fyIIgGNEgOn4URfGPFDOF192nuP77j9ySOcCf07e4A3nX293v0G7AwENR/fvvpF65RI1cRusRY25LTypqdJRtTkCQS1G1ciYpooiALnYkJl8jLCwMU9O7u8Cczz/PocxDTAiegIu5C+UFNcSeyKVZR1cK5OnYLNhM8wyRT/q+wzXBkrbORnRO30HrgcOQGf1pt0MU4cJK2DgMzJ3g5WPQqNtDr1Wt1TNjRyzuNia83r3xjfYz5dX0jEzkclUNi5p68E2AJ6ZSyXV3qrWcO9eTq1dfpqY2lUa+79Ch/WmaBczHwiKwYYCaEtj/PnzXChL2Qqd3YGr09UxV/8y37I6OjgwYMIDMzEyOHz8OwKhWHkzq2ojNF7L4/lT6A40js7HBY/lC9MpL2Fk1pX5XJJlXowFo2dsLc/tAjEycObt1MyE93Gja1pmIPekkRzT8dze11yzq5bBy54NVM78bJo6N0Ld+leGSkyxcv5mdp+agUDjRpc0OVvbZiIjI5KOTqVLf63uZBuGcWZXJnrQ9zLswj2f3PkubzW14Zu8zzL0wl3P55/Cx8uGl4JfQ6O/ulmbAgIH/Th7cydaAgf9O2gIdaQjkni0IQilwANgvimLS/S6+nnlqDXBNFMWFfzq1C3gB+OL6751/at8sCMJCwJWGIPKLoijqBEGoFgShLQ2uWmOBb/+JBRr430FXWUn+53PJ8HLBytGRxm3a3danYm86moIabMY2Y+fWFCzsjFEaN3j/tWt3e/8/0Og1fHHhC9zN3RkXNA6AM9tSMJJLCB/oxU8f9KFTnJ6NXZ4lwsiRUE9rRlQcpMjCkuY9+twcSKuCve80ZK9q0h+GrQTjByva91dWn0ojpUjJ2hcb6oGIosiSzCLmp+fja6pgS0gjAsxNqKlJITtnIwUF29HparGyCqeR3zQc7HshkfxJEKlr4PwyOL0YNDXQ8jno+iFYuj6SffejRYsWZGZmcurUKby8vPDz82Na7yZkldYyd/81PO1M6RPofN9xpOZmuH89hbyPf6eJaz+iP/kU+7VrMLO2od1QPw6taUtl0Q7ijh+m67N9qCyp48iGa1jYGdO0cTu6HvRgh1kSL6XF4ewb+MjrUYX3h6iVfMgGxkfP5Nd2nTE3t8UcWNR1ES8fepl3j7/L0p5LMbp+38vqy4gtieVq8VViS2KJKYm5IU5MZCYE2gXyXMBzBDsEE2wfjJOp0x1rfRgwYODJQLjfdq8BA08S13cn+tEgRBoD50RRfO0e/TsCp4AY4A/H7Ok0iIdfAE8gCxgpimLZ9Ws+AsbTEMD+piiK+6+3hwPrARNgP/C6eJ83YHh4uBgZGflIazXw5JH/8SxS9+/hgq8LPSa8Rove/W85XxtTQtmP1zDv7Ea6kYyz21LoNr4R2w9uJCgoiKFDh9517B/ifuCryK9Y0m0J3Ty7kR1fxq4l0bQb1oiS2EV4rD7AsRbN+dL7eWxM5fw6yosds96h4+ixtBk2qmEQZRFseQ6yL0DnadB1+iO7LGWX1dJr0Qm6+juy4vmGYPj1uSV8kJTDUEdrvvJ3Q1V5iuzsDZSVn0YQ5Dg7DcLdYyyWFn+pD6LTQvQmODYPlAUNgqjHLHBs+ki2PQxqtZrvv/8epVLJK6+8gpWVFfUaHaNXnSehoIpfXmlHc3frBxpLlV5K0YoYNFnnSTeOpfuaHwCBX+ZFUJC4BmPTeiYsWY1GDb9+EYlGrWfkB+EUlMfw1MnxPFXpx6w3f3voNYiijszMlaSlL8arUKBRQj7vSD/gjLQ1v05qh7tNwy7aL4m/8Nn5zwhxCMHZ1JnY0lhylbkASAQJftZ+BNs3CIxgh2B8rXyRPUCq3YdFEIRLoiiG/+MDGzBg4G9jEB4G/mcRBEECtBNF8cy/bcvdMAgPA39Qe+kSmc8+R3SncMrRM3HpWozkN92CtGX1FC65jMzeBNPRTfnps4u4+Vtj3LSEU6dOMWXKFOzt7e84dkldCYN+G0SIYwjLeyxH1Its+TwCrVpHp/A8aj6YSXRjS2YEzkBEwk8T21K8cyVZMVeYuHQtClMzyIuCn5+FunIYugwChz3yWkVRZMKGSM6nlXL47S64WpuQU6+my8UEQi0UfG13ltzcH6iry0KhcMbNbQxurk8jl9v/daAGV6ojs6EkCdxbQ69PwevuOz//CYqLi1m1ahUuLi688MILSKVSiqtVDFt2BpVWz47JHXCzNnmgsSr2pqA8lU/tmUXUdGxMy3nzyU0sZ9uXO9Eof6Xr2JcIGzCUsvwatn15CQtbY4ZPC+WDNUM5rshkd7cfcWv04Glu6+pyiI9/l4rKCBwd+9PU72OMVvdDpdXRtuZ1jK2K6BZSR0rlNZLKk9CJDVm1LOWWtHVpe0NkBNgGYGr0aJmuHhaD8DBg4PHFEONh4IlGEASpIAivCILwmSAIHf5yevrjLDoMGPgDvVpN/sezqPVwI6+qnNC+g24RHaJOT9nPCSCC3TNNObsjFb1OpNVQLy5evEhAQMBdRQfAN5e+oV5XzwetPkAQBOLP5FOWV0NYCwnVM2aR7CbwadBkRCSM7+CDv6KW5IvnaNFnYIPoiPkV1vYFQQLjD/wt0QFwIK6QowlFvNXTH1drE0RR5L3EbPR6HSMrXyMlZQ5yuQNBgUto3+44Pt6TbxcdWedhbR/Y8myDAHl6E0w4+P8uOgAcHBwYNGgQWVlZHD16tKHNoiHNbr1ax/h1EVTfI83un7Hq7YvUzhh5+IsY7/qdjEULcWtig194KFK5J+d/+wV1fR22Lmb0mRhIWX4Nh9bEMbnvp+iksHzPg8d6FBTu5sLFAVQrr+HoM5NCiyF8F7eJxQ4OKCrTGGX5KUrLH9mTth9zI0vGB43nm67f0NGtI0qNkiF+Q3gx6EXCnML+30SHAQMGHm8MwsPAk85KoAtQCiy5HnvxB8P/HZMMGHg4Sr//HnVqKrltQzFSGBPS59bie1WHMlFnVWMzvDH5xXWkRBYR2teL5Iw4VCoVHTt2vOvYV4qvsDN1J883ex5vK29UdVou7k7D2V2B/ovJFFnq+bRrb0xMnGnkYMZ7fZtwcedWZHI5oX0HwqFZsG0CuIbCxGPg8nBF4/5KjUrL7N1xNHW24MUO3gBsLyznaFk1I8Qf8DYzo1X4DsLDfsHJacCtMRwAxYnw05gG0VGeAQO/gdfOQ8Cgh86o9U/SvHlzQkNDOXPmDElJDeFljZ0sWPZcKCnFSqZsjkJ7nzS7AIKRBNunGiNT2KALHUndytUUrlpF++F+yEw6Ul9dRdT+3QB4NrOj06jGZMSUkh9lQZ9aX/aap5GVfPmec+j0Oo7EfcG3595lXZkpnxbaMObkAt46/hY/xP/AOTNL0m09ebtGw+yA76hN+piazAm8HDyZHl49WNBlAU1smvDeyfdIKr9vKJ0BAwb+hzAIDwNPOq1FURwjiuI3QBvAXBCE7YIgKLhzilsDBh4rVGnplC5fgbR3T1JTEwnu0QcT85tpZeuTyqk+noNZa2cUgXac+jkJS3tjmnd35dy5c/j6+uLmdseSMej0OuZemIujiSOvNH8FgEv7M6hTavA89R3VYj1zhjng6zCC6notC0e1QF1ZSsKZEzTv0g3Tva/AmW8gbFxDoT3zv5/++ZvDSeRX1vP5sCCMpBKK1Ro+SkrHjyRGmOfQssUmLC2Db7+wKh92vQHL2kL6Seg2A96IgvBxD1Ws8D9Jv379cHJy4rfffqOyshKATo0dmDM0iBNJxXyy+/5pdgEUvtaYtXbG2r0LZe5NKVu4CO3OH2nRqxUSI18u7txGfU1DlfHgru4Ed3XnyuFs+ni8iygILNv38S3jiaJIWmUaPyX8xJvH3qTjT214M/JHdlXKKRFtaOXchg9af8Cm/ps4P+Y8Pw/ags/IH1GoahhecYgFo1pyMaPshngyNTLl2+7fYiYzY8qRKZTUlfzzN9OAAQP/lRiEh4EnHfkfB6IoakVRfBmIBo4C5v+WUQYMPAiiKFIwaxaCiQnZzRojiiJhA4bcOK+rVlP2SyIyJ1OsBvpy5Ug25QW1dHran9j4GJRK5T13O35L+Y340njeDn8bMyMzKovruHIkG9eaeGR5McwbJRDs8wGRGZVM7uZHiIc1l/buBFEkrHwjpB6BAQth0Dcgk991ngflWn4Va89k8ExrD8K8bAF49+o5lFo975qfJqzleoyM/pIhq74SjnwKS1pC9GZo/XJDatwu00Bu9rdt+icxMjJi5MiR6HQ6tm7dik7XEA/xTGtPXunsy6bzWaw5/WBpdq36+SAxl+PSaQp5NpYUL1yIb8FRTG06o66rIXL39ht9O470w7OZLUm/a+mmbMbvFhlcuvI7O1J28OGpD+m5tSdDdgxh7oW5XC28QKCihik+QRx+6iB7h+9jfuf5PBvwLCEOISik1138XJpDi2fhwkqGeKr5dHAgh68V8t62q+j1Ik5mTizpsYTy+nKmHptKvfbRanwYMGDgycIgPAw86UQKgtD3zw2iKH4KrAO8/xWLDBh4QCq3b6c2IgKrN6YQd/YkTTt0wdLeEQBRL1K2JRFRpcNuTFNqrlcY9wmxx6OZDWfOnMHV1RUfH587j62qZMnlJYQ6htLfpyE71tlfkxC0ajyv/shXwyWY+fXnzDUjgtwseb2zJ3URPxFzYCdNLfKx1Bc37HK0mvCPrFWvF/notxisTIx4v29DtqlNCds5UG3JMyaRDAr9EpnsT0JCq4Jzy2BxCzi1AJr2hykXod98MLt7PMu/jb29PYMHDyYnJ4cjR47caH+/b1P6Bjrz+b5rHIwruO84EhMZNkMaIa+TY9xjAnl2VlQu/YZgOxUSoyZE7tlJbWVFQ1+phNbPuyOz1eOc8RSCKPDFrneYeWYmZ/POEuoUysdtP2ZZ6CCmOxTxduAQXu60CSdzl3sb0X0GSGRw+BOeb+fN27382X45lzl7G6qnB9oFMq/TPK4WX+XjMx8/0G6OAQMGnmwMwsPAE40ois+Jovj7Hdq/F0XR6E7XGDDwOKAtLaXwy68wCQ8j3ViGRlVPq8FP3ThfdSgTVUoFVoN8MXIy48yvyYgidBzZmPj4eMrLy+nUqdMdayKIosiSy0uoVFcyvc10BEEgN7GMtCuleGYc4PvOCpL9zDGuHkp1vYaFnucxWhLMlQ2fo9FBeP/BMDkCvO++m/Kw/BKZzeWsCqb3D8DaVE5s2mo+z7PCR1rKZ63GIZU2VCVHr4erWxuK/x34EJyDGmJLRqwFW99/zJ7/JEFBQYSHh3P27FkSExMBkEgEFj3dguZuVkz9OZqYnMr7jmMSZI9xoB1eNCMnsAXFHi5YbZmLlWUoOo2a7RsX8VXEV4zcPZKeu7qxzuNTZBoTmhW3IcldwpKAmRwbdYwvO88nmFjUxVvwcH+egKZzEQTp/Rdi6QLt34D4HZB1gde7+zGugzdrz6Tz3dEUAHp69eTN0DfZn7GfFVdW/J3bZsCAgScAg/Aw8MQjCIKjIAizBUH4VRCErdePnf5tuwwYuBeF875AX1uLw4yPiDqwB58WYTh4egMN9Tqqj2VjGu6EWStnsuJLSb1cTHg/LyzsjDl9+jT29vY0adLktnF1eh1fXPyCX5J+YUzTMTSxbYKoFzm29AyK+nJO2xVyOqyUnkbPciyhnHeFH/GP+hyNc0su1wbg0yIch6EzwczuH1trqVLFvP0JtPaxZXhLV1LTFjE3o5xKwYalzVthLLsuOipzYXVX2P4SKCzhuW0wdhe4hf5jtvx/0adPH5ydnfntt9+oqKgAwEQuZfUL4diayZmwIYK8irr7jmMzpBGCVELXJs8SYWNCbmNnGl/ZgVTejPzTl9gRtQVLuSVTWkxh2bDFjHq9HaEFg5CIMnacW4uAyLWE6eTmbsLT8yX8/WfRkGn8AenwBpg7w4HpCMDMAc0Y3tKNBYeS2Hg+E4DxQeMZ0mgIy64sY1/avke4WwYMGHhSMAgPA08011PoRlz/5w/ApuvHF+6QXteAgccC5anTVO3Zg/3LL5OSnU5dVSWthowAQFNQQ/nWROSeFtgM9UOvFTn5cxJWDia07OVFSkoKhYWFdOjQAclfivfVaet46/hbbE7YzAvNXmBaq2kAnP/qVyrVZtTVRHG89zUC6qzYHe1Ia1kKE7o2gTevEuf0InU1dbS+bsc/ybz9CdSotHw2JJDU1C/Ym3Gco0JvXvFwJNT6ekyHSgk/PQ2laQ3V0F85CX49/9VMVX+HP+I99Ho9W7duRavVAuBoYczaF1tRp9Yxfn0ESpX2jtfr9DriSuPYkPUju33OIC0GhY8/McbG5LrmYat2QCJKmK55ljV91vBKyCu0cGyBh789A0e3JrCgM8es8jiw7wXy87fi4/06fo0+ePiq4XIz6DETciMhdhsSicD8Ec3pGeDIxztj2XUlD0EQmNVuFmFOYcw8M5Pooui/efcMGDDw34pBeBh40lkADBVFcZYoirtEUdwpiuIsYCiw8N6XGjDw/4++ro6C2bOR+/hgM3ECkbt/w9nPH/eAIPS1Gko2xiMopNg9F4AgkxB1OIvKojo6jfZHaiTh1KlTWFpaEhx8a+ankroSxv8+nhM5J/iw9Ye82+pdJIKE3O17iU2QYlaXRVGnn6kVNWiKnkUvM+HrN15A2vNj9BZuRO7ZjotfE9wCAv/R9V5IK+XXSzlM7OSDWPk1ydkb2SCbhrexnGk+rtdvih5+ewUK42DkOggZ/cgV0R8n7OzsGDJkCLm5uRw+fPhGexNnC5Y+G0pykZLXN19Gq9Pflnmq85bOjN4zmkWXFnHA6gyFdpUMUgzD1SeAUnN3WvpokcqDSTh5lPKC/FvmbdLWhee8RiDVG7ExPpdGvtPw9X3z4UXHH4Q8A07BcHg2aOoxkkr4bkworbxteXtLNMcTizCSGrGo6yKczJyYemzqjYrmf0XU6KiLLaFy/4MF2RswYOC/i//+/7kNGLg3lqIoRv21URTFaMDi9u4GDPy7lCxdiiYnB+fZn5AadYmKwvyG2A4RSn9ORFehwu65ZkgtFVSV1nFpXwa+LR3wCrQjKyuLrKws2rdvj0x2M4VsWmUaz+17jpSKFL7p+g1jAsaAqprydTOJXn0MtcIad++N7LAyoYXsFaJqPZkxqDmejtYAJF04Q2VhAa2GPPXoD6d3QK3VM2NHLG7WxvRy20Bu7o8ctJxLrs6cr5t6YCq9/hF1ZDYk7IE+c6Fxr39s/nuh16upqoohJ+dH4uOncf5CX6KiX6S07PQ/GiQdGBhI69atOX/+PNeuXQOgXluPi0MFYzpKOZZYTL9Va27JPHWt9Bo9PHvwRacvODbqGL8N20HzF7sj1Uvo2ngMOr2eOHkZPvaNAAkHZ0xH1N+sEaLT1eMUuJ7WBU246lhCcpzX31uERAp95kBlFlxYDoCxkZTvXwinibMFr266xKXMMmyMbfiux3do9BqmHJmCUt2Q8lfU6KmLK6H05wTyPrtA6aZr1EQWoKt5sKKKBgwY+O/h8UhubsDAfw5BEAQbURTL/9Joi0F4G3jMqE9IoHTdeqxGPIVpq1ZETH8LGxdX/Fq1pepgJqqkcqyH+aHwskQURU7/kgxCQ0A5wOnTpzE1NSU09GbMQ2RBJFOPTUUmkbGu7zqC7IOgNJW6hQPJ+t2KrLBPsHco5dsWpjjUBnI+oRFdm9jxTGsPoCEQPWLXNmxc3GgU3uYfXe/3p9NILlIys+tlyku2Uuc6g1/yfXje1Y6ONte/F4j68WatkDav/qPz/4EoitTVZVJVdYXKqitUVV1FqYxDr1cDYGRki6Vlc6qr44mOfgELi0C8PF/GwaEvEsmjfYyqdCpyqnPIrMqk0K0QLOGnX3/iku8lsrRZN/oZ2fYnObMzHex6MamdH21c2uBu7n6bADRyMMWyhydVBzLpO3Ayu3/9mrABLTAqCyGn6jIJ779H0/lfohfruXL1ZcrLzzO511tEXknku8SNtAzpiL17wz2vqKggNjaW+Ph4tFot5ubmmJmZ3fG3ubk5pqamSH27gn9fOLUQWj4PZvZYGhuxYXxrRq44x7h1EWx5pR0BLr4s7LqQVw+9yjv73uSzurfQJFQiqnRITGWYtnDAJNgeha81gvS/043OgAEDd8cgPAw86SwCDgqC8C7wR7neMGD+9XMGDDwWiDod+TM/RmptjdO775Idd5XCtBR6TZxCfVwZ1cezMWvtjHkbF0S9yJntKaRfKaHtUF8sbI0pLCwkKSmJbt26IZc31NTYl7aPGWdm4G7hzrIey3C3cIeyNNRLB5K9X0+8zwhEmZzKvkpSk7NwKv8ChUzK/Kea33iwzYq5QlF6Kr1efh2J5AEyHT0g2WW1LDmSTBv3fLzl6/Hy/YCXitrhKNcxs9F1F6vMs7B7Kvh0gf5fodKrOZ1zGkdTRxrbNL4ZdP6QqNUlVFVdvS4yGoSGVtuQRUoiMcHSIgh3t+extAzB0rIFxsauCIKAXq+ioGAnmVmriY2biomxJ56eE3BxGXEz69af59GpyVHmkFWVRWZVZsPv6obfBTUFiNzcOXG2d6ZtTVvCCsIY2G0g3jbeeFp64mLqztPLo8lIa82AoV0wkd/9NbDo7E7dlWLMk7UEdezF5X0/E9j9TWKPxHL+WgIWH0yjYGQWldXRNAv4CheXYYy4cJHNdpf5fsUOOvXyJzE1jqysBuHj5uaGjY0NSqWSsrIylErljViUv2JqaoqboinPqA6SvuYlkv1fvSFQPuvuyFv7cnj++/P80Ksp/pk2TC4azRLxR76tWsXbzac0iI1GVghSw/dBBgw8yRiEh4EnGlEUVwmCkAd8BgQCIhAPzBFFcfe/apwBA3+ifPNP1MfE4Pr110itrYlYthBTK2saN2lL2eo45J4WWA9uhE6n59jGBBLPFxDczZ3Q3g1uMqdPn0Yul9O6dWtEUWRN7BoWX15MmFMYi7stxkphBWXpaFcMJGuvnny5P2UOoTTuasP09Pfw0o0nrlDPkmdCcLK8+RAdsXsbZtY2NOvU7R9bqyiKzNp5FUQ1T/mswN//E37R9CKhpoAfgn2wlEmhLB1+fhZsvGDUBhIr0/jg1AekVDSkaZUKUnytfWlm24wAuwCa2TWjiU0TTI1Mb5lLp6ulqjruusBoEBn19TnXz0owN/fH0aFPg8iwaoGZqd9ddzEkEgWurqNwcRlBSclhMjJXkZg0i+S0RWDVjVxpYzKUxWRVZZFVnUV+TT568aaLk6XcEi9LL0KdQvGy8MLT0hMvSy88LDywUlhx7do1tmzZgleBF/1C+9247rOhQYxedZ6lx1J4t8/tmcr+QJBKsHnKn6Jl0YT79yHXOY70y5swsW5FGWfIPnQERX49gfMX4uwyCJVKRdfgZ9mREM1pu90Y/zoGWeM6unfvTlBQELa2tre9bmq1GqVSSU1NDUql8pbjmpoarqnbEVB2gkORvhRobxZ77KA3Zr86gKd3RjBEnoKjsYJQTUt2WB5FEKzoVdgL8xrzW3ZTLCws/lHXPgMGDPz7GISHgSceURT3AHv+bTsMGLgbmvx8ihctwqxTJywH9KcoI42MK5fpNOIFKn5KvhFMrtWLHFgdS2ZMKW0G+xDWzxtBECgrKyM2NpZ27dphpDBi9rnZbEveRn+f/nzW4TPkUjmUZ6L/fiDZ+7TUVMu50msy5go5OyzWoM91JTGtMQOaOzM4xPWGXYVpKWRejaLTmBeRyf9+ZfI/+D0mk6OJpYz030PHlu9TZTWQbyKSGOZoTW97q4Zq5D+NBlGP7pmfWJeyjaXRS7FWWPN1l6+RClLiS+O5VnaNU7mn2Jm6EwABAS8LV3zMbPCQ63GkBDtdFiaShod/Y2M3LC1DcHe/vpthEYhUanovUwHQ6rXkKfMadi2q/7R7UaXERGNCV4sqArW/YauH9DpTdBI/mjs0Z1CjQXhaNIgLTwtPrI2t7zlPQEAAbdq04cKFC3h5eREY2BDI39bXjmEt3Vh1Mo3hoW74OpjfdQy5hwXmHdxQns6l38g32bJsOnaeCuoqFVwJ9qXLxWuUz9zJqV41JKakoNVq6aS250CTa5TlltHBtAudOgXe8YFfEAQUCgUKhQI7u7ukU67pibikBRPt0im2nEN5QgG1qlrqFDqau8qYXyDjqDSQlzyUdFSZU6wpYUfVDoqTi3GquzXL+YwZM26JVTJgwMB/P4Z3tIEnGkEQvgTSRFFc8Zf2twBnURTf/3csM2CgAVEUKfhsDqJej/OsjxEEgYhd2zAyNsWrvDGaSiUOLzdHI5Wwb3E0+WmVdBnThKDObjfGOHv2LBKJhJDwEKYcncKZ3DNMDJ7IlJZTkAgSqMhCXDOAnN811JbK2NxlKp4qOeZd6zhZcBqzktnYmMmZMyToFtsidm1DbmJCSK9+fzX7kalQljPjtwu4mVcxtc8gnFwG8dLlZCxkEj5r7A46Lfw6HkpTyB6xmo8i5hBVFEUvr17MbDsTG2MbAHp49qC+Po/KqmgySs4TW3SZpMpMslSZXC7O5pjuD5cdY9xM7WlmH0SgRQsCbAPwsG12mwgQRZHS+lIyKjPIrMoksyqT9Kp0Mqsyya7ORqu/6WJkZmSGp4UnQfbBeFoOwMvSCysjEUnlYTqUHKQD8TjZ++Ll2RNz87vvUNyJXr16kZOTw65du3Bxcbmx6/Bh/6Ycji9k1q44fhjf+p47AZa9vaiLK0E4X0v7kSM59dNPyC2CqKmOJTI0lPAzZ7DLyaHla5MIatECa5OXOLetH9c8t+IU6Y2NsxmtB9654v3dEHV6VCkV1F4tQ6oahVXeGmR5x3Fq1hOT5g4Y+1nTUSahSUoJ49ZFsLfak40TWjNGGMPY/WOJkkexvONy7AV7lEoldXV1BtFhwMATiOFdbeBJZyAQdIf2xcBVwCA8DPyrVB86hPLoURynTUPu7k5lUSGJ507RK3Q8mrRqrIf7obVSsGvBZSqKaunzUhB+YY43r6+uJioqiiaBTZhyZgrJ5cnMajeLEf7X621UZCOu60/eURU1eTIWtxhBW6cgpBodK1WfYKEcTX6FjLUvBmNjdnNXo6KwgKTzZwgbOBSFqdk/tt55O36ktM6H1aOtcHfry6rsIi5X1bKsmRf2chnsfx8x5TDbO0zgy6j5SAQJczvOZaDvQPT6ejIyV1JZEUll1RU0mlIAJBI5IRaBdHIbhZVlCJaWzanFgoTyBK6VXiO+NJ74smscyjp+ww4bhQ3WxtYYSYxQaVUU1xVTq629cd5IYoSXpRe+Vr509+iOl6VXw86FpSd2xnZ3efAfQn19HlnZa8nL20JBwQ7s7Lri5fkK1tatHshtSCaTMWLECFauXMnWrVsZP348RkZGOFoY805vfz7ZHc++mAIGNHe56xgSuRTroX6UrotDbp6EhUcN1XnFIJhSZmKGMGE8LmvWYn7gIG79+iGRy3mG1qy0ukiP4Fwi9oC1kwn+rZzvaauo06NKraT2ajH18aXoa7UICikmTV9En3MAO7MfEUa82pD16jod/OxZ8kwLXvvxMpM2XWb12HC+6/4dz+x9hg8jPmTzgM14eHjc9z4ZMGDgvxPhn0wLaMDA44YgCHGiKN6x8MC9zj0uhIeHi5GRkf+2GQb+Q+iqq0kbMBCpnR0+W39BkMk4un4lpadTaWc/CLM2zgjt3di1OJr6Gg39JgXj0fRWv/tDhw5x9uxZIhpFUCwUs6DrAjq6dWw4WZmDuK4/hSdqKb9mxNpm/bHp9yxWlyqo7ZDKOuVO6rMmMSrcgy+ean7LuIfXLCfmyAEmfrcGc9t/pkp5UtYpBq4opoOPmnUTR5NZp6LrxUQ62JizMdgHIXItJfvfZXbjMI6rC2nt3Jo5HebgYu6CTqfi6tWXKSs/jalpo+sCo0FkmJs3RSK5KZr+cI3KqMq4sYORUZVBWmUaJXUld7XP3MgcHysfgu2Dae3cmkD7QJxMnR4pzkCjqSAnZxPZORvQaMqwtGyJl9dEHOx7PVBl8ISEBH7++WdatWrFgAEDGtal0zP4uzOU1ag5/E4XzBW3f3dYXFxMbGwsCYmn6FBrik1xGNF+0WSei0Cv80VTG82AqR/jmJ5M4WdzMOvSGfclS6itLqffL73w0Nswon4RRRnVDH27Jc6+VreML+pEVGkV1F0toS6u5KbYaGaHSbA9xv42CDIJxG5r2Lka/B2EPn+bnT9fzOKD7TEMCnHlm6dbEFcaw/gD4wm0C2R179UN7oGPiCAIl0RRDH/kAQwYMPAfwyA8DDzRCIIQAYwRRTH5L+2NgZ8e9w8ng/B4ssmf9QkVW7fiveVnTIKDqauu4uep0+jh9AzGnjbQ15s9y64iijDo9RAcvSxvub6+vp6vF3xNtiKbFM8UlvZcSlPbpg0nK3Nh/QBKzldRHKXgYLNu7Gw7gokqc2rralnSaBpi3odYyq35/c3OtzzE1lZVsvq1cTTt2IU+r079R9aq06l49fuvOZLRnINvdqSRozWjrqQSVVXLidZNccs9w5HtzzLb0ZEaiZQ3w97k2YBnkQgS9Ho1V2Neo7T0GAFNv8DVdeQtrlEZVTfFRUZlBjnVOWjFm65RlnJLvK288bZs+Pnz7oVOryOhLOFGzMi10mukV6XfCAq3NbYlwLYheL2vT1/8bfwfct315Of/SmbW99TXZ2Nq6ouX50ScnYcgkSjuee2BAwc4d+4cI0aMICioYeP2UmY5Ty0/y8udfZnePwCAyspKYmNjiYmJoaCgABOTSlq2PIZcNMPv/BcY2ZlT10bNtoWLARETUwtenDKJ6gMHKFu/AUUTf2yeeYYf4zexslEmn+d0pSy/H1q9hJ6NM7EN9EUwdUFTYET9tTL0NVoEuRTjZraYBjs0iA2jv4gpUYQ1vaAiG16/BIrb41JWnEjli/0JPNfWk8+GBHEg8wDTTkxjkO8gPu/4+SMHlhuEhwEDjy8G4WHgiUYQhH7At8Ac4NL15nDgQ+BNURT3/Vu2PQgG4fHkUnPhIlkvvIDtuHE4vf8eABd+/hmrCDPMre1goB/7NlxDYSpjyNSWWDvdHgS94rcVFFwpIKlJEgsHL8TZ7LprTFUerB9I+eVyCs4bkxzSkbd9h7CyexAJ29KIa36IM3XmVJW04KeJbWnre+uOxplfNnF+28+8uHA5dm7/jNvL6SvLeeFnV4Y2N2HBM73ZnFfK24nZzPd35ylJLl/sGMVOUzkB1v7M7TwfPxs/APR6DbGxr1NccggX72n8lJ9LcnkymVWZKDXKG+PLJfIbWaL+EBc+Vj54WXphrbB+qIfYWk0tSeVJt4iR1IpUjKRGLO2xlFbOrR56/Xq9luLi38nMXEW1Mg653BFPjxdxcxuDTHbnWqY6nY61q1ZRXFzMmEaNsNTp0NfV82mBOXvrLFlcfRLLglRUlZVItTpMpRLMpHpQFSKowUhvjtQmGOMWL1J/9WfiaqJI82iNXpNCQIEWn8LMW+ZTS+CNSVIcKuGDvW5cCnwDmaaGlleWYFJfCjIFMmcvjAOaYhYehHGzpij8/ZFaWd3RfrIuwNre0OV96Db9jl3m7b/GyhNpvNHdj7d7N2HFlRUsjV7KGy3fYGLziQ99n8EgPAwYeJwxCA8DTzyCIAQB07gZ6xELfC2KYsy/Z9WDYRAeTyb6ujrShg4FEXx37kBiYoK6ro7Yj7ZjL3dH28ObAzvTsHY0ZdDrLTC3ufWb8dSKVL668BXWF6zBEj567SMs5NcfXqsLYP0AquJKyD1pRl1Ia0Z6DGdyD39cL1ZSqixnmc9iajOm8GIHb2YNutXbUF1fx+rXxuEWEMTQaTP+kfXW1qYz4fuNXCpswcn3eoOxlM4Xr9HMzISP7HKYcfQNCgSRCf6jmdTmfYykRg33Sa8lLv4tior2YeH6Mh9cPYBSo6SFQ4sGgWF1cwfDxcwF6T9YZ+SvFNcW89LBl8hT5rGk+xLaubZ7pHFEUaS8/CyZmSspKz+DVGqOu9sYPDzGoVA0xO7olDVUHz5E1e49FF25woHevbCuqKDrseNIRJEKuSkv93wfD2URn0ZvRGFujrG1FRITCdXqa2Asw9alC3JzBwQTE3Q1AehV5iga57E/4hglFTVIZUaMn/wqMjMzas6eo3jxYkxCQjja2oRFtheZWzEe7+xQztVokUpEOnsWYKFKR5WcTH1SEvrKyhtrkrm4oPBvjLF/ExRNmqDwb4zCxwfByAi2vgiJv8Mbl8HS9Y7348PtMfwckc3HA5sxroM3H57+kCOZR9g7fC+Opo63XXM/DMLDgIHHF4PwMGDgMcYgPJ5MCr/8irK1a/Fcvx6ztg3VwJOWHcY0S0Gxh55zsTqcfCwZMDkEYzOjG9eV15ezLHoZW5O2ElQZhF+pH2NfHIuvt29Dh+pCWD+AmqRCso9bIw1oxgsBz2Nrb8XXbRpzZG08RxtvIlPbE1HtwLFpXbE0NrrFtsv7dnJsw2qe+ewrXP0D/vZaRVFk5/GpvHWgFxM6uPLRwFDGxaZzvLSM5+WH2Jm0GXetlrltZtAi+Nk/XacjPv49Cgp3gN0IPog5iq2xLd/1+I7GNo3/tl2PQmldKRMPTSSzMpNvun1DJ/dOf2u8qupYMjNXUVS0H0GQYi+2xfyYEerdkYj19Ri5uWExYACX7Wy5kJ6OXK9HK4qYW1tTZhvIpkQdC0eFMDzUnYrKS0RHj8fIyJrQlpswMbm5U6Utr6dw0SUUPlYYDXRkw7tz0NSnETZwIl2fHwJA5e495L3/PjKPxrzRKwVjwZT1LX9F08ia3cuuotPqGfR6C5y8LRFFEW1REarERFRJSdQnJjUcp6eDRtMwqZERikaNMG3ihJPJL2hdusHQ5cgcHW7bfdLpRaZsvsz+2AIWjAxhYAsHMiozaGL7cBnB/sAgPAwYeHwxCA8DBu6BIAhraciMVSSKYtD1tk+AiUDx9W7T/3DZEgThQ2ACoAPeEEXxwPX2MGA9YALsA6aKD/DmMwiPJ4+6mBgynh6N9YgRuHw6G4CaqELKtySRqUkhSumJV5A9fV8OwkjR8A2+Rqdhc8JmVl5ZSa22lhGeI+AM+Df2Z9SoUQ0DK4tg/QDq0/PJPOaAzMWNbwa/y4HsOnZP7sClVfHkVufys+/vlGYN5YvhwYxu7XmLbTqtljVTJ2Jp78Do2V/+I+stKNzNK5viSK0M5PQHfTiprGVS1An8qtdQVpPOyKpq3u3wGaZ/CkAWRT3XEqaTn7+VCrOuzE6IINg+mMXdF2NvYv+P2PWoVNRX8PKhl0mpSGFBlwV083z0woqiXk9dVBTFhzaTrz9ATWgdyMCizBMv71cxadqP3377jfT0dCQSCaIoMnjwYEJCQgCBYcvPkltex7YJJqQnvYpC4UTLFhsxNr4941X1mVwqd6dhO7oJWfXJ7Fu8HAR4fe06ZEYyaiIKKP1+K7VnVlJla8rrY+uYE/w+vds/T2VxHbsWR1FXrWHAa81xa2Jz5/Wo1ajSM1Al/SFIElElJmHrnIRt0xrSDzigxeH6rog/xk38G479/NAYyRm/PoLzaWWseC6MXs2c7jjHg2AQHgYMPL4YhIcBA/dAEITOgBL44S/CQymK4td/6dsM+AloDbgChwF/URR1giBcBKYC52kQHktEUdx/v/kNwuNWFnw6DXVdIVJdLUZqJYq6Goxr6zGpVWNcryMtwI93vt7xb5t5V0S1mvQRI9FVVuK7ZzdSCwvUeUoKv7tMSW0Op6uUNO3Yge4vBCCVNjxoHss+xoLIBWRVZ9HBrQPTwqcRczyGmJgYJk+e3FDnQVkMGwaizsoh44Qbgok5cR8u4M2jeXzUP4AuxqYcWhvPAb8fyK4fgYuFFbtf74hUcus3z/GnjrH/uwUMfe9jGoW1/tvr1Wiq+GH/eD47O453ezdmTEdvuhxcCKVbsJMa8WluJp3DJ0OPj2/eI1EkMWkWubk/kiUNZmFGKn28+zCnwxyMZcb3mO3/jyp1Fa8eepVrpdeY33k+vb17P9T1quRkKnfvoWrPHjR5eQjGxlh0747JwM6UeySRk/8TWm0l1dUu5GQH0qbNS/j7+7Ny5UosLS156aWXkMlkxORUMvi70/TwPM1LYVG0bLERhcLhjnOKepHi5VfQltXh9HY4P8/7ioLks7g16k1Xhw5oC2uR+1gis8qk8LPpZNrpWT/Ugp/eOo8gCNRUqNi5OJqq4jr6vhyEd/MHF4C6gkwkazqilTpTUtOH+qRkVEnJiHV1DR0EAbmnJ9LG/uyqMiZSaseUl/rRql0QguT+GcD+ikF4GDDw+GKo42HgfwJBEDqIonjmfm1/RRTFk4IgeD/gNEOAn0VRVAHpgiCkAK0FQcgALEVRPHd93h+AocB9hcf/Gl++Pwp5XQXGdbUY1zUICpMaHWY1esxroW8t/PUxRA9Um4KxGgQxmZhrsQQH3Kl0y79PyerVqJKScF+2DKmFBboaDaUb46nX1HKu+Dghg9+m08gmCBKBxLJEvoz4kosFF/G18mV5z+V0dOtIXl4e0dHRtG/fvkF01JTAhkFocrPIuuAHaJEv/JaZO3Jo7WPLi+282PzpOcpN86mxCKa8RGDFmGa3iQ5RFInYtQ07d098W/4zz2ypqQv5Ob4zdmYSBoaaM2zveKi6QlurZnwZcxgb/37Q7WYciSiKJCfPITf3R+J1nqzKTuHl5q8wucXkhkKIjwmWcktW9VrFa0de472T76HRaxjgO+Ce12gKCqjau5fK3XtQJSSARIJZ+/Y4TH0D8x49kZo31Eqx0unIyA4kNXU9np6JBDQ7jF7MQy5fzqBBg9iyZQsnT56ke/fuOCsu0tXjDEey2jNlwGt3FR0AgkTA5qnGFC6JonJPGsMmTWLltARy085SYtSURs+FYhxohyCEYORojn7KFJ7fVcW+gKUM6DcFM2sFw95pyZ5vr7B/RQw9xgXct87HH0idvaDnTIz2T8PluU+hSUPBTE12NvVJSaj+cNVKSqJXVha9RBH9uQ0UnjyDs+Odd1cMGDDw34lBeBj4X+FbIPQB2h6UKYIgjAUigXdEUSwH3GjY0fiDnOttmuvHf22/I4IgvAy8DODp6Xm3bk8U3375EbaRexl0VXWjTSdAlRkozaDaQkKRs4xaUzkqUxNUxmZo5KbopGaYWbvzxvtz2TS6JcEx9Zxa8wPBX/8zbkL/JKrkZEpWrMRywAAsundD1OgoWR+Hprye04Xb8GjdjU6jmlBaX8p3Ud+xPXk7VgorpreZzkj/kcgkMkRR5MCBA5iamtK5c2eoKYUNg9EVZpAdHYy2qgyPtWt56WI1elFkwcgQUiKLUBarueh/hpzsgQwIdqaN7+11OdKjIynJyqDva2890rfMf6Wq6iq/X7lEcsWrPN+9gtH7RqLU1tPVdQzfRixFcAyEYSvh+lyiKJKSOp/snPVcVtmyuaSczzvOZXCjwX/blv8E5nJzVvRcwZSjU/jw1Ido9VqG+A25pY+uqoqqAweo2r2H2ogIEEWMmzfHafp0LPv3Q2Z/665BVVUV27ZtIzMzkxYtxtC1Sy/Kyw+QlDyHy1FjCG25mZCQEE6dOoW9fRElpbMY2zKMK2Vd+WRPFtsnuSOR3D17l5GzGeYdXFGeyqU2uohA23bEFO/lZP4pmjXrcSP2wqJ7d1wXfI3u3Xcom72UdLsAfFr3wMRczpA3W7J32VUOrY1HXacjqPNd/yu7lfBxcHEVHJwJfj0RpEbIvbyQe3lBr143uulra8mNjiMmIp5Ag+gwYOCJwyA8DDzRCILQDmgPOAiC8PafTlkCj5oCZznwGSBe/70AGA/c6RNfvEf7HRFFcRWwChpcrR7Rxv8a5nwwnubRF2icoSeyhTH5Xo3RSMzwC2zH08+9/MDjFHq5ExadQr3mKvuuHqd/867/OaMfElGnI2/GDKTm5jh9NL3B7eXHa6izqzlfcpoaaQ1PvzKYNbFrWH11NWq9muebPc/LzV/GSnEzVWlCQgKZmZkMGDAAY7EefhiCviiVnPjWqLIz8VixnC1KS86l5TD/qWBcrYz5fudZis1ywbQnYpnAB/2a3tHGiF3bMLezp2mHzn9/vaKO+IQZbEsbgJ3XDnbkn0dq7IuJyyQWJ85FMDKGZ34G+c2K6Gnpi8jKWs3FWlP2KuWs7rWccOfH21vG1MiUpT2WMvXoVGaemYlGr2G41yCUx09QtWc3yuMnEDUa5F5e2E+ejNXAAci9ve84VlJSEr/99htarZZhw4Zdj+MAF5fhmJn7ExU1lstRY+jWbT2pqfHs33+Rrt3CCAtdxXRFBe9uvcIvkdm3xe38gagTqbmYT01kAQCCkYSun7xA8vvR1FUlcvyHX+n+4tM3+tv0609BehyK79dSOX4KhXM/x2nwcOQmMga9HsLvq2M5sTkRdZ2W0D5e979ZUiPo/Rn8NBoi10GbO7+3JaameLRvhUf7h09ZbMCAgccfg/Aw8KQjB8xp+Fv/c7L8KmDEowwoimLhH8eCIKwG9lz/Zw7w56IH7kDe9Xb3O7T/zzPvg+dpfzoSx1I41tWB11acfOSxfMNGojw4D5fMXEo3RKL+sv3fqn78T1K2cSP1V67i+vXXSG1sKNuejDqhnJiacrKrz+LYrx3D944gV5lLN49uvBP+Dl6Wtz7MabVaDh48iIODA6EhwbB5BGJhArkZPaiNi8FtwdcUNG7O/CWn6dHUkVHhHlw6kYq2QkJSUA5XM1rwendfPGxvrweSn5xITnwsXZ6fgFRmdNv5hyUnZxNbk/SU2W1BqiglyONpjtGX0+qDSAuuwKiNt6RWTU//joyMpVyoMeKc1osf+y+9bf2PKyYyE5Z0/Yapu15i9rnZ5Hwxlz5n65Da22P9zGisBg3COCjornVEdDodR44c4ezZszg5OTFy5Ejs/7ITYmkRRMuWPxAVNZYrV0fg7WNCbEwvSoq7IZOZ81SoGVsispj/ewJ9Ap2xMbv1774+qZyKPWloi2pR+FphEupIxa/J1J4voO9rk9jx5QyuHLlMk3bNcWtyM5NZwGvTOFSSSsGJEyje+wgxPgmnd99FJpfR79VgjqyL59xvqajqtLQd4nv/Win+fcG7ExyfB81HgYn1I91zAwYM/Pfy+DjNGjDwH0AUxROiKM4G2oqiOPtPPwv/Ws38QREE4c8pY4bRUBcEYBcwWhAEhSAIPkBj4KIoivlAtSAIbYWGT+axwM5HX9WTwZfvPUW3g5FYVcPB/o3+lugAGDZqLFneRjRO12Jv5Mj8I9/+Q5b+PdTZ2RR/sxjzrl2xHNCfisNZ1EUUkqzSkSM7iFYhsEi7BXMjc77v/T1Lui+540P3xYsXKS8vp0/v3kj3TEVMP0V+YW+U56/i9NFHmPTpy9u/XMFULmXeU8HodSJndidSZJZFibYNTpYKXu3S6I42RuzahsLMjOY9+vzt9dbW5bHs8kIOarORGWn5uNNSTksGMcmkEr+Li6DZUGh2030qI3MVaemLiKiRkiJvx6b+P/5XiA5RFKmPj6dw/pdk9+zH5FlRtEoVWNNFw+mvR9P4+DGcp0/HJDj4rg/kFRUVrFu3jrNnzxIeHs5LL710m+j4A0uLIFxcRqDVVmJnV0FoaBMiIi6RlpaGIAh8NjSIqnotXx5IuHGNpqiWknWxlKyNRdTpsXu+GfYTgzEPd8asjTPKM7l4ODbCxrUpek0quxatp16pvGXenh8soTDEnf1hAuXrN5A14SW0ZWVIpRJ6jg+kWSdXLv+eycmfkxD199mgFQTo8znUlcOpBQ93ww0YMPBEYBAeBp5oBEHYJQjCLuDbP47//PMA1/8EnAOaCIKQIwjCBOBLQRBiBEG4CnQD3gIQRTEO+AWIB34HJouiqLs+1CTgeyAFSOV/PLB8yes96bMvHo0RHO4fwtsL9tz/ogeg0NsFMxVE6bfjc0RKWmXaPzLuoyKKIvkzP0aQyXD+ZBZVF/KpOZJFjkZPku8lqvMyiA2o5aPOs9gycAttXNrccZyamhpOnDiBn58ffjlbEaN/Jj+rM5XHorB/bRK2zz3L0mMpXM2p5PNhwThaGLNn/ylkNSYUNlWQVKDig35NMVPcvsldlpdDcsQ5WvQegNzk9t2Qh6GgpoBx+55mT4UeTXUgs8PWsLHCGQuJyPSYz0FuDv1vJoNLy/ye1NT5XK6VUmszkpU9V93iWvY4os7JoWTFCtIGDiJ9+FOUbdqEcVAQ3l8tYPn7Z+jt1Zslpb+y5tr6e45z7do1VqxYQXFxMSNGjGDgwIEYGd19tykjYxnZ2WuwsemARKLAyno5traW7Nixg7q6Opo6WzKuvTc/R2RzOamYil2pFH5zCVVGFVb9fXB+KwyTQLsbIsiqnw8SCznl25Lo88pEEOtR1co4sHIJf852KcjlTHhnPfkuCpYOkKCMukT6UyOoi4lBIhHoOqYJLXt5Ensil8Mb4tHp9Pe+gS4h0GIMXFgB5RkPetsNGDDwhGBwtTLwpNMOyKYhze0F7hxvcVdEUXzmDs1r7tH/c+DzO7RHcrNy+v8sR3fsJm3HHHqdryLDVSCqdSumz9vwj40vOrVAZZSFS0Y67bynMn3fbDaPXvevZUSq+PVXas+fx3n2bOqKpVTuSKZUq2dHwHFMTp/DyNaEBW+sxdHs3tWZjx8/jlqtpreHGv3h+eRdC6E6NgX716dg/9prXM2p4NujKQxt4Ur/YBeqa5UkHS5DaVXLpUJHWniYMiTkzkHAkbu3I5XJaNl30N9a66HMQ8w6MwOVtgZpyXD8TftQaWvDheRc9qsOYJR3CZ5aA+YNmZcS01eRkz6fK7VSbDzf4d3gl+/vqvMvoVepqNy1i8rtv1EXFQWASXgYzp98gkWf3shsbgZBz+88H6MzRiy+vBi1Ts2kkEm3rEur1XLo0CEuXLiAi4sLI0eObMhOdhdEUSQtbSEZmctwdh5KQNP51NQkcjlqLI38DhMZ0Ybff/+dYcOGMbW7H7sic/hw3SVWiaZYtnHBspcXUvPbXQ4lxjJshvhRujEey3wHXPzDyE+KIS2qEVcP7yekV/8bfY3c3Jje7ysmn5rKzDEw53cNmWOexXnWx1iPGEG74Y2Qm8q4sDMNTb2O3i8FIjO6Rwhd9xkQ9xsc/gRGrn/wF8KAAQP/9Rh2PAw86TgD02l46F8M9AJKrrtgnfhXLfsfY/4Hr1Gy/iM6nK8itomMy206Mv2Lf050AEz+YD45HlL809SkS4oYeqU125K2/aNzPCiawkKK5n+JaevWSEN7UvZjAtU6PYu8NpOfdQzzOhnPTv7kvqKjuLiYyMhIwhu7YH9kGjmRflTHFuM0/UMcJk9GpdXz1pZoHMwVzB7coG1X/fILpiorSvxdKFaqmTWo2R2zHSnLy4g/eZSgrj0xs360DEK1mlo+PvMxbx9/GzuJmvZiNyqK2zCxR2PmpOXzlKyMFpELockACHoKgOiUpeSkzye+3oigwG94qfkrj6Xo0FVUULJ8OSnde1Aw82P0ymoc3n4bvyOH8d60CZvRT98iOgBkEhmfd/icIY2GsPzKchZfXnxjB6GsrIw1a9Zw4cIF2rZty4QJE+4pOjSaKuLi3iQjcxmurqNpFvAVEokMC4tAQltuxNy8EG/vVK5cucKVwxHULL/K5HoZiaKOo92csRnW+I6i4w9MAu0wCban6kgWPUY8B2iQSE04vmEDJVkZt/S16dGT2WbPUK0QeWe0GmnL5uTPmEn+rE8QNRrC+3nTebQ/6VdK2PPdVdT12rvfWEtXaP96g/jIvnjf18GAAQNPDgbhYeCJRhRFnSiKv4ui+ALQlgZXp+OCILz+L5v2P8XnH4ynecRxghM0nG1tgd24uXw0b9V/ZK4iXwdslXBc8itt9EGsOL2K4tri+1/4DyKKIgWzP0XUarGcMp3iNXGo9SKfO69D71RGy3RbGrduj1dQyH3HOnjwIHIjGZ2SviLrpAs1mfW4zJuH7dixAHz5eyKpxTV8NbI5VqZGXM6LRnfJmhLHKg6lqxje0o2WnncWFZf370Kv0xM2cNgjrTO2JJaRu0eyI2UHIzyaM95aw/G0fnRr4sCv6ho0eh1fJ85HkClg4EIQBM4nLqA4cyEpamO6hP1Ib5/+95/o/xl1Ti4Fn88luXsPihcvwTiwGZ7r1+Ozaxf2L0/EyO3eKWSlEimfdviUkf4jWRO7hq8ivyImJoYVK1ZQXl7O6NGj6du3LzLZ3Z0OyssvcOFif4qKf6eR77s0bTIH4U87dxYWzQhtuRFPzzgszCrZf/p36nQqRo5tTgc/OxaeS6e4WnXX8f/AenAjBJkU4XwtPi06oK2/itSkJXsWf4lGVX9LX7833+fjOH/KJLV8OLASy/EvULFlC1nPj0VTWEhwV3d6vhhAXnIFuxZHU1+jufvE7d8Ac2c4MB0MhYwNGPifwSA8DDzxXA/2Hg5sAiYDS4Dt/65V/zvM/3AMnY6fw7VA5FBPNyb8cJHuQ/+eW8+9KHNqhk4Al+xUJILIq8kjmXfhi//YfHeiev9+lEePYv7y2xRuL0QQReY7/UjbbqE8U9QGRJHOz42/7zgpKSkkJyfTURtByUEp9aUS3L5ZhPWwoQCcTS1h7Zl0xrbzolNjBzQ6Deu37sBMY02yswNSQeC9vndOn6uqreXKwX00btMeG2fXO/a5Gzq9ju9jvuf5fc+j1qtZ2uUzOgmXOFcyiWqVSIc2buwprmRd3RFMcs5D3y8QzZ2IT/mamtxlZGpNGdh+L82dwh5q3v80dXFx5L79Dql9+lD+009Y9uqFz86deK5ahVnbNg+1KyMRJMxsO5Nn/J9hY/xGZhyfgYOjA6+++ipNm975NQHQ69WkpH7F5ahnkUgUhIdtxdt70m1z62o0aI/K8Tj3Nk39IlALas64JGASYMenQ4Ko1+iYt//afe2UWsixHuCDOr2KTuFDQdCjUyspy6vg2IbVt/QVjIxo+9lS3jpsTHJtBl+H5eO6eBGq5GTShz9FbUQETdq60PflIIqzq9mx8DI1lXcRPwrzBpernAiIM/x3bMDA/woG4WHgiUYQhA3AWRoKBc4WRbGVKIqfiaKY+y+b9j/BgncG0WNfFAo1HOzflDe+O/wfn/PdmUspcBPwT6vjrDqG1mJTopMvcTz7+H98bgBteTkFcz5HDGlHaaY7JiKsdTvIlOdfZYR5bxJOHSdswFCsne5d9Vmn03Hg9/1YC7U47k1AXWuM+4rlWPbuDUBFrZppW6/iY292ozbH6qg1eKS0oMhNz+msCiZ3a4SzlfEdx796eD/qulpaDX7qodYXXRTN8/ufZ/HlxfTw6sGvA3/FtGwL1RoHdiX6MqC5C8sqKugqlNH18kLw64nYfBSJSZ+Qn7WcS7VGdAjbjJul90PN+59CFEWUp06T+eI4Mp4agfLECWxfeAG/w4dwnf8Fxk38H3ns0tJSrKOt8a/wJ80yjeyAbCytLO/av6YmjchLI8nMXIGr6yjatN6NpWXzW+3V6qk+nUvBV5HUXMjHLrANHTssx7dRIimp2UREHKGRgzkTO/my/XIuF9PL7munabgTCl8r1KfLCGrbF239Vey8BhJz5ADXTh+/pa+RiwsDpyzg2aM6Ducc5UenNLx/2YLUwoLMceMp+2EjPiH2DJwcQmVJPb99fZmq0ro7T9xiDDgFNcR6aOrv3MeAAQNPFAbhYeBJ53nAH5gKnBUEoer6T7UgCFX/sm1PNEtf7Uzv/SlUm8PR3mFM++q3/7e5i31scC+F82anEIA30ibw2bk51Ghq/uNzF86dR029hCrPZ7BFygm/FD549QNa2IdwbMMqzGxsaTN05H3HiboUSXFJKcEXL4HWFM916zDv0AEAjU7Paz9eprhaxcJRIZjKZaRWpHL+cALGGgvOKKS4WZvwUiffO45dX6Mkcs9veAQ2x7lR4wdaV3Z1Nu8cf4fn9z9PQU0BczvO5avOX1FbfpSKigscL34LrU5E1sSK/Ho1q1IXIAgSdAO+JDbuTXJzN3G52B4yx7F3/QE2bdrE8ePHSU1Npb7+//+hU9RoqNy5k/Shw8ieOBF1WhqO097F7/gxnN6bhpHzvYXh/bhy5QorV65EWa1kXr95vNz8ZX5L+Y2ZZ2ai0+tu6SuKIjm5m7kYMYj6+lyaBy8noOlcpFLTW/rUXSul8JvLVO5JQ+5hjtPUUGyG+GHlGMygQbOwti7lwMFj5OfHMqW7H27WJszcEYvmPpmmBEHAenhjRJ1IiEV7BImE8twrOPq25/dl35AWFXFLf/POnXmhxUt0vapn+ZXlHJel4r31F8w7d6Zw7lzy3nsfN28ThkxtQX2Nht++vkx5wR3eexIp9J4DFVkNWa4MGDDwxGPIamXgiUYURYO4/n9my6ZVqPatoPvlOlI8Ba6GdeCjeavvf+E/SLFnM+A0HgXpZMozCbXwQlYu8G3Ut3zQ+oP/2LzVx45TdPA0Vd0+ppHUmOxmasaObXCpij95lIKUJPq+9tZ909bW19VxdP8eHEpKcS+uwOvHzRj/yT3n093xnE0t5euRIbT0tEGn1zH75GeE5A4nw0NGSpmSZc+GYnyXzEKnNq+nrqqKLg/g7lWlrmL11dX8eO1HZBIZr4W8xguBL2BqZIpGU0FyylxqJZ3ZHWdMjxAXfqmqZmn9McyzT5PZ4VOSL09BEJJISw2jJrcZjo4WOPs6k5+fT0pKyo15HBwccHd3v/Hj4OCARPLPv311SiUVv2yl7Icf0BYUoGjsh8vcuVgNHIAg//sFJ9VqNfv27SM6OhovLy+eeuopLC0t8ccfI4kRS6OXotFrmNtxLjKJDLW6lGsJ0ykpOYytbSeaBcxHoXC6ZUxNQQ0Ve9JQpVQgczDB7sVAjJvY3OJ+ZWUVyIgRL7Bu3Q5++WUVL730Jh8PasYrGy+x4WzGXUXoHxjZm2DZ05Oq3zPo0HYUp8/+hEQ+CTuPQnYvmMfwDz/BI/Dm7ovjG28wddwl8vNi+OjUdDz6byTgu28pXbmS4iXfokpOxv3bJQx9O5RdS6LZ/vVlBr/RAgdPi1snbtQNGvdpqOvR8jkwu3MdEwMGDDwZCKIhqMuAgceW8PBwMTIy8t8244GZ895LBMadp2mqjqhgBaWtBvD6e7dlF/6Pk5OeRtrYAZTLIblRF0bajCFGLGZ64Gw29d9Ec4fm9x/kISkuziLxqReoa/4uAeaWqJqZ02hsSwDU9XWsffMVLGztGDNnAcJ9Hqj3L5jOhWo5fc+dJHTlWuTe3jfObTyXwcydcbzS2ZcP+zdUmd58bTP7f7tIi+xBbHTS4u9iyZaX294xJiEnIY4ts94nbMBQuo596a42aPQatiZuZfmV5VSqKhniN4QpLabgZHbzofhawkfk52/l15zVHE2qxambKy6lSYy59itpCjd8Ak5ialpBdGYL4rUWfDr0UzycPG5cX19fT25uLjk5OTd+6uoa3HLkcjlubm63iBEzM7P7vxB3W09hEeUbf6B8yy/oq6sxbd0auwnjMevc+R/LqFVUVMTWrVspLi6mc+fOdOnSBan0VvG3NnYtiy4topdXL94LHEBy4kdotZU0avQeHu4v3BJAri2to+poNrWXCxGMZVj19MSsrQuC9O5/P2fP7uPgwYv4+ycwZMhcpvxSwsX0Mo680/Wubnd/IOr0FH0XjbZKxY6kb1GLLvR8aTKXdn9NVUkxI2fMwaVxkxv9NYVFRI0Zwvsj6pDa2fHzoC3Ym9ijPHmS3HengSDg9vVXaP3D2Lk4CnWtlgFTQnD1s7514uJEWNYOwsfDgK/5uwiCcEkUxfC/PZABAwb+cQzCw4CBx5j/JuFxdMduyr//kKapOk51sMV/4Af/0SDy+7F9bBuaXKzim1FhjC8aiLuVK294LUNwV7Nl4BaMJHcv1vawnMw5SdyHn+Jp9iYh1lYIfla4jg9GuJ7C9vTPG7nw2xae+ewrXP0D7jlWzvdzWJupwrswj2c++Qwj15uB32dSShi79iJd/R1YNTYcqUQgX5nPyG1PM/LSh0Q7mHC8tobdUzoS5HZ7IT6tRsPG915Hq1HzwtdLkRub3NZHFEVO5JxgQeQCMqoyaOPchndbvUtT26Y3ztdFR1MRe4yMnOXUK3qzO8YaD0sdMq0SQQATkyoaBV5FUKiJpCl7KtJY2nclAc7NEeRyBOmdd2JEUaSsrOwWIVJQUHAjHa2trS3u7u40a9aMxo0b3/ZQfydUKSmUrl1H5e7doNNh0ac3duMnYBL8z5XVEUWRqKgo9u3bh0KhYPjw4TRqdOcq8QAbYtfx9aWFBBprmezpRcvgxZib33yg15bVU30sm5pLhSARMG/rgmV3DySm9/+bFUWRjRtXk5GRTZs25/AKXMrgFWn0bubEd2NC73u9OqeaoqXRVNlUsf/SckztX2DQ6+05sOwT6pRVjPp4Ho7eN3dPlGfOcGr6RGa+KKeJUyBr+6xFIVWgzsoi5/U3UCUl4TD1DeQjxrL726soy+rp+2owXoF2t0689x2IXAevnQeHR4+rAYPwMGDgccYgPAwYeIz5bxIeq8e3pePZSk62t+aVtef+bXNYNftZOv10ma3drfHL8aF9wKsk6KuZFvQBU0On8lLw3b/tf1ByqnP4Lvo78g9fYEj+W4TbWCN1t8B1UgiCrOFb6cqiAta9PQn/Nh3o//q79xyv6odv2Hr8Knlurkwe9yK2vn43zqWX1DB06RmcLBVsm9QeC2MjRFFk8pHJqCMs8czuyQZrNaPC3Zk3/M47Ome3bubcr5sZ9sEsfFu2uu38tdJrfB35NRcLLuJt6c074e/Qxb0LgiCgq6igctcuKrZuRZWccofRHxzR0gqbIYOwffppFH5+9+yrVqvJz88nOzubnJwcMjMzqaurw9TUlKCgIEJCQnB1db1l10IURWojIihbsxbliRMIxsZYDx+O7bgXkXt43GO2h0elUrFnzx5iYmLw8fFh+PDhWFhY3LW/UplIbNybHChM49dyOR1c2/NNt8UYy4zRVqioPpZFTWQhAOZtXLDo6oHU8uFcwKqrq1m69FuM5EW0Co/gQs0ilp4o5seX2tDB7/6uTBX70lCezOVU2W8U1lVj7/0CfSb6sv2L6eg0GkbPno+tq/uN/sVLlrDnyAoWDpcyuNFg5nSYgyAI6Gtryf94FlV79mDeswc20z9l77oUyvJq6DU+EL+wP9WwqSmBJS3Bqz2M2fJQ6/0rBuFhwMDji0F4GDDwGPPfIjwWvD2IvvtTSPCTYjNh3r+60/EHh/buwPLTD0i3k5Dk2Yae+j542rrxnstOEm2PsX3IdrwsvR5p7LL6MlZdXcWWxC04VTvxQvRrtLW2Rm5njMsboUhMbobP7V44j7ToSMYvWomF3d0f+srXfEfMup852rMnncNb0n3gkBvnKus0DFt2hvIaNTsnd8TTriFGZG/aXj4+NpvxV+eyz0YgS9RybFpX7M0Vt41fmpPNxvdfx691ewZOfe+Wc4U1hXwb9S27UndhpbBiUsgkRjYZiUyQURsRQcXWX6k+cABRrUbn60u0ox359o6Ymim4UmaG3sUJlZ2eL7LmkuEsQy61R249lNXRPxJo5c/Tvk9RXlvHxeIKYssqcc3NpnN0BDKdFn3LlriNeQbLPn2QPECMhU6nIzW1oWheQkICOp0Oe3t7QkJCCA4MRHLxIqVr1lIfE4PUxgab557FZsyY2wr9/RPk5+ezdetWysvL6datGx07drxrXIoo6snO2UBKypcYGVnRLOBLTpSV8MnZT2jt0IpP1W+hj6gAwKyVMxbdPJBZ3f46PihxcXFs3boVH99E3L2y+eT8xxjJ5Oyf2gmF7N47RXq1jsJvLqNR17M9dgHIA2jc9hnaDrHhl9kfIpHJGP3JfKwcG9zuRJ2OrPET+ME4il/a63k77G3GBY27vm6R8h9+oPDLr5B7euL41SIO7VdSkFZJ1+ea0qzDn1I5n17UkOFq7C7w7fLIazcIDwMGHl8MwsOAgceY/wbhMe+D5+lxIBKlKZzt1J4ZX6z5t026wd6nQ/GIrePrke0Yc6IE/1bvkq5X82GLGQQ7BPJ97+8fyr+/VlPLhvgNrI9dT72untGKF3A7EUh7M2OMTSU4v936lofF7PgYfpn9Ie1HPUu7p56567ily5dQuHg5Rwf0ROXkzutvvoX8+kO4Vqdn/IZIzqaUsOmlNrT1bXBRSa1IZdzv42ifNxhNWgjbzNV81D+AiZ1vDyIW9Xq2zP6Q0uxMXly4/EaV8lpNLevj1rM+bj1avZZnA55lYvOJmCq1VP62g4qtW1FnZCCxsMCoZw/Om1uQpFFjY5NPWJiMb2KeJbFESXFbOw6Xv0uRXSbmJo3xDV7GswdeQxAEPuryA5sKa9lTXIEAdJTpcS7OJ1MvxTvyIgNOH8WtuJB6cwvo1QvXUSOwbNoUY2Pj+742dXV1xMfHEx0VRXZODogiToWFNKquJmTAAByGD0difO+4hrshiiK1ah1KlRalSkuNSoubtQl25gpEUSQiIoIDBw5gamrKU089hfef4nD+ikpVSHz8e5SVn8bevicBTecil9uhq1az9cAPzFMuoVmdH187foJTd39kNo9m81/Ztm0bcXGxhIadJEPlwtcXn2FanyZM7nbvXSaA+uRyStbEUmVXyf7IFchMe9Fu+CB8mgv8MvtDFObmjP5kPua2DX+P2uJiUocNY2EfDec86/iux3d0du98Y7yaixfJfettxLo6HD/7nFOpzmTHl9FxZGNCelzfhdLUw3etwMQKXj7RkPXqETAIDwMGHl8MwsOAgceYx114zPnwJdqcOYNtBRweEMr78378t026hY0zhhP+6zU29LXDvcCRluYj8LL3YIZ1FJdcVvNp+08Z1vj+Vbs1Og1bk7ay8upKyurL6OnRk4FVY6g8XE1LUwkyKThPbYWR883gZ71ex6YP3qS+Rsm4RSswkt/+7bUoihQv/JrS1WspDHPjeOOODB06lBYtWtzo8+nueNaeSeeL4cGMbu0JQERBBFOPTsVctGLoxWn8YKnBxErBgTc7I5fd/o371cO/c2j1d/R+9Q2Cu/VGFEV2p+1m8aXFFNUV0durN1NbvoFdXC7lW7dSffgIaDSYhIZi8dRwrpiacjoiAoVCjm+jSFxcSqi1WsfLm+IxCrZmhu0q3BWnsRE8CO64i3dOzeRQSRVuXlOJrQULqYSBJhIcL59DlZ+LjY0Ner2eUpWaZGsH5NW1dLgcQccrkUj1elI9vMjx9abG1wdTS0tMTU0xMzPD1NQUCwsLAgICsLe3R1tWRvmPmynfvJlKjZqsNm1IdvOkVKVFlCpw826Em3cjzK3tqdXoqbkuIv4QEsr668fqPx2rdDfa/vrxaCQV6BXggKcqk7qsWBo39mPYsGH3DHovKj5AQsJH6HT1+Df+CFfX0ehrNFSfzKHmXD6iTs/55inMUS8myD6I5T2XYyG/u6vWw1BXV8eyZcuQywWCgrewNHoUsSUBHH6nK+42986sBlCxJw3l6Vwy5QlcSNqDkfkoBkzpjYl5GVs/m4GlvQOjZs3D1LIhnqjm/AWSXx7P7NesybfU8WP/H2lkfTPWRVNQQM7UqdRfuYrNhJeINu9J6pVSWg3wptVAnwahGfMrbJsAQ5Y2ZLl6BAzCw4CBxxeD8DBg4DHmcRYeR3fspnjDRzS/puH3vt689c3+f9uk2/j2i9l02PIT0V5Sst3a0utSDo6dPyJPr+WL1quo1GWza+gu7Ezs7ni9XtTze/rvfBv1LTnKHMKdwpka8iZlu/XIr1XhrZAgmGhwfK0tRg63Psj98bA/8M33adKu021jqzMyKJg9m5pz5zFrrOLXjhMws7Zn4sSJN9x1fr6YxQfbYxjXwZtZgwIB2JO2h5lnZuJl4cUUzWw2nyrkqKmG78eG07OZ023zKMvLWP/2JBy9fRn58Vzya/KZfW42Z/POEmwfzDTfiXicTKbi11/RZGcjtbLCaugQrEeOJF8mY/fu3ZSWlhIS0hxXtz3U1p6jZchmXlqXz7VaCSPa/EZn6WGcqk3x6Xue92JPsKNUQGfkgpvCiNFWCiwvnSM/LRVbW1t69uxJQEDAjd0MtVpNbW0tKRVVHErJQnfoEB1OHcW5rIQqC0vSwsKpa9yIcpkR1yqlZKhMUYkyBKSgrEerF6g3taDOyIQ63YN9nhlJBcwVMswUMsyv/9x+LMXc+Ga7qVzGoauZ7LxSgEqU4m4h4aWuTRke5o6l8e1B31ptDcnJc8jL/wULiyACmy3CGA+Up3JQns1D1OgxbeGIRQ9PjOxNOJJ5hHdPvktTm6as6LUCK8XtyQEehZSUFDZt2kR4eFPU8u/54MRkOvnZsmbc/V2ZRL1I2U8J1MWUEFV3nNTSaxjbPMfTH3VBWZbG9rmzsHX3YNTHc1GYNoiv4mXLSFj3LTOmWGFqbsNPA37C2tj6xph6tZrCOZ9T8csvmLZvT2rrV7gWraR5d3c6jmiMIADf94TKHHjjMsgfPpOZQXgYMPD4YhAeBgw8xjzOwmPZK53odqKEc63MGb8x4v4X/EscGhqCbZqahU91ZuD5eNydJ+Lj5MPH8nyiG39BT6+efNn5y1uuEUWRs3lnWXx5MdfKruFv48+boW8SZtWaE99cwrtKi7kEFD56HF7qfCOQ/A/qa5SsffMVbF3defqTL25xGdKrVJSuXEXp6lUIgg6H4HKu9n+X48nVjBs3Di+vhriTC2mlPLfmAm197Vj3YiukEoHvY75nSdQSWjm34vMWX7JlbiyrzOoIa2TLD+Nb39E1afeiL0i9dIHn5i/hSPVZFl5aiIjIDOPhtDyWi/L4cdBqMW3dGuuRI7Ho3QuVXs/BgweJiorCxsaGgQMHIgh7yExbTEt9LxSXj2FSX4haIkWjAI0gI9OxF8cFR1LlTlTJ5Qzza4MuJYerMdcwMTGha9euhIWFIZPdv3xUfKWSM/sPYbxjOy2vXEIQIcrJn71e7bjm1AhTTS2iQopUCsZScHGwxdPVCQdr81tEhLEMygrzyUlPJj8rHRk6PFwcCGsRQlBQEObm5g/0N6TT6Th37hxHjx7F2NwSy8Bu7Euu5mpOJaZyKUNauPFcW08CXRvEQlXVVWLj3qKuLhMvr1fxdppEzdlilKfzEDU6TJo7YNnDEyPHW8XqiewTvHX8LRpZN2JVr1XYGP8zcSl79uwhMjKSp0f3ZN35bfyS0JNlo93p3yLknteJooio0VOyNhZ1VhUnCrdSotFi5/0cIz9sTV5CNDu+moOznz8jpn+KkbExok5H9sSXic6NYPazMlo4tWRFrxW3ZZEr37qVwk8/Q+rgQPGQaURdk9O0vQvdnm2CJDcCEvZAl/dA8fC7PwbhYcDA44tBeBgw8BjzuAqPr6YNo+/eBNI8JdQ/N5Wnn3v53zbprvw6rR+BuzNYNsQZr0InusSWYNX9Y0pFHcvbniFKuYVlPZbRyb1hVyK2JJZvLn3DhYILuJm7MbnFZAb4DqAst4aYJZdpJAjoNbXY9LbDqu/tmaEAjv/wPZf27eS5ed/g5HPT1UR5+gwFn36KJisLSx8NTq001A6Yy7eHM/Dz8+Ppp58GILusliFLz2BtasRvr3XATCEw5/wctiVvY4DvAGa1/oS938SyubiUK0Y6fn+zE42dbn9AS710gR1ffkbjkQP4zfwikYWRtHVqwwdxvqhXb0Rqa4vVsKFYjxiBwscHURSJi4tj//791NbW0q5dO7p27Up1xUmKD75Ao1wRo7oarkibkOJii61YjaxWhoW2Htf6Ihw1ZbfZUGlkQ7WFGxVmLpSbOlNm6oLSLoD+bQZjY3S7CNHrRS5nlbMzOo99MfkIxUX0z7lIr8wL2NdUUmppRUVIKF3fmkKBXE5kZCSJiYmIooivry9hYWE0bdr0tlS7SqWS2NhYrly5Qn5+PhKJBD8/P0JCQvD398fI6M6pajMzM9m7dy9FRUU0bdqUIUOGYGLSkIb4ak4Fm85nsutKHvUaPS09rOjrl4q39EvMTGwI8PsSoxg3qk/lIqp0mATbY9nTEyOnu3+Lfzb3LG8cewMPCw9W916NvcnDF9QTRZE6vYhSq6NKp6O8rp6jP6xHp9fjMaAty7YmotEbMWy4D2VGHlTr9FRrdQ0/fzpW6vQoJAKeooSvzlZjp9RwPGsdSqkXsuYDsX3GB6O4KLLWLMGxWTCD352JpYkxurIy0ocN50SAnsXtK3i6ydPMaDvjNjvrrl4l542p6MrLqR0+hXP5vjRq6UCv8YFIjR69eKRBeBgw8PhiEB4GDDzGPI7CY/70F+h88CIaGZzu3JbpX677t026J/M/eY9+23ZxMlBKuUN7Wl+OwMTvDXxdGvMpSnJbL0erV/FNt29YHbOaQ5mHsFHY8ErIK4z0H4lcKif9XB4V25JxkEmor0zHbUo7TAIb33G+srwcNrw7mcAuPej9yhtAQ6G1wi/mUb3/d+R2CpyDczFr2waGLmfH8UvExMQwefJkbG1tqa7X8NTysxRWqdgxuQOOVvDuiXc5nXuaicETeb3l65zZlsKeYxn8YqHm+XZezB5ye00KdV0ta96ZRLxHBec98pBJZLwfMIXQ5SepOXkSq2HDcJ718Y3g68rKSvbu3UtSUhIuLi4MHjwYFwdbNBe/Q3diLsYqLRq3cD6yGMQmh47oJbeLBrlejYuqGPf6QtxURbirCvFQFeGqKsKtvhDX+kJM9fUA/OI2BLOBX9HfuSGlakJBNTuj89h9JY/cijqMjST0CHBiSIgr7eRKsl55hURza6QyKf7xMWhlMujZC7/xL6Lx9iYqKopLly5RVVWFmZkZLVu2JCwsDJs7ZLMqLCzk6tWrXL16lerqaoyNjQkMDCQkJAQPDw8EQUCpVHLo0CGuXLmClZUV/fr1o0mTJnfcVaqs1fDzhTg2nI0nr9oKC7maoS72DMwXcFOLGAfaYdnTC7nLg7kNXci/wOtHX8fZzJnPuywnX2dO5Z/EQNUdREK1TkeVVo/y+rH2Lx/tzpWlDI4+RaKzF9fs7amKlNDb9xjpAT2ok3tjIZViIZNgIZPeODaXSqnT6ynVaNGV1zPpYAlSVR3HM9egNe7M6ZYtOBlkQmDiZfof206yd1MO9RmDtbGC1mmJTJk7k6Vj3DnlkUfHxq/T2ecp7I1k2Mll2BnJsJfLUFRUkPvmW9RGRKDrMoSTYnfcgxzp90owRgpDcLkBA08aBuFhwMBjzOMmPBZ8Oo1mJ/biUiRysH9z3pv/9/Lt/3+g1+s5PbA50iId3w3tTqeEZNrEqzHrMRslIqvCizlX/ykAJjITXgh8gReavYC53LxhB+DHBEyuFiNDRF14Eq/5E5E7O991vt/mzybnWizjv1mFqbkF5Zt/onjxYkS1CrtgFXb+FUj6zII2k8jMzmbdunW0b9+e3r17o9OLvPxDJMeTitkwrjX+bnqmHJlCUnkSM9rOYIT/CNKiivll1VU222mwtzFm5+QOWNwhxmDz+nl8X7GNYhsVXdy78KHdGOqmfYKmoADn6R9iPXp0Q60FvZ6IiAiOHDmCKIp069aNNmEtkF79CfHUAoSqXCosjTnaaj7vappRR4PgMBXreSZ/P6PaP8XhtCukXkrEUm2Gt6cHvbt3x8vdDclfH9JFEerKKT6+AIeL3xFp0Yx5zT6nNFlGRl41UolAp8b2DGnhSq9mzpgrZNTFxpE9cSJIpXh+vxpFkybsi7xC8vof6HT6GKaqemRBQTiOfR6z3r1Jy8oiMjKS5ORkRFGkUaNGhIeH4+/vf9suiF6vJz09nStXrnDt2jU0Gg02NjbY29uTmZmJVqulffv2dO7c+UaWsTtRULCLxKSPGwLmS6axN8qJkzoNOqCDhw3Pd/GlZ4AjsntUHG+4PSIptSouVNbwe/Z5riR+glZqRaXjh+hlN+OQ5ILQIBBkEiylUsyvHzcIBimWMinmUgmWMul1IdEgKDLOnibh4nkGjXqa7y8Vc/haOfO6LKdfx+8wM7t/pit1npLilVdRqso5lL0J0XQYAePbo29sQdaxA1T++gOa5q3IHTqWEq2eltu2MGDrRl57w4tS03wqHd9DYxx4y5gmEgF7icC47T/Sbf8uiryakug2FrWvCy++FYq52cPVMAGD8DBg4HHGIDwMGHiMedyEx6anWxJ2pZ5DvVx549sj/7Y5D8yeN7rT6GA+X430pEmOHd0uR1PX/E0auQUwV1+Lz/AMRGkl44PG33Bt0dSoSVgSjU2lCmW9EnnF73gt+QyppeVd50mPvsT2ebPo/Nx4grz8yP/kE1Tx1zDzt8O5cTzyxs1g+GpwDCAvL48NGzZgZmbGxIkTMTEx4Yv9Caw4kcqnQwJp31TLpCOTqFRVsqDLAjq5d6KyuI7Ncy+y2bSeMqnIzikd8HO81cVKq9ey5ORXbEjfjLGgYGbnT+gYJ1Iw82OkFha4LV6MaWhLoOGb/927d5OTk0OjRo0Y2K83Nhl74eQCqMqhzs6F31y8+NrxU/J0CqzFMioEW5roy9lx7nlqgl9lZ6ENxXnFaE20PDvkWZo2aXrXNLiiKJJZWsuxxCIKL/zClOpF1MqMeaXpbNyd2jErxBN7i5upZGsuXCTntdeQWlnhuXYN8j+lrK3R6vjuWhrZv25nyPEDuBfmI7Wzw+bpUVg/PZpaYwWXL1/m8uXLVFdXY25uTmhoKKGhoVhbW99mm0ql4syZM5w/fx61Wg2Ai4sL4eHhNGvW7IZ71S33WltNQuLHFBbuwpwgnCInICuzwbiJDfXtnNmeW85PF7PIr6zH2dKY0a09eKa1J06WxtdfK5G4mjouVCi5UFnD+YoaSjVaAOyMZATJsklJnY2l3JKvuq/By8IZS5kUxV1qhdwPrVbL6tWrUSqVjHx+AgOWXaCRVRLvtt5MaOgmzM3uvIv3Z+pTyilZG0uJKo9TRftR2Ixh5PQO2DibcXHnr5zavJ7g7r3p9fLrIIpkT5pESeRZZr3nRqleycwua5DKnSnRaClVaynVaCm5/tvz5DFGr/4OldyEc+Ev89o3z6Mwvn9M0F8xCA8DBh5fDMLDgIHHmMdJeHw7uTs9j+QT2cKY53+O+rfNeSi+/uwd+v20l32tZNRZd8arOIKgeGNMes5BI4FvmhizbkKbG/0rk8ooXB+PqV6kuDAJe7MY3BbMR6K4e0E3nVbLD9OmoNdq6GPnTvWWrchsrXAKU2Jhm4PQ8U3o+iHI5BQVFbFu3Trkcjnjxo3D2tqabZdyeGfrFZ5r68ngNnW8eexNFDIFS3sspZldM7QaHdu/usxP5eVESTWseC6MvkG37rwkliUy88xMrpVdo1GpNUuf+wHZmp8p2/ADJuFhuC9ahMzBAZ1Ox8mTJzl16hTGxsb0692DIO1VhFMLoDIb3MKJbNqLqWofUiVNcCUPlWhEKfaE6GrZHPkKx+hArMoFjUxDhn0Gi8cuxt701ngEvV4kuUjJxfRSLqSXcTG9jKJqFQDNXCx5oXE9gxPfw6gqk1m+r5EY+AILAjzxMlFQffQYuW++iZGHB55r12DkdHvGLoCU2npmJGRTc+4sz588RNDVywhSKZa9e2Hz3PPImweTkpLCpUuXSE5OBqBx48aEh4fj5+eHVCqltraWI0eOcOnSJczNzencuTP19fVcvXqVkpISpFIpTZs2JTQ0FF9fXwRBoLwigvi4d6hXFWCfNQzbxH4Y+zW4VCm8bopTrU7P0YQiNl3I4mRSMRIBfH2sMfK0INFEpFbf8BnsaSynjbUZba3MaWNtRiMTBYIgEFcSx4SDE3A1d2VD3w1/O9VuQUEBq1atomnTplS7hPPZ3mu8EfYL4c7XCA398YHER210EWU/J5JVk0BEVRz23k8z8oNWyE1knP55Ixd+20Jo/yF0HfsSuooK0ocNp8BG4MORKuxM7fmx/4+Yy28P7NerdVTuPk/R/I/Q15TR6OBB5G533128GwbhYcDA44tBeBgw8BjzuAiPL98fRe+9MeQ5C1zrPIB3Pv7q3zbpocjLyiJzXF9q6kTW9e9DeFIWPSOvUdJyCo09mrNQX8fQl1rSyc+egr1paM7kodaLlMftwr21Bc4zZiBI7+1vfmnvTo7/sJpWJUoc8oux6eKHg90ppPbuMGwleLUDoLS0lHXrGuJixo0bh52dHZcyy3lm1XnCvGx4ulsRs8/PwsvCi2U9l+Fq3lDZ+cRPiWw+l8kBUw2vdW3Ee32b3phbo9OwKmYV31/9HhMUhF0y4fVB76NY/yO1kZHYPP88Tu9NQzAyorq6ml9//ZXMzExCgprR37UCxYVvoTIL3MIpbP8u76vd+L1Cjw3lhInnOSl0R6uW0qi8lm9yvuGAMgC9zJg693p+53dW9l1JuHM4Or1IfF4VF9JLuZheRkRGGeW1GgCcLY1p42tLax9b2vna4etw/cGzvhLxt1cQEvezw6k3HzZ5l1dUtfSc9gZmAQF4rFp536rjoijye0klM1Ny0Wdl827ESUJPHEasrsa4WTNsnnsOywH9qayt5fLly0RFRaFUKrG0tMTFxYXMzExUKhVt2rSha9euGF+PexFFkby8PK5cuUJMTAx1dXW4uDjj51eByFrkKntcrkzE2j4My15eKLxvTYFbqdFysbKGC5U1XKioIbqwCjFLiTS3FkGjx8JSQY+WLkxq500T67vHf5zPP8+kw5MIdQxlec/lyKUP7370Z06dOsWRI0cYMnQYHx6vpKK2js/az0EhVRHachPm5v73HaP6RDaV+zNIrLxIbJ2axm0G0O+VYBDg2IZVRO3fTdunRtNh1HPUXo4ic+xY0gaHMKNpHO1c2/Ft92+RXi8QqCmupeZ8PjWXihDrtUisRWSmuThMGoVg9PBxHgbhYcDA44tBeBgw8BjzOAiPue+Po9Px80h1cLxXO6bPW/uv2vOoHHq5I+4nS/nk2Ua0zLCmUVk0vmkWyHt9gSCBV2W1fCmzwFMtUqTWoD/3Dd5j+2H36qv3raBdEXOVH+Z8hFV1DZ0UZri2LMFYHw8tn4e+826kBK2oqGDdunWo1WrGjRuHo6MjuRV1DPnuNKYKKU91S+T7uGW0dm7Nom6LsJQ3fHOeHFHIhvUx/Gyppp2fHevHtUYqabApriSOGWdmkFKRQm+XHjj+lE4LBx98z0ehq6rC5dPZWA0eDEBaWhrbtm1DrVLxfIgcz5SN1wVHGNUd3+NraRBr80qQiir6iTtRCpYcoi9CpQqn5FKmiYfJ0Bjh71uCsUc9UaWX8DUPxFSwo7ymlsraWkS0yAQdZnKwNgELhYCZQkQm0aHXaxBFLVKpCfb2PXFyHIC5eVMEUYRTCxCPfU6GqQ9PB83FQimwpGtrAh1tH/g1rtXp+S6rkKVZRZirVMxJuULw3p1oUlOR2thgPXIkNs+MRuLoyPnz5zl16hT19Q3B7t7e3rRv3x4/P78bdVQAdHqREqWK3DIlBclnib18lto6Y8yMNISbtqTVgK6Y+zfs9BSoNJy/7jZ1oULJtZp6RMBIEAixMKGNtTltrMwIMTXmbGIxG89lcjmrAoVMwqAQV55r60WIu9Ud/972pO3hw1Mf0s+7H190/gKJ8OhZn/R6PevWraO4uJh2g57lhY1XmdDelq62byGK+gcSH6IoUrErlZpz+USVHiVd70674d1pNcAHUa/n4KrviD12kM7PjqPV4KcoXbOWoq++4tyHfVnEYV5s9iKTjF+g5kI+qpQKkAqYBNlj3tYFubflfd9z98IgPAwYeHwxCA8DBu6BIAhrgYFAkSiKQdfbbIEtgDeQAYwSRbH8+rkPgQmADnhDFMUD19vDgPWACbAPmCo+wJvv3xYea5fOx2HXD3jl6Pl9QDPe+3Lbv2bL32XpnEl033ScXzoZgUUXXGsS6Hgih/yQV/D3CUOHiChCQkUJTue+IOvpF+k4dQJ25nd2r9IqlShPHqP66GHOX4km28acoU1d8JHuQjCxgMFLoOmAG/2rq6tZt24dNTU1vPjiizg7O5NaXMMbP0WRXVZNz86nOZyzm4G+A/m0/acYSRsCxssLalg/7yLrzeqxsFKwe0pHbMzkqHVqlkYvZX3ceuyN7ZnZbibF6w4iPXeBgOwijJydcf92CcYBAej1ek6dOsXx48dxtjXnBdsojJN3g2tLNF0+YK1JSxZlFlGl1dFdep6u2l/ZJHmNBNEXSUk9IfHRjHA9gY1dBpZWxQiCSJ1WjkYvR6uXotPLkEhkKIwUmMgVmBubIJfJkUiMEAQZWmRUagXKdAKlWgEjbQneuhgEdJiY+OLk1B9Hx/7oVy/FvPgHtHIFk1vM5YB1OK97OTLVywmFqIfSZChNBQsXsPMFkzvvhGTWqZiZnMvB0ioam8j5sqoA913bUR49BoJATWAgFx3sUXp60qxlG8qqqslKjkerqgO5KUpzDzIFJ3KUIkXVKhorShjsdJUmvvsQ9DLK458jRaOgrq4cqakZ1Y2bcdrenbTrqaRMpRJaWZrRxtqMNlZmtLQ0w/QugeXxeVVsupDJjqhcatU6gtwsea6NF4NbuGIqvzW+YV3sOhZeWsjYZmOZ1mraQ78H/kxpaSkrVqzA09OTK8bB7IjKY/vLPlTmjEMUdYS2/PH+4kMvUrIxDtW1cs6X7CdfEs6gKZ3xbm6PXq9j37cLSDx7kh4TXiOkVz9yXpuM8tQpfvw/9u47Tq6rvv//67bpfbb3Kq16lyVZlnsFA8ahE2roISEQDAQILcSQBoQaOsbGYGNjG9u4d/XepV2ttvfpfW47vz9WuAQbMN9AFH73+fA8ZnbmztwyGs99zzmfcz64iTuVbXxw8k1cIZ2P/5wG/GsbUIL/by05v+YED4fj7OUED4fjt5AkaQtQAG54VvD4FyAlhPiCJEkfBaJCiI9IkrQYuBlYDzQBDwELhBCWJEm7gL8FdjAfPP5TCPE7p/r+3w4eP3jjWjbsKfLoBbW891tP/K9tx/+Ee2+/hbov/SOTqsQtF1/JypNj9M0donE6irjoetyyzNDEIdoPf48bLryG2/xrUWWbja1TXNZ1koWxYUQyi7w3g7a/jOuEhWRKZKIa29paWVSb5Kqa41Q71qK84juoka6n110sFvnhD39IJpNh9SUvZ19C5qHjswwliqiKzqp1v+REdvfTw+X++tdeQ7e45Qu7+XYhw7RLcNt7NrG0OcxwdpjrnriO46njvLL3lXxo7YcYe2IbE//4j7Sm8vjPO4/mf/0XlEiEYrHI7bffzuDgIJt6Y1ySugE5NYi44GPc3fdXfH5omuGyznkhiZcXP03KtPmGfB2qMFldepLLKg/SEJlAkgS5cpwnp9exc2olc5bCtctXcH5vE+s6Yk8HtJJlcyRfYl+uxP58if25EqOV+WJtGejze4hpKieyk6ywt7ORbfRxFAmBOilRl+tkgZVATg5xV/freVfzO1igz/ClE9ezJnPguW+qNwbxboh1z18/63ZeePjFRJJ/n5hjxrJYKBTOO3yYVY/dTvfgAG5dZyjUwF1dm3m0ZTWGqtIqZ1iszVEv5QCIBAzaGo4RajyEkGSmChfzUPX1bJU8FP0yLaUUK8f6ackkMFQNqWsRl205l01Ndajyi/vFPl8xuGP/BDfuGOXkTJ6gR+Xa1S28cUPb0wMICCH44u4vctPxm/j7tX/Pm5e8+Q/4JDxj9+7d3HPPPWy++Eo++HCGJU1hvvP6WvYfeCNCmGdaPhb+1tcQhsXUN/dhTBTZmnqAgu8iXv0Pm4jU+7BMk7v+4585vXcXl/zFu2hK15P81gcxZcEX39vIUfU037/8+6ysX4kQglRRZyRVYjRZZHCuyIcu++3rfiFO8HA4zl5O8HA4fgdJkjqAu58VPE4CFwghpiRJagQeE0IsPNPagRDi+jPL3Q98mvlWkUeFEH1n7n/dmee/63et+38zeHz5b6/g8vtHOLhYo+5Nn+eiV1z9v7Id/5Mef/M51O3M8ZE3L2LTUJBW7QSrH84xXbeGquolOreXT294O4V2jRb/NEXdR3XMy6qJU2yZPURncnL+hRoCSKsbGPVoHBvWcWHw5t79jC30MlGvgKQQDC4hFt2IUNdw+y+OUcpleEosYrDiw6XYLO+u0tWUob/yK4Zzg3xywye5dsG1z9neR244zjf2jbLHY/Ifr17BNauauWvwLj6/8/O4FBef3fRZLmq7iFz/SY6//vWECiXi734Xte9/P5KiMDo6yq233kqpVOINy1Q6j/4nksvPzNXf4t3ldrZnivT53bw7sJ+a6c9wP5cxRTOb5Z10W4eRJUG6GGPfzHIen95AwWrFHztGyfsQN7z8CyypWcZAqcK+XIkDufmwcbxYxjrztdLs1lgV8rHBqLBg/04Ku55kYm6SoiThEQKv6kLzKDQo48Q6EhQWSVTjIFuCZcdy1KQNpj21vGrFf3LK3chfedJ8pCWMnRqnONWPlTiFkj6NvzBMSJ99zrGbE2GGRAPDooGRSAv9zb0Me5vwJiyaK2HWTQywdOf9BCeGsQNB5CuuwrWphby0n8nKISaTDUxN92CZbgyPm2NN3eyvb6fictPi0Vju9RKt2lRmSkz3jxLKDtImp7GQyfiaaVywgvULW1nbHiX6IoaEFUKwZyTNjTtG+NXhaXTLZlN3nM9fs4zOGj+2sPnw4x/mgZEH+Jct/8KVnVf+wZ8HIQQ33ngjo6OjxM95BV94aJivvHYll/RW2bf/DQhhsmrVjwkG+n7r69glg4kv78RIl3k8vR1X86W86qPrUIUgu2uCe2/6d2Zyw2xqfSX1rbWUv/0xJlav4B8vm6Zql4lnr2Mq6aFQNaklw8uUrayV+1l/3d3EnzXK2e/LCR4Ox9nLCR4Ox+/wPMEjI4SIPOvxtBAiKknS14AdQogbz9z/PeBXzAePLwghLjlz/3nAR4QQL32B9b0TeCdAW1vbmpGRkT/Wrr2gL37s9Vx6z34SUdi96Tw+fv23/+Tb8Mfw/c/8JRtv3sMPL3Hj81xESzVL+8Gt1CUlSpE4H3zThxgnwrqjR1l36hDrp4/SWJqfjXs6WkO2yUe0pUhMS7J7ppG07qM7mCTc6eLbza+nQIBu7TRxpkmW3JxKNdFWtIjLZbZ7KujxISR3gqxIYmEB4JW8/E333/AXa/7i6aJmgOPbpvj6zYe522/wlk0d/P0V7Xxux+e4d+he1tav5frzrqfB30Bx+3aG3vNehK4T+9QnaX7NaxFCsH37dh566CFiIR9vaziB78StiPZN3L3lS/zdhI4EfLTNy+LZj5IuHGOaRloZQ8YmWanhyORiHpneyHihiQ2uIV5z1aU8Ufo+D4/dx5VLP8UpeQUH8yUKlg1ASJVZFfSzKuhlTS5J896dzD3xABPJBEm3C11TUYA18RidgSI+dQK/MoFbyT29z2UrTEquZSaukW8o0VhJ0HO6SNHt5vNtH+KHTZdTWzS46ESOujmbigQVoCqB323R7E3Q7J6hQZ2hlimixhi+wmm8VvY5/w5m3bWoNT0Ew80kT82R2T2KfbICAlJL/ew873LuXfYSTtgeOhNTrJgepjadQFJVlq5azWVbziMYfO7oUtmSwZNHBtm/eyfG3DAIwZAd57DZQG1tHes6Y6zriLKuI0ZzxPt71TAkClVu3TPOtx4fxLRs/vmVy3j5ymaqVpV3PfguDs0d4luXfIv1jetf/IfhjFwuxze+8Q1i8Rp+WeljJlfh4Q+dj2KPs2/fGzCtAkuXfIWamgt/6+uYqQoTX9pBtVzksfQgodpVbJBAFXBa09kx9jOk8jQH61/C6qlhrjx8B/duuIibzt9FzKzhQ+NvoFMvEMNCEMCQwsQ+vAlftO5F75MTPByOs5cTPByO3+FFBI+vA9v/W/C4FxgFrv9vweM6IcTvbEL432jx+Nx1b2XzUzvwl+GRy9fy0S/8+E+6/j+mr13/T2y+48ccjynct/lKlh47TWdogCWPWpxaU0tHZCnm9p14SiUsRabc7CXWkKe9cRbVa5PRPdw7s4ipQgBL0zjSsJzd3eeg1rqomCpGpoCUH0IVY2juKc4vxamrhtlZt5MJ/wQ+WdCq2TRq4FHi2FYnxWIvoTGFgGXS3t7OggULqA+3cdO3TnKDr8yK9ij/cI2fj2/9KJPFSd6z4j28Y9k7kCWZ1Pe/z+y//Tt5l4r1zrex8X0foFwuc8cdd3Dy5EnWdce5Mv9T5NkjVDZ9gA82/iW3JwqsD3n5O9ct2HM/RmAhAVN2I0O51Tx6vI/T+TY0LF7j3snbtfvpfNdP+NeRe7nh6Pch/gbm/FewNOBlXdjPKr+bBf0HsR69m+rhQ8zmqsz5fOQ9LoQs4VN0ugIZlkUM6r2TKFIFIWQMuwHdbMewOqjQRUV0I0kRZElBls6MduRJYEd/QWv+HmRh8cDCPj4d+xSjSiO9eZ339Zucm5jf/t9GooQqTaFpU0z6phj2T1FnTLKwOErYmg8+RlEmPRggPejHrkqIqAexejHBzRcSb15CSgmz49Rhjg2fQJEV1qxcxbmbNxOKhn9jfdlslqe2bmPfvn1YpkHJU8fOch0j1fm5QBrDHtZ1nAkinTEW1AWRf0vXrIlMmb+5eT97R9K8dl0rn7p6Cboo8Jb73sJ0cZofXvFDFsb+sG5JAIcOHeL222+nc80FfHpbkbdu6uQfr15MpTLFoUPvIl84Rk/XR2iOvwm7bDGXKDI3WySVLJPLVCjlday8TmvRpMMWFMwMT+aKdAZa6fHIeJDQrQqPTv+UvJFkS/2rCB66B3P6IEevvpbPLr6DzfnFfGzq5eD1knRHGDM1XvLhDcjOqFYOx58VJ3g4HL/D/5+6Wv3zx97Gyt07aJ8Q3PeSXj70b3f9ydb9p7L9tasJHCrz929dxgWDAdoDFdqe2E88Bzkv7O2R2N8jcbJDYGsySCo2brqHQiw95QIBh9pkDjS7sSQX5lcZfQAAZa5JREFUCAXkKopnElmb/2VdEhLnTm2mvlrHgH+KlMcHcgvCE6Y+NEObb4Qu1yla5FFkSTApmjmSWYt7pA45o+NNruZmv4TttnnVBce5feJman21fHHLF1lVtwpb15n+5CfJ3nkXibo4g8sX8sb/+CaziQS33HILuVyO1y730nvsK0iywvHLvsIbK73M6AbviE6yOXUdiDJlKcyD+fPZN7qEsfF6QKJRLaCoLj6rfI8L7W1UX3Mzf5+Z4vGT/0E5cBGrOt/HW/sfJvrYvcjDU2SKElPBIFmfG1OWQYJ6d57eQJIef4q4vwSAKeKUywvJDZbJD0whdBkQuOMCbVM7E70djFRMtNlJavMB/Kwm7FqBWwlhi0li7k8RkKcYqI3w077zuVV6PbNSA4uLSd62d4Llhw8TDNrI7e0MmzBuCkLxOpb3LUYrJimmB6laMwgscnac26KLua0xit/K86aREV42M0KNPYXXGKM6OkpuoEg1rSJrNpHOEpGeMgQWMmNdzlNSLQPKLDISfaKZla5ugt4AkkdF9ijIHhXZo1JRDQ4lB9g/dYyKUaU2Uk8wvpChaoA9UzlGijpFBH6PytqOGGs7oqzviLGsJYxbfe4Jt2HZfOnBfr7x2CAL6gN8/fWrCQaKvOHeN4CAG6+6kcZAIwDCtBG6ha3PX//68ty/bexf318xuXfwCQaz40y7NnJ/TucHdbU06wJTL5Lq/S9K9XsITZxHw7E3I4nfnNRPABKgCx0VlUR1kh0lP+ev76S1I4TsVamIMj+/4Z8o51NcWJ8m8OQstg13vauNH3mnaREvo7//XGRR5JzaMl9912tfVDe1X3OCh8Nx9nKCh8PxOzxP8PhXIPms4vKYEOI6SZKWAD/hmeLyh4HeM8Xlu4H3AzuZbwX5qhDi3t+17j9l8Pi3D1/DuY+ewFeBJy5o5P1fe+RPst4/tZ99/JUsv+04X3+Jl1r1UmozM/TV7iCjl0nWC0xFoEsSVUmioKpUs1GC/bV4SwrDjSo7l2l4wjZ+LPK5AiXLwLBVjGojTZUW3lruoGrpjMspVlZ7CFlRkiJLXk6DlCTINBE5g8eqoIgi45Eq1ZUWTZ5JSraHU1s/wF25FkY9GZpbfkjaN0lruZXX17yeFQtX0BmLk/jwhynv20fx/M08np7kVZ+6nulihfvvv5+gz8Pb24YJHr0Ru3ktX1//Bf45pdHqMniP9W+0mXuwhMKtiXfwwNElSLrAr+j0MEew2U08tpjuI9/gb9TbeXLdR3mX1o489UVi7gX8405BdN9JBrQIs+EAxpmTY5ds0uFNscCXpD2YxuO2EAJ0u4eKvZGqvARtSTe+TX1IsszYO9+FMTaKb0kn1cERzLyOHGrCu2ANUsMGJLUWE4tB/wx531Hc+i4a02M0ySnqPCUShpfdoXoeW3o+d3lfTlqKs7I6yDuGp+k8YlEdOYR3dB9aSFDpqlLsgSOdi9hZ+xL2u5Ywy3whfJ/PgwBOliq0uDU+19vMFTXzw9nahkl52xOkf/xD8tv3gm0T7IC6JTOotQ3k6q/l8WI7BxMTSEgsDnWxyr+AgOHCrpjYFQtRMRGGjYHFSWWSw+oIRalK1A6wwmyny65DRsaQoYAgY9sUEZQk0LwawbCbWNxLQ9yHCwlRtdiayPHJoRlKtuC6WITFnjn+LvB54laEf5/8MIGKF+wX8b0ugeRSKCsmP7efwhYeflJdSC0yb8NFCajIgoaFd1PT+kv0yiLQP0GzGSY6WUKaLIEE3mVnhsLtDDP78HGMh5KMlYc5TDOXvbuL8f03Ez31C9qqQ/x0ZAVl282aNcsJf/9uDtf38pmX+9EiB1g98xJWDZ+PLBRe/U+raIjXvujPuBM8HI6zlxM8HI7fQpKkm4ELgBpgBvgUcAdwC9DGfDeqVwkhUmeW/zjwNsAEPvDrkaskSVrLM8Pp/gp4/9kynO4jd/ySk/d8gc1bU6RDsOP85Vz3xZ/9Udf5v+n6T32Sl9x1C3vaFZ5cdxWLTwzxuo9/lpaFi0lkZxnYdhuusW14Mqc5MSQznI4Q0cpc2DBIezDDkL+Z7cFV7AsuIhvoYFP3cl7d08ctu0b5l/tOsEkZokNO0E4LfXoLUctNQHrmF2IhBAVbkEJnRqowhk5eSlHvepJKTSvbhrawK36UUOtNCExa0xfSUekjbCSJpROc98STeCoV9q3fQKI8Q2jZRtydXZzuP8GKjjgvq96OMrWP9Jp38Pq6t7C/ZHKpsoPXml/FQ4XD5Qv5zz3XYpdtYtEiq0rjxLUKx5au5W09y3j4J9/g667/5LbGq/j7lmuJzX6e+pzJB25RmAjHSAV9IGyi7goL/EkWBNPU+HLIkkCofszYZorZVZSyy1Camghd0o5n0fxcHCP7Hif3Nx+BYpn73ruKiZoAveMNbJheSIPdiBAW1uwJjIldKMXd0JBhYKGHA4uaGahbg9uKc/74Xl4y/SR+u8pUOcBjZgePrjqfR5qupCAFWWfv5M2Jwywa6yQ/6qE4dQTf0B7ChQJ5f4C5vsV4V69iwaYNNK9aiezxsC1d4B8GxjlRrHBhLMg/9TbT7Xum3saYmSV9809I/egGsAziawPEm44je31UFl3LVnMpW0/MALB69Wo2b95MJBKZf78t++kQoherHD56lJ1H95AqpAm6AiyP99HjbkOuglE2qBQNrIqJZNhoAjzMzwNiAWgysiaT1OBz5QJ7dZ0rQ35e2jLHJ8U/s0jt5Uuxf8Lj8SBpCrJLQXLLSJqC5FKQ3QqSS8ZWZQYzJfZNZdk5mmHPSJqpbIUWOcMlrgEOqIs4UAjwzvO6eNf5XcT8LiRJYnLoNk6c/jhqNULzvr/Fp3TjX9+Af/1vDoU7+MPHcJ9Q6C8MMiUkXhW/jilXA8meaznl38Tgrd/DNEwqdj1vPPgwhxZewncuHSbhm+C8xFWYtWN8/c0/eM6cKr8vJ3g4HGcvJ3g4HGexP3bw+KePvp2+Y7tY0m9yslPm6PJz+PgX/29OEPj7KhbyHP/LzciDOn//plVcMhRi9ZI+rnjvBwCwTIO999zJjtt+irBtFl91JZ6IiWd0G/HSKaLGFIFqDs2eLw4vyW72hhazvXYz6L2URobYbzRRt3A1n79mKXVBD9lcmtPHB0mdSmJPV/FkFcK6m7BwoZwpMs6Ygu9Vi9zbeReu+FPUqhJvrynhlmPMjPdhbQ+xZfuTGIrKzr4uqqKMEYhQaOxCUWQ8Up6/ln6GS7L42fJP8cnIelyizNvF11jHHgpGkH/r/yRjEy5kv8x54ZN0pjJM1DRwaPE6vrq8l3//5k/4ofVJRtxNnByJ8OUlSXRF4prH65DwEtWK9IUTLKvJEpTOFGzXLkL0XkZVWk/mQC1mUkdr9BO4uJXJpjR7Z/exd2Yvpae28bafpaj4vOx76YWsEufQUZzvGnREm+NhbZrJJsFVzRHW9Q+gPraV6olxEKB6LAItBsG1vfgufgVy3yUUdv8I3+5vYEoyR+Jx9psNPBi+iu0t56PLbjbzONcad9E9245vcg2pCYXJ3GG0yUN0T2RwWTa2IlPtaUZdsZTg2g3c17uW/5grULEF726t5QPt9fhVBb1ikk9WKA5PUP7B17GfehDCEUIbYjREd6JgMOvbwE6xln1lDZCIqq1E6UKqujF0C6M6f0GAQKC7k5T8Y5iuPJKl4Ss14yk1IT+rG5PqkkGVqVgWUtVGeVY1i41gh8dkm9skLiuc3zLIg4H/YpW2gb9v+QTBqA9/2I0/4gaXzIGJDHuH0+weSbN/JE2+agJQH3KztiPGuvYoaztinNrzGAf2H2BPZAtTeZOHP3Q+nqn5mcXLR5OUg6eYXPM1hKazZMmXqa276JkPlxCk+rcz/vgPaJu4l4ncewm7t3AgN0a6qYFji2r45aEpzEqJ85RRFg49gSRcLJmG1umD3HBtH/f2DoAETS6ZG6++k9pAx4v+jDvBw+E4eznBw+E4i/0xg8cXP/Z61j+xn7oUbF8fInjp23nNG9/5R1nX2eaX111Fz11D/Os1ftqkywkOH+P937uJqVP9PPL9b5GaHKd77Tlc+OZ3EK5reM5zhRBMFsY5vedetBNP4qpM0lI6zQFjKdtYixzW2Rs+h6ODGgGXymdfsZSrlzc+7yhGuqEzNjrE9IkZ7nlinDs7b0D2TnBR+lzenHgFO/oGkOsfYOXjJwjdplCp9bG/qYOMMFDjC0nX+pElwYVs4zx2Myy38K4FH+Fg/XKWigP8lf1N4lKC20dfzn2nLsIWErVNZS5OnSSoVtnRs4ahxk5eVhdj271P8NPMPxDWixx5NM4nX+Wmoin8xb4wK7wpFobnqPcUEUhI7efCklcgei6lPOoj9/Ao5lwZq0bh6JIJ7nM9yb65fWSr8+Hk5cf9vO6uHPbiDURWvBWpCFMeiV9UyjwhW5y7uom/Oq+T3vrnjhRlZTIUHnuE/N23Udx9CFMG65wWqqsksq0T6LbMgoESPdlpDgd6ua73g5zU2qgrzDIea0XIEhfYD3ONfAvxqkVoZj2+yTUkJi2GjOPMFA7QksixaFzHPZ8jGWlq5DvXvImtS1cSLulcerRC32nzOQXs4ewgvQO3EiqMkQu3oy6PsbhxK145Q5o29kkb2SHqMVGp9bfTUbOEUCCC5laec1FdMsn8DEf69zI2OYJLc7FyxWo2bDiHaCyC9KyC8/6pHF+65wR7TyRo9ri4ekE9iyN+9k9m+eboNCXbZklkJ6cbf8HC6c00jV/DpCKYUC1mFIF95qWaNI2FIS/LaoOsbonQ2RggEPHgj7hx+1R0Xeeb3/wmCcPNT5JtbHG5+YzuQvNp+NbWE1jfiBnIPF103tvzMZr8FzP62A/xnbiVBmOcitDY6zuXat+1+Le6aJKa2JVP8ZjHJuQtUJ9qQBEqWe04avJ+KprB+lNjhKsWv/hoJw+Xp+mJ9PLjq36MS3FqPByOPydO8HA4zmJ/rODx5b+5jC2PjWEq8OSFPXzoP375P76Os9k/f/6TvOynt/LYYpkDy17CgoFRGupqmRsdJlzfwIVvfifda37/IUrvfuAu9mzbR59yitdYv6Qsu/l57FJ+nL2IgXwDaxfE+fpfrKQ+9JtzEggh+PR3vs7t6g8AhU9u+AyvXnwlwhYYiTynP/JBxPatTHRHOBYKY2sSVkc7ebmBBZbGy6Vf4pePszvcx9uWfpa0EuO14sdcLt3LSK6Zrx17F5lcGBHTWK+N0pedYKC+m13dCzE0jZf0P8T6bTtZ3T7IIkZ48HAXd66Ay0olNpGl2ZcHwGpYibLiNfOBI9BI/uA0qQcHUVOCWX+GH8Xv4lHfToQkaAu2saZ+DWvqVtP1swOoP7kNa/O7iMRWMCHZfFaUGffKvHFDG2/e2EHd8xwXANMskMnsITnyJKnEdorKAMflReyz17FfrGNabQAheNv4nXxk/IeE9TTmqjchX/IpHkgZ/OfJkxyQgyi2xUX5B3iF/zZCag6lHCM8vQHP5GrmpkuMl0bIlMpEyjYN6QSxzBADrQ3852veykBbJ31DJzlv711UIiWMhe3U1bXSHm1i4f5xAjf8ApHOEL7mFdRd1oF64gaYPoztiXI6uoW7ZxrICj/Lly9ny5YtxOPx593Xqakptm7dytGjR5FlmZUrV7Jp06bfWH7PcIov/OoEe0bSdNb4+fvLFlAfcvMPvzhC/0wBf+OvkCOPU529ApG+kAVhHz1eD+2ySqMhQd6kmKlSLZm/sQ2KJlMf0vCrabaxh4SxgLvtMC9riPD5168iUPvMMMBWcYYDu99Gxj5B43SFvoEC+8VipjpfgbHgah4aqvDgkRlajQT/ZrqJuWrZlq8yJVmMN+7jcGwr074JWnIuLtzZgORRuGjPEBNtLj71FxYmFo+++lFqvDW/9+fw15zg4XCcvZzg4XCcxf6ng8dX/+XjhPf/inX7y4w2SBzYsIaP/BkNl/v7On3iGLkPvprKtMVH37CWy0bj+JIjnPOKV7PuZdeiul74V1YhBJlMhqmpKSYnJ5mYmGBoaIgVK1ZwwRUX8Nj9X2HFiXtpqw6gCpsn3Sv4fuFSHlNWs2FTG+87p4ONkQCKJJEsJ/n43Z9ha+lRrGInX9jyBV62dDEAVjbL+Ac+QGn7Doqb1vN4IYHl91Ns6UbRZC6q/xXrEkMoluC7vVfx6YbraBEjvNv8Og2MceOJ17FjcjXCpRBqEVw+e5CJWA27OpdQ1STOGTnEu+76GdHBJLMvq+Ulrt3sTDZT48/T6c4hSzAthdjd1Mn2+hYmNA3d0mlJ13DN0Pm0leoZdk9yU809zLTmWd2wht7QclS9m9E5jeMjCc75+TfZnJ7B3PDXhL1RHvEKji4IsbI7xstWNOF3P3d0pF8HjVRyN4mZPeTzAwhbwbJcTNud6J4Sza7TWJKMbWu4KgJvpoi7UEXToa1SpbGcw5Q1TtetY9i3ghE9zJ3hpeyLt+C2dK5IPchl/IJQPIskC7RCA6HpjXgnVzIzl2W83I/WHqC1q5ugoXNXyeZ7XSspu1y85sF7+Mt7b2WyFo622JxokRithZcccnHpriq2pjD96i00XbSEnsFHcJ96GCSZqeg53JfpZMyuY9myZWzZsoWamuc/oU6lUmzbto39+/dj2zaLFy/m3HPPpampCQDdtDkykeHmXWPce2SKYtV6+rleTaZimARbb0UE9vOZjf/EKxe8/HnXY+oWxaxOMVOlmCpj9qfRhnN4igYW8Jg2wJAyypH8SvZoGudUVC6VXCyoOUYrD9FsbEWjyv62WtIdApOFHDc/za37CmTSeVapGRaX/YRLMSR7iguCAdyKn4dLVW5Y+m8UvEks5s8/GpJuLtldj6zaXL53lKFrVlF908t4ec/LcSvu3/tz/WtO8HA4zl5O8HA4zmL/k8Hji//wZpbt3E3nhGDfMjfj3Zv4yBe+8T/y2v8XPfC3F9F6/xSffl2QReaVNAV7eed1b3zOMrZtk06nnw4ZU1NTTE1NUalUAJBlmbq6Onp6erjwwgtRlPlRnkpGiXv23kDbY79ghXEQj1VmhDp+aFzOT2uuQFnsprv0EKO5BzGFSTVxMR9Y+x7ee8ECAPThYcbe/R708XHG1izjcCWHp62bOW+YZa1hVk3/hC7rFDOBIO9f+A88EdjEZdkHeb33xxxJLuAHx/+SgqEimr2slUeJGkUOtfXRVJrm6p33suHx/ViWxGSjH/8amfWufjTZRpEEKd3D43KYG2ujHPcGUCUNj+qmTdTwxrktrE13kfOU2Lt4lGRznEqhnVPTNofGs8zmqwCEjBKf33cji4PdaItfjvC5CF/bgx3zcXLHNJMDGUzDwjRMjGoFU9cxTRvblBGWCki4o6MEGg8SaDqENzY/iaZRjFGcWYQQCpqmo6k6mlpBIY8iMgipiM/W6Z0sEi0YZIMqJ3sC5IMqY7Tyc17HHukcgiLLy7mdi7kflzDm32wJ3LlWQlOb8CWWUvQOkw7voxAcxgw0cYv/Wh5hBQuMKT5y+Ic0HjqMnLaQ81D0BpkKuQhM5WidsZiMwo8ukcn3uHlHSXBRchyPqTPrbefJ6jKO2d0sXraCLVu2UFv7/KM25fN5du7cya5du9H1KnIgxrjSyNakl6I53+rQHvdRG3AzMJsnWzbZ0lvDy1c28+8PHiMT+iaaf4hvXPJ1zm0+93nXYaYqFHdOUdwzjV00UWu8+Dc04l9dh6XB9773PWZn5xgJrODhWYUPq7fzPvXnlO0Ap8qbOVm5gBljAYG2XTSt+yGVapCju1+Hf2YFMjLTgSH6a3czGN/Pysk4H8m9H1tSeJAShVf00xZroSPUQVuojcrJCX75H/9MTHWxeu9xur73XfwbNvxBn20neDgcZy8neDgcZ7H/qeDxbx9+BZsfOYmnCk+e38j7v/7nOVTui/Fv13+EK398J3evkxnsfQkLZ9cRiQepX+DBchdJZeeYnp6mWp0/mVYUhfr6ehobG5++1NXVoWnaC67DsAzuGbgT41c/5eLCIQZtP98K1bInMoMAQum1TGTOI1LTycaL22nxuOg7cZSFn/4EYHOwu4UZRVBt7MCOxHljb4HWgR9gCZNb2rfw0eaPoWBylXQX55cf5tvH3sKpZCd2QCXYobAx2Y/wxGk9fYQLtj1O/dwcJZeL3EI/jc2zdIeSaLJNznDTX2rgjoBGfsN6/m7DvzA0V+bUbIHT0zk6BwtclLWRgJvQuYkq1TP7KEnQUxtgWXOYpc1hVqhFwtdfj9ZwGUq0C9fiGDP1AY7vnmJuvISETdA/Dt4cwlNGUgxk2USzZLyBHO7IBHL8JJI3ixASk9UeRvNLkUe7CQ12IzHfSiJjgyRhi2fqILwBlai7QnDuGG3pn9PZfQzVbZFL15DznY9oXcPBQB3/Fa9jjz9MrV7mL+dOcGHpIOXgMfTQOJJ3fs886R5C0xvxzS0i7z9FtmY3T9YH+YHnnZiovJnvsYVHn6n9sEEugme/TOABBTUlUezWOP7SACfqKvTOJbgsWaDJsEhKHvaxmr1iGVJLnMXrFrO8czlhtYFjU3kOj2c5NJ7l0ESG6VSeXiXBAiVBRC6DrFLX3sN5G9eztLcTSZKoGBY/3j7C1x49Ra5icOWSBjLVPAfMf0bzpPivi7/HhpYVAAhbUOlPU9wxReVkan4o3EVx/BsbcXdHnqlFKibI77qJm54aYsoM0W8E2W4v5oLoIQILAoxM+smMxGkzBC1yjk5vgc6N30VSK+w/cin5qaXUFtqpeNOo0QpdHe1op4+wpNRH0RZMdtdz6buXPaf26eT2p7jnK1+kxrBZN5Oj9847UF+ga9pv4wQPh+Ps5QQPh+Ms9v8aPB6545cM3H09525Nkwr/+Q+V+2Lc+dMb6frO50kU4TOvWscF+a5nHhQyboLUxOro7G5j0Ypu6hvrUNXfnDjttxFCMDea56FdT/Gz6Zs4HTyMYmvEU8sYT1+IbsXpCCfwbl7AnBpm5cP38zc/+T6JcJijjSHywSBGYwe+OpXXiTtpzo9zNNbKu3o/x6Cng8sDaa7Mf4bdwwu5ffBqLCFjdQdZKk1xwbFtXHB6O9HTWeyKTDbiJ7/ExZL4KE2+PFVL5Vi2lsFCnDnfZn608hHq/C3815bv0tA0P/xt+XCC7L1DWJkq3mU1GFuaGKwaDMzksQUsawmzuDH0dJep0sGDTH/2R2idVyE0jWGqHEnLCGS82ijBnu2Eeneianncs2HcuVZkVxw9mKQcPYFQTEzLw0FWsEtZz2S+kzX9OosGPdSTwS1PU1BTtIycJDA8iKuqY6pe8oE2inULKNQuIOduoGj7AHBJRTYFfswi3wMYppvRwU7UcoH6ZX72XvwOrvesY19F0Klp/J0vxLkViWNzJ0gW7yYceApXIA22hDe5mPDMRtTJHnaXT/HVc/oYaexkQ76fTzYlqKsPoOtJKokhKslhqrkp1B15/I+BpEPxQpv8lRbCK4inDNomysQyBoYkc1AsYidrmAsolAJVsnjJ6wEsyUc0FKG7vp31nYtY2hIlMT3NwQMH6T/Rj2mYxGpj9CztoW1BG7JLJlOucNu+Ee49Mo5lm7THQky6fogsm7yu9b1clKul5rgbT0Gl6jUZ78kx2p0i7ylj2iamZdA828/ikX30zZ5Cw2afEuWX0pWYRgO/sDoomHF8lmC1lGFJJUBID2PIVYbiB5loepKrO/ppdtnM5NZTp78fKxskPV0iM1PCMg2ixlOcV3sRCcPkmNdL27Jaog0+Yo1+oo1+Tu99nPu/9RVaY3W88kvfQPU8f/3Pb+MED4fj7OUED4fjLPb/Ejw+d91bWXx8N4sHLE50KxxbvpGPX/+d/+Et/L/tiXefS+1jKa57c4RLT/fRNT7NVEhwtHkFYWkV9eU6hA2qW6G1L0r70jjtS+MEoi98MiSEIDFWYGDPDI8cf5ynAvcyEenHbfuoty6if2QVpuFjlTzIR5SbOEc5SQ4PM/1hzH0K+ZZGtkfcVKM1GM2tXNGwm1XTuyhpbj7Z835urn0pQXK8ie/RkB3j28fewly+BqvWja9dY934Ec7duYvzDu5Btm1KDSpKp0FXY4qwq0pW97A31cRgIUZvNM1jsZfyYNuDaELlK0PX4dLDlGWJsCbjNm2kmIfYNT14e6MvuM9mIkHi+zdTGAjgifcwpxvsK4MpZwl17iTUtQ1/IEXIWINfLKdcGiPv3o8eHAdAqjQwVFnJbYF1HFCXUJPPsyhTpSkWJ6EZjFkGk5JKzhuYX962CefTxDMJGopZmgsZWlMJ2qbGCc/MoOSqCFc9hruJcqAVrVZhffxnNLpOMqX3sS3xOsws1KinGV4V40erLqFfDrDY7+GjXY1cGg9xqlThvvG9zE38ghXGY0S0DJguwlOb8I9u5rvhADcv6SZQzPHqJ29msy/Dko2baF11KdT2gSxTPn6cmS9+gfKOXQhVxqy1MFpszLCEXqPR6C7QoWdQhWCQVrazhnRMpq3jMIFA+vmPtZifH1AIGWwFyVaxhYQh2eiSiY6FBdhIWAIG023cXclhmwEur2zgsvoDTLuSzKpZLASWALct6NRNunWdoGlTReKk6ua4O0BZboZKI2KsAcpeHrZ6mDODvL1uJw2RfjK+aXKeOXyKoMUfoyPcg6gOU6lM4PW0EQwtBwTCtjCqBvlEhcydzayNXMVY1WCs+xd44oNIkgDJRlYE2SGF1IDMG//pR0TqnOJyh+PPiRM8HI6z2B8aPL74sddyzuMHqU3Dto1hul/2cS56xdV/hC38v+1r17+Pi370MD/bopAXPbTlTGryJRpTecruEvetDJOpvZiL1c3IuSCF1Hw3nHhzYD6ELIvT0BlCkiUS4wUG984ysHeag+Zu9rc8xGxghJAcpVG6nIMnFyEsD9eubuE9F3RTkkb4yhM3U7szyzU7n8Q3VWGq3s+B+noq9Y1sWjLNhsxOPBWdOxov4iOdH6QlHOQN0m34537FHaevZfvEMnDJ6IuirNeH+MiPvkxkMo/pUdi5bjG9NaNsVE/iUmwmS0F2JVtJVr0sic8x4u/gcMPlPBm4lZyU44LJC+gR7ayv9NBghgCQkLCFIG9D1Q1KRCJQZxOtt2B2ktKBg2T7Z8kGVtPQsgJFljlWMZir3U24czvh0AzBxEqCs6txFZpIdf6KdNtDlHAzk1vLRHENe/x97AtHsWQZxbawZOU575GvVCCWT9GiZ+mWCnR4TCZcNQwqUYbVAFOaD/NZk8z5izni6TnimTli6Tlq0nM0ZVPE8wX6ghnWNh7HpVY5njufbeW3oROcn728xeSxFUHmAl6WCpuPdLVzSft8y8/+XImHx57EmP05a+wnUWUTT7qHfPIiPtG4iQmfn437HmPj3sdocmcIRSQmGlfS71vFgF6HcXqctx+8g6XJIaYDNcy1LaCpmiU22o8qVYj0lIj2VdFcOkmi7GAV6fbzaFrqo+oaJV+eRsZGkUAGZMSZ2wKralDNFakWykjCQnXJ+IJuArhQihaSaXLIMPhGdQ6z3Exn6TLeu+pBwu4cGEWEUUBYOkIGEwVblpAkMR8EnkUIiYH+DYxML+Qhu4u87eEDa75KV3gCVXGjyR6QZCRJASQsq4hlFZBlN253PZLkQpJkJEkmOyZjPrCB5dEtDOhVlCtvRvXkMA0wqjZmVaCXBS95za3I8otrZQQneDgcZzMneDgcZ7E/JHh88aPvZctDjyLb8NRFvXzo3+76I23d/31f+dz1XPrADzmtyHz5lZ1cMTFNLhdGqrjwVmUa0hXClRKHOqpsW9LJK+uvYV39+YwcTjI1mEXYArdPxe3XSCcKDNbs5UjnY8wqE9R5moibl7P3WDeK5OK161p51/ndNEe8AAjbprRrF2P/9FmswWGONNcy1hCifrXNpeoeGlIVRr31/PXCTzAZX8KbfdtoS3yH+4av5IGRzVhCwmwP4mlW+NAt3+WC3TsxahXKrSqNtSlaI1kEcCofZ2eyDUMoEPXxpGcd3tEKV57exi2Xz3GoU+Lv7vKwIHQVDfVbUGWN48oEBxggki9RZ8ZpVjuo89SjnjnBr9g2E7rFjCjTIvtpcyvkjDJD7Xfjb+jHk1qMPb2KaqaFMiZ2xyO4+u7muNbNXZW3cNTX8Zz3QbEsYoU0dTPjxNJzNBRnWWROsJwxFnpS1HjKSNggbDB1MMtPP9dEYTS2mIGa1fSHFnLC3cJJOcppyU9JeibEeE2dmlyK1tnTvHvuFi6x9lOy3ewbXcRcYjloDWSCbWxb1sETS/zkfTLd0xWuPjbBSsmmqbeG5s2L2eetcnD0Flqzt1OjzCDpAY5WL+TbwSvwJTQ2P3wrTZlxbCQmvM0YwQDNgTwdoTJ9OYPAE6exkjlCV11FzQf+FiuZpLx3L6W9u1GnniDclsAX0ykLN/ukpUy6N7D6nEtoXbwErSaO9ALd/XRd59CO/ezbuYfJ4hyykOhwNbB6xSr0vk7+4ZFbGXd9C6vQh2f6VXzF/T22WNuZEVF+Zp3P8fqX0bNwKUOJInsOTrPIzLLE9OAzfJTlMoPxA5ys2U2zEaE+u5gH7MXImp9b372JnrrA827T9PRdHD/xEVyuelYs/zaBwIKnH9t79x3k7x2lJ7SKU5rCpo+vx+V58SHj+TjBw+E4eznBw+E4i/2hLR7//qGrMVxRPnr9DX+ErfrzIYRg59vOIbw9z3vep5AMPXeSP8mW8OgyHl3GX5FwmTYZn0pDTTdXLLkSOeWlPApj1VG2BX5FwpylLdCNr3Qpe4614lFdvOGcNt6xpevpOTzMVIrM7beT/tktmGNjWF4ve5vD2Jts1jSP0jeWQbIk/qPtzfyw6eW8rnQrq/2P8tT4Ru4YfCkVS8Nq8kGnjysObOWv7voJZrOGr6ZId+00IbeOYcscSjewO9WCpXk4Gu7jweC5lDrihGIWF8zsQSndzOM1Ov90pIO11msRrjbkymGU3A1MuixGfG0M+drIqmEAYmaaRtPCEAvJFnqISR5We8Ejy6SLT7Jj6UH2FYMkbAlD1lEVkzXxAsvqRziiLuMO8TpG5BZUW2AxP3t3e2qG9cd3UTN1mEnfHDOxKjSHWdm7gU3N53JO4zmE3eH//qZBKQWZYUj/+jLyzO3sOIj5gVqnXTUMBLrpjy9nILSQAU8LA2qMOdwsz5/kCwNfYnX+OPtCy/hBw5swp6p0nuynYa7E3sVruPvctRS9LhaOVbnwSJnanI1lZykGYDDqJek/wbmNT7Ci9jCSJDgiVvK4uIxluxTqhndSqiRR9CooEtGoyTn+IRZq06RPBEieCCAkGd8VK2n+q7ejdm1EqB70wUGqT/0cZfBWvMppAI5LPewvLUYeM2kt5GnSXLjq6lBra1Fq65BcrZi5CFZKBhnKvRr9/lmODB2nXC4TCYdZ1aRxLHUL3/Cl8WSWkJh6A53yLKuWLSca9PPU0Rm8s1mWW4K6ShhLshiLHONk7W7Go0dYFY1wdffVXLbgr9i94wB3PryNB+ylBP0+fv6eTU8H6v8umz3AocPvxrLKLF3yZWpqLnz6s3fPV/6V2tO1NHm7GW3ws+kDq593os0XywkeDsfZywkeDsdZ7I85c7lj3g8+95dsuGkPe85r5dFlL2VaczFWSFAReSSlhKQWcMtZVDkHShGhVjC135x8rTe8FCl7MXuPN+B3KbxyYZCXNtuoxTSFVALzyFF8+w8RGptEFoKU38NUi5fyWpuORXMsHs0Tz1XZHl7BdQv+Dq8xx9u83+F0ooObT76KvO7HirtRut28eeYeXrvzl0y5a+jxjdAUzKDKgpKpsjPRxqFMA1lfDQ8GzyXas4S3be5k5eBO/vXANh5baiPpT1JfDfLXs3/Funwbul8iclkL0aVBJLMMRhmMEkYlxdDkHo4dKZAY6qSaacOlGCyLZGixmrAKM+wrP8Suzgia5EIRCtjgDiVo6TnMcd9C7uRaElLd08cpUszRmp7j3MQoly1awIoNm/BHoozlxtg+tZ1tk9vYObWTglFAlmSWxpeysWkjm5o2sax2GZr8wqOIAWCZkBt//lCSGYFSkowaYMDXzoC3DZ8wuTi5Da9V5rvNf8G/dryVoupDEgJ3sYKwBNWAB2SJ6KzO5kNFlqUEChKWMJHlLA0decyVR/Erj+OT08xRy0T2QhZtDTOXHUX32+QTswhDx9I0qA/Q4cmwfO8gYlig+kxqVhWorOzCbj2HeOdGPB0bAdAf+nfkozej2mVGaGYbaxi2O2jJFmktBWn1rcTtrcEuJTGGHscY2Yowi6jxOFI0yHjcR380xmSoEUnYnAge4EjtEPbsRRSTl+Gx4XyjzJJKGEUoJHzjnKzbxamaPXRFJK7suIRXLHkfYW/Dcw7zzp07ufGex3nAXERTLMDP372JeOD559yoVCY5eOhdFAon6O35GK2tb0WSJIxKhZ9+4jpW2OcR1urJrq5n+WsWvohP7/NzgofDcfZygofDcRZzgscf3+c+/Tlee9+PMbMKhXovmR4PrgUq2opLmQlv4qFtJ8lnsswYEU6JZiwUwEKR8oSZIsQ0ZTXEjLEaj62zInuQ5bnDeGwdzbRoSeVpS+XxV3VMl0JmsY/sBhu1r0DArtAwrdMxVqGg+PhM13tI957LVcUvkEjYfP/4m0gUo9gBFbsrQGN6hk9MfJ2LPQcI2M+En2TVy5NznQzmY5z2dbIvuobVa1fwtnM7WN0WJVPNcMOxG/jZsZ+yLN3N1YmLWVbtRlckftDp4sYOF4YsCJUKNFpp2l1zdJSS1Jx2o4wsQJge5OAUNeFZ1uQXo+GhOvw4B7uL6CtXUtG8nLBgVk7TF3iQKbWBe3kZOSmC27RYmMlxbinF2nofna2tRGNxGhsbX/A9MW2TI4kjbJvcxrbJbRxOHMYWNn7Nz/qG9ZzTeA7nNJxDd6T7eX8hrxgWc/kqc4UqiaevdeYKFRLZIolsgbl8hURJULRkIuS5Tv0pr1UeY1aK8DnfW7k/uhEloGAGPBR9XnhWHUmwWKBvZJS2GYvGfJRI2YcE+PVpYgsOkF1xjCb3cUyhICfX4jvWy+DQNHaNgqyqJMdGsS0T2+tHkzTW9Q8QS+YwGjXaVs4RCZcAyPgayDeuwdu4nHD2NMrgI8ilObJSHU/Zq9gvLUBILtrjjSxoqaPT7cI1N4Vx9EnMwQOYyQxGWaVUdlHRvJzu6uJ0ZwdbW48xEhxh6cnl7DNehcsy+MuhXxFRDlOqqdLW3sOa5ddQ17Ea2fvrlgxp/j9Jmh9DGTh86hTfffwADxoLWBD3ceMrewm6lfnHn7UckoRlVzg58VkSucdoiF5Nb/NHkBUXmblZbvvi9Zwffw0uOUhpc4RyLMdcMsmVL3vZH9QC4gQPh+Ps5QQPh+Ms5gSPP75sMsXOr76GhcdOw7SgnNQQ1vxJZrHWTWGBSnB5mGxjDCM3Q91klsFqK48UV3K6Ws+ktxZLklmR30utuh93fYhNnjZ6jo8j7zmKZNpUOxWKmytIi00iJRMt46c2XyFSyQBwe93F3L70HVxo3YCWH+E7J97GWKoR4ZZxN6u8aughako5Yp4UF3v20CIlKNkaI8UIuxItzFaCnAz0cjy+hHOWFnnFkhwtsTAVvNw5cYQd/f1ckFzDS/Jb8Oke5KCGIUY5NPAABxst5hbWka0JkXXF8I210nE6QFPKxpThWKsLKSbxjpEi9bYXc/YYs4XdHHjFZTwUiHJUcaN7BVfyS4r4eYTLKUs++vIG7xEa12xegCvue/p42yUDq2gg+zRkr4ok/+4Ty0wlwxOju3lidC+7J44zmy9jmwG8Uh01ri58UgNYQfJliUS+Sr76my1SABGfRm3ATU3ATW1w/rom6Jq/L+imrXCEtu3/gJY4DjULoeM8MIqY6VGGiyWeooWb6l7K0Zpe7GcVwQeLBRoSOZoz0Jzy0pSGYv0E1XU7OEd9Ar9UxFVoxj20jtFjNuOFUWo7OrBMg7mRIbBtuqvQOTSOahgcX9rLxLktdGgTrM4dp7mSp2hdRNG6HE0aIajeiksexkCjX+7mEbGJpAgDgham6GOQGpLsMHt4ynUezV1LSBZclA+NsayYwisXuX31g0wF51g+fT7HU5tIyiGuHnqSdx76Jaqwf+/Pz0RzEz/dcBUPmwtYkhrm81u/jct+/uMvJEH+JRaFq2xcAxLRb6uUpCCnG+vIhCJc0vQWLFzc5dqDZc7yt5/+NF7v83fh+m2c4OFwnL2c4OFwnMWc4PGn8ZlP/geFtgBb0ney0BiClExwIo8xI1Oec2Gb80GkHNOoLrCxOm0CtWVqKDI31UDqeISwMYsiC+QZCVdKwvYK9FU2lcVufJpKeyFBxMgBkNAi7AgvZ29kBVN1S+lWnqRD3863+9/OwFQHQpHwNMi8IvMkV2Uep48h6oMFhIDTpTgHs02M5kJUcHE8sJCJ2gVsWTDNBQtO4FJmSZbneDxhYk+t5eLMuSwrLUBIFoXaA+Rbd2E1z6G5Y5RKQxhGkkq6lczJi8iOr0XYHmwpQyYwR7lG4dJ8Pb1mBLsww9TIA/zrBWt5atU6kCRClQxXWY8w7Q2zVToPC5WLCiZ/Xd/I6pYIZqpIMTFBOTNGpTBBtTpNRU6QthWMbDfVXCclKUrBrZDTJPIyZCVBTggylkXOtMnoJtmKifUC31WyUgYlj6Tm8XksWsJBemoaWNHQSXe89umQEfO7cKny877Gc9gW7P0BPPxZ0Iuw8X2w5TpwB+a7bZ1+lNzpp/ivXJRvN7+cvDtALJtGNU1m48/MQl6TLVKfsckFZbqiO3ipcR9N2mkk001gcj2p43UMjJ5GDnioaWmllEmTGx2hdzZNeyIHqkZl6aXYnRcSFwFcQmbKneN4aJTZwDC9xgDrcwdp1BMIoISXg/RxiMVMM9+1LS5SNFglCpUuCoU1yGjM+keZiu8g5t/KvqBEQpa4eO5CDmZW0G/VEaFEu5Xkmp4Grmrz4rZNEMzX1iDOXM//LYQAAWP5PP9+KstjeiebQwZf7jVReGa5Xz9fCJuMYTAq78FsuA9d93LkyIWUShH8iSniWYuLGt9ISQiU0ndY8LWbnBYPh+PPjBM8HI6zmBM8/nRmx8e56Ss/pty5mIi5gwuy99BEkjGtDjVpUjueojKnUZxzY1fnT2D1sILRayBJAu9+GcmUqDRqSAsVOuunCUvzXWbG3fXsDC9lINxBJlyDy5WjzT5MizpA1XbxndNv58joAoQAb43gr4z7ecX0r2gLJnEpNpmqh+O5eg5n6ihYXuTOGvpVG6FfwUbiXCB5kJFIyXkeDO4hKKlsya0hYPvI+wpkeqcpdo6hegtolFHsIla5xPTpPqaO9SGl6jAlg5Haw8S7ClxU9tAwEEdVe8HSKfffy373FCcXLcBQNUouF/6WAgcaW9ktr0XFZkvhOFcXdtMkj2C7MiRkmNB9jJcjTJVDzFX9pAwPOWt+3gm7Wo9VbQT7mboAFwKfLeMV4BUSXiHhEeCxJdxCwm3P3x9CosatUut3EY54EEGTOc8U/eIkh8p7mZOmKGk5OqLtT3fNWtew7jcL1X+bwhw89Ck4cBOEmuGK62HRy57pPmRbFMf3c+PpU3xdb2RWC7NofICNe/cBcLS7h2MdveQD80MTIwTd4hRvKPyKhb5toBh40j14RjbjnVyKX4lQNDKMFI4xl9hLx+gwtYUyVX8A87yX4Xnp5RQCNoV8Fu/kTppnH6a1eBgFi5QIEJZKCOAheS0P6xcg6XGCaglLy4MksBCUXVM0uYdYQ5Y6S0eycnyg1k1Jlvj8pMKvqi/lVmMDEnCNtodXqE/Srk7jV8xn9ptnuk8Bv44XTFtxPld8LVvNbi53HeD6wE0AZAgyYdUybtUxbtVSFPMtGDWhcRYs3oqimNSd8lGfKvLIUDO2uZzN9a8iZ2fp/OBy/I3tv/979ustdIKHw3HWcoKHw3EWc4LHn55lmXzpC1/G7+nCL2eJGfdyUelxPFTZ41pEWguweG6YwESR0pyL/JwX25QIdVSo6crhiZqc8rZwPNjOdDhIJWoS88zgM/OcLndyutDNWLGZuXIN+UqAQsENBjRH0ny4fDMXVPcQ8VQwbJmBXA2HMg1MlEM09vTRt/kCFm7cjD8SpWSU+NXQr5gpzVCdKuA7LujLttJdbcXApCrr+C0vGdtmTMpy0j1Kv2sCXUjUFzpoyHeiCJWMbxZpcYZztvSxuWMj+u5ZsvcOIkwJY2QrsnsOtWMtJTvJeO0oD3dZPOlbR7+0GI8o0VN+FDW1nYpepmRLVLGxJB2UMpKsP/fgCvBaXtyWG13WqSgVgio0qoJWt0mry6RZhVoriFJqRSr0IfLLsIxWhPChCJAMm3LBoFQyKBuCii2oiGdOgJ9DNaioefKuNFl3CjUCzfX19LZ0srx9MTU1YTx+7bd39xrdAff8Pcwchq4L4YovQF3fcxapWDY/G5vkqyPTjNsqfakp3rRtN5tP5UnUt3K0rZUdrbU80uSnrEr4RZ5XVB/hEh7A5Z5G0YNIk+eSHlhAfnKQopUi3N5ANJmmdscefBWddMBLfkkLdfWzdKpDuHxubjXP48fVLVjxBazz5rhw6udcJD+AW64wVl3Kvcoy7o6mCHkrdOQ7UcsRJCQEYMoKRZebOZ/BsdAdaJKLC5SXIlfcPDzSzJwRYJEyw1p1DK9aweOq4NcqeF1lPGp1PnecOejSmRtlQ+P2mXPZb7ay0jXKGmUcS8wPBqDKBgGtiN9VJKgVcMlVZK2Kr2UY2VMhe6qZ1KEO8skcHYHlLIhcQNN7VxDrir/oz7ATPByOs5cTPByOs5gTPP53fe07P6BS9bMkH8VWHqW7ei991hAl4eI+aR1zkRjLGCRq5hkPxRn21XBCbmK43MJcqZZsJUSp4saoymACCHxU8VPGJ1fweyp0auO8RtzNufoYCjBcDXA81cBArhYrEiGwopva1UuJNTUT0AIEXAGCWhC/8KKP5Tn5+D4aRoO4hcZcMEt4Qwttq/vInc4ytnOamZEcmaJJypwfxhbAp4DLbxOJqrR6A2i6QCsZuIpFZMWDmThJZeJRhjZJHOua5rjXxZj/Avq1cylLfmL2NDWlh0ilHwdbR1hesHxgeZFsDy7dT8AMEDR9hEwXIVsmaEv4kJH/W9cZU7LQ5Sq6UsWSTYRko8qCgCKIKBJxRaZWVqnBQ8CK4hZ1aO4IrlAclz+OYnuwqxZ60aBaMKgUTfSSQbliUTVtqjZUhaBiQ1WAJQQ2zF8E2JJA9cmEYj4CMS/+qAd/xD1/Cbvmr0MKrsM/gkc/j6gWsJe+BaP3rzGLHsxkBTNVwUqVKacr/Cou84MuN6N+mc6CxVsHs1xychApk6RSTvK1jX3ctn4NwWKe3uIsTd4Ul/Awtf69gID0Mo5mL+DAbIjY9AlakidYnk9RM5RE0wUZn5tT9VH6Y41UGvoQni5EPkCHHkBGJh04RkfkFq4QR6mxTMyaXtTNH4Kl11K1BCdOnODEiRMMDw9TLs/Ph1KJVrg/fD9RO8rlmcsp5XV2600csxoIShVWq+N0yGl+n15PQsBOs40TVj2r1THWqSO0MkG3GKXOKiEZXsYr7cxUW0ibTRhKgKZNdxFqPUjm9GYmd12Onr0FRanjrf/yGcItLzwIwQtxgofDcfZygofDcRZzgsfZ4Ud33UX/bIGlmTaW5vvJuO9jtf44fioct1t5hFW4bBM/FfxSBT/l+Wu5QpAyAcr4qeARVZ7vx/WMpXE8U8/xTD3Tspex5ionG7PMBSsgQdQM0VVpoavSQnelle5qC016LTIyJbnMqeZpei9cQzTcxmR/hvETaSYHMlRL80W+0QYfDU1+grbAn63izVbR7Pn/91vYVOwsfjmKXUyQHLqNby89yhN9KuHoOZT8FzCt9KAJnW59P/rUJNPjITSjhrCtEpIEcZEnKvIEpAoe2UJ91k5KwsZnl4gJkxgaUSK4RQ0VFEpIlCSbsmRSRqci6ZQlnQrG0z16nk0WEl40QsJLRPiI2gEiIkjE9uHFhfR8T3oRxH8PJYB15lpI4FPz1Mo34lfuw8ZPznwjRekq1JgfNe5Bjc1fiHm4XzX5WirN8VKFdo/Ge4xpLr3nB1SeOsDOpoX88xvfR9YX5K/23sTLEw8w3d6LHo8RiB5CuDPIlRr6SxfzXd+l6CWVSC7JBXse49Ld24nnc8xEohxqb2Yu4MZXrVLxaIw0JYks9/G+LR+jN7YSjvwctn0VZo9BsAk2vBvWvAU8YWzbZnJykhMnTnDy5EkOFQ6xvX47rXYrH2j/AF0dXRyaM/nqE+OcTlXojrnp9lUpzo4QI09U0ZGF9fSxkyQJj8dLxOsjYll8dy7KgB3nJeYkqwphqiIKz5rQUQgTycqi6Sk8Zob4+l14lh9A5NtJn7ycxOmTvPlfvoQv9CK6xz2zLU7wcDjOUk7wcDjOYk7wOLv8/KGHeHR6gq5cD1dPFNBdT+CV7qXbPk0FN1W8WHhBaAgDyqZJ0TDQbRndVs5cVAxbRrdVdFuhbGnMFAMYpo+ZYCN2ayvdgTALRJBGESaoB1CqzxRF60GbQswgHSqS0ipEA73oky7GT6Yp5w0AQjUeWhZGae6L0twdxiVLTE2f4PDIkxxOHud0dZa0oXPJ3DquyG1GtkyKg/dyU/0j3LlhJa6aTcz4VmJIbprtUbry48SSEbypLK5yEs0yUOE5hb9uu0xUZKmVctQgExIRAlYTHrsXiWfmgCjZJQq2jqCMJMpIUgmZApqcxSOn8ctJ3HIKUypTkiwmFJVh1cW44iIh+8kTQDWDBI0gmnhmTg9J0vG58sRceULuMkGfQixaQzTehd/bjUdrwa02IgkFTIFtWoiyRTlTJJmYJp/JYharqLqCz/LitrxIQn4meAAVW5C2BApDLPZ/lzrtCCW5k2L7h/F0nI/sU+cvShnZmoTyJA+XSnzZqOeAXEOTkeK9Yz/j9RO/oKR4+OCCj/BAzblcMLeTf97z76jFCnnNRaVtOaXWMnq8H2yFUmYN+6313OlbzbTHxyW7t/GG++6gdXaa002t3HjlNWxfuhJvpYi/VCAm2TSHgnQ11NPd0MDime30HPgvAmNbEa4g0tq3wDnvgXDz08cvmUzyvV3f48ezP6Y9386axBrCoTDRWJz9GRePzvnIWyotcoY16jghycA0QgSFCy9QpYquFrGV+e51ti3xsNHLpAhxiTHK0mQSfyFJqJigRlg0uGrwRnpRYl3InjAGFkfbn8DVexN5O8IPCu/jzqvfgKI9E1Z+X07wcDjOXk7wcDjOYk7wODvd9uAj3JMeJlLq4y9GbdpKFvD8IyYJIbAlG0uxsDwWpmaSLk4jjBki5UmUvI7XbgBXB3KoBUmdn+HcFjZJo8wEglnVpCzpmLaNZrqRTQ+WoT69Do8XwrEiVf8IQ95+BtRZpkWFLDoVuYgl52kywyzPdbI810Gv3kGjaEVGxhjdzh0c54aLLqHa3kxai+EWZdaae+idGMU14kLmmRN8v12gkVkapTn8NnitBjx2O5LdiSLV45b8Ty9bsGwyFmQsQdYSZEyBCQhspBc4Xs9lIqEjU0GVirikAh4pjyqnKHhyJL0lZjwmSVXGlFx4LS8hPYjH9jz9CjImMdLUyGmiSpqIq0jMbxEL+fCEGlFDnWihLmRfLfhiZGWF/flhtiaPcnjyGHqqSL0Rp1O0sMDVQyPNkHNTyUwRNh5iofd+vHKeohXDEgF8SgpVKjxnLwzh5f7YRXy17S84GOnCr5fpGR3GGM6Ti8WYWt6Bv1ziuhu/x/JTI8yFa0lFagj3xIj1zWI078J2FcCWUTLdFGaXMpJYgTg9w8rj9xDPTjMbqeGejefx+Nr1FAIh8m4futvznO1Ynj/Je8Z+ysvmHkNIEk/VXsoTzW9A9/QQNSy0UpG9hVvZpzzAkuxyFqUWIM7Ub5hC4rhVzyGzEQOFHiXBKmWSkLCRLZBMAaYJVgWYbw3RNTd3u9aQEH4u0QboUUo0+OtoDNbh9gc57lHYK5kcV2XGfH4qmptu0c8HxRfxUGXVyl/SFO/8Pf6dPJcTPByOs5cTPByOs5gTPM5uP7/3Qe4uDzPs6cI2I4QMQdQw6CjnaMlZNOdcNJc0IkJCO9M6YJ/5f+6zax1MLPJ2lYKhkzFVkrabvDX/S/uvyVYRQxTJalVm/DpZT5GSO81MYIh0YAhJy+LVbeqy0J7xszTfRZfeTYPcTsTbiqrNBwJhVrAyI5jpYR5oVPnFRSs4FmnFklS6RT/r8vuoHyqhp8NERJ4aUcRv23jNMB6jFYVW3FKEoOwioDyzD0VLkLEEacsmZRvMUCKrlii7qxh+cMV8tHa0sra3h4DfzUBihl39BxidOE21WMJlSHgtFz7Ljdfy4jH9uE0vmuVGtV2otobM/K/fQojnHWZVYGHKBUquNEVPhoqriFCqyJKFR8goQn1WdyxBlCw1pKghRS0pakhTQxIf1fkl3CEsT5CC4mIOE72SprZaps6ynrNeG3m+wFrAnNnJYPkcxuUWjhBnp4gxLvvxCRm/kHDFNCZ7/MzVeXHpFstO5+icqvL4qhrmIirr+ytcfLCE+us339YxlASe9v00NkwSjUxhhMYAUEoxlIkerJ0h/IcGCBSmKHlqON2ylomInwqTFP0yZY+HvMdL0RfEDDYQ9MAV+mNclXsIr13lkeh6vtH6Wp6KrAYgkPo+3uJjFCNvxvZeiCRAsU28ehl3uYw+bpBPzgfHJn+BXm+SsFnGa1TxmMZzjk1VKNyn95EXbrb4TiPCLsouNyWXh7Lmpuj2UHR5KLt9GB4PLlWlRkqyzN7FN7Zc9xs1Qb8PJ3g4HGcvJ3g4HGcxJ3j83zA0PMkjTz7KuJRkJKwx4KthSOrAlOZbCqJmmrZCmt5ZhTWzATYUVILSmUkKLUFJWFQkg6JcIa8Uyal5MmqWisjgKswRTc7RPJWlaaqErBfBNhmPS5xuUPAbHtqsFmq0NryBdpRoB7J/fj4JIWyy1RlOKRlO+Q1OhRTG4n7molES/hgl1Ydf5NlkP8Wq6ZNET4eQ8vW4zUYCVhNhxUNIkQjJEq5n1W0UbJuk0JmRKsy6dXK1ErVdUdYu62JxawuK/OK7xwBUS0XGThzn6JHjHBg+SX92irTHoOAzqAR0DFcJt7DxWipBPUK4UkuwGsOvR/DpQby6B5epoFryfIuKrCFJHqRfBxZsLLWEqZYw1AKGlsdWywhZf05NiYZJUCkQVdPUqBnqlDz1Uh6PojCt1nNcj3KsFGS4VE+y2kZVBGiVkrzF9XM2azvI2WEeLb2K/vIFeCwv8vO07gzVGGxd7GWoMYRqmrRNDVJ2Cabq+/CUp1l97AbOPzzA2mNFYvkz9TiShAjG0bqXoC/XKLcPU6wZRKhVJEMjsK0ez8MVtESOsjvGSNtlTDauw5BymAxj2eNI+hxypYgEuDTBiro0a4JD+KUSc64ODkYu46Cnk1/xS2bkCRqqm3BVlyLO7INsC9ymhdAlJkpxZswQmgKLeoNEemvpnymQS6eJKDohxUS1DOyyzvCYH8uWudB7iqhcxm395iSDNmCoGpbLg/B6+dTb3kzA7/+N5X4XJ3g4HGcvJ3g4HH8gSZKGgTxgAaYQYq0kSTHgZ0AHMAy8WgiRPrP8x4C3n1n+b4QQ9/+udTjB4/+msfEZtj1yH7P2DOMROBkIMyB3kZTmA4EmDJrLc/SkDZYkPURMmYApE7BlApZM0FLwWOC2BW4LNPHcWmvbNrDNEpJRQfbWUNEUxnwyp91V+r1VTgdgPKww5/NT1J574hYVSRqYop4pestjLOkPER07lwg+QgoEZOnp1gRDCFJCJ6FVyEVt/N0RVmzso7mh7k9yHC3TYHboNBMnjjJx8jgTJ49RzmWxgErQj93RjF4XZc6jMptLMqOnKGplTKWErBTwYRLQ3TSm49TkI4SLPjTbhaXIGC43QvEhSX7cZgCv5UGVwFJKZ8JJGUstIeRnnSALkISKbGtItoZ85iLZZ+4TCg3yMBd5b6VJHWbcauMu/WpOy3VU1BJVtUhFLWMpOpqiEPJ6kGtaOBZbwhEtigqsdWkcMyyqwMcaa3ltLIwxPMLc3qNM7jtGYSqLjR/DFaHqjlD1Boi0TRFtPoJSfxTLN4v7qETobg/aqIXh83G69UIm6y5GKG4EAtOaxK4eRuijIAooks3i8AxrYxPE3GWyupttmSa+3K0yWVOldUbhtY8ptM+BXFdLYM0amq64itpzNnBkMssn7jzMofECLreE3OEm2xZFyPNBJVAs0lDVWe338ejWOSzD4EMr4S9f+RJKpRL5fJ5cLsfU1BTT09OkUiny+TymafKJT3wCRXFqPByOPydO8HA4/kBngsdaIUTiWff9C5ASQnxBkqSPAlEhxEckSVoM3AysB5qAh4AFQgjreV76aU7w+PMwMTnHrkfupVg9zWTU5mTQR7/SyTBdGJLrdz5fEjYu28JlW2i2jcsWeCyBZsOcRyXjem5f/rBI08DU0wGjoZqnuWTTktUIFWvwFJvwlRpx6dGnux7lhEFCrZAN2mitfhat7aatt/W3z3HxJyaEIDM9yfSpfqZO9TN9qp/Z4UEscz4c+CNRGnoWUNu1kJQdYcdgiV25AlOuKmW5jKQUkNQ8kqtAQMlQV00TLxTwViwsBFWXoODXqHq9qCKAVw8SrIaJVsMEzACKUJAkAbINkoWQTIRsgWSB9Mx3qYRgOce5hCcJUuKgWMwj1sUU7diZsKLOBxfx6wCjkvF52dEb40i7b35fKoK8T2bhuM5Ldxfx6QJbMjC1Ira7hKVkMclhajq28sy6G6Q8HZEEvroJpOQowfsk3Kdk7IBCcVELQ4F1zOrdSGojkqQhhI2l92PpxxHWBN3+KdbFx2n25SibCjcoNfyozUNRhdf2vJrXLHw7OydSbJvLcLhqMqx5qGhu5GQF7WQWKW8SCqm8akMj1yxp547dE9y8a5SibrG6NcyxySyaXeFvFpu85bWvRNM0no9hGC/42O/iBA+H4+zlBA+H4w/0AsHjJHCBEGJKkqRG4DEhxMIzrR0IIa4/s9z9wKeFENt/2zqc4PHnaWY6yfaH74HiMfLBMlVNoCugK4KqClVFoiKDLsvokkYVNzou9P92baARJUUDUzTac7SUDNoKKpFiDFexEVexEblYj2GpVCSLimxRlW0MTWC5JZQaNx0rWuhY2oni/cNO8v63mYZBYmSIqVMnnw4k6amJpx+PNbXQ0LMAzd9E/6TKk0XBsCyRkl1UdeWZmQdVgS+coF0+RUNpHF8xSUUqkvdUyQR0EuEqhvbM96WvohCreKg1AjQSoVmNU6/6kRQTSypjiCK2WWBFaZTl5REsJHa6lrFVWkHV8iEsF4jf7IKVd3s50NrLicZ2rDPd8bymyaaxfrrGT6Gc+c72eDzU1tbiEhaF0RFcsxXqrCBmOUGuOk2BIqZboanDQ72SJrRvGveAjRUQVDYFMRrbsU4HKUyFyYY7SPl7sGUfws5j6ieol7ezKnyEdn8a3WXzlWiYnwcDREy4ZDaMqS/jWHgRoraJlvo61nV0sLaxln0nk3zzoQHG0mU299Tw0Sv7aI36uHHnCD/YOkyiUEVGEJFKvLOnzFvf+FrcbvdvHIf/F07wcDjOXk7wcDj+QJIkDQFp5k9d/ksI8W1JkjJCiMizlkkLIaKSJH0N2CGEuPHM/d8DfiWE+PnzvO47gXcCtLW1rRkZGfkT7I3jbGIZFvlsnpmZFJlkikI2SbWUxioXEWYRySojiwoKOpYZwRYN4K7FF/ETrgnS0FxDbV0MV8CN9AcMR/p/XaVQYPr0ANMDJ5kanG8ZKWUzACiaRm1bF75wK8WCn70lF8f9bmbcKjnbg1mSnm68kDRBwF+hgRSt2Wni1TFkMU3BXSLtrzAX0kkEq5i/bnEQECypRAoa0bxGJO8iWtBorVpcXDdETzBJznSzrdLCqBJA8YDklpFcCqbqwpZDYEUxqiGSIsqupj4ONLdhnelu5BE2F+hZrh3bjzTRT3pmjnS2gmE90yolIxNy1RBx1eHBjWv2FOHRI/jsClZzEFm3UCcr2D5B4SKLyiY/3lQ72qiXVNrPKV8XaaUHVz6CS1cQdpqIdIIG7SDlyCluaKtw0qNxTrnCdXMZPDkvE+UQE6UQo0acjCtMOhClP9DHabsFQ6g0RUyWL/ARr4sypWvsPJamOF6gTipwWU2CV73xdXTGIwQV+XkHDXixnODhcJy9nODhcPyBJElqEkJMSpJUBzwIvB+46wWCx9eB7f8teNwrhLjtt63DafFwOP7fCSHIJ+aYOtX/dMvIzNApzOr86FVuf5BArA3bjDJT0jhZ62c0rjErRSmUfIiC9UxPKgkUN7hcNmG7RGNxltbcMWLlfiw1wXTMZLRWZjoK9pnMJ9tQk5e4MFvlbUaKJllnpBxg61QnM9UQ9gsNLSwL9JCbo92rONy+ipnaJpBkEIJ4Zo7VJ/ew4sRRFg8OEipXCVQNNJ9Gpq2WcmwFjYGNhF21FMw0h81dHArPkovW4MnZbNm+n66hUWy3TPF8QeGSKsKn4E124j0VRe+XmSnJCF8Qj+wCKUBRriGn1HKg4QDb2h5Gl01elTL52/w0AWyEgLmqn4lSiIlyiKFyHY8H1nMgtBxbklmeO8LazD68VBgI93Jf+GJalAwXK8eQlTkMrwsR8EEoiBwK8omr3odb+91dEf87J3g4HGcvJ3g4HP8DJEn6NFAA3oHT1crhOOvZlkVibOSZepHBfpJjowgxP46tO1CDJNeiVzWyXSaDjWFmXVHSeph80U+l6MIuiWeXdiBUCcmroLlsfFKRMGOExSgeaRxDmSPjzpH16lxbKPDX6SxB2+Z+xc8juRDhlEZ9SqY2o+CvyBgeBcMlY7gVTFnGWzBxlwUPbrqEe867hGzwmRm9A0aBZeUj9KkHqfdOUcXDNE1MWU3MlBcyqzWQ9Dy3K11EpFg3to+X3/cwS/YPY7llyuuDVC600BtSAGilWlzpBtS8DyXnQs2oKGmQU4KELrhx1RhbO6eJlLz8xWAPm3WbWt8QDa7TuOT5iQRzhofhcpQnpJXcrW1hyK5jXfYQ5+QOcDTQw33xi+mW5jiPk/jH+pGN6tPb+P4f/hSXN/Ci31sneDgcZy8neDgcfwBJkvyALITIn7n9IPBZ4GIg+azi8pgQ4jpJkpYAP+GZ4vKHgV6nuNzhOHvo5RIzQ4NMDZxkenA+kBSSZ0q4JBlJ8uOJlwh3Joh0ppDDNkPlDo7PLmI438t0pY6C7qFqyNj6c19beBRsn4rwmbg9M9RJA/xN9gFeleunJEt8PRLmllAAU5Lw2iodeoi2goeaOZ1IqkLKYzHqKZEJeBHuZaTqLmSgsZeK24Nk2whpfhufzWUZNNol2hSdtqJJ94jCwpyf2nyR6cI+qh2jtHQm8c8NYD2YQxxTQQXrXA/lLXXofh1LLWC5CgjlufNzYCto1ShDhQA3V3JMUGGlWcebM2uoK8RQKjk81jh+ZZiIOoRHyQGQt73sFgs5aHZRzeoctLrYHt3AInmK9dIQRnaQrJal5CryhQ//gvqa+It+H53g4XCcvZzg4XD8ASRJ6gJ+ceZPFfiJEOLzkiTFgVuANmAUeJUQInXmOR8H3gaYwAeEEL/6XetxgofD8b+rkEoyPTjA1KmT5JMJCqkchXSeYjaH7Jkj3DFLpDOLNz7/S30h4Sc53cRkZgGzZjNpfKTwURRB8vjIWRLGs2aG7JHH+ZTrx5zHYQbUej5XcwHbgx40MY5iT4IovuC2CSSqvo2UwtdgaQ0o+jha9RRCiaN7FiJkF5KwCFhjxO0R6uwxYnoCaa6CVlFRbIlyNYel6dQ0NNBVKLL4gVM0Hi4hZEgsspleX89sVwNl1YVezSOqGTxWBb9iEfBI+NwWLs1gV9XkvryKDVwcNLg4ZOKyXWjlOGolhqvqxVPV8VZzuPRJovoMnqqFZSp80nwPP7M3sU4ZY7kyRetMCamc52Vf+iLRePgF9/+FOMHD4Th7OcHD4TiLOcHD4Th72ZbN7Gie8RNpxo7voCzuJdh6FF9NBoBS0kt6qoF0rpeMWY8AZCR8kh/ZiCOZtSRwc1qyaJF38tfKjbTLszxorebz5hsYkRuR/CUIZFBFGU3oeCQTvyYI+VR8HpmqmSVnZpn2NJGNrsd2RXEXp6iZ2o0kDErhFkqhDiqeegAUu0xAH8Jf6cedO4Ksz2LJJgY6pmQiZJvajM01223OPyxQbSi74FSTxKlmhdNNLsYaAxiuAMLyYetuLF3FqspomkE1PErJncIj3CyXalnskahxFYl4cvhd+fnhiJ99DA0VuQI/PvlqHktt4Orax1nimeSccpHF7/wO7lDNi35fnODhcJy9nODhcJzFnODhcPzfYRoW04NZho8fYW7qNtTYbnx1MwCU027S47XkMr3k5VYq1vx3b128ls5gM83lCKEJC0XcSYP6M2TJ5HHtpfxAeyVDhhshqwgkLCGw7PmLaQvsX18LgSkERpMPsysIXhUpVUU7lUNO6wiXjB1zY9e4seIe8MxXvksFAzlZnb+kqkiWAGyQTKLVNMvnRlmcGGNJcpjO3BQyAlOSGY40MBRvZDxex1xNDVI4hFd14zElsgxytPYxCr4CHaMRtuyqoyXQQLS7hfCSOoK9Ydw1INmz6Po0Q9ODJDIT/HzgKo4kF/GOZTewvmEfa9fsJBJxgofD8efECR4Ox1nMCR4Ox/9d1bLJ6PGTjPb/nDKP4qkZR5IF1axGejRKeq6TitxNUVERQuDxeOisb6MbjYVTtxDUH8ASEXK8CT1yJUo0jBJ1o0bcKBEPSsSNGnUjB1xPT/QohKBkWtw4meTrY3PMGiadcp6eiaME+kvUpbpxmX4SIYnT3TazzQEmvF50QBHQkzFZnDBoSyfIDTyJoSfRhEldbZy2xjo6kIhkssinh6geOYow5ms/XDEVb6yEL1bEV6sjwgo/rO/gO+4qkpB4/WANlz2YQ84WAFAiEbwrV+JdtQrvypXYC/v49s4pvvnYAKaAqzum+Pxb3kLI8+Lnl3GCh8Nx9nKCh8NxFnOCh8Px5yObnOLU4dtIJO5GiZxCkgV6XiMzHCI13UTJ7MUIBtHt+SKQFXGLC0v3Eimfwpa8VLXNFPXzqVSWAc+an0WRUMK/DiTuM4HEgxHW+IlV4RvJNAnD5KJYkNfGdLIDxxg9lEIZiRAsxzFlONqWZqzbzVy8hnHJA0BYF3RUbQKWjqeSw05NIKWn8JWLeMtFmrxu+iSZ9lKFwGwCMTCAnZ0vIldCHnxNKnokzY0dcFu7h07T5MNpH0syLZTmVMrDGfSxqTP7oODp62Nu+Tm801hM1oTH/v582mucUa0cjj8nTvBwOM5iTvBwOP48GUaG0cF7GB+8DcN1GEmxMYoameEA6fEacqVOiNZRlqGTMVZI/SxiALeoYLqjWJ0vRbRcg6kswspWMTNVrHQVK1PFylWfmZEdKCtwa7eHH7WqZFWJi0yVD3iCLIn4GC3MceDkMNP9VVyp+ULuyVCSE50FrLo2SlqAgqSS0SQyLgn7BSb481RK+Ep5Fs5MsHJsmMVjo7QPnyaYmAPAdKsMNMHhZptQPbxRnaJWsrCqEuVqK6ViLeUZKA8nSdsudjcs4n23fwMl4AQPh+PPiRM8HI6zmBM8HI4/f6aZZy7xKGMDt5Gv7ADFxCxrZIeDpEdCZDKNCF8tkt9LhzbCMk6wgCFULAquWrKtl6KteQPxhRtRFAVh2Vg5HStdxcxWsdIVrEyVTLbCjS6dG2pl8prExdMG7zyl0120kTQZKeiiiEWiUCafg4oFumRRdWVRXWX8iozHHUKSAxQUF2mXRMYtkfLLJFwWs7LOrKyTUSQKbg9lj494Ns2ywX6WDp5g+cAJuiZHkQVYEgw3xRnrWMNsSy/5+m7criBRXSKqy0R1wdXvX4LmdUa1cjj+nDjBw+E4iznBw+H4/xfLKpFMPsHM5N3MJR5FyBVsXaWcCFBMuCklVAq5KJYZojeYYYl7iE55DAmYoo5TrrWk6y8l3rGcrt4O6ppjKMpz5/fI6CbfGprhu9MJirbgpZKb9xZV2tLGfMtJpopdMJ5/A/8b+0zTigRInKkzkUCoNgWRZ4Q5Bq1pJuw8SdnCkjUixVlqM4fpmczQOynhMudfY7yugUM9fRzuXsjhnoVsv/YyXKryQqt+QU7wcDjOXk7wcDjOYk7wcDj+/8uyqqTST5FMPk4utZ9CqR8hmQAIXaY656OY8GBmVJqrVXrVJE1qCiFgWGrlMAs5YS5GkpoIe2upiTZQX19PuMZHMObBCmvcWMrxg+kUVdvmLxqifKijgXavG2FYmFkdLBsUGUmRqJRNUtNFUtNlpsbTTI/nKCVMsCVkIKhaeL1FIqqgCT9B4QXAUC1KtSZKkxc1IJOtzDA11s/W1C6eqB2gPmOx+YjG6jEX8XwFtaojJImebU/hisZe9HFzgofDcfZygofDcRZzgofD4fg12zYolgbJ546Qyx0iN7eXgj6IkOZbJ4QOrnGZmlmDZr1EmCqmkBkQ7RyWF9NPF6atoupuNCOKZsbQjBBW2M+OpT62NijYElwm3PxVOExvbQDNrSDJEpIkIckgP317/m/bEmQTZdKTRVJTRabH0qQmi5hF8MkQVSRCLoOIJqjBg8x860vClyVXp6M3wH3mwzxUfAq30Dh3vIUFe8qEqiavvus+VM0Z1crh+HPiBA+H4yzmBA+Hw/Hb2LZJqTRIPn+UbHIfubm9FK0hbEknWDBpmK5SP6vjtmx0FAbsRvabSxnUehGSCoAmZDySH12pZ2tTO/vbAwhg9ekqK4eq1GUslD/wVEGSztS5i/lxuCKqREyRiJ25dp0ZBviUa4KvNvyUfv8gzaVWtkxeygf/8b14NPcfsE4neDgcZysneDgcZzEneDgcjhdLCItSaYhc7gjZqR1kE3txZ05RlyxSl9BRLUFVkRl3hTkm2jhV6SRvNmCr812jii4PhzoXcbi+FVuS0YSg07JZJGksVb0sktzU2WfqOez5uUNsWyBsgRDMXz/7thDYNlimTaWoU84ZVAoG5UIVuWgSkSCmSERVid3RXXyv/hcU5DJ3XnEHbY3tL3r/neDhcJy9nODhcJzFnODhcDj+JwhhUy6PkJ3bhbX3p/jG9hHJZJAFlDwy0zVuJpUACb2OTLmWdKmGKTqYDDf8f+3dfWxdd33H8ffXz3b8kDR12zw5KTTdlhVKwSsdYmJiGtA/piIh0FYNKu2P/dVoSKtYy5BWhDSJDo1psP2BtLHuCbYCE9NoqbaJMZ7Wrg+0IclCkjZx3KRJ+mC7duLYvve7P+5pexM7Hva41/dev1/Skc/5nfM7+X0U69hfn3PujzODmzg9sIkX+jdSaq+87N07N8uWqZfYcW6Sa2en2Z1zDPf2Mjg0xNCmTWzavJn+oY30DgzS099Pe8flH5k6NzXHi+PTvDA+zctjU0w89zzPxAF+9xN7X5sYcSUsPKTGZeEhNTALD0m1kudeYu7p++HJL9F1+hABTG3o4Pmruzk93MVcRzs53UZ5vpPyQhez5V6Otb2Bo51v5NmuXRzvHuFs95Wvne+K8xNcPf0iw5MTDE9OcsXUy3TOzxEL87Rnma62Nro7O+jt6aGvp4f+gQEGhwbpHxiib3CQnoEBejYM0DswQHfxNS4zb8hyLDykxmXhITUwCw9JdTF1En70VXLfA8Spp0iCmcGreKWnh/MdcK6zzLmuEud7y5S6S2Rv5XeHGfp4hus4wm6Ocj1H2c1UVObe6Mw5dpaPsWvhOCNzY2ybPcng/Ayl+S4WSl0sLBTLXCelC0Fpto3yLJTPA/Ml7vrs5+jq7llxFAsPqXF1rPUAJEnSGhvcCu/YS7xjL5w9ROz7Cv37v0b/maNcNA16tMHAFnJoO6WezSx09DNHJ7PlGS6UHmNm/vuMZRsHOjdysOcaDvXu5D9738VcdxcMwGB5gus4zBvjMNdxmDewjz7OLRpOqdRBWy7UL7+kuvCOh9TAvOMhaU0tXIDJcZg8ARMnYGKsWB+rbE89B1m6uE//1TC0AzaOwMYdlIZGOBZX8ORCP9+dH+SRUifP8vrEgDvPv8TPToxx/cuH2f3iAbZP/ZjoLnHLpw4QbW2slHc8pMZl4SE1MAsPSQ2ttACvnHy9EJkYg8mx17cnx6F8ySzofZtZGNzOyxu2crznGg62X8kjsZkDHcOc6LmG+a4B3tTXzd/ftJsBZy6XWoqPWkmSpNVp7yjubIwsvb9chunnFxUlHRMnGJ44wvDktxhdmOXDVV3Odw5wpm8L/Td+G+irRwpJdWLhIUmSaqOtrfL+yOBWGHn74v2ZMHO2KEyOw+QJeidOsHP6NHRZdEitxsJDkiStjQjov6qybH/bWo9GUo2t/K0tSZIkSVohCw9JkiRJNWfhIdVRRLwvIg5FxJGIuHutxyNJklQvFh5SnUREO/BnwK3AHuA3ImLP2o5KkiSpPiw8pPq5GTiSmc9k5hzwZeC2NR6TJElSXVh4SPWzDThRtT1etF0kIn47Ih6LiMfOnj1bt8FJkiTVkoWHVD+xRFsuasj8QmaOZubo8PBwHYYlSZJUexYeUv2MAzuqtrcDJ9doLJIkSXUVmYv+4CqpBiKiA/gx8CvAc8B/A7dn5v5l+pwFjq/yn7wSeGGVfZuVmdeH9ZZ5veWF/1/mnZnp7WKpATlzuVQnmbkQEXcCDwPtwF8uV3QUfVb9wzMiHsvM0dX2b0ZmXh/WW+b1lhfWZ2ZpPbDwkOooMx8EHlzrcUiSJNWb73hIkiRJqjkLD6l1fWGtB7AGzLw+rLfM6y0vrM/MUsvz5XJJkiRJNecdD0mSJEk1Z+EhSZIkqeYsPKQmERE7IuJbEXEwIvZHxO8U7VdExL9GxOHi66aqPvdExJGIOBQR761qf1tE7Cv2/WlELDWr+ppbaeaI+NWIeLzI9nhEvLvqXC2ZuarfSERMR8RdVW0tmzki3hwRPyiO3xcRPUV7w2dexfd1Z0TcX+Q6GBH3VJ2r4fPCspk/WGyXI2L0kj5Nff2StITMdHFxaYIF2AK8tVgfoDIZ4R7gPuDuov1u4NPF+h7gKaAbuBY4CrQX+x4FfhEI4CHg1rXO91PKfBOwtVi/AXiu6lwtmbmq31eBB4C7Wj0zlY+Cfxq4sdje3Ezf26vIezvw5WK9DzgG7GqWvP9H5p8Dfgb4D2C06vimv365uLgsXrzjITWJzDyVmU8U668AB4FtwG3A/cVh9wPvL9Zvo/LLyoXMfBY4AtwcEVuAwcz8QWYm8NdVfRrKSjNn5pOZebJo3w/0RER3K2cGiIj3A89QyfxqWytnfg/wdGY+VfR5MTNLzZJ5FXkT2BARHUAvMAdMNUteuHzmzDyYmYeW6NL01y9Ji1l4SE0oInZR+ev+I8DVmXkKKj/cgauKw7YBJ6q6jRdt24r1S9sb2k+YudoHgCcz8wItnDkiNgC/B3zyku4tmxm4HsiIeDginoiIjxXtTZf5J8z7FWAGOAWMAZ/JzJdowrywKPPltNT1S1KFM5dLTSYi+qk8VvPRzJxa5vHmpXbkMu0NawWZXz3+54FPU/nLOLR25k8Cn83M6UuOaeXMHcA7gV8AzgH/HhGPA1NLHNuwmVeQ92agBGwFNgHfiYh/owX+j5c7dIm2prx+SXqddzykJhIRnVR+aP9dZn6taD5dPH7w6uM1Z4r2cWBHVfftwMmiffsS7Q1phZmJiO3APwEfycyjRXMrZ347cF9EHAM+Cnw8Iu6ktTOPA9/OzBcy8xzwIPBWmijzCvPeDnwzM+cz8wzwPWCUJsoLl818OS1x/ZJ0MQsPqUkUn9zyF8DBzPzjql3/DNxRrN8BfL2q/deLdxyuBXYDjxaPcLwSEbcU5/xIVZ+GstLMEbER+AZwT2Z+79WDWzlzZv5SZu7KzF3AnwB/mJmfb+XMwMPAmyOir3jv4V3AgWbJvIq8Y8C7o2IDcAvwP82SF5bNfDlNf/2StJgzl0tNIiLeCXwH2AeUi+aPU3lO+h+BESq/oHyweP6biPh94LeABSqPNjxUtI8Cf0XlRdWHgL3ZgBeDlWaOiE8A9wCHq07znsw806qZL+l7LzCdmZ8ptls2c0T8JpX/6wQezMyPFe0Nn3kV39f9wBepfNJTAF/MzD8qztXweWHZzN3A54BhYAL4YWa+t+jT1NcvSYtZeEiSJEmqOR+1kiRJklRzFh6SJEmSas7CQ5IkSVLNWXhIkiRJqjkLD0mSJEk1Z+EhSQIqcy1ExHcj4taqtg9FxDfXclySpNbgx+lKkl4TETcADwA3Ae3AD4H3Vc0Cv5JztWdm6ac7QklSs7LwkCRdJCLuA2aADcXXncCbgA7g3sz8ekTsAv6mOAbgzsz8fkT8MvAHwCngLZm5p76jlyQ1KgsPSdJFImID8AQwB/wLsD8z/zYiNgKPUrkbkkA5M2cjYjfwpcwcLQqPbwA3ZOazazF+SVJj6ljrAUiSGktmzkTEPwDTwIeAX4uIu4rdPcAIcBL4fES8BSgB11ed4lGLDknSpSw8JElLKRdLAB/IzEPVOyPiXuA0cCOVDyqZrdo9U6cxSpKaiJ9qJUlazsPA3ogIgIi4qWgfAk5lZhn4MJUX0SVJuiwLD0nScj4FdAJPR8SPim2APwfuiIj/ovKYlXc5JEnL8uVySZIkSTXnHQ9JkiRJNWfhIUmSJKnmLDwkSZIk1ZyFhyRJkqSas/CQJEmSVHMWHpIkSZJqzsJDkiRJUs39L+wNbyyQAVG3AAAAAElFTkSuQmCC\n" 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\n" 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\n" 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\n" 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YeazhzeaCoxDNUxvZrD0GNzv+iLaB1s6PxhH3BVq7d9H2sO1x4RVRG9BEdFwzD8hK77aeHBZlbdlR0jy7Y5zq1+7flnZlNqvzo7WPL9A80yehifSfQRPRwD1oXo4wr4ivB5qf+1i2Xow2NHgKmphL9W5vfvyh6yWECETz9javC9A80lYgs1kbCJF/XHBv8zKUAOEt7t9ktL4TtPpParGvOU1oAvIgsc3+LkS7N37NhtY7pdyN9gAbz9GHLo+VzzS0YbCpwJxjiP5jMQNNzCVJKUOAdzjyWh6L31xvUvO03iGl7IDWz/3HO5xciBYS0PxZFSSlPP34itUmLevx1/qhY2cm5RIp5alo3rMsNI9eezl47c5D8+oW/0r69nDE9RBaTPfRnqnHarfHahNttcX29MHHvBda4vXyv482qhTh7ad2NrPDwtHvyaPyR4uyWcAZQpuqwIgmLuzAGrQhFRdwixDCILSA3uZuvveB67wKWAghAoQWzPd7hqNAc1efSttfn12KNoTTu9nvHG8ZItDiPSYIIYYILabmMdrfGfxWlnvtff14DpJa7NVW4BEhhK/QAg57Al95k3yOVpbhXgHzONpwUSvPEmjTLaAF+rcSOUIL+uyK9lCNBV5qts8kDsf++HhtabPOvG9tw9o6h/cN6mvgcW9bGIr2oD0oVr9Bc8uf4z3fw8B22f5hmWMipSwFvgf+K4QIFlqgaLo4PIXHLOBWIUSC903ynmNk9wVwuxAizSsADsaNuP4IW9Ha9m1o99lBVnm3lUktfvCgHQ8KIaKEFnj7MNpLS3v4tfv3t7AIraN8HK0+Dg5jBHnPVYkmzB+mtVf0WASh9TvVaJ3i022kOV1o8Xo+aLFl66Q2vHUIrz3vAy8L7YMJvNf7eGJO2oX33GuAZ7z3TE+0YceD3olZwH1CiDAhRCJwc4sstqINFeqFFjDefCjnQ+AKb7+s85bhoMekHC3G5ljMQIsbPhktlrAtKtGGoVrm9Snal9qXcPRwjEN4y978J9CuZ42U0ia0+LiLfyWb5vzmehPah2cdvTY0oH285AbWAw1C+yjGz3tsdyHEAH4/5UCq94W3Pf3QURHaB3lneft7O9rowtE+wGqrHXyLFi97K+24du1kDnBms3vvcY6uQz4HThFCnO/tcyLE4Q8NjtUm2mqLf0YfHIAm5CoBhBBXoHnKDrIVOFkIkSy00In72pPpHyrKpJTZaDff62hvmRPQpoFwSC0mazJasF0tmlv162bHbkSL33jDu3+/N20rvMKiXfODSC2W4McWwy4IIQajvUG/KaUsa/ab5z33RVIbf78ZLWC1FG1orIKjBN+3g/niyHnKvmnDXum193iGBA9yIdqQ4MEYp3OlN6jWW5br0Bp6BVqjvqHF8Qe/wmpE60wflFIubrb/Au++OrS3lGqgn9fVfZBsNO9CAtqHE1aOfEO523sOC1pn8zGaO74tbkDzoFSg3VTXe8uBt1znAE95yzuI44vpaA+XogWp7vaeYw7aGydoD+vv0b7o3YImMFy03el9hFafK9A+bLDR+uHQJu1s68vRhjOaxzat8m5r/jLyJNo0GtvRvszd7N32q/za/ftbkFpMyddoHq3mHpglaAG+e9G8NDaOb2hhuve4YrRr19YHKzPQgsBr0GJBpxwlr3vQ+oO1QhsKXUqz2DZxlOHZ38hFaH1SCdpLxyNSyh+8+x5DK1MuWrtr6Um/Fa2/rUMry7cHd0gp16MFXb+M5nFczuF78lXgXKF9uXe0eKsvgJHAT/IoHzF5h82eAlYLbShnsHd7EVo7k/y6Rz0Brb9o/ktH6wceF0KY0V4kZv1KPs35zfWG9qHIUjQx8wvwltRimdzeY3p7861Ci/9rHrP4WzkoequFEJu9fx+rHzoWOjTHSAlaOx9B6z7/II8C07zX7nw4FE7zFZDG77zXD+Ltv29Eu/9K0crT5hxnUsoCtBjGO7z2b+Xw6MhR28RR2uJv7oOPUZbdwH/R2kY50ANt5Ofg/h/QvoTfDmwCFrQnX9FCqyiOgVdh1wEZUsrcv9kcxQmE0OLi3pFSnjAT2CoUJwJCiI/Q5nZ6sMX2PGCklDLv77BL8et4vdSdpJSX/GpixR/CP3ny2D8EIcQEIYS/1wX8IpqHIe/vtUrxd+Mdtjjd61ZPQPO6tPJ8KhT/ZoS2jNJktCFUxf8QQpsr8yrgvb/bln8TSpT9OmejuX9L0NzZF7YcClX8KxFoQyO1aMOXe2g2f5VC8W9HCPEEWuDzC0cZWXgFbeRBcYIhtOmaCoHFUsrjWg1E8ftQw5cKhUKhUCgUJwDKU6ZQKBQKhUJxAqAWwVW0m8jISJmamvp3m6FQKBT/U2zatKlKSnlca34q/p0oUaZoN6mpqWzcuPHvNkOhUCj+pxBCHM9KAop/MWr4UqFQKBQKheIEQIkyhUKhUCgUihMAJcoUCoVCoVAoTgCUKFMoFAqFQqE4AVCiTKFQKBQKheIEQIkyhUKhUCgUihMAJcoUCoVCoVAoTgCUKFMoFAqF4hhs27aNPXv2/N1mKP4FKFH2D0QI4SuEWC+E2CaE2CWEeKyNNEII8ZoQYr8QYrsQou/fYatCoVCcqDidTubPn88333zD1q1b/25zFP8C1Iz+/0zswGgpZaMQwgisEkIsllKubZZmPJDh/Q0C3vb+q1AoFP96amtrmTVrFqWlpQwdOpTRo0f/3SYp/gUoUfYPREopgUbvf43en2yR7GxgujftWiFEqBAiTkpZ+heaqlAoFCcc2dnZfPPNN0gpufDCC+nSpcvfbZLiX4ISZf9QhBB6YBPQEXhTSrmuRZIEoLDZ/4u8244QZUKIa4BrAJKTk/80exUKheLvxu128/PPP7Nq1SpiY2M5//zzCQ8P/7vNUvyLUKLsH4qU0g30FkKEAt8IIbpLKXc2SyLaOqyNfN4D3gPo379/q/0KhULxT6CxsZE5c+aQl5dH3759GT9+PEaj8e82S/EvQ4myfzhSyjohxDJgHNBclBUBSc3+nwiU/IWmKRQKxQlBfn4+s2fPxmazMXHiRHr37n3E/jqni3qXmxQ/099joOJfgxJl/0CEEFGA0yvI/IBTgOdaJJsH3CSE+BItwL9exZMpFIp/E1JK1qxZw9KlSwkLC+OSSy4hNjYWl0eyucHCslozy2vMbGlo4tTIYKb16PB3m6z4h6NE2T+TOGCaN65MB8ySUi4QQlwHIKV8B1gEnA7sB5qAK/4uYxUKheKvxmaz8e2335KVlUXXrl3pc9p4llgcLN+Ry8paM2a3Bx3QJ9if21JjOCUi+O82WfEvQGgf3ykUv07//v3lxo0b/24zFAqF4ndRWlrKp3O+YrcwQo++ZJsCyLU6AEgwGRkVHsyI8CCGhwUSavz9vgshxCYpZf/fnZHiH4/ylCkUCoXiH49bSrabrczYmcWPFbWUZQ7BI3T4Cx1D/X25KjGKkeFBpPuZEKKt76AUij8fJcoUCoVC8Y+k2OZgea2ZZTVmVtSYqXO5AR0Jfn5cEx/BqTHh9A8JwKT79cVtpJRKrCn+dJQoUygUCsU/Aovbzdo6C8trzPxc08C+JjsAUQY9KdVl9CvO49wu6Zx96kh07RBiAG6Xi+WffkhAaBiDJp3/Z5qvUChRplAoFIr/TTxSsrvRyrIazRu2vt6CQ0p8dYLBIYFMiYsgua6SrQvnoROCyZMn06lTp3bn31Rfx/yXn6Voz076T5j8J5ZEodBQokyhUCgU/zPUOl0srW5geY2Z5bVmKh0uALoG+HJlYiSjwoMZGBKAD5Iff/yRNWvWEB8fz3nnnUdYWFi7z1N+YD9zX3wKa0M9p990B12Hj/qziqRQHEKJMoVCoVD8T7Cp3sJlO3KpcrqIMBoYER7EiLAgRoQHEWs6PPu+2Wzmi9mzKSgooH///owbNw6Dof2Pu13Lf+SH99/APySUCx9/npgOHf+M4igUrVCiTKFQKBQnPN+W13JrVgGxPkY+7pFBv2B/dG0E3ufm5jJnzhwcDgeTJk2iV69e7T6H2+VixWcfsXnxPJK69eDM2+/FPzjkjyyGQnFMlChTKBQKxQmLlJKX8sp5Ia+MQSEBfNQ9jQif1o8uj8fDmjVr+PHHHwkPD+fSSy8lJiam3edpaqhnwSvPUbhrO33Hn8XJl1yJ/ji8awrFH4FqcQqFQqE4IbG5PdyRXchX5bWcFxvGi52T2py+wmq18s0337B3714yMzM566yzMJnav05leW4Oc198kqb6OsbdcDuZI8aAx4M1dz01Wxcg81bjDk4h5aqP/sjiKRStUKJMoVAoFCccVQ4XV+7MZX29hfvS4rglJbrNecJKSkqYNWsWDQ0NjBs3jkGDBh3XfGJ7Vi3j+3dfxz8ogItvnIqxehWVrz1NcO0u/KSVBKBeBFMdmPQHlk6haBslyhQKhUJxQpFtsTF1+wEqHE7ey0zlrOjQVmmklGzevJlFixYREBDAFVdcQVJS+4WTx25h27SnaNyxmMnpTmJFOcYlcwGoIoy8gJ54kocQ1ut0ojv1J6Sd85opFL8HJcoUCoVCccKwvMbM/+3MxVev4+s+HekbHNAqjcPhYOHChWzbto0OHTpwzjnnEBDQOt0R2BuhcB3uAyuxZf+Ib/VO+uCBCCgjkp3G3rgSBhLcfRzJ3foT6ef3J5VQoTg6SpQpFAqF4oRgWnEV9+8ropO/L5/27ECir0+rNNXV1cycOZOKigpGjBjBiBEj2p6d31oLBWuReatx5SzHULkTIT0IBHVEky97UxfYhaiBk0jt1ofekZFqGSXF344SZQqFQqH4W3FLyWP7S3ivqJJTIoJ5p1sKgQZ9q3S1tbV89NFHeDwepkyZQkZGxuGd5nIoWAP5a/DkrkJU7kEgcaOnhFjy6U+lKZ28colRCiZdfzNDumS2yz4pJU1uDwFt2KRQ/JEoUaZQKBSKv41Gl5vrdueztLqBaxKjeKRjPPo2PFZWq5UZM2bgdru58sorifaxw7YvIX81Mn8Nono/AE7hQ4GMJZ/BlBjT8E0fRmrHzlh3bCbvu3kkdOnGhNvvIyD06LP7OzwedpitrKu3sKHewvp6C8PCAnk3M/XPqgaFAlCiTKFQKBR/E0U2B5duP0B2k43nOiVyWUJkm+lcLhezZs7Ev2o7UzOcBH8+BuoLAHDo/CgggQMMJ58ERFwf0jM60zE9nREJCTisTSx89Xnyt2+h16mnM+ryq9EbjEfkX+t0sdErvjbUW9hqbsLmkQCk+vkwJiKI0eHBf25lKBQoUaZQKBSKv4HNDdqSSTa3h897dmDkUUSPrCtg75ePcmbZj0RQhzvHRI6hI3sZSR6JNPmlkJ7RifT0dIalp+Pv73/o2MqCPOa++CSN1VWces3N9BxzGlJK8qx21tdbWF+nCbG9TTYADAJ6BPpzWUIkA0MCGBAcQLTJiNvtweOSf0m9KP7dKFGmUCgUir+U+RV13Lwnn2gfI3N6d6RzgO+RCRxNsGc+bP0cclfQDUlNcDfW+Z/Nj2WBxCZ0pHPnzkxKTycmJqbNAP3sX1bx3dsvYwgIott9T/NLSCSveuc9O7iIebBBR//gAM6JCWNASAC9g/3x1x/50YC5xsaS93cSHOnH2KvaF4OmUPxWlChTKBQKxV+ClJLX8it4JreUAcEBfNwjjciDSyZJCQW/aEJs11xwmLH7x7GGQTSknk5ho57q8mpOG3faMSeIrXU4+HT+fH7KLaRy4jUUh8diq3RAZQnJvj6MCAvSvGAhAXQO8G1z/cyD5O2oYuknu/G4Jb1PSf4zqkShOAIlyhQKhULxp2P3eLgzu5DZZbVMjgnjpc5J+Op1UFegBexvnQG1uWAMgMxJlMaO4oPvdxMeFUF9ST0Gg4HLLruM1NTUQ3lKKSmwOQ4F46+rNbO3yY4MT0cX1oEeQX5MDQ1kYEggA0MCiDEZj25gMzxuD+vm5bJ5ST6RCQH07qMn0FkNRP85laNQeFGiTKFQKBR/KtUOF1ftzGVtvYW7UmP5T3wAYsdMzSuWt1JLlDocRtwDXSdQZbbxyfvv42MyUVlZSXx8PBdccAEhISFkWaysrGnUYsLqGyn3DkUG6iCuNJ+hhTmc1ac354waQYD++KewaKyy8sv7O6HMwqkJ/vjZHIhVUKUrJGZQFzWXmeJPRYkyhUKhUPxp7LPYmLrjAGU2O7NDyxi+7T2Y8S04LRCWCqMegJ4XQFgKABaLhU8//RSn04nH46FPnz70P+VUvqhuZM7ebHY2WgFI9DUyNCyIASEBxBbsJfudl/H19WXCf+4noXPXdtvnsbtxFDRgz62nYWc1sryJrgKknx6pd7K/bjP2QDvDbrpSCTLFn44SZQqFQqH4U1hZY+bRjau5tHQJV1X/gKmhEHyCoPtk6D0FkgdDM6HjdDqZPn069fX1uA1Ggk85nS/8w7h+/V48QK8gP57MSGB8ZAgJvj54PG7WzJrBum9mEtexM2fdcT+B4RHHtMljdWHPb8B+oB5Hbj2O4kbwSCRQ5/LQ6GsgfVwqVdYsvvvwVeIyOjPp3kfwDQj8cytLoUCJMoVCoVD80djNrFnzOcatM/ixfhsSgegwAsY8BF3PBJ/W61R6PB4+njaNTXY3Od0GkB+dQJMdErBxc0oM58aEkdHsK02bpZFFr79I7paNdB81ljFXXY/B2DpmzN3owJGnecLsB+pxlllAAnqBT2IQvoPj2LGnhr25ZtIHxTLi4s7sWvYdP370NsndezHxrocw+vq2yleh+DNQokyhUCgUvx+PB/JW4tk2A9fOuQxxWykNSMY+8gFMvS+C0KSjHrrb3MTDPyxnS2ImFpMfgTrBxJgwzo0JZ3BoQKsvJKuLCpj74pPUV5Qz5qob6HXq+ENDi+4GO/YD9ZoIy23AVdEEgDDq8EkOInhMMj5pIZiSgyjNa2DJB7uwN7k4eWoXug6JY8O8r1g54xPS+w/izFvvweDTev1NheLPQokyhUKhUPx2qnO0rye3fQH1hdgMgXwVPQZL9wv5v8FnYNC3sVg4UGF38k1FLbNKqtnVZEeExtCxsY7be3VifHQYfkc5LmfTOha+9iJGk4nzHnyS2NgMmjZXeEVYPe5qbSJYYdJjSg3Gv280prQQfBICEQYtT+mRbP4+n3VzDxAc5ceEm3sTkRDA6pmfsu6bWXQZOoJxN9yO3qAekYq/FtXiFAqFQnH82Orh2xsgawEgsKWN4L9p1/Bh4CDu79KB6xKjWh3S5PawpKqeWWU1rKg145YQY6lnaGk+gz12/vN/V6I/xheTuVs38f0rr5GZMIzuXUfimWulrH4DADp/Az6pIQQOjseUFowxLhChbx2Yb2t0svST3eTvrKZj/2hGTemC0aTj52nvsWXxfHqMHsspV9+ITqcWH1f89ShRplAoFIrjozoHvrgQag7AiHvZ3fk8Lsq1YXF7eD8zlTERh5dM8kjJmrpGZpfVsrCyjka3hwSTkfN9QaxeRrC5jvDwcK699tpjCrLSfdlsfvsrxsdfhQEj7kIrpg4hmNISMaWFYIj2R+iO/XVk2YF6lry/kyazg5Mv7ET3EQlI6eH7915n588/0O+Msxkx9f/weKzU12djbtxDo3k35sY9BAV1o0vnJ/6wKlQo2kKJMoVCoVC0nwPLYdal2leTU79lYWAPbtqdT4SPgfm9Muga6AdAlsXKnLJavi6vpcTuJFCvY0J0KJOjQjBv+IX1a9ei1+vxDQjg8ssvx2QyHfWUVXn55LzxM4PCxqNP8CfyvC4YYvzbPUWFlJJtPxbyy9c5BIabOOeufkSnBONyOvju3ScpyVtB/0s6E562nbXrTqWpKQ/tawAwGEIIDOyCn1/K7605heJXUaJMoVAoFO1j/fuw+B6IzICLvuCtxkAe35lH32B/pvVIA+C9wgrmlNWyvdGKXsCo8GAeTo/ntMgQPDYrs2fPJi8vD39/f5xOJ1OnTiU4uO3FyAFqs4uo/GArqX6ZGPuHEj0pE3GUeLO2sDc5+XHaHnK3l9Ohv4Pup9ZS71hB4eZd1FRuxq+bjfRu4KKQhoYkgoK6EhNzNkGBXQkK6obJFKfmJ1P8ZShRplAoFIpj43ZqYmzjh9BpHHLyezxX0sQr+SWcHhnCuMgQbt1TwHJvnFjPID+e6JjAxJhQony0aSpKSkr48ssvaWpqIiYmhoqKCi6++GJiY2OPetra1Xk0zMvFR/hhOD2CmJO7tctcl6uRxsYsSgu2sG/7WvRReXQ5twSEg737QQgjTnMg9UW+JHc6k879zyUosAsGQ1Cb+dl270YYjZgyMo6/7hSK40CJMoVCoVAcnaYabbgybyUMvRU5+mEeOVDOe0WVdAvwZUWtmUVV9SSYjNyYFM25seF0CjhyXq8tW7awYMECAgMDycjIYM+ePZx55plkHEXkeBxuar7KxratmhpHKZFTMont31qQSSmx20tpbMzC7I39amzcjdVacCiNb2QgwcHdiIgaQ2BQN3x0KSx5fTpl+3MYd8PtdBs+6pjFN//0E8V33IlfZiYpn336GypQoWg/SpQpFAqFom0qsrSA/oZimPgO7l4Xcnd2IZ+X1hBnMrLbYmNyTBhT4sI5KTSw1XxiLpeLJUuWsGHDBtLS0khOTmb58uUMHTqU/v37t3lKZ5mF6s/34KxsYnfdL3S4fBhJ/XsB4HbbqK5eTn39JsyNuzGb9+By1R061s8vhQD/rjQWD6dkVziRMT0ZM2UYfkHaXGNN9XXMeeohaooLmXD7vWQMHHLUokspqfnoYypefBHf7t2Jf+m/v7MyFYpfR4kyhUKhULRm7/cw50ow+sHlC3EmDODWPQV8XV5LsF5HjcPF612TOS82vM3DzWYzs2fPpqCggCFDhhAfH8+cOXPo1q0bY8aMaZVeSknThnJq5+Xg9NhYVfo1vS49i/SBA6mqXkZ5+XwqK5fidjei05kIDOhMdPRpBAV2IzCoK4EBnakrgyXv76S+oolBZ3eg79iUQ19kmqurmP3kg5irKpl410Ok9u531KJLp5Oyxx+nbvYcgsaNI/7ZZ9CpWf0VfwFKlCkUCoXiMFLCmtfhh4chtgdc9AX2oHiu25XH4qp6jELgr9fzZe9U+ga3Xi4JoLCwkFmzZmGz2TjnnHMIDQ1l2rRpJCYmMmnSJHS6IwP1PXYXtd/sx7q1Eot/I0uzPqbPhX3xSVjJqtUP4HTWYjAEER09ntiYCYSGDkKnO/z4klKyZ00pK77ci8nfwNm39yGhU9ih/XXlZcx+4gFsjQ2cc/9jJHbtftTiu+vrKbr1NprWriXiumuJuuUWhK79HxYoFL8HJcoUCoVCoeGyw/zbYNsM6HoWTHqHJr0fV2w7wPK6RkAL4v+oexoxptbrTAJs3LiRRYsWERISwlVXXYWPjw8ffPABQUFBXHTRRRhbrE/pKGmkZkYWzuomqjvuYo/5I7pc7sRq2ERpmR9RkWOIiTmTiIiT0elaT5vhtLtZ/kU22WvLSOwSxqlXZuIffHhppOqiAmY/+SBup5PzHnqa2PSjB+s78vMpvPY6HMXFxD37DKETJx5/HSoUvwMlyhQKhUIBjRXw5RQoWg8j7oUR92D2SC7eup8NDdr6kRfGhvNc50RMbXiOXC4XixYtYvPmzXTs2JHJkycD8OGHH+LxeJgyZQoBAYc9a1JKLOtKKf9pJebE9dT1Wo1bX0mUR0dk1EhiYiYQGTkGg6FtbxxATamF797bSW2ZhQFnptH/9FR0zSaQLT+wn6+efhidXs8FjzxDZHLqUfNq2rCBoptuBiFI+fgj/I8S86ZQ/JkoUaZQKBT/dkq3wRcXQ1M1nPcJZE6i1uli8pb97LHY0AGPdUzg/xIj25yzq6GhgZkzZ1JcXMzw4cMZNWoUHo+HTz/9lLq6OqZOnUpkZOSh9Ja6XPKXTadG/Ix9cCEgMBf5o3cM5tQpr+HrF/GrJmevK2PZ51kYTXrOuqU3SV2PjG0rztrN188+iikggPMeeoqw2Pij5lX39TeUPvIIPklJJL3zNj7JyUcmcNrAWgvBcb9ql0Lxe1CiTKFQKP7N7J4L31wHfmFw5XcQ35tKh5MzNu2lwOYkQK/j4+5pnBze9hxe+fn5zJo1C6fTyfnnn0+3bt2QUjJv3jzy8/OZPHkyqamp2O0VlFcspKxwHmbbdgiGADIJDriKFe+vJjy2C+c9+BTGXwmodzncrJy1j92rSojPCGXsVZkEhB45rJm/fSvfvvgEQeGRnPvgkwRHtl6HE0B6PFS+/ArV77+P/0mDSXz1VfTBweC0QtEGyFul/Yo2QKfT4ILPflsdKxTtRImyfyBCiCRgOhALeID3pJSvtkgzEpgL5Ho3fS2lfPwvNFOhUPydSAkrXoCfn4LEAXDB5xAUQ7HVzthNe6l2uknyNTK7d0dS/dpeAmnDhg0sXryYsLAwLrvsMqKjowFYtmwZ27dvZ+TIgURE7mHzlueprV0HeDCZk4muvZDkYVOx6Y3MeuxegiJSmHTvo78qyOxNTua9to2KvAb6jkth0IQ0dC1m99+/YS0LXnmWsPhEzn3gCQJCw9rMy2O1UnLPvZi//57Q8ycRe8kYxMbXNRFWvBHcDhA6iOtNU/+rMaeOIub4a1mhOC6UKPtn4gLukFJuFkIEAZuEED9IKXe3SLdSSnnm32CfQqH4O3E0wdwbYNc30PNCmPAqGH3Za7EyftM+LG4Pg0IC+LxnBwINrRcJl1Ly448/smrVKjp16sTkyZPx9QqqLVt+YffuTzjppBo88kuyspz4+aYQ03Aefjt6EpLcg7DzOtFgruTrh+/G5B/IOfc/jl9g2564g9ibnMx7dStVRY2Mv7YHHfq09n7tWb2cxW/8l5gOHZl832NHzdNZnE/V/Vfg27SXmMuiMDjfQ8x4E4Qe4nvDoOsgdTgyeRDf1Ht4cF8RXW1+fHX8Na1QHBdKlP0DkVKWAqXev81CiD1AAtBSlCkUin8b9cXw5cVaHNkpj8HQW0EI1tQ2csG2HJxSMiUunBc6J7WaDBa0gP558+axfft2+vfvz+mnnw44qaz8npycmTSYV9KlqxsfnxhiYy4lTI7C8bUOj9lJyLg0AofFY6mr5aunH8bj8XD+A48TFBHZ2s5mNBdk467tQVrP1um3/7iEH95/g8SumUy6+2F8/PwP73RYoHAd5K3Cs+cHDBU7iEuTSHSIyM6QOgFSh0PSIPDV1uEsszu5O7uQ76sb6BfszzOdEn9XtSsU7UGJsn84QohUoA+wro3dJwkhtgElwJ1Syl1tHH8NcA1AcsvgV4VC8b9F0UZNkDkscNGX0HkcAHPKarh5TwESeDQ9nuuSo9s83G63M3PmTA4cOMDo0aPp1SuErOz7qKxcgstlxuk00diYybBh/yE6cgiW1aXUf5eHPkRP9HW98EkKwmZp5OtnHqGpro7zHn6KiISkY5pst7qY99o2TZBd071NQbZp4bcsm/4Bab37MeE/92HEBft/PBwTVrIZPC4kOuw1RqyN0QROvQ+fIeeC6UhvmpSSL8tqeGR/MQ6P5NH0eK5OikKvFiVX/AUIKeXfbYPiT0IIEQgsB56SUn7dYl8w4JFSNgohTgdelVIec7Xd/v37y40bN/55BisUij+PbTNh3s0QFKsJshhtLckXDpTy3/xy9MDHPdIYGxnS5uFms5nPP/+c8vJyJkw4g9DQ1eTmvYFe709Y6Cg2bBDU1sZw9dXXEWQMoHb2XmxZNfhlRhB2bid0fgacDjtfP/0IJXuzmHTPw6T26ntMk+1Wl+YhKzRz2tXd6dD7yCFLKSVrv/qSDXOmM6BvAoN6x6MrWAMlW8DjAp0B4vsiU4dhzrZQ+s48fDr3IPGtNzFGtxaeRTYHd2UX8nONmZNNOp7W2YnIO4B9bzbGxCQirrziN1W9EGKTlFLNsaH4VZSn7B+KEMIIfAV83lKQAUgpG5r9vUgI8ZYQIlJKWfVX2qlQKP5kPB746XFY9TKkDIPzp0NABC6P5IbdecyrrMdXJ5jfN4MeQf5tZlFVVcVnn32GxWLh/PPHYGl6nty8TcTGTiIt9X4++2wOFRUVXHHFVPzqBBVfbMbd6CT0rHQCTopDCIHH7Wbhqy9QlLWL02++8/cJMrsZmf8LhYveJqV0A4O6NKJrkrDWAAn9tCHZlKGQNAipM1H2xJPUzZpP0NjTiH/uWXR+foeykh4PjuJilqzZwPpNWxlTmM9dFcX4lZTglJIyQBcYSPCZZ/wRV0OhOCZKlP0DEdpEQh8Ce6SULx0lTSxQLqWUQoiBgA6o/gvNVCgUfzZ2M3x1NexdDP0uh/EvgMGHOqeL87bmsKPRSqhBz9L+nUn082kzi8LCQmbMmIFOp2Py5GQqKm9CSklmt5eJjj6TWbNmUVJSwoUXXEDQXg+VP2xHH+ZL9A298UkIBDSP1g/vv0HOxrWMuvxaug4dcWyzrS7mv7aVqgIzp13TTJDV5sPiu5H7fkBINwlSYA5NRgy4BtKGaTFhPocnm3XX11N02zU0/bKWiGuvJfz/rsKWlYU9Oxtbdjb27L1Ys7PBYiEDSBcCfVISgZmZmM6ZjG/nzpg6d0EfH/tHXA2F4ldRouyfyVBgKrBDCLHVu+1+IBlASvkOcC5wvRDCBViBC6Uay1Yo/jnU5sEXF0FltibGBl4NQrDXYuO8rfspd7hIMBn5vn9nInzafhRkZWUxZ84cQkL8GD68mJLSdwgO7kX3zFfw9U1i8eLFZGVlcfqo04hY46JhXx5+PSMJm5yBzvdwnqu+mMbOn39g8DkX0nf8hGOa7fAKssr8ZoLM44b178OPjyOF4IBpEJuzG4gbcylDp1zTakJb6fHQtGEDJXfdjauqClNmJg2LF1P97ruH0ugCA6lN7cCKAcPIT0xmzEn9OXtQX/SBgUfk1eRs4p7ldxIbEMs9A+85zougUBwfSpT9A5FSrgKOGZUqpXwDeOOvsUihUPyl5K2GmZeAdMMlX0H6KAC+r6rnml152DySLv6+zOuXQXAbU16AtoblwoULSU3V0TFjEdU1+aSkXE+HtFsBPQsXLmTjxo2M6TyExFU67NYGQid1JGBg7BEiaeOCb1g/dw49TxnHkPOmHNNsh9XFvIOC7OCQZUUWzLsJijbgSR/D0tKO7Ni0m2EX3sqgSefjbrRg37sXe3bWIe+Xbc8epM2mZSoE0mLBt2tXQidNxNS5M+XJqdxR72JdQxOjw4N4oXMSCb6tPYXllnJu/ulmsmqyuLP/nb/tWigUx4ESZQqFQvFPYtMnsPAOCEvTAvojOyKl5LX8Cp7NLUUCA4L9+bJXOgFHmYPs559/ZsWK5fTqXUVIyI9ITzh9ek8nPHwIbrebuXO/Zfu27ZyVcDLR243oIvVEXtkdn7gj16ncveInln/6IRmDhjDmquvbXKLpIA6ri/mva4Js7NWZdOgRAsuf1ya49QnEecbrfD1/B7aNGzk1I5OwH1ew/60PcBYWHspDFxSEISIC6XCgj4gg9uGHCRw+DJ2/FivnlpJ3Cyt5PrcUk07Hq12SOT82rE27dlXv4pYfb6HR2cjro19nRNKxh1wVij8CJcoUCoXin4DbBd8/AOvegfQxcO5H4BdKk9vD7VkFzK2oA2BUeBAfd0/DV996UXG3282CBQvYuXMNJw3ZicGQRUTEKXTt8gw+PuG4XC6++uor9u7O5oLw0QQdEPj1jiJsUgY605EC78DmDXz39iskd+/J6TffhU7XtkcODgqybVTkaYIsPboQ3psIFbuQXSdR7Xs6ux98jy5Fpfi4PZBbhiMlBd/MTEInT8LUuQumjAxqZ8+m5r338B88mMRXX0EfcvhL0myLjdv2FLDF3MRpkcE81ymJWJOxTXt+yP+B+5Y/gq+rC2Ojrub9JTqWxezikQmZx39dFIrjQIkyhUKh+F/HWgtzroScn2DwjXDq46A3UGRzcPmOXHY2WgE4MzKEtzJT8NG1FmQOh4PZs2dTWbWcwSdtQKezk9HxMRISpiCEwOl0aouO7yvgYr9RmMogZHwqgScntvI0FWfvYf7LzxKVksZZdzyIwdi2+AFw2DRBVp7XwLjLO9Ch4lXkt2/RZI6hwTmZ+pd2I+vXEabT4XPSYOIvvpiAk0465P0C75JJ996HeckSQs87j9iHH0J4z+n0SN4qqOC/eWUEGnS83S2FidGhrWyusTjYkFvNRxtXsCGvBmm/Dyl1fLavhg7hdrpGugElyhR/LkqUKRQKxf8yVftgxgVQVwBnvQ59LwVgbV0jV+3Mw+xyA3BBbDgvdUlqcxLUxsZGZsyYjq/vYnr02E1AQAbdM18lMLAzoE0a+8UXX2A+UMWF+pMxNAnCL+mMX2briVyrCvP59rnHCAwP55z7HsPk3/Y0G+AVZK9pguzsM2sJnX0mpbvrMZcl4bY4wXcr5cH+lHdJZdgTz5LQo1erPJwVFRTdeBO2nTuJvucewi+/7JDg2tVo5fY9BWxvtDIhKpSnOyUQ5WNESklhTRMb8mq8v1r2VzRqGQpfIkNDOGdQB7pFlOJvfRGdex/xcecD5x3XpVEojhclyhQKheJ/lf1LYfaVoDfCZfMh5SQAphdXcf++Ivx1OhxSckVCJE9lJLS5bFJ1dTWzZr1OfMIiAgOrSUi4hIyO96HXa2tZWq1WPv/8cwwFdia6B2Hw8yHism74xAe2yquhsoKvnn4YvY8P5z7wBP4hoUc13WFzseDVLXi2ruIMOQ/LfcU02PUI33CCRo/B0aMbi3/+Dn2AP+c+8AQRia1XFLFlZVF4/Q246+tJfPMNgkaP1vL2eHg1v5xX88sJNRh4r1sKGR49izcVsyGvlg25NZQ1aB8CBPka6JkUiMt/G+VyDTcNmsAVXc9g/74nqaz6AX//dDp3mk54+NDjvjwKxfGiRJlCoVD8ryElrH0Lvn8QojPhohkQmozD4+GhfcVMK6km1sdAmcPFTcnRPNAhrs1g9qKiIpYseZj0jqsxGv3pnvkOUVGnHtpvsVj4dPqnxJb40t/ZGZ+kICIu7YY+qPWXik0N9cx56iGcNhsXPPosIdFtz+0l3W4a1qxn10uf0ylnNXqHDaveQ2DPFIKn3krgqDHk7t7OgpefIygqmnMfeJzgyNaz75t//pmSO+5EFxRE6uef4du1KwDbzE3cujOf7NIGejn1RFrsPLB0PWabC4DYYF8GpIUzIDWMAanhCJ8yblt2C43WKl4Z+iidRQHr1p0GCNLT7yY56Qp0urbncFMo/miUKFMoFIr/JVx2WPAf2PoZdDkTJr0LpkCqHC7+b2cua+stpPv5kGN1cE9aLLelxLQpyLKzt7Bt212kpuUSGNCPXr1fw9d0WEg1NDTw2bRP6VYeTUdXLH69ogg/NwNhbB2w77A28fUzj2KuquScBx4nKiXtiP3S7aZp0ybM3y2hYckS3NXVhOkFQfFNBPeIIvDG19Cla16+ncuW8v27rxGdms7k+x7FP/jIZZ+klNRMm0bFc8/j260biW+9hTUkjBW7y3lreyGb82vR1TsweSAL6BgdyJk94xiQGs6A1HASw/wO1ceKohXcveRu/A3+vDv0Nhzlr5DTlEtU1Dg6ZTyAr2/8771aCsVxoUSZQqFQ/K/QWKHNP1a4DkbcAyPuBZ2OPY1Wpu44QKXDRWaAH7ssVh7rGM+1SW0vLL5+wxdUVj5DeEQTiQk306nTzQhxWGzV1tYy8+MZDK5OIdodQvCpKQSNTmpT3LmcTub+92kq8nI4+84HSOzaHdAmcLVu3kzD4u9o+H4J7soqhK8vzpho4joVEppkwXDKvdqSSHotKH/D/K9Z8dlHJPfozdl33I+P35HxaNLlouzJJ8n5djE5p11CzvAz2PBlNlmlDUhACoiI8GPCSakM6xBB/9RwwgNae7mklHy+53Ne2PgCfcJTuTExmrrcR/DzSyGj25s0GBL5oXgTufVzyGvII7c+lx6RPXh86OO/9copFO1CiTKFQqH4X6B0uzZDf1M1nPcJZE4C4Ieqeq7bnU+AXkdnf192NFp5oXMiU+NbB+F7PC6WL78Ht2cuBkMIvXrNICpy4BFpqqqqmPvRbEbWdiJQ70f4xZ3x7xnVKi8tPzffvfkSBTu2ctr1t9GhzwCaNm2iYfF3mJcswVVZiTCZCDz5ZPxP6osjaxqxurVYw/piuPhdiOoEaCJpxecfs3H+13QaPIzxN93R6otNj8XCgruf4g1XEnvHPQyA37ZSQiP9cKYHER7tzwuD0jktNuyY1ej0OHl67dN8tW82p0XG4O/O4cOc/TToO1JW5aBy712H0uqFnsSgRNKC0+gc3vnY10eh+ANQokyhUChOdHbPhW+uA78wuPI7iO+NlJL3iip5bH8JXQJ88dEJdjRaeaNrMufEhrfKwmIpYvWaK9Hrc3A6+zBq5Pv4+h4pYMrLy1n6wTxGm7tiDPAh+ooe+CQGtWmSx+1m6Ydvkb1mBSOHjSFyzXr2P/wErooKhI8PgSNOJmjcOAJPHo7c9hksvQcPUNH9YaIn3w7eaTk8bjffv/c6u5YtpdeppzP6ymtbzWm2f28hj702n5VBQ4k1enjg1K74RfnxRm0NuXYnl8VH8GB6PEEtJsO1OC2HPF159Xnsrd3LutJ1WF0WJILvqioAQZAxiLTQSIZEppEakkpaSBppwWkkBSVh1B99Og+F4o9GiTKFQqE4UfF4YMXzsOwZSBwAF3wOQTE4PB7u31vMZ6XVnBYRTKXDxfbGJt7tlsqZ0aGtsikpWcCu3fcipRPpuZzTxj6ArsVcZUVFRWz68CdOtnZGF+1L7FW90IeY2jTL3mRh4QtPYfhpGac1udBvf586Hx8Chg8neNw4AkeNQh8YABVZeL6cjL54A/n2PnjGvUTaiL6H8nE67Cx89XlyNq7jpHMv4qRzLz5iiLTe6uSVrzfw6bYqDL6x3Jxh5LKLRvJSUQUfFZeT4uvDrF5pdPSxsKN8HbkNuYcEWG5DLhVNFYfy0qHDoBNE6Z0MCnIT7xfCwPT/o1fSZMJ9w4+52oBC8VehRJlCoVCciDgs8O31mpes10Vw5itg9KXW6eKqnXmsqWvk+qQoNtRZ2N7YxDttCDK328ruPY9SUTEHS2MEMTEPcNJJZ7c6Vf6BPA58vIF+zjT0nYKJuaQ7Op+2Z+CvKy9lxW03k7ozGz+ni8ARIwg+8wyvEPNOk+FywPIXkCuex+X2ZUX9raRcdAMZAw9/SGCzNPLt809QnL2b0VdcS59xhxcqd7o9zFhXwMvf7abe7uG0ip3cd8MZ1HZO4vQ1M6k276W3sQZ/cxl35BRgc9sOHRtkDCItJI3BcYNJC0kjNTiVRkcDK3Y9ximBjZj0elJTriM15fpD0360hZRuGhv3Ule1ker9a/A1JtJl5APtuHAKxW9HiTKFQqE40agrhC8vgrKdMPZJOOkmEIL9TTambj9Asc3JC50SmVVWy9ajCDKzeQ/bd9yMzZZLcVF3+vV7nMzM1pOv5uzaS+3n2WR4YvEZEk3UmZ0Qura9RgVLvqPo4YfIqG9El5pK0lNP4t+v35GJijfB3JuhYhfFhtH8UDGVoVcMIWPAYUFmqavlq6cfprqokDNuvpMuQ7V1JaWU/JxdwVML95BTaaFn1X7OdazGee0wbqt4mX17tiFwEYBABCYQE5LK4LhBpAZ7hxxD0ojwjTjC6/Xtjv9iLn6HM4I9+IcMpFfXZ/D3T21VNqezlvr6rdTXb6a+YQsN9dtxeywA6N1BRLhOac+VUyh+F0qUKRQKxYlEwTqYOUWb+uLiWdBpLAArasxcvSsPgxB82iONF/PK2WK2tBJkUkqKiqaxb/9zOBwGDuSczhln3ElKSkqrU2Wv3Yl7bglRMhj/iSmED249QSuAu6GB7PvuRf74M4EGA0G33kLCNdcg9M28aY4mWPY0/PImMiCGdX5Psjkvk1Ou6EanZoKsrqyUOU8/RFNdHZPueZjUXtpwZlZZA08t3MPK/SXEBu9lGPMp7d/EC35O2L8HlzGB0IgzeLjneEYn9Mekb3to9SBWWwXz119GhGsveqMvHbs8TXLcWQghDnnB6hu2UF+/mYaGrTQ15QIg0OMnOhBcchKmqg6ERvQjcvRgTG1MlqtQ/NEoUaZQKBQnCls+hwW3QUgiXL4QorQv/qZ5Z+jP8PflnW4p3JVdxGazpVUMmcNRze4991Bd/TO1tUkUF53KRRddTXR066kx9izZjOnnegx6I2GXdSW4U+s00uOh7quvKXn2GYSlidqOqfR++10Ck1qIt9wVMO8WqM3F3edyFuecQ0GOizGXd6NTsyHLirwDfPX0w3jcbs576CniMjpT0WDjiSXLWJK7DFPwPkI678ciPGTH6BmQNBRfQ3e2uDI4NzGD5zsltbmQ+hE2SzcHCj5h7/7nCcFFiU8vJvV7BVtTDgcOvKR5wRq243ZrXjCjMZyQkL7ExpyDb1kqnpUByBqBqUMIwRNTMaUEt/PiKRS/HyGl/LttUPyP0L9/f7lx48a/2wyF4p+Hxw0/PAy/vAFpI7QpL/zDcXkkj+wv5sPiKk6JCOaFTolcsyu/TUFWU7OaXbvvwOGo40BOH1yu4UyZcgnBwUeKCiklWbPWE7DFjsXkIPn6AQTEHjlBK4B1xw5KH3sc+86d1Pj7Yp94BiPufwS9odnXiNY6ze7N0yC8A67xr7BgQRAle+sYc3k3Og86LMgKd+/g2+efwMffnzPueYA9sogPNy1iZ+16hLEGgMQmf3rvaGREl3GkXPYw12SVcMBq57GOCVyVEPmrwfj19VvZsfte7NZ9lDsFoYFdCNVZsVrzABBCT2BgF0KC+xIS0ofg4N74mpKw7aqm4ft8HFVN1McFUNMnghJfHfnVTeRVWThQaaZbbACvXDzguC7rQYQQm6SU/X/TwYp/FcpTplAoFH8ntnqYc6W2juXAa+G0p0BvpMHl5tpdefxcY+baxCj+kxrD1B25rQSZx2MnJ+e/FBR+iCCeTZtOIzqqD1OnXoiv75GB7NLtIfuDXwjK9VAZZKXrLSfjG+R3RBpXTQ0VL71E/Vdf4/Qxsis5mo433cKQCZOPFEW5K7RpOsylMPRWnEPuZuF7+yjZW9tKkO1b/wszPniSqk56mnoF8M7yC3BJJ9JjJMyYyflpFzHw/e8I3rSP2IceZNPYMzh7uzZUO6tXOkPD2p6WA8DhqKG6ZiUF+e/TaNmDlCAExBglRk85AUF9iY87zyvCeiCEH+VmG7lVFvaur2T/thXkW+wUG6BY78FWaobSMgB89DqiA/TordXYhA74baJMoWgvSpQpFArF30V1Dsy4AGpzta8r+18BQL7VziXbD5BrtfNi5yQmRody8fYDbGo4UpA1Wvaxa9ftNDbuwe0eztpfEunWrTcTJ07EYDiye/c0Ocl5cy2B1VAUbabfTadi9Dns9ZIuF7VffEnl66/jsVgoSowlOzqYcbfdS8cBgw9n5LLDT0/AmjcgIh3+bynOqN4sems7xXtrOcUryKwuKxvKNvDtuhmsrViHeai29qSxwkpT7SDifPrw6NgzOclgo/Dqa3DV1BD/5ht80qEbz2w/QPdAPz7qkUaS75Ez8luthVTXrNQC8uu3HPKCSaDRLTjgCmZ0p+sJDhhFWWMou6qbyC+ykFdtIa9qI/k1FmxOz6H8jEBSkC9pCcGMjAwgNTKA1Ah/YvwFW1b/zJ7du4hPimfSpEl/yCVXKI6FEmUKhULxd5DzE8y+HIQeLp0LqcMAWFvXyJU7c/FI+LJXOr2D/FsJMiklxcWfs2//0+j1ATQ1XcWmjQ4GDx7M2LFjW81B5qxsovDtjfg0SXLSGhn2f+PQNwvSt6xfT/mTT2HfuxfRPZM1wo4rMpzz7n6YmLT0wxlVZMHX/wdlO6D/lTD2SZz4suit7RRl15J5UTAbg3/k5R9WsrF8I3a3HYNLkChiiRGnsHVfHBGmOB4/rRPn9kvCvnkTeTfehDAYiJw2jf/oAllwoJRJ0aH8t0sy/s3ix8zmXeTlv0NFxXeAB50+lFpbECUNGRTZ0vihPBBBBiEig3dX27C7sg8d66PXkRzhT0qAiX4OiKt1kuzvQ5dhSaQPT8LQYj3PrKws5s6Yj9VqZfTo0QwdOvSI+lIo/ixUTJmi3aiYMoXiD0BKWPcuLLlfC+S/6AsISwXgy9Jq7souItnXh097diDax3BIkL3TLZUJ0aE4HFXs3nMv1dU/ExY2nJz9w9m1q5CRI0cyYsSIVnFXtv21lH2yA5fLSX53O6MuHndItDnLyqh4/gUaFi3CEB9P4ykj+WnbOmI6ZDDxrgcJDI84bPP697T4MZ9AOPsN6Dwei7WJtz6cxebGDVQm76fcWQKgTVFhDoN15TTFnstKeyIAVw/vwHUj0wk0GWhYtIiSe+7FmJiI5623uabSSrbFxkPp8VyXFOX9SlJSV7eBvLy32VWUxd667hQ0ncyBGh9KzT44PYe9aDqdhw6RQaRGBJIW6U9KRABpkQGkRPgTafNgWVqAdVc1On8DQSOTCDwprtXi6larle+++45t27YRExPDpEmTiI2N5feiYsoU7UV5yhQKheKvwuWARXdqgfGdz4DJ74IpCI+UPHWglDcLKhgeFsj7makYhGglyKqql7F799243WbSO9zP6tUm9u3bz9ixYxkyZEir05l/KaFu3n7MWCgfKBgzaTxCCDwOBzWfTKPqnXfA7Sb8+uvYrnexfeXPdBo0lHE33o7R5I1HM5fB3Bu1mLeMsXD2m1h9g/hs04d8uP1DLEFmfIJNDI4ZxFUJlzMk5iR2zfiGOZtL2Bh7Lg0WPWf3juXucV1ICPVDSkn1hx9S8cKL+PXrR+4zL3B9fhUAM3p1YGR4MB6Pm/XZP/HDtuVsKzGxt24c9fZzAQgzNZASnEvfLuHstOdT4tzNNf0mclP/qRhafJnpqrHR8EM+VVsqED56gk9JJnBYAjrf1o++/fv3M3fuXBobGzn55JM5+eSTDw0By7pS3BVFGDqpmDLFn4sSZQqFQvFXYKmCmVOhYA0MvxNGPQA6HRaXmxv35PNdVQOXxkfwVEYido+HKc0E2ekRvmTvfYyioukEBHSiU8aHzJu3iby8/Zx55pn073+kE0a6JbULc2haU0qRrhrbiGDGjB2NEILG5cspf/oZHPn5BJ4yhpCbb+K7mdMp3LWdQZMuYOj5UxAHhz+zFsK8m7XVBU5/EVufS5i1dzYfbPuQWmcNSeYu3NZ9KhNPHouvwRen3cZLL7zFrOoIqqM60TchlIfO7Eaf5DCvXW7Kn3qK2hlfEHT6eObddCdP5pST4W/ikdho8vZUcWXWcjbm22hw+AMnERXgYUTnGNKDNxHFJ6RFhhGQcBP/WfsKDfoG3hz+HKOSRx1RfneDg4afCrBsKAMhCByeQNCIJPQBrdextNvtLFmyhM2bNxMVFcWFF15IfHw8js3LaVjwOZZ1m7AUWAjsGErit+v+lKahUBxEiTKFQqH4synbCV9cBJYKOOdD6KF5fYpsDi7bcYA9jTaezNCmfbC4NUG20SvIRvmXs2HjbVgse0lKvJyEhJuZMWM2JSUlTJ48mZ49ex5xKo/NRdXne3Dsq2OHvoDAscmMGjEcR0EB5U8/Q+OyZfikppL0/vs4O6Yx57nHqa8oZ9wNt5M5YoyWicMC392nefRie2Kf+BZzarfz4TdnUGmtJKG+E+MbruL/LptEVJL2ZeTu/ArueHcxezydiQqB1yf14cyecYeGUz1NTRTfcSeNP/+M7/9dzT2DT2fZigMkWDw0VNu4sukAAOG+dfSKqWB4546c2mskkb6l7N79Hxot2SQmTqXIOIhbVzxCiCmE6eOn0yW8y6Gyuy1OzCuKsKwpQbolAQNiCB6dfNQ1PHNzc5k7dy51dXWc1Lcv/WsLsD9wDft35OKyaKE9PqE6god0gdMm/mHNQaE4GiqmTNFuVEyZQvEb2LMAvr4GfIPhws8hQVuWaHO9hct25mJze3g3M5XREcE0utyHBNnbXZPp7ZhLTs7zGAzBdOv6PCZTPz799FOqqqo499xz6dq16xGnclVbqfxkF87KJtYYskmb0JMBPXpQ9d571Hz4EcJoJPLGGwifOpWifVnM++/TCJ2Os+58gMQumVomRZvg66uh5gCOITfxVUIXPtj1MRXWCtJlVzJ3n8JJKYM45fJu+AYYqbU4eHHRDr7YWIJBurgkM4i7Lx6Fb7N4LUdlJctve5CNdZJtw85gvdUHj0P7AjI2yEXHkJ1khOykX7KJgV2nEBU1BhAUFU1jf85z6PVBdO7yDF8U7ODDnR/SI7IHr41+jUi/SAA8dheNq0owryhCOtz4944m+JRkDBFHTvdxyB6Hg6VLlrB+0yZCpJuTtq0nJKsAAL2PB/+0IOz9+/HLkLP5NiyD3WVlnBxo4p2TB/6mJqBiyhTtRXnKFAqF4s9ASlj5Ivz0pCbELvgcguMA+La8lluzCojxMTKnd0c6B/geIcjeyQggsfQ/7KtZSWTkGLp2eQar1cDHH39MfX09F198Menp6Ueczp5bT9Wnu3HY7Pzgs53+Zw+lY2UlOWeciau0lOCzJhB9x50YY6LZ8dP3LP3gTUJj45l0zyOExsSC2wWrXoZlz+AMiuObsXfzXvFPlJfOpWdYL07Nu4yg3EQGnplG//GpuKXk49W5vPx9Nmabi+5N+3j8khH0GdQPt0eys7ietQeq+WVnEetzKjEnnQVJIJx6jFGCKcm5dPOZSbipiPDw4aSm3Epo6CCEENjtlezZczfVNSuIjBhNdNpd3P/Lc6wvW895nc7jnoH3YNKbkE4PjWtLMS8rwGNx4dstgpCxKRhjA1pfDo8He1YW+5Z+zw+lZTSYfMnI3kvPndsIDrejH5nIvn7D+SaqLzlltQRXlRK1YDF9a6dxktVCaK/+8BtFmULRXpSnTNFulKdMoWgnjiaYdxPs/Ap6nA9nvQZGLcj9hbwyXsorZ1BIAB92TyPSx3CEIHsvMZ+g0idxu5vIyHiAhPiLqK2tZfr06VitVi6++OJW61haNpZR+/V+GvU2vtNv4ZRB3Qn6ciZN69Zh6tqV2AcfwL9fPzweNytnTGPj/K9J6dmHCbffi8k/AGrz4OtrcRau5dvOJ/O+wUppUzm9o3ozOfgSKr/yRacTnHplJimZEWzKr+XBb3eyp7SBFGcZIxo3MfqK69jnCGRdbjXrcmsw27R5yeKbaujZUIDhjFEsDzBzceBCBrmXIqWD6KhxpKRcS3Bwj0Nlqaxcyp6s+w6Vv8LQhTuX30mDo4GHBj/E2R3PRro9WDaVY/6xAHe9A1PHUEJOS8Un6chJZp1lZVhWr8Gy4ica1q1ja0o62V06E9jUyKDS9bhiwtgWls6BRj1BNZWE1dcg0J6JwsdERGIycalpRCSkEJPckcSemb+pOShPmaK9KFGmaDdKlCkU7aChRIsfK90GpzwCQ28DIbC6PdyaVcC8ijrOjw3jhc5JmHS6Q4Jse30Nb4d9hbHmKwIDu9E982UCAjpSUVHB9OnTcbvdTJ06lfj4+EOnkh5J/Xd5NK4ootJk5if3Bk63W2DRInSBgUTfdiuh55+P0Otx2Kwsev2/5GxcS6+xZzD68mu0qTG2fYlz0V3MDzDxXnQ8xY46ekb25Lpe12PYGMvm7wqISg5i3DXdcfnpeW5xFjM3FhJq8JBYm43VN5SygAQs3uHItMgABqWF06uxiMQ3nyEiKpTPH74BH8sshrAKvdATFzuJ5OSrCQjocKgsbncT+/Y9TXHJFwQFZtKt20t8U/ALL218ibjAOF4e+TKdwjph3VlNw3e5uKpt+CQHETw2Fd+OoVoejRaaNqzXhNjKZTjyi3DodZTGh7M7PR27B3ydjbjsTnRut1aHCNwRUYQkppDeIZ3UDh2ITE4lNDoWodPRtKOcyne+wRDuT8ITl/6mJqFEmaK9qOFLhUKh+KMo2gRfXgyORm3+sc7jASi3O7lsRy7bzE082CGOG5OjEUJg8QqyyvodvOfzJrqaPJKTrya9w+3odCZKSkr49NNP0ev1XHHFFUcsLO6xu6n5Mgvbnhr2+pRSVvQDp+/aCfX1hJ5/PlG33YohTPvq0VxdxTfPP05Vfh6jLr+WvuMnQFMNrgW3s6DgB96Nj6ZIuMkMSuD+3k/RP2QQSz/aTeGeAroOjWPY+Rl8va2EZ7/LoqHJSYKnhiq7PzuDupIS7stZHaMZ3CGcwR0iiA4yUfPxJ1Q8/zx1E3uycryO0ZabcAtfkhMuIyX5Knx9446otoaGHezafTtNTXmkJF9DTNK1PPLLE3yf/z2jk0bzxLAnCLD7Uv3ZHmy7qjHG+hNxaTdMGcHYd+2i8q01NKxaTVX2Hsw+esz+JixBehp6JWPF+8VlbRXSx5eS6AT8E5JJSetAv86d6NYhHWOL5agAmrbvp/Llj7Bu/hGPvQFDn4HAbxNlCkV7UaJMoVAo/gi2z9bm8wqKhanfQEw3AHaYm7hsRy51LjcfdU9lfFQoABaXm4u37Seq4UtuZga+MoJuvacTHj4UgIKCAj7//HN8fX259NJLiYiIOHQqV52N6k924yizsMW6hsj139OnpAS/3r2JeehB/DIPD7OV5ezj2xeewGmzMumeh0nr0x9Xzk8sXnwT7/hKCqIi6BqewRu9b+LkxJOpLDAz+5mNWBucjLqkC7JDABd8uI4tBXUE6VwgBcUijOHJvtxyZl/6p4Qd+sJSut2UPfkkZZs/o/KZSPQhG4lzB+KKvobRna/BaAw7osqkdJOf/z4Hcl/GxyeSPn0+pYYoLl40lQJzAf/p9x8u63YZ1q2VlM3fhXS6CTgpDHPVWna88hYVuTk0CInZz4TFZER21LyIQkicgf7YjUHoDT7YktPpMPYMRmZ0INXP1ObC5larldrKSkqWr6R0xRrqmuqxBAbQdPopWHx96NylC53+uNaiULSJEmUKhULxe/B4tLUgV70EKcPg/OkQoAmoRZV13Li7gHCjnnl9OtI9yB/QBNnVW9czpuE5MtlBVNRpdO3y1CHRkpOTw5dffklwcDCXXnopISEhh05nL2igetpuHI2N7M3+iPS92zAEBRHz5BOETJ58eI4xYO/aVSx+82X8Q0I59/HHCYuNZsG3l/Fu5Trygox0Dkzm1QF3MCpJm+dr96oSVszci3+wD2Nv7cn0PSVMW7AFvVfDWF2S0VFO7p0ymo7x4UdUg7upkexXLqMqaRvOoZJ6HKw1XsVVva6lW3AELbHZSti1+07q6tYRHX06XTo/yQ+Fq3n0l1vxN/jzwdgP6OPXk5rpe7Bl1eA01rI350tydlZjNXm9X+GBBAo7EX5NmCJC2RPTma1pQ+nmhpDsnfj6+jHxrAl06dIFp9NJXV0d+4sLqa2tpa6ujtraWu3vmhpsDsdh4xKi8PFEERoRgX+wHz7OWlyG+t/XThSKdqBEmUKhUPxW7Gb4+lrIXgj9LofxL4DBByklbxRU8NSBUvoE+TOtRxrRXiFhcbl5aON0Lmh6hQCdm66dniUu7txD3pusrCxmz55NREQEl156KYGBgYdO17S1gprZe7GUb6Zxy6ckWRoJnjiRmLvvwhB+WCRJKVn/7WxWfTmduE5dOOuO+1mZ9zVv//g6uToPGX5hvHzSA4zucDo6ocPlcLPiy73sWVNKYtcwmvqHctZnG2i0a8H6Pk4Lo40l3HXlGWR0yTiiCjweO8X7PyN3539xDrbT6IlmBudgCD+DNzMzCDW2fsyUly8gK/tBpPTQrevzRERN4IVNL/JF1hf0je7L8yc/j9/aesp+XEu1pYTswrkU6xvw6HTEmOwMDCnEP8LD5oTerIw7HXfaCPpHRHBaYy2JPy2lurqaqKgooqOjWb16NQsXLsRsNh9hg8FgINhgwK+mhsTiYgIsTQQbIwlN6oG7n4Cm5SRVbSWuthKAbPdJwDW/u8koFMdCBfr/DyKE8JFSOn495R+LCvRXKJpRm68F9FdmwbhnYeDVhwL678ouZE55LROjQ3m5SzJ+3uV/GuwNfLLhXjIdS3D7dWNYr9fw9087lOWOHTv4+uuviY+PZ8qUKfj7a5416ZE0LM2nfuF2GnZ9jr5oO574eFKffYaAgUdO0+ByOvnh3dfYvfJnOg09GcP4TN7b+Dz7HTWkuzxc3/VSTj3pLnTCa1OVlcXv7qCqsJHAodG8X1xBUZ0VgFBnPYMt27hiwlAGjD8Dne7w3GMej52i4hnkH3gHh7sKUWJkXvyNzGQIN6bEcV+HOPQthgldLjPZex+lrOxbgoP7kNntvzRIE3csv4PtlduZmnY+l+V0xLrVTZG7kX3Vq6nx1GKQbroEVhKb6GR17FAqwvrgH5BAtMuOrrGRujrN49X8eSaEIDg4mNDQUMLCwggLCyM0NJTApibEz8twzpuHp64OXWgEhrhhGFL7Ehw0j1CxECE8lPrGUBbTD5/kwYRGd0eEZRCfmPybmooK9Fe0FyXKTnCEEMuAy6WUed7/DwTel1L2+qttUaJMofCStxpmTQWPC877BNJHA1Bsc3DFzly2m63clRrLf1JjDnnAymu3sGrbLQR7SnFGXs647veg0x1e9mfTpk3Mnz+f1NRULrroIkwmbRZ6j8NNzcw9NMydjTXrW6R0Y5o6lY7/uR3h43OEWU0N9cx98SmKs3fhN7k/P/psYV9DLmkOJ9f7pjB24ifogw4H2efvrOaHj3ZRgpsV0bC/rgmAaGnmpLKfGZmZyJgrryM4MurQMVK6KSuby4HcV7DZijEdMFK7uwMPTXiKep2Ol7skMzHmyNgxgLr6TezadQc2WzFpaTeTmnID68o2cPfyu3A4rNyyM5Geu2MojEsgx7ILq8GDn58eU6SJquAEnLoQpNWuzf/mxd/fn8DAQMxmM1arldjYWIYNG0ZcXBwhISGH1q702GyYv1tC7YzpWLfvBp3AJykKQ8LZ6KJ6E2D4geLQNRTFdEWfPJCQ8EycDS4KCvIpKCjA1eBDYnwil990/m9qLkqUKdqLGr488XkG+E4I8RqQAIwHrvh7TVIo/sVsmgYL74CwVLjoS4jsCMCa2kau3pWH3eNhWo80TovU4sCkdLMv923y8l7DTRi21HeY0OGUI7L85ZdfWLJkCR07duSCCy7AaNTEmrveTtnz82hc+j6e+kJqUlLo8t8XierevZVZ1UUFfP3co+w2FLF/kp4829ekNnp4tr6RccMeQD9A8+SB5nnbsCiX2d/lsCbYQ7HHBXUQ4+NkeN5COvrbGXPDdXQceNLhIH4pqa5eRk7OCzRasvE1hxP+iZGV6ZP571nnE23yYUGPNDIDj5xF3+NxkZf3Brl5b+Lrm0D/fjPx96Tyxsw7+MD2Ex0rApi0tQO1kUnM7ezC49OEJ6ELCB1NaB6v8MAIor3DkdHR0URERBASEsK2bdtYunQpBoOByZMn06NHj8NB/C47thVzqZs5k/rVu/HY3BgDXYT39MeZeA349KTav5rdvesh+XxCa8dSnp9P0aoiXC5tdv9wv3giG/tirzEQEBqIQvFnozxl/wMIIUYCPwBVQB8pZdnfYYfylCn+1bhd8P2DsO5tSB8D534EfqFIKfmwuIpH9heT5mfi4+5pZARoUyxYrcXs2PUfzA0bWctQMrs8wcT4wxO/SilZvnw5y5Yto2vXrpxzzjmHvDvWrBJK7n4Kx96fcfj6UzRhPKPvvx8/v9ZLB+3fuI53vniUzR2qqQy0kqzz47ryQsYHdcQw+QOI6nwobUO9neff2cSiqjpq9Fr/H2ISnFK7ivjy7fQ69XSGX3yZNqmsl/r6zezf/zx19RswuSMInO3CvRGm3fUos2KTGRYayLuZqUT4HPme39SUz67d/6G+fitBfmej39eN/O07+SWwHpdPCCGOIITQH6wM9NKDPjqatA4ZZCbGEx0dTWRk5KE6OUhtbS1z584lLy+PjIwMJkyYQLDeAYXr8exfRcPS5dRuLMdWZUToJLpUQW7PzuhCL6JrXTxNvoIDaQ4K7TmUlpXi8XgQCKIj4okNT8LXE07Nfhd15TZ0eoHJ30ByZgSnXN7tNzUd5SlTtBflKTvBEUI8BJwPnAz0BJYJIe6QUi78ey1TKP5FWOtgzhWQ8xMMvgFOfQL0BmxuD3fvLWRWWS2nRQbzetcUgg2ayCgrn09W1kNY3W4+EjczpeulTIw9Mhj/hx9+YM2aNfTq1YuzzjoLvV6PlJKqt7+g+t1XkXYz+RndMFxzGePPOEOb7LUZUkqmz3mBacWzqOxpJ8EUwRO1ds4s34th2G0w8n4waEOctRYH7y3Zx6fr82lEIvRg1AtO8yslecdcohMTOfXxF0jofHg9zUbLPnJyXqSqailGXRiRa9IwflHE3lMn8Owrl5DnllyXFMWDHeIx6ARSShoaGqioqCAn50fy8tZiaUzA2tgNF3qgCIJC8fX4Yqq1obNXEuT2ITaiE72uOJfOHWKPeRmklGzevJklS5aA9HBWr0j6sAYx7SVs+/OozQmgId8Pj1OHJTyYdeP6sGLUOZxmjmTEbiuuGsk6SshpqkXsMhFkiiZJdACnEbvZjbtMUqKVHAC9UUdotD8hUX5EJQcdyzSF4g9BibITn0hgoJTSCvwihPgO+ABQokyh+Cuo2g9fXKAF9p/1OvTVJhAttjm4cmcu28xW7vTGj+mEwO1uIjv7EUrLvqZU35UXuInHug06Is7K4/GwcOFCNm3axIABAxg/fjw6nQ77gVyKb78fe/ZWPOHJrBp1MoMun0Lv3r1bmbWtZDOPLbqXfcZSgkP8eDBsIJO3fIMxKA4uXwCpwwA4UNnIR6tzmb2hELtbHpxKlcFR0GP3bPztdQy+YAoDzpqM3qDttdlKOJD7KqWlX6PX+RFd1B/dyzvxBPgz/YV3mOYXSqLBwMfRJsKri1i8ZzMVFRVUVFRgt9sP2eijiyG0sp64uv2URzRR6hFEV0qElKQEdiUjYijp5w8noG9Mm3OHNcdclse8b2axr7yJNF0JZ3sWEbSxkcqSCCoOBCEqYrH4R7Kh3xBKuw7EaAwiqtTKhB98cHoaWeKRSAQQRQBajJxPkJHACD/8Ao2Ya23UlFjQ6QSdB8XQ//Q0giM1r6Tb48bsMB/DOoXij0ENX/4PIITwA5KllNl/px1q+FLxryPnJ5h9OegMcMFnkDIEODJ+7M1uKYfixyyWA+zYeQMWy37Wmy7iTftE3sjscIQgc7vdzJ07l+3btzNs2DDGjBmDdDioeuc9qt97D4QRc89TWNkzkvMuvpDk5CO/+Mupy+Hltf9leflKTA4dkwOHcLs9H7+81dD9HDjjJaRvCOtya/hg5QF+zKpABwS6BPV6SUKQkbHWzQTuW0VSZk9O+b8bCY9PAMDprCUv722Kij9FSkm0cxiGl7KQ+TUUXnUNjw05haraOs6xVhNTkk9tTQ0Avr6+RBh98DPnQfx+/MJqifulgbj9FrKio9nijMRk0+HyM9An6mQ60I3QnsmETeyIPujIjxUOISVU7IbsxezauoEFNak4pYFBlOJ0xlNTGoBPvR6HTyiWgCicxmAEzYWdxFcI/AyCgAQ/ojpFEBLlT1C4L0ERvgSF+2K3uti0OJ9dq4oB6D48gT6nJePvZ8Ba38j2/K3sKtxOflkuMVFx3HTe3b+pGanhS0V7UaLsBEcIMQF4EfCRUqYJIXoDj0spz/qrbVGiTPGvYudX8NXVENVFWzIpLAUpJR9548dSW8SPlZcvYE/W/QidD1+Z7mKOpTNvdUs5QpC5XC7mzJlDVlYWo0eP5uSTT6Zx9WrKHn0cZ2EBhsSBZPUeQFEHPRddfBGhoaGHji1uLOatrW+xIGc+epegZ1E4Dw44i447XwenFU5/AWePi1i0s4z3Vx5gZ3EDoX5Goh2Q63KCTjAhpom49V/g7+fDiKlXkTliDMLr3Sss/IS8/Hdxuy1EB56K/6cNOH7YjOjenS9v/A+/FBXTtbKY0IZaANJSU+lkMhHxy0qcq1ZgPs2OZYwHnzqJbkMgWx1dsJbZEB4PpRE2BqeNY2DtQAx+RkLP7ohfj8jW3jGXA/JXQfZ3sHcx1rpyFspRZDkH4e8MR98UjcHtXRJJutF5mnAYHbh8nBgDBBExQaSJMCJKPPjpBKGjkwkcHg8uidvixOP92aptFG+roiavHh8JYeEmgvyNSJsTt8WBzt3aa2dJ9tD5hhG/qSkpUaZoL0qUneAIITYBo4FlUso+3m07pJQ9jnFMEjAdiAU8wHtSyldbpBHAq8DpQBPatBubj2WLEmWKfw17FsCsSyF5MFw8E0xB2Nwe7tlbxMyyGsZGBPNGNy1+zOOxs2//MxQVfUpwcF/e1d3Bwnpf3uyWwuRmgszhcDBz5kxycnIYN24c/dPTKX/2ORoWLkQXHINPz4tYHufCNzOCSZMmHZoSo8paxXvb32P23tkID3TODWB4Y0euGBKC3/ZPIKY7DRPe54scEx+vzqOswUaHqAAGR4ewdGcpFULSK9SH4eWLESV76TpsJCMv/T/8Q0LxeJyUlM4mN/c1HI5KIsJGEr4ugaY3v8UZEMCuK67kF4eT0OoKBBATF0fPjAwSs3djm/EZnsp6PEku6q5y0xSuo2xPB2pyk3CVlqMzGdkXb8YeF8L9zlsxVQn8ekYSelY6+sBm3rGmGtj3PWQvQu7/CeEw49D5scgwhX3VfTBZI9B7TAiPi4ia3QQ0ZlMf44PM7EJiYhqxIVFE+IehK3dg21ODtLvRBRjR+Rvw2Fx4LC7wtP2ccwsQgXoaTU2UygoKXMXU6c3oDB7STJGkGkKJduqRVaX4pMQScYVakFzx56Jiyk58XFLK+hZvlL+mpF3AHVLKzUKIIGCTEOIHKeXuZmnGAxne3yDgbe+/CsW/m31LtSHL+D6HBFnz+LE7UmO4IzUWnRBYrcXs3HkTDebtJCZeyQv281lQZeHFzklHCDKbzcaMGTMoLCzkrDPPJHXvXnJuuhmPzY5Pt7PwdB7DAuMOuo3ozahRo9DpdNTb6/lk1yd8vudzHG4HA1ydSFrewMDOXRjbcQu67Vsp7nEjHxsv5Mv3C2m0uzipQwT3juvCvOW5zNhVQrAQXBVahd+W2YRGx3DK/Y+T2qsvUkrKyxeSc+AlrNY8QkL6ku64koZ7viLLU0DRxLMpMBqhtBijfyApAwczLiIM+eU0Gl5/jSaXB79oO5VXm6hMN1KVFU/991E4LDbCE4zUndqFL+RPXO+4hFOLB6DzNxJ2SUf8ukdqw5KV2ZC9GPZ+hyxch5AeakyxLDFdQpZzABEVwYR49KTpPEQ4K4my1+JrMqLL6IJR9EN4gBrvjybsaPOrIUAX7IMh1KQJswAj+kAj0mSgOL+BfTuqMdtcGDo3YIncRm7hGhxFRUTXSdIsAXSz+BJU3YRobDp07WoBGWBAP6wjEWpBcsWfjBJlJz47hRAXA3ohRAZwC7DmWAdIKUuBUu/fZiHEHrQ5zpqLsrOB6VJzla4VQoQKIeK8xyoU/04OLIeZUyC6K1zyFZiC+KWukat35mHzePikexrjorT4saqqn9m1+w6kdNO9+5s8Vd2VBVW1PNYxnkviD6/12NTUxKeffkp5eTnn9OlDwHPPU75jB6aufTEkTKIuNJDvjVsYO2k8PXv2pMnZxOc7P+fjnR/T6GxkbOIpdFhjx7ozn9NHd6JL9Wx2VaXwQdxnzN+kfdF4Zs84rhqaxvaCOh6Ysx2r28NIXwO9S2fhya+k/1nncNK5F2E0+VJTs5r9OS9gNu8gICCDrnFPkf/uWr6vXU3xoIE49XrsJl+yIhNI6dyF26sLcbz1IvX7ixF6D4FpdnaN6YwlzkPtFjv1G7RYro4D+pI2chivVH5CXV4WH1Y/RZg5EP++0YSOT0ZXtQG++w6ZvQhRm4tHBpLjO4RthueQtjRiqnzoKqCfDvwCxOGhzcBE0CVgiA7AEOGHIcIXfYgJYdJj31uLdWcVGHQEj0khaFg8Qq9DOp04S0ux5RWQv2wXpev34LLmEuypIK6hgeAfPQAMPXiRTCZ8kmIwpsQj+/tgC22gzFDBbo+eHbpO7LJ2o0uUiXf/upao+Jeihi9PcIQQ/sADwFhAAEuAJ6SUtnYenwqsALpLKRuabV8APCulXOX9/4/APVLKjS2Ovwbvgm/Jycn98vPzf3eZFIoTkoK18OkkbVLYyxYg/cP5uLiKh1vEj3k8Lg7kvkJ+/tsEBnaje+ZrPFls5OPiKu5MjeXOtMPTOpjNZqZPn05DeTmTrDbkwoXow8IIGH05Hkcn8o1VbAjJ47yLzic6LprZe2fz3vb3qLHVMDJxJBdHT2TH25/hbKzlomFGtheW8b7hYlZbkwnw0XPRwGSuGJaG3enm9hlb2FbaQKJLx9mGUnS5XxObnsGp19xMdGoHGsw7ydn/AjW1q/DxiSPQ7/848FMFe6trsPv64iMEMr0T3wZEYguP5oHdq+n14bvQ6MAY6MKV6cPmU8/CZDBTsWYPTeV++Pib6H3qWfQaO54cTzH3/XQvZxQNYWL1KPQBBoIzqzHU/oKzsBCPIxQH8dSLdIQ7CpM80idgk26sznpM1bno64rRhxgJPnM0weOGoQ8yHTGJbdOWcuq+2oKzvBRjtANjpANXRSnOoiIcJcW4ysq1heK9uHRQFQxVoTpEfCxR6Zmkdx1CRIcuuCKgRu5gW942NuXXkV2TyP7aDlTZNGHtKyT9pYOxUTouvWPib2paavhS0V6UKPsHI4QIBJYDT0kpv26xbyHwTAtRdreUctPR8lMxZYp/LMWbYNrZEBQDly9CBkbz9IFSXi+oOCJ+zG6vZOeuW6mrW0d83Pl06vQIz+XV8FpBBdclRfFIevwh8VBXV8f0adMI2rWLwbt2Q00NIeeejy5yLK5SN5v1ByhNsnHuBeeyomoFb297m1JLKQNiB3BLn1vw3VfPkndeIzpUoIsL4iPzILJlEjFBJq4YlsZFA5MJNBn4eHUuzy/OQrglpzoNdKyZj06WM+zCqfQ+7QxstkJyDrxERcVC7PZE7LazydvroN5qQ+d2k+JyEXPaeF72jWSb1cGpOdu58Z3XCWk04xfvJHdQBmXjryGiPI99S5diq9PhH+bDgLMuoufJZ4BF8uO27ziwdTen1A8iwO2H0NmQHj1weBkpNxKzALtT0uSWNEo3rhA3fkk6LBu+JWHDLwQ0NeGTmUn0jTcSOGrkEULMnpVF/bwl1C/8AXdVgbbE1UGEwBAdDXExFBnd7KKGvVFVlIV5sIQH0Kv7KEaljmFo/FB8hKSs6hfWZm9hY04F+8qDKamNRTp0+HrshEkraW4XCW4X8boqgnT5uOzlREVFMPSZeb+peSlRpmgvSpSdoAgh5nOM2LFf+/pSCGEEFgBLpJQvtbH/XbSPB77w/j8bGHms4UslyhT/SMp2wCdngm8IXLEYGRzPYzklvFNYyaXxETzbKRGdENTWrmPnrltxucx06fw4cXHn8Fp+OU8fKGVqfATPd0o8JCKqqqqY/cYbdF25ipjiYkzduhJ1yz00rhM462wsM+zC1D2coH5BvLPzHXLrc8mMyOSWvrcwKGYga2Z+xsr5c2ns0J2lns5UyDC6hAuuHtOTCb3i8THoyKuycMfMrWwqrCPdqWOcowFd5Vd06NudMVdejykIcvNe58CB+VRVplFb25vaWg9CSmLKykgzN9L7yiv5MqUTLxZWEtDUyG2ff8ioXWuhk56dp51JyPBLMW75hW3fz8fZ5CEwRnLSyCmkhA/DkVOPPbceXM27KQ96yrH5lHPAz58DIhZnvQlTvcDuEQgfN5HpJroOTqRTt2h2vPRfxLdz8bNaoUtnku64g4BhwxBCIB0OLBs20PjTz5h/+glXaSkg0Eem4z+wP/4DOmNMSKAi0MNK8w6W7VtFUUMOPi5BiC2ILv4ZdA1LI1wEUVdTSllFGQ31Fhw2DzqnBx+PA6N0tdkkDmIUbkwGN74BAaT0HsjI6+/7TU1MiTJFe1Gi7ARFCHHw2+vJaF9Rfub9/0VAnpTy/mMcK4BpQI2U8rajpDkDuAnt68tBwGtSyoHHskmJMsU/jsps+Ph0MJg0QRaazMP7i3m/qIqrEiJ5MiMBkOTnv0fOgf/i759Cj+5vEhjYmQ+KKnlwXzGTY8J4vWsyeq8gqywrY9Wdd5GxZQt6k4mY227Fb8B4qr/ci93lYLF+M/qT/PnZ9TN7avaQHpLOzX1uZnTyaBzWJj575TUWFbnZE9yNJkwMDyji6rNGM7xnR4QQeDyST9bk8dziLITbw6gmI53rf8HHtI9TrryOtH492bfvPbZsXU5FeRL19dEAxJpMJGzaTFJeHgmXXkr5ySdza04Ju8LjGLFpLXctfg9btzDKz7meHqn9yf9hAbuW/YC/CCElPo4OsQPxN8chrW4ArH417Nblk25LJ9QdTL3fJr6KbaS0MZPYcl8iGnwQCHwCILlHOD2GpRDXIRRpbaLk40+o/ugjfJqaaExJIe3ee4gYORKP2Uzj8hXU/riU6nVrsTrs2P19cYQl4PALwxUThiNIUm+uwmKux2NzoPMc7eJqOHRGbDpf7DoTdp0PRqOOIJ2JaH0oSeERhMeG4Z8QQWCIE7+SnzHlLMTXVYspOg39wKug02lQuhUMftB53G9qZkqUKdqLEmUnOEKIFVLKk39tW4v9w4CVwA60KTEA7geSAaSU73iF2xvAOLQpMa5oGU/WEiXKFP8oqnM0QYaEKxbjCe/A/fuK+aS4imsTo3i0YzwuVz27d99FVfVPREefTtcuz2AwBPJlaTW3ZRUyLjKY9zPTMOo0QVa6YgUF99xLcG0tPiNGkPTYozhy3NQtOECdvolPg5aS2zGfPY17SAhM4IbeN3BG2hnodXpWbd3HM5//zB5dHDohmaBfy9VDE+k27jrwLq+UX23hrtnbWZ9XQwenjtOsTox18+g2vDuDz7+QrTtms3PnLqqro5BST3h4CJkx8UR+/Q0+27YRMGQIxpN68V5xNR8MOQM/u53bf/qIxCR/ws+7gxSLje1zF2LdW02MfwqxAUn4on3YoA/WYwwqpcD6HTN9i+jXcDpDGntTomvkO18npjodgU1+gCAg3Ein/rF07BdDVHKQNhdaYyMV06ZR9OmnOJx2KqIi0A8dQmBkBPV7szGXldJkbcJm0OPW61pdLoPRB0+QD9UGM3W6JpxGiUkXgr81hijS0MeEki0tZJv9qXKHYteZMPi46exfRw/0DPRNpm9qMsGpofikBGOI8EV4XJC9CDZ8CLnLtUmCO58OSYPAUgW5y6Bkq9ZGOoyCS7/9TU1NiTJFe1Gi7ATH++XkGVLKA97/pwGLpJRdj33kH48SZYp/DHUFmiBzNsHlC/FEdeGevUV8WlLNjcnRPNghDotlH9u2X43dXk5Gxv0kJkxFCMG8ijqu25XH8LAgpvdMw6TT4bFYKHjuOZpmz8Hm50f0Aw+QMHEidfMPYFlbymr/HUyLnU+hqYgI3wiu7XUt52aci14YWL63ktcWbWVLhRM/aecS41KuCNtO/PkvQ9IAADweyadr83l28R6kSzLSYqBrYw6+ftvoNfkc8qv2sXdvCW63AV9fF927d6N3p/7oPv2U+tlzMERFQqc48kqqeG7yNezs2IWBhds5L9rAqQPPxLJ8B9Ubcgi0BRPsowW4u4yNGJIhOLgJZ/VsGmp+4K3QeBKbJjO+egTFTtjvduGwG5DSTUiUh7gOBsLjBGDBUleDpbYGc0U5Dfl5NJobcLUhtnQeDyanG3+DkcCoaEI6dMTPLw7dARcm6UdVf/g5agvfl/+M0+MkM6gXifl9iM3JQODPGpOdLQbwCIj0raZLQCU99XoGhqTSK6UXfqmh+CQFoTM1+7Cgvhg2T4PN08FcCoExENtdm7y2aCO4rJpASxygibEOIyGhH+h/24QFSpQp2osSZSc4QohxwHvAAe+mVOBaKeWSv9oWJcoU/wgaSuDj8WCthcsW4I7twZ3ZhXxRWsNtKTHckxZLQ8M2tm67Cp3OSM8e7xAS0huApdUNXLEjlz7B/nzRqwMBej2NK1dS/NDDeMrKyO3Shb4vv0R0TCLVM7LIKcjm9aiZbA/OIsgYxFU9ruKiLhehFybmbinh/ZUH/p+9+w6T4yjwPv7t7sl5d3Y2513tapVzdpJzjjiCDebggMPAHWDwBeIRTHqBA45wYDA22BgbHCXbOMqyck4raXPOOzl1qPePFQ5gYPEdKLg+z9PPzPZ0t7pm6pn5qbq6iqMjSfxGglscz/Fe+8MEZp8Pl34L3CEAesbT3P7gHjZ3TFBvqZyXUHFkn6dwfoQoFtFoElXVKSubZPnyC5kz+yLiv/kNI1/7OmY8DhEv1liC35x+Pj+68ga8psUnNDeXZD0k9g2gxVUURcEQeRKebnLl2/FWKYQnRiloe444BneGq9Fyazi372wGU3HG86NYxhiqOgnE0bPJP3qbVVXDZbPhiCdwZvNgd6AoKkXpLO5YHJdpUTBrNgVnn0Ng7Vk4amvRR9JMPnSUiZ4hXqjbyxOFG+hIduKx+ahXVlFyYD4zxitJKYItrjzj4R5mBPqZ59RYHq6juWYVntpybEXuP54twLKmWr62/RhanwAshLcUS9fRs1kM4SAZaiZWuoRYuIWEp5JkXieVTJJJpiksDHDlRZe+qSonQ5k0XTKUnQQURXECM4/92SqEyP257f9WZCiTTnrJUfjpRRAfhJsfxqxYxIcP9fDr4Uk+WlvCx2pLmZjcyL5978fhKGLhgp/hdk/NPblxMsFNezto8rj49cJGPPEYw1/6MvFHHyURCrFn9Sou/8QnCDuD7P/J8/xcfYingpuwKTZunn0zt867FWG4uWdLNz99uYvRRI4KW5bTx5/kX0vW4bPlUS74Mix+JxzrO3bvlm6+tK4Vy7A4I6nSSC+mv4ecQ0MICAaHKSvvY+nSK6irvZn84TaGPv0ZMnv3gl0BXTBYUsKvb/wwYbWcc+JQP55HscASJmPZflLuOGpzH5mKB3CpThoOJwgPxTloFXOvow7XeJCSyQB5fRIhMq+8lU5vgOLaWgpKy/EVhvEWFOIrLMSNSn7delIPPIiSyaAHAiipFDbTBLcb/2mn4Vt7Fr4zzsBWMDXArtAtYs92s2PLRp4ofIkXAtvJiRw+qxpzaClzhpawKG1DM9PkPH0UBkYpdxXgdVeiuoKYpsDImxh5E103yeUMcrqBblgYhoWhG1imgoUdS3EgFAdCsSMUByh/3Hr3Rhy5g7znrg++qWonQ5k0XTKUnQQURVnFVAvZK23nQoi7/97nIUOZdFJLT0zdZTnZCW9/EKNqJbcd6uY3I1E+UVfKP9eWMjzyBAcO/AtebyML5t+F0xkBYGcsxdv2tFPhdPDQggbs659g+EtfxkwmOTpvLkfmzuXmd78bD3a+96uvcb93HYZickbwDD51/qfIZD38+KVOfrW9l3TeZFVtkOau33F+6tcsD/dBpBnlmrugZBYAvRNpPvHgXl5uH2O+mmGxMY5wDSNUgc9npyhyiKKifdTXX0hjw8fQ8i76P/1vJB9/CgVQg5VolTMZmXMORUYhHnPqVu6sM03P2EGGUh34ZhZTvQCG4r8mNani7FLJjbrozxdgvO6/fXYULYw/VEb1/Gaals2mpL4OTzD0urdXHx5h9Jv/j9ijj4FhIFAwNScJXwHGvEWUn3cxWmMLug65WJpsNEUumiI+OMngYD8JkQXLhk24UYQHBReoToQ6/UuGqqWjmnlUS0cz86hWHkXkUYQOIg/HHgU6gjyWksdCRyh5TEXHVPOYqo6l6gjNQKg6iqaDqlNY6ueWTz38pqqeDGXSdMlQdoJTFOXnQAOwGzCPrRZCiA/9vc9FhjLppJWNwc8ug5FDcNOv0GvP4AMHu3l0NMq/1ZdxW00Jff2/4PDhTxEMLmb+vB9htwcAOJjMcNWuNoI2jYeK3Zhf/AKpjRuxzZnDs83NxIIBbr75ZnYObeXrO77OiG2ChmQtH175L4SLF/KDFztYt28QVVG4bEE5l1dCz88/xznB7ZS5YrDoFrjgy+DwIITg3i09fOOJPVSLUWaLMey2DIqA+roiIqXbsdk3EAzMo6n5M/g9s+n67EfJP74VW2Q2WvFstOKZqHYfAF1elZGAjj9xiNYDz2CQp6iqGocSYyI6TDpun+qMBQgFci4vqhrEY9aiaEUEnEXMnN/A3Gub8Ra6EJYgGc0RHU4THU4zOhBl/HAnqb4hDF3B1FwI1Y2luRGq6y9/LsLCZmSwmVlsRgbNyILIIMhiKlkMNUNey5K1Z8nYM6QdWVLOLBlnHt1uYNoFllNguRQUh4WNLA49jtPK4lYs/JZFQNEIOoMUOEMEseMxMjizcVzZBK5sHKdl4hIC57Hlde1mriDCXYhwFaDXnIbzgs+9qeonQ5k0XTKUneCOdfSfJU6AD0qGMumklEvCPVdB/064/hfkG8/h/Qe7eXw0xqcbynlfVYTu7v+mvePrhMNnMXfOf6FpbgDa01ku39mGU1j86tBW9P/+Hoqi4HnfP/KreBzTslhz5Rp+ePD77IjuojZbwezJubQsvoEnOnJs6ZzA77Rx44pq3rmqltHtL9J9/2c5r/QIdqcT9fJvw5yrAegeS/DFXz6LPtJJlRpFUcCWc9FYF6RleR9j4/djtxfQ2HA7YfsKur78DWydDmzF89BCU5dYTReMVLr5bXaY9Oheqvr2IZLxP3pLHL48roIcOU+QThYTMUtxxspwGD7siqDcrlDWVIh7Tph4LM/kUIqRvglS4wbCejW2qGYWVy6GZmReE66mApZqZjDVLIaWxbAZGE6dnN0gqqWIOeNEPUli7jxZp5OEFxJek6jLRBy7k1URKqWuMpoKq2lwFFCvOKnVdWpTMYLpSUgOT/UPzEyC8ecnOLHsXkxXAXlHiJwtQMYWJKUFSCh+4viYFD4mLC8jppdR001fxk1vxkEsJ8jqJgIIuGzs/cz5b6oKylAmTZcMZSc4RVEeAD50IsxJKUOZdNLRs/CLt0HXRnjbT8nNvIT3HujiybE4n2+s4B8qw7S1fZme3h9TWnIFLS1fRlWnRqHvzea5YudRSro7+foDd8HBg/jOPBPnh27j5+vWkbJSZBZneKL3CXymm6vHLmRvoow2Ty19MZ3yoItb19Rx3dIqPDaFF3/6PQr2fY8FBYNYpQtQr/0pFNYxODjIr57cwFDnEZyKgWnZ8aZLKFR1zrzFYnD8x5hmksrIO/EfnkfmhQ5sWgOqM4AQFrn8AJkZIbqiBzlwdBv29Kud7hVFwefVqHYMEQrGmGxW0cry9CTnMtp+PeHxQvS4ioWFSxU40MgpAt18TSd5YeLMjuFLj+A5tijmMFl1BEOJ4QiH8S5fybCqcbR/CHuwkLUXX0FVczObejp4+sA2do3vIqp1ojomQHn9b07Q5qHaVUFowkF41KABk6UhkyZ1HPtkL+Rir9s+r3pICQdpUyOJmzgeosLHpPAzbi9j2FHJiChgIO9mIOdiUvjQ/8w0z6oCTpuGpipYQpA3LAzrj38XGyJenvnomX99HUSGMmn6ZCg7wSmK8hywANgKvNLT4y+N6P+3IEOZdFIxDXjgFmh9DK78Idk5b+Pd+7t4ZiLOl5oquaUsRGvrHQwOPURl5S00zfh3lGOdvgdzea7eeogLH7qPq596FFsoROm//xv5pUu562d3cchxiEOFh8gYac6ePB1z9EzWmTZy2JhfGeTdp9Vz4ZxS7JpKOh7jhW/czpLsI0RcacTKD5JZ+XH2HTrM1u07GR8dxhQKk4QoiZYQynqZd24vrqonyI1MEklcirttBloqjKJoiHyK7OhBBu3DDBYL+rtap8KZw4VmGthMg0DAyZxQH0tdhxmxlfNy+QKE6SU+OB892oSZsyEQKLwavlQzgTs9RCAxgjczjDs9gqYPk3ZFyWh5FAE+w0bhhIHD7qTguusoePe7OJId5b6n7qMr0YVWbGfClmEg04uuJl79LITAKaBCdzIn52W+20Ojlqd2cpCC+CDKayYvMVUHMWc5o7ZSekQxR7NBDmYKabdK6RURknhe2daGgV/J4Xc78AeC+Nwu/C47AZcNv8uG32V/5VEgGE/mGUlkGZjM0DWepns8hXnsn7ZrCh6HRjJnYloCt13jtBlFXDK/nDObIwRcr04Z9deSoUyaLhnKTnCvGdn/dYQQL/y9z0WGMumkIQQ8chvs+jlccCeZpe/lXfs6eX4ywVebK7mxxMf+Ax9mbOx31Nd9hNraD74yhMJoXuftG3bx7m/dydwjBwlefRUlH/84Mcvii/d+kc3uzcTsMWY552M/eB6b8mEsBKfVBfjQ+XNYXFPwyrFGOts59J33sMq7C+EI0LXmK+wahsOHD2NZFpPCS4dVRHnSxdxMMf6CHhat6oFeA9/YIuzpIgDMWD+piQMMpNvpcMRI2KaO7ymvpF9o+EYGsIsAgVAJNfY8plJEPw1k9TCK9ZpWIpHHlh/Fkx6iIDaMNz2CJzOMPTdCLKKSLQkinA6co3FCPZN48uCoqUFYFnpvL4rfj/8d13PkvJk8O7aJZzqfIWG9Gr40005Id1BuZqm34rTkcizO5WjI67w20iTsEQasYjpzYTpFhDYRoVsU0yuKGSGEQCVsz1MhhqmwBqhQJ6go8FChRamY2EJEjeNvOg3X0ptRGta+MrguQN6waB9NcmgwfmxJ0DoUZyyZf2WbsqCLuiIvmqowEs/RNpLAFBDxOzl3VgnnzSphZUMYp00jl04xOTiAoqqU1DW8qeooQ5k0XTKUSdMmQ5l00nj607Dxm3DGJ0if/klu2dfBS5NJvj6zimsjDvbs/Uei0a00NX2aqsp3vLLbhG7w/ide4L1f+zxlsUkqvvAFgpdewv7e/Xxi/SfocfRQYCvBPXY5hwfrcKHQ7BjnjmtXsWJO4+tOoe2lpzB+cxthf54d3rXsp5lkKo3L7WHYVszzY16KHA4uGIFmu4f68kl8iQJU04UgjzHaSnS8lR6rm15bnpzdhqIoVLbMIVjfxM7WUVz9OTStHM1eDjgBEFggojhzw3iTQ4Qnh/ClRvCmh9H0GCOFKulQCBEsJTCjhfKqMjxHe0i/uAFzchLsdrzLlmGvqSazbz+5ffvIlhZw5O2r2FKrs3F4E2kjg9NSWJa2WJlJ0WKkqMvrFFoWCpDEzqBaxJBVTpdRwlERoedY6OoTEfI48FkKEaed5roQ9WU+Kqx+KiY2U9G/jorMEdw2BRrPhuJZU/0BO54FuxeWvAtWvB+ClYwlcxwajNM6mODQYJyDg3HaR5Pox5q/HDaV5hI/M0v9tJT58TntdI2l2NA2xr7+qcuiDREvZzeFWR6BMnOC+NAgEz1dTHR3ERsfJZufukBR7gtyw4/vfVPVUYYyabpkKDtBKYqS4I0nJFeYuvsy8Hc+JRnKpJPDxm/B05+Cpf9A6vw7uXlfF5uiSb7ZUs0VhRa7d99KMnWYWS1fpbT01V4AccPkX+95kJu//RW8Tid13/sOzGnm21u/zS+P/hKEhj15HmMDKygRDs7TTBzeVm695UYqKipeOY4Qgh0/+xLptqc5rDXRr5ShKApNTU2k/JX817YktYbCtbkc8whRYJtq5dFd46TVfcT2HKUnNcBQwImpKGimRYnLR+npl5O1qug4OI7IeVGUqRYwzRzEle6gdKiNoslu3JkxVGEyGtAYC3vJVZVytDRNXHNxvX4zxUkXCu1Y8YNkdmxF5HKogQC+00/Hd9ZZCENn8u6fM9R1gB1LguxaFWGn0oshTEImrE2lOCedYnkmS4eoZK9VR5dVfix0TYWvlBqgRKiUCpWqcj+1LUX4TYXRHaMY/WmqS3ycdlUtNe79cPBhaH0c0uNT80s2nQczL50aUX/L96F3M3jC6Evfz+6yt7GhJ8eu3iitQwlGE6+O3VEScNJSFqClLMDMUj+zygJUF3rY0xfjqQNDPHVgiJ7JqXHWmnwms22T1CXbsQ0cIZOa6oeXsztIR/zkwx7SBX4SgSCT/kIm3GGaM738+IqPvqkqKUOZNF0ylEnTJkOZdMLb+XN45IMw+ypSV/yAm/f3sCma5L9aqrkomGHX7pvJ5YaYN/d7hMOv9gxIGSb/7/99jyvu+j5WbR0zf/gDNlitfHbj55nMj2PGFpAZuZA57lKuSUCtI8YWz1Heccs7KCsre+U4w0NDrL/ra/RlHeiKg0jIy8Jlqykpb+ShR9oJDKRZg40gKkII0r4BEuHNDI+1M7wvRVRxIxQFh2ojkFBweJswq9cSS9gABSFMVKOPYKyNysE2QvF2bEaKwSIHQ0VFxL3VmL4yKuYU0HLu2dwz+iCH9u3itrazqe4YwxzfhznSDoC9ogLf2Wvxr12Le/58Ek8/zb57v8tGVy87FjhoLTARClTqJuekUpydTuPNhNlkzWajNYct1kwSrjiqu4facIC19Qs4q2A2wY0j+PqSOOtCFFzZSFpR2PSbdtp3juALKJy1coAqsQHlyBNTQ5U4fNB0Acy6DOrOgCNPTrVyjhykxzufF6vey4u5GWzqiJLIGagKzCwNHAtgU+FrZlmAQq8DPZdlqH+A5/b28Fx7jO3jCglLQxMmFZk+Ks0BCrU4VqGdbNhHyh8g5g8x4Q4z5iwirfpeV51sQicixigRE6y2TfIfZ8rBY6W/LRnKpGmToUw6oR16DH71Dqg/k9R1v+DmA/2vBLLz/ePs3v1OTDPN/Pn/Qyi4+JXd0nmdez/5H6x54mFSy1dS9/XP8eUD32Z91zrMbBm5ocs5vWIBtzj8NB6McdQ1zHZPJzffcjMlJSVYlsWRI0fYvHkTXV3daBg0awMsv+hDFKTC9G4fwjOSwY5CRhiM6TqJ0HYm7E8z0aOSmZgafsPu9OP3NpLNlGE6m4/1S8tjy3ZQPNZO8VgbwXgXObtOb7mL0Yp6/OGlTMZasPDiK99NccseZq16G48MtHPwiQe4dk8ZZd0jiNQoAK45c/GffRa+tWfjbJqBlc+z7cHv8cy+e9hclqErqAHQksuzNp1mZtLDcK6ZF835bLZayNgduELdpB07CYaGuGbmBVzbfC3V7kriz/aSeKEP1aURvKgedWYBO9Z3c+i5dmpcu1hUs4dwcgNKPgnOIMy8CGZdPjW3pLBg1z0kX/oBm6IBXnSewQYW05WcakWsCLk5vSnCGU1FLKsOYsVGmRwaIDo4wOTQAP2Do2yfUNjrLKevoBrD48DryxByp1HdCmmnlwlnIfk/GDvNJdJEGKXYHKNcRKlQc1TbbdT6imiINFNRugCbw/u/rpoylEnTJUOZNG0ylEknrM4NcM/VUDaP1E0PcXPryCuB7BxPL7t334qq2lmw4Kf4fTNf2S2TSPDUP/4TTTu3MXHNtWRuXcVntnyBWC5ObuwsqmIL+eqNp1GzO0dmzygHXP3s8/ZxyztvwefzsXPnTrZt20Y0GsUr0iwTR6l2LsHtOg9jdGrsrA5MDilplNFDmM4j6Jlu9NTUpUebrQBcs1DVGahaIcJK4Em1UzbSRmG0HV+yjzG/wpGqEobqK9GLq5hRdg6ufi8jnQk0u0Gg9gWKmjdRV3cx7ZvGGX5qPQsOGzhyOqg2nE0LCF19If7zzsZeUoJh6mzf/yue3nIXLzLIkFNFFYJF2RxL0hq2eD0H8/PZZM1mnCClzijFVaP0qM+RU7uYFW7h+pnXc2HdhbhtbrJHJpn8bRvmRBbP4hL859ZwaEsnY888SLX6EnXunWgiC+5CmHkxzLoC6k4HmwMrPcH+393Li7sO8GK2kZ2iCQMNj0NjZX2YNY1h5gZ0HGNdjHS00d3VycFUnnF/iJFQMWNFJSQDQTJuD1m7G6For6sWARGjiFGKxCgl+TEqzBhVdpNaj4/6YAWlkQW4IgtRbM43rFZ92TzbYim2xlJUuhz8U3Xxm6qeMpRJ0yVDmTRtMpRJJ6SB3VPTJwUrSd3yGDcfjb4SyM50HJyax9JexMKFr85jCZDu7WPbu99DuK+Hrg+8jxfm9rC+az0iW4E+eBVn2uHzt16G+uQ42dYJdrm7Oeof4ZJLLuHw4cPs3bsXXdepiISZPdBGnTUHU0wFvkTExS8mJ2lPdNIwsZmQHp2a4kdRUWxVaPYmNHs9qpUmFG2nZLydYLQNuz7KSIGdCZeTyVARj5x2OYMNtaxMJrnUqmN0f5bEeBZXIEuw4TGCtZuoYi3xJ8dw/G4bzrxAODzYS+bhWbaGyD9ejqMqTHbkIC8f+AXP9L/Ei/oYUU3FYQmWpnUqU8UMJ5awQ1/AAEWElDTlapTyyjEy5a3sje7Erto5v/Z8rp95PfOK5gGQ74qT2NBP9uA4tiI3oYsiRI8+RX7Hg5QrO7ApeSx3BHX2ZVOXJmvWgGZjKJZlw76jvLh1By+NupkUU5cM5xRpLK0NM9MWp2Cyg76eLvanMgwEixiOlDNaWsa4L4I4NmyJIiwKmJgKXYxSZI5SlhmjPBujRjGpLwhTVDYXT2QZzvA8FO3PD2lhWIKDqQxbYym2RlNsjSYZ0g0AXMCFDg//vbrpTVVRGcqk6ZKhTJo2GcqkE85YG/zkfLB7yLxzHW/vyr4SyNYomzhw8KN/NI8lQHL3blrf9wFELsfLH76GX7vWEcvGyY6eTVF0IRcXxXj/DddhPDxAvjPGZlcbHZ5RCgsL6evrw2az0VLaSPOkl9B4AFDBn0FbVMtPd25i8sgWItk+VCxQnKj2BlRbPU5hp3iim8LJdkKxdkw1Tl6FfEUlHTaYtNuxCt38dsXV9FQ2cEkqz5WxIJ07JtBzJoWVaby19+Ep2kpZ5xKMp8dxHOzB0BTM6vmEys/GNWceBWtdZPObebH9CZ6NH2WjHTKqis+0mBuzYU82sSdzNn1WJQDltknqlShh+wiiKcY+ZR9j2THKvGVc23wtVzZeSdgdRhgW6T2jJDf2ow+k0FxpQjNaUVLP4xh4ERWDNEUYjZfgX3MtSvUKsiZs7ZzgxSOjbGjt5/DY1NAUEaKs8I1Q73fiSEzQkYrT5y9kOFLOSKSciWDRK59XoRijjg7qaCeUG8MzmaE6mabZ5aemZi6BmefiLJqHor2+peyN5LMGqWiO4YkM2yaT7Ehn2G/qHLVb5I6NrBFIm1SOGVQdW0qiJvaqPO/71wveVDWVoUyaLhnKpGmToUw6ocT6pwKZniHzzvW8vV95JZAtN9Zx+MinCQWXMG/eD1+ZxxIg+tjj9N5xByPBIL98dwNb1B2QqyQ/+DYWmnlOLxXcdPV1ZH/VTXxwkicdu5lQk1iWRcDtY46rjvqRAlymHU3px7S2cCTcwIGuVnLj3VMDoap+NFs9btNL+WSUglg7vlQ3tsYy/E0tmIeOEm07QveMarqdKqZQCBRa3Lfyeg5UzuXSqMkF/RpDBydRVYXyWSlclT/Bmd1D0Y56lOcnUONpJoNOnPVnUlx2Fr7wEdIlu3gxv4dn1RzbXS4MRaHQUKiYKKA/tZru7HIQGigKxc5xVriHKMylGXeNM141zkHjIKYwWV2+muuar+P0ytPRVA0znie5eYDU1iHUVCeewE68np2o0Z0owiJuFNMjVuNddS3V551L23iWDUdHeeHIKFs7J8gZFnZMlqkHWWxrI+90sN9WQU+4huFIObFA4SufT5ExQp02FcBqrE4ciQx9k6WMpuqYW7uGq1ctozrs+aPqYFmCTDxPMpoj9QdLIpqjL5On1W7SGVTpLbIxEpx6HxRLUBw3KJtMUxifwJPsxRL9pByxY0uUpD2O19HM8+/8yZuqqjKUSdMlQ5k0bTKUSSeM9ATcdSHE+snc/BhvH/WxKZrkWzOrWZy9l87Ob1JUdDZzZn8bTZvq3C2EYOy732XsO99lX105370qzaQjS3bkHCqttcxP72V2VZjrLrmG+D2H2RE7zC6tAwGUuyLMSpdTnS/E5neREQfoGNzKgXgCMz813pWiFmBXiymN6pSNd+GxOjBrnZSdcTWFKy/E0djA5M/upvtnd9Fe5Kc/6EVBUFaY4b6VN7CtfClnDBic3WmQHczg8tmpXZTGVvQdbPsPEdhcgG1vEkuFjtoCSqsvoypUjBn4HS96tvK4W2WPa6pvVJHphegMuuMrsbJVFGST6KoNmz/P2nAnLS4PvcNRur3dDBQPMGQO4Xf4uaLxCq5rvo6aQA0A+d4EyZe6MQ+8iIsteFw70IxeAGJaI0diC+kxlhM47XQS1R5e7prgxSMjjCSmWsNK1QSNjm7C4QzjRUXs8c9k1PtqC1hxbog6pYNa+1QIqzd60PJF7I7P5sXeKnoTtZzWXMX1S6s5rbGI5ESWsd4ksdE0qWieVDT3SghLx/OIY9MjmQoMF2j0FNnoKVbpC9tIHRuRXzPy+NKDuDKdaPlDmOZ+DMtC6CEsPYhihvBqIfxOLwG3E59TRVMNakNlfO6cW95UdZWhTJouGcqkaZOhTDoh5JJw9+UwtI/sjb/mpnj5sUBWxbzEf9HX93PKSq9i5swvoapTHeqtXI7Bf/034o8/zjMLivjxuZNYVi3Jvqu4vKYGX/dLzGis56q1l3Lwpxt5KX+AmJKm2ipiid5A2BkkXaBycHgvHd0vYxljwFQQ8xheascmKRttJxvUYXmW9CwHxQtuZ9bit6MoCsmXNnLw85/mCAbDQS821aIskmX9kqvZXLySBe15VnXkUdImheVeGpZnEdbX4ZlD+F52ok6YJENODs2IMLv0UiqdnWwPPct6T5KXPG4MRSFoFjI2sYR8fA7ulJ95o204TZ32khJm1Y1xbkUIxSzg5aPb6Qh00BvoJSdytBS+vuO+MC0yOzvQN/wW++SLuNQdqEoSoTqI+ZZyaHw+hyfn0xcoY6LCwYF0kiOTOQQKdnR8jgyOsEKqOsxY6NVLxmXpAeqtDqpdHdRp7dTSSaHmJlS4gjizeba9lPt22kjmBfVFXi5tKma5z4c5mmW0N8FYXxI9a75yPMtuknNmSNnjjLpj9BQKhkJuJgNFJL1lCNUBgKqPY8v0oKVGcKfjhEydoMuJz+HA61Jx2E3S5gSx3DBxfZykESVP5o+qnc+oY9O7H3lTVVaGMmm6ZCiTpk2GMum4M/Lwy+ug43lyb/s5N+ZmHgtk5bRMfJHh4Ueprno3jY2ffGUeS2NsjN5/+iDZPXu45ywnjyyH3Oh5lHEeNzUIBvZupKWlhbPmrebJ+x+jnSF8wsUafSYBZ4hD44foHd2Caf0+iAUIpwQNgwMEkqPsL6onHtGov6gbrT5BbnQNy8/8EuGScvKDg2y9/TbaxicY93twKAahCpXDy65kt3cJZftTzOvJo1pQMzdM4/I8mYOfR6w7jGuPimLBwKwIB6oKOdc/m1HPyzwd6OFpr4uUquKzXGSiC4lHl1Ecc3NW11bmjbXTESqna3k5p80sZNWclew62MVTHU/REehgxDXySsf965qvY35kPoqiYPa2oj//a5SOp3BY+1EUE8teSLrsTI7EF/Py0TqOqg76Q4LDRo6EUAGBw22iR7xkywKIoB0FQW2+jwa9nUqth0pnGzVKJx5y+H0tBEOLCQUXI+zzeOyAwf3bejk6ksSpqSz1e5iXt+EbziGO5S9TNZj0jjHs6WLU28mot49xvyDrrkV3tKA7mzAdpaAoICw8epRCc4KIGCOsjKCJISazQ0zkRonr8akZD15DCAVh+BFGAMsIIHQ/Tpz4NRtFDgflHg81wRCLKus5b/Hlb6raylAmTZcMZdK0yVAmHVeWCQ/+Axx4iNyl3+EmdSUvR5N8s7mUppE7GJ94kcaG26mp+cdXdsns3k3Phz9EdmKcb18KW+vqSPVezXULFrPU3sveHdtYsGABYS3Ihu0bMTCZb9ZSpUc4MLaDkeQWBGDDTeVEkvrBPhyKRXvtXB4KzqGrIMxNlb+hfkkHRroAe+ZDnHHljZjZOM998gP09o4Sd7twoeOsC2JfezNHcnXkt41TO2Kg2FVmryqjYU6K6GOfRqw7im1EQfidtK2qozekssBuscl/kHU+lRGbDZfQcKaaGRxfhTNVxYr+/VzW9hIl6XG2tbRgO2sWl55zNg5/LU+88ATrB9bT6e8kY8tQ4i7h+pbrpzruO4LQtxVz5yPQug4t1wWAYa/HqD2XbudqntoVZHciT4fDoEezMBUFVbEQYQe5Mj9WkYuq9CCzrDbqbUcod3ZQ5ujBTRZN9RAILiAUXEIwtJhgYAG5tIN1W/t4cE8/W0fjmECZoTAvb2NmXsOyZRnz9DLm62XM28eYt4+oPQlmKTbfIgx/E3F3NTltauwwTeTw6t2QOYiSPYQ9344iXh3lH9OFZQSw9OCrocvw41VsFNjtlLpdVPk9VBd6qSoMUBOJUFtUgd9bjqa9OkxGPj+BaWZwu1+dueGvIUOZNF0ylEnTJkOZdNwIAY9/FLb/mPw5n+dG70VsjCb5ZlMRDYMfIRbfQ8vML1Befu2xzQWT9/6CoS9/iVGf4KtX2DmiXYA/dxZ3XjmP+OFN7N27l7lz5zLSOchwcoxys5D5Rh2dk130T/4OENSNpakfGsTt1HGV6hyceR6fVVYzZCtktbmZm9Y8jMOfIdp+Fk3NtzP39Eae/cYdtL+0g4TDicfUcTaVs+Cm29l9RGFo4xDBpIXpt7FsbRV1/k4mfnEnYkM3iq4gZkY4tKqehNVH1jHGU/4MRx0ONAGRXAU9Y2swkrNpiI9wRevznDawl6GCQoaXNbPo+iuYuewChodHuef5e3h24ln6vf0IRbC8ZDk3zbqJ04sWoHU+jzi8DlqfQslPIoSNHHMwK84h2ngeD+zIs6k/RbvdYkyb+n3QHBb5Uh9GiQen12RJ/ghL3AeZqb6AX5lqQXQaNoKBBYQqLiQYXAx6HaO9GY60jrC3fZidkzkOqoKEKnBbMMM0KLEPoPs6pgKYM4bl8BJ2VhD2FODzuBl3FNAhggwqlViqB6wcjux+HJn9ODN9qOkkej6E0IPHAlcQYfgIajaKXXbK/RrlIRuVIR81RSFqikqoLa7C4wq+QRUT5PLDpFJtpFNtpFJtpNLtpFJt6PoEnoK1rFz4ozdVfWUok6ZLhjJp2mQok44Ly4TH/wV2/BR91Ue4IfIONkaTfGNGkLr+95PJdDN79jcpjpw/tXkqxeB/fIr4E0+wo1HlWxeGGR9+J+c0zuNzl7Xw3LpHOHz4MNWRCnpG+3ELB0uNGUzGUnSPPYkQCYpSgrl9fZQurabAvYlRh5vvKe/kAXUpLivDraW/ZMHCfeQTZUwefDdrr7uKzOQ+Nn75MwyrLty6TlFjLav/6Yts3zhC56Yh7LogWuJgzZnltAxuYPzeH0L7BJZTwJn1dMwppCO1nz2uNNtdDoSiUJ4PMjKxnGh8OQV5WNm9h3ccWo/PyNA3o4riq85l7o0fwO7w0Nbdxg82/ICXMy8Td8RxK26uaLyCG6vOoLZ/HxxZh+jaiGLpWATImIvJu1YzWruC+zsn2Dyh0G7TyKoAAjWgTbWGRVxU2sZYYm9ltvoyM8QebBh40gahuEnIPYdMybvZNdpET+cw2ZEszrgDzbDTZjfZ6zDotlmgQEgbx+drx1U0QlEoQoU/QtjrwG6L0588yv7YON1KNVn3QnTXLFDsKGYSe6wTbWIcZdSCdIQCh50yn05ZUKEi6KQq7KemqIjaSBlV4VKc9j89NIYQJplMH+l0O6nUUVKpqeCVTLdjmclXtssID0NWOT1qNb1KFUaikJ9fLqdZkv62ZCiTpk2GMunvTs/Cg++G1sfQV/8LN4RvZGMsxdcb3NT0vQddjzFv3vcpLFgJQK6jg77bbiPX0cl9pyv8ekED2YF38IVLl3PlvGLuu+8+Bjv6UTSFnJWnxazEmw7SNvIcht6DW1eZ39VD7ZrVFNa2445vYlesgtvN93HU00CLOMx71vyCoCfO2MELceg3svbtzTz9uffRNxxHAKWaYO4Hv0bnPpPuvWNYQHuNk9WzbKzZvY7Yb38D6Tx6uUA9p54dpTl2ZPvY6LaTVxUiuh0Rn0fX5Jk4jEKqJvq5+dCTLB05TNblwDpzITM//HG8dbPJ6Tl+te1XPHz4Ydpow1RNKu0VvLP6TC5Np/C0/Q5GDgJgOmtJZxeTyS+j1VXDo4lhtlhe+uweLAVUTcEscqCXetAKNObaO5mrbmO+2EQJw3jyNgrGEoRiBqbRxM7UIg5MLMCVrsBuTd3ZaCoGRz3D7HOa9IkQhrDjduaZV2uyckYSr6uXA2NtHI0OMJQbJ4eOaa8g515E3rUMw1ULgJJN4RqPUpnKMsdp0RDx0VhSzMyKGmqLCnD9mdD1e5aVJ53pJpVqI5E4QizWSjx5FMPoQ0V/ZbuYFaRfqaRXqWaASgaoYEBUILI2gpk0hekERfkMs9027rjpxjdVjWUok6ZLhjJp2mQok/6uMlG470bofpnceV/kJvd5bIwm+UqtQk3fPwCwYMFdBPxzAIivW8fAv/07KVXna5ea7AyuxJ6+hl+9ayW1QTv3/uTnJEZixNU0EStAdaaCnuEDZHM7US2VloFhZjbOpOSGs3Hu/RxWapxfjy/hs+73oKtO3lb2GGfPfRYr3UDPS29n5uKViMx6Dj7yKEmbg6JMmuLzb8AUy+jeP07WobCr1s4qZzfnvfwE+o6dCBtkF5hEF4f5nS/Fi5pBTNMImArhRA1HJ88ln6vDmc+xtmcHt7Suw6fnyFeEKX3XO4hccws4HWzu28y9O+5ly+QWcmoOp+XkdFctN2kOFnVtQUmPIRQNK7KMZHoBifEF7LAiPKmPsEV1MG6bGiZEdWvkS92YERcFwTQL1F3MF1uZwz4KVR8FMYNQXy8F0TxjRjX7M8voi59D1irGwmTM208sEEUvUJh0hOmO+emftKGpFiUFPdj8O0k795FTX72b0TJd5NVl5NxLMAKNWC4/AMWmzlKnjUtKSzirvIiQ1/EXq4gQgnQ6ytjYfiajh0gmjpDMtGOIQZzaOKoy1anfEgpjROhXKhmgkv5j4SueCeHOCIpyGcqsPNVYVDhUipwuNJuNdCbDxMQ4k5OT1NSUc8MNckgM6W9LhjJp2mQok/5u4oNw7zUwepiJS77LteYCDiYzfLE6T23fe7Dbgyxc8DM8njpEPs/w177G5N0/p7vaxRcvNRlMX0ZZ6AIev2U5+WiU3/zwfgbMcWxoNIpqxvrHyaReAitH9XiSWW4fNZ+4Ha++AV78KpN5D5+NX8vDvnMpY4gPrPw5FYFRJg5dyfihtSw638/B+z7NmK7iyesUllTiX/AhOnePYzhVdlQZLBl8kXNe/h3K5CRmRGNicZYdTU6ecgr6bBoOS1CfDNMTO4OR5BJAoTo2zDuPPs6y/lYURcF9+lKK/+E23IsW0TrZym9af8O6jnXErBiapTHXLOEGu5NzBnfjyMYQriB6+RrGJhsZGZzDTop40cqwWTHJKhqKAoTs5Es8WEUOGr1dLBDbWcAOZrlUCijG1ztEUdtuXLrBmFlKe/oMjmZPY8IqZczbR6IwQWGNk+bGPNt6x3mpo4ChyRqEsKM6B7GHtmMP7AIti5WLIPIluCjFUzgbs6CBEXeQNGBXFNaEfFwQCXJ+UZBS5x9PgzQVutJMTk4yMTHBxMQR4rHtZI0uLNsYLucEfkfsle0NoTGslB4LXVPhK5oLY2QCFBmCasWi1qZS4dTwq3mSqRjRySixWIpkUue1P4eKYuFypXG64rhcCSIFYa6+9jtvqjrLUCZNlwxl0rTJUCb9XYy1wT1XQnqC9st+zNWxKpKmyZ0V4xT0fACPp4YFC36Ky1mKPjxM/0f+mcyuXTy93MVPTrMRH7yJFfPP4p5L5rJt/UY2bNlIUslSqRRjDDtIT25AWOMUpPI0jMeoef8/UXPZWpTf/CN0v8SueAUfsW6j21XNGZEt3DDvfry2+Rxedw0OexlO/QEGO9umBpU1BbZVH2e4HbCrtJbrNLQ+zDnbnkcVFtm5LvbPjPF4pZ0DDjuKEDRl3KRii2iNn4PbcpM3Dc4f2sJ17c8QmUigBLwUXH8jhTfcwIA3zxOdT/DIkUfoS/ehCIXadAGXK06uS3TiS48gHF6SkRUc7Q1yZHIhHe4mNquwDwOBgmoDvcSNGXHjLDRYYNvDAnaw0hOnJjQLdQzMnVuon9iFgzwJM0xbdg2t2dUccNlJF6YJ12rUVY+xc6ifTX0eRhMV5NO1YLlAzWLzHcLh6aXAbaPKW01LeBbNZXMYdjvYns3yUjRJxhIEbCrnhIOcXxRgbWEAv03DNE1isdgrwWtkaIjRkWH6k0lSznE8wVEKfUOUuXoIaFMBLI+DASpeCV+TehH5fBiPCFCj5ikTaUIihT2bJJXIkEjoJJMKhvH6y552exaXK4HLlZxaHDlcqsCNDY/lxq4HcORC2PUAeaOcZf9y65uq0jKUSdMlQ5k0bTKUSX9z/Tvg3rcBChsvvou3j4UotGt8tXgvoudTBAPzmT//f7DbQ6Q2baL/ox9DTyf59gWClxrCpIdv5ZpzV3FHXYjf/uwBelJD+C03nnSE7NA+LP0oLl3QNDLBeE0Dqz//OarMdvjNP2JmEtwzsoQveN6DqgneNf9XLC8/jDl6K62/m02gsI9E231kFI1IOgdNV5PKNoMC0SqL4N5fccbODSgOOyNLbDw8O85zhU5MRaE2B/74DPZFL0YzStBQcOWH+YeB37KktRNb1sA5s5nCd9xM/qxlPDn0HI93PM6B8QMgoCwb4qy0wi36COWZQYRqJ1Ewn0N9brZGF9Lum89Wu4cBpr7PVZ9CvtiHGXFRHhhkkbKTFc4RVhSVYRpNtG85QmXvJuaYu3GraTKWn6PZFWxWmzkSKqWoVsFf3MPe0R72jtmZSJehp+qxslUAKFqKkD/G4powNyxexJKqCoJuOx3pHOvHYjw5FmNbLIUFVDjtnFPgZYUd6rNJxgcHGBkeZnxigj7dYsTmIO7ykvHYCfpGKHX3Um3voEE5ipssAKNE6LRmMCaqyVvFeHMqYT2OJxtHS6bIpBWyWSe5nBdQXqlOimLicqVw2XO4bQYeVcGjaHhw4DHdqKYHI+cmr7vJ5h3kBGQV/diSJ4dODgMdk1JngPf967+8qWotQ5k0XTKUSdMmQ5n0N9X2DNz/DoS3iHvW/ojbx50s9Lv5N/f9ZId/RiRyHrNnfR1VcTH+o/9h9FvfIl7q59MXJ+l2NZHKvpPbLlrIkvbDvLT9ZRQB5UYJyf5R9Mw2VMukYSyKWlbDgRmN3PCum6k++jPY+C2iVog7Jt7GOt9Z1Lj6eP+SH1MfqaNnwzsZOpxDyz9MOjuKO2fgDcwjE7gQYYGnRsDOX7Bsz0Z0j4fBlV5+2DLKEb+NEsOiOlFMx+S5jOTm06IYjAuL5eZTXHxkP5EjY6Bq+M89F8d1V/BSeIwnOtexdWgrAkHECLE8pnBTdpQ5+QEECglvM4cGnDwXn0d7YAW7nQWMAooiEIUO9BIvWpHCLNch5uudnOF3Ueyv46XdAnt7B0uye5lj345XmyRvuThgzWaHdy7jNZVkA53snxykK5MlkQ1jphoxks0IMwAI/N40i2sKePfKuaxpLEFRFCwh2B1Ps240yrrRKG3ZqQ70lUaWGdERyocHMLIZok4PMbeXuNtL3OUl7vKguPLMUI7QzCGaaaVWdKApFkIoRJUSUkQwDQciaUBckE6GSCULyWb9r6s2Dk3HZTNwaQInKnY0VMsOhhPdcJC1VHJMBa08BpbyB795AuxouIUDj2nDbR1bhB2PcEytx0HGmmDlN977pqq2DGXSdMlQJk2bDGXS38zeB+C378OKzOSzK77ND6IalxR5uDX/n2RiG6mt+QD19f+MFU8w8IlPknz+eQ4sLOLLaydJJFeTDNzAHS0+zA0vMpmJU2WGsY3bGJt4FkSGsskkC+rm83JDBUP5PLdcsZaqzZ+Cvm3silXxIW6j11nJOVUvce3MhykPv5et984jObYJM7UJhEWhKCFVfB1COKisUVB33Evjvo0k/X6614T4YVMfgx6NmTkTz/hCNseuYSFuqrQUaU8HZ46/wOw94yj9UbSCAnzXXMXB06t4NPEyG/o3oFs6RWoh8ydVrk6MsEbvR0GQsFdwcNjHk8lZHPav4YCrhKiioihghh0YpR78kRwL2c/sySSzCNMV9zI2oFMXS7JQ20OTawNB2xCG0Gi1NXOwqJEtBYL96SgjVpKcpWCmmjESMzHT9YCGXbNYUO3nmkX1nNNSQtjnRLcEW4bHeKqrl63RFEdsTtI2B4oQBNMJ/Nk0Nssk7XARd3nION1Tn68QlDHAXGsfczlAvXKE0LHxzSyhYqKimJBJB5mMlpCMF5NMFJHT3a9UEYdqYlfAtDTyFljKG9SjYxRT4LY03KY29WhpuC3HK0HLgxO34sKpenBqblT1jfuzgYnC1HAeqh/K/+3sN1W9ZSiTpkuGMmnaZCiT/iY2/zes/yR69WpunfMFnk4p/FO5nbPGbkPXB2mZ+WVKSy8ne/AgfR/6MPrQEPefG+TX8+LoE1ehl6/ltnQXsd4OApabhkwpvQMvkTMGCWZM5uX8NHzsYzzQvpPR0VH+YU0ZZVs+h5nP8dOhpdzpuxVNs3j3/HtYXZskoH6K53/cQz6+HsuKEsh70CNXI9QI1eWC4N5fEtm/kbFQkP2rg9zT3EfcqbIircP4KrYmL+VyfFS7B7B8L7O8exDnpn5EIo2zpYXxS1bwSP0ETw08R9pIU+goYGEmwLlDfZyf78aGRUIpoHUsyPrMHHa5V3HYU0VK0VBUMIpcmKVuioqiLLIOMHMoT2ComJGoF1fKRbmZp9H9Eo3uFyi29WCh0Oaq5IVAiAc1NwO2JJaWwUzXYiRmYyVnYeohAKoLXVwwp5y1M4tZVB1iLBrlidYjbByLsV91MOAJYmpT/bI000QoYKmv9tNShCBkpik1J5kl9jNDPUC52k5IHUNTpuZNEgIsSyObKiQxUcnEeCXxjB/d+v1xBAimpk16DZspsOkGAd2Gx9BwChs+4cArnHgVN27Vg0v1YNfc2DTXK9Nsvd4f/t79mWQH6JpFXjXJKSaxsMlpH75gmpX6D/4VGcqkaZKhTJo2Gcqk/1NCwDOfhZf+H6mmi7mk5nbadYXPVCSpH/gAmuZm3tzvEwjMJ3r//Qx/8UsYQS+fuzDPwRINkXwXZb4wZ/QfBtNirlFNfmSA3vjLOA0bsycyNCy6kfDHruDeR+9nbKifD8wYJHT4fib1IB+P3cjvfKdR5+3hfYt+zLyGC4i2Xs2Wh36NmduD3VSx+c5BuOZQFjGo2nc/vtaXGQgX8NIqJ480j2LZYG3SID1+Jtuz53K9CDLPEcPpf4GK1iHyL+8HVcU8fSkvrQzwS8cuJnKT+Ow+VtqrWdPXx4Wxw7jRSQovh6NFPJ2ZxYuOFXR466fumNTAiLgxS9xUhgdYpLfS3Geg9ZWTjhbh14P41FEqPS9R79lAndoJQKsjyKPuEI/5FSbsFpbhw4zPR0nNJ5+pwDQ17JrCyoYizmqOsKBIY2SgjaeHJ9mhuenzBkm4PMfmlBSvC0kuI0elPkGlOUCF2kO5/TCVtg4KmET7g7klLd1JLhUmFi1lbKKMaDKE4I8Dk2JZqLqOkkvjzOYpNtwUKwUUaRECjiJc9gCq6kJR/nyQel0VQ2CqgrxmklVMshhkMclZJjnTIm+CYapYph1MB6aloQtBXoAhXh/hchXj/Mt/vO2vq+O/L5sMZdI0yVAmTZsMZdL/GdOARz8Mu+9haM7bObv4PViKxp3FB/H2/zt+Xwvz5n0fu+5n8D8+RWL9eibm1vHxs/qI2iL4zPewKtZPKJugyiyiKVXIvqFHyRk56mJp5haeSeC8i1EvKOWBhx9EHz7Me0Iv45psZedYNR+wf4QhZynn1zzHdbM2MLv58zz73wMMHF4PVhKPWosZvIRwQKGh9Vf4j75Md2kRT64QPNscxSsEFyRMRsfPZbu1gluNUparOmrRfry7dpDdthtCATrOaODu5hEOasM4NSenhVpYPTzBuX07CIoMGeGkNV3Bs8kmntSW0+ltRFc0sCmYxS6sYieN4U4WZY8yowes7npEvBwVlZC9gxL/MzQ7d1ElhgBos7l43Odivc9Dj+ZATc3BlV+Mla1nPD51ia4k4GR5pYdyJhm1YuxzeunzhYi7vBg22+s+JtUyKconqMsNUqd0UuM8QJXjECFiU21MxtT8kELLHctuCplUiGi0mGi8hHisGF1/9RImYip42XNZfOkcJbqTcmcZYWcZbmchmupBETYQfxy8BAJdtUirOilFJy0MUqZJVggMQ8EybQjTgTDt6IJXlqnfOAOEjhB5TC2NpWVQtCyqkkMlj40cmsijWXlUM49i5FHMPMLIYxl5LDNPSVExl35XTrMk/W3JUCZNmwxl0v+JfBp+/S44sp59iz/MRb6rqPE4+IznIZTRu4hELmD2rK+SP9hO/z//C/rgIE+fW8P/LOhGZGbRkL2ABbEefLhYnmskOnaAI/GdFKcdLIjUEio8H//aBvqrUjzy2KPMsg5xifgdlmHw4+FVfN1/C3abwT/Mv5uzmsOU+P+FBz/7A/K5bmz40XwXYndX0DT+AiW7fk1vdTEPLc2yaUaSYtPkkpige/JCdttmcmu2htOEExEZQG3dQHrDC1ghH787LcDPmoexHDZWFM7mnCyccWQTkfw4eWy0GjW8kGzmMXMZbZ4GTEUDu4JR7EYpsTGn8BALU53Udjmwulsw08WAic+3iWrvRmap+ykRSSxgj9PBsx4Pz7oDjKozKbSW47Ra6Bu1M57SUYByn8DnzpEOaYxFQiTcvtcHMCFwmjpFepyGfB8LlN3Ue3YQ1gam2rQsBQz3VMuRLYOiTv1uGIadRCJMPFZCLBYhkSjCsuwgBA7DwG+qRHBTbYtQYivFJryga6iWgvIHlw4FAhNBTlhkLEHKgISpMGEI0maOjJVAWEmElUCQwVIyoORQlDyqyKOIPIqlw7FFWAaWZWAKc9pVUxECzbSwWRY2IbAhsGNhw6KswMVZd617U1VehjJpumQok6ZNhjLpfy09Ab+8HtG7lfXLP8W7XGtZHXTyIevLGPGXqK39IHW1H2LyZz9n5BvfQA/5+M8LDA5W5PEnz2PZeCEFIscCo4aalIfto09g5mzUhAzmVd6IyHsIXF7PhtFd7Nq2ias922hJb2Ys7eej6Xfygm8lDYEO3r/gXpbNfR/7Hhzn8KaNCEycjgXgWUOVe5ya576J4bH4n7NMNjblmKHrXBqFvZOXcDRYwg3ZCs7IVENBCtHzLMlnHsXye/jdaX5+3jJGUaiUd7iqWXt0G2XxHkyhcpQansvM4WF9GUccdViKinComCVu7MWwqGAPi5J9lHd4yffMw8iGyNsmCASfocG5jQWik5AwyCmw2eXieVcR+31zKAieQVCdQ++ojW1dk5gCNFXgCCjkSjyky/zgfH0LmM0yiORjNOo9LNG2sMD1HDbFmHrRcIDhwlJMFHv6dQEsmQiTSBaSTBSSTIbJZv0oQiGguCgUfsJWkFIjSEQE0N7gEiWAJQQ5ARlLEDcFMSPHiDFBlFEgjmolUK0Uip7CMtKYRhbrTwSr3wcozbKm+pz9/rklsAkLuyKwK+axxcChGjhsJk6biUMzcNoNnI6pR5dTx+7SsAV8qH4/iisIrgA4A+AKQsUiWPoPb6ray1AmTZcMZdK0yVAm/a9MdsG91yImO/nvpV/kc86lXB/RuDL2ISx9mJaWOylyrGHwk3eQfOEFWmeXcOe5YyTVMmYnL2RmLE6JKOD0fBMD0b0cinURykdZ9o4bCewOo9hU7JdV8tCmx8kPHuAWz3ME0j1sHq3lw86PMOwo5qK6p7hhditVRf/C+v/8ESk9i6qVY/OeR8AXZMahewn1bOWZpR7uXp2lWeS5YkJhU/JShiNOLlQcnD54FqoXxORLJJ74OZbXxTOrfNwzd5LiwnLelXVySdsGnAg6RCXPmot4ML+CVrUagYpwTQUxT7HOsuAOlsaHKO4oINY7n3zOQ8LbTjjwJLPte1miT+AWgpiqstEVprVgDrbGq3Goc3n20AT7BjJk8lNvr+W1YUVcU0vQDqrySj+wgJFkhtHNQm0LLbYdlDCEAig5L5Zlx1J11DcKYIkCkqkikompoSjswkah5SciAhRZfgqFj5DwoBy73Pja/l66sEiSJ26lSFiTpI0JUrkhcrlRrHwa3ciRE+YfXalUhMCpG7h0E5du4DYNPELHq+Twqjo+exavXcfhMLE5LDSHhWoXaC4N1edB8/vRAgEUb/DVQOUMvD5gvfI8AM7X/G13/VG1NQ0LPTcVCl3eP75LczpkKJOmS4YyadpkKJPetL7t8IvrsCyD2xd+mXsdLXysNM2i4fdhs3mn+o8d0acGgx0f52dnuXl8UQZ/+kxWjJRQoCqckWuiOOdg6/jL6GmT1NwE7zjtDtJP9mIv8TK+0sbDzz7ObOsgF+nrMQyT70Qv4Pvea3HZc7xn3t2cM2sRR36boHNvG0JxYHOfhs01lxnmAco3/pjhMjvfOjdHvsjk1ok8u+MXE61QON03xoojN2IngMjtI/HY97CcNp5b6eWeeTGKwxXcMBzjmtFDCGw8I5bxPeMS9lk1gILl1rBK3BSWJFju38aKyXHCnaUM9s0jaUEisIeywCMsFT0szGXRgGHNRWvhLOK1F7PTWsCG3jx9UUE2wdQNipqCFXZCoR21wE7e70IoCoqwqDIHaVH30qzsp5lWQlYccgFMS8XS8tgcqVcCmK47SCULj7WAhadawDI+/MJDkfATtvyEhY8C04cLBxrK68JXzkqT1sdJ50ZJZIaJ5UYY18dJKPk/uoNStaxXg5al41XyeBQdvy2Hz5Ej4FHxhpw4Qn5shSG0gkLUYBhcIXCHXvMYPBa2pgKVZfOjCztGzkTPmej5qUfjNc8zmRzxZIZUOkc6kyOb1clnDfI5AzNvYeYtLF2AARgKiqGimiqqmGrxS5TG+ORnrnxT1V+GMmm6bH95E0mSpP+Fgw/DQ+8l7y3hujlfZo+jkq8UH6Z88A68/tnMnf09Uj97hIH/+g6TBR6++HboCntoGb+UWQmTRrOIFdlGeuKHeCGZYijQzcy3XcZVmSWk1/fibClgR2EP29e9zJX2DczWd9OdLuAj+fexyzeXeeH93Nz8MA0FN7Dh80+QQkN1NOLwrCXiU2jYeCeO3BB3n22xcb7BO+Npot1n8FxpAec07WBO2014u2cD3cTXfw5LZHhhjZ97FiYIB53c1gvXD21GFw4ess7m68aVjIjCqZarEjdlxSOs8m5j9UScgqO19PQuZVhN01HwErU1H+Xy7CRNug5Z6HeX8lzFSp5yL+PJXD2xlB2xTUfJ5qbeS4+KvUxBjdhJFQUwbHYUYVFHO7PZT4vYzwyjC0/Ohm4JhC2HzZkCVYA7ivX7ADZSQzI5dQlSzwQICz+Flp+KY+ErJHy4lFd/HoSwSOtRxnNdxPLjTJgTxPUJEvo4hpgaMNZumrgtHQ8mxZpFnR38Xjv+gItAOECgNIK3vBStsATFU/BKwBLOILoaIGt4yGVNxuNZRifijEcTxOIpkgM5Mqk8esbAzFmQA8VIoRoZVHMUzdTQhPbH9e7PMJQ8upbH0HLoah5Dnfpb13IYzjy6+/ev6eiqiaFYaP4g8OZCmSRNl2wpk6ZNtpRJfxUhYNN34Kn/IFqykHNmfAbTE+az3kfwjv+Y4uKLaCr6OEOf/DTpzZvZOMvDDy7IoeorWDlaRYVwcmZ+Fmo6zp54PxPaKAcbB3nf6R+idp+LfGcc24oiHh56EX3oANcbv6XIFuOx+AJut38QU1O5YeZDnFMh6F0nGOiJoygebN5zcDjraRl9ksj+x9k2U+WnZ8N5IkVkbC4vBeZyXtOztEwsJ3zkMlSRJPX89zCSA2xY6uGeJSkCTh/vGIlxXW6AtHBzn7mWbxuXE8eHWewiXJ3g/ODznDGWxt/XTH/fLI6qY2QLn2e28yXOzk1SYuawUDjgbWZdaBW/CZ5OT7IIdSyHOplDEaCoAk/QwB7RiBYXkvV6AagU3cxhH7Oz3TRme3GQAEcKhyuJ+kctYOFX+oCpmULCVoBCy0eB5adQeCnA+0qne9PSSeTHiRnjxPVx4vmpx1R+AoeZx4eJz6kRCHoJlRYTamyiYOYs/GU1WM4IYxkbwxNxRieiTMaSxBNp0qkcubSOkTWwchbkFFRdxWbYsBt2HIYD9U/0PwOwsMjbMuS0NDlbhpwtTV7LoqsGhmpiqCamamJqFpYGliYQmgI2BdWmojpUNLsNu9OO3enA5XTgcbjx2N347G58Tg9+hwe/00Pg2BJyeQm63bjtdjR1+kNw/CmypUyaLhnKpGmToUyaNtOAdR+H7T+hvfZCzqn8Z2YEfXyMr6ElXqSu7sNE+hYy8IlPkkvE+OG58PysAE3jZzIv7WShWUNTNsz+6AGG8gl2tRylQmvm7WddT+C5NGY8R3qFlwf3rmNmdhcX8wy6pfKfmRv4pf0CmkJHeVfzQwQGa+l6boSsakNzzMHmPp1yMUTjpu8S82X50blQWJZlxVgZD2oXc/6sp5ilOSnZewvOdITMtp+RH9zFxsVu7lmawWNz8e5Ykmsyg8SEjx+bF/Jj40JSNjdWpZu5VYe5ytjFnPZaJnsWs1cdJh/czBLXDtbkOvBbOTKqk+cLlrK+YDXPsIjEhB1tLIOVn/rx93myuIoMopEikoWFoCqUWkPMynXTlOmmNncEn20Yty+Gy5WaertNjWQiTDwRmboEmQjjzhRTaAUosLwUCR9FIoALBwB5MzMVuo4Fr0R+jHx6GCU/gc8GAZ+LYCRMsLYWUTOLHn8tXZNpxkdjpGJpzJSJllFx5Zy4DBcOw4nKn26tsjDJ2TLHwlWGnJYjb8uj2wwMm4llF+BQUJ0qmtOGy+vA43Hj93spCPgp8gYp8gQp9oYo9YfwO/+68cqONxnKpOmSoewUpCjKT4BLgBEhxJw3eP1M4GGg89iqh4QQn/tLx5WhTJqWXAIeeBe0Pc2zLe/mpsjbOa9Q4+bkh1GNUVpmfAn1vjbGf/QjeoscfONKk3H3fFaPNjLDCLJab2Ik1kZnIsrhlnYsJczsyGwuXXgO5iODKA6Vww1RXj68gYv1p1hgP0J7tph3K3fQTyFXNz3KarWNkfU2opYbG0FU/wU4KGBO210ERw7y6DKFA0sNrog5edi4kYamA5we3kPx0bcR7DsdveNZcnt/y+a5Gnev1nE53Lw/FufSzBCjVojvWZfzS+MsMj4PjmqVtSUbuWgoinZkMYeyFmrgMAsd21mT34FTGIzbgjwdXsnzjgXsSdWQiWvEUx4soWLTdHzhHOmiIPGiInDbKDATNKd6qU13UqcfosLThT8whs02dakwn3MTj0eIxSOkYiU4EtWEj931GBYBCoQXDZW0kSCuj5PIjZHNjGCkhhD6CG67jq/Aj1lUwURhFX3eUkYsG9lEHtICe9aGO+/Co/uwW47XfbwWFmlHnJQ9SdaZxXSYWA6B6lTQnBoOtx2X14nP7ybo9xIJBSguDFLqLyTiCeJ+g870pzoZyqTpkqHsFKQoyulAErj7z4SyjwkhLvlrjitDmfQXxfrhF9ciRg7xg/mf5DPB8/iHojhnjX0ApyPE7OL/ZPLfv09uzx6enq/x07UeKmNrWJYsYJnZSDCR5WCsh66KHvI1ERyDTlYtX8kSs4HMxiGUYhePp54lb/RxrfEwES3GA+Ic7sjfQnWgj1vq78e1IU//WBhNOFC8Z6LZW6gYfYmm1l9zpNziN+fCeZrBjuRVtNeV809V36VwYgmRfTegjg+TfvkuOgoT/PAci1yhmw9NTnJBdpR+q5hvmVfwW3MN2YiP0uoJLnNuoeWonyPjAUKeTlZqW1iYawWgz1HMFm8Lu5Q69uZr6U2UMJkLAuDxZTHDbhKRMFaBA6+VoyYxQG2mg1nqXho8h/D7JqcmGheQToWIxyMkYsUo0VoC6XKKCRGxpgJY1kiSzAyTTQ9ipIYRxhh5W560P8REoJQxVyExmxfdtOPI2/HkPXh1P5p4fbdiUzFJ2+OkHSlyzjyWx8Lm0/CFPBQXB6mrKGV2TRWlgaKTqqXqeJOhTJouGcpOUYqi1AKPyVAm/d0M7oVfXIuVS/Cxef/JA75F3F6wn1njnyIYXEL98LUMfupL5PIZvn8h7KxtZvVoCwv1cuZmSmibOEyHsxP/VXOIbktiU21csuZ8iraYGKMZEkVxHopuYZZ6gEt4jjx2PsYHeVKfz2X16zgttoXeHSVYpg3VvQybcwmFscPMPnAPeVuKB85QqK3OEI2t5ZdVV/De2u8z3xqneN9N+EbqSG/8EeO5Nn62Fg7OcHLbZJxr0qO0iSq+rV/OY+pKzAoPCysPcXa0A7NPocgaYy0bqdBHsFA45K6lzV3KflsxL8YXcjTWgCU0NM3CVqCRLA5iFjmx2wVVyUFm6EeZq+5mpmcP7tdcikwkiojHirGilXhijRTpJRRZAUKWm2x2nHRmmHRunISVYUxTGHUXkHEEsBQPDtOHV/f/0eVEQ9FJORJknWl0pwFecAYcBAu9lJcW0lRVzsyqStwO9xt9utL/ggxl0nTJUHaKmkYoexDoAwaYCmgH/sRx3gu8F6C6unpxd3f33+iMpZPakafggXeScwa4dtaXOOxv4N89D1IWu4fy8DUEHnISv/8B2kpVvnmZA5exktOSFazUZ5Ce6OVIph39pnrq8/Xs3bGXyopKzo8sR9kSxXSYvJB+mV5viouNp1hgO8JhxyzeEf8nbN4MN1ffh7bVJD7mQ3M0Y3OfTiAxyOxDP0exJnhqsYIyM0MwM4/vF72LuhnD3Gr7LhU9awi3XUJu12Nke17kt8stnlxq5+2pHO9ODNIrKviq/jbWuVbgrFY4K7yVOYOd1EfHODOzFa/IklZd7HM3cNhRzkarnk2TC4nnAwDYPAq5Yg9mxI0S0KjMDNFkHGG+upNZnl04bVkAcjkP8ViEfKwcZ6yRULyBIrMAv6GRzcaIGQkmLYMxxU2cADYCKH/QMV5X86Qdxy4nug1Ur4o35CJcFKC6IsKsmgoqIsWo6p/uUC/97chQJk2XDGWnqL8QygKAJYRIKopyEfAtIcSMv3RM2VImvaGtP4J1tzMZbuHcGZ/HEYjwce4kmN1Gg+N9pL/wO+jo5OHlCr9eVsPK8fmclm+kNGZxZLKVracJPnzR+3jxsWcZHh5mxdylzO0qRoxmabM62ejupsTs4kqeotCW4MdcypezV3NOzQucFn+Z4T1hFKUMm3stvpxOy6F7ceT7eHqRgtaSpijTxN2Bm+icOYN3uX7A2kQfJftvRuzvw9z/G15uzPCLM1XOQuG2WA9xM8I39Kv5jfsMimakuND2AucNbmfV+CE0BEP2QvY7Z7BHrWR9ZgFtiXoECqpNYIRdmBE3VqGDcjFKi3GY+bYdzHbtwKnkEUIhlQqRjZWhRevwRpsJpytx5zVShkHM0ogLF3FTI3vsq1lXc6QcSXLOLKbHxOaz4S9wU1JcQENVCTNrKikIBuTlxBOYDGXSdMlQdor6c6HsDbbtApYIIcb+3HYylEmvY5nw9Kdg03c4WnEm59d9gnlBJ+9JfxgfaRo6ryX6jV+QsBl852Ibw8HlnB+rZ1m2ioHRQ/yuqpdV7/xnlqY0HnvsMTRN47zqVRTtEWRElhccrUxanVysP8NsTzcxrZAPZt7DYWc5b6+6H9uOPNmJCDb3GbjNEDOP/ApvqpXfLQLHzAyB7CzuLbiJgw3NNHnbeb/xPWYdPQ3XvgbEjl/Q5h7grnMVqkMOPhrtwqYH+ZZxJfc5z6agMcP7tF/yjrZncOmCVmct+20NbDAbWJ9YTs50AwIlaCMf8WCFXYTcSWYbrSywbWO+Yzse0hiGjXS8BGJVuKIzCE40Y88GyJo2YqadCfIMaglSrhyW28TunwpcpSWFrwSugN8nA9dJToYyabpkKDtF/YWWslJgWAghFEVZBvwaqBF/oTLIUCa9Ip+Gh94DrY/xTMP1vKPivVwenOTK+G0EqKbwgUrMZ19mb63Cd84roTm9jPOyjQTGk7zMDl582+X8aMlFHHr+GXbu3EllSQUrhisIZf0c1gbYJvaxIruBVYVtaEaGnxtn81XjOpZWbOe05EvE9hWi2VfhVBtoan+UYHQ7zy4WeJrTODML+GnxjbTVNzLHeYCL+S0rR1QK9l2Ouvkpxid3cO9ZCrFGJ7dP9lKRc/Id4zLutl+Ar1Hnn20/5fyuXRxlBvuo5ZHsQg5lGwBQXYJ8kReryIUjaNFCG/O1bSywbSfCKPm8i1y0Enu0Ac94M7ZoNQmhMuLOMxkQUOSgtKyAGdVlNFdV4Pd7j/MHKf09yFAmTZcMZacgRVF+CZwJFAHDwKcBO4AQ4vuKonwQeD9TE4pkgH8RQrz8l44rQ5kEQGps6g7L/p18f9Y/87nI5bzPv5PV8S9QMrEc81vdaOMT3He6ygtNS7hwsonVuVr6Rw7w3ysFa858Hx8MuXnkwV8zMjLCTGcxK6It5BSDl9R9BBLPc25NL6FkBzvMRj5pvBcroHNp4Trsu3LoqSU4HIup736OotENPL/QwN+cQckt4n9KbqS3rpYVjs1cYqxnYX8V3q6VqPsOkG1bz2OLdLYudnJbepClafiBcQk/sV+MvV7hFteDBDqzPJ1ewtbcLAQqiiqwCp0YRW5E2E69o5cF2nbmqbuoo4N8xkM+VoljohH7aCN6voxcjZ+5p8+ksaVatnBJgAxl0vTJUCZNmwxlEvFBuPtyrGg3H53zGR4uWMVHnfcxM/krSretQvx8B+N+wbcuCuGyn8Yl6UaKJ/NstHbz6LVX880Fa/B1t/P444+jmCZLMw20iGqO0kdPfB2nt0xSO/Yi4/j5nH4LmxwzuLR0PcVHhkgPz8ThWkPtwD6Kh55iw/wsoaYMeX05Pyy/kfGaEs60PcNliZ3M7FmI43ApHHoRY3AXW2aYPHKajesY5+Jknp8bF/B97TKsOier7S+ztXUOk3oBqkugh9xYISdWyEGpe5S5tj3MU3bRwkGslBM9WoVtog5ltI60vYzAgkrWnL0Av89zvD8d6QQlQ5k0XTKUSdMmQ9lb3GQ33H0ZRnKUt8/5Eq3h+XxUfInqif1476nCu6+bl1sU7l45l9NTc1ibrWd4tJ0fLU2z9Kz38aHiIBuf+R0HDhzAbzq4WF+CisK+5PNU1XUwN/o0DtXgLvMCfsCFnF62kRm9R0n31GJzraFqrJ/SgSfYPCdG4YwMMWM1P6q+iUyVh/OVp7hyeJTKjiVo+4aw2p9nRB3mmfkKu2cpnK3EuSmW4mH9bL6rXkm61kezs5Uj/XXE/BGskANHwKLe2UWzeoAZHKZBtEHSiRGtQhmrwZioIhepoeWseSye1yBbwaRpk6FMmi4ZyqRpk6HsLWysDe6+jHw2wVVz7iRZ1MhtuY9Q3mrg+HEeLZvjJ2e76So5k2syM6iKamzW9/HQtefxtTmrSW/dyNbt2xFCYaZZziqjmf58BxPOZ1lsPUepO8FWq5lPG7dQUdrFopFdZDtKsDnXUDExRungk2ybNU6kMcuwdTr/U3cjWpnFZeazXNZlp/BgLRx4mezQDrY1mrw4V6G4OMtl6STzMxaPGav5NlcRrSukMdzBATEbr8egydZKk9JKE4cpzg+TjRcjolWI8Uqy8RLUubNYe+FyygoCx/sTkE5iMpRJ0yVDmTRtMpS9RQ0fQNx9BRnT4JLZX8UbDvGPyQ8ReixM+KkxuiPwX+c2MNNaxsXZRiZGe/nJ/AnmnfNe5m7+HW1d/QhVpdgMcKY+G4cFR5NPUG5bz9yCISYI8J/62+kr8LA6vQlx2IvmWEVFNEPJ0Hq2zh6luD7HoHUWP268kcLiSa5J7OS8o2FcO5JY7S/Sax/g2QUqk40G51kJzkzl2G7M4xFzFU85lkKFnXlVrRQ5xqmjnQZxFFtSJR8vR52swRwrJ6eWUXLGfM5cPReHw/aX3xdJmiYZyqTpkqFMmjYZyt6C+nci7rmKOHYumf1Vqgqy3Nr3aZz/E6CoN8n6hTaemnMWV2Vm0JzwsS17kPuvWMbaQz24YnFQFHyWkzPysygWATrj2zDEQ6wobset6PzMPJ/fupewUmzG1WqiaisojQtKhp5k65xhSupy9Ijz+GXDlTSEe7h6dJgVuwJoe/aQGtrO5iadvbMFC4JJLkqn6c4384i5inX2FSRLAswo7WB14GUWGLvIxQsRsUpsE7UYE+UYpeXMP38xs1uqj/e7LJ3iZCiTpkuGMmnaZCh7i+nehLj3bYzZ/Fw85+ss9rdyzbYfEbpbw8Lkv88uxR44k2uyjSTHR7l7xhCZolks7u3Gbpk4LJUV+gwarTK6EnvImg8xr7CdUufUpcpvqpdT6zhMpHUCRVlKacJByfDTbJozSEmdTr91Lrvr53CuSLCy3YV33zhW52ba3X28NE8hVJPmwnwKI1vJo+ZqHrWvYrSkmBklnZzte4ba6DDKRD320UaMdCnOmdWcdskyCsP+4/3OSm8xMpRJ0yVDmTRtMpS9hbQ/i/jljfQ7I1w292ucb1/PmQ+8QPXzGdpLFX5wxjLOMxexKFXIzvRRfnR2Dav6EpTGJlCFwjy9kgWikZHUQSz9HpojnRRoadqscn7EhWRcOWqPdqIaCyhO+ykefoZNc/spqTWYNM/AVjiLCzoFzkN9mIN76bUPsKdeId2YZ7UnSWmqgCeM1TxiW01XSQ0Npd2scW5ixlAW73ATVrQMtbGSM69aRbhIhjDp+JKhTJouGcqkaZOh7C2i9QnEA7fQ7qnibXO+wtuTP2DBD9sp786wfoGTjS0XcGtuNtZEgntr+hkvm8myrnZsQlCvh1lhzSKXbUcxf0ZtYTs+Jcs2q4l7lbPI27PUd3egZZqI5IooHnmel+f0UlJtYuVPp2m4ibqjR0mN72NPWZy2WvCXZllkT1OWDvCCsYyH1TUcKG6iqnSIVbY9zO9y4B2oIVdcyMKLlzBTXo6UTjAylEnTJUOZNG0ylL0F7Ps14qH3csDfxM1zP8+HWr/ErLtGsOsmP1hbjj90HpelqmiN9/L9M4Os6E0QyaSI6B7WWHPw5nvJ239Jo2sfDgyeMhfzW/ty3OYYZe0j2JSZFOaCFI9s4KW5XZRUWXjGTqN+nyCf3s7uWp1klUFtKENLzs5AdiabrDm87J5Pb2EFpZFxVitHWXjYi8MopOKMWaxeMxebTXbMl05cMpRJ0yVDmTRtMpSd4nb+HPHIbWwLzuW22Z/ko498ltm/S9BXpPD9M5dxmVhFVVThoVAHw2UVtIxPEDRUVpmzqDAHMZ2/ok7dgyE0HjLX8KyzheJ4HwX9dpy2mVSMTyL0TWybNUJJucDXt5DEYA8jFRN4S7M0q0CqgV3GXDZ6F9BZWEWkYJJm5yBNkzqVQ27C9U2cc8lK/F738X63JGnaZCiTpkuGMmnaZCg7hW35Aay7nRcKlvLFqnfw4R99k7rOLM/PcvLCvAv5h+xsxsYGeap+jKq8G6elstCoZ441CK5fUkU7ceHml9ZZ7LRXUDHUTyBRR1AvIDK+j4NluxlvzlBh85MerWbQ3Ud9aAJfppze3Hw2+hbSWVhJZXCEOdoIDcNQmCmmcuEsTjttPg67/Xi/Q5L0pslQJk2XbPOXpLe6Dd+AZz7LuvAaHtIW8ekvfhVv1uS/15bhD1/I+6PFbM3vIFnu48xUDTNFAZXqHlTXZym1RhgSBXzNupp2NUB1f5o5Rinlk07its3sax4gskTgSFajADkRJ+cvoMNzPi8UlFBTM8xcfYLrRibwakUsXXYOjbXlx/sdkSRJOi5kKJOktyohyD/1aRybvsVDkbUcbnPw4fW/ZiQE3zxnGVeop+MfTrA91MpZnnqK2YvlfoRKowcbFkeMCn6o3MBozkbNYJCl+SL8yX3srbiHgbkpwkaEwnwz0WQRh32LGCwK0eQdYn4yzrWpLMXlpZx+5iW4XY7j/U5IkiSdEGQok6S3GNMw6N/yGxIbvsXs7D5+GToP5ckBrjgywaYZDp5beDHvy7QwNLqHhqq9rFJbCVsxAFrzVdwlzueoFkHE7VRNVtEUjzLs2cyemV1EPB7cqRa2mGsYKQvR7B1gXizBApudRWtOp7mx8jiXXpIk6cQlQ5kkvQUIIehv20/3+q9TGd9MjT5IQvPwPcclzPnlXgoSBnedVoq39HJuHnfisX+NG6u6SAknG/U57NJqGbB8uKIOSuIltOQcKMZB9lXfzeD8HFp2LodDHyERTjAnnuB9leWcddbFuGQrmCRJ0rTJUCZJp7DRkREOPn0Xov9pVmZ2Uyl0drpncp91Os7uQdZu2knMC1++eClXamfh7DtIZemjbLXP5gllBYmsg8hYgIJcEeXZOFHXETpLnqenNIlHqUK4z6HE5uT8S67iP5sWHe/iSpIkndTk3ZfStMm7L08O8XicvZtfoGv/A6zOb6MmNUBXNMjWWCXugRx1g2nsJpgKbG9w8OKCS3h7qoGO9LMcKfXimHRSFC0hkFfJan0MhQ+hlw6guopwiEpK3FVcd92HKAhXHe+iStJJQd59KU2XbCmTpFNAJpNhz569PL/lQRaae2jq2osy5qRtxEt8qByHAXOVSTpLFNbND9EWqSQeamSNUcc1g6Psyu/Aa82ivi3OZPAw0cbnmfAEcakVNPlWcNWNH8fjKzjexZQkSTqlyVAmSSehXC7H4OAgm3ZsY1vPfprHdlHTc5RlQybhQY2sESIExIrh6TkhuiIViFALS8xaFuoay9NZ9O5xovHdHC5MYlQfYSS0D5dSybziJVz0jl+jKNrxLqYkSdJbigxlknSC03WdQ4cO8OsnH0DL9BOcGCY0OUFwMk1J1ODmSXDqU9v2RGw8PyfAYKQSd2AuC/QKFqbyzEuMkRrrI6nspa0kTawii3D48FohltavZPXVP0NRlONbUEmSpLc4Gcok6QSSzWT4yp0fwZ4bxx8dJxiLE5zMEpq0uDYG2mu6gI4FYLxApbvcznhxCR7XCurSRTSnxihLdBFVd9Ab3o8/5CIcKGHFue+jrFl2xpckSTpRyVAmScfZF29/F8XDBynvT1A4KXhb6tXXdA1GQzAc0Tg6w0U66EdzRQjo9fgzTnQxgeaIUuiF2WuaWXbRDbLFS5Ik6SQlQ5kkHSd3/ustlHbt54L9adx56C2Brho78ZCHtL8AzV6IPe3D8Fo0L17GRRdfj9fvP96nLUmSJP2NyFAmSX9nX/rk26lp388FB3PYLDg0w0ZHyyxuv/P+431qkiRJ0nEkQ5kk/Z3c+YkbqGvdz6VHDIQCB2Y66G6azSe+/IvjfWqSJEnSCUCGMkn6G/vq7dfQeKCVy9pNcnbYNd9FX9087vjSz473qUmSJEknEBnKJOlv4NnfPsquDT+keW87l/QKUi7YvNTHUMUc7vjyXcf79CRJkqQTkAxlkvR/6P57fsjgtoeYtaeHi4YEMS9sWBViuGgm//EVGcYkSZKkP02GMkn6P/DlO26mYPgoTYejzBuHsSA8d0YRk4Ut/NuXfni8T0+SJEk6CchQJklv0hc+cSuhWDu17aNc2itQmRrW4ndnl0PNCm67/QvH+xQlSZKkk4gMZZL0V/jCHe/Fm+6ium2AS7pMHObU4K5blvoYqWzik1+6l/OO90lKkiRJJyUZyiTpL7j/nh/St/sxKjq6uKBdx5ODuAf2zXExWNPA4lW3cOsVlx7v05QkSZJOcjKUSdIbePa3j7J9088p7Wmj8WiGeUnIOKCt3kZfXTX2sgXy8qQkSZL0f0qGMkk65s5PfgDNGCA83E9tR5JLx8FQob1GZdvKMhLuWv79K/9zvE9TkiRJOkXJUCa9Zd1/zw/p3vc7QiO9lA3EOX/AwqmDpUBPucJzZ4SJhhr41zt/erxPVZIkSXoLkKFMest49rePsnXLfQQmeijpn6Su32ReZuq1oTDsb3ExVlZCylXOv935k+N7spIkSdJbjgxl0intzn+9BU+sj8jgKFV9OpfHp9ZP+qGz1s5IeRHJQCV3fOnu43uikiRJ0lueDGXSKeeLn3gX4fHDNLZOctnY1LqUC3oqNPYuKCAWrmbZ8uu5Tt4xKUmSJJ1AZCiTTgnP/vZRtr/8U2qOHuXiozoOA3rKFF5aGWCyuIqi6iXc+k+fON6nKUmSJEl/kgxl0kntPz/5bgqjR2k6MMqlo1PDVhyY6aCvYSa333n/8T49SZIkSZo2GcpOQYqi/AS4BBgRQsx5g9cV4FvARUAaeKcQYuff9yz/d77yieuo6DjMRYdzuPMwUDQ11+R4sFFO/C1JkiSdlGQoOzX9FPgO8Kd6r18IzDi2LAf++9jjCe0Ld7yXQLyNxkNDXDogyGtweIaN7hmNLF59Kx+QfcQkSZKkk5gMZacgIcSLiqLU/plNLgfuFkIIYLOiKCFFUcqEEIN/nzP863z5jpso7zrEua0Z/JmpuSZfXBViPNIsxxCTJEmSThkylL01VQC9r/m779i6PwpliqK8F3gvQHV19d/shO6/54d07d+AzYjhTkbxxZP4Y3mCUZPLx8FU4Ei9RmdzHZWLLuUf3/7ev9m5SJIkSdLxIEPZW5PyBuvEG20ohPgh8EOAJUuWvOE203X/PT+kc/8G7Hr0WPBK4Y9PBa+ZMZhnvLqtrsF4EGIhlY4ZPkbLmuVYYpIkSdIpTYayt6Y+oOo1f1cCA3+rf+zOO65n2Yt7mBmFeear6/MaTIQgGlTpq3aSDHjJ+ELk7SFKqubIISwkSZKktxQZyt6aHgE+qCjKfUx18I/9LfuTCVxEgyq9rwleuj1E3ZzTuE5ehpQkSZIkQIayU5KiKL8EzgSKFEXpAz4N2AGEEN8HnmBqOIw2pobEeNff8nw++aWf/i0PL0mSJEmnBBnKTkFCiBv+wusC+Ke/0+lIkiRJkjQN6vE+AUmSJEmSJEmGMkmSJEmSpBOCDGWSJEmSJEknABnKJEmSJEmSTgAylEmSJEmSJJ0AZCiTJEmSJEk6AchQJkmSJEmSdAJQpoaskqS/TFGUUaD7OPzTRcDYcfh3/95kOU8tspynlv9NOWuEEJH/y5ORTk0ylEknPEVRtgshlhzv8/hbk+U8tchynlreKuWUji95+VKSJEmSJOkEIEOZJEmSJEnSCUCGMulk8MPjfQJ/J7KcpxZZzlPLW6Wc0nEk+5RJkiRJkiSdAGRLmSRJkiRJ0glAhjJJkiRJkqQTgAxl0t+doihViqI8pyjKIUVRDiiK8uFj6wsVRXlaUZSjxx4LXrPPHYqitCmKclhRlPNfs36xoij7jr32bUVRlONRpjfy15ZTUZRzFUXZcaw8OxRFWfuaY50y5XzNftWKoiQVRfnYa9adUuVUFGWeoiibjm2/T1EU17H1p0w5FUWxK4rys2PlOaQoyh2vOdbJWM63HfvbUhRlyR/sc9J9D0knGSGEXOTyd12AMmDRsed+4AgwC/gK8Mlj6z8J3Hns+SxgD+AE6oB2QDv22lZgJaAA64ALj3f5/hflXAiUH3s+B+h/zbFOmXK+Zr8HgQeAj52K5QRswF5g/rG/w6dovb0RuO/Ycw/QBdSexOVsAZqB54Elr9n+pPweksvJtciWMunvTggxKITYeex5AjgEVACXAz87ttnPgCuOPb+cqS/9nBCiE2gDlimKUgYEhBCbhBACuPs1+xx3f205hRC7hBADx9YfAFyKojhPtXICKIpyBdDBVDl/v+5UK+d5wF4hxJ5j+4wLIcxTsJwC8CqKYgPcQB6In6zlFEIcEkIcfoNdTsrvIenkIkOZdFwpilLLVAvRFqBECDEIU1+YQPGxzSqA3tfs1ndsXcWx53+4/oQzzXK+1tXALiFEjlOsnIqieIFPAJ/9g91PqXICTYBQFOVJRVF2Kopy+7H1p1o5fw2kgEGgB/iaEGKCk7ecf8pJ/z0knfhsx/sEpLcuRVF8TF3C+ogQIv5numG80Qviz6w/ofwV5fz99rOBO5lqaYFTr5yfBf6fECL5B9ucauW0AWuApUAaeEZRlB1A/A22PZnLuQwwgXKgANigKMrvOEk/zz+36RusO2m+h6STg2wpk44LRVHsTH0R3iuEeOjY6uFjlwJ+fylr5Nj6PqDqNbtXAgPH1le+wfoTxl9ZThRFqQR+A9wshGg/tvpUK+dy4CuKonQBHwH+VVGUD3LqlbMPeEEIMSaESANPAIs49cp5I7BeCKELIUaAjcASTt5y/ikn7feQdPKQoUz6uzt2Z9KPgUNCiG+85qVHgFuOPb8FePg1668/1r+qDpgBbD12CSWhKMqKY8e8+TX7HHd/bTkVRQkBjwN3CCE2/n7jU62cQojThBC1Qoha4JvAF4UQ3znVygk8CcxTFMVzrL/VGcDBU7CcPcBaZYoXWAG0nsTl/FNOyu8h6SRzPO8ykMtbc2Hqko5g6s603ceWi5i6O+0Z4Oixx8LX7PNvTN3tdJjX3NnE1P/I9x977Tscm6XiRFj+2nIC/85U35zdr1mKT7Vy/sG+n+H1d1+eUuUE3s7UzQz7ga+ciuUEfEzdRXsAOAh8/CQv55VMtX7lgGHgydfsc9J9D8nl5FrkNEuSJEmSJEknAHn5UpIkSZIk6QQgQ5kkSZIkSdIJQIYySZIkSZKkE4AMZZIkSZIkSScAGcokSZIkSZJOADKUSZJ0wjo29tVLiqJc+Jp11yqKsv54npf0/9u7f9UsgigM489LIggpvAOttJAEE/ACLG2sBLvgDWhnbyCddfAGEggpA6bIDYiksBBTpLL0BgwEId+xmAG/zkTyZxaeHyzL7izLKV9md+ZIug5uiSFpaEmWaftgrQELtP2kntffjgeXeddCVZ1fbYWSdDUMZZKGl+QDbWPdpX5+AKzQ+ktuVNV+byq93Z8BeFNVn5M8A97TGmavVtXjm61eki7GUCZpeL19z1fgN/AJOK6qnd6a6og2i1bArKrOkjwEdqvqaQ9lB8ByVf24jfol6SIWb7sASfqXqjpNsgf8Al4BL5K868N3gfu0JtBbSVaBc+DR3CuODGSSRmcokzQVs34EeFlVJ/ODSTZovQqf0BYxnc0Nn95QjZL031x9KWlqDoG3SQKQZK3fvwf8rKoZsE5bFCBJk2EokzQ1m8Ad4FuS7/0a4CPwOskX2qdLZ8ckTYo/+kuSJA3AmTJJkqQBGMokSZIGYCiTJEkagKFMkiRpAIYySZKkARjKJEmSBmAokyRJGsAfnYwH8uVSd1wAAAAASUVORK5CYII=\n" 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ghg1 = \"100\"\n", + "ghg2 = \"4000\"\n", + "bio1 = \"45\"\n", + "bio2 = \"00\"\n", + "scen = [f\"SSP2BIO{bio1}GHG{ghg1}\", f\"SSP2BIO{bio2}GHG{ghg1}\", f\"SSP2BIO{bio1}GHG{ghg2}\", f\"SSP2BIO{bio2}GHG{ghg2}\"]\n", + "print(scen)\n", + "print(scen)\n", + "py_df.filter(variable=\"Emissions|CO2|Land|+|Land-use Change\", ).plot()\n", + "for i in df[df.index.get_level_values(3).str.startswith(\"Demand|Bioenergy|++|2nd generation\")].index.get_level_values(3).unique():\n", + " py_df.filter(variable=i, ).plot()\n", + "py_df.filter(variable=\"Emissions|CO2|Land|Land-use Change|Gross LUC|+|Deforestation\", ).plot()\n", + "py_df.filter(variable=\"Emissions|CO2|Land|Land-use Change|Gross LUC|+|Other land conversion\", ).plot()\n", + "for i in df[df.index.get_level_values(3).str.startswith(\"Resources|Land Cover|+\")].index.get_level_values(3).unique()[:2]:\n", + " py_df.filter(variable=i, ).plot()\n", + "py_df.filter(variable=\"Emissions|CO2|Land|Land-use Change|+|Regrowth\", ).plot()\n", + "py_df.filter(variable=\"Productivity|Landuse Intensity Indicator Tau\", ).plot()\n", + "py_df.filter(variable=\"Productivity|Landuse Intensity Indicator Tau\", ).diff({\"Productivity|Landuse Intensity Indicator Tau\":\"test\"}).plot()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-29T09:34:40.275913800Z", + "start_time": "2023-09-29T09:34:35.941401500Z" + } + }, + "id": "3436432340b73595" + }, + { + "cell_type": "code", + "execution_count": 456, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['SSP2BIO10GHG990', 'SSP2BIO10GHG100', 'SSP2BIO10GHG000']\n" + ] + }, + { + "data": { + "text/plain": "" + }, + "execution_count": 456, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/png": 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\n" 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\n" 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\n" 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\n" 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\n" 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\n" 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "bio = \"10\"\n", + "scen = [f\"SSP2BIO{bio}GHG990\", f\"SSP2BIO{bio}GHG100\", f\"SSP2BIO{bio}GHG000\"]\n", + "print(scen)\n", + "py_df.filter(variable=\"Emissions|CO2|Land|+|Land-use Change\", scenario=scen).plot()\n", + "for i in df[df.index.get_level_values(3).str.startswith(\"Demand|Bioenergy|++|2nd generation\")].index.get_level_values(3).unique():\n", + " py_df.filter(variable=i, scenario=scen).plot()\n", + "py_df.filter(variable=\"Emissions|CO2|Land|Land-use Change|Gross LUC|+|Deforestation\", scenario=scen).plot()\n", + "py_df.filter(variable=\"Emissions|CO2|Land|Land-use Change|Gross LUC|+|Other land conversion\", scenario=scen).plot()\n", + "for i in df[df.index.get_level_values(3).str.startswith(\"Resources|Land Cover|+\")].index.get_level_values(3).unique()[:2]:\n", + " py_df.filter(variable=i, scenario=scen).plot()\n", + "py_df.filter(variable=\"Emissions|CO2|Land|Land-use Change|+|Regrowth\", scenario=scen).plot()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-28T21:20:23.184649700Z", + "start_time": "2023-09-28T21:20:21.731291200Z" + } + }, + "id": "499c0f6fe189657f" + }, + { + "cell_type": "code", + "execution_count": 301, + "outputs": [ + { + "data": { + "text/plain": "array([, , ], dtype=object)" + }, + "execution_count": 301, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ax[0]" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-28T13:32:14.998245800Z", + "start_time": "2023-09-28T13:32:14.929749100Z" + } + }, + "id": "fc6f87238f8589b0" + }, + { + "cell_type": "code", + "execution_count": 403, + "outputs": [ + { + "data": { + "text/plain": "Index(['Demand|Bioenergy|++|1st generation',\n 'Demand|Bioenergy|++|2nd generation',\n 'Demand|Bioenergy|++|Overproduction',\n 'Demand|Bioenergy|++|Traditional Burning'],\n dtype='object', name='Variable')" + }, + "execution_count": 403, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[df.index.get_level_values(3).str.contains(\"Regrowth\")]\n", + "#df[df.index.get_level_values(3).str.contains(\"LUC\")]\n", + "df[df.index.get_level_values(3).str.startswith(\"Resources|Land Cover|For\")].index.get_level_values(3).unique()\n", + "df[df.index.get_level_values(3).str.startswith(\"Demand|Bioenergy|++\")].index.get_level_values(3).unique()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-28T20:11:54.321242500Z", + "start_time": "2023-09-28T20:11:54.133467900Z" + } + }, + "id": "27c384765f61a35c" + }, + { + "cell_type": "code", + "execution_count": 288, + "outputs": [ + { + "data": { + "text/plain": " 2000 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World test1 unknown 4052.726058 \n SSP2BIO05GHG600 World test1 unknown 4052.726058 \n SSP2BIO07GHG400 World test1 unknown 4052.726058 \n SSP2BIO07GHG600 World test1 unknown 4052.726058 \n SSP2BIO10GHG400 World test1 unknown 4052.726058 \n SSP2BIO10GHG600 World test1 unknown 4052.726058 \n\n 2005 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World test1 unknown 4256.643156 \n SSP2BIO05GHG600 World test1 unknown 4256.643156 \n SSP2BIO07GHG400 World test1 unknown 4256.643156 \n SSP2BIO07GHG600 World test1 unknown 4256.643156 \n SSP2BIO10GHG400 World test1 unknown 4256.643156 \n SSP2BIO10GHG600 World test1 unknown 4256.643156 \n\n 2010 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World test1 unknown 4486.586686 \n SSP2BIO05GHG600 World test1 unknown 4486.586686 \n SSP2BIO07GHG400 World test1 unknown 4486.586686 \n SSP2BIO07GHG600 World test1 unknown 4486.586686 \n SSP2BIO10GHG400 World test1 unknown 4486.586686 \n SSP2BIO10GHG600 World test1 unknown 4486.586686 \n\n 2015 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World test1 unknown 4509.69031 \n SSP2BIO05GHG600 World test1 unknown 4509.69031 \n SSP2BIO07GHG400 World test1 unknown 4509.69031 \n SSP2BIO07GHG600 World test1 unknown 4509.69031 \n SSP2BIO10GHG400 World test1 unknown 4509.69031 \n SSP2BIO10GHG600 World test1 unknown 4509.69031 \n\n 2020 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World test1 unknown 4377.733857 \n SSP2BIO05GHG600 World test1 unknown 4377.733857 \n SSP2BIO07GHG400 World test1 unknown 4377.733857 \n SSP2BIO07GHG600 World test1 unknown 4377.733857 \n SSP2BIO10GHG400 World test1 unknown 4377.733857 \n SSP2BIO10GHG600 World test1 unknown 4377.733857 \n\n 2025 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World test1 unknown 2461.082693 \n SSP2BIO05GHG600 World test1 unknown 2263.573047 \n SSP2BIO07GHG400 World test1 unknown 2461.082693 \n SSP2BIO07GHG600 World test1 unknown 2263.573047 \n SSP2BIO10GHG400 World test1 unknown 2461.082693 \n SSP2BIO10GHG600 World test1 unknown 2263.573047 \n\n 2030 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World test1 unknown 1977.076574 \n SSP2BIO05GHG600 World test1 unknown 1750.603598 \n SSP2BIO07GHG400 World test1 unknown 1977.092257 \n SSP2BIO07GHG600 World test1 unknown 1750.729300 \n SSP2BIO10GHG400 World test1 unknown 1976.100871 \n SSP2BIO10GHG600 World test1 unknown 1750.478291 \n\n 2035 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World test1 unknown 1511.002693 \n SSP2BIO05GHG600 World test1 unknown 1325.115834 \n SSP2BIO07GHG400 World test1 unknown 1511.057456 \n SSP2BIO07GHG600 World test1 unknown 1325.869817 \n SSP2BIO10GHG400 World test1 unknown 1505.212414 \n SSP2BIO10GHG600 World test1 unknown 1324.184166 \n\n 2040 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World test1 unknown 1144.898601 \n SSP2BIO05GHG600 World test1 unknown 980.931897 \n SSP2BIO07GHG400 World test1 unknown 1145.040276 \n SSP2BIO07GHG600 World test1 unknown 982.824125 \n SSP2BIO10GHG400 World test1 unknown 1131.402139 \n SSP2BIO10GHG600 World test1 unknown 978.204951 \n\n 2045 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World test1 unknown 850.098155 \n SSP2BIO05GHG600 World test1 unknown 714.367263 \n SSP2BIO07GHG400 World test1 unknown 850.621923 \n SSP2BIO07GHG600 World test1 unknown 716.444098 \n SSP2BIO10GHG400 World test1 unknown 836.319735 \n SSP2BIO10GHG600 World test1 unknown 709.609237 \n\n 2050 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World test1 unknown 701.041775 \n SSP2BIO05GHG600 World test1 unknown 567.564009 \n SSP2BIO07GHG400 World test1 unknown 702.293399 \n SSP2BIO07GHG600 World test1 unknown 571.301686 \n SSP2BIO10GHG400 World test1 unknown 707.649785 \n SSP2BIO10GHG600 World test1 unknown 573.845743 \n\n 2055 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World test1 unknown 634.194597 \n SSP2BIO05GHG600 World test1 unknown 484.302127 \n SSP2BIO07GHG400 World test1 unknown 638.299201 \n SSP2BIO07GHG600 World test1 unknown 484.015596 \n SSP2BIO10GHG400 World test1 unknown 705.379114 \n SSP2BIO10GHG600 World test1 unknown 537.121041 \n\n 2060 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World test1 unknown 545.853597 \n SSP2BIO05GHG600 World test1 unknown 360.564581 \n SSP2BIO07GHG400 World test1 unknown 560.135989 \n SSP2BIO07GHG600 World test1 unknown 359.293849 \n SSP2BIO10GHG400 World test1 unknown 768.754217 \n SSP2BIO10GHG600 World test1 unknown 543.445372 \n\n 2070 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World test1 unknown 305.516081 \n SSP2BIO05GHG600 World test1 unknown 86.317439 \n SSP2BIO07GHG400 World test1 unknown 337.348250 \n SSP2BIO07GHG600 World test1 unknown 90.134435 \n SSP2BIO10GHG400 World test1 unknown 837.185427 \n SSP2BIO10GHG600 World test1 unknown 499.067206 \n\n 2080 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World test1 unknown 33.653413 \n SSP2BIO05GHG600 World test1 unknown -185.858535 \n SSP2BIO07GHG400 World test1 unknown 83.790328 \n SSP2BIO07GHG600 World test1 unknown -155.945305 \n SSP2BIO10GHG400 World test1 unknown 927.962263 \n SSP2BIO10GHG600 World test1 unknown 482.846144 \n\n 2090 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World test1 unknown -179.429390 \n SSP2BIO05GHG600 World test1 unknown -426.932176 \n SSP2BIO07GHG400 World test1 unknown -133.947810 \n SSP2BIO07GHG600 World test1 unknown -345.759334 \n SSP2BIO10GHG400 World test1 unknown 1050.937859 \n SSP2BIO10GHG600 World test1 unknown 549.529695 \n\n 2100 \nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World test1 unknown -316.561534 \n SSP2BIO05GHG600 World test1 unknown -543.305891 \n SSP2BIO07GHG400 World test1 unknown -256.707200 \n SSP2BIO07GHG600 World test1 unknown -436.556404 \n SSP2BIO10GHG400 World test1 unknown 1121.486572 \n SSP2BIO10GHG600 World test1 unknown 563.498261 ", + "text/html": "
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20002005201020152020202520302035204020452050205520602070208020902100
modelscenarioregionvariableunit
MAgPIEMP00BD1BI00SSP2BIO05GHG400Worldtest1unknown4052.7260584256.6431564486.5866864509.690314377.7338572461.0826931977.0765741511.0026931144.898601850.098155701.041775634.194597545.853597305.51608133.653413-179.429390-316.561534
SSP2BIO05GHG600Worldtest1unknown4052.7260584256.6431564486.5866864509.690314377.7338572263.5730471750.6035981325.115834980.931897714.367263567.564009484.302127360.56458186.317439-185.858535-426.932176-543.305891
SSP2BIO07GHG400Worldtest1unknown4052.7260584256.6431564486.5866864509.690314377.7338572461.0826931977.0922571511.0574561145.040276850.621923702.293399638.299201560.135989337.34825083.790328-133.947810-256.707200
SSP2BIO07GHG600Worldtest1unknown4052.7260584256.6431564486.5866864509.690314377.7338572263.5730471750.7293001325.869817982.824125716.444098571.301686484.015596359.29384990.134435-155.945305-345.759334-436.556404
SSP2BIO10GHG400Worldtest1unknown4052.7260584256.6431564486.5866864509.690314377.7338572461.0826931976.1008711505.2124141131.402139836.319735707.649785705.379114768.754217837.185427927.9622631050.9378591121.486572
SSP2BIO10GHG600Worldtest1unknown4052.7260584256.6431564486.5866864509.690314377.7338572263.5730471750.4782911324.184166978.204951709.609237573.845743537.121041543.445372499.067206482.846144549.529695563.498261
\n
" + }, + "execution_count": 288, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "py_df.add(\"Emissions|CO2|Land|Land-use Change|+|Regrowth\", luc_co2, \"test\", ignore_units=True, append=True)\n", + "py_df.add(\"test\", \"Emissions|CO2|Land|Land-use Change|+|Peatland\", \"test1\", ignore_units=True).timeseries() " + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-28T13:21:50.488892900Z", + "start_time": "2023-09-28T13:21:50.235332900Z" + } + }, + "id": "1acca98c6925e8ae" + }, + { + "cell_type": "code", + "execution_count": 273, + "outputs": [ + { + "data": { + "text/plain": " 2000 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4052.726058 \n SSP2BIO05GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4052.726058 \n SSP2BIO07GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4052.726058 \n SSP2BIO07GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4052.726058 \n SSP2BIO10GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4052.726058 \n SSP2BIO10GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4052.726058 \n\n 2005 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4256.643156 \n SSP2BIO05GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4256.643156 \n SSP2BIO07GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4256.643156 \n SSP2BIO07GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4256.643156 \n SSP2BIO10GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4256.643156 \n SSP2BIO10GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4256.643156 \n\n 2010 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4486.586686 \n SSP2BIO05GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4486.586686 \n SSP2BIO07GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4486.586686 \n SSP2BIO07GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4486.586686 \n SSP2BIO10GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4486.586686 \n SSP2BIO10GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4486.586686 \n\n 2015 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4509.69031 \n SSP2BIO05GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4509.69031 \n SSP2BIO07GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4509.69031 \n SSP2BIO07GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4509.69031 \n SSP2BIO10GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4509.69031 \n SSP2BIO10GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4509.69031 \n\n 2020 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4377.733857 \n SSP2BIO05GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4377.733857 \n SSP2BIO07GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4377.733857 \n SSP2BIO07GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4377.733857 \n SSP2BIO10GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4377.733857 \n SSP2BIO10GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4377.733857 \n\n 2025 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 2461.082693 \n SSP2BIO05GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 2263.573047 \n SSP2BIO07GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 2461.082693 \n SSP2BIO07GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 2263.573047 \n SSP2BIO10GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 2461.082693 \n SSP2BIO10GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 2263.573047 \n\n 2030 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 1977.076574 \n SSP2BIO05GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 1750.603598 \n SSP2BIO07GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 1977.092257 \n SSP2BIO07GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 1750.729300 \n SSP2BIO10GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 1976.100871 \n SSP2BIO10GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 1750.478291 \n\n 2035 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 1511.002693 \n SSP2BIO05GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 1325.115834 \n SSP2BIO07GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 1511.057456 \n SSP2BIO07GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 1325.869817 \n SSP2BIO10GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 1505.212414 \n SSP2BIO10GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 1324.184166 \n\n 2040 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 1144.898601 \n SSP2BIO05GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 980.931897 \n SSP2BIO07GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 1145.040276 \n SSP2BIO07GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 982.824125 \n SSP2BIO10GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 1131.402139 \n SSP2BIO10GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 978.204951 \n\n 2045 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 850.098155 \n SSP2BIO05GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 714.367263 \n SSP2BIO07GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 850.621923 \n SSP2BIO07GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 716.444098 \n SSP2BIO10GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 836.319735 \n SSP2BIO10GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 709.609237 \n\n 2050 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 701.041775 \n SSP2BIO05GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 567.564009 \n SSP2BIO07GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 702.293399 \n SSP2BIO07GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 571.301686 \n SSP2BIO10GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 707.649785 \n SSP2BIO10GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 573.845743 \n\n 2055 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 634.194597 \n SSP2BIO05GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 484.302127 \n SSP2BIO07GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 638.299201 \n SSP2BIO07GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 484.015596 \n SSP2BIO10GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 705.379114 \n SSP2BIO10GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 537.121041 \n\n 2060 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 545.853597 \n SSP2BIO05GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 360.564581 \n SSP2BIO07GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 560.135989 \n SSP2BIO07GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 359.293849 \n SSP2BIO10GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 768.754217 \n SSP2BIO10GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 543.445372 \n\n 2070 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 305.516081 \n SSP2BIO05GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 86.317439 \n SSP2BIO07GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 337.348250 \n SSP2BIO07GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 90.134435 \n SSP2BIO10GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 837.185427 \n SSP2BIO10GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 499.067206 \n\n 2080 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 33.653413 \n SSP2BIO05GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr -185.858535 \n SSP2BIO07GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 83.790328 \n SSP2BIO07GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr -155.945305 \n SSP2BIO10GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 927.962263 \n SSP2BIO10GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 482.846144 \n\n 2090 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr -179.429390 \n SSP2BIO05GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr -426.932176 \n SSP2BIO07GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr -133.947810 \n SSP2BIO07GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr -345.759334 \n SSP2BIO10GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 1050.937859 \n SSP2BIO10GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 549.529695 \n\n 2100 \nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr -316.561534 \n SSP2BIO05GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr -543.305891 \n SSP2BIO07GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr -256.707200 \n SSP2BIO07GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr -436.556404 \n SSP2BIO10GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 1121.486572 \n SSP2BIO10GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 563.498261 ", + "text/html": "
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20002005201020152020202520302035204020452050205520602070208020902100
modelscenarioregionvariableunit
MAgPIEMP00BD1BI00SSP2BIO05GHG400WorldEmissions|CO2|Land|+|Land-use ChangeMt CO2/yr4052.7260584256.6431564486.5866864509.690314377.7338572461.0826931977.0765741511.0026931144.898601850.098155701.041775634.194597545.853597305.51608133.653413-179.429390-316.561534
SSP2BIO05GHG600WorldEmissions|CO2|Land|+|Land-use ChangeMt CO2/yr4052.7260584256.6431564486.5866864509.690314377.7338572263.5730471750.6035981325.115834980.931897714.367263567.564009484.302127360.56458186.317439-185.858535-426.932176-543.305891
SSP2BIO07GHG400WorldEmissions|CO2|Land|+|Land-use ChangeMt CO2/yr4052.7260584256.6431564486.5866864509.690314377.7338572461.0826931977.0922571511.0574561145.040276850.621923702.293399638.299201560.135989337.34825083.790328-133.947810-256.707200
SSP2BIO07GHG600WorldEmissions|CO2|Land|+|Land-use ChangeMt CO2/yr4052.7260584256.6431564486.5866864509.690314377.7338572263.5730471750.7293001325.869817982.824125716.444098571.301686484.015596359.29384990.134435-155.945305-345.759334-436.556404
SSP2BIO10GHG400WorldEmissions|CO2|Land|+|Land-use ChangeMt CO2/yr4052.7260584256.6431564486.5866864509.690314377.7338572461.0826931976.1008711505.2124141131.402139836.319735707.649785705.379114768.754217837.185427927.9622631050.9378591121.486572
SSP2BIO10GHG600WorldEmissions|CO2|Land|+|Land-use ChangeMt CO2/yr4052.7260584256.6431564486.5866864509.690314377.7338572263.5730471750.4782911324.184166978.204951709.609237573.845743537.121041543.445372499.067206482.846144549.529695563.498261
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" + }, + "execution_count": 273, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "py_df.filter(variable=afolu_co2).timeseries()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-28T12:38:37.715706600Z", + "start_time": "2023-09-28T12:38:37.584217200Z" + } + }, + "id": "b2bb66c2b0b62efd" + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false + }, + "id": "91bf13dd7e858c2e" + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "collapsed": false + }, + "id": "13270b5c2a86aa39" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/message_ix_models/model/material/magpie notebooks/refactor_emi_drivers_func.ipynb b/message_ix_models/model/material/magpie notebooks/refactor_emi_drivers_func.ipynb new file mode 100644 index 0000000000..50665c6ea1 --- /dev/null +++ b/message_ix_models/model/material/magpie notebooks/refactor_emi_drivers_func.ipynb @@ -0,0 +1,508 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "outputs": [], + "source": [ + "import pandas as pd" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "start_time": "2023-09-25T08:52:47.708814300Z" + } + }, + "id": "initial_id" + }, + { + "cell_type": "code", + "execution_count": 8, + "outputs": [], + "source": [ + "iamc_index = [\"Region\", \"Variable\", \"Unit\"]" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "start_time": "2023-09-25T08:52:49.112676200Z" + } + }, + "id": "14ab9fc74924209c" + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [], + "source": [ + "df = pd.read_csv(\"C:/Users\\maczek\\Desktop/emi_drivers.csv\")\n", + "df = df.set_index([\"Region\", \"Scenario\", \"Unit\"])\n", + "df.columns = [int(i) for i in df.columns]" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "start_time": "2023-09-25T08:52:48.245828200Z" + } + }, + "id": "c2653dcb51d6727c" + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [], + "source": [ + "data = pd.read_csv(\"C:/Users\\maczek\\Desktop/data.csv\").set_index(\"Region\")\n", + "data.columns = [int(i) for i in data.columns]" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "start_time": "2023-09-25T08:52:48.473847700Z" + } + }, + "id": "af947380eec6c2b7" + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "collapsed": false + }, + "id": "d4a75d09fb9d213f" + }, + { + "cell_type": "markdown", + "source": [ + "## current implementation" + ], + "metadata": { + "collapsed": false + }, + "id": "101ca884844ac146" + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [], + "source": [ + "scn_cfg = {}\n", + "scn_cfg[\"region_mapping\"] = df.index.get_level_values(0).unique()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "start_time": "2023-09-25T08:52:48.758487500Z" + } + }, + "id": "bd6451c021ec9be8" + }, + { + "cell_type": "code", + "execution_count": 111, + "outputs": [], + "source": [ + "old_dfs_dict = {}\n", + "new_dfs_dict = {}" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-25T09:17:42.910070900Z", + "start_time": "2023-09-25T09:17:42.825532300Z" + } + }, + "id": "fd8612083bddf1c1" + }, + { + "cell_type": "code", + "execution_count": 123, + "outputs": [], + "source": [ + "for reg in scn_cfg[\"region_mapping\"]:\n", + " #tmp_gr = tmp = self.data.loc[reg, :, driver]\n", + " tmp_gr = tmp = df.loc[reg, :]\n", + " tmp_gr = tmp_gr.pct_change(axis=1)\n", + " for i in range(len(tmp.columns)):\n", + " # 2010 should remain hardcoded - it is the year until\n", + " # which we had calibrated the emi-drivers.\n", + " if tmp.columns[i] <= 2010:\n", + " # If there is no data available, a dataframe with\n", + " # values set to 0 is created.\n", + " if data.empty:\n", + " tmp.loc[:, tmp.columns[i]] = 0\n", + " else:\n", + " tmp.loc[:, tmp.columns[i]] = data.loc[\n", + " reg, tmp.columns[i]\n", + " ]\n", + " else:\n", + " tmp.loc[:, tmp.columns[i]] = (\n", + " tmp.loc[:, tmp.columns[i - 1]]\n", + " + tmp.loc[:, tmp.columns[i - 1]]\n", + " * tmp_gr.loc[:, tmp.columns[i]]\n", + " )\n", + " tmp = tmp.reset_index()\n", + " #tmp[\"Region\"] = reg\n", + " tmp[\"Unit\"] = \"Mt CO2/yr\"\n", + " #tmp[\"Variable\"] = \"test_tec\"\n", + " #tmp = tmp.set_index(iamc_index).fillna(0)\n", + " #self.data = tmp.combine_first(self.data)\n", + " old = tmp.set_index([\"Scenario\", \"Unit\"])\n", + " old.columns = old.columns.astype(int)\n", + " old_dfs_dict[reg] = old" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-25T09:21:52.700786100Z", + "start_time": "2023-09-25T09:21:52.500044200Z" + } + }, + "id": "11598920603d80ae" + }, + { + "cell_type": "code", + "execution_count": 50, + "outputs": [], + "source": [ + "old_post_2010 = old.drop(old.columns[:4], axis=1).copy(deep=True)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-25T09:00:55.358425700Z", + "start_time": "2023-09-25T09:00:55.327185600Z" + } + }, + "id": "a0ddced02ed86c32" + }, + { + "cell_type": "code", + "execution_count": 134, + "outputs": [ + { + "data": { + "text/plain": " 2010 2015 2020 2025 2030 2035 2040 2045 2050 \\\nScenario Unit \nBIO00GHG000 Mt CO2/yr 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 \nBIO00GHG010 Mt CO2/yr 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 \nBIO00GHG020 Mt CO2/yr 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 \nBIO00GHG050 Mt CO2/yr 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 \nBIO00GHG100 Mt CO2/yr 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 \n... ... ... ... ... ... ... ... ... ... \nBIO45GHG3000 Mt CO2/yr 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 \nBIO45GHG400 Mt CO2/yr 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 \nBIO45GHG4000 Mt CO2/yr 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 \nBIO45GHG600 Mt CO2/yr 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 \nBIO45GHG990 Mt CO2/yr 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 \n\n 2055 2060 2070 2080 2090 2100 2110 \nScenario Unit \nBIO00GHG000 Mt CO2/yr 1.0 1.0 1.0 1.0 1.0 1.0 1.0 \nBIO00GHG010 Mt CO2/yr 1.0 1.0 1.0 1.0 1.0 1.0 1.0 \nBIO00GHG020 Mt CO2/yr 1.0 1.0 1.0 1.0 1.0 1.0 1.0 \nBIO00GHG050 Mt CO2/yr 1.0 1.0 1.0 1.0 1.0 1.0 1.0 \nBIO00GHG100 Mt CO2/yr 1.0 1.0 1.0 1.0 1.0 1.0 1.0 \n... ... ... ... ... ... ... ... \nBIO45GHG3000 Mt CO2/yr 1.0 1.0 1.0 1.0 1.0 1.0 1.0 \nBIO45GHG400 Mt CO2/yr 1.0 1.0 1.0 1.0 1.0 1.0 1.0 \nBIO45GHG4000 Mt CO2/yr 1.0 1.0 1.0 1.0 1.0 1.0 1.0 \nBIO45GHG600 Mt CO2/yr 1.0 1.0 1.0 1.0 1.0 1.0 1.0 \nBIO45GHG990 Mt CO2/yr 1.0 1.0 1.0 1.0 1.0 1.0 1.0 \n\n[84 rows x 16 columns]", + "text/html": "
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2010201520202025203020352040204520502055206020702080209021002110
ScenarioUnit
BIO00GHG000Mt CO2/yr1.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.0
BIO00GHG010Mt CO2/yr1.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.0
BIO00GHG020Mt CO2/yr1.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.0
BIO00GHG050Mt CO2/yr1.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.0
BIO00GHG100Mt CO2/yr1.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.0
......................................................
BIO45GHG3000Mt CO2/yr1.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.0
BIO45GHG400Mt CO2/yr1.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.0
BIO45GHG4000Mt CO2/yr1.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.0
BIO45GHG600Mt CO2/yr1.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.0
BIO45GHG990Mt CO2/yr1.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.0
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84 rows × 16 columns

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" + }, + "execution_count": 134, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.loc[\"R12_CHN\", :].divide(df.loc[\"R12_CHN\", :][2010], axis=0).drop(df.loc[\"R12_CHN\", :].columns[:4], axis=1)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-25T09:25:32.774062100Z", + "start_time": "2023-09-25T09:25:32.673880900Z" + } + }, + "id": "bd87dc9331011540" + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "collapsed": false + }, + "id": "1532726880abd30c" + }, + { + "cell_type": "markdown", + "source": [ + "## new vectorized calculation" + ], + "metadata": { + "collapsed": false + }, + "id": "f02833576e276727" + }, + { + "cell_type": "code", + "execution_count": 140, + "outputs": [], + "source": [ + "for reg in scn_cfg[\"region_mapping\"]:\n", + " #tmp = self.data.loc[reg, :, driver]\n", + " tmp = df.loc[reg, :]\n", + "\n", + " tmp_rel_2010 = tmp.divide(tmp[2010], axis=0).drop(tmp.columns[:4], axis=1)\n", + " tec_afr = data.loc[reg]\n", + " new = tmp_rel_2010[tmp_rel_2010.columns] * tec_afr[2010]\n", + " data_bef_2010 = pd.DataFrame(data.loc[reg, :2010]).T\n", + " for col in data_bef_2010:\n", + " new[col] = data_bef_2010[col].values[0]\n", + " new = new[new.columns.sort_values()]\n", + " new = new.fillna(0)\n", + " \n", + " new_dfs_dict[reg] = new" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-25T09:28:47.564726900Z", + "start_time": "2023-09-25T09:28:47.517862500Z" + } + }, + "id": "33da9bcb802ee936" + }, + { + "cell_type": "code", + "execution_count": 99, + "outputs": [], + "source": [ + "tmp_rel_2010 = tmp.divide(tmp[2010], axis=0).drop(tmp.columns[:4], axis=1)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-25T09:12:30.400010800Z", + "start_time": "2023-09-25T09:12:30.368763300Z" + } + }, + "id": "15a0e53d9d20722a" + }, + { + "cell_type": "code", + "execution_count": 100, + "outputs": [], + "source": [ + "tec_afr = data.loc[\"R12_AFR\"]" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-25T09:12:30.747514900Z", + "start_time": "2023-09-25T09:12:30.700449100Z" + } + }, + "id": "3b6086a46dc1e065" + }, + { + "cell_type": "code", + "execution_count": 101, + "outputs": [], + "source": [ + "new = tmp_rel_2010[tmp_rel_2010.columns] * tec_afr[2010]" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-25T09:12:31.101228400Z", + "start_time": "2023-09-25T09:12:31.063474800Z" + } + }, + "id": "1a158eca7eea06ad" + }, + { + "cell_type": "code", + "execution_count": 102, + "outputs": [], + "source": [ + "data_bef_2010 = pd.DataFrame(data.loc[reg, :2010]).T" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-25T09:12:31.502279100Z", + "start_time": "2023-09-25T09:12:31.471032Z" + } + }, + "id": "be8d0f5cc4687fa0" + }, + { + "cell_type": "code", + "execution_count": 103, + "outputs": [], + "source": [ + "for col in data_bef_2010:\n", + " new[col] = data_bef_2010[col].values[0]" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-25T09:12:32.065466800Z", + "start_time": "2023-09-25T09:12:32.002950900Z" + } + }, + "id": "3d05af475086cc82" + }, + { + "cell_type": "code", + "execution_count": 104, + "outputs": [], + "source": [ + "new = new[new.columns.sort_values()]" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-25T09:12:32.573423400Z", + "start_time": "2023-09-25T09:12:32.525508200Z" + } + }, + "id": "a070ba462231478a" + }, + { + "cell_type": "code", + "execution_count": 106, + "outputs": [ + { + "data": { + "text/plain": " 1990 1995 2000 2005 2010 \\\nScenario Unit \nBIO00GHG000 Mt CO2/yr 115.793 115.793 115.793 116.082 116.371 \nBIO00GHG010 Mt CO2/yr 115.793 115.793 115.793 116.082 116.371 \nBIO00GHG020 Mt CO2/yr 115.793 115.793 115.793 116.082 116.371 \nBIO00GHG050 Mt CO2/yr 115.793 115.793 115.793 116.082 116.371 \nBIO00GHG100 Mt CO2/yr 115.793 115.793 115.793 116.082 116.371 \n... ... ... ... ... ... \nBIO45GHG3000 Mt CO2/yr 115.793 115.793 115.793 116.082 116.371 \nBIO45GHG400 Mt CO2/yr 115.793 115.793 115.793 116.082 116.371 \nBIO45GHG4000 Mt CO2/yr 115.793 115.793 115.793 116.082 116.371 \nBIO45GHG600 Mt CO2/yr 115.793 115.793 115.793 116.082 116.371 \nBIO45GHG990 Mt CO2/yr 115.793 115.793 115.793 116.082 116.371 \n\n 2015 2020 2025 2030 \\\nScenario Unit \nBIO00GHG000 Mt CO2/yr 116.862954 117.064786 104.533321 85.470978 \nBIO00GHG010 Mt CO2/yr 116.862954 117.064786 104.882979 86.171215 \nBIO00GHG020 Mt CO2/yr 116.862954 117.064786 104.987858 86.359776 \nBIO00GHG050 Mt CO2/yr 116.862954 117.064786 105.086286 86.579303 \nBIO00GHG100 Mt CO2/yr 116.862954 117.064786 105.308946 86.940757 \n... ... ... ... ... \nBIO45GHG3000 Mt CO2/yr 116.862954 117.064786 105.317794 86.959742 \nBIO45GHG400 Mt CO2/yr 116.862954 117.064786 105.316503 86.956978 \nBIO45GHG4000 Mt CO2/yr 116.862954 117.064786 105.317794 86.959742 \nBIO45GHG600 Mt CO2/yr 116.862954 117.064786 105.317241 86.958452 \nBIO45GHG990 Mt CO2/yr 116.862954 117.064786 105.317425 86.959190 \n\n 2035 2040 2045 2050 2055 \\\nScenario Unit \nBIO00GHG000 Mt CO2/yr 58.128176 31.002874 12.322813 3.466718 0.635540 \nBIO00GHG010 Mt CO2/yr 59.042042 31.812045 12.809237 3.658228 0.680146 \nBIO00GHG020 Mt CO2/yr 59.257329 31.975538 12.893656 3.687536 0.686598 \nBIO00GHG050 Mt CO2/yr 59.559617 32.243910 13.048671 3.744860 0.699316 \nBIO00GHG100 Mt CO2/yr 59.928075 32.492375 13.163503 3.781908 0.707242 \n... ... ... ... ... ... \nBIO45GHG3000 Mt CO2/yr 59.957014 32.522973 13.185622 3.792046 0.710006 \nBIO45GHG400 Mt CO2/yr 59.952959 32.519102 13.183225 3.791124 0.709638 \nBIO45GHG4000 Mt CO2/yr 59.956645 32.522604 13.185253 3.792046 0.709822 \nBIO45GHG600 Mt CO2/yr 59.954986 32.520945 13.184331 3.791677 0.709822 \nBIO45GHG990 Mt CO2/yr 59.956092 32.522051 13.185069 3.792046 0.709822 \n\n 2060 2070 2080 2090 2100 2110 \nScenario Unit \nBIO00GHG000 Mt CO2/yr 0.059536 0.0 0.0 0.0 0.0 0.0 \nBIO00GHG010 Mt CO2/yr 0.064144 0.0 0.0 0.0 0.0 0.0 \nBIO00GHG020 Mt CO2/yr 0.064881 0.0 0.0 0.0 0.0 0.0 \nBIO00GHG050 Mt CO2/yr 0.066171 0.0 0.0 0.0 0.0 0.0 \nBIO00GHG100 Mt CO2/yr 0.067093 0.0 0.0 0.0 0.0 0.0 \n... ... ... ... ... ... ... \nBIO45GHG3000 Mt CO2/yr 0.067277 0.0 0.0 0.0 0.0 0.0 \nBIO45GHG400 Mt CO2/yr 0.067277 0.0 0.0 0.0 0.0 0.0 \nBIO45GHG4000 Mt CO2/yr 0.067277 0.0 0.0 0.0 0.0 0.0 \nBIO45GHG600 Mt CO2/yr 0.067277 0.0 0.0 0.0 0.0 0.0 \nBIO45GHG990 Mt CO2/yr 0.067277 0.0 0.0 0.0 0.0 0.0 \n\n[84 rows x 20 columns]", + "text/html": "
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19901995200020052010201520202025203020352040204520502055206020702080209021002110
ScenarioUnit
BIO00GHG000Mt CO2/yr115.793115.793115.793116.082116.371116.862954117.064786104.53332185.47097858.12817631.00287412.3228133.4667180.6355400.0595360.00.00.00.00.0
BIO00GHG010Mt CO2/yr115.793115.793115.793116.082116.371116.862954117.064786104.88297986.17121559.04204231.81204512.8092373.6582280.6801460.0641440.00.00.00.00.0
BIO00GHG020Mt CO2/yr115.793115.793115.793116.082116.371116.862954117.064786104.98785886.35977659.25732931.97553812.8936563.6875360.6865980.0648810.00.00.00.00.0
BIO00GHG050Mt CO2/yr115.793115.793115.793116.082116.371116.862954117.064786105.08628686.57930359.55961732.24391013.0486713.7448600.6993160.0661710.00.00.00.00.0
BIO00GHG100Mt CO2/yr115.793115.793115.793116.082116.371116.862954117.064786105.30894686.94075759.92807532.49237513.1635033.7819080.7072420.0670930.00.00.00.00.0
..................................................................
BIO45GHG3000Mt CO2/yr115.793115.793115.793116.082116.371116.862954117.064786105.31779486.95974259.95701432.52297313.1856223.7920460.7100060.0672770.00.00.00.00.0
BIO45GHG400Mt CO2/yr115.793115.793115.793116.082116.371116.862954117.064786105.31650386.95697859.95295932.51910213.1832253.7911240.7096380.0672770.00.00.00.00.0
BIO45GHG4000Mt CO2/yr115.793115.793115.793116.082116.371116.862954117.064786105.31779486.95974259.95664532.52260413.1852533.7920460.7098220.0672770.00.00.00.00.0
BIO45GHG600Mt CO2/yr115.793115.793115.793116.082116.371116.862954117.064786105.31724186.95845259.95498632.52094513.1843313.7916770.7098220.0672770.00.00.00.00.0
BIO45GHG990Mt CO2/yr115.793115.793115.793116.082116.371116.862954117.064786105.31742586.95919059.95609232.52205113.1850693.7920460.7098220.0672770.00.00.00.00.0
\n

84 rows × 20 columns

\n
" + }, + "execution_count": 106, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "new.fillna(0)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-25T09:12:48.537962500Z", + "start_time": "2023-09-25T09:12:48.437702200Z" + } + }, + "id": "240d2b12b4968c7d" + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "collapsed": false + }, + "id": "8cf23514ab0de739" + }, + { + "cell_type": "markdown", + "source": [ + "## check if old and new calculation yield same output" + ], + "metadata": { + "collapsed": false + }, + "id": "dd042b4579161338" + }, + { + "cell_type": "code", + "execution_count": 107, + "outputs": [ + { + "data": { + "text/plain": "True" + }, + "execution_count": 107, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "round = 11\n", + "(old.fillna(0).round(round) == new.fillna(0).round(round)).all().all()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-25T09:13:28.864784400Z", + "start_time": "2023-09-25T09:13:28.780131700Z" + } + }, + "id": "9041d5009d0a95b7" + }, + { + "cell_type": "code", + "execution_count": 142, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "R12_AFR\n", + "True\n", + "R12_CHN\n", + "True\n", + "R12_EEU\n", + "True\n", + "R12_FSU\n", + "True\n", + "R12_LAM\n", + "True\n", + "R12_MEA\n", + "True\n", + "R12_NAM\n", + "True\n", + "R12_PAO\n", + "True\n", + "R12_PAS\n", + "True\n", + "R12_RCPA\n", + "True\n", + "R12_SAS\n", + "True\n", + "R12_WEU\n", + "True\n" + ] + } + ], + "source": [ + "for k,v in old_dfs_dict.items():\n", + " print(k)\n", + " #print(new_dfs_dict[k])\n", + " print((v.fillna(0).round(round) == new_dfs_dict[k].fillna(0).round(round)).all().all())\n", + " if not (v.fillna(0).round(round) == new_dfs_dict[k].fillna(0).round(round)).all().all():\n", + " break" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-25T09:28:57.739335300Z", + "start_time": "2023-09-25T09:28:57.407534300Z" + } + }, + "id": "ce3dfad183defef5" + }, + { + "cell_type": "code", + "execution_count": 42, + "outputs": [], + "source": [ + "pd.testing.assert_frame_equal(old, new)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "start_time": "2023-09-25T08:56:04.961689700Z" + } + }, + "id": "8b0f59e3b8202475" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/message_ix_models/model/material/magpie notebooks/tax_emission_setup.ipynb b/message_ix_models/model/material/magpie notebooks/tax_emission_setup.ipynb new file mode 100644 index 0000000000..2495981abe --- /dev/null +++ b/message_ix_models/model/material/magpie notebooks/tax_emission_setup.ipynb @@ -0,0 +1,182 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 18, + "outputs": [], + "source": [ + "import ixmp\n", + "import matplotlib.pyplot as plt\n", + "import message_ix" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T13:52:55.824837700Z", + "start_time": "2023-10-05T13:52:55.787004900Z" + } + }, + "id": "e34c79b21d8c4a80" + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [], + "source": [ + "mp = ixmp.Platform(\"ixmp_dev\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T13:45:38.626355200Z", + "start_time": "2023-10-05T13:45:36.273179900Z" + } + }, + "id": "227c231f7a367485" + }, + { + "cell_type": "code", + "execution_count": 9, + "outputs": [], + "source": [ + "target_model = \"...\"\n", + "target_scenario = \"...\"\n", + "peak_model = \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\"\n", + "peak_scenario = \"EN_NPi2020_1150\"" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T13:50:44.255403100Z", + "start_time": "2023-10-05T13:50:44.239973600Z" + } + }, + "id": "56f8ec02603f447e" + }, + { + "cell_type": "code", + "execution_count": 10, + "outputs": [], + "source": [ + "peak_scen = message_ix.Scenario(mp, peak_model, peak_scenario)\n", + "target_scen = message_ix.Scenario(mp, target_model, target_scenario)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T13:50:46.628590500Z", + "start_time": "2023-10-05T13:50:44.571545200Z" + } + }, + "id": "48d2afc1d5f9a47a" + }, + { + "cell_type": "code", + "execution_count": 13, + "outputs": [], + "source": [ + "# pull and modify tax emission dataframe from peak scenario\n", + "df_emi_tax = peak_scen.par(\"tax_emission\")\n", + "df_emi_tax[\"type_emission\"] = \"TCE\"" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T13:51:35.379890600Z", + "start_time": "2023-10-05T13:51:35.326754400Z" + } + }, + "id": "8b1af490dac35dd" + }, + { + "cell_type": "code", + "execution_count": 21, + "outputs": [ + { + "data": { + "text/plain": "Text(0.5, 0, '')" + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# quick visualization to check the emission tax trajectory\n", + "fig, ax = plt.subplots()\n", + "df_emi_tax.plot(x=\"type_year\", ax=ax)\n", + "ax.set_ylabel(\"USD/tC\")\n", + "ax.set_xlabel(\"\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T13:53:20.623834300Z", + "start_time": "2023-10-05T13:53:20.507446200Z" + } + }, + "id": "57bc9de8707bcde8" + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df_emi_bound = target_scen.par(\"bound_emission\")" + ], + "metadata": { + "collapsed": false + }, + "id": "38e6964989aa4070" + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "# remove emission bound from target scenario\n", + "# and add tax emission from peak scenario\n", + "target_scen.check_out()\n", + "if len(df_emi_bound) != 0:\n", + " target_scen.remove_par(\"bound_emission\", df_emi_bound)\n", + "target_scen.add_par(\"tax_emission\", df_emi_tax)\n", + "target_scen.commit(\"add emission tax to emulate peak budget scenario\")" + ], + "metadata": { + "collapsed": false + }, + "id": "3e49cf0636dab573" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/message_ix_models/model/material/magpie_LU_readin.py b/message_ix_models/model/material/magpie_LU_readin.py new file mode 100644 index 0000000000..f099ebf4eb --- /dev/null +++ b/message_ix_models/model/material/magpie_LU_readin.py @@ -0,0 +1,100 @@ +import ixmp +import message_ix +import data_scp +from message_data.tools.utilities import add_globiom +from message_ix_models.util import private_data_path + +import message_data.tools.post_processing.iamc_report_hackathon + + +magpie_scens = [ + "MP00BD0BI78", + "MP00BD0BI74", + "MP00BD0BI70", + "MP00BD1BI00", + "MP30BD0BI78", + "MP30BD0BI74", + "MP30BD0BI70", + "MP30BD1BI00", + "MP50BD0BI78", + "MP50BD0BI74", + "MP50BD0BI70", + "MP50BD1BI00", + "MP76BD0BI70", + "MP76BD0BI74", + "MP76BD0BI78", + "MP76BD1BI00" +] + + +def build_magpie_baseline_from_r12_base(mp, scen_base, magpie_scen): + scen = scen_base.clone(scen_base.model+"-"+magpie_scen, "baseline") + if scen.has_solution(): + scen.remove_solution() + add_globiom( + mp, + scen, + "SSP2", + private_data_path(), + 2015, + globiom_scenario="noSDG_rcpref", + regenerate_input_data=True, + #regenerate_input_data_only=True, + #allow_empty_drivers=True, + add_reporting_data=True, + config_setup="MAGPIE_"+magpie_scen + ) + scen.set_as_default() + return scen + + +def solve_mp_baseline(): + mp = ixmp.Platform("ixmp_dev") + for magpie_scen in magpie_scens[5:]: + scen = message_ix.Scenario(mp, f"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-{magpie_scen}", "baseline") + scen.solve(model="MESSAGE-MACRO") + + +def run_reporting(): + mp = ixmp.Platform("ixmp_dev") + for magpie_scen in magpie_scens: + print() + print(f"starting reporting of {magpie_scen}") + print() + scen = message_ix.Scenario(mp, f"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-{magpie_scen}", "baseline") + if scen.has_solution(): + message_data.tools.post_processing.iamc_report_hackathon.report( + mp, + scen, + # NB(PNK) this is not an error; .iamc_report_hackathon.report() expects a + # string containing "True" or "False" instead of an actual bool. + "False", + scen.model, + scen.scenario, + merge_hist=True, + merge_ts=True, + # run_config="materials_run_config.yaml", + ) + del scen + + +def main(): + mp = ixmp.Platform("ixmp_dev") + scen_base = message_ix.Scenario(mp, "MESSAGEix-GLOBIOM 1.1-R12-MAGPIE", "baseline_w/o_LU") + + for magpie_scen in magpie_scens[-1:]: + print(f"read-in of {magpie_scen} matrix started") + scen = build_magpie_baseline_from_r12_base(mp, scen_base, magpie_scen) + if "MP00" not in magpie_scen: + print(f"adding SCP model to {magpie_scen}") + scp_dict = data_scp.gen_data_scp(scen) + scen.check_out() + data_scp.add_scp_sets(scen) + for k,v in scp_dict.items(): + scen.add_par(k, v) + scen.commit("add SCP tecs") + scen.set_as_default() + + +if __name__ == '__main__': + run_reporting() \ No newline at end of file diff --git a/message_ix_models/model/material/material_demand/refactor_mat_demand_calc.py b/message_ix_models/model/material/material_demand/refactor_mat_demand_calc.py new file mode 100644 index 0000000000..bdb48d3e86 --- /dev/null +++ b/message_ix_models/model/material/material_demand/refactor_mat_demand_calc.py @@ -0,0 +1,675 @@ +import message_ix_models.util +import pandas as pd +import numpy as np +from scipy.optimize import curve_fit +import yaml + +file_cement = "/CEMENT.BvR2010.xlsx" +file_steel = "/STEEL_database_2012.xlsx" +file_al = "/demand_aluminum.xlsx" +# file_petro = "/demand_petro.xlsx" +file_gdp = "/iamc_db ENGAGE baseline GDP PPP.xlsx" + +giga = 10**9 +mega = 10**6 + +material_data_dirname = { + "aluminum": "aluminum", + "steel": "steel_cement", + "cement": "steel_cement" +} + +def steel_function(x, a, b, m): + gdp_pcap, del_t = x + return a * np.exp(b / gdp_pcap) * (1 - m) ** del_t + + +def cement_function(x, a, b): + gdp_pcap = x + return a * np.exp(b / gdp_pcap) + + +fitting_dict = { + "steel": { + "function": steel_function, + "initial_guess": [600, -10000, 0], + "x_data": ["gdp_pcap", "del_t"], + "phi": 9, + "mu": 0.1, + }, + "cement": { + "function": cement_function, + "initial_guess": [500, -3000], + "x_data": ["gdp_pcap"], + "phi": 10, + "mu": 0.1, + }, + "aluminum": { + "function": cement_function, + "initial_guess": [600, -10000], + "x_data": ["gdp_pcap"], + "phi": 9, + "mu": 0.1, + }, +} + + +def gompertz(phi, mu, y, baseyear=2020): + return 1 - np.exp(-phi * np.exp(-mu * (y - baseyear))) + + +def derive_steel_demand(df_pop, df_demand, datapath): + # Read GDP data + gdp_ppp = pd.read_excel(f"{datapath}/other{file_gdp}", sheet_name="data_R12") + gdp_ppp = ( + gdp_ppp[gdp_ppp["Scenario"] == "baseline"] + .loc[:, ["Region", *[i for i in gdp_ppp.columns if type(i) == int]]] + .melt(id_vars="Region", var_name="year", value_name="gdp_ppp") + .query('Region != "World"') + .assign( + year=lambda x: x["year"].astype(int), region=lambda x: "R12_" + x["Region"] + ) + ) + + # Read raw steel consumption data + df_raw_steel_consumption = pd.read_excel( + f"{datapath}/steel_cement{file_steel}", + sheet_name="Consumption regions", + nrows=26, + ) + df_raw_steel_consumption = ( + df_raw_steel_consumption.rename({"t": "region"}, axis=1) + .drop(columns=["Unnamed: 1"]) + .rename({"TIMER Region": "reg_no"}, axis=1) + ) + df_raw_steel_consumption = df_raw_steel_consumption.melt( + id_vars=["region", "reg_no"], var_name="year", value_name="consumption" + ) + df_raw_steel_consumption["region"] = ( + df_raw_steel_consumption["region"] + .str.replace("(", "") + .str.replace(")", "") + .str.replace("\d+", "") + .str[:-1] + ) + + # Read population data + df_population = pd.read_excel( + f"{datapath}/steel_cement{file_cement}", + sheet_name="Timer_POP", + skiprows=[0, 1, 2, 30], + nrows=26, + ) + df_population = df_population.drop(columns=["Unnamed: 0"]).rename( + {"Unnamed: 1": "reg_no", "Unnamed: 2": "region"}, axis=1 + ) + df_population = df_population.melt( + id_vars=["region", "reg_no"], var_name="year", value_name="pop" + ) + + # Read GDP per capita data + df_gdp = pd.read_excel( + f"{datapath}/steel_cement{file_cement}", + sheet_name="Timer_GDPCAP", + skiprows=[0, 1, 2, 30], + nrows=26, + ) + df_gdp = df_gdp.drop(columns=["Unnamed: 0"]).rename( + {"Unnamed: 1": "reg_no", "Unnamed: 2": "region"}, axis=1 + ) + df_gdp = df_gdp.melt( + id_vars=["region", "reg_no"], var_name="year", value_name="gdp_pcap" + ) + + # Organize data + df_steel_consumption = ( + pd.merge( + df_raw_steel_consumption, + df_population.drop("region", axis=1), + on=["reg_no", "year"], + ) + .merge(df_gdp[["reg_no", "year", "gdp_pcap"]], on=["reg_no", "year"]) + .assign( + cons_pcap=lambda x: x["consumption"] / x["pop"], + del_t=lambda x: x["year"].astype(int) - 2010, + ) + .dropna() + .query("cons_pcap > 0") + ) + + # Assuming df_steel_consumption contains the necessary columns (cons.pcap, gdp.pcap, del_t) + x_data = df_steel_consumption["gdp_pcap"] + y_data = df_steel_consumption["cons_pcap"] + del_t_data = df_steel_consumption["del_t"] + + # Initial guess for parameters + initial_guess = [600, -10000, 0] + + # Perform nonlinear least squares fitting + params_opt, _ = curve_fit( + steel_function, (x_data, del_t_data), y_data, p0=initial_guess + ) + + # Extract the optimized parameters + a_opt, b_opt, m_opt = params_opt + print(f"a: {a_opt}, b: {b_opt}, m: {m_opt}") + + # Merge with steel consumption data + df_in = pd.merge(df_pop, df_demand.drop(columns=["year"]), how="left") + df_in = pd.merge(df_in, gdp_ppp, how="inner") + df_in["del_t"] = df_in["year"] - 2010 + df_in["gdp_pcap"] = df_in["gdp_ppp"] * giga / df_in["pop.mil"] / mega + + df_in["demand_pcap0"] = df_in.apply( + lambda row: steel_function((row["gdp_pcap"], row["del_t"]), *params_opt), axis=1 + ) + + # Demand Prediction and Calculations + df_demand = df_in.groupby("region").apply( + lambda group: group.assign( + demand_pcap_base=group["demand.tot.base"].iloc[0] + * giga + / group["pop.mil"].iloc[0] + / mega + ) + ) + df_demand = df_demand.groupby("region").apply( + lambda group: group.assign( + gap_base=group["demand_pcap_base"].iloc[0] - group["demand_pcap0"].iloc[0] + ) + ) + df_demand = df_demand.groupby("region").apply( + lambda group: group.assign( + demand_pcap=group["demand_pcap0"] + + group["gap_base"] * gompertz(9, 0.1, y=group["year"]) + ) + ) + df_demand = ( + df_demand.groupby("region") + .apply( + lambda group: group.assign( + demand_tot=group["demand_pcap"] * group["pop.mil"] * mega / giga + ) + ) + .reset_index(drop=True) + ) + + # Add 2110 placeholder + # df_demand_components = pd.concat([df_demand_components, df_demand_components[df_demand_components['year'] == 2100].assign(year=2110)]) + + # return df_demand_components[['node', 'year', 'demand_tot']].sort_values(by=['year', 'node']).reset_index(drop=True) + return df_demand + + +def derive_cement_demand(df_pop, df_demand, datapath): + # Read GDP data + gdp_ppp = pd.read_excel(f"{datapath}/other{file_gdp}", sheet_name="data_R12") + gdp_ppp = ( + gdp_ppp[gdp_ppp["Scenario"] == "baseline"] + .loc[:, ["Region", *[i for i in gdp_ppp.columns if type(i) == int]]] + .melt(id_vars="Region", var_name="year", value_name="gdp_ppp") + .query('Region != "World"') + .assign( + year=lambda x: x["year"].astype(int), region=lambda x: "R12_" + x["Region"] + ) + ) + + # Read raw steel consumption data + df_raw_cement_consumption = pd.read_excel( + f"{datapath}/steel_cement{file_cement}", + sheet_name="Regions", + skiprows=122, + nrows=26, + ) + # print(df_raw_steel_consumption.columns) + df_raw_cement_consumption = df_raw_cement_consumption.rename( + {"Region #": "reg_no", "Region": "region"}, axis=1 + ) + df_raw_cement_consumption = df_raw_cement_consumption.melt( + id_vars=["region", "reg_no"], var_name="year", value_name="consumption" + ) + # df_raw_cement_consumption["region"] = df_raw_cement_consumption["region"].str.replace("(","").str.replace(")","").str.replace('\d+', '').str[:-1] + + # Read population data + df_population = pd.read_excel( + f"{datapath}/steel_cement{file_cement}", + sheet_name="Timer_POP", + skiprows=[0, 1, 2, 30], + nrows=26, + ) + df_population = df_population.drop(columns=["Unnamed: 0"]).rename( + {"Unnamed: 1": "reg_no", "Unnamed: 2": "region"}, axis=1 + ) + df_population = df_population.melt( + id_vars=["region", "reg_no"], var_name="year", value_name="pop" + ) + + # Read GDP per capita data + df_gdp = pd.read_excel( + f"{datapath}/steel_cement{file_cement}", + sheet_name="Timer_GDPCAP", + skiprows=[0, 1, 2, 30], + nrows=26, + ) + df_gdp = df_gdp.drop(columns=["Unnamed: 0"]).rename( + {"Unnamed: 1": "reg_no", "Unnamed: 2": "region"}, axis=1 + ) + df_gdp = df_gdp.melt( + id_vars=["region", "reg_no"], var_name="year", value_name="gdp_pcap" + ) + + # Organize data + df_cement_consumption = ( + pd.merge( + df_raw_cement_consumption, + df_population.drop("region", axis=1), + on=["reg_no", "year"], + ) + .merge(df_gdp[["reg_no", "year", "gdp_pcap"]], on=["reg_no", "year"]) + .assign( + cons_pcap=lambda x: x["consumption"] / x["pop"] / 10**6, + del_t=lambda x: x["year"].astype(int) - 2010, + ) + .dropna() + .query("cons_pcap > 0") + ) + + # Assuming df_steel_consumption contains the necessary columns (cons.pcap, gdp.pcap, del_t) + x_data = df_cement_consumption["gdp_pcap"] + y_data = df_cement_consumption["cons_pcap"] + + # Initial guess for parameters + initial_guess = [500, -3000] + # df_steel_consumption.to_csv("py_output.csv") + # Perform nonlinear least squares fitting + params_opt, _ = curve_fit(cement_function, x_data, y_data, p0=initial_guess) + + # Extract the optimized parameters + a_opt, b_opt = params_opt + print(f"a: {a_opt}, b: {b_opt}") + + # Merge with steel consumption data + df_in = pd.merge(df_pop, df_demand.drop(columns=["year"]), how="left") + df_in = pd.merge(df_in, gdp_ppp, how="inner") + df_in["del_t"] = df_in["year"] - 2010 + df_in["gdp_pcap"] = df_in["gdp_ppp"] * giga / df_in["pop.mil"] / mega + + df_in["demand_pcap0"] = df_in.apply( + lambda row: cement_function(row["gdp_pcap"], *params_opt), axis=1 + ) + + # Demand Prediction and Calculations + df_demand = df_in.groupby("region").apply( + lambda group: group.assign( + demand_pcap_base=group["demand.tot.base"].iloc[0] + * giga + / group["pop.mil"].iloc[0] + / mega + ) + ) + df_demand = df_demand.groupby("region").apply( + lambda group: group.assign( + gap_base=group["demand_pcap_base"].iloc[0] - group["demand_pcap0"].iloc[0] + ) + ) + df_demand = df_demand.groupby("region").apply( + lambda group: group.assign( + demand_pcap=group["demand_pcap0"] + + group["gap_base"] * gompertz(10, 0.1, y=group["year"]) + ) + ) + df_demand = ( + df_demand.groupby("region") + .apply( + lambda group: group.assign( + demand_tot=group["demand_pcap"] * group["pop.mil"] * mega / giga + ) + ) + .reset_index(drop=True) + ) + + # Add 2110 placeholder + # df_demand_components = pd.concat([df_demand_components, df_demand_components[df_demand_components['year'] == 2100].assign(year=2110)]) + + # return df_demand_components[['node', 'year', 'demand_tot']].sort_values(by=['year', 'node']).reset_index(drop=True) + return df_demand + + +def derive_alu_demand(df_pop, df_demand, datapath): + # Read GDP data + gdp_ppp = pd.read_excel(f"{datapath}/other{file_gdp}", sheet_name="data_R12") + # print(gdp_ppp.columns) + gdp_ppp = ( + gdp_ppp[gdp_ppp["Scenario"] == "baseline"] + .loc[:, ["Region", *[i for i in gdp_ppp.columns if type(i) == int]]] + .melt(id_vars="Region", var_name="year", value_name="gdp_ppp") + .query('Region != "World"') + .assign( + year=lambda x: x["year"].astype(int), region=lambda x: "R12_" + x["Region"] + ) + ) + + # Read raw steel consumption data + df_raw_alu_consumption = ( + pd.read_excel( + f"{datapath}/aluminum{file_al}", sheet_name="final_table", nrows=378 + ) + .drop(["cons.pcap", "del.t"], axis=1) + .rename({"gdp.pcap": "gdp_pcap"}, axis=1) + ) + + # Organize data + df_alu_consumption = ( + df_raw_alu_consumption.assign( + cons_pcap=lambda x: x["consumption"] / x["pop"], + del_t=lambda x: x["year"].astype(int) - 2010, + ) + .dropna() + .query("cons_pcap > 0") + ) + + # Assuming df_steel_consumption contains the necessary columns (cons.pcap, gdp.pcap, del_t) + x_data = df_alu_consumption["gdp_pcap"] + y_data = df_alu_consumption["cons_pcap"] + + # Initial guess for parameters + initial_guess = [600, -10000] + + # Perform nonlinear least squares fitting + params_opt, _ = curve_fit(cement_function, x_data, y_data, p0=initial_guess) + + # Extract the optimized parameters + a_opt, b_opt = params_opt + print(f"a: {a_opt}, b: {b_opt}") + + # Merge with steel consumption data + df_in = pd.merge(df_pop, df_demand.drop(columns=["year"]), how="left") + df_in = pd.merge(df_in, gdp_ppp, how="inner") + df_in["del_t"] = df_in["year"] - 2010 + df_in["gdp_pcap"] = df_in["gdp_ppp"] * giga / df_in["pop.mil"] / mega + + df_in["demand_pcap0"] = df_in.apply( + lambda row: cement_function(row["gdp_pcap"], *params_opt), axis=1 + ) + + # Demand Prediction and Calculations + df_demand = df_in.groupby("region").apply( + lambda group: group.assign( + demand_pcap_base=group["demand.tot.base"].iloc[0] + * giga + / group["pop.mil"].iloc[0] + / mega + ) + ) + df_demand = df_demand.groupby("region").apply( + lambda group: group.assign( + gap_base=group["demand_pcap_base"].iloc[0] - group["demand_pcap0"].iloc[0] + ) + ) + df_demand = df_demand.groupby("region").apply( + lambda group: group.assign( + demand_pcap=group["demand_pcap0"] + + group["gap_base"] * gompertz(9, 0.1, y=group["year"]) + ) + ) + df_demand = ( + df_demand.groupby("region") + .apply( + lambda group: group.assign( + demand_tot=group["demand_pcap"] * group["pop.mil"] * mega / giga + ) + ) + .reset_index(drop=True) + ) + + # Add 2110 placeholder + # df_demand_components = pd.concat([df_demand_components, df_demand_components[df_demand_components['year'] == 2100].assign(year=2110)]) + + # return df_demand_components[['node', 'year', 'demand_tot']].sort_values(by=['year', 'node']).reset_index(drop=True) + return df_demand + + +def read_pop(datapath, filename): + df_population = pd.read_excel( + f"{datapath}/steel_cement{filename}", + sheet_name="Timer_POP", + skiprows=[0, 1, 2, 30], + nrows=26, + ) + df_population = df_population.drop(columns=["Unnamed: 0"]).rename( + {"Unnamed: 1": "reg_no", "Unnamed: 2": "region"}, axis=1 + ) + df_population = df_population.melt( + id_vars=["region", "reg_no"], var_name="year", value_name="pop" + ) + return df_population + + +def read_hist_gdp(datapath, filename): + # Read GDP per capita data + df_gdp = pd.read_excel( + f"{datapath}/steel_cement{filename}", + sheet_name="Timer_GDPCAP", + skiprows=[0, 1, 2, 30], + nrows=26, + ) + df_gdp = df_gdp.drop(columns=["Unnamed: 0"]).rename( + {"Unnamed: 1": "reg_no", "Unnamed: 2": "region"}, axis=1 + ) + df_gdp = df_gdp.melt( + id_vars=["region", "reg_no"], var_name="year", value_name="gdp_pcap" + ) + return df_gdp + + +def project_demand(df, phi, mu): + df_demand = df.groupby("region").apply( + lambda group: group.assign( + demand_pcap_base=group["demand.tot.base"].iloc[0] + * giga + / group["pop.mil"].iloc[0] + / mega + ) + ) + df_demand = df_demand.groupby("region").apply( + lambda group: group.assign( + gap_base=group["demand_pcap_base"].iloc[0] - group["demand_pcap0"].iloc[0] + ) + ) + df_demand = df_demand.groupby("region").apply( + lambda group: group.assign( + demand_pcap=group["demand_pcap0"] + + group["gap_base"] * gompertz(phi, mu, y=group["year"]) + ) + ) + df_demand = ( + df_demand.groupby("region") + .apply( + lambda group: group.assign( + demand_tot=group["demand_pcap"] * group["pop.mil"] * mega / giga + ) + ) + .reset_index(drop=True) + ) + return df_demand + + +def read_base_demand(filepath): + with open(filepath, "r") as file: + yaml_data = file.read() + + data_list = yaml.safe_load(yaml_data) + + df = pd.DataFrame( + [ + (key, value["year"], value["value"]) + for entry in data_list + for key, value in entry.items() + ], + columns=["region", "year", "value"], + ) + return df + + +def read_hist_mat_demand(material): + datapath = message_ix_models.util.private_data_path("material") + + if material in ["cement", "steel"]: + # Read population data + df_population = pd.read_excel( + f"{datapath}/steel_cement{file_cement}", + sheet_name="Timer_POP", + skiprows=[0, 1, 2, 30], + nrows=26, + ) + df_population = df_population.drop(columns=["Unnamed: 0"]).rename( + {"Unnamed: 1": "reg_no", "Unnamed: 2": "region"}, axis=1 + ) + df_population = df_population.melt( + id_vars=["region", "reg_no"], var_name="year", value_name="pop" + ) + + # Read GDP per capita data + df_gdp = pd.read_excel( + f"{datapath}/steel_cement{file_cement}", + sheet_name="Timer_GDPCAP", + skiprows=[0, 1, 2, 30], + nrows=26, + ) + df_gdp = df_gdp.drop(columns=["Unnamed: 0"]).rename( + {"Unnamed: 1": "reg_no", "Unnamed: 2": "region"}, axis=1 + ) + df_gdp = df_gdp.melt( + id_vars=["region", "reg_no"], var_name="year", value_name="gdp_pcap" + ) + + if material == "aluminum": + df_raw_cons = ( + pd.read_excel( + f"{datapath}/aluminum{file_al}", sheet_name="final_table", nrows=378 + ) + .drop(["cons.pcap", "del.t"], axis=1) + .rename({"gdp.pcap": "gdp_pcap"}, axis=1) + ) + + df_cons = ( + df_raw_cons.assign( + cons_pcap=lambda x: x["consumption"] / x["pop"], + del_t=lambda x: x["year"].astype(int) - 2010, + ) + .dropna() + .query("cons_pcap > 0") + ) + elif material == "steel": + df_raw_cons = pd.read_excel( + f"{datapath}/steel_cement{file_steel}", + sheet_name="Consumption regions", + nrows=26, + ) + df_raw_cons = ( + df_raw_cons.rename({"t": "region"}, axis=1) + .drop(columns=["Unnamed: 1"]) + .rename({"TIMER Region": "reg_no"}, axis=1) + ) + df_raw_cons = df_raw_cons.melt( + id_vars=["region", "reg_no"], var_name="year", value_name="consumption" + ) + df_raw_cons["region"] = ( + df_raw_cons["region"] + .str.replace("(", "") + .str.replace(")", "") + .str.replace("\d+", "") + .str[:-1] + ) + + # Merge and organize data + df_cons = ( + pd.merge( + df_raw_cons, df_population.drop("region", axis=1), on=["reg_no", "year"] + ) + .merge(df_gdp[["reg_no", "year", "gdp_pcap"]], on=["reg_no", "year"]) + .assign( + cons_pcap=lambda x: x["consumption"] / x["pop"], + del_t=lambda x: x["year"].astype(int) - 2010, + ) + .dropna() + .query("cons_pcap > 0") + ) + elif material == "cement": + df_raw_cons = pd.read_excel( + f"{datapath}/steel_cement{file_cement}", + sheet_name="Regions", + skiprows=122, + nrows=26, + ) + df_raw_cons = df_raw_cons.rename( + {"Region #": "reg_no", "Region": "region"}, axis=1 + ) + df_raw_cons = df_raw_cons.melt( + id_vars=["region", "reg_no"], var_name="year", value_name="consumption" + ) + # Merge and organize data + df_cons = ( + pd.merge( + df_raw_cons, df_population.drop("region", axis=1), on=["reg_no", "year"] + ) + .merge(df_gdp[["reg_no", "year", "gdp_pcap"]], on=["reg_no", "year"]) + .assign( + cons_pcap=lambda x: x["consumption"] / x["pop"] / 10**6, + del_t=lambda x: x["year"].astype(int) - 2010, + ) + .dropna() + .query("cons_pcap > 0") + ) + else: + print("non-available material selected. must be one of [aluminum, steel, cement]") + df_cons = None + return df_cons + + +def derive_demand(material, scen): + + datapath = message_ix_models.util.private_data_path("material") + + # read pop from scenario + pop = scen.par("bound_activity_up", {"technology": "Population"}) + pop = pop.loc[pop.year_act >= 2020].rename( + columns={"year_act": "year", "value": "pop.mil", "node_loc": "region"} + ) + + # read gdp from scenario + gdp = scen.par("bound_activity_up", {"technology": "GDP"}) + gdp = gdp.loc[gdp.year_act >= 2020].rename( + columns={"year_act": "year", "value": "gdp_ppp", "node_loc": "region"} + ) + + # get base year demand of material + df_base_demand = read_base_demand(f"{datapath}/{material_data_dirname[material]}/demand_{material}.yaml") + + # get historical material data + df_cons = read_hist_mat_demand(material) + x_data = tuple(pd.Series(df_cons[col]) for col in fitting_dict[material]["x_data"]) + + # run regression on historical data + params_opt, _ = curve_fit( + fitting_dict[material]["function"], + xdata=x_data, + ydata=df_cons["cons_pcap"], + p0=fitting_dict[material]["initial_guess"] + ) + print(f"{params_opt}") + + df_in = pd.merge(pop, df_base_demand.drop(columns=["year"]), how="left") + df_in = pd.merge(df_in, gdp[["region", "year", "gdp_ppp"]], how="inner") + df_in["del_t"] = df_in["year"] - 2010 + df_in["gdp_pcap"] = df_in["gdp_ppp"] * giga / df_in["pop.mil"] / mega + + x = tuple(pd.Series(df_in[col]) for col in fitting_dict[material]["x_data"]) + + df_in["demand_pcap0"] = df_in.apply( + lambda row: fitting_dict[material]["function"](x, *params_opt), axis=1 + ) + + df_final = project_demand(df_in, fitting_dict[material]["phi"], fitting_dict[material]["mu"]) + + return df_final diff --git a/message_ix_models/model/material/modify_materials_for_ssp_update.ipynb b/message_ix_models/model/material/modify_materials_for_ssp_update.ipynb new file mode 100644 index 0000000000..0911896a9a --- /dev/null +++ b/message_ix_models/model/material/modify_materials_for_ssp_update.ipynb @@ -0,0 +1,1256 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "outputs": [ + { + "data": { + "text/plain": "", + "application/javascript": "if (typeof IPython !== 'undefined') { IPython.OutputArea.prototype._should_scroll = function(lines){ return false; }}" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model = \"SSP_dev_SSP2_v0.1\"\n", + "scenario = \"baseline_v0.1_materials_no_pow_sect\"\n", + "\n", + "\n", + "import ixmp\n", + "import message_ix\n", + "mp = ixmp.Platform(\"ixmp_dev\")\n", + "#scen = message_ix.Scenario(mp, model, scenario)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-27T11:02:02.715065500Z", + "start_time": "2023-11-27T11:01:49.501141Z" + } + }, + "id": "5b8dbb8cae325fed" + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [], + "source": [ + "from message_data.model.material import data_petro\n", + "import importlib" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-23T14:25:21.171813800Z", + "start_time": "2023-11-23T14:25:21.140567100Z" + } + }, + "id": "c13b9ce5f19fc9b0" + }, + { + "cell_type": "code", + "execution_count": 47, + "outputs": [], + "source": [ + "importlib.reload(data_petro)\n", + "df = data_petro.gen_mock_demand_petro(scen, 1, 1)\n", + "df_baseyear = df[(df[\"commodity\"]==\"HVC\") & (df[\"year\"]==2025)]" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-23T15:36:06.906718800Z", + "start_time": "2023-11-23T15:36:06.646978100Z" + } + }, + "id": "b7749376f111cb06" + }, + { + "cell_type": "code", + "execution_count": 49, + "outputs": [ + { + "data": { + "text/plain": " node commodity level year time value unit\n0 R12_AFR HVC demand 2030 year 3.257632 t\n1 R12_CHN HVC demand 2030 year 0.614827 t\n2 R12_EEU HVC demand 2030 year 3.551030 t\n3 R12_FSU HVC demand 2030 year 6.284582 t\n4 R12_LAM HVC demand 2030 year 13.712894 t\n.. ... ... ... ... ... ... ...\n151 R12_PAO HVC demand 2025 year 11.000000 t\n152 R12_PAS HVC demand 2025 year 37.500000 t\n153 R12_RCPA HVC demand 2025 year 10.700000 t\n154 R12_SAS HVC demand 2025 year 29.200000 t\n155 R12_WEU HVC demand 2025 year 50.500000 t\n\n[156 rows x 7 columns]", + "text/html": "
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nodecommoditylevelyeartimevalueunit
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" + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-23T15:36:22.137635700Z", + "start_time": "2023-11-23T15:36:22.085727100Z" + } + }, + "id": "2f3408d63ca5432a" + }, + { + "cell_type": "code", + "execution_count": 37, + "outputs": [ + { + "data": { + "text/plain": "2025" + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scen.firstmodelyear" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-23T14:42:10.504171500Z", + "start_time": "2023-11-23T14:42:10.457063400Z" + } + }, + "id": "b841a5e58b768c5b" + }, + { + "cell_type": "code", + "execution_count": 112, + "outputs": [], + "source": [ + "df_gdp = scen.par(\"gdp_calibrate\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-23T16:07:20.225623900Z", + "start_time": "2023-11-23T16:07:20.170634300Z" + } + }, + "id": "40d0282e9d3e7dc2" + }, + { + "cell_type": "code", + "execution_count": 113, + "outputs": [], + "source": [ + "df_gdp = df_gdp.set_index(\"node\")\n", + "df_gdp = df_gdp.join(df_gdp[df_gdp[\"year\"]==2020][\"value\"], rsuffix=\"_2020\")\n", + "df_gdp[\"growth\"] = (df_gdp[\"value\"] / df_gdp[\"value_2020\"])-1" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-23T16:07:20.434690600Z", + "start_time": "2023-11-23T16:07:20.385791200Z" + } + }, + "id": "f2eb8581f5c11c5" + }, + { + "cell_type": "code", + "execution_count": 114, + "outputs": [ + { + "data": { + "text/plain": " year value unit value_2020 growth\nnode \nR12_AFR 2020 998.98924 T$ 998.98924 0.000000\nR12_AFR 2025 1368.01863 T$ 998.98924 0.369403\nR12_AFR 2030 1856.87524 T$ 998.98924 0.858754\nR12_AFR 2035 2479.36677 T$ 998.98924 1.481875\nR12_AFR 2040 3314.60341 T$ 998.98924 2.317957\n... ... ... ... ... ...\nR12_WEU 2070 38793.79891 T$ 13849.19218 1.801160\nR12_WEU 2080 45819.24718 T$ 13849.19218 2.308442\nR12_WEU 2090 52976.57491 T$ 13849.19218 2.825247\nR12_WEU 2100 59671.75882 T$ 13849.19218 3.308682\nR12_WEU 2110 59671.75882 T$ 13849.19218 3.308682\n\n[168 rows x 5 columns]", + "text/html": "
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yearvalueunitvalue_2020growth
node
R12_AFR2020998.98924T$998.989240.000000
R12_AFR20251368.01863T$998.989240.369403
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R12_AFR20352479.36677T$998.989241.481875
R12_AFR20403314.60341T$998.989242.317957
..................
R12_WEU207038793.79891T$13849.192181.801160
R12_WEU208045819.24718T$13849.192182.308442
R12_WEU209052976.57491T$13849.192182.825247
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" + }, + "execution_count": 114, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_gdp" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-23T16:07:20.866898900Z", + "start_time": "2023-11-23T16:07:20.819939400Z" + } + }, + "id": "c7a7c51e9e511da5" + }, + { + "cell_type": "code", + "execution_count": 92, + "outputs": [ + { + "data": { + "text/plain": " node year value unit growth_abs\n0 R12_AFR 2020 998.98924 T$ NaN\n140 R12_CHN 2020 12597.24955 T$ -84206.84352\n14 R12_EEU 2020 1782.52848 T$ -68011.55969\n98 R12_FSU 2020 2824.38360 T$ -5491.21456\n28 R12_LAM 2020 4997.59636 T$ -13906.42139\n42 R12_MEA 2020 3620.98548 T$ -45701.43853\n56 R12_NAM 2020 18030.12282 T$ -30434.35428\n112 R12_PAO 2020 4865.43755 T$ -50091.24951\n126 R12_PAS 2020 4867.54648 T$ -8906.70881\n154 R12_RCPA 2020 354.01999 T$ -43266.03017\n70 R12_SAS 2020 2559.91153 T$ -3893.55831\n84 R12_WEU 2020 13849.19218 T$ -90687.68030", + "text/html": "
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nodeyearvalueunitgrowth_abs
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" + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_gdp[df_gdp[\"year\"]==2020][\"value\"]" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-23T16:03:57.582043300Z", + "start_time": "2023-11-23T16:03:57.505271100Z" + } + }, + "id": "5ddadd5106a8ebf4" + }, + { + "cell_type": "code", + "execution_count": 84, + "outputs": [], + "source": [ + "#df_gdp_gro = pd.concat([df_gdp, ], axis=1)\n", + "df_gdp[\"growth_rel\"] = df_gdp[\"growth_abs\"].cumsum() / df_gdp[\"value\"]" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "start_time": "2023-11-23T16:00:40.108836Z" + } + }, + "id": "f889f4dd6457b0e9" + }, + { + "cell_type": "code", + "execution_count": 73, + "outputs": [ + { + "data": { + "text/plain": " node year value unit\n0 R12_AFR 2020 998.98924 T$\n1 R12_AFR 2025 1368.01863 T$\n2 R12_AFR 2030 1856.87524 T$\n3 R12_AFR 2035 2479.36677 T$\n4 R12_AFR 2040 3314.60341 T$\n.. ... ... ... ...\n163 R12_RCPA 2070 3360.28429 T$\n164 R12_RCPA 2080 4304.25447 T$\n165 R12_RCPA 2090 5373.51508 T$\n166 R12_RCPA 2100 6453.46984 T$\n167 R12_RCPA 2110 6453.46984 T$\n\n[168 rows x 4 columns]", + "text/html": "
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nodeyearvalueunit
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" + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scen.par(\"gdp_calibrate\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "start_time": "2023-11-23T15:50:24.321689200Z" + } + }, + "id": "cb0f0cd0ecb06cb1" + }, + { + "cell_type": "code", + "execution_count": 65, + "outputs": [ + { + "data": { + "text/plain": "0 18.552009\n1 55.053443\n2 96.956958\n3 139.034792\n4 135.760735\n ... \n190 7025.448265\n191 7157.327735\n192 6695.183911\n193 0.000000\n194 NaN\nName: value, Length: 240, dtype: float64" + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_gdp[\"value\"].diff().shift(-1)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-23T15:42:53.399086500Z", + "start_time": "2023-11-23T15:42:53.314295800Z" + } + }, + "id": "ab5bf200d0f3b2c5" + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false + }, + "id": "f1ed71a6bb560d34" + }, + { + "cell_type": "code", + "execution_count": 119, + "outputs": [], + "source": [ + "from message_data.model.material.material_demand import refactor_mat_demand_calc" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-24T09:18:11.864211800Z", + "start_time": "2023-11-24T09:18:11.779779900Z" + } + }, + "id": "681b232fd2af6a05" + }, + { + "cell_type": "code", + "execution_count": 129, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index([ 'Model', 'Scenario', 'Region', 'Variable', 'Unit', 2000,\n", + " 2005, 2010, 2015, 2020, 2025, 2030,\n", + " 2035, 2040, 2045, 2050, 2055, 2060,\n", + " 2070, 2080, 2090, 2100, 2110, 'Notes'],\n", + " dtype='object')\n", + "Index(['TIMER Region', 'Unnamed: 1', 't', 1970,\n", + " 1971, 1972, 1973, 1974,\n", + " 1975, 1976, 1977, 1978,\n", + " 1979, 1980, 1981, 1982,\n", + " 1983, 1984, 1985, 1986,\n", + " 1987, 1988, 1989, 1990,\n", + " 1991, 1992, 1993, 1994,\n", + " 1995, 1996, 1997, 1998,\n", + " 1999, 2000, 2001, 2002,\n", + " 2003, 2004, 2005, 2006,\n", + " 2007, 2008, 2009, 2010,\n", + " 2011, 2012],\n", + " dtype='object')\n" + ] + }, + { + "ename": "KeyError", + "evalue": "'Reg_no'", + "output_type": "error", + "traceback": [ + "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[1;31mKeyError\u001B[0m Traceback (most recent call last)", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3621\u001B[0m, in \u001B[0;36mIndex.get_loc\u001B[1;34m(self, key, method, tolerance)\u001B[0m\n\u001B[0;32m 3620\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m-> 3621\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_engine\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_loc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcasted_key\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 3622\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m err:\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx:136\u001B[0m, in \u001B[0;36mpandas._libs.index.IndexEngine.get_loc\u001B[1;34m()\u001B[0m\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx:163\u001B[0m, in \u001B[0;36mpandas._libs.index.IndexEngine.get_loc\u001B[1;34m()\u001B[0m\n", + "File \u001B[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5198\u001B[0m, in \u001B[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001B[1;34m()\u001B[0m\n", + "File \u001B[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5206\u001B[0m, in \u001B[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001B[1;34m()\u001B[0m\n", + "\u001B[1;31mKeyError\u001B[0m: 'Reg_no'", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001B[1;31mKeyError\u001B[0m Traceback (most recent call last)", + "Input \u001B[1;32mIn [129]\u001B[0m, in \u001B[0;36m\u001B[1;34m()\u001B[0m\n\u001B[0;32m 16\u001B[0m base_demand \u001B[38;5;241m=\u001B[39m gen_mock_demand_aluminum(scen)\n\u001B[0;32m 17\u001B[0m base_demand \u001B[38;5;241m=\u001B[39m base_demand\u001B[38;5;241m.\u001B[39mloc[base_demand\u001B[38;5;241m.\u001B[39myear \u001B[38;5;241m==\u001B[39m \u001B[38;5;241m2020\u001B[39m]\u001B[38;5;241m.\u001B[39mrename(\n\u001B[0;32m 18\u001B[0m columns\u001B[38;5;241m=\u001B[39m{\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mvalue\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mdemand.tot.base\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mnode\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mregion\u001B[39m\u001B[38;5;124m\"\u001B[39m}\n\u001B[0;32m 19\u001B[0m )\n\u001B[1;32m---> 21\u001B[0m \u001B[43mrefactor_mat_demand_calc\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mderive_steel_demand\u001B[49m\u001B[43m(\u001B[49m\u001B[43mpop\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mbase_demand\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mstr\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mprivate_data_path\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mmaterial\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n", + "File \u001B[1;32m~\\PycharmProjects\\message_data\\message_data\\model\\material\\material_demand\\refactor_mat_demand_calc.py:38\u001B[0m, in \u001B[0;36mderive_steel_demand\u001B[1;34m(df_pop, df_demand, datapath)\u001B[0m\n\u001B[0;32m 36\u001B[0m df_raw_steel_consumption.drop(columns='Unnamed: 2')\n\u001B[0;32m 37\u001B[0m .melt(id_vars='Reg_no', var_name='year', value_name='consumption')\n\u001B[1;32m---> 38\u001B[0m )\n\u001B[0;32m 39\u001B[0m \n\u001B[0;32m 40\u001B[0m # Read population data\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:8434\u001B[0m, in \u001B[0;36mDataFrame.melt\u001B[1;34m(self, id_vars, value_vars, var_name, value_name, col_level, ignore_index)\u001B[0m\n\u001B[0;32m 8423\u001B[0m \u001B[38;5;129m@Appender\u001B[39m(_shared_docs[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mmelt\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m%\u001B[39m {\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mcaller\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mdf.melt(\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mother\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mmelt\u001B[39m\u001B[38;5;124m\"\u001B[39m})\n\u001B[0;32m 8424\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mmelt\u001B[39m(\n\u001B[0;32m 8425\u001B[0m \u001B[38;5;28mself\u001B[39m,\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 8431\u001B[0m ignore_index: \u001B[38;5;28mbool\u001B[39m \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mTrue\u001B[39;00m,\n\u001B[0;32m 8432\u001B[0m ) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m DataFrame:\n\u001B[1;32m-> 8434\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mmelt\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m 8435\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[0;32m 8436\u001B[0m \u001B[43m \u001B[49m\u001B[43mid_vars\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mid_vars\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 8437\u001B[0m \u001B[43m \u001B[49m\u001B[43mvalue_vars\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mvalue_vars\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 8438\u001B[0m \u001B[43m \u001B[49m\u001B[43mvar_name\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mvar_name\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 8439\u001B[0m \u001B[43m \u001B[49m\u001B[43mvalue_name\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mvalue_name\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 8440\u001B[0m \u001B[43m \u001B[49m\u001B[43mcol_level\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mcol_level\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 8441\u001B[0m \u001B[43m \u001B[49m\u001B[43mignore_index\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mignore_index\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 8442\u001B[0m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\reshape\\melt.py:132\u001B[0m, in \u001B[0;36mmelt\u001B[1;34m(frame, id_vars, value_vars, var_name, value_name, col_level, ignore_index)\u001B[0m\n\u001B[0;32m 130\u001B[0m mdata \u001B[38;5;241m=\u001B[39m {}\n\u001B[0;32m 131\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m col \u001B[38;5;129;01min\u001B[39;00m id_vars:\n\u001B[1;32m--> 132\u001B[0m id_data \u001B[38;5;241m=\u001B[39m \u001B[43mframe\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mpop\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcol\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 133\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m is_extension_array_dtype(id_data):\n\u001B[0;32m 134\u001B[0m id_data \u001B[38;5;241m=\u001B[39m concat([id_data] \u001B[38;5;241m*\u001B[39m K, ignore_index\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m)\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:5270\u001B[0m, in \u001B[0;36mDataFrame.pop\u001B[1;34m(self, item)\u001B[0m\n\u001B[0;32m 5229\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mpop\u001B[39m(\u001B[38;5;28mself\u001B[39m, item: Hashable) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m Series:\n\u001B[0;32m 5230\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 5231\u001B[0m \u001B[38;5;124;03m Return item and drop from frame. Raise KeyError if not found.\u001B[39;00m\n\u001B[0;32m 5232\u001B[0m \n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 5268\u001B[0m \u001B[38;5;124;03m 3 monkey NaN\u001B[39;00m\n\u001B[0;32m 5269\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[1;32m-> 5270\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43msuper\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mpop\u001B[49m\u001B[43m(\u001B[49m\u001B[43mitem\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mitem\u001B[49m\u001B[43m)\u001B[49m\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:865\u001B[0m, in \u001B[0;36mNDFrame.pop\u001B[1;34m(self, item)\u001B[0m\n\u001B[0;32m 864\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mpop\u001B[39m(\u001B[38;5;28mself\u001B[39m, item: Hashable) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m Series \u001B[38;5;241m|\u001B[39m Any:\n\u001B[1;32m--> 865\u001B[0m result \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m[\u001B[49m\u001B[43mitem\u001B[49m\u001B[43m]\u001B[49m\n\u001B[0;32m 866\u001B[0m \u001B[38;5;28;01mdel\u001B[39;00m \u001B[38;5;28mself\u001B[39m[item]\n\u001B[0;32m 868\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m result\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3505\u001B[0m, in \u001B[0;36mDataFrame.__getitem__\u001B[1;34m(self, key)\u001B[0m\n\u001B[0;32m 3503\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcolumns\u001B[38;5;241m.\u001B[39mnlevels \u001B[38;5;241m>\u001B[39m \u001B[38;5;241m1\u001B[39m:\n\u001B[0;32m 3504\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_getitem_multilevel(key)\n\u001B[1;32m-> 3505\u001B[0m indexer \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcolumns\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_loc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mkey\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 3506\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m is_integer(indexer):\n\u001B[0;32m 3507\u001B[0m indexer \u001B[38;5;241m=\u001B[39m [indexer]\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3623\u001B[0m, in \u001B[0;36mIndex.get_loc\u001B[1;34m(self, key, method, tolerance)\u001B[0m\n\u001B[0;32m 3621\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_engine\u001B[38;5;241m.\u001B[39mget_loc(casted_key)\n\u001B[0;32m 3622\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m err:\n\u001B[1;32m-> 3623\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m(key) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01merr\u001B[39;00m\n\u001B[0;32m 3624\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m:\n\u001B[0;32m 3625\u001B[0m \u001B[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001B[39;00m\n\u001B[0;32m 3626\u001B[0m \u001B[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001B[39;00m\n\u001B[0;32m 3627\u001B[0m \u001B[38;5;66;03m# the TypeError.\u001B[39;00m\n\u001B[0;32m 3628\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_check_indexing_error(key)\n", + "\u001B[1;31mKeyError\u001B[0m: 'Reg_no'" + ] + } + ], + "source": [ + "from message_ix_models.util import private_data_path\n", + "from message_data.model.material.data_aluminum import gen_mock_demand_aluminum\n", + "\n", + "import importlib\n", + "importlib.reload(refactor_mat_demand_calc)\n", + "\n", + "pop = scen.par(\"bound_activity_up\", {\"technology\": \"Population\"})\n", + "pop = pop.loc[pop.year_act >= 2020].rename(\n", + " columns={\"year_act\": \"year\", \"value\": \"pop.mil\", \"node_loc\": \"region\"}\n", + ")\n", + "\n", + "# import pdb; pdb.set_trace()\n", + "\n", + "pop = pop[[\"region\", \"year\", \"pop.mil\"]]\n", + "\n", + "base_demand = gen_mock_demand_aluminum(scen)\n", + "base_demand = base_demand.loc[base_demand.year == 2020].rename(\n", + " columns={\"value\": \"demand.tot.base\", \"node\": \"region\"}\n", + ")\n", + "\n", + "refactor_mat_demand_calc.derive_steel_demand(pop, base_demand, str(private_data_path(\"material\")))" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-24T09:23:43.822801700Z", + "start_time": "2023-11-24T09:23:43.236417500Z" + } + }, + "id": "5ebc89de5e68e22" + }, + { + "cell_type": "code", + "execution_count": 24, + "outputs": [ + { + "data": { + "text/plain": " Model Scenario Region Variable \\\n0 ENGAGE_SSP2_v4.1.5 baseline AFR GDP|PPP \n1 ENGAGE_SSP2_v4.1.5 baseline RCPA GDP|PPP \n2 ENGAGE_SSP2_v4.1.5 baseline EEU GDP|PPP \n3 ENGAGE_SSP2_v4.1.5 baseline FSU GDP|PPP \n4 ENGAGE_SSP2_v4.1.5 baseline LAM GDP|PPP \n5 ENGAGE_SSP2_v4.1.5 baseline MEA GDP|PPP \n6 ENGAGE_SSP2_v4.1.5 baseline NAM GDP|PPP \n7 ENGAGE_SSP2_v4.1.5 baseline PAO GDP|PPP \n8 ENGAGE_SSP2_v4.1.5 baseline PAS GDP|PPP \n9 ENGAGE_SSP2_v4.1.5 baseline SAS GDP|PPP \n10 ENGAGE_SSP2_v4.1.5 baseline WEU GDP|PPP \n11 - baseline CHN GDP|PPP \n\n Unit 2010 2015 \\\n0 billion US$2010/yr OR local currency 1794.2820 2402.228030 \n1 billion US$2010/yr OR local currency 1079.8701 2150.548072 \n2 billion US$2010/yr OR local currency 2076.9590 2618.822609 \n3 billion US$2010/yr OR local currency 3162.3830 4356.569587 \n4 billion US$2010/yr OR local currency 6463.3140 8526.634978 \n5 billion US$2010/yr OR local currency 4119.7470 5191.844904 \n6 billion US$2010/yr OR local currency 15903.0700 16473.205062 \n7 billion US$2010/yr OR local currency 5323.9890 5498.664307 \n8 billion US$2010/yr OR local currency 5112.7630 6593.728910 \n9 billion US$2010/yr OR local currency 4943.5830 7639.789695 \n10 billion US$2010/yr OR local currency 15126.8600 15885.840026 \n11 billion US$2010/yr OR local currency 9718.8309 19354.932650 \n\n 2020 2025 2030 ... 2040 2045 \\\n0 3084.364057 4244.401513 5306.159830 ... 8819.133966 11918.121112 \n1 2373.445674 3270.479945 3893.061971 ... 5073.624444 5537.560712 \n2 2730.706601 3143.074779 3501.253203 ... 4256.420498 4597.068272 \n3 4731.600636 5781.870531 6651.451416 ... 8506.190346 9251.644256 \n4 9420.268593 11361.550043 12958.770551 ... 16842.884509 19144.971803 \n5 6351.518847 8052.544729 9560.925873 ... 13412.104505 15685.103462 \n6 20642.913911 23092.253472 25377.748594 ... 29535.387817 31456.430747 \n7 6170.836262 6626.157558 7058.364196 ... 7782.799460 8172.236645 \n8 8074.694821 10087.804348 11864.037191 ... 15989.457685 18306.797895 \n9 9198.303861 13133.949690 15990.168190 ... 25016.096851 31679.807625 \n10 18046.985482 19720.652041 21315.555794 ... 25135.170593 27351.221411 \n11 21361.011063 29434.319503 35037.557742 ... 45662.620000 49838.046404 \n\n 2050 2055 2060 2070 2080 \\\n0 14602.417875 19755.749042 23807.218699 37616.622442 56865.524017 \n1 5964.216044 6245.074034 6503.801283 6880.935088 7096.848967 \n2 4910.855123 5246.396492 5553.862302 6242.035364 6847.727435 \n3 9950.008692 10722.125627 11446.507830 13192.598513 14735.329180 \n4 21230.787541 23785.112717 26129.992341 31454.646621 37181.975468 \n5 17790.488063 20402.696892 22849.032178 28824.680775 35379.951669 \n6 33290.073322 35187.978277 36999.868551 40983.108844 44752.779080 \n7 8539.725624 8980.617553 9391.395793 10315.595017 11207.031058 \n8 20390.026395 22741.413604 24982.135093 29828.339187 34700.948700 \n9 36496.175849 44208.145112 50386.897810 66407.736884 83868.117540 \n10 29432.165059 31909.731358 34205.015495 39523.950514 44994.729214 \n11 53677.944399 56205.666302 58534.211546 61928.415791 63871.640704 \n\n 2090 2100 2110 \n0 81978.281459 112862.365070 155381.562316 \n1 7207.835099 7292.012794 7377.173569 \n2 7413.134880 7998.343398 8629.749513 \n3 16214.272854 17702.925869 19328.254011 \n4 43231.355239 49620.005864 56952.759595 \n5 42596.412518 50429.418189 59702.826330 \n6 48349.422545 51744.756419 55378.527310 \n7 12090.062676 12969.968251 13913.912686 \n8 39529.321059 44257.183183 49550.516195 \n9 102061.793719 120825.538934 143038.940693 \n10 50555.126628 56171.213456 62411.182238 \n11 64870.515894 65628.115143 66394.562119 \n\n[12 rows x 21 columns]", + "text/html": "
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ModelScenarioRegionVariableUnit20102015202020252030...2040204520502055206020702080209021002110
0ENGAGE_SSP2_v4.1.5baselineAFRGDP|PPPbillion US$2010/yr OR local currency1794.28202402.2280303084.3640574244.4015135306.159830...8819.13396611918.12111214602.41787519755.74904223807.21869937616.62244256865.52401781978.281459112862.365070155381.562316
1ENGAGE_SSP2_v4.1.5baselineRCPAGDP|PPPbillion US$2010/yr OR local currency1079.87012150.5480722373.4456743270.4799453893.061971...5073.6244445537.5607125964.2160446245.0740346503.8012836880.9350887096.8489677207.8350997292.0127947377.173569
2ENGAGE_SSP2_v4.1.5baselineEEUGDP|PPPbillion US$2010/yr OR local currency2076.95902618.8226092730.7066013143.0747793501.253203...4256.4204984597.0682724910.8551235246.3964925553.8623026242.0353646847.7274357413.1348807998.3433988629.749513
3ENGAGE_SSP2_v4.1.5baselineFSUGDP|PPPbillion US$2010/yr OR local currency3162.38304356.5695874731.6006365781.8705316651.451416...8506.1903469251.6442569950.00869210722.12562711446.50783013192.59851314735.32918016214.27285417702.92586919328.254011
4ENGAGE_SSP2_v4.1.5baselineLAMGDP|PPPbillion US$2010/yr OR local currency6463.31408526.6349789420.26859311361.55004312958.770551...16842.88450919144.97180321230.78754123785.11271726129.99234131454.64662137181.97546843231.35523949620.00586456952.759595
5ENGAGE_SSP2_v4.1.5baselineMEAGDP|PPPbillion US$2010/yr OR local currency4119.74705191.8449046351.5188478052.5447299560.925873...13412.10450515685.10346217790.48806320402.69689222849.03217828824.68077535379.95166942596.41251850429.41818959702.826330
6ENGAGE_SSP2_v4.1.5baselineNAMGDP|PPPbillion US$2010/yr OR local currency15903.070016473.20506220642.91391123092.25347225377.748594...29535.38781731456.43074733290.07332235187.97827736999.86855140983.10884444752.77908048349.42254551744.75641955378.527310
7ENGAGE_SSP2_v4.1.5baselinePAOGDP|PPPbillion US$2010/yr OR local currency5323.98905498.6643076170.8362626626.1575587058.364196...7782.7994608172.2366458539.7256248980.6175539391.39579310315.59501711207.03105812090.06267612969.96825113913.912686
8ENGAGE_SSP2_v4.1.5baselinePASGDP|PPPbillion US$2010/yr OR local currency5112.76306593.7289108074.69482110087.80434811864.037191...15989.45768518306.79789520390.02639522741.41360424982.13509329828.33918734700.94870039529.32105944257.18318349550.516195
9ENGAGE_SSP2_v4.1.5baselineSASGDP|PPPbillion US$2010/yr OR local currency4943.58307639.7896959198.30386113133.94969015990.168190...25016.09685131679.80762536496.17584944208.14511250386.89781066407.73688483868.117540102061.793719120825.538934143038.940693
10ENGAGE_SSP2_v4.1.5baselineWEUGDP|PPPbillion US$2010/yr OR local currency15126.860015885.84002618046.98548219720.65204121315.555794...25135.17059327351.22141129432.16505931909.73135834205.01549539523.95051444994.72921450555.12662856171.21345662411.182238
11-baselineCHNGDP|PPPbillion US$2010/yr OR local currency9718.830919354.93265021361.01106329434.31950335037.557742...45662.62000049838.04640453677.94439956205.66630258534.21154661928.41579163871.64070464870.51589465628.11514366394.562119
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12 rows × 21 columns

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" + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from message_ix_models.util import (\n", + " broadcast,\n", + " make_io,\n", + " make_matched_dfs,\n", + " same_node,\n", + " add_par_data,\n", + " private_data_path,\n", + ")\n", + "import pandas as pd\n", + "df_gdp = pd.read_excel(\n", + " private_data_path(\"material\", \"methanol\", \"methanol demand.xlsx\"),\n", + " sheet_name=\"GDP_baseline\",\n", + " ) \n", + "df = df_gdp[(~df_gdp[\"Region\"].isna()) & (df_gdp[\"Region\"] != \"World\")]\n", + "df = df.dropna(axis=1)\n", + "df" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-23T14:34:29.280833300Z", + "start_time": "2023-11-23T14:34:29.164902900Z" + } + }, + "id": "77768b1f5532e68e" + }, + { + "cell_type": "code", + "execution_count": 17, + "outputs": [ + { + "data": { + "text/plain": "year_act 1990 1995 2000 2005 2010 \\\nnode_loc \nR12_AFR 271.455524 290.007532 345.060976 442.017934 581.052726 \nR12_CHN 838.663234 1455.044821 2160.256814 3396.324652 5703.315566 \nR12_EEU 890.917087 802.783706 944.330209 1164.365537 1356.751983 \nR12_FSU 1749.056217 1035.815476 1116.838624 1549.642196 1887.986772 \nR12_LAM 1777.475911 2104.621622 2448.478261 2794.058167 3428.029377 \nR12_MEA 923.149621 1139.373737 1518.708333 1872.857955 2347.592803 \nR12_NAM 8424.876209 9511.891683 11766.032882 13260.831721 13881.779497 \nR12_PAO 3232.875109 3491.610480 3743.429694 4056.182533 4197.181659 \nR12_PAS 984.333333 1414.102258 1631.705179 2061.975432 3085.776892 \nR12_RCPA 41.795276 65.186554 90.467595 130.345245 182.959419 \nR12_SAS 420.882861 537.195129 700.336621 968.940197 1375.864982 \nR12_WEU 8056.231293 8760.541667 10122.277211 11096.446429 11614.874150 \n\nyear_act 2015 2020 2025 2030 \\\nnode_loc \nR12_AFR 716.813461 998.989240 1368.018625 1856.875242 \nR12_CHN 6612.838095 12597.249546 18796.728814 26265.311778 \nR12_EEU 1429.632941 1782.528479 2135.375473 2527.594422 \nR12_FSU 2070.092516 2824.383598 3612.532110 4540.650530 \nR12_LAM 3752.743938 4997.596357 6313.315558 7870.347914 \nR12_MEA 2728.016143 3620.985480 4689.533422 6001.189152 \nR12_NAM 15164.541706 18030.122824 20731.982116 23411.377937 \nR12_PAO 4444.424839 4865.437555 5328.230735 5790.865455 \nR12_PAS 3667.170716 4867.546481 6274.462718 7885.997801 \nR12_RCPA 191.311034 354.019988 525.926102 759.780600 \nR12_SAS 1722.991014 2559.911529 3815.393732 5561.916763 \nR12_WEU 12353.633028 13849.192177 15375.603814 17016.715296 \n\nyear_act 2035 2040 2045 2050 \\\nnode_loc \nR12_AFR 2479.366774 3314.603412 4517.417565 6162.000935 \nR12_CHN 31779.168946 37291.085088 42057.027778 46300.743062 \nR12_EEU 2908.785626 3307.539451 3673.647838 4045.608182 \nR12_FSU 5400.750295 6288.079443 7034.991188 7731.857143 \nR12_LAM 9439.545455 11247.304734 13141.528193 15271.185657 \nR12_MEA 7468.361988 9180.558757 10973.157936 12956.405973 \nR12_NAM 25908.973396 28432.682303 30839.028757 33195.516022 \nR12_PAO 6207.936141 6620.625825 7084.795192 7566.369971 \nR12_PAS 9667.563245 11716.739693 13845.204708 16171.420315 \nR12_RCPA 976.608206 1238.055602 1519.934896 1830.640107 \nR12_SAS 7857.039482 11068.494608 14866.729189 19848.371084 \nR12_WEU 18838.770758 20906.461751 23218.843794 25700.197292 \n\nyear_act 2055 2060 2070 2080 \\\nnode_loc \nR12_AFR 8577.989840 11963.585420 22747.571333 39895.853924 \nR12_CHN 49633.467401 52750.003770 58298.326044 63001.761304 \nR12_EEU 4427.270005 4850.211186 5775.938713 6672.119355 \nR12_FSU 8453.431718 9324.554152 11366.946565 13374.462312 \nR12_LAM 17543.266146 20056.958333 25680.038879 32385.211186 \nR12_MEA 15113.972648 17571.820273 23368.410602 30275.401896 \nR12_NAM 35609.614390 38212.132723 43260.486550 47544.976471 \nR12_PAO 8111.292818 8719.640947 10185.261488 11671.063437 \nR12_PAS 18335.382760 20622.422972 25656.708175 31310.156530 \nR12_RCPA 2157.717172 2515.990575 3360.284294 4304.254469 \nR12_SAS 25211.747491 31827.305808 46668.655253 63617.247899 \nR12_WEU 28475.555666 31585.048057 38793.798913 45819.247178 \n\nyear_act 2090 2100 2110 \nnode_loc \nR12_AFR 64039.060554 96804.093067 96804.093067 \nR12_CHN 67276.308380 69794.088167 69794.088167 \nR12_EEU 7552.177728 8315.598163 8315.598163 \nR12_FSU 15560.537726 17847.927374 18904.017751 \nR12_LAM 40462.277720 49322.424009 49322.424009 \nR12_MEA 38534.015030 48464.477103 48464.477103 \nR12_NAM 51359.255294 54956.687059 54956.687059 \nR12_PAO 12839.161176 13774.255294 13774.255294 \nR12_PAS 37491.349057 43620.050164 43620.050164 \nR12_RCPA 5373.515084 6453.469838 6453.469838 \nR12_SAS 82725.072711 104536.872483 104536.872483 \nR12_WEU 52976.574913 59671.758824 59671.758824 ", + "text/html": "
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year_act19901995200020052010201520202025203020352040204520502055206020702080209021002110
node_loc
R12_AFR271.455524290.007532345.060976442.017934581.052726716.813461998.9892401368.0186251856.8752422479.3667743314.6034124517.4175656162.0009358577.98984011963.58542022747.57133339895.85392464039.06055496804.09306796804.093067
R12_CHN838.6632341455.0448212160.2568143396.3246525703.3155666612.83809512597.24954618796.72881426265.31177831779.16894637291.08508842057.02777846300.74306249633.46740152750.00377058298.32604463001.76130467276.30838069794.08816769794.088167
R12_EEU890.917087802.783706944.3302091164.3655371356.7519831429.6329411782.5284792135.3754732527.5944222908.7856263307.5394513673.6478384045.6081824427.2700054850.2111865775.9387136672.1193557552.1777288315.5981638315.598163
R12_FSU1749.0562171035.8154761116.8386241549.6421961887.9867722070.0925162824.3835983612.5321104540.6505305400.7502956288.0794437034.9911887731.8571438453.4317189324.55415211366.94656513374.46231215560.53772617847.92737418904.017751
R12_LAM1777.4759112104.6216222448.4782612794.0581673428.0293773752.7439384997.5963576313.3155587870.3479149439.54545511247.30473413141.52819315271.18565717543.26614620056.95833325680.03887932385.21118640462.27772049322.42400949322.424009
R12_MEA923.1496211139.3737371518.7083331872.8579552347.5928032728.0161433620.9854804689.5334226001.1891527468.3619889180.55875710973.15793612956.40597315113.97264817571.82027323368.41060230275.40189638534.01503048464.47710348464.477103
R12_NAM8424.8762099511.89168311766.03288213260.83172113881.77949715164.54170618030.12282420731.98211623411.37793725908.97339628432.68230330839.02875733195.51602235609.61439038212.13272343260.48655047544.97647151359.25529454956.68705954956.687059
R12_PAO3232.8751093491.6104803743.4296944056.1825334197.1816594444.4248394865.4375555328.2307355790.8654556207.9361416620.6258257084.7951927566.3699718111.2928188719.64094710185.26148811671.06343712839.16117613774.25529413774.255294
R12_PAS984.3333331414.1022581631.7051792061.9754323085.7768923667.1707164867.5464816274.4627187885.9978019667.56324511716.73969313845.20470816171.42031518335.38276020622.42297225656.70817531310.15653037491.34905743620.05016443620.050164
R12_RCPA41.79527665.18655490.467595130.345245182.959419191.311034354.019988525.926102759.780600976.6082061238.0556021519.9348961830.6401072157.7171722515.9905753360.2842944304.2544695373.5150846453.4698386453.469838
R12_SAS420.882861537.195129700.336621968.9401971375.8649821722.9910142559.9115293815.3937325561.9167637857.03948211068.49460814866.72918919848.37108425211.74749131827.30580846668.65525363617.24789982725.072711104536.872483104536.872483
R12_WEU8056.2312938760.54166710122.27721111096.44642911614.87415012353.63302813849.19217715375.60381417016.71529618838.77075820906.46175123218.84379425700.19729228475.55566631585.04805738793.79891345819.24717852976.57491359671.75882459671.758824
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" + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_gdp.pivot(columns=\"year_act\", values=\"value\", index=\"node_loc\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-23T14:30:40.784322100Z", + "start_time": "2023-11-23T14:30:40.746572900Z" + } + }, + "id": "6c4ab6e19490cdd0" + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [ + { + "data": { + "text/plain": "" + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from message_data.model.material import data_methanol_new\n", + "import importlib\n", + "importlib.reload(data_methanol_new)" + ], + "metadata": { + "collapsed": false + }, + "id": "17e354a124d8a5e9" + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [], + "source": [ + "meth_dict = data_methanol_new.gen_data_methanol_new(scen)" + ], + "metadata": { + "collapsed": false + }, + "id": "9c54b44117ba6e67" + }, + { + "cell_type": "code", + "execution_count": 9, + "outputs": [], + "source": [ + "setlist = scen.set_list()" + ], + "metadata": { + "collapsed": false + }, + "id": "dfaacb0ebce0d997" + }, + { + "cell_type": "code", + "execution_count": 21, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "node :\n", + "\n", + "level :\n", + "final_material\n", + "\n", + "year :\n", + "time :\n", + "\n", + "value :\n", + "\n", + "unit :\n", + "\n", + "node_loc :\n", + "\n", + "year_vtg :\n", + "\n", + "value :\n", + "\n", + "unit :\n", + "\n", + "node_loc :\n", + "\n", + "year_vtg :\n", + "\n", + "year_act :\n", + "\n", + "value :\n", + "\n", + "unit :\n", + "\n", + "node_loc :\n", + "\n", + "year_vtg :\n", + "\n", + "year_act :\n", + "\n", + "mode :\n", + "fuel\n", + "feedstock\n", + "ethanol\n", + "\n", + "time :\n", + "\n", + "value :\n", + "\n", + "unit :\n", + "\n", + "node_loc :\n", + "\n", + "year_vtg :\n", + "\n", + "year_act :\n", + "\n", + "mode :\n", + "fuel\n", + "feedstock\n", + "ethanol\n", + "\n", + "node_origin :\n", + "\n", + "level :\n", + "primary_material\n", + "import_fs\n", + "export_fs\n", + "secondary_material\n", + "final_material\n", + "useful_petro\n", + "useful_resins\n", + "\n", + "time :\n", + "\n", + "time_origin :\n", + "\n", + "value :\n", + "\n", + "unit :\n", + "\n", + "node_loc :\n", + "\n", + "year_vtg :\n", + "\n", + "year_act :\n", + "\n", + "mode :\n", + "fuel\n", + "feedstock\n", + "ethanol\n", + "\n", + "node_dest :\n", + "\n", + "level :\n", + "primary_material\n", + "secondary_material\n", + "import_fs\n", + "export_fs\n", + "final_material\n", + "\n", + "time :\n", + "\n", + "time_dest :\n", + "\n", + "value :\n", + "\n", + "unit :\n", + "\n", + "node_loc :\n", + "\n", + "year_vtg :\n", + "\n", + "value :\n", + "\n", + "unit :\n", + "\n", + "node_loc :\n", + "\n", + "year_vtg :\n", + "\n", + "value :\n", + "\n", + "unit :\n", + "\n", + "node_loc :\n", + "\n", + "year_vtg :\n", + "\n", + "year_act :\n", + "\n", + "time :\n", + "\n", + "value :\n", + "\n", + "unit :\n", + "\n", + "relation :\n", + "CO2_PtX_trans_disp_split\n", + "gas_util\n", + "SO2_red_synf\n", + "global_GSO2\n", + "global_GCO2\n", + "meth_exp_tot\n", + "OilPrices\n", + "PE_export_total\n", + "PE_import_total\n", + "PE_synliquids\n", + "PE_total_direct\n", + "PE_total_engineering\n", + "FE_transport\n", + "SO2_red_ref\n", + "trp_energy\n", + "total_TCO2\n", + "nuc_lc_in_el\n", + "FE_liquids\n", + "FE_final_energy\n", + "\n", + "node_rel :\n", + "\n", + "year_rel :\n", + "\n", + "node_loc :\n", + "\n", + "year_act :\n", + "\n", + "mode :\n", + "fuel\n", + "feedstock\n", + "naphtha\n", + "atm_gasoil\n", + "vacuum_gasoil\n", + "ethane\n", + "propane\n", + "ethanol\n", + "\n", + "value :\n", + "\n", + "unit :\n", + "\n", + "relation :\n", + "meth_exp_tot\n", + "\n", + "node_rel :\n", + "\n", + "year_rel :\n", + "\n", + "value :\n", + "\n", + "unit :\n", + "\n", + "relation :\n", + "CO2_PtX_trans_disp_split\n", + "meth_exp_tot\n", + "\n", + "node_rel :\n", + "\n", + "year_rel :\n", + "\n", + "value :\n", + "\n", + "unit :\n", + "\n", + "node_loc :\n", + "\n", + "year_vtg :\n", + "\n", + "year_act :\n", + "\n", + "mode :\n", + "feedstock\n", + "fuel\n", + "ethanol\n", + "\n", + "value :\n", + "\n", + "unit :\n", + "\n", + "node :\n", + "\n", + "year_vtg :\n", + "\n", + "year_act :\n", + "\n", + "mode :\n", + "feedstock\n", + "fuel\n", + "\n", + "time :\n", + "\n", + "type_addon :\n", + "methanol_synthesis_addon\n", + "\n", + "value :\n", + "\n", + "unit :\n", + "\n", + "node :\n", + "\n", + "year_act :\n", + "\n", + "mode :\n", + "feedstock\n", + "fuel\n", + "\n", + "time :\n", + "\n", + "type_addon :\n", + "methanol_synthesis_addon\n", + "\n", + "value :\n", + "\n", + "unit :\n", + "\n", + "node_loc :\n", + "\n", + "year_act :\n", + "\n", + "mode :\n", + "feedstock\n", + "fuel\n", + "\n", + "time :\n", + "\n", + "value :\n", + "\n", + "unit :\n", + "\n", + "node_loc :\n", + "\n", + "year_vtg :\n", + "\n", + "value :\n", + "\n", + "unit :\n", + "\n", + "node_loc :\n", + "\n", + "year_act :\n", + "\n", + "time :\n", + "\n", + "value :\n", + "\n", + "unit :\n", + "\n", + "node_loc :\n", + "\n", + "year_act :\n", + "\n", + "time :\n", + "\n", + "value :\n", + "\n", + "unit :\n", + "\n", + "node_loc :\n", + "\n", + "year_act :\n", + "\n", + "time :\n", + "\n", + "value :\n", + "\n", + "unit :\n", + "\n", + "node_loc :\n", + "\n", + "year_act :\n", + "\n", + "time :\n", + "\n", + "value :\n", + "\n", + "unit :\n", + "\n", + "node_loc :\n", + "\n", + "year_act :\n", + "\n", + "mode :\n", + "feedstock\n", + "fuel\n", + "\n", + "time :\n", + "\n", + "value :\n", + "\n", + "unit :\n", + "\n", + "node_loc :\n", + "\n", + "year_act :\n", + "\n", + "mode :\n", + "feedstock\n", + "fuel\n", + "\n", + "time :\n", + "\n", + "value :\n", + "\n", + "unit :\n", + "\n", + "node_loc :\n", + "\n", + "year_vtg :\n", + "\n", + "value :\n", + "\n", + "unit :\n", + "\n", + "node_loc :\n", + "\n", + "year_vtg :\n", + "\n", + "value :\n", + "\n", + "unit :\n", + "\n", + "node_loc :\n", + "\n", + "year_act :\n", + "\n", + "value :\n", + "\n", + "unit :\n", + "\n", + "node_loc :\n", + "\n", + "year_act :\n", + "\n", + "time :\n", + "\n", + "value :\n", + "\n", + "unit :\n", + "\n", + "node_loc :\n", + "\n", + "year_act :\n", + "\n", + "time :\n", + "\n", + "value :\n", + "\n", + "unit :\n", + "\n", + "node_loc :\n", + "\n", + "year_act :\n", + "\n", + "time :\n", + "\n", + "value :\n", + "\n", + "unit :\n", + "\n", + "node_loc :\n", + "\n", + "year_act :\n", + "\n", + "time :\n", + "\n", + "value :\n", + "\n", + "unit :\n", + "\n", + "node_loc :\n", + "\n", + "year_act :\n", + "\n", + "time :\n", + "\n", + "value :\n", + "\n", + "unit :\n", + "\n", + "node_loc :\n", + "\n", + "year_act :\n", + "\n", + "time :\n", + "\n", + "value :\n", + "\n", + "unit :\n", + "\n", + "node_loc :\n", + "\n", + "year_act :\n", + "\n", + "mode :\n", + "feedstock\n", + "fuel\n", + "\n", + "time :\n", + "\n", + "value :\n", + "\n", + "unit :\n", + "\n", + "node_loc :\n", + "\n", + "year_vtg :\n", + "\n", + "value :\n", + "\n", + "unit :\n" + ] + } + ], + "source": [ + "for par, df in meth_dict.items():\n", + " for col in df.columns:\n", + " df_set = list(df[col].unique())\n", + " if col in [\"technology\", \"commodity\", \"emission\"]:\n", + " continue\n", + " print(col,\":\")\n", + " if col in setlist:\n", + " for _set in df_set:\n", + " if _set not in list(scen.set(col)):\n", + " print(_set)\n", + " print()" + ], + "metadata": { + "collapsed": false + }, + "id": "57d247938684e749" + }, + { + "cell_type": "code", + "execution_count": 25, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "relation :\n" + ] + } + ], + "source": [ + "relations = []\n", + "for par, df in meth_dict.items():\n", + " if par != \"relation_activity\":\n", + " continue\n", + " for col in df.columns:\n", + " df_set = list(df[col].unique())\n", + " if col not in [\"relation\"]:\n", + " continue\n", + " print(col,\":\")\n", + " if col in setlist:\n", + " for _set in df_set:\n", + " if _set not in list(scen.set(col)):\n", + " relations.append(_set)\n", + " print()" + ], + "metadata": { + "collapsed": false + }, + "id": "618e1291b9c47679" + }, + { + "cell_type": "code", + "execution_count": 33, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'common': {'commodity': {'require': ['coal', 'gas', 'electr', 'ethanol', 'methanol', 'fueloil', 'lightoil', 'hydrogen', 'lh2', 'd_heat'], 'add': ['biomass', 'water', 'fresh_water']}, 'level': {'require': ['primary', 'secondary', 'useful', 'final', 'water_supply', 'export', 'import']}, 'type_tec': {'add': ['industry']}, 'mode': {'add': ['M1', 'M2']}, 'emission': {'add': ['CO2', 'CH4', 'N2O', 'NOx', 'SO2', 'PM2p5', 'CF4']}, 'year': {'add': [1980, 1990, 2000, 2010]}, 'type_year': {'add': [2010]}}, 'generic': {'commodity': {'add': ['ht_heat', 'lt_heat']}, 'level': {'add': ['useful_steel', 'useful_cement', 'useful_aluminum', 'useful_refining', 'useful_petro', 'useful_resins']}, 'technology': {'add': ['furnace_foil_steel', 'furnace_loil_steel', 'furnace_biomass_steel', 'furnace_ethanol_steel', 'furnace_methanol_steel', 'furnace_gas_steel', 'furnace_coal_steel', 'furnace_elec_steel', 'furnace_h2_steel', 'hp_gas_steel', 'hp_elec_steel', 'fc_h2_steel', 'solar_steel', 'dheat_steel', 'furnace_foil_cement', 'furnace_loil_cement', 'furnace_biomass_cement', 'furnace_ethanol_cement', 'furnace_methanol_cement', 'furnace_gas_cement', 'furnace_coal_cement', 'furnace_elec_cement', 'furnace_h2_cement', 'hp_gas_cement', 'hp_elec_cement', 'fc_h2_cement', 'solar_cement', 'dheat_cement', 'furnace_coal_aluminum', 'furnace_foil_aluminum', 'furnace_loil_aluminum', 'furnace_ethanol_aluminum', 'furnace_biomass_aluminum', 'furnace_methanol_aluminum', 'furnace_gas_aluminum', 'furnace_elec_aluminum', 'furnace_h2_aluminum', 'hp_gas_aluminum', 'hp_elec_aluminum', 'fc_h2_aluminum', 'solar_aluminum', 'dheat_aluminum', 'furnace_coke_petro', 'furnace_coal_petro', 'furnace_foil_petro', 'furnace_loil_petro', 'furnace_ethanol_petro', 'furnace_biomass_petro', 'furnace_methanol_petro', 'furnace_gas_petro', 'furnace_elec_petro', 'furnace_h2_petro', 'hp_gas_petro', 'hp_elec_petro', 'fc_h2_petro', 'solar_petro', 'dheat_petro', 'furnace_coke_refining', 'furnace_coal_refining', 'furnace_foil_refining', 'furnace_loil_refining', 'furnace_ethanol_refining', 'furnace_biomass_refining', 'furnace_methanol_refining', 'furnace_gas_refining', 'furnace_elec_refining', 'furnace_h2_refining', 'hp_gas_refining', 'hp_elec_refining', 'fc_h2_refining', 'solar_refining', 'dheat_refining', 'furnace_coal_resins', 'furnace_foil_resins', 'furnace_loil_resins', 'furnace_ethanol_resins', 'furnace_biomass_resins', 'furnace_methanol_resins', 'furnace_gas_resins', 'furnace_elec_resins', 'furnace_h2_resins', 'hp_gas_resins', 'hp_elec_resins', 'fc_h2_resins', 'solar_resins', 'dheat_resins']}, 'mode': {'add': ['low_temp', 'high_temp']}}, 'petro_chemicals': {'commodity': {'require': ['crudeoil'], 'add': ['HVC', 'naphtha', 'kerosene', 'diesel', 'atm_residue', 'refinery_gas', 'atm_gasoil', 'atm_residue', 'vacuum_gasoil', 'vacuum_residue', 'gasoline', 'heavy_foil', 'light_foil', 'propylene', 'pet_coke', 'ethane', 'propane', 'ethylene', 'BTX']}, 'level': {'require': ['secondary', 'final'], 'add': ['pre_intermediate', 'desulfurized', 'intermediate', 'secondary_material', 'final_material', 'demand']}, 'balance_equality': {'add': [['lightoil', 'import'], ['lightoil', 'export'], ['fueloil', 'import'], ['fueloil', 'export']]}, 'mode': {'add': ['atm_gasoil', 'vacuum_gasoil', 'naphtha', 'kerosene', 'diesel', 'cracking_gasoline', 'cracking_loil', 'ethane', 'propane', 'light_foil', 'refinery_gas', 'refinery_gas_int', 'heavy_foil', 'pet_coke', 'atm_residue', 'vacuum_residue', 'gasoline', 'ethylene', 'propylene', 'BTX']}, 'technology': {'add': ['atm_distillation_ref', 'vacuum_distillation_ref', 'hydrotreating_ref', 'catalytic_cracking_ref', 'visbreaker_ref', 'coking_ref', 'catalytic_reforming_ref', 'hydro_cracking_ref', 'steam_cracker_petro', 'ethanol_to_ethylene_petro', 'agg_ref', 'gas_processing_petro', 'trade_petro', 'import_petro', 'export_petro', 'feedstock_t/d', 'production_HVC'], 'remove': ['ref_hil', 'ref_lol']}, 'shares': {'add': ['steam_cracker']}}, 'steel': {'commodity': {'add': ['steel', 'pig_iron', 'sponge_iron', 'sinter_iron', 'pellet_iron', 'ore_iron', 'limestone_iron', 'coke_iron', 'slag_iron', 'co_gas', 'bf_gas']}, 'level': {'add': ['new_scrap', 'old_scrap_1', 'old_scrap_2', 'old_scrap_3', 'primary_material', 'secondary_material', 'tertiary_material', 'final_material', 'useful_material', 'waste_material', 'product', 'demand', 'dummy_emission', 'end_of_life', 'dummy_end_of_life']}, 'technology': {'add': ['cokeoven_steel', 'sinter_steel', 'pellet_steel', 'bf_steel', 'dri_steel', 'sr_steel', 'bof_steel', 'eaf_steel', 'prep_secondary_steel_1', 'prep_secondary_steel_2', 'prep_secondary_steel_3', 'finishing_steel', 'manuf_steel', 'scrap_recovery_steel', 'DUMMY_ore_supply', 'DUMMY_limestone_supply_steel', 'DUMMY_coal_supply', 'DUMMY_gas_supply', 'trade_steel', 'import_steel', 'export_steel', 'other_EOL_steel', 'total_EOL_steel']}, 'relation': {'add': ['minimum_recycling_steel', 'max_global_recycling_steel', 'max_regional_recycling_steel']}, 'balance_equality': {'add': [['steel', 'old_scrap_1'], ['steel', 'old_scrap_2'], ['steel', 'old_scrap_3'], ['steel', 'end_of_life'], ['steel', 'product'], ['steel', 'new_scrap'], ['steel', 'useful_material'], ['steel', 'final_material']]}}, 'cement': {'commodity': {'add': ['cement', 'clinker_cement', 'raw_meal_cement', 'limestone_cement']}, 'level': {'add': ['primary_material', 'secondary_material', 'tertiary_material', 'final_material', 'useful_material', 'demand', 'dummy_end_of_life', 'end_of_life']}, 'technology': {'add': ['raw_meal_prep_cement', 'clinker_dry_cement', 'clinker_wet_cement', 'clinker_dry_ccs_cement', 'clinker_wet_ccs_cement', 'grinding_ballmill_cement', 'grinding_vertmill_cement', 'DUMMY_limestone_supply_cement', 'total_EOL_cement', 'other_EOL_cement', 'scrap_recovery_cement'], 'remove': ['cement_co2scr', 'cement_CO2']}, 'relation': {'remove': ['cement_pro', 'cement_scrub_lim']}, 'balance_equality': {'add': [['cement', 'end_of_life']]}, 'addon': {'add': ['clinker_dry_ccs_cement', 'clinker_wet_ccs_cement']}, 'type_addon': {'add': ['ccs_cement']}, 'map_tec_addon': {'add': [['clinker_dry_cement', 'ccs_cement'], ['clinker_wet_cement', 'ccs_cement']]}, 'cat_addon': {'add': [['ccs_cement', 'clinker_dry_ccs_cement'], ['ccs_cement', 'clinker_wet_ccs_cement']]}}, 'aluminum': {'commodity': {'add': ['aluminum']}, 'level': {'add': ['new_scrap', 'old_scrap_1', 'old_scrap_2', 'old_scrap_3', 'useful_aluminum', 'final_material', 'useful_material', 'product', 'secondary_material', 'demand', 'end_of_life', 'dummy_end_of_life_1', 'dummy_end_of_life_2', 'dummy_end_of_life_3']}, 'technology': {'add': ['soderberg_aluminum', 'prebake_aluminum', 'secondary_aluminum', 'prep_secondary_aluminum_1', 'prep_secondary_aluminum_2', 'prep_secondary_aluminum_3', 'finishing_aluminum', 'manuf_aluminum', 'scrap_recovery_aluminum_1', 'scrap_recovery_aluminum_2', 'scrap_recovery_aluminum_3', 'DUMMY_alumina_supply', 'trade_aluminum', 'import_aluminum', 'export_aluminum', 'other_EOL_aluminum', 'total_EOL_aluminum']}, 'relation': {'add': ['minimum_recycling_aluminum', 'maximum_recycling_aluminum']}, 'balance_equality': {'add': [['aluminum', 'old_scrap_1'], ['aluminum', 'old_scrap_2'], ['aluminum', 'old_scrap_3'], ['aluminum', 'end_of_life'], ['aluminum', 'product'], ['aluminum', 'new_scrap']]}}, 'fertilizer': {'commodity': {'require': ['electr', 'freshwater_supply'], 'add': ['NH3', 'Fertilizer Use|Nitrogen', 'wastewater']}, 'level': {'add': ['secondary_material', 'final_material', 'wastewater']}, 'technology': {'require': ['h2_bio_ccs'], 'add': ['biomass_NH3', 'electr_NH3', 'gas_NH3', 'coal_NH3', 'fueloil_NH3', 'NH3_to_N_fertil', 'trade_NFert', 'export_NFert', 'import_NFert', 'trade_NH3', 'export_NH3', 'import_NH3', 'residual_NH3', 'biomass_NH3_ccs', 'gas_NH3_ccs', 'coal_NH3_ccs', 'fueloil_NH3_ccs']}, 'relation': {'add': ['NH3_trd_cap', 'NFert_trd_cap']}}, 'methanol': {'commodity': {'require': ['electr', 'biomass', 'hydrogen', 'd_heat'], 'add': ['methanol', 'ht_heat', 'formaldehyde', 'ethylene', 'propylene', 'fcoh_resin']}, 'level': {'add': ['final_material', 'secondary_material', 'primary_material', 'export_fs', 'import_fs']}, 'mode': {'add': ['feedstock', 'fuel', 'ethanol']}, 'technology': {'add': ['meth_bio', 'meth_bio_ccs', 'meth_h2', 'meth_t_d_material', 'MTO_petro', 'CH2O_synth', 'CH2O_to_resin', 'meth_coal', 'meth_coal_ccs', 'meth_ng', 'meth_ng_ccs', 'meth_t_d', 'meth_bal', 'meth_trd', 'meth_exp', 'meth_imp'], 'remove': ['sp_meth_I', 'meth_rc', 'meth_ic_trp', 'meth_fc_trp', 'meth_i', 'meth_coal', 'meth_coal_ccs', 'meth_ng', 'meth_ng_ccs', 'meth_t_d', 'meth_bal', 'meth_trd', 'meth_exp', 'meth_imp']}, 'addon': {'add': ['meth_h2']}, 'type_addon': {'add': ['methanol_synthesis_addon']}, 'map_tec_addon': {'add': [['h2_elec', 'methanol_synthesis_addon']]}, 'cat_addon': {'add': [['methanol_synthesis_addon', 'meth_h2']]}, 'relation': {'add': ['CO2_PtX_trans_disp_split', 'meth_exp_tot']}, 'balance_equality': {'add': [['methanol', 'export'], ['methanol', 'export_fs'], ['methanol', 'import'], ['methanol', 'import_fs'], ['methanol', 'final']]}}, 'buildings': {'level': {'add': ['service']}, 'commodity': {'add': ['floor_area']}, 'technology': {'add': ['buildings']}}, 'power_sector': {'unit': {'add': ['t/kW']}}}\n" + ] + }, + { + "data": { + "text/plain": "{'steel': ['minimum_recycling_steel',\n 'max_global_recycling_steel',\n 'max_regional_recycling_steel'],\n 'aluminum': ['minimum_recycling_aluminum', 'maximum_recycling_aluminum'],\n 'fertilizer': ['NH3_trd_cap', 'NFert_trd_cap'],\n 'methanol': ['CO2_PtX_trans_disp_split', 'meth_exp_tot']}" + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import yaml\n", + "\n", + "file_path = \"C:/Users\\maczek\\PycharmProjects\\message_data\\data\\material\\set.yaml\"\n", + "try:\n", + " with open(file_path, 'r') as file:\n", + " yaml_data = yaml.safe_load(file)\n", + " #print(yaml_data)\n", + "except FileNotFoundError:\n", + " print(\"File not found.\")\n", + "except yaml.YAMLError as e:\n", + " print(\"Error reading YAML:\", e)\n", + "for i in yaml_data.values():\n", + " if \"add\" in i.keys():\n", + " i[\"add\"]\n", + "tec_list = {}\n", + "for k, v in list(yaml_data.items()):\n", + " if \"relation\" in v.keys():\n", + " if \"add\" in v[\"relation\"].keys():\n", + " tec_list[k] = v[\"relation\"][\"add\"]\n", + "tec_list" + ], + "metadata": { + "collapsed": false + }, + "id": "63202ecc1092200d" + }, + { + "cell_type": "code", + "execution_count": 34, + "outputs": [ + { + "data": { + "text/plain": "['minimum_recycling_steel',\n 'max_global_recycling_steel',\n 'max_regional_recycling_steel',\n 'minimum_recycling_aluminum',\n 'maximum_recycling_aluminum',\n 'NH3_trd_cap',\n 'NFert_trd_cap',\n 'CO2_PtX_trans_disp_split',\n 'meth_exp_tot']" + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tec_list_flat = [item for sublist in tec_list.values() for item in sublist]\n", + "tec_list_flat" + ], + "metadata": { + "collapsed": false + }, + "id": "ccc0003e590dd93b" + }, + { + "cell_type": "code", + "execution_count": 36, + "outputs": [], + "source": [ + "rels_depr = [i for i in relations if i not in tec_list_flat]" + ], + "metadata": { + "collapsed": false + }, + "id": "184529ea8b44a9d5" + }, + { + "cell_type": "code", + "execution_count": 37, + "outputs": [], + "source": [ + "import yaml\n", + "file_path = 'missing_rels.yaml'\n", + "\n", + "# Dump the list to the YAML file\n", + "with open(file_path, 'w') as file:\n", + " yaml.dump(rels_depr, file, default_flow_style=False)" + ], + "metadata": { + "collapsed": false + }, + "id": "88612c44b58baa7b" + }, + { + "cell_type": "code", + "execution_count": 40, + "outputs": [], + "source": [ + "file.close()" + ], + "metadata": { + "collapsed": false + }, + "id": "361019e1f3b9621f" + }, + { + "cell_type": "code", + "execution_count": 41, + "outputs": [ + { + "data": { + "text/plain": "['gas_util',\n 'SO2_red_synf',\n 'global_GSO2',\n 'global_GCO2',\n 'OilPrices',\n 'PE_export_total',\n 'PE_import_total',\n 'PE_synliquids',\n 'PE_total_direct',\n 'PE_total_engineering',\n 'FE_transport',\n 'SO2_red_ref',\n 'trp_energy',\n 'total_TCO2',\n 'nuc_lc_in_el',\n 'FE_liquids',\n 'FE_final_energy']" + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "file_path = \"C:/Users\\maczek\\PycharmProjects\\message_data\\message_data\\model\\material\\petrochemical model fixes notebooks\\missing_rels.yaml\"\n", + "\n", + "with open(file_path, 'r') as file:\n", + " yaml_data = yaml.safe_load(file)\n", + " \n", + "yaml_data" + ], + "metadata": { + "collapsed": false + }, + "id": "28ceabe429b7e7bf" + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [], + "source": [ + "df_scens = mp.scenario_list(model=\"MESSAGEix-Materials\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-27T11:02:02.883815800Z", + "start_time": "2023-11-27T11:02:02.715065500Z" + } + }, + "id": "e2da838c5fdf1877" + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [ + { + "data": { + "text/plain": " model scenario scheme \\\n221 MESSAGEix-Materials SSP_supply_cost_test_LED MESSAGE \n225 MESSAGEix-Materials SSP_supply_cost_test_SSP4 MESSAGE \n224 MESSAGEix-Materials SSP_supply_cost_test_SSP3 MESSAGE \n222 MESSAGEix-Materials SSP_supply_cost_test_SSP1 MESSAGE \n226 MESSAGEix-Materials SSP_supply_cost_test_SSP5 MESSAGE \n.. ... ... ... \n32 MESSAGEix-Materials NoPolicy MESSAGE \n81 MESSAGEix-Materials NoPolicy_all_with_power_sect_bldg MESSAGE \n80 MESSAGEix-Materials NoPolicy_all_with_power_sect MESSAGE \n220 MESSAGEix-Materials NoPolicypower MESSAGE \n178 MESSAGEix-Materials NoPolicy_power MESSAGE \n\n is_default is_locked cre_user cre_date upd_user \\\n221 1 0 maczek 2023-11-27 01:43:09.000000 None \n225 1 0 maczek 2023-11-27 01:24:36.000000 None \n224 1 0 maczek 2023-11-27 01:17:11.000000 maczek \n222 1 0 maczek 2023-11-27 00:59:57.000000 maczek \n226 1 0 maczek 2023-11-27 00:54:14.000000 None \n.. ... ... ... ... ... \n32 1 0 unlu 2021-04-26 18:00:28.000000 maczek \n81 1 0 min 2021-03-26 17:18:25.000000 min \n80 1 0 min 2021-03-26 14:47:46.000000 None \n220 1 0 min 2021-03-26 11:23:39.000000 None \n178 1 1 min 2021-03-16 17:22:08.000000 None \n\n upd_date lock_user lock_date \\\n221 None None None \n225 None None None \n224 2023-11-27 01:24:20.000000 None None \n222 2023-11-27 01:07:41.000000 None None \n226 None None None \n.. ... ... ... \n32 2022-10-26 18:48:00.000000 None None \n81 2021-03-26 18:38:22.000000 None None \n80 None None None \n220 None None None \n178 None min 2021-03-16 17:23:14.000000 \n\n annotation version \n221 clone Scenario from 'MESSAGEix-Materials|SSP_s... 1 \n225 clone Scenario from 'MESSAGEix-Materials|SSP_s... 1 \n224 clone Scenario from 'MESSAGEix-Materials|SSP_s... 1 \n222 clone Scenario from 'MESSAGEix-Materials|SSP_s... 1 \n226 clone Scenario from 'MESSAGEix-Materials|SSP_s... 1 \n.. ... ... \n32 clone Scenario from 'ENGAGE_SSP2_v4.1.5|baseli... 88 \n81 clone Scenario from 'ENGAGE_SSP2_v4.1.4|baseli... 1 \n80 clone Scenario from 'ENGAGE_SSP2_v4.1.4|baseli... 2 \n220 clone Scenario from 'ENGAGE_SSP2_v4.1.4|baseli... 25 \n178 clone Scenario from 'ENGAGE_SSP2_v4.1.4|baseli... 1 \n\n[375 rows x 13 columns]", + "text/html": "
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modelscenarioschemeis_defaultis_lockedcre_usercre_dateupd_userupd_datelock_userlock_dateannotationversion
221MESSAGEix-MaterialsSSP_supply_cost_test_LEDMESSAGE10maczek2023-11-27 01:43:09.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-Materials|SSP_s...1
225MESSAGEix-MaterialsSSP_supply_cost_test_SSP4MESSAGE10maczek2023-11-27 01:24:36.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-Materials|SSP_s...1
224MESSAGEix-MaterialsSSP_supply_cost_test_SSP3MESSAGE10maczek2023-11-27 01:17:11.000000maczek2023-11-27 01:24:20.000000NoneNoneclone Scenario from 'MESSAGEix-Materials|SSP_s...1
222MESSAGEix-MaterialsSSP_supply_cost_test_SSP1MESSAGE10maczek2023-11-27 00:59:57.000000maczek2023-11-27 01:07:41.000000NoneNoneclone Scenario from 'MESSAGEix-Materials|SSP_s...1
226MESSAGEix-MaterialsSSP_supply_cost_test_SSP5MESSAGE10maczek2023-11-27 00:54:14.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-Materials|SSP_s...1
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32MESSAGEix-MaterialsNoPolicyMESSAGE10unlu2021-04-26 18:00:28.000000maczek2022-10-26 18:48:00.000000NoneNoneclone Scenario from 'ENGAGE_SSP2_v4.1.5|baseli...88
81MESSAGEix-MaterialsNoPolicy_all_with_power_sect_bldgMESSAGE10min2021-03-26 17:18:25.000000min2021-03-26 18:38:22.000000NoneNoneclone Scenario from 'ENGAGE_SSP2_v4.1.4|baseli...1
80MESSAGEix-MaterialsNoPolicy_all_with_power_sectMESSAGE10min2021-03-26 14:47:46.000000NoneNoneNoneNoneclone Scenario from 'ENGAGE_SSP2_v4.1.4|baseli...2
220MESSAGEix-MaterialsNoPolicypowerMESSAGE10min2021-03-26 11:23:39.000000NoneNoneNoneNoneclone Scenario from 'ENGAGE_SSP2_v4.1.4|baseli...25
178MESSAGEix-MaterialsNoPolicy_powerMESSAGE11min2021-03-16 17:22:08.000000NoneNonemin2021-03-16 17:23:14.000000clone Scenario from 'ENGAGE_SSP2_v4.1.4|baseli...1
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375 rows × 13 columns

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" + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_scens.sort_values(\"cre_date\", ascending=False)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-27T11:02:02.930659700Z", + "start_time": "2023-11-27T11:02:02.883815800Z" + } + }, + "id": "db66efc69c0773ab" + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12|baseline_DEFAULT', version: 21" + ], + "metadata": { + "collapsed": false + }, + "id": "d779b88ddf1f6f20" + }, + { + "cell_type": "code", + "execution_count": 138, + "outputs": [ + { + "data": { + "text/plain": " model scenario scheme \\\n0 MESSAGEix-GLOBIOM 1.1-R12 2degrees MESSAGE \n1 MESSAGEix-GLOBIOM 1.1-R12 MACRO_test MESSAGE \n2 MESSAGEix-GLOBIOM 1.1-R12 base_GLOBIOM_noSDG MESSAGE \n3 MESSAGEix-GLOBIOM 1.1-R12 base_GLOBIOM_noSDG_noConst MESSAGE \n4 MESSAGEix-GLOBIOM 1.1-R12 base_GLOBIOM_update MESSAGE \n5 MESSAGEix-GLOBIOM 1.1-R12 baseline_DEFAULT MESSAGE \n6 MESSAGEix-GLOBIOM 1.1-R12 baseline_DEFAULT_2507 MESSAGE \n7 MESSAGEix-GLOBIOM 1.1-R12 baseline_DEFAULT_clone MESSAGE \n8 MESSAGEix-GLOBIOM 1.1-R12 baseline_DEFAULT_gdp_update MESSAGE \n9 MESSAGEix-GLOBIOM 1.1-R12 baseline_DEFULT_check MESSAGE \n10 MESSAGEix-GLOBIOM 1.1-R12 baseline_prep_lu MESSAGE \n11 MESSAGEix-GLOBIOM 1.1-R12 baseline_prep_macro MESSAGE \n12 MESSAGEix-GLOBIOM 1.1-R12 baseline_prep_macro_macro MESSAGE \n13 MESSAGEix-GLOBIOM 1.1-R12 test to confirm MACRO converges MESSAGE \n\n is_default is_locked cre_user cre_date upd_user \\\n0 1 0 unlu 2023-10-09 11:41:56.000000 unlu \n1 1 0 min 2022-05-02 16:57:38.000000 None \n2 1 0 min 2023-08-01 10:58:46.000000 None \n3 1 0 min 2023-08-11 15:17:05.000000 None \n4 1 0 min 2023-04-18 14:25:06.000000 None \n5 1 0 min 2023-07-27 10:24:37.000000 None \n6 1 0 unlu 2023-07-25 17:28:24.000000 None \n7 1 0 unlu 2023-02-01 09:55:44.000000 None \n8 1 0 min 2022-03-03 00:24:43.000000 min \n9 1 0 unlu 2023-07-31 14:17:24.000000 unlu \n10 1 0 min 2022-04-30 17:29:17.000000 min \n11 1 0 min 2022-03-02 14:21:00.000000 None \n12 1 0 min 2022-04-30 20:29:07.000000 min \n13 1 0 min 2022-04-30 20:34:54.000000 min \n\n upd_date lock_user lock_date \\\n0 2023-10-09 12:06:23.000000 None None \n1 None None None \n2 None None None \n3 None None None \n4 None None None \n5 None None None \n6 None None None \n7 None None None \n8 2022-03-03 00:33:38.000000 None None \n9 2023-07-31 14:26:45.000000 None None \n10 2022-04-30 18:18:13.000000 None None \n11 None None None \n12 2022-04-30 20:34:39.000000 None None \n13 2022-04-30 20:40:13.000000 None None \n\n annotation version \n0 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n1 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n2 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 6 \n3 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n4 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 3 \n5 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 21 \n6 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n7 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n8 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 25 \n9 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n10 clone Scenario from 'MESSAGEix-GLOBIOM_R12_CHN... 17 \n11 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n12 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 17 \n13 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 6 ", + "text/html": "
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modelscenarioschemeis_defaultis_lockedcre_usercre_dateupd_userupd_datelock_userlock_dateannotationversion
0MESSAGEix-GLOBIOM 1.1-R122degreesMESSAGE10unlu2023-10-09 11:41:56.000000unlu2023-10-09 12:06:23.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
1MESSAGEix-GLOBIOM 1.1-R12MACRO_testMESSAGE10min2022-05-02 16:57:38.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
2MESSAGEix-GLOBIOM 1.1-R12base_GLOBIOM_noSDGMESSAGE10min2023-08-01 10:58:46.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...6
3MESSAGEix-GLOBIOM 1.1-R12base_GLOBIOM_noSDG_noConstMESSAGE10min2023-08-11 15:17:05.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
4MESSAGEix-GLOBIOM 1.1-R12base_GLOBIOM_updateMESSAGE10min2023-04-18 14:25:06.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...3
5MESSAGEix-GLOBIOM 1.1-R12baseline_DEFAULTMESSAGE10min2023-07-27 10:24:37.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...21
6MESSAGEix-GLOBIOM 1.1-R12baseline_DEFAULT_2507MESSAGE10unlu2023-07-25 17:28:24.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
7MESSAGEix-GLOBIOM 1.1-R12baseline_DEFAULT_cloneMESSAGE10unlu2023-02-01 09:55:44.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
8MESSAGEix-GLOBIOM 1.1-R12baseline_DEFAULT_gdp_updateMESSAGE10min2022-03-03 00:24:43.000000min2022-03-03 00:33:38.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...25
9MESSAGEix-GLOBIOM 1.1-R12baseline_DEFULT_checkMESSAGE10unlu2023-07-31 14:17:24.000000unlu2023-07-31 14:26:45.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
10MESSAGEix-GLOBIOM 1.1-R12baseline_prep_luMESSAGE10min2022-04-30 17:29:17.000000min2022-04-30 18:18:13.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM_R12_CHN...17
11MESSAGEix-GLOBIOM 1.1-R12baseline_prep_macroMESSAGE10min2022-03-02 14:21:00.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
12MESSAGEix-GLOBIOM 1.1-R12baseline_prep_macro_macroMESSAGE10min2022-04-30 20:29:07.000000min2022-04-30 20:34:39.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...17
13MESSAGEix-GLOBIOM 1.1-R12test to confirm MACRO convergesMESSAGE10min2022-04-30 20:34:54.000000min2022-04-30 20:40:13.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...6
\n
" + }, + "execution_count": 138, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#'MESSAGEix-GLOBIOM 1.1-R12|baseline_DEFAULT'\n", + "mp.scenario_list(model=\"MESSAGEix-GLOBIOM 1.1-R12\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-26T23:09:05.055299100Z", + "start_time": "2023-11-26T23:09:04.964181400Z" + } + }, + "id": "a331aff3c9bd8ecf" + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false + }, + "id": "de251c1ec9184d76" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/message_ix_models/model/material/output_file.csv b/message_ix_models/model/material/output_file.csv new file mode 100644 index 0000000000..a25accae8e --- /dev/null +++ b/message_ix_models/model/material/output_file.csv @@ -0,0 +1,1065 @@ +"","reg_no","region","year","consumption","pop","gdp.pcap","cons.pcap","del.t" +"1",1,"Canada (1)","1970",11085,21.71685,17450.8056114366,510.433142928187,-40 +"2",1,"Canada (1)","1971",11753,22.04843,18025.7,533.053827415376,-39 +"3",1,"Canada (1)","1972",12842,22.3441,18755.83,574.737850260248,-38 +"4",1,"Canada (1)","1973",14153,22.61467,19822,625.832700631935,-37 +"5",1,"Canada (1)","1974",15458,22.87666,20318.25,675.710527673183,-36 +"6",1,"Canada (1)","1975",13178,23.14227,20451.19,569.434199842971,-35 +"7",1,"Canada (1)","1976",12570,23.41609,21262.92,536.810372696723,-34 +"8",1,"Canada (1)","1977",12828,23.6952,21739.13,541.375468449306,-33 +"9",1,"Canada (1)","1978",13524,23.97564,22334.27,564.072533621626,-32 +"10",1,"Canada (1)","1979",15525,24.25064,22921.16,640.18928984967,-31 +"11",1,"Canada (1)","1980",13306,24.51628,23163.14,542.741394697727,-30 +"12",1,"Canada (1)","1981",14118,24.76985,23729.14,569.967117281695,-29 +"13",1,"Canada (1)","1982",9418,25.01605,22823.87,376.478300930802,-28 +"14",1,"Canada (1)","1983",10771,25.26695,23211.37,426.288095713966,-27 +"15",1,"Canada (1)","1984",13089,25.53886,24299.49,512.513087898207,-26 +"16",1,"Canada (1)","1985",13183,25.84259,25161.78,510.126887436592,-25 +"17",1,"Canada (1)","1986",12080,26.18316,25435.69,461.365243920138,-24 +"18",1,"Canada (1)","1987",13013,26.55471,26146.49,490.044892224393,-23 +"19",1,"Canada (1)","1988",15091,26.94366,27050.92,560.094656776399,-22 +"20",1,"Canada (1)","1989",13904,27.33053,27366.56,508.735103197779,-21 +"21",1,"Canada (1)","1990",11222,27.70085,27052.81,405.113922496963,-20 +"22",1,"Canada (1)","1991",10688,28.05119,26156.04,381.017703705262,-19 +"23",1,"Canada (1)","1992",11160,28.38487,26074.78,393.167204922904,-18 +"24",1,"Canada (1)","1993",12714,28.7027,26389.06,442.954843969383,-17 +"25",1,"Canada (1)","1994",15324,29.00752,27366.22,528.276805462859,-16 +"26",1,"Canada (1)","1995",15047,29.30209,27851.94,513.512858639094,-15 +"27",1,"Canada (1)","1996",14555,29.58475,28032.4,491.976440564818,-14 +"28",1,"Canada (1)","1997",17722,29.85598,28951.6,593.582927105391,-13 +"29",1,"Canada (1)","1998",18780,30.12362,29870.1,623.431048459647,-12 +"30",1,"Canada (1)","1999",18488,30.39813,31237.79,608.195306750777,-11 +"31",1,"Canada (1)","2000",19800,30.68682,32563.29,645.228146807,-10 +"32",1,"Canada (1)","2001",16900,30.99318,32816.54,545.281252198064,-9 +"33",1,"Canada (1)","2002",17700,31.31491,33429.25,565.225957858413,-8 +"34",1,"Canada (1)","2003",17300,31.64643,33701.3,546.665137268248,-7 +"35",1,"Canada (1)","2004",19314,31.97922,34374.57,603.954693078818,-6 +"36",1,"Canada (1)","2005",18014,32.30709,35069.09,557.586585483248,-5 +"37",1,"Canada (1)","2006",20132,32.62844,35681.66,617.007739260596,-4 +"38",1,"Canada (1)","2007",18248,32.9453,36292.62,553.887807972609,-3 +"39",1,"Canada (1)","2008",16799,33.25827,36102.59,505.107451469965,-2 +"40",1,"Canada (1)","2009",10591,33.57128,34818.05,315.477991902602,-1 +"41",1,"Canada (1)","2010",15656,33.88646,35171.96,462.013441356813,0 +"42",1,"Canada (1)","2011",15747,34.20387,35899.38,460.386500124109,1 +"43",1,"Canada (1)","2012",17305,34.52255,36724.76,501.266563449108,2 +"44",2,"USA (2)","1970",127304,210.1166,20233.0173504408,605.873119972434,-40 +"45",2,"USA (2)","1971",127663,211.3708,20588.72,603.976518989378,-39 +"46",2,"USA (2)","1972",138410,213.279,21545.43,648.962157549501,-38 +"47",2,"USA (2)","1973",149595,215.2026,22608.04,695.135653565524,-37 +"48",2,"USA (2)","1974",144120,217.1463,22299.5,663.700003177581,-36 +"49",2,"USA (2)","1975",116821,219.1142,22060.54,533.151206083403,-35 +"50",2,"USA (2)","1976",129953,221.1212,23036.41,587.700320005499,-34 +"51",2,"USA (2)","1977",133923,223.1746,23887.94,600.081729730892,-33 +"52",2,"USA (2)","1978",146445,225.2625,24995.59,650.108207091726,-32 +"53",2,"USA (2)","1979",140906,227.3659,25552.23,619.732334532135,-31 +"54",2,"USA (2)","1980",114433,229.4748,25256.33,498.673492688522,-30 +"55",2,"USA (2)","1981",128969,231.5823,25658,556.903528464827,-29 +"56",2,"USA (2)","1982",84290,233.7049,24922.98,360.668518289518,-28 +"57",2,"USA (2)","1983",94484,235.8841,25807.67,400.552644285901,-27 +"58",2,"USA (2)","1984",111714,238.1756,27399.11,469.040489453999,-26 +"59",2,"USA (2)","1985",105593,240.6182,28234.18,438.840453465282,-25 +"60",2,"USA (2)","1986",95286,243.2289,28889.27,391.754433786446,-24 +"61",2,"USA (2)","1987",101468,245.994,29519.71,412.481605242404,-23 +"62",2,"USA (2)","1988",110698,248.8842,30379.85,444.777129283418,-22 +"63",2,"USA (2)","1989",102351,251.8545,31081.96,406.389403405538,-21 +"64",2,"USA (2)","1990",103052,254.8715,31284.72,404.329240421153,-20 +"65",2,"USA (2)","1991",89600,257.9169,30855.28,347.398716408269,-19 +"66",2,"USA (2)","1992",97372,260.9965,31508.4,373.077799893868,-18 +"67",2,"USA (2)","1993",104357,264.1288,31972.56,395.098906291173,-17 +"68",2,"USA (2)","1994",117471,267.343,32871.2,439.401817141275,-16 +"69",2,"USA (2)","1995",112584,270.6544,33292.92,415.969590740073,-15 +"70",2,"USA (2)","1996",119433,274.0731,34108.96,435.770602806332,-14 +"71",2,"USA (2)","1997",123589,277.5733,35210.62,445.248156072648,-13 +"72",2,"USA (2)","1998",134634,281.0898,36237.3,478.971488826702,-12 +"73",2,"USA (2)","1999",125143,284.5352,37405.6,439.815530732226,-11 +"74",2,"USA (2)","2000",132891,287.8484,38340.24,461.670101345014,-10 +"75",2,"USA (2)","2001",114261,291.0017,38212.57,392.647190720879,-9 +"76",2,"USA (2)","2002",118226,294.015,38430.96,402.108735948846,-8 +"77",2,"USA (2)","2003",103881,296.9338,39013.13,349.845655833051,-7 +"78",2,"USA (2)","2004",123835,299.8273,40046.78,413.021095810822,-6 +"79",2,"USA (2)","2005",113325,302.7468,40880.21,374.322701346472,-5 +"80",2,"USA (2)","2006",128526,305.7028,41647.99,420.427945049898,-4 +"81",2,"USA (2)","2007",114050,308.6801,42071.21,369.476360802008,-3 +"82",2,"USA (2)","2008",102428,311.6721,41849.97,328.640260068193,-2 +"83",2,"USA (2)","2009",62393,314.665,40435.57,198.283889215515,-1 +"84",2,"USA (2)","2010",86983,317.6473,41046.07,273.835162458488,0 +"85",2,"USA (2)","2011",96344,320.6194,41798.14,300.493357544802,1 +"86",2,"USA (2)","2012",102012,323.5832,42499.48,315.257405205215,2 +"87",3,"Mexico (3)","1970",4168,50.5959,6910.35116215552,82.3782164167452,-40 +"88",3,"Mexico (3)","1971",4051,53.55186,7131.387,75.6462987466729,-39 +"89",3,"Mexico (3)","1972",4684,55.23661,7482.806,84.7988317892789,-38 +"90",3,"Mexico (3)","1973",5174,56.95376,7827.696,90.8456263467065,-37 +"91",3,"Mexico (3)","1974",6112,58.68935,8035.034,104.141552087389,-36 +"92",3,"Mexico (3)","1975",6211,60.43023,8251.832,102.779684935834,-35 +"93",3,"Mexico (3)","1976",5962,62.17798,8374.157,95.8860355386264,-34 +"94",3,"Mexico (3)","1977",6487,63.9267,8421.252,101.475596268852,-33 +"95",3,"Mexico (3)","1978",8060,65.64747,8935.027,122.777008771244,-32 +"96",3,"Mexico (3)","1979",8840,67.30379,9560.348,131.344757850932,-31 +"97",3,"Mexico (3)","1980",10317,68.87221,10205.26,149.799171538128,-30 +"98",3,"Mexico (3)","1981",11369,70.33874,10869.09,161.632124772209,-29 +"99",3,"Mexico (3)","1982",8181,71.71709,10593.25,114.073228570763,-28 +"100",3,"Mexico (3)","1983",6296,73.04627,9964.059,86.1919438186234,-27 +"101",3,"Mexico (3)","1984",7331,74.38186,10138.41,98.5589766106951,-26 +"102",3,"Mexico (3)","1985",7693,75.76497,10211.46,101.537689515353,-25 +"103",3,"Mexico (3)","1986",6608,77.20679,9644.588,85.5883271406569,-24 +"104",3,"Mexico (3)","1987",6485,78.69822,9637.398,82.4033885391563,-23 +"105",3,"Mexico (3)","1988",6969,80.23514,9570.521,86.8572049603204,-22 +"106",3,"Mexico (3)","1989",7256,81.80685,9780.728,88.6967289414028,-21 +"107",3,"Mexico (3)","1990",8804,83.40398,10079.66,105.558511716108,-20 +"108",3,"Mexico (3)","1991",10392,85.02397,10305.09,122.224356260946,-19 +"109",3,"Mexico (3)","1992",11314,86.66608,10476.68,130.547037549177,-18 +"110",3,"Mexico (3)","1993",10095,88.32349,10480.6,114.295755296807,-17 +"111",3,"Mexico (3)","1994",13223,89.98788,10745.36,146.942010412958,-16 +"112",3,"Mexico (3)","1995",7602,91.64993,9894.47,82.946053532174,-15 +"113",3,"Mexico (3)","1996",10588,93.31045,10217.9,113.47067772152,-14 +"114",3,"Mexico (3)","1997",12473,94.96079,10720.61,131.348949392691,-13 +"115",3,"Mexico (3)","1998",13009,96.57095,11059.1,134.709247449673,-12 +"116",3,"Mexico (3)","1999",11929,98.10286,11308.07,121.596862721433,-11 +"117",3,"Mexico (3)","2000",19800,99.53062,11881.7,198.933755260441,-10 +"118",3,"Mexico (3)","2001",18800,100.8404,11708.96,186.433215258964,-9 +"119",3,"Mexico (3)","2002",19800,102.0425,11666.68,194.036798392827,-8 +"120",3,"Mexico (3)","2003",21000,103.1652,11695.68,203.557013411499,-7 +"121",3,"Mexico (3)","2004",22298,104.2505,12038.26,213.888662404497,-6 +"122",3,"Mexico (3)","2005",22403,105.3296,12296.23,212.694247391047,-5 +"123",3,"Mexico (3)","2006",25476,106.4108,12757.33,239.411789028933,-4 +"124",3,"Mexico (3)","2007",25111,107.4868,13034.13,233.619383961566,-3 +"125",3,"Mexico (3)","2008",25984,108.5555,13084.07,239.361432631235,-2 +"126",3,"Mexico (3)","2009",17098,109.6099,11916.03,155.98955933725,-1 +"127",3,"Mexico (3)","2010",20034,110.6451,12121.01,181.065406421071,0 +"128",3,"Mexico (3)","2011",20869,111.6633,12483.56,186.892201824592,1 +"129",3,"Mexico (3)","2012",24059,112.6668,13052.16,213.541167406902,2 +"130",4,"Rest Central America (4)","1970",434,41.86008,5032.14566151399,10.3678731622109,-40 +"131",4,"Rest Central America (4)","1971",441,44.06901,5135.072,10.0070321525262,-39 +"132",4,"Rest Central America (4)","1972",557,45.03952,5338.452,12.3669168765564,-38 +"133",4,"Rest Central America (4)","1973",728,46.01947,5395.166,15.8193912272349,-37 +"134",4,"Rest Central America (4)","1974",925,47.00089,5357.973,19.6804783909411,-36 +"135",4,"Rest Central America (4)","1975",1086,47.9782,5451.598,22.6352801897528,-35 +"136",4,"Rest Central America (4)","1976",1008,48.94946,5626.064,20.5926684380175,-34 +"137",4,"Rest Central America (4)","1977",1000,49.91717,5897.804,20.033186977547,-33 +"138",4,"Rest Central America (4)","1978",1000,50.88577,6102.653,19.6518594491151,-32 +"139",4,"Rest Central America (4)","1979",1000,51.86187,6092.561,19.2819888677365,-31 +"140",4,"Rest Central America (4)","1980",1726,52.8503,6013.287,32.6582819775858,-30 +"141",4,"Rest Central America (4)","1981",1444,53.8526,6111.062,26.813932846325,-29 +"142",4,"Rest Central America (4)","1982",1347,54.8676,6097.064,24.5500076547908,-28 +"143",4,"Rest Central America (4)","1983",1408,55.8943,6153.557,25.1904040304646,-27 +"144",4,"Rest Central America (4)","1984",1570,56.93069,6230.195,27.577392791129,-26 +"145",4,"Rest Central America (4)","1985",1676,57.97546,6246.894,28.9087831299657,-25 +"146",4,"Rest Central America (4)","1986",1736,59.02685,6258.291,29.4103446143577,-24 +"147",4,"Rest Central America (4)","1987",1654,60.08547,6322.337,27.5274538087161,-23 +"148",4,"Rest Central America (4)","1988",1679,61.15466,6374.894,27.4549805362339,-22 +"149",4,"Rest Central America (4)","1989",1697,62.23912,6447.625,27.2658096708308,-21 +"150",4,"Rest Central America (4)","1990",1673,63.34122,6380.926,26.4125004223158,-20 +"151",4,"Rest Central America (4)","1991",1303,64.46304,6276.783,20.2131329828689,-19 +"152",4,"Rest Central America (4)","1992",1574,65.60065,6233.968,23.9936646969199,-18 +"153",4,"Rest Central America (4)","1993",1432,66.74309,6199.818,21.4554045969403,-17 +"154",4,"Rest Central America (4)","1994",1665,67.8755,6284.081,24.5302060389979,-16 +"155",4,"Rest Central America (4)","1995",1617,68.98741,6387.906,23.4390593877926,-15 +"156",4,"Rest Central America (4)","1996",1822,70.07453,6565.691,26.0008879117705,-14 +"157",4,"Rest Central America (4)","1997",2024,71.13991,6767.845,28.4509777985381,-13 +"158",4,"Rest Central America (4)","1998",1884,72.18943,6963.645,26.0980035442862,-12 +"159",4,"Rest Central America (4)","1999",1963,73.23238,7150.771,26.8050826697152,-11 +"160",4,"Rest Central America (4)","2000",3536,74.27577,7374.437,47.6063728454111,-10 +"161",4,"Rest Central America (4)","2001",3019,75.32159,7391.297,40.0814693370121,-9 +"162",4,"Rest Central America (4)","2002",3016,76.36828,7440.828,39.4928365546533,-8 +"163",4,"Rest Central America (4)","2003",2529,77.41533,7585.463,32.6679483249635,-7 +"164",4,"Rest Central America (4)","2004",2632,78.46117,7714.614,33.5452555703668,-6 +"165",4,"Rest Central America (4)","2005",2708,79.50519,7961.29,34.0606694984315,-5 +"166",4,"Rest Central America (4)","2006",2773,80.54659,8289.141,34.4272799134017,-4 +"167",4,"Rest Central America (4)","2007",2895,81.58723,8549.562,35.4834941693694,-3 +"168",4,"Rest Central America (4)","2008",3297,82.63269,8741.552,39.8994635174046,-2 +"169",4,"Rest Central America (4)","2009",2212,83.6889,8566.153,26.4312232566087,-1 +"170",4,"Rest Central America (4)","2010",2599,84.76005,8589.175,30.6630305196847,0 +"171",4,"Rest Central America (4)","2011",2566,85.848,8815.172,29.8900382070636,1 +"172",4,"Rest Central America (4)","2012",3210,86.94998,9076.81,36.9177773243881,2 +"173",5,"Brazil (5)","1970",6088,95.98846,4246.81244601682,63.424290794956,-40 +"174",5,"Brazil (5)","1971",7386,98.3547,4627.022,75.095547035373,-39 +"175",5,"Brazil (5)","1972",7662,100.7355,5062.171,76.0605744747383,-38 +"176",5,"Brazil (5)","1973",9513,103.1473,5634.889,92.2273292660108,-37 +"177",5,"Brazil (5)","1974",12799,105.607,6001.288,121.194617781018,-36 +"178",5,"Brazil (5)","1975",11241,108.1272,6166.741,103.960890506737,-35 +"179",5,"Brazil (5)","1976",10715,110.7081,6612.648,96.7860526917181,-34 +"180",5,"Brazil (5)","1977",11976,113.3463,6756.245,105.658499659892,-33 +"181",5,"Brazil (5)","1978",11932,116.0439,6812.455,102.823155719516,-32 +"182",5,"Brazil (5)","1979",12760,118.8022,7104.538,107.405418418177,-31 +"183",5,"Brazil (5)","1980",14303,121.6184,7572.326,117.605559685048,-30 +"184",5,"Brazil (5)","1981",12063,124.494,7072.421,96.896235963179,-29 +"185",5,"Brazil (5)","1982",10612,127.4183,6950.202,83.284740104051,-28 +"186",5,"Brazil (5)","1983",8580,130.3607,6561.69,65.8173820791082,-27 +"187",5,"Brazil (5)","1984",10673,133.2812,6756.077,80.0788108150287,-26 +"188",5,"Brazil (5)","1985",11995,136.1494,7139.268,88.1017470514009,-25 +"189",5,"Brazil (5)","1986",14528,138.9564,7553.835,104.550779956879,-24 +"190",5,"Brazil (5)","1987",14980,141.7046,7673.975,105.712870294966,-23 +"191",5,"Brazil (5)","1988",11720,144.3898,7523.531,81.1691684592679,-22 +"192",5,"Brazil (5)","1989",12462,147.0111,7631.713,84.7691092713407,-21 +"193",5,"Brazil (5)","1990",11048,149.5705,7178.574,73.8648329717424,-20 +"194",5,"Brazil (5)","1991",11245,152.0602,7167.804,73.950974679765,-19 +"195",5,"Brazil (5)","1992",10712,154.4847,7022.369,69.3402000327541,-18 +"196",5,"Brazil (5)","1993",12658,156.8735,7238.053,80.6892177455083,-17 +"197",5,"Brazil (5)","1994",13472,159.2665,7509.604,84.5877821136272,-16 +"198",5,"Brazil (5)","1995",14639,161.692,7723.665,90.5363283279321,-15 +"199",5,"Brazil (5)","1996",15793,164.1566,7771.271,96.2069146168963,-14 +"200",5,"Brazil (5)","1997",18730,166.6499,7913.353,112.39130656544,-13 +"201",5,"Brazil (5)","1998",17700,169.1618,7798.799,104.633552019428,-12 +"202",5,"Brazil (5)","1999",16302,171.6753,7703.894,94.958331221789,-11 +"203",5,"Brazil (5)","2000",17500,174.1745,7920.493,100.473949975456,-10 +"204",5,"Brazil (5)","2001",18500,176.6591,7911.419,104.721466372239,-9 +"205",5,"Brazil (5)","2002",17437,179.1234,8009.999,97.346298696876,-8 +"206",5,"Brazil (5)","2003",17283,181.5374,7994.121,95.2035228002604,-7 +"207",5,"Brazil (5)","2004",20351,183.8635,8344.161,110.685372572588,-6 +"208",5,"Brazil (5)","2005",18680,186.0746,8505.417,100.389843643356,-5 +"209",5,"Brazil (5)","2006",20592,188.1584,8745.075,109.43970611995,-4 +"210",5,"Brazil (5)","2007",24556,190.12,9145.536,129.160530191458,-3 +"211",5,"Brazil (5)","2008",26720,191.9714,9517.1,139.18739978976,-2 +"212",5,"Brazil (5)","2009",20640,193.7337,9368.056,106.537995196499,-1 +"213",5,"Brazil (5)","2010",29004,195.4232,9608.739,148.416359981824,0 +"214",5,"Brazil (5)","2011",27813,197.0412,9864.866,141.153220747742,1 +"215",5,"Brazil (5)","2012",27979,198.5855,10132.95,140.891454814173,2 +"216",6,"Rest South America (6)","1970",7530,96.40464,6420.1296626765,78.1082736266636,-40 +"217",6,"Rest South America (6)","1971",8192,97.62685,6490.836,83.9113420129811,-39 +"218",6,"Rest South America (6)","1972",8651,99.84344,6497.796,86.6456524334498,-38 +"219",6,"Rest South America (6)","1973",9260,102.0981,6636.509,90.6970844707198,-37 +"220",6,"Rest South America (6)","1974",11024,104.4029,6804.255,105.590936650227,-36 +"221",6,"Rest South America (6)","1975",10182,106.7655,6732.178,95.3678856934122,-35 +"222",6,"Rest South America (6)","1976",9474,109.1912,6781.431,86.7652338283671,-34 +"223",6,"Rest South America (6)","1977",11054,111.6749,6982.911,98.9837465715215,-33 +"224",6,"Rest South America (6)","1978",10553,114.201,6943.699,92.4072468717437,-32 +"225",6,"Rest South America (6)","1979",11003,116.7471,7185.399,94.2464523744059,-31 +"226",6,"Rest South America (6)","1980",9579,119.2977,7190.426,80.2949260547353,-30 +"227",6,"Rest South America (6)","1981",8534,121.845,7047.316,70.0398046698675,-29 +"228",6,"Rest South America (6)","1982",8777,124.3942,6683.035,70.557952058858,-28 +"229",6,"Rest South America (6)","1983",7322,126.9583,6425.478,57.6724798614978,-27 +"230",6,"Rest South America (6)","1984",8336,129.5569,6485.092,64.3423854692417,-26 +"231",6,"Rest South America (6)","1985",7397,132.2033,6324.582,55.9517046851327,-25 +"232",6,"Rest South America (6)","1986",8537,134.8992,6619.767,63.2842893063858,-24 +"233",6,"Rest South America (6)","1987",10036,137.6364,6768.011,72.9167574856651,-23 +"234",6,"Rest South America (6)","1988",10468,140.4059,6733.679,74.5552715377345,-22 +"235",6,"Rest South America (6)","1989",9049,143.1948,6376.971,63.1936355230777,-21 +"236",6,"Rest South America (6)","1990",7717,145.9918,6390.963,52.8591331841925,-20 +"237",6,"Rest South America (6)","1991",9158,148.7932,6732.243,61.5485116255313,-19 +"238",6,"Rest South America (6)","1992",11131,151.5962,7080.655,73.4253233260464,-18 +"239",6,"Rest South America (6)","1993",11151,154.3925,7195.21,72.2250109299351,-17 +"240",6,"Rest South America (6)","1994",11543,157.1728,7386.602,73.4414606089603,-16 +"241",6,"Rest South America (6)","1995",13276,159.9293,7507.502,83.0116807864475,-15 +"242",6,"Rest South America (6)","1996",13529,162.6594,7628.711,83.1737975180039,-14 +"243",6,"Rest South America (6)","1997",14629,165.3608,7964.193,88.4671578753852,-13 +"244",6,"Rest South America (6)","1998",14820,168.0274,7983.648,88.1999007304761,-12 +"245",6,"Rest South America (6)","1999",11116,170.6525,7600.39,65.1382194811093,-11 +"246",6,"Rest South America (6)","2000",12463,173.2326,7644.868,71.9437334543267,-10 +"247",6,"Rest South America (6)","2001",12119,175.7636,7586.33,68.9505676943349,-9 +"248",6,"Rest South America (6)","2002",10858,178.249,7237.485,60.9147877407447,-8 +"249",6,"Rest South America (6)","2003",12058,180.7024,7342.196,66.7284994554582,-7 +"250",6,"Rest South America (6)","2004",14313,183.1424,7874.344,78.1523011601901,-6 +"251",6,"Rest South America (6)","2005",15034,185.5829,8358.868,81.0096188819121,-5 +"252",6,"Rest South America (6)","2006",18067,188.0279,8862.133,96.086804139173,-4 +"253",6,"Rest South America (6)","2007",19758,190.4744,9389.734,103.730475066466,-3 +"254",6,"Rest South America (6)","2008",19704,192.9207,9785.698,102.135229656538,-2 +"255",6,"Rest South America (6)","2009",15322,195.3631,9543.877,78.4283214179136,-1 +"256",6,"Rest South America (6)","2010",18668,197.798,9644.25,94.3791140456425,0 +"257",6,"Rest South America (6)","2011",21212,200.2255,9827.167,105.940552027589,1 +"258",6,"Rest South America (6)","2012",22053,202.6449,10055.4,108.825832774474,2 +"259",7,"Northern Africa (7)","1970",2572,71.53033,2614.15184141864,35.9567752588308,-40 +"260",7,"Northern Africa (7)","1971",2470,73.61912,2693.479,33.5510666250833,-39 +"261",7,"Northern Africa (7)","1972",2896,75.41985,2964.886,38.3983792065352,-38 +"262",7,"Northern Africa (7)","1973",3199,77.25272,2966.555,41.4095451914185,-37 +"263",7,"Northern Africa (7)","1974",4221,79.14845,3170.684,53.3301662887902,-36 +"264",7,"Northern Africa (7)","1975",4579,81.13089,3279.306,56.439661884641,-35 +"265",7,"Northern Africa (7)","1976",4326,83.20176,3621.714,51.9940924326601,-34 +"266",7,"Northern Africa (7)","1977",4800,85.35855,3794.459,56.2333825961195,-33 +"267",7,"Northern Africa (7)","1978",5086,87.61505,3907.863,58.0493876337456,-32 +"268",7,"Northern Africa (7)","1979",5621,89.98675,4075.367,62.4647517551195,-31 +"269",7,"Northern Africa (7)","1980",6452,92.48152,4095.043,69.7652893248294,-30 +"270",7,"Northern Africa (7)","1981",7006,95.10136,3853.739,73.6687677231955,-29 +"271",7,"Northern Africa (7)","1982",6707,97.83386,3984.952,68.55499721671,-28 +"272",7,"Northern Africa (7)","1983",7065,100.653,3995.509,70.191648535066,-27 +"273",7,"Northern Africa (7)","1984",6668,103.5235,4019.197,64.4104961675368,-26 +"274",7,"Northern Africa (7)","1985",8322,106.4149,4133.836,78.2033343075077,-25 +"275",7,"Northern Africa (7)","1986",10142,109.3194,4022.011,92.7740181523133,-24 +"276",7,"Northern Africa (7)","1987",8271,112.229,3838.14,73.69752915913,-23 +"277",7,"Northern Africa (7)","1988",8953,115.1134,3884.93,77.7754805261594,-22 +"278",7,"Northern Africa (7)","1989",9002,117.9373,3957.57,76.3286932972011,-21 +"279",7,"Northern Africa (7)","1990",8051,120.6754,4004.541,66.7161658465603,-20 +"280",7,"Northern Africa (7)","1991",8367,123.3155,4053.06,67.8503513345849,-19 +"281",7,"Northern Africa (7)","1992",8259,125.8616,4021.332,65.6196965555817,-18 +"282",7,"Northern Africa (7)","1993",8359,128.3269,3965.028,65.1383303111039,-17 +"283",7,"Northern Africa (7)","1994",9684,130.7339,4006.419,74.0741307342625,-16 +"284",7,"Northern Africa (7)","1995",8949,133.1018,3971.964,67.2342522790826,-15 +"285",7,"Northern Africa (7)","1996",8464,135.4332,4127.618,62.4957543645133,-14 +"286",7,"Northern Africa (7)","1997",9022,137.7305,4164.798,65.5047356976124,-13 +"287",7,"Northern Africa (7)","1998",10913,140.0129,4257.673,77.9428181260441,-12 +"288",7,"Northern Africa (7)","1999",9557,142.3036,4331.432,67.1592285788975,-11 +"289",7,"Northern Africa (7)","2000",8706,144.6207,4392.518,60.1988512017989,-10 +"290",7,"Northern Africa (7)","2001",9037,146.9718,4505.486,61.4879861306727,-9 +"291",7,"Northern Africa (7)","2002",9303,149.3582,4580.779,62.2865031849607,-8 +"292",7,"Northern Africa (7)","2003",9926,151.7827,4702.743,65.3961222194624,-7 +"293",7,"Northern Africa (7)","2004",11452,154.2455,4853.247,74.2452778200985,-6 +"294",7,"Northern Africa (7)","2005",14699,156.7456,4994.974,93.7761570340731,-5 +"295",7,"Northern Africa (7)","2006",15271,159.2843,5169.681,95.8726001244316,-4 +"296",7,"Northern Africa (7)","2007",16197,161.8604,5336.363,100.067712670919,-3 +"297",7,"Northern Africa (7)","2008",18892,164.4664,5541.492,114.868447293794,-2 +"298",7,"Northern Africa (7)","2009",23483,167.0921,5643.445,140.539259486235,-1 +"299",7,"Northern Africa (7)","2010",19208,169.7283,5783.958,113.169106153776,0 +"300",7,"Northern Africa (7)","2011",16512,172.3709,5968.287,95.793431489886,1 +"301",7,"Northern Africa (7)","2012",19798,175.0158,6195.265,113.121215341701,2 +"302",8,"Western Africa (8)","1970",650,131.74,1363.06138638973,4.9339608319417,-40 +"303",8,"Western Africa (8)","1971",540,144.355,1385.493,3.74077794326487,-39 +"304",8,"Western Africa (8)","1972",571,148.0385,1391.376,3.85710473964543,-38 +"305",8,"Western Africa (8)","1973",710,151.873,1412.128,4.67495868258347,-37 +"306",8,"Western Africa (8)","1974",884,155.9128,1502.656,5.66983595958767,-36 +"307",8,"Western Africa (8)","1975",1380,160.195,1452.761,8.61450107681264,-35 +"308",8,"Western Africa (8)","1976",1327,164.7402,1515.54,8.05510737512763,-34 +"309",8,"Western Africa (8)","1977",1799,169.534,1525.414,10.6114407729423,-33 +"310",8,"Western Africa (8)","1978",1380,174.5343,1461.469,7.90675529108032,-32 +"311",8,"Western Africa (8)","1979",1025,179.6796,1479.4,5.70459863000586,-31 +"312",8,"Western Africa (8)","1980",1573,184.9263,1452.905,8.50609134557929,-30 +"313",8,"Western Africa (8)","1981",1362,190.2622,1362.24,7.15854226430683,-29 +"314",8,"Western Africa (8)","1982",1462,195.7041,1341.225,7.470461783887,-28 +"315",8,"Western Africa (8)","1983",859,201.2735,1282.053,4.26782462668955,-27 +"316",8,"Western Africa (8)","1984",1085,207.0039,1248.601,5.24144714181713,-26 +"317",8,"Western Africa (8)","1985",728,212.9206,1284.188,3.41911491889465,-25 +"318",8,"Western Africa (8)","1986",622,219.0201,1285.169,2.83992199802666,-24 +"319",8,"Western Africa (8)","1987",831,225.2956,1240.093,3.68848748044791,-23 +"320",8,"Western Africa (8)","1988",568,231.7652,1261,2.45075619635735,-22 +"321",8,"Western Africa (8)","1989",653,238.4503,1274.071,2.73851616039066,-21 +"322",8,"Western Africa (8)","1990",953,245.3616,1265.348,3.88406335791746,-20 +"323",8,"Western Africa (8)","1991",1251,252.5202,1257.347,4.95405912081489,-19 +"324",8,"Western Africa (8)","1992",1568,259.9104,1227.554,6.03284824308685,-18 +"325",8,"Western Africa (8)","1993",1061,267.4611,1194.141,3.96693201366479,-17 +"326",8,"Western Africa (8)","1994",775,275.0754,1166.051,2.81740933576758,-16 +"327",8,"Western Africa (8)","1995",801,282.6889,1174.865,2.8335035440019,-15 +"328",8,"Western Africa (8)","1996",770,290.269,1195.513,2.65271179492126,-14 +"329",8,"Western Africa (8)","1997",1047,297.85,1207.545,3.51519221084438,-13 +"330",8,"Western Africa (8)","1998",1092,305.5169,1215.845,3.57427035951203,-12 +"331",8,"Western Africa (8)","1999",1347,313.3915,1205.784,4.29813827114009,-11 +"332",8,"Western Africa (8)","2000",1518,321.5615,1211.977,4.7207143890049,-10 +"333",8,"Western Africa (8)","2001",2339,330.0531,1229.888,7.08673846723452,-9 +"334",8,"Western Africa (8)","2002",2126,338.8388,1228.412,6.27436999540785,-8 +"335",8,"Western Africa (8)","2003",2364,347.8856,1279.238,6.79533731778493,-7 +"336",8,"Western Africa (8)","2004",2024,357.1391,1350.488,5.6672596195712,-6 +"337",8,"Western Africa (8)","2005",2696,366.5572,1382.886,7.35492305157285,-5 +"338",8,"Western Africa (8)","2006",2226,376.1295,1412.682,5.9181744585309,-4 +"339",8,"Western Africa (8)","2007",2221,385.8629,1453.617,5.75593040947964,-3 +"340",8,"Western Africa (8)","2008",3009,395.5942,1486.149,7.60627936405539,-2 +"341",8,"Western Africa (8)","2009",3200,405.4698,1485.888,7.89207975538499,-1 +"342",8,"Western Africa (8)","2010",2917,415.4669,1512.174,7.02101659602726,0 +"343",8,"Western Africa (8)","2011",3754,425.5756,1563.176,8.82099443671113,1 +"344",8,"Western Africa (8)","2012",3732,435.7884,1610.389,8.56378921513285,2 +"345",9,"Eastern Africa (9)","1980",219,113.8755,828.705,1.92315291700146,-30 +"346",9,"Eastern Africa (9)","1981",159,117.2407,832.1068,1.35618432847979,-29 +"347",9,"Eastern Africa (9)","1982",115,120.7025,831.9492,0.952755742424556,-28 +"348",9,"Eastern Africa (9)","1983",190,124.2837,831.0629,1.5287604086457,-27 +"349",9,"Eastern Africa (9)","1984",200,128.0149,796.799,1.56231813640443,-26 +"350",9,"Eastern Africa (9)","1985",254,131.9122,761.3655,1.92552318890899,-25 +"351",9,"Eastern Africa (9)","1986",173,136.0031,779.3807,1.27202982873185,-24 +"352",9,"Eastern Africa (9)","1987",266,140.2672,820.2136,1.89638062212691,-23 +"353",9,"Eastern Africa (9)","1988",213,144.6171,821.2789,1.47285486985979,-22 +"354",9,"Eastern Africa (9)","1989",204,148.9328,834.4102,1.36974528109322,-21 +"355",9,"Eastern Africa (9)","1990",576,153.1369,821.6528,3.76134034318313,-20 +"356",9,"Eastern Africa (9)","1991",356,157.1817,812.7501,2.26489470466346,-19 +"357",9,"Eastern Africa (9)","1992",294,161.114,803.2378,1.82479486574723,-18 +"358",9,"Eastern Africa (9)","1993",268,165.0652,814.662,1.62360085590421,-17 +"359",9,"Eastern Africa (9)","1994",348,169.2208,798.5344,2.05648478201261,-16 +"360",9,"Eastern Africa (9)","1995",450,173.7107,820.5782,2.59051399827414,-15 +"361",9,"Eastern Africa (9)","1996",386,178.5897,846.8434,2.1613788477163,-14 +"362",9,"Eastern Africa (9)","1997",445,183.8085,865.4262,2.42099794079164,-13 +"363",9,"Eastern Africa (9)","1998",744,189.2655,867.0915,3.93098583735546,-12 +"364",9,"Eastern Africa (9)","1999",522,194.8088,875.5386,2.67955041045374,-11 +"365",9,"Eastern Africa (9)","2000",481,200.3306,890.777,2.40103109559897,-10 +"366",9,"Eastern Africa (9)","2001",645,205.7974,915.4696,3.13415038285226,-9 +"367",9,"Eastern Africa (9)","2002",800,211.2515,913.5626,3.78695535889686,-8 +"368",9,"Eastern Africa (9)","2003",895,216.7518,926.085,4.12914679370598,-7 +"369",9,"Eastern Africa (9)","2004",1216,222.3888,957.9957,5.46790126121459,-6 +"370",9,"Eastern Africa (9)","2005",1129,228.2275,994.8387,4.94681841583508,-5 +"371",9,"Eastern Africa (9)","2006",1182,234.2803,1052.696,5.04523854545175,-4 +"372",9,"Eastern Africa (9)","2007",1180,240.5218,1111.72,4.90600020455526,-3 +"373",9,"Eastern Africa (9)","2008",1131,246.9325,1159.686,4.58019904224839,-2 +"374",9,"Eastern Africa (9)","2009",1416,253.4809,1172.51,5.58621971122874,-1 +"375",9,"Eastern Africa (9)","2010",1314,260.1408,1196.758,5.05111078308362,0 +"376",9,"Eastern Africa (9)","2011",1729,266.9061,1228.543,6.47793362534614,1 +"377",9,"Eastern Africa (9)","2012",1478,273.7767,1264.589,5.39856021348785,2 +"378",10,"South Africa (10)","1970",4783,22.65705,7507.24598655225,211.104269973364,-40 +"379",10,"South Africa (10)","1971",5532,23.10612,7680.814,239.417089498367,-39 +"380",10,"South Africa (10)","1972",4881,23.73564,7600.829,205.640125987755,-38 +"381",10,"South Africa (10)","1973",5636,24.38357,7737.135,231.139246632056,-37 +"382",10,"South Africa (10)","1974",6494,25.03996,7994.749,259.345462213198,-36 +"383",10,"South Africa (10)","1975",7506,25.69802,7922.092,292.084759837528,-35 +"384",10,"South Africa (10)","1976",6066,26.35369,7898.8,230.176495208071,-34 +"385",10,"South Africa (10)","1977",4871,27.00982,7699.677,180.341816420842,-33 +"386",10,"South Africa (10)","1978",5111,27.67476,7741.208,184.680915028712,-32 +"387",10,"South Africa (10)","1979",6300,28.3606,7840.325,222.139164897781,-31 +"388",10,"South Africa (10)","1980",7197,29.07527,8153.932,247.529945551666,-30 +"389",10,"South Africa (10)","1981",7264,29.82413,8375.337,243.561170099513,-29 +"390",10,"South Africa (10)","1982",6146,30.60253,8131.013,200.833068377026,-28 +"391",10,"South Africa (10)","1983",5345,31.39648,7779.051,170.242014391422,-27 +"392",10,"South Africa (10)","1984",6013,32.18631,7975.078,186.818557330741,-26 +"393",10,"South Africa (10)","1985",5267,32.95903,7693.75,159.804460264759,-25 +"394",10,"South Africa (10)","1986",5302,33.70417,7525,157.309911503532,-24 +"395",10,"South Africa (10)","1987",6042,34.42834,7521.477,175.494955609245,-23 +"396",10,"South Africa (10)","1988",6573,35.15575,7675.216,186.96799243367,-22 +"397",10,"South Africa (10)","1989",7644,35.92072,7691.661,212.801970561837,-21 +"398",10,"South Africa (10)","1990",5525,36.74528,7495.161,150.359447526322,-20 +"399",10,"South Africa (10)","1991",5070,37.64043,7242.42,134.695591947276,-19 +"400",10,"South Africa (10)","1992",4146,38.59106,6913.046,107.434208855626,-18 +"401",10,"South Africa (10)","1993",4422,39.56142,6826.668,111.775563162293,-17 +"402",10,"South Africa (10)","1994",4702,40.50132,6883.903,116.094981595662,-16 +"403",10,"South Africa (10)","1995",4879,41.37482,6948.519,117.921963165036,-15 +"404",10,"South Africa (10)","1996",4479,42.16738,7111.549,106.219546957862,-14 +"405",10,"South Africa (10)","1997",4964,42.88969,7176.842,115.738770786173,-13 +"406",10,"South Africa (10)","1998",4481,43.56173,7102.682,102.865519803736,-12 +"407",10,"South Africa (10)","1999",4223,44.215,7162.754,95.5105733348411,-11 +"408",10,"South Africa (10)","2000",4488,44.87189,7351.122,100.01807367597,-10 +"409",10,"South Africa (10)","2001",4661,45.53606,7442.058,102.358438564953,-9 +"410",10,"South Africa (10)","2002",5391,46.19748,7604.567,116.694676852504,-8 +"411",10,"South Africa (10)","2003",4562,46.84852,7732.848,97.3776759650038,-7 +"412",10,"South Africa (10)","2004",5471,47.47708,8001.647,115.234551071801,-6 +"413",10,"South Africa (10)","2005",5175,48.07344,8294.74,107.647798867732,-5 +"414",10,"South Africa (10)","2006",6680,48.63852,8634.635,137.339705237742,-4 +"415",10,"South Africa (10)","2007",6708,49.17316,8976.143,136.415882160105,-3 +"416",10,"South Africa (10)","2008",6779,49.66764,9158.946,136.487258102056,-2 +"417",10,"South Africa (10)","2009",4931,50.10981,8880.243,98.4038853869133,-1 +"418",10,"South Africa (10)","2010",5535,50.49241,9047.664,109.620436021968,0 +"419",10,"South Africa (10)","2011",5884,50.81151,9392.609,115.800534170309,1 +"420",10,"South Africa (10)","2012",5998,51.07314,9748.672,117.439421190865,2 +"421",11,"Western Europe (11)","1970",158709,350.5968,14487.0506357884,452.682397557536,-40 +"422",11,"Western Europe (11)","1971",140220,352.9577,15199.47,397.27140107724,-39 +"423",11,"Western Europe (11)","1972",155769,354.9143,15796.79,438.891867698766,-38 +"424",11,"Western Europe (11)","1973",164677,356.7897,16672.98,461.552001080749,-37 +"425",11,"Western Europe (11)","1974",162340,358.5519,16982.68,452.765694450371,-36 +"426",11,"Western Europe (11)","1975",135061,360.1783,16776.05,374.983723339246,-35 +"427",11,"Western Europe (11)","1976",153791,361.6743,17432.06,425.219596747682,-34 +"428",11,"Western Europe (11)","1977",138762,363.0525,17852.02,382.209184622059,-33 +"429",11,"Western Europe (11)","1978",133784,364.308,18323.95,367.227730381984,-32 +"430",11,"Western Europe (11)","1979",144906,365.4356,18929.63,396.529511629409,-31 +"431",11,"Western Europe (11)","1980",143617,366.4388,19177.92,391.926291648155,-30 +"432",11,"Western Europe (11)","1981",129204,367.3138,19175.8,351.75373209501,-29 +"433",11,"Western Europe (11)","1982",125621,368.0829,19304.97,341.284531283578,-28 +"434",11,"Western Europe (11)","1983",118828,368.8095,19603.88,322.193435906613,-27 +"435",11,"Western Europe (11)","1984",123526,369.5762,20068.35,334.236890795457,-26 +"436",11,"Western Europe (11)","1985",122110,370.4444,20546.55,329.631113333067,-25 +"437",11,"Western Europe (11)","1986",125792,371.4311,21072.92,338.668463680074,-24 +"438",11,"Western Europe (11)","1987",123760,372.5264,21595.54,332.218065618974,-23 +"439",11,"Western Europe (11)","1988",143386,373.729,22415.35,383.663028558126,-22 +"440",11,"Western Europe (11)","1989",147491,375.0283,23141.94,393.279653828791,-21 +"441",11,"Western Europe (11)","1990",143477,376.4099,23740.99,381.172227404221,-20 +"442",11,"Western Europe (11)","1991",134363,377.9008,24072.15,355.550980574796,-19 +"443",11,"Western Europe (11)","1992",131771,379.4931,24256.64,347.228974650659,-18 +"444",11,"Western Europe (11)","1993",114697,381.0957,24107.88,300.966397679113,-17 +"445",11,"Western Europe (11)","1994",133985,382.5862,24704.82,350.208658859102,-16 +"446",11,"Western Europe (11)","1995",150106,383.8869,25250.52,391.016208159226,-15 +"447",11,"Western Europe (11)","1996",118678,384.9419,25646.1,308.30107088888,-14 +"448",11,"Western Europe (11)","1997",147043,385.801,26292.55,381.136907369343,-13 +"449",11,"Western Europe (11)","1998",160390,386.6206,27006.55,414.851148645468,-12 +"450",11,"Western Europe (11)","1999",155188,387.6207,27729.33,400.360455465872,-11 +"451",11,"Western Europe (11)","2000",164860,388.9529,28701.79,423.85594759674,-10 +"452",11,"Western Europe (11)","2001",159700,390.6713,29129.45,408.783547703658,-9 +"453",11,"Western Europe (11)","2002",156401,392.7094,29307.49,398.261411618871,-8 +"454",11,"Western Europe (11)","2003",157194,394.9489,29469.26,398.010983193016,-7 +"455",11,"Western Europe (11)","2004",164963,397.2142,30002.44,415.29985584604,-6 +"456",11,"Western Europe (11)","2005",157008,399.3727,30374.05,393.136536373167,-5 +"457",11,"Western Europe (11)","2006",177442,401.39,31089.81,442.068810882184,-4 +"458",11,"Western Europe (11)","2007",185593,403.2907,31788.91,460.196577803555,-3 +"459",11,"Western Europe (11)","2008",171693,405.0706,31809.39,423.859445736126,-2 +"460",11,"Western Europe (11)","2009",107207,406.7432,30401.88,263.574166697808,-1 +"461",11,"Western Europe (11)","2010",135654,408.3169,30592.05,332.227248002716,0 +"462",11,"Western Europe (11)","2011",142538,409.7802,31045.56,347.840134784453,1 +"463",11,"Western Europe (11)","2012",127518,411.1214,31544.86,310.171156257008,2 +"464",12,"Central Europe (12)","1970",35854,115.354,6190.69877807217,310.817136813635,-40 +"465",12,"Central Europe (12)","1971",38376,116.2557,6603.938,330.099943486642,-39 +"466",12,"Central Europe (12)","1972",40080,117.1717,6871.404,342.062119095311,-38 +"467",12,"Central Europe (12)","1973",43049,118.0999,7164.145,364.51343311891,-37 +"468",12,"Central Europe (12)","1974",46801,119.0415,7718.882,393.148607838443,-36 +"469",12,"Central Europe (12)","1975",49900,119.9958,8040.493,415.847888009414,-35 +"470",12,"Central Europe (12)","1976",49914,120.964,8480.507,412.635164181079,-34 +"471",12,"Central Europe (12)","1977",52359,121.9404,9000.721,429.381894761703,-33 +"472",12,"Central Europe (12)","1978",54631,122.907,9468.698,444.49054976527,-32 +"473",12,"Central Europe (12)","1979",54927,123.8406,9789.172,443.529827859361,-31 +"474",12,"Central Europe (12)","1980",54949,124.7236,9744.632,440.566179937077,-30 +"475",12,"Central Europe (12)","1981",51436,125.5422,9523.311,409.710838267929,-29 +"476",12,"Central Europe (12)","1982",50013,126.2965,9487.436,395.996721999422,-28 +"477",12,"Central Europe (12)","1983",49717,126.9993,9685.796,391.47459867889,-27 +"478",12,"Central Europe (12)","1984",50000,127.6711,9998.942,391.631308886663,-26 +"479",12,"Central Europe (12)","1985",49644,128.3229,10119.9,386.867815487337,-25 +"480",12,"Central Europe (12)","1986",52258,128.9657,10397.69,405.208516683118,-24 +"481",12,"Central Europe (12)","1987",52915,129.5838,10568.02,408.345796310959,-23 +"482",12,"Central Europe (12)","1988",49841,130.1302,10695.18,383.0087097384,-22 +"483",12,"Central Europe (12)","1989",47252,130.5418,10679.2,361.968350367469,-21 +"484",12,"Central Europe (12)","1990",36619,130.7764,9924.173,280.012295796489,-20 +"485",12,"Central Europe (12)","1991",22688,130.8156,8845.062,173.434972587367,-19 +"486",12,"Central Europe (12)","1992",18881,130.682,8251.99,144.480494635834,-18 +"487",12,"Central Europe (12)","1993",18831,130.4263,8068.822,144.380389538,-17 +"488",12,"Central Europe (12)","1994",19766,130.1213,8381.351,151.904415341685,-16 +"489",12,"Central Europe (12)","1995",23288,129.8215,8868.516,179.384770627361,-15 +"490",12,"Central Europe (12)","1996",22516,129.5468,9292.298,173.80591415612,-14 +"491",12,"Central Europe (12)","1997",24339,129.2877,9634.274,188.254567139798,-13 +"492",12,"Central Europe (12)","1998",24117,129.0358,9914.649,186.901619550543,-12 +"493",12,"Central Europe (12)","1999",21799,128.772,10121.81,169.283695213245,-11 +"494",12,"Central Europe (12)","2000",24043,128.4843,10555.81,187.127921465891,-10 +"495",12,"Central Europe (12)","2001",24462,128.1723,10920.03,190.852469683387,-9 +"496",12,"Central Europe (12)","2002",25809,127.8486,11302.74,201.871588738555,-8 +"497",12,"Central Europe (12)","2003",27243,127.5286,11814.58,213.622669738396,-7 +"498",12,"Central Europe (12)","2004",31184,127.2323,12516.89,245.094995531795,-6 +"499",12,"Central Europe (12)","2005",31202,126.9734,13142.54,245.736508591563,-5 +"500",12,"Central Europe (12)","2006",37644,126.7586,14000.54,296.973933129586,-4 +"501",12,"Central Europe (12)","2007",42199,126.582,14878.34,333.372833420234,-3 +"502",12,"Central Europe (12)","2008",39935,126.4339,15532.45,315.856744116886,-2 +"503",12,"Central Europe (12)","2009",25396,126.2898,14880.61,201.093041559968,-1 +"504",12,"Central Europe (12)","2010",30871,126.1351,15071.57,244.745514928041,0 +"505",12,"Central Europe (12)","2011",33688,125.965,15582.96,267.439368078434,1 +"506",12,"Central Europe (12)","2012",31519,125.7821,16274.66,250.584145120808,2 +"507",13,"Turkey (13)","1970",1781,36.20744,4664.74278930178,49.1887855092765,-40 +"508",13,"Turkey (13)","1971",1976,37.16068,4822.903,53.1744844281644,-39 +"509",13,"Turkey (13)","1972",2131,38.15393,5046.164,55.852699839833,-38 +"510",13,"Turkey (13)","1973",2232,39.17263,5075.279,56.9785587539055,-37 +"511",13,"Turkey (13)","1974",3478,40.19642,5222.716,86.525118406067,-36 +"512",13,"Turkey (13)","1975",3078,41.21053,5459.658,74.6896484951783,-35 +"513",13,"Turkey (13)","1976",3701,42.20812,5888.265,87.6845497975271,-34 +"514",13,"Turkey (13)","1977",4720,43.19261,5950.076,109.277952872031,-33 +"515",13,"Turkey (13)","1978",3501,44.17188,5905.608,79.2585690262674,-32 +"516",13,"Turkey (13)","1979",3465,45.15865,5740.513,76.7294859345884,-31 +"517",13,"Turkey (13)","1980",3374,46.16132,5478.383,73.0914973835237,-30 +"518",13,"Turkey (13)","1981",3316,47.18342,5620.011,70.2789242492384,-29 +"519",13,"Turkey (13)","1982",4049,48.2195,5695.207,83.9701780400046,-28 +"520",13,"Turkey (13)","1983",4252,49.25853,5852.216,86.3200749190039,-27 +"521",13,"Turkey (13)","1984",5271,50.28509,6117.528,104.822324072603,-26 +"522",13,"Turkey (13)","1985",5275,51.28881,6252.194,102.848945023291,-25 +"523",13,"Turkey (13)","1986",5381,52.26511,6565.622,102.955872474008,-24 +"524",13,"Turkey (13)","1987",6724,53.21939,7059.511,126.344928042204,-23 +"525",13,"Turkey (13)","1988",6605,54.16364,7097.417,121.945275465238,-22 +"526",13,"Turkey (13)","1989",7390,55.11508,6995.141,134.083085790677,-21 +"527",13,"Turkey (13)","1990",6593,56.08619,7510.979,117.551218936426,-20 +"528",13,"Turkey (13)","1991",7380,57.07924,7433.463,129.293942946683,-19 +"529",13,"Turkey (13)","1992",7510,58.09025,7671.898,129.281592005543,-18 +"530",13,"Turkey (13)","1993",10270,59.11751,8115.384,173.72179579282,-17 +"531",13,"Turkey (13)","1994",6410,60.15739,7602.812,106.553824891672,-16 +"532",13,"Turkey (13)","1995",10750,61.2061,8061.251,175.6360885598,-15 +"533",13,"Turkey (13)","1996",10480,62.2649,8508.948,168.313126657234,-14 +"534",13,"Turkey (13)","1997",11690,63.33183,8999.519,184.583328793752,-13 +"535",13,"Turkey (13)","1998",13020,64.39564,9055.145,202.187601520848,-12 +"536",13,"Turkey (13)","1999",12160,65.44167,8610.54,185.814329004746,-11 +"537",13,"Turkey (13)","2000",13370,66.45958,9053.042,201.174909621758,-10 +"538",13,"Turkey (13)","2001",11580,67.44411,8412.621,171.697721268766,-9 +"539",13,"Turkey (13)","2002",12870,68.39813,8806.59,188.163038960276,-8 +"540",13,"Turkey (13)","2003",15320,69.32945,9145.75,220.973915125535,-7 +"541",13,"Turkey (13)","2004",18563,70.25018,9870.96,264.241315822963,-6 +"542",13,"Turkey (13)","2005",20582,71.16904,10562.13,289.198786438597,-5 +"543",13,"Turkey (13)","2006",24299,72.08793,11146.31,337.074458928145,-4 +"544",13,"Turkey (13)","2007",28117,73.00374,11515.06,385.144651493198,-3 +"545",13,"Turkey (13)","2008",22862,73.91425,11475.32,309.304362825842,-2 +"546",13,"Turkey (13)","2009",19198,74.81569,10600.55,256.603928935227,-1 +"547",13,"Turkey (13)","2010",25131,75.70514,10866.41,331.958966062278,0 +"548",13,"Turkey (13)","2011",28665,76.58212,11240.01,374.304080378031,1 +"549",13,"Turkey (13)","2012",30286,77.44726,11503.49,391.053214794171,2 +"550",14,"Ukraine + (14)","1992",34926,66.24849,6198.245,527.196921771349,-18 +"551",14,"Ukraine + (14)","1993",22228,66.15531,5407.86,335.997216247645,-17 +"552",14,"Ukraine + (14)","1994",10287,65.96564,4266.155,155.944822183185,-16 +"553",14,"Ukraine + (14)","1995",9501,65.67137,3788.413,144.67491693869,-15 +"554",14,"Ukraine + (14)","1996",9543,65.26628,3526.296,146.216392293233,-14 +"555",14,"Ukraine + (14)","1997",7571,64.76211,3557.207,116.904776573833,-13 +"556",14,"Ukraine + (14)","1998",5945,64.19197,3600.348,92.6128299224965,-12 +"557",14,"Ukraine + (14)","1999",5104,63.60104,3655.677,80.2502600586405,-11 +"558",14,"Ukraine + (14)","2000",14112,63.02394,3902.145,223.914912333313,-10 +"559",14,"Ukraine + (14)","2001",14759,62.473,4250.785,236.246058297184,-9 +"560",14,"Ukraine + (14)","2002",15754,61.94598,4511.045,254.318359318878,-8 +"561",14,"Ukraine + (14)","2003",18590,61.44504,4945.633,302.54679629145,-7 +"562",14,"Ukraine + (14)","2004",9517,60.96768,5573.686,156.099100375806,-6 +"563",14,"Ukraine + (14)","2005",9335,60.51197,5865.295,154.266998744215,-5 +"564",14,"Ukraine + (14)","2006",10729,60.082,6375.124,178.572617422855,-4 +"565",14,"Ukraine + (14)","2007",12246,59.68111,6928.893,205.190553593926,-3 +"566",14,"Ukraine + (14)","2008",11090,59.30458,7281.334,187.000734176011,-2 +"567",14,"Ukraine + (14)","2009",6704,58.94514,6583.867,113.732870937282,-1 +"568",14,"Ukraine + (14)","2010",9234,58.59694,6776.641,157.585020651249,0 +"569",14,"Ukraine + (14)","2011",10465,58.25822,7111.536,179.631303531073,1 +"570",14,"Ukraine + (14)","2012",10449,57.92915,7526.385,180.375510429551,2 +"571",15,"Central Asia (15)","1992",4838,51.81463,3146.03,93.371312310828,-18 +"572",15,"Central Asia (15)","1993",4157,52.401,2840.36,79.3305471269632,-17 +"573",15,"Central Asia (15)","1994",2742,52.92537,2467.293,51.8088017145652,-16 +"574",15,"Central Asia (15)","1995",1472,53.40026,2278.397,27.5654088575599,-15 +"575",15,"Central Asia (15)","1996",1417,53.82479,2258.805,26.3261593774913,-14 +"576",15,"Central Asia (15)","1997",877,54.20422,2285.418,16.179552071776,-13 +"577",15,"Central Asia (15)","1998",848,54.56543,2274.89,15.5409753024946,-12 +"578",15,"Central Asia (15)","1999",554,54.94284,2347.512,10.0832064742194,-11 +"579",15,"Central Asia (15)","2000",1267,55.36184,2535.983,22.885800038438,-10 +"580",15,"Central Asia (15)","2001",1351,55.83427,2807.675,24.1966090001714,-9 +"581",15,"Central Asia (15)","2002",1484,56.35691,3025.344,26.3321747058169,-8 +"582",15,"Central Asia (15)","2003",1598,56.92098,3263.119,28.0740071586961,-7 +"583",15,"Central Asia (15)","2004",3375,57.51052,3548.419,58.6849153859155,-6 +"584",15,"Central Asia (15)","2005",3722,58.1136,3832.548,64.0469700724099,-5 +"585",15,"Central Asia (15)","2006",4054,58.72855,4166.622,69.0294584150298,-4 +"586",15,"Central Asia (15)","2007",5018,59.3594,4502.187,84.5358949045981,-3 +"587",15,"Central Asia (15)","2008",4083,60.00612,4681.461,68.04305960792,-2 +"588",15,"Central Asia (15)","2009",4566,60.66952,4654.056,75.2601965533929,-1 +"589",15,"Central Asia (15)","2010",3702,61.34934,4804.037,60.3429474546914,0 +"590",15,"Central Asia (15)","2011",3960,62.0439,5057.375,63.8257749754609,1 +"591",15,"Central Asia (15)","2012",5070,62.7498,5305.246,80.7970702695467,2 +"592",16,"Russia + (16)","1970",110234,142.7897,6231.84786332018,772.002462362481,-40 +"593",16,"Russia + (16)","1971",115364,143.7069,6596.91,802.772866160219,-39 +"594",16,"Russia + (16)","1972",121243,144.6594,6713.989,838.12735294077,-38 +"595",16,"Russia + (16)","1973",129391,145.6433,7157.659,888.410246128727,-37 +"596",16,"Russia + (16)","1974",137554,146.645,7431.844,938.006750997306,-36 +"597",16,"Russia + (16)","1975",141031,147.6552,7560.977,955.137374098576,-35 +"598",16,"Russia + (16)","1976",145171,148.6757,7954.266,976.427217090621,-34 +"599",16,"Russia + (16)","1977",146330,149.7151,8293.8,977.389722212389,-33 +"600",16,"Russia + (16)","1978",153451,150.7774,8645.484,1017.73210043415,-32 +"601",16,"Russia + (16)","1979",152200,151.8668,8864.585,1002.19402792447,-31 +"602",16,"Russia + (16)","1980",150330,152.9852,9216.292,982.644072759979,-30 +"603",16,"Russia + (16)","1981",150849,154.1217,9617.574,978.765482083315,-29 +"604",16,"Russia + (16)","1982",150343,155.2687,10248.43,968.276284917694,-28 +"605",16,"Russia + (16)","1983",157578,156.4324,10627.06,1007.32329108292,-27 +"606",16,"Russia + (16)","1984",159369,157.6224,10957.45,1011.08091235763,-26 +"607",16,"Russia + (16)","1985",157161,158.8373,11014.04,989.446433551817,-25 +"608",16,"Russia + (16)","1986",161544,160.0866,11055.36,1009.10382255604,-24 +"609",16,"Russia + (16)","1987",162989,161.3439,11175.18,1010.19623301532,-23 +"610",16,"Russia + (16)","1988",164619,162.528,11651.14,1012.8654754873,-22 +"611",16,"Russia + (16)","1989",166319,163.5323,12317.96,1017.04067025291,-21 +"612",16,"Russia + (16)","1990",152577,164.2816,11860.85,928.752824418559,-20 +"613",16,"Russia + (16)","1991",131865,164.743,11207.94,800.428546281177,-19 +"614",16,"Russia + (16)","1992",59892,164.9401,9502.627,363.113639436377,-18 +"615",16,"Russia + (16)","1993",40510,164.9262,8647.017,245.625012884551,-17 +"616",16,"Russia + (16)","1994",21104,164.7832,7565.564,128.07130824016,-16 +"617",16,"Russia + (16)","1995",22062,164.573,7261.488,134.056011617945,-15 +"618",16,"Russia + (16)","1996",20124,164.3162,7023.942,122.471186651103,-14 +"619",16,"Russia + (16)","1997",20364,164.0016,7144.26,124.169520297363,-13 +"620",16,"Russia + (16)","1998",14949,163.6224,6801.33,91.3627962919502,-12 +"621",16,"Russia + (16)","1999",19581,163.1617,7254.708,120.009781707349,-11 +"622",16,"Russia + (16)","2000",28796,162.6118,8001.696,177.084319834108,-10 +"623",16,"Russia + (16)","2001",31404,161.9744,8448.438,193.882490072505,-9 +"624",16,"Russia + (16)","2002",28393,161.2711,8898.082,176.057582542687,-8 +"625",16,"Russia + (16)","2003",27402,160.5378,9605.862,170.688772363892,-7 +"626",16,"Russia + (16)","2004",32214,159.8201,10343.2,201.564133672798,-6 +"627",16,"Russia + (16)","2005",35625,159.1522,11087.96,223.842334570304,-5 +"628",16,"Russia + (16)","2006",42337,158.548,12010.28,267.029543103666,-4 +"629",16,"Russia + (16)","2007",47942,158.003,13079.33,303.424618519902,-3 +"630",16,"Russia + (16)","2008",42241,157.5089,13871.51,268.181671003988,-2 +"631",16,"Russia + (16)","2009",29530,157.0491,12757.81,188.030367572944,-1 +"632",16,"Russia + (16)","2010",42680,156.6101,13424.54,272.52393044893,0 +"633",16,"Russia + (16)","2011",48565,156.1913,14018.42,310.932811238526,1 +"634",16,"Russia + (16)","2012",50105,155.7946,14560.81,321.609349746397,2 +"635",17,"Middle East (17)","1970",4486,65.63198,6984.26967389179,68.3508253141228,-40 +"636",17,"Middle East (17)","1971",4334,68.67008,7619.628,63.1133675685248,-39 +"637",17,"Middle East (17)","1972",4868,70.88831,8453.769,68.6714071755978,-38 +"638",17,"Middle East (17)","1973",6759,73.19949,9221.72,92.3367089032997,-37 +"639",17,"Middle East (17)","1974",9101,75.62141,10048.69,120.34951477366,-36 +"640",17,"Middle East (17)","1975",11525,78.17059,10053.5,147.433964615081,-35 +"641",17,"Middle East (17)","1976",12445,80.83937,10780.2,153.947266041287,-34 +"642",17,"Middle East (17)","1977",11388,83.63344,10917.79,136.165629442003,-33 +"643",17,"Middle East (17)","1978",13230,86.59713,10536.47,152.77642573143,-32 +"644",17,"Middle East (17)","1979",12865,89.7875,10947.24,143.282750939719,-31 +"645",17,"Middle East (17)","1980",13648,93.23826,10746.25,146.377678004716,-30 +"646",17,"Middle East (17)","1981",13028,96.96494,10356.82,134.357841091842,-29 +"647",17,"Middle East (17)","1982",16839,100.935,9462.221,166.830138207757,-28 +"648",17,"Middle East (17)","1983",18610,105.0733,9060.028,177.11445248222,-27 +"649",17,"Middle East (17)","1984",16866,109.2758,8780.68,154.343413637786,-26 +"650",17,"Middle East (17)","1985",17274,113.462,8386.904,152.244804427914,-25 +"651",17,"Middle East (17)","1986",11469,117.6081,8004.08,97.5187933484173,-24 +"652",17,"Middle East (17)","1987",11968,121.7178,7868.794,98.3257995133004,-23 +"653",17,"Middle East (17)","1988",11303,125.7715,7680.812,89.8693265167387,-22 +"654",17,"Middle East (17)","1989",11715,129.7554,7655.955,90.2852598042162,-21 +"655",17,"Middle East (17)","1990",10897,133.6615,7916.439,81.5268420599799,-20 +"656",17,"Middle East (17)","1991",11752,137.4736,8032.402,85.4855041258831,-19 +"657",17,"Middle East (17)","1992",13804,141.1928,8477.415,97.7670249474477,-18 +"658",17,"Middle East (17)","1993",15096,144.8516,8408.716,104.217005542224,-17 +"659",17,"Middle East (17)","1994",14761,148.497,8525.458,99.4026815356539,-16 +"660",17,"Middle East (17)","1995",14254,152.1644,8525.457,93.6749988827873,-15 +"661",17,"Middle East (17)","1996",15733,155.8667,8765.96,100.938815025916,-14 +"662",17,"Middle East (17)","1997",16920,159.5982,8829.913,106.016233265789,-13 +"663",17,"Middle East (17)","1998",17511,163.3544,9009.427,107.19637793656,-12 +"664",17,"Middle East (17)","1999",18333,167.1241,8940.858,109.696925817402,-11 +"665",17,"Middle East (17)","2000",21382,170.901,9213.073,125.113369728673,-10 +"666",17,"Middle East (17)","2001",25040,174.681,9180.806,143.347015416674,-9 +"667",17,"Middle East (17)","2002",27470,178.4721,9188.435,153.917615134242,-8 +"668",17,"Middle East (17)","2003",31405,182.2935,9526.202,172.277124527205,-7 +"669",17,"Middle East (17)","2004",32804,186.1707,10049.02,176.203881706412,-6 +"670",17,"Middle East (17)","2005",36528,190.1193,10431.33,192.131992911819,-5 +"671",17,"Middle East (17)","2006",38253,194.1498,10810.37,197.028274044063,-4 +"672",17,"Middle East (17)","2007",46614,198.249,11188.48,235.128550459271,-3 +"673",17,"Middle East (17)","2008",47720,202.4243,11591.75,235.742447917567,-2 +"674",17,"Middle East (17)","2009",44001,206.5805,11474.69,212.996870469381,-1 +"675",17,"Middle East (17)","2010",47723,210.6817,11688.86,226.517063418417,0 +"676",17,"Middle East (17)","2011",53298,214.712,11970.35,248.230187413838,1 +"677",17,"Middle East (17)","2012",53446,218.6812,12268.1,244.40143917264,2 +"678",18,"India (18)","1970",6432,554.9108,791.734291330751,11.5910521114385,-40 +"679",18,"India (18)","1971",7710,565.1073,796.2614,13.6434266554334,-39 +"680",18,"India (18)","1972",9227,577.574,774.7964,15.9754421078511,-38 +"681",18,"India (18)","1973",8221,590.4122,783.1032,13.9241702661293,-37 +"682",18,"India (18)","1974",8551,603.6847,774.9598,14.1646790120737,-36 +"683",18,"India (18)","1975",8500,617.4316,827.0153,13.766707113792,-35 +"684",18,"India (18)","1976",8202,631.6699,821.7555,12.9846301050596,-34 +"685",18,"India (18)","1977",10185,646.3734,861.3548,15.7571459469093,-33 +"686",18,"India (18)","1978",10060,661.4861,889.7242,15.2081804893557,-32 +"687",18,"India (18)","1979",12050,676.9282,823.8906,17.8010016424194,-31 +"688",18,"India (18)","1980",10900,692.6373,859.506,15.7369520815584,-30 +"689",18,"India (18)","1981",14000,708.589,890.5605,19.757574560147,-29 +"690",18,"India (18)","1982",13900,724.7841,900.8348,19.1781249064377,-28 +"691",18,"India (18)","1983",11600,741.2145,945.1629,15.6499906572254,-27 +"692",18,"India (18)","1984",13900,757.8796,959.7208,18.3406440811971,-26 +"693",18,"India (18)","1985",14400,774.7748,987.9305,18.5860459065008,-25 +"694",18,"India (18)","1986",15290,791.8759,1012.685,19.3085810541778,-24 +"695",18,"India (18)","1987",17640,809.162,1030.271,21.8003317011921,-23 +"696",18,"India (18)","1988",19040,826.6349,1105.686,23.0331431687677,-22 +"697",18,"India (18)","1989",20036,844.3027,1146.969,23.7308254492139,-21 +"698",18,"India (18)","1990",21700,862.1616,1185.312,25.1692954081926,-20 +"699",18,"India (18)","1991",20300,880.2089,1173.358,23.0627070460206,-19 +"700",18,"India (18)","1992",18540,898.4102,1212.587,20.6364531480164,-18 +"701",18,"India (18)","1993",18850,916.6924,1245.062,20.5630591024863,-17 +"702",18,"India (18)","1994",21880,934.9619,1301.963,23.4020231198726,-16 +"703",18,"India (18)","1995",26080,953.1479,1373.79,27.3619655459557,-15 +"704",18,"India (18)","1996",27100,971.2095,1450.131,27.9033514396224,-14 +"705",18,"India (18)","1997",27300,989.1498,1481.559,27.5994596571723,-13 +"706",18,"India (18)","1998",27600,1006.996,1545.437,27.4082518699181,-12 +"707",18,"India (18)","1999",29700,1024.799,1630.772,28.9812929169525,-11 +"708",18,"India (18)","2000",30200,1042.59,1667.55,28.9663242501846,-10 +"709",18,"India (18)","2001",31200,1060.371,1725.123,29.4236639817573,-9 +"710",18,"India (18)","2002",33360,1078.111,1760.65,30.9430105063393,-8 +"711",18,"India (18)","2003",35000,1095.767,1877.288,31.9410969667822,-7 +"712",18,"India (18)","2004",39220,1113.283,2000.71,35.2291376047241,-6 +"713",18,"India (18)","2005",44330,1130.618,2154.28,39.2086451834307,-5 +"714",18,"India (18)","2006",50670,1147.746,2327.327,44.1473984662112,-4 +"715",18,"India (18)","2007",55020,1164.67,2501.308,47.2408493392978,-3 +"716",18,"India (18)","2008",56209,1181.412,2635.268,47.5778136670357,-2 +"717",18,"India (18)","2009",64360,1198.004,2744.491,53.7226920778228,-1 +"718",18,"India (18)","2010",69244,1214.464,2901.218,57.0160992833052,0 +"719",18,"India (18)","2011",75856,1230.793,3079.418,61.6318097356745,1 +"720",18,"India (18)","2012",77039,1246.96,3271.291,61.7814524924617,2 +"721",19,"Korea (19)","1970",3507,46.31954,2528.76124545009,75.7131871344145,-40 +"722",19,"Korea (19)","1971",3847,46.7346,2695.758,82.3158858747052,-39 +"723",19,"Korea (19)","1972",4031,47.79924,2768.704,84.331884774737,-38 +"724",19,"Korea (19)","1973",5906,48.85305,3030.457,120.893168389691,-37 +"725",19,"Korea (19)","1974",7664,49.85923,3192.208,153.712762912704,-36 +"726",19,"Korea (19)","1975",6498,50.79347,3334.332,127.92983035024,-35 +"727",19,"Korea (19)","1976",8445,51.64096,3603.506,163.532978472902,-34 +"728",19,"Korea (19)","1977",9162,52.4142,3883.855,174.799958789794,-33 +"729",19,"Korea (19)","1978",11688,53.14951,4166.839,219.907953996189,-32 +"730",19,"Korea (19)","1979",10347,53.89885,4377.384,191.970700673577,-31 +"731",19,"Korea (19)","1980",6100,54.6987,4271.043,111.520017843203,-30 +"732",19,"Korea (19)","1981",7480,55.56164,4453.97,134.62525584198,-29 +"733",19,"Korea (19)","1982",7630,56.47328,4688.231,135.108143178508,-28 +"734",19,"Korea (19)","1983",8620,57.41008,5079.116,150.147848600803,-27 +"735",19,"Korea (19)","1984",10620,58.33636,5384.712,182.04769718234,-26 +"736",19,"Korea (19)","1985",11310,59.22562,5651.163,190.964653472602,-25 +"737",19,"Korea (19)","1986",12190,60.07206,6122.17,202.922956196275,-24 +"738",19,"Korea (19)","1987",15050,60.88259,6668.251,247.197105116586,-23 +"739",19,"Korea (19)","1988",15830,61.65868,7243.802,256.735953478083,-22 +"740",19,"Korea (19)","1989",18300,62.40517,7616.01,293.244934674483,-21 +"741",19,"Korea (19)","1990",28613,63.12606,8160.819,453.267636218703,-20 +"742",19,"Korea (19)","1991",31203,63.81775,8775.825,488.939205785224,-19 +"743",19,"Korea (19)","1992",27369,64.47853,9147.686,424.466872926538,-18 +"744",19,"Korea (19)","1993",30659,65.11692,9574.646,470.83000854463,-17 +"745",19,"Korea (19)","1994",33871,65.74452,10254.54,515.1912281054,-16 +"746",19,"Korea (19)","1995",37990,66.36863,11043.38,572.408982978856,-15 +"747",19,"Korea (19)","1996",40054,66.99483,11670.62,597.867029440929,-14 +"748",19,"Korea (19)","1997",40568,67.61752,12066.25,599.962849864946,-13 +"749",19,"Korea (19)","1998",26385,68.22087,11156.84,386.758480212873,-12 +"750",19,"Korea (19)","1999",35831,68.78278,12106.01,520.929802488355,-11 +"751",19,"Korea (19)","2000",40367,69.28803,13015.59,582.597022891256,-10 +"752",19,"Korea (19)","2001",40094,69.73186,13445.67,574.973907192494,-9 +"753",19,"Korea (19)","2002",45767,70.12032,14308.51,652.692400719221,-8 +"754",19,"Korea (19)","2003",47567,70.46568,14634.21,675.03783402076,-7 +"755",19,"Korea (19)","2004",49454,70.78619,15233.18,698.639098954189,-6 +"756",19,"Korea (19)","2005",49354,71.09551,15766.58,694.192924419559,-5 +"757",19,"Korea (19)","2006",52574,71.39767,16491.62,736.354561710487,-4 +"758",19,"Korea (19)","2007",57678,71.68975,17238.48,804.550162331435,-3 +"759",19,"Korea (19)","2008",62266,71.97407,17552.23,865.117117873145,-2 +"760",19,"Korea (19)","2009",48619,72.24392,17505.51,672.983968754741,-1 +"761",19,"Korea (19)","2010",55835,72.49487,18212.32,770.192428788409,0 +"762",19,"Korea (19)","2011",60094,72.7268,18909.09,826.297870936161,1 +"763",19,"Korea (19)","2012",57650,72.94119,19790.77,790.362756626263,2 +"764",20,"China (20)","1970",24884,850.7245,545.028678374734,29.2503624851524,-40 +"765",20,"China (20)","1971",26517,859.5515,566.5772,30.8498094645871,-39 +"766",20,"China (20)","1972",28022,879.7271,591.6257,31.8530598864125,-38 +"767",20,"China (20)","1973",31665,899.2614,636.1968,35.2122308374406,-37 +"768",20,"China (20)","1974",33796,917.7689,633.6603,36.824085017481,-36 +"769",20,"China (20)","1975",34891,935.0037,658.5936,37.3164298708123,-35 +"770",20,"China (20)","1976",32159,950.8076,675.8064,33.8228259849837,-34 +"771",20,"China (20)","1977",38497,965.3068,720.6266,39.8805851155301,-33 +"772",20,"China (20)","1978",50741,978.9117,790.7791,51.8340929013311,-32 +"773",20,"China (20)","1979",52797,992.2164,840.6167,53.2111744978212,-31 +"774",20,"China (20)","1980",52599,1005.687,890.6027,52.3015610224652,-30 +"775",20,"China (20)","1981",47791,1019.611,931.0032,46.8717971853972,-29 +"776",20,"China (20)","1982",49047,1033.708,976.3886,47.4476351155258,-28 +"777",20,"China (20)","1983",60490,1048.272,1053.122,57.7044889112749,-27 +"778",20,"China (20)","1984",69162,1063.636,1174.035,65.0241247945726,-26 +"779",20,"China (20)","1985",80823,1079.994,1264.867,74.8365268695937,-25 +"780",20,"China (20)","1986",87605,1097.527,1367.061,79.8203597724703,-24 +"781",20,"China (20)","1987",84231,1116.062,1508.311,75.4716135841916,-23 +"782",20,"China (20)","1988",84698,1134.983,1632.842,74.6249062761293,-22 +"783",20,"China (20)","1989",87467,1153.432,1689.233,75.8319519486194,-21 +"784",20,"China (20)","1990",85655,1170.784,1736.462,73.1603780031159,-20 +"785",20,"China (20)","1991",91402,1186.737,1852.535,77.0195923780922,-19 +"786",20,"China (20)","1992",109472,1201.508,2040.276,91.1121690409053,-18 +"787",20,"China (20)","1993",162257,1215.291,2243.661,133.51287880845,-17 +"788",20,"China (20)","1994",148335,1228.437,2463.473,120.751003103944,-16 +"789",20,"China (20)","1995",127735,1241.203,2654.993,102.912255287814,-15 +"790",20,"China (20)","1996",135717,1253.63,2851.902,108.259215238946,-14 +"791",20,"China (20)","1997",144946,1265.624,3056.159,114.525325057047,-13 +"792",20,"China (20)","1998",152318,1277.165,3204.725,119.262585492086,-12 +"793",20,"China (20)","1999",166037,1288.206,3395.04,128.890099875331,-11 +"794",20,"China (20)","2000",170347,1298.727,3625.594,131.16459425268,-10 +"795",20,"China (20)","2001",197765,1308.65,3789.196,151.121384633019,-9 +"796",20,"China (20)","2002",236609,1318.131,4047.024,179.503402924292,-8 +"797",20,"China (20)","2003",286437,1327.264,4346.401,215.810117655568,-7 +"798",20,"China (20)","2004",328205,1336.162,4716.491,245.63264035349,-6 +"799",20,"China (20)","2005",390036,1344.92,5105.734,290.006840555572,-5 +"800",20,"China (20)","2006",421134,1353.561,5583.551,311.130418207971,-4 +"801",20,"China (20)","2007",461505,1362.097,6179.665,338.819482019269,-3 +"802",20,"China (20)","2008",488206,1370.518,6600.259,356.220056941974,-2 +"803",20,"China (20)","2009",590890,1379.084,6953.668,428.465561198593,-1 +"804",20,"China (20)","2010",635339,1387.711,7535.852,457.832358466568,0 +"805",20,"China (20)","2011",692705,1396.417,8122.802,496.058842022118,1 +"806",20,"China (20)","2012",712188,1405.172,8800.189,506.833327165642,2 +"807",21,"South Eastern Asia (21)","1970",3550,165.873,1226.811612426,21.4019159236283,-40 +"808",21,"South Eastern Asia (21)","1971",3167,170.5723,1269.914,18.5669068189853,-39 +"809",21,"South Eastern Asia (21)","1972",4044,174.8429,1319.411,23.1293349629868,-38 +"810",21,"South Eastern Asia (21)","1973",4722,179.1311,1407.655,26.360581719199,-37 +"811",21,"South Eastern Asia (21)","1974",5197,183.4014,1445.375,28.3367520640519,-36 +"812",21,"South Eastern Asia (21)","1975",4559,187.6357,1469.266,24.2970820584782,-35 +"813",21,"South Eastern Asia (21)","1976",4953,191.8074,1568.291,25.8227784746574,-34 +"814",21,"South Eastern Asia (21)","1977",5176,195.9422,1638.323,26.4159532760171,-33 +"815",21,"South Eastern Asia (21)","1978",5617,200.137,1714.774,28.0657749441632,-32 +"816",21,"South Eastern Asia (21)","1979",6260,204.5237,1794.445,30.6076997433549,-31 +"817",21,"South Eastern Asia (21)","1980",12922,209.1916,1855.656,61.7711227410661,-30 +"818",21,"South Eastern Asia (21)","1981",12863,214.1794,1906.573,60.0571296772706,-29 +"819",21,"South Eastern Asia (21)","1982",14914,219.4439,2020.039,67.9627002618893,-28 +"820",21,"South Eastern Asia (21)","1983",16741,224.892,2062.688,74.4401757287943,-27 +"821",21,"South Eastern Asia (21)","1984",14991,230.3877,2067.952,65.0685778798087,-26 +"822",21,"South Eastern Asia (21)","1985",14627,235.8302,2004.524,62.0234388979868,-25 +"823",21,"South Eastern Asia (21)","1986",14204,241.1824,2017.115,58.8931862358116,-24 +"824",21,"South Eastern Asia (21)","1987",15653,246.4646,2101.155,63.5101349240418,-23 +"825",21,"South Eastern Asia (21)","1988",16808,251.7013,2244.821,66.7775653125351,-22 +"826",21,"South Eastern Asia (21)","1989",18333,256.9387,2393.997,71.3516492455204,-21 +"827",21,"South Eastern Asia (21)","1990",15767,262.206,2526.197,60.1321098678139,-20 +"828",21,"South Eastern Asia (21)","1991",17248,267.5092,2623.859,64.4762871706842,-19 +"829",21,"South Eastern Asia (21)","1992",19684,272.8203,2737.396,72.1500562824687,-18 +"830",21,"South Eastern Asia (21)","1993",21807,278.0967,2893.716,78.4151699750482,-17 +"831",21,"South Eastern Asia (21)","1994",24440,283.2807,3082.369,86.2748503516124,-16 +"832",21,"South Eastern Asia (21)","1995",30819,288.3328,3277.861,106.88690291219,-15 +"833",21,"South Eastern Asia (21)","1996",32886,293.235,3458.425,112.148959026037,-14 +"834",21,"South Eastern Asia (21)","1997",29930,298.003,3547.629,100.435230517814,-13 +"835",21,"South Eastern Asia (21)","1998",17324,302.6725,3331.017,57.236782330737,-12 +"836",21,"South Eastern Asia (21)","1999",23374,307.2962,3452.535,76.0634202440512,-11 +"837",21,"South Eastern Asia (21)","2000",24947,311.9131,3648.302,79.9806099839987,-10 +"838",21,"South Eastern Asia (21)","2001",28441,316.5344,3671.916,89.8512136437619,-9 +"839",21,"South Eastern Asia (21)","2002",33001,321.1503,3796.933,102.758739443806,-8 +"840",21,"South Eastern Asia (21)","2003",32797,325.7535,3948.602,100.680422466681,-7 +"841",21,"South Eastern Asia (21)","2004",35856,330.3298,4163.552,108.546065174865,-6 +"842",21,"South Eastern Asia (21)","2005",37478,334.869,4335.763,111.918391968202,-5 +"843",21,"South Eastern Asia (21)","2006",36843,339.3705,4556.155,108.562765473133,-4 +"844",21,"South Eastern Asia (21)","2007",44376,343.8399,4788.853,129.060065454882,-3 +"845",21,"South Eastern Asia (21)","2008",43942,348.2811,4879.189,126.168201490118,-2 +"846",21,"South Eastern Asia (21)","2009",41344,352.6996,4725.93,117.221567588962,-1 +"847",21,"South Eastern Asia (21)","2010",47338,357.0985,4833.895,132.562864307747,0 +"848",21,"South Eastern Asia (21)","2011",49020,361.4778,4984.516,135.609987667292,1 +"849",21,"South Eastern Asia (21)","2012",55458,365.8338,5169.88,151.593428491299,2 +"850",22,"Indonesia (22)","1970",597,122.5516,815.96851168522,4.87141742743465,-40 +"851",22,"Indonesia (22)","1971",612,122.3185,861.4948,5.00333146662198,-39 +"852",22,"Indonesia (22)","1972",1030,125.2198,906.8879,8.22553621711582,-38 +"853",22,"Indonesia (22)","1973",1442,128.1714,971.1509,11.2505597972715,-37 +"854",22,"Indonesia (22)","1974",1280,131.1653,1024.68,9.75867855294045,-36 +"855",22,"Indonesia (22)","1975",1446,134.1949,1060.138,10.7753722384383,-35 +"856",22,"Indonesia (22)","1976",1382,137.2527,1094.294,10.0690186786854,-34 +"857",22,"Indonesia (22)","1977",1744,140.3358,1159.308,12.4273350064631,-33 +"858",22,"Indonesia (22)","1978",1964,143.4478,1238.283,13.6913915724047,-32 +"859",22,"Indonesia (22)","1979",2005,146.5954,1295.252,13.677100372863,-31 +"860",22,"Indonesia (22)","1980",3013,149.781,1373.326,20.1160360793425,-30 +"861",22,"Indonesia (22)","1981",3125,153.0044,1450.297,20.4242492372768,-29 +"862",22,"Indonesia (22)","1982",3140,156.256,1435.479,20.0952283432316,-28 +"863",22,"Indonesia (22)","1983",2883,159.5178,1522.775,18.0732181612334,-27 +"864",22,"Indonesia (22)","1984",2666,162.7665,1596.258,16.3792918075894,-26 +"865",22,"Indonesia (22)","1985",2392,165.9843,1619.961,14.4110015224331,-25 +"866",22,"Indonesia (22)","1986",2847,169.1649,1683.776,16.8297324090281,-24 +"867",22,"Indonesia (22)","1987",2569,172.309,1739.577,14.909261849352,-23 +"868",22,"Indonesia (22)","1988",2539,175.4149,1815.884,14.4742550376279,-22 +"869",22,"Indonesia (22)","1989",2334,178.4835,1942.182,13.0768390355411,-21 +"870",22,"Indonesia (22)","1990",4690,181.5162,2076.524,25.837914191681,-20 +"871",22,"Indonesia (22)","1991",4468,184.5114,2225.445,24.2153059377361,-19 +"872",22,"Indonesia (22)","1992",4441,187.47,2351.38,23.6891235931082,-18 +"873",22,"Indonesia (22)","1993",5819,190.3995,2488.523,30.5620550474135,-17 +"874",22,"Indonesia (22)","1994",5268,193.3102,2634.95,27.2515366493853,-16 +"875",22,"Indonesia (22)","1995",7250,196.2107,2806.983,36.950074588185,-15 +"876",22,"Indonesia (22)","1996",7169,199.1018,2977.691,36.0067061171722,-14 +"877",22,"Indonesia (22)","1997",7268,201.9845,3067.95,35.9829590884449,-13 +"878",22,"Indonesia (22)","1998",2650,204.8671,2633.08,12.935215073577,-12 +"879",22,"Indonesia (22)","1999",2954,207.7595,2617.542,14.2183630592103,-11 +"880",22,"Indonesia (22)","2000",5425,210.6679,2704.367,25.751431518518,-10 +"881",22,"Indonesia (22)","2001",5598,213.5939,2762.531,26.2086136355018,-9 +"882",22,"Indonesia (22)","2002",5415,216.5329,2845.241,25.0077470906269,-8 +"883",22,"Indonesia (22)","2003",5225,219.4762,2939.942,23.8066815445137,-7 +"884",22,"Indonesia (22)","2004",6404,222.4118,3045.907,28.7934363194759,-6 +"885",22,"Indonesia (22)","2005",8051,225.3285,3176.415,35.7300563399659,-5 +"886",22,"Indonesia (22)","2006",6987,228.2233,3307.078,30.6147531825191,-4 +"887",22,"Indonesia (22)","2007",8063,231.0922,3471.032,34.8908357789661,-3 +"888",22,"Indonesia (22)","2008",10587,233.8442,3638.303,45.2737335371157,-2 +"889",22,"Indonesia (22)","2009",8908,236.5386,3760.015,37.659815353604,-1 +"890",22,"Indonesia (22)","2010",10744,239.1637,3914.834,44.9232053192019,0 +"891",22,"Indonesia (22)","2011",13148,241.7141,4090.793,54.3948408470999,1 +"892",22,"Indonesia (22)","2012",15006,244.1897,4272.029,61.4522234148287,2 +"893",23,"Japan (23)","1970",69882,104.331,13475.1039580717,669.810506944245,-40 +"894",23,"Japan (23)","1971",57699,105.8952,13916.97,544.868889241439,-39 +"895",23,"Japan (23)","1972",68888,107.383,14878.82,641.516813648343,-38 +"896",23,"Japan (23)","1973",87181,108.868,15854.73,800.795458720653,-37 +"897",23,"Japan (23)","1974",75753,110.2935,15458.06,686.831046253859,-36 +"898",23,"Japan (23)","1975",64736,111.6189,15746.74,579.973463275485,-35 +"899",23,"Japan (23)","1976",60176,112.8224,16198.01,533.369260005105,-34 +"900",23,"Japan (23)","1977",58243,113.9121,16747.41,511.297746244692,-33 +"901",23,"Japan (23)","1978",61507,114.9126,17476.81,535.250268464903,-32 +"902",23,"Japan (23)","1979",72329,115.864,18283.88,624.257750466064,-31 +"903",23,"Japan (23)","1980",79007,116.7942,18649.32,676.463386024306,-30 +"904",23,"Japan (23)","1981",71136,117.7135,19046.46,604.314713265683,-29 +"905",23,"Japan (23)","1982",69504,118.6093,19425.15,585.991149092019,-28 +"906",23,"Japan (23)","1983",65614,119.4596,19597.75,549.256819878854,-27 +"907",23,"Japan (23)","1984",74367,120.2328,20079.04,618.525061380921,-26 +"908",23,"Japan (23)","1985",73377,120.9082,20981.66,606.881915370504,-25 +"909",23,"Japan (23)","1986",69941,121.4764,21501.41,575.757924996131,-24 +"910",23,"Japan (23)","1987",75751,121.9516,22230.42,621.156261992463,-23 +"911",23,"Japan (23)","1988",86871,122.3671,23653.66,709.921212482767,-22 +"912",23,"Japan (23)","1989",93278,122.7693,24823.57,759.782779571114,-21 +"913",23,"Japan (23)","1990",99032,123.191,26025.36,803.889894553985,-20 +"914",23,"Japan (23)","1991",99151,123.646,26798.44,801.894117076169,-19 +"915",23,"Japan (23)","1992",84040,124.1233,26954.84,677.068688956868,-18 +"916",23,"Japan (23)","1993",80589,124.602,26917.76,646.771319882506,-17 +"917",23,"Japan (23)","1994",79333,125.0493,27116.15,634.413787202327,-16 +"918",23,"Japan (23)","1995",84340,125.4419,27561.15,672.343132557782,-15 +"919",23,"Japan (23)","1996",83612,125.7723,28243.62,664.788669683229,-14 +"920",23,"Japan (23)","1997",86002,126.0494,28624.13,682.288055317994,-13 +"921",23,"Japan (23)","1998",71187,126.2856,27985.07,563.69847393527,-12 +"922",23,"Japan (23)","1999",70632,126.5001,27898.04,558.355289837716,-11 +"923",23,"Japan (23)","2000",80561,126.7058,28649.37,635.811462458704,-10 +"924",23,"Japan (23)","2001",74998,126.9073,28656.62,590.966792296424,-9 +"925",23,"Japan (23)","2002",72778,127.0974,28688.81,572.615962246277,-8 +"926",23,"Japan (23)","2003",77013,127.2626,29056.55,605.150295530659,-7 +"927",23,"Japan (23)","2004",80500,127.3842,29825.46,631.94650513957,-6 +"928",23,"Japan (23)","2005",83000,127.4487,30386.92,651.242421460556,-5 +"929",23,"Japan (23)","2006",83300,127.4509,31114.88,653.58502764594,-4 +"930",23,"Japan (23)","2007",85900,127.396,31781.99,674.275487456435,-3 +"931",23,"Japan (23)","2008",83200,127.2931,31586.76,653.609661482044,-2 +"932",23,"Japan (23)","2009",56000,127.1561,29950.3,440.403566954318,-1 +"933",23,"Japan (23)","2010",67400,126.9954,30521.48,530.727884632042,0 +"934",23,"Japan (23)","2011",69600,126.8138,31181.63,548.836167672603,1 +"935",23,"Japan (23)","2012",68800,126.6079,31957.19,543.410008380204,2 +"936",24,"Oceania (24)","1970",7212,16.80333,15508.5470070863,429.200640587312,-40 +"937",24,"Oceania (24)","1971",8190,17.2816,15691.06,473.914452365522,-39 +"938",24,"Oceania (24)","1972",8721,17.55112,16085.19,496.891366476897,-38 +"939",24,"Oceania (24)","1973",8152,17.81179,16413.83,457.6743830912,-37 +"940",24,"Oceania (24)","1974",8843,18.06842,16917.32,489.417447679432,-36 +"941",24,"Oceania (24)","1975",7328,18.32478,16790.77,399.895660411748,-35 +"942",24,"Oceania (24)","1976",7530,18.58204,16948.99,405.229996275974,-34 +"943",24,"Oceania (24)","1977",6097,18.84033,17074.64,323.614289134001,-33 +"944",24,"Oceania (24)","1978",6075,19.10138,16971.29,318.039848429799,-32 +"945",24,"Oceania (24)","1979",7645,19.36676,17308.82,394.748527890055,-31 +"946",24,"Oceania (24)","1980",6883,19.63778,17521.98,350.497866866825,-30 +"947",24,"Oceania (24)","1981",7414,19.91511,17855.64,372.280143067249,-29 +"948",24,"Oceania (24)","1982",6645,20.19899,18193.99,328.976844881848,-28 +"949",24,"Oceania (24)","1983",5562,20.4894,17668.38,271.457436528156,-27 +"950",24,"Oceania (24)","1984",6861,20.78607,18240.45,330.076825489378,-26 +"951",24,"Oceania (24)","1985",6432,21.08854,18793.35,304.999777130138,-25 +"952",24,"Oceania (24)","1986",6570,21.39682,19283.07,307.054973589533,-24 +"953",24,"Oceania (24)","1987",6710,21.71029,19407.46,309.070030847124,-23 +"954",24,"Oceania (24)","1988",7380,22.02703,19983.34,335.042899564762,-22 +"955",24,"Oceania (24)","1989",6985,22.34453,20351.25,312.604471877457,-21 +"956",24,"Oceania (24)","1990",6353,22.66077,20744.13,280.352344602589,-20 +"957",24,"Oceania (24)","1991",5933,22.97585,20326.68,258.227660782953,-19 +"958",24,"Oceania (24)","1992",5842,23.28964,20101.77,250.841146535541,-18 +"959",24,"Oceania (24)","1993",6652,23.59976,20636.26,281.867273226507,-17 +"960",24,"Oceania (24)","1994",7276,23.9033,21240.62,304.393117268327,-16 +"961",24,"Oceania (24)","1995",7259,24.1987,21882.24,299.974792034283,-15 +"962",24,"Oceania (24)","1996",7097,24.48367,22486.01,289.866674399712,-14 +"963",24,"Oceania (24)","1997",7522,24.75996,23018.93,303.796936667103,-13 +"964",24,"Oceania (24)","1998",9258,25.03483,23650.4,369.804787969401,-12 +"965",24,"Oceania (24)","1999",7376,25.31832,24598.03,291.330546418562,-11 +"966",24,"Oceania (24)","2000",7496,25.61716,25221.73,292.616355599137,-10 +"967",24,"Oceania (24)","2001",8894,25.93458,25443.69,342.939812404905,-9 +"968",24,"Oceania (24)","2002",9209,26.26733,26105.29,350.587593029059,-8 +"969",24,"Oceania (24)","2003",10108,26.60799,26603.28,379.88589141833,-7 +"970",24,"Oceania (24)","2004",8700,26.94575,27316.85,322.87095367544,-6 +"971",24,"Oceania (24)","2005",8653,27.27279,27736.18,317.275936932012,-5 +"972",24,"Oceania (24)","2006",8587,27.58661,28185.85,311.274201505731,-4 +"973",24,"Oceania (24)","2007",9340,27.88964,28774.07,334.891379021027,-3 +"974",24,"Oceania (24)","2008",9414,28.2549,28941.88,333.181147340815,-2 +"975",24,"Oceania (24)","2009",6656,28.61869,28742.47,232.575285591339,-1 +"976",24,"Oceania (24)","2010",8636,28.9838,29041.5,297.959549817484,0 +"977",24,"Oceania (24)","2011",7552,29.3506,29645.03,257.303087500767,1 +"978",24,"Oceania (24)","2012",7945,29.71791,30264.62,267.347199045962,2 +"979",25,"Other South Asia (25)","1970",696,166.4336,883.206850213264,4.1818478960979,-40 +"980",25,"Other South Asia (25)","1971",662,172.0365,877.3312,3.84802062353047,-39 +"981",25,"Other South Asia (25)","1972",461,176.6455,805.4592,2.60974663945586,-38 +"982",25,"Other South Asia (25)","1973",554,181.4043,828.6378,3.05395186332408,-37 +"983",25,"Other South Asia (25)","1974",536,186.282,853.4778,2.87735798413159,-36 +"984",25,"Other South Asia (25)","1975",630,191.2564,846.13,3.29400741622241,-35 +"985",25,"Other South Asia (25)","1976",599,196.3199,866.3409,3.05114254846299,-34 +"986",25,"Other South Asia (25)","1977",752,201.4788,867.564,3.73240261506422,-33 +"987",25,"Other South Asia (25)","1978",700,206.7432,906.8549,3.38584292010572,-32 +"988",25,"Other South Asia (25)","1979",880,212.1292,916.955,4.14841521110719,-31 +"989",25,"Other South Asia (25)","1980",1208,217.6501,939.1577,5.55019271757743,-30 +"990",25,"Other South Asia (25)","1981",1157,223.303,970.4668,5.18130074383237,-29 +"991",25,"Other South Asia (25)","1982",1561,229.0865,989.3985,6.81402003173474,-28 +"992",25,"Other South Asia (25)","1983",1658,235.0167,1015.109,7.05481780656438,-27 +"993",25,"Other South Asia (25)","1984",1550,241.1136,1039.595,6.42850506980942,-26 +"994",25,"Other South Asia (25)","1985",2146,247.3895,1068.855,8.67457996398392,-25 +"995",25,"Other South Asia (25)","1986",2145,253.8464,1091.995,8.44999180606855,-24 +"996",25,"Other South Asia (25)","1987",2104,260.4727,1105.74,8.07762195423935,-23 +"997",25,"Other South Asia (25)","1988",2348,267.2489,1127.968,8.78581726622635,-22 +"998",25,"Other South Asia (25)","1989",2308,274.1485,1137.222,8.41879492318944,-21 +"999",25,"Other South Asia (25)","1990",1772,281.1478,1161.731,6.30273471818026,-20 +"1000",25,"Other South Asia (25)","1991",2019,288.2426,1179.253,7.00451633450434,-19 +"1001",25,"Other South Asia (25)","1992",1996,295.4254,1223.416,6.75635879650159,-18 +"1002",25,"Other South Asia (25)","1993",2252,302.6656,1220.913,7.44055485658099,-17 +"1003",25,"Other South Asia (25)","1994",2396,309.9265,1234.786,7.73086522127021,-16 +"1004",25,"Other South Asia (25)","1995",2312,317.182,1278.556,7.28919043325283,-15 +"1005",25,"Other South Asia (25)","1996",2040,324.4149,1305.664,6.2882438506986,-14 +"1006",25,"Other South Asia (25)","1997",2209,331.6305,1314.06,6.66102786082704,-13 +"1007",25,"Other South Asia (25)","1998",2418,338.8539,1330.169,7.13581871124989,-12 +"1008",25,"Other South Asia (25)","1999",2417,346.1237,1353.693,6.98305259073562,-11 +"1009",25,"Other South Asia (25)","2000",2620,353.4673,1389.991,7.41228396516453,-10 +"1010",25,"Other South Asia (25)","2001",3135,360.8943,1395.691,8.68675398863324,-9 +"1011",25,"Other South Asia (25)","2002",3345,368.3938,1437.076,9.07995737170387,-8 +"1012",25,"Other South Asia (25)","2003",3250,375.9497,1484.305,8.64477348964502,-7 +"1013",25,"Other South Asia (25)","2004",3869,383.537,1553.889,10.0876838479729,-6 +"1014",25,"Other South Asia (25)","2005",4577,391.1392,1630.904,11.7017164221842,-5 +"1015",25,"Other South Asia (25)","2006",4432,398.7456,1705.815,11.1148561890087,-4 +"1016",25,"Other South Asia (25)","2007",4460,406.365,1784.052,10.9753546688322,-3 +"1017",25,"Other South Asia (25)","2008",3542,414.0226,1855.233,8.55508853864499,-2 +"1018",25,"Other South Asia (25)","2009",4810,421.7539,1887.607,11.4047552375923,-1 +"1019",25,"Other South Asia (25)","2010",5047,429.5813,1931.21,11.7486492079613,0 +"1020",25,"Other South Asia (25)","2011",5238,437.511,1992.753,11.9722704114868,1 +"1021",25,"Other South Asia (25)","2012",5959,445.5251,2062.911,13.3752284663647,2 +"1022",26,"Other South Africa (26)","1970",2005,45.62302,1392.89646093913,43.9471126637386,-40 +"1023",26,"Other South Africa (26)","1971",1909,47.25866,1408.711,40.3947128420484,-39 +"1024",26,"Other South Africa (26)","1972",2327,48.63551,1423.226,47.845699572185,-38 +"1025",26,"Other South Africa (26)","1973",3034,50.0741,1457.276,60.5902053157221,-37 +"1026",26,"Other South Africa (26)","1974",3254,51.5736,1480.218,63.0942963066375,-36 +"1027",26,"Other South Africa (26)","1975",2692,53.13327,1421.38,50.6650541176931,-35 +"1028",26,"Other South Africa (26)","1976",2775,54.75267,1387.807,50.6824598690804,-34 +"1029",26,"Other South Africa (26)","1977",3701,56.43172,1346.514,65.5836823687104,-33 +"1030",26,"Other South Africa (26)","1978",3636,58.17012,1298.553,62.5063176764978,-32 +"1031",26,"Other South Africa (26)","1979",3307,59.96738,1281.161,55.1466480609958,-31 +"1032",26,"Other South Africa (26)","1980",1410,61.82227,1295.589,22.8073152279915,-30 +"1033",26,"Other South Africa (26)","1981",1453,63.73701,1267.775,22.7968020464091,-29 +"1034",26,"Other South Africa (26)","1982",887,65.70934,1231.324,13.4988420215452,-28 +"1035",26,"Other South Africa (26)","1983",1161,67.72829,1201.375,17.1420244036871,-27 +"1036",26,"Other South Africa (26)","1984",1459,69.7794,1202.17,20.9087495736564,-26 +"1037",26,"Other South Africa (26)","1985",1492,71.85332,1209.845,20.7645241722999,-25 +"1038",26,"Other South Africa (26)","1986",1239,73.941,1206.85,16.7566032377166,-24 +"1039",26,"Other South Africa (26)","1987",978,76.04729,1246.922,12.860418826233,-23 +"1040",26,"Other South Africa (26)","1988",997,78.1937,1289.007,12.7503878189675,-22 +"1041",26,"Other South Africa (26)","1989",1038,80.41052,1291.151,12.9087587047068,-21 +"1042",26,"Other South Africa (26)","1990",1072,82.71741,1291.267,12.9597868211783,-20 +"1043",26,"Other South Africa (26)","1991",1435,85.12392,1284.605,16.8577762866184,-19 +"1044",26,"Other South Africa (26)","1992",1297,87.61742,1213.922,14.8029923729779,-18 +"1045",26,"Other South Africa (26)","1993",1306,90.16825,1108.931,14.4840340141901,-17 +"1046",26,"Other South Africa (26)","1994",1358,92.73466,1101.903,14.6439314060137,-16 +"1047",26,"Other South Africa (26)","1995",1387,95.28664,1125.655,14.5560804746605,-15 +"1048",26,"Other South Africa (26)","1996",1293,97.81644,1177.455,13.2186368671769,-14 +"1049",26,"Other South Africa (26)","1997",1690,100.3338,1215.532,16.8437754774563,-13 +"1050",26,"Other South Africa (26)","1998",1765,102.848,1246.188,17.1612476664592,-12 +"1051",26,"Other South Africa (26)","1999",1553,105.3752,1257.437,14.7378130717664,-11 +"1052",26,"Other South Africa (26)","2000",1552,107.9291,1267.886,14.3798104496378,-10 +"1053",26,"Other South Africa (26)","2001",1692,110.5141,1283.399,15.3102635772268,-9 +"1054",26,"Other South Africa (26)","2002",2220,113.1327,1339.247,19.622973729081,-8 +"1055",26,"Other South Africa (26)","2003",1892,115.7948,1359.277,16.3392483945739,-7 +"1056",26,"Other South Africa (26)","2004",181,118.5119,1431.622,1.52727278863979,-6 +"1057",26,"Other South Africa (26)","2005",217,121.295,1534.786,1.78902675295767,-5 +"1058",26,"Other South Africa (26)","2006",225,124.1446,1650.745,1.81240263370296,-4 +"1059",26,"Other South Africa (26)","2007",225,127.067,1789.49,1.77071938426185,-3 +"1060",26,"Other South Africa (26)","2008",272,130.2515,1871.848,2.08826769749293,-2 +"1061",26,"Other South Africa (26)","2009",344,133.5386,1843.252,2.57603419535625,-1 +"1062",26,"Other South Africa (26)","2010",355,136.949,1909.069,2.59220585765504,0 +"1063",26,"Other South Africa (26)","2011",474,140.4888,1988.006,3.37393443463109,1 +"1064",26,"Other South Africa (26)","2012",439,144.1459,2063.026,3.0455254016937,2 diff --git a/message_ix_models/model/material/petrochemical model fixes notebooks/add_new_meth_modes.ipynb b/message_ix_models/model/material/petrochemical model fixes notebooks/add_new_meth_modes.ipynb new file mode 100644 index 0000000000..57a6cba0a4 --- /dev/null +++ b/message_ix_models/model/material/petrochemical model fixes notebooks/add_new_meth_modes.ipynb @@ -0,0 +1,112 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import ixmp as ix\n", + "\n", + "mp = ix.Platform(\"ixmp_dev\")\n", + "\n", + "import message_ix\n", + "scenario = message_ix.Scenario(mp, model=\"MESSAGEix-Materials\", scenario=\"NoPolicy_methanol_wood_new\")\n", + "#scenario = message_ix.Scenario(mp, model=\"SHAPE_SSP2_v4.1.8\", scenario=\"baseline_no_globiom\")\n", + "#scenario.has_solution()\n", + "#scenario.version" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [], + "source": [ + "import os\n", + "from message_ix_models import Context\n", + "from pathlib import Path\n", + "Context.get_instance(-1).handle_cli_args(local_data=Path(os.getcwd()).parents[2].joinpath(\"data\"))\n", + "#Path(os.getcwd()).parents[2].joinpath(\"data\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\model\\material\\data_methanol.py:121: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df_rel_meth[\"value\"] = df_rel_meth.apply(lambda x: get_embodied_emi(x, pars, \"meth_plastics_share\"), axis=1)\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\model\\material\\data_methanol.py:123: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df_rel_hvc[\"value\"] = df_rel_hvc.apply(lambda x: get_embodied_emi(x, pars, \"hvc_plastics_share\"), axis=1)\n" + ] + }, + { + "data": { + "text/plain": " node_loc technology year_vtg year_act mode node_dest commodity \\\n0 R12_AFR meth_h2 2020 2020 fuel R12_AFR ht_heat \n1 R12_AFR meth_h2 2020 2025 fuel R12_AFR ht_heat \n2 R12_AFR meth_h2 2020 2030 fuel R12_AFR ht_heat \n3 R12_AFR meth_h2 2020 2035 fuel R12_AFR ht_heat \n4 R12_AFR meth_h2 2020 2040 fuel R12_AFR ht_heat \n.. ... ... ... ... ... ... ... \n235 R12_SAS meth_coal 2015 2035 M1 R12_SAS methanol \n236 R12_WEU meth_coal 2015 2035 M1 R12_WEU electr \n237 R12_WEU meth_coal 2015 2035 M1 R12_WEU methanol \n238 R12_CHN meth_coal 2015 2035 M1 R12_CHN electr \n239 R12_CHN meth_coal 2015 2035 M1 R12_CHN methanol \n\n level time time_dest value unit \n0 secondary year year 0.050000 GWa \n1 secondary year year 0.050000 GWa \n2 secondary year year 0.050000 GWa \n3 secondary year year 0.050000 GWa \n4 secondary year year 0.050000 GWa \n.. ... ... ... ... ... \n235 primary year year 1.000000 GWa \n236 secondary year year 0.038462 GWa \n237 primary year year 1.000000 GWa \n238 secondary year year 0.038462 GWa \n239 primary year year 1.000000 GWa \n\n[25934 rows x 12 columns]", + "text/html": "
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node_loctechnologyyear_vtgyear_actmodenode_destcommodityleveltimetime_destvalueunit
0R12_AFRmeth_h220202020fuelR12_AFRht_heatsecondaryyearyear0.050000GWa
1R12_AFRmeth_h220202025fuelR12_AFRht_heatsecondaryyearyear0.050000GWa
2R12_AFRmeth_h220202030fuelR12_AFRht_heatsecondaryyearyear0.050000GWa
3R12_AFRmeth_h220202035fuelR12_AFRht_heatsecondaryyearyear0.050000GWa
4R12_AFRmeth_h220202040fuelR12_AFRht_heatsecondaryyearyear0.050000GWa
.......................................
235R12_SASmeth_coal20152035M1R12_SASmethanolprimaryyearyear1.000000GWa
236R12_WEUmeth_coal20152035M1R12_WEUelectrsecondaryyearyear0.038462GWa
237R12_WEUmeth_coal20152035M1R12_WEUmethanolprimaryyearyear1.000000GWa
238R12_CHNmeth_coal20152035M1R12_CHNelectrsecondaryyearyear0.038462GWa
239R12_CHNmeth_coal20152035M1R12_CHNmethanolprimaryyearyear1.000000GWa
\n

25934 rows × 12 columns

\n
" + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import data_methanol\n", + "import importlib\n", + "importlib.reload(data_methanol)\n", + "df = data_methanol.gen_data_methanol(scenario)\n", + "\n", + "df[\"output\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/message_ix_models/model/material/petrochemical model fixes notebooks/methanol_modifications.ipynb b/message_ix_models/model/material/petrochemical model fixes notebooks/methanol_modifications.ipynb new file mode 100644 index 0000000000..422dfd91ae --- /dev/null +++ b/message_ix_models/model/material/petrochemical model fixes notebooks/methanol_modifications.ipynb @@ -0,0 +1,890 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "import ixmp as ix\n", + "import message_ix\n", + "import pandas as pd\n", + "\n", + "mp = ix.Platform(\"ixmp_dev\")\n", + "scenario = message_ix.Scenario(mp, model=\"MESSAGEix-Materials\", scenario=\"NoPolicy_petro_thesis_2\")#, version=37)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## Generate new mode for methanol technologies" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "par_dict = {}\n", + "for i in scenario.par_list():\n", + " try: par_dict[i] = scenario.par(i, filters={\"technology\":[\"meth_ng\", \"meth_h2\", \"meth_coal\", \"meth_bio\", \"meth_bio_ccs\", \"meth_coal_ccs\", \"meth_ng_ccs\"]})\n", + " except:\n", + " pass" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "par_dict = {}\n", + "for i in scenario.par_list():\n", + " try:\n", + " df = scenario.par(i, filters={\"technology\":\"meth_bal\"})\n", + " if df.size != 0:\n", + " par_dict[i] = df\n", + " except:\n", + " pass" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "for i in par_dict.keys():\n", + " if \"mode\" in par_dict[i].columns:\n", + " par_dict[i][\"mode\"] = \"feedstock\"" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "keys = list(par_dict.keys())\n", + "for i in keys:\n", + " if par_dict[i].size == 0:\n", + " par_dict.pop(i)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "for j in par_dict.keys():\n", + " df = par_dict[j]\n", + " if \"mode\" in par_dict[j].columns:\n", + " df[\"mode\"] = \"M2\"\n", + "\n", + "df = par_dict[\"output\"]\n", + "df.loc[df[\"commodity\"]==\"methanol\",\"level\"] = \"final_material\"\n", + "par_dict[\"output\"] = df" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "#scenario.remove_solution()\n", + "#scenario.check_out()\n", + "for i in par_dict.keys():\n", + " print(i)\n", + " scenario.check_out()\n", + " scenario.add_par(i, par_dict[i])\n", + " scenario.commit(\"add new mode for meth tecs\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scenario.par(\"input\", filters={\"technology\":\"meth_coal\", \"mode\":\"M2\"})" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## update meth_bio(ccs)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "#df = scenario.par(\"output\", filters={\"technology\":\"meth_bio_ccs\"})\n", + "#scenario.remove_solution()\n", + "scenario.check_out()\n", + "scenario.add_par(\"output\", df[df[\"year_act\"] > 2025])\n", + "#df = scenario.par(\"input\", filters={\"technology\":\"meth_bio_ccs\"})\n", + "#scenario.add_par(\"input\", df[df[\"year_act\"] > 2025])\n", + "scenario.commit(comment=\"fix meth_bio_ccs issue\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scenario.check_out()\n", + "df = scenario.par(\"input\", filters={\"technology\":\"meth_bio_ccs\"})\n", + "scenario.remove_par(\"input\", df)\n", + "scenario.add_par(\"input\", df[df[\"year_act\"] > 2025])" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scenario.commit(\"fix meth_bio_ccs input\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df1 = scenario.par(\"input\", filters={\"technology\":\"meth_bio_ccs\"})\n", + "df1" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## add missing years to methanol trade tecs" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "paramList_tec = [x for x in scenario.par_list() if \"technology\" in scenario.idx_sets(x)]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "par_dict_exp = {}\n", + "df_all_rows = pd.DataFrame()\n", + "for p in paramList_tec:\n", + " df = scenario.par(p, filters={\"technology\":\"eth_bio_ccs\"})\n", + " if df.index.size:\n", + " par_dict_exp[p] = df\n", + "\n", + "list(par_dict_exp.keys())" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "par_dict_exp.keys()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "par_dict_exp[\"relation_activity\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "for p in par_dict_exp.keys():\n", + " print(p, len(par_dict_exp[p].value.unique()))\n", + " try:\n", + " print(par_dict_exp[p][\"year_act\"].min())\n", + " print(par_dict_exp[p][\"year_vtg\"].min())\n", + " if par_dict_exp[p][\"year_act\"].min() < 2020:\n", + " print(par_dict_exp[p])\n", + " except:\n", + " continue" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "import numpy as np\n", + "par_dict_bc = {}\n", + "for p in list(par_dict_exp.keys())[:-2]:\n", + " df = par_dict_exp[p].copy(deep=True)\n", + " if \"year_act\" in df.columns:\n", + " year_name = \"year_act\"\n", + " else:\n", + " year_name = \"year_vtg\"\n", + " min_year = df[year_name].min()\n", + " min_year = 2040\n", + " new_lowest_y = 2000\n", + " tec_lt = 20\n", + " new_years = np.linspace(new_lowest_y, min_year, int((min_year-new_lowest_y)/5)+1).astype(int)[:-1]\n", + " df_min_year = df[df[year_name] == min_year].copy(deep=True)\n", + " print(p, new_years)\n", + " if len(new_years) == 0:\n", + " continue\n", + " df_min_year_new = df_min_year.copy(deep=True)\n", + " for i in new_years:\n", + " df_min_year_add = df_min_year.copy(deep=True)\n", + " df_min_year_add[year_name] = i\n", + " if (year_name == \"year_act\") & (\"year_vtg\" in df.columns):\n", + " df_min_year_add = df_min_year_add[df_min_year_add[\"year_vtg\"] == min_year]\n", + " df_min_year_add[\"year_vtg\"] = i\n", + " old_years = np.linspace(i-tec_lt, i, int((tec_lt)/5)+1).astype(int)\n", + " print(i, old_years)\n", + " for j in old_years:\n", + " df_min_year_add_vtg = df_min_year_add[df_min_year_add[\"year_vtg\"]==df_min_year_add[\"year_act\"]].copy(deep=True)\n", + " df_min_year_add_vtg[\"year_vtg\"] = j\n", + " df_min_year_add = pd.concat([df_min_year_add, df_min_year_add_vtg])\n", + " df_min_year_new = pd.concat([df_min_year_new, df_min_year_add])\n", + " par_dict_bc[p] = df_min_year_new" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "for i in par_dict_bc.keys():\n", + " par_dict_bc[i] = par_dict_bc[i].drop_duplicates()\n", + " if \"year_act\" in par_dict_bc[i].columns:\n", + " par_dict_bc[i] = par_dict_bc[i][par_dict_bc[i][\"year_act\"] >= 2020]\n", + " if \"year_vtg\" in par_dict_bc[i].columns:\n", + " par_dict_bc[i] = par_dict_bc[i][par_dict_bc[i][\"year_vtg\"] < 2020]\n", + "\n", + "\n", + "keys = list(par_dict_bc.keys())\n", + "for i in keys:\n", + " if \"year_vtg\" not in par_dict_bc[i].columns:\n", + " par_dict_bc.pop(i)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df = par_dict_bc[\"input\"]\n", + "df[df[\"year_act\"]==2035]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "par_dict_imp = {}\n", + "df_all_rows = pd.DataFrame()\n", + "for p in paramList_tec:\n", + " df = scenario.par(p, filters={\"technology\":\"meth_imp\"})\n", + " if df.index.size:\n", + " par_dict_imp[p] = df\n", + "par_dict_imp.keys()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "par_dict_bc_imp = {}\n", + "for p in list(par_dict_imp.keys())[:-2]:\n", + " df = par_dict_imp[p].copy(deep=True)\n", + " if \"year_act\" in df.columns:\n", + " year_name = \"year_act\"\n", + " else:\n", + " year_name = \"year_vtg\"\n", + " min_year = df[year_name].min()\n", + " min_year = 2045\n", + " print(p)\n", + " new_years = np.linspace(2020, min_year, int((min_year-2020)/5)+1).astype(int)[:-1]\n", + " df_min_year = df[df[year_name] == min_year].copy(deep=True)\n", + " if len(new_years) == 0:\n", + " continue\n", + " for i in new_years:\n", + " df_min_year_add = df_min_year.copy(deep=True)\n", + " df_min_year_add[year_name] = i\n", + " print(new_years)\n", + " if (year_name == \"year_act\") & (\"year_vtg\" in df.columns):\n", + " df_min_year_add[\"year_vtg\"] = i\n", + " if (year_name == \"year_act\") & (\"year_rel\" in df.columns):\n", + " df_min_year_add[\"year_rel\"] = i\n", + " df_min_year = pd.concat([df_min_year, df_min_year_add])\n", + " par_dict_bc_imp[p] = df_min_year" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "for i in par_dict_bc_imp.keys():\n", + " par_dict_bc_imp[i] = par_dict_bc_imp[i].drop_duplicates()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "par_dict_trd = {}\n", + "df_all_rows = pd.DataFrame()\n", + "for p in paramList_tec:\n", + " df = scenario.par(p, filters={\"technology\":\"meth_trd\"})\n", + " if df.index.size:\n", + " par_dict_trd[p] = df\n", + "par_dict_trd.keys()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "par_dict_bc_trd = {}\n", + "for p in list(par_dict_trd.keys())[:-1]:\n", + " df = par_dict_trd[p].copy(deep=True)\n", + " if \"year_act\" in df.columns:\n", + " year_name = \"year_act\"\n", + " else:\n", + " year_name = \"year_vtg\"\n", + " min_year = df[year_name].min()\n", + " min_year = 2045\n", + " print(p)\n", + " new_years = np.linspace(2020, min_year, int((min_year-2020)/5)+1).astype(int)[:-1]\n", + " df_min_year = df[df[year_name] == min_year].copy(deep=True)\n", + " if len(new_years) == 0:\n", + " continue\n", + " for i in new_years:\n", + " df_min_year_add = df_min_year.copy(deep=True)\n", + " df_min_year_add[year_name] = i\n", + " print(new_years)\n", + " if (year_name == \"year_act\") & (\"year_vtg\" in df.columns):\n", + " df_min_year_add[\"year_vtg\"] = i\n", + " if (year_name == \"year_act\") & (\"year_rel\" in df.columns):\n", + " df_min_year_add[\"year_rel\"] = i\n", + " df_min_year = pd.concat([df_min_year, df_min_year_add])\n", + " par_dict_bc_trd[p] = df_min_year" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "for i in par_dict_bc_trd.keys():\n", + " par_dict_bc_trd[i] = par_dict_bc_trd[i].drop_duplicates()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "list(par_dict_exp.keys())[7:-3]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "#par_dict_bc_imp.keys()\n", + "#par_dict_bc_trd.keys()\n", + "#par_dict_bc.keys()\n", + "\n", + "with pd.ExcelWriter('meth_bio_ccs_additions.xlsx') as writer:\n", + " for i in list(par_dict_exp.keys())[7:-3]:\n", + " par_dict_exp[i].to_excel(writer, sheet_name=i, index=False)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scenario.commit(\"update meth costs\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df_bio = scenario.par(\"inv_cost\", filters={\"technology\": \"meth_coal\"})\n", + "df_bio_ccs = scenario.par(\"inv_cost\", filters={\"technology\": \"meth_coal_ccs\"})\n", + "df_bio.set_index([\"node_loc\", \"year_vtg\"])[\"value\"]-df_bio_ccs.set_index([\"node_loc\", \"year_vtg\"])[\"value\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df = scenario.par(\"land_input\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df.groupby([\"land_scenario\", \"year\"]).sum().reset_index().set_index(\"year\").loc[2050].sort_values(\"value\")#.plot(x=\"year\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scenario.par(\"output\", filters={\"technology\":\"\"})" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "from message_ix_models.util import broadcast, same_node\n", + "bio_dict = data_methanol.gen_data_meth_bio(scenario)\n", + "\n", + "df_all = bio_dict[\"input\"]\n", + "yv_ya = scenario.vintage_and_active_years()\n", + "yv_ya = yv_ya[(yv_ya[\"year_act\"]>2015) & (yv_ya[\"year_act\"]-yv_ya[\"year_vtg\"]<20)]\n", + "nodes=df_all[\"node_loc\"].unique()\n", + "\n", + "import pandas as pd\n", + "\n", + "df=df_all\n", + "df_new = message_ix.make_df(\"input\", **yv_ya, **df.drop([\"year_vtg\", \"year_act\", \"node_loc\"], axis=1).iloc[0]).pipe(broadcast, node_loc=nodes).pipe(same_node)\n", + "df_new.loc[df_new[\"year_vtg\"]<2030, \"value\"] = 1.7241\n", + "df_new.loc[df_new[\"year_vtg\"].isin([2030, 2035]), \"value\"] = 1.6393\n", + "df_new.loc[df_new[\"year_vtg\"].isin([2040, 2045]), \"value\"] = 1.5873\n", + "df_new.loc[df_new[\"year_vtg\"]>2045, \"value\"] = 1.5385\n", + "\n", + "df_input_new = df_new.copy(deep=True)\n", + "df_input_new" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df_all = bio_dict[\"output\"]\n", + "df_output_new = pd.DataFrame()\n", + "\n", + "df = df_all[df_all[\"commodity\"]==\"d_heat\"]\n", + "df_new = message_ix.make_df(\"output\", **yv_ya, **df.drop([\"year_vtg\", \"year_act\", \"node_loc\"], axis=1).iloc[0]).pipe(broadcast, node_loc=nodes).pipe(same_node)\n", + "df_new.loc[df_new[\"year_vtg\"]<2030, \"value\"] = 0.3793\n", + "df_new.loc[df_new[\"year_vtg\"].isin([2030, 2035]), \"value\"] = 0.3607\n", + "df_new.loc[df_new[\"year_vtg\"].isin([2040, 2045]), \"value\"] = 0.3492\n", + "df_new.loc[df_new[\"year_vtg\"]>2045, \"value\"] = 0.3385\n", + "df_output_new = pd.concat([df_output_new, df_new])\n", + "\n", + "df = df_all[df_all[\"commodity\"]==\"electr\"]\n", + "df_new = message_ix.make_df(\"output\", **yv_ya, **df.drop([\"year_vtg\", \"year_act\", \"node_loc\"], axis=1).iloc[0]).pipe(broadcast, node_loc=nodes).pipe(same_node)\n", + "df_new.loc[df_new[\"year_vtg\"]<2030, \"value\"] = 0.03448\n", + "df_new.loc[df_new[\"year_vtg\"].isin([2030, 2035]), \"value\"] = 0.0328\n", + "df_new.loc[df_new[\"year_vtg\"].isin([2040, 2045]), \"value\"] = 0.03175\n", + "df_new.loc[df_new[\"year_vtg\"]>2045, \"value\"] = 0.03077\n", + "df_output_new = pd.concat([df_output_new, df_new])\n", + "\n", + "df = df_all[df_all[\"commodity\"]==\"methanol\"]\n", + "df_new = message_ix.make_df(\"output\", **yv_ya, **df.drop([\"year_vtg\", \"year_act\", \"node_loc\"], axis=1).iloc[0]).pipe(broadcast, node_loc=nodes).pipe(same_node)\n", + "df_output_new = pd.concat([df_output_new, df_new])" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df_all = bio_dict[\"capacity_factor\"]\n", + "df = df_all\n", + "df_cap = message_ix.make_df(\"capacity_factor\", **yv_ya,\n", + " **df.drop([\"year_vtg\", \"year_act\", \"node_loc\"],\n", + " axis=1).iloc[0]).pipe(broadcast, node_loc=nodes).pipe(same_node)\n", + "df_cap.loc[df_new[\"year_vtg\"]<2030, \"value\"] = 0.9215\n", + "\n", + "\n", + "df_all = bio_dict[\"var_cost\"]\n", + "df = df_all\n", + "df_var = message_ix.make_df(\"var_cost\", **yv_ya,\n", + " **df.drop([\"year_vtg\", \"year_act\", \"node_loc\"]\n", + " , axis=1).iloc[0]).pipe(broadcast, node_loc=nodes).pipe(same_node)\n", + "df_var.loc[df_new[\"year_vtg\"]<2030, \"value\"] = 18.89" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df_all = bio_dict[\"fix_cost\"]\n", + "df = df_all\n", + "\n", + "df_fix = message_ix.make_df(\"fix_cost\", **yv_ya, **df.drop([\"year_vtg\", \"year_act\", \"node_loc\"], axis=1).iloc[0]).pipe(broadcast, node_loc=nodes).pipe(same_node)\n", + "df_fix.loc[df_fix[\"year_vtg\"]<2030, \"value\"] = 54.101\n", + "df_fix.loc[df_fix[\"year_vtg\"].isin([2030, 2035]), \"value\"] = 36.067\n", + "df_fix.loc[df_fix[\"year_vtg\"].isin([2040, 2045]), \"value\"] = 35.521\n", + "df_fix.loc[df_fix[\"year_vtg\"]>2045, \"value\"] = 36.067\n", + "\n", + "df_fix" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "new_par_dict = {\n", + " \"capacity_factor\":df_cap,\n", + " \"input\":df_input_new,\n", + " \"output\":df_output_new,\n", + " \"fix_cost\":df_fix,\n", + " \"var_cost\":df_var,\n", + " \"inv_cost\":bio_dict[\"inv_cost\"],\n", + " \"technical_lifetime\":bio_dict[\"technical_lifetime\"]\n", + "}\n", + "with pd.ExcelWriter('meth_bio_techno_economic.xlsx') as writer:\n", + " for i in new_par_dict.keys():\n", + " new_par_dict[i].to_excel(writer, sheet_name=i, index=False)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "#data_methanol.gen_data_methanol(scenario)[\"historical_new_capacity\"]\n", + "#data_methanol.add_methanol_trp_additives(scenario)\n", + "#data_methanol.get_cost_ratio_2020(scenario, \"meth_coal\", \"inv_cost\", year=2020, ref_reg=\"R12_WEU\")\n", + "df_bio = data_methanol.gen_data_meth_bio(scenario)[\"inv_cost\"]\n", + "df_bio_ccs = data_methanol.gen_meth_bio_ccs(scenario)[\"inv_cost\"]\n", + "df_bio.set_index([\"node_loc\", \"year_vtg\"])[\"value\"]-df_bio_ccs.set_index([\"node_loc\", \"year_vtg\"])[\"value\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "meth_dict = data_methanol.gen_data_methanol(scenario)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "meth_dict[\"relation_activity\"][\"technology\"].unique()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "\n", + "arr = [\"meth_ng\", \"meth_coal\", \"meth_coal_ccs\", \"meth_ng_ccs\"]\n", + "df = scenario.par(\"relation_activity\", filters={\"relation\": \"CO2_cc\", \"technology\":arr})\n", + "df = df.rename({\"year_rel\":\"year_vtg\"}, axis=1)\n", + "values = dict(zip(df[\"technology\"], df[\"value\"]))\n", + "\n", + "df_em = scenario.par(\"emission_factor\", filters={\"emission\": \"CO2_transformation\", \"technology\":arr})\n", + "for i in arr:\n", + " df_em.loc[df_em[\"technology\"]==i,\"value\"] = values[i]\n", + "df_em[\"emission\"] = \"CO2_industry\"\n", + "df_em\n", + "#scenario.add_par(df_em)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df = scenario.par(\"input\", filters={\"technology\":\"meth_t_d\"})\n", + "df[\"value\"] = 1\n", + "df" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df = scenario.par(\"technical_lifetime\")\n", + "dict = df.loc[df[\"technology\"].str.contains(\"NH3\"),[\"technology\",\"value\"]].set_index(\"technology\").to_dict()[\"value\"]\n", + "for i in df.keys():\n", + " if (\"year_vtg\" in df[i].columns) & (\"year_act\" in df[i].columns):\n", + " df_temp = df[i]\n", + " df[i] = df_temp[(df_temp[\"year_act\"]-df_temp[\"year_vtg\"])<=dict[df_temp[\"technology\"]]]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## modify meth exp limit" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df = scenario.par(\"relation_activity\", filters={\"relation\":\"meth_exp_limit\"})\n", + "df_mea = df[(df[\"technology\"]==\"meth_exp\") & (df[\"node_rel\"]==\"R12_MEA\")]\n", + "df_mea[df_mea[\"value\"]] = -1.15\n", + "df_lam = df[(df[\"technology\"]==\"meth_exp\") & (df[\"node_rel\"]==\"R12_LAM\")]\n", + "df_lam[df_lam[\"value\"]] = -1.6\n", + "df_rel = pd.concat([df_mea, df_lam])\n", + "scenario.check_out()\n", + "scenario.add_par(\"relation_activity\", df_rel)\n", + "scenario.commit(\"modify meth exp share constraint for LAM and MEA\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "#scenario.par(\"relation_lower\", filters={\"relation\":\"meth_exp_limit\"})\n", + "rel_dict = {\n", + " \"relation\": \"meth_exp_tot\",\n", + " \"year_rel\": 2020,\n", + " \"year_act\": 2020,\n", + " \"technology\": \"meth_exp_tot\",\n", + " \"mode\": [\"fuel\", \"feedstock\"],\n", + " \"unit\": \"-\",\n", + " \"value\": 1\n", + "\n", + "}\n", + "from message_ix_models.util import broadcast, same_node\n", + "nodes = scenario.set(\"node\")[1:]\n", + "nodes_share = nodes.drop(5).reset_index(drop=True)\n", + "df = message_ix.make_df(\"relation_activity\", **rel_dict).pipe(broadcast, node_loc=nodes_share)\n", + "df[\"node_rel\"] = df[\"node_loc\"]\n", + "df[[\"relation\", \"node_rel\", \"year_rel\", \"node_loc\", \"technology\", \"year_act\", \"mode\", \"value\", \"unit\"]]\n", + "# rel_dict = {\n", + "# \"relation\": \"meth_exp_tot\",\n", + "# \"year_rel\": 2020,\n", + "# \"year_act\": 2020,\n", + "# \"technology\": \"meth_exp_tot\",\n", + "# \"mode\": [\"fuel\", \"feedstock\"],\n", + "# \"unit\": \"-\",\n", + "# }\n", + "# df_up = message_ix.make_df(\"relation_upper\", **rel_dict).pipe(broadcast, node_rel=nodes_share)\n", + "# df_lo = message_ix.make_df(\"relation_lower\", **rel_dict).pipe(broadcast, node_rel=nodes_share)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df = scenario.par(\"relation_lower\", filters={\"relation\":\"meth_exp_tot\"})\n", + "scenario.check_out()\n", + "scenario.remove_par(\"relation_lower\", df)\n" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scenario.commit(\"remove meth exp tot upper\")" + ], + "metadata": { + "collapsed": false + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/message_ix_models/model/material/petrochemical model fixes notebooks/missing_rels.yaml b/message_ix_models/model/material/petrochemical model fixes notebooks/missing_rels.yaml new file mode 100644 index 0000000000..39a87231e3 --- /dev/null +++ b/message_ix_models/model/material/petrochemical model fixes notebooks/missing_rels.yaml @@ -0,0 +1,17 @@ +- gas_util +- SO2_red_synf +- global_GSO2 +- global_GCO2 +- OilPrices +- PE_export_total +- PE_import_total +- PE_synliquids +- PE_total_direct +- PE_total_engineering +- FE_transport +- SO2_red_ref +- trp_energy +- total_TCO2 +- nuc_lc_in_el +- FE_liquids +- FE_final_energy diff --git a/message_ix_models/model/material/petrochemical model fixes notebooks/split_h2_elec.ipynb b/message_ix_models/model/material/petrochemical model fixes notebooks/split_h2_elec.ipynb new file mode 100644 index 0000000000..8288e521b9 --- /dev/null +++ b/message_ix_models/model/material/petrochemical model fixes notebooks/split_h2_elec.ipynb @@ -0,0 +1,208 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 30, + "outputs": [], + "source": [ + "import copy" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 19, + "outputs": [], + "source": [ + "import ixmp as ix\n", + "import message_ix\n", + "mp = ix.Platform(\"ixmp_dev\")\n", + "\n", + "scenario = message_ix.Scenario(mp, model=\"MESSAGEix-Materials\", scenario=\"NoPolicy_petro_thesis_2_macro\")\n", + "#scenario = message_ix.Scenario(mp, model=\"MESSAGEix-Materials\", scenario=\"baseline_DEFAULT_rebase2\")\n", + "#scenario = message_ix.Scenario(mp, model=\"MESSAGEix-Materials\", scenario=\"baseline_VK_Transport-Materials_VK\")#, version=37)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 17, + "outputs": [ + { + "data": { + "text/plain": " model scenario scheme \\\n6155 MESSAGEix-Materials 2.0deg_petro_thesis_1 MESSAGE \n6156 MESSAGEix-Materials 2.0deg_petro_thesis_2 MESSAGE \n6157 MESSAGEix-Materials 2.0deg_petro_thesis_2_macro MESSAGE \n6158 MESSAGEix-Materials 2degree_petro_thesis_2 MESSAGE \n6282 MESSAGEix-Materials NoPolicy_no_SDGs_petro_thesis_1 MESSAGE \n6287 MESSAGEix-Materials NoPolicy_petro_biomass MESSAGE \n6288 MESSAGEix-Materials NoPolicy_petro_test MESSAGE \n6289 MESSAGEix-Materials NoPolicy_petro_test_2 MESSAGE \n6290 MESSAGEix-Materials NoPolicy_petro_test_3 MESSAGE \n6291 MESSAGEix-Materials NoPolicy_petro_test_4 MESSAGE \n6292 MESSAGEix-Materials NoPolicy_petro_test_5 MESSAGE \n6293 MESSAGEix-Materials NoPolicy_petro_test_6 MESSAGE \n6294 MESSAGEix-Materials NoPolicy_petro_thesis_2 MESSAGE \n6295 MESSAGEix-Materials NoPolicy_petro_thesis_2_macro MESSAGE \n\n is_default is_locked cre_user cre_date upd_user \\\n6155 1 0 maczek 2023-02-08 23:38:03.000000 maczek \n6156 1 0 maczek 2023-04-06 16:44:33.000000 None \n6157 1 0 maczek 2023-04-14 11:43:37.000000 maczek \n6158 1 0 unlu 2023-04-06 15:35:51.000000 None \n6282 1 0 maczek 2023-04-12 22:12:42.000000 None \n6287 1 0 unlu 2022-08-09 14:51:56.000000 unlu \n6288 1 0 unlu 2022-04-14 11:08:53.000000 None \n6289 1 0 unlu 2022-04-14 14:05:39.000000 None \n6290 1 0 unlu 2022-04-14 14:40:02.000000 None \n6291 1 0 unlu 2022-04-14 15:13:25.000000 None \n6292 1 0 unlu 2022-04-14 15:29:02.000000 None \n6293 1 0 unlu 2022-04-14 15:38:19.000000 None \n6294 1 0 maczek 2023-04-13 15:29:47.000000 maczek \n6295 1 0 maczek 2023-04-14 11:18:27.000000 maczek \n\n upd_date lock_user lock_date \\\n6155 2023-02-08 23:44:53.000000 None None \n6156 None None None \n6157 2023-04-14 15:59:01.000000 None None \n6158 None None None \n6282 None None None \n6287 2022-08-09 15:14:46.000000 None None \n6288 None None None \n6289 None None None \n6290 None None None \n6291 None None None \n6292 None None None \n6293 None None None \n6294 2023-04-14 11:18:16.000000 None None \n6295 2023-04-14 11:28:34.000000 None None \n\n annotation version \n6155 clone Scenario from 'MESSAGEix-Materials|NoPol... 1 \n6156 clone Scenario from 'MESSAGEix-Materials|NoPol... 6 \n6157 clone Scenario from 'MESSAGEix-Materials|NoPol... 2 \n6158 clone Scenario from 'MESSAGEix-Materials|NoPol... 1 \n6282 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 9 \n6287 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 3 \n6288 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 4 \n6289 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 1 \n6290 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 1 \n6291 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 2 \n6292 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 2 \n6293 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 1 \n6294 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 65 \n6295 clone Scenario from 'MESSAGEix-Materials|NoPol... 5 ", + "text/html": "
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modelscenarioschemeis_defaultis_lockedcre_usercre_dateupd_userupd_datelock_userlock_dateannotationversion
6155MESSAGEix-Materials2.0deg_petro_thesis_1MESSAGE10maczek2023-02-08 23:38:03.000000maczek2023-02-08 23:44:53.000000NoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...1
6156MESSAGEix-Materials2.0deg_petro_thesis_2MESSAGE10maczek2023-04-06 16:44:33.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...6
6157MESSAGEix-Materials2.0deg_petro_thesis_2_macroMESSAGE10maczek2023-04-14 11:43:37.000000maczek2023-04-14 15:59:01.000000NoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...2
6158MESSAGEix-Materials2degree_petro_thesis_2MESSAGE10unlu2023-04-06 15:35:51.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...1
6282MESSAGEix-MaterialsNoPolicy_no_SDGs_petro_thesis_1MESSAGE10maczek2023-04-12 22:12:42.000000NoneNoneNoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...9
6287MESSAGEix-MaterialsNoPolicy_petro_biomassMESSAGE10unlu2022-08-09 14:51:56.000000unlu2022-08-09 15:14:46.000000NoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...3
6288MESSAGEix-MaterialsNoPolicy_petro_testMESSAGE10unlu2022-04-14 11:08:53.000000NoneNoneNoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...4
6289MESSAGEix-MaterialsNoPolicy_petro_test_2MESSAGE10unlu2022-04-14 14:05:39.000000NoneNoneNoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...1
6290MESSAGEix-MaterialsNoPolicy_petro_test_3MESSAGE10unlu2022-04-14 14:40:02.000000NoneNoneNoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...1
6291MESSAGEix-MaterialsNoPolicy_petro_test_4MESSAGE10unlu2022-04-14 15:13:25.000000NoneNoneNoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...2
6292MESSAGEix-MaterialsNoPolicy_petro_test_5MESSAGE10unlu2022-04-14 15:29:02.000000NoneNoneNoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...2
6293MESSAGEix-MaterialsNoPolicy_petro_test_6MESSAGE10unlu2022-04-14 15:38:19.000000NoneNoneNoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...1
6294MESSAGEix-MaterialsNoPolicy_petro_thesis_2MESSAGE10maczek2023-04-13 15:29:47.000000maczek2023-04-14 11:18:16.000000NoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...65
6295MESSAGEix-MaterialsNoPolicy_petro_thesis_2_macroMESSAGE10maczek2023-04-14 11:18:27.000000maczek2023-04-14 11:28:34.000000NoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...5
\n
" + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = mp.scenario_list()\n", + "df[df[\"scenario\"].str.contains(\"petro\")]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 20, + "outputs": [], + "source": [ + "paramList_tec = [x for x in scenario.par_list() if ((\"technology\" in scenario.idx_sets(x)) & (\"mode\" in scenario.idx_sets(x)))]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 27, + "outputs": [], + "source": [ + "import pandas as pd\n", + "par_dict_meth = {}\n", + "df_all_rows = pd.DataFrame()\n", + "for p in paramList_tec:\n", + " df = scenario.par(p, filters={\"technology\":\"h2_elec\"})\n", + " if df.index.size:\n", + " par_dict_meth[p] = df" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 31, + "outputs": [], + "source": [ + "par_dict_fuel = copy.deepcopy(par_dict_meth)\n", + "par_dict_fs = copy.deepcopy(par_dict_meth)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 32, + "outputs": [], + "source": [ + "for k,v in par_dict_fuel.items():\n", + " v[\"mode\"] = \"fuel\"\n", + "for k,v in par_dict_fs.items():\n", + " v[\"mode\"] = \"feedstock\"" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 35, + "outputs": [], + "source": [ + "for k,v in par_dict_meth.items():\n", + " scenario.remove_par(k, v)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 36, + "outputs": [], + "source": [ + "for k,v in par_dict_fuel.items():\n", + " scenario.add_par(k, v)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 37, + "outputs": [], + "source": [ + "for k,v in par_dict_fs.items():\n", + " scenario.add_par(k, v)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 38, + "outputs": [], + "source": [ + "scenario.commit(\"split h2_elec modes\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 42, + "outputs": [ + { + "data": { + "text/plain": "['year',\n 'node',\n 'technology',\n 'relation',\n 'emission',\n 'land_scenario',\n 'land_type',\n 'lvl_spatial',\n 'time',\n 'lvl_temporal',\n 'type_node',\n 'type_tec',\n 'type_year',\n 'type_emission',\n 'type_relation',\n 'mode',\n 'grade',\n 'level',\n 'commodity',\n 'rating',\n 'shares',\n 'type_addon',\n 'level_storage',\n 'storage_tec',\n 'sector',\n 'time_relative',\n 'map_spatial_hierarchy',\n 'map_node',\n 'map_temporal_hierarchy',\n 'map_time',\n 'cat_node',\n 'cat_tec',\n 'cat_year',\n 'cat_emission',\n 'type_tec_land',\n 'cat_relation',\n 'level_resource',\n 'level_renewable',\n 'level_stocks',\n 'map_shares_commodity_total',\n 'map_shares_commodity_share',\n 'addon',\n 'cat_addon',\n 'map_tec_addon',\n 'balance_equality',\n 'mapping_macro_sector',\n 'is_capacity_factor',\n 'map_tec_storage']" + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scenario.set_list()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "for k,v in par_dict_meth.items():\n", + " print(k, v[\"mode\"].unique())" + ], + "metadata": { + "collapsed": false + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/message_ix_models/model/material/petrochemical model fixes notebooks/water_tec_pars.xlsx b/message_ix_models/model/material/petrochemical model fixes notebooks/water_tec_pars.xlsx new file mode 100644 index 0000000000..0e7a24bb7a --- /dev/null +++ b/message_ix_models/model/material/petrochemical model fixes notebooks/water_tec_pars.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0bf0882eafa9b184a1e3a44c61c36e1756a91bb60e22d47e999cda6f1b14ca96 +size 88386 diff --git a/message_ix_models/model/material/report/materials.yaml b/message_ix_models/model/material/report/materials.yaml new file mode 100644 index 0000000000..73744836cb --- /dev/null +++ b/message_ix_models/model/material/report/materials.yaml @@ -0,0 +1,21 @@ + +combine: + # Name and dimensions of quantity to be created +- key: coal:nl-ya + # Inputs to sum + inputs: + # Input quantity. If dimensions are none ('name::tag') then the necessary + # dimensions are inferred: the union of the dimensions of 'key:' above, + # plus any dimensions appearing in 'select:'' + - quantity: in::pe # e.g. 'in:nl-t-ya:pe' is inferred + # Values to select + select: {t: [coal, lignite]} + # Weight for these values in the weighted sum + - quantity: in::import + select: {t: coal} + - quantity: in::export + select: {t: coal} + weight: -1 + # commented (PNK 2019-10-07): doesn't exist + # - quantity: in::bunker + # select: {t: coal} diff --git a/message_ix_models/model/material/tax_emission_1000f.csv b/message_ix_models/model/material/tax_emission_1000f.csv new file mode 100644 index 0000000000..a4a6fc8198 --- /dev/null +++ b/message_ix_models/model/material/tax_emission_1000f.csv @@ -0,0 +1,14 @@ +node,type_emission,type_tec,year,lvl,mrg +World,TCE,all,2025,102.1439278727925,0.0 +World,TCE,all,2030,130.36441186537496,0.0 +World,TCE,all,2035,166.3816952699343,0.0 +World,TCE,all,2040,212.34989001051068,0.0 +World,TCE,all,2045,271.0182494193178,0.0 +World,TCE,all,2050,345.89559483490166,0.0 +World,TCE,all,2055,441.4601702377553,0.0 +World,TCE,all,2060,563.4274758525585,0.0 +World,TCE,all,2070,917.7639879950095,0.0 +World,TCE,all,2080,1494.9408286949079,0.0 +World,TCE,all,2090,2435.101083211352,0.0 +World,TCE,all,2100,3966.5230701029004,0.0 +World,TCE,all,2110,6461.048115879377,0.0 diff --git a/message_ix_models/model/material/tax_emission_600f.csv b/message_ix_models/model/material/tax_emission_600f.csv new file mode 100644 index 0000000000..a8b90340c9 --- /dev/null +++ b/message_ix_models/model/material/tax_emission_600f.csv @@ -0,0 +1,14 @@ +node,type_emission,type_tec,year,lvl,mrg +World,TCE,all,2025,191.1174531180089,0.0 +World,TCE,all,2030,243.91968168647298,0.0 +World,TCE,all,2035,311.31019246731444,0.0 +World,TCE,all,2040,397.3194588643599,0.0 +World,TCE,all,2045,507.09149977105983,0.0 +World,TCE,all,2050,647.1915316582767,0.0 +World,TCE,all,2055,825.9986192615938,0.0 +World,TCE,all,2060,1054.2068084140296,0.0 +World,TCE,all,2070,1717.1918057378095,0.0 +World,TCE,all,2080,2797.124505512571,0.0 +World,TCE,all,2090,4556.221077456938,0.0 +World,TCE,all,2100,7421.604031479733,0.0 +World,TCE,all,2110,12089.010928947138,0.0 diff --git a/message_ix_models/model/material/test_SSP_costs.ipynb b/message_ix_models/model/material/test_SSP_costs.ipynb new file mode 100644 index 0000000000..d22ead04f6 --- /dev/null +++ b/message_ix_models/model/material/test_SSP_costs.ipynb @@ -0,0 +1,704 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 7, + "outputs": [ + { + "data": { + "text/plain": "", + "application/javascript": "if (typeof IPython !== 'undefined') { IPython.OutputArea.prototype._should_scroll = function(lines){ return false; }}" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model = \"MESSAGEix-Materials\"\n", + "scenario = \"baseline_DEFAULT_test_0310_macro\"\n", + "\n", + "\n", + "import ixmp\n", + "import message_ix\n", + "mp = ixmp.Platform(\"ixmp_dev\")\n", + "scen = message_ix.Scenario(mp, model, scenario)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-28T21:04:48.800572400Z", + "start_time": "2023-11-28T21:04:39.706723100Z" + } + }, + "id": "64cd795b05a0bdd" + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [], + "source": [ + "model = \"MESSAGEix-Materials\"\n", + "scenario = \"baseline_DEFAULT_0108_macro\"\n", + "\n", + "\n", + "import ixmp\n", + "import message_ix\n", + "mp = ixmp.Platform(\"ixmp_dev\")\n", + "scen = message_ix.Scenario(mp, model, scenario)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-29T07:51:33.404668500Z", + "start_time": "2023-11-29T07:51:32.023778600Z" + } + }, + "id": "46f136d4c3eeff9d" + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [], + "source": [ + "scen.to_excel(\"C:/Users\\maczek\\PycharmProjects\\message_data/macro_dump.xlsx\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-29T09:15:51.773767900Z", + "start_time": "2023-11-29T07:53:47.494599700Z" + } + }, + "id": "5dbbbdf07d5feade" + }, + { + "cell_type": "code", + "execution_count": 8, + "outputs": [ + { + "data": { + "text/plain": "True" + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scen.has_solution()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-28T21:04:51.769066400Z", + "start_time": "2023-11-28T21:04:51.706749500Z" + } + }, + "id": "5bf2f475d4a0cd34" + }, + { + "cell_type": "code", + "execution_count": 9, + "outputs": [], + "source": [ + "base = scen.clone(model=scen.model, scenario=\"SSP_supply_cost_test_baseline_macro\", keep_solution=False)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-28T21:05:36.800203700Z", + "start_time": "2023-11-28T21:05:18.456553600Z" + } + }, + "id": "2f393bdf1d5d3b0e" + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [], + "source": [ + "from message_ix_models.tools.costs.config import Config\n", + "from message_ix_models.tools.costs.projections import create_cost_projections" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-26T23:16:20.039581700Z", + "start_time": "2023-11-26T23:16:16.086701100Z" + } + }, + "id": "a8aca9ff39fadf22" + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Selected module: materials\n", + "Selected node: R12\n", + "Selected reference region: R12_NAM\n", + "Selected base year: 2021\n", + "Selected method: convergence\n", + "Selected fixed O&M rate: 0.025\n", + "Selected format: message\n", + "Selected scenario: SSP2\n", + "Selected convergence year: 2050\n", + "For the convergence method, only the SSP scenario(s) itself needs to be specified. No scenario version (previous vs. updated) is needed.\n", + "...Calculating regional differentiation in base year+region...\n", + "The following technologies are not projected due to insufficient data:\n", + "CH2O_synth\n", + "CH2O_to_resin\n", + "DUMMY_alumina_supply\n", + "DUMMY_coal_supply\n", + "DUMMY_gas_supply\n", + "DUMMY_limestone_supply_cement\n", + "DUMMY_limestone_supply_steel\n", + "DUMMY_ore_supply\n", + "agg_ref\n", + "feedstock_t/d\n", + "finishing_aluminum\n", + "gas_processing_petro\n", + "hydrotreating_ref\n", + "import_NFert\n", + "import_NH3\n", + "import_aluminum\n", + "import_petro\n", + "import_steel\n", + "manuf_aluminum\n", + "meth_bal\n", + "meth_imp\n", + "meth_t_d\n", + "meth_t_d_material\n", + "meth_trd\n", + "other_EOL_aluminum\n", + "other_EOL_cement\n", + "other_EOL_steel\n", + "prep_secondary_aluminum_1\n", + "prep_secondary_aluminum_2\n", + "prep_secondary_aluminum_3\n", + "prep_secondary_steel_1\n", + "prep_secondary_steel_2\n", + "prep_secondary_steel_3\n", + "production_HVC\n", + "residual_NH3\n", + "scrap_recovery_aluminum\n", + "scrap_recovery_cement\n", + "scrap_recovery_steel\n", + "secondary_aluminum\n", + "total_EOL_aluminum\n", + "total_EOL_cement\n", + "total_EOL_steel\n", + "trade_NFert\n", + "trade_NH3\n", + "trade_aluminum\n", + "trade_petro\n", + "trade_steel\n", + "......(Using year 2021 data from WEO.)\n", + "...Applying learning rates to reference region...\n", + "...Applying splines to converge...\n", + "...Creating MESSAGE outputs...\n" + ] + } + ], + "source": [ + "cfg = Config(module=\"materials\", ref_region=\"R12_NAM\", method=\"convergence\", format=\"message\", scenario=\"SSP2\")\n", + "\n", + "out_materials = create_cost_projections(\n", + " node=cfg.node,\n", + " ref_region=cfg.ref_region,\n", + " base_year=cfg.base_year,\n", + " module=cfg.module,\n", + " method=cfg.method,\n", + " scenario_version=cfg.scenario_version,\n", + " scenario=cfg.scenario,\n", + " convergence_year=cfg.convergence_year,\n", + " fom_rate=cfg.fom_rate,\n", + " format=cfg.format,\n", + ")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-26T23:22:58.008834200Z", + "start_time": "2023-11-26T23:21:43.039935700Z" + } + }, + "id": "d038d7d62bea30a2" + }, + { + "cell_type": "code", + "execution_count": 10, + "outputs": [ + { + "data": { + "text/plain": " node_loc technology year_vtg year_act value unit\n0 R12_AFR bio_hpl 1960 1960 9.519231 USD/kWa\n1 R12_AFR bio_hpl 1960 1965 9.519231 USD/kWa\n2 R12_AFR bio_hpl 1960 1970 9.519231 USD/kWa\n3 R12_AFR bio_hpl 1960 1975 9.519231 USD/kWa\n4 R12_AFR bio_hpl 1960 1980 9.519231 USD/kWa\n... ... ... ... ... ... ...\n11799575 R12_WEU visbreaker_ref 2090 2100 1.478498 USD/kWa\n11799576 R12_WEU visbreaker_ref 2090 2110 1.892602 USD/kWa\n11799577 R12_WEU visbreaker_ref 2100 2100 1.155000 USD/kWa\n11799578 R12_WEU visbreaker_ref 2100 2110 1.478498 USD/kWa\n11799579 R12_WEU visbreaker_ref 2110 2110 1.155000 USD/kWa\n\n[892848 rows x 6 columns]", + "text/html": "
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" + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-26T23:24:06.305578100Z", + "start_time": "2023-11-26T23:23:59.945987500Z" + } + }, + "id": "849530ddbdb9a6bd" + }, + { + "cell_type": "code", + "execution_count": 11, + "outputs": [ + { + "ename": "ValueError", + "evalue": "The index set 'technology' does not have an element 'meth_i'!", + "output_type": "error", + "traceback": [ + "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[1;31mValueError\u001B[0m Traceback (most recent call last)", + "Input \u001B[1;32mIn [11]\u001B[0m, in \u001B[0;36m\u001B[1;34m()\u001B[0m\n\u001B[0;32m 1\u001B[0m base\u001B[38;5;241m.\u001B[39mcheck_out()\n\u001B[1;32m----> 2\u001B[0m \u001B[43mbase\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43madd_par\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mfix_cost\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mout_materials\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfix_cost\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mdrop_duplicates\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mdrop\u001B[49m\u001B[43m(\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mscenario_version\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mscenario\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43maxis\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m1\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\ixmp\\core\\scenario.py:535\u001B[0m, in \u001B[0;36mScenario.add_par\u001B[1;34m(self, name, key_or_data, value, unit, comment)\u001B[0m\n\u001B[0;32m 529\u001B[0m elements \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mmap\u001B[39m(\n\u001B[0;32m 530\u001B[0m \u001B[38;5;28;01mlambda\u001B[39;00m e: (e\u001B[38;5;241m.\u001B[39mkey, e\u001B[38;5;241m.\u001B[39mvalue, e\u001B[38;5;241m.\u001B[39munit, e\u001B[38;5;241m.\u001B[39mcomment),\n\u001B[0;32m 531\u001B[0m data\u001B[38;5;241m.\u001B[39mastype(types)\u001B[38;5;241m.\u001B[39mitertuples(),\n\u001B[0;32m 532\u001B[0m )\n\u001B[0;32m 534\u001B[0m \u001B[38;5;66;03m# Store\u001B[39;00m\n\u001B[1;32m--> 535\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_backend\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mitem_set_elements\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mpar\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mname\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43melements\u001B[49m\u001B[43m)\u001B[49m\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\ixmp\\core\\timeseries.py:108\u001B[0m, in \u001B[0;36mTimeSeries._backend\u001B[1;34m(self, method, *args, **kwargs)\u001B[0m\n\u001B[0;32m 102\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m_backend\u001B[39m(\u001B[38;5;28mself\u001B[39m, method, \u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[0;32m 103\u001B[0m \u001B[38;5;124;03m\"\"\"Convenience for calling *method* on the backend.\u001B[39;00m\n\u001B[0;32m 104\u001B[0m \n\u001B[0;32m 105\u001B[0m \u001B[38;5;124;03m The weak reference to the Platform object is used, if the Platform is still\u001B[39;00m\n\u001B[0;32m 106\u001B[0m \u001B[38;5;124;03m alive.\u001B[39;00m\n\u001B[0;32m 107\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[1;32m--> 108\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mplatform\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_backend\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmethod\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\ixmp\\backend\\base.py:53\u001B[0m, in \u001B[0;36mBackend.__call__\u001B[1;34m(self, obj, method, *args, **kwargs)\u001B[0m\n\u001B[0;32m 47\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m__call__\u001B[39m(\u001B[38;5;28mself\u001B[39m, obj, method, \u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[0;32m 48\u001B[0m \u001B[38;5;124;03m\"\"\"Call the backend method `method` for `obj`.\u001B[39;00m\n\u001B[0;32m 49\u001B[0m \n\u001B[0;32m 50\u001B[0m \u001B[38;5;124;03m The class attribute obj._backend_prefix is used to determine a prefix for the\u001B[39;00m\n\u001B[0;32m 51\u001B[0m \u001B[38;5;124;03m method name, e.g. 'ts_{method}'.\u001B[39;00m\n\u001B[0;32m 52\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[1;32m---> 53\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mgetattr\u001B[39;49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmethod\u001B[49m\u001B[43m)\u001B[49m\u001B[43m(\u001B[49m\u001B[43mobj\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\ixmp\\backend\\jdbc.py:1047\u001B[0m, in \u001B[0;36mJDBCBackend.item_set_elements\u001B[1;34m(self, s, type, name, elements)\u001B[0m\n\u001B[0;32m 1044\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m java\u001B[38;5;241m.\u001B[39mIxException \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[0;32m 1045\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28many\u001B[39m(s \u001B[38;5;129;01min\u001B[39;00m e\u001B[38;5;241m.\u001B[39margs[\u001B[38;5;241m0\u001B[39m] \u001B[38;5;28;01mfor\u001B[39;00m s \u001B[38;5;129;01min\u001B[39;00m (\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mdoes not have an element\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mThe unit\u001B[39m\u001B[38;5;124m\"\u001B[39m)):\n\u001B[0;32m 1046\u001B[0m \u001B[38;5;66;03m# Re-raise as Python ValueError\u001B[39;00m\n\u001B[1;32m-> 1047\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(e\u001B[38;5;241m.\u001B[39margs[\u001B[38;5;241m0\u001B[39m]) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;28mNone\u001B[39m\n\u001B[0;32m 1048\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mcannot be edited\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;129;01min\u001B[39;00m e\u001B[38;5;241m.\u001B[39margs[\u001B[38;5;241m0\u001B[39m]:\n\u001B[0;32m 1049\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mRuntimeError\u001B[39;00m(e\u001B[38;5;241m.\u001B[39margs[\u001B[38;5;241m0\u001B[39m])\n", + "\u001B[1;31mValueError\u001B[0m: The index set 'technology' does not have an element 'meth_i'!" + ] + } + ], + "source": [ + "base.check_out()\n", + "base.add_par(\"fix_cost\", )" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-26T23:26:51.149516500Z", + "start_time": "2023-11-26T23:26:26.039933300Z" + } + }, + "id": "b032114dc82a0126" + }, + { + "cell_type": "code", + "execution_count": 13, + "outputs": [], + "source": [ + "tec_set = list(base.set(\"technology\"))" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-26T23:27:32.868165400Z", + "start_time": "2023-11-26T23:27:32.836970100Z" + } + }, + "id": "2315f0de398752dd" + }, + { + "cell_type": "code", + "execution_count": 14, + "outputs": [], + "source": [ + "fix_cost = out_materials.fix_cost.drop_duplicates().drop([\"scenario_version\", \"scenario\"], axis=1)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-26T23:27:59.915107400Z", + "start_time": "2023-11-26T23:27:53.024521700Z" + } + }, + "id": "ebf07c7956772e0f" + }, + { + "cell_type": "code", + "execution_count": 17, + "outputs": [], + "source": [ + "base.add_par(\"fix_cost\", fix_cost[fix_cost[\"technology\"].isin(tec_set)])" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-26T23:29:13.055635600Z", + "start_time": "2023-11-26T23:28:40.039748300Z" + } + }, + "id": "4e534e7cbf802e51" + }, + { + "cell_type": "code", + "execution_count": 18, + "outputs": [], + "source": [ + "base.commit(\"update cost assumption with tool\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-26T23:30:05.027074Z", + "start_time": "2023-11-26T23:29:19.164705500Z" + } + }, + "id": "869f9b234e212056" + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "def modify_costs_with_tool(scen_name, ssp):\n", + " mp = ixmp.Platform(\"ixmp_dev\")\n", + " base = message_ix.Scenario(mp, \"SSP_supply_cost_test_baseline\", scenario=scen_name)\n", + " scen = base.clone(model=base.model, scenario=base.scenario.replace(\"baseline\", ssp))\n", + " \n", + " tec_set = list(scen.set(\"technology\"))\n", + " \n", + " cfg = Config(module=\"materials\", ref_region=\"R12_NAM\", method=\"convergence\", format=\"message\", scenario=ssp)\n", + " \n", + " out_materials = create_cost_projections(\n", + " node=cfg.node,\n", + " ref_region=cfg.ref_region,\n", + " base_year=cfg.base_year,\n", + " module=cfg.module,\n", + " method=cfg.method,\n", + " scenario_version=cfg.scenario_version,\n", + " scenario=cfg.scenario,\n", + " convergence_year=cfg.convergence_year,\n", + " fom_rate=cfg.fom_rate,\n", + " format=cfg.format,\n", + " )\n", + " \n", + " fix_cost = out_materials.fix_cost.drop_duplicates().drop([\"scenario_version\", \"scenario\"], axis=1)\n", + " scen.add_par(\"fix_cost\", fix_cost[fix_cost[\"technology\"].isin(tec_set)])\n", + " inv_cost = out_materials.inv_cost.drop_duplicates().drop([\"scenario_version\", \"scenario\"], axis=1)\n", + " scen.add_par(\"fix_cost\", inv_cost[inv_cost[\"technology\"].isin(tec_set)])\n", + " \n", + " scen.commit(\"update cost assumption with tool\")\n", + " #scen.solve(model=\"MESSAGE\")" + ], + "metadata": { + "collapsed": false + }, + "id": "25a079df2acd518c" + }, + { + "cell_type": "code", + "execution_count": 22, + "outputs": [ + { + "data": { + "text/plain": " model scenario scheme \\\n0 MESSAGEix-Materials 2.0deg_no_SDGs_petro_thesis_1_final_macro MESSAGE \n1 MESSAGEix-Materials 2.0deg_no_SDGs_petro_thesis_1_macro MESSAGE \n2 MESSAGEix-Materials 2.0deg_petro_thesis_1 MESSAGE \n3 MESSAGEix-Materials 2.0deg_petro_thesis_2 MESSAGE \n4 MESSAGEix-Materials 2.0deg_petro_thesis_2_final_macro MESSAGE \n.. ... ... ... \n366 MESSAGEix-Materials test_ccs MESSAGE \n367 MESSAGEix-Materials test_foil_trp_constraint MESSAGE \n368 MESSAGEix-Materials test_foil_trp_constraint_IEA_calib MESSAGE \n369 MESSAGEix-Materials test_report_JM MESSAGE \n370 MESSAGEix-Materials timeseries_test MESSAGE \n\n is_default is_locked cre_user cre_date upd_user \\\n0 1 0 maczek 2023-06-27 23:24:23.000000 maczek \n1 1 0 maczek 2023-05-09 09:34:33.000000 maczek \n2 1 0 maczek 2023-02-08 23:38:03.000000 maczek \n3 1 0 maczek 2023-04-06 16:44:33.000000 None \n4 1 0 maczek 2023-06-21 23:48:48.000000 maczek \n.. ... ... ... ... ... \n366 1 0 unlu 2022-07-12 15:32:10.000000 None \n367 1 0 maczek 2023-10-12 16:37:45.000000 None \n368 1 0 maczek 2023-10-16 13:51:53.000000 None \n369 1 1 min 2021-08-02 16:17:16.000000 None \n370 1 0 unlu 2022-06-09 13:59:42.000000 None \n\n upd_date lock_user lock_date \\\n0 2023-07-03 17:01:57.000000 None None \n1 2023-05-09 10:28:12.000000 None None \n2 2023-02-08 23:44:53.000000 None None \n3 None None None \n4 2023-06-22 00:36:08.000000 None None \n.. ... ... ... \n366 None None None \n367 None None None \n368 None None None \n369 None min 2021-08-02 16:39:25.000000 \n370 None None None \n\n annotation version \n0 clone Scenario from 'MESSAGEix-Materials|NoPol... 1 \n1 clone Scenario from 'MESSAGEix-Materials|NoPol... 4 \n2 clone Scenario from 'MESSAGEix-Materials|NoPol... 1 \n3 clone Scenario from 'MESSAGEix-Materials|NoPol... 6 \n4 clone Scenario from 'MESSAGEix-Materials|NoPol... 1 \n.. ... ... \n366 clone Scenario from 'MESSAGEix-Materials|NoPol... 1 \n367 clone Scenario from 'MESSAGEix-Materials|NoPol... 1 \n368 clone Scenario from 'MESSAGEix-Materials|test_... 1 \n369 clone Scenario from 'Buildings_SSP2|test', ver... 1 \n370 clone Scenario from 'MESSAGEix-Materials|NoPol... 1 \n\n[371 rows x 13 columns]", + "text/html": "
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modelscenarioschemeis_defaultis_lockedcre_usercre_dateupd_userupd_datelock_userlock_dateannotationversion
0MESSAGEix-Materials2.0deg_no_SDGs_petro_thesis_1_final_macroMESSAGE10maczek2023-06-27 23:24:23.000000maczek2023-07-03 17:01:57.000000NoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...1
1MESSAGEix-Materials2.0deg_no_SDGs_petro_thesis_1_macroMESSAGE10maczek2023-05-09 09:34:33.000000maczek2023-05-09 10:28:12.000000NoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...4
2MESSAGEix-Materials2.0deg_petro_thesis_1MESSAGE10maczek2023-02-08 23:38:03.000000maczek2023-02-08 23:44:53.000000NoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...1
3MESSAGEix-Materials2.0deg_petro_thesis_2MESSAGE10maczek2023-04-06 16:44:33.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...6
4MESSAGEix-Materials2.0deg_petro_thesis_2_final_macroMESSAGE10maczek2023-06-21 23:48:48.000000maczek2023-06-22 00:36:08.000000NoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...1
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366MESSAGEix-Materialstest_ccsMESSAGE10unlu2022-07-12 15:32:10.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...1
367MESSAGEix-Materialstest_foil_trp_constraintMESSAGE10maczek2023-10-12 16:37:45.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...1
368MESSAGEix-Materialstest_foil_trp_constraint_IEA_calibMESSAGE10maczek2023-10-16 13:51:53.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-Materials|test_...1
369MESSAGEix-Materialstest_report_JMMESSAGE11min2021-08-02 16:17:16.000000NoneNonemin2021-08-02 16:39:25.000000clone Scenario from 'Buildings_SSP2|test', ver...1
370MESSAGEix-Materialstimeseries_testMESSAGE10unlu2022-06-09 13:59:42.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...1
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371 rows × 13 columns

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" + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mp.scenario_list(model=\"MESSAGEix-Materials\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-26T23:56:29.295406400Z", + "start_time": "2023-11-26T23:56:28.795734900Z" + } + }, + "id": "8f0f995ddaaeb4e4" + }, + { + "cell_type": "code", + "execution_count": 77, + "outputs": [], + "source": [ + "import pandas as pd" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-27T13:13:25.213762300Z", + "start_time": "2023-11-27T13:13:25.160590500Z" + } + }, + "id": "d8c358646f3093fa" + }, + { + "cell_type": "code", + "execution_count": 78, + "outputs": [], + "source": [ + "r_df = pd.read_csv(\"C:/Users\\maczek\\PycharmProjects\\message_data\\message_data\\model\\material/output_file.csv\")\n", + "py_df = pd.read_csv(\"C:/Users\\maczek\\PycharmProjects\\message_data\\message_data\\model\\material/py_output.csv\")#, usecols=[1,2,3])" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-27T13:13:25.461244100Z", + "start_time": "2023-11-27T13:13:25.414353300Z" + } + }, + "id": "b9e448c393a4c115" + }, + { + "cell_type": "code", + "execution_count": 79, + "outputs": [ + { + "data": { + "text/plain": " Unnamed: 0 region reg_no year consumption pop \\\n0 0 Canada 1 1970 11085 21.71685 \n1 1 USA 2 1970 127304 210.11660 \n2 2 Mexico 3 1970 4168 50.59590 \n3 3 Rest Central America 4 1970 434 41.86008 \n4 4 Brazil 5 1970 6088 95.98846 \n... ... ... ... ... ... ... \n1059 1113 Indonesia 22 2012 15006 244.18970 \n1060 1114 Japan 23 2012 68800 126.60790 \n1061 1115 Oceania 24 2012 7945 29.71791 \n1062 1116 Other South Asia 25 2012 5959 445.52510 \n1063 1117 Other South Africa 26 2012 439 144.14590 \n\n gdp_pcap cons_pcap del_t \n0 17450.805611 510.433143 -40 \n1 20233.017350 605.873120 -40 \n2 6910.351162 82.378216 -40 \n3 5032.145662 10.367873 -40 \n4 4246.812446 63.424291 -40 \n... ... ... ... \n1059 4272.029000 61.452223 2 \n1060 31957.190000 543.410008 2 \n1061 30264.620000 267.347199 2 \n1062 2062.911000 13.375228 2 \n1063 2063.026000 3.045525 2 \n\n[1064 rows x 9 columns]", + "text/html": "
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Unnamed: 0regionreg_noyearconsumptionpopgdp_pcapcons_pcapdel_t
00Canada119701108521.7168517450.805611510.433143-40
11USA21970127304210.1166020233.017350605.873120-40
22Mexico31970416850.595906910.35116282.378216-40
33Rest Central America4197043441.860085032.14566210.367873-40
44Brazil51970608895.988464246.81244663.424291-40
..............................
10591113Indonesia22201215006244.189704272.02900061.4522232
10601114Japan23201268800126.6079031957.190000543.4100082
10611115Oceania242012794529.7179130264.620000267.3471992
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" + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "py_df" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-27T13:13:25.915333400Z", + "start_time": "2023-11-27T13:13:25.877536100Z" + } + }, + "id": "70bcaef1ace99b44" + }, + { + "cell_type": "code", + "execution_count": 88, + "outputs": [], + "source": [ + "df = r_df.set_index([\"reg_no\", \"year\"]).join(py_df.set_index([\"reg_no\", \"year\"])[\"gdp_pcap\"])" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-27T13:14:20.010108900Z", + "start_time": "2023-11-27T13:14:19.940929600Z" + } + }, + "id": "cd43f48dc605ec77" + }, + { + "cell_type": "code", + "execution_count": 85, + "outputs": [ + { + "data": { + "text/plain": " Unnamed: 0 reg_no region year consumption \\\n0 1 1 Canada (1) 1970 11085 \n1 2 1 Canada (1) 1971 11753 \n2 3 1 Canada (1) 1972 12842 \n3 4 1 Canada (1) 1973 14153 \n4 5 1 Canada (1) 1974 15458 \n... ... ... ... ... ... \n1059 1060 26 Other South Africa (26) 2008 272 \n1060 1061 26 Other South Africa (26) 2009 344 \n1061 1062 26 Other South Africa (26) 2010 355 \n1062 1063 26 Other South Africa (26) 2011 474 \n1063 1064 26 Other South Africa (26) 2012 439 \n\n pop gdp.pcap cons.pcap del.t \n0 21.71685 17450.805611 510.433143 -40 \n1 22.04843 18025.700000 533.053827 -39 \n2 22.34410 18755.830000 574.737850 -38 \n3 22.61467 19822.000000 625.832701 -37 \n4 22.87666 20318.250000 675.710528 -36 \n... ... ... ... ... \n1059 130.25150 1871.848000 2.088268 -2 \n1060 133.53860 1843.252000 2.576034 -1 \n1061 136.94900 1909.069000 2.592206 0 \n1062 140.48880 1988.006000 3.373934 1 \n1063 144.14590 2063.026000 3.045525 2 \n\n[1064 rows x 9 columns]", + "text/html": "
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Unnamed: 0reg_noregionyearconsumptionpopgdp.pcapcons.pcapdel.t
011Canada (1)19701108521.7168517450.805611510.433143-40
121Canada (1)19711175322.0484318025.700000533.053827-39
231Canada (1)19721284222.3441018755.830000574.737850-38
341Canada (1)19731415322.6146719822.000000625.832701-37
451Canada (1)19741545822.8766620318.250000675.710528-36
..............................
1059106026Other South Africa (26)2008272130.251501871.8480002.088268-2
1060106126Other South Africa (26)2009344133.538601843.2520002.576034-1
1061106226Other South Africa (26)2010355136.949001909.0690002.5922060
1062106326Other South Africa (26)2011474140.488801988.0060003.3739341
1063106426Other South Africa (26)2012439144.145902063.0260003.0455252
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1064 rows × 9 columns

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" + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "r_df" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-27T13:13:55.052664Z", + "start_time": "2023-11-27T13:13:54.991222800Z" + } + }, + "id": "273365e96cb009d6" + }, + { + "cell_type": "code", + "execution_count": 90, + "outputs": [ + { + "data": { + "text/plain": " Unnamed: 0 region consumption pop \\\nreg_no year \n1 1970 1 Canada (1) 11085 21.71685 \n 1971 2 Canada (1) 11753 22.04843 \n 1972 3 Canada (1) 12842 22.34410 \n 1973 4 Canada (1) 14153 22.61467 \n 1974 5 Canada (1) 15458 22.87666 \n... ... ... ... ... \n26 2008 1060 Other South Africa (26) 272 130.25150 \n 2009 1061 Other South Africa (26) 344 133.53860 \n 2010 1062 Other South Africa (26) 355 136.94900 \n 2011 1063 Other South Africa (26) 474 140.48880 \n 2012 1064 Other South Africa (26) 439 144.14590 \n\n gdp.pcap cons.pcap del.t gdp_pcap \nreg_no year \n1 1970 17450.805611 510.433143 -40 17450.805611 \n 1971 18025.700000 533.053827 -39 18025.700000 \n 1972 18755.830000 574.737850 -38 18755.830000 \n 1973 19822.000000 625.832701 -37 19822.000000 \n 1974 20318.250000 675.710528 -36 20318.250000 \n... ... ... ... ... \n26 2008 1871.848000 2.088268 -2 1871.848000 \n 2009 1843.252000 2.576034 -1 1843.252000 \n 2010 1909.069000 2.592206 0 1909.069000 \n 2011 1988.006000 3.373934 1 1988.006000 \n 2012 2063.026000 3.045525 2 2063.026000 \n\n[1064 rows x 8 columns]", + "text/html": "
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Unnamed: 0regionconsumptionpopgdp.pcapcons.pcapdel.tgdp_pcap
reg_noyear
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19734Canada (1)1415322.6146719822.000000625.832701-3719822.000000
19745Canada (1)1545822.8766620318.250000675.710528-3620318.250000
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2620081060Other South Africa (26)272130.251501871.8480002.088268-21871.848000
20091061Other South Africa (26)344133.538601843.2520002.576034-11843.252000
20101062Other South Africa (26)355136.949001909.0690002.59220601909.069000
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1064 rows × 8 columns

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" + }, + "execution_count": 90, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "df[np.isclose(df[\"gdp.pcap\"], df[\"gdp_pcap\"])]" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-27T13:14:27.669971100Z", + "start_time": "2023-11-27T13:14:27.621595600Z" + } + }, + "id": "e735a3b902fd3cd0" + }, + { + "cell_type": "code", + "execution_count": 91, + "outputs": [ + { + "data": { + "text/plain": " Unnamed: 0 reg_no region year consumption \\\n0 1 1 Canada (1) 1970 11085 \n1 2 1 Canada (1) 1971 11753 \n2 3 1 Canada (1) 1972 12842 \n3 4 1 Canada (1) 1973 14153 \n4 5 1 Canada (1) 1974 15458 \n... ... ... ... ... ... \n1059 1060 26 Other South Africa (26) 2008 272 \n1060 1061 26 Other South Africa (26) 2009 344 \n1061 1062 26 Other South Africa (26) 2010 355 \n1062 1063 26 Other South Africa (26) 2011 474 \n1063 1064 26 Other South Africa (26) 2012 439 \n\n pop gdp.pcap cons.pcap del.t \n0 21.71685 17450.805611 510.433143 -40 \n1 22.04843 18025.700000 533.053827 -39 \n2 22.34410 18755.830000 574.737850 -38 \n3 22.61467 19822.000000 625.832701 -37 \n4 22.87666 20318.250000 675.710528 -36 \n... ... ... ... ... \n1059 130.25150 1871.848000 2.088268 -2 \n1060 133.53860 1843.252000 2.576034 -1 \n1061 136.94900 1909.069000 2.592206 0 \n1062 140.48880 1988.006000 3.373934 1 \n1063 144.14590 2063.026000 3.045525 2 \n\n[1064 rows x 9 columns]", + "text/html": "
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Unnamed: 0reg_noregionyearconsumptionpopgdp.pcapcons.pcapdel.t
011Canada (1)19701108521.7168517450.805611510.433143-40
121Canada (1)19711175322.0484318025.700000533.053827-39
231Canada (1)19721284222.3441018755.830000574.737850-38
341Canada (1)19731415322.6146719822.000000625.832701-37
451Canada (1)19741545822.8766620318.250000675.710528-36
..............................
1059106026Other South Africa (26)2008272130.251501871.8480002.088268-2
1060106126Other South Africa (26)2009344133.538601843.2520002.576034-1
1061106226Other South Africa (26)2010355136.949001909.0690002.5922060
1062106326Other South Africa (26)2011474140.488801988.0060003.3739341
1063106426Other South Africa (26)2012439144.145902063.0260003.0455252
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1064 rows × 9 columns

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" + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "r_df" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-27T13:14:32.996061100Z", + "start_time": "2023-11-27T13:14:32.895541600Z" + } + }, + "id": "76887683edf06dc4" + }, + { + "cell_type": "code", + "execution_count": 51, + "outputs": [ + { + "data": { + "text/plain": " region year consumption\n0 Canada 1970 11085\n1 USA 1970 127304\n2 Mexico 1970 4168\n3 Rest Central America 1970 434\n4 Brazil 1970 6088\n.. ... ... ...\n866 Korea 2012 57650\n867 Japan 2012 68800\n868 Oceania 2012 7945\n869 Other South Asia 2012 5959\n870 Other South Africa 2012 439\n\n[871 rows x 3 columns]", + "text/html": "
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regionyearconsumption
0Canada197011085
1USA1970127304
2Mexico19704168
3Rest Central America1970434
4Brazil19706088
............
866Korea201257650
867Japan201268800
868Oceania20127945
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871 rows × 3 columns

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" + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "py_df#[\"region\"].unique()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-27T12:43:19.069975600Z", + "start_time": "2023-11-27T12:43:18.969677200Z" + } + }, + "id": "f67fd7ab71039148" + }, + { + "cell_type": "code", + "execution_count": 54, + "outputs": [ + { + "data": { + "text/plain": " Unnamed: 0 region year consumption pop \\\n0 0 Canada 1970 11085 21.71685 \n1 1 USA 1970 127304 210.11660 \n2 2 Mexico 1970 4168 50.59590 \n3 3 Rest Central America 1970 434 41.86008 \n4 4 Brazil 1970 6088 95.98846 \n.. ... ... ... ... ... \n866 898 Korea 2012 57650 72.94119 \n867 899 Japan 2012 68800 126.60790 \n868 900 Oceania 2012 7945 29.71791 \n869 901 Other South Asia 2012 5959 445.52510 \n870 902 Other South Africa 2012 439 144.14590 \n\n gdp_pcap cons_pcap del_t \n0 17450.805611 510.433143 -40 \n1 20233.017350 605.873120 -40 \n2 6910.351162 82.378216 -40 \n3 5032.145662 10.367873 -40 \n4 4246.812446 63.424291 -40 \n.. ... ... ... \n866 19790.770000 790.362757 2 \n867 31957.190000 543.410008 2 \n868 30264.620000 267.347199 2 \n869 2062.911000 13.375228 2 \n870 2063.026000 3.045525 2 \n\n[871 rows x 8 columns]", + "text/html": "
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Unnamed: 0regionyearconsumptionpopgdp_pcapcons_pcapdel_t
00Canada19701108521.7168517450.805611510.433143-40
11USA1970127304210.1166020233.017350605.873120-40
22Mexico1970416850.595906910.35116282.378216-40
33Rest Central America197043441.860085032.14566210.367873-40
44Brazil1970608895.988464246.81244663.424291-40
...........................
866898Korea20125765072.9411919790.770000790.3627572
867899Japan201268800126.6079031957.190000543.4100082
868900Oceania2012794529.7179130264.620000267.3471992
869901Other South Asia20125959445.525102062.91100013.3752282
870902Other South Africa2012439144.145902063.0260003.0455252
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" + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "py_df" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-27T12:43:39.390431200Z", + "start_time": "2023-11-27T12:43:39.352903100Z" + } + }, + "id": "777d42af7aa34009" + }, + { + "cell_type": "code", + "execution_count": 67, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\maczek\\AppData\\Local\\Temp\\ipykernel_5280\\2693521517.py:1: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n", + " r_df[\"region\"] = r_df[\"region\"].str.replace(\"(\",\"\").str.replace(\")\",\"\").str.replace('\\d+', '').str[:-1]\n", + "C:\\Users\\maczek\\AppData\\Local\\Temp\\ipykernel_5280\\2693521517.py:1: FutureWarning: The default value of regex will change from True to False in a future version.\n", + " r_df[\"region\"] = r_df[\"region\"].str.replace(\"(\",\"\").str.replace(\")\",\"\").str.replace('\\d+', '').str[:-1]\n" + ] + } + ], + "source": [ + "r_df[\"region\"] = r_df[\"region\"].str.replace(\"(\",\"\").str.replace(\")\",\"\").str.replace('\\d+', '').str[:-1]" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-27T12:46:37.385766700Z", + "start_time": "2023-11-27T12:46:37.307654400Z" + } + }, + "id": "7450d215c7e87cb3" + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import yaml\n", + "\n", + "\n", + "path = \"C:/Users\\maczek\\PycharmProjects\\message_data\\data\\material/aluminum/demand_alu.yaml\"\n", + "path = \"C:/Users\\maczek\\PycharmProjects\\message_data\\data\\material/steel_cement/demand_cement.yaml\"\n", + "path = \"C:/Users\\maczek\\PycharmProjects\\message_data\\data\\material/steel_cement/demand_steel.yaml\"\n", + "\n", + "with open(path, 'r') as file:\n", + " yaml_data = file.read()\n", + "\n", + "data_list = yaml.safe_load(yaml_data)\n", + "\n", + "# Convert to DataFrame\n", + "df = pd.DataFrame([(key, value['year'], value['value']) for entry in data_list for key, value in entry.items()],\n", + " columns=['Region', 'Year', 'Value'])" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-28T14:30:34.630137Z", + "start_time": "2023-11-28T14:30:34.561139100Z" + } + }, + "id": "69ac530864bb3606" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/message_ix_models/model/material/test_db.ipynb b/message_ix_models/model/material/test_db.ipynb new file mode 100644 index 0000000000..7b25999492 --- /dev/null +++ b/message_ix_models/model/material/test_db.ipynb @@ -0,0 +1,3677 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "## create thesis scenarios" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 1, + "outputs": [ + { + "data": { + "text/plain": " GLOBIOM resin_conc MTBE NH3_residual_elasticity \\\n0 baseline 0.01 baseline 0.50 \n1 baseline 0.01 baseline 0.50 \n2 baseline 0.01 baseline 0.50 \n3 baseline 0.01 baseline 0.50 \n4 baseline 0.01 baseline 0.50 \n.. ... ... ... ... \n251 no_SDGs 0.03 phase-out 0.25 \n252 no_SDGs 0.03 phase-out 0.25 \n253 no_SDGs 0.03 phase-out 0.25 \n254 no_SDGs 0.03 phase-out 0.25 \n255 no_SDGs 0.03 phase-out 0.25 \n\n HVC_elasticity_2020 HVC _elasticity_2030 meth_residual_elasticity_2020 \\\n0 1.00 0.59 1.4 \n1 1.00 0.59 1.4 \n2 1.00 0.59 1.2 \n3 1.00 0.59 1.2 \n4 1.00 0.25 1.4 \n.. ... ... ... \n251 0.75 0.59 1.2 \n252 0.75 0.25 1.4 \n253 0.75 0.25 1.4 \n254 0.75 0.25 1.2 \n255 0.75 0.25 1.2 \n\n meth_elasticity_2030 \n0 0.54 \n1 0.25 \n2 0.54 \n3 0.25 \n4 0.54 \n.. ... \n251 0.25 \n252 0.54 \n253 0.25 \n254 0.54 \n255 0.25 \n\n[256 rows x 8 columns]", + "text/html": "
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256 rows × 8 columns

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" + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "df = pd.read_excel(\"C:/Users\\maczek\\PycharmProjects\\message_data\\data\\material\\methanol/scenario_list.xlsx\")\n", + "df_new = df.copy(deep=True).iloc[:,:1]\n", + "for i in range(len(df.columns)):\n", + " df_new = df_new.merge(df.iloc[:,i+2:i+3], how=\"cross\") #.merge(df.iloc[:,3:4], how=\"cross\")\n", + "df_new.drop_duplicates().reset_index(drop=True)\n", + "#df_new.drop_duplicates().reset_index(drop=True).to_excel(\"test.xlsx\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [ + { + "data": { + "text/plain": "", + "application/javascript": "if (typeof IPython !== 'undefined') { IPython.OutputArea.prototype._should_scroll = function(lines){ return false; }}" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Unable to determine R home: [WinError 2] The system cannot find the file specified\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rpy2 version:\n", + "3.5.3\n", + "Python version:\n", + "3.8.8 (default, Apr 13 2021, 15:08:03) [MSC v.1916 64 bit (AMD64)]\n", + "Looking for R's HOME:\n", + " Environment variable R_HOME: None\n", + " InstallPath in the registry: C:\\Users\\maczek\\AppData\\Local\\Programs\\R\\R-4.2.1\n", + " Environment variable R_USER: None\n", + " Environment variable R_LIBS_USER: None\n", + "R version:\n", + " In the PATH: R version 4.2.1 (2022-06-23 ucrt) -- \"Funny-Looking Kid\"\n", + " Loading R library from rpy2: OK\n", + "Additional directories to load R packages from:\n", + "None\n", + "C extension compilation:\n", + " Warning: Unable to get R compilation flags.\n" + ] + } + ], + "source": [ + "import message_data.model.material.data_methanol" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [ + { + "data": { + "text/plain": "" + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import importlib\n", + "importlib.reload(message_data.model.material.data_methanol)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 21, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "True\n", + "phase_out\n" + ] + } + ], + "source": [ + "#add_mtbe_act_bound(scenario)\n", + "pardict = message_data.model.material.data_methanol.gen_data_methanol(scenario)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 23, + "outputs": [ + { + "data": { + "text/plain": "'NoPolicy_petro_thesis_2_macro'" + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scenario.scenario" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 24, + "outputs": [], + "source": [ + "scenario.remove_solution()\n", + "scenario.check_out()\n", + "scenario.add_par(\"bound_activity_up\", pardict[\"bound_activity_up\"])\n", + "#scenario.commit()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 25, + "outputs": [], + "source": [ + "scenario.commit(\"fix mtbe phase out\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 22, + "outputs": [ + { + "data": { + "text/plain": " node_loc technology year_act mode time value unit\n0 R12_AFR meth_coal 2015 feedstock year 0.0 GWa\n1 R12_AFR meth_ng 2015 feedstock year 0.0 GWa\n2 R12_RCPA meth_coal 2015 feedstock year 0.0 GWa\n3 R12_RCPA meth_ng 2015 feedstock year 0.0 GWa\n4 R12_EEU meth_coal 2015 feedstock year 0.0 GWa\n.. ... ... ... ... ... ... ...\n139 R12_CHN loil_trp 2070 M1 year 0.0 -\n140 R12_CHN loil_trp 2080 M1 year 0.0 -\n141 R12_CHN loil_trp 2090 M1 year 0.0 -\n142 R12_CHN loil_trp 2100 M1 year 0.0 -\n143 R12_CHN loil_trp 2110 M1 year 0.0 -\n\n[193 rows x 7 columns]", + "text/html": "
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" + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pardict[\"bound_activity_up\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [ + { + "data": { + "text/plain": "Empty DataFrame\nColumns: [relation, node_rel, year_rel, value, unit]\nIndex: []", + "text/html": "
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relationnode_relyear_relvalueunit
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" + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = scenario.par(\"relation_upper\", filters={\"relation\":\"CO2_PtX_trans_disp_split\"})\n", + "df" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [ + { + "data": { + "text/plain": " model scenario scheme \\\n6410 MESSAGEix-Materials 2.0deg_no_SDGs_petro_thesis_1_macro MESSAGE \n6411 MESSAGEix-Materials 2.0deg_petro_thesis_1 MESSAGE \n6412 MESSAGEix-Materials 2.0deg_petro_thesis_2 MESSAGE \n6413 MESSAGEix-Materials 2.0deg_petro_thesis_2_macro MESSAGE \n6414 MESSAGEix-Materials 2degree_petro_thesis_2 MESSAGE \n6543 MESSAGEix-Materials NoPolicy_no_SDGs_petro_thesis_1 MESSAGE \n6544 MESSAGEix-Materials NoPolicy_no_SDGs_petro_thesis_1_macro MESSAGE \n6548 MESSAGEix-Materials NoPolicy_no_petro MESSAGE \n6550 MESSAGEix-Materials NoPolicy_petro_biomass MESSAGE \n6551 MESSAGEix-Materials NoPolicy_petro_test MESSAGE \n6552 MESSAGEix-Materials NoPolicy_petro_test_2 MESSAGE \n6553 MESSAGEix-Materials NoPolicy_petro_test_3 MESSAGE \n6554 MESSAGEix-Materials NoPolicy_petro_test_4 MESSAGE \n6555 MESSAGEix-Materials NoPolicy_petro_test_5 MESSAGE \n6556 MESSAGEix-Materials NoPolicy_petro_test_6 MESSAGE \n6557 MESSAGEix-Materials NoPolicy_petro_thesis_2 MESSAGE \n6558 MESSAGEix-Materials NoPolicy_petro_thesis_2_macro MESSAGE \n6559 MESSAGEix-Materials NoPolicy_petro_thesis_2_test_meth_h2 MESSAGE \n\n is_default is_locked cre_user cre_date upd_user \\\n6410 1 0 maczek 2023-05-09 09:34:33.000000 maczek \n6411 1 0 maczek 2023-02-08 23:38:03.000000 maczek \n6412 1 0 maczek 2023-04-06 16:44:33.000000 None \n6413 1 0 maczek 2023-05-12 08:31:34.000000 maczek \n6414 1 0 unlu 2023-04-06 15:35:51.000000 None \n6543 1 0 maczek 2023-05-08 22:47:58.000000 maczek \n6544 1 0 maczek 2023-05-09 04:15:13.000000 maczek \n6548 1 0 maczek 2023-05-04 09:18:27.000000 maczek \n6550 1 0 unlu 2022-08-09 14:51:56.000000 unlu \n6551 1 0 unlu 2022-04-14 11:08:53.000000 None \n6552 1 0 unlu 2022-04-14 14:05:39.000000 None \n6553 1 0 unlu 2022-04-14 14:40:02.000000 None \n6554 1 0 unlu 2022-04-14 15:13:25.000000 None \n6555 1 0 unlu 2022-04-14 15:29:02.000000 None \n6556 1 0 unlu 2022-04-14 15:38:19.000000 None \n6557 1 0 maczek 2023-05-09 11:02:33.000000 maczek \n6558 1 0 maczek 2023-05-10 08:04:11.000000 maczek \n6559 1 0 maczek 2023-05-15 10:42:54.000000 maczek \n\n upd_date lock_user lock_date \\\n6410 2023-05-09 10:28:12.000000 None None \n6411 2023-02-08 23:44:53.000000 None None \n6412 None None None \n6413 2023-05-12 09:20:07.000000 None None \n6414 None None None \n6543 2023-05-09 04:15:01.000000 None None \n6544 2023-05-09 04:47:38.000000 None None \n6548 2023-05-04 09:44:52.000000 None None \n6550 2022-08-09 15:14:46.000000 None None \n6551 None None None \n6552 None None None \n6553 None None None \n6554 None None None \n6555 None None None \n6556 None None None \n6557 2023-05-10 08:03:55.000000 None None \n6558 2023-05-11 10:08:56.000000 None None \n6559 2023-05-15 11:44:21.000000 None None \n\n annotation version \n6410 clone Scenario from 'MESSAGEix-Materials|NoPol... 4 \n6411 clone Scenario from 'MESSAGEix-Materials|NoPol... 1 \n6412 clone Scenario from 'MESSAGEix-Materials|NoPol... 6 \n6413 clone Scenario from 'MESSAGEix-Materials|NoPol... 10 \n6414 clone Scenario from 'MESSAGEix-Materials|NoPol... 1 \n6543 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 11 \n6544 clone Scenario from 'MESSAGEix-Materials|NoPol... 4 \n6548 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 3 \n6550 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 3 \n6551 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 4 \n6552 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 1 \n6553 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 1 \n6554 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 2 \n6555 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 2 \n6556 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 1 \n6557 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 70 \n6558 clone Scenario from 'MESSAGEix-Materials|NoPol... 9 \n6559 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 1 ", + "text/html": "
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modelscenarioschemeis_defaultis_lockedcre_usercre_dateupd_userupd_datelock_userlock_dateannotationversion
6410MESSAGEix-Materials2.0deg_no_SDGs_petro_thesis_1_macroMESSAGE10maczek2023-05-09 09:34:33.000000maczek2023-05-09 10:28:12.000000NoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...4
6411MESSAGEix-Materials2.0deg_petro_thesis_1MESSAGE10maczek2023-02-08 23:38:03.000000maczek2023-02-08 23:44:53.000000NoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...1
6412MESSAGEix-Materials2.0deg_petro_thesis_2MESSAGE10maczek2023-04-06 16:44:33.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...6
6413MESSAGEix-Materials2.0deg_petro_thesis_2_macroMESSAGE10maczek2023-05-12 08:31:34.000000maczek2023-05-12 09:20:07.000000NoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...10
6414MESSAGEix-Materials2degree_petro_thesis_2MESSAGE10unlu2023-04-06 15:35:51.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...1
6543MESSAGEix-MaterialsNoPolicy_no_SDGs_petro_thesis_1MESSAGE10maczek2023-05-08 22:47:58.000000maczek2023-05-09 04:15:01.000000NoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...11
6544MESSAGEix-MaterialsNoPolicy_no_SDGs_petro_thesis_1_macroMESSAGE10maczek2023-05-09 04:15:13.000000maczek2023-05-09 04:47:38.000000NoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...4
6548MESSAGEix-MaterialsNoPolicy_no_petroMESSAGE10maczek2023-05-04 09:18:27.000000maczek2023-05-04 09:44:52.000000NoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...3
6550MESSAGEix-MaterialsNoPolicy_petro_biomassMESSAGE10unlu2022-08-09 14:51:56.000000unlu2022-08-09 15:14:46.000000NoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...3
6551MESSAGEix-MaterialsNoPolicy_petro_testMESSAGE10unlu2022-04-14 11:08:53.000000NoneNoneNoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...4
6552MESSAGEix-MaterialsNoPolicy_petro_test_2MESSAGE10unlu2022-04-14 14:05:39.000000NoneNoneNoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...1
6553MESSAGEix-MaterialsNoPolicy_petro_test_3MESSAGE10unlu2022-04-14 14:40:02.000000NoneNoneNoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...1
6554MESSAGEix-MaterialsNoPolicy_petro_test_4MESSAGE10unlu2022-04-14 15:13:25.000000NoneNoneNoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...2
6555MESSAGEix-MaterialsNoPolicy_petro_test_5MESSAGE10unlu2022-04-14 15:29:02.000000NoneNoneNoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...2
6556MESSAGEix-MaterialsNoPolicy_petro_test_6MESSAGE10unlu2022-04-14 15:38:19.000000NoneNoneNoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...1
6557MESSAGEix-MaterialsNoPolicy_petro_thesis_2MESSAGE10maczek2023-05-09 11:02:33.000000maczek2023-05-10 08:03:55.000000NoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...70
6558MESSAGEix-MaterialsNoPolicy_petro_thesis_2_macroMESSAGE10maczek2023-05-10 08:04:11.000000maczek2023-05-11 10:08:56.000000NoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...9
6559MESSAGEix-MaterialsNoPolicy_petro_thesis_2_test_meth_h2MESSAGE10maczek2023-05-15 10:42:54.000000maczek2023-05-15 11:44:21.000000NoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...1
\n
" + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = mp.scenario_list()\n", + "df[df[\"scenario\"].str.contains(\"petro\")]\n", + "#2023-04-24 22:27:03.000000,None,None,None,None,\"clone Scenario from 'MESSAGEix-Materials|NoPolicy_no_SDGs_petro_thesis_1_macro', version: 2\",2" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [], + "source": [ + "scenario.remove_solution()\n", + "scenario.check_out()\n", + "scenario.remove_par(\"relation_upper\", df)\n", + "scenario.commit(\"remove CCS constraint\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## check for conflicts with growth constraints" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 1, + "outputs": [ + { + "data": { + "text/plain": "", + "application/javascript": "if (typeof IPython !== 'undefined') { IPython.OutputArea.prototype._should_scroll = function(lines){ return false; }}" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import ixmp as ix\n", + "import message_ix\n", + "\n", + "mp = ix.Platform(\"ixmp_dev\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-11T11:19:28.326312700Z", + "start_time": "2023-10-11T11:19:04.611575400Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [], + "source": [ + "scen = message_ix.Scenario(mp, \"MESSAGEix-Materials\", \"baseline_DEFAULT_0108_updated_SSP1_chemicals_Y\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-11T11:19:30.819515500Z", + "start_time": "2023-10-11T11:19:28.326312700Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [ + { + "data": { + "text/plain": " model scenario \\\n0 MESSAGEix-Materials baseline_DEFAULT_0108_updated_SSP1_chemicals_Y \n1 MESSAGEix-Materials baseline_DEFAULT_0108_updated_SSP1_chemicals_Y \n2 MESSAGEix-Materials baseline_DEFAULT_0108_updated_SSP1_chemicals_Y \n3 MESSAGEix-Materials baseline_DEFAULT_0108_updated_SSP1_chemicals_Y \n4 MESSAGEix-Materials baseline_DEFAULT_0108_updated_SSP1_chemicals_Y \n... ... ... \n32758 MESSAGEix-Materials baseline_DEFAULT_0108_updated_SSP1_chemicals_Y \n32759 MESSAGEix-Materials baseline_DEFAULT_0108_updated_SSP1_chemicals_Y \n32760 MESSAGEix-Materials baseline_DEFAULT_0108_updated_SSP1_chemicals_Y \n32761 MESSAGEix-Materials baseline_DEFAULT_0108_updated_SSP1_chemicals_Y \n32762 MESSAGEix-Materials baseline_DEFAULT_0108_updated_SSP1_chemicals_Y \n\n region variable unit \\\n0 CPA Population million \n1 CPA Population|Rural million \n2 CPA Population|Urban million \n3 China (R12) Agricultural Demand million t DM/yr \n4 China (R12) Agricultural Demand|Energy million t DM/yr \n... ... ... ... \n32758 Western Europe (R12) Yield|Cereal t DM/ha/yr \n32759 Western Europe (R12) Yield|Energy Crops t DM/ha/yr \n32760 Western Europe (R12) Yield|Non-Energy Crops t DM/ha/yr \n32761 Western Europe (R12) Yield|Oilcrops t DM/ha/yr \n32762 Western Europe (R12) Yield|Sugarcrops t DM/ha/yr \n\n 2020 2025 2030 2035 2040 2045 2050 \\\n0 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.000000 \n1 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.000000 \n2 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.000000 \n3 773.1930 750.3587 726.2460 710.6230 695.0001 659.6072 625.019042 \n4 48.7939 3.0609 3.2875 3.1393 2.9911 2.9911 2.985015 \n... ... ... ... ... ... ... ... \n32758 4.7784 4.8983 5.0183 5.0927 5.1671 5.2521 5.337100 \n32759 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.000000 \n32760 2.0796 2.0958 2.1121 2.1701 2.2280 2.2677 2.307500 \n32761 2.7800 2.8554 2.9308 2.9534 2.9760 3.0187 3.061300 \n32762 22.0911 22.4336 22.7761 0.0000 0.0000 0.0000 0.000000 \n\n 2055 2060 2070 2080 2090 2100 \\\n0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n1 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n2 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n3 603.654901 582.473065 483.324285 457.339577 419.300834 377.512951 \n4 3.030702 3.076596 2.496485 34.491232 40.984672 36.500276 \n... ... ... ... ... ... ... \n32758 5.460600 5.584200 5.762029 5.925012 6.008445 6.024653 \n32759 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n32760 2.367900 2.428300 2.529550 2.617236 2.666845 2.669336 \n32761 2.982600 2.903900 3.104353 3.190358 3.363317 3.593627 \n32762 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n\n 2110 \n0 0.000000 \n1 0.000000 \n2 0.000000 \n3 362.301770 \n4 22.167891 \n... ... \n32758 6.011952 \n32759 0.000000 \n32760 2.673214 \n32761 3.594700 \n32762 0.000000 \n\n[32763 rows x 19 columns]", + "text/html": "
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modelscenarioregionvariableunit20202025203020352040204520502055206020702080209021002110
0MESSAGEix-Materialsbaseline_DEFAULT_0108_updated_SSP1_chemicals_YCPAPopulationmillion0.00000.00000.00000.00000.00000.00000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
1MESSAGEix-Materialsbaseline_DEFAULT_0108_updated_SSP1_chemicals_YCPAPopulation|Ruralmillion0.00000.00000.00000.00000.00000.00000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
2MESSAGEix-Materialsbaseline_DEFAULT_0108_updated_SSP1_chemicals_YCPAPopulation|Urbanmillion0.00000.00000.00000.00000.00000.00000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
3MESSAGEix-Materialsbaseline_DEFAULT_0108_updated_SSP1_chemicals_YChina (R12)Agricultural Demandmillion t DM/yr773.1930750.3587726.2460710.6230695.0001659.6072625.019042603.654901582.473065483.324285457.339577419.300834377.512951362.301770
4MESSAGEix-Materialsbaseline_DEFAULT_0108_updated_SSP1_chemicals_YChina (R12)Agricultural Demand|Energymillion t DM/yr48.79393.06093.28753.13932.99112.99112.9850153.0307023.0765962.49648534.49123240.98467236.50027622.167891
............................................................
32758MESSAGEix-Materialsbaseline_DEFAULT_0108_updated_SSP1_chemicals_YWestern Europe (R12)Yield|Cerealt DM/ha/yr4.77844.89835.01835.09275.16715.25215.3371005.4606005.5842005.7620295.9250126.0084456.0246536.011952
32759MESSAGEix-Materialsbaseline_DEFAULT_0108_updated_SSP1_chemicals_YWestern Europe (R12)Yield|Energy Cropst DM/ha/yr0.00000.00000.00000.00000.00000.00000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
32760MESSAGEix-Materialsbaseline_DEFAULT_0108_updated_SSP1_chemicals_YWestern Europe (R12)Yield|Non-Energy Cropst DM/ha/yr2.07962.09582.11212.17012.22802.26772.3075002.3679002.4283002.5295502.6172362.6668452.6693362.673214
32761MESSAGEix-Materialsbaseline_DEFAULT_0108_updated_SSP1_chemicals_YWestern Europe (R12)Yield|Oilcropst DM/ha/yr2.78002.85542.93082.95342.97603.01873.0613002.9826002.9039003.1043533.1903583.3633173.5936273.594700
32762MESSAGEix-Materialsbaseline_DEFAULT_0108_updated_SSP1_chemicals_YWestern Europe (R12)Yield|Sugarcropst DM/ha/yr22.091122.433622.77610.00000.00000.00000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
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32763 rows × 19 columns

\n
" + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scen.timeseries(iamc=True)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-11T11:21:22.267110400Z", + "start_time": "2023-10-11T11:20:51.496907900Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 14, + "outputs": [ + { + "data": { + "text/plain": "True" + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scen.has_solution()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 15, + "outputs": [], + "source": [ + "import message_ix_models" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 20, + "outputs": [ + { + "data": { + "text/plain": "'C:\\\\Users\\\\maczek\\\\PycharmProjects\\\\message_data\\\\data\\\\material\\\\yuheng/MESSAGEix-Materials_baseline_DEFAULT_0108_updated_SSP1_chemicals_Y'" + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f\"{message_ix_models.util.private_data_path()}\\material\\yuheng/{scen.model}_{scen.scenario}.xlsx\"" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Unexpected exception formatting exception. Falling back to standard exception\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Traceback (most recent call last):\n", + " File \"C:\\Users\\maczek\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3398, in run_code\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + " File \"C:\\Users\\maczek\\AppData\\Local\\Temp\\ipykernel_8832\\1922672665.py\", line 1, in \n", + " scen.to_excel(f\"{message_ix_models.util.private_data_path()}\\material\\yuheng/{scen.model}_{scen.scenario}.xlsx\")\n", + " File \"C:\\Users\\maczek\\Anaconda3\\lib\\site-packages\\ixmp\\core\\scenario.py\", line 896, in to_excel\n", + " self.platform._backend.write_file(\n", + " File \"C:\\Users\\maczek\\Anaconda3\\lib\\site-packages\\ixmp\\backend\\jdbc.py\", line 565, in write_file\n", + " super().write_file(path, item_type, **kwargs)\n" + ] + } + ], + "source": [ + "scen.to_excel(f\"{message_ix_models.util.private_data_path()}\\material\\yuheng/{scen.model}_{scen.scenario}.xlsx\")" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "is_executing": true + } + } + }, + { + "cell_type": "code", + "execution_count": 10, + "outputs": [], + "source": [ + "df = mp.scenario_list()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [ + { + "data": { + "text/plain": "True" + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scen.has_solution()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scen.clone(\"\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scen.remove_solution()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scen.check_out()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scen.commit()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 11, + "outputs": [ + { + "ename": "KeyError", + "evalue": "\"None of [Index(['node_loc', 'technology', 'year_vtg', 'year_act', 'mode', 'node_origin',\\n 'commodity', 'level', 'time', 'time_origin'],\\n dtype='object')] are in the [columns]\"", + "output_type": "error", + "traceback": [ + "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[1;31mKeyError\u001B[0m Traceback (most recent call last)", + "Input \u001B[1;32mIn [11]\u001B[0m, in \u001B[0;36m\u001B[1;34m()\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[43mscen\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43madd_par\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43minput\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdf\u001B[49m\u001B[43m)\u001B[49m\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\ixmp\\core\\scenario.py:505\u001B[0m, in \u001B[0;36mScenario.add_par\u001B[1;34m(self, name, key_or_data, value, unit, comment)\u001B[0m\n\u001B[0;32m 501\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mkey\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;129;01min\u001B[39;00m data\u001B[38;5;241m.\u001B[39mcolumns:\n\u001B[0;32m 502\u001B[0m \u001B[38;5;66;03m# Form the 'key' column from other columns\u001B[39;00m\n\u001B[0;32m 503\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m N_dim \u001B[38;5;241m>\u001B[39m \u001B[38;5;241m1\u001B[39m \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(data):\n\u001B[0;32m 504\u001B[0m data[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mkey\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m=\u001B[39m (\n\u001B[1;32m--> 505\u001B[0m \u001B[43mdata\u001B[49m\u001B[43m[\u001B[49m\u001B[43midx_names\u001B[49m\u001B[43m]\u001B[49m\u001B[38;5;241m.\u001B[39mastype(\u001B[38;5;28mstr\u001B[39m)\u001B[38;5;241m.\u001B[39magg(\u001B[38;5;28;01mlambda\u001B[39;00m s: s\u001B[38;5;241m.\u001B[39mtolist(), axis\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m1\u001B[39m)\n\u001B[0;32m 506\u001B[0m )\n\u001B[0;32m 507\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m 508\u001B[0m data[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mkey\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m=\u001B[39m data[idx_names[\u001B[38;5;241m0\u001B[39m]]\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3511\u001B[0m, in \u001B[0;36mDataFrame.__getitem__\u001B[1;34m(self, key)\u001B[0m\n\u001B[0;32m 3509\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m is_iterator(key):\n\u001B[0;32m 3510\u001B[0m key \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mlist\u001B[39m(key)\n\u001B[1;32m-> 3511\u001B[0m indexer \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcolumns\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_get_indexer_strict\u001B[49m\u001B[43m(\u001B[49m\u001B[43mkey\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mcolumns\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m[\u001B[38;5;241m1\u001B[39m]\n\u001B[0;32m 3513\u001B[0m \u001B[38;5;66;03m# take() does not accept boolean indexers\u001B[39;00m\n\u001B[0;32m 3514\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mgetattr\u001B[39m(indexer, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mdtype\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;28;01mNone\u001B[39;00m) \u001B[38;5;241m==\u001B[39m \u001B[38;5;28mbool\u001B[39m:\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py:5782\u001B[0m, in \u001B[0;36mIndex._get_indexer_strict\u001B[1;34m(self, key, axis_name)\u001B[0m\n\u001B[0;32m 5779\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m 5780\u001B[0m keyarr, indexer, new_indexer \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_reindex_non_unique(keyarr)\n\u001B[1;32m-> 5782\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_raise_if_missing\u001B[49m\u001B[43m(\u001B[49m\u001B[43mkeyarr\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mindexer\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43maxis_name\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 5784\u001B[0m keyarr \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mtake(indexer)\n\u001B[0;32m 5785\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(key, Index):\n\u001B[0;32m 5786\u001B[0m \u001B[38;5;66;03m# GH 42790 - Preserve name from an Index\u001B[39;00m\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py:5842\u001B[0m, in \u001B[0;36mIndex._raise_if_missing\u001B[1;34m(self, key, indexer, axis_name)\u001B[0m\n\u001B[0;32m 5840\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m use_interval_msg:\n\u001B[0;32m 5841\u001B[0m key \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mlist\u001B[39m(key)\n\u001B[1;32m-> 5842\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mNone of [\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mkey\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m] are in the [\u001B[39m\u001B[38;5;132;01m{\u001B[39;00maxis_name\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m]\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m 5844\u001B[0m not_found \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mlist\u001B[39m(ensure_index(key)[missing_mask\u001B[38;5;241m.\u001B[39mnonzero()[\u001B[38;5;241m0\u001B[39m]]\u001B[38;5;241m.\u001B[39munique())\n\u001B[0;32m 5845\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mnot_found\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m not in index\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n", + "\u001B[1;31mKeyError\u001B[0m: \"None of [Index(['node_loc', 'technology', 'year_vtg', 'year_act', 'mode', 'node_origin',\\n 'commodity', 'level', 'time', 'time_origin'],\\n dtype='object')] are in the [columns]\"" + ] + } + ], + "source": [ + "scen.add_par(\"input\", df)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [ + { + "data": { + "text/plain": "True" + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-M-R12-NAVIGATE\",\"NPi2020-con-prim-dir-ncr\").has_solution()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [ + { + "data": { + "text/plain": " model \\\n6280 MESSAGEix-GLOBIOM 1.1-BM-R12 (NAVIGATE) \n6281 MESSAGEix-GLOBIOM 1.1-BM-R12 (NAVIGATE) \n6282 MESSAGEix-GLOBIOM 1.1-BM-R12 (NAVIGATE) \n6283 MESSAGEix-GLOBIOM 1.1-BM-R12 (NAVIGATE) \n6284 MESSAGEix-GLOBIOM 1.1-BM-R12 (NAVIGATE) \n... ... \n7736 NAVIGATE_SSP2_v4.1.7_noSDG_rcpref_medrel \n7737 NAVIGATE_SSP2_v4.1.7_noSDG_rcpref_medrel \n7738 NAVIGATE_hydro_test \n7739 NAVIGATE_hydro_test \n7740 NAVIGATE_hydro_test \n\n scenario scheme is_default is_locked \\\n6280 Ctax-ref MESSAGE 1 0 \n6281 Ctax-ref+B MESSAGE 1 0 \n6282 NPi-AdvPE MESSAGE 1 0 \n6283 NPi-AdvPE_ENGAGE_20C_step-0 MESSAGE 1 0 \n6284 NPi-AdvPE_ENGAGE_20C_step-3 MESSAGE 1 0 \n... ... ... ... ... \n7736 NPi2020-con-prim-dir-ncr_nexus MESSAGE 1 0 \n7737 NPi2020_nexus MESSAGE 1 0 \n7738 NoPolicy_R12_update_hydro MESSAGE 1 0 \n7739 NoPolicy_R12_update_hydro_1000 MESSAGE 1 0 \n7740 NoPolicy_R12_update_hydro_1500 MESSAGE 1 0 \n\n cre_user cre_date upd_user \\\n6280 kishimot 2023-08-02 17:03:51.000000 kishimot \n6281 kishimot 2023-08-02 17:19:35.000000 kishimot \n6282 kishimot 2023-05-08 17:24:09.000000 kishimot \n6283 kishimot 2023-05-17 11:32:35.000000 None \n6284 kishimot 2023-05-17 11:39:15.000000 kishimot \n... ... ... ... \n7736 awais 2022-11-24 13:13:38.000000 awais \n7737 awais 2023-04-09 17:02:46.000000 awais \n7738 min 2022-01-24 23:33:58.000000 None \n7739 min 2022-01-26 11:04:24.000000 None \n7740 min 2022-01-27 15:18:57.000000 None \n\n upd_date lock_user lock_date \\\n6280 2023-08-02 17:19:24.000000 None None \n6281 2023-08-02 18:17:39.000000 None None \n6282 2023-05-08 19:48:42.000000 None None \n6283 None None None \n6284 2023-05-17 11:54:29.000000 None None \n... ... ... ... \n7736 2022-11-24 19:26:29.000000 None None \n7737 2023-04-09 20:29:00.000000 None None \n7738 None None None \n7739 None None None \n7740 None None None \n\n annotation version \n6280 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-BM-... 1 \n6281 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-BM-... 1 \n6282 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-M-R... 2 \n6283 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-BM-... 1 \n6284 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-BM-... 2 \n... ... ... \n7736 clone Scenario from 'NAVIGATE_SSP2_v4.1.7_noSD... 13 \n7737 clone Scenario from 'NAVIGATE_SSP2_v4.1.7_noSD... 4 \n7738 clone Scenario from 'MESSAGEix-Materials|NoPol... 1 \n7739 clone Scenario from 'NAVIGATE_hydro_test|NoPol... 1 \n7740 clone Scenario from 'NAVIGATE_hydro_test|NoPol... 1 \n\n[623 rows x 13 columns]", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
modelscenarioschemeis_defaultis_lockedcre_usercre_dateupd_userupd_datelock_userlock_dateannotationversion
6280MESSAGEix-GLOBIOM 1.1-BM-R12 (NAVIGATE)Ctax-refMESSAGE10kishimot2023-08-02 17:03:51.000000kishimot2023-08-02 17:19:24.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-BM-...1
6281MESSAGEix-GLOBIOM 1.1-BM-R12 (NAVIGATE)Ctax-ref+BMESSAGE10kishimot2023-08-02 17:19:35.000000kishimot2023-08-02 18:17:39.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-BM-...1
6282MESSAGEix-GLOBIOM 1.1-BM-R12 (NAVIGATE)NPi-AdvPEMESSAGE10kishimot2023-05-08 17:24:09.000000kishimot2023-05-08 19:48:42.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-M-R...2
6283MESSAGEix-GLOBIOM 1.1-BM-R12 (NAVIGATE)NPi-AdvPE_ENGAGE_20C_step-0MESSAGE10kishimot2023-05-17 11:32:35.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-BM-...1
6284MESSAGEix-GLOBIOM 1.1-BM-R12 (NAVIGATE)NPi-AdvPE_ENGAGE_20C_step-3MESSAGE10kishimot2023-05-17 11:39:15.000000kishimot2023-05-17 11:54:29.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-BM-...2
..........................................
7736NAVIGATE_SSP2_v4.1.7_noSDG_rcpref_medrelNPi2020-con-prim-dir-ncr_nexusMESSAGE10awais2022-11-24 13:13:38.000000awais2022-11-24 19:26:29.000000NoneNoneclone Scenario from 'NAVIGATE_SSP2_v4.1.7_noSD...13
7737NAVIGATE_SSP2_v4.1.7_noSDG_rcpref_medrelNPi2020_nexusMESSAGE10awais2023-04-09 17:02:46.000000awais2023-04-09 20:29:00.000000NoneNoneclone Scenario from 'NAVIGATE_SSP2_v4.1.7_noSD...4
7738NAVIGATE_hydro_testNoPolicy_R12_update_hydroMESSAGE10min2022-01-24 23:33:58.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...1
7739NAVIGATE_hydro_testNoPolicy_R12_update_hydro_1000MESSAGE10min2022-01-26 11:04:24.000000NoneNoneNoneNoneclone Scenario from 'NAVIGATE_hydro_test|NoPol...1
7740NAVIGATE_hydro_testNoPolicy_R12_update_hydro_1500MESSAGE10min2022-01-27 15:18:57.000000NoneNoneNoneNoneclone Scenario from 'NAVIGATE_hydro_test|NoPol...1
\n

623 rows × 13 columns

\n
" + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = mp.scenario_list()\n", + "df[df[\"model\"].str.contains(\"NAVIGATE\")]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 8, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'common': {'commodity': {'require': ['coal', 'gas', 'electr', 'ethanol', 'methanol', 'fueloil', 'lightoil', 'hydrogen', 'lh2', 'd_heat'], 'add': ['biomass', 'water', 'fresh_water']}, 'level': {'require': ['primary', 'secondary', 'useful', 'final', 'water_supply', 'export', 'import']}, 'type_tec': {'add': ['industry']}, 'mode': {'add': ['M1', 'M2']}, 'emission': {'add': ['CO2', 'CH4', 'N2O', 'NOx', 'SO2', 'PM2p5', 'CF4']}, 'year': {'add': [1980, 1990, 2000, 2010]}, 'type_year': {'add': [2010]}}, 'generic': {'commodity': {'add': ['ht_heat', 'lt_heat']}, 'level': {'add': ['useful_steel', 'useful_cement', 'useful_aluminum', 'useful_refining', 'useful_petro', 'useful_resins']}, 'technology': {'add': ['furnace_foil_steel', 'furnace_loil_steel', 'furnace_biomass_steel', 'furnace_ethanol_steel', 'furnace_methanol_steel', 'furnace_gas_steel', 'furnace_coal_steel', 'furnace_elec_steel', 'furnace_h2_steel', 'hp_gas_steel', 'hp_elec_steel', 'fc_h2_steel', 'solar_steel', 'dheat_steel', 'furnace_foil_cement', 'furnace_loil_cement', 'furnace_biomass_cement', 'furnace_ethanol_cement', 'furnace_methanol_cement', 'furnace_gas_cement', 'furnace_coal_cement', 'furnace_elec_cement', 'furnace_h2_cement', 'hp_gas_cement', 'hp_elec_cement', 'fc_h2_cement', 'solar_cement', 'dheat_cement', 'furnace_coal_aluminum', 'furnace_foil_aluminum', 'furnace_loil_aluminum', 'furnace_ethanol_aluminum', 'furnace_biomass_aluminum', 'furnace_methanol_aluminum', 'furnace_gas_aluminum', 'furnace_elec_aluminum', 'furnace_h2_aluminum', 'hp_gas_aluminum', 'hp_elec_aluminum', 'fc_h2_aluminum', 'solar_aluminum', 'dheat_aluminum', 'furnace_coke_petro', 'furnace_coal_petro', 'furnace_foil_petro', 'furnace_loil_petro', 'furnace_ethanol_petro', 'furnace_biomass_petro', 'furnace_methanol_petro', 'furnace_gas_petro', 'furnace_elec_petro', 'furnace_h2_petro', 'hp_gas_petro', 'hp_elec_petro', 'fc_h2_petro', 'solar_petro', 'dheat_petro', 'furnace_coke_refining', 'furnace_coal_refining', 'furnace_foil_refining', 'furnace_loil_refining', 'furnace_ethanol_refining', 'furnace_biomass_refining', 'furnace_methanol_refining', 'furnace_gas_refining', 'furnace_elec_refining', 'furnace_h2_refining', 'hp_gas_refining', 'hp_elec_refining', 'fc_h2_refining', 'solar_refining', 'dheat_refining', 'furnace_coal_resins', 'furnace_foil_resins', 'furnace_loil_resins', 'furnace_ethanol_resins', 'furnace_biomass_resins', 'furnace_methanol_resins', 'furnace_gas_resins', 'furnace_elec_resins', 'furnace_h2_resins', 'hp_gas_resins', 'hp_elec_resins', 'fc_h2_resins', 'solar_resins', 'dheat_resins']}, 'mode': {'add': ['low_temp', 'high_temp']}}, 'petro_chemicals': {'commodity': {'require': ['crudeoil'], 'add': ['HVC', 'naphtha', 'kerosene', 'diesel', 'atm_residue', 'refinery_gas', 'atm_gasoil', 'atm_residue', 'vacuum_gasoil', 'vacuum_residue', 'gasoline', 'heavy_foil', 'light_foil', 'propylene', 'pet_coke', 'ethane', 'propane', 'ethylene', 'BTX']}, 'level': {'require': ['secondary', 'final'], 'add': ['pre_intermediate', 'desulfurized', 'intermediate', 'secondary_material', 'final_material', 'demand']}, 'mode': {'add': ['atm_gasoil', 'vacuum_gasoil', 'naphtha', 'kerosene', 'diesel', 'cracking_gasoline', 'cracking_loil', 'ethane', 'propane', 'light_foil', 'refinery_gas', 'refinery_gas_int', 'heavy_foil', 'pet_coke', 'atm_residue', 'vacuum_residue', 'gasoline', 'ethylene', 'propylene', 'BTX']}, 'technology': {'add': ['atm_distillation_ref', 'vacuum_distillation_ref', 'hydrotreating_ref', 'catalytic_cracking_ref', 'visbreaker_ref', 'coking_ref', 'catalytic_reforming_ref', 'hydro_cracking_ref', 'steam_cracker_petro', 'ethanol_to_ethylene_petro', 'agg_ref', 'gas_processing_petro', 'trade_petro', 'import_petro', 'export_petro', 'feedstock_t/d', 'production_HVC'], 'remove': ['ref_hil', 'ref_lol']}, 'shares': {'add': ['steam_cracker']}}, 'steel': {'commodity': {'add': ['steel', 'pig_iron', 'sponge_iron', 'sinter_iron', 'pellet_iron', 'ore_iron', 'limestone_iron', 'coke_iron', 'slag_iron', 'co_gas', 'bf_gas']}, 'level': {'add': ['new_scrap', 'old_scrap_1', 'old_scrap_2', 'old_scrap_3', 'primary_material', 'secondary_material', 'tertiary_material', 'final_material', 'useful_material', 'waste_material', 'product', 'demand', 'dummy_emission', 'end_of_life', 'dummy_end_of_life']}, 'technology': {'add': ['cokeoven_steel', 'sinter_steel', 'pellet_steel', 'bf_steel', 'dri_steel', 'sr_steel', 'bof_steel', 'eaf_steel', 'prep_secondary_steel_1', 'prep_secondary_steel_2', 'prep_secondary_steel_3', 'finishing_steel', 'manuf_steel', 'scrap_recovery_steel', 'DUMMY_ore_supply', 'DUMMY_limestone_supply_steel', 'DUMMY_coal_supply', 'DUMMY_gas_supply', 'trade_steel', 'import_steel', 'export_steel', 'other_EOL_steel', 'total_EOL_steel']}, 'relation': {'add': ['minimum_recycling_steel', 'max_global_recycling_steel']}, 'balance_equality': {'add': [['steel', 'old_scrap_1'], ['steel', 'old_scrap_2'], ['steel', 'old_scrap_3'], ['steel', 'end_of_life'], ['steel', 'product'], ['steel', 'new_scrap'], ['steel', 'useful_material'], ['steel', 'final_material']]}}, 'cement': {'commodity': {'add': ['cement', 'clinker_cement', 'raw_meal_cement', 'limestone_cement']}, 'level': {'add': ['primary_material', 'secondary_material', 'tertiary_material', 'final_material', 'useful_material', 'demand', 'dummy_end_of_life', 'end_of_life']}, 'technology': {'add': ['raw_meal_prep_cement', 'clinker_dry_cement', 'clinker_wet_cement', 'clinker_dry_ccs_cement', 'clinker_wet_ccs_cement', 'grinding_ballmill_cement', 'grinding_vertmill_cement', 'DUMMY_limestone_supply_cement', 'total_EOL_cement', 'other_EOL_cement', 'scrap_recovery_cement'], 'remove': ['cement_co2scr', 'cement_CO2']}, 'relation': {'remove': ['cement_pro', 'cement_scrub_lim']}, 'addon': {'add': ['clinker_dry_ccs_cement', 'clinker_wet_ccs_cement']}, 'type_addon': {'add': ['ccs_cement']}, 'map_tec_addon': {'add': [['clinker_dry_cement', 'ccs_cement'], ['clinker_wet_cement', 'ccs_cement']]}, 'cat_addon': {'add': [['ccs_cement', 'clinker_dry_ccs_cement'], ['ccs_cement', 'clinker_wet_ccs_cement']]}}, 'aluminum': {'commodity': {'add': ['aluminum']}, 'level': {'add': ['new_scrap', 'old_scrap_1', 'old_scrap_2', 'old_scrap_3', 'useful_aluminum', 'final_material', 'useful_material', 'product', 'secondary_material', 'demand', 'end_of_life', 'dummy_end_of_life']}, 'technology': {'add': ['soderberg_aluminum', 'prebake_aluminum', 'secondary_aluminum', 'prep_secondary_aluminum_1', 'prep_secondary_aluminum_2', 'prep_secondary_aluminum_3', 'finishing_aluminum', 'manuf_aluminum', 'scrap_recovery_aluminum', 'DUMMY_alumina_supply', 'trade_aluminum', 'import_aluminum', 'export_aluminum', 'other_EOL_aluminum', 'total_EOL_aluminum']}, 'relation': {'add': ['minimum_recycling_aluminum']}, 'balance_equality': {'add': [['aluminum', 'old_scrap_1'], ['aluminum', 'old_scrap_2'], ['aluminum', 'old_scrap_3'], ['aluminum', 'end_of_life'], ['aluminum', 'product'], ['aluminum', 'new_scrap']]}}, 'fertilizer': {'commodity': {'require': ['electr', 'freshwater_supply'], 'add': ['NH3', 'Fertilizer Use|Nitrogen', 'wastewater']}, 'level': {'add': ['secondary_material', 'final_material', 'wastewater']}, 'technology': {'require': ['h2_bio_ccs'], 'add': ['biomass_NH3', 'electr_NH3', 'gas_NH3', 'coal_NH3', 'fueloil_NH3', 'NH3_to_N_fertil', 'trade_NFert', 'export_NFert', 'import_NFert', 'trade_NH3', 'export_NH3', 'import_NH3', 'residual_NH3', 'biomass_NH3_ccs', 'gas_NH3_ccs', 'coal_NH3_ccs', 'fueloil_NH3_ccs']}, 'relation': {'add': ['NH3_trd_cap', 'NFert_trd_cap']}}, 'methanol': {'commodity': {'require': ['electr', 'biomass', 'hydrogen', 'd_heat'], 'add': ['methanol', 'ht_heat', 'formaldehyde', 'ethylene', 'propylene', 'fcoh_resin']}, 'level': {'add': ['final_material', 'secondary_material', 'primary_material', 'export_fs', 'import_fs']}, 'mode': {'add': ['feedstock', 'fuel', 'ethanol']}, 'technology': {'add': ['meth_bio', 'meth_bio_ccs', 'meth_h2', 'meth_t_d_material', 'MTO_petro', 'CH2O_synth', 'CH2O_to_resin', 'meth_coal', 'meth_coal_ccs', 'meth_ng', 'meth_ng_ccs', 'meth_t_d', 'meth_bal', 'meth_trd', 'meth_exp', 'meth_imp'], 'remove': ['sp_meth_I', 'meth_rc', 'meth_ic_trp', 'meth_fc_trp', 'meth_i', 'meth_coal', 'meth_coal_ccs', 'meth_ng', 'meth_ng_ccs', 'meth_t_d', 'meth_bal', 'meth_trd', 'meth_exp', 'meth_imp']}, 'addon': {'add': ['meth_h2']}, 'type_addon': {'add': ['methanol_synthesis_addon']}, 'map_tec_addon': {'add': [['h2_elec', 'methanol_synthesis_addon']]}, 'cat_addon': {'add': [['methanol_synthesis_addon', 'meth_h2']]}, 'relation': {'add': ['CO2_PtX_trans_disp_split', 'meth_exp_tot', 'meth_trade_balance', 'loil_trade_balance']}}, 'buildings': {'level': {'add': ['service']}, 'commodity': {'add': ['floor_area']}, 'technology': {'add': ['buildings']}}, 'power_sector': {'unit': {'add': ['t/kW']}}}\n" + ] + } + ], + "source": [ + "import yaml\n", + "file_path = \"C:/Users\\maczek\\PycharmProjects\\message_data\\data\\material\\set.yaml\"\n", + "try:\n", + " with open(file_path, 'r') as file:\n", + " yaml_data = yaml.safe_load(file)\n", + " print(yaml_data)\n", + "except FileNotFoundError: print(\"File not found.\")\n", + "except yaml.YAMLError as e: print(\"Error reading YAML:\", e)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 18, + "outputs": [ + { + "ename": "KeyError", + "evalue": "'add'", + "output_type": "error", + "traceback": [ + "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[1;31mKeyError\u001B[0m Traceback (most recent call last)", + "Input \u001B[1;32mIn [18]\u001B[0m, in \u001B[0;36m\u001B[1;34m()\u001B[0m\n\u001B[0;32m 1\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m i \u001B[38;5;129;01min\u001B[39;00m yaml_data\u001B[38;5;241m.\u001B[39mvalues():\n\u001B[1;32m----> 2\u001B[0m \u001B[43mi\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43madd\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\n", + "\u001B[1;31mKeyError\u001B[0m: 'add'" + ] + } + ], + "source": [ + "for i in yaml_data.values():\n", + " i[\"add\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 29, + "outputs": [], + "source": [ + "tec_list = {}\n", + "for k,v in list(yaml_data.items()):\n", + " if \"technology\" in v.keys():\n", + " tec_list[k] = v[\"technology\"][\"add\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 40, + "outputs": [ + { + "data": { + "text/plain": "0 meth_bio\n1 meth_bio_ccs\n2 meth_h2\n3 meth_t_d_material\n4 MTO_petro\n5 CH2O_synth\n6 CH2O_to_resin\n7 meth_coal\n8 meth_coal_ccs\n9 meth_ng\n10 meth_ng_ccs\n11 meth_t_d\n12 meth_bal\n13 meth_trd\n14 meth_exp\n15 meth_imp\nName: technology, dtype: object" + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s = pd.Series(tec_list[\"methanol\"])#to_excel(\"test.xlsx\")\n", + "s.name = \"technology\"\n", + "s.to_excel(\"\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "with pd.ExcelWriter('test.xlsx') as writer:\n", + " for i in list(par_dict_fs.keys()):\n", + " par_dict_fs[i].to_excel(writer, sheet_name=i, index=False)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 41, + "outputs": [], + "source": [ + "import pandas as pd\n", + "with pd.ExcelWriter('test.xlsx') as writer:\n", + " for k,v in tec_list.items():\n", + " s = pd.Series(tec_list[k])#to_excel(\"test.xlsx\")\n", + " s.name = \"technology\"\n", + " s.to_excel(writer, sheet_name=k, index=False)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [], + "source": [ + "import pandas as pd" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 11, + "outputs": [ + { + "data": { + "text/plain": " year_vtg node_loc technology year_act mode node_origin \\\n0 2030 R11_AFR syn_micr_beef NaN hydrogen R11_AFR \n1 2030 R11_CPA syn_micr_beef NaN hydrogen R11_CPA \n2 2030 R11_EEU syn_micr_beef NaN hydrogen R11_EEU \n3 2030 R11_FSU syn_micr_beef NaN hydrogen R11_FSU \n4 2030 R11_LAM syn_micr_beef NaN hydrogen R11_LAM \n.. ... ... ... ... ... ... \n61 2035 R11_NAM syn_micr_beef NaN hydrogen R11_NAM \n62 2035 R11_PAO syn_micr_beef NaN hydrogen R11_PAO \n63 2035 R11_PAS syn_micr_beef NaN hydrogen R11_PAS \n64 2035 R11_SAS syn_micr_beef NaN hydrogen R11_SAS \n65 2035 R11_WEU syn_micr_beef NaN hydrogen R11_WEU \n\n commodity level time time_origin value unit \n0 hydrogen final year year 1.000000 GWa \n1 hydrogen final year year 1.000000 GWa \n2 hydrogen final year year 1.000000 GWa \n3 hydrogen final year year 1.000000 GWa \n4 hydrogen final year year 1.000000 GWa \n.. ... ... ... ... ... ... \n61 electr final year year 0.936073 - \n62 electr final year year 0.936073 - \n63 electr final year year 0.936073 - \n64 electr final year year 0.936073 - \n65 electr final year year 0.936073 - \n\n[66 rows x 12 columns]", + "text/html": "
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year_vtgnode_loctechnologyyear_actmodenode_origincommodityleveltimetime_originvalueunit
02030R11_AFRsyn_micr_beefNaNhydrogenR11_AFRhydrogenfinalyearyear1.000000GWa
12030R11_CPAsyn_micr_beefNaNhydrogenR11_CPAhydrogenfinalyearyear1.000000GWa
22030R11_EEUsyn_micr_beefNaNhydrogenR11_EEUhydrogenfinalyearyear1.000000GWa
32030R11_FSUsyn_micr_beefNaNhydrogenR11_FSUhydrogenfinalyearyear1.000000GWa
42030R11_LAMsyn_micr_beefNaNhydrogenR11_LAMhydrogenfinalyearyear1.000000GWa
.......................................
612035R11_NAMsyn_micr_beefNaNhydrogenR11_NAMelectrfinalyearyear0.936073-
622035R11_PAOsyn_micr_beefNaNhydrogenR11_PAOelectrfinalyearyear0.936073-
632035R11_PASsyn_micr_beefNaNhydrogenR11_PASelectrfinalyearyear0.936073-
642035R11_SASsyn_micr_beefNaNhydrogenR11_SASelectrfinalyearyear0.936073-
652035R11_WEUsyn_micr_beefNaNhydrogenR11_WEUelectrfinalyearyear0.936073-
\n

66 rows × 12 columns

\n
" + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from message_ix_models.util import broadcast\n", + "scp_demand = pd.read_excel(\"C:/Users\\maczek\\PycharmProjects\\message_data\\data\\scp/scp_techno_economic.xlsx\", sheet_name=\"input\")\n", + "scp_demand.pipe(broadcast, year_vtg=[2030, 2035])" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 12, + "outputs": [ + { + "data": { + "text/plain": " commodity level time value unit node year\n0 scp demand year 1.589159 ??? R11_AFR 2020\n1 scp demand year 1.765396 ??? R11_AFR 2025\n2 scp demand year 1.941633 ??? R11_AFR 2030\n3 scp demand year 2.119691 ??? R11_AFR 2035\n4 scp demand year 2.297748 ??? R11_AFR 2040\n.. ... ... ... ... ... ... ...\n138 scp demand year 1.921039 ??? R11_WEU 2060\n139 scp demand year 1.923622 ??? R11_WEU 2070\n140 scp demand year 1.913528 ??? R11_WEU 2080\n141 scp demand year 1.889773 ??? R11_WEU 2090\n142 scp demand year 1.849365 ??? R11_WEU 2100\n\n[143 rows x 7 columns]", + "text/html": "
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commodityleveltimevalueunitnodeyear
0scpdemandyear1.589159???R11_AFR2020
1scpdemandyear1.765396???R11_AFR2025
2scpdemandyear1.941633???R11_AFR2030
3scpdemandyear2.119691???R11_AFR2035
4scpdemandyear2.297748???R11_AFR2040
........................
138scpdemandyear1.921039???R11_WEU2060
139scpdemandyear1.923622???R11_WEU2070
140scpdemandyear1.913528???R11_WEU2080
141scpdemandyear1.889773???R11_WEU2090
142scpdemandyear1.849365???R11_WEU2100
\n

143 rows × 7 columns

\n
" + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scp_demand = pd.read_excel(\"C:/Users\\maczek\\PycharmProjects\\message_data\\data\\scp/population_beef_demand.xlsx\", sheet_name=\"demand_R11\")\n", + "scp_demand[\"value\"] = scp_demand[\"value\"] / 1000\n", + "scp_demand" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 14, + "outputs": [ + { + "data": { + "text/plain": " relation node_rel year_rel node_loc technology \\\n0 CO2_PtX_trans_disp_split R12_AFR 2020 R12_AFR syn_micr_beef \n1 CO2_PtX_trans_disp_split R12_CHN 2020 R12_CHN syn_micr_beef \n2 CO2_PtX_trans_disp_split R12_EEU 2020 R12_EEU syn_micr_beef \n3 CO2_PtX_trans_disp_split R12_FSU 2020 R12_FSU syn_micr_beef \n4 CO2_PtX_trans_disp_split R12_LAM 2020 R12_LAM syn_micr_beef \n.. ... ... ... ... ... \n487 CO2_PtX_trans_disp_split R12_EEU 2025 R12_EEU co2_tr_dis \n488 CO2_PtX_trans_disp_split R12_NAM 2025 R12_NAM co2_tr_dis \n489 CO2_PtX_trans_disp_split R12_PAO 2025 R12_PAO co2_tr_dis \n490 CO2_PtX_trans_disp_split R12_RCPA 2025 R12_RCPA co2_tr_dis \n491 CO2_PtX_trans_disp_split R12_WEU 2025 R12_WEU co2_tr_dis \n\n year_act mode value unit \n0 2020 hydrogen 0.597213 ??? \n1 2020 hydrogen 0.597213 ??? \n2 2020 hydrogen 0.597213 ??? \n3 2020 hydrogen 0.597213 ??? \n4 2020 hydrogen 0.597213 ??? \n.. ... ... ... ... \n487 2025 M1 1.000000 ??? \n488 2025 M1 1.000000 ??? \n489 2025 M1 1.000000 ??? \n490 2025 M1 1.000000 ??? \n491 2025 M1 1.000000 ??? \n\n[492 rows x 9 columns]", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
relationnode_relyear_relnode_loctechnologyyear_actmodevalueunit
0CO2_PtX_trans_disp_splitR12_AFR2020R12_AFRsyn_micr_beef2020hydrogen0.597213???
1CO2_PtX_trans_disp_splitR12_CHN2020R12_CHNsyn_micr_beef2020hydrogen0.597213???
2CO2_PtX_trans_disp_splitR12_EEU2020R12_EEUsyn_micr_beef2020hydrogen0.597213???
3CO2_PtX_trans_disp_splitR12_FSU2020R12_FSUsyn_micr_beef2020hydrogen0.597213???
4CO2_PtX_trans_disp_splitR12_LAM2020R12_LAMsyn_micr_beef2020hydrogen0.597213???
..............................
487CO2_PtX_trans_disp_splitR12_EEU2025R12_EEUco2_tr_dis2025M11.000000???
488CO2_PtX_trans_disp_splitR12_NAM2025R12_NAMco2_tr_dis2025M11.000000???
489CO2_PtX_trans_disp_splitR12_PAO2025R12_PAOco2_tr_dis2025M11.000000???
490CO2_PtX_trans_disp_splitR12_RCPA2025R12_RCPAco2_tr_dis2025M11.000000???
491CO2_PtX_trans_disp_splitR12_WEU2025R12_WEUco2_tr_dis2025M11.000000???
\n

492 rows × 9 columns

\n
" + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from message_data.model.material.data_methanol_new import broadcast_reduced_df\n", + "\n", + "broadcast_reduced_df(pd.read_excel(\"C:/Users\\maczek\\PycharmProjects\\message_data\\data\\scp/scp_techno_economic.xlsx\", sheet_name=\"relation_activity\"), \"relation_activity\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [], + "source": [ + "scenario = message_ix.Scenario(mp, model=\"MESSAGEix-Materials\", scenario=\"NoPolicy_no_SDGs_petro_thesis_1_final\")\n", + "#scenario = message_ix.Scenario(mp, model=\"MESSAGEix-Materials\", scenario=\"NoPolicy_no_SDGs_petro_thesis_1\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [], + "source": [ + "premise_clone = scenario.clone(model=scenario.model,\n", + " scenario=scenario.scenario.replace(\"final\", \"final_premise\"))" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [ + { + "data": { + "text/plain": "2" + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "premise_clone.version" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [ + { + "data": { + "text/plain": "'NoPolicy_no_SDGs_petro_thesis_1_final_premise'" + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "premise_clone.scenario" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [], + "source": [ + "premise_clone.set_as_default()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 20, + "outputs": [], + "source": [ + "df_rcpa = scenario.par(\"initial_activity_up\", filters={\"technology\":\"steam_cracker_petro\", \"node_loc\":\"R12_RCPA\", \"year_act\":\"2020\"})\n", + "scenario.check_out()\n", + "scenario.remove_par(\"initial_activity_up\", df_rcpa)\n", + "scenario.commit(\"remove RCPA growth up sc 2020\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 14, + "outputs": [], + "source": [ + "sc_clone = scenario.clone(\"MESSAGEix-Materials\", \"NoPolicy_no_SDGs_petro_thesis_1_solve_test\", keep_solution=False)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "sc_clone.set_as_default()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 13, + "outputs": [ + { + "data": { + "text/plain": "'2023-05-09 04:15:01.0'" + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scenario.last_update()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [], + "source": [ + "df_loil_rel = scenario.par(\"relation_activity\", filters={\"relation\":\"loil_trade_balance\"})" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [], + "source": [ + "scenario.check_out()\n", + "scenario.remove_par(\"relation_activity\", df_loil_rel)\n", + "scenario.commit(\"remove loil trade balance constraint\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 20, + "outputs": [], + "source": [ + "scenario.par(\"demand\", filters={\"commodity\":[\"methanol\", \"HVC\", \"NH3\", \"fcoh_resin\"]}).to_excel(\"high_demand_output.xlsx\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [], + "source": [ + "scenario = message_ix.Scenario(mp, model=\"MESSAGEix-Materials\", scenario=\"NoPolicy_petro_thesis_2_w_ENGAGE_fixes_test_build\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 11, + "outputs": [], + "source": [ + "df_remove = scenario.par(\"relation_activity\", filters={\"technology\":\"loil_exp\", \"relation\":\"loil_trade_balance\", \"node_loc\":\"R12_EEU\", \"year_rel\":2020})" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 12, + "outputs": [], + "source": [ + "scenario.check_out()\n", + "scenario.remove_par(\"relation_activity\", df_remove)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 13, + "outputs": [], + "source": [ + "scenario.commit(\"remove loil_exp from EEU 2020\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 15, + "outputs": [], + "source": [ + "mp.close_db()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [], + "source": [ + "sc_ls = mp.scenario_list()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 45, + "outputs": [], + "source": [ + "scenario = message_ix.Scenario(mp, model=\"MESSAGEix-Materials\", scenario=\"2.0deg_petro_thesis_2_macro_w_ENGAGE_fixes\")#, version=9)\n", + "#scenario = message_ix.Scenario(mp, model=\"MESSAGEix-Materials\", scenario=\"NoPolicy_no_SDGs_petro_thesis_1\")#_macro\")#, version=1)\n", + "#scenario = message_ix.Scenario(mp, model=\"MESSAGEix-Materials\", scenario=\"NoPolicy_no_SDGs_petro_thesis_1_macro\")\n", + "\n", + "\n", + "#scenario = message_ix.Scenario(mp, model=\"MESSAGEix-Materials\", scenario=\"NoPolicy_petro_thesis_2_test_meth_h2\")\n", + "#scenario = message_ix.Scenario(mp, model=\"MESSAGEix-GLOBIOM 1.1-BM-R12 (NAVIGATE)\", scenario=\"NPi-Default\")\n", + "#scenario = message_ix.Scenario(mp, model=\"MESSAGEix-Materials\", scenario=\"baseline_DEFAULT_rebase2\")\n", + "#scenario = message_ix.Scenario(mp, model=\"MESSAGEix-Materials\", scenario=\"baseline_VK_Transport-Materials_VK\")#, version=37)\n", + "#scenario = message_ix.Scenario(mp, model=\"MESSAGEix-GLOBIOM 1.1-R12\", scenario='baseline_DEFAULT')\n", + "#scenario = message_ix.Scenario(mp, model=\"MESSAGEix-GLOBIOM 1.1-M-R12 (NGFS)\", scenario='NPi2030')\n", + "#scenario = message_ix.Scenario(mp, model=\"ENGAGE_SSP2_v4.1.8.3.1_T3.4v2_r3.1\", scenario='EN_NPi2020_techb_1000')\n", + "#scenario = message_ix.Scenario(mp, model=\"SHAPE_SSP2_v4.1.8\", scenario=\"baseline\", version=1)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [], + "source": [ + "scenario.last_update()\n", + "#scenario.version" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "### update scenario with ENGAGE fixes" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 49, + "outputs": [], + "source": [ + "sc_clone = message_ix.Scenario(mp, model=\"MESSAGEix-Materials\", scenario=\"2.0deg_petro_thesis_2_macro_w_ENGAGE_fixes\")\n", + "sc_clone2 = sc_clone.clone(model=\"MESSAGEix-Materials\", scenario=\"2.0deg_petro_thesis_2_macro_w_ENGAGE_fixes_test_loil_exp_fix\")\n", + "#sc_clone = message_ix.Scenario(mp, model=\"MESSAGEix-Materials\", scenario=\"NoPolicy_petro_thesis_2_macro_w_ENGAGE_fixes\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [], + "source": [ + "scenario = message_ix.Scenario(mp, model=\"MESSAGEix-Materials\", scenario=\"2.0deg_petro_thesis_2_macro_w_ENGAGE_fixes_test_loil_exp_fix\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [], + "source": [ + "import importlib\n", + "from message_data.model.material import data_methanol_new" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [ + { + "data": { + "text/plain": "" + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "importlib.reload(data_methanol_new)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 28, + "outputs": [], + "source": [ + "meth_dict = data_methanol_new.gen_data_methanol_new(scenario)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 29, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "historical_activity\n", + "initial_activity_up\n", + "ref_activity\n", + "historical_activity\n", + "ref_activity\n" + ] + } + ], + "source": [ + "for k,v in meth_dict.items():\n", + " if (\"technology\" in v.columns) & (\"year_act\" in v.columns):\n", + " #print(v[v.get(\"technology\")==\"meth_exp\"].year_act.unique())\n", + " df = v[v.get(\"technology\")==\"meth_exp\"]\n", + " if df.size == 0:\n", + " continue\n", + " if 2020 not in df.year_act.unique():\n", + " print(k)\n", + "\n", + "for k,v in meth_dict.items():\n", + " if (\"technology\" in v.columns) & (\"year_act\" in v.columns):\n", + " #print(v[v.get(\"technology\")==\"meth_exp\"].year_act.unique())\n", + " df = v[v.get(\"technology\")==\"meth_imp\"]\n", + " if df.size == 0:\n", + " continue\n", + " if 2020 not in df.year_act.unique():\n", + " print(k)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 9, + "outputs": [], + "source": [ + "df = scenario.par(\"relation_activity\", filters={\"relation\":\"meth_trade_balance\"})\n", + "df_new = df.copy(deep=True)\n", + "df_new[\"mode\"] = \"M1\"\n", + "df_new = df_new.drop_duplicates()\n", + "df_new = df_new[~df_new[\"node_loc\"].isin([\"R12_FSU\", \"R12_LAM\"])]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 12, + "outputs": [], + "source": [ + "df_new[\"relation\"] = \"loil_trade_balance\"" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 19, + "outputs": [], + "source": [ + "df_new.loc[df_new[\"technology\"]==\"meth_exp\",\"value\"] = 0.989" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 20, + "outputs": [ + { + "data": { + "text/plain": " relation node_rel year_rel node_loc technology year_act \\\n0 loil_trade_balance R12_GLB 2030 R12_AFR meth_exp 2030 \n1 loil_trade_balance R12_GLB 2030 R12_RCPA meth_exp 2030 \n2 loil_trade_balance R12_GLB 2030 R12_EEU meth_exp 2030 \n5 loil_trade_balance R12_GLB 2030 R12_MEA meth_exp 2030 \n6 loil_trade_balance R12_GLB 2030 R12_NAM meth_exp 2030 \n.. ... ... ... ... ... ... \n653 loil_trade_balance R12_GLB 2020 R12_PAS meth_imp 2020 \n654 loil_trade_balance R12_GLB 2020 R12_SAS meth_imp 2020 \n655 loil_trade_balance R12_GLB 2020 R12_WEU meth_imp 2020 \n656 loil_trade_balance R12_GLB 2020 R12_CHN meth_imp 2020 \n668 loil_trade_balance R12_GLB 2020 R12_MEA meth_imp 2020 \n\n mode value unit \n0 M1 0.989 ??? \n1 M1 0.989 ??? \n2 M1 0.989 ??? \n5 M1 0.989 ??? \n6 M1 0.989 ??? \n.. ... ... ... \n653 M1 -1.000 ??? \n654 M1 -1.000 ??? \n655 M1 -1.000 ??? \n656 M1 -1.000 ??? \n668 M1 -1.000 ??? \n\n[280 rows x 9 columns]", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
relationnode_relyear_relnode_loctechnologyyear_actmodevalueunit
0loil_trade_balanceR12_GLB2030R12_AFRmeth_exp2030M10.989???
1loil_trade_balanceR12_GLB2030R12_RCPAmeth_exp2030M10.989???
2loil_trade_balanceR12_GLB2030R12_EEUmeth_exp2030M10.989???
5loil_trade_balanceR12_GLB2030R12_MEAmeth_exp2030M10.989???
6loil_trade_balanceR12_GLB2030R12_NAMmeth_exp2030M10.989???
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653loil_trade_balanceR12_GLB2020R12_PASmeth_imp2020M1-1.000???
654loil_trade_balanceR12_GLB2020R12_SASmeth_imp2020M1-1.000???
655loil_trade_balanceR12_GLB2020R12_WEUmeth_imp2020M1-1.000???
656loil_trade_balanceR12_GLB2020R12_CHNmeth_imp2020M1-1.000???
668loil_trade_balanceR12_GLB2020R12_MEAmeth_imp2020M1-1.000???
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280 rows × 9 columns

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" + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_new" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 21, + "outputs": [], + "source": [ + "scenario.remove_solution()\n", + "scenario.check_out()\n", + "scenario.add_par(\"relation_activity\", df_new)\n", + "#scenario.remove_par(\"relation_activity\", df)\n", + "scenario.commit(\"fix loil trade balance relation tec modes\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 44, + "outputs": [], + "source": [ + "df = scenario.par(\"relation_activity\", filters={\"relation\":\"loil_trade_balance\", \"technology\":\"loil_exp\"})" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 47, + "outputs": [], + "source": [ + "df_fsu = df[df[\"node_loc\"]==\"R12_AFR\"].copy(deep=True)\n", + "df_lam = df[df[\"node_loc\"]==\"R12_AFR\"].copy(deep=True)\n", + "df_fsu[\"node_loc\"] = \"R12_FSU\"\n", + "df_lam[\"node_loc\"] = \"R12_LAM\"" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 50, + "outputs": [], + "source": [ + "scenario.remove_solution()\n", + "scenario.check_out()\n", + "#scenario.add_set(\"relation\", \"loil_trade_balance\")\n", + "scenario.add_par(\"relation_activity\", df_fsu)\n", + "scenario.add_par(\"relation_activity\", df_lam)\n", + "scenario.commit(\"add new relation to limit loil_exp ACT with loil_imp ACT\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 51, + "outputs": [ + { + "data": { + "text/plain": " relation node_rel year_rel node_loc technology year_act \\\n0 loil_trade_balance R12_GLB 2030 R12_AFR loil_exp 2030 \n1 loil_trade_balance R12_GLB 2030 R12_RCPA loil_exp 2030 \n2 loil_trade_balance R12_GLB 2030 R12_EEU loil_exp 2030 \n3 loil_trade_balance R12_GLB 2030 R12_MEA loil_exp 2030 \n4 loil_trade_balance R12_GLB 2030 R12_NAM loil_exp 2030 \n.. ... ... ... ... ... ... \n163 loil_trade_balance R12_GLB 2090 R12_LAM loil_exp 2090 \n164 loil_trade_balance R12_GLB 2100 R12_LAM loil_exp 2100 \n165 loil_trade_balance R12_GLB 2110 R12_LAM loil_exp 2110 \n166 loil_trade_balance R12_GLB 2025 R12_LAM loil_exp 2025 \n167 loil_trade_balance R12_GLB 2020 R12_LAM loil_exp 2020 \n\n mode value unit \n0 M1 0.989 ??? \n1 M1 0.989 ??? \n2 M1 0.989 ??? \n3 M1 0.989 ??? \n4 M1 0.989 ??? \n.. ... ... ... \n163 M1 0.989 ??? \n164 M1 0.989 ??? \n165 M1 0.989 ??? \n166 M1 0.989 ??? \n167 M1 0.989 ??? \n\n[168 rows x 9 columns]", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
relationnode_relyear_relnode_loctechnologyyear_actmodevalueunit
0loil_trade_balanceR12_GLB2030R12_AFRloil_exp2030M10.989???
1loil_trade_balanceR12_GLB2030R12_RCPAloil_exp2030M10.989???
2loil_trade_balanceR12_GLB2030R12_EEUloil_exp2030M10.989???
3loil_trade_balanceR12_GLB2030R12_MEAloil_exp2030M10.989???
4loil_trade_balanceR12_GLB2030R12_NAMloil_exp2030M10.989???
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163loil_trade_balanceR12_GLB2090R12_LAMloil_exp2090M10.989???
164loil_trade_balanceR12_GLB2100R12_LAMloil_exp2100M10.989???
165loil_trade_balanceR12_GLB2110R12_LAMloil_exp2110M10.989???
166loil_trade_balanceR12_GLB2025R12_LAMloil_exp2025M10.989???
167loil_trade_balanceR12_GLB2020R12_LAMloil_exp2020M10.989???
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168 rows × 9 columns

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" + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scenario.par(\"relation_activity\", filters={\"relation\":\"loil_trade_balance\", \"technology\":\"loil_exp\"})" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 28, + "outputs": [], + "source": [ + "df = scenario.par(\"relation_activity\", filters={\"relation\":\"loil_trade_balance\"})" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 37, + "outputs": [], + "source": [ + "#df.loc[df[\"technology\"] == \"meth_exp\", \"technology\"] = \"loil_exp\"\n", + "df.loc[df[\"technology\"] == \"meth_imp\", \"technology\"] = \"loil_imp\"" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 42, + "outputs": [ + { + "data": { + "text/plain": " relation node_rel year_rel node_loc technology year_act \\\n0 loil_trade_balance R12_GLB 2030 R12_AFR loil_exp 2030 \n1 loil_trade_balance R12_GLB 2030 R12_RCPA loil_exp 2030 \n2 loil_trade_balance R12_GLB 2030 R12_EEU loil_exp 2030 \n3 loil_trade_balance R12_GLB 2030 R12_MEA loil_exp 2030 \n4 loil_trade_balance R12_GLB 2030 R12_NAM loil_exp 2030 \n.. ... ... ... ... ... ... \n275 loil_trade_balance R12_GLB 2020 R12_PAS loil_imp 2020 \n276 loil_trade_balance R12_GLB 2020 R12_SAS loil_imp 2020 \n277 loil_trade_balance R12_GLB 2020 R12_WEU loil_imp 2020 \n278 loil_trade_balance R12_GLB 2020 R12_CHN loil_imp 2020 \n279 loil_trade_balance R12_GLB 2020 R12_MEA loil_imp 2020 \n\n mode value unit \n0 M1 0.989 ??? \n1 M1 0.989 ??? \n2 M1 0.989 ??? \n3 M1 0.989 ??? \n4 M1 0.989 ??? \n.. ... ... ... \n275 M1 -1.000 ??? \n276 M1 -1.000 ??? \n277 M1 -1.000 ??? \n278 M1 -1.000 ??? \n279 M1 -1.000 ??? \n\n[280 rows x 9 columns]", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
relationnode_relyear_relnode_loctechnologyyear_actmodevalueunit
0loil_trade_balanceR12_GLB2030R12_AFRloil_exp2030M10.989???
1loil_trade_balanceR12_GLB2030R12_RCPAloil_exp2030M10.989???
2loil_trade_balanceR12_GLB2030R12_EEUloil_exp2030M10.989???
3loil_trade_balanceR12_GLB2030R12_MEAloil_exp2030M10.989???
4loil_trade_balanceR12_GLB2030R12_NAMloil_exp2030M10.989???
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275loil_trade_balanceR12_GLB2020R12_PASloil_imp2020M1-1.000???
276loil_trade_balanceR12_GLB2020R12_SASloil_imp2020M1-1.000???
277loil_trade_balanceR12_GLB2020R12_WEUloil_imp2020M1-1.000???
278loil_trade_balanceR12_GLB2020R12_CHNloil_imp2020M1-1.000???
279loil_trade_balanceR12_GLB2020R12_MEAloil_imp2020M1-1.000???
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280 rows × 9 columns

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" + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 40, + "outputs": [], + "source": [ + "scenario.add_par(\"relation_activity\", df)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 41, + "outputs": [], + "source": [ + "scenario.commit(\"fix loil trade balance relation tec names\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "### copy meth_exp constraint relation for loil_exp" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 57, + "outputs": [], + "source": [ + "df_act_exp = sc_clone2.par(\"relation_activity\", filters={\"relation\": \"meth_trade_balance\", \"technology\":\"meth_exp\"})\n", + "df_act_imp = sc_clone2.par(\"relation_activity\", filters={\"relation\": \"meth_trade_balance\", \"technology\":\"meth_imp\"})\n", + "\n", + "df_act_exp[\"relation\"] = \"loil_trade_balance\"\n", + "df_act_imp[\"relation\"] = \"loil_trade_balance\"\n", + "\n", + "df_act_exp[\"technology\"] = \"loil_exp\"\n", + "df_act_imp[\"technology\"] = \"loil_imp\"\n", + "\n", + "df_act_exp[\"value\"] = 0.9899\n", + "\n", + "df_up_loil = sc_clone2.par(\"relation_upper\", filters={\"relation\": \"meth_trade_balance\"})\n", + "df_up_loil[\"relation\"] = \"loil_trade_balance\"" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 60, + "outputs": [], + "source": [ + "#sc_clone2.remove_solution()\n", + "#sc_clone2.check_out()\n", + "sc_clone2.add_set(\"relation\", \"loil_trade_balance\")\n", + "sc_clone2.add_par(\"relation_activity\", df_act_exp)\n", + "sc_clone2.add_par(\"relation_activity\", df_act_imp)\n", + "sc_clone2.add_par(\"relation_upper\", df_up_loil)\n", + "sc_clone2.commit(\"add new relation to limit loil_exp ACT with loil_imp ACT\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 17, + "outputs": [], + "source": [ + "df_upper = scenario.par(\"relation_lower\", filters={\"relation\": \"meth_exp_limit\", \"node_rel\":\"R12_AFR\"})\n", + "df_upper[\"relation\"] = \"meth_trade_balance\"\n", + "df_upper[\"node_rel\"] = \"R12_GLB\"" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 47, + "outputs": [ + { + "data": { + "text/plain": " relation node_rel year_rel node_loc technology year_act \\\n0 meth_trade_balance R12_GLB 2030 R12_AFR meth_exp 2030 \n1 meth_trade_balance R12_GLB 2030 R12_RCPA meth_exp 2030 \n2 meth_trade_balance R12_GLB 2030 R12_EEU meth_exp 2030 \n3 meth_trade_balance R12_GLB 2030 R12_FSU meth_exp 2030 \n4 meth_trade_balance R12_GLB 2030 R12_LAM meth_exp 2030 \n.. ... ... ... ... ... ... \n330 meth_trade_balance R12_GLB 2020 R12_CHN meth_exp 2020 \n331 meth_trade_balance R12_GLB 2020 R12_LAM meth_exp 2020 \n332 meth_trade_balance R12_GLB 2020 R12_LAM meth_exp 2020 \n333 meth_trade_balance R12_GLB 2020 R12_MEA meth_exp 2020 \n334 meth_trade_balance R12_GLB 2020 R12_MEA meth_exp 2020 \n\n mode value unit \n0 fuel 0.99 ??? \n1 fuel 0.99 ??? \n2 fuel 0.99 ??? \n3 fuel 0.99 ??? \n4 fuel 0.99 ??? \n.. ... ... ... \n330 feedstock 0.99 ??? \n331 feedstock 0.99 ??? \n332 fuel 0.99 ??? \n333 fuel 0.99 ??? \n334 feedstock 0.99 ??? \n\n[335 rows x 9 columns]", + "text/html": "
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relationnode_relyear_relnode_loctechnologyyear_actmodevalueunit
0meth_trade_balanceR12_GLB2030R12_AFRmeth_exp2030fuel0.99???
1meth_trade_balanceR12_GLB2030R12_RCPAmeth_exp2030fuel0.99???
2meth_trade_balanceR12_GLB2030R12_EEUmeth_exp2030fuel0.99???
3meth_trade_balanceR12_GLB2030R12_FSUmeth_exp2030fuel0.99???
4meth_trade_balanceR12_GLB2030R12_LAMmeth_exp2030fuel0.99???
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330meth_trade_balanceR12_GLB2020R12_CHNmeth_exp2020feedstock0.99???
331meth_trade_balanceR12_GLB2020R12_LAMmeth_exp2020feedstock0.99???
332meth_trade_balanceR12_GLB2020R12_LAMmeth_exp2020fuel0.99???
333meth_trade_balanceR12_GLB2020R12_MEAmeth_exp2020fuel0.99???
334meth_trade_balanceR12_GLB2020R12_MEAmeth_exp2020feedstock0.99???
\n

335 rows × 9 columns

\n
" + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_exp = scenario.par(\"relation_activity\", filters={\"relation\": \"meth_exp_limit\", \"technology\":\"meth_exp\"})\n", + "df_exp[\"value\"] = 0.99 # 1.010**-1\n", + "df_exp[\"relation\"] = \"meth_trade_balance\"\n", + "df_exp[\"node_rel\"] = \"R12_GLB\"\n", + "df_exp" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 28, + "outputs": [ + { + "data": { + "text/plain": " relation node_rel year_rel node_loc technology year_act \\\n0 meth_trade_balance R12_GLB 2030 R12_AFR meth_imp 2030 \n1 meth_trade_balance R12_GLB 2030 R12_RCPA meth_imp 2030 \n2 meth_trade_balance R12_GLB 2030 R12_EEU meth_imp 2030 \n3 meth_trade_balance R12_GLB 2030 R12_FSU meth_imp 2030 \n4 meth_trade_balance R12_GLB 2030 R12_LAM meth_imp 2030 \n.. ... ... ... ... ... ... \n330 meth_trade_balance R12_GLB 2020 R12_CHN meth_imp 2020 \n331 meth_trade_balance R12_GLB 2020 R12_LAM meth_imp 2020 \n332 meth_trade_balance R12_GLB 2020 R12_LAM meth_imp 2020 \n333 meth_trade_balance R12_GLB 2020 R12_MEA meth_imp 2020 \n334 meth_trade_balance R12_GLB 2020 R12_MEA meth_imp 2020 \n\n mode value unit \n0 fuel -1 ??? \n1 fuel -1 ??? \n2 fuel -1 ??? \n3 fuel -1 ??? \n4 fuel -1 ??? \n.. ... ... ... \n330 feedstock -1 ??? \n331 feedstock -1 ??? \n332 fuel -1 ??? \n333 fuel -1 ??? \n334 feedstock -1 ??? \n\n[335 rows x 9 columns]", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
relationnode_relyear_relnode_loctechnologyyear_actmodevalueunit
0meth_trade_balanceR12_GLB2030R12_AFRmeth_imp2030fuel-1???
1meth_trade_balanceR12_GLB2030R12_RCPAmeth_imp2030fuel-1???
2meth_trade_balanceR12_GLB2030R12_EEUmeth_imp2030fuel-1???
3meth_trade_balanceR12_GLB2030R12_FSUmeth_imp2030fuel-1???
4meth_trade_balanceR12_GLB2030R12_LAMmeth_imp2030fuel-1???
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330meth_trade_balanceR12_GLB2020R12_CHNmeth_imp2020feedstock-1???
331meth_trade_balanceR12_GLB2020R12_LAMmeth_imp2020feedstock-1???
332meth_trade_balanceR12_GLB2020R12_LAMmeth_imp2020fuel-1???
333meth_trade_balanceR12_GLB2020R12_MEAmeth_imp2020fuel-1???
334meth_trade_balanceR12_GLB2020R12_MEAmeth_imp2020feedstock-1???
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335 rows × 9 columns

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" + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_imp = df_exp.copy(deep=True)\n", + "df_imp[\"technology\"] = \"meth_imp\"\n", + "df_imp[\"value\"] = -1\n", + "df_imp" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 48, + "outputs": [], + "source": [ + "scenario.remove_solution()\n", + "scenario.check_out()\n", + "scenario.add_set(\"relation\", \"meth_trade_balance\")\n", + "scenario.add_par(\"relation_activity\", df_exp)\n", + "scenario.add_par(\"relation_activity\", df_imp)\n", + "scenario.add_par(\"relation_upper\", df_upper)\n", + "scenario.commit(\"add new relation to limit meth_exp ACT with meth_imp ACT\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 38, + "outputs": [], + "source": [ + "scenario.remove_solution()\n", + "scenario.check_out()\n", + "scenario.remove_par(\"relation_upper\", df_upper)\n", + "scenario.commit(\"remove meth_exp upper constraint\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 49, + "outputs": [ + { + "data": { + "text/plain": "'2023-06-09 14:42:46.0'" + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sc_clone.last_update()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 56, + "outputs": [], + "source": [ + "bound = sc_clone.par(\"bound_activity_lo\", filters={\"technology\":\"bio_hpl\", \"year_act\":2100, \"node_loc\":\"R12_PAO\"})" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 58, + "outputs": [ + { + "data": { + "text/plain": " node_loc technology year_act mode time value unit\n0 R12_CHN meth_exp 2100 feedstock year 30 GWa", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
node_loctechnologyyear_actmodetimevalueunit
0R12_CHNmeth_exp2100feedstockyear30GWa
\n
" + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bound[\"node_loc\"] = \"R12_CHN\"\n", + "bound[\"technology\"] = \"meth_exp\"\n", + "bound[\"mode\"] = \"feedstock\"\n", + "bound[\"value\"] = 30\n", + "bound" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 61, + "outputs": [], + "source": [ + "sc_clone2.remove_solution()\n", + "sc_clone2.check_out()\n", + "sc_clone2.add_par(\"bound_activity_lo\", bound)\n", + "sc_clone2.commit(\"add experimental meth_exp bound_lo\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [], + "source": [ + "from message_data.tools.utilities import manual_updates_ENGAGE_SSP2_v417_to_v418, update_h2_blending" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Updating efficiencies for technologies: ['coal_bal' 'gas_bal']\n", + "Updating efficiencies for technologies: ['biomass_t_d' 'coal_t_d']\n" + ] + } + ], + "source": [ + "sc_clone.remove_solution()\n", + "sc_clone.check_out()\n", + "manual_updates_ENGAGE_SSP2_v417_to_v418._correct_balance_td_efficiencies(sc_clone)\n", + "update_h2_blending.main(sc_clone)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "### add gas_processing_petro to h2_scrub_limit" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 8, + "outputs": [ + { + "data": { + "text/plain": "array(['g_ppl_co2scr', 'gas_bio', 'gas_cc', 'gas_ct', 'gas_hpl',\n 'gas_ppl', 'gas_t_d', 'gas_t_d_ch4', 'h2_mix'], dtype=object)" + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#sc_clone.commit(\"add ENGAGE fixes\")\n", + "sc_clone.par(\"relation_activity\", filters={\"relation\":\"h2_scrub_limit\"})[\"technology\"].unique()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 10, + "outputs": [], + "source": [ + "df = sc_clone.par(\"relation_activity\", filters={\"relation\":\"h2_scrub_limit\", \"technology\":\"gas_bio\"})" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 12, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "R12_AFR\n", + "16\n", + "R12_RCPA\n", + "16\n", + "R12_EEU\n", + "16\n", + "R12_FSU\n", + "16\n", + "R12_LAM\n", + "16\n", + "R12_MEA\n", + "16\n", + "R12_NAM\n", + "16\n", + "R12_PAO\n", + "16\n", + "R12_PAS\n", + "16\n", + "R12_SAS\n", + "16\n", + "R12_WEU\n", + "20\n", + "R12_CHN\n", + "16\n" + ] + } + ], + "source": [ + "for i in df[\"node_rel\"].unique().tolist():\n", + " print(i)\n", + " print(df[df[\"node_rel\"]==i].index.size)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 24, + "outputs": [], + "source": [ + "sc_clone.set_as_default()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 23, + "outputs": [ + { + "data": { + "text/plain": " model scenario \\\n6488 MESSAGEix-Materials 2.0deg_no_SDGs_petro_thesis_1_macro \n6489 MESSAGEix-Materials 2.0deg_petro_thesis_1 \n6490 MESSAGEix-Materials 2.0deg_petro_thesis_2 \n6491 MESSAGEix-Materials 2.0deg_petro_thesis_2_macro \n6492 MESSAGEix-Materials 2.0deg_petro_thesis_2_macro_w_ENGAGE_fixes \n6493 MESSAGEix-Materials 2degree_petro_thesis_2 \n6623 MESSAGEix-Materials NoPolicy_no_SDGs_petro_thesis_1 \n6624 MESSAGEix-Materials NoPolicy_no_SDGs_petro_thesis_1_macro \n6628 MESSAGEix-Materials NoPolicy_no_petro \n6630 MESSAGEix-Materials NoPolicy_petro_biomass \n6631 MESSAGEix-Materials NoPolicy_petro_test \n6632 MESSAGEix-Materials NoPolicy_petro_test_2 \n6633 MESSAGEix-Materials NoPolicy_petro_test_3 \n6634 MESSAGEix-Materials NoPolicy_petro_test_4 \n6635 MESSAGEix-Materials NoPolicy_petro_test_5 \n6636 MESSAGEix-Materials NoPolicy_petro_test_6 \n6637 MESSAGEix-Materials NoPolicy_petro_thesis_2 \n6638 MESSAGEix-Materials NoPolicy_petro_thesis_2_macro \n6639 MESSAGEix-Materials NoPolicy_petro_thesis_2_macro_w_ENGAGE_fixes \n6640 MESSAGEix-Materials NoPolicy_petro_thesis_2_test_meth_h2 \n6641 MESSAGEix-Materials NoPolicy_petro_thesis_2_test_new_input_files \n\n scheme is_default is_locked cre_user cre_date \\\n6488 MESSAGE 1 0 maczek 2023-05-09 09:34:33.000000 \n6489 MESSAGE 1 0 maczek 2023-02-08 23:38:03.000000 \n6490 MESSAGE 1 0 maczek 2023-04-06 16:44:33.000000 \n6491 MESSAGE 1 0 maczek 2023-05-12 08:31:34.000000 \n6492 MESSAGE 1 0 maczek 2023-06-07 17:39:46.000000 \n6493 MESSAGE 1 0 unlu 2023-04-06 15:35:51.000000 \n6623 MESSAGE 1 0 maczek 2023-05-08 22:47:58.000000 \n6624 MESSAGE 1 0 maczek 2023-05-09 04:15:13.000000 \n6628 MESSAGE 1 0 maczek 2023-05-04 09:18:27.000000 \n6630 MESSAGE 1 0 unlu 2022-08-09 14:51:56.000000 \n6631 MESSAGE 1 0 unlu 2022-04-14 11:08:53.000000 \n6632 MESSAGE 1 0 unlu 2022-04-14 14:05:39.000000 \n6633 MESSAGE 1 0 unlu 2022-04-14 14:40:02.000000 \n6634 MESSAGE 1 0 unlu 2022-04-14 15:13:25.000000 \n6635 MESSAGE 1 0 unlu 2022-04-14 15:29:02.000000 \n6636 MESSAGE 1 0 unlu 2022-04-14 15:38:19.000000 \n6637 MESSAGE 1 0 maczek 2023-06-07 10:11:12.000000 \n6638 MESSAGE 1 0 maczek 2023-06-07 11:11:09.000000 \n6639 MESSAGE 1 0 maczek 2023-06-07 15:19:13.000000 \n6640 MESSAGE 1 0 maczek 2023-05-15 10:42:54.000000 \n6641 MESSAGE 1 0 maczek 2023-06-07 17:11:26.000000 \n\n upd_user upd_date lock_user lock_date \\\n6488 maczek 2023-05-09 10:28:12.000000 None None \n6489 maczek 2023-02-08 23:44:53.000000 None None \n6490 None None None None \n6491 maczek 2023-05-12 09:20:07.000000 None None \n6492 maczek 2023-06-07 18:37:17.000000 None None \n6493 None None None None \n6623 maczek 2023-05-09 04:15:01.000000 None None \n6624 maczek 2023-05-09 04:47:38.000000 None None \n6628 maczek 2023-05-04 09:44:52.000000 None None \n6630 unlu 2022-08-09 15:14:46.000000 None None \n6631 None None None None \n6632 None None None None \n6633 None None None None \n6634 None None None None \n6635 None None None None \n6636 None None None None \n6637 maczek 2023-06-07 11:10:57.000000 None None \n6638 maczek 2023-06-07 11:21:49.000000 None None \n6639 maczek 2023-06-07 17:33:51.000000 None None \n6640 maczek 2023-05-15 11:44:21.000000 None None \n6641 None None None None \n\n annotation version \n6488 clone Scenario from 'MESSAGEix-Materials|NoPol... 4 \n6489 clone Scenario from 'MESSAGEix-Materials|NoPol... 1 \n6490 clone Scenario from 'MESSAGEix-Materials|NoPol... 6 \n6491 clone Scenario from 'MESSAGEix-Materials|NoPol... 10 \n6492 clone Scenario from 'MESSAGEix-Materials|NoPol... 1 \n6493 clone Scenario from 'MESSAGEix-Materials|NoPol... 1 \n6623 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 11 \n6624 clone Scenario from 'MESSAGEix-Materials|NoPol... 4 \n6628 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 3 \n6630 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 3 \n6631 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 4 \n6632 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 1 \n6633 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 1 \n6634 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 2 \n6635 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 2 \n6636 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 1 \n6637 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 112 \n6638 clone Scenario from 'MESSAGEix-Materials|NoPol... 12 \n6639 clone Scenario from 'MESSAGEix-Materials|NoPol... 1 \n6640 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 1 \n6641 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 1 ", + "text/html": "
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modelscenarioschemeis_defaultis_lockedcre_usercre_dateupd_userupd_datelock_userlock_dateannotationversion
6488MESSAGEix-Materials2.0deg_no_SDGs_petro_thesis_1_macroMESSAGE10maczek2023-05-09 09:34:33.000000maczek2023-05-09 10:28:12.000000NoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...4
6489MESSAGEix-Materials2.0deg_petro_thesis_1MESSAGE10maczek2023-02-08 23:38:03.000000maczek2023-02-08 23:44:53.000000NoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...1
6490MESSAGEix-Materials2.0deg_petro_thesis_2MESSAGE10maczek2023-04-06 16:44:33.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...6
6491MESSAGEix-Materials2.0deg_petro_thesis_2_macroMESSAGE10maczek2023-05-12 08:31:34.000000maczek2023-05-12 09:20:07.000000NoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...10
6492MESSAGEix-Materials2.0deg_petro_thesis_2_macro_w_ENGAGE_fixesMESSAGE10maczek2023-06-07 17:39:46.000000maczek2023-06-07 18:37:17.000000NoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...1
6493MESSAGEix-Materials2degree_petro_thesis_2MESSAGE10unlu2023-04-06 15:35:51.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...1
6623MESSAGEix-MaterialsNoPolicy_no_SDGs_petro_thesis_1MESSAGE10maczek2023-05-08 22:47:58.000000maczek2023-05-09 04:15:01.000000NoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...11
6624MESSAGEix-MaterialsNoPolicy_no_SDGs_petro_thesis_1_macroMESSAGE10maczek2023-05-09 04:15:13.000000maczek2023-05-09 04:47:38.000000NoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...4
6628MESSAGEix-MaterialsNoPolicy_no_petroMESSAGE10maczek2023-05-04 09:18:27.000000maczek2023-05-04 09:44:52.000000NoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...3
6630MESSAGEix-MaterialsNoPolicy_petro_biomassMESSAGE10unlu2022-08-09 14:51:56.000000unlu2022-08-09 15:14:46.000000NoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...3
6631MESSAGEix-MaterialsNoPolicy_petro_testMESSAGE10unlu2022-04-14 11:08:53.000000NoneNoneNoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...4
6632MESSAGEix-MaterialsNoPolicy_petro_test_2MESSAGE10unlu2022-04-14 14:05:39.000000NoneNoneNoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...1
6633MESSAGEix-MaterialsNoPolicy_petro_test_3MESSAGE10unlu2022-04-14 14:40:02.000000NoneNoneNoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...1
6634MESSAGEix-MaterialsNoPolicy_petro_test_4MESSAGE10unlu2022-04-14 15:13:25.000000NoneNoneNoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...2
6635MESSAGEix-MaterialsNoPolicy_petro_test_5MESSAGE10unlu2022-04-14 15:29:02.000000NoneNoneNoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...2
6636MESSAGEix-MaterialsNoPolicy_petro_test_6MESSAGE10unlu2022-04-14 15:38:19.000000NoneNoneNoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...1
6637MESSAGEix-MaterialsNoPolicy_petro_thesis_2MESSAGE10maczek2023-06-07 10:11:12.000000maczek2023-06-07 11:10:57.000000NoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...112
6638MESSAGEix-MaterialsNoPolicy_petro_thesis_2_macroMESSAGE10maczek2023-06-07 11:11:09.000000maczek2023-06-07 11:21:49.000000NoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...12
6639MESSAGEix-MaterialsNoPolicy_petro_thesis_2_macro_w_ENGAGE_fixesMESSAGE10maczek2023-06-07 15:19:13.000000maczek2023-06-07 17:33:51.000000NoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...1
6640MESSAGEix-MaterialsNoPolicy_petro_thesis_2_test_meth_h2MESSAGE10maczek2023-05-15 10:42:54.000000maczek2023-05-15 11:44:21.000000NoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...1
6641MESSAGEix-MaterialsNoPolicy_petro_thesis_2_test_new_input_filesMESSAGE10maczek2023-06-07 17:11:26.000000NoneNoneNoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...1
\n
" + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sc_list = mp.scenario_list()\n", + "\n", + "sc_list[sc_list[\"scenario\"].str.contains(\"petro\")]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 39, + "outputs": [], + "source": [ + "df_set = scenario.set(\"balance_equality\").iloc[0]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 40, + "outputs": [ + { + "data": { + "text/plain": "commodity BIO01GHG000\nlevel LU\nName: 0, dtype: object" + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_set" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 41, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\maczek\\AppData\\Local\\Temp\\ipykernel_21000\\2936832773.py:1: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df_set[\"commodity\"] = \"methanol\"\n", + "C:\\Users\\maczek\\AppData\\Local\\Temp\\ipykernel_21000\\2936832773.py:2: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df_set[\"level\"] = \"export_fs\"\n" + ] + }, + { + "data": { + "text/plain": "commodity methanol\nlevel export_fs\nName: 0, dtype: object" + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_set[\"commodity\"] = \"methanol\"\n", + "df_set[\"level\"] = \"export_fs\"\n", + "df_set" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 42, + "outputs": [ + { + "data": { + "text/plain": "commodity methanol\nlevel export\nName: 0, dtype: object" + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_set" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 42, + "outputs": [], + "source": [ + "scenario.check_out()\n", + "scenario.add_set(\"balance_equality\", df_set)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 44, + "outputs": [ + { + "ename": "RuntimeError", + "evalue": "this Scenario is not checked out, no changes to be committed!", + "output_type": "error", + "traceback": [ + "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[1;31mException\u001B[0m Traceback (most recent call last)", + "File \u001B[1;32mMsgScenario.java:1277\u001B[0m, in \u001B[0;36mat.ac.iiasa.ixmp.objects.MsgScenario.commit\u001B[1;34m()\u001B[0m\n", + "File \u001B[1;32mMsgScenario.java:1289\u001B[0m, in \u001B[0;36mat.ac.iiasa.ixmp.objects.MsgScenario.commit\u001B[1;34m()\u001B[0m\n", + "File \u001B[1;32mScenario.java:2013\u001B[0m, in \u001B[0;36mat.ac.iiasa.ixmp.objects.Scenario.commit\u001B[1;34m()\u001B[0m\n", + "File \u001B[1;32mScenario.java:2026\u001B[0m, in \u001B[0;36mat.ac.iiasa.ixmp.objects.Scenario.commit\u001B[1;34m()\u001B[0m\n", + "File \u001B[1;32mScenario.java:2042\u001B[0m, in \u001B[0;36mat.ac.iiasa.ixmp.objects.Scenario.commit\u001B[1;34m()\u001B[0m\n", + "\u001B[1;31mException\u001B[0m: Java Exception", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001B[1;31mat.ac.iiasa.ixmp.exceptions.IxException\u001B[0m Traceback (most recent call last)", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\ixmp\\backend\\jdbc.py:697\u001B[0m, in \u001B[0;36mJDBCBackend.commit\u001B[1;34m(self, ts, comment)\u001B[0m\n\u001B[0;32m 696\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m--> 697\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mjindex\u001B[49m\u001B[43m[\u001B[49m\u001B[43mts\u001B[49m\u001B[43m]\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcommit\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcomment\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 698\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m java\u001B[38;5;241m.\u001B[39mException \u001B[38;5;28;01mas\u001B[39;00m e:\n", + "\u001B[1;31mat.ac.iiasa.ixmp.exceptions.IxException\u001B[0m: at.ac.iiasa.ixmp.exceptions.IxException: this Scenario is not checked out, no changes to be committed!", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001B[1;31mRuntimeError\u001B[0m Traceback (most recent call last)", + "Input \u001B[1;32mIn [44]\u001B[0m, in \u001B[0;36m\u001B[1;34m()\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[43mscenario\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcommit\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43madd methanol export constraint for fs mode\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\ixmp\\core\\timeseries.py:199\u001B[0m, in \u001B[0;36mTimeSeries.commit\u001B[1;34m(self, comment)\u001B[0m\n\u001B[0;32m 183\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mcommit\u001B[39m(\u001B[38;5;28mself\u001B[39m, comment: \u001B[38;5;28mstr\u001B[39m) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m 184\u001B[0m \u001B[38;5;124;03m\"\"\"Commit all changed data to the database.\u001B[39;00m\n\u001B[0;32m 185\u001B[0m \n\u001B[0;32m 186\u001B[0m \u001B[38;5;124;03m If the TimeSeries was newly created (with ``version='new'``), :attr:`version`\u001B[39;00m\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 197\u001B[0m \u001B[38;5;124;03m utils.maybe_commit\u001B[39;00m\n\u001B[0;32m 198\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[1;32m--> 199\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_backend\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mcommit\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcomment\u001B[49m\u001B[43m)\u001B[49m\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\ixmp\\core\\timeseries.py:108\u001B[0m, in \u001B[0;36mTimeSeries._backend\u001B[1;34m(self, method, *args, **kwargs)\u001B[0m\n\u001B[0;32m 102\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m_backend\u001B[39m(\u001B[38;5;28mself\u001B[39m, method, \u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[0;32m 103\u001B[0m \u001B[38;5;124;03m\"\"\"Convenience for calling *method* on the backend.\u001B[39;00m\n\u001B[0;32m 104\u001B[0m \n\u001B[0;32m 105\u001B[0m \u001B[38;5;124;03m The weak reference to the Platform object is used, if the Platform is still\u001B[39;00m\n\u001B[0;32m 106\u001B[0m \u001B[38;5;124;03m alive.\u001B[39;00m\n\u001B[0;32m 107\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[1;32m--> 108\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mplatform\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_backend\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmethod\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\ixmp\\backend\\base.py:53\u001B[0m, in \u001B[0;36mBackend.__call__\u001B[1;34m(self, obj, method, *args, **kwargs)\u001B[0m\n\u001B[0;32m 47\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m__call__\u001B[39m(\u001B[38;5;28mself\u001B[39m, obj, method, \u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[0;32m 48\u001B[0m \u001B[38;5;124;03m\"\"\"Call the backend method `method` for `obj`.\u001B[39;00m\n\u001B[0;32m 49\u001B[0m \n\u001B[0;32m 50\u001B[0m \u001B[38;5;124;03m The class attribute obj._backend_prefix is used to determine a prefix for the\u001B[39;00m\n\u001B[0;32m 51\u001B[0m \u001B[38;5;124;03m method name, e.g. 'ts_{method}'.\u001B[39;00m\n\u001B[0;32m 52\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[1;32m---> 53\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mgetattr\u001B[39;49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmethod\u001B[49m\u001B[43m)\u001B[49m\u001B[43m(\u001B[49m\u001B[43mobj\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\ixmp\\backend\\jdbc.py:701\u001B[0m, in \u001B[0;36mJDBCBackend.commit\u001B[1;34m(self, ts, comment)\u001B[0m\n\u001B[0;32m 699\u001B[0m arg \u001B[38;5;241m=\u001B[39m e\u001B[38;5;241m.\u001B[39margs[\u001B[38;5;241m0\u001B[39m]\n\u001B[0;32m 700\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(arg, \u001B[38;5;28mstr\u001B[39m) \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mthis Scenario is not checked out\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;129;01min\u001B[39;00m arg:\n\u001B[1;32m--> 701\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mRuntimeError\u001B[39;00m(arg)\n\u001B[0;32m 702\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m: \u001B[38;5;66;03m# pragma: no cover\u001B[39;00m\n\u001B[0;32m 703\u001B[0m _raise_jexception(e)\n", + "\u001B[1;31mRuntimeError\u001B[0m: this Scenario is not checked out, no changes to be committed!" + ] + } + ], + "source": [ + "scenario.commit(\"add methanol export constraint for fs mode\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 16, + "outputs": [ + { + "data": { + "text/plain": " relation node_rel year_rel node_loc technology \\\n0 h2_scrub_limit R12_AFR 2010 R12_AFR gas_processing_petro \n1 h2_scrub_limit R12_AFR 2015 R12_AFR gas_processing_petro \n2 h2_scrub_limit R12_AFR 2020 R12_AFR 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" + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[\"value\"] = -(1.33181 * 0.482)\n", + "df[\"technology\"] = \"gas_processing_petro\"\n", + "df" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 17, + "outputs": [], + "source": [ + "sc_clone.check_out()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 18, + "outputs": [], + "source": [ + "sc_clone.add_par(\"relation_activity\", df)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 19, + "outputs": [], + "source": [ + "sc_clone.commit(\"add gas_processing_petro to h2_scrub_limit\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 20, + "outputs": [ + { + "data": { + "text/plain": "'NoPolicy_petro_thesis_2_macro_w_ENGAGE_fixes'" + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sc_clone.scenario" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 21, + "outputs": [ + { + "data": { + "text/plain": " relation node_rel year_rel node_loc technology \\\n0 h2_scrub_limit R12_AFR 2010 R12_AFR gas_processing_petro \n1 h2_scrub_limit R12_AFR 2015 R12_AFR gas_processing_petro \n2 h2_scrub_limit R12_AFR 2020 R12_AFR gas_processing_petro \n3 h2_scrub_limit R12_AFR 2025 R12_AFR gas_processing_petro \n4 h2_scrub_limit R12_AFR 2030 R12_AFR gas_processing_petro \n.. ... ... ... ... ... \n191 h2_scrub_limit R12_CHN 2070 R12_CHN gas_processing_petro \n192 h2_scrub_limit R12_CHN 2080 R12_CHN gas_processing_petro \n193 h2_scrub_limit R12_CHN 2090 R12_CHN gas_processing_petro \n194 h2_scrub_limit R12_CHN 2100 R12_CHN gas_processing_petro \n195 h2_scrub_limit R12_CHN 2110 R12_CHN gas_processing_petro \n\n year_act mode value unit \n0 2010 M1 -0.641932 ??? \n1 2015 M1 -0.641932 ??? \n2 2020 M1 -0.641932 ??? \n3 2025 M1 -0.641932 ??? \n4 2030 M1 -0.641932 ??? \n.. ... ... ... ... \n191 2070 M1 -0.641932 ??? \n192 2080 M1 -0.641932 ??? \n193 2090 M1 -0.641932 ??? \n194 2100 M1 -0.641932 ??? \n195 2110 M1 -0.641932 ??? \n\n[196 rows x 9 columns]", + "text/html": "
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196 rows × 9 columns

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" + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sc_clone.par(\"relation_activity\", filters={\"relation\":\"h2_scrub_limit\", \"technology\":\"gas_processing_petro\"})" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 13, + "outputs": [ + { + "data": { + "text/plain": " relation node_rel year_rel node_loc technology year_act mode \\\n160 h2_scrub_limit R12_WEU 1990 R12_WEU gas_bio 1990 M1 \n161 h2_scrub_limit R12_WEU 1995 R12_WEU gas_bio 1995 M1 \n162 h2_scrub_limit R12_WEU 2000 R12_WEU gas_bio 2000 M1 \n163 h2_scrub_limit R12_WEU 2005 R12_WEU gas_bio 2005 M1 \n164 h2_scrub_limit R12_WEU 2010 R12_WEU gas_bio 2010 M1 \n165 h2_scrub_limit R12_WEU 2015 R12_WEU gas_bio 2015 M1 \n166 h2_scrub_limit R12_WEU 2020 R12_WEU gas_bio 2020 M1 \n167 h2_scrub_limit R12_WEU 2025 R12_WEU gas_bio 2025 M1 \n168 h2_scrub_limit R12_WEU 2030 R12_WEU gas_bio 2030 M1 \n169 h2_scrub_limit R12_WEU 2035 R12_WEU gas_bio 2035 M1 \n170 h2_scrub_limit R12_WEU 2040 R12_WEU gas_bio 2040 M1 \n171 h2_scrub_limit R12_WEU 2045 R12_WEU gas_bio 2045 M1 \n172 h2_scrub_limit R12_WEU 2050 R12_WEU gas_bio 2050 M1 \n173 h2_scrub_limit R12_WEU 2055 R12_WEU gas_bio 2055 M1 \n174 h2_scrub_limit R12_WEU 2060 R12_WEU gas_bio 2060 M1 \n175 h2_scrub_limit R12_WEU 2070 R12_WEU gas_bio 2070 M1 \n176 h2_scrub_limit R12_WEU 2080 R12_WEU gas_bio 2080 M1 \n177 h2_scrub_limit R12_WEU 2090 R12_WEU gas_bio 2090 M1 \n178 h2_scrub_limit R12_WEU 2100 R12_WEU gas_bio 2100 M1 \n179 h2_scrub_limit R12_WEU 2110 R12_WEU gas_bio 2110 M1 \n\n value unit \n160 -0.537884 ??? \n161 -0.531719 ??? \n162 -0.525694 ??? \n163 -0.519804 ??? \n164 -0.514044 ??? \n165 -0.508472 ??? \n166 -0.502900 ??? \n167 -0.497564 ??? \n168 -0.492228 ??? \n169 -0.487114 ??? \n170 -0.482000 ??? \n171 -0.482000 ??? \n172 -0.482000 ??? \n173 -0.482000 ??? \n174 -0.482000 ??? \n175 -0.482000 ??? \n176 -0.482000 ??? \n177 -0.482000 ??? \n178 -0.482000 ??? \n179 -0.482000 ??? ", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
relationnode_relyear_relnode_loctechnologyyear_actmodevalueunit
160h2_scrub_limitR12_WEU1990R12_WEUgas_bio1990M1-0.537884???
161h2_scrub_limitR12_WEU1995R12_WEUgas_bio1995M1-0.531719???
162h2_scrub_limitR12_WEU2000R12_WEUgas_bio2000M1-0.525694???
163h2_scrub_limitR12_WEU2005R12_WEUgas_bio2005M1-0.519804???
164h2_scrub_limitR12_WEU2010R12_WEUgas_bio2010M1-0.514044???
165h2_scrub_limitR12_WEU2015R12_WEUgas_bio2015M1-0.508472???
166h2_scrub_limitR12_WEU2020R12_WEUgas_bio2020M1-0.502900???
167h2_scrub_limitR12_WEU2025R12_WEUgas_bio2025M1-0.497564???
168h2_scrub_limitR12_WEU2030R12_WEUgas_bio2030M1-0.492228???
169h2_scrub_limitR12_WEU2035R12_WEUgas_bio2035M1-0.487114???
170h2_scrub_limitR12_WEU2040R12_WEUgas_bio2040M1-0.482000???
171h2_scrub_limitR12_WEU2045R12_WEUgas_bio2045M1-0.482000???
172h2_scrub_limitR12_WEU2050R12_WEUgas_bio2050M1-0.482000???
173h2_scrub_limitR12_WEU2055R12_WEUgas_bio2055M1-0.482000???
174h2_scrub_limitR12_WEU2060R12_WEUgas_bio2060M1-0.482000???
175h2_scrub_limitR12_WEU2070R12_WEUgas_bio2070M1-0.482000???
176h2_scrub_limitR12_WEU2080R12_WEUgas_bio2080M1-0.482000???
177h2_scrub_limitR12_WEU2090R12_WEUgas_bio2090M1-0.482000???
178h2_scrub_limitR12_WEU2100R12_WEUgas_bio2100M1-0.482000???
179h2_scrub_limitR12_WEU2110R12_WEUgas_bio2110M1-0.482000???
\n
" + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[df[\"node_rel\"]==\"R12_WEU\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 65, + "outputs": [ + { + "data": { + "text/plain": "True" + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scenario.has_solution()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [ + { + "data": { + "text/plain": "Empty DataFrame\nColumns: [node_loc, technology, year_act, mode, time, value, unit]\nIndex: []", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
node_loctechnologyyear_actmodetimevalueunit
\n
" + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = scenario.par(\"historical_activity\", filters={\"technology\":\"meth_coal\", \"mode\":\"M1\"})\n", + "df" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [], + "source": [ + "mode_pars_list = [x for x in scenario.par_list() if \"mode\" in scenario.idx_sets(x)]\n", + "meth_tecs_list = [\"meth_bio\",\n", + " \"meth_bio_ccs\",\n", + " \"meth_h2\",\n", + " \"meth_coal\",\n", + " \"meth_coal_ccs\"\n", + " \"meth_ng\",\n", + " \"meth_ng_ccs\",\n", + " \"meth_bal\",\n", + " \"meth_imp\",\n", + " \"meth_trd\",\n", + " \"meth_exp\",\n", + " \"meth_t_d\"]\n", + "scenario.remove_solution()\n", + "scenario.check_out()\n", + "for p in mode_pars_list:\n", + " df = scenario.par(p, filters={\"technology\":meth_tecs_list})\n", + " scenario.remove_par(p, df)\n", + "scenario.commit(\"remove M1 mode of meth tecs\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 8, + "outputs": [ + { + "data": { + "text/plain": "array(['eth_bio', 'eth_bio_ccs', 'eth_exp', 'meth_coal', 'meth_coal_ccs',\n 'meth_ng', 'meth_ng_ccs', 'meth_exp'], dtype=object)" + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scenario.par(\"relation_activity\", filters={\"relation\":\"meth_exp_limit\"})[\"technology\"].unique()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 9, + "outputs": [], + "source": [ + "df = scenario.var(\"ACT\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 13, + "outputs": [], + "source": [ + "df = df[df[\"lvl\"]!=0]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 14, + "outputs": [ + { + "data": { + "text/plain": " node_loc technology year_vtg year_act mode time \\\n26481 R12_AFR steam_cracker_petro 1995 2020 atm_gasoil year \n26499 R12_AFR steam_cracker_petro 2000 2020 vacuum_gasoil year \n26503 R12_AFR steam_cracker_petro 2000 2025 atm_gasoil year \n26518 R12_AFR steam_cracker_petro 2005 2020 vacuum_gasoil year \n26522 R12_AFR steam_cracker_petro 2005 2025 atm_gasoil year \n... ... ... ... ... ... ... \n356981 R12_CHN steam_cracker_petro 2090 2110 vacuum_gasoil year \n356985 R12_CHN steam_cracker_petro 2100 2100 atm_gasoil year \n356991 R12_CHN steam_cracker_petro 2100 2110 vacuum_gasoil year \n356995 R12_CHN steam_cracker_petro 2110 2110 atm_gasoil year \n356996 R12_CHN steam_cracker_petro 2110 2110 vacuum_gasoil year \n\n lvl mrg \n26481 1.720463 0.0 \n26499 3.375000 0.0 \n26503 3.375000 0.0 \n26518 1.125000 0.0 \n26522 1.125000 0.0 \n... ... ... \n356981 123.553838 0.0 \n356985 73.377037 0.0 \n356991 73.377037 0.0 \n356995 139.193010 0.0 \n356996 83.698062 0.0 \n\n[641 rows x 8 columns]", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
node_loctechnologyyear_vtgyear_actmodetimelvlmrg
26481R12_AFRsteam_cracker_petro19952020atm_gasoilyear1.7204630.0
26499R12_AFRsteam_cracker_petro20002020vacuum_gasoilyear3.3750000.0
26503R12_AFRsteam_cracker_petro20002025atm_gasoilyear3.3750000.0
26518R12_AFRsteam_cracker_petro20052020vacuum_gasoilyear1.1250000.0
26522R12_AFRsteam_cracker_petro20052025atm_gasoilyear1.1250000.0
...........................
356981R12_CHNsteam_cracker_petro20902110vacuum_gasoilyear123.5538380.0
356985R12_CHNsteam_cracker_petro21002100atm_gasoilyear73.3770370.0
356991R12_CHNsteam_cracker_petro21002110vacuum_gasoilyear73.3770370.0
356995R12_CHNsteam_cracker_petro21102110atm_gasoilyear139.1930100.0
356996R12_CHNsteam_cracker_petro21102110vacuum_gasoilyear83.6980620.0
\n

641 rows × 8 columns

\n
" + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[df[\"technology\"]==\"steam_cracker_petro\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 43, + "outputs": [ + { + "data": { + "text/plain": " relation node_rel year_rel node_loc technology year_act \\\n0 meth_exp_limit R12_AFR 2020 R12_AFR meth_bio_ccs 2020 \n1 meth_exp_limit R12_AFR 2025 R12_AFR meth_bio_ccs 2025 \n2 meth_exp_limit R12_RCPA 2020 R12_RCPA meth_bio_ccs 2020 \n3 meth_exp_limit R12_RCPA 2025 R12_RCPA meth_bio_ccs 2025 \n4 meth_exp_limit R12_EEU 2020 R12_EEU meth_bio_ccs 2020 \n5 meth_exp_limit R12_EEU 2025 R12_EEU meth_bio_ccs 2025 \n6 meth_exp_limit R12_FSU 2020 R12_FSU meth_bio_ccs 2020 \n7 meth_exp_limit R12_FSU 2025 R12_FSU meth_bio_ccs 2025 \n8 meth_exp_limit R12_LAM 2020 R12_LAM meth_bio_ccs 2020 \n9 meth_exp_limit R12_LAM 2025 R12_LAM meth_bio_ccs 2025 \n10 meth_exp_limit R12_MEA 2020 R12_MEA meth_bio_ccs 2020 \n11 meth_exp_limit R12_MEA 2025 R12_MEA meth_bio_ccs 2025 \n12 meth_exp_limit R12_NAM 2020 R12_NAM meth_bio_ccs 2020 \n13 meth_exp_limit R12_NAM 2025 R12_NAM meth_bio_ccs 2025 \n14 meth_exp_limit R12_PAO 2020 R12_PAO meth_bio_ccs 2020 \n15 meth_exp_limit R12_PAO 2025 R12_PAO meth_bio_ccs 2025 \n16 meth_exp_limit R12_PAS 2020 R12_PAS meth_bio_ccs 2020 \n17 meth_exp_limit R12_PAS 2025 R12_PAS meth_bio_ccs 2025 \n18 meth_exp_limit R12_SAS 2020 R12_SAS meth_bio_ccs 2020 \n19 meth_exp_limit R12_SAS 2025 R12_SAS meth_bio_ccs 2025 \n20 meth_exp_limit R12_WEU 2020 R12_WEU meth_bio_ccs 2020 \n21 meth_exp_limit R12_WEU 2025 R12_WEU meth_bio_ccs 2025 \n22 meth_exp_limit R12_CHN 2020 R12_CHN meth_bio_ccs 2020 \n23 meth_exp_limit R12_CHN 2025 R12_CHN meth_bio_ccs 2025 \n24 meth_exp_limit R12_AFR 2020 R12_AFR meth_bio_ccs 2020 \n25 meth_exp_limit R12_AFR 2025 R12_AFR meth_bio_ccs 2025 \n26 meth_exp_limit R12_RCPA 2020 R12_RCPA meth_bio_ccs 2020 \n27 meth_exp_limit R12_RCPA 2025 R12_RCPA meth_bio_ccs 2025 \n28 meth_exp_limit R12_EEU 2020 R12_EEU meth_bio_ccs 2020 \n29 meth_exp_limit R12_EEU 2025 R12_EEU meth_bio_ccs 2025 \n30 meth_exp_limit R12_FSU 2020 R12_FSU meth_bio_ccs 2020 \n31 meth_exp_limit R12_FSU 2025 R12_FSU meth_bio_ccs 2025 \n32 meth_exp_limit R12_LAM 2020 R12_LAM meth_bio_ccs 2020 \n33 meth_exp_limit R12_LAM 2025 R12_LAM meth_bio_ccs 2025 \n34 meth_exp_limit R12_MEA 2020 R12_MEA meth_bio_ccs 2020 \n35 meth_exp_limit R12_MEA 2025 R12_MEA meth_bio_ccs 2025 \n36 meth_exp_limit R12_NAM 2020 R12_NAM meth_bio_ccs 2020 \n37 meth_exp_limit R12_NAM 2025 R12_NAM meth_bio_ccs 2025 \n38 meth_exp_limit R12_PAO 2020 R12_PAO meth_bio_ccs 2020 \n39 meth_exp_limit R12_PAO 2025 R12_PAO meth_bio_ccs 2025 \n40 meth_exp_limit R12_PAS 2020 R12_PAS meth_bio_ccs 2020 \n41 meth_exp_limit R12_PAS 2025 R12_PAS meth_bio_ccs 2025 \n42 meth_exp_limit R12_SAS 2020 R12_SAS meth_bio_ccs 2020 \n43 meth_exp_limit R12_SAS 2025 R12_SAS meth_bio_ccs 2025 \n44 meth_exp_limit R12_WEU 2020 R12_WEU meth_bio_ccs 2020 \n45 meth_exp_limit R12_WEU 2025 R12_WEU meth_bio_ccs 2025 \n46 meth_exp_limit R12_CHN 2020 R12_CHN meth_bio_ccs 2020 \n47 meth_exp_limit R12_CHN 2025 R12_CHN meth_bio_ccs 2025 \n\n mode value unit \n0 fuel 1.0 ??? \n1 fuel 1.0 ??? \n2 fuel 1.0 ??? \n3 fuel 1.0 ??? \n4 fuel 1.0 ??? \n5 fuel 1.0 ??? \n6 fuel 1.0 ??? \n7 fuel 1.0 ??? \n8 fuel 1.0 ??? \n9 fuel 1.0 ??? \n10 fuel 1.0 ??? \n11 fuel 1.0 ??? \n12 fuel 1.0 ??? \n13 fuel 1.0 ??? \n14 fuel 1.0 ??? \n15 fuel 1.0 ??? \n16 fuel 1.0 ??? \n17 fuel 1.0 ??? \n18 fuel 1.0 ??? \n19 fuel 1.0 ??? \n20 fuel 1.0 ??? \n21 fuel 1.0 ??? \n22 fuel 1.0 ??? \n23 fuel 1.0 ??? \n24 feedstock 1.0 ??? \n25 feedstock 1.0 ??? \n26 feedstock 1.0 ??? \n27 feedstock 1.0 ??? \n28 feedstock 1.0 ??? \n29 feedstock 1.0 ??? \n30 feedstock 1.0 ??? \n31 feedstock 1.0 ??? \n32 feedstock 1.0 ??? \n33 feedstock 1.0 ??? \n34 feedstock 1.0 ??? \n35 feedstock 1.0 ??? \n36 feedstock 1.0 ??? \n37 feedstock 1.0 ??? \n38 feedstock 1.0 ??? \n39 feedstock 1.0 ??? \n40 feedstock 1.0 ??? \n41 feedstock 1.0 ??? \n42 feedstock 1.0 ??? \n43 feedstock 1.0 ??? \n44 feedstock 1.0 ??? \n45 feedstock 1.0 ??? \n46 feedstock 1.0 ??? \n47 feedstock 1.0 ??? ", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
relationnode_relyear_relnode_loctechnologyyear_actmodevalueunit
0meth_exp_limitR12_AFR2020R12_AFRmeth_bio_ccs2020fuel1.0???
1meth_exp_limitR12_AFR2025R12_AFRmeth_bio_ccs2025fuel1.0???
2meth_exp_limitR12_RCPA2020R12_RCPAmeth_bio_ccs2020fuel1.0???
3meth_exp_limitR12_RCPA2025R12_RCPAmeth_bio_ccs2025fuel1.0???
4meth_exp_limitR12_EEU2020R12_EEUmeth_bio_ccs2020fuel1.0???
5meth_exp_limitR12_EEU2025R12_EEUmeth_bio_ccs2025fuel1.0???
6meth_exp_limitR12_FSU2020R12_FSUmeth_bio_ccs2020fuel1.0???
7meth_exp_limitR12_FSU2025R12_FSUmeth_bio_ccs2025fuel1.0???
8meth_exp_limitR12_LAM2020R12_LAMmeth_bio_ccs2020fuel1.0???
9meth_exp_limitR12_LAM2025R12_LAMmeth_bio_ccs2025fuel1.0???
10meth_exp_limitR12_MEA2020R12_MEAmeth_bio_ccs2020fuel1.0???
11meth_exp_limitR12_MEA2025R12_MEAmeth_bio_ccs2025fuel1.0???
12meth_exp_limitR12_NAM2020R12_NAMmeth_bio_ccs2020fuel1.0???
13meth_exp_limitR12_NAM2025R12_NAMmeth_bio_ccs2025fuel1.0???
14meth_exp_limitR12_PAO2020R12_PAOmeth_bio_ccs2020fuel1.0???
15meth_exp_limitR12_PAO2025R12_PAOmeth_bio_ccs2025fuel1.0???
16meth_exp_limitR12_PAS2020R12_PASmeth_bio_ccs2020fuel1.0???
17meth_exp_limitR12_PAS2025R12_PASmeth_bio_ccs2025fuel1.0???
18meth_exp_limitR12_SAS2020R12_SASmeth_bio_ccs2020fuel1.0???
19meth_exp_limitR12_SAS2025R12_SASmeth_bio_ccs2025fuel1.0???
20meth_exp_limitR12_WEU2020R12_WEUmeth_bio_ccs2020fuel1.0???
21meth_exp_limitR12_WEU2025R12_WEUmeth_bio_ccs2025fuel1.0???
22meth_exp_limitR12_CHN2020R12_CHNmeth_bio_ccs2020fuel1.0???
23meth_exp_limitR12_CHN2025R12_CHNmeth_bio_ccs2025fuel1.0???
24meth_exp_limitR12_AFR2020R12_AFRmeth_bio_ccs2020feedstock1.0???
25meth_exp_limitR12_AFR2025R12_AFRmeth_bio_ccs2025feedstock1.0???
26meth_exp_limitR12_RCPA2020R12_RCPAmeth_bio_ccs2020feedstock1.0???
27meth_exp_limitR12_RCPA2025R12_RCPAmeth_bio_ccs2025feedstock1.0???
28meth_exp_limitR12_EEU2020R12_EEUmeth_bio_ccs2020feedstock1.0???
29meth_exp_limitR12_EEU2025R12_EEUmeth_bio_ccs2025feedstock1.0???
30meth_exp_limitR12_FSU2020R12_FSUmeth_bio_ccs2020feedstock1.0???
31meth_exp_limitR12_FSU2025R12_FSUmeth_bio_ccs2025feedstock1.0???
32meth_exp_limitR12_LAM2020R12_LAMmeth_bio_ccs2020feedstock1.0???
33meth_exp_limitR12_LAM2025R12_LAMmeth_bio_ccs2025feedstock1.0???
34meth_exp_limitR12_MEA2020R12_MEAmeth_bio_ccs2020feedstock1.0???
35meth_exp_limitR12_MEA2025R12_MEAmeth_bio_ccs2025feedstock1.0???
36meth_exp_limitR12_NAM2020R12_NAMmeth_bio_ccs2020feedstock1.0???
37meth_exp_limitR12_NAM2025R12_NAMmeth_bio_ccs2025feedstock1.0???
38meth_exp_limitR12_PAO2020R12_PAOmeth_bio_ccs2020feedstock1.0???
39meth_exp_limitR12_PAO2025R12_PAOmeth_bio_ccs2025feedstock1.0???
40meth_exp_limitR12_PAS2020R12_PASmeth_bio_ccs2020feedstock1.0???
41meth_exp_limitR12_PAS2025R12_PASmeth_bio_ccs2025feedstock1.0???
42meth_exp_limitR12_SAS2020R12_SASmeth_bio_ccs2020feedstock1.0???
43meth_exp_limitR12_SAS2025R12_SASmeth_bio_ccs2025feedstock1.0???
44meth_exp_limitR12_WEU2020R12_WEUmeth_bio_ccs2020feedstock1.0???
45meth_exp_limitR12_WEU2025R12_WEUmeth_bio_ccs2025feedstock1.0???
46meth_exp_limitR12_CHN2020R12_CHNmeth_bio_ccs2020feedstock1.0???
47meth_exp_limitR12_CHN2025R12_CHNmeth_bio_ccs2025feedstock1.0???
\n
" + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = scenario.par(\"relation_activity\", filters={\"technology\":\"meth_bio_ccs\", \"year_act\":[2020, 2025]})\n", + "df" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 44, + "outputs": [], + "source": [ + "scenario.check_out()\n", + "scenario.remove_par(\"relation_activity\", df)\n", + "scenario.commit(\"remove 2020, 2025 meth_bio_ccs pars\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 30, + "outputs": [], + "source": [ + "import copy\n", + "paramList_tec = [x for x in scenario.par_list() if (\"technology\" in scenario.idx_sets(x))]\n", + "par_dict_meth = {}\n", + "for p in paramList_tec:\n", + " df = scenario.par(p, filters={\"technology\": \"meth_h2\"})\n", + " if df.index.size:\n", + " par_dict_meth[p] = df" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [], + "source": [ + "import copy\n", + "paramList_tec_mode = [x for x in scenario.par_list() if (\"mode\" in scenario.idx_sets(x))]\n", + "par_dict_meth_mode = {}\n", + "for p in paramList_tec_mode:\n", + " df = scenario.par(p, filters={\"technology\": \"meth_imp\"})\n", + " if df.index.size:\n", + " par_dict_meth_mode[p] = df" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 22, + "outputs": [], + "source": [ + "df = scenario.par(\"relation_activity\", filters= {\"technology\":\"meth_coal\"})" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 27, + "outputs": [], + "source": [ + "df[df.relation == \"meth_exp_limit\"].to_excel(\"meth_exp_limit_coal.xlsx\", index=False)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 32, + "outputs": [ + { + "data": { + "text/plain": " relation node_rel year_rel node_loc technology \\\n0 CO2_Emission R12_AFR 2030 R12_AFR meth_h2 \n1 CO2_Emission R12_AFR 2035 R12_AFR meth_h2 \n2 CO2_Emission R12_AFR 2040 R12_AFR meth_h2 \n3 CO2_Emission R12_AFR 2045 R12_AFR meth_h2 \n4 CO2_Emission R12_AFR 2050 R12_AFR meth_h2 \n.. ... ... ... ... ... \n619 CO2_PtX_trans_disp_split R12_WEU 2070 R12_WEU meth_h2 \n620 CO2_PtX_trans_disp_split R12_WEU 2080 R12_WEU meth_h2 \n621 CO2_PtX_trans_disp_split R12_WEU 2090 R12_WEU meth_h2 \n622 CO2_PtX_trans_disp_split R12_WEU 2100 R12_WEU meth_h2 \n623 CO2_PtX_trans_disp_split R12_WEU 2110 R12_WEU meth_h2 \n\n year_act mode value unit \n0 2030 fuel 0.549 ??? \n1 2035 fuel 0.549 ??? \n2 2040 fuel 0.549 ??? \n3 2045 fuel 0.549 ??? \n4 2050 fuel 0.549 ??? \n.. ... ... ... ... \n619 2070 feedstock -0.549 ??? \n620 2080 feedstock -0.549 ??? \n621 2090 feedstock -0.549 ??? \n622 2100 feedstock -0.549 ??? \n623 2110 feedstock -0.549 ??? \n\n[624 rows x 9 columns]", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
relationnode_relyear_relnode_loctechnologyyear_actmodevalueunit
0CO2_EmissionR12_AFR2030R12_AFRmeth_h22030fuel0.549???
1CO2_EmissionR12_AFR2035R12_AFRmeth_h22035fuel0.549???
2CO2_EmissionR12_AFR2040R12_AFRmeth_h22040fuel0.549???
3CO2_EmissionR12_AFR2045R12_AFRmeth_h22045fuel0.549???
4CO2_EmissionR12_AFR2050R12_AFRmeth_h22050fuel0.549???
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619CO2_PtX_trans_disp_splitR12_WEU2070R12_WEUmeth_h22070feedstock-0.549???
620CO2_PtX_trans_disp_splitR12_WEU2080R12_WEUmeth_h22080feedstock-0.549???
621CO2_PtX_trans_disp_splitR12_WEU2090R12_WEUmeth_h22090feedstock-0.549???
622CO2_PtX_trans_disp_splitR12_WEU2100R12_WEUmeth_h22100feedstock-0.549???
623CO2_PtX_trans_disp_splitR12_WEU2110R12_WEUmeth_h22110feedstock-0.549???
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624 rows × 9 columns

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" + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "par_dict_meth.get(\"relation_activity\")\n", + "#par_dict_meth[\"fix_cost\"]#.year_act.unique()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 20, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "initial_activity_up 2025\n", + "initial_activity_lo 2025\n", + "relation_activity 2030\n", + "ref_activity 2025\n" + ] + } + ], + "source": [ + "for k, vs in par_dict_meth.items():\n", + " if len(vs[\"value\"].unique()) != 1:\n", + " print(k, vs[\"year_act\"].min())" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 37, + "outputs": [], + "source": [ + "df = df[(df.technology == \"wind_ppl\") & (df.year_act == 2050)]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 38, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\maczek\\AppData\\Local\\Temp\\ipykernel_10940\\856668087.py:1: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df[\"value\"] = 0.1\n" + ] + } + ], + "source": [ + "df[\"value\"] = 0.1" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 41, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\maczek\\AppData\\Local\\Temp\\ipykernel_10940\\1042674758.py:1: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df[\"technology\"] = \"meth_h2\"\n", + "C:\\Users\\maczek\\AppData\\Local\\Temp\\ipykernel_10940\\1042674758.py:2: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df[\"mode\"] = \"feedstock\"\n" + ] + } + ], + "source": [ + "df[\"technology\"] = \"meth_h2\"\n", + "df[\"mode\"] = \"feedstock\"\n", + "df.to_excel(\"meth_h2_bound_test.xlsx\", index=False)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 42, + "outputs": [], + "source": [ + "#df_tot = pd.read_clipboard(sep=\",\").T\n", + "#df[\"test\"] = df.index\n", + "df_tot = df_tot.reset_index()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 43, + "outputs": [], + "source": [ + "#df_bio = pd.read_clipboard(sep=\",\").T\n", + "#df[\"test\"] = df.index\n", + "df_bio = df_bio.reset_index()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 44, + "outputs": [], + "source": [ + "#df_h2 = pd.read_clipboard(sep=\",\").T\n", + "#df[\"test\"] = df.index\n", + "df_h2 = df_h2.reset_index()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 52, + "outputs": [ + { + "data": { + "text/plain": " index\n3 gas_cc_ccs\n6 meth_ng\n7 meth_ng_ccs\n8 h2_smr\n9 h2_smr_ccs\n23 gas_NH3_ccs", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
index
3gas_cc_ccs
6meth_ng
7meth_ng_ccs
8h2_smr
9h2_smr_ccs
23gas_NH3_ccs
\n
" + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_h2[~df_h2[\"index\"].isin(df_bio[\"index\"])]\n", + "df_bio[~df_bio[\"index\"].isin(df_h2[\"index\"])]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 64, + "outputs": [], + "source": [ + "import pyperclip" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 82, + "outputs": [ + { + "data": { + "text/plain": " index\n0 gas_ppl\n2 gas_cc\n3 gas_cc_ccs\n6 meth_ng\n7 meth_ng_ccs\n8 h2_smr\n9 h2_smr_ccs\n12 dri_steel\n13 eaf_steel\n14 furnace_gas_steel\n15 hp_gas_steel\n16 furnace_gas_cement\n17 hp_gas_cement\n18 furnace_gas_aluminum\n19 hp_gas_aluminum\n20 furnace_gas_petro\n21 hp_gas_petro\n22 gas_NH3\n23 gas_NH3_ccs\n24 furnace_gas_refining\n25 hp_gas_refining", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
index
0gas_ppl
2gas_cc
3gas_cc_ccs
6meth_ng
7meth_ng_ccs
8h2_smr
9h2_smr_ccs
12dri_steel
13eaf_steel
14furnace_gas_steel
15hp_gas_steel
16furnace_gas_cement
17hp_gas_cement
18furnace_gas_aluminum
19hp_gas_aluminum
20furnace_gas_petro
21hp_gas_petro
22gas_NH3
23gas_NH3_ccs
24furnace_gas_refining
25hp_gas_refining
\n
" + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_bio[~df_bio[\"index\"].isin(df_tot[\"index\"])]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 73, + "outputs": [], + "source": [ + "x= list(df_bio[~df_bio[\"index\"].isin(df_tot[\"index\"])][\"index\"].values)\n", + "pyperclip.copy(\",\\n\".join(x))" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 8, + "outputs": [ + { + "data": { + "text/plain": "'2023-04-19 10:12:12.0'" + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scenario.last_update()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 15, + "outputs": [ + { + "data": { + "text/plain": " model scenario scheme is_default is_locked \\\n6379 MESSAGEix-Materials NoPolicy_no_petro MESSAGE 1 0 \n\n cre_user cre_date upd_user upd_date lock_user \\\n6379 maczek 2023-05-03 23:10:38.000000 None None None \n\n lock_date annotation version \n6379 None clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 1 ", + "text/html": "
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modelscenarioschemeis_defaultis_lockedcre_usercre_dateupd_userupd_datelock_userlock_dateannotationversion
6379MESSAGEix-MaterialsNoPolicy_no_petroMESSAGE10maczek2023-05-03 23:10:38.000000NoneNoneNoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...1
\n
" + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = mp.scenario_list()\n", + "df[df[\"scenario\"].str.contains(\"no_petro\")]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 9, + "outputs": [], + "source": [ + "df_gro = scenario.par(\"growth_activity_up\")\n", + "df_ini = scenario.par(\"initial_activity_up\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 11, + "outputs": [], + "source": [ + "df_hist = scenario.par(\"historical_activity\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 16, + "outputs": [], + "source": [ + "hist_list = df_hist[df_hist[\"year_act\"] == 2015][\"technology\"].unique()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 17, + "outputs": [], + "source": [ + "ini_list = df_ini[\"technology\"].unique()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 21, + "outputs": [ + { + "data": { + "text/plain": "array(['furnace_gas_cement', 'furnace_coal_refining',\n 'furnace_ethanol_steel', 'hp_elec_refining', 'dheat_refining',\n 'hp_gas_refining', 'furnace_biomass_cement', 'furnace_foil_petro',\n 'furnace_gas_aluminum', 'fc_h2_aluminum', 'hp_gas_steel',\n 'dheat_petro', 'furnace_coal_petro', 'furnace_coal_steel',\n 'furnace_coke_petro', 'furnace_methanol_petro',\n 'furnace_h2_aluminum', 'furnace_methanol_aluminum',\n 'furnace_gas_petro', 'furnace_h2_petro',\n 'furnace_methanol_refining', 'furnace_gas_refining',\n 'furnace_biomass_petro', 'furnace_elec_steel', 'hp_elec_aluminum',\n 'furnace_loil_refining', 'furnace_methanol_cement',\n 'furnace_methanol_steel', 'solar_petro', 'furnace_loil_cement',\n 'furnace_biomass_aluminum', 'furnace_elec_aluminum',\n 'dheat_aluminum', 'fc_h2_refining', 'furnace_elec_refining',\n 'hp_gas_petro', 'dheat_steel', 'furnace_elec_petro',\n 'furnace_loil_petro', 'furnace_ethanol_refining',\n 'furnace_foil_refining', 'furnace_foil_aluminum', 'solar_aluminum',\n 'furnace_foil_steel', 'solar_refining', 'furnace_foil_cement',\n 'furnace_coke_refining', 'furnace_coal_aluminum',\n 'furnace_gas_steel', 'furnace_coal_cement', 'furnace_h2_steel',\n 'fc_h2_steel', 'furnace_elec_cement', 'furnace_ethanol_aluminum',\n 'solar_steel', 'fc_h2_petro', 'furnace_ethanol_cement',\n 'hp_elec_petro', 'furnace_biomass_refining',\n 'furnace_biomass_steel', 'furnace_loil_steel', 'furnace_h2_cement',\n 'furnace_ethanol_petro', 'hp_elec_steel', 'hp_gas_aluminum',\n 'furnace_h2_refining', 'furnace_loil_aluminum'], dtype=object)" + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_gro[(~df_gro[\"technology\"].isin(hist_list)) & (~df_gro[\"technology\"].isin(ini_list))][\"technology\"].unique()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## add ini act up to MTO" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [], + "source": [ + "import ixmp as ix\n", + "import message_ix\n", + "mp = ix.Platform(\"ixmp_dev\")\n", + "\n", + "#scenario = message_ix.Scenario(mp, model=\"MESSAGEix-Materials\", scenario=\"NoPolicy_petro_thesis_2\")#, version=64)\n", + "scenario = message_ix.Scenario(mp, model=\"MESSAGEix-Materials\", scenario=\"2.0deg_no_SDGs_petro_thesis_1_macro\")#, version=64)\n", + "#scenario = message_ix.Scenario(mp, model=\"MESSAGEix-Materials\", scenario=\"2.0deg_petro_thesis_2_macro\")#, version=64)\n", + "#scenario = message_ix.Scenario(mp, model=\"MESSAGEix-Materials\", scenario=\"baseline_DEFAULT_rebase2\")\n", + "#scenario = message_ix.Scenario(mp, model=\"MESSAGEix-Materials\", scenario=\"baseline_VK_Transport-Materials_VK\")#, version=37)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [ + { + "data": { + "text/plain": " model scenario scheme \\\n6242 MESSAGEix-Materials 2.0deg_no_SDGs_petro_thesis_1_macro MESSAGE \n6373 MESSAGEix-Materials NoPolicy_no_SDGs_petro_thesis_1_macro MESSAGE \n\n is_default is_locked cre_user cre_date \\\n6242 1 0 maczek 2023-04-25 10:15:05.000000 \n6373 1 0 maczek 2023-04-24 22:13:46.000000 \n\n upd_user upd_date lock_user lock_date \\\n6242 Florian Maczek 2023-05-02 13:04:08.000000 None None \n6373 maczek 2023-04-24 22:23:46.000000 None None \n\n annotation version \n6242 clone Scenario from 'MESSAGEix-Materials|NoPol... 3 \n6373 clone Scenario from 'MESSAGEix-Materials|NoPol... 2 ", + "text/html": "
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modelscenarioschemeis_defaultis_lockedcre_usercre_dateupd_userupd_datelock_userlock_dateannotationversion
6242MESSAGEix-Materials2.0deg_no_SDGs_petro_thesis_1_macroMESSAGE10maczek2023-04-25 10:15:05.000000Florian Maczek2023-05-02 13:04:08.000000NoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...3
6373MESSAGEix-MaterialsNoPolicy_no_SDGs_petro_thesis_1_macroMESSAGE10maczek2023-04-24 22:13:46.000000maczek2023-04-24 22:23:46.000000NoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...2
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" + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = mp.scenario_list()\n", + "df[df[\"scenario\"].str.contains(\"thesis_1_macro\")]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 13, + "outputs": [ + { + "data": { + "text/plain": " model scenario \\\n4944 GENIE_SSP2_v4.1.7_test EN_NPi2020-DAC-MP-new-gov-less_1000 \n4945 GENIE_SSP2_v4.1.7_test EN_NPi2020-DAC-MP-new-gov-less_1000_step1 \n4946 GENIE_SSP2_v4.1.7_test EN_NPi2020-DAC-MP-new-gov-less_1000_step2 \n5258 GENIE_SSP2_v4.1.7_test NPi2020-DAC-MP-new-gov-less \n\n scheme is_default is_locked cre_user cre_date \\\n4944 MESSAGE 1 0 unlu 2021-10-21 11:10:07.000000 \n4945 MESSAGE 1 0 unlu 2021-10-27 09:34:30.000000 \n4946 MESSAGE 1 0 unlu 2021-10-21 10:47:16.000000 \n5258 MESSAGE 1 0 unlu 2021-10-21 10:04:49.000000 \n\n upd_user upd_date lock_user lock_date \\\n4944 unlu 2021-10-21 11:33:34.000000 None None \n4945 None None None None \n4946 unlu 2021-10-21 10:54:27.000000 None None \n5258 None None None None \n\n annotation version \n4944 clone Scenario from 'GENIE_SSP2_v4.1.7_test|EN... 1 \n4945 clone Scenario from 'GENIE_SSP2_v4.1.7_test|NP... 2 \n4946 clone Scenario from 'GENIE_SSP2_v4.1.7_test|EN... 1 \n5258 clone Scenario from 'GENIE_SSP2_v4.1.7_test|NP... 3 ", + "text/html": "
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modelscenarioschemeis_defaultis_lockedcre_usercre_dateupd_userupd_datelock_userlock_dateannotationversion
4944GENIE_SSP2_v4.1.7_testEN_NPi2020-DAC-MP-new-gov-less_1000MESSAGE10unlu2021-10-21 11:10:07.000000unlu2021-10-21 11:33:34.000000NoneNoneclone Scenario from 'GENIE_SSP2_v4.1.7_test|EN...1
4945GENIE_SSP2_v4.1.7_testEN_NPi2020-DAC-MP-new-gov-less_1000_step1MESSAGE10unlu2021-10-27 09:34:30.000000NoneNoneNoneNoneclone Scenario from 'GENIE_SSP2_v4.1.7_test|NP...2
4946GENIE_SSP2_v4.1.7_testEN_NPi2020-DAC-MP-new-gov-less_1000_step2MESSAGE10unlu2021-10-21 10:47:16.000000unlu2021-10-21 10:54:27.000000NoneNoneclone Scenario from 'GENIE_SSP2_v4.1.7_test|EN...1
5258GENIE_SSP2_v4.1.7_testNPi2020-DAC-MP-new-gov-lessMESSAGE10unlu2021-10-21 10:04:49.000000NoneNoneNoneNoneclone Scenario from 'GENIE_SSP2_v4.1.7_test|NP...3
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" + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[df[\"scenario\"].str.contains(\"2020-DAC-MP-new-gov-less\")]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 14, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "This Scenario has a solution, use `Scenario.remove_solution()` or `Scenario.clone(..., keep_solution=False)`\n" + ] + }, + { + "data": { + "text/plain": "0 CF4_TCE\n1 CH4_TCE\n2 CH4n_TCE\n3 CH4o_TCE\n4 CH4s_TCE\n ... \n628 BIO00GHG600_pcapita\n629 BIO00GHG1000_pcapita\n630 BIO00GHG1500_pcapita\n631 BIO00GHG2000_pcapita\n632 BIO00GHG3000_pcapita\nLength: 633, dtype: object" + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scenario = message_ix.Scenario(mp, model=\"GENIE_SSP2_v4.1.7_test\", scenario=\"EN_NPi2020-DAC-MP-new-gov-less_1000\")\n", + "scenario.set(\"technology\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [ + { + "data": { + "text/plain": " relation node_rel year_rel value unit\n0 CO2_Emission R12_AFR 1990 0.0 ???\n1 CO2_Emission R12_AFR 1995 0.0 ???\n2 CO2_Emission R12_AFR 2000 0.0 ???\n3 CO2_Emission R12_AFR 2005 0.0 ???\n4 CO2_Emission R12_AFR 2010 0.0 ???\n.. ... ... ... ... ...\n235 CO2_Emission R12_CHN 2015 0.0 ???\n236 CO2_Emission R12_CHN 2025 0.0 ???\n237 CO2_Emission R12_CHN 2035 0.0 ???\n238 CO2_Emission R12_CHN 2045 0.0 ???\n239 CO2_Emission R12_CHN 2055 0.0 ???\n\n[240 rows x 5 columns]", + "text/html": "
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relationnode_relyear_relvalueunit
0CO2_EmissionR12_AFR19900.0???
1CO2_EmissionR12_AFR19950.0???
2CO2_EmissionR12_AFR20000.0???
3CO2_EmissionR12_AFR20050.0???
4CO2_EmissionR12_AFR20100.0???
..................
235CO2_EmissionR12_CHN20150.0???
236CO2_EmissionR12_CHN20250.0???
237CO2_EmissionR12_CHN20350.0???
238CO2_EmissionR12_CHN20450.0???
239CO2_EmissionR12_CHN20550.0???
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240 rows × 5 columns

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" + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scenario.par(\"relation_lower\", filters={\"relation\":\"CO2_Emission\"})" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [ + { + "data": { + "text/plain": "True" + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scenario.has_solution()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 12, + "outputs": [ + { + "data": { + "text/plain": " model scenario \\\n633 COMMIT_SSP2_v1 baseline_DEFAULT \n994 Carbon_pool_SSP2_v4.1.7 baseline_DEFAULT \n1175 ENGAGE_SSP2_test baseline_DEFAULT \n1354 ENGAGE_SSP2_v2 baseline_DEFAULT \n1549 ENGAGE_SSP2_v4 baseline_DEFAULT \n... ... ... \n7769 tmp_test baseline_DEFAULT_ENGAGEv10.8_Sec6_2_3b \n7770 tmp_test baseline_DEFAULT_ENGAGEv10.8_Sec6_2_3c \n7771 tmp_test baseline_DEFAULT_ENGAGEv10.8_Sec6_2_3d \n7772 tmp_test baseline_DEFAULT_ENGAGEv10.8_Sec6_clean \n7773 tmp_test baseline_DEFAULT_ENGAGEv10.8_Sec7 \n\n scheme is_default is_locked cre_user cre_date \\\n633 MESSAGE 1 0 unlu 2019-12-05 10:37:20.000000 \n994 MESSAGE 1 0 fricko 2021-10-08 09:30:23.000000 \n1175 MESSAGE 1 0 fricko 2020-03-27 14:07:11.000000 \n1354 MESSAGE 1 0 fricko 2019-10-31 16:25:43.000000 \n1549 MESSAGE 1 0 fricko 2020-04-16 22:03:18.000000 \n... ... ... ... ... ... \n7769 MESSAGE 1 0 fricko 2022-04-22 11:00:00.000000 \n7770 MESSAGE 1 0 fricko 2022-04-25 10:51:29.000000 \n7771 MESSAGE 1 0 fricko 2022-04-26 15:25:45.000000 \n7772 MESSAGE 1 0 fricko 2022-04-04 13:37:38.000000 \n7773 MESSAGE 1 0 fricko 2022-04-04 11:32:35.000000 \n\n upd_user upd_date lock_user lock_date \\\n633 None None None None \n994 None None None None \n1175 None None None None \n1354 None None None None \n1549 None None None None \n... ... ... ... ... \n7769 None None None None \n7770 None None None None \n7771 None None None None \n7772 None None None None \n7773 None None None None \n\n annotation version \n633 clone Scenario from 'SSP2|baseline_DEFAULT', v... 35 \n994 clone Scenario from 'SSP2_test|baseline_DEFAUL... 15 \n1175 clone Scenario from 'SSP2_test|baseline_DEFAUL... 4 \n1354 clone Scenario from 'SSP2|baseline_DEFAULT', v... 2 \n1549 clone Scenario from 'SSP2_test|baseline_DEFAUL... 9 \n... ... ... \n7769 clone Scenario from 'SSP2_test|baseline_DEFAUL... 35 \n7770 clone Scenario from 'SSP2_test|baseline_DEFAUL... 29 \n7771 clone Scenario from 'SSP2_test|baseline_DEFAUL... 22 \n7772 clone Scenario from 'SSP2_test|baseline_DEFAUL... 1 \n7773 clone Scenario from 'SSP2_test|baseline_DEFAUL... 1 \n\n[498 rows x 13 columns]", + "text/html": "
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modelscenarioschemeis_defaultis_lockedcre_usercre_dateupd_userupd_datelock_userlock_dateannotationversion
633COMMIT_SSP2_v1baseline_DEFAULTMESSAGE10unlu2019-12-05 10:37:20.000000NoneNoneNoneNoneclone Scenario from 'SSP2|baseline_DEFAULT', v...35
994Carbon_pool_SSP2_v4.1.7baseline_DEFAULTMESSAGE10fricko2021-10-08 09:30:23.000000NoneNoneNoneNoneclone Scenario from 'SSP2_test|baseline_DEFAUL...15
1175ENGAGE_SSP2_testbaseline_DEFAULTMESSAGE10fricko2020-03-27 14:07:11.000000NoneNoneNoneNoneclone Scenario from 'SSP2_test|baseline_DEFAUL...4
1354ENGAGE_SSP2_v2baseline_DEFAULTMESSAGE10fricko2019-10-31 16:25:43.000000NoneNoneNoneNoneclone Scenario from 'SSP2|baseline_DEFAULT', v...2
1549ENGAGE_SSP2_v4baseline_DEFAULTMESSAGE10fricko2020-04-16 22:03:18.000000NoneNoneNoneNoneclone Scenario from 'SSP2_test|baseline_DEFAUL...9
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7769tmp_testbaseline_DEFAULT_ENGAGEv10.8_Sec6_2_3bMESSAGE10fricko2022-04-22 11:00:00.000000NoneNoneNoneNoneclone Scenario from 'SSP2_test|baseline_DEFAUL...35
7770tmp_testbaseline_DEFAULT_ENGAGEv10.8_Sec6_2_3cMESSAGE10fricko2022-04-25 10:51:29.000000NoneNoneNoneNoneclone Scenario from 'SSP2_test|baseline_DEFAUL...29
7771tmp_testbaseline_DEFAULT_ENGAGEv10.8_Sec6_2_3dMESSAGE10fricko2022-04-26 15:25:45.000000NoneNoneNoneNoneclone Scenario from 'SSP2_test|baseline_DEFAUL...22
7772tmp_testbaseline_DEFAULT_ENGAGEv10.8_Sec6_cleanMESSAGE10fricko2022-04-04 13:37:38.000000NoneNoneNoneNoneclone Scenario from 'SSP2_test|baseline_DEFAUL...1
7773tmp_testbaseline_DEFAULT_ENGAGEv10.8_Sec7MESSAGE10fricko2022-04-04 11:32:35.000000NoneNoneNoneNoneclone Scenario from 'SSP2_test|baseline_DEFAUL...1
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498 rows × 13 columns

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" + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#df = mp.scenario_list()\n", + "df[df[\"scenario\"].str.contains(\"baseline_DEFAULT\")]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [], + "source": [ + "df = scenario.par(\"growth_activity_lo\", filters={\"technology\":\"furnace_gas_petro\"})\n", + "df = df[df[\"year_act\"]==2030]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [ + { + "data": { + "text/plain": " node_loc technology year_act time value unit\n0 R12_AFR furnace_gas_petro 2025 year -0.05 t\n12 R12_RCPA furnace_gas_petro 2025 year -0.05 t\n24 R12_EEU furnace_gas_petro 2025 year -0.05 t\n36 R12_FSU furnace_gas_petro 2025 year -0.05 t\n48 R12_LAM furnace_gas_petro 2025 year -0.05 t\n60 R12_MEA furnace_gas_petro 2025 year -0.05 t\n72 R12_NAM furnace_gas_petro 2025 year -0.05 t\n84 R12_PAO furnace_gas_petro 2025 year -0.05 t\n96 R12_PAS furnace_gas_petro 2025 year -0.05 t\n108 R12_SAS furnace_gas_petro 2025 year -0.05 t\n120 R12_WEU furnace_gas_petro 2025 year -0.05 t\n132 R12_CHN furnace_gas_petro 2025 year -0.05 t", + "text/html": "
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node_loctechnologyyear_acttimevalueunit
0R12_AFRfurnace_gas_petro2025year-0.05t
12R12_RCPAfurnace_gas_petro2025year-0.05t
24R12_EEUfurnace_gas_petro2025year-0.05t
36R12_FSUfurnace_gas_petro2025year-0.05t
48R12_LAMfurnace_gas_petro2025year-0.05t
60R12_MEAfurnace_gas_petro2025year-0.05t
72R12_NAMfurnace_gas_petro2025year-0.05t
84R12_PAOfurnace_gas_petro2025year-0.05t
96R12_PASfurnace_gas_petro2025year-0.05t
108R12_SASfurnace_gas_petro2025year-0.05t
120R12_WEUfurnace_gas_petro2025year-0.05t
132R12_CHNfurnace_gas_petro2025year-0.05t
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" + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[\"year_act\"] = 2025\n", + "df" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 9, + "outputs": [ + { + "data": { + "text/plain": "{'var_cost': node_loc technology year_vtg year_act mode time value unit\n 0 R12_AFR h2_elec 2010 2010 feedstock year 4.0 USD/GWa\n 1 R12_AFR h2_elec 2010 2015 feedstock year 4.0 USD/GWa\n 2 R12_AFR h2_elec 2010 2020 feedstock year 4.0 USD/GWa\n 3 R12_AFR h2_elec 2010 2025 feedstock year 4.0 USD/GWa\n 4 R12_AFR h2_elec 2010 2030 feedstock year 4.0 USD/GWa\n ... ... ... ... ... ... ... ... ...\n 1723 R12_CHN h2_elec 2090 2100 feedstock year 4.0 USD/GWa\n 1724 R12_CHN h2_elec 2090 2110 feedstock year 4.0 USD/GWa\n 1725 R12_CHN h2_elec 2100 2100 feedstock year 4.0 USD/GWa\n 1726 R12_CHN h2_elec 2100 2110 feedstock year 4.0 USD/GWa\n 1727 R12_CHN h2_elec 2110 2110 feedstock year 4.0 USD/GWa\n \n [1728 rows x 8 columns],\n 'output': node_loc technology year_vtg year_act mode node_dest commodity \\\n 0 R12_AFR h2_elec 2010 2010 feedstock R12_AFR hydrogen \n 1 R12_AFR h2_elec 2010 2015 feedstock R12_AFR hydrogen \n 2 R12_AFR h2_elec 2010 2020 feedstock R12_AFR hydrogen \n 3 R12_AFR h2_elec 2010 2025 feedstock R12_AFR hydrogen \n 4 R12_AFR h2_elec 2010 2030 feedstock R12_AFR hydrogen \n ... ... ... ... ... ... ... ... \n 1723 R12_CHN h2_elec 2090 2100 feedstock R12_CHN hydrogen \n 1724 R12_CHN h2_elec 2090 2110 feedstock R12_CHN hydrogen \n 1725 R12_CHN h2_elec 2100 2100 feedstock R12_CHN hydrogen \n 1726 R12_CHN h2_elec 2100 2110 feedstock R12_CHN hydrogen \n 1727 R12_CHN h2_elec 2110 2110 feedstock R12_CHN hydrogen \n \n level time time_dest value unit \n 0 secondary year year 1.0 GWa \n 1 secondary year year 1.0 GWa \n 2 secondary year year 1.0 GWa \n 3 secondary year year 1.0 GWa \n 4 secondary year year 1.0 GWa \n ... ... ... ... ... ... \n 1723 secondary year year 1.0 GWa \n 1724 secondary year year 1.0 GWa \n 1725 secondary year year 1.0 GWa \n 1726 secondary year year 1.0 GWa \n 1727 secondary year year 1.0 GWa \n \n [1728 rows x 12 columns],\n 'input': node_loc technology year_vtg year_act mode node_origin commodity \\\n 0 R12_AFR h2_elec 2010 2010 feedstock R12_AFR electr \n 1 R12_AFR h2_elec 2010 2015 feedstock R12_AFR electr \n 2 R12_AFR h2_elec 2010 2020 feedstock R12_AFR electr \n 3 R12_AFR h2_elec 2010 2025 feedstock R12_AFR electr \n 4 R12_AFR h2_elec 2010 2030 feedstock R12_AFR electr \n ... ... ... ... ... ... ... ... \n 1723 R12_CHN h2_elec 2090 2100 feedstock R12_CHN electr \n 1724 R12_CHN h2_elec 2090 2110 feedstock R12_CHN electr \n 1725 R12_CHN h2_elec 2100 2100 feedstock R12_CHN electr \n 1726 R12_CHN h2_elec 2100 2110 feedstock R12_CHN electr \n 1727 R12_CHN h2_elec 2110 2110 feedstock R12_CHN electr \n \n level time time_origin value unit \n 0 secondary year year 1.25 GWa \n 1 secondary year year 1.25 GWa \n 2 secondary year year 1.25 GWa \n 3 secondary year year 1.25 GWa \n 4 secondary year year 1.25 GWa \n ... ... ... ... ... ... \n 1723 secondary year year 1.25 GWa \n 1724 secondary year year 1.25 GWa \n 1725 secondary year year 1.25 GWa \n 1726 secondary year year 1.25 GWa \n 1727 secondary year year 1.25 GWa \n \n [1728 rows x 12 columns],\n 'relation_activity': relation node_rel year_rel node_loc technology year_act \\\n 0 RES_variable_limt R12_AFR 2010 R12_AFR h2_elec 2010 \n 1 h2_exp_limit R12_AFR 2010 R12_AFR h2_elec 2010 \n 2 oper_res R12_AFR 2010 R12_AFR h2_elec 2010 \n 3 solar_step R12_AFR 2010 R12_AFR h2_elec 2010 \n 4 solar_step2 R12_AFR 2010 R12_AFR h2_elec 2010 \n ... ... ... ... ... ... ... \n 5787 wind_curtailment_3 R12_CHN 2015 R12_CHN h2_elec 2015 \n 5788 wind_curtailment_3 R12_CHN 2025 R12_CHN h2_elec 2025 \n 5789 wind_curtailment_3 R12_CHN 2035 R12_CHN h2_elec 2035 \n 5790 wind_curtailment_3 R12_CHN 2045 R12_CHN h2_elec 2045 \n 5791 wind_curtailment_3 R12_CHN 2055 R12_CHN h2_elec 2055 \n \n mode value unit \n 0 feedstock -1.250000 ??? \n 1 feedstock 1.000000 ??? \n 2 feedstock 0.312500 ??? \n 3 feedstock 0.062500 ??? \n 4 feedstock 0.187500 ??? \n ... ... ... ... \n 5787 feedstock -0.105263 ??? \n 5788 feedstock -0.105263 ??? \n 5789 feedstock -0.105263 ??? \n 5790 feedstock -0.105263 ??? \n 5791 feedstock -0.105263 ??? \n \n [5792 rows x 9 columns],\n 'historical_activity': node_loc technology year_act mode time value unit\n 0 R12_AFR h2_elec 2010 feedstock year 0.000000 GWa\n 1 R12_RCPA h2_elec 2010 feedstock year 0.312201 GWa\n 2 R12_EEU h2_elec 2010 feedstock year 0.000000 GWa\n 3 R12_FSU h2_elec 2010 feedstock year 0.000000 GWa\n 4 R12_LAM h2_elec 2010 feedstock year 0.000000 GWa\n 5 R12_MEA h2_elec 2010 feedstock year 0.000000 GWa\n 6 R12_NAM h2_elec 2010 feedstock year 0.000000 GWa\n 7 R12_PAO h2_elec 2010 feedstock year 3.122010 GWa\n 8 R12_PAS h2_elec 2010 feedstock year 0.000000 GWa\n 9 R12_SAS h2_elec 2010 feedstock year 0.000000 GWa\n 10 R12_WEU h2_elec 2010 feedstock year 0.000000 GWa\n 11 R12_SAS h2_elec 2015 feedstock year 0.000000 ???\n 12 R12_MEA h2_elec 2015 feedstock year 0.000000 ???\n 13 R12_FSU h2_elec 2015 feedstock year 0.000000 ???\n 14 R12_PAS h2_elec 2015 feedstock year 0.000000 ???\n 15 R12_EEU h2_elec 2015 feedstock year 0.000000 ???\n 16 R12_PAO h2_elec 2015 feedstock year 0.000000 ???\n 17 R12_LAM h2_elec 2015 feedstock year 0.000000 ???\n 18 R12_RCPA h2_elec 2015 feedstock year 0.000000 ???\n 19 R12_WEU h2_elec 2015 feedstock year 0.000000 ???\n 20 R12_NAM h2_elec 2015 feedstock year 0.000000 ???\n 21 R12_AFR h2_elec 2015 feedstock year 0.000000 ???\n 22 R12_CHN h2_elec 2010 feedstock year 2.809809 GWa\n 23 R12_CHN h2_elec 2015 feedstock year 0.000000 ???\n 24 R12_AFR h2_elec 2010 feedstock year 0.000000 GWa\n 25 R12_RCPA h2_elec 2010 feedstock year 0.312201 GWa\n 26 R12_EEU h2_elec 2010 feedstock year 0.000000 GWa\n 27 R12_FSU h2_elec 2010 feedstock year 0.000000 GWa\n 28 R12_LAM h2_elec 2010 feedstock year 0.000000 GWa\n 29 R12_MEA h2_elec 2010 feedstock year 0.000000 GWa\n 30 R12_NAM h2_elec 2010 feedstock year 0.000000 GWa\n 31 R12_PAO h2_elec 2010 feedstock year 3.122010 GWa\n 32 R12_PAS h2_elec 2010 feedstock year 0.000000 GWa\n 33 R12_SAS h2_elec 2010 feedstock year 0.000000 GWa\n 34 R12_WEU h2_elec 2010 feedstock year 0.000000 GWa\n 35 R12_SAS h2_elec 2015 feedstock year 0.000000 ???\n 36 R12_MEA h2_elec 2015 feedstock year 0.000000 ???\n 37 R12_FSU h2_elec 2015 feedstock year 0.000000 ???\n 38 R12_PAS h2_elec 2015 feedstock year 0.000000 ???\n 39 R12_EEU h2_elec 2015 feedstock year 0.000000 ???\n 40 R12_PAO h2_elec 2015 feedstock year 0.000000 ???\n 41 R12_LAM h2_elec 2015 feedstock year 0.000000 ???\n 42 R12_RCPA h2_elec 2015 feedstock year 0.000000 ???\n 43 R12_WEU h2_elec 2015 feedstock year 0.000000 ???\n 44 R12_NAM h2_elec 2015 feedstock year 0.000000 ???\n 45 R12_AFR h2_elec 2015 feedstock year 0.000000 ???\n 46 R12_CHN h2_elec 2010 feedstock year 2.809809 GWa\n 47 R12_CHN h2_elec 2015 feedstock year 0.000000 ???,\n 'ref_activity': node_loc technology year_act mode time value unit\n 0 R12_AFR h2_elec 2010 feedstock year 0.000000 GWa\n 1 R12_AFR h2_elec 2020 feedstock year 0.000000 GWa\n 2 R12_AFR h2_elec 2030 feedstock year 0.000000 GWa\n 3 R12_AFR h2_elec 2040 feedstock year 0.000000 GWa\n 4 R12_AFR h2_elec 2050 feedstock year 0.000000 GWa\n .. ... ... ... ... ... ... ...\n 379 R12_CHN h2_elec 2015 feedstock year 1.561005 GWa\n 380 R12_CHN h2_elec 2025 feedstock year 0.000000 GWa\n 381 R12_CHN h2_elec 2035 feedstock year 0.000000 GWa\n 382 R12_CHN h2_elec 2045 feedstock year 0.000000 GWa\n 383 R12_CHN h2_elec 2055 feedstock year 0.000000 GWa\n \n [384 rows x 7 columns],\n 'addon_conversion': node technology year_vtg year_act mode time \\\n 0 R12_AFR h2_elec 2020 2020 feedstock year \n 1 R12_AFR h2_elec 2020 2025 feedstock year \n 2 R12_AFR h2_elec 2020 2030 feedstock year \n 3 R12_AFR h2_elec 2020 2035 feedstock year \n 4 R12_AFR h2_elec 2020 2040 feedstock year \n .. ... ... ... ... ... ... \n 775 R12_CHN h2_elec 2090 2100 feedstock year \n 776 R12_CHN h2_elec 2090 2110 feedstock year \n 777 R12_CHN h2_elec 2100 2100 feedstock year \n 778 R12_CHN h2_elec 2100 2110 feedstock year \n 779 R12_CHN h2_elec 2110 2110 feedstock year \n \n type_addon value unit \n 0 methanol_synthesis_addon 0.897 - \n 1 methanol_synthesis_addon 0.897 - \n 2 methanol_synthesis_addon 0.897 - \n 3 methanol_synthesis_addon 0.897 - \n 4 methanol_synthesis_addon 0.897 - \n .. ... ... ... \n 775 methanol_synthesis_addon 0.897 - \n 776 methanol_synthesis_addon 0.897 - \n 777 methanol_synthesis_addon 0.897 - \n 778 methanol_synthesis_addon 0.897 - \n 779 methanol_synthesis_addon 0.897 - \n \n [780 rows x 9 columns],\n 'addon_up': node technology year_act mode time type_addon \\\n 0 R12_AFR h2_elec 2020 feedstock year methanol_synthesis_addon \n 1 R12_AFR h2_elec 2025 feedstock year methanol_synthesis_addon \n 2 R12_AFR h2_elec 2030 feedstock year methanol_synthesis_addon \n 3 R12_AFR h2_elec 2035 feedstock year methanol_synthesis_addon \n 4 R12_AFR h2_elec 2040 feedstock year methanol_synthesis_addon \n .. ... ... ... ... ... ... \n 177 R12_CHN h2_elec 2070 feedstock year methanol_synthesis_addon \n 178 R12_CHN h2_elec 2080 feedstock year methanol_synthesis_addon \n 179 R12_CHN h2_elec 2090 feedstock year methanol_synthesis_addon \n 180 R12_CHN h2_elec 2100 feedstock year methanol_synthesis_addon \n 181 R12_CHN h2_elec 2110 feedstock year methanol_synthesis_addon \n \n value unit \n 0 1.0 - \n 1 1.0 - \n 2 1.0 - \n 3 1.0 - \n 4 1.0 - \n .. ... ... \n 177 1.0 - \n 178 1.0 - \n 179 1.0 - \n 180 1.0 - \n 181 1.0 - \n \n [182 rows x 8 columns]}" + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import copy\n", + "paramList_tec = [x for x in scenario.par_list() if\n", + " ((\"technology\" in scenario.idx_sets(x)) & (\"mode\" in scenario.idx_sets(x)))]\n", + "par_dict_meth = {}\n", + "for p in paramList_tec:\n", + " df = scenario.par(p, filters={\"technology\": \"h2_elec\"})\n", + " if df.index.size:\n", + " par_dict_meth[p] = df\n", + "par_dict_fuel = copy.deepcopy(par_dict_meth)\n", + "par_dict_fs = copy.deepcopy(par_dict_meth)\n", + "for k, v in par_dict_fuel.items():\n", + " v[\"mode\"] = \"fuel\"\n", + "for k, v in par_dict_fs.items():\n", + " v[\"mode\"] = \"feedstock\"\n", + "par_dict_fs" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 11, + "outputs": [], + "source": [ + "import pandas as pd\n", + "with pd.ExcelWriter('h2_elec_fs.xlsx') as writer:\n", + " for i in list(par_dict_fs.keys()):\n", + " par_dict_fs[i].to_excel(writer, sheet_name=i, index=False)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 12, + "outputs": [], + "source": [ + "with pd.ExcelWriter('h2_elec_fuel.xlsx') as writer:\n", + " for i in list(par_dict_fuel.keys()):\n", + " par_dict_fuel[i].to_excel(writer, sheet_name=i, index=False)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "for k, v in par_dict_meth.items():\n", + " scenario.remove_par(k, v)\n", + "for k, v in par_dict_fuel.items():\n", + " scenario.add_par(k, v)\n", + "for k, v in par_dict_fs.items():\n", + " scenario.add_par(k, v)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [], + "source": [ + "scenario.remove_solution()\n", + "scenario.check_out()\n", + "scenario.add_par(\"growth_activity_lo\", df)\n", + "scenario.commit(\"modify gas petro furnace constraints\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 34, + "outputs": [ + { + "data": { + "text/plain": " node_loc technology year_act time value unit\n2944 R12_RCPA steam_cracker_petro 2020 year 0.2 t\n2945 R12_FSU ethanol_to_ethylene_petro 2020 year 0.1 t\n2946 R12_AFR ethanol_to_ethylene_petro 2020 year 0.1 t\n2947 R12_PAO ethanol_to_ethylene_petro 2020 year 0.1 t\n2948 R12_EEU ethanol_to_ethylene_petro 2020 year 0.1 t\n... ... ... ... ... ... ...\n7211 R12_PAO ethanol_to_ethylene_petro 2110 year 0.1 t\n7212 R12_EEU ethanol_to_ethylene_petro 2110 year 0.1 t\n7213 R12_MEA ethanol_to_ethylene_petro 2110 year 0.1 t\n7214 R12_WEU ethanol_to_ethylene_petro 2110 year 0.1 t\n7215 R12_NAM ethanol_to_ethylene_petro 2110 year 0.1 t\n\n[99 rows x 6 columns]", + "text/html": "
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node_loctechnologyyear_acttimevalueunit
2944R12_RCPAsteam_cracker_petro2020year0.2t
2945R12_FSUethanol_to_ethylene_petro2020year0.1t
2946R12_AFRethanol_to_ethylene_petro2020year0.1t
2947R12_PAOethanol_to_ethylene_petro2020year0.1t
2948R12_EEUethanol_to_ethylene_petro2020year0.1t
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7211R12_PAOethanol_to_ethylene_petro2110year0.1t
7212R12_EEUethanol_to_ethylene_petro2110year0.1t
7213R12_MEAethanol_to_ethylene_petro2110year0.1t
7214R12_WEUethanol_to_ethylene_petro2110year0.1t
7215R12_NAMethanol_to_ethylene_petro2110year0.1t
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99 rows × 6 columns

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" + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "par_petro[\"initial_activity_up\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 35, + "outputs": [], + "source": [ + "from message_data.model.material import data_methanol\n", + "par_mto = data_methanol.gen_data_meth_chemicals(scenario2, \"MTO\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 38, + "outputs": [ + { + "data": { + "text/plain": " node_loc technology year_act time value unit\n0 R12_AFR MTO_petro 2020 year 0.1 ???\n1 R12_AFR MTO_petro 2025 year 0.1 ???\n2 R12_AFR MTO_petro 2030 year 0.1 ???\n3 R12_AFR MTO_petro 2035 year 0.1 ???\n4 R12_AFR MTO_petro 2040 year 0.1 ???\n... ... ... ... ... ... ...\n1759 R12_WEU MTO_petro 2070 year 0.1 ???\n1760 R12_WEU MTO_petro 2080 year 0.1 ???\n1761 R12_WEU MTO_petro 2090 year 0.1 ???\n1762 R12_WEU MTO_petro 2100 year 0.1 ???\n1763 R12_WEU MTO_petro 2110 year 0.1 ???\n\n[154 rows x 6 columns]", + "text/html": "
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node_loctechnologyyear_acttimevalueunit
0R12_AFRMTO_petro2020year0.1???
1R12_AFRMTO_petro2025year0.1???
2R12_AFRMTO_petro2030year0.1???
3R12_AFRMTO_petro2035year0.1???
4R12_AFRMTO_petro2040year0.1???
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1759R12_WEUMTO_petro2070year0.1???
1760R12_WEUMTO_petro2080year0.1???
1761R12_WEUMTO_petro2090year0.1???
1762R12_WEUMTO_petro2100year0.1???
1763R12_WEUMTO_petro2110year0.1???
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154 rows × 6 columns

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" + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "par_mto[\"initial_activity_up\"].drop_duplicates()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 25, + "outputs": [ + { + "data": { + "text/plain": "'2023-04-13 15:29:47.0'" + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scenario2.last_update()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 11, + "outputs": [], + "source": [ + "scenario2 = message_ix.Scenario(mp, model=\"MESSAGEix-Materials\", scenario=\"NoPolicy_petro_thesis_2\")#, version=65)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 26, + "outputs": [], + "source": [ + "df = scenario2.par(\"initial_activity_up\", filters={\"technology\":\"MTO_petro\", \"node_loc\":\"R12_CHN\"})\n", + "scenario2.check_out()\n", + "scenario2.remove_par(\"initial_activity_up\", df)\n", + "\n", + "#df[\"level\"] = \"secondary\"\n", + "#scenario2.add_par(\"output\", df)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 27, + "outputs": [], + "source": [ + "df_eth = scenario2.par(\"initial_activity_up\", filters={\"technology\":\"ethanol_to_ethylene_petro\"})\n", + "df_eth\n", + "df_eth_hist = scenario2.par(\"historical_activity\", filters={\"technology\":\"ethanol_to_ethylene_petro\", \"year_act\":2015})[\"node_loc\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 28, + "outputs": [], + "source": [ + "scenario2.remove_par(\"initial_activity_up\" ,df_eth[df_eth[\"node_loc\"].isin(df_eth_hist)])" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 29, + "outputs": [], + "source": [ + "scenario2.commit(\"remove ini act up for historical regions\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "paramList_tec = [x for x in scenario.par_list() if \"mode\" in scenario.idx_sets(x)]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "import pandas as pd\n", + "par_dict_meth = {}\n", + "df_all_rows = pd.DataFrame()\n", + "for p in paramList_tec:\n", + " df = scenario.par(p, filters={\"technology\":\"meth_h2\"})\n", + " if df.index.size:\n", + " par_dict_meth[p] = df" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "for k,v in par_dict_meth.items():\n", + " print(k, v[\"mode\"].unique())" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scenario.remove_solution()\n", + "scenario.check_out()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "par = \"addon_up\" #\"addon_conversion\" #\n", + "df = scenario.par(par, filters={\"technology\":\"h2_elec\"})\n", + "df_remove = df.copy(deep=True)\n", + "scenario.remove_par(par, df_remove)\n", + "\n", + "df[\"mode\"] = \"M1\"\n", + "scenario.add_par(par, df.drop_duplicates())" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scenario.commit(\"fix meth_h2 addon parameters\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df = mp.scenario_list()\n", + "df[df[\"scenario\"].str.contains(\"petro\")]\n", + "#clone Scenario from 'SHAPE_SSP2_v4.1.8|baseline_no_SDGs', version: 1" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df = scenario.par(\"initial_activity_up\", filters={\"technology\":\"geo_ppl\"})" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df = df[df.year_act > 2015]\n", + "df[\"value\"] = 0.1\n", + "df[\"technology\"] = \"MTO_petro\"\n", + "df[\"unit\"] = \"-\"\n", + "scenario.check_out()\n", + "scenario.add_par(\"initial_activity_up\", df[df[\"node_loc\"]!=\"R12_CHN\"])" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scenario.commit(\"add MTO ini_act_up\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df_growth_up = scenario.par(\"growth_activity_up\", filters={\"technology\": \"meth_exp\"})\n", + "df_growth_up = df_growth_up[df_growth_up[\"node_loc\"]==\"R12_NAM\"]\n", + "df_growth_up = df_growth_up[df_growth_up[\"year_act\"]==2020]\n", + "df_growth_up[\"value\"] = 0.2\n", + "df_growth_up" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df_growth_up = scenario.par(\"initial_activity_up\", filters={\"technology\": \"meth_imp\"})\n", + "#df_growth_up = df_growth_up[df_growth_up[\"node_loc\"]==\"R12_NAM\"]\n", + "df_growth_up = df_growth_up[df_growth_up[\"year_act\"]==2020]\n", + "#df_growth_up[\"value\"] = 0.2\n", + "df_growth_up" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scenario.remove_solution()\n", + "scenario.check_out()\n", + "scenario.remove_par(\"initial_activity_up\", df_growth_up)\n", + "scenario.commit(\"modify meth exp growth constraint\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df_growth_up = scenario.par(\"growth_activity_up\", filters={\"technology\": \"steam_cracker_petro\"})\n", + "df_growth_up[\"technology\"] = \"import_petro\"" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## Unlock scenario" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scenario.run_id()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "#mp._backend.jobj.unlockRunid(scenario.run_id())\n", + "mp._backend.jobj.unlockRunid(33129)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## set local data path" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [], + "source": [ + "import os\n", + "from message_ix_models import Context\n", + "from pathlib import Path\n", + "Context.get_instance(-1).handle_cli_args(local_data=Path(os.getcwd()).parents[2].joinpath(\"data\"))\n", + "#Path(os.getcwd()).parents[2].joinpath(\"data\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## get all parameters for given technology" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "paramList_tec = [x for x in scenario.par_list() if \"technology\" in scenario.idx_sets(x)]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "import numpy as np" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "par_dict_bunker = {}\n", + "df_all_rows = pd.DataFrame()\n", + "for p in paramList_tec:\n", + " df = scenario.par(p, filters={\"technology\":\"meth_exp\"})\n", + " if df.index.size:\n", + " par_dict_bunker[p] = df\n", + "\n", + "df = par_dict_bunker[\"relation_activity\"]\n", + "par_dict_bunker[\"relation_activity\"] = df[~df[\"relation\"].str.contains(\"CO\")]\n", + "\n", + "for k in par_dict_bunker.keys():\n", + " par_dict_bunker[k][\"technology\"] = \"NH3_bunker\"\n", + " if \"level\" in par_dict_bunker[k].columns:\n", + " par_dict_bunker[k][\"level\"] = \"secondary_material\"\n", + " if \"commodity\" in par_dict_bunker[k].columns:\n", + " par_dict_bunker[k][\"commodity\"] = \"NH3\"" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df_all_rows = pd.DataFrame()\n", + "for p in paramList_tec:\n", + " print(p)\n", + " df = scenario.par(p)\n", + " df = df[df[\"technology\"]==\"meth_bunker\"]\n", + " for v in df[\"value\"].unique():\n", + " if v == 0:\n", + " continue\n", + " df_row = df[np.isclose(df[\"value\"], v)].iloc[0]\n", + " df_row[\"parameter\"] = p\n", + " df_all_rows = pd.concat([df_all_rows, pd.DataFrame(df_row.copy(deep=True)).T])" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df_all_rows[df_all_rows[\"parameter\"] ==\"relation_activity\"].dropna(axis=1)\n", + "#df_all_rows[\"parameter\"].unique()\n", + "#df_all_rows[df_all_rows[\"node_loc\"]==\"R12_CHN\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df_all_rows.to_excel(\"meth_rc_all_unique_par_vals.xlsx\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df_all_rows = pd.DataFrame()\n", + "for p in paramList_tec:\n", + " print(p)\n", + " df = scenario.par(p)\n", + " df = df[df[\"technology\"]==\"meth_bunker\"]\n", + " for v in df[\"value\"].unique():\n", + " if v == 0:\n", + " continue\n", + " df_row = df[np.isclose(df[\"value\"], v)].iloc[0]\n", + " df_row[\"parameter\"] = p\n", + " df_all_rows = pd.concat([df_all_rows, pd.DataFrame(df_row.copy(deep=True)).T])" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df_all_rows[df_all_rows[\"parameter\"] ==\"relation_activity\"].dropna(axis=1)\n", + "#df_all_rows[\"parameter\"].unique()\n", + "#df_all_rows[df_all_rows[\"node_loc\"]==\"R12_CHN\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "df_all_rows.to_excel(\"meth_rc_all_unique_par_vals.xlsx\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## investigate CCS issues" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "scen_org = message_ix.Scenario(mp,model = \"SHAPE_SSP2_v4.1.8\", scenario=\"baseline\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "co2_dict = {}\n", + "for k in scen_org.par_list():\n", + " df = scen_org.par(k)\n", + " print(k)\n", + " co2_dict[k] = df.get(df.get(\"technology\")==\"co2_tr_dis\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "new_dict = {}\n", + "for i in co2_dict.keys():\n", + " if co2_dict[i] is not None:\n", + " if len(co2_dict[i]):\n", + " new_dict[i] = co2_dict[i]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## get all ethanol pars" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "par_dict = {}\n", + "for p in scenario.par_list():\n", + " try:\n", + " par_dict[p] = scenario.par(p, filters={\"technology\":\"ethanol_to_ethylene_petro\"})\n", + " except:\n", + " continue" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "for i in list(par_dict.keys()):\n", + " if par_dict[i].size == 0:\n", + " par_dict.pop(i)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## check local and private path" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "from message_data.model.material.util import read_config" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "import ixmp\n", + "ixmp.config.get(\"message local data\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "from message_ix_models.util import local_data_path, private_data_path" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "private_data_path()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "import importlib" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "importlib.reload(data_methanol)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "import ixmp as ix\n", + "import message_ix\n", + "mp = ix.Platform(\"ixmp_dev\")\n", + "scenario = message_ix.Scenario(mp, model=\"MESSAGEix-Materials\", scenario=\"NoPolicy_petro_thesis_2_macro\")#, version=37)\n", + "#scenario = message_ix.Scenario(mp, model=\"MESSAGEix-Materials\", scenario=\"baseline_VK_Transport-Materials_VK\")#, version=37)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "import os\n", + "from message_ix_models import Context\n", + "from pathlib import Path\n", + "Context.get_instance(-1).handle_cli_args(local_data=Path(os.getcwd()).parents[2].joinpath(\"data\"))\n", + "#Path(os.getcwd()).parents[2].joinpath(\"data\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "from message_data.model.material import data_methanol" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "par_dict = data_methanol.gen_data_methanol(scenario)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "for i in par_dict.keys():\n", + " df = par_dict[i]\n", + " if \"mode\" in par_dict[i].columns:\n", + " print(i)\n", + " print(df[df[\"mode\"] == \"M1\"][\"technology\"].unique())\n", + " #print(df[[\"mode\", \"technology\"]].drop_duplicates())\n", + " print()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "for i in par_dict.keys():\n", + " df = par_dict[i]\n", + " if \"level\" in par_dict[i].columns:\n", + " print(i)\n", + " #print(df[df[\"mode\"] == \"M1\"][\"technology\"].unique())\n", + " print(df[[\"level\", \"technology\", \"commodity\"]].drop_duplicates())\n", + " print()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "data_methanol.gen_data_meth_chemicals(scenario, \"MTO\")[\"initial_activity_up\"]" + ], + "metadata": { + "collapsed": false + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/message_ix_models/model/material/util notebooks/fetch_run_id_from_scenario.ipynb b/message_ix_models/model/material/util notebooks/fetch_run_id_from_scenario.ipynb new file mode 100644 index 0000000000..f8c3812e02 --- /dev/null +++ b/message_ix_models/model/material/util notebooks/fetch_run_id_from_scenario.ipynb @@ -0,0 +1,183 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": true, + "ExecuteTime": { + "start_time": "2023-04-24T13:57:51.695781Z", + "end_time": "2023-04-24T13:57:53.881033Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": "162 8\n167 10\n180 1\n206 2\n208 1\n ..\n9019 1\n9030 1\n9042 1\n9055 1\n9059 1\nName: version, Length: 192, dtype: int64" + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import ixmp\n", + "from jaydebeapi import connect\n", + "\n", + "platform = \"ixmp_dev\"\n", + "# model = \"MESSAGEix-Materials\"\n", + "# scenario = \"2.0deg_petro_thesis_2_final_macro\"\n", + "model = \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\"\n", + "model = \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP50BD0BI78\"\n", + "scenario = \"600f_MP00BD0BI78_test_calib_macro\"\n", + "#scenario = \"2.0deg_no_SDGs_petro_thesis_1_macro\"\n", + "scenario = \"baseline\"\n", + "\n", + "# Connect to the platform\n", + "# This also starts the JVM with the correct classpath to find ojdbc*.jar\n", + "mp = ixmp.Platform(platform)\n", + "df = mp.scenario_list()\n", + "version = df[df[\"scenario\"]==scenario][\"version\"]\n", + "version" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "outputs": [], + "source": [ + "version = df[(df[\"scenario\"]==scenario) & (df[\"model\"]==model)][\"version\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 15, + "outputs": [ + { + "data": { + "text/plain": "7019 1\nName: version, dtype: int64" + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "version" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 16, + "outputs": [], + "source": [ + "version = version.values[0]" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "start_time": "2023-04-24T13:58:14.533265Z", + "end_time": "2023-04-24T13:58:14.566259Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 17, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[(37485, 0, 'maczek', '2023-08-28 08:53:34')]\n" + ] + } + ], + "source": [ + "#version = 7\n", + "\n", + "\n", + "conn = connect(\n", + " \"oracle.jdbc.driver.OracleDriver\",\n", + " \"jdbc:oracle:thin:@x8oda.iiasa.ac.at:1521/PIXMP2.iiasa.ac.at\",\n", + " driver_args=[\"ixmp_dev\", \"ixmp_dev\"]\n", + ")\n", + "curs = conn.cursor()\n", + "\n", + "curs.execute(f\"\"\"\n", + " SELECT RUN.id, status, lock_user, lock_date\n", + " FROM\n", + "\tRUN\n", + "\tJOIN MODEL ON RUN.MODEL_ID = MODEL.ID\n", + "\tJOIN SCENARIO ON RUN.SCEN_ID = SCENARIO.ID\n", + " WHERE\n", + " MODEL.NAME = {model!r}\n", + "\tAND SCENARIO.NAME = {scenario!r}\n", + "\tAND VERSION = {version}\"\"\"\n", + " )\n", + "print(curs.fetchall())\n", + "conn.close()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "start_time": "2023-04-24T13:58:30.457765Z", + "end_time": "2023-04-24T13:58:31.358340Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 19, + "outputs": [], + "source": [ + "mp._backend.jobj.unlockRunid(37485)\n", + "#for i in mp._backend.get_scenarios(False, \"Materials\", \"baseline\"):\n", + "# mp._backend.check_out(i, False)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "start_time": "2023-04-24T13:58:37.686915Z", + "end_time": "2023-04-24T13:58:37.721569Z" + } + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false + } + } + ], + "metadata": { + "kernelspec": { + "name": "python3", + "language": "python", + "display_name": "Python 3 (ipykernel)" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/message_ix_models/model/material/util notebooks/format reporting output.ipynb b/message_ix_models/model/material/util notebooks/format reporting output.ipynb new file mode 100644 index 0000000000..0ebd330ea1 --- /dev/null +++ b/message_ix_models/model/material/util notebooks/format reporting output.ipynb @@ -0,0 +1,1306 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "## prepare reporting output" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [], + "source": [ + "def prepare_xlsx_for_explorer(filepath):\n", + " import pandas as pd\n", + " df = pd.read_excel(filepath)\n", + "\n", + " def add_R12(str):\n", + " if len(str) < 5:\n", + " return \"R12_\" + str\n", + " else:\n", + " return str\n", + "\n", + " df = df[~df[\"Region\"].isna()]\n", + " df[\"Region\"] = df[\"Region\"].map(add_R12)\n", + " df.to_excel(filepath, index=False)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-20T17:39:56.168197700Z", + "start_time": "2023-09-20T17:39:56.141984400Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 8, + "outputs": [], + "source": [ + "import os\n", + "f_list = os.listdir(\"C:/Users\\maczek\\PycharmProjects\\message_data/reporting_output\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 11, + "outputs": [], + "source": [ + "rep_list = [i for i in f_list if (\"1150\" in i) and (\"step\" not in i)]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 8, + "outputs": [], + "source": [ + "#path = \"C:/Users\\maczek\\PycharmProjects\\message_data/reporting_output/MESSAGEix-Materials_NoPolicy_petro_thesis_2_final_macro.xlsx\"\n", + "#path = \"C:/Users\\maczek\\PycharmProjects\\message_data/reporting_output/MESSAGEix-Materials_NoPolicy_petro_thesis_2_macro_w_ENGAGE_fixes.xlsx\"\n", + "#path = \"C:/Users\\maczek\\PycharmProjects\\message_data/reporting_output/MESSAGEix-Materials_2.0deg_petro_thesis_2_macro_w_ENGAGE_fixes.xlsx\"\n", + "#path = \"C:/Users\\maczek\\PycharmProjects\\message_data/reporting_output/MESSAGEix-Materials_2.0deg_petro_thesis_2_final_macro.xlsx\"\n", + "#path = \"C:/Users\\maczek\\PycharmProjects\\message_data/reporting_output/MESSAGEix-Materials_2.0deg_no_SDGs_petro_thesis_1_final_macro.xlsx\"\n", + "#path = \"C:/Users\\maczek\\PycharmProjects\\message_data/reporting_output/MESSAGEix-GLOBIOM 1.1-R12-MAGPIE_baseline_MP00BD1BI00.xlsx\"\n", + "#path = \"C:/Users\\maczek\\PycharmProjects\\message_data/reporting_output/MESSAGEix-GLOBIOM 1.1-R12-MAGPIE_600f_MP00BD1BI00.xlsx\"\n", + "#path = \"C:/Users\\maczek\\PycharmProjects\\message_data/reporting_output/MESSAGEix-GLOBIOM 1.1-R12-MAGPIE_baseline_MP00BD0BI78.xlsx\"\n", + "#path = \"C:/Users\\maczek\\PycharmProjects\\message_data/reporting_output/MESSAGEix-GLOBIOM 1.1-R12-MAGPIE_1000f_MP76BD0BI78.xlsx\"\n", + "path = \"C:/Users\\maczek\\PycharmProjects\\message_data/reporting_output/MESSAGEix-GLOBIOM 1.1-R12-MAGPIE_600f_MP00BD0BI78.xlsx\"\n", + "#path = \"C:/Users\\maczek\\PycharmProjects\\message_data/reporting_output/MESSAGEix-GLOBIOM 1.1-R12-MAGPIE_1000f_MP76BD1BI00.xlsx\"\n", + "#path = \"C:/Users\\maczek\\PycharmProjects\\message_data/reporting_output/MESSAGEix-GLOBIOM 1.1-R12-MAGPIE_1000f_MP00BD1BI00.xlsx\"\n", + "#path = \"C:/Users\\maczek\\PycharmProjects\\message_data/reporting_output/MESSAGEix-GLOBIOM 1.1-R12-MAGPIE_600f_MP00BD0BI78.xlsx\"\n", + "#path = \"C:/Users\\maczek\\PycharmProjects\\message_data/reporting_output/MESSAGEix-Materials_NoPolicy_no_SDGs_petro_thesis_1_final_macro.xlsx\"\n", + "dir = \"C:/Users\\maczek\\PycharmProjects\\message_data/reporting_output\"\n", + "file = \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00_new-mapping_with-smoothing_primary-forest-constraint_1000f\"\n", + "file=\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00_new-mapping_no-smoothing_1000f\"\n", + "file = \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00_bugfix_1000f\"\n", + "#file=\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00_new-mapping_with-smoothing_1000f\"\n", + "path= dir+\"/\"+file+\".xlsx\"" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-20T17:48:44.841011500Z", + "start_time": "2023-09-20T17:48:44.746936800Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 1, + "outputs": [], + "source": [ + "#rep_list = ['baseline_MP76BD0BI78', '600f_MP76BD0BI78', 'baseline_MP76BD1BI00', '600f_MP76BD1BI00']\n", + "rep_list = ['1000f_MP76BD1BI00test_scp', '1000f_MP00BD1BI00', '1000f_MP76BD0BI78test_scp']\n", + "#rep_list = ['1000f_MP00BD0BI78']\n", + "# 'baseline_MP76BD0BI70' still to build scp and 600f" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 8, + "outputs": [], + "source": [ + "for scen_name in rep_list:\n", + " path = f\"C:/Users\\maczek\\PycharmProjects\\message_data/reporting_output/MESSAGEix-GLOBIOM 1.1-R12-MAGPIE_{scen_name}.xlsx\"\n", + " prepare_xlsx_for_explorer(path)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 13, + "outputs": [], + "source": [ + "for scen_name in rep_list:\n", + " path = f\"C:/Users\\maczek\\PycharmProjects\\message_data/reporting_output/{scen_name}\"\n", + " prepare_xlsx_for_explorer(path)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [], + "source": [ + "for scen_name in os.listdir(\"H:/pcenv\\message_data/reporting_output\"):\n", + " path = f\"H:/pcenv\\message_data/reporting_output/{scen_name}\"\n", + " prepare_xlsx_for_explorer(path)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 9, + "outputs": [], + "source": [ + "import os\n", + "#import message_data.model.material.util as util\n", + "from pathlib import Path\n", + "\"\"\"util.prepare_xlsx_for_explorer(\n", + " Path(os.getcwd()).parents[0].joinpath(\n", + " \"reporting_output\", scenario.model+\"_\"+scenario.scenario+\".xlsx\"))\"\"\"\n", + "prepare_xlsx_for_explorer(path)\n", + "#prepare_xlsx_for_explorer(\"C:/Users\\maczek\\PycharmProjects\\message_data/reporting_output/MESSAGEix-Materials_2.0deg_petro_thesis_2_macro.xlsx\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-09-20T17:49:04.450247400Z", + "start_time": "2023-09-20T17:48:46.746948Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 11, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import xlwings as xw\n", + "import openpyxl as pxl\n", + "import xarray" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 12, + "outputs": [], + "source": [ + "#df = pd.read_excel(path)\n", + "#wb = xw.Book(path)\n", + "#sht = wb.sheets[0]\n", + "#for i in wb.sheets:\n", + "# i.range(f\"F2:Y{df.index.size+1}\").api.Replace(None, 0.0)\n", + "#wb.save()\n", + "#wb.close()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 13, + "outputs": [], + "source": [ + "wb = pxl.load_workbook(path)\n", + "ws = wb[\"Sheet1\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 14, + "outputs": [], + "source": [ + "ws.auto_filter.ref = ws.dimensions\n", + "c = ws['B2']\n", + "ws.freeze_panes = c" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 15, + "outputs": [], + "source": [ + "for i in range(70, 76): #iterate from column \"F\" to \"K\"\n", + " ws.column_dimensions[chr(i)].hidden= True\n", + "\n", + "ws.column_dimensions[\"E\"].width = 11\n", + "ws.column_dimensions[\"D\"].width = 71\n", + "wb.save(path)\n", + "wb.close()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [ + { + "data": { + "text/plain": "", + "application/javascript": "if (typeof IPython !== 'undefined') { IPython.OutputArea.prototype._should_scroll = function(lines){ return false; }}" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import ixmp\n", + "import message_ix\n", + "\n", + "mp = ixmp.Platform(\"ixmp_dev\")\n", + "#scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\", \"600f_MP00BD0BI78\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [], + "source": [ + "sc_clone = scen.clone(\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\", \"600f_MP00BD0BI78_test_calib\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [], + "source": [ + "import ixmp\n", + "import message_ix\n", + "\n", + "mp = ixmp.Platform(\"ixmp_dev\")\n", + "scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\", \"baseline_MP00BD0BI78\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 9, + "outputs": [], + "source": [ + "import ixmp\n", + "import message_ix\n", + "\n", + "mp = ixmp.Platform(\"ixmp_dev\")\n", + "scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\", \"600f_MP00BD0BI78_test_calib_macro\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 10, + "outputs": [ + { + "ename": "ValueError", + "evalue": "This Scenario does not have a solution!", + "output_type": "error", + "traceback": [ + "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[1;31mValueError\u001B[0m Traceback (most recent call last)", + "Input \u001B[1;32mIn [10]\u001B[0m, in \u001B[0;36m\u001B[1;34m()\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[43mscen\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mremove_solution\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\ixmp\\core\\scenario.py:757\u001B[0m, in \u001B[0;36mScenario.remove_solution\u001B[1;34m(self, first_model_year)\u001B[0m\n\u001B[0;32m 755\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_backend(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mclear_solution\u001B[39m\u001B[38;5;124m\"\u001B[39m, first_model_year)\n\u001B[0;32m 756\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m--> 757\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mThis Scenario does not have a solution!\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n", + "\u001B[1;31mValueError\u001B[0m: This Scenario does not have a solution!" + ] + } + ], + "source": [ + "scen.remove_solution()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [ + { + "data": { + "text/plain": "'600f_MP00BD0BI78_test_calib'" + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scen.scenario" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 8, + "outputs": [], + "source": [ + "sc_clone = scen.clone(\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\", \"baseline_MP00BD0BI78_test_calib\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 9, + "outputs": [], + "source": [ + "sc_clone.remove_solution()\n", + "sc_clone.solve()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 29, + "outputs": [ + { + "data": { + "text/plain": "'baseline_MP00BD0BI78_test_calib_macro'" + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sc_clone.scenario" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 10, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "This Scenario has a solution, use `Scenario.remove_solution()` or `Scenario.clone(..., keep_solution=False)`\n", + "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\model\\material\\data_util.py:100: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " data[parname] = df_gdphist.append(\n", + "\n", + "======================= WARNING =======================\n", + "\n", + "You are using *experimental*, incomplete features from\n", + "`message_ix.macro`—please exercise caution. Read more:\n", + "- https://github.com/iiasa/message_ix/issues/317\n", + "- https://github.com/iiasa/message_ix/issues/318\n", + "- https://github.com/iiasa/message_ix/issues/319\n", + "- https://github.com/iiasa/message_ix/issues/320\n", + "\n", + "======================================================\n", + "\n" + ] + } + ], + "source": [ + "from message_data.model.material.data_util import add_coal_lowerbound_2020, add_macro_COVID\n", + "sc_clone = add_macro_COVID(sc_clone,'R12-CHN-5y_macro_data_NGFS_w_rc_ind_adj_mat.xlsx')" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 11, + "outputs": [], + "source": [ + "sc_clone.solve(model=\"MESSAGE-MACRO\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 30, + "outputs": [ + { + "data": { + "text/plain": "'baseline_MP00BD0BI78'" + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scen.scenario" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [ + { + "ename": "ModelError", + "evalue": "GAMS errored with return code 3:\n There was an execution error\n\nFor details, see the terminal output above, plus:\nListing : C:\\Users\\maczek\\PycharmProjects\\message_ix\\message_ix\\model\\MESSAGE-MACRO_run.lst\nInput data: C:\\Users\\maczek\\PycharmProjects\\message_ix\\message_ix\\model\\data\\MsgData_MESSAGEix-GLOBIOM_1.1-R12-MAGPIE_600f_MP00BD0BI78.gdx", + "output_type": "error", + "traceback": [ + "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[1;31mModelError\u001B[0m Traceback (most recent call last)", + "Input \u001B[1;32mIn [3]\u001B[0m, in \u001B[0;36m\u001B[1;34m()\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[43mscen\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msolve\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmodel\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mMESSAGE-MACRO\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\n", + "File \u001B[1;32m~\\PycharmProjects\\message_ix\\message_ix\\core.py:677\u001B[0m, in \u001B[0;36mScenario.solve\u001B[1;34m(self, model, solve_options, **kwargs)\u001B[0m\n\u001B[0;32m 657\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21msolve\u001B[39m(\u001B[38;5;28mself\u001B[39m, model\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mMESSAGE\u001B[39m\u001B[38;5;124m\"\u001B[39m, solve_options\u001B[38;5;241m=\u001B[39m{}, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[0;32m 658\u001B[0m \u001B[38;5;124;03m\"\"\"Solve MESSAGE or MESSAGE-MACRO for the Scenario.\u001B[39;00m\n\u001B[0;32m 659\u001B[0m \n\u001B[0;32m 660\u001B[0m \u001B[38;5;124;03m By default, :meth:`ixmp.Scenario.solve` is called with 'MESSAGE' as the\u001B[39;00m\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 675\u001B[0m \u001B[38;5;124;03m :class:`.GAMSModel`.\u001B[39;00m\n\u001B[0;32m 676\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[1;32m--> 677\u001B[0m \u001B[38;5;28;43msuper\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msolve\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmodel\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mmodel\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43msolve_options\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43msolve_options\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\ixmp\\core\\scenario.py:835\u001B[0m, in \u001B[0;36mScenario.solve\u001B[1;34m(self, model, callback, cb_kwargs, **model_options)\u001B[0m\n\u001B[0;32m 833\u001B[0m \u001B[38;5;66;03m# Iterate until convergence\u001B[39;00m\n\u001B[0;32m 834\u001B[0m \u001B[38;5;28;01mwhile\u001B[39;00m \u001B[38;5;28;01mTrue\u001B[39;00m:\n\u001B[1;32m--> 835\u001B[0m \u001B[43mmodel_obj\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrun\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[0;32m 837\u001B[0m \u001B[38;5;66;03m# Store an iteration number to help the callback\u001B[39;00m\n\u001B[0;32m 838\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28mhasattr\u001B[39m(\u001B[38;5;28mself\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124miteration\u001B[39m\u001B[38;5;124m\"\u001B[39m):\n", + "File \u001B[1;32m~\\PycharmProjects\\message_ix\\message_ix\\models.py:373\u001B[0m, in \u001B[0;36mGAMSModel.run\u001B[1;34m(self, scenario)\u001B[0m\n\u001B[0;32m 370\u001B[0m lines2 \u001B[38;5;241m=\u001B[39m (\u001B[38;5;124m\"\u001B[39m\u001B[38;5;132;01m{}\u001B[39;00m\u001B[38;5;124m = \u001B[39m\u001B[38;5;132;01m{}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;241m.\u001B[39mformat(\u001B[38;5;241m*\u001B[39mkv) \u001B[38;5;28;01mfor\u001B[39;00m kv \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcplex_opts\u001B[38;5;241m.\u001B[39mitems())\n\u001B[0;32m 371\u001B[0m optfile2\u001B[38;5;241m.\u001B[39mwrite_text(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;130;01m\\n\u001B[39;00m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;241m.\u001B[39mjoin(lines2))\n\u001B[1;32m--> 373\u001B[0m result \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43msuper\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrun\u001B[49m\u001B[43m(\u001B[49m\u001B[43mscenario\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 375\u001B[0m \u001B[38;5;66;03m# In previous versions, the `cplex.opt` file(s) were removed at this point\u001B[39;00m\n\u001B[0;32m 376\u001B[0m \u001B[38;5;66;03m# in the workflow. This has been removed due to issues when running\u001B[39;00m\n\u001B[0;32m 377\u001B[0m \u001B[38;5;66;03m# scenarios asynchronously.\u001B[39;00m\n\u001B[0;32m 379\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m result\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\ixmp\\model\\gams.py:296\u001B[0m, in \u001B[0;36mGAMSModel.run\u001B[1;34m(self, scenario)\u001B[0m\n\u001B[0;32m 293\u001B[0m check_call(command, shell\u001B[38;5;241m=\u001B[39mos\u001B[38;5;241m.\u001B[39mname \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mnt\u001B[39m\u001B[38;5;124m\"\u001B[39m, cwd\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcwd)\n\u001B[0;32m 294\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m CalledProcessError \u001B[38;5;28;01mas\u001B[39;00m exc:\n\u001B[0;32m 295\u001B[0m \u001B[38;5;66;03m# Do not remove self.temp_dir; the user may want to inspect the GDX file\u001B[39;00m\n\u001B[1;32m--> 296\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mformat_exception(exc, model_file) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;28mNone\u001B[39m\n\u001B[0;32m 298\u001B[0m \u001B[38;5;66;03m# Read model solution\u001B[39;00m\n\u001B[0;32m 299\u001B[0m scenario\u001B[38;5;241m.\u001B[39mplatform\u001B[38;5;241m.\u001B[39m_backend\u001B[38;5;241m.\u001B[39mread_file(\n\u001B[0;32m 300\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mout_file,\n\u001B[0;32m 301\u001B[0m ItemType\u001B[38;5;241m.\u001B[39mMODEL,\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 306\u001B[0m var_list\u001B[38;5;241m=\u001B[39mas_str_list(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mvar_list) \u001B[38;5;129;01mor\u001B[39;00m [],\n\u001B[0;32m 307\u001B[0m )\n", + "\u001B[1;31mModelError\u001B[0m: GAMS errored with return code 3:\n There was an execution error\n\nFor details, see the terminal output above, plus:\nListing : C:\\Users\\maczek\\PycharmProjects\\message_ix\\message_ix\\model\\MESSAGE-MACRO_run.lst\nInput data: C:\\Users\\maczek\\PycharmProjects\\message_ix\\message_ix\\model\\data\\MsgData_MESSAGEix-GLOBIOM_1.1-R12-MAGPIE_600f_MP00BD0BI78.gdx" + ] + } + ], + "source": [ + "scen.solve(model=\"MESSAGE-MACRO\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "processing Table: Resource|Extraction\n", + "processing Table: Resource|Cumulative Extraction\n", + "processing Table: Primary Energy\n", + "processing Table: Primary Energy (substitution method)\n", + "processing Table: Final Energy\n", + "processing Table: Secondary Energy|Electricity\n", + "processing Table: Secondary Energy|Heat\n", + "processing Table: Secondary Energy\n", + "processing Table: Secondary Energy|Gases\n", + "processing Table: Secondary Energy|Solids\n", + "processing Table: Emissions|CO2\n", + "processing Table: Carbon Sequestration\n", + "processing Table: Emissions|BC\n", + "processing Table: Emissions|OC\n", + "processing Table: Emissions|CO\n", + "processing Table: Emissions|N2O\n", + "processing Table: Emissions|CH4\n", + "processing Table: Emissions|NH3\n", + "processing Table: Emissions|Sulfur\n", + "processing Table: Emissions|NOx\n", + "processing Table: Emissions|VOC\n", + "processing Table: Emissions|HFC\n", + "processing Table: Emissions\n", + "processing Table: Emissions\n", + "processing Table: Agricultural Demand\n", + "processing Table: Agricultural Production\n", + "processing Table: Fertilizer Use\n", + "processing Table: Fertilizer\n", + "processing Table: Food Waste\n", + "processing Table: Food Demand\n", + "processing Table: Forestry Demand\n", + "processing Table: Forestry Production\n", + "processing Table: Land Cover\n", + "processing Table: Yield\n", + "processing Table: Capacity\n", + "processing Table: Capacity Additions\n", + "processing Table: Cumulative Capacity\n", + "processing Table: Capital Cost\n", + "processing Table: OM Cost|Fixed\n", + "processing Table: OM Cost|Variable\n", + "processing Table: Lifetime\n", + "processing Table: Efficiency\n", + "processing Table: Population\n", + "processing Table: Price\n", + "processing Table: Useful Energy\n", + "processing Table: Useful Energy\n", + "processing Table: Trade\n", + "processing Table: Investment|Energy Supply\n", + "processing Table: Water Consumption\n", + "processing Table: Water Withdrawal\n", + "processing Table: GDP\n", + "processing Table: Cost\n", + "processing Table: GLOBIOM Feedback\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "no emissions included\n", + "Starting to upload timeseries\n", + " region variable unit 2025 \\\n", + "0 R12_AFR Resource|Extraction EJ/yr 21.705672 \n", + "1 R12_AFR Resource|Extraction|Coal EJ/yr 5.802705 \n", + "2 R12_AFR Resource|Extraction|Gas EJ/yr 6.834491 \n", + "3 R12_AFR Resource|Extraction|Gas|Conventional EJ/yr 6.834491 \n", + "4 R12_AFR Resource|Extraction|Gas|Unconventional EJ/yr 0.000000 \n", + "\n", + " 2030 2035 2040 2045 2050 2055 \\\n", + "0 18.782491 18.141649 18.134179 18.305262 18.455276 20.166486 \n", + "1 0.925383 0.796329 0.050280 0.024543 0.014460 0.022022 \n", + "2 9.044117 10.318515 12.553804 14.147722 15.378979 17.854765 \n", + "3 9.044117 10.318515 12.553804 14.147722 15.378979 17.854765 \n", + "4 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "\n", + " 2060 2070 2080 2090 2100 2110 \n", + "0 19.231046 18.867719 10.906651 6.138375 3.398055 2.379544 \n", + "1 0.042275 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "2 17.285903 18.014806 10.663254 6.138375 3.398055 2.379544 \n", + "3 17.285903 18.014806 3.928724 1.153387 0.848893 0.624785 \n", + "4 0.000000 0.000000 6.734530 4.984987 2.549162 1.754759 \n", + "Finished uploading timeseries\n" + ] + } + ], + "source": [ + "from message_data.tools.post_processing.iamc_report_hackathon import report as reporting\n", + "\n", + "for scen_name in rep_list[2:]:\n", + " import ixmp\n", + " import message_ix\n", + " mp = ixmp.Platform(\"ixmp_dev\")\n", + " scen = message_ix.Scenario(mp, model, scen_name)\n", + " reporting(\n", + " mp,\n", + " scen,\n", + " # NB(PNK) this is not an error; .iamc_report_hackathon.report() expects a\n", + " # string containing \"True\" or \"False\" instead of an actual bool.\n", + " \"False\",\n", + " scen.model,\n", + " scen.scenario,\n", + " merge_hist=True,\n", + " merge_ts=True,\n", + " #run_config=\"materials_run_config.yaml\",\n", + " )\n", + " mp.close_db()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "processing Table: Resource|Extraction\n", + "processing Table: Resource|Cumulative Extraction\n", + "processing Table: Primary Energy\n", + "processing Table: Primary Energy (substitution method)\n", + "processing Table: Final Energy\n", + "processing Table: Secondary Energy|Electricity\n", + "processing Table: Secondary Energy|Heat\n", + "processing Table: Secondary Energy\n", + "processing Table: Secondary Energy|Gases\n", + "processing Table: Secondary Energy|Solids\n", + "processing Table: Emissions|CO2\n", + "processing Table: Carbon Sequestration\n", + "processing Table: Emissions|BC\n", + "processing Table: Emissions|OC\n", + "processing Table: Emissions|CO\n", + "processing Table: Emissions|N2O\n", + "processing Table: Emissions|CH4\n", + "processing Table: Emissions|NH3\n", + "processing Table: Emissions|Sulfur\n", + "processing Table: Emissions|NOx\n", + "processing Table: Emissions|VOC\n", + "processing Table: Emissions|HFC\n", + "processing Table: Emissions\n", + "processing Table: Emissions\n", + "processing Table: Agricultural Demand\n", + "processing Table: Agricultural Production\n", + "processing Table: Fertilizer Use\n", + "processing Table: Fertilizer\n", + "processing Table: Food Waste\n", + "processing Table: Food Demand\n", + "processing Table: Forestry Demand\n", + "processing Table: Forestry Production\n", + "processing Table: Land Cover\n", + "processing Table: Yield\n", + "processing Table: Capacity\n", + "processing Table: Capacity Additions\n", + "processing Table: Cumulative Capacity\n", + "processing Table: Capital Cost\n", + "processing Table: OM Cost|Fixed\n", + "processing Table: OM Cost|Variable\n", + "processing Table: Lifetime\n", + "processing Table: Efficiency\n", + "processing Table: Population\n", + "processing Table: Price\n", + "processing Table: Useful Energy\n", + "processing Table: Useful Energy\n", + "processing Table: Trade\n", + "processing Table: Investment|Energy Supply\n", + "processing Table: Water Consumption\n", + "processing Table: Water Withdrawal\n", + "processing Table: GDP\n", + "processing Table: Cost\n", + "processing Table: GLOBIOM Feedback\n", + "Starting to upload timeseries\n", + " region variable unit 2025 \\\n", + "0 R12_AFR Resource|Extraction EJ/yr 18.425755 \n", + "1 R12_AFR Resource|Extraction|Coal EJ/yr 2.374063 \n", + "2 R12_AFR Resource|Extraction|Gas EJ/yr 7.104881 \n", + "3 R12_AFR Resource|Extraction|Gas|Conventional EJ/yr 7.104881 \n", + "4 R12_AFR Resource|Extraction|Gas|Unconventional EJ/yr 0.000000 \n", + "\n", + " 2030 2035 2040 2045 2050 2055 2060 \\\n", + "0 13.297529 9.323047 6.774340 4.936031 3.528415e+00 2.43978 1.589079 \n", + "1 1.158082 0.216647 0.018782 0.000170 3.322613e-07 0.00000 0.000000 \n", + "2 5.350509 3.993009 2.942602 2.131803 1.506807e+00 1.02482 0.644342 \n", + "3 5.350509 3.993009 2.942602 2.131803 1.506807e+00 1.02482 0.644342 \n", + "4 0.000000 0.000000 0.000000 0.000000 0.000000e+00 0.00000 0.000000 \n", + "\n", + " 2070 2080 2090 2100 2110 \n", + "0 0.417925 0.00286 0.005642 0.01113 0.021957 \n", + "1 0.000000 0.00000 0.000000 0.00000 0.000000 \n", + "2 0.122128 0.00000 0.000000 0.00000 0.000000 \n", + "3 0.122128 0.00000 0.000000 0.00000 0.000000 \n", + "4 0.000000 0.00000 0.000000 0.00000 0.000000 \n", + "Finished uploading timeseries\n" + ] + } + ], + "source": [ + "# report baseline\n", + "from message_data.tools.post_processing.iamc_report_hackathon import report as reporting\n", + "reporting(\n", + " mp,\n", + " scen,\n", + " # NB(PNK) this is not an error; .iamc_report_hackathon.report() expects a\n", + " # string containing \"True\" or \"False\" instead of an actual bool.\n", + " \"False\",\n", + " scen.model,\n", + " scen.scenario,\n", + " merge_hist=True,\n", + " merge_ts=True,\n", + " #run_config=\"materials_run_config.yaml\",\n", + ")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 14, + "outputs": [ + { + "data": { + "text/plain": "True" + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scen.has_solution()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 14, + "outputs": [], + "source": [ + "scen_name = \"600f_MP00BD1BI00\"" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [], + "source": [ + "model = \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\"" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 9, + "outputs": [ + { + "data": { + "text/plain": " model \\\n6871 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6870 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6891 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00 \n6892 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00 \n6893 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE_MP00BD1BI00 \n6878 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6888 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6876 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6877 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6887 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6886 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6869 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6885 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6882 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6881 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6874 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6873 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6884 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6872 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6880 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6875 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6883 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6890 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6889 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6879 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n\n scenario scheme is_default is_locked \\\n6871 1000f_MP76BD1BI00test_scp MESSAGE 1 0 \n6870 1000f_MP76BD0BI78test_scp MESSAGE 1 0 \n6891 NPi2020-con-prim-dir-ncr MESSAGE 1 0 \n6892 baseline MESSAGE 1 0 \n6893 baseline MESSAGE 1 0 \n6878 600f_MP76BD1BI00test_scp MESSAGE 1 0 \n6888 baseline_MP76BD1BI00test_scp MESSAGE 1 0 \n6876 600f_MP76BD0BI78test_scp MESSAGE 1 0 \n6877 600f_MP76BD1BI00 MESSAGE 1 0 \n6887 baseline_MP76BD1BI00 MESSAGE 1 0 \n6886 baseline_MP76BD0BI78test_scp MESSAGE 1 0 \n6869 1000f_MP00BD0BI78_test_calib_macro MESSAGE 1 0 \n6885 baseline_MP76BD0BI78 MESSAGE 1 0 \n6882 baseline_MP00BD0BI78_test_calib_macro MESSAGE 1 0 \n6881 baseline_MP00BD0BI78_test_calib MESSAGE 1 0 \n6874 600f_MP00BD0BI78_test_calib_macro MESSAGE 1 0 \n6873 600f_MP00BD0BI78_test_calib MESSAGE 1 0 \n6884 baseline_MP76BD0BI70 MESSAGE 1 0 \n6872 600f_MP00BD0BI78 MESSAGE 1 0 \n6880 baseline_MP00BD0BI78 MESSAGE 1 0 \n6875 600f_MP00BD1BI00 MESSAGE 1 0 \n6883 baseline_MP00BD1BI00 MESSAGE 1 0 \n6890 baseline_w/o_LU MESSAGE 1 0 \n6889 baseline_cum_LU_emis MESSAGE 1 0 \n6879 baseline MESSAGE 1 0 \n\n cre_user cre_date upd_user \\\n6871 maczek 2023-08-16 14:43:19.000000 maczek \n6870 maczek 2023-08-16 14:04:03.000000 maczek \n6891 maczek 2023-08-14 13:21:06.000000 None \n6892 maczek 2023-08-14 13:08:04.000000 maczek \n6893 maczek 2023-08-14 13:07:05.000000 None \n6878 maczek 2023-08-09 17:27:58.000000 maczek \n6888 maczek 2023-08-09 16:57:44.000000 maczek \n6876 maczek 2023-08-09 16:27:09.000000 maczek \n6877 maczek 2023-08-09 16:12:11.000000 None \n6887 maczek 2023-08-09 15:20:37.000000 None \n6886 maczek 2023-08-09 15:00:57.000000 maczek \n6869 maczek 2023-08-09 13:45:52.000000 maczek \n6885 maczek 2023-08-09 09:56:43.000000 None \n6882 maczek 2023-08-09 09:37:24.000000 maczek \n6881 maczek 2023-08-09 09:30:09.000000 maczek \n6874 maczek 2023-08-09 09:23:31.000000 None \n6873 maczek 2023-08-09 09:16:15.000000 maczek \n6884 maczek 2023-08-09 09:10:13.000000 None \n6872 maczek 2023-08-08 15:52:17.000000 maczek \n6880 maczek 2023-08-08 13:39:45.000000 maczek \n6875 maczek 2023-08-08 11:53:14.000000 maczek \n6883 maczek 2023-08-08 10:17:35.000000 maczek \n6890 maczek 2023-08-08 09:50:56.000000 None \n6889 maczek 2023-08-06 20:02:21.000000 maczek \n6879 maczek 2023-08-03 11:49:57.000000 maczek \n\n upd_date lock_user lock_date \\\n6871 2023-08-16 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clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6891 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6892 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6893 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6878 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6888 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6876 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6877 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6887 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6886 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6869 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6885 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6882 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6881 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6874 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6873 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6884 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6872 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6880 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6875 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 2 \n6883 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6890 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6889 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6879 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 2 ", + "text/html": "
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modelscenarioschemeis_defaultis_lockedcre_usercre_dateupd_userupd_datelock_userlock_dateannotationversion
6871MESSAGEix-GLOBIOM 1.1-R12-MAGPIE1000f_MP76BD1BI00test_scpMESSAGE10maczek2023-08-16 14:43:19.000000maczek2023-08-16 15:24:22.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6870MESSAGEix-GLOBIOM 1.1-R12-MAGPIE1000f_MP76BD0BI78test_scpMESSAGE10maczek2023-08-16 14:04:03.000000maczek2023-08-16 14:38:21.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6891MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00NPi2020-con-prim-dir-ncrMESSAGE10maczek2023-08-14 13:21:06.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6892MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00baselineMESSAGE10maczek2023-08-14 13:08:04.000000maczek2023-08-16 14:22:47.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6893MESSAGEix-GLOBIOM 1.1-R12-MAGPIE_MP00BD1BI00baselineMESSAGE10maczek2023-08-14 13:07:05.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6878MESSAGEix-GLOBIOM 1.1-R12-MAGPIE600f_MP76BD1BI00test_scpMESSAGE10maczek2023-08-09 17:27:58.000000maczek2023-08-09 18:00:37.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6888MESSAGEix-GLOBIOM 1.1-R12-MAGPIEbaseline_MP76BD1BI00test_scpMESSAGE10maczek2023-08-09 16:57:44.000000maczek2023-08-09 17:26:01.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6876MESSAGEix-GLOBIOM 1.1-R12-MAGPIE600f_MP76BD0BI78test_scpMESSAGE10maczek2023-08-09 16:27:09.000000maczek2023-08-09 16:53:19.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6877MESSAGEix-GLOBIOM 1.1-R12-MAGPIE600f_MP76BD1BI00MESSAGE10maczek2023-08-09 16:12:11.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6887MESSAGEix-GLOBIOM 1.1-R12-MAGPIEbaseline_MP76BD1BI00MESSAGE10maczek2023-08-09 15:20:37.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6886MESSAGEix-GLOBIOM 1.1-R12-MAGPIEbaseline_MP76BD0BI78test_scpMESSAGE10maczek2023-08-09 15:00:57.000000maczek2023-08-09 15:59:13.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6869MESSAGEix-GLOBIOM 1.1-R12-MAGPIE1000f_MP00BD0BI78_test_calib_macroMESSAGE10maczek2023-08-09 13:45:52.000000maczek2023-08-09 14:12:46.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6885MESSAGEix-GLOBIOM 1.1-R12-MAGPIEbaseline_MP76BD0BI78MESSAGE10maczek2023-08-09 09:56:43.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6882MESSAGEix-GLOBIOM 1.1-R12-MAGPIEbaseline_MP00BD0BI78_test_calib_macroMESSAGE10maczek2023-08-09 09:37:24.000000maczek2023-08-09 09:47:10.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6881MESSAGEix-GLOBIOM 1.1-R12-MAGPIEbaseline_MP00BD0BI78_test_calibMESSAGE10maczek2023-08-09 09:30:09.000000maczek2023-08-09 09:35:09.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6874MESSAGEix-GLOBIOM 1.1-R12-MAGPIE600f_MP00BD0BI78_test_calib_macroMESSAGE10maczek2023-08-09 09:23:31.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6873MESSAGEix-GLOBIOM 1.1-R12-MAGPIE600f_MP00BD0BI78_test_calibMESSAGE10maczek2023-08-09 09:16:15.000000maczek2023-08-09 09:22:45.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6884MESSAGEix-GLOBIOM 1.1-R12-MAGPIEbaseline_MP76BD0BI70MESSAGE10maczek2023-08-09 09:10:13.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6872MESSAGEix-GLOBIOM 1.1-R12-MAGPIE600f_MP00BD0BI78MESSAGE10maczek2023-08-08 15:52:17.000000maczek2023-08-08 16:31:06.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6880MESSAGEix-GLOBIOM 1.1-R12-MAGPIEbaseline_MP00BD0BI78MESSAGE10maczek2023-08-08 13:39:45.000000maczek2023-08-08 15:37:34.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6875MESSAGEix-GLOBIOM 1.1-R12-MAGPIE600f_MP00BD1BI00MESSAGE10maczek2023-08-08 11:53:14.000000maczek2023-08-08 12:22:46.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...2
6883MESSAGEix-GLOBIOM 1.1-R12-MAGPIEbaseline_MP00BD1BI00MESSAGE10maczek2023-08-08 10:17:35.000000maczek2023-08-08 11:52:04.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6890MESSAGEix-GLOBIOM 1.1-R12-MAGPIEbaseline_w/o_LUMESSAGE10maczek2023-08-08 09:50:56.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6889MESSAGEix-GLOBIOM 1.1-R12-MAGPIEbaseline_cum_LU_emisMESSAGE10maczek2023-08-06 20:02:21.000000maczek2023-08-06 20:05:37.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6879MESSAGEix-GLOBIOM 1.1-R12-MAGPIEbaselineMESSAGE10maczek2023-08-03 11:49:57.000000maczek2023-08-04 16:29:36.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...2
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" + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = mp.scenario_list()\n", + "df[df[\"model\"].str.contains(\"R12-MAGPIE\")].sort_values(\"cre_date\", ascending=False)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 15, + "outputs": [], + "source": [ + "scen = message_ix.Scenario(mp, model, scen_name)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 16, + "outputs": [], + "source": [ + "sc_clone = scen" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## create budget scenario" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 18, + "outputs": [], + "source": [ + "mode = \"clone\"\n", + "#mode = \"load\"\n", + "\n", + "budget = \"1000f\"\n", + "#budget = \"600f\"" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 19, + "outputs": [ + { + "data": { + "text/plain": "'1000f_MP00BD1BI00'" + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scen.scenario.replace(\"600f\",budget)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 20, + "outputs": [], + "source": [ + "if mode == \"clone\":\n", + " scen_budget = sc_clone.clone(model=sc_clone.model, scenario=sc_clone.scenario.replace(\"600f\",budget), keep_solution=False)\n", + "else:\n", + " scen_budget = message_ix.Scenario(mp, model=\"ENGAGE-MAGPIE_SSP2_v4.1.8.3_T4.5_r3.1\", scenario=budget)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 21, + "outputs": [ + { + "data": { + "text/plain": "'1000f_MP00BD1BI00'" + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scen_budget.scenario" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 22, + "outputs": [ + { + "data": { + "text/plain": " type_year year\n0 cumulative 2025\n1 cumulative 2030\n2 cumulative 2035\n3 cumulative 2040\n4 cumulative 2045\n5 cumulative 2050\n6 cumulative 2055\n7 cumulative 2060\n8 cumulative 2070\n9 cumulative 2080\n10 cumulative 2090\n11 cumulative 2100\n12 cumulative 2110", + "text/html": "
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type_yearyear
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" + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "extra = scen_budget.set(\"cat_year\", {\"type_year\": \"cumulative\", \"year\": [2020, 2015, 2010]})\n", + "scen_budget.check_out()\n", + "scen_budget.remove_set(\"cat_year\", extra)\n", + "scen_budget.commit(\"remove cumulative years from cat_year set\")\n", + "scen_budget.set(\"cat_year\", {\"type_year\": \"cumulative\"})" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 23, + "outputs": [ + { + "data": { + "text/plain": " node type_emission type_tec type_year value unit\n0 World TCE all cumulative 4046.0 ???", + "text/html": "
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nodetype_emissiontype_tectype_yearvalueunit
0WorldTCEallcumulative4046.0???
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" + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "emission_dict = {\n", + " \"node\": \"World\",\n", + " \"type_emission\": \"TCE\",\n", + " \"type_tec\": \"all\",\n", + " \"type_year\": \"cumulative\",\n", + " \"unit\": \"???\",\n", + " }\n", + "if budget == \"1000f\":\n", + " value = 4046\n", + "if budget == \"600f\":\n", + " value = 2605\n", + "\n", + "df = message_ix.make_df(\n", + " \"bound_emission\", value=value, **emission_dict\n", + ")\n", + "scen_budget.check_out()\n", + "scen_budget.add_par(\"bound_emission\", df)\n", + "scen_budget.commit(\"add emission bound\")\n", + "scen_budget.par(\"bound_emission\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 24, + "outputs": [], + "source": [ + "scen_budget.solve(model=\"MESSAGE-MACRO\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 12, + "outputs": [], + "source": [ + "scen.check_out()\n", + "scen.add_set(\"balance_equality\", (\"i_feed\", \"useful\"))" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 14, + "outputs": [], + "source": [ + "scen.commit(\"add i_feed to balance_equality\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 17, + "outputs": [ + { + "data": { + "text/plain": " commodity level\n0 BIO01GHG000 LU\n1 BIO02GHG000 LU\n2 BIO03GHG000 LU\n3 BIO04GHG000 LU\n4 BIO05GHG000 LU\n.. ... ...\n128 BIO60GHG2000 LU\n129 BIO60GHG3000 LU\n130 BIO60GHG400 LU\n131 BIO60GHG600 LU\n132 i_feed useful\n\n[133 rows x 2 columns]", + "text/html": "
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" + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scen.set(\"balance_equality\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 18, + "outputs": [ + { + "ename": "ModelError", + "evalue": "GAMS errored with return code 3:\n There was an execution error\n\nFor details, see the terminal output above, plus:\nListing : C:\\Users\\maczek\\PycharmProjects\\message_ix\\message_ix\\model\\MESSAGE-MACRO_run.lst\nInput data: C:\\Users\\maczek\\PycharmProjects\\message_ix\\message_ix\\model\\data\\MsgData_MESSAGEix-GLOBIOM_1.1-R12-MAGPIE_600f_MP00BD0BI78_test_calib_macro.gdx", + "output_type": "error", + "traceback": [ + "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[1;31mModelError\u001B[0m Traceback (most recent call last)", + "Input \u001B[1;32mIn [18]\u001B[0m, in \u001B[0;36m\u001B[1;34m()\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[43mscen\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msolve\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmodel\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mMESSAGE-MACRO\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43msolve_options\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m{\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mscaind\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m:\u001B[49m\u001B[38;5;241;43m-\u001B[39;49m\u001B[38;5;241;43m1\u001B[39;49m\u001B[43m}\u001B[49m\u001B[43m)\u001B[49m\n", + "File \u001B[1;32m~\\PycharmProjects\\message_ix\\message_ix\\core.py:677\u001B[0m, in \u001B[0;36mScenario.solve\u001B[1;34m(self, model, solve_options, **kwargs)\u001B[0m\n\u001B[0;32m 657\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21msolve\u001B[39m(\u001B[38;5;28mself\u001B[39m, model\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mMESSAGE\u001B[39m\u001B[38;5;124m\"\u001B[39m, solve_options\u001B[38;5;241m=\u001B[39m{}, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[0;32m 658\u001B[0m \u001B[38;5;124;03m\"\"\"Solve MESSAGE or MESSAGE-MACRO for the Scenario.\u001B[39;00m\n\u001B[0;32m 659\u001B[0m \n\u001B[0;32m 660\u001B[0m \u001B[38;5;124;03m By default, :meth:`ixmp.Scenario.solve` is called with 'MESSAGE' as the\u001B[39;00m\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 675\u001B[0m \u001B[38;5;124;03m :class:`.GAMSModel`.\u001B[39;00m\n\u001B[0;32m 676\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[1;32m--> 677\u001B[0m \u001B[38;5;28;43msuper\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msolve\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmodel\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mmodel\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43msolve_options\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43msolve_options\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\ixmp\\core\\scenario.py:835\u001B[0m, in \u001B[0;36mScenario.solve\u001B[1;34m(self, model, callback, cb_kwargs, **model_options)\u001B[0m\n\u001B[0;32m 833\u001B[0m \u001B[38;5;66;03m# Iterate until convergence\u001B[39;00m\n\u001B[0;32m 834\u001B[0m \u001B[38;5;28;01mwhile\u001B[39;00m \u001B[38;5;28;01mTrue\u001B[39;00m:\n\u001B[1;32m--> 835\u001B[0m \u001B[43mmodel_obj\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrun\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[0;32m 837\u001B[0m \u001B[38;5;66;03m# Store an iteration number to help the callback\u001B[39;00m\n\u001B[0;32m 838\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28mhasattr\u001B[39m(\u001B[38;5;28mself\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124miteration\u001B[39m\u001B[38;5;124m\"\u001B[39m):\n", + "File \u001B[1;32m~\\PycharmProjects\\message_ix\\message_ix\\models.py:373\u001B[0m, in \u001B[0;36mGAMSModel.run\u001B[1;34m(self, scenario)\u001B[0m\n\u001B[0;32m 370\u001B[0m lines2 \u001B[38;5;241m=\u001B[39m (\u001B[38;5;124m\"\u001B[39m\u001B[38;5;132;01m{}\u001B[39;00m\u001B[38;5;124m = \u001B[39m\u001B[38;5;132;01m{}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;241m.\u001B[39mformat(\u001B[38;5;241m*\u001B[39mkv) \u001B[38;5;28;01mfor\u001B[39;00m kv \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcplex_opts\u001B[38;5;241m.\u001B[39mitems())\n\u001B[0;32m 371\u001B[0m optfile2\u001B[38;5;241m.\u001B[39mwrite_text(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;130;01m\\n\u001B[39;00m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;241m.\u001B[39mjoin(lines2))\n\u001B[1;32m--> 373\u001B[0m result \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43msuper\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrun\u001B[49m\u001B[43m(\u001B[49m\u001B[43mscenario\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 375\u001B[0m \u001B[38;5;66;03m# In previous versions, the `cplex.opt` file(s) were removed at this point\u001B[39;00m\n\u001B[0;32m 376\u001B[0m \u001B[38;5;66;03m# in the workflow. This has been removed due to issues when running\u001B[39;00m\n\u001B[0;32m 377\u001B[0m \u001B[38;5;66;03m# scenarios asynchronously.\u001B[39;00m\n\u001B[0;32m 379\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m result\n", + "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\ixmp\\model\\gams.py:296\u001B[0m, in \u001B[0;36mGAMSModel.run\u001B[1;34m(self, scenario)\u001B[0m\n\u001B[0;32m 293\u001B[0m check_call(command, shell\u001B[38;5;241m=\u001B[39mos\u001B[38;5;241m.\u001B[39mname \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mnt\u001B[39m\u001B[38;5;124m\"\u001B[39m, cwd\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcwd)\n\u001B[0;32m 294\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m CalledProcessError \u001B[38;5;28;01mas\u001B[39;00m exc:\n\u001B[0;32m 295\u001B[0m \u001B[38;5;66;03m# Do not remove self.temp_dir; the user may want to inspect the GDX file\u001B[39;00m\n\u001B[1;32m--> 296\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mformat_exception(exc, model_file) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;28mNone\u001B[39m\n\u001B[0;32m 298\u001B[0m \u001B[38;5;66;03m# Read model solution\u001B[39;00m\n\u001B[0;32m 299\u001B[0m scenario\u001B[38;5;241m.\u001B[39mplatform\u001B[38;5;241m.\u001B[39m_backend\u001B[38;5;241m.\u001B[39mread_file(\n\u001B[0;32m 300\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mout_file,\n\u001B[0;32m 301\u001B[0m ItemType\u001B[38;5;241m.\u001B[39mMODEL,\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 306\u001B[0m var_list\u001B[38;5;241m=\u001B[39mas_str_list(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mvar_list) \u001B[38;5;129;01mor\u001B[39;00m [],\n\u001B[0;32m 307\u001B[0m )\n", + "\u001B[1;31mModelError\u001B[0m: GAMS errored with return code 3:\n There was an execution error\n\nFor details, see the terminal output above, plus:\nListing : C:\\Users\\maczek\\PycharmProjects\\message_ix\\message_ix\\model\\MESSAGE-MACRO_run.lst\nInput data: C:\\Users\\maczek\\PycharmProjects\\message_ix\\message_ix\\model\\data\\MsgData_MESSAGEix-GLOBIOM_1.1-R12-MAGPIE_600f_MP00BD0BI78_test_calib_macro.gdx" + ] + } + ], + "source": [ + "scen.solve(model=\"MESSAGE-MACRO\", solve_options={\"scaind\":-1})" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 21, + "outputs": [ + { + "data": { + "text/plain": "'1000f_MP00BD0BI78_test_calib_macro'" + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scen.scenario.replace(\"6\", \"10\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 22, + "outputs": [], + "source": [ + "sc_clone = scen.clone(scen.model, scen.scenario.replace(\"6\", \"10\"))" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 23, + "outputs": [], + "source": [ + "scen_budget = sc_clone" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 25, + "outputs": [], + "source": [ + "df = scen_budget.par(\"bound_emission\")\n", + "scen_budget.check_out()\n", + "scen_budget.remove_par(\"bound_emission\", df)\n", + "scen_budget.commit(\"remove emission bound\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 26, + "outputs": [], + "source": [ + "budget = \"1000f\"" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 27, + "outputs": [ + { + "data": { + "text/plain": " node type_emission type_tec type_year value unit\n0 World TCE all cumulative 4046.0 ???", + "text/html": "
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nodetype_emissiontype_tectype_yearvalueunit
0WorldTCEallcumulative4046.0???
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" + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "3953\n", + "emission_dict = {\n", + " \"node\": \"World\",\n", + " \"type_emission\": \"TCE\",\n", + " \"type_tec\": \"all\",\n", + " \"type_year\": \"cumulative\",\n", + " \"unit\": \"???\",\n", + "}\n", + "if budget == \"1000f\":\n", + " value = 4046\n", + "if budget == \"600f\":\n", + " value = 2605\n", + "\n", + "df = message_ix.make_df(\n", + " \"bound_emission\", value=value, **emission_dict\n", + ")\n", + "scen_budget.check_out()\n", + "scen_budget.add_par(\"bound_emission\", df)\n", + "scen_budget.commit(\"add emission bound\")\n", + "scen_budget.par(\"bound_emission\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 29, + "outputs": [], + "source": [ + "scen_budget.solve(model=\"MESSAGE-MACRO\", solve_options={\"scaind\":-1})" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 1, + "outputs": [ + { + "data": { + "text/plain": "", + "application/javascript": "if (typeof IPython !== 'undefined') { IPython.OutputArea.prototype._should_scroll = function(lines){ return false; }}" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import ixmp\n", + "import message_ix\n", + "\n", + "mp = ixmp.Platform(\"ixmp_dev\")\n", + "#scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\", \"baseline_MP76BD0BI78\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 48, + "outputs": [ + { + "data": { + "text/plain": "\"clone Scenario from 'MESSAGEix-GLOBIOM 1.1-M-R12-NAVIGATE|baseline', version: 19\"" + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#df = mp.scenario_list()\n", + "df[(df[\"model\"]==\"MESSAGEix-GLOBIOM 1.1-M-R12-NAVIGATE\") & (df[\"scenario\"].str.contains(\"NPi2020\"))]#[\"annotation\"][6632]\n", + "#df = mp.scenario_list()\n", + "df[(df[\"model\"]==\"MESSAGEix-GLOBIOM 1.1-M-R12-NAVIGATE\") & (df[\"scenario\"]==\"NPi2020\")][\"annotation\"][6632]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "NPi2020-con-prim-dir-ncr" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 24, + "outputs": [ + { + "data": { + "text/plain": "array(['ENGAGE_SSP2_v4.1.2_R12', 'ENGAGE_SSP2_v4.1.4_R12_CHN',\n 'ENGAGE_SSP2_v4.1.4_R12_CHN_FG',\n 'ENGAGE_SSP2_v4.1.4_R12_CHN_FG_test', 'ENGAGE_SSP2_v4.1.4_R12_NOR',\n 'ENGAGE_SSP2_v4.1.7_R12', 'ENGAGE_SSP2_v4.1.7_R12_CHN',\n 'ENGAGE_SSP2_v4.1.7_R12_CHN_FG',\n 'ENGAGE_SSP2_v4.1.7_R12_CHN_FG_test', 'MESSAGE-SCP_R12',\n 'MESSAGE-_exDemand_SCP_R12',\n 'MESSAGEix-GLOBIOM 1.1-BM-R12 (NAVIGATE)',\n 'MESSAGEix-GLOBIOM 1.1-BM-R12 (SHAPE)',\n 'MESSAGEix-GLOBIOM 1.1-BM-R12-EFC',\n 'MESSAGEix-GLOBIOM 1.1-BM-R12-EFC_test_AM',\n 'MESSAGEix-GLOBIOM 1.1-BM-R12-NGFS',\n 'MESSAGEix-GLOBIOM 1.1-BMT-R12 (NAVIGATE)',\n 'MESSAGEix-GLOBIOM 1.1-BMT-R12-NAVIGATE',\n 'MESSAGEix-GLOBIOM 1.1-M-R12',\n 'MESSAGEix-GLOBIOM 1.1-M-R12 (NAVIGATE)',\n 'MESSAGEix-GLOBIOM 1.1-M-R12 (NGFS)',\n 'MESSAGEix-GLOBIOM 1.1-M-R12-NAVIGATE',\n 'MESSAGEix-GLOBIOM 1.1-M-R12-NAVIGATE-MAGPIE',\n 'MESSAGEix-GLOBIOM 1.1-M-R12-NAVIGATE_test',\n 'MESSAGEix-GLOBIOM 1.1-M-R12-NGFS',\n 'MESSAGEix-GLOBIOM 1.1-MT-B-R12-EFC',\n 'MESSAGEix-GLOBIOM 1.1-MT-R12',\n 'MESSAGEix-GLOBIOM 1.1-MT-R12 (NAVIGATE)',\n 'MESSAGEix-GLOBIOM 1.1-MT-R12-NGFS', 'MESSAGEix-GLOBIOM 1.1-R12',\n 'MESSAGEix-GLOBIOM 1.1-R12 (SHAPE)',\n 'MESSAGEix-GLOBIOM 1.1-R12 (agaur)',\n 'MESSAGEix-GLOBIOM 1.1-R12-MAGPIE',\n 'MESSAGEix-GLOBIOM 1.1-R12-MC (SHAPE)',\n 'MESSAGEix-GLOBIOM 1.1-R12-NGFS', 'MESSAGEix-GLOBIOM 1.1-T-R12',\n 'MESSAGEix-GLOBIOM1.1-BM-R12-NGFS',\n 'MESSAGEix-GLOBIOM_1.1-M-R12-NAVIGATE',\n 'MESSAGEix-GLOBIOM_R12_CHN', 'MESSAGEix-Materials_R12',\n 'MESSAGEix-R12',\n 'ixmp://local/MESSAGEix-Materials/NoPolicy_GLOBIOM_R12_local'],\n dtype=object)" + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[(df[\"model\"].str.contains(\"R12\"))][\"model\"].unique()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 42, + "outputs": [ + { + "data": { + "text/plain": " model scenario scheme \\\n6878 MESSAGEix-GLOBIOM 1.1-R12-NGFS NPi2020-con-prim-dir-ncr MESSAGE \n6879 MESSAGEix-GLOBIOM 1.1-R12-NGFS baseline MESSAGE \n6880 MESSAGEix-GLOBIOM 1.1-R12-NGFS baseline_DEFAULT MESSAGE \n6881 MESSAGEix-GLOBIOM 1.1-R12-NGFS baseline_prep_gdp MESSAGE \n6882 MESSAGEix-GLOBIOM 1.1-R12-NGFS baseline_prep_gdp_macro MESSAGE \n\n is_default is_locked cre_user cre_date upd_user \\\n6878 1 0 fricko 2022-03-08 09:15:51.000000 fricko \n6879 1 0 fricko 2022-03-08 10:43:11.000000 fricko \n6880 1 0 min 2022-03-04 15:16:04.000000 None \n6881 1 0 min 2022-03-04 13:21:50.000000 min \n6882 1 0 min 2022-03-04 14:45:49.000000 min \n\n upd_date lock_user lock_date \\\n6878 2022-03-08 09:22:36.000000 None None \n6879 2022-03-08 10:47:08.000000 None None \n6880 None None None \n6881 2022-03-04 13:30:30.000000 None None \n6882 2022-03-04 14:54:27.000000 None None \n\n annotation version \n6878 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 29 \n6879 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 4 \n6880 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 3 \n6881 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 5 \n6882 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 5 ", + "text/html": "
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modelscenarioschemeis_defaultis_lockedcre_usercre_dateupd_userupd_datelock_userlock_dateannotationversion
6878MESSAGEix-GLOBIOM 1.1-R12-NGFSNPi2020-con-prim-dir-ncrMESSAGE10fricko2022-03-08 09:15:51.000000fricko2022-03-08 09:22:36.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...29
6879MESSAGEix-GLOBIOM 1.1-R12-NGFSbaselineMESSAGE10fricko2022-03-08 10:43:11.000000fricko2022-03-08 10:47:08.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...4
6880MESSAGEix-GLOBIOM 1.1-R12-NGFSbaseline_DEFAULTMESSAGE10min2022-03-04 15:16:04.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...3
6881MESSAGEix-GLOBIOM 1.1-R12-NGFSbaseline_prep_gdpMESSAGE10min2022-03-04 13:21:50.000000min2022-03-04 13:30:30.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...5
6882MESSAGEix-GLOBIOM 1.1-R12-NGFSbaseline_prep_gdp_macroMESSAGE10min2022-03-04 14:45:49.000000min2022-03-04 14:54:27.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...5
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" + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[(df[\"model\"]==\"MESSAGEix-GLOBIOM 1.1-R12-NGFS\")]#[\"annotation\"][6881]" + ], + "metadata": { + "collapsed": false + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} From e9796166fddf64dfb182e1b6d2abe9f3e67dbedd Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 30 Nov 2023 16:08:53 +0100 Subject: [PATCH 646/774] =?UTF-8?q?Add=202=C2=B0c=20build=20cli=20command?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- message_ix_models/model/material/__init__.py | 37 +++++++++++++++++++- 1 file changed, 36 insertions(+), 1 deletion(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 3fc599900f..4d7f416609 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -589,4 +589,39 @@ def modify_costs_with_tool(context, scen_name, ssp): scen.add_par("inv_cost", inv_cost[inv_cost["technology"].isin(tec_set)]) scen.commit(f"update cost assumption to: {ssp}") - scen.solve(model="MESSAGE-MACRO", solve_options={"scaind": -1}) \ No newline at end of file + scen.solve(model="MESSAGE-MACRO", solve_options={"scaind": -1}) + + +@cli.command("run_2C_scenario") +@click.option("--ssp", default="SSP2", help="Suffix to the scenario name") +@click.pass_obj +def modify_costs_with_tool(context, scen_name, ssp): + import message_ix + from message_ix_models.tools.costs.config import Config + from message_ix_models.tools.costs.projections import create_cost_projections + + mp = ixmp.Platform("ixmp_dev") + base = message_ix.Scenario(mp, "MESSAGEix-Materials", scenario=f"SSP_supply_cost_test_{ssp}_macro") + scenario_cbud = base.clone(model=base.model, scenario=base.scenario + "_1000f", shift_first_model_year=2025) + + emission_dict = { + "node": "World", + "type_emission": "TCE", + "type_tec": "all", + "type_year": "cumulative", + "unit": "???", + } + df = message_ix.make_df( + "bound_emission", value=3667, **emission_dict + ) + scenario_cbud.check_out() + scenario_cbud.add_par("bound_emission", df) + scenario_cbud.commit("add emission bound") + pre_model_yrs = scenario_cbud.set("cat_year", {"type_year": "cumulative", "year": [2020, 2015, 2010]}) + scenario_cbud.check_out() + scenario_cbud.remove_set("cat_year", pre_model_yrs) + scenario_cbud.commit("remove cumulative years from cat_year set") + scenario_cbud.set("cat_year", {"type_year": "cumulative"}) + + scen_bud.solve(model="MESSAGE-MACRO",solve_options={"scaind":-1}) + return From 642c4fbed1a47f9222c307626f6cbb420f0e73bf Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 30 Nov 2023 16:37:06 +0100 Subject: [PATCH 647/774] Undo tracking of some files --- .../ammonia/FAOSTAT_data_ammonia_trade.csv | 3 - .../fert_techno_economic_iea_data.xlsx | 3 - ...nol production statistics (version 1).xlsx | 3 - .../reduced/location factor collection.xlsx | 3 - .../reduced/meth_bio_techno_economic_new.xlsx | 3 - .../meth_bio_techno_economic_new_ccs.xlsx | 3 - .../methanol/reduced/meth_coal_additions.xlsx | 3 - .../reduced/meth_coal_additions_fs.xlsx | 3 - .../reduced/meth_h2_techno_economic_fs.xlsx | 3 - .../reduced/meth_ng_techno_economic.xlsx | 3 - .../reduced/meth_ng_techno_economic_fs.xlsx | 3 - .../methanol/reduced/meth_t_d_fuel.xlsx | 3 - .../meth_trade_techno_economic_fs.xlsx | 3 - .../methanol/reduced/methanol demand.xlsx | 3 - .../methanol/reduced/methanol_all_pars.xlsx | 3 - .../reduced/methanol_all_pars_new2.xlsx | 3 - .../reduced/methanol_sensitivity_pars.xlsx | 3 - ...methanol_sensitivity_pars_high_demand.xlsx | 3 - .../model/material/CO2 diff files/_Diff.xlsx | 3 - .../CO2 diff files/_Diff_baseline_low.xlsx | 3 - .../_Diff_w_ENGAGE_fix_and_meth_exp_rel.xlsx | 3 - .../material/TE-tool stuff/fix_om_ratios.csv | 138 - .../technology_materials_first_year.csv | 138 - .../data analysis ipynbs/gdx_pandas.ipynb | 3026 -------- .../data analysis ipynbs/gdx_pandas_3.ipynb | 2059 ------ .../MESSAGE-magpie test.ipynb | 2590 ------- .../magpie notebooks/data_scp_final.ipynb | 744 -- .../material/magpie notebooks/magpie.ipynb | 6434 ----------------- .../plot_LU_carbon_price.ipynb | 1088 --- .../read_magpie_raw_files.ipynb | 1785 ----- .../refactor_emi_drivers_func.ipynb | 508 -- .../magpie notebooks/tax_emission_setup.ipynb | 182 - .../add_new_meth_modes.ipynb | 112 - .../methanol_modifications.ipynb | 890 --- .../missing_rels.yaml | 17 - .../split_h2_elec.ipynb | 208 - .../water_tec_pars.xlsx | 3 - .../fetch_run_id_from_scenario.ipynb | 183 - .../format reporting output.ipynb | 1306 ---- 39 files changed, 21474 deletions(-) delete mode 100644 message_ix_models/data/material/ammonia/FAOSTAT_data_ammonia_trade.csv delete mode 100644 message_ix_models/data/material/ammonia/fert_techno_economic_iea_data.xlsx delete mode 100644 message_ix_models/data/material/methanol/reduced/Methanol production statistics (version 1).xlsx delete mode 100644 message_ix_models/data/material/methanol/reduced/location factor collection.xlsx delete mode 100644 message_ix_models/data/material/methanol/reduced/meth_bio_techno_economic_new.xlsx delete mode 100644 message_ix_models/data/material/methanol/reduced/meth_bio_techno_economic_new_ccs.xlsx delete mode 100644 message_ix_models/data/material/methanol/reduced/meth_coal_additions.xlsx delete mode 100644 message_ix_models/data/material/methanol/reduced/meth_coal_additions_fs.xlsx delete mode 100644 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a/message_ix_models/model/material/TE-tool stuff/technology_materials_first_year.csv b/message_ix_models/model/material/TE-tool stuff/technology_materials_first_year.csv deleted file mode 100644 index 62c7a34ff6..0000000000 --- a/message_ix_models/model/material/TE-tool stuff/technology_materials_first_year.csv +++ /dev/null @@ -1,138 +0,0 @@ -message_technology,first_year_original -MTO_petro,2020 -NH3_to_N_fertil,1995 -atm_distillation_ref,1980 -bf_steel,1990 -biomass_NH3,1995 -biomass_NH3_ccs,1995 -bof_steel,1990 -catalytic_cracking_ref,1980 -catalytic_reforming_ref,1980 -clinker_dry_ccs_cement,1980 -clinker_dry_cement,1980 -clinker_wet_ccs_cement,1980 -clinker_wet_cement,1980 -coal_NH3,1995 -coal_NH3_ccs,1995 -cokeoven_steel,1990 -coking_ref,1980 -dheat_aluminum,2030 -dheat_cement,2030 -dheat_petro,2030 -dheat_refining,2030 -dheat_resins,2030 -dheat_steel,2030 -eaf_steel,1990 -electr_NH3,1995 -ethanol_to_ethylene_petro,1980 -finishing_steel,1990 -fueloil_NH3,1995 -fueloil_NH3_ccs,1995 -furnace_biomass_aluminum,1980 -furnace_biomass_cement,1980 -furnace_biomass_petro,1980 -furnace_biomass_refining,1980 -furnace_biomass_resins,1980 -furnace_biomass_steel,1980 -furnace_coal_aluminum,1980 -furnace_coal_cement,1980 -furnace_coal_petro,1980 -furnace_coal_refining,1980 -furnace_coal_resins,1980 -furnace_coal_steel,1980 -furnace_coke_petro,1980 -furnace_coke_refining,1980 -furnace_elec_aluminum,1980 -furnace_elec_cement,1980 -furnace_elec_petro,1980 -furnace_elec_refining,1980 -furnace_elec_resins,1980 -furnace_elec_steel,1980 -furnace_ethanol_aluminum,1980 -furnace_ethanol_cement,1980 -furnace_ethanol_petro,1980 -furnace_ethanol_refining,1980 -furnace_ethanol_resins,1980 -furnace_ethanol_steel,1980 -furnace_foil_aluminum,1980 -furnace_foil_cement,1980 -furnace_foil_petro,1980 -furnace_foil_refining,1980 -furnace_foil_resins,1980 -furnace_foil_steel,1980 -furnace_gas_aluminum,1980 -furnace_gas_cement,1980 -furnace_gas_petro,1980 -furnace_gas_refining,1980 -furnace_gas_resins,1980 -furnace_gas_steel,1980 -furnace_h2_aluminum,2030 -furnace_h2_cement,2030 -furnace_h2_petro,2030 -furnace_h2_refining,2030 -furnace_h2_resins,2030 -furnace_h2_steel,2030 -furnace_loil_aluminum,1980 -furnace_loil_cement,1980 -furnace_loil_petro,1980 -furnace_loil_refining,1980 -furnace_loil_resins,1980 -furnace_loil_steel,1980 -furnace_methanol_aluminum,2020 -furnace_methanol_cement,2020 -furnace_methanol_petro,2020 -furnace_methanol_refining,2020 -furnace_methanol_resins,2020 -furnace_methanol_steel,2020 -gas_NH3,1995 -gas_NH3_ccs,1995 -grinding_ballmill_cement,1980 -grinding_vertmill_cement,1980 -hp_elec_aluminum,1980 -hp_elec_cement,1980 -hp_elec_petro,1980 -hp_elec_refining,1980 -hp_elec_resins,1980 -hp_elec_steel,1980 -hp_gas_aluminum,1980 -hp_gas_cement,1980 -hp_gas_petro,1980 -hp_gas_refining,1980 -hp_gas_resins,1980 -hp_gas_steel,1980 -hydro_cracking_ref,1980 -meth_bio,2020 -meth_bio_ccs,2030 -meth_coal,2000 -meth_coal_ccs,2030 -meth_h2,2020 -meth_ng,2000 -meth_ng_ccs,2030 -pellet_steel,1990 -prebake_aluminum,1985 -raw_meal_prep_cement,1980 -sinter_steel,1990 -soderberg_aluminum,1985 -solar_aluminum,2030 -solar_cement,2030 -solar_petro,2030 -solar_refining,2030 -solar_resins,2030 -solar_steel,2030 -steam_cracker_petro,1980 -vacuum_distillation_ref,1980 -visbreaker_ref,1980 -dri_steel,1990 -export_NFert,2005 -export_NH3,2005 -export_aluminum,1985 -export_petro,1980 -export_steel,1990 -fc_h2_aluminum,2030 -fc_h2_cement,2030 -fc_h2_petro,2030 -fc_h2_refining,2030 -fc_h2_resins,2030 -fc_h2_steel,2030 -manuf_steel,1990 -meth_exp,2020 diff --git a/message_ix_models/model/material/data analysis ipynbs/gdx_pandas.ipynb b/message_ix_models/model/material/data analysis ipynbs/gdx_pandas.ipynb deleted file mode 100644 index c2e06924fd..0000000000 --- a/message_ix_models/model/material/data analysis ipynbs/gdx_pandas.ipynb +++ /dev/null @@ -1,3026 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import gdxpds\n", - "import pandas as pd\n", - "co2_c_factor = 44/12" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "outputs": [], - "source": [ - "#gdx_file = 'C:/Users\\maczek\\PycharmProjects\\message_ix\\message_ix\\model\\output\\MsgOutput_MESSAGEix-Materials_2.0deg_petro_thesis_2_macro.gdx'\n", - "#gdx_file = 'C:/Users\\maczek\\PycharmProjects\\message_ix\\message_ix\\model\\output\\MsgOutput_MESSAGEix-Materials_2.0deg_petro_thesis_2_macro_w_ENGAGE_fixes_test_loil_exp_fix.gdx'\n", - "#gdx_file = 'C:/Users\\maczek\\PycharmProjects\\message_ix\\message_ix\\model\\output\\MsgOutput_ENGAGE_SSP2_v4.1.8.3_T4.5_r3.1_baseline_magpie.gdx'\n", - "#gdx_file = 'C:/Users\\maczek\\PycharmProjects\\message_ix\\message_ix\\model\\output\\MsgOutput_MESSAGEix-Materials_NoPolicy_petro_thesis_2_w_ENGAGE_fixes_test_build_2_macro.gdx'\n", - "#gdx_file = 'C:/Users\\maczek\\PycharmProjects\\message_ix\\message_ix\\model\\output/thesis final/MsgOutput_MESSAGEix-Materials_NoPolicy_petro_thesis_2_final_macro.gdx'\n", - "gdx_file = 'C:/Users\\maczek\\PycharmProjects\\message_ix\\message_ix\\model\\output/MsgOutput_MESSAGEix-GLOBIOM_1.1-R12-MAGPIE-MP00BD1BI00_baseline.gdx'\n", - "#gdx_file = 'C:/Users\\maczek\\PycharmProjects\\message_ix\\message_ix\\model\\output/MsgOutput_MESSAGEix-GLOBIOM_1.1-R12-MAGPIE-MP00BD1BI00_NPi2020-con-prim-dir-ncr_master.gdx'\n", - "#dataframes = gdxpds.to_dataframes(gdx_file)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 3, - "outputs": [], - "source": [ - "mps = [\"00\", \"30\", \"50\", \"76\"]\n", - "bis = [\"00\", \"70\", \"74\", \"78\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 28, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MP00BD1BI00 []\n", - "MP00BD0BI70 [0.03030621]\n", - "MP00BD0BI74 []\n", - "MP00BD0BI78 []\n", - "MP30BD1BI00 [0.08847191]\n", - "MP30BD0BI70 []\n", - "MP30BD0BI74 [0.02933949]\n", - "MP50BD1BI00 []\n", - "MP50BD0BI70 [0.0890051]\n", - "MP50BD0BI74 [0.01935185]\n", - "MP50BD0BI78 []\n", - "MP76BD1BI00 [0.12013137]\n", - "MP76BD0BI70 [0.0857669]\n", - "MP76BD0BI74 [0.02513131]\n", - "MP76BD0BI78 [0.04660288]\n" - ] - } - ], - "source": [ - "import os\n", - "\n", - "for mp in mps:\n", - " for bi in bis:\n", - " if bi == \"00\":\n", - " bd = 1\n", - " else:\n", - " bd = 0\n", - " model = f\"MP{mp}BD{bd}BI{bi}\"\n", - " gdx_name = f\"C:/Users\\maczek\\PycharmProjects\\message_ix\\message_ix\\model\\output/MsgOutput_MESSAGEix-GLOBIOM_1.1-R12-MAGPIE-MP{mp}BD{bd}BI{bi}_NPi2020-con-prim-dir-ncr.gdx\"\n", - " gdx_name = f\"C:/Users\\maczek\\PycharmProjects\\message_ix\\message_ix\\model\\output/MsgOutput_MESSAGEix-GLOBIOM_1.1-R12-MAGPIE-MP{mp}BD{bd}BI{bi}_EN_NPi2020_650.gdx\"\n", - " #print(os.path.exists(gdx_name))\n", - " if not os.path.exists(gdx_name):\n", - " continue\n", - " #print(\"test\")\n", - " df_act = gdxpds.to_dataframe(gdx_name, \"ACT\")[\"ACT\"]\n", - " #print(df_act.columns)\n", - " print(model, df_act[(df_act[\"Level\"]!=0) & (df_act[\"tec\"]==\"bio_backstop\")][\"Level\"].values)\n", - " #break\n", - " #break" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 16, - "outputs": [], - "source": [ - "#df_all = gdxpds.to_dataframes(gdx_file)\n", - "df_all = gdxpds.to_dataframe(gdx_file, \"land_output\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 4, - "outputs": [], - "source": [ - "df_gro = gdxpds.to_dataframe(gdx_file, \"growth_activity_lo\")[\"growth_activity_lo\"]\n", - "df_soft = gdxpds.to_dataframe(gdx_file, \"soft_activity_lo\")[\"soft_activity_lo\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 13, - "outputs": [], - "source": [ - "df_var = gdxpds.to_dataframe(gdx_file, \"ACTIVITY_CONSTRAINT_LO\")[\"ACTIVITY_CONSTRAINT_LO\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 20, - "outputs": [], - "source": [ - "df_var.columns = [\"node\", \"tec\", \"year_all\", \"time\", \"Level\", \"Marginal\", \"Lower\", \"Upper\", \"Scale\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 21, - "outputs": [ - { - "data": { - "text/plain": " node tec year_all time Level Marginal Lower \\\n0 R12_AFR LNG_exp 2020 year 64.134989 0.0 29.753100 \n1 R12_AFR LNG_exp 2025 year 32.884912 0.0 -9.048762 \n2 R12_AFR LNG_exp 2030 year 31.082772 0.0 -9.048762 \n3 R12_AFR LNG_exp 2035 year 12.542668 0.0 -9.048762 \n4 R12_AFR LNG_exp 2040 year 32.041154 0.0 -9.048762 \n... ... ... ... ... ... ... ... \n13732 R12_CHN biomass_exp 2070 year 83.981427 0.0 -16.050522 \n13733 R12_CHN biomass_exp 2080 year 81.488368 0.0 -16.050522 \n13734 R12_CHN biomass_exp 2090 year 80.277312 0.0 -16.050522 \n13735 R12_CHN biomass_exp 2100 year 102.884565 0.0 -16.050522 \n13736 R12_CHN biomass_exp 2110 year 65.048786 0.0 -16.050522 \n\n Upper Scale \n0 3.000000e+300 1.0 \n1 3.000000e+300 1.0 \n2 3.000000e+300 1.0 \n3 3.000000e+300 1.0 \n4 3.000000e+300 1.0 \n... ... ... \n13732 3.000000e+300 1.0 \n13733 3.000000e+300 1.0 \n13734 3.000000e+300 1.0 \n13735 3.000000e+300 1.0 \n13736 3.000000e+300 1.0 \n\n[13737 rows x 9 columns]", - "text/html": "
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nodetecyear_alltimeLevelMarginalLowerUpperScale
0R12_AFRLNG_exp2020year64.1349890.029.7531003.000000e+3001.0
1R12_AFRLNG_exp2025year32.8849120.0-9.0487623.000000e+3001.0
2R12_AFRLNG_exp2030year31.0827720.0-9.0487623.000000e+3001.0
3R12_AFRLNG_exp2035year12.5426680.0-9.0487623.000000e+3001.0
4R12_AFRLNG_exp2040year32.0411540.0-9.0487623.000000e+3001.0
..............................
13732R12_CHNbiomass_exp2070year83.9814270.0-16.0505223.000000e+3001.0
13733R12_CHNbiomass_exp2080year81.4883680.0-16.0505223.000000e+3001.0
13734R12_CHNbiomass_exp2090year80.2773120.0-16.0505223.000000e+3001.0
13735R12_CHNbiomass_exp2100year102.8845650.0-16.0505223.000000e+3001.0
13736R12_CHNbiomass_exp2110year65.0487860.0-16.0505223.000000e+3001.0
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13737 rows × 9 columns

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" - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_var" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 34, - "outputs": [ - { - "data": { - "text/plain": "['wind_ppf',\n 'biomass_imp',\n 'biomass_exp',\n 'LH2_bunker',\n 'LNG_bunker',\n 'eth_bunker',\n 'foil_bunker',\n 'loil_bunker',\n 'meth_bunker']" - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "[i for i in list(df_gro.tec.unique()) if i not in list(df_soft.tec.unique())]\n", - "#[i for i in list(df_soft.tec.unique()) if i not in list(df_gro.tec.unique())]\n", - "[i for i in list(df_soft.tec.unique()) if i not in list(df_var.tec.unique())]\n", - "#[i for i in list(df_var.tec.unique()) if i not in list(df_soft.tec.unique())]\n", - "#[i for i in list(df_var.tec.unique()) if ((i not in list(df_gro.tec.unique())) & (i not in list(df_soft.tec.unique())))]\n", - "[i for i in list(df_var.tec.unique()) if (i not in list(df_soft.tec.unique()))]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 17, - "outputs": [], - "source": [ - "df_all = df_all[\"land_output\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 18, - "outputs": [ - { - "data": { - "text/plain": " node land_scenario year_all commodity level time \\\n2201157 R12_CHN BIO05GHG000 2020 bioenergy land_use year \n2201158 R12_CHN BIO05GHG000 2020 bioenergy land_use_reporting year \n2203425 R12_CHN BIO05GHG010 2020 bioenergy land_use year \n2203426 R12_CHN BIO05GHG010 2020 bioenergy land_use_reporting year \n2205735 R12_CHN BIO05GHG020 2020 bioenergy land_use year \n... ... ... ... ... ... ... \n2388263 R12_CHN BIO45GHG4000 2020 bioenergy land_use_reporting year \n2390583 R12_CHN BIO45GHG600 2020 bioenergy land_use year \n2390584 R12_CHN BIO45GHG600 2020 bioenergy land_use_reporting year \n2392903 R12_CHN BIO45GHG990 2020 bioenergy land_use year \n2392904 R12_CHN BIO45GHG990 2020 bioenergy land_use_reporting year \n\n Value \n2201157 238.5000 \n2201158 238.5029 \n2203425 238.5000 \n2203426 238.5029 \n2205735 238.5000 \n... ... \n2388263 238.5029 \n2390583 238.5000 \n2390584 238.5029 \n2392903 238.5000 \n2392904 238.5029 \n\n[168 rows x 7 columns]", - "text/html": "
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nodeland_scenarioyear_allcommodityleveltimeValue
2201157R12_CHNBIO05GHG0002020bioenergyland_useyear238.5000
2201158R12_CHNBIO05GHG0002020bioenergyland_use_reportingyear238.5029
2203425R12_CHNBIO05GHG0102020bioenergyland_useyear238.5000
2203426R12_CHNBIO05GHG0102020bioenergyland_use_reportingyear238.5029
2205735R12_CHNBIO05GHG0202020bioenergyland_useyear238.5000
........................
2388263R12_CHNBIO45GHG40002020bioenergyland_use_reportingyear238.5029
2390583R12_CHNBIO45GHG6002020bioenergyland_useyear238.5000
2390584R12_CHNBIO45GHG6002020bioenergyland_use_reportingyear238.5029
2392903R12_CHNBIO45GHG9902020bioenergyland_useyear238.5000
2392904R12_CHNBIO45GHG9902020bioenergyland_use_reportingyear238.5029
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168 rows × 7 columns

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" - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_all[(df_all[\"commodity\"]==\"bioenergy\") & ( df_all[\"node\"]==\"R12_CHN\") & (df_all[\"year_all\"]==\"2020\")]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 33, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\maczek\\AppData\\Local\\Temp\\ipykernel_24400\\878313158.py:1: FutureWarning: reindexing with a non-unique Index is deprecated and will raise in a future version.\n", - " v[v.node.isin([\"R12_CHN\"])]\n" - ] - }, - { - "ename": "ValueError", - "evalue": "cannot reindex on an axis with duplicate labels", - "output_type": "error", - "traceback": [ - "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[1;31mValueError\u001B[0m Traceback (most recent call last)", - "Input \u001B[1;32mIn [33]\u001B[0m, in \u001B[0;36m\u001B[1;34m()\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[43mv\u001B[49m\u001B[43m[\u001B[49m\u001B[43mv\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mnode\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43misin\u001B[49m\u001B[43m(\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mR12_CHN\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m)\u001B[49m\u001B[43m]\u001B[49m\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3492\u001B[0m, in \u001B[0;36mDataFrame.__getitem__\u001B[1;34m(self, key)\u001B[0m\n\u001B[0;32m 3490\u001B[0m \u001B[38;5;66;03m# Do we have a (boolean) DataFrame?\u001B[39;00m\n\u001B[0;32m 3491\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(key, DataFrame):\n\u001B[1;32m-> 3492\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mwhere\u001B[49m\u001B[43m(\u001B[49m\u001B[43mkey\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 3494\u001B[0m \u001B[38;5;66;03m# Do we have a (boolean) 1d indexer?\u001B[39;00m\n\u001B[0;32m 3495\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m com\u001B[38;5;241m.\u001B[39mis_bool_indexer(key):\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\util\\_decorators.py:311\u001B[0m, in \u001B[0;36mdeprecate_nonkeyword_arguments..decorate..wrapper\u001B[1;34m(*args, **kwargs)\u001B[0m\n\u001B[0;32m 305\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(args) \u001B[38;5;241m>\u001B[39m num_allow_args:\n\u001B[0;32m 306\u001B[0m warnings\u001B[38;5;241m.\u001B[39mwarn(\n\u001B[0;32m 307\u001B[0m msg\u001B[38;5;241m.\u001B[39mformat(arguments\u001B[38;5;241m=\u001B[39marguments),\n\u001B[0;32m 308\u001B[0m \u001B[38;5;167;01mFutureWarning\u001B[39;00m,\n\u001B[0;32m 309\u001B[0m stacklevel\u001B[38;5;241m=\u001B[39mstacklevel,\n\u001B[0;32m 310\u001B[0m )\n\u001B[1;32m--> 311\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfunc\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:10955\u001B[0m, in \u001B[0;36mDataFrame.where\u001B[1;34m(self, cond, other, inplace, axis, level, errors, try_cast)\u001B[0m\n\u001B[0;32m 10942\u001B[0m \u001B[38;5;129m@deprecate_nonkeyword_arguments\u001B[39m(\n\u001B[0;32m 10943\u001B[0m version\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, allowed_args\u001B[38;5;241m=\u001B[39m[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mself\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mcond\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mother\u001B[39m\u001B[38;5;124m\"\u001B[39m]\n\u001B[0;32m 10944\u001B[0m )\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 10953\u001B[0m try_cast\u001B[38;5;241m=\u001B[39mlib\u001B[38;5;241m.\u001B[39mno_default,\n\u001B[0;32m 10954\u001B[0m ):\n\u001B[1;32m> 10955\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43msuper\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mwhere\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcond\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mother\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43minplace\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43maxis\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mlevel\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43merrors\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtry_cast\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:9308\u001B[0m, in \u001B[0;36mNDFrame.where\u001B[1;34m(self, cond, other, inplace, axis, level, errors, try_cast)\u001B[0m\n\u001B[0;32m 9300\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m try_cast \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m lib\u001B[38;5;241m.\u001B[39mno_default:\n\u001B[0;32m 9301\u001B[0m warnings\u001B[38;5;241m.\u001B[39mwarn(\n\u001B[0;32m 9302\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mtry_cast keyword is deprecated and will be removed in a \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m 9303\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mfuture version.\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[0;32m 9304\u001B[0m \u001B[38;5;167;01mFutureWarning\u001B[39;00m,\n\u001B[0;32m 9305\u001B[0m stacklevel\u001B[38;5;241m=\u001B[39mfind_stack_level(),\n\u001B[0;32m 9306\u001B[0m )\n\u001B[1;32m-> 9308\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_where\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcond\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mother\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43minplace\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43maxis\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mlevel\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43merrors\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43merrors\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:9075\u001B[0m, in \u001B[0;36mNDFrame._where\u001B[1;34m(self, cond, other, inplace, axis, level, errors)\u001B[0m\n\u001B[0;32m 9072\u001B[0m cond \u001B[38;5;241m=\u001B[39m cond\u001B[38;5;241m.\u001B[39mastype(\u001B[38;5;28mbool\u001B[39m)\n\u001B[0;32m 9074\u001B[0m cond \u001B[38;5;241m=\u001B[39m \u001B[38;5;241m-\u001B[39mcond \u001B[38;5;28;01mif\u001B[39;00m inplace \u001B[38;5;28;01melse\u001B[39;00m cond\n\u001B[1;32m-> 9075\u001B[0m cond \u001B[38;5;241m=\u001B[39m \u001B[43mcond\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mreindex\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_info_axis\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43maxis\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_info_axis_number\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcopy\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m)\u001B[49m\n\u001B[0;32m 9077\u001B[0m \u001B[38;5;66;03m# try to align with other\u001B[39;00m\n\u001B[0;32m 9078\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(other, NDFrame):\n\u001B[0;32m 9079\u001B[0m \n\u001B[0;32m 9080\u001B[0m \u001B[38;5;66;03m# align with me\u001B[39;00m\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\util\\_decorators.py:324\u001B[0m, in \u001B[0;36mrewrite_axis_style_signature..decorate..wrapper\u001B[1;34m(*args, **kwargs)\u001B[0m\n\u001B[0;32m 322\u001B[0m \u001B[38;5;129m@wraps\u001B[39m(func)\n\u001B[0;32m 323\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mwrapper\u001B[39m(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m Callable[\u001B[38;5;241m.\u001B[39m\u001B[38;5;241m.\u001B[39m\u001B[38;5;241m.\u001B[39m, Any]:\n\u001B[1;32m--> 324\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfunc\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:4804\u001B[0m, in \u001B[0;36mDataFrame.reindex\u001B[1;34m(self, *args, **kwargs)\u001B[0m\n\u001B[0;32m 4802\u001B[0m kwargs\u001B[38;5;241m.\u001B[39mpop(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124maxis\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;28;01mNone\u001B[39;00m)\n\u001B[0;32m 4803\u001B[0m kwargs\u001B[38;5;241m.\u001B[39mpop(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mlabels\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;28;01mNone\u001B[39;00m)\n\u001B[1;32m-> 4804\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43msuper\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mreindex\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:4966\u001B[0m, in \u001B[0;36mNDFrame.reindex\u001B[1;34m(self, *args, **kwargs)\u001B[0m\n\u001B[0;32m 4963\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_reindex_multi(axes, copy, fill_value)\n\u001B[0;32m 4965\u001B[0m \u001B[38;5;66;03m# perform the reindex on the axes\u001B[39;00m\n\u001B[1;32m-> 4966\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_reindex_axes\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m 4967\u001B[0m \u001B[43m \u001B[49m\u001B[43maxes\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mlevel\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mlimit\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtolerance\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmethod\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfill_value\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcopy\u001B[49m\n\u001B[0;32m 4968\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241m.\u001B[39m__finalize__(\u001B[38;5;28mself\u001B[39m, method\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mreindex\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:4617\u001B[0m, in \u001B[0;36mDataFrame._reindex_axes\u001B[1;34m(self, axes, level, limit, tolerance, method, fill_value, copy)\u001B[0m\n\u001B[0;32m 4615\u001B[0m columns \u001B[38;5;241m=\u001B[39m axes[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mcolumns\u001B[39m\u001B[38;5;124m\"\u001B[39m]\n\u001B[0;32m 4616\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m columns \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[1;32m-> 4617\u001B[0m frame \u001B[38;5;241m=\u001B[39m \u001B[43mframe\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_reindex_columns\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m 4618\u001B[0m \u001B[43m \u001B[49m\u001B[43mcolumns\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmethod\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcopy\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mlevel\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfill_value\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mlimit\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtolerance\u001B[49m\n\u001B[0;32m 4619\u001B[0m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 4621\u001B[0m index \u001B[38;5;241m=\u001B[39m axes[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mindex\u001B[39m\u001B[38;5;124m\"\u001B[39m]\n\u001B[0;32m 4622\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m index \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:4662\u001B[0m, in \u001B[0;36mDataFrame._reindex_columns\u001B[1;34m(self, new_columns, method, copy, level, fill_value, limit, tolerance)\u001B[0m\n\u001B[0;32m 4649\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m_reindex_columns\u001B[39m(\n\u001B[0;32m 4650\u001B[0m \u001B[38;5;28mself\u001B[39m,\n\u001B[0;32m 4651\u001B[0m new_columns,\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 4657\u001B[0m tolerance\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[0;32m 4658\u001B[0m ):\n\u001B[0;32m 4659\u001B[0m new_columns, indexer \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcolumns\u001B[38;5;241m.\u001B[39mreindex(\n\u001B[0;32m 4660\u001B[0m new_columns, method\u001B[38;5;241m=\u001B[39mmethod, level\u001B[38;5;241m=\u001B[39mlevel, limit\u001B[38;5;241m=\u001B[39mlimit, tolerance\u001B[38;5;241m=\u001B[39mtolerance\n\u001B[0;32m 4661\u001B[0m )\n\u001B[1;32m-> 4662\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_reindex_with_indexers\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m 4663\u001B[0m \u001B[43m \u001B[49m\u001B[43m{\u001B[49m\u001B[38;5;241;43m1\u001B[39;49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[43m[\u001B[49m\u001B[43mnew_columns\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mindexer\u001B[49m\u001B[43m]\u001B[49m\u001B[43m}\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 4664\u001B[0m \u001B[43m \u001B[49m\u001B[43mcopy\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mcopy\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 4665\u001B[0m \u001B[43m \u001B[49m\u001B[43mfill_value\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mfill_value\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 4666\u001B[0m \u001B[43m \u001B[49m\u001B[43mallow_dups\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[0;32m 4667\u001B[0m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:5032\u001B[0m, in \u001B[0;36mNDFrame._reindex_with_indexers\u001B[1;34m(self, reindexers, fill_value, copy, allow_dups)\u001B[0m\n\u001B[0;32m 5029\u001B[0m indexer \u001B[38;5;241m=\u001B[39m ensure_platform_int(indexer)\n\u001B[0;32m 5031\u001B[0m \u001B[38;5;66;03m# TODO: speed up on homogeneous DataFrame objects (see _reindex_multi)\u001B[39;00m\n\u001B[1;32m-> 5032\u001B[0m new_data \u001B[38;5;241m=\u001B[39m \u001B[43mnew_data\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mreindex_indexer\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m 5033\u001B[0m \u001B[43m \u001B[49m\u001B[43mindex\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 5034\u001B[0m \u001B[43m \u001B[49m\u001B[43mindexer\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 5035\u001B[0m \u001B[43m \u001B[49m\u001B[43maxis\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mbaxis\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 5036\u001B[0m \u001B[43m \u001B[49m\u001B[43mfill_value\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mfill_value\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 5037\u001B[0m \u001B[43m \u001B[49m\u001B[43mallow_dups\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mallow_dups\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 5038\u001B[0m \u001B[43m \u001B[49m\u001B[43mcopy\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mcopy\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 5039\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 5040\u001B[0m \u001B[38;5;66;03m# If we've made a copy once, no need to make another one\u001B[39;00m\n\u001B[0;32m 5041\u001B[0m copy \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mFalse\u001B[39;00m\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\managers.py:679\u001B[0m, in \u001B[0;36mBaseBlockManager.reindex_indexer\u001B[1;34m(self, new_axis, indexer, axis, fill_value, allow_dups, copy, consolidate, only_slice, use_na_proxy)\u001B[0m\n\u001B[0;32m 677\u001B[0m \u001B[38;5;66;03m# some axes don't allow reindexing with dups\u001B[39;00m\n\u001B[0;32m 678\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m allow_dups:\n\u001B[1;32m--> 679\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43maxes\u001B[49m\u001B[43m[\u001B[49m\u001B[43maxis\u001B[49m\u001B[43m]\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_validate_can_reindex\u001B[49m\u001B[43m(\u001B[49m\u001B[43mindexer\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 681\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m axis \u001B[38;5;241m>\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mndim:\n\u001B[0;32m 682\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mIndexError\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mRequested axis not found in manager\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py:4107\u001B[0m, in \u001B[0;36mIndex._validate_can_reindex\u001B[1;34m(self, indexer)\u001B[0m\n\u001B[0;32m 4105\u001B[0m \u001B[38;5;66;03m# trying to reindex on an axis with duplicates\u001B[39;00m\n\u001B[0;32m 4106\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_index_as_unique \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(indexer):\n\u001B[1;32m-> 4107\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mcannot reindex on an axis with duplicate labels\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n", - "\u001B[1;31mValueError\u001B[0m: cannot reindex on an axis with duplicate labels" - ] - } - ], - "source": [ - "v[v.node.isin([\"R12_CHN\"])]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 57, - "outputs": [], - "source": [ - "bio_dict = {}\n", - "for k,v in df_all.items():\n", - " if \"tec\" in v.columns:\n", - " if len(v[v.tec.str.contains(\"bio\")][\"tec\"].unique()):\n", - " #print(v[v.tec.str.contains(\"bio\")][\"tec\"].unique())\n", - " if \"node\" in v.columns:\n", - " v_temp = v.loc[:,~v.columns.duplicated()].copy()\n", - " bio_dict[k] = v_temp[(v_temp.tec.str.contains(\"bio\")) & (v_temp.node ==\"R12_CHN\")]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 69, - "outputs": [ - { - "data": { - "text/plain": "{'is_bound_new_capacity_lo': array(['bio_ppl', 'eth_bio'], dtype=object),\n 'is_dynamic_activity_lo': array(['bio_ppl', 'biomass_i', 'biomass_rc', 'biomass_imp', 'biomass_exp'],\n dtype=object),\n 'bound_new_capacity_lo': array(['bio_ppl', 'eth_bio'], dtype=object),\n 'bound_activity_lo': array(['bio_hpl', 'bio_ppl', 'biomass_i', 'eth_bio'], dtype=object),\n 'initial_activity_lo': array(['bio_ppl', 'biomass_i', 'biomass_rc', 'biomass_imp', 'biomass_exp'],\n dtype=object),\n 'growth_activity_lo': array(['bio_ppl', 'biomass_i', 'biomass_rc', 'biomass_imp', 'biomass_exp'],\n dtype=object),\n 'soft_activity_lo': array(['bio_extr_mpen', 'bio_hpl', 'bio_istig', 'bio_istig_ccs',\n 'bio_ppl', 'bio_ppl_co2scr', 'biomass_i', 'biomass_rc',\n 'biomass_t_d', 'eth_bio', 'eth_bio_ccs', 'gas_bio', 'h2_bio',\n 'h2_bio_ccs', 'landfill_mechbio', 'liq_bio', 'liq_bio_ccs'],\n dtype=object),\n 'abs_cost_activity_soft_lo': array(['bio_extr_mpen'], dtype=object),\n 'level_cost_activity_soft_lo': array(['bio_hpl', 'bio_istig', 'bio_istig_ccs', 'bio_ppl',\n 'bio_ppl_co2scr', 'biomass_i', 'biomass_rc', 'biomass_t_d',\n 'eth_bio', 'eth_bio_ccs', 'gas_bio', 'h2_bio', 'h2_bio_ccs',\n 'landfill_mechbio', 'liq_bio', 'liq_bio_ccs'], dtype=object)}" - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lo_dict = {k:v for k,v in bio_dict.items() if ((\"lo\" in k))}\n", - "{k:i.tec.unique() for k,i in lo_dict.items()}" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 42, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "is_bound_new_capacity_lo ['bio_ppl' 'eth_bio' 'bio_istig']\n", - "is_dynamic_activity_lo ['biomass_i' 'biomass_rc' 'biomass_imp' 'biomass_exp' 'bio_ppl']\n", - "bound_new_capacity_lo ['bio_ppl' 'eth_bio']\n", - "bound_activity_lo ['bio_ppl' 'biomass_i' 'eth_bio' 'bio_hpl' 'biomass_rc' 'biomass_t_d'\n", - " 'biomass_nc' 'bio_istig' 'gas_bio']\n", - "initial_activity_lo ['biomass_i' 'biomass_rc' 'biomass_imp' 'biomass_exp' 'bio_ppl']\n", - "growth_activity_lo ['biomass_i' 'biomass_rc' 'biomass_imp' 'biomass_exp' 'bio_ppl']\n", - "soft_activity_lo ['bio_extr_mpen' 'bio_hpl' 'bio_istig' 'bio_istig_ccs' 'bio_ppl'\n", - " 'bio_ppl_co2scr' 'biomass_i' 'biomass_rc' 'biomass_t_d' 'eth_bio'\n", - " 'eth_bio_ccs' 'gas_bio' 'h2_bio' 'h2_bio_ccs' 'landfill_mechbio'\n", - " 'liq_bio' 'liq_bio_ccs']\n", - "abs_cost_activity_soft_lo ['bio_extr_mpen']\n", - "level_cost_activity_soft_lo ['bio_hpl' 'bio_istig' 'bio_istig_ccs' 'bio_ppl' 'bio_ppl_co2scr'\n", - " 'biomass_i' 'biomass_rc' 'biomass_t_d' 'eth_bio' 'eth_bio_ccs' 'gas_bio'\n", - " 'h2_bio' 'h2_bio_ccs' 'landfill_mechbio' 'liq_bio' 'liq_bio_ccs']\n" - ] - } - ], - "source": [ - "for k,v in lo_dict.items():\n", - " print(k,v)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 12, - "outputs": [ - { - "data": { - "text/plain": "", - "application/javascript": "if (typeof IPython !== 'undefined') { IPython.OutputArea.prototype._should_scroll = function(lines){ return false; }}" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import ixmp\n", - "import message_ix\n", - "\n", - "mp = ixmp.Platform(\"ixmp_dev\")\n", - "df = mp.scenario_list()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 16, - "outputs": [ - { - "data": { - "text/plain": " model scenario scheme \\\n1249 ENGAGE-MAGPIE_SSP2 1000f MESSAGE \n1250 ENGAGE-MAGPIE_SSP2 EN_NPi2020_1000 MESSAGE \n1251 ENGAGE-MAGPIE_SSP2 EN_NPi2020_1000_step1 MESSAGE \n1252 ENGAGE-MAGPIE_SSP2 EN_NPi2020_1000_step2 MESSAGE \n1253 ENGAGE-MAGPIE_SSP2 EN_NPi2020_1000f MESSAGE \n... ... ... ... \n1319 ENGAGE-MAGPIE_SSP2 npi_low_dem_scen2 MESSAGE \n1320 ENGAGE-MAGPIE_SSP2 orgCode_1000f MESSAGE \n1321 ENGAGE-MAGPIE_SSP2 orgCode_1000f_slackLandUp MESSAGE \n1322 ENGAGE-MAGPIE_SSP2 orgCode_baseline MESSAGE \n1323 ENGAGE-MAGPIE_SSP2 test to confirm MACRO converges MESSAGE \n\n is_default is_locked cre_user cre_date \\\n1249 1 0 steinhauser 2023-05-05 13:33:33.000000 \n1250 1 0 steinhauser 2023-07-24 17:05:34.000000 \n1251 1 0 steinhauser 2023-07-24 16:20:13.000000 \n1252 1 0 steinhauser 2023-07-24 16:47:04.000000 \n1253 1 0 steinhauser 2023-07-24 19:49:23.000000 \n... ... ... ... ... \n1319 1 0 steinhauser 2023-07-12 11:09:57.000000 \n1320 1 1 steinhauser 2023-05-13 19:37:52.000000 \n1321 1 0 steinhauser 2023-05-16 09:18:19.000000 \n1322 1 0 steinhauser 2023-05-15 13:04:26.000000 \n1323 1 0 fricko 2023-07-06 07:34:01.000000 \n\n upd_user upd_date lock_user \\\n1249 steinhauser 2023-05-12 17:07:36.000000 None \n1250 steinhauser 2023-07-24 17:12:10.000000 None \n1251 steinhauser 2023-07-24 16:32:50.000000 None \n1252 steinhauser 2023-07-24 16:50:29.000000 None \n1253 steinhauser 2023-07-24 20:05:26.000000 None \n... ... ... ... \n1319 steinhauser 2023-07-12 11:19:35.000000 None \n1320 None None steinhauser \n1321 None None None \n1322 steinhauser 2023-05-15 13:43:43.000000 None \n1323 fricko 2023-07-06 07:36:41.000000 None \n\n lock_date \\\n1249 None \n1250 None \n1251 None \n1252 None \n1253 None \n... ... \n1319 None \n1320 2023-05-13 20:47:53.000000 \n1321 None \n1322 None \n1323 None \n\n annotation version \n1249 clone Scenario from 'ENGAGE-MAGPIE_SSP2|baseli... 1 \n1250 clone Scenario from 'ENGAGE-MAGPIE_SSP2|EN_NPi... 1 \n1251 clone Scenario from 'ENGAGE-MAGPIE_SSP2|NPi202... 1 \n1252 clone Scenario from 'ENGAGE-MAGPIE_SSP2|EN_NPi... 1 \n1253 clone Scenario from 'ENGAGE-MAGPIE_SSP2|NPi202... 1 \n... ... ... \n1319 clone Scenario from 'ENGAGE-MAGPIE_SSP2|NPi202... 1 \n1320 clone Scenario from 'ENGAGE-MAGPIE_SSP2|orgCod... 1 \n1321 clone Scenario from 'ENGAGE-MAGPIE_SSP2|orgCod... 1 \n1322 clone Scenario from 'JST_test|ENGAGE_baseline_... 2 \n1323 clone Scenario from 'ENGAGE-MAGPIE_SSP2|baseli... 2 \n\n[75 rows x 13 columns]", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
modelscenarioschemeis_defaultis_lockedcre_usercre_dateupd_userupd_datelock_userlock_dateannotationversion
1249ENGAGE-MAGPIE_SSP21000fMESSAGE10steinhauser2023-05-05 13:33:33.000000steinhauser2023-05-12 17:07:36.000000NoneNoneclone Scenario from 'ENGAGE-MAGPIE_SSP2|baseli...1
1250ENGAGE-MAGPIE_SSP2EN_NPi2020_1000MESSAGE10steinhauser2023-07-24 17:05:34.000000steinhauser2023-07-24 17:12:10.000000NoneNoneclone Scenario from 'ENGAGE-MAGPIE_SSP2|EN_NPi...1
1251ENGAGE-MAGPIE_SSP2EN_NPi2020_1000_step1MESSAGE10steinhauser2023-07-24 16:20:13.000000steinhauser2023-07-24 16:32:50.000000NoneNoneclone Scenario from 'ENGAGE-MAGPIE_SSP2|NPi202...1
1252ENGAGE-MAGPIE_SSP2EN_NPi2020_1000_step2MESSAGE10steinhauser2023-07-24 16:47:04.000000steinhauser2023-07-24 16:50:29.000000NoneNoneclone Scenario from 'ENGAGE-MAGPIE_SSP2|EN_NPi...1
1253ENGAGE-MAGPIE_SSP2EN_NPi2020_1000fMESSAGE10steinhauser2023-07-24 19:49:23.000000steinhauser2023-07-24 20:05:26.000000NoneNoneclone Scenario from 'ENGAGE-MAGPIE_SSP2|NPi202...1
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1319ENGAGE-MAGPIE_SSP2npi_low_dem_scen2MESSAGE10steinhauser2023-07-12 11:09:57.000000steinhauser2023-07-12 11:19:35.000000NoneNoneclone Scenario from 'ENGAGE-MAGPIE_SSP2|NPi202...1
1320ENGAGE-MAGPIE_SSP2orgCode_1000fMESSAGE11steinhauser2023-05-13 19:37:52.000000NoneNonesteinhauser2023-05-13 20:47:53.000000clone Scenario from 'ENGAGE-MAGPIE_SSP2|orgCod...1
1321ENGAGE-MAGPIE_SSP2orgCode_1000f_slackLandUpMESSAGE10steinhauser2023-05-16 09:18:19.000000NoneNoneNoneNoneclone Scenario from 'ENGAGE-MAGPIE_SSP2|orgCod...1
1322ENGAGE-MAGPIE_SSP2orgCode_baselineMESSAGE10steinhauser2023-05-15 13:04:26.000000steinhauser2023-05-15 13:43:43.000000NoneNoneclone Scenario from 'JST_test|ENGAGE_baseline_...2
1323ENGAGE-MAGPIE_SSP2test to confirm MACRO convergesMESSAGE10fricko2023-07-06 07:34:01.000000fricko2023-07-06 07:36:41.000000NoneNoneclone Scenario from 'ENGAGE-MAGPIE_SSP2|baseli...2
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" - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df[df[\"model\"].str.contains(\"MAGPIE\")][\"model\"].unique()\n", - "df[df[\"model\"]==\"ENGAGE-MAGPIE_SSP2\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 17, - "outputs": [], - "source": [ - "scen = message_ix.Scenario(mp, \"ENGAGE-MAGPIE_SSP2\", \"baseline\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 19, - "outputs": [], - "source": [ - "df_lu_old = scen.par(\"land_use\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 22, - "outputs": [ - { - "data": { - "text/plain": " value\nnode land_scenario year \nR11_AFR BIO00GHG000 1990 0.7953\n 1995 0.7953\n 2000 0.7951\n 2005 0.7975\n 2010 0.8051\n... ...\nR11_WEU BIO45GHG600 2070 0.7248\n 2080 0.7245\n 2090 0.7221\n 2100 0.7225\n 2110 0.7225\n\n[18480 rows x 1 columns]", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
value
nodeland_scenarioyear
R11_AFRBIO00GHG00019900.7953
19950.7953
20000.7951
20050.7975
20100.8051
............
R11_WEUBIO45GHG60020700.7248
20800.7245
20900.7221
21000.7225
21100.7225
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18480 rows × 1 columns

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" - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_lu_old.groupby([\"node\", \"land_scenario\", \"year\"]).sum()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 10, - "outputs": [ - { - "data": { - "text/plain": "Index(['node', 'land_scenario', 'year_all', 'land_type', 'Value'], dtype='object')" - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_lu_act = gdxpds.to_dataframe(gdx_file, \"land_use\")[\"land_use\"]\n", - "df_lu_act.columns" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 11, - "outputs": [ - { - "data": { - "text/plain": " Value\nnode land_scenario year_all \nR12_AFR BIO00GHG000 1990 0.7945\n 1995 0.7945\n 2000 0.7943\n 2005 0.7941\n 2010 0.8016\n... ...\nR12_WEU BIO45GHG990 2070 0.7251\n 2080 0.7249\n 2090 0.7231\n 2100 0.7216\n 2110 0.7216\n\n[20160 rows x 1 columns]", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Value
nodeland_scenarioyear_all
R12_AFRBIO00GHG00019900.7945
19950.7945
20000.7943
20050.7941
20100.8016
............
R12_WEUBIO45GHG99020700.7251
20800.7249
20900.7231
21000.7216
21100.7216
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20160 rows × 1 columns

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" - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_lu_act.groupby([\"node\", \"land_scenario\", \"year_all\"]).sum()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 57, - "outputs": [], - "source": [ - "df_lu_act = gdxpds.to_dataframe(gdx_file, \"LAND\")\n", - "df_lu_out = gdxpds.to_dataframe(gdx_file, \"land_output\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 58, - "outputs": [], - "source": [ - "df_lu_out = df_lu_out[\"land_output\"]\n", - "df_lu_act = df_lu_act[\"LAND\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 59, - "outputs": [], - "source": [ - "df_lu_act = df_lu_act[df_lu_act.columns[:-4]]\n", - "df_lu_act = df_lu_act[df_lu_act[\"Level\"] != 0]\n", - "df_lu_out = df_lu_out.set_index([\"node\", \"land_scenario\", \"year_all\"])\n", - "df_lu_act = df_lu_act.set_index([\"node\", \"land_scenario\", \"year_all\"])\n", - "df_lu = df_lu_act.join(df_lu_out)\n", - "df_lu[\"value\"] = df_lu[\"Level\"] * df_lu[\"Value\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 68, - "outputs": [], - "source": [ - "df_matrix = pd.read_csv(\"C:/Users\\maczek\\PycharmProjects\\message_data\\data\\globiom/magpie_input_MP00BD1BI00.csv\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 70, - "outputs": [], - "source": [ - "data_swapped = {\n", - " 'SubSaharanAfrica': 'R12_AFR',\n", - " 'PlannedAsiaChina': 'R12_RCPA',\n", - " 'ChinaReg': 'R12_CHN',\n", - " 'CentralEastEurope': 'R12_EEU',\n", - " 'FormerSovietUnion': 'R12_FSU',\n", - " 'LatinAmericaCarib': 'R12_LAM',\n", - " 'MidEastNorthAfrica': 'R12_MEA',\n", - " 'NorthAmerica': 'R12_NAM',\n", - " 'PacificOECD': 'R12_PAO',\n", - " 'OtherPacificAsia': 'R12_PAS',\n", - " 'SouthAsia': 'R12_SAS',\n", - " 'WesternEurope': 'R12_WEU'\n", - "}\n" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 77, - "outputs": [], - "source": [ - "df_matrix = df_matrix.drop([\"SSPscen\", \"SDGscen\"], axis=1)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 80, - "outputs": [], - "source": [ - "df_matrix[\"land_scenario\"] = df_matrix[\"BIOscen\"] + df_matrix[\"GHGscen\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 82, - "outputs": [], - "source": [ - "df_matrix = df_matrix.drop([\"BIOscen\", \"GHGscen\"], axis=1)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 87, - "outputs": [], - "source": [ - "df_matrix = df_matrix.melt(id_vars=[\"Region\", \"Variable\", \"Unit\", \"land_scenario\"], var_name=\"year_all\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 88, - "outputs": [], - "source": [ - "df_matrix = df_matrix.replace({'Region': data_swapped})" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 93, - "outputs": [], - "source": [ - "df_matrix = df_matrix.rename({\"Region\":\"node\", \"Variable\":\"commodity\"}, axis=1)\n", - "df_matrix = df_matrix.drop(\"Unit\", axis=1)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 97, - "outputs": [], - "source": [ - "df_matrix = df_matrix.set_index([\"node\", \"land_scenario\", \"year_all\"])" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 112, - "outputs": [], - "source": [ - "df_lu = df_lu_act.join(df_matrix)#.swaplevel(0,2).loc[\"2035\"]#.groupby([\"node\", \"year_all\"]).sum()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 114, - "outputs": [], - "source": [ - "df_lu[\"val_eff\"] = df_lu[\"Level\"] * df_lu[\"value\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 137, - "outputs": [], - "source": [ - "indic = \"Biodiversity|BII\"" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "legend1 = []\n", - "import matplotlib.pyplot as plt\n", - "fig,ax = plt.subplots(figsize=(15,10))\n", - "for i, reg in df_lu.index.get_level_values(0).unique()[:6]:\n", - " df_temp = df_lu.loc[reg]\n", - " df_temp[(df_temp[\"commodity\"]==indic)][\"val_eff\"].groupby([\"year_all\"]).sum().plot(x=\"year_all\")\n", - " legend.append(reg)\n", - "ax.legend(legend, loc=2, ncol=3)" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "is_executing": true - } - } - }, - { - "cell_type": "code", - "execution_count": 1, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'df_lu' is not defined", - "output_type": "error", - "traceback": [ - "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[1;31mNameError\u001B[0m Traceback (most recent call last)", - "Input \u001B[1;32mIn [1]\u001B[0m, in \u001B[0;36m\u001B[1;34m()\u001B[0m\n\u001B[0;32m 2\u001B[0m \u001B[38;5;28;01mimport\u001B[39;00m \u001B[38;5;21;01mmatplotlib\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mpyplot\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m \u001B[38;5;21;01mplt\u001B[39;00m\n\u001B[0;32m 3\u001B[0m fig,ax \u001B[38;5;241m=\u001B[39m plt\u001B[38;5;241m.\u001B[39msubplots(figsize\u001B[38;5;241m=\u001B[39m(\u001B[38;5;241m15\u001B[39m,\u001B[38;5;241m10\u001B[39m))\n\u001B[1;32m----> 4\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m i, reg \u001B[38;5;129;01min\u001B[39;00m \u001B[43mdf_lu\u001B[49m\u001B[38;5;241m.\u001B[39mindex\u001B[38;5;241m.\u001B[39mget_level_values(\u001B[38;5;241m0\u001B[39m)\u001B[38;5;241m.\u001B[39munique()[:\u001B[38;5;241m6\u001B[39m]:\n\u001B[0;32m 5\u001B[0m df_temp \u001B[38;5;241m=\u001B[39m df_lu\u001B[38;5;241m.\u001B[39mloc[reg]\n\u001B[0;32m 6\u001B[0m df_temp[(df_temp[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mcommodity\u001B[39m\u001B[38;5;124m\"\u001B[39m]\u001B[38;5;241m==\u001B[39mindic)][\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mval_eff\u001B[39m\u001B[38;5;124m\"\u001B[39m]\u001B[38;5;241m.\u001B[39mgroupby([\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124myear_all\u001B[39m\u001B[38;5;124m\"\u001B[39m])\u001B[38;5;241m.\u001B[39msum()\u001B[38;5;241m.\u001B[39mplot(x\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124myear_all\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n", - "\u001B[1;31mNameError\u001B[0m: name 'df_lu' is not defined" - ] - }, - { - "data": { - "text/plain": "
", - "image/png": 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- }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "legend1 = []\n", - "import matplotlib.pyplot as plt\n", - "fig,ax = plt.subplots(figsize=(15,10))\n", - "for i, reg in df_lu.index.get_level_values(0).unique()[6:]:\n", - " df_temp = df_lu.loc[reg]\n", - " df_temp[(df_temp[\"commodity\"]==indic)][\"val_eff\"].groupby([\"year_all\"]).sum().plot(x=\"year_all\")\n", - " legend.append(reg)\n", - "ax.legend(legend, loc=2, ncol=3)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 121, - "outputs": [ - { - "data": { - "text/plain": "node land_scenario year_all\nR12_AFR BIO00GHG000 2035 0.167425\n 2040 0.295972\n 2045 0.394696\n 2050 0.471256\n 2055 0.528590\n ... \nR12_WEU BIO00GHG990 2070 0.036868\n 2080 0.022109\n 2090 0.013248\n 2100 0.007938\n BIO05GHG050 2020 0.759600\nName: val_eff, Length: 305, dtype: float64" - }, - "execution_count": 121, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_lu[(df_lu[\"commodity\"]==\"Biodiversity|BII\")][\"val_eff\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 90, - "outputs": [ - { - "data": { - "text/plain": " Level commodity \\\nnode land_scenario year_all \nR12_AFR BIO00GHG000 2035 0.226219 Population \n 2035 0.226219 TCE \n 2035 0.226219 LU_CO2 \n 2035 0.226219 BCA_LandUseChangeEM \n 2035 0.226219 BCA_SavanBurnEM \n... ... ... \nR12_WEU BIO05GHG050 2020 1.000000 Costs|TC \n 2020 1.000000 Emissions|CO2|AFOLU|Agriculture \n 2020 1.000000 Emissions|CO2|AFOLU|Soil Carbon \n 2020 1.000000 Emissions|GHG|AFOLU \n 2020 1.000000 Landuse intensity indicator Tau \n\n level time Value \\\nnode land_scenario year_all \nR12_AFR BIO00GHG000 2035 land_use_reporting year 1605.5862 \n 2035 land_use_reporting year 863.1115 \n 2035 land_use_reporting year 469.4045 \n 2035 land_use_reporting year 58.1282 \n 2035 land_use_reporting year 715.0514 \n... ... ... ... \nR12_WEU BIO05GHG050 2020 land_use_reporting year 5418.4543 \n 2020 land_use_reporting year -5.1668 \n 2020 land_use_reporting year -40.2557 \n 2020 land_use_reporting year 480.2134 \n 2020 land_use_reporting year 2.0366 \n\n value \nnode land_scenario year_all \nR12_AFR BIO00GHG000 2035 363.214205 \n 2035 195.252274 \n 2035 106.188246 \n 2035 13.149707 \n 2035 161.758257 \n... ... \nR12_WEU BIO05GHG050 2020 5418.454300 \n 2020 -5.166800 \n 2020 -40.255700 \n 2020 480.213400 \n 2020 2.036600 \n\n[39628 rows x 6 columns]", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
LevelcommodityleveltimeValuevalue
nodeland_scenarioyear_all
R12_AFRBIO00GHG00020350.226219Populationland_use_reportingyear1605.5862363.214205
20350.226219TCEland_use_reportingyear863.1115195.252274
20350.226219LU_CO2land_use_reportingyear469.4045106.188246
20350.226219BCA_LandUseChangeEMland_use_reportingyear58.128213.149707
20350.226219BCA_SavanBurnEMland_use_reportingyear715.0514161.758257
...........................
R12_WEUBIO05GHG05020201.000000Costs|TCland_use_reportingyear5418.45435418.454300
20201.000000Emissions|CO2|AFOLU|Agricultureland_use_reportingyear-5.1668-5.166800
20201.000000Emissions|CO2|AFOLU|Soil Carbonland_use_reportingyear-40.2557-40.255700
20201.000000Emissions|GHG|AFOLUland_use_reportingyear480.2134480.213400
20201.000000Landuse intensity indicator Tauland_use_reportingyear2.03662.036600
\n

39628 rows × 6 columns

\n
" - }, - "execution_count": 90, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_matrix[df_matrix[\"commodity\"]==\"Biodiversity|BII\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 66, - "outputs": [ - { - "data": { - "text/plain": "Empty DataFrame\nColumns: [Level, commodity, level, time, Value, value]\nIndex: []", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
LevelcommodityleveltimeValuevalue
land_scenarioyear_all
\n
" - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#df_afr = df_lu.loc[\"R12_AFR\"]\n", - "df_afr[(df_afr[\"commodity\"]==\"Biodiversity|BII\")]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 50, - "outputs": [ - { - "data": { - "text/plain": "node year_all\nR12_AFR 2020 494.475077\n 2025 475.040000\n 2030 458.300000\n 2035 441.650000\n 2040 425.000000\n ... \nR12_WEU 2070 82.237854\n 2080 75.834860\n 2090 70.045717\n 2100 64.567990\n 2110 59.274784\nName: value, Length: 168, dtype: float64" - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_lu[(df_lu[\"commodity\"]==\"bioenergy\") & (df_lu[\"level\"]==\"land_use\")][\"value\"].groupby([\"node\", \"year_all\"]).sum()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 3, - "outputs": [], - "source": [ - "df = gdxpds.to_dataframe(gdx_file, \"inv_cost\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [ - { - "ename": "KeyError", - "evalue": "'inv_cost'", - "output_type": "error", - "traceback": [ - "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[1;31mKeyError\u001B[0m Traceback (most recent call last)", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3621\u001B[0m, in \u001B[0;36mIndex.get_loc\u001B[1;34m(self, key, method, tolerance)\u001B[0m\n\u001B[0;32m 3620\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m-> 3621\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_engine\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_loc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcasted_key\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 3622\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m err:\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx:136\u001B[0m, in \u001B[0;36mpandas._libs.index.IndexEngine.get_loc\u001B[1;34m()\u001B[0m\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx:163\u001B[0m, in \u001B[0;36mpandas._libs.index.IndexEngine.get_loc\u001B[1;34m()\u001B[0m\n", - "File \u001B[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5198\u001B[0m, in \u001B[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001B[1;34m()\u001B[0m\n", - "File \u001B[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5206\u001B[0m, in \u001B[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001B[1;34m()\u001B[0m\n", - "\u001B[1;31mKeyError\u001B[0m: 'inv_cost'", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001B[1;31mKeyError\u001B[0m Traceback (most recent call last)", - "Input \u001B[1;32mIn [5]\u001B[0m, in \u001B[0;36m\u001B[1;34m()\u001B[0m\n\u001B[1;32m----> 1\u001B[0m df\u001B[38;5;241m=\u001B[39m\u001B[43mdf\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43minv_cost\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3505\u001B[0m, in \u001B[0;36mDataFrame.__getitem__\u001B[1;34m(self, key)\u001B[0m\n\u001B[0;32m 3503\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcolumns\u001B[38;5;241m.\u001B[39mnlevels \u001B[38;5;241m>\u001B[39m \u001B[38;5;241m1\u001B[39m:\n\u001B[0;32m 3504\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_getitem_multilevel(key)\n\u001B[1;32m-> 3505\u001B[0m indexer \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcolumns\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_loc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mkey\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 3506\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m is_integer(indexer):\n\u001B[0;32m 3507\u001B[0m indexer \u001B[38;5;241m=\u001B[39m [indexer]\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3623\u001B[0m, in \u001B[0;36mIndex.get_loc\u001B[1;34m(self, key, method, tolerance)\u001B[0m\n\u001B[0;32m 3621\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_engine\u001B[38;5;241m.\u001B[39mget_loc(casted_key)\n\u001B[0;32m 3622\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m err:\n\u001B[1;32m-> 3623\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m(key) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01merr\u001B[39;00m\n\u001B[0;32m 3624\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m:\n\u001B[0;32m 3625\u001B[0m \u001B[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001B[39;00m\n\u001B[0;32m 3626\u001B[0m \u001B[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001B[39;00m\n\u001B[0;32m 3627\u001B[0m \u001B[38;5;66;03m# the TypeError.\u001B[39;00m\n\u001B[0;32m 3628\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_check_indexing_error(key)\n", - "\u001B[1;31mKeyError\u001B[0m: 'inv_cost'" - ] - } - ], - "source": [ - "df=df[\"inv_cost\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 14, - "outputs": [ - { - "data": { - "text/plain": " node tec year_all Value\n0 R12_AFR LNG_exp 1995 235.0\n1 R12_AFR LNG_exp 2000 235.0\n2 R12_AFR LNG_exp 2005 235.0\n3 R12_AFR LNG_exp 2010 235.0\n4 R12_AFR LNG_exp 2015 235.0\n... ... ... ... ...\n66344 R12_CHN meth_exp 2070 235.0\n66345 R12_CHN meth_exp 2080 235.0\n66346 R12_CHN meth_exp 2090 235.0\n66347 R12_CHN meth_exp 2100 235.0\n66348 R12_CHN meth_exp 2110 235.0\n\n[66349 rows x 4 columns]", - "text/html": "
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nodetecyear_allValue
0R12_AFRLNG_exp1995235.0
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66344R12_CHNmeth_exp2070235.0
66345R12_CHNmeth_exp2080235.0
66346R12_CHNmeth_exp2090235.0
66347R12_CHNmeth_exp2100235.0
66348R12_CHNmeth_exp2110235.0
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66349 rows × 4 columns

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" - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 7, - "outputs": [], - "source": [ - "df.year_all = df.year_all.astype(\"int\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 17, - "outputs": [], - "source": [ - "df_nam = df[df[\"node\"]==\"R12_NAM\"].set_index([\"tec\", \"year_all\"])" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 26, - "outputs": [], - "source": [ - "df_merge = df.set_index([\"tec\", \"year_all\"]).merge(df_nam, left_index=True, right_index=True)\n", - "df_merge[\"ratio\"]=df_merge[\"Value_x\"] / df_merge[\"Value_y\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 33, - "outputs": [], - "source": [ - "df_merge = df_merge.reset_index()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 38, - "outputs": [ - { - "data": { - "text/plain": "(2018.0, 2055.0)" - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": "
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61dZWTCCwLjpthNHNmRRWa1rHlq9ol2OXcV/WDCwWt3Q5CiGGPAlTQgwDSiksFjcWi/uI47zaaR0mGGzo2OIV7XoMxgWw5uYtBIN1hEJNh3lea1zwSu/U/XioNcxmy4yO95IuRyHE4CNhSgjRhVLmWNDpCcPwEwjWH+pmPMy4r7a2vQQCtV2WAWpnNifHwtWhVq7MuFavzFi95CpHIcRAIWFKCHHcTCY7DnsuDntuj8qHQq2HxnoFaiPdj8FD9yPBax8NgbXRLkfd5RxKWTp2L8aFr9hYL1tmbLyXDLQXQvQXCVNCiBPOYknCYkk67ALX8QwjRDDUEG31qolr7ao51O0YqIm2etV0mbn+0HN6YmO5OrZ8xbV+RffJWC8hRG9ImBJCDGgmkwW7LRO7LRPouuB1vMhA+9ZoN2Nc+ArUEggeut/WtoeGho+7rM/YLjbWq8NA+8zuW7+sGZhM1n545UKIwULClBBiyIgMtE/GYkkGul+TMZ5hhKJze9XETS3RtfWrtXU3gWANhnG46SVSDo316jZ8HWoBM5uTpdVLiCFGwpQQYtgymSzY7VnY7VlHLXtoeonuuhkPDbQ/2oz2JpMtbl6vzI7zecU9jlzhmCatXkIMAhKmhBCiBzpML8Hoo5aPzGhf12WQfbDDgPsaWlq2H2VS1VSs1hTM5iQs5iTMFhdmc1L05sJidmE2J0e3uw6Va79vOXTfZHJIq5gQ/UDClBBC9IPIjPY52O05Ry3bZVLVDq1fNYRCTYTDbYRDrQQCdYTDJYTDrdFb22EX1O6mVtEAloQ5LmQdCl+u6PakaEiLhjbL4UOaUjYJaGLYkzAlhBAJ1ttJVeNprTGMAOFwSyRwhdsIh1sJhVoP3Q8fuh+Obg/FhTF/oDKufFt03ceu01F0X3dLLGDFQpglvuXsUABrD3CH3xa5L12bYrCRMCWEEIOYUgqz2R6dPb5nk6wejdYGhuEjFG4jHOoU0sKthENtsZaxUKeQ1l4mGGzoEOoMw9vj5zeZbLFg1TWkuToFtaRYi5ql8zFx5ZUy9cl7I0R3JEwJIYToQClTrNsPW2afnFPrcFwoayPUXUiLdmV2CGlx4c0fqI47puWwV1d2x2RyxoWr5EgYi7WgdQpnlqRDY9G6K2dJku5N0YGEKSGEEP1OKXOsK7OvGEaQcNjboYvzUEhrPdSVGer0OFouMv7sYIfjul/gu7vXY4kLV8mdWsySO1woYOnSepbU8X40uErr2eB11DCllPorsAyo0lpP72a/Au4GlgJtwFe01p/2dUWFEEKIeCaTFZPJitXq6ZPzRcaf+bqMOevYvdnSccxZqGMrWjBQ2+Hx4Wbk706XQBbXtdnxcceA1vHigGQslvarN2198r6Io+tJy9SjwL3AY4fZfz4wIXo7FfhT9KcQQggxaETGnzkxm53Y+qh7s3PrWSg2vqy1SyiLPG6JdXeGwq0EAjUdHvfu4gBrl4sCOocwk8mGUhZMyoJqv5msKGWObovcVyYLpvb7yooyWaJl2rfFH2eNnitS1mQ6dL/9Odq3DRVHDVNa63eVUqOPUORi4DGttQY+UkqlKqXytNblfVVJIYQQYjDqr9azrqHsUAjrfgxa++PIFByhWMtZEK2DaB3qxRQbfUV1Clcdb523xQKf6fDb0GaI/TSjsaAwA2Y0ZtAmUGaUtqCVCaLbtDYDJiKxyIQ2FJrIfm0owvrI4+P6YsxUAXAw7nFJdNuAC1PvPb2TmoMtia6GEEKIQSZshAlrAwODMJqwEcYgjKE1IW1E5grTBmGtI2W0gQEd72tNGI2hNQY6WjZSxtAQRqOjPyPbNWEdaYcy0ISJ3G/fFiYywqv9vgYMDQa26C0NjYqeC4zo/fhtmkhIiG/r0rGf0XvqMPtV120djz907kP7Vaeyqmt53c226PZDx6u4bUSDz7HSeGgjQzWRQRUZqpkM1UQ6TZFtqokMmo54hr4IU93FtW7bIJVSNwA3AIwcefTV4oUQQpwYYSOMJhoIMDCMMOH2IBANCgYaIxoYwhhxocCIHqtjX/pGNEC0bzPi9nUODJEAEL1P9H5sW8eb7mZfTwJD7KdWXbdFv5gjPzvej5Q5ni9qU/TWtyLtJjpWYwWYYtuI7TPFykZ/KlA6Wk4RKw+RhBaJK+3PEdmudPzjQ/fjY9GhY6LbdNdtHc8Tfd4Ozxl5rvbH7cdrNIZJETYpwkpF7ptNkccmE2Fz9KfJRMhkwjAp7OYgqaqFNFpIoYU0mkg3msg0GkkPN5JpNJIZbiDdaCIj3IiNULfvc7Ny0mDy0GA68oUTfRGmSoARcY8LgbLuCmqtHwIeApgzZ07POn370MIrJ57opxRCDANGOEwg4KetrRWft422Ni9t3jZ8Ph9enx9fIIDXH8DnD+IPhvAFQ/iDRuRnyCAQ1vhDmoAB/hAEDAgaEDAUgWgXQ1hHg4JWhFHRlgyFjj1W0SDR6X57KNBx9zv8jJy3+7+Le0IB/TP2JT4wdPmpOm4zo1HRbSY0ZhUJEFalMWFEOmxU+3aNWYFJRY4zqfZ9RO+DRWlMJrBEt1lMCrMCsylyP7LNhMUEZpOK7DcpLGZT5L7ZhNVkwmIxYTaZItvNZixmM1aLGbPJhNUaeWyxWLBarFjMJqxWa+Sx1YrVasNsNmMoM14DfIaJtoCJlqBBa1DT5A3S5AvS5A3R7AvS5AvR5A12uN/kC+ILGlgMH6mhGlJDVaQFaskI1JPmb8QRCmAPB7EaIeyhEFYjjC1sYA+HsYYN7KEw1rDGFjKwhjS2kI4+BtNRvsX9FjNNriQak5JpTnLR7EyixeWi2emi2eWi1eWkxemi1eHC63Dgszvx2p347Q78Ngd+mx2/zUHYbMEV9pIRbCAz0EBWsIHMYD0ZgQYyo/czAw1kBerJCDaSHqzHroPd1imgnPhMKfhNqYTN2YSdk6i1Z2BJysKRkos9LQ+VlovKyMGcmYvb4cRNNOT8v8P/H+mLMPUicItS6ikiA88bZbyUEKK/hYIhfL42Wlua8Hp9eL1efH4fXq+PNr8fvz+ANxDEHwjhCwbxBSLBxR8M44+GF39YEwhrAmEIGopANMAEDUVQmwjqyM9Q3C2IiTCR+2FMhFGEo3/3H5kJsEdvRyplREd3GJGbiv6MhgVzNECYVeQve7PS2DAiwUDpLgGhPUTEBwazOrTNYjoUFkwqEh7aA4I5GiDig4JZKSzm6H2TCas5PiyYsLQHBUskJFjMJqwWayRcWKxYLGasVgsWswWr1RbZZjNjtdixWC1YrXasVitmsxmTefAPUDYMTUsgRHN7uPEGI/d9wWjYCdHcFAlETb4gzT4/Tb6WDuWC4e5Ti0mH8IRqyA5Xk6fryAzVMyHQRKqvCU9bK+5WL+5WP56WEG5v9+cImiFogaDVRMiiCFtN+Ow2mtzJNCYl05Lkps2ZRKsjiTaniza7E5/Nhc/mwGtz4LPa8Vns+Kx2/FYbfosdn9WG32onbDn8TPZ2w09GoJEsfy35vlqm+MrJDdST3dRIZrCRjGAjqaFGUsKNJIcbsOnur4o0sBFWaWBKRdsy0c6J6PRMfEmZKHcWeLIxpeWgMnIxZ+ZicyXTH9c49mRqhOXAYiBTKVUC/ASwAmitHwBeJjItwm4iUyN8tR/qKYRIMCMcpq2tFW9rC21eLz6fF6/Xi9fvx+f34/MH8QUC+ALR4BIMR26hcIfWF38YAuGOrS/BaAtMUKtIYNEmQphiQaZjeIkEGKNHXSeW6M1xhDK6Q3ixRMOLJXrfqiI/naYQFqWxKQOrSWMzaawmsJk0NjPYzAq7WUV+WhQOqxm7xYTdasFhteCwW3HarNjtNpx2Ow67HZfTidPpxOl04HIl4XS5sVhl+r+BJBQ24sJPewvQofAT3wIUC0y+aDlvkGZ/CH2UFhyXzYzbYcHjsOJ2mMkxNzLJVoFHV5Nk1JLUUoujpQFHcxPOplZczV6Sm4MktxqYuzm33wrNbgtej51AQTpV6R5qMtIJZuZS6cmnzJFGmSWJMpOdJpOFNmXGa7bgM1vxW22EjhCC2ikjjCMYwB4K4AyHcId8jA41kudtIjfUTE6wicxgM2mhJlKDjXhCTbhCjTjDDdiMBix0PyO+1hYMlYphTkNb09GucRjODHxJWajkTEjJRqXkYMrIxZSZi0pyYzUlfn6unlzNd81R9mvg5j6rkRACgGAgQFtbC15vK942H15fG15vJLh4u2l5ORRejEiXUdg41PpiQCCsDoUXHQkwoWjrS3yA6RpeIi0vPQsvZnrS5aMwsHRqfWkPLxYiAcahQrhNBlalsZoiN5uKBhcT0QATCTEOi4oEF4sZu9WM02aJhBi7FYfNisNuw+Vw4HA4cDocOB0uXMlJuFxJ2Gz2IdECIrrnC4a7BJ3OrUNdWoriwlJb4MhXuCkFyfZIEPI4rbgdFgpSnXic7sg2hwWP04pLt2JpPoi5qQxTYwWqoRpVX4upvh5zfRO2xjacjX7cLWGs3TxlyARNbjNet41AhpuaCR5qM9KwZWXjyMrFnTuC1PzRpOePozoAe0rL2FrbwO42H/sNRbnNRaPr0LgfZRiktTWTEvKTYoTI10GSwl6SQybcZhMpZsgOe8kJtZIZbCE10IzH30iyvxGHvx5roB4VqMMUqsOkGzHR/cVdWpsxVEokHFnS0M6RhB3phFxZkJyJ8uSgUrMxpediyspFuVMxm0z91HHcf+RPICGOwgiHaW5upKG+nsamRhqbm2lqaaO51Uuz10+zL0iLP4Q3eKjr6FCLS9duo1hw0eow4SVy0z0aw9KTlpf2rqND3UcWFT4UXpTGgoHLFIq2xHQML1azPhReTJFWF5vZFAkw0dYXh9WC3WbBYbNit1lx2m04bDacTgcOu0NaX8QxMQxNs/9QuGkfC9QcF4Ka/Z3CkS9Ec/tYIl+IQOjIM5pbTCoWgiKByEK2OznucSQQubvct2DHj6+mmMaK/TSV78BbWYa/tBKjtg5V14i1oRVno4/k5hCObobwGEBzsok2tw1/qhNfQSYNGalYMrNwZOeSnDuClLyRZBSMIyWzEFNcC0yr38+mg6VsrKxiR2Mze1tCHNzVSlXJPgK2aFeyJRWb0092WzNTAy2MVT4mpbiZkZ3JdH8jtp2b0PUHoa0G5a9DBWoxhepRRgMm3YxSXZu9tDZhKA+GKRKOwslTCNkzICkTkrNQnmxUajYqLQdzZh4qNWNQhqPekt9oYkhpDz71dXU0NjXSFA0+TW1emr0Bmr1BWvxhWgNhWoOatpDCG1Z4wya8hgm/Ycavzfi1hYA2EyRy6z7YHG78y5G7jdpvDpPG2h5eol1HnbuNbCai4aW928iM3RoNL51aXhx2e7TVxYHT6YoEGFcSTqdLWl7ECae1xh8yDrUGxbUONfsOdZc1d2opin/cEjh6F5nTGukic0dbgFKcVkakOaPBxxrb7okLS/GtSE6rucMae6FggLqKfdSW7qWpfD9tpWX4qyoI1dQSqGugoaEFb6OPtqYgSb5I5cxAWvQG0OpQtHis+FMcNI7PoTE9FUtGOvbsXJJyCvDkjSI9fwxpOaOw2o78h1BJXR2rS0rZurmYXS1eikOaMquDOpcHbTIBDnA6SNHN5AXamOarZ7zJwZT0VGYV5jEuKwsVDBDc9BHG9vdQmz/B0rYec/RSf60VhnKjTakYlnTCrvFoRwY4MyLhyJ2FSs1BpedGutbSszCbzUM+HPWWhCmRMEY4TFNjPfX19TQ2NtDY3EJTaxvNbT6avX5aon91tgYM2qLBpy2s8EWDj+8wwaf7gcAOOrfeKDRWwlgJY1Mh7CqMXYVJMQdwmHw4zQZOsybJqkmyKpJsJpLtFpIdVtxOGx6XE487iRS3mxRPCm6PR7qNxJARChu0+ENxA6O7hp3I42irkf9Q69HRBk63M5tU3FihyM+R6a4OLUXxLUZuR8eA5HZYsJqP3v1sGAYNVQepK99Jw55iSitK2FtVQai2Bl3bgKW+GVtjG67mIO5WI3aVmid6A/DaoMVtwedx0FqQRsuMFMwZGdiysnHm5OPJG0l6/lgy8sdidyb36r0OhEJsLStjY1kl2xqa2OMNcBAzFY5kvA5XpJDyYHa6yGprYnTQy5neAJM8SczIymTWyELSkpIOvd6GOkKfvYPx4hOEqtdg9W/FpiKLQodUAaHUMwiNnIdp+iIsY6Zgth59jJQ4MglTokdCwRBNTfU0NjTQ0NgQ7eryxgWfIM3+cCT4hKLBJ6TwGSZ8hgmvYcGvzQSityAWgoe9AurowcehwthNYdJMARwmbyT4WDQuCyR3CT523ElOUt3JpLiTSUlJJTU1laRkj4QeMSRprWkLhDu0AHUeK9S1lahjd1rrUcYKASTZzLEuL7fDSmayjTGZSbHHHQJRXOtQ+zGdW4V6wzAMmusrqSndTWP5floqS/FVlROsqUbX1mOqb8bW0IqrKYC7JYwl2tuXFL1B5Eq25mQz3hQ7vkw3bRM81GWmY83MxpmTR3JuIWl5Y8goGEdyyvEvL1PT1Mz6gyVsrq5lZ3Mr+4KaMrONmiQPYbMFsII9gyTdSq6vlXn+RsaZfExNS2FGXg6T83Oxmrt+bYdLi/G//Q/0vg8w16/DEtqLTRlobSJknYA/5/OosQuwFJ2BJXeEfPH3A3lPh5j2K66aGusjoaepmaaW1tj4nhZfMHKLtfZovCFFW9iEL6ziWnvM0fBjIYiZ0GEbdQ8ffGwqhI1Ia4/dFCLdFIyEHpOB0wIuiybZZuoSfDxJTjzJSaR63KR4UklNT8flSpLgI4YVrTVNvhBlDV7qWgMdBkW3B59uu8iiP8PGkVuFrGbVpZUnfqyQu9O+9rIp0cfJ9si0B33JMAxaGqqoLdtLQ9m+SECqriBQXRUNSNGB2k1+kpvD2KJ5zxm9ARgKmpNMtHki45DqRmZTHx2H5MyOBCRP/igy8seSklHQYRxSXwiHw+yqrGRDWSXb6hrY3ebngFaU25JodrW3WCWjXE4y2prJD/o4ta2WiW4n0zIzKBpRQF5q6mHPr8NhQjs3EN78Durgx5ibP8OiKzADhnYQckzHX/ANTBNOx1K0EKs7BWl36n8SphLI522jsaGOxsZGGpuaaG5ti+vmCtDiC9IaMDq09njjurn8RtfQc/jxPdborSMzRqTFR4WxE8IWbfFJNgdxmAxc0a4ulxWSrCaS24OPMy74JCWR4nGTlpZGSmq6jNERogd8wTDljT7KGryUNXgP3Y/+LG/wHrF1yG3vGHRyPQ4m5lgPjR+KawFyOzoOnPY4rNgtpmNuFeoNwzBobaqhtnQPDRX7aSk/iLeqgmBNFUY0IFkbWnE2RwKSPToRdfyfaQbQkqRo9dgIeJzU52fQkJ6KOSMjOg4pH09uZKB2WvZILNb+mEmooyavl40HS9lUVc2Oxhb2BUKUKBtVLjdBqw1QYE3D7vSR421mZqCZsSYfk1PdzMzNYXpBHk7bkeccA9A+L8ENqzF2vI+p/BMs3g1YacEKhEkjlDybUP5XMU9diGXaXGzSZZcQEqZ6IBQM0dhQR1NTY5exPS2+SOhp6TS2xxsCr2HCGzZFAk80+ASIjO8JYY5O9NeZCXBFb4cceXyPgcNs4DJrXBaNy6pwWU0k280k2y2Rbi6XA3eSE09yMikeN6kpqXhS07E7jjz4UQjRe6GwQVWzPxaOyqOBqazRR3mjl7IGH3WtgS7HZSbbKUh1MD4rmYUTMilIdZKX4iQz2XYoODmtJNssmEz9H4SOpKWxltrS3TSU76e54kA0IFVj1NYdCkhNfpJbQtijV7LFX7LRHpDa3Db8KU4a8tJpTE/FnJmBIzsXV3YkIKUXjCU9Z/QJCUidGYbBwdo6PistY2tNPbvbvOwPQZnNSb3LDcoEOMFpJ1U3kx/0MsNbxwSTg2kZaRQVFjAyI71XrV9GXTXB9e+id7+PqXot1sA2bCryBoZMIwmmnQ2j5mGeuRjz6EnYB8AcS2KYhak1n3zI6+u2R1p7gjoWfHxhhdcw4zPag4+FAObYoObedHGBxoqBNdrKY4uGHpcpRLrJHxvU7LRokiyKJJsiyWYm2WEhyW7F7XLEDWz2kJLikdYeIQYQrTV1rQHKG32URluQYq1J0Z+VTT4697K5HRbyU5zkpzqYWZhKfoqD/GhYKkh1kpNix25J7P/x1uY6akp201BeTEtlCd7KcgI11Ri1tZjqm7C0B6S4S/1tQEbcOZpdkRYkv8dBw4TUyKX+GRnYs3JIyinAnTuSjIKxPbqS7UTxBYJsKStlY3kV2xua2OMLclBZqHQk47NHOxDNKVicLrLbmpgQbGNMm59JKcnMyM5i1ogCPC7XkZ/kMEIHdhPe8Da6+EPM9Z9iCe/DrjRamwlZJ+HPvRo17jQsRYuxZOcPry/tQWRYfS5vr9/JQ/uzY4/NhLFFu7hsRLq37CqExxrp4nK2t/ZYFUnWyCy18WN7kl0OPMmu2NVcqWlpuN0pEnqEGMRa/CHKG7yRoBRtVSptiLQotYclf6e5i2wWE/kpDvJSnJw2LpP81Mj9/NT2wOTA7UhM90tbSwO1ZXuoL9tHc0Wkiy1QHZkLyVTXhKWxFUdTZC4kZ7SxzAakx52jxalodVvxpThpHJ8au9TfFg1IntwRpOWPIT13NDb7sYWKE6GysZHPDpaypbqWnS1tFAc1pRY7dUkeDJMZsIEjk2TdQp6/lQW+RsabvExNT2VmXi4Tc3MwH8fvdx0KEdr+GeEt76BKPsbS/BkWqrEAhnYScs7En7MU08TTsRSdjjXJLeOdBgmljzaJRz+ZM2eOXrt27Ql9zrraaupqa0lNTSUlNR2r7cQ3GwshEscfClPZ6KessdM4pej90gYvzb6Oq8ebFOR4HOSlOMhLdUa73iIhKT/FSV6qg4wk2wkZf9TO29ZEbeke6kv30lxZgreqHH80IKn6Jqz1LTia/CQ3B2MBqbP2gORPcRBKTUanpWDOjLQguaJjkNLyRpORP3ZAB6TOwuEw28rL2Vheyba6RvZ4/RzQZsrtSbQ6D00fYAqHyWxrIj/kZ4xVMdHtYnpWJkUjCsjyeI7wDD2n21oIbvgAY8d7mCo+weLbhInWSD3JIOSeDQXzME1diGXqHJRlWLVvDDpKqXVa6znd7RtWn1x6RhbpGVmJroYQoh8Yhqa6JTpOqeHQ2KRIUIp0xVU3d10sNc1lJT/VSWGai7lj0mMtSQWpTvJSneS47X1+1drRtLU0ULzxfSo3r6Ftx3Yor8LaEGlBSmoO4fJH/gi20HWyyFa3BV+Kk+YxWTSlRwNSZjau7HzceSNIyxtDZt44bM7BE5C60z7NwJaaOnY1t7IvEKbUbKfG5Y6uLWcGWzoO7SXX28LJ/ibGKR+T0zzMzMtmWkEBtj4OL+GaSkLr30Hvfh9zzVoswR3YVCScB02jCKZ/Dj16AZaZZ2AeOV7GOw0hwypMCSEGJ601jd5gXEiKH9jto6zRS0Wjj1CngUoumznWijQ51xMJSqmO2NilvBQnTlviuuUD/jYObP2Ysk0f0bJ9K+w9QPLBejJqI7Ow5QIBMzSmWfGmOGgelRkJSNHJIl3ZeXjyRkbmQjqGySIHumA4xPayCjaVV7Ktvr2VyUSFPYmW2GtNQjmdpOsm8kI+TvbWMiHJybTMdIoKCyhIS+3z6Q8AtGEQ3r+L8Ma3Yf+HmBo+xWrsx0xksd6gbTL+/C+ixkXmd7Jm5vR5HcTAIWFKCJFw3kCYskYv5Q3t0wNE78d1x3VecNZiUuSmRILRnFFp5KU6o11vjtigbo/TckK73w4nHA5RuuszSjZ+SOP2TYR378N1sIbMKj/WMGQBGQpqMq00j0yn5czReCZPI2/mPMZPPmXADNTuL5WNjWw4WMaWmhp2NbdRHDSirUwewhYL8a1MOb4WZvmbGat8TE5xMz0nm+mF+STZjz7NwPHQoRChLWsxtr4DpR9jaVmPhdrIeCeSCDln4su9BNOk07HOPA2ba2gFW3FkEqaEEP0qEDKobDp0tVt5Y9cuuPq2rqvAZrnt5Kc4mJDt5oyJ2R0Gc+enOslMtmNO8PQAnRmGQU3pLvavf4+6rRsI7t6DY38lGeVtOIKHuuTqUsw0jUjl4JzJuCZOIXfGXMbOXMA0V9+M1RmI2pdM2VRexfb6Rvb4AhzUZio6jGVyo1wuMtqayQv6mOutZXySk2lZacwq6L9Wpu4Yrc2E1r+HsfN9TJVrsHg3YVVeAMJkEfLMIVh4KuZpZ2CZPBubXHg0rEmYEkIcs2A4EpQqGn2xbrf2sBQZ3O2jpqXrOCW3wxIbyF00MrXLoO6BME3A0TTUlFK84T1qNq/Dt3Mn1uJy0staSPZqkoFkoClJUZ/vofTMKTgnTCRz2kmMKVrElPTcRFe/31Q2NPJZSeSKuV3RK+bKLDZqXe1LpljAnoFTt5Hja+VkfxNjlJfJqR6m52b3eDLLvhauKiP02VvovR9grl2HJbgTmwqjtSJkHkMg8wLU6PmYZy3GMnK8LPQrOpAwJYToVtjQVDX7YuOUKhoP3S9r9FHR6KWq2U/nC4KT7RbyUhzkpjiYkuuJjVHKTXGQn+ogN8VJsn3w/Oppa2lg34Z3qdq8lrYd2zEVl5BS0kRaUxg7UEBkEdza/CQqTxlD/YTxpE8tYlTRQqYUjE909ftFIBRic0kpmyoi8zLtbW9lciTR5jjUymRyuchojVwxN6+tlgntS6YU5pOflnbE5+hP2jAI791OeNM76AMfYmn8FItxMDreyUrQNgV/wVdQ4xdgLVqENT1LpigQRzR4fqMJIfpM+5Vv7fMotYej+NalqmZ/l/XdnFZzLBxNnJAVmy6gvVUpN8WBJ0HzKR2vng4Gr81xUDc1n6bxY0idMpMRsxYwaezME9b9dCKV1dezvqSMrTW17Gz2sj+kKbPYqXV5MMxm2hfmdUUX5j3F3xQZy5TqYUZuNlPz83HYEv/vQQeDhDZ/THjbe6jSj7G0rsdCfXS8UzJBVxHh3CtQkxdinTkfm8N51HMKEU/ClBBDjGFoalsDsa629nAUC0wNPiqbul75ZreYIoHI42D+uIzYHEp50QHd+SkDZ0D38ehuMHjSgchgcIvRaTD4qAxazhw1pAeD+wJBNpWWsjnayrTPF+QgZiocyXgd7dMneDC7XGS0NVEQ8rPAW8NEdxJTM9OZXVhATmpKQl9DZ0ZzI6HP3sXY9T6mqjVYfFuwKh9WIKRyCaXMI1g4D/P0M7BMnIldxjuJ4yRhSohBRGtNfVswbjC3t0PrUnmjl8pGP4Fwpxm6zSZyUyLBaO6Y9OhVcJGQlBedIiDNZR30QSlel8Hgu3bjOFDVdTB4qpmmwlQOnDJ0B4MbhkFpfQMbSkvZWlPPrhYvxSFNudVBncvdYfbvJN1Krr+Vef5Gxiovk9NSmJEXaWXq63mZ+kq4tJjQhrdh30eY6tZhCe3GpozIeCfLOAJZF6PGnIZ51hlYCsfIF5/oc/JvSogBIn4upYqmQxNPljf4OgSnzkuZWM2KHE+k6232iDTyZsSNUUrQDN0nWkNNKcWfvUvNlk/x7erZYPCsGXMYPfP0ITUY3Bvws6mkLDKWqbGZfb4gJcpCRfwac8qD2ekiq62JkUEvi9r8THQnMS0rg5mF+eSkDKxWps5iS7JsfRdV8gnm5vVYdAVmwNB2Qvap+PO+hmn86ViKFmFNTZfxTqLfSZgS4gTQWtPsD8XmTipviBujFGtd8uENdpxLyWxS5Ljt5KU6mV6QwjlTc2JrvuWlRMYqZSbbMQ2wKQL6S2tzHcUb3z/iYPA2O9TmJVE5dyz1E8aTMXU2I2ctGDKDwQ3D4GBtHRtKy9laW8euVi/7w1BuibQyaZMZsIPDHrfGXANjlZcpaSnMyMthcn4uVvPg+PVvtDRFpijYtRpT5droFAVtWIEw6YSSZhHK/zLmKQuxTJ+LLQFXAgoxOP43CTHA+YJhSurbOixjEpku4FA3XGunSSdNCrLdDvJSHUzOdXPmpOzY+KT2Qd5Z7oE3l9KJ0NPB4DW5Q3sweIvPx783bWFtdT17/ZFWpqr4ViZTCpZoK9PooJczvYFYK9OswgIyPe7EvoBjcMQuO/Nogpnnwch5mGecgXn0RFmSRQwIEqaE6AWtNZVNfrZVNLGtvInt5c1sr2hiT3VrhyvflILM5Mikk+Ozklk4IfPQQO7o9ADZbjvWE7zm20DTk8Hg6QpqOw0Gz581n/GT5gy5weAAm0tKeG7bbt5p8bE9OSOyzpwzE49uJjfQxum+RsaZIq1Ms/LzmJibg3mQDqDWoRChbesIb30PVfoJ5qb1WKjs2GWX+zXU+NOwFp2BNS0j0VUWolsSpoQ4DF8wzK7KFraVN7Gt4lBwip+tuyDVyZQ8N5+bmsv47OTYDN05Hgc2y/AOSvEMw6CieAslGz+gfttGgnv2HmUw+BSSJk8hd/pcxs04jelDaDB4Z61+Py9v2sarZRV8gp1qdxpYUkm3NLKkrZZz87M4b8ok0pMH//IkR+2ySy4ilP9VzJNPly47MahImBLDntaa8kYf2yua2FbeHGlxqmhmb3UL7Y1NTquZSbluzpuey+RcD1PyPEzKdZPilKGt8QzDoPrgDg5sXE39to0Edu/BdiCynIrLr3EDbqKDwQuig8EnTiJr+slDbjD4kWwvK+PZrbt4p8nL1uR0glYbZmcmE5pruTRQx6WTxzF7VFGiq3ncpMtODBcSpsSw4g2E2Vl5KDC1/2z0HmptGpHuZHKuh6XTc5mS52FynoeR6a5hOXbpSGrK9rB//fvUbltPYPdubPsrSSvveAVdi1NRl59M+YLx2MdPIH1qEaNnnc6UvDGJrv4J5QsEeXXLVl46WMEabaXCkw7mFFJtikVtdZybm8HFM6aRkuQ6+skGKOmyE8NZj8KUUuo84G7ADDystf5Np/1pwF+BcYAPuF5rvbmP6ypEj2mtKW3wsq28me1xwWlfbWts+ROXzczkXDcXzMxjSp6HKbluJua6B+0M3v2lrmI/xRveo3brevy7d2HdX0FqeTOeVo0LcAGtDkVdXhKVp46jfvw40qfMYuSsBUzKHz9kBoP31p7KKv65ZTtvNbSxNSkVv82ByZXJ+OYalvpruWTiWOaMHryD5Y3mxkNddlVrsXg3S5edGLaOGqaUUmbgPuAcoARYo5R6UWu9Na7YD4H1WutLlVKTo+WX9EeFheisLRBie0VzbExT+8DwZn8oVmZUhovJuW4uKsqPdtO5GZHmGjZTCvREbOHeLZ/h270LS3EZqWXNpLQYOIFComvQ5SVRffIYGsaNJW3KzEhoGjF50IaCvhIIhXht6zZe2l/GR4aFck8GKA8eu+I0bz1ne9K5dMY00pNPTnRVj0lPuuz0qNOwzFiEedQE6bITw0pPWqbmAru11nsBlFJPARcD8WFqKvBrAK31dqXUaKVUjta6sq8rLIYvw4i0Nm2Nu4puW3kT++vaYq1NyXYLk3PdXDw7P9JFlxsZ2zSYFtbtb011FRRvfJ/qLZ/i3bUTS3EZnrJG0pqM2FxNPivU5rqomTWSxvFjSJ08kxEzT2PSmOnDPjTF219dwz+3bOfN+mY2O1Px2Z0oVybjmmr5sq+Gi8aPYf64wdf6JF12QvROT75hCoCDcY9LgFM7ldkAXAa8r5SaC4wi8oeshClxTFr8IXZEB4S3DwzfUdFMS7S1SSkYnZHElDwPl51UyORcN1PyPBSmOYf0TN+90dJYS/HG96jasg7vzh2YikvxlDaS3hjGCuQDfkt04d5phTSNG03q5JkUzJzHxHGzMA+SSR1PpGA4xBtbd/DS/lI+CpkoSckEkkm2w1xvA2e7FZfOmEqWZ3C1PkmXnRDHpye/Lbv7ZtKdHv8GuFsptR7YBHwGhDofpJS6AbgBYOTIkb2qqBiaDENzoK6ty5V0B+raYmXcDgtTcj1cflIBk/M8TM51MynXjcsmX/YAbS0NkVnBt6ylded21L5SPKUNZNSHMAN5RCa4rM12UD8pl6bxY0iZNJ2CGfOYMGE2Fqst0S9hQDtYW8ezm7fxZl0TGx0peB0ulDOT0U01fNFbw7KxI1k0cXC1PoVK9hHe8A4Uf4ip7tNOXXZjCGaejx41X7rshOihnnwblQAj4h4XAmXxBbTWTcBXAVSkWWBf9Eancg8BDwHMmTOncyATQ1yzLxgd29TE1miL046KZtqiM4ObFIzOTGJGYQpXzimMjG3K95Cf4pDWJsDb1sT+TR9QsXlNNDQdJLm0nozaECYgBwiZoCbbTuO4bJrHjsIzeTp5M+YO2Qku+0M4HOat7Tv5d3EJHwThgCcDVBIuB8z2NXJ2Mlw6Yyp5qScluqo9crguOwvSZSdEX+lJmFoDTFBKjQFKgauBL8QXUEqlAm1a6wDwH8C70YAlhqGwodlf29olOJXUe2NlUpxWJue6uXLOCKbkuZmc62FijhunbXDO5NyXAt42irdEQlPLzq2w9yBJpXVk1gQx6UOhqTbTRvOoTFrOHIV70lTyps9l3NS5zLAP3svrE6W8oYFnN25lVW0j6x0e2hxJ4MxgZLCWq721XDhmBIsnzRgUM41Ll50QJ95Rw5TWOqSUugVYSWRqhL9qrbcopW6M7n8AmAI8ppQKExmY/rV+rLMYQBq9wQ5TD2yraGZnRXNswV6TgrFZycwemcY1c0cyJS8ytinXI61NkfXnPqF88yc079iK3rufpJI6MmoCsaVUMhTUZlhpKUyjdeFIkidOJXf6KYybdho2p4SmY2UYBu/u3M2/9u5ntR/2ezLQJhdOB8z0NbLEBZdOn8KIjIHf+iRddkIkntI6Mb1tc+bM0WvXrk3Ic4veCxuafTWtbI8uq9I+tqm04VBrU6rLypTo7OCT89xMzfMwPjsZh3Xg/zXfn0LBAAe2f0L5pk9o2r4ZY99+XAdrY+vPARhAXbqF5sJU9JiRJE2cTO70Uxg14zScQ3gplROpuqmJ5zZt4fWqBj6zu2lxRpZnKWysYZ7FYNnoQpZMmYh1AA+816EQoa1rMba9ByWfYG5ej4Uq4FCXnZE1B9P4BViKFmGSLjsh+oxSap3Wek53+wbubw2RMLUtfnZUNEe66SoioWlHRTP+UOSb32xSjMtKYs7oNL6YOyoWnLLd9mHd2hQOhyjZsY7STR/RuGMz4T3FuA7WkFHpwxaGdCK32jQzzQWpHJgziaRJU8ieNofRM05jmjs90S9hSDEMgw927+Vfe4p532ew15OBNjlxOGG6t4GzXHD5tMmMyipKdFWPKPjZ+4Q/eynaZbcJq4r8AXOoy26udNkJkWASpoYxbyDMrqrmWFhqD1A1Lf5YmYwkG1PyPHxp3igm50UmuxyfnYzdMrxbmyDS4rT9o5c5uOrfmNZuImd/E/b4RXtTzDQVeCiZNR7npMlkT4uuP5eSmeiqD1l1LS2R1qfKOj61JtPkcoMjk/xALZe01XDBqHw+N/UUbJaB/atPB/wEXnsC0/qHsYa2Yol12S2VLjshBqCB/RtF9In2AeHtYWlHRTM7KpspjltaxW4xMTHHzeJJWbGpBybluslKHt6tTZ0d3LmOnSv/ge/Dj8neVkmyVzMaKM93UHLmFJyTJpM17aRhtWhvIhmGwZp9+3lh117e94bY7c7EMNuxOdOZ3lrHYqfm0mmTmJBTlOiq9ki4qozgS/dh3b8cO7WEVAH+ST/Eeu71WNOzEl09IcRhSJgaQrTWVEe76OKD066qZnzBSBdd+2SXk3LcXDQrPxacRmUkyUK+3WioKWXLayuof/9tPBv2kVUbIh+o95ionD2S1gULmHbeNUwpGJ/oqg4bja1tPL9pC69V1LLO4qIhyQP2DHL9dSxrq2HpiFzOnVaEcxB1eQU3foTx5h+x1a/EoYIEHHMJzf1fbGdcgmUQXEEoxHAnYWqQavWH2FnZ3KW1qa41ECuTmWxncq6ba08dxaRcN5Nz3UzIlukHjiTgb2Pruy9Q9vYrWNduJf9AK+kaXFYon5SJ75KTGHv2pcybtWhQTdI4mBmGwfoDB3luxx7eaw2yy5NB2GzF6kpnaksdX3ZqLp0ygcn5RYmuaq/oYJDAqhWoTx/CFtiAoe34My/GfM63sE0uSnT1hBC9IGFqgAuFDYqjczbFB6f4GcJdNjMTctycMyUnFpom5brJSB48f5knimEY7N30Hntff47Qx+vI3VGDMwCjFJSNcFF8ycnkLT6P6YsuY7ZMRXDCNHm9/HvTVl4tq2at2UldcgrY0sny13NeWw3n5eeydMYskuyD79+4UVtF8KU/Yd77BHaqCKlcfOPvwLb0GzikK0+IQUnC1AChtaayyR+bFbw9OO2ubiEQvYrOpGBMZhIzClL4/MmFseA0Is2FSbroeqymbA9bV66gafV7pG06SHpjmBFATbqFsgXjST39DKZ97iqmZY046rlE39l0sIRnt+3i3RY/O9wZhCxWLEkZTG6p5ZqQ5tIp45leWJToah6z4LZPMd74I9aal7ArPwHbbPxzfobtrCuxDPAB8UKII5P/wQnQ7Auys7K5S2tTozcYK5PjsTMp18PpEzKZlBNpaZI5m46Nt62Jzauepuqd13F8tpP8Uh9ZgMuhqJySjXfeKUz43BUsnDI30VUdVlr9/kjrU2kla0wOapJTwZpGhrWBJW21nJ+fxdLp0/E4nYmu6jHT4TCBt/6JWvMgNv9atLbiT1+K+aybsc3ovF68EGKwkjDVj4Jhg73VrV1am+Inuky2W5iYk8zSGXmx7rnJuW5SXbL47LEyDIOda16j+I0XYM0G8nbXkxwChwnKxrgpvvokCs9cRtFpF8givyfYpoMlvLBjN+80+dienE7QasPsymRiSy2XB+u4ZNI4Zo8qSnQ1j5vRWEfgpYew7Pobdl1GmEx8Y76NdelNOLLkKk8hhhoJU31Aa01Zo48dcRNc7qhoZk91C8FwZO4Bi0kxNiuJk0al8YVTR8ZamwrTnDL1QB8o37eZ7a89TevqD8jaUoanVTMKqMy2cfCsKWQuWsL0c65ihszxdEI1tbXx783bWFlezbr21idzKqm2Rs5oq+PcvEwunjEVj2tojEcL7t6CsfIerFUv4FBeAtbp+Iu+h+2ca3AMoqsLhRC9I2Gqlxq9wWhYaupwFV2zLxQrk5/iYFKum8WTsmOtTeOykrFZ5OqvvtLSWMPm11dQ8+4qktfvIacqQC7QlKSonpZP62nzmXLuVUwZMz3RVR1WDMNgbfF+Xty1j/dbg+xyZxC2WLC4MpjUHGl9unjiOIpGzhwyV0NqwyD43r/goz9h836I1hYCKedgWnwztpMWJrp6QogTQMLUYfhDYfZUtbKjsmNrU3mjL1bG47AwOdfDxUX5TMr1MDnXzcQcNylOawJrPjSFggG2ffASJav+hWndZvL3NZNigNMC5ePT2H/uaYw++2LmzDkb8wBeW20oqmlq5oXNW3mjqo5PLUk0JnnAlk62v55zvTWcm5/N+dMG99in7hgtTQRe/jOW7Y9iMw4QJg3fiBuxXnAL9ly5eEGI4WTYf+sYhqa0wRsNTIeC096aVsJGpIvOZjYxLjuZeWMzYjODT851k+txSBddP9q/7RN2vfYP/B+tIWdbFUm+yGzjZQUODiydSfYZ5zB9yZXMkoWAT6hwOMx7u/bw0t79fODX7PNkYJgis45Pba3nDKfmkskTmFJQlOiq9otQ8U5Cr9yDrfI5HLQQNE/EP/t32M69DodjaAVGIUTPDKsw1eQLsqW0iR0VTeyIXk23s6KZ1kA4VmZEupNJOR7OnZYbC02jM5OwmodGl8RA1lBTyuaVy2l4/x1SNhSTWReiAKj3mKmYMwrPgoVMPfcqpuSPS3RVh52y+nqe27SNVbWNbLB7aHUmgTOL/GAtF7XVcN6IPM6dOrhmHe8NbRgEP1yJXn0/ttb3MQMB95moRbdgnbMYNUS6LIUQx2ZYhakX1pfxo+c3A5DmsjIp180Vc0bEWpsm5rhJtg+rtyShAt42Nr/7LOVvv4pt3TbyD7aRocFlg4pJmXgvO5mx51zKvBkLh8z4msEiGA6xattOXt5fwkdBxQFPBigXTqdihreBxS7NJVMnMTa7KNFV7Ve6rYXAK49g2vJXbMZewnjwF3wVy9JvYS8ck+jqCSEGiGGVHM6eks2o9LlMznWT5ZYFfE80wzDYu+Fd9r7xHKGPPyV3Z2S28dEKykYmsf+yU8hffD7TF13MSfahcXXXYLK3qprntmzn7foWNrlS8dmd4MxkVLCWq9pquGD0CM6cMh3rMBiTFirZR+jlP2It+wd2mgiaxuKf8T/Yzv8qDldyoqsnhBhghv5vxTh5KU7yUmRMw4lUXbqbLa8up2X1atI3HyStyWAEUJ1hoez0CaSdvjgy23hmQaKrOux4A35WbtnOKyUVfGJYKPdkAG6SHYo53gbOStJcMn0K+WknJbqqJ4Q2DIJr30a/dx+2pjcxowkkLSS84Cas88+VrjwhxGENqzAl+l9bSwOb3/wH1e+8gfOzneSV+cgBkh2Kyqk5eOefysRzr2DKxJMTXdVhaUtJKc9v38W7TT62JacRsNoxuTIZ21TDl301LBs7igUThs60BT2hfV4CKx/DtOkv2EI7MEjCn3stlvO+hX3MpERXTwgxCEiYEsclHA6xc83r7H/jeVizkbw9DbhD4DRB2VgPxVefzIglyyiat1RmG0+AJq+XlzdvZWVZNWuVnWp3WnTSzCYWttVxdk4GF06bQqZn+IXbcMVBgi/di/XgCuzUEzKNxDf1x9jO/w8c7pREV08IMYhImBK9VrZ3E9tXrqDtw486zjaeY+PgkqlkLjqL6WfLbOOJYBgGnx04wAs79vJ+W4CdydEFg10ZTGyp5dJAHRdPGsvsITRpZm8FP3sf4+17sTW8jkOFCDjnEz71RqwLL8RilrUvhRC9J2FKHFVzQxWbX19B7btv4l6/h+zqIHlAY7KJ6hkFtJ12WmS28VFTE13VYamupYUXNm3ljcpa1llcNCSlgC2dTH89Z7fV8rn8LJYN8gWDj5cO+Am8sRz12Z+xBTdjaCeB7M9j+twt2CbMSHT1hBCDnIQp0UXA38a29/9N6TuvYFm7hbziZlLbZxufkMaBpUWMWnIxc+ecM2xbNxLJMAxW79rDv/fuZ7UvzF53JobZhs2VzpSWer7k0Fw8eTzTC4sSXdWEC1dXEHz5fqz7nsRODSGVj2/iD7AtvQF7anqiqyeEGCIkTAkMw2Dn2tfZ/+a/MNauJ2dnLc4AjAIqChwcuKCInMWfY9qZl1Mks40nRHlDA89v3saq6gbW2920OJPBkUleoJYLvTWcX5jLudOG7qSZvRXcvIbwqj9ir3sFhwoQsM8hdMqvsS2+DItFfu0JIfqW/FYZpg5sX8OuN57F9/EnZG4tx9OqGQlUZ1gpWzCelNMWMu2cq5iWOyrRVR2WguEQb2/fxUvFJXwUhP3uDLTJicMJM9oaWOyMTJo5Lqco0VUdMHQoRODNp1FrH8IW+AyzthPIuADTObdimzI8pncQQiSGhKlhorp0N9tee5qmD1eTuukAGfUh8omOe5peQOv8eUw6+3KmjC9KdFWHreLqGp7bso2361vY6EjB63CBM4ORwVqu8NWwdFQhS4bJpJm9YdTXEnjpT1j2PI5dVxAiG//Y72C94JvYM7ITXT0hxDAgv5WHqOaGKra88Q9q3n8L14bd5JX7yQKS7IrKSZm0XX4SY5dczNxZZ8i4pwTxBYK8tnU7rxws45OwhdKUyKSZSXYTJ/kaOSsJLp4+mcJ0aVXpTnDHBsKv3YOt5l84lJ+AdSb+k3+EbclVWKzWRFdPCDGMSJgaIvzeFja/8xyV776G5dNt5B1oJcUApxnKx6ZQ/IVTKDxjKTPnn4/V5kh0dYet7WVlPLdtF+80etmalEbAFpk0c0xTLV/yVnPB2FEsnDADs1yi3y0dDhN453nUJw9g832CRVsIpJ2H6cxvYZs1L9HVE0IMUz0KU0qp84C7ATPwsNb6N532pwBPACOj57xTa/1IH9dVxAmHQ+z4+FUOvPVv9NqN5O2uxxWEUQrKR7g4sGw22YvOZtriy5iVnJro6g5bLT4fL23exmtllazBRpU7HUwppNgUC7x1nO1O56LpU8gahpNm9obRVE/wpYcw7/wbdl1KmHR8o27BuvQm7DmyFJEQIrGOGqaUUmbgPuAcoARYo5R6UWu9Na7YzcBWrfWFSqksYIdS6kmtdaBfaj0MGYZB8ZYP2PPG8wQ+WUv29iqSvdHJMrNtlJwxibTTz2Da2VfKOncJZBgGGw4e5IUde3mvxc8Od2TSTLMzkwnNtVwUqOOiCWOYM3r4TprZG6G92wi/ejfWyuexKy9By1T8RbdjO+daHHZpYRVCDAw9aZmaC+zWWu8FUEo9BVwMxIcpDbiVUgpIBuqAUB/Xddip3L+Nra89TeuHH5K2pYT0xjCFQF2KmcrZI2mZP58pn7tCJstMMMMweGXzVpbvLWGd2Ul9cgpY08i0NrCkrZZz87JYNn0qHpcr0VUdFLRhEHz/JfRHD2BrXY0ZE4GUs1GLbsY254xEV08IIbroSZgqAA7GPS4BTu1U5l7gRaAMcANXaa2NPqnhMNJQU8qW11dQv/pdkjfuI6cqQC7Q4lRUTc6mbe4cxp9zGfOnzpNWjQEgEArx8Ief8GhtKwdSsrC60pnSUse1Yc3Fk8YzY0RRoqs4qBitzQRe/gvmbY9gM4oJk4J/xNexLr0Fe75M0SGEGLh6EqZUN9t0p8fnAuuBs4BxwOtKqfe01k0dTqTUDcANACNHjux1ZYeatpYGtrz9LFXvvoH9sx3klbSRriHJCuXj09h/7nxGnrmMk049D7NcDj9g1DQ184cP1/BM0EJDkocMc4Bbwg3cevpcaX06BqH9uwi9ei+28mdw0ELQPAF/0W+wnftlHE55P4UQA19PvqFLgBFxjwuJtEDF+yrwG621BnYrpfYBk4FP4gtprR8CHgKYM2dO50A25AUDPrZ/8BIlb7+CWreZvL2NJIfBqaBsVBL7L51D7hnnMm3RJRQ5kxNdXdHJjrJy7ly7kdfsKfht6Yxtq+J7DgfXLVooV9/1kjYMgp+8gX7vfmwt72AGAu7FqIU3YT1lCUpaXoUQg0hPwtQaYIJSagxQClwNfKFTmQPAEuA9pVQOMAnY25cVHYwMw2DvhnfYu+oFQp+sI2dHLS6/ZjRQnmfn4DnTyDz9TKadfQXTU2VywYHqrW07+MP2vXziyYakTE5pquLWwtGcfebnEl21QUe3tRB49VFMWx7FFt6FQTL+/C9jOf8W7CPHJ7p6QghxTI4aprTWIaXULcBKIlMj/FVrvUUpdWN0/wPAL4BHlVKbiHQLfk9rXdOP9R6wSnevZ8frz9D20cdkbC0jtdlgBFCbZqH81DF45i9gyueuZEqBfHEMZOFwmMfXfMrD5fXsTs3G5kpnaWs13z15JlMKZBqD3goV7yS08j5s5f/ETjNB0xh8036GbenXcCS5E109IYQ4Lj0aiKO1fhl4udO2B+LulwHD8s/0uor9bHl9BY0fvIdnYzFZtSHygKYkRc3UPNpOncuEsy9jyuRTEl1V0QNNbW388YNPWO6FmuRUUqwOvuav5bbT5pLpkS/93ohclfdv9EcPRq/KU5GuvNO+gXXe57BKV54QYoiQUc291NJYy5Y3/0HNe2/i2LCL3FIfmUCSDSonZuC9aDajz7qQU04+W664G0T2V9fwvx9/ykvmZLyOVArDNfzI3MbXzzsNm0X+m/SG0VhH8OU/Y971ODbjIGFS8Y/4Dyzn3Yy9cEyiqyeEEH1OviWOIuBvY+v7L1L21qtYPt1KXnEzHgNcJigb42b/lUUUnHE+009fxkl2ufJosPlo917+b9MOVruzCLsymdVQxS35bi444ywJw70U3L6e8Bv3Yav+V2yCTV/Rndg/90UcDmeiqyeEEP1GwlQn4XCIXWvfYP+b/8JYu56cXXU4AzAKqChwcOD8mWQtWsK0sz7PDHd6oqsrjoFhGPzj0w08eLCSram5WJIzWdxSw+1FUzhptCwq3Bs6GCTw1j9Q6x7G5l8XWSsv9XOYzrgJ60kLkeWGhRDDgYQpYP+2T9j1+j/xf7yGrG0VuNs0I4HqTCtlp08g9bSFTD37SqblysSBg1mr38+DH67h8UY/5Z4Mku3JXOut5vb5c8hPkzFtvRGuriD48p+w7Ps7dqoIk4VvzLexnn8j9uz8RFdPCCFOqGEZpqpKdrLttadp/vADUjcfIKM+TAHQ4DZRNbOA1vnzmXT255kybmaiqyr6QHlDA7//YC3P46DFmUwuAe6gmRuXzCXJbk909QaV4PrVGG/fj61+JQ4VJGA/Cf9JP8V21pU4rNIOJYQYnoZVmPpgxd34//QIuRV+soFku6JychZtnz+JsWddzKmzFsk4mSFk/YED/P6zrbztTCfozGRyfSXfzHZxxRmL5XPuBe33EVj5OKaNf8Ua2oqhHfgzL8Z89s3Ypki3qBBCDKswZXUl0eRxUHzWKYw48wJmzVuKxWpLdLVEHzIMg5c3b+He3aWsT83GnJTJac3VfGf6ROafWZTo6g0q4dJigq/ch7XkH9ipJ6QK8E36Abbzb8CRKuMFhRCinYqsAHPizZkzR69duzYhzy2Gns6LDjv9XpYGm7lj3kmMzspMdPUGDW0YBD9+A736AWzNbwGaQNIC1Kk3YD19GUqWzRFCDFNKqXVa6znd7RtWLVNi6DnsosOLZNHh3jBamgi88lfM2/6GzdgbWeYl74tYzr0F+5hJia6eEEIMaBKmxKAkiw73jdDebYRW3out8nkctBA0j8M/43+wnvcVWeZFCCF6SMKUGFRk0eHjp8NhAu++gPrkIaxtH2HGRMBzFur0G7GechZKBucLIUSvSJgSA54sOtw3jPpaAi8/iGX3E9h1KWHS8Y/8Btbzb8aePzLR1RNCiEFLwpQYsGTR4b4R3LIW4837sNa8hEP5CVhn4C/6Lrazv4DD7kh09YQQYtCTMCUGHFl0+PjpYJDAqhWoT/+MLbAerW3408/HvPgmbLPmJbp6QggxpMg3kxgwZNHh4xeuLCX4yp+wFi/HTg0hlYtv3O1Yz/8GjsycRFdPCCGGJAlTIqHaFx1+4GAV21JzsCRncmZLDd+RRYd7JbD2HfS7f8LW+DoOFSLgmEtozq+xLb4Mi7TmCSFEv5LfsiIhui46nCSLDveS9rYRWPkYps2PYAttx9BOAtmfx3TOzdgmyrqSQghxokiYEieULDp8/EIHdhN69X6sZc9gp5GQaSS+KT/Cdv7XsHvSEl09IYQYdiRMiRNCFh0+PtowCH7wCvrDB7G1vIcZCCQvJDz/G1hPOx+LvIdCCJEwEqZEv5FFh4+f0dxI4JWHMW//GzZjP2E8+Au+guXcm7CPmpDo6gkhhEDClOgHXRYddqVweWtNdNHhbteIFJ0Ed27EeP1+rFUv4FBtBM0T8Rf9Btu5X8bhlDUHhRBiIJEwJfqMLDp8fHQoROCdZ1Fr/ozN9wlaWwiknINa9E2sJy2UZV6EEGKAkjAljpssOnx8jNoqAq88iGXPE9h1BWEy8Y26Bev538SeW5jo6gkhhDgKCVPimMmiw8cnuPEjwm/dj73uFRwqQMBWhH/2f2NbchUOm1zZKIQQg4WEKdErsujw8dEBP4HXl6PW/wVbcCNmbSeQuQzTWTdjmybjyYQQYjCSMCV6RBYdPj7hsgMEX70f64EV2KkjpPLxTfgetvO/jj09K9HVE0IIcRwkTIkjkkWHj502DIJr30a/dz+2pjdxqDAB53xCc2/AtuhiLDKeTAghhoQefRsqpc4D7gbMwMNa69902n8HcG3cOacAWVrruj6sqziB3t+5m3u27JJFh4+Bbmsh8OqjmLY8ii28C4Mk/LlXYznnFmzjpya6ekIIIfrYUcOUUsoM3AecA5QAa5RSL2qtt7aX0Vr/L/C/0fIXArdJkBp8guEQT3zyKX+tqGdXag7WpAwWt9Tw3dlTmD1KFh0+mtC+HYRW3oet4lnsNBM0jcU37WfYzr8eR7In0dUTQgjRT3rSMjUX2K213guglHoKuBjYepjy1wDL+6Z64kSoaWrm7g/X8EzATH1yCik2F1/21fCf8+eQl5qa6OoNeEZ9LcG//Se2+n9hRhFwn4lacCPWU8/GKq14Qggx5PUkTBUAB+MelwCndldQKeUCzgNuOf6qif62/sAB7vpsK2850gjY0hnTVsVtNi9fWThfxkP1UOCt5zC/811sug5/7rVYLvwu9sIxia6WEEKIE6gn35iqm236MGUvBFYfrotPKXUDcAPAyJEje1RB0bcMw+DZzzby4P4KNqVmY0rK4tTmKm4ZMYYlMj9UjxmNdQQfvQ17/fMETaMwlj2K46SFia6WEEKIBOhJmCoBRsQ9LgTKDlP2ao7Qxae1fgh4CGDOnDmHC2SiHzR5vfzpgzUsbwlR4UnH5XRzRVsNt82dzdhsmR+qNwLvvoj5ze9g07X48r+K/bpfoWS9PCGEGLZ6EqbWABOUUmOAUiKB6QudCymlUoAzgC/2aQ3FcdlVWcldn6znFYsbr8NDPrXcQTPfOPMUkh2ORFdvUDGa6gn+7TvYa58lZBpBcOlzOOYsTnS1hBBCJNhRw5TWOqSUugVYSWRqhL9qrbcopW6M7n8gWvRS4DWtdWu/1Vb02Gubt3HvrmLWerIxkrIoaqjixrxkLjrjTJna4BgE3n8J0xu3YdNV+PKuw37dr7G4khNdLSGEEAOA0joxvW1z5szRa9euTchzD1W+QJC/fLSGv9W1ciAlC3vAx9n+Bm47aTrTC2XB3GNhNDcS+NvtOGr+QUgVYJx/L7a5ZyW6WkIIIU4wpdQ6rXW3637JJVtDQEldHX/46FNe1HaaXG4yzAG+GaznWwtOIT1ZWk+OVeCDVzG9/m3sRiW+3GuxXfdbLEmydI4QQoiOJEwNYh/u2sMftuxkdVImIWcmkxsq+VGmky8sWohZlio5ZkZLE8G//Rf26uWEVD7B85/GMU+udBRCCNE9CVODTDgc5ok1n/KX8jp2puZgScpkYWsN354+iXlnFiW6eoNe4OPXMb16K3Zdhj/raqxf/l8sMnu5EEKII5AwNUjUtbRw9wef8IzfRG1yKh6biy/5arht3snkp52S6OoNekZrM4HHvoe94u+ETTkEznkK+4LzE10tIYQQg4CEqQFu08ES/u/TzayKzlI+uq2aW6xevnrOPBw2a6KrNyQE1ryF6eVbcOgSfFlXYPvy77G4UxJdLSGEEIOEhKkByDAMnl+/iQeLy9mQmo0pOYtTmqq4pXA055x5TqKrN2Tothb8j/0Ae/kThFUWgbP/juP0CxJdLSGEEIOMhKkBpMXn408ffsLfm4KUezJwOT18vq2G204pYlyOzFLelwJr34m0RhkH8GdehvXL/4fFk5boagkhhBiEJEwNAHsqq7hrzXpeNifT5vCQSx2308Q3Fp+Cx+lMdPWGFO3z4n/sh9hLH8VQGQTOfAz7GRcnulpCCCEGMQlTCfTGlm3cu7OYTzzZGK5MZjVU8Y2cJC45Y7HMUt4Pgp++B/++GYexH3/6JVi/fBe21PREV0sIIcQgJ2HqBPMFgjzyyVr+VtNCcUoWNlc657ZUc9vsacwceVKiqzckaZ8X/+M/wl7yFwyVjv+MR7CfeVmiqyWEEGKIkDB1gpTV13PXR+t4wYjMUp5uDvCNYB3fmn8KmR6ZVbu/BDd8CC98E4exD3/aMqxfvgd7WkaiqyWEEGIIkTDVzz7es48/bNrOe0mZhByZTGyo5L8zHHxh0QKsZnn7+4v2+/A/8WPsB/6MoVLxL/oL9rM+n+hqCSGEGILk27wfhMNh/r72M/5SVsv26CzlC1pruHXaRBbILOX9LrjpY3j+mzjCe/Cnno/1y3/Enp6V6GoJIYQYoiRM9aH61lbuWf0JT/sVtcmpuG1JfNFbw3/OO4nCdJmlvL/pgB//kz/DXvwAhvLgP+1B7J+7OtHVEkIIMcRJmOoDm0tKuOvTzbxhT8VvS2NkWzXfNLfxH+ecKrOUnyDBLWvh2W/iCO/En3Iuluv+iD0zJ9HVEkIIMQxImDpGhmHw4sbNPLC3jA0p2aikLOY0VXFTwSjOk1nKTxgdDEZao/b9CUMl4Z9/P/Zzr010tYQQQgwjEqZ6qcXn48EP1/BEU4ByTwZOp4dL26q5bW4RE2SW8hMquO1T+OeNOEI78LuXYLnuXuzZ+YmulhBCiGFGwlQPFVfX8PuPP+UlcxJtDjc51HGb0cQ3ZZbyE04Hg/iX/xL7nj9GWqPm3oN96ZcTXS0hhBDDlISpo3hr2w7u2b6Xj92RWcpnNFTxjewkLpNZyhMiuGMD/ONGHKGt+JPPxHLdfdhzChJdLSGEEMOYhKluBEIhHv14DY9UNbMvNRtbUjrntFZz2+ypFMks5QmhQyH8T/0a+6670cqB/5S7sJ3/FZQEWiGEEAkmYSpOZUMjf/hwLc+GrTQmeUizBvh6oI5b588hy+NJdPWGreCuTbDiGzhCW/AnLcJy3f3Yc0ckulpCCCEEIGEKgE/27uPujdt515VB0JHBhIZKvu9w8EWZpTyhdCiEf8XvsO+8C61s+E+6E9uyr0lrlBBCiAFl2CaFcDjMinXr+XNpDdtSczAnZbGgpZpbp03gdJmlPOFCu7dirPgGjuBGAq4FmL/0APb8kYmulhBCCNHFsAtT9a2t3PvBGlZ4NTXuNJLtSXyhrZrb5p3MiIw5ia7esKfDYQJP34l1++8xKQv+ot9iu+gGaY0SQggxYA2rMHX/ex/wu1aFz57KCKOGH5pa+frZp+C02RNdNQGE9m7HeOob2APrCTjnYf7Sg9gLRie6WkIIIcQRDaswNSUzg+k1e7m5wMO5Z5wlUxsMEDocxv/MXdi2/i8mTPhn/RLbJTdJa5QQQohBYViFqTOnTOLMKZMSXQ0RJ1S8E2P5N3D4PyXgnIvp2gexjxib6GoJIYQQPdajP/2VUucppXYopXYrpb5/mDKLlVLrlVJblFLv9G01xVCjDQPfP+7C9MgiLL5t+Kb/HOt/rcQiQUoIIcQgc9SWKaWUGbgPOAcoAdYopV7UWm+NK5MK3A+cp7U+oJTK7qf6iiEgdGA3xpM34vCvIeA4GdMXHsQxakKiqyWEEEIck550880Fdmut9wIopZ4CLga2xpX5AvCs1voAgNa6qq8rKgY/bRgEnrsP68ZfYkLjm/ZT7J//toyNEkIIMaj1JEwVAAfjHpcAp3YqMxGwKqXeBtzA3VrrxzqfSCl1A3ADwMiRMmfQcBIq2YfxxDew+z4mYD8J0zUP4Bgj49eEEEIMfj0JU6qbbbqb85wMLAGcwIdKqY+01js7HKT1Q8BDAHPmzOl8DjEEacMg8MKfsG74JSYdwj/1/2H7/HdQZnOiqyaEEEL0iZ6EqRIgfiG0QqCsmzI1WutWoFUp9S4wC9iJGLbCZfsJP/4N7N4PCVhnYrrmIexjpyS6WkIIIUSf6slglTXABKXUGKWUDbgaeLFTmReAhUopi1LKRaQbcFvfVlUMFtow8L/wIOqh07C0fYpv0g+wfv9tLBKkhBBCDEFHbZnSWoeUUrcAKwEz8Fet9Ral1I3R/Q9orbcppV4FNgIG8LDWenN/VlwMTOGKg4QeuxF72/sErDNQVz2IY/y0RFdLDAPBYJCSkhJ8Pl+iqyKEGMQcDgeFhYVYrdYeH6O0TszQpTlz5ui1a9cm5LlF39OGQeClv2Jd91OU9uOf+G3sV30fZRlW88KKBNq3bx9ut5uMjAyU6m6opxBCHJnWmtraWpqbmxkzZkyHfUqpdVrrbhfxlW86cdzCFSWEHr8Je+s7BC1T4coHcUycmehqiWHG5/MxevRoCVJCiGOmlCIjI4Pq6upeHSdhShwX/0uPYlnzI2zai2/87div+aG0RomEkSAlhDhex/J7RL71xDEJV5UReuxm7C1vErRMxvj8gzgmFyW6WkIIIcQJJ1NPi17zv/oE3D8PW/O7+Mb8J5bvvY9VgpQQmM1mioqKmD59OhdeeCENDQ2xfeeddx6pqaksW7aswzHXXnstkyZNYvr06Vx//fUEg8GjPs/FF1/M/PnzO2z76U9/SkFBAUVFRRQVFfH970eWUV28eDGTJk1i1qxZnHLKKaxfv/64X+dA09/v+6OPPorJZGLjxo2xbdOnT6e4uDj2+LPPPkMpxcqVKzscq5TiS1/6UuxxKBQiKyurS32GghPxOWRlZVFUVMTUqVP585//HNv33HPPoZRi+/btHY55//33mTt3LpMnT2by5Mk89NBDffNiO5EwJXosXF2B//eXY//oZgxzDqGrXsPx5Z+henHFgxBDmdPpZP369WzevJn09HTuu+++2L477riDxx9/vMsx1157Ldu3b2fTpk14vV4efvjhIz5HQ0MDn376KQ0NDezbt6/Dvttuu43169ezfv16fvOb38S2P/nkk2zYsIGbbrqJO+644zhf5cBzIt73wsJCfvnLXx52//Llyzn99NNZvnx5h+1JSUls3rwZr9cLwOuvv05BQUFvXt6gcSI+h6uuuor169fz9ttv88Mf/pDKykrg0Pv/1FNPxcpWVFTwhS98gQceeIDt27fz/vvv8+CDD/LSSy/10Ss+RMKU6BH/a3+H+07F1vQ2vlG3RFqjpp6c6GoJMWDNnz+f0tLS2OMlS5bgdru7lFu6dClKKZRSzJ07l5KSkiOe95///CcXXnghV199dYcvjmOp01DUX+/7smXL2LJlCzt27OiyT2vNM888w6OPPsprr73WZXqO888/P/YFvnz5cq655ppjeWmDSn99Du2ys7MZN24c+/fvp6WlhdWrV/OXv/ylw/+J++67j6985SucdNJJAGRmZvK73/2uwx8afUXGTIkjMmqrCP7tW9ibXiVoHo9x6TM4pp+S6GoJcUQ/+9cWtpY19ek5p+Z7+MmFPZszLRwOs2rVKr72ta/1+PzBYJDHH3+cu++++4jlli9fzk9+8hNycnL4/Oc/zw9+8IPYvrvuuosnnngCgN/+9rece+65HY599dVXueSSS3pcp9567+md1Bxs6dNzZo5IZuGVE3tUtj/fd5PJxH/913/xq1/9ir/97W8d9q1evZoxY8Ywbtw4Fi9ezMsvv8xll10W23/11Vfz85//nGXLlrFx40auv/563nvvvR7Xsbd27vwFzS19O2+2O3kKEyf+qEdl+/NzaLd371727t3L+PHjef755znvvPOYOHEi6enpfPrpp5x00kls2bKFL3/5yx2OmzNnDlu2bOlxvXpKWqbEYfnfeBr9x1OxNb6Ob+Q3sXzvA6wSpIQ4LK/XS1FRERkZGdTV1XHOOef0+NibbrqJRYsWsXDhwsOWqaysZPfu3Zx++ulMnDgRi8XC5s2H5keO7+aLD1LXXnsthYWF/Pa3v+Vb3/rWsb24Aay/3/d2X/jCF/joo4+6dK8uX76cq6++GogEp85dfTNnzqS4uJjly5ezdOnSHtdtsDkRn8OKFSsoKirimmuu4cEHHyQ9Pf2w77/Wutsr8/rjql9pmRJdhKvKCD3+bezNrxE0jcW4ZDmOmfMSXS0heqynLUh9rX3MSGNjI8uWLeO+++7j1ltvPepxP/vZz6iurubBBx88YrkVK1ZQX18fm0ywqamJp556iv/5n/854nFPPvkks2bN4vvf/z4333wzzz77bM9fVC/0tAWpr/X3+97OYrFw++2389vf/ja2LRwO889//pMXX3yRX/7ylx0mfYzv1rrooov47ne/y9tvv01tbW3vX2Qv9LQFqa+diM/hqquu4t577409rq2t5c0332Tz5s0opQiHwyil+N3vfse0adNYu3YtF110Uaz8unXrmDp16rG9wCOQlikRow0D/0t/jVyp1/RmtDXqQ6wSpITolZSUFO655x7uvPPOo16d9/DDD7Ny5UqWL1+OyXTkX8nLly/n1Vdfpbi4mOLiYtatW9fjcVNWq5X/+Z//4aOPPmLbtqG5dGp/ve/xvvKVr/DGG2/EJnV84403mDVrFgcPHqS4uJj9+/dz+eWX8/zzz3c47vrrr+fHP/4xM2bM6PXrGmxOxOfQ7plnnuG6665j//79FBcXc/DgQcaMGcP777/PzTffzKOPPhq7grW2tpbvfe97/Nd//dexvKwjkjAlAAiXFhO480Lsa27DsORFrtS7/jcouyPRVRNiUJo9ezazZs2KhZ2FCxdyxRVXsGrVKgoLC2OX0N94441UVlYyf/58ioqK+PnPf97t+YqLizlw4ADz5h3642bMmDF4PB4+/vjjHtXJ6XRy++23c+eddx7nqxu4+vp978xms3HrrbdSVVUFRALupZde2qHM5Zdfzt///vcO2woLC/n2t799vC9v0Ojvz6Hdkd7/vLw8nnjiCb7+9a8zefJkTjvtNK6//nouvPDCvnmRcWRtvmFOGwaB5+7DuunXKB3EP+Hb2K+WNfXE4LNt2zamTJmS6GoIIYaA7n6fyNp8oluhvdsxVtyE3b+OgK0I05V/wjG+7/uShRBCiKFMwtQwpEMh/E//L7Ydd2HChG/6T7FfdivKbE501YQQwCOPPNLlEvEFCxZ0mARR9D153weGwfg5SDffMBPcvh6euQlraAsB53xMX7gfy4ixia6WEMdNuvmEEH1FuvlEt3TAj//vv8C+709o5cR30p3Yl30NdQxXTwghhBDiEAlTw0Bww4fw4s04wnvwu5dg+dIfceQMzbWhhBBCiBNNwtQQpr1t+B//b+ylj2KoVPyn/Qn7576Q6GoJIYQQQ4qEqSEq8PHrmF79Txy6BH/ahVivuwt7elaiqyWEEEIMOTJgZogxmhvx3XsD1pevAEIEznoc+38+gUmClBD9zmw2U1RUxPTp07nwwgtpaGiI7TvvvPNITU1l2bJlHY659tprmTRpEtOnT+f6668/4ozRjz76KFlZWRQVFVFUVMR1110HwL///e/YJIlTp06NLcvxla98hWeeeabDOZKTk/vo1Q4cJ/J9nzp1Kn/+859j+5577jmUUmzfvj22zTAMbr31VqZPn86MGTM45ZRTuqznNxT19+dQWVnJsmXLYv/OO69zmMjPQsLUEBJ45wWM35+CvfppAtlXY/rOJ9gWXXT0A4UQfaJ9bbLNmzeTnp7e4VLuO+64g8cff7zLMddeey3bt29n06ZNeL1eHn744SM+x1VXXRVbzPixxx4jGAxyww038K9//YsNGzbw2WefsXjx4r5+aQPaiXzf3377bX74wx9SWVkJRGbgPv300zss67NixQrKysrYuHEjmzZt4rnnniM1NbVvXuwA1t+fw49//GPOOeccNmzYwNatW/nNb37TYX8iPwsJU0OAUVeN/w/XYnvrOjDZCC59BvvND2BypyS6akIMW/Pnz6e0tDT2eMmSJR0Wvm23dOlSlFIopZg7dy4lJSW9ep7m5mZCoRAZGRkA2O12Jk2adHyVH8T6+33Pzs5m3Lhx7N+/n5aWFlavXs1f/vKXDl/g5eXl5OXlxdaaKywsJC0t7Thf2eDSH59DeXk5hYWFscczZ86M3U/0ZyFjpgY5/8onsXz4/7DpBnyF/4H9i7/A4nQlulpCJNYr34eKTX17ztwZcP5vjl4OCIfDrFq1iq997Ws9Pn0wGOTxxx/vMllhZytWrOD9998H4Nvf/jZf/epXueiiixg1ahRLlixh2bJlXHPNNce0aOzxeuvRh6jav7dPz5k9aixnfuWGHpXtz/e93d69e9m7dy/jx4/n+eef57zzzmPixImkp6fz6aefctJJJ3HllVdy+umn895777FkyRK++MUvMnv27B7X6Xj9aFcJm1u8fXrO6clOfjGh8OgF6b/P4eabb+aqq67i3nvv5eyzz+arX/0q+fn5AAn/LKRlapAKV5Tgv/NS7B/ehGFOJ3TZKzi+/nuUBCkhEsbr9VJUVERGRgZ1dXWcc845PT72pptuYtGiRSxcuPCI5eK7+b761a8C8PDDD7Nq1Srmzp3LnXfeyfXXXw+AUqrL8d1tG+xOxPu+YsUKioqKuOaaa3jwwQdJT09n+fLlXH311QBcffXVLF++HIi0fuzYsYNf//rXmEwmlixZwqpVq479BQ4S/f05nHvuuezdu5evf/3rbN++ndmzZ1NdXQ2Q+M9Ca52Q28knn6xF7xnhsPY9/4AO/6RAGz9O195H/p82/L5EV0uIhNu6dWuiq6CTkpK01lo3NDTo008/Xd99990d9r/11lv6ggsu6HLcT3/6U33xxRfrcDh8xPM/8sgj+uabbz5imerqap2cnKy11vr222/XDz30UGxfbW2tHj16dI9ey2CSiPe9pqZGOxwOPXLkSD1q1ChdWFioR4wYoQ3D6HL8//7v/+pbbrmlty9r0Onvz6GzCy64QD/zzDP98ll09/sEWKsPk2mkZWoQCR3YTfB352P/7L8IWUcR+sKbOL7yC5TNnuiqCSHipKSkcM8993DnnXce8eokiLQqrVy5kuXLlx9T11xLSwtvv/127PH69esZNWoUAIsXL2bFihUEAgEgclXamWee2evnGCxO5Pv+zDPPcN1117F//36Ki4s5ePAgY8aM4f333+fTTz+lrKwMiFxNtnHjxthnMhz01+fw5ptv0tbWBkTGCu7Zs4eRI0cOiM+iR/+ClFLnKaV2KKV2K6W+383+xUqpRqXU+ujtx31e02FMh8P4nv49pr+cjsX7Gb5JP8D6vXewTpqV6KoJIQ6jfaqC9sGwCxcu5IorrmDVqlUUFhaycuVKAG688UYqKyuZP38+RUVF/PznP+/V82it+d3vfsekSZMoKiriJz/5CY8++igAy5YtY+HChZx88skUFRWxevVqfvvb3/bp6xxoTtT7vnz5ci699NIO2y6//HL+/ve/U1VVxYUXXsj06dOZOXMmFouFW265pW9e4CDRH5/DunXrmDNnDjNnzmT+/Pn8x3/8B6eccsqA+CyOutCxUsoM7ATOAUqANcA1WuutcWUWA9/VWi/r7hzdkYWOeya4ewv66W9iC2wgYJ+D6er7sYwZvlfqCHE4stCxEKKv9MdCx3OB3VrrvdGTPQVcDGw94lHiuOhgEP9Tv8a++49oZcU/85fYLrlJFiYWQgghBpiehKkC4GDc4xLg1G7KzVdKbQDKiLRSbemD+g1LwS1r4bmbcIR24E9ahOWL92LPHz797UIMd4888kiXS8QXLFjQYRJE0ffkfR8YBuPn0JNuviuAc7XW/xF9/CVgrtb6W3FlPIChtW5RSi0F7tZaT+jmXDcANwCMHDny5P379/fdKxkCtN+H/4kfYz/wMIZKJnTKz7Gdf520RgnRA9LNJ4ToK73t5uvJt3QJMCLucSGR1qcYrXWT1rolev9lwKqUyux8Iq31Q1rrOVrrOVlZslZcvOCn7xL67ak4Dj5IIOUcuPkT7Bd8RYKUEEIIMcD1pJtvDTBBKTUGKAWuBr4QX0AplQtUaq21UmoukZBW29eVHYqM1mYCj/0Ae8WTGCod/6K/YD/r84mulhBCCCF66KhhSmsdUkrdAqwEzMBftdZblFI3Rvc/AHwe+KZSKgR4gav10foPBYHVr2B64zs4dBn+zMuwfun32FPTE10tIYQQQvRCj9bmi3bdvdxp2wNx9+8F7u3bqg1dRmMdwcdux177LCGVT+Cc5dgXLE10tYQQQghxDGRAzgnmf/Of6LtOwVbzPL6cL2L67ifYJEgJMSSYzWaKioqYPn06F154IQ0NDbF95513HqmpqSxb1nE6vmuvvZZJkyYxffp0rr/++iPOGP3oo48edsLBUChEZmYmP/jBDzpsX7x4MSNHjiS+s+CSSy4hOTn5GF7hwHQi3vesrCyKioqYPHkyd911V2xfRUUFV199NePGjWPq1KksXbqUnTt3UlxcjNPppKioiKlTp3LjjTdiGAYA1dXVWK1WHnzwwb59IxKsPz8HrTWZmZnU19cDUF5ejlIqtug3QFZWFrW1tfz0pz+loKCAoqKi2K2hoaHb/z+LFy+mL+a8lDB1goSrK/D/31XY370ew5RM8MLncXzzPkxJ7kRXTQjRR5xOJ+vXr2fz5s2kp6d3uJT7jjvu4PHHH+9yzLXXXsv27dvZtGkTXq+Xhx9++Jie+7XXXmPSpEk8/fTTdB5lkZqayurVqwFoaGigvLz8mJ5joDoR73v7AtOrV6/ml7/8JQcPHkRrzaWXXsrixYvZs2cPW7du5Ve/+hWVlZUAjBs3jvXr17Nx40a2bt3K888/D8A//vEP5s2bF1uMd6joz89BKcWpp57Khx9+CMAHH3zA7Nmz+eCDDwDYsWMHmZmZZGRkAHDbbbfFFgRfv349qampffxqO5Iw1c+0YeB/+VG4by62xtfxjfgGlv/6CNucMxJdNSFEP5o/fz6lpaWxx0uWLMHt7vrH09KlS1FKoZRi7ty5lJSUHNPzLV++nG9/+9uMHDmSjz76qMO+q6++Orasx7PPPstll112TM8xGPT3+56RkcH48eMpLy/nrbfewmq1cuONN8b2FxUVsXDhwg7HWCwWTjvtNHbv3g1EPqvf//73lJSUdKjrUNIfn8OCBQti4emDDz7gO9/5Todwddppp/Xxq+i5Ho2ZEscmXLaf0BO3YG97l6B5Isal/8Qx/ZREV0uIIe+3n/yW7XXb+/Sck9Mn87253+tR2XA4zKpVq/ja177W4/MHg0Eef/zxLpMV9oTX62XVqlU8+OCDNDQ0sHz5cubPnx/bv2TJEr7+9a8TDod56qmneOihh/jFL37R6+c5moZ/7SFQ1tqn57TlJ5F64bgelT0R7/uBAwfw+XzMnDmThx56iJNPPvmox7S1tbFq1Sp+/vOfc/DgQSoqKpg7dy5XXnklK1as4Dvf+U6P69sTP/vXFraWNfXpOafme/jJhdN6VLa/PofTTjsttnbfJ598ws9+9jP+8Ic/AJEwtWDBgljZu+66iyeeeAKAtLQ03nrrrR7X5VhIy1Q/0IaB/59/RD00H1vrR/jG3Y7l+x9glSAlxJDm9XopKioiIyODuro6zjnnnB4fe9NNN7Fo0aIurRo98e9//5szzzwTl8vF5ZdfznPPPUc4HI7tN5vNnH766axYsQKv18vo0aN7/RwD2Yl431esWMG0adMYO3Ys3/72t3E4HEc99549eygqKmLBggVccMEFnH/++Tz11FNceeWVQKTFcCh19fX35zB37lw+++wzWltbCQaDJCcnM3bsWHbv3t2lZSq+m689SCmluj3v4bb3hrRM9bHQvh0YT92E3b+WgHUm6sr7cUyYkehqCTGs9LQFqa+1jxlpbGxk2bJl3Hfffdx6661HPe5nP/sZ1dXVxzwgefny5axevToWkmpra3nrrbc4++yzY2WuvvpqLr30Un76058e03P0RE9bkPraiXjfr7rqKu69914+/PDDWDCaNm0azzzzzGGPaR8zFW/58uVUVlby5JNPAlBWVsauXbuYMKHLoiHHrKctSH2tvz8Hl8vF+PHj+etf/8pJJ50EwLx583j55Zepqqpi0qRJRzw+IyMjNoC9XV1dHZmZXeYY7zVpmeojOhTCt/w3mB5diMW3Bd/UH2P9/ttYJUgJMeykpKRwzz33cOeddx7xKjGAhx9+mJUrV7J8+XJMx7DiQVNTE++//z4HDhyguLiY4uJi7rvvvi4tHgsXLuQHP/gB11xzTa+fY7A4Ee/7/Pnz+dKXvsTdd9/NWWedhd/v589//nNs/5o1a3jnnXe6PXbHjh20trZSWloa+6x+8IMfxMazDRX9+TksWLCAP/zhD7Fu7Pnz53P33Xczb968o7YwnXLKKaxevZqKigoA1q5di9/vZ8SIEUc8rke01gm5nXzyyXqoCGz7TPv/5zStf+LR/l9/Tgf370p0lYQYdrZu3ZroKuikpKQOj5ctW6Yfe+wxrbXWp59+us7MzNQOh0MXFBToV199VWuttdls1mPHjtWzZs3Ss2bN0j/72c8Oe/5HHnlEJyUl6YKCgtjtF7/4hb7qqqs6lKutrdWZmZna5/PpM844Q69Zs+aodR3MTsT7fvPNN8cel5aW6pycHN3U1KRLS0v1FVdcoceOHaunTp2qly5dqnfu3Kn37dunp02b1uE8P/nJT/T3vve9Dts2bNigp0yZclyvf6Do789Ba62ffvppDehduyLfsz6fT9tsNv2rX/0qVuYnP/mJzs/Pj51z1qxZet++fVprrZ9//nk9e/ZsPWvWLL1gwQK9bt26bp+nu98nwFp9mExz1IWO+8ucOXN0X8ztkEg64Mf/9//Bvu9+tHISnP0jbBd+XdbTEyIBZKFjIURf6e1CxzJm6hgFN3wEL96MI7wbv/ssLF/8I/bcwkRXSwghhBAnmISpXtLeNvyP/z/spY9gqFT88+/Hfu61ia6WEGIIeeSRR7pcIr5gwYIOkyCKvifv+8AwGD8H6ebrhcAnb2J69dtYjAP4U5dhve4PmNKzEl0tIQTSzSeE6DvSzdcPjOZGgo99D1vVU4RVFoHFf8O++JJEV0sIIYQQA4CEqaMIvPsiprfuwK4r8GVdhe2632LxpCW6WkIIIYQYICRMHYZRX0vwb/+JveFFQqqQwPnP4Di157O5CiGEEGJ4kDDVDf9rf8fywX9j0w34Cq7H/sVfYHElJ7paQgghhBiAZEKkOOHKUvx3Xob9g29imNMIXfpvHDfchZIgJYToAbPZTFFREdOnT+fCCy+koaEhtu+8884jNTWVZcuWdTjm2muvZdKkSUyfPp3rr7/+iDNGP/rooyilWLVqVWzbc889h1IqtqzJ4sWLmTRpEkVFRRQVFfH5z3++wzlmzZo15GZBPxHve1ZWVuw9LSoqYuvWrRQXF+N0Ojtsf+yxxwBITk7uco5bbrml7170ANTfnwPAK6+8wpw5c5gyZQqTJ0/mu9/9LgA//elPufPOOzuUHT16NDU1NUBk/b3bb789tu/OO+/s06WVJEwRXZj4xT/Dn+Zha34H36hbsHzvQ6xFC45+sBBCRLWvTbZ582bS09M7XMp9xx138Pjjj3c55tprr2X79u1s2rQJr9fLww8/fMTnmDFjRoelYp566ilmzZrVocyTTz4ZW+Q1fu24bdu2YRgG7777Lq2trcf6MgecE/G+X3XVVbH3dP369UydOhU4tP5e++26667r2xc3iPT357B582ZuueUWnnjiCbZt28bmzZsZO3Zsj+pmt9t59tlnY+Gqrw37br7Qwb0Yf78Zu/cDgpapGJffj2PK7ERXSwhxHCp+9Sv827b36TntUyaT+8Mf9rj8/Pnz2bhxY+zxkiVLePvtt7uUW7p0aez+3LlzKSkpOeJ5Fy5cyHvvvUcwGMTv97N7926Kiop6VKe///3vfOlLX2Lbtm28+OKLfd5C9corr8TWPesrubm5nH/++T0u31/v+6DyyvehYlPfnjN3Bpz/mx4X74/P4Xe/+x3//d//zeTJkwGwWCzcdNNNPaqPxWLhhhtu4K677uKXv/xlD19Fzw3blikdDuP7x12YHj4NS9s6fBO/j+X772GVICWEOE7hcJhVq1Zx0UUX9fiYYDDI448/znnnnXfEckopzj77bFauXMkLL7zQ7XNce+21sW6nO+64I7Z9xYoVXHXVVVxzzTVdFkIeCvrzfV+xYkWH7jyv1wvAnj17Omx/7733jus1DAX99Tls3ryZk08++bD777rrrg6fRVlZWYf9N998M08++SSNjY09rldPDcuWqdDurRhP34Qj8BkBx0mYrvoTjrGTE10tIUQf6U0LUl/yer0UFRVRXFzMySefzDnn9PwK4JtuuolFixaxcOHCo5a9+uqrueeee2hsbOT3v/89v/rVrzrsf/LJJ5kzp+PcgmvWrCErK4tRo0ZRWFjI9ddfT319PWlpfTfVS29akPrSiXjfr7rqKu69994u29u7+XpCKdXjeh2XXrQg9aUT9e//cG677bbYGCqIjJmK5/F4uO6667jnnntwOp3H/DzdGVYtUzoUwvfELzA/fgYW/w78M/4H6/dWYZEgJYToA+1jRvbv308gEOjx8hc/+9nPqK6u5v/+7/96VH7u3Lls3ryZmpoaJk6c2KNjli9fzvbt2xk9ejTjxo2jqamJf/7znz06dqA7Ue97b+sUCARij+vq6sjMzOzz5xlI+vtzmDZtGuvWrTuuOv7nf/4nf/nLX/p8zOCwClP+p36DY/edBJLmom/4EPvl30KZhtVbIIQ4AVJSUrjnnnu48847j3p10sMPP8zKlStZvnw5pl78Pvr1r3/dpUXqcAzD4B//+AcbN26kuLiY4uJiXnjhhSHX1Xci3veeOuOMM3jiiSeASIvN008/zZlnntnnzzMQ9dfncMcdd/CrX/2KnTt3ApF/170Nwunp6Vx55ZX85S9/6dVxRzOskoTtklvxz7sP23f/hblgdKKrI4QYwmbPns2sWbN46qmngMjA8SuuuIJVq1ZRWFjIypUrAbjxxhuprKxk/vz5FBUV8fOf/7xH5z///PMP++UcP2bq7LPP5t1336WgoICCgoJYmUWLFrF161bKy8uP85UOLP31vnceM/XBBx8AXcdM3XPPPQDcfffdPPvssxQVFTFv3jyuuOIKFi1a1I+vfGDpj89h5syZ/OEPf+Caa65hypQpTJ8+/Zj+/d5+++19flWfLHQshBgSZKFjIURf6e1Cx8OqZUoIIYQQoq8Ny6v5hBBiIHvkkUe4++67O2xbsGBBjwf0imMj7/vAMBg/hx518ymlzgPuBszAw1rrbq+7VEqdAnwEXKW1fqa7Mu2km08I0Zekm08I0Vf6vJtPKWUG7gPOB6YC1yilph6m3G+BlcdQbyGEOG6JGgMqhBg6juX3SE/GTM0Fdmut92qtA8BTwMXdlPsW8E+gqte1EEKI4+RwOKitrZVAJYQ4ZlpramtrcTgcvTquJ2OmCoCDcY9LgFPjCyilCoBLgbOAU3pVAyGE6AOFhYWUlJRQXV2d6KoIIQYxh8NBYWFhr47pSZjqbv77zn/6/QH43v9v7/5D7a7rOI4/X9nolqPM/JG4XXeDVVODya5uQkY/h0SR4gSJsvCPMag06IcZUW1RUURGSOlK2YogAtvKwiyG9gsba67cLiPmZNVyaK0kb6a1+eqP7+dcvh7u9Z7vOfd27rnn9YDDOefz/Xx/nPHie9/7/vrYPvlcj8uXtBHYCDA6OtrhJkZEzG7JkiWMjY31ezMiYgh1UkwdBZbXvi8DHmnrMw58txRSZwBvlXTC9s56J9tbga1QXYDe5TZHRERELBidFFN7gJWSxoC/ANcA76x3sD3130FJ24AftRdSEREREYvRrMWU7ROS3k91l94pwB22JyRtKtNvnedtjIiIiFiw+jacjKS/An/sw6rPAOZ2UJ6ISrIV8yXZivmSbHXuPNtnTjehb8VUv0j67UwP3YroRbIV8yXZivmSbM2NjM0XERER0YMUUxERERE9GMZiamu/NyAWrWQr5kuyFfMl2ZoDQ3fNVERERMRcGsYjUxERERFzZuCLKUnLJd0r6aCkCUk3lPbTJf1M0qHy/tLS/hZJeyXtL+9vrC1rTWl/SNJX9Vxj48Si10W2LpH0u/L6vaQra8tKtmJK02zV5huVNCnpw7W2ZCumdLHfWiHp37V91621ZSVbHRr4Ygo4AXzI9ipgHfA+SecDHwN22V4J7CrfoXqextttvwZ4D/Dt2rK+TjV24Mryuvz/8xNigWqarQPAuO3VVNm5TVLrwbjJVtQ1zVbLzcDdbW3JVtR1k63DtleX16Zae7LVoYEvpmwfs/1A+fwEcBA4F3gHsL102w5cUfrss90aW3ACGJH0AknnAC+2fb+rC8m+1ZonhlMX2XrS9onSPkIZEDzZinZNswUg6QrgYar9Vqst2Ypn6SZb00m2mhn4YqpO0grgImA3cLbtY1CFCzhrmlmuAvbZfpoqbEdr046WtoiOsyVpraQJYD+wqRRXyVbMqJNsSToVuBHY3DZ7shUzavA3cUzSPkk/l3RZaUu2GuhkoOOBIGkpcCfwQdv/nO3UrqQLgC8A61tN03TLrY7RKFu2dwMXSFoFbJd0N8lWzKBBtjYDN9uebOuTbMW0GmTrGDBq+7ikNcDO8vcx2WpgURRTkpZQheY7tr9fmh+VdI7tY+Vw5WO1/suAHcC1tg+X5qPAstpilwGPEEOtabZabB+U9C/gQpKtmEbDbK0FNkj6InAa8Iykp8r8yVY8S5NslTMzT5fPeyUdBl5J9luNDPxpvnJ3we3AQdtfrk36IdUF5pT3H5T+pwE/Bm6y/etW53LY8wlJ68oyr23NE8Opi2yNtS44l3Qe8CrgSLIV7Zpmy/ZltlfYXgF8Bfic7VuSrWjXxX7rTEmnlM+voLrQ/OFkq5mBf2inpNcCv6S6RuWZ0vxxqnPE3wNGgT8BV9v+u6RPADcBh2qLWW/7MUnjwDbghVR3zHzAg/4PFF3rIlvvprpD5r+l/xbbO8uykq2Y0jRbbfN+Gpi0/aXyPdmKKV3st64CtlDdBXgS+JTtu8qykq0ODXwxFREREdFPA3+aLyIiIqKfUkxFRERE9CDFVEREREQPUkxFRERE9CDFVEREREQPUkxFRERE9CDFVEQEIGmbpA3l833lGTsREbNKMRURi1Lrqc4REfMtxVRE9J2kz0i6ofb9s5Kul/QRSXskPShpc236Tkl7JU1I2lhrn5S0RdJu4NIZ1vXJsswDkrZqtlHRIyJmkWIqIhaC2ynjhkl6HnAN8CjVOGGXAKuBNZJeV/pfZ3sNMA5cL+llpf1U4IDttbZ/NcO6brF9se0LqYbJeNt8/KCIGB7P7/cGRETYPiLpuKSLgLOBfcDFwPryGWApVXH1C6oC6srSvry0H6caW+zOWVb3BkkfBV4EnA5MAHfN4c+JiCGTYioiFopvAu8FXg7cAbwJ+Lzt2+qdJL0eeDNwqe0nJd0HjJTJT9k+OdMKJI0AXwPGbf+5DBo8MlP/iIhO5DRfRCwUO4DLqY5I3VNe10laCiDpXElnAS8B/lEKqVcD6xqso1U4/a0sd8OcbX1EDK0cmYqIBcH2fyTdCzxeji79VNIq4P5yjfgk8C7gJ8AmSQ8CfwB+02Adj0v6BrAfOALsmdtfERHDSLb7vQ0REa0Lzx8ArrZ9qN/bExHRqZzmi4i+k3Q+8BCwK4VURAyaHJmKiEVJ0g5grK35Rtv39GN7ImLxSjEVERER0YOc5ouIiIjoQYqpiIiIiB6kmIqIiIjoQYqpiIiIiB6kmIqIiIjowf8AK0pwTVliO7QAAAAASUVORK5CYII=\n" - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "tec_name = \"meth_bio\"\n", - "\n", - "import matplotlib.pyplot as plt\n", - "fig, ax = plt.subplots(figsize=(10,5))\n", - "legend = []\n", - "for reg in df_merge.node_x.unique():\n", - " df_merge[(df_merge[\"tec\"]==tec_name) & (df_merge[\"node_x\"]==reg) & (df_merge[\"year_all\"] > 2015)].plot(x=\"year_all\", y=\"ratio\",ax=ax)\n", - " legend.append(reg)\n", - "ax.legend(legend, ncol=3)\n", - "ax.set_title(f\"inv_cost - {tec_name}\", fontsize=16)\n", - "ax.set_xticks([i for i in range(2020, 2110, 10)])\n", - "ax.set_xlim(2018, 2055)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 12, - "outputs": [ - { - "data": { - "text/plain": "(2018.0, 2060.0)" - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "tec_name = \"meth_bio\"\n", - "\n", - "import matplotlib.pyplot as plt\n", - "fig, ax = plt.subplots(figsize=(10,5))\n", - "legend = []\n", - "for reg in df.node.unique():\n", - " df[(df[\"tec\"]==tec_name) & (df[\"node\"]==reg) & (df[\"year_all\"] > 2015)].plot(x=\"year_all\", y=\"Value\",ax=ax)\n", - " legend.append(reg)\n", - "ax.legend(legend, ncol=3)\n", - "ax.set_title(f\"inv_cost - {tec_name}\", fontsize=16)\n", - "ax.set_xticks([i for i in range(2020, 2110, 10)])\n", - "ax.set_xlim(2018, 2060)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 4, - "outputs": [], - "source": [ - "hist_act = dataframes[\"historical_activity\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 15, - "outputs": [], - "source": [ - "#h\n", - "hist_act = hist_act[hist_act[\"year_all\"]==\"2015\"]\n", - "hist_act_rcpa = hist_act[hist_act[\"node\"]==\"R12_RCPA\"]\n", - "hist_act_chn = hist_act[hist_act[\"node\"]==\"R12_CHN\"]\n", - "hist_act_merge = hist_act_rcpa.merge(hist_act_chn, left_on=\"tec\", right_on=\"tec\")\n", - "hist_act_merge[\"ratio\"] = hist_act_merge[\"Value_x\"] / hist_act_merge[\"Value_y\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 21, - "outputs": [ - { - "data": { - "text/plain": " node_x tec year_all_x mode_x time_x Value_x \\\n35 R12_RCPA elec_trp 2015 M1 year 5.000000 \n54 R12_RCPA gas_extr_mpen 2015 M1 year 0.100000 \n81 R12_RCPA oil_exp 2015 M1 year 1.000000 \n85 R12_RCPA oil_extr_mpen 2015 M1 year 0.100000 \n156 R12_RCPA eaf_steel 2015 M2 year 0.893333 \n165 R12_RCPA catalytic_cracking_ref 2015 atm_gasoil year 5.953922 \n173 R12_RCPA import_petro 2015 M1 year 0.120969 \n174 R12_RCPA export_petro 2015 M1 year 0.004573 \n175 R12_RCPA gas_NH3 2015 M1 year 0.165746 \n176 R12_RCPA coal_NH3 2015 M1 year 0.834254 \n177 R12_RCPA export_NFert 2015 M1 year 0.041194 \n180 R12_RCPA meth_exp 2015 feedstock year 0.000272 \n181 R12_RCPA meth_exp 2015 feedstock year 0.000272 \n182 R12_RCPA meth_exp 2015 fuel year 0.000272 \n183 R12_RCPA meth_exp 2015 fuel year 0.000272 \n184 R12_RCPA meth_imp 2015 feedstock year 0.177914 \n185 R12_RCPA meth_imp 2015 feedstock year 0.177914 \n186 R12_RCPA meth_imp 2015 fuel year 0.177914 \n187 R12_RCPA meth_imp 2015 fuel year 0.177914 \n\n node_y year_all_y mode_y time_y Value_y ratio \n35 R12_CHN 2015 M1 year 54.270254 0.092132 \n54 R12_CHN 2015 M1 year 153.154140 0.000653 \n81 R12_CHN 2015 M1 year 51.318611 0.019486 \n85 R12_CHN 2015 M1 year 250.804477 0.000399 \n156 R12_CHN 2015 M1 year 72.360000 0.012346 \n165 R12_CHN 2015 vacuum_gasoil year 112.350506 0.052994 \n173 R12_CHN 2015 M1 year 18.270686 0.006621 \n174 R12_CHN 2015 M1 year 0.221271 0.020667 \n175 R12_CHN 2015 M1 year 9.662983 0.017153 \n176 R12_CHN 2015 M1 year 48.637017 0.017153 \n177 R12_CHN 2015 M1 year 20.080371 0.002051 \n180 R12_CHN 2015 feedstock year 0.096487 0.002823 \n181 R12_CHN 2015 fuel year 0.096487 0.002823 \n182 R12_CHN 2015 feedstock year 0.096487 0.002823 \n183 R12_CHN 2015 fuel year 0.096487 0.002823 \n184 R12_CHN 2015 feedstock year 3.405133 0.052249 \n185 R12_CHN 2015 fuel year 3.405133 0.052249 \n186 R12_CHN 2015 feedstock year 3.405133 0.052249 \n187 R12_CHN 2015 fuel year 3.405133 0.052249 ", - "text/html": "
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node_xtecyear_all_xmode_xtime_xValue_xnode_yyear_all_ymode_ytime_yValue_yratio
35R12_RCPAelec_trp2015M1year5.000000R12_CHN2015M1year54.2702540.092132
54R12_RCPAgas_extr_mpen2015M1year0.100000R12_CHN2015M1year153.1541400.000653
81R12_RCPAoil_exp2015M1year1.000000R12_CHN2015M1year51.3186110.019486
85R12_RCPAoil_extr_mpen2015M1year0.100000R12_CHN2015M1year250.8044770.000399
156R12_RCPAeaf_steel2015M2year0.893333R12_CHN2015M1year72.3600000.012346
165R12_RCPAcatalytic_cracking_ref2015atm_gasoilyear5.953922R12_CHN2015vacuum_gasoilyear112.3505060.052994
173R12_RCPAimport_petro2015M1year0.120969R12_CHN2015M1year18.2706860.006621
174R12_RCPAexport_petro2015M1year0.004573R12_CHN2015M1year0.2212710.020667
175R12_RCPAgas_NH32015M1year0.165746R12_CHN2015M1year9.6629830.017153
176R12_RCPAcoal_NH32015M1year0.834254R12_CHN2015M1year48.6370170.017153
177R12_RCPAexport_NFert2015M1year0.041194R12_CHN2015M1year20.0803710.002051
180R12_RCPAmeth_exp2015feedstockyear0.000272R12_CHN2015feedstockyear0.0964870.002823
181R12_RCPAmeth_exp2015feedstockyear0.000272R12_CHN2015fuelyear0.0964870.002823
182R12_RCPAmeth_exp2015fuelyear0.000272R12_CHN2015feedstockyear0.0964870.002823
183R12_RCPAmeth_exp2015fuelyear0.000272R12_CHN2015fuelyear0.0964870.002823
184R12_RCPAmeth_imp2015feedstockyear0.177914R12_CHN2015feedstockyear3.4051330.052249
185R12_RCPAmeth_imp2015feedstockyear0.177914R12_CHN2015fuelyear3.4051330.052249
186R12_RCPAmeth_imp2015fuelyear0.177914R12_CHN2015feedstockyear3.4051330.052249
187R12_RCPAmeth_imp2015fuelyear0.177914R12_CHN2015fuelyear3.4051330.052249
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" - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "hist_act_merge[hist_act_merge[\"ratio\"]<0.111]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 38, - "outputs": [ - { - "data": { - "text/plain": " node commodity level year_all time Level Marginal \\\n0 R12_AFR NH3 final_material 2020 year 2.500000 0.0 \n1 R12_AFR NH3 final_material 2025 year 2.735064 0.0 \n2 R12_AFR NH3 final_material 2030 year 2.906112 0.0 \n3 R12_AFR NH3 final_material 2035 year 3.156448 0.0 \n4 R12_AFR NH3 final_material 2040 year 3.342780 0.0 \n... ... ... ... ... ... ... ... \n2060 R12_CHN fcoh_resin final_material 2060 year 0.282172 0.0 \n2061 R12_CHN fcoh_resin final_material 2070 year 0.215595 0.0 \n2062 R12_CHN fcoh_resin final_material 2080 year 0.094929 0.0 \n2063 R12_CHN fcoh_resin final_material 2090 year 0.030637 0.0 \n2064 R12_CHN fcoh_resin final_material 2100 year 0.011176 0.0 \n\n Lower Upper Scale \n0 4.000000e+300 3.000000e+300 1.0 \n1 4.000000e+300 3.000000e+300 1.0 \n2 4.000000e+300 3.000000e+300 1.0 \n3 4.000000e+300 3.000000e+300 1.0 \n4 4.000000e+300 3.000000e+300 1.0 \n... ... ... ... \n2060 4.000000e+300 3.000000e+300 1.0 \n2061 4.000000e+300 3.000000e+300 1.0 \n2062 4.000000e+300 3.000000e+300 1.0 \n2063 4.000000e+300 3.000000e+300 1.0 \n2064 4.000000e+300 3.000000e+300 1.0 \n\n[2065 rows x 10 columns]", - "text/html": "
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nodecommoditylevelyear_alltimeLevelMarginalLowerUpperScale
0R12_AFRNH3final_material2020year2.5000000.04.000000e+3003.000000e+3001.0
1R12_AFRNH3final_material2025year2.7350640.04.000000e+3003.000000e+3001.0
2R12_AFRNH3final_material2030year2.9061120.04.000000e+3003.000000e+3001.0
3R12_AFRNH3final_material2035year3.1564480.04.000000e+3003.000000e+3001.0
4R12_AFRNH3final_material2040year3.3427800.04.000000e+3003.000000e+3001.0
.................................
2060R12_CHNfcoh_resinfinal_material2060year0.2821720.04.000000e+3003.000000e+3001.0
2061R12_CHNfcoh_resinfinal_material2070year0.2155950.04.000000e+3003.000000e+3001.0
2062R12_CHNfcoh_resinfinal_material2080year0.0949290.04.000000e+3003.000000e+3001.0
2063R12_CHNfcoh_resinfinal_material2090year0.0306370.04.000000e+3003.000000e+3001.0
2064R12_CHNfcoh_resinfinal_material2100year0.0111760.04.000000e+3003.000000e+3001.0
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2065 rows × 10 columns

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" - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "hist_act = dataframes[\"DEMAND\"]\n", - "hist_act" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 70, - "outputs": [], - "source": [ - "#h\n", - "hist_act = hist_act[hist_act[\"year_all\"]==\"2020\"]\n", - "#hist_act = hist_act.set_index([\"commodity\", \"level\"])\n", - "hist_act_rcpa = hist_act[hist_act[\"node\"]==\"R12_RCPA\"]\n", - "hist_act_chn = hist_act[hist_act[\"node\"]==\"R12_CHN\"]\n", - "\n", - "hist_act_merge = hist_act_rcpa.join(hist_act_chn[\"Level\"], rsuffix=\"_x\")\n", - "hist_act_merge[\"ratio\"] = hist_act_merge[\"Level\"] / hist_act_merge[\"Level_x\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 71, - "outputs": [ - { - "data": { - "text/plain": " node year_all time Level Marginal \\\ncommodity level \nNH3 final_material R12_RCPA 2020 year 2.500000 0.0 \nmethanol final_material R12_RCPA 2020 year 0.204400 0.0 \ni_spec useful R12_RCPA 2020 year 16.366848 0.0 \ni_therm useful R12_RCPA 2020 year 25.932783 0.0 \nnon-comm useful R12_RCPA 2020 year 2.142900 0.0 \nrc_spec useful R12_RCPA 2020 year 9.493912 0.0 \nrc_therm useful R12_RCPA 2020 year 34.041000 0.0 \ntransport useful R12_RCPA 2020 year 55.257000 0.0 \nsteel demand R12_RCPA 2020 year 12.083479 0.0 \ncement demand R12_RCPA 2020 year 237.872109 0.0 \naluminum demand R12_RCPA 2020 year 1.999989 0.0 \nHVC demand R12_RCPA 2020 year 0.440000 0.0 \nfcoh_resin final_material R12_RCPA 2020 year 0.058829 0.0 \n\n Lower Upper Scale Level_x \\\ncommodity level \nNH3 final_material 4.000000e+300 3.000000e+300 1.0 18.000000 \nmethanol final_material 4.000000e+300 3.000000e+300 1.0 24.732400 \ni_spec useful 4.000000e+300 3.000000e+300 1.0 147.301632 \ni_therm useful 4.000000e+300 3.000000e+300 1.0 233.395048 \nnon-comm useful 4.000000e+300 3.000000e+300 1.0 19.286100 \nrc_spec useful 4.000000e+300 3.000000e+300 1.0 85.445212 \nrc_therm useful 4.000000e+300 3.000000e+300 1.0 306.369000 \ntransport useful 4.000000e+300 3.000000e+300 1.0 497.313000 \nsteel demand 4.000000e+300 3.000000e+300 1.0 980.023775 \ncement demand 4.000000e+300 3.000000e+300 1.0 2140.848982 \naluminum demand 4.000000e+300 3.000000e+300 1.0 25.998916 \nHVC demand 4.000000e+300 3.000000e+300 1.0 50.500000 \nfcoh_resin final_material 4.000000e+300 3.000000e+300 1.0 0.724910 \n\n ratio \ncommodity level \nNH3 final_material 0.138889 \nmethanol final_material 0.008264 \ni_spec useful 0.111111 \ni_therm useful 0.111111 \nnon-comm useful 0.111111 \nrc_spec useful 0.111111 \nrc_therm useful 0.111111 \ntransport useful 0.111111 \nsteel demand 0.012330 \ncement demand 0.111111 \naluminum demand 0.076926 \nHVC demand 0.008713 \nfcoh_resin final_material 0.081154 ", - "text/html": "
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nodeyear_alltimeLevelMarginalLowerUpperScaleLevel_xratio
commoditylevel
NH3final_materialR12_RCPA2020year2.5000000.04.000000e+3003.000000e+3001.018.0000000.138889
methanolfinal_materialR12_RCPA2020year0.2044000.04.000000e+3003.000000e+3001.024.7324000.008264
i_specusefulR12_RCPA2020year16.3668480.04.000000e+3003.000000e+3001.0147.3016320.111111
i_thermusefulR12_RCPA2020year25.9327830.04.000000e+3003.000000e+3001.0233.3950480.111111
non-commusefulR12_RCPA2020year2.1429000.04.000000e+3003.000000e+3001.019.2861000.111111
rc_specusefulR12_RCPA2020year9.4939120.04.000000e+3003.000000e+3001.085.4452120.111111
rc_thermusefulR12_RCPA2020year34.0410000.04.000000e+3003.000000e+3001.0306.3690000.111111
transportusefulR12_RCPA2020year55.2570000.04.000000e+3003.000000e+3001.0497.3130000.111111
steeldemandR12_RCPA2020year12.0834790.04.000000e+3003.000000e+3001.0980.0237750.012330
cementdemandR12_RCPA2020year237.8721090.04.000000e+3003.000000e+3001.02140.8489820.111111
aluminumdemandR12_RCPA2020year1.9999890.04.000000e+3003.000000e+3001.025.9989160.076926
HVCdemandR12_RCPA2020year0.4400000.04.000000e+3003.000000e+3001.050.5000000.008713
fcoh_resinfinal_materialR12_RCPA2020year0.0588290.04.000000e+3003.000000e+3001.00.7249100.081154
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" - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "hist_act_merge" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "hist_act_merge[hist_act_merge[\"ratio\"]<0.111]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 18, - "outputs": [ - { - "data": { - "text/plain": " tec ratio\n85 oil_extr_mpen 0.000399\n54 gas_extr_mpen 0.000653\n177 export_NFert 0.002051\n183 meth_exp 0.002823\n182 meth_exp 0.002823\n.. ... ...\n166 catalytic_cracking_ref 0.232963\n142 extract__freshwater_supply 0.250000\n179 import_NH3 0.331096\n155 eaf_steel 1.000000\n178 import_NFert 49.573951\n\n[188 rows x 2 columns]", - "text/html": "
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tecratio
85oil_extr_mpen0.000399
54gas_extr_mpen0.000653
177export_NFert0.002051
183meth_exp0.002823
182meth_exp0.002823
.........
166catalytic_cracking_ref0.232963
142extract__freshwater_supply0.250000
179import_NH30.331096
155eaf_steel1.000000
178import_NFert49.573951
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188 rows × 2 columns

\n
" - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "hist_act_merge[[\"tec\", \"ratio\"]].sort_values(\"ratio\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 3, - "outputs": [], - "source": [ - "act = dataframes[\"ACT\"].drop([\"time\", \"Marginal\", \"Lower\", \"Upper\", \"Scale\"], axis=1)\n", - "act = act[act[\"Level\"] != 0]\n", - "act = act.rename({\"vintage\":\"year_vtg\", \"year_all\":\"year_act\", \"node\":\"node_loc\"}, axis=1)\n", - "act = act.set_index([\"node_loc\", \"year_act\", \"tec\", \"mode\", \"year_vtg\"])" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 4, - "outputs": [], - "source": [ - "inp = dataframes[\"input\"].drop(\"time\", axis=1)\n", - "node_cols = inp[\"node\"]\n", - "node_cols.columns = [\"node_loc\", \"node_origin\"]\n", - "inp[\"node_loc\"] = node_cols[\"node_loc\"]\n", - "inp[\"node_origin\"] = node_cols[\"node_origin\"]\n", - "inp = inp.rename({\"vintage\":\"year_vtg\", \"year_all\":\"year_act\"}, axis=1)\n", - "inp = inp.drop(\"node\", axis=1)\n", - "inp = inp.set_index([\"node_loc\", \"year_act\", \"tec\", \"mode\", \"year_vtg\"])" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 5, - "outputs": [], - "source": [ - "act_in = act.join(inp)\n", - "act_in = act_in.dropna()\n", - "act_in[\"input\"] = act_in[\"Level\"] * act_in[\"Value\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 6, - "outputs": [], - "source": [ - "out = dataframes[\"output\"].drop(\"time\", axis=1)\n", - "node_cols = out[\"node\"]\n", - "node_cols.columns = [\"node_loc\", \"node_dest\"]\n", - "out[\"node_loc\"] = node_cols[\"node_loc\"]\n", - "out[\"node_origin\"] = node_cols[\"node_dest\"]\n", - "out = out.rename({\"vintage\":\"year_vtg\", \"year_all\":\"year_act\"}, axis=1)\n", - "out = out.drop(\"node\", axis=1)\n", - "out = out.set_index([\"node_loc\", \"year_act\", \"year_vtg\", \"tec\", \"mode\"])" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 7, - "outputs": [], - "source": [ - "act_out = act.join(out)\n", - "act_out = act_out.dropna()\n", - "act_out[\"output\"] = act_out[\"Level\"] * act_out[\"Value\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 8, - "outputs": [], - "source": [ - "rel = dataframes[\"relation_activity\"]\n", - "node_cols = rel[\"node\"]\n", - "node_cols.columns = [\"node_loc\", \"node_dest\"]\n", - "rel[\"node_loc\"] = node_cols[\"node_loc\"]\n", - "rel[\"node_origin\"] = node_cols[\"node_dest\"]\n", - "\n", - "year_cols = rel[\"year_all\"]\n", - "year_cols.columns = [\"year_act\", \"year_rel\"]\n", - "rel[\"year_act\"] = year_cols[\"year_act\"]\n", - "rel[\"year_rel\"] = year_cols[\"year_rel\"]\n", - "\n", - "rel = rel.drop(\"node\", axis=1)\n", - "rel = rel.drop(\"year_all\", axis=1)\n", - "rel = rel.set_index([\"node_loc\", \"year_act\", \"tec\", \"mode\"])" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 9, - "outputs": [], - "source": [ - "rel_co2emi = rel[rel[\"relation\"]==\"CO2_Emission\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 10, - "outputs": [], - "source": [ - "emi_top_down = rel_co2emi.join(act).dropna()\n", - "emi_top_down[\"emi\"] = emi_top_down[\"Value\"] * emi_top_down[\"Level\"] #<" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 11, - "outputs": [], - "source": [ - "emi_bott_up_names = [\"CO2_ind\", \"CO2_r_c\", \"CO2_trp\", \"CO2_trade\", \"CO2_shipping\", \"CO2_cc\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 12, - "outputs": [], - "source": [ - "emi_bott_up = rel[rel[\"relation\"].isin(emi_bott_up_names).values]\n", - "emi_bott_up = emi_bott_up.join(act).dropna()\n", - "emi_bott_up[\"emi\"] = emi_bott_up[\"Value\"] * emi_bott_up[\"Level\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 38, - "outputs": [ - { - "data": { - "text/plain": " Level\ntec \nbiomass_i 167.172537\ncoal_i 646.478318\nelec_i 87.922704\nfoil_i 100.367546\ngas_i 569.083035\nheat_i 176.669585\nhp_gas_i 5.982707\nloil_i 96.592091\nsolar_i 96.415719", - "text/html": "
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Level
tec
biomass_i167.172537
coal_i646.478318
elec_i87.922704
foil_i100.367546
gas_i569.083035
heat_i176.669585
hp_gas_i5.982707
loil_i96.592091
solar_i96.415719
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Level
tecyear_actnode_locmodeyear_vtg
CH4_TCE2020R12_AFRM1202010.356186
2025R12_AFRM1202511.408378
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2035R12_AFRM1203513.711018
2040R12_AFRM1204014.759977
..................
meth_imp2035R12_CHNfeedstock203524.245616
2040R12_CHNfeedstock204031.090559
2045R12_CHNfeedstock204525.668182
2050R12_CHNfeedstock205011.717664
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66620 rows × 1 columns

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" - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "act.index = act.index.swaplevel(0, 2)\n", - "act" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 6, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Level\n", - "year_act \n", - "2020 3215.410027\n", - "2025 3639.927256\n", - "2030 4032.417858\n", - "2035 4384.430552\n", - "2040 4582.811404\n", - "2045 4625.292203\n", - "2050 4473.551896\n", - "2055 4183.626996\n", - "2060 3755.588088\n", - "2070 2300.091380\n", - "2080 1324.577029\n", - "2090 857.266707\n", - "2100 796.318673\n", - "2110 1468.300602\n", - " Level\n", - "year_act \n", - "2020 39.794360\n", - "2025 68.371773\n", - "2030 116.419351\n", - "2035 192.174476\n", - "2040 317.179604\n", - "2045 476.168020\n", - "2050 703.454829\n", - "2055 1018.044083\n", - "2060 1447.998813\n", - "2070 2065.563856\n", - "2080 2384.014642\n", - "2090 3410.512507\n", - "2100 4269.243252\n", - "2110 5530.804486\n" - ] - }, - { - "data": { - "text/plain": "" - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": "
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\n" - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "fig, ax = plt.subplots()\n", - "legend = []\n", - "for i in [\"loil_trp\", \"foil_trp\"]:\n", - " print(act.loc[i].groupby([\"year_act\"]).sum())\n", - " act.loc[i].groupby([\"year_act\"]).sum().reset_index().plot(ax=ax, x=\"year_act\", y=\"Level\")\n", - " legend.append(i)\n", - "ax.legend(legend)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## identify missing relations" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 16, - "outputs": [], - "source": [ - "rel[\"count\"] = 1\n", - "tecs = list(rel[rel.index.get_level_values(2).str.startswith(\"meth_\")].index.get_level_values(2).unique())\n", - "\n", - "meth_rel = rel.swaplevel(0,2).loc[tecs]\n", - "counts = meth_rel[~meth_rel.index.get_level_values(1).isin([\"1990\", \"1995\", \"2000\", \"2005\", \"2010\", \"2015\"])].groupby([\"tec\",\"year_act\", \"node_loc\", \"relation\"]).sum()[\"count\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 17, - "outputs": [], - "source": [ - "fuel = meth_rel.swaplevel(0,3).loc[\"fuel\"]\n", - "fuel[\"df\"] = \"fuel\"\n", - "\n", - "m1 = meth_rel.swaplevel(0,3).loc[\"M1\"]\n", - "m1[\"df\"] = \"M1\"\n", - "\n", - "w_labels = pd.concat([fuel, m1]).reset_index()\n", - "wo_labels = pd.concat([fuel.drop(\"df\", axis=1), m1.drop(\"df\", axis=1)]).reset_index()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 18, - "outputs": [ - { - "data": { - "text/plain": " year_act node_loc tec relation Value node_origin year_rel \\\n0 1990 R12_GLB meth_trd CO2_trade 0.005545 R12_GLB 1990 \n1 1995 R12_GLB meth_trd CO2_trade 0.005545 R12_GLB 1995 \n2 2000 R12_GLB meth_trd CO2_trade 0.005545 R12_GLB 2000 \n3 2005 R12_GLB meth_trd CO2_trade 0.005545 R12_GLB 2005 \n4 2010 R12_GLB meth_trd CO2_trade 0.005545 R12_GLB 2010 \n... ... ... ... ... ... ... ... \n13679 2070 R12_CHN meth_t_d FE_liquids 1.000000 R12_CHN 2070 \n13680 2080 R12_CHN meth_t_d FE_liquids 1.000000 R12_CHN 2080 \n13681 2090 R12_CHN meth_t_d FE_liquids 1.000000 R12_CHN 2090 \n13682 2100 R12_CHN meth_t_d FE_liquids 1.000000 R12_CHN 2100 \n13683 2110 R12_CHN meth_t_d FE_liquids 1.000000 R12_CHN 2110 \n\n count df \n0 1 fuel \n1 1 fuel \n2 1 fuel \n3 1 fuel \n4 1 fuel \n... ... ... \n13679 1 fuel \n13680 1 fuel \n13681 1 fuel \n13682 1 fuel \n13683 1 fuel \n\n[13684 rows x 9 columns]", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
year_actnode_loctecrelationValuenode_originyear_relcountdf
01990R12_GLBmeth_trdCO2_trade0.005545R12_GLB19901fuel
11995R12_GLBmeth_trdCO2_trade0.005545R12_GLB19951fuel
22000R12_GLBmeth_trdCO2_trade0.005545R12_GLB20001fuel
32005R12_GLBmeth_trdCO2_trade0.005545R12_GLB20051fuel
42010R12_GLBmeth_trdCO2_trade0.005545R12_GLB20101fuel
..............................
136792070R12_CHNmeth_t_dFE_liquids1.000000R12_CHN20701fuel
136802080R12_CHNmeth_t_dFE_liquids1.000000R12_CHN20801fuel
136812090R12_CHNmeth_t_dFE_liquids1.000000R12_CHN20901fuel
136822100R12_CHNmeth_t_dFE_liquids1.000000R12_CHN21001fuel
136832110R12_CHNmeth_t_dFE_liquids1.000000R12_CHN21101fuel
\n

13684 rows × 9 columns

\n
" - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "diff = wo_labels.drop_duplicates(keep=False).merge(w_labels[\"df\"], left_index=True, right_index=True)\n", - "diff[diff[\"df\"]==\"fuel\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## co2 emissions analysis" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 19, - "outputs": [], - "source": [ - "def get_bot_up_emi_by_fuel(df, node, year):\n", - " #df = df.loc[node, year]\n", - "\n", - " df_oil = df[df.index.get_level_values(0).str.contains(\"oil\") |\n", - " (df.index.get_level_values(0).str.contains(\"liq\")) |\n", - " (df.index.get_level_values(0).str.contains(\"treat\")) |\n", - " (df.index.get_level_values(0).str.contains(\"coki\")) |\n", - " (df.index.get_level_values(0).str.contains(\"cata\"))\n", - " ].sort_values(\"emi\")\n", - " df_oil = df_oil[df_oil.index.get_level_values(0) != \"liq_bio_ccs\"]\n", - "\n", - " df_gas = df[(\n", - " (df.index.get_level_values(0).str.contains(\"gas\")) |\n", - " (df.index.get_level_values(0).str.contains(\"meth_ng\")) |\n", - " (df.index.get_level_values(0).str.contains(\"smr\")) |\n", - " (df.index.get_level_values(0) == \"h2_mix\") |\n", - " (df.index.get_level_values(0).str.contains(\"flar\"))\n", - " )].sort_values(\"emi\")\n", - "\n", - " df_coal = df[(df.index.get_level_values(0).str.contains(\"coal\")) | (df.index.get_level_values(0).str.contains(\"igcc\"))].sort_values(\"emi\")\n", - "\n", - " return df_gas, df_oil, df_coal" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 20, - "outputs": [], - "source": [ - "def get_top_dn_emi_by_fuel(df, node, year):\n", - " #df = df.loc[node, year]\n", - "\n", - " top_gas = df[((df.index.get_level_values(0).str.contains(\"gas\")) |\n", - " (df.index.get_level_values(0).str.contains(\"LNG\")) |\n", - " (df.index.get_level_values(1).str.contains(\"ane\")) |\n", - " (df.index.get_level_values(0).str.contains(\"h2_smr\")) |\n", - " (df.index.get_level_values(0).str.contains(\"flar\")))]#.sum()\n", - "\n", - " top_oil = df[(df.index.get_level_values(0).str.contains(\"oil\"))\n", - " |(df.index.get_level_values(1).str.contains(\"gasoil\"))].sort_values(\"emi\")#.sum()\n", - " #top_oil = top_oil[top_oil.index.get_level_values(0) != \"liq_bio_ccs\"]\n", - " top_coal = df[(df.index.get_level_values(0).str.contains(\"coal\")) |\n", - " (df.index.get_level_values(0).str.contains(\"lign\"))].sort_values(\"emi\")#.sum()\n", - "\n", - " return top_gas, top_oil, top_coal" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 21, - "outputs": [], - "source": [ - "elec_list = [\n", - " \"coal_ppl_u\",\n", - " \"coal_ppl\",\n", - " \"coal_adv\",\n", - " \"coal_adv_ccs\",\n", - " \"igcc\",\n", - " \"igcc_ccs\",\n", - " \"foil_ppl\",\n", - " \"loil_ppl\",\n", - " \"loil_cc\",\n", - " \"oil_ppl\",\n", - " \"gas_ppl\",\n", - " \"gas_cc\",\n", - " \"gas_cc_ccs\",\n", - " \"gas_ct\",\n", - " \"gas_htfc\",\n", - " \"bio_istig\",\n", - " \"g_ppl_co2scr\",\n", - " \"c_ppl_co2scr\",\n", - " \"bio_ppl_co2scr\",\n", - " \"igcc_co2scr\",\n", - " \"gfc_co2scr\",\n", - " \"cfc_co2scr\",\n", - " \"bio_istig_ccs\",\n", - " ]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 22, - "outputs": [], - "source": [ - "region = \"R12_LAM\"\n", - "year = \"2020\"" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## get global emissions for specific year" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 23, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\maczek\\AppData\\Local\\Temp\\ipykernel_17352\\2128802406.py:7: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " emi_bot_up_new = emi_bot_up_new.append(emi_top_down_new[emi_top_down_new.index.get_level_values(0) == \"meth_bio_ccs\"])\n" - ] - } - ], - "source": [ - "emi_top_down_new = emi_top_down.reset_index().set_index([\"tec\", \"mode\"])\n", - "emi_bot_up_new = emi_bott_up.reset_index().set_index([\"tec\", \"mode\"])\n", - "\n", - "emi_top_down_new = emi_top_down_new[emi_top_down_new[\"year_act\"]==year]\n", - "emi_bot_up_new = emi_bot_up_new[emi_bot_up_new[\"year_act\"]==year]\n", - "\n", - "emi_bot_up_new = emi_bot_up_new.append(emi_top_down_new[emi_top_down_new.index.get_level_values(0) == \"meth_bio_ccs\"])\n", - "emi_top_down_new = emi_top_down_new.drop(\"CO2_TCE\")\n", - "emi_bot_up_new = emi_bot_up_new.drop([\"CO2t_TCE\", \"CO2s_TCE\"])\n", - "\n", - "emi_bot_up_new = pd.concat([emi_bot_up_new, emi_top_down_new[emi_top_down_new.index.get_level_values(0).str.contains(\"alu\")]])\n", - "emi_bot_up_new = pd.concat([emi_bot_up_new, emi_top_down_new[emi_top_down_new.index.get_level_values(0).str.contains(\"clink\")]])\n", - "\n", - "top_new_gas, top_new_oil, top_new_coal = get_top_dn_emi_by_fuel(emi_top_down_new[emi_top_down_new[\"year_act\"]==year], \"test\", \"test\")\n", - "bot_new_gas, bot_new_oil, bot_new_coal = get_bot_up_emi_by_fuel(emi_bot_up_new[emi_bot_up_new[\"year_act\"]==year], \"test\", \"test\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 24, - "outputs": [], - "source": [ - "oil_tecs = ['hydrotreating_ref', 'catalytic_reforming_ref', 'oil_extr_1_ch4',\n", - " 'furnace_foil_petro', 'loil_t_d', 'oil_extr_2_ch4', 'foil_hpl',\n", - " 'foil_t_d', 'furnace_foil_refining', 'oil_extr_2', 'oil_extr_1',\n", - " 'oil_extr_3', 'furnace_foil_aluminum', 'loil_rc', 'oil_extr_3_ch4',\n", - " 'furnace_foil_cement', 'oil_extr_4_ch4', 'foil_trd', 'oil_trd',\n", - "\n", - " 'fueloil_NH3', 'fueloil_NH3_ccs', 'loil_i', 'foil_i', 'foil_trp', 'loil_trp', 'loil_bunker',\n", - " 'foil_bunker', 'oil_ppl', 'loil_ppl', 'loil_cc', 'foil_rc', 'foil_ppl',\n", - " 'catalytic_cracking_ref', 'loil_trd', 'oil_extr_4', \"coking_ref\", 'LNG_bunker', 'sp_liq_I']\n", - "bot_new_oil = emi_bot_up_new[(emi_bot_up_new.index.get_level_values(0).isin(oil_tecs))]\n", - "\n", - "coal_tecs = ['furnace_coal_aluminum', 'coal_hpl', 'furnace_coal_refining',\n", - " 'furnace_coal_cement', 'DUMMY_coal_supply', 'h2_coal_ccs',\n", - " 'coal_NH3_ccs',\n", - "\n", - " 'coal_ppl', 'coal_ppl_u', 'meth_coal', 'meth_coal_ccs', 'coal_rc', 'coal_NH3', 'coal_adv', 'coal_i', 'coal_gas'\n", - " ]\n", - "bot_new_coal = emi_bot_up_new[(emi_bot_up_new.index.get_level_values(0).isin(coal_tecs))]\n", - "\n", - "gas = ['h2_mix', 'gas_t_d', 'gas_hpl', 'furnace_gas_petro', 'gas_extr_2',\n", - " 'gas_extr_1', 'gas_t_d_ch4', 'gas_NH3_ccs', 'furnace_gas_refining',\n", - " 'gas_extr_3', 'DUMMY_gas_supply', 'gas_cc', 'gas_extr_4', 'gas_extr_5',\n", - " 'gas_extr_6', 'gas_i', 'gas_trp',\n", - "\n", - " 'gas_NH3','gas_cc_ccs', 'hp_gas_rc', 'hp_gas_i', 'gas_rc', 'gas_ct',\n", - " 'gas_ppl', 'meth_ng_ccs', 'h2_smr_ccs', 'meth_ng', 'LNG_trd', 'h2_smr',\n", - "]\n", - "bot_new_gas = emi_bot_up_new[(emi_bot_up_new.index.get_level_values(0).isin(gas))]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 25, - "outputs": [], - "source": [ - "top_new_trd = emi_top_down_new[(emi_top_down_new.index.get_level_values(0).str.contains(\"exp\")) | (emi_top_down_new.index.get_level_values(0).str.contains(\"imp\"))]\n", - "bot_new_trd = emi_bot_up_new[(emi_bot_up_new.index.get_level_values(0).str.contains(\"exp\")) | (emi_bot_up_new.index.get_level_values(0).str.contains(\"imp\"))]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## residual carbon emissions" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 26, - "outputs": [], - "source": [ - "bot_new_rest = emi_bot_up_new[~(emi_bot_up_new.index.get_level_values(0).isin(bot_new_coal.index.get_level_values(0))) &\n", - " ~(emi_bot_up_new.index.get_level_values(0).isin(bot_new_oil.index.get_level_values(0))) &\n", - " ~(emi_bot_up_new.index.get_level_values(0).isin(bot_new_gas.index.get_level_values(0)))\n", - "].sort_values(\"emi\")\n", - "\n", - "top_new_rest = emi_top_down_new[~(emi_top_down_new.index.get_level_values(0).isin(top_new_oil.index.get_level_values(0))) &\n", - " ~(emi_top_down_new.index.get_level_values(0).isin(top_new_gas.index.get_level_values(0))) &\n", - " ~(emi_top_down_new.index.get_level_values(0).isin(top_new_coal.index.get_level_values(0))) &\n", - " ~(emi_top_down_new.index.get_level_values(0).isin(top_new_trd.index.get_level_values(0)))\n", - "].sort_values(\"emi\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 27, - "outputs": [ - { - "data": { - "text/plain": "211.23699782794165" - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bot_new_rest.sum()[\"emi\"] * (44/12) - top_new_rest.sum()[\"emi\"] * (44/12)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 28, - "outputs": [ - { - "data": { - "text/plain": "423.78991342878" - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "emi_bot_up_new.sum().emi * (44/12) - emi_top_down_new.sum().emi * (44/12)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 29, - "outputs": [ - { - "data": { - "text/plain": "-12.097574083883933" - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "top_new_rest[top_new_rest[\"emi\"]<0].loc[\"meth_t_d\"].sum()[\"emi\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 30, - "outputs": [ - { - "data": { - "text/plain": "-44.35777164090775" - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "top_new_rest.loc[\"meth_t_d\"].sum().emi * co2_c_factor\n", - "#top_new_rest.loc[\"clinker_dry_cement\"].sum().emi * co2_c_factor" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 31, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Index([], dtype='object', name='tec')\n", - "Index(['meth_t_d'], dtype='object', name='tec')\n", - "Index(['CH2O_synth', 'soderberg_aluminum', 'eth_t_d', 'flaring_CO2',\n", - " 'prebake_aluminum', 'meth_trd', 'furnace_methanol_cement',\n", - " 'DUMMY_limestone_supply_steel', 'MTO_petro', 'h2_coal',\n", - " 'clinker_wet_cement', 'clinker_dry_cement', 'igcc'],\n", - " dtype='object', name='tec')\n", - "Index(['soderberg_aluminum', 'prebake_aluminum', 'clinker_wet_cement',\n", - " 'clinker_dry_cement'],\n", - " dtype='object', name='tec')\n" - ] - } - ], - "source": [ - "print(bot_new_rest[bot_new_rest[\"emi\"]<0].drop_duplicates().index.get_level_values(0).unique())\n", - "print(top_new_rest[top_new_rest[\"emi\"]<0].drop_duplicates().index.get_level_values(0).unique())\n", - "print(bot_new_rest[bot_new_rest[\"emi\"]>0].drop_duplicates().index.get_level_values(0).unique())\n", - "print(top_new_rest[top_new_rest[\"emi\"]>0].drop_duplicates().index.get_level_values(0).unique())" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 32, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "negative residual emission bottom-up: 0.0\n", - "negative residual emission top-down: -44.35777164090775\n", - "positive residual emission bottom-up: 2137.403984064366\n", - "positive residual emission top-down: 1970.5247578773321\n" - ] - } - ], - "source": [ - "print(\"negative residual emission bottom-up: \", bot_new_rest[bot_new_rest[\"emi\"]<0].drop_duplicates().sum()[\"emi\"] * (44/12))\n", - "print(\"negative residual emission top-down: \", top_new_rest[top_new_rest[\"emi\"]<0].sum()[\"emi\"] * (44/12))\n", - "print(\"positive residual emission bottom-up: \", bot_new_rest[bot_new_rest[\"emi\"]>0].sum()[\"emi\"] * (44/12))\n", - "print(\"positive residual emission top-down: \", top_new_rest[top_new_rest[\"emi\"]>0].sum()[\"emi\"] * (44/12))" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## check trade carbon balances" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 33, - "outputs": [ - { - "data": { - "text/plain": " node_loc year_act year_vtg relation Value node_origin \\\ntec mode \nloil_exp M1 R12_AFR 2020 2015 CO2_Emission -0.631 R12_AFR \nloil_imp M1 R12_AFR 2020 2020 CO2_Emission 0.631 R12_AFR \n M1 R12_CHN 2020 2020 CO2_Emission 0.631 R12_CHN \nloil_exp M1 R12_FSU 2020 2000 CO2_Emission -0.631 R12_FSU \n M1 R12_FSU 2020 2005 CO2_Emission -0.631 R12_FSU \n M1 R12_FSU 2020 2015 CO2_Emission -0.631 R12_FSU \n M1 R12_FSU 2020 2020 CO2_Emission -0.631 R12_FSU \n M1 R12_LAM 2020 2015 CO2_Emission -0.631 R12_LAM \n M1 R12_MEA 2020 2000 CO2_Emission -0.631 R12_MEA \n M1 R12_MEA 2020 2005 CO2_Emission -0.631 R12_MEA \n M1 R12_MEA 2020 2010 CO2_Emission -0.631 R12_MEA \n M1 R12_MEA 2020 2020 CO2_Emission -0.631 R12_MEA \n M1 R12_NAM 2020 2000 CO2_Emission -0.631 R12_NAM \n M1 R12_NAM 2020 2015 CO2_Emission -0.631 R12_NAM \nloil_imp M1 R12_NAM 2020 2020 CO2_Emission 0.631 R12_NAM \n M1 R12_PAO 2020 2020 CO2_Emission 0.631 R12_PAO \n M1 R12_RCPA 2020 2020 CO2_Emission 0.631 R12_RCPA \nloil_exp M1 R12_SAS 2020 2015 CO2_Emission -0.631 R12_SAS \nloil_imp M1 R12_SAS 2020 2020 CO2_Emission 0.631 R12_SAS \n M1 R12_WEU 2020 2020 CO2_Emission 0.631 R12_WEU \n\n year_rel Level emi \ntec mode \nloil_exp M1 2020 0.399258 -0.251931 \nloil_imp M1 2020 47.653442 30.069322 \n M1 2020 39.268683 24.778539 \nloil_exp M1 2020 10.142300 -6.399791 \n M1 2020 40.339789 -25.454407 \n M1 2020 13.855435 -8.742779 \n M1 2020 39.340764 -24.824022 \n M1 2020 40.069433 -25.283812 \n M1 2020 51.848149 -32.716182 \n M1 2020 23.071599 -14.558179 \n M1 2020 51.197000 -32.305307 \n M1 2020 127.024324 -80.152349 \n M1 2020 10.000000 -6.310000 \n M1 2020 54.575404 -34.437080 \nloil_imp M1 2020 302.049754 190.593395 \n M1 2020 9.866751 6.225920 \n M1 2020 5.307703 3.349160 \nloil_exp M1 2020 15.521467 -9.794046 \nloil_imp M1 2020 33.309633 21.018378 \n M1 2020 23.414262 14.774399 ", - 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node_locyear_actyear_vtgrelationValuenode_originyear_relLevelemi
tecmode
loil_expM1R12_AFR20202015CO2_Emission-0.631R12_AFR20200.399258-0.251931
loil_impM1R12_AFR20202020CO2_Emission0.631R12_AFR202047.65344230.069322
M1R12_CHN20202020CO2_Emission0.631R12_CHN202039.26868324.778539
loil_expM1R12_FSU20202000CO2_Emission-0.631R12_FSU202010.142300-6.399791
M1R12_FSU20202005CO2_Emission-0.631R12_FSU202040.339789-25.454407
M1R12_FSU20202015CO2_Emission-0.631R12_FSU202013.855435-8.742779
M1R12_FSU20202020CO2_Emission-0.631R12_FSU202039.340764-24.824022
M1R12_LAM20202015CO2_Emission-0.631R12_LAM202040.069433-25.283812
M1R12_MEA20202000CO2_Emission-0.631R12_MEA202051.848149-32.716182
M1R12_MEA20202005CO2_Emission-0.631R12_MEA202023.071599-14.558179
M1R12_MEA20202010CO2_Emission-0.631R12_MEA202051.197000-32.305307
M1R12_MEA20202020CO2_Emission-0.631R12_MEA2020127.024324-80.152349
M1R12_NAM20202000CO2_Emission-0.631R12_NAM202010.000000-6.310000
M1R12_NAM20202015CO2_Emission-0.631R12_NAM202054.575404-34.437080
loil_impM1R12_NAM20202020CO2_Emission0.631R12_NAM2020302.049754190.593395
M1R12_PAO20202020CO2_Emission0.631R12_PAO20209.8667516.225920
M1R12_RCPA20202020CO2_Emission0.631R12_RCPA20205.3077033.349160
loil_expM1R12_SAS20202015CO2_Emission-0.631R12_SAS202015.521467-9.794046
loil_impM1R12_SAS20202020CO2_Emission0.631R12_SAS202033.30963321.018378
M1R12_WEU20202020CO2_Emission0.631R12_WEU202023.41426214.774399
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" - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "emi_top_down_new[emi_top_down_new.index.get_level_values(0).str.contains(\"loil\")]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 34, - "outputs": [ - { - "data": { - "text/plain": "\"['gas_exp_chn', 'gas_exp_cpa', 'gas_exp_eeu', 'gas_exp_sas', 'gas_exp_weu', 'gas_imp', 'loil_exp']\"" - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "trd_tecs = sorted(list(emi_top_down_new.loc[(emi_top_down_new.index.get_level_values(0).str.contains(\"exp\")) | (emi_top_down_new.index.get_level_values(0).str.contains(\"imp\")) | (emi_top_down_new.index.get_level_values(0).str.contains(\"LNG\"))].index.get_level_values(0).unique()))#.sum()\n", - "comm_trd = [*trd_tecs[6:13]]#, trd_tecs[0]]\n", - "str(comm_trd)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 35, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "total top-down: 10106.387931650237\n" - ] - } - ], - "source": [ - "print(\"total top-down: \", emi_top_down_new.sum()[\"emi\"])" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 36, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "top-down trade balance: -309.90262543642564\n" - ] - } - ], - "source": [ - "print(\"top-down trade balance: \", emi_top_down_new.loc[(emi_top_down_new.index.get_level_values(0).isin(comm_trd))].sum()[\"emi\"])" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 37, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " top-down bottom-up\n", - "coal: 15310.756509423185 15367.135755810177\n", - "oil: 12224.570955760117 12550.335173360832\n", - "gas: 7622.870492500809 7425.670749577608\n", - "total: 35158.19795768411 35343.141678748616\n" - ] - } - ], - "source": [ - "print(\" top-down bottom-up\")\n", - "print(\"coal: \", top_new_coal.sum()[\"emi\"] * co2_c_factor,\" \", bot_new_coal.sum()[\"emi\"] * co2_c_factor)\n", - "print(\"oil: \", top_new_oil.sum()[\"emi\"] * co2_c_factor, \" \", bot_new_oil.sum()[\"emi\"] * co2_c_factor)\n", - "print(\"gas: \", top_new_gas.sum()[\"emi\"] * co2_c_factor,\" \", bot_new_gas.sum()[\"emi\"] * co2_c_factor)\n", - "print(\"total: \", (top_new_coal.sum()[\"emi\"]+top_new_oil.sum()[\"emi\"]+top_new_gas.sum()[\"emi\"]) * co2_c_factor,\" \", (bot_new_coal.sum()[\"emi\"]+bot_new_oil.sum()[\"emi\"]+bot_new_gas.sum()[\"emi\"]) * co2_c_factor)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## carbon IO gas" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 38, - "outputs": [], - "source": [ - "emi_factor_gas = 0.482" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 39, - "outputs": [ - { - "data": { - "text/plain": "node_loc year_act tec mode year_vtg\nR12_LAM 2020 gas_extr_mpen M1 2020 139.81507\nName: output, dtype: float64" - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "act_out.loc[region, year, [\"gas_extr_mpen\"]][\"output\"] * emi_factor_gas" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 40, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\maczek\\AppData\\Local\\Temp\\ipykernel_17352\\2951075765.py:1: FutureWarning: The behavior of indexing on a MultiIndex with a nested sequence of labels is deprecated and will change in a future version. `series.loc[label, sequence]` will raise if any members of 'sequence' or not present in the index's second level. To retain the old behavior, use `series.index.isin(sequence, level=1)`\n", - " inp_atm_dist = act_in.loc[region, year, [\"gas_bal\", \"gas_exp_nam\", \"LNG_prod\", \"gas_exp_cpa\", \"gas_exp_chn\", \"gas_exp_eeu\", \"gas_exp_sas\",\"gas_exp_weu\"]]\n" - ] - }, - { - "data": { - "text/plain": " Level commodity level \\\nnode_loc year_act tec mode year_vtg \nR12_LAM 2020 gas_bal M1 2020 252.783802 gas primary \n LNG_prod M1 2020 37.305296 gas primary \n\n Value node_origin input \nnode_loc year_act tec mode year_vtg \nR12_LAM 2020 gas_bal M1 2020 1.0 R12_LAM 252.783802 \n LNG_prod M1 2020 1.0 R12_LAM 37.305296 ", - "text/html": "
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LevelcommoditylevelValuenode_origininput
node_locyear_acttecmodeyear_vtg
R12_LAM2020gas_balM12020252.783802gasprimary1.0R12_LAM252.783802
LNG_prodM1202037.305296gasprimary1.0R12_LAM37.305296
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" - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "inp_atm_dist = act_in.loc[region, year, [\"gas_bal\", \"gas_exp_nam\", \"LNG_prod\", \"gas_exp_cpa\", \"gas_exp_chn\", \"gas_exp_eeu\", \"gas_exp_sas\",\"gas_exp_weu\"]]\n", - "inp_atm_dist#.sum()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 41, - "outputs": [ - { - "data": { - "text/plain": "node_loc year_act tec mode year_vtg\nR12_LAM 2020 gas_bal M1 2020 121.841792\n LNG_prod M1 2020 17.981153\nName: input, dtype: float64" - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#in_gas_proc = act_in.loc[region, year, \"gas_processing_petro\"]\n", - "(inp_atm_dist[\"input\"] * emi_factor_gas)#.sum()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 42, - "outputs": [ - { - "data": { - "text/plain": "tec\nfurnace_gas_refining 22.566977\ngas_NH3 2.610735\ngas_cc 38.983500\ngas_ppl 2.953142\ngas_t_d 47.083041\nh2_smr 3.473940\nmeth_ng 4.170458\nName: input, dtype: float64" - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(act_in.loc[region, year, act_in[\"level\"]==\"secondary\", act_in[\"commodity\"]==\"gas\"][\"input\"]\n", - " * emi_factor_gas).groupby(\"tec\").sum()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 43, - "outputs": [ - { - "data": { - "text/plain": "tec\ndri_steel 0.428896\neaf_steel 0.019407\nfurnace_gas_petro 2.164642\ngas_i 18.358918\ngas_processing_petro 2.809953\ngas_rc 15.804027\nhp_gas_aluminum 0.014086\nhp_gas_rc 1.386845\nName: input, dtype: float64" - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(act_in.loc[region, year, act_in[\"level\"]==\"final\", act_in[\"commodity\"]==\"gas\"][\"input\"] * emi_factor_gas).groupby(\"tec\").sum()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 44, - "outputs": [ - { - "data": { - "text/plain": "2.932242552118641" - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(act_out.loc[region, year, act_out[\"commodity\"].isin([\"ethane\", \"propane\"])][\"output\"] * 0.81).sum()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 45, - "outputs": [ - { - "data": { - "text/plain": "2.0096798752590517" - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(act_out.loc[region, year, act_out.index.get_level_values(3).isin([\"ethane\", \"propane\"])][\"output\"] * 0.5).sum()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 46, - "outputs": [ - { - "data": { - "text/plain": "1.2016059460355528" - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "act_out.loc[region, year, \"steam_cracker_petro\", act_out[\"commodity\"]==\"gas\"].sum()[\"output\"] * emi_factor_gas" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## carbon IO oil" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 47, - "outputs": [ - { - "ename": "KeyError", - "evalue": "('R12_LAM', '2020', 'oil_imp')", - "output_type": "error", - "traceback": [ - "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[1;31mKeyError\u001B[0m Traceback (most recent call last)", - "Input \u001B[1;32mIn [47]\u001B[0m, in \u001B[0;36m\u001B[1;34m()\u001B[0m\n\u001B[0;32m 2\u001B[0m out_oil_extr \u001B[38;5;241m=\u001B[39m act_out\u001B[38;5;241m.\u001B[39mloc[region, year, extr_tec]\n\u001B[0;32m 3\u001B[0m out_oil_exp \u001B[38;5;241m=\u001B[39m act_out\u001B[38;5;241m.\u001B[39mloc[region, year, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124moil_exp\u001B[39m\u001B[38;5;124m\"\u001B[39m]\n\u001B[1;32m----> 4\u001B[0m in_oil_imp \u001B[38;5;241m=\u001B[39m \u001B[43mact_in\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mloc\u001B[49m\u001B[43m[\u001B[49m\u001B[43mregion\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43myear\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43moil_imp\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\n\u001B[0;32m 6\u001B[0m carbon_in_extr \u001B[38;5;241m=\u001B[39m (out_oil_extr\u001B[38;5;241m.\u001B[39msum()[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124moutput\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m-\u001B[39m out_oil_exp\u001B[38;5;241m.\u001B[39msum()[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124moutput\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m+\u001B[39m in_oil_imp\u001B[38;5;241m.\u001B[39msum()[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124minput\u001B[39m\u001B[38;5;124m\"\u001B[39m]) \u001B[38;5;241m*\u001B[39m \u001B[38;5;241m0.631\u001B[39m\n\u001B[0;32m 7\u001B[0m carbon_in_extr\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:961\u001B[0m, in \u001B[0;36m_LocationIndexer.__getitem__\u001B[1;34m(self, key)\u001B[0m\n\u001B[0;32m 959\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_is_scalar_access(key):\n\u001B[0;32m 960\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mobj\u001B[38;5;241m.\u001B[39m_get_value(\u001B[38;5;241m*\u001B[39mkey, takeable\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_takeable)\n\u001B[1;32m--> 961\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_getitem_tuple\u001B[49m\u001B[43m(\u001B[49m\u001B[43mkey\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 962\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m 963\u001B[0m \u001B[38;5;66;03m# we by definition only have the 0th axis\u001B[39;00m\n\u001B[0;32m 964\u001B[0m axis \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39maxis \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;241m0\u001B[39m\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1140\u001B[0m, in \u001B[0;36m_LocIndexer._getitem_tuple\u001B[1;34m(self, tup)\u001B[0m\n\u001B[0;32m 1138\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m suppress(IndexingError):\n\u001B[0;32m 1139\u001B[0m tup \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_expand_ellipsis(tup)\n\u001B[1;32m-> 1140\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_getitem_lowerdim\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtup\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 1142\u001B[0m \u001B[38;5;66;03m# no multi-index, so validate all of the indexers\u001B[39;00m\n\u001B[0;32m 1143\u001B[0m tup \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_validate_tuple_indexer(tup)\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:859\u001B[0m, in \u001B[0;36m_LocationIndexer._getitem_lowerdim\u001B[1;34m(self, tup)\u001B[0m\n\u001B[0;32m 849\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m (\n\u001B[0;32m 850\u001B[0m \u001B[38;5;28misinstance\u001B[39m(ax0, MultiIndex)\n\u001B[0;32m 851\u001B[0m \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mname \u001B[38;5;241m!=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124miloc\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 856\u001B[0m \u001B[38;5;66;03m# is equivalent.\u001B[39;00m\n\u001B[0;32m 857\u001B[0m \u001B[38;5;66;03m# (see the other place where we call _handle_lowerdim_multi_index_axis0)\u001B[39;00m\n\u001B[0;32m 858\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m suppress(IndexingError):\n\u001B[1;32m--> 859\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_handle_lowerdim_multi_index_axis0\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtup\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 861\u001B[0m tup \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_validate_key_length(tup)\n\u001B[0;32m 863\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m i, key \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28menumerate\u001B[39m(tup):\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1166\u001B[0m, in \u001B[0;36m_LocIndexer._handle_lowerdim_multi_index_axis0\u001B[1;34m(self, tup)\u001B[0m\n\u001B[0;32m 1162\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m ek:\n\u001B[0;32m 1163\u001B[0m \u001B[38;5;66;03m# raise KeyError if number of indexers match\u001B[39;00m\n\u001B[0;32m 1164\u001B[0m \u001B[38;5;66;03m# else IndexingError will be raised\u001B[39;00m\n\u001B[0;32m 1165\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mndim \u001B[38;5;241m<\u001B[39m \u001B[38;5;28mlen\u001B[39m(tup) \u001B[38;5;241m<\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mobj\u001B[38;5;241m.\u001B[39mindex\u001B[38;5;241m.\u001B[39mnlevels:\n\u001B[1;32m-> 1166\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m ek\n\u001B[0;32m 1167\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m IndexingError(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mNo label returned\u001B[39m\u001B[38;5;124m\"\u001B[39m) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01mek\u001B[39;00m\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1160\u001B[0m, in \u001B[0;36m_LocIndexer._handle_lowerdim_multi_index_axis0\u001B[1;34m(self, tup)\u001B[0m\n\u001B[0;32m 1157\u001B[0m axis \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39maxis \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;241m0\u001B[39m\n\u001B[0;32m 1158\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m 1159\u001B[0m \u001B[38;5;66;03m# fast path for series or for tup devoid of slices\u001B[39;00m\n\u001B[1;32m-> 1160\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_get_label\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtup\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43maxis\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43maxis\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 1162\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m ek:\n\u001B[0;32m 1163\u001B[0m \u001B[38;5;66;03m# raise KeyError if number of indexers match\u001B[39;00m\n\u001B[0;32m 1164\u001B[0m \u001B[38;5;66;03m# else IndexingError will be raised\u001B[39;00m\n\u001B[0;32m 1165\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mndim \u001B[38;5;241m<\u001B[39m \u001B[38;5;28mlen\u001B[39m(tup) \u001B[38;5;241m<\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mobj\u001B[38;5;241m.\u001B[39mindex\u001B[38;5;241m.\u001B[39mnlevels:\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1153\u001B[0m, in \u001B[0;36m_LocIndexer._get_label\u001B[1;34m(self, label, axis)\u001B[0m\n\u001B[0;32m 1151\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m_get_label\u001B[39m(\u001B[38;5;28mself\u001B[39m, label, axis: \u001B[38;5;28mint\u001B[39m):\n\u001B[0;32m 1152\u001B[0m \u001B[38;5;66;03m# GH#5667 this will fail if the label is not present in the axis.\u001B[39;00m\n\u001B[1;32m-> 1153\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mobj\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mxs\u001B[49m\u001B[43m(\u001B[49m\u001B[43mlabel\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43maxis\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43maxis\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:3857\u001B[0m, in \u001B[0;36mNDFrame.xs\u001B[1;34m(self, key, axis, level, drop_level)\u001B[0m\n\u001B[0;32m 3854\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_consolidate_inplace()\n\u001B[0;32m 3856\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(index, MultiIndex):\n\u001B[1;32m-> 3857\u001B[0m loc, new_index \u001B[38;5;241m=\u001B[39m \u001B[43mindex\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_get_loc_level\u001B[49m\u001B[43m(\u001B[49m\u001B[43mkey\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mlevel\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m0\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[0;32m 3858\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m drop_level:\n\u001B[0;32m 3859\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m lib\u001B[38;5;241m.\u001B[39mis_integer(loc):\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\multi.py:3052\u001B[0m, in \u001B[0;36mMultiIndex._get_loc_level\u001B[1;34m(self, key, level)\u001B[0m\n\u001B[0;32m 3049\u001B[0m \u001B[38;5;28;01mpass\u001B[39;00m\n\u001B[0;32m 3051\u001B[0m \u001B[38;5;66;03m# partial selection\u001B[39;00m\n\u001B[1;32m-> 3052\u001B[0m indexer \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_loc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mkey\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 3053\u001B[0m ilevels \u001B[38;5;241m=\u001B[39m [i \u001B[38;5;28;01mfor\u001B[39;00m i \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mrange\u001B[39m(\u001B[38;5;28mlen\u001B[39m(key)) \u001B[38;5;28;01mif\u001B[39;00m key[i] \u001B[38;5;241m!=\u001B[39m \u001B[38;5;28mslice\u001B[39m(\u001B[38;5;28;01mNone\u001B[39;00m, \u001B[38;5;28;01mNone\u001B[39;00m)]\n\u001B[0;32m 3054\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(ilevels) \u001B[38;5;241m==\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mnlevels:\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\multi.py:2905\u001B[0m, in \u001B[0;36mMultiIndex.get_loc\u001B[1;34m(self, key, method)\u001B[0m\n\u001B[0;32m 2902\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m(key) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01merr\u001B[39;00m\n\u001B[0;32m 2904\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m start \u001B[38;5;241m==\u001B[39m stop:\n\u001B[1;32m-> 2905\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m(key)\n\u001B[0;32m 2907\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m follow_key:\n\u001B[0;32m 2908\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mslice\u001B[39m(start, stop)\n", - "\u001B[1;31mKeyError\u001B[0m: ('R12_LAM', '2020', 'oil_imp')" - ] - } - ], - "source": [ - "extr_tec = \"oil_extr_mpen\"\n", - "out_oil_extr = act_out.loc[region, year, extr_tec]\n", - "out_oil_exp = act_out.loc[region, year, \"oil_exp\"]\n", - "in_oil_imp = act_in.loc[region, year, \"oil_imp\"]\n", - "\n", - "carbon_in_extr = (out_oil_extr.sum()[\"output\"] - out_oil_exp.sum()[\"output\"] + in_oil_imp.sum()[\"input\"]) * 0.631\n", - "carbon_in_extr" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 48, - "outputs": [ - { - "data": { - "text/plain": "319.2508439033615" - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "inp_atm_dist = act_in.loc[region, year, \"atm_distillation_ref\"]\n", - "carbon_in_ref = inp_atm_dist[inp_atm_dist[\"commodity\"] == \"crudeoil\"].sum()[\"input\"] * 0.631\n", - "carbon_in_ref" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 49, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "10.089172440637014\n" - ] - } - ], - "source": [ - "out_agg_ref = act_out.loc[region, year, \"agg_ref\"]\n", - "carbon_out_agg_ref = out_agg_ref.loc[out_agg_ref[\"commodity\"].isin([\"fueloil\"])][\"output\"].sum() * 0.665 \\\n", - "+ out_agg_ref[~out_agg_ref[\"commodity\"].isin([\"fueloil\"])].sum()[\"output\"] * 0.631\n", - "\n", - "print(carbon_in_ref - carbon_out_agg_ref)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 50, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\maczek\\AppData\\Local\\Temp\\ipykernel_17352\\3261112422.py:1: PerformanceWarning: indexing past lexsort depth may impact performance.\n", - " inp_atm_dist = act_in.swaplevel(0,2).loc[\"atm_distillation_ref\", year]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "252.94682658537567\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\maczek\\AppData\\Local\\Temp\\ipykernel_17352\\3261112422.py:4: PerformanceWarning: indexing past lexsort depth may impact performance.\n", - " out_agg_ref = act_out.swaplevel(0,2).loc[\"agg_ref\", year]\n" - ] - } - ], - "source": [ - "inp_atm_dist = act_in.swaplevel(0,2).loc[\"atm_distillation_ref\", year]\n", - "carbon_in_ref = inp_atm_dist[inp_atm_dist[\"commodity\"] == \"crudeoil\"].sum()[\"input\"] * 0.631\n", - "\n", - "out_agg_ref = act_out.swaplevel(0,2).loc[\"agg_ref\", year]\n", - "carbon_out_agg_ref = out_agg_ref.loc[out_agg_ref[\"commodity\"].isin([\"fueloil\"])][\"output\"].sum() * 0.665 + out_agg_ref[~out_agg_ref[\"commodity\"].isin([\"fueloil\"])].sum()[\"output\"] * 0.631\n", - "\n", - "print(carbon_in_ref - carbon_out_agg_ref)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 51, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\maczek\\AppData\\Local\\Temp\\ipykernel_17352\\2343031665.py:1: PerformanceWarning: indexing past lexsort depth may impact performance.\n", - " sc_in = act_in.swaplevel(0,2).loc[\"steam_cracker_petro\", year]\n" - ] - }, - { - "data": { - "text/plain": "301.4771887846489" - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sc_in = act_in.swaplevel(0,2).loc[\"steam_cracker_petro\", year]\n", - "sc_in[~sc_in.index.get_level_values(1).isin([\"ethane\", \"propane\"])].sum()[\"input\"] * 0.631" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## carbon IO coal" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 52, - "outputs": [ - { - "data": { - "text/plain": " Level commodity level Value \\\ntec mode year_vtg \nLNG_prod M1 2020 37.305296 gas primary 1.000000 \ndri_steel M1 2020 2.696444 gas final 0.330000 \n 2020 2.696444 gas dummy_emission 0.330000 \neaf_steel M2 2005 0.265603 gas final 0.002000 \n 2005 0.265603 gas dummy_emission 0.002000 \n... ... ... ... ... \nmeth_ng fuel 2015 2.359075 gas secondary 1.666667 \n 2015 2.359075 gas secondary 1.666667 \n 2015 2.359075 gas secondary 1.666667 \n 2015 2.359075 gas secondary 1.666667 \n 2015 2.359075 gas secondary 1.666667 \n\n node_origin input emi_factor \ntec mode year_vtg \nLNG_prod M1 2020 R12_LAM 37.305296 NaN \ndri_steel M1 2020 R12_LAM 0.889827 NaN \n 2020 R12_LAM 0.889827 NaN \neaf_steel M2 2005 R12_LAM 0.000531 NaN \n 2005 R12_LAM 0.000531 NaN \n... ... ... ... \nmeth_ng fuel 2015 R12_LAM 3.931792 0.253333 \n 2015 R12_LAM 3.931792 0.253333 \n 2015 R12_LAM 3.931792 0.253333 \n 2015 R12_LAM 3.931792 0.253333 \n 2015 R12_LAM 3.931792 0.253333 \n\n[2135 rows x 7 columns]", - "text/html": "
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LevelcommoditylevelValuenode_origininputemi_factor
tecmodeyear_vtg
LNG_prodM1202037.305296gasprimary1.000000R12_LAM37.305296NaN
dri_steelM120202.696444gasfinal0.330000R12_LAM0.889827NaN
20202.696444gasdummy_emission0.330000R12_LAM0.889827NaN
eaf_steelM220050.265603gasfinal0.002000R12_LAM0.000531NaN
20050.265603gasdummy_emission0.002000R12_LAM0.000531NaN
..............................
meth_ngfuel20152.359075gassecondary1.666667R12_LAM3.9317920.253333
20152.359075gassecondary1.666667R12_LAM3.9317920.253333
20152.359075gassecondary1.666667R12_LAM3.9317920.253333
20152.359075gassecondary1.666667R12_LAM3.9317920.253333
20152.359075gassecondary1.666667R12_LAM3.9317920.253333
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2135 rows × 7 columns

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" - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "act_in_yr_reg = act_in.loc[region, year]\n", - "comm = \"gas\"\n", - "bott_dict = {\n", - " \"coal\":bot_new_coal,\n", - " \"gas\":bot_new_gas\n", - "}\n", - "bott_yr_reg_coal_merge = bott_dict[comm].copy(deep=True)\n", - "x = act_in_yr_reg[(act_in_yr_reg.index.get_level_values(0).str.contains(comm)) & (act_in_yr_reg[\"commodity\"]==comm) & (act_in_yr_reg[\"level\"]==\"final\")].join(bott_yr_reg_coal_merge.rename({\"Value\": \"emi_factor\"}, axis=1)[\"emi_factor\"])\n", - "x = act_in_yr_reg[(act_in_yr_reg[\"commodity\"]==comm)].join(bott_yr_reg_coal_merge.rename({\"Value\": \"emi_factor\"}, axis=1)[\"emi_factor\"])\n", - "x" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 53, - "outputs": [ - { - "data": { - "text/plain": " Level commodity level \\\ntec mode node_loc year_act year_vtg \nLNG_prod M1 R12_AFR 2020 2020 60.182113 gas primary \n 2025 2025 69.629561 gas primary \n 2030 2030 94.292866 gas primary \n 2035 2035 101.502785 gas primary \n 2040 2040 100.225163 gas primary \n... ... ... ... \nmeth_ng fuel R12_WEU 2025 2015 0.331997 gas secondary \n 2015 0.331997 gas secondary \n 2015 0.331997 gas secondary \n 2015 0.331997 gas secondary \n 2015 0.331997 gas secondary \n\n Value node_origin input \\\ntec mode node_loc year_act year_vtg \nLNG_prod M1 R12_AFR 2020 2020 1.000000 R12_AFR 60.182113 \n 2025 2025 1.000000 R12_AFR 69.629561 \n 2030 2030 1.000000 R12_AFR 94.292866 \n 2035 2035 1.000000 R12_AFR 101.502785 \n 2040 2040 1.000000 R12_AFR 100.225163 \n... ... ... ... \nmeth_ng fuel R12_WEU 2025 2015 1.666667 R12_WEU 0.553329 \n 2015 1.666667 R12_WEU 0.553329 \n 2015 1.666667 R12_WEU 0.553329 \n 2015 1.666667 R12_WEU 0.553329 \n 2015 1.666667 R12_WEU 0.553329 \n\n emi_factor \ntec mode node_loc year_act year_vtg \nLNG_prod M1 R12_AFR 2020 2020 NaN \n 2025 2025 NaN \n 2030 2030 NaN \n 2035 2035 NaN \n 2040 2040 NaN \n... ... \nmeth_ng fuel R12_WEU 2025 2015 0.253333 \n 2015 0.253333 \n 2015 0.253333 \n 2015 0.253333 \n 2015 0.253333 \n\n[204796 rows x 7 columns]", - "text/html": "
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LevelcommoditylevelValuenode_origininputemi_factor
tecmodenode_locyear_actyear_vtg
LNG_prodM1R12_AFR2020202060.182113gasprimary1.000000R12_AFR60.182113NaN
2025202569.629561gasprimary1.000000R12_AFR69.629561NaN
2030203094.292866gasprimary1.000000R12_AFR94.292866NaN
20352035101.502785gasprimary1.000000R12_AFR101.502785NaN
20402040100.225163gasprimary1.000000R12_AFR100.225163NaN
....................................
meth_ngfuelR12_WEU202520150.331997gassecondary1.666667R12_WEU0.5533290.253333
20150.331997gassecondary1.666667R12_WEU0.5533290.253333
20150.331997gassecondary1.666667R12_WEU0.5533290.253333
20150.331997gassecondary1.666667R12_WEU0.5533290.253333
20150.331997gassecondary1.666667R12_WEU0.5533290.253333
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204796 rows × 7 columns

\n
" - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "act_in_yr_reg = act_in#.swaplevel(0,1).loc[year]\n", - "comm = \"gas\"\n", - "bott_dict = {\n", - " \"coal\":bot_new_coal,\n", - " \"gas\":bot_new_gas\n", - "}\n", - "bott_yr_reg_coal_merge = bott_dict[comm].copy(deep=True)\n", - "x = act_in_yr_reg[(act_in_yr_reg.index.get_level_values(0).str.contains(comm)) & (act_in_yr_reg[\"commodity\"]==comm) & (act_in_yr_reg[\"level\"]==\"final\")].join(bott_yr_reg_coal_merge.rename({\"Value\": \"emi_factor\"}, axis=1)[\"emi_factor\"])\n", - "x = act_in_yr_reg[(act_in_yr_reg[\"commodity\"]==comm)].join(bott_yr_reg_coal_merge.rename({\"Value\": \"emi_factor\"}, axis=1)[\"emi_factor\"])\n", - "x" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 54, - "outputs": [ - { - "data": { - "text/plain": " node_loc year_act year_vtg relation Value \\\ntec mode \nDUMMY_gas_supply M1 R12_AFR 2020 2020 CO2_ind 0.482000 \nfurnace_gas_petro high_temp R12_AFR 2020 2020 CO2_ind 0.523913 \nfurnace_gas_refining high_temp R12_AFR 2020 2020 CO2_cc 0.800120 \ngas_NH3 M1 R12_AFR 2020 2005 CO2_cc 0.534804 \n M1 R12_AFR 2020 2010 CO2_cc 0.534804 \n... ... ... ... ... ... \ngas_t_d M1 R12_WEU 2020 2005 CO2_cc 0.034737 \n M1 R12_WEU 2020 2010 CO2_cc 0.034737 \nhp_gas_rc M1 R12_WEU 2020 2020 CO2_r_c 0.321333 \nmeth_ng feedstock R12_WEU 2020 2015 CO2_cc 0.253333 \n fuel R12_WEU 2020 2015 CO2_cc 0.253333 \n\n node_origin year_rel Level emi \ntec mode \nDUMMY_gas_supply M1 R12_AFR 2020 1.660615 0.800417 \nfurnace_gas_petro high_temp R12_AFR 2020 1.179212 0.617805 \nfurnace_gas_refining high_temp R12_AFR 2020 4.492885 3.594847 \ngas_NH3 M1 R12_AFR 2020 1.065932 0.570065 \n M1 R12_AFR 2020 2.041205 1.091645 \n... ... ... ... ... \ngas_t_d M1 R12_WEU 2020 64.785421 2.250441 \n M1 R12_WEU 2020 15.089721 0.524169 \nhp_gas_rc M1 R12_WEU 2020 33.288225 10.696616 \nmeth_ng feedstock R12_WEU 2020 1.287200 0.326091 \n fuel R12_WEU 2020 1.468320 0.371975 \n\n[520 rows x 9 columns]", - "text/html": "
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node_locyear_actyear_vtgrelationValuenode_originyear_relLevelemi
tecmode
DUMMY_gas_supplyM1R12_AFR20202020CO2_ind0.482000R12_AFR20201.6606150.800417
furnace_gas_petrohigh_tempR12_AFR20202020CO2_ind0.523913R12_AFR20201.1792120.617805
furnace_gas_refininghigh_tempR12_AFR20202020CO2_cc0.800120R12_AFR20204.4928853.594847
gas_NH3M1R12_AFR20202005CO2_cc0.534804R12_AFR20201.0659320.570065
M1R12_AFR20202010CO2_cc0.534804R12_AFR20202.0412051.091645
.................................
gas_t_dM1R12_WEU20202005CO2_cc0.034737R12_WEU202064.7854212.250441
M1R12_WEU20202010CO2_cc0.034737R12_WEU202015.0897210.524169
hp_gas_rcM1R12_WEU20202020CO2_r_c0.321333R12_WEU202033.28822510.696616
meth_ngfeedstockR12_WEU20202015CO2_cc0.253333R12_WEU20201.2872000.326091
fuelR12_WEU20202015CO2_cc0.253333R12_WEU20201.4683200.371975
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520 rows × 9 columns

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" - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bot_new_gas.loc[\"gas_i\"]\n", - "bott_yr_reg_coal_merge" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 55, - "outputs": [ - { - "data": { - "text/plain": "mode node_loc year_act year_vtg\nM1 R12_AFR 2020 2005 0.482000\n 2005 0.482000\n 2005 0.482000\n 2005 0.482000\n 2005 0.482000\n ... \n R12_WEU 2060 2040 0.637941\n 2040 0.637941\n 2040 0.637941\n 2040 0.516429\n 2040 0.516429\nLength: 4557, dtype: float64" - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(x[\"emi_factor\"] / x[\"Value\"]).dropna().loc[\"gas_i\"]#.reset_index()#.drop([\"year_vtg\", \"node_loc\", \"year_act\"], axis=1).drop_duplicates()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 56, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\maczek\\AppData\\Local\\Temp\\ipykernel_17352\\3091354.py:1: PerformanceWarning: indexing past lexsort depth may impact performance.\n", - " x.loc[\"gas_i\", \"M1\", \"R12_CHN\"]\n" - ] - }, - { - "data": { - "text/plain": " Level commodity level Value node_origin input \\\nyear_act year_vtg \n2020 2005 1.228475 gas final 1.408451 R12_CHN 1.730247 \n 2005 1.228475 gas final 1.408451 R12_CHN 1.730247 \n 2005 1.228475 gas final 1.408451 R12_CHN 1.730247 \n 2005 1.228475 gas final 1.408451 R12_CHN 1.730247 \n 2005 1.228475 gas final 1.408451 R12_CHN 1.730247 \n... ... ... ... ... ... ... \n 2015 3.930682 gas final 1.408451 R12_CHN 5.536172 \n 2015 3.930682 gas final 1.408451 R12_CHN 5.536172 \n 2015 3.930682 gas final 1.408451 R12_CHN 5.536172 \n 2015 3.930682 gas final 1.408451 R12_CHN 5.536172 \n 2015 3.930682 gas final 1.408451 R12_CHN 5.536172 \n\n emi_factor \nyear_act year_vtg \n2020 2005 0.678873 \n 2005 0.678873 \n 2005 0.678873 \n 2005 0.678873 \n 2005 0.678873 \n... ... \n 2015 0.708824 \n 2015 0.708824 \n 2015 0.708824 \n 2015 0.573810 \n 2015 0.573810 \n\n[62 rows x 7 columns]", - "text/html": "
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LevelcommoditylevelValuenode_origininputemi_factor
year_actyear_vtg
202020051.228475gasfinal1.408451R12_CHN1.7302470.678873
20051.228475gasfinal1.408451R12_CHN1.7302470.678873
20051.228475gasfinal1.408451R12_CHN1.7302470.678873
20051.228475gasfinal1.408451R12_CHN1.7302470.678873
20051.228475gasfinal1.408451R12_CHN1.7302470.678873
........................
20153.930682gasfinal1.408451R12_CHN5.5361720.708824
20153.930682gasfinal1.408451R12_CHN5.5361720.708824
20153.930682gasfinal1.408451R12_CHN5.5361720.708824
20153.930682gasfinal1.408451R12_CHN5.5361720.573810
20153.930682gasfinal1.408451R12_CHN5.5361720.573810
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62 rows × 7 columns

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" - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x.loc[\"gas_i\", \"M1\", \"R12_CHN\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## check *_t_d tecs emission factors" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 92, - "outputs": [], - "source": [ - "df_gas_t_d = act_in[act_in.index.get_level_values(2).str.contains(\"coal\")].join(emi_bott_up.rename({\"Value\": \"emi_factor\"}, axis=1)[\"emi_factor\"])" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 58, - "outputs": [], - "source": [ - "df_gas_t_d = act_in.join(emi_bott_up.rename({\"Value\": \"emi_factor\"}, axis=1)[\"emi_factor\"]).dropna()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 93, - "outputs": [], - "source": [ - "ef_dict = {\n", - " 'biomass':0,\n", - " 'coal':0.814,\n", - " 'electr':0,\n", - " 'ethanol':0,\n", - " 'fueloil':0.665,\n", - " 'gas':0.482,\n", - " 'gas_1':0.482,\n", - " 'gas_2':0.482,\n", - " 'gas_3':0.482,\n", - " 'gas_4':0.482,\n", - " 'gas_5':0.482,\n", - " 'gas_6':0.482,\n", - " 'gas_afr':0.482,\n", - " 'gas_chn':0.482,\n", - " 'gas_cpa':0.482,\n", - " 'gas_weu':0.482,\n", - " 'gas_eeu':0.482,\n", - " 'gas_sas':0.482,\n", - " 'LNG':0.482,\n", - " 'd_heat':0,\n", - " 'lightoil':0.631,\n", - " 'methanol':0.549,\n", - " 'hydrogen':0,\n", - " 'freshwater_supply':0,\n", - " \"crudeoil\":0.63,\n", - " \"crude_1\":0.665,\n", - " \"crude_2\":0.665,\n", - " \"crude_3\":0.665,\n", - " \"crude_4\":0.665,\n", - " \"crude_5\":0.665,\n", - " \"crude_6\":0.665,\n", - " \"crude_7\":0.665,\n", - " \"ht_heat\":0,\n", - " \"lh2\":0,\n", - "}\n", - "\n", - "def get_ef(df):\n", - " df[\"ef\"] = ef_dict[df[\"commodity\"]]\n", - " return df\n", - "\n", - "df_gas_t_d = df_gas_t_d.apply(lambda x: get_ef(x), axis=1)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 67, - "outputs": [], - "source": [ - "df_gas_t_d = df_gas_t_d.join(act_out.rename({\"commodity\":\"comm_out\"}, axis=1)[\"comm_out\"])\n", - "df_gas_t_d = df_gas_t_d[df_gas_t_d[\"commodity\"]==df_gas_t_d[\"comm_out\"]]\n", - "#df_gas_t_d = df_gas_t_d[~df_gas_t_d.index.get_level_values(2).str.contains(\"_extr\")]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 94, - "outputs": [], - "source": [ - "# for IO tecs\n", - "ef_false = (df_gas_t_d[\"emi_factor\"] / (df_gas_t_d[\"Value\"] - 1)).dropna()\n", - "# for input only tecs\n", - "ef_false = (df_gas_t_d[\"emi_factor\"] / df_gas_t_d[\"Value\"]).dropna()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 95, - "outputs": [], - "source": [ - "tec_comm_map = df_gas_t_d.reset_index()[[\"tec\", \"commodity\"]].drop_duplicates().set_index(\"tec\").to_dict()[\"commodity\"]\n", - "tec_list = [i for i in list(tec_comm_map.keys())]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 99, - "outputs": [ - { - "data": { - "text/plain": " 0\nyear_act node_loc mode year_vtg \n2045 R12_PAS M1 2010 0.823070\n2050 R12_NAM M1 2010 0.816775\n2035 R12_CHN M1 2000 0.816429\n R12_RCPA M1 2000 0.816429\n2025 R12_LAM M1 1980 0.814000\n... ...\n2045 R12_SAS M1 2020 0.814000\n 2025 0.814000\n 2030 0.814000\n 2035 0.814000\n2035 R12_PAS M1 2015 0.814000\n\n[476 rows x 1 columns]", - "text/html": "
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" - }, - "execution_count": 99, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pd.DataFrame(ef_false).swaplevel(0,2).loc[\"coal_ppl\"].sort_values(0, ascending=False)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 98, - "outputs": [ - { - "data": { - "text/plain": "year_act node_loc mode year_vtg\n2020 R12_AFR M1 1960 NaN\n 1965 NaN\n 1970 NaN\n 1975 NaN\n 1980 NaN\n ..\n2090 R12_AFR M1 2080 NaN\n2100 R12_AFR M1 2080 NaN\n 2100 NaN\n2110 R12_AFR M1 2100 NaN\n 2110 NaN\nLength: 632, dtype: float64" - }, - "execution_count": 98, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_gas_t_d.swaplevel(0,2).loc[\"coal_ppl_u\"][\"emi_factor\"] / df_gas_t_d.swaplevel(0,2).loc[\"coal_ppl_u\"][\"Value\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 96, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "coal_adv\n", - "coal_bal\n", - "coal_exp\n", - "coal_extr\n", - "coal_extr_ch4\n", - "coal_i\n", - "coal_ppl\n", - "coal_ppl_u\n", - "coal_rc\n", - "coal_t_d\n", - "furnace_coal_aluminum\n", - "furnace_coal_cement\n", - "furnace_coal_refining\n", - "furnace_coal_petro\n", - "coal_hpl\n", - "furnace_coal_resins\n", - "coal_imp\n", - "meth_coal\n", - "coal_NH3\n", - "coal_trd\n", - "sp_coal_I\n", - "coal_gas\n", - "h2_coal\n" - ] - }, - { - "data": { - "text/plain": "
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- }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import math\n", - "import matplotlib.pyplot as plt\n", - "fig, axs = plt.subplots(int(math.sqrt(len(tec_list))), int(math.sqrt(len(tec_list))), figsize=(40,20), facecolor=\"w\")\n", - "ax_it = iter(fig.axes)\n", - "for k in tec_list:\n", - " print(k)\n", - " try:\n", - " tec = k\n", - " y = ef_false.swaplevel(0,2).loc[tec].droplevel([3,2]).reset_index()\n", - " ax = next((ax_it))\n", - " y.plot.scatter(x=\"year_act\", y=0, ax=ax)\n", - " ax.plot([ef_dict[tec_comm_map[tec]]]*len(y.year_act.unique()), color=\"red\")\n", - " ax.set_ylabel(\"Mt C / GWa\")\n", - " ax.set_title(tec)\n", - " ax.set_xlabel(\"\")\n", - " except:\n", - " pass" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 64, - "outputs": [], - "source": [ - "fig.savefig(\"test.png\", facecolor=\"w\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "ef_false.swaplevel(0,2).loc[\"hp_gas_i\"].droplevel([3,2]).reset_index()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## aggregated emissions comparison" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df_gas_t_d[\"loss\"] = (df_gas_t_d[\"Value\"] - 1) * df_gas_t_d[\"Level\"]\n", - "t_d_emi = (df_gas_t_d[\"emi_factor\"] * df_gas_t_d[\"Level\"]).groupby([\"year_act\"]).sum() * co2_c_factor" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df_gas_t_d[\"emi_factor_corr\"] = (df_gas_t_d[\"Value\"] - 1) * df_gas_t_d[\"ef\"]\n", - "t_d_emi_corr =(df_gas_t_d[\"emi_factor_corr\"] * df_gas_t_d[\"Level\"]).groupby([\"year_act\"]).sum() * co2_c_factor" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "fig,ax = plt.subplots(figsize=(10,5))\n", - "t_d_emi.plot(ax=ax)\n", - "t_d_emi_corr.plot(ax=ax)\n", - "ax.set_ylabel(\"Mt CO2\")\n", - "ax.set_title(\"CO2 Emissions from t_d technologies\")\n", - "ax.legend([\"current CO2_cc\", \"corrected CO2_cc\"])" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df_gas_t_d.swaplevel(0,2).loc[\"gas_t_d\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "act_in_yr_reg = act_in.loc[region, year]\n", - "comm = \"coal\"\n", - "bott_dict = {\n", - " \"coal\":top_yr_reg_gas,\n", - " \"gas\":bott_yr_reg_gas\n", - "}\n", - "bott_yr_reg_coal_merge = bott_dict[comm].copy(deep=True)\n", - "x = act_in_yr_reg[(act_in_yr_reg.index.get_level_values(0).str.contains(comm)) & (act_in_yr_reg[\"commodity\"]==comm) & (act_in_yr_reg[\"level\"]==\"final\")].join(bott_yr_reg_coal_merge.rename({\"Value\": \"emi_factor\"}, axis=1)[\"emi_factor\"])\n", - "x = act_in_yr_reg[(act_in_yr_reg[\"commodity\"]==comm)].join(bott_yr_reg_coal_merge.rename({\"Value\": \"emi_factor\"}, axis=1)[\"emi_factor\"])\n", - "x#.sum()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "x#(x[\"emi_factor\"]/ x[\"Value\"]).dropna()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## oil emissions" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df_bot_oil = get_bot_up_emi_by_fuel(emi_bott_up, region, year)[1]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df_bot_oil[df_bot_oil.index.get_level_values(0).str.contains(\"extr\")].sum()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "emi_bot_up_refining = df_bot_oil.loc[df_bot_oil.index.get_level_values(0).str.endswith(\"_ref\")]#.sum()\n", - "emi_bot_up_extr = df_bot_oil.loc[df_bot_oil.index.get_level_values(0).str.contains(\"extr\")]\n", - "emi_bot_up_final = df_bot_oil.loc[(df_bot_oil.index.difference(emi_bot_up_refining.index)) & (df_bot_oil.index.difference(emi_bot_up_extr.index))]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "emi_bot_up_refining.sum()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "emi_bot_up_final.sort_values(\"emi\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "print(\"extraction emissions: \",emi_bot_up_extr.sum()[\"emi\"])\n", - "print(\"refining emissions: \",emi_bot_up_refining.sum()[\"emi\"])\n", - "print(\"combustion emissions: \",emi_bot_up_final.sum()[\"emi\"])" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "inp_hyd_crack = act_in.loc[region, year, \"hydro_cracking_ref\"]\n", - "inp_hyd_crack[inp_hyd_crack[\"commodity\"] == \"hydrogen\"].sum()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "inp_steam_crack = act_in.loc[region, year, \"steam_cracker_petro\"]\n", - "inp_steam_crack[~inp_steam_crack[\"commodity\"].isin([\"ethane\",\"propane\", \"electr\", \"ht_heat\"])].sum()[\"input\"] * 0.64" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df_top_oil = get_top_dn_emi_by_fuel(emi_top_down, region, year)[1]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df_top_oil.loc[df_top_oil.index.get_level_values(0).str.startswith(\"oil\")].sum()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df_top_oil.loc[~df_top_oil.index.get_level_values(0).str.endswith(\"ref\")]#.sum()#.sort_index()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "carbon_out_foil_loil_exp = df_top_oil[df_top_oil.index.get_level_values(0).str.contains(\"loil\")].sum()[\"emi\"] + df_top_oil[df_top_oil.index.get_level_values(0).str.contains(\"foil\")].sum()[\"emi\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "act_out.swaplevel(0,2).loc[\"MTO_petro\"].loc[\"2025\", \"R12_CHN\"].sum()#.groupby(\"year_act\").sum()" - ], - "metadata": { - "collapsed": false - } - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/message_ix_models/model/material/data analysis ipynbs/gdx_pandas_3.ipynb b/message_ix_models/model/material/data analysis ipynbs/gdx_pandas_3.ipynb deleted file mode 100644 index d6fbd04921..0000000000 --- a/message_ix_models/model/material/data analysis ipynbs/gdx_pandas_3.ipynb +++ /dev/null @@ -1,2059 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import gdxpds\n", - "import pandas as pd\n", - "co2_c_factor = 44/12" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "outputs": [], - "source": [ - "#gdx_file = 'C:/Users\\maczek\\PycharmProjects\\message_ix\\message_ix\\model\\output\\MsgOutput_MESSAGEix-Materials_2.0deg_petro_thesis_2_macro.gdx'\n", - "#gdx_file = 'C:/Users\\maczek\\PycharmProjects\\message_ix\\message_ix\\model\\output\\MsgOutput_MESSAGEix-Materials_2.0deg_petro_thesis_2_macro_w_ENGAGE_fixes_test_loil_exp_fix.gdx'\n", - "gdx_file = 'C:/Users\\maczek\\PycharmProjects\\message_ix\\message_ix\\model\\output\\MsgOutput_ENGAGE_SSP2_v4.1.8.3_T4.5_r3.1_baseline_magpie.gdx'\n", - "#gdx_file = 'C:/Users\\maczek\\PycharmProjects\\message_ix\\message_ix\\model\\output\\MsgOutput_MESSAGEix-Materials_NoPolicy_petro_thesis_2_w_ENGAGE_fixes_test_build_2_macro.gdx'\n", - "#gdx_file = 'C:/Users\\maczek\\PycharmProjects\\message_ix\\message_ix\\model\\output/thesis final/MsgOutput_MESSAGEix-Materials_NoPolicy_petro_thesis_2_final_macro.gdx'\n", - "dataframes = gdxpds.to_dataframes(gdx_file)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 5, - "outputs": [], - "source": [ - "hist_act = dataframes[\"historical_activity\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 15, - "outputs": [], - "source": [ - "#h\n", - "hist_act = hist_act[hist_act[\"year_all\"]==\"2015\"]\n", - "hist_act_rcpa = hist_act[hist_act[\"node\"]==\"R12_RCPA\"]\n", - "hist_act_chn = hist_act[hist_act[\"node\"]==\"R12_CHN\"]\n", - "hist_act_merge = hist_act_rcpa.merge(hist_act_chn, left_on=\"tec\", right_on=\"tec\")\n", - "hist_act_merge[\"ratio\"] = hist_act_merge[\"Value_x\"] / hist_act_merge[\"Value_y\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 21, - "outputs": [ - { - "data": { - "text/plain": " node_x tec year_all_x mode_x time_x Value_x \\\n35 R12_RCPA elec_trp 2015 M1 year 5.000000 \n54 R12_RCPA gas_extr_mpen 2015 M1 year 0.100000 \n81 R12_RCPA oil_exp 2015 M1 year 1.000000 \n85 R12_RCPA oil_extr_mpen 2015 M1 year 0.100000 \n156 R12_RCPA eaf_steel 2015 M2 year 0.893333 \n165 R12_RCPA catalytic_cracking_ref 2015 atm_gasoil year 5.953922 \n173 R12_RCPA import_petro 2015 M1 year 0.120969 \n174 R12_RCPA export_petro 2015 M1 year 0.004573 \n175 R12_RCPA gas_NH3 2015 M1 year 0.165746 \n176 R12_RCPA coal_NH3 2015 M1 year 0.834254 \n177 R12_RCPA export_NFert 2015 M1 year 0.041194 \n180 R12_RCPA meth_exp 2015 feedstock year 0.000272 \n181 R12_RCPA meth_exp 2015 feedstock year 0.000272 \n182 R12_RCPA meth_exp 2015 fuel year 0.000272 \n183 R12_RCPA meth_exp 2015 fuel year 0.000272 \n184 R12_RCPA meth_imp 2015 feedstock year 0.177914 \n185 R12_RCPA meth_imp 2015 feedstock year 0.177914 \n186 R12_RCPA meth_imp 2015 fuel year 0.177914 \n187 R12_RCPA meth_imp 2015 fuel year 0.177914 \n\n node_y year_all_y mode_y time_y Value_y ratio \n35 R12_CHN 2015 M1 year 54.270254 0.092132 \n54 R12_CHN 2015 M1 year 153.154140 0.000653 \n81 R12_CHN 2015 M1 year 51.318611 0.019486 \n85 R12_CHN 2015 M1 year 250.804477 0.000399 \n156 R12_CHN 2015 M1 year 72.360000 0.012346 \n165 R12_CHN 2015 vacuum_gasoil year 112.350506 0.052994 \n173 R12_CHN 2015 M1 year 18.270686 0.006621 \n174 R12_CHN 2015 M1 year 0.221271 0.020667 \n175 R12_CHN 2015 M1 year 9.662983 0.017153 \n176 R12_CHN 2015 M1 year 48.637017 0.017153 \n177 R12_CHN 2015 M1 year 20.080371 0.002051 \n180 R12_CHN 2015 feedstock year 0.096487 0.002823 \n181 R12_CHN 2015 fuel year 0.096487 0.002823 \n182 R12_CHN 2015 feedstock year 0.096487 0.002823 \n183 R12_CHN 2015 fuel year 0.096487 0.002823 \n184 R12_CHN 2015 feedstock year 3.405133 0.052249 \n185 R12_CHN 2015 fuel year 3.405133 0.052249 \n186 R12_CHN 2015 feedstock year 3.405133 0.052249 \n187 R12_CHN 2015 fuel year 3.405133 0.052249 ", - "text/html": "
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node_xtecyear_all_xmode_xtime_xValue_xnode_yyear_all_ymode_ytime_yValue_yratio
35R12_RCPAelec_trp2015M1year5.000000R12_CHN2015M1year54.2702540.092132
54R12_RCPAgas_extr_mpen2015M1year0.100000R12_CHN2015M1year153.1541400.000653
81R12_RCPAoil_exp2015M1year1.000000R12_CHN2015M1year51.3186110.019486
85R12_RCPAoil_extr_mpen2015M1year0.100000R12_CHN2015M1year250.8044770.000399
156R12_RCPAeaf_steel2015M2year0.893333R12_CHN2015M1year72.3600000.012346
165R12_RCPAcatalytic_cracking_ref2015atm_gasoilyear5.953922R12_CHN2015vacuum_gasoilyear112.3505060.052994
173R12_RCPAimport_petro2015M1year0.120969R12_CHN2015M1year18.2706860.006621
174R12_RCPAexport_petro2015M1year0.004573R12_CHN2015M1year0.2212710.020667
175R12_RCPAgas_NH32015M1year0.165746R12_CHN2015M1year9.6629830.017153
176R12_RCPAcoal_NH32015M1year0.834254R12_CHN2015M1year48.6370170.017153
177R12_RCPAexport_NFert2015M1year0.041194R12_CHN2015M1year20.0803710.002051
180R12_RCPAmeth_exp2015feedstockyear0.000272R12_CHN2015feedstockyear0.0964870.002823
181R12_RCPAmeth_exp2015feedstockyear0.000272R12_CHN2015fuelyear0.0964870.002823
182R12_RCPAmeth_exp2015fuelyear0.000272R12_CHN2015feedstockyear0.0964870.002823
183R12_RCPAmeth_exp2015fuelyear0.000272R12_CHN2015fuelyear0.0964870.002823
184R12_RCPAmeth_imp2015feedstockyear0.177914R12_CHN2015feedstockyear3.4051330.052249
185R12_RCPAmeth_imp2015feedstockyear0.177914R12_CHN2015fuelyear3.4051330.052249
186R12_RCPAmeth_imp2015fuelyear0.177914R12_CHN2015feedstockyear3.4051330.052249
187R12_RCPAmeth_imp2015fuelyear0.177914R12_CHN2015fuelyear3.4051330.052249
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" - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "hist_act_merge[hist_act_merge[\"ratio\"]<0.111]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 38, - "outputs": [ - { - "data": { - "text/plain": " node commodity level year_all time Level Marginal \\\n0 R12_AFR NH3 final_material 2020 year 2.500000 0.0 \n1 R12_AFR NH3 final_material 2025 year 2.735064 0.0 \n2 R12_AFR NH3 final_material 2030 year 2.906112 0.0 \n3 R12_AFR NH3 final_material 2035 year 3.156448 0.0 \n4 R12_AFR NH3 final_material 2040 year 3.342780 0.0 \n... ... ... ... ... ... ... ... \n2060 R12_CHN fcoh_resin final_material 2060 year 0.282172 0.0 \n2061 R12_CHN fcoh_resin final_material 2070 year 0.215595 0.0 \n2062 R12_CHN fcoh_resin final_material 2080 year 0.094929 0.0 \n2063 R12_CHN fcoh_resin final_material 2090 year 0.030637 0.0 \n2064 R12_CHN fcoh_resin final_material 2100 year 0.011176 0.0 \n\n Lower Upper Scale \n0 4.000000e+300 3.000000e+300 1.0 \n1 4.000000e+300 3.000000e+300 1.0 \n2 4.000000e+300 3.000000e+300 1.0 \n3 4.000000e+300 3.000000e+300 1.0 \n4 4.000000e+300 3.000000e+300 1.0 \n... ... ... ... \n2060 4.000000e+300 3.000000e+300 1.0 \n2061 4.000000e+300 3.000000e+300 1.0 \n2062 4.000000e+300 3.000000e+300 1.0 \n2063 4.000000e+300 3.000000e+300 1.0 \n2064 4.000000e+300 3.000000e+300 1.0 \n\n[2065 rows x 10 columns]", - "text/html": "
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nodecommoditylevelyear_alltimeLevelMarginalLowerUpperScale
0R12_AFRNH3final_material2020year2.5000000.04.000000e+3003.000000e+3001.0
1R12_AFRNH3final_material2025year2.7350640.04.000000e+3003.000000e+3001.0
2R12_AFRNH3final_material2030year2.9061120.04.000000e+3003.000000e+3001.0
3R12_AFRNH3final_material2035year3.1564480.04.000000e+3003.000000e+3001.0
4R12_AFRNH3final_material2040year3.3427800.04.000000e+3003.000000e+3001.0
.................................
2060R12_CHNfcoh_resinfinal_material2060year0.2821720.04.000000e+3003.000000e+3001.0
2061R12_CHNfcoh_resinfinal_material2070year0.2155950.04.000000e+3003.000000e+3001.0
2062R12_CHNfcoh_resinfinal_material2080year0.0949290.04.000000e+3003.000000e+3001.0
2063R12_CHNfcoh_resinfinal_material2090year0.0306370.04.000000e+3003.000000e+3001.0
2064R12_CHNfcoh_resinfinal_material2100year0.0111760.04.000000e+3003.000000e+3001.0
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" - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "hist_act = dataframes[\"DEMAND\"]\n", - "hist_act" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 70, - "outputs": [], - "source": [ - "#h\n", - "hist_act = hist_act[hist_act[\"year_all\"]==\"2020\"]\n", - "#hist_act = hist_act.set_index([\"commodity\", \"level\"])\n", - "hist_act_rcpa = hist_act[hist_act[\"node\"]==\"R12_RCPA\"]\n", - "hist_act_chn = hist_act[hist_act[\"node\"]==\"R12_CHN\"]\n", - "\n", - "hist_act_merge = hist_act_rcpa.join(hist_act_chn[\"Level\"], rsuffix=\"_x\")\n", - "hist_act_merge[\"ratio\"] = hist_act_merge[\"Level\"] / hist_act_merge[\"Level_x\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 71, - "outputs": [ - { - "data": { - "text/plain": " node year_all time Level Marginal \\\ncommodity level \nNH3 final_material R12_RCPA 2020 year 2.500000 0.0 \nmethanol final_material R12_RCPA 2020 year 0.204400 0.0 \ni_spec useful R12_RCPA 2020 year 16.366848 0.0 \ni_therm useful R12_RCPA 2020 year 25.932783 0.0 \nnon-comm useful R12_RCPA 2020 year 2.142900 0.0 \nrc_spec useful R12_RCPA 2020 year 9.493912 0.0 \nrc_therm useful R12_RCPA 2020 year 34.041000 0.0 \ntransport useful R12_RCPA 2020 year 55.257000 0.0 \nsteel demand R12_RCPA 2020 year 12.083479 0.0 \ncement demand R12_RCPA 2020 year 237.872109 0.0 \naluminum demand R12_RCPA 2020 year 1.999989 0.0 \nHVC demand R12_RCPA 2020 year 0.440000 0.0 \nfcoh_resin final_material R12_RCPA 2020 year 0.058829 0.0 \n\n Lower Upper Scale Level_x \\\ncommodity level \nNH3 final_material 4.000000e+300 3.000000e+300 1.0 18.000000 \nmethanol final_material 4.000000e+300 3.000000e+300 1.0 24.732400 \ni_spec useful 4.000000e+300 3.000000e+300 1.0 147.301632 \ni_therm useful 4.000000e+300 3.000000e+300 1.0 233.395048 \nnon-comm useful 4.000000e+300 3.000000e+300 1.0 19.286100 \nrc_spec useful 4.000000e+300 3.000000e+300 1.0 85.445212 \nrc_therm useful 4.000000e+300 3.000000e+300 1.0 306.369000 \ntransport useful 4.000000e+300 3.000000e+300 1.0 497.313000 \nsteel demand 4.000000e+300 3.000000e+300 1.0 980.023775 \ncement demand 4.000000e+300 3.000000e+300 1.0 2140.848982 \naluminum demand 4.000000e+300 3.000000e+300 1.0 25.998916 \nHVC demand 4.000000e+300 3.000000e+300 1.0 50.500000 \nfcoh_resin final_material 4.000000e+300 3.000000e+300 1.0 0.724910 \n\n ratio \ncommodity level \nNH3 final_material 0.138889 \nmethanol final_material 0.008264 \ni_spec useful 0.111111 \ni_therm useful 0.111111 \nnon-comm useful 0.111111 \nrc_spec useful 0.111111 \nrc_therm useful 0.111111 \ntransport useful 0.111111 \nsteel demand 0.012330 \ncement demand 0.111111 \naluminum demand 0.076926 \nHVC demand 0.008713 \nfcoh_resin final_material 0.081154 ", - "text/html": "
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nodeyear_alltimeLevelMarginalLowerUpperScaleLevel_xratio
commoditylevel
NH3final_materialR12_RCPA2020year2.5000000.04.000000e+3003.000000e+3001.018.0000000.138889
methanolfinal_materialR12_RCPA2020year0.2044000.04.000000e+3003.000000e+3001.024.7324000.008264
i_specusefulR12_RCPA2020year16.3668480.04.000000e+3003.000000e+3001.0147.3016320.111111
i_thermusefulR12_RCPA2020year25.9327830.04.000000e+3003.000000e+3001.0233.3950480.111111
non-commusefulR12_RCPA2020year2.1429000.04.000000e+3003.000000e+3001.019.2861000.111111
rc_specusefulR12_RCPA2020year9.4939120.04.000000e+3003.000000e+3001.085.4452120.111111
rc_thermusefulR12_RCPA2020year34.0410000.04.000000e+3003.000000e+3001.0306.3690000.111111
transportusefulR12_RCPA2020year55.2570000.04.000000e+3003.000000e+3001.0497.3130000.111111
steeldemandR12_RCPA2020year12.0834790.04.000000e+3003.000000e+3001.0980.0237750.012330
cementdemandR12_RCPA2020year237.8721090.04.000000e+3003.000000e+3001.02140.8489820.111111
aluminumdemandR12_RCPA2020year1.9999890.04.000000e+3003.000000e+3001.025.9989160.076926
HVCdemandR12_RCPA2020year0.4400000.04.000000e+3003.000000e+3001.050.5000000.008713
fcoh_resinfinal_materialR12_RCPA2020year0.0588290.04.000000e+3003.000000e+3001.00.7249100.081154
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tecratio
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" - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "hist_act_merge[[\"tec\", \"ratio\"]].sort_values(\"ratio\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 7, - "outputs": [], - "source": [ - "act = dataframes[\"ACT\"].drop([\"time\", \"Marginal\", \"Lower\", \"Upper\", \"Scale\"], axis=1)\n", - "act = act[act[\"Level\"] != 0]\n", - "act = act.rename({\"vintage\":\"year_vtg\", \"year_all\":\"year_act\", \"node\":\"node_loc\"}, axis=1)\n", - "act = act.set_index([\"node_loc\", \"year_act\", \"tec\", \"mode\", \"year_vtg\"])" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 4, - "outputs": [], - "source": [ - "inp = dataframes[\"input\"].drop(\"time\", axis=1)\n", - "node_cols = inp[\"node\"]\n", - "node_cols.columns = [\"node_loc\", \"node_origin\"]\n", - "inp[\"node_loc\"] = node_cols[\"node_loc\"]\n", - "inp[\"node_origin\"] = node_cols[\"node_origin\"]\n", - "inp = inp.rename({\"vintage\":\"year_vtg\", \"year_all\":\"year_act\"}, axis=1)\n", - "inp = inp.drop(\"node\", axis=1)\n", - "inp = inp.set_index([\"node_loc\", \"year_act\", \"tec\", \"mode\", \"year_vtg\"])" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 5, - "outputs": [], - "source": [ - "act_in = act.join(inp)\n", - "act_in = act_in.dropna()\n", - "act_in[\"input\"] = act_in[\"Level\"] * act_in[\"Value\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 6, - "outputs": [], - "source": [ - "out = dataframes[\"output\"].drop(\"time\", axis=1)\n", - "node_cols = out[\"node\"]\n", - "node_cols.columns = [\"node_loc\", \"node_dest\"]\n", - "out[\"node_loc\"] = node_cols[\"node_loc\"]\n", - "out[\"node_origin\"] = node_cols[\"node_dest\"]\n", - "out = out.rename({\"vintage\":\"year_vtg\", \"year_all\":\"year_act\"}, axis=1)\n", - "out = out.drop(\"node\", axis=1)\n", - "out = out.set_index([\"node_loc\", \"year_act\", \"year_vtg\", \"tec\", \"mode\"])" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 7, - "outputs": [], - "source": [ - "act_out = act.join(out)\n", - "act_out = act_out.dropna()\n", - "act_out[\"output\"] = act_out[\"Level\"] * act_out[\"Value\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 8, - "outputs": [], - "source": [ - "rel = dataframes[\"relation_activity\"]\n", - "node_cols = rel[\"node\"]\n", - "node_cols.columns = [\"node_loc\", \"node_dest\"]\n", - "rel[\"node_loc\"] = node_cols[\"node_loc\"]\n", - "rel[\"node_origin\"] = node_cols[\"node_dest\"]\n", - "\n", - "year_cols = rel[\"year_all\"]\n", - "year_cols.columns = [\"year_act\", \"year_rel\"]\n", - "rel[\"year_act\"] = year_cols[\"year_act\"]\n", - "rel[\"year_rel\"] = year_cols[\"year_rel\"]\n", - "\n", - "rel = rel.drop(\"node\", axis=1)\n", - "rel = rel.drop(\"year_all\", axis=1)\n", - "rel = rel.set_index([\"node_loc\", \"year_act\", \"tec\", \"mode\"])" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 9, - "outputs": [], - "source": [ - "rel_co2emi = rel[rel[\"relation\"]==\"CO2_Emission\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 10, - "outputs": [], - "source": [ - "emi_top_down = rel_co2emi.join(act).dropna()\n", - "emi_top_down[\"emi\"] = emi_top_down[\"Value\"] * emi_top_down[\"Level\"] #<" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 11, - "outputs": [], - "source": [ - "emi_bott_up_names = [\"CO2_ind\", \"CO2_r_c\", \"CO2_trp\", \"CO2_trade\", \"CO2_shipping\", \"CO2_cc\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 12, - "outputs": [], - "source": [ - "emi_bott_up = rel[rel[\"relation\"].isin(emi_bott_up_names).values]\n", - "emi_bott_up = emi_bott_up.join(act).dropna()\n", - "emi_bott_up[\"emi\"] = emi_bott_up[\"Value\"] * emi_bott_up[\"Level\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 9, - "outputs": [ - { - "data": { - "text/plain": " Level\ntec year_act node_loc mode year_vtg \nCF4_TCE 2020 R11_AFR M1 2020 0.000214\n 2025 R11_AFR M1 2025 0.000244\n 2030 R11_AFR M1 2030 0.000259\n 2035 R11_AFR M1 2035 0.000283\n 2040 R11_AFR M1 2040 0.000288\n... ...\nbiomass_exp 2045 R11_WEU M1 2025 0.603323\n 2050 R11_WEU M1 2025 0.603323\n 2100 R11_WEU M1 2100 2.542521\n 2110 R11_WEU M1 2100 2.542521\n 2110 20.742661\n\n[39186 rows x 1 columns]", - "text/html": "
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biomass_exp2045R11_WEUM120250.603323
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" - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "act.index = act.index.swaplevel(0, 2)\n", - "act" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 31, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Level\n", - "year_act \n", - "2020 3245.894213\n", - "2025 3695.781712\n", - "2030 4134.097917\n", - "2035 4584.361981\n", - "2040 4981.643773\n", - "2045 5219.056006\n", - "2050 5334.279704\n", - "2055 5294.281771\n", - "2060 5022.616845\n", - "2070 4145.596276\n", - "2080 3708.114331\n", - "2090 4277.202731\n", - "2100 5114.284017\n", - "2110 7212.136752\n", - " Level\n", - "year_act \n", - "2020 16.657540\n", - "2025 19.424365\n", - "2030 18.510678\n", - "2035 2.421083\n", - "2040 0.085181\n", - "2045 0.125159\n", - "2050 0.179682\n" - ] - }, - { - "data": { - "text/plain": "" - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": "
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\n" - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "fig, ax = plt.subplots()\n", - "legend = []\n", - "for i in [\"loil_trp\", \"foil_trp\"]:\n", - " print(act.loc[i].groupby([\"year_act\"]).sum())\n", - " act.loc[i].groupby([\"year_act\"]).sum().reset_index().plot(ax=ax, x=\"year_act\", y=\"Level\")\n", - " legend.append(i)\n", - "ax.legend(legend)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 38, - "outputs": [ - { - "data": { - "text/plain": " Level\ntec \nbiomass_i 167.172537\ncoal_i 646.478318\nelec_i 87.922704\nfoil_i 100.367546\ngas_i 569.083035\nheat_i 176.669585\nhp_gas_i 5.982707\nloil_i 96.592091\nsolar_i 96.415719", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Level
tec
biomass_i167.172537
coal_i646.478318
elec_i87.922704
foil_i100.367546
gas_i569.083035
heat_i176.669585
hp_gas_i5.982707
loil_i96.592091
solar_i96.415719
\n
" - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#act.index = act.index.swaplevel(0,2)\n", - "#act.index = act.index.swaplevel(1,2)\n", - "#act2020 = act.loc[\"2020\"]\n", - "act2020[act2020.index.get_level_values(0).str.startswith(\"furnace\")].groupby(\"tec\").sum()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## identify missing relations" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 16, - "outputs": [], - "source": [ - "rel[\"count\"] = 1\n", - "tecs = list(rel[rel.index.get_level_values(2).str.startswith(\"meth_\")].index.get_level_values(2).unique())\n", - "\n", - "meth_rel = rel.swaplevel(0,2).loc[tecs]\n", - "counts = meth_rel[~meth_rel.index.get_level_values(1).isin([\"1990\", \"1995\", \"2000\", \"2005\", \"2010\", \"2015\"])].groupby([\"tec\",\"year_act\", \"node_loc\", \"relation\"]).sum()[\"count\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 17, - "outputs": [], - "source": [ - "fuel = meth_rel.swaplevel(0,3).loc[\"fuel\"]\n", - "fuel[\"df\"] = \"fuel\"\n", - "\n", - "m1 = meth_rel.swaplevel(0,3).loc[\"M1\"]\n", - "m1[\"df\"] = \"M1\"\n", - "\n", - "w_labels = pd.concat([fuel, m1]).reset_index()\n", - "wo_labels = pd.concat([fuel.drop(\"df\", axis=1), m1.drop(\"df\", axis=1)]).reset_index()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 18, - "outputs": [ - { - "data": { - "text/plain": " year_act node_loc tec relation Value node_origin year_rel \\\n0 1990 R12_GLB meth_trd CO2_trade 0.005545 R12_GLB 1990 \n1 1995 R12_GLB meth_trd CO2_trade 0.005545 R12_GLB 1995 \n2 2000 R12_GLB meth_trd CO2_trade 0.005545 R12_GLB 2000 \n3 2005 R12_GLB meth_trd CO2_trade 0.005545 R12_GLB 2005 \n4 2010 R12_GLB meth_trd CO2_trade 0.005545 R12_GLB 2010 \n... ... ... ... ... ... ... ... \n13679 2070 R12_CHN meth_t_d FE_liquids 1.000000 R12_CHN 2070 \n13680 2080 R12_CHN meth_t_d FE_liquids 1.000000 R12_CHN 2080 \n13681 2090 R12_CHN meth_t_d FE_liquids 1.000000 R12_CHN 2090 \n13682 2100 R12_CHN meth_t_d FE_liquids 1.000000 R12_CHN 2100 \n13683 2110 R12_CHN meth_t_d FE_liquids 1.000000 R12_CHN 2110 \n\n count df \n0 1 fuel \n1 1 fuel \n2 1 fuel \n3 1 fuel \n4 1 fuel \n... ... ... \n13679 1 fuel \n13680 1 fuel \n13681 1 fuel \n13682 1 fuel \n13683 1 fuel \n\n[13684 rows x 9 columns]", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
year_actnode_loctecrelationValuenode_originyear_relcountdf
01990R12_GLBmeth_trdCO2_trade0.005545R12_GLB19901fuel
11995R12_GLBmeth_trdCO2_trade0.005545R12_GLB19951fuel
22000R12_GLBmeth_trdCO2_trade0.005545R12_GLB20001fuel
32005R12_GLBmeth_trdCO2_trade0.005545R12_GLB20051fuel
42010R12_GLBmeth_trdCO2_trade0.005545R12_GLB20101fuel
..............................
136792070R12_CHNmeth_t_dFE_liquids1.000000R12_CHN20701fuel
136802080R12_CHNmeth_t_dFE_liquids1.000000R12_CHN20801fuel
136812090R12_CHNmeth_t_dFE_liquids1.000000R12_CHN20901fuel
136822100R12_CHNmeth_t_dFE_liquids1.000000R12_CHN21001fuel
136832110R12_CHNmeth_t_dFE_liquids1.000000R12_CHN21101fuel
\n

13684 rows × 9 columns

\n
" - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "diff = wo_labels.drop_duplicates(keep=False).merge(w_labels[\"df\"], left_index=True, right_index=True)\n", - "diff[diff[\"df\"]==\"fuel\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## co2 emissions analysis" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 19, - "outputs": [], - "source": [ - "def get_bot_up_emi_by_fuel(df, node, year):\n", - " #df = df.loc[node, year]\n", - "\n", - " df_oil = df[df.index.get_level_values(0).str.contains(\"oil\") |\n", - " (df.index.get_level_values(0).str.contains(\"liq\")) |\n", - " (df.index.get_level_values(0).str.contains(\"treat\")) |\n", - " (df.index.get_level_values(0).str.contains(\"coki\")) |\n", - " (df.index.get_level_values(0).str.contains(\"cata\"))\n", - " ].sort_values(\"emi\")\n", - " df_oil = df_oil[df_oil.index.get_level_values(0) != \"liq_bio_ccs\"]\n", - "\n", - " df_gas = df[(\n", - " (df.index.get_level_values(0).str.contains(\"gas\")) |\n", - " (df.index.get_level_values(0).str.contains(\"meth_ng\")) |\n", - " (df.index.get_level_values(0).str.contains(\"smr\")) |\n", - " (df.index.get_level_values(0) == \"h2_mix\") |\n", - " (df.index.get_level_values(0).str.contains(\"flar\"))\n", - " )].sort_values(\"emi\")\n", - "\n", - " df_coal = df[(df.index.get_level_values(0).str.contains(\"coal\")) | (df.index.get_level_values(0).str.contains(\"igcc\"))].sort_values(\"emi\")\n", - "\n", - " return df_gas, df_oil, df_coal" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 20, - "outputs": [], - "source": [ - "def get_top_dn_emi_by_fuel(df, node, year):\n", - " #df = df.loc[node, year]\n", - "\n", - " top_gas = df[((df.index.get_level_values(0).str.contains(\"gas\")) |\n", - " (df.index.get_level_values(0).str.contains(\"LNG\")) |\n", - " (df.index.get_level_values(1).str.contains(\"ane\")) |\n", - " (df.index.get_level_values(0).str.contains(\"h2_smr\")) |\n", - " (df.index.get_level_values(0).str.contains(\"flar\")))]#.sum()\n", - "\n", - " top_oil = df[(df.index.get_level_values(0).str.contains(\"oil\"))\n", - " |(df.index.get_level_values(1).str.contains(\"gasoil\"))].sort_values(\"emi\")#.sum()\n", - " #top_oil = top_oil[top_oil.index.get_level_values(0) != \"liq_bio_ccs\"]\n", - " top_coal = df[(df.index.get_level_values(0).str.contains(\"coal\")) |\n", - " (df.index.get_level_values(0).str.contains(\"lign\"))].sort_values(\"emi\")#.sum()\n", - "\n", - " return top_gas, top_oil, top_coal" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 21, - "outputs": [], - "source": [ - "elec_list = [\n", - " \"coal_ppl_u\",\n", - " \"coal_ppl\",\n", - " \"coal_adv\",\n", - " \"coal_adv_ccs\",\n", - " \"igcc\",\n", - " \"igcc_ccs\",\n", - " \"foil_ppl\",\n", - " \"loil_ppl\",\n", - " \"loil_cc\",\n", - " \"oil_ppl\",\n", - " \"gas_ppl\",\n", - " \"gas_cc\",\n", - " \"gas_cc_ccs\",\n", - " \"gas_ct\",\n", - " \"gas_htfc\",\n", - " \"bio_istig\",\n", - " \"g_ppl_co2scr\",\n", - " \"c_ppl_co2scr\",\n", - " \"bio_ppl_co2scr\",\n", - " \"igcc_co2scr\",\n", - " \"gfc_co2scr\",\n", - " \"cfc_co2scr\",\n", - " \"bio_istig_ccs\",\n", - " ]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 22, - "outputs": [], - "source": [ - "region = \"R12_LAM\"\n", - "year = \"2020\"" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## get global emissions for specific year" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 23, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\maczek\\AppData\\Local\\Temp\\ipykernel_17352\\2128802406.py:7: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " emi_bot_up_new = emi_bot_up_new.append(emi_top_down_new[emi_top_down_new.index.get_level_values(0) == \"meth_bio_ccs\"])\n" - ] - } - ], - "source": [ - "emi_top_down_new = emi_top_down.reset_index().set_index([\"tec\", \"mode\"])\n", - "emi_bot_up_new = emi_bott_up.reset_index().set_index([\"tec\", \"mode\"])\n", - "\n", - "emi_top_down_new = emi_top_down_new[emi_top_down_new[\"year_act\"]==year]\n", - "emi_bot_up_new = emi_bot_up_new[emi_bot_up_new[\"year_act\"]==year]\n", - "\n", - "emi_bot_up_new = emi_bot_up_new.append(emi_top_down_new[emi_top_down_new.index.get_level_values(0) == \"meth_bio_ccs\"])\n", - "emi_top_down_new = emi_top_down_new.drop(\"CO2_TCE\")\n", - "emi_bot_up_new = emi_bot_up_new.drop([\"CO2t_TCE\", \"CO2s_TCE\"])\n", - "\n", - "emi_bot_up_new = pd.concat([emi_bot_up_new, emi_top_down_new[emi_top_down_new.index.get_level_values(0).str.contains(\"alu\")]])\n", - "emi_bot_up_new = pd.concat([emi_bot_up_new, emi_top_down_new[emi_top_down_new.index.get_level_values(0).str.contains(\"clink\")]])\n", - "\n", - "top_new_gas, top_new_oil, top_new_coal = get_top_dn_emi_by_fuel(emi_top_down_new[emi_top_down_new[\"year_act\"]==year], \"test\", \"test\")\n", - "bot_new_gas, bot_new_oil, bot_new_coal = get_bot_up_emi_by_fuel(emi_bot_up_new[emi_bot_up_new[\"year_act\"]==year], \"test\", \"test\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 24, - "outputs": [], - "source": [ - "oil_tecs = ['hydrotreating_ref', 'catalytic_reforming_ref', 'oil_extr_1_ch4',\n", - " 'furnace_foil_petro', 'loil_t_d', 'oil_extr_2_ch4', 'foil_hpl',\n", - " 'foil_t_d', 'furnace_foil_refining', 'oil_extr_2', 'oil_extr_1',\n", - " 'oil_extr_3', 'furnace_foil_aluminum', 'loil_rc', 'oil_extr_3_ch4',\n", - " 'furnace_foil_cement', 'oil_extr_4_ch4', 'foil_trd', 'oil_trd',\n", - "\n", - " 'fueloil_NH3', 'fueloil_NH3_ccs', 'loil_i', 'foil_i', 'foil_trp', 'loil_trp', 'loil_bunker',\n", - " 'foil_bunker', 'oil_ppl', 'loil_ppl', 'loil_cc', 'foil_rc', 'foil_ppl',\n", - " 'catalytic_cracking_ref', 'loil_trd', 'oil_extr_4', \"coking_ref\", 'LNG_bunker', 'sp_liq_I']\n", - "bot_new_oil = emi_bot_up_new[(emi_bot_up_new.index.get_level_values(0).isin(oil_tecs))]\n", - "\n", - "coal_tecs = ['furnace_coal_aluminum', 'coal_hpl', 'furnace_coal_refining',\n", - " 'furnace_coal_cement', 'DUMMY_coal_supply', 'h2_coal_ccs',\n", - " 'coal_NH3_ccs',\n", - "\n", - " 'coal_ppl', 'coal_ppl_u', 'meth_coal', 'meth_coal_ccs', 'coal_rc', 'coal_NH3', 'coal_adv', 'coal_i', 'coal_gas'\n", - " ]\n", - "bot_new_coal = emi_bot_up_new[(emi_bot_up_new.index.get_level_values(0).isin(coal_tecs))]\n", - "\n", - "gas = ['h2_mix', 'gas_t_d', 'gas_hpl', 'furnace_gas_petro', 'gas_extr_2',\n", - " 'gas_extr_1', 'gas_t_d_ch4', 'gas_NH3_ccs', 'furnace_gas_refining',\n", - " 'gas_extr_3', 'DUMMY_gas_supply', 'gas_cc', 'gas_extr_4', 'gas_extr_5',\n", - " 'gas_extr_6', 'gas_i', 'gas_trp',\n", - "\n", - " 'gas_NH3','gas_cc_ccs', 'hp_gas_rc', 'hp_gas_i', 'gas_rc', 'gas_ct',\n", - " 'gas_ppl', 'meth_ng_ccs', 'h2_smr_ccs', 'meth_ng', 'LNG_trd', 'h2_smr',\n", - "]\n", - "bot_new_gas = emi_bot_up_new[(emi_bot_up_new.index.get_level_values(0).isin(gas))]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 25, - "outputs": [], - "source": [ - "top_new_trd = emi_top_down_new[(emi_top_down_new.index.get_level_values(0).str.contains(\"exp\")) | (emi_top_down_new.index.get_level_values(0).str.contains(\"imp\"))]\n", - "bot_new_trd = emi_bot_up_new[(emi_bot_up_new.index.get_level_values(0).str.contains(\"exp\")) | (emi_bot_up_new.index.get_level_values(0).str.contains(\"imp\"))]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## residual carbon emissions" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 26, - "outputs": [], - "source": [ - "bot_new_rest = emi_bot_up_new[~(emi_bot_up_new.index.get_level_values(0).isin(bot_new_coal.index.get_level_values(0))) &\n", - " ~(emi_bot_up_new.index.get_level_values(0).isin(bot_new_oil.index.get_level_values(0))) &\n", - " ~(emi_bot_up_new.index.get_level_values(0).isin(bot_new_gas.index.get_level_values(0)))\n", - "].sort_values(\"emi\")\n", - "\n", - "top_new_rest = emi_top_down_new[~(emi_top_down_new.index.get_level_values(0).isin(top_new_oil.index.get_level_values(0))) &\n", - " ~(emi_top_down_new.index.get_level_values(0).isin(top_new_gas.index.get_level_values(0))) &\n", - " ~(emi_top_down_new.index.get_level_values(0).isin(top_new_coal.index.get_level_values(0))) &\n", - " ~(emi_top_down_new.index.get_level_values(0).isin(top_new_trd.index.get_level_values(0)))\n", - "].sort_values(\"emi\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 27, - "outputs": [ - { - "data": { - "text/plain": "211.23699782794165" - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bot_new_rest.sum()[\"emi\"] * (44/12) - top_new_rest.sum()[\"emi\"] * (44/12)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 28, - "outputs": [ - { - "data": { - "text/plain": "423.78991342878" - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "emi_bot_up_new.sum().emi * (44/12) - emi_top_down_new.sum().emi * (44/12)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 29, - "outputs": [ - { - "data": { - "text/plain": "-12.097574083883933" - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "top_new_rest[top_new_rest[\"emi\"]<0].loc[\"meth_t_d\"].sum()[\"emi\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 30, - "outputs": [ - { - "data": { - "text/plain": "-44.35777164090775" - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "top_new_rest.loc[\"meth_t_d\"].sum().emi * co2_c_factor\n", - "#top_new_rest.loc[\"clinker_dry_cement\"].sum().emi * co2_c_factor" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 31, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Index([], dtype='object', name='tec')\n", - "Index(['meth_t_d'], dtype='object', name='tec')\n", - "Index(['CH2O_synth', 'soderberg_aluminum', 'eth_t_d', 'flaring_CO2',\n", - " 'prebake_aluminum', 'meth_trd', 'furnace_methanol_cement',\n", - " 'DUMMY_limestone_supply_steel', 'MTO_petro', 'h2_coal',\n", - " 'clinker_wet_cement', 'clinker_dry_cement', 'igcc'],\n", - " dtype='object', name='tec')\n", - "Index(['soderberg_aluminum', 'prebake_aluminum', 'clinker_wet_cement',\n", - " 'clinker_dry_cement'],\n", - " dtype='object', name='tec')\n" - ] - } - ], - "source": [ - "print(bot_new_rest[bot_new_rest[\"emi\"]<0].drop_duplicates().index.get_level_values(0).unique())\n", - "print(top_new_rest[top_new_rest[\"emi\"]<0].drop_duplicates().index.get_level_values(0).unique())\n", - "print(bot_new_rest[bot_new_rest[\"emi\"]>0].drop_duplicates().index.get_level_values(0).unique())\n", - "print(top_new_rest[top_new_rest[\"emi\"]>0].drop_duplicates().index.get_level_values(0).unique())" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 32, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "negative residual emission bottom-up: 0.0\n", - "negative residual emission top-down: -44.35777164090775\n", - "positive residual emission bottom-up: 2137.403984064366\n", - "positive residual emission top-down: 1970.5247578773321\n" - ] - } - ], - "source": [ - "print(\"negative residual emission bottom-up: \", bot_new_rest[bot_new_rest[\"emi\"]<0].drop_duplicates().sum()[\"emi\"] * (44/12))\n", - "print(\"negative residual emission top-down: \", top_new_rest[top_new_rest[\"emi\"]<0].sum()[\"emi\"] * (44/12))\n", - "print(\"positive residual emission bottom-up: \", bot_new_rest[bot_new_rest[\"emi\"]>0].sum()[\"emi\"] * (44/12))\n", - "print(\"positive residual emission top-down: \", top_new_rest[top_new_rest[\"emi\"]>0].sum()[\"emi\"] * (44/12))" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## check trade carbon balances" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 33, - "outputs": [ - { - "data": { - "text/plain": " node_loc year_act year_vtg relation Value node_origin \\\ntec mode \nloil_exp M1 R12_AFR 2020 2015 CO2_Emission -0.631 R12_AFR \nloil_imp M1 R12_AFR 2020 2020 CO2_Emission 0.631 R12_AFR \n M1 R12_CHN 2020 2020 CO2_Emission 0.631 R12_CHN \nloil_exp M1 R12_FSU 2020 2000 CO2_Emission -0.631 R12_FSU \n M1 R12_FSU 2020 2005 CO2_Emission -0.631 R12_FSU \n M1 R12_FSU 2020 2015 CO2_Emission -0.631 R12_FSU \n M1 R12_FSU 2020 2020 CO2_Emission -0.631 R12_FSU \n M1 R12_LAM 2020 2015 CO2_Emission -0.631 R12_LAM \n M1 R12_MEA 2020 2000 CO2_Emission -0.631 R12_MEA \n M1 R12_MEA 2020 2005 CO2_Emission -0.631 R12_MEA \n M1 R12_MEA 2020 2010 CO2_Emission -0.631 R12_MEA \n M1 R12_MEA 2020 2020 CO2_Emission -0.631 R12_MEA \n M1 R12_NAM 2020 2000 CO2_Emission -0.631 R12_NAM \n M1 R12_NAM 2020 2015 CO2_Emission -0.631 R12_NAM \nloil_imp M1 R12_NAM 2020 2020 CO2_Emission 0.631 R12_NAM \n M1 R12_PAO 2020 2020 CO2_Emission 0.631 R12_PAO \n M1 R12_RCPA 2020 2020 CO2_Emission 0.631 R12_RCPA \nloil_exp M1 R12_SAS 2020 2015 CO2_Emission -0.631 R12_SAS \nloil_imp M1 R12_SAS 2020 2020 CO2_Emission 0.631 R12_SAS \n M1 R12_WEU 2020 2020 CO2_Emission 0.631 R12_WEU \n\n year_rel Level emi \ntec mode \nloil_exp M1 2020 0.399258 -0.251931 \nloil_imp M1 2020 47.653442 30.069322 \n M1 2020 39.268683 24.778539 \nloil_exp M1 2020 10.142300 -6.399791 \n M1 2020 40.339789 -25.454407 \n M1 2020 13.855435 -8.742779 \n M1 2020 39.340764 -24.824022 \n M1 2020 40.069433 -25.283812 \n M1 2020 51.848149 -32.716182 \n M1 2020 23.071599 -14.558179 \n M1 2020 51.197000 -32.305307 \n M1 2020 127.024324 -80.152349 \n M1 2020 10.000000 -6.310000 \n M1 2020 54.575404 -34.437080 \nloil_imp M1 2020 302.049754 190.593395 \n M1 2020 9.866751 6.225920 \n M1 2020 5.307703 3.349160 \nloil_exp M1 2020 15.521467 -9.794046 \nloil_imp M1 2020 33.309633 21.018378 \n M1 2020 23.414262 14.774399 ", - 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node_locyear_actyear_vtgrelationValuenode_originyear_relLevelemi
tecmode
loil_expM1R12_AFR20202015CO2_Emission-0.631R12_AFR20200.399258-0.251931
loil_impM1R12_AFR20202020CO2_Emission0.631R12_AFR202047.65344230.069322
M1R12_CHN20202020CO2_Emission0.631R12_CHN202039.26868324.778539
loil_expM1R12_FSU20202000CO2_Emission-0.631R12_FSU202010.142300-6.399791
M1R12_FSU20202005CO2_Emission-0.631R12_FSU202040.339789-25.454407
M1R12_FSU20202015CO2_Emission-0.631R12_FSU202013.855435-8.742779
M1R12_FSU20202020CO2_Emission-0.631R12_FSU202039.340764-24.824022
M1R12_LAM20202015CO2_Emission-0.631R12_LAM202040.069433-25.283812
M1R12_MEA20202000CO2_Emission-0.631R12_MEA202051.848149-32.716182
M1R12_MEA20202005CO2_Emission-0.631R12_MEA202023.071599-14.558179
M1R12_MEA20202010CO2_Emission-0.631R12_MEA202051.197000-32.305307
M1R12_MEA20202020CO2_Emission-0.631R12_MEA2020127.024324-80.152349
M1R12_NAM20202000CO2_Emission-0.631R12_NAM202010.000000-6.310000
M1R12_NAM20202015CO2_Emission-0.631R12_NAM202054.575404-34.437080
loil_impM1R12_NAM20202020CO2_Emission0.631R12_NAM2020302.049754190.593395
M1R12_PAO20202020CO2_Emission0.631R12_PAO20209.8667516.225920
M1R12_RCPA20202020CO2_Emission0.631R12_RCPA20205.3077033.349160
loil_expM1R12_SAS20202015CO2_Emission-0.631R12_SAS202015.521467-9.794046
loil_impM1R12_SAS20202020CO2_Emission0.631R12_SAS202033.30963321.018378
M1R12_WEU20202020CO2_Emission0.631R12_WEU202023.41426214.774399
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" - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "emi_top_down_new[emi_top_down_new.index.get_level_values(0).str.contains(\"loil\")]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 34, - "outputs": [ - { - "data": { - "text/plain": "\"['gas_exp_chn', 'gas_exp_cpa', 'gas_exp_eeu', 'gas_exp_sas', 'gas_exp_weu', 'gas_imp', 'loil_exp']\"" - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "trd_tecs = sorted(list(emi_top_down_new.loc[(emi_top_down_new.index.get_level_values(0).str.contains(\"exp\")) | (emi_top_down_new.index.get_level_values(0).str.contains(\"imp\")) | (emi_top_down_new.index.get_level_values(0).str.contains(\"LNG\"))].index.get_level_values(0).unique()))#.sum()\n", - "comm_trd = [*trd_tecs[6:13]]#, trd_tecs[0]]\n", - "str(comm_trd)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 35, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "total top-down: 10106.387931650237\n" - ] - } - ], - "source": [ - "print(\"total top-down: \", emi_top_down_new.sum()[\"emi\"])" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 36, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "top-down trade balance: -309.90262543642564\n" - ] - } - ], - "source": [ - "print(\"top-down trade balance: \", emi_top_down_new.loc[(emi_top_down_new.index.get_level_values(0).isin(comm_trd))].sum()[\"emi\"])" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 37, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " top-down bottom-up\n", - "coal: 15310.756509423185 15367.135755810177\n", - "oil: 12224.570955760117 12550.335173360832\n", - "gas: 7622.870492500809 7425.670749577608\n", - "total: 35158.19795768411 35343.141678748616\n" - ] - } - ], - "source": [ - "print(\" top-down bottom-up\")\n", - "print(\"coal: \", top_new_coal.sum()[\"emi\"] * co2_c_factor,\" \", bot_new_coal.sum()[\"emi\"] * co2_c_factor)\n", - "print(\"oil: \", top_new_oil.sum()[\"emi\"] * co2_c_factor, \" \", bot_new_oil.sum()[\"emi\"] * co2_c_factor)\n", - "print(\"gas: \", top_new_gas.sum()[\"emi\"] * co2_c_factor,\" \", bot_new_gas.sum()[\"emi\"] * co2_c_factor)\n", - "print(\"total: \", (top_new_coal.sum()[\"emi\"]+top_new_oil.sum()[\"emi\"]+top_new_gas.sum()[\"emi\"]) * co2_c_factor,\" \", (bot_new_coal.sum()[\"emi\"]+bot_new_oil.sum()[\"emi\"]+bot_new_gas.sum()[\"emi\"]) * co2_c_factor)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## carbon IO gas" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 38, - "outputs": [], - "source": [ - "emi_factor_gas = 0.482" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 39, - "outputs": [ - { - "data": { - "text/plain": "node_loc year_act tec mode year_vtg\nR12_LAM 2020 gas_extr_mpen M1 2020 139.81507\nName: output, dtype: float64" - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "act_out.loc[region, year, [\"gas_extr_mpen\"]][\"output\"] * emi_factor_gas" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 40, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\maczek\\AppData\\Local\\Temp\\ipykernel_17352\\2951075765.py:1: FutureWarning: The behavior of indexing on a MultiIndex with a nested sequence of labels is deprecated and will change in a future version. `series.loc[label, sequence]` will raise if any members of 'sequence' or not present in the index's second level. To retain the old behavior, use `series.index.isin(sequence, level=1)`\n", - " inp_atm_dist = act_in.loc[region, year, [\"gas_bal\", \"gas_exp_nam\", \"LNG_prod\", \"gas_exp_cpa\", \"gas_exp_chn\", \"gas_exp_eeu\", \"gas_exp_sas\",\"gas_exp_weu\"]]\n" - ] - }, - { - "data": { - "text/plain": " Level commodity level \\\nnode_loc year_act tec mode year_vtg \nR12_LAM 2020 gas_bal M1 2020 252.783802 gas primary \n LNG_prod M1 2020 37.305296 gas primary \n\n Value node_origin input \nnode_loc year_act tec mode year_vtg \nR12_LAM 2020 gas_bal M1 2020 1.0 R12_LAM 252.783802 \n LNG_prod M1 2020 1.0 R12_LAM 37.305296 ", - "text/html": "
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LevelcommoditylevelValuenode_origininput
node_locyear_acttecmodeyear_vtg
R12_LAM2020gas_balM12020252.783802gasprimary1.0R12_LAM252.783802
LNG_prodM1202037.305296gasprimary1.0R12_LAM37.305296
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" - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "inp_atm_dist = act_in.loc[region, year, [\"gas_bal\", \"gas_exp_nam\", \"LNG_prod\", \"gas_exp_cpa\", \"gas_exp_chn\", \"gas_exp_eeu\", \"gas_exp_sas\",\"gas_exp_weu\"]]\n", - "inp_atm_dist#.sum()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 41, - "outputs": [ - { - "data": { - "text/plain": "node_loc year_act tec mode year_vtg\nR12_LAM 2020 gas_bal M1 2020 121.841792\n LNG_prod M1 2020 17.981153\nName: input, dtype: float64" - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#in_gas_proc = act_in.loc[region, year, \"gas_processing_petro\"]\n", - "(inp_atm_dist[\"input\"] * emi_factor_gas)#.sum()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 42, - "outputs": [ - { - "data": { - "text/plain": "tec\nfurnace_gas_refining 22.566977\ngas_NH3 2.610735\ngas_cc 38.983500\ngas_ppl 2.953142\ngas_t_d 47.083041\nh2_smr 3.473940\nmeth_ng 4.170458\nName: input, dtype: float64" - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(act_in.loc[region, year, act_in[\"level\"]==\"secondary\", act_in[\"commodity\"]==\"gas\"][\"input\"]\n", - " * emi_factor_gas).groupby(\"tec\").sum()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 43, - "outputs": [ - { - "data": { - "text/plain": "tec\ndri_steel 0.428896\neaf_steel 0.019407\nfurnace_gas_petro 2.164642\ngas_i 18.358918\ngas_processing_petro 2.809953\ngas_rc 15.804027\nhp_gas_aluminum 0.014086\nhp_gas_rc 1.386845\nName: input, dtype: float64" - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(act_in.loc[region, year, act_in[\"level\"]==\"final\", act_in[\"commodity\"]==\"gas\"][\"input\"] * emi_factor_gas).groupby(\"tec\").sum()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 44, - "outputs": [ - { - "data": { - "text/plain": "2.932242552118641" - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(act_out.loc[region, year, act_out[\"commodity\"].isin([\"ethane\", \"propane\"])][\"output\"] * 0.81).sum()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 45, - "outputs": [ - { - "data": { - "text/plain": "2.0096798752590517" - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(act_out.loc[region, year, act_out.index.get_level_values(3).isin([\"ethane\", \"propane\"])][\"output\"] * 0.5).sum()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 46, - "outputs": [ - { - "data": { - "text/plain": "1.2016059460355528" - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "act_out.loc[region, year, \"steam_cracker_petro\", act_out[\"commodity\"]==\"gas\"].sum()[\"output\"] * emi_factor_gas" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## carbon IO oil" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 47, - "outputs": [ - { - "ename": "KeyError", - "evalue": "('R12_LAM', '2020', 'oil_imp')", - "output_type": "error", - "traceback": [ - "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[1;31mKeyError\u001B[0m Traceback (most recent call last)", - "Input \u001B[1;32mIn [47]\u001B[0m, in \u001B[0;36m\u001B[1;34m()\u001B[0m\n\u001B[0;32m 2\u001B[0m out_oil_extr \u001B[38;5;241m=\u001B[39m act_out\u001B[38;5;241m.\u001B[39mloc[region, year, extr_tec]\n\u001B[0;32m 3\u001B[0m out_oil_exp \u001B[38;5;241m=\u001B[39m act_out\u001B[38;5;241m.\u001B[39mloc[region, year, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124moil_exp\u001B[39m\u001B[38;5;124m\"\u001B[39m]\n\u001B[1;32m----> 4\u001B[0m in_oil_imp \u001B[38;5;241m=\u001B[39m \u001B[43mact_in\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mloc\u001B[49m\u001B[43m[\u001B[49m\u001B[43mregion\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43myear\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43moil_imp\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\n\u001B[0;32m 6\u001B[0m carbon_in_extr \u001B[38;5;241m=\u001B[39m (out_oil_extr\u001B[38;5;241m.\u001B[39msum()[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124moutput\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m-\u001B[39m out_oil_exp\u001B[38;5;241m.\u001B[39msum()[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124moutput\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m+\u001B[39m in_oil_imp\u001B[38;5;241m.\u001B[39msum()[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124minput\u001B[39m\u001B[38;5;124m\"\u001B[39m]) \u001B[38;5;241m*\u001B[39m \u001B[38;5;241m0.631\u001B[39m\n\u001B[0;32m 7\u001B[0m carbon_in_extr\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:961\u001B[0m, in \u001B[0;36m_LocationIndexer.__getitem__\u001B[1;34m(self, key)\u001B[0m\n\u001B[0;32m 959\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_is_scalar_access(key):\n\u001B[0;32m 960\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mobj\u001B[38;5;241m.\u001B[39m_get_value(\u001B[38;5;241m*\u001B[39mkey, takeable\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_takeable)\n\u001B[1;32m--> 961\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_getitem_tuple\u001B[49m\u001B[43m(\u001B[49m\u001B[43mkey\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 962\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m 963\u001B[0m \u001B[38;5;66;03m# we by definition only have the 0th axis\u001B[39;00m\n\u001B[0;32m 964\u001B[0m axis \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39maxis \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;241m0\u001B[39m\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1140\u001B[0m, in \u001B[0;36m_LocIndexer._getitem_tuple\u001B[1;34m(self, tup)\u001B[0m\n\u001B[0;32m 1138\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m suppress(IndexingError):\n\u001B[0;32m 1139\u001B[0m tup \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_expand_ellipsis(tup)\n\u001B[1;32m-> 1140\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_getitem_lowerdim\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtup\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 1142\u001B[0m \u001B[38;5;66;03m# no multi-index, so validate all of the indexers\u001B[39;00m\n\u001B[0;32m 1143\u001B[0m tup \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_validate_tuple_indexer(tup)\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:859\u001B[0m, in \u001B[0;36m_LocationIndexer._getitem_lowerdim\u001B[1;34m(self, tup)\u001B[0m\n\u001B[0;32m 849\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m (\n\u001B[0;32m 850\u001B[0m \u001B[38;5;28misinstance\u001B[39m(ax0, MultiIndex)\n\u001B[0;32m 851\u001B[0m \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mname \u001B[38;5;241m!=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124miloc\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 856\u001B[0m \u001B[38;5;66;03m# is equivalent.\u001B[39;00m\n\u001B[0;32m 857\u001B[0m \u001B[38;5;66;03m# (see the other place where we call _handle_lowerdim_multi_index_axis0)\u001B[39;00m\n\u001B[0;32m 858\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m suppress(IndexingError):\n\u001B[1;32m--> 859\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_handle_lowerdim_multi_index_axis0\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtup\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 861\u001B[0m tup \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_validate_key_length(tup)\n\u001B[0;32m 863\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m i, key \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28menumerate\u001B[39m(tup):\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1166\u001B[0m, in \u001B[0;36m_LocIndexer._handle_lowerdim_multi_index_axis0\u001B[1;34m(self, tup)\u001B[0m\n\u001B[0;32m 1162\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m ek:\n\u001B[0;32m 1163\u001B[0m \u001B[38;5;66;03m# raise KeyError if number of indexers match\u001B[39;00m\n\u001B[0;32m 1164\u001B[0m \u001B[38;5;66;03m# else IndexingError will be raised\u001B[39;00m\n\u001B[0;32m 1165\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mndim \u001B[38;5;241m<\u001B[39m \u001B[38;5;28mlen\u001B[39m(tup) \u001B[38;5;241m<\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mobj\u001B[38;5;241m.\u001B[39mindex\u001B[38;5;241m.\u001B[39mnlevels:\n\u001B[1;32m-> 1166\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m ek\n\u001B[0;32m 1167\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m IndexingError(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mNo label returned\u001B[39m\u001B[38;5;124m\"\u001B[39m) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01mek\u001B[39;00m\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1160\u001B[0m, in \u001B[0;36m_LocIndexer._handle_lowerdim_multi_index_axis0\u001B[1;34m(self, tup)\u001B[0m\n\u001B[0;32m 1157\u001B[0m axis \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39maxis \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;241m0\u001B[39m\n\u001B[0;32m 1158\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m 1159\u001B[0m \u001B[38;5;66;03m# fast path for series or for tup devoid of slices\u001B[39;00m\n\u001B[1;32m-> 1160\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_get_label\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtup\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43maxis\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43maxis\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 1162\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m ek:\n\u001B[0;32m 1163\u001B[0m \u001B[38;5;66;03m# raise KeyError if number of indexers match\u001B[39;00m\n\u001B[0;32m 1164\u001B[0m \u001B[38;5;66;03m# else IndexingError will be raised\u001B[39;00m\n\u001B[0;32m 1165\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mndim \u001B[38;5;241m<\u001B[39m \u001B[38;5;28mlen\u001B[39m(tup) \u001B[38;5;241m<\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mobj\u001B[38;5;241m.\u001B[39mindex\u001B[38;5;241m.\u001B[39mnlevels:\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1153\u001B[0m, in \u001B[0;36m_LocIndexer._get_label\u001B[1;34m(self, label, axis)\u001B[0m\n\u001B[0;32m 1151\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m_get_label\u001B[39m(\u001B[38;5;28mself\u001B[39m, label, axis: \u001B[38;5;28mint\u001B[39m):\n\u001B[0;32m 1152\u001B[0m \u001B[38;5;66;03m# GH#5667 this will fail if the label is not present in the axis.\u001B[39;00m\n\u001B[1;32m-> 1153\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mobj\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mxs\u001B[49m\u001B[43m(\u001B[49m\u001B[43mlabel\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43maxis\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43maxis\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:3857\u001B[0m, in \u001B[0;36mNDFrame.xs\u001B[1;34m(self, key, axis, level, drop_level)\u001B[0m\n\u001B[0;32m 3854\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_consolidate_inplace()\n\u001B[0;32m 3856\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(index, MultiIndex):\n\u001B[1;32m-> 3857\u001B[0m loc, new_index \u001B[38;5;241m=\u001B[39m \u001B[43mindex\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_get_loc_level\u001B[49m\u001B[43m(\u001B[49m\u001B[43mkey\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mlevel\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m0\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[0;32m 3858\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m drop_level:\n\u001B[0;32m 3859\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m lib\u001B[38;5;241m.\u001B[39mis_integer(loc):\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\multi.py:3052\u001B[0m, in \u001B[0;36mMultiIndex._get_loc_level\u001B[1;34m(self, key, level)\u001B[0m\n\u001B[0;32m 3049\u001B[0m \u001B[38;5;28;01mpass\u001B[39;00m\n\u001B[0;32m 3051\u001B[0m \u001B[38;5;66;03m# partial selection\u001B[39;00m\n\u001B[1;32m-> 3052\u001B[0m indexer \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_loc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mkey\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 3053\u001B[0m ilevels \u001B[38;5;241m=\u001B[39m [i \u001B[38;5;28;01mfor\u001B[39;00m i \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mrange\u001B[39m(\u001B[38;5;28mlen\u001B[39m(key)) \u001B[38;5;28;01mif\u001B[39;00m key[i] \u001B[38;5;241m!=\u001B[39m \u001B[38;5;28mslice\u001B[39m(\u001B[38;5;28;01mNone\u001B[39;00m, \u001B[38;5;28;01mNone\u001B[39;00m)]\n\u001B[0;32m 3054\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(ilevels) \u001B[38;5;241m==\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mnlevels:\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\multi.py:2905\u001B[0m, in \u001B[0;36mMultiIndex.get_loc\u001B[1;34m(self, key, method)\u001B[0m\n\u001B[0;32m 2902\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m(key) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01merr\u001B[39;00m\n\u001B[0;32m 2904\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m start \u001B[38;5;241m==\u001B[39m stop:\n\u001B[1;32m-> 2905\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m(key)\n\u001B[0;32m 2907\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m follow_key:\n\u001B[0;32m 2908\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mslice\u001B[39m(start, stop)\n", - "\u001B[1;31mKeyError\u001B[0m: ('R12_LAM', '2020', 'oil_imp')" - ] - } - ], - "source": [ - "extr_tec = \"oil_extr_mpen\"\n", - "out_oil_extr = act_out.loc[region, year, extr_tec]\n", - "out_oil_exp = act_out.loc[region, year, \"oil_exp\"]\n", - "in_oil_imp = act_in.loc[region, year, \"oil_imp\"]\n", - "\n", - "carbon_in_extr = (out_oil_extr.sum()[\"output\"] - out_oil_exp.sum()[\"output\"] + in_oil_imp.sum()[\"input\"]) * 0.631\n", - "carbon_in_extr" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 48, - "outputs": [ - { - "data": { - "text/plain": "319.2508439033615" - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "inp_atm_dist = act_in.loc[region, year, \"atm_distillation_ref\"]\n", - "carbon_in_ref = inp_atm_dist[inp_atm_dist[\"commodity\"] == \"crudeoil\"].sum()[\"input\"] * 0.631\n", - "carbon_in_ref" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 49, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "10.089172440637014\n" - ] - } - ], - "source": [ - "out_agg_ref = act_out.loc[region, year, \"agg_ref\"]\n", - "carbon_out_agg_ref = out_agg_ref.loc[out_agg_ref[\"commodity\"].isin([\"fueloil\"])][\"output\"].sum() * 0.665 \\\n", - "+ out_agg_ref[~out_agg_ref[\"commodity\"].isin([\"fueloil\"])].sum()[\"output\"] * 0.631\n", - "\n", - "print(carbon_in_ref - carbon_out_agg_ref)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 50, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\maczek\\AppData\\Local\\Temp\\ipykernel_17352\\3261112422.py:1: PerformanceWarning: indexing past lexsort depth may impact performance.\n", - " inp_atm_dist = act_in.swaplevel(0,2).loc[\"atm_distillation_ref\", year]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "252.94682658537567\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\maczek\\AppData\\Local\\Temp\\ipykernel_17352\\3261112422.py:4: PerformanceWarning: indexing past lexsort depth may impact performance.\n", - " out_agg_ref = act_out.swaplevel(0,2).loc[\"agg_ref\", year]\n" - ] - } - ], - "source": [ - "inp_atm_dist = act_in.swaplevel(0,2).loc[\"atm_distillation_ref\", year]\n", - "carbon_in_ref = inp_atm_dist[inp_atm_dist[\"commodity\"] == \"crudeoil\"].sum()[\"input\"] * 0.631\n", - "\n", - "out_agg_ref = act_out.swaplevel(0,2).loc[\"agg_ref\", year]\n", - "carbon_out_agg_ref = out_agg_ref.loc[out_agg_ref[\"commodity\"].isin([\"fueloil\"])][\"output\"].sum() * 0.665 + out_agg_ref[~out_agg_ref[\"commodity\"].isin([\"fueloil\"])].sum()[\"output\"] * 0.631\n", - "\n", - "print(carbon_in_ref - carbon_out_agg_ref)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 51, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\maczek\\AppData\\Local\\Temp\\ipykernel_17352\\2343031665.py:1: PerformanceWarning: indexing past lexsort depth may impact performance.\n", - " sc_in = act_in.swaplevel(0,2).loc[\"steam_cracker_petro\", year]\n" - ] - }, - { - "data": { - "text/plain": "301.4771887846489" - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sc_in = act_in.swaplevel(0,2).loc[\"steam_cracker_petro\", year]\n", - "sc_in[~sc_in.index.get_level_values(1).isin([\"ethane\", \"propane\"])].sum()[\"input\"] * 0.631" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## carbon IO coal" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 52, - "outputs": [ - { - "data": { - "text/plain": " Level commodity level Value \\\ntec mode year_vtg \nLNG_prod M1 2020 37.305296 gas primary 1.000000 \ndri_steel M1 2020 2.696444 gas final 0.330000 \n 2020 2.696444 gas dummy_emission 0.330000 \neaf_steel M2 2005 0.265603 gas final 0.002000 \n 2005 0.265603 gas dummy_emission 0.002000 \n... ... ... ... ... \nmeth_ng fuel 2015 2.359075 gas secondary 1.666667 \n 2015 2.359075 gas secondary 1.666667 \n 2015 2.359075 gas secondary 1.666667 \n 2015 2.359075 gas secondary 1.666667 \n 2015 2.359075 gas secondary 1.666667 \n\n node_origin input emi_factor \ntec mode year_vtg \nLNG_prod M1 2020 R12_LAM 37.305296 NaN \ndri_steel M1 2020 R12_LAM 0.889827 NaN \n 2020 R12_LAM 0.889827 NaN \neaf_steel M2 2005 R12_LAM 0.000531 NaN \n 2005 R12_LAM 0.000531 NaN \n... ... ... ... \nmeth_ng fuel 2015 R12_LAM 3.931792 0.253333 \n 2015 R12_LAM 3.931792 0.253333 \n 2015 R12_LAM 3.931792 0.253333 \n 2015 R12_LAM 3.931792 0.253333 \n 2015 R12_LAM 3.931792 0.253333 \n\n[2135 rows x 7 columns]", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
LevelcommoditylevelValuenode_origininputemi_factor
tecmodeyear_vtg
LNG_prodM1202037.305296gasprimary1.000000R12_LAM37.305296NaN
dri_steelM120202.696444gasfinal0.330000R12_LAM0.889827NaN
20202.696444gasdummy_emission0.330000R12_LAM0.889827NaN
eaf_steelM220050.265603gasfinal0.002000R12_LAM0.000531NaN
20050.265603gasdummy_emission0.002000R12_LAM0.000531NaN
..............................
meth_ngfuel20152.359075gassecondary1.666667R12_LAM3.9317920.253333
20152.359075gassecondary1.666667R12_LAM3.9317920.253333
20152.359075gassecondary1.666667R12_LAM3.9317920.253333
20152.359075gassecondary1.666667R12_LAM3.9317920.253333
20152.359075gassecondary1.666667R12_LAM3.9317920.253333
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2135 rows × 7 columns

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" - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "act_in_yr_reg = act_in.loc[region, year]\n", - "comm = \"gas\"\n", - "bott_dict = {\n", - " \"coal\":bot_new_coal,\n", - " \"gas\":bot_new_gas\n", - "}\n", - "bott_yr_reg_coal_merge = bott_dict[comm].copy(deep=True)\n", - "x = act_in_yr_reg[(act_in_yr_reg.index.get_level_values(0).str.contains(comm)) & (act_in_yr_reg[\"commodity\"]==comm) & (act_in_yr_reg[\"level\"]==\"final\")].join(bott_yr_reg_coal_merge.rename({\"Value\": \"emi_factor\"}, axis=1)[\"emi_factor\"])\n", - "x = act_in_yr_reg[(act_in_yr_reg[\"commodity\"]==comm)].join(bott_yr_reg_coal_merge.rename({\"Value\": \"emi_factor\"}, axis=1)[\"emi_factor\"])\n", - "x" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 53, - "outputs": [ - { - "data": { - "text/plain": " Level commodity level \\\ntec mode node_loc year_act year_vtg \nLNG_prod M1 R12_AFR 2020 2020 60.182113 gas primary \n 2025 2025 69.629561 gas primary \n 2030 2030 94.292866 gas primary \n 2035 2035 101.502785 gas primary \n 2040 2040 100.225163 gas primary \n... ... ... ... \nmeth_ng fuel R12_WEU 2025 2015 0.331997 gas secondary \n 2015 0.331997 gas secondary \n 2015 0.331997 gas secondary \n 2015 0.331997 gas secondary \n 2015 0.331997 gas secondary \n\n Value node_origin input \\\ntec mode node_loc year_act year_vtg \nLNG_prod M1 R12_AFR 2020 2020 1.000000 R12_AFR 60.182113 \n 2025 2025 1.000000 R12_AFR 69.629561 \n 2030 2030 1.000000 R12_AFR 94.292866 \n 2035 2035 1.000000 R12_AFR 101.502785 \n 2040 2040 1.000000 R12_AFR 100.225163 \n... ... ... ... \nmeth_ng fuel R12_WEU 2025 2015 1.666667 R12_WEU 0.553329 \n 2015 1.666667 R12_WEU 0.553329 \n 2015 1.666667 R12_WEU 0.553329 \n 2015 1.666667 R12_WEU 0.553329 \n 2015 1.666667 R12_WEU 0.553329 \n\n emi_factor \ntec mode node_loc year_act year_vtg \nLNG_prod M1 R12_AFR 2020 2020 NaN \n 2025 2025 NaN \n 2030 2030 NaN \n 2035 2035 NaN \n 2040 2040 NaN \n... ... \nmeth_ng fuel R12_WEU 2025 2015 0.253333 \n 2015 0.253333 \n 2015 0.253333 \n 2015 0.253333 \n 2015 0.253333 \n\n[204796 rows x 7 columns]", - "text/html": "
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LevelcommoditylevelValuenode_origininputemi_factor
tecmodenode_locyear_actyear_vtg
LNG_prodM1R12_AFR2020202060.182113gasprimary1.000000R12_AFR60.182113NaN
2025202569.629561gasprimary1.000000R12_AFR69.629561NaN
2030203094.292866gasprimary1.000000R12_AFR94.292866NaN
20352035101.502785gasprimary1.000000R12_AFR101.502785NaN
20402040100.225163gasprimary1.000000R12_AFR100.225163NaN
....................................
meth_ngfuelR12_WEU202520150.331997gassecondary1.666667R12_WEU0.5533290.253333
20150.331997gassecondary1.666667R12_WEU0.5533290.253333
20150.331997gassecondary1.666667R12_WEU0.5533290.253333
20150.331997gassecondary1.666667R12_WEU0.5533290.253333
20150.331997gassecondary1.666667R12_WEU0.5533290.253333
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204796 rows × 7 columns

\n
" - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "act_in_yr_reg = act_in#.swaplevel(0,1).loc[year]\n", - "comm = \"gas\"\n", - "bott_dict = {\n", - " \"coal\":bot_new_coal,\n", - " \"gas\":bot_new_gas\n", - "}\n", - "bott_yr_reg_coal_merge = bott_dict[comm].copy(deep=True)\n", - "x = act_in_yr_reg[(act_in_yr_reg.index.get_level_values(0).str.contains(comm)) & (act_in_yr_reg[\"commodity\"]==comm) & (act_in_yr_reg[\"level\"]==\"final\")].join(bott_yr_reg_coal_merge.rename({\"Value\": \"emi_factor\"}, axis=1)[\"emi_factor\"])\n", - "x = act_in_yr_reg[(act_in_yr_reg[\"commodity\"]==comm)].join(bott_yr_reg_coal_merge.rename({\"Value\": \"emi_factor\"}, axis=1)[\"emi_factor\"])\n", - "x" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 54, - "outputs": [ - { - "data": { - "text/plain": " node_loc year_act year_vtg relation Value \\\ntec mode \nDUMMY_gas_supply M1 R12_AFR 2020 2020 CO2_ind 0.482000 \nfurnace_gas_petro high_temp R12_AFR 2020 2020 CO2_ind 0.523913 \nfurnace_gas_refining high_temp R12_AFR 2020 2020 CO2_cc 0.800120 \ngas_NH3 M1 R12_AFR 2020 2005 CO2_cc 0.534804 \n M1 R12_AFR 2020 2010 CO2_cc 0.534804 \n... ... ... ... ... ... \ngas_t_d M1 R12_WEU 2020 2005 CO2_cc 0.034737 \n M1 R12_WEU 2020 2010 CO2_cc 0.034737 \nhp_gas_rc M1 R12_WEU 2020 2020 CO2_r_c 0.321333 \nmeth_ng feedstock R12_WEU 2020 2015 CO2_cc 0.253333 \n fuel R12_WEU 2020 2015 CO2_cc 0.253333 \n\n node_origin year_rel Level emi \ntec mode \nDUMMY_gas_supply M1 R12_AFR 2020 1.660615 0.800417 \nfurnace_gas_petro high_temp R12_AFR 2020 1.179212 0.617805 \nfurnace_gas_refining high_temp R12_AFR 2020 4.492885 3.594847 \ngas_NH3 M1 R12_AFR 2020 1.065932 0.570065 \n M1 R12_AFR 2020 2.041205 1.091645 \n... ... ... ... ... \ngas_t_d M1 R12_WEU 2020 64.785421 2.250441 \n M1 R12_WEU 2020 15.089721 0.524169 \nhp_gas_rc M1 R12_WEU 2020 33.288225 10.696616 \nmeth_ng feedstock R12_WEU 2020 1.287200 0.326091 \n fuel R12_WEU 2020 1.468320 0.371975 \n\n[520 rows x 9 columns]", - "text/html": "
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node_locyear_actyear_vtgrelationValuenode_originyear_relLevelemi
tecmode
DUMMY_gas_supplyM1R12_AFR20202020CO2_ind0.482000R12_AFR20201.6606150.800417
furnace_gas_petrohigh_tempR12_AFR20202020CO2_ind0.523913R12_AFR20201.1792120.617805
furnace_gas_refininghigh_tempR12_AFR20202020CO2_cc0.800120R12_AFR20204.4928853.594847
gas_NH3M1R12_AFR20202005CO2_cc0.534804R12_AFR20201.0659320.570065
M1R12_AFR20202010CO2_cc0.534804R12_AFR20202.0412051.091645
.................................
gas_t_dM1R12_WEU20202005CO2_cc0.034737R12_WEU202064.7854212.250441
M1R12_WEU20202010CO2_cc0.034737R12_WEU202015.0897210.524169
hp_gas_rcM1R12_WEU20202020CO2_r_c0.321333R12_WEU202033.28822510.696616
meth_ngfeedstockR12_WEU20202015CO2_cc0.253333R12_WEU20201.2872000.326091
fuelR12_WEU20202015CO2_cc0.253333R12_WEU20201.4683200.371975
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520 rows × 9 columns

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" - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bot_new_gas.loc[\"gas_i\"]\n", - "bott_yr_reg_coal_merge" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 55, - "outputs": [ - { - "data": { - "text/plain": "mode node_loc year_act year_vtg\nM1 R12_AFR 2020 2005 0.482000\n 2005 0.482000\n 2005 0.482000\n 2005 0.482000\n 2005 0.482000\n ... \n R12_WEU 2060 2040 0.637941\n 2040 0.637941\n 2040 0.637941\n 2040 0.516429\n 2040 0.516429\nLength: 4557, dtype: float64" - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(x[\"emi_factor\"] / x[\"Value\"]).dropna().loc[\"gas_i\"]#.reset_index()#.drop([\"year_vtg\", \"node_loc\", \"year_act\"], axis=1).drop_duplicates()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 56, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\maczek\\AppData\\Local\\Temp\\ipykernel_17352\\3091354.py:1: PerformanceWarning: indexing past lexsort depth may impact performance.\n", - " x.loc[\"gas_i\", \"M1\", \"R12_CHN\"]\n" - ] - }, - { - "data": { - "text/plain": " Level commodity level Value node_origin input \\\nyear_act year_vtg \n2020 2005 1.228475 gas final 1.408451 R12_CHN 1.730247 \n 2005 1.228475 gas final 1.408451 R12_CHN 1.730247 \n 2005 1.228475 gas final 1.408451 R12_CHN 1.730247 \n 2005 1.228475 gas final 1.408451 R12_CHN 1.730247 \n 2005 1.228475 gas final 1.408451 R12_CHN 1.730247 \n... ... ... ... ... ... ... \n 2015 3.930682 gas final 1.408451 R12_CHN 5.536172 \n 2015 3.930682 gas final 1.408451 R12_CHN 5.536172 \n 2015 3.930682 gas final 1.408451 R12_CHN 5.536172 \n 2015 3.930682 gas final 1.408451 R12_CHN 5.536172 \n 2015 3.930682 gas final 1.408451 R12_CHN 5.536172 \n\n emi_factor \nyear_act year_vtg \n2020 2005 0.678873 \n 2005 0.678873 \n 2005 0.678873 \n 2005 0.678873 \n 2005 0.678873 \n... ... \n 2015 0.708824 \n 2015 0.708824 \n 2015 0.708824 \n 2015 0.573810 \n 2015 0.573810 \n\n[62 rows x 7 columns]", - "text/html": "
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LevelcommoditylevelValuenode_origininputemi_factor
year_actyear_vtg
202020051.228475gasfinal1.408451R12_CHN1.7302470.678873
20051.228475gasfinal1.408451R12_CHN1.7302470.678873
20051.228475gasfinal1.408451R12_CHN1.7302470.678873
20051.228475gasfinal1.408451R12_CHN1.7302470.678873
20051.228475gasfinal1.408451R12_CHN1.7302470.678873
........................
20153.930682gasfinal1.408451R12_CHN5.5361720.708824
20153.930682gasfinal1.408451R12_CHN5.5361720.708824
20153.930682gasfinal1.408451R12_CHN5.5361720.708824
20153.930682gasfinal1.408451R12_CHN5.5361720.573810
20153.930682gasfinal1.408451R12_CHN5.5361720.573810
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62 rows × 7 columns

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" - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x.loc[\"gas_i\", \"M1\", \"R12_CHN\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## check *_t_d tecs emission factors" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 92, - "outputs": [], - "source": [ - "df_gas_t_d = act_in[act_in.index.get_level_values(2).str.contains(\"coal\")].join(emi_bott_up.rename({\"Value\": \"emi_factor\"}, axis=1)[\"emi_factor\"])" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 58, - "outputs": [], - "source": [ - "df_gas_t_d = act_in.join(emi_bott_up.rename({\"Value\": \"emi_factor\"}, axis=1)[\"emi_factor\"]).dropna()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 93, - "outputs": [], - "source": [ - "ef_dict = {\n", - " 'biomass':0,\n", - " 'coal':0.814,\n", - " 'electr':0,\n", - " 'ethanol':0,\n", - " 'fueloil':0.665,\n", - " 'gas':0.482,\n", - " 'gas_1':0.482,\n", - " 'gas_2':0.482,\n", - " 'gas_3':0.482,\n", - " 'gas_4':0.482,\n", - " 'gas_5':0.482,\n", - " 'gas_6':0.482,\n", - " 'gas_afr':0.482,\n", - " 'gas_chn':0.482,\n", - " 'gas_cpa':0.482,\n", - " 'gas_weu':0.482,\n", - " 'gas_eeu':0.482,\n", - " 'gas_sas':0.482,\n", - " 'LNG':0.482,\n", - " 'd_heat':0,\n", - " 'lightoil':0.631,\n", - " 'methanol':0.549,\n", - " 'hydrogen':0,\n", - " 'freshwater_supply':0,\n", - " \"crudeoil\":0.63,\n", - " \"crude_1\":0.665,\n", - " \"crude_2\":0.665,\n", - " \"crude_3\":0.665,\n", - " \"crude_4\":0.665,\n", - " \"crude_5\":0.665,\n", - " \"crude_6\":0.665,\n", - " \"crude_7\":0.665,\n", - " \"ht_heat\":0,\n", - " \"lh2\":0,\n", - "}\n", - "\n", - "def get_ef(df):\n", - " df[\"ef\"] = ef_dict[df[\"commodity\"]]\n", - " return df\n", - "\n", - "df_gas_t_d = df_gas_t_d.apply(lambda x: get_ef(x), axis=1)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 67, - "outputs": [], - "source": [ - "df_gas_t_d = df_gas_t_d.join(act_out.rename({\"commodity\":\"comm_out\"}, axis=1)[\"comm_out\"])\n", - "df_gas_t_d = df_gas_t_d[df_gas_t_d[\"commodity\"]==df_gas_t_d[\"comm_out\"]]\n", - "#df_gas_t_d = df_gas_t_d[~df_gas_t_d.index.get_level_values(2).str.contains(\"_extr\")]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 94, - "outputs": [], - "source": [ - "# for IO tecs\n", - "ef_false = (df_gas_t_d[\"emi_factor\"] / (df_gas_t_d[\"Value\"] - 1)).dropna()\n", - "# for input only tecs\n", - "ef_false = (df_gas_t_d[\"emi_factor\"] / df_gas_t_d[\"Value\"]).dropna()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 95, - "outputs": [], - "source": [ - "tec_comm_map = df_gas_t_d.reset_index()[[\"tec\", \"commodity\"]].drop_duplicates().set_index(\"tec\").to_dict()[\"commodity\"]\n", - "tec_list = [i for i in list(tec_comm_map.keys())]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 99, - "outputs": [ - { - "data": { - "text/plain": " 0\nyear_act node_loc mode year_vtg \n2045 R12_PAS M1 2010 0.823070\n2050 R12_NAM M1 2010 0.816775\n2035 R12_CHN M1 2000 0.816429\n R12_RCPA M1 2000 0.816429\n2025 R12_LAM M1 1980 0.814000\n... ...\n2045 R12_SAS M1 2020 0.814000\n 2025 0.814000\n 2030 0.814000\n 2035 0.814000\n2035 R12_PAS M1 2015 0.814000\n\n[476 rows x 1 columns]", - "text/html": "
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476 rows × 1 columns

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" - }, - "execution_count": 99, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pd.DataFrame(ef_false).swaplevel(0,2).loc[\"coal_ppl\"].sort_values(0, ascending=False)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 98, - "outputs": [ - { - "data": { - "text/plain": "year_act node_loc mode year_vtg\n2020 R12_AFR M1 1960 NaN\n 1965 NaN\n 1970 NaN\n 1975 NaN\n 1980 NaN\n ..\n2090 R12_AFR M1 2080 NaN\n2100 R12_AFR M1 2080 NaN\n 2100 NaN\n2110 R12_AFR M1 2100 NaN\n 2110 NaN\nLength: 632, dtype: float64" - }, - "execution_count": 98, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_gas_t_d.swaplevel(0,2).loc[\"coal_ppl_u\"][\"emi_factor\"] / df_gas_t_d.swaplevel(0,2).loc[\"coal_ppl_u\"][\"Value\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 96, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "coal_adv\n", - "coal_bal\n", - "coal_exp\n", - "coal_extr\n", - "coal_extr_ch4\n", - "coal_i\n", - "coal_ppl\n", - "coal_ppl_u\n", - "coal_rc\n", - "coal_t_d\n", - "furnace_coal_aluminum\n", - "furnace_coal_cement\n", - "furnace_coal_refining\n", - "furnace_coal_petro\n", - "coal_hpl\n", - "furnace_coal_resins\n", - "coal_imp\n", - "meth_coal\n", - "coal_NH3\n", - "coal_trd\n", - "sp_coal_I\n", - "coal_gas\n", - "h2_coal\n" - ] - }, - { - "data": { - "text/plain": "
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- }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import math\n", - "import matplotlib.pyplot as plt\n", - "fig, axs = plt.subplots(int(math.sqrt(len(tec_list))), int(math.sqrt(len(tec_list))), figsize=(40,20), facecolor=\"w\")\n", - "ax_it = iter(fig.axes)\n", - "for k in tec_list:\n", - " print(k)\n", - " try:\n", - " tec = k\n", - " y = ef_false.swaplevel(0,2).loc[tec].droplevel([3,2]).reset_index()\n", - " ax = next((ax_it))\n", - " y.plot.scatter(x=\"year_act\", y=0, ax=ax)\n", - " ax.plot([ef_dict[tec_comm_map[tec]]]*len(y.year_act.unique()), color=\"red\")\n", - " ax.set_ylabel(\"Mt C / GWa\")\n", - " ax.set_title(tec)\n", - " ax.set_xlabel(\"\")\n", - " except:\n", - " pass" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 64, - "outputs": [], - "source": [ - "fig.savefig(\"test.png\", facecolor=\"w\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "ef_false.swaplevel(0,2).loc[\"hp_gas_i\"].droplevel([3,2]).reset_index()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## aggregated emissions comparison" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df_gas_t_d[\"loss\"] = (df_gas_t_d[\"Value\"] - 1) * df_gas_t_d[\"Level\"]\n", - "t_d_emi = (df_gas_t_d[\"emi_factor\"] * df_gas_t_d[\"Level\"]).groupby([\"year_act\"]).sum() * co2_c_factor" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df_gas_t_d[\"emi_factor_corr\"] = (df_gas_t_d[\"Value\"] - 1) * df_gas_t_d[\"ef\"]\n", - "t_d_emi_corr =(df_gas_t_d[\"emi_factor_corr\"] * df_gas_t_d[\"Level\"]).groupby([\"year_act\"]).sum() * co2_c_factor" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "fig,ax = plt.subplots(figsize=(10,5))\n", - "t_d_emi.plot(ax=ax)\n", - "t_d_emi_corr.plot(ax=ax)\n", - "ax.set_ylabel(\"Mt CO2\")\n", - "ax.set_title(\"CO2 Emissions from t_d technologies\")\n", - "ax.legend([\"current CO2_cc\", \"corrected CO2_cc\"])" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df_gas_t_d.swaplevel(0,2).loc[\"gas_t_d\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "act_in_yr_reg = act_in.loc[region, year]\n", - "comm = \"coal\"\n", - "bott_dict = {\n", - " \"coal\":top_yr_reg_gas,\n", - " \"gas\":bott_yr_reg_gas\n", - "}\n", - "bott_yr_reg_coal_merge = bott_dict[comm].copy(deep=True)\n", - "x = act_in_yr_reg[(act_in_yr_reg.index.get_level_values(0).str.contains(comm)) & (act_in_yr_reg[\"commodity\"]==comm) & (act_in_yr_reg[\"level\"]==\"final\")].join(bott_yr_reg_coal_merge.rename({\"Value\": \"emi_factor\"}, axis=1)[\"emi_factor\"])\n", - "x = act_in_yr_reg[(act_in_yr_reg[\"commodity\"]==comm)].join(bott_yr_reg_coal_merge.rename({\"Value\": \"emi_factor\"}, axis=1)[\"emi_factor\"])\n", - "x#.sum()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "x#(x[\"emi_factor\"]/ x[\"Value\"]).dropna()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## oil emissions" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df_bot_oil = get_bot_up_emi_by_fuel(emi_bott_up, region, year)[1]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df_bot_oil[df_bot_oil.index.get_level_values(0).str.contains(\"extr\")].sum()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "emi_bot_up_refining = df_bot_oil.loc[df_bot_oil.index.get_level_values(0).str.endswith(\"_ref\")]#.sum()\n", - "emi_bot_up_extr = df_bot_oil.loc[df_bot_oil.index.get_level_values(0).str.contains(\"extr\")]\n", - "emi_bot_up_final = df_bot_oil.loc[(df_bot_oil.index.difference(emi_bot_up_refining.index)) & (df_bot_oil.index.difference(emi_bot_up_extr.index))]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "emi_bot_up_refining.sum()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "emi_bot_up_final.sort_values(\"emi\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "print(\"extraction emissions: \",emi_bot_up_extr.sum()[\"emi\"])\n", - "print(\"refining emissions: \",emi_bot_up_refining.sum()[\"emi\"])\n", - "print(\"combustion emissions: \",emi_bot_up_final.sum()[\"emi\"])" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "inp_hyd_crack = act_in.loc[region, year, \"hydro_cracking_ref\"]\n", - "inp_hyd_crack[inp_hyd_crack[\"commodity\"] == \"hydrogen\"].sum()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "inp_steam_crack = act_in.loc[region, year, \"steam_cracker_petro\"]\n", - "inp_steam_crack[~inp_steam_crack[\"commodity\"].isin([\"ethane\",\"propane\", \"electr\", \"ht_heat\"])].sum()[\"input\"] * 0.64" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df_top_oil = get_top_dn_emi_by_fuel(emi_top_down, region, year)[1]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df_top_oil.loc[df_top_oil.index.get_level_values(0).str.startswith(\"oil\")].sum()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df_top_oil.loc[~df_top_oil.index.get_level_values(0).str.endswith(\"ref\")]#.sum()#.sort_index()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "carbon_out_foil_loil_exp = df_top_oil[df_top_oil.index.get_level_values(0).str.contains(\"loil\")].sum()[\"emi\"] + df_top_oil[df_top_oil.index.get_level_values(0).str.contains(\"foil\")].sum()[\"emi\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "act_out.swaplevel(0,2).loc[\"MTO_petro\"].loc[\"2025\", \"R12_CHN\"].sum()#.groupby(\"year_act\").sum()" - ], - "metadata": { - "collapsed": false - } - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/message_ix_models/model/material/magpie notebooks/MESSAGE-magpie test.ipynb b/message_ix_models/model/material/magpie notebooks/MESSAGE-magpie test.ipynb deleted file mode 100644 index eabd4da1f3..0000000000 --- a/message_ix_models/model/material/magpie notebooks/MESSAGE-magpie test.ipynb +++ /dev/null @@ -1,2590 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "source": [ - "# MAGPIE setup baseline" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 1, - "outputs": [ - { - "data": { - "text/plain": "", - "application/javascript": "if (typeof IPython !== 'undefined') { IPython.OutputArea.prototype._should_scroll = function(lines){ return false; }}" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": "" - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import ixmp\n", - "import message_ix\n", - "from message_data.tools.utilities import calibrate_UE_gr_to_demand, add_globiom, transfer_demands\n", - "from message_ix_models.util import private_data_path\n", - "import message_data\n", - "import importlib\n", - "importlib.reload(message_data.tools.utilities)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-03T12:03:36.363202200Z", - "start_time": "2023-10-03T12:03:20.633202800Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 2, - "outputs": [ - { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'data_scp'", - "output_type": "error", - "traceback": [ - "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[1;31mModuleNotFoundError\u001B[0m Traceback (most recent call last)", - "Input \u001B[1;32mIn [2]\u001B[0m, in \u001B[0;36m\u001B[1;34m()\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[38;5;28;01mimport\u001B[39;00m \u001B[38;5;21;01mdata_scp\u001B[39;00m\n\u001B[0;32m 2\u001B[0m \u001B[38;5;28;01mimport\u001B[39;00m \u001B[38;5;21;01mimportlib\u001B[39;00m\n\u001B[0;32m 3\u001B[0m importlib\u001B[38;5;241m.\u001B[39mreload(data_scp)\n", - "\u001B[1;31mModuleNotFoundError\u001B[0m: No module named 'data_scp'" - ] - } - ], - "source": [ - "import data_scp\n", - "import importlib\n", - "importlib.reload(data_scp)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-03T12:03:36.942308700Z", - "start_time": "2023-10-03T12:03:36.336284800Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 2, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'scen' is not defined", - "output_type": "error", - "traceback": [ - "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[1;31mNameError\u001B[0m Traceback (most recent call last)", - "Input \u001B[1;32mIn [2]\u001B[0m, in \u001B[0;36m\u001B[1;34m()\u001B[0m\n\u001B[1;32m----> 1\u001B[0m df \u001B[38;5;241m=\u001B[39m \u001B[43mscen\u001B[49m\u001B[38;5;241m.\u001B[39mtimeseries()\n\u001B[0;32m 2\u001B[0m df\u001B[38;5;241m.\u001B[39myear\u001B[38;5;241m.\u001B[39munique()\n", - "\u001B[1;31mNameError\u001B[0m: name 'scen' is not defined" - ] - } - ], - "source": [ - "df = scen.timeseries()\n", - "df.year.unique()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 4, - "outputs": [], - "source": [ - "mp = ixmp.Platform(\"ixmp_dev\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-03T12:04:20.124790500Z", - "start_time": "2023-10-03T12:04:13.917186600Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 4, - "outputs": [], - "source": [ - "df = mp.scenario_list()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 7, - "outputs": [ - { - "data": { - "text/plain": "array(['ENGAGE-MAGPIE_SSP2', 'ENGAGE-MAGPIE_SSP2_v4.1.8.3_T4.5_r3.1',\n 'ENGAGE_SSP2_MAGPIE_SCP', 'ENGAGE_SSP2_PtX_FL', 'ENGAGE_SSP2_test',\n 'ENGAGE_SSP2_test_FL', 'ENGAGE_SSP2_v1', 'ENGAGE_SSP2_v2',\n 'ENGAGE_SSP2_v2.1', 'ENGAGE_SSP2_v2_test', 'ENGAGE_SSP2_v4',\n 'ENGAGE_SSP2_v4.1', 'ENGAGE_SSP2_v4.1.1', 'ENGAGE_SSP2_v4.1.2',\n 'ENGAGE_SSP2_v4.1.2_NAV_test', 'ENGAGE_SSP2_v4.1.2_R12',\n 'ENGAGE_SSP2_v4.1.2_sens', 'ENGAGE_SSP2_v4.1.3',\n 'ENGAGE_SSP2_v4.1.4', 'ENGAGE_SSP2_v4.1.4_BZ',\n 'ENGAGE_SSP2_v4.1.4_DAC_PtX_FL', 'ENGAGE_SSP2_v4.1.4_FG',\n 'ENGAGE_SSP2_v4.1.4_R12_CHN', 'ENGAGE_SSP2_v4.1.4_R12_CHN_FG',\n 'ENGAGE_SSP2_v4.1.4_R12_CHN_FG_test', 'ENGAGE_SSP2_v4.1.4_R12_NOR',\n 'ENGAGE_SSP2_v4.1.4_sens', 'ENGAGE_SSP2_v4.1.5',\n 'ENGAGE_SSP2_v4.1.6', 'ENGAGE_SSP2_v4.1.7',\n 'ENGAGE_SSP2_v4.1.7 +transport', 'ENGAGE_SSP2_v4.1.7_DR10p',\n 'ENGAGE_SSP2_v4.1.7_DR1p', 'ENGAGE_SSP2_v4.1.7_DR_10p',\n 'ENGAGE_SSP2_v4.1.7_DR_1p', 'ENGAGE_SSP2_v4.1.7_DR_2p',\n 'ENGAGE_SSP2_v4.1.7_DR_3p', 'ENGAGE_SSP2_v4.1.7_DR_4p',\n 'ENGAGE_SSP2_v4.1.7_GLOBIOMupd', 'ENGAGE_SSP2_v4.1.7_R12',\n 'ENGAGE_SSP2_v4.1.7_R12_CHN', 'ENGAGE_SSP2_v4.1.7_R12_CHN_FG',\n 'ENGAGE_SSP2_v4.1.7_R12_CHN_FG_test', 'ENGAGE_SSP2_v4.1.7_T4.5',\n 'ENGAGE_SSP2_v4.1.7_T4.5_r1', 'ENGAGE_SSP2_v4.1.7_T4.5_r2',\n 'ENGAGE_SSP2_v4.1.7_T4.5_r3', 'ENGAGE_SSP2_v4.1.7_T4.5_r3.1',\n 'ENGAGE_SSP2_v4.1.7_T4.5_r3.1_test',\n 'ENGAGE_SSP2_v4.1.7_T4.5_r3.1_test1',\n 'ENGAGE_SSP2_v4.1.7_T4.5_r3.2', 'ENGAGE_SSP2_v4.1.7_WAT',\n 'ENGAGE_SSP2_v4.1.7_ar5_gwp100', 'ENGAGE_SSP2_v4.1.7_clean',\n 'ENGAGE_SSP2_v4.1.7_clean_FG', 'ENGAGE_SSP2_v4.1.7_clean_R4',\n 'ENGAGE_SSP2_v4.1.7_clean_R4_t12', 'ENGAGE_SSP2_v4.1.7_clean_t12',\n 'ENGAGE_SSP2_v4.1.7_t12', 'ENGAGE_SSP2_v4.1.7_test',\n 'ENGAGE_SSP2_v4.1.7_wat', 'ENGAGE_SSP2_v4.1.7_wat_wat',\n 'ENGAGE_SSP2_v4.1.8', 'ENGAGE_SSP2_v4.1.8.1',\n 'ENGAGE_SSP2_v4.1.8.1_test', 'ENGAGE_SSP2_v4.1.8.2',\n 'ENGAGE_SSP2_v4.1.8.2_test', 'ENGAGE_SSP2_v4.1.8.3',\n 'ENGAGE_SSP2_v4.1.8.3.1_T3.4_r3.1',\n 'ENGAGE_SSP2_v4.1.8.3.1_T3.4v2_r3.1',\n 'ENGAGE_SSP2_v4.1.8.3.1_T4.5_r3.1',\n 'ENGAGE_SSP2_v4.1.8.3.1_T4.5_r3.1_fe_h2_reporting',\n 'ENGAGE_SSP2_v4.1.8.3.1_T4.5_r3.1_test',\n 'ENGAGE_SSP2_v4.1.8.3.1_T4.5v2_r3.1',\n 'ENGAGE_SSP2_v4.1.8.3.1_test_T4.5_r3.1',\n 'ENGAGE_SSP2_v4.1.8.3_T4.5_r3.1', 'ENGAGE_SSP2_v4.1.8.3_test',\n 'ENGAGE_SSP2_v4.1.8.4', 'ENGAGE_SSP2_v4.1.8.5',\n 'ENGAGE_SSP2_v4.1.8_JT', 'ENGAGE_SSP2_v4.1.8_T4.5_test',\n 'ENGAGE_SSP2_v4.1.8_T4.5_test_test', 'ENGAGE_SSP2_v4.1.8_t12',\n 'ENGAGE_SSP2_v4_test', 'ENGAGE_V417_FG', 'ENGAGE_Yoga',\n 'ENGAGE_baseline_jsk', 'ENGAGE_experimental',\n 'ENGAGE_test_hydro_v4.1.7', 'ENGAGE_test_hydro_v4.1.8',\n 'ENGAGE_v4.1.7_SSP2_DAC_PtX_FL'], dtype=object)" - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df[df[\"model\"].str.startswith(\"ENGAGE\")].model.unique()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 11, - "outputs": [], - "source": [ - "scen = message_ix.Scenario(mp, \"MESSAGEix-Materials\", \"baseline_DEFAULT_0108\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 13, - "outputs": [], - "source": [ - "df = scen.set(\"balance_equality\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 16, - "outputs": [], - "source": [ - "df_eth = df[df[\"commodity\"]==\"methanol\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 18, - "outputs": [], - "source": [ - "df_eth[\"commodity\"] = \"ethanol\"" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 22, - "outputs": [], - "source": [ - "df_eth = df_eth[~df_eth.level.str.contains(\"fs\")]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 23, - "outputs": [ - { - "data": { - "text/plain": " commodity level\n151 ethanol export\n153 ethanol import\n155 ethanol final", - "text/html": "
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151ethanolexport
153ethanolimport
155ethanolfinal
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commoditylevel
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135 rows × 2 columns

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" - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "scen_new.set(\"balance_equality\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 38, - "outputs": [], - "source": [ - "scen_new.check_out()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 39, - "outputs": [], - "source": [ - "scen_new.add_set(\"balance_equality\", (\"ethanol\", \"primary\"))\n", - "scen_new.add_set(\"balance_equality\", (\"ethanol\", \"secondary\"))" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 41, - "outputs": [], - "source": [ - "scen_new.commit(\"add further ethanol levels to bal_eq\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 43, - "outputs": [], - "source": [ - "scen_new.remove_solution()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 25, - "outputs": [], - "source": [ - "scen_new = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP50BD1BI00\", \"EN_NPi2020_500f\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 5, - "outputs": [ - { - "data": { - "text/plain": "True" - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "scen_new = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\", \"cumu_cost_test_step7_1000f\")\n", - "scen_new.has_solution()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-03T12:04:22.625833700Z", - "start_time": "2023-10-03T12:04:20.127783400Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 6, - "outputs": [], - "source": [ - "df = scen_new.timeseries()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-03T12:04:50.109013400Z", - "start_time": "2023-10-03T12:04:34.399154600Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 8, - "outputs": [ - { - "data": { - "text/plain": " region variable unit year value \\\n132 CPA Emissions|CO2 Mt CO2/yr 1990 0.000000 \n133 CPA Emissions|CO2 Mt CO2/yr 1995 0.000000 \n134 CPA Emissions|CO2 Mt CO2/yr 2000 0.000000 \n135 CPA Emissions|CO2 Mt CO2/yr 2005 0.000000 \n136 CPA Emissions|CO2 Mt CO2/yr 2010 0.000000 \n... ... ... ... ... ... \n201441 GLB region (R12) Emissions|CO2 Mt CO2/yr 2070 2569.568282 \n201442 GLB region (R12) Emissions|CO2 Mt CO2/yr 2080 -8925.499016 \n201443 GLB region (R12) Emissions|CO2 Mt CO2/yr 2090 -19558.089012 \n201444 GLB region (R12) Emissions|CO2 Mt CO2/yr 2100 -20221.539211 \n201445 GLB region (R12) Emissions|CO2 Mt CO2/yr 2110 -20562.001412 \n\n model \\\n132 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00 \n133 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00 \n134 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00 \n135 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00 \n136 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00 \n... ... \n201441 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00 \n201442 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00 \n201443 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00 \n201444 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00 \n201445 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00 \n\n scenario \n132 cumu_cost_test_step7_1000f \n133 cumu_cost_test_step7_1000f \n134 cumu_cost_test_step7_1000f \n135 cumu_cost_test_step7_1000f \n136 cumu_cost_test_step7_1000f \n... ... \n201441 cumu_cost_test_step7_1000f \n201442 cumu_cost_test_step7_1000f \n201443 cumu_cost_test_step7_1000f \n201444 cumu_cost_test_step7_1000f \n201445 cumu_cost_test_step7_1000f \n\n[266 rows x 7 columns]", - "text/html": "
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regionvariableunityearvaluemodelscenario
132CPAEmissions|CO2Mt CO2/yr19900.000000MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00cumu_cost_test_step7_1000f
133CPAEmissions|CO2Mt CO2/yr19950.000000MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00cumu_cost_test_step7_1000f
134CPAEmissions|CO2Mt CO2/yr20000.000000MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00cumu_cost_test_step7_1000f
135CPAEmissions|CO2Mt CO2/yr20050.000000MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00cumu_cost_test_step7_1000f
136CPAEmissions|CO2Mt CO2/yr20100.000000MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00cumu_cost_test_step7_1000f
........................
201441GLB region (R12)Emissions|CO2Mt CO2/yr20702569.568282MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00cumu_cost_test_step7_1000f
201442GLB region (R12)Emissions|CO2Mt CO2/yr2080-8925.499016MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00cumu_cost_test_step7_1000f
201443GLB region (R12)Emissions|CO2Mt CO2/yr2090-19558.089012MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00cumu_cost_test_step7_1000f
201444GLB region (R12)Emissions|CO2Mt CO2/yr2100-20221.539211MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00cumu_cost_test_step7_1000f
201445GLB region (R12)Emissions|CO2Mt CO2/yr2110-20562.001412MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00cumu_cost_test_step7_1000f
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" - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df[df[\"variable\"]==\"Emissions|CO2\"]" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-03T12:06:58.382788Z", - "start_time": "2023-10-03T12:06:58.229677600Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 28, - "outputs": [], - "source": [ - "scen_new.remove_solution()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 31, - "outputs": [], - "source": [ - "scen_new.check_out()\n", - "scen_new.add_set(\"balance_equality\", df_eth)\n", - "scen_new.commit(\"add equality to ethanol supply\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 44, - "outputs": [], - "source": [ - "scen_new.solve(\"MESSAGE-MACRO\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 8, - "outputs": [ - { - "data": { - "text/plain": " model scenario scheme \\\n4390 ENGAGE_SSP2_v4.1.8.5 EN_NPi2020_1000 MESSAGE \n4391 ENGAGE_SSP2_v4.1.8.5 EN_NPi2020_1000_LU_fbk MESSAGE \n4392 ENGAGE_SSP2_v4.1.8.5 EN_NPi2020_1000_step1 MESSAGE \n4393 ENGAGE_SSP2_v4.1.8.5 EN_NPi2020_1000_step2 MESSAGE \n4394 ENGAGE_SSP2_v4.1.8.5 EN_NPi2020_1000f MESSAGE \n... ... ... ... \n4483 ENGAGE_SSP2_v4.1.8.5 baseline_with_lu_bkp MESSAGE \n4484 ENGAGE_SSP2_v4.1.8.5 indci_low_dem_scen MESSAGE \n4485 ENGAGE_SSP2_v4.1.8.5 npi_low_dem_scen MESSAGE \n4486 ENGAGE_SSP2_v4.1.8.5 npi_low_dem_scen2 MESSAGE \n4487 ENGAGE_SSP2_v4.1.8.5 test to confirm MACRO converges MESSAGE \n\n is_default is_locked cre_user cre_date upd_user \\\n4390 1 0 fricko 2023-07-12 10:58:00.000000 fricko \n4391 1 0 fricko 2023-05-10 13:48:13.000000 None \n4392 1 0 fricko 2023-07-12 10:13:53.000000 fricko \n4393 1 0 fricko 2023-07-12 10:37:15.000000 fricko \n4394 1 0 fricko 2023-07-12 13:13:05.000000 fricko \n... ... ... ... ... ... \n4483 1 0 fricko 2023-07-07 13:14:40.000000 None \n4484 1 0 fricko 2023-07-07 22:19:40.000000 fricko \n4485 1 0 fricko 2023-07-11 17:35:34.000000 fricko \n4486 1 0 fricko 2023-07-11 18:08:18.000000 fricko \n4487 1 0 fricko 2023-07-10 16:15:17.000000 fricko \n\n upd_date lock_user lock_date \\\n4390 2023-07-12 11:02:41.000000 None None \n4391 None None None \n4392 2023-07-12 10:21:09.000000 None None \n4393 2023-07-12 10:44:02.000000 None None \n4394 2023-07-12 13:28:49.000000 None None \n... ... ... ... \n4483 None None None \n4484 2023-07-07 22:27:53.000000 None None \n4485 2023-07-11 17:51:18.000000 None None \n4486 2023-07-11 18:20:51.000000 None None \n4487 2023-07-10 16:18:01.000000 None None \n\n annotation version \n4390 clone Scenario from 'ENGAGE_SSP2_v4.1.8.5|EN_N... 3 \n4391 clone Scenario from 'ENGAGE_SSP2_v4.1.8.5|EN_N... 3 \n4392 clone Scenario from 'ENGAGE_SSP2_v4.1.8.5|NPi2... 3 \n4393 clone Scenario from 'ENGAGE_SSP2_v4.1.8.5|EN_N... 6 \n4394 clone Scenario from 'ENGAGE_SSP2_v4.1.8.5|NPi2... 2 \n... ... ... \n4483 clone Scenario from 'ENGAGE_SSP2_v4.1.8.5|base... 2 \n4484 clone Scenario from 'ENGAGE_SSP2_v4.1.8.5|INDC... 1 \n4485 clone Scenario from 'ENGAGE_SSP2_v4.1.8.5|NPi2... 7 \n4486 clone Scenario from 'ENGAGE_SSP2_v4.1.8.5|NPi2... 3 \n4487 clone Scenario from 'ENGAGE_SSP2_v4.1.8.5|base... 8 \n\n[98 rows x 13 columns]", - "text/html": "
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modelscenarioschemeis_defaultis_lockedcre_usercre_dateupd_userupd_datelock_userlock_dateannotationversion
4390ENGAGE_SSP2_v4.1.8.5EN_NPi2020_1000MESSAGE10fricko2023-07-12 10:58:00.000000fricko2023-07-12 11:02:41.000000NoneNoneclone Scenario from 'ENGAGE_SSP2_v4.1.8.5|EN_N...3
4391ENGAGE_SSP2_v4.1.8.5EN_NPi2020_1000_LU_fbkMESSAGE10fricko2023-05-10 13:48:13.000000NoneNoneNoneNoneclone Scenario from 'ENGAGE_SSP2_v4.1.8.5|EN_N...3
4392ENGAGE_SSP2_v4.1.8.5EN_NPi2020_1000_step1MESSAGE10fricko2023-07-12 10:13:53.000000fricko2023-07-12 10:21:09.000000NoneNoneclone Scenario from 'ENGAGE_SSP2_v4.1.8.5|NPi2...3
4393ENGAGE_SSP2_v4.1.8.5EN_NPi2020_1000_step2MESSAGE10fricko2023-07-12 10:37:15.000000fricko2023-07-12 10:44:02.000000NoneNoneclone Scenario from 'ENGAGE_SSP2_v4.1.8.5|EN_N...6
4394ENGAGE_SSP2_v4.1.8.5EN_NPi2020_1000fMESSAGE10fricko2023-07-12 13:13:05.000000fricko2023-07-12 13:28:49.000000NoneNoneclone Scenario from 'ENGAGE_SSP2_v4.1.8.5|NPi2...2
..........................................
4483ENGAGE_SSP2_v4.1.8.5baseline_with_lu_bkpMESSAGE10fricko2023-07-07 13:14:40.000000NoneNoneNoneNoneclone Scenario from 'ENGAGE_SSP2_v4.1.8.5|base...2
4484ENGAGE_SSP2_v4.1.8.5indci_low_dem_scenMESSAGE10fricko2023-07-07 22:19:40.000000fricko2023-07-07 22:27:53.000000NoneNoneclone Scenario from 'ENGAGE_SSP2_v4.1.8.5|INDC...1
4485ENGAGE_SSP2_v4.1.8.5npi_low_dem_scenMESSAGE10fricko2023-07-11 17:35:34.000000fricko2023-07-11 17:51:18.000000NoneNoneclone Scenario from 'ENGAGE_SSP2_v4.1.8.5|NPi2...7
4486ENGAGE_SSP2_v4.1.8.5npi_low_dem_scen2MESSAGE10fricko2023-07-11 18:08:18.000000fricko2023-07-11 18:20:51.000000NoneNoneclone Scenario from 'ENGAGE_SSP2_v4.1.8.5|NPi2...3
4487ENGAGE_SSP2_v4.1.8.5test to confirm MACRO convergesMESSAGE10fricko2023-07-10 16:15:17.000000fricko2023-07-10 16:18:01.000000NoneNoneclone Scenario from 'ENGAGE_SSP2_v4.1.8.5|base...8
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" - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df[df[\"model\"].str.startswith(\"ENGAGE_SSP2_v4.1.8.5\") & (==\"EN_NPi2020_1000\")]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 6, - "outputs": [], - "source": [ - "#scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD0BI70\", \"baseline\")\n", - "scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\", \"600f_MP00BD0BI78_test_calib_macro\")\n", - "scen_base = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\", \"1000f_MP00BD0BI78_test_calib_macro\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 9, - "outputs": [], - "source": [ - "scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12\", \"baseline_DEFULT_check\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 10, - "outputs": [ - { - "data": { - "text/plain": " node land_scenario year commodity level time \\\n0 R12_CHN BIO00GHG000 2020 bioenergy land_use year \n1 R12_CHN BIO00GHG010 2020 bioenergy land_use year \n2 R12_CHN BIO00GHG020 2020 bioenergy land_use year \n3 R12_CHN BIO00GHG050 2020 bioenergy land_use year \n4 R12_CHN BIO00GHG100 2020 bioenergy land_use year \n.. ... ... ... ... ... ... \n163 R12_CHN BIO60GHG200 2020 bioenergy land_use_reporting year \n164 R12_CHN BIO60GHG2000 2020 bioenergy land_use_reporting year \n165 R12_CHN BIO60GHG3000 2020 bioenergy land_use_reporting year \n166 R12_CHN BIO60GHG400 2020 bioenergy land_use_reporting year \n167 R12_CHN BIO60GHG600 2020 bioenergy land_use_reporting year \n\n value unit \n0 161.6800 GWa \n1 162.6100 GWa \n2 162.4100 GWa \n3 162.3600 GWa \n4 161.6800 GWa \n.. ... ... \n163 260.2013 GWa \n164 260.3617 GWa \n165 260.3803 GWa \n166 260.0390 GWa \n167 259.9724 GWa \n\n[168 rows x 8 columns]", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
nodeland_scenarioyearcommodityleveltimevalueunit
0R12_CHNBIO00GHG0002020bioenergyland_useyear161.6800GWa
1R12_CHNBIO00GHG0102020bioenergyland_useyear162.6100GWa
2R12_CHNBIO00GHG0202020bioenergyland_useyear162.4100GWa
3R12_CHNBIO00GHG0502020bioenergyland_useyear162.3600GWa
4R12_CHNBIO00GHG1002020bioenergyland_useyear161.6800GWa
...........................
163R12_CHNBIO60GHG2002020bioenergyland_use_reportingyear260.2013GWa
164R12_CHNBIO60GHG20002020bioenergyland_use_reportingyear260.3617GWa
165R12_CHNBIO60GHG30002020bioenergyland_use_reportingyear260.3803GWa
166R12_CHNBIO60GHG4002020bioenergyland_use_reportingyear260.0390GWa
167R12_CHNBIO60GHG6002020bioenergyland_use_reportingyear259.9724GWa
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" - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "scen.par(\"land_output\", filters={\"commodity\":\"bioenergy\", \"node\":\"R12_CHN\", \"year\":\"2020\"})" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 6, - "outputs": [], - "source": [ - "scen_base = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP30BD0BI78\", \"NPi2020-con-prim-dir-ncr\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 9, - "outputs": [ - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[1;31mKeyboardInterrupt\u001B[0m Traceback (most recent call last)", - "Input \u001B[1;32mIn [9]\u001B[0m, in \u001B[0;36m\u001B[1;34m()\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[43mscen_base\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msolve\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmodel\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mMESSAGE-MACRO\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32m~\\PycharmProjects\\message_ix\\message_ix\\core.py:677\u001B[0m, in \u001B[0;36mScenario.solve\u001B[1;34m(self, model, solve_options, **kwargs)\u001B[0m\n\u001B[0;32m 657\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21msolve\u001B[39m(\u001B[38;5;28mself\u001B[39m, model\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mMESSAGE\u001B[39m\u001B[38;5;124m\"\u001B[39m, solve_options\u001B[38;5;241m=\u001B[39m{}, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[0;32m 658\u001B[0m \u001B[38;5;124;03m\"\"\"Solve MESSAGE or MESSAGE-MACRO for the Scenario.\u001B[39;00m\n\u001B[0;32m 659\u001B[0m \n\u001B[0;32m 660\u001B[0m \u001B[38;5;124;03m By default, :meth:`ixmp.Scenario.solve` is called with 'MESSAGE' as the\u001B[39;00m\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 675\u001B[0m \u001B[38;5;124;03m :class:`.GAMSModel`.\u001B[39;00m\n\u001B[0;32m 676\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[1;32m--> 677\u001B[0m \u001B[38;5;28;43msuper\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msolve\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmodel\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mmodel\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43msolve_options\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43msolve_options\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\ixmp\\core\\scenario.py:835\u001B[0m, in \u001B[0;36mScenario.solve\u001B[1;34m(self, model, callback, cb_kwargs, **model_options)\u001B[0m\n\u001B[0;32m 833\u001B[0m \u001B[38;5;66;03m# Iterate until convergence\u001B[39;00m\n\u001B[0;32m 834\u001B[0m \u001B[38;5;28;01mwhile\u001B[39;00m \u001B[38;5;28;01mTrue\u001B[39;00m:\n\u001B[1;32m--> 835\u001B[0m \u001B[43mmodel_obj\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrun\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[0;32m 837\u001B[0m \u001B[38;5;66;03m# Store an iteration number to help the callback\u001B[39;00m\n\u001B[0;32m 838\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28mhasattr\u001B[39m(\u001B[38;5;28mself\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124miteration\u001B[39m\u001B[38;5;124m\"\u001B[39m):\n", - "File \u001B[1;32m~\\PycharmProjects\\message_ix\\message_ix\\models.py:373\u001B[0m, in \u001B[0;36mGAMSModel.run\u001B[1;34m(self, scenario)\u001B[0m\n\u001B[0;32m 370\u001B[0m lines2 \u001B[38;5;241m=\u001B[39m (\u001B[38;5;124m\"\u001B[39m\u001B[38;5;132;01m{}\u001B[39;00m\u001B[38;5;124m = \u001B[39m\u001B[38;5;132;01m{}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;241m.\u001B[39mformat(\u001B[38;5;241m*\u001B[39mkv) \u001B[38;5;28;01mfor\u001B[39;00m kv \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcplex_opts\u001B[38;5;241m.\u001B[39mitems())\n\u001B[0;32m 371\u001B[0m optfile2\u001B[38;5;241m.\u001B[39mwrite_text(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;130;01m\\n\u001B[39;00m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;241m.\u001B[39mjoin(lines2))\n\u001B[1;32m--> 373\u001B[0m result \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43msuper\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrun\u001B[49m\u001B[43m(\u001B[49m\u001B[43mscenario\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 375\u001B[0m \u001B[38;5;66;03m# In previous versions, the `cplex.opt` file(s) were removed at this point\u001B[39;00m\n\u001B[0;32m 376\u001B[0m \u001B[38;5;66;03m# in the workflow. This has been removed due to issues when running\u001B[39;00m\n\u001B[0;32m 377\u001B[0m \u001B[38;5;66;03m# scenarios asynchronously.\u001B[39;00m\n\u001B[0;32m 379\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m result\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\ixmp\\model\\gams.py:293\u001B[0m, in \u001B[0;36mGAMSModel.run\u001B[1;34m(self, scenario)\u001B[0m\n\u001B[0;32m 286\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mNotImplementedError\u001B[39;00m(\n\u001B[0;32m 287\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mGAMSModel requires a Backend that can write to GDX files, e.g. \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m 288\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mJDBCBackend\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m 289\u001B[0m )\n\u001B[0;32m 291\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m 292\u001B[0m \u001B[38;5;66;03m# Invoke GAMS\u001B[39;00m\n\u001B[1;32m--> 293\u001B[0m \u001B[43mcheck_call\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcommand\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mshell\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mos\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mname\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m==\u001B[39;49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mnt\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcwd\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcwd\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 294\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m CalledProcessError \u001B[38;5;28;01mas\u001B[39;00m exc:\n\u001B[0;32m 295\u001B[0m \u001B[38;5;66;03m# Do not remove self.temp_dir; the user may want to inspect the GDX file\u001B[39;00m\n\u001B[0;32m 296\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mformat_exception(exc, model_file) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;28mNone\u001B[39m\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\subprocess.py:359\u001B[0m, in \u001B[0;36mcheck_call\u001B[1;34m(*popenargs, **kwargs)\u001B[0m\n\u001B[0;32m 349\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mcheck_call\u001B[39m(\u001B[38;5;241m*\u001B[39mpopenargs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[0;32m 350\u001B[0m \u001B[38;5;124;03m\"\"\"Run command with arguments. Wait for command to complete. If\u001B[39;00m\n\u001B[0;32m 351\u001B[0m \u001B[38;5;124;03m the exit code was zero then return, otherwise raise\u001B[39;00m\n\u001B[0;32m 352\u001B[0m \u001B[38;5;124;03m CalledProcessError. The CalledProcessError object will have the\u001B[39;00m\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 357\u001B[0m \u001B[38;5;124;03m check_call([\"ls\", \"-l\"])\u001B[39;00m\n\u001B[0;32m 358\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[1;32m--> 359\u001B[0m retcode \u001B[38;5;241m=\u001B[39m \u001B[43mcall\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mpopenargs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 360\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m retcode:\n\u001B[0;32m 361\u001B[0m cmd \u001B[38;5;241m=\u001B[39m kwargs\u001B[38;5;241m.\u001B[39mget(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124margs\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\subprocess.py:342\u001B[0m, in \u001B[0;36mcall\u001B[1;34m(timeout, *popenargs, **kwargs)\u001B[0m\n\u001B[0;32m 340\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m Popen(\u001B[38;5;241m*\u001B[39mpopenargs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs) \u001B[38;5;28;01mas\u001B[39;00m p:\n\u001B[0;32m 341\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m--> 342\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mp\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mwait\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtimeout\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mtimeout\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 343\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m: \u001B[38;5;66;03m# Including KeyboardInterrupt, wait handled that.\u001B[39;00m\n\u001B[0;32m 344\u001B[0m p\u001B[38;5;241m.\u001B[39mkill()\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\subprocess.py:1083\u001B[0m, in \u001B[0;36mPopen.wait\u001B[1;34m(self, timeout)\u001B[0m\n\u001B[0;32m 1081\u001B[0m endtime \u001B[38;5;241m=\u001B[39m _time() \u001B[38;5;241m+\u001B[39m timeout\n\u001B[0;32m 1082\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m-> 1083\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_wait\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtimeout\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mtimeout\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 1084\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyboardInterrupt\u001B[39;00m:\n\u001B[0;32m 1085\u001B[0m \u001B[38;5;66;03m# https://bugs.python.org/issue25942\u001B[39;00m\n\u001B[0;32m 1086\u001B[0m \u001B[38;5;66;03m# The first keyboard interrupt waits briefly for the child to\u001B[39;00m\n\u001B[0;32m 1087\u001B[0m \u001B[38;5;66;03m# exit under the common assumption that it also received the ^C\u001B[39;00m\n\u001B[0;32m 1088\u001B[0m \u001B[38;5;66;03m# generated SIGINT and will exit rapidly.\u001B[39;00m\n\u001B[0;32m 1089\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m timeout \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\subprocess.py:1361\u001B[0m, in \u001B[0;36mPopen._wait\u001B[1;34m(self, timeout)\u001B[0m\n\u001B[0;32m 1358\u001B[0m timeout_millis \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mint\u001B[39m(timeout \u001B[38;5;241m*\u001B[39m \u001B[38;5;241m1000\u001B[39m)\n\u001B[0;32m 1359\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mreturncode \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m 1360\u001B[0m \u001B[38;5;66;03m# API note: Returns immediately if timeout_millis == 0.\u001B[39;00m\n\u001B[1;32m-> 1361\u001B[0m result \u001B[38;5;241m=\u001B[39m \u001B[43m_winapi\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mWaitForSingleObject\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_handle\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 1362\u001B[0m \u001B[43m \u001B[49m\u001B[43mtimeout_millis\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 1363\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m result \u001B[38;5;241m==\u001B[39m _winapi\u001B[38;5;241m.\u001B[39mWAIT_TIMEOUT:\n\u001B[0;32m 1364\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m TimeoutExpired(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39margs, timeout)\n", - "\u001B[1;31mKeyboardInterrupt\u001B[0m: " - ] - } - ], - "source": [ - "scen_base.solve(model=\"MESSAGE-MACRO\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 12, - "outputs": [ - { - "data": { - "text/plain": "array([1990, 1995, 2000, 2005, 2010, 2015], dtype=int64)" - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = scen_base.timeseries()\n", - "df.year.unique()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 14, - "outputs": [ - { - "data": { - "text/plain": " node type_emission type_tec year lvl mrg\n0 World TCE_CO2 all 2030 508.879384 0.0\n1 World TCE_CO2 all 2035 713.809308 0.0\n2 World TCE_CO2 all 2040 907.404817 0.0\n3 World TCE_CO2 all 2045 1151.119609 0.0\n4 World TCE_CO2 all 2050 1561.134700 0.0\n5 World TCE_CO2 all 2055 1653.599833 0.0\n6 World TCE_CO2 all 2060 2351.461667 0.0\n7 World TCE_CO2 all 2070 2019.232897 0.0\n8 World TCE_CO2 all 2080 1192.303190 0.0\n9 World TCE_CO2 all 2090 1041.983798 0.0\n10 World TCE_CO2 all 2100 1241.847644 0.0\n11 World TCE_CO2 all 2110 2058.269348 0.0", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
nodetype_emissiontype_tecyearlvlmrg
0WorldTCE_CO2all2030508.8793840.0
1WorldTCE_CO2all2035713.8093080.0
2WorldTCE_CO2all2040907.4048170.0
3WorldTCE_CO2all20451151.1196090.0
4WorldTCE_CO2all20501561.1347000.0
5WorldTCE_CO2all20551653.5998330.0
6WorldTCE_CO2all20602351.4616670.0
7WorldTCE_CO2all20702019.2328970.0
8WorldTCE_CO2all20801192.3031900.0
9WorldTCE_CO2all20901041.9837980.0
10WorldTCE_CO2all21001241.8476440.0
11WorldTCE_CO2all21102058.2693480.0
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" - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-M-R12-NAVIGATE\", \"NAV_Ind_Comb_800\")\n", - "scen.var(\"PRICE_EMISSION\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 7, - "outputs": [], - "source": [ - "transfer_demands(scen_base, scen)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 8, - "outputs": [], - "source": [ - "scen.solve(model=\"MESSAGE-MACRO\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 3, - "outputs": [], - "source": [ - "df = mp.scenario_list()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 8, - "outputs": [ - { - "data": { - "text/plain": " model scenario scheme \\\n6923 MESSAGEix-GLOBIOM 1.1-R12 base_GLOBIOM_noSDG_noConst MESSAGE \n6922 MESSAGEix-GLOBIOM 1.1-R12 base_GLOBIOM_noSDG MESSAGE \n6929 MESSAGEix-GLOBIOM 1.1-R12 baseline_DEFULT_check MESSAGE \n6925 MESSAGEix-GLOBIOM 1.1-R12 baseline_DEFAULT MESSAGE \n6926 MESSAGEix-GLOBIOM 1.1-R12 baseline_DEFAULT_2507 MESSAGE \n6924 MESSAGEix-GLOBIOM 1.1-R12 base_GLOBIOM_update MESSAGE \n6927 MESSAGEix-GLOBIOM 1.1-R12 baseline_DEFAULT_clone MESSAGE \n6921 MESSAGEix-GLOBIOM 1.1-R12 MACRO_test MESSAGE \n6933 MESSAGEix-GLOBIOM 1.1-R12 test to confirm MACRO converges MESSAGE \n6932 MESSAGEix-GLOBIOM 1.1-R12 baseline_prep_macro_macro MESSAGE \n6930 MESSAGEix-GLOBIOM 1.1-R12 baseline_prep_lu MESSAGE \n6928 MESSAGEix-GLOBIOM 1.1-R12 baseline_DEFAULT_gdp_update MESSAGE \n6931 MESSAGEix-GLOBIOM 1.1-R12 baseline_prep_macro MESSAGE \n\n is_default is_locked cre_user cre_date upd_user \\\n6923 1 0 min 2023-08-11 15:17:05.000000 None \n6922 1 0 min 2023-08-01 10:58:46.000000 None \n6929 1 0 unlu 2023-07-31 14:17:24.000000 unlu \n6925 1 0 min 2023-07-27 10:24:37.000000 None \n6926 1 0 unlu 2023-07-25 17:28:24.000000 None \n6924 1 0 min 2023-04-18 14:25:06.000000 None \n6927 1 0 unlu 2023-02-01 09:55:44.000000 None \n6921 1 0 min 2022-05-02 16:57:38.000000 None \n6933 1 0 min 2022-04-30 20:34:54.000000 min \n6932 1 0 min 2022-04-30 20:29:07.000000 min \n6930 1 0 min 2022-04-30 17:29:17.000000 min \n6928 1 0 min 2022-03-03 00:24:43.000000 min \n6931 1 0 min 2022-03-02 14:21:00.000000 None \n\n upd_date lock_user lock_date \\\n6923 None None None \n6922 None None None \n6929 2023-07-31 14:26:45.000000 None None \n6925 None None None \n6926 None None None \n6924 None None None \n6927 None None None \n6921 None None None \n6933 2022-04-30 20:40:13.000000 None None \n6932 2022-04-30 20:34:39.000000 None None \n6930 2022-04-30 18:18:13.000000 None None \n6928 2022-03-03 00:33:38.000000 None None \n6931 None None None \n\n annotation version \n6923 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6922 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 6 \n6929 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6925 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 21 \n6926 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6924 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 3 \n6927 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6921 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6933 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 6 \n6932 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 17 \n6930 clone Scenario from 'MESSAGEix-GLOBIOM_R12_CHN... 17 \n6928 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 25 \n6931 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 ", - "text/html": "
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modelscenarioschemeis_defaultis_lockedcre_usercre_dateupd_userupd_datelock_userlock_dateannotationversion
6923MESSAGEix-GLOBIOM 1.1-R12base_GLOBIOM_noSDG_noConstMESSAGE10min2023-08-11 15:17:05.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6922MESSAGEix-GLOBIOM 1.1-R12base_GLOBIOM_noSDGMESSAGE10min2023-08-01 10:58:46.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...6
6929MESSAGEix-GLOBIOM 1.1-R12baseline_DEFULT_checkMESSAGE10unlu2023-07-31 14:17:24.000000unlu2023-07-31 14:26:45.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6925MESSAGEix-GLOBIOM 1.1-R12baseline_DEFAULTMESSAGE10min2023-07-27 10:24:37.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...21
6926MESSAGEix-GLOBIOM 1.1-R12baseline_DEFAULT_2507MESSAGE10unlu2023-07-25 17:28:24.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6924MESSAGEix-GLOBIOM 1.1-R12base_GLOBIOM_updateMESSAGE10min2023-04-18 14:25:06.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...3
6927MESSAGEix-GLOBIOM 1.1-R12baseline_DEFAULT_cloneMESSAGE10unlu2023-02-01 09:55:44.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6921MESSAGEix-GLOBIOM 1.1-R12MACRO_testMESSAGE10min2022-05-02 16:57:38.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6933MESSAGEix-GLOBIOM 1.1-R12test to confirm MACRO convergesMESSAGE10min2022-04-30 20:34:54.000000min2022-04-30 20:40:13.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...6
6932MESSAGEix-GLOBIOM 1.1-R12baseline_prep_macro_macroMESSAGE10min2022-04-30 20:29:07.000000min2022-04-30 20:34:39.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...17
6930MESSAGEix-GLOBIOM 1.1-R12baseline_prep_luMESSAGE10min2022-04-30 17:29:17.000000min2022-04-30 18:18:13.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM_R12_CHN...17
6928MESSAGEix-GLOBIOM 1.1-R12baseline_DEFAULT_gdp_updateMESSAGE10min2022-03-03 00:24:43.000000min2022-03-03 00:33:38.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...25
6931MESSAGEix-GLOBIOM 1.1-R12baseline_prep_macroMESSAGE10min2022-03-02 14:21:00.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
\n
" - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df[(df[\"model\"]==(\"MESSAGEix-GLOBIOM 1.1-R12\"))].sort_values(\"cre_date\", ascending=False)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "from message_ix_models import workflow" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 5, - "outputs": [ - { - "data": { - "text/plain": " model \\\n6950 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6951 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6952 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6953 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6954 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6955 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6956 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6957 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6958 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6959 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6960 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6961 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6962 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-M \n6971 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD0BI70 \n6977 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD0BI74 \n6987 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD0BI78 \n6998 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00 \n7005 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP30BD0BI70 \n7011 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP30BD0BI74 \n7013 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP30BD0BI78 \n7020 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP30BD1BI00 \n7028 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP50BD0BI70 \n7036 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP50BD0BI74 \n7044 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP50BD0BI78 \n7052 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP50BD1BI00 \n7060 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP76BD0BI70 \n7068 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP76BD0BI74 \n7075 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP76BD0BI78 \n7083 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP76BD1BI00 \n7084 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE_MP00BD1BI00 \n\n scenario scheme is_default is_locked \\\n6950 baseline MESSAGE 1 0 \n6951 baseline_MP00BD0BI78 MESSAGE 1 0 \n6952 baseline_MP00BD0BI78_test_calib MESSAGE 1 0 \n6953 baseline_MP00BD0BI78_test_calib_macro MESSAGE 1 0 \n6954 baseline_MP00BD1BI00 MESSAGE 1 0 \n6955 baseline_MP76BD0BI70 MESSAGE 1 0 \n6956 baseline_MP76BD0BI78 MESSAGE 1 0 \n6957 baseline_MP76BD0BI78test_scp MESSAGE 1 0 \n6958 baseline_MP76BD1BI00 MESSAGE 1 0 \n6959 baseline_MP76BD1BI00test_scp MESSAGE 1 0 \n6960 baseline_cum_LU_emis MESSAGE 1 0 \n6961 baseline_w/o_LU MESSAGE 1 0 \n6962 baseline MESSAGE 1 0 \n6971 baseline MESSAGE 1 0 \n6977 baseline MESSAGE 1 0 \n6987 baseline MESSAGE 1 0 \n6998 baseline MESSAGE 1 0 \n7005 baseline MESSAGE 1 0 \n7011 baseline MESSAGE 1 0 \n7013 baseline MESSAGE 1 0 \n7020 baseline MESSAGE 1 0 \n7028 baseline MESSAGE 1 0 \n7036 baseline MESSAGE 1 0 \n7044 baseline MESSAGE 1 0 \n7052 baseline MESSAGE 1 0 \n7060 baseline MESSAGE 1 0 \n7068 baseline MESSAGE 1 0 \n7075 baseline MESSAGE 1 0 \n7083 baseline MESSAGE 1 0 \n7084 baseline MESSAGE 1 0 \n\n cre_user cre_date upd_user \\\n6950 maczek 2023-08-03 11:49:57.000000 maczek \n6951 maczek 2023-08-08 13:39:45.000000 maczek \n6952 maczek 2023-08-09 09:30:09.000000 maczek \n6953 maczek 2023-08-09 09:37:24.000000 maczek \n6954 maczek 2023-08-08 10:17:35.000000 maczek \n6955 maczek 2023-08-09 09:10:13.000000 None \n6956 maczek 2023-08-09 09:56:43.000000 None \n6957 maczek 2023-08-09 15:00:57.000000 maczek \n6958 maczek 2023-08-09 15:20:37.000000 None \n6959 maczek 2023-08-09 16:57:44.000000 maczek \n6960 maczek 2023-08-06 20:02:21.000000 maczek \n6961 maczek 2023-08-08 09:50:56.000000 None \n6962 maczek 2023-08-24 09:43:03.000000 None \n6971 maczek 2023-08-23 13:40:54.000000 maczek \n6977 maczek 2023-08-23 13:30:41.000000 maczek \n6987 maczek 2023-08-22 23:50:24.000000 maczek \n6998 maczek 2023-08-23 14:39:04.000000 maczek \n7005 maczek 2023-08-23 16:23:21.000000 maczek \n7011 maczek 2023-08-23 16:12:44.000000 maczek \n7013 maczek 2023-08-23 16:00:04.000000 maczek \n7020 maczek 2023-08-23 17:53:09.000000 maczek \n7028 maczek 2023-08-23 22:52:13.000000 maczek \n7036 maczek 2023-08-23 22:42:02.000000 maczek \n7044 maczek 2023-08-23 18:03:02.000000 maczek \n7052 maczek 2023-08-24 00:05:58.000000 maczek \n7060 maczek 2023-08-24 00:16:04.000000 maczek \n7068 maczek 2023-08-24 08:22:27.000000 maczek \n7075 maczek 2023-08-24 08:32:00.000000 maczek \n7083 maczek 2023-08-24 09:44:01.000000 maczek \n7084 maczek 2023-08-14 13:07:05.000000 None \n\n upd_date lock_user lock_date \\\n6950 2023-08-04 16:29:36.000000 None None \n6951 2023-08-08 15:37:34.000000 None None \n6952 2023-08-09 09:35:09.000000 None None \n6953 2023-08-09 09:47:10.000000 None None \n6954 2023-08-08 11:52:04.000000 None None \n6955 None None None \n6956 None None None \n6957 2023-08-09 15:59:13.000000 None None \n6958 None None None \n6959 2023-08-09 17:26:01.000000 None None \n6960 2023-08-06 20:05:37.000000 None None \n6961 None None None \n6962 None None None \n6971 2023-08-28 11:25:05.000000 None None \n6977 2023-08-28 11:17:38.000000 None None \n6987 2023-08-28 11:09:29.000000 None None \n6998 2023-08-28 11:48:42.000000 None None \n7005 2023-08-28 12:12:04.000000 None None \n7011 2023-08-28 12:03:48.000000 None None \n7013 2023-08-28 11:55:59.000000 None None \n7020 2023-08-28 12:20:17.000000 None None \n7028 2023-08-28 12:43:31.000000 None None \n7036 2023-08-28 12:35:35.000000 None None \n7044 2023-08-28 12:27:41.000000 None None \n7052 2023-08-28 13:10:43.000000 None None \n7060 2023-08-28 13:18:38.000000 None None \n7068 2023-08-28 13:26:36.000000 None None \n7075 2023-08-28 13:34:56.000000 None None \n7083 2023-08-28 13:44:05.000000 None None \n7084 None None None \n\n annotation version \n6950 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 2 \n6951 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6952 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6953 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6954 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6955 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6956 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6957 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6958 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6959 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6960 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6961 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6962 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6971 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6977 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6987 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 2 \n6998 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 4 \n7005 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n7011 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n7013 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 2 \n7020 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 2 \n7028 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n7036 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n7044 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n7052 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n7060 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n7068 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n7075 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n7083 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n7084 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 ", - "text/html": "
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modelscenarioschemeis_defaultis_lockedcre_usercre_dateupd_userupd_datelock_userlock_dateannotationversion
6950MESSAGEix-GLOBIOM 1.1-R12-MAGPIEbaselineMESSAGE10maczek2023-08-03 11:49:57.000000maczek2023-08-04 16:29:36.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...2
6951MESSAGEix-GLOBIOM 1.1-R12-MAGPIEbaseline_MP00BD0BI78MESSAGE10maczek2023-08-08 13:39:45.000000maczek2023-08-08 15:37:34.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6952MESSAGEix-GLOBIOM 1.1-R12-MAGPIEbaseline_MP00BD0BI78_test_calibMESSAGE10maczek2023-08-09 09:30:09.000000maczek2023-08-09 09:35:09.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6953MESSAGEix-GLOBIOM 1.1-R12-MAGPIEbaseline_MP00BD0BI78_test_calib_macroMESSAGE10maczek2023-08-09 09:37:24.000000maczek2023-08-09 09:47:10.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6954MESSAGEix-GLOBIOM 1.1-R12-MAGPIEbaseline_MP00BD1BI00MESSAGE10maczek2023-08-08 10:17:35.000000maczek2023-08-08 11:52:04.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6955MESSAGEix-GLOBIOM 1.1-R12-MAGPIEbaseline_MP76BD0BI70MESSAGE10maczek2023-08-09 09:10:13.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6956MESSAGEix-GLOBIOM 1.1-R12-MAGPIEbaseline_MP76BD0BI78MESSAGE10maczek2023-08-09 09:56:43.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6957MESSAGEix-GLOBIOM 1.1-R12-MAGPIEbaseline_MP76BD0BI78test_scpMESSAGE10maczek2023-08-09 15:00:57.000000maczek2023-08-09 15:59:13.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6958MESSAGEix-GLOBIOM 1.1-R12-MAGPIEbaseline_MP76BD1BI00MESSAGE10maczek2023-08-09 15:20:37.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6959MESSAGEix-GLOBIOM 1.1-R12-MAGPIEbaseline_MP76BD1BI00test_scpMESSAGE10maczek2023-08-09 16:57:44.000000maczek2023-08-09 17:26:01.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6960MESSAGEix-GLOBIOM 1.1-R12-MAGPIEbaseline_cum_LU_emisMESSAGE10maczek2023-08-06 20:02:21.000000maczek2023-08-06 20:05:37.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6961MESSAGEix-GLOBIOM 1.1-R12-MAGPIEbaseline_w/o_LUMESSAGE10maczek2023-08-08 09:50:56.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6962MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MbaselineMESSAGE10maczek2023-08-24 09:43:03.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6971MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD0BI70baselineMESSAGE10maczek2023-08-23 13:40:54.000000maczek2023-08-28 11:25:05.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6977MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD0BI74baselineMESSAGE10maczek2023-08-23 13:30:41.000000maczek2023-08-28 11:17:38.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6987MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD0BI78baselineMESSAGE10maczek2023-08-22 23:50:24.000000maczek2023-08-28 11:09:29.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...2
6998MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00baselineMESSAGE10maczek2023-08-23 14:39:04.000000maczek2023-08-28 11:48:42.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...4
7005MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP30BD0BI70baselineMESSAGE10maczek2023-08-23 16:23:21.000000maczek2023-08-28 12:12:04.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
7011MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP30BD0BI74baselineMESSAGE10maczek2023-08-23 16:12:44.000000maczek2023-08-28 12:03:48.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
7013MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP30BD0BI78baselineMESSAGE10maczek2023-08-23 16:00:04.000000maczek2023-08-28 11:55:59.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...2
7020MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP30BD1BI00baselineMESSAGE10maczek2023-08-23 17:53:09.000000maczek2023-08-28 12:20:17.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...2
7028MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP50BD0BI70baselineMESSAGE10maczek2023-08-23 22:52:13.000000maczek2023-08-28 12:43:31.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
7036MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP50BD0BI74baselineMESSAGE10maczek2023-08-23 22:42:02.000000maczek2023-08-28 12:35:35.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
7044MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP50BD0BI78baselineMESSAGE10maczek2023-08-23 18:03:02.000000maczek2023-08-28 12:27:41.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
7052MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP50BD1BI00baselineMESSAGE10maczek2023-08-24 00:05:58.000000maczek2023-08-28 13:10:43.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
7060MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP76BD0BI70baselineMESSAGE10maczek2023-08-24 00:16:04.000000maczek2023-08-28 13:18:38.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
7068MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP76BD0BI74baselineMESSAGE10maczek2023-08-24 08:22:27.000000maczek2023-08-28 13:26:36.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
7075MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP76BD0BI78baselineMESSAGE10maczek2023-08-24 08:32:00.000000maczek2023-08-28 13:34:56.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
7083MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP76BD1BI00baselineMESSAGE10maczek2023-08-24 09:44:01.000000maczek2023-08-28 13:44:05.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
7084MESSAGEix-GLOBIOM 1.1-R12-MAGPIE_MP00BD1BI00baselineMESSAGE10maczek2023-08-14 13:07:05.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
\n
" - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df[(df[\"model\"].str.startswith(\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\")) & (df[\"scenario\"].str.contains(\"baseline\"))].sort_values(\"model\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 5, - "outputs": [], - "source": [ - "#scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\", \"baseline\")\n", - "#scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12\", \"baseline_DEFAULT\")\n", - "#scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\", \"baseline\")\n", - "#scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\", \"NPi2020-con-prim-dir-ncr\")\n", - "#scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\", \"npi_low_dem_scen\")\n", - "#scen = message_ix.Scenario(mp, \"ENGAGE_SSP2_v4.1.8.1\", \"baseline\")\n", - "scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\", \"baseline_w/o_LU\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-11T08:01:14.451774300Z", - "start_time": "2023-09-11T08:01:11.092726Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 4, - "outputs": [], - "source": [ - "def build_magpie_baseline_from_r12_base(scen_base, magpie_scen):\n", - " scen = scen_base.clone(scen_base.model+\"-\"+magpie_scen, \"baseline\")\n", - " if scen.has_solution():\n", - " scen.remove_solution()\n", - " add_globiom(\n", - " mp,\n", - " scen,\n", - " \"SSP2\",\n", - " private_data_path(),\n", - " 2015,\n", - " globiom_scenario=\"noSDG_rcpref\",\n", - " regenerate_input_data=True,\n", - " #regenerate_input_data_only=True,\n", - " #allow_empty_drivers=True,\n", - " add_reporting_data=True,\n", - " config_setup=\"MAGPIE_\"+magpie_scen\n", - " )\n", - " scen.set_as_default()\n", - " return scen" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 5, - "outputs": [], - "source": [ - "magpie_scens = [\n", - " \"MP00BD0BI78\",\n", - " \"MP00BD0BI74\",\n", - " \"MP00BD0BI70\",\n", - " \"MP00BD1BI00\",\n", - " \"MP30BD0BI78\",\n", - " \"MP30BD0BI74\",\n", - " \"MP30BD0BI70\",\n", - " \"MP30BD1BI00\",\n", - " \"MP50BD0BI78\",\n", - " \"MP50BD0BI74\",\n", - " \"MP50BD0BI70\",\n", - " \"MP50BD1BI00\",\n", - " \"MP76BD0BI70\",\n", - " \"MP76BD0BI74\",\n", - " \"MP76BD0BI78\",\n", - " \"MP76BD1BI00\"\n", - "]\n", - "magpie_scen = magpie_scens[0]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 6, - "outputs": [], - "source": [ - "mp = ixmp.Platform(\"ixmp_dev\")\n", - "#scen_base = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\", \"baseline_w/o_LU\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "read-in of MP00BD0BI74 matrix started\n", - "Starting to process data input from csv file.\n", - "Agricultural Demand\n", - "Agricultural Demand|Energy\n", - "Agricultural Demand|Energy|Crops\n", - "Agricultural Demand|Energy|Crops|1st generation\n", - "Agricultural Demand|Energy|Crops|2nd generation\n", - "Agricultural Demand|Non-Energy\n", - "Agricultural Demand|Non-Energy|Crops\n", - "Agricultural Demand|Non-Energy|Crops|Feed\n", - "Agricultural Demand|Non-Energy|Crops|Food\n", - "Agricultural Demand|Non-Energy|Crops|Other\n", - "Agricultural Demand|Non-Energy|Crops|Other|Waste\n", - "Agricultural Demand|Non-Energy|Livestock\n", - "Agricultural Demand|Non-Energy|Livestock|Food\n", - "Agricultural Demand|Non-Energy|Livestock|Other\n", - "Agricultural Demand|Non-Energy|Livestock|Other|Waste\n", - "Agricultural Production\n", - "Agricultural Production|Energy\n", - "Agricultural Production|Energy|Crops\n", - "Agricultural Production|Energy|Crops|1st generation\n", - "Agricultural Production|Energy|Crops|2nd generation\n", - "Agricultural Production|Non-Energy\n", - "Agricultural Production|Non-Energy|Crops\n", - "Agricultural Production|Non-Energy|Crops|Cereals\n", - "Agricultural Production|Non-Energy|Livestock\n", - "BCA_LandUseChangeEM\n", - "BCA_SavanBurnEM\n", - "Biodiversity|BII\n", - "Biodiversity|BII|Biodiversity hotspots\n", - "CH4_LandUseChangeEM\n", - "CH4_SavanBurnEM\n", - "CO_LandUseChangeEM\n", - "CO_SavanBurnEM\n", - "Costs|TC\n", - "CrpLnd\n", - "Emissions|CH4|AFOLU\n", - "Emissions|CH4|AFOLU|Agriculture\n", - "Emissions|CH4|AFOLU|Agriculture|Livestock\n", - "Emissions|CH4|AFOLU|Agriculture|Livestock|Enteric Fermentation\n", - "Emissions|CH4|AFOLU|Agriculture|Livestock|Manure Management\n", - "Emissions|CH4|AFOLU|Agriculture|Rice\n", - "Emissions|CH4|AFOLU|Biomass Burning\n", - "Emissions|CH4|AFOLU|Land\n", - "Emissions|CH4|AFOLU|Land|Grassland Burning\n", - "Emissions|CO2|AFOLU\n", - "Emissions|CO2|AFOLU|Afforestation\n", - "Emissions|CO2|AFOLU|Agriculture\n", - "Emissions|CO2|AFOLU|Deforestation\n", - "Emissions|CO2|AFOLU|Forest Management\n", - "Emissions|CO2|AFOLU|Negative\n", - "Emissions|CO2|AFOLU|Other LUC\n", - "Emissions|CO2|AFOLU|Positive\n", - "Emissions|CO2|AFOLU|Soil Carbon\n", - "Emissions|GHG|AFOLU\n", - "Emissions|N2O|AFOLU\n", - "Emissions|N2O|AFOLU|Agriculture\n", - "Emissions|N2O|AFOLU|Agriculture|Livestock\n", - "Emissions|N2O|AFOLU|Agriculture|Livestock|Manure Management\n", - "Emissions|N2O|AFOLU|Agriculture|Managed Soils\n", - "Emissions|N2O|AFOLU|Biomass Burning\n", - "Emissions|N2O|AFOLU|Land\n", - "Emissions|N2O|AFOLU|Land|Grassland Burning\n", - "Emissions|N2O|AFOLU|Land|Grassland Pastures\n", - "EneCrpLnd\n", - "EneCrpLnd_gr\n", - "Fertilizer Use|Nitrogen\n", - "Fertilizer Use|Phosphorus\n", - "Fertilizer|Nitrogen|Intensity\n", - "Fertilizer|Phosphorus|Intensity\n", - "Food Demand\n", - "Food Demand|Crops\n", - "Food Demand|Livestock\n", - "Food Demand|Microbial protein\n", - "Forestry Demand|Roundwood\n", - "Forestry Demand|Roundwood|Industrial Roundwood\n", - "Forestry Demand|Roundwood|Wood Fuel\n", - "Forestry Production|Forest Residues\n", - "Forestry Production|Roundwood\n", - "Forestry Production|Roundwood|Industrial Roundwood\n", - "Forestry Production|Roundwood|Wood Fuel\n", - "GrassLnd\n", - "LU_CH4\n", - "LU_CH4_Agri\n", - "LU_CH4_BioBurn\n", - "LU_CO2\n", - "LU_GHG\n", - "LU_N2O\n", - "Land Cover\n", - "Land Cover|Cropland\n", - "Land Cover|Cropland|Cereals\n", - "Land Cover|Cropland|Energy Crops\n", - "Land Cover|Cropland|Irrigated\n", - "Land Cover|Cropland|Non-Energy Crops\n", - "Land Cover|Cropland|Oilcrops\n", - "Land Cover|Cropland|Sugarcrops\n", - "Land Cover|Forest\n", - "Land Cover|Forest|Afforestation and Reforestation\n", - "Land Cover|Forest|Forest old\n", - "Land Cover|Forest|Forestry\n", - "Land Cover|Forest|Managed\n", - "Land Cover|Forest|Natural Forest\n", - "Land Cover|Other Land\n", - "Land Cover|Pasture\n", - "Land Cover|Protected\n", - "Landuse intensity indicator Tau\n", - "NH3_LandUseChangeEM\n", - "NH3_ManureEM\n", - "NH3_RiceEM\n", - "NH3_SavanBurnEM\n", - "NH3_SoilEM\n", - "NOx_LandUseChangeEM\n", - "NOx_SavanBurnEM\n", - "NOx_SoilEM\n", - "NewForLnd\n", - "OCA_LandUseChangeEM\n", - "OCA_SavanBurnEM\n", - "OldForLnd\n", - "OtherLnd\n", - "Population\n", - "Population|Risk of Hunger\n", - "Price|Agriculture|Non-Energy Crops and Livestock|Index\n", - "Price|Agriculture|Non-Energy Crops|Index\n", - "Price|Carbon|CH4\n", - "Price|Carbon|CO2\n", - "Price|Carbon|N2O\n", - "Price|Water\n", - "Primary Energy|Biomass\n", - "Primary Energy|Biomass|1st Generation\n", - "Primary Energy|Biomass|1st Generation|Biodiesel\n", - "Primary Energy|Biomass|1st Generation|Bioethanol\n", - "Primary Energy|Biomass|Energy Crops\n", - "Primary Energy|Biomass|Fuelwood\n", - "Primary Energy|Biomass|Other\n", - "Primary Energy|Biomass|Residues\n", - "Primary Energy|Biomass|Residues|Forest industry\n", - "Primary Energy|Biomass|Residues|Logging\n", - "Primary Energy|Biomass|Roundwood harvest\n", - "SO2_LandUseChangeEM\n", - "SO2_SavanBurnEM\n", - "TCE\n", - "TotalLnd\n", - "VOC_LandUseChangeEM\n", - "VOC_SavanBurnEM\n", - "Water|Withdrawal|Irrigation\n", - "Water|Withdrawal|Irrigation|Cereals\n", - "Water|Withdrawal|Irrigation|Oilcrops\n", - "Water|Withdrawal|Irrigation|Sugarcrops\n", - "Yield|Cereals\n", - "Yield|Energy Crops\n", - "Yield|Non-Energy Crops\n", - "Yield|Oilcrops\n", - "Yield|Sugarcrops\n", - "bioenergy\n", - "total_cost\n", - "Price|Primary Energy|Biomass\n", - "read-in of MP00BD0BI70 matrix started\n", - "Starting to process data input from csv file.\n", - "Agricultural Demand\n", - "Agricultural Demand|Energy\n", - "Agricultural Demand|Energy|Crops\n", - "Agricultural Demand|Energy|Crops|1st generation\n", - "Agricultural Demand|Energy|Crops|2nd generation\n", - "Agricultural Demand|Non-Energy\n", - "Agricultural Demand|Non-Energy|Crops\n", - "Agricultural Demand|Non-Energy|Crops|Feed\n", - "Agricultural Demand|Non-Energy|Crops|Food\n", - "Agricultural Demand|Non-Energy|Crops|Other\n", - "Agricultural Demand|Non-Energy|Crops|Other|Waste\n", - "Agricultural Demand|Non-Energy|Livestock\n", - "Agricultural Demand|Non-Energy|Livestock|Food\n", - "Agricultural Demand|Non-Energy|Livestock|Other\n", - "Agricultural Demand|Non-Energy|Livestock|Other|Waste\n", - "Agricultural Production\n", - "Agricultural Production|Energy\n", - "Agricultural Production|Energy|Crops\n", - "Agricultural Production|Energy|Crops|1st generation\n", - "Agricultural Production|Energy|Crops|2nd generation\n", - "Agricultural Production|Non-Energy\n", - "Agricultural Production|Non-Energy|Crops\n", - "Agricultural Production|Non-Energy|Crops|Cereals\n", - "Agricultural Production|Non-Energy|Livestock\n", - "BCA_LandUseChangeEM\n", - "BCA_SavanBurnEM\n", - "Biodiversity|BII\n", - "Biodiversity|BII|Biodiversity hotspots\n", - "CH4_LandUseChangeEM\n", - "CH4_SavanBurnEM\n", - "CO_LandUseChangeEM\n", - "CO_SavanBurnEM\n", - "Costs|TC\n", - "CrpLnd\n", - "Emissions|CH4|AFOLU\n", - "Emissions|CH4|AFOLU|Agriculture\n", - "Emissions|CH4|AFOLU|Agriculture|Livestock\n", - "Emissions|CH4|AFOLU|Agriculture|Livestock|Enteric Fermentation\n", - "Emissions|CH4|AFOLU|Agriculture|Livestock|Manure Management\n", - "Emissions|CH4|AFOLU|Agriculture|Rice\n", - "Emissions|CH4|AFOLU|Biomass Burning\n", - "Emissions|CH4|AFOLU|Land\n", - "Emissions|CH4|AFOLU|Land|Grassland Burning\n", - "Emissions|CO2|AFOLU\n", - "Emissions|CO2|AFOLU|Afforestation\n", - "Emissions|CO2|AFOLU|Agriculture\n", - "Emissions|CO2|AFOLU|Deforestation\n", - "Emissions|CO2|AFOLU|Forest Management\n", - "Emissions|CO2|AFOLU|Negative\n", - "Emissions|CO2|AFOLU|Other LUC\n", - "Emissions|CO2|AFOLU|Positive\n", - "Emissions|CO2|AFOLU|Soil Carbon\n", - "Emissions|GHG|AFOLU\n", - "Emissions|N2O|AFOLU\n", - "Emissions|N2O|AFOLU|Agriculture\n", - "Emissions|N2O|AFOLU|Agriculture|Livestock\n", - "Emissions|N2O|AFOLU|Agriculture|Livestock|Manure Management\n", - "Emissions|N2O|AFOLU|Agriculture|Managed Soils\n", - "Emissions|N2O|AFOLU|Biomass Burning\n", - "Emissions|N2O|AFOLU|Land\n", - "Emissions|N2O|AFOLU|Land|Grassland Burning\n", - "Emissions|N2O|AFOLU|Land|Grassland Pastures\n", - "EneCrpLnd\n", - "EneCrpLnd_gr\n", - "Fertilizer Use|Nitrogen\n", - "Fertilizer Use|Phosphorus\n", - "Fertilizer|Nitrogen|Intensity\n", - "Fertilizer|Phosphorus|Intensity\n", - "Food Demand\n", - "Food Demand|Crops\n", - "Food Demand|Livestock\n", - "Food Demand|Microbial protein\n", - "Forestry Demand|Roundwood\n", - "Forestry Demand|Roundwood|Industrial Roundwood\n", - "Forestry Demand|Roundwood|Wood Fuel\n", - "Forestry Production|Forest Residues\n", - "Forestry Production|Roundwood\n", - "Forestry Production|Roundwood|Industrial Roundwood\n", - "Forestry Production|Roundwood|Wood Fuel\n", - "GrassLnd\n", - "LU_CH4\n", - "LU_CH4_Agri\n", - "LU_CH4_BioBurn\n", - "LU_CO2\n", - "LU_GHG\n", - "LU_N2O\n", - "Land Cover\n", - "Land Cover|Cropland\n", - "Land Cover|Cropland|Cereals\n", - "Land Cover|Cropland|Energy Crops\n", - "Land Cover|Cropland|Irrigated\n", - "Land Cover|Cropland|Non-Energy Crops\n", - "Land Cover|Cropland|Oilcrops\n", - "Land Cover|Cropland|Sugarcrops\n", - "Land Cover|Forest\n", - "Land Cover|Forest|Afforestation and Reforestation\n", - "Land Cover|Forest|Forest old\n", - "Land Cover|Forest|Forestry\n", - "Land Cover|Forest|Managed\n", - "Land Cover|Forest|Natural Forest\n", - "Land Cover|Other Land\n", - "Land Cover|Pasture\n", - "Land Cover|Protected\n", - "Landuse intensity indicator Tau\n", - "NH3_LandUseChangeEM\n", - "NH3_ManureEM\n", - "NH3_RiceEM\n", - "NH3_SavanBurnEM\n", - "NH3_SoilEM\n", - "NOx_LandUseChangeEM\n", - "NOx_SavanBurnEM\n", - "NOx_SoilEM\n", - "NewForLnd\n", - "OCA_LandUseChangeEM\n", - "OCA_SavanBurnEM\n", - "OldForLnd\n", - "OtherLnd\n", - "Population\n", - "Population|Risk of Hunger\n", - "Price|Agriculture|Non-Energy Crops and Livestock|Index\n", - "Price|Agriculture|Non-Energy Crops|Index\n", - "Price|Carbon|CH4\n", - "Price|Carbon|CO2\n", - "Price|Carbon|N2O\n", - "Price|Water\n", - "Primary Energy|Biomass\n", - "Primary Energy|Biomass|1st Generation\n", - "Primary Energy|Biomass|1st Generation|Biodiesel\n", - "Primary Energy|Biomass|1st Generation|Bioethanol\n", - "Primary Energy|Biomass|Energy Crops\n", - "Primary Energy|Biomass|Fuelwood\n", - "Primary Energy|Biomass|Other\n", - "Primary Energy|Biomass|Residues\n", - "Primary Energy|Biomass|Residues|Forest industry\n", - "Primary Energy|Biomass|Residues|Logging\n", - "Primary Energy|Biomass|Roundwood harvest\n", - "SO2_LandUseChangeEM\n", - "SO2_SavanBurnEM\n", - "TCE\n", - "TotalLnd\n", - "VOC_LandUseChangeEM\n", - "VOC_SavanBurnEM\n", - "Water|Withdrawal|Irrigation\n", - "Water|Withdrawal|Irrigation|Cereals\n", - "Water|Withdrawal|Irrigation|Oilcrops\n", - "Water|Withdrawal|Irrigation|Sugarcrops\n", - "Yield|Cereals\n", - "Yield|Energy Crops\n", - "Yield|Non-Energy Crops\n", - "Yield|Oilcrops\n", - "Yield|Sugarcrops\n", - "bioenergy\n", - "total_cost\n", - "Price|Primary Energy|Biomass\n", - "read-in of MP00BD1BI00 matrix started\n", - "Starting to process data input from csv file.\n" - ] - } - ], - "source": [ - "for magpie_scen in magpie_scens[1:]:\n", - " print(f\"read-in of {magpie_scen} matrix started\")\n", - " scen = build_magpie_baseline_from_r12_base(scen_base, magpie_scen)\n", - " if \"MP00\" not in magpie_scen:\n", - " print(f\"adding SCP model to {magpie_scen}\")\n", - " scp_dict = data_scp.gen_data_scp(scen)\n", - " scen.check_out()\n", - " for k,v in scp_dict.items():\n", - " scen.add_par(k, v)\n", - " scen.commit(\"add SCP tecs\")\n", - " scen.set_as_default()" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "is_executing": true - } - } - }, - { - "cell_type": "code", - "execution_count": 9, - "outputs": [ - { - "data": { - "text/plain": "'MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD0BI78'" - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "scen.model" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 37, - "outputs": [], - "source": [ - "#scen.remove_solution()\n", - "scen.solve(model=\"MESSAGE-MACRO\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 28, - "outputs": [], - "source": [ - "if \"MP00\" not in magpie_scen:\n", - " scp_dict = data_scp.gen_data_scp(scen)\n", - " scen.check_out()\n", - " for k,v in scp_dict.items():\n", - " scen.add_par(k, v)\n", - " scen.commit()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scen.check_out()\n", - "scen.add_set(\"balance_equality\", (\"i_feed\", \"useful\"))\n", - "scen.commit(\"add ifeed to balance eqÄ\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scen.solve(model=\"MESSAGE-MACRO\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 16, - "outputs": [], - "source": [ - "df_ts = scen.timeseries()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 17, - "outputs": [ - { - "data": { - "text/plain": "array([], dtype=object)" - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_ts[\"year\"].unique()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 18, - "outputs": [ - { - "data": { - "text/plain": " model scenario \\\n6969 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD0BI70 NPi2020-con-prim-dir-ncr \n6970 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD0BI70 NPi2030-con-prim-dir-ncr \n6976 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD0BI74 NPi2020-con-prim-dir-ncr \n6985 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD0BI78 NPi2020-con-prim-dir-ncr \n6986 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD0BI78 NPi2030-con-prim-dir-ncr \n6997 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00 NPi2020-con-prim-dir-ncr \n7004 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP30BD0BI70 NPi2020-con-prim-dir-ncr \n7010 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP30BD0BI74 NPi2020-con-prim-dir-ncr \n7012 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP30BD0BI78 NPi2020-con-prim-dir-ncr \n7019 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP30BD1BI00 NPi2020-con-prim-dir-ncr \n7027 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP50BD0BI70 NPi2020-con-prim-dir-ncr \n7035 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP50BD0BI74 NPi2020-con-prim-dir-ncr \n7043 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP50BD0BI78 NPi2020-con-prim-dir-ncr \n7051 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP50BD1BI00 NPi2020-con-prim-dir-ncr \n7059 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP76BD0BI70 NPi2020-con-prim-dir-ncr \n7067 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP76BD0BI74 NPi2020-con-prim-dir-ncr \n7074 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP76BD0BI78 NPi2020-con-prim-dir-ncr \n7082 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP76BD1BI00 NPi2020-con-prim-dir-ncr \n\n scheme is_default is_locked cre_user cre_date \\\n6969 MESSAGE 1 0 maczek 2023-08-25 10:07:00.000000 \n6970 MESSAGE 1 0 maczek 2023-08-24 22:55:18.000000 \n6976 MESSAGE 1 0 maczek 2023-08-24 23:51:00.000000 \n6985 MESSAGE 1 0 maczek 2023-08-24 23:14:26.000000 \n6986 MESSAGE 1 0 maczek 2023-08-22 13:43:19.000000 \n6997 MESSAGE 1 0 maczek 2023-08-25 10:36:08.000000 \n7004 MESSAGE 1 0 maczek 2023-08-25 13:38:00.000000 \n7010 MESSAGE 1 0 maczek 2023-08-25 11:22:25.000000 \n7012 MESSAGE 1 0 maczek 2023-08-28 17:00:51.000000 \n7019 MESSAGE 1 0 maczek 2023-08-25 14:38:55.000000 \n7027 MESSAGE 1 0 maczek 2023-08-25 15:49:51.000000 \n7035 MESSAGE 1 0 maczek 2023-08-25 15:27:05.000000 \n7043 MESSAGE 1 0 maczek 2023-08-25 15:04:01.000000 \n7051 MESSAGE 1 0 maczek 2023-08-25 16:17:51.000000 \n7059 MESSAGE 1 0 maczek 2023-08-25 16:41:07.000000 \n7067 MESSAGE 1 0 maczek 2023-08-25 17:06:02.000000 \n7074 MESSAGE 1 0 maczek 2023-08-25 17:29:13.000000 \n7082 MESSAGE 1 0 maczek 2023-08-25 17:51:18.000000 \n\n upd_user upd_date lock_user lock_date \\\n6969 maczek 2023-08-25 10:29:11.000000 None None \n6970 None None None None \n6976 maczek 2023-08-25 00:11:14.000000 None None \n6985 maczek 2023-08-24 23:34:49.000000 None None \n6986 None None None None \n6997 maczek 2023-08-25 11:05:51.000000 None None \n7004 maczek 2023-08-25 14:06:06.000000 None None \n7010 None None None None \n7012 None None None None \n7019 maczek 2023-08-25 15:03:05.000000 None None \n7027 maczek 2023-08-25 16:16:58.000000 None None \n7035 maczek 2023-08-25 15:49:10.000000 None None \n7043 maczek 2023-08-25 15:26:11.000000 None None \n7051 maczek 2023-08-25 16:40:26.000000 None None \n7059 maczek 2023-08-25 17:05:11.000000 None None \n7067 maczek 2023-08-25 17:28:15.000000 None None \n7074 maczek 2023-08-25 17:50:34.000000 None None \n7082 maczek 2023-08-25 18:12:37.000000 None None \n\n annotation version \n6969 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 4 \n6970 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6976 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6985 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 5 \n6986 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6997 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 23 \n7004 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n7010 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n7012 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n7019 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n7027 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n7035 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n7043 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n7051 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n7059 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n7067 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n7074 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n7082 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 ", - "text/html": "
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modelscenarioschemeis_defaultis_lockedcre_usercre_dateupd_userupd_datelock_userlock_dateannotationversion
6969MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD0BI70NPi2020-con-prim-dir-ncrMESSAGE10maczek2023-08-25 10:07:00.000000maczek2023-08-25 10:29:11.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...4
6970MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD0BI70NPi2030-con-prim-dir-ncrMESSAGE10maczek2023-08-24 22:55:18.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6976MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD0BI74NPi2020-con-prim-dir-ncrMESSAGE10maczek2023-08-24 23:51:00.000000maczek2023-08-25 00:11:14.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6985MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD0BI78NPi2020-con-prim-dir-ncrMESSAGE10maczek2023-08-24 23:14:26.000000maczek2023-08-24 23:34:49.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...5
6986MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD0BI78NPi2030-con-prim-dir-ncrMESSAGE10maczek2023-08-22 13:43:19.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6997MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00NPi2020-con-prim-dir-ncrMESSAGE10maczek2023-08-25 10:36:08.000000maczek2023-08-25 11:05:51.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...23
7004MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP30BD0BI70NPi2020-con-prim-dir-ncrMESSAGE10maczek2023-08-25 13:38:00.000000maczek2023-08-25 14:06:06.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
7010MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP30BD0BI74NPi2020-con-prim-dir-ncrMESSAGE10maczek2023-08-25 11:22:25.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
7012MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP30BD0BI78NPi2020-con-prim-dir-ncrMESSAGE10maczek2023-08-28 17:00:51.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
7019MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP30BD1BI00NPi2020-con-prim-dir-ncrMESSAGE10maczek2023-08-25 14:38:55.000000maczek2023-08-25 15:03:05.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
7027MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP50BD0BI70NPi2020-con-prim-dir-ncrMESSAGE10maczek2023-08-25 15:49:51.000000maczek2023-08-25 16:16:58.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
7035MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP50BD0BI74NPi2020-con-prim-dir-ncrMESSAGE10maczek2023-08-25 15:27:05.000000maczek2023-08-25 15:49:10.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
7043MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP50BD0BI78NPi2020-con-prim-dir-ncrMESSAGE10maczek2023-08-25 15:04:01.000000maczek2023-08-25 15:26:11.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
7051MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP50BD1BI00NPi2020-con-prim-dir-ncrMESSAGE10maczek2023-08-25 16:17:51.000000maczek2023-08-25 16:40:26.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
7059MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP76BD0BI70NPi2020-con-prim-dir-ncrMESSAGE10maczek2023-08-25 16:41:07.000000maczek2023-08-25 17:05:11.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
7067MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP76BD0BI74NPi2020-con-prim-dir-ncrMESSAGE10maczek2023-08-25 17:06:02.000000maczek2023-08-25 17:28:15.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
7074MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP76BD0BI78NPi2020-con-prim-dir-ncrMESSAGE10maczek2023-08-25 17:29:13.000000maczek2023-08-25 17:50:34.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
7082MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP76BD1BI00NPi2020-con-prim-dir-ncrMESSAGE10maczek2023-08-25 17:51:18.000000maczek2023-08-25 18:12:37.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
\n
" - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df[(df[\"model\"].str.startswith(\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\")) & (df[\"scenario\"].str.startswith(\"NP\")) & (df[\"model\"].str.contains(\"MP\"))]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\", \"baseline_MP00BD1BI00\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scen_new = scen.clone(scen.model+\"-\"+scen.scenario.split(\"_\")[1], scen.scenario.split(\"_\")[0])" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "import pyperclip\n", - "pyperclip.copy(scen_new.model)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scen_no_lu = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\", \"baseline_w/o_LU\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\", \"baseline_MP00BD1BI00\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "mp.scenario_list().sort_values(\"cre_date\", ascending=False)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "sc_clone = scen.clone(\"MESSAGEix-GLOBIOM 1.1-M-R12-NAVIGATE_test\",\"NPi2020-con-prim-dir-ncr_test\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "sc_clone.remove_solution()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "sc_clone.solve()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df = mp.scenario_list()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df = scen.par(\"land_output\", filters={\"commodity\":\"bioenergy\"})" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df_act = scen.var(\"ACT\", filters={\"node_loc\":\"R12_AFR\", \"year_act\":2020})\n", - "df_act[df_act[\"technology\"].str.contains(\"biomass\")].sort_values(\"lvl\", ascending=False).head(12)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df.sort_values(\"cre_date\", ascending=False).head(20).loc[6764][\"annotation\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df[df[\"model\"]==\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "sc_base = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\", \"baseline\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "sc_base.set(\"technology\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scen = sc_base.clone(\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\", \"baseline_w/o_LU\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "#df[(df[\"year\"]==2020) & (df[\"land_scenario\"]==\"BIO00GHG000\")]\n", - "#df[(df[\"year\"]==2020) & (df[\"node\"]==\"R12_CHN\")]\n", - "df[(df[\"year\"]==2020) & (df[\"node\"]==\"R12_AFR\")]# & (df[\"land_scenario\"]==\"BIO00GHG2000\")]\n", - "#scen.var(\"LAND\", filters={\"node\":\"R12_CHN\", \"year\":2020})\n", - "#scen.var(\"LAND\", filters={\"node\":\"R12_AFR\", \"year\":2020}).sort_values(\"lvl\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df[(df[\"model\"]==\"MESSAGEix-GLOBIOM 1.1-M-R12-NAVIGATE\") & (df[\"scenario\"].str.contains(\"baseline\"))]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-M-R12-NAVIGATE\",\"NPi2020-con-prim-dir-ncr\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-M-R12-NAVIGATE-MAGPIE\",\"NPi2020-con-prim-dir-ncr_MP00BD1BI00\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scen.scenario" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scen.solve(solve_options={\"scaind\":-1})" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "### solve new MAGPIE base (without materials) on LU cum message_ix branch" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\", \"baseline\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "sc_clone = scen.clone(\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\", \"baseline_cum_LU_emis\", keep_solution=False)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "sc_clone.solve()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scen.has_solution()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df_scen_list = mp.scenario_list()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "### get old R11 scenario of Jan" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "sc_base_old = message_ix.Scenario(mp, 'ENGAGE-MAGPIE_SSP2', 'baseline')\n", - "sc_base_old_clone = sc_base_old.clone(\"MAGPIE_SSP2_clone\", \"baseline_old_clone\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "sc_base_old_clone.remove_solution()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "sc_base_old_clone.scenario" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "sc_base_old_clone.solve()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "### get biomass trade parametrization from NGFS scenario" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "sc_ngfs = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-M-R12 (NGFS)\", \"baseline_DEFAULT\")\n", - "#sc_ngfs = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12\", \"baseline_DEFAULT\", version=2)\n", - "sc_ngfs.par(\"input\", filters={\"technology\":\"biomass_imp\"})" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "import copy\n", - "paramList_tec = [x for x in sc_ngfs.par_list() if (\"technology\" in sc_ngfs.idx_sets(x))]\n", - "par_dict_meth = {}\n", - "for p in paramList_tec:\n", - " df = sc_ngfs.par(p, filters={\"technology\": [\"biomass_imp\", \"biomass_exp\", \"biomass_trd\"]})\n", - " if df.index.size:\n", - " par_dict_meth[p] = df" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "par_dict_meth.keys()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "par_dict_meth[\"input\"]#.relation.unique()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "### list all MAGPIE models" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df_scen_list[(df_scen_list[\"model\"].str.contains(\"MAGPIE\")) & (df_scen_list[\"scenario\"].str.contains(\"NP\"))]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scen = message_ix.Scenario(mp, model=\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\", scenario=\"baseline\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "### R12 base (missing biomass trade!!)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "model_name = \"MESSAGEix-GLOBIOM 1.1-R12\"\n", - "scenario_name = \"baseline_DEFAULT\"\n", - "version = 21" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scen_ref = message_ix.Scenario(mp, model=model_name, scenario=scenario_name, version=21)\n", - "scen = scen_ref.clone(\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\", \"baseline\", keep_solution=False)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "#scen.check_out()\n", - "#scen.add_set(\"technology\", \"biomass_exp\")\n", - "#scen.add_set(\"technology\", \"biomass_trd\")\n", - "for k,v in par_dict_meth.items():\n", - " scen.add_par(k, v)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scen.commit(\"add biomass trade to R12 MAGPIE base model\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scen.par(\"land_output\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "### R12-M base" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "model_name = \"MESSAGEix-GLOBIOM 1.1-M-R12-NAVIGATE\"\n", - "#scenario_name = \"baseline\"\n", - "scenario_name = \"NPi2020-con-prim-dir-ncr\"" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df_scen_list[(df_scen_list[\"model\"].str.contains(\"NAVIGATE\")) & (df_scen_list[\"scenario\"].str.contains(\"NP\"))]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "#scen_ref = message_ix.Scenario(mp, model=model_name, scenario=scenario_name)\n", - "magpie_scen_name = \"MESSAGEix-GLOBIOM 1.1-M-R12-NAVIGATE-MAGPIE\"\n", - "scen = scen_ref.clone(magpie_scen_name, scenario_name, keep_solution=False)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "### R11 base" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "model_name = \"ENGAGE_SSP2_v4.1.8.3_T4.5_r3.1\"\n", - "scenario_name = \"baseline_magpie\"" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df = mp.scenario_list()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## remove land use matrix from scenario" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scen_no_lu = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-M-R12-NAVIGATE-MAGPIE\", \"NPi2020-con-prim-dir-ncr_no_LU\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scen_no_lu.scenario" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "#scen_no_lu = scen.clone(scen.model, scenario_name+\"_no_LU\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "import importlib\n", - "importlib.reload(message_data.tools.utilities)\n", - "if scen.has_solution():\n", - " scen.remove_solution()\n", - "add_globiom(\n", - " mp,\n", - " scen,\n", - " \"SSP2\",\n", - " private_data_path(),\n", - " 2015,\n", - " globiom_scenario=\"magpie\",\n", - " #regenerate_input_data_only=True,\n", - " clean=True,\n", - " clean_only=True,\n", - " #globiom_scenario=\"no\",\n", - " #allow_empty_drivers=True,\n", - " add_reporting_data=True,\n", - " config_setup=\"MAGPIE\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "#magpie_scen = \"MP00BD1BI00\"\n", - "magpie_scen = \"MP00BD0BI78\"\n", - "magpie_scen = \"MP76BD0BI78\"\n", - "magpie_scen = \"MP76BD1BI00\"" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scen_no_lu = scen" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scenario_name = \"baseline\"" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scen = scen_no_lu.clone(scen_no_lu.model, scenario_name+\"_\"+magpie_scen)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scen.set_as_default()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df = mp.scenario_list()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df[df[\"model\"]==\"MESSAGEix-GLOBIOM 1.1-R12\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## add MAGPIE matrix to scenario" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "add_globiom(\n", - " mp,\n", - " scen,\n", - " \"SSP2\",\n", - " private_data_path(),\n", - " 2015,\n", - " globiom_scenario=\"noSDG_rcpref\",\n", - " regenerate_input_data=True,\n", - " #regenerate_input_data_only=True,\n", - " #allow_empty_drivers=True,\n", - " add_reporting_data=True,\n", - " config_setup=\"MAGPIE_\"+magpie_scen)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scen.solve()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scen.set_as_default()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scen.solve()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "#scen.remove_solution()\n", - "scen.solve(\"MESSAGE-MACRO\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "### add biomass backstop to calibrate read-in of new matrix" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scen.check_out()\n", - "\n", - "# Add a new technology\n", - "scen.add_set(\"technology\", \"bio_backstop\")\n", - "\n", - "# Retrieve technology for which will be used to create the backstop\n", - "filters = {\"technology\": \"elec_rc\"} #, \"node_loc\": \"R11_NAM\"}\n", - "\n", - "#for node in [\"R11_NAM\", \"R11_CPA\"]:\n", - "for par in [\"output\", \"var_cost\"]:\n", - " df = scen.par(par, filters=filters)\n", - " df.technology = \"bio_backstop\"\n", - " #df.node_loc = node\n", - "\n", - " # Make change to output\n", - " if par == \"output\":\n", - " #df.node_dest = node\n", - " df.commodity = \"biomass\"\n", - " df.level = \"primary\"\n", - "\n", - " elif par == \"var_cost\":\n", - " df.value = 100000\n", - "\n", - " #print(df)\n", - " scen.add_par(par, df)\n", - "\n", - "scen.commit(\"Add biomass dummy\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scen.check_out()\n", - "scen.remove_set(\"technology\", \"bio_backstop\")\n", - "scen.commit(\"remove backstop\")\n", - "scen.par(\"output\", filters={\"technology\":\"bio_backstop\"})" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## create budget scenario" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "mode = \"clone\"\n", - "#mode = \"load\"\n", - "\n", - "#budget = \"1000f\"\n", - "budget = \"600f\"" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scen.scenario.replace(\"baseline\",budget)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "if mode == \"clone\":\n", - " scen_budget = scen.clone(model=scen.model, scenario=scen.scenario.replace(\"baseline\",budget), shift_first_model_year=2025)\n", - "else:\n", - " scen_budget = message_ix.Scenario(mp, model=\"ENGAGE-MAGPIE_SSP2_v4.1.8.3_T4.5_r3.1\", scenario=budget)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "if mode == \"clone\":\n", - " scen_budget = scen.clone(model=\"ENGAGE-MAGPIE_SSP2_v4.1.8.3_T4.5_r3.1\", scenario=budget, keep_solution=False,\n", - " shift_first_model_year=2025)\n", - "else:\n", - " scen_budget = message_ix.Scenario(mp, model=\"ENGAGE-MAGPIE_SSP2_v4.1.8.3_T4.5_r3.1\", scenario=budget)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "extra = scen_budget.set(\"cat_year\", {\"type_year\": \"cumulative\", \"year\": [2020, 2015, 2010]})\n", - "scen_budget.check_out()\n", - "scen_budget.remove_set(\"cat_year\", extra)\n", - "scen_budget.commit(\"remove cumulative years from cat_year set\")\n", - "scen_budget.set(\"cat_year\", {\"type_year\": \"cumulative\"})" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "emission_dict = {\n", - " \"node\": \"World\",\n", - " \"type_emission\": \"TCE\",\n", - " \"type_tec\": \"all\",\n", - " \"type_year\": \"cumulative\",\n", - " \"unit\": \"???\",\n", - " }\n", - "if budget == \"1000f\":\n", - " value = 4046\n", - "if budget == \"600f\":\n", - " value = 2605\n", - "\n", - "df = message_ix.make_df(\n", - " \"bound_emission\", value=value, **emission_dict\n", - ")\n", - "scen_budget.check_out()\n", - "scen_budget.add_par(\"bound_emission\", df)\n", - "scen_budget.commit(\"add emission bound\")\n", - "scen_budget.par(\"bound_emission\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scen_budget.set_as_default()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scen_budget.solve()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 14, - "outputs": [ - { - "data": { - "text/plain": "'600f_MP00BD0BI78'" - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## run reporting" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 3, - "outputs": [], - "source": [ - "import ixmp\n", - "import ixmp as ix\n", - "import message_ix\n", - "\n", - "model_name=\"MESSAGEix-Materials\"\n", - "scenario_name=\"NoPolicy_petro_thesis_2_final_macro\"\n", - "#scenario_name=\"NoPolicy_petro_thesis_2_macro_w_ENGAGE_fixes\"\n", - "\n", - "mp = ix.Platform(\"ixmp_dev\")\n", - "scen = message_ix.Scenario(mp, model=model_name, scenario=scenario_name)#, version=37)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-22T09:43:44.817648400Z", - "start_time": "2023-11-22T09:43:43.081087200Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 4, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "processing Table: Resource|Extraction\n", - "processing Table: Resource|Cumulative Extraction\n", - "processing Table: Primary Energy\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "KeyboardInterrupt\n", - "\n" - ] - } - ], - "source": [ - "import message_data.tools.post_processing.iamc_report_hackathon\n", - "import importlib\n", - "importlib.reload(message_data.tools.post_processing.iamc_report_hackathon)\n", - "message_data.tools.post_processing.iamc_report_hackathon.report(\n", - " mp,\n", - " scen,\n", - " # NB(PNK) this is not an error; .iamc_report_hackathon.report() expects a\n", - " # string containing \"True\" or \"False\" instead of an actual bool.\n", - " \"False\",\n", - " scen.model,\n", - " scen.scenario.replace(\"_test_calib_macro\", \"\"),\n", - " merge_hist=True,\n", - " merge_ts=True,\n", - " run_config=\"materials_run_config.yaml\",\n", - ")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-22T09:43:57.385985Z", - "start_time": "2023-11-22T09:43:45.456907800Z" - } - } - }, - { - "cell_type": "markdown", - "source": [], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## check if historic TS is missing" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df = scen.timeseries(iamc=True)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "(\n", - "df[df.variable == \"Emissions|Kyoto Gases\"]\n", - " .drop(\"variable\", axis=1)\n", - " .set_index(\"region\")\n", - " )" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scen.set_as_default()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## add missing historic TS to SCP baseline scenario" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 18, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "This Scenario has a solution, use `Scenario.remove_solution()` or `Scenario.clone(..., keep_solution=False)`\n" - ] - } - ], - "source": [ - "scen_ref = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-NGFS\", \"baseline\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 19, - "outputs": [], - "source": [ - "df_ref = scen_ref.timeseries(iamc=True)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "(\n", - "df_ref[df_ref.variable == \"Emissions|Kyoto Gases\"]\n", - " .drop(\"variable\", axis=1)\n", - " .set_index(\"region\")\n", - " )" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 23, - "outputs": [], - "source": [ - "#scen.remove_solution()\n", - "scen.check_out()\n", - "scen.add_timeseries(df_ref)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 24, - "outputs": [], - "source": [ - "scen.commit(\"add historic TS\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 25, - "outputs": [], - "source": [ - "scen.set_as_default()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## add missing biomass slack for NPi2020 scenario" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df = scen.par(\"land_output\", filters={\"node\":\"R12_CHN\", \"commodity\":\"bioenergy\", \"year\":2020})" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df[\"value\"] = df[\"value\"] + 11.3741" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scen.check_out()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scen.add_par(\"land_output\", df)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scen.commit(\"add R12_CHN biomass slack 2020\")" - ], - "metadata": { - "collapsed": false - } - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/message_ix_models/model/material/magpie notebooks/data_scp_final.ipynb b/message_ix_models/model/material/magpie notebooks/data_scp_final.ipynb deleted file mode 100644 index e8857bb102..0000000000 --- a/message_ix_models/model/material/magpie notebooks/data_scp_final.ipynb +++ /dev/null @@ -1,744 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true, - "ExecuteTime": { - "end_time": "2023-07-20T15:27:10.206336800Z", - "start_time": "2023-07-20T15:27:03.454843500Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": "", - "application/javascript": "if (typeof IPython !== 'undefined') { IPython.OutputArea.prototype._should_scroll = function(lines){ return false; }}" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Unable to determine R home: [WinError 2] The system cannot find the file specified\n" - ] - } - ], - "source": [ - "import ixmp as ix\n", - "import message_ix\n", - "import pandas as pd\n", - "\n", - "from message_ix_models.util import broadcast, private_data_path\n", - "from message_data.model.material.data_methanol_new import broadcast_reduced_df" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "outputs": [], - "source": [ - "mp = ix.Platform(\"ixmp_dev\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## add SCP tech and demand" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 4, - "outputs": [], - "source": [ - "model_name = \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\"\n", - "scen_name = \"baseline_MP76BD1BI00\"\n", - "\n", - "scenario = message_ix.Scenario(mp, model_name, scen_name)\n", - "\n", - "sc_clone = scenario.clone(model_name, scen_name+\"test_scp\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-07-20T15:29:34.441051600Z", - "start_time": "2023-07-20T15:27:10.212887200Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 11, - "outputs": [], - "source": [ - "all_years = scenario.vintage_and_active_years()\n", - "years = all_years[all_years[\"year_act\"]==all_years[\"year_vtg\"]]\n", - "first_year = 2025 # when should scp be available?? 2025? 2030?\n", - "years = years[years[\"year_act\"] >= first_year]" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-07-20T12:39:50.996074800Z", - "start_time": "2023-07-20T12:39:50.974724Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 3, - "outputs": [], - "source": [ - "region_setup = \"R12\"" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 13, - "outputs": [], - "source": [ - "scp_demand = pd.read_excel(private_data_path(\"scp\", \"population_beef_demand.xlsx\"),\n", - " sheet_name=f\"demand_{region_setup}\")\n", - "scp_demand[\"value\"] = scp_demand[\"value\"] / 1000\n", - "scp_demand = scp_demand[scp_demand[\"year\"]>=first_year]\n", - "\n", - "input = pd.read_excel(private_data_path(\"scp\", \"scp_techno_economic.xlsx\"),\n", - " sheet_name=f\"input_{region_setup}\")\n", - "output = pd.read_excel(private_data_path(\"scp\", \"scp_techno_economic.xlsx\"),\n", - " sheet_name=f\"output_{region_setup}\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-07-20T12:39:52.665801700Z", - "start_time": "2023-07-20T12:39:52.590786200Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 14, - "outputs": [], - "source": [ - "input = input.pipe(broadcast, year_vtg=years[\"year_vtg\"])\n", - "input[\"year_act\"] = input[\"year_vtg\"]\n", - "output = output.pipe(broadcast, year_vtg=years[\"year_vtg\"])\n", - "output[\"year_act\"] = output[\"year_vtg\"]" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-07-20T12:39:54.146585400Z", - "start_time": "2023-07-20T12:39:54.122675300Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 15, - "outputs": [], - "source": [ - "input = input[input[\"commodity\"]!=\"NH3\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 16, - "outputs": [], - "source": [ - "df_ccu_rel_act = broadcast_reduced_df(pd.read_excel(private_data_path(\"scp\", \"scp_techno_economic.xlsx\"),\n", - " sheet_name=f\"relation_activity_{region_setup}\"), \"relation_activity\")\n", - "\n", - "df_ccu_rel_lo = broadcast_reduced_df(pd.read_excel(private_data_path(\"scp\", \"scp_techno_economic.xlsx\"),\n", - " sheet_name=f\"relation_lower_{region_setup}\"), \"relation_lower\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-07-20T12:39:55.547689700Z", - "start_time": "2023-07-20T12:39:55.465648600Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 18, - "outputs": [], - "source": [ - "df = sc_clone.par(\"land_output\", {\"commodity\": \"Food Demand|Microbial protein\"})\n", - "df[\"level\"] = \"final_material\"" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 19, - "outputs": [], - "source": [ - "#sc_clone.check_out()\n", - "\n", - "sc_clone.add_set(\"technology\", \"syn_micr_beef\")\n", - "sc_clone.add_set(\"commodity\", \"Food Demand|Microbial protein\")\n", - "sc_clone.add_set(\"mode\", \"hydrogen\")\n", - "\n", - "if region_setup == \"R11\":\n", - " sc_clone.add_set(\"level\", \"demand\")\n", - " sc_clone.add_set(\"relation\", \"CO2_PtX_trans_disp_split\") # add relation to mimic CO2 input requirements for SCP\n", - " input = input[input[\"commodity\"]!=\"NH3\"] # remove NH3 input since it is not in R11 base model\n", - "\n", - "sc_clone.add_par(\"input\", input)\n", - "sc_clone.add_set(\"level\", \"final_material\")\n", - "sc_clone.add_par(\"output\", output)\n", - "sc_clone.add_par(\"land_input\", df)\n", - "sc_clone.add_set(\"relation\", \"CO2_PtX_trans_disp_split\")\n", - "sc_clone.add_par(\"relation_activity\", df_ccu_rel_act)\n", - "sc_clone.add_par(\"relation_lower\", df_ccu_rel_lo)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-07-20T12:40:30.081008500Z", - "start_time": "2023-07-20T12:40:05.693199Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 20, - "outputs": [], - "source": [ - "sc_clone.commit(\"add scp model\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 23, - "outputs": [], - "source": [ - "#sc_clone.remove_solution()\n", - "sc_clone.solve(model=\"MESSAGE-MACRO\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-07-20T15:33:12.158785600Z", - "start_time": "2023-07-20T15:29:42.459097500Z" - } - } - }, - { - "cell_type": "markdown", - "source": [], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## build 1000f/600f scenario" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 24, - "outputs": [], - "source": [ - "scen = sc_clone" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 25, - "outputs": [], - "source": [ - "mode = \"clone\"\n", - "#mode = \"load\"\n", - "\n", - "#budget = \"1000f\"\n", - "budget = \"600f\"" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 26, - "outputs": [ - { - "data": { - "text/plain": "'600f_MP76BD1BI00test_scp'" - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "scen.scenario.replace(\"baseline\",budget)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 27, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Override keep_solution=True for shift_first_model_year\n" - ] - } - ], - "source": [ - "if mode == \"clone\":\n", - " scen_budget = scen.clone(model=scen.model, scenario=scen.scenario.replace(\"baseline\",budget), shift_first_model_year=2025)\n", - "else:\n", - " scen_budget = message_ix.Scenario(mp, model=\"ENGAGE-MAGPIE_SSP2_v4.1.8.3_T4.5_r3.1\", scenario=budget)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "if mode == \"clone\":\n", - " scen_budget = scen.clone(model=\"ENGAGE-MAGPIE_SSP2_v4.1.8.3_T4.5_r3.1\", scenario=budget, keep_solution=False,\n", - " shift_first_model_year=2025)\n", - "else:\n", - " scen_budget = message_ix.Scenario(mp, model=\"ENGAGE-MAGPIE_SSP2_v4.1.8.3_T4.5_r3.1\", scenario=budget)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 28, - "outputs": [ - { - "data": { - "text/plain": " type_year year\n0 cumulative 2025\n1 cumulative 2030\n2 cumulative 2035\n3 cumulative 2040\n4 cumulative 2045\n5 cumulative 2050\n6 cumulative 2055\n7 cumulative 2060\n8 cumulative 2070\n9 cumulative 2080\n10 cumulative 2090\n11 cumulative 2100\n12 cumulative 2110", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
type_yearyear
0cumulative2025
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\n
" - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "extra = scen_budget.set(\"cat_year\", {\"type_year\": \"cumulative\", \"year\": [2020, 2015, 2010]})\n", - "scen_budget.check_out()\n", - "scen_budget.remove_set(\"cat_year\", extra)\n", - "scen_budget.commit(\"remove cumulative years from cat_year set\")\n", - "scen_budget.set(\"cat_year\", {\"type_year\": \"cumulative\"})" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 29, - "outputs": [ - { - "data": { - "text/plain": " node type_emission type_tec type_year value unit\n0 World TCE all cumulative 2605.0 ???", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
nodetype_emissiontype_tectype_yearvalueunit
0WorldTCEallcumulative2605.0???
\n
" - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "emission_dict = {\n", - " \"node\": \"World\",\n", - " \"type_emission\": \"TCE\",\n", - " \"type_tec\": \"all\",\n", - " \"type_year\": \"cumulative\",\n", - " \"unit\": \"???\",\n", - " }\n", - "if budget == \"1000f\":\n", - " value = 4046\n", - "if budget == \"600f\":\n", - " value = 2605\n", - "\n", - "df = message_ix.make_df(\n", - " \"bound_emission\", value=value, **emission_dict\n", - ")\n", - "scen_budget.check_out()\n", - "scen_budget.add_par(\"bound_emission\", df)\n", - "scen_budget.commit(\"add emission bound\")\n", - "scen_budget.par(\"bound_emission\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 31, - "outputs": [], - "source": [ - "scen_budget.solve(model=\"MESSAGE-MACRO\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## reporting" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 3, - "outputs": [], - "source": [ - "df = mp.scenario_list()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 12, - "outputs": [], - "source": [ - "import pyperclip" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 21, - "outputs": [], - "source": [ - "scen_list = str(df[df[\"model\"]==\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\"].sort_values(\"cre_date\")[\"scenario\"].tolist())" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 22, - "outputs": [], - "source": [ - "pyperclip.copy(scen_list)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 3, - "outputs": [], - "source": [ - "model_name = \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\"" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 3, - "outputs": [], - "source": [ - "rep_list = ['baseline_MP76BD0BI78test_scp', '600f_MP76BD0BI78test_scp', 'baseline_MP76BD1BI00test_scp', '600f_MP76BD1BI00test_scp']\n", - "# 'baseline_MP76BD0BI70' still to build scp and 600f" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 7, - "outputs": [ - { - "data": { - "text/plain": "True" - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "scen = message_ix.Scenario(mp, model_name, \"1000f_MP00BD0BI78_test_calib_macro\")\n", - "scen.has_solution()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 10, - "outputs": [], - "source": [ - "from message_data.tools.post_processing.iamc_report_hackathon import report as reporting" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 5, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "baseline_MP76BD0BI78\n", - "600f_MP76BD0BI78\n", - "baseline_MP76BD1BI00\n", - "600f_MP76BD1BI00\n" - ] - } - ], - "source": [ - "for scen_name in rep_list:\n", - " print(scen_name.replace(\"test_scp\", \"\"))" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 7, - "outputs": [ - { - "data": { - "text/plain": "['baseline_MP76BD1BI00test_scp', '600f_MP76BD1BI00test_scp']" - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "rep_list[2:]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 8, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'rep_list' is not defined", - "output_type": "error", - "traceback": [ - "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[1;31mNameError\u001B[0m Traceback (most recent call last)", - "Input \u001B[1;32mIn [8]\u001B[0m, in \u001B[0;36m\u001B[1;34m()\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m scen_name \u001B[38;5;129;01min\u001B[39;00m \u001B[43mrep_list\u001B[49m[\u001B[38;5;241m2\u001B[39m:]:\n\u001B[0;32m 2\u001B[0m scen \u001B[38;5;241m=\u001B[39m message_ix\u001B[38;5;241m.\u001B[39mScenario(mp, model_name, scen_name)\n\u001B[0;32m 3\u001B[0m reporting(\n\u001B[0;32m 4\u001B[0m mp,\n\u001B[0;32m 5\u001B[0m scen,\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 13\u001B[0m \u001B[38;5;66;03m#run_config=\"materials_run_config.yaml\",\u001B[39;00m\n\u001B[0;32m 14\u001B[0m )\n", - "\u001B[1;31mNameError\u001B[0m: name 'rep_list' is not defined" - ] - } - ], - "source": [ - "for scen_name in rep_list[2:]:\n", - " scen = message_ix.Scenario(mp, model_name, scen_name)\n", - " reporting(\n", - " mp,\n", - " scen,\n", - " # NB(PNK) this is not an error; .iamc_report_hackathon.report() expects a\n", - " # string containing \"True\" or \"False\" instead of an actual bool.\n", - " \"False\",\n", - " scen.model,\n", - " scen_name.replace(\"_test_calib_macro\", \"\"),\n", - " merge_hist=True,\n", - " merge_ts=True,\n", - " #run_config=\"materials_run_config.yaml\",\n", - " )" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 12, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "processing Table: Resource|Extraction\n", - "processing Table: Resource|Cumulative Extraction\n", - "processing Table: Primary Energy\n", - "processing Table: Primary Energy (substitution method)\n", - "processing Table: Final Energy\n", - "processing Table: Secondary Energy|Electricity\n", - "processing Table: Secondary Energy|Heat\n", - "processing Table: Secondary Energy\n", - "processing Table: Secondary Energy|Gases\n", - "processing Table: Secondary Energy|Solids\n", - "processing Table: Emissions|CO2\n", - "processing Table: Carbon Sequestration\n", - "processing Table: Emissions|BC\n", - "processing Table: Emissions|OC\n", - "processing Table: Emissions|CO\n", - "processing Table: Emissions|N2O\n", - "processing Table: Emissions|CH4\n", - "processing Table: Emissions|NH3\n", - "processing Table: Emissions|Sulfur\n", - "processing Table: Emissions|NOx\n", - "processing Table: Emissions|VOC\n", - "processing Table: Emissions|HFC\n", - "processing Table: Emissions\n", - "processing Table: Emissions\n", - "processing Table: Agricultural Demand\n", - "processing Table: Agricultural Production\n", - "processing Table: Fertilizer Use\n", - "processing Table: Fertilizer\n", - "processing Table: Food Waste\n", - "processing Table: Food Demand\n", - "processing Table: Forestry Demand\n", - "processing Table: Forestry Production\n", - "processing Table: Land Cover\n", - "processing Table: Yield\n", - "processing Table: Capacity\n", - "processing Table: Capacity Additions\n", - "processing Table: Cumulative Capacity\n", - "processing Table: Capital Cost\n", - "processing Table: OM Cost|Fixed\n", - "processing Table: OM Cost|Variable\n", - "processing Table: Lifetime\n", - "processing Table: Efficiency\n", - "processing Table: Population\n", - "processing Table: Price\n", - "processing Table: Useful Energy\n", - "processing Table: Useful Energy\n", - "processing Table: Trade\n", - "processing Table: Investment|Energy Supply\n", - "processing Table: Water Consumption\n", - "processing Table: Water Withdrawal\n", - "processing Table: GDP\n", - "processing Table: Cost\n", - "processing Table: GLOBIOM Feedback\n", - "Starting to upload timeseries\n", - " region variable unit 2025 \\\n", - "0 R12_AFR Resource|Extraction EJ/yr 17.710206 \n", - "1 R12_AFR Resource|Extraction|Coal EJ/yr 2.964129 \n", - "2 R12_AFR Resource|Extraction|Gas EJ/yr 6.834491 \n", - "3 R12_AFR Resource|Extraction|Gas|Conventional EJ/yr 6.834491 \n", - "4 R12_AFR Resource|Extraction|Gas|Unconventional EJ/yr 0.000000 \n", - "\n", - " 2030 2035 2040 2045 2050 2055 \\\n", - "0 14.190102 13.207377 12.677562 12.923691 13.127013 15.616553 \n", - "1 0.686702 0.192814 0.000340 0.000170 0.001140 0.000000 \n", - "2 7.515499 8.521001 9.343876 10.490576 11.391426 14.423792 \n", - "3 7.515499 8.521001 9.343876 10.490576 11.391426 14.423792 \n", - "4 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "\n", - " 2060 2070 2080 2090 2100 2110 \n", - "0 15.670154 14.318625 8.187958 4.633826 2.532917 1.246648 \n", - "1 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "2 14.897350 14.123627 8.187064 4.632625 2.530546 1.243462 \n", - "3 14.897350 14.123627 8.187064 4.632625 2.530546 1.243462 \n", - "4 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "Finished uploading timeseries\n" - ] - } - ], - "source": [ - "reporting(\n", - " mp,\n", - " scen,\n", - " # NB(PNK) this is not an error; .iamc_report_hackathon.report() expects a\n", - " # string containing \"True\" or \"False\" instead of an actual bool.\n", - " \"False\",\n", - " scen.model,\n", - " scen.scenario.replace(\"_test_calib_macro\", \"\"),\n", - " merge_hist=True,\n", - " merge_ts=True,\n", - " #run_config=\"materials_run_config.yaml\",\n", - ")" - ], - "metadata": { - "collapsed": false - } - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/message_ix_models/model/material/magpie notebooks/magpie.ipynb b/message_ix_models/model/material/magpie notebooks/magpie.ipynb deleted file mode 100644 index 69ce0f5087..0000000000 --- a/message_ix_models/model/material/magpie notebooks/magpie.ipynb +++ /dev/null @@ -1,6434 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "source": [ - "## MAGPIE setup baseline" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 1, - "outputs": [ - { - "data": { - "text/plain": "", - "application/javascript": "if (typeof IPython !== 'undefined') { IPython.OutputArea.prototype._should_scroll = function(lines){ return false; }}" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import ixmp\n", - "import message_ix\n", - "from message_ix_models.util import private_data_path\n", - "import message_data\n", - "import importlib\n", - "#importlib.reload(message_data.tools.utilities)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-16T14:05:34.002478400Z", - "start_time": "2023-10-16T14:05:25.844644400Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 2, - "outputs": [], - "source": [ - "import message_data.tools.utilities" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-16T14:05:34.449804800Z", - "start_time": "2023-10-16T14:05:33.971237200Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 3, - "outputs": [], - "source": [ - "mp = ixmp.Platform(\"ixmp_dev\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-16T14:05:39.928527300Z", - "start_time": "2023-10-16T14:05:34.449804800Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 4, - "outputs": [], - "source": [ - "scen = message_ix.Scenario(mp, \"MESSAGEix-Materials\", \"NoPolicy_no_SDGs_petro_thesis_1_final_premise\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-09T07:43:02.244569500Z", - "start_time": "2023-10-09T07:42:59.922975200Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 22, - "outputs": [], - "source": [ - "sc_clone = scen.clone(scen.model, scen.scenario+\"_updated\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-09T07:49:04.893243700Z", - "start_time": "2023-10-09T07:48:41.128939100Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 25, - "outputs": [], - "source": [ - "sc_clone.set_as_default()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-09T07:49:47.134774700Z", - "start_time": "2023-10-09T07:49:47.068038200Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 24, - "outputs": [ - { - "data": { - "text/plain": "'MESSAGEix-Materials'" - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sc_clone.model" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-09T07:49:15.315295200Z", - "start_time": "2023-10-09T07:49:15.199304500Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 17, - "outputs": [ - { - "data": { - "text/plain": " model scenario \\\n7935 MESSAGEix-Materials NoPolicy_no_SDGs_petro_thesis_1_final_premise \n\n scheme is_default is_locked cre_user cre_date \\\n7935 MESSAGE 1 0 maczek 2023-07-20 11:57:30.000000 \n\n upd_user upd_date lock_user lock_date \\\n7935 None None None None \n\n annotation version \n7935 clone Scenario from 'MESSAGEix-Materials|NoPol... 2 ", - "text/html": "
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modelscenarioschemeis_defaultis_lockedcre_usercre_dateupd_userupd_datelock_userlock_dateannotationversion
7935MESSAGEix-MaterialsNoPolicy_no_SDGs_petro_thesis_1_final_premiseMESSAGE10maczek2023-07-20 11:57:30.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...2
\n
" - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model_pattern = \"MESSAGEix-Material\"\n", - "scen_pattern = \"NoPolicy_no_SDGs_petro_thesis_1_final_premise\"\n", - "df = mp.scenario_list()\n", - "df[(df[\"model\"].str.contains(model_pattern)) & (df[\"scenario\"].str.startswith(scen_pattern))]" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-09T07:46:39.014607300Z", - "start_time": "2023-10-09T07:46:37.682873200Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 19, - "outputs": [], - "source": [ - "def get_scen_list(mp, scen_pattern, model_pattern):\n", - " df = mp.scenario_list()\n", - " print(df[(df[\"model\"].str.contains(model_pattern)) & (df[\"scenario\"].str.startswith(scen_pattern))])\n", - " return df[(df[\"model\"].str.contains(model_pattern)) & (df[\"scenario\"].str.startswith(scen_pattern))]" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-09T07:47:19.226034900Z", - "start_time": "2023-10-09T07:47:19.125116900Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 20, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Empty DataFrame\n", - "Columns: [model, scenario, scheme, is_default, is_locked, cre_user, cre_date, upd_user, upd_date, lock_user, lock_date, annotation, version]\n", - "Index: []\n" - ] - }, - { - "data": { - "text/plain": "Empty DataFrame\nColumns: [model, scenario, scheme, is_default, is_locked, cre_user, cre_date, upd_user, upd_date, lock_user, lock_date, annotation, version]\nIndex: []", - "text/html": "
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modelscenarioschemeis_defaultis_lockedcre_usercre_dateupd_userupd_datelock_userlock_dateannotationversion
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" - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "get_scen_list(mp, model_pattern, scen_pattern)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-09T07:47:21.515731100Z", - "start_time": "2023-10-09T07:47:20.537571400Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 19, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'StringMethods' object has no attribute 'startwith'", - "output_type": "error", - "traceback": [ - "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[1;31mAttributeError\u001B[0m Traceback (most recent call last)", - "Input \u001B[1;32mIn [19]\u001B[0m, in \u001B[0;36m\u001B[1;34m()\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[43mget_scen_list\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmp\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mMESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mnew\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\n", - "Input \u001B[1;32mIn [18]\u001B[0m, in \u001B[0;36mget_scen_list\u001B[1;34m(mp, scen_pattern, model_pattern)\u001B[0m\n\u001B[0;32m 1\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mget_scen_list\u001B[39m(mp, scen_pattern, model_pattern):\n\u001B[0;32m 2\u001B[0m df \u001B[38;5;241m=\u001B[39m mp\u001B[38;5;241m.\u001B[39mscenario_list()\n\u001B[1;32m----> 3\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m df[(df[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mmodel\u001B[39m\u001B[38;5;124m\"\u001B[39m]\u001B[38;5;241m.\u001B[39mstr\u001B[38;5;241m.\u001B[39mstartswith(model_pattern)) \u001B[38;5;241m&\u001B[39m (\u001B[43mdf\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mscenario\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mstr\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mstartwith\u001B[49m(scen_pattern))]\n", - "\u001B[1;31mAttributeError\u001B[0m: 'StringMethods' object has no attribute 'startwith'" - ] - } - ], - "source": [ - "get_scen_list(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\", \"new\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-19T16:44:44.127087500Z", - "start_time": "2023-09-19T16:44:37.699794100Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 12, - "outputs": [ - { - "data": { - "text/plain": " model \\\n7101 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00 \n7103 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00 \n7104 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00 \n7105 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00 \n\n scenario scheme is_default is_locked \\\n7101 bugfix_cost-correction_new-matrix MESSAGE 1 0 \n7103 new-mapping_no-smoothing MESSAGE 1 0 \n7104 new-mapping_no-smoothing_1000f MESSAGE 1 0 \n7105 new-mapping_with-smoothing MESSAGE 1 0 \n\n cre_user cre_date upd_user \\\n7101 maczek 2023-09-18 16:26:29.000000 None \n7103 maczek 2023-09-19 14:37:44.000000 steinhauser \n7104 steinhauser 2023-09-19 15:03:24.000000 None \n7105 maczek 2023-09-19 14:51:21.000000 None \n\n upd_date lock_user lock_date \\\n7101 None None None \n7103 2023-09-19 15:46:39.000000 None None \n7104 None None None \n7105 None None None \n\n annotation version \n7101 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n7103 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 2 \n7104 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n7105 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 ", - "text/html": "
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modelscenarioschemeis_defaultis_lockedcre_usercre_dateupd_userupd_datelock_userlock_dateannotationversion
7101MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00bugfix_cost-correction_new-matrixMESSAGE10maczek2023-09-18 16:26:29.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
7103MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00new-mapping_no-smoothingMESSAGE10maczek2023-09-19 14:37:44.000000steinhauser2023-09-19 15:46:39.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...2
7104MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00new-mapping_no-smoothing_1000fMESSAGE10steinhauser2023-09-19 15:03:24.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
7105MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00new-mapping_with-smoothingMESSAGE10maczek2023-09-19 14:51:21.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
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" - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = mp.scenario_list()\n", - "df[(df[\"model\"].str.startswith(\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\")) & (df[\"scenario\"].str.contains(\"new\"))]" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-19T16:18:24.427634800Z", - "start_time": "2023-09-19T16:18:23.037601600Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 7, - "outputs": [], - "source": [ - "#scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD0BI78\", \"EN_NPi2020_1000f\")\n", - "models = [\"MESSAGEix-MAgPIE_Forest-test\", ]\n", - "scenarios = [\"primary_constraint_1000f_landScenLo_002\"]\n", - "#scen = message_ix.Scenario(mp, models[0], scenarios[0])" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-14T15:35:05.007149100Z", - "start_time": "2023-09-14T15:35:02.841426700Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 4, - "outputs": [], - "source": [ - "#scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\", \"EN_NPi2020_1000f\")\n", - "scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\", \"baseline_w/o_LU\")\n", - "#scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD0BI78\", \"test to confirm MACRO converges\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-16T14:15:07.752450300Z", - "start_time": "2023-10-16T14:15:04.240307100Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 6, - "outputs": [], - "source": [ - "scen.remove_solution()\n", - "scen.solve()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-02T09:32:34.261763900Z", - "start_time": "2023-10-02T09:29:05.034233600Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 4, - "outputs": [], - "source": [ - "#scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\", \"new-mapping_no-smoothing_1000f\")\n", - "#scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\", \"new-mapping_with-smoothing\")\n", - "#scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\", \"new-mapping_with-smoothing_primary-forest-constraint_tax_100\")\n", - "scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\", \"new-mapping_with-smoothing_primary-forest-constraint_tax_1000\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-20T20:22:20.385118700Z", - "start_time": "2023-09-20T20:22:17.697414900Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 6, - "outputs": [], - "source": [ - "#sc_clone = scen.clone(\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\", \"new-mapping_with-smoothing_primary-forest-constraint_tax_100\")\n", - "sc_clone = scen.clone(\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\", \"new-mapping_with-smoothing_primary-forest-constraint_tax_1000\", keep_solution=False)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-20T19:12:30.273833500Z", - "start_time": "2023-09-20T19:12:11.848257400Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 5, - "outputs": [], - "source": [ - "#sc_clone = scen.clone(\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\", \"exp_matrix_test\")\n", - "sc_clone = scen.clone(\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\", \"exp2110_matrix_test\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-16T14:18:16.956351400Z", - "start_time": "2023-10-16T14:17:58.254478800Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 19, - "outputs": [], - "source": [ - "sc_clone.remove_solution()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-20T18:22:13.578298800Z", - "start_time": "2023-09-20T18:22:11.750390500Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 5, - "outputs": [], - "source": [ - "scen.solve(model=\"MESSAGE-MACRO\", solve_options={\"scaind\":-1})" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-20T20:47:05.194037100Z", - "start_time": "2023-09-20T20:22:24.447338300Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 23, - "outputs": [ - { - "data": { - "text/plain": "True" - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sc_clone.has_solution()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-20T18:53:55.190420400Z", - "start_time": "2023-09-20T18:53:55.018939700Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 10, - "outputs": [], - "source": [ - "sc_clone.check_out()\n", - "sc_clone.add_par(\"tax_emission\", df)\n", - "sc_clone.commit(\"add emi tax\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-20T19:12:51.645775700Z", - "start_time": "2023-09-20T19:12:51.058281800Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 7, - "outputs": [], - "source": [ - "scen2 = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP30BD0BI70\", \"EN_NPi2020_1150\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-20T18:01:46.858015600Z", - "start_time": "2023-09-20T18:01:45.373668100Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 11, - "outputs": [ - { - "data": { - "text/plain": " node type_emission type_tec type_year value unit\n0 World TCE all 2020 3310.036812 USD/tC\n1 World TCE all 2025 3310.036812 USD/tC\n2 World TCE all 2030 3310.036812 USD/tC\n3 World TCE all 2035 3310.036812 USD/tC\n4 World TCE all 2040 3310.036812 USD/tC\n5 World TCE all 2045 3310.036812 USD/tC\n6 World TCE all 2050 3310.036812 USD/tC\n7 World TCE all 2055 3310.036812 USD/tC\n8 World TCE all 2060 3310.036812 USD/tC\n9 World TCE all 2070 3310.036812 USD/tC\n10 World TCE all 2080 3310.036812 USD/tC\n11 World TCE all 2090 3310.036812 USD/tC\n12 World TCE all 2100 3310.036812 USD/tC\n13 World TCE all 2110 3310.036812 USD/tC", - 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" - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = sc_clone.par(\"tax_emission\")\n", - "df" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-20T19:12:53.660686600Z", - "start_time": "2023-09-20T19:12:53.550006800Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 8, - "outputs": [], - "source": [ - "#df[\"type_emission\"] = \"TCE\"\n", - "df[\"value\"] = (1000 * 3.310036812)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-20T19:12:37.910946600Z", - "start_time": "2023-09-20T19:12:37.770609200Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 9, - "outputs": [ - { - "data": { - "text/plain": " node type_emission type_tec type_year value unit\n0 World TCE all 2020 3310.036812 USD/tC\n1 World TCE all 2025 3310.036812 USD/tC\n2 World TCE all 2030 3310.036812 USD/tC\n3 World TCE all 2035 3310.036812 USD/tC\n4 World TCE all 2040 3310.036812 USD/tC\n5 World TCE all 2045 3310.036812 USD/tC\n6 World TCE all 2050 3310.036812 USD/tC\n7 World TCE all 2055 3310.036812 USD/tC\n8 World TCE all 2060 3310.036812 USD/tC\n9 World TCE all 2070 3310.036812 USD/tC\n10 World TCE all 2080 3310.036812 USD/tC\n11 World TCE all 2090 3310.036812 USD/tC\n12 World TCE all 2100 3310.036812 USD/tC\n13 World TCE all 2110 3310.036812 USD/tC", - "text/html": "
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" - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-20T19:12:39.535922500Z", - "start_time": "2023-09-20T19:12:39.422454400Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 5, - "outputs": [ - { - "data": { - "text/plain": "True" - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "scen.has_solution()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-20T12:09:33.535380800Z", - "start_time": "2023-09-20T12:09:33.462699Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 8, - "outputs": [], - "source": [ - "scen.remove_solution()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-20T11:59:15.670992300Z", - "start_time": "2023-09-20T11:58:32.559401200Z" - } - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scen.solve(\"MESSAGE-MACRO\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "start_time": "2023-09-20T12:10:04.268266300Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 5, - "outputs": [], - "source": [ - "#sc_clone3 = scen.clone(\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00_uncorrected\", \"baseline\")\n", - "#sc_clone = scen.clone(\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00_incOldFor+PrimFor\", \"baseline\")\n", - "#sc_clone = scen.clone(\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00_OldForSplit\", \"baseline\")\n", - "#sc_clone = scen.clone(\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\", \"bugfix\")\n", - "#sc_clone2 = scen.clone(\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\", \"bugfix_cost-correction\")\n", - "#sc_clone2 = scen.clone(\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\", \"bugfix_cost-correction_new-matrix\")\n", - "#sc_clone2 = scen.clone(\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\", \"new-mapping_no-smoothing\")\n", - "sc_clone2 = scen.clone(\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\", \"new-mapping_with-smoothing\")\n", - "#sc_clone = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00_corrected\", \"baseline\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-19T12:51:40.341511700Z", - "start_time": "2023-09-19T12:51:21.425252Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 4, - "outputs": [], - "source": [ - "#scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00_uncorrected\", \"baseline\")\n", - "#scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\", \"bugfix_1000f\")\n", - "scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\", \"bugfix_cost-correction_new-matrix\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-19T09:24:37.660479Z", - "start_time": "2023-09-19T09:24:34.009163800Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 10, - "outputs": [ - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[1;31mKeyboardInterrupt\u001B[0m Traceback (most recent call last)", - "Input \u001B[1;32mIn [10]\u001B[0m, in \u001B[0;36m\u001B[1;34m()\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[43mscen\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msolve\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmodel\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mMESSAGE-MACRO\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32m~\\PycharmProjects\\message_ix\\message_ix\\core.py:677\u001B[0m, in \u001B[0;36mScenario.solve\u001B[1;34m(self, model, solve_options, **kwargs)\u001B[0m\n\u001B[0;32m 657\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21msolve\u001B[39m(\u001B[38;5;28mself\u001B[39m, model\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mMESSAGE\u001B[39m\u001B[38;5;124m\"\u001B[39m, solve_options\u001B[38;5;241m=\u001B[39m{}, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[0;32m 658\u001B[0m \u001B[38;5;124;03m\"\"\"Solve MESSAGE or MESSAGE-MACRO for the Scenario.\u001B[39;00m\n\u001B[0;32m 659\u001B[0m \n\u001B[0;32m 660\u001B[0m \u001B[38;5;124;03m By default, :meth:`ixmp.Scenario.solve` is called with 'MESSAGE' as the\u001B[39;00m\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 675\u001B[0m \u001B[38;5;124;03m :class:`.GAMSModel`.\u001B[39;00m\n\u001B[0;32m 676\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[1;32m--> 677\u001B[0m \u001B[38;5;28;43msuper\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msolve\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmodel\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mmodel\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43msolve_options\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43msolve_options\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\ixmp\\core\\scenario.py:835\u001B[0m, in \u001B[0;36mScenario.solve\u001B[1;34m(self, model, callback, cb_kwargs, **model_options)\u001B[0m\n\u001B[0;32m 833\u001B[0m \u001B[38;5;66;03m# Iterate until convergence\u001B[39;00m\n\u001B[0;32m 834\u001B[0m \u001B[38;5;28;01mwhile\u001B[39;00m \u001B[38;5;28;01mTrue\u001B[39;00m:\n\u001B[1;32m--> 835\u001B[0m \u001B[43mmodel_obj\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrun\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[0;32m 837\u001B[0m \u001B[38;5;66;03m# Store an iteration number to help the callback\u001B[39;00m\n\u001B[0;32m 838\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28mhasattr\u001B[39m(\u001B[38;5;28mself\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124miteration\u001B[39m\u001B[38;5;124m\"\u001B[39m):\n", - "File \u001B[1;32m~\\PycharmProjects\\message_ix\\message_ix\\models.py:373\u001B[0m, in \u001B[0;36mGAMSModel.run\u001B[1;34m(self, scenario)\u001B[0m\n\u001B[0;32m 370\u001B[0m lines2 \u001B[38;5;241m=\u001B[39m (\u001B[38;5;124m\"\u001B[39m\u001B[38;5;132;01m{}\u001B[39;00m\u001B[38;5;124m = \u001B[39m\u001B[38;5;132;01m{}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;241m.\u001B[39mformat(\u001B[38;5;241m*\u001B[39mkv) \u001B[38;5;28;01mfor\u001B[39;00m kv \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcplex_opts\u001B[38;5;241m.\u001B[39mitems())\n\u001B[0;32m 371\u001B[0m optfile2\u001B[38;5;241m.\u001B[39mwrite_text(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;130;01m\\n\u001B[39;00m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;241m.\u001B[39mjoin(lines2))\n\u001B[1;32m--> 373\u001B[0m result \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43msuper\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrun\u001B[49m\u001B[43m(\u001B[49m\u001B[43mscenario\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 375\u001B[0m \u001B[38;5;66;03m# In previous versions, the `cplex.opt` file(s) were removed at this point\u001B[39;00m\n\u001B[0;32m 376\u001B[0m \u001B[38;5;66;03m# in the workflow. This has been removed due to issues when running\u001B[39;00m\n\u001B[0;32m 377\u001B[0m \u001B[38;5;66;03m# scenarios asynchronously.\u001B[39;00m\n\u001B[0;32m 379\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m result\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\ixmp\\model\\gams.py:293\u001B[0m, in \u001B[0;36mGAMSModel.run\u001B[1;34m(self, scenario)\u001B[0m\n\u001B[0;32m 286\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mNotImplementedError\u001B[39;00m(\n\u001B[0;32m 287\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mGAMSModel requires a Backend that can write to GDX files, e.g. \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m 288\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mJDBCBackend\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m 289\u001B[0m )\n\u001B[0;32m 291\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m 292\u001B[0m \u001B[38;5;66;03m# Invoke GAMS\u001B[39;00m\n\u001B[1;32m--> 293\u001B[0m \u001B[43mcheck_call\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcommand\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mshell\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mos\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mname\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m==\u001B[39;49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mnt\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcwd\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcwd\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 294\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m CalledProcessError \u001B[38;5;28;01mas\u001B[39;00m exc:\n\u001B[0;32m 295\u001B[0m \u001B[38;5;66;03m# Do not remove self.temp_dir; the user may want to inspect the GDX file\u001B[39;00m\n\u001B[0;32m 296\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mformat_exception(exc, model_file) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;28mNone\u001B[39m\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\subprocess.py:359\u001B[0m, in \u001B[0;36mcheck_call\u001B[1;34m(*popenargs, **kwargs)\u001B[0m\n\u001B[0;32m 349\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mcheck_call\u001B[39m(\u001B[38;5;241m*\u001B[39mpopenargs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[0;32m 350\u001B[0m \u001B[38;5;124;03m\"\"\"Run command with arguments. Wait for command to complete. If\u001B[39;00m\n\u001B[0;32m 351\u001B[0m \u001B[38;5;124;03m the exit code was zero then return, otherwise raise\u001B[39;00m\n\u001B[0;32m 352\u001B[0m \u001B[38;5;124;03m CalledProcessError. The CalledProcessError object will have the\u001B[39;00m\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 357\u001B[0m \u001B[38;5;124;03m check_call([\"ls\", \"-l\"])\u001B[39;00m\n\u001B[0;32m 358\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[1;32m--> 359\u001B[0m retcode \u001B[38;5;241m=\u001B[39m \u001B[43mcall\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mpopenargs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 360\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m retcode:\n\u001B[0;32m 361\u001B[0m cmd \u001B[38;5;241m=\u001B[39m kwargs\u001B[38;5;241m.\u001B[39mget(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124margs\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\subprocess.py:342\u001B[0m, in \u001B[0;36mcall\u001B[1;34m(timeout, *popenargs, **kwargs)\u001B[0m\n\u001B[0;32m 340\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m Popen(\u001B[38;5;241m*\u001B[39mpopenargs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs) \u001B[38;5;28;01mas\u001B[39;00m p:\n\u001B[0;32m 341\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m--> 342\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mp\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mwait\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtimeout\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mtimeout\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 343\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m: \u001B[38;5;66;03m# Including KeyboardInterrupt, wait handled that.\u001B[39;00m\n\u001B[0;32m 344\u001B[0m p\u001B[38;5;241m.\u001B[39mkill()\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\subprocess.py:1083\u001B[0m, in \u001B[0;36mPopen.wait\u001B[1;34m(self, timeout)\u001B[0m\n\u001B[0;32m 1081\u001B[0m endtime \u001B[38;5;241m=\u001B[39m _time() \u001B[38;5;241m+\u001B[39m timeout\n\u001B[0;32m 1082\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m-> 1083\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_wait\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtimeout\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mtimeout\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 1084\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyboardInterrupt\u001B[39;00m:\n\u001B[0;32m 1085\u001B[0m \u001B[38;5;66;03m# https://bugs.python.org/issue25942\u001B[39;00m\n\u001B[0;32m 1086\u001B[0m \u001B[38;5;66;03m# The first keyboard interrupt waits briefly for the child to\u001B[39;00m\n\u001B[0;32m 1087\u001B[0m \u001B[38;5;66;03m# exit under the common assumption that it also received the ^C\u001B[39;00m\n\u001B[0;32m 1088\u001B[0m \u001B[38;5;66;03m# generated SIGINT and will exit rapidly.\u001B[39;00m\n\u001B[0;32m 1089\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m timeout \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\subprocess.py:1361\u001B[0m, in \u001B[0;36mPopen._wait\u001B[1;34m(self, timeout)\u001B[0m\n\u001B[0;32m 1358\u001B[0m timeout_millis \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mint\u001B[39m(timeout \u001B[38;5;241m*\u001B[39m \u001B[38;5;241m1000\u001B[39m)\n\u001B[0;32m 1359\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mreturncode \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m 1360\u001B[0m \u001B[38;5;66;03m# API note: Returns immediately if timeout_millis == 0.\u001B[39;00m\n\u001B[1;32m-> 1361\u001B[0m result \u001B[38;5;241m=\u001B[39m \u001B[43m_winapi\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mWaitForSingleObject\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_handle\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 1362\u001B[0m \u001B[43m \u001B[49m\u001B[43mtimeout_millis\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 1363\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m result \u001B[38;5;241m==\u001B[39m _winapi\u001B[38;5;241m.\u001B[39mWAIT_TIMEOUT:\n\u001B[0;32m 1364\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m TimeoutExpired(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39margs, timeout)\n", - "\u001B[1;31mKeyboardInterrupt\u001B[0m: " - ] - } - ], - "source": [ - "scen.solve(model=\"MESSAGE-MACRO\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-20T20:21:54.259873900Z", - "start_time": "2023-09-20T19:52:18.976484100Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 7, - "outputs": [], - "source": [ - "scen.check_out()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-20T19:52:01.382482400Z", - "start_time": "2023-09-20T19:52:01.242075700Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 8, - "outputs": [], - "source": [ - "scen.add_set(\"balance_equality\", (\"ethanol\", \"import\"))\n", - "scen.add_set(\"balance_equality\", (\"ethanol\", \"export\"))\n", - "scen.add_set(\"balance_equality\", (\"ethanol\", \"primary\"))\n", - "scen.add_set(\"balance_equality\", (\"ethanol\", \"secondary\"))\n", - "scen.add_set(\"balance_equality\", (\"ethanol\", \"final\"))" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-20T19:52:03.914047Z", - "start_time": "2023-09-20T19:52:03.835957300Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 9, - "outputs": [], - "source": [ - "scen.commit(\"add ethanol to bal eq\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-20T19:52:16.492564900Z", - "start_time": "2023-09-20T19:52:15.101269300Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 6, - "outputs": [ - { - "data": { - "text/plain": " commodity level\n0 BIO01GHG000 LU\n1 BIO02GHG000 LU\n2 BIO03GHG000 LU\n3 BIO04GHG000 LU\n4 BIO05GHG000 LU\n.. ... ...\n127 BIO60GHG200 LU\n128 BIO60GHG2000 LU\n129 BIO60GHG3000 LU\n130 BIO60GHG400 LU\n131 BIO60GHG600 LU\n\n[132 rows x 2 columns]", - "text/html": "
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130BIO60GHG400LU
131BIO60GHG600LU
\n

132 rows × 2 columns

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" - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "scen.set(\"balance_equality\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-20T19:50:49.710622900Z", - "start_time": "2023-09-20T19:50:49.523427Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 5, - "outputs": [ - { - "data": { - "text/plain": "False" - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "scen.has_solution()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-19T09:24:39.501008400Z", - "start_time": "2023-09-19T09:24:39.385411300Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 40, - "outputs": [ - { - "data": { - "text/plain": " value\nnode year BIOscen GHGscen \nR12_AFR 2050 BIO05 20 22420.20\n 2055 BIO05 20 25769.78\n 2070 BIO45 10 3437540.72\n 2080 BIO25 10 2551086.76\n BIO45 10 5378379.02\n... ...\nR12_WEU 2110 BIO25 20 297068.80\n 50 296870.50\n 4000 513705.10\n BIO45 50 541051.08\n 3000 765584.27\n\n[1096 rows x 1 columns]", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
value
nodeyearBIOscenGHGscen
R12_AFR2050BIO052022420.20
2055BIO052025769.78
2070BIO45103437540.72
2080BIO25102551086.76
BIO45105378379.02
...............
R12_WEU2110BIO2520297068.80
50296870.50
4000513705.10
BIO4550541051.08
3000765584.27
\n

1096 rows × 1 columns

\n
" - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_cost = scen3.par(\"land_cost\")\n", - "df_cost[df_cost[\"year\"] == 2030]\n", - "df_cost_split = df_cost.join(df_cost[\"land_scenario\"].str.split(\"GHG\", expand=True)).drop(\"land_scenario\", axis=1)\n", - "df_cost_split = df_cost_split.rename({0: \"BIOscen\", 1: \"GHGscen\"}, axis=1)\n", - "df_cost_split[\"year\"] = df_cost_split[\"year\"].astype(int)\n", - "df_cost_split[\"GHGscen\"] = df_cost_split[\"GHGscen\"].astype(int)\n", - "df_cost_split = df_cost_split.set_index([\"node\", \"year\", \"BIOscen\", \"GHGscen\"]).sort_index().drop(\"unit\",\n", - " axis=1) #.diff()\n", - "df_cost_split[(df_cost_split.diff().value < 0) & (df_cost_split.index.get_level_values(3) != 0)]" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-18T10:59:09.311612200Z", - "start_time": "2023-09-18T10:59:08.765307200Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 9, - "outputs": [], - "source": [ - "df_cost = sc_clone2.par(\"land_cost\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-18T10:44:16.060673400Z", - "start_time": "2023-09-18T10:44:15.623115400Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 7, - "outputs": [], - "source": [ - "df_emi = scen3.par(\"land_emission\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-18T12:42:55.460232900Z", - "start_time": "2023-09-18T12:42:46.257002900Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 8, - "outputs": [], - "source": [ - "df_emi = df_emi[df_emi[\"emission\"]==\"TCE\"]" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-18T12:42:55.585650600Z", - "start_time": "2023-09-18T12:42:55.475924Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 9, - "outputs": [], - "source": [ - "df_emi_split = df_emi.join(df_emi[\"land_scenario\"].str.split(\"GHG\", expand=True)).drop(\"land_scenario\", axis=1)\n", - "df_emi_split = df_emi_split.rename({0: \"BIOscen\", 1: \"GHGscen\"}, axis=1)\n", - "df_emi_split[\"year\"] = df_emi_split[\"year\"].astype(int)\n", - "df_emi_split[\"GHGscen\"] = df_emi_split[\"GHGscen\"].astype(int)\n", - "df_emi_split = df_emi_split[df_emi_split[\"year\"] > 2020]" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-18T12:43:04.335485900Z", - "start_time": "2023-09-18T12:43:04.069623400Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 10, - "outputs": [], - "source": [ - "df_emi_split = df_emi_split.set_index([\"node\", \"year\", \"BIOscen\"]).join(df_emi_split.groupby([\"node\", \"year\", \"BIOscen\"]).min()[\"value\"], rsuffix=\"_min\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-18T12:43:07.788537500Z", - "start_time": "2023-09-18T12:43:07.554254700Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 11, - "outputs": [ - { - "data": { - "text/plain": " emission value unit GHGscen value_min\nnode year BIOscen \nR12_AFR 2025 BIO00 TCE 618.938 Mt C/yr 0 339.305\n BIO00 TCE 491.613 Mt C/yr 10 339.305\n BIO00 TCE 469.864 Mt C/yr 20 339.305\n BIO00 TCE 429.854 Mt C/yr 50 339.305\n BIO00 TCE 388.521 Mt C/yr 100 339.305\n... ... ... ... ... ...\nR12_WEU 2110 BIO45 TCE 91.815 Mt C/yr 3000 89.084\n BIO45 TCE 144.318 Mt C/yr 400 89.084\n BIO45 TCE 89.084 Mt C/yr 4000 89.084\n BIO45 TCE 131.225 Mt C/yr 600 89.084\n BIO45 TCE 106.620 Mt C/yr 990 89.084\n\n[13104 rows x 5 columns]", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
emissionvalueunitGHGscenvalue_min
nodeyearBIOscen
R12_AFR2025BIO00TCE618.938Mt C/yr0339.305
BIO00TCE491.613Mt C/yr10339.305
BIO00TCE469.864Mt C/yr20339.305
BIO00TCE429.854Mt C/yr50339.305
BIO00TCE388.521Mt C/yr100339.305
........................
R12_WEU2110BIO45TCE91.815Mt C/yr300089.084
BIO45TCE144.318Mt C/yr40089.084
BIO45TCE89.084Mt C/yr400089.084
BIO45TCE131.225Mt C/yr60089.084
BIO45TCE106.620Mt C/yr99089.084
\n

13104 rows × 5 columns

\n
" - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_emi_split" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-18T12:43:08.507091900Z", - "start_time": "2023-09-18T12:43:08.351074300Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 12, - "outputs": [], - "source": [ - "df_emi_split_final = df_emi_split.join(df_emi_split[df_emi_split[\"value\"] == df_emi_split[\"value_min\"]][[\"GHGscen\"]], rsuffix=\"_min\")#.groupby([\"node\", \"year\", \"BIOscen\"], as_index=False).min()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-18T12:43:22.583840200Z", - "start_time": "2023-09-18T12:43:22.366484800Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 13, - "outputs": [ - { - "data": { - "text/plain": " emission value unit GHGscen value_min \\\nnode year BIOscen \nR12_AFR 2025 BIO00 TCE 618.938 Mt C/yr 0 339.305 \n BIO00 TCE 491.613 Mt C/yr 10 339.305 \n BIO00 TCE 469.864 Mt C/yr 20 339.305 \n BIO00 TCE 429.854 Mt C/yr 50 339.305 \n BIO00 TCE 388.521 Mt C/yr 100 339.305 \n... ... ... ... ... ... \nR12_WEU 2110 BIO45 TCE 91.815 Mt C/yr 3000 89.084 \n BIO45 TCE 144.318 Mt C/yr 400 89.084 \n BIO45 TCE 89.084 Mt C/yr 4000 89.084 \n BIO45 TCE 131.225 Mt C/yr 600 89.084 \n BIO45 TCE 106.620 Mt C/yr 990 89.084 \n\n GHGscen_min \nnode year BIOscen \nR12_AFR 2025 BIO00 4000 \n BIO00 4000 \n BIO00 4000 \n BIO00 4000 \n BIO00 4000 \n... ... \nR12_WEU 2110 BIO45 4000 \n BIO45 4000 \n BIO45 4000 \n BIO45 4000 \n BIO45 4000 \n\n[13104 rows x 6 columns]", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
emissionvalueunitGHGscenvalue_minGHGscen_min
nodeyearBIOscen
R12_AFR2025BIO00TCE618.938Mt C/yr0339.3054000
BIO00TCE491.613Mt C/yr10339.3054000
BIO00TCE469.864Mt C/yr20339.3054000
BIO00TCE429.854Mt C/yr50339.3054000
BIO00TCE388.521Mt C/yr100339.3054000
...........................
R12_WEU2110BIO45TCE91.815Mt C/yr300089.0844000
BIO45TCE144.318Mt C/yr40089.0844000
BIO45TCE89.084Mt C/yr400089.0844000
BIO45TCE131.225Mt C/yr60089.0844000
BIO45TCE106.620Mt C/yr99089.0844000
\n

13104 rows × 6 columns

\n
" - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_emi_split_final" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-18T12:43:23.577548300Z", - "start_time": "2023-09-18T12:43:23.426726100Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 98, - "outputs": [], - "source": [ - "def set_val(df):\n", - " #print(df)\n", - " if df[\"GHG\"] > df.GHG_min:\n", - " df.value = df.value_min\n", - " return df" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-18T13:37:08.494865900Z", - "start_time": "2023-09-18T13:37:08.368149700Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 16, - "outputs": [ - { - "data": { - "text/plain": " emission value unit GHGscen value_min\nnode year BIOscen \nR12_AFR 2025 BIO00 TCE 618.938 Mt C/yr 0 339.305\n BIO00 TCE 491.613 Mt C/yr 10 339.305\n BIO00 TCE 469.864 Mt C/yr 20 339.305\n BIO00 TCE 429.854 Mt C/yr 50 339.305\n BIO00 TCE 388.521 Mt C/yr 100 339.305\n... ... ... ... ... ...\nR12_WEU 2110 BIO45 TCE 91.815 Mt C/yr 3000 89.084\n BIO45 TCE 144.318 Mt C/yr 400 89.084\n BIO45 TCE 89.084 Mt C/yr 4000 89.084\n BIO45 TCE 131.225 Mt C/yr 600 89.084\n BIO45 TCE 106.620 Mt C/yr 990 89.084\n\n[13104 rows x 5 columns]", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
emissionvalueunitGHGscenvalue_min
nodeyearBIOscen
R12_AFR2025BIO00TCE618.938Mt C/yr0339.305
BIO00TCE491.613Mt C/yr10339.305
BIO00TCE469.864Mt C/yr20339.305
BIO00TCE429.854Mt C/yr50339.305
BIO00TCE388.521Mt C/yr100339.305
........................
R12_WEU2110BIO45TCE91.815Mt C/yr300089.084
BIO45TCE144.318Mt C/yr40089.084
BIO45TCE89.084Mt C/yr400089.084
BIO45TCE131.225Mt C/yr60089.084
BIO45TCE106.620Mt C/yr99089.084
\n

13104 rows × 5 columns

\n
" - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_emi_split" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-18T12:43:59.600890200Z", - "start_time": "2023-09-18T12:43:59.397789600Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 21, - "outputs": [], - "source": [ - "import pandas as pd" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-18T13:08:23.743522200Z", - "start_time": "2023-09-18T13:08:23.558846300Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 22, - "outputs": [], - "source": [ - "df_ghg = pd.read_csv(\"C:/Users\\maczek\\Desktop/ghg.csv\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-18T13:10:04.508971400Z", - "start_time": "2023-09-18T13:10:04.332044300Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 40, - "outputs": [], - "source": [ - "df_ghg_long = df_ghg.melt(id_vars=[\"Region\", \"Scenario\", \"BIO\", \"GHG\", \"Variable\", \"Unit\"], var_name=\"Year\")#.groupby([\"Region\", \"Year\", \"BIO\"]))" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-18T13:14:32.821827700Z", - "start_time": "2023-09-18T13:14:32.649846Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 63, - "outputs": [], - "source": [ - "df_ghg_long[\"Year\"] = df_ghg_long[\"Year\"].astype(int)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-18T13:25:04.369234Z", - "start_time": "2023-09-18T13:25:04.181566500Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 89, - "outputs": [], - "source": [ - "df_ghg_long = df_ghg_long[df_ghg_long[\"Year\"]>2020]" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-18T13:34:26.369940Z", - "start_time": "2023-09-18T13:34:26.244644200Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 90, - "outputs": [], - "source": [ - "df_ghg_long_min = df_ghg_long.groupby([\"Region\", \"Year\", \"BIO\"]).min()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-18T13:34:27.392222600Z", - "start_time": "2023-09-18T13:34:27.104163700Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 92, - "outputs": [ - { - "data": { - "text/plain": " Scenario GHG Variable Unit value \\\nRegion Year BIO \nR12_AFR 2025 BIO00 BIO00GHG000 0 LU_GHG Mt CO2/yr 2138.487660 \n BIO00 BIO00GHG010 10 LU_GHG Mt CO2/yr 2063.784906 \n BIO00 BIO00GHG020 20 LU_GHG Mt CO2/yr 1822.973364 \n BIO00 BIO00GHG050 50 LU_GHG Mt CO2/yr 1599.195430 \n BIO00 BIO00GHG100 100 LU_GHG Mt CO2/yr 1621.774631 \n... ... ... ... ... ... \nR12_WEU 2110 BIO45 BIO45GHG600 600 LU_GHG Mt CO2/yr 506.625325 \n BIO45 BIO45GHG990 990 LU_GHG Mt CO2/yr 419.190754 \n BIO45 BIO45GHG2000 2000 LU_GHG Mt CO2/yr 369.390249 \n BIO45 BIO45GHG3000 3000 LU_GHG Mt CO2/yr 361.430013 \n BIO45 BIO45GHG4000 4000 LU_GHG Mt CO2/yr 355.936202 \n\n value_min \nRegion Year BIO \nR12_AFR 2025 BIO00 1270.916475 \n BIO00 1270.916475 \n BIO00 1270.916475 \n BIO00 1270.916475 \n BIO00 1270.916475 \n... ... \nR12_WEU 2110 BIO45 355.936202 \n BIO45 355.936202 \n BIO45 355.936202 \n BIO45 355.936202 \n BIO45 355.936202 \n\n[13104 rows x 6 columns]", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
ScenarioGHGVariableUnitvaluevalue_min
RegionYearBIO
R12_AFR2025BIO00BIO00GHG0000LU_GHGMt CO2/yr2138.4876601270.916475
BIO00BIO00GHG01010LU_GHGMt CO2/yr2063.7849061270.916475
BIO00BIO00GHG02020LU_GHGMt CO2/yr1822.9733641270.916475
BIO00BIO00GHG05050LU_GHGMt CO2/yr1599.1954301270.916475
BIO00BIO00GHG100100LU_GHGMt CO2/yr1621.7746311270.916475
...........................
R12_WEU2110BIO45BIO45GHG600600LU_GHGMt CO2/yr506.625325355.936202
BIO45BIO45GHG990990LU_GHGMt CO2/yr419.190754355.936202
BIO45BIO45GHG20002000LU_GHGMt CO2/yr369.390249355.936202
BIO45BIO45GHG30003000LU_GHGMt CO2/yr361.430013355.936202
BIO45BIO45GHG40004000LU_GHGMt CO2/yr355.936202355.936202
\n

13104 rows × 6 columns

\n
" - }, - "execution_count": 92, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_ghg_long_join = (df_ghg_long.set_index([\"Region\", \"Year\", \"BIO\"]).join(df_ghg_long_min[[\"value\"]], rsuffix=\"_min\"))\n", - "df_ghg_long_join" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-18T13:34:36.698101200Z", - "start_time": "2023-09-18T13:34:36.447726100Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 93, - "outputs": [ - { - "data": { - "text/plain": " Scenario GHG Variable Unit value \\\nRegion Year BIO \nR12_AFR 2025 BIO00 BIO00GHG990 990 LU_GHG Mt CO2/yr 1270.916475 \n BIO05 BIO05GHG990 990 LU_GHG Mt CO2/yr 1270.946775 \n BIO07 BIO07GHG990 990 LU_GHG Mt CO2/yr 1270.946775 \n BIO10 BIO10GHG990 990 LU_GHG Mt CO2/yr 1270.946775 \n BIO15 BIO15GHG990 990 LU_GHG Mt CO2/yr 1271.956851 \n... ... ... ... ... ... \nR12_WEU 2110 BIO07 BIO07GHG2000 2000 LU_GHG Mt CO2/yr 360.259355 \n BIO10 BIO10GHG2000 2000 LU_GHG Mt CO2/yr 368.318550 \n BIO15 BIO15GHG3000 3000 LU_GHG Mt CO2/yr 388.080595 \n BIO25 BIO25GHG3000 3000 LU_GHG Mt CO2/yr 364.388988 \n BIO45 BIO45GHG4000 4000 LU_GHG Mt CO2/yr 355.936202 \n\n value_min \nRegion Year BIO \nR12_AFR 2025 BIO00 1270.916475 \n BIO05 1270.946775 \n BIO07 1270.946775 \n BIO10 1270.946775 \n BIO15 1271.956851 \n... ... \nR12_WEU 2110 BIO07 360.259355 \n BIO10 368.318550 \n BIO15 388.080595 \n BIO25 364.388988 \n BIO45 355.936202 \n\n[1092 rows x 6 columns]", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
ScenarioGHGVariableUnitvaluevalue_min
RegionYearBIO
R12_AFR2025BIO00BIO00GHG990990LU_GHGMt CO2/yr1270.9164751270.916475
BIO05BIO05GHG990990LU_GHGMt CO2/yr1270.9467751270.946775
BIO07BIO07GHG990990LU_GHGMt CO2/yr1270.9467751270.946775
BIO10BIO10GHG990990LU_GHGMt CO2/yr1270.9467751270.946775
BIO15BIO15GHG990990LU_GHGMt CO2/yr1271.9568511271.956851
...........................
R12_WEU2110BIO07BIO07GHG20002000LU_GHGMt CO2/yr360.259355360.259355
BIO10BIO10GHG20002000LU_GHGMt CO2/yr368.318550368.318550
BIO15BIO15GHG30003000LU_GHGMt CO2/yr388.080595388.080595
BIO25BIO25GHG30003000LU_GHGMt CO2/yr364.388988364.388988
BIO45BIO45GHG40004000LU_GHGMt CO2/yr355.936202355.936202
\n

1092 rows × 6 columns

\n
" - }, - "execution_count": 93, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_ghg_long_join[df_ghg_long_join[\"value\"]==df_ghg_long_join[\"value_min\"]]" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-18T13:34:51.104101600Z", - "start_time": "2023-09-18T13:34:50.932343700Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 95, - "outputs": [], - "source": [ - "df_ghg_long_join = df_ghg_long_join.join(df_ghg_long_join[df_ghg_long_join[\"value\"] == df_ghg_long_join[\"value_min\"]][\"GHG\"], rsuffix=\"_min\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-18T13:36:13.979560800Z", - "start_time": "2023-09-18T13:36:13.792048700Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 100, - "outputs": [], - "source": [ - "df_ghg_long_join_final = df_ghg_long_join.apply(lambda x: set_val(x), axis=1)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-18T13:37:57.749108900Z", - "start_time": "2023-09-18T13:37:56.432295600Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 117, - "outputs": [], - "source": [ - "df_ghg_final = df_ghg_long_join_final.drop([\"value_min\", \"GHG_min\"], axis=1).reset_index().pivot(columns=\"Year\", values=\"value\", index=[\"Region\",\"Scenario\", \"Variable\", \"Unit\", \"BIO\", \"GHG\"])" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-18T13:46:44.808123400Z", - "start_time": "2023-09-18T13:46:44.620360800Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 120, - "outputs": [ - { - "data": { - "text/plain": "Year 2025 2030 \\\nRegion Scenario Variable Unit BIO GHG \nR12_AFR BIO00GHG000 LU_GHG Mt CO2/yr BIO00 0 2138.487660 2618.828003 \n BIO00GHG010 LU_GHG Mt CO2/yr BIO00 10 2063.784906 2285.350912 \n BIO00GHG020 LU_GHG Mt CO2/yr BIO00 20 1822.973364 1905.371082 \n BIO00GHG050 LU_GHG Mt CO2/yr BIO00 50 1599.195430 1579.643292 \n BIO00GHG100 LU_GHG Mt CO2/yr BIO00 100 1621.774631 1545.687581 \n... ... ... \nR12_WEU BIO45GHG3000 LU_GHG Mt CO2/yr BIO45 3000 644.372113 632.714191 \n BIO45GHG400 LU_GHG Mt CO2/yr BIO45 400 732.111950 678.303025 \n BIO45GHG4000 LU_GHG Mt CO2/yr BIO45 4000 644.372113 631.093216 \n BIO45GHG600 LU_GHG Mt CO2/yr BIO45 600 727.080232 672.949900 \n BIO45GHG990 LU_GHG Mt CO2/yr BIO45 990 644.372113 653.500355 \n\nYear 2035 2040 \\\nRegion Scenario Variable Unit BIO GHG \nR12_AFR BIO00GHG000 LU_GHG Mt CO2/yr BIO00 0 3046.831786 3348.098027 \n BIO00GHG010 LU_GHG Mt CO2/yr BIO00 10 2373.001104 2362.038757 \n BIO00GHG020 LU_GHG Mt CO2/yr BIO00 20 2003.213618 2122.678213 \n BIO00GHG050 LU_GHG Mt CO2/yr BIO00 50 1678.182227 1856.242320 \n BIO00GHG100 LU_GHG Mt CO2/yr BIO00 100 1575.732706 1695.510497 \n... ... ... \nR12_WEU BIO45GHG3000 LU_GHG Mt CO2/yr BIO45 3000 590.803423 599.932465 \n BIO45GHG400 LU_GHG Mt CO2/yr BIO45 400 636.289335 682.739098 \n BIO45GHG4000 LU_GHG Mt CO2/yr BIO45 4000 590.803423 570.956727 \n BIO45GHG600 LU_GHG Mt CO2/yr BIO45 600 626.724454 680.439542 \n BIO45GHG990 LU_GHG Mt CO2/yr BIO45 990 639.014404 680.333981 \n\nYear 2045 2050 \\\nRegion Scenario Variable Unit BIO GHG \nR12_AFR BIO00GHG000 LU_GHG Mt CO2/yr BIO00 0 3651.476518 4182.934047 \n BIO00GHG010 LU_GHG Mt CO2/yr BIO00 10 2403.273844 2557.756949 \n BIO00GHG020 LU_GHG Mt CO2/yr BIO00 20 2230.066139 2385.444408 \n BIO00GHG050 LU_GHG Mt CO2/yr BIO00 50 2029.538087 2205.718056 \n BIO00GHG100 LU_GHG Mt CO2/yr BIO00 100 1857.875817 2051.872649 \n... ... ... \nR12_WEU BIO45GHG3000 LU_GHG Mt CO2/yr BIO45 3000 593.256927 551.721501 \n BIO45GHG400 LU_GHG Mt CO2/yr BIO45 400 725.368357 700.713245 \n BIO45GHG4000 LU_GHG Mt CO2/yr BIO45 4000 515.454258 497.398756 \n BIO45GHG600 LU_GHG Mt CO2/yr BIO45 600 708.291402 708.254727 \n BIO45GHG990 LU_GHG Mt CO2/yr BIO45 990 692.023218 674.601341 \n\nYear 2055 2060 \\\nRegion Scenario Variable Unit BIO GHG \nR12_AFR BIO00GHG000 LU_GHG Mt CO2/yr BIO00 0 4920.254792 5741.654839 \n BIO00GHG010 LU_GHG Mt CO2/yr BIO00 10 2844.690571 3302.594530 \n BIO00GHG020 LU_GHG Mt CO2/yr BIO00 20 2639.371534 2977.705404 \n BIO00GHG050 LU_GHG Mt CO2/yr BIO00 50 2378.937662 2594.796580 \n BIO00GHG100 LU_GHG Mt CO2/yr BIO00 100 2241.188245 2467.624768 \n... ... ... \nR12_WEU BIO45GHG3000 LU_GHG Mt CO2/yr BIO45 3000 527.806798 504.809004 \n BIO45GHG400 LU_GHG Mt CO2/yr BIO45 400 660.701527 617.525820 \n BIO45GHG4000 LU_GHG Mt CO2/yr BIO45 4000 481.081028 469.672034 \n BIO45GHG600 LU_GHG Mt CO2/yr BIO45 600 670.617239 626.624804 \n BIO45GHG990 LU_GHG Mt CO2/yr BIO45 990 637.176056 594.923460 \n\nYear 2070 2080 \\\nRegion Scenario Variable Unit BIO GHG \nR12_AFR BIO00GHG000 LU_GHG Mt CO2/yr BIO00 0 6357.920924 6103.922402 \n BIO00GHG010 LU_GHG Mt CO2/yr BIO00 10 3862.491747 4119.423536 \n BIO00GHG020 LU_GHG Mt CO2/yr BIO00 20 3308.837646 3314.616552 \n BIO00GHG050 LU_GHG Mt CO2/yr BIO00 50 2856.081743 2933.449334 \n BIO00GHG100 LU_GHG Mt CO2/yr BIO00 100 2703.323406 2711.529005 \n... ... ... \nR12_WEU BIO45GHG3000 LU_GHG Mt CO2/yr BIO45 3000 467.423790 402.684836 \n BIO45GHG400 LU_GHG Mt CO2/yr BIO45 400 594.705974 576.482651 \n BIO45GHG4000 LU_GHG Mt CO2/yr BIO45 4000 431.316345 399.294218 \n BIO45GHG600 LU_GHG Mt CO2/yr BIO45 600 589.199300 541.679505 \n BIO45GHG990 LU_GHG Mt CO2/yr BIO45 990 561.125293 510.976352 \n\nYear 2090 2100 \\\nRegion Scenario Variable Unit BIO GHG \nR12_AFR BIO00GHG000 LU_GHG Mt CO2/yr BIO00 0 5280.287635 4726.518905 \n BIO00GHG010 LU_GHG Mt CO2/yr BIO00 10 4024.376079 3969.405081 \n BIO00GHG020 LU_GHG Mt CO2/yr BIO00 20 3085.746555 2958.811283 \n BIO00GHG050 LU_GHG Mt CO2/yr BIO00 50 2805.581073 2710.875387 \n BIO00GHG100 LU_GHG Mt CO2/yr BIO00 100 2533.542612 2481.916204 \n... ... ... \nR12_WEU BIO45GHG3000 LU_GHG Mt CO2/yr BIO45 3000 369.753056 361.430013 \n BIO45GHG400 LU_GHG Mt CO2/yr BIO45 400 535.765561 537.274005 \n BIO45GHG4000 LU_GHG Mt CO2/yr BIO45 4000 368.741419 355.936202 \n BIO45GHG600 LU_GHG Mt CO2/yr BIO45 600 506.046646 506.625325 \n BIO45GHG990 LU_GHG Mt CO2/yr BIO45 990 456.903431 419.190754 \n\nYear 2110 \nRegion Scenario Variable Unit BIO GHG \nR12_AFR BIO00GHG000 LU_GHG Mt CO2/yr BIO00 0 4726.518905 \n BIO00GHG010 LU_GHG Mt CO2/yr BIO00 10 3969.405081 \n BIO00GHG020 LU_GHG Mt CO2/yr BIO00 20 2958.811283 \n BIO00GHG050 LU_GHG Mt CO2/yr BIO00 50 2710.875387 \n BIO00GHG100 LU_GHG Mt CO2/yr BIO00 100 2481.916204 \n... ... \nR12_WEU BIO45GHG3000 LU_GHG Mt CO2/yr BIO45 3000 361.430013 \n BIO45GHG400 LU_GHG Mt CO2/yr BIO45 400 537.274005 \n BIO45GHG4000 LU_GHG Mt CO2/yr BIO45 4000 355.936202 \n BIO45GHG600 LU_GHG Mt CO2/yr BIO45 600 506.625325 \n BIO45GHG990 LU_GHG Mt CO2/yr BIO45 990 419.190754 \n\n[1008 rows x 13 columns]", - "text/html": "
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Year2025203020352040204520502055206020702080209021002110
RegionScenarioVariableUnitBIOGHG
R12_AFRBIO00GHG000LU_GHGMt CO2/yrBIO0002138.4876602618.8280033046.8317863348.0980273651.4765184182.9340474920.2547925741.6548396357.9209246103.9224025280.2876354726.5189054726.518905
BIO00GHG010LU_GHGMt CO2/yrBIO00102063.7849062285.3509122373.0011042362.0387572403.2738442557.7569492844.6905713302.5945303862.4917474119.4235364024.3760793969.4050813969.405081
BIO00GHG020LU_GHGMt CO2/yrBIO00201822.9733641905.3710822003.2136182122.6782132230.0661392385.4444082639.3715342977.7054043308.8376463314.6165523085.7465552958.8112832958.811283
BIO00GHG050LU_GHGMt CO2/yrBIO00501599.1954301579.6432921678.1822271856.2423202029.5380872205.7180562378.9376622594.7965802856.0817432933.4493342805.5810732710.8753872710.875387
BIO00GHG100LU_GHGMt CO2/yrBIO001001621.7746311545.6875811575.7327061695.5104971857.8758172051.8726492241.1882452467.6247682703.3234062711.5290052533.5426122481.9162042481.916204
.........................................................
R12_WEUBIO45GHG3000LU_GHGMt CO2/yrBIO453000644.372113632.714191590.803423599.932465593.256927551.721501527.806798504.809004467.423790402.684836369.753056361.430013361.430013
BIO45GHG400LU_GHGMt CO2/yrBIO45400732.111950678.303025636.289335682.739098725.368357700.713245660.701527617.525820594.705974576.482651535.765561537.274005537.274005
BIO45GHG4000LU_GHGMt CO2/yrBIO454000644.372113631.093216590.803423570.956727515.454258497.398756481.081028469.672034431.316345399.294218368.741419355.936202355.936202
BIO45GHG600LU_GHGMt CO2/yrBIO45600727.080232672.949900626.724454680.439542708.291402708.254727670.617239626.624804589.199300541.679505506.046646506.625325506.625325
BIO45GHG990LU_GHGMt CO2/yrBIO45990644.372113653.500355639.014404680.333981692.023218674.601341637.176056594.923460561.125293510.976352456.903431419.190754419.190754
\n

1008 rows × 13 columns

\n
" - }, - "execution_count": 120, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_ghg_long = df_ghg.melt(id_vars=[\"Region\", \"Scenario\", \"BIO\", \"GHG\", \"Variable\", \"Unit\"],\n", - " var_name=\"Year\")\n", - "df_ghg_long[\"Year\"] = df_ghg_long[\"Year\"].astype(int)\n", - "df_ghg_long = df_ghg_long[df_ghg_long[\"Year\"] > 2020]\n", - "df_ghg_long_min = df_ghg_long.groupby([\"Region\", \"Year\", \"BIO\"]).min()\n", - "df_ghg_long_join = (df_ghg_long.set_index([\"Region\", \"Year\", \"BIO\"]).join(df_ghg_long_min[[\"value\"]], rsuffix=\"_min\"))\n", - "\n", - "df_ghg_long_join = df_ghg_long_join.join(df_ghg_long_join[df_ghg_long_join[\"value\"] == df_ghg_long_join[\"value_min\"]][\"GHG\"], rsuffix=\"_min\")\n", - "df_ghg_long_join_final = df_ghg_long_join.apply(lambda x: set_val(x), axis=1)\n", - "df_ghg_final = df_ghg_long_join_final.drop([\"value_min\", \"GHG_min\"], axis=1).reset_index().pivot(columns=\"Year\", values=\"value\", index=[\"Region\",\"Scenario\", \"Variable\", \"Unit\", \"BIO\", \"GHG\"])\n", - "df_ghg_final" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-18T13:50:52.230367300Z", - "start_time": "2023-09-18T13:50:50.480351700Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 129, - "outputs": [], - "source": [ - "df_ghg_new = df_ghg.set_index([\"Region\",\"Scenario\", \"Variable\", \"Unit\", \"BIO\", \"GHG\"])[[\"1990\", \"1995\", \"2000\", \"2005\", \"2010\", \"2015\", \"2020\"]].join(df_ghg_final)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-18T13:57:40.402198Z", - "start_time": "2023-09-18T13:57:40.167794200Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 114, - "outputs": [ - { - "data": { - "text/plain": " 1990 1995 2000 2005 \\\nRegion Scenario Variable Unit BIO GHG \nR12_AFR BIO00GHG000 LU_GHG Mt CO2/yr BIO00 0 NaN NaN NaN NaN \n BIO00GHG010 LU_GHG Mt CO2/yr BIO00 10 0.0 0.0 0.0 0.0 \n BIO00GHG020 LU_GHG Mt CO2/yr BIO00 20 0.0 0.0 0.0 0.0 \n BIO00GHG050 LU_GHG Mt CO2/yr BIO00 50 0.0 0.0 0.0 0.0 \n BIO00GHG100 LU_GHG Mt CO2/yr BIO00 100 0.0 0.0 0.0 0.0 \n... ... ... ... ... \nR12_WEU BIO45GHG600 LU_GHG Mt CO2/yr BIO45 600 0.0 0.0 0.0 0.0 \n BIO45GHG990 LU_GHG Mt CO2/yr BIO45 990 0.0 0.0 0.0 0.0 \n BIO45GHG2000 LU_GHG Mt CO2/yr BIO45 2000 0.0 0.0 0.0 0.0 \n BIO45GHG3000 LU_GHG Mt CO2/yr BIO45 3000 0.0 0.0 0.0 0.0 \n BIO45GHG4000 LU_GHG Mt CO2/yr BIO45 4000 0.0 0.0 0.0 0.0 \n\n 2010 2015 2020 \\\nRegion Scenario Variable Unit BIO GHG \nR12_AFR BIO00GHG000 LU_GHG Mt CO2/yr BIO00 0 NaN NaN NaN \n BIO00GHG010 LU_GHG Mt CO2/yr BIO00 10 0.0 0.0 0.0 \n BIO00GHG020 LU_GHG Mt CO2/yr BIO00 20 0.0 0.0 0.0 \n BIO00GHG050 LU_GHG Mt CO2/yr BIO00 50 0.0 0.0 0.0 \n BIO00GHG100 LU_GHG Mt CO2/yr BIO00 100 0.0 0.0 0.0 \n... ... ... ... \nR12_WEU BIO45GHG600 LU_GHG Mt CO2/yr BIO45 600 0.0 0.0 0.0 \n BIO45GHG990 LU_GHG Mt CO2/yr BIO45 990 0.0 0.0 0.0 \n BIO45GHG2000 LU_GHG Mt CO2/yr BIO45 2000 0.0 0.0 0.0 \n BIO45GHG3000 LU_GHG Mt CO2/yr BIO45 3000 0.0 0.0 0.0 \n BIO45GHG4000 LU_GHG Mt CO2/yr BIO45 4000 0.0 0.0 0.0 \n\n 2025 2030 \\\nRegion Scenario Variable Unit BIO GHG \nR12_AFR BIO00GHG000 LU_GHG Mt CO2/yr BIO00 0 NaN NaN \n BIO00GHG010 LU_GHG Mt CO2/yr BIO00 10 -74.702754 -333.477090 \n BIO00GHG020 LU_GHG Mt CO2/yr BIO00 20 -240.811542 -379.979831 \n BIO00GHG050 LU_GHG Mt CO2/yr BIO00 50 -223.777934 -325.727790 \n BIO00GHG100 LU_GHG Mt CO2/yr BIO00 100 22.579200 -33.955711 \n... ... ... \nR12_WEU BIO45GHG600 LU_GHG Mt CO2/yr BIO45 600 -5.031718 -5.353125 \n BIO45GHG990 LU_GHG Mt CO2/yr BIO45 990 -82.708119 -19.449545 \n BIO45GHG2000 LU_GHG Mt CO2/yr BIO45 2000 83.945207 12.145148 \n BIO45GHG3000 LU_GHG Mt CO2/yr BIO45 3000 -3.327431 -32.931312 \n BIO45GHG4000 LU_GHG Mt CO2/yr BIO45 4000 1.869618 -1.620975 \n\n 2035 2040 \\\nRegion Scenario Variable Unit BIO GHG \nR12_AFR BIO00GHG000 LU_GHG Mt CO2/yr BIO00 0 NaN NaN \n BIO00GHG010 LU_GHG Mt CO2/yr BIO00 10 -673.830682 -986.059271 \n BIO00GHG020 LU_GHG Mt CO2/yr BIO00 20 -369.787486 -239.360543 \n BIO00GHG050 LU_GHG Mt CO2/yr BIO00 50 -325.031391 -266.435894 \n BIO00GHG100 LU_GHG Mt CO2/yr BIO00 100 -102.449521 -160.731823 \n... ... ... \nR12_WEU BIO45GHG600 LU_GHG Mt CO2/yr BIO45 600 -9.564881 -2.299557 \n BIO45GHG990 LU_GHG Mt CO2/yr BIO45 990 12.289950 -0.105561 \n BIO45GHG2000 LU_GHG Mt CO2/yr BIO45 2000 5.249088 -45.788472 \n BIO45GHG3000 LU_GHG Mt CO2/yr BIO45 3000 -53.460068 -34.613044 \n BIO45GHG4000 LU_GHG Mt CO2/yr BIO45 4000 3.235089 -28.975738 \n\n 2045 2050 \\\nRegion Scenario Variable Unit BIO GHG \nR12_AFR BIO00GHG000 LU_GHG Mt CO2/yr BIO00 0 NaN NaN \n BIO00GHG010 LU_GHG Mt CO2/yr BIO00 10 -1248.202674 -1625.177098 \n BIO00GHG020 LU_GHG Mt CO2/yr BIO00 20 -173.207706 -172.312541 \n BIO00GHG050 LU_GHG Mt CO2/yr BIO00 50 -200.528052 -179.726352 \n BIO00GHG100 LU_GHG Mt CO2/yr BIO00 100 -171.662270 -153.845408 \n... ... ... \nR12_WEU BIO45GHG600 LU_GHG Mt CO2/yr BIO45 600 -17.076956 7.541481 \n BIO45GHG990 LU_GHG Mt CO2/yr BIO45 990 -16.268184 -33.653386 \n BIO45GHG2000 LU_GHG Mt CO2/yr BIO45 2000 -71.048467 -78.928898 \n BIO45GHG3000 LU_GHG Mt CO2/yr BIO45 3000 -27.717824 -43.950942 \n BIO45GHG4000 LU_GHG Mt CO2/yr BIO45 4000 -77.802669 -54.322745 \n\n 2055 2060 \\\nRegion Scenario Variable Unit BIO GHG \nR12_AFR BIO00GHG000 LU_GHG Mt CO2/yr BIO00 0 NaN NaN \n BIO00GHG010 LU_GHG Mt CO2/yr BIO00 10 -2075.564221 -2439.060308 \n BIO00GHG020 LU_GHG Mt CO2/yr BIO00 20 -205.319036 -324.889127 \n BIO00GHG050 LU_GHG Mt CO2/yr BIO00 50 -260.433872 -382.908823 \n BIO00GHG100 LU_GHG Mt CO2/yr BIO00 100 -137.749417 -127.171812 \n... ... ... \nR12_WEU BIO45GHG600 LU_GHG Mt CO2/yr BIO45 600 9.915712 9.098984 \n BIO45GHG990 LU_GHG Mt CO2/yr BIO45 990 -33.441183 -31.701343 \n BIO45GHG2000 LU_GHG Mt CO2/yr BIO45 2000 -82.656784 -85.508885 \n BIO45GHG3000 LU_GHG Mt CO2/yr BIO45 3000 -26.712474 -4.605572 \n BIO45GHG4000 LU_GHG Mt CO2/yr BIO45 4000 -46.725770 -35.136969 \n\n 2070 2080 \\\nRegion Scenario Variable Unit BIO GHG \nR12_AFR BIO00GHG000 LU_GHG Mt CO2/yr BIO00 0 NaN NaN \n BIO00GHG010 LU_GHG Mt CO2/yr BIO00 10 -2495.429177 -1984.498866 \n BIO00GHG020 LU_GHG Mt CO2/yr BIO00 20 -553.654101 -804.806984 \n BIO00GHG050 LU_GHG Mt CO2/yr BIO00 50 -452.755903 -381.167218 \n BIO00GHG100 LU_GHG Mt CO2/yr BIO00 100 -152.758337 -221.920329 \n... ... ... \nR12_WEU BIO45GHG600 LU_GHG Mt CO2/yr BIO45 600 -5.506675 -34.803147 \n BIO45GHG990 LU_GHG Mt CO2/yr BIO45 990 -28.074007 -30.703152 \n BIO45GHG2000 LU_GHG Mt CO2/yr BIO45 2000 -99.702109 -102.103303 \n BIO45GHG3000 LU_GHG Mt CO2/yr BIO45 3000 6.000606 -6.188214 \n BIO45GHG4000 LU_GHG Mt CO2/yr BIO45 4000 -36.107445 -3.390618 \n\n 2090 2100 \\\nRegion Scenario Variable Unit BIO GHG \nR12_AFR BIO00GHG000 LU_GHG Mt CO2/yr BIO00 0 NaN NaN \n BIO00GHG010 LU_GHG Mt CO2/yr BIO00 10 -1255.911556 -757.113824 \n BIO00GHG020 LU_GHG Mt CO2/yr BIO00 20 -938.629524 -1010.593798 \n BIO00GHG050 LU_GHG Mt CO2/yr BIO00 50 -280.165482 -247.935896 \n BIO00GHG100 LU_GHG Mt CO2/yr BIO00 100 -272.038461 -228.959183 \n... ... ... \nR12_WEU BIO45GHG600 LU_GHG Mt CO2/yr BIO45 600 -29.718914 -30.648680 \n BIO45GHG990 LU_GHG Mt CO2/yr BIO45 990 -49.143216 -87.434571 \n BIO45GHG2000 LU_GHG Mt CO2/yr BIO45 2000 -80.323076 -49.800505 \n BIO45GHG3000 LU_GHG Mt CO2/yr BIO45 3000 -6.827298 -7.960236 \n BIO45GHG4000 LU_GHG Mt CO2/yr BIO45 4000 -1.011637 -5.493810 \n\n 2110 \nRegion Scenario Variable Unit BIO GHG \nR12_AFR BIO00GHG000 LU_GHG Mt CO2/yr BIO00 0 NaN \n BIO00GHG010 LU_GHG Mt CO2/yr BIO00 10 -757.113824 \n BIO00GHG020 LU_GHG Mt CO2/yr BIO00 20 -1010.593798 \n BIO00GHG050 LU_GHG Mt CO2/yr BIO00 50 -247.935896 \n BIO00GHG100 LU_GHG Mt CO2/yr BIO00 100 -228.959183 \n... ... \nR12_WEU BIO45GHG600 LU_GHG Mt CO2/yr BIO45 600 -30.648680 \n BIO45GHG990 LU_GHG Mt CO2/yr BIO45 990 -87.434571 \n BIO45GHG2000 LU_GHG Mt CO2/yr BIO45 2000 -49.800505 \n BIO45GHG3000 LU_GHG Mt CO2/yr BIO45 3000 -7.960236 \n BIO45GHG4000 LU_GHG Mt CO2/yr BIO45 4000 -5.493810 \n\n[1008 rows x 20 columns]", - "text/html": "
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19901995200020052010201520202025203020352040204520502055206020702080209021002110
RegionScenarioVariableUnitBIOGHG
R12_AFRBIO00GHG000LU_GHGMt CO2/yrBIO000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
BIO00GHG010LU_GHGMt CO2/yrBIO00100.00.00.00.00.00.00.0-74.702754-333.477090-673.830682-986.059271-1248.202674-1625.177098-2075.564221-2439.060308-2495.429177-1984.498866-1255.911556-757.113824-757.113824
BIO00GHG020LU_GHGMt CO2/yrBIO00200.00.00.00.00.00.00.0-240.811542-379.979831-369.787486-239.360543-173.207706-172.312541-205.319036-324.889127-553.654101-804.806984-938.629524-1010.593798-1010.593798
BIO00GHG050LU_GHGMt CO2/yrBIO00500.00.00.00.00.00.00.0-223.777934-325.727790-325.031391-266.435894-200.528052-179.726352-260.433872-382.908823-452.755903-381.167218-280.165482-247.935896-247.935896
BIO00GHG100LU_GHGMt CO2/yrBIO001000.00.00.00.00.00.00.022.579200-33.955711-102.449521-160.731823-171.662270-153.845408-137.749417-127.171812-152.758337-221.920329-272.038461-228.959183-228.959183
..............................................................................
R12_WEUBIO45GHG600LU_GHGMt CO2/yrBIO456000.00.00.00.00.00.00.0-5.031718-5.353125-9.564881-2.299557-17.0769567.5414819.9157129.098984-5.506675-34.803147-29.718914-30.648680-30.648680
BIO45GHG990LU_GHGMt CO2/yrBIO459900.00.00.00.00.00.00.0-82.708119-19.44954512.289950-0.105561-16.268184-33.653386-33.441183-31.701343-28.074007-30.703152-49.143216-87.434571-87.434571
BIO45GHG2000LU_GHGMt CO2/yrBIO4520000.00.00.00.00.00.00.083.94520712.1451485.249088-45.788472-71.048467-78.928898-82.656784-85.508885-99.702109-102.103303-80.323076-49.800505-49.800505
BIO45GHG3000LU_GHGMt CO2/yrBIO4530000.00.00.00.00.00.00.0-3.327431-32.931312-53.460068-34.613044-27.717824-43.950942-26.712474-4.6055726.000606-6.188214-6.827298-7.960236-7.960236
BIO45GHG4000LU_GHGMt CO2/yrBIO4540000.00.00.00.00.00.00.01.869618-1.6209753.235089-28.975738-77.802669-54.322745-46.725770-35.136969-36.107445-3.390618-1.011637-5.493810-5.493810
\n

1008 rows × 20 columns

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" - }, - "execution_count": 114, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_ghg.set_index([\"Region\",\"Scenario\", \"Variable\", \"Unit\", \"BIO\", \"GHG\"]).diff()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-18T13:44:41.870578200Z", - "start_time": "2023-09-18T13:44:41.667382800Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 103, - "outputs": [ - { - "ename": "KeyError", - "evalue": "'GHG'", - "output_type": "error", - "traceback": [ - "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[1;31mKeyError\u001B[0m Traceback (most recent call last)", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3621\u001B[0m, in \u001B[0;36mIndex.get_loc\u001B[1;34m(self, key, method, tolerance)\u001B[0m\n\u001B[0;32m 3620\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m-> 3621\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_engine\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_loc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcasted_key\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 3622\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m err:\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx:136\u001B[0m, in \u001B[0;36mpandas._libs.index.IndexEngine.get_loc\u001B[1;34m()\u001B[0m\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx:163\u001B[0m, in \u001B[0;36mpandas._libs.index.IndexEngine.get_loc\u001B[1;34m()\u001B[0m\n", - "File \u001B[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5198\u001B[0m, in \u001B[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001B[1;34m()\u001B[0m\n", - "File \u001B[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5206\u001B[0m, in \u001B[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001B[1;34m()\u001B[0m\n", - "\u001B[1;31mKeyError\u001B[0m: 'GHG'", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001B[1;31mKeyError\u001B[0m Traceback (most recent call last)", - "Input \u001B[1;32mIn [103]\u001B[0m, in \u001B[0;36m\u001B[1;34m()\u001B[0m\n\u001B[1;32m----> 1\u001B[0m df_emi_split_final_min \u001B[38;5;241m=\u001B[39m \u001B[43mdf_emi_split_final\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mapply\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43;01mlambda\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mx\u001B[49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[43mset_val\u001B[49m\u001B[43m(\u001B[49m\u001B[43mx\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43maxis\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m1\u001B[39;49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:8839\u001B[0m, in \u001B[0;36mDataFrame.apply\u001B[1;34m(self, func, axis, raw, result_type, args, **kwargs)\u001B[0m\n\u001B[0;32m 8828\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01mpandas\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mcore\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mapply\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m frame_apply\n\u001B[0;32m 8830\u001B[0m op \u001B[38;5;241m=\u001B[39m frame_apply(\n\u001B[0;32m 8831\u001B[0m \u001B[38;5;28mself\u001B[39m,\n\u001B[0;32m 8832\u001B[0m func\u001B[38;5;241m=\u001B[39mfunc,\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 8837\u001B[0m kwargs\u001B[38;5;241m=\u001B[39mkwargs,\n\u001B[0;32m 8838\u001B[0m )\n\u001B[1;32m-> 8839\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mop\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mapply\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241m.\u001B[39m__finalize__(\u001B[38;5;28mself\u001B[39m, method\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mapply\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\apply.py:727\u001B[0m, in \u001B[0;36mFrameApply.apply\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m 724\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mraw:\n\u001B[0;32m 725\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mapply_raw()\n\u001B[1;32m--> 727\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mapply_standard\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\apply.py:851\u001B[0m, in \u001B[0;36mFrameApply.apply_standard\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m 850\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mapply_standard\u001B[39m(\u001B[38;5;28mself\u001B[39m):\n\u001B[1;32m--> 851\u001B[0m results, res_index \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mapply_series_generator\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 853\u001B[0m \u001B[38;5;66;03m# wrap results\u001B[39;00m\n\u001B[0;32m 854\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mwrap_results(results, res_index)\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\apply.py:867\u001B[0m, in \u001B[0;36mFrameApply.apply_series_generator\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m 864\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m option_context(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mmode.chained_assignment\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;28;01mNone\u001B[39;00m):\n\u001B[0;32m 865\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m i, v \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28menumerate\u001B[39m(series_gen):\n\u001B[0;32m 866\u001B[0m \u001B[38;5;66;03m# ignore SettingWithCopy here in case the user mutates\u001B[39;00m\n\u001B[1;32m--> 867\u001B[0m results[i] \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mf\u001B[49m\u001B[43m(\u001B[49m\u001B[43mv\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 868\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(results[i], ABCSeries):\n\u001B[0;32m 869\u001B[0m \u001B[38;5;66;03m# If we have a view on v, we need to make a copy because\u001B[39;00m\n\u001B[0;32m 870\u001B[0m \u001B[38;5;66;03m# series_generator will swap out the underlying data\u001B[39;00m\n\u001B[0;32m 871\u001B[0m results[i] \u001B[38;5;241m=\u001B[39m results[i]\u001B[38;5;241m.\u001B[39mcopy(deep\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m)\n", - "Input \u001B[1;32mIn [103]\u001B[0m, in \u001B[0;36m\u001B[1;34m(x)\u001B[0m\n\u001B[1;32m----> 1\u001B[0m df_emi_split_final_min \u001B[38;5;241m=\u001B[39m df_emi_split_final\u001B[38;5;241m.\u001B[39mapply(\u001B[38;5;28;01mlambda\u001B[39;00m x: \u001B[43mset_val\u001B[49m\u001B[43m(\u001B[49m\u001B[43mx\u001B[49m\u001B[43m)\u001B[49m, axis\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m1\u001B[39m)\n", - "Input \u001B[1;32mIn [98]\u001B[0m, in \u001B[0;36mset_val\u001B[1;34m(df)\u001B[0m\n\u001B[0;32m 1\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mset_val\u001B[39m(df):\n\u001B[0;32m 2\u001B[0m \u001B[38;5;66;03m#print(df)\u001B[39;00m\n\u001B[1;32m----> 3\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[43mdf\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mGHG\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m \u001B[38;5;241m>\u001B[39m df\u001B[38;5;241m.\u001B[39mGHG_min:\n\u001B[0;32m 4\u001B[0m df\u001B[38;5;241m.\u001B[39mvalue \u001B[38;5;241m=\u001B[39m df\u001B[38;5;241m.\u001B[39mvalue_min\n\u001B[0;32m 5\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m df\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\series.py:958\u001B[0m, in \u001B[0;36mSeries.__getitem__\u001B[1;34m(self, key)\u001B[0m\n\u001B[0;32m 955\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_values[key]\n\u001B[0;32m 957\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m key_is_scalar:\n\u001B[1;32m--> 958\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_get_value\u001B[49m\u001B[43m(\u001B[49m\u001B[43mkey\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 960\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m is_hashable(key):\n\u001B[0;32m 961\u001B[0m \u001B[38;5;66;03m# Otherwise index.get_value will raise InvalidIndexError\u001B[39;00m\n\u001B[0;32m 962\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m 963\u001B[0m \u001B[38;5;66;03m# For labels that don't resolve as scalars like tuples and frozensets\u001B[39;00m\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\series.py:1069\u001B[0m, in \u001B[0;36mSeries._get_value\u001B[1;34m(self, label, takeable)\u001B[0m\n\u001B[0;32m 1066\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_values[label]\n\u001B[0;32m 1068\u001B[0m \u001B[38;5;66;03m# Similar to Index.get_value, but we do not fall back to positional\u001B[39;00m\n\u001B[1;32m-> 1069\u001B[0m loc \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mindex\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_loc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mlabel\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 1070\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mindex\u001B[38;5;241m.\u001B[39m_get_values_for_loc(\u001B[38;5;28mself\u001B[39m, loc, label)\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3623\u001B[0m, in \u001B[0;36mIndex.get_loc\u001B[1;34m(self, key, method, tolerance)\u001B[0m\n\u001B[0;32m 3621\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_engine\u001B[38;5;241m.\u001B[39mget_loc(casted_key)\n\u001B[0;32m 3622\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m err:\n\u001B[1;32m-> 3623\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m(key) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01merr\u001B[39;00m\n\u001B[0;32m 3624\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m:\n\u001B[0;32m 3625\u001B[0m \u001B[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001B[39;00m\n\u001B[0;32m 3626\u001B[0m \u001B[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001B[39;00m\n\u001B[0;32m 3627\u001B[0m \u001B[38;5;66;03m# the TypeError.\u001B[39;00m\n\u001B[0;32m 3628\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_check_indexing_error(key)\n", - "\u001B[1;31mKeyError\u001B[0m: 'GHG'" - ] - } - ], - "source": [ - "df_emi_split_final_min = df_emi_split_final.apply(lambda x: set_val(x), axis=1)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-18T13:38:26.714015100Z", - "start_time": "2023-09-18T13:38:26.416722600Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 20, - "outputs": [ - { - "data": { - "text/plain": " emission value unit GHGscen value_min \\\nnode year BIOscen \nR12_CHN 2030 BIO00 TCE 370.205 Mt C/yr 0 276.073 \n BIO00 TCE 359.754 Mt C/yr 10 276.073 \n BIO00 TCE 359.133 Mt C/yr 20 276.073 \n BIO00 TCE 356.055 Mt C/yr 50 276.073 \n BIO00 TCE 352.996 Mt C/yr 100 276.073 \n... ... ... ... ... ... \nR12_WEU 2110 BIO25 TCE 92.979 Mt C/yr 3000 92.979 \n BIO25 TCE 146.292 Mt C/yr 400 92.979 \n BIO25 TCE 92.979 Mt C/yr 4000 92.979 \n BIO25 TCE 137.590 Mt C/yr 600 92.979 \n BIO25 TCE 114.979 Mt C/yr 990 92.979 \n\n GHGscen_min \nnode year BIOscen \nR12_CHN 2030 BIO00 3000 \n BIO00 3000 \n BIO00 3000 \n BIO00 3000 \n BIO00 3000 \n... ... \nR12_WEU 2110 BIO25 3000 \n BIO25 3000 \n BIO25 3000 \n BIO25 3000 \n BIO25 3000 \n\n[2832 rows x 6 columns]", - "text/html": "
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emissionvalueunitGHGscenvalue_minGHGscen_min
nodeyearBIOscen
R12_CHN2030BIO00TCE370.205Mt C/yr0276.0733000
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BIO00TCE359.133Mt C/yr20276.0733000
BIO00TCE356.055Mt C/yr50276.0733000
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...........................
R12_WEU2110BIO25TCE92.979Mt C/yr300092.9793000
BIO25TCE146.292Mt C/yr40092.9793000
BIO25TCE92.979Mt C/yr400092.9793000
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" - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_emi_split_final_min[df_emi_split_final_min[\"GHGscen_min\"]!=4000]" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-18T12:45:21.805484800Z", - "start_time": "2023-09-18T12:45:21.616944900Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 67, - "outputs": [], - "source": [ - "df_emi_split = df_emi_split.reset_index().set_index([\"node\", \"year\", \"BIOscen\", \"GHGscen\"]).sort_index()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-18T12:08:41.007587100Z", - "start_time": "2023-09-18T12:08:40.886337600Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 58, - "outputs": [ - { - "data": { - "text/plain": " node land_scenario year value unit\n8064 R12_AFR BIO00GHG000 2030 131.20 USD\n8065 R12_AFR BIO00GHG010 2030 1111.61 USD\n8066 R12_AFR BIO00GHG020 2030 1426.91 USD\n8067 R12_AFR BIO00GHG050 2030 2918.26 USD\n8068 R12_AFR BIO00GHG100 2030 4789.62 USD\n... ... ... ... ... ...\n9067 R12_WEU BIO45GHG3000 2030 39074.74 USD\n9068 R12_WEU BIO45GHG400 2030 28098.57 USD\n9069 R12_WEU BIO45GHG4000 2030 37804.04 USD\n9070 R12_WEU BIO45GHG600 2030 28762.85 USD\n9071 R12_WEU BIO45GHG990 2030 34236.04 USD\n\n[1008 rows x 5 columns]", - "text/html": "
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nodeland_scenarioyearvalueunit
8064R12_AFRBIO00GHG0002030131.20USD
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9067R12_WEUBIO45GHG3000203039074.74USD
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9070R12_WEUBIO45GHG600203028762.85USD
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1008 rows × 5 columns

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" - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_cost[df_cost[\"year\"] == 2030]" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-18T12:04:53.176338500Z", - "start_time": "2023-09-18T12:04:52.910680700Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 19, - "outputs": [], - "source": [ - "df_cost_split = df_cost.join(df_cost[\"land_scenario\"].str.split(\"GHG\", expand=True)).drop(\"land_scenario\", axis=1)\n", - "df_cost_split = df_cost_split.rename({0: \"BIOscen\", 1: \"GHGscen\"}, axis=1)\n", - "df_cost_split[\"year\"] = df_cost_split[\"year\"].astype(int)\n", - "df_cost_split[\"GHGscen\"] = df_cost_split[\"GHGscen\"].astype(int)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-18T10:48:13.331126500Z", - "start_time": "2023-09-18T10:48:13.154426500Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 25, - "outputs": [], - "source": [ - "df_cost_split = df_cost_split.set_index([\"node\", \"year\", \"BIOscen\", \"GHGscen\"]).sort_index().drop(\"unit\", axis=1)#.diff()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-18T10:49:27.788734900Z", - "start_time": "2023-09-18T10:49:27.623550500Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 29, - "outputs": [ - { - "data": { - "text/plain": " value\nnode year BIOscen GHGscen \nR12_AFR 2030 BIO00 400 -1327.67\n 2000 -22741.68\n BIO05 400 6732.67\n 2000 -14678.76\n BIO07 400 10035.83\n... ...\nR12_WEU 2110 BIO15 100 190049.36\n 4000 442089.01\n BIO25 50 297005.83\n 4000 551895.39\n BIO45 50 541116.47\n\n[1122 rows x 1 columns]", - "text/html": "
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value
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R12_WEU2110BIO15100190049.36
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1122 rows × 1 columns

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" - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_cost_split[(df_cost_split.diff().value < 0) & (df_cost_split.index.get_level_values(3) != 0)]" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-18T10:50:54.654657800Z", - "start_time": "2023-09-18T10:50:54.514383Z" - } - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "sc_clone.solve(model=\"MESSAGE-MACRO\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "start_time": "2023-09-12T11:59:37.621512400Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 6, - "outputs": [], - "source": [ - "import message_data.tools.utilities" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-13T15:36:09.534732900Z", - "start_time": "2023-09-13T15:36:09.519035Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 10, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "processing Table: Resource|Extraction\n", - "processing Table: Resource|Cumulative Extraction\n", - "processing Table: Primary Energy\n", - "processing Table: Primary Energy (substitution method)\n", - "processing Table: Final Energy\n", - "processing Table: Secondary Energy|Electricity\n", - "processing Table: Secondary Energy|Heat\n", - "processing Table: Secondary Energy\n", - "processing Table: Secondary Energy|Gases\n", - "processing Table: Secondary Energy|Solids\n", - "processing Table: Emissions|CO2\n", - "processing Table: Carbon Sequestration\n", - "processing Table: Emissions|BC\n", - "processing Table: Emissions|OC\n", - "processing Table: Emissions|CO\n", - "processing Table: Emissions|N2O\n", - "processing Table: Emissions|CH4\n", - "processing Table: Emissions|NH3\n", - "processing Table: Emissions|Sulfur\n", - "processing Table: Emissions|NOx\n", - "processing Table: Emissions|VOC\n", - "processing Table: Emissions|HFC\n", - "processing Table: Emissions\n", - "processing Table: Emissions\n", - "processing Table: Agricultural Demand\n", - "processing Table: Agricultural Production\n", - "processing Table: Fertilizer Use\n", - "processing Table: Fertilizer\n", - "processing Table: Food Waste\n", - "processing Table: Food Demand\n", - "processing Table: Forestry Demand\n", - "processing Table: Forestry Production\n", - "processing Table: Land Cover\n", - "processing Table: Yield\n", - "processing Table: Capacity\n", - "processing Table: Capacity Additions\n", - "processing Table: Cumulative Capacity\n", - "processing Table: Capital Cost\n", - "processing Table: OM Cost|Fixed\n", - "processing Table: OM Cost|Variable\n", - "processing Table: Lifetime\n", - "processing Table: Efficiency\n", - "processing Table: Population\n", - "processing Table: Price\n", - "processing Table: Useful Energy\n", - "processing Table: Useful Energy\n", - "processing Table: Trade\n", - "processing Table: Investment|Energy Supply\n", - "processing Table: Water Consumption\n", - "processing Table: Water Withdrawal\n", - "processing Table: GDP\n", - "processing Table: Cost\n", - "processing Table: GLOBIOM Feedback\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "Starting to upload timeseries\n", - " region variable unit 2020 \\\n", - "0 R12_AFR Resource|Extraction EJ/yr 22.425189 \n", - "1 R12_AFR Resource|Extraction|Coal EJ/yr 7.762483 \n", - "2 R12_AFR Resource|Extraction|Gas EJ/yr 4.243681 \n", - "3 R12_AFR Resource|Extraction|Gas|Conventional EJ/yr 4.243681 \n", - "4 R12_AFR Resource|Extraction|Gas|Unconventional EJ/yr 0.000000 \n", - "\n", - " 2025 2030 2035 2040 2045 2050 \\\n", - "0 25.994892 29.432686 32.148193 36.073644 39.695341 42.961276 \n", - "1 9.690720 11.680136 13.889250 16.118193 18.702148 21.697660 \n", - "2 6.834491 8.212897 9.515340 9.853557 11.543875 11.805179 \n", - "3 6.834491 8.212897 9.515340 9.853557 11.543875 11.805179 \n", - "4 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "\n", - " 2055 2060 2070 2080 2090 2100 2110 \n", - "0 47.483945 56.304720 67.500799 75.436121 83.513448 72.080130 38.622405 \n", - "1 25.170280 29.195998 39.273137 45.796136 52.383412 49.979985 17.393034 \n", - "2 13.330084 15.774029 18.048166 22.433829 20.245962 14.901028 10.967157 \n", - "3 13.330084 15.774029 18.048166 11.505394 1.328787 0.977987 0.719799 \n", - "4 0.000000 0.000000 0.000000 10.928435 18.917174 13.923040 10.247358 \n", - "Finished uploading timeseries\n" - ] - } - ], - "source": [ - "# report baseline\n", - "from message_data.tools.post_processing.iamc_report_hackathon import report as reporting\n", - "reporting(\n", - " mp,\n", - " scen,\n", - " # NB(PNK) this is not an error; .iamc_report_hackathon.report() expects a\n", - " # string containing \"True\" or \"False\" instead of an actual bool.\n", - " \"False\",\n", - " scen.model,\n", - " scen.scenario,\n", - " merge_hist=True,\n", - " merge_ts=True,\n", - " #run_config=\"materials_run_config.yaml\",\n", - ")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "start_time": "2023-09-20T11:41:19.658236900Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 11, - "outputs": [ - { - "data": { - "text/plain": "" - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import importlib\n", - "importlib.reload(message_data.tools.utilities)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-13T15:21:15.534349300Z", - "start_time": "2023-09-13T15:21:15.487434500Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 6, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting to process data input from csv file.\n", - "Agricultural Demand\n", - "Agricultural Demand|Energy\n", - "Agricultural Demand|Energy|Crops\n", - "Agricultural Demand|Energy|Crops|1st generation\n", - "Agricultural Demand|Energy|Crops|2nd generation\n", - "Agricultural Demand|Non-Energy\n", - "Agricultural Demand|Non-Energy|Crops\n", - "Agricultural Demand|Non-Energy|Crops|Feed\n", - "Agricultural Demand|Non-Energy|Crops|Food\n", - "Agricultural Demand|Non-Energy|Crops|Other\n", - "Agricultural Demand|Non-Energy|Crops|Other|Waste\n", - "Agricultural Demand|Non-Energy|Livestock\n", - "Agricultural Demand|Non-Energy|Livestock|Food\n", - "Agricultural Demand|Non-Energy|Livestock|Other\n", - "Agricultural Demand|Non-Energy|Livestock|Other|Waste\n", - "Agricultural Production\n", - "Agricultural Production|Energy\n", - "Agricultural Production|Energy|Crops\n", - "Agricultural Production|Energy|Crops|1st generation\n", - "Agricultural Production|Energy|Crops|2nd generation\n", - "Agricultural Production|Non-Energy\n", - "Agricultural Production|Non-Energy|Crops\n", - "Agricultural Production|Non-Energy|Crops|Cereals\n", - "Agricultural Production|Non-Energy|Livestock\n", - "BCA_LandUseChangeEM\n", - "BCA_SavanBurnEM\n", - "Biodiversity|BII\n", - "Biodiversity|BII|Biodiversity hotspots\n", - "CH4_LandUseChangeEM\n", - "CH4_SavanBurnEM\n", - "CO_LandUseChangeEM\n", - "CO_SavanBurnEM\n", - "Costs|TC\n", - "CrpLnd\n", - "Emissions|CH4|AFOLU\n", - "Emissions|CH4|AFOLU|Agriculture\n", - "Emissions|CH4|AFOLU|Agriculture|Livestock\n", - "Emissions|CH4|AFOLU|Agriculture|Livestock|Enteric Fermentation\n", - "Emissions|CH4|AFOLU|Agriculture|Livestock|Manure Management\n", - "Emissions|CH4|AFOLU|Agriculture|Rice\n", - "Emissions|CH4|AFOLU|Biomass Burning\n", - "Emissions|CH4|AFOLU|Land\n", - "Emissions|CH4|AFOLU|Land|Grassland Burning\n", - "Emissions|CO2|AFOLU\n", - "Emissions|CO2|AFOLU|Afforestation\n", - "Emissions|CO2|AFOLU|Agriculture\n", - "Emissions|CO2|AFOLU|Deforestation\n", - "Emissions|CO2|AFOLU|Forest Management\n", - "Emissions|CO2|AFOLU|Negative\n", - "Emissions|CO2|AFOLU|Other LUC\n", - "Emissions|CO2|AFOLU|Positive\n", - "Emissions|CO2|AFOLU|Soil Carbon\n", - "Emissions|GHG|AFOLU\n", - "Emissions|N2O|AFOLU\n", - "Emissions|N2O|AFOLU|Agriculture\n", - "Emissions|N2O|AFOLU|Agriculture|Livestock\n", - "Emissions|N2O|AFOLU|Agriculture|Livestock|Manure Management\n", - "Emissions|N2O|AFOLU|Agriculture|Managed Soils\n", - "Emissions|N2O|AFOLU|Biomass Burning\n", - "Emissions|N2O|AFOLU|Land\n", - "Emissions|N2O|AFOLU|Land|Grassland Burning\n", - "Emissions|N2O|AFOLU|Land|Grassland Pastures\n", - "Fertilizer Use|Nitrogen\n", - "Fertilizer Use|Phosphorus\n", - "Fertilizer|Nitrogen|Intensity\n", - "Fertilizer|Phosphorus|Intensity\n", - "Food Demand\n", - "Food Demand|Crops\n", - "Food Demand|Livestock\n", - "Food Demand|Microbial protein\n", - "Forestry Demand|Roundwood\n", - "Forestry Demand|Roundwood|Industrial Roundwood\n", - "Forestry Demand|Roundwood|Wood Fuel\n", - "Forestry Production|Forest Residues\n", - "Forestry Production|Roundwood\n", - "Forestry Production|Roundwood|Industrial Roundwood\n", - "Forestry Production|Roundwood|Wood Fuel\n", - "GrassLnd\n", - "LU_CH4\n", - "LU_CH4_Agri\n", - "LU_CH4_BioBurn\n", - "LU_CO2\n", - "LU_GHG\n", - "LU_N2O\n", - "Land Cover\n", - "Land Cover|Cropland\n", - "Land Cover|Cropland|Cereals\n", - "Land Cover|Cropland|Energy Crops\n", - "Land Cover|Cropland|Irrigated\n", - "Land Cover|Cropland|Non-Energy Crops\n", - "Land Cover|Cropland|Oilcrops\n", - "Land Cover|Cropland|Sugarcrops\n", - "Land Cover|Forest\n", - "Land Cover|Forest|Afforestation and Reforestation\n", - "Land Cover|Forest|Forest old\n", - "Land Cover|Forest|Forest old|Other forest\n", - "Land Cover|Forest|Forest old|Primary forest\n", - "Land Cover|Forest|Forestry\n", - "Land Cover|Forest|Managed\n", - "Land Cover|Forest|Natural Forest\n", - "Land Cover|Other Land\n", - "Land Cover|Pasture\n", - "Land Cover|Protected\n", - "Landuse intensity indicator Tau\n", - "NH3_LandUseChangeEM\n", - "NH3_ManureEM\n", - "NH3_RiceEM\n", - "NH3_SavanBurnEM\n", - "NH3_SoilEM\n", - "NOx_LandUseChangeEM\n", - "NOx_SavanBurnEM\n", - "NOx_SoilEM\n", - "NewForLnd\n", - "OCA_LandUseChangeEM\n", - "OCA_SavanBurnEM\n", - "OldForLnd_Prim\n", - "OldForLnd_SndPlant\n", - "OtherLnd\n", - "Population\n", - "Population|Risk of Hunger\n", - "Price|Agriculture|Non-Energy Crops and Livestock|Index\n", - "Price|Agriculture|Non-Energy Crops|Index\n", - "Price|Carbon|CH4\n", - "Price|Carbon|CO2\n", - "Price|Carbon|N2O\n", - "Price|Water\n", - "Primary Energy|Biomass\n", - "Primary Energy|Biomass|1st Generation\n", - "Primary Energy|Biomass|1st Generation|Biodiesel\n", - "Primary Energy|Biomass|1st Generation|Bioethanol\n", - "Primary Energy|Biomass|Energy Crops\n", - "Primary Energy|Biomass|Fuelwood\n", - "Primary Energy|Biomass|Other\n", - "Primary Energy|Biomass|Residues\n", - "Primary Energy|Biomass|Residues|Forest industry\n", - "Primary Energy|Biomass|Residues|Logging\n", - "Primary Energy|Biomass|Roundwood harvest\n", - "SO2_LandUseChangeEM\n", - "SO2_SavanBurnEM\n", - "TCE\n", - "TotalLnd\n", - "VOC_LandUseChangeEM\n", - "VOC_SavanBurnEM\n", - "Water|Withdrawal|Irrigation\n", - "Water|Withdrawal|Irrigation|Cereals\n", - "Water|Withdrawal|Irrigation|Oilcrops\n", - "Water|Withdrawal|Irrigation|Sugarcrops\n", - "Yield|Cereals\n", - "Yield|Energy Crops\n", - "Yield|Non-Energy Crops\n", - "Yield|Oilcrops\n", - "Yield|Sugarcrops\n", - "bioenergy\n", - "total_cost\n", - "Price|Primary Energy|Biomass\n" - ] - }, - { - "data": { - "text/plain": "" - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "message_data.tools.utilities.add_globiom(\n", - " mp,\n", - " sc_clone,\n", - " \"SSP2\",\n", - " private_data_path(),\n", - " 2015,\n", - " globiom_scenario=\"noSDG_rcpref\",\n", - " regenerate_input_data=True,\n", - " #regenerate_input_data_only=True,\n", - " #allow_empty_drivers=True,\n", - " add_reporting_data=True,\n", - " config_file=\"magpie_config.yaml\",\n", - " config_setup=\"MAGPIE_MP00BI00_exp2110\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-16T14:31:09.875880100Z", - "start_time": "2023-10-16T14:20:00.060555200Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 14, - "outputs": [ - { - "data": { - "text/plain": " node land_scenario year emission value unit\n532896 R12_AFR BIO00GHG000 2100 TCE 1153.565 Mt C/yr\n532987 R12_CHN BIO00GHG000 2100 TCE 211.385 Mt C/yr\n533078 R12_EEU BIO00GHG000 2100 TCE 51.079 Mt C/yr\n533169 R12_FSU BIO00GHG000 2100 TCE 108.907 Mt C/yr\n533260 R12_LAM BIO00GHG000 2100 TCE 275.042 Mt C/yr\n533351 R12_MEA BIO00GHG000 2100 TCE 76.391 Mt C/yr\n533442 R12_NAM BIO00GHG000 2100 TCE 124.316 Mt C/yr\n533533 R12_PAO BIO00GHG000 2100 TCE 72.617 Mt C/yr\n533624 R12_PAS BIO00GHG000 2100 TCE 324.909 Mt C/yr\n533715 R12_RCPA BIO00GHG000 2100 TCE 30.069 Mt C/yr\n533806 R12_SAS BIO00GHG000 2100 TCE 582.181 Mt C/yr\n533897 R12_WEU BIO00GHG000 2100 TCE 173.137 Mt C/yr\n561288 R12_AFR BIO00GHG000 2110 TCE 986.762 Mt C/yr\n561379 R12_CHN BIO00GHG000 2110 TCE 204.090 Mt C/yr\n561470 R12_EEU BIO00GHG000 2110 TCE 51.117 Mt C/yr\n561561 R12_FSU BIO00GHG000 2110 TCE 107.176 Mt C/yr\n561652 R12_LAM BIO00GHG000 2110 TCE 214.274 Mt C/yr\n561743 R12_MEA BIO00GHG000 2110 TCE 78.029 Mt C/yr\n561834 R12_NAM BIO00GHG000 2110 TCE 124.822 Mt C/yr\n561925 R12_PAO BIO00GHG000 2110 TCE 75.186 Mt C/yr\n562016 R12_PAS BIO00GHG000 2110 TCE 307.080 Mt C/yr\n562107 R12_RCPA BIO00GHG000 2110 TCE 29.367 Mt C/yr\n562198 R12_SAS BIO00GHG000 2110 TCE 569.786 Mt C/yr\n562289 R12_WEU BIO00GHG000 2110 TCE 176.483 Mt C/yr", - "text/html": "
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nodeland_scenarioyearemissionvalueunit
532896R12_AFRBIO00GHG0002100TCE1153.565Mt C/yr
532987R12_CHNBIO00GHG0002100TCE211.385Mt C/yr
533078R12_EEUBIO00GHG0002100TCE51.079Mt C/yr
533169R12_FSUBIO00GHG0002100TCE108.907Mt C/yr
533260R12_LAMBIO00GHG0002100TCE275.042Mt C/yr
533351R12_MEABIO00GHG0002100TCE76.391Mt C/yr
533442R12_NAMBIO00GHG0002100TCE124.316Mt C/yr
533533R12_PAOBIO00GHG0002100TCE72.617Mt C/yr
533624R12_PASBIO00GHG0002100TCE324.909Mt C/yr
533715R12_RCPABIO00GHG0002100TCE30.069Mt C/yr
533806R12_SASBIO00GHG0002100TCE582.181Mt C/yr
533897R12_WEUBIO00GHG0002100TCE173.137Mt C/yr
561288R12_AFRBIO00GHG0002110TCE986.762Mt C/yr
561379R12_CHNBIO00GHG0002110TCE204.090Mt C/yr
561470R12_EEUBIO00GHG0002110TCE51.117Mt C/yr
561561R12_FSUBIO00GHG0002110TCE107.176Mt C/yr
561652R12_LAMBIO00GHG0002110TCE214.274Mt C/yr
561743R12_MEABIO00GHG0002110TCE78.029Mt C/yr
561834R12_NAMBIO00GHG0002110TCE124.822Mt C/yr
561925R12_PAOBIO00GHG0002110TCE75.186Mt C/yr
562016R12_PASBIO00GHG0002110TCE307.080Mt C/yr
562107R12_RCPABIO00GHG0002110TCE29.367Mt C/yr
562198R12_SASBIO00GHG0002110TCE569.786Mt C/yr
562289R12_WEUBIO00GHG0002110TCE176.483Mt C/yr
\n
" - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sc_clone.par(\"land_emission\", filters={\"year\":[2100,2110], \"emission\":\"TCE\", \"land_scenario\":\"BIO00GHG000\"})" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-16T14:51:01.363158200Z", - "start_time": "2023-10-16T14:51:01.162574400Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 7, - "outputs": [], - "source": [ - "sc_clone.set_as_default()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-16T14:47:48.101224600Z", - "start_time": "2023-10-16T14:47:48.022080100Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 9, - "outputs": [ - { - "data": { - "text/plain": "'MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00'" - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sc_clone.model" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-16T14:48:27.832391Z", - "start_time": "2023-10-16T14:48:27.768777Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 10, - "outputs": [ - { - "data": { - "text/plain": "'exp2110_matrix_test'" - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sc_clone.scenario" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-16T14:48:43.500081500Z", - "start_time": "2023-10-16T14:48:43.437548100Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 6, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting to process data input from csv file.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:481: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = data.loc[reg][tmp.columns[i]]\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\tools\\utilities\\add_globiom.py:483: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tmp[tmp.columns[i]] = (\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Agricultural Demand\n", - "Agricultural Demand|Energy\n", - "Agricultural Demand|Energy|Crops\n", - "Agricultural Demand|Energy|Crops|1st generation\n", - "Agricultural Demand|Energy|Crops|2nd generation\n", - "Agricultural Demand|Non-Energy\n", - "Agricultural Demand|Non-Energy|Crops\n", - "Agricultural Demand|Non-Energy|Crops|Feed\n", - "Agricultural Demand|Non-Energy|Crops|Food\n", - "Agricultural Demand|Non-Energy|Crops|Other\n", - "Agricultural Demand|Non-Energy|Crops|Other|Waste\n", - "Agricultural Demand|Non-Energy|Livestock\n", - "Agricultural Demand|Non-Energy|Livestock|Food\n", - "Agricultural Demand|Non-Energy|Livestock|Other\n", - "Agricultural Demand|Non-Energy|Livestock|Other|Waste\n", - "Agricultural Production\n", - "Agricultural Production|Energy\n", - "Agricultural Production|Energy|Crops\n", - "Agricultural Production|Energy|Crops|1st generation\n", - "Agricultural Production|Energy|Crops|2nd generation\n", - "Agricultural Production|Non-Energy\n", - "Agricultural Production|Non-Energy|Crops\n", - "Agricultural Production|Non-Energy|Crops|Cereals\n", - "Agricultural Production|Non-Energy|Livestock\n", - "BCA_LandUseChangeEM\n", - "BCA_SavanBurnEM\n", - "Biodiversity|BII\n", - "Biodiversity|BII|Biodiversity hotspots\n", - "CH4_LandUseChangeEM\n", - "CH4_SavanBurnEM\n", - "CO_LandUseChangeEM\n", - "CO_SavanBurnEM\n", - "Costs|TC\n", - "CrpLnd\n", - "Emissions|CH4|AFOLU\n", - "Emissions|CH4|AFOLU|Agriculture\n", - "Emissions|CH4|AFOLU|Agriculture|Livestock\n", - "Emissions|CH4|AFOLU|Agriculture|Livestock|Enteric Fermentation\n", - "Emissions|CH4|AFOLU|Agriculture|Livestock|Manure Management\n", - "Emissions|CH4|AFOLU|Agriculture|Rice\n", - "Emissions|CH4|AFOLU|Biomass Burning\n", - "Emissions|CH4|AFOLU|Land\n", - "Emissions|CH4|AFOLU|Land|Grassland Burning\n", - "Emissions|CO2|AFOLU\n", - "Emissions|CO2|AFOLU|Afforestation\n", - "Emissions|CO2|AFOLU|Agriculture\n", - "Emissions|CO2|AFOLU|Deforestation\n", - "Emissions|CO2|AFOLU|Forest Management\n", - "Emissions|CO2|AFOLU|Negative\n", - "Emissions|CO2|AFOLU|Other LUC\n", - "Emissions|CO2|AFOLU|Positive\n", - "Emissions|CO2|AFOLU|Soil Carbon\n", - "Emissions|GHG|AFOLU\n", - "Emissions|N2O|AFOLU\n", - "Emissions|N2O|AFOLU|Agriculture\n", - "Emissions|N2O|AFOLU|Agriculture|Livestock\n", - "Emissions|N2O|AFOLU|Agriculture|Livestock|Manure Management\n", - "Emissions|N2O|AFOLU|Agriculture|Managed Soils\n", - "Emissions|N2O|AFOLU|Biomass Burning\n", - "Emissions|N2O|AFOLU|Land\n", - "Emissions|N2O|AFOLU|Land|Grassland Burning\n", - "Emissions|N2O|AFOLU|Land|Grassland Pastures\n", - "Fertilizer Use|Nitrogen\n", - "Fertilizer Use|Phosphorus\n", - "Fertilizer|Nitrogen|Intensity\n", - "Fertilizer|Phosphorus|Intensity\n", - "Food Demand\n", - "Food Demand|Crops\n", - "Food Demand|Livestock\n", - "Food Demand|Microbial protein\n", - "Forestry Demand|Roundwood\n", - "Forestry Demand|Roundwood|Industrial Roundwood\n", - "Forestry Demand|Roundwood|Wood Fuel\n", - "Forestry Production|Forest Residues\n", - "Forestry Production|Roundwood\n", - "Forestry Production|Roundwood|Industrial Roundwood\n", - "Forestry Production|Roundwood|Wood Fuel\n", - "GrassLnd\n", - "LU_CH4\n", - "LU_CH4_Agri\n", - "LU_CH4_BioBurn\n", - "LU_CO2\n", - "LU_GHG\n", - "LU_N2O\n", - "Land Cover\n", - "Land Cover|Cropland\n", - "Land Cover|Cropland|Cereals\n", - "Land Cover|Cropland|Energy Crops\n", - "Land Cover|Cropland|Irrigated\n", - "Land Cover|Cropland|Non-Energy Crops\n", - "Land Cover|Cropland|Oilcrops\n", - "Land Cover|Cropland|Sugarcrops\n", - "Land Cover|Forest\n", - "Land Cover|Forest|Afforestation and Reforestation\n", - "Land Cover|Forest|Forest old\n", - "Land Cover|Forest|Forest old|Other forest\n", - "Land Cover|Forest|Forest old|Primary forest\n", - "Land Cover|Forest|Forestry\n", - "Land Cover|Forest|Managed\n", - "Land Cover|Forest|Natural Forest\n", - "Land Cover|Other Land\n", - "Land Cover|Pasture\n", - "Land Cover|Protected\n", - "Landuse intensity indicator Tau\n", - "NH3_LandUseChangeEM\n", - "NH3_ManureEM\n", - "NH3_RiceEM\n", - "NH3_SavanBurnEM\n", - "NH3_SoilEM\n", - "NOx_LandUseChangeEM\n", - "NOx_SavanBurnEM\n", - "NOx_SoilEM\n", - "NewForLnd\n", - "OCA_LandUseChangeEM\n", - "OCA_SavanBurnEM\n", - "OldForLnd_Prim\n", - "OldForLnd_SndPlant\n", - "OtherLnd\n", - "Population\n", - "Population|Risk of Hunger\n", - "Price|Agriculture|Non-Energy Crops and Livestock|Index\n", - "Price|Agriculture|Non-Energy Crops|Index\n", - "Price|Carbon|CH4\n", - "Price|Carbon|CO2\n", - "Price|Carbon|N2O\n", - "Price|Water\n", - "Primary Energy|Biomass\n", - "Primary Energy|Biomass|1st Generation\n", - "Primary Energy|Biomass|1st Generation|Biodiesel\n", - "Primary Energy|Biomass|1st Generation|Bioethanol\n", - "Primary Energy|Biomass|Energy Crops\n", - "Primary Energy|Biomass|Fuelwood\n", - "Primary Energy|Biomass|Other\n", - "Primary Energy|Biomass|Residues\n", - "Primary Energy|Biomass|Residues|Forest industry\n", - "Primary Energy|Biomass|Residues|Logging\n", - "Primary Energy|Biomass|Roundwood harvest\n", - "SO2_LandUseChangeEM\n", - "SO2_SavanBurnEM\n", - "TCE\n", - "TotalLnd\n", - "VOC_LandUseChangeEM\n", - "VOC_SavanBurnEM\n", - "Water|Withdrawal|Irrigation\n", - "Water|Withdrawal|Irrigation|Cereals\n", - "Water|Withdrawal|Irrigation|Oilcrops\n", - "Water|Withdrawal|Irrigation|Sugarcrops\n", - "Yield|Cereals\n", - "Yield|Energy Crops\n", - "Yield|Non-Energy Crops\n", - "Yield|Oilcrops\n", - "Yield|Sugarcrops\n", - "bioenergy\n", - "total_cost\n", - "Price|Primary Energy|Biomass\n" - ] - }, - { - "data": { - "text/plain": "" - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "message_data.tools.utilities.add_globiom(\n", - " mp,\n", - " sc_clone2,\n", - " \"SSP2\",\n", - " private_data_path(),\n", - " 2015,\n", - " globiom_scenario=\"noSDG_rcpref\",\n", - " regenerate_input_data=True,\n", - " #regenerate_input_data_only=True,\n", - " #allow_empty_drivers=True,\n", - " add_reporting_data=True,\n", - " config_file=\"magpie_config.yaml\",\n", - " config_setup=\"MAGPIE_MP00BI00_OldForSplit\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-19T13:02:56.778310400Z", - "start_time": "2023-09-19T12:51:40.341511700Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 7, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00 new-mapping_with-smoothing\n" - ] - } - ], - "source": [ - "sc_clone2.set_as_default()\n", - "print(sc_clone2.model, sc_clone2.scenario)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-19T13:02:56.809581600Z", - "start_time": "2023-09-19T13:02:56.778310400Z" - } - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scen.remove_solution()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "extra = scen.set(\"cat_year\", {\"type_year\": \"cumulative\", \"year\": [2015, 2010]})\n", - "scen.check_out()\n", - "scen.remove_set(\"cat_year\", extra)\n", - "scen.commit(\"remove cumulative years from cat_year set\")\n", - "scen.set(\"cat_year\", {\"type_year\": \"cumulative\"})" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 8, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "KeyboardInterrupt\n", - "\n", - "KeyboardInterrupt\n", - "\n" - ] - } - ], - "source": [ - "scen.solve(\"MESSAGE-MACRO\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-11T14:50:37.754227400Z", - "start_time": "2023-09-11T14:50:27.403030200Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 4, - "outputs": [], - "source": [ - "df = mp.scenario_list()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-12T11:43:23.281436900Z", - "start_time": "2023-09-12T11:43:21.916538800Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 5, - "outputs": [ - { - "data": { - "text/plain": " model scenario scheme \\\n7072 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00_c... baseline MESSAGE \n7073 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00_c... baseline MESSAGE \n\n is_default is_locked cre_user cre_date upd_user \\\n7072 1 0 maczek 2023-09-11 16:34:23.000000 None \n7073 1 0 maczek 2023-09-11 13:19:28.000000 None \n\n upd_date lock_user lock_date \\\n7072 None None None \n7073 None None None \n\n annotation version \n7072 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 2 \n7073 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 ", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
modelscenarioschemeis_defaultis_lockedcre_usercre_dateupd_userupd_datelock_userlock_dateannotationversion
7072MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00_c...baselineMESSAGE10maczek2023-09-11 16:34:23.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...2
7073MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00_c...baselineMESSAGE10maczek2023-09-11 13:19:28.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
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" - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df[df[\"model\"].str.contains(\"corr\")]" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-12T11:44:04.887557400Z", - "start_time": "2023-09-12T11:44:04.816399100Z" - } - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df.scheme.unique()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scen.scheme" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df[df[\"scenario\"].str.contains(\"_macro\")]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "model_name = \"ENGAGE_SSP2_v4.1.8.3_T4.5_r3.1\"\n", - "scenario_name = \"baseline_magpie\"" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scen = message_ix.Scenario(mp, model=model_name, scenario=scenario_name)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "import importlib\n", - "importlib.reload(message_data.tools.utilities)\n", - "if scen.has_solution():\n", - " scen.remove_solution()\n", - "add_globiom(\n", - " mp,\n", - " scen,\n", - " \"SSP2\",\n", - " private_data_path(),\n", - " 2015,\n", - " #globiom_scenario=\"magpie\",\n", - " #regenerate_input_data=True,\n", - " #regenerate_input_data_only=True,\n", - " clean=True,\n", - " clean_only=True,\n", - " #globiom_scenario=\"no\",\n", - " #allow_empty_drivers=True,\n", - " add_reporting_data=True,\n", - " config_setup=\"MAGPIE\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "add_globiom(\n", - " mp,\n", - " scen,\n", - " \"SSP2\",\n", - " private_data_path(),\n", - " 2015,\n", - " globiom_scenario=\"noSDG_rcpref\",\n", - " regenerate_input_data=True,\n", - " #regenerate_input_data_only=True,\n", - " #globiom_scenario=\"no\",\n", - " #allow_empty_drivers=True,\n", - " add_reporting_data=True,\n", - " config_setup=\"MAGPIE\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "mps = [\"50\"]\n", - "bis = [\"00\", \"70\", \"74\", \"78\"]\n", - "bis = [\"00\", \"70\", \"74\", \"78\"]\n", - "for mp in mps:\n", - " for bi in bis:\n", - " if bi == \"00\":\n", - " bd = 1\n", - " else:\n", - " bd = 0\n", - " model = f\"MP{mp}BD{bd}BI{bi}\"\n", - " print(model)\n", - " scen = message_ix.Scenario(mp_obj, f\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-{model}\", \"baseline\")\n", - " if scen.has_solution():\n", - " scen.remove_solution()\n", - " if \"bio_backstop\" in scen.set(\"technology\").tolist():\n", - " scen.check_out()\n", - " scen.remove_set(\"technology\", \"bio_backstop\")\n", - " scen.commit(\"remove backstop\")\n", - " #print(scen.par(\"output\", filters={\"technology\":\"bio_backstop\"}))\n", - " del scen" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scen.check_out()\n", - "scen.remove_set(\"technology\", \"bio_backstop\")\n", - "scen.commit(\"remove backstop\")\n", - "scen.par(\"output\", filters={\"technology\":\"bio_backstop\"})" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scen.remove_solution()\n", - "scen.solve(\"MESSAGE-MACRO\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## SCP setup" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "base = message_ix.Scenario(mp, \"ENGAGE_SSP2_v4.1.8.3.1_T4.5_r3.1\", \"baseline\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "#base_clone = base.clone(\"ENGAGE_SSP2_MAGPIE_SCP\", \"baseline_MP00\")\n", - "base_clone = base.clone(\"ENGAGE_SSP2_MAGPIE_SCP\", \"baseline_MP80\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "import importlib\n", - "importlib.reload(message_data.tools.utilities)\n", - "if base_clone.has_solution():\n", - " base_clone.remove_solution()\n", - "add_globiom(\n", - " mp,\n", - " base_clone,\n", - " \"SSP2\",\n", - " private_data_path(),\n", - " 2015,\n", - " #globiom_scenario=\"magpie\",\n", - " #regenerate_input_data=True,\n", - " #regenerate_input_data_only=True,\n", - " clean=True,\n", - " clean_only=True,\n", - " #globiom_scenario=\"no\",\n", - " #allow_empty_drivers=True,\n", - " add_reporting_data=True,\n", - " config_setup=\"MAGPIE\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "add_globiom(\n", - " mp,\n", - " base_clone,\n", - " \"SSP2\",\n", - " private_data_path(),\n", - " 2015,\n", - " globiom_scenario=\"noSDG_rcpref\",\n", - " regenerate_input_data=True,\n", - " #regenerate_input_data_only=True,\n", - " #globiom_scenario=\"no\",\n", - " #allow_empty_drivers=True,\n", - " add_reporting_data=True,\n", - " config_setup=\"MAGPIE\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "base_clone.set_as_default()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df = mp.scenario_list()\n", - "df[df[\"model\"].str.contains(\"SCP\")]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## create budget scenario" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 34, - "outputs": [], - "source": [ - "mode = \"clone\"\n", - "#mode = \"load\"\n", - "\n", - "budget = \"1000f\"\n", - "#budget = \"600f\"" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-13T07:27:28.476248600Z", - "start_time": "2023-09-13T07:27:28.422805300Z" - } - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "if mode == \"clone\":\n", - " scen_budget = scen.clone(model=\"ENGAGE-MAGPIE_SSP2_v4.1.8.3_T4.5_r3.1\", scenario=budget, keep_solution=False,\n", - " shift_first_model_year=2025)\n", - "else:\n", - " scen_budget = message_ix.Scenario(mp, model=\"ENGAGE-MAGPIE_SSP2_v4.1.8.3_T4.5_r3.1\", scenario=budget)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "extra = scen_budget.set(\"cat_year\", {\"type_year\": \"cumulative\", \"year\": [2020, 2015, 2010]})\n", - "scen_budget.check_out()\n", - "scen_budget.remove_set(\"cat_year\", extra)\n", - "scen_budget.commit(\"remove cumulative years from cat_year set\")\n", - "scen_budget.set(\"cat_year\", {\"type_year\": \"cumulative\"})" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "emission_dict = {\n", - " \"node\": \"World\",\n", - " \"type_emission\": \"TCE\",\n", - " \"type_tec\": \"all\",\n", - " \"type_year\": \"cumulative\",\n", - " \"unit\": \"???\",\n", - " }\n", - "if budget == \"1000f\":\n", - " value = 4046\n", - "if budget == \"600f\":\n", - " value = 2605\n", - "\n", - "df = message_ix.make_df(\n", - " \"bound_emission\", value=value, **emission_dict\n", - ")\n", - "scen_budget.check_out()\n", - "scen_budget.add_par(\"bound_emission\", df)\n", - "scen_budget.commit(\"add emission bound\")\n", - "scen_budget.par(\"bound_emission\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scen_budget.remove_solution()\n", - "scen_budget.solve(model=\"MESSAGE-MACRO\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scen_budget.set_as_default()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## run reporting" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 10, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "processing Table: Resource|Extraction\n", - "processing Table: Resource|Cumulative Extraction\n", - "processing Table: Primary Energy\n", - "processing Table: Primary Energy (substitution method)\n", - "processing Table: Final Energy\n", - "processing Table: Secondary Energy|Electricity\n", - "processing Table: Secondary Energy|Heat\n", - "processing Table: Secondary Energy\n", - "processing Table: Secondary Energy|Gases\n", - "processing Table: Secondary Energy|Solids\n", - "processing Table: Emissions|CO2\n", - "processing Table: Carbon Sequestration\n", - "processing Table: Emissions|BC\n", - "processing Table: Emissions|OC\n", - "processing Table: Emissions|CO\n", - "processing Table: Emissions|N2O\n", - "processing Table: Emissions|CH4\n", - "processing Table: Emissions|NH3\n", - "processing Table: Emissions|Sulfur\n", - "processing Table: Emissions|NOx\n", - "processing Table: Emissions|VOC\n", - "processing Table: Emissions|HFC\n", - "processing Table: Emissions\n", - "processing Table: Emissions\n", - "processing Table: Agricultural Demand\n", - "processing Table: Agricultural Production\n", - "processing Table: Fertilizer Use\n", - "processing Table: Fertilizer\n", - "processing Table: Food Waste\n", - "processing Table: Food Demand\n", - "processing Table: Forestry Demand\n", - "processing Table: Forestry Production\n", - "processing Table: Land Cover\n", - "processing Table: Yield\n", - "processing Table: Capacity\n", - "processing Table: Capacity Additions\n", - "processing Table: Cumulative Capacity\n", - "processing Table: Capital Cost\n", - "processing Table: OM Cost|Fixed\n", - "processing Table: OM Cost|Variable\n", - "processing Table: Lifetime\n", - "processing Table: Efficiency\n", - "processing Table: Population\n", - "processing Table: Price\n", - "processing Table: Useful Energy\n", - "processing Table: Useful Energy\n", - "processing Table: Trade\n", - "processing Table: Investment|Energy Supply\n", - "processing Table: Water Consumption\n", - "processing Table: Water Withdrawal\n", - "processing Table: GDP\n", - "processing Table: Cost\n", - "processing Table: GLOBIOM Feedback\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "Starting to upload timeseries\n", - " region variable unit 2025 \\\n", - "0 R12_AFR Resource|Extraction EJ/yr 23.180454 \n", - "1 R12_AFR Resource|Extraction|Coal EJ/yr 7.266278 \n", - "2 R12_AFR Resource|Extraction|Gas EJ/yr 6.834491 \n", - "3 R12_AFR Resource|Extraction|Gas|Conventional EJ/yr 6.834491 \n", - "4 R12_AFR Resource|Extraction|Gas|Unconventional EJ/yr 0.000000 \n", - "\n", - " 2030 2035 2040 2045 2050 2055 \\\n", - "0 20.273657 19.952535 18.933601 20.577608 20.405360 22.329736 \n", - "1 2.423100 1.583713 0.867861 0.184598 0.230745 0.253654 \n", - "2 9.028904 10.612705 12.200300 14.689182 15.898805 18.912043 \n", - "3 9.028904 10.612705 12.200300 14.689182 15.898805 18.912043 \n", - "4 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "\n", - " 2060 2070 2080 2090 2100 2110 \n", - "0 21.929689 22.723112 21.102234 12.027721 6.912506 3.818970 \n", - "1 0.310799 0.160775 0.070950 0.017169 0.030747 0.000000 \n", - "2 18.931974 21.239637 20.509762 11.973180 6.868753 3.817505 \n", - "3 18.931974 18.531374 1.567102 1.153387 0.848893 0.624785 \n", - "4 0.000000 2.708263 18.942660 10.819793 6.019860 3.192720 \n", - "Finished uploading timeseries\n" - ] - } - ], - "source": [ - "# report budget scenario\n", - "from message_data.tools.post_processing.iamc_report_hackathon import report as reporting\n", - "reporting(\n", - " mp,\n", - " scen_budget,\n", - " # NB(PNK) this is not an error; .iamc_report_hackathon.report() expects a\n", - " # string containing \"True\" or \"False\" instead of an actual bool.\n", - " \"False\",\n", - " scen_budget.model,\n", - " scen_budget.scenario,\n", - " merge_hist=True,\n", - " merge_ts=True,\n", - " #run_config=\"materials_run_config.yaml\",\n", - ")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-20T14:53:33.158659800Z", - "start_time": "2023-09-20T14:38:43.624078500Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 13, - "outputs": [ - { - "data": { - "text/plain": "'new-mapping_with-smoothing_primary-forest-constraint_1000f'" - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "scen_budget.scenario" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-20T14:55:38.002761800Z", - "start_time": "2023-09-20T14:55:37.940314800Z" - } - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scen = message_ix.Scenario(mp, \"MESSAGE-SCP_R11\", \"baseline_MP00\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "# report baseline\n", - "from message_data.tools.post_processing.iamc_report_hackathon import report as reporting\n", - "reporting(\n", - " mp,\n", - " scen,\n", - " # NB(PNK) this is not an error; .iamc_report_hackathon.report() expects a\n", - " # string containing \"True\" or \"False\" instead of an actual bool.\n", - " \"False\",\n", - " scen.model,\n", - " scen.scenario,\n", - " merge_hist=True,\n", - " merge_ts=True,\n", - " #run_config=\"materials_run_config.yaml\",\n", - ")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scen.set(\"is_test\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df = mp.scenario_list()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df[df[\"model\"].str.contains(\"SCP\")]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "base = message_ix.Scenario(mp, \"ENGAGE_SSP2_MAGPIE_SCP\", \"baseline_MP00\")\n", - "base_solved = base.clone(\"ENGAGE_SSP2_MAGPIE_SCP\", \"baseline_MP00_solved\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "base_solved.solve()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "# report baseline\n", - "from message_data.tools.post_processing.iamc_report_hackathon import report as reporting\n", - "reporting(\n", - " mp,\n", - " base_solved,\n", - " # NB(PNK) this is not an error; .iamc_report_hackathon.report() expects a\n", - " # string containing \"True\" or \"False\" instead of an actual bool.\n", - " \"False\",\n", - " base_solved.model,\n", - " base_solved.scenario,\n", - " merge_hist=True,\n", - " merge_ts=True,\n", - " #run_config=\"materials_run_config.yaml\",\n", - ")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "#scen.read_excel(\"sceniario.xlsx\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "#scen.to_excel" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "import pandas as pd" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df_mag = pd.read_csv(\"C:/Users\\maczek\\PycharmProjects\\message_data\\data\\globiom/magpie_input_MP76BD1BI00.csv\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df_glo = pd.read_csv(\"C:/Users\\maczek\\PycharmProjects\\message_data\\data\\globiom/SSP2_globiom_input_noSDG_rcpref.csv\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df_mag[\"GHGscen\"]\n", - "df_mag[\"BIOscen\"]\n", - "df_mag[\"GHGscen\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df_mag[df_mag.Variable.str.startswith(\"Land Cover\")]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df_glo[df_glo.Variable.str.startswith(\"Land Cover\") & (df_glo.Variable.str.count(\"\\\\|\")==1)][\"Variable\"].unique()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "vars = [#\"Land Cover\",\n", - "\"Land Cover|Cropland|Non-Energy Crops\",\n", - "\"Land Cover|Other Land\",\n", - "\"Land Cover|Pasture\",\n", - "\"Land Cover|Cropland|Energy Crops\",\n", - "\"Land Cover|Forest|Forest old\",\n", - "\"Land Cover|Forest|Afforestation and Reforestation\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df_mag[df_mag.Variable.isin(vars)].groupby([\"Region\", \"GHGscen\", \"BIOscen\"]).sum()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df_mag[df_mag.Variable.isin([\"Land Cover\"])].groupby([\"Region\", \"GHGscen\", \"BIOscen\"]).sum()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df_glo[df_glo.Variable.isin([\"Land Cover\"])].groupby([\"Region\", \"Scenario\"]).sum()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df_glo[df_glo.Variable.isin([vars])].groupby([\"Region\", \"Scenario\"]).sum()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "### modify emulator constraints" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 4, - "outputs": [], - "source": [ - "scen = message_ix.Scenario(mp,\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00_uncorrected\", \"baseline\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-12T15:27:28.137030200Z", - "start_time": "2023-09-12T15:27:24.630401300Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 11, - "outputs": [], - "source": [ - "df1 = scen.par(\"growth_land_up\", filters={\"land_type\":\"OldForLnd\",\"node\":\"R12_EEU\", \"year\":[\"2050\"]})\n", - "df2 = scen.par(\"growth_land_up\", filters={\"land_type\":\"OldForLnd\",\"node\":\"R12_EEU\", \"year\":[\"2090\"]})\n", - "\n", - "df3 = scen.par(\"growth_land_up\", filters={\"land_type\":\"OldForLnd\",\"node\":\"R12_NAM\", \"year\":[\"2060\"]})\n", - "df4 = scen.par(\"growth_land_up\", filters={\"land_type\":\"OldForLnd\",\"node\":\"R12_NAM\", \"year\":[\"2090\"]})\n", - "\n", - "df5 = scen.par(\"growth_land_up\", filters={\"land_type\":\"OldForLnd\",\"node\":\"R12_PAS\", \"year\":[\"2060\"]})\n", - "df6 = scen.par(\"growth_land_up\", filters={\"land_type\":\"OldForLnd\",\"node\":\"R12_PAS\", \"year\":[\"2070\"]})\n", - "df7 = scen.par(\"growth_land_up\", filters={\"land_type\":\"OldForLnd\",\"node\":\"R12_PAS\", \"year\":[\"2080\"]})\n", - "df8 = scen.par(\"growth_land_up\", filters={\"land_type\":\"OldForLnd\",\"node\":\"R12_PAS\", \"year\":[\"2090\"]})" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-12T14:26:50.088412100Z", - "start_time": "2023-09-12T14:26:50.057167200Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 13, - "outputs": [], - "source": [ - "df1[\"value\"] = 0.0019\n", - "df2[\"value\"] = 0.0039\n", - "\n", - "df3[\"value\"] = 0.0034\n", - "df4[\"value\"] = 0.0023\n", - "\n", - "df5[\"value\"] = 0.0021\n", - "df6[\"value\"] = 0.0297\n", - "df7[\"value\"] = 0.0088\n", - "df8[\"value\"] = 0.0143" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-12T14:30:36.093022600Z", - "start_time": "2023-09-12T14:30:36.061779800Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 16, - "outputs": [], - "source": [ - "scen.check_out()\n", - "scen.add_par(\"growth_land_up\", df1)\n", - "scen.add_par(\"growth_land_up\", df2)\n", - "\n", - "scen.add_par(\"growth_land_up\", df3)\n", - "scen.add_par(\"growth_land_up\", df4)\n", - "\n", - "scen.add_par(\"growth_land_up\", df5)\n", - "scen.add_par(\"growth_land_up\", df6)\n", - "scen.add_par(\"growth_land_up\", df7)\n", - "scen.add_par(\"growth_land_up\", df8)\n", - "scen.commit(\"fix forrest constraint\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-12T14:32:05.221078500Z", - "start_time": "2023-09-12T14:32:04.065505800Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 17, - "outputs": [], - "source": [ - "df1 = scen.par(\"growth_land_up\", filters={\"land_type\":\"CrpLnd\", \"node\":['R12_WEU', 'R12_PAO', 'R12_AFR', 'R12_MEA', \n", - " 'R12_LAM', 'R12_RCPA', 'R12_SAS']})\n", - "df2 = scen.par(\"growth_land_up\", filters={\"land_type\":\"CrpLnd\", \"node\":\"R12_WEU\", \"year\":[\"2055\"]})" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-12T14:38:34.032636400Z", - "start_time": "2023-09-12T14:38:33.871687200Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 20, - "outputs": [], - "source": [ - "df2[\"value\"] = 0.026" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-12T14:39:03.935959500Z", - "start_time": "2023-09-12T14:39:03.898154200Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 21, - "outputs": [], - "source": [ - "df1[\"value\"] = 0.0206" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-12T14:39:26.166895500Z", - "start_time": "2023-09-12T14:39:26.104375900Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 22, - "outputs": [], - "source": [ - "scen.check_out()\n", - "scen.add_par(\"growth_land_up\", df1)\n", - "scen.add_par(\"growth_land_up\", df2)\n", - "scen.commit(\"fix cropland constraint\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-12T14:39:44.977825400Z", - "start_time": "2023-09-12T14:39:43.958900Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 31, - "outputs": [], - "source": [ - "scen.solve(model=\"MESSAGE-MACRO\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-12T15:47:21.294914600Z", - "start_time": "2023-09-12T15:41:40.318234400Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 20, - "outputs": [], - "source": [ - "df3 = scen.par(\"growth_land_up\", filters={\"land_type\":\"OldForLnd\",\"node\":\"R12_EEU\", \"year\":[\"2050\"]})\n", - "df4 = scen.par(\"growth_land_up\", filters={\"land_type\":\"OldForLnd\",\"node\":\"R12_NAM\", \"year\":[\"2060\"]})" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-12T15:37:19.744066Z", - "start_time": "2023-09-12T15:37:19.628247400Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 15, - "outputs": [], - "source": [ - "df2 = scen.par(\"growth_land_up\", filters={\"land_type\":\"OldForLnd\",\"node\":\"R12_EEU\", \"year\":[\"2055\"]})" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-12T15:31:25.020306Z", - "start_time": "2023-09-12T15:31:24.938051600Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 13, - "outputs": [], - "source": [ - "df3[\"year\"] = 2055" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-12T15:31:11.709356Z", - "start_time": "2023-09-12T15:31:11.668956600Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 17, - "outputs": [], - "source": [ - "df2[\"year\"]= 2050" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-12T15:31:37.731761Z", - "start_time": "2023-09-12T15:31:37.647046400Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 18, - "outputs": [], - "source": [ - "scen.check_out()\n", - "scen.add_par(\"growth_land_up\", df2)\n", - "scen.add_par(\"growth_land_up\", df3)\n", - "scen.commit(\"fix cropland constraint\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-12T15:31:48.421502200Z", - "start_time": "2023-09-12T15:31:47.366177100Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 22, - "outputs": [], - "source": [ - "df4 = scen.par(\"growth_land_up\", filters={\"land_type\":\"OldForLnd\",\"node\":\"R12_NAM\"})" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-12T15:37:44.159751400Z", - "start_time": "2023-09-12T15:37:44.041331200Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 30, - "outputs": [], - "source": [ - "scen.check_out()\n", - "scen.add_par(\"growth_land_up\", df4.set_index([\"node\", \"year\", \"land_type\"]).shift().reset_index().drop(0))\n", - "scen.commit(\"fix forrest again\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-12T15:41:29.477690900Z", - "start_time": "2023-09-12T15:41:28.143364100Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 33, - "outputs": [ - { - "data": { - "text/plain": "'MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00_uncorrected'" - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "scen.model" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-13T07:26:49.127432200Z", - "start_time": "2023-09-13T07:26:49.064948100Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 5, - "outputs": [], - "source": [ - "mode = \"clone\"\n", - "#mode = \"load\"\n", - "\n", - "budget = \"1000f\"\n", - "#budget = \"600f\"" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-20T12:10:56.172808300Z", - "start_time": "2023-09-20T12:10:56.110318400Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 6, - "outputs": [], - "source": [ - "if mode == \"clone\":\n", - " scen_budget = scen.clone(model=scen.model, scenario=scen.scenario+\"_\"+budget, \n", - " keep_solution=False, shift_first_model_year=2025)\n", - "else:\n", - " scen_budget = message_ix.Scenario(mp, model=\"ENGAGE-MAGPIE_SSP2_v4.1.8.3_T4.5_r3.1\", scenario=budget)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-20T12:11:22.993257Z", - "start_time": "2023-09-20T12:10:57.074124400Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 7, - "outputs": [ - { - "data": { - "text/plain": " type_year year\n0 cumulative 2025\n1 cumulative 2030\n2 cumulative 2035\n3 cumulative 2040\n4 cumulative 2045\n5 cumulative 2050\n6 cumulative 2055\n7 cumulative 2060\n8 cumulative 2070\n9 cumulative 2080\n10 cumulative 2090\n11 cumulative 2100\n12 cumulative 2110", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
type_yearyear
0cumulative2025
1cumulative2030
2cumulative2035
3cumulative2040
4cumulative2045
5cumulative2050
6cumulative2055
7cumulative2060
8cumulative2070
9cumulative2080
10cumulative2090
11cumulative2100
12cumulative2110
\n
" - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "extra = scen_budget.set(\"cat_year\", {\"type_year\": \"cumulative\", \"year\": [2020, 2015, 2010]})\n", - "scen_budget.check_out()\n", - "scen_budget.remove_set(\"cat_year\", extra)\n", - "scen_budget.commit(\"remove cumulative years from cat_year set\")\n", - "scen_budget.set(\"cat_year\", {\"type_year\": \"cumulative\"})" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-20T12:11:24.095437500Z", - "start_time": "2023-09-20T12:11:22.993257Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 8, - "outputs": [ - { - "data": { - "text/plain": " node type_emission type_tec type_year value unit\n0 World TCE all cumulative 4046.0 ???", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
nodetype_emissiontype_tectype_yearvalueunit
0WorldTCEallcumulative4046.0???
\n
" - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "emission_dict = {\n", - " \"node\": \"World\",\n", - " \"type_emission\": \"TCE\",\n", - " \"type_tec\": \"all\",\n", - " \"type_year\": \"cumulative\",\n", - " \"unit\": \"???\",\n", - " }\n", - "if budget == \"1000f\":\n", - " value = 4046\n", - "if budget == \"600f\":\n", - " value = 2605\n", - "\n", - "df = message_ix.make_df(\n", - " \"bound_emission\", value=value, **emission_dict\n", - ")\n", - "scen_budget.check_out()\n", - "scen_budget.add_par(\"bound_emission\", df)\n", - "scen_budget.commit(\"add emission bound\")\n", - "scen_budget.par(\"bound_emission\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-20T12:11:25.569954500Z", - "start_time": "2023-09-20T12:11:24.100427Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 9, - "outputs": [], - "source": [ - "#scen_budget.remove_solution()\n", - "scen_budget.solve(model=\"MESSAGE-MACRO\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-20T12:38:01.821675600Z", - "start_time": "2023-09-20T12:11:25.553999600Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 14, - "outputs": [], - "source": [ - "scen_budget.set_as_default()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-20T14:55:52.252811800Z", - "start_time": "2023-09-20T14:55:52.168235Z" - } - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "sc" - ], - "metadata": { - "collapsed": false - } - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/message_ix_models/model/material/magpie notebooks/plot_LU_carbon_price.ipynb b/message_ix_models/model/material/magpie notebooks/plot_LU_carbon_price.ipynb deleted file mode 100644 index 77b6de01fe..0000000000 --- a/message_ix_models/model/material/magpie notebooks/plot_LU_carbon_price.ipynb +++ /dev/null @@ -1,1088 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "outputs": [ - { - "data": { - "text/plain": "", - "application/javascript": "if (typeof IPython !== 'undefined') { IPython.OutputArea.prototype._should_scroll = function(lines){ return false; }}" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import message_ix\n", - "import ixmp\n", - "import matplotlib.pyplot as plt\n", - "mp = ixmp.Platform(\"ixmp_dev\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-27T11:17:32.616052700Z", - "start_time": "2023-09-27T11:17:12.853249500Z" - } - }, - "id": "3e36b5b631702f36" - }, - { - "cell_type": "code", - "execution_count": 10, - "outputs": [], - "source": [ - "# insert model and scenario names here\n", - "models = [\"ENGAGE_SSP2_v4.1.8.3.1_T4.5_r3.1\", \n", - " #\"MESSAGEix-MAgPIE_Forest-test\", \n", - " #\"MESSAGEix-MAgPIE_Forest-test\", \n", - " #\"MESSAGEix-MAgPIE_Forest-test\"\n", - " ]\n", - "scenarios = [\"EN_NPi2020_1000f\", \n", - " #\"primary_constraint_1000f\", \n", - " #\"primary_constraint_1000f_landScenLo_002\", \n", - " #\"forest_split-primary_constraint_1000f\"\n", - " ]\n" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-20T17:54:56.009268900Z", - "start_time": "2023-09-20T17:54:55.966256800Z" - } - }, - "id": "7f9af46eb7b85b94" - }, - { - "cell_type": "code", - "execution_count": 2, - "outputs": [], - "source": [ - "#models = [\"MESSAGEix-MAgPIE_Forest-test\"]#, \"MESSAGEix-MAgPIE_Forest-test\"]\n", - "#scenarios = [\"forest_split-primary_constraint_1000f\"]#\"forest_split-primary_constraint_1000f_landScenLo_0.02\", \"forest_split-primary_constraint_1000f_landScenLo_0.03\"]\n", - "#models = [\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\"]\n", - "#scenarios = [\"EN_NPi2020_1000f\"]\n", - "models = [\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\"#, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\", \n", - " #\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\"\n", - " ]\n", - "scenarios = [#\"new-mapping_no-smoothing_1000f\", \"new-mapping_with-smoothing_1000f\", \n", - " \"new-mapping_with-smoothing_primary-forest-constraint_tax_100\", \n", - " #\"1000f_MP00BD1BI00\",\n", - "]\n", - "models = [\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\"]\n", - "scenarios = [\"cumu_cost_test_step5_1000f\"]" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-27T11:17:50.354024700Z", - "start_time": "2023-09-27T11:17:50.275913100Z" - } - }, - "id": "616851e3869badf4" - }, - { - "cell_type": "code", - "execution_count": 3, - "outputs": [], - "source": [ - "def get_globiom_prices(scen):\n", - " df_land = scen.par(\"land_output\", filters={\"commodity\":\"Price|Carbon|CO2\"})\n", - " df_land = df_land.drop([\"level\", \"time\", \"commodity\", \"unit\"], axis=1)\n", - " return df_land.set_index([\"node\", \"land_scenario\", \"year\"])" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-27T11:17:51.163084300Z", - "start_time": "2023-09-27T11:17:51.131715900Z" - } - }, - "id": "47e8a5295c7add02" - }, - { - "cell_type": "code", - "execution_count": 4, - "outputs": [], - "source": [ - "def get_globiom_mix(scen):\n", - " df_land = scen.var(\"LAND\")\n", - " return df_land[df_land[\"lvl\"]!=0].drop(\"mrg\", axis=1).set_index([\"node\", \"land_scenario\", \"year\"])" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-27T11:17:51.647940900Z", - "start_time": "2023-09-27T11:17:51.616699800Z" - } - }, - "id": "c074eb254294a384" - }, - { - "cell_type": "code", - "execution_count": 5, - "outputs": [], - "source": [ - "def calc_prices(scen):\n", - " df_prc = get_globiom_prices(scen)\n", - " df_mix = get_globiom_mix(scen)\n", - " df_prc = df_mix.join(df_prc)\n", - " df_prc[\"cost\"] = df_prc[\"lvl\"] * df_prc[\"value\"]\n", - " df_prc_sum = df_prc.groupby([\"node\", \"year\"]).sum()\n", - " return df_prc_sum" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-27T11:17:52.059027400Z", - "start_time": "2023-09-27T11:17:52.023117200Z" - } - }, - "id": "8a0665c807be6bde" - }, - { - "cell_type": "code", - "execution_count": 6, - "outputs": [], - "source": [ - "def plot_globiom_prices(df_glob, df_msg, ax):\n", - " legend = []\n", - " for reg in df_glob.index.get_level_values(0).unique():\n", - " df_glob.loc[reg].plot(y=\"cost\", ax=ax)\n", - " legend.append(reg)\n", - " ax.set_xticks([i for i in range(2020,2111, 10)])\n", - "\n", - " df_msg[\"lvl\"] = df_msg[\"lvl\"] / 3.310036812\n", - " df_msg.plot(x=\"year\", y=\"lvl\", ax= ax, linestyle=\"--\", color=\"black\", legend=False)\n", - " legend.append(\"PRICE_EMISSION\")\n", - " ax.legend(legend)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-27T11:17:52.539309100Z", - "start_time": "2023-09-27T11:17:52.508064Z" - } - }, - "id": "ddc5345118d19091" - }, - { - "cell_type": "code", - "execution_count": 7, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00 cumu_cost_test_step5_1000f\n" - ] - }, - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "for m,s in zip(models, scenarios):\n", - " print(m, s)\n", - " scen = message_ix.Scenario(mp, m, s)\n", - " df_glob = calc_prices(scen)\n", - " df_tce_price = scen.var(\"PRICE_EMISSION\")\n", - " fig, ax = plt.subplots(figsize=(10.33, 6))\n", - " plot_globiom_prices(df_glob, df_tce_price, ax)\n", - " ax.set_ylim((0,4000))\n", - " del scen\n", - " #ax.set_title(f\"{m} - {s}\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-27T11:18:10.842871100Z", - "start_time": "2023-09-27T11:17:52.992065400Z" - } - }, - "id": "2573f378ce63adde" - }, - { - "cell_type": "code", - "execution_count": 12, - "outputs": [], - "source": [ - "fig.savefig(\"carbon_prices_plot_globiom.svg\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-20T17:56:33.108389900Z", - "start_time": "2023-09-20T17:56:32.997671300Z" - } - }, - "id": "fee3a0df28f3a6f8" - }, - { - "cell_type": "code", - "execution_count": 10, - "outputs": [ - { - "data": { - "text/plain": "False" - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "scen.has_solution()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-20T14:55:14.455973500Z", - "start_time": "2023-09-20T14:55:14.331058Z" - } - }, - "id": "187c5c060fc88813" - }, - { - "cell_type": "code", - "execution_count": 5, - "outputs": [], - "source": [ - "import gdxpds\n", - "import matplotlib.pyplot as plt" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-02T12:11:46.417648500Z", - "start_time": "2023-10-02T12:11:40.952642400Z" - } - }, - "id": "736eb42c45d5161e" - }, - { - "cell_type": "code", - "execution_count": 1, - "outputs": [], - "source": [ - "#file = \"MsgOutput_MESSAGEix-GLOBIOM_1.1-R12-MAGPIE-MP00BD1BI00_new-mapping_with-smoothing_1000f\"\n", - "#file = \"MsgOutput_MESSAGEix-MAgPIE_Forest-test_primary_constraint_1000f_landScenLo_002\"\n", - "file = \"MsgOutput_MESSAGEix-GLOBIOM_1.1-R12-MAGPIE-MP00BD1BI00_new-mapping_with-smoothing_primary-forest-constraint_tax_1000\"\n", - "#file = \"MsgOutput_MESSAGEix-GLOBIOM_1.1-R12-MAGPIE-MP00BD1BI00_new-mapping_with-smoothing_primary-forest-constraint_1000f\"\n", - "file = \"MsgOutput_MESSAGEix-GLOBIOM_1.1-R12-MAGPIE-MP00BD1BI00_cumu_cost_test_step7_1000f\"" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-02T12:11:36.429920600Z", - "start_time": "2023-10-02T12:11:36.407785100Z" - } - }, - "id": "3eb6618656496c4d" - }, - { - "cell_type": "code", - "execution_count": 2, - "outputs": [], - "source": [ - "dir = \"C:/Users\\maczek\\PycharmProjects\\message_ix\\message_ix\\model\\output/\"\n", - "dir = \"C:/Users\\maczek\\Downloads/\"" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-02T12:11:36.462738300Z", - "start_time": "2023-10-02T12:11:36.423385700Z" - } - }, - "id": "ad637f993ac4f5ba" - }, - { - "cell_type": "code", - "execution_count": 6, - "outputs": [], - "source": [ - "df_cum = gdxpds.to_dataframe(f\"{dir}{file}.gdx\", \"EMISS_LU\")[\"EMISS_LU\"]" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-02T12:11:50.340321700Z", - "start_time": "2023-10-02T12:11:46.417648500Z" - } - }, - "id": "474e5cadab13f594" - }, - { - "cell_type": "code", - "execution_count": 7, - "outputs": [], - "source": [ - "df_cum = df_cum[df_cum[\"emission\"]==\"LU_CO2\"]" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-02T12:11:50.716852400Z", - "start_time": "2023-10-02T12:11:50.342314700Z" - } - }, - "id": "65795a510c265309" - }, - { - "cell_type": "code", - "execution_count": 8, - "outputs": [], - "source": [ - "df_cum = df_cum[df_cum.columns[:-4]]" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-02T12:11:51.055284400Z", - "start_time": "2023-10-02T12:11:50.731406300Z" - } - }, - "id": "e44534a69a00974b" - }, - { - "cell_type": "code", - "execution_count": 9, - "outputs": [], - "source": [ - "df_cum = df_cum[(df_cum[\"type_tec\"]==\"all\") & (df_cum[\"node\"]!=\"World\")]" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-02T12:11:51.126810700Z", - "start_time": "2023-10-02T12:11:51.055284400Z" - } - }, - "id": "49f8a4894a1d4e46" - }, - { - "cell_type": "code", - "execution_count": 10, - "outputs": [], - "source": [ - "# df_emi = gdxpds.to_dataframe(\"C:/Users\\maczek\\PycharmProjects\\message_ix\\message_ix\\model\\output/MsgOutput_MESSAGEix-GLOBIOM_1.1-R12-MAGPIE-MP00BD1BI00_new-mapping_with-smoothing_1000f.gdx\", \"land_emission\")[\"land_emission\"]\n", - "# df_land = gdxpds.to_dataframe(\"C:/Users\\maczek\\PycharmProjects\\message_ix\\message_ix\\model\\output/MsgOutput_MESSAGEix-GLOBIOM_1.1-R12-MAGPIE-MP00BD1BI00_new-mapping_with-smoothing_1000f.gdx\", \"LAND\")[\"LAND\"]\n", - "df_emi = gdxpds.to_dataframe(f\"{dir}{file}.gdx\", \"land_emission\")[\"land_emission\"]\n", - "df_land = gdxpds.to_dataframe(f\"{dir}{file}.gdx\", \"LAND\")[\"LAND\"]" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-02T12:11:56.668307100Z", - "start_time": "2023-10-02T12:11:51.131796700Z" - } - }, - "id": "108b3eaef65d5342" - }, - { - "cell_type": "code", - "execution_count": 11, - "outputs": [], - "source": [ - "df_emi = df_emi[df_emi[\"emission\"] == \"LU_CO2\"]\n", - "df_emi = df_emi.drop(\"emission\", axis=1)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-02T12:11:56.757553800Z", - "start_time": "2023-10-02T12:11:56.668307100Z" - } - }, - "id": "316e1dc219e8fcd5" - }, - { - "cell_type": "code", - "execution_count": 12, - "outputs": [], - "source": [ - "df_land = df_land[df_land.columns[:-4]]\n", - "df_land = df_land[df_land[\"Level\"]!=0]\n", - "df_land = df_land.set_index([\"node\", \"year_all\", \"land_scenario\"])" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-02T12:11:56.906444800Z", - "start_time": "2023-10-02T12:11:56.757553800Z" - } - }, - "id": "f48da74d29f29cff" - }, - { - "cell_type": "code", - "execution_count": 13, - "outputs": [], - "source": [ - "df_emi = df_emi.set_index([\"node\", \"year_all\", \"land_scenario\"])" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-02T12:11:56.937522100Z", - "start_time": "2023-10-02T12:11:56.906444800Z" - } - }, - "id": "e3c0040b092ec5b9" - }, - { - "cell_type": "code", - "execution_count": 14, - "outputs": [], - "source": [ - "df_join = df_land.join(df_emi)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-02T12:11:56.984340900Z", - "start_time": "2023-10-02T12:11:56.937522100Z" - } - }, - "id": "498d645b7df704f2" - }, - { - "cell_type": "code", - "execution_count": 140, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "BIO00GHG200 234.0\n", - "BIO00GHG400 210.0\n", - "BIO15GHG600 203.0\n", - "mix: 234.0\n" - ] - }, - { - "data": { - "text/plain": "
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\n" - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(16,9))\n", - "df_plot = df_emi.loc[\"R12_WEU\"]\n", - "legend=[]\n", - "for i in df_plot.index.get_level_values(1).unique():\n", - " #print(i)\n", - " if i in [\"BIO15GHG600\", \"BIO00GHG400\", \"BIO00GHG200\"]:\n", - " df_plot.index = df_plot.index.set_levels(df_plot.index.levels[0].astype(int), level=0)\n", - " df_scen = df_plot.swaplevel(0,1).loc[i]\n", - " df_scen.cumsum().plot(ax=ax)\n", - " print(i, df_scen.loc[:2060].sum()[0].round())\n", - " legend.append(i)\n", - "ax.set_xlim((2025,2110))\n", - "\n", - "df_mix = df_join.loc[\"R12_WEU\"]\n", - "df_mix.index = df_mix.index.astype(int)\n", - "df_mix.cumsum().plot(ax=ax, legend=False, y=\"value\", color=\"black\")\n", - "print(\"mix: \", df_mix.loc[:2060, \"value\"].sum().round())\n", - "legend.append(\"reported CO2 emissions\")\n", - "\n", - "ax.legend(legend, fontsize=14)\n", - "ax.set_title(\"CO2 Emissions WEU\", fontsize=20)\n", - "fig.savefig(\"CO2_Emissions_WEU_cum.svg\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-02T13:35:38.478618700Z", - "start_time": "2023-10-02T13:35:38.078003100Z" - } - }, - "id": "a3defc0b51072d05" - }, - { - "cell_type": "code", - "execution_count": 129, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "BIO00GHG200 234.0\n", - "BIO00GHG400 210.0\n", - "BIO00GHG990 156.0\n", - "BIO15GHG600 203.0\n", - "BIO15GHG990 181.0\n", - "mix: 234.0\n" - ] - }, - { - "data": { - "text/plain": "Text(0.5, 1.0, 'CO2 Emissions WEU')" - }, - "execution_count": 129, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(16,9))\n", - "df_plot = df_emi.loc[\"R12_WEU\"]\n", - "legend=[]\n", - "for i in df_plot.index.get_level_values(1).unique():\n", - " #print(i)\n", - " if i in [\"BIO15GHG600\", \"BIO00GHG400\", \"BIO00GHG200\", \"BIO15GHG990\", \"BIO00GHG990\"]:\n", - " df_plot.index = df_plot.index.set_levels(df_plot.index.levels[0].astype(int), level=0)\n", - " df_scen = df_plot.swaplevel(0,1).loc[i]\n", - " df_scen.plot(ax=ax)\n", - " print(i, df_scen.loc[:2060].sum()[0].round())\n", - " legend.append(i)\n", - "ax.set_xlim((2025,2110))\n", - "\n", - "df_mix = df_join.loc[\"R12_WEU\"]\n", - "df_mix.index = df_mix.index.astype(int)\n", - "df_mix.plot(ax=ax, legend=False, y=\"value\", color=\"black\")\n", - "print(\"mix: \", df_mix.loc[:2060, \"value\"].sum().round())\n", - "legend.append(\"reported CO2 emissions\")\n", - "\n", - "ax.legend(legend, fontsize=14)\n", - "ax.set_title(\"CO2 Emissions WEU\", fontsize=20)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-02T13:24:12.925982Z", - "start_time": "2023-10-02T13:24:12.687657700Z" - } - }, - "id": "b93080974064f3d6" - }, - { - "cell_type": "code", - "execution_count": 138, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "BIO00GHG200 234.0\n", - "BIO00GHG400 210.0\n", - "BIO15GHG600 203.0\n", - "mix: 234.0\n" - ] - }, - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(16,9))\n", - "df_plot = df_emi.loc[\"R12_WEU\"]\n", - "legend=[]\n", - "for i in df_plot.index.get_level_values(1).unique():\n", - " #print(i)\n", - " if i in [\"BIO15GHG600\", \"BIO00GHG400\", \"BIO00GHG200\"]:\n", - " df_plot.index = df_plot.index.set_levels(df_plot.index.levels[0].astype(int), level=0)\n", - " df_scen = df_plot.swaplevel(0,1).loc[i]\n", - " df_scen.plot(ax=ax)\n", - " print(i, df_scen.loc[:2060].sum()[0].round())\n", - " legend.append(i)\n", - "ax.set_xlim((2025,2110))\n", - "\n", - "df_mix = df_join.loc[\"R12_WEU\"]\n", - "df_mix.index = df_mix.index.astype(int)\n", - "df_mix.plot(ax=ax, legend=False, y=\"value\", color=\"black\")\n", - "print(\"mix: \", df_mix.loc[:2060, \"value\"].sum().round())\n", - "legend.append(\"reported CO2 emissions\")\n", - "\n", - "ax.legend(legend, fontsize=14)\n", - "ax.set_title(\"CO2 Emissions WEU\", fontsize=20)\n", - "fig.savefig(\"CO2_Emissions_WEU.svg\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-02T13:35:13.039124500Z", - "start_time": "2023-10-02T13:35:12.623213800Z" - } - }, - "id": "d32c1c4001ba1275" - }, - { - "cell_type": "code", - "execution_count": 99, - "outputs": [ - { - "data": { - "text/plain": " Value\nnode year_all land_scenario \nR12_AFR 2025 BIO05GHG000 313.340\n 2030 BIO05GHG000 405.057\n 2035 BIO05GHG000 467.444\n 2040 BIO05GHG000 466.584\n 2045 BIO05GHG000 438.874\n... ...\nR12_CHN 2070 BIO45GHG990 31.567\n 2080 BIO45GHG990 19.381\n 2090 BIO45GHG990 5.939\n 2100 BIO45GHG990 -3.808\n 2110 BIO45GHG990 -3.808\n\n[13104 rows x 1 columns]", - "text/html": "
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Value
nodeyear_allland_scenario
R12_AFR2025BIO05GHG000313.340
2030BIO05GHG000405.057
2035BIO05GHG000467.444
2040BIO05GHG000466.584
2045BIO05GHG000438.874
............
R12_CHN2070BIO45GHG99031.567
2080BIO45GHG99019.381
2090BIO45GHG9905.939
2100BIO45GHG990-3.808
2110BIO45GHG990-3.808
\n

13104 rows × 1 columns

\n
" - }, - "execution_count": 99, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_emi = df_emi[~df_emi.index.get_level_values(1).isin([\"2000\", \"2005\", \"2010\", \"2015\", \"2020\"])]\n", - "df_emi" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-02T12:40:11.755879500Z", - "start_time": "2023-10-02T12:40:11.628813800Z" - } - }, - "id": "549dbfa61e6d5953" - }, - { - "cell_type": "code", - "execution_count": 15, - "outputs": [], - "source": [ - "df_join[\"value\"] = df_join[\"Value\"] * df_join[\"Level\"]" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-02T12:11:57.006473800Z", - "start_time": "2023-10-02T12:11:56.984340900Z" - } - }, - "id": "904a5ffdcc47f767" - }, - { - "cell_type": "code", - "execution_count": 16, - "outputs": [], - "source": [ - "df_join = df_join.groupby([\"node\", \"year_all\"]).sum()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-02T12:11:57.053365400Z", - "start_time": "2023-10-02T12:11:57.006473800Z" - } - }, - "id": "ded9a528ca861c11" - }, - { - "cell_type": "code", - "execution_count": 17, - "outputs": [ - { - "data": { - "text/plain": "" - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": "
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\n" - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(3,4, figsize=(32,18))\n", - "ax_it = iter(fig.axes)\n", - "for reg in df_cum.node.unique():\n", - " if reg == \"R12_GLB\":\n", - " continue \n", - " ax = next(ax_it)\n", - " df_tmp = df_cum[df_cum[\"node\"]==reg]\n", - " df_tmp.plot(x=\"year_all\", y=\"Level\", ax=ax, legend=False)\n", - " df_tmp2 = df_join.loc[reg]\n", - " df_tmp2.plot(ax=ax, y=\"value\", legend=False)\n", - " ax.set_title(reg)\n", - " ax.set_xlabel(\"\")\n", - "fig.legend([\"LU_CO2 from EMISS equation\", \"LU_CO2 from land_use_reporting\"], fontsize=16, loc=\"lower center\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-02T12:11:58.939921100Z", - "start_time": "2023-10-02T12:11:57.037716900Z" - } - }, - "id": "3a4f18e634f69e86" - }, - { - "cell_type": "code", - "execution_count": 139, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(16,9))\n", - "ax_it = iter(fig.axes)\n", - "for reg in df_cum.node.unique():\n", - " if reg != \"R12_WEU\":\n", - " continue \n", - " ax = next(ax_it)\n", - " df_tmp = df_cum[df_cum[\"node\"]==reg]\n", - " df_tmp.plot(x=\"year_all\", y=\"Level\", ax=ax, legend=False)\n", - " df_tmp2 = df_join.loc[reg]\n", - " df_tmp2.plot(ax=ax, y=\"value\", legend=False)\n", - " ax.set_title(\"CO2 Emissions WEU\", fontsize=20)\n", - " ax.set_xlabel(\"\")\n", - "fig.legend([\"LU_CO2 from EMISS equation\", \"LU_CO2 from land_use_reporting\"], fontsize=16, loc=\"lower center\")\n", - "fig.savefig(\"CO2_Emissions_WEU_diff.svg\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-02T13:35:28.003253Z", - "start_time": "2023-10-02T13:35:27.787273900Z" - } - }, - "id": "c3c35acb7d0953f6" - }, - { - "cell_type": "code", - "execution_count": 23, - "outputs": [ - { - "data": { - "text/plain": "" - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(12,7))\n", - "df_cum.groupby(\"year_all\").sum().plot(ax=ax, legend=False)\n", - "df_join.groupby(\"year_all\").sum().plot(y=\"value\", ax=ax, legend=False)\n", - "ax.set_xlabel(\"\")\n", - "#ax.set_ylim((-2000, 1500))\n", - "ax.set_title(\"World\", fontsize=18)\n", - "ax.legend([\"LU_CO2 from EMISS equation\", \"LU_CO2 from land_use_reporting\"], fontsize=14, loc=\"lower center\")\n", - "ax.axhline(-650, color=\"black\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-27T11:22:24.291734500Z", - "start_time": "2023-09-27T11:22:24.071522800Z" - } - }, - "id": "f8d4759cdcc20916" - }, - { - "cell_type": "code", - "execution_count": 30, - "outputs": [ - { - "data": { - "text/plain": "" - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots()\n", - "for i in df_emi.index.get_level_values(2).unique():\n", - " if \"4000\" not in i:\n", - " continue\n", - " df_emi.swaplevel(0,2).loc[i].groupby(\"year_all\").sum().plot(ax=ax)\n", - "ax.axhline(-650)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-22T13:14:10.745588300Z", - "start_time": "2023-09-22T13:14:10.542162300Z" - } - }, - "id": "6fd9ebb44395518a" - }, - { - "cell_type": "code", - "execution_count": 16, - "outputs": [], - "source": [ - "fig.savefig(\"emi_accounting_comparison_tax_1000.svg\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-20T20:50:18.277635300Z", - "start_time": "2023-09-20T20:50:18.011832700Z" - } - }, - "id": "e15122db2fa07444" - }, - { - "cell_type": "code", - "execution_count": 48, - "outputs": [ - { - "ename": "TypeError", - "evalue": "'<' not supported between instances of 'str' and 'int'", - "output_type": "error", - "traceback": [ - "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[1;31mTypeError\u001B[0m Traceback (most recent call last)", - "Input \u001B[1;32mIn [48]\u001B[0m, in \u001B[0;36m\u001B[1;34m()\u001B[0m\n\u001B[1;32m----> 1\u001B[0m df_join[\u001B[43mdf_join\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mindex\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_level_values\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m1\u001B[39;49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m<\u001B[39;49m\u001B[38;5;241;43m2055\u001B[39;49m]\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\ops\\common.py:70\u001B[0m, in \u001B[0;36m_unpack_zerodim_and_defer..new_method\u001B[1;34m(self, other)\u001B[0m\n\u001B[0;32m 66\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mNotImplemented\u001B[39m\n\u001B[0;32m 68\u001B[0m other \u001B[38;5;241m=\u001B[39m item_from_zerodim(other)\n\u001B[1;32m---> 70\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mmethod\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mother\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\arraylike.py:48\u001B[0m, in \u001B[0;36mOpsMixin.__lt__\u001B[1;34m(self, other)\u001B[0m\n\u001B[0;32m 46\u001B[0m \u001B[38;5;129m@unpack_zerodim_and_defer\u001B[39m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m__lt__\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m 47\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m__lt__\u001B[39m(\u001B[38;5;28mself\u001B[39m, other):\n\u001B[1;32m---> 48\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_cmp_method\u001B[49m\u001B[43m(\u001B[49m\u001B[43mother\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43moperator\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mlt\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py:6690\u001B[0m, in \u001B[0;36mIndex._cmp_method\u001B[1;34m(self, other, op)\u001B[0m\n\u001B[0;32m 6687\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m is_object_dtype(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mdtype) \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(\u001B[38;5;28mself\u001B[39m, ABCMultiIndex):\n\u001B[0;32m 6688\u001B[0m \u001B[38;5;66;03m# don't pass MultiIndex\u001B[39;00m\n\u001B[0;32m 6689\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m np\u001B[38;5;241m.\u001B[39merrstate(\u001B[38;5;28mall\u001B[39m\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mignore\u001B[39m\u001B[38;5;124m\"\u001B[39m):\n\u001B[1;32m-> 6690\u001B[0m result \u001B[38;5;241m=\u001B[39m \u001B[43mops\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcomp_method_OBJECT_ARRAY\u001B[49m\u001B[43m(\u001B[49m\u001B[43mop\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_values\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mother\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 6692\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m 6693\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m np\u001B[38;5;241m.\u001B[39merrstate(\u001B[38;5;28mall\u001B[39m\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mignore\u001B[39m\u001B[38;5;124m\"\u001B[39m):\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\ops\\array_ops.py:73\u001B[0m, in \u001B[0;36mcomp_method_OBJECT_ARRAY\u001B[1;34m(op, x, y)\u001B[0m\n\u001B[0;32m 71\u001B[0m result \u001B[38;5;241m=\u001B[39m libops\u001B[38;5;241m.\u001B[39mvec_compare(x\u001B[38;5;241m.\u001B[39mravel(), y\u001B[38;5;241m.\u001B[39mravel(), op)\n\u001B[0;32m 72\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m---> 73\u001B[0m result \u001B[38;5;241m=\u001B[39m \u001B[43mlibops\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mscalar_compare\u001B[49m\u001B[43m(\u001B[49m\u001B[43mx\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mravel\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43my\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mop\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 74\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m result\u001B[38;5;241m.\u001B[39mreshape(x\u001B[38;5;241m.\u001B[39mshape)\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\_libs\\ops.pyx:107\u001B[0m, in \u001B[0;36mpandas._libs.ops.scalar_compare\u001B[1;34m()\u001B[0m\n", - "\u001B[1;31mTypeError\u001B[0m: '<' not supported between instances of 'str' and 'int'" - ] - } - ], - "source": [ - "df_join[df_join.index.get_level_values(1)<2055]" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-20T13:36:58.865218500Z", - "start_time": "2023-09-20T13:36:57.609076600Z" - } - }, - "id": "487778a8578950ac" - }, - { - "cell_type": "code", - "execution_count": 64, - "outputs": [ - { - "data": { - "text/plain": " Level\nnode \nR12_AFR 1360.246539\nR12_CHN -234.583538\nR12_EEU 94.750907\nR12_FSU -36.503831\nR12_GLB 0.000000\nR12_LAM -461.315355\nR12_MEA 43.783812\nR12_NAM -191.983143\nR12_PAO -43.486300\nR12_PAS 827.924115\nR12_RCPA 45.160749\nR12_SAS 165.671837\nR12_WEU 259.030739", - 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R12_WEU259.030739
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" - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_cum.groupby(\"node\").sum()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-19T20:43:34.345832800Z", - "start_time": "2023-09-19T20:43:34.220548200Z" - } - }, - "id": "c1c60ffe2084680e" - }, - { - "cell_type": "code", - "execution_count": 17, - "outputs": [], - "source": [ - "df_land2 = df_land.reset_index()\n", - "histo = df_land2.join(df_land2[\"land_scenario\"].str.split(\"GHG\", expand=True)).drop(\"land_scenario\", axis=1).groupby(1).sum()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-20T20:50:25.949573200Z", - "start_time": "2023-09-20T20:50:25.902343Z" - } - }, - "id": "41486c4325eefb24" - }, - { - "cell_type": "code", - "execution_count": 18, - "outputs": [], - "source": [ - "histo.index = histo.index.astype(int)#.astype(int)#.plot.bar()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-20T20:50:26.199374100Z", - "start_time": "2023-09-20T20:50:26.152619900Z" - } - }, - "id": "912bd4b1cf1d1f6a" - }, - { - "cell_type": "code", - "execution_count": 19, - "outputs": [ - { - "data": { - "text/plain": "Text(0.5, 0, 'GHG category')" - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": "
", - "image/png": 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- }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(11,7))\n", - "(histo/168).sort_index().plot.bar(ax=ax, legend=False)\n", - "ax.set_xlabel(\"GHG category\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-20T20:50:26.715403200Z", - "start_time": "2023-09-20T20:50:26.530818800Z" - } - }, - "id": "df3db1c98d46d40e" - }, - { - "cell_type": "code", - "execution_count": 20, - "outputs": [], - "source": [ - "fig.savefig(\"histo_tax_1000.svg\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-20T20:50:47.621212Z", - "start_time": "2023-09-20T20:50:47.512023100Z" - } - }, - "id": "fd85a693a9cd1b69" - }, - { - "cell_type": "code", - "execution_count": 28, - "outputs": [ - { - "data": { - "text/plain": " Level\nnode year_all land_scenario \nR12_AFR 2020 BIO05GHG200 1.000000\n 2100 BIO00GHG000 0.001690\n 2110 BIO00GHG000 0.002765\n 2040 BIO00GHG100 0.226219\n 2045 BIO00GHG100 0.401263\n... ...\nR12_CHN 2020 BIO07GHG100 1.000000\n 2080 BIO07GHG3000 0.401263\n 2090 BIO07GHG3000 0.240251\n 2100 BIO07GHG3000 0.143847\n 2110 BIO07GHG3000 0.086127\n\n[314 rows x 1 columns]", - "text/html": "
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" - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_land" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-20T19:00:02.956541Z", - "start_time": "2023-09-20T19:00:02.847331400Z" - } - }, - "id": "d887d1506a4622df" - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [], - "metadata": { - "collapsed": false - }, - "id": "6343055268f44a34" - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/message_ix_models/model/material/magpie notebooks/read_magpie_raw_files.ipynb b/message_ix_models/model/material/magpie notebooks/read_magpie_raw_files.ipynb deleted file mode 100644 index 67696f2987..0000000000 --- a/message_ix_models/model/material/magpie notebooks/read_magpie_raw_files.ipynb +++ /dev/null @@ -1,1785 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "initial_id", - "metadata": { - "collapsed": true, - "ExecuteTime": { - "end_time": "2023-10-05T08:35:26.746929400Z", - "start_time": "2023-10-05T08:35:25.398719100Z" - } - }, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "import os" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "outputs": [], - "source": [ - "dir = \"C:/Users\\maczek\\Desktop/MP00BI00/MP00BI00/raw-exp\"\n", - "dir = \"C:/Users\\maczek\\Desktop/MP00BI00/MP00BI00/raw-linear\"" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-05T08:35:26.747927200Z", - "start_time": "2023-10-05T08:35:26.632018700Z" - } - }, - "id": "a75aaa89554ad632" - }, - { - "cell_type": "code", - "execution_count": 3, - "outputs": [], - "source": [ - "f_list = os.listdir(dir)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-05T08:35:26.748925Z", - "start_time": "2023-10-05T08:35:26.647118400Z" - } - }, - "id": "c626ebcfa537a454" - }, - { - "cell_type": "code", - "execution_count": 4, - "outputs": [], - "source": [ - "df = pd.DataFrame()\n", - "for scen_name in f_list[:2]:\n", - " # if \"BIO00\" in scen_name:\n", - " # continue\n", - " # if \"BIO45\" in scen_name:\n", - " # continue\n", - " # if \"BIO15\" in scen_name:\n", - " # continue\n", - " # if \"BIO25\" in scen_name:\n", - " # continue\n", - " # if \"GHG400raw\" in scen_name:\n", - " # df_tmp = pd.read_csv(dir+\"/\"+scen_name, sep=\";\")\n", - " # df_tmp = df_tmp[df_tmp[\"Region\"]==\"World\"]\n", - " # df = pd.concat([df, df_tmp])\n", - " # del df_tmp\n", - " # if \"GHG600raw\" in scen_name:\n", - " df_tmp = pd.read_csv(dir+\"/\"+scen_name, sep=\";\")\n", - " #df_tmp = df_tmp[df_tmp[\"Region\"]==\"World\"]\n", - " df = pd.concat([df, df_tmp])\n", - " del df_tmp\n", - " #if \"BIO00\" in scen_name:\n", - " # df_tmp = pd.read_csv(dir+\"/\"+scen_name, sep=\";\")\n", - " # df_tmp = df_tmp[df_tmp[\"Region\"]==\"World\"]\n", - " # df = pd.concat([df, df_tmp])\n", - " # del df_tmp" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-05T08:35:26.864063500Z", - "start_time": "2023-10-05T08:35:26.658212300Z" - } - }, - "id": "293986cfd209e770" - }, - { - "cell_type": "code", - "execution_count": 5, - "outputs": [], - "source": [ - "df = df.reset_index(drop=True)\n", - "split_cols = df.reset_index()[\"Scenario\"].str.split(\"GHG\", expand=True)#.drop(\"Scenario\", axis=1)\n", - "split_cols[0] = split_cols[0].str.replace(\"SSP2\", \"\")\n", - "split_cols[1] = \"GHG\" + split_cols[1]\n", - "df[[\"BIO\", \"GHG\"]] = split_cols" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-05T08:35:26.986242Z", - "start_time": "2023-10-05T08:35:26.828612800Z" - } - }, - "id": "3ba1d6cb86af3036" - }, - { - "cell_type": "code", - "execution_count": 6, - "outputs": [], - "source": [ - "df = df.drop([\"Unnamed: 23\", \"Scenario\", \"Model\"], axis=1)\n", - "df = df.set_index([\"Variable\", \"Region\", \"BIO\", \"GHG\", \"Unit\"])\n", - "df.columns = [int(i) for i in df.columns]" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-05T08:35:30.501889700Z", - "start_time": "2023-10-05T08:35:29.196964500Z" - } - }, - "id": "a686e64adaf8d750" - }, - { - "cell_type": "code", - "execution_count": 7, - "outputs": [], - "source": [ - "df = df.melt(ignore_index=False)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-05T08:35:30.590539800Z", - "start_time": "2023-10-05T08:35:30.396805600Z" - } - }, - "id": "d8ab94cc9465baa5" - }, - { - "cell_type": "code", - "execution_count": 24, - "outputs": [], - "source": [ - "from bokeh.models import CDSView, ColumnDataSource, IndexFilter\n", - "\n", - "source = ColumnDataSource(df.loc[\"Biodiversity|BII\"])" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-05T08:53:41.407389500Z", - "start_time": "2023-10-05T08:53:40.301937400Z" - } - }, - "id": "d030e89d85a02396" - }, - { - "cell_type": "code", - "execution_count": 26, - "outputs": [ - { - "data": { - "text/plain": "{'Region_BIO_GHG_Unit': array([('AFR', 'BIO00', 'GHG000', 'unitless'),\n ('CHA', 'BIO00', 'GHG000', 'unitless'),\n ('CPA', 'BIO00', 'GHG000', 'unitless'),\n ('EEU', 'BIO00', 'GHG000', 'unitless'),\n ('FSU', 'BIO00', 'GHG000', 'unitless'),\n ('LAM', 'BIO00', 'GHG000', 'unitless'),\n ('MEA', 'BIO00', 'GHG000', 'unitless'),\n ('NAM', 'BIO00', 'GHG000', 'unitless'),\n ('PAO', 'BIO00', 'GHG000', 'unitless'),\n ('PAS', 'BIO00', 'GHG000', 'unitless'),\n ('SAS', 'BIO00', 'GHG000', 'unitless'),\n ('WEU', 'BIO00', 'GHG000', 'unitless'),\n ('World', 'BIO00', 'GHG000', 'unitless'),\n ('AFR', 'BIO00', 'GHG010', 'unitless'),\n ('CHA', 'BIO00', 'GHG010', 'unitless'),\n ('CPA', 'BIO00', 'GHG010', 'unitless'),\n ('EEU', 'BIO00', 'GHG010', 'unitless'),\n ('FSU', 'BIO00', 'GHG010', 'unitless'),\n ('LAM', 'BIO00', 'GHG010', 'unitless'),\n ('MEA', 'BIO00', 'GHG010', 'unitless'),\n ('NAM', 'BIO00', 'GHG010', 'unitless'),\n ('PAO', 'BIO00', 'GHG010', 'unitless'),\n ('PAS', 'BIO00', 'GHG010', 'unitless'),\n ('SAS', 'BIO00', 'GHG010', 'unitless'),\n ('WEU', 'BIO00', 'GHG010', 'unitless'),\n ('World', 'BIO00', 'GHG010', 'unitless'),\n ('AFR', 'BIO00', 'GHG000', 'unitless'),\n ('CHA', 'BIO00', 'GHG000', 'unitless'),\n ('CPA', 'BIO00', 'GHG000', 'unitless'),\n ('EEU', 'BIO00', 'GHG000', 'unitless'),\n ('FSU', 'BIO00', 'GHG000', 'unitless'),\n ('LAM', 'BIO00', 'GHG000', 'unitless'),\n ('MEA', 'BIO00', 'GHG000', 'unitless'),\n ('NAM', 'BIO00', 'GHG000', 'unitless'),\n ('PAO', 'BIO00', 'GHG000', 'unitless'),\n ('PAS', 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0.76384163, 0.78275925,\n 0.72232247, 0.74720353, 0.81586527, 0.67675379, 0.84941208,\n 0.82764936, 0.73470648, 0.85399713, 0.75783254, 0.84354412,\n 0.689077 , 0.76278054, 0.78820097])}" - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "source.data" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-05T08:53:56.842755Z", - "start_time": "2023-10-05T08:53:55.728271500Z" - } - }, - "id": "70feae3dda563d29" - }, - { - "cell_type": "code", - "execution_count": 20, - "outputs": [ - { - "ename": "TypeError", - "evalue": "unhashable type: 'list'", - "output_type": "error", - "traceback": [ - "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[1;31mTypeError\u001B[0m Traceback (most recent call last)", - "Input \u001B[1;32mIn [20]\u001B[0m, in \u001B[0;36m\u001B[1;34m()\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[43msource\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mdata\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mBIO\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mGHG\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m)\u001B[49m\n", - "\u001B[1;31mTypeError\u001B[0m: unhashable type: 'list'" - ] - } - ], - "source": [ - "source.data.get([\"BIO\", \"GHG\"])" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-05T08:47:53.905643300Z", - "start_time": "2023-10-05T08:47:52.734166100Z" - } - }, - "id": "b230b30f89d3fc65" - }, - { - "cell_type": "code", - "execution_count": 28, - "outputs": [ - { - "data": { - "text/plain": "[False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n 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False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True]" - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\"Region_BIO_GHG_Unit\"\n", - "bools = [True if ((y_val[1] == \"BIO00\") & (y_val[2] == \"GHG010\")) else False for y_val in source.data[\"Region_BIO_GHG_Unit\"]]\n", - "bools" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-05T08:55:33.326373900Z", - "start_time": "2023-10-05T08:55:32.140865300Z" - } - }, - "id": "89154b81107d7375" - }, - { - "cell_type": "code", - "execution_count": 22, - "outputs": [ - { - "data": { - "text/plain": "[False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n False,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True,\n True]" - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from bokeh.models import BooleanFilter\n", - "\n", - "bools = [True if ((y_val[0] == \"BIO00\") & (y_val[1] == \"GHG010\")) else False for y_val in zip(source.data['BIO'], source.data['GHG'])]\n", - "view = CDSView(filter=BooleanFilter(bools))\n", - "\n", - "#p2.circle(x=\"x\", y=\"y\", size=10, hover_color=\"red\", source=source, view=view)\n", - "bools" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-05T08:49:45.882385900Z", - "start_time": "2023-10-05T08:49:44.648191600Z" - } - }, - "id": "d02de070eea817a2" - }, - { - "cell_type": "code", - "execution_count": 66, - "outputs": [ - { - "data": { - "text/plain": "4" - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import math\n", - "regs = 15\n", - "rows = math.ceil(math.sqrt(regs))\n", - "if (rows * (rows-1)) >= (regs):\n", - " cols = rows - 1\n", - "else:\n", - " cols = rows\n", - "cols" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-04T21:27:13.662548400Z", - "start_time": "2023-10-04T21:27:12.428171400Z" - } - }, - "id": "daf81ed2700bf664" - }, - { - "cell_type": "code", - "execution_count": 10, - "outputs": [ - { - "ename": "ValueError", - "evalue": "invalid literal for int() with base 10: 'Unnamed: 23'", - "output_type": "error", - "traceback": [ - "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[1;31mValueError\u001B[0m Traceback (most recent call last)", - "Input \u001B[1;32mIn [10]\u001B[0m, in \u001B[0;36m\u001B[1;34m()\u001B[0m\n\u001B[0;32m 1\u001B[0m df \u001B[38;5;241m=\u001B[39m df\u001B[38;5;241m.\u001B[39mset_index([\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mModel\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mScenario\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mRegion\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mVariable\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mUnit\u001B[39m\u001B[38;5;124m\"\u001B[39m])\n\u001B[0;32m 2\u001B[0m df \u001B[38;5;241m=\u001B[39m df\u001B[38;5;241m.\u001B[39mdrop(df\u001B[38;5;241m.\u001B[39mcolumns[\u001B[38;5;241m-\u001B[39m\u001B[38;5;241m1\u001B[39m], axis\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m1\u001B[39m)\n\u001B[1;32m----> 3\u001B[0m df\u001B[38;5;241m.\u001B[39mcolumns \u001B[38;5;241m=\u001B[39m [\u001B[38;5;28mint\u001B[39m(i) \u001B[38;5;28;01mfor\u001B[39;00m i \u001B[38;5;129;01min\u001B[39;00m df\u001B[38;5;241m.\u001B[39mcolumns]\n", - "Input \u001B[1;32mIn [10]\u001B[0m, in \u001B[0;36m\u001B[1;34m(.0)\u001B[0m\n\u001B[0;32m 1\u001B[0m df \u001B[38;5;241m=\u001B[39m df\u001B[38;5;241m.\u001B[39mset_index([\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mModel\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mScenario\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mRegion\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mVariable\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mUnit\u001B[39m\u001B[38;5;124m\"\u001B[39m])\n\u001B[0;32m 2\u001B[0m df \u001B[38;5;241m=\u001B[39m df\u001B[38;5;241m.\u001B[39mdrop(df\u001B[38;5;241m.\u001B[39mcolumns[\u001B[38;5;241m-\u001B[39m\u001B[38;5;241m1\u001B[39m], axis\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m1\u001B[39m)\n\u001B[1;32m----> 3\u001B[0m df\u001B[38;5;241m.\u001B[39mcolumns \u001B[38;5;241m=\u001B[39m [\u001B[38;5;28;43mint\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mi\u001B[49m\u001B[43m)\u001B[49m \u001B[38;5;28;01mfor\u001B[39;00m i \u001B[38;5;129;01min\u001B[39;00m df\u001B[38;5;241m.\u001B[39mcolumns]\n", - "\u001B[1;31mValueError\u001B[0m: invalid literal for int() with base 10: 'Unnamed: 23'" - ] - } - ], - "source": [ - "df = df.set_index([\"Model\", \"Scenario\", \"Region\", \"Variable\", \"Unit\"])\n", - "df = df.drop(df.columns[-1], axis=1)\n", - "df.columns = [int(i) for i in df.columns]" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-04T12:09:31.501717400Z", - "start_time": "2023-10-04T12:09:31.410396100Z" - } - }, - "id": "ca445c3673ae5909" - }, - { - "cell_type": "code", - "execution_count": 44, - "outputs": [ - { - "data": { - "text/plain": " variable value\nVariable Region BIO GHG Unit \nBiodiversity|BII AFR BIO00 GHG000 unitless 1995 0.753832\n CHA BIO00 GHG000 unitless 1995 0.750001\n CPA BIO00 GHG000 unitless 1995 0.832715\n EEU BIO00 GHG000 unitless 1995 0.673826\n FSU BIO00 GHG000 unitless 1995 0.849367\n... ... ...\nZERO PAO BIO00 GHG010 NaN 2100 0.000000\n PAS BIO00 GHG010 NaN 2100 0.000000\n SAS BIO00 GHG010 NaN 2100 0.000000\n WEU BIO00 GHG010 NaN 2100 0.000000\n World BIO00 GHG010 NaN 2100 0.000000\n\n[593892 rows x 2 columns]", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
variablevalue
VariableRegionBIOGHGUnit
Biodiversity|BIIAFRBIO00GHG000unitless19950.753832
CHABIO00GHG000unitless19950.750001
CPABIO00GHG000unitless19950.832715
EEUBIO00GHG000unitless19950.673826
FSUBIO00GHG000unitless19950.849367
.....................
ZEROPAOBIO00GHG010NaN21000.000000
PASBIO00GHG010NaN21000.000000
SASBIO00GHG010NaN21000.000000
WEUBIO00GHG010NaN21000.000000
WorldBIO00GHG010NaN21000.000000
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593892 rows × 2 columns

\n
" - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-04T15:19:36.302301400Z", - "start_time": "2023-10-04T15:19:36.244917800Z" - } - }, - "id": "511f547533222d48" - }, - { - "cell_type": "code", - "execution_count": 27, - "outputs": [], - "source": [ - "afolu_co2 = \"Emissions|CO2|Land|+|Land-use Change\"\n", - "for_cov = \"Resources|Land Cover|+|Forest\"\n", - "cro_cov = \"Resources|Land Cover|+|Cropland\"\n", - "#afolu_co2 = \"Emissions|CO2|Land\"\n", - "luc_co2 = \"Emissions|CO2|Land|Cumulative|Land-use Change|+|Gross LUC\"\n", - "luc_co2 = \"Emissions|CO2|Land|Land-use Change|+|Gross LUC\"\n", - "luc_co2_def = \"Emissions|CO2|Land|Land-use Change|+|Gross LUC\"\n", - "luc_co2_all = \"Emissions|CO2|Land|Land-use Change|Gross LUC\"" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-03T12:23:15.398752700Z", - "start_time": "2023-10-03T12:23:15.383133200Z" - } - }, - "id": "54e85d03d17f6bc3" - }, - { - "cell_type": "code", - "execution_count": 75, - "outputs": [ - { - "data": { - "text/plain": "{'Emissions|CO2|Land|Cumulative|Land-use Change|+|Regrowth',\n 'Emissions|CO2|Land|Cumulative|Land-use Change|Regrowth|CO2-price AR',\n 'Emissions|CO2|Land|Cumulative|Land-use Change|Regrowth|NPI_NDC AR',\n 'Emissions|CO2|Land|Cumulative|Land-use Change|Regrowth|Other Land',\n 'Emissions|CO2|Land|Cumulative|Land-use Change|Regrowth|Secondary Forest',\n 'Emissions|CO2|Land|Cumulative|Land-use Change|Regrowth|Timber Plantations',\n 'Emissions|CO2|Land|Land-use Change|+|Regrowth',\n 'Emissions|CO2|Land|Land-use Change|Regrowth|CO2-price AR',\n 'Emissions|CO2|Land|Land-use Change|Regrowth|NPI_NDC AR',\n 'Emissions|CO2|Land|Land-use Change|Regrowth|Other Land',\n 'Emissions|CO2|Land|Land-use Change|Regrowth|Secondary Forest',\n 'Emissions|CO2|Land|Land-use Change|Regrowth|Timber Plantations'}" - }, - "execution_count": 75, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "set([i for i in df.index.get_level_values(3) if \"Regr\" in i])" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-03T13:46:16.752142400Z", - "start_time": "2023-10-03T13:46:16.215256700Z" - } - }, - "id": "4a4e98b9a599d263" - }, - { - "cell_type": "code", - "execution_count": 63, - "outputs": [], - "source": [ - "from matplotlib.backends.backend_pdf import PdfPages" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-03T13:25:45.312912600Z", - "start_time": "2023-10-03T13:25:44.547475600Z" - } - }, - "id": "9a18e5541143276a" - }, - { - "cell_type": "code", - "execution_count": 69, - "outputs": [], - "source": [ - "import time" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-03T13:33:37.364362800Z", - "start_time": "2023-10-03T13:33:36.829228100Z" - } - }, - "id": "924d048bedc50e0a" - }, - { - "cell_type": "code", - "execution_count": 82, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "187.0278775691986\n", - "373.2970609664917\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\maczek\\AppData\\Local\\Temp\\ipykernel_28236\\1713529792.py:13: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n", - " fig, axs = plt.subplots(4, 3, figsize=(25,18))\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "560.124425649643\n", - "749.7232000827789\n" - ] - }, - { - "data": { - "text/plain": "
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\n" 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\n" 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\n" 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\n" 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\n" 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\n" 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\n" 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zD7AE+KK19kXgGPCRaav8Wrl32IgxZuCCzb9mjBkHzgB9wP97Qw5aRERkYWkABqy1pUus84Fp4/WIMWbkIut8FPhC+fUXUPsSERGRmXbFYzb+efStwLvLyz8K7LHWHgD+EbjJGHPzdTxWkQVNQbfI/PQx4F+stRMh9oUnvn9orU2Wp8YLtn23tbYa/6rzWuDC90VEROT1GwQajTHBS6zzxWnjddJam5z+pjHmbmAZ8E/lRV8ANhpjtlyPAxYREVmgrmbMbrbWPlguOIOpO62x1nYBT6CL0iLXjYJukXnGGBMDPgDcb4zpMcb0AL8KbDbGbL7S/Vhrn8Dv7/2H1+VARUREFrbtQI6piq9r8THAALvK4/3O8nLdFi0iIjJzrmnMNsbcBawCfn3aufkdwIcvE5qLyDXS/1gi88+7ARfYCBSmLf8iV3/i+yngpDFmi7V210wcnIiIiIC1dtQY85vAZ4wxJeBfgCLwEPAGIHOp7Y0xUfwL258Avj3trfcBv2mM+U+XucVaRERErsDrGLM/BvyA88/DY/gPpXwr8M3rdtAiC5QqukXmn48Bf2OtPW2t7ZmYgD/Df0DVFV/gstb2A38H/D/X51BFREQWLmvtHwOfBP5/QD9+X89fBL52BZu/G8gCf3fBeP/XQAB4+Hocs4iIyEJ0tWP2tAvS/2v6OG2tPQH8PWpfInJdGGvtbB+DiIiIiIiIiIiIiMg1U0W3iIiIiIiIiIiIiFQ0Bd0iIiIiIiIiIiIiUtEUdIuIiIiIiIiIiIhIRVPQLSIiIiIiIiIiIiIVLTjbBzCTGhsb7dKlS2f7MEREZB578cUXB6y1TbN9HJVOY7aIiFxPGq9nhsZrERG5nmZ6vJ5XQffSpUt54YUXZvswRERkHjPGnJrtY5gPNGaLiMj1pPF6Zmi8FhGR62mmx2u1LhERERERERERERGRiqagW0REREREREREREQqmoJuEREREREREREREaloCrpFREREREREREREpKLN+aDbGPOwMeawMeaoMea/zPbxiIiILCSXG4eN79Pl9/cYY26ZjeMUERFZ6DRmi4jIQjeng25jTAD4DPBWYD3wYWPM+tk9KhERkYXhCsfhtwKrytMngL+4oQcpIiIiGrNFRESY40E3cDtw1Fp73FpbAP4JeNcsH5OIiMhCcSXj8LuAv7O+HUDSGNN2ow9URERkgdOYLSIiC95cD7o7gDPTvj5bXiYiIiLX35WMwxqrRUREZp/GbBERWfCCs30Al2Eussyet4Ixn8C/7Yrmjg5+8399mqgTIB4MUR0KkoiEScRixBNxampqqElWU9/QTG2ynnA4fCO+BxERkUp12XH4CtfxV5w2Zi9evPj1HZmIiIhMN2NjtsZrERGpVHM96D4LdE77ehHQNX0Fa+1ngc8CRNpW2b87t+I1duUBI+WpfBHbWIyxGAOOsRjj4TgWx3gEjDc5DxiXgHEJ4hE0LkFKRCj67+HhUN4Gi4P1lxmLA/4y6/mvjYdjIVB+z18XHAMBAwEMAccQMIZgwCEUDBAMOoRDQYLhIKFQiHA0TCQSJRKLEY1HiUSixCNxotEY8UicWDhKIpogEooSDERwnMBM/VuIiMjCc9lx+ArXAc4fs7du3XrRMFxERESuyYyN2RqvRUSkUs31oPt5YJUxZhlwDvgQ8JHXWrk6mOee1lMUnQBFAhRMgGJ5KuHPXQIUcSgRwMXBtRNzf/JwMNb6ubi1/vVtb2pu7NRyx3X9ENtzMdaWt/Pfs9ZgrcGzDp69Xh1iLJAtT+czTAT17lRo77g4xsPBI+CU5+X3/GB+Ktz337Pl9afNmVjPTm3DxOtpc/yLCH6IP/XacTh/bgwBBxzH4DiGoGMIOA6BgCHoBAgEAn7oHwgQdIKEQgGCgRChQIhwOEg4ECYSDhEKRYiGIkTCESKhMLFQlHAoQsAJ4ThBHCeAEwjhmACBQAhjApPL/ee2OBhzsQIHEZEF7UrG4W8Av2iM+SfgDmDUWtt9Yw9TRERkwdOYLSIiC96cDrqttSVjzC8C3wcCwOestftfa/2lLc38w6/8/KV3WkhDegAyA5AeLM+nvvYyA2QzY2Ry42TzGTKuSyYQJRuIkglEyQRijAar6atdSX/NEvpjbfSHmugzUfpKkPW8V32ksZZqz1LjeVQXCyTyRRKFHLF8nng+R7SQJVbIEivmCRVyWOvhYfGsxTP4r43Fw+BBeRn+1xOvjcHF4JnyOoCLM7Ue/vu2PPcwuNafe9aU1/VfezZEcfI9P6j31y+/tmbyosDE++7kfDb/kyqUJ5+ZDOTLAb+ZHuxf/OvpYb8xE5X6nl/tb/zwf3K/5bmZ3M4P+s20CwPG2PI2/jID5c8qz6G8HZN3ATjluwwChvLdBv4FAWfa64DxLw4EjH+BwL9gMLHc8ZebqeX+Nv4+jGMIYHAcx/8cTPlzynPHweBvgzGYcit/Yxz8ux0NGMe/77G8zF/HlC8WmMl1X/11+aKP8T9jYj65fPKCw4XbTK1z0eXG4Ewe3/Rj5VXHAgbjOBPvTvtsc/525Z/Dxb+fi2/nL5+6YHK511ez7o3a3/U6Jl1IqkyvNQ4bY/5d+f2/BL4DvA04CmSAn56t4xUREVmoNGaLiIjM8aAbwFr7HfwBeWaEE/5Ut+SibztAojwBUMxBZlognh6AoePQuw+OfBWGT0xtHKkh3bKF/pab6W9YT1/tCvrjbfR5AQYKJfoKRfoLJXoLJQYKRbLeq+8Cc4CGcJDmcJCV8SjrElHWV8VYm4jSGQ1fVVhkraXoWnIll3zRI19yyZe8yde5okeu6JLKl0jlS4xni6SyJdK5IplMkWwmTz5ToJArUMzk8XIlbL6ILZQIW4+o9YhYl7C1hKwlDISxhICA9QPeoDE4AYvj4FeRO4CxWMdiHbDG88P6aXO3HN67BjxjcY31C+rLob+/fOrrifDfNX6NuwuTYf/kxQEm9ulX2rv4Fwc8O7H+RPgPng3gEfRDfwwFO3VhYPJCwUS1fvm1xZl63zrlzyjPrV/jXokmQn2DH+gb8Nv9TH5tp71frtwvvwdMhv3+uh6OKU2ty9T6mKm7AC63b5j+OVPr+tuCKe9v8nX5mCl/PbEMc/77/v9ZE5838bo8n1z3/Pf8z5r23rTPmvi88+bGv+Pj/P2/+n3/dflNy2Q3xYm3/c8w/pvT38e/4eRV20+tMo2/ocFiy5H+RdnyB5fXxZry11N7Nba8j4nl1kx98LSfxvTvxzB1BK/6vIscjf/9mGm7vXAd8+r1Jj/ETB3yxIeft6U5bx/n7e81li8kFxuHyyfLE68t8As3+rhERETkfBqzRURkoZvzQfesC0WhtsOfLiY/Dn2HoHcv9O4n0bufxJ6/YWl+bGqduqXQsgFabvLnS2/CJteQttBXKNFfKE7O+wsl+gsluvNFXhrL8PW+kcndVAUc1k4LvtdVxViXiJIMXfyf0RhDOGgIBx2IztyPxFpLrugxni+SypUmg/Lpr8dzJdL5EoPl10PpAn3jefrHcwymC5Nh3HTJWIjm6gjN1VGaqiM0V0f8eU2UpqoIzTX+sqpIEGMM1rN4rsV1PbxSee5a3KKLmy/i5ouUynO3UPKnidf5Il7BxSu4uEUPr+Biix5eyZ+sa7Elf//WtVgP8CzeRGJuy4GiNVPTRLXztKBxMkArh6F+/OtX6k/8CCZ3U9415aAf/AB/Ipiz5dAeY8rBfzmGNReE+VhcbDn0t/7FA8oXCcp3B/jreJMXC+zEBQPAmonX5QsM09+bXDZtsuULBXb6xQUzua3f9Wfie7aT603tx5Zj4qllF36GndxfuXOQMVOdhZj42UzE0tN/tobzfs7T4tUL12Pya/Ma25tXz+3UNpdeZ/ryhReUzj+/P9sHICIiIiIiIiIXUND9ekWqofM2f5pgLYyegd79fuV3zz7/9eHv4CemYEJxqprXU9VyE8snQ/CbINZ03u7HSy6H0zkOpLIcTOc4mMry9b4R/q7kTq7THglNBt/ry/OV8Qhh5/pUERtjiIUDxMIBmquvfvuS6zGYLtA3lqdvPFcOwMuvx/L0jec5MZCmfzxPwX11K5hYKDAZhDfXRMoheJTm6ghLGxMsa0zQ0ByfU60SLhbKu6VpAX3JUiq6eOUgvpQrTIXzk5OLW3TxiiU/zC+6uCUPtzAR0FtseV/etHDeun4IXU6x8azBL6cPgnHKQexU6w2HiZAeJpt3TH7N5NfTGplMVjc7ZqJquvz15LzcygUm55OvJz4T/7+tibkz0QoEvzWI4cKWIXOTtf5lg4m5h8Xa8uWEiTkWb7JFkVe+0DD12uLh+VdSyttPXGKYdrGk/L4tf+bERYrJ7Se2NRNbTlwA8S92WLzyfKL9kTftvYlty3dbMNE+qfy+AW/aZ/jLJl77d2L4203sd/oygzXe5L7t9LszyvuxpnxhxBgm/nPFKf//7PffwTj+fxuUW/T4XWWm2ttMtpExBmeiJY5hssXN9HXMROub8n9fjpnYR3mCyf/2jDH85o37z0lERERERERErpCC7uvBGEgu9qc1b51aXshA/6FyAF4OwQ9+A176/NQ6tZ1Tld8r3kB15za21ibYWjvZTAVrLd35IgfLAfihcgD+1HCKYrlUOmiYbH0yUfm9ripGRyQ06wFwMODQUhOlpSYK1L7metZaRrPFiwbhE18f6hnnqbEBxvOl87atiQZZ3lTF8sYEy5sSLGusYnlTgqUNCWLhwHX+Dl/NOOUe2qG5G9Jaa7GexXUtXsnDLVk/jHf9157r4RYngnlvcr1S0fMD+qIfxpfyRT+EL5TKwfy0UL5cNe+WJ2/iszyLWwLPs3gu/kUBr1z97Rk86072hp+ok58eul/s6/OmcnX99AYV561nLQ7lh8taD8e6fghvXQJYjPVwrB/Ym/LcseV+7QB24mGt5fpyW26lYu20z5nWqsSZHrD6YWzQmKm+4M60nuITfdLLBz291zjmgvnk64mf0vnLpu4xMBe8nnhvov2HOf9rpr6+6O+P6eXxC4CCbhEREREREZG5R0H3jRSOQ8ct/jTBWhjvngq+J0LwIz+Ap/4QoklY/RY/MF/xRojWYIyhPRqmPRrmjQ01k7sqepajmdxk8H0gneO50TRfndb+pCbosDYxFXxvqoqxqTpO0LlIeDXLjDEk42GS8TCrWy5dOp4tuPSM5Tg5mOZ4f5oTAymO96fZfnyQr7x87rx1O5KxcvidYHljgmXlQLwjGcOZgz+HG8UYgwkYnAAwCxcDrpTn2fOCdve8UN5OBfMl7/y2NtPn5XWngnxvsmJ+YvJKflubyYB+Yr1pVfOuW557fjjveUxN9ir/W5poMM+0+Wsw1sWx/mNlHeuWg/iSP/dKGK/kr+OVMF557hanllnXX24n1nOnzUv+PieXlaY+z5t6z8H1g3yv/Hhb6zfOwfgXBigH/ZNNb8oV136nHwfrBLCBADgONhDAluc4Dl7A8Zc7DhgHz0z/2vjrGgfrlLuuO+VLCaY8lV9PtPmxZnoX9alc3q+GL7cOKvcVn1xmy19N9B+HaRcMZCac7e3lrz73f3jo/vtZumL1bB+OiIiIiIiIVDhjL9YsuUJt3brVvvDCC7N9GDMjn4Jjj8Hh78Ir34PsEDghWHoPrHkbrHnYrxi/AqPFkh9+lwPwifl4uS1ITdDh3rpq7q+r5v76apbEItfzO7vh0vkSJwbSnBiYFoKXX6emVYKHgw7LGhJTIXiTXwW+vDFBMh6exe9AKpG1dlqYfpEg/rxA/sK5Xz3vuRPbTNvPZNDuTQbt3mu+Ny3Y9+xku5zJiwAT23oWt2Qv2jt/Jjlm6gGj/tx/2OnEA0z9qnh3smrer5ifCPK9acH+RAjvTQXzE+G8V5oM7P2Qv4hxS+CVcNwSxitiXNefl0o4bgHcEo5bhFLRvyDgFjDu1H4o79+We+VvPnjgRWvt1uv705r/Im2rbNvHPgWAE7KEw0US4Ry1ToZGk6LFTdFoPVoSCVYvW8m2Bx4kXlU1uwctIiIVwxij8XoGzKtzbBERmXNmerxW0F0JPBfOPOf3+D78XRg84i9v2ehXeq95K7RtgavoyW2t5Vy+yItjaZ4cGufxoXHO5YsALIuFub++hgfqqrm7rorq4Nyt7n09rLX0p/Kc6E9zfDII90Pw04MZSt7U/xv1ifBkBfjK5io2dNRyU3uNAnCZV87rJX/RcP78oP68h8BOW9f/evrraYH9tPDemxbMT1XIn//+9M+95D68GzCWGQgEDD/3mQd14jwDGhcvttv+439j0MQZ82JkC2FKBQeTdyHvcWH9vDEe8XCOmlCa+sAYdSZLDUVq8agLBGivb+CWW7ay/mb904iIiILumTJvz7FFRGROUNB9CQtmEB44MlXpfXq7/4DL6jZY/bBf7b3sPghFr2qX1lqOZfM8PjTOE0PjPDOSIuN6BAzcWpPg/rpqHqivZvMcbXMy04qux5mhzGQV+PFyCH5iIE3feH5yvY5kjA0dNdzUXsuGjho2tNfSXHN1P3sRef2steUe71MV7udXuVs879Uh/HlBvDdR5X5BxfwFwfpd712lE+cZcLExO5MZ5fC5Y7xw/ATPd49w2o3T71QxauPkikFM3oO8i8m5OPkSlF6930ggTzI8Rk0wQ00gS5UpUmVLJPCoNQ718RiLWlrZtPV2lixfdYO+WxERudEUdM+MBXOOLSIis0JB9yUsyEE4MwRH/sWv9j76r1BIQSgOKx70Q+/Vb4FE41XvtuB5vDCa4YnhcR4fGmPPeBYL1AYD3FNXxQP1fquTxfOszcmVGE4XONA9xr5zo+zrGmP/uVGOD6Qn32+sikyG3hMh+KK62Kw/BFREZoZOnGfG1Y7Z6fEBTvWd5JWBHg6PjHIy73KkGOe0aSRlY5BzMeUQPJzLE8znIe/hFQ0l9+KPJIkGclSHUlQFs1QF8lSZAgmKVONShaU2GKK5tpYVy1dw8x13q3WKiEgF0Xg9MxbkObaIiNwwCrovYcEPwqU8nHzKr/Y+/F0YOwcY6Ly93OLkbdC4+poeqDZULPHUsF/t/fjQOF3lNifLYxHur/erve9Kzt82J5eTypc4OBF+nxtjf9coR/pSuOV2CjXR4GS7E39ey7LGBIEFUB0vMt/oxHlmzNiYbS3psV5O9p7k+HAvx8ZTHM26nCLK0UgLw6FaKHkE8kUaR4epT41QkxsnVkgTdAtkbJCUF2HcjZEqxkkVE1he3QrMMS5VoTTVwQyJQI5qJ0eVKZLArxaPA4lAgGQsTlNjI8uWrmDNxs3EEonX/z2KiMhV03g9Mxb8ObaIiFxXCrovQYPwNNZCz55y6P0d6N7tL69fXn6Y5VuhcxsELl7lduldW45m8uVq73GeGU6R9TyCE21O6qt5oK6azTVxAgu4ijlXdDncM86+Lj/8PtA1ysGecQol/yGg8XCAdW01bGiv4aZyCL6quZpw8Mp7rYvIjacT55lxI8ZsW8xztusgu7uOsXtknN2lMHsiixgJ1QAQ8oosyfaRTKVJDVvODlXhjGbZaLroCI6RCLl4IUvWcciYIClCpGyYlBslVYoxXkyQd1+7XZXBIx7MEg/miAXyxAN5YqZAzJT8yXrE8IgbQ1UgQDIWo6mhiRUrVrJq4xaiUbXCEhG5VhqvZ4bOsUVE5HpS0H0JGoQvYfQcvPJdOPw9OPEEuAWIJv3Q+9aPQecd11TpDZD3PF4YTfvV3sPj7C23OUlOtjmp4aGGGlojoRn9lipR0fU42pdi37lR9nf5ld/7u8bIFFwAwgGHNa3V3NRew6ZFSTZ31rKmpZpgQOG3yFyhE+eZMVtjts2Ncfr0Hnb1nGLPWJrdXpw9sSWMBasBCHtFVhYHaSsWMLkofcMxuroypHJTDcGNgfbaGEsa4ixnkLrCOYKFFAU3T9otkbGWLE55CpK1QbI2RNYNk/GiZEtR0qUYnn3tu6Ac4xIL5ogHsuWQvEDEFIkYlzAuEeMSsR5hLBE8oo4h6hjioTC18RjJulpaWtpoX7qCtpZOwmE9PFlEFhaN1zND59giInI9Kei+BA3CVyg/Dsd+5Fd7H/oW5Meg+Sa47Wdg0wchUv26dj9QKPH08PhkxXd3vogB7kxW8Z6WJG9vSlIfuvpK8vnK8ywnBtN+8H1udLICfDTrt4eJhQJs7Khly+IkWzr9qa02qp7fIrNEJ84zYy6N2d5YN6dO72F371l2pfLsppq98eWkgn7bkahXYL0dY3nAUBeoxcslGB/KcXoww8nBNCOZ4uS+mqojrG2tLk81rG2rZmVzFZELWnvlsjmOH9rP0VcO0Tc4wEgmQ+oiIXlmMiSPkPdC5L0weTdM3o3gXiIon84xLpFAgWggT9gpEnEKRJwiEadEhBJh45YDdI8wLiFjCRtL2BhCQYdoKEgkHCaeiFFVVUV1VTV1ySRNDa001bdQV10/c/8YIiIzROP1zJhL47WIiMw/CrovQYPwNcinYN+X4Pm/9ludhKtg0wdg68ehdcPr3r21lsOZHN/qG+VrfcMczeQJGri/rob3tCR5uLGWqgXa1/tSrLWcGsyw68zI5HSga4yC67c9aaqOTIbeN3cm2bioluqoKuZFbgSdOM+MOT1mex7e4DGOn9nL7v4e9mSK7DZ17KlaSSYQByDuFdjopNlcFWV9/SIaSxFO9qU42D3O4d4xXulNTbaqCjiG5Y0J1rbVTIXgbTW0X+NFS2stmXSW/rNnOXXsOAMDPYyMj5HK5ci4RXKeRw4oGMjjUMApzwPkbYC8DVKwQfJuyA/P3Ug5QL+2B0wHjEvIKRB2SoScIiGn5E+mRNgpETQuIeMSwiVoPILGI+R45deWoGMJBSAYMIRCDtFQgGg4TCwcJhGJk4jHqYlXk6xOUl9TT3NdK/FYLcGgLpqLyGvTeD0z5vR4LSIiFU9B9yVoEH4drIVzL/qB9/6vQCkHi26H2z4O698NodffJ9Ray75Ulq/2jvD1vmHO5YvEHMNDDbW8pyXJg/U1RNWi4zXlSy4Hu8fZdXqY3WdH2XVmhBMDacC/jX5lU5Uffpcrv9XyROT60InzzKi4MbtUwO3Zx7Gz+9k90M/unGV3sJl9VavIBqJEbZH7wzneungZb2ptpTbgcHIwzaGecQ51j3OoZ4xDPeOcHc5O7rI6Gjyv8nttazWrW6pv2IVLz7MUcyUKOZd8usBITz89p08zONjPWCpFJpch5xbIey5541EyHkUDJQNFx1ICisahaKCIoWgDU/OJyQtOzgteaNr89bVSCTmFcqDuB+vBcrAeckoEjR+uB8tBe8gUy3OXsOMScjzCjkckYAkFDLGgIRoKEo+EiEcj1MRiJOLVxONJqmL1VFc3UVPdRHWiicA1PNtERGaHxuuZUXHjtYiIVBQF3ZegQXiGZIZg1xfghc/B0DGI1cPNPw5bf8Z/mOUM8Kzl+dE0X+0b4Zt9IwwWS1QHHN7WlOQ9LUnuSVYTdNSa43JGMgV2nRlh95lRdp0ZZteZEYYz57c82dxZy5bOOrYsTl5z9aCITNGJ88yYF2N2bpTS2ZfYcWwX3xvJ892aLZyLtuJYjztCWd7asYiH21pZHJuqlB7LFXmlZ9wPwHvGyiH4OKn8VA/wzvqYH35PC8GXNiQIzJFxsZh3yY4XyKaKZMcL5FJFsuNFsqnysrE82dG8/166RLHo/63pAAEDAcBQIhZKEXSGMN4YrknjeTmKAZd8KEQ+FCAfDFIMOuQdh4LjUHQMBQyFcrBesKYcrBuKOBStUw7Vp4L2gjvV7sW1Vx5SB4xLJJAvTwUiwXy59UuBcKDoz02JcLkNTDyQIRHIknCyxAM54oECQWPBhMCEcAJRnECUYChGOFxFOFJDJFJDJFpLLJqkKt5IItFITaKZWDRJIKC73UReL43XM2NejNciIjJnKei+BA3CM8zz/AdXvvA5OPRtsC6seNBva7L6YZihqqaSZ3lqeJyv9g3z3f5Rxl2PxlCQdzYneU9zkq21CRyFs1fEWsvpofNbnuzvGpu8fX56y5MtnUk2qeWJyFXTifPMmHdjtudhz77A3oOP893BFN+r3szBqhUAbAhkebitjbe1tbAu8eoLjtZazo1kz6v8PtQzzvH+FF75z7RoyGHToiS3La3jtqX13LKkjpoK+f1dKrpkx4vlQLxAdrzASH+W4a40Q91pRvqy2PI3agzU1IWorYHaSJ5qZ4yq/ADxVDfeUD/uwCCloWFKo2koggnFMMEYhGLl11Gc6jp/iidxqhow4RqwEQoFSxZLiiLjTp6UKZIKFkhHSowHiqQCJTKUSOOStR45a8l5kPcMBS9AwQtS8IIUrV+VnvdCFNwQHhcPpcNOnppwiurwODXhFFXhFDXhcaqnzae/Djr+g6mtBc91cEsBf+46eK6D5xlc18HaIB4hLGEwEYwTxQnECATjhELVhCPVRCJJYrE64vEmquNN1Na0Up1oIRSO37B/d5HZpvF6ZlR1Lrfv++QnefuiJj7w/g/O9uGIiMg8o6D7EubdSfNcMtYNL/0dvPi3MN4FNR1wy8fglo9CTduMfUzO9fjXoTG+2jvMDwfHyHmWjkiId7fU8e7mJBuqYqpIvkqFksehnjE/+D7th9/HX6PlyeZFSda2quWJyKXoxHlmzOsx2/Pg3IucPPADvts/zPeqNvJczQascVjsFHhrcwMPt7Vwe22CwCXGtFzR5WhfikM94xzoGuPFU0Ps6xrD9SyOgbWtNX7wvaye25bW01Lz+tuMzQa36DHSl2GoHHxPzEf7Mkz8meo4htrmGPXtCerbEtS3V5Gsc6hyMnhDg5QGB3AHBykNlF8PDFLq6yN/8iTe6Ki/ExPAqW0mvGwtofYVBBoXEahuxISqsV4YN1XEHS1A+eLwdE4iRKAmTKA28qp5oSrIaCTAUKbAYDpP/1iO3tEM/WNZ+sZy9I9nGErnGcl5jBcMrr34v3nUyRMP5EgEs1QFM/4UTlEVSlMTHqcxOkRbopdEOEUgUCTglAgEXYxz5X/Ley54JQe3PHleANcN4HkBP0A3YSAEJorjxHCCMYLBBKFQtV+BHqkjHq8nEW+ipqqFqkQzsUQdwWDsav/ZRa47jdczI9K2yrZ97FMABGKWhqpxVphB7gx7fPxjP02iunp2D1BERCqagu5LmNcnzXOFW4JXvgcv/DUcewxMANa+3e/lvex+PzmdIamSy/cGRvlq7whPDI9RsrAqHuFdzXW8pyXJinhlntDPBaOZIrvPjpxX+T2ULgB+1eDGjtpy1Xcdmztr6UjqAoPIBJ04z4wFM2ZbC+deov/Ad/iX7l6+m1jPk3VbKThh6k2JtzTW8tbWZu6tqyZ2BRcZM4USL58e4fmTQ7xwcpiXTg+TKfiVwJ31MW5bWj85rWhKVPTv7lLRZaS3HIBPC8FHB7IwEYAHDMmWeDn8TkzOa5tiOAEHay3u8DCF48fJHzvuz08cp3D8BMVz55hM0o0h1N5OePlywktXE+pYTqChHaeqEeuG8MYKuGMF3NE87lgeL10671hN2CHUmiDUXkW4vYpQe4JQawITPP/f1FrLeL7EYKrAYCrPQKrAUNp/PZguMJDKT72XzjOSKU5W9U+oDVnqQwVqbIZYcZSkk6E+OE5VMEswVCQWM4TDHsFwgUCggOPkwORwyILN4zeAKeKYIo5xCTgugYDnT8GpyVxF95TpAbrnBvwqdC+A5wWxNoQlBCbsB+iBGMGAH57HYnXEog1Ux1uormomFm8kGq8nlmgkEFS7NXl9NF7PjA0bN9iHfvYT7KWOc4VasmMhTMG/IGiClrrqNEuDA2wpjfORd72blWvXzvIRi4hIJVHQfQkL5qR5rhg8Bi/+Dbz8fyE7BA0r4dafhi0fgXj9zH5UocS3+0f4at8wO0bSWGBTVYx3t9TxruYkHdHX91Crhc5ay5mhLLvOTlR9D7NvWsuTxiq/5cnNi9XyREQnzjNjQY7Z1kLXS6T2f4vHzp3me7G1/LDhTsaCVcRxeUNdFW9tbeKhhhqSoStrD1Z0PQ52j/H8yWGePzHEC6eGGEj5Fy7rE2FuXVLH7Uvr2bq0jg0dtYTmwR07pYLLcE/mvOrvoa4UYwO5yXWcoKGuNcGitXUs3dBA28okgQtCZy+Xo3Dq1AUh+AkKJ05gc9P2VVtLZPlywsuX+fNlywkvWYpT3YSXcSkN5Ch2pSh0pSh2p7F5t7yhIdQc90PvaQG4E73y1m+uZxnJFBhIFTg1mOZof4qjfSmO9fnzdPkiB0BVyNASh4ZQkRoyRAujRHLDVJn8ZB2C4zjU1taSTCZfNdXV1VFVVQUGssUso+kBxsa7SaX7SKcHyWaHyOdHKebHKBXHcUsZ3FIGvBzYAsYWcCjhmKIfnjsegUA5RA/aqw7QrQduMeCH56UA1gvh2RDYMIYoASdGIJAgHKwmEk4SiSQJhWoIR2qJRGsJx+qIxuqIxhuJqup8QdJ4PTMuHK8Hhwf51Jf+mecKCc6UkqTHQph0+XeRsVRXZVkSHuQmd5AHN27mLW9/xywduYiIVAIF3ZewIE+a54JiDg583a/yPrMTglG46b1+lXfHrTNa5Q3QnS/w9d4RvtY3wq7xDADbahOToXfdFYYDcmkTLU92nxnh5XLV9/H+qZYnKyZanpSnNa3V8yJAEbkcnTjPjAU/ZlsLXS9TOPB1tp96he9E1/D9xnvoiTQSxHJXTZSHWxp5uLGW9qu4mGut5cRAmhdODvP8ySGePznEyUF/rIyGHG7urJtsd3Lz4jqqIvNnzCzmXYZ70pMV4P1nxuk6OoJXsoSjATrX17NkQyNLNjQQr3ntn6n1PErd3eSPn6Bw/Fh57ofg7sDA1IqhEOEli4msWkVs82biW7YQXrsOm/ImQ++JANwbL05uFqiPEi6H3xMBeOASx/Oax2ktPWM5jpZD74npWH9q8mIHQCRoWFQTpjUBjaESNWSIFccIZAbJpsfP22cgEHjNIDyZTFJVVYXjXP1Yb60l5+ZIF9KMjQ8wMn6OsVQPqXQf2cwQ+dwwhcIobjGNdTPg5jA2j0ORgCkRdFxCAY9gwPpTyCMY9HBCHs4VBueea/CKDp4bxLpBrBfCEMEh5ofmJo4TSBAMJggGqwiFqglFagiHa4lE66YF5/VE4/UEgpHLf6jMqoU0Xhtj6oFHgaXASeAD1trhC9bpBP4OaAU84LPW2j+93L4vN16n81k++82v8sMBl5NuHeOpMGa0iCl3gYpG8yyKDbDeHWBjfZKf+OhPE4vrwpOIiPgUdF/Cgj9pngt69voPr9zzRSikoHWTH3hvfD+EEzP+cScyeb7WN8xXe0d4JZMjbAxvbarlQ6313Fdffcnep3L1Jlqe7J7W8mRwWsuTDe21k/2+t3Qm1fJE5qWFcuJsjPkD4J1AATgG/LS1duQi650ExgEXKF3pz0Zj9jTWQvcuvP1fY9eJPXw3soLvNd7LkfgSALYkQry1uZEPtdXTErn6u2n6xnPnBd8HusbwLAQcw/q2GrYunaj6rqepen6Fd4VcibOHhjm1d4CT+wbJjBbAQPOSGpZubGDpxkYaO6uueKxyR0cpnDhxXgieO3SQUlc3ACYcJrp+PbEtW/zp5i2EWlpwxwt++N2VotiVptCVwh2cVjVeFTqv6jvcXkWgPopxrm0MHckUzg/Ay5Xg50ayk91aAo6hsy7G4mSYtoShMVyihizx0hi58RFGRkZIp9Pn7TcQCLxmCD4RhF+vcb/gFhgrjDFeGJ+a58cYHe8jne4nmx4gnx2mmB/DK6SglAU3R8ArELBFwo4l4ljCDoQDHqGAJRSwBIOWYNAjEPInc4U5vh+cB7BuELwweBEMkXK1eRzHiRMMVBEMVRMMVhOO1BKOJIlE64jE/UrzeKKRSLwex5k/F5zmkoUyXgMYY34fGLLW/p4x5r8Addba/3zBOm1Am7X2JWNMNfAi8G5r7YFL7ftqx+t0PsdXd/yIL70ywFFbz1gqghkpTrY7CQZLtCcGWUMvKwOGD7zv/SxbseIqv2MREZkvFHRfgk6a55DcGOz9Ijz/OejbD7E6uOPfwe2fmPG2JuBXCu1PZfmnniG+3DPMcMmlPRLiA631fKitnqWx+XXiPldYazk7nOXlM1Ph975zo+TPa3ky1e97U2ctNWp5IhVuoZw4G2PeDDxmrS0ZY/4nwIUnzeX1TgJbrbUDF753KRqzX4O10LMH9n+NI8d28L3gUr7beC8v1awnjOWR1gZ+bnEzqxLX/pyKVL7Ey6f9VifPnxzm5TPD5Ir+7+3lTQkeWtfCm9a3cMviOgLXGLTORdZaBs6kOLl3gFP7Buk9OQYW4rVhlm5oYMnGRhatrSN8Fa1FJhR7+8ju2jU55fbvxxb8C8HBtjZimzcT2+JXfUfWr8cJh/FypcnQe6L6u9ibYaIpt4kECLUlJsPvyLJagg2vrwoyW3A51u9XfR+bFoCfGEhTdKfOCVpqIqxsrmJZQ5z2KofGcImkyWGzo4yOjjIy4gfhmUzmvP0Hg8FLBuGJxOz0jbfWkillGC+MM5ofPT8sL8/HC+OM5UZIZ4YpZEbw8mO4uRSmlCPoFogBUTMxWSIO5eDcEglCOAChoF9pHgh6OCGXQOjKzrO8koNXDE6F5jYyWW3uOHECgcRkaB4K1RCK1BKJJInE6onG6okmGohVNRKO1KrAYJqFMl4DGGMOAw9Ya7vLgfbj1to1l9nm68CfWWt/cKn1Xu94PZ7L8tjB5/m/e05z0K1nJB3FjBRxys86MMajKTHM8kAf7V6OlTU1vPd976O1veOaP1NERCqHgu5L0EnzHGQtnN4Bz34aDn8HQnG49afgzl+A2kXX5SPznsf3B8b4p+5BHh8axwPuTCb4UGsD72iuJRG4iqcryVUruh6HusfZdWZ4MgA/dkHLk82L/Krvm9XyRCrQQjpxnmCMeQ/wiLX2xy/y3kkUdF8f1vp3Sh34GqcOPc5f1t7DP7a9g7wT4uGGWn5hSQtba1//3VJF12PfuVFeODnMU0cH2H5sgKJraUiEeXBtM29a38K9q5qIhefX+JkZK3B6/yAn9w5y5sAghZyLEzR0rK5jyYYGlm5soLYpfk37toUCuUOHJoPvzK5dl636BrAlj2JvZqrnd1eaYncKO1EJ2RQjuq6e2Np6wktqMDM0fpZcj9NDmfOqvy/WB7wmGmRlc9XktKQuSnPEJeJmGB+bCsAnpmw2e97nhEIhkskkjY2NLFmyhCVLltDS0nJN7VBupJJXmgrDC2OT00RwPpgdZCA7QH+2n8HsIP3ZftKFNCHPEvccqj1DwkI1Dg3BGMlAlJpAkETAIYoh4ngETYmAKeE4RRynCE4B45QwwSJOyL2iFi3Ww+9nXgpiXb+fOTaCY6MYJ0bASRBw4lOB+URoHvXbskRi/oNAY1VNBEOV31piIY3XxpgRa21y2tfD1tq6S6y/FHgS2GCtHbvUvmd6vB7N59hxbC//uO8oL+WSDGXjOCMFzHgRUypf6DMeLdEBFoWHabVZloYjPPymN7Fh8+YZOw4REZkbFHRfgk6a57i+g/DMn/ptTYyBTR+Eu/89NF2y2OB16c4X+OeeYf6xe5AT2QKJgMO7mpN8uK2BrTVxVb3cIKPZInsmH3R5fsuTSNBhY0ctm6f1+15Up5YnMnctpBPnCcaYbwKPWmv/4SLvnQCGAQv8b2vtZ69knxqzr5Jbgr3/zMCzf8nnYjfzN4seYThYxR01cX5hSQsPNdTgzNDvzfFckSde6ecHB3p57FAf47kS0ZDDPSubePP6Fh5c10xj1fy6U8p1PbqPjvotTvYOMtLrVyrXtcbLoXcjrStrCbyOYPlaqr4BrGcpDWTJHRkmd2iI/PFRcC0mGiS6Okl0XQPR1XUEEjN/x9SV9wF3WNaYOC8EX9lcRXtVkGx6nOHh4fMC8J6eHkZGRvxtIxEWL148GXy3tbURDFZ+K49MMeMH4LkBPwTP9DOQHXjVNJgbxLPeq7avClXRGGukIdZAU6SBpmAtDU6UJCGqcYhZS8grEXALlIpjlIopSu44rpvB8zJ4ZLHkwOTBKfqBebCEE/Ku6PE5fmuWcpW5G/YfAmqjOKZcZe5UEZjoZR6qIRytIxKp9avM4w1EE03EqpoIhWbv7+35Nl4bY36I31/7Qr8BfP5Kg25jTBXwBPC71tqvvMY6nwA+AbB48eJbT5069TqP/rWN5HLsOLGPH54+xY9GE/S4NZjxEs5YntB4Djc/9Xu3PjJMR3iINpNmkWO4c8stvPHND835i2UiIvLaFHRfgk6aK8TIadj+GXjx837/xLXvgHt+FRZdv79DrbXsHE3zT91DfKN/hIzrsTIe4YOt9Xyg9dp6nsq1m2h5smtar+/zW56EJ0PvzZ1JNi1KUhvTv5HMDfPpxPlSJ83W2q+X1/kNYCvwXnuRPxqMMe3W2i5jTDPwA+CXrLVPvsbn3bAT53nLc+HA10g/9Wn+MbiCv1j8Ec6FG1kdj/Dzi5t5b0sd4Rk84S+6Hs+dGOIHB3r5wYFezo1kMQZuXVzHm9b7LU6WN1XN2OfNFSN9GU7tHeTUvgHOHSk/0DIWpHNdPUs3NbDkpgZi1Vf/AMnpbKFA7uBBP/jevfuKq769fIn8kRGyB4fIHR7CSxXBQHhxzWS1d7Dl+oeLF/YBP9bvV4OfHZ7qA+4YWFwfZ2VzFSuaq1jZ5Afgq1uqKeXSnDp1anIaKD/oMxgM0tnZORl8d3R0EA6/vp/1XOZ6LsP54clK8AuD8P5MP4O5Qfoz/WRKmVdtHzABGqINNMYbaYw10hRroiHWMPm6MdY4OUWDUdxSkVxmmGy6n1x6kFx2mEJumEJ+hEJhlFJhjGJpHNdN47ppPJvFksWaPJg8JljEBEoEwqUr6mfuuQZbCuCVzg/MjZn2ANCJtizhGsLhJJFI0n/4Z7zBrzBPNBGKVF91mDmfxuvLudLWJcaYEPAt4PvW2j++kn3f6HPskVyenaf28UxPN09nHA7ShEm5BEbzVI+NQKpIJhMF/N9xiWCajugAbU6KDkpsXLKE97zvEaLR+XVBVkRkvlLQfQkKuitMegCe+yzs/N+QG4Gl98I9vwIr3sgVlZpco1TJ5Rv9IzzaPcTO0TQBA2+or+HDbfW8qaFmRgMCuXJF1+Nwzzgvn5mo/B6ebHkCsKIpwZbOOv9Bl4uSrG1TyxOZHQvsxPljwL8D3mitfXXC8ur1fwtIWWv/8HLrasx+nTwPDn+b4hN/yDe9Rj6z9KPsjy2hLRzkZzub+cn2BqqDM9tqxFrLge6xydB7f5d/t/uKpgRvWt/Km9a3cHNnEmce9fWG8gMtDw5zct8Ap/YOkhnzH2jZstR/oOWyzU00dMxM2H+pqu9QZyeJO+8kcec24tu2Eayrw3qW4rkU2UND5A4NUTyXAiCQjBBdW090XT3R5UlM6MaNl9mCy/GBae1PLtIHPOAYNrTXcPuyem5f1sDtS+sJeHlOnz49GXz39PQA4DgOHR0dk8F3Z2cn0ei196ivZBNV4hOB+PRWKQPZgcnXQ7mhi1aJV4eqzwvBG2INNMXLYXi0kcZ4I22JNqrD1Zc9Fs/zKObGyaT6yWUGyGWGyGWH/NC8MEqxMEapNE6plMLz0ng244fmE4F5oIAJlnCCLk7w8uej1gOvGMBzQ+CGyr3Mo+U+5uW2LNP6mIcjtWy5+1cW0nj9B8DgtIdR1ltr/9MF6xjg8/gPrfyVK933bI/XI/kCz53azzM9XTybC7Av2Ix1ITiWo3FwgHhqjGLGYzhdS8n6d4OEnALt0QHag6O02QKr6ut47yOP0NLUNGvfh4iIXJyC7kuY7UFYrlE+BS99Hp79MxjvgtaNfoX3+ndzRQ0JX4djmRyPdg/xxZ5hegpF6kMBHmmp58Nt9ayrqvzehJVuNFtk79lRdp0Znqz8nrhVOhJ02NBRO1n5rZYncqMslKDbGPMw8MfA/dba/tdYJwE41trx8usfAL9trf3e5favMXuGWAtH/gX7xO/zeMbwmaU/xdM1G6gJOHyso5F/s6jput21dG4kyw/LofeO44OUPEtjVYSH1vl9ve9e2Ug0NL/6elvP0n9mnFP7Bjm5Z4C+U+MAtCyr4aZ721l5awuhyMx9zxNV35mXXybz3PNkdu7ES/sXgSPr15HYdieJO+8kvvVWnFgMdyxP7tAw2UND5I8MY4seJuQQWZkkutav9g7Uzk6V4/Q+4HvPjbLz+BC7zoxQcP1WGmtaqrljWT13LG/gtqX1VAU9zpw5Mxl8d3V14XkexhhaW1sng+/FixeTSLz+XvXzyUSV+PSWKRNV4edVi2f7yZayr9q+OlxNR1UH7Yl22qva/dfT5lcShF8pay3FfJpsup9serBcZT5IIT9CMT9CoThGqTA+1ZalHJhjctiJwDxQwgmVXhWYP/TG4wtivAYwxjQAXwQWA6eB91trh4wx7cBfWWvfZoy5B3gK2AtMXAn5r9ba71xq33NtvB4tFNh56iDPdp8tB99NeCZAuJinvb+XutEhgtkUmbxDV6aJbMl/1oJjXJoiQzSHxmk0WZoo0Vmb5MEH38hN665fK00REbk0Bd2XMNcGYblKpQLsedTv4z14BOqW+T28N38YQte3cqfkWR4fHuefugf5/sAYRWvZVB3jw20NvKc5STJU+b0i54OJlie7p/X73jut5UlDIszmziSbFyXZ3FnL5kVJ6hLz93ZnmR0LKOg+CkSAwfKiHdbaf3fBSfNy4Kvl94PAF6y1v3sl+9eYPcOsheOPw5N/wK7BAf582U/xrfo7CRrD+1vr+bnFzayMX7+xdDRb5PHDffzgQC+PH+4nlS8RCwW4b3Ujb1rfyoNrm6mfh7+PM2MFjjzfy/6nzjHckyEcDbDmjlbW39tB46KZb+liSyWye/eS2bGD9LPbyezaBcUiJhQitmULibv84Du6YQNYh/zxEb/a++AQ7kgegFB7wg+91zUQ6qjCzGIFfq7osvvMCM+dGGLniSFePDVMtug//HJ5U4I7ljVwx7J6bl9WT2M8wNmzZyeD77Nnz1IqlQBoamqaDL6XLFlCTU3NrH1PlSZTzEyG3v3ZfnpSPZxLnaMr3UVXqotzqXOvCsNvZBB+NYqFTDksHyCbHmDp2jcviPH6epvr4/VooegH3z3n2J417C0H3xEvz02jx6kdTBFPjWFLWfrdBH35JAO5Blw7dVEyEUzTFBmh0UnTSJ6WoGHdsmW89a1vo7Z6/rXnEhGZSxR0X8JcH4TlCnkuHPo2PP3H0PUyVLXAtp+HrT8D0et/4jJYKPHVvmG+0DXIgXSOiGN4a2MtH25r4N66qhl72JfMjOktT/acGWH32RGO9KUm+4MuaYizeVGSTYv86u+b2muJhedXhaHcWAsl6L7eNGZfRyefgSd/nxNdR/jLpT/Joy1vJk+AtzbW8guLm7m19vpWvxZKHjuOD062OOkZy+EY2Lq0njevb+GhdS0sbZxfFbjWWrqPjrL/6XMce7Eft+RNVXlvbSF0ncYdL5Mh8+JLpLdvJ719O/mDBwFwqqqI3377ZKuT0PLluP1Zv6/3oSEKp8bAglMVIrqm3m9zsiqJE53dC/tF12PfuVF2nhjiuRNDPH9iiPG8H2Z31se4fWlDueq7nvaaMN3d3ZPB9+nTpymU27zU1dVNht5Lly4lmUzqjq9rZK1lJD8yGXpPzisgCNd4PTMqbbweKxTZeeYwz3af4dnMVPAd9gpsSR1lXXYQp7dEaGgEGykxGgoyYGP0l6roy9UzWqid3JfBoz4ySlNotFwFXqC9qoq7776LbbfcoodgiojMAAXdl1Bpg7BchrVw4kl4+k/g+I8gUgu3fRy2/RxUNd+Aj7fsTWX5p+4hvtI7zEjJpSMS4gOt9XywrZ6lMT3gZK5K5UvsPTvK7rMj7D7jT12jOcDvC7qmpZrNnUm2dNayuTPJquZqAvOsp6xcPzpxnhkas2+AM8/Dk39A/8mdfG7xh/mbRe9lhDDbahP8/OJmHmqoue4Xb6217Ds3xg8O9PAvB3o51OO3+tjQUcMHt3byY5s7qI3Pr4cN51JFDu/smaryjgVZc0crN93bPmO9vF9LaWiIzM6dpJ/dTnrHDopnzgAQbGoicdedxLf5wbdT3UD+yHD5gZbD2FwJAobIstrJFifBxtlv4eZ6loPdY+WK70GeOzHEcKYIQEtNhNvLFd93LKtnWUOMvr6+8x5wmc36AWxNTc15Fd+NjY0KvmfIXA7CNV7PjEofr8eKJXaeeYXtXad5OmPYE/If7JssjnFX7gRrbQnyDTw/0MiRcwNsc1+hLT5KLmIYDITodxP0F2rpyzZS8KbuTIoE8jSFh2kMpmiwOZoDsHJxOw+/6WE6mtULXETkSinovoRKH4TlErpehqc/BQe+DoEw3PwTcNcvQf2yG/LxOdfjewOjPNozxOND41hgW22CD7bV82NNSRIz/MAvmXl94zn2nPHD713l8Hss51eJxcMBNrTX+u1Oyq1P1O9bXotOnGeGxuwbqGsXPPkHpF/5V76w6D385dKf5JyJsyYR5ec7m3lPS/KGPYj5zFCG7+/v4SsvneNA9xiRoMPDG1r54NZOti1vmFcPspys8n7qHMde8qu8W5fXsP6eDlZubb5uVd7TFc6cIb19u9/qZPsO3OFhAMLLl5PYto3EXXcS23ob7hCTD7Qs9fnPnQ02xfxK77X1RJbWYObAA6A9z3KsP8WOcsX3zuOD9I37LVnqE2FuX1pffsBlPWtaqhgeGuTUqVOcPHmSU6dOkUr5D+uMx+PnBd8tLS2qzLxOZjMI13g9M+bbeD2QzfH08T083tvLE6VqukNJAJbnuriPATbHE5jAcvYNBNl7dpSD3eMUXI+VpQHuTpwkFC0wHAwwQIS+UhV9+TqGcnVYpn6HVIdS1IfGqHMyJE2eOuPSXBVjw0038cC2u6lOxGfpuxcRmXsWTNBtjPkt4GeBiQdgVdyDMuQ6GDgKz34adv8jeCW46b1wz6/4D7C8QbpyBf65Z5hHe4Y4ns0TDzi8synJB1vr2ZZMqLVJhbDWcnIww+7yQy53nx1hf9cYBfX7lsvQifPM0Jg9C3oPwFN/SHH/1/l661v4zMpPcNBJ0hYJ8YlFTfxkewNVN/DC7b5zozz6/Bm+tusc47kSnfUxPnBrJ++7dRHtydmvJp5JuVSRQzu62f9UFyO9N7bKe4L1PPKHD5PevsMPv194AZvNguMQ3bCh3ObkTkJL1lI4nvIfaHl8FFyLiQaIrq7zg+819QQSc6MK31rL6aEMO4/7Pb53nhjk7LAfmlZHg9xWDr7vWFbPTe01jI+OnFfxPTIyAkAkEmHx4sWTwXdbWxvBoJ7PciNczyBc4/XMmM/jtbWWI4M9PHFsH4+PZnjWaSEbiBL0StyaO8X90SJ3ty8mEl/F/p5M+Y7RUV7pHcf1/BylIxbg7dFTJGw3w06JfifIkI0wVKpiqFDDUK7uvH7gBo+aUIq60Dj1gQy15Kl3LC3JKjZv2sS9t95OPHp9n08lIjKXLLSgO2Wt/cMr3WY+D8JygbFu2PHn8MLnoJCClW+Ce34VltwFNyhottby/GiaR3uG+HrfCCnXY0k0zAda6/lAWz2dUYWilaZQ8vt9T7Y8uaDf9+L6eDn8Vr/vhUwnzjNDY/YsGjgCT/0Rds8X+VH9Nj6z7pd5JtBKTdDh4x1N/OLi5ht6p1Ku6PL9/T08+vwZnj02iGPg3lVNfPC2Th5a10I4OH8qbf0q7xH2PdnFsZf78EqW1uW1fi/vW5sJ3sAxxRYKZHfvLvf33kF2zx5wXUwkQvzWW/1q71u3QbCZ3OERcoeH8MaLYCDcWU10XT3RtQ2EWuNz6g6ocyNZni+H3jtPDHG8Pw1ALBTg1iV1kw+33NyZJJ9JnRd8DwwMABAMBuns7JwMvhctWkQoNDfC/YXm9QThX/qxL2m8ngELabzOl0q8cHIfT5w9weO5EHvD7VjjUFtKcU+piweSMe5bvpGW+k4OdI+x58wIe86NsvfsKEf7p84ZFtXFuG1pPVtb46wYP8HZ0/s4mx6h38CQCTLkRRkqJhgq1DKUS55XDW7wSIbHqQ+OkwxkqLUF6oMerY1Jtt58C3duvJlIWOeZIjJ/KOi+hIU0CEtZdhie/2vY8ReQGYDObXDvf4BVb7phgTdA2nX5Tv8oj3YP8fSIf1vsPckqPtRWz9uaksTnwO2+cm3U71supKB7ZmjMngOGTvjPwdj1BV6uWsNnNvwa3wotoy0S4r+t7OCdTbU3PMA8PZjhn188w5dePEv3aI76RJh3b+ngg7d1sqb1xjzA7kbJpgoc3tEzWeUdiQdZPVHl3X5jqrync1MpMs89T3rHdjLbt5M/chSAQG0t8W3biG/bRmTlLZRGIuQOD1E8myq/HymH3vVEV9RiQnPrAnD/eJ7nTgzxXDn4nugTHw46bOlMlnt8N3DLkiReIcfp06cng++enh4AHMeho6NjMvju7OwkqorLOeFSQfifP/TnGq9nwEIerwfHh3jqyEs80T/EEzTQFW4AYFm+l/uD49zf0sLdK2+hJpYglS+x/9woe86O8vKZYZ47McxAym+tVBsLceuSOrYureO2pfVs7KglGgqQHxzl+Pan2X38AOfy4wwYOy0Ir2Ion2QkX3NeEO4Yl7rwGHXBFLUmS63JUxeA5oYaNt10E3dvupWaqhs/hoiIXKuFFnT/FDAGvAD8B2vt8EXW+wTwCYDFixffeurUqRt4lDJnFLPw0t/DM38KY2ehdRPc92uw9p1wg3suns7mJ1ubnM4VqAo4vKs5yYfaGthaM7eqnuTa9I3l2H12dLLqW/2+FxYF3TNjIZ84zzkjZ/zx86W/44WqNfz6lt9hL7XcV1fF765axKrEjQ/0XM/y1JF+vvjCGX5woJeia9ncmeSDWzt55+Y2qqPzp7rWWkvXkRH2P3VBlfd97ay85cZWeU9X7OuberDl9u2UyqFvqL2d+J3biN16N4G61RTOFMgfHcYWPEzIIbIiORl8B2vn3oO7RzIFnj85PBl87zs3imch6Bg2dNRyx3K/1cmtS+oJU+LMmTOTwXdXVxee52GMobW1lSVLlrB06VJWrFihiu85SOP1zNB47bOex9Guwzxx4iCPj5d4NtxJJhAjYF1uKXRxfwIeWLyaLYtWEQw4k62Vnj85zAsnh3j+5BDHyneYhAMOmxbVsnVpPVuX1HHrkrpXtUi01pI508vRnc+w/+wrnCtmGHRgkBDDXpTBYjXDhRpGLwjCJ1qjJEMpkk6GWpOnxrg0VsdYvWoF922+nUVtLTf0ZycicinzKug2xvwQaL3IW78B7AAGAAv8d6DNWvszl9qfBmGhVIA9j/oVakPHoHEN3PtJ2PAIBG5sr0XPWnaMpPmnnkG+2TdK1vNYEYvwwbZ6Hmmpo12tTeYN9fteWHTiPDM0Zs9BY93wg9/E3fslPr/q4/zPRT9Oxjr8284mfnVpC4nA7ASug6k8X9vVxRefP8Ph3nGiIYe3bWzjg1s7uX1Z/by6kJhNFTi0vYcDT09Vea+5o5X1s1TlPcFaS+HkSb+39/btpHc+hzc2BkBk9Wri2+4isnobmBZyx8Zxh/w7n0JtCb/Se1094UXVmDl4x1MqX+LFU8PsPD7IcyeG2H12hKJrMQbWtdZMBt+3La2nOmw4e/bsZPB99uxZSqUS0WiUm266ic2bN9PZ2Tmv/pusZBqvZ4bG64sr5NK8cPR5nug+x+OFOHtiS7DGocZNcz+DvH3RIh5asYmq0NQ56GAqz4unhnnx1DDPnxxi77lRiq6fx6xqrmLr0npuW1rH1iX1dNZfulDGcz1Sr5zi2O7nONR1gm43zWC5NcqwF2G4FJ/WI/z88+B4IEsyPOa3RiFHjSlSHw+xZHEHd6zfwqbVqwnM0pgvIgvPvAq6r5QxZinwLWvthkutp0FYJnku7P8qPPXH0Lcfkkv8h1Zu+XEI3vjqolTJ5Zv9IzzaPcSO0TQOcH99NR9srefhxlqiam0y70z0+941reXJ9N596vdduXTiPDM0Zs9hR/8VvvWr9KdH+Z2tv8+j4TV0REL81soO3jEL7UwmWGvZfdZ/gOU3d3eRypdY1pjg/VsX8b5bFtFSM39aSVhr6XplhP1PT1V5t69KsvmNnSzd1Igzy4GxdV1yBw741d47tpN98SVsoQDBILHNm4ltfYBg8wbc8SiF0+PggZMIEV1TfqDl6jqc6Nx82GOu6PLS6eFyu5MhXjo9TK7oX7he1VzlP9xyeQN3LKunIR7k5MmT7Nmzh4MHD1IsFqmrq2PTpk1s3ryZ+vr6Wf5uFjaN1zND4/WVGeo7xlNHd/H4cIofBBczEK4j4hV5wBni7Z1LePOSlSRD5//eyxVddp8Z4YVy8P3iqWHGy3eJNldH/D7f5XYna1urCV7FOaPnemROdtO152UOnj7E2cI4/cZl2Akw7IUZcmMMF2oYzNWRc88fP0NOkWRonORke5Qi9VHDks427lx/M1vWriWgB/aKyAxZMEG3MabNWttdfv2rwB3W2g9dahsNwvIqngevfA+e+kM49yJUt8FdvwS3/hSEE7NySCcyeb7YM8QXe4Y4ly9SGwyUW5vUc3O1WpvMZ+O5IvvOjanfd4XTifPM0Jg9xxUy8MT/hGf/F881bePXN/wm+90oD9RV87urO1gRn91QOVMo8d29PTz6whmeOzFEwDE8sLqJD9zWyYNrmwnNowvI2fECB7d3s+/xc4wP5ahpjLLpDZ2su6uNcGxuBA1eLkf2pZdIb99Bevt2cvv3g7U48Tjx2+8mvP4+nOgSCt0lbLYEQYfY+nrit7QQXVWHCczdsa5Q8th7boSdJ4bYedwPolJ5P4ha0hBn27IG7l/TxO2Lazh74gi7d+/mxIkTAHR2drJ582ZuuukmYrHYbH4bC5LG65mh8frquZlhntv3GN/u7uU74ZV0RZsJWpd7AuO8vXMJDy9aRFP41e2OPM/ySt/4ZLuTF04Oc27Ef9hqIhzglnKbk9uW1rOlM0kicu1jgLWW3NkB+nbv5ejJA5zMDNDnlBh0DMM25Afh5T7hY4Wa87YNOUXqQmMkA2mSJkuNKdJQFWL1kiXctelmVi9doopwEbliCyno/ntgC37rkpPAv50Ivl+LBmF5TdbC8cfhqT+Ck09BvAG2/Rzc9rMQS87KIXnW8vRwikd7hvh2/wg5z7I6HuWDbfW8v6WO5oh6PS4El+v3vbGjli2Lk9zcmWRLZx2ttfOnYrFS6cR5ZmjMrhA9++Cb/57SuZf5203/kf/Z8FZy1vDzi5v55SXNs9bOZLoTA2n++QX/AZZ943kaq8K895ZFfGBrJyub588DuTzX48TuAXY/dobuo6OEogHW3dXGpjcsorYpPtuHdx53ZIT0c8+VW53soHDyJACBxmbi295CqH0rpbEqbM7FqQoR39JM/JZmwrPYnuVKlVyPg93j7Cz3+N55fJCxXImgY9i6tI43rGnmtkVx0l1H2bNnD/39/QQCAdasWcPmzZtZuXKlAqAbROP1zNB4/fp4I2fZtfcHfKt3gG/H13Mq1oFjPe4I5Xh752Le1tp8yZaW50ayvFCu9n7+5DCHesaw1i+SuXWJ/zvnwbXNrG6pmtGCKWsthb4RBncd5sTx3RxN9dJjCuU+4X5F+GChmsFcPeni+QVkESdPXWicZCBNrclR65Roqo2zbuly7tm0mSWd7SruEpFJCybovhYahOWKnN7pV3gf+ReI1MDtPwvbfh4SjbN2SGMll6/3DfNo9xAvjGUIGLi/rpr3t9bzlsZa4vOoMk0uzfMsJwfT7D47wq7Tfs/vA91jk/37WmuibOlMsmWx3/N706La11XNIVdPJ84zQ2N2BfFceP6v4V9/m/5Agt++41P8s22jIxLiv6/q4K2Ns9fOZLqS6/HEK/08+vwZHjvUR8mz3LOykZ+5ZykPrG6e9XYfM6nv1Bi7HzvD0ef78Kxl2aZGNj/YSfvq5Jz4t7hQsavLr/be4Vd8uwMDYALEbnsb4eX34hWS4EGoNUH8lmbiW5oJ1FTGsyxKrsdLp0f40eE+fnSoj0M94wB0JGO8YW0Tm5uChIdPcPjAPjKZDPF4nA0bNrB582ba2xX2XE8ar2eGxuuZY3sPcGDv9/lW/wjfrt7CK4llANwSKvD2RZ28o6WeJbFLt9kcyxV56dQwO08M8fjhfg52+89L6EjGeGBNE29Y08xdKxuIh6/v+YHnemRO9NK/dw8nzh7kVG6I3kCJAWMYtGGG3DiD+VoGsg2vao0SC2T9INzJ+EF4wKO9oY6bV63mvlu2UJuseY1PFZH5SEH3JWgQlqvSvduv8D7wDQjF/HYmd/0S1LTP6mEdSef4Ys8QX+kd5ly+SCLg8PamWt7fUs9ddVUEdEK04OSKLge6xyaD711nRjg9lAHAMbC6pdoPv8sBuFqeXF86cZ4ZGrMr0FgXfOc/wqFvsWPpu/j1Vb/KwYLhDfXV/O6qRSyP3/hnYLyW/vE8X3zhDH+//RQ9YzmWNyX46buX8b5bOq77yf+NlB7Js/eJs+x/sotcukhjZxWbH+xk1dYWAqG5eZHcWkv+0CFSTzxJ6sknye7aBYEYkdX3EV75AJg6MBBZVUfilmai6xtwKugZFl0jWR4/3M9jh/p45ugA2aJLJOiwbXk9G+sN1anT9J04iOu6NDY2snnzZjZt2kRtbe1sH/q8o/F6Zmi8vg48D87s5Mi+7/HtwTTfSd7Gnuo1AGwIe7y9vZ23NydZnbj8nZw9o7nJC21PHx0gU3AJBx3uXN7AG9Y08eDaFhY33Pi7frx8kdH9J+k7uIcT/Uc5UxqjL+AyaAIM2giDxThD+SQD2QYK3tSFTYOlJjROfSBF0mRJOkVaklVsWraMezbfROciXSAUmW8UdF+CBmG5Jv2H4ek/gT1fBCcAWz4Cd/8K1C+b1cPyrOXZkRRf7h3mm30jpFyPtkiI9zTX8f7WOtZVqdfjQjaULrD7zAgvl4Pv3WdGGM0WAb+H38ZFtWzprGNLZ61answwnTjPDI3ZFezQt+Hbv0ZpvI+/ufP3+J+x2yl4lNuZtMypu5CKrsd39nbzuadPsPvsKLWxEB++fTEfu2sJbbXzZxwtFVxeea6X3Y+dYagrTaw6xIb7F7Hhvg7ic7wyujQ8TPqZZ0k9+QTpJ5/ClqIEF99JePk9mGA1JmSIbW4mcUsL4aU1mAq6kJsrujx3YmgyhDo56F+kXt4YZ0M91GbOQv9xAsaybNkyNm3axPr164lE5s5Fo0qm8XpmaLy+zkoFOPpDTu3/Ht8ZLfHt+rt4oXYDAKsihne0NvO2plo2VMUuG/DmSy7PnxjmsUN9PH64j+MDaQCWNyUmW5zctrSecHD2x+niaIahlw/Sd3wfJ0dPcdZm6HMs/SbIgBujP19Lf7bxVf3B44Es9cFxkiZD0hRpSkRYu2QR29at5qa1ywkG1f5TpNIo6L4EDcLyugyfhGf+FF7+B/827Y2PwD2fhOa1s31kZF2P7w+M8uXeYX40NEbJwvpElEda63lvSx2t6ue94FlrOTGQnqz43nVmhIOv0fJkS2eSjR1qeXKtdOI8MzRmV7j8ODz2O7Dzf9Nbt5b/fvsf8aVcgkXREP99ZQcPz5F2JhOstbx4api/fvoE39/fgzGGt21s4+P3LGNLZ3K2D2/GWGs5e2iY3Y+d4dTeQZygYfVtLWx6sJOmzurZPrzLsq5Lbu9eUk8+SerxJyj2uYQ6txFctBUTiGCilsTWNhLbFhFqrLwLFScG0pMB1M7jQxRcj0Q4wNo6aMh3k8x2UROyrFu3js2bN7N8+XIcZ/YDqUql8XpmaLy+gXJjcOhbdO//Lt/JBPl2433sqN2MZxyWhAO8raWedzQlubkmjnMFY+zJgbR/oe1wPzuODU7+zrlnVSNvWNPMG9Y201Iz94phvJLH2IHj9B58ibO9hzntjdPjuOUQPEp/oYb+TBODuTosU78jQ06RuuA4dSZD0hSojwRY3dHMratXcMuG1VRVJS7xqSIyWxR0X4IGYZkRY92w/c/ghc9BMQvr3gH3/hq0b5ntIwNgoFDi633DfKlnmJfHMxjg3roqHmmt5+2NtSSClXN7r1xfV9Ly5OZyr2+1PLlyOnGeGRqz54mzL8I3/z307mX7hn/Lf+n4SQ7nXN5YX8PvrOpg2RxqZzLhzFCGzz97kkefP8N4vsStS+r4+D3LePP6FoJzqBr99RrpzbDnsTMc3N5NqeDRsTrJpgc7WbqpsWL6lRf7+kg/9RSpJ54hdyxFsPkWAs3rMMbBieWIb22l+g1rCcQr74J/Ol/imaMD5WrvfnrGcgAsSliaS/202gGWVhu2bN7E5s2baWlpmeUjrjwar2eGxutZMt4D+77CwIFv8/1iLd9qup+n626laIK0hQO8ramOR1rr2VJ9+UpvgEyhxLNHB3msfIdJ96j/O2d9Ww0Prm3mDWub2NJZN+fPBTzXI33iLH27X6Cn7yCnCsP0BEr0mQADXoSBQg19mUb6sw0Up7VEcfCoDaWoc9IkbZ6maIDV7U3cvnYFt25aQzxeeRdPReYLBd2XoEFYZlR6EHb+Bez8LORHYeVDfuC95M7ZPrJJRzM5vtwzzJd7hzmdKxBzHN7aVMsjLXXcV1dNcI7/oSI33mAq7z/o8szoZVqeJLl5cXJOVnnMNp04zwyN2fOIW4Ttn4HHf49iIMzn7v00v+8tp+hZfqHcziQ2BwPkVL7EP79whr955iSnhzJ0JGP81F1L+eDtndREKy84fS25dJGDz3Sz5/EzpIby1DRG2fSGTtbd3UY4Wjl39thCgcxLLzP++HZyh8Yw8TUEatqxXgknNEz85iZq3raVQAWGFdZaDvWMT1Z7v3hqGM9CPODRxjAdzghbWiJsu2UjGzdupLp67lfnzwUar2eGxus5YOAI7Pkio/u/xb8EFvGd5gf4Uf0d5EyQdYkoH2lr4H2tddSHrux3urWWw73j/OhQPz861MeLp4dxPUsyHuL+1U08uLaZ+1Y1UZeY262vLmStJdvVS8/LO+k7t5+ufD/dgRJ9jqHfi9BfqKYv00xvpum8B2QGjEsykPIrwSnQUhXhps4Wbl27lFs2rCEUnj9/E4jMRQq6L0GDsFwXuVF4/q/8k/jMICy5G+79JKx4I8yR27KttTw/muZLvcN8o2+EkZJLUzjIe5rreKS1jo1X0NNNFqbLtTxpq41OVnyr5YlPJ84zQ2P2PDR8Er71STj2r/R0PsBvb/lvfGUMOqNhfndVB29unJsP23M9yw8P9vLXT5/guRNDJMIB3r+1k5++eylLGubPbc6e63F81wB7HjtD97FRwtEA6+5uZ9MbFlFTgW1A8mfOMP79HWT3jWBNB06kGi8/jqGL2MY6at6yjXBHx2wf5jUZyRR48sgAPyoH38OZIgZLk0mxKDDKnUtqeMsdN7F27VrC4coKom4kjdczQ+P1HGItnH0B9n6RsQPf4avVt/CFxe9nd3QxYWN4a1MtH2lr4N66qitqbTJhNFPkySP9/OhwH08c7mcwXcAxcPPiOh5c28w7N7XPygMtZ1quf4Dul56h9/Qeeov9nDNFeh2HPjdKX66e3kwTfZmm8x6OGTQl6gIpkuRImhKLkjE2dDZz67qlbFi3Qj3BRWaAgu5L0CAs11UhAy99Hp75NIx3Qdtmv4f3unf6D7GcI/Kex78OjvGlnmF+MDhG0VpWx6M80lrHe1vqWBTVCZFc2pW2PNnSmWRLZx0rm6vm/G2OM0knzjNDY/Y8ZS3s+zJ8779AZohn7vp/+fXqN/FKtsBDDX47k6WxudfOZMK+c6N87ukTfHNPFyXP8tC6Fj5+zzLuWFY/ry4Y954YY/djZzj2Yh/WWpZtbmLzGxfRtjJZkd+nm84w9p0XyOwawCvWYZwg7lgXNvsKsbU1VL3hTuI334wJVV4g4XqW3WdHePxQH/+yr4tDff54HKfA4tA4dy9P8r67b2LtymXq530BjdczQ+P1HFXMwu5/hGf/jP15+MKSD/HlpgcZIciiaIgPtTbwobb6qz738zzLnnOjk3eY7Dk7CsCWziTv2tLO2ze10Vw9v+749DyP9KmjnNn9NEN9h+jxRugOePQRpK8Up7dcBd6faaRkpwp+QqZIXSBN0uaoczyWNSXYsLiZ225aycrlnQQCcycjEJnLFHRfggZhuSFKedjzKDz9KRg6Bg0r4Z5fhY0fgODcCpGHiyW+2TfCl3qHeW7Uf+r2nckE72+p5x3NSWrUz1uu0GTLk9MjvFxueTKWKwELr+WJTpxnhsbseS4zBD/4TXj57ykml/F/7vsMf5SqpmQtv7i4mV9cPDfbmUzoHcvx99tP8X93nmI4U+Sm9hp+5u5lvGNzG5F5NHamhvPse+Is+546Rz5dorGzis1v7GTVrS0EQnP33+dS3EyR8ccOkXmhGy8Xw1oPt/8Qpf6Xia6oouq+u6m69x6CTU2zfajXpG8sx48O9/Htl06y8/QYedfg4NERznLX0lo+cM96blm1qCIvWMy0hTReG2PqgUeBpcBJ4APW2uHXWDcAvACcs9a+43L71ng9x3kevPJdePZ/kTvzIt9rezNfWPkxnjTNGOD+umo+0t7AWxpriFzDxbCzwxm+taebr+/q4mD3GI6Bu1Y08mOb23nLhlZqY5V3AfFqlIp5Bg8+R9fBnYyNnaCXDN2ORy9h+vLJySrwgWwDrp36+yBiCtQ5aepsgeYIrG6rYeOydu68eS1NjfWz+B2JzD0Kui9Bg7DcUJ4LB74OT/8x9OyFmkVw1y/BLR+F8Ny7tetUNs+Xe/2HWB7P5ok4hjc31PL+1jreUF9DaAFV5MrrdyUtT/yKb3/atChJLDw/wqGFdOJ8PWnMXiBOPuM/rHLwCN2bPspvrfgFvj6UY2U8wqfXLuaW2rndGiRXdPnqy+f43NMnONKXoqk6wk9uW8KP37GYhqq5W5l+tYoFl1d29rD7sbMMd6eJ1YTZeH8HN93bQbxmbl3EvxrFgSzpnWdJP9+FzTlYN0/p3IsUT28n1Bqi6r77qLr/PqIbN2IqsBq6UPLYcbSPr+58haePj9Cf98fZumCJbUuqefcdq7h/XTvR0PwYf6/WQhqvjTG/DwxZa3/PGPNfgDpr7X9+jXU/CWwFahR0zzNnnodnPw0Hv8npWAf/tOmTPFp9K+eKUB8K8EhLPR9uq2dd1bW1qzrSO843dnfxjd1dnBrMEA44PLCmiXdt6eCN65oX3O+aXKqP7j1P03viZVL5c/SZHD3GodeL0ptppDfTTE+6meF83XnbVTsZkuSop8iiZJB17fXcvKaTWzeuJRqdP39biFwpBd2XoEFYZoW1cPRf4ak/gtPPQrwB7vg5uP3fQKzu8tvfYNZaXh7P8KWeYb7WN8xQ0aUhFOQ9LUkeaaln8xU+uVvkQrmiy/6uscmHXE5veRJwDOvaqrllcR03L05yy+I6FtfHK/K/tYVy4myM+S3gZ4H+8qL/aq39zkXWexj4UyAA/JW19veuZP8asxeQUh6e/hN/nAzFeOINf8In3bV054v80pIW/sPSFsJzPGS01vLkkQE+9/QJnniln0jQ4T03d/DTdy9jTev8eTCgtZazB4fZ9a9nOL1/ECdoWH17K5sfXETjosr9Pq1nKZwcI/1SL9ndfdiixZbGKRx/mtLp7Zhwgap77yFx331U3XMPgdq52U/+cg6e6ePRJ/fx5NEhTmUjuDiEHMst7QnecctSHlzfSkey8vqxX6uFMl4DGGMOAw9Ya7uNMW3A49baNRdZbxHweeB3gU8q6J6nho7D9j+Hl/8Bt5TniZt+li8sfj/fz4YpWsvN1XF+vL2BdzUnqb6Gu5Sstew+O8o3dnXxzT1d9I/nSYQDvOWmVt65pZ17VjYSmsN3bV1vnlci1f8Kp1/+ESN9h0h5A3Q7Lj0E6C0k6Um30JtuoifTQrY09Ts5gEutkyVp8zSHXJY1xVjf2cBtN61k1fLFaoUi85aC7kvQICyz7tR2v8L7yL9AuBpu+xnY9gtQ3TLbR3ZRRc/yo6ExvtgzxL8MjFGwllXxCO9vrVc/b5kRg6k8u86M8PLpEV46PczuMyOkCy4ADYkwNy9OcnM5/N68KFkRD7pcKCfO5aA7Za39w0usEwBeAd4EnAWeBz5srT1wuf1rzF6A+l+Bb/0KnHqGsWUP8f9s+R0eHcqzPhHlz9YvYf01VpjdaEd6x/ncMyf5yktnyZc87l3VyMfvWcb9q5sq8uLdaxnuSbPnsbMc2tFNqeDRsSbJ5gc7WbKxEaeC7wKzRZfsgUEyL/WRe2UYLGCHKRx5nPyRJ8HNErv5Zr/a+4H7iaxeXZH/rifPdPHlp/bw+Cv9HM8lSONXCS5viPLmDe28YU0zty6pIziPw6iFMl4DGGNGrLXJaV8PW2tfVXFjjPkS8D+AauDXFHTPc5kheP6v4bn/Del+BhbdzZc3/SpfsIs4nMkTcxx+rDnJR9rqub02cU2/61zPsvP4IF/f1cV393UzlitRnwjzto2t/NjmDrYuqavoMWMmWWvJ5wfoO7KdrsM7yaZPM+qM0+0Yetz4ZAV4b6aZvkwj7rR+4BFTKFeBF2hLwMqWajYta+OOLatpamyYxe9K5PVT0H0JGoRlzuje41evHfgaOCG4+Sfg7l+GuqWzfWSvaaRY4pv9I3ypZ5ido2kMcFeyikda63hH07Vd7Re5kOtZXukd56XTw5Ph9/F+v3+8Y2Bta81kxfctS+pY2jD3qr4XyonzFQbddwK/Za19S/nrXwew1v6Py+1fY/YC5Xn+g52//xsQCPL9N32WX8stYqTo8h+XtfLznc0EK+SEeChd4As7T/F320/RN55nZXMVH79nGe+5uWNe3b6dSxc58HQXex8/S2o4T01TjM0PLmLtnW2Eo3P/4uSluGMFMrv7yLzYR7EnDQ6Y0AiFE0+Se/67YF2CLS2ToXdi2zacxNxut3Mh13U5fvw4/7JzL0+8MsDpUjV9XjUehupIgPvWNPPgmmbuX9NE4zxqxwPzb7w2xvwQaL3IW78BfP5yQbcx5h3A26y1P2+MeYBLBN3GmE8AnwBYvHjxradOnZqR70FmSTEHe/4Jnv0zGDyCre3kpdv/I19I3sPXBtOkXY+V8Qgfaq3ng231NIWvre92vuTyxOF+vrG7ix8e7CVX9GivjfLOLe382OZ21rfVzLm/6+cK180xPnyI03t+xEjPIQpeL/2BIj2E6M010JNuKgfhLQznk+dtW22y1No8jcEii2tDrG6r5eY1i7l14xq1QpGKoKD7EnTSLHPO4DF45lOw6x/BerDxEf/Blc3rZvvILulUNs+Xeob5Uu8QJ7IFYo7h4cZa3t9az3111RUTQkhlGMkUePnMCC+fGual037Lk1Tef9BlXTzEzYvruKVc+b25M0nVLFd9z7cT59dSDrp/ChjDf2jVf7jwwVbGmEeAh621/6b89U8Cd1hrf/E19qkTZ/ENHoOv/Cyce5HBzT/Ff17+i3xrKM0tNXE+vW4xK+OV80DbQsnjW3u6+KunTnCge4z6RJifuGMxP3HnEpqrK+f7uBzX9Tj+cj97HjtDz/ExwtEA6+5pZ9MDi6hprIxq/EspdKXIvNRHZlcfXqqIiToEa1IUTz1L+ulv46XTmFCI+G1bqbr/fhL33Udk2bLZPuyrks/nOXDgAM+9vJftJ4Y569XSTT1pN4ABNnUmeXBNM29c18xN7ZUfSC2U8RqurHWJMeZ/AD8JlIAoUAN8xVr7E5fat86x5xHPgyPfh2f/F5x6BiK1pLf+G76x4sf5xxGP50bTBA28qaGWD7fV82B9zTWf96XyJX54oJev7zrHU0cGKHmWlc1V/NhmP/Re2lhZFw1ni7UeuVwXvSefo+uV7eTTp8k6I/Q6ht5SFT3pZnoyzfSm/WrwrHt+K5Qak6Pe5mmJuixtiLGus4HbN6xk5bJOtUKROUNB9yVoEJY5a/QcbP8MvPg3UMzAmrfDvZ+ERXP7b29rLS+OZfjnniG+3jfCSMmlKRzkvS11vL+ljpuq1M9bZp7rWY72pcpV3374fbQvBYAxsKal+rzwe3lj4obeEjmfTpwvUx22AxjAv7H/vwNt1tqfuWD79wNvuSDovt1a+0uX+2yN2YJbhCd+H576Q2ztIr72pr/m14di5D2P31jRzs90NOJU0BhjrWXH8SH++unj/PBgH+GAw49taefj9yxjXVvNbB/ejOo5Mcqex85y7MU+rLUs29LE5gc7aVtZW/F/F1jXkjs6TOalPrL7B6HkEWyMEmwqUOp6nvTTP6Rw7BgAoSWLqbrvfqrf+CDxrVsxwcqpcB8ZGWHv3r3s2rWbw/0Zumwd/aEWzmYCWKC1Jsob1jbz0Lpm7lrRWJEPlJ5P4/XlGGP+ABic9jDKemvtf7rE+g+g1iUL29kX/MD74DfABGDj+zly6y/whUId/9wzzECxRGs4xIfa6vloewPtr6Ol5VC6wHf2dvON3V08d2IIgM2Lannn5nbeubmdlpr5c1H4RioWRxgdPcyZg08y2n2AktfLeDBDt43Ql22kp9wKpSfdQn+2EddO/R6PUCRJjganQHsVrGiqYuOyNrbdvIbGhvpZ/K5kIVLQfQkahGXOywzBzv8NO/8SciOw7D6455Ow/AE/wZvD8p7Hvw6O8c89w/xwcIyitaxLRHmktZ73tdTRGrm2W9xErsRopsiusyO8dGqYl04Ps+vMCOM5v+q7Nhbye3131nHLkiSbO5PURK/ff48L6cR5gjFmKfAta+2GC5ardYm8fqd3+tXdo2foufe/8sn69/DYcIq7k1V8at1iOivweRHH+1P8zTMn+ecXz5AretyzspGP37uM+1c1zatepanhHHufOMf+p86RT5doWlzN5gcXsXJrC4Fg5fd+9nIlsnsGSL/US+HkGBiILKslvCxEqftl0s88QWbHTmw+T6C2lqoHHqD6TQ+RuPtunFhlVLlba+nq6mL37t3s3buXoUyRsarFDMc62NtXJF1wiQQd7lnZyIPrmnlwbTNttZXxvS2k8doY0wB8EVgMnAbeb60dMsa04z8o+m0XrP8ACroFYOgE7PgLePnv/YKsFW+keNcv84PqLXyhZ4jHBscwBt7RlORnFzVxa83rayvYNZLlW3u6+PquLvZ3+fvetqyB99zcwds2tc36nZvzgeflyWRP03PuBXqO7KCQOk0pOMhgwNKTT5ZboDTRk26lJ93MSOH8BzBXkaeOHE2hIotqg6xpS3Lz6k5u3bSWSKTy/iaTuU9B9yVoEJaKkR+HF//W75OW6oH2W+De/wBr3gbO3D8xHCqW+HrfCF/qGeLFsQwOcG9dNY+01vG2xloS6uct15nnWY71p87r9X2kL4W1/jWjVc1V3FJ+yOUti+tY0VQ1Y+HSQjlxNsa0WWu7y69/Fb8lyYcuWCeI/zDKNwLn8B9G+RFr7f7L7V9jtpwnNwbf/c+w+wvYjlv5wv2f4Td7ihjgt1d28OG2+oqsFB7JFPjCc6f5/LMn6R3Ls6IpwcfvWc57b5lffbyLBZfDO3rY89gZhnsyxGvCbLi/g5vu7SBeMz9OikuDWTIv95F+uQ93MIcJOcQ2NBJdX0uxaw/px/6V8R89jjc2holGSdxzN9VvfIiqB+4nWPeqZwLOScVikb1797Jjxw76+vqIxquoWr6FLtPIk0eHODucBeCm9hreuLaZB9e1sKmjds5evFko4/X1pvF6gcgMwQt/DTs/C+k+aN0Id/0yp5a/nc/1jPCP3YOMlTy2VMf52UWNvLM5Sfh1nrce60/xjV1dfH3XOU4OZoiHA7xtYxvvv3URty+rzHF/LrPWki/0Mjp+mDNHnmK8+yCe10s+PEavF518GGZPupnuVBu9mSZy3lSP7wAe/x979x0e51Xmffz7zKj3KZpRdy9y73Z63OL04iSkQhoEFliW3YWlk1CXpb2wwNISkkAKaU4vjntsxyWucZHcm9p0SaPp5bx/PLITJ47sJCOPNLo/18UFtkajI2PrN+ee+9ynhDBmLUxFXpJB5jzG1FlPjEIx9IM6huibpNDdAwlh0e/EwrD9CX2Ot+8wlI+G876qz/I29o8O6QPBcPc8bx/HwlEKjAYut5byqQoz55mKMMoLFHGWdIZjbD/WzpYj7Ww9phfAO0IxAIrzsphUW3ai+D251kRpwcf7NzZQNs6apv0DmIQ+uuQw8Pnu2Z8ndYdpmnY58BvACPxNKfWTM3l+yWxxSrueg5e+CokoRy/5FV/Nms5b7QHmWUr41aha7P309FA0nuSVHS08uOYQO5s7MRVkc/usQXw6w+Z4q6TiWIOX7cuPcXSXF2OWgZEz7EyYU4u1pijdy0sJpRTRo36CWxwEt7tR4TiGkhwKJpVTMM5MrLkB/5Kl+JctI+5wgNFIwbRpFM+dS/G8uWRXVaX7WzgtpRQHDx5k/fr17Nu3D6PRyLhx47GPnMh2V5LljQ42H/GRVGAtymXO6HLmjLZzwQgrhX2oG3Og5HVvk7weYGJh2PGUPtbEvRdK6+DibxIYdyNPOjp4sMnNgVAEe04Wd1Rb+XSV5WNfXnmcUootR308vamJl7a3EIgmGGQp4IYpNVw/tYaqsv5xiqQ/i8e7CAQO0ubegmPfBmJdx1BZLjqzYzjCFn0GeNBGq7+StoAdd8R0ilEoESzGCFVFGsPLCxk/tJJzJtdjNpWl7xsT/YIUunsgISz6rUQcdj8Pq38Nzl36C4rz/w0mfxqy+sdNyUml2NgR4Jk2Hy+6fHTGk1TkZHN9hYkb7Cbqi+QFiji7kknFQXfgxJzvrUd97HH4OR57w21FTK4tY8ogE1PqTAy3FWE8g6402TinhmS2+FAdzfD8v8ChVSRHX8mD03/CT5o6yDcY+O+RNVxr7x/dsaeilGLDIS8PrjnE0gYHWQaNqydWc8/5QxhTlVlzvL2tAd5Z0cSeda3EY0mqR5mYOLeWweMsaH20A/ijUrEkoUYPwS1Ownt8kFRkVxRQMNlO/kQrsWP78S9din/ZUqL79bneeWPGUDx/HkVz55I7YkSf71h0u91s2LCBbdu2EYvFGDJkCLNmzcJSNYjV+90sa3Cyaq8LfzhOjtHArGEW5o7WL7SsMRWkde2S16kheT1AHb+4cuXPoHUblNfD3O+THHkpK31d/LXJxQqvnxxN4zq7ic/VWBlX/Mn/zQejcV7b0cbTm4+x/qAXTYPzh1u5YWoNC8ZWZNRpqP4gmYwTDjfR0bWHpiPr8LfoXeDJXB+eZB5tARuOoI3WLjttXZW0BcvpiBWf9BxFRDBpYcqz49SWZjGyspSpowczedwIcnMy49SX+GSk0N0DCWHR7ykFexfD6l9B00YoroLz/g2m3gHZ/adQHE4kecPTydNtXlZ4O4krGF+Uzw0VJq6zmbD104480f91ReLdXd8+th7Ti9++oN71XZR7vOtbv+Rycl0ZZQUffPElG+fUkMwWPUomYf0fYNkPId/Evsv/zFeC1Wz1B7naVsZ/j6jBktN3Okc/jkPuAA+vPcRTm5oIxRKcO8zCZy8YwsUjbX12FMTHEQ7E2L2mhR0rm+jyRSgtz2f8xTWMmlVBXmHmvB5IBGKE3nER3OIkesyvz/MeVkbBZBv54yzEWo7RtWwZ/iVLCW3fDuiXWRbPnUfxvLnkT5qE1oePfQeDQbZs2cLGjRvp7OzEbDYza9YsJk6ciCErm7cPe1ne4GRZo5ND7gCgXx49t14vek+qNZ3Rm8mpJHmdGpLXA1wyCQ0vwLIfgfcA1M6CeffDoHPYFwjzYLObJ1u9hJJJZpUW8rnachZYSslKwb/3o54gz2xp4tnNTTS3hyjOy+LqiVXcOK2WiTX9//Lj/i4W8+ld4J6tOA9tIuI/gpblIZIdwBm20Ba04QjYaPVX0Rqw4whZiJxqFIohQkV+ksGmPMbUlTNj/HCGDaqWUSgDiBS6eyAhLDKGUnBoFaz6ORxZC4U2veA97S7IKUz36j4SVzTGC852nm7zst0fwqjBRaZibqwws8BaSoFRAkykj1KKw57giUsutx5tp7Gtk2R3NA61FjK5Tr/kcnKtiVEVxWQZDbJxTgHJbHFG2nbAs58DVwPxGV/gD6P/lV8e9VCWbeRXo2q5xFp6+ufo4zqCsRNzvNs6wwwtL+Tu84Zw/ZQa8nMyp3MtkUhycKuL7cuO4TjUiTHbwPCpNsaeX0XFsMwqWMRcQYJbnQS3uUh49XneeWMtFE62kTvcRNzjomv5CvxLlxLYsAFiMYxWK8WzZ1M8fx4Fs2Zh6KNdbolEgt27d7N+/Xqam5vJy8tjypQpzJw5k9JS/d/jQVcXyxqcLGt08PZhH4mkwlyYw8Wjypk72s4FI629emn0cVLoTg3JawFAIqZfWLnyf/Q7pkZeCnPvA/sYOmJxHm/18mCzi6ZwjJq8bO6uLufWSjNl2Z/8TelkUrHuoIenNx3jtZ1tROJJRtqLuHFqLddOrqa8uH+cgB4okskYodAxOrv2cKxlA/7m3STjbZDrwU82jmC5XgDvqqCtq4rWgA13pJQk777mySGOiTDWrChVRRrDyouY0D0KxVSWWSfghBS6eyQhLDLS4TV6wfvQKiiwwrlfhumfhdzi039uH7M3EOaZNi/POnw0R2IUGQ1cWV7GjRUmzikrwpBBm1zRfwUicbY3tbO1e9zJlqPteANRAApzjOz+0WWycU4ByWxxxmIhWPoD2PBHKK9n1xV/4V+dOewOhLml0swPh1dTnAGXIMcSSV7d0coDqw+xo7mDsoJsbptZx2fOGYy9JHPmeAO4jvrZtaaFvRvbiIUTmCoLGXt+VcZ1eSuliB7p1Ivex+d5F2dTMNFGwWQb2VWFJLu66Fr1Jv5lSwmsepNkMIihsJCiiy6kaO5cii68EGNx33zNd+zYMdatW0dDQwMAY8aMYdasWdTW1p54TEcoxqq9LpY3OFixx0VHKEaWQWPmUDNzRtuZO9rGYGvvNHFIoTs1JK/FSaIB2PAnWPNbiHTCxJth9rehrI6EUix2d/DXJhfr2gPkGwx8qsLEZ2vKGVGYmhzrDMd4eXsrT28+xtaj7RgNGrNH2bhxWg1zRtvIliaqPkspRSzmJRA4gKP9HRxHNxFtP4xmdJPM7cQdMeE4Pgqls1ovhIcsdMZPvuPj+CgUW06cmtIsRlWamDJ6EFPGjiDnE86LF+khhe4eSAiLjHZ0vV7wPrAM8k1wzpdgxr2Q1/+62ZJK8VZ7F0+3+XjZ1U4gkaQ6N5sbKszcYDel7IWQEKmglOKoN8iWoz62HGnnx9eNl41zCkhmi49s/1J4/osQ8hGZ/X1+VbGQ3x9zUZmbzW/r6zjf1DeLgR+VUoq3D/t4YPVBlnTP8b5qQhV3njeYCTVl6V5eSkXDcfZvdrJrdQvOw50YswwMm1rO2POrqRyeWV3eKp4k3OglsMVJeI8XEoosewEFk20UTLKRVZZLMholuG4d/qXL8C9fTsLjgexsCmfN0i+znDuHrPLydH8rH9De3s7GjRvZvHkzkUiEmpoaZs2aRX19PUbju29CxRNJthxtZ1mjg+UNTvY5uwAYVl7I3Hq96D11kImsFBWqpNCdGpLX4pSCXljza9jwF0DpjVgXfA0KLQDs9Ad5oMnNc04fkaRitrmYz9aUM9tcnLLmpv1OP09vbmLRlmZc/giWwhyunVzNjdNqGF0hXb/9STIZIRg6Sqd/D83OTXQ070TF2tByvUQ0hTNYTlvQRltXBa2dNfps8IiJSPLd00/HR6FYDFHs+UkGm/VRKLMmjGBIbaWMQunDpNDdAwlhMSA0bYY3fw57X9eL3LO+CDM/rxe/+6FgIsnr7g6ebvOyyusnCUwqLuCGChPX2kxY+/kMVpF5ZOOcGpLZ4mMJeOClr0DjyzDkQjbP/x1fORriQCjCPdVWvjOsKqNGYh3xBHho7WGe2nSMYDTBuOoSbplRxzWTqinKzax8dB3zs3tNC3s3tBENJzBVFDDm/CpGz6okryizOrQSgRihHW6CW51Ej3Tq87yHlFIwxUb+OCuGvCxUIkFo+3a96L10KbGjR0HTyJ84keL58yieO5ecwYPT/a2cJBKJsG3bNjZs2IDX66WkpISZM2cyZcoU8vM/eNfMEU+A5Y1Oljc6WX/QQyyhKMnL4uJR+lzvi0faKC34+P/fS16nhuS16FFHE6z8b9j2OGQXwnlf0fenuXoXrisa49EWDw83u3FE4wzLz+XuGis3V5gpTNFprHgiyZv7XDz1dhPLGh3EEorx1aXcOK2GqydWnfLOHdE/KKWIRt0EAvtxduzA0bKFiPcgmsENuR34Y8U4guW0Bey0ddbQ2lVJa9CCJ1pyylEolu5RKEOthYwdUsGsiaOwW81p/A4FSKG7RxLCYkBp2Qpv/lLf7OeW6N3d53wJCvrvD2pHJMZzDh/POHzs7AqRpcEccwk3VJi5xFJCXgYVL0T/JRvn1JDMFh+bUvqc0Ne+CcZsglf+lp/mTuOBJjdD83P53/o6ppX2r/ssTqczHOP5rc08vuEojW1+CnOMXD2pmltn1DG+pv+d7OpJLJJg/2YHu1a36LO8swwMm1LO2AuqqBxellFd3gBxT4jgVieBrU4SnjBkGcgfa6Fgso28ESY0o4ZSisi+ffiXLqVr6TLCu3cDkDtiOEVz51I8dx5548b2mT+bZDLJ3r17Wb9+PYcPHyY7O5tJkyYxa9YsLBbLKT/HH46xZp+bZY1OVjQ68QSiGA0aUweZmFdvY85oO8PKCz/S9yh5nRqS1+KMOBth+Y/0vWmhDS76L5hyB2TpReZoMsnLrg7+eszFVn+QYqOBWyst3F1jZVB+6mZsewNRnt/azNObm2ho7STHaGD+WDufmlbL+cOtZ/1SXNF7EokwodAROrr20OLeQkfrTpLhVgy5XpKGOO6wufsyzEraOmtp7e4C74yf/BoxnygmLYw1O05VkZHh9iImDKtmxoRRlJUUfchXF6kkhe4eSAiLAaltB7z5C9j9on5R5fTPwjlfhqK+d7T1o2joCvF0m49nHV4c0TglWQauLjdxY4WJGaUfbaMjRCrJxjk1JLPFJ+Y5AIs+B82bYeKtrDn3fr560E1LOMaX6mx8bUgFuRl2TFUpxdZj7Ty+4Sgvv9NCOJZkfHUpt86s4+qJVRRmWJe3u6mL3Wta2LOhjWgoTpm9gLEX6LO884syq0NPKUX0mJ/gFiehd1wkg3EMRdkUTCinYIqN7OqiE699Ys3N+Jctx79sGcFNmyCRIKuiQh9vMn8eBVOnomX3jS741tZWNmzYwI4dO0gkEowcOZJZs2YxZMiQD30tl0gqtje1s7zBydIGB41tfgAGWQqYO9rO3Hob0webycnq+d+35HVqSF6Lj+TYRlh6PxxZC6bBMOd7MHYhvCePN3cE+GuTi5dd7SQULLCW8Lmacs4tK0rpHm9ncwfPbG7i+W3NtAdjVJTkcd2Uaq6fUs1wW2aMOxMfpJQiEnUQ6DqAu3MnbY4thL0HMeDCkOcnksjGGSzXO8E7amjrqqItaMUZKSOQeO/pI0URUUxahPLueeAjKsqYNLKWaeNGkJ8nl6CmihS6eyAhLAY0Z4Pe4b3zWcjOh2l3w7lfgWJ7ulf2iSSUYo2vi6fbvLzi6iCUTFKXl8MNFSZutJsZUiABI84u2TinhmS2SIlETL+/YvUvobQW/7V/5b5wJY+3eqkvzON/6+sYX1yQ7lX2io7Qu13eexx6l/c1k/Uu73HVGdblHU2wf5OT3WuaaTvYiSFLY9hkG2PPr6JqZOZ1eat4kvAeH8GtDkIN3fO8y/MpmNI9z9v07l0mcZ+PrpWr8C9dSmDNGlQkgqG0lOKLL6Jo3jyKzjsPQ0H6/w34/X42bdrE22+/TTAYxG63M2vWLMaNG0f2aYryze0hljc6Wdbg4K0DHqLxJMW5WVw4spw5o23MHm3DXPjBNz4kr1ND8lp8ZErBviWw7Afg2AkVE2DefTBsLrzn53VrJMrDzR7+0eLGG0swriife2vLudZWRk4K36iOxBMsa3Dy9KZjvLnPTSKpmFhTysIpNVw1seqUPz9EZkokQoRCR/EH9tPmewevYwfxriYMRi+GnBDBWJ4+Dzxgp62zlrauCtqCFlzRMsLvmQeuoSgmgtkQwZaXoLY0h5FVZqaNGcL4kYPJyc6sxoPeJoXuHkgICwG49sLqX8GOp8CYA1PvhPP+DUqq0r2yTywQT/CKu4Nn2rys9nWhgGklBdxQYeYaWxkmCRRxFsjGOTUks0VKHd2gd3d3HIMLvsaScf/Cf+5rwRuL85VBdr46yJ7STXNfopRiy9F3u7wj8SQTa0q5ZUYdV2Vgl7enuYtda1rYs17v8i615TP2/GpGn1NBfnHmFSuSwRjBnW6CW5xED3cCkDOklMIpNvLH6/O8331skMBbb+FfshT/ypUkOzrQ8vIoPO88iufOpWj2xWSZ0nunSywWY8eOHaxfvx6n00lhYSHTp09n2rRpFBWd/oh4MBpn7X4PyxocLGt04vJH0DSYUmdizmgb8+rtjLTrXaGS16kheS0+tmQSdjwNK34M7Udh8AUw7wdQM/Wkh4USSZ51+PjLMRd7g2FsOVncVW3lM1VWLCm+r8npD/PithYWbWlmd2sn2UaN2aNsLJxSw5zRttOeFBGZKxZrJxg8RHvXHtq879DhaIBIG4YcLxjidMUKcXRfiOnoqKU1YKctZMYdLSWm3v17aiBJKRHMxgj2PMUgcx71tVamjx3GqCHVcinmKUihuwcSwkK8h+eAfhP29n+CZoApn4HzvgplteleWUq0hKMscvh42uFjTyBMtqaxwFrCrZUWLjIXY8yw7i7Rd8jGOTUks0XKhTvhtW/A9seheiq+q//C9zzZPOPwMbYoj9+OrmNchnZ3H9cRjPHc1iYe33iUvY4uinKzuHZyFbfOGMSYqpJ0Ly+lYtEEB7Y42b26hdYDHRiMGkMnlzP2/CqqR5rQMnAOa9wbJrjVSXCrk7g7BFka+fXd87xHmdDec5eJisUIbt6sX2a5bBnx1lYwGCiYNo3ieXMpnjuX7OrqtH0vSikOHTrEunXr2LdvH0ajkfHjxzNr1iwqKirO6DmSScXOlg6WNegXWu5o7gCguiyfufU2fnTteMnrFJC8Fp9YPAKbH9ZPYAXdUH81zP0+WEec9DClFCu9fv7S5GKF10+eQeN6u4nP1ZYzuvCDF9p+Ug2tnSza0sRzW1twd0UoK8jm6olVLJxSw8Sa0ow7LSQ+HqWSRCIOgsFDeDobcHjeIeDeh5ZwYshpB03RHinBEbTh6KymzV+jzwMPm/BEi0m851LMLBKUEsGSFaWiAIZY8hk7yM708SMYVGkdsEVwKXT3QEJYiFPwHYY1/w+2Pqb/etKtcMF/6DPTMoBSip3d87yfcXjxxhJU52ZzU6WZmyvM1KXwchMhQArdqSKZLXrNrufgpa/qY00W/JjFgxby9b1NeGNxvjqogq8MsmVsd/dxSik2H/HpXd47WonGk0ysLeO2GXVcObGSghR3yKWbp6V7lvf6NiLBOKXl+Yw5v4rR51RSUJJ5Xd5KKWJNXQS2OPR53oE4hsIs8ieUUzjFTnbNyXNulVKEd+3Gv2wpXUuXEtm3H4DcMfUUz5tH8dx55I4ckbaijtvtZsOGDWzbto1YLMaQIUOYNWsWI0aM+Eib/raOMCv2OFnW4GTNfhd7fny55HUKSF6LlIn4Yd0f4K3fQSwEk2+Hi795ypPHewJhHmhy8XSbl3BScbGpmHtry5ltLk75z6p4Isnq/W6e3dzEG7sdRONJhpUXsnBKDddNrqaqLPVFdpEZkskY4XATgcBBnB07cLl2Em4/hBE3htwukkrDGzZ1zwOvo62zmtZgOY5wGb54IYp3My6HOGVaBGtWjMoijaHWQsYPrWLmhJHYLZk1ku79pNDdAwlhIXrQfgzW/ha2PALJBEy8RS94W4ale2UpE0kmWezu5PEWD6t8+sVFF5qKubXKzKXW0oy7lEykhxS6U0MyW/SqjmZ44YtwcCUMn4fv8t/y3bYEz3Z3d/9v/SDGFg2MjWt7MMqiLc08vvEo+51dFOdmce3kam6dWUd9ZWZ1ecejCQ5sdbFrdTOt+/Uu7yETyxl7QRU1ozKzy1slkoT3+ghudRLa7YG4IsuaT8FkGwWTbWSZ8z7wOdHDh/EvW4Z/6TJC27aBUmTX1emXWV4yn/yJE9HS8JopGAyyZcsWNm7cSGdnJ2azmVmzZjFx4kRycz9a40I4liA/J0vyOgUkr0XKdbn0uzXefhAMRpj5eTj/3yH/g6OVPNE4/2hx81CzG0c0zoiCXO6tLecGu5l8Y+p/TnWEYry2o5VntzTx9mEfmgbnDrOwcHINl46ryLhxYKL3xOMBQqEj+AP7cfh24HHtIt51DKPRgyE7QjxpwB2y4AhU0NZeR1tXJa1BK85IKR2JwpOeK48YJi2CNTtOdYmB4bYSJgyvZvq4EZhKCj9kBf1HRhW6NU27EbgfqAdmKKU2vedj3wLuARLAV5RSi0/3fBLCQpyBzhZY+7+w+SFIRGHSbXDxt6A0fcdXe8OxcJR/tnr4Z6uX5kgMc7aRG+xmbqk0Uz9Aihuid0ihOzUks0WvSyZh04PwxvcgKxeu+BWvV8zj63ub8HV3d//bIDvZGVj8PBWlFJu6u7xf6e7ynlxXps/ynlBFfo7x9E/Sj3hbA+xe00Lj+lYigTgl5fmMzeAub4BkOE5oh5vAFifRQ/oYj5zBJXrRe7wVQ8EHL32Mu1z4l6/Av2wpwXXrUbEYWRUVlCy4hOIFl5I/6ewXvROJBLt372b9+vU0NzeTl5fHlClTmDFjBmVlZWf8PJLXqSF5LXqN7zCs+Cm88xTkl8FF34Tp94Dxgz+roskkLzjb+csxFzu6QpizjXy6yspd1VYqcnu+0PbjOuIJ8NzWZhZtaeaoN0hBjpFLx1Vw/ZQazhlqwTBAXj+I1FJKEYt5CQYP0dG1jzbvdjrce1DhFozZPjRjgmgiG2fQiqOrkrb2QbQFKmgNmXFFSulKnvwGdgFRyrQI5TkJKouNDLeXMH5oNVPHDcNaevq7L/qCTCt01wNJ4M/A144XujVNGwM8AcwAqoClwEilVKKn55MQFuIj8Dv0kSabHgQ0mHkvnP8fUGBO98pSKqEUb3r9PN7q5XV3BzGlmFJSwK2VFq61lVGUlVkbe9H7ZOOcGpLZ4qzxHIDnvgBNG2HMtXgv+QXfbQmxyOFjfFE+v62vY8wAewPUF4jy7JYmnth4lAOuAMV5WSycXM0tM+sYXZFhXd6xBAe3uti1uoWWfe0YDBpDJlkZe341NaMzs8sbIO4LE9zmIrjVQdwZAqNGfr2Zgsl2fZ73KS5cS/j9dK1YQefriwmsXq0Xve12ihdcQsmll5I/adJZL3ofO3aMdevW0dDQAMCYMWOYNWsWtbWnv3NG8jo1JK9Fr2vbAYu/A4dWgWU4zP8hjLocTjGiRCnF+o4Afznm4nV3B1maxjW2Mu6tLWdCL93DcfyN4mc3N/HKO634I3GqSvO4dnI1C6fUMNzWP4qJou9TKkk43EowdAhv524c7h10efejJRwYczrRNEUonoszWI6jo4Y2fy1tARuOkAlXtITA+4rg+UQxaVEsOXGqi40MtZcwYVg108YMw1rWd/7eZlSh+8QiNG0lJxe6vwWglPrv7l8vBu5XSq3r6XkkhIX4GHxHYOXPYPsTkFsM530FZn0Rcvr/EZj3c0fjPNPm5fFWL3uDYQqMBq6xlXFrpYVpJQVy4Yg4I7JxTg3JbHFWJRP6+K4VP9WPRl/9O14zz+Lre5roiCf498F2/rVu4HR3H6eUYuMhL09sPMqrO9uIxpNMqSvj1pmDuHJCJXnZmfVmsK8twK41LexZ10Y4EKPEmndilndhaWbe6aGUItbcpV9iud1FsiuGoUCf510w2UZO3ann3Sa6uk4uekejZNlsFF9yCSWXLiB/ypSzWvRub29n48aNbN68mUgkQnV1Neeccw719fUYjaf+eyp5nRqS1+KsUAr2vQFvfBfce2HwBXDJj6Fq0od+yuFQhAeaXDzR6iWQSDKrtJDP15ZzibUUYy/t68KxBG/sdrBoSxNv7nWRVDCxtozrp1Rz1YQqTIWZeWJIpF8yGSEUOnZiHrjbvZtwx2EMyo0xtwvgRBHc2VmNo7OW1oC9uwheTFfy5KaOE+NQchJUFRsZZitm/LBqpo0dSnlZ8Vn93gZKofv3wHql1KPdv34QeE0p9UxPzyMhLMQn4NgNy38Ee16FQhtc9F8w5Q7IyrywVkqxpTPIY60enne2E0wkGVGQy62VFm6oMFGe0zvH30RmkI1zakhmi7Ro2wGLPg/OXTD503jn/IjvHO3gOWc7E4ry+c0A7O4+7niX9+Mbj3LQFaAkL4uFU2q4dWYdI+1nd8PT2+KxBAe3udi9uoXmvXqX9+CJVsaeX0VtvTlju7xVQhHep8/zDu/2oGJJsix5787ztpz6775e9F5J5+LXCbzZXfQuLz+56P0hxeZUi0QibNu2jQ0bNuD1eikpKWHGjBlMnTqV/PyT1y95nRqS1+KsSsRg88Ow8r8h6NXvlZr7vVNeWHlcRyzO461eHmhy0RyJMTg/h8/WlHNzhblXT+86O8O8sK2FZ7c00djmJ9uoMWe0jesm13DxqPKMe7NY9F2JRFAvggcP4WrficvTQKSzuwieoxfBw/EcXCErbR21ODpraAvYaevuBD9VEbzsPUXw4fZixg2tZkr9YCrMqb8Ys98VujVNWwpUnOJD31FKvdD9mJWcXOj+A7DufYXuV5VSz57i+e8F7gWoq6ubeuTIkV75PoQYMI5ugKX3w9G3wDQYZn8Xxl0PGXqRY1c8wYvOdh5r9bC5M0i2pnGJtYTbKi1cZC7utW4A0X/Jxjk1ZOMs0iYe0TfQa38LJTVw7f/xStF4vjHAu7uPU0qx4ZCXxzcc5fWdbUQTSaYNMnHLjDquyNAu791rW2lc10q4K0axJY8x51VRf17mdnlD9zzvnR6CWx1EDnaAgpxBJeSPt5I/zkJW2QcvsQRIdAXoWrkS/+LX6XpzNSoSwVhupWT+JRRfuoCCqVPPStE7mUyyb98+1q1bx+HDh8nOzmbSpEnMnDkTq9UKSF6niuS1SItwB6z+Faz/Ixiy4Nyv6CePezh1HE8qXnN38JdjLt7uDFBsNHBblYV7asqpzevd5q1dLR0s2tLMC9uacXdFKcwxMm+MnSvGV3LhSCl6i/RJJIIEQ0cJBg7h7NiF272bSOcRDLxbBI/Ec3CGrDg6anH4a2jrstEWNuOKlOA/ZRFcH4fy3k7wKaMHU2n5eEXwflfoPqNFyOgSIfoWpWDfElj2A3DsBPt4mHcfDJ93yllpmaIxEOKJFi9PO7x4Ywmqc7O5qdLMzRVm6vIzd7MrPhrZOKeGZLZIu6Mb4PkvgPcgzPoingu/zbcPeXihu7v7t/V1A/7yYm8gyrOb9VneB90BSvOzWTilmltn1DEiw7q8E7EkB7frs7yb9/jQDBpDJlgZc4He5Z3Jl47F2yMEtzkJbXMSawsCkF1TRP5YveidXX7qubfJQICuVavofH0xXW++iQqHu4ve8ylecCkF085O0butrY3169ezY8cOEokEI0eOZNasWQwbNmzA5LWmaWbgSWAwcBj4lFLKd4rHlQEPAOMABdwte2zRp/kO601Yu56Dogq9u3viLWDo+WfLlk59jvdLrnaUgsvLS/l8ra3Xx1XGEknWHfDw6o5WXt/VRnswRlFuFvPqbVwxoYoLRlil6C36jEQiSDB4hGDwMM6OnbhdDUT8RzDgwpgTACCSyMYVtOLorMXZWUvr8SJ4tJjOxMmvD3KJv9sJXmRkqL2I8UNrmDy6jipL6Yf+2xsohe6xwOO8exnlMmCEXEYpxFmWTMLOZ2HFj/UXGYPOg3n3Q+2MdK+sV0WSSRa7O3mi1cNKrx+AC03F3FJp5rLyUnIztLtdnBkpdKeGZLboE6IBWHIfvP1XsI6E6/7My9lD+cbeJjrjCf5zsJ0v19nJyuAi55lQSrHuoIfHNxxl8a42YgnF9MEmbp1Zx2XjMq/Lu90RZPeaFhqOd3mb8xhzfiX151ZRWJbZb3zH3CHCu9yEdnqIHtNfA2XZCsgfZyF/rJXsqsJTblSTgQBdb76pF71XrdKL3lYrxfPnUbLgUgqmT+v1orff72fTpk28/fbbBINBfvCDHwyYvNY07eeAVyn1M03TvgmYlFLfOMXjHgFWK6Ue0DQtByhQSrX39NyS16JPOLoBFn8bmjdBxXi45Ccw9KLTflpzOMrfmt082uKhI55gQlE+d1ZbudZuosDYu3u6WCLJWwc8vPqOXvTuCOlF7/ndnd4XjLSS24ujVYT4JOLxAKHQUYLBQzjbd+JyNxL1H+7uBNeL4NFENq6QBUd7LU5/La1ddtrCJpzREjoT+cC7rxdy3lsELzYytLyY8cOrmDSijjq7OXMK3ZqmXQf8DigH2oFtSqkF3R/7DnA3EAe+qpR67XTPJyEsRC+JR2HLI7Dq5xBwwqgr9HfTbfXpXlmvOxaO8mSrlydaPTRHYpiyjNxQYeLWSsuA7/QbqKTQnRqS2aJPObAcnv8SdDngwq/hnvXvfPuggxed7Uwozue3o6W7+zhPV4Rnuru8D3uClBVks3CyPst7uK0o3ctLqeNd3rvXtNDUqHd5Dx5vYcz5VdSNtWR0lzdAvCNCeJeH0E43kUP6eBNjWS754/RO75y6klPOM08GgycXvUMhjBZLd9F7AQXTp6NlZfXaumOxGDt27GDq1KkDJq81TdsDXKyUatU0rRJYqZQa9b7HlADbgaHqIxQBJK9Fn6GU3oS19AfQcRRGXgaX/AisI077qYF4gqcdPh5pdtMQCFOSZeCmCjN3VFsZXnDqUU2pFEskWbvfzas7Wlm8y0FHKEbx8aL3hErOHyFFb9F/6EXw7k5w305cbr0T3Ki9WwSPJbL0InhH3XtmgptxRovpSBTw3iL4kf+5MnMK3akmISxEL4t0wYY/wtr/hYhfPzY2+1tQVpfulfW6hFKs9vl5rMXL6+4OYkoxubiA26osXGMro1hemAwYA6XQrWnak8DxTXIZ0K6UmnSKxx0G/EACiJ/pn41ktuhzQu3w2jfgnX9C5US47s+8RCXf2HuMrniS/xxcwZfqbAO+u/u4ZFKx/qCHxzYe5Y3uLu8ZQ8zcNrOOBWMrMq/L2xmkYW0LDW+1EvLHKDLlMub8KurPraTI1PtFknRLBGKEGzyEdnoI7/NBQmEoyiZ/rN7pnTu0FC3rg92RetF7NZ2LX6drZXfR22ymeN48Si5dQMGMGb1W9B4oeQ2gaVq7UqrsPb/2KaVM73vMJOAvwG5gIrAZ+DelVKCn55a8Fn1OLKzvSd/8FcRDMO1uuOibUGg57acqpdjYEeDhZjcvu/Q93QWmIu6strLAUnpWMj4aT7L2gJtX32ll8a42OsNxivP0oveVEyo5f3g5Oaf4eSpEfxCPd53oBHd4d+L2NBL1H8Hw3iJ4Mgt3yIyjvQ5HZy2/+PKvpND9YSSEhThLgl79cpCNfwUUTP8sXPCfUGhN98rOCnc0zrMOL4+3etkTCJNvMHCNrYxbK81MLz31kV6ROQbSxvk4TdN+BXQopX54io8dBqYppdwf5Tkls0WftftFePmr+pu7c7+He8q9fGt/Ky+52plYrM/uHl0o3d3v5X5Pl/cRTxBTQTbXT6nhlpl1DCvPsC7veJJD293sXtPMsQYfaFA5tJQhk8oZOqmc0vLM/7uRDMcJ7/ER2uUm3OhFRZNoeUby6y3kj7WQO9KEIeeDb3QkQyG63lyNf/Hr+FeuQgWDGE0miufNo/jSBRTOnJnSonem5bWmaUuBilN86DvAI2dQ6J4GrAfOU0pt0DTtt0CnUup7p/ha9wL3AtTV1U09cuRI6r4RIVKly6VfLr35Icgphou+DjPuhawzGzHlisZ4otXLI81umiMxKnKyub3Kwu1VFipys3t58bpoXO/0fmWHXvT2dxe9LxlTwZUTKjlvuFWK3iJj6EXwIwSCh3B4d+FxNxDtOsJll6+UQveHkU2zEGdZRxOs/BlsewyyC+Dcf4VzvgS5mXVB1YdRSrGlM8jjrR6ed7YTSCQZUZDLLZUWbqwwUZ5zdl4gibMr0zbOp6Pp79wcBeYopfad4uOHkUK3yDRdTnjpq7DnFag7F679P16Mm/hmd3f314ZU8MVa6e5+v2RS8dYBD09s1Gd5x5OKWUPN3DKjjkvHVWTcsewOV4g9G9o4uM2Fp6kLAEt14Ymit7WmKOPf/FaxJOF9PkK7PIQbPCSDcbRsA3kjTeSPs5I32owh/4PF62Q4TNebb+J/fTH+lSv1ondZGcXz51G84FIKZ85Ay/5kr6MGUl6f4eiSCmC9Umpw968vAL6plLqip+eWvBZ9nrMB3vge7F8CpsEw7wcw5ho4w5+/CaVY5unkoWY3K7x+jBpcZi3lzmor55WdvZ/j0XiSNftdvPJOG2/s1oveJXlZXDK2gismVHLeMCl6i8yUkZdRpoqEsBBp4toDy38EDS9BgRUu/DpMu+uM303PBIF4ghdc7Tze4mFTZ5AsDRZYS7m10sLF5mKMGb7RHUgG0sYZQNO0C4Fff9j3rGnaIcAHKODPSqm/9PBc0iEm+g+lYPsT+jiTZAIW/ATX+Nv41r5mXnZ1MKm4gN/W1zGqMPPHVnwcLn+Epzcf458bj3HUG8RcmMMNU2u4eXotQzOsyxug0x3i4DYXB7e5aD2gz7MuseYxZKJe9K4YVprxM71VQhE51EFol5vQLg/JzigYNXKHlekjTsZYMBbnfODzkuEwXatX4399MV0rVpAMBjGWllLUfZFl4ayZH6voPZDyWtO0XwCe91xGaVZK/dcpHrca+KxSao+mafcDhUqpr/f03LLHFv3G/mXwxnfBuRtqZ8GCn0LN1I/0FIdDEf7e7OGJVg++eIIRBbncUW3lRruJ0uzeu1vg/SLxBGv26Z3eS3Y58Ef0oveC40Xv4Vaye/kyTSHOFil090BCWIg0a9oMS++Dw6v1ud2zvwPjbwRDZnVwnc6eQJjHWz083ebFG0tQlZvNTRVmbq40Myh/4BT/M1UmbZx7OgatlHqh+zF/BPYrpX71Ic9RpZRq0TTNBiwB/lUp9ebpvrZktug32o/BC1+CQ6tg+Hy4+ne8EM7jW3ubTszu/kJdObkG2XCeSjKpWHvAzeMbjrJkt4N4Up/lfc2kKi4fV4mp8IOFz/4u2Bnl8DtuDm5zcazRSzKuyC/OZvAEK0MnlVM72owxO7P/vqikItrkJ7TTQ2iXm4QnDBrkDCrRL7McayHrFLPNk+EwgTVr6Fz8Bl3Ll5MMBPSi97y5lCxYQOGsWWg5Z/Z3JpPy+nQ0TbMATwF16KewblRKeTVNqwIeUEpd3v24ScADQA5wELhLKeXr6bklr0W/kkzA1n/A8p9AwKnvRefeB2W1H+lpQokkL7naebjZzZbOIPkGA9fbTdxRbWF8cUEvLf7UIvEEq/fqF1ku2a0XvUvzs1kw1s4VE6o4d5hFit6iX5NCdw8khIXoA5SCA8th6f3Q9g7YxsDc78PIS8/4+FimiCaTLHZ38nirh5VePwq40FTErZUWLrWWkicvSPqlAbZxzgKagalKqaYzePz9QJdS6pene6xktuhXkkl4+6+w5D79tNIVv8I16hq+ubeJV1wdDMrL4b7hVVxmLc34URWfhNMf5ulNTSza0sQBV4Asg8YFI6xcPamK+WMqKMo9e91yZ0s0FOfILg+Htrk4vNNDLJwgO9fIoHEWhk4qZ9A4CzmnGO2RSZRSxNqChHe5Ce30EGvTL6PKri7SO73HWcm2fbBwlIxECKxdS+frr9O1fAXJri4MpaUUz5lDyaULKDznnB6L3gMpr3uT5LXolyJ+WPMbWPd7/dezvgjn/zvklXzkp3rHH+SRZjeLHD5CScXUkgLurLZyVXnZWd/PhWMJVu97t+jdFYlTVpDNgjF6p/c5UvQW/ZAUunsgISxEH5JMwu7n9ZEm3oP68bF598Ogc9K9srRoCkd5stXLE20emsIxTFlGrq8wcVulhfqizL+4KpMMpI2zpmmXAt9SSl30IR8vBAxKKX/3/14C/FAp9frpnlsyW/RL7v3w3OeheROMvQ6u+DUrw1l8f18Le4Nhzisr4ocjqhkrP9d7pJRid2snL25v4eXtrTS3h8jNMjC33sbVE6u4eJSNvOzMOw2WiCVp2uPj4DYXh7a7CPljGLI0akaZGTrJypCJ5RSUZF6H+/vF3SFCu/RO7+hRPwBZ5fknOr2zqz84EzcZjRJYs1a/yHLZcr3oXVJC8Zw5FF+6gKJzz/1A0Xsg5XVvkrwW/VpHEyz7IbzzJBSWw4X/BVPvhKyP/rO2IxbnqTYfDze7ORCKYM42ckulhc9UWdJyajccS/DmXteJoncgmqCsIJtLu8ebnDPUQpYUvUU/IIXuHkgIC9EHJWL68bGV/wNdbTBigd7hXTEu3StLi6RSrPZ18Virh9dcHcSUYlJxAbdVmbnWZqI4wy7qykQDaeOsadrD6BdX/ek9v3fiGLSmaUOB57o/lAU8rpT6yZk8t2S26LcScVj7G/0y5gIzXPW/xEcs4O8tbn5xqI2OeILbqiz815AKuZT4DCSTiq3HfLy4rYVXdrTi7opSlJvFJWPtXD2xKmPnkCaTiraDHXrRe5uLTrc+2qNyaOmJyyxLyzP/DZNER4TQbg+hXR4iB9shCcayXL3Te6yVnMElaIZTFL3XrtUvsly+nKTfj6G4+ETRu/C88zDk5AyovO5NktciIzRvhje+D0fWQNkgmP3tjz1iUynFGl8XD7e4ed3dQVLBbHMxd1ZbmWspScvdTOFYglXdRe+l3UVvU0E2l46r4IrxVcwaapait+izpNDdAwlhIfqwaBA2/hnW/D8Id8KET8HF3wLzkHSvLG080TjPOrw81uplTyBMvsHA1bYybqs0M720UI6/91GycU4NyWzR77W+o3d3O3fr47kW/BRfySB+fbiNh5rd5BsM/PvgCj5bYyVH5nefkXgiybqDHl7a3sJrO9vwh+OYC3O4fHwFV02oYvpgc0Ze6KiUwtMcOHGZpaepCwBLdRFDJ1kZOrkcyym6nDNNIhAj3OAltMtNeJ8P4gpDYTb5Yy3kjbWQN6wMLevkf0vJaJTAW2+9W/Tu7MRQVETx3DlU//znktcpIHktMoZScGAZLP3BuyM253wXRl3+sUdstkaiPNri4dEWD45onJq8bD5daeXGChNVeek5oROOJVi5p7vo3eAgGE1gLsxhwdgKrpxQycwhUvQWfYsUunsgISxEPxDy6fPSNvxJvyxk2l1w4dehyJbulaWNUoqtnUEeb/XynNNHIJE8ccP3pyrMlEiXd58ihe7UkMwWGSEehQ1/hFW/gHgYzvkiXPA19iVyuH9/C8u8nQzJz+G+YdUssJZkfKEylSLxBKv2uHhxewtLGxyEY0kqS/O4ckIlV0+sZlx15v55drhCHNquF71bD3SAghJrnt7pPbGcimGlGVnwf69kJE54j4/QTjfhRh8qmkDLNZJXbyZ/rJW8USYMOSe/PlLRKIF16+h8fTH+ZcsY/fZGyesUkLwWGef4iM0VPwHPfqiZrl9YOeSCj/2UsaRisbuDh5rdrG3vwgBcZC7mpgozC6yl5KepsKwXvZ28sqONZd1Fb0thDgvGVXDl+EpmSNFb9AFS6O6BhLAQ/UhnC6z6OWz5O2TlwTlfgnO/DHml6V5ZWgXiCV5wtfOPZg9b/foN3zdUmLijysK4s3zDtzg1KXSnhmS2yCh+Byz7AWx7DIrs+oZ54i0s93Vx3/5m9gUjXGAq4ofDq+Veho8hEImztMHBS9tbWLXXRSyhGGIt5KoJlVw9qYrhtuJ0L7HXBDujHH7HzcFtLo41eknGFfnF2QyZYGXIpHJqR5sxZmd2kULFkoQPtOtF790eksE4ZBnIG2kif5yF/NFmDAUnjwlS0SiG3FzJ6xSQvBYZKxHXc3vlz8DfAsPm6CM2qyZ/oqc9HIrwZKuXp9q8NEdilGQZuNZm4qYKM1NKCtL2Jm0oerzo3cqyBiehmF70vnScPtN75hALxgx/E1X0TVLo7oGEsBD9kHs/rPgx7HoO8s1wwX/C9M9Cdl66V5Z22zqDPNLi5jmHj3BSMa37hu8r03DDt3iXFLpTQzJbZKSmzfDaf+mXVVZPhct+TqxqKo+0uPnloTY64wlur7LwX0MqseZkpXu1/VJ7MMriXW28uL2FdQc8JBXUV5Zw1cRKrppQRa05c98UjobiHNnl4eA2F0d2eoiFE2TnGRk0zsLQSeUMGmshJz+z/16phCJyuEMveu/ykOiMgkEjd1gp+WP1yyyNxfq4AMnr1JC8FhkvFoK3H4DVv9JPH4+5Vh9pYh3xiZ42qRRvtXfxz1Yvr7jaCSUVIwpy+VSFmRsrzFTkpu8ej1A0wYruovfy7qK3tSjnxEzvGUPMUvQWZ40UunsgISxEP9ayVb8R+8ByKKmB2d+CCTeDMbM3bGfCF4vzVJuXR5o9HOy+4fvWSgufTtMN3wOdbJxTQzJbZKxkEnY8BUvu0y9hnngLzLsfX56VX3XP7y40GviPQRXcLfO7PxGnP8wr77Ty4vYWth5tB2BKXRlXT6ziiglVlBdnbkYmYkma9vj0yyy3uwj5YxiyNGpHmxky0cqQieUUlKRnPuzZopKKaJOf0C4P4Z1u4h79Qs+cuhLyx1koubBW8joFJK/FgBHugLd+D+v+oI8jm3wbXPRNKK3+xE/tjyd4ydnOk21eNnQETow2ubnSzAJLaVqbmILROCsa9Zneyxr1UWHWolwuG1fB5eMrmT7YJONNRK+SQncPJISFyAAHV8HS+6FlC1hHwdzvwegrP/YFIZkkefyG72b9hm8FzLWUcEeVhTlpuuF7IJJCd2pIZouMF/Hr3WHr/gDGHLjwazDri+yNKO7b38wKr59h+bncN7yK+ZbMnTd9thzzBnnpnRZe3NZCY5sfgwbnDLNw9cQqLh1bSWlB+jrnelsyqWg72KEXvbe56HTrBd/KYaUMnVTO0EnllFgze2SOUoq4I0hop5vQLg+x1gC1/3Oh5HUKSF6LAafLpef3pgcBDWZ8Ds7/Dyi0pOTpDwYjPNWmjzZpicQoyzJyrV0fbTKpOD+trweC0TjLG528uqOV5Y1OwrEkpoJsZo+2Mb/ezoUjyynMlUY0kVpS6O6BhLAQGUIpaHhJ7/D27IPqaTDv/k90QUimaQ7rN3w/1urBGY1Tm5fDZ6os3FJpkePwvUwK3akhmS0GDM8BeOO7sOdVMA2BBT+FUZexzOvn/u753ReZivnBiCpGF2Z2MfJs2efw8+L2Fl7c3sIRT5Bso8ZFI21cNbGS+WPsFGRwTiql8DR3cXCbPtfb09QFgKWm6ETR21JdmPFvrMQ9IbKtBZLXKSB5LQas9qP6/O7tT0B2IZz7r/ql07mpuRcioRRrfV38s83Lq652wknFyII8bqo0c4PdhD2No01Avx9j5R4XSxscLG900hGKkWM0cO5wC/PH2JlXb8deIuNGxScnhe4eSAgLkWEScdj+OKz47+4LQubCvPugcmK6V9ZnRJNJXnN38Eizh7fau8jRNK62lXFntZWpabzsJJNJoTs1JLPFgLN/Gbz+LXDv0S+8uvRnxCwjeaTFzS8OteGPJ/hMtZWvD67AksGF2LNJKcWO5g5e3NbCy++00tYZJj/byLwxdq6eWMWFI63kZhnTvcxe1eEKcWi7i4PbXLQe6AAFJdY8hnQXvSuGlmLI0DmsktepIXktBjxnIyz/ETS+DAVW/YTWtLshK3XjsTrjCV50tvNkq5e3OwMYNZhtLuGmCjOXWEvITfOYs1giyabDPpY2OFiy28FRbxCAiTWlzKu3M2+MndEVxbL3FB+LFLp7ICEsRIaKhWDjX/UjZOF2GHc9zP4OWIale2V9yp5AmEea3TzV5qUrkWRsUR53VltZaDNRmOEb+bNJNs6pIZktBqRETL/wasV/QywAM+6Fi76BN6uIXx5q45EWN0VGI/852M5d1eVkZ2gBMh2SScXbh728uL2FV3e04gvGKMnL4tJxFVw9sZpzhlky/uKtYGeUQ9tdHNru5lijl2RckV+czZCJetG7ZpQJY3bmzGGVvE4NyWshujVthmU/gEOroLQWLv5mr9wptT8Y5qlWL087fLR2jza5zm7i5kozE4rSO9oE9DeR9zm7WLJbL3pvO9YOQI0pn3n1di4ZY2f6EDPZMtdbnCEpdPdAQliIDBdqh7d+B+v/DxJRmPIZuOgbUFyR7pX1KYF4gmcdPh5udrM7EKbYaOBTFWbuqLYyslCOl31SsnFODclsMaAF3LD8x7D5YSgww5zvwZTP0BiKcv++Flb6/AwvyOW+YVXMk/ndKRdLJFmz381L21p4Y7eDrkgca1EuV4yv4OpJVUypM2X8n3k0FOfILg8Ht7k4stNDLJwgO89I9UgT1SPLqB5pwlJT1K+7vSWvU0PyWoj3ObBCL3i3bAXrSD3D669K+Z1SCaVY7fPzz1Yvr7k7iCQVowvzuKnCzPV2E7Y0jzY5ztkZZlmjk6W7Haze7yYaT1KSl8Xs0Tbm1du5aFQ5JXl9Y62ib5JCdw8khIUYIPwOePPneoHAkA2z/gXO+zfIL0v3yvoUpRSbOoM83OzmJWc7UaU4r6yIO6qtXGYtlU7Bj0k2zqkhmS0E0PoOvPYNOPoWVEyAy36OqpvFUk8n9+9v4UAowmxzMfcPr2aUvFHZK8KxBCsanby4vYVljU6i8STVZflcNbGKqydWUV+Z+UexE7Ekxxq9HHrHTfMeHx3OEAA5+VlUjSjrt4VvyevUkLwW4hSO3ym1/Mf6SLKqKTD3+zBsdq98uY5YnBec7fyzzcuWziBGDeaaS7ip0sx8Swk5aR5tclwwGmf1PjdLdutzvb2BKNlGjVlDLSdGnFSXyX0k4mRS6O6BhLAQA4z3IKz4Kex4GvLK4MKv68fAs3LSvbI+xx2N80Srh0da3DSFY9hzsritysLtlRaq8uTP66OQjXNqSGYL0U0p2LUI3vg+dDbp47nm/5BocRUPN7v51WEHXYkEn6ow8y+1NjmZ04v84Rhv7HLw0jstrN7nJpFUDCsv5OqJ1Vw9qYoh1sJ0L/Gs6PJFaNnno3lvO817+2/hW/I6NSSvhehBIg7v/FO/tLLjGAy5EObeBzW996NnbyDMU21enm7z4ojGMWcbWWg3cVOFmXF9YLTJcYmkYutRnz7ipMHBQVcAgDGVJcwbo484GVslp9aEFLp7JCEsxADV+g4svR8OLAPzULjkJzDqspQfH8sECaVY7unk4WYPy72dGDS41FrKHVVWLjAVyQuNMyAb59SQzBbifaJBWPsbWPtb0Axw/r/Duf+KR2Xz/4608ViLh1BSMd9SwhfrbMwqLZSf2b3IG4jy6o5WXtzewtuHvSgF46tLuWpiJVdOqKJqAHWk9dfCt+R1akheC3EG4hHY9Dd48xcQ9MCQi/QcH3pxr+1J40nFKp+fJ9u8vO7qIKoUYwrzuKnSzEK7ifKcvjUu5ICri6W7HSxtcLDpiA+loLI070Sn96yh5oy/IFqcmhS6eyAhLMQAt28JLP42uPfqLy4W/BQqxqV7VX3WkVCEv7d4eKLVgzeWYFh+LndUW/hUhZmy7NReqpJJZOOcGpLZQnyI9qPwxvdg9/NQWgeX/AjGXIMnluChZjd/a3bhjSWYXFzAl+psXFZeilEK3r2qtSPEK+/oRe93mjoAmDHYzFWTqrh8XAWWotw0r/Ds6i+Fb8nr1JC8FuIjiPhh00Ow7g/Q1QZVk/WC9+iroBfHi/i6R5s82eplqz9IlgbzLCXcVGFmbh8abXKcpyvC8kYnS3Y7WL3PTSiWoCg3i4tGljNvjI3Zo2yUFcip44FCCt09kBAWQpCI6S8uVv4Uwh36hZWzvwtF5eleWZ8VTiR52dXOw81uNnUGyTdoXGc3cWe1lQnFBeleXp8jG+fUkMwW4jQOrYbXvwmOnTD4Apj/Q6ieQjCR5Kk2L3865uRwKMqQ/Bw+X2vjpgoz+ca+tZHNRIfcAV7e3sKL21vY5+zCaNCYNsjE/DF25o+xM8gyMMabvFdfLXxLXqeG5LUQH0MsrI80WftbfdymZQSc/1UY/6leH7PZGAjxVKuPZxxenN2jTW6wm7mp0szYor53GikcS7B2v5ulDQ6WNjhx+SMYDRrTB5uYP6aC+fV26iyyJ81kUujugYSwEOKEoBdW/Rze/itkF8CFX4OZX4CsgdV19VHt8Ad5pNnDsw4foWSSKSUF3Flt5eryMvKkgALIxjlVJLOFOAOJOGx5WL/sKuTTZ3+e91UYNocE8Jqrgz8cdbLVH8ScbeTu6nLuqrZiyZFTOb1NKUVjm59X3mllyW4Hexx+AEbai7qL3hVMqC7tU6M8zpZAe4Tm7sJ3y9522h1B4OwXviWvU0PyWohPIJmA3S/Aml9D2w4oqYZzvqw3Y+UW9eqXjicVK7ydPNnm5Q13J1GlGFeUz00VZq6zm7D2wdcKyaRie1M7SxscLNntYK+jC4BR9mLmjbExr97OxJqyAZmtmUwK3T2QEBZCfIBrL7zxXdi3GEyD4ZIfw+grZX73aXTE4jzt8PFIs5t9wQimLCM3V5q5o9rK4PyB/WaBbJxTQzJbiI8g3AmbH4b1/wf+VqiYAOf9G4y5FmUwsr4jwP8ddbLE00m+QeOmSgtfqC0f8D+vz6ajniBLGhws2d3G24d9JJIKe0kuc+v1Tu9zh1kG7OzRdBW+Ja9TQ/JaiBRQCvYvgzX/D46sgXyT3oQ1414oMPf6l/fG4jzn8PFkm5d3/CGyNY35lhJuqjQzx1xCdh8tHB/xBFjSPdf7eLaWF+cyr97Wna1W8rIHZrZmEil090BCWAjxofYvhcXfAVejfgR8wU+hckK6V9XnKaVY297Fw81uXnN3kFAw21zMXdVW5lpKBuRcWNk4p4ZkthAfQzwC7zylH4X27NPfwD3nyzD5dsjOZ08gzJ+OOXm2zUdcKS4vL+WLdTamlAy8cRrp5AtEWbFHnz26aq+LYDRBYY6Ri0aVM3+MnTmj7JQW9K1Lws6mHgvfw0upGmmiemQZ1triT1T4Hkh5rWmaGXgSGAwcBj6llPKd4nH/DnwWUMAO4C6lVLin55a8FiLFjm3UC957XoXsQph6J5zzJSitPitfvqErxJNtXp5p8+GOxbFmZ3F9hYmbK8zU98HRJse1B/VsXbrbyco9TgLRBPnZRi4YYdWzdbRtwN2ZkSmk0N0DCWEhRI8Scdj8EKz4qX4EfMqn9fndxfZ0r6xfaIvEeLTFw6MtHtqiMapzs/lMlZVbq8x97lbv3jSQNs69STJbiE8gmdQ3yGt/A01vQ4FV7wybfg8UmHFEYjzQ5OKRFjed8SSzSgv5Up2NuZYSDAPwDcp0CscSrDvg4Y3ujrTjs0dnDDafmOtdax7Ys0d7q/A9kPJa07SfA16l1M80TfsmYFJKfeN9j6kG1gBjlFIhTdOeAl5VSj3c03NLXgvRSxy79TeudzwNmgEm3qSPJ7OOOCtfPva+0SYxpZhQlM+nKs0stJswZ/e90SbHReIJ1h/0srQ7W1s7whg0mDrIxLzuk1RDy3t3NIxIHSl090BCWAhxRkI+ePOXsOFPkJUHF/wnzPoiZOele2X9QiypeMPTwcPNblb7usjWNK4sL+XOaiszSgvRMryIMpA2zr1JMluIFFAKjrylF7z3vfGezrAvQmkNXfEEj7V6+MsxF82RGCML8viXunIW2k3kGuTehbPt+OzR48ewj88eHV1RfKLoPb66NONz9HQ+tPCdZ6RqRNkZF74HUl5rmrYHuFgp1appWiWwUik16n2PqQbWAxOBTuB54H+VUm/09NyS10L0Mt8ReOt3sPUf+smt+qvggv+AqslnbQmeaJznnD6ebPWyo0sfbXKJtYSbKszM7sOjTUA/gbyrpVN/Q3m3g92tnQAMLS9kfnfRe3KdCWMf/h4GOil090BCWAjxkbj3w5Lv6V1xZXUw/0cw5hqZ3/0R7A+G+Xuzh3+2eeiMJ6kvzOPOaivX200UZegs0kzbOGuadiNwP1APzFBKbXrPx74F3AMkgK8opRaf4vPP6Lj0+0lmC5Fijl3dnWHP6Dk2/lP6HG/baGJJxYtOH/93zMmurjD2nCw+W1POZ6oslPbhjq1Md9gdYGmDgzd2O9h02EtSQUVJHvPG2Jg/poJzhlrIyZI3JD5u4TvT8ronmqa1K6XK3vNrn1LKdIrH/RvwEyAEvKGUuu10zy15LcRZ0uWCDX+EjQ9ApAOGXgzn/4d+EfVZ3J/u6grxVKuXZxw+PLE45mwjV5WXcb3dxLTSwj5/MqzJF2RZg5OlDQ7WHfAQTyoshTnMGW1jbr2d80dYKcqV1z59iRS6eyAhLIT4WA6sgMXfBuduqDsXLv1vqJqU7lX1K4FEghcc7TzU7GZHV4gio4EbKszcUWXp07PePo5M2zhrmlYPJIE/A187XujWNG0M8AQwA6gClgIjlVKJ933+aY9Ln4pkthC9pP0orPsDbPk7xIIw8jI4/6tQNwulFG/6uvjDUQdv+rooNBq4vcrCvTXlVOflpHvlA5o3EGV5o5Mlu9t4c6+bUCxBUW4WF40q55Ixdi4eZaM0f+CMCevJmRa+7YNLMy2vlwIVp/jQd4BHTlfo1jTNBDwL3AS0A08DzyilHj3F17oXuBegrq5u6pEjR1L0XQghTivcCZv+pl9A3eWA6qlw/r/DqCvgLJ7GiiaTrPD6WeTw8Ya7g1BSUZOXzXU2Ewvtpn6xx+sMx1i1x8WS3Q5W7HHiD8fJMRqYOdTM3O7C90AfH9YXSKG7B7JpFkJ8bIk4bP07LP8JBD0w6TaY+z0oPtV+QnwYpRRbO4M81OLmRWc7kaRiVmkhd9eUc5m1tE8feztTmVboPk7TtJWcXOj+FoBS6r+7f70YuF8pte59n3fa49KnIpktRC8LeODtv8KGP0PIC7Wz9IL3iAVgMLDDH+SPx1y84PShAdfaTHyxzsaYfrBxzXThWIK1+93dI06cuLsiZBk0Zg41M7/ezrwxdmpMsjE/7sMK31/+89yMzOtTOcPRJTcClyql7un+9WeAWUqpL/b03JLXQqRJLAzbH9dPa/kOg3WkPsN7wqfAeHbf+OyKJ3jN3cEih483fX4SCuoL81hoN3Gt3URtP3izPJZIsumwj+WNDpY1OjnoCgAwwlbEnHobc0fbmVJXRpZRTlKdbVLo7oGEsBDiEwt36PO71/8RjDlwwb/DOV+GbNn4f1TeWJx/tnp5uNnN0XCUytxs7qyycluVBWtO/z0uNoAK3b8H1h/v9NI07UHgNaXUM+/7vDM6Lv1+ktlCnCXRAGx9FN76PXQchfLR+kiTcTdAVg7HwlH+eszFo60egokkF5uK+WKdjQtMRQN+VnRfkEwqth5rZ2mDgyW7Hex36nO9x1SWnJjrPbaqRP6/eo/jhe9RMyozMq9PRdO0XwCe95yuMiul/ut9j5kJ/A2Yjj665GFgk1Lqdz09t+S1EGmWiMPu52HNb8CxA0pq4Nx/hSmfhpzCs74cVzTGi852nnP42NSpv7E4s7SQhXYTV9nK+vQllu91yB1geaOT5Y0ONhz0Ek8qSvOzuXhUOXNG27h4pI3SAjlJdTZkVKH7w+aCapo2GGgA9nQ/dL1S6gunez4JYSFEyngOwJLvQ+PLUFoL838AYxfK/O6PIaEUyzydPNjkZpXPT65B4xpbGffUlDOxuP91pPXHQndPx52VUi90P2YlJxe6/wCse1+h+1Wl1LPve+4zLnTLUWgh0igRg13P6Rtl5y4oqYZzvgRT7oDcItpjcf7e4uGBJhfOaJzxRfn8S52Nq8vLyMqA0ziZ4pA7wJLdbSzZ7WDTER9KQXVZPvPqbcwbY2fmEJnrfVx/zOuPS9M0C/AUUAccBW5USnk1TasCHlBKXd79uB+gjy6JA1uBzyqlIj09t+yxhegjlIL9S2H1r+HoW5Bvhln/AtM/CwXmtCzpSCjCcw4fzzp87AtGyNJgtrmEhXYTl1hLKDT2jzubOsMx1uxzs6zByYo9TryBKEaDxtRBpu4RJzaGlUsDQG/JtEL3h80FHQy8rJQa91GeT0JYCJFyh96E17+tv3teOwsu/ak+J018LHsDYf7W7OapNi/BRJJpJQV8tqacK8rL+s1Yk0zdOMvoEiEGiOMb5TW/gSNrIK8MZnwOZnweisqJJJM82+bjj8ec7AtGqMnL5vM1Nm6tNFOYoZcM91eergjLGp0s2e1g9T4X4ViS4rwsLh5lY/4YOxePKqckb+B2o2VqXp9tktdC9EFH18Oa/wd7X4ecIph6p/7mdUlVWpajlGJXV4hFjnaed/poicQoMBq4zFrKQruJC03F/Wavl0gqtje1s7zBybJGJw2tnQDUmQu6L7S0MWOImVx5TZQyGVXoPrGID26uByOFbiFEX5FM6Me+l/8IAi6YeAvM/X7aXkhkgs54gidbvfyt2cWhUBR7ThafqbLymWoL5Tl9e1OeqRvnU2TxWOBx3r2Mchkw4hSXUZ72uPSpSGYL0Qc0bdI3yo2vQFYuTL5dH9dlHkJSKZZ6OvnDUScbOgKUZRm5o9rKPdVWbLl9++f0QBSKJliz382S3W0sa3DiCUTJNmrMGmph/hg78+rtVJUNrDFsmZrXZ5vktRB9mGOX/sb1zmfBYISJN8O5/wbW4WlbUlIp1rcHWOTw8ZKrnY54AnO2kattJq63m5hWUtCvOqNb2kPdI06crN3vJhJPUphj5IIR5cyptzF7lI3y4tx0L7NfG0iF7l3AXqAT+K5SavXpnkdCWAjRq8KdsObXsO4PYMjSb78+58uQ0//Gb/QVSaVY7vXzYJOLFV4/2ZrG1bYy7qm2MqX07M+cOxOZtnHWNO064HdAOdAObFNKLej+2HeAu9GPOH9VKfVa9+8/APxJKbXpw45Ln+7rSmYL0Ye49+mXXW3/J6gEjL1On+NdORGAzR0B/u+Yk1ddHWRrGjdWmPhCrY0RhXlpXrg4lURSsfWojyW79bneB936hVvjqkuYX1/B/DF26iuL+1Wh4ePItLxOF8lrIfoB7yFY93vY8g9IRGHMNfpetWpSWpcVSSZZ6fXzrMPHG+4OwklFbV4OC+0mrrOXMbqwf70BG4omeOuAm2WNTpY3OGnrDAMwsbaMuaNtzBltk3szPoZ+V+j+mHNBc4EipZRH07SpwPPAWKVU5ymeX+Z9CiHOLu8hWHof7H5Bvwxk/g9g3PUyv/sTOhAM81Czm3+2eulKJJlcXMA9NVauspWRa+g780Zl45wasnEWog/qbIX1/webHoKoH4bNgfO+CkMuBE3jYDDCn445earNSzipWGAt4Yu1NmaUFsqmrg874Oo6UfTecvTdud7zx9i5ZIyd6UPMZBv7Ts6miuR1akheC9GPdDn1HH/7QYh06jl+/n/A4PPTvlftiid41d3Bcw4fq7x+ksDYojyus5m41m6iJi8nrev7qJRS7G7tPDHiZHtTO0pBRUkes0fbmDvaxnnDreTnyIiT0+l3he4zWsT7Ct0f9ePHSQgLIc6qw2vh9W9C2ztQMx0u/RnUyH7qk+qKJ3iqzcvfmt3sD0awZmfx6SoLd1RbqegDx+Vl45waktlC9GGhdtj0N1j/Rwg4oWqy3uFdfzUYjLiiMR5qdvNwsxtvLMHUkgK+VGdjgbUUoxS8+zSXP8LyRkf3XG/9CHZJXhazR+tzvS8cmTlzvSWvU0PyWoh+KNyhF7vX/58+erN6GlzwHzDyMugDDUSuaIwXnO085/CxuTMIwKzSQhbaTVxlK8OUnZXmFX50Ln+ElXv0ESdv7nURiCbIzTJw7jALc+rtzBlto3qAjRA7UwOi0K1pWjngVUolNE0bCqwGxp/uKLSEsBDirEsmYPsTsOyH0OWA8Z+CefdBaU26V9bvJZXiTZ+fB5vcLPV0YtTgyvIy7qkpT+tsN9k4p4ZkthD9QCysZ9xb/wveg2AeCuf+K0y8FbLzCCaS/LPVw5+PuTgSjjI0P5cv1JZzY4WZ/AzsEM40wWic1fvcLNntYHmjE28gSpZBY/pgM7NHlzNntI1h5UX9tltf8jo1JK+F6MdiIdj2GKz9X2g/AuWj9ZNa428AY994U/NwKMJzDh+LHD72BSNkaxqzzcUstJu4xFpKQT98PRGJJ3j7kI9ljQ6WNTg56tWL+aMriplbb2POaDuTassw9pMLOntbRhW6P2wuqKZp1wM/RJ8JmgDuU0q9dLrnkxAWQqRNxK9fBPLW70Az6J1v530FcvrmnOn+5nAowkPNbp5o9dAZTzKhKJ+7a6xcazORd5Zf/MjGOTUks4XoR5IJaHgJ1v4GWrZCoQ1mfQGm3QP5ZSSU4hVXB3846mC7P4QlO4t7aqzcWW3F3A+7sgaiRFKx5aiP5Y1OVjQ6aWzzA1Brzmf2KBuzR9s4Z6iFvOz+cwRb8jo1JK+FyACJOOx6Tr+A2rkLSmv1N64nf7rP3DellGJnV4hnHT6ed7TTFo1RaDRwmbWUhXYTF5qKyeqHhWGlFAdcAZZ3F703HfGRSCrMhTlcPKqcuaPtXDDSmjGnqT6OjCp0p5qEsBAi7XxHYOn9sGsRFFfBvPth/I194ohYJgjEEzzj8PFgk5u9wTDmbCOfrrJyR5WFqrM01002zqkhmS1EP6QUHHpTL3gfWA45xTDtTpj1JSipRCnFW+1d/N9RF8u8neQbDNxSaebzteUMys9N9+rFR9DSHmLFHr3ovXa/h1AsQV62gXOHWZk92sbsUeXUmPpGceTDSF6nhuS1EBlEKdj3Bqz+NRxbDwUWmPkvMOOzkG9K9+pOSCjF+vYuFjl8vOzqoCOewJKdxTW2MhbaTUxN4+neT6ojGGPVPhfLGxys2OOiIxQjy6AxY4iZOaNtzK23M8Q6sJrlpNDdAwlhIUSfcWQdLP6W3vlWPVWf3107I92ryhhKKda2d/Fgk5vF7g40DS6zlnJPTTmzevlSNNk4p4ZkthD9XOt2WPtbvUNMM8LEm+Dcf4PykQA0BkL88aiLRQ4fCaW40lbGF2ttTCrp28VR8UHhWIINh7ysaNRnjx4/gj3SXtRd9LYxdZCpz11oKXmdGpLXQmSoI+tgza/1wndOEUy7C875MhRXpHtlJ4kkk6zw+HnW4WOJp4NwUlGXl8NCu4mFdhMjC/PSvcSPLZ5IsvVYO8sanCxvdLDX0QXAUGshc0bbmFNvY/rgzLww+r2k0N0DCWEhRJ+STMI7T8KyH4C/FcbdoHd4l9Wme2UZ5WgowsPNHh5v9dAeTzC2KI97qsu5zm7qlRmxsnFODclsITKE9xCs+z1sfRTiERh9hT7/s3Y6AK2RKA80ufl7sxt/Ism5ZUV8obacuZYSubiyH1JKcdAdYEWjkxV7nGw85CWWUBTnZXHhyHJmj7Jx8ahyrEXp7+CXvE4NyWshMlzbDn0E565FYMiCSbfCuV8By7B0r+wD/PEEr7o6eM7h402fnyQwriif6+wmrrOVnbUTvr3lmDfI8kYnyxqdrD/gIZpInsjXuaNtXDzKhrmwf3+PpyKF7h5ICAsh+qRIl9719tb/6r8+91/1IkBuUVqXlWmCiSTPOXw82ORidyCMKcvIrVUW7qy2UpvCFz2ycU4NyWwhMkyXCzb+GTb+FcLtMOg8PetGzAdNwx9P8GiLh782uWiJxKjMzebmCjO3VllS+jNanF3+cIy1+z0nCt9OfwRNgwk1ZcwepV9oOa6qFEMa5qpKXqeG5LUQA4T3kL5f3foYJGMw5lo4/6tQOTHdKzslVzTGC852Fjl8bOkMogGzygq53m7mivJSTP38jpBAJM6a/W6WNzhZvseJyx/BoMHkOlP3iBMbo+zF/XaEy3tJobsHEsJCiD6t/Zje3b3jaSiqgHn3wYSbZX53iimlWNce4MFmF6+7O1AKFlhLuafGynllRZ/4xYBsnFNDMluIDBXpgi2PwLo/QGcz2MbqFzSPWwjGbGJJxRueDh5t8bDSq194eLG5mFsrLSywlpAjmdhvKaXY1dKpjzjZ42TbsXaUAmtRLhd3F73PH3H2LtySvE4NyWshBhh/G6z/I7z9IET9MPgCmPkFGHUZGPrmhcSHghGec/pY5PCxPxghW9OYYylmod3EfEspBf189EcyqdjZ0tE94sTJjuYOAKrL8plbb2POaBuz+tmF0e8lhe4eSAgLIfqFYxvh9W9C82aonKTP7x50TrpXlZGaw1EeaXbzaKsHbyzB6MI87q62cn2FiULjx3shIBvn1JDMFiLDxaOw8xn9RJOrEUpr9dmfUz4NOfolS03hKE+0evhnq5fmSAxLdhY3VZi5rcrMsIL+O3NT6LyBKKv2Olne6OLNve9euDVtsN6NNnuUjeG2T/4G9IeRvE4NyWshBqhQO2x+GN5+ADqOQVkdzLgXJt/epy6ufC+lFDu6Qjzr8PGCo522aIxCo4HLy0tZaDNxgamYrDScMEo1R2eYFY1OljY4WbPfRTiWpCDHyPnDrcyt1/PVVtJ/XkdJobsHEsJCiH4jmdQLAEvuA38LjL0O5v0ATIPSvbKMFE4ked7p48EmNzu6QpRmGbm50szd1VYG5X+0OaKycU4NyWwhBohkEvYt1ud/HlsP+WaYfg9MuePEnRUJpVjp9fNYi4c3PB3EFcwqLeT2KgtXlJf1yn0L4uw6fuHW8kYnKxqdNLbp3fw1pvwTRe9zhqW2G03yOjUkr4UY4BJx2PMKbPgzHFkL2QUw8Wa9y7t8VLpX96ESSrGuvYtFDh8vu9rpjCexZmdxta2MhXYTU0sKMmLsRziWYN1BD8saHCxvcNLSEQZgYk0pc0bbmVtvY2xVSZ/+XqXQ3QMJYSFEvxMNwFu/0wsAKgnnfAku+A/ILU73yjKSUoq3OwI82OzmFVc7CQXzLSXcU1POhaYz6yqTjXNqSGYLMQAdXa/n3d7XQdNg+HyYdheMuOTEcWhnJMaTbV4eb/VwKBSlNMvI9XYTt1dZGFOUn971i5RpaQ+xYo+TFY0u1u53E4olyMs2cO4wK7NHlTN7tI0aU8En+hqS16kheS2EOKF1O2z4iz6KMxGBobNh1r/oed6HR49FkkmWeTp5ztHOEk8H4aSiNi+H62xlXGc3UZ8hry+UUjS2+Vne6GRpg+PECDF7Sa5e9B5t47zhVvJz+taIEyl090BCWAjRb3U06/O733kSCm36/O6Jt/bpFwz9XWskyt+bPfyjxYM7FmdEQS53VVv5VIWZoqwPD3/ZOKeGZLYQA5jvMGz5O2x9FLocUFINUz6j/6ekCoCkUrzV3sVjLR5ecXUQVYrJxQXcVmXhWltZjz+nRf8SjiXYcMh74kLLI54gACNsRXq392gbUweZyP6Inf2S16kheS2E+ICA+92xJv5WMA/Vx5pMug3yStK9uh754wled3ewyOHjTZ+fhILRhXkstJu4xlb2kU/79mXurggr97hY1uDgzb0uAtEEuVkGzhtuZc5ofbZ3VVn6i/xS6O6BhLAQot9r2qzP727aCNVT4fJf6P8tek0kmeQlZzsPNLnZ5g9SbDRwc6WZu6rLGVrwwRc6snFODclsIQSJGOx5FTY9BAdXgGaAkZfC1Ltg+NwTXd7eWJxn23w82uphTyBMgdHAdbYybqu0MDlDjh4LnVKKg+7AiaL3xkNeYglFcV4WF47QO70vGllOefHpCxGS16kheS2E+FCJGDS8COv/pO9fc4r0YveMe8E6PN2rOy13NM5Lrnaec/jY2BEAYFpJAdd2F73Lc87O5clnQzSeZOMhL8saHSxrcHLUq7+pXF9ZwrzuCy0n1pRhSMMMcyl090BCWAiREZTSO7vf+B4EXPqFH/Puh0JruleW8bZ0jzV50dlOTCnmmIu5p6ac2eZiDN2FFNk4p4ZkthDiJN6DsPkR2PaYnn2ldd1d3p+G4gpAL4Ju6QzyaKuH5x3thJJJ6gvzuK3Kwg12E2XZWWn+JkSqdUXirNnnPlH4dvojgD57dHb3bO/x1aWn3JhLXqeG5LUQ4ow0b9bneO9cBMmYPpZs5udh6Jx+cUr5WDjK8w4fzzl87A6EMQAXmIq5zl7G5eVllGTQSTKlFAdcXSxrcLKs0cmmw16SCqxFOVw8ysa8ehvnjyinKPfsvK6SQncPJISFEBkl3Amr/gc2/AlyCmH2d2DaPWCUjXxvc0Zi/L3Fw99b3DijcYbm62NNbqo0U5qdJRvnFJDMFkKcUjwKjS/D5ofg0JugGWHUZfos7/dslv3xBM87fTza4mG7P0SuQePKcr3L+5yyQunyzkBKKXa1dJ4oem/tnj1qLcrl4lHlzB5l44KRVkry9A48KXSnhuS1EOIj8Tv0DH/7QQg4wTpS7/CeeAvkFqV7dWekMRDieYfe6X0kHCXXoDHPUsJ1NhNzLSUZd0l2ezDKqr0uljU4WbnHSWc4TrZRY9ZQC3NH25hbb6fW/MnuzeiJFLp7ICEshMhIrj3w2n/BwZVgGwuX/xwGn5/uVQ0I0WSSV1wdPNjkYlNnkEKjgYMXTZSNcwpIZgshTstzQN8sb3scgh4oGwRT74DJn4Yi24mH7fQHebTVyyKHl854kqH5udxaaeamSnNGHTsWJ/MGoqzaq19ouWqvi45QjCyDxtRBJuaMtvGFi4dLXqeA5LUQ4mOJR2DX87Dhj9CyFXJL9VNa0z8L5iHpXt0ZUUqxtTPIIqePF5ztuKJxiowGLi8vZaHdxPllxWSlYdRHb4olkmw+4mN5o5NlDQ4OuPSRLiNsRcyptzGv3s7k2jKyUljsl0J3DySEhRAZSyloeAkWfwc6jsK462H+j6C0Ot0rGzC2dQZ5sNnF78cMlo1zCkhmCyHOWDyiZ+Cmh+DIGjBkwegr9FneQy460eUdTCR52dXOYy0eNnQEyNJggbWU2yotXGQuxihd3hkrnkiy9Vg7KxqdLG900tjm58j/XDlg8lrTtBuB+4F6YIZS6pQBq2napcBvASPwgFLqZ6d7bslrIcQnohQ0va2fUt79AiQT+kmtmV+AIRdCP8nmhFKs9XXxnNPHK652OuNJrNlZXG0r4zq7iWkZemfIYXeAZY1Oljc62HDQSzypKCvI5uKR5cytt3PhyHJK8z9ZU4EUunsgISyEyHjRIKz9Daz5jb7Rv/BrcM6XICtzbofu6+QodGpIZgshPhbXXtj8MGx/HEI+MA+FKXfo91m85y6LvYEwj7d6eKrNizeWoDo3m1srLdxSaaYqLyd96xdnRUt7iGpTwYDJa03T6oEk8Gfga6cqdGuaZgT2AvOBJuBt4Bal1O6enlvyWgiRMp0t+kiTzQ/pJ7XK6/U53hNugpzeG42RauFEkuXeTp5ztLPE00E4qajNy+FaWxkL7Sbqi/LTvcRe0RmOsXqvm2WNDlbuceENRDEaNKYPNjGv3s6c0TaGln/08TRS6O6BhLAQYsDwHda7uxtfBvMwuPRnMPKSdK9qQJBCd2pIZgshPpFYWO8M2/wwHH0LDNlQf5U+y3vwBSc6xCLJJK+7O3i8xcsqnx8DMNtcwu1VZuZZSsnOsCPH4l0DMa81TVvJhxe6zwHuV0ot6P71twCUUv/d03NKXgshUi4Whp3PwPo/gWMH5JXpo8mmfw7KatO9uo/EH0/wuruDRQ4fb/r8JBSMLszjOpuJa+1lDMrPzIa0RFKx7ZiPZQ3vnqQCGGItZM5oG3PrbUwfbCb7DEacSKG7BxLCQogBZ/9SeO0b4NkPIy+DS3+qd7eJXjMQN869QTJbCJEyzsZ3u7zDHWAZDlPvhIm3QqHlxMOOhCI80erln61e2qIxbDlZ3FRh5rYqC4MzdCM6kA3EvD5NofsG4FKl1Ge7f/1pYKZS6ss9PafktRCi1ygFR9fB+j/qDVwAo6+EWf8Cdef0m7Emx7mjcV5y6ZdYbuzQZ1tPLSngOruJa2xlGX1vyDFvkBV7nCxrcLLugIdoIklxbhYXjipn7mgbF4+yYS489Yk6KXT3QEJYCDEgxaP6JR+rfg6JGJz3FTj/P/rV8a/+JNM2zh8211PTtPnAz4AcIAp8XSm1/BSffz/wOcDV/VvfVkq9erqvK5kthEi5WEi/+GrzQ3BsAxhzYMw1+izvQeee2DDHk4rl3k4ebfGw1NNJEji/rIjbqixcZi0lL4UXLIn0ycC8XgpUnOJD31FKvdD9mJV8eKH7RmDB+wrdM5RS/3qKx94L3AtQV1c39ciRIyn7PoQQ4pTaj8LbD8DmRyDcDhXj9Tne426A7Lx0r+4jOxaO8rzDx/NOH7u6whiAC0zFXGcv4/LyMkqyjOleYq8JROKs2e9meYOTZY1O3F0RDBpMqTMxp97G3NF2RtqLTsw0l0J3D2TTLIQY0DpbYcn3YcdTUFIDC34MY67td++E93UZuHE+5VxPTdMmAw6lVIumaeOAxUqpD9x+2l3o7lJK/fKjfF3JbCFEr3Ls6u7yfhIiHWAd1d3lfTMUmE88rDUS5clWL4+1ejkWjmLKMnJjhZlbq8yMLszMGZsDRabl9ZmQ0SVCiH4vGtT3s+v/BK4GKLDob1hPvwdKqtK9uo+lMRDieYfe6X0kHCXXoDHPUsK1NhPzLCXkZ/Ab7MmkYkdzx4kLLXc2dwJQY8pn7mgbc+rtXDzKJoXuDyMhLIQQwJF18OrX9XlnQy6Ey34Otvp0rypjZOrG+TSbYw1wA1VKqcj7PnY/UugWQvRV0QDseg42PQTNmyArT38TeNpdUDvzxJvBSaVY7evisVYPr7k6iCnFtJICbquycLWtjEJj5nZeZapMzeuenCbLs9Avo5wLNKNfRnmrUmpXT88peS2ESAul4NCbsOFPsOc1MBj1U1ozvwA10/tlM5dSiq2dQRY5fbzgbMcVjVNkNHB5eSnX2UxcYComK8PvDmnrCLO8u+i9Zr+bcCzJkf+5UgrdH0ZCWAghuiUTsOlvsPzHEPHrt1lf/E3IK033yvq9TN04n8Fczy8opead4mP3A3cCncAm4D+VUr7TfT3JbCHEWde2Qy94v/MURP1gG6N3eU+4CfLLTjzMHY3zdJuXx1s97AtGKDYauM5u4rYqCxOLZSxYf5GpeX0qmqZdB/wOKAfagW1KqQWaplUBDyilLu9+3OXAbwAj8Del1E9O99yS10KItPMe0seabPmHfkqrajLM/BcYey1k9c87NhJKsdbXxXNOH6+42umMJ7FmZ3GVrYyFdhPTSgpOjPbIVOFYgrcOuJlbXyGF7g8jISyEEO8T8MDyH+nHtwutMO8HMPEWMGTu8aje1h83zp9krqemaWOBF4FLlFIHTvHcdvRubwX8CKhUSt39IeuQmZ9CiPSLdMHOZ/VZ3i1bISsfxi3Uj0bXTDvRJaaUYmNHgEdbPbzkbCecVIwvyue2KgsL7aaMnq+ZCfpjXvdFsscWQvQZkS7Y/gRs+DN49kGhTR9pMvUuKLane3UfWziRZLm3k+cc7SzxdBBOKmrzcri2u+hdX5TZo9RkRncPJISFEOJDtGyFV/8LmjbqR70u+zlUT0n3qvqlTN04n6rQrWlaDbAcuEsptfYMnmMw8LJSatzpHiuZLYToE1q26QXvHc9AtAvs47q7vD910imojlicRc52HmvxsLMrRL5B4ypbGbdXWpheWpjxXVf9Uabm9dkmeS2E6HOSSTi4XJ/jvX8JGLL1N6xnfqHf73H98QSvuztY5PDxps9PQsGowjwW2kxcay9jUH7/7GDviRS6eyAhLIQQPUgm4Z0n9QsrAy6Y8hmYex8UWtK9sn4lUzfO7y90a5pWBqwCfqiUeraHz6tUSrV2/+9/B2YqpW4+3deTzBZC9CkRP+x4Wh9t0vYOZBfAuOv1Wd5VU07q8t7uD/F4q4dFDh9diSQjCnK5vcrCjRVmzNlZaf5GxHGZmtdnm+S1EKJPc++HjX+BbY/pb1jXzNDHdo65BozZ6V7dJ+KOxnnJpV9iubEjAMDUkgKus5u4xlZGeU7//v6Ok0J3DySEhRDiDIQ7YNXP9Ys9cgphzvf0415G2ZyfiUzbOPcw1/O7wLeAfe95+CVKKaemaQ8Af1JKbdI07R/AJPTRJYeBzx8vfPdEMlsI0ScpBS1b9IL3zmchFoSKCXrBe/yNkFt84qGBeIIXXHqX9+bOIDmaxhXlpdxeZeHcsiLp8k6zTMvrdJG8FkL0C+EO2Pa4PtbEdwiKK98da1JoTffqPrFj4SjPO3w87/SxqyuMAbjQVMzCChOXWUsp7sfj1KTQ3QMJYSGE+AicjfDaf8GhVfpR7ct/AYPOTfeq+jzZOKeGZLYQos8Ld+gXV25+GBw7IacIxt+gb5qrJp300IauEI+2eHjG4aMjnmBofi63VVn4VIUpYzqu+hvJ69SQvBZC9CvJJOx7Q2/qOrgCjLn6G9UzPw+VE9K9upRoDIR43tHOsw4fx8JR8gwal1hLud5uYra5mJx+dh+XFLp7ICEshBAfkVLQ8CIs/g50HNNfBMz/IZRUpXtlfZZsnFNDMlsI0W8oBU2b9FneOxdBPARVk/WC97jrIbfoxENDiSQvu9p5tMXDho4A2ZrGpVa9y/sCUxEG6fI+aySvU0PyWgjRbzkbYeOfYfs/9RNadefAjHuh/qp+P9YE9HFqmzqDPOvw8aLThzeWoCzLyFXdl1jOLC3sF687pNDdAwlhIYT4mKJBWPP/YO1vwZAFF/0XzPoiZOWke2V9jmycU0MyWwjRL4Xa9fsuNj0ErgbIKdYvrpx2F1SMP+mhewNhHmvx8LTDizeWoC4vh9sqLdxcacae2/832H2d5HVqSF4LIfq9kA+2/APefgDaj0BRBUy7W798utie7tWlRCypWOXz85zDx6uuDkLJJNW52VxrN3G93UR9YV6fHakmhe4eSAgLIcQn5D0Ei78Ne14Fy3C49H9gxLx0r6pPkY1zakhmCyH6NaXg2EbY9DfY9RwkIlAzXe/yHnsd5BSceGgkmeQ1Vwf/aPGwtr0LowaXWEq5rcrCbHMxxj668ezvJK9TQ/JaCJExkgnYt0S/vPLAMjBk65dWzvy8nuEZkseBRILF7k4WOXys9HYSVzCqMI/r7SautZVRl5+b7iWeRArdPZAQFkKIFNm3VJ/f7T0Ao66ABT8B85B0r6pPkI1zakhmCyEyRtCrH4ve9Dfw7IO8Uph4i170to0+6aEHgxEeb/Xwz1Yv7lic6txsbqm0cEulmeo8OUWVSpLXqSF5LYTISO79eof3tscg0gmVE/WxJuOuh+z8dK8uZdzROC+52nnO4WNjRwCAGaWFLLSbuKq8DEtOVppXKIXuHkkICyFECsUjsP7/YNUvIBmH8/4Nzv/3k7rUBiLZOKeGZLYQIuMoBUfW6gXv3S9CMgZ15+rHo8dcDVnvdlBFk0necHfyaIuHVT4/GjDHUsLtlRbmWUrIMmRGV1k6SV6nhuS1ECKjRbr0kWQb/6qPJMs3wZTPwLR7wDQo3atLqSOhyIlLLPcGw2RpcLG5hOvtJi6xllBoNKZlXVLo7oGEsBBC9ILOFljyfdjxNJTW6t3d9VdnzNGuj0o2zqkhmS2EyGgBt94ltukh8B2CfDNMulXv8rYOP+mhR0IRnmj18kSrB0c0TkVONjdXmrml0sygPna8uD+RvE4NyWshxICgFBxerY81aXxF/72Rl8GMz8HQizNq76uUYncgzLNtPp53+miJxCgwGrjMWspCu4kLTcVkn8U33DOq0K1p2i+Aq4AocAC4SynV3v2xbwH3AAngK0qpxad7PglhIYToRYfX6uNMHDthyEVw2c8/cCR7IJCNc2pIZgshBoRkEg6t0ru897yqn5AacqFe8B595UmXPseTiqWeTh5t9bDc04kCLjIVc1uVhQXWEnIMhvR9H/2Q5HVqSF4LIQac9mOw+SHY/DAEPWAdCdM/BxNvhrySdK8upZJKsb49wHNOHy8522mPJ7BkZ3G1rYzr7SamlhT0+iWWmVbovgRYrpSKa5r2PwBKqW9omjYGeAKYAVQBS4GRSqlET88nISyEEL0sEddDf/mPIBqAmV+Ai76RcYHfE9k4p4ZkthBiwPG3wdZ/wOa/Q8dRKCyHybfD1DvBNPikhzaHoye6vJsjMazZWdxUaea2SgtDC6TL+0xIXqeG5LUQYsCKhWH387Dhz9CyBXKK9Ds4ZtwL5SPTvbqUiySTrPD4WeT08Ya7g3BSUZeXw0K7iYV2EyML83rl62ZUofu9NE27DrhBKXVbdzc3Sqn/7v7YYuB+pdS6np5DQlgIIc6SgBuW/RC2/F3fqM//IUy4CQZAt5lsnFNDMlsIMWAlE3Bgud7lvfd1/bj0sDn6LO+Rl4Lx3YuhEkqxwuvnsRYPb3g6SCg4r6yIT1dZuKy8lNwBkLsfl+R1akheCyEE0LRZH2uyaxEkovo4kxn36rltSM9s697kjyd41dXBcw4fb/r8JIFxRfkstJu4zl5GZW7qLtDO5EL3S8CTSqlHNU37PbBeKfVo98ceBF5TSj1zis+7F7gXoK6ubuqRI0fO5rKFEGJga94Cr34dmjdBzQy4/BdQNSndq+pVsnFODdk4CyEE0NGsv2m85e/gb4HiSpj8af0irLLakx7aFonxZKuXx1o9HA1HMWcbubHCzO2VFkb0UpdVfyZ5nRqS10II8R5dLtjyiP5mdWczlNbB9Lth8meg0JLu1fUKZyTGC852Fjl8bPUH0YBzyoq43m7iivJSyrKzTvscPel3hW5N05YCFaf40HeUUi90P+Y7wDRgoVJKaZr2B2Dd+wrdryqlnu3pa0kICyFEGiSTsP0JWHqf3uk99U6Y+30oMKd7Zb1CNs6pIZkthBDvkYjDvsX65ZX7l+qXXo24RJ/lPWL+Sd1iSaVY7evi0RYPr7s7iCnFzNJCbq+ycGV5GflG6fIGyetUkbwWQohTSMT1uzc2/kW/xNKYC+Nv0Lu8M7jx62AwwiKHj0UOHwdDEXI0jbmWEhbaTcy3lJD3MV6D9LtC92kXoGl3AF8A5iqlgt2/J6NLhBCivwl3wMqf6TPMcoth7vf0DXqGHeWSjXNqSGYLIcSH8B3Ru8W2/AMCTiit1Tu8J38aSipPeqgrGuOpNh+PtXg4GIpQmmXkBruJ26ss1Bflp+kb6Bskr1ND8loIIU7DsRve/itsfxJiAf2k84x7Ycw1J106nUmUUmz3h1jk8PG804czGqfYaODycv0Sy/NMRRjP8BLLjCp0a5p2KfBr4CKllOs9vz8WeJx3L6NcBoyQyyiFEKIfcDbo40wOr4aK8XDZL2DQOeleVcrIxjk1JLOFEOI0EjG9W2zT3+DgStCMMOoyfZb30Nkn3YuhlOKt9i4ea/XysrOdqFJMLSngtioL19jKKDRm1pvOZ0LyOjUkr4UQ4gyF2vWTzhv/Ct4DUGjTTztPuwtKqtK9ul6TUIq1vi4WOXy84mrHn0hiy8niWpt+ieXE4ny0HoremVbo3g/kAp7u31qvlPpC98e+A9wNxIGvKqVeO93zSQgLIUQfoZR+Q/Xi70JnE0y4Wb+wstie7pV9YrJxTg3JbCGE+Ag8B2Dzw7DtMQh6wDQYptwBk2+HIttJD/XG4jzT5uUfLR72BSMUGQ0s7O7ynlBckJblp4PkdWpIXgshxEeUTMLB5XrBe+9i0AxQfxXM/DzUnaOPJ8tQoUSSpZ5OnnP4WOrpJKoUw/Jzuc6uF72HFuR+4HMyqtCdahLCQgjRx0QDsPrX8Nb/QlYezP4OTP8sGD/ZhRXpJBvn1JDMFkKIjyEegYaX9FneR9aAIRvqr9S7vAdfcNLmWSnF2x0B/tHq4SVnO+GkYkJxPrdXWrjObqI4K7O7vCWvU0PyWgghPgHvIdj0oD6OLNwO9nEw43Mw/kbIKUz36npVeyzOK64OFjl8vNXehQImFRdwvd3ENbYybLnZgBS6eyQhLIQQfZR7P7z2dTiwXA/3y3/Zb8eZyMY5NSSzhRDiE3Lt6e7yflzfPFuG60ekJ932gQuh22NxnnXos7x3B8IUGA1cayvj9ioLk4sLejxS3F8NpLzWNO1G4H6gHpihlPpAwGqaVgv8HagAksBflFK/Pd1zS14LIUQKRIOw42m9y9uxA/JK9bs3pt8D5qHpXl2vawlHed7ZznMOHzu6QhiAC0zFLLSbuLnKIoXuDyMhLIQQfZhSehfa69/Sx5lMvBXm/+ADR677uoG0ce5NktlCCJEisRDsfkGf5X1sAxhz9Quwpt0NdbM+0OW91R/k0RYPzzvbCSaSjCnM47YqCzfYTZRm998TV+83kPJa07R69OL1n4GvfUihuxKoVEpt0TStGNgMXKuU2t3Tc0teCyFECikFR9fDxr9Aw4uQTMCIS/TLK4fNOen+jUy1NxBmkcPHIoePo+EojjmTpdD9YSSEhRCiH4gG4M1fwlu/g+wCmPMdmHZPvxlnMpA2zr1JMlsIIXqBY5c+1uSdJyHSCeWj9YL3hJsgv+ykh/rjCZ5z+Hi01cM7/hD5Bo1r7SburrYyPgNmeQ/EvNY0bSUfUug+xWNfAH6vlFrS0+Mkr4UQopd0tuonszY/BF0OvbN7+udg0q0fyOxMpJRic2eQ6WVFUuj+MBLCQgjRj7j3watfh4MrwD4ervil3nnWxw3EjXNvkMwWQoheFA3Azmf1onfLFsjKh3EL9aJ39dQPXIT1TneX99NtPkLJJNNLCrm7xsoV5aXk9NPusoGY12da6NY0bTDwJjBOKdXZ02Mlr4UQopfFo3p398a/6Cezsgth4k160ds+Jt2r63Uyo7sHEsJCCNHPKKUft178behs1ueKzvsBFJWne2UfaiBunHuDZLYQQpwlLdv0brF3noZYQH9zedqdMP5TkFdy0kM7YnGeavPxULObg6EI5TlZfLrKwmeqrFR0XxrVX2RaXmuathR9vvb7fUcp9UL3Y1ZymkK3pmlFwCrgJ0qpRR/ymHuBewHq6uqmHjly5BOuXgghxBlp2QZv/xV2PAPxsH7R9IzPwagr+s0J6I9KCt09kE2zEEL0U5EuWP1LeOv33eNMvqt3nfXBMM/AjfMpL7Dq7vZqAPZ0P3S9UuoLp/h8M/AkMBg4DHxKKeU73deVzBZCiLMs3KlfhLX5IWjboXeMjb8Bpt0FVZNPemhSKVZ5/fyt2c1STydGDS4vL+OuaiuzSgv7xeWVmZbXZ+J0hW5N07KBl4HFSqlfn8lzSl4LIUQaBL2w9R/w9gPQfhRKqvW8nnJnn24K+zik0N0DCWEhhOjnXHvhta/DwZVQMR4u/xXUzUz3qk6SaRvnD7vAqrvQ/bJSatxpPv/ngFcp9TNN074JmJRS3zjd15XMFkKINFEKmrfol1fufBbiIb3QPfUuvfCdU3jSw4+EIjzc7OaJVi/t8QT1hXncXWNlod1EodGYpm/i9DItr89ET4VuTX934hH0zP7qmT6n5LUQQqRRMgF7F+tjTQ6uAGMOjF2oX15ZMzXdq0uJVOd1/xy4JoQQIjOVj4RPPw83PgwBD/ztEnj+S9DlSvfKMpZSqkEptef0j/xQ16BvnOn+72s/8aKEEEL0Hk3TN8fX/gH+sxEu+znEwvDSV+DX9bD4O+A7fOLhg/JzuW94NVvOHcuvR9Vi1DS+vqeJyW/t4r59zRwKRtL3vQgANE27TtO0JuAc4BVN0xZ3/36Vpmmvdj/sPODTwBxN07Z1/+fyNC1ZCCHEmTAYYfTl8Jnn4Utv629KN74CD8yBv86Bd57SZ3yLE6SjWwghRN8U6YI3fw7r/qB3l835nj7OxJDe7rFM7RB7fxdYd0f3LmAv0Al8Vym1+hSf166UKnvPr31KKdOHfA2Z+SmEEH2RUnB0nd4xtvtFUEkYeSnMvBeGzj7p8kqlFG93BPhbs5uXXe3EFcwxF3N3TTlzzMUY+shYk0zN67NN9thCCNHHRPyw/Z+w4c/g2QdFdr0APu0uKD7VVQ59m4wu6YGEsBBCZCDXHnj1a3DoTaiYAFf8CmpnpG05/XHj/HEusNI0LRcoUkp5NE2bCjwPjFVKdb7vuc+40P1ektlCCNFHdTTrY002PwxBN1hH6RdhTbwFcotOeqgjEuPRFg9/b3HjiMYZnJ/DnVVWbq40U5ad3ns2+mNe90WS10II0Uclk3BwOWz4C+xbDIZsGHstzPwC1PSf+JNCdw8khIUQIkMpBbueg8XfBn8rTL4d5v0ACq1nfSmZunE+gwusTvlxTdP2ABcrpVo1TasEViqlRp3u60lmCyFEHxcL69m78c/QshVyS/4/e+8dJ8lV3us/p0Ln7slhd2aTNmmTJCQhQGQRBSaDMybZ4GyuDbb5Yfti+9q+GNvYxlwTbGGwMZgkkA2YICQyBgmFTdqoDbOzk0P3dKquqvP7o6p7umdmR7ur2Zme2ff5fGqr6tSpqnNmeuft8623vgdu+LlA9O7Y2ljV13xpbIqPDIzxg+k8cUPxqp523tDfyZ5UfEWav1bj9XIj8VoQBGEVMH4CfvhhePDjUM5C301wy1sC4duKrnTrFkU8ugVBEISrD6Vg7yvh138Et/5m8KrW+24MZqH2vZVu3ZpEKdWllDLD7WuA7cDJBareBbwu3H4d8IXlaaEgCIJwRbFjcMPPwC/dA2/6Oux4Afzow0H8/bdXw7GvBdlkgG0oXtbdxudv3M7dT9zJq3ra+ezwBM/50RFe9uNjfH54koq/dhKsBEEQBKGp6NgKt/9f+O1D8KK/glIW7nwzvHcv3PPnkBta6RYuG5LRLQiCIKw+Rh4J7ExOfRvWXQ8v/ptlez1rrWWIKaVeAbwP6AKmgAe11i9QSr0K+BPABTzgf2ut/zM855+AD2it71NKdQCfAjYCZ4DXaK0nHuu+ErMFQRBWIbkhuO8jcP9HYGYY2rfCLW+GG34WYpmGqlMVl0+en+Aj58Y4XXLoiVi8dn0nr13fQU/UvuJNXWvxeqWQeC0IgrAK8X04eU/g433sq8E8V7tfPmtr0iTzaYBYlyyKBGFBEISrCK3h4OfgK+8M7UxeC8991xW3M5GB89IgMVsQBGEV4zpw6AuBrcnAjyCSCjy8b3kzdO1oqOprzTcmctwxMMo3JnJYCn6iq5U39nXyxJYk6goNtiVeLw0SrwVBEFY54yeCN6Ef+LfA1mT9jfCkt8CeVzSFrYkI3YsgQVgQBOEqpJyDb74bfvCPwUD7OX8EN70+eGp9BZCB89IgMVsQBGGNcO7H8MMPwYHPgufA1tsCX9Dtz5sXi08Wynz03BifGBon6/rsTcV5Y18nL+9pI2EuraumxOulQeK1IAjCGqE8Aw99IojZY0ch2QU3vQFufiNk1q1Ys0ToXgQJwoIgCFcxI4fhS28P7UxuCO1Mblry28jAeWmQmC0IgrDGmBmF+/8F7vvn4E2rts3wxF8KJpCOtzZUzXsenxue5I6BMQ7nS7RaJj+zrp3X93WyKb402WUSr5cGideCIAhrDK1nbU2OfmXFbU1E6F4ECcKCIAhXOVoHGWVfeWfgHXrjL8Bz/jckO5bsFjJwXhokZguCIKxRvAoc/s8gY+zM98FOwHU/Fbwm3b2roarWmh9M57ljYIwvjU3ha3huR4Y39nXyzPY0xuMYbEu8XhokXguCIKxhJk7CD6u2JtOw/gmB4L2MtiYidC+CBGFBEAQBCGaZrtqZxDKB2H3jLyyJnYkMnJcGidmCIAhXAecfCgTvhz8NXhm2PCOwNdl5+7yYfL7s8LFz4/zb+XFGHZdr4lHe0NfJT/a20WJbl3xriddLg8RrQRCEq4DyDDz8SfifD8HYkWW1NRGhexEkCAuCIAgNDB8K7ExOfyeYdOPFfwV9j8/ORAbOS4PEbEEQhKuI/Dj8+KPwo3+G7AC0bIQnvil4CJ1ob6jq+D5fHJ3mjoExfpTNkzANXt3Txhv6OtmVil/0LSVeLw0SrwVBEK4itIaT94a2Jv8d2pq8LLQ1eeIVsTURoXsRJAgLgiAI89Aa9n8GvvpOmBmBm14XZHjPGVhfLDJwXhokZguCIFyFeC4c+VKQ5X3q22DFYN9rAluT3n3zqj+cK/CRc2PcOTxJydc8pTXJG/u6eGFnC7ax+GBb4vXSIPFaEAThKmXiUfjRP8GP/zWwNVl3QyB4733lktqaiNC9CBKEBUEQhAsy187kue+CJ/wCGMYlXUYGzkuDxGxBEISrnOGDgeD90H+AW4RNT4Vb3gzX/gSYjVYlExWXT5yf4F/OjXG25LAuavML6zv4+fUddEXsBS8v8XppkHgtCIJwlVOegYf/I8jyrtmavD60NVn/uC8vQvciSBAWBEEQHpPhQ/Clt8Hp7wY2Ji/6K+i78aJPl4Hz0iAxWxAEQQCgMBFMgvWjD8PUGcj0BYPnm14Pyc6Gqp7W3D2e5Y6BMe6dzGErxUu7W3ljXyc3ZhKouleqJV4vDRKvBUEQBCB4U/rRbwaC95EvB7Ymu14aZHlvuOWybU1E6F4ECcKCIAjCRaE17P80fPUPAjuTm98At/3hRdmZyMB5aZCYLQiCIDTge3D0K/DDDwb+oGYU9r06yPJef8O86scLJf7l3BifPD/BjOdzXTrOG/s6eVl3G3HTkHi9REi8FgRBEOaxoK3JW2DPK8GOXdKlROheBAnCgiAIwiVRmoZ7/2/wVDrWEtqZvHZROxMZOC8NErMFQRCECzJ6JLA1efATUMnDhicFgvful4HZaFUy43p8ZniSOwbGOFoo0W6b/Oy6Dv5wW5/E6yVA4rUgCIJwQZz8rK3J6COQ6AzeyHrimy7a1kSE7kWQICwIgiBcFsMH4YtvgzPfg76b4cV/BeufsGBVEbqXBonZgiAIwmNSmoYHPh6I3pOPQqo3sDW5+Q2Q6m6oqrXmu1MzfOTcGF8eneb8bU+QeL0ESLwWBEEQHhOt4dFvhbYmXwptTV4S2po8aVFbExG6F0GCsCAIgnDZaA0PfyqwM8mPBgPp2/5gnp2JCN1Lg8RsQRAE4aLxfTj+9cDW5PjXwbBh7yvhlrdA/03zqg+UHDbEoxKvlwCJ14IgCMIlMXkqtDX5WPDAet31Qbze+6oFbU2Wenx94XezBUEQBOFqQim4/qfgN+4Lnjzf/xH4h5uDAO37K906QRAEQbh6MQzY8Xz4+c/Cr98fvBL9yJfgn26DD98GD/0HuOVa9f5YZAUbKwiCIAhXMW2b4fn/B377MPzE34LrwBd+Fd67G+7+E5g+d0VvL0K3IAiCINQTa4Hb/y+85dvQuQPu+g345+fB4IMr3TJBEARBEDq3we3vht85DLe/B0pZuPPN8N698I0/g+z5lW6hIAiCIAiRZGA19qvfh1+4CzY+Bb7zXvjbffDp18Pp7wdvVS8x1pJfURAEQRDWAr174Q1fhoc+CV/7Q/jQs4IMMkEQBEEQVp5oGp70ZnjiL8LJewIf72+9B77zN8GklVcRSqnXAO8CdgG3aK0v6DWilDKB+4BzWuufWJ4WCoIgCFctSsE1zwyWydOhrclH4eCd0Hvdkt9OMroFQRAE4UIoBTf8DPz6fXDLm+G+O1a6RYIgCIIg1GMYsO058LP/Ab/548B+7NjXV7pVy80B4JXAty6i7m8Bh69scwRBEARhAdo2wfP/dNbWxHeX/BYrKnQrpd6jlHpEKfWwUupOpVRrWL5ZKVVUSj0YLh9YyXYKgiAIVznxVnjRX8KvfG+lWyIIgiAIwoVovwZe8Gfw24dWuiXLitb6sNb6yGPVU0r1Ay8G/unKt0oQBEEQLkDV1uQKjK9XOqP7a8BerfV1wFHgHXXHTmitbwiXX16Z5gmCIAhCHd27VroFgiAIgiA8FtHUSregWflb4HcBmWVbEARBWHmUWvJLrqjQrbX+qta6mqf+A6B/JdsjCIIgCIIgCIIgCM2GUurrSqkDCywXZUiulPoJYERrff9F1H2zUuo+pdR9o6Ojj7vtgiAIgrBcNNNklG8E/qNuf4tS6gEgC/yB1vrbK9MsQRAEQRAEQRAEQVg5tNbPfZyXeCrwUqXUi4AYkFFK/ZvW+ucXuNeHgA8B3Hzzzfpx3lcQBEEQlo0rntF9MU+elVLvBFzg42HReWCj1voJwG8D/66Uylzg+vK0WRAEQRAuE6XUa5RSB5VSvlLq5rryn6ubK+PB8PgNC5z/LqXUubp6L1rWDgiCIAiC8Jhord+hte7XWm8Gfhr4xkIityAIgiCsZq54RvdjPXlWSr0O+AngOVprHZ5TBsrh9v1KqRPADuC+Ba4vT5sFQRAE4fI5ALwS+GB9odb644QPoJVS+4AvaK0fvMA13qu1/qsr2UhBEARBEBZGKfUK4H1AF/BFpdSDWusXKKXWA/+ktZaH0IIgCMJVwYpalyilXgj8HvBMrXWhrrwLmNBae0qpa4DtwMkVaqYgCIIgrFm01ocB1OITgfwM8IllaZAgCIIgCJeE1vpO4M4FygeBeSK31vpe4N4r3jBBEARBWGZWdDJK4B+ANPC18HXnD4TlzwAeVko9BHwG+GWt9cRKNVIQBEEQrnJ+isWF7l9XSj2slLpDKdV2oUpiNyYIgiAIgiAIgiBcKVY0o1trve0C5Z8FPrvMzREEQRCENYlS6utALIevygABAABJREFU7wKH3qm1/sJjnPskoKC1PnCBKv8I/Cmgw/VfE0wwPQ+xGxMEQRAEQRAEQRCuFCq0xV4TKKVywJGVbscS0gmMrXQjlgjpS3OylvoCa6s/0pfmZafWOr3SjVhqlFL3Am/TWt83p/y9wKjW+s8v4hqbgf/SWu+9iLprKWavpc/4WuoLrK3+SF+ak7XUF1hb/VmT8Xq5kXjd1Kyl/khfmpO11BdYW/1ZS31Z0ni9ohndV4AjWuubV7oRS4VS6r610h/pS3OylvoCa6s/0pfmRSk1b2LktYpSygBeQ2ApdqE667TW58PdVxBMbnkxrJmYvZY+42upL7C2+iN9aU7WUl9gbfXnaorXVxiJ103KWuqP9KU5WUt9gbXVn7XWl6W83kp7dAuCIAiCsIIopV6hlBoAngJ8USn1lbrDzwAGtNYn55zzT0qp6herv1RK7VdKPQw8G/hfy9JwQRAEQRAEQRAEQahjrWV0C4IgCIJwCWit7wTuvMCxe4EnL1D+i3Xbr71ijRMEQRAEQRAEQRCEi2StZXR/aKUbsMSspf5IX5qTtdQXWFv9kb40L2utPyvFWvo5Sl+al7XUH+lLc7KW+gJrqz9rqS8ryVr6Oa6lvsDa6o/0pTlZS32BtdUf6csFWFOTUQqCIAiCIAiCIAiCIAiCIAhXH2sto1sQBEEQBEEQBEEQBEEQBEG4yhChWxAEQRAEQRAEQRAEQRAEQVjVNLXQrZTaoJS6Ryl1WCl1UCn1W2F5u1Lqa0qpY+G6LSx/nlLqfqXU/nB9W921bgrLjyul/l4ppVZBf25RSj0YLg8ppV7RLP251L7UnbdRKTWjlHrbau2LUmqzUqpY97v5wGrtS3jsOqXU98P6+5VSsWboy+X0Ryn1c3W/lweVUr5S6oZm6M9l9MVWSn00bPNhpdQ76q612voSUUp9JGzzQ0qpZzVLXx6jP68J932l1M1zznlH2OYjSqkXNFN/VorL+Fw0bcy+jL5IvG7S/iiJ2U3ZFyXxupn707Qxe5G+SLy+BC7jMyHxukn7U3de08Xsy/jdSLxuwr6oJo7Xl9mfpo3Zl9EXidcXQmvdtAuwDrgx3E4DR4HdwF8Cvx+W/z7w7nD7CcD6cHsvcK7uWj8EngIo4MvA7augPwnAqjt3pG5/RftzqX2pO++zwKeBtzXL7+Yyfi+bgQMXuNZq64sFPAxcH+53AGYz9OXxfM7C8n3AyVX8u/lZ4JPhdgI4BWxepX35NeAj4XY3cD9gNENfHqM/u4CdwL3AzXX1dwMPAVFgC3Cimf7frNRyGZ+Lpo3Zl9EXiddN2h8kZjdlX+acK/G6ufrTtDF7kb5IvL6ynwmJ103an7rzmi5mX8bvZjMSr5uuL3PObap4fZm/m6aN2ZfRF4nXF7r/cn8QH+cP6wvA84AjwLq6H+CRBeoqYDz8Qa0DHqk79jPAB1dZf7YAwwR/NJuuPxfTF+DlwHuAdxEG4dXYFy4QhFdpX14E/Ntq6MvFfs7q6v458GfN2p+L+N38DPCf4f/5DoLg0L5K+/J+4Ofr6t8N3NKMfanvT93+vTQG4ncA76jb/wpB8G3K/qz0z/Ei/782dcy+xL5IvG6i/iAxuyn7MqeuxOvm6s+qidlIvF6Wz8ScuhKvm6w/rJKYfRF/ezYj8brp+jKnblPH64v83ayamH0RfZF4fYGlqa1L6lFKbSZ4mvw/QI/W+jxAuO5e4JRXAQ9orctAHzBQd2wgLFsxLrY/SqknKaUOAvuBX9ZauzRZfy6mL0qpJPB7wB/POX3V9SVki1LqAaXUN5VSTw/LVmNfdgBaKfUVpdSPlVK/G5Y3VV/gsv4G/BTwiXC7qfpzkX35DJAHzgNngL/SWk+wOvvyEPAypZSllNoC3ARsoMn6AvP6cyH6gLN1+9V2N11/Voq1FLMlXtdoqr6AxOxmjdkSr5szXsPaitkSr5cGidfNGa9hbcVsidcSr5eDtRSzJV4/vnhtXXIrVwClVIrgdZy3aq2zj2XJopTaA7wbeH61aIFqekkbeQlcSn+01v8D7FFK7QI+qpT6Mk3Un0voyx8D79Vaz8ypsxr7ch7YqLUeV0rdBHw+/Mytxr5YwNOAJwIF4G6l1P1AdoG6q+L/TFj/SUBBa32gWrRAtWb/3dwCeMB6oA34tlLq66zOvtxB8JrSfcBp4HuASxP1Beb3Z7GqC5TpRcqvKtZSzJZ43ZzxGiRm06QxW+J1c8ZrWFsxW+L10iDxujnjNaytmC3xWuL1crCWYrbE6xqXHa+bXuhWStkEP5iPa60/FxYPK6XWaa3PK6XWEXhrVev3A3cCv6C1PhEWDwD9dZftBwavfOvnc6n9qaK1PqyUyhP4ojVFfy6xL08CXq2U+kugFfCVUqXw/FXVlzCDoRxu36+UOkHw1HY1/l4GgG9qrcfCc78E3Aj8G03Ql7BNl/N/5qeZfdoMq/N387PAf2utK8CIUuq7wM3At1llfQkzZf5X3bnfA44BkzRBX8I2LdSfCzFA8LS8SrXdTfE5W0nWUsyWeN2c8RokZjdrzJZ43ZzxGtZWzJZ4vTRIvG7OeA1rK2ZLvJZ4vRyspZgt8brG44rXTW1dooJHF/8MHNZa/03dobuA14XbryPwe0Ep1Qp8kcDb5bvVymF6f04p9eTwmr9QPWc5uYz+bFFKWeH2JgLT9lPN0J9L7YvW+ula681a683A3wJ/rrX+h9XYF6VUl1LKDLevAbYTTMqw6vpC4H10nVIqEX7Wngkcaoa+wGX1B6WUAbwG+GS1rBn6cxl9OQPcpgKSwJMJ/KlWXV/Cz1cy3H4e4GqtV8Pn7ELcBfy0UiqqgtfEtgM/bJb+rBRrKWZLvG7OeA0Ss2nSmC3xujnjNaytmC3xemmQeN2c8Tps05qJ2RKvJV4vB2spZku8XsJ4rVfYLH6xheB1D00wY+2D4fIiAtP4uwmeVtwNtIf1/4DAb+fBuqU7PHYzcIBg9s5/ANQq6M9rgYNhvR8DL6+71or251L7Mufcd9E4I/Sq6guBN91BAk+kHwMvWa19Cc/5+bA/B4C/bJa+PI7+PAv4wQLXWlW/GyBFMHv6QeAQ8PZV3JfNBJNoHAa+Dmxqlr48Rn9eQfAUuUwwWdFX6s55Z9jmI9TN/NwM/Vmp5TI+F00bsy+jLxKvm7Q/SMxu5r48C4nXzdifzTRpzF6kLxKvr+xnQuJ1k/Znzrnvooli9mX8biReN29fnkUTxuvL/Jw1bcy+jL5sRuL1gosKTxQEQRAEQRAEQRAEQRAEQRCEVUlTW5cIgiAIgiAIgiAIgiAIgiAIwmMhQrcgCIIgCIIgCIIgCIIgCIKwqhGhWxAEQRAEQRAEQRAEQRAEQVjViNAtCIIgCIIgCIIgCIIgCIIgrGpE6BYEQRAEQRAEQRAEQRAEQRBWNSJ0C4IgCIIgCIIgCIIgCIIgCKsaEboFQRAEQRAEQRAEQRAEQRCEVY0I3YIgCIIgCIIgCIIgCIIgCMKqRoRuQRAEQRAEQRAEQRAEQRAEYVUjQrcgCIIgCIIgCIIgCIIgCIKwqhGhWxAEQRAEQRAEQRAEQRAEQVjViNAtCGsQpdQppVRRKTVTt/yDUur1SilvTvmMUmp9eJ5WSm2bc613KaX+bWV6IgiCIAhrmwvE7I1Kqb9WSg2E+48qpd5bd47Ea0EQBEFYJsJY/dwLHFNKqZNKqUMLHLs3jNnXzyn/fFj+rCvTYkG4ehGhWxDWLi/RWqfqll8Py78/pzyltR5c0ZYKgiAIwtVNQ8wG3gDcDNwCpIFnAw+sZAMFQRAEQViQZwDdwDVKqScucPwo8AvVHaVUB/BkYHR5micIVxcidAuCIAiCIAhCc/FE4E6t9aAOOKW1/thKN0oQBEEQhHm8DvgC8KVwey4fB35KKWWG+z8D3Ak4y9M8Qbi6EKFbEARBEARBEJqLHwC/rZT6VaXUPqWUWukGCYIgCILQiFIqAbyaQMz+OPDTSqnInGqDwCHg+eH+LwDy8FoQrhDWSjdAEIQrxueVUm7d/tuBCvBkpdRUXfm41nrrsrZMEARBEIR66mP2vcCrgEng54D3AuNKqXdorT+6Qu0TBEEQBGE+rwTKwFcBk0BjezFBxnY9HwN+QSl1EmjVWn9fnmELwpVBMroFYe3ycq11a93y4bD8B3PK60VuD7DnXMcmEMgFQRAEQbgy1Mfsl2utPa31+7XWTwVagT8D7lBK7QrrS7wWBEEQhJXndcCntNau1roMfI6F7Us+B9wG/Abwr8vYPkG46hChWxCEes4Am+eUbQFOL39TBEEQBEHQWhe11u8nyPDeHRZLvBYEQRCEFUQp1U8gXv+8UmpIKTVEYGPyIqVUZ31drXUB+DLwK4jQLQhXFBG6BUGo5z+AP1BK9SulDKXUc4GXAJ9Z4XYJgiAIwlWDUuqtSqlnKaXiSilLKfU6IA08EFaReC0IgiAIy4utlIpVF+ANwFFgJ3BDuOwABggmnJzL/wc8U2t9allaKwhXKeLRLQhrl/9USnl1+18jmA36KUqpmTl1n621/hHwJ+HyHaANOAH8nNb6wHI0WBAEQRAEAIrAXwPbAE0wkH6V1vpkeFzitSAIgiAsL1+as38C+Dut9VB9oVLqAwT2Je+rL9daDxJMTCkIwhVEaa1Xug2CIAiCIAiCIAiCIAiCIAiCcNmIdYkgCIIgCIIgCIIgCIIgCIKwqhGhWxAEQRAEQRAEQRAEQRAEQVjViNAtCIIgCIIgCIIgCIIgCIIgrGpE6BYEQRAEQRAEQRAEQRAEQRBWNSJ0C4IgCIIgCIIgCIIgCIIgCKsaa6UbsJR0dnbqzZs3r3QzBEEQhDXM/fffP6a17lrpdqx2JGYLgiAIVxKJ10uDxGtBEAThSrLU8XpNCd2bN2/mvvvuW+lmCIIgCGsYpdTplW7DWkBitiAIgnAlkXi9NEi8FgRBEK4kSx2vxbpEEARBEARBEARBEARBEARBWNWI0C0IgiAIgiAIgiAIgiAIgiCsakToFgRBEARBEARBEARBEARBEFY1InQLgiAIgiAIgiAIgiAIgiAIq5qmF7qVUi9USh1RSh1XSv3+SrdHEARBEIT5SLwWBEEQhOZH4rUgCIKwlmlqoVspZQLvB24HdgM/o5TavbKtEgRBEAShHonXgiAIgtD8SLwWBEEQ1jpNLXQDtwDHtdYntdYO8EngZSvcJkEQBEEQGpF4LQiCIAjNj8RrQRAEYU1jrXQDHoM+4Gzd/gDwpPoKSqk3A28G2Lhx4/K1TBAEQRCEKo8Zr6ExZvf0refd730PKVMRsQ0iUZuobWNbNnbEJmJHiESiRGJRotEE0ViUeDxJPJEhmUwQjcSwIxGUYWIYBsowUEotT28FQRAEYXVyyfFaxtiCIAjCaqLZhe6FRqy6YUfrDwEfAoj1b9O73v15DKUxlI+Jj6E0pvIx8DHDMhONiYdFsG/jYelg29IeNj6WDspt7WNrj4j2sZTGBCylMJXCMhSWacyuTRPLNLEtG8uyiNkR7GiESDRGPBojGosTS8SJx9MkUymS6Vbi8SSRSGQZfpSCIAiCcMV4zHgNjTE7um67/sfhi3lbWgP5cJloOGIoL4j3ykeFcd6oLcF3AYP67WCt0OE5OjgWrhUaEx9VLUM31qmV0VimgjJVOxa02wh/MNWyap3atiK8ngq36+ophaHAUKq2KEW4BJWUAsNQtX3DMIJ7GgYKhRkeM5QRXMNQKGViVuuqYFHKwKp7WGBgYBgKg/DaGBimQkF4TRMIj5sGJtW2mUFfDTM8P7hv7XxlYGBgmgpDBV9BDcOY7Ut4vlJG8HNQCpQR/vyCSgowTav2kTOM4OXE2kOOcF37QFb3lZotrVWt1lLMblbPV3Xn1i42e94FytTsxWvXn9e2uv3gPgu0pXqseota3WpbVeM96+4nCIKwCJccr2+++eZ5xwVBEAShWWl2oXsA2FC33w8MXri6wvVMfAy0D1qrcCEI39XF13X7GrVsobsULpMNpYbyMOsG69VBenWwXStbYLBuqHBQHg60q+K+CgffJrP1FYT7OrgWzA7iw22jYTsYcAcDdR0MjAkG3ibVwTeYSmEawWDWMoLBsWUYmIaJaZlYpoVtmZiWjW1HsC0LOxrBsqPE4vEgY8+OkYjHiEZiRGMxIpEo6WSLPAQQBEFYHVxivIaU7fCEDaPMWFFyZpwZK07ejFI2IrOxOYzTUc8h7paJu2ViXomY7xD1ykR9h6jvYPuV2RCPqi2eVg1lWs8e87UiiKwKVxv4frU8iJC+DrY1Ck8b4blGcLxuu3ZMVyPn1Yw3Z33lUfiN++FDh4DwAYTyw7WuKwu+/4CunaPCbQi+B1Er90NBWYd1Zrer9zSVj6p76NL4oKVuv+E7XPgJVD6m0qgFjlXPU2gs5WEaLpbygiXcNsPFMjwsNVtWO2Z4WLjhcQ8z7E/wI6r2ItgOfmrVf8J9XaeL1SqH36+50Dmz29XyWh1Vd9+6a8/eR4Gu3qq6Xd8GNXu9ujoNbVL1166/hzGnbHatUSgdnqaDBwpaBw9/gmar4KCqnle/P3tdVb2WCv7mqOoTqup28OgmrG0EX7a1UffQwqitqw80FMbsNYzg4VTwcMMIHjmp4Nt89cGWInhwRfggK3iYZqAMc7aOMsAwa9uGMlBGWE+p2gMzw6yeP1tWfbBihHVr+4ZReyijVN21atuE7TBqD7Vq59WfE7bDCPuujLpjKJQR3jt80FXtgzKC+kbYVmFBLjleC4IgCMJqotmF7h8B25VSW4BzwE8DP3uhynt7W7jv/3tJsOP7UM5CcQKKk1CYxCtMUC5OU8hPUSjMUCzOkC8WyRaK5B2PKc8k75vkVIK8kaRoRCkRpWhGKakIjopQMoOlYMaCcsOmZNiUDQvHsINv9j7BWoPSs6K65VWIeC62V8H2K0R8F6u6aBfTd7G0h+F7mNol6juAxtfBF+bqoN0Pv0B7ujpAD6TuYJBeNwgPj1cH4rODd6OxTJtX/Bd5Yari//SCR6uDxbmDx+qAsnFAGAxQGwaRddl7s1l7/pwsvAW2a0J/fVnjfpDPRviAIdg2VZCZZwKGoYPMNYMg280IsutMpTBNE9M0MI3qmwA2pqmCtwFME8uyidoWth0lGrGJRePEIsFbAdFojEQ8QTyekIcBgiA0C5cUrwG2dHdx56+9frZAa8gNkR87yfnxAQanxziXzzFYdhn0TQbtds5FuxmMbmDGSjZcy9CaVu3R6hukfYtkRZMqa+IzLu0jDmqqgl/0MB2fuA9JXxFZMKkNtK0w4ybRhEUqbdPaYpFOWcTjingUYlGfmOUTs31MKlCpoJ0ybr6Al8/hzORxCgWcQh7XKVEpl6iUHFzHxXfLVCouruviuS6er/F8Dw+NNkw808Q3DDzDwDdn174y8EwDrRS+baEtO1xb+JaJNk1800QbBr6h0IaJb4QSrap+JdHBw/9QQfTDMnRVlAy+voQqX00wVDWRMpD6/Dp5Udet9QJlfigWawjagUaHa8LjvtLosE5wXOMravep3jfYUrUb1Oco6PB4g/ip69uiasJqrUxX8++rz1NUrW5VgK0/X8/br0nmte36712+r/CwqIQPQLww+aK+jl70gYpC1z10WeqHKGYoiFuGF2xXBXRjtryhzHBDMd3Fris3DQ9LVWr17TnnWIaLbbqYKlgHxypEzAq24WAbFaKWg6FmHxrUfsqq+ttZuFwS2Jee4DMKtQcMWoGvZv9/VB8eVP//VP8f6Nn/Z8Ds/6FFzqsv11rN3nPOuv6hSMPDlfBY7fyFzhUW4pLjtSAIgiCsJppa6NZau0qpXwe+QpBEfIfW+uBFnWwYEG8NlhATSITLBfFcKE2F4ngokhcnwu3hYHviURg5BDPDs+fF2/C69zLdcz3TnbuZbN3OVHoj00aUyYrLlOsxVfGYqrhMVFwmnKBs2vXIeh6VC3wZS3nQ6mjSRZ9E3iOerRCbdrGyFbyKj/I0tlZYgK3B1gobsMLtiAr2ba2wNBgL3MfTLlr5oDxQFbThoZSLrzwMywPbQxk+WB4YwaKVD4aPVl44SPXwgyFrsI8fDNwhLAdPBQNnLxys+hAuwbZG4YVrv3Zczdk36urOHtO6cd+veyAQDCrB1wYVDLRv4VEdTM5m72m9eNZeMBg18VbkwUAxXCbnHZl9XX/uWwGzDwHMWkaZX8voN+u2q0J/9Y2A6n44DA+3Cc5Dh5n+9W8NUHsQYBBk+Qev3AeZ/6YBpjJmhX4jFPkNA9M0sE0T27SwLAvbsmpCfzSSIBqNEItEiEbjxGMJEvEEiXiSRDJFxI6K0C8ITcDjitdVlILMOpKZdWy7BrY13gByQzBxEiZOkJ04w7mpMQYLBQYdl0GrhcFoN4PRLgajPRyNd1NMRqHdQm002ZNq56ltaW5tSbHNssjNVDg3mmdwJM/4WIHJyTKF6TKlmQq66BIvVkjkFYkRRVIr4nphRU1ZimjKpqWjlfbe9bR2J2i9NkFLT5yWrjiWfXHxQrsufrGIXyjiF/LoYrFuv4BfLOAXCnhTU3gTk3iTk3hjI7jV7YkJdKWycBttG7O9HbOtDau9DbMt2Dbb27Da2zFbw+22tqBeayvKbGy39jXa9dGOh674dYuHdsJ1xUc7Pn5te5G6jo9fcvELLn6xwpyk7Mb2x0yMhI2RsGprs7oftzCS9uzxcF9FzTVn4+H7morvU/E0juvPLp6H42oc7zHKXA/HC84vzzm/Ule37PpBvXBdLct5Pk7Fn3MfH89fGjUxYhpEbYO4bRKzTWLhdtQ2wzKDWG3brNWNWkZ4zCBqKiIWRC2CbRuiBkRtsE2NbUDECr7/BA97XHzt42sfXV18Lyzz0OEx8PF9H9/30FTr+Witw3pBOX64jw7q6uAbqw6vVa0PfvB/irr7EtwbrYN7Vu8dHsOv1gueTFWvidazx5VG+0F7ta4+wdK1dlA7P2xX3fbsE6/gO7uq1q2Wq9njAbPbCj8UoMM3IhrqV9+MmPtYrPo2RVhm1L1hEdabfcNi9hiKhu3asfpyZt/eEOazJPFaEARBEJoYpfXa+RJw88036/vuu2/5bpgfDwTvkUMwfDDcPgzOzGydlo3Qsxu6w6VnN3RsB2tWnNNaU/Q1U3WC+HjF5VSxzLFCiROFMscLZabd2VeB44bimkSULbEofbZFr2nSrg0yLpTLHtlShelCheliJdguBkuuUGGm4FIsVCiXvVAED0VxIIqiIxahPWrRalukLZOkaRA3DCIobB+Up3Edj0rZwy0H60rZ41I+SqZlYEfNYIkFaytiziuzoyZ25AJltX0rON8OXlm8HLTvg+uiPQ/tebPbrgdedduFcF0q5ikVCxQKBcrlMsVygZJTpuJWKFccKhWHiufhVDw87VLxNK7vBQNV7eNpH0+DpzU+wUveDdthFp6vq9thRn9V8G/YD4/raj1qGWBetTx4CRuvmj0W5MaHbwhc4A2AunIvzPz35rwN4OmVf1YW2P7MivyNHr31Ir8O6tJo+1MT+1Wj1U+j2K8xG/x451j9KLDCN5PNqrgfiv+mobDC13Ito17kN8PsfZOIZTcI/Atl8ifiCdLJjAj8TYBS6n6t9c0r3Y7VzpLFbK2DB8/jJ2pCuJ44yeTkMEdLDt9L7+U7nU/i/tQuysrEBK7PJHhaa4qntqV5YkuShDmbLev5mtFcmcHpIuenSpyfLnJ+ssjYWJHJiSIz0w5OvkLCVyR0kB2e0QZd2iBa79ihIN0Wo7UnTmt3gpbuBK09CVq642Q6Yhjm0mXoaq3x8wW8yQm8iQncyclQEJ/Am5xsEMTdyWDbz+UWvphpYq9bh93fj923nkh/f7jdj93fh9XVtaQistYaXfbw85VQ+HbxCxX8fAWv4KKLLl4hPFa31qVF7FEMMOI2RtLCzEQxMxHMlnCdiWK2BPtG0r7s7w1CgOdrKp4/K54vIJI7rk/Fmz0elHuUKj6likexMrs9u/hhuUfJ9Sk5HiU32C86QZnjLvKEZBFMQxGzjFBQD0X1iEnMMhvKYnNE9tgcEb56PFo9bs2taxCzAr96YWWQeL00LPsYWxAEQbiqWOp4LUL3UuP7MH22UfwePgTjx8B3gzqGDZ3bZ4XvqgjeupELvYeptWas4nK8UOZEoV4AL3Gm6DQkQ62P2mxNRNmWiLE1EWV7uF4ftcNJoQI8XzNZcBiaLjE0XeJ8tsT5qWKwPV1iKBsM8EuVxoGEaSi601F6W2Ksa4nRm4nTm4nSm4rSFY/QEbNpsS1wNZWyGwjhjkelNCuKzxXJq4uzwLFLwYoYDaK5ZYf7oThuRU1s2wjW9fUixuzxurKa+B4xMay1M9GT1jrI/nHdQNSvCvsVd1bYr267Ltr10G4lFPqron9wrFQqUsjnKVZKlByHslOi5FZw3Apl16PiuVR8j4qvqWgPV4MbCv0uGl+DWxX1VZDV74aCvVcV9Oesa/67dfteneDvKQNfV98AUIHAz6zoX7UAmifo12Xve9qctfmpt/zxA8F/pb145wr8F8zenyP2B6J9nU8s/qyIv1AW/xyxP3AWDT37Q9ueub79Ve/+4AHA4iJ/1cPftmwiVoRIxCYaiRKNxohGosRjcWJhJn88niAZSxGLx1f0Zy8D56VhWWJ2OQdHvwKH76J44pvcH9/Cdzqfynd7ns4DVg8uClspbsokeGpbiqe1pbkxkyD6GN6uruczkitzfrrE4FSRI0M5Hjw7xeHTU1hFj3bfoFeZbIpGaPcN7IKH78zGUsNUZDrjtHbHaelJBJng3XFaexIkW6LLIr5qx8GdmqoJ4FVB3B0dpTIwQGVgAGfwHN7oWMN5KhrF7uvD7u8LRPC+qhDeR6S/D6OlZVlipfY0fnGuAN4ohvv5Cl7OwZt28HLl+ZnjhgrF7zohvCUUwjPRWpmyxOu3GfF8vYBgPiuUX5SIXn/MDUX0ih+I6qGgXr3O5Q6ZIpYxT1ivCuPRCwnqlkF0jmAes03iESM8b3796rYpwnoNiddLQ1OMsQVBEIQ1iwjdi9DUQdh1ArF7+BCMHAzXhwJRvEokDd27gqVnL2x/LrRf85iXLvs+jxYDAfx4vszxYonj+TIniiWyddkuccMIBfBoTQC/NhVjZyJ2wUGp1prpYiUQvqsC+HSxJoQPTgXbBadRkFYKOlPRUAiPsbkzybbuFNu7U2zvSZOKXlwmsPY1bsUPRe9QNC/7ddtzBHTHw3X8QCh3vNm1E1yjmolecTx899I++8pQs4J4xMC0TeyIgWkHgri1wNoM65u2MVseMbDsOetw27SNNSeqryRa61oWvnY9cCuz2fnubKZ+w/5cgd+rPgyYL/CXnDLFsoPjlilWHEpuhYpbwfE1ju/iaI3r+VSUxtUalyBz3yXIyncBN8zebxDzVVhe2w+WWgY/RuP+BQT+msg/R+CvZu/PFfrnivqN2fvNk8Wv6sR8c856oUz+6qRu5kIe/mG5qs/ar5+wdwGh/59/950ycF4Clj1mOwU4cTccuguO/jczboX/6Xwy3938Mr6T2sN+N4ImeGPqiS1JntaW5mmtKa5LJ7AuUjjyfc3JsRkeODPFg2eD5ZGhHJ6nSWjYkYizOxNnoxUh4wIzLtnRIl7dA2UrYtDSlZiXCd7aEyeeWv43OfxikcrgYCB8DwxQGTgXbJ8boHJuEH+6cY4NI5UKhO/+PiJVEbwmivdhJBY1kLtiaF/jz1TwsmW86TJeNhTAp8thmYOXLaOd+VnCtczwehG8XhRviWLEVv5vo3Dl0Dqwd2kUzf154nq5JpbPiuTVc8pzhfSGY3PF+svLVodZG5i5InlVGI9aZpjBbjQK76EVTLS6tozatSKmGa6NurXZuG8ZTffdVYTupaF142b98v/1O/TisKujhac+7alsu2bXSjdLEARBWCOI0L0ITS10X4jSNIw80ih+Dx8MfMIB1t8Ie18Fe14BLX2XdGmtNaNOkAV+PMwAr2aCnyk5Nee63ojNszvS3Nae4RltqSAb+xLvkyu784XwcP/8dJHT4wXKdaL7+pYY23rS7OhOsb0nEL+3dafIxOxLuvfjwff8QACv2rDUCeL1oni1Tr1o7lbCtePjVTwq4Too83ArPm7FbxAvLglFIJTbswK4FS5VYd20QoHcMjAjZriuqzPn3HqxvXpusDZrxw1TBPbVyFzrHV2pXEDgr8/MdxvseBr36+pV3EDsD7edchmn4lB2KxRdh7LrBH6wXlXg96mgcbWP62sqVUFfEWbr61DANwKbHqVqS028b9g3avuaxnpVr/5A7J9v11PL/l9Cod/XBqfe/VIZOC8BKxqz3TKcvDcQvY98EYqTTMV6+MG1P8d3ep/Jd1Q3jxQcAFKmwZNbU6HVSYo9qXjD21GPRdHxODA4zUNnp3jg7BQPnpni3FQRAMtQXNub4sbODDuTcdaZFnbBY3q0yNRwgexYCV3ngZxsidC1KUPXhhRdmzJ0b0yTaIms6N9tL5ulcu5cgwheL4TrYrGhvtnejt3fT3TLZqI7dhDdsZPojh1Y3UtriXI5VG1UvOlZ4bthHQrkfn6+H7qKmVitMczWKGZbdN62kRKbFOHi0TrwUi/PEcUXEtYbM9Nn69Rnu9fqhGJ72Z1fvhRDwohpEKmK5A1rk4j1GEK5bRA1G+vPPb+6X92OXqBuNatdhO6lIbpuu173ur9tKFOmxo64xG2HlFWkxSjQofN06QIdEUVvZxvbdl/Ljdc+kXSiZWUaLgiCIKwKROhehFUpdC+E1jB5Cg7fBQc+C+cfCso33gp7Xwm7Xw6prsd1i5IXZIE/kCtwz3iOb03mmHY9TAU3Z5Lc1p7h2R1p9l7igP5CeL7m7ESBYyMzHB3OcXxkhmMjwbo+a6U3EwuE7+4023tS7OhJsa07TUt8+QTwpUT7Gtf18UKx3KvUi+ShIF4nnHuVunq1+rOiebD25pTN1vMcH/9xTAylFIFo3iCOm3Wi+ALCey2Lfb5wXhPbI/OvV903mzADSGhO5mXo12XeX8x2LTt/AZueejH/wtvBA4B175SM7qWgaWK2V4FT3wli7uH/gvwImFFGd7yE7215Bd9N7uK7OYcTxTIAbZbJrW0pbm0NrE52JKKX/DdsJFfiobPTPHh2kgfPTvHw2Wly5cDeLB2zuL6/lRs2tHL9+ha2pWKoGZfJoQJjAzlGT+eYHC7U5lmLZyJ0b0zTVbek2i69TVcCrTXexMS8bPDKuQHKJ07iDs9O6m22tITCd3XZTnT7DsxUcgV7sDDa9YOM8KoAPlXGnSrhTZWD7ckyuuQ2nmQqzNYoVmsUMxTBrbZoWBbsi0WKsFLUMtYdn7LnNfirz64by6tl5UXqOl4g1jvehc6vLrPHlgLTUEQtg8N/ervE6yXgmq1b9U++9a0M+jZjRpwpFSenoxQrEZyKCWWNcjzUgm/LamKRMim7SIs1Q5vKkVFl0kaFtA3tbQk2btjAdduvZ1vfLozHsA4TBEEQ1h4idC9C0wyal5qx43Dwc7D/MzB2BJQBW54RZHrvegnE2x73LVxf8+Nsnm9M5PjGRJaHc0EGVlfE4lntaZ7TnuEZ7WnaLzHb+7HwfM25ySJHh3McC8XvY8MzHB+ZoViZtUPpTkfZEWZ97+hJh2J4itaETMg3F98LRW/Xr4nn7lxBvNIors8T0J3G+u5Cwrvj1UR893G8YotiNhvdMuYL7QsI71XhPCibFdrr7V9qdertZMKypZwETrj6kAyxpaEpY7bvwdn/CTK9D98F2XPBvBrXPJPzO1/Jd7ufxncKiu9M5RgoBVm9XRGL2ztb+KX+LrYnY5d3W19zYnQmyPgOs76PDOfwwgeX/W1xnrCxjefu6ua5u3qwNYwPzDByJsdouEyez9cyMuNpm64NjeJ3uuPCNmUrhTc1RenoUcpHj1E+ejRYjh3Dz+drdey+PqI7dxLdsZ1YKIJHNm9GWc1tFeKX3FAAL+NNlhq23akyfs6pPayoYqTthbPCW6NYbTFUzGy636EgLCVaayqeXlgor/g4nke54lOuK19IhK/u/8FP7JF4vQQsFq+9SokT5x5l/8lHOXBqlOMODJkJJs04WR2l6NnoqhBe9lFlD+X48/7+AUTNEq2RHBkrT9oskDZKpG2HTFzR0Zpky/oNXL/jBjZ378Q0zCvca0EQBGG5EKF7EZpy0LyUaB1Ymxz4XJDpPfloMADf9pxA9N55O0TTS3KrUafCvRM5vjGe5d6JHJOuhwE8IZOoZXvfkE4sSbb3Qvi+5txUsSZ8Hx2e4fhIIIbX+4F3paOB73fo/X1dfwvX9maISFbUsqK1xnPrRHVnfqa5GwrpXp1w3pCp7szPVHedWcF+rvDuOZf/mq1hqlo2un2RfuvVyUlrQnpd2UL1qmViB7P2EKF7aWj6mK01nPsxHPp8IHpPngoeNG96Kux+Gae33M53K3G+NZnjy2PTlH3Nc9oz/PKGLp7Wlnrc/++rlicPhn7fPzo1wUiuTMw2eM6uHl56/XqeuaOLmB0M9iuOx/jADKNncoEAfjrHxPl8zfokmrTo2pCme1Oaro0ZujamyHTGm+7vk9aayrlBykeP1MTv0tGjOI+eAi+I/8q2iWzd2iB+R3fswOrpabr+XAjt+njTofhdJ4DXssKnSjAnO1JFjJo/uJGp8wqvLTJ5piDUI/F6aXg88dorTnN66FEeOT/AyalJzmQrDFTinDMzjFhJskTRjg4E8LJHpFQmUi5B2aPiWHj+fEE7aeVpCQXxlFUgZRZJ2hVa4iY9bSmu6dvAddtupK99G5bZ3A9FBUEQBBG6F6XpB81LidYw+EAgeB+8M8g6s2Kw4wWB6L39+WDHl+RWntY8lC1w90SWeyZyPJAtoIF22+RZ7Rlua0/zrPYMnZEr/0XC9zWD00WOjcxwfHimlgl+fGSGmfC174hpsHt9hhs2hK9+b2hlc0di1Qx+hYtDa43v6QWF84rjXcAupm67bh3Urctqd5bOb71qB1MT0udNRDonI71uwtOGOnNF9bqJTqv1JVN9eZCB89KwqmK21jC0PxC8D90VvF2Fgg23wK6XMnbtK/lY1uCOgTHGKi67kzHevKGLV/S0EV2i17B9X3Pf6Un+86FBvrT/PON5h3TU4vl7ennpDeu5dWsH9py/AW7FY3wgz+jZHKOns4ycyTExmMf3gu9+kbhF18YUXRszNfuTlq54U3pJ+46Dc/Jkg/hdPnK0wf7EaGkhtn37rP3Jzh3Edu5csQkwHw/a1/j5SmiFEtqiTJfxcuEkmrnAMxxv/vf42uSZofhtLCCGG0nxDBfWPhKvl4YrFq+1xs0NMTByhkNDA+wfH+doRXE00svJZB+eUlDxieUdOrM5WgpZEuUZzEoR1/XIeVGmK0mylRS+bhTEFT7pyAwtdo60lSdl5UmaJdIRl7aExbqOFnZu3sbuLU+gK70RQ8l3aEEQhJVChO5FWFWD5qXE94NXrQ9+LhC986MQScG1Lw5E72ueDdbSWXyMOy7fnAyyve+ZyDFecVHAdek4t7VnuK0jw42ZBOYyCstaawYmi+w/Nzvh1/6B6Zr9SUvc5rr+lkD47g/E7650dNnaJ6x+qn7rVRF8rp+66zQK7m79xKVhlrvnePMF9WoGvNNYttArnY+FYar5meVzstatxUT0unI7PDc4NnstK2JgXuWCugycl4ZVHbNHj4T2Jl8IBHArDk/5VUpP+Q3unPb50NlRDudLdEUs3tDXyS+s71zSh8Gu5/O9E+P850OD/PfBIXIll/ZkhNv39vLS69fzxM3tGBcQMb2Kz/jgTM3yZPRMjrFzM/hh9nAkZtKzJUPPlhZ6r2mhZ0uGWLJ558nwpqdnhe86C5Sa/YlhEN22jdjevcT27iG+dy/Ra6/FiKx+6zOtNX7BDSbIzJZD3/DQO7xu25+pzI8phsJMRzBbIuH6AoJ4TLIhhdWLxOulYdnjte9RGjnCobOHuW/oPIdLmqN2O4dS11A0g0Quy/NYN11gw5TH5imPrnyepDdOyZxhynKZVJopDKZ9m6wXY7qSZKaSRNP4HdY2HFqjWVrsLBlrhpRVIG2VycQ0nS1xNq3vY/c1e9jYuY9UVCbVFARBuBKI0L0Iq3rQvFR4Lpz+TpDpfeguKE1BrDXw8t77Ktj8dFjCV7h8rdk/U+Qb41m+MZ7j/mweH2i1TJ7ZnubZ7Wlua8/QHV3+QbLr+RwfneGhs1M8eDYQwOs9T/ta42HGdwvX97eyt6+FZFQGdMLKU7WCqWWhN4jngYBeL7JXnDnCurNAVnp9veq1ypdn/6IMVRO/FxTR5/qiR+qPLyao15c1b4a6DJyXhjUTs8eOwzffDfs/BYkOeObvoW98Pd/OOXzg7AjfmMgRMxSv7mnnlzZ0sfMyfbwvRNn1+OaRUf7z4fN8/dAwxYpHbybGi69bx0uvX891/S2P+UaT5/lMDOYD25NTWYYezTJxbqb296G1J0Hvlgw9ofDdsT7ZtP8/Ifgb6g4OUjpyhNKBgxQP7Kd04CDexERQwbaJbd8+K37v20d02zaU3byC/uNBez7eTAVvuoxfE8DrBfFgrUvevHNVxGywRzFaorMCeTVrPC12KUJzIvF6aWiKeO1V8IYPcXLgMA+MDnEgX+ERq40DyW1MRFoBUNqnLZ+jdbJMz7TPzinYNe3T5VjYho2pXKaiWSYjWcYiRUaNChNKM6lNprwoWTfBtJPG1Y3jQUN5tESytEayZGoZ4kXSEZfWpMX6rja2bdrGpt599Ga2EjFX/4NUQRCE5USE7kVoiiDcTLgOnLwnEL0f+SI4M5Dsgt0vD0TvDU+CJZ7ZeqoSZHvfM57jnoksw05gJ/KkliRv6OvkRV0tRFZwNu2C43JwMBuK31M8NDDF2Ylg4k1DwY6edM3u5Pr+Vnb0pLCaeDAvCI+Hqv3L3Cz0ijNHLF9IPHd8KqHtS6PYvoD9i+Pj+5cea2oZ6vPE8qqYvkBZre7FlVmWccmv78vAeWlYczF78AH42h/Bo9+Cti3w3P8Nu1/O0UKZDw+M8umhCUq+5rb2NG/Z0M0zlsDHey4Fx+Xrh0f4z4cG+eaRURzPZ2N7gpdcv46XXt/Hzt6Ln8fDKbmMns4x9Og0QyezDD86TTEXTMBpRU16NqXp2RII373XtJDINPfAXmuNe/48xQMHKO0/QOngAYoHDuJnswCoSITormuJ79lLbN8+4nv3ELnmGpR59Ux45jteIHxPB5NletPzs8O9rHMBuxS7IRu8lh1eJ4wbCbFLEZYXiddLQ9PGa7eMHjrA+XMH2D86xEN5h0NGGweTWzkbX1erlirNEM8VMLImm8pRnj5Voi/nYhUUlo6QNBRJQ5FWHhGjwoTlcdbKct7OMWKVGFMeE1qR9SNMe0mmnRRlf/6bwWk7R2tsihYrR9qeCSbUjJTJxKGnLcXm/n42rt/HutZdtMY6xFZTEAQhRITuRWjaINwMVIpw7KvBRJZH/xvcEmT6YN9r4NbfgGTnkt9Sa82hfImvj2X5xNA4p4oOXRGLn1/XwWvXd7A+1hyD4vGZMg8NzGZ9PzQwxVQhGMzHbIN9fUHG9w0bA/G7v635Ju8ShGbH8/yab/pc0byyoKA+J1N9ofp151XrXo7ly9zs8/qscmtOxrkdMbj1ldtl4LwErMmYrTUcvzsQvEcOQt9N8Lw/hc1PZdxx+djgGHecG2PUcdlV9fHubiN2BR6oThcrfOXgEP/50CDfOzGO52t29KR4yXXrecn169ncmbzErmly4yWGTk4z9GiW4ZPTjJ2dqT3EynTGQruTwPaksz+F2eRZvlprKmfPUtwfZHyXDhygdPAgfqEAgIrHie3eTXzvHmJ79xHbu4fIpk2oFXxgv9I8LrsUM7RLyUQwUhGMhIWRsMO1hRG3a2VmWKbsq+dBg7D0iNC9NKyqeO0UYGg/U+ce5sDoIPvzZQ6oVg6ktnE8sRFPWRi+T2Z6itKYwS4iPCeZYKsZJT4+w9S5HNlJTUwpUqYiqTStfpGU8knYEexIAh+fUcqcUXnORac4bxcYMxwmtWLaj5B142TdFHl3/vwQaTtHW2yK1sh0aJWSJ2WVyMQ8Olrj9Pf20rduF+va99KT2oRlyNvGgiCsfUToXoRVFYRXknIOjvw3HPhMIH7bSXjab8GTfxUilzbwvVh8rblnIsdHzo1x93gWQ8ELO1t4Q18nT21d+qy2x4PWmjMTBR6sZn2fneLAYBbHDSYj7ExFuHFjGzdtCpa9fS3EZCAmCCtOo+XLQtnpFy5zHY/KvEz0+WJ7xfH41fffJgPnJWBNx2zfg4c+Cd/4P5AbhB23w3PfBd3XUvZ9Pj88xQfPjnAoX6LTDny8X9e3tD7e9YzNlPny/vPc9dAgPzo1CcB1/S285Lr1vPi6daxvvbzJq13HY/RMLhC+w8zv/FQZANM26N6YbvD7TrU1/9wY2vNwTp2idCDI+C7t30/p8GF0OeiXkU4T27MnFL/3Etu7F7uvr6m+xzQDj2mXknPwiy5+oQLuhcciyjYw4tasKB63MJLhuiqS12+Ha7FSEUCE7qVi1cfrcg7OP0zx3IM8eP4U9xQM7m29kYfTOwGIOiUYczAmPG6Ix3jmxnae0Jamy1NMDeYZe3SSsYEZisXA4TtpQKsu0lbJkVYuCdMkGktjxmY9vH000zGHsxmfM2qKM/4kI16JSVeT9SJk3QRZN0XRmx9/M5Es7bHJUAzPkbJmSFkl0tEKbRmbdV1ddHXvoLdjD+syO0hHM8v1kxQEQbgiiNC9CKs+CK8EI4/A3X8CR74IqV541u/DE167pD7eczldLPPRc+N84vw4k67HjkSM1/d18JredtJWcwrGFc/nyFCOB89O8eMzk/z49CSnxoOMr4hpsLcvUxO+b9zURnd6aT1YBUFoHmTgvDRcFTG7UoQf/CN8572BfdgTXgvPegdk1qG15juTM3xwYJSvj2eJGopX97Tx5g3dS+7jXc/gVJEvPhyI3vvPTQNwy+Z2XnL9Om7ft47O1OMTo2cmSzWrk6GTWUbP5PDCB8WptmhN+O7ZnKFzQ4rIKpjsULsu5RMnAvG7mv195AhUgre/zJYWort2Edu9m9iuXcR27yKyefNVZXvyeNAVD6/g4hcC4bu2Ltbvzy9byEKliooYdRni9aL4QmWzIroI5GsLiddLw5qL104BTt7L6NFv8M3Rce5J7uKetluYiLSitCaSLeKOucSnHJ7UlubWazq4dWsH2zJxpgYLjA3MMHYux9iZLJPDRXQQ4rBx6XSnaC2O06rLpEyDaKodI70OZcy+yaziJnZ3ErMrxkTC45Q3w4ncGKemhxieyTPlQNaLkPPiTFfm26QofDLRHO3RSdpiU7RYWdJWjqRZIB11aE1bdLS10daxjZ7O3axr3U1nogdDyd83QRCaExG6F2HNBeHl5PT34ev/G87+D3RsD7xFr/0JuIIZSkXP5wsjk3zk3BgP5YokTYPX9Lbz+r4Ork1eXnbZcjI2U+bHpye5/8wk95+a5OFz07Ws7w3tcW7e1M6Nm9q4aWMbO3vTmOJLKQhrAhk4Lw1XVczOj8O33gM/+icwbXjKr8NTfxOigWf2sXyp5uNd9DXPbk/zlg1dPLMtfUUzhR8dy/NfDw1y10ODHBuZwTQUT9/eyS89/Rpu3bo0/qGe6zM2MFMTvocfnSY7VgoOKmjrTdK9MU3XpjTdG9N0bkhjR5tfIPYdh/LRY5QO7Kd08CClQ4cpHz2KDsVvFY8T27GD6O5dgfi9azfRHdsxos2f1b4a0FqjHR+/WJkjkLtBWd5tFMqLs8dZZM4IFTXnZI3XZZIn5ojk8VnLFWXKd7xmROL10rCm47XvwcB9+Ee+yMOnD3GP0cs97bdwX2YvvjKwXRdvzMEYK5POuTxlfSu3bu3gydd0sHtdBu1pJobyjA/MBAL4wAzjAzlK+WCeKlu5tFUGaR89Tlt5ipRpYGV6sTo3YyS7gdmYoCIGVlcCuzuB1Z3A7o6jOqKM+iWOnhvm2JkhTo0MMTSTY7KsyXo2WS+wSan4jZMpm8qlLTZFe2yK9tgkLdY0aXOGhJUnZZdpSSrSLW1kWq+hs/Na1rXupjezmagpMUoQhOVHhO5FWNNBeDnQOpi08u4/hrGjwWSVz/sT2PjkK37rH2fzfOTcGHeNTFH2Nbe2pnhDXycv7GzBXiUCcdn1ODiYDcTv05Pcd3qS0VzwqnMqavGEja01y5MbNraSidmPcUVBEJoRGTgvDVdlzJ54FL7xp8Ek0YnO4C2qm14fiN/AuOPyr6GP94jjsjMZ4y39Xbyy58r4eFfRWnNkOMddDw7y6fsHGM2V2dfXwlueeQ2371235A9qC1mHkdNBtvfI6Rwjp7MUph0geL7etq5O/N6UoaM/hR1pfvFbVyqUT56kdOgwpcOHKB86TOmRR/BnZoIKpkl069Za1nd0VyCCm+mLnyRUeHxordFlb06G+EKZ5PNF8kXngLAMjKgZCOWRYB1sG6ioFWSYRy1U1JhzPNyOmLPnR02wDLHDWQIkXi8NV1W8HjsOR77E1LF7+Hbe5962J/KNjqdwPtIBQLTo4g4VMcbKtJd9nry5nVu3dgYZ392BHafWmunRIuePTzN0YorzJ6aZHAreBDYMTVukQGvxDKmzD5AZOkYsnsFIr8datx2rYxNEO8CrGyeaCqsz3iCAW10J7K4EyjaoVCoMjExwdGCUY2dHOD06ylAux0TZJ+sGYnjOTeDTGEejZpn22GRtabWnSZmBVUrcKpNO+MST7SQzG2nr3El3y056M9tpi7XL3ydBEJYUEboX4aoKwlcSz4UH/w3u+QuYGYKdL4Ln/G/ovvaK33rccfn38+N8dHCMgVKF3ojNz68PJq/sia4uYVhrzcBkkfvrhO8jQ1l8HQzkd/aka3YnN21qY2N7Qr40CMIqQAbOS8NVHbPP3Q9f/SM4/R1o3xq8RbXrpbW3qMq+zxdGAh/vgzMlOmyL1/d18JYN3WSusMVXqeJx5wPn+OA3T3BqvMDmjgS/9IxreNWN/Vd0Por8VJmRMzlGT2cZCQXwYjYUvw1F+7oEXRsD4btrY5rO/hTWahC/fZ/KwEAofgcCeOnwYbzRsVode8OGmvgd2xUI4HZ39wq2WpiL9qsCeVX8niOOOz667AZZ5mUP7XjBOlx8J1gvlk3egEEgfi8mikdmhfELiea18oh5VWadX63xWin1GuBdwC7gFq31fXXH3gG8CfCA39Raf+WxrnfVxuv8GBz9CvrIlzgyeJx7MtdxT8et/KBlH46ysLQmMlXBGSpgjJXoMixu3drBU67p4Jk7u+irm/+iOOMwdGKa88enOX9iipHTOfzQfqklAx32NC3Zk6RO/gjrxEMoK4qR6sVat53Ihl0Yrf1gZtAlc/ahmwKrI47Vk8DuSWD3JrF7ElidcdSch+PFUpmTg2OcOD/OycFxTo9MMDSTYzwUw3NejLw3f/LMlD1TE8I74pO0RSdJGzkSxgxJq0AsqjFjbUST68m0b6WzfSc9mR30pjZIdrggCBeNCN2LcNUG4SuFkw+8Rb/7d4G36A0/B8/+/yCz/orf2tOau8ezfOTcGPdM5LAUvKirlTf0dfLkluSqFYRzpQoPnZ0OxO8zkzxwepJcOXi1rTMV5aZNrTXhe896meRSEJqRq3XgvNRc9TFbazj6lcA2bPQR6L8leItq01Pqqmi+OzXDB8+O8rXxLN0Riz/Z1sfLuluveBz0fM1XDg7xgW+e4OGBaTpTUd7w1M38/JM30RK/8g+etdbkpxxGz2QZOZ0Ls7+zFHOhNYihaF8fZH53b0rTtTFDR38Sa5XETXd0NBC+awL4YSpnztSOm52doeXJrABub9iAMsRjdbWitQZPzwrg9WJ4dfsCAvmF6mrHv/gGXGzWuW0EwngkWBv1+/b8ckzVtN/Lr9Z4rZTaBfjAB4G3VYVupdRu4BPALcB64OvADq21t9j1rvp4DcGcGye/CUe+SP7YPXzfXs897U/mnu5ncNLuBCDpatRoifL5AsZEmRvWt/Civb28aN86NrQ3isiu4zFyOsf5MON76MQ05UIwJoynbbo7Ne1M0DLxCNFj91E5djSYF8KwMDs3Ed12A9b6HRjp9eAn8LLerABuKuyuOFZPErs3gd0TCOBmWwx1gTe0tNZkZwocHxzj5NAkJ89PcHYsy3BuJhDDPYusG8fRkYbzDOXRFp2qCeGBKD5FxpomqXLEjTymZeDbLVjxbpKtm2hv30F3Zic96Wtol+xwQRAQoXtRJAhfIfLj8O2/gh9+GAwLnvwr8NTfgnjrstz+ZKHMR8+N8cmhCaZdj13JGK/v6+TVPW0km3TyyovF8zXHRnK1rO/6SS4tQ3HtujTX97cGy4ZWtnWnxOtbEFaYq3XgvNRIzA7xXHjw43DPnwdvUV37E8FbVF07Gqo9lCvw9iNneThX5Nntaf5iRz+b41c+W0przfdPjPOP3zzBt4+NkYpa/OyTNvLGp26ht2V5J17WWjMzWa6J3lXrk9JMIH4bhqK9r2p7EmR+t/UmVsWElwBeLkf5kUcaBPDyiRPgBuKHikSwN24gsmkzkU2biGzcSGTzJiKbNmH19IgIfhWifY2uhKL4BUXzINt8ybPOq6gg+7wqkBv1gvictVFXb75wPrtdq2MbwXKZQtjVHq+VUvfSKHS/A0Br/Rfh/leAd2mtv7/YdSRez8H3gzezjnwRjnyZU7ks97Tfwj29z+E7qd0UlIUNZCYrZI9PY0yUua6vhdv39fKivevY3Jmcd0ntB17f1Yzv88enyY0H81lYEYOeTWm62j3aKsOkhw/hHzlA6fBh/HweAJXKEN/3FCJbrsPs2AhGK960jzdVrt1D2UaY/V0ngPcmMNKRi/o/prVmZDLH0XNjPDo8xemRac6O5RjOFZlwvFpmuE9jLIqZJdpjEw1CeHtsktbIJCk1Q0Tl8bBwjTRGtINIah2Zti20t2yjO72dnlQ/KTslgrggrGFE6F4ECcJXmMlT8I0/g/2fgngbPOPt8MRfBGt5XksqeD53DgeTVx6YKZI2DX6yt53X93WyPbm8g+0ryWiuzI/PTPLQ2SkeGpji4YFpcqVgkJuImOzta+GGDa1c19/C9f2t9LfFJfALwjJytQ+clwqJ2XNw8vCD/wff+TuoFOCm18Ezfx/SPbUqntZ85NwY//fkeVyteeumHn51YzeRZRI4D5yb5oPfOskXHx7ENBSveEIfb37GVrZ1p5bl/gtRFb9HTmcZPZ0L7U9ylPKVWp1ES4TW7gStPYlwHae1J0GmM45pNbc47JfLlI8dp3T4EM6jp3BOn8Y5fYrKmbNox6nVU9EokY0bsDdtIrIxEL8jmzYR2bwJq7tbRHDhotGuj674gRAerrXj1cp0xQ9EcccPRHbHbzju15fXrf2wHt5lCOn2wuK5MVcYn1OevrXvqo7XCwjd/wD8QGv9b+H+PwNf1lp/ZoFz3wy8GWDjxo03nT59etnaveoYPwFHvgxHvkT57H38MLOHL/S/jLs6nkYWiwyK5FiZ8UcmMfIuu9dlePF167h9by/XdF04fs5MlhsyvsfO5tChBWZ7X4p1WzP0tlVonzmJ98gBSvv3Uzp8uBYbzNZWYtfdSHTHE7B6tqGinXhZTWUojz8zGyNV3GqwPqkJ4IlLf3vL9XzOjExx/Pw4p4anOTOeY2B8huGcw2TZY9qzKfqNWeEKTTqSoyM2QXt8ko463/CO2CQpM4vtFyl5Fo6O41kZjHgHsXQ/mdbNdKS20Z3ZRk9yPUl7/kMEQRCaHxG6F0EGzcvE+Yfg6++CE9+Alo1w2x/AvtfAMg2itNbcly3wL+HklRWteXpbMHnlCzpbMNeY6Ov7mkfH8zx0NhC9Hzw7xaHzWRw3eF21PRnh+v4WrutvrQngHSnxRBOEK4UI3UuDxOwLMDMK3/pLuO8OMKNw628ES3R2MHy+7PCHx87xX6PTbE9EefeODdzatnxi85nxAh/+9kk+dd9ZHM/nebt6+OVnbeXGjW3L1obF0FqTGy8xdnaGyeE8UyNFpocLTI0UatYnEIgF6Y5YnQAerFt64qQXecW7GdC+jzs0FArfZ8J1sFTOnEFX6voZixHZsIHI5k3YGzeGIvhmIps2igguLDvaC7PQFxLOHa9BPPcr/jyRfTExPVjP2rlsePcz1my8Vkp9Hehd4NA7tdZfCOvcS6PQ/X7g+3OE7i9prT+72L0kXl8C+XE4+t/w4Mcpnv0RX+16Bp++5rXcE9mEh6IPg8hQkcFDE6iKz7W9aV60bx0v2tfLtu7FJyZ2Si7Dj2Y5fzwUvx/N4pY9lIKeLRn6d7WzYXuGFmcI59BBivsfprT/AOXjx4MsdMBat4743r1E99yA3XctRrIXb9qjMlSgMpxHl2ZdbIx0pMH6xO5NYvUkMB7n/BiFssuJoQlOnp/g1GiWs+MznJssMDpTYaKsyboW7pyJMy3l0hqdpj06SWdinPb4rBDeHpuiLTqB8jyKjknJi+CoOL6dwYx3E89soCW9mfb0NXSnttKd7CFhydxYgtBMiNC9CBKEl5kT98DX/giGHoaeffC8d8HW59Qm01oORp0KHx8c52OD4wyWK+xIxPjtzT28tLsVYw0HL8f1OTKU46GBqZoAfnQkeMoP0N8WD+1OgqzvvX0tJKOr49VtQWh2ROheGiRmPwbjJ+DuP4ZDX4BkNzzr9+Gm14MxO/j7+niWdxwd4GzJ4ad62/mjrevpiCzf3/qxmTIf/d4pPvb900wXK9yypZ1feeZWnrWzq2kHkKV8hemRIlMjgfAdCOBFpoYLVMqzA3zTNmjpijdmgYdieCxlN23/ALTnzYrgZ87gnKoTwc+enS+C18TvjUFGeLhY3d1N3U9BWAjt6yAr3fGw0tGrOl6LdckKM/II3P8RePATjPoGd17zs3x63YvZr5NYwDZlwUCeRw+NoXzY0ZPi9r3reNG+dezoeWyrDs/1GX50mrOHJzlzaILR01m0Bjtm0rejjY2729mwq510SlN+5BGKD++ntH8/xQMHGuaEiFxzDfF9e4nu2Ud0226MTB/eRIXKUJ7KcIHKcAHc2QdIZnusMQO8NxlMgLlEb0hprZnIOzw6Ms3JoQlOj+Y4N5FncLrESM5hoqTJeSaaxp9PzCjRFs2GfuHjdCbG6IhP0R6bCKxSotMY+JQdk2LFDDLEVRzfSmPE2omkekml+snEN9CR2kJXciOdiU6ZVFMQlgERuhdBgvAK4Ptw8HNw95/A1GnY8kx43h/D+icsazNcX/Nfo1P8zalhjhZK7EzG+J3NvfxEV8uaFrzryZddDpybDsTvgWkeOjvFwGQRAEPB9u50YHeyIfD83tmbJtLkr2wLQjMiQvfSIDH7Ijn7I/jaH8KZ78Omp8IrPwQt/bXDBc/nvaeG+MezI2Qskz/cup6f7l3eyZ3yZZdP/PAM//ydRzk/XeLa3jRveeY1/MR167HN1RFntNYUsg5Tw4VgCcXv6ZEC06NF/Dq7hWjCmhXBQwuURCZCIhMhno4QT9lNmw2uPY/K+aHQ/qRRBHcGBoLJzkJUPB6I4HVe4JFNm7A3bsLqbt6HGYJQ5WqP1wsI3XuAf2d2Msq7ge0yGeUVxikE4+X77oBz93M4cy2f2f2rfDZxHUOeIm0a7NYWpVM5jjwyBhq2diXDTO91XNubvqi/t6V8hXNHJjlzeIKBwxNkxwKP73R7jA272tiwu4P+a9uIJW3cyUlKBw5SOrCf4v4DFPc/jDc6FlzIsojt2EFs376aAG519OGOlELhO09lqIA7VgimPAUwFFZXvM76JLA/WWwCzMeD6/kM58qcHZvh5PAkp0dzDEzkOT9dYmTGYbykKXqN3z8UPimrQKs9Q2t1As3EOJ3J0VAQn6QlksU0gk55nqLoGBQrJmUiVFQCP9KClegkllxHKr6etsQm2lNb6E720xZrwzIksUwQLgcRuhdBgvAK4pbhvo8Er1sXxmHvq+C2P4T2LcvaDE9r/nNkir8+NcSxQpldoeD9oqtI8K5nfKZcszt5OBTAJ/KBb1vEMti9LsOudWl29qTZ0Zvm2t4M7cnIY1xVEK5urvaB81IhMfsS0Boe+iR86W3BpNAv+wfY9ZKGKodnivze0QF+OJ3nyS1J3r1zAzuXef4Kx/W566FBPvjNExwbmaGvNc4vPX0LP/nEDSSWMdN8qfE9n9xEaVb8Dm1QpoaL5CZLMOertDIU8ZRNPBMhmYkQz0RIpCMkWgIhvCqKJzIRYsnmEcUDEfx8IH6fOU3l9OlZIXxgoDYpJoBKJGZF8NALPBDBN2J1iQguNAdXa7xWSr0CeB/QBUwBD2qtXxAeeyfwRsAF3qq1/vJjXU/i9RIy+GCQ5f3wp/EqRb6z9dV8esvP8EWvg6Kv2RC1udYzyR2b5uFjY/gatnQmedG+Xm7fu4496zMX/fd1erTA2UMTnD08ycAjEzglDxR0b0yzYVc7G3a303tNC6ZloLXGHR6muH8/pYf3Uzywn9KBg/i5HBD8zY/v3k3suuuIX7eP2N59WD29eOOlIPM7tD6pDBfwJkq1NjRMgFnNAr+ECTAfDwXHZXCqxLnJAqdHs5wamebcZIHz2RKjMxXGiz6O39gGhSZpFslYeVoi2TBDfILO+BidyTG6UiO0xqaxDL/hvIqrKDgGRc+iTAzPSqAjGax4B9FoJ4loN5nYeloSfbQnN9KR6CETufjfpSCsdUToXgQJwk1AKQvf+3v4/vvBq8DNb4Rn/i4kO5e1GZ7WfGFkir85NcTxQpndyRhv29LL7Z0tV3VA0VozMFmsTXL58MAUR4ZyTBZmM7i60lF29qTZ2RsuPWl29KSJP04/NkFYK1ytA+cLoZR6D/ASwAFOAG/QWk891nkSsy+D8RPwmTfC+Qfh5jfBC/4M7HjtsK81nzw/wZ+eGGTG8/nVjd28dVMP8WXOqvZ9zTceGeED3zzBfacnaUvYvO7WzbzuKZtpW2MPU13HIzdRophzKGQrFLIOhWyZYtahkKtQmC5TyDkUsxU81593flUUT7QEYni8TgSPh+J4IhTHo0kbY4VEce26DSJ4zQrl9Jl5IriRSISTYm6cnRRzU7BtdnZe1d/DhOVF4vXSIPH6ClDKwv5PwY/ugJGDzMS6+eINb+XTbU/juwWNBm5MxdnqKEaOTHLf8XE8X7OpIxHam/Syr+/ix7W+5zNyOsfZwxOcPTTB0KNZtK+xoiZ921sD4XtXO23rZr2rte/jnD4d2J3sP0Dp4YcbJ7tsbye+bx+x6/YRv+46Ynv3YrW14Zc93JHCrPXJUJ7KcB4/t8gEmOH6cibAvFy01mRLLkPTJQani5yfKjIwPsPZ8RnOTxcZzpYZzbuU5r3voEmoMhmrQMbO02LnaItO0R4PBPGuxCidqRFisfIF7+24imLFoKQtHCJ4ZgIdSWNGWolGOolHu0jF15GJrac9uYmOZD+tsVYMtTrekhOES0WE7kWQINxE5Ibg3v8LP/4Y2Al41u/Bk3+1wVt0OfC05s7hSf7m1DAni2X2puK8bXMvL+iUJ6hVtNaM5so8MpTj6HCOR4ZyHBnKcWwkRymc0Ecp2NSeYEdPmmt70+zszbCzN83mjgTWKnktXRCWChk4N6KUej7wDa21q5R6N4DW+vce6zyJ2ZeJ68A3/gS+9z7o3g2vvgO6dzVUGXNc/uTEOT41NMmmWIS/2NHPbR2ZFWnuj05N8IF7T3D3IyPEbZOfeuIGfvHpW+hvS6xIe1YKrTVO0aWQdSjmHPLTTiiOB0sxO7tdyDn47sLfz+2YSTRhEY3bRBMWkbgV7ltEwnX1eCQxeyyasIjErCuSPa5dl8rg4JyJMU8FIvi5c40ieDKJvWkjkb4+rK4urK4uzM7OYLuzC6urE6ujA2Wt3jcAhOZB4vXSIPH6CqI1DPwosDU58DnwygxsfgGfu/YX+TT9HCs6RA3Fs1tSbChoTh4e5wcnAtF7Q3ucl13fx6tu6mdLZ/KSbusUXc4dnQwyvh+ZZGq4AECyNRranLSz4dp24unGh9PacSgdPUZp/8MUH95Pcf/DOCdOUp0oyt64kfi+fUHW977riO3ehREL3i7z8hXc4Trxe8EJMO0g+7s7EWaCJ7C7l1cAb+iv1uTKLuenSpyfLoaieInByQIDE3mGposM5xyKC8TsuKqQUGWSZomUWSBjz9ASmaElOl0Txltj48TsEnakjB0pY5jzH4hD4BZbdBUlPxDGK2YcbacwrRbsSDuxSCep2DoyifW0xjfQkdpEe7wbc5m1F0G4XEToXgQJwk3I6NHAW/Tofwfeoi//R2jbtOzNcH3N50Ymee+pIR4tOuxLxXnbll6e3yGC94XwfM2ZiQJHhrIcGZrhyHCWR4ZynBrL44d/NiKWwbauFNf2BtYnO3sDIbw3E5Ofq7BmkYHzhQlfl3611vrnHquuxOzHyfGvw52/DOUcvPAv4KY3zJsM+ruTOX7v6ADHC2Ve2t3Kn27roye6MoPFI0M5PvitE9z14CAaeOUT+vit526/6gTvi6FeFK9fygUXp+BSLlQoF13KBZdyMSwrujhFd/ELK4jEFhLFw/2EPSuKV4/Vtm0iUfOShXJdqSwggp+mMjiIOzaGPz29QDsVZltbKH6HInhXZ8N+II53YyQT8n1DuCASr5cGidfLRGECHvz3QPSeOIGOt/HQE36dT/fezp1Zn4mKR6dtcXt7ht6sxwMHR/je8cDe5KZNbbz6pn5efN06MrFLj/PZ8SIDhyeDjO9HJijng3jSuSHFhl3tbNrbQe/WFswFEpy8mRlKBw5S3P9wYHuyfz/u0FBw0DSJ7txBfF9oebJvH9GtW1FmIMBqrfGyDpWhPG448WVlpIA7XEA79QJ4JPT/DgXw7sAOxYg3x0PRbKnC0HSJ89Mlzk8VA6/wXJmRbCkQw7MlJgqV2hi6ikKTMDySRoW4doirEkkzyBTP2Dlaollao9O0xKaJ2kVsq4htlbDtQBi3IpW5X/1qOJ6i6BmUsQNh3ExhWBksO8gaT0R7SMd7aYn305naTEdyA7a5tt66E1YPInQvggThJkXrIGh/OUzwe9F74PqfnjcgXw5cX/OZ4Qnee2qY0yWH69JBhvfzRPC+aEoVj+MjMxwZynFkOMj+PjKUYyg768eWiVkN1ic7ezPs7EnTskJP4wVhKZGB84VRSv0n8B9a63+7wPE3A28G2Lhx402nT59ezuatPXLDcOdb4OQ9sOul8NK/h3hbQ5Wy7/P+MyP83elhIkrxjmvW8bq+TswVinnnpor807dP8vH/OYPWmp970iZ+7dnb6EpHV6Q9awnf11RKoQDeIIJX5oni5UIgjJcLs8cq89/PbkQRiOJ1Ingtc3yuQF7bt2t17ag577uW7zh4o6O4Y2O4tXX9drgeG2uYKLPWpHh8VgxfTBTv6KgJK8LVg8TrpUHG2MuM1vDotwLB+5H/At/F2fJs7tn3K3zK2sbXJmZwtGZnMsbL2zKogQL/9cA5jo/MELUMXrCnl1fd1M/TtnViXsZbPL6vGTs7a3Ny/sQ0vqeJxEw27Gpn494ONu3pINl64bhdGR4JJ7rcXxO/G/y+9+wJJru8bh/xffuw1q9viA/a13jTZSrDhVAAz88K4JXZrGcjE6llfds9yVoWuBFrDgG8HtfzGc87DGdLDGfLDGdLjFS3c8F6JFtiPJxPqx5DQdrSpEyXBA4xXSamS0T8EikrzBaPZslEckQiZWy7hGUViVilUBgvYUUc7IiDaS0c631NKIxbOCqGbyZQZhrLasO2W4hG2klEOknGusjE19ESX0dboo+YfXVbwwpLw1UjdCulXgO8C9gF3FKdLXoxJAg3OZOn4M5fgTPfCwbkP/G3kOxYkaZU6gTvMyWHG9IJ3rall+e0X9ys1sJ8pgoOR4dnODKUbbBByZVmM8x6M7Fa1vfO3sD7e1t3ipgtg09h9XA1DpyVUl8Hehc49E6t9RfCOu8EbgZeqS/iy4XE7CXC9+H774O7/wRSvfCqf4JNT5lX7WShzO8fPcu3Jme4IZ3gPTv72ZdeuWzqwaki7/vGMT513wAR0+CNT9vMm5++VR6IriC+5+OUvEYRvDgrnDu1LPLKAoK5S6W8uFCuFHWi+KwAHk3YxJL1ZcE6lqwTySMGfi6LOzqKFwrf7ujogqK4n83Ov7lhYLa3NwrinZ3B0t1YZiQvzQJAaF6uxnh9JZB4vYLkhuGBf4X7PwrTZyDZzeSNb+Suja/iE9PwYK5A0jR4TU8bt1pRfrR/hLseGmS6WKEnE+UVT+jn1Tf1sa07fdlNcEouA49McvrAOGcOjjMzGXhPd25IsWlPB5v2dtCzJYOxiJ1lg993aHlSPnQYHT7ANDs6iO/dS2zvXmL79hLfuxerc/4cX9rXeFPlUPQObVCGC7gjjQK4mYk0TIJZzQJvRgF8Lo7rMzZTrgniI7lSgzg+mgvW9XNsVbEMaI0aZGyfpKqQoEzULxHxikS8EhFdJmkWSdh5InYZO1LCtgJhvJYtbhXrMsbL2ItkjQN4Gkq+iYOFq6JoIw5GAsNKYVktRO1WYpFOErFOUtFuMvF1tCbWk4p2Y5px0V4E4OoSuncBPvBB4G0idK8RfC/wFf3G/4FEO7zs/bD9eSvWnIqv+fTQBO89PczZksONmQRv29zLs0XwXhK01pyfLjVkfh8ZynF8ZAbHC76MmIZic0cizP7O1DLBN7YnLisLQRCuNDJwno9S6nXALwPP0VoXLuYcidlLzLn74TNvgqnT8Mzfh2e8bd68GFprPj8yxR8dP8e44/KL/V387pZeUtbKPWx8dCzPe792lLseGiQTs3jLM7fyhqduJhFp/sGo0Ijn+TXR23kMgbyUr8smD9e+d+ExiVLUssVnBfCFhXHb1ljlGazCFMbMBMbUGN7YHFG8miXuzrd7MRIJzK563/A52eLVTPG2NskSb3IkXi8NEq+bAN+D43cHWd7HvhJkfW9/Hg/c+JvcoTfyhZEpHK15WmuKX1jXjhop8fkfn+Peo6N4vub6Da28+sY+XnL9eloTl29PobVmYjDP6QPjnD4wzvkT02hfE01YbNgdWJxs3N1BIvPY99COQ+nI0ZrlSengAcrHT9T8vq3eXmJ79wQC+J69xPbuwWprW/havsabLDVYn1RGFhDAWyJYoQd4zQe8K75iHuCPh1LFYzRXFcLrhPFsYJsS7JfIlubHuYip6EjatMUUGdMnjkNcF4m4RSJuHtsrYLtF8F1QGtOsYFlOsNhlLMvBthwss4hllLGtMpZZxrIdTNvBsh0s28Wy3UVFcldDWVtUsPGMGBhxlJHCtNLYdivRSBvxSAeJaBfpWA8t8XW0xNdj262YprwJuJa4aoTuKkqpexGhe+0xtB8+92YYOQQ3vwme/6cQWbkMGsf3+dRQ4OF9rlzhpkyCt2/p5ZltInhfCVzP59R4Psj8HgonwBzOcWaiUP1uQ8w22NGTrpsAM7BB6UpH5XcirCgycG5EKfVC4G+AZ2qtRy/2PInZV4BSFr74O7D/U8G8GK/8MLT0zas2XXH585Pn+djgOOuiNv9nex+3d67sq6eHBrP89VePcPcjI3SmovzGbdv46Vs2EF1BEV5YPrTWuI5PuVBZUAQvF1xK+fll5UKFct7Fn2t8Wocy1KwfecIimrSJhTYrEdPD9kpYlTxmKYtVmMLMjWNMjmBMDKFHz+ONjdVeuW/ANLHa2wNrlLZWzNZWzNa2cN2K2Va3H26Lp/jyIvF6aZB43WRMnYUffwzu/xfIj8D6JzB26+/w76mb+ejgBOfKFfqiNq/v6+T56STfPjjCZ+4f4JGhHBHT4Lm7u3n1Tf08Y3sX1iJZ2BdDuVDh7OFJTh8c58yBcQrZwHaje1M6sDjZ20H3pgzGRSYv+fk8pcOHKR44QOnAQUoHDuCcOlU7bvf3E9u7l/jePUH29+7dmJkLT7bdIIAPh1ngo8V5AnjNA7w7gVUVwbvjmKnV71lddLw5Yvisf3jVNmUkW2amPF8Qj9smXakIbQmLlqhB2tYkDJeo7xD1CliVApabx3QK6EqRiutS8Tw8rQEFhCK5XQ6EccsJBHGzhGUGtipVgbwqkpt2Bdt2se3F3xJztQqyyIngG3G0kcAwU1hWhkiklZjdQSLaSSrWQybWSybeSzTSjmVlMIyIxOImQ4TuRZAgvMqolOAbfwrffz+0XwOv/BD0r+x3Ucf3+eT5Cf7u9DDnyhWemEnyti29PKMtJX8Ml4GC43JsOPD/rrc/GZsp1+q0JezQ/iTDjp50LQM8FZXsP2F5kIFzI0qp40AUGA+LfqC1/uXHOk9i9hXkoU/Cf/02WJHgzalrX7xgtfun87z9yFkO5Us8ryPDn+/oZ0NsZQd195+e4C//+wj/8+gEfa1x3vrc7bzyxn55w0e4IFprKmWvQfguF1xKte1GgbxUcCnnZ73J9SIiuWGo0G7FJBoB23CxtYPtFbGcPFYpi5mfwJqZwJoewZoawpw4j+nN91gFwLYxW1uw5orira2YbXNF8nBpaUEZj0+MulqReL00SLxuUiolePiT8N2/g4mT0LEN99bf4qt9t3PH+Wm+MzVD1FC8rLuVN6zvxJ5x+eyPB/jCg4NM5B06U1Fe8YT1vOqmfq7tvbBYfLFoXzM2MFPL9h5+dBqtIZa02bgnyPbesLud+CWKx14uR+lgIHoXQ/G7MjBQOx7ZtKnB8iS2a9djWlA1WqAE2d8LToKZtGrCdy0DvDuJkbbXnDYwU3Zr4nfVLmUkW2Y4V2YsV2Y8X2ZsxmGy4LCQhGibio5klM50hI5khLa4RTqiyNgQNz1ibhHTmcGs5FGlHE6pQLFYouw4OK5LxfPxG67oY1l1meRWCdsoYZvFUCwPM8gj5VAgn80ij1j+/AbW4WlFBTMQyVUUbcRQRhzDSmJZaWwrQ8RuIR5pI253kIx1kIp2kYx2YllpLCuNaSbX3GdgJVlTQvdFen7eyyJCt0xstQZ49Nvw+V+B7CA84+3B69bmyr4+VPZ9PnF+gr8/PcxgucKTWpK8bXMvTxPBe0UYnyk32p8MB5ng+bovIn2tcbZ1p7imK8k1nUmu6UqxpTNJbyZ20VkEgnAxyMB5aZCB8xVm/AR85g1w/iF44i8Fb07Z8XnVXF/z4YFR/vLRIQDevqWXt2zoWrHJKiEQLr99bIz3fOUI+89Ns607xe88bwcv3NsrMVhYUrTWVEpeIIrXC+BVobyaOZ6vNArlhcCG5ULDKMs2iCUMYhFN1PKIqjIRv0TEncEuZ7ELE1i5cazsMMb4EEwubKMCgFKYLS0XFsXnCONWW1sgjkdWfybi40Xi9dIg8brJ8T04fBd8+29g6GFIr4On/BqP7PpZPjJS4NPDkxQ8nxszCd7U18nz2zN8/9gYn7l/gG88MoLra/b2ZXjVjf287IY+2pNL87ejlK9w9tBE4O19aJxirgIKejZn2BRme3dtSKMuY5zmTk5SOngomPAyzP52h4LvMShFZOs1xPcEnt/xfXuJXnstRiz2mNfVWuNNO7gjs97flXBb11mAqJjVmAHeE6zNlrWfKex6PhMFh/EZh7GZcm09VtsPtqvrql3pXNoSNh2pKJ2pCB2pKF2pKG0Jm5aIImVrEoZHxC9hlrJUCjlmslkKhTzFQpFiuUTZqVBxXVxfM/8O/qxAbpawjSK2WcI2S5iWEy4VTNvBsCoYdgXTqmBZHpblYVs+j/Wx9DVUsPCUja8iaBUDM4FpJjHNNLaVJmK3EAvtV1KxTpKRTuLRdmwrEwrmKZSSNxdhjQndF4NkdF8llKbhS78bPJVef2OQ3d25faVbRdn3+fjgOO87M8L5coUntwQZ3k9ru/wJPYSlwfc156aKNeH7kaEcJ0dneHQsT6FOAI/bJps7k3UCeJItnYEI3hJffX5swsojA+elQWL2MuCWg0kqv/8P0L0HXn0HdF+7YNWBksM7jw3wlbEsT21N8Q+7N7IuurJCmdaa/z4wxF999QgnRvPs62vh7S/YydO3d675gaTQ/Ghf45Q9SjMVijMOpVyFQs6hNBOsi7n5Zb678LjLjpnEkxaxmCJm+0TNChHKRLwCkUoOuziNVQiyxs2pEfTUBN7UFLpYvGD7jGTyMUXxmjAebqv42poYTOL10iDxepWgNZy8B77zXnj0WxBrgVveTPamN/OpnOKOgTFOFst02havXd/Ba9d3EHU1dz00yGd/PMCBc1lsU/HsnYG1ybOv7cZ+nNYmtab5mpEzuVq298jpLGiIp2027elg494ONuxqJ5a8/LGZOzpK8eDBwPJkfyCAe+Phy4amSXT79sDze88eYnv2EN25EyN6cT7PWmv8XIXKSD7IAB8t1oRwPz87KaSKmgtkgCcwW6OXJeivdrTW5MpumBHuMJYrMxaux/NlxnJOLVN8bKZMbgE/cQjG80GmeJTOUBzvTEXpCNetMZOU5ZMwPSzfIZ+dJjs5RS47TX4mRz6fp1gsUSqXqVQqVDzvAgJ5reWYpotplLFUaK9ilLCMMpYZWqxUhfI6sdywKlh2IJRHTJ+L+e9T0SausvGJBJnlZgJlJMLM8nqxvJ1EpIN0rJtYpK0mlJtmek1YsYjQvQgShNcAB++E//pfwatYz/9TeOIvsugMBstEyfP5+Plx3nd6hCGnwq2tKd6+pZentKZWumnCHLTWDGfLnByb4eRonkfH8jUB/OxkEa/uFeXOVIQtnUmu6UyxpU4I39ieJGLJa8LCwsjAeWmQmL2MHPsa3PnL4OThhX8BN71+wdiqteaTQxP8f0fPETcVf3vtRp7f2bL87Z2D52vufOAc7/3aUc5NFXnSlnZ+94U7uWlT+0o3TRAummr2eCCCVyiGYnhxprpdtw6F8wt5j0cTFvF0hFjCJBYlyBo3HKK6iO3miZRzgTA+M46ZHUVPTuJNTeFNTS3sNx6iotEFMsdb5gni9eK5kW7e+WwkXi8NEq9XIQP3w3ffC4f/C6wY3Pha/Cf/Gt+inTsGxvjaeBZDwYs6W3ljfydPbklyZDjHZ+8f4M4HBhmbKdOejPCyG9bz6pv62bN+ab8LFHMOZ+qyvcv5YNLC3mtaat7enf2P701qrTXu8HBoeXKgJoB709NBBcsium0bsT27ie0JBPDotddetPhdxZtxcEeKtckvgwzwPH6uTgC3DayueIP4bXUnsNrjKLM5/36uBKWKx0R+NlN8tC5jfLw+azzvMJF3Gsb1VUxD0Z6MNAriyQid6WDdnozQmgjWmahB1PBxymWKxSKlUomZbJZcdprCzAz5mRmKhQKlUolSNYu8Tih/LCVV4WCrUiCSq6o3eZBpbthVobyCYTuoqmBuuTWxPGr62BeR8O1rhVvLLo+CEQUjjhFmmFtmioidIWJniNltxCOtJKIdJCMdROxMKJgnsawUhrEyk3xeNUK3UuoVwPuALmAKeFBr/YLFzpEgvEbInocv/BqcuBu2PTfwF00v5HCz/BRDwfvvTw8z4rg8vS3F2zf3cosI3qsCx/U5M1GoCd81IXxshrGZWT9NQ8GG9gTXdAbZ3/V2KD0ZmQzzakcGzkuDxOxlJjcMd74lyPba/TJ4yd9BvG3BqscLJX7l4Gn2zxR5Y18nf7R1PbElyup6PJRdj//40Vn+/u7jjM2Uue3abt72/J3sXv/4vUUFodnQWlMuuIuK4fVlpZnKwlYqCmIJm3jaJp6OEE9aRCOaqFkhqhwiXh67MoNdmsbOT2Bmx/GnA1Hcqwrk09PgXyD3zbJmrVXqLVTm+Y7Xbbe0oMwr/7q2xOulQeL1Kmb0aODh/fB/gPZh36vhqW/ldGYr/3JujE+cn2DK9diVjPHG/k5e2dNGFMW3jo3y2fvP8bVDwziez651GV59Uz8vv2E9HamlFcJ8XzP8aJYzB4Ns79EzwcO4REskyPbeE3h7R+OPfz4mrTWVc4OB53fd4k1NBRWqmd/14vfOnRdlezKvX4VKMPFlvQf4SAFvanbuKUyF3RWvmwAztEHpiKMk6WpRfF8zVayEtin1dinz7VTGZsqUKgvHMENBS9ymLRGhNVFdR2hP2rQmIrQlIrQlwu3kbL2oZVKpVAIRPFyKxSKFfJ6Z7DQzuRyFfJ5ioUCxWKRcLlN2nNqEne4i84PU8FwMv9IolBtlrAaRPFiUVUHZFQyzug4zyy0f29RELzLkeqFo7tdE81gomscxzRSWlcK20kTtFmJ2K/FIO/FIG6loBxG7ZY5ofvFvhl41QvflIEF4DaE1/Oif4Kt/CHYsGJDvftlKt6pGwfP518Ex3nd6hLGKy7Pa0rx9Sy83tSw+8YXQvEwXKzw6lufRMBP8ZE0In2kIjImIyZbOZJAJ3pWqs0NJko6JFcrVgAyclwaJ2SuA78P33xfYmaTXwav+GTY+acGqZd/nz0+c54MDo+xKxvjAns3sTF76QO9KUHBc/uV7p/jAvSfIllxecv16fvt5O9jSKTFYuHrxfU25UKGYrYrgdVnjubllFUqFCguloxmGIp6JkGyJkGiJkmiJkEjbxCM+sTBrPOLmiJSmITtHEJ+zrSuV+TcAUAojkwkn5ryAGL6A5Ypxib7jEq+XBonXa4DpAfj+/4P7/wUqedjxQnja/6LQdwufH57kn8+NcnCmRMYy+JneDl7f18mWRJSpgsN/Pnyez9x3locGprEMxW3XLr21ST356TJnDk5w5uA4Zw5N4BRdDEPRu7WFTXsD4bujb+kmAtRa4w4OBrYnBw9ROnBgvvi9bRuxPXuI7dk9m/l9GeI3gF92axng9ZNhepOl2b/JBlgd8zPA7a446mJSfIV55MsuYzNlJgsVJvPBpJqThQpThTnb+Uq471xQHAdIRsw54vesIN6esGlLzpZVxfFU1Kp9brXWlMvlBqG8VCoFQnkuRz6Xo1DIU8jnKZcC25Wy41CpBEK5dzE6ru+hfA/leeC7mDjYZhnTKGMaFSw7WAzbxYgEgjlhhjmWi7IcDMvDtDxsU2ObPpHLEM09FcFXEahO+lnNNLdSRMKJP2/d80cidF8ICcJrkLFj8Lk3w+CP4fqfgdvfHfiNNQl5z+Oj58b5hzPDTFQ8bmtP8/Yt63hCJrHSTROWCN/XDGVLNQuUk3WZ4AOTBeofxnalo6EVyqwXeGCFkrgiXwSFlUEGzkuDxOwVZOB++OwbYeosPOsd8PTfBmPhb613j2f5zcNnKHgef7q9n59b1940b7VMFyt8+Fsn+efvPIrj+fzkzf38xm3bWd86f9JNQRAa8T2fUt5tEMMLOYdCNlymy+Sng3VxZmFRPJa0AyE8EyHZEiWRiZBoCbbjGZu47RPVBYxCFr8qhC8giLtT1e1pdKFwwTYbicTigvicSTkjfX0Sr5cAiddriMIE/PDD8D8fgOIEbHwKPO1/obc9jx9lC9xxboz/Gp3C03Bbe4Y39nfy7PY0hlIcDa1NPvfAOUZzZTqSEV52Qx+vubmfXeuuzJtVvucz9Gg2sDg5OM7Y2RkAkq3RYELLPR3072ojEnv82d71zBO/q5nfk5NBhSUWvwF8x8MdK9YmwqxmgLvjRWpm0grM9tisB3g1A7wrgXGx6qNw0ZQqXiB656uCeCiC5y8gkhcqTBcv8IAXsE1FS7w+W7xRJG9LRGhL1mWQJ2xa4jbWAjqC53k1oXzuulgsUJiZacwoL5UpO2Ucx8FxXSquh3/JYnmwVr6LZZQxlRN6lrtYURfTdjEiLioSZJUr2wGrgrJdlOUGvueWXxPMI6YmEnbtuc85KUL3hZAgvEbxKvCtv4JvvQcy6+EVH4DNT1vpVjWQdz3uODfG/zszwqTr8byODG/b0sv1aRG81zJl1+PMeKEh+7sqgo/nZ61QTEOxsT1RE8EDP/BABO9OixXKakOE7qVBYvYKU8oGc2Ic+AxsfnowCXRm/YJVR8oVfuPwGb45mePFXS389c4NtNpLO6h8PIzmyrz/nuP8+/+cAQWvffImfvVZW5f89WpBuFrxPZ9irkJ+ukxhOhDCq9v56XIojDvks+UFJ9y0IgaJlijJUAhPhKJ4NWs82RIhkYkST9noirOgGN4ojE/hTc6K5n42u2C7dx95ROL1EiDxeg3i5OHH/wrfex9kB4IJq5/2VtjzSoZdzccGx/jXwXFGHJct8Qhv6u/ip3vbSVkmrufzrWOjfOb+Ab52aJiKp9mzPrA2edkNfbQnr9xE1vmpMqcPjnPmwDhnD0/glDwMU7FuWwub9nSycW877euWLtu7Hq017vnzofg9K4B7ExNBBdMkunVrIH7v3UN8797L8vyed1/Xxx0LM8CHC7ij4XqsCN7s31uzNTo7EWZPorZtLIHli3DxuJ7PdLHSIH5PFpza9lQh8BevPz5VcKh4F9ZlMzGrliHeXieOt4ZCeHXJxBv3H2vOscXE8nLoW14IfcqLxQKlYpFSORTLKxUqrod3IVuzehYUy4MFv4JlOPz+331UhO4LIUF4jTNwX5DdPXESbv11uO0PwWquQeyM6/HPA2P849kRplyPF3ZmeNvmXvaK4H3VMVVw5vmAV7fL7mxASEbMmvC9JcwEr06OmYrKF5NmRITupUFidhOgNTz0Cfji28CKwMv+H1z7ogWr+lrzgbOj/PnJQXoiNv9v9yae1GTzUwxMFvi7rx/jsz8eIG6bvOnp1/Cmp22hJS62UoKwHFR9xauid2HambNdDkVyB6fozjtfGYpE2q5ZpgTCeHQ2Y7wlzBjPRDHt2QG8dl286el5gnjba14j8XoJkHi9hvEqsP8z8N2/hdFHoHUj3PqbcMPP4Vgxvjg6zT8PjHJftkDKNPjZdR28qb+TTfFgDD6Zd7jroUE+c/8A+89NY5uK51zbw6tv6ueZO7uu6ButnuczdGK65u09fi4PQKo9yqY9wYSWfTuXPtu7Hq017tBQMNnlQuK3ZRHdvp343j3E9uwltncvsR3bUZdow7TgvT0fd6LU6AE+XKAyWoS6saaRiTRmgId2KGZSvhs1C1pr8o7HZN5hqlBhoiqMzxHEA8G8wkQ+OJ53vEWvG7fNRYXwlrhFywXE8qh1cW8IPJZYXm/JUizkA7G8ll1ewalU8HyfP/7jPxah+0JIEL4KcPLw1T+A++4Injy/8kPQu3elWzWPnOvx4YFRPnh2lGnX48VdLbxtcy+7UvI69dWO72vOZ0sNE2KeDG1Rzk0VGyaS6k5H2VzNAq/5gifZ0J646OAjLD0idC8NErObiLHj8Jk3wNDDcMtb4Pn/JxC+F+CBbIFfOXSKM0WH397cy1s39WAZzfVWyvGRGd77taN8cf950jGLNzx1C2966hZaEjKoE4RmoeJ4tezwepuUqhBeyAZlxZyzoG1KNGFdIDN8drt9XeqqjNdKqfcALwEc4ATwBq31VHjsHcCbAA/4Ta31Vx7rehKvrwJ8H459Bb79NzDwQ0h0wpN/GZ74ixBv48fZPP80MMZdI5N4Gl7QmeEX+7t4amuqlj39yFCWz9w3wOcfPMfYjENnKsornrCeV9+0gZ296SvehdxEqSZ6DzwySaXsYViK9dtaA5uTvR209iSu+Ju0DZnfBw4Gnt8HDgST+gLKtonu3FnL+o7t3Ut061aUvTTfUbSv8SZLNeuTehsU7dQJ4Em7Zn1Sb4NipGx523iV4Lg+2VJgl1JdstXtQmP53OOPJZLHbGOOKL6QUD5/ycRtYpfoI+95HpZlidB9ISQIX0Uc/Sp84degNAW3/QE85dcv6C+6kkxXXD40MMqHzo6S83xe0tXK72zp4dqkCN7CfEoVj9PjhUAEH8/zaJgBPtcKxVDQ35ZoEL+r2+tb4hhNJjqtNUToXhokZjcZbhm+/sfwg/fDhifDT34M0j0LVp1xPd5xbIBPD03y5JYk/7B7E/2xK/eq8uVycHCa9919nP8+OEQ6avH6p27mTU/bQmui+doqCMLC+J5PcaYyxyYlFMYbRHIHz218hfrXP/icqzJeK6WeD3xDa+0qpd4NoLX+PaXUbuATwC3AeuDrwA6t9aKKh8Trqwit4cz34TvvhWNfhUgKbv7/2bvr8LjuK//j7zti1kgzYjbbkjm2Y4eZ0aEmDbVN07RN+iun2cJut9stbiFN0wbbhuOAQ47DbDsxyJaZxDgsGr7f3x93TKkt0wjnvJ4nT2Jr4MbbzZnvmXM/5xbjrJ1RQIc/yKOtdv7ZZscZDDM1LZkvl1q5Is9McmR6OxjWeX+bjWfXNPP2li5CuqKmOIvFc0q4ZEYR5kGMNtkjHNJp3+mmcZOTxo0OXO3GtHemJZmyqbmUVedSMslMwhDlWiulCLa27m16eyMNcL3XyBzXkpJInjzZmPiurialehqJVVVocdG7PqUUYY8/MgEeyQLv6ifY2Yfy7ftPgJYc/2/N7/i8FOKyJG5zLAmG9X1N8YM1yv/tr9Den/X6//2OrP0lxf97k/yQjfLUBLJTEphYkCmN7kORIhxj+hzw8p2w9RUoXwSX/RXM5cN9VQflDob4W7ONB1ps9IV1Ls3L5tsVBUxMO/aFFSK2ePqDRvPb3kt9ZAq8IdIM3/8b2cR4ExW5e5rg6XszwSstaeSmJcoHlCiQRnd0SM0eoTY+By9+HVLMcO1jUDznkA9d0uHkB9tbiNc0fjeplIvysofuOo/ClvZu/vzODl6r6yAtMY6bFlbw5ZOrBjVHVAgxtJRSBLyhvZPhfZ4AkxcUxny91jTtcmCxUur6yDQ3SqlfRn62HPiZUmrFQK8h9TpGddTBR3+ATc+DKQFm3QCL7gJzOd6wzgudLh5osbGlz0dOQhw3FVm4qdhCQdK+yWRHr5+X1rfx7OoWNrd3kxhn4qypeSyeU8IpE6wHXbI3GLodXpoiTe+WbS5Cn5v2LpuWi7lg8Ke996d0nWBT096mt2/jRrybN+9dyKulpJA8daoRe1JdTfK0ahIrytFM0f0zU0qh9wQJdvUdGIPS5UXv27dYUUuMIz4v5YAIlIS8VOJyktFkyCqmhMI63b7QgBPjB5so7/YG6TlEk7zxVxdJo/tQpAjHoD35oq993/j1xX+AmsXDekkDcQZD3N/UxYOtdrxhnSvyzXy7Ip9xqdLwFsdGKYWtx89u+77p7z2LMZuc/QcstshIjt8vBiU9kg2eRoVF8sCPhjS6o0Nq9gjWUQdPfQF6OuGi/4NZ1x/yoQ1eP7dvaqS2p58bi3L52fhiUofo4Hq0tnX08Kd3dvBaXTspCXHceGIFXzm5UpZWCjFGSb0GTdNeBp5WSj2madq9wEql1GORnz0ELFNKLRnoNaRexzjHLvj4j1D7BCgdpl8NJ30brBNRSvGxu5cHWmy8Ye8mToNL88x8ucTKrMwDd1RtavPw3JpWXqxtxdkXwJqRxBWzilk8p4QJ+YMfbbJHOKjTttO9d6mlq8NoLGfkJFNWnUv5tJxBz/Y+FBUOE2hoOGDq27dlC8rnA8CUlhZZdlm9twGeUFo6aA36cG8gMvm9bwI81NVPuHvfncbEayRYUg9sguenEp+bgnaYZYgi9oTCOj0HaZJfMrNYGt2HIkU4hrka4fmvQPMqmH0jnPcrSBy5CyAdgRB/be7ioRY7fl3nygIz3y4voDJVDtsiekJhnVa312iC7xeDUm/vo81zYB64NSPJiEGRPPDDkoNzdEjNHuH6HLDkZqj/AObfbuR2xx08PzKg6/y6voN7m7qYmJrM/dPKmTqCd1Ls6Ozhz+/s5OUNbSTHx/HFE8u57ZQqLNLwFmJMGcv1WtO0t4CCg/zoHqXU0shj7gHmAlcopZSmaX8BVnyu0f2aUuq5g7z+bcBtAGVlZXMaGxsH6d9EjBqeVvjkz7DmUQj5YOolcPJ3oHAGYHzx/XCLnSfaHfSGdeZmpvKVUisXWrIP2OURCOm8u62LJWtaeHerEW0yoySLK2aXcNH0wiH/8nnPtHfTJgfNWw+c9i6blkv5tFzMhUM77b0/FQrh37XbaHpvMhrg/i1bUEFj2tqUlUXKtKl7l12mVE8jvqhoUK9X94X2ZYBHpr+DXf2EXb59OxVMEJ+bcsD0d3xeKvHWFEyJcrYUB4p2vZZGtxg7wkF493+MTDHrZLjqEcibMtxXNSBbIMh9TV082monoBSL83P4dkX+3k3WQgyWPXng9fbef2uESx74wMbywXkoSc0eBcIheOunsOJeqDgZrnoU0iyHfPgHzh6+saURTyjMT8cVcUuxZUTHJe3s6uXed3bw0vo2EuNN3DC/nNtOrSIvQ+6yEmIsiOV6rWnaTcDtwJlKqf7I70l0iTh+fXZYeR98+gD4u2H82XDKd6FsAQA9oTBPdzh5sMVGgzdAUVICtxRbuL4ol5yEA6ek7b1+XlzXypI1LWzt6CHOpHHKBAuXzSrmnKkFpAxxQ3T/bO+mTQ6cbUa2d3pOEuXTjIiTksnDM+29PxUI4N+5E+/GjfsWXm7fDiEjFiLObI7kfe9beBmflzfon8n0QJiQzUvIduASzJDDC3tWKGgQl510QARKfCQT3DTMf65i+EijewBShAUAO9+GF74K/l644DdGntgIPmgDdPmD/KWpi3+02QkpxdUFOdxZnk+FNLzFMJA88IHF8sE5mqRmjyLrnzZ2YqRZ4ZrHoGjmIR9qD4S4a0sTbzu7OdeSye8nlZGbOLIPLrttvdz7zk5erG0lIc7E9fPLuf3UKvIypeEtxGgWq/Va07TzgN8DpyqlbPv9/jTgCfYto3wbmCDLKMUx8brhswdg5V+h3wHlJ8Ep34Gq00HTCCvF245uHmix8aGrlxSTxuKCHL5cYmXSQfZUbWnv5sXaVl6qbaPd4yM1MY7zphVw2axiFo7LHbI87/31OH00bXIY2d5bXQT9YUxxGoXjs43Gd3UOOYVpI+LMo/v9+Ldvj8SeGA1w/86dEDb+3zvOaiElMvW9pwEebzn08EI0qZBOyOE9YPo71NVP0NYPoX39SFNm4n7T3/uiUOLSZafKWCeN7gFIERZ79XQaUSb170PNVUbGaNLQZX8dq05/kD83dfKvNgcBXXGOJZOvlFhZlJ0+IgqoiG3HkwdeZU1jnNX4e3LC6L5dLVYPztEmNXuUaVsHT90A/Xa45M9GRuchKKV4sMXOz3e1kZMQz71TyzjJPPJrcL29j7+8u5MX1rUSb9K4bl4Zt586joIsaXgLMRrFar3WNG0nkAQ4Ir+1Uil1e+Rn9wC3AiHgW0qpZYd7PanXYkCBPljzD/jkT9DTDkWzjQnviedDZHHill4vD7bYeK7ThU9XnGrO4CulVs7IycD0uTOuritW1TtZWtvKq3Xt9PhCWNKTuHhGIZfPKqamOGtYzsXhkE77Lg9NGx007j/tbU7aG3FSMmX4p733p3u9+LZu3Tv17d20kcCu3ezJr4wvKNg39T3NaIDHm81Ddn1KV4RdvgOmv/c0w9V+w1Wm1Ph909/7TYHHZY7dwapYI43uAUgRFgfQw/DR7404E3MFLH5kwCm0kaTTH+TRVjv/bHPgCIaYkpbMl0usXJFvJmWELvkSse3zeeANjn2N8P3zwDUNirNT9ja9x1nTjb/y0rCmJ42KDyuxenCONqnZo1CvDZ69CRo/hhO/AWf9J8Qd+kC3saef2zc3sqvfz53l+Xy3ooCEURB31OgwGt7PrW0lzqRx7QmlfO20cRRmjdzccSHEv5N6HR1Sr8URCflh/ZNGjKirAfKmGksrp12+97OCIxDisTYHj7Ta6QgEGZeSxJdKLFxTkEPaQXYC+YJh3tvWxQvrWnl3q41AWKfKksZls4q5bGYxZbnDt5Nrz7R30yYnzVudBH1hTCaNwvFZxlLL6twRM+29v3BvH/6tWw6IPQk0NOz9eUJx8YGxJ9OmEZeZOaTXqJQi7AkcsABzz9/1/tDex2lJcUbj25piLMC0Gosw48zJaKPg86bYRxrdA5AiLA6q4WN47svGFNo5v4B5XxnxUSZ7+MI6L3a5eKDFxqZeHzkJcXyxyMLNxbkUJsktPGJ08AbCRtPb3suurj522XrZZetlt60Pb3Dft/UZSfFU5aUzbv8GuDWN8tw0EkfQ1m45OEeH1OxRKhyE5T+CT/8OVacZXyKn5hzy4X3hMD/e0coT7U7mZKZy39TyUbOHotnZz1/e3cmSNS2YNI2rTyjha6eNpzhbGt5CjAZSr6ND6rU4KuEQbHoePvwd2LaCuRJO+hbMuA7ijfof1BWv2Nz8vdnGup5+MuNNXFeQy83FFipTD/4ZwdMfZNnGdl5Y18qqeicAs8uyuWxWMRfWDP0Sy/2FQzoduzw0bnLQtMmBo9WY9s7ISaa8xmh6l0wyEz9ClzCGe3rwbdocWXZpNMCDzc17f55YXk7y9Omk1FSTXFND8pQpmJKH/m43pRR6X5BgZ//eHPCQzUuwsx+9Z9+OKeJNJFhTPjcFnkJ8bgraCDpTin2k0T0AKcLikPoc8OLXYMdymHwRXHovpAzdbTnHSynFCncfD7bYeN3uwaTBRdZsvlJiZU5W2nBfnhDHRNcVHd0+o/Hd1csu275meEe3b+/j4kwaZTmpexvg+0+Cm9OG/gsfOThHh9TsUW7tv+DVb0NGIVz7BBRUD/jwpV0uvretGaXgN5NKuSx/9NTgZmc/f31/F8+uNg59V80t5Y7TxlFiHr5JMiHE4Um9jg6p1+KY6DpsexU++C2010JGESy6E2bfBIn76ucaTx8PtNh4xeYmpOCMnAxuKbZwZm7mv8Wa7NHq9vJSbRtLa1vZ2tFDvEnjlIlWLptVzNlT8od8ieXn7Zn2bqhz0LLVSSigE5dgomSSmfLItHemZWR/aR5yufBt3oyvbiPejXX4NtQR6uoyfhgfT9LECaRU15BcU03K9OkkjRuHFj98sS26N/Rv09/Brn7CLv++B5k04nOTjcnvvJTI31OJt6bIIsxhJo3uAUgRFgNSClb8Bd76qVFoFz8EpfOG+6qOWqPXz8Otdp5sd9Ad0pmVkcpXSq1cZM0i0STfUIqxodcfYvd+k99GM9yIQwmE9b2Py0lLpMqStjf+ZE8DvMScMmhLa+TgHB1Ss8eAltXw9A3g88Bl9xm3Jw+gyevnjs2NrO7u55qCHH4xoZj0g9yqPFK1ur389b2dPPNZC7pSLJ5TwtdPH09pjjS8hRiJpF5Hh9RrcVyUgl3vGBPejR9Dai4suMO4yzo5a+/DOv1B/tXm4F9tdjoDIcqTE7m52MK1hTmYEw7dhPz8Esu0xDjOrS7gspnDt8Ryf6FgmLbtbho3OmjY6KDb5gXAXJhGRXUu5TW5FIzLIm4UxJMGO7vwbazDu6EOX10d3o0b0bu7AdBSUkieOtWIO5leQ0pNDQmlpcMe3aIHwsbUd1c/oU5jAWbI1k/I7gP9c4sw90yBWyPLMK2pmCQHfEhIo3sAUoTFEWlZA0tuAU8LnPkTWHjn3kUZo0lfKMzTHU4earGzy+unIDGBm4tzuaHIgiVRvpEUY1NYV7S4+vc1vyMN8N32Xuy9+25ZS4jTqMjd1wCvsqQzLs+YBs9MTjiua5CDc3RIzR4jejrhmS9C8yoji/OM/wDToZvXIV3xu4YO/tjYSVlKIvdNLWd25ui6M6nN7eX+93fx1KfN6EpxycwiblhQzqzSbDkMCTGCSL2ODqnXImoaV8CHv4Wdb0FSltHsXnAHpOXufUhQV7xmd/NIi52Vnj5STBqX55u5tdhCdcahv1g+1BLLS2YUcdmsomFbYrk/pRTuzn4aNzpo3OigbYcbPaxITImndEoOFTW5lE3LJTVzdESUKqUINjbiravDW1eHr24jvs2bUX5jijouK8uIOqmpJqXGiD6Jt1qH+aoNKqwTcvoIdXmN5ndX/96GuPLvi9bUkuKIt6ZEmt+pe5vh8bnJaKPgy4nRQhrdA5AiLI6Y1w0v3wmbl8L4s+Dyv0GaZbiv6pjoSvGus4cHmm285+ohyaRxRb6Zr5RYmZo+sm+JEiKa3P0BdtkOzADfZeul0dFPeL9v7PMykg6MQInkghdlpWA6gsUlcnCODqnZY0goAMu+B2sehfFnw5UPHDYebKW7l69vbqQjEOR7FQV8szyfuFHWJO7w+Lj//V08s7qZ/kCYSfkZXDevlMtnlZCVenxfqAkhjp/U6+iQei2irq3WmPDe8jIkpMCcm40l11nFBzxsc6+XR1rtLOlw4dV15mWlcUuxhQsPcyfzQZdYWtO4bGYxl84sojx3ZHzBHvCGaN7q3Nv47vcEQIO8sgzKayxU1ORiLc0YVYsVVTCIf+dOY+p7Yx3euo34d+yAsNE8ji8oIKWmhuSaGlKm1xjLLjMyhvmq91FKofcEjQlw277md8jWT9izXw64xKBElTS6ByBFWBwVpWD1w/D63caB/MoHofLk4b6q47K9z8eDLTaejXwYWJidzm0lVs62ZI66BoIQ0RII6TQ5+yNRKPtPgvfS7du3uTs5wUSlZb9lmHnpVFnSqLKmkbrfXRJycI4Oqdlj0OqH4bXvQ3apkdudN2XAh3uCIX64vYUXutwsyErj3qnllCSPjimm/fX6Q7xU28ZTnzWxocVDUryJC2oKuW5eGSdUmId9gkyIWCX1OjqkXotBY9sGH/0fbHgGNBPUXAULvwn5Uw94mDsY4ukOJ4+02mnwBrAmxvPFolxuLLJQkDTwF8ue/iCvbWznxf2WWM4szebSmUVcOL2QvIyhX6p4MEpX2Ft6aaiz07jRQWdDNyhIyUykfFoO5dUWSqfmkJQy+pqouteLb8sWvBs27M38DjY27f15YlWVseiy2mh+J02ejClp5C0u1/0ho/Ft8xoT4F39xj87vBDeLwYlI/Hfmt/xeanESQzKIUmjewBShMUx6aiDZ28B5y449QdwyvcGvO16NHAFQzze5uCRVjut/iBlyYl8qcTCdYW5ZI6iPFQhBpNSCkdfYO8izP0nwZtd/exfHouzU/ZOgP/npdVycI4CqdljVNNKePqLEOw37paactGAD1dKsaTTxd3bWzBp8OuJo2tR5edtbPXw1GdNLF3XRo8/xDhrGteeUMaVc0rIGYbluULEMml0R4fUazHoXA2w4j5Y9y/j88OEc43FleWLYL/GoK4U7zl7eLjVztuObuI0uMCazS3FFhZkpR22idjm9vLy+jaW1raxub0bkwaLxlu4dGYx507LJ+M44w2jydsToGmTMendtNmJvz+EyaRROD6L8moL5TW5mAtSR23jNOx24924CV/dBrx1G/HWbSBssxs/TEggeeLEvZEnyTXVxrLLuJHZx/i3GJRII/zfYlAS4/Zmf+/7eyrxOclo8bEdgyKN7gFIERbHzN8Lr30X1j8JFSfDFQ9AZuFwX9VxC+mKZXYPD7bYWOXpIy3OxLUFOXypxEpV6sj7llSIkcIXDNPg6DPyv/dMgEea4Vt+fr4cnA9C07TvAr8BrEop++EeLzV7DOtuM5ZUtq4xvkA+9YeH3YXRGFlUuaa7n6sLzPzPhJJRtajy8/oDIV7d0M6TnzaxtslNQpzGudMKuG5eGSdW5R5RTJIQ4vhIozs6pF6LIdPvhM8ehFX3Q78DiufAortg8kX/NojW6PXzaKudJ9uduENhpqQlc2uJhSvyzaQdQUN0R2cPS2vbWLq+lWanl8R4E2dNyeOSGcWcNslKcsLI+Qyih3U6dnfTuNGY9na09gGQaUmmfFou5TUWiidmE584cq75aCmlCHV2GlnfG+rwbjQyv/XeXgC01FRSpk7dF3lSU0NCcfGIbvQrpdB794tB2ZsH7iXs8e97oAnic1IOzAC3ppCQlxozMSjS6B6AFGFx3GqfgFe/Y2SFXf53mHDWcF9R1Kzv6eeBZhtLu9yElOLM3Ey+XGLhFHMGphFcIIQYSZRSmEwmOTh/jqZppcCDwGRgjjS6BUEfvPptqH0cJl1gTHcnZw74lJCu+H1jB39o6KQ0OZG/Ti1ndtbIyNE8Hts6enjqsyaeX9uKxxukPDeVa04oZfGckhFzy7QQY5E0uqND6rUYckGvcS7/5M/gqoecKiPDe+YXjHP6fvrDOi92uni41c7GXi+Z8SauLcjl5mLLEQ12KaVY1+zmpdo2XtnQhr03QEZyPOdXF3DZzGLmV+USN8K+nO5x+oxc7zo7LVtdhII68Qkmiiebqag2Gt8ZOaP/84XSdQINjUbW94Y6fHV1+LZsQQWMrOw4s9mY+q6uIXl6DSk1NcTn5h7mVUcG3R/+twzwYNchYlCsn2uCj8EYFGl0D0CKsIgK23ZYcgt0boSFd8KZP4G4kXMb0/Hq8gf5R5udf7Q6sAdDFCQmcGleNpflm5mZkTKm/oMpxGCQg/O/0zRtCfBzYCkwVxrdAjB2YXz6ALz+Q8gdZ+R2WyYc9mmr3L18fUsj7f4g360o4M5RuKjyYHzBMK9v7OCJT5v4tN5JvEnjrCn5XDuvlJMnWEfcQVqI0U7qdXRIvRbDRg8bCys//iO0rYVUC8z/KpzwZUjNOeChSik+8/TxSKudl21uQgpOz8nglmILZ+Ye2b6qUFjnk10Olta2sXxTB73+EHkZSVw8o4hLZxZRU5w14s7KoWCY1u1uGuscNG600233AZBTlEZ5dS4VNbkUVGVhihsb0RgqEMC3Y4eR9V1nZH77d+4EXQcgoaho39R3dWTZZfroGZpQYUXI5TMywPc0v22RGBTf52JQrCnG5Lcl0gC3pBBvScE0Cif7pdE9ACnCImqCXlj+I2OxVskJcOVDYC4f7quKKr+us8zm4YUuF+84eggqRWVKIpflmbk0P5vJaSmHfxEhYpAcnA+kadolwJlKqbs0TWtggEa3pmm3AbcBlJWVzWlsbBy6CxXDp/5DePYmCAeNxc8Tzz3sUz6/qPLPU8spHYWLKg9ll62Xpz9rZsmaFpx9AYqzU7jmhFKumltCYZbUXyGiQep1dMgZWww7paDxY/j4T7BjOSSkwqwvwolfP+gZvdMf5LE2B/9qc9ARMPZV3Vxs4brCHMwJRxYF4QuGeXtLF0trW3lvm41AWKfSksYlkaZ3lTU92v+Wx00phbuzn4ZI07t9hwddVySlxlM6NYfy6lzKp+WSkjF2Pk8B6P39+DZvxlu3cW/md7C52fihppE4ruqAqe+kSZMwJY6uP4MDY1AiGeCRifCwxw/7tXXjspOMBrg1sgjTMvKXYUqjewBShEXUbXoBXrrTWIJx6V9gysXDfUWDwh0M8Zrdw4udLj5y9aIDU9KSuTzfzKV52ZSnSJ63EHvE4sFZ07S3gIKD/Oge4EfAOUopz+Ea3fuTmh1j3M3w1BeMBdCn3wMnf+ewud0ASzqc/HCMLKo8GH8ozJubO3nq02Y+2mnHpMHpk/K4bl4Zp02yEj9GJrCEGA6xWK8Hg9RrMaJ0bjYiTeqeBaXDtMuMu7CLZv7bQ4ORfVUPt9hY6ekj2aRxWZ6ZW0oszMhIPeK39PQHeX1TO0tr21ix24FSML0ki0tmFHHxjCLyM0dmTEjAG6J5i5OGjcZSS293ADTIr8g0mt7VuVhLM9DG4B1lIZcL38aN+2V+byRsN44nWkICSZMnk1Kzr/mdWFmJdgSfS0ciFQwTtPv2RqGEbP0EbV5CNi8qsP8UuMloelsjMSjWFOItRjN8uKfApdE9ACnCYlA4640ok7Z1MO82OPvnkDAyi1k0dPmDvGRz82Kni9Xd/QDMyUzl8nwzl1izyUsaOzEuQhwLOTjvo2laDfA20B/5rRKgDZinlOoY6LlSs2NQoB9evgvqnoFJF8Jlf4GUwzeuG71+vr65kdXd/VwVWVSZMYoXVR5Ko6OPpz9r5tk1Ldh6/ORnJnH13FKunltKac6RH8iFEAap19Eh9VqMSJ5WWPVXWP0oBHqg6jSj4T3uDGNI7XO29Hp5pNXOsx0uvLrO7MxUbim2cEleNklH0eDs8Ph4ZUMbS2vbqGv1oGlwYlUul84s4rzqQrJSRuZZWekKW3MPjRsdNNQ56GrsBgWpmYl7m96lU3JITBmbyw+VUoTa2w+Y+vZt3IjeZyz2NKWnkzxt2t5Flyk1NcQXFIzYCegjoZRC7wlEmt6RPHCbl5DdS9jlO3AKPCvp36NQrCnEZSYNyRch0ugegBRhMWhCAXj7P2HFvZBfA5ffDwXVw31Vg67J62dpl5sXOl1s7vNhAhZmp3N5vpkLrVlkH+GtX0KMJXJwPjSZ6BaHpRSs/Cu8+WPIKIKrHoWSOYd9WkhX/F9jB/8XWVR539Ry5oyBRZUHEwzrvL2li6c+a+L97TYATp5g5ZIZRZw60Yo1Q+6yEuJISL2ODqnXYkTzeWD1I8Zni94O46y+6C5j0vsge7a6Q2Ge6XDyaKudnf1+chLiuL4wlxuLLUcdkbbL1stLtW0srW2lwdFPYpyJ0yZZuWxWMWdOySNpBH8p398doGmzg8Y6B02bnQS8IUxxGoXjs6moMRrf2fmpo7rRezgqHCZQX39g83vrVggGAYizWkipjuR910wnpXoacdnZw3vRUaKCYUIOnxF/0mU0v/dEoSj/flPgCXumwPebBI9MhZuSove/b2l0D0CKsBh0216Hl74BXjec+gM46VtjalHlQLb1+Xix08WLXS7qvQESNI3TczK4PN/MOZZM0uJGbiEXIprk4Hxo0ugWR6xlNTx7C/S0wdn/BQvuOOgE1ud96u7ljsiiyu9UFHDXGFlUeSitbi/PfNbMM6ubafcYC6aqizM5bWIep02yMrM0W+JNhDgEqdfRIfVajAohP2x4xog1sW+DrFIjw3vWFyHp3/O0lVJ86OrlkVY7y+0eAM62ZHJLsYVTzBmYjuKzhVKKulYPL65r4+UNbdh6/GSlJHDpzCIWzykZkUss9xcO63Tu9kSyvR0424wp50xLMuU1FiprLBRNzCYufux/3tADAfxbt+6LPKmrI7B7996fJ5SXHdD8Tp46BVPy2Lnb35gCDxpNb7uRBW40wQ8yBZ6ZuLcBvjcT3JJCXPbRT4HHTKNb07TfABcDAWAXcItSyj3Qc6QIiyHR54Bl34ONz0HhTLjsr5A/dbivasgopVjf4+WFLhcvdblp9wdJMZk415LJ5flmTsvJOKrbv4QYbeTgHB1SswVeF7z4ddj2Kky6wNiFkZpz2Kd1h8L8cHsLz3e6mJ+Vxp+nlFE2xndJ6Lpic3s372+38d62LtY2uQnriszkeE6eYOXUSVZOm2glb4TmhAoxHGK1Xmua9nPgUkAHuoCblVJtkZ/dDXwJCAN3KqWWH+71pF6LUUXXjYWVH/8RmlZAcjac8GWY/1VIzzvoU1p8Af7V5uCxNgeOYIiqlCRuLs7lmoIcso7yDuawrvh4p50la1pYvqkDf0hnYn46i+eUcNmsYvIyRn6d7nZ4adrooGGjg5atLsJBncTkOMqm5VIx3UJ5dS7JabEx7AcQ7unBt2nTAc3vUEckoTEujqSJE0mprjbyvqdPJ2ncOLT4sXfnuwrqhBx74k/2i0Kx9aN8n5sCz03ZF4WyZymmNQVT0sH/XGKp0X0O8I5SKqRp2q8AlFI/GOg5UoTFkNq8FF75Nvi74bQfwsK7IG7s/QdtILpSrHT38WKXi1dsbpzBMFnxcVxgzeLyPDOLzOljetJOxKZYPThHm9RsARhRJqvuhzd+DBkFsPgRKD3hiJ76XIeTH2xvQQN+PamUy8fYosqBeLxBPtph571tXby/3UZXjx+AqYWZnDbJymmT8phdJtPeIrbFar3WNC1TKdUd+ec7galKqds1TZsKPAnMA4qAt4CJSqnwoV9N6rUYxZo/NRreW1+FuESYeR2c+E2wjD/ow/26zitdbh5ptbO6u58Uk4kr843lldPSU4767T3eIK9uaGfJmmbWNrmJM2mcOtHK4jklIz7aZI9gIEzLVhcN623U1xkLLTWTRtH4LCqmW6icYSHLGnt7RIJdXQcuu6yrQ+/uBkBLSSF56lRj2WVNNSnTp5NQUjKip/qPh1IKvTcYaXxHFmJGolDCzgOnwE0ZifsWYe43CZ6QmxIbje79aZp2ObBYKXX9QI+TIiyGXJ8dXv0ObH4RimYb0915k4f7qoZFUFd84OrhhU4Xy+we+sI61sR4LrFmc3m+mTmZYzvjS8SOWD04R5vUbHGA1jXw7M3Q3QZn/adxu/ER1Iz9F1Uuzjfzy4ljc1HlQJQypr3f22bj/W021jS5COuKjOR4Tp5g4bSJeZw6yUq+THuLGCP1eu8Ed5lS6muRf0Yp9cvIz5YDP1NKrRjoNaRei1HPvhNW/Blqn4RwACZfCAu/CaXzD/lZo66nn0da7bzQ6cKrK+ZlpXFLsYULrVkkHsPdy7tsvTy3poXn17bS0e0bVdEmeyhd0dnYTcN6O/Ub7HsjTsyFaVRGmt55FZmYhmB54UijlCLY1IR3Qx3eug346jbi27wZ5TcGEeKys/cuuUyuqTaWXVosw3zVg0+FjCnw/ae/Q3YvwS4vyhfa+7jSX50Sk43ul4GnlVKPDfQ4KcJi2Gx83mh4B/rg9B8ZhdMUWwft/XnDOm85unmxy8Vbjm78uqI0OZHL8oym95S05FFRzIU4GDk4R4fUbPFvvG5Y+nXY+gpMPB8uu++IokxCuuIPjZ38vqGDksiiyrljdFHlkej2Bfl4h533ttl4b3sXnd3GIWtyQQanTTKyveeUm0mQaW8xxsVyvdY07RfAjYAHOF0pZdM07V5g5Z4ztaZpDwHLlFJLBnotqddizOjtgk8fgM8eMOLTSk4wzu2TLzrk2d0dDPFUu5NH2+w0eANYE+O5oTCXLxblUnSUyythbESb7OGxeWnYYDS923e40XVFSkYCFTUWKqZbKJ2SQ0IUFxaONioYxL9z5wHNb/+OHUa8DhBfVEhKzXRSaqqNvO9p04hLj43Pr0op9D5jCjxk85I+v3DsNLo1TXsLKDjIj+5RSi2NPOYeYC5whTrIxWqadhtwG0BZWdmcxsbGQbxiIQbQ2wWv/D/jgF5yAlx6H1gnDvdVDbvuUJhlNg8vdrn4wNVDWMGE1CQuzzdzeZ6ZytSxnasqxp5YPjhHkxycxUEpBav+Bm/8RyTK5GEonXdET/3M08cdmxtp8wf4drmxqDI+BqeK9qeUYmtHj9H03tbFmkYXIV2RkRTPovGWvTEnBVmj52AtxJEay/X6SM7RkcfdDSQrpX6qadpfgBWfa3S/ppR67iCvL2dsMXYF+qD2CVhxL7gawFxp3Ek283pIPHgMh64U7zl7eKTVzluObkwanGfJ4pZiC4uy049piGssRJvs4e8P0rjJQcN6O42bnAS8IeISTJRONlMx3Wh8p2XJuV/v78e3eTPeuo346jbgrdtIsLnZ+KGmkTiuymh+T68hubqG5EkT0RKP/guV0SZmMroBNE27CbgdOFMp1X+4x8uhWQw7pYwlla99FwL9cOaPYcEdMT3dvT97IMQrNjcvdrpY6TFudZqRkcLleWYuzc+mMGns/0dcjH5j+eA8lKRmiwG1roFnb4HuVjjzp3DiN+AIbhXuDoW5e3sLz3W6mJeVxu8mlTIhTZq4e/T4gny8MzLtvc1GR7cPMKa9jYWWecytkGlvMTZIvQZN08qBV5VS1RJdIsTn6GHY8jJ88ifjc0dKjrG4ct5tkG495NMavX7+2ebgyXYHzmCYCalJ3Fxs4eqCnGOOTxsL0SZ7hEM6bTvdxrT3ejs9DuOzRl5FJpXTc6mYbiW3OG1U/TsNppDLha+u7oC877DTCYCWkEDSlCmk1NQYze+aGhIrKtCOIT5nJIuZRremaecBvwdOVUrZjuQ5UoTFiNHTaUx3b3vVyP669L5DLr2IVa2+AEu7jKb3hl4vGrAgO43L8sxcZM0mNzG2FnuK0UMOztEhNVscltcNL33DOIROPM/Yg3EEUSZgLKq8e0cL3rDiq6VW/l95PmmjaDJqKCil2N7Zy3vbunhvm43VjU6CYUV6UjyLxufujTkpzDr6BVxCjASxWq81TZuglNoR+edvYpynF2uaNg14gn3LKN8GJsgyShHzlIKmlfDJn2Hba/strvwGWCYc8mm+sM5LNjePtNhZ19NPWpyJxZHllZPTjq12jqVoEzA+azjb+qjfYKdhg53OemNhY0ZOMhUzLFROt1A0IZu4+LHVuD0eSilCbW14929+b9qE6jdmf03p6SRXV0ciT4zc7/iCglH9xUEsNbp3AkmAI/JbK5VStw/0HCnCYkRRCjY8A8u+ByG/MZE2//YjmkiLNbv6fbzQ6ebFLhc7+/3Ea3CKOYPL882cb8kiXZoTYgSJ1YNztEnNFkdEKSNP8417IC3PiDIpm39ET7UFgvz3rnae7nBSmJTAT8cVcWle9qg+CAymXn9o77T3+9u6aPMYE1iT8jM4bZKVUydamVuRQ6IcRsUoEav1WtO054BJgA40ArcrpVojP7sHuBUIAd9SSi073OtJvRYxxb7DiDTZs7hy0gVGjnfZggGXZNd2G8srX+xy4dcVp5kzuK3Uyuk5Gcf8uWMsRZvs0efx01jnoH6DneYtTsJBncTkOMqqc6mcbqFsWi7JaQnDfZkjjgqHCezebeR9b6zDt34Dvu3bIWQsdIyzWEiprt676DK5poZ4s3mYr/rIxUyj+1hIERYjUnc7vPIt2P46lC2ES++F3HHDfVUjklKKTb1eXohMerf6gySbNM7KzeTyfDNn5mSSLLdTi2EWqwfnaJOaLY5K2zp49mZwN8NZP4UTv3nEXxyv9vRx9/YW6nq9nJSdzi8mljBJ4kwGpJRiR9e+ae/PGoxp77TEOBbul+1dnC3T3mLkknodHVKvRUzqtRlLKz99ALxOKJ5rNLynXDxgLKkzGOKxNgcPtdjoDISYmJrMV0utXJFvJuU4zrFjKdpkj2AgTMsW595pb29PEM2kUTQhi8rpViqmW8iyyueMQ9H9fvxbt0byvuvwbtxIYPduY0gESCgujkx8Vxt53yN42aU0ugcgRViMWErB+idh2Q+Nb4bP/k844Ssy3T0AXSlWe/p4ocvNS11uHMEQ6XEmzrdmcXmemZPNGSTE+JIxMTzk4BwdUrPFUfN54KVvwualMOEcuOx+SMs9oqeGleKfbQ7+d3c7feEwXy6x8t2KArlj6Aj1+kN8stPO+9uNbO9WtxeACXnpe5vecyvMo3K6TIxdUq+jQ+q1iGmBfqh9HFb8BVz1YK4wIk1mfgESD900DOg6L3W5+VuzjbpeLzkJcdxcbOGWYgvWxGOfWB5r0SZ7KF3R2dBt5HpvsONsM/Z55RSlUTHdiDjJr8hEk/P/gMK9vfg2bca3sW5vAzzY2mr8UNNIrKqKTH4bmd9JkydjGgHLLqXRPQApwmLE626Dl+6EnW9C+UnGdHdO5XBf1YgX0hUfuXt4sdPNa3Y33SGdnIQ4LrZmc3m+mXlZaZhG4bfYYnSSg3N0SM0Wx0Qp+OxBWP4jSLNGokwWHPHT7YEQv9zdxhPtTvIS4/np+GIulziTo6KUYpetd+9Cy0/rnQTCOqmJcSwct2fa20qJOXW4L1XEOKnX0SH1WgiMxZVbX4GP/wStqyHFbAyuzfsKpOcd8mlKKT5x9/L3Fhtv2LtJ0DSuyDfz1VIrU9KPb1p5LEab7OGxefc2vdt2uFG6IiUjYW/Tu2RKDgmJo/ffbyiFnE58Gzcaed91G/Fu3EjYbjd+mJBA8sSJ+yJPqmtIGj8OLW5o/2yl0T0AKcJiVFAK1j1mHNL1sDHdPfdLMt19hHxhnXed3bzQ5eZNuwevrihKSuCSvGwutmYzKzNVmt5iUMnBOTqkZovj0lYbiTJpgjN/DAvvOqo6urbbiDNZ3+NlQVYav5xYctwHzljV5w+xYpeD97YbMSctLmPae5w1be9Cy3mVOaP6wC1GJ6nX0SH1Woj9KAXNq4zFlVtfNRZXzrjWmPK2Thzwqbv6fTzQYufpdideXecUczpfLc3j9JyM4z6/jsVokz18fUGaNjtoWG+ncaODgC9MXIKJ0slmKqZbqJhuIS0rabgvc9RQShHq6Ig0viOT3xs3ovf2AqClpJA8deq+ye+aahLKygb1f0PS6B6AFGExqnhajFuwd70DlafAJfeCuXy4r2pU6Q2FWW738EKXm/ec3YQUWBPjOTs3k/MsWZxkziBVMr1FlMnBOTqkZovj5vMYd0ltfhHGnw2X/+2Io0zAiDN5ot3B/+xqpzsc5tZiC9+rLCRTGrLHzJj27uO9bV28v93Gqt3GtHdKQhwLx+XujTkpzZFpbzH4pF5Hh9RrIQ7BvsOINKl9AsJ+mHg+LLoTyk4ccHGla2+Ot52OQJAJqUl8tTSPK48zxxvGbrTJHuGQTttONw3rjWnvHoexODuvIpPK6RYqZ1jIKUob1Y394aB0nUBD4wGRJ74tW1B+PwCmrCxSpk3bl/ldU0NCfn7U3l8a3QOQIixGHaVg7T9g+X8ACs75Ocy5ZcDCKA7OHQzxtqOb5Y5u3nF00xvWSTFpnJKTwbm5WZxtyTyuPDQh9pCDc3RIzRZRoRSsfghevxtSLUaUSfmJR/USzmCI/93dzr/aHFgS4/nxuCKuyjfLISkK+gMhVu527I05aXL2A1Cem8qcMjOzy83MLjMzqSCDOMndFFEm9To6pF4LcRgHW1x5yndh4nkDnusDus7LkRzvDZEc75uKjBzvvKTjP7eO5WgTML5cd7b1UR9penc1dAOQkZtM5XQLFTMsFE3IJk4G346JCgbx79x5QOSJf/t2CIcBiLda92Z9J1fXkFI9jbjs7GN6L2l0D0CKsBi13E2w9BtQ/z5UnWZMd2eXDvdVjVoBXWeFu4/ldg/L7R5a/UE0YHZmKudasjjHksmk1GRpYohjIgfn6JCaLaKqfb0RZeJqhDP+AxZ966gjwWq7+/nRjhbWdvczLxJnMk3iTKJGKUW9vY/3ttlYudvB2iY39l5jUigtMY6ZZdnM3tP8LjWTlSpfTovjI/U6OqReC3GE9iyu/ORPxvk+v8ZoeE+5ZMDPJEopVnr6+FtzF8sjOd6XR3K8p0bpc8hYjjbZo8/jp2GDnYYNdpq3uggHdRJT4imflkPFDAvl03JJks8Wx0X3+fBt2WI0viPRJ4GGhr0/TygrOyDyJHnqVEyph7+LTxrdA5AiLEY1pWD1w/DGj0EzGQf1ubdC/PBvwR3NlFJs7vPtbXqv7zGyQ8uTE/c2vednpZMgk2TiCMnBOTqkZouo83XDy3fBpudh/FmRKBPLUb2ErhRPdTj5711tuINhbi628IPKArIS4gfpomOXUopmp5e1TS7WNLpY2+Ria0cPYd04m4zPS2d2pPk9p9zMOGs6JqnV4ihIvY4OqddCHKVwEOqehQ9/B46dYJkEJ38Hqq+EuIE/T+zu9/Ngi40nIzneJ0dyvM+IQo43jP1okz2CgTAtW5zURxrf3p4gJpNG0cRsKmdYqZppId08Nv5dh1u4uxvfpk17I0+8GzcSam83fmgykTRu3L7Gd3UNyZMmoiUe2OOSRvcApAiLMcHVYBzUd78H5ko466cw9TKJM4mSdn+AN+3dLLd385G7B7+uyI6P48zcTM6xZHJ6Tqbks4oBycE5OqRmi0Gx50vj1++G1By48Hcw6YKjrqHuYIhf1Xfwj1Y75oR47hlXyLUFObLseJD1+UOsb3Gzrsm9t/nt7g8CkJkcz6wyc2TqO5uZpdlkJMtkljg0qdfRIfVaiGOkh409Ih/8Fro2g7kCTvo2zLjusMNs7mCIf7U5eLjVTrvfyPH+SomVxQU5UdtBNdajTfZQuqKzoduIOFlvw9VhxKjllWdQOdNK1UwrOYVpw3yVY0vIbt8v8sT4e9jlAkBLSCBp8uS9je+U6TUkT5ggje5DkSIsxgylYOdb8OZPjKJYPAfO/jlULBruKxtT+kJh3nP1sNzu4S1HN85gmARNY2F2OudYMjnHkkVpskzUiwPJwTk6pGaLQdW+AZ6/DWxbjEWV5/8Kcscd9cts7Onn7u2tfNbdx5zMVP5nYgkzMmSR4lDZE3diNL3drG10sb2rB6WM7y4m5WfszfmeXZZNpUUWUIl9pF5Hh9RrIY6TrsP2ZfDBb6BtHWSWwKK7YPYXIWHgaJKgrnjZ5ub+5i429Bg53jdGcrzzo5DjvUcsRJvs4eroY3etjd21+3K9s/NTqZppoXKmlfzyTDS5gyyqlFIEW9siyy6Nxrdv0yb0vj4Apm7bKo3uQ5EiLMYcPQzrn4R3fgE9bcYm57N+BnmTh/vKxpywUqz29LHc3s0bDg87+43c0GnpyZyTm8W5liymZ6TINJ+Qg3OUSM0Wgy4chE//Du/+EsJ+WPhN49bhxKOb2tGV4tkOFz/f1YYjGOKLRbncXVWIWeJMhkW3L0htk5u1TUbze12jix5/CICctERmlWbvbX7PKM0iNVH+7xSrpF5Hh9RrIaJEKdj1Nrz/G2heCen5xmeTObdAUvphnqpY5enjb802Xrd7iNc0Ls/P5quleVHdJ3KwaJNpRZlcP7+cS2cWkZY0tmpqr8tP/Xobu2tttG13o+uKtKzESLyJlaKJ2cTFyzLLwaB0nUB9Pd66OsyXXy6N7kORIizGrEA/rLofPvo/CPTCrBvgtB9BZuFwX9mYtbPfxxv2bt6we/jU04cOFCQmcI4lk3MtWSzKTidZNjjHJDk4R4fUbDFkejqNO6Q2PGVMUZ37C5h66VHHmXiCIX7b0MHDrXay4uP4UVURXyiUOJPhpuuKnbZeY+o7Eneyy2ZMCMWZNKYUZuzN+Z5dZqbEnDKmJtPEoUm9jg6p10JEmVLQ8JEx4V3/PqTkwIl3wLzbIDnrsE+v35Pj3eGkP6xzUnY6Xy21cmZuZlQ/k3i8QV6qbeXxVU1s7eghPSmey2cVc/2CMiYXZEbtfUYKX1+Qxo0O6mttNG5yEAoYyywranKpnGGlbFoOicljq9E/UkhG9wCkCIsxr89hFMTPHgRTPCz8Biy8E5LHXqEZSRyBEG87u1lu9/Cus4f+sE5qnInTczI4JzeLs3IzyZWJsZghB+fokJothlzjCnjte9BZB1Wnwfm/AevEo36Zzb1efrS9hZWePmZmpPKLCcXMyZJsx5HE3R84IOe7ttlNfyAMgDUj6YAll9XFWSQnjI0cUnEgqdfRIfVaiEHU/KmR4b1jOSRlwfyvwoKvGXtGDsMdDPF4u5OHWmy0+YOMj+R4XxXFHG8wpsnXNrl4fGUTr9S1EwjpzC03c/2CMs6vLhyTNTQUCNO8xcnu9XYa1tvx9QWJizdROsVM5UwrldMtpGRIxGm0SKN7AFKERcxw1sM7P4eNz0GqBU77Icy5GeJkKdNg84V1PnH38rrdwxv2bjoCQUzACVlpnGPJ4lxLJuNTZYPzWCYH5+iQmi2GRTgEax4xamigDxbcAad+H5IyjupllFI83+niP3e10RUIsSg7nTvK8jgjJ0OmhUegUFhnW2fP3pzvtU0uGh3GMqqEOI1pRVl7l1zOKTdTmBW928DF8JF6HR1Sr4UYAu3rjYb3lpcgIQ1O+BKc+A3IyD/sU4O64pVIjvf6Hi/m+DhuLDZyvAuimOMN4OoLsGRNC4+vaqTB0Y85NYGr5pZy3bwyKi1j80t/PazTvssTyfW20ev0o2lQOD6bqplWKmdYyLTI54bjIY3uAUgRFjGndQ28+VNo+BByxsGZPzmm27HFsVFKsaHXy/JI03tjrxeAcSlJnBtpes/NSiNO/u8xpsjBOTqkZoth1WuDt38G6x6DjEI457+h+sqjrp+9oTD/bHPwQIuNdn+QSWnJ3F5q5Yp8M0kmibcayey9/kjT22h+r29x4w/pABRmJR+w5HJaURaJktE56ki9jg6p10IMoa4t8OHvYeMSiEuE2TfBojshq+SwT1VK8Wkkx3tZJMf7svxsvlpipTrKi7R1XbFit4PHVjbyxuZOwrripPEWblhQxplT8kkYoxGfSinszb3srrVRv96Go9WISrOUpkea3lZyi2Ux9tGSRvcApAiLmKQU7HjTyB+1bYGSE+Ds/4LyhcN9ZTGnxRfgjUjT+2N3L0GlyEmI46xcI9f7NHMGafFj79auWCMH5+iQmi1GhJbV8Op3oL0WKk6G838N+VOP+mUCus7SLjd/bepic5+P/MR4vlxi5YtFuWTL0spRIRDS2dLezdomF2saXaxrctPqNr7AToo3UVOcxZxyM7Mik995GXL31kgn9To6pF4LMQwcu+Cj38P6pwANZn4BTvp/kFN5RE9v8EZyvNud9IV1FkVyvM+Kco43QGe3j2c+a+bJT5to8/jIy0ji2hNKuXZeGUXZY3vS2d3VT/16O/W1Ntp3e0BBpiWZypnGMsuCqixMJml6H440ugcgRVjEND0MtU/Au7+AnnaYdCGc9bNjyh8Vx68nFOZdZw9v2D285ejGHQqTqGmcZE7nXEsW51gyKUySXK/RSA7O/07TtG8C3wBCwKtKqe8f7jlSs8WIoYdh7T/g7f8CX7eRj3naD49oIdTnKaX4wNXLfU1dvO/qITXOxPWFOXylxEpZStIgXLwYTB0eH2ubjCWXa5pcbGrtJhA2pr5Lc1IOWHI5uSCD+DE6wTZaxXq91jTtu8BvAKtSyh75vbuBLwFh4E6l1PLDvY7UayGGkbsJPv4jrP0X6CGouQpO/s4Rn/E9++V4t/qDjEtJ4iulVq4qMJMWF90BrFBY571tNh5f1ch7221owBmT87l+QRmnTrCO+YZvn8dPwwY7u2vttGxzoocUKRkJVE63UDnTSunkHOIS5HPCwUijewBShIUAAv2w8j746A8Q7IfZNxoH9oyC4b6ymBXSFas8vbxh72a5w0ODNwDA9IwUzs01Ik6mpafILU6jRKwfnD9P07TTgXuAC5VSfk3T8pRSXYd7ntRsMeL0O41m95pHIc1q3B0149pjjgPb1Ovlr01dvNjlQgEXW7P5WlkeM6J8+7AYOr5gmE1t3Xtzvtc0uujq8QOQmhjHjBIj49uY/M4mO1W+0B5OsVyvNU0rBR4EJgNzlFJ2TdOmAk8C84Ai4C1golIqPNBrSb0WYgTobocV98LqhyHohWmXwcnfhYLqI3p6UFe8anNzf7ON2p5+suPjuLEol1tKLIMyfNXs7OfJT5t4ZnUz9t4AJeYUvjC/jKvnlmJJH/tf/Ae8IRo3Odhda6Nxo4OgL0xCUhzl1blUzbRSVp1LUorc8beHNLoHIEVYiP302eGD38BnDxr5Xgu/afx1lAu3RHQppdje7+cNu4fldg9ruvtRQHFSQiTXO4sTs9NIlGzXESuWD84Ho2naM8DflVJvHc3zpGaLEattHbz6XWhdDaUL4ILfQOH0Y385X4AHWmz8q81Bb+T24a9FFldG+/ZhMbSUUrS4vHunvtc2udnc3k1YN85X46xpexvfc8rNVFnSx/xE20gSy/Va07QlwM+BpcDcSKP7bgCl1C8jj1kO/EwptWKg15J6LcQI0meHFX+BTx+AQA9MugBO+S4Uzzmipyul+MzTx99abCyzeTBpcFmemdtKrUwfhC/iAyGd5Zs6eHxVIyt3O0mI0zivupDr55cxvzInJga9wkGdlm0udq+3Ub/ejrc7gClOo2SymcoZxjLLtKyx3/wfiDS6ByBFWIiDcOyCd34Om14wJtRO+6Gx1CIuuhuYxbGxBYK86ejmDbuH9509eHVFepyJM3IzucCSxRm5mWRKrveIEssH54PRNK0W4yB9HuADvquU+uwQj70NuA2grKxsTmNj41BdphBHR9dh/RPGwmevE+Z+Cc64B1LMx/yS3aEwj0cWV7b5g0xMTeZrZbK4cqzpD4RY3+w5IPLE3R8EIDM5ntnlZuZEIk9mlGaTliQTXYMlVuu1pmmXAGcqpe7SNK2BfY3ue4GVSqnHIo97CFimlFoy0OvJGVuIEcjrglV/N+7k9rlh3Jlwyveg/MQjfonGSI73E5Ec74WRHO+zByHHG2BnVy+Pr2rkuTUtdPtCjM9L5/r5ZVwxq4Ss1NjoTei6onO3h93r7eyutdFt84IGBZWZe3O9s/Ni784/aXQPQIqwEANoWQNv/hgaP4bc8XDmT2HKxcd8S7aIPm9Y50NXD8vtHpbbu7EHQyREcr3Pj0x75yfFxoeAkSwWD86apr0FHCz/6B7gF8A7wF3ACcDTQJU6zAcMqdliVPC64N3/Me6OSjEbuy9m3gDH0ZgO6oqlXS7+2tzFpl4feZHFlTfK4soxSSnFbntfZMGlEXeyvbMXAJMGkwsy9058zy4zU5ojUWbRMpbr9WHq8o+Ac5RSns81uv8CrPhco/s1pdRzB3l9+WJaiNHA3wOfPWTEmvTZoPwkY8K76rQjPufv+SL+wUiOd1VKEl8usXBNYU7Uc7wBvIEwr2xo47FVTaxvdpOcYOLi6UVcv6CcGSVZMVMDlVI42/rYXWtjd60Ne7Px2SCnKI2qmcakt7UsIyb+PKTRPQA5NAtxGErB9uXw5k/Avg1K5hnLLCacc1yHdhF9YaVY293PazY3y+xGrrcGzMlM5TxLFhdYs6lKje1bnIbLWD44HwtN014H/lcp9V7k17uABUop20DPk5otRpWOOnjte9C0wrg9+ILfQvHs43rJPYsr/9rUxXuyuDKmePqDrGveF3eyrslFX8CISbakJzGnPHtv47u6OIvkBLmz61jEYr3WNK0GeBvoj/xWCdCGkct9C0h0iRBjUqDfWKz98R+hpx2K5xoT3hPPPeKGd0hXvGp387dmG2u7jRzvLxbl8pUSK3mDNGy1sdXD46uaWFrbSn8gTHVxJtfPL+eSGUUxd8dTt8NLfa0x6d2+041SkJ6TRNUMK5UzrRSNz8I0RpdeS6N7AFKEhThC4RDUPg7v/wq6W8EyEU78Oky/FhKSh/vqxOcopdja5+N1u4dlNg8ber0ATExN5nxrFudbspiRIRNgQyUWD84D0TTtdqBIKfUTTdMmYhywy2SiW4w5SsGGZ4y7o3q7YM5Nxt1RqTnH/dL7L67UFVycl80dsrgyZoR1xbaOHtY0uVgXiTtpdBh9yoQ4jeriLOaUmY3Yk3Iz+ZnyWe1ISL2Gz010TwOeYN8yyreBCbKMUogxJOQ3zvkf/R+4m6Cgxmh4T774qAbbVnv6uL+5i9dsHhJNGtcV5vL1sjxKkgdnyXK3L8jSda08trKJbZ09ZCTFc/nsYq6fX86kgtjbMebtDdCwwc7uWjvNW5yEgzpJafFU1lionGmlbGoO8Ylj50twaXQPQIqwEEcpHIRNL8Inf4KODUaG9wlfgRO+DGm5w3114hBafIG9Te+Vnl7CCoqSEjjPYjS9F2SnkyDLrgaNHJwPpGlaIvAwMBMIYGR0v3O450nNFqOWr9v4onjlXyE5E077Ecy6ARKPvynd5gvwYIudf7XZ6YnkZX6t1MqZg5SXKUYuW4/fyPmOZH2vb/EQCOkAFGenRCa+s5lTnsPkwgwSxuiU1/GQen1gozvy63uAW4EQ8C2l1LLDvYbUayFGoXAQ6p6FD38Hjp1gmWREmky7AuKOfFK6vt/Pn5s6ebbDhUJxVUEO3yzLH7Q7i5VSrGl08fiqJl7d0E4grDO33MwNC8o5r7ogJu9wCvrDNG12sLvWRmOdA39/iPhEE2XTcqmaaaWiJpekUZ5xLo3uAUgRFuIYKQUNH8In98KO5RCfDDOuM6a8LROG++rEAJzBEG85ullm8/CesxuvrsiOj+Os3EzOt2ZxWk7GoGSrxTI5OEeH1Gwx6nVtMeJMGj6ElBw44UvGl8UZ+cf90gdbXHlTcS6X5ZnJTYytW3mFIRDS2dTmYW2Tm7WNLlY3Ouns9gOQkhDHjNKsvXEns8vMmNMGZ+puNJF6HR1Sr4UYxfQwbH4RPvgtdG0GcyWc/G3jTu74I68Trb4A9zV18Xi7g4CuuDQvmzvL85mSnjJol+7sC7BkTTNPrGqiwdGPOTWBq+eWct28MiosaYP2viNZOKzTtt3N7lob9bU2+jwBTHEaJZPMVM2yUjnDSmrm6Kv/0ugegBRhIaLAts1YZrH+aQgHYNL5cOI3oHyhLK4c4frDOh84e3jN7uZNezeuUJhkk8apORmcZ8ninNwsaZBEgRyco0NqthgTlILGT2DFX2DbaxCXADVXwYI7oKD6uF8+qCte6nJxf7ONul4v8RqckZPJ4oIczsnNJFmmeGOWUoo2j481jXuyvl1sausmrBtnuypr2gFxJ+Ot6Zhi7G4vqdfRIfVaiDFA143PKR/8BtprIbMETvoWzPriUUWX2gJB7m+28Wirnb6wznmWTO4qL2BW5uBFrem64pNdDh5b2cibWzoJ64qTJ1i4fn4ZZ07Jj9k7mpSu6GzoZvc6G7tqbXTbvKBB4bgsxs3Ko3KmhczcwfsiIpqk0T0AKcJCRFFvF3z2IHz6AHidUDQbFn4Dplx6VLc7ieER0hUrPb17I05a/UFMwPzsNM63ZHGeJUuWnR0jOThHh9RsMeY4dhlxJrWPQ7AfKk81vigef1ZUFj5v7vXybIeTFzrddASCZMSZuDgvm8X5OSzITpNoE0F/IMSGFs/euJM1jS5c/UEAMpLj9057zyk3M7Msm/QxvuhL6nV0SL0WYgxRCna+DR/8GppXQXo+LLwT5t4CiUc+Je0KhniwxcZDLXbcoTCnmTO4qyKfE7PTB/HiobPbx9OfNfPkp020e3zkZyZxzQllXHtCKUXZo6OpOxiUUjjb+ti1zsbudTYcrb0AWMsyqJpppWqWlZzCkTsFL43uAUgRFmIQBPph/ZPGtJpzF2SVwYLbYfaNkBR7iyFGI6UUdb1eltk8LLN72NrnA6A6PYXzLVmcb81iSlqyLLM8QnJwjg6p2WLM6nfC2n/Aqr9BT7ux8HnBHTDjWkg4/kNYWCk+dvXybKeTV20e+sM6JckJXJmfw5X5ZiamyaJCYVBKUW/vY22Te+/k9/auHpQCkwaTCjIjOd9G87ssJ3VMfRaQeh0dUq+FGIOUgoaPjAnv+vchNdf4rDLvK5CcdcQv0xsK82irnfubbdiDIeZnpfGt8nxOy8kY1HoSCuu8u83G46saeX+7DQ04c0o+188v45QJ1pi7g+nz3F39e+NNOnZ3A2AuSKVyppVxs6xYywb3/z5HSxrdA5AiLMQg0nXYvszI8W76BJKyYM5NMP92yCoe7qsTR6G+329Mets9fObpQwHlyYmcZzWWWZ6QlUbcCCp8I40cnKNDarYY80IBIxdzxb3Qvt44RM79krHwOQo53gB94TCv2zws6XTxvrMHHZiekcJV+Tlclp+NNXF0LycS0dftC1K7p/Hd5GJdk5tefwgAS3oiJ1TkML8yhwXjcpmYlzGqmwVSr6ND6rUQY1zzp0aG947lxhl//ldhwdcgNeeIX8Ib1nm83cF9TV20+YNMz0jhW+X5nGfJGvQ7zpoc/Tz5WRPPfNaMoy9AaU4KX5hXzlVzS7Ckyx3MvS4/9ett7K610brdjdIV6TlJVEWa3gXjsoe91sdMo1vTtJ8DlwI60AXcrJRqG+g5UoSFGCKta4yG9+YXQTNB9ZXG7dmF04f7ysRRsgWCLLcbyyw/dPUQUIrchHjOtWRyniWLU8wZkgH7OXJwjg6p2SJmDHKO9x5d/iAvdLlY0uGirtdLnAanmjO4qiCHcy1ZpMp/y8VBhHXFjq4e1jS6WNPgYlW9k1a3F4Ds1ATmVeSwoCqX+VU5TCnIHPbD8NGQeh0dUq+FiBFttfDhb2HLy5CYbizZPvEbkJ53xC8R0HWe7XDx56ZOGrwBJqUlc1d5PpdYs4kf5PrhD4VZvqmTx1c2sqreSUKcxnnVhdwwv4x5lTkjaoJ5uPh6gzTU2dm1zkbzZifhkE5KRgKV0y1UzcqjZJKZuISh/7wYS43uTKVUd+Sf7wSmKqVuH+g5UoSFGGKuRlh1P6z9JwR6ofIUI+Nr/FmyuHIU6g2FedvZzes2D285uukJ66TGmTgjJ4PzLVmclZtJVsLYzvM8EnJwjg6p2SImfT7Hu+o04xA57syo5HjvsbXPy3MdLp7vdNHqD5IeZ+JCazaL880sNKfLXTtiQM3OflbVO1m528GqegfNTqPxnZWSwAkVOSyoMprfUwoziRvBjW+p19Eh9VqIGNO1BT78HWx8DuISjQnvRd86qgnvkK54yebmj42dbOvzUZGSyDfL8rmqwExiFD/vHMrOrh4eW9nEc2tb6PGFGJ+XzvXzy7hyTgmZyXK3G0DAF6Jpk5Pd67po2Ogg6AuTmBxHeY2FcbOslE3LJSEpbkiuJWYa3fvTNO1uoEwp9bWBHidFWIhh4nUbeaQr74eeNrBOhhO/DjVXH9UWZzFyBHSdj129LLN7eN3uoSsQIl6DRdkZnG/N4lxLJoVJicN9mcNCDs7RITVbxLR+J6x5FD79eyTHexKceAdMvyYqOd576ErxibuX5zpdvNzlpjesU5iUwBX5Zhbnm5mSHruLm8SRa3V7WbXbwardTlbWO2h09APGgst5FTnMr8phfmUu04oyiR9Bdw5IvY4OqddCxCjHLnj/17DhaUjKhJPuMmJLj2Jppa4Ur9s9/KGxkw09XoqTEvhaWR7XF+aSMgT1whsI8/KGNh5f1cT6ZjepiXFcObuEmxZWMD5vcBdnjibhoE7zVqeR673ejq83SFyCibKpOVTNslJRYyE5bfC+IIipRremab8AbgQ8wOlKKdtBHnMbcBtAWVnZnMbGxqG9SCHEPqEAbHoBPvkzdNZBmhXmfdW47ekovgEWI4uuFOu6+1lm97DM5mGX1w/A7MxUzrdkcZ4liwkxtPxMDs7RIQdnIdiX4/3Jn6Fjw6DkeO/hDesstxt53u86uwkrmJaezOL8HK7IN5OfJBNO4sh0eHysqncYE9+7ney29wGQnhTP3AqzEXVSmUN1cRYJw9j4lnodHVKvhYhxnZvgnf824tfS8+HU78Psm4wotiOklOI9Zw9/aOxklacPS0I8t5daubnYQnr80EwN17V4ePSTBl5e30YgrHPyBAu3LKrgtIl5oyqWa7DpYZ32nR52RZZZ9rr8mEwaxZOyqZqVR+UMC2lZ0c0+H1ONbk3T3gIKDvKje5RSS/d73N1AslLqpwO9nhRhIUYIpYztzZ/cCzvfhPgUmPkFY8o7d9xwX504DkopdvT7WWYzllnW9hhTXRNSkzjPksX51ixmZqQO+tKR4SQH5+iQmi3EfpSCxo8jOd7LIjneVxtT3vnTov52tkCQpV1ulnS4qO3pxwScYs5gcYGZ8y1ZpA3RoVOMDV3dPlbWO1m122h+77IZje/UxDjm7lluWZVDTXE2ifFD1/iWeh0dUq+FEAA0rYK3fgZNn4C5Ek6/x9jVdZRRJCvcvfyxoZP3XD1kx8fxpRILXy6xYh6iiEx7r58nVzXxr5WNdPX4qchN5cYTK1g8V2JNPk8pRVdjD7vX2di1rgtPlxc0KKjMomqWlaqZVrKsx3934JhqdB8pTdPKgVeVUgNu7JEiLMQI1LUFVtwLG56BcBAmX2jkkZYtkBzvMaDNF+D1SLzJJ+5eQgoKEhM415LJBdZsTsxOG5IctqEkB+fokJotxCHYd8Kqv0LtE4Oa473Hjj4fz3W6WNLppMUXJDXOxAWWLBYXmDnZnCF53uKodfX4+LTeyardTlbVO9je2QtASkIcc8rNLKjKYX5VLtNLskgaxC9VpF5Hh9RrIcReSsHOt+Ct/zTu4M6vgTN/AhPOPuqz/brufv7Y2MHr9m7S4kzcXGzh9lIr1sShaTYHwzrLNnbw6Mf1rG1yk5YYx+I5Jdy4sIJxVok1+TylFM72Pnavs7G71oa92ajtltJ0qmYaTe+corRjWvoZM41uTdMmKKV2RP75m8CpSqnFAz1HirAQI1hPJ3z2AHz2IHhdUDwXFn4DJl8McbLgcCxwB0O85ehmmd3DO44evLpOZryJs3OzuNiazWk5GSSPoOzOYyUH5+iQmi3EYXw+xzu7HGZcBzOugZyqqL+drhSfevpY0uHiJZuL7pBOfmI8l+WbuSrfzLT0lGM6vAjh6PXz6d7llk62dvQAkJxgYnaZmfmVuSyoymFGaTbJCdFrfEu9jg6p10KIf6PrsOl5eOfn4GqAsoVw1k+NYbajtKXXy58aO1na5SbRpHF9YS53lOVRnDx0+6A2tLh59JMGXlnfTiCsc+pEKzcvrODUiVaJNTmEbruX3bU2dq+z0b7bAwqy8lIYN8tK1cw88soz0I7wzy6WGt3PAZMAHWgEbldKtQ70HCnCQowCgT5jSm3lfeDcDdllsOAOmHUDJGUM99WJKPGGdT509bDM7mG53YMzGCY9zsR5liwuzjOa3kmjdNJbDs7RITVbiCMUCsDmpVD7GOx+H1BQdiLMuBamXQ7JWVF/S19Y5y1HN0s6nbzt6CGoFJPTklmcb+aKfDNFQ3j4FGOPsy9gTHzXO1i528nWjm6UgsR4E7PLsplfmcv8qhxml5mPq/Et9To6pF4LIQ4pFIB1/zSWVvZ2wsTz4cwfH1Ps2u5+P39u6uTZDicaGlcVmPlmWT6VqdHNgx6IrcfPk5828Vgk1qTSksaNJ5azeE4JGRJrckh9Hj/16+3srrXRutWFrivSzUlUzrBSNctK0fgsTAMMvMVMo/tYSBEWYhTRw0YO6Sd/huaVxkF9zi0w/6uQWTTcVyeiKKgrPnb38FKXm2U2D65QmIw4E+dasrgkL5tTR1nTWw7O0SE1W4hj4GkxosDWPwn27RCfDJMuMPZgVJ0+KHdIOYOhSJ63kzXd/WjAoux0FheYuciaPWRLpMTY5ekP8mnDnolvB5vbutEVJMaZmFmazfyqHBZU5TK7zExK4pH/703qdXRIvRZCHFagD1bdDx/9EfzdMP0aOP1uMFcc9Us1+wLc19TFE+0Ogrrisnwzd5bnMTnt+LOgj1QgpLNsYzuPftLAukisyVVzS7nxxHKqJNZkQL6+II11dnats9G02Uk4qJOclkDlDAtVs6yUTDYT/7kvsaXRPQApwkKMUi2rjYb3lpdAi4OaxUYeacGAsfxiFArqio9cPbxkM5re7lCYzPhI09tqNL1Heqa3HJyjQ2q2EMdBKWhbC7VPwsYlRiRYWh5Mv9qINxmk+lnf72dJp5PnOl00eAOkmDTOtWSxuCCH08wZxMvtvSIKPN4gqxucrIrEnWxs9aArSIjTmFFiNL7nV+Yyp9xMWtKhv9yReh0dUq+FEEes3wkf/wFW/c0YbJt7C5zyPUjPO+qX6vIHub/ZxqNtdvrDOudbsrirPJ+ZmanRv+4B1Da7+ccnDbyyoY1gWHHaJCPW5JQJEmtyOEF/mKbNDnavs9GwwU7AFyYhKY7ymlyqZlopr84lMTleGt0DkSIsxCjnaoCVf4W1/4JgX2QB1zdh/JmyuHIMCug6H7p6ebnLzTK7B08oTFZ8HOdFJr1PNqePyKa3HJyjQ2q2EFESCsCO5bD+Kdi+HPSgsRxq5nVQc9UxHS4PRynF6u5+lnQ4eanLjSsUxpIQz+X52VyZn8OMDMnzFtHT4wuyusHFynoHq3Y7qWv1ENYV8SaNmpIsFlTlMr8yh7kVOaTv1/iWeh0dUq+FEEetu82IM1n7T+PusxPvgIXfPKa4NWcwxIMtNh5qseMJhTk9J4O7yvNZkD20k9VdPT6eWNXEYyubsPf6qbKkcdPCCq6cU3JA7REHFw7ptGxzsXudjfr1Nrw9QeLiTZROzeGir8+QRvehSBEWYozwumD1I/sWcOVNhRO/bhzY44cuo0sMnYCu84Grl5e6XLxu99Ad0smKj+P8vU3vDBJGyDfmcnCODqnZQgyCPgdsfM6INmlba9wlNf4sI8970gWQkBz1twzoOm87ulnS6eJNezcBpZiQmsTi/ByuKDBTKnneIsp6/SHWNLpYtdvByt0ONrR4COmKOJNGdXEWCyqNqJMzpuRLvY4CqddCiGPm2AXv/LexuDLFDCd/B074MiQcfQxJTyjMo6127m+24QiGWJCVxrcq8jnVnDGkX64HQjqv1RmxJrXNbtKT4lk8p4SbFlZQaUkbsusYzXRd0bHLw+51NnbVdnHzL0+SRvehSBEWYowJBYwD+4p7oXMjpOfDvNtg7q2QmjPcVycGiV/X+cBpxJu8bvPQE9Yxx8dxvjWLi63ZnDTMTW9pdEeH1GwhBlnXVtjwFKx/GnraICkLqi+HGV+A0nmDcqeUOxjiZZubJR0uVnn6ADgxO43F+TlcZM0iK0EmnkT09Qf2NL6NBZe1zW6CYUXjry6KyXqtadrPgK8Atshv/Ugp9VrkZ3cDXwLCwJ1KqeWHez2p10KI49ZWC2//F+x6GzKL4bQfGp9HjmG3SH9Y54l2B39p6qLdH2RGRgr/r7yAcyyZmIb4brJ1TS7+8UkDr9a1EwwrTp9k5eZFlZw83iKxJkdIKYXJZJJG96FIERZijFIKdr8Ln9xrFMeEVJh5vXELVE7VcF+dGER+Xed9p7HI8nW7h95I0/sCaxaX5JlZlJ0+5Jmw0uiODqnZQgwRPQz1HxjRJltegmC/UTtnXGcsizKXD8rbNnr9PN/pYkmHi11eP0kmjXNys7iqwMxpo2Afgxi9vIEw65pcLJpgjcl6HWl09yqlfvu5358KPAnMA4qAt4CJSqnwQK8n9VoIETX1H8Bb/wmtqyF3Apz5Y5hyyTF9+e7XdZ7tcPHnxk4afQEmpyVzV3k+l+RlEzfEDe+ubh+Pr2ri8VWRWBNrGjcvrOCK2RJrciQko3sAUoSFiAGdm2HFX2DD06CHYMpFRo532fzhvjIxyHxhnfdd+5refWGdnIQ4LrBkc0leNguHqOktje7okJotxDDw98CWl6H2CWj40Pi98kVG03vqpZCcGfW3VEqxrqefJR0uXuxy4QyGMcfHcaE1m0vzslloTh/yA6mIDbFarwdodN8NoJT6ZeTXy4GfKaVWDPR6Uq+FEFGlFGx91Zjwtm+Dollw5k9h3OnH9HIhXbG0y8UfGjvZ0e+nMiWRb5bnszjfPORfqvtDYV6ra+eRjxvY0OIhIymeq+aWcuOJ5VRIrMkhSaN7AFKEhYghPR1GhvdnD4HPDSUnGDneky8+plugxOjiDeu85+zmZZuH5fs1vS+yZnOxNZsTB7HpHasH52iTmi3EMHM3GV8ar38KHDshPsX48njGtVB1Opjiov6WQV3xrrObFyNfWPaHdayJ8VwUaXrPy0ob8tuOxdgVq/U60ui+GegGVgPfUUq5NE27F1iplHos8riHgGVKqSUHeY3bgNsAysrK5jQ2Ng7R1QshYoYeNj6DvPdL8DRD5alw1k+heM6xvZxSLLN7+GNDJxt6vRQnJXBHWR5fKMwlJW5oG95KKdY1u3n04wZeq2snrBSnT8rj5oUVnDzBIgu7P0ca3QOQQ7MQMSjQB+seh5X3gaveyPw64Usw+2ZIyx3uqxNDwBvWedfZzUtdbt5wdNMf1slNiOdCq7HI8sTs6E4LxurBOdqkZgsxQigFLauNBZYbnzO+PE4vgOlXG5Pe+VMH5W37w8YSyxe7XLzt6ManKwqTErjEms2l+dnMykiVg6A4LmO5Xmua9hZQcJAf3QOsBOyAAn4OFCqlbtU07S/Ais81ul9TSj030HtJvRZCDKqQH1Y/DB/8BvodRpTJGT8G68RjejmlFO84e/hjYyefevqwJsZze2keNxXlkh4f/S/xD6czEmvyxKpG7L0Bxu0Xa5ImsSaANLoHJEVYiBimh2HHG7Dqftj9HsQnQ81VMP92KKge7qsTQ6R//6a3vRuvrmPZr+m9IApN77F8cB5KUrOFGIFCftj+OtQ+CTvfNCLCCmcYC6NqFkOaZVDetjcU5g1HNy92unjX2UNQKcqSE7kkL5vL8rKZlp4iTW9x1KReg6ZpFcArSqlqiS4RQoxo/h4jovSTPxv7RGZebyytzCo5ppdTSrHC3ccfGjv4wNVLdnwcXy218pUS67A0vP2hMK9uMGJN6lo9ZCTHc3Uk1qQ8N7ZjTaTRPQApwkIIALq2GLEm658yimT5SbDgdph4vsSaxJA904Iv29y8GWl677lF/mJrNvOz046p6S0H5+iQmi3ECNdrMya81z8B7evBFA8TzjGiTSaeB/FJg/K2nmCIZXYPS7vcfODqIaxgXEoSl+Znc2memUlpyYPyvmLsidV6rWlaoVKqPfLP/w+Yr5S6VtO0acAT7FtG+TYwQZZRCiFGlD47fPg7+OxBQIN5X4GTvn1cd2uv7e7j/xo6edPRTU5CHF8rzePWEgtpcUPf8FZKsbbJzaOfNLAsEmty5uQ8bl5YyaLxuTH5xb40ugcgRVgIcQCvC9b+Cz59ADxNkFUG874Ms74IqTnDfXViCPWFw7zt6OGlyC3yXl2RF2l6X3KUubCxenA+FE3TZgL3A8lACLhDKfXp4Z4nNVuIUaRzsxFtsuEZ6O2A5GyovtKINimZC4N0KHMEQrxqc7O0y80n7l4UMDktmcvyjKZ3ZergNNvF2BCr9VrTtH8BMzGiSxqAr+7X+L4HuBWjXn9LKbXscK8n9VoIMSzcTfDe/xqfPxLSYNGdsOAOSEo/5pdc193Pb+rbecfZQ25CPN8sy+PGYgupQ5zhvUeHx8fjqxp5YlUTjr4A4/PSI7EmxaQmxs6AnjS6ByBFWAhxUHoYtr0Gq/4GDR8aC7dmXAvzvwp5U4b76sQQ6wuFeTMy6b0nF7YgMYGL8rK42JrNCYdpesfqwflQNE17A/g/pdQyTdMuAL6vlDrtcM+Tmi3EKKSHYfe7xh1TW16BkBdyxxs1dfq1kF06aG/d6Q/yss3NS11uPvX0ATA9PYVL881ckpdNaXLioL23GJ2kXkeH1GshxLDq2grv/By2vgJpVjjlezDn5uO6s+wzTx+/qW/nA1cveYnx3Fmezw2FuSQPU8PbF4zEmnxSz8bWbjKS47lmbik3nlhBWW7qsFzTUJJG9wCkCAshDqtjI3z6N2MqLeQztjvPvx0mngumob91SQyvPU3vl7rcvO3sxh9ZhnaRNYtL8szMyUz9t6a3HJwPFMn4fFgp9bSmadcBFyulvnC450nNFmKU83XD5qVG07vxI+P3Kk42prynXgJJGYP21q2+AC93uXmxy01tTz8AczJTuSzPzMV52RQkJQzae4vRQ+p1dEi9FkKMCM2fwdv/aQyuZZfB6fcYO7mO4wy/wt3Lr+vbWeHuoygpgbvK87muMIdE0/A0vI1YExePfNzAso0d6Epx5uR8bllUwcJxYzfWRBrdA5AiLIQ4Yn0OWPsPI/uruxXMFTDvNmPpRUr2cF+dGAa9+zW934k0vYuSEvbGm8yONL3l4HwgTdOmAMsBDTABC5VSjYd47G3AbQBlZWVzGhsP+jAhxGjjajC+QF7/JDh3Q0IqTLnYaHpXnjKoXyQ3ev0s7XKztMvFpl4fGjA/K43L8s1caM3CmihN71gl9To65IwthBgxlIJd78BbP4OODZA3Fc74MUw6/5hj1JRSfOzu5Ve7O/isu4/ipAS+XVHA1QU5JJiGr7Hc7vHy+Momnvi0CWdfgIn56dy0sILLZ429WBNpdA9AirAQ4qiFQ8ZtUKv+Bk2fGPlfM6+DeV8F68ThvjoxTHpCYd6we3jJ5uZdRw8BpShOSuCivGz+a0JJzB2cNU17Cyg4yI/uAc4E3ldKPadp2tXAbUqpsw73mlKzhRiDlILmT42G98bnwe+BzGKYfrXR9LZOGtS339Hn29v03tHvxwScbM7g0rxszrdmYU4YWwdDMTBpdEeH1GshxIij67D5RXjnv8G5C0rmwVk/g4pFx/ySSinec/bw6/oO1vX0U56cyLcrCrgy30z8MDa8fcEwL69v49FPGtjU1k1mcjzXzivjiwvKKc0ZG7Em0ugegBRhIcRxaauFT/8Odc9COADjzjRiTcafBcN0+5IYft17mt5dbt5z9tB8+kw5OO9H0zQPkK2UUppxP51HKZV5uOdJzRZijAv6YPsyqH0Sdr4FKgxFs42Gd/WVkJY7aG+tlGLLfk3vBm+ABE3j1Byj6X2eJYuMeIkrG+uk0R0dUq+FECNWOAi1jxtLK3vaYfzZcOZPoHD6Mb+kUoq3HN38pr6DDb1eqlKS+E5FPpflm4kbxugQpRSrG108+nEDr2/qQCnFOVMLuPWkSk6oMI/qWBNpdA9AirAQIip6bbDmUSPWpLcDcqqMCe+ZX4Dkw/bvxBjmCYbITkyQg/N+NE3bAnxNKfWepmlnAr9WSs053POkZgsRQ3o6YeMSY9K7ow5MCcZujBnXwoRzIX7wFkkqpVjf42Vpl4uXuty0+oMkmTTOzMnkkrxszrZkkhYnTe+xSBrd0SH1Wggx4gW9xsDah78HnxuqF8PpP4Lcccf8kkopXrd7+E19B5v7fExITeK7lQVcbM3+tx1OQ63N7eVfKxt5YlUTHm+Q6uJMbl1UyUXTi0iMH30DetLoHoAUYSFEVIUCsOUlI9ak5VNIzIBZ1xtZ3sdRNMXoJgfnA2madhLwRyAe8AF3KKXWHO55UrOFiFEdG42Gd92z0NsJKWbjQDrjOiiefcwZm0dCV4q13f282OXi5S43nYEQKSYT51gyuTQvmzNyMkmOG30HRHFwUq+jQ+q1EGLU8Lrhkz/BivtAD8Lsm+DU70PGwRIYj4yuFK/YPPymvp0d/X6mpCXzvcoCzrdkDfsUdX8gxAvrWnn4o3p22fqwZiTxxQXlfGF+GZb0pGG9tqMhje4BSBEWQgya1jVGw3vj80bRnHCOEWsy7oxBPZSLkUcOztEhNVuIGBcOwe73YP0TsPVVCPkgd4Ix5T39GsguHdy3V4qV7l6Wdrl5xebGGQyTHmfiPEsWl+Zlc2pOBokSWzaqSb2ODqnXQohRp6cDPviNcZe2KQEWfA0W3QUp2cf8kmGlWNrl5nf1Hezy+qlJT+F7lQWcnZs57A1vXVd8uNPOwx/V8/52G4nxJi6bWcQtiyqZUjjy70iXRvcApAgLIQZdTyeseQQ+ewj6usAy0ZjwnnEdJKUP99WJISAH5+iQmi2E2Mvngc1LYf1T0PgxoEHFSUZk2JSLISljUN8+pCs+cvewtMvNazYPnlCY7Pg4LrBmcVmemYXZ6cO6iEocG6nX0SH1Wggxajl3w7v/Y9xFlpwFJ38X5n8V4o992jmkK57vcvG7+g4afQFmZaTy/coCTsvJGPaGN8DOrh4e+biB59a24AvqLByXy62LKjljch6mEfpZRhrdA5AiLIQYMiE/bHoRVv0V2tZBUhbMugHmfQVyKof76sQgkoNzdEjNFkIclKsBNjxjxJs4d0NCqtHsnnEtVJ4KpsHN0w7oOu85e3ipy83rdg+9YZ3chHgusmZxaZ6ZBdlpw57NKY6M1OvokHothBj1OurgrZ8Zy7HNlXD2fxmfLY6jngd1xbMdTn7f2EGLL8gJmWl8v7KAk8zpI6Lh7e4P8OSnzfxzRQPtHh8VuancvLCCxXNLSU+KH+7LO4A0ugcgRVgIMeSUgpbPYNX9xjSaHoZJ5xvfFFeeKrEmY5AcnKNDarYQYkBKQfOnRsN70/PG1HdGEUy/2riLKm/yoF+CN6zzjrObpV1u3rR78OqKgsQELs4zJr1nZ6aOiMOsODip19Eh9VoIMWbsfAuW/wfYtkD5Ijj3F1A067heMqDrPNXu5A+NnbT5gyzISuMHVYWcmD0y7vYOhnVe39jBwx/Xs67JTUZSPNecUMpNCysozUkd7ssDpNE9ICnCQohh1d0Gqx+G1Y9Avx2sU4yG9/RrIHFkFBFx/OTgHB1Ss4UQRyzog+2vG03vHW+CCkPhTKPhXbMY0iyDfgl9oTBvOoym99uObgJKUZKcwKV5Zhbnm5mSnjLo1yCOjtTr6JB6LYQYU8IhWPdPeOcXxpl9xnVw5k8gs+i4XtYX1nm83cGfGjvpDIQ42ZzODyoLmZuVFqULP35rm1w88nEDr9W1o5TinKkF3HpSJSdUmIf1i3tpdA9AirAQYkQI+mDjc0asSUcdJGfD7BuNWJPssuG+OnGc5OAcHVKzhRDHpNcGG5cYTe/29WCKNxZEz7gWJp53XLmbR6o7FOZ1u4elnW7ed3UTUlCdnsJVBWYuzzOTl5Qw6NcgDk/qdXRIvRZCjEk+D3z4e1h5H2hxxrLKRXdC4vE1pr1hnX+22flzYxf2YIjTczL4XmUBszNHTsO73ePlnysaeWJVEx5vkOriTG5dVMmF0wtJih/ciLiDkUb3AKQICyFGFKWgaaURa7LlZUDB5Ath/u3GrVJyu/OoJAfn6JCaLYQ4bp2bjYb3hmegt8P4Yrn6CpjxBSiZOyR11h4IsbTLxbMdLmp7+onT4FRzBlcX5HCuJYuUONOgX4M4OKnX0SH1WggxprkajPzuTS9ARqEx3T39WjAdX/3uC4d5pMXOfc1dOINhzsnN5HuVBdRkjJw7vb2BMM+va+GRjxvY2dWLNSOJLy4o5wvzy7CkD/7gwB7S6B6AFGEhxIjlbobVD8GaR8HrgvxqI9ak5ipIkNudRxM5OEeH1GwhRNToYdj9Hqx/yvhiOeSFnHHG7cjTrwZz+ZBcxvY+H0s6nDzX6aLVHyQjzsRFedlclZ8jSyyHgdTr6JB6LYSICU0r4fW7oW0tFM6Ac38JFYuO+2V7Q2EearHz1+Yu3KEwF1iy+F5lwYiKPFNK8eEOOw9/XM9722wkxpu4bGYRtyyqZEph5qC/vzS6ByBFWAgx4gW9xuTZqr9B1yZIyYE5N8MJX4KskuG+OnEE5OAcHVKzhRCDwt9jLIde/xQ0fGj8XvlJRrTJ1EshefAPbLpSfOLu5dkOF6/Y3PSFdUqSE1icn8PiAjPjU5MH/RqE1OtokXothIgZum7Eo731M+huhSkXw1n/Cbnjjvulu0Nh/t5s42/NXfSEdS7Jy+Y7FQVMShtZnwl2dvXy6Cf1LFnTgi+os3BcLrcuquSMyXmYTIPzhX3MNbo1Tfsu8BvAqpSyD/RYKcJCiFFDKWj4yIg12fYaoBmFdP7tULZAYk1GMDk4R4fUbCHEoHM3wYanofZJcO6C+BSYcpHR9K46HUyDn0PZH9Z53e7h2Q4n7zt70IHZmakszjdzWb6ZnIT4Qb+GWBXL9VrTtG8C3wBCwKtKqe9Hfv9u4EtAGLhTKbX8cK8l9VoIEXMC/bDiL/DR/0E4YNyJfcr3ICX7uF/aFQzxt2YbD7TY6A/rXJFv5tsV+YwbYV+Cu/sDPPVZM//4pIF2j4+K3FRuXljB4rmlpCdF97NLTDW6NU0rBR4EJgNzpNEthBiTXI3w2QOw9p/GUozCGbDgDqi+EuJkodVIE8sH52iSmi2EGDJKQesaqH3CWBbtc0N6AUy/ysjzzp86JJfR6Q/yfKeLZzucbO7zkaBpnJWbyeICM2flZpJ0nHmg4kCxWq81TTsduAe4UCnl1zQtTynVpWnaVOBJYB5QBLwFTFRKhQd6PanXQoiY1dMB7/w3rHsMUsxw+o+Mu7GjcEZ3BELc19zFwy12/LrOVQU5fLsin/KUocvGPhLBsM7yTR08/FE9a5vcZCTFc80Jpdy0sILSnOjkjcdao3sJ8HNgKTBXGt1CiDEt0GdMnq36G9i2QmYJnPh1mH0jJKUP99WJiFg9OEeb1GwhxLAI+WH7ciPaZMdy0ENQMN3I865ZDOl5Q3IZm3q9PNvh5PlOF12BENnxcVyal81VBTnMyUxFkzu7jlus1mtN054B/q6Ueutzv383gFLql5FfLwd+ppRaMdDrSb0WQsS89g2w/EdGJJplIpzzC5hwdlTuwrYFgtzb1MU/Wu2ElOKaghy+VVFAaXJiFC48utY1uXjk4wZeq2tHV4pzphZw60mVnFBhPq7PLTHT6NY07RLgTKXUXZqmNXCIRremabcBtwGUlZXNaWxsHNoLFUKIaFMKdrwBH/0Bmj6B5GyYd5txy1SaZbivLubF6sE52uTgLIQYdn12Y8J7/ZPQtg60OBh/Fsy8DiaeDwmDfxtxSFd84OphSaeLZTY3Xl1RmZLIVQU5XJlvHnGTXaNJrNZrTdNqMQbFzgN8wHeVUp9pmnYvsFIp9VjkcQ8By5RSSw7yGnLGFkKI/SkF25bBG/9hxKFVnQ7n/gLyp0Xl5Tv8Qf7c2Mm/2hwo4AuFOdxVnk/RCGx4t3u8/GtFI0982oS7P0h1cSa3LqrkwumFJMUffSzcmGp0a5r2FlBwkB/dA/wIOEcp5Rmo0b0/OTQLIcac5k+Nhve2V41s0Vk3wMJvgLliuK8sZsXqwTnapGYLIUaUrq2w4SlY/zT0tEFSFlRfbkx6l84fkt0ZPaEwr9jcLOlw8bG7F4AFWWlcVZDDxXnZZB7D4TGWjeV6fZhz9C+Ad4C7gBOAp4Eq4F5gxeca3a8ppZ4b6L2kXgshxH5CAVj9ELz3v+DvNu6+Pv2eqN0R1uoL8MfGTp5sd2LS4ItFuXyzLJ/8pJEXaeoNhHlhXSsPf1zPzq5erBlJfHFBOV+YX4Yl/ci/qB9Tje5D0TStBngb6I/8VgnQBsxTSnUc6nlShIUQY5ZtG3zyJ+MArnSYdjksugsKpw/3lcWcsXxwHkpSs4UQI5IehvoPjGiTLS9BsB/MlUbDe/rVkFM5JJfR7AvwfIeLZzud7Oz3k2zSOMeSxVX5Zk7LySTBJNEmhxOr9VrTtNeB/1VKvRf59S5gAfBlkOgSIYSIin4nvP9rY9dWfAqc/G1jz1aU7gZr8vr5Y2MnT3U4SdA0biq28I2yPKyJI6/hrZTiwx12Hv64nve22UiMN3HZzCJuWVTJlMLMwz4/JhrdnycT3UIIEdHdBivvg9WPQKAXxp1pNLwrTxmSaTMRuwfnaJOaLYQY8fy9sOVlI9qk/gNAQdlCmHEtTLsMkrMG/RKUUtT2GHneL3a5cAbDWBLiuSLfzOICMzXpKZLnfQixWq81TbsdKFJK/UTTtIkYA2RlwFTgCfYto3wbmCDLKIUQ4jjYd8CbP4Ftr0FWGZz9M5h2RdTO5g1eP79v6GBJh4skk4kvlVi4oyyPnIT4qLx+tO3s6uXRT+p5bk0r3mCYheNyuXVRJWdMzsN0iC/ppdE9ACnCQoiY4XUbt0ytvB/6uqBottHwnnIxmOTW5sEUqwfnaJOaLYQYVdzNUPcM1D4Jjh0QnwyTLoCZX4BxZwxJ7Q3oOu86e3imw8mb9m4CSjEpLZmr8s1cWWCmMGnk5XgOp1it15qmJQIPAzOBAEZG9zuRn90D3AqEgG8ppZYd7vWkXgshxBHY/R4s/w/orIOSeXDeL6EkeiVoV7+P3zV08kKni9Q4E7eVWLm91ErWCG14u/sDPPVZM//4pIF2j4+K3FRuXljB4rmlpCcdeM0x2eg+UlKEhRAxJ+iD9U/Ax38CVz3kjIOF3zRusR6CJVqxKFYPztEmNVsIMSopBW1rjWiTuiXgdUJWKcy+CWZ/ETIOFpscfa5giJe6jDzvz7r70ICTzelcVZDDBZYs0iTPW+p1lEi9FkKII6SHofYJeOfn0NsJNVfBmT+F7NKovcW2Ph+/re/gZZub7Pg4vl6Wx60lFtLiRmbdD4Z1lm/q4OGP6lnb5CYjKZ5rTijlpoUVlOakAtLoHpAUYSFEzNLDRpboR3+A9lpIz4f5t8MJXxqSW6tjiRyco0NqthBi1AsFjFuV1zxiTHJpcTD5AphzC1SdDibTkFxGfb+fZzudLOlw0eQLkBpn4gJLFlcX5LDInE5cjEabSL2ODqnXQghxlPw9xrl8xb3Gr0/8Bpz0LUjKiNpbbOzp53/rO3jL0Y01MZ67yvP5YlEuSUP02eNYrGty8cjHDbxW146uFOdMLeDWkyqZX5Urje5DkSIshIh5SkH9+0Zh3f0uJGbA3FuMxRiZhcN9dWOCHJyjQ2q2EGJMceyCNY9C7ePQ7wBzBcy5GWbeAOnWIbkEpRSfevp4tsPFSzYX3SGdgsQEriwwc1WBmclpKUNyHSOF1OvokHothBDHyN0Mb/+XEX2Wng9n/AfMvD6qcWefefr45e52PnH3UpyUwHcqCri6IIf4Eby0ut3j5V8rGnni0ybc/UEaf3WRNLoPRYqwEELsp60WPv4jbH4RTPEw/Rojx9syYbivbFSTg3N0SM0WQoxJIb+xwHL1I9D4EZgSjP0Zc2+BipOHbHG0L6zzhqObZzucvOPsJqxgenoKiwvMXJ5vxpqYMCTXMZykXkeH1GshhDhOLath+Y+geRXk18C5v4CqU6P28kopPnT18svd7azr6acqJYnvVxZwSV42phF8V5c3EOaFda1cv6BcGt2HIkVYCCEOwrkbPrnXmDIL+WHyhXDS/4vqcoxYIgfn6JCaLYQY82zbIlPeT4DPDbkTIlPeX4DUnKG7jECQpV1unulwsqHHS5wGp+dksjjfzLmWLFLiRu5tzsdD6nV0SL0WQogoUAo2vQBv/RTcTTDxfDjnv8EyPopvoXjD0c0vd7eztc/H1LRkflhVyNm5mWgjuOEtGd0DkCIshBAD6LXBp3+DTx8wDtzlJxlZYePPGrIJs7FADs7RITVbCBEzgl7Y9KKR5d28CuKSYNplMPdWKJ0/pDV4W5+PJR1Onut00eYPkhFn4pK8bBYX5DA/K21ET34dLanX0SH1Wgghoijog1V/hQ9+ByEvnPAVOPX7Uf0CXFeKpV1ufl3fTr03wOzMVH5UVchJ5uhlhEeTNLoHIEVYCCGOgL8H1v4TVvwFulshv9qINJl2BcTFD/fVjXhycI4OqdlCiJjUucmINdnwNPi7wTrFiDWZfg2kZA/ZZYSV4hNXL892OnnF5qE/rFOanMg1BTlcV5hDcXLikF3LYJF6HR1Sr4UQYhD0dsG7/wNr/wFJmXDqD+CEL0N89OpvUFc80+Hk9w0dtPqDnJSdzt1VhczJSovae0SDNLoHIEVYCCGOQigAG5cYOd62rZBVBgu/AbNugMSRVfxGklg9OGuadhXwM2AKME8ptXq/n90NfAkIA3cqpZYf7vWkZgshYlqgDzY+B6sfhrZ1EJ8C1VcaU97Fs4d0yrsvHGaZzcMzHU4+cPViAs7IzeSGwlzOys0c0QutBhKr9TrapF4LIcQg6twMb9wDu96BnHFGnMmk86P6OcAX1vlXm4M/NnZiD4Y4OzeTH1YVMi19ZCyplkb3AKQICyHEMdB12LEcPvoDNK+ElByY/1WYd9uQZoiOFrF6cNY0bQqgA38Dvrun0a1p2lTgSWAeUAS8BUxUSoUHej2p2UIIEdFWa8SabHgWgn1QUGM0vGuugqShvc240evniXYnT7U76AyEyE+M59rCXL5QmEN5StKQXsvxitV6HW1Sr4UQYpApBTvfguX3gH2bsbz63P+BwulRfZu+UJiHWu38pakLTyjMpXnZfK+ygPGpyVF9n6Mlje4BSBEWQojj1LTSaHhvXwYJqTD7Rjjx65BdNtxXNmLE+sFZ07T3OLDRfTeAUuqXkV8vB36mlFox0OtIzRZCiM/xdUPds0a0SWcdJKZDzWKj6V04Y0gvJaQr3nJ081i7g3cc3ejAKeZ0biiycJ4lk0TTyF9gGev1OlqkXgshxBAJB40l1u/+D3hdMOt6OOPHkFEQ1bfxBEP8tdnG31ts+MI61xTm8O2KAkqHKbZMGt0DkCIshBBR0rUFPv4T1D1jfMNcfaWR411QPdxXNuxi/eB8kEb3vcBKpdRjkV8/BCxTSi05yHNvA24DKCsrm9PY2Dhk1y2EEKOGUtC6xog12fi8sayqaLbR8K6+Ysjjxdp8AZ5sd/JEu4NWf5DchHiuLjBzfVHusE+BDSTW63W0yBlbCCGGmNcNH/4WVt4PcYlw0v8zIkYTohs1YgsE+XNjF/9os6MU3FCUy7fK88lLSojq+xyONLoHIEVYCCGizNMCK+4zvlkO9sH4s+Gkb0H5oiHNDx1JxvLBWdO0t4CDjQzco5RaGnnMexzY6P4LsOJzje7XlFLPDfReUrOFEOIIeN3G4srVDxv7NJIyYca1MOcWyJ86pJcSVor3nT083u5gud1DSMGCrDRuKMrlQms2KXEja8p7LNfroST1WgghholzN7z5U9jyEmSWwFk/herFEOW7qlp9Af6voZMnOxwkahpfKrHy9bI8zAnxUX2fQ5FG9wCkCAshxCDpd8JnD8Gq+6HfDsVzjYb3pAujXmhHulg/OEt0iRBCDAOljHix1Q/D5qUQ9kPpAph7C0y9NOpTXofT5Q/ydIeTx9sdNHgDZMXHsTjfzA1FuUwZo8utYpXUayGEGGYNH8PyH0F7LRTPMfK7yxZE/W3q+/38tqGD5ztdpMeZ+FpZHreVWEmPj4v6e+1PGt0DkCIshBCDLOiF2sfhkz+DqwFyJ8CiO2H6NRA/upZUHatYPzgfpNE9DXiCfcso3wYmyDJKIYQYJH0OWP+EkeXt3AXJ2TDzephzM1gnDuml6ErxibuXx9ocvGbzEFCK2Zmp3FCYy6V52aQN8uF4ILFer6NF6rUQQowAum7c4fX2f0JPO0y7HM7+r0HZpbWl18uv6ztYZveQkxDHnWX53FRsGbQ7t6TRPQApwkIIMUTCIdiy1Fhc2bEBMgphwdeMW6mTM4f76gZVrB6cNU27HPgzYAXcQK1S6tzIz+4BbgVCwLeUUssO93pSs4UQ4jgpBfUfwJpHYMsroAeh4mSj4T3l4iH/AtoRCLGk08ljbQ529PtJjzNxRb6R5T0jI3VIrwVit15Hm9RrIYQYQQJ9xtDZR38AlLFHa9G3IDH6dXZtdx+/2t3B+64eChIT+HZFPtcV5pJgim6EqTS6ByBFWAghhphSsPtdo9DWvw9JWXDCrTD/a5CRP9xXNyjk4BwdUrOFECKKertg3WPGTg13I6TmwqwbjKZ3TtWQXopSis88fTzW7uDlLjdeXVGTnsL1RblckW8mc4imvKVeR4fUayGEGIE8LUZ+98YlkFlsTHdXXzkoe7Q+dvXwv7s7+Ky7j/LkRL5bWcAV+WbiovRe0ugegBRhIYQYRq1r4eM/GssyTAkw8zpYeCfkjhvuK4sqOThHh9RsIYQYBLoOu98xYk22LQMVhqrTjSzvSRdAXMKQXo4nGOL5LjePtznY2OslxWTikrxsbijKZW5mKtogLraO1XqtadrTwKTIL7MBt1JqZuRndwNfAsLAnUqp5Yd7PanXQggxgjWugNd/AO3rjd0d5/8vFM2K+tsopXjb2cOvdrdT1+tlYmoyP6gq4AJL1nHXcml0D0CKsBBCjACOXcbtVLVPQDgAUy+BU74HBTXDfWVREasH52iTmi2EEIOsux3W/QvW/AO6WyA9H2Z9EebcNCiZngNRSrG+x8vj7Q6e73TRF9aZlJbM9YU5LC7IISchPurvKfUaNE37HeBRSv2XpmlTgSfZt1PjLWCi7NQQQohRTg8be7Te/i/osxt3dJ35E0jPi/5bKcUrNg+/qW9nR7+f6Rkp/LCykNNzMo654S2N7gFIERZCiBGkpxNW3Q+fPQj+bph8EZz6AyicPtxXdlzk4BwdUrOFEGKI6GHY8aaR5b09MsA74Wxjr8aEcyAu+k3mgfSFwiztcvOvNgfrevpJMmlcaM3m+sIcFmanR23KO9brtWb8QTYBZyildkSmuVFK/TLy8+XAz5RSKwZ6HanXQggxSvg88MFvYOX9kJBiDJvNvx3iE6P+ViFd8Vyni982dNDsC7AgK40fVhWyIDv9qF9LGt0DkCIshBAjkNdlFNuVfwW/Z9Q3vGP94BwtUrOFEGIYuJth7T+Nv3o7jFzP2Tcaf2UWDfnlbO718libg+c6XXhCYapSkvhCYQ7XFOZgTTy+mJVYr9eapp0C/H7Pn4GmafcCK5VSj0V+/RCwTCm15CDPvQ24DaCsrGxOY2Pj0F24EEKI42PfCW/cA9tfh5xxcN4vjS+2ByEuLKDrPN7u5A8NHXQGQpyek8EPqwqPagm1NLoHIIdmIYQYwbxuY8J7xX1Gw3vShXDaD6BwxnBf2VGJ9YNztEjNFkKIYRQOGgfg1Q/DrndAi4OJ58G8r0DVaYNyGB6IN6zzis3I8l7p6SNeg3Mt/5+9+46P5K7vP/76zsx2rXqXTqfrvdlXbNwrLoBNMTEQagKB0DsEfgmEECCQhARICKGZEoxxBxdwO9u43/l670Xt1NvWmfn+/piVTrqiO5/3blfS5/l47EO7szOr71eS/bl573c/U8Rf1pRxWWkU4wzGM5HrtVLqUaD6BE99SWt9X2af/wZ2a63/NfP4B8BzxwTdD2qt7xrre0m9FkKIcWrXo/DHL0LHTph5Nbz2G1Ax+6x8q5jj8rOmDr5/oI1u2+HGiiI+N62GOZHgKY+VoHsMUoSFEGIciPfAC/8Dz//A+3jVnBu8Fd61S3M9stMykU+czyWp2UIIkSe69sHLt8HLv4RYB1TMgws+BIvf6n30+RzbNZjg1y2d3NHaRVfaoT7o4+01ZbytppSawOl//Hoy12ullAU0AedrrQ9ntknrEiGEmGycNLz4v7D6m5AehJUf8M69Q8Vn5dv12w4/PHSE/znUzqDj8uaqEj47rZqpocBJj5GgewxShIUQYhxJ9HqB93Pf9+7Pvt5b4X0WrhKdTZP5xDmbpGYLIUSesZOw+S7vk1dtmyBUCsvfByv+Ggprzvlwkq7Lwx29/Kq5k6e7BzCAq8sK+cvaMq4sLcQyxl7lPZnrtVLqOuCLWuvLRmxbAPwfRy9G+RgwSy5GKYQQk8BgBzz+T7D25xAuhSu/DOe9GwzzrHy7zpTNDw4e4adN7dha8/aaMj7ZWHXCN6wl6B6DFGEhhBiHEr3wwo8ygXeP99Hpyz4PdeflemQnNJlPnLNJarYQQuQpreHAM961NbY/4J0EL3gTXPBBqDs/J0PaH0/yf82d3N7axZGUTbXfx9tqSnlbTSkNJ1klNpnrtVLq53j9uH94zPYvAe8DbOATWuuHTvVaUq+FEGICadkID3/Bq/NVi+D6b0LjxWft27Um03z3QBu/bu7EVPCeunI+2lBFmf/ohbAl6B6DFGEhhBjHEn3w4v/Acz/wLmA567XeCu8cnVSfzGQ+cc4mqdlCCDEOdO2DF3/ktTVJ9cOUVV5bk7mvB9M69fFZlnY1j3b28qvmLp7o6kMDl5VEeUdtGa8tL8RvGMP7Sr3ODqnXQggxwWgNW++FP/0/6D0E82+Ga78GxQ1n7VseiCf51/2t3NnaTcg0+EB9BR9qqKTQMiXoHosUYSGEmAASfd5J9XPfzwTe18JlX4D6/Ai85cQ5O6RmCyHEOJLog/W/9i4q3b0fiqZ4F648710QKsnJkJoSKX7T0sVvWjppSqYp91m8tbqUd9SWMiMclHqdJVKvhRBigkrH4dnvwdP/Bmh4zcfg4k+AP3LWvuXOwQTf3tfK79t7KLZMPtxQyccaqyXoPhkpwkIIMYEk+73A+9nvQ7wLZl4Dl38B6nN7zionztkhNVsIIcYh14GdD3ttTfY/Db4wLH07rPoglM/KyZAcrVnd1c+vmzv5Y2cvjobXFBdwz3mzpF5ngdRrIYSY4HoPw6NfgU2/g8I6uPqrsOgtoMa+Fsarsak/xjf3tvJYVx9tVy6ToPtkpAgLIcQElOz3rhT97PcygffV3grvKStyMhwJurNDarYQQoxzrZvg+R/CpjvASXmfwLrgQzD9irN6cjyWI8k0v23t4lfNnbz4mgVSr7NA6rUQQkwSB5+Hhz4PLeu9VmXXfwtql53Vb/lizwCrSqJZrdfGqXfJDaXUV5RSTUqp9ZnbDbkekxBCiBwIROGST8EnNsHVX4HmdfCTq+GXb4JDL+Z6dEIIIcTkVL0Ibv4BfHILXP530LwefvlG+K8LYO3PIRU750OqDPj46NQqnrtg3jn/3kIIIcS41nABvP8JeMP3oWsv/OgKuO/D0N921r7lyuKCrL9m3gbdGf+utV6auT2Y68EIIYTIoUABXPxJ+PhG7+NULevhJ9d4J9UHX8j16IQQQojJqaDSu3j0JzfDzT8E0w+//zj8+3x49KvQ13zOh2TkaEW5EEIIMa4ZBpz3Tvjoy/Caj8KG38L3zodn/gPsZK5Hd1ryPegWQgghRgsUeBfJ+PhGuOYfoWUj/PRa+MXN3sethBBCCHHuWQFY+jb4m6fgPQ/C1Ivgz/8O310Ed/4VHF6b6xEKIYQQ4nQEC+Har8GHX4DGi+GRv/c+sbXjIcjzFtj5HnR/RCm1USn1U6VUbi7nLYQQIj8FCuCij8MnNsK1/wRtm+Gnr4Vf3AQHnsv16IQQQojJSSlovAhu/TV8bB2s/BvY9Sf48ZXw42tg893g2LkepRBCCCFOpWwGvP12+Mu7wPDBb26FX70Z2nfkemQnldOgWyn1qFJq8wluNwH/DcwAlgItwL+e5DU+oJRao5Ra097efu4GL4QQIj/4I97Hqj6+Ea79OrRtgZ9dB7e9AQ48m+vRCSGEEJNX6TS47p/hU1vh+n+BwXa4873wH0vgz9+FWFeuRyiEEEKIU5l5NXzoGbjum3B4DfzXhfDQFyDeneuRHUfpPF9yDqCUagT+oLVeONZ+ckVoIYQQpGKw9mfeCfTgEZh2KVz2BW91WRYopbJ6VejJSmq2EEJMQq7jre5+/r9g31PgC8OSt8GqD0LF7Kx+K6nX2SH1WgghxCiDHfDE170LTweL4covw/nvAcM8o5fLdr3O29YlSqmaEQ/fCGzO1ViEEEKMI/4wXPhh+PgGeO03vI9V/fwG+PnrYP+fcz06IYQQYvIyTJhzPbz79/DBZ2Dhm2Ddr+AHK7yPQu9+NO97fwohhBCTWqQcXvfv3jU5KufDA5+C/7kU9j2d65EBeRx0A/+ilNqklNoIXAF8MtcDEkIIMY74w3Dh33qB93XfhI6d8PMb4Wc35k0RFkIIISat6oVw0w/gk1vgii9B6yYv7P7BKljzU+8TWkIIIYTIT9WL4D1/gFtug0Qf3PY6uONd0H0gp8PK26Bba/1OrfUirfVirfUbtNYtuR6TEEKIccgXggs+lAm8vwWdu70i/LMbvY9Ny8oxIYQQIncKKuCyz8EnNsEb/wd8QfjDJ+Hf58OjX4HeplyPUAghhBAnohQsuBk+8iJc8WXY9Qh8fwU8/k+QGszJkPI26BZCCCGyyheCCz4IH1/vXRCrczfc9npvlffeJyXwFkIIIXLJCsCSW+EDT8J7H4bGS+CZ/4DvLoI73+dd/EoIIYQQ+ccXgss+Cx9ZA/Nvgqe+Dd9bDhvvOOfn2RJ0CyGEmFx8IVj1N94K7+u/DV374BdvgJ/dAHtXS+AthBBC5JJSMPVC+ItfwsfWe5/K2vUI/Pgq+PHVsOlOcNK5HqUQQgghjlVUB2/+X3jfnyBaBXe/H35yLTS9fM6GIEG3EEKIyckXhFUfgI+tgxu+A9374Rc3wc+uhz1PSOB9DKXULUqpLUopVym1fMT2a5RSazPX1VirlLoyl+MUQggxgZRMhdd+HT611XtzOtYJd/0VfHcxPP1vEOvK9QiFEEIIcayGVfDXj3vX4ujeD/97Bdz7YehvO+vfWoJuIYQQk5svCCvf77U0ueE70HMQfnkz/PQ62PO4BN5HbQbeBDx1zPYO4PVa60XAu4FfnuuBCSGEmOACUe/N6Y+shbf9Fipmw2NfhX+bD7//BLTvyPUI84ZSaqlS6nml1Hql1Bql1MoRz31RKbVbKbVDKfXaXI5TCCHEBMXL69EAAQAASURBVGcYsOwv4aNr4aKPw8bfwvfOhz9/F+zk2fu2Z+2VhRBCiPHECniB98fWwY3/Cr2H4ZdvhJ++FnY/NukDb631Nq31cUmC1nqd1ro583ALEFRKBc7t6IQQQkwKhgFzroN33QcfehYW3wLr/w9+sBJ++SbY9Si4bq5HmWv/AnxVa70U+PvMY5RS84FbgQXAdcB/KaXMXA1SCCHEJBEshGv+ET78Aky7BB79B/jBKtj+4Fk5x5agWwghhBjJCsCKv4aPvQw3/hv0NsGv3gQ/uQZ2P5rr0eW7NwPrtNYnfIteKfWBzOqyNe3t7ed4aEIIISaUqgXwhu95bU2u/DK0bYFfvxn+axW89JNcjy6XNFCYuV8EDL0ZfRNwu9Y6qbXeB+wGVp7geCGEECL7ymbA234Df3k3mH64/W3eeXaWSdAthBBCnIgVgBV/5QXer/t36G+FX70516M6q5RSjyqlNp/gdtNpHLsA+BbwNyfbR2v9I631cq318oqKimwOXQghxGQVKYdLPwuf2ARv+l/wheGBT+V6VLn0CeDbSqlDwHeAL2a21wGHRux3OLPtOPLGtBBCiLNm5lXwoWfgum9B09qsv7yV9VcUQgghJhIrAMvfB0v/Etb/Gr76vlyP6KzRWl99JscppeqBe4B3aa33ZHdUQgghxGmw/LD4rbDoFjj0Anz1wlyP6KxRSj0KVJ/gqS8BVwGf1FrfpZR6K/AT4GpAnWD/E35mXGv9I+BHAMuXL5/cvduEEEJkn+mDCz7o1ewvlmf1pSXoFkIIIU6H5Yfl7wUmbtB9JpRSxcADwBe11s/keDhCCCEmO6Wg4YJcj+KsGuuNaaXUL4CPZx7+Dvhx5v5hYMqIXes52tZECCGEOPciZVl/SWldIoQQQohTUkq9USl1GLgQeEAp9cfMUx8BZgL/Tym1PnOrzNlAhRBCiMmtGbgsc/9KYFfm/v3ArUqpgFJqGjALeDEH4xNCCCHOGlnRLYQQQohT0lrfg9ee5Njt/wT807kfkRBCCCFO4P3AfyilLCABfABAa71FKXUHsBWwgQ9rrZ3cDVMIIYTIPgm6hRBCCCGEEEKICUBr/Wfg/JM893Xg6+d2REIIIcS5I61LhBBCCCGEEEIIIYQQQoxrEnQLIYQQQgghhBBCCCGEGNck6BZCCCGEEEIIIYQQQggxrknQLYQQQgghhBBCCCGEEGJcU1rrXI8ha5RS/cCOXI8ji8qBjlwPIktkLvlpIs0FJtZ8ZC75a47WOprrQYx3E6xmT6S/8Yk0F5hY85G55KeJNBeYWPORep0FUq/z2kSaj8wlP02kucDEms9EmktW67WVrRfKEzu01stzPYhsUUqtmSjzkbnkp4k0F5hY85G55C+l1Jpcj2GCmDA1eyL9jU+kucDEmo/MJT9NpLnAxJqP1OuskXqdpybSfGQu+WkizQUm1nwm2lyy+XrSukQIIYQQQgghhBBCCCHEuCZBtxBCCCGEEEIIIYQQQohxbaIF3T/K9QCybCLNR+aSnybSXGBizUfmkr8m2nxyZSL9HGUu+WsizUfmkp8m0lxgYs1nIs0llybSz3EizQUm1nxkLvlpIs0FJtZ8ZC4nMaEuRimEEEIIIYQQQgghhBBi8ploK7qFEEIIIYQQQgghhBBCTDISdAshhBBCCCGEEEIIIYQY1/I66FZKTVFKPaGU2qaU2qKU+nhme6lS6hGl1K7M15LM9muUUmuVUpsyX68c8VrnZ7bvVkr9p1JKjYP5rFRKrc/cNiil3pgv83mlcxlxXINSakAp9ZnxOhelVKNSKj7id/PD8TqXzHOLlVLPZfbfpJQK5sNczmQ+Sql3jPi9rFdKuUqppfkwnzOYi08pdVtmzNuUUl8c8VrjbS5+pdTPMmPeoJS6PF/mcor53JJ57Cqllh9zzBczY96hlHptPs0nV87g7yJva/YZzEXqdZ7OR0nNzsu5KKnX+TyfvK3ZY8xF6vUrcAZ/E1Kv83Q+I47Lu5p9Br8bqdd5OBeVx/X6DOeTtzX7DOYi9fpktNZ5ewNqgPMy96PATmA+8C/AFzLbvwB8K3N/GVCbub8QaBrxWi8CFwIKeAi4fhzMJwxYI449MuJxTufzSucy4ri7gN8Bn8mX380Z/F4agc0nea3xNhcL2AgsyTwuA8x8mMur+TvLbF8E7B3Hv5u3A7dn7oeB/UDjOJ3Lh4GfZe5XAmsBIx/mcor5zAPmAKuB5SP2nw9sAALANGBPPv13k6vbGfxd5G3NPoO5SL3O0/kgNTsv53LMsVKv82s+eVuzx5iL1Ouz+zch9TpP5zPiuLyr2Wfwu2lE6nXezeWYY/OqXp/h7yZva/YZzEXq9cm+/7n+Q3yVP6z7gGuAHUDNiB/gjhPsq4DOzA+qBtg+4rm3Af8zzuYzDWjD+59m3s3ndOYC3Ax8G/gKmSI8HufCSYrwOJ3LDcCvxsNcTvfvbMS+/wx8PV/ncxq/m7cBv8/8N1+GVxxKx+lcfgD85Yj9HwNW5uNcRs5nxOPVjC7EXwS+OOLxH/GKb17OJ9c/x9P87zWva/YrnIvU6zyaD1Kz83Iux+wr9Tq/5jNuajZSr8/J38Qx+0q9zrP5ME5q9mn8v6cRqdd5N5dj9s3ren2av5txU7NPYy5Sr09yy+vWJSMppRrx3k1+AajSWrcAZL5WnuCQNwPrtNZJoA44POK5w5ltOXO681FKrVJKbQE2AR/UWtvk2XxOZy5KqQjweeCrxxw+7uaSMU0ptU4p9aRS6pLMtvE4l9mAVkr9USn1slLqc5nteTUXOKP/B/wF8JvM/byaz2nO5U5gEGgBDgLf0Vp3MT7nsgG4SSllKaWmAecDU8izucBx8zmZOuDQiMdD4867+eTKRKrZUq+H5dVcQGp2vtZsqdf5Wa9hYtVsqdfZIfU6P+s1TKyaLfVa6vW5MJFqttTrV1evrVc8yhxQShXgfRznE1rrvlO1ZFFKLQC+BVw7tOkEu+msDvIVeCXz0Vq/ACxQSs0DblNKPUQezecVzOWrwL9rrQeO2Wc8zqUFaNBadyqlzgfuzfzNjce5WMDFwAogBjymlFoL9J1g33Hx30xm/1VATGu9eWjTCXbL99/NSsABaoES4Gml1KOMz7n8FO9jSmuAA8CzgE0ezQWOn89Yu55gmx5j+6QykWq21Ov8rNcgNZs8rdlSr/OzXsPEqtlSr7ND6nV+1muYWDVb6rXU63NhItVsqdfDzrhe533QrZTy4f1gfq21vjuzuU0pVaO1blFK1eD11hravx64B3iX1npPZvNhoH7Ey9YDzWd/9Md7pfMZorXeppQaxOuLlhfzeYVzWQW8RSn1L0Ax4CqlEpnjx9VcMisYkpn7a5VSe/DetR2Pv5fDwJNa647MsQ8C5wG/Ig/mkhnTmfw3cytH322G8fm7eTvwsNY6DRxRSj0DLAeeZpzNJbNS5pMjjn0W2AV0kwdzyYzpRPM5mcN475YPGRp3Xvyd5dJEqtlSr/OzXoPU7Hyt2VKv87New8Sq2VKvs0PqdX7Wa5hYNVvqtdTrc2Ei1Wyp18NeVb3O69Ylynvr4ifANq31v4146n7g3Zn778br94JSqhh4AK+3yzNDO2eW9/crpS7IvOa7ho45l85gPtOUUlbm/lS8pu3782E+r3QuWutLtNaNWutG4LvAP2utvz8e56KUqlBKmZn704FZeBdlGHdzwet9tFgpFc78rV0GbM2HucAZzQellAHcAtw+tC0f5nMGczkIXKk8EeACvP5U424umb+vSOb+NYCttR4Pf2cncz9wq1IqoLyPic0CXsyX+eTKRKrZUq/zs16D1GzytGZLvc7Peg0Tq2ZLvc4Oqdf5Wa8zY5owNVvqtdTrc2Ei1Wyp11ms1zrHzeLHuuF93EPjXbF2feZ2A17T+Mfw3q14DCjN7P9lvH4760fcKjPPLQc241298/uAGgfzeSewJbPfy8DNI14rp/N5pXM55tivMPqK0ONqLni96bbg9UR6GXj9eJ1L5pi/zMxnM/Av+TKXVzGfy4HnT/Ba4+p3AxTgXT19C7AV+Ow4nksj3kU0tgGPAlPzZS6nmM8b8d5FTuJdrOiPI475UmbMOxhx5ed8mE+ubmfwd5G3NfsM5iL1Ok/ng9TsfJ7L5Ui9zsf5NJKnNXuMuUi9Prt/E1Kv83Q+xxz7FfKoZp/B70bqdf7O5XLysF6f4d9Z3tbsM5hLI1KvT3hTmQOFEEIIIYQQQgghhBBCiHEpr1uXCCGEEEIIIYQQQgghhBCnIkG3EEIIIYQQQgghhBBCiHFNgm4hhBBCCCGEEEIIIYQQ45oE3UIIIYQQQgghhBBCCCHGNQm6hRBCCCGEEEIIIYQQQoxrEnQLIYQQQgghhBBCCCGEGNck6BZCCCGEEEIIIYQQQggxrknQLYQQQgghhBBCCCGEEGJck6BbCCGEEEIIIYQQQgghxLgmQbcQQgghhBBCCCGEEEKIcU2CbiGEEEIIIYQQQgghhBDjmgTdQkwwSqn9SqmUUqr8mO3rlVJaKdWolPp5Zp+BEbcNx+wfyWx/8NzOQAghhJjYMrW6TSkVGbHtr5VSq0c8VkqpvUqprSc4fnWmpi85Zvu9me2Xn8XhCyGEEJNKpm7HM+fHbUqpnymlCjLPvSdTe996guPmK6XuV0r1KqX6lVJPKKVec+5nIMTkIUG3EBPTPuBtQw+UUouA0DH7/IvWumDEbckxz78FSALXKqVqzu5whRBCiEnHAj4+xvOXApXAdKXUihM8vxN419ADpVQZcAHQns1BCiGEEAKA12utC4DzgBXAlzPb3w10Zb4OU0rNAJ4BNgHTgFrgHuBPSqkLz9WghZhsJOgWYmL6JSNOfvGK7i9e4Wu8G/ghsBF4R5bGJYQQQgjPt4HPKKWKT/L8u4H7gAc55uQ549fAXyilzMzjt+GdQKeyPE4hhBBCZGitm4CHgIVKqanAZcAHgNcqpapG7PoV4Dmt9Ze01l1a636t9X/inat/61yPW4jJQoJuISam54FCpdS8zAnwXwC/Ot2DlVINwOV4J9G/ZnRoLoQQQohXbw2wGvjMsU8opcJ4n6waqsO3KqX8x+zWDGwFrs08fhev/E1tIYQQQrwCSqkpwA3AOrzau0ZrfRewjdELxK4BfneCl7gDuChT64UQWSZBtxAT19Cq7muA7UDTMc9/RinVM+J224jn3gVs1FpvBX4DLFBKLTsnoxZCCCEmj78HPqqUqjhm+5vw2of9CfgDXpuTG09w/C+Adyml5gDFWuvnzuZghRBCiEnsXqVUD/Bn4Engn/HOm/8v8/z/MfoTWOVAywlepwUviys5ayMVYhKToFuIieuXwNuB93DiFV7f0VoXj7iNLMrvwltBhta6Ga+Qn+hj00IIIYQ4Q1rrzXhB9heOeerdwB1aa1trnQTu5sR1+G7gSuCjeHVfCCGEEGfHzZnz5qla67/F69U9Dbg98/z/AYuUUkszjzuAE13rqgZwge6zPF4hJiUJuoWYoLTWB/AuSnkD3onwaclcBXoW8EWlVKtSqhVYBbxNKWWdlcEKIYQQk9c/AO8H6gCUUvV44fVfjqjDbwFuUEqVjzxQax3D6xP6ISToFkIIIc6ldwMKWJ+p1S9ktg+1/XwUuOUEx70Vr3d37OwPUYjJR4JuISa2vwKu1FoPvoJj3g08AswHlmZuC4EwcH2WxyeEEEJMalrr3cBvgY9lNr0T2AnM4Wgdng0cxrvg5LH+DrhMa73/LA9VCCGEEIBSKogXWH+Ao7V6Kd4nrN6RWSD2VeA1SqmvK6VKlVJRpdRH8YLwz+di3EJMBhJ0CzGBaa33aK3XnOTpzymlBkbcOkYU7O9prVtH3PbhrRST9iVCCCFE9v0jEMncfzfwX8fU4Vbgh5ygDmutm7XWfz6HYxVCCCEmu5uBOPCLY2r1TwATuE5rvQu4GFgC7Mfrzf1m4LVa62dyMmohJgGltc71GIQQQgghhBBCCCGEEEKIMyYruoUQQgghhBBCCCGEEEKMaxJ0CyGEEEIIIYQQQgghhBjXJOgWQgghhBBCCCGEEEIIMa5J0C2EEEIIIYQQQgghhBBiXJOgWwghhBBCCCGEEEIIIcS4ZuV6ANlUXl6uGxsbcz0MIYQQE9jatWs7tNYVuR7HeCc1WwghxNkk9To7pF4LIYQ4m7JdrydU0N3Y2MiaNWtyPQwhhBATmFLqQK7HMBFIzRZCCHE2Sb3ODqnXQgghzqZs12tpXSKEEEIIIYQQQgghhBBiXJOgWwghhBBCCCGEEEIIIcS4JkG3EEIIIYQQQgghhBBCiHFNgm4hhBBCCCGEEEIIIYQQ45oE3UIIIYR41ZRS1ymldiildiulvpDr8QghhBCTzalqsfL8Z+b5jUqp83IxTiGEEOJskaBbCCGEEK+KUsoEfgBcD8wH3qaUmp/bUQkhhBCTx2nW4uuBWZnbB4D/PqeDFEIIIc4yCbqFEEII8WqtBHZrrfdqrVPA7cBNOR6TEEIIMZmcTi2+CfiF9jwPFCulas71QIUQQoizZUIF3a3t7ax+6jES8XiuhyKEEEJMJnXAoRGPD2e2CSGEEOLcOJ1aLPVaCCHEhGblegDZ1J7y854HE+iHHscIgOVzCfhsQmaKiJGikCRFOkmVCXOqy7l4xUpmT52KaU6ovF8IIYQ419QJtunjdlLqA3gflaahoeFsj0kIIYSYTE6nFku9FkIIMaFNqKA7YqWYU9vOgA4w6PhI2D7iaT8DgwGOpDRqZAlvAdZtBbUVw6fx+ZxMKJ6mQCUp1ClKlE1dNMDSmdNZNG8e0yrL8VlmrqYnhBBC5KvDwJQRj+uB5mN30lr/CPgRwPLly487sRZCCCHEGTudWiz1WgghxIQ2oYLu6VUV/PFj7zluu+u6bN+8gT+vfZF9/TGOYNJtBulVQQZ0gJjtJ5n20Z/y05tQkHJQbubgDvjlvkF4ZA2gMXwav88haKUzq8RTlBoOU6JBZk+bwqKZ05hZXUlxJIBSJ3rDXAghhJhwXgJmKaWmAU3ArcDbczskIYQQYlI5nVp8P/ARpdTtwCqgV2vdMtaL7mzr4JZ//T7Fdj8hbWNC5qYx8QIFQyl8KEwFPtPEMgz8loXPtAj4fQR8fkKhEKFgiEi0kEg0SlFpKcVl5RQVlWT75yCEEGISm1BB98kYhsH8xcuYv3jZmPs5js3BfQfYuPZ5dna20KIVnWaAbiNEL0EGnABx208iHSCWDNOVdFHJTCjeBRwYgNWbAFCGxm/ZhKw0EZWmUKUp9ymmVESZNqWaObU1TKsuo6Y4giWtU4QQQoxjWmtbKfUR4I94578/1VpvyfGwhBBCiEnjZLVYKfXBzPM/BB4EbgB2AzHgvad63aTt46X2aVkcaTJz6wB2onAxDQdLOZiGg6ky95WDqdzMzcEavu9iMvKr9r5y9KuFxkBntmkvjM98HQrqLcBUCksZ+AyFzzTxmSZ+nw+/5SMY8BMOhQiFwhQUFlJYXEy0qIiyihpC4UgWfx5CCCGyaVIE3afLNC2mzZzBtJkzxtwvnUqxZ89uNq9/icNdzbQYijZ/hE4zTA9h+p0Qg6kA8VSIWFLTlXK8QHxAQzewswsvGQfQ+CyHkGETUWmKDIeKsEFNZSHzG2pZ1FDD7LoyokHf2Z6+EEIIcca01g/inUALIYQQIgdOVIszAffQfQ18+JW85ozyAB+8OM72vl4OEuJwqIy2QCmgwNWEUwkqB7soj/dRnhygIh2nyE7gagdHa2zAydxswEXhoDLbMveVwtUKG4WDgcPQY8N7rDM3DNKODxszs83EcTNftYmtTWzXxM08zp4E0Jq57cAYDuIdLONoKD8yoDeUi5X5OhzID93PPDaGAnqVCeV1ZtuIx5bSGIClMkG9AkspL6Q3FJZhYCoDyzTxWyaWaeH3+bAsi2AgQDAYJhAIEA5FCITDRAsKiRaVUVBURDAYzuLPSAgh8oME3WfA5/czd9585s6bf8Lntdb09fSyZ89udu1aR2tPGz2GS2cwSLs/QoeK0utG6EuHiaWCxJOQSLn0JB2aky7buhxUJ7CtmaGWaX7DptBIUeF3qC8PMbe+kvn1VcytL2dKWYGsChdCCCGEEEIIkVXhQJCPve4to7a1drXzzLb1bOpoZZ9rc6ComA21c0gZfgCCToKpsVYa0oM0BoIsr2ngirmLKI0Ez9m4YwMD9HR10NPZwUBfPwN9/QzG+kkkkyRSCZKpNKl0mpTrYjsOtutio7G1d3M12ApsFC5HQ/nhsF55wbyrjcx2YzicH9o2FM4ntQ/H9bbb2vSezwT19oiwPvsB/UhDK+m7gQMAGMrxgvgRq+eNUfePBvXefS+INzKr6Ec+NoZW0St3+P7wLRPcj97O8Kp7bx8wtBfkG2gUZN4cUCjttcUxjcxXZWAqhWEYWIaBZRpYhonpM/GZFpZp4fP58Pt8+Px+Av4A/kCQQDBEMBImFAgRihQSjkQJR6JYpiwqFGIiUd6buhPD8uXL9Zo1a3I9jNPi2GlaW9rYv28f+w5tpKP3CH2mS28oSE8oTLuvkFa3hI5EMcm4DxW3UXEHI2ajEs6oa2MrNAVGmlJfmppiHzOqSpldU8a8KRXMqimhOOyTfuFCCJElSqm1WuvluR7HeDeearYQQojxR+p1dpxuvY7FBnh22zrWtxxidzzOAV+EPeE6+nxRAAztMCXeRn2yj0bTYEF5DZfNXcSMsuKzPIP8orXGtTXplIOTdrHTDnbKxU67OGmHgb4BBnr7GejvJTbYSzweJ5GKkUqmSTpJbNsm7TqkXRvH1diui4PGReMAWrk4ClwFWnnbXKVxFbgw/NUZEeC7w/eNo1+1yjw+Gua7WqEzq+5d7W0ffl4bmceZY4cej3xOeyvxvZg7P6hMiG8qFzX8dSiwd7wQfyj0Rx8T+LvDQb4xMuAfuZ3j3wwYfk6Pvn805Pf634/eX414c8AL/RWZVjwGmIaBoTIr/A0DM9OOxzQtLNPE5/PCf5/Ph98fxB/wEwiFCATDBMIhgsEI4UgB4VAEvy+IZQbwm37JkcQ5ke16LSu6c8S0fNRNqaduSj0Xcclxz2ut6TzSzpYtW9m99xnaVTddFUE6CotoC5bS5JbTmSjBjYOKO/TEbPrjKQ522TzfNgAbBxh6p9aHQ7GVpiJqMLUsyqyqYmbWlDK3voyGsgKCvrP1rrEQQgghhBBCiIkuHC7g6vMv4eoR29x0knW7NvPSwd3sGOjnoOFnb6iaZ4KVkAI27qcy2UlDvJOLCgr5+GVXE/ZP7IhCKYXpU5i+k4W9Z3ZxTu1qErE0sb4U8b4U8X7vfqw/Rbzf2xbL3OJ9aRzbPeHrBCM+QlEf4UI/oUI/BSVBKhoKqJxaSFFFKCvBZyIWJxEbZHCgj8HBfmIDfSSTCRKJBGk7SSqRIm2nSKXTpNM2jmOTdmxv5b3j4rgujh76indfg+NmAn0NrtaZcN4L9x3trcTXjA733cx9PSL415nV+6420Bxtt6OHAv6h4zNvBmitsLWVCfGHQn5vf2f4DYGhsH/kGwPmiDcLcpXJxDK3zuEtpnJGrPYfsbJ/xH2Docd6+LG3+v/YNwO81f8q06LnZMH/UCsfA42hGfUpAC/Q91b9m+C9EcHQ6n+FYXj99k2lvFX/hsI0TSxTYRomlmVhWT78Pguf34cvEPDC/lCAUDhCMBIhGAgRCgTxWyF8vgABK4TPCuIzfZjKlMB/nMnKim6l1HXAf+D93f1Ya/3NY55XmedvwPuv6D1a65fHOlYpVQr8FmgE9gNv1Vp3jzWOybI6bLC/n23b97B350s0de2g1+fSXVRER0EJrcEyWnQF3YkSbxV4zEHFbfyxOEbMxkkotHv0P1KFpsyXYlpZkKVTylg+s4blM6ooKwjkcIZCCJG/ZIVYdkyWmi2EECI3pF5nR9brteuy/9BOntm9lW09nezXJtvDtRwOVVOR7OLKdC+fvvhqGkqi2fueYhStNemE44Xe/ZkwfCgE708ffdyfYqArORyKB8IWlVOjVEwtpGpqIZWNUSLFAQkBs8BOpbGTSRKxBPGBfmKD/STicWKJGMlknGQ8STKdJJXKrOq3bdJ2mrTjYLsOtuPguC62q3FwcVyNo3XmjQCvFY+36l8Nf3W0Qiuvf/7RAP9o+D8U8HvbjGMC/pGBv/eco9Xwiv7hlf3HBvwcu8r/6H1H5/5NrtEBv3OCoN85GvCP+DTAcNue4dD/mNX9I9v6nKC9z/AbBBy7kh8MyPT1Z/ixoUYE/EamrY+ZWc2fubCu6fPh81lYPgu/P4A/6IX7gUAQv89PwO8n6Avi9/vxWyEsK0DADOHzB/GZAfxWAFOZGMo46/+N592KbqWUCfwAuAY4DLyklLpfa711xG7XA7Myt1XAfwOrTnHsF4DHtNbfVEp9IfP48692vBNBJBpl+YqlLF+xdNR2O51iz56D7Ni2hbam1bTrbvoiYbqrSmgPl9Hqr6CZahLpECrmtULxDSbp79Ws77J5qbWD/32pA9hEgZGmodhiQU0R502vYsWsGqaXF2AYUsSEEEIIIYQQQpwBw6Bx6lwap84d3mQn4/zyiXu4J+7y2+KF3L92Mxf1N/G3572G10ytzeFgJyalFP6QhT9kUVw19gUpHcelq2mQIwf6OHKgnyMH+lj3p4No11swGS70Uzk1SmVjIZWZ8DtU4D8X05hQLL8Py+8jGC2guKo818M557TWuKk06WSS1GCCVCJFYnCQRDxOIh4jEY8RTyVIJpMkkwlSdho7nSJlO6Qyq/29dj6u12ffdb2g39U4ZC6K62pcpTKr+xkV+h8N9Y+G/ce39VEjAv2hWyaox8BxTVIjV/3robDfPL6dz1Dg7w61/DnXK/pdvIvsJoATr+I3jwn6h7Z5X/WoUN8c2ebnRKH+iNX8R/vyDwX+2ZeNt0xWAru11nsBlFK3AzcBI4Pum4BfZK7y/LxSqlgpVYO3Wvtkx94EXJ45/jZgNRJ0j8ny+ZkzdyZz5s7E+/F5tNa0NLWyZdtOmve9QM/ALnoCDr1FxRypreLAjCnsV9NIpy2M/jTBvgF0j8O+PpttW3r53ZY+YBc+5VAbgblVUZZMLWPlrFoW1JUQ8kvrEyGEEEIIIYQQr5wVCPHe697Oe12XR597gF83N/No6Xk8tqeVZS+/wDumz+Fti+ZiGPnT23myME2DioYoFQ1RFmQ6rtoph47DA174vd8Lv/dv7hy+jli0LOiF31MLvQC8IYo/lPvVuiJ/KaUwA37MgJ9g4eT4NIfrOOh0GjuRxI7FSSdSpOJJUokkdiJNIpEgnoiTTKa8Vf3pFKlUiqSdIu3YpOw0acfF1g5p11vRn85cTHfkan4n07rH4Zj+/EqNaO+jThD4q8wxQ/ePhviOVriuwsVHWhskMyv9j13JP9TGZ/gNgXO0gj8br1wHHBrx+DDequ1T7VN3imOrtNYtAFrrFqVUZRbGOikppaitr6G2vga4bNRzbUfaeeH5tbTs/i0dVift5RU01UxhX+NUmpiC1gZqIE20t5dI9yDdvS6P7e3nj3ti8PghFJrygMvM8iCL6ktYMbOGJY3lVEbP3RW1hRBCCCGEEEKMc4bB1Re9nquB7Vv+zE82v8zvS5by6a4UP3jgYW4sKuXTr1lB0JKFVrlk+U2qpxdRPb1oeFsqbtN+sJ+2A320Z1Z+73m5ffj54qowlY1e+F3VWEh5fQGWLJgTk5hhmmCamMEggeKiUx8wjrm2jZtO4yST2KkkOpHCTiSwEymSsST138ru98tG0H2iXhbHNv4+2T6nc+zY31ypDwAfAGhoaHglhwqgqrKCN7zhOuA6ABzbYePW3WxY+xz9R+6nK+rSVlHD4eoG9tVPo11VgdaoeJry7i7CXTHsXpf1LQ7PNaX50QtHAIhYLtOKLObXFHLe9EpeM6eWKaVh6d8lhBBCCCGEEGJMcxdczLcXXMwXmrbx42ce5p7ILL7nBPnNI09xudJ88eLXUFcgi6vyhT9kUTenhLo5Ry+mGR9IceRAP+0H+mjb38/h7d3sfKENAGUoSmsjVGV6fk+ZV0pRRShXwxdCnEWGZWFYFlYoxLm4GmA2gu7DwJQRj+uB5tPcxz/GsW1KqZrMau4a4MiJvrnW+kfAj8C7UMaZTkJ4TMtk2eI5LFs8B3gPAJ29gzz34gYObnySZGoTnaVhWivqOVg9lb21MxhQhQD4UkmquzoId8ZJ92gO9Si2dtrcsbkH2EmRz2VJTZjL5tVw1aIGppZFJPgWQgghhBBCCHFCZXXz+Pxb5/Hp3lZ+/fgd3EUxdxYv5oHn1rEy0cNnVlzAiuqSU7+QOOdCBX6mLihj6oKy4W2DPUna9vcN9/zes76drc+0AFA1rZDZK6uZtbySUFT6fAshzozy2ma/ihdQygJ2AlcBTcBLwNu11ltG7HMj8BHgBrzWJP+ptV451rFKqW8DnSMuRlmqtf7cWGPJ+hWhxQm5rmbz3mZefmktffseJeVvpb2yjOayKRwITWM/00kp732asB2jvrOTYFuCgU6XrlSABD6A4eD70nm1XLVwCo3lEnwLIfJftq8KPVlJzRZCCHE2Sb3Ojryr18kBHln9G27vTfBw+YVoDBb1t/LeOQu5da58wnu80VrT2x5n77p2dr7YRmfTAMpQTJlXyuyVVUxfWoEvIC1OhJjIsl2vX3XQDaCUugH4LmACP9Vaf10p9UEArfUPlZdefh+vP0YMeK/Wes3Jjs1sLwPuABqAg8AtWuuuscaRd0V4EukeTPL8hp3sXPccquMp4sVp2qqqaSppYJ9vBgdVIwARZ5CZ7e34m1L09tgcscPDwXfhUPA911vxPU2CbyFEHpIT5+yQmi2EEOJsknqdHXlbr500W1+4m1/s38vdFRfTZ0WZOnCEmyvr+fTyufhNuXDleNTZNMDOF9vY+VIrA11JLL/BtCUVzF5ZxZT5pZjyexViwsnLoDtf5G0RnoQcV7O9uYuXXtpE+7bV+NQGWqZUs6dyDputxfSoUgDqkh1Mb+uFJpuOgTStboQ43seUCn2axdUhLp1bw5WLpjCjokCCbyFEzsmJc3ZIzRZCCHE2Sb3Ojryv11rTvvUxblv/DHeUrOBgqJbSRA+X+yP8/YWLqQ6fi46wItu0q2nZ08OOF9vYs/YIyZhNKOpj5nmVzF5VTdW0QskGhJggJOgeQ94X4UlMa83mw12sfvJ5krvuwS3pYf+U6ewqns92NY+0CmBpm3mDbTS0xEm1pGhOODTrguHgO5oJvi+bW8OVC6cwo1KCbyHEuScnztkxc+YM/dgjD1JTNw2/X/owCiGEyC6p19kxns6xU4fXccfT93J7aBZrihYSsuOcn7b58oolLK0ozPXwxBlybJcDmzvZ+WIb+zd14KRdCsuDzF5ZzeyVVZRUR3I9RCHEqyBB9xjGUxGe7LoHUzyxcT9bnn6IgsHV9E4pYm/NTLaGFnFYTQWg2BlgaW8nNU02A+2D7HOg2Y0SGxF8L8oE31csqGdWVVSCbyHEWScnztkRqJmla979XVAaw9L4LAe/mSZoJAkbSQpUnELiFJAkqFyChiZsKMIBP4WRMCXFxVSVVdM4ZRp1tVMlLBdCCDGK1OvsGJfn2N37eeSJX/HbdJQHKy5GaZgb6+Uj8xbwxplVuR6deBVScZs969rZ+WIrTTu60RoqGqLMXlnFrOVVRIplBb8Q440E3WMYl0VY4LiadQe7ePL5jcQ33UO0YD8Hpzawp3wuW6xF9KsiAKYl2zmva5CKwy5HunvZqQya3cLh4LvA0lzQUMDNK2Zw1YJaQn65aIUQIvvkxDk7ihoa9eLPfYNBHSCu/SRdH2nbRNkabHf011MwlU3IShAykwTNJEEjRdBIE1R25uYQMlyCpiJsGRQE/BSFw5QVl1BbXcf0xllUlFXLm6VCCDGBSL3OjnF9jh3rYstTP+PXHQPcUXU1g2aIy1MJbrvmAunhPQEM9ibZ9VIbO19so/1gP0pB3ZwSZq+sZsayCvwhK9dDFEKcBgm6xzCui7AY1tqb4ImtzWx65nFK2/+IU2ewZ8pMdhbOZ5eag6MsAjrF4sF2lh2xKW5Os2+wh62Gn4NuEWksfEqzpDrA68+byo3LplJeIO/sCiGyQ06cs+PYmq1dh7aWw2zatY+9R7poHkzRnlZ0GAE6/EG6/QH6LD8xw/LC77SLsl2wNb50ikAqic9OYdppDNvBdcCxFbZtkXCCpxxPwEwQthIEzSQhI0XQTBFUaUKGTUA5hJRD0NCETIOIzyIaClASLaKyrIKpDTNonDIDv19qjRBC5Aup19kxIc6x03HaXvw1/3Swi99VX0v9QDc/u3A5i8qjuR6ZyJLu1kHvIpYvttLXkcD0GTQuKmf2yiqmLizDtOSNDSHylQTdY5gQRViMkrQdXtrXzer1u4i9fB+1gc20NVSxp3o2W4OLaFW1AJTbvazo7eX8Q4qe9i5ewGGvW8wgAUAzq8Ti+kU13LxiBtMrCnI7KSHEuCYnztnximu26xLrbubQvl3sOtzKwe4YbSnoVH46fRG6AgV0BiN0hYLEgqM/0VM4kKK2p5+a/i6qB9spjrXjsweImRZx0yJm+IgbFnF8xLWPhPYRd/3EnSBxO0DcCeLqsT8l5DdShKw4ITNJ2EwQNNOEVIqQkc4E5Zn2K5aiIOAjGg5TXlJCXVUtjbUzqaysxfJJ+xUhhMgWqdfZMaHOsbsP8PP7/o2v1t+K4Wg+UV3HR5dMzfWoRBZprWnb18fOF9vYvbaNeH+aQNhixvmVzFlZRc3MYvkEnxB5RoLuMUyoIixOaH/HII9vb2PzS89RdvhhCqp62dcwnV1l89hqLiSuIhS4g1za3cGlB0wGO7p5iiS7KKRLexepqAnDlXMqeNOqGSxrKMUwpNAJIU6fnDhnR9ZrtuuS6m2ls2kXbYf3sf9ILwcHbPYZheyL1LC3sJwj0QBkTm6CKZeaQYeZhp+lYYtlAYdF9OHv7cLu6MTu7MDp6MTu7CTV2UFPPE6HZdJdHKUvGqU/EmIwEGDAHyBm+oibPmL4vLDc9RFzAsSdQCYsD6I5+UoiQzmZ1isJwmaCkJkiaKQIqTRBw2u/ElAuYRMifpNoKEhpcSF1FbU0Vs+gumoKoQK5ToUQQowk9To7Jtw5djrBy/f9A58KrGJHpJHXpF1+fdUygpa0vZxoHMfl8LZudr7Yyt717dgpl9LaCEuvnsLsFdWYPlnlLUQ+kKB7DBOuCIsxxVI2z+zu5MktBxl4+QFmsYa2mVVsrF/GOt/5JFWIIqefS7vbufpAELuzh8cZYCtRWnUUjaLQp7l4WhFvXDWDS2ZXEfTJP3CEEGOTE+fsOJc12x3opG3bn2na8SI7O5PsM8vZHpnO7mg1h4sKSGc+zmo4mqpBl2lYLIqEWFZewIVTS6gqCQGgXRentxenowO7sxO7oxOns4N02xHs1hbSzS2kW1qw29vBdYe/v2P4GJxST3dDHR1lRfRGQvT6LAYMk0FlENMGMW2R0CYx10fc8RN3A8TsADE7hKNP3mPSVA5hK07YimeC8iQhI0lQpb2b4RA0XMI+RSTgoyRSQFVxOfWVU6mraqSkrIJAKCwhuRBiwpF6nR0T9Ry794Wf85U9zfym5gaqBge4bdVillYU5npY4ixJJx12r21jw2OH6WwaIFToZ9FldSy8tI5QVD5RJ0QuSdA9holahMWpaa3Z0tzHfS8fpOXZe1monufgzDo21J3PRt9S0ipAqdPDZV0dXLe/AKu7n0d0FxuMMIfcImxM/Ibm/NoQNy2fxnWL6ykOS8ETQhxPTpyzI6c123VIt26jbeufGdj7As29cXb5GtgYmceOgnr2F5cwEDwaLhfHXBrTBvMCfhaXRFhVX8zcmijGSS5kpdNp7CNHSLe0kG5p9cLvEUF4urUVt7d39EGGgVVZia+mBl9NNVZNDVZVDVZlNYloMa3K5lBPO62dHXQP9tObTDFguwy6iphrEHMtYq6fmONn0PEC8rF6kytcQlaciBUnZGVWkhspgipFSGUu5mnYRCyDwoCfskghNSWV1FfNoLJyCkVlZRKQCyHymtTr7JjQ59hNL/Obh37Al6f+FY42+XhVDZ9c0pDrUYmzSGvN4R3dbHj0EAc2d2L6DOasqmbJlVMorY3kenhCTEoSdI9hQhdhcdocV/Psng7uW7uf9Nq7WODfwJ6Z09hQdx6brCU4ykeF3cllnR287kAR4Z4Ej+o2XjL87HeLieNHoZlX7uPGJfW84fxpTCkN53paQog8ISfO2ZF3NTveQ3z/S3Rsfxp96CXi/Z2sDyxhfWQxWwqmsbewgo6of1TrkylxmGX6WFFSwBvmVlFXfvq1whkY9MLvllbSLc3Yra0jgvAW7OYWdDo96hijsBD/1Kn4GxvxNw59bcQ/tRGzwDs501qjkw7uYJpEX5Ijne0cOnKYpq42Ogf66E0k6LNtBl3NoGswqE0GXYuY62PQCTBoh4nZoZOO228kKfANEvHFiZjeKvKQShJSSYLKJmQ4hE0o8PkoCYepKiqnunQa5eX1lFRUEi0ull7kQohzQup1duRdvc62wU423/UZPlZyI1sLZrIyDbdfsYiwfNJ3wutqGWTD44fY8XwrTtqlYUEZS6+aQv28EnkjX4hzSILuMUz4IixesVjK5pGtbTy4ZhdF2+9lbnA7W2bOZkPteWy1FuEqk2q7ncs6OnjD/mKKe12e1M382YS9uoQe7Z3sT4kqrplXxRtXzmBhXZEUPiEmMTlxHk0p9W3g9UAK2AO8V2vdc6rj8r5muy507qZ/z3P07XoGf8vL+ONtvBRcydrwMtYWzGdvYRWthWFsS4HWNPRrVpoBrq4t5tp5VYRDJ285ciradXG6uoaD8HRzM+mDB0nt309y/37s5pZR+5sV5QSmNuKf1jgiAJ+Kr6EBwz92sKxdjRtL48Zs3IEUyd4U7R1dNHW10NzbTnusl+5Ukl7XpV9Dv1b0a4sBxwvHB+wQcefk4XjQTFDgHyBixQkP9SFXCcJGkpBKE1Y2EVNRFPBTUVBEZdEUykunUVpZRWlFFaGo9B8XQrxyUq+zI+/rdTa4Dv2Pf5Ovtdn8ovYmSmMJfrViPudVSiuTySDen2LzU01serKJeF+KsroIS66SPt5CnCsSdI9hUhRhccba+5P8YWMzj63ZwoyDv2daeB/rZy5kfc357LDmoZXBlHQLl3R0cvP+Msr7DV5wm3jcTLFTF3NEF6BRlAbg9YureOclc5hZGc31tIQQ55icOI+mlLoWeFxrbSulvgWgtf78qY4blzU72Y9uWkvvrmdJ7nuBaMd6fPYAT4Yu43dFr2dt+RyaSkJoQxFIa+YOwkWRMDdOK2fZjOxe/NhNJEhlgu/U/gOZr/tJHTiA09l5dEfDwFdbezT8zgTg/mmN+GpqUOYrW7GmHRd3II3Tnxq+uf1pEr1JOnr6aevvpD3RS2d6gC6VokfZ9OLSh0G/azLg+ui3gwzaQZJu4ITfI2Amifr7ifoGiVgxImacsEoQMpKESRNSjheM+32UhoooK6qjuGgqpeXVlFRUUlhaimn5Xs2PVwgxAUi9zo5xWa/P1I6HufOJX/D5GR8hrS0+WlHFZ5dNzfWoxDnipF12vtTGhscO0tk0KH28hThHJOgew6QqwuJV2dM+wH3rmnh27cus6PgjNZFmXpy5hPU157PbmgtAY/oQl7R3cfO+MioHQmzWzfzR6GOTKqHZLUSjmFFs8hcrG7ll1XRKIlL8hJgM5MT55JRSbwTeorV+x6n2nRA1W2vo2ktiyx+IrbuT0u6NdJol/LjgXTxechH7KsrpC3tBctmgw9K0xeWlUW6cW0Vt1dnrA+n09ZE6kAm/9x0NwFP79+MODg7vp3w+fFMbjobfjY0Epk8nMHs2ZvTVvZGrtUYnnBFh+IhgvC+F05eivydBR+8gnWqQDmuQI0aCTiNFp3LoQdPrmvQ7fvqdIAPpMC7Hh/IKl4gvRtQ/QNTnrRo/GoynCJMkrBwKTEWxL0hxpILi6BSKSxooq6ymtLKKSGERypAVW0JMNJOpXiulSoHfAo3AfuCtWuvuE+y3H+gHHMA+nZ/PhKjXr0TXXrbf+Qk+XP1OtkRncV7a4M4rFkgrk0lEa83h7d2sf/QQB7dk+nhfkOnjXSN9vIXINgm6xzDpirB41bTWvHywm3vWNbF9/XNcPvA4xQVd/HnGcjZUn8c+ayYAM1P7ubi9m5v3lFERL2CNPsTvzTibdRk9OoyJZkV9iHdePJtrFtbht+SEWYiJajKdOL9SSqnfA7/VWv/qVPtOyJrdfQB7873E199JtHMjGrjXdxV3FL2ZfRUzaCoNkLYUhqtp7HVZaQW4praEK+dXEgqf/RXIWmucjo7h9icjA/D0gYOjeoL76uoIzJ1LcM4cAnPnEJw7F199fdYDYe1q3ME0Tk8SpzeJ3ZMcvu/0eI/dgRSu1gwAXTh0mXHag3Ha/HGOqDid2qbXgX7HYsAOMGCHT9pKJWTFKPT3U+TvJ+oboMCKEzG8YDxMirCyKVBQYAUoCJYRjdRRVFxPaVk9ZZWVFJaW4wue/CKfQoj8MZnqtVLqX4AurfU3lVJfAEpO9OmqTNC9XGvdcbqvPSHr9amkYgz+/jN8LV7Bz+veSFEsxa+Wz2FFVVGuRybOsa7mEX287Uwf76unUD9X+ngLkS0SdI9hUhZhkTUp22X1jiPct+4Qndv+zLXJJ/EVxHhq5irWV53PIasRpV3mpPZyY1MPN+9uIKldHtP7edSy2OWUk8BH2NJcM7uU91w2l6UNUgCFmGgm04nzEKXUo0D1CZ76ktb6vsw+XwKWA2/SJ/nHhVLqA8AHABoaGs4/cODAWRpxHujej95yH4kNdxJq3wjAC+5cfh55K9srVtBVHqEt6q0OCyVd5g/AxZEwN0wrZ+GsUkzz3L5hqh2HdEsLyd27SW7fQWLHdpLbd5A6cMDrVw4YkQiBOXMIzp1DYM5c7+usWRjhs3vBZm27OH0pL/jOBOBOTwKnN4XTk8DuSaET9qhj0mj6ohadEUVrYJAmq48Wu58jqQQ9SYe+lMVgOsRAOkzSPT64VrgU+AYpCvRRGOin0Nd/fChOmrCCiOEn6C+jIFRFYbSekrKpVFTXEi0rIxgpkH8HCJEjk6leK6V2AJdrrVuUUjXAaq31nBPstx8Juk+P1rDmp9z34gN8avZnSGo/Hy4v54vnNeZ6ZCIHhvt4rz5MvD+d6ePdwOwVVdLHW4hXSYLuMUzaIiyyrjee5uHNLdz/8gF8+1dznf0M6aji8RkX8FLVhXSYlRTqHi7t2cEtO/3M65lOM93cx2GeNwo56JbgYFAVVrxpWR3vuHgW9SVnNwgQQpwbk+nE+XQppd4NfBC4SmsdO51jJlXN7toHW+8ltfFu/Ee80PtldxZ3qCt4qexqnOoyDheaDAS8QLSyz2GpbXJFaSHXz62kqjqSs7DUjcdJ7tpFYvv2TAC+g+SOHbgDA94OSuGfOtVb/T13TiYIn4tVXX1Ox+wm7UwAPjIMT2J3JXC6Ejh9qVH7K5+BWRrEKg2SLvLTHTZoNpMcdHo4lOykta+Xjp44vYMuA0kfg+kgg+kItj7+AqOmsikK9FEc6KMo0Euxv48CK0bEiBNRCSJkeosDliok6CsjEqomWlhPeXkjpZW1RIpLCBVEpYWKEFk0meq1UqpHa1084nG31rrkBPvtA7oBDfyP1vpHp3rtSVWvT+TwGnbf/Wk+2PhhNkdnsyRtcNfl8ynwn/kFp8X4Zacddr3UxvpHD9HVLH28hciGvAq6X0EvsOuA/wBM4Mda62+OdbxSqhHYBuzIvMTzWusPnmo8k74Ii7OiqSfOfeubeGjtHqZ1rOb69DNsqp3KI7OuZHNkMaBYmN7Ka5s7ef2OegKUsVUf5h6jl42U0qa9q3XPL/fx9gunc9P5U4kG5SJZQoxXk+nE+XRkavy/AZdprdtP97hJW7M798DWe3E23YN5ZBMALzOH+9KreKzgcqJTGugrNDkQVtimwrI103ocVloB3txYwaqFlTlfOaS1Jt3URHL7dhLbd5DcsZ3Etu2kDx8e3scsKiIw1PZkzlyC8+binzkTw5+bk0CddrG7E8PBt901dD+O3ZVAp9xR+xtRP1YmCB8KxM2SAPGIRbvrsK+nk/1dnRzu6qClp4eO3gS9g5r+hI/BVOiErVNM5XhtUwK9mWDc+xq1YhQYccIkCOsUQe1guEEMogR95UTC1RQV1lFaNp3iijoiRcUEo1EMQ/rFCjGWiVavx/p0FXDbaQbdtVrrZqVUJfAI8FGt9VMn2G/yfALrdAy0E7/z/fyTuYif1L+ZgpjNr86bxQU10spkstJac3hbN+sfO8jBLV3Sx1uIVyHfgu5T9gJTSpnATuAa4DDwEvA2rfXWkx2fCbr/oLVe+ErGM2lPmsU5obVmS3Mft790kA1rn+ct9kNUGO38bs41PFt7Cb1mCWW6g0t6N3LTLpMF7QtJmJo/u3t5yKfZ7pbTr4P4lOaixgLedelcLp1diXWOP54uhHh1JtqJ86ullNoNBIDOzCZ5c/p0de6BLffgbrkHo20zLoqNah73pFbwoF5F2YzpBEpDHAhqWoPe6uj6bofLtJ+3Tq9geR6E3iM5AwMkd+wYvfp75050IuHtYFkEpk3zVn/Pn09oyWKC8+ZhhE7cT/tc0drrE358CJ752pv01j4OsRRWycgQPIRVHsQqC2GVBlGWQSLt0N6fpKUvxr7OTvZ3dXO4u5u2nn66+lP0xgwGk35i9vFzV7iZQLxvOBAvznyN+vqJGnHCxAnpNNr2oZ0QBgUEfKUUhKspKqynsHgK0eIpREtqCBUWYVqy8lBMPpOpXp9u65JjjvkKMKC1/s5Y+0m9znBseOKfeGD7Gj425++IE+DDpWV86fzGXI9M5NjxfbxLWXhZPVMXlmEY0r5MiFPJt6D7lAVVKXUh8BWt9Wszj78IoLX+xsmOl6Bb5LveWJrfrT3Enc9uYVXfI7xB/5lHypby0Kyr2BFdgKEdltkvc1XLEa7ZVksJM+nQvTzAPp62CtjrlJLCotCvuWF+Be+6dC7za2VFgBDjwWQ6cT6bpGYfo2MXbLkXveVu1JGtaBSbrQXcEV/Ow85KgrX1TJlSzO6g5mCmpfTUDpvLlZ9bpleydFEFli//VvhqxyF14KC36nv7jswq8O3YbW3eDqZJYM5sQosWE1q8mNDiRfinT0eZ+TMXbbveRTJPsBLc7kygk87RnRWYJUGsskzwXZYJwctDWCVeCD5SynbpGEhypD9JS+8g+zq7ONTTR1N3H0d6B+kZdOmPmwymA96LM/JbeYF4SbAnE4b3UhzspWTofqCXqG8Av2PjpH04KT+uE0TpCJZZ5PUUL6gnEq2loKieaEkDBSW1+HK06l6IbJtM9Vop9W2gc8QCslKt9eeO2ScCGFrr/sz9R4B/1Fo/PNZrS70+xrY/sO/Bf+ADMz/PpsLZLEwZ3H3FPAr98ondyS7W5/Xx3vJ0E7HeFAUlARZcUsu8i2qJFAVyPTwh8la+Bd2n7AWmlHoLcJ3W+q8zj98JrNJaf+Rkx2eC7i14K8H7gC9rrZ8+1XikCItzzXE1T2w/wm3P7EXve5J3GY+gVYpfzbqRZ2svYtCMUqVbuLR/DdfvNZl7eAGWr4R9bjN3m0dYp0o55BahMWiIGrxleQO3vmYGldHjL4wlhMgPk+nE+WySmj2G9h2w5V7Ycg+0b0Oj2OpfxP8MXMqD7koWza6iuKaAjYZDk1+jXM20DpvLVZC3zqxk4cJyLH/+BMUnkj5yhMTmzcQ3bCSxaSPxjZuG+34bkQjBhQsJLV5EcLEXgPuqqnI84hPTWuPGbOzOOHZHHLszkfkax+5IjL5I5rEheHnIu3+SEHwk23HpHEzR2pugrS9Ba2+cgz29HOrup6UnRntfkp6YJp4+fuW2pWwKff0UBfopDvRQGuqmNNhNcXBotbgXlAetFI5tYKd82Ck/jh0AN4RpRAn4yygI1xItqCMSraOgeAqFpY0Ew6VysU2RtyZTvVZKlQF3AA3AQeAWrXWXUqoWr3XoDUqp6cA9mUMs4P+01l8/1WtLvT6Bjt0kfvsuvl54Bf9bfwvhmMOvl83gwtriXI9M5AHHcdm/oYPNTzVxeHs3hqGYtrScBZfWUT+7BCWrvIUY5ZwH3a+2F5hS6hbgtccE3Su11h8dI+gOAAVa606l1PnAvcACrXXfCcYn/cNEXtjV1s9tz+3nubXreIv+E9eql7i95DIemHkNe6OzsHSale4LXN56mIt21FCZWETaVKx1d3O/P8U2p5wOXYBCs6w6yHsum80Ni+ultYkQeWYynTifTXLifJqObIet98LGO6BrDwP+Sn6tr+W/+y/BDpRw0ZIqVEmA59Ip2nwaw9XMPGJzhRHkzbMqmL+wIu9DbwDtuqT27ye+cSOJjV7wndixA9JpAKzKSq/VSWbld3DhAsyCghyPemzHheBDQXjmsU6MsRK8fEQQfooQfKShliltfQna+pK09iU40pegpTfG4e4BWnrjdA7YpJzjT7L9RoqoNUiRv59ifx/FQS8ULwt1UhruHu4p7jOOhveuY5BO+nDSPrQTBArwWcUEAxUUROqIRuuIFHqrxSPRWiwrKsG4OGekXmeH1OuTSA3C/R/joZZWPjLnS8RUkA+VlPD3y6flemQij/S0xdjydBPbnmshOWhTVBli4aV1zL2ghmCBfApACMi/Fd1npXXJCb7PauAzWusxK6wUYZEPeuNpfrfmEL95dgfL+p7gfb5HOEQhP5t+E8/XXkDCDFOvD3DZ4HNcedBk5u7ZRALT6SPGI3o7T/jC7LTLieGn2A9vX1nP+y6fS3mBfNxJiHwgJ87ZITX7FXJd2PMYPP9fsOdxXDPAC4XX8o3Oy9iYqmVaeYSLllXTEzBYHY/RYYHpaGa32VxhBHjz7EpmL6rANw5C7yFuMkly2zbiGzcR37iR+KaNpA8c9J5UCv+M6YQWLyG0eBGhxYsJzJqF8o2Pk8bhEHx49fdphuDlR0NwX3kIsziIMl95cDyQtL0wvDdBW78XinvheIKWnjitvXHaB1LY7vHHBo0kUTNGoW+QQl8/xYE+SoI9lIa6KA13Uh5ppyTUg6GOP8dwHYWd8uHYfrQOolQBlllIIFBKOFxNYUEtkcJaCgprCQTL8fmKsKxiDEN6jItXTup1dki9HoPW8ML/cPDJ/+R9c7/K5sJZzE8q7r5iPsWB8VGPxLlhpx32rD3C5qeaad3bi2kZzDy/koWX1VE1rVDeBBaTWr4F3afTC8zCa0FyFdCEdzHKt2utt5zseKVUBd5FKp3MR6yeBhZprbvGGo8UYZFPhtqa/PyZfQzufZ73+B5hudrCT4tv4L6Z13G4oIGAjnOhfoZL2vZy/r56qo8swAyV0OQe4XariReooMUtwlSay6dH+ci1i1g2tTTXUxNiUpMT5+yQmv0qHNkGL/wQNtwOdoLW8gv5mXM9P2qZjlIGF82qYNmiSvbZNo8MDtBjgi+tmdOa5ioryE2zq5i1qBxfYPyE3kPs7m6v5cnGjZnV35twursBUIGAd5HLxYsJLl5EaMkSfHV14+7kcVQIPhSED7VE6YiP7gluZi6MORyAH71vFgZe1cejtdZ0x9LDAfiRTBjeOrRavDdOW1+czsE07jGnEwpN2EgRMeIUmHEKfQMU+fspCvRSEuylNNRJaaCTomAPfn8Ky59irF+TY5s4th9XB1EqgmlF8QfKCIcrKYzWU1hYR8Bfhs9XPHyzrEKUGn9/4yJ7pF5nh9Tr03DweZK/ex9fr3wjP6q/hYKYw+OXLKChUNpRiuN1HB5gy1NN7HihlXTSoayugIWX1jJ7VTX+oLyxKyaffAu6T9kLLLPfDcB3ARP46VAvsDGOfzPwj4ANOMA/aK1/f6rxSBEW+WqorcnqtVu4yX2M9/ofY43ZyI8b38ILNeeTNgJM17u4LPEUr2lSNO6cQ7GaT8JweUBv4U++Anba5diYTC82+cDls3nj8qkELDmBE+JckxPn7JCanQWDnfDyz+HF/4X+FtLF03mq5C18o3kpu3uhMGjxuqU1zJlbwQs9gzwyOEC/AYGUy7wWmyutIK+fU8mMReXj9sRKa0368OHRLU+2bkUnkwCYZWWEliwhtHQpoaVLCC1ciBEO53jUZ05rjTuQHt0OpcPrB253xtHpEUuwLWPUKnDfiJYoRtSXtTcAhvqHH9supW1UIJ6gJ24fd6ypNBEjTYgkYZJErRhRc5Cof5BiX58XjAe6CPv68ZkJfGYKnz+J5Utj+dOYfuekAbnW4Dg+XB0AI4xhFuILlBAKVVIQqaYgVE3I760a9/lKsKyhrwUoJW3jJgKp19kh9fo09bfBne/l94MWH5z3D/iT8PCFc5lTEsn1yESeSiVsdr3Uxuanmug4NIAvYDJ7ZRULL6ujvD6a6+EJcc7kVdCdb6QIi3w31Nbk18/uZU7v0/x14FGmcIj/Knkz9814La3hasJ6kItZzaXt21m8Zw5VR5aggoVscPbw20CcjU4VfTpEgaV587Jq/uaqBdQWh3I9NSEmDTlxzg6p2VnkpGHrfV5bk6a16GARzdNu4cepq/m/HZC0XWZVFvDG8+qonl7MQ83dPDo4SNyAUNJlfnOaq6wQN8ytZPri8Rt6D9HpNImdO73ge/0G4uvXkxq6hotpEpgzm/DSpcMBuK+hYdyt+j4R7Wqc/tQxAXjm1pUA5+i/+ZXfPLr6e2RP8PIQRtg6Kz+Pkf3Dh1aFHxm+n6C117ufSB/fLyVouEQMmxBJgjpBmDRhlSKsUkStGIW+Pgr9PfitGD4jhk/F8RsJfGYSy5fC8qfx+dOYAQcr4GAGTtCTZejnqBWO9qNVCGVFsfzFBAJlBP1lhAMVFASrCPhLsYZWj1ve6nEJyPOP1OvskHr9CjhpePiLPLF7Pe9a8A1U2uDe82dxXmVhrkcm8pjWmrb9fWx5qolda47gpF2qphWy8LI6Zp5XOS6utSLEqyFB9xikCIvxYritybP7aduznvf4HuFN5p9ZHVjMj6beyouVi3GVwUqe4/qBx1lwcBY1O+YSjMzgiNvBb6z9vKAqOOQUA3DBlDAfvmYhF82qmBAn60LkMzlxzg6p2WfJoZe8wHvrfYAmPft1rC55Cz/cW87agz2YhuLy2RXcdF4dqiLEnQfaWT0YI2lAQdxlQVOa6/1hrllUxdRFZVi+iXFyZXd3E9/ghd7xDRtIbNiIG4sBYJaUjF71vWgRRmRircDTrsbpSY4Kv9OZtihOdwJG5L4qZGVWgI/uCW6VhzDO8psgWmv6k7bXOzyzOrxtxK01s0K8oz85Mrf3xo2mwHIJqzRBnSSok14YToqw8oLxAtMmZDiYRgpDDWLRj8Uglh7ETxz/cDBuYwYzoXjQ8QLyoIPpO/l5k9bgKj/aCIEZwTQLsKxC/L4iAv5SQv5SwoFyAr5iLF8RllWIzyrEsqJYViGmGZF/w2WZ1OvskHr9CmkND3+R57c/w60Lv4PtWPx28XQuqi3J9cjEOJAYTLPj+VY2P9VET1uMQNhi7oU1LLy0juKq8fuJNCHGIkH3GKQIi/FoqK3JH9fu4kb3CT4QfAxNgm/Wvp/7G64gaQVZqtdyY+oPzG+ponrzXArNJaQMzZ/0Jh70h9mZriSJRV2B4n0Xz+DWC2cQCYzvFXlC5Cs5cc4OqdlnWe9hr6XJ2p9Dogdql9E2/738ou887lzfRltfkpKwj5uW1vG6ZbU0+eG3+9p5KhbDVlDRa7OsyeYNRYWsOq+aujnFGObEWa2qHYfk7j1e8J0Jv1N793pPGgaB2bOPht9LluCf1jhhQ0htu9jdiVEtUIbCcKc3CSNOFYwC3zErwINY5WGssiDGOVxx5riazoHkScNwLyhP0BNPH3esz9AUmI4XiLuJo4F4JgwPk6bQ5xIJWFimQmkb7aRwU3F0qh9l92Kl+/G5g/h8NmbAwfR7q8SH7htBBxVwMo9dfH4Xyxr7nEujcFQAjBBqOCgvIuArJuAvIRwoI+Qvx58JyS1f4YiwvBDDCE7Yv9EzJfU6O6Ren4FM2L1hy2O8cfF/kNQBfjq3kddOLcv1yMQ4obWmaWcPW55qYu+6dlxXUzenhIWX1jFtaTnmBPo3mRASdI9BirAYz4bamvzy2X009r7Ap4L306AO8a/V7+M3Ddcx6I8wR2/lDe7dzG9XVO9cSlm319Zkq7Ob24MDbLCr6dIRgqbmdQvK+dtrFjK9oiDXUxNiQpET5+yQmn2OpAa9i1a+8EPo2AkFVbjL/5rnSl7Pb7bE+dPWNlK2y7yaQt5yfj1XLKzi6f5BfrW/nU12CqU101vTLG91eH1tKQtXVlM1rXBCBmpOTw/xTZuIr/OC7/iGDbgDAwCYRUUEly4htGQJ4aVLCS5ejFkw8eurTjtHL4TZGSfdnrk4ZkcCtz81al+z0D96BXhZCKsihFUaRFm5OSFPpB2OnCIMb+1LkLSPb2USNrW3AlylCDgJQjpBSKWJqBShTChe5DcoiIQJ+f34LROfAcpJY6fiOPFB0rF+7P5e0gMDOLE4hpHE9LtHg3G/F4YbAQcVctAhGyPz2Aq4WH4Xn+Xit1xO9eEKFwNXBYeD8qGV4gFfCUF/aSYoL8PyFeKzMmF5JjD3WVEMI3CWfgu5I/U6O6Ren6FM2L1900O8YfEP6DfCfG96HW+ZWZXrkYlxZrA3ybZnW9j6dDP9XQnChX7mXVTDgkvqiJbKBU/F+CdB9xikCIuJwHE1j21r43uP7SLS+jyfDd7LXHbzw5p38OMpN9MdKGSqu5eb1F0s6WmmoukiKrbNwlc4nS6nizt8u3hWVbLPKUFjsKw6wIeuns/V82swjIkXTAhxrsmJc3ZIzT7HXBf2Pg7P/zfsfhTMACy+hf4l7+fe1hLuXHOIDYd78ZmKa+ZXceuKBmpqo/yutZPfHu6iTTsE0pr5B5Nc0A3XzCxn9opqyuombrsF7bqk9uwhvmEDsfXrSWzYQHL3Hi+8UIrAzJnD7U6CixYRmDEDZU6MVi+nw03aR1eADwfg3s2NjbjwpAKzODA6AC8P4SsPYZYEUWZu/3601vTF7aO9wjMX02ztS9Dam+RIv9c/vGMgiXvMaZMBRH1D/cNT+J0YIVLDK8PDmVA8GrAoLIxSUFBAKOAnYFn4DYWpXZRtQyqBG4+R6O9lsK+Hwd4eEv19pGOx4RX1ynC9kDzTY1yFXYi4EHLQwTQEU+C3MQMuls/B59P4LZegpQmaGusUP2YXC9fwgnLDCGNaR1uvBP0lhHylhAKlo9qteF+9m2kWYBj59WlCqdfZIfX6VdAaHv4C+zfcz/VLfkiPFeWf66t577zaXI9MjEOuqzm4pZMtTzWxf3MnCpi6sIwFl9bRsKBMzvXFuCVB9xikCIuJRGvN49uP8N1HdxFsfoHPhu5jqd7CL2vewvfrb6UlVEKV08xNxt2siq+jqONiKtc2EgouxTbgMb2BBwMhtqUqieOnIgR/ecFU3nPJHIrCvlxPT4hxS06cs0Nqdg617/BWeK//DdhxmHYpXPC37Cx6DXesaeKulw/THUtTVxziL1ZM4S3n17NX29x+uJM/dPSSQFMy4LBof5JLYiYXLqpk1ooqiiomfu9Ip6+P+MZNxDes9y50uWEDbl8fACocJrRgAcFFiwgtXkRo0SKs2toJ+0bAWNxYGrsz4fUBP+amk87RHQ2FVRrEGtkPPBOEm8UBVB6dtNuOS8dA6qRh+NAFNfsS9nHH+gy8digqTZAUfjtGiOTRFeKZULwgFCAa9QLxoa9Bvw+/YWLhYjhpSKZIDfYT7+8j1tdLvL+PeF8fsf5e4n29OOnj27WARvnALDIwoqDDDjpi4wbTuP4kri+F9iUx/TY+S+OzXAKWJmh4t5AC/2ksynfwoYfCcjOCaRXgGwrLfSWE/GWE/CWZtisjwnJzKDDP7gU9pV5nh9TrV0lreOjzNK+/m+uW/IgjgWK+WFnBxxdPyfXIxDjW1xln65+b2fpMC/G+FNHSIPMvqWX+RbWEC/25Hp4Qr4gE3WOQIiwmIq01T+zwAm+zaQ2fC93PSncd91XfwHca3sfeUBkldievM+/lMns14Z7zqdo4i+iA19Zkt7OX/wt2s9Gpoc2N4lOaa+cU8+FrFzG/tijX0xNi3JET5+yQmp0HYl3w8m1eL+++JiidDqs+RHLx2/nTzj5uf+kgz+zuxFBwxZxKbl3ZwMqZpfyxq5/fHu7k2f5BNNBwJM3i/UkuNYIsOa+KmcsriRRNvDYIJ6Jdl9T+AyQ2b/IC8E0bSW7dhs6EjWZZGaFFiwguXkRo0WJCixZiFhfndtA5pLXGHUwf7QfeMWIleGccnRrRQsTMhOAjwu+hnuBmoT+vQvCR4ilneBV4W3/SC8Qz94dbp/SepF2KBVHLIaJsAjqBz44RzgTiQz3EQ6QJh4JEo9FRt6FwPBTwY6ExHIdUbIB4Xx/xvl5imUA83t9LrK+XRCYoTw4OnnQuZjCAEQ6gwn4IGeiIixv2AnLHn8T2JXF8cRwzjmsm0UaCgHIJGhAyNCEDgkoTMjRBA3yn8Stz8KONIMoIHx+W+0sJ+0sJ+EtGrCyPHrOyPDIclku9zg6p11mgNTz0OdrX3cF1S35MU6icjxQX8+Xzp+V6ZGKcc2yXfRs62PxUE007ujEMxfRlFSy4tI662cWT8s12Mf5I0D0GKcJiIhsZeOumdXwudB8Xuy/xaOXlfLvxb9kYqqLA7uN69QeuMR4i2DeV6v3LiO6Yia9oOr1OD3f6tvGMUckeuxQHk7llFp+8biHXLpycK86EOBNy4pwdUrPziJOGbb+H5/8LDr8EBdVw8Sfh/PdwoM/hjjWH+N2awxzpT1IZDXDL8nr+YnkDRsTirtZubm/uZF8yhc/RzDmcYvH+JBcXFTB3RTXTl1UQjEyuTxHpVIrEjp2jwu/Unr1e0AH4GhoIZVZ9BxctJjh/HkZQemxqrXH7U8MheHpEKxS7MwEjg2HLGLUK3Fc2FIKHMKL+vP83jdaavoQ9Kvg+kgnCRwbkR/qTOMf0S1FA1OcF4mGVJqgT+OxBgjo14qKaaQLYhMOh4QD8ZMF4OBTCTsS91eF9vcT7vdXi8f6jq8W9x0dvdjJ54okpRSASwR+J4IuEMMJBVNiHG7SwAwo76JD2J0n746SsGGlzkJQawHVj4MZQbgJDJ72V5GooLD96P2hwyhYsGnBVAK2CXHfFOqnXWSD1Oku0hgc/S8+633DDkh+zN1zNuyNRvnXBzFyPTEwQ3a2DbHm6me3PtZCM2RRXhVl4aR1zLqiedP8WE+OLBN1jkCIsJgOtNat3tPPdR3diN23gs6H7udx9nufKVvGdmR/nmWAdQTvOVe7D3Oi7n0gsQFnXpRQ/X0OgYCmOgqdYz++DAbYmqxkkQEPU4BOvncdN503FzNMVUkLkCwm6s0Nqdp7a9zSs/gYceAaiNXDxp+C8d2Ebfh7ffoTbXzrE6h1HcDVcPLOcv1gxhWvmV7I1luS3rV3c29pNr+tSmNQs2Jtg2cEU5zeUMGtFJdMWV+ALTJ4e1iM5AwMkNm8hvmkjiU2biW/ahN3S4j1pmgRmzx4VfgdmTq5+36eiXY3Tlzq6AnxkT/CuBDhHz2eU3zi6AnxEAG6VhTAKfHkfgo/kuJrOwaR3Qc3eBG39icxFNJO09R8NyLsGU8cdaxlQ5IMCyyFMioAbx2fHCJIc7h8eVml8yiUUCh0XgJ/ovs/nBSXpZIJ4fz+Jgf7hVeKjwvBjw/G+Xhz7+JYuAEoZBKNRQtHCzC2KFQljRoLooIUOeSF5yq9J+hzivjhx+kimu0navaTTfTjOAK4zgHbiGDqBqVMEDc3fvWGX1OsskHqdRZmwe3Dtr7hxyf+yvXAKb7RC/Pclc3I9MjGB2CmH3WuPsPmpJtr29WH6DGYtr2TBpXVUNU7MC4qL8U2C7jFIERaTycjAO9G0ic+G/8BVzjNsKl7Iv8/9LA/7GzBdm0sSj3NT+B7K0n1EBi+j/MV6goklGMEidjl7uC3Uy7p0Hb06RFVY8dGrZvPWVdPxW9nrkSjERCJBd3ZIzc5jWsP+p+GJb8DBZyFaC5d8Cpa9E3xBWnrj/G7NYX770iGaeuKURvy8aVkdt66cwpTyCH/q6OOO1i4e7+zDAep7HRbuTrCk1WbBvHJmraiiYX4p5iSvM+kjR0hs3kx840YSGzcR37x5VL/v4Px5XruTTPjtq5NPX52IdjVOT3J0G5TMKnC7K8HIK0iqgDmiF/jovuDmOF7tlrQdjvQN9Qo/cSje1ptgMOUcd2zQhCK/psB0CGUCcSs9mLmoZnr4q6k0wWDwlGF4NBrF7x/dH1Zr7YXjxwXgfaNXj/f3kRheUd6H6xw/XgDDNEcE45lb4dH7gYICCPtZuOJSqddZIPU6y7SGBz9DYs0vuHnJj1hfNI2r8PPrK+bnemRiAmo/1M+Wp5rY8WIbdtKhfEoBCy+tY9aKKvzB/LqAsJi8JOgegxRhMRlprVm9s53vPrqLgcNb+Fzo91zj/pk90el8b+EXucs3A61dVgw8y5sjv6NWNWPFl1OzYw6BPbOxChs4ZB/kp+E21qbr6dIRSgLwN5dO5z2XzibokxVlQowkQXd2SM0eB7SGfU95K7wPPnc08D7vXWAFcFzNn3d3cPuLB3lkaxu2q1nRWMJfrGjgxkU1DOByT1s3d7R2sXkggalhTmuaBXsSzO9ymbGwnOlLK2hYUConW2TCwAMHiG/yWp4kNm4ksW0bOuWt1jWLiwnOn0dw/vzhm6+hAWVM7jcMxqIdF6c7OboNSiYEd7oTXp+LDBWyMm1Qjr8wphGaGH+fA8lMu5ShIDwTih855n7aOf78sMCnKPRpCkybECn8mUA8qJMj+ofbGEoTCAROGYZHo1ECgZP38tdak4rHTrg6/NhWKsP7DPQPtwgC+MwdD0i9zgKp12eB1vDAp0mv/Tm3LP4fni+exSrH5J4rF2DI/9PFWZCK2+x8qY3NTzbR2TSAL2gyZ1U1Cy+to6yuINfDE5OcBN1jkCIsJrORgXfv4W18NvwA17tP0hSq4YdLvsyvfHNIoVjU8zJvCt3BrMBudGIqNQdXEF4/H1/xdNqcFn4aOshLdj1H3ChRn+a9r5nK+6+YSzQ4flc6CZFNEnRnh9TscURr2Pekt8L70PNQWHd0hbflBVUdA0nuWuut8t7bMUg0aHHz0jr+YsUUFtYVsXUgzh2tXdzV2k172qbAgfmHU8zfnWBqr0vDvFJmLKugcXG59JEcQadSJHbt8kLvrVtJbN1GcufO4YtdGpEIgXlzj4bf8+YTmDEdZU2MYPZs0raL3XXsBTG9x05vclQIbkSsERfEHN0WxQhMrJ+11pruWHq4f/iRvsxFNDNh+JE+72v7wIn7hxcGvEA8YtiZFeIJrPQAATcxHIgHsVEKfD7fabVMCQaDp/VpBtd1SA4ODoff9fMWSL3OAqnXZ4nrwoOfwVnzM965+L94vGQeC1MGD1+9EMuUsFucHVpr2vb1sfnJJnavPYJju9TMKGLBpXXMOK8CSxa5iRyQoHsMUoSF8IrXk5nAu/PwDj4bfoDXuavp9Bfzk/P+nh/7FzGAweyurdxs/Y4l0Y2QqqX68IWE18zBVzybLrudn4V38YI9hWa3iJCpedvyWj5y7UJKI/5TD0KICUyC7uyQmj0OaQ17n/AC78MvQmH9iMDbn9lF88K+Ln770iEe2NRCynZZVFfErSun8IYltYT8Fqu7+7mrtYuHOnpJuJoKR7HoQJLZ22NUDmrqZhczfWkF05dWECk++YrPyUqnUiT37BkOvhNbt5LYvh0djwOgAgECc+YQnHd09Xdg9iyMMVbPitF02sXuig9fGHNkSxSnb3Q/bCPqGx2Cl4XwVYQwS4MY/okbGIzsH96WCb/b+o6uDh/a1jmY5NjTTUNB8VAgnlkhHnDimOlBAk7ca5miUgRwUAosyzqtlimhUGhUIC71OjukXp9FrgsPfhq95qd8YNH3+X3pIqYnYPW1i/FL2C3OssRAmu3Pt7D5qSZ6j8QJRnzMfU0NCy6ppbgynOvhiUlEgu4xSBEW4qiRgXf7oV18OvIQN7mPMWCF+cV5X+aH4RV0uopZHdt4u3kbs4t3QbqamtYLCT07C1/JfHqdLn4Z3MZzbj0HnBJ8hubNiyv55A2LqSoM5nqKQuSEnDhnh9TscUxr2PO419Lk8EtQNAUu+TQsfcdw4A3QG0tzz7rD3P7SIba39hPymbxucQ23rmzgvIZiBh2Xhzp6ubutmye7+nGB6Y7Bwn1Jpm8ZIJrQVE0r9ELvZRVy0jUG7Tik9u8/Gnxnbm5/v7eDZRGYMePoyu8F8wnOmYMRieR24OOQm3JGrAIfvSLcHUiP2tcs8h+/Erw8hFUaRE2SHvVpx6W9/2jw7QXhI4LxTA/xnlj6uGMtA0qCBoWWS8R0CJHE73gtU3x2LLNCPI0vE4ibpjkqAL/11lulXmeB1OuzzHXhgU+h1/6MTy36Lr8pXUZdXPPk1Yso8E+sT4yI/KRdzeGd3Wx5qol96ztwXc2UeSUsuLSOxsXlmPKmizjLJOgegxRhIY6nteapXR38+yM7aT20h09HHuZN+hGSyuS28/+e70ZeQ5+jWdr8ErcGfkl9WTPYFdS0v4bgU9PxlyxhwO7jN6GN/FnXstcuw1Bww9xSPvO6JUwtk5NkMblI0J0dUrMnAK1hz2PeCu+mNVDUAJd+Gpa8fVTgrbVmw+Febn/xIPdvaCaWcphREeGmpXXctLSWqWURjiTT3Hekh7vaulnfH0MBix2LhfsS1G3oI2BDaW2E6cu8ld7l9QVyYcZT0FqTbmoisSUTfG/bSmLLVpzOTm8HpfA3NmbC78zq73nzMIuLczru8cxN2Jn2J7HjQ/CYfXRHBWZxYFQf8OFbSQA1CUOFRNoZFYgPtUs5umLcu9+ftI87NmAqSoJHW6YEMyvEb/u7d0m9zgKp1+eA68IDn4S1P+f/LfoO/1u6gvKYy5+vWkSxtI8U59Bgb5JtzzSz5elmBrqThAv9NC4uZ+rCMurnlsg1VcRZIUH3GKQIC3FyQ4H3dx/dyeGD+/h05GHeov9Ev1XA91Z9ix+bs9COwwX7VvPmgtupqOwCp4zqrosIPtGIv2gpSTfOHf51PGlUsytdjkZx5YxCPvf6pcypLsz1FIU4JyTozg6p2ROI1rD7MVj9z9C0Foob4JLPwNK3gzn6BH0gafOHDc3cs66JF/Z1AbCsoZibltTyuiW1lBcE2B1LcHdbN3e1dnMgkSKgFKtcH/P3Jihd14PpQGF5cLi9SfX0IpQhoffp0FpjH2knsXULiW1HV3/bzS3D+/jq6o676KVVUZHDUU8MbixNekQf8JEhuE44R3c0FFZJ4AT9wEOYxYFJ/7c+mLQ50p88QQ/xo9va+hIk0i4HvvU6qddZIPX6HHFd+MMn4OXb+Obib/Hd4lUUxV2euGwBtQXySVpxbrmOy4EtXex4roWD27pIJxwMU1Ezs5ipC8uYuqCMkpqwLDoQWZFXQbdSqhT4LdAI7AfeqrXuPsF+1wH/AZjAj7XW38xsvwX4CjAPWKm1XjPimC8CfwU4wMe01n881XikCAtxalprnt7Vwb8/upPWg3v4WsGdXG0/yaGS+Xzr/K9zZ7qUiJ3ikh0P8vqSeymu6Ue5xVT1XEzwkan4i88nrZPcY63hcauKHakKbEwunBLm869fytKGklxPUYizSoLuE1NKfQb4NlChte441f5SsycgrWH3o/DEP0Pzy17gfelnYcnbjgu8AZp74ty/oZn71jezraUP01BcNLOcm5fWcu2CaiJ+k5f7YtzZ1s19R7rpSjsUmyaXaD/zdscJvtyNdjThQj/TlpQzfVkFdbNLMCdJS4hssru7SW4b2fZkG6n9+4efNyvKRwXfwXnz8dXVygluFmitcQfTJ+wHbnfG0Sn36M6mwioNnjgEL/RP+hB8iNaavoRNcdgv9ToLpF6fQyPC7h8s/me+VvQaIgmXxy6ZT2NhKNejE5OUY7u07unlwJZODmzupKt5EIBoaZCpC8toWFhG/ZwSfIGJe10KcXblW9D9L0CX1vqbSqkvACVa688fs48J7ASuAQ4DLwFv01pvVUrNA1zgf4DPDAXdSqn5wG+AlUAt8CgwW2vtMAYpwkKcPq01j247wj8/uI3Czg18p/B2ZiW3sqnhOv5x3md4OuGjPBnn8k13clXVIxTWD6J0IVX9lxB8uB5/4UpcHP5gvsgj/jK2JqtIYbG0OsDnX7eEC2aUywmwmJAk6D6eUmoK8GNgLnC+BN2TnNaw6xFvhXfzOiiemgm8bz1h4A2ws62f+9Y3cd/6Zg53xwn6DK6eV8XNS+u4dHYFylA8mbmI5cMdvcRdTX3AxxWun7m749jrurGTDv6QRePiMqYvraBhfpmcdL0KzsAAye3bR130MrlnDzjeP8eNoiJv5fe8owG4v3EqypA3GrJFa43bnxoOwdOjQvAE2EdDcOUzsMqCx10Y0yoPYUR9k/LfZFKvs0Pq9TnmuvCHj8PLv+C2Jf/E56MXE0y5PHjBHOaXFuR6dELQ35XgYCb0PrTd+/eXYSnqZhUzdWE5DQtKKa6S1d7i9OVb0L0DuFxr3aKUqgFWa63nHLPPhcBXtNavzTz+IoDW+hsj9lnN6KB71D5KqT9mXuO5scYjRViIVy5lu/ziuf38x2M7udx+hq+F76Ao1crqhX/L1+rextaEy9SBXi5/+ddcMPUFChsGULqAqtglBB6sJ1CwEtdQPKJe5KFQIVviNSTwMafM4rM3LOKq+TVS5MSEIifOx1NK3Ql8DbgPWC5BtwAygfefvItWNq+DkkYv8F58K5gn7vGotWbtgW7uW9/MHzY20x1LUxz2ccOiGm5eWsfyqSXE3OMvYrkwEuQK18+c3XF61nWSHLSxfAa1s4qpn1fKlHmllNVFpB69Sm4iQXLnzlEXvUzu2IFOexcSNMJhAnPnjrroZWD6dJRPesxmm3Y1Tl/y6Erwke1QuhLgHD3HU34Tqzx4wp7gRtiasP9dSL3ODqnXOTAi7L5r2T/y4cgl+NOau8+fxfJKaRcp8oeTdmne08OBzZ0c3NxJd2sM8FrMDYXedXNK8Pll4YE4uXwLunu01sUjHndrrUuO2ectwHVa67/OPH4nsEpr/ZER+6xmdND9feB5rfWvMo9/Ajyktb7zBGP4APABgIaGhvMPHDhwxvMRYjLrHEjyr4/s5O4Xd/Oh4J/4kHEfhk5z16qv8a2Ci2hOOSzqbufiF3/OollbKJrWj9JhKlOXEvpDHf7QKrRp8rR6kfvDETbHahgkQGOhySevm8/rlk7BlI/UiglATpxHU0q9AbhKa/1xpdR+JOgWx9Iadv7RW+HdsgFKpsFln4fFbwXj5Cc+acfl6V3t3Le+mT9taSOedqgrDvH6JbXctLSWeTWFJ7yI5UXFBVyp/UzfHadra/fwSVeo0M+UuSVMmVdK/dxSCkoC5+gHMLHpdJrk3r0jLnq5jcS2beiY93NXfj+B2bNHXfQyMHs2RlB6zp4t2tE4PYlR/cDTmRDc6U54n6fNUEFrVAjuqxgRgo/zi45Jvc4Oqdc54rrw+4/Bul/y0Plf4X3ByzAczW8WTefSOmkVKfJTX0d8eLX34e3d2GkX02dQN9vr7d2woIziynCuhynyzDkPupVSjwLVJ3jqS8BtpxF03wK89pige6XW+qMj9lnN6KD7B8BzxwTdD2qt7xprrFKEhXj1trX08bU/bGXnnr18JXovN9qPkAiW8+PXfIf/1NMYdFwuaDvEyhd+zsz5eyme3oehglTalxJ8oJaAdQH4Ajyv13BvgcWmWC19OkR1RPGxq+dwy8pp+Ez5WLMYvybjifMp/i3wd8C1WuveUwXd8ub0JKc17HzY6+HduhHKZ8PlX4T5N8Mp2l0MJm0e3dbGveuaeGpXB46rmVMV5Q1LvdC7viR8/EUsDcUVpVGuDEWY25amZ3svh7d3Ee/3Vh+X1ESYMs8LvmtnFeMf56FePtGOQ+rAwUzwfbTvt9vb6+1gGPinTyM4dx7BuXMIZL5a5eW5HfgkoG0Xu3t0CD50c3qTMOLU0CjwHV0FXhHCN9wSJYjy5f/qvMlUr8e69tUx+53w2lljkXPsHHJd+P1HYd2veGr533Or7woAfjR7Cq+bJhcJFvnNTjs078qs9t7SRU+b9wZ4UWVo+IKWtbOLscZBPRFnV76t6JbWJUJMQFprHtnaxtcf3Eawazv/VnQHCxIv01m5lP9Y/nV+Fotiobns4G4WvXAbDYubKJnZh1I+KvVlhP9Qg1+tQvkjrHPXcWfUZmNsCt06TFkQPnjZTN558UyCUtTEODSZTpxPRSm1CHgMiGU21QPNeCfZrWMdKzV7EtMatv0envg6tG+HqkVw5Zdg9nVwGi0UOgeSPLiphXvXN7P2gHcN9BWNJbxhaR03LqqhJOzj5b4Yd7d182BHLy3JNJaCi4uj3FBexMq0SWxXH4e3ddG8uxcn7WKYiurpRUyZV0L9vFIqpxZiyKeQskprjd3cTHzLFq/39/YdJLZvw25uGd7HrCgfEX7PJThvHv6pU1Gm/HvhXNBpF7trZPidIN0Rw+6I42beIBpiFgdGXxRz6FYSQOXJgobJVK9Pdu2rY/Y56bWzxnptqdc5NiLsXrPi77nJvBzXUHx3Wh1/Masq16MT4rT1tsc4sLmLA5s7adrZjZN2sXwGdXNLmLqgjKkLyygsl4uuTkb5FnR/G+gccTHKUq31547Zx8IrqFcBTXgF9e1a6y0j9lnN6KB7AfB/HL0Y5WPALLkYpRDnVtJ2uO3Z/XzvsV1cYK/hGwW/pTx5kP2zbuYbcz/FfX2aUlNx1Y7NzHjpl9Qubad0di/KsKhUlxL6Qy1+dyVGIMo2dxO3Fw6yMdZAu1tA1Afvu2gq779iLgUBWUUnxo/JdOL8SknrEvGKuA5svttradK1F+rOhyu/DNOvOK3AG+BQV4z7NzRz77omdh0ZwDIUl82u4A1La7lmfhVBn8n6vhgPdPTyQHsP++MpDGBlUYTXVRZzbXEUdSjG4e1dHNrWTfvBfgACYYu6Od5q7ynzSiiqkI/Zni1OTw+JHTtJbt9GYtt2Ejt2kNy9GzJ9v1Uw6LU+mTuXwNw5BOfOIzB7NmZBJMcjn1zchJ1ZBR4b7gme7ohjt8fRCfvojobCKg2OCL+H7ocxC/2oc/gG0mSs18eeVx/z3CkXoJ2I1Os84Lpw/0dh/a/YfMH/43p9BWlL8bXaKt6/oC7XoxPiFbNTDk07vdXeBzZ30NeRAKCkOkzD0GrvmcWYvvx441ScXfkWdJcBdwANwEHgFq11l1KqFu+jUDdk9rsB+C7eR6R+qrX+emb7G4HvARVAD7B+ROH9EvA+wAY+obV+6FTjkSIsxNnRMZDkX/+0kztf2stfB1fzCesu/Ok+1i3/JP9YdQvPD6Ro9Jlcue5FqtffQdWyTsrm9KJMRYV1KaEHagkkVmCEitllb+P2kk42DU6l2S0ibGresbKev716PiURf66nKsQpTcYT59MlQbc4I04aNvwGnvwX6D0EUy/yAu+przntl9Bas62ln/vWN3H/hmZaehOE/SYXzyznirmVXDGnkqrCAP+fvfsOj6M69zj+ne2rtrvSrnrvcu/GxoAxzRgI3ZSEQEJCGiSkXAghCQkJoaURQiDUUI3pplcX3Hu3eu911bfPuX+scMPIxl5bsnQ+z8MD0o7Gs5t7/dN555337Olz815rJ++1dlHcF1xUTY4M4wKHhQscVuJVhboiJ7WFHdQWdtDb4QGCmyol50cPzPe2YQqXmyseT8LrDc79LirC80Xxu7CQwBejTwB9WuqXur91cXEjdmPF4UoIgdrv/9IYlC/+Eb59A8EVvQZdjOmQneCacH3I/7cbjXl9mEL3YffOOhSZ18OEqsLbN8O2Fymf9TvmBc7EY9BwuyOGn09MHeqrk6SjJoSgq8UVLHrvDnZ7q36BzqglOc8WHHMyLobIaLm3x0g1rArdw40MYUk6vvY0dHP3u7vZU1HDXVHvcanvPRS9iU9OvYc/GWdQ6vIyyaTjtFUriCx8h9iJHcSM6USjFdj1cwj/IAlD73Q0YdHU+Mt4wVbPrv50agI2DBrBFZPiuXX+OGKjZIhJw9doXDgfDzKzpS/xe2DLc/D5g9DbDFlnBUeaJE39WqdRVcGGqg7e3dHAsqJW6jtdAIxJiOLMfAfz8mOZlGKj0u3h/dYu3m3tZEfPwDHhJi5wWFngsJAXZqS71b236F1f7MTrDoACsamRJBcEC98JmRbZcXQCCCHwNzUFi99FRQPd30X4qmv2HqO1WDAWFOzr/i4owJiZiaKXNyaGglAFgR7vvsJ368C/2134292g7luHKibt3qK3/uAi+FHOzx9peT3YfhlCiCUDxyznqwvdh907a79j5Z4aw5EagCU3w/aXqDv1t5zmm4fLqOEnFiu/m5Yx1FcnSSHh8wSoL3YOdHu309MRbEyITgwnbWwMqeNiSMiyoNXJ371GClnoHoRcNEvS8SeE4KPdzfzl/UI0znL+YXudyf1r8FvSWTznQR7wJtLs9XN2uIFpn3yIUvoJsRM7sI/tRKMLEGM8lYiPkjE4p6IJd9Dor+IFWwW7XBlU+KPRKnDBmBh+dcFEUqLlo+LS8DPSFs5DRWa29JW8/bDxSVj1D3B1QN4FcOZvIH7c1z6VEIKS5l6WFbewtKiFzdVOAqrAGqbnjNxg0fv0HAe9Wni/tZP3W7vY0NWHALLMRi5wWFjgsDIx0oxQBc1VPdQWdlBX2EFTZTdCFej0GuIyLSTlWknMsRKXESU3VjqBAr19eEpKcBcV7uv+Li5GeILd+IpejyE7G1N+/gEbX2otliG+8tFNBAQBpxtf+0EF8NZBNsU8uBB+mE0xR2Ney9Elo8B+xe7m037PHPdceswavmWO5K+zsof66iQppIQQOJv6Bza0bKehtBM1INCbtKTkR5M2LobUsTFE2IxDfanSMZCF7kHIEJakE8fjD/DM6ir+vbSMyYHtPBj5CvGuUvpSTuW/0/7EI1163KrKFRFG8t99G0/lcmInOHGM70Sj8xFtnEXkpynoWyajjUyg1VfPi7Y97PRkUuqLQaBwdraF2y6aRE5c5FC/XUnaazQunI8HmdnSYXl6YN1jsOZh8HTDuMtg7h1gzznqU3b1+1hZ1srSohZWFLfS3udFo8DkVBvzBkacREeb+Ki9m/daO1nd2UtAQJJRzwUOKxc4LEy3hKNRFLwuP/UlTuqKnTSUdtJW1wsCtDoNcRlRJOZaScqxEp9pQWeQhe8TSfj9eKurv9T9HWjdN1VJl5gwMPpkX/e3PikJRSM7xIba3k0xWwfmgO/3j9q736aYCmgtX94UU283o7WZ0Og0oy6vD1PoPuzeWYci83oYUgPw1o9hx8t0zv0js/pOwxmm5VKdmUdPyxvqq5Ok48br9lNX5KR6dzs1u9rpdQZvasckRQyMOIkmLtOCdphsiiwdGVnoHoQMYUk68Vp7PPzt42Je3VTNt02ruc3wKmZPG63jr+NvuT/h+XYPJo2G6yMMJL7+Kr01a7GPdxI3sRON3ovNNIOo5anoayehtSTj9DfxknUbO3xZFHvs+NFwaloEt104kYkptqF+u5IkC90hIjNbOmIuJ6z5N6x7FPwumHgNnHEb2NKP6bSqKthR38XSohaWFbWwsz44/zk+ysSZ+Q7m5sUyNs3Kqp4+3mvtYkVHD14hiDXomG+3cKHDyixrBPqBzfXcfT4ay7uoL3HSUNJJW20PQoBGpxCXHkVijpWkHBvxWRb0Rln4Hgr+1lbcRcV4ivcVv70VlcHZt4AmPDw473v/jS9zstGY5Ei14UJ1f3ke+BfFcOEO7DtQo5By72mjJq+/au+rI907azAyr4cpNQBvfB92vU7PWfcwp3s2zWEazlYMvDB3zFBfnSQdd0IIOhr69nZ7N5Z1oaoCg1lHSoFtb7d3uEV2ew93stA9CBnCkjR0dtV3cfe7e9hdWc+dlo+4yr8EraJQNut27rVfxHsdfcQZdNwUrse8aBFd9Ruwj+0kbnIXWoMbi3kKlpXpGComoLWm0+Nv4yXLZnYE0tnjduBDx5QEE7ddOJFTsuxD/XalUUwWukNDZrb0tfW1BceZbHgCRACmfBtO/z+ISgzJ6Vt63CwvbmVZUQsrS9vo9fgxaDXMzIzmzLxYZuTEUEaAd1s7+ay9B5eqYtNpOccexXl2C3NtkYTr9hWwPS4/jWWdNJR0Ul/aSWtND0IVaDQKsemRJObYSMy1kpBlwXCUM4ilY6e63XhKS/dtfFlUhKe4GLWvL3iAVoshIz3Y/V2QjzEvH1NBPrqYmKG9cOkAQgjUPt9A8duNv82F9fwMmdchIPN6GAv44bXvQOHbuM97kNOc06k1K8wK6Hh93hg08gkVaRTxuPzUFXZQvTs427u/ywuAPWWg23tsDLHpUXK29zAkC92DkCEsSUNLCMGHu5q45/1CVGctf49Zwil9n0FEHBtP/wt/1E5gU3c/uWEmfmRUcL/wIp2Nm4gZ0038lE60RhdR5olY12WhKyxAH51Dv7+TxVFr2UYau/vjcKOnwK7nVwvGM68gHkVRhvptS6OMLHSHhsxs6ah1N8DKv8HmZ0HRwPTvwZyfQ4QjZH+E16+yqbqDZUUtLCtupaylF4AMezhn5sUyO89Of5SOjzt6+KS9my5/AIOicKotgvPsFs6JiSLJZDjwnG4/TeVd1Jd20lDipKWqB1UVKBoFR2okSTnWYOE724rRLAvfQ0moKr66ui+NPvE3NO49Ruuw7x19YirIx5hfgCEtFUUru/WHC5nXoSHzepgL+OCV66H4PXwL/sm89smUmmC8V8NH54yTxW5pVBJC0F7fu3dDy6aK4L4qGq2CLT4ce3IE9pQIYpIjsCdHYI4wHP6k0nEjC92DkCEsScOD2xfg6dWVPLK0jDFqMf+wvkpy705E/Hjen/MA9/TaqHB5mGUN5wcalYZnX6CzaTMx+T0kTO9Ca+wj0jwW66Yc9Nvz0NsL8AR6eDViFVu1yezsTaAfAxkWLb+YP5YLJiaj0ciCt3RiyIVzaMjMlo6ZsxpWPADbXwKdGWb+AGbfAmHRIf+jatr7WVbcwrLiFtaUt+P1q4QZtJyabef0XAfhCWFs93r5uL2LSlewg2h8hHlvt/eECPOXbsz6PAGaKrpoKO2kvsRJc1U3ql+gKGBPidw74zsh24opXB/y9yR9fYHOzoNGnxTjKSsDX3ButGI2Y8zN2dv9bcrPx5ibiyZMbq49FGReh4bM65OA3wOLvwWlH6Ne9DDz2yaxw6CS44bPzp2AQc4rlkY5d5+PuiInrTU9tNX10lbXs7fjGyDcagwWv5P3Fb8tsWGyxnCCyEL3IGQIS9Lw0tLt5q8fF/Pq5lquMm3it6bFRLga8OVdxAtT7uSvrQHafX4ujrVyvd9D4f+ep6tpC9H5vSRO70Zr6iHCnId1Wz6GTTnoHePwBvpYEraSTYZ4dvYm0i1MJEZo+OnZ+VwxPQ2d/EVOOs7kwjk0ZGZLIdNWBsvvhV2vgzEyWOye+UMwRR2XP87lDbCmvC1Y+C5qpb7TBUCS1czs7GiyM2x0RelZ3dPHxq4+VCDeoOdcexTnxEQxxxaJ+RBZ5fcGaKrspqEkuLllU0U3Ab8KCtiTI0jKs5GSH01ijlXO+B5GhNeLp6ICd2ERnqJC3EXFuIuKULuCM99RFAxpaRgL8veNP8nPR+dwyKfSjjOZ16Eh8/ok4XPDy9dA+TLUix/l8vbxrNX6SXEJVpwzgTC9zA1J2p+rxxssetf20lbfQ3tdL87GflQ1WCPVGTTEJAUL347kCGKSI4lJCpfj5o4DWegehAxhSRqedtZ18ad397C9qolfW5fybf/raFUPvTN+zCOZ3+Wxxh78QvCdJDuX9fSy6dln6WrZTnROL0kze9Cauwg3ZWPbU4BhbRZ6xyT8ATfvmj9nvTmGXd1JOEUY8WEKv75wHN+YlCLvvkrHjVw4h4bMbCnkmnfDsr9A0btgjoY5twbHmhjCj9sfKYSgvLWPNeVtrC5rY215O91uPwB5cZFMzo7GmBhOtU6wsquXvoCKWaPhjOgIzo2xcI49Cofh0N3afl+Alqpu6ks6qS/ppKm8i4BfRaNTSMi0kJwfTUpBNI60SJl5w4wQAn9jI+6iItyFhXvHn/jq6vYeo42JwZSXd0AB3JCejqKTC+hQkXkdGjKvTyI+F7y0EKpWwWVP8K32MXwqvMT1q6w4ezxWo3w6SJIGE/CpdDT20VYX7Pxur+ulra4XT3/wdzsUsNjN+40+icSeHEGEzShvXh8DWegehAxhSRq+hBC8v7OJv7xfiLezkb873mVOzwcoZitNp/+ev1rP4qUmJxE6DbekxjGvtZ01zz5Pd+sObNn9JJ/SizasgzBTOraScRhWpKOPnYIq/HxsWMnqiEi2daXSKcLItOr4/SUTmZsfP9RvWxqB5MI5NGRmS8dN/RZYdg+UfQphMTDrJzD9+8etw3t/AVWwu6GL1WXtrC5rY2NVBx6/ilajMC7FQnKWjX6bnu0+Lw0eHwowJSqMc2MsnGuPIj/c9JULJb83QGNZF7VFHdQWdtBWG5wbbgzTDXR720guiMbi+PKYFGl4CPT04CkuDo49KSrEU1iEp7QU8cXoE6MRY25ucORJfh6mggKMuXloI47fzZqRTOZ1aMi8Psl4++DFK6FmHVz5DD9qz+dNvwtbf4AVZ44jNsw41FcoSScVIQS9Ts9A93ew87u1rpfuVtfeY4xhuv3GngSL39EJ4Wj18mnzIyEL3YOQISxJw5/bF+CpVZU8sqyM7EAVD8W8Skb3JrDnUnzmvdwTyOTj9m4SjXpuz0hgQk0jK597np62nVgyXaSe2oc2rA2TMYWYyokYliajt09HoPKh4XM+N0eztSeZPoxMjDPyh8unMDk19PNapdFLLpxDQ2a2dNzVbgjO8C77BExWOOVHwTneZtsJuwS3L8CWGidrytpZXd7GjrouAqrAoNNQkBuDOSWCBiOUeIJzIlNMBs6zR3FujIVTrOEYBtlEzNXjpa7YSW1hsPDd2+EBIDLaREpBsOidnG+TGywNc8Lnw1NRGRx7MrDppWdPIYEvRp8A+rTUfWNP8oIFcF1cnLyhcRgyr0ND5vVJyNMDL1wO9Zth4XPc5szjOVcPkS6VpaePJSXSNNRXKEknPa/bT3t9H221PbTVB7u/2+t68ftUADQaBVtC2AHFb3tyBOZI+XvZwWShexAyhCXp5NHS7eaBj4p5bXMtl4bt5I/ml4nqq4LMuaye82fu7jCwvcfFmHATv8tKJL64mhXPv0Bfxy4s6R5S5/SjDW/GaEjAXjcFw0fJ6B0z8AsPr0YsZ7Mmle19CbjRMyc9gj9cOoXsuMihftvSCCAXzqEhM1s6Yeq3wOd/heL3wBgFM26CU34M4TEn/FJ63D7WV3SwuryNNWXtFDf3ABAeZSA5PxpvjJFyEcArBJFaDWfGRHFuTBRnxURh03/1SAshBF0tLuqKOqgtdFJX7MTrCj5ma0+JIKUgmpT8aBKyLegMck7rcCeEwN/cfMDYE3dREb6amr3HaK3WL839NmZkoOjlaIIvyLwODZnXJyl3Nzx/CTTugKtf4k/dOTzS1YnZo/LxrHxybPJJEUkKNVUVdLX0D2x4uW/0SV+nZ+8x4RZDcORJSsTe4vdo3/hSFroHIUNYkk4+O+o6ufudPWyrbuVXtlV8L7AYna8Hdcq3eXvCL/hLQx81bi9n2CL5bVYCmq1lfP7SC/Q7dxOV6iXtNDfaiAYM+ljs1ZMxLc1HHzsZl7+T56JXssuXw05XHAE0LCiI5reXTCbBYh7qty2dxOTCOTRkZksnXNPOYMF7zxLQh8H0G4MbV0bEDtkltfS4WVseHHOyuqyd+k4XQqMQlRJBVFoULWEKPUKgVWCmJYL59ijOs1tIMw/+6LkaUGmp6aGuMNjx3VTRhRoQaHUaErItwcJ3QTT25AiUUbywOtkEenvxlJQcUAD3lJQgvMEnAhSDAWN29oEF8Lw8tJGj80a/zOvQkHl9EnN1wnPfgJYiuGYR/+jL4f7WdoxelXdn5DLePjr/bpCkE83Vu2/jyy+K387Gvn0bX+o1RCftK3x/MQZltGx8KQvdg5AhLEknJyEE7+5o5L4PiujtbOXvcR8yr+dtFJ0Zz5xf8lzqVfy9tp1Of4DL42zclhFP57o9rFr0Eq6uPUQm+0g/w4M2og6zIYWYbeMx7JiMPjqHDn8Dzzo2U9SfT6HHgUZRuHpKAr9cMB5buHxsSPr65MI5NGRmS0OmpQhW/hV2vQ5aI0y9AU79KUQlDullCSGo6egPzvcuD25s2d7nRVj0RKVFocaa6Bhoxh4bYWK+3cJ8u4VxEYefye11+4PzvQs7qCvqoL2+DwBTuJ7kfBvJ+TZSCqKJsssbwScb4ffjrawc2PiyaKAAXkjA6dx7jD4lBVN+Hsb8fEwFBZjy89ElJIz40Scyr0ND5vVJrr8Dnv0GtJfCta/wpDub39Y3o/cLXpuczcx4y1BfoSSNSgGfSkdTX7DwXdtLW30PbbX7bXwJRDnMXyp+R0Z/9X4uJytZ6B6EDGFJOrm5fQGe+LyC/ywvJ1Wt59+xb5LjXAnWVLrm/Yl/h8/gibpWBHBjkoNbUh1UrdjJmldewt1diCXdT8aZPWBoJsKYj215DsaG2WgjE6gJlPJiXCklnQWU+qMxaeHGU9P4ydkFhBlGx51SKTTkwjk0ZGZLQ669HFb+Dba/DBotTL4O5twK1tShvjIg+PhrUVMPa8rbWFXWxrqKdvr1Ckp8GOaUCLpMGgSQZNRzviNY9D7FEoHuCDq0+7o81BU5qRuY793XFewIjnKYB7q9bSTl2jCFyzEYJyMhBP6W1n1zv4uK8BQW4q2pgYG1n8ZiwZSfP1AAH+j+zsxEMYycJgCZ16Eh83oE6GuHZy8EZxV88zUWebP4RVUDGlXw3NgMzkqR+xlJ0nDwxcaXwa7vnr0jULpaXTBQujWG6YhJiiA6MRxrbBiWWDPW2DAi7Sa02pNz88thVehWFCUaWAykA1XAQiGE8xDHzQceArTAk0KI+wa+fyXwB6AAmCGE2DTw/XSgECgeOMU6IcQPD3c9MoQlaWRo7nZz/4dFvLGlnvPDirk3fBHWnhJImUn9vHu5323n1SYnFp2WW9PiuCExhp0fbWbtK8/i81ThGBsgZXYbquLEqp9C5DupmDxnoDHb2MlW3rK3U+jMpSZgJUoPPz07h2+fmo1Bd3IGg3RiyYVzaMjMloYNZxWs+gdsfREQMPEaOO0XEJ051Fd2ALcvwPrKDlYUt7K8pIXyLhcBhwljUjhuq4GAAladlrNjojjfYWFudCTh2sPP4xZC4GzqH+j2dlJf7MTnCaAo4EiLImWg2zs+04JWL3PyZKb29eEuKTlg7renuBjhGZgdqtdjzM7e2/VtGlOAMT8fbUTE0F74UZJ5HRoyr0eI3hb43wXQVQ/XvcG7gSxuKqkF4L85KVyU6RjiC5Qk6at43X46GvoGxp/07B194nUH9h6jaBQiY0xYY81YYsOwOMx7C+FRMSY0w7gIPtwK3Q8AHUKI+xRF+TVgE0LcftAxWqAEOAeoAzYC1wgh9iiKUgCowH+BXx1U6H5XCDHu61yPDGFJGlm213Zy97t72Frdzs+i1/MjdREGdxuMX8ju2Xfy55YAyzp6SDEZuCMzgQssESx/7iN2L38VaCV5poJjQi2q6CdGzCL8jSRMpnmgM7JSv4blVsH2jkya1UgcZoXbF4zlsqmpo3ojCOnw5MI5NGRmS8NOVx2sfgg2PwuqHyYshNN+Cfacob6yQ6pp72dFSQvLi1tZXdlBn0UH8WZErBm/VsGgKMyNjmS+w8I5MVE4DEfWnR0IqLRUdlNbGNzYsrmqG6EKdAYNiTlWkvOD871jksJH3KOzo5Hw+/FWVw+MPRnoAC8sJNDRsfcYfWpqsPhdEBx9YiwoQOdwDPv//WVeh4bM6xGkpwmeWRAsen/7LZaSyXW7KlE1Cn9LT+Ta3PihvkJJko6QEAJ3r4/OFhddLf10tvTT1eLa+2+fZ18RXKNRiLSbgoVvR7AQ/kVBPDLGNOT1j+FW6C4G5gohGhVFSQCWCyHyDjpmFvAHIcR5A1/fASCEuHe/Y5YjC92SJB2CEIK3tzdw/wdFdHU5+VvCUs7rfg1F0cDsW1gx9vv8qcbJrl4X06LCeCAvhRSv4P1HXqZu94do9T1kzNMRmV6GAtj7TsH8VhrG6DNQUXnHvJStEdFs6UjFKcJIs2j5/cUTmVcQP+wXcNLQkAvn0JCZLQ1bPU2w5mHY9DT4XDDuMjjtVxA3Zqiv7Cu5fQE2VnWwvLiVZcUtlAZ8qLFmlHgzfqMWBZgaFcYFDivz7RYywgbfzHJ/Xpef+hIntQOjTpxN/QCYI/V7i94pBTYibKbj9O6kE+2L0Sfuwj14CvcVv321tXuP0cbEDBS/B8aeFBRgSEtD0QyfjjGZ16Eh83qE6aqH/y2Afidcv4T1mkyu2FqGT6fwp8Q4vj82aaivUJKkYySEwNXjGyh69+8thne1uuhsceHfvwiuVYiym4MjUBz7RqFYYs1ERJ+YIvhwK3R3CiGs+33tFELYDjrmCmC+EOJ7A19fB8wUQty83zHL+XKhezfBTvBu4LdCiJWHux4ZwpI0crm8AR7/vILHVpQTJ1p4NO5tCto/gYh41Hm/45X4+dxd0UiXP8BNyQ5+lR5PZ3kbHz76HJ2Nq9FHeMg7X48ueg86bQT2xumYPs7FGHsK3kAvi6yfUaJNY0tXIj3CxNhYI3+8bDLT0mOG+q1Lw4xcOIeGzGxp2Otrg7X/hg1PgLcXCi6C0/8PEiYO9ZUdVm1HP8tLWlle3MLnTV30RxsQcWbUyGBXd4ZRzzfibJzvsDIx8vCbWe6v1+mmttBJXVEHtUVOXN3B+d62+LCBwndwvrfBLPe/GGkCPT17N7v8ovjtKSsDf3DjLCUsDFNe3kDXdz6mgjEYc7LRGI/8xkooybwODZnXI1BnbbDY7e6G699hpz6DCzeU4DFouM0ewy8mDY+9KiRJCj0hBP3d3gML4C3BAnhXaz9+r7r3WI1OwWIfGIWyXwHcGhtGhNWIEqIi+AkvdCuK8ilwqGdY7gSePYJC95XAeQcVumcIIW7Z75jlHFjoNgIRQoh2RVGmAm8BY4UQ3Ye4vpuAmwBSU1OnVldXH+49S5J0EmvscvHAh8W8ubWeeeFVPBj5MjGdOyB+Ah3n/ZW/eBJ5obGdJKOee3KSOc8exc7lxax88XncPdsxO6DgAoFqLMKgj8VeOBHT+onoHePoCbTxnH051b4CtvTG40bP7NRw7rpsCnnxUUP91qVhQi6cQ0MunKWTRn8HrH8M1j0Gni7InQ+n3wbJU4f6yo6Ixx9gY6WT5cUtfFzVRoVGRY0zo9oMoChEazQsiLVyUZyNWdZwDF+jI1cIQXt9X7DoXdhBQ2knfq+KolGIS48iuSA43zsuI+qk3SBJGpzq9eItKzuw+F1YiNof7PxHp8OYmXlA8dtUkI826vj/XiXzOjRkXo9QzqrgGBOfC254j3JjOuesKaTfqOHHFiu/n5Yx1FcoSdIJJoSgr9O7bxRKq2vfOJRWFwHfviK4VqchymHeOwJl/3+HW75eEXy4dXQfl9Elh/hzBn39CzKEJWn02FrjDM7vrnHyE/s2fipexNjXCNNvZMP027m9qoPCPjfn2aP4c04yCRotq15Zx7aPFhHwVmHL0JF9bh8+KggzpGNbm4epfDY6axotgRqeS9hAXc94trsc+NAyPz+a3148iWRb2FC/dWmIyYVzaMjMlk467i5Y/zisewRcTsiaFyx4p80a6iv7Wuqc/awoaeWj0lZWd/fiijai2k2gVTABp0VFcGVyDPNioojQHX4zy/0FfCpNFV3UFgXne7dWdyME6I1aknKtJBdEk5IfjS0hTI4HG8GEquKrrd2v+L0nOPe7tW3vMfqkpL2bXZoKxmAaU4AuLi6k/3ch8zo0ZF6PYO3lwQ0qVT/c8B715jTOXLGbbrOGb5kj+eus7KG+QkmShgmhCno7Pfs6wVtde/+7u9VFwL+vCK7Tf1EEP7AT3OIII9xq+FLWD7dC94NA+36bUUYLIW476BgdwREkZwH1BDejvFYIsXu/Y5ZzYEe3g+AmlwFFUTKBlcB4IUQHg5AhLEmjyxfzu+/7oIiurk4eTfyA0zteQ4lMwLfgrzwePp2/VjYB8KuMeG5KduDt9vLxE+9TuXUJItBK4uQwkmY14wvUE6kfg+WjdMxdZ6IJd1Am9vBaQgk1znHs8thRFIWFk+P51YLxxEQMzWO40tCTC+fQkJktnbQ8PbDxqeBYk75WSJsDp/4Mss+GYTSf+Eh4/Sqbqjr4pKSF9xud1BkgEGsCgxaNgHFGAwuT7Xwj3kas8cg2s9yfu88XnO9dGJzv3dXqAiDCZiR9gp2MiXaScm1odSfX5yYdHX9rK+6ior3Fb8+eQrz7PY2rtdn2zvs25RdgGlOAIT0dRfv1brh8QeZ1aMi8HuHaSoOd3YoCN7xPW3gqpy/dRUeYhkt0Zh47Le/w55AkaVRTVUGv003XQAH8iw0xu1r66Wpzofr31Z11Bg0Wx74OcEusmbFzkoZVoTsGeAVIBWqAK4UQHYqiJAJPCiEWDBy3APgnoAWeFkLcM/D9S4GHAQfQCWwTQpynKMrlwN2AHwgAdwkh3jnc9cgQlqTRqd/r59Hl5Ty6vJzZpioejngGS3cJFHyD2rPu47eNHj5q6yY/3MQDucnMsEbQVNnJR4+9Slv1p6D0kj3XgjW/FH+gAxtTiFiSgklzDhpjBJu0G/g4ro2y1nxKfTEYNPDd2Wn85JwCIoxyBuloIxfOX6Yoyi3AzQRz+72Db3ofisxs6aTn7YfN/wsWvLvrwZEPs2+B8VeC7uS8GVrf6WJZUQtvVrWy2ePBYzciwnQgBMmKlgscVr6V4SAn3HxU5+9uc1Fb2EH1rnZq93Tg96kYTFpSx8WQMdFO2jg7Rjnbe1QJ9PbhKSke6P4uxLOnEE9pKcLnA0AxmTDm5QY3vRwofhtzc9GYDr/5qczr0JB5PQq0FAU7u7UG+M57dIancManO2kO03AmBl48Ix/NSXYjV5Kk4UFVBb0d7v2K3y46W4P/3d3qQlUFN//3rOFT6B5uZAhL0uhW1NTN7a/tYHddO/fHL+eynhdRdCY4924+TLmYO8saqPf4+GZCNHdmJWLTaSlaW8vy5xbR71yHRu9j7EU2DHHbUVU3MZ4ZhL2ZiinqLIRWw6fmFWyOVtjdnEVVwEakHm6el8MNp2Vh/JqPd0snL7lwPpCiKGcS3LfjAiGER1GUWCFEy+F+Tma2NGIEfLDrDVjzMDTvhIh4mPkDmPYdMNsO//PDlNevsrGqnTdKW/jM2UNzmAZhMQBgCcCpEeHckBXLHHsUmqMYN+H3BqgtclK5vZWqHW24enxoNAqJuVYyJjrImGgnMvrwxUxp5BE+H56Kir3zvt17CnEXFaH29AQP0GgwZGYER57k5+8dgaKzHfj/bzKvQ0Pm9SjRvBv+dyHow+A779MfkczcT3ZQY1ZIcgnePr2ApAj5d7IkSaGjBlR6OtxYY8NlofuryBCWJCmgCp5ZXcnfPi4hU9PIU9EvEO/cBGmn0rfgn/y1J5zH61qx6LTclZXEwngbAZ/Khnd2sfGdV/C7dmCI1DDhsigC5o0oihZ72zTC3s/GYD+NAF5ej/qU8ogYtrak0qhGYTcr/N/5Y7hiWhraEO08LA1fcuF8IEVRXgEeF0J8+nV+Tma2NOIIARXLYc2/oHwp6MNh6vVwyo/AmjrUV3fMGjpdvFXUxJsNHRQJPz6rATQKBr9gvM7AwpQYrspwYDqKMROqKmiu7KZyeyuV29vobA5uZGhPidhb9LYnR8i53qOYEAJfff2Xit/+pqa9x+gSEgY6v4PF76hzzpF5HQIyr0eRxu3w7EVgssB3PsAfkcj1nxfxmepB71X5V24Kl2bFDvVVSpI0wgyrGd3DjQxhSZK+UNvRzx1v7GRVWSu3x27kJs8zaP1uOOP/2DPxB9xW1sSm7n5mWcO5PzeF3HATfZ0elj+/hpL1b6D6KomMD2PsJVrcYgM6rRV7xQTMKydiiJ2KK9DF8zEf06LPYGNbEu0inJQoLb/7xkTOGRsvF+MjmCx0H0hRlG3AEmA+4Ca458bGw/2czGxpRGvaFezw3vVasAA+9tLgWJPESUN9ZSHh9at8XtnOCxUtrO3vpytCCzoNil8lLaBhvj2Km/ITSTzK7j9nUx+V29uo3N5GU2UXCIiMNpE+MTjXOzHHilYrH6OXwN/RESx+FxXtLX57KytBVRlTXCTzOgRkXo8y9VvguYshLAa+8z5EJfK/ogburGokoNdwqTGcR2Zny1EmkiSFjCx0D0KGsCRJ+xNC8NrmOv78XiHh3naeTXydnNZPIHYM6kUP85I2gz+XN9AXUPlxaiw/S4sjTKuhuaqbT578iJaKDxCBFuIKosk6u5d+73aM+gSiN+dj3j0bfUwOzkAT/0tYSpd/HBuc8XQLEwV2A3ddOplTsuxD/RFIx8FoLHQrivIpEH+Il+4E7gGWAj8DpgOLgUxxiF8wFEW5CbgJIDU1dWr1fpuQSdKI1FUP6x+FTf8Dbw9knA6zfxrcuHIE3RCtdvbzVFEDH7d3U60XCIMWVIHNpTIrPIwbsuI4LcV2VDeB+7u9VO0MFr1rCzsI+FSMYTpSxw7M9R4bg0HO9Zb2o/b34ykpIWzy5FGX18eDXGOPQrUb4flLIDIebngfIuModfZx6Zpi2sI0JA+MMjnam5mSJEn7k4XuQcgQliTpUFp63Pzx7T28t7OR62MKuZMnMfQ1wYybaD3tDu6u6+bVJiepJgP35iZzVkwUQghKNzax7Lkl9LYtB9FL5pxE4qbU4PKUEq7LxLIsE3PzmeiikqgVZbyYtJ6u3sls7o6lHwMzUsL5w6WTGZNoGeqPQAqh0VjoHoyiKB8C9wkhlg98XQ6cIoRoHeznZGZLo4q7CzY/C+sehZ4GcBQMbFx5xUm7ceVXcfsDvFzSzKv17ewK+PAYg11/hl4/YzV6LkuMZmF+HBaz4Wuf2+cJUFvYMTDXux13nw+NTiE510bGRDvpExxE2EbW5ykdPZnXoSHzepSqXgsvXA6WJLj2FYjOwB9Que7zIpYJDwaPyiN5qVyU6RjqK5Uk6SQnC92DkCEsSdJgPt7dxO+W7MLV4+SplA+Z1vI6SlQSXPh3Vjtmc3tJHWX9Hi50WPhTThIJRgN+b4DNH5axYclbePvWAz4mfCMNc+p2PN4GopSxRL2TgjlwHhqzjd2abSxJKqG9fSJb+x140XJuro3fXjyZ1Jiwof4IpBCQC+cDKYryQyBRCPF7RVFygc+A1EN1dO9PZrY0Kvm9sPuLjSt3BTeuPOWHMPU7YLYO9dWFnBCCdS3dPFXWzOrePpyGYEe30ucn069wZWIMN4xLxBr29YveqipoKu/aO9e7q9UFQGxaJBkT7WRMdBCdGC5HiY1ioymvFUW5EvgDUADMEEIcMmAVRakCeoAA4D+Sz0fm9ShWtRpevhYUDSx8DjJOA+DpPfX8rraJgFbDleZwHpolR5lIknT0ZKF7EDKEJUk6nG63j3vfL2LRhhrOt9TwV9NThHeVwtjL8Jx3H492CP5Z3YxOUfh1ZgI3JNrRaRR6nR5WvbKdotVLCHh2oDPqmbIwGTVqNX5/JzbfFCLeSsUcdh7ojKw2rmFVQjt1TWPY6bEj0HD5pDj+74LxxEbKx/xOZqNp4XwkFEUxAE8DkwAvwRndSw/3czKzpVFNiOCGlWsehoplYIiAKdcHi94jYOPKr1LX7+HJ0ibebe2kTqOCogSL3j6FhYnRXD8uCWv41y96CyFwNvZTuSNY9G6u7AYgym4iY0JwM8uEbAsaOdd7VBlNea0oSgGgAv8lmMODFbqnCSHajvTcMq9HufZyeOkqcFbCggdh2ncBKB4YZdIRpiHVJXh37hhiw+QTNZIkfX2y0D0IGcKSJB2pdRXt/Pr1HdS3d/Pv1BWc2/48ij4MzruHqrwruKO0nmUdPUyIMHN/XgqTo4Ld2M2V3Sx7fg2NJR+g+soJj4lk0pUOXMpShPAT0zWF8HcyMUbPQ1UE70YspdihUNKQTbHPjl4D15+Syi3nFhBl0g/xpyAdjdG0cD6eZGZL0oDGHbD237Dr9WABfNxlwbEmCROH+sqOq1aPl8dLmniz2bm36K3p85Hh07AwMZobxidjCTu6nOzr8lC1IzjXu67IScCvYgzXkT4uuJllyphoDCY513ukG415rSjKcmShWwo1dxe89l0o+xSmfx/m3wtaPf6AyrdWFLIcLwaPyn/yU7kwQ44ykSTp65GF7kHIEJYk6etw+wL889NSnlhZwSRzC0/Ynie6bRNknI644B+8o9r5XWkdLV4/NyTZuSMzgSidFiEEZZtaWPHiMrpbPkEEmnFkxVNwoZ4ez2doFCMxdRMJ+2wcxrhT8ah9LIr+kLYoB9vrU6kMRBOugx+fmcWNp+dg0muH+qOQvobRuHA+HmRmS9JBuuqCM7w3PzuwceUZAxtXnjWiNq48lBaPl8dLm3izyUn9F53evT4yfRoWJkVz/fikoxpvAuB1+6nd00Hl9jaqdrbh6fej1WlIzv9irredcIvsQhyJRmNeH0GhuxJwAgL4rxDi8cOdU+a1BIAagE9+H7wxm3EGXPk/CIsG4Ind9dxV14yqVbjKHM4/5CgTSZK+BlnoHoQMYUmSjsau+i5uf30Hexo6+VPyJq7tfgqN6oMzbqdnxo+5v6aNp+vasBt03J2dxMWxVhRFwecNsPXjKja8/Qnens8RajcZM7JIndNFV99K9LpooneNIXzzDPSOCfSo7TwT9yEeYzYbGxJpUC1EmxR+eV4BV81IQycfqT4pjMaF8/EgM1uSvoK7Czb/D9Y9Fty4MnZMsMN73BWgO7pi78mk2e3libIDi96aXh8ZPoWFSTF8e1wStqMYbwKgBlQay7qo3N5G5Y5WutvcAMRlRAXnek9wYEsIk3O9R4iRlteKonwKxB/ipTuFEEsGjlnO4IXuRCFEg6IoscAnwC1CiM8PcdxNwE0AqampU6urq0P0LqST3tYX4d1bwZIM17wMjjwACjv6uGxtEc4wLeluePuMAjnKRJKkIyIL3YOQi2ZJko6WP6DyxMpK/vlpCUm6Tp6Ne42U5k8hbjx8419sj8rntuJatve4OMMWyb25yWQO/PLW63Sz5o0SCld+SMCzHiG8jJs/DltBCT192zDpkrCtyiKsZh46azrNopZnkj9D8U9ibUssbSKCpEgtd140nvPHJ8oF9jA30hbOQ0VmtiQdht8bHGey5mFo2R3cuHLG92DqdyE8Zqiv7oRocnt5oqyZt5o7qFcGOr17fMGZ3kkxXDc+ieijLHoLIeho6Nu7mWVLdQ8AFod572aW8VkWNBqZySer0ZjXhyt0H3TsH4BeIcRfBztO5rX0JTXrYfE3we+BK56GnHMA8AZUvrm8kJVKcJTJfwvSOD/dPsQXK0nScCcL3YOQISxJ0rGqbOvj16/vYH1lB7ckFPMz73/R9bfAzB8ROPM3PNvq4t6KRrxC8NPUOG5Oi8U48Ghec2U3KxZto6HoUwLebWh1WqZeMQFd3Dr6XWWEa7KwfJBOeP98NOEOypRCFqWsQ+mbztoOB13CTG6Mgd9fMok5OXK+3XA1GhfOx4PMbEk6QkJA+Wew9j/Bf+tMMPFqOOXHezvpRoNGt5cny4IzvRsGit6aHh8ZXoUrk2K4bnwiMRFH3z3Y63Tvm+td7EQNCEwRetLHx5Ax0UHKmGj0Bjlq7GQyGvN6sEK3oijhgEYI0TPw358AdwshPhzsnDKvpUPqrIVF1wRvxJ5zN8y6ee+Yrf/uquOP9S2oWoVvhkfwt1k5Q3yxkiQNZ7LQPQgZwpIkhYKqChZvquUv7xViCPTyXOoHjG14FSypcOE/aE49g7vK6nmrpZMss5H7cpM5LToSCHaIlW5qZtXiTXQ1LUX1lWC2WJh2VQ5e0yd4PE1YAmOJeDuFMN0FaIyRbNRv4KOUEjxtU1nf7aAfA9OTw7l34VSyYyOH+NOQDjYaF87Hg8xsSToKLUWw7j+wYzH43ZB9drDgnTVvxM/x3l+D28tTXxS9NcG1TLDozUDRO+mYit5el5/q3e1Ubm+jelc7XpcfrV5DSkF0cK73eDthUSN/jMzJbjTltaIolwIPAw6gE9gmhDhPUZRE4EkhxAJFUTKBNwd+RAe8JIS453DnlnktfSVvH7z5Qyh8GyZeCxf9E3TBv3v3dPRy2dpiOsO0ZLrh7bljsJvl35uSJH2ZLHQPQoawJEmh1Nzt5ndv7eLjPc0sjK3lT5onMHaWwfiFMP9elnsM/LqkjiqXl8vjbPwhOxGHQQ+Azxtg2yc1bHxnLZ6e5aj+BuypSUy4LJZu37v4/T3Y+iYRuSQDk/U8hFbLp2GfsyWpg/aG8WzqdxBAy8IpCdxx4QQsYfqh/TCkvUbTwvl4kpktScegrw02PQMbn4DeZnAUwCk/ggkLQW8e6qs7oepdHp4qb+bNZieNA0VvpdtLhjc43uRbE5KwH0PROxBQaSjtDM713t5Kb4cHFIjPsJAx0U7mJAfWuLBQvR0phGReh4bMa2lQqgor7ocV90HyDLjqBYiMA4KjTK5evoc1ig+jW+WJcemcmzo6Rm9JknTkZKF7EDKEJUkKNSEEH+xq4vdLdtHX388TGZ9zauOzKMZIOO8vuMYt5OHaFv5d3YJJq3BnZiLXJcagGeis63W6WfNmGUWrVhHwrEL1d5I2qYCcc7V09CxBCIhpnkD4RwUYHXPx4+M168fU240U1WZT7LMTrlf45bn5XDc7Q25YOQzIhXNoyMyWpBDwe2DXG7DuEWjaCWExMO1GmP69vYWG0aRuoOj91lcUvb85PglH5NEXvYUQtNX17i16t9X2AhCTHEHOtFhypsURZR9dNxqGM5nXoSHzWjoiu9+EN38UzKFrXoKEiXtf+s/OOv7c0IKqhesjorj/lOwhvFBJkoYbWegehAxhSZKOl85+L39+r5DXNtdxpq2df0X8j8jWzZB5Jlz4D8pMCfy6uI5Vnb1MiQrjgdxkxkXu6/Bqquxi5cuFNBSvQvWuR1XdjJ03nYTpbbQ530erCSe6dCzha6dhjJ1On9rJEwnvIAz5rK5PoEmNIs2i454rpsj53UNMLpxDQ2a2JIWQEFC1KjjWpPgD0Oph3BUw68cQP36or25I1Lg8PHNQ0VvT5SXdq3BVUjTXTEgiNtJ0TH9GT4ebiq2tlG5qprmyG4DY9ChypsWSPTWOCNvRF9WlYyfzOjRkXktHrGEbvHwtuJxwyaMw9pK9L+1s6+GK9SV0hWnJdsPbc8cSbZZPrEqSNMwK3YqiRAOLgXSgClgohHAe4rj5wEOAluCMsPsGvv8gcBHgBcqB7wghOgdeuwO4EQgAPxVCfHS465EhLEnS8baqtI073txBXUcf/8zawjdaH0dRA3DmbxAzf8QbbT3cVdZAh8/P95Md/F9GPBG64OZVQghKNzaz+rXddDWtRPVuRaPTMuXi2URk7cLZuRKD1o5tYzYRZeeis2ZSRSnPpa9C0zODVR2x9Aojp2dG8efLp5IaIx+VHgpy4RwaMrMl6ThpL4f1j8HWF8HXB+mnwayfQM55oBmdTwXV9Ht4urKZt5qcNH3R6b236B3DteMTiY06tqJ3d5uLss0tlG5qDnZ6K5CYbSV7aixZU2LlTO8hIPM6NGReS19LTzMs/ibUbYS5d8Dpt+3NHrc/wNXLC1mn8WFyqzw1PoOzUqKH+IIlSRpqw63Q/QDQIYS4T1GUXwM2IcTtBx2jBUqAc4A6YCNwjRBij6Io5wJLhRB+RVHuBxBC3K4oyhhgETADSAQ+BXKFEIHBrkeGsCRJJ0K/18/fPy7h6dWVjI3o5SnHYmIbPgs+onfRv+h0jOMvFY0839BOvFHPn3OSWGC3oAyMM/F5A2z9uIbN7+/A07uKgKcQc6SF6VfNQFiX09OzA7OSjPW9bCICl6MYwllq/pxNKe00141ni8uBUDTccEoKP58/lgijbog/kdFFLpxDQ2a2JB1nLidseQ7WPw7ddRCdFZzjPelaMIQP9dUNmep+N89UtvBWcydNigqA0vnFeJNgp3fcMRa9O5v7Kd3UTOnGZpxN/SgKJOfbyJ4WR+YkB6Zw2cV4Isi8Dg2Z19LX5nPDu7fC9kUw5uJgd/d+ufPQjlrub2pFVeBGi4V7ZmQN3bVKkjTkhluhuxiYK4RoVBQlAVguhMg76JhZwB+EEOcNfH0HgBDi3oOOuxS4QgjxzYOPURTlo4FzrB3semQIS5J0Im2r7eTXr++gqKmbOzNK+W73f9D2twcfFZ/7Gza7BLeV1LK7183ZMVHck5NEmnnfY8y9Tjdr3yynaM12VO9K/J5aohOTmLJwDL3iDdzuOiy9Y4h6dxxm63l4cfOM4x28tkS2VKVQ5o/BYlT4zQVjuHJaGhqNMoSfxughF86hITNbkk6QgA8K34a1j0D9ZjBZYOoNMOMmsCQP9dUNqWqXh6crguNNmpWBTu9OLxleuDIphmvGJxFvOfqitxCCjoY+Sjc2U7q5he5WFxqtQuqYaLKnxZEx0Y7BJG9WHy8yr0ND5rV0VISAtf+Gj38XHKF19UtgTdn78vbWHq7cUEJ3mJZcT3CUidUkbwJK0mg03ArdnUII635fO4UQtoOOuQKYL4T43sDX1wEzhRA3H3TcO8BiIcQLiqL8G1gnhHhh4LWngA+EEK8Ndj0yhCVJOtG8fpX/rijn4aVlxOpdPJf6Hpk1r4EtHS78J/6MuTxV38r9lU0IIfhFejw/SHFg2O/x8aaKLla+UkJjyTaEfxV+Tzsp48cw9sJIWjpfBiGILh1D5KZ5GKLH0yzqeCztA6LUGayod9AmIsiNMXDvlVOZmi4f/zve5MI5NGRmS9IJJgTUbghuXFn4DqAE56ee8hNInjrUVzfkqlwenjm46O30kD6wkeXVExJJsBz9RpNCCFpreijd2EzZ5hZ6nR60eg3p42PInhpH+vgYdAZtqN6OhMzrUJF5LR2Tko/h9RtBZ4SrXoTUmXtfcvsDLFy2hw26AFpPgHPNYdw/PZPYMLm/gSSNJie80K0oyqdA/CFeuhN49ggK3VcC5x1U6J4hhLhlv2PuBKYBlwkhhKIojwBrDyp0vy+EeP0Q13cTcBNAamrq1Orq6sO/a0mSpBAra+nh16/vZFO1k++nNHCb71H0neUw8Vo47x7qNRH8vqye91q7yA0z8UBeMqdYI/b+vFAFJRubWfNGCd3NmxD+dagBN5MuOJ2YcRW0dXyEQXFgXZpFVNeVaMNiWG/YyEfphagt01nT6aAfA+flR/OHSycd02JcGpxcOIeGXDhL0hByVsOGx4OjTTzdkDITTvkx5F8IWtlhXNHv4X+VzSxp7qT5i/EmoSx6q4LGii7KNrVQtrkZV48PvVFLxkQ7OdPiSBkTjVY3Oueph5LM69CQeS0ds5YiWHQ1dNfDRQ8FR2jt54nd9fytqonOMC2KT+UUjYEHpmaQYxu9Y7YkaTQZbh3dxzy6RFGU64EfAmcJIfq/4hg5ukSSpGFPVQUvrK/m/g+K0Akvz2atYGLN/1BMVph/H4y/go/bu/lNaR11bh9Xx0fzu6xEYgz7igo+T4AtH1ez5YNifK5V+Pp3EBZlYeY3Z+M1v0tfXzERniwsS/IJj7yEgKKyKPoD2hIMVFTksMPjQKtRuOm0DG4+Ox+TXnaHhZpcOIeGzGxJGgY8PbD1BVj3KHRWgyUVZv4AplwXHHEiUd7v5tmBmd4tigpCoDi9pA+MN7lqQhJJ1qMveqsBlfrSTso2NlO+tRVPvx9jmI7MSQ5ypsWRlGdFo5VF76Mh8zo0ZF5LIdHfAa9eD5Wfw6yb4Zy7QXPgOuWlkiYeKG2gKUwDAcG4gJZ7J6UxPU7mkSSNZMOt0P0g0L7fZpTRQojbDjpGR3AzyrOAeoKbUV4rhNitKMp84O/AGUKI1v1+ZizwEvs2o/wMyJGbUUqSdDKo73Tx2zd3sqy4lYsTnNynfxJzy1bIPhsu/Ad9kUn8s6qZR2tbiNRq+V12IlfHR6NR9s3Y7mzuZ8WiYmp2FUFgBd7+OhLycpl0WTKt3S/g9/cSXVdA5OdzMNlPoVO08kjyEmzmCayuiKVajcZu1nDXxRO4cGLi3o0wpWMnF86hITNbkoYRNQDF78Pa/0DNGjBEwuRvBYve0RlDfXXDRtlA0XtJs5MWRewteuf4FG7MiuOKCUmEH8MG0QG/Sm1hB2WbWqjY3orPHcAcqSdrciw502NJyLKiyP04jpjM69CQeS2FTMAHH94BG5+AnHPh8icPeVP109p27tpVS7khWKtK9yrcVZDM+en2E33FkiSdAMOt0B0DvAKkAjXAlUKIDkVREoEnhRALBo5bAPwT0AJPCyHuGfh+GWAE2gdOuU4I8cOB1+4Evgv4gVuFEB8c7npkCEuSNFwIIXh7ewN/fGcPfW4Pj+VuYW7dYygA834LM39AkcvL7cV1rO/qY6YlnPvzkskPNx9wjtKNzax8pYS+ju0I/2r83j4mnHM6CTPaaG59HZ0mEtuaHKLqL0MXmcQu3U4WZ6whuncOS5ticIowJsSbuXfhVMYmym6IUJAL59CQmS1Jw1TD1mDBe/cbIFTIWwCzfgKps0DeNN2rpM/Nc1UtvNnspF0RoAr0bR5OMZq4eWwSp2XZj2mTaL8vQM2uDko3NVO1ow2/TyXcaiR7aiw50+KITY+UN7EPQ+Z1aMi8lkJu09Pw/v9BdBZcswhisg552JaWbu7YVsV2xQ86DbH9Kr/KSuDb+Qkn+IIlSTqehlWhe7iRISxJ0nDT3uvhT+/u4a1tDcy2u/iP9UWsdUshcQp841+oceN4uamDP5U10BMI8MOUWH6eHke4dt+jfO4+H+uWVLBrRQWKugFPz2aM4eGccvVcRPRndHVtJiyQgvWdHCL0VyJ0epZEfUZxahe9NRNZ22PHi45LJsRy5zcmYo+QG7wcC7lwDg2Z2ZI0zHU3wIYnYPMz4HJC4uTgHO+xl4JWP9RXN2wIIdjZ089/Spv4qLMHlwbwqUQ6vSywRfHTyalkOSIOe57BeN1+qne2U7qpmerd7ah+QZTdRPbUOHKmxxKTFCGL3ocg8zo0ZF5Lx0XlSnjluuBGyQufg8wzvvLQqm4Xt22sYGXAg9BriOoP8IMkOz+fkIJGI0c7SdLJTha6ByFDWJKk4WpZcQt3vrGTxm4XD+SVc3nLw2jcTph9C5xxO+1Cz58rGljU2EGySc99uSmcHRN1wDmaKrpY/mIxrTVV6JSV9HdVEpueybRrxtDe9zweTxOWtjwsn0zHHHM2faKTRxPeIjwmkx2lSezy2jFqFX52Vg43np6NQW50dVTkwjk0ZGZL0knC2w/bFwXneLeXQmQCzPg+TP0OhEUP9dUNKwEhWNraxSOljWx0uwloFHD5SepTuTbZzvcmpWAxH9tNAk+/j4ptbZRtaqa2yIlQBbb4sGCn9/Q4bPFy87YvyLwODZnX0nHTUQmLroG2Epj76+DTQ4av/juszeXljo0VfNDfh9+oxeQKcE2MlbumpmPSyX2JJOlkJQvdg5AhLEnScNbr8fPgh0U8t66avCg/zyQuIaHydYjODO5AnnE66zp7ua24jpJ+N1fFR/OnnCSi9vvFTQ2obF9ax/p3KvC7i8C3Ck9/J2Pnnkba6T4aW15EgxbrtmysJRejs2RTqSnnifSPSONUllXZqFOtJEZqufvSSZxVECe7wL4muXAODZnZknSSUVUo+xTWPQIVy0FnhknXBLu87TlDfXXDTl8gwKu17TxV2Uyp8IOioOnxMVZo+WFOAhfnxaE7xk0mXT1eyre2UrapmfrSThAQkxxBzrTgeJMo+9FvkjkSyLwODZnX0nHl7oa3b4E9b0F4LJz+K5h6A+i++gnUfl+AP26u5OWOLjxmLTpPgAvCI7h3WibRx3gzUZKkE08WugchQ1iSpJPB5uoObnttB+WtffxfbjM/7H4IbWdVcOOvc/6Ex2TlH1XNPFzTTJxBz9/zU5gbfWB3d0+Hm5WLS6jY1oBet5V+5zp0BiMzF56HIXEt7R3LMIpYbB9kExm4GsUQwWcRq1iVXk5E6yyWttroFmZmpETwlyunkB0bOTQfxklILpxDQ2a2JJ3EmnfDuv/Ajlch4AluKnbKjyFzrpzjfQitHi+PljbxWlMHLVpACIxdPk4PC+MX41KYnHTse2j0dXoo29JC2aZmmiq6AYhNjyJnWizZU+OIsI2+sWUyr0ND5rV0QtSsh8/uhupVYEmFM++ACVeB5qs7tf0Blb/vqOWJhjZ6wrRofCqnaY08OD2L1CjTCbx4SZKOhSx0D0KGsCRJJwuPP8AjS8v4z/JyHCaV57KWkV32DEpYNJz/AIy9lK09Ln5aWE1pv4dvJ8bw+6xEIg56LK9iWysrF5fQ3daEybiG7tZiYpJTmXntdDp9L9LfX0lkTyaW9yYSZr0Qr+LhaccSvEkWGsqy2dBnJ6BouWZqErctGIclTHZBHI5cOIeGzGxJGgF6W2HTU7DxSehrhdixcMqPYPyVoJdFhkMp7O7nn4X1fNLVS79egYBKdE+Ai+wWfjEpjbjIYy9Id7e5KNvcQtnmFlprekCBxGwr2VNjyZoSS1iUIQTvZPiTeR0aMq+lE0YIKF8aLHg3bgN7Hsz7LRRcdNibqP8rauBv5U20hmnArzJJ6LhvcjqTHFGD/pwkSUNPFroHIUNYkqSTTVFTN7e/vpPttZ18O6OL36qPYWjeDnkL4KKHcJvt3F/ZyGO1rSSbDPwjP4U5tgO7r71uPxvfrWTbZ7VoNVUI7+f0d7eRO3s2ueeG0djyDGrAja0kB8vW8zDYJtGo1PFI6hIyI2ewpiiGYn8M4XqF/5ufzzdPyTjmx6lHMrlwPpCiKJOAxwAT4Ad+LITYcLifk5ktSSOIzw07Xw3O8W7ZDeEOmHYjTL8RImKH+uqGJSEEnzV38nBxI5u8bgI6DXgDpHsUvp1q58axSRj1xz5ztrO5n9JNzZRuasHZ2IeiQHK+jexpcWROcmAKH7k3uGVeh4bMa+mEEwIK34alfw7O706cDGf9HjLPPGzB+93KVv5UWEf1wD3DbK/CH8elclaK3FNCkoYrWegehAxhSZJORgFV8MzqSv72cQl6JcBTBZuZVvZvFGMkfONhyF/Axq4+flpYTaXLy41Jdn6TlUC49sAFcFtdD8tfLKapop2wiN10N69E0WiYcdkCInMKaW5+Ez1WrEszsXRfizbMzjrjRpZkbSKjfy4fVUfSLKJIt+r5yxVTmJ1tH6JPZHiTC+cDKYryMfAPIcQHiqIsAG4TQsw93M/JzJakEUiI4Pzudf+B0o9Ba4DxC2HWjyFu7FBf3bDlVVVeKG/mmapWSpUAaBW0Lj+TNAZuzk1kfkZMSPbTaK/v3Vv07m51odEqpI6JJntaHBkT7RhMuhC8m+FD5nVoyLyWhkzADztehuX3QVctpJ8GZ90FKdMP+6Prm7r4zfYqdmtV0ECCC27LSeCq7Fg0GtnQI0nDiSx0D0KGsCRJJ7Pajn7ueGMnq8rauDS5h/t5GEPbLph8Hcy/l35dOH+paODJujYyzAYeyk9lhjXigHMIVbB7VQNr3yzH6+4gPHwD7bU7sMYnMPtb8+jlFbp7dhDmTsb6zlgiIq4goBG8HPMBdaluqJnEcmc0vcLI3Cwrd182mdSYsCH6RIYnuXA+kKIoHwFPCyEWK4pyDXCREOLaw/2czGxJGuFaS2D9o7BtEfhdkHEGzPoJZJ8DssjwlZweHw/truf1FietBkBRCOvzMzcinNvGp5AfE3HYcxyOEILWmh5KNwVnevc6PWj1GtLHxZA7I560cTFo9Sf//0Yyr0ND5rU05Pwe2PQMfP4g9LcFn3yd99sjuoFa7Ozj15srWad6EXoNWk+ADKFlbkwU38mNJ8sq1zmSNNRkoXsQMoQlSTrZCSF4dXMdd7+zBz1+Xs5dRm7ZUyjWVLj0cUidyRpnL7cW1VDr9vKDFAe3ZyRgPmjUSF+Xh9WvlVG6sRlTWAMB93J62pvImjaDcRcl0dD6X3y+Diy1WVhXzcUUMwcnrTyc9DqJsWPYU5jAFrcdFA03zErl1vPGEGEcWZ1eR0sunA+kKEoB8BGgABpgthCi+nA/JzNbkkaJ/g7Y/AxseAJ6GiEmJzjHe+I1YJAFhsGUdPbzwM5alvb20m/SgipwuAUXO6z8ckIKNtOxjx0RqqCpoitY9N7SgqvbizFMR9bUWPJmxpOQaUHRnJwbjMq8Dg2Z19Kw4ekN3kBd/S/w9AT3gzjzDojOPOyPNvV7uGdrNau7+mjUCYReA0IQ5lYZozdwQYKNa3LisBpH7jgnSRquZKF7EDKEJUkaKWo7+vn54m1sqnby05w2bu3+G5ruWpjzczjj1/Sh5Y/lDTzX0E5OmJGH8lOZYgn/8nn2dLBiUTGdLb3YHCW01SxDqAGmXXwh9vH1NDS+iEYYsK7LwFp/NbrIFHbpd/G/9E+ZoD+Lz4ojKA/EYDUq3HnhWC6fmormJF3whspoXDgrivIpEH+Il+4EzgJWCCFeVxRlIXCTEOLsrzjPTcBNAKmpqVOrqw9bD5ckaaTwe2HPW7D2keAmY+ZomP49mPF9Ocf7CHxc287DxQ1s8XsJGLUofpWsgJYb0hzckBePLgRd8mpApa7ISfGGJiq2tuL3qkTGmMidEUfezHhs8V/+PWM4G415fTzINbY07PR3wOqHYP1/QfXBlG/D6bdBVMIR/bg3oPJmRStv1LSxze2my6QBjQIBQYxHMDXczJWpdi5Ij5FjTiTpBJCF7kHIEJYkaSQJqILHVpTzj09KSIsI8FLKEuLKX4WEiXDZE+DIY0VHD78oqqHR4+Pm1Fh+mRGP8aBfyPy+AJs/rGbLR9VoNP1ERm2msXQDkXYHc761ALfpHZzO1Zj8sVjfyyVK902ETs8S62dsTa8jsWMOH9ZF0iYiyHeY+MuVU5iSahuiT2XoyYXzgRRF6QKsQgihBIfIdgkhDrvFvcxsSRqlhIDqNbD231D8QXCO94SFMOtmiM0f6qsb9vwBlacLG3m2ppUKnYrQa9B5VabqDdySl8hZibaQzPP2uv1Ubm+jZH0TtYUdCAGxaZHkzognZ3ocYVGGELyb40vmdWjIvJaGre7G4DiTLc+CRg8zb4JTb4Wwr7fxZEu/h+dLmviwuYuSgA+PObgPksarkhJQOM0WyfU5cYy3Rx6HNyFJkix0D0KGsCRJI9H22k5+vngble19PDiulsvrH0Dx9sHZf4QZN9GtCu4qq2dRYwf54Sb+VZDKhMgvPw7ubOpjxaJi6os7sdg78PZ+hrOxlrQJk5hy+VgaO/6L211LZGs61s9OISx6Pn1085+E1wlLjqe9JJOVXTG4MHD+mBjuungS8RbTEHwiQ0sunA+kKEoh8CMhxHJFUc4CHhBCTD3cz8nMliSJttJgh/f2ReB3B+d3z745OM87BMXaka7d5eXv22pY0tZFW1iwIzHMo3JWVAT/NzaZXEtoRsP0dXko3dhMyYZmWmt6UDQKKQXR5M2MI2OiA71Re/iTDAGZ16Eh81oa9joqYfm9sOMVMEbC7J8GR2QZj25Pg+2tPTxX1sRKZy91WoFqCDYRGV0BcrV65sdZuC43ntgwYyjfhSSNWrLQPQgZwpIkjVT9Xj/3vFfIi+trmB0X4HHr/4io/gwyz4RL/gNRiXzS1sWvimtp9/n5WVoct6bFoz9ozIgQgpL1Tax+vQxXr5f41CoaSj7C7/EwecEFJM/sp7b+SVADWLanYyu9An1ULpXaCh5JX8Jky+ms2x3FDo8DrUbDD8/I4Mfz8jDph+ci93iQC+cDKYoyB3gI0AFu4MdCiM2H+zmZ2ZIk7dXXBhufgo1PQF8rxI+HWbfAuMtAK+elHoldLT38dWctK/r6cEUGP7NYL1web+WW/CSiQzR3tqOhj+INTZRsaKK3w4PeqCVzsoO8GfEk5duG1XgzmdehIfNaOmk074al90DxexBmh9N/BVO/A/qjb8zxB1Q+qG7n1Zo2Nve5aDcqoFVAFVjcKpNMJi5LtXNppgODVo45kaSjIQvdg5AhLEnSSPfpnmZuf30HPR4fz4zfzeyyv6NoDXDh32Hc5Th9fn5XWs9rzU7GR5j5V0EqBRHmL53H3edj7Zvl7FnVQFiUn0jrVqq3ryTcFs2cb16CsK2gpeVdDKoNy8eZWHzfRmOI4tPIlXycsZ1JvnN5v8RItRqNI0zDXRdP4IIJiSF5XHq4kwvn0JCZLUnSl/jcsGNxsMu7rRgiE2HmD2DqDWC2DvXVnRSEELxb1sp/ShrZIXwEwnWgCnLQ8t30OK7NcHxpxNlR/TmqoLG8k+J1TZRtacXr8hNmMZAzPTjP254cMeS/E8i8Dg2Z19JJp3YjfPZHqFoJUckw99fBDZC1umM+dafHx6LSZt5tdLLH58U1MOZE8akk+BVmR4VzVYaDUxMscr63JB0hWegehAxhSZJGg9YeD7e/voOlRS1cmeHhHvEwhqYtMH4hLHgQzFbeb+3ktuI6uvwBfpUez09SY9EdosuqsayT5S8V09HQR3yGi772j2mtLicpfywzr5lNS/fj9PYWEd6dhPXDyYRHXYpX8fBU3Nv0pGkIq5vEx01ROEUYExPC+MuVUxibaBmCT+XEkQvn0JCZLUnSV1JVKPsU1j4MlZ+DIQImXxd8FN2WNtRXd9Jwef08saOOF+vaqTYBRi3agGC6wcTNeQnMi7WgCUEx2u8LUL2zneL1TVTvakcNCKITw8mdEUfujHgio4dmzNloymtFUR4ELgK8QDnwHSFE5yGOm0/wKSwt8KQQ4r7DnVvmtXRSEgIqlsNnd0PDFojJgTN/AwUXhfRJoVJnH8+WNrO8vZtKJUDAuG++t8MPY8JMzIu1cHGGXY46kaSvIAvdg5AhLEnSaCGE4MX1Nfz5vT1E6GBRwRpyCh+ByHi45FHIPIN2r59fl9TxTmsnkyPDeKggldzwLy82AwGV7Z/WsvHdSoQCKbmNVG19G09fHxPPnU/mGSZq6h/B7+/GUpKObdtFGK2TadTU86/k18lPnELZ7ljW9MTgRcdlk+L5zYXjiYkYmb/MjaaF8/EkM1uSpCPSuD3Y4b3rdRAqjLk4ONYk+bBbAUj7qe9y8Y+tNbzb3kVnlA50GsICgnMskdyan0RB5Jef/joa7l4fZVtaKF7XRFNFFyiQlGMld2Y8WVNiMZqPvaPySI2mvFYU5VxgqRDCryjK/QBCiNsPOkYLlADnAHXARuAaIcSewc4t81o6qQkBRe/C0j9DaxEYLZBzDuSdH/y3KXQNOqqqsqy+k7dq2tjc3U8dAbwm7d7rMLtV0jU6plvCuTA1hjmy61uSgGFW6FYUJRpYDKQDVcBCIYTzEMcd8s7xV915VhQlHSgEigdOsU4I8cPDXY8MYUmSRpuyll5uXbyVXfXd/GJMDzd3PoCmoxxm3Qzzfgd6E0tanNxRUkdfQOX2jAR+kOJAe4gOru42F58vLqF6Zzu2eC2R1m2UrvsMU0QEp157BcbEbdQ3LEInzFhWZGDrvB5tmIO15o28nL6COabz+WyXkd0+Byadws/OyuW7p2Vh0I2sX+BG08L5eJKZLUnS19JVD+sfg83PgqcLUmcFsy7vfNCMnn0ijpUQgg21Th7aWccqtwuv1QAaBYeqsDAhmh9kxRMbonneXa0uSjY0Uby+ia4WF1qdhvQJdvJmxpE6Ngbtcf79YLTmtaIolwJXCCG+edD3ZwF/EEKcN/D1HQBCiHsHO5/Ma2lEUANQ8lFwfnfxh9DfBhodpM+BvAXBLLGmhvyPrep28WZlK5+3dlPs8eI0gBj4u0/jU7H7YIzZyJmxFi7NdMiub2lUGm6F7geADiHEfYqi/BqwfZ07x19153mg0P2uEGLc17keGcKSJI1GXr/KQ5+V8J/l5eTYtLyQ9h6xhc+BowAufwLix9Pq9fF/xbV82NbNDEs4/8xPJfMQv0gJIajY1srKxaX0dXnImACd9e/TWFpEfFYOp37rPNrdz9LZuQGTKxbb++OIDLuagAYW2T+gJLOdvM7TeafcQL1qJSlSx58um8S8grgh+GSOj9G6cA41mdmSJB0VTw9seR7WPQpdNRCdCaf8GCZ9EwxhQ311JxWPP8Bbexp5vLSZQm0A1WIAIRijM/CjrHi+kWALzTxvIWip6qF4QxNlm5px9fgwhevJnhZL3sx44jKijss879Ga14qivAMsFkK8cND3rwDmCyG+N/D1dcBMIcTNhzjHTcBNAKmpqVOrq6uP/4VL0omiBqBu00DR+wNoKwl+P258sOCdvwASJsFx+HvJG1D5rLaDD+o72NzdT+0hur5Tv+j6Tonm9ESr7PqWRrzhVuguBuYKIRoVRUkAlgsh8g465ojuHO9/51kWuiVJkr6+DZUd/HzxNpq63Tw4qYVLa/6C0t8B834Ls29BKBpeb3ZyZ2k9XlXlzqxEvptkP+R8Tq/bz4a3K9mxrBZjhJ6McW2UrHmDvq5Oxp95NvnnJVBT/xAeTxORtanY1p6N2Xo6TqWdh5JeIT41E1dZJp+0RtEtzJyWaeEvV0whJfrkL0KM1oVzqMnMliTpmAT8UPg2rP031G8Gsw2mfw+mfx8iR87N1ROlrdfDk1trebG+jVaLHkxaDCqca43gJ9kJTIoKC0kxOhBQqd3TQcn6Jiq2txHwqUQ5zOQNzPO2xoXu94SRlteKonwKxB/ipTuFEEsGjrkTmAZcJg5a6CuKciVw3kGF7hlCiFsG+3NlXksjXlsZFL8fLHrXrguOyYpMhLz5kHcBZJwGuuPXaV3T7eaNyhY+b+umyH1g17fyRde3ycjcuGDXd7zs+pZGmOFW6O4UQlj3+9ophLAddMwR3Tne/87zQKF7N8FO8G7gt0KIlV9xDfJusyRJ0oBut48/LNnNG1vrOS1Jw2PW5wkvfw9SZ8Olj4EtjUaPl18W1bK0o4fZ1gj+kZ9CmvnQvzC11vSw/MUiWqp7SMwxExaxnT0rPkBvMjH7qoVEZVVRW/ckqAqWdalEN1yHLiKVncbdPJH6Hqfbz2X9tjA2uhwoGi0/PiOTH5+Vi1F38j5mPtIWzkNFLpwlSQoJIaBmXbDgXfRecJOxCQuDY01iC4b66k46Qgg21Th5aFstn/f343WYQKsQq2i4PtnOt1IcxIVotInX5ad8ayslG5qoK3aCgLiMKHJnxJMzLRZzpOGYzj/a8lpRlOuBHwJnCSH6D/G6HF0iSYfT1w6lHwUL32VLwdcX3BA5ax7kXwA550JY9HG9BH9A5bM6J+/XtQe7vkUAj3lf17dpb9d3GBekxDBXdn1LJ7kTXuge7M4x8OwRFLoPe+f44DvPiqIYgQghRLuiKFOBt4CxQojuwa5VhrAkSVLQO9sbuPPNnfhVlacnVzCz6D4UIeD8+2HStQhgUVMHvy+tRwXuykrk24kxh+zWUlXB7s/rWfdWOQG/oGCWkebyd6ndvR1Hajpzvn0JveI1Wts+weCzYf0gF4v2etAZWWL7jFXpxZziP58le3RUqdEkROi498rJzM2LPeGfSyiMtoXz8SIzW5KkkGsvD25cue0l8Lsg++xgwTtz7nF5BH2k6/X4eWVbHU+UNVFlVhA2I4oQTDabuSkjjvkOCyZtaIorvU43JRubKVnfTHt9LxqNQurYaHJnxpMxwY7O8PVvkI+mvB7YE+vvwBlCiNavOEZHsJHsLKCe4EjRa4UQuwc7t8xradTyuaHy833d3r1NoGiDe0TknR/8JybrhFxKbY+bNypb+by1iyK3l46Dur5jvuj6jrVwSYadxAjTCbkuSQqF4dbRfcyjSw5353ngmOXAr4QQgyasDGFJkqR9Gjpd/PKV7aytaOeaXLhbfRh93VoouAgufAjCY6hze/lFUQ2fO3s5wxbJ3/NTSDIduoOqr9PDqtdKKdvUgiXWTNakHnZ+uoie9lYK5sxlwkUF1DY9RH9/OeGtCdiWzSHcegF9dPNw4qtEpMcTKM/lo5bgOJN5OTb+fPlkEq3mE/zJHJvRtHA+nmRmS5J03PS1w6anYcPj0NcSnLs66ycw7nLQHVuX8Gi1p6GbxzfX8HZ7J/2xJjDrMAHfcFi5IdXB5MjQjDYBaKvrpWR9EyUbmujr8qI3acmaEkvejDgSc21oNEf254ymvFYUpQwwAu0D31onhPihoiiJwJNCiAUDxy0A/glogaeFEPcc7twyryUJUFVo3BoseBe9Dy0D94fsecGZ3nkLIGkanKDOan9AZWm9k/frOtjc1UeNCOAxaYI3dfd2fWuZGhXOBSnRzE20oQvRjUlJCrXhVuh+EGjfbzPKaCHEbQcd85V3jr/qzrOiKA6Cm1wGFEXJBFYC44UQHYNdjwxhSZKkA6mq4KlVlTz4UTHRZg0vjt9C1o6/g8kKFz8CuecihODZhnbuLm9AC/wxJ4lr4qO/csFavbudzxcV093mJmd6NAbDNrZ9+BYanZ5TrlhI7PhOqqr/jRpwEbU9hZiyq9FH5rPHUMR/0pcwz76AVVuMbHbHotVouGVeNj+Ym4NBd3L88jWaFs7Hk8xsSZKOO58bdr4aHGvSWgSRCTDjJpj2neBMb+lrc/sCvL+zkcd217NLE0CNM4NWIUmn4/oUOwsTYogP0WgTVRU0lDgpXt9E+dZWfO4A4VYjuTPiyJsZT0xSxKA/L/M6NGReS9IhOKug+MPghpZVq0EEINwBufMh9zxIO/W4jzg5WH2vmzcqWvm8tZtCt4d2PQj9gV3f+SYjZziiuCzTQZLs+paGieFW6I4BXgFSgRrgSiFEx5HeOR7kzvPlwN2AHwgAdwkh3jnc9cgQliRJOrQ9Dd387OWtlLb0cvskPz9ovxdNayFMuxHO/RMYwql2ebi1qIa1nX2cHRPFX/NSvnKx6vcG2PRBFVs/rkFv1DJxXhR1u5dQsWUj0YnJnHb9VXiNH9HY+Cq6QATWT3KxBb6H0Bl4OeZDdmbVM7X/HN4s1FKj2kiO0nP/wsmcmu04wZ/M1ycXzqEhM1uSpBNGCCj7DNb8CypXgD4cplwHp/wIbOlDfXUnrcq2Pp7bWMPLdW10xhgGRpvArKgwrk91cF5M6Eab+LwBqna0Uby+iZrdHQhVEJMUQd7MeHKmxxFh+/JeIzKvQ0PmtSQdhssZzJii96DsU/AMTNyNHQNpswf+ORUiDzUR+PjxB1SWNwS7vjd19VGjBnDv1/VtdKukKlqmWsJZkBzNvCTZ9S0NjWFV6B5uZAhLkiR9NbcvwP0fFvHM6irGOAw8l/EJ9h2PQ3QmXPYEJE9FFYKn6tr4S0UDRo2Ge3KSuCzO9pXd3R0NfaxYVExDaScJWRYyJ/ay+d0X6GxuJGfmbKZdPou6lofo6dlJWGsc0SvOIjzqXNo1bfwt+SUyM8bSU5jKx60WeoSJc/NjuPvSScRbhm+HgVw4h4bMbEmShkTjjuAc712vgVCD47xm3QIp04f6yk5avoDKZ4UtPL21hjVeN4HEMIRJR5iicFmcjWsTY5gcFbrRJq4eL6WbWijZ0ERzZTcokJxnI29mPJmTHRhMOkDmdajIvJakr8HvhfrNUL0aqtdA7Xrw9gZfi84cKHrPCf7bmnrC94+o73WzpLKN5S1d7Dm469uvEu2FfJOB0x0WLstwkBI5fNdk0sghC92DkCEsSZJ0eCtKWvnVq9vp7Pfy9xk9XFh+N0pPI5xxG5z2S9DqKe9387PCGjZ197PAbuH+vGQchkN3dwshKFrbxJrXy/C6/EyYF48itrPx7VcBmHHJ5SRO81FV/Q8CfheWDSnENNyILiyZ1WGbeCH9ExZYL+XTzRq2eWLRaTX8/Oxcbjw9C/0w7CqQC+fQkJktSdKQ6m6A9f+Fzc+AuwtSZsLsW4JzVjVff+NDKaih08XiTbU8V9JEs0WHiDMjtAppBj3fSrZzRbyNBGPo5qR3NvdTvKGJkvVNdLe50ek1ZEy0D2xi6ZB5HQIyryXpGAT80LQ9WPT+4h93Z/C1qOR9Hd/pcyAm+4QXvv0BlRUNnbxf187Grn5qVP++rm/A6AqQqdFxekwk12bHkWcLP6HXJ40OstA9CBnCkiRJR6ajz8sdb+zgo93NnJVu5GHbIsIKX4OkqXDp42DPJiAEj9W28kBlI+FaDfflpvCNWOtXntPV62XtG+UUrmkkMtrEtAUxlG94g5L1q7ElJHHGd67GrXuHpuYlGDwWrB9NwGq4Hp/Gz5Nxb9GU6WJc51zeKNZSp1pJtxq4b+EUTsmMOXEfzBGQhe7QkJktSdKw4OmFrS/Auv9AZzXYMoIbV066FgxyQX+0AqpgVVkbL2yq4WNnD94Ec3C0CXC6NYJrEmOYbw/daBMhBE0V3ZSsb6J0czOePj83//csmdchIPNakkJIVaG1MFjwrloV/HdfS/C1cMe+MSdpsyF27Anb3HJ/Db1ullS1sby5i91uD20GYGAvJaMrQJZGx1x7FNdkxZIjC99SCMhC9yBkCEuSJB05IQSvbqrjD+/sRqdReGpGPdN3/BECXjj3zzDtu6AoFPcFu7u39fRzcayVv+QkE2PQfeV5G0qdLH+xGGdTP5mTHaSP62PN4qfobG4k/9QzmHzpRKrrH6S/v4Lwqjjsmy/DFDmTGn0tf0texPS0OTTtcvBJm5U+jCwYY+cPl04kdpg8OicL3aEhM1uSpGEl4Ieid4MbV9ZtDG7aPP3G4OaVJ3iu6kjT1uvhjS11PLujnioTiKRwVJOWcI2Gy+NtXBUfzZQQjjYJ+FWqd7WTNTlW5nUIyLyWpONICGgv3zfqpHo1dNUGXzNZIHW/Gd8JE0Abms1+vw63P8CbFa28W9/Btn437fsVvk2uAFlaHXNjorg2O44sa9gJvz7p5CcL3YOQISxJkvT1Vbf3cevibWyt6eSGcQZ+G3gEXeUyyD4HLn4EIuPwq4J/1zTzt6pmrHotD+amMN9h+cpzBvwq2z6tYeN7VWi0CjMuTKGvfQ0b334NncHIqddciyWnlurq/0AALJ+nEdP9QzQGK+9ZV/Be+gYuCr+CDzapbPc6MOo0/PLcPG44NXPIN0mRhe7QkJktSdKwVbM+uHFl0XvBosL4K2HWzRA3Zqiv7KQmhGBjlZNFG2t4u7YdV7wZEW9GaBQyTAauTYwJ6WgTmdehIfNakk6wzpp9Re/qNdBeFvy+PhxSZkD6qcHCd+IU0J/4RqB+X4C3Klt5p76DHf1u2o2Adl/hO1ur58yBju9MWfiWjoAsdA9ChrAkSdLR8QdU/r2sjIeXlhEfaeTFiTtI33If6MPgoodgzDcA2NPr4qeFNezqdXFFnI0/5yRh1X91d3dXq4sVLxVRW+gkIdvClHOj2Ljkf9Ts2k58Vg6nX38Z7e5naG9fgbHHRvQnpxAZfiX9mj7+lfgKZIST2XYKr5doaVAtZEYbefCqKUxNiz5RH82XyIVzaMjMliRp2Gsvh3WPwrYXwdcPWWfB7Jsh88wTPkd1pOly+Xh7Wz0vbKplN35EchgBqxENcLotkqsTojnPbsF8DDe3ZV6HhsxrSRpiPc37dXyvgZbdwe9rjZA8bd+c7+QZYIw44ZfX6/XzZmUr7zc42d7vpsOogDaYkSZXgBytnjMdwY7v9CjzCb8+afiThe5ByBCWJEk6NltqnPx88TZqOvq5c4aW77bci6ZxG0z6Jsy/D0xReFWVf1Y381B1Mw69nr/lp3BWTNRXnvOLzSpXv1aK36sy7YI0zOFVfP7CU7i6u5k4fwH5ZyVQWfMAHk8zkbsTsJd8C0P4GHabivl3ymuclXI+ZdsiWNoRTT8GLh4fy+8unoA9wnjiPpwBcuEcGjKzJUk6afR3wKanYcPj0NsMceOCc7zHXQG60G2sOFrtrOvi5Y01vFHcTLfdgCY5Ap9RQ6RWwyVxwdEmU49itInM69CQeS1Jw0x/B9SsGyh+r4bG7SBU0OggYdK+USepM8FsO+GX1+v180ZFK+83dLDd5cZp1AQL30Jgdqt7C9/fzI4nNWp4jKaUhpYsdA9ChrAkSdKx6/P4ufudPSzeVMvkxDCeylhG9JaHwZIMl/43+MsTsL2nn58W1lDc5+bahGj+mJ1EpE771eft8rDy5RLKt7ZiT4lgzhWpFK56k+2fvE+41cYZ374OrWMDdbXPovEbsH2Wi83/A4TewKKYD1mfXsxFpitZstHDDm8sZp3C7ecX8K1ZGWg1J66zTi6cQ0NmtiRJJx2/B3a+Fpzj3bIHIuJh5k0w7UYwW4f66k56/V4/7+1o5OWNtWzo6UMkhyPizAQ0CplmI1cnRHNFnI1E05HdXJB5HRoyryVpmPP0QO36fR3f9ZuDey6hBG/Mps0OjjtJnQ0RjhN+ed1eX7DwXe9kp/vLhe9cnZ6zHBauyY4jZZjsySSdWLLQPQgZwpIkSaHz4a4m7nhjBy5fgH/M9jG/5Pcozio49Wdw5m9AZ8Sjqvy1solHalpIMOr5Z34qp0VHDnre8i0trHi5BHevj8nnppKS72XZM4/RUlVO+qSpzLrmbBraHqKreyvmlmhiVp1LeMR82rXt/DXpBeKzMoirm8hr5Vqa1Chy7SYeuGoqk1KsJ+RzkQvn0JCZLUnSSUsIKP8M1vwbKpaBITK4ceUpP4bIuKG+uhGhrKWHlzfU8ur2etojdejSInBH6VGAM2yRXJUQzfzDjDaReR0aMq8l6STjcwWL3VUDHd+1G8DvCr5mz93X8Z02O9jIdIJ1e328VtbCB42d7HS76dyv8B3mVsnT6ZnnsHBtThxJEbLwPRrIQvcgZAhLkiSFVku3m1+9toPPS1o5PzeSv1tewbzzeYgbD5c9vndjrs1dffysqIayfg/XJ8bw+6xEwgfp7nb3+Vj9ehlFaxqxxoVxxrU5tFSsZvXi51H9AWZcegXJM6Ci8m8EfN1EbkzC0XgTOnMKqyM280zKe1ycdDk7N+lZ1hmNGz2XTUrgtxeNxxZ+fB8jlwvn0JCZLUnSiNC4A1b9A/a8BRo9TP4WnPpTsKUP9ZWNCB5/gE/2NLN4Yy0r6pyoSWHo0yJx6RQitRoujrVxVUI00w4x2kTmdWjIvJakk5zfGxxv8sWok5p14OkOvmZN21f0TpsN0ZknfA+KTo+P18pb+KDRyS63hy6TBjTBwne4WyVPZ+DsWAtXZ8eSKAvfI5IsdA9ChrAkSVLoCSF4dk0V935QRIRRx1OntDJp6++Cj8mdfRfM/BFoNLgCKvdVNvJ4bSupJgP/LEhllnXwDVFq93Sw7MUietrdjD8jiXFnWFm9+BlK1q3ClpjMmd/5Ji79uzQ2vobeHY7tkwlY9Tfi0wZ4IvZNytNbWaC5jNc29rPbH0u4QcNvFozh6hlpaI7TOBO5cA4NmdmSJI0o7eWw+iHYvgjUAIy7HOb8fO8NYenY1Xb088qmWhZvqqVRJ9CnReJ1mPArkGk2clV8NFfE20gaGG0i8zo0ZF5L0gijBqB518Cok4FNLvvbg69FJuwreqedCvY80Bz9psBHo9PtY3F5Mx81dbL74MK3SyXfECx8X5MTR3zYid+vSQo9WegehAxhSZKk46ekuYefvbyNwsZuvj8lktt9j6Ar/RDST4NLH9v76Nu6zl5uLaqh2uXle8l27shMJGyQR4u9bj8b3q5k+7JaIqxG5n4zH9VfxWdPP0pXcxMFp53J5EumUF1/P319pYRVxWDfegXm8FnUGOp4MPF5JmbPIKwym9crdDSLSApizTx41VTGJVlC/jmM1oWzoihXAn8ACoAZQohN+712B3AjEAB+KoT46HDnk5ktSdKI1N0Aax+BTc+Arw9yz4fTfgEpM4b6ykYMf0BlRUkrL2+s5bPSVrwOE+FZUXSFaVGA0wdGm1weHz0q8zrUZF5L0ggnBLQW7yt6V6+Gnsbga+ZoyDgdss6EzDPBlnbCL6/D5ePV8mY+bOpkt8dD9xeFb1UQ4Q4Wvs+Js3JNdiyxsvB9UpKF7kHIEJYkSTq+PP4Af/+khMc/ryAjOoznJheRvP5PwV2+L/gbjL8CFIW+QIB7yht5ur6NLLORf49JY3JU2KDnbqroYunzRTgb+8idGccpF6ey49MlbHjrNfQmI3Ou+RbWnAYqqx4Gv5+oVanYu3+CxmDlPetK3khezjUJ32Ltej+fd9vxoGPh1ER+c8F4LGH6kH0Go7jQXQCowH+BX31R6FYUZQywCJgBJAKfArlCiMBg55OZLUnSiNbfARuegPWPgssJaXPgtJ9D1lkn/LHwkayl282rm+tYvLGWKpcHfVokmpRwejTQPG/yqMzrUJN5LUmjjBDgrAoWvatWQsXyfYXv6MxgwTvrzGCz0xBsxNzh2tfxvecQhe8xXxS+c+Kwm4/vSEspNGShexAyhCVJkk6MNeVt/PKV7bT2ePj9qWaua/wLSt0GGHtZsOAdFg3AKmcPPyusodnr41fp8dySFod2kAV+wKey6cMqtnxQjTFcx2lX5RId7+Gzpx+jdvcOErLzOP07V9LR/z9a2z7B0B1J9LLZRJmupl/bx78SFtObLpgXuIDFm/oo9DuINGi486KxXDk1NSTjTEZrofsLiqIs58BC9x0AQoh7B77+CPiDEGLtYOeRmS1J0qjg7YPNz8Kah6GnAeInBEeajLkYNF+9l4X09aiqYF1FOy9vrOWDXU24o3TU/frcUZ3XoSLzWpJGuS86viuWQfkyqFoVfGJJ0UDS1GDhO3MuJE8H3YkvLLe5vLxc1swnzZ3s8Xjp2a/wHelWGWMMFr6vzY4n2hy65icpdGShexAyhCVJkk6crn4fv12yi3e2NzAjNYrHs1ZiXf83CI+FSx6BrHkAdPr8/LqkjrdaOplpCefhglRSzYM/VtZe38vS5wppqe4hfYKd06/OpW73WpY//xSu7m4mn38R+eckU1F1H253HeF7YoktvQGDeQy7w0p5KGERZ2SfjVqSyBtVBlpFBOMTwnhg4VQKEqKO6X3LQveXCt3/BtYJIV4Y+Pop4AMhxGuDnUdmtiRJo4rfCzsWw+p/QnsZRGfBnFthwtVDUhgYyZx9Xt7cWs+Np2WO6rwOFZnXkiQdwO+Fuo3BwnfFcqjfDEIFQ0RwrvcXY04ceUPyBFNLv4fFZS183NxJoddL736Fb6tbZVq4mWvTY5mfGo3mBM8flw5NFroHIUNYkiTpxHtraz2/e2sXAnjodJhX+FuUthKY+UM4+w+gNyOE4PVmJ3eU1AFwb24yl8fZUAb55UdVBTuW1rJ+SQUarcLsy7PJnBjF6leeY/unHxJhi+aMb38bnWMLNbVPovFpsSzLIcb3Y4TOwEv2D1mRvI1vxV3PsjX9rOqx40PHtTOSuW3BWKJMR3dHfyQXuhVF+RSIP8RLdwohlgwcs5wDC92PAGsPKnS/L4R4/RDnvwm4CSA1NXVqdXX1cXkfkiRJw5YagMJ3YNXfoXE7RCbC7JthyvVgHHwDZ+nrGcl5fSLJNbYkSYNydQZHnJQvCxa/OyqC349MDHZ6Zw10fEfEDsnlNfV7eLm0mU+au9jt9+I2B5+m0noCZAot58Va+G5eAokRpiG5PmmYFboVRYkGFgPpQBWwUAjhPMRx84GHAC3wpBDivoHv/wm4mODMzxbgBiFEw8BrcmMrSZKkk0Sds59fvLKdDZUdXDIumvujXsO45cngTt2XPQ6JkwCocXm4pbCG9V19XBJr5f7cZCx63aDn7mrtZ9kLRdQXd5KUa2Xut/JxddXyyZOP0FpVQcakqcz65vk0tP2Lzs71GFuisK+ZT0TYAtr1Hfw14XkiMhzMcp3FS1t6KPI7sJq0/P4b47h0cvKgxfZDGe0LZzm6RJIkKQSEgPKlsOofwQKB2QYzfgAzf7B3/Jd0bEZ7XoeKzGtJkr4WZ/W+MSeVK4L7VADEjt3X7Z02GwyD7990vGxv7eGZ0iY+7+ylUS8QOg2oAotbZVqYmWvSHSxIi5Hd3ifQcCt0PwB0CCHuUxTl14BNCHH7QcdogRLgHKAO2AhcI4TYoyhKlBCie+C4nwJjhBA/lBtbSZIknXwCquC/n5fz949LsEcYeWpON2M33gF9rXDWXTDrZtBoCAjBw9XN/LWqiTiDnocL0phtG7yLTQhB4epGVr9WSiAgmHlRJuPnJrD9k/dZ/coLiECAGZctJHWGjvLK+/F5nURujie28UfojKmsjtrC4/Fvckn25XTttvBWjZE2EcHkpHDuXziV3LjII36fo33hfIhC91jgJfZl9mdAjsxsSZKkI1S7MdjhXfw+6MNh2ndg1k8gKnGor+ykNtrzOlRkXkuSdNRUFZq27+v2rlkHAS9oDZAyc1/hO2HikOxb0e8L8HJpE2/Wd7DL58V1ULf3uQ4L381PIEl2ex9Xw63QXQzMFUI0KoqSACwXQuQddMwsgp1d5w18fUDn137H3QGkCiF+JLvDJEmSTl676rv42ctbKW/t45ZZ0dzq+jfaonch6yy49LG9j61t7e7nJ3uqqXR5+ElqLLdlxGM4zJ3zXqeHFYuKqdrRRmxaJGdeV4DR7Gb5s09Qsn410YnJnPndb+M2fEB9/SJ0bhO2pROwar5HQCt4PPYNdqZUc739O7y3soO1fbH4FC3Xn5LKL+ePIcI4eHc5jN6Fs6IolwIPAw6gE9i2X7bfCXwX8AO3CiE+ONz5ZGZLkiQdpHlPcIb3zteCm3xNugZOvRVisob6yk5KozWvQ03mtSRJIePth5o1A4Xv5dC8K/h9sw0yTg8WvbPOBFv6kFzezrYeni4Jdns3HNTtPTXMzDVpDi5Il93eoTbcCt2dQgjrfl87hRC2g465ApgvhPjewNfXATOFEDcPfH0P8G2gCzhTCNH6dTa2kvM+JUmShh+XN8Bf3i/k+XXV5MdF8NzE3cSu/gOYLHDZf/duVNnnD3BXWQMvNLYzIcLMI2PSyAkf/I65EIKyzS2sXFyCp8/PlPlpTDs/nepdm1n69GN0tTQz5rQzmXLZDKrrHqSndzfmKguObQsxh51KramBB+KfJTO7gAnds3lxcw8lAQcxZg1/uHgCF05MHHSciVw4h4ZcOEuSJH0FZxWseRi2PB/sfBt7Ccz5ebDjTTpiMq9DQ+a1JEnHTW8LVKzYN+qkpyH4fVvGvm7vjNPBbD3hl9bvC7C4rJk369rZuX+3tzdAhhrs9r5RdnuHxAkvdA+2MRXw7BEUuq8Ezjuo0D1DCHHLQcfdAZiEEHd9nY2t9idDWJIkaXhZVtTCr17dTr83wD/P1HNu4W9QWouCHWrzfgva4IaQH7R28sviWlwBlT9kJ/HtxJjDzs529/pY9WopxeubsMWHceZ1BdiTjax/81U2vv06BpOJOddehy23hYrKf6D63EStTsbe/VO0ehvvRa9icfzHXJv1Leq3Gni73kyHCGd6aiT3XTmFLMehx6nIhXNoyMyWJEk6jN4WWPcf2PgUeLoh+2yY84vgbNOvub/EaCTzOjRkXkuSdEIIAW0l+8acVK0Cb2/wCafEKfsK38nTQWc44Ze3u72Xp0oaWeE8sNs7aqDb++o0Oxel22W391EYbh3doRxdkga8J4QYJ0eXSJIkjRzN3W5ufXkbayvaWTgxhnvML6Hf9iwkTYMrntr7aFqzx8fPCmtY7uzh3Jgo/pafgsOgP+z5q3e3s/zFInqdHibMTWbmxZn0tDXy6VOPULdnFwk5eZxxw1V0uF+gpeU99N1hxKw4lSjDtfTr+vlX/Ms0pfTwTet1vPl5G+v7HQQUHTeemsat5+YTZjhwnIlcOIeGzGxJkqQj5OqETU/B2v9Af1twrumcn0PufFnwHoTM69CQeS1J0pAI+KBu075u7/rNIALBvSzST9035sSRf8Kz0O0P8PJAt/cO74Hd3umqlnPswW7vlEjZ7X0khluh+0Ggfb/NKKOFELcddIyO4GaUZwH1BDejvFYIsVtRlBwhROnAcbcAZwghrpAbW0mSJI0sAVXw76VlPPRZCekx4Tx7Sj0pK+8ABFz0Txh3OQCqEDxV18afKxqI1Gr5Z0EqZ8dEHfb8XrefdW9VsHNFHZE2E3O/lUdKQTSFK5ex/PmncPf2MOX8iyg4J4Pyqr/gclUTVhhNbOl3MZrGsTu8jL/FP88pOXPIbJvI81t6KVftOMK0/OmyiZw3Nn5vh7lcOIeGzGxJkqSvyeeCrS/A6n9BVw3Ejgl2eI+9FLSH32NitBlNeT2wLr8I8ALlwHeEEJ2HOK4K6AECgP9IPh+Z15IkDQvuLqhcua/w3VEe/H5kAmTODRa+M8+AyEMNpDi+drf38vRAt3e9TiD0B3Z7L0y1c3GG7Pb+KsOt0B0DvAKkAjXAlUKIDkVREoEnhRALBo5bAPwT0AJPCyHuGfj+60AeoALVwA+FEPUDr8mNrSRJkkaYdRXt/OzlrTj7fdw7L4rLKv6AUrcBJl8H598PhnAACntd/HhPNYV9br6TZOf3WYmYtYf/xaCxrJOlzxfR2dxP/qx4Tr0iByHcrHrpWXZ89iER0TGcef130MVup6r6MRSfwLIiG7vnFtAZWWT/f/buO76t6v7/+OtcTVveK15J7CTOIoQsSJgJGxIoUAotXXRS2gKl89uW9tdJB92DTkp3SwelUAgbstjZIcPZw3tbtixr3Ht+f1zFcRzHcRLZluXPk4cekq7uvTrHNvn4vH107lM8WfwKHyz/EDvfMHm81kerTuXcsky+9ba5lOf5xtTAeShJzRZCiFNkRuDNh2HNj6Bxh/3JqPPugjnvApfMHjtsLNVrpdQVwAta66hS6rsAWuv/62e//cACrXXTYM8t9VoIkZDaDtoXtDx8Yctgi729YOaR2d4Tz+sZXw6X7qjJP3c38J+qZjaHQ3TFZnsbYYsyy+DyvAw+OK2YCRlSrw9LqKA70UgRFkKIxNcSCPOZf23ihR0NLJ2Zxw8LnsT76o8hrwLe9iAUnglAt2nx7b21/LqqkYpUD7+cOZFZ6aknPH80YrJ2+X7WP30Qb5qLxe+YyuR5BdTs3M5zv72fxoP7mTTvbM5751Jqmu+npWU17gYfea9eTbr3GlrcbXyv8I/oiV5u9t3CQytreCM4Dq0c3HZROf+39IwxM3AeSlKzhRDiNFkW7HwSVv8QqteCrwDO/Rgs+CB4T/xpqGQ3loLu3pRSNwBv01q/q5/X9iNBtxAi2VgW1G0+Mtv74KtghsDhtpf7mrTEDr6L5oDhGNambWvp5MHKOla0dhwz23teipe3T8zn2rI8nIOY1JWsJOgegBRhIYQYHSxL8+BL+/jOkzsYl+HlD0u6qFjzaQi2whXfhHM+3LPW2sqWDu7afoCWiMnnJxXx0fH5GINYh63xUAcv/nkHjQc7mDQnn4tumUpKmpP1Tz7Gy//8K1prFr71ZiYuSmH3nm8RDjWStr6Agto7cLkn8HLWJn5R8A+unnYNBVVT+POmAHutXA5895oxOXCON6nZQggRJ1rD/tV24L33RfBk2nV00UfBlzfSrRsxYzjo/h/wD631X/p5bR/QCmjg11rr3xznHLcBtwFMmDBh/oEDB4awxUIIEWeRIBx42Z7pvfdFqNtib/flw9QrYdpSO/wegdne/9rTwH8ONbOpn9nel+Vm8KHpY2+2twTdA5BBsxBCjC4bD7Vx59/XU9vWzZcuLuDW+u+idj8D05bBdT+H1BwAWiJRPlt5iCca2zk/K42fzphAiffEV9u2TIuNzx3i9cf34XQZnHfjFGacV0RHcxMr/vhbdr3+Mjkl47n4A+8j7H6GQ1V/wtHtInvlbLL1bZhOzW/zH+H14h18pPwjbHqlk3vv/sCYHDjHm9RsIYQYAtXr7SVNtv8PnF6Y91447w7ImjDSLRt2yRZ0K6WeA/pbfPYerfWjsX3uARYAb9X9DPSVUsVa6xqlVAHwLHCn1nrVQO8r9VoIMep1Ntqh986nYNezEGq3a+SkJTDtavviziOwtvf2lgC/r6zlxdYOqg7P9taa9KDFPK+Xmyfmc1158s/2lqB7AFKEhRBi9GkPRvjCfzazfEsdSypy+fnk10hb9Q1IK4C3/ta+qjagtebvdS18aVc1LqW4b1op1xVkD+o92uq7ePEvO6jZ1Ubp9GyWvGs6mfkp7F3/Bs8/+Cv8jfWcsfhS5r/1PPZXfRe/fxPeA+nkb7qZVO+FVKXU8d1xv6dg0nh+c+VvkmrgPFKkZgshxBBq3Akv/QQ2P2Q/P/MmOP9uKJg+os0aTskWdJ+IUupW4HbgUq111yD2/yrQqbX+/kD7Sb0WQiSVaBgOvgyVT0Llcnutb4CSBXboPW0pFMzo+XTxcAmbFv/c3cDDh5rYHA4R6DXbe6JlcGluBh+aXkRZRsqwtms4SNA9ACnCQggxOmmt+etrB/n649vISnHx28sdnPXqp6B1P1z0Objos+BwArCvK8THtx9gvb+Lmwqz+VZFKenOE6+1pi3N1jU1vPyf3WhLs/Atk5h9yXjMSIhX//MP1v7vEdxeLxe8673kTmtlz57vYUYCpL9SQkH73RjOHJ7MfYmPfO6LY2rgPFSkZgshxDBor4KXfw7r/gDRIEy/Bi74FJTOH+mWDbmxFHQrpa4Cfggs1lo3HmcfH2BorTtij58Fvq61fmqgc0u9FkIkLa2hYZsdeFc+CdXr7O1ZE+3Ae9rV9gUtHa5hb1pla4AHK2t5saWDQ31me8/1ermlzJ7tbRijf7a3BN0DkCIshBCj2/ZaP3f8bT17mwJ86qJiPh78Jcbmf8CE8+DG30JmKQARS/OjA3X8eH89pV4398+cyNmZg1tjraOlm5V/r+TAlmbGlWdw8bunk1uSRnPVQZ574BdUbX+ToqnTufh976Ql9Dfq6h7B6feQu+YCMh3vZsJ9i8fMwHkoSc0WQohhFGiG134Fr/8autuhfDFc8En7Y9vDPGttuIyxoHs34AGaY5te1VrfrpQqBh7QWi9VSk0CHom97gT+prW+90TnlnothBgzOurs5U0qn7SXOol229e9qLjcDr2nXAYpWcPerLBp8e89Dfz7UBObukMEUgxQCkfIZApOrivM5gMzisjyDH8gHw8SdA9AirAQQox+gVCU//foVh5eX8XC8hx+PXs3WS9+3v5L+nX3w/RlPfu+0R7g49sOUNUd5hMTx/GpskJcxokH7Fprdq2tZ/U/dhEORpl/dRnzr5qI4VBsW/UCK//8O7oDncxbeh0zr5jCnn330tW1h5TtGZx/x8YxM3AeSlKzhRBiBIQ6YO3v4ZX7obMOiufZgff0ayAJZoX1NpaC7qEk9VoIMSaFA3bYXbkcKp+CriYwnDDx/Nhs76sgu2xEmlbZGuC3O2p5obWDGpcGpwGmxbgQXJKdzm3Ti5mRM7wX2jwdEnQPQIqwEEIkj/+sr+JL/30Tr8vBL67KZNH6z0DtJjj7w3DFN8FlX426I2ryxV1V/KuulXkZqdw/YyLlqZ5BvUewI8zqf+5i1xv15BT7uOQ9MxhXnkGww8/qv/+RLc8/TVpuHhff+gFc495k/777ueSSbTJwjgOp2UIIMYIi3bDp7/Y63q37IG+qvYb37JtH5CPaQ0GC7viQei2EGPMs017W5PASJ4077O0FZxxZ17t47oj8wdgfjvD7HXU8WtNCJVFMjwO0Ji1ocXZqCu8pL+CqCTkJvcSJBN0DkCIshBDJZU9jJ3f8bQPba/3cfn4Jn3X+E8dr98O4WfC2ByF/Ws++jza08rnKKiJa880pJdxSlIMa5Mex929uYsXfKulqDzH70vEsvHYSLo+D6srtPPfA/TQd3M+keWdzwbtvpKD0TBk4x4HUbCGESABmFLb9F9b8COrfhKwJcOGn4ax3gtM90q07LRJ0x4fUayGE6KN5j73EyY7lcPAV0CakFdqzvKcthfKLwDX8F420LIsn9jfz530NrOvuJpBqX8fKGTKZrlxcX5zN+6YVkeZ2DnvbBiJB9wCkCAshRPLpjpjc+8R2/vzqAc4an8UD57aQ/9zdEOmCq78Lc9/Ts75odXeYu7Yf5KW2TpblZ/K9aePJcQ2ukIeDUV5+ZA9bV1WTkefl4ndPp3R6DmY0yoYnH+Plf/0NrTV3/+U/MnCOA6nZQgiRQLSGnU/Dyu9CzXrIKIULP2nXWOfgPiWVaCTojg+p10IIMYCuFtj1rD3be/fzEO4AVypMvsSe7V1xJaTlj0jTtjZ38usdNaxs66Teo8ChUFGL4ojispx0PjK9mElZqSPStt4k6B6AFGEhhEheT26p5XMPbwbgx0sLuXT7l2HfSjjjrXDtj8GbCYClNb881Mh39taS63LykxkTWJyTPuj3qd7Zyot/2UF7Q5CZ5xdx3o1T8KS68Dc1sPLPD/KWT31BBs5xIDVbCCESkNb2QH3ld6DqDUgvttfwnvfeniXDRgsJuuND6rUQQgxSNAT719jLm1Q+Cf4qQMH4c44scZI3dUQuAt0SjPDAjhoer29ljzIx3fYSJxlBi4W+VN4/ZRyXlOYMe7tAgu4BSREWQojkdqilizv/voGNh9p49zklfCXnOVwrvwWZpfZSJqVH6uOWji4+tu0Au7pCfKQ0ny9MKsLrGNzaZNGwyRtP7GPDs4dISXex+JZpTJpj/yVeBs7xITVbCCESmNb2RbhWftf+WHZaIVxwN8x/34h8HPtUSL2OD6nXQghxCrSGui2x0Hs51G60t+dMil3M8moYvwgcw7+MSNS0+M/eRv5+oJGN4RDBFHuJE3e3yRkOFzeW5vLuqYV4nY5haY8E3QOQIiyEEMkvYlp8/+lKfr1qL9ML03ngEovS5++Ejhq45Etw3id6LgTSZVp8Y08Nv69uYobPyy9mTmRG2uAH6A0H/Lzw5x00V3UyeV4BF71jKr5Mjwyc40BqthBCjAJaw/7VsOK7cGAN+Arg/E/Agg+Ae+Q/7jwQCbrjQ+q1EELEQXu1va535ZP2p5LNMHizYOqVdug9+VLwZoxI09Y2+PldZS2r/AGavQoMhYpYTIgqrszL5CMziylJG7pPdUnQPQApwkIIMXa8WNnAp/+5iWDY5DvLJnDdwe/aF9SatARu+A2kj+vZ99mmdj654xAdpsmXJhXzwdI8jEF+ZMw0LTY8c5A3ntiHy+3gwz9aLAPnOJCaLYQQo8z+NfYM732rwJcP590JCz4InrSRblm/JOiOD6nXQggRZ6EO2POiHXrvfAqCLWC4oPzCI7O9M0tHpGl1XSEe2F7D8oZ29jssLJcBlia72+K8NB8fqijk3OKsuL6nBN0DkCIshBBjS72/m088tIFX97bw1jnFfLt8A55nvwhuH9zwa6i4rGffxnCET+44xHPNfpZkp/OTGRMY53EN+r1a6wKs/FslN3x6vgyc40BqthBCjFIHXrED770vQmounHsHnPNh8Az+ehjDQYLu+JB6LYQQQ8gy4dBr9vImO5ZDyx57e+HsI6F30Vkjsq532LT4x+56/nGwiS2RMKHYEieeoMlsl5u3T8jn5ikFuAe5POjxJFTQrZTKAf4BlAH7gZu11q397HcV8BPAATygtf5ObPs3gOsAC2gA3qe1rlFKlQHbgcrYKV7VWt9+ovZIERZCiLHHtDQ/e2EXP31+F2W5Pn5ztY8pK++Chm324PvSr4DTDYDWmj/WNPO13dWkOAx+MG08V+dnndT7ycA5PqRmCyHEKHfodVh5H+x+FlKy4dyPwzm39VwceqRJvY4PqddCCDGMmnbZoXflk3YAri37wtCHL2ZZfiE4PSPStJdqWvndrnpe6QzQ6jXAUBhhizLLYFlBFh+eUURB6sm3LdGC7vuAFq31d5RSnweytdb/12cfB7ATuByoAt4AbtFab1NKZWit/bH97gJmaq1vjwXdj2utZ51Me6QICyHE2PXKnmY+8dAG2oIRvnpVObe0/Ra19gEongs3/g5yJ/fsuyvQzce3HWBzZ5B3FeXw9Skl+AZ5sQ0ZOMeH1GwhhEgSVetg1X32x6+9mbDo47DwI5CSNaLNknodH1KvhRBihASaYNczdvC9+wWIBMCdBpMvgenLYOpVI1ZrD3V08+vtNTzT1M4hp0bHljjJ69ZcmJnGh6cWMq9gcGuOJ1rQXQks0VrXKqWKgBVa62l99jkX+KrW+srY8y8AaK2/3We/LwATtNYflaBbCCHEqWjuDPHpf21iRWUjV88q5PtnHsL35CfAisI1P4LZN/fsG7Ys7ttXx/0HGyhLcfOLmWXMzTjxhbXG6sBZKXUT8FVgBnCO1nptbPvlwHcANxAGPqu1fuFE55OaLYQQSaZmA6z8HlQ+AZ5MWHQ7LLwdUnNGpDljtV7Hm9RrIYRIAJFu++LQh2d7d9Ta63pPWgwzroXp14Avb0Sa1hUx+evOOv5d3cw2M0LEa08gSwmazPV4eOfEfK4vz8d5nCVOEi3obtNaZ/V63qq1zu6zz9uAq7TWH4o9fw+wUGt9R+z5vcB7gXbgYq11Yyzo3oo9E9wPfElrvfpE7ZEiLIQQwrI0D6zZy31PVVKY6eVX1xYw69XPwMFX4Kx3wtLvHXXhrJdaO7hr+0HqwxE+U1bInRPH4RhgDbSxOnBWSs3AXmrs18BnegXdc4H62NJjs4CntdYlJzqf1GwhhEhStZvtGd7b/wfudHt297kfH/bAe6zW63iTei2EEAnGsqBmPWx7FLY/Bq37QRkw8XyY8RaYcQ1kFI9Q0yxerG7j93vqeD0QxJ9igFI4wiaTtZNrx2Xx4RnFZHmPXCtr2INupdRzQGE/L90D/HEQQfdNwJV9gu5ztNZ39tnvC4BXa/0VpZQHSNNaNyul5gP/Bc44vMxJn+NuA24DmDBhwvwDBw6coMtCCCHGgg0HW7nz7xuoa+/mc5dP5sP636jV34ecSfC2B+2LesS0RaJ8fmcV/21oY2Gmj5/NmMCElP7XFxvrA2el1Ap6Bd19XlNAE1CstQ4NdB4ZOAshRJKr32qv4b3tUfsi0Wd/CM67c9hmnI31eh0vUq+FECKBaQ11W+w/Lm9/DBp32NtLz4GZb7Fne2eXjVjz9rR18evtNTzf2kG1S4PTAFMzLqRZkpXGR2YUc0ZuekLN6I7n0iUTgSf6W65koEF1b1KEhRBC9NYejPD5hzfz5Jt1LJmWz08WBchc/jHoaoLLv25/pDo2e1trzcP1rXxhZxUA355ayo3jslF9ZneP9YHzCYLutwG3a60vO9F5pGYLIcQY0bAdVn0P3vwPuFLg7A/CeXdBWsGQvu1Yr9fxIvVaCCFGkcadsP1R2PYY1G22txXOjoXe10H+1BFrmj8c4Y876vhvbQuVOkrUYy9xUn/J3IQKur8HNPe6GGWO1vpzffZxYi9BcilQjX0xyndqrbcqpSq01rti+90JLNZav00plY99kUtTKTUJWA2cqbVuGag9UoSFEEL0pbXmL68e4BtPbCc71cXPr5vI2Zu+DDufhIor4fpfHDW77GAwxJ3bD/Jae4DrC7L47tRSMl3OnteTeeA80Ke4tNaPxvZZQT9Bt1LqDOAx4Aqt9Z7jnF8+hSWEEGNV405Y/X3Y8i9weGDBB+D8uyC9v7Jz+pK5Xg8nGWMLIcQo1bIPdjxuh95Vr9vb8qfHlje5FgrP7Jn0Ndwsy2L5gWb+sq+Bf1w6K6GC7lzgn8AE4CBwk9a6RSlVDDygtV4a228p8GPAATyotb43tv1hYBr2mp8HsGeBVSulbgS+DkQBE/iK1vp/J2qPFGEhhBDHs7WmnTv/toH9zQHuvHgKn8h4EePZL0NKDtz4Wyi/qGdfU2t+dqCe7++vY5zbxc9mTOS8bHtd77E+cO4v6FZKlQIvAO/XWr80mPNIzRZCiDGqaTes/gFs/gc4XDD/fXD+J+K+nuhYr9fxIvVaCCGSgL8Gtj9uL29y4CXQFmSX24H3zOugZP6Ihd4JdTHKRCNFWAghxEACoShffvRN/rO+moXlOdx/iYu8p26H5t1w0Wdg8efBcWT29gZ/Fx/fdoB9wRAfn1DA58oL8TgcY3rg3DfoVkplASuBr2utHx7seaRmCyHEGNe8B9b8EDb+HQwnzHsvXHA3ZJbG5fQSdMeH1GshhEgynY1Q+YS9rvfelWBFIKPEDr1nXAsTzgXDMWzNkaB7AFKEhRBCDMa/11Xx5f++SYrbwY/eWsHi3d+DDX+B8Qvhxgcga0LPvoGoyVd21/CX2mZmp6Xw7DnTx+TAWSl1A/AzIB9oAzZqra9USn0J+AKwq9fuV2itGwY6n9RsIYQQALTuh9U/hI1/BWXA3HfDBZ88qhafCgm640PqtRBCJLFgG+x8yl7eZM/zEO0GXz5MX2YvcVJ+kf3pqyEkQfcApAgLIYQYrN0Nndzxt/XsqOvgtosm8bniLTiXfwoMA97yM/sjXL082djGpysPsf3C2TJwjgOp2UIIIY7SdhDW/AjW/9l+PuedcOGnILvslE4nQXd8SL0WQogxItQJu56xlzfZ+QxEAuDNgmlL7Zneky8BlzfubytB9wCkCAshhDgZ3RGTbzy+jb++dpCzxmfxy6uzKX7u41CzHua/H676NrhSevbviJpkuJwycI4DqdlCCCH61V4Fa34M6/9oryF61jvgwk9DzqSTOo0E3fEh9VoIIcagSBD2vGiH3pXLobsd3GlQcQXMfAtMuRw8aXF5Kwm6ByBFWAghxKl4YnMtn394Myj43g3Tuarhd/DSTyB/Btz0eyiY0bOvDJzjQ2q2EEKIAflr7Fq87g9gRmD22+3AO2/KoA6Xeh0fUq+FEGKMi4Zh/yp7eZMdT0BXEzi9MOUye6b31KsgJeuUTy9B9wCkCAshhDhVh1q6uOPvG9h0qI13L5rA/5tRh/uxj0Kow57ZPf/9oJQMnONEarYQQohB6aiDl34Kax8EMwSz3gYXfRbypw54mNTr+JB6LYQQoodlwsFX7NB7+/+gowYMF0xabK/pPX0Z+PJO6pQSdA9AirAQQojTEY5afO/pHfx29T5mFGXwy+tLKVv1Kdjzgl243/JTVGqODJzjQGq2EEKIk9LZAC//FN74nf2R6llvhYs+BwXT+91dgu74kHothBCiX5YF1etg+6N28N12wL6o9MTz7bHzjGsgo/iEp4l3vTbidSIhhBBitHM7De5ZNpMH37eAuvYgS39XycMzfgKXf91em+xXF450E4UQQoixKa0Arvgm3L0FLrgbdj4Nv1gE/7wV6reOdOtGlFLqG0qpzUqpjUqpZ5RS/SYLSqmrlFKVSqndSqnPD3c7hRBCJBHDgPFn27X5E5vgI6uTbYTgAAEAAElEQVTtJcY6G+DJz8IPZ8ADl8PLP4PW/cPWLJnRLYQQQvSjtj3IJx7ayOv7WnjrvBLuPTtEyv8+ivrEBpkhFgdSs4UQQpyWrhZ45X547dcQ7rDXCb3oc1A0GxhbM7qVUhlaa3/s8V3ATK317X32cQA7gcuBKuAN4Bat9baBzi31WgghxElrrIwtb/Io1G2xtxWdZdfqGdcdtfyYzOgWQgghhkFRZgp/+9BC7rq0gkc2VLPsP0G23/jcSDdLCCGEEACpOXDpl+HuzbD4/2DvKvj1hfD3d0LNhpFu3bA6HHLH+ID+ZrOdA+zWWu/VWoeBh4DrhqN9Qgghxpj8abD4s3D7GrhrI1z+DXC44YVvwv1nw/0L4YV7j4TgcSRBtxBCCHEcTofBpy6fyl8/tJDO7ijX/fK1kW6SEEIIIXpLzYGLv2gH3ku+CAfWwG+WjHSrhp1S6l6l1CHgXcD/62eXEuBQr+dVsW39nes2pdRapdTaxsbG+DdWCCHE2JFTDuffBR96Dj65Da6+D1LzYPX34VcXxP3tJOgWQgghTuC8yXks/8SFXDq9YKSbIoQQQoj+pGTBkv+Du9+ES7400q2JO6XUc0qpN/u5XQegtb5Haz0e+CtwR3+n6Gdbv+uYaq1/o7VeoLVekJ+fH79OCCGEGNsyS2DhR+D9T8Cnd8I1P477W0jQLYQQQgxCXpqHX757/kg3QwghhBAD8WbARZ8d6VbEndb6Mq31rH5uj/bZ9W/Ajf2cogoY3+t5KVAzVO0VQgghBpSWDwveH/fTStAthBBCCCGEEEKMUkqpil5P3wLs6Ge3N4AKpVS5UsoNvAN4bDjaJ4QQQgwX50g3QAghhBBCCCGEEKfsO0qpaYAFHABuB1BKFQMPaK2Xaq2jSqk7gKcBB/Cg1nrriLVYCCGEGAISdAshhBBCCCGEEKOU1rq/pUrQWtcAS3s9Xw4sH652CSGEEMPttJYuUUrlKKWeVUrtit1nH2e/q5RSlUqp3Uqpz/fz+meUUloplddr2xdi+1cqpa48nXYKIYQQQgghhBBCCCGESF6nu0b354HntdYVwPOx50dRSjmA+4GrgZnALUqpmb1eHw9cDhzstW0m9pphZwBXAb+InUcIIYQQQgghhBBCCCGEOMrpBt3XAX+MPf4jcH0/+5wD7NZa79Vah4GHYscd9iPgc4Duc96HtNYhrfU+YHfsPEIIIYQQQgghhBBCCCHEUU436B6nta4FiN0X9LNPCXCo1/Oq2DaUUm8BqrXWmwZ7TF9KqduUUmuVUmsbGxtPrRdCCCGEEEIIIYQQQgghRq0TXoxSKfUcUNjPS/cM8j1UP9u0Uio1do4rBntMfyfXWv8G+A3AggUL+t1HCCGEEEIIIYQQQgghRPI6YdCttb7seK8ppeqVUkVa61qlVBHQ0M9uVcD4Xs9LgRpgMlAObFJKHd6+Xil1zgDHDGjdunWdSqnKE+03iuQBTSPdiDiRviSmZOoLJFd/pC+Ja9pINyAZJFnNTqaf8WTqCyRXf6QviSmZ+gLJ1R+p13Eg9TqhJVN/pC+JKZn6AsnVn2TqS1zr9QmD7hN4DLgV+E7s/tF+9nkDqFBKlQPV2BeZfKfWeiu9ljpRSu0HFmitm5RSjwF/U0r9ECgGKoDXB9GeSq31gtPoT0JRSq1Nlv5IXxJTMvUFkqs/0pfEpZRaO9JtSBJJU7OT6Wc8mfoCydUf6UtiSqa+QHL1R+p13Ei9TlDJ1B/pS2JKpr5AcvUn2foSz/Od7hrd3wEuV0rtAi6PPUcpVayUWg6gtY4CdwBPA9uBf8ZC7uOKvf5PYBvwFPBxrbV5mm0VQgghhBBCCCGEEEIIkYROa0a31roZuLSf7TXA0l7PlwPLT3Cusj7P7wXuPZ32CSGEEEIIIYQQQgghhEh+pzujO9H8ZqQbEGfJ1B/pS2JKpr5AcvVH+pK4kq0/IyWZvo7Sl8SVTP2RviSmZOoLJFd/kqkvIymZvo7J1BdIrv5IXxJTMvUFkqs/0pfjUFrreJ5PCCGEEEIIIYQQQgghhBhWyTajWwghhBBCCCGEEEIIIcQYk9BBt1JqvFLqRaXUdqXUVqXUJ2Lbc5RSzyqldsXus2PbL1dKrVNKbYndX9LrXPNj23crpX6qlFKjoD/nKKU2xm6blFI3JEp/TrYvvY6boJTqVEp9ZrT2RSlVppQK9vre/Gq09iX22myl1Cux/bcopbyJ0JdT6Y9S6l29vi8blVKWUmpOIvTnFPriUkr9Mdbm7UqpL/Q612jri1sp9ftYmzcppZYkSl9O0J+bYs8tpdSCPsd8IdbmSqXUlYnUn5FyCj8XCVuzT6EvUq8TtD9KanZC9kVJvU7k/iRszR6gL1KvT8Ip/ExIvU7Q/vQ6LuFq9il8b6ReJ2BfVALX61PsT8LW7FPoi9Tr49FaJ+wNKALmxR6nAzuBmcB9wOdj2z8PfDf2eC5QHHs8C6juda7XgXMBBTwJXD0K+pMKOHsd29Dr+Yj252T70uu4h4F/AZ9JlO/NKXxfyoA3j3Ou0dYXJ7AZOCv2PBdwJEJfTufnLLb9TGDvKP7evBN4KPY4FdgPlI3Svnwc+H3scQGwDjASoS8n6M8MYBqwAljQa/+ZwCbAA5QDexLp/5uRup3Cz0XC1uxT6IvU6wTtD1KzE7IvfY6Vep1Y/UnYmj1AX6ReD+3PhNTrBO1Pr+MSrmafwvemDKnXCdeXPscmVL0+xe9NwtbsU+iL1Ovjvf9w/yCe5hfrUeByoBIo6vUFrOxnXwU0x75QRcCOXq/dAvx6lPWnHKjH/kcz4fozmL4A1wPfA75KrAiPxr5wnCI8SvuyFPjLaOjLYH/Oeu37LeDeRO3PIL43twD/i/0/n4tdHHJGaV/uB97da//ngXMSsS+9+9Pr+QqOLsRfAL7Q6/nT2MU3Ifsz0l/HQf7/mtA1+yT7IvU6gfqD1OyE7EuffaVeJ1Z/Rk3NRur1sPxM9NlX6nWC9YdRUrMH8W9PGVKvE64vffZN6Ho9yO/NqKnZg+iL1Ovj3BJ66ZLelFJl2H9Nfg0Yp7WuBYjdF/RzyI3ABq11CCgBqnq9VhXbNmIG2x+l1EKl1FZgC3C71jpKgvVnMH1RSvmA/wO+1ufwUdeXmHKl1Aal1Eql1IWxbaOxL1MBrZR6Wim1Xin1udj2hOoLnNK/AW8H/h57nFD9GWRf/g0EgFrgIPB9rXULo7Mvm4DrlFJOpVQ5MB8YT4L1BY7pz/GUAId6PT/c7oTrz0hJppot9bpHQvUFpGYnas2Wep2Y9RqSq2ZLvY4PqdeJWa8huWq21Gup18MhmWq21OvTq9fOk27lCFBKpWF/HOdurbX/REuyKKXOAL4LXHF4Uz+76bg28iScTH+01q8BZyilZgB/VEo9SQL15yT68jXgR1rrzj77jMa+1AITtNbNSqn5wH9jP3OjsS9O4ALgbKALeF4ptQ7w97PvqPh/Jrb/QqBLa/3m4U397Jbo35tzABMoBrKB1Uqp5xidfXkQ+2NKa4EDwMtAlATqCxzbn4F27WebHmD7mJJMNVvqdWLWa5CaTYLWbKnXiVmvIblqttTr+JB6nZj1GpKrZku9lno9HJKpZku97nHK9Trhg26llAv7C/NXrfV/YpvrlVJFWutapVQR9tpah/cvBR4B3qu13hPbXAWU9jptKVAz9K0/1sn25zCt9XalVAB7XbSE6M9J9mUh8Dal1H1AFmAppbpjx4+qvsRmMIRij9cppfZg/9V2NH5fqoCVWuum2LHLgXnAX0iAvsTadCr/z7yDI39thtH5vXkn8JTWOgI0KKVeAhYAqxllfYnNlPlkr2NfBnYBrSRAX2Jt6q8/x1OF/dfyww63OyF+zkZSMtVsqdeJWa9Banai1myp14lZryG5arbU6/iQep2Y9RqSq2ZLvZZ6PRySqWZLve5xWvU6oZcuUfafLn4HbNda/7DXS48Bt8Ye34q93gtKqSzgCey1XV46vHNsen+HUmpR7JzvPXzMcDqF/pQrpZyxxxOxF23fnwj9Odm+aK0v1FqXaa3LgB8D39Ja/3w09kUpla+UcsQeTwIqsC/KMOr6gr320WylVGrsZ20xsC0R+gKn1B+UUgZwE/DQ4W2J0J9T6MtB4BJl8wGLsNenGnV9if18+WKPLweiWuvR8HN2PI8B71BKeZT9MbEK4PVE6c9ISaaaLfU6Mes1SM0mQWu21OvErNeQXDVb6nV8SL1OzHoda1PS1Gyp11Kvh0My1Wyp13Gs13qEF4sf6Ib9cQ+NfcXajbHbUuxF45/H/mvF80BObP8vYa+3s7HXrSD22gLgTeyrd/4cUKOgP+8Btsb2Ww9c3+tcI9qfk+1Ln2O/ytFXhB5VfcFem24r9ppI64FrR2tfYse8O9afN4H7EqUvp9GfJcCr/ZxrVH1vgDTsq6dvBbYBnx3FfSnDvojGduA5YGKi9OUE/bkB+6/IIeyLFT3d65h7Ym2upNeVnxOhPyN1O4Wfi4St2afQF6nXCdofpGYncl+WIPU6EftTRoLW7AH6IvV6aH8mpF4naH/6HPtVEqhmn8L3Rup14vZlCQlYr0/x5yxha/Yp9KUMqdf93lTsQCGEEEIIIYQQQgghhBBiVEropUuEEEIIIYQQQgghhBBCiBORoFsIIYQQQgghhBBCCCHEqCZBtxBCCCGEEEIIIYQQQohRTYJuIYQQQgghhBBCCCGEEKOaBN1CCCGEEEIIIYQQQgghRjUJuoUQQgghhBBCCCGEEEKMahJ0CyGEEEIIIYQQQgghhBjVJOgWQgghhBBCCCGEEEIIMapJ0C2EEEIIIYQQQgghhBBiVJOgWwghhBBCCCGEEEIIIcSoJkG3EEIIIYQQQgghhBBCiFFNgm4hhBBCCCGEEEIIIYQQo5oE3UIkKaXUfqVUUCnVqZSqV0r9XimVFnvtfUoprZS6uZ/jvqiU2hc7rkop9Y/hb70QQggxNiilLlBKvayUaldKtSilXlJKnd3rdV+sJi8/2WOFEEIIceqUUl/oW3+VUruOs+0dsTF2IFa3D98+F9vnD0qpb/Y5rix2jHPoeyPE2CBBtxDJ7VqtdRowDzgb+FJs+61AS+y+h1LqVuA9wGWx4xYAzw9fc4UQQoixQymVATwO/AzIAUqArwGhXru9Lfb8CqVU0UkeK4QQQohTtwo4XynlAFBKFQIuYF6fbVNi+wKcpbVO63W7byQaLsRYJUG3EGOA1roaeBKYpZSaCCwGbgOuVEqN67Xr2cDTWus9sePqtNa/GfYGCyGEEGPDVACt9d+11qbWOqi1fkZrvbnXPrcCvwI2A+86yWOFEEIIcerewA6258SeXwS8CFT22bZHa10z3I0TQhxLgm4hxgCl1HhgKbABeC+wVmv9MLCdowfNrwLvVUp9Vim14PBfqYUQQggxJHYCplLqj0qpq5VS2b1fVEpNAJYAf43d3jvYY4UQQghxerTWYeA17DCb2P1qYE2fbauOPVoIMRIk6BYiuf1XKdWGXYhXAt/CHiT/Lfb63+i1fInW+i/AncCVsf0blFKfH84GCyGEEGOF1toPXABo4LdAo1LqsV6ftnovsFlrvQ34O3CGUmruII8VQgghxOlbyZFQ+0LsoHt1n20re+2/XinV1ut25fA1VQihtNYj3QYhxBBQSu0HPqS1fq7XtvOxi3Cp1routozJPmCe1npjn+NdwPXYM8iu1Vo/PUxNF0IIIcYkpdR04C/ALq31LUqpncBvtdbfi73+AnbwffeJjh3GZgshhBBJSyl1CfAP7CXDtmqti2PXydgFTAeagCla631KKQ1UaK1393OeB4BmrfX/9dpWAewAXFpraxi6I0TSkxndQowttwIK2KiUqsP+GBYc/VFoALTWEa31v7DXBJ01fE0UQgghxiat9Q7gD9jX1DgPqAC+oJSqi9XthcAtSinnQMcOX4uFEEKIpPcKkIl9jauXoOdTVTWxbTVa632DOM9BoKzPtnLgkITcQsSPBN1CjBFKKS9wM3YxntPrdifwLqWUUyn1PqXUMqVUulLKUEpdDZzBkUBcCCGEEHGilJqulPq0Uqo09nw8cAv2NTNuBZ4FZnKkZs8CUoGrT3CsEEIIIeJAax0E1gKfwl6y5LA1sW2DXZ/7YWCZUuoKpZRDKVUMfAl4KJ7tFWKsk6BbiLHjeiAI/ElrXXf4BvwOcABXAX7gi9h/bW4D7gM+qrVeMyItFkIIIZJbB/Ys7deUUgHskPpN4NPYf5z+We+aHZsx9mfsEHygY4UQQggRPyuBAuxw+7DVsW19g+5NSqnOXrcfA2itt2L/QfrbQAv2TPHXgK8NcduFGFNkjW4hhBBCCCGEEEIIIYQQo5rM6BZCCCGEEEIIIYQQQggxqknQLYQQQgghhBBCCCGEEGJUk6BbCCGEEEIIIYQQQgghxKgmQbcQQgghhBBCCCGEEEKIUc050g2Ip7y8PF1WVjbSzRBCCJHE1q1b16S1zh/pdox2UrOFEEIMJanX8SH1WgghxFCKd71OqqC7rKyMtWvXjnQzhBBCJDGl1IGRbkMykJothBBiKEm9jg+p10IIIYZSvOu1LF0ihBBCCCGEEEIIIYQQYlSToFsIIYQQQgghhBBCCCHEqCZBtxBCCCGEEEIIIYQQQohRTYJuIYQQQgghhBBCCCGEEKNawgfdSqmrlFKVSqndSqnPj3R7hBBCiGR0onqrbD+Nvb5ZKTVvsMcKIYQQIjFIzRZCCJHMEjroVko5gPuBq4GZwC1KqZkj2yohhBAiuQyy3l4NVMRutwG/PIljhRBCCDHCpGYLIYRIdgkddAPnALu11nu11mHgIeC6EW6TEEIIkWwGU2+vA/6kba8CWUqpokEeK4QQQoiRJzVbCCFEUkv0oLsEONTreVVsmxBCCCHiZzD19nj7SK0WQgghRgep2UIIIZJaogfdqp9t+qgdlLpNKbVWKbW2sbFxmJolhBBCJJUT1tsB9hnMsfYJpGYLIYQQI2lQNVvqtRBCiNEq0YPuKmB8r+elQE3vHbTWv9FaL9BaL8jPzx/WxgkhhBBJ4oT1doB9BnMsIDVbCCGEGGGDqtlSr4UQQoxWiR50vwFUKKXKlVJu4B3AYyPcJiGEECLZDKbePga8V9kWAe1a69pBHiuEEEKIkSc1WwghRFJzjnQDBqK1jiql7gCeBhzAg1rrrSPcLCGEECKpHK/eKqVuj73+K2A5sBTYDXQB7x/o2BHohhBCCCEGIDVbCCFEskvooBtAa70ce3AthBBCiCHSX72NBdyHH2vg44M9VgghhBCJR2q2EEKIZJboS5cIIYQQQgghhBBCCCGEEANK+BndJ2NXQxPv/+FPmaDCnFlYwAUXLaGwZMJIN0sIIYQQQgghhBBCCCHEEEqqoLs74uLFhsn2k3pg0xYcro2kuENkuLrIMToo0H5yiJDlUBSk+Zg2tYIFC5fg82WMaNuFEEIIIYQQQgghhBBCnJqkCrpLfRY3lu7noHbT4PDRSiodZgrt4VT8oUyquwogbKEOH9AE7Af17ErSXZ1ku/1kOoJkGEEyVIQMZZLjclCSl8uZc+YwrWIuHk/qyHVQCCGEEEIIIYQQQgghxDGSKujOzsrmvjuOvU5WMNBKZfVuNtcc4s1GP1uDXmocWTQrH2ZEoUImLd1p+EM5OLvD6JDGjDqOnKAOeLMNt/E0mW4/ma5OMp1dZKoQmUaYbBeMy0xj6pQK5s1eSEZ6EUqpY9ohhBBCCCGEEEIIIYQQIv6SKug+nhRfNnOmns2cqWcftV1Huqlt2MuuhoNsa25mV1eY/XjY58qn1pWHCpkQMjG6o6QFAqQEI+iQl/awg5auLPzhNKI69iWsBXaA8cRaMt1+stwdZDoDZDq6yTRCZLks8tJTKS8pZt6s+RQWTMXh8A7/F0MIIYQQQgghhBBCCCGSzJgIuo9HubwUl8ykuGQmi3u/YFkEWg6yp34fu1sa2NkZYJfPYK8jk33eMrodHgCK2+sYV+NnXGs748KthJwav2Hgx4Vfe6gJ5rM9kk7I9Bw59w7g+UOku7aR5Wkny9VJpiNoB+Iuk1yfm9K8HM6cNpNJE2bhdudjGGP62ySEEEIIIYQQQgghhBADkgS1P4aBL6+M2XllzO69XWvMzka27FnL6toqVqoUXp82jQ2GG5cVobS9FppD1DemkRdysDA7hRlESe9soa27hVYVoN1p4neCXzlotzw0d+ewN5JGINpr7e9K4KUwKc6X7DDc7bdnhzuDZDjCZHk0+WkeyorymV42k5y8yXi9hTidvmH+QgkhhBBCCCGEEEIIIcTIk6D7ZCiFI72AOXOWMmcO3Kk1XXVbeX3XOla1+1nlKuLNKRUwBVqiQTb7m1hRB+3ah2F5mVqQxtzx2Vw6PpPpGalkWwb+Q820HWimvqaZWn8bra4AHZ4wHS7Tnh2unfi7M6mJFtER8WFhHGnPJnCobjI9L5PtaT+yfrijmwxnmGyPZlxWCpMLiynKn0xGziRSfUW4XDkoZRy3m0IIIYQQQgghhBBCCDGaSNB9OpQitWgWS4pmsQQg1EHT7jWsObiTVQHNSt90GmcWAlAQ6cAKdPF4bSMPbapGRSzSPU7mTMhi7qRc5i6ZzGXjs8hKdRPujuJvCtJcHaBpbxNN+1ppaQ0RDEHY0U3AGaDL00mnsxu/U9OmHPhDKRwKZvNmNJWw5T6mqT5XgGzPK/YMcU87mc4AGc4gmc4wmR6L/DQ3xZlZZKQXkpJaiC+jCF9GMR5PLk5nliyfIoQQQgghhBBCCCGESFiSXsaTJ528M67m+jOu5nqt0Y072bdrDavq61hlZrIm6yz8M9JhBkyO+skJGeytN1mzYhfatE8xKd/H3PHZzJuYxWUzxjFtYWHP6bsDEVpqOu0AvLqT5oN+WmoCRCK6Z580y487WkuX1U67W9HkNWhNddKKk7aIi5ZwIfvbygiYKWjUUc1XWKS7O8n0+Ml0V9v3Hj9ZHj/pjgBpRpBMV4RsD2R4MnC7s3G5c/CmFJDiG4cvvbgnHHe5slDKMSxfdiGEEEIIIYQQQgghxNgmQfdQUQpVMI1JBdOYBLwv3EV03xo2732ZVW2drPJO4o2MWUQmufCUe5ipQuRYPkINEV6sbODh9VV86b9vcv7kPK6fW8JVswpJ87korsimuCK75220pelo6aapqpOWmk6aqgK01BQSqu/Co6EkDBMiFunOAOmhBlJbdpBavwu3GaQ5K4/arHE0ZubR4Muk2eXEbxn4u1NpCaZzQJcTMD1HL5cS43V09wThmZ52Mt01ZHpWkuVpJ8PdQaa7nXQjiM+wcJCKoXw4jFQcjjScznRcrgxc7kzc3iw8nmy8qXmkpObhTs3G5czA6UzDMI6dmS6EEEIIIYQQQgghhBB9Ka31ifcaJRYsWKDXrl070s0YnOY9BHa9yGtVu1nZ7WJ15llsS5sCQBYR5nmdZHV62LyhjqqWIF6XweUzC3nr3BIuqMjD5Rh4je1o2KS1roumqk6aazppruqkuSZA0B/u2cfrhWxvkNxwFVm1G3HveB2CXfaLysBVPhXnlDNoGjeRgykZ1OCkLqJp7jZpi2j8lsKvFZ0YdGoXkX7+bmJgkuEKkO7uJN3VSYbHH7t12Nvc9n1G7HWP80j7tGmgTRfacqMsL0qlYKgUHEYaDocPlysDpysDtycLjycLT0ounpRMPN4s3N5MnE4fDkcqhpEiS68IIeJGKbVOa71gpNsx2o2qmi2EEGLUkXodH1KvhRBCDKV412tJ/0ZK7mR8uZO5BLgk0g0HX6Zx92OsbmhglaOYF3IW0uDJZeKiDN6dWU5oXwfPbq7jf5tqyPW5ufasYq6fW8JZpZkopY45vdPtIH9COvkT0o/a3uUPHxV81+/z82ZbCmRWkHLZOyme4GFcip+cwF4c+7YT2rkB33OPMMOymAEotxv3lMl4K6bimToV95QKXCWTUO50Wurb2V3dwL7GFmraAzR1RWiJWLSaDjq7smghh0PaIIiTaD+zxAFcKkqqoxufowufM0i60w7A091+0t1+Mt3tZHpbyUrdT2aqH48VgdCJv9zaNNCWEywnaDdoNwoPhvJgGCk4jBQcjlQcjlScrjScrjRcrjRcnnQ8nkxcngw8Kdm4POk4HT4cDm8sRPfKhT2FEEIIIYQQQgghhBhhMqM7EbUdJLLtcZZXruPB7MW8ljmbFCxuHJfNrKiLN7Y08Oz2esJRi/I8H9fPKeH6ucVMzPWd0tt1toao2tHCwW0tVO1oIdgRASC7MJXxM3IomZJOvqMZc/9uQjt3Edq5k9DOnUQbGnrO4cjMxDN1aq9bBZ6KqTjS7DbpiIUZCGN1RDA7wrS3BKiqb6e6tY2a9k6agiGawybtUYt2DR0oOrRBAIMuHJjHC8Yx8aowKUaIFEeQFEcQryMYC8q7SHN2keYMkOEMkOHqJN3VgdsIYxhR++aIYjgsDJd9czgtTnZp8Z4QXbtAu1C4UbjtEF15MQxvTzDucKbidPpwuny43Gm4PGm43Rm4PBm43Gk4nakYjsPB++FjU2S9cyESiMwQi4+kqdlCCCESktTr+JB6LYQQYijFu15L0J3IzChs+y9b1v6b33lm88i4ywgZbi7M9HJLYT7hqgCPbqzh1X3NaA3zJmRxw9wSrpldTLbv1Na31pamuaaTQ9taObSjhZpdbZgRC8NQjJuUwfgZOYyfmUPBxAys9jZCu3YdFX6Hdu3CCgR6zucqKcFTUYF31ixS580l5ayzMHwDB/JWyMTqDGN2hDE7IpgdIdpbg9S3Bqhp7aSuM0BDd4iWSJRWbdEOtAOdWhHAIIjjuDPGAVxE8agoHkw8KoxThXEZIZxGN07VjcfRjc9p39Jd3WQ6u/G5wngMjdswcSsTJxZOFUURxSB2r6KgIigVBSOKcpgYTit20xhOC+W0ME4hs9aWAdoZC9JjYbo6PCPdDsSdjhQczpRYkH54ZroPlycNlzsdhyMFh+HFcHhjQXoKhuGJPffajw2PzFAX4gRk4BwfSVezhRBCJBSp1/Eh9VoIIcRQkqB7AElbhLWGvS/S/Mpv+Wsomz+U3ECNJ5/xboP3jy/kktRUXnyznkc2VLGzvhOnoVgyrYAb5pZw6YwCvK5Tnw0cjZjU7mmnansLh7a30nioAzR4Up2UTMu2g+8Z2WTmp8aaqolU1xwJvnfuJLRrJ6Hde+x+GAbe6dNJmTfPDr7nzcNVWHiKXxaN7jZjgXgYqzOM1RnBDEQItIdo8Ydo9gdp6ArSFOymJRqlTZm0aU270nTEZo4HUHRhEBogHHdg4sHEqyI9IblbRXCqEA4jhMPoxuHoxjCCKEcAnF04nWGcTnA6HLgcBm7lxGM48RoGKYYixQAv4EHj1hYObeK0ojh0FEUEdASl7XsIo+l1b0TtAN3RK0CPhek9ofqp5tXacWRmunJhKHcsTLeDcEcsULdD9VQ7VHem2jPQHfZ+jlj4bjg8vUL0wyG7p+f54VnrEq6L0UQGzvGRtDVbCCFEQpB6HR9Sr4UQQgwlCboHMCaKcO0moi/9jCfrm/hdyQ28mnkWKQreVpTLB4pz0R1R/ruxmkc3VlPvD5HucXL1mYVcP7eEReW5GMax63mfjGBnmKodrRza3sKh7S10ttgLZGfkeSmdkcP46TmUTs/G63MddZzZ0UFw4yaCG9bTtX4DwU2b0MEgAM7iIlLnzSdl3lxS583DU1GBcsR/qQ4dMTEDETsM7zx8b4fjViBCd0eINn+IlkCY1q4IbdqkmSgtmLRh0Y5FG9BhQAB7BnmXVmiO/zV198wej+JWJt7YczdRvCqKS0XskNzoxnAEUUYXOLqJOiJEjAhRFbXvjSiWw8JwGjhdTpwuB16nG4/DjVe5SDXceLULr+XCYzlxayeuKLgtC6eO4jQtDCuCYdnhuTJDoCNoK4S2wliE0Dps34igDBPl1PayLk6NigXqhkPHZqXr2PY4BOsAOOzlXozDs9RjM80d9tItR4L0WEBu9A7Vj4ToRk+I7j4SzB+z7ci9Uu5+17gXYiAycI6PMVGzhRBCjBip1/Eh9VoIIcRQkqB7AGOqCLfuh1d+wdbKNfxu3FL+U3gl3crFeVk+PlSaz6U5Gazd18IjG6p5ckstgbBJUaaXt8wp5oa5JUwvzDjtJmitaW8I9oTe1ZWthLtNUFAwIT022zuHwsmZOJxHp6A6EqF7R+WR4HvdOqKNjQAYaWmkzJnTE3ynzJ6NkZp62u092b7pYNQOxjsi9vriPQF5GCtgP452hvF3hmnvjuJH047GH7u1Y9GmLNocGr+Bvd3SdFqagHX891ZoPMrsCcQ9h2/q6NDcc1SIHsUwwpiOKBF1bEje+/Hh1w4/tgwLh9OBw+3A7XLjdrvxGm5SlIcU7SEFNymWuydAd1sOXJYDl2ngNBWOKDhMUFETw4qiohGUFQYzjDbDaB3CMkNYVjeWFcLSdqjeE5b3npke22Y4rJ6gvSdwdxEL1WPbHRbKOP1/v5Ryx2atu4/MQnd4jwrLjwTk/W3r/7nD8KBizyVsTy4ycI6PMVWzhRBCDDup1/Eh9VoIIcRQkqB7AGOyCHe1wBsP0LL2L/w16zz+MP5mql05lHhcvK8kj3cV55KiFc9tr+e/G6pZubORqKWZUZTBDXOLuW5OCeMyvHFpimlaNOzzx4LvVur3+9GWxuk2mDgrjzMuKKZ0ejaqn1nl9pIn1QTXr6dr/XqC69YT2r3bXu7E4cA7Y8aR4HvuPFzjCuLS5njRUQur68hM8cNBuBUIH5k9Hoj0LK8SiZh0oOnoE477HYoOl8LvVHQoe1ubtvBH7Qt1dkWPn5AbQKpTk+LQeI0jQblLh3FaYVxWuCc0d6sj4bkLi745q0ajHRrTMDENsycYDxMmrMI9zw/fm8o86nnf7ZZh4XQ58bg8pDhT8BoefCqFFDykaDcpuPFqNx4zFqabRk+Y7jCVfYtqVOxGxISIiY5E0GYIbYYwo0EsK4RphbFiM9WVw4qF4tqeje6IzUJ36FiAHput3mcfO1hXOFzE9qNnZrsyDofsJijzmK/dqeg/OLcDeNUTsLt7bXMd9dww3KjeYbw6sn/P8cp19DbV57yGxw79DZcsJXMCMnCOjzFZs4UQQgwbqdfxIfVaCCHEUJKgewBjugiHu2DT34i+fD/PGCU8UPYuXk6bgddQvHVcNh8szeeMtBSaO0M8saWWRzZUs+FgG05D8fazx3PHJVMoykyJa5NCwSjVla0c2tbCrnX1hAJRMvK8zDi/mBnnFeHL9Ax4vOn3E9y4sSf4Dm7Zgu7uBuyLXKbMn9cTfHsqpqCM0RPOWWHzmKVTDs8WP7y8ih2O27PHieXbkVg4fnj2eKfXQYfbDsc7HIoOhR2cWxbtpkl72KQ9FCUQMY/bFkNBmtvA51KkOiHVCV6HxmtYeAwLD1FcRHHpCE7LDs2NaDcqGoRwN3AS/4Yo0A47RLcMyw7RD884VxHCKkwo9l9ERY4K2Xvf992OAkMZeBwePA4Pbocbr+HFo1yk4MGr3Xhx4cGFW9vLu7i0o2d2utMycFoKh2XgMMHodSNqoaIWmBZELHQ0ihUx0ZEoZiSMGQ1jmiF0bMa6xjwSnDv7D9KPDdsPz2pXONzgcCkMJxgO+hwXC9pjNwwTlBUL3OP3b7lSzp7Z5ka/Qfux246E6q4BwndXLGR39XnsscP7nqD9cCgfey32PFECeBk4x8eYrtlCCCGGnNTr+JB6LYQQYihJ0D0AKcKAZcL2x+Cln7C9vZ3fTXwnD+dfQhAHizJ9fLA0n6vzMnEain1NAR5cs4+H3jiIUop3njOBj108mYL0+Mzw7i0aMdm7sZFta2qormxDGYqyM3OZeUExE84Y3NrhOhKhe/v2nuC7a8MGzKYmAIz0dFLmziHtggtJu3gJ7vHj496HkaItjRWM2iF4LBTvHZIf2Wa/rkPHhtoRNJ1ORUeqgw6vg06Xgd9FTzjeiabdtOgwTXv2eCiKvztKezCCaR3/3whDQYbXRbrXSbrHQbrH6AnNU5yQ2jsw75lhHsVphXFYESKRMOFw/7eToRwKHIADtKHRhh2kW8rCMiw7HFcmEWJLuhCxZ6cTJqRDhAljKtMO35XZs7+lrJ5Z6aYyMZW9NE9fbsNth+xOD15lL/fi7RWwu7UTj3bh1g7csZDdDtgVTtPAYdmz1u1wXaNMUKZlz143LbA0OmqhTRMdNdFmLHCPmpjRKFYkgmlGsKxwbCb70QG7MvqG5f2E7oY+NlR3aBxO7NDdqXpmthsOe6a7cmgMQx9ZRsawUMoEI/7hu82BoZy9AnI7XHf0Cdl7ZrwrV5/tx9vHFVsf/vA5+wvd3T3vm5Y2RQbOcSA1WwghxFCSoDs+pk6dqnfu3DnSzRBCCJGkJOgegAyae9Ea9q+Bl35C6/5X+XvJ9Tw48RaqlI8Sj4tbS/J4V1EuuW4nVa1d/Oz53fx7fRUuh+K955bxkYsmkZs28IzrU9VW38W2l2rY8UotwY4IadkeZpxXxIzzi0nPGXzIrrUmcuiQHXyv30DX668T3r8fAPfkyaRfvIS0JUtImTMH5XQOSV8S0YkuutkTkMf24ThBtvI6MXxOulOdBLwOOj0GnS5Fp8O+IGcH0IFFh2nhj5i0d0dpC0bwByO0x24nDMlTXGT2uvU89zpJczvwuXvPMrdIMSw8ysKhI0QjEcLhMJHYfSgUIhqNEolEeu57P+677VT/7VOGQjnsGwZHheva0HY4bpg9gfnhYL13uB7W9sz1bt3dE6Cbyuw57vDNUhYa3W+43pfbcONyuHArN27DhUe78KjYDPbYY5d24MIO2504cGkHTh0L3LXDDtx1r+Bdw+Hc+sjkcQ2WBdHD91YseD8cwptY0ShWNIoZjaB1LIDXkaNDdqNv8G4d2dY7dB9oW6/z2bPfY6F8T1DPkf2NXmH84Rnxp+iyS/fKwDkOpGYLIYQYShJ0x4enqELP+sy3uMSo4Rsf/Qheb/wnRQkhhBi7JOgegAyaj6PuTXj5Z5hv/odncxbywNTbWeMqxWMo3jYum8+WF1HocbG/KcBPn9/FfzdW43U5eP/5ZXz4wklkpbqHpFlm1GL/5ia2ranh4PYWACaeYc/ynnhmLg7HyS9TED5wgM4VK+hYsYKuN9ZCNIqRmUnahfZM77QLLsCRmRnnnoxeR11086i1xWOBeKDX+uKxW78rlSgwUpwYaS4MnwtHmhuV6qTb6yDgNuhwxgJyhb30StTsmTHe++YPRmg7jZA8w+skzeMkzeMizesk3eMk7fC22HOfx4nP48DrUGjLPGEgHq9tp8NwGEcCdhWbva6wg3YDtIoF7cq+WcrquZnEgvPYfVTHwncdIRr7L6IjPQF8WIePOr7nhtUzw/3wtsGE8A7lwGW4cCkXLuXETSx0x4kbJy6cuHDg0va9s+fesEN4DByW0XPv0ApDGzgsMLRhh/CWsu91LIyP3TAtlKXtP+aYvWbHmxaYJtqKonUUdBR0BK2jaOznFhF7O9GjQnXl0HzoW2tk4BwHUrOFEEIMJQm648M3frLOe+/PUKbGmWpxRlodn1owk8UXnT/STRNCCJEExkzQrZT6HnAtEAb2AO/XWrcNdIwMmk+gvQpe/SWs+wM7nHk8OONOHkqbj9Ph4O6J47itNB+vw2B3Qyc/fm4nj2+uJd3j5IMXlvOBC8rJ8LqGrGn+piDbX65l+0s1BNrDpGa4mX5eETPPLyYz/9TWDjc7Ogi89DKdK1bQuXIlZmsrOBykzptH2pIlpF18Me7yMjs4FIOiLY3VdXQgftTj3muMByJYXdH+T6TA8B0OxY+E44bPZYflqU6CHoNOh6JTgV9btAejR80W7y8k7whF6eyOEhxgTfLefG5HryDcZQfjvcLxdO/Rz9M8vUNzV892t3PgP8porYlGoycVkkejUUzT7Lnv+/hkXuv9PN6UQ2EYsSDeUD3h+1EhfJ9bT3DOkRDdxOy5N5VJlCimtgP6w4F8VB8J5qM62nMujT4qfO/7uN9tSmNh9RvUKxQuw4XTcOIyXLiVCzd2SO/WTh5+5/9k4BwHUrOFEEIMJQm642PBggX6B9+/l+9vqWZb5zjMdkBBfnYHlzub+Nodt+Nyj51PzwohhIivsRR0XwG8oLWOKqW+C6C1/r+BjpFB8yAFW2Htg/Dqr9hvOvna3G/ypHsyE7xuvjqlmKvzMlFKsaPOz4+e3cnTW+vJTHFx20WTeN95Zfg8Q/eLjGVaHNjawrbV1Rx4sxmtoXR6NjMvKGbSnHwcJwgUj0ebJsHNm+lcsZLOFSsIVVYC4Jo4gfQl9hInqfPno9xDM3t9rNKmhdUV7XfJlGPD8jC6+zhBrEPZYfjhILznsfuobYbPhSPVieky6IpYdIQidMbC78MheN/ngZC9zX7eZ/9QlMH8E+l2GkfPHo+F5D7P0bPJDwfqfUP0w8emuBxD+ocXrTWWZQ06FD+ZAH0w+1qWddz7vtuGjcL+mhtH7lF2QN/7cU9Ij8W9n7pXBs5xIDVbCCHEUJKgOz561+tQl58vPvgAz+syWhu8qIjG5TWZ7avh7nPncuEFC0e4tUIIIUabMRN096aUugF4m9b6XQPtJ4PmkxTphpd+DKt/wKrcRXz5jC9SaaVwflYa36goYWaaPZP6zep2fvjsTl7Y0UCuz83tiyfznnMn4nU5hrR5na3dbH+5lm0v1dDZEsKb5mL6uUXMPL+I7ELfaZ07UlNDx4oVdK5YQderr6HDYYy0NHwXXEDaksWkXXQRzpycOPVEDJaOWkcvn9ITive5EGfsXoePE4gqMFKdGKmu2C322Gc/dqS6MHzOI6/5XBgpLnvd7d7t0ZqusGkH4bGQPNDr8eFg/Hghes/+oSjh6InXhDYU+Dx28J3qsYNvr8vA63LgdTmOep7icuDptS0lto+3n+c9x7kdeJ0OXIeXQElgA4Xig72Pxzn6u//ABz4gA+c4kJothBBiKEnQHR/91mutefLxP/LjKoOd/gJ0q/07+bisdi53tfPlOz+MR2Z5CyGEGISxGnT/D/iH1vovA+0ng+ZT1LQLHv8k0f0v8+cz7uS+cTfQbsJ7inP5XHkRubFfUtYfbOVHz+5k9a4m8tM93HHxFN5xzng8zqENvC1Lc2h7C9vW1LB/UxOWpSmuyGLmBcVMnpuP03167291dRF49VU6X7SD72hjIyhFypw59hInS5bgmVqR8MHgWHTMhTe7IliBqH3fFemZTW512dvMroh9EcXjUF5HLAy3Z4YfHZIfHZw7Yvsp1+A+ZRCKmgRCZiwIjxwJxo8Kzo9sC4SidEdMuiMWwYgZe3z089AgwvP+OAyF12mQ4nbgcTpIcR8djvdsi+3TX4B+ODw/ErYfHbof3j4aQvWTJQPn+JCaLYQQYihJvY6PE9Xrtqrt3PPYU6xgCp11TlTYwuWOMielhjsuXMDiC84extYKIYQYbZIq6FZKPQcU9vPSPVrrR2P73AMsAN6q+2msUuo24DaACRMmzD9w4MAQtjiJaQ0b/wbP3EOrqfj+oh/wBzWJNKeDz5YXcmtxHi7DDqte29vMD57dyev7WijO9HLHJRXctKAU1ylcPPJkBdpDVL5ax9Y1Nfgbg3hSnUxbWMjMC4rJLUk77fNry6J723Z7Xe8VK+h+800AnMVF9hInF19M6jnnYHg8p/1eYvhprdER69hAvOdxFLMrclQ4bgWix585DiiX0TNT/KgZ4n0C8d6BuXLHZ4kSy9KEohbdEbMn/A7GwvDuPs+DEZNQP9uOCtDDJt1Rk2DYPOa83ZFTC9UNBV6XA5fDwOUwcDsULqdx9PPYY5ezz3OHgdvZ5/nh152neryBq9c2t8PA5VA4jMEH8sk2cFZK5QD/AMqA/cDNWuvWfva7CvgJ9uVQH9Bafye2/avAh4HG2K5f1FovP9H7StAthBBiKCVbvR4pg67X3e384z+/5pddE9jXlgtNYRSawvR2Lvd08oU7PkDqEF7zSQghxOiUVEH3iSilbgVuBy7VWnedaH8ZNMdBoAme+RJs+js7ii7g/83+Kqu6XVSkevj6lBIuzs0A7MDwpd3N/ODZSjYcbGN8Tgp3XVLBDXNLcA5D4K0tTfXOVratqWHPxkasqKZwUgbzrpxI2ey8uM0gjTQ00LlyJZ0rVhJ4+WV0MIjh85F+2aVkLFuG79xzUS75hS3Z6ah1ZFZ47xC8T0jeE5YHIujuKBzvn1eHOmppFUefcNxeSsWJ4XXas8wPP/Y47As+jgCt7VC9dxjeHbHojpp092zrG44fCd0jpkXY1ERMq+cWjtrPo5ZFJKoJ93otYmrC0T7PTWtQS8CcCqU4Kvg+EpT3ee4w+NdHz0uqgbNS6j6gRWv9HaXU54HsvtfEUEo5gJ3A5UAV8AZwi9Z6Wyzo7tRaf/9k3ldqthBCiKEkQXd8nHS9tkyqNvyPr6w7yGo9jVCtRnWbuFxR5npq+MjiRVx64dyha7AQQohRZcwE3bGZYz8EFmutG0+0P8igOa72roDHP4lu2csz8/+Pr+Rey/6QyeW5GXxtSgmTUu0ZzVprVlQ28sNnd7Klup1JeT4+cVkF18wuxjFMgVywM0zlq3VsWVmNvzFIwcR0zr6mnImzcuO6ZIIVCtH12mv4n3mGjmeexfL7cWRlkX7VlWQuW0bK/PkoY+hDfjE6aEtjBQ/PCj96hnjvQLxvSI41wL/JCpT7cPDtQHntANzwOlAphx/3Cce9jp7tRooDnMaoXkpEa41p6Z7guycIPyYoPxKk9zw3NZFon+em1bPteEF83+Mf+si5STVwVkpVAku01rVKqSJghdZ6Wp99zgW+qrW+Mvb8CwBa629L0C2EECIRSdAdH6dTr62ajTzw1H/4veMsqpqzMBq7URoKU1u5JCXEFz/2HtJ87ji3WAghxGgyloLu3YAHaI5telVrfftAx8igOc4i3bD6B7DmR4S8Wfz2wp/zo0gxYUvzodI8PllWSEZsfW6tNc9sq+dHz+5kR10HFQVpfPLyqVx1RiHGMAXelmlR+Vodbzyxn47mbsaVZ7Dw2kmUzsiOe7BnhcME1qzB//gTdLz4IjoYxFlYSMbVV5OxbBneM2aO6jBRjAytNTpk2sF4MIrVHUV3m7HHZux5tOe57rb36f2YE014dqh+wvE+obnXeWyYfvi5x3nMRTvHmmQbOCul2rTWWb2et2qts/vs8zbgKq31h2LP3wMs1FrfEQu63wf4gbXAp/tb+iR2nCw3JoQQYlgkW70eKXEZY3fUs3XNH/lmXQavmhWYNRGMLhOXw57l/f7F53L14jlxaa8QQojRZcwE3adCgu4h0rADHr8bDr5Cw6Sr+PaZX+KhNpNcl5MvTiri7UU5OGKhrmVplr9Zy4+e3cmexgAzijL41OVTuWxGwbAFv6ZpsePlWtYu309na4iiKZmcc+0kSqdln/jgU2B1ddHxwov4n3iCzjVrIBLBPXEiGcuWkXHNMjyTJg3J+wrRl9YaHbaOCsCt7ih6wKD86H0GWo/8MOV29B+UDzDTXLkdGG4D5XFgeEb3zPLROHAe6JoYwB8HEXTfBFzZJ+g+R2t9p1JqHNCEvVjPN4AirfUHTtQmqdlCCCGG0mis14korvU60k3Xlof5+dot/C3tQpobU3DUB8GCAm8b57q7+fT738aEosz4vJ8QQoiEJ0H3AGTQPIQsCzb8GZ79MkSCbLzgq3w5/TLe6AgyOy2Fb1SUsDDryMUgTUvz2KZqfvLcLvY3d3F2WTbfvP5MphWmD1uTzYjF9pdrWPvkAQJtIUqmZnHOtZMorsgauvdsa8P/7LP4n1hO12uvgdZ4Zswgc9lSMpYuxVVcPGTvLUQ8aFOjQ9FjZ5HHZpYfE46Hjg3TMQdRVw4vw+Jx2GuPux1HPTfcDlSvYLzv6z2P3Uf2xzn4C0qejmQbOJ/u0iV99isDHtdazzrR+0rNFkIIMZSSrV6frtgfrb8KzMD+Y/WgivCQ1Gut0fvWsOaVf3Ef89kYKUfVdGP4IygspnnqWTJ+HHe9+yq5gKUQQiQ5CboHIIPmYdDZAE9/Ebb8C507hf8u+Tnf6MigJhTh+oIsvjS5mFLvkXXWoqbFv9ZVcd9TO+jojvLBC8v5xKUVpLqdw9bkaMRk6+oa1j91gC5/mNLp2Sx8yyQKJw3tTIFIQwMdTz1F+xNP0L1pMwAp8+aRsWwpGVddhTM3d0jfX4iRoLWGqIUVNGOzyu3lV3TYxAodfa9DvR6HLaxQ1J6RHjKxYq/rsHn8i3r2ZdBvAH68YFx5jBPuj+PY8DzZBs5Kqe8Bzb0uRpmjtf5cn32c2BejvBSoxr4Y5Tu11luVUkVa69rYfp/EXtLkHSd6X6nZQgghhlKy1evTpZSagb3I3a+Bz4xo0N1by14aX32Qn1dHeSj3SgKNBq6aTnQIvEaI2a5mblmyiOuXzBq1nwYUQghxfBJ0D0AGzcNo93Pw+Keg7QCBOe/j/pmf4Be1HSjg4xPG8bEJBaQ6jlyYsTUQ5jtP7uAfaw9RkpXCV66dyRVn9Pcp+qETCZtsXVXN+qcPEOyIMOGMXM65tpxxZRlD/t7hQ4fwP7Ec/xNPENq1CxwOfIsWkbFsGemXX4Yjffhmugsxmmit0RHr+MF4yH6tJxg/HJL3u3+v8HywDHVMMF5457ykGjgrpXKBfwITgIPATVrrFqVUMfCA1nppbL+lwI8BB/Cg1vre2PY/A3Ow/ySxH/jI4eB7IFKzhRBCDCUJuvunlFpBIgXdhwWaib7+G5bv2MhPCm5ku56As7oTZ0MQyzTIcXYwLyXM3e+8mlnl+UPfHiGEEMNCgu4ByKB5mIW7YNV98PLPwJvJoUu/yzc8C3issZ0Sj4svTy7muoKso/7yvnZ/C/c88iaV9R1cNmMcX33LTEqzU4e12ZGQyZYVVWx45iDdgQhls/M455py8icMT9jcXbkT/xNP4F++nEhVFcrtJm3xRWQsW0bakiUYXu+wtEOIsUpbGh21Bg7GQyZWbHZ539noBR88UwbOcZA5oUz/+Nvf4P3ves9IN0UIIUQSkqC7f4MJukf04tGhTlj/J3asf4TfZFzEv/OvINpskl7dSKjFASgmupq5sCSXT73rMnLSPcPXNiGEEHEnQfcAJOgeIfVb4X93Q9XrUL6YV5Z8jy83wJudQRZm+vhGRQmz04+E2RHT4sE1+/jxc7sA+MRlFXzwgnJcvWaAD4dwd5TNL1ax8dmDhLqiTJqTz9nXlJNXmnbig+NAa033pk20P7Ec/1NPYjY2YaSmknbZpWQuW4bvvPNQLlmTTohEIwPn+PAUVeiiW3+MJ9OkIrWBWwq9vOvt7x7pZgkhhEgSY7FeD3Txaa31o7F9VpCIM7r7ioZhy79of+U3/NM1mV8Wv4MaIxdfdQsptW10BlJxKJMZrhZuXDSHd11xJm6nY/jbKYQQ4rRI0D0ACbpHkGXBut/Dc1+DaDfmhZ/l75Pfy7cPNNISifKe4ly+PLmY9F6/fFS3BfnqY1t5dls9U8el8c3rz+Sc8pxhb3ooGGXT84fY9NxBwt0mk+cVcM415eQU+4atDdo06Xr9ddqfeIKOZ57F8vtxZGWRftWVZC5bRsr8+ShjeP8QIITo31gcOA+F0okTdcWnvsmBjhysTnvdk5SsKNNS6nlPaSZvu/HtI91EIYQQo5jU6/6NmqD7MMuCnU9irf4RK7oUvy19Oyuy5mF0hCmq2UdXrZOuSCo+o5uzUkLc8daLOXdmoaznLYQQo4QE3QMY8SIsoKMOnvo8bH0E8qbhX/YTvh+dwG+rGin2uPj+tPFcnHv0mtjPbqvnq49tpbotyE3zS/nC0hnk+NzHeYOh0x2I2IH384eIhE0qFozj7GVlZBcOX+ANYIXDBNaswf/4E3S8+CI6GMRZWEjG0qVkLFuKd+ZM+cVNiBEkA+f4OFyzLdPk3l//iueimRxqz8YK2KF3alaEGd56PjAlj2uuuXGkmyuEEGKUkXrdv1EXdB+mNRx4Cdb8iH1V2/n9+Lfxl4KldBkeChqqyK6rpbphHFHtpMDZwflFGXzy5ouYkD88n9YVQghxaiToHkDCFGEBO5+BJz4N7Qdh3ntZt+ge7t7Xyq6uEO8ozOFrU4rJdDl7du8KR/np87t5YPVe0rxOvnD1dG6aPx7DGP5AN9gZZuOzB9n8YhVmxGLqwkIWLC0jq2B41xIHsAIBOl5cgf/xx+lcswaiUdxlZWRccw0Zy5biKS8f9jYJMdbJwDk++qvZZjTKV3/9a1aY2VS1ZaG7NBrwZYc5w1PHh6YXc+VV141Mg4UQQowqUq+PppS6AfgZkA+0ARu11lee6LiEHGPXboaXfkJg+3IeHncFv5rwHvZ68kjpDlBRt4VQPRxoG49CU+7yc938aXzwqtmkeWVZSCGESDQSdA8gIYvwWBYOwIpvwyu/gLRxdF//a37IFO4/1EC+y8V900q5Ii/zqEMq6zr48n/f5PX9LSyYmM03b5jF9MKM47zB0Oryh9nwzAG2rKzGMjXTF9mBd0Zeyoi0x2xrw//MM/gff4KuN94ArfHOnEnGsmVkLFuKq7C/5fiEEPEmA+f4OFHNDgQCfOPB37Na51LTmokOarSC9KwQs9y13H5mOUsuWzqMLRZCCDGaSL2Oj4QeY7fsg1d+jt7wF17xTeNXFR/j2dRpaGB6ayUFTbvZVzeexmAeLhXlDG+Im5ecxfXnTibV7Tzh6YUQQgw9CboHkNBFeCyr2QAPfwia98D5n2DTgk9x965atge6uXFcNt+oKCGn1+xurTX/XlfFt5Zvp6M7ygcvKOcTl1WM2C8jgfYQ658+wNZVNWhLM+OCYs5eVoYvc+Su8B2pr8f/5JP4n1hO95YtoBSp8+eTcc01pF95Bc7s7BFrmxDJTgbO8XEyNdvv7+Drf/wTa3Qeda3pENSgID27m9muGj42fwbnX3TZELdYCCHEaCL1Oj5GxRi7swFe+xW8/gA12s0fpn+c32deSIfhJK+rifntbxBtCbKhbhZdUR9OZTLF3c21i6bxjgunk5s2cuM6IYQY6yToHsCoKMJjVTgAT38R1v0BiuYQvuEBfhJI4ycH6slyOvnO1FKuKcg66pDWQJjvPrWDh944RHGml6++5QyuOGPkZi13toZY99R+tq2uweEymHflBM66bAIu98he3Tu8fz/ty5fjf/wJwnv3gtOJ7/zzyFy2jLRLLsWRNrxrjAuR7GTgHB+nWrPb2tv5yh//yqs6j/rWdOi2QEFmdpCzXNXctWgOC85dPAQtFkIIMZpIvY6PUTXG7vbb481X7icUaOaxilv5ReFb2a58OKMRFrVtZFpoHXX+dDY0zKalOweFxQRXkMvOmsCtF89mQu7wL1cphBBjmQTdAxhVRXis2v4/eOxOiIbgqu+wdepN3L3jEFs6g1ybn8W3ppaQ7z567bS1+1u455E3qazv4LIZBXz1LWdQmj1yv4C01XfxyiN72LuxkbRsDwuvm8S0cwpRI7CeeG9aa0I7duB/4gnaly8nWlOL8npJu3gJmcuW4bvoIgz38F/kU4hkIwPn+IhHzW5saeUrf3mI13U+Ta2+ntA7K6eLuc5q7rrwHOYuOC9OLRZCCDGaSL2Oj1E5xo6GYNND8NJPoGUPG0ou5VcVH+NxKw9TQUFjPdd2rqLYvY0dXSVsaJhNVWcJAOOcXZw/NZ/3XXwWZ5ZmodTIjvGEECLZSdA9gFFZhMcifw08cjvsWwkzriWy7Cf8oinKD/bXkeY0uLeilOsLjv6lImJa/P6lffzo2V0A3HVpBR+6sByXwxipXlCzq5WX/r2bhgMd5E9I5/wbp1AyLTGWDNGWRXDjRvyPP47/qacxW1ow0tNJv+JyMpctI3XhQpRjZGeiCzFaycA5PuJds2sbGvnq3x9mLfk0taSiQhYYkJPdyULHIe6+/BKmnTkvbu8nhBAisUm9jo9RPca2TNjxOKz+IdRupCmrgr/O+xK/1eU0WRoVjDJ9z3ZutZ5H5TWyNVTIxsbZ7GqdhMYg0wgxf4KP918yh0WT80d07CmEEMlKgu4BjOoiPNZYFrzyc3j+6+DLhxt+xY5x5/DJ7YfY0NHFVXkZfHfqeMZ5jp7dXd0W5GuPbeWZbfVMHZfGN68/k3PKc0aoE6Atzc436nn1v3vobA1RNjuP8946mezCxFkuREejBF55Ff/jj9Px3HNYgQCOvDwyrr6azGVL8c6ejTLklzYhBksGzvExlDV7f3UV33j4CdbpAlpbvD2h97jsNs4zDnH3W69jYvmMIXlvIYQQiUHqdXwkxRhba3uS1arvw/7VmL5xPLvoK/zKO49XO0NgaRx1XSzZ9QY3pr5CqLidSiuXjY1nsrV5OhHLRYqKMGuci/dcfCaXzSiWi1kKIUScSNA9gKQowmNNzcbYhSp3w/l3YS65h1/XtnPfvlo8hsHXp5Rwc2H2MR8Ze25bPV95bCvVbUFuml/KF5bOIMc3cstyRMMmm144xLqnDhANW8y6sJizryknJT2xlgqxurvpXLkK/+OP07lyJTocxpGXR9rii0hbsgTfuefJmt5CnIAMnONjuGr25spdfPfplWzUBXQ2u1BhC+XQlGS3cBEHueuWd1FYUj7k7RBCCDG8pF7HR9KNsQ+8DCvvg70vQmouu8/9HL/Pu4K/1XcQ1BrDH8F1wM/76texIG0D4fHN7DHS2Ng0i00NswhEfTgxmZqtufnC6Vw7u0wuZimEEKdBgu4BJF0RHivCAXj6Hlj3eyiaAzc+wJ7U8XxqxyFeaw9wSU4635s2nhLv0aFxVzjKT5/fzQOr95LmdfL5q6Zz84LxGCO4VnaXP8wbj+9j65oaXG6D+VeXMfuSUpyuxFsmxOzooHPFCjpfXEHnmjVYfj+4XPjOPpu0JUtIW7IY94QJI91MIRKODJzjYyRq9oo33uBnr23lTTOf7kaFimoMp8XErAYuNqr52Ls/SF5BybC2SQghxNCQeh0fSTvGPvQ6rPwu7H4OUrIJLLqLh8tu4re1newKhjCiFqoqwLj6Lj6ud1NivUz3+AYOedxsap7J+p6LWWom+CJcf+4k3jp3ChNzZdKQEEKcDAm6B5C0RXis2P547EKV3XDVt7HmvpcHa5q5d08tDgVfnVLCu4pyjpndvbO+gy898iav72/h7LJsvnXDmVSMSx+hTthaagO88p/d7N/STHqOl0U3TKJiwbiEvZiJjkTo2rCBzpUr6VyxkvCePQC4J03qCb1T585FuVwnOJMQyU8GzvEx0jX738+/wO8ra6kM5xJpslBRjcNlMjmzlksd9dz+vo+SmZ0/Yu0TQghxeqRex8dI1+shV7UOVt0HO58CbyZ64cd4deb7eLAxyPLGdkzA0dSNcaCTi1Jc3GrswdH0AoGSRhozTDa1TGd9r4tZFnjCLD6zgKvOmMKCshwyU2T8JIQQA5GgewBJX4THAn8tPPIRew216dfAW37GAeXjUzsO8VJbJxdmp/GDaeOZkHL0x8O01vxrXRXfWr6dQCjKRy6azB2XTME7wjOpD+1o4eWHd9N0qJOCsgzOf9sUiqdkjWibBiN88CCdK1bSuXIlXa+/jo5EMDIySLvgfHuJkwsvxJmdGBfeFGK4ycA5PhKlZlumyW8fX84/a7vYG8zGaoqiTI3LHWVaRhWXuZv58Ac+gS8tc6SbKoQQ4iRIvY6PRKnXQ65mI6z6nn3xSk8GLLydunkf5s+tJn+qbqYxEsXVbaIPdJLbEuYd0/N4S/cuGisfo2N8M/6cTra0T2V9/Wz2tJdjagegKUyNcvbUbK6aOYWFk/LIk2VOhBDiKBJ0D2DMFOFkZ1nw6v3w3NfAlwc3/AqrfDF/qWnm63tqsIAvTSrifSV5GH1mSDd3hrh3+Xb+s76aibmp3Hv9mVxQkTcy/YixLE3lq3W89ugeAu1hJs3N59wbJpNVkDqi7RosszNA4JWX7WVOVq7CbGoCwyBlzhx7tvfixXimViTsbHUh4k0GzvGRiDW7q7ubn/73fzze7uBQIB2aIihL4/GEmZV+gEtTArz/A58gJSVtpJsqhBDiBKRex0ci1ushVbvZDry3PwbuNDjnNiILP87yoIPfVzXxansAQ2uMmiDGwU4W5aTzzrPyWFT/JrveeJi20lasca1UWensbJ1CZcsU9rSXE7Hsmd153ghzJ2dw5cwpnDs5n5KslBHusBBCjCwJugcw5opwsqvdZF+osmkXnHcnXPJlqqLwmR2HWNHawaJMHz+aPoHy1GP/Kv7y7ibu+e+b7GsKcMPcEu5ZNmPE/3oeCZlsfO4g6585iBW1OHNxKQuWleH1jZ6Ps2nLonvrVnu294oVdG/dCoCzuIj0JUtIW7KE1HPOwfB6R7ilQgwdGTjHR6LX7Ma2Fn746NM8H/ZR509FNYVQGlK83cxJ28ulGVHec+vdeDwyQBVCiEQk9To+Er1eD5n6bXbgvfURcKXCOR+Cc+9kO2n8vrqJf9W1ELQ03s4o5l4/2e1Rbpxbwjtm5VK8500OrVzO/vBeopMiqIJmarWPna2TqWyZwq62yXSb9ngp0x1hdpmPK2ZO4vzJ4yjP88kEIiHEmCJB9wDGbBFOZuEuePqLsQtVngU3/g6dO4WH6lr4yu5qIpbm85OK+FBpPo4+vxB0R0x+8eJufrlyD6luJ19cOp2b5o/sxSoBAu0hXn9sL9tfrsWd4mTB0jLOXFKKw2mMaLtORaS+gc5V9hIngZdfQXd1obxefOee27O2t2vcuJFuphBxJQPn+BhNNXtX1UF+8swqVls5tLS5MZrt0DstpYv5qbtZnG3wrvfcicczOj6pI4QQY4HU6/gYTfV6SDRWwqrvw5v/BocHzv4gnHcX7d5c/lnXyu+rm9gbDOExNfpgJ+pggPkF6dy8YDxLzyzEfWg/gVWr2P/qKqqy/FARwTmukUblYWfrFHa0TGFnSwWdUfsilj5nlJnjPVw+s5wLpxQxbVz6iI9fhRBiKEnQPYAxX4ST2Y4n4NE7ei5UybxbqQ1H+FxlFc82+1mQkcqPpk+gwnfsTOLdDR188T/2xSrPKcvhW2+dxZSCkb1YJUBzdScvPbybQ9tayMhP4bwbJjNpbv6o/Qu+FQrR9fob9hInK1YQqa4GwDNzBmmLF5O+ZAneM89EGaMv0BeiNxk4x8dordmvVW7jVy9t4FWdT2eLgdFih94+bxdnpe5lgaeLW995G7n5JSPdVCGEGNOkXsfHaK3Xcde0G1Z/Hzb/ExwumP8+OP8TWOlFrG7t5MHqRp5t8qOBtLYI3XvaSW2PsGxWETctGM/C8hx0oJPAK6/gX7maPdu30zxZYUzpxl1QR6vTSWXrZCpbKqhsqaAtbF8bxGOYTCt2cumMiVxUUcyskkxcDhlPCSGShwTdA5AinOT8tfDf22Hvip4LVeqUbP5T38qXdlXTZVl8amIhH5tQgKvPX70tS/OvdYf41vIddIWjfHTxZD528chfrBLg4NZmXnp4Ny01AYomZ3Le26ZQWD66L3qmtSa8ezedK1fSsWIFwfUbwLJw5OaSdtFF9gUtzz8PR5qscytGHxk4x8dor9mWafLUhjf4w6b9bDDy6W4Bo7kbZYHbGeaM9L3Mcbbw9iuuYfqZ5450c4UQYsyReh0fo71ex13zHljzQ9j0ECgD5r0XLvgkZJZyqDvMn6qb+FttC82RKGkmWAc6MA90MiHNw9vmjefG+SWUZqeitSZUWUnnqtU0rX6dfa0BOs4wcE3y4x1Xi9+le9b4rmyZSmN3LgAuZTF5nGLx9FIWlhUxvTCDokzvqJ0sJYQQEnQPQIrwGNDPhSqZtITGcIQv7Kzi8cZ2Zvq8/HD6BOZkHPsR8qbOEPc+sZ1HNlRTnufjm9fP4vwpI3uxSgDLtNj+ci2v/W8fQX+YigUFLLp+Mhl5ybH2q9nWRufqNXSuXEnn6tVY7e3gcpG6YH7P2t7uiRNHuplCDIoMnOMjmWq2ZZo8/Opq/r27nnVGAd2t4GgMoiIaQ5lMy9jPma5Glp41i8WX3SyDUSGEGAZSr+Mjmep1XLXuhzU/gg1/tZ/PfbcdeGdPJGxZLG9s5881zbzU1okB5HaatG1vxdEa4oLJedy0oJQrzyjsmXhl+v0EXn4Z/8o11KzbS01aJl3TQ6SUNeEtqCXoibArtsb3juZp1AYL0Ni/T3gdJqW5BrNKczh7YjEzizKZVphOqts5Ml8bIYQ4CWMu6FZKfQb4HpCvtW4aaF8pwmNI7WZ4+INHXagSp5unGtv5/M4qGsIRbhufz2fLC/E5jp21vWZXE/f8dwsHmrt467wS7lk6g9wRvlglQLg7yoZnDrLx2YNYlmbm+cUsWFqGL2vk2xYvOholuHGjvcTJypWEdu0GwF1WFlvXewmp8+ehXKPnIp1ibJGBc3wka82ORqP869WV/O9AE685igi1WjgagqigBUBZ+iFmeWq5sDCLt91yBw6H/FsnhBBDQep1fCRrvY6btkOxwPvPoC046xa48NOQUw7A7q5u/lzTzD9rW2iNmmRrhToUILC7nQyHwXVzirlp/nhml2b2/CFcWxahHTvoXLWK5lVvUF1j0VBURKSiDV/JIVLG1WKmBqnqKKKqs4QDbRM46C+ltquAsHbHGqbJTbWYXJTCggmFnFWaz4zCDEqzU2TNbyFEQhlTQbdSajzwADAdmC9BtzhKuAueuQfWPgiFs+HG30H+VPxRk2/uqeFPNc1M8Lr53rTxLM45dk3u7ojJz1/Yza9X7cHncfLFpTO4aX5pQsy0C7SFWLt8P9vW1KAcijMXlzDvqomkpLlPfPAoE66qonPFSjpXrKDrtdfQkQhGWhq+Cy4gbcli0i66CGdOzkg3U4geMnCOj7FQs8ORCP945UWeqGrldU8JoVYLV0MX+E0AClIamZV6iAVpFu951x2kZ+SOcIuFECJ5SL2Oj7FQr+OivRpe+gms+wNoE+a8Ey78DGTbn1rtNi2eaGzjTzXNvNYewAkUBzXN21qINnUzbVwaNy8Yz/VzS8jrMwHLbGuj86WX6Fy5mur1+2l0ltJUWEi0qIXUnIOk5jXgzm7B4WunJZRFVWcJB9tL2d9WRnWgiOZwJsRmf7sNKzb7O5v544uYWWzP/s7wyh/ehRAjY6wF3f8GvgE8CiyQoFv06/CFKiNBuOIbcPaHQCleaevkMzsOsScY4ubCbL42pYRs17Ef39pZ38EX/7OFtQdaWView703nMmUgsRYO7q9McgbT+xj52t1ON0OzrpsPHMum4AnJTk/hmYFAgReeaVnbW+zsQmUIuWss+zQe8kSPNOmJcQfI8TYJQPn+BhrNbs7HOJvL73AMw1+XvOOJ9xm4WoIoFoiaK1Id3UwK30fs90B3nn9O5hYNmukmyyEEKOa1Ov4GGv1+rT5a+GlH9uTsbSGee+xZ3hnlvbssiMQ5M/VzfyrvgV/1KJAGaTUBqnb1ozLhEumF3DTgvEsmZZ/zIUntWXRvXUbgTWraXt9A3V7O2j1ldOaVUFHehaujB2kZu0irbABd2YzrowWIhhUBwo55C9lf2sZhzpKqQ3m020dCdSzU+zZ3/PGj2N2SS4zijIoy/XhkNnfQoghNmaCbqXUW4BLtdafUErtR4JuMZCOOvjvx2DP8zD5Urju55BRTLdp8aMD9dx/sJ4sp5NvVpRwXUHWMUGpZWn+ufYQ31q+ne6IxUeXTOajSyYnxMUqAVpqA7z+v73sWd+Ix+dk3hUTOfPiUlzuxGjfUNCWRfe27T1LnHRv2QKAs7CwJ/T2LVqE4fWOcEvFWCMD5/gYyzW7Mxjkby+/wHPNnbyeNpFwm4WnvhPV1I1lOnAbIWZk7uNMZzPXnXs+Z5+3bKSbLIQQo47U6/gYy/X6tLRX2xetXPdHUArm3QoXfgoyint26TItHmto5U81zaz3d+FWikkRRfO2Fvy1AfLTPLx1Xgk3zS+lYtyxn1AGsMJhujdvJvD667S/vpG6fZ20pJXTllVBR9p4tIrg9u0mq2QPKTlVGKkNONObcbiDtIayqOooZl9rGQfbJ1IdKKQpnInGDtedSlOcrZhUkM6Mwjym5GdQnu+jPNdHti/5PmkshBgZSRV0K6WeAwr7eeke4IvAFVrr9oGCbqXUbcBtABMmTJh/4MCBIWyxSGha2385f+ZL4HDBsh/CrBtBKbZ2BvnUjoNs6ghyRW4G35laSrH32OLc2BHim09s49GNNUzK8/HNG2Zx3uSRv1jlYY0HO3j10b0c3NpMaoab+VeXccYFxThcxokPHuWijY10rlpF54oVBF56GaurC+X14lu4kLSLl5C2eDGuoqKRbqYYA2TgHB8ycLa1BTr428sv8EJbkLUZ5YT9Gm9dB47GLiJhJ4YymZx+kFnuehYV53DdWz+M15sYnzoSQohEJvU6PqRen6a2Q7D6+7DhL6AcsOAD9kUr08cdtdvWziB/qm7i4fpWOk2L8U4nWY0h9m5swAxbnDU+i5sXlHLtWcUDLjPSN/iu3R+g1VdGa9ZUOtNKQSkMQ5Nf1kp64W5w78Q0qjBSGnH5/ERMJ7WBcexvm8i+1nJqOotoDuXQFvX1XPwSIMWpKc5xMqUgg+njcpmUn0ZZro+yPB+ZKbIMihBi8JIq6D4epdSZwPNAV2xTKVADnKO1rjvecVKEBQDNe+CR26HqdTjjBjvwTs0hamkeqGrku/tqcSjFPZOLubU4F6OfZTBW7WzkS/99k4MtXdw4r5R7ls0gJ4H+al2zu43XHt1Lza420nI8nL2snOmLCjEcyR94g/0LXNcbb9hre7/4IpGqKgBcEyaQctZZPTfvtKkod+J830RykIFzfEjNPlZbp58/vfw8K/wh1mZOIhKAlFo/nkY/wS7748XZnlam+aqY6vRz5dw5nLfkJlnOSQgh+iH1Oj6kXsdJ635Y9T3Y+HdwuOHsD8L5d0Na/lG7BaImjzS08afqJjZ3BkkxFDO1E/+OVg7tb8fjNLh6ViE3LRjPuZNyT3hhSSsU6gm+217fRN2BQGzGdyz4BpwOKJyUQf5kjSt1K6HoFjoDO9HOepxpbRgOk6jloCmYS3V7MVX+8dR1jqMxmE9LKIt2ywu9QvA0t6Ykx8O0wiymFmRRluejLNdHeZ4Pnyc5l+AUQpy6MRF09yVLl4iTZkbh5Z/Ai9+G1Bx4y89h6hUAHAiG+GzlIVa1dnJOpo8fTBtPhe/Y5S+6IyY/fX4Xv1m1l3Svk3uWzeTGeSUJEyhorTm0vYXXHt1Lw4EOssalcs615UyZV4AaQ2upaa0J791L58pVBDesJ7hxE9HGRgCUx4P3jDNiwfdsUs46C2dhYcJ8D8XoJAPn+JCaPbCGthb++uqLrAxEWJc5hWgIUus7SG9uJthuEIrawXepr4ap3jpmeELcePUNTKqYP8ItF0KIxCD1Oj6kXsdZ8x5Y9X3Y/BA4vXDObXDeXeA79oLUG/1d/Lmmif/UtxG0LKZ43BS2Rtixrp7OrghFmV7eclYx188tYUZRxqDe/ujgezO1BwK0pk2iNauCQFoJAE6nHXyXzsyjcJIPXPtpqV9Pe8s2Orv2YxmNOFLacHpCAERMJ3WBAqraJlDtL6EuMI7m7lxawxl06KMvrJnphQm5KUwrzGZSfjqT8uxZ4BNzfKQk8bKcQojjk6B7AFKExTFqN8MjH4GGbTD/fXDFveBJQ2vNP+ta+cruarpMi0+WjePjEwpwG8fOiK6s6+CLj2xh3YFWFk2yL1Y5OT9xPjautWbfxiZe+99eWmoC5Jamsegtk5h4Zu6YDHS11kTr6ghu2kRw4yaCmzbRvXUrOhwGwFlQYAffc2Kzvs84AyMlZYRbLUaTZBs4K6VygH8AZcB+4GatdWs/+z0IXAM0aK1nnezxfUnNHryalib++uqLvBoMsz5jMkGHB09rkILaBpS/k+aOdEztxKmiTM44SIWrkTmZHm5++21kZOSf+A2EECIJJVu9HilSr4dI025Y+V3Y8i9w+2DhR+DcO+xJWn34oyYP17fyp+omtge6SXMYzHd6MPf72fBmI6almV6YznVzSrhuTjHFWYMf2/QE36/ZM75rDwZpTSunNXsqAZ+9nrjDoRk3IY3iGfkUTc6kcFIm7hQn7S0HaaxaS0vTZjo6dhGKVmO4W3GmBFCGnTOFom6q2sdzqG08tR3FNHTl0xLKoTWaRkAfvcRJdoqiNCeFKQVZlOWmMTE3lQm5qUzMSSXH5x6TY1shxoIxGXQPlhRh0a9oCF68F176KWRPhOt/BRPPBaAxHOFLu6p5tKGNGT4vP5g+nnkZvmNOYVmah944xHee3E4wYvLuRRO565KKhLoIh2Vpdr1Rz+uP78PfGGRceQaLrptE6fRjf1kaa3Q4THdlJcFNm+0AfNMmIgcP2i86HHimTT1qyRN3WZn8IiWOK9kGzkqp+4AWrfV3lFKfB7K11v/Xz34XAZ3An/oE3YM6vi+p2aemLdDBv15bxZqWJt5IK6PFk4kRiVJc1URGawOBTqgP2rPCfK4AU9MOMtXZygXlpSy7/sMYhnxkWAgxNiRbvR4pUq+HWMMOO/De+gh40mHRR2HRxyAl65hdtdas83fxp5omHmtoo9vSTEv1MD1sULOtiS372lAKzinL4Ya5JVx9ZtFJr5dthUIEN22i6/U3aH19M3UHu2jzTaQtczKd6ePRykChySnwUDwzn6IpWRRXZOHLPDJzOxIO0FC1nqa6DfjbdtDVvR/TaMSZ4sfhivbs19GdxqHWMqraS6gPFNIczKM9nEVrNJUOjm53ihNKsjxMKsikPC8tFoD7mJibSlGmF+cYWcJTiGQkQfcApAiLAR14xZ7d3XYQzr8LLr4HnHZBfrqpnc/vrKIuFOHDpfn8X3khPuexH51q7Ajxw2d38o83DuLzOPn4xVN433lleF2J8zEr07TY8XItbzyxn0BbiJJp2Sy6bhKFkzJHumkJJdrS0hN6d2/eTHDTZqxAAABHZibes2aTMjsWfs8+E0emfP2ELdkGzkqpSmCJ1rpWKVUErNBaTzvOvmXA432C7kEf35vU7NMXjUZ5dO0anqs+wDpvAQdT7QvyFjX4KayvQQXaqA5m0h62P86c521imu//s/ffYXad53kufq/dey/TKzrRAYK9gJ0URbGTIlVti1ISJ07sHDsuJ8kvcU/OSeI4J5Ydy6o0KYmURFkiRVLspFjQe5k+e9ruva/1/f7YgwFAopIDTMF3X9dcA2DWWvtbw+E8+33W+z3vGCsMee66+lo2brlTPtSTSCSLlsWm13OF1OuLxNR+eO3P4eBzYHbD1b8JV3wNLKeOJEnX6vw4mubJiQR7ciVMisL1LjuBVI3dO6cYjBcw6XXctCLEvRta2LoihPkU9e3Z0KpVyvv3U9qxk+z2PUweTZDUh8i4l5BxdaPpG41fTqeO5pUBWpf5aF7ixhO2feQ9hqZpZBP9TI19QDqxj3y+j6o2jmJKYbSWTzo2W3AxlulgItvCVCFMqhQgU/WSrttJYUA9IRNcr0CT00h3wElXyEGnz94wwv02Onw2bCb5kF8imc9Io/sMSBGWnJVKDl78I9j+TQhdBvd/HZrWAJCrq/zJwATfHIvTZjHyX5a1s9V/6jcWR6Zy/Pnzh3jlUJRWj5X/6/bl3LOu5azDQC4m9ZrK/jfG2f7CEKVcja61Aa64p4dA2/yJXZlPCFWlOjAwY36Xdu2m0tcH078jTT09J2V9m5cuRTHIN02XIoutcFYUJS2E8Jzw95QQwnuaY7v4qNF9Puc/ATwB0NHRsWl4eHhW7kHS4PV9u/ino3vZrrNzwNUDgD+XpyuSwZUfJVNVGSg1UVYtKGh0OMdYZp5itbXOQ595nJa2FXN8BxKJRDJ7LDa9nitkjX2RmdjTMLwP/wwsnkaD1pYnGt3ep2F/vsQ/TiR4ZjJFqq7Sajay1W5DjBZ4fdck8XwFl8XAp9Y285n1rWzp8n3sulUIQXVwiNLOHeS37SS6f4xY0UrGvYS0p5easbFOsxmae92NuJMlboIdTvRn6LqulDNEI9tJRHeTyxyhVB6mrkQxWHLoTce7wDVNR7HoYiLbzES2jalCkHQpRLbiI6PaSGKgxMmv47Po6fTb6Q45G3ngfhtd/sZnj23+7NCWSC5VpNF9BqQIS86ZIy/Cc78JxSRs/QO45rdA13jC/V46z+8cHqWvWOHBsJf/tLQVn/HUhuY7fXH+5OcH2T+eZXWriz+4ayVX9wYu5p2clWq5zp5XIux8aYRqqc7SzSG2fLoHT9g210ub96j5POV9+2ayvku7d6MmkwAoVivW1auPZ32vXYsxFJrjFUsuBguxcFYU5WWg6RRf+kPgWxfL6D4RqdkXloMjg/xg17t8oMJuVy9VnQlXpcSSiSLhqUmEOsmw5mS42IQm9Jh0FZa4RlhqiHOZ28R9n/kcwVDPXN+GRCKRfGwWol7PR6RezxHjOxuG95EXwOZvDKzc8pVGnvdpqGgaL8Qz/ON4ktdTOQCu9TjYoBiZPJzi5f2TFKsqLW4L96xv5b4NrSxvOr2Bfq7UEwlKO3dS2L6T+K4+pqIaaUcXGXcvJVujPtLrBKF2K62XhWle4mnkfFvO3jQkhCCXHiEa2UYqvo9Cvp9yLYIwJDBYC+j0x/2set1IqegikQsynmslmg+RLoXJVXxkhZWkMJL+kAnuNOro8tkaJnjQTqffTlfARqffjl/mgkskFwVpdJ8BKcKS86KYhH/6N3Dgx9B+Bdz7v8HfC0BZ1fgfw1P8z5Ep3AYDf7y0lXtDnlMKnaYJfrJ7jP/ywmHGM2VuXhHi3925gqXhT/6mYTYpF2rsemmE3a+MotYFK65sYvOnunD55SDGc0UIQS0SOSnru3zwINRqABhamk/K+rasWoXObD7LVSULjcVWOMvoksXPZCrBU++9wa8KeXa4uskZHFjqVZbFs3SM1HAV+5gyVTlaCxAtNx7WmnQVup1jdBkTLDfX+dTNd7Fs5VWy4JNIJAuGxabXc4XU6zkmsh1e+1PoexnsQbjmX8PmXwPTmZuWIuUqT08keWoyyWi5itug556Am46Cxva9Ud48GkfVBCubXdy7voV71rfQ7J6dulArlynv3Utxx06SO/YzOZgnZWom415CztEGx3K+gwZaVoVpmY47OTHn+5xeR62RmNxPbHw76dRBSsUhqtoEiimN0XZyFEqlYqNUdJEpeJnIthDNN5ErNZOveclqZhIYiKMgTohEsRkUOj02ukMOukKOmS7wroCdkNMs3xNJJLOENLrPgBRhyXkjBOz9Ifz8d0CtwW1/3HjjMC1aB/MlfvvQKDtzRW72ufiL5W20WU69valcU/mHt4f4/17to1Ct88jlHfybW5cSclou5h2dlWK2yvYXhtj3xhgIWLIpxJqtbTR1ywzqj4NWqVA+cOC48b17D7Xx8cYXjUYsK1dO53yvxbp+Hca2NvmmaIGz2ApnRVH+C5A4YZikTwjxu6c5touPGt3nfP6JSM2eG/KlEs+8/wavxybZ5mglag6g11SWpuIsiRpoGkxSs40SMcFQ3cN4KYhAh05RabdP0G2O0msscdP69Vx9wwMoyvyZUSGRSCQnstj0eq6Qej1PGHmvYXgPvAaOMFz727DpS2A8c62pCcHbqTz/OJnkZ7E0FU2w2mHlHp8Lw0SRF3dPsmu0McTyym4/921o5Y41Tbgs5zfE8kwITaPa309x+w6yO3YzcTBGouYm7e4le0LOt8MOTUu9tKwI0tTrxt/q+NgRK+VSmujodpLRPWQzRyhXGlEoemsWg0mdOU5V9ZSKLkolF7mil8lME8liC8ViMwXNQUaYiaMnioJ6wvXNOoVOt5XOgJ2uJgddAfuMEd7stqKfR5GmEsl8RxrdZ0CKsORjkxmDn/wLGHgVltwC9/xPcLUAoArB30di/NnAJDoFfr+nmS+2BDCeRryShSp/9cujfPfdYUwGHV+9vpevXN8974Zg5JJldr40wqFfTVArq4Q6nazZ2sbSTWH0Rjm1+pNQi0anB1w2sr5L+/YhSiUA9D7f8a7v9euwrF6D3nH6LYiS+cdiK5wVRfED3wc6gBHgISFEUlGUFuD/CCHumj7uH4EbgQAwBfwHIcTfn+78s72u1Oy5R1NVfr7zXX4x3Md2s48BezsA7fkYy1IVlsQduPozpD1DjNlqDGtORkphalqj+A1bo/TYJunW59jS0codd38Bi2V+7WaSSCSXLotNr+cKqdfzjOF34NU/haE3wdkC1/8ObPgCGM6eNZ2u1flRNM0/jifYky9h1incGXCz1WZjvD/FT3aOM5QoYjLouGVliHvXt3Lj8hAmw+zXhrWpaCPne/tOpvaMEk3qyDi7yLh7qJo9ABj0GqFWKy2rm2ju9RDucWO2frKaWtM00rE+opEPSMX3Uij0U1XHUcxpjPYyJ/Yjlct2SkUXxZKLQsFDLB8mW2ilUglTEFbSwkQcPRNA7YTXMOkU2l0WegJ2eppddAftdAfsdAftBB2yE1wi+TDS6D4DUoQlnwgh4IP/Ay/+32Aww6f+H1jz4MyXR0oVfu9IhFeTOXqtZv6wt5k7A+7TCtVgvMBfvnCI5/dNEnKa+Z3blvHgpvZ593S3Wq5z+N1J9r4WITVZxOo0suqaFlbf0IrDO7+60Rcqol6ncvToSZEn1YGBxhcVBfOSJTNZ39Z16zD19qLo5MOG+YosnGcHqdnzj3cP7+O5g7vYLQzsdfVQ1Zmw14tclp5ked7C8pSXymCGhHWQCWeeYcXGUDlEsd7YPu02Zeixj9NtSLPGa+O+e7+Ix9c6x3clkUguVaRezw5Sr+cpg2/AK38Co++CuwNu+L9g3WdBf26d2PtyRf5xIskzUynSdZU2i5FHm3ys1gy8uz/KT3ePkyhUcVkM3LqqiU+tbeKaJQHMhguzk0srFint20dx526SOw8zOZwnZQiTcfWQd7SCogMEXo+O5hUBWlYEaOp14wpYZ804LmRiTI58QHJqF7ncEcrVUTRDApOjgN6ozRyn1g0US+4ZE7xYcJPKN1EoNlPX/BQ0G0lhYgqFccRJJrjdoKPTbaUn5KCn2TltgjvoDthxW2evi14iWUhIo/sMSBGWzAqJfvjRVyHyAVx2f8PwtvmARkbzS4ks/7l/nKPFCpe77Pz7JS1c7j59R+62oSR/8vOD7BxJszzs5N/dtYIblwXn3ZNcIQSRQyn2vBphaG8cRVHoWRdgzdY2WpaeOp9c8vFRMxlKe/Y2jO89uynt3oOWyQCgs9sxr1yBubsbU2cnpq4uTF1dGDs60JnkZPC5RhbOs4PU7PlNNJ3k2W1v86tMkt22FiYtQQCW5kdYmS+xph6iPeVhsj9JSj/EpCfFiN7IUCVAquoBwKIv0+OI0GVMsMIGn77jXrp7Ns7hXUkkkksJqdezg9TreYwQ0P/LhuE9vgO83XDjv4M1D4Hu3Azpsjo9wHIiyRvTAyyv9zp5KOzFma7xiz0TvHRwily5jtNs4JZVYe5a08x1SwNYjBcuvkwIQW14mOKuXWR37GXy4BTxnJmMq5uMqxvV0MgTt5g0wp0OWtY009zrIdjhwDDL66qUikRHdhOb2E42dYhSZQiVKfT2PCZ79YQ1Q7nsoFR0UzzhI1tooVYLodW9ZIWFGHoiCKYQaCe8js9soNNjpSfspKfZQU/geCSK1SSj4iSLF2l0nwEpwpJZQ63D2/8dXvszsAXgM/8Llt4y8+W6JnhqMslfDk4Qrdb5VNDNH/a00GM79QANIQTP75vkL144xHCiyLVLAvz+XSu4rGV+5mJn4yX2vT7GgbfHqRTr+FvtrLmxjWVbmjCapcheCIQQVIeGZjq+K0eOUh0aQk0kjh+k02FsaZkxvo9/dGJsbkbRy/82FwNZOM8OUrMXDpqq8sr+HbzYf4jdioV9rh5URY+7lmNdbpi1ONhk6EGMQ+RoklR9jIR3klGzwlDNx0SpYZLrlTqdjjG6zXF6DWWuXbuGa298CJ1ufkV7SSSSxYHU69lB6vUCQAg48gK8+icwuRcCyxqG96r74Dx2iY7ODLBMECnX8Bj03B/2cl/QTW6yyPP7JnnxwBSZUg27Sc/NK8PctaaJG5eHLqjpfQw1n6e8Zw/5nbuI7RpkaqxM2tRMxtVDyRYCQKdoBIIGmleFaVne6Pq2uS5Mo1C9ViMx1kd0bDup+D6KxX5q6gSKNY3ZVUanP+61VSrWkwzwUtlNqeilWg4hVD/1uocUJsaAUTQSnOzTNdlMdHmt9DQ5Gx/BhgHe7rNh1MudwJKFjTS6z4AUYcmsM7Ebnv0qxA42hlTe+p/B7Jj5cqGu8jejMf7XaJSqpvGFlgC/3dVE4DR53NW6xnffHeavXjlKplTj/g1t/Nvbl83ahOvZplZVOfrBFHtejZCI5DHbDKy8upnVN7ThDs7PNS821GyW6vAw1aEhqkPHPjc+tEJh5jjFaMTY2YGpqwvzh4xwvd8vO/JnEVk4zw5Ssxcuo7EoP9rxNu/lc+xytJMweVGExsr8EGtrea5097BcaWeqP8f40RTpwhQJ3xhj1hpDmpvRYghVNHTSb0nQZZuiQ59hpcvEPXc+TFPbijm+Q4lEshiQen0y08OjPw1UgX7gy0KI9NnOk3q9gNA0OPRTePXPGvVr6DLY+vuw4m44j1pAE4K3UnmenEjwfDxDRRMss1l4qMnLZ4IeBiNZnt87wS/2T5Iq1rCZ9GxdEeKu1c1sXRG8aLOphKZRHRigtGsXyR37mTySJFG0kXH3kHV2IHSNKBCHTaOp103L6maaut34W+3oLqA5rKkqyYlRpka2k4zuIp8/SlWNoFgyWDzlk2JQajXTSd3fpYqbctlNreSCuh+lHqCmephAYRSNEVTyJ7yWXoE2h5luv53uYyZ4wE5XwE6zy/Kxh3lKJBcTaXSfASnCkgtCrQyv/jG889fg7YJ7/zd0XnXSIdFKjf86NMn3JhJYdTr+ZUeYr7QHsZ1GQDOlGv/fq338wztDKMCvX9vNP7uxF+csTreeTYQQTPRl2PtahP6dMYQQdK32s2ZrG+0rfChSQC86QgjUeJzq0BCVGfN7mOrwELXhEUTteBqczuFomN4nxKAc6wTXO+XguPNFFs6zg9TsxUG9XueFXe/xy5F+dusdHHR2IRQd/mqadfkRLrc7uK1rM8T0jB1JM340TTqdJuseZtJRIKIzM1IJkK42djgZdDU67ON0mBJ0GUpcsbSXW27/LEajbY7vVCKRLDSkXp+Moii3Aa8IIeqKovwFgBDi9852ntTrBYimwr5nG7uTk/3QvA62/iEsve28DG+ATK3Oc7E0P5hM8X6mgI7paJMmL7f5XOwZSfOzvRP8Yt8kiUIVi1HH1uUh7lzTzE0rQjjMF3fXViMecg/57buY3DtKdKJG2treGHJpcgGg12n4A3qaV4RoWh4k3O3C4b3wQyLr1SqJsVFikd0korvI5440DHBrFoungtFSP34fqoFi0UWx6KFYdFGqeihWnFRKNgx1B/paCKUeJKfZiKAxOv1ROeH1zDqFTpeF7oC9kQcebpjg3QE7PrtJNkJJ5g3S6D4DUoQlF5Sht+HHX4P0CKx9FG75j+BqPumQI4UyfzowzgvxLM1mI7/b3cTDTT70pxGRSKrIf/3FYX68axy/3cRv3bKUz27pmNfbj/KpCvvfHGP/m2OUcjU8YRtrbmxlxZXNmD7hFGzJ7CBUldrEBNXBoZM6wKtDQ9TGxxvbG6fRBwKYujpnOsGNnZ2Nzx0d6MynjuK51JGF8+wgNXtxMjAxxjM732FbqcwuZxcZoxO9ULksN8A6rcxt3cvY3HwZ0aE8kwMZJgcyxCI5yvooCd8EY2aNUc1FpBSipjUe/rpNGbrsE3Tq0yy16bjzlrvoXbpFFmgSieSMSL0+PYqi3Ac8KIR4/GzHSr1ewKh12PM0vP4XkB6Gtsth6x9Az9bzNrwBBosVvj+Z5AdTSSLlGg69jk+HPDwU9nG5y8a2oRTP75vg+X2TxHIVTAYdNywL8qk1zdy0MoRrDpq6hKpS6eujuGMniR2HmRzMkixbyTq7yDnbZ7q+rSaVYKuV5tXNNC3xEep0YrJcnNq2Wi6RjIwSHdlHIrqLXL5hgOttOSzeKibb8RxwTdNRmh6AWSy5KdXcFGpOimUbuqoOc92PqRqmVveRECZG0YigMYaGesJrOg06ujxWuoPTQzFDxzLBbfO2+U6yeJFG9xmQIiy54FTy8Ob/A7/6a9AZ4brfhqt+E4yWkw77VTrPf+obZ2euyEq7hf+7t4WtPudpi/I9kTR/+vODvDuQpCdg5/fuXMFtq8LzuohXaxp9O6LsfS3C1GAWo1nPiiubWLO1DW/T6YdzSuYWrVKhNjo6Y3yf2A2uxuPHD1SUU+eBd3dd8nngsnCeHaRmL36qtRrPbXuL18ZH2GPycMTRCUC4HGddcYwtTjf3bbqGkMNHdDg7bXxnmRrMkMvmyTlHiLqzRHQmRqo+4pXGYGidotJmn6DTHKdTn2dDWxN33v0FbDbvXN6uRCKZZ0i9Pj2KovwUeFoI8d3TfP0J4AmAjo6OTcPDwxdzeZLZRq3Bzu/CG/8VshHovKbR4d11zce6nCYE76YLfH8yyU9jaQqqRrvFxENNXh4K++iwmNg+nOLneyd4Yd8kk9kyJr2O65YGuGtNM7esCuO2zp2ZquZylPftI79zD1P7IkTHiqSVIFlXJyVbePoogcehEep207KmhXCPB1+L/aJGgZTzeeKRYWKjh0hO7SafO0JFjWBwFDB7K5gdlZnnFUIolErHB2GW614KwkmuYqVWFViqBmy1ZvTVMOW6mymhnzHBpxAnJYL7zQa6PbaG+d3qpDfooCfooNMv88AlFwZpdJ8BWTRLLhrJQXjxj+DQP4GnA277Y1h5z0lPxoUQPBdL86f9EwyXq1zndfDve1tY4zz19mshBL88GOXPnj9If6zAmlY3T1zfw52rmzDMc0GZGsqy99UIR7dPodUF7Su9rLmxjc41AZkLtoBQc7mTc8CPZYMPDp46D7yz66RucFNXF/pAYF4/oJkNZOE8O0jNvvTYPzLAT/Z8wLZqnT3OLvIGO3qhsio3yBq1yNb2Hu7ccCV6vZ5svMzkQIapgQyTg1nikTxFJUHKG2HCWmdUszNaDlFWGw+a7cYCXfYxOg0pes0at1xzA2s33LLofx9JJJLTcynqtaIoLwNNp/jSHwohfjJ9zB8Cm4H7xTmYAVKvFxH1Cmz/VqNxKz8JPTfC1j+C9ss/9iWLqsbzsTTfn0zxRiqHALa47Tzc5OPTQTdOvZ6doyl+vneS5/dOMJ4pY9QrXLMkwF2rm7ntsjAe24UZFnmuCCGoT01R2r2H7K79TByMEksIMpZWsq5O6sbGjC69ohII6GlaEaR5VZhwlxuH9+LugBVCUMykiY8OE4/0kZjaQz53mEo9gtFVwuKtNgZh6o7/r10u2ygWPZSKLip1PyXcpFQLxZqKKNfw1jyYK82IaoCS6mZCKDNRKKkTLHCDAm0OCz0BO0taXPQ2OWZMcJ99bv8bShY20ug+A1KEJRedgdfhhd+H6H7ovBbu/HNoWnPSIVVN41tjCf7foUlSdZUHw15+r6eZdsupxaCuavxwe4SvvzHAYLxAm9fKr1/bzcOb27Ff5Iyz86WYrXLgrXH2vTFGIV3BFbCw+vo2Vl7TjMUut0AtVIQQqInESREoxzrBP5IHbrcf7/7u7MTU3YWpowNDUzOGgH9RdIJfioXzhUBq9qVNsVzmn3a8wxsTo+w1ujns7ALAW82wLj/CBquFz6zdwoq2Rhd4raoSG84yOTDd+T2YJZcpUrCPEnOnGDPoGal5mSr7EehQ0GiyRem0ROnU51gb8vDpe76Iyx0+w6okEsliQur1R1EU5YvA14CbhRDFczlH6vUipFaCD/4e3vpvUIw3sru3/gG0bPhEl52oVPnhZIrvTyY5Wqxg1incEXDzcJOPG7xO9ArsGk3z/L5Jfr53gkiqhEGncFWvn9sua2Lr8iBt3vkxk0OoKpX+fkp79hLfeZTJgTTJgpmMo5O8ow2ha9TlVmOdUKuF5suaaV4RINjhwmi++PWOEIJcIkZidITY6ACJqb3kc4epqmOY3BXMvgoWVxm94fggzGrVMjMEs1b3U1Z8ZBQrqXqNSrmEqQL+ShPmSiv1qp+samcUwfB0J3jthNf3mPR0e2wsaXbS09zoAu8NOejwyS5wydmRRvcZkCIsmRPUOuz4JrzyJ1BOw8Yvwk1/BPbASYdlanX+50iUv4vEAPj11iD/qjOEx3hq81rTBC8fnOJv3xhg23AKl8XA567s5EtXdxFyWU55znxBVTUGd8XZ+1qE8aNpDEYdy7aEWbO1jUCbHH64mGjkgU9+JAu8OjREbWzspDxw9HoMgQCGpjDGUBhDOIyxqfHZEDr+Z51lfv98y8J5dpCaLTmRI2Mj/GT3e+woldntaCdp8gCwLD/EmmqGa5va+Myma7BN/35oFHTlk+JO4qN5ylqalG+UCVuFCHZGygEK9UaclkVfptMxRqcxSbexxo0bN3LltfeiKAv/AZxEIvkoUq9PRlGUO4D/F7hBCBE71/OkXi9iKnl4/2/hnb+CUgpW3A03/j40rf5ElxVCsDtX4vuTSX4cTZGsqYRMBu4Pe3mkycdKhxUhBPvGsvx83wQ/3zvBcKLx3GVpyMHWFSFuXB5kc6cPk2H+mKRaqUT54EHyu/YwtXuUaKRISvOQcXVRtgYBUNBw2zWC7Q7Cl7UQ6vESaHPMifkNoGkqmehUowN8ZJDk5AHy+SNUtTFM3ioWbxWLu4zBdHwQZr1uPMEAD1DTBcjpnExRI1/OQKFKUyWAo9yGUglTqnuYEDpG0BhBI/mhLvB2p4WeoIMlLU56Q056Q3Z6Ag68sgtcMo00us+AFGHJnFJKwet/2XizYLTDjb8Hl38FDCf/Ao+Uq/zF4AQ/nEzhMej5111hvtQawKw7vYhvH07xf94c4IX9kxh1Oj6zvoWvXN/DsvD8N43jkTx7X4tw5L1J6jWN5iVu1m5tp3t9AL18uruomckDHxmlHp2iNjlJfSpKfWqK2tQU9akptHz+I+fp3G6M4WkDPBzCGG6a/hzG0NSEIRRC7/HMWSSBLJxnB6nZktNRr9f5xe73eWW4nz06Kwdc3aiKHnu9wNrcEOuNOj61Yj2bl6486bxG13euEXky2Oj8zmVKlCzjxLwxxow6RutuxstBNNEoOIPWOF3WSTr0WVZ5bNxz92cJhnvm4rYlEsksI/X6ZBRF6QPMQGL6n94VQnztbOdJvb4EKGfh3f/dmENVycJl9zUM7+DyT3zpqqbxciLLDyZTvJTIUBew2mHl4SYv94W9BE1GhBAMxAu8eijKa4djvDeYoKYKHGYD1y4JsHVFkBuXhwjPw2avejJJee9eUtv3M3lwilhUJWMKk3N2UDMdq9UFLmudYKuN8Kpmwkv9BNov3rDLU6HWa6QmxqcjUIZJjh8inz9MVUxg8dUwe2tYPWWMluODMFXVQLHgplD0oIoQqi5EXu9kUqmRrMap5gqESh6C5VaM5Rbq9QBJ1TxjgJ+qC7zHZ6e3ydnoBA866A3aaZdd4Jcc0ug+A1KEJfOC2BH4xe9D38vgXwK3/xksu+0jh+3LFfnP/RO8nsrRYTHxBz3N3BPyoDuDeTcUL/CNtwf5/rZRyjWNrcuDfOX6Hq7q8c/7HNJyocbBdybY93qEbLyM3WNm9fUtrLq2FZtLPs29VFHzBerRqePm9+RUwxSfilKfnKQWnUKNJ07uDAcUs7nRER4KNQzxpnDDCD+hM9wQDKIYZv8NpCycZwep2ZJzZSwR40fb3+GDbJo99hYmLI2uqc7iOGvKMa70Bbh/07X4XO6TzhNCkEuWZ0zvqcEssZEcZbVA2jNM1F4gorMyXA6SrTWKUaOuSodjnE5Tgm5DmatWruCmWx9Fp5M6JZEsNKRezw5Sry8hSil456/hvb+BWhHWPAQ3/B74e2fl8olqnR9FU/xgMsnuXAm9Ajf5XDzc5ONWvwvLtLmZr9R5py/Oq4djvHY4ykSmDMCqZhdbVwTZujzE+nbPvJxjJYSgFolQ2n+Q1J6jRPtiJGIqGX2AnLOdqtlz7EiclhqBZivhlU2El4cItjsw2+Y27rNWrZCMjJKIjBAfHSYxdphc/giqbgpLoI7ZV8PqKWE6hQFeLHkQNKPqQxQMbqL6OpP1SUqZDK68jdZyGEelFVFpolh3MYpg9HRd4C5LI/6kxUVvqGGA9wYdc57nLrkwSKP7DEgRlswrjrzYMLwTfbDkVrj9TyG47COHvZbM8p/6xjlQKLPeaePf97ZwtddxxksnC1W+++4w33pniEShyupWF09c38tdC2BwpaYJRvYl2PNahNEDSXQGhSWbQqy9sZ1wt2uulyeZh4hajXosNt0FHqU+NdkwwqemqE0d7xIX1erJJypKIyplujv8pC7xpqaGKR4OobPbz2s9snCeHaRmSz4Omqry5sG9/KJvH3uEgb3Obip6M2atymXZQdYpNW7rXckNq9ajO8VMgHpNJT6aP971PZghkyhSMkdJeCeZMMGo6iJSClEXjQdlXnOKLtskHfo0yxwG7r7jXjq71l/kO5dIJOeL1OvZQer1JUghAW//d3j/70CtwrpH4brfmTXDG+BQocQPJlM8M5lislrDZdBxd9DD/WEvV3scM81fQggOT+V49VCMVw9H2T6cQtUEbquR65cF2bo8yA3LgvgdF3co5PkghKAejVE+sJ/U7sNEj8SIR2tkhIecs4OKxTdzrMNUxR82E14eomlVM8EOJxbH3M+6qhQLxEdHiI8MER8dJjlxhFz+KIo5hTlQx+KrYvWUMVkqM+eoqoFi0U255AVdC8LQRNHoJaZXiWhjZNMJTGmFtmqYQLkZQ6WFejXAlNAzPG2Aj32oC9xrNtDjt9Hb5KI3fGwYpp0On23e+yCS0yON7jMgRVgy76hX4YO/g9f+AmoF2PIE3PC7YPWedJgqBD+cTPEXgxOMV2rc6nfxe91NrHaeeRhHuabyo51j/N2bAwzECrR6rPzatd08cnk7jnk+uBIgNVlg7+tjHPrVBLWySqjTydqtbSzZFEZvlEIlOXeEEKjpNPXpSJTa5NRHjPDa1BRaNvuRc3VO5wkRKaeOS9F7vTO7JmThPDtIzZbMBul8lmc/eIt3kzH2WAIM2VoBCJfjrCuMscXl5v7N19LiC5z2GoV0Zabre3IwQ2w4R6laIOceYcqZY0xnZqTqJ1FpaLdeqdPmmKDTFKdbX2Rzdwd33P1FjEbrRblniURybki9nh2kXl/C5Kbg7f8B2/4e1NoFMbxVIXgzleOZqRQ/j2UoqBrNZiP3hhqm92qH9aSdy5lSjbeOxnn1cCPmJJ6voCiwts3D1uWNbu81rW50uvm92xmgnkpROXiQ9O5DRA9NEpuokK47yTk7KFuPv2+xGav4gwZCS0M0rW4h1OmaFzuihRAUM2niI8PERxsGeGL8CIViHwZnAbNfPcEAL8+cd8wAr1R86HRtKKYWSkYvcaNgVIsQy06ixSu0lIO0VMLYym0otTCZum0mBmUEjdRJXeAKHW5Lo/t7ehjmsSgU2QU+/5FG9xmQIiyZtxTi8Mofw/ZvNkzum/6oMbRSf7IZXVI1/k8kxl8NT5FTNa71OPhqe5Cb/a4zRppomuCXh6L83RsDvD+UxGUx8Pj04Mr5mGX2YaqlOoffm2TvaxFSk0WsTiOrrm1h9fWtOLzzf/2ShYNWKh03wqOnjkupx+OgaSedpxiNGEIhDE1NdD/5PVk4zwJSsyUXgu19B/nZgV3sqmvscXaRN9gxaHUuyw2wTlS4rXcFN1228ZTd3sdQVY3kWGHG+J4ayJKJlSgZYiQ840xY64xqTkZLIapao4PMa07RYx+nR59hY5OXz9z369hs3tO+hkQiufBIo3t2kHotITc5bXh/44IZ3gBFVePFeIZnp1K8ksxSF7DUZuaBcCPPu9N6cte2pgn2j2d59XCUVw9H2TWaRgjw203cMG16X780iHuO40DOBzWfp3LoENk9B5naP0FsvESqYiPnaKNkC88cZ9VX8QX0hJf4aVrTRrDTjd1jmhdxpkLTyMSiM93fDQP8KOXqIEZPFct0B7jNU8JoPtEA1zcGYFaD6A3t6MytVMx+kgaFCGOMZkYox3L4iy7aK00EKs0Yqq1Uqh7GhHJSFnj9hPX4zAZ6AnZ6m50sCTlZEmp0grd6regXwAORSwFpdJ8BKcKSec/EHnjh92H4LQhdBnf8GfTc8JHD0rU63x1P8PdjcSYqNZbazDzRHuTBsA/rWbbk7BxJ8XdvDvDCvkn0OoXPrG/lK9f1sLxp/g+uFEIQOZhiz2sRhvbGURSFnvVB1m5tpXnJ3A0flFxaiHqdeiLRyAg/RVxK13e+LQvnWUBqtuRCUyyX+fG2t3hzaoy9Ji99jg6g0e29vjDGFreHBzZfS5PXf9ZrlXLVmaiTqcEsU4NZSuUSeccoU540IzozA+XQTNa3w5in1xGhR59ijc/K/fd9GY+35YLer0QiORlpdM8OUq8lM5zK8L7+34Jv9oc4J2t1fhpN8+xUivcyBQAud9m5v8nLp4MeAqaP7l5OFqq8caQRcfLGkRipYg2dAps6vdy4PMSNy4OsbHItiG7vE9HKZSpHjpDbc5DJfWPEIwVSRRNZextFWxiUhj9g1lXx+xRCPV6a1rYT6vHi8JrnTQ2t1mskx8caxvfoMLGRIVKTfVTqkUb8yUwHeAmTuXT8vGkDXK2HMRg7MFg6qFr8pI16ImKM4ewQ6XgCe8ZEWzVMa7kJW7UVUQ0RV00zBvgwGukTusDNeoVur40lTS6WNDlmDPDugB2L8fQNEZLZRxrdZ0CKsGRBIAQcfA5e/CNIj8CKu+G2PwZf90cOrWmC56Ipvj4aY0++hM+o54stAb7cGiBkPvOT6eFEgW+8Ncj3t0Uo1VRuWBbkq9f3cFXv/B9cCZCNl9j7+hgH3x6nUqzjb3Ww5sZWll3RhNEkhUcyd8jCeXaQmi252OwZPMpz+7ezo6qy29lFwWDHqNVYnRtkHRVu71112mzvD6NpgtREo+t7oj/DRF+aVKxA0TJBzDfFsMHIYDUwE3di0ZfpcY7SY0iy0qHj/rsfpbltxYW+ZYnkkkbq9ewg9VryET5ieH8Wrv+dC2J4A4yWq/x4KsUzUykOFcoYFLjB6+KBJi+3B1zYT6HbqibYHUnz2qEorx6OsXcsA4DHZuTyLh9XdPvY0u1jVbNrQWY7i1qNSn8/uT0Hmdo7SmwkRypvJGttpmhvQiiN74lJqeLzCEJdHprWtRPq9eMKWOaVH1Atl0iMNoZfzmSAT/aj6aOYgxpmfx2bv4rFVfyIAV4qelC1MEZjJxZbN3VriIxJz5g2wWB2kInEGLqUSms5RHu1iUClBUOllUzNzgiCYTSGUZlAzFjgOgXaXBZ6w06WNjlZEnTQG7KzJOhcULsDFhLS6D4DUoQlC4paCX711/DmfwOtBlf9Jlz322D+aOe1EIJfpQt8PRLlxXgWo6Jwf9jLV9uDrHScORM0dWxw5a+GiOerXNbi4onre7hrTTPGBSDqtarK0fen2PNqhMRYHrPNwMprWlhzQyuugMxDlVx8ZOE8O0jNlswl+VKJn2x7izej4+wx+xiwtwPQXI6xrjDOFR4vD2y+lpDHd5YrnXDNVJnxvjTjRzOMH02THM9TNE8R904wYlIYqvmYLAcBMOqqdDnG6DHFWW5WuefWu1my4ooLcq8SyaWK1OvZQeq15LRcZMMb4EC+xDNTKX48lWKsUsOm13FnwM39YS/Xe50YT9OtHc2VefNInPcHk7w3mGAoUQTAbtKzadr4vqLbx5o2N2bDwmyqEqpKdXiE/N4DTO0eIjacJZnRkTWHKdhbELpGF7yRGj63SrDDRdPadsLLgriDVpR51ulezGam87+PZ4CnJvvR2bKYAxpmfw1boI7FWcBkLs6cp6p6SiUPQmvCZOrC7liCZm8mZzYyWhtjIDPASGqYerxESzlIR6WJ5nIL5mo7xarzBANcYxSN6glr8luNLAk5WNLUyAFfEmp8NLvn18ODhcYlZXQrivIvgd8E6sDPhBC/e6bjpQhLFiTZcXj5/wd7ngJHGG75j7D2UdCd2oTuL5b5u0icpycSlDTBDV4nX2sPcqPPecZfruWayo93jvG3Jwyu/PI1XTy4qW1BDGgQQjDRl2HPqxEGdsUQQtC1JsDaG9toW+mVwiK5aMjCeXaQmi2ZT+waOMJz+7azs66xx9lNwWDDpFVZnR1knVLjjiWruW7lmnPq9j5GKV9loq9heo8fTRMfzVHUJUj4I0TMGoOqh7FiCIEOvaLS7hij1xxjqbHC7ddcx/pNt0ttk0g+AVKvZwep15KzMgeGtyYE72UKPDuV4qfRNOm6it9o4J6QhwfCXja5bGfU0KlsmfcHkzMfh6dyAJgNOjZ0eNjS7eeKbh8bOjzYThGTslAQQlAbG6ewdz/R3YNEB9MkUpA1BCk4WtF0jQ5lAzW8jjqBNgdNa9toWtGEp8k272JehBDk4jHi09Enieku8HR8GKOrhCmoYQnUsflqWJz5jxjg5bIXRDMmczdO13IUeytZi57h0ij9mX4Gk4NU4wWaS37aq020lZtxVTsoVz2MChiejkEZQiN3QgyKzaCjJ+hgaZOT3qB9xgDv8NkxGeZ/c+Fcc8kY3YqibAX+EPiUEKKiKEpICBE90zlShCULmsg2eP73YGwbtGyEW/4DdN8ApxHoZK3Od8YS/P1YjGi1zjKbha+1B7k/7MVyhk5tTRO8cijK3745wPuDSUwGHXeubuKRy9u5sts/78TsVORTFfa/Ocb+N8co5Wp4wjbW3NjGsi1hLHa5nUhyYZGF8+wgNVsyX8mXSvzogzd4OzbJbrOfQXsbAC3lKOuKE1zp8fHA5msJuM9v2GS1VGdioGF8T/SlmRrKUq5nSPqHidiqDGluRoth6sKAgkarfZIeyxRLDEVuXLeW67Y+jKLIYkkiOVekXs8OUq8l58wcGN4AFU3j1USOZ6ZSvJTIUNYEnRYT94e93B/2stRuOes1UoUqHwwleW/a+N4/nkETYNAprGlzc8W08b2py4vLsvDrzVo0SnHffqK7Bon2J0kkNDKKj7yjDU3faILTU8djqxJothJc3kR4dSv+Nue8jBFV63XSkxMznd+NTvAhcqkxTL4a5qCGNaBi81cx23MnGeD1uoFKxYeitGKx9OByrUTvaiNtgqHCMP3pfgZSAxRjWcIlLx2VZtorzQRq7dTLPsaEjiE0RlAZUjSiJ3isBkWhw2elN9wYgtmIQXHQG7TjXAQ/R7PFpWR0fx/4WyHEy+d6jhRhyYJH02DvD+Dl/wC5CWheD9f8Fqy8B/SnfpJc0TR+Ek3z9dEo+/Nl/EYDX24N8MVWP0HTmX957h/P8PQHo/xo5xi5cp1Ov42HN7fz0KY2Qq6zvyGYa9SaRt+OKHtejRAdyqLTKbSu8NK7IUj3uiA21/zvVJcsPGThPDtIzZYsFLb3HeSnB3axqy7Y4+qmqLdi1qqszg6wTqnzqeVruWbl2vO+br2qMjWUnen4nhzIUKwWyHgGGXOUGMLBcLGJqtbQsmbbJCus46yxVHnkvsdobVs527cqkSwqpF7PDlKvJedNbhLe+u+w/R8uquENkKur/DyW4dmpFG+mcmjAWoeV+8Je7g17aDafW32YK9fYPpya6fjeHUlTUwU6BVY2u9gyHXVyeZcPv8N8YW/qIlFPpSgdOEhsZx9TR+Ik4nXSmoe8oxXVMB0ZKjQcxjI+n55gj4/w2g5CPT5sbtO83IVWq5RJjkWIjQwRHxkiEWlkgRfz0Ub8SUjFFlSxeitY7BmMpvLMufW6kWo1gE7Xhs26BI9nFXp3O0m9ymBukP50P4PpATKxJMGim45KMx2VZlqqbYhKiAlNNxOBMqxojAoN9YS1hZ3mmc7vY4Mwl4QcBJ3zZ4DoxeJSMrp3AT8B7gDKwL8VQnxwpnOkCEsWDbVyI8rknf8JiT7wdjUyvNc/DibbKU8RQvB2Os/fjMZ4OZHFrFN4KOzjK+1Blp/lKXa5pvL8vgmeen+U9waT6HUKN60I8ejl7dywLLggBnTERnL0bZ+if0eMTKyEokDzEg+9G4P0rA/h8C6ONyCSuUcWzrOD1GzJQiRbzPOjD97m7fgkeywBhmytAHQXImyqJLijcwl3bbjyvCJOjqGqGvGRfMP47mt0fRcKBbLuYcZdOfpwMFhooS4MmHQVlrmGWW6Mc21HC5++7ysYDPLhrkRyIlKvZwep15KPzRwa3gBTlRo/iTaGWO7OlVCAK9x27gl5uDvoIWQ+947aUlVl5+hx43vHSIpyTQNgacjBlunhlld0+2lyz/+GsXNFKxQoHT5Mcncf0cNTJCbLpItmctYmytbAzHEmKngdKv42O6FVLYRWtuBrsaOfpz5COZ8nHhluRJ8c+xgZplpPNwZgBlXsQQ2rp4TZnsForMycW6uZqdeD6PXt2G1L8flWY/J2MSWKDGQGGMgM0J/qJxWP4Ss4ZwzwjmobxkoTUdVwkgE+jEbxBF/WaTbQewoDvN1rXRC+zMdhURndiqK8DDSd4kt/CPwJ8ArwW8DlwNNAj/jQghVFeQJ4AqCjo2PT8PDwBV2zRHJR0VQ4/PPGFrDIB2Dzw5Yn4PKvgN1/2tOOFsr8bSTGDyaTlDXBTT4nX2sPcZ3XcdangwOxPN/fFuGH2yPE8xXCLjMPbWrn4c3tdPhPbbLPJ4QQJMYK9O+MMrAzRnK8AEC420XvhhC9G4NyiKXkEyEL59lBFs6SxcAHRw7wk4M72SaM7HX1oip6mssxNhXG2Rpu4YErrsdi+ngPWoUmSIwXmOhLM3YkTeRQklwxTTwwwKBV5Ug1RLzSGJYZtMZZYYtwmanIA7ffw1I52FIikXo9S0i9lnxijhne274BWh3Wfxau+7fg675oS+gvlvnRVJqfxtIcLpRRgKs8Dj4d8nB30H3WndAfplrX2DuWmRluuW0oRb5SB6DNa2V9u2fm47IWN9Z5GPfxcRGaRm10lOzew0T3jhAfyZJMQ1bxUrA3z0Sf6ISKy1TGFzQSXBKgaW0HgW7vvI0aFUJQzKRPGIB5zAgfQtOXMIc0zAEVW0jF6ipisWcwGI6PqqzVLKhqGKOhE7tjGQH/Gsy+biZq2Yb5ne6nP91PNDGJN2uno9pER6WZrmorjmoziZplOv9bZVjRGFEEcU2bub5Rr9Dtt7MkfDwC5ZgRbjEu7J+vRWV0nwlFUV4A/lwI8dr03/uBK4UQsdOdI0VYsmgRAkbebRjeR54HgxU2fh6u+heNbu/TEK/W+fZ4nG9E4sRrdVbaLXy1Pch9YS/m0wy7PEZN1XjlUJSnPxjltcNRNAHXLPHzyOUd3LYqvGB+maYmC/TvjDGwM0ZspDFkJNDuoHdDiJ4NQXzN9jleoWShIQvn2UFqtmSxMTg5zlPb3+S9ispOVy8VvRlPLcOm3DDXuj189sob8ThcH/v6miaIDmUZ2Z9g5ECSicE0RfM4Y4Epjio2+ostVDUTBqVOr2uYFcYYV4Td3P/QV7FYnLN4pxLJwkDq9ewg9Voya2Qnjmd4z5HhDXCoUOKn0TTPRdMcLVbQcdz0/tTHML0BVE1wcCLLe4NJtg8n2T2aYSxdAkCvU1gedrKu3cOGdg/r2j0sCTnQL4DZWOeDms1SOniI2K4Bon0xklNV0lUreWszVbN75jirUsLrEgQ6nIRXtxFa0Yw7aEWZp98PoWlkpwdgHje/h0mOjYC1jjkkMAfq2IMqVmcesy2DwVCbOb9Ws6FpTZiMXThdKwj412LzLyFSjs+Y3wOZASbiYziyZjoqTXRUm+muthKotpCp2mYGYA6jMaIXjKkqxyxwhcbDlWMd4EtDzhkT3G2dnw8VPsylZHR/DWgRQvx7RVGWAb8EOj7c0X0iUoQllwTRQ41Ikz1Pg1Dhsvvg6n8FLetPe0pZ1fhRNMXXR2McKpQJmQz8WmuAL7QG8BnPPkV6IlPih9siPL1tlEiqhMdm5L4NrTx6eQfLmxZO8ZyNl6ZN7yiTA1kAvE02ejc2TO9A29k73iUSWTjPDlKzJYuZeCbFU++9ztu5HNudXWSNTmxqiQ2ZAa60mnj08htoD4Y+0WuU8zVGDyYZ2Z9g+ECSXCZDwj/AkL3C0bqfiVLj+h5zmpWOEVYa8tx7w1bWbrxlNm5RIpn3SL2eHaReS2adUxreFy/S5EQOFUo8F03z0xNM76unTe+7PqbpfYxorsye0Qy7I2l2jTY+cuVG17fNpGdNq5v1HR7WtzXM72a3ZdHVoqJepzo8TGr3IaL7xolHcqRyOnKGAEVbGKE0muf0oobHWsEfMhNcFiK8rpNAhxujef4212mqSmpy/OT4k9ERUhNj6N0Cc2h6AGaghsWRx2JLo9cfT+mu1ZwI0YzZ3I3btYJQaANWbw8jhSn60n0zXeDjiTEsGd10/EkTXdVW2ittFKp2hhEMozGIyogBRlSV6gmWadBhZmn4eAzKknmaA34pGd0m4BvAeqBKI6P7lTOdI0VYckmRHYd3/zds+weo5qDnxobh3XsTnOaXlhCC11M5vj4a49VkDotO4e6gh8+1+LnCbT/rLztNE7zTn+CpD0Z4cf8UVVVjQ4eHRy9v5+61LdjNZzfN5wv5VIWBXQ3Te/xoGiHAFbTSuz5Iz8Yg4S7XvPrlL5k/yMJ5dpCaLblUKJbLfP+9V3k9FmWbo52Y2YdBq7M2288WvcpD66/kso5PVtwLTRAfyze6vfcnmezPkDOMM+Efp99g4WixmZJqRaeodDsjrDBNstFr4dFHv4r9DFFoEslCRur17CD1WnLBOMnwrsGqz8A1//qMDVwXCiEEhwrlhukdS9M3bXpf43Xw6aCHu4IeAqZPVutqmmAoUWgY3yNpdkUyHBzPUlUbvblBp3km7mRdm4c1be4F05F7vtRTKQr7DxLdNUisP0EyXidTd5C3t1A3TMelCoFDX8TrVQh2ehqDL5eHsXvm5+DLY9SrVZLjkY90gGdiUQw+pWGAB1VsvioWRw6rLY1OdzyipFZzo9CCxdqLx72KcHgDZlcnw/mJkyJQRpPDGFMKndVGBnh3tZWOahv1inOm+3sIjREDDGl1Ctpx79dlMZw0CHNpyMmSkINWjxXdHHTWXzJG98dBirDkkqScge3fhF/9f5CfhPAauOa3Gp3e+tOL8cF8iX8Yi/PsVIq8qrHEZuaxZj8PN/nOScSThSo/2jnGU++PcDSax27S8+l1LTxyeTvr2z3zWnw+TDFbZXB3I94kciiFpgkcXjM964P0bgzS1OuZk1/4kvmJLJxnB6nZkkuRer3OT7e/zYuRIbZbgozYWgBYlR1gs1bgnuVruHbV+k/8OtVSncjhVKPbe3+CTCJHytvPsLPEUdXLaLExIsdhzLPSOcRKY4Y7Nm3kquseWFD6LZGcCanXs4PUa8kFJzc53cD1DahkGw1c1/zrxuc50KQTTe/nomn6SxX0Clwz3el9Z+CTm97HqNRVDk7k2D2aZvdoml2RNAOxwszXe4N21p1gfq9sdmEyLM6BhKJapTwwQHLXEaIHJkmMF0gVjGRNQcrW4MxxJip4bFX8zTZCK8KE13bia3Win+ffl0qxSCIycpL5HR8dppDLYPTrMAVV7CEVq7eC2Z7Fas3MGOBCQL3uQ6drxWZdgse7iuamTRjt7QznxmY6wPvSfUQSI+jTGh2VFjorzXRVW+iutkPFOdMBPoTKsBGGhUayfrzL3GLU0RNwNLrAg8eN8E6//YL+3Emj+wxIEZZc0tQrsPcH8PZfQfwwuDsaGd4bPw+m0+dQF1SV56JpvjeeYFu2iFFRuCPg5vEWH9d7nejO8uZCCMGOkTRPfzDCT3dPUKqpLA87eeTydu7b0IrXbprtO72glAs1hvbG6d8RY/RAErWuYXWZ6FkXoHdDiJblnnk7PVpycZCF8+wgNVtyqaOpKq8f2MVPj+5nu8HFYWcXAN2FCJsqCe7oXMJdG65Ep/9k23aFEKSniozsb8ScjB1JUyDKhH+EAZORI6Um8nUHAB2OCCssE6xzwGcf+Q183rZPepsSyZwh9Xp2kHotuWiUM43dyu/+70YDV/O6huG96jOgm5sICyEEB08wvQemTe9rPc5p09uNf5ZM72NkijX2jE0b36MZdo2miecrAJj0Ola1uBrGd7ub9e1euvy2RfuQWghBPRYjt+8QU7uHiQ+mSSZVMsJNwXZ88KUiVFzGEl6fnlCPl/C6LkJLg1gc878jvpjNkBgdJjZysgFerlUwBRTMQRVbUMXqKWGxZ7BYsuh0DR9XCAVV9aPXd2C3LcXnX01T0yZ05jDDuUjDAE83DPDR1Aj6pNqIQJnuAO+utWEuu2e6v4fQGDHCMBoTtfrMGg06hU6/7SNd4D1BO7ZZ+PmXRvcZkCIskQCaBkd/0dgGNvIrsHrh8q/AlifAETzjqYcKJZ4cT/KDySSpukqbxchjzX4ebfLRYjm7YZ0r1/inPRM89cEou0fTmPQ6bl/dxKOXt3NVj3/BdUVXy3WG9yUY2BljaF+CekXFbDfQvbZherev9KE3StP7UmOxFc6KoviAp4EuYAh4WAiROsVx3wDuBqJCiNUn/Pt/BL4CHBsW/QdCiJ+f7XWlZkskJ7Nr4Ag/3PsB24SRva5eVEVPcznGpsI4N4abefCKG7CYzJ/4dWpVlfGj6ZmYk8RklqxrgBF3lqPCw3CxCU3osRqKLHcOs8KQ4uZVy7jljs+jKFLzJAuHxabXc4XUa8lFp16B3U/BO38FiT7wdsHV/xLWPw5G65wtSwjBgRnTO8VgqYpegeuOmd5B9znNv/o4rzueKc90fe8cTbM3kqFUa3Tiuq1G1ra5ZwZdrmv3EHB88vcL8xmtUqF0+CjxnUeJHomSmCiRLpnJWcJUzZ6Z4ywU8TpVAq0OQqtaCK/twBOyzdvBl8cQQpBLxGeM72Od4PHREeo6DeO0AW4PqlhdBSy2NBZrHkU5ZoDrUNUQRmMnTscy/P61hEIbUEwBhrIj9KX76M80IlBGksPokiqd0wZ4V7WFnlo79rKbUQTDqA0D3KQwhEakVkM9wUZu9Zw4CPO4Ee6xnXvDozS6z4AUYYnkQ4y813iDcOhnYDDD+sfgqt8Ef+8ZT6toGs/HMnx3PMFb6Tw64Ga/i8eb/dzid2E4B2E4OJHl6Q9G+dHOMTKlGu0+K49sbufBTe00uS2zdIMXj3pVZeRAkoGdMQb3xKmW6hgterrWBOjdEKTjMv+8HpYhmT0WW+GsKMpfAkkhxJ8rivLvAK8Q4vdOcdz1QB749imM7rwQ4r+ez+tKzZZITs/g5DhPbX+T9yt1driWUNGbCVSSXF0Y5d6uZdyxfssn7vQ+RjZeYuRAo9s7cihFrhYnFhhiwKzjSCVMuuoGoMU+wQrrOGssNR6993Ga21bMyutLJBeKxabXc4XUa8mcoamNOvbt/w5j28EehCu+Cpf/RqOZaw4RQrA/3xhk+VwszdCHTO/bAq5PNMjybNRVjb5Ynl0j6elhlxkOT2Y5FsPc6rGeNOhydatrVjpv5zNCCOoTE6R2HSK6b5T4cJZUBjJ4KdpPHnzpNpXxBQ0ElwQIr+si2OvHZJn/3x9NU8lEp4iPDJEYHZnp/k5OjKOa9ZiDAnNAxR6oYXHmsdrTWCyFE843IEQTZlMXTtcKgsF1BPzr0PQehnJDM/nfJxrg7ZUwnZUWOqvN9NY6cJfdjM8MwdQYMcOIIhiq1qickAMecJg+NATTydKwg9ApBmFKo/sMSBGWSE5D7Aj86n82noyrNVh1TyPHu3XTWU8dKlV4cjzBU5NJotU6YZOBR5p8PNbip8t69ifF5ZrKL/ZP8vQHo7zTn0CnwNblIR65vJ2tK0IYF2AMiFrXiBxOMbAjysDuOOV8DYNRR8dqP70bgnStCWCyzn+hlHw8FlvhrCjKYeBGIcSEoijNwGtCiOWnObYL+CdpdEskF494JsWT773Ga/kiH7iXUdMZ6S5EuKaW5HPrrmR9z7JZey21rjHZn2F4uts7GslQcAwz6k1xFAeDhRbqwoBJV2GZa5gVxjjXdbXwqc98BYNhYUWVSRY/i02v5wqp15I5RwgYeqthePe9DCYHbPoSXPnPwd0616tDCMG+Y6Z3NM1wuYoCbHHbuTPg5s6gm85zqJs/KcVqnX1j2Zms792jaSKpEgB6ncKysJP17W7WTZvfy8JO9PO8s3k20IpFigcPM7VjgGhfjGS0SqZiIWdtpm48HvFqUwr4XIJAh5PQ6jZCl7Xi8lsXRCyMWq+Rmhg/If97hERkmGR0CqxGTCGBJVDH7q9iceaw2jKYzcWZ8zXNBLRgMXfj9qwiFFyP13sZqs7JUPYEAzzTz3ByCCVRp6PSdFIHuL/sIQoMHesANyuM6ASDtRq5+vFhm06zgd6TIlAc3LKqSRrdp0OKsERyFnKT8N7X4YO/h0oGOq+FLV+BFZ8C/ZmfONc1wcuJLN+dSPBKIosGXOd18HiznzuDbsy6sxvWw4kC3982yg+2RYjmKgSdZh7c1MYjm9vpCpw+R3w+o6ka430ZBnZE6d8Vo5ipojMotK/w0bMhSM+6hZENJjl3FlvhrChKWgjhOeHvKSHEKdtkzmB0fwnIAtuA3zlV9MmHkZotkZw/w1MTfPP913hTM7LPtQRFaKzN9nGDQfClq2+mxReY1dcrpCuMHGiY3qMHk+SKKeKBAQZtGkcqIeIVHwAha4wVtgiXmUs8cOe9LFl6+ayuQyL5OCw2vZ4rpF5L5hWTexszqfY9A4oO1j7caOAKnrJH46JzLN7k57E0L8Qz7M+XAVhpt3Bn0M2dATerHRfPPI3lKuyJHI882T2aJltuZC/bTHpWt7pnBl2u7/DQ4rYsCGP3kyI0jeroKMmdh4nuHyMeyZPK6sjpfRStwcbPFmAQVTyWMr6whdCyIOF1XQQ6PRhMC2Mnd61aIRkZPSH6ZJh4ZIRMMoniMGIOCayBGlZfFYsjg82WwWQqz5yvaRZ0ShsWay9e72WEgutxuVag6ewMZgZn4k/60w0DXCSq0xngTXRVW+mttRMoe0jBTA74iEVhWCcYqteJVxs/i8N/cbc0uk+HFGGJ5Byp5GD7t+C9v4HMKNhDsOFzsOmLjQy0szBervLUZJInJxJEyjW8Bj0PNfl4vMXPcvvZY0nqqsZrh2M89cEorx6OomqCq3r8PLqlndsva8JiXBjC8WGEJpgczNK/M8rAjhi5ZBlFp9C6zEPvhiDd64PY3Ys7L+1SYCEWzoqivAw0neJLfwh86xMa3WEgDgjgPwPNQohfO835TwBPAHR0dGwaHh7+WPcjkUjgvcMHeGr/Nt42BxmxNWNWK1yROcrNHjefv+ZWbJbZjQnTVI3ocG6m23tyKE3BGmHMF6NPsdFfbKGqmTAodZa4hllhjLKlycsDD30Ns3lhPsyWLGwWol7PR2SNLZmXpIbhV/8Ldnwb6iVYfldjcGXHFXO9spMYLlV4IZ7h+ViG9zMFNKDNYuTOgJs7Am6ucDvOKRZ0thBCMJQoTg+6bHwcGM9SVRsdtwGHeabre32Hh7WtHty2S6dpS81mye87xNSuQWIDCZKxOpmanbytGdUw/b5KaDj0RXwehUCnm/DaDkIrmrF7TAvmIUGlWJwxv080wfOFIorL0BiAGahj9Zax2DPYbGmMxurM+ZpmR6/vwGZbgt+3hkBgLQ7HMlTFfHIHeLqf4VTDAG8vh+moNNNZbaG31k6o4iUvFEZQufMvbpJG9+mQIiyRnCea2tj+te0fGgMshYAlN8OmL8OyO0B/5vgNTQjeSOX43niSF+IZakJwucvOYy0+7gl5sJ9DfuhUtswPt0d4+oNRRpJF3FYj921o5ZHL21nZ7JqtO73oCCGIjeQY2Bmjf2eM9FQRFGjuddO7IUTPhiBO38LLKpcsvsL5k0aXnM/XT0RqtkQyO2iqyk+2vcU/jQ7xjrOLlMmNr5rmqtwwn+7o4Z5NV89anveJlPJVRg8mGdnfyPfO5rIkfX0M2WscrfuZKIUA8JpTrLCPsMqU5zM33MzaDTfP+lokklOx2PR6rpB6LZnXFBLw/t/C+1+HUgo6rmoY3ktvg3PYcXwxiVfrvJjI8EIsw+upHBVN4DPqudXv5q6gm+u9TqxzEOtZrWscmszOGN+7R9P0x47nOvcE7I0hl21u1rV7WNnsWrCNaR8HUa9TGRoivuMI0YMTJMYKpPIGcsYAZevxnXQmUcZjq+FvthBa3kR4XSe+Nhd6w/z6OTwTpVz2pOzvRGSE2OgIxVoVvUePOTBtgLuLMwa4wVCfOV/T3BiNHTjsywgE1uH1XobdvgRVMTKUaRjgfek+BjIDDCeHUBMVOsph/tdvfUsa3adDirBE8gnIRBpPxHd8G3IT4GyBjZ+HjV8Ad9tZT49Va/xwMsX3JhL0FSs49DruD3t5vMXPOqftrOdrmuDdgQRPfTDKC/smqaoa69rcPHJ5B59e14zTsnCfJAshSE4UGqb3jhiJsTwAoU4nvRsbprcndPbvkWR+sNgKZ0VR/guQOGEYpU8I8bunObaLj3Z0NwshJqb//G+AK4QQj57tdaVmSySzT7Fc5ttvvcQvMxne9yyjojPRURznmkqcx1Zv5vJlqy7I6wpNEI/kZ2JOxvvTFAzjTAQm6NebOVpsoaRa0Skq3c5RVpqmuLHdzwMP/yaKcukUy5KLy2LT67lC6rVkQVAtwI7vwK/+urFjObiyEWmy5sGzRnTOBYW6yqvJHC/EM7yUyJKpq1h1Orb6nNwZdHOL34XXOHcznzKlGnsjGXaNptgdybBrNE0sVwHAqFdY2exiXZuHtW2N6JPeoAPdJZD3fSL1ZJLcnoNM7h4mPpgimVTJqE4KtmY0fWNuiSJUnIYiPp+eYLeX8LpOQstCWJ0LZ66JEIJCOvUhA3yYaCRCFYHeq8MSqGML1LG4ClhtaWy2DHq9esI1fJhMXTidywkE1uF2r8RuW4KKjsHMICv8K6TRfTqkCEsks4BahyMvwPZ/gL5fgqLA0tth85dhyS2gO3NBKoTgvUyB700k+Gk0TVkTrHFYeazFz/0hD+5zEOxUocqPd43x1PujHJ7KYTXquXttM49uaWdjh3fBbAk6HempIgO7YvTviBIdzgHgb3XQuzFIz4Ygvmb7gr/HxcxiK5wVRfED3wc6gBHgISFEUlGUFuD/CCHumj7uH4EbgQAwBfwHIcTfK4ryHWA9jeiSIeCrx4zvMyE1WyK5sIwlYnzrV6/wel1ht7sxsHJNto9rdTW+fMVNdITCF+y1K6U6Y4dSDB9IMLIvQSaZI+ntY8RZ4ojqI1JsJCm12CZZaxvh2pCdRz77rzAa5U4nyeyx2PR6rpB6LVlQqDXY9yy8/T8guh9cbXDVv2g0b5kdc726U1LTBL9K5/l5vNHtPVmtoVfgKreDO4ONiJNWy9wao0IIJrPl6ciTDHsiafZEMuQrjW5eh9nAmtZGx/f69sbnJtelkfd9IqJapdTXT2zHUWKHJolPlkkXjeTMYapmz8xxZkp47XX8rXbCK5sJre3A22RHNwcd/R8XIQS5eOy4+T06TGx0hMTkBFWDHqMXzIE6Nn8NizOH3ZbGasui02nT5ysoShCzuYfrrn1SGt2nQ4qwRDLLpIamu7y/A4UouNth4xcbnd7OU8X9nkymVufZaJrvjSfYly9h1SncHfLwuWY/W9xnN3OFEOyOZHj6gxGe2zVOoaqyNOTgkcvbuX9jGz77wnkSejpyyfJ0vEmUif4MCPCEbXSvDdC63EvzEjcmy9w9zZd8FFk4zw5SsyWSi8fO/sN8b897vG30MWhvw6RV2Zw5yk0OB1+49mZctgtX/AshSE0WGdmfYORAkvEjabK6CCPBcQ7iZqDQgkBHyBpjrX2YKz16Pve538JicV6wNUkuDaRezw5SryULEiHg6Evw9n+H4bfB4oEtX4HNvw6u5rle3WnRhGB3rsTzsTTPxzMcLTa6qNc6rdwVcHNH0M1y2/wwkDVNMBDPs2s0w+7RNLsjaQ5OZKmpDY8x5DRPG9+NYZdr2ty4rfOvu/5CI4SgHo2S2X2QqT2jxIfTJFOQxU3B1oTQNWp9najjMpXw+w0Ee/2E13cR7PVjXmAZ6ZqmkpmanDa/G13gsdFhEvE4qsmA0Q8Wfw2br4rVkcVmT3PPp3dJo/t0SBGWSC4Q9Soc/jls+wYMvg6KHlbc1cjy7tl61vwzMS3YT04keHYqRV7VWGoz81izn4eafARMZzdyC5U6P9szwVMfjLBjJI1Rr3DbZU08enk71/QGFsVWqUKmwuCuRqb3+NE0mipQdAqhTiety7y0LvPQ1CuN77lGFs6zg9RsieTio6kqz+96jx8PHuUdZwcJkxd3LcdV2QE+1dLBA1dcf0HyvE+kVlUZPZCkf2eUoT0JMvUxRoMRDuoc9BVa0YQenznJWscQlztVvvi5f4nD4b+ga5IsTqRezw5SryULntEPGob3oZ+BooMVn4LLfx26b2jsXp7H9BXLPB/L8EI8w/ZsEYAeq5k7Am7uDLrZ5LKhm0f3UK6pHJzIThvfDQN8IH5C3nfQzrq2Szfv+0S0Uoni4aNEdxwldiRGIlohU7aQszRRMx1/2G+liNelEWhzELqslfDqNtxBG8oC8z/Ueo3UxPhM93d8dIR4ZJhEKs3vffsH0ug+HVKEJZKLQKIftn8Tdn0PignwdjW6vDd8Dhyhs55eqKs8F2t0eW/LFjEqCncE3Hyuxc91Xsc5CfWRqRxPfzDKszsipIo1Wj1WHrm8nQc2tdHqsX7ye5wH1KoqkwMZxg6nGD+SZmowi6YJdDqFUNe08b3cS1OvG6Pp0nxzMFfIwnl2kJotkcwt5WqF7739S15MJnjPvZSy3kJraZJrylM8snID16xce8HXoNY1IodS9O+MMrgrTro0wXh4mAN6G0cLbdSFAbcpyxrnAJusFb74+FfxeVsv+LokiwOp17OD1GvJoiHR34jn3PndxuBK/xLY/Guw/jGweud6dWdlslLjF/GG6f1WKk9NCIImQ8P0Dri5xuvAPM8GcMLxvO/dkfTMwMtT5X0fiz3pCVx6ed/HEEJQjURI7zrM1L4I8dEcqYxCVuelaAs3HtQAelHDYynjC5kJLQvStL6LQKcHwwL0BWrVCiazRRrdp0OKsERyEalX4OBPYds/wPBboDPCyrsbbxa6rjunp+MH8yX+cSLJDyaTpOoq7RYTjzX7eLTZR7P57LEklbrKSwemePqDUd48GkdR4OpePw9sbOOO1U3YzqFTfKFQq6hM9meIHEkxdjhFdDiH0AQ6vUK423W847vHvSAFbiEhC+fZQWq2RDJ/mEwl+NY7v+T1qmCneylC0bEqN8C1lPnS5TfQ03zhzWVN1Rg/mqZ/Z4yBnTHSuSjjoQEOmUwcLrRT1Uw4jHlWOwfYaCnwxYe+RLh5yQVfl2ThIvV6dpB6LVl01Mpw4Mfwwd9D5H0wWGD1A40u79ZNc726cyJTq/NKMsfz8Qy/TGQpqBoOvY6b/S7uDLi52e/CaZifNeGH8753j6bZO3Y879tpNrBmuuN7XVsj+qTJfWnP8FDzeQr7DzG1c4B4X5xEvEamaiNna0E1TDf6CQ2nvoTPpxDs9hBe10VoWQiba/7Hvc62XkujWyKRfHJih493eZczjafjm74E6x8Hm++sp5dVjRfiGb47nuCtdB4dcLPfxeda/Nzsc2E4hye6o8kiz+4Y45kdEUaSRewmPXeuaebBTW1s6fItuqfC1XKdif4M40dSRA6niQ1nEQJ0BoWmbjetyzy0LvMS7nFhuES3g10oZOE8O0jNlkjmJ3uH+vnernd4S++mz9GBQauzKXOErTYrX7ruFjz2C5+dLTTB5ECG/ukZFslkgqlQH4fNeg4W2yirFqyGEqtdA6w3ZfjcvQ/T2bXugq9LsrCQej07SL2WLGom9sC2v4c9P4BaAZrXNwzv1Q+AyT7XqzsnyqrGW+k8z8fS/CKeJV6rY1QUrvU6uCvo5na/m5B5fuc8q5pgIJZn13TW955IRuZ9nwWhqlSGhkjsOEx0/xjxsSKpvIGcOUTZcjzyzUwZr6NOoM1B+LIWQqvb8YRt88ofkUb3GZAiLJHMMbUS7P9xY0vY6HugN8Oqz8DmL0PHVefU5T1YrPCPEwmemkwSrdYJmww82uznsWYfnVbzWc8XQvDBUIpntkf42d4J8pU6bV4r929s44GNrXT6F8YblvOlWqoz3pdm7EiascMp4qM5hAC9QUdTj4vW5Y2O73CXG71x/m1pW0jIwnl2kJotkcx/Xtz1Ps/2H+QdextRsx9HvcBV2X7uDLfw8JU3YjBc+J1TQghiIzn6dzRM73g0SSxwhMM2OFBso1i3YdZXWOUaYL0pyWdv/zTLVl11wdclmf9IvZ4dpF5LLgnKWdjzdKPLO3YQzG5Y/9nGbuXg8rle3TmjCsH2TIGfxzM8H8swXK6iAJtcNu4Mergz4KbHdvaaej5wLnnf66cjTxp5307M87SL/WIhhKAei5Hdc5CpXSPEhlPTgy89Jw2+1IsabnMFX9hEaGmIpvWdBDo9GM1z8/2TRvcZkCIskcwjpvY3Yk32PA2VLARXNIZXrnvknDLQaprgl4ks351I8EoiiwZc53XweLOfO4Puc8ofK1VVXjwwyQ+3R3irL44QsKXLxwObWrlrTTNOy+J9Clwp1hjvyzA2HXUSj+RBgMGoo6n3eMd3qMuF3iCN7/NBFs6zg9RsiWThUK3VeOqdV3ghPsV7riUUDDaayzGuLo7z0LI13Lhm40VZhxCC5HiB/h1R+nfGiI4liPuPctSusr/cQq7mxKCrsdI1yDpjnAdv2Mr6zbdclLVJ5h9Sr2cHqdeSSwohYORXDcP7wE9AqzViOTf/Gqy4GwzzPwbiGEIIDhXKPB/P8EIsw558CYDldgt3BtzcEXCzzmlFmUfDLM9Gplhjz1h6JvZk12iaeP543veqZtdM5Mm6Szzv+0S0QoHCwcPEdvYTPRIjEauSrtjI21qoG22Ng4TAoS/i8+qmo086ZqJPLvTPiDS6z4AUYYlkHlItwL5nYds3YHxHIwPtsvsbbxbaNp9Tl/d4ucpTk0menEgQKdfwGfU8FPbxWIuf5fZzy+uayJT40c4xntkeoT9WwGLUcftlTTy4qY2rewPoF7kAlgs1xo+mG8b3kTSJSB4Ag0lHc697uuPbS7DTiV4vje8zIQvn2UFqtkSyMElmM/zD2y/xWrnGDvcyVEXPsvwQ16p5vrD5Ola0dV60taQmCwzsitG/I8bEcJKU9whHnVX2V5pJV93oFZVlrkHWmqLce/kmrrr+/ou2NsncI/V6dpB6Lblkycdg53cau5XTI2APwcYvNCI6Pe1zvbrzZrRc5RfTnd7vZvKoAlrMxplhlld6HBgXWE0shGAiUz6p63tPJE2hqgIfzfve2Okh5Ly0876PIer1RvTJ9sNED46RiBRI5Q1kzWHK1sDMcWbKeBwqgVb7dPRJG96wDd0segbS6D4DUoQlknnOxO5Gl/feH0A1D+HVjViTNQ+DxXXW0zUheCOV47vjCX4Rz1ITgstddh5r8XFPyINdf/atNkIIdo2meWZHhJ/uniBTqtHksnDfxlYe2NjGkpBjNu503lPO1xg7mpqJOkmON7aBGcx6WqaN75ZlHkIdzlkVscWALJxnB6nZEsnC51BkmO9se4u3dHYOO7vQC5UNmaPcaDHwpatvJuA++w6u2SIbL00Psowy1p8i7T1Kv7PI/mqYeMWHgsYS1zBrzZN8as0Ktt76+ILqYpOcP1KvT0ZRlP8MfAbQgCjwJSHE+NnOk3otueTRVOj7ZSPL+8gvGo1aS2+Hy38Dem+Cc9hpPN9I1uq8FM/yQjzDa8ksJU3gMei5xe/irqCbG3zOc6qt5yMfzvvePdrI+65rDe+z3Wdlc6ePjZ1eNnd6WRZ2Lvqmt3NFCEE9GiO35wCTu4eJDWVIpTSyeMjbmxG6xo54najjNpfxh0wEl4VoWtdBoNODyfLxIu2k0X0GpAhLJAuESq5hdm/7BkzuBaMd1jzQ6PJu2XBOl4hVa/xgMsWTEwn6ihUceh33h7081uw/5y1YlbrKLw9G+eH2CK8fiaFqgvXtHh7Y1MY9a1tw2xZvtMmHKeWqjB1JN4ZbHkmTmmgY30aLnpYlHlqWeWhb7iXQ7rzkt3/Jwnl2kJotkSwuXtu7gx8c2cs7thYmLEHs9SJXZPu4PRDms1ffhMl48TQ1n6owsKtheo8eSZB1DNLvzbC/FmSqHASg2znCWvM4ty5t51P3/AaKsvCMCsmZkXp9MoqiuIQQ2ek//ytglRDia2c7T+q1RHIC6RHY/k3Y8W0oxMDb1Yjn3PA5sAfOdva8pKhqvJ7M8nw8w0vxLKm6ikWncIPPyR0BN7f63QRMF34mx4WkXFPZP55l50iKbUMptg2nZiJPnGYD6zs8bO70sanTy/oODw7zwr7f2UbNFygdOkx0Zx+xIzES0SrpipWcvYW68XijoF1XxOdVCHR5aFrXQWhpELvHfFZvRhrdZ0CKsESywBACxnbA9m/A3megXmpMut78ZVj9IJjP3l0thOC9TIHvjif4WSxNSRNc5rDwWLOfB8JePMZzE6lorsxzu8b54fYIhyZzmPQ6blkV4oGNbdywLIjhEutqLmarMzEn40dSpCaLAJgselqWemaiTvxtl17umSycZwep2RLJ4qRer/P9d1/j+alxfuXqJW+wE6okuLoQ4f7eldy2fstFXU8pV502vWOMHEyQtQ4y6E+yvx5grBQGoMMxxlpLhBvaAjz4yL9AURZmF5vkZKRenx5FUX4f6BBC/LOzHSv1WiI5BfUqHHyu0bg1/DboTbDqXrj816H9inOK55yP1DXBu5k8L0xHnIxVajPDLG8LuLnV72KF3bLgd0QJIRhNltg+kmTbUIrtwykOT+UQAnQKrGhysbnLy6bOxkerZ2FlmV8MRL1OZWCA5K4jTO0fJzGWJ5U3kDOHKVmDM8eZqOB11PG32gmtaiF8WSveZvtJcanS6D4DUoQlkgVMKQ17vt/IQIseAJMT1j7cML2b1pzTJTK1Oj+KpnlyPMGefAmzTuHuoIfPNvu42uNAdw7iJIRg/3iWZ3ZE+MmucZKFKgGHmXvXt/DApjZWNp89YmUxUshUZozvscMpMtHGMBOzzUDzkka3d+tyD/4WB8oiN75l4Tw7SM2WSBY/6UKOb775Mq8WS2x3L6OuM7AkP8K1apavXnkT3U0tF3U95UKNob1x+nfEGDmQIGMcZtgf5YDmZbjYWEuzbYp1thGuDdl5+NHfxGSyXtQ1SmYPqdcfRVGUPwG+AGSArUKI2NnOkXotkZyF6MGG4b37KahkIbgS1j3SaNxagFnexxBCsDdf4sV4lhcTGfbkGvVfu8XEbX4XtwXcXOWxY1qA0S2nIluusXMkzfbhFNuHk+wcSVOczvpucllmTO9NnV5WtbgwXmKNcOdCI/okSnbPAaK7h4kNpUmmBFnhoeBoQZuJPlFxHYs+WRpk86ObpNF9OqQISySLACFg9L1Glvf+H4FagbbLG1vCLrsPTLZzuszeXJEnJ5I8M5UkW9fospp4rNnPw00+mszntn26pmq8djjGD7eP8sqhKDVVsKrZxYOb2vjM+hb8DvMnudMFTT51zPhumN/Z2LTxbTfQutQ7E3Xia7YvOuNbFs6zg9RsieTSYmBijG9+8DpvYeGAswezVuWa9CEeaOvkvs3XorvIWaDVcp3hfQn6d8QY3p8gLUYZDY5zEBcDhVYEOkLWGGvtI1zp0fH5z/9rzOew00wyf7gU9VpRlJeBplN86Q+FED854bjfByxCiP9wmus8ATwB0NHRsWl4ePhCLFciWVxU8rDvh7DzexB5v/Fvndc0mrdWfQasF29uxYVgslLjpUQj3uTNVI6SJnDoddzoc3JbwM3NPhf+BR5xciJ1VePQZG7a+G58jKUbNa/FqGNdm2em63tjhxePzTTHK56/NKJPDhHb0UfsaJTEVJV01UbO1kLN5OQ3v36zNLpPhyyaJZJFRjHZeDK+7RuQOAoWN6z7bMP0Dq04p0uUVI2fxdJ8byLBr9IF9Arc4nfxeLOfm3wuDOdowiYLVX66e5xndkTYE8lg0CncuDzEg5tauWlFGJPh0n6im0uWZ/K9x4+kyMbLAFgcRlqWegh3u2jqdhHscGE0L+wt4Zdi4XwhkJotkVy6vLT7fZ48epA33EspGGwszQ9zqyjytetvJ+TxXfT11KsqIweS9O+MMrQnQaY+zmhwhIM6J32FVjShx2tOsc4xxOVOlS889i9wuhZmFuulhNTr06MoSifwMyHE6rMdK/VaIvkYJAdg7w8bO5YTRxvRJktva5jeS28Ho2WuV/iJKKoab6VyvJTI8lI8y2S1EXGy2WXntoCLWwMultsWfsTJh5nIlE4yvvePZ1Gnh1wuCTnYfELXd3fAvujufzY5Fn2S2nWYlkfukUb36ZAiLJEsUoRoZJ9t+wYceA60GnRc1RheufKec36j0F8s848TSZ6eTBKr1gmbDDzS5OOzzX66befenX1kKscz2yP8aOcY0VwFr83IPesa0SZrWt1S0IBsvDST7z3el54xvhUFfC0Owt2uxkeXC2+zfUHlfMvCeXaQmi2RSCZTCf7mzRd5WbHT5+jAXi9wQ6aPzy27jJvWzs2vWbWuETmUon9nlMFdcdKlCcbCwxzU2zhaaKMuDLhNWdY4B9lsLfOFx57A52ubk7VKzozU65NRFGWpEOLo9J//JXCDEOLBs50n9Voi+QQIAeM7Ye8PYN8zkJ8CsxtW3dMwvTuvhQUe/SGEYE++xIvTwyz35Btdzx0WE7cFXNzmd3PlIoo4OZFitc7u0Qzbh5Mz5ne2XAfAZzexscM70/W9ptWNxbiwG74uFJdMRreiKOuBvwEsQB3450KI9890jhRhieQSoBCHXd9rRJukBsHqg/WPNbq8A0vO6RI1TfByIsOTE0l+mciiAdd4HDze4ueugBvLOeZt1VWNt/ri/HB7hBcPTFGtaywNOXhwUxv3bWgl5FrYT+pnk1KuytRQlqnBLFNDWaJDWSrFxpsAo1lPqNM5bXy7CXe7sHvmbyyMLJxnB6nZEonkGJqq8uz7b/DMeIS3Pcup6kysyfRxhxm+csMduGxzExmiqRrjR9P074wxsCtGKhtlItzPIaOZw4V2qpoJhzHPelcf17pVvvzF38Fsts/JWiUfRer1ySiK8gywHNCAYeBrQoixs50n9VoimSXUOgy90ejyPvhTqObB1QqrH4C1j0DTWTdYLAgmKlVeimd5MZHlrVSOsiZw6nXc6HNxW8DFzX4XPuPiiTg5EU0T9MfybDuh63swXgDApNexutU13fHtY1Onl6Bz/ta8F5NLyeh+EfhvQojnFUW5C/hdIcSNZzpHirBEcgmhaTD4emN45aGfgVaHrutg05dg5afBcG6iMVGp8vREkicnkoyUq3gMeu4Pe3m8xc9ljnMfQJUp1fjZngme2RFh+3AKnQLXLQ3ywKY2blsVlk9vP4TQBJlYianBzIz5HY/k0dSGJjm8ZkJdjY7vcLeLYIcTk2V+vCGShfPsIDVbIpGcioGJMb7+7iu8bA4yZm3CV01zY36YX1u7hc1LV87ZuoQmmBzINEzvnTESyThToaMcNBs5UOigphkJWBJssg9wS7uPBx/5TRRl8XWvLSSkXs8OUq8lkgtAtQiHf97o9O57uVHLhlbBmocaHwt4iOWJFFWNN1O5Rrd3Iku0WkcHXO62c+v0QMulNvOi3hEdz1fYcYLxvSeSoapqAHT6bWzq8LKpy8vmTh9LQ44FtdN5triUjO5fAN8QQjytKMpngU8LIR470zlShCWSS5TcFOz8Duz4FqRHwOaH9Y83TG9/7zldQhOCt1N5npxI8LNYhqoQrHNaebzZz31hL07DuRvVA7E8z+4Y49kdEcYzZZwWA3evbeHBTa1s7PAuaiH/JNRrKvHR/IzxPTWYOXXkybT5PVeRJ7Jwnh2kZkskkjNRr9f59psv8tNUivc8KxAoXJ45zKdcDr58/R2YjOc2WPpCIIQgNpKjf0eMvu1TxBOTRJr62at3cDTfhkBHhyPCJsso929ax3VbH5qztV7KSL2eHaReSyQXmEIC9j/bML1H32v82yIaYnkMTQh250ozpve+6YiTLquJ2/xubgu4uMLtwLjIjd5KXWXfWJbtw0m2DaXYMZIinq8C4LQY2NjRiDrZ3OllXbsHu3l+NHtdSC4lo3sl8AtAAXTA1UKIM457liIskVziaBoMvNKINTn8PAgVuq9vGN4rPg2Gc5uEnKzVeXYqxXfHExwqlLHqdHw65ObxZj9b3Oc+VELTBO8OJPjh9gjP75ukVFPpDth5YGMr921so9Vz7h3jlyozkSdDWaLTBvjpIk9CXS4c3gu//UsWzrOD1GyJRHKu7Bo4wjd2/opX7O3EzT6ay1FuLk/yxJYbWdbaMadrE0IwNZjlyHuTHN0eJVUdZiA0zh7NT6TYhILGCvcAm8xRvnDnPSxbecWcrvdSQur17CD1WiK5iCQHp4dYPr0oh1ieyFi5OjPM8q10joomcBl0bPW5uM3v4ia/C+8ijTg5ESEEw4ki24dTbBtOsWM4xZFoDiFAr1NY2eyc7vr2sbnTS8si9BAWldGtKMrLQNMpvvSHwM3A60KIZxRFeRh4Qghxyymu8QTwBEBHR8em4eEzeuESieRSITsBu74L278NmRGwBWDD47Dxi+fc5S2EYGeuyJPjSX4UTVFQNZbYzDzW7OehJi9B07l3k+UrdZ7fO8EPt0d4bzCJosBVPX4e2NjGnWuasJkWv4jPBkIIMtHpyJOhHFODmZMiT+we80ld3xci8kQWzrODLJwlEsn5UiyX+dvXnueFco1d7mUYtRpXpQ9xX1MLj1x5Izr93MaEqarG6IEkR96fYmBXlJSpjyO+NLurzSQrXky6KmvcR9lizfIbj30Nf0AOsbyQSL2eHaReSyRzgBAwsQv2/AD2/XBRDrE8kYKq8mYyz4uJRrd3bDriZIvbzq0BN7f5XSxZ5BEnJ5Ip1dgx0jC9tw2l2DWaplRTAWh2W2Y6vq/o8bM87FzwcSeLyug+E4qiZACPEEIojZ/mjBDCdaZzpAhLJJKPoKnQ/2ojy3umy/sG2PxlWP6pc+7yLtRVnouleXI8yQfZAgYFbg+4eazZz40+J/rzEN3RZJFnd4zxzI4II8kidpOeO9c088DGNq7o9i14obrYfCTyZChLNtbYCteIPLFPG9/uWYk8kYXz7CA1WyKRfBJe37eL7xzezeuuXnIGBz2FCLeoGb567W20+oNzvTyq5TqDu2Icfn+K4QMxEp5DHHLV2FNsp1i34TDm2eDs4zqP4Itf/DdyiOUFQOr17CD1WiKZYzS1MZtqzw/g4HONIZbOFljzYMP0Dq9uFD2LBE0IduWK0wMtM+zPN6Isu6cjTm69RCJOTqSuahycyDXiTqazvicyje+Lx2bk8i4fV3T7uKLbz6oWF/oF9r25lIzug8A/E0K8pijKzcBfCiE2nekcKcISieSMZMdh5/caWd6ZUbAHp7O8vwi+nnO+zOFCmScnEvxgMkmyptJiNvJos49Hm3x0WM89OkMIwQdDKZ7ZHuFneyfIV+q0ea3cv7GNBza20umXRe/HpZSvzhjf0aEsU4PHI08MZj2hjunIk+nYk/OJPJGF8+wgNVsikcwGyWyGv3njBV7UTBxydmNTS1ybOcKjXcu4a9NVc708AAqZCn3bohx+b5KJkShTocPsNxtmhlgGrXE22Qa5rSvIfQ/+80umY+1CI/V6dpB6LZHMI6pFOPJ8w/Tue2nRDrE8kch0xMmL8Qxvp/JUhcBt0LPV5+S2gJubfE48l0DEyYcZTRZ5bzDJ+4MJ3htMMpwoAuA0G9jc1ej23tLtY02rG6N+fnf/X0pG97XA/wAMQBn450KI7Wc6R4qwRCI5JzQV+qezvI+80Ojy7rkRNn0Zlt91zl3eVU3jF/EsT04keC2ZA+AGr5PPtvi4I+DGfB7byUpVlRcPTPLD7RHe6osjBFze5eX+jW18am0zLsvcDd1aDMxEnkyb3lNDWeKjuZMjT7pcM7Enwc7TR57Iwnl2kJotkUhmm59+8BZPDw/wlmcZZb2FVbkBbtPX+doNd+BxnHFj6EUjNVngyPtTHHl/klhiYnqIpZO+6SGWnY4Imy2jPLB5I1ffeP9cL3dBI/V6dpB6LZHMUwoJOPAj2PP9k4dYrnmoMcTS5pvb9V0ACnWV11M5XoxneTmRJV6ro1caESfHBlr22hZPjvn5MJkp89606f3eQIL+WAEAm0nPpk4vV3T72NLtZ127G7NhbqPePswlY3R/HKQISySS8yY7Dju/C9u/BdkI2EPHs7x93ed8mdFylacmEjw1kWSsUsNn1PNQ2MdnW3yssJ/fwIiJTIkf7xznmR0R+qJ5zAYdt64K88CmNq5bEsAwz5/ILhTUmkYskmsY34ONzu/MKSJPQtOxJ76WRuSJLJxnB6nZEonkQjESneLr77zMy0Yvw7YW3LUsN+QG+eKqDVyzcu1cLw9oPICdHGgMsezbHiVZOz7EcqzYhE5RWeEaZLM5yhfvvp/eZWfc2Co5BVKvZwep1xLJAiA1BHt/0DC940dAZ4RltzdM72V3LKohlsfQhGBntsiL093eBwuNKI9eq5lbAy5u87vZ4rZjWGAxHrNFLFfhg6GG6f3eYJJDk43GPLNBx4YOD1d0+7mi28eGDi9W09wa39LoPgNShCUSycdGU6HvZdj+zekubw16b4JNX2p0eevPraNaFYI3kjm+N5HgF/EsNSHY7LLxWIufzwQ92M/j6akQgj2RDM/uiPDc7nFSxRpBp5l717dw/8Y2VjbPj+60xUQ5X5vu+s7M5H1XCidHntz/bzfJwnkWkJotkUguNJqq8uQ7v+TH0SjvelZQ1xnYmD7MXXYLv3HjHVhM5x5bdSFR6xojB5IceX+S/l1RMuZ+DvvT7Cm3kKx6MOkqrHP3scWa4zc+/8/welvmeskLAml0zw5SryWSBYQQMLG7YXjPDLF0NYZYrnkYuq4F3fzq5p0tRstVXoxneCme5Z308YiTm06IOHFfghEnx0gVqrw/lOT9wSTvDSY4MJ5FE2DUK6xr83BFT6Pje3OnF7v54n6fpNF9BqQISySSWSEzBju/Azu+Ddmx6S7vzzWyvL1d53yZWLXGDydTPDmR4Gixgl2v496Qh8eb/Wxw2c4rg7Na13j1cJRntkd49XCUmipY1ezi/o2tfGZ9K0Hn/CjWFxtCCDKx0vFBl4NZHv79y2XhPAtIzZZIJBeTgyOD/O22N3nF2sKUJUCoEuem4ji/vvEa1nT1zvXyZqiW6vTvjHHk/UmGD0VJeI9wyFlj7/QQS6cxx0ZXP9d6BV/4/G9jNtvmesnzFml0zw5SryWSBYqmwuAbDdP74E+hmpseYvlAw/RuWrOohlieSL6u8loyx4uJDC8nsiRrKgYFtrgd3OZ3ceslHHFyjGy5xrah5HTUSZK9YxlUTaDXKaxudXNlt48renxs6vThtl7YGFVpdJ8BKcISiWRW0VQ4+lKjy/voLxpPyHu3Tmd533nOXd5CCD7IFPjeRJLnomlKmsYKu4XHm/080OTFd55PlpOFKj/d3Yg22RPJoNcp3LAsyAMb27h5ZQiLcXE+pZ8vyMJ5dpCaLZFI5oJytcI3Xn+Bn+dLbPOsQC9Urkgf4jP+AI9fczMGw/zp9iqkKxzdNtUYYjkaYyp0iP1mIwcKHdQ1IyFrjE32Ie7oDnPP/V+TQyw/hNTr2UHqtUSyCDjVEMvgSlh7bIhlx1yv8IKhHos4iWd4MZHl0AkRJ7cEXNzqd3GF24HxEo04OUahUmf7cGqm43v3aIaqqqEosKrZ1Yg66fGxpcuH135uM83OFWl0nwEpwhKJ5IKRicCO6S7v3Dg4wo0u741fBG/nOV8mV1f5cTTF98aT7MoVMSkKdwbdPN7s51qvA915FqlHp3I8u3OMH+0YYzJbxmUxcPe6Fh7Y2MbGDo8sei8AsnCeHaRmSySSuebdw/v45r7tvObsIm1001Gc4OZagq9ddTOd4ea5Xt5JJMcLHHl/kiPvTxFPjzMaHmCv3kV/vhWBji7nKJvNYzx4xWauvO4zc73ceYHU69lB6rVEssgoJmH/sSGW7zb+rePqhum96t5FOcTyREZKFV5OZHkpkeXtVCPixGXQsdXn4ja/i61+13k3oi1GyjWVHSPTxvdAkh0jKSp1DYDlYSdX9Pi4otvPlm7fJ95dLo3uMyBFWCKRXHDUeuMp+LZ/aHwWApbc3MjyXnbHOXd5A+zPl3hyPMEzUynSdZV2i4nHmn080uSjxXJ+T0lVTfCr/gTP7Ijwwr5JSjWV7oCd+ze0ct/GVtq8cmvzbLHYCmdFUXzA00AXMAQ8LIRIfeiYduDbQBOgAX8rhPgf53r+qZCaLZFI5gvpQo6/e/0FflHTsc/Vi1mtcE3mMA+1dfKZzdei08+fnVJCE0wMZI4PsVSHGQxNsLseYLwURqeorHQPsNkU44v3PETPkvVzveQ5Y7Hp9Vwh9VoiWcTMDLH8AcQPN4ZYLr0N1j68aIdYnkihrvJ6KsdLiSwvJ7LEqnV0wBa3nVv8Lm4NuFlmM8vmMaBSV9kTyfD+YJJ3BxJsH05RrKoA9ATtXNHt58pp87vJfX4/N9LoPgNShCUSyUUlPTqd5f2d6S7vJtj4edj4hfPa/lVWNZ6PZ/jeeIK30nl0wE1+F19o8XOTz3Xek6LzlTrP753gmR0R3h1IAnBlj48HNrZx55pmHBd5uMRiY7EVzoqi/CWQFEL8uaIo/w7wCiF+70PHNAPNQogdiqI4ge3AvUKIA+dy/qmQmi2RSOYjL+x8j6f6D/GmeykFg41l+WFupcQ/u/52Am7vXC/vJNS6xvC+BEfen2Jgd5SU9ShHfVl2l1tJVd2Y9Y0hlldY8/zG538Ttyc010u+qCw2vZ4rpF5LJJcAQsDknkaX994fQn6yMcRy5T1w2b3QfQMYZjeuYr6hCcGuXJGX4o1u7335EgCdFhO3Blzc5ndzpceOSaeb45XOD2qqxr6xzHTUSZIPBpPkKnUAOnw2ruj2cUWPnyu6fbT7ztx0J43uMyBFWCKRzAlqHY6+CNv/oZHpDbDklhO6vM/dWB4qVXhyPMFTk0mi1TotZiOPNft5rPn8u7wBIqkiP9oxxrM7xxiMF7Aa9dyxuon7N7ZydW8A/SWeRfZxWGyFs6Ioh4EbhRAT04b2a0KI5Wc55yfAXwshXvo454PUbIlEMr8ZT8b527de5CWdk357O456gRsyfXxu+Wq2rtk018v7CJVSnYGdUQ6/N8XI4SgJ/2EO2uvsLXZQqltxmbJscPZzg0/P577wbzAt8i49WHx6PVdIvZZILjGODbHc+wM48FxjiKXZ3ZhRteoe6L0JjNa5XuUFZ7xc5aXpiJO3UjnKmsCh13Gjz8mtfjc3+10ETLKB7BiqJjg4kZ0ebpng/aEk6WINgFaPlS3dvhnzu8tvO6lLXhrdZ0CKsEQimXPSI40O753fgdwEOJthw+cbnd7n0eVd0wQvJjJ8eyzB66kcOuDWgIvPtwTY6nOiP8/tU0IIdoykeWZHhH/aPU62XKfJZeHeDa08uKmVJSHned7opctiK5wVRUkLITwn/D0lhDht26KiKF3AG8BqIUT2fM8/htRsiUSyENBUlWfee4MfTkT4lWc5VZ2Jtdmj3G7W8dUb7sJhnX/Ffj5V5ugHUQ6/P8lkJMpU6BB7zWYO5TuoCwNha5RNtiHuXNLK3fd+ZdFuyV5sej1XSL2WSC5h6hUYeA0O/AQO/QzKaTDaYdltsOozsORWMDvmepUXnKKq8dZ0xMlL8SyT1RoKsMll41a/m9sCLlbYLYtWTz8OmiY4Es3x3kByZsBlPF8FIOQ0N4zvHj9XdvtY1uSSRvfpkCIskUjmDWodjv5iOsv75ca/Lb0VNn25kXt2Hl3ew6UK3x1P8I8TSeK1Oq1mI59r8fPZZj9N5nPPBD9Guabyy4NRnt0R4bUjMVRNsLbNzQMb2/j0uhZ8szxFebGxEAtnRVFeppGv/WH+EPjWuRrViqI4gNeBPxFCPDv9b+dsdCuK8gTwBEBHR8em4eHhj3dDEolEMgf0jY/y9fde4xVziDFrGH81zY35Yb626VrWdPXO9fJOSWIsz5H3pzjywSTJzDj///buPD7K+tz///uTTPZ9IZCEJeyLIBAWC0pVLKCoCFjXulRbq6fW1i7ne6zWWk9Pt9PfsWo9rbX2KGi1WkUERAHXuoMEkCUL+5IA2fd95v79cU8wBghJmGTmHl7PxyMPkjv33PlcfDJz5b7mc1/3/v57tTUkXrtrB0mShsUd0JSIQl1z7kxNnTHfz6P1LSfm60DEOTYASZK7Rdr3vr3KO2+VVFciuSLtK5nHLpBGXyxFJvh7lL3OsixtrW3QutJqrS2r0pYau8XJwMgwzUlJ0JyUeM1MjFVkKC1O2rMsS7tL6vTp3rJjN7g8Ut0oSdr/u8sodJ8MSRhAQKrY/0Uv79ojUlzGF728EwZ2+TDNHo/eKK3WM0Wler+iVqFGmpeSoBszUnR+cpxCevAOcklNk1ZsKdLLGw9px+FqhYUaXTg6TYuzB2r2mDSFu0jQHQXbiXNXW48YY8IkrZK0xrKsh7r7+I7I2QCcqrW1VU+/v0YrK6q0IXG0Qi2PzqvM1U1DR+qS7Bn+Ht4JWR5Lh3dXKv/To9qdU6xy9z7tSTusLa1pOtyQplDj1tiE3ZoeUabvXP1NDcgc4e8hn7Zgy9f+Qr4GcByPWzrwsV30zl1p368qJEwafqFd9B5zqRSd7O9R9omjTS16s6xa68qq9F55rRo8HkWHhuj8pDjNSYnX11LildaDxWnBzrIsHSiv16d7ynXN9MEUuk+GJAwgoLlbpII3pI1PS7vekoyxL/eaeov9bzdWee+pt1d5/+NImcpb3BocGe5d5Z2sfuE9S6S5h6u1LOeQlm8uUklNk5Kiw7RgYoYWZw/U2QMTuBTLK9hOnI0xv5dU1u5mksmWZf2/DvsYSUtk33Ty7u4+/kTI2QCCwaf5O/TXrRv0duIo1YdGaXJVgRbHx+iWr86TyxWYvTvdLW03sTyi3Z8XqzJ6lwqS7JtYVjYnKNpVpynxu3R+stFNNzu3n3ew5Wt/IV8D6JTHIxVulHYsl3JX2K08TaiUdZ7d03vM5VJcf3+Psk80uj36sLLW2+KkSoVNdo/qSXHRmpsarzkp8RofG8V5dQf06O4ESRiAY1Ts+6KXd+1RKT7zi17e3Vjl3eTxaHVJlZYWlerjyjq5jHRxaoJuykjVeUmxPVrl3er26P1dpVqWU6i124+oqdWjEWmxWpydqUWTM5WeEHj9SPtSsJ04G2NSJL0oabCkA5Kusiyr3BiTIelJy7LmG2POk/S+pK2SPN6H3mtZ1uqTPf5UP5ecDSCYHCg+qsc+XKs3otJVHJGqrPpCXeqp0V0XzldibLy/h3dSTfUt2r2pRAWfHtH+/GKV9svVtmijbbVZavGEKT36qKZH79OV2RP01dlX+Xu43RJs+dpfyNcAusyypMNb7IL3jlelsl2SjDR4hl30Hnt5t851ncyyLOXWNWptaZXWlVUrp7pelqT0iLBjK71nJcUpihYnFLo7QxIG4DjuFin/dXuV9+637VXeI+dKU2+1+52FhHb5UDvrGvXs4TK9eLhcFa1uZUWF64b0FF1zGqu8qxpatHrrYS3LOaQN+ypkjHTu8FRdOSVT884aoOgz8E7TnDj7BjkbQDCqbWjQn99ZpZXucBXEDlFyc6Xm1B3QnedcoFGZXb8ptT/UlDcq/5Mjyvv4sIrLCnVgwB5tNonaV5epEOPWuPg9OieyRLddfYsjWpuQr32DfA2gRyxLKs71Fr1XSMXb7e2ZU71F7wVS8lD/jrEPlTS36K2yaq0rq9a75TWqc3sUFWJ0XlKc5qbahe/0iDPzXlkUujtBEgbgaBX7pI1LpE3PSnXFUuJgu+A9+UYpJrXLh2l0e/RaSaWeKSrTJ1V1CjNG8/vZvbzPTYzt8aVS+8vqtCynUMs2HdLB8gbFhIfqkgnpujJ7oM4ZmqyQkDPjEixOnH2DnA0gmHncbj3/0dt6saREnyaNU4SnWedX5OqW0eN14YQp/h5epyzL0pHdVcr7+LAKNh5VRcgu5adWaNOXWpvs1AXJIbrp5h8pLCzC30M+IfK1b5CvAfhE6S4p91W76H14s71twARp3BXS2CukfqP8Ory+1OTx6JPKOq0trdLasmodbGyWJJ0dG6WvpcZrTkqCJsZF9ejqbCei0N0JkjCAoOBuse9kveFv9p2tQ8OlcQulad+WBk23V313UX5do54tKtWLRypU1erW8KgI3ZCRoqsHJCulh6uxPR5Ln+2v0MsbD+m1rYdV29SqzMSoY61NhvWL7dFxnYITZ98gZwM4U7y/fbOezNuidxPGqCk0QtMqc3VVcrJuOO9rCgnt+pVb/tDS7NaeTSXK+/iw9uUdVUlqrrZFh2h77RC1eMKUEX1E06L26+tTztas2V/393C/hHztG+RrAD5Xsd++iWXuCungp/a2fmPsVd7jrpD6n9Wtc14nsyxLBfVNWltapTfLqrWhqk4eSWnhLn0tJV5zUxI0KzlWMQH+98LpoNDdCZIwgKBTnCd99jdp8/NSc43Uf4I07VvShKukiK4XlBvcHq0sqdQzhWXaUF2ncGN0WVqibsxI0VcSYnq8yruh2a21O45oWU6h3t9ZIo8lZQ9O1OLsgbr87AwlRAffHaY5cfYNcjaAM82ew4X648dvaW3MYJWFJ2pE7QFdHtKoO2dfrtiowL//xZdam5QX6sCA3dqspC+3Noko1W3X3qIBGcP9PVzytY+QrwH0quoiKXeVXfTe/6FkeaTkYd6i9wIpI/uMKXpLUllzq94pr9basmq9U1atGrdHESFG5ybGak5qguakxGtgZHC1OKHQ3QmSMICg1VQrbX3RXuV9dJsUES9NvM4uevcb3a1D5dY26JmiMr10tFzVrR6NjI7QjRkpumpAspLCet5z+2h1o5ZvKtTLOYdUcLRW4aEh+tq4NF2ZPVBfHdVPYUFyow1OnH2DnA3gTFVZV6M/vbNaqxSjPTED1a+pXPMaDumumXM0pH+6v4d3Sh1bm1SGFCgvtapDa5NdujDFbm3icvnnhJx87RvkawB9prbEvrI5d4W091+Sp1VKGPRF0XvgdCkkOM4pu6LFY+nTqlqtK63W2rIq7W2wW5yMi4nUnNQEzUuJ16T4aMe3OKHQ3QmSMICgZ1nSwfXShielHcsld7OUNctuazLmUim06yuo69xurSi2e3nnVNcrMsTosn6JuikjRdNOY5W3ZVnaXlStl3MOacXmIpXVNSslJlyXT8zQ4uxMTchM6PGxAwEnzr5BzgZwpvO43Xr6X2v0UmWNchJHK8rdqAsr83XbWdmaMXaCv4fXJV9ubXJEpSl52hZjtK0264vWJtH79fXsiZo1+8o+HRv52jfI1wD8or5cKnjD7um9+y37vDd2gDT2MrvwPeRcKbTni7ScaHd9o9Z6i97rq+rktqR+4S7NSYnXvNQEzUqKU7QDF5dR6O4ESRjAGaW2RNr0jPTZU1LVATvxT/mmNOVmKT6jW4faVlOvpUVlWna0QrVuj8bEROqGjBRd1T9JCaexyrvF7dF7+SV6ZVOh1uUeVXOrRyPSYrU4O1MLJ2UqIzHwL9XuiBNn3yBnA8AX1m1Zr6d35elfiWPVakL1lcpcXTcgXV8/5/yA7+PdprqsQQWfHlHux0dUcrLWJpGluu2aWzUgY1ivj4d87RvkawB+11gt7Vwr7XhV2rlOam2QolPshV5jr5CGflXy09VD/lLZ0qq3y2u0prRKb3tbnESGGM1KitNc7w0tB0Q4o40ohe5OkIQBnJE8bjvhb3hS2vWmZELspD/t23bS78bq6bpWt5YXV2ppUam21DQoKsRoQVqSbspIUXZ89GmtxK5qaNHqrYe1LOeQNuyrkDHSjGEpWjQ5U5dMSFdshDPekefE2TfI2QBwvNwDe/WnDf/S2rihqgqL15iavboi3KN/m32ZIsMj/D28LrEsS4e9rU12fnZUFaFtrU0Gqqo5XjGuOk1J2KkLk0N1Yy+2NiFf+wb5GkBAaa63z3l3vCoVrLHvYxWZII2eb6/0Hj5bCov09yj7VLPHo08r67SmrEprS6t1oNFucTIpLlpzU+3V3uNiIgP2qmoK3Z0gCQM445XvsVd4b3pGaqiQUkbafbwnXidFJXbrUJ/X1OuZojK9fLRC9W6PxsVE6sbMVH29f5LiXKe3uuxAWb1e2VSoVzYd0r6yekWGhWjeWQO0OHugzh2eIlcAX3LFibNvkLMB4OTKq6v06LuvaXVokg5Epyu9sUQXNx3V92bNU2ZKP38Pr8tamtzas7lda5PUXG2NDtW22iFqbWttErVfX586SbMuXOzTn02+9g3yNYCA1dIo7XnX7umd95rUWCmFx0oj50rjrpBGzpHCY/w9yj5lWZby6hq1rqxaa0qrlFNdL0tSZkSY5qYmaG5KvGYmxSoigHqdU+juBEkYALxaGqTty+1V3oWfSWHR0oSv26u80yd261C1rW4tO1qhZ4rKtLW2QVEhIVrUP1E3ZaRqUnz0aQ3TsizlHKjUspxDWvX5YVU1tKhfXIQWTsrQoskDNS4j/rSO3xs4cfYNcjYAnFpra6v++u5qLa9t0paEkYpprdNF1bt0+8TpmjJirL+H1y0dW5vsH7BHm5Wk/XUZdmuThD06J7xU37n2W+qfMfS0fx752jfI1wAcwd1i38Ayd4WUu0qqL5VcUdKIi+yi96h59srvM0xJc4vWlVVrXWm13i2vUYPHo5jQEF2QHKd5qQm6KDleKeH+vbKaQncnSMIAcAJFm6XP/iZ9/k+7n9nAaXbBe9zCbl3WZVmWNnlXeS8/WqkGj0dnx0bpxswULUpLUuxprvJuanXrnbxiLcsp1Dv5xWpxWxozIE6LszN1xaRM9Y8PjEvQOHH2DXI2AHTPyg0f6NkDe/VB0jhJ0rkVubp+0BAtnD7LzyPrnuNam7gKlJ9SpZz2rU3id+rCFJduvPmHPW5tQr72DfI1AMfxuKX9H3mL3iulmsNSaLg07AJp7OV2m5OYVH+Pss81uD36oKJG68qqtba0WkeaWxQiaVpCzLHV3iOiI/q8xQmF7k6QhAGgEw0V0pZ/2Ku8y3ZJUclS9o3SlFuk5O6tnKpudevloxV6prBUO+oaFRMaoiv7J+mGjBSdHXd6q7wlqaKuWas+L9LLOYXafLBSIUY6b2Q/LZ6cqbln9Ve0H9915sTZN8jZANAzm/cU6PFNH+ut+OGqccVqQvUuLYwO020XzFd4mDNuPNWmpcmtPZuKlfvxEe3PO6LStFxtjQrRttqsL7U2uXpats69YGG3jk2+9g3yNQBH83jsK5x3vGoXvisP2Pe0GnKu3dN7zKVSQqa/R9nnLMvS57UNWlNapXWl1dpa2yBJGhYVoTmp8ZqXkqDpCTFyhfR+0TuoCt3GmKsk/ULSWEnTLcv6rN33firpW5Lckr5vWdaaUx2PJAwAXWBZ0t737IJ33mrJ8tj9y6Z9WxrxNSmk6yuzLctSTnW9lhaVaUVxhRo8libGRemmjFQt7J+omNDTW+UtSXtKar39vAt1qKJBMeGhunh8uq7MztRXhqUopA+Sb3ucOPsGORsATs+RijL98b039HpEPxVFpmlQw2Fd0lqh759/iVITkvw9vG6rLmtQ/idHlPfxYZVW2K1NNin5i9Ym8Xt0TkSpbr/+20obkHXK45GvfYN8DSBoWJZ0ZKu9yjt3hVSSZ2/PnGqv9B57uZQy3L9j9JPCxuZjfb0/rKhVs2Up0RWqi1LiNTc1Xhcmxyv+NK/gPplgK3SPleSR9BdJP2krdBtjxkl6XtJ0SRmS3pQ0yrIsd2fHIwkDQDdVFUo5S6SNT0u1R6WEwdLUW6Tsm7p9OVdlS6teOlqhpYVlKqhvVKx3lffNmakaFxt12kP1eCxt2FeuVzYV6rXPD6umqVXpCZG6YlKmrszO1Mj+caf9M7qCE2ffIGcDgG80NjfpiXdWa3mjpR3xw5TQUqOv1ezRd6fN0lmDh/l7eN1mWZYO77Jbm+zaWKxyV77yUqqU0zBQ1S1trU126cKUUN1484/kcp14FTv52jfI1wCCVkmBlLfSLnwXbbK3pZ1lF7zHLZDSxkl93MYjENS2uvVeRY3WlFbpzbJqlbe45TLSzMTYYy1OBkdF+OznBVWh+9ggjHlXXy50/1SSLMv6jffrNZJ+YVnWx50dhyQMAD3kbpHyVkkb/ibte9/uYTZuob3Ke9D0biV4y7K0vqpOzxSVaWVJpZo8lqbGR+vmzFRd3i9RkaGnf4fnxha31u04qlc2Feq9ghK5PZYmZCZo0eRMLZiUodRY3yXejjhx9g1yNgD43ksfv6Pni4r0cdIYhVoezarM1U3DRuviyef4e2g9cuLWJqHaVjukXWuTA7p62uTjWpuQr0/MGPMTSb+X1M+yrNJT7U++BnBGqDwg5b1mF733fyTJkpKHeVd6L5AysqWQ0z+PdRq3ZWljVZ3WlFVrbWmVdtY3SZLGxkRqbmqC5qXEa1J8tEJO4w2BM6XQ/ZikTyzLetb79d8kvW5Z1kudHYckDAA+UJxn37xy8/NSc43Uf4I07VvShKukiNhuHaqipVUvHinX0sIy7W5oUnJYqK4ZkKybMlI1NNo3xeiSmiat3FKkZZsOaVthtUJDjM4f1U+LszP1tbH9FRnm20usOHH2DXI2APSeT/N36K9bN+jtxFGqD43S5Kp8LY6P1S1fnSeXy3/3uTgd1aUNyv+0rbXJIe0fsPfLrU0SduuciHL92/XfVmr/IeTrEzDGDJL0pKQxkqZQ6AaAE6gtlvJX20XvPe9JnhYpLkMae5ld+B48Uwp1Zi49XXvrm7S2rEprSqv1aVWt3JbUL9ylOSnxmpuSoFnJsd1uX+q4Qrcx5k1JA07wrfssy3rVu8+7+nKh+38lfdyh0L3asqyXT3D870j6jiQNHjx4yv79+3slDgA44zTVSltftFd5H90mRcRLE6+zi979RnfrUJZl6YOKWj1dVKo3SqvktqTzk+J0c2aK5qYk+OwmFwVHa7Qsp1DLNxXqSHWj4iJcuvTsdC2anKlpWck+6efNibNvcOIMAL3vYEmx/vjBGr0Rla7iiFRl1RfqUk+N7pp9qRJj+qbll6+dtLVJ40BVN8crJqxWU+J26Zn/+Bn5ugNjzEuSfinpVUlTKXQDwCk0VEoFa+ye3rveklobpKhkacx8e6X3sAskV+9dTRzIKlta9Xa53eLk7bJq1bg9igwxmpUUp7mp8ZqTkqABEae+SbbjCt1dGgStSwAgcFmWdHC9ffPKHcsld7OUNcsueI+5TAo9dfJq70hTi547XKZni8pU1NSi9IgwfSM9Rd/ISFZ6RLhPhuz2WPpkT5mW5RTq9W2HVd/s1sCkKC2anKlFkzM1rF/3Vqa3R6HbN8jZANB3ahsa9Od3VmmlO1wFsUOU3FypuXUH9N1zLtCozMH+Hl6PdWxtUuJtbbK9Jku7f7uQfN2OMWaBpIssy/qBMWafKHQDQPc019nF7tyVUsEbUlO1FB4njZpnr/Qe8bVuXwEdLJo9Hn1aWXdstfeBxmZJ0sS4KM3z9vU+KzZK5gQtTs6UQvdZkp7TFzejfEvSSG5GCQB+VlsibXpG+uwpqeqAFDtAmnKzNOWbUnxGtw7V6rH0Zlm1lhSV6p3yGoUaaV5Kgm7OTNWspNjT6vPVXn1zq9ZuP6qXcw7pw12l8ljSpEGJujI7U5ednaGkmO4V1yl0+wY5GwD6nsft1vMfva0XS0r0adI4RXiadX5lrm4dNUEXTMj29/BOS8fWJj/60zVnXL7u7GpqSfdKmmtZVtWpCt1cNQ0Ap9DaLO39l5T7qt3bu75MckVKwy+yi96jL5aikvw9Sr+wLEv59Y1aW2r39d5YXS9LUmZEmOZ4+3rPTIpVhLfneVAVuo0xiyT9UVI/SZWSNluWNc/7vfsk3SqpVdLdlmW9fqrjcdIMAH3E45Z2rrNXee96UzIh9uVb074tDT2/23en3tfQpGeKyvT84TKVt7g1NCpcN2Wk6pr0ZCWH+a7/2dHqRr26uVDLcgqVd6RGYaFGF45O0+LsgbpwTD9FuE7dT4xCt2+QswHAv97fvllP5m3Ruwlj1BQaoemVubomNVXXzZytkG721wwklsfS4d2VyhyVTL72MsZMkL14rN67aaCkIknTLcs60tljydcAcAoet3TgY3uld+5KqbpQCnHZV0GPvdy+Cjquv79H6TclzS1aV1atdaXVere8Rg0ej2JCQ3RBcpzmpSbomvSU4Cl0+xpJGAD8oHyPvcJ70zNSQ4WUMtJuazLxOikqsVuHavJ4tKq4UkuKyrS+qk4RIUYL0hJ1c0aqpsRHn/BSp57aUVStZTmH9OqWIpXUNCkhKkyXT0zXoskDlT048aQ/i0K3b5CzASAw7DlcqD9+/JbWxgxWWXiixtbs0ZWRobpj9qWOvXGlRL7uDK1LAKCXWJZ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\n" 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\n" 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\n" - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "start = time.time()\n", - "\n", - "vars = [\"Resources|Land Cover|+|Forest\",\n", - " \"Resources|Land Cover|+|Cropland\",\n", - " \"Emissions|CO2|Land|+|Land-use Change\",\n", - " \"Emissions|CO2|Land|Cumulative|Land-use Change|+|Regrowth\"]\n", - "for var in vars:\n", - " df_var = df[df.index.get_level_values(3)==(var)].melt(ignore_index=False)\n", - " with PdfPages(f'{var.split(\"|\")[-1]}.pdf') as pdf:\n", - " for bio in set([i[0] for i in df.index.get_level_values(1).str.split(\"G\")]):\n", - " #print(bio)\n", - " df_var_bio = df_var[df_var.index.get_level_values(1).str.startswith(bio)]\n", - " fig, axs = plt.subplots(4, 3, figsize=(25,18))\n", - " ax_it = iter(fig.axes)\n", - " for reg in df_var_bio.index.get_level_values(2).unique():\n", - " if reg == \"World\":\n", - " continue\n", - " legend = []\n", - " ax = next(ax_it)\n", - " df_reg = df_var_bio[df_var_bio.index.get_level_values(2)==reg]\n", - " for ghg in set([i[2] for i in df.index.get_level_values(1).str.split(\"G\")]):\n", - " #print(df_reg[df_reg.index.get_level_values(1).str.endswith(ghg)])\n", - " if ghg == \"000\":\n", - " continue\n", - " df_reg[df_reg.index.get_level_values(1).str.endswith(ghg)].plot(x=\"variable\", y=\"value\", ax=ax, legend=False)\n", - " legend.append(ghg)\n", - " #ax.legend(legend)\n", - " ax.set_xlabel(\"\")\n", - " ax.set_title(reg)\n", - " ax.set_xlim((2020,2100))\n", - " #ax.set_ylim((3000,5000))\n", - " \n", - " fig.suptitle(bio.replace(\"SSP2\",\"\"), fontsize=30)\n", - " fig.legend(legend, loc=\"lower center\", fontsize=16, ncol=12)\n", - " #fig.savefig(f\"example_{bio}.svg\")\n", - " pdf.savefig()\n", - " \n", - " end = time.time()\n", - " print(end - start)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-03T14:07:26.337893200Z", - "start_time": "2023-10-03T13:54:31.461068600Z" - } - }, - "id": "18b06ce1d852eeaa" - }, - { - "cell_type": "code", - "execution_count": 241, - "outputs": [ - { - "data": { - "text/plain": "(2020.0, 2100.0)" - }, - "execution_count": 241, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "var = cro_cov\n", - "\n", - "fig, ax = plt.subplots(figsize=(16,9))\n", - "df_var = df[df.index.get_level_values(3)==(var)].melt(ignore_index=False)\n", - "legend = []\n", - "for reg in df_var.index.get_level_values(1).unique():\n", - " df_reg = df_var[df_var.index.get_level_values(1)==reg]\n", - " df_reg.plot(x = \"variable\", y=\"value\", ax = ax)\n", - " legend.append(reg[-6:])\n", - "\n", - "ax.legend(legend)\n", - "ax.set_xlim((2020,2100))" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-28T12:19:47.138608200Z", - "start_time": "2023-09-28T12:19:46.953653Z" - } - }, - "id": "ffb479ab6d5173e" - }, - { - "cell_type": "code", - "execution_count": 240, - "outputs": [ - { - "data": { - "text/plain": "(2020.0, 2100.0)" - }, - "execution_count": 240, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "var = luc_co2\n", - "\n", - "fig, ax = plt.subplots(figsize=(16,9))\n", - "df_var = df[df.index.get_level_values(3)==(var)].melt(ignore_index=False)\n", - "legend = []\n", - "for reg in df_var.index.get_level_values(1).unique():\n", - " df_reg = df_var[df_var.index.get_level_values(1)==reg]\n", - " df_reg.plot(x = \"variable\", y=\"value\", ax = ax)\n", - " legend.append(reg)#[-6:])\n", - "\n", - "ax.legend(legend)\n", - "ax.set_xlim((2020,2100))" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "start_time": "2023-09-28T12:19:09.645799400Z" - } - }, - "id": "7d19d7ab80b97de1" - }, - { - "cell_type": "code", - "execution_count": 368, - "outputs": [], - "source": [ - "import pyam" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-28T14:39:07.445167500Z", - "start_time": "2023-09-28T14:39:07.398032200Z" - } - }, - "id": "2bcc5a675de60409" - }, - { - "cell_type": "code", - "execution_count": 369, - "outputs": [], - "source": [ - "py_df = pyam.IamDataFrame(df)\n", - "#df_lands = df[df.index.get_level_values(3).str.startswith(\"Resources|Land Cover|+|\")]\n", - "#df_lands_tot = df[df.index.get_level_values(3)==(\"Resources|Land Cover\")]" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-28T14:39:10.165945300Z", - "start_time": "2023-09-28T14:39:07.845642500Z" - } - }, - "id": "5dde75e29e69b3f2" - }, - { - "cell_type": "code", - "execution_count": 286, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Resources|Land Cover|+|Cropland\n", - "Resources|Land Cover|+|Forest\n", - "Resources|Land Cover|+|Other Land\n", - "Resources|Land Cover|+|Pastures and Rangelands\n", - "Resources|Land Cover|+|Urban Area\n" - ] - } - ], - "source": [ - "for i in py_df.filter(variable=\"Resources|Land Cover|+|*\").variable:\n", - " print(i)\n", - " py_df.divide(i, \"Resources|Land Cover\", f\"Share|{i}\", append=True)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-28T13:21:46.066795200Z", - "start_time": "2023-09-28T13:21:45.242754400Z" - } - }, - "id": "55e3ebea09dc9b6c" - }, - { - "cell_type": "code", - "execution_count": 309, - "outputs": [ - { - "data": { - "text/plain": "" - }, - "execution_count": 309, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig,ax = plt.subplots(figsize=(16,9))\n", - "py_df.filter(variable=\"Share|Resources|Land Cover|+|*\", year=[i for i in range(2020,2110,5)], scenario=\"SSP2BIO10GHG400\").plot(ax=ax)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-28T13:35:24.153172200Z", - "start_time": "2023-09-28T13:35:23.878086800Z" - } - }, - "id": "314905a7f7f1dc6a" - }, - { - "cell_type": "code", - "execution_count": 313, - "outputs": [ - { - "data": { - "text/plain": "" - }, - "execution_count": 313, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": "
", - "image/png": 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LuOzcOsrL5vNff3qMxqZmPvySKaUOS5LUjTQ3Jx5ZtSmbK3HRGuY+uYHG5kT/qnJOmjSM95x6GKdNGcHE4QNKHaokSZIkqYfrW5mN1PbP/zef//7zQjZt381nXnm4xUWpC7KwKHVTFeVlfP0t06koC7560yIamxMfe+kUP2wlSe1au2Unf1+8htsXreXvi9ewdssuAI4YPZh3n3oYp08dwawJNVRVlJU4UkmSJElSb1NZXsbX3jKDwf0q+cHtj9OwbTf/+YajKXcKDqlLsbAodWMV5WX8z5unU14WfPOvi2lsbuaTL59mcVGSBMDupmbuf3JDdlXi4jU89NQmAIYOqOLUKcM5bcoITp06nJGD+pY4UkmSJEmSoKws+MLrjqS6XyWX3bKEzTt38/W3zqBPRXmpQ5OUs7AodXPlZcFX3ngMleXBd/62lMam5DABktSLrVi/jdsWZfMk3rV0HVt2NlJeFsyqreGTL5/KaVNHcNSYIZTZ41OSJEmS1AVFBB9/+TQG96vkP/7wKJt3zOEHF8yif5XlDKkr8J0o9QBlZcEXz8mGBfjB7Y+zuynxude8yOKiJPUC23Y1cvfj67ht4RpuX7yWJ9ZuBWBsdT9eN2MMp00ZwUmThzG4b2WJI5UkSZIkqfPefephDO5byWd+uYDzf3QPP7n4OIb0N7eVSs3CotRDlJUF/+/so6goK+PHdzxBU3Mzl77uSIuLktTDpJR47OnN3J5flThn2QZ2NTXTt7KMEw4bxoUnTuC0qSM4bPgAPwMkSZIkSd3aW44dz+B+FXzk2gd46+V38dN3Hed0HlKJWViUepCI4POvPYKKsuBH/3iC3c2J/zj7KIe7k6RubsPWXfx9yVpuX7SG2xetYfXmnQBMGzWIi06awOlTRzJ7Yg19K51zQpIkSZLUs5x11Gh+fHEll1w9h7d8/y6uftfxjB/av9RhSb2WhUWph4kI/vXVL6KivIzv37aUpqbEl95wtMVFSepGGpuaeWBFQ3ZV4uK1LKhvICUY0q+SU6YM5/QpIzh16nBGD+lX6lAlSZIkSSq6U6YM55p3H887fnIfb/r+nVzzruOZMmpQqcOSeiULi1IPFBF8+qxpVJYH37plCbubm/nvN02n3OKiJHVZTzVsf/aKxH8sWcvmHY2UBcwYX81HXzKF06aOYPq4ao/lknqdiPgw8CGgEfhDSulTETEReBRYmK92d0rpffn6s4ArgX7AH4GPppTSwY5bkiRJB9bM2hquf+8JXHDFvbzlB3dx5TuOY/r46lKHJfU6FhalHioi+MTLp1FRVsbXb15EU3Piq2+eTkV5WalDkyQBO3Y3cc8T67lt4RpuX7yGJau3AHDI4L686qjRnDZ1BCdPHkZ1/6oSRypJpRMRLwbOBo5JKe2MiJEFNy9NKc1o427fAy4B7iYrLJ4F3FjsWCVJklR8hx8ymJ+/70TOv+Ie3vbDu/nhRbM5adLwUocl9SoWFqUe7qMvnUJFefDff15IY3PiG2+dQaXFRUk66FJKLHpmC39fvIbbFq3h3ifWs7OxmaqKMo4/dCjnHjue06aOYMrIgUR4VaIk5d4PfDmltBMgpbR6bytHxGhgcErprvz/nwLnYGFRkiSpx5gwbAA/f99JXHDFPVz8k/v49nl1vPzIQ0odltRrWFiUeoEPvngyFWXBl258jKamxGXn1VFVYXFRkortqYbt3LFkLXcsWcudS9exZvNOACaNGMDbjq/l9KkjOP7QYfSrKi9xpJLUZU0FTo2ILwI7gE+mlO7Lbzs0IuYBm4B/Syn9HRgL1Bfcvz5fJkmSpB5k1OC+XH/JiVx85X28/3/v57/fdAxvmDmu1GFJvYKFRamXeO/pkygvC/7jD4/ygf+9n++8vY4+FX6RLUkH0oatu7jr8XXPFhOXrdsGwPCBVZw0aTgnTx7GyZOHM66mf4kjlaSuIyJuBtrqYv6vZDlrDXACcCxwQ0QcBqwCalNK6/I5FX8dEUcCbV3y3e78ihFxCdmwqdTW1r6g5yFJkqSDq2ZAFT979/FccvUcPn7DfDZt383FJx9a6rCkHs/CotSLvPvUw6gsL+Pzv32Y9109l++dP4u+lRYXJWl/bd/VxL3L1nPnkrXcsXQtD6/cREowoKqc4w8bxgUnTuTkycOYNmqQw5tKUjtSSi9t77aIeD/wy5RSAu6NiGZgeEppDdAyPOrciFhKdnVjPVDYVX0csHIvj305cDnA7Nmz2y1ASpIkqWsa0KeCKy46lo9cO49Lf/cIG7c38pGXTDYHl4rIwqLUy1x00kQqyoN//dVDvOenc/jhhbMtLkpSJzU2NTO/fiN3LlnLP5asZd7yBnY1NVNZHtTV1vCxl0zllCnDOGZctfPZStKB8WvgTODWiJgKVAFrI2IEsD6l1JRfwTgFeDyltD4iNkfECcA9wIXAt0oUuyRJkg6CvpXlfPftM/nMLx/k6zcvomH7Lj736iMoK7O4KBWDhUWpF3r78ROoKAs+88sHeddV9/GjC491fi9JakNKicWrt/CPxWu5c+la7n58PVt2NgJw5JjBXHzyRE6aNIzjDh1K/ypPqySpCH4M/DgiHgJ2ARellFJEnAb8e0Q0Ak3A+1JK6/P7vB+4EugH3Jj/SJIkqQerKC/jK288hsF9K/nxHU+weUcjX37D0VTY6Vc64PwGTOql3npsLeVlZfzzz+dz8U/u5ccXH8uAPh4SJOmphu3PzpF459J1rNm8E4AJw/rzuhljOHnScE6cNIyhA6pKHKkk9XwppV3A+W0s/wXwi3buMwc4qsihSZIkqYspKws+95oXUd2/kq/dtIhN23dz2Xl1jtYmHWBWEaRe7E2zxlFZHvzT9Q9w8U/u5SfvOI6BFhcl9TIbtu7irsfXPVtMXLZuGwDDB1Zx0qThnDx5GCdNGs74of1LHKkkSZIkSdqbiOAjL5nC4L4VXPq7R3jnlfdx+YWz/c5TOoB8N0m93NkzxlJeFnz0uge44Ip7uOqdxzG4b2Wpw5Kkotm+q4n7lq3PColL1/Lwyk2kBAOqyjn+sGFccOJETp48jGmjBjnZuyRJkiRJ3dDFJx/K4H6V/PPPF/D2H93DlRcfS40jD0kHhIVFSbzmmDGUR/Dha+fx9h/eww8vnM0hQ/qWOixJOiAam5qZX7+RO5es5R9L1jJveQO7mpqpLA/qamv42EumcsqUYRwzrppK516QJEmSJKlHeMPMcQzqW8kHf3Y/b738Lq5+1/GMGux3ntILFSmlUsdQFLNnz05z5swpdRhSt/LXR5/hw9fOo39VOd9520yOP2xYqUOSpH2WUmLx6i38Y/Fa7ly6lrsfX8+WnY0AHDlmMCdPHs5Jk4Zx3KFD6V9lHyupO4mIuSml2aWOQz2TOaQkSVLPdOfStbznqjkMHVjFNe86ngnDBpQ6JB0k5pDFYWFR0h4WP7OZ9149l+Xrt/Gvr34RF5800aEAJXV5TzVsf3aOxDuXrmPN5p0ATBjWn5MmDeeUycM5cdIwhjrsidStmRSqmMwhJUmSeq75Kxq4+Cf3UlFextXvOo7DDxlc6pB0EJhDFofd9CXtYcqoQfz6Qyfz8evn84XfPcL8FQ186Q3H0K+qvNShSdKzNmzdxV2Pr3u2kPjE2q0ADB9YxUmThnPy5GGcNGk444f2L3GkkiRJkiSp1KaPr+aG957IBVfcy1t/cDc/ecexzKytKXVYUrdkYVHS8wzuW8nlF8ziO39bwtduXsTCZ7bwg/NnUTvML+gllcb2XU3ct2x9dlXi0rU8vHITKcGAqnKOP2wY558wgZMnD2PaqEFeZS1JkiRJkp5nyqhB/N/7TuT8K+7h7T+8h8++6nDeeux4+lR4QYW0LxwKVdJe/W3haj567Twigm+eO4Mzpo0sdUiSeoHGpmbm12/kziVr+ceStcxb3sCupmYqy4O62hpOnjScU6YM45hx1VSWl5U6XEkHicPYqJjMISVJknqH1Zt38JFr53H34+sZW92PD585mTfOGuf3Cz2QOWRxWFiU1KEn123lvVfPZeEzm/nEy6bygTMmU1bmFUGSDpyUEotXb+Efi9dy59K13PP4ejbvbATgyDGDOXnycE6aNIzjDh1K/yoHXJB6K5NCFZM5pCRJUu+RUuIfS9by1b8s4oEVDdQO7c9HXzKFc+rGUu73nj2GOWRxWFiU1CnbdjXy2V8+yG8eWMnLjhjFV98yncF9K0sdlqRu7KmG7dkciUvWcsfSdazZvBOACcP6c9Kk4ZwyeTgnThrG0AFVJY5UUldhUqhiMoeUJEnqfVJK3PLYar520yIeXrmJSSMG8LGXTuXVR4/2wooewByyOCwsSuq0lBI/uWMZX/zjo0wY2p8fXDCLKaMGlTosSd3Ehq27uOvxdVkxcek6nli7FYDhA6s4adJwTp48jJMmDWf8UOdzldQ2k0IVkzmkJElS79XcnPjLI0/ztZsWseiZLRx+yCD+6WVTefkRo4iwwNhdmUMWh4VFSfvs7sfX8aGf3c/2XU38z5un88qjR5c6JEld0PZdTdy3bD13LF3LHUvW8vDKTaQEA6rKOf6wYZw8OSsmThs1yJN0SZ1iUqhiMoeUJElSU3Pi9wtW8s2bF/P42q0cPXYIH3/ZVM6YNsLvLrohc8jisLAoab+s2rid919zPw+saOB9p0/in18xzfHHpV6usamZ+fUb86FN13L/kw3samqmsjyoq63h5EnDOWXKMI4ZV+2E6JL2i0mhiskcUpIkSS0am5r59QMr+eZfF7Fi/XZm1lbziZdP46RJwywwdiPmkMVhYVHSftvZ2MQXfvcIP7tnOadMHs5l59U5F5rUi6SUWLx6C/9YvJY7l67lnsfXs3lnIwBHjB7MKVOGc9KkYRx36FD6V1WUOFpJPYFJoYrJHFKSJEmt7W5q5v/m1POtWxazauMOjj90KJ94+TSOO3RoqUNTJ5hDFoeFRUkv2PX3Ledzv36YEYP68IMLZnHU2CGlDklSkTzVsD2bI3HJWu5Yuo41m3cCMGFYf06aNJxTJg/nxEnD7GQgqShMClVM5pCSJElqz47dTVx373K+c+tS1mzeyalThvOJl09jxvjqUoemvTCHLA4Li5IOiPkrGnjfNXNZv3UXX3z90bxp1rhShyTpAGjYtou7lq7jH0vWcufSdTyxdisAwwdWcdKkbI7EkyYNZ/zQ/iWOVFJvYFKoYjKHlCRJUke272rimruf5Hu3LWX91l285PCR/NPLpnqhRRdlDlkcFhYlHTBrt+zkwz+bx12Pr+OCEybwudccQVWF86hJ3UVKiZUbdzB/RQMPrGjgzqVreXjlJlKCAVXlHH/YME6enBUTp40a5JwCkg46k0IVkzmkJEmSOmvLzkauunMZP7htKZt2NPLKow7hn142lamjBpU6NBUwhywOC4uSDqjGpma+8ueFXH7748ysreZ7589i1OC+pQ5LUhvWbdnJgvqNzK9vYEH9RhbUN7B2yy4AKsuDutoaTp40nFOmDOOYcdVUlttRQFJpmRSqmMwhJUmStK82bt/NFf94gh//4wm27mrktceM4WMvncJhIwaWOjRhDlksFhYlFcXv5q/kUz9fwMC+FXz37TM5dqITGkultGVnIw/mxcOWYmL9hu0ARMDkEQM5Zlw108cPYfq4ag4fPYg+FeUljlqS9mRSqGIyh5QkSdL+2rB1F5f//XGuvGMZOxubeMPMcXz0JVOcOqbEzCGLw8KipKJZ+PRm3nv1HOo3bOdzrzmCC0+c4NCJ0kGws7GJR1dtZkF9NqTpgvqNLF2zhZaP/HE1/ZieFxGPGVfNUWOHMLBPRWmDlqROMClUMZlDSpIk6YVas3kn379tKVff/STNzYm3HDueD714MmOq+5U6tF7JHLI4LCxKKqqN23fz8esf4K+PreYNdWP54uuPpl+VV0FJB0pTc2Lx6s0sWPHckKaPPb2J3U3Z5/vwgX2YPi4rIB4zfgjHjB3CsIF9Shy1JO0fk0IVkzmkJEmSDpRnNu3gO39bwrX3LicI3nZ8LR84YxIjnTLqoDKHLI6SFBYjYhmwGWgCGlNKsyNiKHA9MBFYBrwlpbQhX/+zwLvy9T+SUvpzR49hUih1Hc3NictuWcw3bl7MEaMH84MLZjkMgLQfUkosX7+N+fUbWZBfifjgUxvZvrsJgEF9Kjg6LyJOHzeEY8ZXM2ZIX68UltRjmBSqmMwhJUmSdKDVb9jGt29Zwv/NraeyPLjwxIm897TD7PR9kJhDFkcpC4uzU0prC5Z9BVifUvpyRHwGqEkpfToijgCuBY4DxgA3A1NTSk17ewyTQqnr+eujz/Cx6x+gvCy47Nw6Tps6otQhSV3a6k07mF+/kfkrGphf38CDT22kYdtuAKoqyjhyzGCmj6vmmHFDmD6+mkOHDaCszCKipJ7LpFDFZA4pSZKkYnly3Va++dfF/HreU/StLOcdJ0/kklMnMaR/ZalD69HMIYujKxUWFwJnpJRWRcRo4NaU0rT8akVSSl/K1/szcGlK6a69PYZJodQ1LVu7lfdePZdFqzfzyZdP4wNnTPJqKgnYuG03C57KrkKcn1+N+PSmHQCUlwVTRg5kxvjqbEjTcUOYdsggKsvLShy1JB1cJoUqJnNISZIkFduS1Vv4xs2L+P2CVQzqW8G7TzmMd54ykUF9LTAWgzlkcVSU6HET8JeISMAPUkqXA6NSSqsA8uLiyHzdscDdBfetz5dJ6oYmDh/ALz9wEp/+xQL++88LWVDfwP+8ebofnupVtu9q4uGVG7MhTfN5EZ9Yu/XZ2w8dPoDjDxv67JCmR44Z4tykkiRJkiRJ3dzkkQP59ttm8sEXb+LrNy3i6zcv4id3PsF7T5vERSdNoH9VqUo2UueVai89OaW0Mi8e3hQRj+1l3bYuZWrzMsuIuAS4BKC2tvaFRympKAb0qeBb59UxY3w1X7rxMc75zh384ILZTB45sNShSQfc7qZmFj69mQV5EfGBFQ0sXr2Fpubso+yQwX05ZtwQ3jRrHMeMG8IxY6sdBkOSJEmSJKkHe9HowVx+4WwerN/I125ayH/96TGu+MfjvO/0SZx/wgT6VtrBXF1XSYZC3SOAiEuBLcB7cChUqde5c+laPvyzeexsbOZ/3jyds446pNQhSfutuTnxxLqtLKhvYP6Kjcyvb+CRlZvY2dgMwJB+lRwzbsizQ5pOHzeEkYP7ljhqSeo+HMZGxWQOKUmSpFKZ++QGvnbTQu5Yso5Rg/vwoRdP5i3HjqdPhQXGF8IcsjgOemExIgYAZSmlzfnfNwH/DrwEWJdS+nJEfAYYmlL6VEQcCfwMOA4YA/wVmJJSatrb45gUSt3HyobtvP+aucyv38gHXzyJj79sGuVlzruori2lxMqNO1iwouHZIU0frN/I5p2NAPSrLOfosUOyqxDHZ0XE2qH9nVNUkl4Ak0IVkzmkJEmSSu2upev42k0LuW/ZBsZW9+MjL5nMG2aOo7K8rNShdUvmkMVRiqFQRwG/yr9YrQB+llL6U0TcB9wQEe8ClgNvBkgpPRwRNwCPAI3ABzsqKkrqXsZU9+P6957I53/zMN/521IW1G/ksnPrqBlQVerQpGet37qL+fUNLFiRFRHn129k7ZadAFSWB4cfMpjXzRjD9HHVHDN+CJNHDKTCkz5JkiRJkiR10omThnHDYSfy98Vr+epNi/j0Lx7ku7cu5aMvmcLZM8Z6MYa6hJIPhVos9jaVuqef3bOcz//2IUYN7sv3z5/FUWOHlDok9UJbdjby0FMb9xjStH7DdgAiYNKIgXsMaXr4IYMc+16SDgJ7m6qYzCElSZLUlaSU+Oujq/naTYt4ZNUmJo0YwMdeOpVXHz2aMguMnWIOWRwWFiV1Ofcv38AHrrmfDdt28eU3Hs3r68aVOiT1YDsbm3h01eZni4gL6htYsmYLLR+PY6v75QXEIRwzrpqjxg5mUN/K0gYtSb2USaGKyRxSkiRJXVFzc+LPDz/N129exKJntnD4IYP4p5dN5eVHjHLKnQ6YQxaHhUVJXdKazTv54M/u594n1nPxSRP511e/yLHE9YI1NSeWrN6SDWla38CC+o08umoTu5uyz8LhA6s4ZlxWRJye/x42sE+Jo5YktTApVDGZQ0qSJKkra2pO/H7BSr5x82KeWLuVo8cO4eMvn8oZU0dYYGyHOWRxWFiU1GXtbmrmS398jB/f8QTHTqzhO2+fychBfUsdlrqJlBIr1m9/tog4f8VGHlq5kW27sml6B/ap4OixQzhmfFZEnD6+mjFD+noiJkldmEmhiskcUpIkSd1BY1Mzv5r3FN/862LqN2xnZm01n3z5NE6aPLzUoXU55pDFYWFRUpf3mwee4tO/WMDgvpV87/xZzJpQU+qQ1AWt3rSD+fX5vIj1G3mwvoEN23YDUFVRxhGjBzN93BCm5/MiHjZ8gOPRS1I3Y1KoYjKHlCRJUneyq7GZ/5u7gm/fsoRVG3dwwmFD+cTLp3HsxKGlDq3LMIcsDguLkrqFR1dt4r1Xz2XVxu38f689kvOPr/XKsl5u2dqt3PLYau55Yh0L6jeyauMOAMoCpo4alA1lml+NOHXUIKoqHEpXkro7k0IVkzmkJEmSuqMdu5u49t7lfOdvS1m7ZSenTR3Bx182lRnjq0sdWsmZQxaHhUVJ3cbGbbv56PXzuHXhGt40axz/cc5R9K0sL3VYOkh2NTZz7xPr+dvC1fztsdU8vnYrALVD+zNjfD4v4vhqjhwzmP5VFSWOVpJUDCaFKiZzSEmSJHVn23c1cfXdy/jerUvZsG03L33RSP7pZVM5csyQUodWMuaQxWFhUVK30tSc+ObNi7jsliUcOWYw7zn1MF7yopEM6ltZ6tBUBKs37eBvC1dzy2Or+cfitWzd1URVRRknHDaMM6eN4MWHj2TCsAGlDlOSdJCYFKqYzCElSZLUE2zZ2ciVdzzB5bc/zqYdjbzyqEP4p5dNZeqoQaUO7aAzhywOC4uSuqWbHnmGz/36IZ7etIOq8jJOmzqcVx41mpceMYoh/SwydldNzYn59Q387bHV/G3hah56ahMAo4f05cWHj+TMaSM5afIwr0iUpF7KpFDFZA4pSZKknmTj9t1c8ffH+fEdy9i6q5HXTR/DR18yhcNGDCx1aAeNOWRxWFiU1G01NyfmrdjAHx98mhsfXMXKjTuoLA9OmTycVx49mpcfMYrq/lWlDlMd2LhtN7ctXsPfHlvNbYvWsH7rLsoCZk2o4YxpIznz8JEcfsgg59SUJJkUqqjMISVJktQTbdi6ix/c/jhX3bmMXU3NvKFuLB95yRTGD+1f6tCKzhyyOCwsSuoRmvMr3W586Gn+sGAVTzVsp6IsOGnycF511CG8/MhDGDrAImNXkFJi0TNbuOWxbK7Eucs30NScqOlfyelTs+FNT586wqKwJOl5TApVTOaQkiRJ6snWbN7J929bytV3P0lzc+Itx47nw2dOZvSQfqUOrWjMIYvDwqKkHielxINPbeSPDz7NHx9cxfL12ygvC048bBivOno0Lz9yFMMH9il1mL3K9l1N3Ll0Lbc8tppbF67hqYbtABwxejBnHj6SFx8+khnjqykv86pESVL7TApVTOaQkiRJ6g2e3riD7/xtCdfdt5yI4G3H1fKBF09i5KC+pQ7tgDOHLA4Li5J6tJQSD6/cxI0PreKPDz7NE2u3UhZw/KHDeNUxo3nFkaN65IdmV7Bi/bbsqsSFq7lr6Tp2NjbTv6qcUyYP58zDR3LGtJEcMsS2lyR1nklh7xMR1wPT8n+rgYaU0oz8ts8C7wKagI+klP6cL58FXAn0A/4IfDR1IvE1h5QkSVJvUr9hG9/66xJ+fn89leXBRSdO5L2nT+pRo76ZQxaHhUVJvUZKicee3syND67iDw+uYumarUTAsROH8uqjR3PWUYcwarCFrv21u6mZ+5at59aFa7jlsdUsWb0FgEOHD+DF00by4sNHcNyhQ+lTUV7iSCVJ3ZVJYe8WEV8FNqaU/j0ijgCuBY4DxgA3A1NTSk0RcS/wUeBussLiZSmlGzvavjmkJEmSeqNla7dy2V8X86sHnqJ/ZTnvOPlQ3nPqYQzpX1nq0F4wc8jisLAoqVdKKbF49Rb+sGAVNz60ikXPbCECZtXW8Kq8yDimuueOL36grNm8k1sXZlcl/n3RWjbvbKSyPDj+0GG8+PCRnHn4SA4dPqDUYUqSegiTwt4rIgJYDpyZUlqcX61ISulL+e1/Bi4FlgF/Sykdni8/DzgjpfTejh7DHFKSJEm92ZLVm/n6zYv5w4JVDOpbwXtOPYx3nDyRQX27b4HRHLI4LCxKEtkHZ8ucjI89vRmAutrqZ69kHFfTv8QRdg3Nzdn8lS1DnC6o3wjAqMF98qsSR3Ly5OEM7FNR4kglST2RSWHvFRGnAV9ref0j4tvA3Smla/L/rwBuJCssfjml9NJ8+anAp1NKr2lnu5cAlwDU1tbOevLJJ4v9VCRJkqQu7ZGVm/j6zYu46ZFnqO5fyXtPm8RFJ02gf1X3+77PHLI4LCxKUiuPr9nCjQ9lRcaHV24CYPq4Ibzq6NG88qjR1A7rXUXGTTt28/dFa/nbwtXcunANa7fsJALqxlc/W0w8csxgsgsJJEkqHpPCnikibgYOaeOmf00p/SZf53vAkpTSV/P/vwPc1aqw+Eeyqxq/1Kqw+KmU0ms7isMcUpIkSXrOgvoGvnbTIm5duIbhA6t4/xmTefvxtfSt7D7THJlDFoeFRUnaiyfXbeWPDz7NjQ+tevbqvKPGDuZVR4/mVUeNZmIPHOYzpcSS1Vv428LV3PLYauYs20Bjc2JIv0pOmzqCMw8fwelTR/aoiZwlSd2DSWHvFBEVwFPArJRSfb7MoVAlSZKkg2Duk+v56l8WcefSdYwa3IcPnTmFt84eT1VFWalD65A5ZHFYWJSkTlqxfhs3PrSKPz74NA+saADgRaMH8+qjD+GVR49m0oiBpQ1wL3Y2NrF+6y7WbdnF+q3Zz7qtu1i/defzlq/dspNNOxoBOPyQQc/OlVg3vpqK8q5/wiBJ6rlMCnuniDgL+GxK6fSCZUcCPwOOA8YAfwWmpJSaIuI+4MPAPWRXMX4rpfTHjh7HHFKSJElq351L1/K1vyxizpMbGFvdj4+8ZDJvmDmOyi78faE5ZHFYWJSk/fBUw3ZufHAVNz70NHOf3ADAtFGDsisZjz6EKaMGFfXxt+1qbLNIuG7rLtZvKVyW/WzZ2djmdsrLgpr+VQwbUMXQAVUMHZj9PXVUVlAcW92vqM9DkqR9YVLYO0XElWTzKX6/1fJ/Bd4JNAIfSyndmC+fDVwJ9CObd/HDqROJrzmkJEmStHcpJW5fvJav/WUh8+s3MnFYfz760im8bvpYysu63jRJ5pDFYWFRkl6gVRu386eHnubGB5/mvifXkxJMGTmQVx49mlcfPZqpowbudf7BlBKbdzayfkthMbD9IuG6rTvZsbu5zW1VlZdlBcIBVQwbWPXc3wOqGDqgzx7Lhw2oYnDfSsq64Ie+JEltMSlUMZlDSpIkSZ2TUuKvj67mqzct4tFVm5g8ciAfe+kUXnXU6C71XaM5ZHFYWJSkA+iZTTv488NP88cHV3HvE+tpTnDYiAG84shD6F9Z3qpAmBUQN2zdza6mtguF/SrL2y0Str7KcOiAKgb2qdhrEVOSpO7MpFDFZA4pSZIk7Zvm5sSfHn6ar9+0iMWrt3D4IYP4+Mum8rIjRnWJ7yjNIYvDwqIkFcmazTv588NPc+NDq7hr6TqaEwzqU8HQgYUFwvaLhMMG9KFfVXmpn4YkSV2GSaGKyRxSkiRJ2j9NzYnfzV/JN25eBMDNHz+dii4w96I5ZHFUlDoASeqpRgzqw/knTOD8EyawbVcj5WVBnwoLhZIkSZIkSZJ6jvKy4Jy6sbzmmNE81bC9SxQVVTwWFiXpIOhf5eFWkiRJkiRJUs9VUV7GhGEDSh2GisyysSRJkiRJkiRJkqQOWViUJEmSJEmSJEmS1CELi5IkSZIkSZIkSZI6ZGFRkiRJkiRJkiRJUocsLEqSJEmSJEmSJEnqkIVFSZIkSZIkSZIkSR2ysChJkiRJkiRJkiSpQxYWJUmSJEmSJEmSJHXIwqIkSZIkSZIkSZKkDllYlCRJkiRJkiRJktQhC4uSJEmSJEmSJEmSOmRhUZIkSZIkSZIkSVKHLCxKkiRJkiRJkiRJ6pCFRUmSJEmSJEmSJEkdsrAoSZIkSZIkSZIkqUMWFiVJkiRJkiRJkiR1KFJKpY6hKCJiDfBkqeMAhgNrSx1ED2XbFoftWjy2bXHYrsVj2xaH7Vo8tm1xdKV2nZBSGlHqINQzmUP2CrZtcdiuxWPbFoftWjy2bXHYrsVj2xZHV2pXc8gi6LGFxa4iIuaklGaXOo6eyLYtDtu1eGzb4rBdi8e2LQ7btXhs2+KwXaWDy/dc8di2xWG7Fo9tWxy2a/HYtsVhuxaPbVsctmvP51CokiRJkiRJkiRJkjpkYVGSJEmSJEmSJElShywsFt/lpQ6gB7Nti8N2LR7btjhs1+KxbYvDdi0e27Y4bFfp4PI9Vzy2bXHYrsVj2xaH7Vo8tm1x2K7FY9sWh+3awznHoiRJkiRJkiRJkqQOecWiJEmSJEmSJEmSpA5ZWNxHETE+Iv4WEY9GxMMR8dF8+dCIuCkiFue/a/LlL4uIuRHxYP77zIJtzcqXL4mIyyIiSvW8uoL9aNvjIuKB/Gd+RLy+YFu2bW5f27XgfrURsSUiPlmwzHYtsB/77MSI2F6w336/YFu2bW5/9tmIOCYi7srXfzAi+ubLbdcC+7HPvr1gf30gIpojYkZ+m22b2492rYyIq/L2ezQiPluwLdu1wH60bVVE/CRvw/kRcUbBtmzb3F7a9c35/80RMbvVfT6bt93CiHhFwXLbVerAfhzLzCE7aT/a1hyyE/a1XQvuZw7Zgf3YZ80hO2F/9tkwh+yU/dhnzSE7YT/a1Ryyk/ajbc0hO2Ev7WoO2VullPzZhx9gNDAz/3sQsAg4AvgK8Jl8+WeA/8r/rgPG5H8fBTxVsK17gROBAG4EXlnq59fN2rY/UFFw39UF/9u2+9muBff7BfB/wCcLltmuL6BtgYnAQ+1sy7bd/3atABYA0/P/hwHltusLb9tW9z0aeLzgf9t2P9sVeBtwXf53f2AZMNF2PSBt+0HgJ/nfI4G5QJlt2+l2fREwDbgVmF2w/hHAfKAPcCiw1OOsP/50/mc/jmXmkMVrW3PIIrRrwf3MIQ9w22IOWax2NYcsUtu2uq855AFqV8whi9m25pAvrF3NIXvpj1cs7qOU0qqU0v3535uBR4GxwNnAVflqVwHn5OvMSymtzJc/DPSNiD4RMRoYnFK6K2XvqJ+23Ke32o+23ZZSasyX9wUSgG27p31tV4CIOAd4nGyfbVlmu7ayP23bFtt2T/vRri8HFqSU5uf3WZdSarJdn+8F7rPnAdeC+2xr+9GuCRgQERVAP2AXsMl2fb79aNsjgL/m668GGoDZtu2e2mvXlNKjKaWFbdzlbLIvMnamlJ4AlgDH2a5S55hDFo85ZHGYQxaPOWRxmEMWjzlkcZhDFo85ZHGYQ6o1C4svQERMJOtNeg8wKqW0CrI3GlkPh9beCMxLKe0kO6DVF9xWny8TnW/biDg+Ih4GHgTelyeJtm07OtOuETEA+DTwhVZ3t133Yh+OB4dGxLyIuC0iTs2X2bbt6GS7TgVSRPw5Iu6PiE/ly23XvdiPz7C3kieF2Lbt6mS7/hzYCqwClgP/k1Jaj+26V51s2/nA2RFRERGHArOA8di27WrVru0ZC6wo+L+l/WxXaR+ZQxaPOWRxmEMWjzlkcZhDFo85ZHGYQxaPOWRxmEMKskv/tR8iYiDZMB8fSylt6mgo4Ig4Evgvsl5RkF3q21o6oEF2U/vStimle4AjI+JFwFURcSO2bZv2oV2/AHw9pbSl1Tq2azv2oW1XAbUppXURMQv4dX5ssG3bsA/tWgGcAhwLbAP+GhFzgU1trNvr2xX26zPseGBbSumhlkVtrNbr23Yf2vU4oAkYA9QAf4+Im7Fd27UPbftjsqFY5gBPAncCjdi2bWrdrntbtY1laS/LJbXBHLJ4zCGLwxyyeMwhi8McsnjMIYvDHLJ4zCGLwxxSLSws7oeIqCR7A/1vSumX+eJnImJ0SmlVfknv6oL1xwG/Ai5MKS3NF9cD4wo2Ow5YSS+3r23bIqX0aERsJZuDxLZtZR/b9XjgTRHxFaAaaI6IHfn9bddW9qVt857mO/O/50bEUrKeku6zrezjPlsP3JZSWpvf94/ATOAabNfn2c/j7Lk819MU3GefZx/b9W3An1JKu4HVEXEHMBv4O7br8+zjcbYR+KeC+94JLAY2YNvuoZ12bU89Wa/dFi3t57FA6iRzyOIxhywOc8jiMYcsDnPI4jGHLA5zyOIxhywOc0gVcijUfRRZ94YrgEdTSl8ruOm3wEX53xcBv8nXrwb+AHw2pXRHy8r5JdebI+KEfJsXttynt9qPtj00srHFiYgJZBPFLrNt97Sv7ZpSOjWlNDGlNBH4BvCfKaVv267Ptx/77IiIKM//PgyYQjaRuW1bYF/bFfgzcExE9M+PCacDj9iuz7cfbUtElAFvBq5rWWbb7mk/2nU5cGZkBgAnAI/Zrs+3H8fZ/nmbEhEvAxpTSh4PWtlLu7bnt8C5kc3xdijZ59e9tqvUOeaQxWMOWRzmkMVjDlkc5pDFYw5ZHOaQxWMOWRzmkHqelJI/+/BDNlRCAhYAD+Q/rwKGkU30ujj/PTRf/9/IxsB+oOBnZH7bbOAhYCnwbSBK/fy6WdteQDYx/APA/cA5BduybfezXVvd91Lgk7brAdtn35jvs/Pzffa1tu2B2WeB8/O2fQj4iu16QNv2DODuNrZl2+5nuwIDgf/L99lHgH+2XQ9Y204EFpJNJH8zMMG23ad2fT1ZD9KdwDPAnwvu86952y0EXmm7+uNP53/241hmDlm8tjWHLEK7trrvpZhDHrC2xRyyaPss5pDFbNszMIc8oO2KOWQx23Yi5pAvpF3NIXvpT+QvpiRJkiRJkiRJkiS1y6FQJUmSJEmSJEmSJHXIwqIkSZIkSZIkSZKkDllYlCRJkiRJkiRJktQhC4uSJEmSJEmSJEmSOmRhUZIkSZIkSZIkSVKHLCxKkrqkyPwjIl5ZsOwtEfGnUsYlSZIkSep6zCElSTo4IqVU6hgkSWpTRBwF/B9QB5QDDwBnpZSW7se2ylNKTQc2QkmSJElSV2EOKUlS8VlYlCR1aRHxFWArMCD/PQE4GqgALk0p/SYiJgJX5+sAfCildGdEnAF8HlgFzEgpHXFwo5ckSZIkHUzmkJIkFZeFRUlSlxYRA4D7gV3A74GHU0rXREQ1cC9ZT9QENKeUdkTEFODalNLsPCn8A3BUSumJUsQvSZIkSTp4zCElSSquilIHIEnS3qSUtkbE9cAW4C3AayPik/nNfYFaYCXw7YiYATQBUws2ca8JoSRJkiT1DuaQkiQVl4VFSVJ30Jz/BPDGlNLCwhsj4lLgGWA6UAbsKLh560GKUZIkSZLUNZhDSpJUJGWlDkCSpH3wZ+DDEREAEVGXLx8CrEopNQMXAOUlik+SJEmS1HWYQ0qSdIBZWJQkdSf/D6gEFkTEQ/n/AN8FLoqIu8mGsLGHqSRJkiTJHFKSpAMsUkqljkGSJEmSJEmSJElSF+cVi5IkSZIkSZIkSZI6ZGFRkiRJkiRJkiRJUocsLEqSJEmSJEmSJEnqkIVFSZIkSZIkSZIkSR2ysChJkiRJkiRJkiSpQxYWJUmSJEmSJEmSJHXIwqIkSZIkSZIkSZKkDllYlCRJkiRJkiRJktQhC4uSJEmSJEmSJEmSOmRhUZIkSZIkSZIkSVKHLCxKkiRJkiRJkiRJ6pCFRUmSJEmSJEmSJEkdsrAoSZIkSZIkSZIkqUMWFiVJkiRJkiRJkiR1yMKiJEmSJEmSJEmSpA5ZWJQkSZIkSZIkSZLUIQuLkiRJkiRJkiRJkjpkYVGSJEmSJEmSJElShywsSpIkSZIkSZIkSeqQhUVJkiRJkiRJkiRJHbKwKEmSJEmSJEmSJKlDFhYlSZIkSZIkSZIkdcjCoiRJkiRJkiRJkqQOWViUJEmSJEmSJEmS1CELi5IkSZIkSZIkSZI6ZGFRkiRJkiRJkiRJUocsLEqSJEmSJEmSJEnqkIVFSZIkSZIkSZIkSR2ysChJkiRJkiRJkiSpQxYWJUmSJEmSJEmSJHXIwqIkSZIkSZIkSZKkDllYlCRJkiRJkiRJktQhC4uSJEmSJEmSJEmSOmRhUZIkSZIkSZIkSVKHumVhMSKujIj/6OS6yyLipcWOSeqKIqI2IrZERHmpY5EKRcSpEbGw1HG8UBFxa0S8u53bJkZEioiKgx1XW/JjwWGdWG+vcUfEpRFxzYGP8IWJiO9HxOdewP3/JSJ+dCBjauMxLo2IS4v5GG085sSIWHYwHzN/3D32k/y9csZBjqFLvQfbs7fjSCnl59sXH6z7vVD7kh/sx7bNJ7RfzFulzjFvVVdl3nrwmbd2eH/z1gP7uAckbzXfbV9XzXf1wnXLwmKx5IlfiojXtVr+jXz5xa2Wn5Ev/9Q+Ps6lEbE7/7BsiIg7I+LE/LaLI+IfBesui4jt+botP98uWDdFxNdabf+cfPmV+f8tB5qW+y+LiM8UrD8xIv4WEdsi4rHWCW1EvC0inoyIrRHx64gYWnDbrRGxI9/uxoi4PSKObuO5bs5/FkXEtyNidME6VRHx8zyu1PpAnL8uu/LH2BwRcyPi9ILbW7fZ0Ij4VR7vkxHxtlbbe0n+PLflz3tCJ1+6bieltDylNDCl1PRCtxUR4yLiFxGxNn+tHyx8T0TEu/J23RwRz0TEHyJiUH5b4Wu4PiJuiojD89suyl/TTRFRHxFfKfxQbPUe2JBvd3zB7Xt8YRMRfSLiSxGxPL/f4oj454iIgnXekr/vtkXErW081xl5TNvy3zNa3T4lIq6LiDV53Isj4lsRMS6//YyIqG9ju3t8mEbEoIj4Wv4ct+Yx/zwijmvjvqfn74//aLV8b+/PPhHx4zzGpyPi4897YUskpfT3lNK0Yj9ORPw5Co7RETE2Wh23C5YdUux4Sik/Fjxe6jgKxV4+4/ZVSul9KaX/t7+xpJT+M6V00E928/fwnPy5r4qIGyPilILbj4iI3+bH3c3559ZJBbdPjYjf5Mej9fk+3+57K7pAgh3dJAE6UCI7z7k0/6zYmu/3P46IiaWO7WDIX+ut+T7+VP6594K+OI5W535SbxPmreatPYx5q3lrmLeat+bMWzu8v3lrFxfmu70q3+3NLCw+3yLgopZ/8oPAm4Glbax7EbC+cP19cH1KaSAwAvgH8MvCE8hWXpt/sLb8fKjgtqXAW1sdrC7Mn0dr1fljngf8fxFxVr78WmAeMAz4V+DnETECICKOBH4AXACMArYB32213Q/l2x0G3Apc3cZzHQQMBV4PHALMjYIkLW+D84Gn22mDr+SPMQT4Hll7tfel1HeAXXm8bwe+lz8PImI48Evgc3k8c4Dr29lOt1aED7CrgRXABLLX+kLgmfyxTgf+Ezgvf61fBNzQ6v4tr+E4YDVwZb68P/AxYDhwPPAS4JOt7vva/L6j88f81l7i/L98G68CBpHtu5cA3yxYZz3wDeDLre8cEVXAb4BrgBrgKuA3+XIiYjJwD7ASqEspDQZOJnsvntJ6e+2JiD7ALcDRwGuAwWTtdl0ee+G6lXn897Ra3tH781JgCtlr9mLgUwXv+xekG50g3Q6cXvD/acBjbSxbnFJq7/jzPJHpFp+h3eC12ttnXI+Wf2nyDbLj5yigluw9fHZ++yTgDuBB4FBgDPAr4C+Rf7ELVAO/Babl27iX7BimruPnwOuAt5Gdx0wH5pJ9VnVLse9XJU7PP8dfQtYO7ylKYFLvYt5q3tojmLeat2LeCuat3eG1Mm/t5XlrlOCqxG6ix+W76pyifbjk1el/jogFebX6iogYlfdo2BwRN0dETcH6r4uIhyPrCXlrRLyo4La6iLg/v9/1QN9Wj/WaiHggnutFecwLCP13wMkFsZ0FLKBV4hAR/YE3AR8EpkTE7Fa3XxhZb6x1EfG5aGdom5TSbrITwEPITnr31dNkB+5X5I87FDiJ7GDdppTSXcDDwFERMRWYCXw+pbQ9pfSLfHtvzFd/O/C7lNLtKaUtZInNGyLv0ddqu41kJ5dHtPO4u1NKDwNvBdYAn8iX70opfSOl9A9grz0UU0rNwM/IkqtRrW+PiAF57J9LKW3Jt/lbshNYgDcAD6eU/i+ltIPsBHZ65L0Q91VEHBdZj51NkfV2/FrBbafk+2NDRKxo+QIust54/xNZL79nIhsGoV9+2xmR9X78RESsjqwX0DsKtvnqiJiXP96KKBiaIJ7rEfOuiFgO3BKteslExJjIehCtj4glEbEvX+wdC1yZUtqaUmpMKc1LKd1YcNtdKaV5ACml9Smlq1JKm1tvJKW0jew1PCr//3t5L8BdKaWngP8lS3ieJ3/Nfk47+1hEvAR4OfDGlNJDeZx3kyX/H8yTK1JKN6eUbiBLslo7A6gAvpFS2plSugwI4Mz89kuBO1JKH08p1efbW53vw9e123rPdwFZsnpOHmtT3rY/Tyld2mrdTwB/IUssCnX0/rwQ+H8ppQ0ppUeBHwIX70OMe8j3pQ9GxGJgcb6s3eNvRMzM99fNEfF/EXF95D1Xo1Xv2Ih4UX7sb8g/C15XcNuVEfGdyHr9bo6Ie/IT1864neyY3vJ5dyrZCfHsVstuzx/rpIi4L7JedvfFnj3sbo2IL0bEHWTJ8B7Ds0REef7eXhsRjwOv7mSMzxMRJ0TWW7e8YNnrI2JB/vdxEXFX3l6rIutRX1WwbluvVWp5D+ztWFLgnRGxMt/+JzqIteVYNz8O0Il2ZL3674iIr+fbfjx/fS7OY14dEYVfqD7bCzwihkfE7/P7rY+Iv7e83hHx6ciumtocEQvz48bzekTG3s9LlkXEJyM7x9mY79t9O3rsVs9vCPDvwAdTSr/M3/+7U0q/Syn9c77apWTH1n/Nj6ub82PS1cB/AaSU7k0pXZHfvhv4OjAtIvb5nCIiPhMRS/O2eSQiXt/q9fhHvo9viIgnIuKVBbcfGhG35fe9iexLt33WyX37fZH1htyQHxsiv22f3oNtvOatPzMvzve7zfnzfXvBuu+MiEfzGP4c7VxFEtm538uAs1NK9+WfSxtTSt9JKV1RsOqEfH/fHBF/iewL5ZZt/F9kx4ONkV1hc2TBbXs9PkbEy/P9fGNEfDd/jQqvQOjU8zhQUkqPAX8nPweIvX+GtLk/5u/F7wMnRn4VVevHiYia/H24Jn9uv4/86oz89lsj4v/tpc0viOfO4/+1aA2iLiHMW81b23iu5q3mreatzzJvNW9tV5i3mrdmLqWX5a37q5PvCfPd527rVvlub1PsXitvJNu5pgKvBW4E/oXsTVsGfAQgsiThWrLeXyOAPwK/i+xS2irg12QHo6FkvbpakgciYibwY+C9ZAnOD4DfRtarag+RnTA3dBDzDrIT+nPz/y8EftrOc9uSx/PnfL2WxzmCrOfG28l6qg0Bxrb1YHmcFwP1KaW1HcTWnp8WPP65ZD0+drbzeBERJwNHkvX2PBJ4vNWJ9Px8Ofnv+S03pJSWkvWqnNrGtqvInvPdews2ZcOb/IbspGifRHayciHwBHmvw1amAk0ppcKer3t7PlvJeuwdyf75JvDNlPX+m0Te2zEiasn292+R7dMzgAfy+/xXHucMYDLZvvH/FWzzEJ7bZ94FfCee+8JgK9nzryb7AHl/RJzTKqbTyXoQvqKNeK8F6sl6D70J+M+Ck5OO3h9357Gcmz+/QvcAr4iIL0TEyW29/1pExECy/WReO6ucRvYFQlv37U+W4Le3j70MuCeltKJwYUrpHrLn3ZneMkcCC1JKqWDZAp7bR14K/KIT2+nIS4E/5/tgu/IPvXeSnci1FWub7898nxlTeDt7vhf21zlkPXSP2NvxNz8e/Iqsh+9Qsn3v9W1tMLKerb8jS0JHAh8G/jf2HBbjPOALZL1xlwBfLLj/76NgmKxW7gX6kPWYgmz/uinfRuGy2yP7gusPwGX58/ka8IdWJ7otPYkHAU+2eqz3kPXirQNmk73H9kv+xcJWnvtiALLeXz/L/24C/ons8/REsn37A602cw75a9XGQ3TmWPJisp7DLwc+E218yRgRY8na7D/IXudPAr+I564e+ExE/L6j57sXx5O9/4aRPffryL4Qmkz2xcu382NKa58ge8+PIPsy71+AlO9THwKOTVkv9VcAy9p4Xu2elxSs9hayL3EPBY7huS8/2nzsNmI8kewL51/t5fm/jOw8o7UbyL546N/GbacBT6eU1u1lu+1ZSvbZPITs/XZN7HmVxvHAQrL97ivAFS1JDtnrMze/7f+xf1fFQOf27deQ7QfTyV6Hls+7A/YejOwL38uAV+b7yknkn+P5e+VfyL70HUFWKLu2nU29FLi39edSG94GvIPsGFjFnldA3Ej2XhwJ3E/2RWahNo+PebL2c+CzZO+hhfnzaHmO+/I8Doj8HPlUYF4nzuHb3B/zLxzfR/blxcCUUnUbD1UG/ITsyodaYDvQeriqNts8j/F7ZMf7MXls41BPZ95q3vo85q3mrZi3tmzHvBXz1raYtz7LvLX35a37y3y3B+e7vU2xC4vfSik9k/fm+jvZidO8lNJOsgNSXb7eW4E/pJRuynst/A/Qj2xnOAGoJOuFtTul9HPgvoLHeA/wg5TSPXnPqavIkpMTWgeTUvpHO18+tPZT4MLIemWcTpYgtnYR2VApTWQHpfPykwzI3ti/yx9vF9nJd+uD81vyk+EVwCyyD9L2/DqyngwtP6176/0KOCOPt72EEmAt2VAaPwI+k1L6KzAQ2NhqvY1kJx904naAy/LnsoXsg+8Le3kuLVaSfZh31ifzx9hK1mvrc6nt+RcOxPPZF7uByRExPO9p2pI4vB24OaV0bb7frkspPZB/kL0H+KeWXjxkQwmc22qb/57f749k7ToNIKV0a0rpwZRSc0ppAdkBsXBoDIBL8x5E2wsXRja/wynAp1NKO1JKD5DtCxfk2+7o/fFmsvfx54AnIuvtd2x+37+THahnkp2srYvnz6HU8houIXsdLm79AJH1cp1Ndgwo9Ov8vpvITlj+u50YhwOr2rltFZ3ridTRPjKcgp7gEfGh/H25JSJ+WHCfMa3etw3sOeRM6+3MyNfbFHtODn8ZeU/mfYx1YMH/bT2P/fWlfN/dzt6PvyeQ9aC9LN+Xf0mWLLXlhDzeL6esB/AtwO/JTh5a/DLv4dZIdpIxo+WGlNJrUkrPGx4ov20n2RcIp+UJWHXK5mv4e8GyI4DbyBKVxSmlq/NeVteS9bZ9bcEmr0wpPZzfvrvVw72F7LNqRUppPfCl9puxU64lb4PIevO+Kl9GSmluSunuPI5lZMlx62NB4Wu1h04eS76QH0seJPuC/rzW2yFLkv6YUvpjvq2byIbqelX+OF9OKb2mg+e5t8+4J1JKP8mP99cD48mOjztTSn8h+0Jichvb3E32BemEfP/7e/6lSxNZwn5ERFSmlJblX2y0trfzkhaXpZRW5q/173hun2zvsVsbBqzN9+n2tHdMW0V2DldTuDCyK7K+A+zXvDQpuypiZf5aXk/Wa7hw7pwnU0o/zF+Pq8ie56j8S7tjyY5VO1NKt5O1yf7E0Jl9+8sppYaU0nLgbzzX9gf6PdhMdpVMv5TSqpRdvQLZl1JfSik9mr9+/wnMiLZ7Pw6j/c+lQj9JKS3K3683sOcx7scp6/W7k+euWBlScN/2jo+vIrva5Zf5bZex55VM+/I8Xqj7I2ID2X7xI7Jjyl7P4TuxP7YrP+/6RUppW36u9UWevx+11+ZvAn6fsqsqdpKd9zS/gOeu7sG81by1Peat5q0Xt34A81bz1mTeWsi81by11+Wt+6uT7wnz3e6X7/ZKxS4sFvbM297G/y0nEGMo6EGTsmFDVpD1fBsDPNXq4FbY22YC8IlWJ0Hj8/vtl5QNQzIC+DeyLxbaOtF9Mc9V0H9D1nuj5RLkMXn8LdvbBrTugXFDSqk6pTQypXRmSmnuXkI6J1+35afwJJA8vj/k8Q5PKd3RznaGp5RqUkovStkl6ZCd/A9utd5gYHMnbwf4SH5i35es58TPo+NhfcaSJYud9T/5Y/QjO4H/7yi4lL3AgXg+AETEqfHcpMxt9kQk65k5FXgssuEnWk5ExtP2/CYjyOZmmFuwv/4pX95iXasP7G3k75WIOD6yCZDXRMRGsh77rZOO9nqJjAFaksIWT9JOr+TWUjYsyWdSSkeS9WZ6gOzEKvLbb0wpvZYs8T6bLAF7d8Em/ifffw9JKb2u9UlR3pPky2S9ZVr3gj4nf/37kH0JcFu0PWH5WrIThraMzm/vSEf7yLrCx0gpfTuP7RtkXya1WNnqfVtNNicL7WzngXydN5A9TyLitcCg/ERpX2PdUvB/W89jD5EN99Wyv7+9rXVyhfvX3o6/bR2797ZvrsiP/S1a75uFJwfPvic66Xay3nCn8txr8I+CZStSSk/S6rOonTj21gtrTKvbW2/rWRHx9oL2vrGd1X5GNkRQH7L94v48TiKb+Pz3kQ0XsYnsBKmzx4L9OZa0tE9rE4A3t/FFRHvvw7bs7TOu9XkDKaX2ziUK/TfZl0F/iWxoj8/k911C1pvzUmB1RFwXEW09r72dl7Rob59s87HbsA4YHnufS6S9Y9posiRgQ8uCyHrb/gX4bsq+XNhnkQ2H90DBa3kUe+4Xzz7n/NwGsuc9BtiQ9uzJ3u7+30EMndm322v7dt+DnfxMf1b+XN5K9t5YFdnQKy3Dz00AvlnQTuvJhh5r6/N0j2P9XrT5nCIb7ubLkQ31s4nneiq3+bqwl/bIj8f1Bevuy/MgsiGUWtZ9G/Ddgvd+63nMWpuZn4NOSin9W/6e2us5fCf2x3ZFRP+I+EFkwztuIvscqG71xXFn220rzz+PV89j3op5azvMW81bzVvNW1uYt7bNvNW8tcfmrW3sM78vWNZem+1te+a7XTTf1b4rdmGxs1aSvdhANuwJ2Yf8U2RV77EtJ4G5wuEsVgBfbHVw77+/B6cC15BdFt5WL8oLyNrudxHxNPA4WXLSMqzLKgqGTIpsHoJhrTdygP2ULN6r9/F+DwOHxZ5zT0znuSE9Hua5YReIiMPITh4Lh2wBsg+wlPUAXEI2BEGbIhuz+7VkPa/2Sco8RDYpcFtjSS8CKiJiSsGyvT2fAWRDwTzvoJuy3jotkzK3OQxHSmlxSuk8ssu1/4ssOR1Atl9OauMua8lOKI4s2F+HpGyC9874GdmQR+NTSkPI5hiKVuu01cMI8t62rV7rWrL32T7JE6j/ITuID211W3PKehXfQj4fRUcim5j9h2STYT+4l8dtSlkPwibannD+ZuD4/EuUwu0fR3ZMuaUT4TwMHNPqmHMMz+0jfyU7WX6h/gq8PN9f2vMSsjkVns6PNW8FPhYRvymItc33Z0ppA9mxaHrB9grfC3tIKb2yYH9vPezAHqsW/L23429bx+49XpcCK4Hxsed4/vu1b7bjdrJE7DSeO+7cQTYnymn57S1xtO651DqO9t5fkD3nwufYeuil5zaS0v8WtHdbXzaRUnqE7ETxlew5nAxkw/Q9BkxJ2ZBW/0LnjwXQuWNJ6+fS1twuK4CrW+0DA1I7PXEPlpT1dvtESukwss+bj0c+fFZK6WcppVPIXutEPudDK3s7L9nvx27lLrJh7M7Zy+ZuJut139pbyIaB3JbHV0OWnP02pfTFNtbvUGS99n5I9iXYsPwLo4d4/n7RllVATavjWbv7fwc6s2/vLY4234PtfKZvJfvStMUeX/yllP6cUnoZWaL0GFn7QLbfv7fVft8vpXRnGzHdDBwXBfP77aO3kX3p+VKyoX4m5ss7+7oUno8Gew7puS/Pg5TSMQVfOP4M+EDB/VoP39MZ7X6GdGJ/3NvxDbJz4mnA8fl+dFq+vLPt9ux+FNnQTcU+j1f3Yd76wpm3ZsxbzVvNW81bWzNvfT7zVvPWPRzsvLVwnyEr9L+mYNn+7D/mu3vqMvmu9l1XKSzeALw6Il4S2bAsnyAbluBOsgNYI/CRiKiIiDew5+XNPwTel/diiYgYENnkvi902ITLyIauuL2N2y4kGzZlRsHPG/PnMIxsfN/XRjZZb1W+bmcPEvvrtjzeb+3LnVI2p8MDwOcjom9kE94ew3Nj8f8v2XM5NT/w/jvZJcjt9R47kWx4huedBEZEZWSTCF9LdjD7WsFtfSKfQBioymNps83yHhSntPUYKetx8Uvg3/N94WSyA1RL4vorssu835g/3v9HNi9B68nFOyUizo+IESnrFdSQL24ia7eXRsRb8v12WETMyNf7IfD1iBiZb2NsRLyizQd4vkFkvTd35EnH2zoba8rGu74T+FLevseQ9Vzd28l44XP9r4g4Kn8+g4D3A0tSSusi4uzI5rCoyd+Hx5Fdyr/XeUvy7Z6Zx/DGlFJ7Q460rBsRcTbZMAqPtvEcbyZLfH4REUdG1vPlhHz730spLc63U56//hVAWd4eLb02byV7DT+S75cfype3JHeXAqdGNmTO2Hx7w8nmB9kXPyX7EPxV3q4tMc0uWOdzPDevyQyyE+ofko1LDh2/P38K/Fv+uhxONgTMlfsY597s7fh7F1k7fijfZ86m/SHs7iE74flUfpw4g+zE9roDFOedZHMynE+eoOUJ7Jp8Wctx/o9k83y8LY/5rWTHs87OtXAD2X4zLj9h3ufea234Gdm8Tqex55wFg8iGWNqSv7bv38ftduZY8rnIrvo5kmyfa6sH8jVk++ArWvbhiDjjBZxUHhAR8ZqImJx/jmwi2xebImJaRJwZWW/aHWRfmLU1PNnezkv267Fbr5dS2kj2GfSdiDgnb+vKiHhlRHwlX+0LwEkR8cWIGBoRgyLiw2TnIZ/OH28w2ZxZd6SUOrvPtRz3Wn76AAPIEtY1+XbfQSe/ZEtZj+Q5wBcim2fsFPYciqk9fVrFUcYL27f39T34ANnQUrWRDbXy2ZYbImJURLwuP7buJOtN3/I6fh/4bP7eICKGRERbiXTL59JNZMf6WS2foRHxvoh4Zyee06D88deRJYX/2Yn7tPgDcHS+f1UAH2TPZLLTz6NI9vYZ0tH++AwwLvacP6bQILL3d0NkQ4d9fh/i+jnwmsjm8Koi+2ztKjmTSs+89YUzb8W8dW/MW81bMW81b93z/uat5q2lzlv3l/lux3pyvtvjdYkkOaW0kOxD8ltkPeNeS9YDbFfK5np4A9nwFBvIej79suC+c8hOOr6d376ENsbAh+cuC+5kTOtTSn9Nac/xpfOTvYnAd1JKTxf8/DZ/7PNSNibxh8lOLlaRDeGwmnYmpu+E38VzlzNviYhftRFvyuPdl2FaWpxLdlK4gWxIjzellNbk232Y7LLo/82fwyCeP6nst1tiI0uE/i2lVDhEwlvz2xrITjDXAbNSSoW9iBaSfVCOJfug2c6ePbA+lT/GVrLeLT8hG4e6LR8gG3pmNVky+P78eZA/rzeSzbWzgWxC33Pb2U5nnAU8nD+/bwLnpmweiOVkYz1/guxS6wd4rgfep8n2lbsju8z7ZvK5KDrhA2TJ52ayD/Yb9jHe88j235VkyernUza2fGfeH/3z+zSQ9XaeALwuv20D2ftwMdkH5DXAf3fQg7DF58h6pfwx2h9i43d5bJvIXruL0nNjf7f2RrIxyP9E9sF4DXAF2XuyxQVk+9j3yHoEbifvmZMfc84hOwFqIJuA/px8ecuXGieQ9YKZn78Wd5C16ec68XzJt7ODbGiqR8g+DDeRvQ+OJevV1dKD7NnjTB7n1pb3eSfen58nG9roSbIvcf47pfSnzsbYiefQ7vG34Nj9LrJ2PJ8s0XnecTBf93VkPRzXAt8FLuzsFyeRDYfzL3uJcxvZ5Nx9yHqytfg7Wa/t2/P11pENi/UJsuPUp8h6o3VmKCLI9qE/A/PJJpz+5d5X75RrgTOAW1rF8UmypGpz/rjtDTvUns4cS24je03/SjYk1F9ar5B/8XM2WS+7NWQ9wv6Z/PwiIv6ljfdzax1+xu2HKWTH1i1kXxZ8N6V0K9k+8GWy/expstf/efvO3s5LXsBjP09K6Wtk80r8G8+134fI58jKv1Q6hezzYxnZOcUbgVek54aPez3ZceMdrdpxbz0vzyM7nrT8LE1ZT+Ov5jE/AxxNdmzrrLeRfaauJzv2tDdvVqEtreI4kxe2b+/TezD//LseWEB2jCj8MqaM7Fiwkuw5nU5+fE0p/Yqsx/B1+ef4Q2THr/a8iewLoOvJ5gx6iOy86+ZOPKefkh3DnyL7vOjwi88W+THjzcBXyI5pR5Al0jv383kcUB18hnS0P95C9kX90xHR1jH6G2TngmvJ2qzTn335Z+sHyb4gW5XHVr/XO6nXMG/tkHmreWvhczNvNW81by1g3mre2gbz1u6Rt+4v892O9dh8tzeI1Oa8rDqQImIg2QnKlJTSEyUOR5JKIiLuAb6fUvpJqWORSiEiLgVIKV16EB9zInBrSmniwXrMduK4Fbi0vWRVxZf3kK0H3p5S+lsn1r+SbN+5ch8fZ7/uJ6n0zFslybxVMm/d97zVfLf09jXf1QvXJa5Y7Iki4rWRXR4+gGxM/wd5bgJSSerxIuL0iDgkHwrhIrLhqg5Yz1NJ0t5FNtxTdWTDBrXM39HpXqCSej7zVkm9nXmrJHVP5rulVVHqAHqwlvkRguwy3HNbD08jST3cNLKhSgaSDW3zppTSqtKGJJXUrSV4zAayYSFL7Ur8oroUTiQb0rOKbGiZc1JK2zt531+zf6/Z/t5PUmmYt0rq7cxbpT3dWoLHbKB75637ez+9MC8k39UL5FCokiRJkiRJkiRJkjrkUKiSJEmSJEmSJEmSOtRjh0IdPnx4mjhxYqnDkCRJknSAzJ07d21KaUSp41DPZA4pSZIk9SzmkMXRYwuLEydOZM6cOaUOQ5IkSdIBEhFPljoG9VzmkJIkSVLPYg5ZHA6FKkmSJEmSJEmSJKlDFhYlSZIkSV1GRJRHxLyI+H3+/6UR8VREPJD/vKpg3c9GxJKIWBgRryhYPisiHsxvuywiohTPRZIkSZJ6GguLkiRJkqSu5KPAo62WfT2lNCP/+SNARBwBnAscCZwFfDciyvP1vwdcAkzJf846KJFLkiRJUg/XY+dYlCRJktR17d69m/r6enbs2PG82/r27cu4ceOorKwsQWQqpYgYB7wa+CLw8Q5WPxu4LqW0E3giIpYAx0XEMmBwSumufJs/Bc4BbixW3JIkSZKKyxyy67CwKEmSJOmgq6+vZ9CgQUycOJHCUSpTSqxbt476+noOPfTQEkaoEvkG8ClgUKvlH4qIC4E5wCdSShuAscDdBevU58t253+3Xi5JkiSpmzKH7DocClWSJEnSQbdjxw6GDRtG66nvIoJhw4a12QtVPVtEvAZYnVKa2+qm7wGTgBnAKuCrLXdpYzNpL8vbesxLImJORMxZs2bNfsUtSZIkqfjMIbsOC4uSJEmSSqJ1QtjRcvV4JwOvy4cyvQ44MyKuSSk9k1JqSik1Az8EjsvXrwfGF9x/HLAyXz6ujeXPk1K6PKU0O6U0e8SIEQf22UiSJEk6oMwhuwYLi5IkSZKkkkspfTalNC6lNBE4F7glpXR+RIwuWO31wEP5378Fzo2IPhFxKDAFuDeltArYHBEnRPYNw4XAbw7eM5EkSZKknss5FiVJkiRJXdlXImIG2XCmy4D3AqSUHo6IG4BHgEbggymlpvw+7weuBPoBN+Y/kiRJkqQXyMKiJEmSpJJIKbU5ZE1KbU6Hp14kpXQrcGv+9wV7We+LwBfbWD4HOKpI4UmSJEkqAXPIrsGhUCVJkiQddH379mXdunXPSwBTSqxbt46+ffuWKDJJkiRJUldjDtl1eMWiJEmSpINu3Lhx1NfXs2bNmufd1rdvX8aNG1eCqCRJkiRJXZE5ZNdhYVGSJEnSQVdZWcmhhx5a6jAkSZIkSd2AOWTX4VCokiRJkiRJkiRJkjpkYVGSJEmSJEmSJElShywsFtHupmYWPbO51GFIkiRJkrqBpubEY09vIqVU6lAkSZIkqU0WFovoW7cs4axv3M62XY2lDkWSJEmS1MX97J4nOesbf2flxh2lDkWSJEmS2mRhsYjqxlfTnGD+io2lDkWSJEmS1MXV1dYAMGfZ+hJHIkmSJElts7BYRDPGVwMwb8WG0gYiSZIkSeryDj9kEP2ryrn/SXNISZIkSV2ThcUiqhlQxWHDBzBveUOpQ5EkSZIkdXEV5WXU1VYzd7mFRUmSJEldk4XFIptRW8285Q2klEodiiRJkiSpi5tVW8OjqzazdWdjqUORJEmSpOexsFhkdbU1rN2yk/oN20sdiiRJkiSpi5s5oYam5sT8FQ2lDkWSJEmSnsfCYpHV5fMs3u9QNpIkSZKkDtTV1hABc51nUZIkSVIXZGGxyA4/ZBD9KsudZ1GSJEmS1KEh/SqZOnIQcywsSpIkSeqCLCwWWUV5GceMG8I8h7GRJEmSJHXCzAk13L98A83NqdShSJIkSdIeLCweBHW1NTyyciM7djeVOhRJkiRJUhc3e0INm3c0snj1llKHIkmSJEl7sLB4ENTVVrO7KfHwyo2lDkWSJEmS1MXNmlADOM+iJEmSpK7HwuJBUFdbDeA8i5IkSZKkDk0Y1p9hA6osLEqSJEnqciwsHgQjB/VlXE0/C4uSJEmSpA5FBLMm1DD3yfWlDkWSJEmS9mBh8SCpq61h3nJ7m0qSJEmSOjZrQg3L1m1j7ZadpQ5FkiRJkp5lYfEgqRtfzcqNO3h6445ShyJJkiRJ6uJmT8zmWbzf4VAlSZIkdSEWFg+SmROypNCrFiVJkiRJHTlyzBCqysucZ1GSJElSl2Jh8SA5YvRgqirKmLeiodShSJIkSZK6uL6V5Rw1drCFRUmSJEldioXFg6Sqooyjxgz2ikVJkiRJUqfMnjiUBU9tZGdjU6lDkSRJkiTAwuJBVVdbw4L6jexuai51KJIkSZKkLm5mbQ27Gpt56KlNpQ5FkiRJkgALiwdVXW01OxubeXSVSaEkSZIkae9mTagB4H6HQ5UkSZLURVhYPIhm1mZJ4bzlDaUNRJIkSZLU5Y0Y1IcJw/oz58n1pQ5FkiRJkgALiwfV6CF9GTW4j/MsSpIkSZI6ZVZtDXOfbCClVOpQJEmSJMnC4sEUEdSNr2HeioZShyJJkiRJ6gZmTqhh7ZadrFi/vdShSJIkSZKFxYOtrraaJ9dtY+2WnaUORZIkSZLUxc2emE2p4XCokiRJkroCC4sH2cwJWVL4gPMsSpIkSZI6MGXkIAb1qWDuk06pIUmSJKn0ilZYjIgfR8TqiHioYNl/R8RjEbEgIn4VEdUFt302IpZExMKIeEXB8lkR8WB+22UREcWK+WA4aswQKsqCeStMCiVJkiRJe1deFsyorbawKEmSJKlLKOYVi1cCZ7VadhNwVErpGGAR8FmAiDgCOBc4Mr/PdyOiPL/P94BLgCn5T+ttdiv9qsp50ejBzPOKRUmSJElSJ8yeMJSFz2xm047dpQ5FkiRJUi9XtMJiSul2YH2rZX9JKTXm/94NjMv/Phu4LqW0M6X0BLAEOC4iRgODU0p3pZQS8FPgnGLFfLDU1VYzf0UDTc2p1KFIkiRJkrq4WRNqSMkpNSRJkiSVXinnWHwncGP+91hgRcFt9fmysfnfrZd3azNra9i6q4lFz2wudSiSJEmSpC5uRm01ZYHDoUqSJEkquZIUFiPiX4FG4H9bFrWxWtrL8va2e0lEzImIOWvWrHnhgRZJXW01gMOhSpIkSZI6NLBPBYcfMtjCoiRJkqSSO+iFxYi4CHgN8PZ8eFPIrkQcX7DaOGBlvnxcG8vblFK6PKU0O6U0e8SIEQc28AOodmh/hg6oYt5yk0JJkiRJUsdmTahh3vINTqkhSZIkqaQOamExIs4CPg28LqW0reCm3wLnRkSfiDgUmALcm1JaBWyOiBMiIoALgd8czJiLISKoG1/N/RYWJUmSJEmdMHtiNqXGY09vKnUokiRJknqxohUWI+Ja4C5gWkTUR8S7gG8Dg4CbIuKBiPg+QErpYeAG4BHgT8AHU0pN+abeD/wIWAIs5bl5Gbu1utpqlq7ZysZtu0sdiiRJkiSpi5tZWwPA/Q6HKkmSJKmEKoq14ZTSeW0svmIv638R+GIby+cARx3A0LqElqTwgfoGTp/adYdtlSRJkiSV3riafowc1Ie5T27gghMnljocSZIkSb3UQZ9jUZljxlcTgfMsSpIkSVKBiCiPiHkR8fv8/6ERcVNELM5/1xSs+9mIWBIRCyPiFQXLZ0XEg/ltl+VTa3RrEcHsiTXM8YpFSZIkSSVkYbFEBvapYNqoQcxb3lDqUCRJkiSpK/ko8GjB/58B/ppSmgL8Nf+fiDgCOBc4EjgL+G5ElOf3+R5wCTAl/znr4IReXDNra6jfsJ1nNu0odSiSJEmSeikLiyVUV1vNvOUbaG5OpQ5FkiRJkkouIsYBrwZ+VLD4bOCq/O+rgHMKll+XUtqZUnoCWAIcFxGjgcEppbtSSgn4acF9urVZE5xnUZIkSVJpWVgsobraGjbtaOTxtVtLHYokSZIkdQXfAD4FNBcsG5VSWgWQ/x6ZLx8LrChYrz5fNjb/u/Xybu/IMUPoU1HmcKiSJEmSSsbCYgnNrK0GnGdRkiRJkiLiNcDqlNLczt6ljWVpL8vbesxLImJORMxZs2ZNJx+2dKoqypg+rpq5FhYlSZIklYiFxRI6bPhABvWtYN6KhlKHIkmSJEmldjLwuohYBlwHnBkR1wDP5MObkv9ena9fD4wvuP84YGW+fFwby58npXR5Sml2Smn2iBEjDuRzKZqZE2p4eOVGduxuKnUokiRJknohC4slVFYWzBhf7fwYkiRJknq9lNJnU0rjUkoTgXOBW1JK5wO/BS7KV7sI+E3+92+BcyOiT0QcCkwB7s2HS90cESdERAAXFtyn25s9oYbdTYkF9RtLHYokSZKkXsjCYonNrK1h0TOb2bKzsdShSJIkSVJX9GXgZRGxGHhZ/j8ppYeBG4BHgD8BH0wptVzG937gR8ASYClw48EOulhmTqgBcDhUSZIkSSVRUeoAeru62mqaEyyob+CkScNLHY4kSZIklVxK6Vbg1vzvdcBL2lnvi8AX21g+BziqeBGWztABVRw2YgBzn1wPTCp1OJIkSZJ6Ga9YLLEZ46sBmLe8oaRxSJIkSZK6h1m1Ncx9cgMppVKHIkmSJKmXsbBYYtX9s96m85Y7jI0kSZIkqWOzJtSwYdtunli7tdShSJIkSeplLCx2AXXja5i3vMHeppIkSZKkDs2emM2zOMd5FiVJkiQdZBYWu4CZE6pZt3UXK9ZvL3UokiRJkqQu7rDhAxnSr5L7LSxKkiRJOsgsLHYBdeOz3qbzVpgUSpIkSZL2rqwsmFlbzVwLi5IkSZIOMguLXcDUUQPpX1XOvOUNpQ5FkiRJktQNzJ44lMWrt9CwbVepQ5EkSZLUi1hY7AIqyss4ZtwQ7l9ub1NJkiRJUsdm1uYj39hBVZIkSdJBZGGxi5hZW8MjKzexY3dTqUORJEmSJHVx08cPobwsHA5VkiRJ0kFlYbGLqKutobE58dBTG0sdiiRJkiSpi+tfVcGRYwYz58n1pQ5FkiRJUi9iYbGLmDG+GnAYG0mSJElS58ysrWH+io3sbmoudSiSJEmSegkLi13EiEF9GD+0n/MsSpIkSZI6ZdaEGrbvbuLRVZtKHYokSZKkXsLCYhcys7bGKxYlSZIkSZ0ye2INgPMsSpIkSTpoLCx2IXXjq3l60w5Wbdxe6lAkSZIkSV3c6CH9GDOkr4VFSZIkSQeNhcUupK42623qVYuSJEmSpM6YNXGohUVJkiRJB42FxS7kRaMH06eijPtNCiVJkiRJnTCrtppVG3ewssGRbyRJkiQVn4XFLqSqooyjxw5h3oqGUociSZIkSeoGZk0YCjjPoiRJkqSDw8JiF1NXW82DT21kV2NzqUORJEmSJHVxLxo9iH6V5RYWJUmSJB0UFha7mLraGnY1NvPoqk2lDkWSJEmS1MVVlJcxY3y1hUVJkiRJB4WFxS6mrrYagHnLTQolSZIkSR2bNaGGR1ZtYtuuxlKHIkmSJKmHs7DYxYwe0o9DBvfl/uUNpQ5FkiRJktQNzJpYQ1Nz4oEVDaUORZIkSVIPZ2GxC5o5oZp5K7xiUZIkSZLUsZnjawC43+FQJUmSJBWZhcUuqG58DSvWb2fN5p2lDkWSJEmS1MUN6V/JlJEDmWNhUZIkSVKRWVjsglrmWXQYG0mSJElSZ8yeWMP9T26guTmVOhRJkiRJPZiFxS7oqLFDqCgL7l9ub1NJkiRJUsdm1tawaUcjS9dsKXUokiRJknowC4tdUN/Kco4cM5h5FhYlSZIkSZ0wa0I2z6LDoUqSJEkqJguLXVRdbQ0L6jfS2NRc6lAkSZIkSV3cocMHMHRAFXMtLEqSJEkqIguLXVRdbTXbdjWx6BmHsZEkSZIk7V1EMLM2m2dRkiRJkorFwmIXVTc+G8bGeRYlSZIkSZ0xe2INj6/dyrotO0sdiiRJkqQeysJiFzV+aD+GD6xi3vKGUociSZIkSeoGWuZZvN88UpIkSVKRWFjsoiKCGeNrmLfCKxYlSZIkSR07euwQKsvDeRYlSZIkFY2FxS6srraax9dspWHbrlKHIkmSJEnq4vpWlnPU2CHMfXJ9qUORJEmS1ENZWOzC6mqrAXhgRUNJ45AkSZIkdQ+zamuYX7+RXY3NpQ5FkiRJUg9kYbELmz6umrJwfgxJkiRJUufMmlDDrsZmHlq5sdShSJIkSeqBLCx2YQP6VDDtkMHMW+78GJIkSZKkjs2aUAPA/c6zKEmSJKkILCx2cXW11TywooHm5lTqUCRJkiRJXdzIwX0ZP7Qfcy0sSpIkSSoCC4tdXN34ajbvaOTxtVtKHYokSZIkqRuYVVvDnCc3kJIdVCVJkiQdWBYWu7i62pZhbBpKG4gkSZIkqVuYNXEoazbvpH7D9lKHIkmSJKmHsbDYxR02fABD+lUyb4XD2EiSJEmSOjYr76DqcKiSJEmSDjQLi11cWVkwY3w185Y3lDoUSZIkSVI3MO2QQQzsU8GcJ9eXOhRJkiRJPYyFxW6grraahc9sZsvOxlKHIkmSJEnq4srLgrraauY6pYYkSZKkA8zCYjdQV1tDSjB/RUOpQ5EkSZIkdQMza2tY+PQmNu/YXepQJEmSJPUgFha7gRnjqwGYt9z5MSRJkiT1TBHRNyLujYj5EfFwRHwhX35pRDwVEQ/kP68quM9nI2JJRCyMiFcULJ8VEQ/mt10WEVGK51RKsyfW0JzgATuoSpIkSTqALCx2A0P6VTJ55EDnWZQkSZLUk+0EzkwpTQdmAGdFxAn5bV9PKc3If/4IEBFHAOcCRwJnAd+NiPJ8/e8BlwBT8p+zDt7T6BpmjK8mAuY+aQdVSZIkSQeOhcVuom58NfNWNJBSKnUokiRJknTApcyW/N/K/GdvCdDZwHUppZ0ppSeAJcBxETEaGJxSuitlCdRPgXOKGHqXNKhvJdNGDbKwKEmSJOmAsrDYTdTV1rB+6y6Wr99W6lAkSZIkqSgiojwiHgBWAzellO7Jb/pQRCyIiB9HRE2+bCywouDu9fmysfnfrZe39XiXRMSciJizZs2aA/lUuoTZE2uYt7yBpmY7qEqSJEk6MCwsdhN1tdUA3O88i5IkSZJ6qJRSU0ppBjCO7OrDo8iGNZ1ENjzqKuCr+eptzZuY9rK8rce7PKU0O6U0e8SIES8w+q5n1oQatuxsZNEzm0sdiiRJkqQewsJiNzF11CAGVJU7z6IkSZKkHi+l1ADcCpyVUnomLzg2Az8EjstXqwfGF9xtHLAyXz6ujeW9zqzaoQDMcThUSZIkSQeIhcVuorwsmD6+2sKiJEmSpB4pIkZERHX+dz/gpcBj+ZyJLV4PPJT//Vvg3IjoExGHAlOAe1NKq4DNEXFCRARwIfCbg/U8upLxQ/sxYlAf7rewKEmSJOkAqSh1AOq8utpqfnDb42zf1US/qvJShyNJkiRJB9Jo4KqIKCfrBHtDSun3EXF1RMwgG850GfBegJTSwxFxA/AI0Ah8MKXUlG/r/cCVQD/gxvyn14kIZtXWMNfCoiRJkqQDpGhXLEbEjyNidUQ8VLDszRHxcEQ0R8TsVut/NiKWRMTCiHhFwfJZEfFgfttleY/TXqlufA2NzYkHn9pY6lAkSZIk6YBKKS1IKdWllI5JKR2VUvr3fPkFKaWj8+Wvy69IbLnPF1NKk1JK01JKNxYsn5NvY1JK6UMppTbnWOwNZk2oYfn6bazevKPUoUiSJEnqAYo5FOqVwFmtlj0EvAG4vXBhRBwBnAscmd/nu3kvVYDvAZeQDWszpY1t9hp1tdUAzFtub1NJkiRJUsdmTawBcDhUSZIkSQdE0QqLKaXbgfWtlj2aUlrYxupnA9ellHamlJ4AlgDH5XNpDE4p3ZX3MP0pcE6xYu7qhg3sw4Rh/Z1nUZIkSZLUKUeOGUxVRZnDoUqSJEk6IIp5xeK+GAusKPi/Pl82Nv+79fI2RcQlETEnIuasWbOmKIGWWt34au5fvoFePJKPJEmSJKmT+lSUc8zYIcyxsChJkiTpAOgqhcW25k1Me1neppTS5Sml2Sml2SNGjDhgwXUldbU1rN68k5UbnR9DkiRJktSxWRNreOipjezY3VTqUCRJkiR1c12lsFgPjC/4fxywMl8+ro3lvdbM2mx+DOdZlCRJkiR1xqzaGnY3JR58amOpQ5EkSZLUzXWVwuJvgXMjok9EHApMAe5NKa0CNkfECRERwIXAb0oZaKkdPnoQfSrKnGdRkiRJktQpsyZkHVSdZ1GSJEnSC1VRrA1HxLXAGcDwiKgHPg+sB74FjAD+EBEPpJRekVJ6OCJuAB4BGoEPppRaxmh5P3Al0A+4Mf/ptSrLyzhm3BCvWJQkSZIkdcqwgX04dPgAC4uSJEmSXrCiFRZTSue1c9Ov2ln/i8AX21g+BzjqAIbW7dXV1nDlncvY2dhEn4ryUocjSZIkSeriZtbWcOvC1aSUyAYEkiRJkqR911WGQtU+qBtfza7GZh5ZuanUoUiSJEmSuoHZE2tYt3UXy9ZtK3UokiRJkroxC4vd0Mx8fgznWZQkSZIkdYbzLEqSJEk6ECwsdkOjBvdlzJC+zFvRUOpQJEmSJEndwOQRAxnct4K5T64vdSiSJEmSujELi91UXW0N85bb01SSJEmS1LGysmDmhBqvWJQkSZL0glhY7Kbqaqup37Cd1Zt3lDoUSZIkSVI3MKu2hkXPbGHj9t2lDkWSJElSN2VhsZuqq3WeRUmSJElS57XMs3i/o99IkiRJ2k8WFrupI8cMprI8LCxKkiRJkjpl+vhqysuC+x0OVZIkSdJ+srDYTfWtLOeIMUOcZ1GSJEmS1CkD+lTwotGDmLPMPFKSJEnS/rGw2I3Vja9mQf1GGpuaSx2KJEmSJKkbmFVbwwMrGswjJUmSJO0XC4vd2MwJNWzf3cRjT28udSiSJEmSpG5g1sSh5pGSJEmS9puFxW6sbnw1APNWNJQ0DkmSJElS9zBrQg0Ac5atL3EkkiRJkrojC4vd2Liafgwf2Md5FiVJkiRJnTK2uh+jh/Rl7vKGUociSZIkqRuysNiNRQR1tdXMMyGUJEmSJHXSzAk13P+kHVQlSZIk7TsLi91cXW01T6zdyoatu0odiiRJkiSpG5hVW8NTDdtZtXF7qUORJEmS1M1YWOzmZtZm82M84DyLkiRJkqROmD0xyyPnetWiJEmSpH1kYbGbO2bcEMoC51mUJEmSJHXKi0YPpm9lmYVFSZIkSfvMwmI317+qgsMPGcw8r1iUJEmSJHVCZXkZ08dVW1iUJEmStM8sLPYAdbXVPLC8gebmVOpQJEmSJEndwOyJNTy8chPbdjWWOhRJkiRJ3YiFxR5gZm0Nm3c2smTNllKHIkmSJEnqBmZNqKGpOTF/xcZShyJJkiSpG7Gw2APU1VYDzrMoSZIkSeqcmbU1ANxvHilJkiRpH1hY7AEOHT6AIf0qmbe8odShSJIkSZK6ger+VUweOdB5FiVJkiTtEwuLPUBEUFdbbU9TSZIkSVKnzaqtYe6TG2huTqUORZIkSVI3YWGxh5hZW8Pi1VvYtGN3qUORJEmSJHUDsybUsHH7bh5fu6XUoUiSJEnqJiws9hB1tdWkBAtWbCx1KJIkSZKkbmDWxGyeRYdDlSRJktRZFhZ7iOnjq4mAeQ6HKkmSJEnqhMOGD6CmfyVzlplHSpIkSeocC4s9xOC+lUweMdB5FiVJkiRJnRIRzJpQw1zzSEmSJEmdZGGxB6mrrWbeigZSSqUORZIkSZLUDcycUMPja7ayfuuuUociSZIkqRuwsNiDzKytoWHbbpat21bqUCRJkiRJ3cCs2myexfudZ1GSJElSJ1hY7EHq8oTQeRYlSZIkSZ0xfXw1FWXhcKiSJEmSOsXCYg8yeeRABvapYN7yhlKHIkmSJEnqBvpWlnPk2CHMXWZhUZIkSVLHLCz2IOVlwfTxQ7jfnqaSJEmSpE6aVVvD/PoGdjU2lzoUSZIkSV2chcUeZmZtDY89vZltuxpLHYokSZIkqRuYPbGGnY3NPLJqU6lDkSRJktTFWVjsYepqq2lqTjxYv7HUoUiSJElSp0VE34i4NyLmR8TDEfGFfPnQiLgpIhbnv2sK7vPZiFgSEQsj4hUFy2dFxIP5bZdFRJTiOXUXsyZkTTpn2foSRyJJkiSpq7Ow2MPMGJ8lhPNWNJQ2EEmSJEnaNzuBM1NK04EZwFkRcQLwGeCvKaUpwF/z/4mII4BzgSOBs4DvRkR5vq3vAZcAU/Kfsw7i8+h2Rg3uy9jqfk6rIUmSJKlDFhZ7mKEDqpg4rD/3P2lCKEmSJKn7SJkt+b+V+U8CzgauypdfBZyT/302cF1KaWdK6QlgCXBcRIwGBqeU7kopJeCnBfdRO2ZPrGHukxvImkySJEmS2mZhsQeaWVvDvBUNJoSSJEmSupWIKI+IB4DVwE0ppXuAUSmlVQD575H56mOBFQV3r8+Xjc3/br1cezFrQg3PbNpJ/YbtpQ5FkiRJUhdmYbEHqqutZs3mnTzVYEIoSZIkqftIKTWllGYA48iuPjxqL6u3NW9i2svy528g4pKImBMRc9asWbPP8fYkM2uzaTUcDlWSJEnS3lhY7IHq8oRw3vKG0gYiSZIkSfshpdQA3Eo2N+Iz+fCm5L9X56vVA+ML7jYOWJkvH9fG8rYe5/KU0uyU0uwRI0YcyKfQ7Rx+yCAGVJUz12k1JEmSJO2FhcUeaNohg+hbWWZPU0mSJEndRkSMiIjq/O9+wEuBx4DfAhflq10E/Cb/+7fAuRHRJyIOBaYA9+bDpW6OiBMiIoALC+6jdlSUlzGjtpo5y8wjJUmSJLWvotQB6MCrLC/jmLHVXrEoSZIkqTsZDVwVEeVknWBvSCn9PiLuAm6IiHcBy4E3A6SUHo6IG4BHgEbggymlpnxb7weuBPoBN+Y/6sCsCUP59i2L2bKzkYF9/LpAkiTp/2fvvuPkKsv+j3+v7b1vejYFEkoCSciSBJAiihQVgiJioSiPiGJ7rODPggULKiqPgoBUC0gVkKIIIj0hFQihhPRedpNsdrP9+v1xzm5mN7MtyezZ8nm/XvPa2dPmOvecOTP3uc593wD2Rk1hgJo2pkC3PrdSdY1NSk9JjjocAAAAAOiUu78iaVqc6dskvaeDda6SdFWc6fMkdTY+I+KYPqZQzS4tWr1d75pQEnU4AAAAAPogukIdoKaNLlR9U7OWrN8ZdSgAAAAAgH5gWlmBzMQ4iwAAAAA6RGJxgJpWViBJdIcKAAAAAOiWvIxUHTI0V/NXk1gEAAAAEB+JxQFqaF6GRhZkagEVQgAAAABANx01plALV1WqqdmjDgUAAABAH0RicQCbVlagRbRYBAAAAAB00/SyQlXVNertzVVRhwIAAACgDyKxOIBNKyvUuu27tWlnbdShAAAAAAD6gfKxhZIYZxEAAABAfCQWBzDGWQQAAAAA9ERZUZZKctI0fyWJRQAAAAB7I7E4gE0akae05CQtZJxFAAAAAEA3mJmOKivUfOqRAAAAAOIgsTiApacka9LIPFosAgAAAAC6rXxsoVZtq9GWqrqoQwEAAADQx5BYHOCmjS7UK+u2q6GpOepQAAAAAAD9wPQxjLMIAAAAID4SiwPctLIC1TY0682NVVGHAgAAAADoByaNyFdacpIW0B0qAAAAgHZILA5w08oKJIkKIQAAAACgWzJSk3XEqHzNW1kRdSgAAAAA+hgSiwPcyIJMleamM84iAAAAAKDbpo8p1Gvrdqq2oSnqUAAAAAD0ISQWBzgz01FlBVpIi0UAAAAAQDdNH1Oo+qZmLVm/I+pQAAAAAPQhJBYHgWllhVq5rUYV1fVRhwIAAAAA6AeOKiuUJM1byU2qAAAAAPYgsTgITBtdIElatIYKIQAAAACga6W56RpTnKX5q6hHAgAAANiDxOIgcMSofCUnmRas2h51KAAAAACAfmL6mEItWF0pd486FAAAAAB9RMISi2Z2i5ltNrPXYqYVmdkTZvZ2+LcwZt4VZrbMzN40s1Njpk83s1fDedeamSUq5oEqKy1Fhw3P1UJaLAIAAAAAumn6mEJt3VWvVdtqog4FAAAAQB/RaWLRzJJiE4M9dJuk09pNu1zSk+4+QdKT4f8ys8MlnSdpUrjOdWaWHK5zvaRLJE0IH+23iW6YNrpQi9fsUFMzd5oCAAAASIz9rEOij5k+JrgXmO5QAQAAALToNLHo7s2SFptZWU837O7PSKpoN/ksSbeHz2+XNDtm+l3uXufuKyQtkzTDzIZLynP3Fz3oe+WOmHXQA9PKCrSrrlHLNu+KOhQAAAAAA9T+1CHR90wckqvc9BTNX01iEQAAAEAgpRvLDJe0xMzmSqpumejuZ+7D6w119w3h+hvMbEg4faSkl2KWWxtOawift5+OHppWFtxpumB1pQ4ZlhtxNAAAAAAGsANZh0SEkpJM08YUav5KEosAAAAAAt1JLP4g4VFI8cZN9E6mx9+I2SUKuk1VWRk3yMYaW5ylwqxULVxdqY/NoGwAAAAAJExv1CHRS6aXFeo3T76lHbsblJ+ZGnU4AAAAACLWncTiEZL+4u4H4hbFTWY2PGytOFzS5nD6WkmjY5YbJWl9OH1UnOlxufuNkm6UpPLycgYTjGFmmlZWqIWrt0cdCgAAAICB7UDWIRGx8rGFcpcWrq7USYcM6XoFAAAAAANap2MshoZJetnM7jaz08wsXivC7npI0oXh8wslPRgz/TwzSzezcZImSJobdptaZWazwte9IGYd9NC00QV6e/Mu7djdEHUoAAAAAAauA1mHRMSmjC5QkkkLVpEnBgAAANCNxKK7f0dBou9mSRdJetvMfmJmB3W2npndKelFSYeY2Vozu1jSzySdYmZvSzol/F/uvkTS3ZJel/S4pMvcvSnc1Ock/VHSMknvSHqspzuJQMs4i4vXbI82EAAAAAAD1r7WIdE35aSn6NBheZq/msQiAAAAgO51hSp3dzPbKGmjpEZJhZLuNbMn3P2bHazzsQ42954Olr9K0lVxps+TNLk7caJzU0bny0xauHq7TphYGnU4AAAAAAaofalDou8qH1uoe+evVWNTs1KSu9PxEQAAAICBqssagZl9yczmS7pa0vOSjnD3z0maLunDCY4PB1BuRqomDsnVwjXcaQoAAAAgMahDDjzTxxSqpr5Jb2ysijoUAAAAABHrTovFEkkfcvdVsRPdvdnMPpCYsJAo08oK9NhrG+XuYqgTAAAAAAlAHXKAmT4mGFZjwepKTR6ZH3E0AAAAAKLUYYtFM5tnZr+V9IKkTfGWcfeliQoMiTGtrEA7djdoxdbqqEMBAAAAMIBQhxy4RhZkamheuuatpPcbAAAAYLDrrCvUWZIekHSSpP+a2aNm9mUzm9grkSEhppW13Gm6PdpAAAAAAAw01CEHKDPT9DGFmr+KxCIAAAAw2HWYWHT3Rnd/2t0vd/eZki6WVCXpx2a20Myu67UoccAcXJqj3PQULVxNhRAAAADAgUMdcmCbPqZI67bv1sYdtVGHAgAAACBC3RljUZLk7hsk3SLpFjNLknRMwqJCwiQlmaaWFWghLRYBAAAAJBB1yIGlZZzF+asq9f4jh0ccDQAAAICodDbGYrKZfdbMfmRmx7Wb/W13fz7BsSFBpo0u0Bsbd6qmvjHqUAAAAAAMENQhB7bDh+cpPSWJ7lABAACAQa6zMRZvkHSipG2SrjWza2LmfSihUSGhppUVqtmlxWt2RB0KAAAAgIGDOuQAlpaSpCmjCzR/VUXUoQAAAACIUGeJxRnu/nF3/42kmZJyzOx+M0uXZL0SHRJi6ugCSdLCNdxpCgAAAOCAoQ45wE0fU6gl63dqd31T1KEAAAAAiEhnicW0lifu3ujul0haJOkpSTkJjgsJVJidpvEl2YyzCAAAAOBAog45wE0vK1Rjs+uVtdujDgUAAABARDpLLM4zs9NiJ7j7DyXdKmlsIoNC4k0tK9DC1dvl7lGHAgAAAGBgoA45wB01plCSNI9xFgEAAIBBq8PEort/0t0fjzP9j+6emtiwkGjTygq1dVed1lbujjoUAAAAAAMAdciBryg7TeNLs7WAxCIAAAAwaKV0NtPMhki6TNIkSS7pdUnXufumXogNCXRUWYEkacHqSo0uyoo2GAAAAAADAnXIgW96WaGeWLpJ7i4zhs4EAAAABpsOWyya2XGSXg7/vUPSn8Pnc8J56McOGZqrzNRkxlkEAAAAcEBQhxwcyscWantNg97ZUh11KAAAAAAi0FmLxV9Jmu3uC2OmPWhmD0i6QdLMhEaGhEpJTtKRo/K1cM32qEMBAAAAMDBQhxwEpofjLC5YVamDh+REHA0AAACA3tZhi0VJee0qhJIkd18kKTdhEaHXTCsr1Ovrd6i2oSnqUAAAAAD0f9QhB4HxJTkqyErVfMZZBAAAAAalzhKLZmaFcSYWdbEe+olpZQVqaHItWb8j6lAAAAAA9H/7VYc0s9Fm9h8zW2pmS8zsy+H0K81snZktCh9nxKxzhZktM7M3zezUmOnTzezVcN61xmCAB0xSkumoskLNW1URdSgAAAAAItBZ5e7Xkv5lZieaWW74OEnSY+E89HPTygokiXEWAQAAABwI+1uHbJT0NXc/TNIsSZeZ2eEt23b3qeHjUUkK550naZKk0yRdZ2bJ4fLXS7pE0oTwcdoB2UNICrpDfWdLtSqr66MOBQAAAEAv63CMRXe/0czWS/qRgoqaS3pd0o/d/eFeig8JNCQ3Q6MKM0ksAgAAANhv+1uHdPcNkjaEz6vMbKmkkZ2scpaku9y9TtIKM1smaYaZrVTQLeuLkmRmd0iarSDBiQOgdZzF1ZV6z2FDI44GAAAAQG/qMLEoSe7+D0n/6KVYEIFpZYWav5IubAAAAADsvwNVhzSzsZKmSZoj6ThJXzCzCyTNU9CqsVJB0vGlmNXWhtMawuftp8d7nUsUtGxUWVnZ/oY9aEwZVaDkJNP8VSQWAQAAgMGmw65QzexqM7s0zvT/NbOfJzYs9JZpowu0fketNu6ojToUAAAAAP3YgapDmlmOpPskfcXddyro1vQgSVMVtGj8VcuicVb3TqbvPdH9Rncvd/fy0tLS7oY46GWmJWvSiDzNX1UZdSgAAAAAellnYyx+QNKNcab/VtL7ExMOettRYRc2C1dTIQQAAACwX/a7DmlmqQqSin9x9/slyd03uXuTuzdLuknSjHDxtZJGx6w+StL6cPqoONNxAE0fU6jFa7eroak56lAAAAAA9KLOEoseVtzaT2xW/DtA0Q8dPjxPaSlJWrhme9ShAAAAAOjf9qsOaWYm6WZJS939mpjpw2MWO1vSa+HzhySdZ2bpZjZO0gRJc8OxGqvMbFa4zQskPbivO4X4po8pVG1Ds5as3xl1KAAAAAB6UWeJxRozm9B+Yjhtd+JCQm9KS0nS5BF5tFgEAAAAsL/2tw55nKTzJZ1sZovCxxmSrjazV83sFUnvlvS/kuTuSyTdLel1SY9Luszdm8JtfU7SHyUtk/SOpMf2b9fQ3sxxxcpITdL3H3xN1XWNUYcDAAAAoJekdDLve5IeM7MfS5ofTiuXdIWkryQ4LvSiaWWF+vNLq1Tf2Ky0lM5yzQAAAADQof2qQ7r7c4rfsvHRTta5StJVcabPkzS565Cxr0pz0/W7jx2lS/40T5/7ywLdfGG5UpOpTwIAAAADXYe/+t39MUmzFdwRelv4OEnSh929w4od+p+jygpV19isNzbShQ0AAACAfUMdcvB57+FD9ZOzj9Azb23Rt+59Rc3NHnVIAAAAABKssxaLcvfXJF3YS7EgItPKCiRJC1dv15GjCiKNBQAAAED/RR1y8DlvRpm2VNXpcbBSRwAA4GFJREFUV0+8pdLcdF1xxmFRhwQAAAAggeinBBqen6GheemMswgAAAAA6LEvnHywzp81Rjc8s1x/fHZ51OEAAAAASKBOWyxicDAzTRtdqAWrt0cdCgAAAACgnzEzXXnmJG3dVacfP7JUpbnpOmvqyKjDAgAAAJAAXbZYNLPjujMN/du0sgKtrqjR1l11UYcCAAAAoB+jDjk4JSeZfv3RqZoxrkhfv2exnn17S9QhAQAAAEiA7nSF+n/dnIZ+bNb4YknSN+5ZrKrahoijAQAAANCPUYccpDJSk3XTBeU6qDRHl/5pvl5btyPqkAAAAAAcYB0mFs3sGDP7mqRSM/tqzONKScm9FiF6xZTRBfrR7Ml65u2tOuf6F7WmoibqkAAAAAD0I9QhIUn5mam6/dMzVJCVpotunatV26qjDgkAAADAAdRZi8U0STkKxmHMjXnslHRO4kNDbzt/1hjd9qmjtX7Hbp193fOav6oy6pAAAAAA9B/UISFJGpqXods/PUONza4LbpmrLVUMuQEAAAAMFObunS9gNsbdV/VSPAdMeXm5z5s3L+ow+qVlm3fp4ttf1oYdtfrFOUfqrKkjow4JAAAAkJnNd/fyqONA56hDosWC1ZX6+E0vacKQXN15ySzlpKdEHRIAAAAGEeqQidHhr3ozeyjm+V7z3f3MBMWEiB08JEd///xx+uyf5+vLdy3SO5t36SvvnaikpL2PAwAAAACQqENib0eVFeq6Txylz9wxX5/783zdfOHRSkvprOMkAAAAAH1dZ7cLHiNpjaQ7Jc2RRFZpECnMTtOfL56p//fAq7r2qWV6Z2u1fvWRKcpIZWgUAAAAAHFRh8ReTj50qH76oSP0zXtf0TfvXaxrzp3KTasAAABAP9ZZYnGYpFMkfUzSxyU9IulOd1/SG4EhemkpSbr6nCN10JAc/fzxN7S2crduOn+6huRlRB0aAAAAgL6HOiTiOrd8tLZU1ekX/3xTpbnp+n/vPzzqkAAAAADsow77IHH3Jnd/3N0vlDRL0jJJT5vZF3stOkTOzHTpiQfp+k9M11sbq3TW75/XkvU7og4LAAAAQB9DHRKd+fxJB+nCY8bopmdX6KZnlkcdDgAAAIB91OngBmaWbmYfkvRnSZdJulbS/b0RGPqW0yYP0z2XHiN36SN/eFFPvL4p6pAAAAAA9DHUIdERM9P3PjhJ7z9iuK56dKn+vnBd1CEBAAAA2AcdJhbN7HZJL0g6StIP3P1od/+Ru/Prf5CaPDJfD37hOB1UmqNL/jRPNz7zjtw96rAAAAAA9AHUIdGV5CTTr86dolnji/T1exbrmbe2RB0SAAAAgB7qrMXi+ZImSvqypBfMbGf4qDKznb0THvqaoXkZuvuzx+j0ycP0k0ff0OX3var6xuaowwIAAAAQPeqQ6FJGarJuvKBcBw/J0aV/nq9X1m6POiQAAAAAPdDZGItJ7p4bPvJiHrnuntebQaJvyUxL1u8+dpS+8O6D9bd5a3TBLXO0vaY+6rAAAAAARIg6JLorLyNVt396hgqz0vSpW1/Wyq3VUYcEAAAAoJs6HWMR6EhSkunrpx6ia86dogWrtuvs617Q8i27og4LAAAAANAPDM3L0B0Xz1Czuy64Za42V9VGHRIAAACAbiCxiP3yoaNG6a+fmakduxs0+/fP6/llW6MOCQAAAADQDxxUmqNbLjpaW6rq9KlbX1ZVbUPUIQEAAADoAolF7LfysUV68LLjNDQvQxfeMld/nbM66pAAAAAAAP3AtLJCXffJo/TGxipd+uf5qm9sjjokAAAAAJ0gsYgDYnRRlu77/LE67uASffuBV/Wjf7yupmaPOiwAAAAAQB/37kOG6OcfPlLPL9umr9+zWM3UJQEAAIA+i8QiDpi8jFTdfGG5Ljp2rG5+boU+c8c87aprjDosAAAAAEAfd870UfrWaYfqocXr9eNHlsqd5CIAAADQF5FYxAGVkpykK8+cpB+dNUn/fWuLzrn+Ba2trIk6LAAAAABAH3fpieN10bFjdcvzK3TjM8ujDgcAAABAHCQWkRDnHzNWt33qaK3bvluzf/+8FqyujDokAAAAAEAfZmb63gcO1weOHK6fPvaG7l+wNuqQAAAAALRDYhEJc/yEUj3w+WOVlZai8258SQ8uWhd1SAAAAACAPiwpyfSrc6fo2IOK9c17X9HTb26OOiQAAAAAMUgsIqEOHpKrv192nKaOKtCX71qka554i7EyAAAAAAAdSk9J1g3nT9fEobn6/F8WaPGa7VGHBAAAACBEYhEJV5Sdpj/9zwydM32Urn3ybX3xzoWqbWiKOiwAAAAAQB+Vm5Gq2z59tIpz0vSp217W8i27og4JAAAAgEgsopekpyTrF+ccqctPP1SPvLpBH73xJW2uqo06LAAAAABAHzUkN0N3fHqmJOmCW+ZShwQAAAD6ABKL6DVmpktPPEjXf2K63tpYpdm/e16vr98ZdVgAAAAAgD5qXEm2br3oaFVU1+uiW15WVW1D1CEBAAAAgxqJRfS60yYP0z2XHqNml875wwv69+ubog4JAAAAANBHTRldoOs+cZTe2lSlz/5pvuoaGVoDAAAAiAqJRURi8sh8PfiF43RQaY4+86d5uumZ5XL3qMMCAAAAAPRBJx0yRFefc6ReeGebvnr3YjU3U38EAAAAokBiEZEZmpehuz97jE6fPExXPbpUl9/3quobm6MOCwAAAADQB33oqFG64vRD9cgrG/TDf7zOzakAAABABFKiDgCDW2Zasn73saN0Tclb+t1/lmlVRbX+8MnpKshKizo0AAAAAEAfc8kJ47W5qk43P7dCQ/LS9fmTDo46JAAAAGBQiaTFopl92cxeM7MlZvaVcFqRmT1hZm+Hfwtjlr/CzJaZ2ZtmdmoUMSNxkpJMXz/1EF1z7hQtWLVdZ1/3gpZv2RV1WAAAAACAPsbM9P/OOExnThmhqx9/U/fMWxN1SAAAAMCg0uuJRTObLOkzkmZImiLpA2Y2QdLlkp509wmSngz/l5kdLuk8SZMknSbpOjNL7u24kXgfOmqU/vKZmdqxu0FnX/eCXli2NeqQAAAAAAB9TFKS6ZcfmaJ3HVyiy+9/Vf95Y3PUIQEAAACDRhQtFg+T9JK717h7o6T/Sjpb0lmSbg+XuV3S7PD5WZLucvc6d18haZmCpCQGoKPHFunvnz9OQ3LTdcEtc3Xn3NVRhwQAAAAA6GPSUpL0h/On67Dhufr8XxZo4erKqEMCAAAABoUoEouvSTrBzIrNLEvSGZJGSxrq7hskKfw7JFx+pKTYvk3WhtP2YmaXmNk8M5u3ZcuWhO0AEqusOEv3ff5YHXtwia64/1X9+B+vq6nZow4LAAAAANCH5KSn6NaLZqg0N12fvu1lvcOQGgAAAEDC9Xpi0d2XSvq5pCckPS5psaTGTlaxeJvpYNs3unu5u5eXlpbud6yITl5Gqm65sFwXHTtWf3xuhS65Y5521XV2mAAAAAAABpvS3HTd8ekZSk4yXXDzXG3aWRt1SAAAAMCAFkWLRbn7ze5+lLufIKlC0tuSNpnZcEkK/7YMkrBWQYvGFqMkre/NeBGNlOQkXXnmJP3orEl6+q0tOuf6F7S2sibqsAAAAAAAfcjYkmzdetEMVdbU68Jb5mpnbUPUIQEAAAADViSJRTMbEv4tk/QhSXdKekjSheEiF0p6MHz+kKTzzCzdzMZJmiBpbu9GjCidf8xY3XrR0VpXuVuzf/+8FjB2BgAAAAAgxhGj8vWHT07Xss27dMkd81Tb0BR1SAAAAMCAFEliUdJ9Zva6pIclXebulZJ+JukUM3tb0inh/3L3JZLulvS6gq5TL3N3agiDzAkTS3X/549VVlqKzrvxJT20mEarAAAAwEBiZqPN7D9mttTMlpjZl8PpRWb2hJm9Hf4tjFnnCjNbZmZvmtmpMdOnm9mr4bxrzSzeEBsYYE6YWKpffmSKXlpeoa/evUhNzXFHUQEAAACwH6LqCvV4dz/c3ae4+5PhtG3u/h53nxD+rYhZ/ip3P8jdD3H3x6KIGdGbMDRXf7/sOE0dVaAv3blQd81dHXVIAAAAAA6cRklfc/fDJM2SdJmZHS7pcklPuvsESU+G/yucd56kSZJOk3SdmSWH27pe0iUKeryZEM7HIDB72kh95/2H6dFXN+oHDy+RO8lFAAAA4ECKqsUisE+KstP0p/+ZoeMnlOjKh5doxdbqqEMCAAAAcAC4+wZ3XxA+r5K0VNJISWdJuj1c7HZJs8PnZ0m6y93r3H2FpGWSZpjZcEl57v6iB1mlO2LWwSDwP8eP12eOH6c7Xlyl655+J+pwAAAAgAGFxCL6nfSUZP3inClKT0nWV+9epMam5qhDAgAAAHAAmdlYSdMkzZE01N03SEHyUdKQcLGRktbErLY2nDYyfN5+OgaRK04/TLOnjtAv/vmm7n55TdcrAAAAAOgWEovol4blZ+hHsydr4ertuuGZ5VGHAwAAAOAAMbMcSfdJ+oq77+xs0TjTvJPp8V7rEjObZ2bztmzZ0vNg0WclJZmuPmeKjp9QoiseeFVPLt0UdUgAAADAgEBiEf3WmVNG6ANHDtevn3hLr63bEXU4AAAAAPaTmaUqSCr+xd3vDydvCrs3Vfh3czh9raTRMauPkrQ+nD4qzvS9uPuN7l7u7uWlpaUHbkfQJ6SlJOn6T07X4cPzdNlfF2j+qsqoQwIAAAD6PRKL6Nd+dNZkFWWn6Wt3L1ZdY1PU4QAAAADYR2Zmkm6WtNTdr4mZ9ZCkC8PnF0p6MGb6eWaWbmbjJE2QNDfsLrXKzGaF27wgZh0MMjnpKbr1U0drWF6GPn3by3pzY1XUIQEAAAD9GolF9GuF2Wn6+TlH6s1NVbrmibeiDgcAAADAvjtO0vmSTjazReHjDEk/k3SKmb0t6ZTwf7n7Ekl3S3pd0uOSLnP3lrsNPyfpj5KWSXpH0mO9uifoU0py0vWni2cqIzVJ5988R6u31UQdEgAAANBvmXvcoSb6vfLycp83b17UYaCXfPuBV3Xn3NW6+7PH6OixRVGHAwAAgAQws/nuXh51HBiYqEMOfG9tqtK5N7yovIxU3XvpMRqSlxF1SAAAAEgg6pCJQYtFDAj/74zDNLowS1+9e5F21TVGHQ4AAAAAoI+ZODRXt150tLbuqtMFt8zVjpqGqEMCAAAA+h0SixgQstNT9Ktzp2ht5W5d9cjSqMMBAAAAAPRB08oKdeP55Vq+pVqfum2uauq5MRUAAADoCRKLGDCOHlukS04YrzvnrtZ/3tgcdTgAAAAAgD7oXRNKdO3HpmrRmu269M8LVN/YHHVIAAAAQL9BYhEDyldPmahDhubqm/e9osrq+qjDAQAAAAD0QadNHq6ffugIPfPWFv3v3YvU1OxRhwQAAAD0CyQWMaCkpyTrmo9O0faaen3n76/JncohAAAAAGBvHz26TN8+41A98soGffdB6o8AAABAd5BYxIAzaUS+vvLeiXrk1Q16aPH6qMMBAAAAAPRRl5xwkD530kH665zV+sU/34w6HAAAAKDPI7GIAemzJ4zXUWUF+t6DS7RxR23U4QAAAAAA+qhvnnqIPjajTNc9/Y5ufOadqMMBAAAA+jQSixiQUpKT9Ktzp6q+sVnfvO8VurQBAAAAAMRlZvrx7Ml6/5HD9ZNH39DfXl4ddUgAAABAn0ViEQPWuJJsffuMQ/XMW1v0lzlUDAEAAAAA8SUnmX597lQdP6FEV9z/qh5/bUPUIQEAAAB9EolFDGifnDVGx08o0VWPLNXKrdVRhwMAAAAA6KPSUpJ0w/nTNXV0gb505yI99/bWqEMCAAAA+hwSixjQzExXn3OkUpNNX717kZqa6RIVAAAAABBfVlqKbr1ohsaXZuuSP83TwtWVUYcEAAAA9CkkFjHgDc/P1I9mT9aC1dt1wzPvRB0OAAAAAKAPy89K1R2fnqGSnHR96raX9damqqhDAgAAAPoMEosYFM6cMkLvP2K4fv3EW3p9/c6owwEAAAAA9GFD8jL054tnKi05SeffPEdrKmqiDgkAAADoE0gsYlAwM/1o9mQVZKXpq3cvUl1jU9QhAQAAAAD6sLLiLP3p4pmqbWjW+TfP0eaq2qhDAgAAACJHYhGDRlF2mn7+4SP0xsYq/ebfb0cdDgAAAACgjztkWK5uuehobdpZpwtveVk7djdEHRIAAAAQKRKLGFROPnSozjt6tG747zuat7Ii6nAAAAAAAH3c9DGFuuH86Vq2uUoX3/aydtfTAw4AAAAGLxKLGHS+84HDNaIgU1+7Z7Gq6xqjDgcAAAAA0MedMLFUv/noNM1fXanP/WW+6hubow4JAAAAiASJRQw6Oekp+tVHpmh1RY1+8ujSqMMBAAAAAPQD7z9yuH5y9hF6+s0t+to9i9XU7FGHBAAAAPQ6EosYlGaOL9Znjh+vv8xZrf+8uTnqcAAAAAAA/cDHZpTpW6cdqocXr9f3H3pN7iQXAQAAMLiQWMSg9dVTJmri0Bx9695XtL2mPupwAAAAAAD9wOdOOkifPXG8/vzSal3zxFtRhwMAAAD0KhKLGLQyUpN1zblTVVFdr+8+uCTqcAAAAAAA/cTlpx2q844erf97apn++OzyqMMBAAAAeg2JRQxqk0fm6yvvnaCHF6/Xw4vXRx0OAAAAAKAfMDNddfYROuOIYfrxI0t1z7w1UYcEAAAA9AoSixj0Lj3xIE0dXaDvPviaNu2sjTocAAAAAEA/kJxk+vVHp+r4CSX61n2v6J9LNkYdEgAAAJBwJBYx6KUkJ+lX505RbUOTvnXfK3L3qEMCAAAAAPQD6SnJ+sMnp2vK6AJ98a8L9cKyrVGHBAAAACQUiUVA0kGlObri9MP09JtbdOdcurABAAAAAHRPdnqKbr3oaI0rydZn7pinxWu2Rx0SAAAAkDAkFoHQ+bPG6F0Hl+jHj7yuVduqow4HAAAAANBPFGSl6Y6LZ6goJ00X3TpXyzZXRR0SAAAAkBAkFoFQUpLp6nOOVHKS6Wt3L1ZTM12iAgAAAAC6Z2hehv588UylJCfpk3+cq7WVNVGHBAAAABxwJBaBGCMKMvXDsyZp3qpK3fTs8qjDAQAAAAD0I2OKs3XHp2eopr5R5988V1uq6qIOCQAAADigSCwC7cyeOlKnTx6ma/71lpZu2Bl1OAAAAACAfuSw4Xm69VNHa8OO3brwlrnaWdsQdUgAAADAAUNiEWjHzPTj2ZOVl5mir969WPWNzVGHBAAAAADoR6aPKdIfPjldb2+u0v/cNk+765uiDgkAAAA4IEgsAnEU56Trpx86Uks37NRvn3wr6nAAAAAAAP3MSYcM0TXnTtXLqyp02V8XqKGJm1YBAADQ/5FYBDpwyuFDdW75KF3/9Duav6oy6nAAAAAAAP3MB6eM0I9nT9ZTb2zW1+9ZrOZmjzokAAAAYL+QWAQ68d0PHK7h+Zn62t2LVFPfGHU4AAAAAIB+5hMzx+gbpx6iBxet1w8eXiJ3kosAAADov0gsAp3IzUjVr86dolUVNfrpo29EHQ4AAAAAoB/6/EkH6ZITxuv2F1fp1/9+O+pwAAAAgH1GYhHowqzxxbr4uHH600ur9N+3tkQdDgAAAACgnzEzXXH6oTq3fJSuffJt3fLciqhDAgAAAPYJiUWgG75+6iGaMCRH37x3sXbUNEQdDgAAAACgnzEz/eTsI3TapGH64T9e133z10YdEgAAANBjJBaBbshITdY1507Vtl31+t5Dr0UdDgAAAACgH0pJTtJvPzZVxx1crG/e94qeeH1T1CEBAAAAPUJiEeimI0bl64snT9CDi9brkVc2RB0OAAAAAKAfSk9J1g3nl2vyyHxd9tcFevGdbVGHBAAAAHQbiUWgBz7/7oM0ZVS+vvP3V7V5Z23U4QAAAAAA+qGc9BTddtHRGlOUpc/cMU+vrt0RdUgAAABAt5BYBHogNTlJvzp3qmrqm3T5/a/K3aMOCQAAAADQDxVmp+lPF89UfmaqLrx1rpZt3hV1SAAAAECXSCwCPXTwkBxdfvqheuqNzfrby2uiDgcAAAAA0E8Ny8/Qn/9nppJMuuDmOVq3fXfUIQEAAACdIrEI7IMLjxmrYw8q1o/+8bpWb6uJOhwAAACg3zOzW8xss5m9FjPtSjNbZ2aLwscZMfOuMLNlZvammZ0aM326mb0azrvWzKy39wXoiXEl2br90zNUVdeo8/84R1t31UUdEgAAANAhEovAPkhKMv3iI1OUZKav37NYTc10iQoAAADsp9sknRZn+q/dfWr4eFSSzOxwSedJmhSuc52ZJYfLXy/pEkkTwke8bQJ9yqQR+brloqO1fsduXXTrXFXVNkQdEgAAABAXiUVgH40syNT3z5ykuSsrdPNzy6MOBwAAAOjX3P0ZSRXdXPwsSXe5e527r5C0TNIMMxsuKc/dX/RgQPQ7JM1OSMDAAXb02CJd/4npemNDlS669WVVVtdHHRIAAACwFxKLwH748FEj9b7Dh+qX/3xLb26sijocAAAAYCD6gpm9EnaVWhhOGykpdsDzteG0keHz9tPjMrNLzGyemc3bsmXLgY4b6LF3HzpE//exaXp13Q6dfd3zWr5lV9QhAQAAAG2QWAT2g5npJx86QrkZKfrfvy1SfWNz1CEBAAAAA8n1kg6SNFXSBkm/CqfHGzfRO5kel7vf6O7l7l5eWlq6n6ECB8bpRwzXnZ+ZqZ21jfrQ9S9ozvJtUYcEAAAAtCKxCOynkpx0/fRDR+j1DTv1f0+9HXU4AAAAwIDh7pvcvcndmyXdJGlGOGutpNExi46StD6cPirOdKBfmT6mSA98/lgVZafpkzfP0f0L1na9EgAAANALSCwCB8D7Jg3TOdNH6ff/WaYFqyujDgcAAAAYEMIxE1ucLem18PlDks4zs3QzGydpgqS57r5BUpWZzTIzk3SBpAd7NWjgABlTnK0HPnecyscU6at3L9Y1/3pTwdChAAAAQHRILAIHyPc+eLiG52fqa3cv1u76pqjDAQAAAPoVM7tT0ouSDjGztWZ2saSrzexVM3tF0rsl/a8kufsSSXdLel3S45Iuc/eWH+Gfk/RHScskvSPpsd7dE+DAyc9K1e2fnqFzy0fp2qeW6ct3LVJtA/VNAAAARCcl6gCAgSIvI1W/+MiR+vhNc/Szx5bqB2dNjjokAAAAoN9w94/FmXxzJ8tfJemqONPnSeLHOAaMtJQk/fzDR2pMcbZ+8c83tX77bt1w/nQV56RHHRoAAAAGoUhaLJrZ/5rZEjN7zczuNLMMMysysyfM7O3wb2HM8leY2TIze9PMTo0iZqA7jj2oRJ86bqxuf3GVnn17S9ThAAAAAAAGADPTZe8+WL/7+DS9sm6Hzr7uBb2zZVfUYQEAAGAQ6vXEopmNlPQlSeXuPllSsqTzJF0u6Ul3nyDpyfB/mdnh4fxJkk6TdJ2ZJfd23EB3feu0Q3VQaba+cc8r2lHTEHU4AAAAAIAB4gNHjtBdl8xSdV2jPnTdC3rxnW1RhwQAAIBBJqoxFlMkZZpZiqQsSeslnSXp9nD+7ZJmh8/PknSXu9e5+woF42TM6N1wge7LSE3WNedO1ZZddbry4SVRhwMAAAAAGECOKivU3y87TqW56brgljm6Z96aqEMCAADAINLriUV3Xyfpl5JWS9ogaYe7/0vSUHffEC6zQdKQcJWRkmJ/Ja8NpwF91pTRBfrCuw/WAwvX6bFXN0QdDgAAAABgABldlKX7PnesZowr0jfufUW/+Ocbam72qMMCAADAIBBFV6iFClohjpM0QlK2mX2ys1XiTIv7a9nMLjGzeWY2b8sWxrdDtL5w8sE6YmS+vv3Aq9pcVRt1OAAAAACAASQ/M1W3fWqGzjt6tH7/n3f0pbsWqrahKeqwAAAAMMClRPCa75W0wt23SJKZ3S/pWEmbzGy4u28ws+GSNofLr5U0Omb9UQq6Tt2Lu98o6UZJKi8v51Y9RCo1OUm//ugUnXHtczrtN8/qkKG5GlearfEl2Rpfmq3xJTkaVZiplOSoeiQGAAAAAPRnqclJ+umHjtDYkmz97LE3tH77bt10QbmKc9KjDg0AAAADVBSJxdWSZplZlqTdkt4jaZ6kakkXSvpZ+PfBcPmHJP3VzK5R0MJxgqS5vR00sC8OHpKrmy4o14OL1mnF1mo98soG7djd0Do/NdlUVpSlcSU5Oqg0W+NKsjW+NEfjSrJVkpMms3gNdgEAAAAACJiZLj3xII0pytJX/rZIs697XrdedLQOHpIbdWgAAAAYgHo9sejuc8zsXkkLJDVKWqiglWGOpLvN7GIFycePhMsvMbO7Jb0eLn+Zu9O3B/qNEyeW6sSJpZIkd1dlTYOWb9ml5VurtXxLtVZs3aXlW6r1zFtbVN/U3LpebkZK2LoxJ0w4BonHcSXZykqL4p4AAAAAAEBfdfoRwzW8IFP/c/s8nX3dC/rDJ6fruINLog4LAAAAA4y5D8weQ8vLy33evHlRhwF0W1Oza13lbi0PE40rtlZr+dZdWrGlWut3tB2jcXh+RmuicXxJjsaVZuugkhyNLMxUchKtHAEAwMBkZvPdvTzqODAwUYfEQLG2skafvu1lLd9SravOnqyPHl0WdUgAAACRoA6ZGDR7AvqI5CRTWXGWyoqzdNIhbefV1Ddq5daa1kTj8q3B48FF61VV29i6XFpyksYUZ7V2qTo+pqVjUTZdqwIAAADAQDeqMEv3fu5YXfaXBfrWfa9q5bYafeN9hyiJm1ABAABwAJBYBPqBrLQUHT4iT4ePyGsz3d21rbq+TZeqy7dW650tu/SfNzeroWlPi+T8zNTWLlVju1gdV5KtjNTk3t4lAAAAAECC5GWk6paLjtb3H1qi659+R6u31ehX506h7gcAAID9RmIR6MfMTCU56SrJSdeMcUVt5jU2NWtt5e6wS9VqLd+ySyu2VuuFZdt0/4J1bZYdWZDZ2rJxREGmUpOTlJpsSklKUkqytT5vOy1JKUmmlJhlU5OD/1OSwvnJptRw+Zbn3CULAAAAAImXmpykq2ZP1rjibP3ksaVat323brqgXKW56VGHBgAAgH6MxCIwQKUkJ2lsSbbGlmTr3e3mVdc1asXWcBzHltaOW6t1/4J12lXXGHd7B0qSBbGlxiQlk5O6k5hsWcfarJ9kksmUlCRJJjPtmWZB8tXC/1vnxUwL/peSzGSSZNZu/Y6XD/anZZ6UlBRso2X5lm0mmSkpyTSuJFuTRuRxlzAAAACAXmFm+swJ41VWnKUv37VQs3//vG791NGaODQ36tAAAADQT5FYBAah7PQUTR6Zr8kj89tMd3fV1DepscnV0Nwc/G1qVmOzq7GpWQ1Nrsbm8G84vaEpWK51euv8jpdt2XZjU7Mawm0H09u+TlO4Tn1js6rrm2KW27O+S2p2l7vU7JLU8tyDec3BX8VOC5f3cJ9jl3dXQqUmmw4bnqepows0ZVSBppYVaFxxNi05AQAAACTMqZOG6e7PHqOLb5+nD1/3gq775FE6fkJp1GEBAACgHyKxCKCVmSk7ndNC+2RjSyJSik1ihonIZsnbJzPDrGXsug1NzXpjY5UWrdmuRau36775a3XHi6skSXkZKZoyukDTRhdoyugCTR1doOIcuicCAAAAcOAcOapAf7/sOF1828u66NaX9ePZk/WxGWVRhwUAAIB+hgwCALTT2pWpDmwrwjHF2Tp10jBJUlOz650tu7Ro9XYtXLNdi9Zs1+/+syxsdSmNLsrU1NGFmhomGulCFQAAAMD+GlmQqXsuPUZf+OtCXXH/q1q5tVrfOu1QelABAABAt5FYBIAIJCeZJg7N1cShuTr36NGSpJr6Rr22bqcWranUojXbNX9lhR5evF6SlJK0pwvVqaPpQhUAAADAvsnNSNXNF5brBw+/rhueWa5V22r0649OVWYaNzICAACgayQWAaCPyEpL0YxxRZoxrqh12uadtVq4ZrsWh60aH1i4Tn96qW0XqlNjHnShCgAAAKArKclJ+uFZkzS2JFs/fuR1nXfji7rpwnINyc2IOjQAAAD0cSQWAaAPG5KXoVMnDYvbheqitcF4jdc9/Y6awj5URxVmtiYZp5UVaNKIfLpQBQAAALAXM9PF7xqnsqIsfenOhTr79y/olouO1iHDcqMODQAAAH2YuXvUMSREeXm5z5s3L+owACDhYrtQXbxmhxat2a5123dL2tOF6pTR+a1jNo4voQtVAED/ZGbz3b086jgwMFGHxGD22roduvj2l1Vd16Tff+IonTixNOqQAAAA9ht1yMQgsQgAA9DmnbVaFHafumjNdr2ydod21TVKknIzUjR1dIGmjNozXmMJXagCAPoBKoVIJOqQGOw27NitT982T29tqtIPzpykT84aE3VIAAAA+4U6ZGLQFSoADEBD8jL0vknD9L6wC9XmsAvVhS3JxtXbdf1/23ahOmV0gaaF3aiOKsxSRmqSMlKTlZ6SJDNaOAIAAAAD2fD8TN1z6TH60p0L9Z2/v6aVW6t1xRmHKZneTgAAABCDxCIADAJJSaYJQ3M1YWiuzi0fLUnaXd+k19bvCMZrDJONj7yyIe766SlJSk8JEo3BY0/SMfi797SM1CRlpATLp4fP01P3bCPucil7tkF3rQAAAEDvyklP0Y3nT9ePH1mqPz63QqsqavTb86YqK43LRwAAAAjwyxAABqnMtGQdPbZIR48tap22uapWi9fs0JaqOtU2NKm2sUm1Dc2qa2hSXWNzMK0hmFbb2KS6hmbtqmvU1l31qmuZ1xgsX9vY3Noicl+kpSQpIyVJ6R0kHzNSg3l5GSkakpuhYfkZGpqXrqF5GRqWl6Gi7DRaWgIAAAA9lJKcpCvPnKQxxVn60T9e10dveEl/vLBcQ/Myog4NAAAAfQCJRQBAqyG5GTrl8AN3waChqbk1EVkXJilrG5raPd+zTEsysy4mcRmbzGxZr7q+UduqgwTmztoGbd1Vv9drpyUnaUheuoblZWho+BiWvyfxGCQiM5SRmnzA9hcAAAAYKD513DiVFWXpi3cu1OzfP69bLjpahw3PizosAAAARIzEIgAgYVKTk5SanKTcBN/cXN/YrC276rRxR6027axt/btpZ6027qzV6xt26qk3Nmt3Q9Ne6+ZnpgbJx/wMDQsTkUPaJR+Ls9PomhUAAACDznsOG6p7Lj1GF982T+dc/4J+9/Gj9O5Dh0QdFgAAACJEYhEA0O+lpSRpZEGmRhZkdriMu2tnbaM2h8nG1iTkzlpt3FGnzVW1emPDTm3ZVSdv14NrarJpSG5GmxaQw/IzYlpDpmtYfgZjzwAAAGDAmTQiX3+/7DhdfPvLuvj2l3XlmZN0wTFjow4LAAAAEeEKKABgUDAz5WemKj8zVROG5na4XGNT29aPm3bWaePOWm3aESQh39xUpWff3qpddY17rZubkdKmpePQOInI4px0JdP6EQAAAP3IsPwM3f3ZY/Tluxbqew8u0Yqt1frO+w/ndy0AAMAgRGIRAIAYKclJGp6fqeH5Hbd+lKRddY1tul7duLN2T2vInXV6e9NWbdlVp6bmts0fk5NMpTnpbbpeHdqm9WOQhMxJ5ysaAAAAfUd2eopuOL9cVz2yVLc8v0JrKmr02/OmKZvfrQAAAIMKv/4AANgHOekpOnhIjg4ektPhMk3Nrm276jrsenX5lmq98M42VdXu3foxJz2ltYvVobltk48trR9LctKUkpyUyN0EAAAAWiUnmb73wcM1riRL339oic694UXdfOHRGpaf4EHVAQAA0GeQWAQAIEGSk0xD8jI0JC9DR47qeLma+pbWj3Uxycc9icg5Kyq0aWetGtu1fkwyqSQnvbXr1ZZuWIfkpu8ZAzI/Q7npKTKjmyoAAAAcGOcfM1ajirL0hb8s0Fm/f05XnzNFJ04sjTosAAAA9AISiwAARCwrLUXjS3M0vrTj1o/Nza5t1fWtXa9uqtoz7uPGnXVava1Gc1dUaMfuhjjbT2475mOYdGx5PjQvSEam0voRAAAA3fTuQ4bo3s8dqy/euVAX3jJXH5tRpv/3/sPo0h8AgAGmsalZ23c3qKK6Xtt21auypl7bqutVEfu8uk4V1Q2qqK5TUXa6Hvvy8VGHjQTi1x4AAP1AUpKpNDddpbnpmjwyv8Plahua2oz7uGln0BJy484gETlvVaU276xTfVNzm/XMpOLsdA3LT2/tcnVUYZbGFGeprChLZcVZystITfRuAgAAoB85bHie/vHFd+nXT7ylG59drmff3qKrzzlSxx5UEnVoAACgA7vrm1RREyQGt1XXBcnBXfWqqK5v87yiJvi7Y3eD3ONvKzcjRcXZaSrMTtPIggwdMTJPowqzeneH0OtILAIAMIBkpCZrTHG2xhRnd7iMu6uiuj5u16ubdtZq3fZazV9Vqcqatq0fC7JSNaYoS6OLYhKORdkqK87SsLwMJSfR3SoAAMBgk5GarCvOOEzvmzRUX7t7sT5+0xxddOxYffO0Q5SVxmUnAAASqbnZtbM2aE1YUR20HqysbmlF2PZ5y2N3Q1PcbSUnmQqz0sJEYaoOG5anojBpWJydpqKYR3F2mgqy0pSWQu9XgxG/8AAAGGTMTMU56SrOSdfhI/I6XK6qtkGrK2q0pqJGq7bVaHVF8Hh13Q49/trGNmM+piUnaVRhpspaE47BY0xxtkYXZXJRCQAAYICbPqZIj335BP388Td02wsr9fSbm/XLj0xR+diiqEMDAKDfqG9sVmVNfQeJwjpVVjdoW3VdOL9BlTX1amqO35wwMzU5SALmBMnACUNyOkkUpis3I0VJ3DSObuAqHwAAiCs3I1WTRuRr0oi9u15tbGrWhh21rQnHVRXVrQnI+SsrVVXX2Gb50tz0INHYvsVjcZZKc9Jlxg9XAACA/i4zLVlXnjlJp04apm/cu1gfueFFfeb48frqKROVkZocdXgAAPQqd1d1fZMqdrV0K1rXyRiFwaOqtrHD7RVkpba2FhxbnK3pY4KkYGFWS/IwXUVZaSrKSVNRVpoy0/juRWKQWAQAAD2Wkpyk0WGSsD13147dDW1aOa7eFiQf56yo0AOL1rXpmz8zNVll4bbK2iUdRxVmKj2FH8IAAAD9yTEHFevxr5ygnzy6VDc+s1xPLt2kX507VVNHF0QdGgAA+6yp2bW9pm23ont1PdpujML6xua420pNtrC1YLqKs9M0qjCrtRVhvBaFBZmpSkmm21H0DSQWAQDAAWVmKsgK+tqfEufiUV1jk9ZV7taq9t2sbqvR88u2tunr30wanpfRrpVjdmvrx4KsVFo7AgAA9EE56Sn6ydlH6LRJw/St+17Rh69/QZeeOF5fes8EbhwDAPQJtQ1NbZKEXXU9un13Q5sbpWPlpqeoMEwCDs/P0OEj8jpNFOakp3A9A/0WiUUAANCr0lOSNb40R+NLc/aa5+7asqtur4Tj6ooa/efNLdpSVddm+dz0lD3jOoZ/xxQFiccRBRnczQcAABCxEyaW6vGvnKAf/+N1/f4/7+jJpcHYi5NH7t3dPgAAB5K7a3NVnVZurdaqbTVauW3P39XbavYaxqVFkqm1i9Gi7DQdMiw36G60TaIwvTVJWJidyk0zGFRILAIAgD7DzDQkN0NDcjM0fUzRXvNr6hu1pmJ3MK7jtnBcx4oavbmpSk8u3az6pj1djCQnmUYWZGpMcTiuY9jVakvrx9yM1N7cNQAAgEErPzNVv/jIFJ02eZguv/9Vzf798/rCyQfrsncfrFRuBAMA7IfmZteGnbVatbVaK7cF1wpiE4i1DXuuE6QkWes1gfIxhRqSl9FujMJgbML8zFQlJdGaEOgIiUUAANBvZKWl6JBhuTpkWO5e85qbXRt31rZp5bgqHOPxsVc3qLKmoc3yhVmpbbpVjW31OCwvg0oEAPQyM7tF0gckbXb3yeG0Ikl/kzRW0kpJ57p7ZTjvCkkXS2qS9CV3/2c4fbqk2yRlSnpU0pfdO+q0CkBves9hQ/XE/xbq+w8t0W/+/bb+vXSTfvWRqXF/2wEA0KKxqVnrt9eGCcPYBGJQ548dxzAtOUllxVkaW5yl4w4u0djiLI0pztbY4mx6NgIOEBuo9avy8nKfN29e1GEAAIA+Ymdtg1Zvq2lt5RibgFy3fbeamvf8JkpLTtKoosyYhGOYgCzO0ujCLGWm0cUJEAUzm+/u5VHHgcQwsxMk7ZJ0R0xi8WpJFe7+MzO7XFKhu3/LzA6XdKekGZJGSPq3pInu3mRmcyV9WdJLChKL17r7Y129PnVIoHc9/toG/b8HXlNVbaO+csoEXXL8eC72AsAgVt/YrLWVNXt1WboqrMc3xtTZM1KTNLY4W2OKs8K/2UECsSRbw/IylMyNwghRh0wMWiwCAIBBIS8jVZNH5scdz6ehqVkbttdqVUV12xaP22r08spK7Wo37sKQ3PQ2LRxbk45FWSrNSWcAdgDYB+7+jJmNbTf5LEknhc9vl/S0pG+F0+9y9zpJK8xsmaQZZrZSUp67vyhJZnaHpNmSukwsAuhdp00erqPHFuk7f39NVz/+pv61ZJN+de4UHRRnHG4AwMBQ29CkNRU1cbssXVe5WzG5Q2WnJWtsSbYOH56n0ycP25NILMnWkFzq3UCUSCwCAIBBLzXsKqWsOGuvee6uypqGtuM6honHF9/ZpgcWrlNsBxCZqcltko6xYzyOLMxkQHcA6Jmh7r5Bktx9g5kNCaePVNAiscXacFpD+Lz9dAB9UHFOuq77xFF6+JUN+t6Dr+mM3z6rb5x6iD593Di6pQeAfqqmvlGrK2q0cuue7kpXbg26MN2ws7ZN/Tk3I0XjSrI1dXShZk8dqTHF2RpXEnRdWpydRvIQ6KNILAIAAHTCzIIB3LPTNHV0wV7zaxuatG777jatHFuSkM++vaXNQPFmUmlOeuvg8EXZaSrMTlVRVpoKw9coyEoL/09VUXaaMlOTqUwBwN7inRi9k+nxN2J2iaRLJKmsrOzARAagR8xMZ04ZoVnjivTtB17Vjx9Zqn8t2aRffORIjSnOjjo8AEAcVbUNWrUtttvSPeMebtpZ12bZouw0jSnO0szxxTFdlwZ/C7JSqe8C/RCJRQAAgP2QkZqsg0pz4nbb5e7aUlXXJuG4cUetKmvqVVlTrzc27lRlTYMqa+rV0bDX6SlJ7RKRaSrKSm1NRBZmhY8wEVmYlaaMVFpFAhgwNpnZ8LC14nBJm8PpayWNjllulKT14fRRcabH5e43SrpRCsZYPJCBA+iZIXkZuumCct23YJ1+8PASnfabZ/XtMw7VJ2aOofUiAERgR02DVrbrrnRVmDzcuqu+zbKluekaW5yl4yeUBmMdFmdrbHG2yoqzlJ+ZGtEeAEgUEosAAAAJYmYakpehIXkZKh9b1OFyTc2unbsbVFFTr8rqelVUB4nHiuoGba+J/b9e67bvVkV1vXbsbuhwe1lpyfETkW1aRgaJyKKsoJVkWkpSIooAAPbXQ5IulPSz8O+DMdP/ambXSBohaYKkue7eZGZVZjZL0hxJF0j6v94PG8C+MDOdM32Ujju4WN+89xV998ElenzJRv38w0dqVOHeXdYDAPadu6uiuj5mvMO2f7fXtK1zDs/P0JjiLL33sKFh4jBIII4pzlJ2OmkGYDDhEw8AABCx5CRTYZgEVGn31mlsatb23Q17JSIrYxKRldX1qqhp0Mqt1aqsrldVXWOH28tNT2mNIV4isjArtU2ysiAzVSnJJCMBHDhmdqekkySVmNlaSd9XkFC828wulrRa0kckyd2XmNndkl6X1CjpMndvCjf1OUm3ScqU9Fj4ANCPDM/P1B2fnqE7567RVY+8rtN+86y+8/7D9NGjR9NlHgD0QEsvOivjdFm6amtNmzqimTSyIFNji7P1/iOG7+mytCRbZUVZ9IwDoJV5R/1u9XPl5eU+b968qMMAAADoM+obm4MWkC3Jx+q2rSSDeW2TlTX1TR1uLz8ztTXp2L671vaJyKKsNOVnptKVGfaLmc139/Ko48DARB0S6JvWVNTom/e+oheXb9NJh5TqZx86UsPyM6IOCwD6jOZm18adtW27LN0a/F1dUdOmTpecZBpVmNmmxeHYMHk4qjBT6SkkDzGwUIdMDFosAgAADBJpKUmtXbN2V21D055WkNV7d9daGSYi12+v1ZL1O7Wtul71jc1xt5VkUkFW/ERk0CVrapv/C7PTlJeRQssEAAAGsdFFWfrL/8zUn15apZ899obe9+v/6sozJ+nsaSP5jQBg0Ghqdq3fvjtmrMOYlofbalQXUwdLTTaNLsrS2OJsHXNQ8Z6Wh8XZGlmYqVR6ngGwn0gsAgAAoEMZqckanp+p4fmZ3Vre3bW7oanDRGTQMrJBFdX1Wl1Ro0Vrtquypl4NTfF70UhJMhVkpakoe+8WkLGJyJLsdE0clsMdtgAADEBJSaYLjx2rEyeW6uv3LNZX716sx17bqKvOnqwhubReBNC/NTY1a+uuem3aWauNO2u1Ofy7cUedNlfVat323VpTUdOmzpSekqQxYYvDEyeWhi0PgwTiiIJMJdNTDIAEIrEIAACAA8bMlJWWoqy0FI0q7N467q5ddY0dJiKD8SKDecs272ptKdnU3DYZmZ6SpKPKCjVrfLFmji/S1NEFjAMCAMAAMrYkW3/77DG65bkV+sW/3tSpv35GPzxrsj44ZUTUoQHAXtxdO3c3alNVrTbuqNWmnbWtycNNO+ta/99SVad2VRslJ5lKc9I1ND9DE4fk6n2HD9vTdWlJlobmZjDMBIDIkFgEAABApMxMuRmpys1IVVlxVrfWaW52VdU2to4XuWlnreatrNRLy7fpN0++Jf930PXrtNEFrYnGo8oKSTQCANDPJSeZPnPCeL370FJ97e7F+uKdC/X4axv1o9mTVZSdFnV4AAaJ2oYmbamqC1sW7kkabtpZ16bVYW3D3sNE5GemalhehobkpeuQobkalh8MVzEsL0ND89I1LC9DxTnptDoE0GeZe/xup/q78vJynzdvXtRhAAAAoJftqGnQ3JUVmrN8m15asU2vr9+pZpfSkpM0dXSBZo0v0szxxTqqrFCZaSQa+xMzm+/u5VHHgYGJOiTQ/zQ2NeuGZ5brN/9+S/mZqbrq7CN06qRhUYcFoB9rbnZtC29c3LijVpuqarVpx56EYUsCsbKmYa9101KSWpODQ1sThRkamp+hobnpGpYf/M/NjkDvoQ6ZGCQWAQAAMKDt2N2geSsrNGdFhV5avk2vrduhZpdSk01TRhVo5vgizRpfrOljCpWVRocefRmVQiQSdUig/3pj40597e7FWrJ+p86eNlJXfnCS8rNSow4LQB+zq65xry5JN++s08Yde8Y23FxVp8Z2/ZKaSSU56W2Shq2Jw/w9rQzzM1NlRitDoC+hDpkYJBYBAAAwqFTVNgTdpq7YppeWV+i1dTvU1OxKSTIdOSpfM8cXa9b4YpWPKVR2OonGvoRKIRKJOiTQvzU0Net3Ty3T7/+zTMU5afrZh4/Uuw8ZEnVYAHpBQ1OzNlcFCcLN7cYwjG11WF3ftNe6uekprcnBNgnDlm5J8zNUkpOu1OSkCPYMwP6iDpkYJBYBAAAwqO2qa9T8VcH4jHOWb9Mra3eosdmVnGSaPDJfs8YXada4YpWPLVRuBq0fokSlEIlEHRIYGF5bt0NfvXuR3tq0Sx8tH63vfOAwvr+BfsrdVVnT0GmXpJt21mpbdb3aX+JOTTYNyd2THBySm6Fh+RmtYxu2JA+5kRAY2KhDJgaJRQAAACBGTX1sorFCi9duV0OTK8kUJhqLNXNckcrHFik/kwuVvYlKIRKJOiQwcNQ1Nuk3/35bN/z3HQ3Ly9DV50zRuyaURB0WgBi765vaJAg37tjTyjC2m9L6pua91i3OTtOQvAwNa5c0jB3bsDArTUlJdEsKDHbUIRODxCIAAADQid31TVqwulJzlgddpy5as131Tc1KMunwEXmaNa5YM8cXa8bYIsZzSjAqhUgk6pDAwLNgdaW+fs9iLd9SrU/OKtMVpx9G6yQgwRqbmrV1V31rcnBP4rBOm6tqW8czrKpt3GvdzNTk1gThnu5Iw+5J84OkYWluutJTkiPYMwD9EXXIxCCxCAAAAPRAbUNLorFCLy3fpoVrtqu+sVlm0mHD8jRzfFFrq8aCrLSowx1QqBQikahDAgNTbUOTfvnPN3Xz8ys0qjBT5x1dppMPHaJDh+XKjNZMQFcampq1vaZBlTX1qqiuV2V1vSpqwr/VDaqorlNFTYMqq4Nk4tZddWpud7k5Ock0JDe9tZVhm4RhmDQckpeh3PQUPpcADijqkIlBYhEAAADYD7UNTVq0ZntronHB6krVNQZdNh06LFezxhdr1vgizRhXrKJsEo37g0ohEok6JDCwzV1RoaseXarFa7ZLkobnZ+jdhw7Rew4domMPKlFmGi2gMPA1N7t27G6ISQzWhwnDjhKH9doZp2Vhi5z0FBVmp6ooK00FWWkakpsetjjckzQcmpeu4px0JdMtKYAIUIdMDBKLAAAAwAFU19ikV9bu0EvvbNOcFRWat6pCtQ1BovGQobmaOb5IM8cVa+b4IpXkpEccbf9CpRCJRB0SGBw276zV029u0ZNvbNJzb29VdX2T0lOSdOxBxTr50CF696FDNKowK+owgS65u3bVNaqyOl6iMOZvzPzKmvq9WhO2SEtJUnF2mgqz0lSUnabC7DQVZaUGf1seWWmt/xdkpdIlKYA+jzpkYpBYBAAAABKovrFZr67brpfCFo3zVlZqd0OTJOngITmaFZNoHJKbEXG0fRuVQiQSdUhg8KlrbNLcFRV66o3NeuqNzVq1rUZScCPQuw8dopMPHaKjygqUkpwUcaQYDGobmlTRPjkY/h8kBhv2Shw2NMW/rpuSZGFiMC1oURibMGyTONwzPzM1mW5IAQw41CETg8QiAAAA0Isampr16rodrV2nzltZoer6INE4vjS7dXzGWeOLNTSPRGMsKoVIJOqQwODm7lq+tVr/eWOznly6WS+vrFBjsys/M1UnTizVew4bohMnljJ+MrqlvrFZ22uChGD7VoPtk4MtCcOWG8/aM5MKMlNjEoGxLQdT90oUFuWkMVYhAISoQyYGiUUAAAAgQo1NzXpt/U7NWb6ttUVjVV0wls24kuw2LRqH52dGHG20qBQikahDAoi1s7ZBz729VU8u3ayn39ysbdX1SjLpqLJCnXxY0JrxkKG5JG8GgaaWcQnbtyRsTRQ2tO1+dFd962+5eHLTU9p0LxokBtslDmNaFuZnpjI+IQDsI+qQiUFiEQAAAOhDGpua9fqGnZqzvEJzVgTjNFbVBhenxhRntbZmnDm+WCMLBleikUohEok6JICONDe7Fq/drv+8sVlPvblZr63bKUkaWZCpdx9aqvccOlTHHFSsjFTGm+vr3F1VdY3tWg42qKK6LkgQxiYMw7/bdzeoo8unmanJYWvB1C67Gi3KSlNBVprSUuhaFwB6C3XIxCCxCAAAAPRhTc2upRt26qXlQZJx7ooK7djdIEkaXZSpmeOKW7tPHV2UFXG0iUWlEIlEHRJAd23aWRt0mfrGZj2/bKtq6puUkZqkYw8q0cnh2IwjBtnNP33N9pp6rdharZXbqrVia41Wtj6vbr1hq73UZIuTHExVUXa6irJS92pJWJiVpsw0kskA0JdRh0yMXk8smtkhkv4WM2m8pO9JuiOcPlbSSknnuntluM4Vki6W1CTpS+7+z65eh0ohAAAABqLmZtcbG6vCRGOQbNxeEyQaRxZkaub4oEXjrHHFGl2UOaC6aKNSiESiDglgX9Q1NmnO8go99cZmPfnGJq2p2C1JOnRYbmuScVpZIV1ZJkBVbYNWbq3Rim3VQeJwa3Xr88rwt5EUjFE4siBT40qyNbY4W6OLMlWcnb5Xq8IcxiUEgAGHOmRiRNpi0cySJa2TNFPSZZIq3P1nZna5pEJ3/5aZHS7pTkkzJI2Q9G9JE909/ojGISqFAAAAGAyam11vba7SS+8EScY5KypUUV0vSRqen9HamnHW+GKNKc7q1xfMqBQikahDAthf7q53tuwKkoxLN2veqko1NbsKs1J14sRSnXzYUJ04oVT5WalRh9pv1NQ3auXWmtbWhrEtD7fuqm+z7PD8DI0tztbYkmyNLwn+jivJ0uiiLKWn0LIQAAYj6pCJEXVi8X2Svu/ux5nZm5JOcvcNZjZc0tPufkjYWlHu/tNwnX9KutLdX+xs21QKAQAAMBi5u97evEtzlm/TS+E4jS0X3obmpYeJxmLNGl+kcSXZ/SrRSKUQiUQdEsCBtmN3g559e4ueWrpZT7+1RRXV9UpOMk0fU9jamnHCkJx+9V2cCLUNTVpdUdMmcbh8S/B30866NsuW5qZrXEm2xhXvSRyOLcnWmKJsuiUFAOyFOmRiRJ1YvEXSAnf/nZltd/eCmHmV7l5oZr+T9JK7/zmcfrOkx9z93jjbu0TSJZJUVlY2fdWqVb2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\n" - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(3,2, figsize=(30,25))\n", - "\n", - "py_df.filter(variable=luc_co2+\"*\", year=[i for i in range(2020,2110,5)], scenario=\"SSP2BIO10GHG400\").plot(ax=ax[0][0])\n", - "py_df.filter(variable=afolu_co2, year=[i for i in range(2020,2110,5)], scenario=\"SSP2BIO10GHG400\").plot(ax=ax[1][1])\n", - "py_df.filter(variable=\"Emissions|CO2|Land|Land-use Change|+|Regrowth\",\n", - " year=[i for i in range(2020,2110,5)], scenario=\"SSP2BIO10GHG400\").plot(ax=ax[0][1])\n", - "py_df.filter(variable=\"Emissions|CO2|Land|Land-use Change|+|Peatland\",\n", - " year=[i for i in range(2020,2110,5)], scenario=\"SSP2BIO10GHG400\").plot(ax=ax[1][0])\n", - "py_df.filter(variable=\"Emissions|CO2|Land|Land-use Change|Gross LUC|+|Deforestation\",\n", - " year=[i for i in range(2020,2110,5)], scenario=\"SSP2BIO10GHG400\").plot(ax=ax[2][1])" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-28T13:40:11.560359900Z", - "start_time": "2023-09-28T13:40:10.103852500Z" - } - }, - "id": "7d9f5b6ecce0f8e8" - }, - { - "cell_type": "code", - "execution_count": 378, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(3,2, figsize=(30,25))\n", - "vars = [\"Emissions|CO2|Land|Land-use Change|+|Regrowth\",\n", - "\"Emissions|CO2|Land|Land-use Change|Regrowth|CO2-price AR\",\n", - "\"Emissions|CO2|Land|Land-use Change|Regrowth|NPI_NDC AR\",\n", - "\"Emissions|CO2|Land|Land-use Change|Regrowth|Other Land\",\n", - "\"Emissions|CO2|Land|Land-use Change|Regrowth|Secondary Forest\",\n", - "\"Emissions|CO2|Land|Land-use Change|Regrowth|Timber Plantations\"]\n", - "ax_it = iter(fig.axes)\n", - "for var in vars:\n", - " ax = next(ax_it)\n", - " py_df.filter(variable=var,\n", - " year=[i for i in range(2020,2110,5)], \n", - " scenario=[\"SSP2BIO10GHG400\", \"SSP2BIO00GHG400\", \"SSP2BIO45GHG400\"]).plot(ax=ax)\n", - " ax.set_title(var)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-28T14:42:52.735931400Z", - "start_time": "2023-09-28T14:42:51.235604300Z" - } - }, - "id": "ac9126c2a5e09ccd" - }, - { - "cell_type": "code", - "execution_count": 384, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "fig, ax = plt.subplots(3,4, figsize=(30,25))\n", - "\n", - "ax_it = iter(fig.axes)\n", - "for var in df[df.index.get_level_values(3).str.startswith(\"Resources|Land Cover|For\")].index.get_level_values(3).unique():\n", - " ax = next(ax_it)\n", - " py_df.filter(variable=var,\n", - " year=[i for i in range(1995,2110,5)], \n", - " scenario=[\"SSP2BIO10GHG400\", \"SSP2BIO00GHG400\", \"SSP2BIO10GHG000\"]).plot(ax=ax) ##.diff({var:\"test\"})\n", - " ax.set_title(var)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-28T14:50:44.149458Z", - "start_time": "2023-09-28T14:50:41.744151500Z" - } - }, - "id": "dae0386acc8f0156" - }, - { - "cell_type": "code", - "execution_count": 381, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Emissions|CO2|Land|Land-use Change|Gross LUC|+|Deforestation\n", - "Emissions|CO2|Land|Land-use Change|Gross LUC|+|Forest Degradation\n", - "Emissions|CO2|Land|Land-use Change|Gross LUC|+|Other land conversion\n" - ] - }, - { - "data": { - "text/plain": "
", - "image/png": 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- }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "fig, ax = plt.subplots(3,3, figsize=(30,25))\n", - "\n", - "ax_it = iter(fig.axes)\n", - "for var in df[df.index.get_level_values(3).str.startswith(\"Emissions|CO2|Land|Land-use Change|Gross LUC\")].index.get_level_values(3).unique():\n", - " ax = next(ax_it)\n", - " py_df.filter(variable=var,\n", - " #year=[i for i in range(2020,2110,5)], \n", - " scenario=[\"SSP2BIO10GHG400\", \"SSP2BIO00GHG400\", \"SSP2BIO10GHG000\"]).plot(ax=ax) ##.diff({var:\"test\"})\n", - " print(var)\n", - " ax.set_title(var)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-28T14:48:36.476892800Z", - "start_time": "2023-09-28T14:48:35.260721Z" - } - }, - "id": "352a10845b469e43" - }, - { - "cell_type": "code", - "execution_count": 322, - "outputs": [ - { - "data": { - "text/plain": "" - }, - "execution_count": 322, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": "
", - "image/png": 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CvjPzlp5LU0oP58ut7fhfThbk7hQRVSml1/PAe72krDX1lHz7/0nW2rC0C8Y3Ukq/y78P/0z2I55e+Y8e9iQ7dyxOKT1CFojVpxv1nzth3Z/Reo9VssDx2ymlyfnn8SLghFrvR53fWxvJjJLz8FfX9V2YKz0GOpEdA1/Ja5xGFqrWfG8vJetWt3e+vsdoAQzUJEmSJEna+EpbEL1Xx+OaC769Kfmlf34BYxJZC53ewFsprdZapLRVwJbA+bUuWvfLn7dB8oshPYDvAHfWE0x8kOyiLmStrapZ1b1X77z+mvUtJBuHpNQNKaUuKaWeKaWDU0oj11LSMfmyNbfSi/bk9d2V19s9pfS/etbTPaW0WUppx5R1YwdZWNOp1nKdgHkNnA/wpTyIqSZrnXFjrLvbzT5k4V5D/SJ/jbZkgcvPo6RrqxIbY3sAiIj985BkfkTU2WKKrAXZdsD4yLqHq7lw3I+6x93rQdbSYWTJ8XpvPr3GzDw0qLGQ/LMSEXtHxIMRMT0i3gXOZM2QaBJ16w3UhHg13qCe1pO15ReTL0gp7UzW3eYosgvhkc+/J6V0JFlQejRZYPb/Slbxi/z43TyldFTti8t565ifkrU4qd1a85j8/W9DFto+HBGb11HmDLILynXZIp+/Lus6RmaWvkZK6cq8tkvJwv8aU2p9bruQjRVIPesZlS9zHNl2EhFHAh1T/S0b1lbr/JLHdW3HaiLrjrfmeD+tnteD1Y+vtZ1/6zp3r+3YnJSf+2vUPjZLu91d+ZlooEfIWqHtz6r34LGSaZNSSm9Q67uonjrq2waode6vY10rRcRpJfv7nnoW+wdZF55tyI6LZ/M6iYjt8pZ5b0fWLd2Pafi5YEPOJTX7p7YtgRPrCI7r+xzW5ZiUteI6CNihpI56152ylp0n5XVPjaw70Jque2u/j2+QhbtrdBNcj9Lt7kd23pxdx3L1Hv8ppQlkre4vAqZFxPURsd7/Loms++hRJesfxOrv08rPRf5vDcg+G72B2Wn1FrD1Ho/UOh/VYV2f0XqPVbL9dEvJNrxIFjiWvh9r+1xRxzFwZ8m0+lqn1uhech7+BQ37Lqx9nqsiO85qavgtWUs9gK+TdQv8dGRdYX5mHfU0CwZqkiRJkiQVZwrZBQsg65aQ7KLUW2S/mO5T0oURZON81JgE/KjWhet2KWtZ8H78jWzcorpae32C7FrCHRHxNjCRLEyq6XZxKlm3PzXb05YNGx9tffyFrN6/rufzxgFbx+pjog1mVZd741jVLRoRsTXZxf7SLhWBLAhNWUulCWTdIdUpsnGLjiRrIbJeUuZ54H/UPT7Ry0CriBhYMm1t29OerKvGNQKzlI3r1SG/1dlNXkrplZTSKWQX1n5GFia2Jzsu6xrjaQZZmLxzyfHaOaXU0HDgH2RdkvZLKXUGria7kLdaWfU8dwrQtdZ73Z/sc7Ze8sCr5sJk11rzVqSs9eN/ycdJW5eIGE42vteRKWsNU9/rLk9ZS6flrN7aq8YDwN556F26/r3IzikNGZ9nHLBrrXPOrqw6Rv5DdsH4/foPcFh+vNTnQ2Rjfb2dn2tOAr4SEbeV1Frn5zMPAKaWzmf1z8JqUkqHlxzvf69rmZpFS+6v7fxb17l7tfelxBSgX6wa0ww28NisxyNkwdkBrDrv/I9srL4D8vk1dWxZ67m166jv8wXZNpduY+2uUVetJKW/l+zvun4cQErpBbIL/Yezehd6AL8haz03MGVdzn6Lhp8LoGHnktrbUteYg5OAv9Y6BtqneloMrk3KWn79iVUtVNe67pTSfSmlQ8mCoPFk5xBY833sT9Zd3ztkrZzb1cyIrBVt6Q8aYM1jvGtEdKln2+v990dK6R8ppf3yWhLZd0SDRdbd6+/IfkTQLQ/cn2fN96kuU4HNap1f6j0eyc6dH17L+Witn9F1HKuTyH4oUbqfqlPWe0GNtR2r1PHDhCNKpq3vsdaQ78Lax8BiVg/mOtX8uyCl9HZK6XMppd5krfF+HflYhc2ZgZokSZIkScW5AfhoRHwosm4Tzye7ePE48ATZhbAvRUSriDiO1bs7+h1wZv5r+4iI9hHx0VoXSjbE5WRdyz1Sx7zTybo1HFJyOz7fhm5k424cGREfiGwcoYtp2AWw9+PhvN4r1udJKRtrbBTwvYiojohjycKDmjGi/k62LfvnF9q+T9b1Wn2tXPYh6z5tjYv2EVEVETuSdQO1Odn4RDXz2kREdf6wdV5Lnfssb4WwX12vkf8a/2bg+/mxsC9Za6maoPEWsi7Cjs9f77tk42WNr2cXrVVEfDwieuS/2p+TT15Ott8OiYiP5cdtt4gYki/3O+BXEdEzX0efiGjouDEdyX5ZvygPiU5taK0pG1fsceAn+f7dlayF3drCk9Jt/VlEDMq3pyPwBWBCSmlmRBwd2dhqm+Wfw73Iuo9b63h6+XoPzms4PqVUX5eANctGRBxNNlbNi3Vs4wNkQdVNEbFzRFRGxLB8/b9JKb2Sr6cyf/9bARX5/qhpXfYQ2Xv4pfy4PDufXhPGXQTsH1mXln3y9XUnG/9nffyF7KL3Lfl+ralpaMkyF7JqvL0hZAHI74BP5/PX9fn8C/Cd/H3ZgayLuj+tZ51rs7bz7xNk+/Hs/Jg5mtXP3aWeIgs6vp6fJw4iC92v30h1Pk7Wdd/HyQO1PHCcnk+rOc/fTTb+3Kl5zSeRnc8aNAYY2XfZlyKib2Rj2K2r5UxD/INsDKoDyMbsrNGRrAvU+fl7+4X1XG9DziUXRkS7iNiZ7Jirq6Xk38iOwQ/XHMMRcVBk46FtiEuBQyNiyNrWHRG9IuKo/LhfTNYis6Yb4OuAcyNiq8jGbPwxWRfNy8h+dFGdH6dVZC276x3zMaU0lWw8zF/nn6OqiKgZG6ve4z8ito+IgyNrsbWI7IcUdXVTXKPmPFRzawO0Jwt2psPKLnEb9COFvHXYCODiyMaj3Y/Vuy6t7a9kwdFNEbFDRFTk31vfioiP0LDPaH3H6tXAjyILCIlsDLyjG7IdjWF9vwvzY+DfwC8jolO+b7aJiAMBIuLEkuN9Ntl7VvNev0M2jl+zY6AmSZIkSVJBUkovkV3UvIKsBc+RZC1VlqRsDLLjyLqPm03WQuPmkueOILtIfGU+fwJ1jM0Eq7rwa2BNs1JK/0lpte7KyC/ODwCuyn+VXHO7PX/tU1I2jss5ZBeappJ1sTaN7KLfhrgjVnUNNj8ibqmj3pTXuz7dKNY4mewi/myyLvdOSClNz9c7jqxLrb/n29AR+GKt519ZUxvZRbnvpJRKuzA7KZ83hywQmAnskVIqbe3wEtkFxz7Affn90hYGX89fYwHZha1rybpcqssXybqGnEZ2YfUL+XaQb9fxwI/y7d2b1ccvW1/DgXH59l0GnJyyMVTeJBtD5nyyri1Hsaql0DfIjpUnI+uq7QHyMdIa4ItkYeE8sjDwhvWs9xSy43cKWbj4vZSNS9SQz0e7/DlzyFplbgkclc+bTfY5fIXsIv/fgJ+vo6VTjQuBzsDdUX8XeHfktc0le+8+mVaNl1Tb8cCDZF1pzs9r+QPZZ7LGJ8iOsd+QtVx6j7x1S37OOYYsOJ8DfIasS7ol+fyXycYT6guMzt+L/5Ht0wsbsL3k61lE1nXsC2Rdts4l+xzsSTYWFymleaXnmbzOBTWf8wZ8Pr9H1vXoG2Sh+89TSvc2tMYGbEO959+Sc/dnyfbjx8mCqTXOg/myR5G1bpkB/Bo4vaFBd2TdVX5rLXUuBEaSBSfPl8x6lKx16SP5cjPJuq09n+w89XWyljAN6SoUsmPoPmA08Cwl31Xvw3VkXSH+t1YdXyULweblr1tft6D1aci55GGy9/Q/ZF22/rv2Ank4cTRZC7npZKHM18ivt+eBTH1dWq4hP0f/hWzsr7Wtu4LsfZpCdo49kFXH/h/JvoseAV4jC7TOydf/br7c78laJC0AJq+jrE+QjZM1nuxz9pV8XWv790cbsu/TGWTdMvbMt6M+p5B9vmtur+atvn5JFk6/A+xCdq5pqFPJvuNmkZ0L6htftWaswUPybbyf7Hz0NFn3kk818DNa37F6Gdl3/7/z4+3JvK4i1ftdWI/TgdZk5+vZZD+cqukic0/gqfw76nbgyyml1/J5FwF/jqyryI9t7I0oUtT697EkSZIkSdJGkf9Cfg5Z11yvrWNxSWqWIuIp4OqU0rVF1yIVJSIGAA+llAYUXMomKyIeAi5KKT1Ujudp/dlCTZIkSZIkbTQRcWTeVVZ7svFgxgKvF1uVJJVPRBwYEZvn3Sd+kqw72Y3WQk6SVIyyBGoR8ceImBYRz9czPyLi8oiYEBFjImL3ctQlSZIkSZI2uqPJuhKaAgwk6wrQ7nEktSTbk3V/+C5Z93wn5OMRSS3ZHLKx4lS/P7FhP0La0OdpPZWly8d8wML5wF9SSmsMIJgP8HcOWR/bewOXpZSK7k9UkiRJkiRJkiRJKk8LtZTSI2SDANbnaLKwLaWUngS6RMQWa1lekiRJkiRJkiRJKotNZQy1PsCkkseT82mSJEmSJEmSJElSoVoVXUAu6phWZ1+UEXEGcAZA+/bt99hhhx0asy5JkiRJZTRy5MgZKaUeRdeh5ql79+5pwIABRZchSZIkaSMp5/8hN5VAbTLQr+RxX7LBi9eQUroGuAZg6NChacSIEY1fnSRJkqSyiIg3iq5BzdeAAQPw/5CSJElS81HO/0NuKl0+3g6cHplhwLsppalFFyVJkiRJkiRJkiSVpYVaRFwHHAR0j4jJwPeAKoCU0tXA3cBHgAnAQuDT5ahLkiRJkiRJkiRJWpeyBGoppVPWMT8BZ5WjFkmSJEmSJEmSJGl9bCpjqEmSJEmSJEmSJKmFWrp0KZMnT2bRokVrzKuurqZv375UVVUVUFnGQE2SJEmSJEmSJEmFmjx5Mh07dmTAgAFExMrpKSVmzpzJ5MmT2WqrrQqrr6KwV5YkSZIkSZIkSZKARYsW0a1bt9XCNICIoFu3bnW2XCsnAzVJkiRJkiRJkiQVrnaYtq7p5WSgJkmSJEmSJEmSJK2FgZokSZIkSZIkSZK0FgZqkiRJkiRJkiRJKlxKab2ml5OBmiRJkiRJkiRJkgpVXV3NzJkz1wjPUkrMnDmT6urqgirLtCr01SVJkiRJkiRJktTi9e3bl8mTJzN9+vQ15lVXV9O3b98CqlrFQE2SJEmSJEmSJEmFqqqqYquttiq6jHrZ5aMkSZIkSZIkSZK0FgZqkiRJkiRJkiRJ0lrY5aMkSZIkSWX07VvGMn3eYvbZphvDtu7G9r06UlERRZclSZIkaS0M1CRJkiRJKqMO1a149JUZ/PuFdwDo0q6KvbfqyrCtu7HPNt3YrqcBmyRJkrSpMVCTJEmSJKmMvnn4jnzz8B15a857PDVxJk+8OpMnX5vJfeOygG2zdlXsvVU3hm3dlWEGbJIkSdImwUBNkiRJkqQC9OnSluN278txu/cFYPLshTw1cRZPTswCtnvHvQ1A1/atV7ZgG7Z1Nwb27GDAJkmSJJWZgZokSZIkSZuAvpu1o+8e7Th+jyxgmzRrIU+9lgdsE2dyz/OrB2w1Y7AN7NmBCAM2SZIkqTEZqEmSJEmStAnq17Ud/bq244SSgC0L12atFrB1a9+avbfOx2DbuhvbGrBJkiRJG52BmiRJkiRJTUBNwHbi0H7AqoDtiYkzefLVmdw9dlXAlnUPmYVsBmySJEnS+2egJkmSJElSE1QasKWUmDz7vZXh2hMTZ3LX2KkAdO/Qmr3z8df22bor2/QwYJMkSZLWl4GaJEmSJElNXESsDNg+lgdsk2a9t3L8tScmzuSuMXUFbN3Ypkd7AzZJkiRpHQzUJEmSJElqZiKC/t3a0b9bOz62ZxawvVkyBtsTr5YGbG1Wdg85zIBNkiRJqpOBmiRJkiRJzVxEsGW39mzZrT0n7dl/ZcD2xKurWrDdmQdsPTq2WW0Mtq27G7BJkiRJBmqSJEmSJLUwpQHbyXtlAdsbMxeuDNeenDiTO0ZPAWCzdlUM6N6eAd3a079rOwZ0b0f/ru0Z0K0dXdu3NmyTJElSi2CgJkmSJElSCxcRWWjWfVXA9noesI2ZPIc3Zi7k6ddmceuot0hp1fM6tmlF/27tsrCtWzsGdMvDtu7t6NWxmooKwzZJkiQ1DwZqkiRJkiRpNRHBVt3bs1X39pyyV/+V0xctXc7k2e/xxswFvDFzYfZ31kJemDqX+8a9zbIVq9K2Nq0q6N+1Xd4SLg/bumUt2/p0aUuryooiNk2SJEnaIAZqkiRJkiSpQaqrKtm2Zwe27dlhjXnLlq9g6ruLeL0kbHt95kLenLmQxyZMZ9HSFSuXrawI+m7WNgvburZjy25Z8DagWzv6dW1HdVVlOTdLkiRJWicDNUmSJEmS9L61qqygX9csENt/4OrzUkpMm7eY12dkLdpKw7bn3pzNvEXLVlt+i87V2Xht3dqzZfd2bNm1fR66taNjdVUZt0qSJEnKGKhJkiRJkqRGFRH06lRNr07V7L11t9XmpZSYs3Apr89cwJuzFvL6jFVdSf5n/DvMmL9kteW7tW+9aty2ru0Y0L3dypZuXdu3JsJx2yRJkrTxGahJkiRJkqTCRASbtW/NZu1bs1v/zdaYP3/xspIx2xauvP/UxJncOuot0qph2+jWvjU/OGYQH9llizJugSRJkloCAzVJkiRJkrTJ6tCmFTv37szOvTuvMW/R0uVMnp23apu1kNtHT+GLf3+WLx60Decftj2VFbZWkyRJ0sZhoCZJkiRJkpqk6qpKtu3ZkW17dgTg48P6c9HtL/Drh15l3JS5XH7ybnRu55hrkiRJev8qii5AkiRJktSyRERlRDwXEXfmj7tGxP0R8Ur+d7OSZb8ZERMi4qWI+HDJ9D0iYmw+7/Jw4CwBbVpV8pPjduHHx+7C46/O4MgrH2P823OLLkuSJEnNgIGaJEmSJKncvgy8WPL4AuA/KaWBwH/yx0TETsDJwM7AcODXEVGZP+c3wBnAwPw2vDylqyk4de/+XH/GPixaupxjr3qcO8dMKbokSZIkNXEGapIkSZKksomIvsBHgd+XTD4a+HN+/8/AMSXTr08pLU4pvQZMAPaKiC2ATimlJ1JKCfhLyXMkAPbYcjPuPGc/durdibP/8Rw/uedFlq9IRZclSZKkJspATZIkSZJUTpcCXwdWlEzrlVKaCpD/7ZlP7wNMKllucj6tT36/9nRpNT07VXPd54bx8WH9+e3DE/nUtU8ze8GSosuSJElSE2SgJkmSJEkqi4g4ApiWUhrZ0KfUMS2tZXpdr3lGRIyIiBHTp09v4MuqOWndqoIfHrMLPzt+F56aOIujrnqMF6Y4rpokSZLWj4GaJEmSJKlc9gWOiojXgeuBgyPib8A7eTeO5H+n5ctPBvqVPL8vMCWf3reO6WtIKV2TUhqaUhrao0ePjbktamJO2rM///z8MJYuSxz3m/9x26i3ii5JkiRJTYiBmiRJkiSpLFJK30wp9U0pDQBOBv6bUvo4cDvwyXyxTwK35fdvB06OiDYRsRUwEHg67xZyXkQMi4gATi95jlSv3fpvxu3n7MsufTrz5etH8aO7XmDZ8hXrfqIkSZJaPAM1SZIkSVLRfgocGhGvAIfmj0kpjQNuAF4A7gXOSiktz5/zBeD3wATgVeCechetpqlnx2r+/v+G8cl9tuR3j77GJ699mlmOqyZJkqR1iJTq7Ga+SRg6dGgaMWJE0WVIkiRJ2kgiYmRKaWjRdah58v+Qqu1fIybx7Vufp0eHNvz2E3swqE/nokuSJEnSeijn/yFtoSZJkiRJklqkE4f241+f34cVKXH8bx7n1uccV02SJEl1M1CTJEmSJEkt1uB+XbjjnP0Y3K8LX/nnKL5/h+OqSZIkaU0GapIkSZIkqUXr3qENf/9/e/OpDwzgj/97jY//4Slmzl9cdFmSJEnahBioSZIkSZKkFq+qsoKLjtqZX544mOfenMORVzzG2MnvFl2WJEmSNhEGapIkSZIkSbnj9+jLjWd+gIjg+Ksf56aRk4suSZIkSZsAAzVJkiRJkqQSu/TtzO1n78vu/btw/r9Gc9Ht41jquGqSJEktmoGaJEmSJElSLd06tOFvn92bz+63FX96/HVO+/1TzHBcNUmSpBbLQE2SJEmSJKkOrSoruPCInbj0pCGMnpSNqzZ60pyiy5IkSVIBDNQkSZIkSZLW4pjd+nDTFz5ARQQn/vYJ/jViUtElSZIkqcwM1CRJkiRJktZhUJ/O3HHOfuw5YDO+duMYvnvb8yxZ5rhqkiRJLYWBmiRJkiRJUgN0bd+aP396L844YGv+8sQbnPb7J5k2b1HRZUmSJKkMDNQkSZIkSZIaqFVlBd/6yI5cdvIQxr71Lkde8RjPvTm76LIkSZLUyAzUJEmSJEmS1tPRQ/pw8xf2paqygpN++yT/fObNokuSJElSIzJQkyRJkiRJ2gA79e7EHWfvx95bd+UbN43l27eMdVw1SZKkZspATZIkSZIkaQNt1r41135qTz5/4Nb8/ak3OeV3TzJtruOqSZIkNTcGapIkSZIkSe9Dq8oKvnn4jlx56m68MGUuR1zxGCPfcFw1SZKk5sRATZIkSZIkaSM4Ytfe3HLWB6iuquTka57gH085rpokSVJzYaAmSZIkSZK0keyweSduP3tf9tmmO9+6ZSzfvHkMi5ctL7osSZIkvU8GapIkSZIkSRtRl3bZuGpfPGgbrnt6Eidf8yTvOK6aJElSk2agJkmSJEmStJFVVgRfH74Dvz5td156ex5HXPEYI16fVXRZkiRJ2kAGapIkSZIkSY3kI7tswS1f3Jd2rSs5+Zon+euTb5BSKrosSZIkrScDNUmSJEmSpEa0/eYduf2s/dhvYHcuvPV5vnHTGBYtdVw1SZKkpsRATZIkSZIkqZF1blfFHz65J+ccvC03jJjMSb99gmnzHFdNkiSpqTBQkyRJkiRJKoPKiuD8w7bn6o/vwcvvzOfL141ixQq7f5QkSWoKDNQkSZIkSZLKaPigzfnekTvxxMSZXPv460WXI0mSpAYwUJMkSZIkSSqzk/bsxyE79uRn947nlXfmFV2OJEmS1sFATZIkSZIkqcwigp8ctysd2rTi3BtGsWTZiqJLkiRJ0loYqEmSJEmSJBWgR8c2/PjYXXj+rblc8d9Xii5HkiRJa2GgJkmSJEmSVJDhgzbn+N37ctWDE3j2zdlFlyNJkqR6GKhJkiRJkiQV6HtH7cQWndty3j9HsXDJsqLLkSRJUh3KFqhFxPCIeCkiJkTEBXXM7xwRd0TE6IgYFxGfLldtkiRJkiRJRelUXcUvThzMG7MW8uO7Xyy6HEmSJNWhLIFaRFQCVwGHAzsBp0TETrUWOwt4IaU0GDgI+GVEtC5HfZIkSZIkSUXaZ5tufHbfrfjbk2/y4EvTii5HkiRJtZSrhdpewISU0sSU0hLgeuDoWsskoGNEBNABmAXYz4EkSZIkSWoRvvrh7dmuVwe+fuMYZi9YUnQ5kiRJKlGuQK0PMKnk8eR8WqkrgR2BKcBY4MsppRXlKU+SJEmSJKlY1VWVXPKxIcxZuITv3Po8KaWiS5IkSVKuXIFa1DGt9r8KPwyMAnoDQ4ArI6LTGiuKOCMiRkTEiOnTp2/sOiVJkiRJkgozqE9nvnLIdtw1diq3jZpSdDmSJEnKlStQmwz0K3ncl6wlWqlPAzenzATgNWCH2itKKV2TUhqaUhrao0ePRitYkiRJkiSpCJ8/YGt279+FC297nilz3iu6HEmSJFG+QO0ZYGBEbBURrYGTgdtrLfMm8CGAiOgFbA9MLFN9kiRJkiRJm4RWlRVc8rEhLF+R+NqNo1mxwq4fJUmSilaWQC2ltAw4G7gPeBG4IaU0LiLOjIgz88V+AHwgIsYC/wG+kVKaUY76JEmSJEmSNiUDurfnOx/dif9NmMmfn3i96HIkSZJavFbleqGU0t3A3bWmXV1yfwpwWLnqkSRJkiRJ2pSdslc/HnjxHX56z3j2H9idbXt2LLokSZKkFqtcXT5KkiRJkiRpPUQEPz1+F9q1ruTcf45m6fIVRZckSZLUYhmoSZIkSZIkbaJ6dqzmx8fuwti33uWK/04ouhxJkqQWy0BNkiRJkiRpE3b4Lltw3G59uOrBCYyaNKfociRJklokAzVJkiRJkqRN3EVH70yvjm0475+jeG/J8qLLkSRJanEM1CRJkiRJkjZxnaqr+MWJg5k4YwE/uefFosuRJElqcQzUJEmSJEmSmoAPbNudz+y7FX954g0eeXl60eVIkiS1KAZqkiRJkiRJTcTXh2/Ptj078LUbRzNn4ZKiy5EkSWoxDNQkSZIkSZKaiOqqSi49aQgz5y/hwtvGFV2OJElSi2GgJkmSJEmS1IQM6tOZL39oIHeMnsLto6cUXY4kSVKLYKAmSZIkSZLUxHzhoG3YrX8XvnPLWN5+d1HR5UiSJDV7BmqSJEmSpLKJiOqIeDoiRkfEuIi4OJ8+OCKeiIixEXFHRHTKp1dFxJ/z6S9GxDdL1rVHPn1CRFweEVHUdknl1qqygks+NoSlyxNfu3E0KaWiS5IkSWrWDNQkSZIkSeW0GDg4pTQYGAIMj4hhwO+BC1JKuwC3AF/Llz8RaJNP3wP4fEQMyOf9BjgDGJjfhpdrI6RNwVbd2/Otj+7Io6/M4K9PvlF0OZIkSc2agZokSZIkqWxSZn7+sCq/JWB74JF8+v3A8TVPAdpHRCugLbAEmBsRWwCdUkpPpKxpzl+AY8qzFdKm4+N79+fA7Xrw47tf5NXp89f9BEmSJG0QAzVJkiRJUllFRGVEjAKmAfenlJ4CngeOyhc5EeiX378RWABMBd4EfpFSmgX0ASaXrHZyPk1qUSKC/zthV6qrKjnvhtEsW76i6JIkSZKaJQM1SZIkSVJZpZSWp5SGAH2BvSJiEPAZ4KyIGAl0JGuJBrAXsBzoDWwFnB8RWwN1jZe2xiBSEXFGRIyIiBHTp0/f+BsjbQJ6darmh8cMYvSkOVz14KtFlyNJktQsGahJkiRJkgqRUpoDPAQMTymNTykdllLaA7gOqEkFTgXuTSktTSlNA/4HDCVrkda3ZHV9gSl1vMY1KaWhKaWhPXr0aLyNkQp2xK69OXpIby7/7yuMmTyn6HIkSZKaHQM1SZIkSVLZRESPiOiS328LHAKMj4ie+bQK4DvA1flT3gQOjkx7YBgwPqU0FZgXEcMiIoDTgdvKuzXSpuX7Rw2iR4c2nPvPUSxaurzociRJkpoVAzVJkiRJUjltATwYEWOAZ8jGULsTOCUiXgbGk7U0uzZf/iqgA9kYa88A16aUxuTzvgD8HphA1qLtnrJthbQJ6tyuil+cOJhXpy/gp/eML7ocSZKkZqVV0QVIkiRJklqOPAzbrY7plwGX1TF9PnBiPesaAQza2DVKTdl+A7vzqQ8M4E+Pv84hO/Ziv4Hdiy5JkiSpWbCFmiRJkiRJUjPyjeE7sHWP9nztxtG8u3Bp0eVIkiQ1CwZqkiRJkiRJzUjb1pX86mNDmDZvMd+9/fmiy5EkSWoWDNQkSZIkSZKamcH9unDOwdty26gp3DlmStHlSJIkNXkGapIkSZIkSc3QWR/clsH9uvDtW57nnbmLii5HkiSpSTNQkyRJkiRJaoaqKiv41ccGs3jZcr524xhSSkWXJEmS1GQZqEmSJEmSJDVTW/fowLc+siOPvDydvz31ZtHlSJIkNVkGapIkSZIkSc3YJ4Ztyf4Du/Pju17ktRkLii5HkiSpSTJQkyRJkiRJasYigp+fMJjWrSo495+jWLZ8RdElSZIkNTkGapIkSZIkSc3c5p2r+cExgxg1aQ6/eejVosuRJElqcgzUJEmSJEmSWoCjBvfmyMG9uew/rzB28rtFlyNJktSkGKhJkiRJkiS1ED84eme6dWjNuTeMYtHS5UWXI0mS1GQYqEmSJEmSJLUQXdq15ucnDGbCtPn8370vFV2OJElSk2GgJkmSJEmS1IIcsF0PTt9nS/74v9d4fMKMosuRJElqEgzUJEmSJEmSWphvHr4jW3dvz1f/NZp331tadDmSJEmbPAM1SZIkSZKkFqZt60ouOWkI78xbzMW3jyu6HEmSpE2egZokSZIkSVILNKRfF8764Lbc/Nxb3DN2atHlSJIkbdIM1CRJkiRJklqocw7ell37duZbt4xl2txFRZcjSZK0yTJQkyRJkiRJaqGqKiu45GNDWLhkOd+4aQwppaJLkiRJ2iQZqEmSJEmSJLVg2/bswAWH78CDL03nuqcnFV2OJEnSJslATZIkSZIkqYX75D4D2Hfbbvzwrhd4Y+aCosuRJEna5BioSZIkSZIktXAVFcHPTxhMZUVw3g2jWb7Crh8lSZJKGahJkiRJkiSJ3l3a8oOjBzHyjdlc/fCrRZcjSZK0STFQkyRJkiRJEgBHD+nNR3fZgksfeJlxU94tuhxJkqRNhoGaJEmSJEmSAIgIfnjMIDZr15pz/zmKRUuXF12SJEnSJsFATZIkSZIkSStt1r41PzthV15+Zz6//PdLRZcjSZK0STBQkyRJkiRJ0mo+uH1PTtu7P79/7DWenDiz6HIkSZIKZ6AmSZIkSZKkNXz7ozuyZdd2nH/DaOYtWlp0OZIkSYVqVXQBkiRJkiRJ2vS0a92KS04awgm/eZwP/uJh+m7Wlp4d29CzUxt6dKimZ6c22eOO1fTo2IbuHVrTqtLfbkuSpObJQE2SJEmSJEl12r3/Zlx56u7c/8I7TJ+3mNdnLuDp12cxZ+GaLdYioFv71nTv0Iaenarp2bENPTquHrrVBHLtWntJSpIkNS3+60WSJEmSJEn1+sguW/CRXbZYbdriZcuZMX8J0+YuYvq8xUzLb9PnLWb6vEVMm7eYV96Zx/R5i1m2Iq2xzvatK+nZKQvZ6gvdenRow2btWlNREeXaVEmSpHoZqEmSJEmSJGm9tGlVSZ8ubenTpe1al1uxIjF74RKmz1/MtLmrQrdpeeg2fd5iXpgyl4fmLmLBkuVrPL9VRawM2bLwrVbLt5pQrkMbWreyu0lJktR4DNQkSZIkSZLUKCoqgm4d2tCtQxt22Hztyy5YvGxla7faodu0eYuZPPs9Rk2aw8wFS0hrNnpjs3ZV9OjYhm7t29C+TSs6tKnM/2a39iV/27epzKZXt6J961XTDeUkSVJ9DNQkSZIkSZJUuPZ5qDWge/u1Lrd0+Qpmzl9SR+i2iGlzFzN74RLemvMeCxYvY35+W7JsRYNqaF1ZkYVttYK2DitDuKqVQV37WkFdh5Kgrn2bVrRrXUmE3VVKktRcGKhJkiRJkiSpyaiqrGDzztVs3rka6Nyg5yxdvmJlwLZg8XLmL17K/MXLS6Zlt3kr7y9fOX3OwiVMnr1w5XMXLFlWZwu52iKgQ+vVW8TVBHEd87+d2raif9d2DOjWnq26t6dHxzaGcJIkbaIM1CRJkiRJktSsVVVW0KVda7q0a/2+17ViReK9pctXawG3MmwrCejmrxbWrQroZi1YuPL+3EXLWL5iVTrXvnUlW+bh2pbd2jGge3Z/QLf2dO/Q2rBNkqQCGahJkiRJkiRJDVRREStbmvV8n+tatnwFU+Ys4rWZC3h9xgJez/++MHUu9417m2UlYVuHNq0Y0L1dFrh1a5+HbVnrtq7tDdskSWpsBmqSJEmSJElSAVpVVtC/Wzv6d2vHgdv1WG3e0uUreGv2e7w2cwFvzFjA6zMX8tqMBTz/1rvc+/zbq7Vs61jdigE1IVvesi27354u7aoM2yRJ2ggM1CRJkiRJkqRNTFVlxcpgjO1Xn7d0+QomzVrIG3nI9vrMBbw2YwGjJs3mrjFTKMna6FTdKus2Mu86ctX9dhulC0xJkloKAzVJkiRJkiSpCamqrGDrHh3YukcHPlhr3uJly5k8+z1en7FgZdj2+oyFjHh9NrePnkIqCdu6tKtaFbJ1a8+AvAvJAd3b07ltVVm3SZKkTZ2BmiRJkiRJktRMtGlVyTY9OrBNjw5rzFu8bDmTZi3ktRkLs8AtH7Pt6ddmceuot1YL27q2b82Amu4ju7Vny27t6N6hDR2rW9GpuopObavoWN2KqsqKMm6dJEnFMVCTJEmSJEmSWoA2rSrZtmdHtu3ZcY15i5Yu581ZeReSJS3bnnh1Jjc/+1a962xbVUmntquHbNn97G/Hkvt1za+uqmzMTZYkaaMxUJMkSZIkSZJauOqqSrbr1ZHteq0Ztr23ZDmTZi9kzsKlzH1vKXMXZX/nLVqW38/+zlu0jFkLlvD6jAXMXbSMue8tZVnpgG51aF1ZsSp8a1tFp5LArWN1/rhtVR7OrbpfM79960oiorF2iyRJKxmoSZIkSZIkSapX29aVdQZt65JSYtHSFSsDuLkrA7js/rySMK40oJsy572V9xctXbHW16isiJWt3kpbvx28Q09O3KMfFRWGbZKkjcNATZIkSZIkSdJGFxG0bV1J29aV9OpUvUHrWLxsOfMWLcsCtpWt4/IwrlbruJr549+ex33j3uGmkW/x4+N2Yduea44nJ0nS+jJQkyRJkiRJkrRJatOqkjYdKuneoU2Dn5NS4l8jJvOju1/kI5c9yhc/uA1fOGgb2rRyvDZJ0oarKLoASZIkSZIkSdpYIoKP7dmPB847kOGDNufSB17ho5c/xjOvzyq6NElSE2agJkmSJEmSJKnZ6dGxDZefshvXfmpP3luynBOvfoJv3zKWuYuWFl2aJKkJMlCTJEmSJEmS1Gx9cIee/PvcA/jsfltx3dNvcsgvH+be56eSUiq6NElSE2KgJkmSJEmSJKlZa9+mFRcesRO3nrUv3Tu04cy/PcsZfx3J1HffK7o0SVITYaAmSZIkSZIkqUXYtW8Xbjt7X755+A48+sp0Dr3kEf78+OssX2FrNUnS2hmoSZIkSZLKJiKqI+LpiBgdEeMi4uJ8+uCIeCIixkbEHRHRqeQ5u+bzxuXzq/Ppe+SPJ0TE5RERRW2XJKnpqKqs4PMHbsO/v3Igu/XvwvduH8cJVz/O+LfnFl2aJGkTZqAmSZIkSSqnxcDBKaXBwBBgeEQMA34PXJBS2gW4BfgaQES0Av4GnJlS2hk4CFiar+s3wBnAwPw2vHybIUlq6vp3a8dfPrMXvzppMG/MXMgRlz/GL+57iUVLlxddmiRpE1S2QC0ihkfES/kvBy+oZ5mDImJU/qvDh8tVmyRJkiSpPFJmfv6wKr8lYHvgkXz6/cDx+f3DgDEppdH582emlJZHxBZAp5TSEymlBPwFOKZMmyFJaiYigmN368sD5x3IUUN6c+WDEzj8skd54tWZRZcmSdrElCVQi4hK4CrgcGAn4JSI2KnWMl2AXwNH5b86PLEctUmSJEmSyisiKiNiFDANuD+l9BTwPHBUvsiJQL/8/nZAioj7IuLZiPh6Pr0PMLlktZPzabVf64yIGBERI6ZPn94IWyNJag66tm/NJR8bwt8+uzfLVyRO+d2TfP3G0cxZuKTo0iRJm4hytVDbC5iQUpqYUloCXA8cXWuZU4GbU0pvAqSUppWpNkmSJElSGaWUlqeUhgB9gb0iYhDwGeCsiBgJdARqrmC2AvYDTsv/HhsRHwLqGi8t1fFa16SUhqaUhvbo0WPjb4wkqVnZb2B37vvKAZx54Dbc9OxbHHLJw9w+egpZY2hJUktWrkCtDzCp5HFdvxzcDtgsIh6KiJERcXqZapMkSZIkFSClNAd4CBieUhqfUjospbQHcB3war7YZODhlNKMlNJC4G5g93x635LV9QWmlKt2SVLz1bZ1JRccvgN3nL0ffbq05UvXPcen//QMk2YtLLo0SVKByhWoNeSXg62APYCPAh8GLoyI7dZYkd11SJIkSVKTFRE98i7/iYi2wCHA+IjomU+rAL4DXJ0/5T5g14hoFxGtgAOBF1JKU4F5ETEsIgI4HbitvFsjSWrOdurdiZu/uC/fPWInnn5tFof96hF+/+hEli1fUXRpkqQClCtQm8yq/u+h7l8OTgbuTSktSCnNIBuMenDtFdldhyRJkiQ1aVsAD0bEGOAZsjHU7iQba/tlYDzZ/xevBUgpzQYuyZcdBTybUrorX9cXgN8DE8hatN1Txu2QJLUAlRXBZ/bbivvPO5B9tunGD+96kWN//TjPv/Vu0aVJksosytH/b/4rwpeBDwFvkf1H6NSU0riSZXYEriRrndYaeBo4OaX0fH3rHTp0aBoxYkRjli5JkiSpjCJiZEppaNF1qHny/5CSpPcjpcRdY6dy0e0vMHvhEv7fflvxlUO2o23ryqJLk6QWq5z/hyxLC7WU0jLgbLKuOl4EbkgpjYuIMyPizHyZF4F7gTFkYdrv1xamSZIkSZIkSVK5RARH7Nqb/5x3ICfu0ZffPjKRwy59mEdedlgaSWoJytJCrbH460JJkiSpebGFmhqT/4eUJG1MT06cybduHsvEGQs4drc+fOejO9KtQ5uiy5KkFqXZtVCTJEmSJEmSpOZk2NbduPvL+/Olg7flzjFTOOSSh7lp5GSacgMGSVL9DNQkSZIkSZIkaQNUV1Vy3mHbc9eX9mfrHh04/1+j+cQfnuaNmQuKLk2StJEZqEmSJEmSJEnS+7Bdr4786/P78INjBjF60hwO+9Uj/OahV1m6fEXRpUmSNhIDNUmSJEmSJEl6nyoqgk8M25L7zzuQg7bvwc/uHc9RV/6P0ZPmFF2aJGkjMFCTJEmSJEmSpI1k887V/PYTQ7n643swa8Fijv31/7j4jnHMX7ys6NIkSe+DgZokSZIkSZIkbWTDB23O/ecdyGl7b8mfHn+dwy55mP+Of6fosiRJG8hATZIkSZIkSZIaQafqKn5wzCBuPHMfOlS34jN/GsFZ/3iWafMWFV2aJGk9GahJkiRJkiRJUiPaY8uu3HnO/px/6HbcP+4dDvnlw1z/9JuklIouTZLUQAZqkiRJkiRJktTIWreq4JwPDeSer+zPjlt04oKbx3LyNU/y6vT5RZcmSWoAAzVJkiRJkiRJKpNtenTgus8N42fH78KLU+dy+KWPcvl/XmHJshVFlyZJWgsDNUmSJEmSJEkqo4qK4KQ9+/PA+Qdy2M69uOT+l/no5Y8y8o1ZRZcmSaqHgZokSZIkSZIkFaBnx2quPHV3/vipoSxcspwTrn6C79w6lrmLlhZdmiSpllZFFyBJkiRJapoiYjNgIFBdMy2l9EhxFUmS1DQdvEMv9j63G7/898v86fHXuP+Fd7j4qEEMH7R50aVJknK2UJMkSZIkrbeI+H/AI8B9wMX534uKrEmSpKasfZtWfPfInbjli/vStX0bzvzbSD7/1xG8/e6iokuTJGGgJkmSJEnaMF8G9gTeSCl9ENgNmF5sSZIkNX2D+3Xh9rP35YLDd+Chl6ZzyCUP89cnXmfFilR0aZLUohmoSZIkSZI2xKKU0iKAiGiTUhoPbF9wTZIkNQtVlRWceeA2/PvcAxjSrwsX3jaOE65+nJffmVd0aZLUYhmoSZIkSZI2xOSI6ALcCtwfEbcBUwqtSJKkZmbLbu3562f34pKPDea1GQv46OWP8st/v8SipcuLLk2SWpxWRRcgSZIkSWp6UkrH5ncviogHgc7AvQWWJElSsxQRHLd7Xw7crgc/uutFrvjvBO4aM5UfH7cLw7buVnR5ktRi2EJNkiRJkrRBIqIyInoDrwGjgM2LrUiSpOarW4c2XHLSEP762b1YumIFJ1/zJBfcNIZ3Fy4tujRJahEM1CRJkiRJ6y0izgHeAe4H7spvdxZalCRJLcD+A3vw768cyOcP3Jp/jZzMhy55mDtGTyGlVHRpktSsGahJkiRJkjbEl4HtU0o7p5R2yW+7Fl2UJEktQdvWlXzz8B25/ex96d2lmnOue47P/nkEb815r+jSJKnZMlCTJEmSJG2IScC7RRchSVJLtnPvztzyxX258IideHLiTA695GH++NhrLF9hazVJ2thaFV2AJEmSJKnpiIjz8rsTgYci4i5gcc38lNIlhRQmSVILVVkRfHa/rfjwzr34zq3P8/07X+DWUW/x0+N2ZafenYouT5KaDVuoSZIkSZLWR8f89ibZ+GmtS6Z1LLAuSZJatL6btePaT+3JFafsxpQ573HklY/x03vG896S5UWXJknNgi3UJEmSJEkNllK6uOgaJElS3SKCIwf3Zv+B3fnJ3eO5+uFXuXvsVH587C7sN7B70eVJUpNmCzVJkiRJkiRJaka6tGvNz07Yles+N4xWFcHH//AU590wilkLlhRdmiQ1WQZqkiRJkiRJktQM7bNNN+7+8v6cc/C23D5qCodc8jC3PDeZlFLRpUlSk2OgJkmSJEmSJEnNVHVVJecftj13fWl/tuzWjnP/OZrT//g0b85cWHRpktSkOIaaJEmSJGm9RUQP4HPAAEr+b5lS+kxRNUmSpPptv3lHbjrzA/ztqTf4v3tf4rBLH+bcQ7bjs/ttRatK211I0roYqEmSJEmSNsRtwKPAA8DygmuRJEkNUFERnL7PAA7dqRffvW0cP7lnPLeNmsJPj9+FXft2Kbo8SdqkGahJkiRJkjZEu5TSN4ouQpIkrb8tOrfld6cP5d7n3+a7tz3PMVf9j0/vuxXnHbod7dt4yViS6mJbXkmSJEnShrgzIj5SdBGSJGnDDR+0OQ+cfyCn7t2fPzz2Gof96hEeHD+t6LIkaZNkoCZJkiRJ2hBfJgvVFkXEvPw2t+iiJEnS+ulUXcUPj9mFG8/ch7atK/n0n57hnOueY/q8xUWXJkmbFAM1SZIkSdJ6Syl1TClVpJSq8/sdU0qdiq5LkiRtmKEDunLXl/bj3EO2477n3+aQSx7mhmcmkVIqujRJ2iQYqEmSJEmSNkhEHBURv8hvRxRdjyRJen/atKrky4cM5O4v78/2vTry9ZvGcMrvnmTi9PlFlyZJhTNQkyRJkiStt4j4KVm3jy/kty/n0yRJUhO3bc8OXH/GMH563C68MGUuwy97lCv/+wpLlq0oujRJKoyBmiRJkiRpQ3wEODSl9MeU0h+B4fk0SZLUDFRUBCfv1Z8Hzj+QQ3fqxS/+/TJHXvEYz745u+jSJKkQBmqSJEmSpA3VpeR+56KKkCRJjadnx2quOnV3/vDJocxbtJTjf/M4F98xjgWLlxVdmiSVVauiC5AkSZIkNUk/AZ6LiAeBAA4AvllsSZIkqbF8aMde7L11N35+73j+9Pjr/HvcO/zo2EEctH3PokuTpLKwhZokSZIkab2llK4DhgE357d9UkrXF1uVJElqTB3atOLiowdx45n70LZ1JZ+69hm+cv1zzFqwpOjSJKnRGahJkiRJkhosInbI/+4ObAFMBiYBvfNpkiSpmdtjy67c9aX9+PKHBnLX2KkccsnD3PrcW6SUii5NkhqNXT5KkiRJktbH+cDngF/WMS8BB5e3HEmSVIQ2rSo599Dt+OiuW/CNm8bwlX+O4pbn3uJHxw6i72btii5PkjY6AzVJkiRJUoOllD6X//1g0bVIkqTibderIzee+QH++sTr/N99L3HYrx7hax/entP3GUBlRRRdniRtNAZqkiRJkqQGi4jj1jY/pXRzuWqRJEmbhsqK4FP7bsWhO2/Ot28Zy8V3vMBto6bws+N3ZfvNOxZdniRtFAZqkiRJkqT1ceRa5iXAQE2SpBaqT5e2XPupPbl99BQuvuMFjrjiUb5w4DacdfC2tGlVWXR5kvS+GKhJkiRJkhospfTpomuQJEmbrojg6CF92H9gD3545wtc/t8J3DV2Kj87fleGDuhadHmStMEM1CRJkiRJDRYR561tfkrpknLVIkmSNl1d27fmkpOGcPRuffjWzWM54eon+MSwLfn68O3pWF1VdHmStN4qii5AkiRJktSkdFzHba0iojoino6I0RExLiIuzqcPjognImJsRNwREZ1qPa9/RMyPiK+WTNsjX35CRFweEbERt1OSJG0EB27Xg3+fewCf2Xcr/vbUGxz2q0f4z4vvFF2WJK03W6hJkiRJkhospXTx+1zFYuDglNL8iKgCHouIe4ArgK+mlB6OiM8AXwMuLHner4B7aq3rN8AZwJPA3cDwOpaRJEkFa9+mFd89cieOHLwFF9w0ls/+eQRH7LoF3ztyZ3p0bFN0eZLUIAZqkiRJkqQGi4ivp5T+LyKuAFLt+SmlL63t+SmlBMzPH1bltwRsDzyST78fuI88UIuIY4CJwIKSOrYAOqWUnsgf/wU4BgM1SZI2Wbv134w7ztmPax55lcv/M4FHX5nBdz66Iyfs0Rcbmkva1NnloyRJkiRpfbyY/x0BjKzjtk4RURkRo4BpwP0ppaeA54Gj8kVOBPrly7YHvgHUbhnXB5hc8nhyPq32a50RESMiYsT06dMbUp4kSWpErVtVcPbBA7n7y/uzXa8OfO3GMXziD0/z5syFRZcmSWtloCZJkiRJarCU0h353z/XdWvgOpanlIYAfYG9ImIQ8BngrIgYSTYW25J88YuBX6WU5tdaTV0/Y6+rxdw1KaWhKaWhPXr0aNA2SpKkxrdtzw7884x9+OExgxg1aQ6HXfowv3tkIsuWryi6NEmqk10+SpIkSZLWW0QMBb4NbEnJ/y1TSrs2dB0ppTkR8RAwPKX0C+CwfN3bAR/NF9sbOCEi/g/oAqyIiEXATWSBXI2+wJQN3R5JklR+FRXBx4dtyYd27MmFtz7Pj+5+kTvGTOGnx+3KTr07FV2eJK3GFmqSJEmSpA3xd+Ba4HjgyJLbWkVEj4jokt9vCxwCjI+Invm0CuA7wNUAKaX9U0oDUkoDgEuBH6eUrkwpTQXmRcSwyAZdOR24baNuoSRJKostOrfld6cP5cpTd2PKnPc48srH+L97x7No6fKiS5OklQzUJEmSJEkbYnpK6faU0msppTdqbg143hbAgxExBniGbAy1O4FTIuJlYDxZS7NrG7CuLwC/ByYArwL3bNCWSJKkwkUER+zamwfOO5DjduvDrx96lcMve5QnJ84sujRJAiBSWqOL+SZj6NChacSIEUWXIUmSJGkjiYiRKaWhRdehdYuIDwGnAP8BFtdMTyndXFhR6+D/ISVJajoee2UG37plLG/OWsgpe/XngsN3oHPbqqLLkrSJKef/IR1DTZIkSZK0IT4N7ABUASvyaQnYZAM1SZLUdOw3sDv3feUAfvXAy/z+0Yn858V3+P7Rgxg+aPOiS5PUQhmoSZIkSZI2xOCU0i5FFyFJkpqvtq0r+dZHduTIXXvz9ZvGcObfRjJ85835/tE707NTddHlSWphHENNkiRJkrQhnoyInYouQpIkNX+79O3M7Wfvy9eHb89/X5rGhy55mOuffpOmPJyRpKbHQE2SJEmStCH2A0ZFxEsRMSYixkbEmKKLkiRJzVNVZQVfPGhb7vvKAey0RScuuHksp/zuSV6bsaDo0iS1EHb5KEmSJEnaEMOLLkCSJLU8W3Vvz3WfG8YNIybxo7tfZPilj/CVQ7bj/+2/FVWVth+R1HgM1CRJkiRJ6y2l9EbRNUiSpJapoiI4ea/+fHCHnnzvtnH87N7x3DF6Cj87fld26du56PIkNVNG9pIkSZIkSZKkJqdXp2qu/sQeXP3x3ZkxfzFHX/UYP777Rd5bsrzo0iQ1QwZqkiRJkiRJkqQma/igLbj/vAM5ac9+XPPIRD586SP8b8KMosuS1MwYqEmSJEmSJEmSmrTObav4yXG7ct3nhlFZEZz2+6f42r9GM2fhkqJLk9RMGKhJkiRJkiRJkpqFfbbpxj1f3p8vHrQNNz/3Fodc8gh3jZlKSqno0iQ1cQZqkiRJkiRJkqRmo7qqkq8P34Hbz96XLTpXc9Y/nuVzfxnJ1HffK7o0SU2YgZokSZIkSZIkqdnZuXdnbvniB/j2R3bksQnTOfSSR/jrk2+wYoWt1SStPwM1SZIkSZIkSVKz1Kqygs8dsDX//sqBDOnXhQtvfZ6TrnmCCdPmF12apCbGQE2SJEmSJEmS1Kz179aOv352L35+wq68/M58PnLZo1zxn1dYsmxF0aVJaiIM1CRJkiRJkiRJzV5EcOLQfjxw3oEctnMvfnn/yxx15WOMmjSn6NIkNQFlC9QiYnhEvBQREyLigrUst2dELI+IE8pVmyRJkiRJkiSpZejRsQ1Xnro7vzt9KHMWLuXYX/+P79/xAgsWLyu6NEmbsLIEahFRCVwFHA7sBJwSETvVs9zPgPvKUZckSZIkSZIkqWU6dKde3H/eAXx87y354/9e47BfPcLDL08vuixJm6hytVDbC5iQUpqYUloCXA8cXcdy5wA3AdPKVJckSZIkSZIkqYXqWF3FD44ZxL/O3Ifqqgo++cenOe+fo5i1YEnRpUnaxJQrUOsDTCp5PDmftlJE9AGOBa4uU02SJEmSJEmSJLHngK7c9aX9+dLB23L76CkccsnD3DbqLVJKRZcmaRNRrkAt6phW+0x0KfCNlNLyta4o4oyIGBERI6ZPt/mtJEmSJEmSJOn9q66q5LzDtufOL+1H/67t+PL1o/jMn57hrTnvFV2apE1AuQK1yUC/ksd9gSm1lhkKXB8RrwMnAL+OiGNqryildE1KaWhKaWiPHj0aqVxJkiRJkiRJUku0w+aduOkLH+C7R+zEU6/N4tBLHuZP/3uN5StsrSa1ZOUK1J4BBkbEVhHRGjgZuL10gZTSVimlASmlAcCNwBdTSreWqT5JkiRJkiRJkgCorAg+s99W3PeVAxg6oCsX3fECJ1z9OC+/M6/o0iQVpCyBWkppGXA2cB/wInBDSmlcRJwZEWeWowZJkiRJkiRJktZHv67t+POn9+TSk4bw+owFfPTyR/nV/S+zeNlaRy6S1Ay1KtcLpZTuBu6uNe3qepb9VDlqkiRJkiRJkiRpbSKCY3brw/4Du/ODO1/gsv+8wl1jp/Kz43dhjy27Fl2epDIpV5ePkiRJkiRJkiQ1Wd06tOHSk3fj2k/vyXtLlnPC1U/w3dueZ/7iZUWXJqkMDNQkSZIkSZIkSWqgD27fk3+fewCf3GcAf33yDQ695GH+O/6dosuS1MgM1CRJkiRJkiRJWg/t27TioqN25qYvfICO1a34zJ9GcM51zzFj/uKiS5PUSAzUJEmSJEmSJEnaALv334w7z9mf8w7djvuef5tDLnmYm0ZOJqVUdGmSNjIDNUmSJEmSJEmSNlDrVhV86UMDuetL+7FNjw6c/6/RnP7Hp5k0a2HRpUnaiAzUJEmSJEmSJEl6nwb26si/Pr8P3z96Z559YzaH/eoRfv/oRJavsLWa1BwYqEmSJEmSJEmStBFUVASn7zOA+887kH226cYP73qR4379P16cOrfo0iS9TwZqkiRJkiRJkiRtRL27tOUPnxzK5afsxuTZ73HkFY/x8/vGs2jp8qJLk7SBDNQkSZIkSZIkSdrIIoKjBvfmgfMO5OghfbjqwVf5yGWP8tTEmUWXJmkDGKhJkiRJkiRJktRINmvfml9+bDB//exeLFm+gpOueZJv3TKWuYuWFl2apPVgoCZJkiRJkiRJUiPbf2AP/n3uAfy//bbi+qff5NBLHua+cW8XXZakBjJQkyRJkiRJkiSpDNq1bsV3jtiJW764L5u1a83n/zqSL/59JI+9MoNX3pnH3EVLSSkVXaakOrQqugBJkiRJkiRJklqSwf26cMc5+3HNIxO57D+vcPfYVS3V2lZVsnnnanp2bMPmnavp1Wn1+5t3qqZHxzZUV1UWuAVSy2OgJkmSJEmSJElSmVVVVnDWB7flpD378eq0+bw9dxHT5i7mnbmLVt5/7s05vDN3EYuXrVjj+Zu1q8rCtk7VbN6pDb06Va+8bd6pml6d2tCtQxsqK6KArZOaHwM1SZIkSZIkSZIK0r1DG7p3aFPv/JQS7763lHdWC9uyvzXTXnp7LtPnLWZFrd4iKyuCHh3a0Gu1wG3V/c07V9OrYzWd2rYiwuBNWhsDNUmSJEmSJEmSNlERQZd2renSrjXbb96x3uWWr0jMmJ+Hbu8u4p15i7PgLb//xsyFPP36LOYsXLrGc6urKrKQrWM1vTpX0yvvYrJnp1X3e3WqtptJtWgGapIkSZIkSZIkNXGVFbGy5dmufetfbtHS5VnXkvPysG1uzW0xb89dxNjJc7h/7iIWLV2zm8nObavWaO22+cpuJ7Np3Tu0plVlRSNuqVQMAzVJkiRJkiRJklqI6qpK+ndrR/9u7epdJqXE3EXL1uhasub29tzFTJg2g2nzFrO8Vj+TFZF1Y1k7dOvVKW/91qkNvTpW06Vdld1MqkkxUJMkSZIkSZIkSStFBJ3bVtG5bRUDe629m8mZCxYzbe7ivGvJRbzz7qrWbpNnL+TZN2cza8GSNZ7bulXFai3cenWsZvPOpUFc1uqtbWu7mdSmwUBNkiRJkiRJkiStt8qKoGfHanp2rGZQn871Lrd4Wd7NZEnXktNWtnZbxItT5vLg3GksXLJ8jed2rG61MlzrWdraLW/91qtTNT06tqHKbibVyAzUJEmSJEmSJElSo2nTqpJ+XdvRr+vau5mcv3jZat1LZsHbqtZvT01cwDtzF7GsVjeTEdCtfZushVvHVWO67dqvMx/cvmdjb55aCAM1SZIkSZIkSZJUqIigY3UVHaur2LZnh3qXW7EiMWvhEt5+dxHT5i3i7XezAC67v4ip7y5i1KQ5zMy7mfz1abvzkV22KNdmqBkzUJMkSZIkSZIkSU1CRUXQvUMbundoA9TfzeSipcs5+Zon+caNY9i5dye27Na+fEWqWbJTUUmSJEmSJEmS1KxUV1Vy5am7UVERfPHvz7Jo6Zrjs0nrw0BNkiRJkiRJkiQ1O303a8cvTxzMuClz+dFdLxZdjpo4AzVJkiRJUtlERHVEPB0RoyNiXERcnE8fHBFPRMTYiLgjIjrl0w+NiJH59JERcXDJuvbIp0+IiMsjIoraLkmSJG2aDtmpF5/bfyv++uQb3DlmStHlqAkzUJMkSZIkldNi4OCU0mBgCDA8IoYBvwcuSCntAtwCfC1ffgZwZD79k8BfS9b1G+AMYGB+G16WLZAkSVKT8vXhO7Bb/y5ccNNYXpuxoOhy1EQZqEmSJEmSyiZl5ucPq/JbArYHHsmn3w8cny//XEqp5qfE44DqiGgTEVsAnVJKT6SUEvAX4JgybYYkSZKakKrKCq48dXdaVQZnOZ6aNpCBmiRJkiSprCKiMiJGAdOA+1NKTwHPA0fli5wI9KvjqccDz6WUFgN9gMkl8ybn0yRJkqQ19OnSll+eOJgXps7lB3e+UHQ5aoIM1CRJkiRJZZVSWp5SGgL0BfaKiEHAZ4CzImIk0BFYUvqciNgZ+Bnw+ZpJda269oSIOCMiRkTEiOnTp2/ErZAkSVJT86Ede/H5A7bm70+9ye2jHU9N68dATZIkSZJUiJTSHOAhYHhKaXxK6bCU0h7AdcCrNctFRF+ycdVOTynVTJ9MFsjV6AuscVUkpXRNSmloSmlojx49GmdDJEmS1GR89cPbs8eWm/HNm8Ywcfr8dT9ByhmoSZIkSZLKJiJ6RESX/H5b4BBgfET0zKdVAN8Brs4fdwHuAr6ZUvpfzXpSSlOBeRExLCICOB24rYybIkmSpCaoqrKCK07ZjdatKjjrH885npoazEBNkiRJklROWwAPRsQY4BmyMdTuBE6JiJeB8WQtza7Nlz8b2Ba4MCJG5bee+bwvAL8HJpC1aLunjNshSZKkJqp3l7Zc8rEhvDh1Lhff4XhqaphWRRcgSZIkSWo5UkpjgN3qmH4ZcFkd038I/LCedY0ABm3sGiVJktT8fXCHnpx54DZc/fCrDNu6K0cP6VN0SdrE2UJNkiRJkiRJkiS1OF89bDuGbrkZ37p5LK86nprWwUBNkiRJkiRJkiS1OK0qK7ji1N1oU1XJWX9/1vHUtFYGapIkSZIkSZIkqUXaonNbLvnYYMa/PY+Lbh9XdDnahBmoSZIkSZIkSZKkFuug7XvyxYO24fpnJnHLc5OLLkebKAM1SZIkSZIkSZLUop136HbsNaAr377leSZMczw1rclATZIkSZIkSZIktWitKiu4/JTdaJuPp/beEsdT0+oM1CRJkiRJkiRJUou3eedqfnXSEF6eNo/v3f580eVoE2OgJkmSJEmSJEmSBBywXQ/OOmhbbhgxmZtGOp6aVjFQkyRJkiRJkiRJyn3lkIHsvVVXvnPr87zyzryiy9EmwkBNkiRJkiRJkiQpVzOeWrvWlXzx78+ycMmyokvSJsBATZIkSZIkSZIkqUSvTtVcevIQJkyfz3dvG1d0OdoEGKhJkiRJkiRJkiTVsv/AHpzzwW25ceRk/jViUtHlqGAGapIkSZIkSZIkSXX48iHbMWzrrlx42/O87HhqLZqBmiRJkiRJkiRJUh0qK4LLT96NDm2qHE+thTNQkyRJkiRJkiRJqkfPTtVcdvIQXp0+n+/c+jwppaJLUgEM1CRJkiRJkiRJktZi322786WDB3Lzs2/xr5GTiy5HBTBQkyRJkiRJkiRJWocvfWggH9imG9+97Xleetvx1FoaAzVJkiRJkiRJkqR1qKwILj15SD6e2kgWLHY8tZbEQE2SJEmSJEmSJKkBenas5vJThvDajAWOp9bCGKhJkiRJkiRJkiQ10Ae26c6XP7Qdtzz3FjeMmFR0OSoTAzVJkiRJkiRJkqT1cPbB27Lftt357m3jeHHq3KLLURkYqEmSJEmSJEmSJK2HyorgVycNoVPbKs76+7PMdzy1Zs9ATZIkSZIkSZIkaT316NiGy0/ejddnLuDbt4x1PLVmzkBNkiRJkiRJkiRpA+yzTTfOPWQ7bhs1heufcTy15sxATZIkSZIkSZIkaQOd9cFt2X9gd753+zhemOJ4as2VgZokSZIkSZIkSdIGqsjHU9usXRVn/cPx1JorAzVJkiRJkiRJkqT3oXuHbDy1N2Yu4Js3O55ac2SgJkmSJEmSJEmS9D7tvXU3zj9se+4YPYV/PP1m0eVoIzNQkyRJkiRJkiRJ2gi+cOA2HLBdDy6+4wWef+vdosvRRmSgJkmSJEmSJEmStBFUVAS/+thgurZrzdn/eJZ5i5YWXZI2EgM1SZIkSZIkSZKkjaRbhzZccepuTJr9Hhc4nlqzYaAmSZIkSZIkSZK0Ee05oCvnH7Ydd42Zyt+ecjy15sBATZIkSZIkSZIkaSM784BtOGj7HvzA8dSaBQM1SZIkSZIkSZKkjayiIrjkY0Po1qE1Z/3jWeY6nlqTZqAmSZIkSZIkSZLUCLq2b80Vp+zG5NnvccFNYxxPrQkzUJMkSZIkSZIkSWokQwd05Wsf3p67x77NX598o+hytIEM1CRJkiRJkiRJkhrRGftvzcE79OSHd77I2MmOp9YUlS1Qi4jhEfFSREyIiAvqmH9aRIzJb49HxOBy1SZJkiRJkiRJktRYKiqCX544mO4dWvPFf4zk3fccT62pKUugFhGVwFXA4cBOwCkRsVOtxV4DDkwp7Qr8ALimHLVJkiRJkiRJkiQ1ts3at+aKU3dn6pxFfONGx1NrasrVQm0vYEJKaWJKaQlwPXB06QIppcdTSrPzh08CfctUmyRJkiRJkiRJUqPbY8vN+Prw7bl33Nv8+fHXiy5H66FcgVofYFLJ48n5tPp8FrinUSuSJEmSJEmSJEkqs8/tvzWH7NiTH939IqMnzSm6HDVQuQK1qGNanW0ZI+KDZIHaN+qZf0ZEjIiIEdOnT9+IJUqSJEmSJEmSJDWuiOAXJw6mZ8dqzvrHs46n1kSUK1CbDPQredwXmFJ7oYjYFfg9cHRKaWZdK0opXZNSGppSGtqjR49GKVaSJEmSJEmSJKmxdGnXmitO3Y23313E128c7XhqTUC5ArVngIERsVVEtAZOBm4vXSAi+gM3A59IKb1cprokSZIkSZIkSZLKbvf+m3HB4Ttw37h3uPZ/rxddjtahLIFaSmkZcDZwH/AicENKaVxEnBkRZ+aLfRfoBvw6IkZFxIhy1CZJkiRJkiRJklSEz+63FYfs2Iuf3PMioxxPbZNWrhZqpJTuTiltl1LaJqX0o3za1Smlq/P7/y+ltFlKaUh+G1qu2iRJkiRJkiRJksotIvhlzXhqf3+Wdxc6ntqmqmyBmiRJkiRJkiRJklbXuV0VV522O9PmLeKrjqe2yTJQkyRJkiRJkiRJKtCQfl244PAduf+Fd/jDY68VXY7qYKAmSZIkSZIkSZJUsM/sO4DDdurFT+8Zz7Nvzi66HNVioCZJkiRJkiRJklSwiODnJwxm887VnPOP55izcEnRJamEgZokSZIkqWwiojoino6I0RExLiIuzqcPjognImJsRNwREZ1KnvPNiJgQES9FxIdLpu+RLz8hIi6PiChimyRJkqSNpXO7Kq46NR9P7V+Op7YpMVCTJEmSJJXTYuDglNJgYAgwPCKGAb8HLkgp7QLcAnwNICJ2Ak4GdgaGA7+OiMp8Xb8BzgAG5rfhZdwOSZIkqVEM7teFb31kRx54cRq/f9Tx1DYVBmqSJEmSpLJJmfn5w6r8loDtgUfy6fcDx+f3jwauTyktTim9BkwA9oqILYBOKaUnUvaz3b8Ax5RpMyRJkqRG9akPDGD4zpvzs3vHM/INx1PbFBioSZIkSZLKKiIqI2IUMA24P6X0FPA8cFS+yIlAv/x+H2BSydMn59P65PdrT6/9WmdExIiIGDF9+vSNuh2SJElSY4kIfnbCrmzRpZpz/vEssxc4nlrRDNQkSZIkSWWVUlqeUhoC9CVrbTYI+AxwVkSMBDoCNVcM6hoXLa1leu3XuialNDSlNLRHjx4bpX5JkiSpHDq3reLXp+7BjPlLOP9fo1mxwvHUimSgJkmSJEkqREppDvAQMDylND6ldFhKaQ/gOuDVfLHJrGqtBlkINyWf3reO6ZIkSVKzsUvfznz7ozvy3/HT+N2jE4sup0UzUJMkSZIklU1E9IiILvn9tsAhwPiI6JlPqwC+A1ydP+V24OSIaBMRWwEDgadTSlOBeRExLCICOB24rbxbI0mSJDW+0/fZko/ssjn/d99LjHh9VtHltFgGapIkSZKkctoCeDAixgDPkI2hdidwSkS8DIwna2l2LUBKaRxwA/ACcC9wVkppeb6uLwC/ByaQtWi7p5wbIkmSJJVDRPDT43el72ZtOee655jleGqFiJSabp+bQ4cOTSNGjCi6DEmSJEkbSUSMTCkNLboONU/+H1KSJElN2fNvvctxv36cD2zbjT9+ck8qKuoaVrhlKef/IW2hJkmSJEmSJEmStIkb1KczFx6xIw+9NJ3fPuJ4auVmoCZJkiRJkiRJktQEfHzYlnx01y34xb9f4hnHUysrAzVJkiRJkiRJkqQmICL46XG70G+ztpzzj+eYOX9x0SW1GAZqkiRJkiRJkiRJTUTH6iquPHV3Zi1cwrk3jGbFilR0SS2CgZokSZIkSZIkSVITMqhPZ757xE488vJ0fvPwq0WX0yIYqEmSJEmSJEmSJDUxp+3dnyMH9+aX/36JpybOLLqcZs9ATZIkSZIkSZIkqYmJCH587CC27NaeL13/HDMcT61RGahJkiRJkiRJkiQ1QR2rq7jq1N2ZvXAp5/5zlOOpNSIDNUmSJEmSJEmSpCZqp96duOjInXn0lRn8+qEJRZfTbBmoSZIkSZIkSZIkNWGn7NWPo4f05pL7X+aJVx1PrTEYqEmSJEmSJEmSJDVhEcGPjt2FAfl4atPnOZ7axmagJkmSJEmSJEmS1MR1aNOKq07bnbnvZeOpLXc8tY3KQE2SJEmSJEmSJKkZ2HGLTlx81M48NmEGVz3oeGobk4GaJEmSJEmSJElSM3HSnv04drc+XPrAyzz+6oyiy2k2DNQkSZIkSZIkSZKaiYjgh8cMYqvu7fnSdaOYNm9R0SU1CwZqkiRJkiRJkiRJzUj7Nq349Wl7MH/xUr5yveOpbQwGapIkSZIkSZIkSc3M9pt35PtHDeLxV2dyxX9fKbqcJs9ATZIkSZIkSZIkqRk6cWhfjtu9D5f95xX+N8Hx1N4PAzVJkiRJkiRJkqRmqGY8tW16dODL1zue2vthoCZJkiRJkiRJktRMtWvdil+ftjvzFy/ly9c5ntqGMlCTJEmSJEmSJElqxrbr1ZEfHD2IJybO5LL/OJ7ahjBQkyRJkiRJkiRJauZOHNqPE/boyxX/fYVHX5ledDlNjoGaJEmSJEmSJElSC/D9o3dm2x4d+Mr1o3hnruOprQ8DNUmSJEmSJEmSpBagZjy1hUuW86XrnmPZ8hVFl9RkGKhJkiRJkiRJkiS1EAN7deSHxwziqddmOZ7aejBQkyRJkiRJkiRJakGO36MvHxvalysfnMAjLzueWkMYqEmSJEmSJEmSJLUwFx81iO16duQr/xzF2+86ntq6GKhJkiRJkiRJkiS1MG1bV3LVabuzaKnjqTWEgZokSZIkSZIkSVILtG3PDvzo2EE8/fosfvXAy0WXs0kzUJMkSZIkSZIkSWqhjt2tLyfv2Y+rHnyVh16aVnQ5mywDNUmSJEmSJEmSpBbsoqN2ZofNO3LeDaOZ+u57RZezSTJQkyRJkiRJkiRJasGqqxxPbV0M1CRJkiRJkiRJklq4bXp04CfH7cIzr8/ml/c7nlptBmqSJEmSJEmSJEni6CF9OGWv/vzmoVd5cLzjqZUyUJMkSZIkSZIkSRIA3ztyJ3bcohPn3TCKKXMcT62GgZokSZIkSZIkSZKAfDy1U3djybIVnHPdcyx1PDXAQE2SJEmSJEmSJEkltu7RgZ8cvysj35jNL/79UtHlbBIM1CRJkiRJkiRJkrSaowb35rS9+/PbhyfynxffKbqcwhmoSZIkSZIkSZIkaQ0XHrETO23RifP/NZq3Wvh4agZqkiRJkiRJkiRJWkN1VSVXnbY7y5YnzvnHsy16PDUDNUmSJEmSJEmSJNVpq+7t+enxu/Dsm3P4+X0tdzw1AzVJkiRJkiRJkiTV64hde/OJYVtyzSMTeeCFljmemoGaJEmSJEmSJEmS1urbH92RnXtn46lNnr2w6HLKzkBNkiRJkiRJkiRJa1VdVcmvT9udFSsS/3jqzaLLKbtWRRcgSZIkSZIkSZKkTd+W3dpz69n7snX39kWXUnYGapIkSZIkSZIkSWqQbXp0KLqEQtjloyRJkiRJkiRJkrQWBmqSJEmSJEmSJEnSWhioSZIkSZIkSZIkSWthoCZJkiRJKpuIqI6IpyNidESMi4iL8+lDIuLJiBgVESMiYq98elVE/DkixkbEixHxzZJ17ZFPnxARl0dEFLVdkiRJkpo3AzVJkiRJUjktBg5OKQ0GhgDDI2IY8H/AxSmlIcB388cAJwJtUkq7AHsAn4+IAfm83wBnAAPz2/AybYMkSZKkFsZATZIkSZJUNikzP39Yld9SfuuUT+8MTKl5CtA+IloBbYElwNyI2ALolFJ6IqWUgL8Ax5RnKyRJkiS1NAZqkiRJkqSyiojKiBgFTAPuTyk9BXwF+HlETAJ+AdR07XgjsACYCrwJ/CKlNAvoA0wuWe3kfFrt1zoj70JyxPTp0xtpiyRJkiQ1dwZqkiRJkqSySiktz7t27AvsFRGDgC8A56aU+gHnAn/IF98LWA70BrYCzo+IrYG6xktLdbzWNSmloSmloT169Nj4GyNJkiSpRTBQkyRJkiQVIqU0B3iIbOyzTwI357P+RRakAZwK3JtSWppSmgb8DxhK1iKtb8nq+rKqm0hJkiRJ2qgM1CRJkiRJZRMRPSKiS36/LXAIMJ4sDDswX+xg4JX8/pvAwZFpDwwDxqeUpgLzImJYRARwOnBb+bZEkiRJUkvSqugCJEmSJEktyhbAnyOikuxHnjeklO6MiDnAZRHRClgEnJEvfxVwLfA8WTeP16aUxuTzvgD8CWgL3JPfJEmSJGmjM1CTJEmSJJVNHobtVsf0x4A96pg+HzixnnWNAAZt7BolSZIkqbaydfkYEcMj4qWImBARF9QxPyLi8nz+mIjYvVy1SZIkSZIkSZIkSfUpS6CWd+VxFXA4sBNwSkTsVGuxw4GB+e0M4DflqE2SJEmSJEmSJElam3K1UNsLmJBSmphSWgJcDxxda5mjgb+kzJNAl4jYokz1SZIkSZIkSZIkSXUqV6DWB5hU8nhyPm19l5EkSZIkSZIkSZLKqlWZXifqmJY2YBki4gyyLiEBFkfE8++zNmltugMzii5CzZbHlxqTx5cak8eXGtP2RReg5mvkyJEzIuKNouvIeS5tHO7XxuO+bRzu18bjvm0c7tfG475tHO7XxrOp7Nsty/VC5QrUJgP9Sh73BaZswDKklK4BrgGIiBEppaEbt1RpFY8xNSaPLzUmjy81Jo8vNaaIGFF0DWq+Uko9iq6hhufSxuF+bTzu28bhfm087tvG4X5tPO7bxuF+bTwtcd+Wq8vHZ4CBEbFVRLQGTgZur7XM7cDpkRkGvJtSmlqm+iRJkiRJkiRJkqQ6laWFWkppWUScDdwHVAJ/TCmNi4gz8/lXA3cDHwEmAAuBT5ejNkmSJEmSJEmSJGltytXlIymlu8lCs9JpV5fcT8BZ67naazZCadLaeIypMXl8qTF5fKkxeXypMXl8qaXwWG8c7tfG475tHO7XxuO+bRzu18bjvm0c7tfG0+L2bWQ5liRJkiRJkiRJkqS6lGsMNUmSJEmSJEmSJKlJahKBWkQMj4iXImJCRFxQx/yIiMvz+WMiYvci6lTT1IDj67T8uBoTEY9HxOAi6lTTtK7jq2S5PSNieUScUM761LQ15PiKiIMiYlREjIuIh8tdo5q2BnxHdo6IOyJidH6MOQauGiQi/hgR0yLi+Xrm++97NTkR0S8iHoyIF/Nz4pfz6V0j4v6IeCX/u1k+/dCIGBkRY/O/B5esa498+oT8sxBFbVfRNmC/7pX/22dU/v10bMm63K8l1nffljyvf0TMj4ivlkxz3+Y24JgdEBHvlRy3V5esy/1aYkOO2YjYNSKeyJcfGxHV+XT3bW4DjtnTSo7XURGxIiKG5PPcryU2YN9WRcSf8334YkR8s2Rd7tvcBuzX1hFxbb7/RkfEQSXrcr+WWMu+PTF/vCIihtZ6zjfz/fdSRHy4ZHrz3LcppU36BlQCrwJbA62B0cBOtZb5CHAPEMAw4Kmi6/bWNG4NPL4+AGyW3///7N13nFx1vf/x1yfZFNJISKGkkEDoHUJROhYUCzYUUBCvyrWA5Yfd6xXvtberUlSsKAgooqKiWKgiNRBBCIGQhCQkhCSQXnf38/vjnA2Tze5mEzI7W17Px2PJzGnzmTNnzgznPd/v95UeX/619689x1fFcjdRjDP5plrX7V/X+Gvn+Wso8Agwrrw/qtZ1+9d1/tp5jH0K+Ep5eyTwLNC31rX71/n/gOOAQ4F/tzLf7/f+dbk/YGfg0PL2YOAxYF/gq8AnyumfqDhvHgLsUt7eH3iqYlv3AC8q3wN/Al5Z6+fXhfbrAKCuYt1nKu67X1/Avq1Y79fAr4CPVExz327lfgXGt/F56H59Yfu2DngQOKi8Pxzo7b59Yfu12boHADMq7rtfX8C+Bc4Eri5vDwBmAePdty94v74f+El5exQwGejlft2ifbsPsBdwCzCpYvl9Ka4V9AMmUFxD6Nbn2a7QQu0IYHpmzsjMdcDVwKnNljkV+FkW7gKGRsTOHV2ouqTNHl+Z+c/MfK68excwpoNrVNfVnvMXwPkU/1P6TEcWpy6vPcfXmcB1mTkbIDM9xrQl2nOMJTC4/KXZIIpArb5jy1RXlJm3URwvrfH7vbqczJyfmfeXt5cDU4HRFMfz5eVilwOvK5d5IDPnldMfBvpHRL/yWB+SmXdmcTXiZ03r9ERbsV9XZWbTZ1F/is8q3K+b2tJ9CxARrwNmUByzTdPctxW2Zr+2xP26qa3Yty8HHszMf5XrLM7MBvftxl7gMXsGcBV4zLZkK/ZtAgMjog7YDlgHLHPfbmwr9uu+wN/L5Z8BlgCT3K+bam3fZubUzJzWwiqnUoTAazNzJjAdOKI779uuEKiNBuZU3J9bTtvSZaSWbOmx806KRF1qj80eXxExGng98D2kLdOe89eewLCIuCWKrqTO7rDq1B205xi7mOKXavOAh4APZmZjx5Snbs7v9+rSImI8RQu0u4EdM3M+FBcpKH4Z3dwbgQcycy3FsT63Yp7Hf6m9+zUijoyIhyk+m95TBmzu1za0Z99GxEDg48Dnmq3uvm3FFpwLJkTEAxFxa0QcW05zv7ahnft2TyAj4saIuD8iPlZOd9+2Yis+v95CGajhfm1TO/fttcBKYD4wG/h6Zj6L+7ZV7dyv/wJOjYi6iJgAHAaMxf3apmb7tjWt/X9bt923dbUuoB1a6lszt2IZqSXtPnYi4kSKQO2Yqlak7qQ9x9e3gI+Xv5KrfkXqTtpzfNVRfFF8CcWv2+6MiLsy87FqF6duoT3H2MnAFOAkYHfgrxFxe2Yuq3Jt6v78fq8uKyIGUfQ+8KHMXLa573gRsR/wFYqWFODx36It2a+ZeTewX0TsA1weEU1dyG6yaFWK7WK2YN9+Dvi/zFzRbBn3bQu2YL/Op+iifXFEHAb8tjwvuF9bsQX7to7iGs7hwCrg7xExGWjpu2qP37db8fl1JLAqM5vGxPWYbcUW7NsjgAZgF2AYcHtE/A33bYu2YL/+mOKHoPcBTwL/pOhZxf3aiub7tq1FW5iWbUzv8rpCoDaXIjFuMobiV9BbuozUknYdOxFxIPBDir5eF3dQber62nN8TQKuLj/0RwCnRER9Zv62QypUV9bez8dFmbkSWBkRtwEHUfSBLW1Oe46xdwBfLrtwmB4RM4G9KfpKl14Iv9+rS4qIPhQXH67MzOvKyQsiYufMnF92f/NMxfJjgN8AZ2fmE+XkuWzczXyPP/63dL82ycypEbGSYow692sLtnDfHgm8KSK+SjFWb2NErCnXd99W2JL9WrZMXVvenhwRT1C0rPKYbcEWHrNzgVszc1G57g0UY7hegft2I1t5nj2d51ungcdsi7Zw354J/Dkz1wPPRMQdFNeNbsd9u5EtPM/WAx+uWPefwOPAc7hfN9HKvm1Na//f1m3PB12hy8d7gT0iYkJE9KU4WV/fbJnrgbOjcBSwtKl5p7QZmz2+ImIccB1wlq06tIU2e3xl5oTMHJ+Z4yma9r/PME3t1J7Px98Bx5bdGgyguAgytYPrVNfVnmNsNkULSCJiR4pBimd0aJXqrvx+ry4nil9I/QiYmpnfrJh1PfD28vbbKT6fiYihwB+BT2bmHU0Ll8f68og4qtzm2U3r9ERbsV8nRDH2DBGxK8Vn0yz366a2dN9m5rEV/+/yLeCLmXmx+3ZjW3HMjoyI3uXt3YA9gBnu101t6b4FbgQOjIgB5XnheOAR9+3GtmK/EhG9gNMoxlkG/PxqyVbs29nASeV34IHAUcCj7tuNbcV5dkC5P4mIlwH1mem5oAVt7NvWXA+cHsU4wBMoPsPu6c77NoofFHduEXEKxZe13sCPM/MLEfEegMz8XvmiXAy8gqIJ9zsy875a1auupR3H1w8pxjR4slylPjMn1aRYdTmbO76aLftT4A+ZeW1H16muqT3HV0R8lKIVUSPww8z8Vm2qVVfUjs/IXYCfAjtTdOnw5cy8okblqguJiKuAEyhaZy8APgv0Ab/fq+uKiGMofkH+EMXnLsCnKMad+CUwjuJC2WmZ+WxE/BfwSYpfSDd5eWY+ExGTKM6v21GM4Xx+doX/ea+CrdivZwGfANaXy/9P0w/W3K8b29J922zdC4EVmfn18r77trQVx+wbgf+h6H6sAfhsZv6+3Jb7tcLWHLMR8TaKc20CN2Tmx8rp7tvSVu7XEyi++x/VbFvu1wpbcT4YBPwE2Jfi/69+kplfK7flvi1txX4dTxGwNwJPAe/MzCfLbblfK7Sxb/sBFwEjgSXAlMw8uVzn08B/UHyOfSgz/1RO75b7tksEapIkSZIkSZIkSVKtdIUuHyVJkiRJkiRJkqSaMVCTJEmSJEmSJEmS2mCgJkmSJEmSJEmSJLXBQE2SJEmSJEmSJElqg4GaJEmSJEmSJEmS1AYDNUmSJEmSJEmdXhT+ERGvrJj25oj4cy3rkiT1DJGZta5BkiRJkiRJkjYrIvYHfgUcAvQGpgCvyMwntmJbvTOzYdtWKEnqrgzUJEmSJEmSJHUZEfFVYCUwsPx3V+AAoA64MDN/FxHjgZ+XywCcl5n/jIgTgM8C84GDM3Pfjq1ektRVGahJkiRJkiRJ6jIiYiBwP7AO+APwcGZeERFDgXsoWq8l0JiZayJiD+CqzJxUBmp/BPbPzJm1qF+S1DXV1boASZIkSZIkSWqvzFwZEdcAK4A3A6+JiI+Us/sD44B5wMURcTDQAOxZsYl7DNMkSVvKQE2SJEmSJElSV9NY/gXwxsycVjkzIi4EFgAHAb2ANRWzV3ZQjZKkbqRXrQuQJEmSJEmSpK10I3B+RARARBxSTt8emJ+ZjcBZQO8a1SdJ6iYM1CRJkiRJkiR1Vf8L9AEejIh/l/cBLgXeHhF3UXT3aKs0SdILEplZ6xokSZIkSZIkSZKkTssWapIkSZIkSZIkSVIbDNQkSZIkSZIkSZKkNhioSZIkSZIkSZIkSW0wUJMkSZIkSZIkSZLaYKAmSZIkSZIkSZIktcFATZIkSZIkSZIkSWqDgZokSZIkSZIkSZLUBgM1SZIkSZIkSZIkqQ0GapIkSZIkSZIkSVIbDNQkSZIkSZIkSZKkNhioSZIkSZIkSZIkSW0wUJMkSZIkSZIkSZLaYKAmSZIkSZIkSZIktcFATZIkSZIkSZIkSWqDgZokSZIkSZIkSZLUBgM1SZIkSZIkSZIkqQ0GapIkSZIkSZIkSVIbDNQkSZIkSZIkSZKkNhioSZIkSZIkSZIkSW0wUJMkSZIkSZIkSZLaYKAmSZIkSZIkSZIktcFATZIkSZIkSZIkSWqDgZokSZIkSZIkSZLUBgM1SZIkSZIkSZIkqQ0GapIkSZIkSZIkSVIbDNQkSZIkSZIkSZKkNhioSZIkSZIkSZIkSW0wUJMkSZIkSZIkSZLaYKAmSZIkSZIkSZIktcFATZIkSZIkSZIkSWqDgZokSZIkSZIkSZLUBgM1SZIkSZIkSZIkqQ0GapIkSZIkSZIkSVIbDNQkSZIkSZIkSZKkNnTZQC0ifhoRn2/nsrMi4qXVrknqjCJiXESsiIjeta5FqhQRx0bEtFrX8UJFxC0R8a5W5o2PiIyIuo6uqyXluWC3dizXZt0RcWFEXLHtK+wZyv07qwaP22Vet/J9dUKt62hL+f1yfEet1xV0tnOeJEmSJEnbUpcN1KqlDOoyIl7bbPq3yunnNJt+Qjn9Y1v4OBdGxPry4uaSiPhnRLyonHdORPyjYtlZEbG6XLbp7+KKZTMivtls+68rp/+0vN90gaNp/VkR8YmK5cdHxM0RsSoiHm0eQEbEmRHxZESsjIjfRsQOFfNuiYg15XaXRsRtEXFAC891efn3WERcHBE7VyzTNyKuLevK5hfRytdlXfkYyyNickQcXzG/+T7bISJ+U9b7ZESc2Wx7Lymf56ryee/azpeuy8nM2Zk5KDMbXui2ImJMRPw6IhaVr/VDle+JiHhnuV+XR8SCiPhjRAwu51W+hs9GxF8jYu9y3tvL13RZRMyNiK9WXoxr9h54rtzu2Ir5GwXsEdEvIr4UEbPL9R6PiI9GRFQs8+byfbcqIm5p4bkeXNa0qvz34Gbz94iIqyNiYVn34xFxUUSMKeefEBFzW9juRuFLRAyOiG+Wz3FlWfO1EXFEC+seX74/Pt9selvvz34R8eOyxqcj4v9t8sLWSGbenpl7VftxIuLGqDhHR8ToaHberpi2U7XrqaXyXDCj1nVUavb+frp8Pw+qdV0dKTpJ0BURQ6L4vjO7fD2ml/dH1Liu5t9hVkTEvzq4hoyIiVXYbtP3yOuaTT+onH7Ltn7MWoqWv9Pu0oGP3+oPICRJkiRJ2hwDtZY9Bry96U55Yf804IkWln078Gzl8lvgmswcBIwE/gFcV3nBv5nXlBdCm/7Oq5j3BPCW2PjXwGeXz6O5oeVjngH8d0S8opx+FfAAMBz4NHBtRIwEiIj9gO8DZwE7AquAS5tt97xyu8OBW4Cft/BcBwM7AK8HdgImR0WoVu6DtwFPt7IPvlo+xvbAdyn2V2utri4B1pX1vhX4bvk8KC8MXgd8pqznPuCaVrbTpcW2/4X4z4E5wK4Ur/XZwILysY4HvgicUb7W+wC/bLZ+02s4BngG+Gk5fQDwIWAEcCTwEuAjzdZ9TbnuzuVjXtRGnb8qt3EKMJji2D0X+HbFMs8C3wK+3HzliOgL/A64AhgGXA78rpxOeVH1bmAecEhmDgGOpngvHtNGXc0fpx9wE3AA8GpgCMV+u7qsvXLZPmX9dzebvrn354XAHhSv2YnAxyre9y9IFY6varkNOL7i/nHAoy1MezwzWzv/bCIKXeJztAu8Vk3v74OBQ4BP1rac53WBfbdNlOe3vwP7Aa+gOB+9GFgMbBLwV7GOtvb30IrvQQdt421vU7FlrdAWAi+OiOEV095Oy9/juoPm32nnbcnKPeU9KUmSJEnqfKp6IbC8mPDRiHiwbDnxo4jYMSL+FEULlr9FxLCK5V8bEQ9H0WLrlojYp2LeIRFxf7neNUD/Zo/16oiYEs+39jrwBZT+e+DoitpeATxIs6AnIgYAbwLeD+wREZOazT+7bDWyOCI+E610PZmZ6yku2O9EEVJsqaeBh4CTy8fdgeIi2PWtrZCZdwIPA/tHxJ7AocBnM3N1Zv663N4by8XfCvw+M2/LzBUUQdQbomx51Gy79RRhwL6tPO76zHwYeAvFBaQLyunrMvNbmfkPoM2WVJnZCPyCIgzbsfn8iBhY1v6ZzFxRbvN6isAB4A3Aw5n5q8xcQxE4HBRla6ktFRFHRMR9ZQugBVHRWjAijimPxyURMSfK1lxRtBr6etkKYEFEfC8itivnnRBFK60LIuKZiJgfEe+o2OarIuKB8vHmRMSFFfOafsX/zoiYDdwUzbpfiohdIuL6KFqJTY+Id2/B0z0c+GlmrszM+sx8IDP/VDHvzsx8ACAzn83MyzNzefONZOYqitdw//L+d8vWSusy8yngSoqAahPla3YtrRxjEfES4OXAGzPz32Wdd1GEte8vwzAy82+Z+UuKUKy5E4A64FuZuTYzvwMEcFI5/0Lgjsz8f5k5t9zeM+UxfHWre29TZ1GEi68ra20o9+21mXlhs2UvAP5CEQRV2tz782zgfzPzucycCvwAOGcLatxIeSy9PyIeBx4vp7V6/o2IQ8vjdXlE/CoiromyhV00a8UXEfuU5/4l5WfBayvm/TQiLomideLyiLg7InZvZ9m3UZzTmz7zjqUIUyc1m3Zb+Vgvjoh7o2iFeW9EvLiijlsi4gsRcQdFeLlR94kR0bt8by+KiBnAq9pZ4yYi4qgoWmz1rpj2+oh4sLx9RETcWe6v+VG0/O1bsWxLr9WGVjZtnUsq/EdEzCu3f8Fmam061/0rtrKrvjLQvJEiWNvstqNonTyjPCZmRsRby+m9IuK/ovgMfiYifhYR25fzNmk9GhWfz1G0Frs2Iq6IiGXAOVG0ev5JuS+ei4jfVqzb1vH/8Yh4qqxvWnl+2iIR8YmIeKLcxiMR8fpmz/8f5TH3XLkPXlkxf0JE3Fqu+1eKHy205mxgHPD6zHwkMxvL89r/ZuYN5fZafI+241jtVfE8FkfEL6NsSRstfG5t4f5p9TOtlddy+yi+j84vX5vPN9UdERPL/bU0ivfwNeX028pN/iuKFlVv2ZIa22Ed8Fvg9PLxegNvpvgsrHyu3y7fq8uiaDl9bLPn+svyWF9evj6TKua3dRz1johvlM95ZkScFxt/b2hrn22Tc14U34u+Vb7H5pW3+5Xzmr4XfTwingZ+spljqn/5mi8uj9V7o/j/jy9QnOsvjoreHiRJkiRJaq+O+GX9G4GXAXsCrwH+BHyK4qJOL+ADAFGEOldRtFIZCdwA/D6KrgD7Ulxo+DlFiPIrng97iIhDgR8D/0kRSH0fuL7pf8QrRRFwLNlMzWsoApjTy/tnAz9r5bmtKOu5sVyu6XH2pWgl8laKFjXbA6NberCyznOAuZm5aDO1teZnFY9/OkXrmrWtPF5ExNEUv0J/oPx3RrPg41/ldMp/N3StlJlPUFz82bOFbfeleM53tVVs2f3g7ygubGyR8iLO2cBMytZRzewJNGRm5S+723o+KylaFu3H1vk28O2yldLulK2yImIcxfF+EcUxfTAwpVznK2WdBwMTKY6N/67Y5k48f8y8E7gkng94V1I8/6EUF67eGxGva1bT8RQtnU5uod6rgLnALhSB8BejvMjbjvfHXWUtp5fPr9LdwMkR8bmIOLql91+TKLpzeyvF8deS4ygC35bWHUARyLZ2jL0MuDsz51ROzMy7KZ53ey5o7wc8mJlZMe1Bnj9GXgr8uh3b2ZyXAjeWx2CrouiS9D+A/2ml1hbfn+Uxs0vlfDZ+L2yt11G0JNy3rfNveT74DUVLxB0ojr3Xt7TBKFrg/Z4iNBwFnA9cGRGVXUKeAXyOotXgdOALFev/ISq6sW3mHqAf0NSi5Tjgr+U2KqfdVl6Q/SPwnfL5fBP4Y2zccqSpxeNg4Mlmj/VuitaGhwCTKN5jW6UMglfyfJALcCZFGA3Fjw8+TPF5+iKKY/t9zTbzOsrXqoWHaM+55ESKFo4vBz4RLfwoJCJGU+yzz1O8zh8Bfh3Pt3L+RET8YXPPt1x2DPBKitemzW1H8eOJ7wCvLFvFvpjnz7HnlH8nUoSeg4AtuXh+KkVwP5Qi1Pg5RUva/SiOz/8r62vr+N8LOA84vKzvZGDWFtTQ5AmKz8rtKY7/K2Lj1t1HAtMojoOvAj+K2NDa/RfA5HLe/9J2a/qXAn8ug/lNtPUebcex+gGKY/F4inPScxQtySu19bnVllY/00rNX8vLgXqKz95DKI7tpi4A/7d8fsMofuxwEUBmHlfOP6hsUVWNVu2V3+NOpvgMbP6Dj3spvjfsQLFvfxURlT8wey3Fj5qGUnyPrTzm2zqO3k3xvjuY4gdWr2v2uG3ts211zvs0cFRZw0EUrSL/q2L+ThTPe1eK829bx9Tby+c5luJ9+R5gdWZ+GridsleFZr09SJIkSZK0WR0RqF2UmQvKVie3U1zofiAz11JcaD2kXO4twB8z869li62vA9tRXCA7CuhD0VpkfWZeS3FRocm7ge9n5t1lC4/LKcKko5oXk5n/yMyh7aj7Z8DZUfyi/XiKQK+5t1N0ZdhAcWHjjPKCExQXFH5fPt46irAkm63/5jK8mAMcxqYXMCr9tvyVbdNf81ZFvwFOKOttLQAEWETR1d0PgU9k5t8pLjQubbbcUoqLxbRjPsB3yueyguIC4ufaeC5N5lFcHGmvj5SPsZKidclnsuVxwbbF89kS64GJETGibBHXFPS8FfhbZl5VHreLM3NKeaHz3cCHy1Zcyym6Sjy92Tb/p1zvBor9uhdAZt6SmQ+VrQcepLiYWNl1HcCFZUun1ZUToxh37Bjg45m5JjOnUBwLZ5Xb3tz74zSK9/FngJlRtMo4vFz3dorWf4dSXABfHMXYYJXdcja9htMpXodzmj9AFK3xJlGcAyr9tlx3GUVo9rVWahwBzG9l3nzabqHRZHPHyAgqWqyWv+ZfUv7i/QcV6+zS7H27hI27hGy+nYPL5ZZFxLSK5b5D2eJyC2sdVHG/peextb5UHrurafv8exRFS7/vlMfydRThVkuOKuv9ctlS8SbgDxQhWpPrMvOeLFrCXklFK6bMfHVmbtJ9ZzlvLUXge1wZmA3NYhyx2yum7QvcShEsPZ6ZPy9bN15F0SrwNRWb/GlmPlzOX9/s4d5M8Vk1JzOfBb7U+m5sl6so90EUrQ5PKaeRmZMz866yjlkUYU7zc0Hla7WRdp5LPleeSx4CfsLGr0eTtwE3ZOYN5bb+StGV7inl43w5M1+9mef524hYTvF5+Azw2fZsG2ikaGm9XWbOz6IFNBTn329m5ozyffNJ4PRof1dxd2bmb7NoET2UImx4T9nSc31m3lou19bx30AR5O4bEX0yc1YZeG+RLFpTzyuf/zUUrQ0ru2B8MjN/UH4eXk7xI54dyx89HE5x7libmbdRBGKtGU7r507Y/Hu01WOVInD8dGbOLd+PFwJvavZ6tPi51cyiivPpRzb3mVaqfC2HULyWHyof6xmKcLTp83c9RWCzS7m9f9BBMvOfwA5lENvi97jMvKL8LlGfmd+gOL4qf3Twj/K90kARAh9UsW5bx9GbKX4YNDczn6OiK+SI2JG299nWnPMqv9P+tpz2VorvPc9k5kKK75GVr2MjRU8Ka8tjpK1jaj3F8TyxfF9Ozsxl7ahLkiRJkqQ2dUSgVtmCaHUL95su+O5CxS/9ywsfcyha6OwCPJW5UWuRylYBuwIXNLtoPbZcb6uUF1FGUvw69g+tBBMn8nx3PL+j6IayqaubXcr6m7a3imIckkq/zMyhmTkqM0/KzMltlPS6ctmmv8qL9pT1/bGsd0Rm3tHKdkZk5rDM3CeLbuygCGuGNFtuCLC8nfMBPpBFENOf4pfK18bmu90cTRHutdfXy8fYjiJw+VpUdG1VYVs8HwAi4tgyJFkRES22mKJoQbYn8GgU3Qo1XTgeS8vj7o2kaOkwueJ4/XM5vcniMjRosoryvRIRR0bEzRGxMCKWUvzyunlINIeW7QI0hXhNnqSV1pPNlReTP5GZ+1F0tzmF4sJYlPP/lJmvoQhKT6UIzN5VsYmvl8fvTpn52uYXl8vWMV+maHHSvLXm68rXvx9FaHtrROzUQpmLKC4ot2Tncv7mbO4YWVz5GJl5cVnbtyjC/ybzmr1vh1KMFUgr25lSLvMGiudJRLwGGJytt4hoq9YVFfdbeh4biaI73qbj/a2tPB5sfHy1df5t6dzd1rE5pzz3N2l+bFZ2u7vhPdFOt1G0QjuW51+Df1RMm5OZT9Lss6iVOlp7DtDs3N/CtjaIiLdW7O8/tbLYLyi68OxHcVzcX9ZJROxZtsx7Ooru7L5I+88FW3Muado/ze0KnNZCcNza+7Alr8uiFdcJwN4VdbS67Sxadr6lrHt+FN2BNnXd2/x1fJIi3N2km+BWVD7vsRTnzedaWK7V4z8zp1O0ur8QeCYiro6ILf5eEkX30VMqtr8/G79OG94X5XcNKN4buwDP5cYtYFs9Hml2PmrB5t6jrR6rFPvpNxXPYSpF4Fj5erT1vmoyouJ8+nXa95nW/HzVh+J4aarl+xQt7gA+RtG97z1RdJn4H+2oCShapTc7DsYBD1ZMO7Mdm/k5xefbiRQ/lGr+GBdExNQouqRcQtEKq8VjgeIc2T+e77axreOo+XlrS/ZZu895FSq/076uYjvN37OV75eFWXT5XFlXa8fUzyl6jrg6iu4jvxrP/+BNkiRJkqSt1hGBWnvNo/ifY6DolpDiotRTFL+YHt100b5U2d3cHOALzS5cD8iiZcELcQXFuEUttfY6i2L//T6K8RxmUIRJTd31zKfoLqjp+WzH1o2PtiV+RlHvz7dwvYeB3WLjMdEO4vku9x6m4lfOEbEbxcX+yi4VgSIIzaKl0nSKLoFaFMW4Ra+haCGyRbLwb+AOWh6r4zGgLiL2qJjW1vMZSNFV4yaBWRbjeg0q/1rsJi8zH8/MMyguLn2FIkwcSHFctjTG0yKKMHm/iuN1+8xsbzjwC4qunMZm5vbA9yguAG5UVivrzqP4BXzlaz2O4n22RcrAq+mC5g7N5jVm0frxJspx0jYnIl5BMb7Xa7JoDdPa4zZk0dKpgY1bezX5G3BkGXpXbv8IinNKe8bneRg4sNk550CeP0b+TnHB+IX6O/Dy8nhpzUsoxvp6ujzXvAX4UET8rqLWFt+fZQAwv3I+G78XNpKZr6w43q9saZmmRStut3X+bencvdHrUmEeMDaeH9MMtvLYbMVtFMHZcTx/3rmDYqy+48r5TXXs2mzd5nW09v6C4jlXPsfmXaM+v5HMKyv2d0s/DiAzH6G4sPxKNu5CD+C7FK3n9siiy9lP0f5zAbTvXNL8ubQ05uAc4OfNjoGB2UqLwbZk0fLrpzzfQrXNbWfmjZn5Moog6FGKcwhs+jqOo+iybgFFK+cBTTOiaEVb+YMG2PQY3yEihrby3Fv9/pGZv8jMY8pakuIzot2i6O71BxQhy/AycP83m75OLZkPDGt2fmn1eKQ4d57cxvmozffoZo7VORQ/lKjcT/2z6L2gSVvHamva85nW/LVcy8bB3JCmz/fMfDoz352Zu1C0gLo0yjEHNyczZzf78cRs4MCKab/YzCag+P72PopWmasqZ0QxXtrHKVqEDSsfYyntOBbacRxt9J2Vjd/3be4ztuCctxktvWcrzzfNj49Wj6ksWpF+LjP3pejp4tU8//18a44zSZIkSZKAzhWo/RJ4VUS8pPwV6QUU/wP/T+BOigthH4iIuoh4Axt3d/QD4D3lr+0jIgZGxKuaXWDZGt+h6FruthbmnU3RHc3BFX9vLJ/DcIrxOl4TES+OYhyhz9G+C2AvxK1lvRdtyUpZjDU2BfhsFAO5v54iPGgaI+pKiudybHmh7X8oul5rrZXLiyi6T9vkon1E9ImIfSi6gdqJYnyipnn94vmxQPqWtbS4z8pWCMe09Bjlr/GvA/6nPBaOpmgt1RQ0/oaii7A3lo/33xTjZT3ayi5qU0S8LSJGlr/aX1JObqDYby+NiDeXx+3wiDi4XO4HwP9FxKhyG6Mjor3jxgym+EX+mjIkas+v3gHIYlyxfwJfKvfvgRQt7NoKTyqf61ciYv/y+QwG3gtMz8zFEXFqFGOrDSvfh0dQdB/X5nh65XZPKmt4Y2a21iVg07IREadSjHEztYXn+DeKoOrXEbFfRPSOiKPK7X83Mx8vt9O7fP3rgF7l/mj6BfstFK/hB8rjsmmclaYw7kLg2Ci6tBxdbm8Exfg/W+JnFBcjf1Pu16aaJlUs8xmeH2/vYIoA5AfAO8r5m3t//gz4r/J12Zuii7qfbmGdbWnr/HsnxX48rzxmTmXjc3eluymCjo+V54kTKEL3q7dRnf+k6LrvbZSBWhk4LiynNZ3nb6AYf+7Msua3UJzP2jUGGMVn2QciYkwUY9i1Nq7blvgFxXhBx1GM2dlkMEUXqCvK1/a9W7jd9pxLPhMRAyJiP4pjrqWWkldQHIMnNx3DEXFCFOOhbY1vAS+LiIPb2nZE7BgRry2P+7UULTKbugG+CvhwREyIYszGL1J00VxP8aOL/uVx2oeiZXerYz5m5nyK8TAvLd9HfSKiaUytVo//iNgrIk6KosXWGoofUrTUTXGTpvNQ018/YCBFALAQNnSJ264fKWTROuw+4HNRjEd7DBt3XdrczylCil9HxN4R0av83PpURJxC+96jrR2r3wO+EEWwQxRj4J3anuexmee4RZ9p5Wv5F+AbETGkfI67R8TxZV2nVRy3z1Hs+6bXbAHFeHxVk5kzKT43P93C7MEU34UXUvxo6L/ZtHVyazZ3HP0S+GD5XWQoRXDXVFOb+4xtd867iuKzamT5efrfFO//1rR6TEXEiRFxQBRh+TKKLiA77HWUJEmSJHVfnSZQy8xpFBc1L6JowfMaipYq67IYg+wNFN3HPUfRQuO6inXvo7hIfHE5fzotjM0Ez3fh186ans3Mv2du1F0Z5cX58cAl5a+Zm/6uLx/7jCzGcTmf4kLTfIou1p6huOi3NX4fz3cNtiIiNukKKAt/z2IMiy11OsVF/KaxM96UxRgWlM/lPRQXqJ6huKjzvmbrX9xUG8VFuf/KzMouzN5SzltCEQgsBg7LzMpfH0+juOA4mqKrntVs/Gvlj5WPsZLi4s5PKLodasn7KLqGfIbiIs17y+dB+bzeCHyhfL5HsvH4ZVvqFcDD5fP7NnB6FmOvzKYYQ+YCiq4tp/B8S6GPUxwrd0XRVdvf2HgclLa8jyIsXE5xwemXW1jvGRTH7zyKcPGzWYxL1J73x4BynSUUrTJ3BV5bznuO4n34OMUFrCuAr22mpVOTz1B0XXVDtN4F3u/L2pZRvHZvz+fHS2rujcDNFF1prihr+RHFe7LJWRTH2HcpWi6tpmzdUp5zXkcRnC8B/oOii6p15fzHKMYTGgP8q3wt7qDYp59px/Ol3M4aiq69HqHosnUZxfvgcIpWCGTm8srzTFnnyqb3eTven5+l6Hr0SYrQ/WuZ+ef21tiO59Dq+bfi3P1Oiv34NopgapPzYLnsaylatywCLgXObm/QHUV3lZ9qo85VwGSK4OTfFbNup2hdelu53GKK1gwXUJynPga8OjftgrQ1P6A4f/0LuJ+Kz6oX4CqKrhBvalbHRyhCsOXl47bWLWhr2nMuuZXiNf07RZetf2m+QBlqnErRQm4hRSjzUcrvGGUg01qXlpsoz9E/oxj7q61t96J4neZRnGOP5/lj/8cUn0W3ATMpAq3zy+0vLZf7IUVLppXA3M2UdRbFRflHKd5nHyq31db3j34Un6eLKLriG1U+j9acQfH+bvp7omz19Q2KcHoBcADFuaa9zqT4jHuW4lzQ2viqTWMNvrR8jn+lOB/dQ9Et4N3tfI+2dqx+m+Kz/y/l8XZXWde20OpnWivOBvpSnHefo/gBVFNXl4cDd5efNdcDHyxDLih+SHF5FF0Mvnkb1b6JLMYybakl6I0Uwe5jFOfzNbSvm0zacRz9gOJ71YPAAxQ/LKjn+RCqrX22rc55n6cIgB8EHiq39fk2lm/rmNqprHEZxQ9vbuX5cO7bFGOtPRcR30GSJEmSpC0QzbIiVUn5C/klFF1zzdzM4pLULUXE3cD3MvMnta5FqpWIGA/ckpnja1xKpxURtwAXZuYtNS6lVRExCzghM2d1xHodJYrxab+Xmc27v5UkSZIkqUfrNC3UuqOIeE3ZVdZAivFgHgJm1bYqSeo4EXF8ROwURfeJb6foTnabtZCTJL0wEbFdRJxSnqdHU7Rm3KQnBEmSJEmSejoDteo6laILonnAHhRdAdokUFJPshdFV2BLKbrne1M5Jo/Uky2hGCtOrfspnf9HSN/i+bFTO2K9agmKsX6fo+jycSpFN7BSzUXEjyPimYj4dyvzIyK+ExHTI+LBiDi0o2uUJEmS1HPY5aMkSZIkqdOJiOMoxsP9WWbu38L8UyjGyDyFYhy9b2fmthqjUZIkSZI2Ygs1SZIkSVKnk5m3Ac+2scipFGFbZuZdwNCI2LljqpMkSZLU09TVuoAXYsSIETl+/PhalyFJkiRpG5k8efKizBxZ6zrUJYwG5lTcn1tO26h76Yg4FzgXYODAgYftvffeHVagJEmSpOrqyP+H7NKB2vjx47nvvvtqXYYkSZKkbSQinqx1DeoyooVpm4xpkJmXAZcBTJo0Kf1/SEmSJKn76Mj/h7TLR0mSJElSVzQXGFtxfwwwr0a1SJIkSermDNQkSZIkSV3R9cDZUTgKWJqZ8ze3kiRJkiRtjS7d5aMkSZIkqXuKiKuAE4ARETEX+CzQByAzvwfcAJwCTAdWAe+oTaWSJEmSegIDNUmSJElVsX79eubOncuaNWs2mde/f3/GjBlDnz59alCZuoLMPGMz8xN4fweVI0mSJKmHM1CTJEmSVBVz585l8ODBjB8/nojYMD0zWbx4MXPnzmXChAk1rFCSJEmSpPap2hhqETE2Im6OiKkR8XBEfLCcfnBE3BURUyLivog4opz+soiYHBEPlf+eVK3aJEmSJFXfmjVrGD58+EZhGkBEMHz48BZbrkmSJEmS1BlVs4VaPXBBZt4fEYOByRHxV+CrwOcy808RcUp5/wRgEfCazJwXEfsDNwKjq1ifJEmSpCprHqZtbrokSZIkSZ1R1QK1zJwPzC9vL4+IqRQBWQJDysW2B+aVyzxQsfrDQP+I6JeZa6tVoyRJkiRJkiRJkrQ5HTKGWkSMBw4B7gY+BNwYEV+n6HLyxS2s8kbgAcM0SZIkSZIkSZIk1VrVxlBrEhGDgF8DH8rMZcB7gQ9n5ljgw8CPmi2/H/AV4D9b2d655dhr9y1cuLC6xUuSJEl6QTJzi6ZLkiRJktQZVTVQi4g+FGHalZl5XTn57UDT7V8BR1QsPwb4DXB2Zj7R0jYz87LMnJSZk0aOHFm94iVJkiS9IP3792fx4sWbhGeZyeLFi+nfv3+NKpMkSZIkactUrcvHKEYZ/xEwNTO/WTFrHnA8cAtwEvB4ufxQ4I/AJzPzjmrVJUmSJKljjBkzhrlz59JSzxL9+/dnzJgxNahKkiRJkqQtV80x1I4GzgIeiogp5bRPAe8Gvh0RdcAa4Nxy3nnAROAzEfGZctrLM/OZKtYoSZIkqUr69OnDhAkTal2GJEmSJEkvWNUCtcz8BxCtzD6sheU/D3y+WvVIkiRJkiRJkiRJW6OqY6hJkiRJkiRJkiRJXV01u3yUJEmStpmGxmTektVk1rqS7mnYwD4M7t+n1mVIkiRJktQpGahJkiSpS/js9f/mirtm17qMbuuLrz+AM48cV+syJEmSJEnqlAzUJEmS1OnNW7Kaa+6dw8v33ZGT99up1uV0S4eMG1rrEiRJkiRJ6rQM1CRJktTpff/WJ8iE/37NvowZNqDW5UiSJEmSpB6mV60LkCRJktryzPI1XHXvHN546BjDNEmSJEmSVBMGapIkSerUfnj7TOobGnnvCbvXuhRJkiRJktRDGahJkiSp03p25TquuOtJXnvQLowfMbDW5UiSJEmSpB7KQE2SJEmd1o//MZPV6xt4/4kTa12KJEmSJEnqwQzUJEmS1CktXb2ey/85i1fuvxN77Di41uVIkiRJkqQezEBNkiRJndLl/5zF8rX1tk6TJEmSJEk1Z6AmSZKkTmfF2np+fMdMXrrPKPbbZftalyNJkiRJkno4AzVJkiR1Olfe9SRLVq23dZokSZIkSeoUDNQkSZLUqaxe18APbp/BsXuM4JBxw2pdjiRJkiRJkoGaJEmSOper753NohXrOP+kPWpdiiRJkiRJEmCgJkmSpE5kbX0D3791BkdM2IEjJuxQ63IkSZIkSZIAAzVJkiR1ItdOnsvTy9Zw/kmOnSZJkiRJkjoPAzVJkiR1CusbGvnuLU9w8NihHDNxRK3LkSRJkiRJ2sBATZIkSZ3C76bMY+5zqzn/pIlERK3LkSRJkiRJ2sBATZIkSTXX0JhcevN09t15CCftParW5UiSJEmSJG3EQE2SJEk198eH5jNj0Upbp0mSJEmSpE7JQE2SJEk11diYXHLTdCaOGsTJ++1U63IkSZIkSZI2YaAmSZKkmvrLIwuYtmA55504kV69bJ0mSZIkSZI6HwM1SZIk1UxmcvHNjzN++ABefeDOtS5HkiRJkiSpRQZqkiRJqplbHlvIv59axvtOmEhdb7+aSpIkSZKkzsmrFpIkSaqJzOSivz/O6KHb8bpDRte6HEmSJEmSpFYZqEmSJKkm7nxiMffPXsJ7TtidvnV+LZUkSZIkSZ2XVy4kSZJUExfdNJ1Rg/tx2mFjal2KJEmSJElSmwzUJEmS1OHum/Usd85YzLnH7Ub/Pr1rXY4kSZIkSVKbDNQkSZLU4S66aTrDB/blzCPH1boUSZIkSZKkzTJQkyRJUod6cO4Sbn1sIe88dgID+tbVuhxJkiRJkqTNMlCTJElSh7ropulsv10fzjpq11qXIkmSJEmS1C4GapIkSeowU+cv46+PLOAdR49ncP8+tS5HkiRJkiSpXQzUJEmS1GEuuXk6g/rVcc6Lx9e6FEmSJEmSpHYzUJMkSVKHmP7MCv740HzOetGuDB3Qt9blSJIkSZIktZuBmiRJkjrEpbdMp19dL951zIRalyJJkiRJkrRFDNQkSZJUdbMXr+J3U+bx1iN3ZfigfrUuR5IkSZIkaYsYqEmSJKnqvnvrdHr3Cs49brdalyJJkiRJkrTFDNQkSZJUVfOWrObayXN5y6Sx7Dikf63LkSRJkiRJ2mIGapIkSaqqy26bQSb85/G2TpMkSZIkSV2TgZokSZKq5pnla7jqntm84dDRjBk2oNblSJIkSZIkbRUDNUmSJFXND2+fyfqGRt53wsRalyJJkiRJkrTVDNQkSZJUFc+uXMcVdz3Jaw/ahfEjBta6HEmSJEmSpK1moCZJkqSq+PE/ZrJ6fQPvP9HWaZIkSZIkqWurWqAWEWMj4uaImBoRD0fEB8vpB0fEXRExJSLui4gjKtb5ZERMj4hpEXFytWqTJElSdS1dvZ7L/zmLV+6/E3vsOLjW5UiSJEmSJL0gdVXcdj1wQWbeHxGDgckR8Vfgq8DnMvNPEXFKef+EiNgXOB3YD9gF+FtE7JmZDVWsUZIkSVXws3/OYvnaelunSZIkSZKkbqFqLdQyc35m3l/eXg5MBUYDCQwpF9semFfePhW4OjPXZuZMYDpwBJIkSepSVqyt50d3zOQle49iv122r3U5kiRJkiRJL1g1W6htEBHjgUOAu4EPATdGxNcpAr0Xl4uNBu6qWG1uOa35ts4FzgUYN25c1WqWJEnS1rnyridZsmo9551k6zRJkiRJktQ9VK2FWpOIGAT8GvhQZi4D3gt8ODPHAh8GftS0aAur5yYTMi/LzEmZOWnkyJHVKluSJElbYc36Bn5w+wyO3WMEh4wbVutyJEmSJEmStomqBmoR0YciTLsyM68rJ78daLr9K57v1nEuMLZi9TE83x2kJEmSuoCr7pnNohXrOM+x0yRJkiRJUjdStUAtIoKi9dnUzPxmxax5wPHl7ZOAx8vb1wOnR0S/iJgA7AHcU636JEmStG2trW/g+7fO4IgJO3DkbsNrXY4kSZIkSdI2U80x1I4GzgIeiogp5bRPAe8Gvh0RdcAayvHQMvPhiPgl8AhQD7w/MxuqWJ8kSZK2oV9Pfoqnl63ha6cdWOtSJEmSJEmStqmqBWqZ+Q9aHhcN4LBW1vkC8IVq1SRJkqTqWN/QyKW3TOfgsUM5ZuKIWpcjSZIkSZK0TVV1DDVJkiT1DL+bMo+5z63m/JMmUvT8LUmSJEmS1H0YqEmSJOkFaWhMLr15OvvuPIST9h5V63IkSZIkSZK2OQM1SZIkvSB/fGg+MxattHWaJEmSJEnqtgzUJEmStNUaG5NLbprOxFGDOHm/nWpdjiRJkiRJUlUYqEmSJGmr/XXqAqYtWM55J06kVy9bp0mSJEmSpO7JQE2SJElbJTO56KbHGT98AK8+cOdalyNJkiRJklQ1BmqSJEnaKrc8tpB/P7WM950wkbrefq2UJEmSJEndl1c+JEmStMUyk4v+/jijh27H6w4ZXetyJEmSJEmSqspATZIkSVvszicWc//sJbznhN3pW+dXSknbXkS8IiKmRcT0iPhEC/O3j4jfR8S/IuLhiHhHLeqUJEmS1DN49UOSJElb7KKbpjNqcD9OO2xMrUuR1A1FRG/gEuCVwL7AGRGxb7PF3g88kpkHAScA34iIvh1aqCRJkqQew0BNkiRJW+S+Wc9y54zFnHvcbvTv07vW5Ujqno4ApmfmjMxcB1wNnNpsmQQGR0QAg4BngfqOLVOSJElST2GgJkmSpC1y0U3TGT6wL2ceOa7WpUjqvkYDcyruzy2nVboY2AeYBzwEfDAzG5tvKCLOjYj7IuK+hQsXVqteSZIkSd2cgZokSZLa7cG5S7j1sYW889gJDOhbV+tyJHVf0cK0bHb/ZGAKsAtwMHBxRAzZZKXMyzJzUmZOGjly5LauU5IkSVIPYaAmSZKkdrv4pulsv10fzjpq11qXIql7mwuMrbg/hqIlWqV3ANdlYTowE9i7g+qTJEmS1MMYqEmSJKldps5fxl8eWcA7jh7P4P59al2OpO7tXmCPiJgQEX2B04Hrmy0zG3gJQETsCOwFzOjQKiVJkiT1GPbTI0mSpHa55ObpDOpXxzkvHl/rUiR1c5lZHxHnATcCvYEfZ+bDEfGecv73gP8FfhoRD1F0EfnxzFxUs6IlSZIkdWsGapIkSdqsJxau4I8Pzec9x+/O0AF9a12OpB4gM28Abmg27XsVt+cBL+/ouiRJkiT1THb5KEmSpM265Obp9KvrxbuOmVDrUiRJkiRJkjqcgZokSZLaNHvxKn43ZR5vPXJXhg/qV+tyJEmSJEmSOpyBmiRJktr03VufoHev4Nzjdqt1KZIkSZIkSTVhoCZJkqRWzVuymmsnz+Etk8ay45D+tS5HkiRJkiSpJgzUJEmS1KrLbptBJvzn8bZOkyRJkiRJPZeBmiRJklr0zPI1XHXPbN5w6GjGDBtQ63IkSZIkSZJqxkBNkiRJLfrh7TNZ39DI+06YWOtSJEmSJEmSaspATZIkSZt4duU6rrjrSV570C6MHzGw1uVIkiRJkiTVlIGaJEmSNvGTO2ayen0D7z/R1mmSJEmSJEkGapIkSdrI0tXr+ekds3jl/juxx46Da12OJEmSJElSzRmoSZIkaSM/++cslq+tt3WaJEmSJElSyUBNkiRJG6xcW8+P7pjJS/YexX67bF/rciRJkiRJkjoFAzVJkiRtcMVdT7Jk1XrOO8nWaZIkSZIkSU0M1CRJkgTAmvUN/OD2GRy7xwgOGTes1uVIkiRJkiR1GgZqkiRJAuDqe2azaMU6znPsNEmSJEmSpI0YqEmSJIm19Q1879YZHDFhB47cbXity5EkSZIkSepUDNQkSZLEryc/xdPL1nC+Y6dJkiRJkiRtwkBNkiSph1vf0Milt0znoLFDOWbiiFqXI0mSJEmS1OkYqEmSJPVwv5syj7nPreYDJ00kImpdjiRJkiRJUqdjoCZJktSDNTQml948nX12HsJJe4+qdTmSJEmSJEmdkoGaJElSD3bDQ/OZsWgl59s6TZIkSZIkqVUGapIkST1UY2Ny8U3TmThqEK/Yb6dalyNJkiRJktRpGahJkiT1UH+duoBpC5Zz3okT6dXL1mmSJEmSJEmtMVCTJEnqgTKL1mm7Dh/Aqw/cudblSJIkSZIkdWoGapIkST3QLY8t5KGnlvL+EyZS19uvhJIkSZIkSW3x6okkSVIPk5lc9PfHGT10O153yOhalyNJkiRJktTpGahJkiT1MHfOWMz9s5fwnuN3o2+dXwclSZIkSZI2p2pXUCJibETcHBFTI+LhiPhgOf2aiJhS/s2KiCnl9D4RcXlEPFSu88lq1SZJktSTXfT36Ywa3I/TJo2tdSmSJEmSJEldQl0Vt10PXJCZ90fEYGByRPw1M9/StEBEfANYWt49DeiXmQdExADgkYi4KjNnVbFGSZKkHuW+Wc9y54zF/Ner9qF/n961LkeSJEmSJKlLqFqglpnzgfnl7eURMRUYDTwCEBEBvBk4qWkVYGBE1AHbAeuAZdWqT5IkqSe6+Obp7DCwL2ceOa7WpUiSJEmSJHUZHTJoRkSMBw4B7q6YfCywIDMfL+9fC6ykCOFmA1/PzGdb2Na5EXFfRNy3cOHC6hYuSZLUjTw4dwm3TFvIu46dwIC+1eyoQJIkSZIkqXupeqAWEYOAXwMfyszKFmdnAFdV3D8CaAB2ASYAF0TEbs23l5mXZeakzJw0cuTIKlYuSZLUvVx803S2364PZx21a61LkSRJkiRJ6lKqGqhFRB+KMO3KzLyuYnod8AbgmorFzwT+nJnrM/MZ4A5gUjXrkyRJ6ikefXoZf3lkAe84ejyD+/epdTmSJEmSJEldStUCtXKMtB8BUzPzm81mvxR4NDPnVkybDZwUhYHAUcCj1apPkiSpJ7n4pukM6lfHOS8eX+tSJEmSJEmSupxqtlA7GjiLIiSbUv6dUs47nY27ewS4BBgE/Bu4F/hJZj5YxfokSZJ6hCcWruCPD83nrBftytABfWtdjiRJkiRJUpdTtdHoM/MfQLQy75wWpq0ATqtWPZIkST3VpTc/Qb+6XrzrmAm1LkWSJEmSJKlLquoYapIkSaqt2YtX8dspT/HWI3dl+KB+tS5HkiRJkiSpSzJQkyRJ6sa+e+sT9O4VnHvcbrUuRZIkSZIkqcsyUJMkSeqm5i9dzbWT5/CWSWPZcUj/WpcjSZIkSZLUZRmoSZIkdVPfv3UGmfCfx9s6TZIkSZIk6YUwUJMkSeqGnlm+hqvumc0bDh3NmGEDal2OJEmSJElSl2agJkmS1A396PaZrG9o5H0nTKx1KZIkSZIkSV2egZokSVI38+zKdfz8rid57UG7MH7EwFqXI0mSJEmS1OUZqEmSJHUzP7ljJqvXN/D+E22dJkmSJEmStC0YqEmSJHUjS1ev56d3zOKV++/EHjsOrnU5kiRJkiRJ3YKBmiRJUjfys3/OYvnaelunSZIkSZIkbUMGapIkSd3EyrX1/OiOmbxk71Hst8v2tS5HkiRJkiSp2zBQkyRJ6iauvPtJlqxaz3kn2TpNkiRJkiRpWzJQkyRJ6gbWrG/gsttmcuweIzhk3LBalyNJkiRJktStGKhJkiR1A1ffM5tFK9ZynmOnSZIkSZIkbXMGapIkSV3c2voGvn/bDI6YsANH7ja81uVIkiRJkiR1OwZqkiRJXdyvJz/F/KVrON+x0yRJkiRJkqrCQE2SJKkLW9/QyKW3TOegsUM5ZuKIWpcjSZIkSZLULRmoSZIkdWHXT5nH3OdW84GTJhIRtS5HkiRJkiSpWzJQkyRJ6qIaGpNLbp7OPjsP4aS9R9W6HEmSJEmSpG7LQE2SJKmLuuGh+cxYtJLzbZ0mSZIkSZJUVQZqkiRJXVBjY3LxTdOZOGoQr9hvp1qXI0mSJEmS1K0ZqEmSJHVBf526gGkLlnPeiRPp1cvWaZIkSZIkSdVkoCZJktTFZBat03YdPoBXH7hzrcuRJEmSJEnq9gzUJEmSuphbH1vIQ08t5f0nTKSut1/nJEmSJEmSqs0rMJIkSV1IZnLRTdMZPXQ7XnfI6FqXI0mSJEmS1CMYqEmSJHUhd85YzOQnn+M9x+9G3zq/ykmSJEmSJHUEr8JIkiR1IRffNJ1Rg/tx2qSxtS5FkiRJkiSpxzBQkyRJ6iImP/ks/3xiMecetxv9+/SudTmSJEmSJEk9hoGaJElSF3HRTdPZYWBfzjxyXK1LkSRJkiRJ6lEM1CRJkrqAh+Yu5ZZpC3nXsRMY0Leu1uVIkiRJkiT1KAZqkiRJXcBFNz3OkP51nHXUrrUuRZIkSZIkqccxUJMkSerkHn16GX95ZAHvOHoCg/v3qXU5kiRJkiRJPY6BmiRJUid3yc1PMKhfHe84enytS5EkSZIkSeqRDNQkSZI6sScWruAPD87jrBftytABfWtdjiRJkiRJUo9koCZJktSJXXrzE/Sr68U7j5lQ61IkSZIkSZJ6LAM1SZKkTmrOs6v47ZSneOuRuzJiUL9alyNJHSoiXhER0yJiekR8opVlToiIKRHxcETc2tE1SpIkSeo56mpdgCRJklp26S1P0DuCc4/brdalSFKHiojewCXAy4C5wL0RcX1mPlKxzFDgUuAVmTk7IkbVpFhJkiRJPYIt1CRJkjqh+UtXc+3kObz58DHsOKR/rcuRpI52BDA9M2dk5jrgauDUZsucCVyXmbMBMvOZDq5RkiRJUg9ioCZJktQJff/WGWTCe47fvdalSFItjAbmVNyfW06rtCcwLCJuiYjJEXF2SxuKiHMj4r6IuG/hwoVVKleSJElSd2egJkmS1Mk8s3wNV90zmzccOpoxwwbUuhxJqoVoYVo2u18HHAa8CjgZ+ExE7LnJSpmXZeakzJw0cuTIbV+pJEmSpB7BMdQkSZI6mR/dPpP1DY2874SJtS5FkmplLjC24v4YYF4LyyzKzJXAyoi4DTgIeKxjSpQkSZLUk9hCTZIkqRN5buU6fn7Xk7z2oF0YP2JgrcuRpFq5F9gjIiZERF/gdOD6Zsv8Djg2IuoiYgBwJDC1g+uUJEmS1EPYQk2SJKkT+fEdM1m9voH3n2jrNEk9V2bWR8R5wI1Ab+DHmflwRLynnP+9zJwaEX8GHgQagR9m5r9rV7UkSZKk7qxqgVpEjAV+BuxE8T83l2XmtyPiGmCvcrGhwJLMPLhc50Dg+8CQcp3DM3NNtWqUJEnqTJauXs9P75jFK/ffiT12HFzrciSppjLzBuCGZtO+1+z+14CvdWRdkiRJknqmarZQqwcuyMz7I2IwMDki/pqZb2laICK+ASwtb9cBVwBnZea/ImI4sL6K9UmSJHUqP79zFsvX1ts6TZIkSZIkqZOpWqCWmfOB+eXt5RExFRgNPAIQEQG8GTipXOXlwIOZ+a9yncXVqk2SJKmzWbm2nh/9YyYv2XsU++2yfa3LkSRJkiRJUoVeHfEgETEeOAS4u2LyscCCzHy8vL8nkBFxY0TcHxEf64jaJEmSOoMr736S51at57yTbJ0mSZIkSZLU2VSzy0cAImIQ8GvgQ5m5rGLWGcBVzWo5BjgcWAX8PSImZ+bfm23vXOBcgHHjxlWzdEmSpA6xZn0Dl902k2P3GMEh44bVuhxJkiRJkiQ1U9UWahHRhyJMuzIzr6uYXge8AbimYvG5wK2ZuSgzV1EMPn1o821m5mWZOSkzJ40cObKa5UuSJHWIq++ZzaIVaznPsdMkSZIkSZI6paoFauUYaT8CpmbmN5vNfinwaGbOrZh2I3BgRAwoA7fjKcdbkyRJ6q7W1jfw/dtmcMSEHThyt+G1LkeSJEmSJEktqGYLtaOBs4CTImJK+XdKOe90Nu7ukcx8DvgmcC8wBbg/M/9YxfokSZJq7rr7n2L+0jWc79hpkiRJkiRJnVbVxlDLzH8A0cq8c1qZfgVwRbVqkiRJ6kzWNzRy6S3TOWjsUI6ZOKLW5UiSJEmSJKkVVR1DTZIkSa27fso85jy7mg+cNJGit2xJkiRJkiR1RgZqkiRJNdDQmFxyy3T22XkIJ+09qtblSJIkSZIkqQ0GapIkSTVww0PzmbFwJefbOk2SJEmSJKnTa9cYahExDNgD6N80LTNvq1ZRkiRJ3VljY3LxTdOZOGoQr9hvp1qXI0mSJEmSpM3YbKAWEe8CPgiMAaYARwF3AidVtTJJkqRu6m9TFzBtwXK+9ZaD6dXL1mmSJEmSJEmdXXu6fPwgcDjwZGaeCBwCLKxqVZIkSd1UZnLRTdPZdfgAXn3gzrUuR5IkSZIkSe3QnkBtTWauAYiIfpn5KLBXdcuSJEnqnm59bCEPPbWU958wkbreDmcrSZIkSZLUFbRnDLW5ETEU+C3w14h4DphXzaIkSZK6o6bWaaOHbsfrDhld63IkSZIkSZLUTpsN1DLz9eXNCyPiZmB74M9VrUqSJKkbunPGYiY/+Rz/e+p+9K2zdZokSZIkSVJX0Z4WakREb2BHYGY5aSdgdrWKkiRJ6o4uvmk6owb347RJY2tdiiRJkiRJkrbAZgO1iDgf+CywAGgsJydwYBXrkiRJ6lYmP/ks/3xiMf/1qn3o36d3rcuRJEmSJEnSFmhPC7UPAntl5uJqFyNJktRdXXTTdHYY2JczjxxX61IkSZIkSZK0hdozeMccYGm1C5EkSequHpq7lFumLeRdx05gQN929bgtSZIkSZKkTqTVKzoR8f/KmzOAWyLij8DapvmZ+c0q1yZJktQtXHzz4wzpX8dZR+1a61IkSZIkSZK0Fdr6ifTg8t/Z5V/f8k+SJEnt9OjTy7jx4QV88CV7MLh/n1qXI0mSJEmSpK3QaqCWmZ/ryEIkSZK6o0tufoJB/ep4x9Hja12KJEmSJEmStlJ7xlCTJEnSVnhi4Qr+8OA8znrRrgwdYEN/SZIkSZKkrspATZIkqUouvfkJ+tX14p3HTKh1KZIkSZIkSXoBDNQkSZKqYM6zq/jtlKd465G7MmJQv1qXI0mSJEmSpBeg1THUmkTESODdwPjK5TPzP6pXliRJUtf23VufoHcE5x63W61LkSRJkiRJ0gu02UAN+B1wO/A3oKG65UiSJHV985eu5tr75vLmw8ew45D+tS5HkiRJkiRJL1B7ArUBmfnxqlciSZLUTXz/1hk0ZvKe43evdSmSJEmSJEnaBtozhtofIuKUqlciSZLUxS1dtZ4v/PERrrjrSd5w6GjGDBtQ65IkSZIkSZK0DbSnhdoHgU9FxDpgfTktM3NI9cqSJEnqOtbWN/DzO5/kopums2zNek47bAyfPmXfWpclSZIkSZKkbWSzgVpmDu6IQiRJkrqazOSPD83nK39+lDnPrua4PUfyyVfuzT47+7sjSZIkSZKk7qQ9LdSIiNcCx5V3b8nMP1SvJEmSpM7v3lnP8oU/TmXKnCXsvdNgfv7OIzh2j5G1LkuSJEmSJElVsNlALSK+DBwOXFlO+mBEHJOZn6hqZZIkSZ3QjIUr+MqfH+XGhxew05D+fO1NB/KGQ8fQu1fUujRJkiRJkiRVSXtaqJ0CHJyZjQARcTnwAGCgJkmSeozFK9bynb8/zpV3z6ZfXS8+8vI9eecxu7Fd3961Lk2SJEmSJElV1q4uH4GhwLPl7e2rU4okSVLns2Z9Az++YybfvfkJVq1v4IwjxvLBl+zJyMH9al2aJEmSJEmSOkh7ArUvAQ9ExM1AUIyl9smqViVJklRjjY3Jb6c8xddvnMa8pWt46T478olX7s3EUYNqXZokSZIkSZI62GYDtcy8KiJuoRhHLYCPZ+bT1S5MkiSpVu6Yvogv3jCVh+ct46Ax2/PNtxzMUbsNr3VZkiRJkiRJqpFWA7WI2DszH42IQ8tJc8t/d4mIXTLz/uqXJ0mS1HEeW7CcL90wlZunLWT00O349ukH85oDd6FXr6h1aZIkSZIkSaqhtlqoXQC8G/hGC/MSOKkqFUmSJHWwZ5at4f/+9hjX3DuHQf3q+NQpe3P2i8bTv0/vWpcmSZIkSZKkTqDVQC0z313+e2LHlSNJktRxVq6t5we3z+Cy22awvqGRc148gfNPmsiwgX1rXZokSZIkSZI6kba6fHxDWytm5nXbvhxJkqTqa2hMfnXfHL7x18dYuHwtrzpwZz528l7sOnxgrUuTJEmSJElSJ9RWl4+vaWNeAgZqkiSpS8lMbpm2kC/9aSqPLVjBYbsO43tvO4zDdh1W69IkSZIkSZLUibXV5eM7OrIQSZKkavr3U0v50p+mcsf0xYwfPoDvve1QTt5vJyKi1qVJkiRJkiSpk2ury8f/19aKmfnNbV+OJEnStjVvyWq+/pdp/OaBpxi6XR8ufM2+nHnkrvSt61Xr0iRJkiRJktRFtNXl4+AOq0KSJGkbW7ZmPd+95Ql+/I+ZJPCfx+3O+07cnSH9+9S6NEmSJEmSJHUxbXX5+LmOLESSJGlbWN/QyFX3zOZbf3ucZ1eu4/WHjOaCl+/JmGEDal2aJEmSJEmSuqi2unz8WGZ+NSIuArL5/Mz8QFUrkyRJ2gKZyY0PL+Crf36UGYtW8qLdhvPpV+3D/qO3r3VpkiRJkiRJ6uLa6vJxavnvfR1RiCRJ0tZ6YPZzfPGGqdw76zn2GDWIH58ziRP3GkVE1Lo0SZIkSZIkdQNtdfn4+/LfyzuuHEmSpPabvXgVX73xUf7w4HxGDOrHF19/AG+eNIa63r1qXZokSZIkSZK6kbZaqAEQEZOATwO7Vi6fmQdWsS5JkqRWLVm1jotums7P7pxFXa9efOAle/Cfx+3GwH6b/WojSZIkSZIkbbH2XHW6Evgo8BDQ2N4NR8RY4GfATuV6l2XmtyPiGmCvcrGhwJLMPLhivXHAI8CFmfn19j6eJEnq/tbWN/Czfz7JRTc9zoq19Zx22Fj+38v3ZMch/WtdmiRJkiRJkrqx9gRqCzPz+q3Ydj1wQWbeHxGDgckR8dfMfEvTAhHxDWBps/X+D/jTVjyeJEnqpjKT3z84n6/d+Chznl3N8XuO5JOn7M3eOw2pdWmSJEmSJEnqAdoTqH02In4I/B1Y2zQxM69ra6XMnA/ML28vj4ipwGiK1mdERABvBk5qWiciXgfMAFZu0bOQJEnd1t0zFvPFG6byr7lL2WfnIVzxzgM5Zo8RtS5LkiRJkiRJPUh7ArV3AHsDfXi+y8cE2gzUKkXEeOAQ4O6KyccCCzLz8XKZgcDHgZcBH2ljW+cC5wKMGzeuvSVIkqQu5omFK/jKnx7lL48sYKch/fn6aQfx+kNG07tX1Lo0SZIkSZIk9TDtCdQOyswDtvYBImIQ8GvgQ5m5rGLWGcBVFfc/B/xfZq4oGq+1LDMvAy4DmDRpUm5tXZIkqXNatGIt3/7b4/zintls16c3Hz15L/7j6Als17d3rUuTJEmSJElSD9WeQO2uiNg3Mx/Z0o1HRB+KMO3Kyi4iI6IOeANwWMXiRwJvioivAkOBxohYk5kXb+njSpKkrmf1ugZ+fMdMvnvLE6xe38BbjxzHB16yByMG9at1aZIkSZIkSerh2hOoHQO8PSJmUoyhFkBm5oFtrVSOkfYjYGpmfrPZ7JcCj2bm3KYJmXlsxboXAisM0yRJ6v4aG5PrHniKb/xlGvOXruFl++7IJ165N7uPHFTr0iRJkiRJkiSgfYHaK7Zy20cDZwEPRcSUctqnMvMG4HQ27u5RkiT1QP94fBFfvGEqj8xfxkFjtudbbzmYI3cbXuuyJEmSJEmSpI1sNlDLzCe3ZsOZ+Q+K1mwtzTtnM+teuDWPKUmSuoZpTy/nizdM5dbHFjJm2HZ854xDePUBO9OrV+vjqEqSJEmSJEm10p4WapIkSdvEgmVr+OZfHuNXk+cwqF8dnz5lH85+8a70q+td69IkSZIkSZKkVhmoSZKkqlu5tp7v3zaDH9w2g/rGRt5x9ATOP2kiQwf0rXVpkiRJkiRJ0mYZqEmSpKqpb2jkl/fN5Zt/fYxFK9by6gN35mMn78244QNqXZokSZIkSZLUbgZqkiRpm8tMbp72DF+64VEef2YFh48fxg/OPoxDxg2rdWmSJEmSJEnSFjNQkyRJ29S/n1rKF/44lTtnLGbCiIF8722HcfJ+OxIRtS5NkiRJkiRJ2ioGapIkaZt4aslqvn7jNH7zwFPsMLAvn3vtfpx55Dj69O5V69IkSZIkSZKkF8RATZIkvSDL1qzn0puf4Md3zCSA956wO+89YXeG9O9T69IkSZIkSZKkbcJATZIkbZV19Y384u4n+fbfH2fJ6vW8/uDRXHDyXoweul2tS5MkSZIkSZK2KQM1SZK0RTKTP//7ab7y50eZtXgVL959OJ86ZR/2H719rUuTJEmSJEmSqsJATZIktdv9s5/jC3+cyuQnn2OPUYP4yTmHc8JeI4mIWpcmSZIkSZIkVY2BmiRJ2qwnF6/kq3+exh8fms/Iwf340hsO4LTDxlDXu1etS5MkdVMR8Qrg20Bv4IeZ+eVWljscuAt4S2Ze24ElSpIkSepBDNQkSVKrnlu5jotums7P75pFXa9efOile/DuY3djYD+/QkiSqiciegOXAC8D5gL3RsT1mflIC8t9Bbix46uUJEmS1JN4NUySJG1izfoGfnbnLC66aTor19bz5klj+X8v25NRQ/rXujRJUs9wBDA9M2cARMTVwKnAI82WOx/4NXB4x5YnSZIkqacxUJMkSRs0Nia/f3AeX/3zNJ5aspoT9xrJJ165D3vtNLjWpUmSepbRwJyK+3OBIysXiIjRwOuBk2gjUIuIc4FzAcaNG7fNC5UkSZLUMxioSZIkAO6asZgv3jCVB+cuZd+dh/DVNx3I0RNH1LosSVLPFC1My2b3vwV8PDMbIlpavFwp8zLgMoBJkyY134YkSZIktYuBmiRJPdz0Z1bw5T89yt+mLmDn7fvzjdMO4vWHjKZXr9YvTkqSVGVzgbEV98cA85otMwm4ugzTRgCnRER9Zv62QyqUJEmS1KMYqEmS1AM0NCYr1tazfM16lq+pL//Wc/O0Z7jqnjls16c3Hz15L955zAT69+ld63IlSboX2CMiJgBPAacDZ1YukJkTmm5HxE+BPximSZIkSaoWAzVJkjq5+obGMgyrZ1mzQKy16Rv/W8+KtfUtbruuV/C2I8fxgZfswfBB/Tr4mUmS1LLMrI+I84Abgd7AjzPz4Yh4Tzn/ezUtUJIkSVKPY6AmSVIV1Tc0bgi1lm0SdJX/li3HlrUSiK1a17DZx+lb14sh/esY3L8Pg/vXMbh/HSMHDWJQeXtw/z7l/Mpl+rDL0P6MGty/A/aEJElbJjNvAG5oNq3FIC0zz+mImiRJkiT1XAZqkiS1Yl194yZdJC4rW3u11Aps08CsntXrNx+G9avrtUngtdOQ/puEX4P71zULzZ4Pz/rV2U2jJEmSJEmSVC0GapKkbmltfUOLXSC21gqs8vay8vba+sbNPs52fXpvCLWaAq7RQ7fbZNqGf/ttHIQN7t+HvnW9OmCPSJIkSZIkSdpaBmqSpE7viYUreOzp5a2PFbZ201Zk69oRhg3s27vsErEIuLYf0JcxOwx4vhVYv5ZDsSHlv4P619Gnt2GYJEmSJEmS1N0ZqEmSOrW7ZizmbT+8m/rG3Gj6oA1hVxF07TCwL7sOH7hh2pDKVmD9Ng3EBvWvo3evqNGzkiRJkiRJktSVGKhJkjqtOc+u4r1XTGbX4QP49umHMHRAHwb378OgfoZhkiRJkiRJkjqOgZokqVNasbaed11+H40JP3z74UwYMbDWJUmSJEmSJEnqoQzUJEmdTmNj8qGrpzB94Qouf8cRhmmSJEmSJEmSaqpXrQuQJKm5r/9lGn+buoD/fvW+HLPHiFqXI0mSJEmSJKmHM1CTJHUqv5vyFJfe8gRnHDGOs1+0a63LkSRJkiRJkiQDNUlS5zFlzhI+eu2DHDlhBz732v2IiFqXJEmSJEmSJEkGapKkzuHppWs492f3seOQfnz3bYfRt86PKEmSJEmSJEmdg1crJUk1t2Z9A+f+/D5Wrq3nh2cfzg4D+9a6JEmSJEmSJEnaoK7WBUiSerbM5GPXPshDTy3lsrMmsddOg2tdkiRJkiRJkiRtxBZqkqSauvSWJ7j+X/P46Ml78bJ9d6x1OZIkSZIkSZK0CQM1SVLN/OXhp/najdN43cG78N7jd691OZIkSZIkSZLUIgM1SVJNTJ2/jA9dM4WDxg7ly288kIiodUmSJEmSJEmS1CIDNUlSh1u8Yi3vuvw+Bvev4wdnHUb/Pr1rXZIkSZIkSZIktaqu1gVIknqWdfWNvPeK+1m0Yi2/es+LGDWkf61LkiRJkiRJkqQ2GahJkjpMZvLfv/s398x6lu+ccQgHjhla65IkSZIkSZIkabPs8lGS1GF++s9ZXH3vHM47cSKvPWiXWpcjSZIkSZIkSe1ioCZJ6hC3PbaQ//3DI7x83x35fy/bs9blSJIkSZIkSVK7GahJkqpuxsIVnPeL+9lzx8H831sOplevqHVJkiRJkiRJktRuBmqSpKpauno977r8Pup69+IHZ09iYD+H75QkSZIkSZLUtRioSZKqpr6hkfOveoA5z63ie287jLE7DKh1SZIkSZIkSZK0xaoWqEXE2Ii4OSKmRsTDEfHBcvo1ETGl/JsVEVPK6S+LiMkR8VD570nVqk2S1DG+eMOj3PbYQj7/uv05YsIOtS5HkiRJkiRJkrZKNfvdqgcuyMz7I2IwMDki/pqZb2laICK+ASwt7y4CXpOZ8yJif+BGYHQV65MkVdE1987mx3fM5B1Hj+cth4+rdTmSJEmSJEmStNWqFqhl5nxgfnl7eURMpQjIHgGIiADeDJxULvNAxeoPA/0jol9mrq1WjZKk6rh31rP812//zbF7jODTp+xT63IkSZIkSZIk6QXpkDHUImI8cAhwd8XkY4EFmfl4C6u8EXjAME2Sup65z63iPT+fzNhhA7j4zEOp6+1wnZIkSZIkSZK6tmp2+QhARAwCfg18KDOXVcw6A7iqheX3A74CvLyV7Z0LnAswbpxdiElSZ7JybT3vuvw+1jU08oO3T2L77frUuiRJkiRJkiRJesGq2mwgIvpQhGlXZuZ1FdPrgDcA1zRbfgzwG+DszHyipW1m5mWZOSkzJ40cObJ6xUuStkhjY/Lha6bw2ILlXHLmoew+clCtS5IkSZIkSZKkbaJqgVo5RtqPgKmZ+c1ms18KPJqZcyuWHwr8EfhkZt5RrbokSdXxf397jL88soD/etW+HLenP3iQJEmSJEmS1H1Us4Xa0cBZwEkRMaX8O6Wcdzqbdvd4HjAR+EzF8qOqWJ8kaRv5/b/mcdFN0zn98LG84+jxtS5HkiRJkiRJkrapqo2hlpn/AKKVeee0MO3zwOerVY8kqToenLuEj/zqXxwxfgf+59T9KRooS5IkSZIkSVL3UdUx1CRJ3duCZWt498/uY8Sgfnz3bYfSt86PFUmSJEmSJEndT9VaqEmSurc16xs49+eTWb6mnl+/98UMH9Sv1iVJkiRJkiRJUlUYqEmStlhm8olfP8i/5izh+2cdxj47D6l1SZIkSZIkSZJUNfbNJUnaYt+7dQa/nTKPj7x8T07eb6dalyNJkiRJkiRJVWWgJknaIn97ZAFfvfFRXnPQLrz/xIm1LkeSJEmSJEmSqs5ATZLUbtOeXs4Hr36AA0Zvz9fedCARUeuSJEmSJEmSJKnqDNQkSe3y7Mp1vOtn9zKwXx2XnTWJ/n1617okSZIkSZIkSeoQdbUuQJLU+a2rb+S9V0xmwbK1/PI/X8RO2/evdUmSJEmSJEmS1GFsoSZJalNmcuHvH+bumc/ytTcdyMFjh9a6JEmSJEmSJEnqUAZqkqQ2/fyuJ/nF3bN53wm7c+rBo2tdjiRJkiRJkiR1OAM1SVKr7pi+iM/9/hFeus+OfOTle9W6HEmSJEmSJEmqCQM1SVKLZi5ayfuuvJ+JIwfxrdMPplevqHVJkiRJkiRJklQTBmqSpE0sW7Oed11+L70Cfvj2SQzqV1frkiRJkiRJkiSpZrxCKknaSENjcv4vHuDJxau44l1HMnaHAbUuSZIkSZIkSZJqykBNkrSRL/9pKrc+tpAvvv4AjtpteK3LkSRJkiRJkqSas8tHSdIGv7pvDj+4fSbnvHg8Zx45rtblSJIkSZIkSVKnYKAmSQJg8pPP8unf/JtjJo7gv161T63LkSRJkiRJkqROw0BNksRTS1bznz+fzC5D+3PxmYdQ19uPB0mSJEmSJElq4hhqktTDrVpXz7suv4+19Y1cfe7hDB3Qt9YlSZIkSZIkSVKnYhMESerBGhuTC375L6Y9vYyLzjiEiaMG1bokSZIkSZIkSep0DNQkqQf71t8f50//fppPnbIPJ+w1qtblSJIkSZIkSVKnZKAmST3UHx+cz3f+/jinHTaGdx4zodblSJIkSZIkSVKn5RhqktSN1Tc0Mn/pGuY8u4rZFX9znlvN1HnLOGzXYXz+9fsTEbUuVZIkSZIkSZI6LQM1Serilq5av1FYNvvZVRsCtHlLVlPfmBuWresVjB62HeN2GMCZR47j/JMm0q+udw2rlyRJkiRJkqTOz0BNkjq5dfWNPLVk9YaQrHlrs+Vr6jdafoeBfRm7wwAOGjuU1xy0M+N2GMDYYQMYu8MAdt6+P3W97e1XkiRJkiRJkraEgZok1Vhmsnjlug1h2ZyNWpqtZv7S1VQ0MqNv716M2aFoZXbYrsOKwGxDaLYdg/v3qd2TkSRJkiRJkqRuyEBNkjrAmvUNzH2uDMoWr2L2s6uZ89zz4dmqdQ0bLT9qcD/G7jCAIybswNgdBjCu4m/U4H706uWYZ5IkSZIkSZLUUQzUJGkrNTQma9Y3sGpdw4Z/l65ev0nXjHOeW8WCZWs3Wne7Pr03tCx70e7DNwrMxgwbwHZ9HddMkiRJkiRJkjoLAzVJ3da6+kZWr29g9boGVq9vYNW6+g3BV9O01evK+xst1xSQ1bN6fSOr19U/P31dA6vKZdfWN7b62BGw85D+jN1hAMftMfL5bhnL0GzEoL5E2MpMkiRJkiRJkroCAzVpG2jqzm9dfZIUg11lxZhXTbeTrLjdNC833H9+nWy2XsXtiuWblml6TFpavsVtbVgD8vm6im1tPL9YLyvmbfo8NtTUbFubPF7l/Bb2S5Yb2fA45QL1jblx6FURaq1eX79pKFberq8ceKwdImBAn95s17f869Ob7frWsV2fXowa3L+8X0wf0Lc3/ct/n1+2N4P792HssO0YPWw7+tXZykySJEmSJEmSugMDNamdMpOnl61hxsKVzFi4gicWrmTGouL2U0tWbxSgqTr69I4NwdWAvnUbAq0BfesYPqjfpkFXRTg2YKOArOXl+tX1stWYJEmSJEmSJGkTBmpSMyvW1jNz4UpmLCpDs4UrmLloJTMXrWTVuoYNyw3o25vdRg7k0HHDeNNhYxg/fCD9+xQtkpoymYANAU1TTLNhXkBQseDmlic22u7G6206L6Klac+vWDx+xbIVNVXWWLn9yvnRyvaab2uT59zaY5X/iRa21bsM0vr07oUkSZIkSZIkSR3NQE09UkNj8tRzq3li0YoNLc5mlCHagmVrNywXAWOGbcduIwZxxIQd2G3kIHYfMZDdRg5ixyH9bM0kSZIkSZIkSVIPYKCmbm3pqvUthmazFq9iXX3jhuWG9K9jt5GDOHriCHYfOYjdytBs1+EDNrQ6kyRJkiRJkiRJPZOBmrq89Q2NPLl4VRGYLXq+i8YZC1eyeOW6DcvV9QrGDR/AbiMGceJeo9htZBGa7TZiIDsM7GtrM0mSJEmSJEmS1CIDNXUJmcmiFes2Cs2K1mYrmf3sKhoac8OyIwb1ZbcRg3jZvjsWodmIQew2ciBjdxjgGFySJEmSJEmSJGmLGaip03pswXIuu20Gjz+zghkLV7B8Tf2Gef3qejFhxED22XkwrzpgZyaMGLihxdn22/WpYdWSJEmSJEmSJKm7MVBTp/TryXP59G8fok/vXhw4Znted/DojbpoHD10O3r1sotGSZIkSZIkSZJUfQZq6lTWrG/gwusf5up753DUbjvwnTMOYdTg/rUuS5IkSVIHi4hXAN8GegM/zMwvN5v/VuDj5d0VwHsz818dW6UkSZKknsJATZ3GrEUred+V9/PI/GW8/8Td+fBL96TOMc8kSZKkHiciegOXAC8D5gL3RsT1mflIxWIzgeMz87mIeCVwGXBkx1crSZIkqScwUFOn8Od/z+ejv3qQ3r2Dn5xzOCfuParWJUmSJEmqnSOA6Zk5AyAirgZOBTYEapn5z4rl7wLGdGiFkiRJknoUAzXV1Lr6Rr78p0f58R0zOXjsUC5566GMHrpdrcuSJEmSVFujgTkV9+fSduuzdwJ/amlGRJwLnAswbty4bVWfJEmSpB7GQE01M2/Jas77xf3cP3sJ57x4PJ86ZR/61tnFoyRJkiSihWnZ4oIRJ1IEase0ND8zL6PoDpJJkya1uA1JkiRJ2hwDNdXELdOe4cPXTGF9Q3LJmYfyqgN3rnVJkiRJkjqPucDYivtjgHnNF4qIA4EfAq/MzMUdVJskSZKkHqhqzYEiYmxE3BwRUyPi4Yj4YDn9moiYUv7NiogpFet8MiKmR8S0iDi5WrWpdhoak2/+ZRrv+Om97DikP9efd7RhmiRJkqTm7gX2iIgJEdEXOB24vnKBiBgHXAeclZmP1aBGSZIkST1INVuo1QMXZOb9ETEYmBwRf83MtzQtEBHfAJaWt/el+J+k/YBdgL9FxJ6Z2VDFGtWBFi5fy4eueYA7pi/mtMPG8D+n7s92fXvXuixJkiRJnUxm1kfEecCNQG/gx5n5cES8p5z/PeC/geHApREBUJ+Zk2pVsyRJkqTurWqBWmbOB+aXt5dHxFSKgaUfAYji/3jeDJxUrnIqcHVmrgVmRsR04AjgzmrVqI5zz8xnOe8X97N09Xq++qYDefOksZtfSZIkSVKPlZk3ADc0m/a9itvvAt7V0XVJkiRJ6pk6ZAy1iBgPHALcXTH5WGBBZj5e3h8N3FUxf245TV1YY2Ny2e0z+NqN0xi3wwAu/48j2GfnIbUuS5IkSZIkSZIkqd2qHqhFxCDg18CHMnNZxawzgKsqF21h9Wxhe+cC5wKMGzduG1aqbW3pqvVc8Ksp/G3qM7zqgJ358hsPYHD/PrUuS5IkSZIkSZIkaYtUNVCLiD4UYdqVmXldxfQ64A3AYRWLzwUq+wEcA8xrvs3MvAy4DGDSpEmbBG7qHB6cu4T3XXk/C5at4cLX7MvbXzyeclwDSZIkSZIkSZKkLqVXtTZcjpH2I2BqZn6z2eyXAo9m5tyKadcDp0dEv4iYAOwB3FOt+lQdmcnP75zFm757J5nwy/98EeccPcEwTZIkSZIkSZIkdVnVbKF2NHAW8FBETCmnfaocWPp0Nu7ukcx8OCJ+CTwC1APvz8yGKtanbWzF2no+ed1D/P5f8zhxr5F8880HM2xg31qXJUmSJEmSJEmS9IJULVDLzH/Q8rhoZOY5rUz/AvCFatWk6pn29HLee+VkZi1ayUdP3ov3Hr87vXrZKk2SJEmSJEmSJHV9VR1DTT3DryfP5dO/fYhB/fpw5buO4kW7D691SZIkSZIkSZIkSduMgZq22pr1DVx4/cNcfe8cjtptB75zxiGMGty/1mVJkiRJkiRJkiRtUwZq2iqzFq3kfVfezyPzl/H+E3fnwy/dk7revWpdliRJkiRJkiRJ0jZnoKYt9ud/z+ejv3qQ3r2Dn5xzOCfuParWJUmSJEmSJEmSJFWNgZrabV19I1/+06P8+I6ZHDR2KJeceQhjhg2odVmSJEmSJEmSJElVZaCmdpm3ZDXn/eJ+7p+9hHNePJ5PnbIPfevs4lGSJEmSJEmSJHV/BmrarFumPcOHr5nC+obkkjMP5VUH7lzrkiRJkiRJkiRJkjqMgZpa1dCYfPtvj3HRzdPZa8fBXPrWQ9lt5KBalyVJkiRJkiRJktShDNTUooXL1/Khax7gjumLOe2wMfzPqfuzXd/etS5LkiRJkiRJkiSpwxmoaRP3zHyW835xP0tXr+erbzqQN08aW+uSJEmSJEmSJEmSasZATRs0NiaX3T6Dr904jXE7DODy/ziCfXYeUuuyJEmSJEmSJEmSaspATQAsXbWeC341hb9NfYZXHbAzX37jAQzu36fWZUmSJEmSJEmSJNWcgZp4cO4S3nfl/SxYtoYLX7Mvb3/xeCKi1mVJkiRJkiRJkiR1CgZqPVhmcsVdT/K/f5jKyMH9+OV/vohDxg2rdVmSJEmSJEmSJEmdioFaD7VibT2fvO4hfv+veZy410i++eaDGTawb63LkiRJkiRJkiRJ6nQM1HqgaU8v571XTmbWopV89OS9eO/xu9Orl108SpIkSZIkSZIktcRArYe57v65fOo3DzGoXx+ufNdRvGj34bUuSZIkSZIkSZIkqVMzUOsh1qxv4MLrH+bqe+dw5IQduOiMQxg1pH+ty5IkSZIkSZIkSer0DNR6gJmLVvK+K+9n6vxlvP/E3fnwS/ekrnevWpclSZIkSZIkSZLUJRiodXN/emg+H732Qep6Bz8553BO3HtUrUuSJEmSJEmSJEnqUgzUuql19Y186U9T+ckdszh47FAueeuhjB66Xa3LkiRJkiRJkiRJ6nIM1Lqhp5as5v1X3s+UOUs458Xj+dQp+9C3zi4eJUmSJEmSJEmStoaBWjdz87Rn+PA1U6hvSC5966GccsDOtS5JkiRJkiRJkiSpSzNQ6ybqGxr5v789xiU3P8HeOw3mu287jAkjBta6LEmSJEmSJEmSpC7PQK0beGb5Gj5w1QPcNeNZTj98LBe+dj/69+ld67IkSZIkSZIkSZK6BQO1Lu7OJxZz/lUPsGLter5+2kG86bAxtS5JkiRJkiRJkiSpWzFQ66IaG5Pv3voE3/jLNMaPGMiV7zqSvXYaXOuyJEmSJEmSJEmSuh0DtS7ouZXr+PAvp3DLtIW85qBd+NIbDmBQP19KSZIkSZIkSZKkajCF6WLun/0c5115P4tWrON/X7c/bztyHBFR67IkSZIkSZIkSZK6LQO1LiIz+ckds/jSn6ay45D+XPveF3HgmKG1LkuSJEmSJEmSJKnbM1DrApatWc/Hr32QP/37aV66z45847SD2H5An1qXJUmSJEmSJEmS1CMYqHVyD89byvuvvJ85z63mk6/cm3OP280uHiVJkiRJkiRJkjqQgVonlZlcc+8c/vv6hxk2oA9XvfsojpiwQ63LkiRJkiRJkiRJ6nEM1DqhVevq+a/f/pvr7n+KYyaO4FunH8yIQf1qXZYkSZIkSZIkSVKPZKDWyUx/ZgXvu3Iyjz+zgg++ZA8+8JI96N3LLh4lSZIkSZIkSZJqxUCtE/ndlKf45HUP0b9Pby5/xxEct+fIWpckSZIkSZIkSZLU4xmodQJr6xv43z88whV3zWbSrsO4+MxD2Wn7/rUuS5IkSZIkSZIkSRio1dzsxat4/y/u56GnlnLucbvx0ZP3ok/vXrUuS5IkSZIkSZIkSSUDtRr6y8NPc8Gv/gXAZWcdxsv326nGFUmSJEmSJEmSJKk5A7UaWN/QyNdunMZlt83ggNHbc8mZhzJu+IBalyVJkiRJkiRJkqQWGKh1sKeXruG8X9zPfU8+x9uOGsd/vWpf+vfpXeuyJEmSJEmSJEmS1AoDtQ50++ML+eDVU1izvoFvn34wpx48utYlSZIkSZIkSZIkaTMM1DpAQ2Pynb8/znduepw9Rg3i0rcexsRRg2pdliRJkiRJkiRJktrBQK3KFq1Yy4eunsI/pi/iDYeM5vOv358Bfd3tkiRJkiRJkiRJXUWvam04IsZGxM0RMTUiHo6ID1bMOz8ippXTv1pO6xMRl0fEQ+U6n6xWbR3l3lnP8qrv3M49s57ly284gG+8+SDDNEmSJEmSJEmSpC6mmulOPXBBZt4fEYOByRHxV2BH4FTgwMxcGxGjyuVPA/pl5gERMQB4JCKuysxZVayxKjKTy26bwVdvnMaYYdvxm/cdzn67bF/rsiRJkiRJkiRJkrQVqhaoZeZ8YH55e3lETAVGA+8GvpyZa8t5zzStAgyMiDpgO2AdsKxa9VXL0lXrueBX/+JvUxfwyv134itvOpAh/fvUuixJkiRJkiRJkiRtpap1+VgpIsYDhwB3A3sCx0bE3RFxa0QcXi52LbCSIoSbDXw9M59tYVvnRsR9EXHfwoULO6L8dnvo/7N332FWVOcDx7/vFnqHBaUJFpoNFY29gMYSjRpjojGabjTRaHqPpudnEhNbTExiotFYEnuPYm8o2AABxQqCNEWa9PP7Y2bxsuwuS9m9W76f57nP7j0zd+47556ZuzvvnHOmv8dHLnqEB6fM5idHDuNPJ+1qMk2SJEmSJEmSJKmJq/cJvSKiA3ADcHZKaUHeA60rsCewO3B9RGwN7AGsAnrnyx+JiPtSSq8Wbi+ldBlwGcCIESNSfcdfFyklrnryDX5++yR6dGjF9aftxa79uxY7LEmSJEmSJEmSJG0G9ZpQi4hysmTa1SmlG/Pi6cCNKaUEPBURq4EewKeAu1NKK4DZEfEYMAJ4tZpNNxrLV67mm/95ntuen8GBgyv4wyeG07V9q2KHJUmSJEmSJEmSpM2k3oZ8jIgA/g5MSimdX7DoZmBkvs4goBUwl2yYx5GRaU/Wg21yfcW3uZSXBq1KS/j2oYO5/DO7m0yTJEmSJEmSJElqZuqzh9o+wMnA+Ih4Li/7AXA5cHlETACWA59JKaWIuAT4BzABCOAfKaUX6jG+zSIi+N3xO5HlDyVJkiRJkiRJktTc1FtCLaX0KFlirDqfrmb9RcDx9RVPfTKZJkmSJEmSJEmS1HzV25CPkiRJkiRJkiRJUnNgQk2SJEmSJEmSJEmqhQk1SZIkSZIkSZIkqRYm1CRJkiRJkiRJkqRamFCTJEmSJEmSJEmSamFCTZIkSZIkSZIkSaqFCTVJkiRJkiRJkiSpFibUJEmSJEmSJEmSpFqYUJMkSZIkSZIkSZJqYUJNkiRJkiRJkiRJqoUJNUmSJEmSJEmSJKkWJtQkSZIkSZIkSZKkWphQkyRJkiQ1OhFxWERMiYipEfG9apZHRFyYL38hInYtRpySJEmSWgYTapIkSZKkRiUiSoFLgMOBYcCJETGsymqHA9vlj1OBSxs0SEmSJEktigk1SZIkSVJjswcwNaX0akppOXAtcHSVdY4GrkyZJ4EuEbFlQwcqSZIkqWUoK3YAm2LcuHFzI+KNYseR6wHMLXYQzZD1Wn+s2/phvdYf67Z+WK/1x7qtH9Zr/WksdbtVsQNQo9AHmFbwfDrwoTqs0weYWbhSRJxK1oMNYFlETNi8oUpraSznUjVPti/VJ9uX6pPtS/VpcEO9UZNOqKWUKoodQ6WIGJtSGlHsOJob67X+WLf1w3qtP9Zt/bBe6491Wz+s1/pj3aqRiWrK0kasQ0rpMuAysJ2r/tnGVJ9sX6pPti/VJ9uX6lNEjG2o93LIR0mSJElSYzMd6FfwvC8wYyPWkSRJkqTNwoSaJEmSJKmxeRrYLiIGRkQr4ATg1irr3AqcEpk9gfdSSjOrbkiSJEmSNocmPeRjI3NZsQNopqzX+mPd1g/rtf5Yt/XDeq0/1m39sF7rj3WrRiOltDIizgDuAUqBy1NKEyPitHz5n4E7gSOAqcAS4HN12LTtXPXNNqb6ZPtSfbJ9qT7ZvlSfGqx9RUrrDDEvSZIkSZIkSZIkKeeQj5IkSZIkSZIkSVItTKhJkiRJkiRJkiRJtTChVoOI6BcRD0TEpIiYGBFn5eXdIuLeiHg5/9k1Lz8kIsZFxPj858iCbe2Wl0+NiAsjIoq1X8W2EfW6R0Q8lz+ej4hjC7ZlvRbY0LoteF3/iFgUEd8qKLNucxvRZgdExPsF7fbPBduyXgtsTJuNiJ0i4ol8/fER0SYvt25zG9FmTypor89FxOqIGJ4vs14LbETdlkfEFXkdToqI7xdsy7rNbUS9toqIf+T193xEHFiwLeu1QC11e3z+fHVEjKjymu/n9TclIg4tKLdu1aRExGF5O54aEd+rZnnkbXlqRLwQEbsWI041TXVoXyfl7eqFiHg8InYuRpxqmtbXvgrW2z0iVkXExxsyPjV9dWljEXFg/v/hxIh4qKFjVNNVh+/IzhFxW/6/3MSIqMscuBIRcXlEzI6ICTUsb5i/71NKPqp5AFsCu+a/dwReAoYB5wHfy8u/B/xf/vsuQO/89x2Atwq29RSwFxDAXcDhxd6/JlSv7YCygtfOLnhuvW5C3Ra87gbgP8C3Csqs242sV2AAMKGGbVmvm1a3ZcALwM758+5AqXW7afVa5bU7Aq8WPLdeN6FugU8B1+a/twNeBwZYt5tcr18F/pH/3hMYB5RYrxtUt0OBwcCDwIiC9YcBzwOtgYHAK55nfTTFB1Cat9+tgVZ5ux5WZZ0j8rYcwJ7AmGLH7aNpPOrYvvYGuua/H2778lHXR13aV8F69wN3Ah8vdtw+ms6jjuewLsCLQP/8ec9ix+2jaTzq2L5+UPC/XQXwDtCq2LH7aPwPYH9gV2q+7togf9/bQ60GKaWZKaVn8t8XApOAPsDRwBX5alcAx+TrPJtSmpGXTwTaRETriNgS6JRSeiJln+yVla9piTaiXpeklFbm5W2ABGC9rmtD6xYgIo4BXiVrs5Vl1m2BjanX6liv69qIuv0w8EJK6fn8NfNSSqus27VtYps9EbgGbLPV2Yi6TUD7iCgD2gLLgQXW7do2ol6HAaPz9WcD84ER1uu6aqrblNKklNKUal5yNFkSeFlK6TVgKrCHdasmaA9gakrp1ZTScuBasvZd6GjgypR5EuiSt3VpfdbbvlJKj6eU3s2fPgn0beAY1XTV5fwFcCbZzbGzGzI4NQt1aWOfAm5MKb0Ja/7mluqiLu0rAR3zES86kCXUViKtR0rpYbL2UpMG+fvehFodRMQAsh5oY4BeKaWZkF2kILszuqrjgGdTSsvILghNL1g2PS9r8eparxHxoYiYCIwHTssTbNZrLepStxHRHvgu8NMqL7dua7AB54KBEfFsRDwUEfvlZdZrLepYt4OAFBH3RMQzEfGdvNy6rcFGfH99kjyhhvVaqzrW7X+BxcBM4E3gdymld7Bua1THen0eODoiyiJiILAb0A/rtVZV6rYmfYBpBc8r69C6VVNTU1ve0HWk6mxo2/kC2d3SUl2st31FRB/gWODPSBuuLuewQUDXiHgwsmltTmmw6NTU1aV9XUw2YsYMsmu9Z6WUVjdMeGrmGuTv+7LNvcHmJiI6kN31c3ZKacH6pouIiO2B/yPrSQFZF8Oq0mYNsgnakHpNKY0Bto+IocAVEVHZdXOdVesl2CZmA+r2p8AfUkqLqqxj3VZjA+p1JtmwCPMiYjfg5vy8YL3WYAPqtgzYF9gdWAKMjohxwIJq1m3xdbsR318fApaklCrHorbN1mAD6nYPYBXQG+gKPBIR92HdVmsD6vVysn/AxgJvAI+T3dFovdagat3Wtmo1ZamWcqmxqkubtV1rY9W57UTEQWQJtX3rNSI1J3VpX38EvpuP1lH/Eam5qUsbKyO7aW0U2UgbT0TEkymll+o7ODV5dWlfhwLPASOBbYB7I+KR9fyfItVFg/x9b0KtFhFRTnbx4eqU0o158ayI2DKlNDPvMji7YP2+wE3AKSmlV/Li6aw9vENfsgx8i7Wh9VoppTQpIhaTzVFnvVZjA+v2Q8DHI+I8svGxV0fE0vz11m2BDanXvGfqsvz3cRHxCtndXbbZamxgm50OPJRSmpu/9k6ysZOvwrpdy0aeZ0/gg95pYJut1gbW7aeAu1NKK4DZEfEYMAJ4BOt2LRt4nl0JfL3gtY8DLwPvYr2uo4a6rcl0st5+lSrr0POBmpqa2vKGriNVp05tJyJ2Av5GNufkvAaKTU1fXdrXCODaPJnWAzgiIlamlG5ukAjV1NX1O3JuSmkxsDgiHgZ2JpuPV6pNXdrX54Df5EPJT42I14AhZHM2S5uiQf6+d8jHGuTjuP4dmJRSOr9g0a3AZ/LfPwPckq/fBbgD+H5K6bHKlfMhihZGxJ75Nk+pfE1LtBH1OjCfe4aI2AoYDLxuva5rQ+s2pbRfSmlASmkA2R1uv0opXWzdrm0j2mxFRJTmv28NbAe8ar2ua0PrFrgH2Cki2uXnhQOAF63btW1EvRIRJcDxZOObA35/VWcj6vZNYGRk2pNNijvZul3bRpxn2+X1SUQcAqxMKXkuqEYtdVuTW4ETIpsHeCDZd9hT1q2aoKeB7fL/JVqR3TRya5V1bgVOyc/RewLvVQ4zK63HettXRPQHbgROtkeHNtB621dKaWDB//L/Bb5iMk0boC7fkbcA+0U2xHo7shuyJzVwnGqa6tK+3iTr/UhE9CK71vtqg0ap5qpB/r6PLBmsqiJiX7I7yMcDleO4/oBs3onrgf5kJ4DjU0rvRMSPgO+T3SFd6cMppdkRMQL4J1k36buAM1MLrfiNqNeTge8BK/L1f1b5h6L1urYNrdsqrz0XWJRS+l3+3LrNbUSbPQ74GdnwY6uAc1JKt+Xbsl4LbEybjYhPk51rE3BnSuk7ebl1m9vIej2Q7A6xPatsy3otsBHngw7AP4BhZEMP/COl9Nt8W9ZtbiPqdQBZgn018BbwhZTSG/m2rNcCtdRta+AioAKYDzyXUjo0f80Pgc+TfY+dnVK6Ky+3btWkRMQRZDeNlQKXp5R+GRGnAaSU/pwnhy8GDiMbSvpzKaWxxYpXTUsd2tffyOZWfyN/ycqU0oiiBKsmZ33tq8q6/wRuTyn9t6HjVNNVlzYWEd8m60m0GvhbSumPxYlWTU0dviN7k/1fsSXZ/8m/SSldVaRw1YRExDXAgWS9s2cB5wDl0LB/35tQkyRJkiRJkiRJkmrhkI+SJEmSJEmSJElSLUyoSZIkSZIkSZIkSbUwoSZJkiRJkiRJkiTVwoSaJEmSJEmSJEmSVAsTapIkSZIkSZIkSVItTKhJkoomMo9GxOEFZZ+IiLuLGZckSZIkSZIkFYqUUrFjkCS1YBGxA/AfYBegFHgOOCyl9MpGbKs0pbRq80YoSZIkSZIkqaUzoSZJKrqIOA9YDLTPf24F7AiUAeemlG6JiAHAv/J1AM5IKT0eEQcC5wAzgeEppWENG70kSZIkSZKk5s6EmiSp6CKiPfAMsBy4HZiYUroqIroAT5H1XkvA6pTS0ojYDrgmpTQiT6jdAeyQUnqtGPFLkiRJkiRJat7Kih2AJEkppcURcR2wCPgEcFREfCtf3AboD8wALo6I4cAqYFDBJp4ymSZJkiRJkiSpvphQkyQ1FqvzRwDHpZSmFC6MiHOBWcDOQAmwtGDx4gaKUZIkSZIkSVILVFLsACRJquIe4MyICICI2CUv7wzMTCmtBk4GSosUnyRJkiRJkqQWxoSaJKmx+TlQDrwQERPy5wB/Aj4TEU+SDfdorzRJkiRJkiRJDSJSSsWOQZIkSZIkSZIkSWq07KEmSZIkSZIkSZIk1cKEmiRJkiRJkiRJklQLE2qSJEmSJEmSJElSLUyoSZIkSZIkSZIkSbUwoSZJkiRJkiRJkiTVwoSaJEmSJEmSJEmSVAsTapIkSZIkSZIkSVItTKhJkiRJkiRJkiRJtTChJkmSJEmSJEmSJNXChJokSZIkSZIkSZJUCxNqkiRJkiRJkiRJUi1MqEmSJEmSJEmSJEm1MKEmSZIkSZIkSZIk1cKEmiRJkiRJkiRJklQLE2qSJEmSJEmSJElSLUyoSZIkSZIkSZIkSbUwoSZJkiRJkiRJkiTVwoSaJEmSJEmSJEmSVAsTapIkSZIkSZIkSVItTKhJkiRJkiRJkiRJtTChJkmSJEmSJEmSJNXChJokSZIkSZIkSZJUCxNqkiRJkiRJkiRJUi1MqEmSJEmSJEmSJEm1MKEmSZIkSZIkSZIk1cKEmiRJkiRJkiRJklQLE2qSJEmSJEmSJElSLUyoSZIkSZIkSZIkSbUwoSZJkiRJkiRJkiTVwoSaJEmSJEmSJEmSVAsTapIkSZIkSZIkSVItTKhJkiRJkiRJkiRJtTChJkmSJEmSJEmSJNXChJokSZIkqUWKiH9GxC/quO7rEXFwfcckNUYR0T8iFkVEabFjkQpFxH4RMaXYcWyqiHgwIr5Yw7IBEZEioqyh46pOfi7Yug7r1Rp3RJwbEVdt/ghbhrx+Xy/C+zaZzy0/rg4sdhy1yf++HNBQryum/HywbbHj2FQm1CRJkiRJ2ozyRF2KiI9WKf9jXv7ZKuUH5uXf2cD3OTciVuQXN+dHxOMRsVe+7LMR8WjBuq9HxPv5upWPiwvWTRFxfpXtH5OX/zN/XnlxtPL1r0fE9wrWHxARD0TEkoiYXDUBGRGfiog3ImJxRNwcEd0Klj0YEUvz7b4XEQ9HxI7V7OvC/PFSRFwcEVsWrNMqIv6bx5WqXkTLP5fl+XssjIhxEXFAwfKqddYtIm7K430jIj5VZXuj8v1cku/3VnX86JqclNKbKaUOKaVVm7qtiOgbETdExNz8sx5feExExBfyel0YEbMi4o6I6JgvK/wM34mIeyNiSL7sM/lnuiAipkfEeYUX8qscA+/m2+1XsHytBHtEtI6IX0fEm/nrXo6Ib0dEFKzzify4WxIRD1azr8PzmJbkP4dXWb5dRFwbEXPyuF+OiIsiom++/MCImF7NdtdKvkREx4g4P9/HxXnM/42IPap57QH58fGLKuW1HZ+tI+LyPMa3I+Ib63ywRZJSeiSlNLi+3yci7omCc3RE9Ikq5+2Csi3qO55iys8FrxY7jkJVju+38+O5Q7HjakjRSBJdEdEpsr933sw/j6n58x5Fjqvq3zCLIuL5Bo6hXhJK8cHfkZdUKX80qvzNWcs2inrjWKz9d2DlY68GfP8632RnQk2SJEmSpM3vJeAzlU/yC/vHA69Us+5ngHcK198A16WUOgAVwKPAjYUX/Ks4Kr8QWvk4o2DZK8AnY+2eBKfk+1FVl/w9TwR+EhGH5eXXAM8C3YEfAv+NiAqAiNge+AtwMtALWAL8qcp2z8i32x14EPhXNfvaEegGHAtsAYyLgqRaXgefBt6uoQ7Oy9+jM3ApWX3V1OvqEmB5Hu9JwKX5fpBfGLwR+HEez1jguhq206TF5u8V8y9gGrAV2Wd9CjArf68DgF8BJ+af9VDg+iqvr/wM+wKzgX/m5e2As4EewIeAUcC3qrz2qPy1W+bveVEtcf4n38YRQEeytnsqcEHBOu8AfwR+U/XFEdEKuAW4CugKXAHckpeTX1QdA8wAdkkpdQL2ITsW960lrqrv0xq4H9gROBLoRFZv1+axF65bnsc/pkr5+o7Pc4HtyD6zg4DvFBz3m6Qe2ld9eRg4oOD5/sDkaspeTinVdP5ZR2SaxPXZJvBZVR7fw4FdgO8XN5wPNIG62yzy89toYHvgMLLz0d7APGCdBH89xlFbfXcp+Dto58287c0qNqwX2mLglA1Yf7PaTPVyRpW/U58oQgzr1SRO2JIkSZKklim/mPDtiHgh7znx94joFRF3RdaD5b6I6Fqw/kcjYmJkPbYejIihBct2iYhn8tddB7Sp8l5HRsRz8UFvr502IfTbgH0KYjsMeIEqiZ6IaAd8HPgqsF1EjKiy/JS818i8iPhxTXcQp5RWkF2w34IsSbGh3gbGA4fm79uN7CLYrTW9IL/QMRHYISIGAbsC56SU3k8p3ZBv77h89ZOA21JKD6eUFpEloj4Wec+jKttdSZYMGFbD+65IKU0EPgnMAb6Zly9PKf0xpfQoUGtPqpTSauDfZMmwXlWXR0T7PPYfp5QW5du8lSzhAPAxYGJK6T8ppaVkCYedI+8ttaEiYo+IGJv3AJoVBb0FI2LfvD3Oj4hplXebR9Zr6Hd5L4BZEfHniGibLzswsl5a34yI2RExMyI+V7DNj0TEs/n7TYuIcwuWVd7F/4WIeBO4P6oM3RYRvSPi1sh6iU2NiC9twO7uDvwzpbQ4pbQypfRsSumugmVPpJSeBUgpvZNSuiKltLDqRlJKS8g+wx3y55fmvZWWp5TeAq4mS1CtI//M/ksNbSwiRgEfBo5LKU3I43ySLFn71TwZRkrpvpTS9WRJsaoOBMqAP6aUlqWULgQCGJkvPxd4LKX0jZTS9Hx7s/M2fG2Ntbeuk8mSi8fksa7K6/a/KaVzq6z7TeB/ZImgQus7Pk8Bfp5SejelNAn4K/DZDYhxLXlb+mpEvAy8nJfVeP6NiF3z9rowIv4TEddF3pMgqvTii4ih+bl/fv5d8NGCZf+MiEsi6524MCLGRMQ2dQz7YbJzeuW11P3IkqkjqpQ9nL/X3hHxdGS9MJ+OiL0L4ngwIn4ZEY+RJS/XGj4xIkrzY3tuRLwKfKSOMa4jIvaMrMdWaUHZsRHxQv77HhHxRF5fMyPr+duqYN3qPqs1vWxqO5cU+HxEzMi3/831xFp5rns+NnKovjyheQ9ZYm29246sd/KreZt4LSJOystLIuJHkX0Hz46IKyOic75snd6jUfD9HFlvsf9GxFURsQD4bGS9nv+R18W7EXFzwWtra//fjYi38vim5OenDRIR34uIV/JtvBgRx1bZ/0fzNvduXgeHFywfGBEP5a+9l+ymhZqcAvQHjk0pvZhSWp2f136eUroz3161x2gd2mpJwX7Mi4jrI+9JG9V8b21g/dT4nVbDZ9k5sr9HZ+afzS8q446IbfP6ei+yY/i6vPzhfJPPR9b76pMbEmMdzCe7weScGvZxm4i4P6+7uRFxdUR0yZf9i+xzuy2P7Tsb2cZrPZ9sjPUch9V+7hHx+YiYlLfneyIfQSAyf8i3815k/1vsEBGnkn0Pfiff/9tqi8mEmiRJkiSpsTsOOAQYBBwF3AX8gOyiTgnwNYDIkjrXkPVSqQDuJLs40Cr/h/5mst4x3ch6n1Qme4iIXYHLgS+TJaT+AtwaWe+PtUSW4Ji/npiXkiVgTsifnwJcWcO+LcrjuSdfr/J9hpH1EjmJrEdNZ6BPdW+Wx/lZYHpKae56YqvJlQXvfwJZ75plNbxfRMQ+ZHehP5v/fLVK4uP5vJz855qhlVJKr5D1/hpUzbZbke3zk7UFmw8/eAvZRewNkl/4OgV4jbx3VBWDgFUppcIeerXtz2KynkXbs3EuAC7IeyltQ94rKyL6k7X3i8ja9HDgufw1/5fHORzYlqxt/KRgm1vwQZv5AnBJfJDgXUy2/13ILtafHhHHVInpALKeTodWE+81wHSgN1lC+FeRX+Stw/HxZB7LCfn+FRoDHBoRP42Ifao7/ipFNpzbSWTtrzr7kyV8q3ttO7KEbE1t7BBgTEppWmFhSmkM2X7X5YL29sALKaVUUPYCH7SRg4Eb6rCd9TkYuCdvgzXKLyh+HvhZDbFWe3zmbaZ34XLWPhY21jFkPQmH1Xb+zc8HN5FdKO5G1vaOrW6DkfXAu40sadgTOBO4OiIKh4Q8EfgpWa/BqcAvC15/exQMY1vFU0BroLJHy/7Avfk2Cssezi/y3wFcmO/P+cAdEVF4s0Nlj8eOwBtV3utLZL0NdwFGkB1jGyVPBC/mg0QuwKfIktGQ3XzwdbLv073I2vZXqmzmGPLPqpq3qMu55CCyHo4fBr4X1dwUEhF9yOrsF2Sf87eAG+KDXs7fi4jb17e/+bp9gcPJPptatx3ZzRMXAofnvWL35oNz7Gfzx0FkSc8OwMV1iSF3NFnivgtZgv9fZD1ptydrn3/I46ut/Q8GzgB2z+M7FHh9A2Ko9ArZd2VnsvZ/Vazdu/tDwBSydnAe8PeINb3d/w2My5f9nNp70x8M3J0n5tdR2zFah7b6NbK2eADZOeldsp7khWr73qpNjd9puaqf5RXASrLv3l3I2nblMLw/z/evK9nNDhcBpJT2z5fvnPe+qo9e7b8EjqtyzqsUwK/J9nEo0I/sxg5SSicDb/LBSAbn1fH9qtZLXc4nG+qzrP84XPO55+efH5Dd+FQBPEL2+UL2Oe1P9rdTF7K/A+allC7L4z8v3/+jagvIhJokSZIkqbG7KKU0K+918gjZhe5nU0rLyC607pKv90ngjpTSvXmPrd8BbckukO0JlJP1FlmRUvov8HTBe3wJ+EtKaUzew+MKsmTSnlWDSSk9mlLqUoe4ryQbfqcz2T/7N1ezzmfIhjJcRXbR6MT8ghNkF3Vuy99vOVmyJFV5/Sfy5MU0YDeyi001uTm/a7jyUbVX0U3AgXm8NSUAAeaSDXX3N+B7KaXRZBc43quy3ntkF4upw3KAC/N9WUR2AfGntexLpRlkF0jr6lv5eywm613y41T9vGCbY382xApg24jokfeIq0z0nATcl1K6Jm+381JKz+UXOr8EfD3vxbWQbKjEE6ps82f56+4kq9fBACmlB1NK4/PeAy+QXWwqHLoO4Ny8p9P7hYWRzTu2L/DdlNLSlNJzZG3h5Hzb6zs+jic7jn8MvBZZr4zd89c+QnYRbFeyC+DzIpsbrHBYzsrPcCrZ5/DZqm8QWW+8EWTngEI3569dQJY0+20NMfYAZtawbCa199CotL420oOCHqsRcUZ+XC6KiL8WvKZ3leN2PmsPCVl1O8Pz9RZExJSC9S4k73G5gbF2KHhe3X5srF/nbfd9aj//7knW0+/CvC3fSJbcqs6eeby/yXsq3g/cTpZEq3RjSumplPWEvZqCXkwppSNTSusM35kvW0aW8N0/T5h1Sdk8Yo8UlA0DHiJLLL2cUvpX3rvxGrJegYUXaf+ZUpqYL19R5e0+QfZdNS2l9A7ZRfBNcQ15HUTW6/CIvIyU0riU0pN5HK+TJXOqngsKP6u11PFc8tP8XDIe+Adrfx6VPg3cmVK6M9/WvWRD6R6Rv89vUkpHrmc/b46IhWTfh7P5oKdOrdsGVpP1tG6bUpqZsh7QkJ1/z08pvZofN98HToi6Dyv3RErp5pT1iO5CluQ7Le/puSKl9FC+Xm3tfxVZIndYRJSnlF7PE94bJGW9qWfk+38dWW/DwiEY30gp/TX/PryC7CaeXvlND7uTnTuWpZQeJkuI1aQ7NZ87Yf3HaI1tlSzh+MOU0vT8eDwX+HiVz6Pa760q5hacT7+1vu+0XOFn2Ynsszw7f6/ZZMnRyu/fFWTD4/bOt/coDSRlvTP/TDU3TqSUpuZ/Hy9LKc0hS/RXPVY31Jp6SdnoBHU5n9TmwoLP5pm8rC7HYeHn/mWyc9ak/Dz/K2B4flPJCrLvriFA5OvU1l6rZUJNkiRJktTYFfYger+a55UXfHtTcKd/fuFjGlkPnd7AWymt1VuksFfAVsA3q1y07pe/bqPkF1EqgB8Bt9eQmDiI7KIuZL2t2vDB8F698/grt7eEbB6SQtenlLqklHqmlEamlMbVEtIx+bqVj8KL9uTx3ZHH2yOl9FgN2+mRUuqaUhqasmHsIEvWdKqyXidgYR2XA3wtT8S0Ieud8d9Y/7CbfciSe3X1u/w92pIlXH4bBUNbFdgc+wNAROyXJ0kWRUS1PabIepANAiZHNjxc5YXjflQ/714FWU+HcQXt9e68vNK8/GJSpSXkx0pEfCgiHoiIORHxHnAa6yaJplG93kBlEq/SG9TQe7Kq/GLy91JK25MNt/kc2YXwyJfflbK7w7uR3f3+WT648x/yzzCltEVK6aNVLy7nd6f/hqzHSdXemsfkn39rsqTtQxGxRTVhziW7oFydLfPl67O+NjKv8D1SShfnsf2RLPlfaUaV47YL2VyB1LCd5/J1Pka2n0TEUUDHVHOPiNpiXVTwvLr9WEtkw/FWtveTang/WLt91Xb+re7cXVvbnJaf+ytVbZuFw+6uOSbq6GGy3g378cFn8GhB2bSU0htU+S6qIY6a9gGqnPur2dYaEXFSQX3fVcNq/yYbwrM1Wbt4Jo+TiBiU98x7O7Jh235F3c8FG3MuqayfGvAmKQAAcCVJREFUqrYCjq8mcVzTcVidY1LWi+tAsgvmlXHUuO2U9ez8ZB73zMiGA60curfq5/gGWXJ3nWGCa1C43/3IzpvvVrNeje0/pTSVrNf9ucDsiLg2Ijb475LIho9+rmD7O7D257TmuMj/1oDs2OgNvJvW7gFbY3ukyvmoGus7Rmtsq2T1dFPBPkwiSzgWfh61HVeVehScT39H3b7Tqp6vysnaS2UsfyHrcQfwHbLeYE9FNqTl5+sQE5D1Sq/SDvoDLxSUfaoOm/k/sp5aa80PFxE98/bzVn6sX0Xdbg6pzVr1XcfzSW2+VvDZ7JqX1eU4rPr5XFBQh++QfR598gTuxWQ9G2dFxGURUfW7b71MqEmSJEmSmosZZP9IA9mwhGQXpd4iu2O6T+VF+1zhcHPTgF9WuXDdLmU9CzbFVWTzFlXX2+tksv/Lb4uIt4FXyZJJlcMuziQbLqhyf9qycfOjbYgryeL91wa+biKwdaw9J9rOfDDk3kQ+GBaNiNia7GJ/4ZCKQJYITVlPpalkw/NUK7J5i44i6yGyQVJmAvAY1c9P9BJQFhHbFZTVtj/tyYZqXCdhlrJ5vTrkj2qHyUspvZxSOpHsgtz/kSUT25O1y+rmeJpLlkzevqC9dk4p1TU58G+yIUn7pZQ6k93RHlXWqdobstIMoFuVz7o/2XG2QfKEV+UFzW5Vlq1OWe/H+8nnSVufiDiMbH6vo1LWG6am912Vsp5Oq1i7t1el+4AP5Unvwu3vQXZOqcv8PBOBnaqcc3bigzYymuyC8aYaDXw4by81GUU219fb+bnmk8DZEXFLQazVHp95AmBm4XLWPhbWklI6vKC9X13dOpWrFvxe2/m3unP3Wp9LgRlAv/hgTjPYyLZZg4fJEmf788F55zGyufr2z5dXxrFVlddWjaOm4wuyfS7cx6pDo36wkZSuLqjv6m4OIKX0ItlF6MNZewg9gEvJes9tl7IhZ39A3c8FULdzSdV9qW7OwWnAv6q0gfaphh6DtUlZz69/8kEP1Vq3nVK6J6V0CFkiaDLZOQTW/Rz7kw3zN4usl3O7ygWR9aItvKEB1m3j3SKfs6qafa/x74+U0r9TSvvmsSSy74g6y3vm/JXsJoLuecJ9Aut+TtWZCXStcn6psT2SnTsPreV8VOsxup62Oo3sRonCemqTstELKtXWVmtSl++0qp/lMtZOzHWq/H5PKb2dUvpSSqk3WW+pP0U+5+D6pJTerHLzxJvATgVl/17PJkgpzSO7MePnVRb9Ot+PnfJj/dOs3Qaq1t2GtnGo2/lkQ9V2HFYXxzTgy1XaSduU0uMAKaULU0q7kQ29Ogj4dg37UiMTapIkSZKk5uJ64CMRMSqyYRO/SXbR43HgCbJ/wL8WEWUR8THWHu7or8Bp+d32ERHtI+IjVS6wbIwLyYaWe7iaZaeQDWs4vOBxXL4P3cnmpTgqIvaObB6hn7LpFybW56E83os25EUpm2vsOeCciGgTEceSJQ8q54i6mmxf9ssvtP2MbOi1mnq57EU2fNo6F+0jojwihpINA7UF2bBFlctaR0Sb/GmrPJZq6yzvhbBvde+R341/I/CzvC3sQ9ZbqjLReBPZEGHH5e/3E7L5sibXUEW1iohPR0RFftf+/Lx4FVm9HRwRn8jbbfeIGJ6v91fgDxHRM99Gn4io67wxHcnuyF+aJ4nqctc7ACmbV+xx4Nd5/e5E1sOutuRJ4b7+X0TskO9PR+B0YGpKaV5EHB3Z3Gpd8+NwD7LhomqdTy/f7sg8huNSSjUNCVi5bkTE0WRz3EyqZh/vI0tU3RAR20dEaUTsmW//0pTSy/l2SvPPvwwoyeujsnfZg2Sf4dfydnlGXl6ZjDsX2C+yIS375NvrQTYPzIa4kuyi9015vVbGNKJgnR/zwXx7w8kSIH8FPpcvX9/xeSXwo/xzGUI2RN0/NzDO2tR2/n2CrB7PyNvM0ax97i40huwi8Hfy88SBZEn3azdTnI+TDd33afKEWp5wnJOXVZ7n7ySbf+5TecyfJDuf1WkOMLLvsq9FRN/I5rCraV63DfFvsjmo9iebs7NSR7IhUBfln+3pG7jdupxLfhwR7SJie7I2V11PyavI2uChlW04Ig6MbD60jfFH4JCIGF7btiOiV0R8NG/3y8h6ZFYOA3wN8PWIGBjZnI2/IhuieSXZTRdt8nZaTtazu8Y5H1M2rNxdZMmVrnn7rJxTq8b2HxGDI2JkZD22lpLdSFHdMMWVKs9DlY/WQHuyZMEcWDMkbp1uUsh7h40FfhrZfLT7svbQpVX9iyyhcUNEDImIkvx76wcRcQR1O0Zraqt/Bn4ZWYKQyObAO7ou+7Gefdyg77T8s/wf8PuI6JTv4zYRcUAe1/EF7fZdsrqv/Mxmkc0DVt/OJxvuvPD7pCNZ+56ff+d8u8prqsa2QW284D025XxSndqOw+r8Gfh+fr4hIjpHxPH577vnx1k5WTtcykZ8NibUJEmSJEnNQkppCtlFzYvIevAcRdZTZXnK5iD7GNnwce+S9dC4seC1Y8kuEl+cL59KNXMzwQdD+NUxpndSSqNTWmu4MvKL8wOAS/K7mSsft+bvfWLK5nE5k+xC00yyIdZmk1302xi3xQdDgy2KiJuqiTfl8W7IMIqVTiC7iP8u2ZB7H0/ZPB3k+3Ia2QWq2WQXXapOVH9xZWxkF+V+lFIqHMLsk/my+WQJgXnAbimlwt4OU8guOPYB7sl/L7yz+Tv5eywmuyD2D7KhmqrzFbKhIWeTXdA5Pd8P8v06Dvhlvr8fYu35yzbUYcDEfP8uAE5I2dwrb5LNIfNNsmGLnuODnkLfJWsrT0Y2tNJ95HOk1cFXyJKFC8mSgddvYLwnkrXfGWTJxXNSNi9RXY6Pdvlr5pP1ytwK+Gi+7F2y4/BlsotyVwG/XU9Pp0o/BjoDd0bNQ+Ddlse2gOyz+0z6YL6kqo4DHiAbSnNRHsvfyY7JSieTtbFLyXouvU/euyU/5xxDljifD3yebEi65fnyl8jmE+oLPJ9/Fo+R1emP67C/5NtZSjZ07ItkQ7YuIDsOdiebi4uU0sLC80we5+LK47wOx+c5ZEOPvkGWdP9tSunuusZYh32o8fxbcO7+Alk9fposMbXOeTBf96NkvVvmAn8CTqlrojuy4Sp/UEucS4BxZBeVJxQseoSsd+nD+XrzyIat/SbZeeo7wJFp3SFIa/JXsvPX88AzFHxXbYJryIZCvL9KHN8iS4ItzN+3pmFBa1KXc8lDZJ/paLIhW/9XdYU8qXE0WY+WOWRJmW+TX7vOEzI1DWm5jvwcfSXZ3F+1bbuE7HOaQXaOPYAP2v7lZN9FDwOvkV18PzPf/nv5en8j68m0GJi+nrBOJpvDaTLZcXZ2vq3a/v5oTfZ9OpdsWMae+X7U5ESy47vy8Ure6+v3ZMnpWcCOZOeauvoU2XfcO2TngprmV62ca/DgfB/vJTsfPUU27N+YOh6jNbXVC8i++/+Xt7cn87g2hxq/02pwCtCK7Lz7LtkNUJVDXe4OjMm/a24FzkopvZYvOxe4IrLhCD+xmWJfR0ppAXAea/f8/inZ/KTvkX1XVD2v/Jrsxon5EfGtjWzjm3o+qU6Nx2F1Uko3kfXivDb/22gCWXuDbLjiv5J9Zm+QnZ8re7L+nWyuwvkRcXNtAUWVv+klSZIkSVIjlN+ZO59sKJ3X1rO6JDVLETEG+HNK6R/FjkUqlogYADyYUhpQ5FAarYh4EDg3pfRgkUOpUUS8DhyYUnq9IV6nTWcPNUmSJEmSGqmIOCofKqs92V2044HXixuVJDWciDggIraIbPjEz5ANJ7vZeshJklRXDZJQi4jLI2J2REyoYXlExIURMTUiXoiIXRsiLkmSJEmSGrmjyYYgmgFsRzYUoEPNSGpJBpMNf/ge2fB8H8/nMZJasvlkc8WpZv+k8d+E9Ec+mDu1IV6nTdQgQz7mkywuAq5MKa0z6WE+KeGZZOOCfwi4IKW0ucZAlSRJkiRJkiRJkjZag/RQSyk9TDZxYU2OJku2pZTSk0CXiNiylvUlSZIkSZIkSZKkBtFY5lDrA0wreD49L5MkSZIkSZIkSZKKqqzYAeSimrJqx6KMiFOBUwHat2+/25AhQ+ozLkmSJEkNaNy4cXNTShXFjkPNU48ePdKAAQOKHYYkSZKkzaQh/4dsLAm16UC/gud9ySZcXkdK6TLgMoARI0aksWPH1n90kiRJkhpERLxR7BjUfA0YMAD/h5QkSZKaj4b8H7KxDPl4K3BKZPYE3kspzSx2UJIkSZIkSZIkSVKD9FCLiGuAA4EeETEdOAcoB0gp/Rm4EzgCmAosAT7XEHFJkiRJkiRJkiRJ69MgCbWU0onrWZ6ArzZELJIkSZIkSZIkSdKGaCxzqEmSJEmSJEmSJKmFWrFiBdOnT2fp0qXrLGvTpg19+/alvLy8CJFlTKhJkiRJkiRJkiSpqKZPn07Hjh0ZMGAAEbGmPKXEvHnzmD59OgMHDixafCVFe2dJkiRJkiRJkiQJWLp0Kd27d18rmQYQEXTv3r3anmsNyYSaJEmSJEmSJEmSiq5qMm195Q3JhJokSZIkSZIkSZJUCxNqkiRJkiRJkiRJUi1MqEmSJEmSJEmSJKnoUkobVN6QTKhJkiRJkiRJkiSpqNq0acO8efPWSZ6llJg3bx5t2rQpUmSZsqK+uyRJkiRJkiRJklq8vn37Mn36dObMmbPOsjZt2tC3b98iRPUBE2qSJEmSpAYTEW2Ah4HWZP+T/jeldE5EXAcMzlfrAsxPKQ0veF1/4EXg3JTS7/Ky3YB/Am2BO4GzUmMYC0aSJEnSBisvL2fgwIHFDqNGJtQkSZIkSQ1pGTAypbQoIsqBRyPirpTSJytXiIjfA+9Ved0fgLuqlF0KnAo8SZZQO6yadSRJkiRpkzmHmiRJkiSpwaTMovxpef5Y06ssIgL4BHBNQdkxwKvAxIKyLYFOKaUn8l5pVwLH1Hf8kiRJklomE2qSJEmSpAYVEaUR8RwwG7g3pTSmYPF+wKyU0sv5uu2B7wI/rbKZPsD0gufT87JG74Zx0/nP2GnMW7Ss2KFIkiRJqiOHfJQkSZIkNaiU0ipgeER0AW6KiB1SShPyxSdS0DuNLJH2h3yIyMLNrPWkctNVCyLiVLJhIenfv/9miH7TXff0NJ56/R0iYNf+XRk1tCcHD+3Fdj07UGUfJUmSJDUSJtQkSZIkSUWRUpofEQ+SzX02ISLKgI8BuxWs9iHg4xFxHtAFWB0RS4EbgL4F6/UFZlTzHpcBlwGMGDFinYRbMVz35T2Z8NYC7ps0i9GTZ3He3VM47+4p9OvWloOH9uLgob3YY2A3yksdVEaSJElqLEyoSZIkSZIaTERUACvyZFpb4GDg//LFBwOTU0prhnJMKe1X8NpzgUUppYvz5wsjYk9gDHAKcFHD7MWmiQh27NuZHft25uuHDGLme+8zetJsRk+axdVj3uQfj71Ox9Zl7D+4gkOG9uLAwRV0adeq2GFLkiRJLZoJNUmSJElSQ9oSuCIiSsnm9b4+pXR7vuwE1h7ucX1OB/4JtAXuyh9Nzpad2/LpPbfi03tuxZLlK3n05blZgm3ybO54YSalJcFuW3Xl4KE9GTW0F9tUdCh2yJIkSVKLEyk1ihEvNsqIESPS2LFjix2GJEmSpM0kIsallEYUOw41T03tf8jVqxPPT5/P6EmzuW/SLCa/vRCArXu0Z1SeXBuxVVfKHBpSkiRJLVRD/g9pDzVJkiRJkhqhkpJgl/5d2aV/V7516GCmv7tkTXLtn4+/zl8feY3Obcs5aHAFo4b24oDBFXRqU17ssCVJkqRmyYSaJEmSJElNQN+u7fjM3gP4zN4DWLRsJY+8NIf7Js3mgSmzufm5GZSVBHsM7Maoob04eGhPturevtghS5IkSc2GCTVJkiRJkpqYDq3LOHzHLTl8xy1ZtTrx7Jvvct+k2YyeNIuf3/4iP7/9Rbbr2WFNcm2X/l0pLYlihy1JkiQ1WSbUJEmSJElqwkpLghEDujFiQDe+d/gQ3pi3eM3QkH975FX+/NArdGvfioMG9+TgoT3Zb1AFHVp7OUCSJEnaEP4FLUmSJElSM7JV9/Z8ft+BfH7fgbz3/goefmkOoyfN4r5Js7jhmem0Ki3hQ1t34+ChvRg1tCd9u7YrdsiSJElSo2dCTZIkSZKkZqpz23KO2rk3R+3cm5WrVjP2jXcZPWkWoyfN5pxbJ3LOrRMZskXHNcm1nft2ocShISVJkqR1mFCTJEmSJKkFKCstYc+tu7Pn1t354UeG8eqcRYyeNJt7J83i0ode4eIHptKjQ2tGDenJqKE92Xe7HrRr5WUDSZIkCUyoSZIkSZLUIm1d0YGtKzrwpf23Zv6S5Tw4ZQ73TZrFneNnct3YabQuK2Hvbbpz4OCe7NyvC0O26Eib8tJihy1JkiQVhQk1SZIkSZJauC7tWnHMLn04Zpc+LF+5mqdff4f78qEhH5gyEYCykmBQr47s2KczO/btzE59OzN4i460LjPJJkmSpObPhJokSZIkSVqjVVkJ+2zbg3227cFPjhzGjPeWMn76e4x/az7j31rA/158m+vGTgOgvDQYvEWWZNuhT2d26tOFwVt0pFVZSZH3QpIkSdq8TKhJkiRJkqRqRQR9urSlT5e2HLbDFgCklJj+7vtMeOs9xuePuya8zTVPfZBkG7JFpyzB1rczO/bpzKBeJtkkSZLUtJlQkyRJkiRJdRYR9OvWjn7d2nH4jlsCHyTZxr/1Hi9Mf48Jb73HHS/M4Jqn3gSgVWkJQ7bMh4vMh4wc1Ksj5aUm2SRJktQ0mFCTJEmSJEmbpDDJdkRBkm3aO+/zwlvzs55s09/j1udncPWYPMlWVsLQLTuxY59O7NSnCzv06cx2vTqYZJMkSVKjZEJNkiRJkiRtdhFB/+7t6N+9HUfu1BuA1asTb76zZM1QkeOnv8ctz87gqiezJFvrPMm2U9/Oa4aM3LaiA2Um2SRJklRkJtQkSZIkSVKDKCkJBvRoz4Ae7Tlq5w+SbG+8s4QXps9nQj5k5I3PvMWVT7wBQJvyEoZt2SkfKrILO/bpzDYV7U2ySZIkqUGZUJMkSZIkSUVTUhIM7NGegT3ac/TwPkCWZHtt3uI1Cbbxb73Hf8dN54qCJNv2vTuvNSfbNhUdKC2JYu6KJEmSmjETapIkSZIkqVEpKQm2qejANhUd1iTZVq1OvDZ3MePfms/46QsY/9Z8rh87jX8+/joA7VqVslPfzvzgiKHs1LdL8YKXJElSs2RCTZIkSZIkNXqlJcG2PTuwbc8OHLtLVrZqdeLVOYsYn/dku3vC2xx36eN897AhfH6fgZTYY02SJEmbiQOOS5IkSZKkJqm0JNiuV0c+tmtfzv3o9tx99n4cNLgnv7hjEp+/4mnmLVpW7BAlSZLUTJhQkyRJkiRJzUKXdq34y8m78fOjt+fxV+Zx+AWP8PjUucUOS5IkSc2ACTVJkiRJktRsRAQn7zWAm7+yDx3alHHS38fw+/9NYeWq1cUOTZIkSU2YCTVJkiRJktTsDOvdidvP3Jfjd+vLRfdP5YTLnuSt+e8XOyxJkiQ1USbUJEmSJElSs9SuVRnnfXxnLjhhOJPfXsgRFzzC3RPeLnZYkiRJaoJMqEmSJEmSpGbt6OF9uP3MfenfrR2nXTWOn9wygaUrVhU7LEmSJDUhJtQkSZIkSVKzN6BHe244fW++tN9ArnziDY655DGmzl5U7LAkSZLURJhQkyRJkiRJLUKrshJ++JFh/OOzuzN74TKOuuhRrh87jZRSsUOTJElSI2dCTZIkSZIktSgHDenJXWftx/B+XfjOf1/g7OueY+HSFcUOS5IkSY2YCTVJkiRJktTi9OrUhqu++CG+ecggbnt+Bkde9CgvTJ9f7LAkSZLUSJlQkyRJkiRJLVJpSXDmqO247st7sWLlao679HH+9sirrF7tEJCSJElamwk1SZIkSZLUou0+oBt3nrUfBw3uyS/umMTnr3iaeYuWFTssSZIkNSIm1CRJkiRJUovXpV0r/nLybvzs6O15/JV5HH7BIzw+dW6xw5IkSVIjYUJNkiRJkiQJiAhO2WsAN39lHzq0KeOkv4/h9/+bwspVq4sdmiRJkorMhJokSZIkSVKBYb07cfuZ+/LxXfty0f1TOeGyJ3lr/vvFDkuSJElFZEJNkiRJkiSpinatyvjt8Tvzx08OZ9LMBRxxwSPcPeHtYoclSZKkIjGhJkmSJEmSVINjdunDHV/bj/7d2nHaVeP4yS0TWLpiVbHDkiRJUgMzoSZJkiRJklSLAT3ac8Ppe/PFfQdy5RNvcMwljzF19qJihyVJkqQGZEJNkiRJkiRpPVqVlfCjI4dx+WdHMHvhMo666FGuHzuNlFKxQ5MkSVIDMKEmSZIkSZJURyOH9OLOr+3Hzv06853/vsDZ1z3HwqUrih2WJEmS6pkJNUmSJEmSpA2wRec2XP3FPfnmIYO47fkZHHnRo7wwfX6xw5IkSVI9MqEmSZIkSZK0gUpLgjNHbce1p+7F8pWrOe7Sx/nbI6+yerVDQEqSJDVHJtQkSZIkSZI20h4Du3HXWftx4OCe/OKOSXzhiqeZt2hZscOSJEnSZmZCTZIkSZIkaRN0adeKy07ejZ8dvT2PTZ3H4Rc8wuOvzC12WJIkSdqMTKhJkiRJkiRtoojglL0GcNNX96ZDmzJO+tsYzv/fFFauWl3s0CRJkrQZmFCTJEmSJEnaTLbv3ZnbztiX43bty4X3T+XEvz7JW/PfL3ZYkiRJ2kQm1CRJkiRJDSYi2kTEUxHxfERMjIif5uXXRcRz+eP1iHguLz8kIsZFxPj858iCbe2Wl0+NiAsjIoq0W9Ja2rcu43fH78wfPzmcF2cs4IgLHuGeiW8XOyxJkiRtAhNqkiRJkqSGtAwYmVLaGRgOHBYRe6aUPplSGp5SGg7cANyYrz8XOCqltCPwGeBfBdu6FDgV2C5/HNYwuyDVzTG79OH2r+1Hv25t+fK/xnHOLRNYumJVscOSJEnSRjChJkmSJElqMCmzKH9anj9S5fK8l9kngGvy9Z9NKc3IF08E2kRE64jYEuiUUnoipZSAK4FjGmg3pDob2KM9N5y+N1/YdyBXPPEGx/7pcabOXrT+F0qSJKlRMaEmSZIkSWpQEVGaD+k4G7g3pTSmYPF+wKyU0svVvPQ44NmU0jKgDzC9YNn0vKzqe50aEWMjYuycOXM22z5IG6J1WSk/PnIYl392BLMWLOWoix7lP2OnkeWCJUmS1BSYUJMkSZIkNaiU0qp8aMe+wB4RsUPB4hPJe6cViojtgf8DvlxZVN2mq3mvy1JKI1JKIyoqKjY5dmlTjBzSizu/th879+vMt//7Apc8MLXYIUmSJKmOTKhJkiRJkooipTQfeJB87rOIKAM+BlxXuF5E9AVuAk5JKb2SF08nS8hV6gvMQGrktujchqu/uCdHD+/N7+99iUdetuekJElSU2BCTZIkSZLUYCKiIiK65L+3BQ4GJueLDwYmp5SmF6zfBbgD+H5K6bHK8pTSTGBhROyZz7t2CnBLg+yEtIlKS4Jff2xHtuvZgbOufY6Z771f7JAkSZK0HibUJEmSJEkNaUvggYh4AXiabA612/NlJ7DucI9nANsCP46I5/JHz3zZ6cDfgKnAK8Bd9R69tJm0a1XGpZ/ejWUrVvHVq59h+crVxQ5JkiRJtSgrdgCSJEmSpJYjpfQCsEsNyz5bTdkvgF/UsP5YYIfqlklNwTYVHTjv4zvz1X8/w6/vmsQ5R21f7JAkSZJUgwbroRYRh0XElIiYGhHfq2Z554i4LSKej4iJEfG5hopNkiRJkiSpGD6y05Z8bp8B/OOx17njhZnFDkeSJEk1aJCEWkSUApcAhwPDgBMjYliV1b4KvJhS2hk4EPh9RLRqiPgkSZIkSZKK5fuHD2XX/l34zn+f55U5i4odjiRJkqrRUD3U9gCmppReTSktB64Fjq6yTgI65pNJdwDeAVY2UHySJEmSJElF0aqshIs/tSuty0s5/apxLFnu5RBJkqTGpqESan2AaQXPp+dlhS4GhgIzgPHAWSklZ+SVJEmSJEnNXu8ubbnghOG8PHsRP7ppAimlYockSZKkAg2VUItqyqr+ZXgo8BzQGxgOXBwRndbZUMSpETE2IsbOmTNnc8cpSZIkSZJUFPttV8HZowZx47Nv8e+n3ix2OJIkSSrQUAm16UC/gud9yXqiFfoccGPKTAVeA4ZU3VBK6bKU0oiU0oiKiop6C1iSJEmSJKmhnTlyW/YfVMFPb32R8dPfK3Y4kiRJyjVUQu1pYLuIGBgRrYATgFurrPMmMAogInoBg4FXGyg+SZIkSZKkoispCf74yeH06NCK068ex/wly4sdkiRJkmighFpKaSVwBnAPMAm4PqU0MSJOi4jT8tV+DuwdEeOB0cB3U0pzGyI+SZIkSZKkxqJb+1ZcctKuzFqwlG9c/zyrVzufmiRJUrGVNdQbpZTuBO6sUvbngt9nAB9uqHgkSZIkSZIaq136d+VHHxnGObdO5NKHXuGrB21b7JAkSZJatIYa8lGSJEmSJEkb4JS9tuKonXvz+/9N4fFXHMRHkiSpmEyoSZIkSZIkNUIRwW8+tiNbV3Tga9c8y9vvLS12SJIkSS2WCTVJkiRJkqRGqn3rMi49aVeWLF/Fmdc8w4pVq4sdkiRJUotkQk2SJEmSJKkR265XR379sR15+vV3Oe/uycUOR5IkqUUyoSZJkiRJktTIHT28D6fstRV/feQ17p4ws9jhSJIktTgm1CRJkiRJkpqAH35kKDv368K3//MCr81dXOxwJEmSWhQTapIkSZIkSU1A67JSLvnULpSWBqdfNY73l68qdkiSJEkthgk1SZIkSZKkJqJv13b84ZPDmTJrIT+5ZUKxw5EkSWoxTKhJkiRJkiQ1IQcN7smZB23Lf8ZN57qn3yx2OJIkSS2CCTVJkiRJkqQm5qyDB7Hvtj348S0TmfDWe8UOR5IkqdkzoSZJkiRJktTElJYEF5wwnG7tWvGVq5/hvfdXFDskSZKkZs2EmiRJkiRJUhPUvUNrLjlpF2bMf59v/ed5UkrFDkmSJKnZMqEmSZIkSZLURO22VTe+f8RQ7n1xFn95+NVihyNJktRsmVCTJEmSJElqwj6/zwA+suOW/PaeKTz56rxihyNJktQsmVCTJEmSJElqwiKC3xy3I1t1a8eZ1zzL7AVLix2SJElSs2NCTZIkSZIkqYnr2KacP316VxYuXcGZ1zzLylWrix2SJElSs2JCTZIkSZIkqRkYskUnfnXsjox57R1+97+Xih2OJElSs2JCTZIkSZIkqZn42K59OXGP/vz5oVe498VZxQ5HkiSp2TChJkmSJEmS1Iycc9QwdujTiW9c/xxvzltS7HAkSZKaBRNqkiRJkiRJzUib8lIuPWk3Ajj96nEsXbGq2CFJkiQ1eSbUJEmSJEmSmpl+3drxh08OZ+KMBZx768RihyNJktTkmVCTJEmSJElqhkYN7cVXDtyGa5+exn/GTit2OJIkSU2aCTVJkiRJkqRm6huHDGKvrbvzo5snMGnmgmKHI0mS1GSZUJMkSZIkSWqmykpLuPDEXejctpzTrxrHgqUrih2SJElSk2RCTZIkSZIkqRmr6Niaiz+1K9PefZ/v/OcFUkrFDkmSJKnJMaEmSZIkSZLUzO0xsBvfPWwwd098m78/+lqxw5EkSWpyTKhJkiRJkiS1AF/ab2sO3b4Xv75rMk+//k6xw5EkSWpSTKhJkiRJkiS1ABHBb4/fmb5d23LGv59h7qJlxQ5JkiSpyTChJkmSJEmS1EJ0alPOpSftxvwlK/jaNc+yarXzqUmSJNWFCTVJkiRJkqQWZFjvTvz8mB14/JV5/OHel4odjiRJUpNgQk2SJEmSJKmF+cSIfnxiRF8ufmAq90+eVexwJEmSGj0TapIkSZIkSS3Qz47egWFbduLr1z3PtHeWFDscSZKkRs2EmiRJkiRJUgvUpryUSz+9K6tT4itXP8OylauKHZIkSVKjZUJNkiRJkiSphdqqe3t+d/zOjH/rPX5224vFDkeSJKnRMqEmSZIkSWowEdEmIp6KiOcjYmJE/DQvvy4inssfr0fEcwWv+X5ETI2IKRFxaEH5bhExPl92YUREEXZJavIO3X4Lvrz/1lw95k1uenZ6scORJElqlMqKHYAkSZIkqUVZBoxMKS2KiHLg0Yi4K6X0ycoVIuL3wHv578OAE4Dtgd7AfRExKKW0CrgUOBV4ErgTOAy4q0H3Rmomvn3oYJ6dNp8f3DiBYVt2ZvAWHYsdkiRJUqNiDzVJkiRJUoNJmUX50/L8kSqX573MPgFckxcdDVybUlqWUnoNmArsERFbAp1SSk+klBJwJXBMA+2G1OyUlZZw8Ym70L51GadfPY5Fy1YWOyRJkqRGxYSaJEmSJKlBRURpPqTjbODelNKYgsX7AbNSSi/nz/sA0wqWT8/L+uS/Vy2XtJF6dmrDRSfuwutzF/PdG14gy1VLkiQJTKhJkiRJkhpYSmlVSmk40Jest9kOBYtP5IPeaQDVzYuWailfS0ScGhFjI2LsnDlzNiFqqWXYa5vufOvQwdzxwkz++fjrxQ5HkiSp0TChJkmSJEkqipTSfOBBsrnPiIgy4GPAdQWrTQf6FTzvC8zIy/tWU171PS5LKY1IKY2oqKjYnOFLzdZp+2/DwUN78ss7JjHujXeLHY4kSVKjYEJNkiRJktRgIqIiIrrkv7cFDgYm54sPBianlAqHcrwVOCEiWkfEQGA74KmU0kxgYUTsmc+7dgpwS0Pth9SclZQEvz9+OFt2acMZ/36GeYuWFTskSZKkojOhJkmSJElqSFsCD0TEC8DTZHOo3Z4vO4G1h3skpTQRuB54Ebgb+GpKaVW++HTgb8BU4BXgrvoPX2oZOrcr59KTdmPe4uWcfd1zrFrtfGqSJKlli6Y8weyIESPS2LFjix2GJEmSpM0kIsallEYUOw41T/4PKW24a556k+/fOJ7dturKgO7t6dGxFT3at6Z7h1b06PDBz27tW1Fe6n3bkiSpYTXk/5BlDfEmkiRJkiRJanpO2L0fsxYsZfSk2TzxylzmLlrO8lWrq123S7tyurfPEmyFybbuHVrRvX1rehQ879C6jGy0VkmSpKbBhJokSZIkSZKqFRGcffAgzj54EAApJRYuW8m8RcuZt2gZcxctY+6i5cxbtJy5i5Yxb3H2fPLbC5i7aDnvvb+i2u22LispSLZVJtrWTrp1b9+aHh1b0a1dK8rs/SZJkorMhJokSZIkSZLqJCLo1KacTm3KGdij/XrXX75yNe8uWc6chcuYt/iDJFyWgMuScHMWLWPy2wuZu2gZK1ZVPzVJ13blaxJu3Tu0pkdBEq6yJ1zlsvatSu39JkmSNjsTapIkSZIkSaoXrcpK6NWpDb06tVnvuiklFixdybxFWfJt7sJlzK2ShJu3aDmTZixg7qJlLFi6svr3LC2hfetS2rUqo0PrMtq3LqV968rfqylr9cHztdbLy+0dJ0mSwISaJEmSJEmSGoGIoHPbcjq3LWfrivWvv2zlKt5Z/MFwk3PzYSjfXbKCxctWsnjZShYtW8ni5StZuHQlb7+3tKBsFatWV98brqrWZSVrkmxZwq3mZF3l8vatCss+SNa1b1VGaYm95yRJaopMqEmSJEmSJKnJaV1Wypad27Jl57Yb/NqUEstWrs6Sa5VJtmWr1vy+ZPlKFuXPP1j+Qdm7S5Yz7d0lLF62kiXLVrFo+UpS3fJztC0vXZN469S2nH5d2zGgRzsGdG/PgB7tGdC9PT06tHLYSkmSGhkTapIkSZIkSWpRIoI25aW0KS+lR4fWm7y9lBLvr1i1TmKuarJu8fJ1E3MTZrzH3RPfXqvHXIfWZWzVvTLJ1o6turdnYI/2bNW9HRUdWptskySpCEyoSZIkSZIkSZsgImjXqox2rcqg44a/fsWq1Ux/931en7eY1+cu5o15S3ht7mImVpNsa9+qdE2CzWSbJEkNx4SaJEmSJEmSVETlpSUM7JElxhi89rIVq1bz1rvv89q8xbwxdzGvz1vC6/PWn2zbqnu7NUNIDuhhsk2SpE1lQk2SJEmSJElqpMpLS7LEWC3JtsqebZXJthdnLuCeiW+z0mSbJEmbjQk1SZIkSZIkqQlaX7Jtxvz3ea1gCMk31pNsG9Ajn7ete/s84daOio4m2yRJAhNqkiRJkiRJUrNTXlrCVt3bs1X39ussW7lqNW/Nfz/r0TZ38Zpk26SZC/nfxFlrJdvarenZ1o7tenZkyBYdGbxFR7bq3p7SEhNtkqSWw4SaJEmSJEmS1IKUFSTbDhhUsdayqsm2yuEkX5yxgLsnvE1lrq1NeQnb9cySa5VJtiFbdKKiY+si7JEkSfXPhJokSZIkSZIkoPZk29IVq3h51iImv72AyW8vZMrbC3lwyhz+O276mnW6t2/F4C0KE22dGNSrA+1aeRlSktS0+U0mSZIkSZIkab3alJeyY9/O7Ni381rl8xYtY8rbC9ck2SbPWsi1T03j/RWrAIiA/t3aMbjXB0m2wVt0ZED3dpSVlhRjVyRJ2mAm1CRJkiRJkiRttO4dWrP3tq3Ze9sea8pWr05Me3cJk99eyOSZC5kyK+vVdt+kWWuGjWxdVsJ2vTowuFengmEjO1LRsTURzs8mSWpcTKhJkiRJkiRJ2qxKSmLN0JGHbr/FmvKlK1YxdfaivDdblmR75OU53PDMB8NGdm1XvmZOtsrhIwf36kj71l7KlCQVj99CkiRJkiRJkhpEm/JSdujTmR36rD1s5DuLl+fDRi5YM3zk9WOnsWT5qjXr9O/WrmButuzngO7tHTZSktQgTKhJkiRJkiRJKqpu7Vux1zbd2Wub7mvKVq9OTH/3/Q+SbLOyOdrunzybVfm4ka3KSti2osOaJNvgLToydMtO9HTYSEnSZmZCTZIkSZIkSVKjU1IS9O/ejv7d2/HhKsNGvjJnUT43W9ab7bFX5nLjs2+tWWe3rbrykyOHsXO/LkWIXJLUHJlQkyRJkiRJktRktCkvZfvendm+99rDRs5fspzJby/khenzuezh1zj6ksf4+G59+c6hg+nZqU2RopUkNRcm1CRJkiRJkiQ1eV3atWLPrbuz59bdOXGP/lzywCtc/uhr3DV+Jl8duS2f32cgbcpLix2mJKmJcsZOSZIkSZIkSc1KxzblfO/wIfzv6/uz97Y9OO/uKXz4Dw9z94S3SSkVOzxJUhNkQk2SJEmSJElSszSgR3v+esoIrvrCh2hTXsJpV43jpL+NYfLbC4odmiSpiTGhJkmSJEmSJKlZ23e7Htz5tf34+dHb8+LMBRxxwSP86ObxvLN4ebFDkyQ1ESbUJEmSJEmSJDV7ZaUlnLzXAB781oGcstcArnlqGgf+9gH+8dhrrFi1utjhSZIaORNqkiRJkiRJklqMLu1ace5Ht+eus/Zj535d+OltL3L4BY/w0Etzih2aJKkRM6EmSZIkSZIkqcUZ1KsjV35+D/52yghWrlrNZy5/ii/882lenbOo2KFJkhqhBkuoRcRhETElIqZGxPdqWOfAiHguIiZGxEMNFZskSZIkSZKkliciOHhYL+75+v784IghjHntHQ7948P88o4XWbB0RbHDkyQ1Ig2SUIuIUuAS4HBgGHBiRAyrsk4X4E/AR1NK2wPHN0RskiRJkiRJklq21mWlnLr/NjzwrQM5bte+/O3R1zjotw9yzVNvsmp1KnZ4kqRGoKF6qO0BTE0pvZpSWg5cCxxdZZ1PATemlN4ESCnNbqDYJEmSJEmSJImKjq35zXE7cdsZ+7J1RXu+f+N4jrroUca8Oq/YoUmSiqyhEmp9gGkFz6fnZYUGAV0j4sGIGBcRpzRQbJIkSZIkSZK0xg59OnP9l/fiohN3Yf6S5Xzysif56tXPMP3dJcUOTZJUJGUN9D5RTVnVvtJlwG7AKKAt8EREPJlSemmtDUWcCpwK0L9//3oIVZIkSZIkSVJLFxEctXNvDh7ai8sefpVLH5rKfZNm8eX9t+a0A7ehXauGurQqSWoMGqqH2nSgX8HzvsCMata5O6W0OKU0F3gY2LnqhlJKl6WURqSURlRUVNRbwJIkSZIkSZLUtlUpZx28Hfd/80AO22ELLrx/KiN/9xA3P/sWKTm/miS1FA2VUHsa2C4iBkZEK+AE4NYq69wC7BcRZRHRDvgQMKmB4pMkSZIkSZKkGvXu0pYLTtiFG07fi56dWnP2dc9x3KWP8/y0+cUOTZLUABokoZZSWgmcAdxDliS7PqU0MSJOi4jT8nUmAXcDLwBPAX9LKU1oiPgkSZIkSZIkqS5226obN39lH3778Z148533OfqSx/jm9c8ze8HSYocmSapH0ZS7JY8YMSKNHTu22GFIkiRJ2kwiYlxKaUSx41Dz5P+QkqTNbeHSFVzywCtc/uhrlJcGXzloW76w70DalJcWOzRJahEa8n/IhhryUZIkSZIkSZKalY5tyvne4UO49xv7s8+2PfjtPVM45A8PcfeEt51fTZKaGRNqkiRJkiRJkrQJturenstOGcHVX/wQ7crLOO2qcXzqr2OYNHNBsUOTJG0mJtQkSZIkSZIkaTPYZ9se3PG1ffn50dsz6e0FfOTCR/jRzeN5Z/HyYocmSdpEJtQkSZIkSZIkaTMpKy3h5L0G8OC3DuSUvQZwzVPTOPC3D3D5o6+xYtXqYocnSdpIJtQkSZIkSZIkaTPr0q4V5350e+4+az927teFn93+Iof98WEenDK72KFJkjaCCTVJkiRJkiRJqifb9erIlZ/fg79/ZgSrVic++4+n+fw/n+bVOYuKHZokaQOYUJMkSZIkSZKkehQRjBrai/99/QB+cMQQnn7tHT78h4f55R0vsmDpimKHJ0mqAxNqkiRJkqQGExFtIuKpiHg+IiZGxE8Llp0ZEVPy8vPysvKIuCIixkfEpIj4fsH6u+XlUyPiwoiIYuyTJEl11aqshFP334b7v3UgH9+tL3979DUO+u2DXPPUm6xanYodniSpFibUJEmSJEkNaRkwMqW0MzAcOCwi9oyIg4CjgZ1SStsDv8vXPx5onVLaEdgN+HJEDMiXXQqcCmyXPw5rsL2QJGkTVHRszW+O24nbztiXrSva8/0bx3PURY8y5tV5xQ5NklQDE2qSJEmSpAaTMpWTxpTnjwScDvwmpbQsX2925UuA9hFRBrQFlgMLImJLoFNK6YmUUgKuBI5puD2RJGnT7dCnM9d/eS8u/tQuvPf+Cj552ZN89epnmPbOkmKHJkmqwoSaJEmSJKlBRURpRDwHzAbuTSmNAQYB+0XEmIh4KCJ2z1f/L7AYmAm8CfwupfQO0AeYXrDZ6XlZ1fc6NSLGRsTYOXPm1N9OSZK0kSKCI3fqzehvHsA3DhnE6MmzGHX+Q/z+f1NYsnxlscOTJOVMqEmSJEmSGlRKaVVKaTjQF9gjInYAyoCuwJ7At4Hr8znR9gBWAb2BgcA3I2JroLr50taZfCaldFlKaURKaURFRUW97I8kSZtDm/JSvjZqO+7/5oEcvsMWXHT/VEb+7iFufvYtss7YkqRiMqEmSZIkSdooEdE1IvaIiP0rHxvy+pTSfOBBsrnPpgM35kNCPgWsBnoAnwLuTimtyIeBfAwYka/ft2BzfYEZm7pPkiQVW+8ubbnghF244fS96NmpNWdf9xwfu/Rxnps2v9ihSVKLZkJNkiRJkrTBIuKLwMPAPcBP85/n1uF1FRHRJf+9LXAwMBm4GRiZlw8CWgFzyYZ5HBmZ9mQ92CanlGYCCyNiz7wn2ynALZtxFyVJKqrdturGzV/Zh99+fCemv/s+x1zyGN+8/nlmLVha7NAkqUUyoSZJkiRJ2hhnAbsDb6SUDgJ2AeoySdmWwAMR8QLwNNkcarcDlwNbR8QE4FrgMykb3+oSoAMwIV//HymlF/JtnQ78DZgKvALctbl2TpKkxqCkJDh+RD8e+NaBnH7gNtz2/AwO+t2DXPLAVJauWFXs8CSpRSkrdgCSJEmSpCZpaUppaUQQEa1TSpMjYvD6XpQnw3appnw58OlqyhcBx9ewrbHADhseuiRJTUuH1mV897AhnLB7P3515yR+e88Urn36TX54xFAO3X4Lss7akqT6ZA81SZIkSdLGmJ4P3XgzcG9E3IJzmEmSVK+26t6ev5w8gqu/+CHalZdx2lXP8Km/jmHSzAXFDk2Smj0TapIkSZKkDZZSOjalND+ldC7wY+DvwDFFDUqSpBZin217cMfX9uXnx+zApLcX8JELH+GHN43nncXLix2aJDVbJtQkSZIkSRslIkojojfwGvAcsEVxI5IkqeUoKy3h5D234sFvHcgpew3g2qenceBvH+DyR19jxarVxQ5PkpodE2qSJEmSpA0WEWcCs4B7gTvyx+1FDUqSpBaoS7tWnPvR7bn7rP3YuV8Xfnb7ixz2x4d5cMrsYocmSc2KCTVJkiRJ0sY4CxicUto+pbRj/tip2EFJktRSbderI1d+fg/+/pkRrE7w2X88zef/+TSvzllU7NAkqVkwoSZJkiRJ2hjTgPeKHYQkSfpARDBqaC/uOXt/fnjEUJ5+7R0+/IeH+cXtL/Le+yuKHZ4kNWllxQ5AkiRJktR0RMQ38l9fBR6MiDuAZZXLU0rnFyUwSZK0RquyEr60/9Ycu2sffv+/Kfz9sde46dm3+OaHB/PJ3ftRWhLFDlGSmhx7qEmSJEmSNkTH/PEm2fxprQrKOhYxLkmSVEWPDq359cd24rYz9mWbig784KbxHHnRozz56rxihyZJTY491CRJkiRJdZZS+mmxY5AkSRtmhz6due7Le3LH+Jn8+s7JnHDZkxyx4xZ8//Ch9OvWrtjhSVKTYA81SZIkSZIkSWrmIoIjd+rN6G8ewDcOGcQDk+cw6vyH+P3/prBk+cpihydJjZ4JNUmSJEmSJElqIdqUl/K1Udtx/7cO4IgdtuCi+6cy8ncPcdOz01m9OhU7PElqtEyoSZIkSZIkSVILs2XntvzxhF244fS96NmpNV+/7nmO+/PjPDdtfrFDk6RGyTnUJEmSJEkbLCIqgC8BAyj43zKl9PlixSRJkjbcblt14+av7MONz77F/909mWMueYyP7dqH7x42hF6d2hQ7PElqNEyoSZIkSZI2xi3AI8B9wKoixyJJkjZBSUnw8d36ctgOW3DJA1P5+yOvcfeEt/nqQdvyhX0H0qa8tNghSlLRmVCTJEmSJG2Mdiml7xY7CEmStPl0aF3Gdw8bwom79+eXd77Ib++ZwjVPvcmPPjKUQ7ffgogodoiSVDTOoSZJkiRJ2hi3R8QRxQ5CkiRtfv27t+MvJ4/g31/8EO1blXHaVc9w4l+f5MUZC4odmiQVjQk1SZIkSdLGOIssqbY0IhbmD6+ySZLUjOy9bQ/u+Nq+/PyYHZjy9kKOvOgRfnnHiyxZvrLYoUlSgzOhJkmSJEnaYCmljimlkpRSm/z3jimlTsWOS5IkbV5lpSWcvOdWPPitgzhhj/789ZHXOOT8h3lgyuxihyZJDcqEmiRJkiRpo0TERyPid/njyGLHI0mS6k/nduX86tgd+c9pe9G2VSmf+8fTfO2aZ5mzcFmxQ5OkBmFCTZIkSZK0wSLiN2TDPr6YP87KyyRJUjO2+4Bu3PG1ffn6wYO4e8LbHHz+Q1z/9DRSSsUOTZLqlQk1SZIkSdLGOAI4JKV0eUrpcuCwvEySJDVzrctKOevg7bjzrP0Y3Ksj37nhBU7865O8OmdRsUOTpHpjQk2SJEmStLG6FPzeuVhBSJKk4ti2ZweuPXVPfvOxHXlxxgIOu+ARLhr9MstXri52aJK02ZlQkyRJkiRtjF8Dz0bEPyPiCmAc8KsixyRJkhpYSUlwwh79ue+bB/DhYb34/b0v8ZELH2HcG+8UOzRJ2qxMqEmSJEmSNlhK6RpgT+DG/LFXSuna4kYlSZKKpWfHNlz8qV25/LMjWLJ8Fcdd+gQ/unk8C5auKHZokrRZmFCTJEmSJNVZRAzJf+4KbAlMB6YBvfMySZLUgo0c0ov/fX1/vrDvQP495k0O/v1D3D1hJimlYocmSZukrNgBSJIkSZKalG8CXwJ+X82yBIxs2HAkSVJj0751GT8+chhHD+/N924Yz2lXPcMhw3rxs6O3Z8vObYsdniRtFBNqkiRJkqQ6Syl9Kf95ULFjkSRJjdtOfbtw6xn7cPljr3H+vS9x8O8f4juHDeHTe25FaUkUOzxJ2iAm1CRJkiRJdRYRH6tteUrpxoaKRZIkNX5lpSWcuv82HL7Dlvzw5gmcc+tEbnr2LX79sR0ZumWnYocnSXVmQk2SJEmStCGOqmVZAkyoSZKkdfTr1o4rPrc7tz4/g5/d9iJHXfQoX9p/a84atR1tykuLHZ4krZcJNUmSJElSnaWUPlfsGCRJUtMUERw9vA/7b1fBr+6cxKUPvsKd42fyy2N2ZN/tehQ7PEmqlQk1SZIkSVKdRcQ3alueUjq/oWKRJElNU9f2rfjt8Ttz7K59+MGN4/n038fwsV378KOPDKNb+1bFDk+SqlVS7AAkSZIkSU1Kx/U8JEmS6mTvbXpw99n7c8ZB23LrczM4+PyHuOnZ6aSUih2aJK3DHmqSJEmSpDpLKf202DFIkqTmo015Kd86dDBH7dyb7934Al+/7nlufOYtfnHMDmzVvX2xw5OkNUyoSZIkSZLqLCK+k1I6LyIuAta5fTyl9LUihCVJkpq4wVt05IbT9ubqMW/wf3dP4cN/eJizDx7EF/cbSHmpA61JKj4TapIkSZKkDTEp/zm2qFFIkqRmp6QkOHmvARwybAvOuXUC/3f3ZG557i1+c9xODO/XpdjhSWrhTKhJkiRJkuospXRb/vOKYsciSZKapy06t+EvJ4/g7glvc86tEzj2T4/xmb0G8K1DB9OhtZe0JRWHZx9JkiRJ0gaLiBHAD4GtKPjfMqW0U9GCkiRJzcphO2zBPtt257f3TOGKJ17nnolv8/Ojd+DgYb2KHZqkFsjBZyVJkiRJG+Nq4B/AccBRBQ9JkqTNpmObcn529A7ccPredGpTzhevHMtXrh7H7AVLix2apBbGhJokSZIkaWPMSSndmlJ6LaX0RuWj2EFJkqTmadf+XbntzH359qGDuW/SbEad/xBXj3mD1atTsUOT1EKYUJMkSZIkbYxzIuJvEXFiRHys8lHsoCRJUvPVqqyErx60LfecvT879unMD2+awCf+8gQvz1pY7NAktQAm1CRJkiRJG+NzwHDgMD4Y7vHIYgYkSZJahoE92nP1Fz/Ebz++E1PnLOKICx/h/HtfYumKVcUOTVIzVrb+VSRJkiRJWsfOKaUdix2EJElqmSKC40f0Y+SQnvzijklcOPplbn9hBr86dkf23Lp7scOT1AzZQ02SJEmStDGejIhhxQ5CkiS1bN07tOYPnxzOlZ/fgxWrVnPCZU/yvRte4L0lK4odmqRmxoSaJEmSJGlj7As8FxFTIuKFiBgfES+s70UR0SYinoqI5yNiYkT8tGDZmfn2JkbEeQXlO0XEE3n5+Ihok5fvlj+fGhEXRkTUy55KkqRGb/9BFfzv7AP48gFb859x0xl1/kPc9vwMUkrFDk1SM+GQj5IkSZKkjXHYRr5uGTAypbQoIsqBRyPiLqAtcDSwU0ppWUT0BIiIMuAq4OSU0vMR0R2ovOX8UuBU4EngzjymuzZ6jyRJUpPWtlUp3z98KB/duTffv3E8Z17zLDc+M52fH7MDfbu2K3Z4kpo4e6hJkiRJkjZYSumN6h51eF1KKS3Kn5bnjwScDvwmpbQsX292vs6HgRdSSs/n5fNSSqsiYkugU0rpiZTden4lcMxm3UlJktQkbd+7Mzd9ZR9+cuQwxrz2Doec/zB/e+RVVq5aXezQJDVhJtQkSZIkSQ0qIkoj4jlgNnBvSmkMMAjYLyLGRMRDEbF7vvogIEXEPRHxTER8Jy/vA0wv2Oz0vEySJInSkuDz+w7k3m8cwF7bdOcXd0zi2D89zoS33it2aJKaKBNqkiRJkqQGlVJalVIaDvQF9oiIHcimJOgK7Al8G7g+nxOtjGy+tpPyn8dGxCiguvnS1pkkJSJOjYixETF2zpw59bI/kiSp8erTpS1//8wILv7ULsx8bylHX/IYv7pzEkuWryx2aJKaGBNqkiRJkqSiSCnNBx4km/tsOnBjPiTkU8BqoEde/lBKaW5KaQnZXGm75uV9CzbXF5hRzXtcllIakVIaUVFRUZ+7I0mSGqmI4MidejP6GwfwiRH9uOzhV/nwHx7mwSmz1/9iScqZUJMkSZIkNZiIqIiILvnvbYGDgcnAzcDIvHwQ0AqYC9wD7BQR7SKiDDgAeDGlNBNYGBF75j3ZTgFuaeDdkSRJTUjnduX8+mM7cv2X96J1WQmf/cfTnHXts8xdtKzYoUlqAsqKHYAkSZIkqUXZErgiIkrJbvK8PqV0e0S0Ai6PiAnAcuAzKaUEvBsR5wNPkw3peGdK6Y58W6cD/wTaAnflD0mSpFrtMbAbd561H5c++Ap/euAVHpwyhx9+ZCjH79aX7D4dSVpXZP+fNE0jRoxIY8eOLXYYkiRJkjaTiBiXUhpR7DjUPPk/pCRJqmrq7IV8/8bxPP36u+y5dTd+deyObF3RodhhSaqjhvwf0iEfJUmSJEmSJEkt0rY9O3LdqXvx64/tyMQZCzjsgke4aPTLLF+5utihSWpkTKhJkiRJkiRJklqskpLgxD36M/obB3DIsF78/t6XOPKiRxj3xjvFDk1SI2JCTZIkSZIkSZLU4vXs1IZLPrUrf//MCBYtXcnH//wEP7p5PAuWrih2aJIagQZLqEXEYRExJSKmRsT3allv94hYFREfb6jYJEmSJEmSJEkCGDW0F/d+4wA+t/dA/j3mTQ45/yHunvB2scOSVGQNklCLiFLgEuBwYBhwYkQMq2G9/wPuaYi4JEmSJEmSJEmqqn3rMn5y1DBu+so+dGvfmtOuGseXrhzLzPfeL3ZokoqkoXqo7QFMTSm9mlJaDlwLHF3NemcCNwCzGyguSZIkSZIkSZKqtXO/Ltx6xj58//AhPPLyHA45/2GuePx1Vq1OxQ5NUgNrqIRaH2BawfPpedkaEdEHOBb4cwPFJEmSJEmSJElSrcpLS/jyAdvwv7MPYJf+XTjn1okcd+njTH57QbFDk9SAGiqhFtWUVU3h/xH4bkppVa0bijg1IsZGxNg5c+ZsrvgkSZIkSZIkSapR/+7tuPLze/DHTw7nzXeWcOSFj3Le3ZNZuqLWS9qSmomGSqhNB/oVPO8LzKiyzgjg2oh4Hfg48KeIOKbqhlJKl6WURqSURlRUVNRTuJIkSZIkSZIkrS0iOGaXPoz+xgEcs0sf/vTgKxz6x4d5bOrcYocmqZ41VELtaWC7iBgYEa2AE4BbC1dIKQ1MKQ1IKQ0A/gt8JaV0cwPFJ0mSJEmSJElSnXRt34rfHb8z//7ihwjgpL+N4ZvXP887i5cXOzRJ9aRBEmoppZXAGcA9wCTg+pTSxIg4LSJOa4gYJEmSJEmSJEnanPbetgd3n70/Xz1oG2557i0OPv8hbnp2OilVnfFIUlMXTfnAHjFiRBo7dmyxw5AkSZK0mUTEuJTSiGLHoebJ/yElSVJ9mvz2Ar53w3iemzaf/bbrwS+P2ZH+3dsVOyypWWvI/yEbashHSZIkSZIkSZKarSFbdOKG0/fmZ0dvz7NvzufDf3yIPz/0CitWrS52aJI2AxNqkiRJkiRJkiRtBqUlwSl7DeDeb+zP/ttV8Ju7JvPRix/j+Wnzix2apE1kQk2SJEmSJEmSpM1oy85tueyUEfz507vxzuJlHPunx/jpbRNZtGxlsUOTtJFMqEmSJEmSJEmSVA8O22EL7v3GAZz0oa345+Ov8+HzH2L0pFnFDkvSRjChJkmSJEmSJElSPenUppyfH7MD/z1tLzq0KeMLV4zlq1c/w+wFS4sdmqQNYEJNkiRJkiRJkqR6tttW3bj9zP341ocHce+kWYw6/yH+PeZNVq9OxQ5NUh2YUJMkSZIkSZIkqQG0KivhjJHbcfdZ+7F970784KbxfPKyJ3j05bksX7m62OFJqkVZsQOQJEmSJEmSJKkl2bqiA9d8aU/+M246v7pzEp/++xg6ti5j/0EVjBzSkwMHV9C9Q+tihympgAk1SZIkSZIkSZIaWETwiRH9OHKnLXls6jxGT5rF6MmzuWP8TCJg1/5dGTmkJ6OG9mRwr45ERLFDllo0E2qSJEmSJEmSJBVJu1ZlHDKsF4cM68Xq1YmJMxZw36RZ3D95Nr+9Zwq/vWcKfbq0ZdTQnowc0pM9t+5Om/LSYocttTgm1CRJkiRJkiRJagRKSoId+3Zmx76d+fohg5i1YCn3T57N6EmzuX7sNK584g3atSpl3217MGpoTw4a0pOeHdsUO2ypRTChJkmSJEmSJElSI9SrUxtO3KM/J+7Rn6UrVvHEK/MYPXkWoyfN5n8vzgJg576dGTW0FyOH9GT73p0cGlKqJybUJEmSJEmSJElq5NqUl3LQkKxX2s+PTkyauZD7J8/ivkmz+cN9L3H+vS+xRac2jBzak1FDerLPtj0cGlLajEyoSZIkSZIkSZLUhEQEw3p3YljvTpwxcjvmLFzGg1OyoSFvefYt/j3mTdqUl7DPNj3yBFsvtujs0JDSpjChJkmSJEmSJElSE1bRsTXHj+jH8SP6sWzlKsa8+g73T57NfZNmMXrybH7IBLbv3YlRQ3oyamgvduzTmZISh4aUNoQJNUmSJEmSJEmSmonWZaXsP6iC/QdVcM5Rw3h59iJGT5rN/ZNncfEDU7nw/qlUdGzNyME9GTm0J/tu24P2rU0VSOvjUSJJkiRJkiRJUjMUEQzq1ZFBvTpy+oHb8M7i5Tz0UjY05J0TZnLd2Gm0Kithr627M2poT0YO6Unfru2KHbbUKJlQkyRJkiRJkiSpBejWvhXH7tKXY3fpy4pVq3n69Xe4f9JsRk+ezU9umchPbpnIkC06MnJIT0YN7cnwfl0pdWhICTChJkmSJEmSJElSi1NeWsLe2/Rg72168KMjh/HqnGxoyNGTZ/GXh1/lTw++Qrf2rThwcAWjhvRi/0E96NimvNhhS0VjQk2SJEmSJEmSpBZu64oObF3RgS/tvzXvvb+Ch16aw/2TZjF60mxufOYtykuDPQZ2Y9SQXowa2pOturcvdshSgzKhJkmSJEmSJEmS1ujctpyP7tybj+7cm5WrVvPMm/MZPXkW90+azc9uf5Gf3f4i21S05+ChvRg5pCe7bdWVstKSYoct1SsTapIkSZIkSZIkqVplpSXsMbAbewzsxvcPH8ob8xZz/+TZ3D95Npc/9hp/efhVOrct58DBFYwc0pMDB/WkczuHhlTzY0JNkiRJkiRJkiTVyVbd2/O5fQbyuX0GsnDpCh59eS73TZrNA1Nmc8tzMygtCUZs1ZVRQ3syamgvtu7RnogodtjSJjOhJkmSJEmSJEmSNljHNuUcvuOWHL7jlqxanXh++nxG5/Ou/erOyfzqzskM6N6OUUN7MWpIT3Yf2I1yh4ZUE2VCTZIkSZIkSZIkbZLSkmDX/l3ZtX9Xvn3oEN6a/z73T5rF6Mmz+deTb/D3R1+jY+sy9h9cwaghPTlwcE+6tW9V7LClOjOhJkmSJEmSJEmSNqs+Xdpy8l4DOHmvASxZvpJHX57L/ZNnM3rybO54YSYlAbv278rIoT0ZNaQXg3p1cGhINWom1CRJkiRJkiRJUr1p16qMD2+/BR/efgtWr05MmPEeoyfNZvTkWZx39xTOu3sKfbu2ZdSQnowc2os9t+5G67LSYoctrcWEmiRJkiRJkiRJahAlJcFOfbuwU98ufP2QQbz93lIemDKb0ZNmcd3YaVzxxBu0a1XKftv1YNTQXhw0uCcVHVsXO2zJhJokSZIkSZIkSSqOLTq34cQ9+nPiHv1ZumIVT7wyj/smzeL+ybO5Z+IsAHbu14WDh/Rk5NCeDNuyk0NDqihMqEmSJEmSJEmSpKJrU17KQUN6ctCQnqSUmDRzIaMnzWL05Nmcf99L/P7el9iycxtGDunJqKE92XubHrQpd2hINQwTapIkSZIkSZIkqVGJCIb17sSw3p04c9R2zFm4jAemzOb+SbO5+dm3uHrMm7QpL2HfbXswckgvRg3tSa9ObYodtpoxE2qSJEmSJEmSJKlRq+jYmk+M6McnRvRj2cpVjHn1He6fPJv7Js3ivkmz4SbYoU8nRuXJtR16d6akxKEhtfmYUJMkSZIkNZiIaAM8DLQm+5/0vymlc/JlZwJnACuBO1JK3yl4XX/gReDclNLv8rLdgH8CbYE7gbNSSqnh9kaSJEnF0LqslP0HVbD/oArOOWoYL89exOhJsxk9aRYX3f8yF4x+mZ4dW3PUzr35/uFDKCstKXbIagZMqEmSJEmSGtIyYGRKaVFElAOPRsRdZEmxo4GdUkrLIqJnldf9AbirStmlwKnAk2QJtcOqWUeSJEnNWEQwqFdHBvXqyOkHbsM7i5fz0Euz+d/EWfz90ddYuWo1Pz16h2KHqWbAtKwkSZIkqcGkzKL8aXn+SMDpwG9SSsvy9WZXviYijgFeBSYWlG0JdEopPZH3SrsSOKYh9kGSJEmNV7f2rTh2l75c+und+NJ+A7niiTf415NvFDssNQMm1CRJkiRJDSoiSiPiOWA2cG9KaQwwCNgvIsZExEMRsXu+bnvgu8BPq2ymDzC94Pn0vKzqe50aEWMjYuycOXPqYW8kSZLUWH3v8KGMHNKTc2+dyKMvzy12OGriTKhJkiRJkhpUSmlVSmk40BfYIyJ2IJuSoCuwJ/Bt4PqICLJE2h8KerVVqm6G+XXmT0spXZZSGpFSGlFRUbE5d0OSJEmNXGlJcMEJw9m2ogNfuXocr86p+ielVHcm1CRJkiRJRZFSmg88SDb32XTgxnxIyKeA1UAP4EPAeRHxOnA28IOIOCNfv2/B5voCMxoqdkmSJDUNHduU87fPjKC8tIQvXDGW95asKHZIaqL+v717j7OrLu89/nkykwtJuARIMJCEBEiAcEvIBKOWihTl0mNBBAWEpB4QQeVYT8WCrUW0WkRbVCwgRGwQES9FRIRSLwUvyCWBEAi3DKAQiUy4Z4hJSOY5f+yV080wmcxMZvaaPfN5v17rlb1/a6093/3wY+898+y1lg01SZIkSVLNRMTYiNiuuL0VcBjwMHA9cGgxPg0YBjybmQdn5uTMnAx8Gfh8Zn4tM1cAqyJiTnEk21zgRzV+OpIkSaoDE7cfyWWnzOIPL/yJD12ziFc3tJUdSXXIhpokSZIkqZbGA/8dEUuAu6lcQ+1G4Epgt4h4ALgWmJeZrzuFYztnAvOBZuAx4Oa+iy1JkqR6Nnvy9nz+2P34TfNzfPqGpWz+o6b0Wo1lB5AkSZIkDR6ZuQSY2cH4OuDkzez76Xb3FwL79mY+SZIkDVzHzZrAspZVfP22x5m209bMe/PksiOpjniEmiRJkiRJkiRJGhQ+cfheHLb3Tnzmxgf55aMry46jOmJDTZIkSZIkSZIkDQoNQ4IvnzCDqeNG8+Fr7qG5pbXsSKoTNtQkSZIkSZIkSdKgMXp4I/PnNTG8cQinLribF15ZV3Yk1QEbapIkSZIkSZIkaVCZMGYkXz+liRUvruHMby9i3fq2siOpn7OhJkmSJEmSJEmSBp1Zu47hC8ftxx2PP895NywlM8uOpH6ssewAkiRJkiRJkiRJZXjXzAkse6aVS259jGk7jeb9b5lSdiT1UzbUJEmSJEmSJEnSoPXxd+xJc0srn73xQabsOIpD9hxXdiT1Q57yUZIkSZIkSZIkDVpDhgQXvXcGe71hG8665l6WPbOq7Ejqh2yoSZIkSZIkSZKkQW3U8Ebmz2ti+NAGTl2wkOdfWVd2JPUzNtQkSZIkSZIkSdKgt/N2W3HF3Fn88eU1nHH1Itatbys7kvoRG2qSJEmSJEmSJEnAzElj+OJx+3PXE8/zqesfIDPLjqR+orHsAJIkSZIkSZIkSf3F0TN2obmllYt/0czUnUZz2sG7lR1J/YANNUmSJEmSJEmSpCofO2wazS2tfP6mh9ht7CgO3WunsiOpZJ7yUZIkSZIkSZIkqcqQIcG/vOcApu+8Df/nO4t55I+ryo6kktlQkyRJkiRJkiRJamfksEaumNvEyGENnLrgbp5rXVt2JJXIhpokSZIkSZIkSVIHxm+7FVfMbWLlqrWccfUi1q7fUHYklcSGmiRJkiRJkiRJ0iYcMHE7vnT8Adz9uxf4hx8+QGaWHUklaCw7gCRJkiRJkiRJUn/2zgN2prmlla/8fBlTdxrN6X++e9mRVGM21CRJkiRJkiRJkjbjo38xleaVrfzzzQ+z246jOWz6TmVHUg15ykdJkiRJkiRJkqTNGDIk+NJxB7DfLtvy0Wvv5aEVL5cdSTVkQ02SJEmSJEmSJKkLthrWwBVzmxg9opHTFixk5aq1ZUdSjdhQkyRJkiRJkiRJ6qKdthnB/Lmzee6VtZxx9SLWrt9QdiTVgA01SZIkSZIkSZKkbthvwrb8y/EzWPT7Fzj3uvvJzLIjqY/ZUJMkSZIkSZIkSeqmv9x/PP/37dO47p4/cNltj5cdR32ssewAkiRJkiRJkiRJ9eisQ/dgWUsrF97yMLuNHcXh+7yh7EjqIx6hJkmSJEmSJEmS1AMRwReP25/9J2zHx767mKVPv1R2JPURG2qSJEmSJEmSJEk9NGJoA1ecMotttxrKBxYspGXVmrIjqQ/YUJMkSZIkSZIkSdoC47YZwRVzm3hh9at88FuLWPPqhrIjqZfZUJMkSZIkSZIkSdpC++6yLRe99wDuffJFzvmPJWRm2ZHUi2yoSZIkSZIkSZIk9YIj9h3P2YfvyfWLn+aSWx8rO456Uc0aahFxREQ8EhHNEXFOB+vfFxFLiuX2iDigVtkkSZIkSZIkSZJ6w4cO2Z1jZuzMF295hP98YEXZcdRLatJQi4gG4N+AI4HpwIkRMb3dZk8Ab83M/YHPApfXIpskSZIkSZIkSVJviQguePf+zJy0HR/77n088IeXyo6kXlCrI9QOApoz8/HMXAdcCxxdvUFm3p6ZLxR37wAm1CibJEmSJEmSJElSrxkxtIHLT2lizMihnLZgIS0vryk7krZQrRpquwBPVd1fXoxtyqnAzX2aSJIkSZIkSZIkqY+M3Xo48+fN5uU1r/KBby1izasbyo6kLVCrhlp0MJYdbhjxNioNtb/bxPrTI2JhRCxcuXJlL0aUJEmSJEmSJEnqPdN33oaL3juDJctf5OwfLCGzw9aI6kCtGmrLgYlV9ycAT7ffKCL2B+YDR2fmcx09UGZenplNmdk0duzYPgkrSZIkSZIkSZLUGw7f5w184vC9+PF9T3PxL5rLjqMeqlVD7W5gakRMiYhhwAnADdUbRMQk4DrglMx8tEa5JEmSJEmSJEmS+tQZb92NYw/chX/96aP8ZMmKsuOoBxpr8UMyc31EfAS4BWgArszMpRFxRrH+MuAfgR2ASyICYH1mNtUinyRJkiRJkiRJUl+JCP752P148rnV/O33FzNp+5HsN2HbsmOpG2p1hBqZeVNmTsvM3TPzc8XYZUUzjcw8LTPHZOaMYrGZJkmSJEmSJEmSBoThjQ1cdsosdhg1nNOuuptnXl5TdiR1Q80aapIkSZIkSZIkSYPZjqOHM39eE61r1vOBqxbyp3Ubyo6kLrKhJkmSJEmSJEmSVCN7j9+Gr5wwk/v/8BIf//59tLVl2ZHUBTbUJEmSJEmSJEmSauiw6Ttx7pF78ZP7V/CVny8rO466oLHsAJIkSZIkSZIkSYPNBw7ejWXPtPKVny9j93Gj+asDdi47kjrhEWqSJEmSpJqJiBERcVdE3BcRSyPi/Kp1Z0XEI8X4hcXY2yNiUUTcX/x7aNX2s4rx5oj4akREGc9JkiRJ6omI4J/etS8HTd6es79/H4uferHsSOqEDTVJkiRJUi2tBQ7NzAOAGcARETEnIt4GHA3sn5n7AF8qtn8WeGdm7gfMA75V9ViXAqcDU4vliNo8BUmSJKl3DG9s4NKTD2Ts1sM5/aqFrHjpT2VH0ibYUJMkSZIk1UxWtBZ3hxZLAmcCF2Tm2mK7luLfezPz6WL7pcCIiBgeEeOBbTLzt5mZwFXAMTV8KpIkSVKv2GH0cL4xbzar123gA1ctZPW69WVHUgdsqEmSJEmSaioiGiJiMdAC/DQz7wSmAQdHxJ0RcVtEzO5g13cD9xZNt12A5VXrlhdjkiRJUt3Z8w1bc/GJM3nw6Zf52+/dR1tblh1J7dhQkyRJkiTVVGZuyMwZwATgoIjYF2gExgBzgLOB71VfEy0i9gG+AHxw41BHD91+ICJOj4iFEbFw5cqVvftEJEmSpF70tr3G8cmj9ubmB/7IRT97tOw4aseGmiRJkiSpFJn5InArlWufLQeuK04JeRfQBuwIEBETgB8CczPzsWL35VQachtNAJ6mncy8PDObMrNp7NixffVUJEmSpF5x6p9N4YTZE7n4F838aPEfyo6jKjbUJEmSJEk1ExFjI2K74vZWwGHAw8D1wKHF+DRgGPBsse1PgHMz8zcbHyczVwCrImJOcSTbXOBHtXsmkiRJUu+LCD5z9L68ccr2nP2DJdz75AtlR1LBhpokSZIkqZbGA/8dEUuAu6lcQ+1G4Epgt4h4ALgWmJeZCXwE2AP4VEQsLpZxxWOdCcwHmoHHgJtr/FwkSZKkXjescQiXnTyLN2wzgg9ctYinX/xT2ZEEROX3k/rU1NSUCxcuLDuGJEmSpF4SEYsys6nsHBqY/B1SkiRJ9WTZM6s49pLbmbD9SH5wxpsYNbyx7Ej9Ti1/h/QINUmSJEmSJEmSpH5m6k5bc/FJM3nkjy/zse8upq2tfg+QGghsqEmSJEmSJEmSJPVDh+w5jk/9r+n814PP8KX/eqTsOIOaxwdKkiRJkiRJkiT1U3/95sksa2nlklsfY49xozn2wAllRxqUPEJNkiRJkiRJkiSpn4oIzv+rfXjTbjtwzn/cz6Lfv1B2pEHJhpokSZIkSZIkSVI/NrRhCJe870B23m4EH/zWQpa/sLrsSIOODTVJkiRJkiRJkqR+bsyoYcyfN5u169s4bcFCWteuLzvSoGJDTZIkSZIkSZIkqQ7sMW40l7zvQJa1tPI31y5mQ1uWHWnQsKEmSZIkSZIkSZJUJw6eOpbz3jmdnz30DBfe8nDZcQaNxrIDSJIkSZIkSZIkqevmvmkyy55p5eu3Pc4eY0dzfNPEsiMNeB6hJkmSJEmSJEmSVGf+8Z3TecseO/DJH97P3b97vuw4A54NNUmSJEmSJEmSpDoztGEIl5w0i4ljRvLBby3iqedXlx1pQLOhJkmSJEmSJEmSVIe2HTmU+fOaWL+hjdMWLGTVmlfLjjRg2VCTJEmSJEmSJEmqU7uNHc2lJ8+ieWUrH712MRvasuxIA5INNUmSJEmSJEmSpDr2lj125Py/2odfPNzCBTc/VHacAamx7ACSJEmSJEmSJEnaMifP2ZXmllau+NUTTB23Ne+ZPbHsSAOKR6hJkiRJkiRJkiQNAP/wl3tz8NQd+fvr7+fOx58rO86AYkNNkiRJkiRJkiRpAGhsGMLXTjqQiduP5IyrF/Hkc6vLjjRg2FCTJEmSJEmSJEkaILbdaihXzptNW8KpC+7m5TWvlh1pQLChJkmSJEmSJEmSNIBM3nEUl558IE88+wpnXXMv6ze0lR2p7tlQkyRJkiRJkiRJGmDevPuOfPaYfbnt0ZV8/qaHy45T9xrLDiBJkiRJkiRJkqTed+JBk1j2TCtX/uYJpu40mhMPmlR2pLrlEWqSJEmSJEmSJEkD1CeP2ou3ThvLp65/gN8+9lzZceqWDTVJkiRJkiRJkqQBqrFhCBefNJPJO47izG8v4nfPvlJ2pLpkQ02SJEmSJEmSJGkA22bEUL4xr4kATl1wNy/96dWyI9UdG2qSJEmSJEmSJEkD3K47jOKyk2fx5POr+cg197B+Q1vZkeqKDTVJkiRJkiRJkqRB4I277cA/HbMvv1r2LP/0k4fKjlNXGssOIEmSJEmSJEmSpNp47+xJLHumlfm/foI9xo3m5Dm7lh2pLniEmiRJkiRJkiRJ0iBy7lF787Y9x3LeDUu5vfnZsuPUBRtqkiRJkiRJkiRJg0jDkOCrJ85k97GjOPPb9/D4ytayI/V7NtQkSZIkSZIkSZIGma1HDOUb82bTMCQ4bcFCXlr9atmR+jUbapIkSZIkSZIkSYPQxO1H8vVTZvHUC6v58DX38OqGtrIj9Vs21CRJkiRJkiRJkgap2ZO35/Pv2o9fNz/LZ298sOw4/VZj2QEkSZIkSZIkSZJUnuObJtLc0srXf/k4e4wbzdw3TS47Ur/jEWqSJEmSJEmSJEmD3CeO2IvD9h7H+T9+kF8tW1l2nH7HhpokSZIkSZIkSdIg1zAk+PIJM5k6bjQf+vY9NLe0lh2pX7GhJkmSJEmSJEmSJEYPb2T+vCaGNw7htAV38+LqdWVH6jdsqEmSJEmSJEmSJAmACWNG8vVTZvH0i2v40Lfv4dUNbWVH6hdsqEmSJEmSJEmSJOn/m7Xr9lzw7v24/bHnOO+GpWRm2ZFK11h2AEmSJEmSJEmSJPUvxx44gWUtrVx662NMGzeav37LlLIjlcqGmiRJkiRJkiRJkl7n7HfsyWMtrXzmxgeZvOMoDtlzXNmRSuMpHyVJkiRJkiRJkvQ6Q4YEF713Bnu+YRvOuuZemltWlR2pNDbUJEmSJEmSJEmS1KFRwxuZP6+J4UMbOHXBQl54ZV3ZkUphQ02SJEmSJEmSJEmbtMt2W3H53FmseGkNZ1y9iHXr28qOVHM21CRJkiRJkiRJktSpAyeN4YvH7c+dTzzPRT97tOw4NWdDTZIkSZJUMxExIiLuioj7ImJpRJxfte6siHikGL+wavzciGgu1h1eNT4rIu4v1n01IqLWz0eSJEkaTI6esQsXHLsf73/L5LKj1Fxj2QEkSZIkSYPKWuDQzGyNiKHAryPiZmAr4Ghg/8xcGxHjACJiOnACsA+wM/CziJiWmRuAS4HTgTuAm4AjgJtr/owkSZKkQeSEgyaVHaEUHqEmSZIkSaqZrGgt7g4tlgTOBC7IzLXFdi3FNkcD12bm2sx8AmgGDoqI8cA2mfnbzEzgKuCYGj4VSZIkSYOIDTVJkiRJUk1FRENELAZagJ9m5p3ANODgiLgzIm6LiNnF5rsAT1XtvrwY26W43X68/c86PSIWRsTClStX9sGzkSRJkjQY2FCTJEmSJNVUZm7IzBnABCpHm+1L5ZIEY4A5wNnA94pronV0XbTsZLz9z7o8M5sys2ns2LG99RQkSZIkDTI21CRJkiRJpcjMF4FbqVz7bDlwXXFKyLuANmDHYnxi1W4TgKeL8QkdjEuSJElSr7OhJkmSJEmqmYgYGxHbFbe3Ag4DHgauBw4txqcBw4BngRuAEyJieERMAaYCd2XmCmBVRMwpjmSbC/yoxk9HkiRJ0iDRWHYASZIkSdKgMh5YEBENVL7k+b3MvDEihgFXRsQDwDpgXmYmsDQivgc8CKwHPpyZG4rHOhP4d2Ar4OZikSRJkqReZ0NNkiRJklQzmbkEmNnB+Drg5E3s8zngcx2MLwT27e2MkiRJktSep3yUJEmSJEmSJEmSOmFDTZIkSZIkSZIkSeqEDTVJkiRJkiRJkiSpEzbUJEmSJEmSJEmSpE7YUJMkSZIkSZIkSZI6UbOGWkQcERGPRERzRJzTwfqIiK8W65dExIG1yiZJkiRJkiRJkiRtSk0aahHRAPwbcCQwHTgxIqa32+xIYGqxnA5cWotskiRJkiRJkiRJUmdqdYTaQUBzZj6emeuAa4Gj221zNHBVVtwBbBcR42uUT5IkSZIkSZIkSepQrRpquwBPVd1fXox1dxtJkiRJkiRJkiSpphpr9HOig7HswTZExOlUTgkJsDYiHtjCbFJndgSeLTuEBiznl/qS80t9yfmlvrRn2QE0cC1atOjZiPh92TkKvpb2Devad6xt37Cufcfa9g3r2nesbd+wrn2nv9R211r9oFo11JYDE6vuTwCe7sE2ZOblwOUAEbEwM5t6N6r0P5xj6kvOL/Ul55f6kvNLfSkiFpadQQNXZo4tO8NGvpb2Devad6xt37Cufcfa9g3r2nesbd+wrn1nMNa2Vqd8vBuYGhFTImIYcAJwQ7ttbgDmRsUc4KXMXFGjfJIkSZIkSZIkSVKHanKEWmauj4iPALcADcCVmbk0Is4o1l8G3AQcBTQDq4H31yKbJEmSJEmSJEmS1JlanfKRzLyJStOseuyyqtsJfLibD3t5L0STOuMcU19yfqkvOb/Ul5xf6kvOLw0WzvW+YV37jrXtG9a171jbvmFd+4617RvWte8MutpGpY8lSZIkSZIkSZIkqSO1uoaaJEmSJEmSJEmSVJfqoqEWEUdExCMR0RwR53SwPiLiq8X6JRFxYBk5VZ+6ML/eV8yrJRFxe0QcUEZO1afNza+q7WZHxIaIOK6W+VTfujK/IuKQiFgcEUsj4rZaZ1R968J75LYR8eOIuK+YY14DV10SEVdGREtEPLCJ9X6+V92JiIkR8d8R8VDxmvjRYnz7iPhpRCwr/h1TjL89IhZFxP3Fv4dWPdasYry5+H8hynpeZetBXQ8qPvssLt6f3lX1WNa1SndrW7XfpIhojYiPV41Z20IP5uzkiPhT1by9rOqxrGuVnszZiNg/In5bbH9/RIwoxq1toQdz9n1V83VxRLRFxIxinXWt0oPaDo2IBUUNH4qIc6sey9oWelDXYRHxzaJ+90XEIVWPZV2rdFLb44v7bRHR1G6fc4v6PRIRh1eND8zaZma/XoAG4DFgN2AYcB8wvd02RwE3AwHMAe4sO7dLfSxdnF9vBsYUt490frl0denK/Kra7hdUrjN5XNm5Xepj6eLr13bAg8Ck4v64snO71M/SxTn2SeALxe2xwPPAsLKzu/T/Bfhz4EDggU2s9/O9S90twHjgwOL21sCjwHTgQuCcYvycqtfNmcDOxe19gT9UPdZdwJuK/wduBo4s+/nVUV1HAo1V+7ZU3beuW1Dbqv3+A/g+8PGqMWvbw7oCkzt5P7SuW1bbRmAJcEBxfwegwdpuWV3b7bsf8HjVfeu6BbUFTgKuLW6PBH4HTLa2W1zXDwPfLG6PAxYBQ6xrt2q7N7AncCvQVLX9dCp/KxgOTKHyN4QB/TpbD0eoHQQ0Z+bjmbkOuBY4ut02RwNXZcUdwHYRMb7WQVWXNju/MvP2zHyhuHsHMKHGGVW/uvL6BXAWlV9KW2oZTnWvK/PrJOC6zHwSIDOdY+qOrsyxBLYuvmk2mkpDbX1tY6oeZeYvqcyXTfHzvepOZq7IzHuK26uAh4BdqMznBcVmC4Bjim3uzcyni/GlwIiIGF7M9W0y87dZ+WvEVRv3GYx6UNfVmbnxvWgElfcqrOvrdbe2ABFxDPA4lTm7cczaVulJXTtiXV+vB7V9B7AkM+8r9nkuMzdY29fawjl7IvAdcM52pAe1TWBURDQCWwHrgJet7Wv1oK7TgZ8X27cALwJN1vX1NlXbzHwoMx/pYJejqTSB12bmE0AzcNBArm09NNR2AZ6qur+8GOvuNlJHujt3TqXSUZe6YrPzKyJ2Ad4FXIbUPV15/ZoGjImIW6NyKqm5NUungaArc+xrVL6p9jRwP/DRzGyrTTwNcH6+V12LiMlUjkC7E9gpM1dA5Y8UVL4Z3d67gXszcy2Vub68ap3zv9DVukbEGyNiKZX3pjOKBpt17URXahsRo4C/A85vt7u13YRuvBZMiYh7I+K2iDi4GLOunehibacBGRG3RMQ9EfGJYtzabkIP3r/eS9FQw7p2qou1/QHwCrACeBL4UmY+j7XdpC7W9T7g6IhojIgpwCxgIta1U+1quymb+r1twNa2sewAXdDRuTWzB9tIHeny3ImIt1FpqP1ZnybSQNKV+fVl4O+Kb8n1fSINJF2ZX41UPij+BZVvt/02Iu7IzEf7OpwGhK7MscOBxcChwO7ATyPiV5n5ch9n08Dn53vVrYgYTeXsA3+TmS9v7jNeROwDfIHKkRTg/O9Qd+qamXcC+0TE3sCCiNh4CtnXbdonYetMN2p7PnBRZra228badqAbdV1B5RTtz0XELOD64nXBum5CN2rbSOVvOLOB1cDPI2IR0NFn1UFf2x68f70RWJ2ZG6+J65zdhG7U9iBgA7AzMAb4VUT8DGvboW7U9UoqXwRdCPweuJ3KmVWs6ya0r21nm3Ywlp2M1716aKgtp9Ix3mgClW9Bd3cbqSNdmjsRsT8wn8q5Xp+rUTbVv67Mrybg2uJNf0fgqIhYn5nX1ySh6llX3x+fzcxXgFci4pfAAVTOgS1tTlfm2PuBC4pTODRHxBPAXlTOlS5tCT/fqy5FxFAqf3z4dmZeVww/ExHjM3NFcfqblqrtJwA/BOZm5mPF8HJee5r5QT//u1vXjTLzoYh4hco16qxrB7pZ2zcCx0XEhVSu1dsWEWuK/a1tle7UtTgydW1xe1FEPEblyCrnbAe6OWeXA7dl5rPFvjdRuYbr1Vjb1+jh6+wJ/M/RaeCc7VA3a3sS8J+Z+SrQEhG/ofJ3o19hbV+jm6+z64GPVe17O7AMeAHr+jqbqO2mbOr3tgH7elAPp3y8G5gaEVMiYhiVF+sb2m1zAzA3KuYAL208vFPajM3Or4iYBFwHnOJRHeqmzc6vzJySmZMzczKVQ/s/ZDNNXdSV98cfAQcXpzUYSeWPIA/VOKfqV1fm2JNUjoAkInaicpHix2uaUgOVn+9Vd6LyDalvAA9l5r9WrboBmFfcnkfl/ZmI2A74CXBuZv5m48bFXF8VEXOKx5y7cZ/BqAd1nRKVa88QEbtSeW/6nXV9ve7WNjMPrvrd5cvA5zPza9b2tXowZ8dGRENxezdgKvC4dX297tYWuAXYPyJGFq8LbwUetLav1YO6EhFDgOOpXGcZ8P2rIz2o7ZPAocVn4FHAHOBha/taPXidHVnUk4h4O7A+M30t6EAntd2UG4ATonId4ClU3sPuGsi1jcoXivu3iDiKyoe1BuDKzPxcRJwBkJmXFf9RvgYcQeUQ7vdn5sKy8qq+dGF+zadyTYPfF7usz8ymUsKq7mxufrXb9t+BGzPzB7XOqfrUlfkVEWdTOYqoDZifmV8uJ63qURfeI3cG/h0YT+WUDhdk5tUlxVUdiYjvAIdQOTr7GeA8YCj4+V71KyL+jMo3yO+n8r4L8Ekq1534HjCJyh/Kjs/M5yPiH4BzqXxDeqN3ZGZLRDRReX3diso1nM/KevjlvQ/0oK6nAOcArxbbf2bjF9as62t1t7bt9v000JqZXyruW9tCD+bsu4HPUDn92AbgvMz8cfFY1rVKT+ZsRJxM5bU2gZsy8xPFuLUt9LCuh1D57D+n3WNZ1yo9eD0YDXwTmE7l96tvZuYXi8eytoUe1HUylQZ7G/AH4NTM/H3xWNa1Sie1HQ5cDIwFXgQWZ+bhxT5/D/xvKu9jf5OZNxfjA7K2ddFQkyRJkiRJkiRJkspSD6d8lCRJkiRJkiRJkkpjQ02SJEmSJEmSJEnqhA01SZIkSZIkSZIkqRM21CRJkiRJkiRJkqRO2FCTJEmSJEmSJEmSOmFDTZIkSZIkSVK/FxW/jogjq8beExH/WWYuSdLgEJlZdgZJkiRJkiRJ2qyI2Bf4PjATaAAWA0dk5mM9eKyGzNzQuwklSQOVDTVJkiRJkiRJdSMiLgReAUYV/+4K7Ac0Ap/OzB9FxGTgW8U2AB/JzNsj4hDgPGAFMCMzp9c2vSSpXtlQkyRJkiRJklQ3ImIUcA+wDrgRWJqZV0fEdsBdVI5eS6AtM9dExFTgO5nZVDTUfgLsm5lPlJFfklSfGssOIEmSJEmSJEldlZmvRMR3gVbgPcA7I+LjxeoRwCTgaeBrETED2ABMq3qIu2ymSZK6y4aaJEmSJEmSpHrTViwBvDszH6leGRGfBp4BDgCGAGuqVr9So4ySpAFkSNkBJEmSJEmSJKmHbgHOiogAiIiZxfi2wIrMbANOARpKyidJGiBsqEmSJEmSJEmqV58FhgJLIuKB4j7AJcC8iLiDyukePSpNkrRFIjPLziBJkiRJkiRJkiT1Wx6hJkmSJEmSJEmSJHXChpokSZIkSZIkSZLUCRtqkiRJkiRJkiRJUidsqEmSJEmSJEmSJEmdsKEmSZIkSZIkSZIkdcKGmiRJkiRJkiRJktQJG2qSJEmSJEmSJElSJ2yoSZIkSZIkSZIkSZ34f8zdB/ZZKPksAAAAAElFTkSuQmCC\n" - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(3,2, figsize=(30,25))\n", - "\n", - "py_df.filter(variable=\"Resources|Land Cover|+|Forest\", year=[i for i in range(2020,2110,5)], scenario=\"SSP2BIO10GHG400\").plot(ax=ax[0][1])\n", - "py_df.filter(variable=\"Resources|Land Cover|Forest|+|Managed Forest\", year=[i for i in range(2020,2110,5)], scenario=\"SSP2BIO10GHG400\").plot(ax=ax[1][0])\n", - "py_df.filter(variable=\"Resources|Land Cover|Forest|+|Natural Forest\", year=[i for i in range(2020,2110,5)], scenario=\"SSP2BIO10GHG400\").plot(ax=ax[2][1])" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-28T13:47:32.577397500Z", - "start_time": "2023-09-28T13:47:31.575902900Z" - } - }, - "id": "35a72ce4cdc7bf17" - }, - { - "cell_type": "code", - "execution_count": 337, - "outputs": [ - { - "data": { - "text/plain": "" - }, - "execution_count": 337, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": "
", - "image/png": 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\n" 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\n" - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "py_df.filter(variable=\"Resources|Land Cover|+|Forest\", year=[i for i in range(2020,2110,5)], scenario=\"SSP2BIO10GHG400\")\n", - "py_df.filter(variable=\"Resources|Land Cover|Forest|+|Managed Forest\", year=[i for i in range(2020,2110,5)], scenario=\"SSP2BIO10GHG400\")\n", - "py_df.filter(variable=\"Resources|Land Cover|+|Forest\", \n", - " #year=[i for i in range(2020,2110,5)], \n", - " scenario=\"SSP2BIO10GHG400\").diff({\"Resources|Land Cover|+|Forest\":\"test\"}).plot()\n", - "py_df.filter(variable=\"Emissions|CO2|Land|Land-use Change|Gross LUC|+|Deforestation\",\n", - " #year=[i for i in range(2020,2110,5)], \n", - " scenario=\"SSP2BIO10GHG400\").diff({\"Emissions|CO2|Land|Land-use Change|Gross LUC|+|Deforestation\":\"test\"}).plot()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-28T13:59:54.979218500Z", - "start_time": "2023-09-28T13:59:54.625535100Z" - } - }, - "id": "16c24c855d33b6f4" - }, - { - "cell_type": "code", - "execution_count": 486, - "outputs": [ - { - "data": { - "text/plain": "" - }, - "execution_count": 486, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": "
", - "image/png": 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\n" 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\n" 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\n" 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\n" 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HcFrrKrpF4n6coio3WW/A6VJ5Hs6H6Lru8qzbH/17GWPK4rSMZX0uwCkUDgORWY6BCtaZSOq0uIX0PcDNxphWp9q3dVqhB1lr6+G8xh42xvRwM9ZyX3tH1MZ5jwWnh0SZLLdlLbaOxslyfRPO6+Z4OR7/1trt1tq7rLU1cIaY/Nec/gzKr7hZWlhry+NMWHeySfuOt41jX3MnPRaB1e7jyal766leo9keq+5+px33f6ustXZAln3ldKxirX31yLY4r9kZWfd3km0O4fTYuDqbm68BJufmvrOxCWd4Q9bHU85ae2FuH8/J5PJ1fTKn9f4jUlSoyBUpOr4BLjLOqV38cYq1FOBvnH/I6cD9xhg/40za0S7Lth8C/Y0x7Y0jyBhzkclmYqTT9A7OuK3sZpe9BafLZlSWy5XuY6iEM+7oEuOcpqGUu25uP4Tl1TQ372mNcbPO2NVFwLPGmEBjzOU4Bc537ipf4DyWzm5B+AJOl7sTWj7h6MydTcmmaDTG+BtnzOhXOB+ih2a5LcD8O8aqlJsl2+fMOJNEnZPdfbgf4sYBL7jHQiecwuVI8f89zoRbV7r39wywJEuRd0astdtwxhy+aYwpb5yJaeqbf0959A3wgDGmpnEmy3ksh919BTxkjAl3C6qXcbrIp+dHVpxj+0Gc19kRM9xl260z/vpIjqeMMaHGmZ36GZwvgXLjVK/fvPgVp7B4Aef5ONI6Wc69r104X3Q8w4mt9jkph/O+swen4Hs5m3UuNM5451I440vnWGuPaaFy83wIvGWcCbxw/969TtzdqVlr9+AMMXjmVPs2ziR8Ee5r5wDORG4ZwBycQvZR93XYDacIHuPezSKcCeXKuEXnHaeI9RFwm/ue7eNmaHyq498Yc7Ux5siXPPtwCp+cJps78l5w5OKL83dKBBKMMTVxxgXn1jc4E441Nc741GdPtqLb+vcw8LQx5rYsj+ccY8wH7mqneo2e7Fj9GWhojLnZ/Xv4G2PamiwTLhagx4G+xpj7jTHljDEVjXPqng78O9RkB1DJHNtVPydzgQPGmYCutHF6RTQzxrQ9zWy+x/29S3Fmr+uCeP8R8ZyKXJEiwlq7Gufb+HdxWiouwTltTqp1xrRegTO5xz6c8bvjsmwbizNG7T339jhOMnOjW6gl5jLTXmvt5OO6OWGMicFp4Rnutkocufzo3vf11plB+D6cD5DbcLrC7uQkk0Hlwk/m2PPkfp9NXuvmPZ0uwEdch9MF+MgY0ausM9YN97H0xyl2d+J84LjnuO3fO5INp5h8ylr7W5bbr3VvS8DperkHaGP/ncwEnFaTwzjdJye617O2kDzq3schnA/Rn+B0g8zOPTitJjtxPoQOcB8H7uO6EmdCl304E+lcd5L95NUtOLMVr3Dv41v+7R74oZt/Cc7EX7/ifAjL7oP+xzjP5184E20l4xxXp5TLY30aTjfSrOMiZ7jLsn658yLOaYeW4My8vcBddkqnev3mhXXGNI7DaXH9MstNE3EmjvoHp0tiMqfXRfIzd7stOH+77CZQ+xKnMNqLM5b+xpPs6zGc94PZxulS+wdZxhCak3THzsEwnAK7xSn23cD9PRHnA/5/rbVT3b9DH5zxsrtxJsa7JcuXO2/hTCi3A2fiqBwnBrLWzsWZEOstnFa1afz7es3p+G8LzHGPzR+BB6y163K4q+U47wVHLrfhFGKt3fv9hdM4ntz3pWHAnzjP4Z+nWP9bnGP2dpxW2x04x/54d5UcX6MnO1bdLwnPx3nv2YrT3fY1nF4yBco6w3F64bwut+Ec862Ac6y1a9x1VuG8d641TvfjHIf/WGcs7CU4X/iuwznGRuH0iDgdj3Ps3/tPzuB1XRDvPyKFgTnus6mIiCfcb/gTgAan+EAnJYxxxhWPtNZmeyoakZLCGPMpMNVa+6nHUQolt/X9OWttt7OxnYgUXmrJFRHPGGMucbv+BeHMjrqUfyeykRLK7cp3odt1riZOq+AJLfMiIiIi2VGRKyJeuhSnG9pWnC6E1x3f9VlKJIPT3XIfTnfllWQ5f6pICfYDzthgyd564NOzuJ2IFFLqriwiIiIiIiLFhlpyRUREREREpNhQkSsiIiIiIiLFhp/XAQpK5cqVbd26db2OISIiIiIiIgVg/vz5u621occvL7ZFbt26dYmNjfU6hoiIiIiIiBQAY8yG7Jaru7KIiIiIiIgUGypyRUREREREpNhQkSsiIiIiIiLFRrEdk5udtLQ0Nm/eTHJystdRpBAIDAwkLCwMf39/r6OIiIiIiEg+KVFF7ubNmylXrhx169bFGON1HPGQtZY9e/awefNmwsPDvY4jIiIiIiL5pER1V05OTqZSpUoqcAVjDJUqVVKrvoiIiIhIMVOiilxABa4cpWNBRERERKT4KXFFrtdeeuklIiMjadGiBVFRUcyZM4eff/6ZVq1a0bJlS5o2bcr7778PwHPPPUfNmjWJioqiWbNm/PjjjwAMHTqUpk2b0qJFC3r06MGGDc7podavX0/p0qWJioqiZcuWdOzYkdWrVwMwdepULr744qM5fvjhB1q0aEHjxo1p3rw5P/zww9Hb9u7dS8+ePWnQoAE9e/Zk3759R2975ZVXiIiIoFGjRkycOPHo8sTERAYMGED9+vVp1aoVbdq04cMPPzyaq1mzZsc8D8899xxDhgw5+vvQoUOPZmnZsiUPP/wwaWlpx2zTp0+fY/aTkpLCtddeS0REBO3bt2f9+vVHbxs9ejQNGjSgQYMGjB49Ovd/IBERERERKdJU5J5Fs2bN4ueff2bBggUsWbKEP/74g2rVqnH33Xfz008/sXjxYhYuXEi3bt2ObvPQQw+xaNEixo4dy+23305mZiatWrUiNjaWJUuWcNVVV/Hoo48eXb9+/fosWrSIxYsX07dvX15++eUTcixevJjBgwczfvx4Vq1axY8//sjgwYNZsmQJAK+++io9evRgzZo19OjRg1dffRWAFStWMGbMGJYvX86ECRO45557yMjIAODOO++kYsWKrFmzhoULFzJhwgT27t2bq+dl5MiRTJo0idmzZ7N06VLmzZtHlSpVOHz48NF1xo0bR9myZY/Z7qOPPqJixYrExcXx0EMP8dhjjwFOkf78888zZ84c5s6dy/PPP39MoS4iIiIiIsWXityzaNu2bVSuXJmAgAAAKleuTLly5UhPT6dSpUoABAQE0KhRoxO2bdKkCX5+fuzevZvu3btTpkwZAGJiYti8eXO293fgwAEqVqx4wvIhQ4bwn//85+iES+Hh4TzxxBO88cYbAIwfP56+ffsC0Ldv36OtvOPHj+e6664jICCA8PBwIiIimDt3LvHx8cydO5cXX3wRHx/nkAoNDT1adJ7KSy+9xIgRIwgODgagVKlSPP7445QvXx5wWomHDh3KU089dcx2WXNeddVVTJ48GWstEydOpGfPnoSEhFCxYkV69uzJhAkTcpVFRERERESKthI1u3JWz/+0nBVbD+TrPpvWKM+zl0Se9Pbzzz+fF154gYYNG3Leeedx7bXX0rVrV/r06UOdOnXo0aMHF198Mddff/3RYvGIOXPm4OPjQ2ho6DHLP/roIy644IKjv8fHxxMVFcXBgwdJSkpizpw5J+RYvnw5gwcPPmZZdHQ0w4cPB2DHjh1Ur14dgOrVq7Nz504AtmzZQkxMzNFtwsLC2LJlC7t27aJly5YnZM7qSK4jtm/fzuDBgzl48CCJiYk5znD89NNPM2jQoKOF/RFbtmyhVq1aAPj5+VGhQgX27NlzzPKsOUVEREREpPhTS+5ZVLZsWebPn88HH3xAaGgo1157LZ9++imjRo1i8uTJtGvXjiFDhnD77bcf3eatt94iKiqKwYMH8/XXXx8zWdLnn39ObGwsjzzyyNFlR7orx8fHM2zYMO6+++4TclhrT5h0Kbtl2W13vOy2eemll4iKiqJGjRon5Dpy6d+/f7b3O3HiRKKioqhbty5///03ixYtIi4ujssvvzzXeXKbU0REREREip8S25KbU4trQfL19aVbt25069aN5s2bM3r0aG699VaaN29O8+bNufnmmwkPD+fTTz8FnDG5x7e6Avzxxx+89NJLTJs27Wj35+P16dOH22677YTlkZGRxMbG0qJFi6PLFixYQNOmTQGoWrUq27Zto3r16mzbto0qVaoATovopk2bjm6zefNmatSoQWhoKIsXLyYzMxMfHx+efPJJnnzyyRPG0GanfPnyBAUFsW7dOsLDw+nVqxe9evXi4osvJjU1lcWLFzN//nzq1q1Leno6O3fupFu3bkydOvVonrCwMNLT09m/fz8hISGEhYUxderUY3JmHecsIiIiIiLFl1pyz6LVq1ezZs2ao78vWrSIqlWrHlOQLVq0iDp16uS4n4ULF9KvXz9+/PHHowVodmbMmEH9+vVPWD548GBeeeWVo7MRr1+/npdffplBgwYBTnF8ZEbi0aNHc+mllx5dPmbMGFJSUli3bh1r1qyhXbt2REREEB0dzVNPPXV0Iqrk5ORsW1Sz88QTTzBgwAASEhIAp4X2yPlrBwwYwNatW1m/fj0zZsygYcOGR5+vrDm//fZbzj33XIwx9OrVi0mTJrFv3z727dvHpEmT6NWrV66yiIiIiIhI0VZiW3K9kJiYyH333UdCQgJ+fn5ERETw9ttv069fP/r160fp0qUJCgo62op7Mo888giJiYlcffXVANSuXfvo6YWOjH211lKqVClGjRp1wvZRUVG89tprXHLJJaSlpeHv78/rr79+dMzs448/zjXXXMNHH31E7dq1GTt2LOC0AF9zzTU0bdoUPz8/hg8fjq+vLwCjRo3ikUceISIigpCQEEqXLs1rr72Wq+dlwIABJCUl0b59ewICAihbtiydOnWiVatWOW53xx13cPPNNx+9zzFjxgAQEhLC008/Tdu2bQF45plnCAkJyVUWEREREREp2kxuW9uKmujoaBsbG3vMspUrV9KkSROPEklhpGNCRERERKRoMsbMt9ZGH79c3ZVFRERERESk2FCRKyIiIiIiIsWGilwRERERERE5av/hNDIzi+6wVhW5IiIiIiIiAkByWgZ9P57Lw98s8jpKnhVYkWuMCTTGzDXGLDbGLDfGPO8ub2mMmWWMWWqM+ckYU95dXtcYc9gYs8i9jMyyrzbu+nHGmHeMMaagcouIiIiIiJRE1loe/24JizYl0LtZda/j5FlBtuSmAOdaa1sCUUBvY0wMMAp43FrbHPgeeCTLNvHW2ij30j/L8hHA3UAD99K7AHOLiIiIiIiUOCOmxfPDoq0MPr8hvZtV8zpOnhVYkWsdie6v/u7FAo2Av9zlvwNX5rQfY0x1oLy1dpZ1znf0GXBZgYQ+C1566SUiIyNp0aIFUVFRzJkzh59//plWrVrRsmVLmjZtyvvvvw/Ac889R82aNYmKiqJZs2ZHz4U7dOhQmjZtSosWLejRowcbNmwAYP369ZQuXZqoqChatmxJx44dWb16NQBTp07l4osvPprjhx9+oEWLFjRu3JjmzZvzww8/HL1t7NixREZG4uPjw/GnYXrllVeIiIigUaNGTJw48ejyxMREBgwYQP369WnVqhVt2rThww8/PJqrWbNmx+znueeeY8iQIUd/Hzp06NEsLVu25OGHHyYtLe2Ybfr06XPMflJSUrj22muJiIigffv2rF+//uhto0ePpkGDBjRo0IDRo0fn7o8jIiIiIlJCTVq+nTcmrqZPyxrc2z3C6zhnxK8gd26M8QXmAxHAcGvtHGPMMqAPMB64GqiVZZNwY8xC4ADwlLV2OlAT2Jxlnc3usiJn1qxZ/PzzzyxYsICAgAB2797NoUOHuPzyy5k7dy5hYWGkpKQcU6w99NBDDB48mJUrV9K5c2d27txJq1atiI2NpUyZMowYMYJHH32Ur7/+GoD69euzaNEiAN5//31efvnlE4q8xYsXM3jwYH7//XfCw8NZt24dPXv2pF69erRo0YJmzZoxbtw4+vXrd8x2K1asYMyYMSxfvpytW7dy3nnn8c8//+Dr68udd95JvXr1WLNmDT4+PuzatYuPP/44V8/LyJEjmTRpErNnzyY4OJjU1FSGDh3K4cOH8ff3B2DcuHGULVv2mO0++ugjKlasSFxcHGPGjOGxxx7j66+/Zu/evTz//PPExsZijKFNmzb06dOHihUrns6fS0RERESkRFi57QAPfr2IFjUr8PpVLSjqo0MLdOIpa22GtTYKCAPaGWOaAbcD9xpj5gPlgFR39W1AbWttK+Bh4Et3vG52z3C2U30ZY+42xsQaY2J37dqVz4/mzG3bto3KlSsTEBAAQOXKlSlXrhzp6elUqlQJgICAABo1anTCtk2aNMHPz4/du3fTvXt3ypQpA0BMTAybN28+YX2AAwcOZFvYDRkyhP/85z+Eh4cDEB4ezhNPPMEbb7xx9L6yyzB+/Hiuu+46AgICCA8PJyIigrlz5xIfH8/cuXN58cUX8fFxDqnQ0FAee+yxXD0vL730EiNGjCA4OBiAUqVK8fjjj1O+fHnAaSUeOnQoTz311Al5+vbtC8BVV13F5MmTsdYyceJEevbsSUhICBUrVqRnz55MmDAhV1lEREREREqSPYkp3Dk6lnKBfnxwSzSB/r5eRzpjBdqSe4S1NsEYMxXoba0dApwPYIxpCFzkrpOCM44Xa+18Y0w80BCn5TYsy+7CgK0nuZ8PgA8AoqOjc57z+rfHYfvSvD+o7FRrDhe8etKbzz//fF544QUaNmzIeeedx7XXXkvXrl3p06cPderUoUePHlx88cVcf/31R4vFI+bMmYOPjw+hoaHHLP/oo4+44IILjv4eHx9PVFQUBw8eJCkpiTlz5pyQY/ny5QwePPiYZdHR0QwfPjzHh7dlyxZiYmKO/h4WFsaWLVvYtWsXLVu2PCFzVkdyHbF9+3YGDx7MwYMHSUxMPFpwZ+fpp59m0KBBRwv7rHlq1XI6Avj5+VGhQgX27NlzzPKsOUVERERE5F+p6Zn0/3w+uxNTGNu/A1XLB3odKV8U5OzKocaYYPd6aeA8YJUxpoq7zAd4ChiZZX1f93o9nAmm1lprtwEHjTEx7qzKt+B0dS5yypYty/z58/nggw8IDQ3l2muv5dNPP2XUqFFMnjyZdu3aMWTIEG6//faj27z11ltERUUxePBgvv7662O6Dnz++efExsbyyCP/zt11pLtyfHw8w4YN4+677z4hh7X2hC4I2S3LbrvjZbfNSy+9RFRUFDVq1Dgh15FL//79s73fiRMnEhUVRd26dfn7779ZtGgRcXFxXH755bnOk9ucIiIiIiIllbWWp35Yyrz1+xhydUtahAV7HSnfFGRLbnVgtFu4+gDfWGt/NsY8YIy5111nHPCJe70L8IIxJh3IAPpba/e6tw0APgVKA7+5lzOTQ4trQfL19aVbt25069aN5s2bM3r0aG699VaaN29O8+bNufnmmwkPD+fTTz8F/h2Te7w//viDl156iWnTph3t/ny8Pn36cNttt52wPDIyktjYWFq0aHF02YIFC2jatGmO2cPCwti0adPR3zdv3kyNGjUIDQ1l8eLFZGZm4uPjw5NPPsmTTz55whja7JQvX56goCDWrVtHeHg4vXr1olevXlx88cWkpqayePFi5s+fT926dUlPT2fnzp1069aNqVOnHs0TFhZGeno6+/fvJyQkhLCwMKZOnXpMzm7dup0yi4iIiIhISfHRjHV8E7uZ+8+N4JKWNU69QRFSkLMrL7HWtrLWtrDWNrPWvuAuf9ta29C9PO7OmIy19jtrbaS1tqW1trW19qcs+4p191HfWjvQZtdUVwSsXr2aNWvWHP190aJFVK1a9ZiCbNGiRdSpUyfH/SxcuJB+/frx448/UqVKlZOuN2PGDOrXr3/C8sGDB/PKK68cneBq/fr1vPzyywwaNCjH++3Tpw9jxowhJSWFdevWsWbNGtq1a0dERATR0dE89dRTZGRkAJCcnJxti2p2nnjiCQYMGEBCQgLgfKuUnJwMwIABA9i6dSvr169nxowZNGzY8Ojz1adPn6OTan377bece+65GGPo1asXkyZNYt++fezbt49JkybRq1evXGURERERESnupqzeycu/ruSCZtV48LyGXsfJd2dlTK44EhMTue+++0hISMDPz4+IiAjefvtt+vXrR79+/ShdujRBQUFHW3FP5pFHHiExMZGrr74agNq1ax89vdCRsa/WWkqVKsWoUaNO2D4qKorXXnuNSy65hLS0NPz9/Xn99dePjpn9/vvvue+++9i1axcXXXQRUVFRTJw4kcjISK655hqaNm2Kn58fw4cPx9fXGZg+atQoHnnkESIiIggJCaF06dK89tpruXpeBgwYQFJSEu3btycgIICyZcvSqVMnWrVqleN2d9xxBzfffPPR+xwzZgwAISEhPP3007Rt2xaAZ555hpCQkFxlEREREREpzuJ2HuT+LxfSuFp53rymJT4+xW9YnymijaKnFB0dbY8/x+vKlStp0qSJR4mkMNIxISIiIiIlxb5DqVz235kcSslg/MBO1Awu7XWkM2KMmW+tjT5+eYGeQkhERERERES8l5aRyb1fLmBbQjLv39ymyBe4OVF3ZRERERERkWLu+Z+W83f8Ht68uiVt6lT0Ok6BUkuuiIiIiIhIMfa/Wev5fPZG+nWtx5VtwryOU+BKXJFbXMcgy+nTsSAiIiIixd3MuN0899MKzmtShUd7NfY6zllRoorcwMBA9uzZo+JGsNayZ88eAgMDvY4iIiIiIlIg1u0+xD1fLCAitCzDrmuFbzGcSTk7JWpMblhYGJs3b2bXrl1eR5FCIDAwkLCw4t9dQ0RERERKnv2H07hj9Dx8DIzqG03ZgJJT+pWcRwr4+/sTHh7udQwREREREZECk56RyX1fLWTjniS+uLM9tULKeB3prCpRRa6IiIiIiEhx9/Kvq/jrn128ekVz2ter5HWcs65EjckVEREREREpzsbM3cjHM9dxW6e6XNeuttdxPKEiV0REREREpBiYs3YPT49fRpeGoTx5YROv43hGRa6IiIiIiEgRt2lvEgO+WECtkDK8e30r/HxLbqlXch+5iIiIiIhIMZCYks6do2PJyLR81LctFUr7ex3JU5p4SkREREREpIjKyLQ8OGYhcbsSGX1bO8IrB3kdyXNqyRURERERESmi3pi4mj9W7uTZS5pyToPKXscpFFTkioiIiIiIFEHjFmxm5LR4bmxfm5tj6ngdp9BQkSsiIiIiIlLELNi4j8e/W0qHepV4rk8kxhivIxUaKnJFRERERESKkK0Jh7n7s/lUDw7kvze2xr8Ez6ScHU08JSIiIiIiUkQkpaZz12expKRlMObu9lQMKuV1pEJHRa6IiIiIiEgRkJlpGfTNYlZuO8BHt7Yloko5ryMVSmrXFhERERERKQKGTV7Db8u2858Lm9C9URWv4xRaKnJFREREREQKuZ8Wb+WdyWu4uk0Yd5wT7nWcQk1FroiIiIiISCG2ZHMCg8cupm3dirx4eTPNpHwKKnJFREREREQKqR0Hkrnrs1gqlw1gxE1tCPDz9TpSoaeJp0RERERERAqh5LQM7v4sloPJ6Xw3oCOVywZ4HalIUJErIiIiIiJSyFhreey7JSzZsp/3b2pDk+rlvY5UZKi7soiIiIiISCHz36nxjF+0lcHnN+L8yGpexylSVOSKiIiIiIgUIhOWbeeNiau5LKoG93Sr73WcIkdFroiIiIiISCGxYusBHv5mES1rBfPqlS00k3IeqMgVEREREREpBHYnpnDXZ7GUD/Tnw5vbEOivmZTzQhNPiYiIiIiIeCwlPYP+/5vPnkMpjO3XkSrlA72OVGSpyBUREREREfGQtZanvl9G7IZ9DL+hNc3DKngdqUhTd2UPrJr7O/OHXErigX1eRxEREREREY+Nmr6OsfM380CPBlzUorrXcYo8FbkeOLBpGS0P/sXuYZ3ZFLfU6zgiIiIiIuKRKat28vJvK7mweTUe6NHA6zjFgopcD7S78iFW9RxNhcwEKnx+Pov//MbrSCIiIiIicpat2XGQ+75aSNPq5RlydUt8fDSTcn5QkeuRZuf04fCtk9nlW43m0+5m9qf/wWZmeh1LRERERETOgn2HUrljdCylS/kyqm80ZUppuqT8oiLXQzXqNqLGw3+xoPy5xKwfzsI3L+XQwQSvY4mIiIiISAFKy8hkwBfz2X4gmQ9ubkP1CqW9jlSsqMj1WOmgcrR56FtmRzxEy8Tp7HyrC1vWLvc6loiIiIiIFABrLc/+uJzZa/fy2pXNaVW7oteRih0VuYWA8fEh5qbnWNHjEypm7qHcZz1ZMvU7r2OJiIiIiEg++2zWBr6cs5EB3epzeaswr+MUSypyC5HmXS7n0C1/sMcnlMgpdzDrs6c1TldEREREpJiYvmYXL/y8gvOaVOGR8xt5HafYUpFbyNSs14SqD//FonJd6bD2HRYMvZykxP1exxIRERERkTOwdlci936xgAZVyjLsulaaSbkAqcgthMqUrUDrh79nVr37aXVwGtuHdmHL2pVexxIRERERkTzYn5TGnaNj8fP14cNboikboJmUC5KK3ELK+PjQ4Zb/Y1m3UVTO3EXQZ+ex9K/xXscSEREREZHTkJ6RycCvFrBpXxIjb2pDrZAyXkcq9lTkFnItul/FgZsmkeATQtPJfZn9+XMapysiIiIiUkS89OtKpq/ZzYuXNaNdeIjXcUoEFblFQFhEMyo/+BeLy55DTNxbzB92NYcPHfQ6loiIiIiI5OCruRv5ZOZ67jgnnGvb1vY6TomhIreIKFu+Iq0G/cisugNovX8yW4d2YduG1V7HEhERERGRbMxeu4enf1hG14ahPHFBY6/jlCgqcosQ4+NDh1tfZWnXkYRmbCfwk/NYNvMnr2OJiIiIiEgWG/ckMeDz+dSpVIZ3b2iFn6/KrrNJz3YR1PLc69h/4wQO+FSg8aRbmP3l/2mcroiIiIhIIXAwOY07P5tHpoWP+ralfKC/15FKHBW5RVStBi0JeeAvlgZ1IOafIcS+fS3JSYlexxIRERERKbEyMi0PjllE/K5DjLixNXUrB3kdqURSkVuElasQQstBPzGrdj/a7p/Epje7sn3jGq9jiYiIiIiUSK9PXMXkVTt5rk8kHSMqex2nxFKRW8T5+PrS4fbXWdRpBNXTt1Dq43NZ/vevXscSERERESlRvp2/mfenreXmmDrcHFPH6zglmorcYiKq5w3suWECiT7laDjxJuaMeUXjdEVEREREzoL5G/byn3FL6Vi/Es9c0tTrOCWeitxipE6jKILvn87yMm1pv+pVYt+5geTDh7yOJSIiIiJSbG1JOEy//82nRnAg/72xNf6aSdlz+gsUM+WDK9Fi8K/MqnUnbRN+Y+Ob3dixOd7rWCIiIiIixc6hlHTuHB1LSnomo/q2JbhMKa8jCSpyiyUfX1863PEmCzsOp2baRvxGdWflnIlexxIRERERKTYyMy2DvlnM6u0HePf6VkRUKet1JHGpyC3GWp1/E7uv+5UkE0TEr9cz55s3NE5XRERERCQfDPvjHyYs386TFzWlW6MqXseRLFTkFnN1mrSh/P3TWVGmDe1XvMi8d28mJTnJ61giIiIiIkXWj4u38s6fcVwbXYvbO9X1Oo4cR0VuCVChYmWaDfqNWTVvo92+n1k/pDu7tq73OpaIiIiISJGzeFMCj4xdTLu6IfzfZc0wxngdSY6jIreE8PXzo8Ndw1gQ8za10tZhPujKqnl/eB1LRERERKTI2L4/mbs+iyW0XAAjbmpNKT+VU4WR/iolTOvet7Lj2l9INoHU+/ka5n471OtIIiIiIiKFXnJaBnf/L5ZDKemM6htNpbIBXkeSk1CRWwKFN21LuftmsKp0K9ote545795Cakqy17FERERERAolay2PfLuEpVv2M+y6VjSuVt7rSJIDFbklVIWQUCIHT2RWjVtov2c88UO6s3v7Rq9jiYiIiIgUOsOnxPHT4q082qsxPZtW9TqOnIKK3BLM18+PDne/y/x2Q6mTGk/myK6sjv3T61giIiIiIoXGhGXbGDLpHy5vVZP+Xet5HUdyQUWu0ObCO9h+9U+kG3/Cf7qa2V+9RHpaqtexREREREQ8tXzrfh76ejFRtYJ55Yrmmkm5iFCRKwDUa9aeMvf+xarSrYhZ/TobX2nL8pm/eB1LRERERMQTuw6mcNfoWILL+PPBLW0I9Pf1OpLkkopcOSq4cjWaPzqJBTFvUzrzEJG/38CCIX3YtmG119FERERERM6alPQM+v0vln1JaXx4SzRVygV6HUlOg4pcOYbx8aF171up+OgiZtXuR5ODs6j4cSdmffwIyUmJXscTERERESlQ1lqeGLeUBRsTePOaljSrWcHrSHKaVORKtgLLlKXD7a+z/46/WV6uEx02fkDC61Es+O0TbGam1/FERERERArEh9PXMm7BFh46ryEXNq/udRzJAxW5kqNqtRvQZvB4lp//FUk+QbSe8yArXu3KuuVzvI4mIiIiIpKvJq/cwSu/reKiFtW5v0eE13Ekj1TkSq5EdryQ2k/MY07TJ6mZupba3/Riznu3sX/PDq+jiYiIiIicsdXbD3L/VwuJrFGeIVe11EzKRZiKXMk1P/9StL/mUcx9C4gNvYLoXd9j323NnG9eJyM93et4IiIiIiJ5svdQKnd+No+gAD8+vCWa0qU0k3JRpiJXTluFSlVpP/BjNl4zkS2l6tN+xUtseLkNy//+1etoIiIiIiKnJTU9kwGfz2fHgRQ+uCWa6hVKex1JzpCKXMmz8Mj2NH18KgvaD6NM5iEiJ13P/CGXsn3jGq+jiYiIiIickrWWZ39cxpx1e3njqhZE1Qr2OpLkAxW5ckaMjw+tL7iNYPeUQ00P/k2FjzrqlEMiIiIiUuiN/ns9X83dxL3d63NpVE2v40g+UZEr+SLrKYdWlOtIh40fsE+nHBIRERGRQuqvf3bxws8r6Nm0KoN6NvI6juSjAityjTGBxpi5xpjFxpjlxpjn3eUtjTGzjDFLjTE/GWPKZ9nmCWNMnDFmtTGmV5blbdz144wx7xhNdVZoZT3l0OGjpxzqplMOiYiIiEihEb8rkXu/XEDDquUYdm0UPj4qL4qTgmzJTQHOtda2BKKA3saYGGAU8Li1tjnwPfAIgDGmKXAdEAn0Bv5rjDkyrdkI4G6ggXvpXYC5JR8ce8qheJ1ySEREREQKhf1Jadw5OpZSvj6M6htNUICf15EknxVYkWsdRwZl+rsXCzQC/nKX/w5c6V6/FBhjrU2x1q4D4oB2xpjqQHlr7SxrrQU+Ay4rqNySf4495dDlOuWQiIiIiHgqPSOTe79cwOZ9SYy8uQ1hFct4HUkKQIGOyTXG+BpjFgE7gd+ttXOAZUAfd5WrgVru9ZrApiybb3aX1XSvH788u/u72xgTa4yJ3bVrV749DjkzzimHPmHD1RPZUqqeTjkkIiIiIp548ZeVzIjbzcuXN6dt3RCv40gBKdAi11qbYa2NAsJwWmWbAbcD9xpj5gPlgFR39ew6wtsclmd3fx9Ya6OttdGhoaFnnF/yV71m7Wn6+DSdckhEREREzrov5mzg07/Xc1fncK6OrnXqDaTIOiuzK1trE4CpQG9r7Spr7fnW2jbAV0C8u9pm/m3VBacw3uouD8tmuRRBR045VOGRhcyqfTeRB2e6pxx6VKccEhEREZEC8Xf8bp4dv5xujUJ5/IImXseRAlaQsyuHGmOC3eulgfOAVcaYKu4yH+ApYKS7yY/AdcaYAGNMOM4EU3OttduAg8aYGHdW5VuA8QWVW86O0kHl6HD7G+y7fSYry3Wgw8b3dcohEREREcl3G/Yc4p4vFlC3chDvXN8KX82kXOwVZEtudWCKMWYJMA9nTO7PwPXGmH+AVTgtsp8AWGuXA98AK4AJwL3W2gx3XwNwZmWOw2n5/a0Ac8tZVL1OI1oP/pHlPb/UKYdEREREJF/tOJDMHaNjARh1SzTlA/09TiRng3EmLC5+oqOjbWxsrNcx5DSkp6Uy//thNFrxNuXsIWJDr6Dx9a9QoVJVr6OJiIiISBEzf8M++n8+n0Mp6XzUty0d6lfyOpLkM2PMfGtt9PHLz8qYXJHcOPGUQ+PIfLeNTjkkIiIiIqdlzNyNXPfBLEr7+zLuno4qcEsYFblS6GQ95dDWUuG0X/ES61+JZsUs9VIXERERkZNLTc/k6R+W8fi4pcTUq8SPAzvRuFp5r2PJWaYiVwqtrKccCspIpOnE65j/60dexxIRERGRQmjXwRRuGjWH/83eQL8u9fjk1rYElynldSzxgIpcKdSynnLoH7+G1Jn7AgcS9ngdS0REREQKkSWbE+jz3gyWbEng7euieOLCJvj5qtQpqfSXlyKhdFA5zEVvEmL3s+LLx72OIyIiIiKFxHfzN3PVyFn4GMO3/TtyaVRNryOJx1TkSpHRoFUX5lW+lLY7xhK/dLbXcURERETEQ+kZmbzw0woGjV1M69rB/DiwE81qVvA6lhQCKnKlSGl84xD2m3Kk/fgQmRkZp95ARERERIqdvYdSueXjuXw8cx23darL/+5oT6WyAV7HkkJCRa4UKRVCQolr+SiN01Ywf/x7XscRERERkbNs+db9XPLuDGI37GPI1S159pJI/DX+VrLQ0SBFTnSfe1jpH0nEkiHs37PD6zgiIiIicpb8uHgrV474m4xMy9h+HbiqTZjXkaQQUpErRY6Pry+Bl71FOZvIqi8f8TqOiIiIiBSwjEzLK7+t5P6vFtK8ZgV+uu8cWtYK9jqWFFIqcqVICo9sT2y1a2i7+0f+WTDV6zgiIiIiUkD2J6Vx26fzeH/aWm6Kqc0Xd8YQWk7jb+XkVORKkRV5wyvsMcH4/PIwGenpXscRERERkXz2z46D9Bk+g1nxu3nliua8eFlzSvmphJGc6QiRIqtchRA2tn2KiIx4Yr970+s4IiIiIpKPJizbxmXDZ5KUmsGYu2O4vl1tryNJEaEiV4q01hfcztKAVjRZ+TZ7dmz2Oo6IiIiInKHMTMvQSavp//kCGlYtx8/3nUObOiFex5IiREWuFGnGx4cKVw4j0Caz9stBXscRERERkTNwIDmNuz6L5Z0/47gmOoyv+8VQtXyg17GkiFGRK0Ve7YZRzK95E233T2DF7AlexxERERGRPIjbmchlw2cy7Z9dvHBpJK9d2YIAP1+vY0kRpCJXioWWN/wf2wml9KRHSUtN8TqOiIiIiJyGP1bs4LLhM9mflMbnd7bnlg51McZ4HUuKKBW5UiyUKVuBbR2fIzxzA/PHvup1HBERERHJhcxMyzuT13DnZ7HUrVyGH+87h5h6lbyOJUWcilwpNqLOu4HFpdvT/J//snPLOq/jiIiIiEgOElPSueeLBQz9/R8ub1WTb/t3pGZwaa9jSTGgIleKDePjQ+Wr38aXDDaNecjrOCIiIiJyEut3H+KK/85k0ortPHVRE4Ze05JAf42/lfyhIleKlZr1mrCwzu20OTiFpX+N9zqOiIiIiBxn2j+76PPeDHYeTOGz29tzZ+d6Gn8r+UpFrhQ7ra5/ls2mGsFTHiclOcnrOCIiIiICWGsZOS2e2z6ZS43g0vw08BzOaVDZ61hSDKnIlWInsHQQe7u8RC27lYVjXvQ6joiIiEiJl5Sazn1fLeTV31ZxQfPqjLunI7VCyngdS4opFblSLLXofhULgrrQct2HbF2/2us4IiIiIiXWpr1JXDliFr8s3cZjvRvz3vWtKFPKz+tYUoypyJViq8Z1b2Ex7PjmQa+jiIiIiJRIf8ftps97M9iyL4lPbm3LgG71Nf5WCpyKXCm2qtWKYEn9frRK+ptFk8d4HUdERESkxLDW8tGMddz88Vwqlw1g/MBz6NaoitexpIRQkSvFWutrn2S9Ty2qzHiG5KREr+OIiIiIFHvJaRkMGruY//t5BT0aV+H7ezsRXjnI61hSgqjIlWKtVEAgh3q8Rg27g0VfPuN1HBEREZFibWvCYa55fxbjFmzh4Z4NGXlTG8oGaPytnF0qcqXYi+x0EbHlz6P1ptFsilvqdRwRERGRYmnuur30eW8Ga3cd4sNborm/RwN8fDT+Vs4+FblSItS94S1S8Gfftw9iMzO9jiMiIiJSbFhr+d/sDdzw4WzKB/rzw72d6Nm0qtexpARTkSslQuVqtVne+D5aJMeycOJor+OIiIiIFAsp6Rk8MW4pT/+wjC4NQ/lhYCciqpT1OpaUcCpypcSIvuoR4n3rETbnBQ4dTPA6joiIiEiRtuNAMtd9MJsx8zYxsHsEo26Jpnygv9exRFTkSsnh51+KtN5vUIW9LP3iP17HERERESmy5m/YxyXvzmD19oP898bWDO7VSONvpdBQkSslSuO25zG34kW02TaG9StjvY4jIiIiUuSMmbuR6z6YRaC/L+Pu6ciFzat7HUnkGCpypcRpcMObHDKlSfpek1CJiIiI5FZqeiZP/7CMx8ctJaZeJX4c2InG1cp7HUvkBCpypcSpGFqd1ZEP0zR1KfN/ft/rOCIiIiKF3q6DKdw0ag7/m72Bfl3q8cmtbQkuU8rrWCLZUpErJVLbKx7kH7+G1F3wKvv37fY6joiIiEihtWRzAn3em8GSLQm8c30rnriwCX6+KiOk8NLRKSWSj68vPpe8RUW7n1VfPuZ1HBEREZFC6bv5m7lq5Cx8jOG7AR3p07KG15FETklFrpRYES3PITb0cqJ3fkfc4plexxEREREpNNIzMnnhpxUMGruYNrUr8tN95xBZo4LXsURyRUWulGiNb3yDBFOejJ8eIjMjw+s4IiIiIp7beyiVWz6ey8cz13Fbp7p8dkc7QoI0/laKDhW5UqJVqFiZta0ep1H6amJ/eMfrOCIiIiKeWr51P5e8O4PYDfsYcnVLnr0kEn+Nv5UiRkeslHjRl/RnRanmNFj6Jgm7t3sdR0RERMQTPy7eypUj/iYj0zK2XweuahPmdSSRPFGRKyWe8fGh9GVvUc4e4p8vB3sdR0REROSsysi0vPLbSu7/aiHNa1bgp/vOoWWtYK9jieSZilwRILxpW2KrXUu7vT+xKnay13FEREREzor9SWnc9uk83p+2lptiavPFnTGElgvwOpbIGVGRK+JqduMr7CQEv98eISM93es4IiIiIgXqnx0H6TN8BrPid/PKFc158bLmlPJTeSBFn45iEVfZ8hXZ1O4ZIjLiif32Da/jiIiIiBSYCcu2cdnwmSSlZjDm7hiub1fb60gi+UZFrkgWrXv3ZWlAa5qseofd2zd5HUdEREQkX2VmWoZOWk3/zxfQsGo5fr7vHNrUCfE6lki+UpErkoXx8SH4qmEE2lTWffWw13FERERE8s2B5DTu+iyWd/6M45roML7uF0PV8oFexxLJdypyRY5Tq0FL5ofdTNv9k1j+969exxERERE5Y/G7Erls+Eym/bOLFy6N5LUrWxDg5+t1LJECoSJXJBtRN/wfW00Vgv54jLTUFK/jiIiIiOTZHyt2cNl7M9mflMbnd7bnlg51McZ4HUukwKjIFclG6aBy7Oz0AnUzNzL/m5e9jiMiIiJy2jIzLe9MXsOdn8VSt3IQP953DjH1KnkdS6TAqcgVOYmo865nUZkOtFgzgh2b472OIyIiIpJriSnp3PPFAob+/g+Xt6rJ2P4dqBlc2utYImeFilyRHFS5ehg+ZLJlzENeRxERERHJlfW7D3HFf2fy+8odPH1xU4Ze05JAf42/lZJDRa5IDmqEN2Zh3TtonTiNpdPGeR1HREREJEfT/tlFn/dmsPNgCp/d3o47zgnX+FspcVTkipxC6+ufZZOpQfDUJ0lJTvI6joiIiMgJrLWMnBbPbZ/MpUZwaX4aeA6dIip7HUvEEypyRU4hILAMCd1fppbdyoKvnvc6joiIiMgxklLTue+rhbz62youaF6dcfd0pFZIGa9jiXhGRa5ILjTvcjkLynal1fqP2LpulddxRERERADYtDeJK0fM4pel23isd2Peu74VZUr5eR1LxFMqckVyqeZ1b5GJDzu/ecDrKCIiIiL8HbebPu/NYMu+JD65tS0DutXX+FsRVOSK5FrVsPosaTCAqMOzWfT7l17HERERkRLKWstHM9Zx88dzqVw2gPEDz6FboypexxIpNFTkipyGNtf8h/U+tak68xkOHzrodRwREREpYZLTMhg0djH/9/MKejSuwvf3diK8cpDXsUQKFRW5IqfBv1QAST1fpzq7WPTl017HERERkRJk18EUrnl/FuMWbOHhng0ZeVMbygZo/K3I8VTkipymph0uYF6F82mz+X9s/GeR13FERESkhHhtwipWbz/Ih7dEc3+PBvj4aPytSHZU5IrkQfj1Q0k2pdj/3YPYzEyv44iIiEgxt3lfEj8s3ML17WrTs2lVr+OIFGoqckXyoHK1Wqxs8gDNUxayYMInXscRERGRYu6Dv9ZiDPTrWs/rKCKFnopckTyKvnIwcb71qTX3RRIP7PM6joiIiBRTOw8mM2beJq5sHUb1CqW9jiNS6KnIFckjXz8/Mi4YQmW7j2VfPO51HBERESmmPpq+jvSMTPp3re91FJEiQUWuyBloFH0u8yr3IWbHGOa9dS379+32OpKIiIgUIwlJqXw+ewMXt6hBXZ0qSCRXVOSKnKFWd3/ArLDbaZUwieS327F02jivI4mIiEgx8cnM9RxKzeDe7hFeRxEpMlTkipyhUgGBdLjzLdZe+gPJPqVpPuU25rzbl0MHE7yOJiIiIkVYYko6n/69np5Nq9KoWjmv44gUGSpyRfJJw9ZdqTp4DrOrXk/b3eNJGNqOFbN+8zqWiIiIFFGfz97A/sNpDFQrrshpUZErko8Cy5QlZsBIVl0wBoDGE65n9oh+JCclepxMREREipLktAxGTV9H5waVaVkr2Os4IkWKilyRAtA0pjfBD89lXujlxOwYw84h7fhnwVSvY4mIiEgR8U3sJnYnpmgsrkgeqMgVKSBB5YJpP/ATlp77KQGZydQffxmzPnyQ1JRkr6OJiIhIIZaWkcn709YSXaci7cNDvI4jUuSoyBUpYM27XE7pB+cxv+IFdNjyCZtfa0/80tlexxIREZFC6vuFW9iScJh7u0dgjPE6jkiRoyJX5CwoH1yJdg9+xaJzRlI+M4Fa317IrE8fJz0t1etoIiIiUohkZFpGTI0nskZ5ujUK9TqOSJGkIlfkLIo673r8Bs5hafkudFg/grWvdWLDqgVexxIREZFC4tel21i3+5BacUXOQIEVucaYQGPMXGPMYmPMcmPM8+7yKGPMbGPMImNMrDGmnbu8rjHmsLt8kTFmZJZ9tTHGLDXGxBlj3jF6xUsRFly5Gm0G/cD8dkMJTd9Gta/OZ/YXz5ORnu51NBEREfGQtZbhU+KoHxpE78hqXscRKbIKsiU3BTjXWtsSiAJ6G2NigNeB5621UcAz7u9HxFtro9xL/yzLRwB3Aw3cS+8CzC1yVrS58A4y+v/NiqC2xKwZyurXurJl7XKvY4mIiIhHJq/cyartB7mnWwQ+PmrTEcmrAityrePIyUH93Yt1L+Xd5RWArTntxxhTHShvrZ1lrbXAZ8BlBRJa5CyrXK02UYN/YV7US9RKjafi6O7M+eYNbGam19FERETkLLLW8t6UOMIqlqZPVA2v44gUaQU6JtcY42uMWQTsBH631s4BHgTeMMZsAoYAT2TZJNwYs9AYM80Y09ldVhPYnGWdze6y7O7vbrcLdOyuXbvy+dGIFAzj40PbywZy6M4ZxAdG0n7Fiyx7rQfbN8V5HU1ERETOklnxe1i0KYH+Xevj76tpc0TORIG+gqy1GW635DCgnTGmGTAAeMhaWwt4CPjIXX0bUNta2wp4GPjSGFMeyK6vhj3J/X1grY221kaHhmo2OilaqtWKoNljk5nT9CnqJy8naNQ5zP3+XbXqioiIlADvTYmjSrkArmoT5nUUkSLvrHxNZK1NAKbijKXtC4xzbxoLtHPXSbHW7nGvzwfigYY4LbdZX+1hnKKLs0hRZXx8aH/NI+zrO41NperTbvFTLB5yIbu3b/Q6moiIiBSQBRv38Xf8Hu7qXI9Af1+v44gUeQU5u3KoMSbYvV4aOA9YhVOgdnVXOxdYk2V9X/d6PZwJptZaa7cBB40xMe6syrcA4wsqt0hhULNeExo//hezGwyiyaFY/EZ2YP6vH516QxERESlyhv8ZR3AZf25oX9vrKCLFgl8B7rs6MNotXH2Ab6y1PxtjEoC3jTF+QDLOrMkAXYAXjDHpQAbQ31q7171tAPApUBr4zb2IFGs+vr7E3PgMG1ZdTMq3/Wgz92Hmr/yJ+n1HElxZpxUQEREpDlZsPcDkVTt5uGdDggIK8qO5SMlhnAmLi5/o6GgbGxvrdQyRfJGelkrsF8/Ret1IDphybD7nVaLOu97rWCIiInKG7v1yAdNW72LmY+dSoYy/13FEihRjzHxrbfTxyzV1m0gR4OdfiphbX2bTVb+y37ciUTP6M2/YdRxI2ON1NBEREcmj+F2J/Lp0Gzd3qKMCVyQfqcgVKULqN4+h1qOzmRV2O632TeTwsLYs/et7r2OJiIhIHoyYGk+Anw93nBPudRSRYkVFrkgRUyogkA53vsXaS78n2ac0zf+8lTnv9uXQwQSvo4mIiEgubd6XxA8Lt3Bd29pULhvgdRyRYkVFrkgR1bB1N6oOnsPsqtfTdvd4Eoa2Y8XsCV7HEhERkVz44K+1GAN3d6nndRSRYkdFrkgRFlimLDEDRrLqgjEANP7tOmaP6E9yUqLHyURERORkdh5MZsy8TVzRKowawaW9jiNS7KjIFSkGmsb0JvjhucyrfCkxO75ix5D2/LNgqtexREREJBsfTV9HekYmA7rV9zqKSLGkIlekmAgqF0z7+0az9NxPCcw8TL3xlzPrwwdJTUn2OpqIiIi4EpJS+Xz2Bi5uUYO6lYO8jiNSLKnIFSlmmne5nMAH5rKwYi86bPmEuCE9dKohERGRQuKTmes5lJrBvd0jvI4iUmypyBUphipUrEzbB8cQG/0GDVJXsvPdnuzducXrWCIiIiVaYko6n/69np5Nq9KoWjmv44gUWypyRYqx6IvvZkXXkYSlb+TgyPPZuWWd15FERERKrM9nb2D/4TQGqhVXpECpyBUp5lqeew1re/+PShl7SB91PpvjlnkdSUREpMRJTstg1PR1dG5QmZa1gr2OI1KsqcgVKQGadriAbZePpbQ9TODnF7FuxTyvI4mIiJQo38RuYndiisbiipwFKnJFSogGUZ05cO14MvEh5JtLdYohERGRsyQtI5P3p60luk5F2oeHeB1HpNhTkStSgtRp0ob0vr+RaMpSc/y1LJv5k9eRREREir3vF25hS8Jh7u0egTHG6zgixZ6KXJESpkZ4Y0rdNYldvlVoMOk2Fv3xldeRREREiq2MTMuIqfFE1ihPt0ahXscRKRFU5IqUQKE16hJ8z+9s8A+n2fR7iP3pfa8jiYiIFEu/Lt3Gut2H1IorchapyBUpoYIrV6PG/ZNYHRBJ69jHmPPNG15HEhERKVastQyfEkf90CB6R1bzOo5IiaEiV6QEK1u+IvUfnMCSMu1pv+JFZn32tNeRREREio3JK3eyavtB7ukWgY+PWnFFzhYVuSIlXGCZskQ+9CPzy51Lh7XvMOuD+7CZmV7HEhERKdKstbw3JY6wiqXpE1XD6zgiJYqKXBHBv1QAUQ+MZU6lS+mw9TPmDr+NzIwMr2OJiIgUWbPi97BoUwL9u9bH31cfuUXOply94owxFY0x7YwxXY5cCjqYiJxdvn5+tLv3U2ZVv4n2e35gwdvXkJaa4nUsERGRIum9KXFUKRfAVW3CvI4iUuKcssg1xtwJ/AVMBJ53fz5XsLFExAvGx4eYu95lVvi9RB/4g2XDLiP58CGvY4mIiBQpCzbu4+/4PdzVuR6B/r5exxEpcXLTkvsA0BbYYK3tDrQCdhVoKhHxjPHxoUPfl5nT5AlaJf1N3LALOXQwwetYIiIiRcbwP+MILuPPDe1rex1FpETKTZGbbK1NBjDGBFhrVwGNCjaWiHit/bWPM6/VKzROXsLmt89n/54dXkcSEREp9FZsPcDkVTu5vVM4QQF+XscRKZFyU+RuNsYEAz8AvxtjxgNbCzKUiBQObS+9h6Wd3iU8LZ69w89n9/aNXkcSEREp1IZPjaNsgB99O9T1OopIiXXKItdae7m1NsFa+xzwNPARcFkB5xKRQqLV+TexusfHVM3YRvL757Ntw2qvI4mIiBRK8bsS+XXpNm7uUIcKZfy9jiNSYuV2dmVfY0wNYB2wCKhWkKFEpHBp3uVSNl78JeXtAXw+uYANqxd5HUlERKTQGTk1ngA/H+44J9zrKCIlWm5mV74P2AH8DvziXn4u4FwiUsg0bnseu64chx/plPvqEuIWz/Q6koiISKGxeV8S3y/cwnVta1O5bIDXcURKtNzOrtzIWhtprW3uXloUdDARKXzqN48h6aZfSCWAquOuZOWciV5HEhERKRQ++GstxsDdXep5HUWkxMtNkbsJ2F/QQUSkaKgV0Rxu/40En4rU/fUmlkz9zutIIiIintp5MJkx8zZxRaswagSX9jqOSIl30nnNjTEPu1fXAlONMb8AKUdut9YOLeBsIlJIVavdgD39f2frB5fQeMpdLEg+SOvet3odS0RExBMfTV9HekYmA7rV9zqKiJBzS24597IRZzxuqSzLyhV8NBEpzCpVDaPywN9ZW6oRLWc9yNzv3/E6koiIyFmXkJTK57M3cHGLGtStHOR1HBEhh5Zca+3zZzOIiBQ9FSpWxv+BCax49zLaLX6a2YcPEHPDU17HEhEROWs+mbmeQ6kZ3Ns9wusoIuLK1SmEREROpkzZCjR86BcWBHUm5p83mPXxo9jMTK9jiYiIFLjElHQ+/Xs9PZtWpVE1dXQUKSxU5IrIGQsILEOLB8cxL/gCOmx8nznvD1ChKyIixd4Xszew/3AaA9WKK1KoqMgVkXzh51+KNvd9wezQq4nZMYZ5795ERnq617FEREQKRHJaBh9OX0fnBpVpWSvY6zgiksVJx+QeYYwJBe4C6mZd31p7e8HFEpGiyMfXl/YDPmDWJxXosGkUC4ZdQbP7vqFUQKDX0URERPLVN7Gb2J2Ywr3dW3kdRUSOk5uW3PFABeAP4JcsFxGRExgfHzrc8SazGzxM68RprHrrYg4fOuh1LBERkXyTlpHJ+9PW0qZORdqHh3gdR0SOc8qWXKCMtfaxAk8iIsVKzI3PMve78kQveZ7Vw3pR896fKB9cyetYIiIiZ+z7hVvYknCYFy9rhjHG6zgicpzctOT+bIy5sMCTiEix0+7Kh1jY/k0iUlex892e7N25xetIIiIiZyQj0zJiajyRNcrTrVGo13FEJBu5KXIfwCl0k40xB93LgYIOJiLFQ5sL72BF15GEpW/k4Mjz2bE53utIIiIiefbr0m2s232Ie7tHqBVXpJA6ZZFrrS1nrfWx1ga618tZa8ufjXAiUjy0PPca1vb+H5Uy9pA5qheb45Z5HUlEROS0WWsZPiWO+qFB9I6s5nUcETmJXJ1CyBjTxxgzxL1cXNChRKT4adrhArZdPpZAkgn8/CLWLZ/jdSQREZHT8ueqnazafpB7ukXg46NWXJHC6pRFrjHmVZwuyyvcywPuMhGR09IgqjMHrh1PJj6EjL2c+b+MwmZmeh1LRETklKy1vDcljrCKpekTVcPrOCKSg9y05F4I9LTWfmyt/Rjo7S4TETltdZq0Ib3vb+zyrUabeYNY/lp3Nqyc73UsERGRHM2K38PCjQn071off99cdYYUEY/k9hUanOV6hQLIISIlSI3wxoQ/MZc5Tf5D7ZQ11BjTk9kj+pN4YJ/X0URERLL13pQ4qpQL4Ko2YV5HEZFTyE2R+wqw0BjzqTFmNDAfeLlgY4lIcefr50f7ax8j/Z55LAy5gHbbx3B4aCtif3pfXZhFRKRQWbBxH3/H7+GuzvUI9Pf1Oo6InEJuZlf+CogBxrmXDtbaMQUdTERKhpAqNWn3wBfEXfoDCX6ViZ7/KCtf7aKJqUREpNAY/mccwWX8uaF9ba+jiEgunLTINcY0dn+2BqoDm4FNQA13mYhIvmnYuhv1Hp/N3GbPUj11PbW+6c3s/97FgYQ9XkcTEZESbMXWA0xetZPbO4UTFODndRwRyYWcXqmDgLuAN7O5zQLnFkgiESmxfP38aHfVwyR0u4H5Xz1Gux1j2TdsIvOiHqXNJQPw8VUXMRERObuGT42jbIAffTvU9TqKiOTSSYtca+1d7s/uZy+OiAgEV65G+/tGs2bRdDJ/HkTbRU+yavkX+F/yJvVbdPQ6noiIlBBrdyXy69Jt9O9anwpl/L2OIyK5dNIi1xhzRU4bWmvH5X8cEZF/NYjqTGbzWcwb/x4RS4ZQ/rsLmfPX5TS+4XUqhIR6HU9ERIq5EVPjCfDz4Y5zwr2OIiKnIafuypfkcJvFmYRKRKRA+fj60vaKB9jf7QZiv3yM6F3j2P/OH8xrMZg2lw5UF2YRESkQm/cl8f3CLdwUU4fKZQO8jiMipyGn7sq3nc0gIiI5qRASSvuBHxO/5E5SfxpE2yXPsHrFF/hc/CYNojp7HU9ERIqZD/5aizFwd5d6XkcRkdOUU3flh3Pa0Fo7NP/jiIjkrH6LjthmM5n34wjCF71OyPeXMGd6Hxpd/zrBlat5HU9ERIqBnQeTGTNvE1e0CqNGcGmv44jIacrpPLnlTnEREfGE8fGh7WX3UurBBcytejVtdv8E77Vhztg3yUhP9zqeiIgUcR9NX0d6RiYDutX3OoqI5IGx1nqdoUBER0fb2NhYr2OIyFmwbvkcDo8fRNPUpazxa4C9cAgNW3fzOpaIiBRBCUmpdHr1T3o0qco717fyOo6I5MAYM99aG3388py6Kz9qrX3dGPMuzkRTx7DW3p/PGUVE8iQ8sj22yV/E/vIhdea/QqXxlzF3+kVEXP86IVVqeh1PRESKkE9mrudQagb3do/wOoqI5FFO3ZVXuj9jgfnZXERECg3j40P0Jf0IfGgBc6tdR6u9v+H737bM+fo1dWEWEZFcSUxJ59O/19OzaVUaVdPoPJGiSt2VRaRY2rByPgd/eJhmKYuI861Peu/Xadz2PK9jiYhIIfb+tHhe+W0V4+/tRMtawV7HEZFTOFl35Zxaco9sGG2M+d4Ys8AYs+TIpWBiiojkjzpN2hD52BTmtxtK+Yx9NP7lSua9dS27t2/yOpqIiBRCyWkZfDh9HZ0bVFaBK1LEnbLIBb4APgGuBC7JchERKdSMjw9tLryDoEELmVX9Flom/E6pke2Y/dVLpKeleh1PREQKkW9iN7E7MUVjcUWKgdwUubustT9aa9dZazccuRR4MhGRfBJULpgO/d5l+41/siGgMTGrX2fjK21ZMXuC19FERKQQSMvI5P1pa2lTpyLtw0O8jiMiZyg3Re6zxphRxpjrjTFXHLkUeDIRkXxWu2EUzR6bzMIO71Am8xBNJ1xL7NCr2L1V39uJiJRk3y/cwpaEwwzsHoExxus4InKGclPk3gZEAb35t6vyxQWYSUSkwBgfH1r16kuFRxYyq+ZttNg/hcD32zP7i+dJS03xOp6IiJxlGZmWEVPjiaxRnm6NQr2OIyL54KTnyc2ipbW2eYEnERE5i0oHlaPDXcPYFHcHe799iJg1Q1n/6rcc6vEqkZ0u8jqeiIicJb8u3ca63Yf4742t1YorUkzkpiV3tjGmaYEnERHxQK2I5rR4dBILOw6nlE0m8vcbmDvsep1bV0SkBLDWMnxKHPVDg+gdWc3rOCKST3JT5J4DLDLGrHZPH7RUpxASkeLE+PjQ6vybCHnEmYW5XcKvzBt1v9exRESkgP25aierth/knm4R+PioFVekuMhNd+XeBZ5CRKQQCCxTlg793mXOe4eI2f4F88Y1ou0VD3gdS0RECoC1lvemxBFWsTR9omp4HUdE8tEpW3KznjZIpxASkZKgTb+RLA1oTcvFz7Ni1m9exxERkQIwK34PCzcm0L9rffx9c9O5UUSKCr2iRUSO4+dfitr9vmG7bzWqT7yLLWtXeh1JRETy2XtT4qhSLoCr2oR5HUVE8pmKXBGRbFQICcXc8DU+ZJL2+dUc3L/X60giIpJPFmzcx9/xe7ircz0C/X29jiMi+UxFrojISdSKaM6m80ZSM2Mra0deqxmXRUSKieF/xhFcxp8b2tf2OoqIFAAVuSIiOWh2Th8WNPsPLQ/PZd6HA72OIyIiZ2jF1gNMXrWT2zuFExSQmzlYRaSoUZErInIK7a8ezOzQq4nZ8RVzv3vL6zgiInIGhk+No2yAH3071PU6ikjhlJoEe+Jh7zqvk+RZgX19ZYwJBP4CAtz7+dZa+6wxJgoYCQQC6cA91tq57jZPAHcAGcD91tqJ7vI2wKdAaeBX4AFrrS2o7CIix4u++78seXMdrZb8H8urNyKy44VeRxIRkdO0dlcivy7dRv+u9alQxt/rOCJnV0YaJO6Ag9vhwFbn58FtWS7u78n7nfWbXgbXjPY0cl4VZB+NFOBca22iMcYfmGGM+Q14AXjeWvubMeZC4HWgmzGmKXAdEAnUAP4wxjS01mYAI4C7gdk4RW5vQOf1EJGzxs+/FHX6fc3Wd7tSY9LdbKk2kZr1Ir2OJSIip2HE1HgC/Hy445xwr6OI5J/MTEjak33BeiDL74d2Ace1E/r4QdlqUL46VG4A4V2gXDUoVwNCG3rycPJDgRW5bktrovurv3ux7qW8u7wCsNW9fikwxlqbAqwzxsQB7Ywx64Hy1tpZAMaYz4DLUJErImdZhYqVOXjD15jPe5H++bUcuH8a5YMreR1LRERyYfO+JL5fuIWbYupQuWyA13FETs1aSDmQfcF68EhLrHvJTDtx+6DQfwvWGq2gXHWnmC1X/d/lZSqBT/EbwVqgo+2NMb7AfCACGG6tnWOMeRCYaIwZgjMmuKO7ek2cltojNrvL0tzrxy8XETnrwiKasbznBzScdDMrR15L00G/4udfyutYIiJyCh/8tRZj4O4u9byOIgJph7MUqVm7Dm8/tphNO3TitgEVnCK1fHWoe86/BWu5av8WskFVwK/kfj4p0CLX7WocZYwJBr43xjTD6Xb8kLX2O2PMNcBHwHmAyW4XOSw/gTHmbnf/1K6tKeFFpGBEdrqIudueot2y55n94UBi7vnA60giIpKDnQeTGTNvE1e0CqNGcGmv4+RN2mFY/Rtsmgt+AVAqCPzLQKky4B/k/iyTZXnQsdd9dD7gsyIjHQ7tzFKwbs2+mD2878Rt/QL/LVirt4SGvY9reXV/lgo6+4+riDkr86ZbaxOMMVNxxtL2BR5wbxoLjHKvbwZqZdksDKcr82b3+vHLs7ufD4APAKKjozUxlYgUmHZXPczsnauI2fk1c8Y2ov3Vg7yOJCIiJ/HR9HWkZ2QyoFt9r6OcnswMWPcXLB0LK36E1IPgVxoy07PvnpoT34AcCuIjy4NysU6Wn6WCnOu+JeBUTNZC0t5sugsfP+51J9jMY7c1vlC2qlOwhtSDOh3dgrX6sa2vgcFgsmvfk9NVkLMrhwJpboFbGqe19jWcArUrMBU4F1jjbvIj8KUxZijOxFMNgLnW2gxjzEFjTAwwB7gFeLegcouI5Fb0Xe+x5M21tF72EstqNKRZp0u8jiQiIsdJSErl89kbuLhFDepWLgItYNbC9iWw5BtY+i0kbodS5aDppdDiaqjb2WmVzUiD1EOQluSc8iXt0HE/k5zbj66T3bpJzmRECcctz0g5vcy+pXIoiE/WupxN0ZzdPs5Gl9uUg6cY9+r+npF64rZlKv3bVbhacyhf49hW13I1IKiyWtLPsoL82qU6MNodl+sDfGOt/dkYkwC8bYzxA5Jxuxdba5cbY74BVuCcWuhet7szwAD+PYXQb2jSKREpBPz8S1G3/9dsfacrYb/3Z3PVcMIimnkdS0REsvhk5noOpWZwb/cIr6PkbN8Gp8V26VjYtcqZ9bbB+dD8amh0Afgf183a1x9KBzuX/JaRfmKxnFOhfMJy97akPZC66dh10pNPL4uPfy5bl7Ppon3kp19AllbYbLoRpx488X5LlXO7CleD2h2yH/datqqzbyl0THE93Wx0dLSNjY31OoaIlABb1i6nzGe9OOBTgeD7plGhYmWvI4mICJCYkk6nV/+kXXgIH94S7XWcEyXthRU/OK22G2c5y2rFQItrIPJyKBPiabwCkZlx8kI5x2I6p6LaXZZ++NT371sq+4I1a8truaoQUK7gnws5Y8aY+dbaE17cJaADvYhIwapZL5Ll539Aw4k3sfL9a2g6aIJmXBYRKQS+mL2B/YfTGFiYWnHTkuGfCU5hu2aSM7a2ckM49ymn1bZiXa8TFiwfX6eALIgiMjPTKX6PL4TTD7vdiqtD6Yoa91oCqMgVEckHkR0vZO62Z2i39Flmf3APMfeOOvVGIiJSYJLTMvhw+jo6N6hMy1rB3obJzID1M2DpN84EUikHnK6u7fs5hW31liq88oOPDwSUdS5SoqnIFRHJJ+2ufNCZcXnHV8z5phHtr3nE60giIiXWN7Gb2J2Ywr3dW3kTwFrYsQyWfA1Lv3MmMSpVFpr0cbojh3fRZEQiBURFrohIPmp713ssfjOeNstfZtmMRjQ7p4/XkURESpy0jEzen7aWNnUq0j78LI9rTdjkTB615BvYtdKZQCriPOj1IjS8wJkkSUQKlIpcEZF85OvnR73+X7P57S7U+qM/m6qFUyuiudexRERKlO8XbmFLwmFevKwZ5mx0Az68D5b/4BS3G2Y6y2q1hwuHQOQVEFSp4DOIyFEqckVE8lm5CiEcuGksmZ+dD19cw/77/qJCSKjXsURESoSMTMuIqfFE1ihPt0YF+N6blgxrJv47gVRGKlRqAN2fguZXQUh4wd23iORIRa6ISAGoWa8JK3p/SMRvN7D6/atpMniSZlwWETkLfl26jXW7D/HfG1vnfytuZqbTUrvka3cCqf0QVAXa3umMs60epQmkRAoBFbkiIgWkaUxv5m19lrZLnmHO+/1pP/BjryOJiBRr1lqGT4mjfmgQvSOr5d+Odyx3J5D6Fg5sAf8gaHKJO4FUV/DVR2qRwkSvSBGRAtT2igeYvXM1Mdu/YM7XjWh/7WNeRxIRKbb+XLWTVdsP8ubVLfHxOcMW1f2bnaJ2yTewczkYX2cCqZ4vQKMLoFRQ/oQWkXynIldEpIC1vfMdFr0ZT5sVr7L0r8Y073Kp15FERIoday3vTYkjrGJp+kTVyNtODifAivHOBFLrZwAWwtq6E0hdDkGV8zOyiBQQFbkiIgXM18+P+v2/YtPb3ajz5wA2Va9LrQYtvY4lIlKszIrfw8KNCbx4WTP8fX1yv2F6ijNx1JKv4Z+J7gRSEdDtCWcCqUr1Cy60iBQIFbkiImdBuQohHLxlLOmfngdfXqcZl0VE8tl7U+KoUi6Aq9qEnXrlzEzY+LfTFXnFD5C8H4JCIfp2Z5xtjdaaQEqkCFORKyJyltSo24iVF4yi/q/X88/7V1Fm0CT8SwV4HUtEpMhbsHEff8fv4ckLmxDo73vyFXesyDKB1GZ3AqmLofk1UK+bJpASKSb0ShYROYuatO/F3K3P027xU8z5oD/tB37idSQRkSJv+J9xBJfx54b2tU+8cf8WWPYtLBkLO5Y6E0jVPxfOew4aX6gJpESKIRW5IiJnWbvL72PWzlV02Pa5O+Py415HEhEpslZsPcDkVTt5uGdDggLcj7bJ+53z2C75+t8JpGpGwwWvQ+QVUFbDRUSKMxW5IiIeaHfH2ywaGk+bFa+x9K9GNO9yudeRRESKpP9OjaNsgB9921aHlT/D0m9g9QTISIGQetDtcWh+tSaQEilBVOSKiHjA18+PiP5fsWlYN+r8eQ8bqoZTp1GU17FERIqUtTsPsHPZFD4PW0KF/94NyQlQpjK0uRVaXAs1NYGUSEmkIldExCNly1fkYN9vSPukJ75jrmP/wGlUqFTV61giIoXfzlWw5GsqzvmCb0rtwO4tA00ucgrbet3A19/rhCLiIRW5IiIeql6nEasu/Ih6v1zHPx9coxmXRURO5sA2dwKpr2H7UqzxYUlGc/aE9+OKG/pBQFmvE4pIIaEiV0TEY43b9WTe1hdou+g/zHn/Ltrd+ynGx8frWCIi3ks+ACt/dM5nu+4vwDrnsO39Gq9vbsqohYlMu7w7BJT2OqmIFCIqckVECoG2l93rzLi89TNmf/0qMdf/x+tIIiLeSE+FuD/cCaR+g/RkqBgOXR91zmdbOYKdB5P56OcpXNEqjBrBKnBF5FgqckVECon2dwxj4ZtxtF31OkumNqJFtyu9jiQicnZYC5vmOF2Rl38Ph/dBmUrQ+hansA2LPmYCqY+mryM9I5MB3TRjsoicSEWuiEgh4ePrS8MBX7F+WDfqTh3Ihmrh1Gnc2utYIiIFZ9dqpyvy0m8gYSP4lYbG7gRS9btnO4FUQlIqn8/ewMUtalC3cpAHoUWksFORKyJSiASVCyao71jSPu6B79fXk3DvNIIrV/M6lohI/vvxflgwGoyPMyNy9yedAjegXI6bffr3eg6lZnBv94izk1NEihzNbCIiUshUq92AXRd9TJXM3Wz54GpSU5K9jiQikr/iJjsFbvTt8PAquPl7aHndKQvcxJR0Ppm5np5Nq9KoWs7rikjJpSJXRKQQatz2PJa0eZHI1CUsev9ObGam15FERPJHWjL8OhgqRUDvV6Fc7s8P/sXsDew/nMZAteKKSA5U5IqIFFLRfQYwq+attNv7E3PGvOx1HBGR/PH3O7B3LVz4Bvjl/rzgyWkZfDh9HZ0bVKZlreCCyyciRZ6KXBGRQqz97UNZGHQObVcPYfGUsV7HERE5M/vWw/Q3IfJyqH/uaW36TewmdiemcE83teKKSM5U5IqIFGI+vr407P8F6/3CqTf1PtatmOd1JBGRvLEWfn0UfPyg1+n1TknLyOT9aWtpU6ciMfVCCiigiBQXKnJFRAq5oHLBBN06lmQTSOWvL2HhpM+9jiQicvpW/wprJkK3J6B8jdPa9PuFW9iScJiB3SMwWc6XKyKSHRW5IiJFQLVaEWTc/jvb/cNo9fe9zPrwATLS072OJSKSO6lJ8NvjUKUptO93WptmZFpGTI0nskZ5ujUKLaCAIlKcqMgVESkiqtVuQK1B05hb8WI6bPmU5UPOJ2H3dq9jiYic2vQhsH8jXDQUfP1Pa9Pflm1j3e5D3KtWXBHJJRW5IiJFSGDpINo98AVzmz9H48OLOfzeOcQtnuF1LBGRk9v1D8x8B1reAHU6nNam1lqGT4mnfmgQvSOrFVBAESluVOSKiBRB7a58iPWXjsNgqTXuMuZ+/47XkURETmStc07cUmWg5wunvfmfq3ayctsB7ukWgY+PWnFFJHdU5IqIFFENW3el1D1/sSYwknaLn2bOu7eQkpzkdSwRkX8t+w7WTYMez0DZ0xtPa63lvSlxhFUsTZ+o05uoSkRKNhW5IiJFWEiVmjQe/Duzqt9C+z3jWT+kGzs2x3sdS0QEkg/AxCehRitoc9tpbz4rfg8LNybQr2t9/H31kVVEck/vGCIiRZyffyk69HuXhR3eISxtA/6jurFs5k9exxKRkm7qq5C4Ay56E3x8T3vz96bEUaVcAFe3CSuAcCJSnKnIFREpJlr16svuGyZy0KcCTSbdzOzPn8VmZnodS0RKou3LYM5IiL4NarY57c0XbNzH3/F7uKtzPQL9T79AFpGSTUWuiEgxUqdRFJUenM7icp2JiRvGwqGXkXhgn9exRKQkycyEXx6G0sFw7tN52sXwP+MILuPPDe1r5282ESkRVOSKiBQzZctXpNXD45kd8SAtD/7FnmGd2fjPIq9jiUhJsfhL2DQHev4flAk57c1XbD3A5FU7ub1TOEEBfgUQUESKOxW5IiLFkPHxIeam51l5/v8ol7mfkC96s2Di/7yOJSLFXdJe+P0ZqBUDLa/P0y7+OzWOsgF+9O1QN3+ziUiJoSJXRKQYa9bpElLvmMJW/1q0njWQWR/cR0Z6utexRKS4+vP/4HCCO9nU6X/MXLsrkV+WbuPmDnWoUMY///OJSImgIldEpJirViuCOoOnMafSpXTY+hkr3jiPfbu2eR1LRIqbLfMh9hNo3x+qNcvTLkZMjSfAz4c7zgnP53AiUpKoyBURKQECAsvQ/r7PmNvy/2iYvIyU4Z1Zs/Avr2OJSHGRmQE/Pwxlq0K3x/O0i837kvh+4Raua1ubymUD8jmgiJQkKnJFREqQdpffz8bLv8cCtX+4grnfDfM6kogUB7Efw7ZF0PtlCCyfp1188NdajIG7u9TL32wiUuKoyBURKWEaRHUm8N7p/BPYnHZLn2XuOzeRfPiQ17FEpKhK3OWMxa3XDSKvyNMudh5MZsy8TVzRKowawaXzN5+IlDgqckVESqCKodVp+sjvzKp5K+32/sSmN7uyfeMar2OJSFH0+zOQmgQXDgFj8rSLj6avIz0jkwHd6udzOBEpiVTkioiUUL5+fnS4620WdhxOtbTNBHx8Lsumj/c6logUJRv+ds6L2+l+qNwgT7tISErl89kbuLhFDepWDsrngCJSEqnIFREp4VqdfxMJN01kv08FmvzRl1mfPY3NzPQ6logUdhlp8MsgqFAbOg/O824+/Xs9h1IzuLd7RD6GE5GSTEWuiIhQq0FLQh+awaJyXemw9h0WvtmHg/v3eh1LRAqzOSNh5wq44DUoVSZPu0hMSeeTmevp2bQqjaqVy+eAIlJSqcgVEREAgsoF0/rh75nd4GFaJM5k79ud2bBqgdexRKQwOrAVpr4KDXtD4wvzvJsvZm9g/+E0BqoVV0TykYpcERE5yvj4EHPjs6w+/3PKZh6k8lcXsGDCp17HEpHCZuJ/IDPdacXNo+S0DD6cvo7ODSrTslZw/mUTkRJPRa6IiJwgstNFpN85hc3+dWk9+wFmj7yH9LRUr2OJSGEQNxmWf++Mw61YN8+7+SZ2E7sTU7inm1pxRSR/qcgVEZFsVQ2rT93BU5hT6TJitn/BqiE92btzi9exRMRL6Snw6yMQUt+ZUTmPDiSn8c7kONrWrUhMvZB8DCgioiJXRERyEBBYhvb3jWZe1EtEJC8n7b+d+WfBVK9jiYhXZr4De+PhwjfALyDPuxk66R/2HErh2UsiMXk8t66IyMmoyBURkVNqe9lANl8xngzjS93xVzJn7Js6zZBISbNvPUwfAk0vg4geed7Niq0H+GzWem5qX4dmNSvkWzwRkSNU5IqISK5EtOxE0MAZrCrdivbLX2DeOzeSfPiQ17FE5Gz57XEwvtDr5TzvIjPT8sz4ZVQsU4rB5zfKx3AiIv9SkSsiIrlWoVJVIgdPYHbYHbRL+JVNQ7qwbcNqr2OJSEFb9Sv88xt0fwIq1MzzbsYt3ELshn08fkFjKpTxz8eAIiL/UpErIiKnxdfPj5g7h7LonJFUzdhK4Cc9WPrX917HEpGCkpoEvz0GoU2gff8872Z/Uhqv/LqSNnUqcmXrsHwMKCJyLBW5IiKSJ1HnXc/+myaR4BNC5OTbmPXhg6SlpngdS0Ty2/QhsH8jXDwUfPPe+vrm76vZl5TKC5dG4uOjyaZEpOCoyBURkTyrFdGcqg9PZ37FC+iw5RPWvt6ZTfHLvY4lIvll9xpnRuWW10OdjnnezbIt+/l89gZu6VCXyBqabEpECpaKXBEROSNlylag7YNfsbD9MKqnbybks3OZPe5dzb4sUtRZC78OBv8y0POFPO8mM9Py1A/LCAkK4KGeDfMxoIhI9lTkiohIvmh1wW0k3zmdDQENiVnyFLFvXs6+Pbu8jiUiebV8HKydCj2ehrJV8rybsfM3sWhTAv+5sDEVSmuyKREpeCpyRUQk31QJq0/jR6cwr95AohKnk/xuDItm/Op1LBE5XSkHYeKTUD0Kom/P824SklJ59bdVtKsbwuWt8j4rs4jI6VCRKyIi+crHz4+2t7zEpsu/J9PHn+a/38BfIx8gOTnZ62gikltTX4WD2+GioeDjm+fdvD5xNQeS03nhskiM0WRTInJ2qMgVEZECUS+qK5UGzWFxpQvpsv1T1r3RhbWrl3odS0ROZcdymD0C2twKYW3yvJvFmxL4au5Gbu1Yl8bVyudfPhGRU1CRKyIiBSYwqAKt7/+SZR3foWbGZqp8eR5/ffM2mRmalEqkUMrMhJ8fhtLB0OOZPO8mI9Py9PhlhJYN4MHzGuRfPhGRXFCRKyIiBa7Z+X1Jv3s6WwIb0mXFM8x983J27dzhdSwROd7ir2DTbGc25TIhed7NmHkbWbJ5P09e1IRygZpsSkTOLhW5IiJyVoTUqE/DR6ewsMF9tDk0nfT/dmTu1J+9jiUiRxzeB78/A7XaQ8sb8rybvYdSeX3CamLqhdCnZY18DCgikjsqckVE5Kwxvn60uvFFtl81HuvjT5spN/Hnf+8n6fBhr6OJyOT/g8N74aI3wSfvHxFfn7CKQynp/N+lzTTZlIh4QkWuiIicdbWad6byoDksD72Qc3eOZv0bXVi1YrHXsURKri3zIfZjaN8fqjXP824WbNzHmHmbuOOccBpULZePAUVEck9FroiIeKJUUAVaDPyS1Z3fISxzC2Ffn88fXw0jQ5NSiZxdmRnwyyAoWxW6PZHn3WRkWp4Zv4xq5QO5r4cmmxIR76jIFRERTzXq0Rf6z2RbmYact/pZZr9xGVu2b/M6lkjJMf8T2LoQer0EgXk/1c+XczawbMsBnrq4CWUD/PIxoIjI6VGRKyIinitfLZyIwVNY1vgB2h+ejhl5DtP/GO91LJHiL3EXTH4BwrtAsyvzvJvdiSm8MXE150RU5qLm1fMxoIjI6VORKyIihYLx9aPZdS+w+5qfMb7+dJzel4nv3seBJE1KJVJg/ngWUpPgwjfhDCaJevW3VRxOy+C5PpGabEpEPKciV0RECpVqkZ0IHTSHVVUvoteez9j4RheWLFnodSyR4mfDLFj0BXS8D0Ib5nk3sev38u38zdzZuR4RVcrmY0ARkbwpsCLXGBNojJlrjFlsjFlujHneXf61MWaRe1lvjFnkLq9rjDmc5baRWfbVxhiz1BgTZ4x5x+grQhGRYs2vTAUi7/mCtd3eo47dTL3vevPb50NJS8/wOppI8ZCRBr88DBVqQZfBed5NekYmT49fTo0Kgdx3bkQ+BhQRybuCbMlNAc611rYEooDexpgYa+211tooa20U8B0wLss28Udus9b2z7J8BHA30MC99C7A3CIiUkjU63YzPvfMYkdQIy6Ie55Zb1zOhi1bvY4lUvTNeR92roDer0KpoDzv5n+zN7By2wGeuaQpZUppsikRKRwKrMi1jkT3V3/3Yo/c7rbGXgN8ldN+jDHVgfLW2lnWWgt8BlxWIKFFRKTQCapSl/qDp7Cq6YN0TJmO3wddmDzxB5x/CSJy2g5shamvQINe0PiiPO9m58Fkhk76hy4NQ+kVWS0fA4qInJkCHZNrjPF1uyPvBH631s7JcnNnYIe1dk2WZeHGmIXGmGnGmM7usprA5izrbHaXiYhISeHjS+Nrnifh+p/x9fOj29+38ss7A9l3MMnrZCJFz8QnITMdLnjtzCab+nUVKemZPK/JpkSkkCnQItdam+F2Sw4D2hljmmW5+XqObcXdBtS21rYCHga+NMaUB7J718z263tjzN3GmFhjTOyuXbvy5TGIiEjhUblRJ6oMnktc9Yu5eN/nbB7alXkL5nsdS6ToiJ8Cy8dB50EQEp7n3cxZu4dxC7fQr2s9wivnvbuziEhBOCuzK1trE4CpuGNpjTF+wBXA11nWSbHW7nGvzwfigYY4LbdhWXYXBmQ7IMta+4G1NtpaGx0aGpr/D0RERDznU7o8jfp/zsZz3yPcbqHx+Iv4YfSbJKemex1NpHBLT4FfB0NIPeh4f553k5aRyTPjl1MzuDT3dNNkUyJS+BTk7Mqhxphg93pp4DxglXvzecAqa+3m49b3da/Xw5lgaq21dhtw0BgT447jvQUYX1C5RUSkaKjd5Wb87p3FnrINuWzdC8x643LiNm4+9YYiJdXf78CeOLjwDfAPzPNuRv+9ntU7DvLsJU0pXco3HwOKiOSPgmzJrQ5MMcYsAebhjMn92b3tOk6ccKoLsMQYsxj4Fuhvrd3r3jYAGAXE4bTw/laAuUVEpIgIDK1D3UFTiG/+EJ3TZhD4UVd++XmcJqUSOd6+DfDXm9D0Uog4L8+72XEgmWF/rKF7o1B6Nq2ajwFFRPKPKa4fBKKjo21sbKzXMURE5CzZ98/fpH5zB5XTtvFThRvoeMfrVKlQ1utYIoXDV9fD2mkwcB5UyPv8nfd/tZAJy7fz+0NdqFNJY3FFxFvGmPnW2ujjl5+VMbkiIiIFrWLDjlQZPId1NS/hsgNfsP2t7syYO8/rWCLeW/0brP4Vuj12RgXu3/G7+XHxVgZ0ra8CV0QKNRW5IiJSbJjA8kTc/T+29fwv9cwWWv5yCWM+eJV1uxJPvbFIcZSaBL89CqGNIeaePO/myGRTtUJKM6Bb/XwMKCKS/1TkiohIsVO90434D5zFvvKNuW7rK+x5tzuvfPQVizcleB1N5Oya/iYkbISL3gRf/zzv5uMZ64jbmcjzfSIJ9NdkUyJSuKnIFRGRYimgUh1qP/QnB85/iyYBu3hs0wCWv38rd4+YwNTVOzU5lRR/u+OcGZVbXAd1z8nzbrbtP8zbk9dwXpOqnNtYk02JSOGnIldERIovHx/Kd7ydoEGLSW/bn+v8p/PmjtuY9tkLXDxsCj8s3EJ6RqbXKUXyn7Xw6yDwKw3n/98Z7erFX1aSkWl59pKm+RRORKRgqcgVEZHiL7ACpS56FZ97/iaoXgzP+v+P4QfvZ+zY/9H1jal8MnMdSanpXqcUyT/Lv4e1U+Hcp6BslTzvZsaa3fyyZBsDu0dQK6RM/uUTESlAOoWQiIiULNbC6l+xE57AJGxgdkAnBh+4hsTSNbilQ11u7ViXkKBSXqcUybuUg/BeWwgKhbungk/extCmpGdwwbDpZFrLhAe7aCyuiBQ6OoWQiIgIgDHQ+CLMvXPh3KeJyVzIX2Ue5f/Kj+eDycvo+Opknhm/jE17k7xOKpI3U1+Fg9vh4rfyXOACfDRjHWt3H+I5TTYlIkWMilwRESmZ/AOhy2AYGItPk0u4JOFzllZ+kifrrOKruRvoNmQq93+1kGVb9nudVCT3diyH2SOgTV8IO6FxI9e2JBzm3clx9IqsSrdGee/uLCLiBRW5IiJSslWoCVd9BLf9hn/ZSty8+TmWhb/L463T+XPVTi5+dwY3fzSHmXG7NSOzFG7Wwi+DILAC9Hj2jHb1fz+twGJ55pLIfAonInL2qMgVEREBqNMR7p4GF79FwJ7V3LW8L/NbT+DpHlVZue0gN46aQ5/3ZvLzkq1kZKrYlUJo8VewcRb0fB7KhOR5N1NX72TC8u3cd24DagaXzseAIiJnhyaeEhEROV7SXpj6CswbBYEVSOv6JN+Z83h/+gbW7T5E7ZAy3NWlHle3CdNYRSkcDu+Dd6MhpB7cPhF88taOkZKeQa+3/sLHGH57sDMBfjq+RaTw0sRTIiIiuVUmBC58A/rPgKrN8J8wmOsW3MwfV/ox8qbWVAwqxdM/LKPTq3/y7uQ1JCSlep1YSro/X4TDe+GiN/Nc4AJ8MG0t6/ck8fylkSpwRaTIUpErIiJyMlUjoe9PcPVoSE7Ad/RF9F75H364sTZj7o6hRVgF3vz9Hzq++icv/LSCLQmHvU4sJdGWBTDvI2h3N1RvkefdbNqbxHtT4rioeXU6NwjNx4AiImeXuiuLiIjkRmoSzHwbZg4DDHQeBB3vY9WeVD6YtpYfF28FoE/LGvTrWp9G1cp5GldKiMwMGHUeHNgCA+c5k07l0V2fxTIzbjeTB3WlegWNxRWRwk/dlUVERM5EqTLQ/QmnkGh4Pkx5EYa3o/G+aQy9piXTHu3OLR3qMmH5dnoN+4vbPpnLnLV7NCOzFKz5n8LWBXD+S2dU4P65age/r9jBAz0aqMAVkSJPLbkiIiJ5sXYa/PYY7FoJ9bpB79egSmMSklL536wNfPr3evYcSiWqVjD9u9bn/KZV8fExXqeW4uTQbni3DVRr7nSrN3k7vpLTMuj51jQC/Hz59f7OlPJTG4iIFA1qyRUREclP9bo6E1Nd8AZsXQgjOsKEJwg2SdzXowEzHz+X/7usGXsPpdL/8/mcN3QaY+ZuJCU9w+vkUlz8/iykHnImm8pjgQswclo8m/Ye5oU+kSpwRaRY0DuZiIhIXvn6Qfu74b6F0PoWmD3CaVmbP5pAX7g5pg5/DurKeze0okyAL4+PW8o5r01hxNR4DiSneZ1eirKNs2HR/7d35+FRVnf/x9+HJIQdZBUCigqKgCyCgLsPomJVYvVx3+qCu1ZbtbW1Vat9HrVqbfXR1n23/tyK+4ILagXCjqyyyRpZZYdAkvP7Y8YaKSAMSYZM3q/rmovk3DNnvnPxvWb4cO77zLNw4BXQbJ+Up5m9dA0PfjKDE7q24qB2TcuxQElKH09XliSpvBSOS5zCPGcotOwGx94Fu/UGIMbIFzOW8rchM/hs2hLq5WaT360V+d3y6Ln7Lp7KrG1XUgx/PwzWr4ArC6Bm3ZSmiTFywZMjKJi1jI+uO4IWDWqVc6GSVLG2dLpydjqKkSQpI7XsCue/AxNegfd/B48fDV1Og363Ehq05OB2TTm4XVMmzF/BY5/P4tXR83lu+BxaNazFCd1akd81j31b1ifswKmnqgYK/g6LJsJpz6YccAEGT17Ex1MXc9Nx+xpwJWUUV3IlSaoIRavh83vhi/uhRg4cfj30uRyyc/99lzVFxQyevJBBYxfw6VeLKS6NtG9ej/xurRjQNY/dmtRJ4wvQTmllITxwAOzWB856KeVrcddtKKHfvUOom5vFW1cfSk6WV7BJqnq2tJJryJUkqSItmwnv3QRT34LGe0L/O2DvY/7zbms28PaXhbw+dgEFXy8DoPtujcjv2orjurSiWf3c/3iMqqGXL4DJb8IVwxL9lKJ73p/K/R9N58WL+9B7zyblWKAkVR5DriRJ6TR9MLzza1g6DdodlQi7Tdtt9q7zl6/jjXELGDR2AZMLV1IjwMHtmpLfLY9jOrWgfq2cSi5eO4WZn8DT+XDEjXDEr1OeZtaSNRzz5085rktL/nxat3IrT5IqmyFXkqR0K94ABQ/DkDth4zrY51hof1Qi9DZoudmHfLVwFa+PXcCgcfOZu2wdudk1OHLf5gzomscR+zSjVk5WJb8IpUVxETx0MJQWw+XDICe1a2hjjJz3xAjGzP6WD687nOb1vRZXUtVlyJUkaWexehF8+ieY/AasKkyMtdgP2vdLBN42vSDrh6u1MUbGzF3OoDHzeXN8IUvXbKB+rWyO7bwr+d3y6LNnE7LcoTlzfXo3fHQbnPVKok9S9O6EQi59djQ3n9CR8w/eoxwLlKTKZ8iVJGlnEyMsnAjTP4Bpg2HusMRKXW4D2POI5CpvP2jQ6gcPKy4p5YsZSxk0dgHvTfyG1UXFNK+fy/FdWpHfrRVdWjd0h+ZM8u1s+L/eiXB72rMpT7N2QzH97hlCg9o5vHnVIWS72ZSkKs6QK0nSzm79Cpg55PvQu2pBYrxF50TYbX8UtOn9g1Xe9RtL+GjKIgaNnc/HUxazoaSUtk3qMKBbHvndWrFXs3ppejEqNy+cmbge98oCaNg65WnuencKD34yg5cvPZCebRuXX32SlCaGXEmSqpIYYdEkmPZBYtOqOUPLrPIenjituf1RP1jlXbFuI+9N+IZB4+bzxYylxAid8xqQ3zWPE7q2YteGXn9Z5Ux9F15IfNcyh1yT8jTTF63m2L98Sn63PO4+pWv51SdJaWTIlSSpKlu/EmYN+T70rpyfGG/e6ftreXfr8+9V3oUr1/Pm+EJeHzufcfNWEAL03qMx+d3yOLbzrjSqUzONL0bbZMNaeLA35NSBSz6D7NT+zmKMnPNYAePmLefj646gaT2/jkpSZjDkSpKUKWKERZOTpzV/8P0qb836iVXe73ZsbpgHJL4y5vWxCxg0dj4zl6whJytw+N7Nye/Win77tqB2TXdo3il9dHtig7Lz3oQ9Dk15mrfGF3LF86P5Q34nzj2wbfnVJ0lpZsiVJClTFa1KXMs77f1NVnk7lrmWtw8xK4cJ81cyaOx83hi/gIUri6hbM4ujO+3KgG6tOKRdU3LcjGjnsHQGPNgHOp4IJz+S8jSrixKbTTWpV5PXrzzEHbglZRRDriRJ1cF/rPIOg9KN/7HKW1K/FcNnLeX1sQt4+8tCVq4vpnHdmhy3X0tO7J5Hj913Sfcrqb5ihGdPgnkj4cqRUL9FylP979uT+funM3n18oPYfzf/TiVlFkOuJEnV0XervN/t2LxyXmK8zCpvUasDGDJ9BYPGLWDwpIUUFZdydMcW3DygE3mNaqe3/upo4mvw0s/g2Lug9yUpTzNt4SqO/ctnnLx/a+787y7lV58k7SQMuZIkVXcxwuIpyc2rPoDZQ5OrvPUS38vbrh9rdu/LUxM38tcPpxEI/Lxfey48ZA9PY64sRavggV5QtwkM/ASyslOaJsbImY8MZ1LhSj765eE0cbMpSRloSyE3tXdOSZJU9YQAzfdN3A6+OhGoZn36/Y7NU96kLnB5i86cdsLP+dWkPbjjnSm8Mmoet5/Ymd57Nkn3K8h8n9yR+H7kU59OOeACvD5uAUNnLuWPP+1swJVU7biSK0mSkqu8UxMrvGOeg8WToU0fhu/9S37xr2zmL1/Hyfu35jc/6WBoqigLJ8HfDoHuZ8GA+1OeZtX6jRx5zxB2bViL1y4/2M2mJGWsLa3keu6RJElKrvJ2gIOugks/hxP+Astm0vvDUxiy57P86sA6DBo7n773DOG54bMpLc3M/yRPmxjhrV9CrYbQ79Ydmuovg6exeHURt+V3NuBKqpYMuZIk6YeysqHHz+Dq0XDY9WR/9RaXjT+Ngt6f0715DX772gR++tAXTJi/It2VZo5x/4A5X0C/W6BO45SnmfLNSp744mvO6LUbXds0KrfyJKkqMeRKkqTNy60PfW+Cq0ZBp5/SeMz/8cSqS3it1xS+WbaKAQ98zi2vT2TV+o3prrRqW7ccPvgdtD4Aup+T8jQxRn7/z4k0qJXN9UfvU371SVIVY8iVJElb17A1nPR3GPgxodk+dB//B75o9Htu2Xc+Tw2dxZH3DOGNcQvI1H0+KtxHt8PapXDcPVAj9X+a/XPsfAq+Xsav+ndgl7o1y7FASapaDLmSJGnb5O0PP3sLTnuOrFjMuTOvZ/weD9G7TiFXvTCGcx8vYObi1emusmpZMAZGPAoHDISWXVOeZsW6jfzxrSl0a9OIU3u2KccCJanqMeRKkqRtFwLsezxcPgz630H9byfy1xVX8f5eLzFvziz63/cZ974/lfUbS9Jd6c6vtATe/AXUbQZ9f7tDU/35g69YuqaI20/sTA03m5JUzRlyJUnS9suuCX0ug6vHEA68gr0L3+TDmtdyX8v3eOSjCRxz36d8MnVRuqvcuY1+ChaMhmP+mNhVOUWTFqzk6aFfc3bv3emcl/o8kpQpDLmSJCl1tXdJhLQrC6jRrh8/WfIEYxv/hmNLPub8J4Zz+XOjKFyxLt1V7nzWLIHBt0LbQ2G/U1KeprQ08rtBE9ilTk2uc7MpSQIMuZIkqTw03hNOewbOf5fcXfL49fq/MLzpbaye8jH97hnCo5/NpLikNN1V7jwG3wwbVsNP7k6cAp6iV0bPY9Tsb/n1sR1oWCenHAuUpKrLkCtJksrP7gfChYPh5MdonrWWp7Nu49m69/H82x9y/P2fM2r2snRXmH5zhsGYZ+HAK6B5h5SnWbF2I3e8M4Ueu+/Cyfu3LscCJalqM+RKkqTyVaMG7PffcOUIOPJmupVMYHCtX3PBqoe46KH3+NXL4/l2zYZ0V5keJcXw1i+hQR4cdsMOTXXPB1P5du0G/pDfyc2mJKkMQ64kSaoYObXh0F8Qrh5DjR7ncUrpuwytex2Nxv6NY+7+gBdHzKG0tJp9t27Bw7BwAvS/A3LrpTzNhPkreHbYbM49sC2dWrnZlCSVZciVJEkVq14zOP5ewmVDqbXnQdyY/Rxv1PgFQ157hFP+9gWTC1emu8LKsbIQPv4faNcP9j0h5WlKSyM3/XMCjevmcu1Re5djgZKUGQy5kiSpcjTvAGe9BOe8RvMmjXmw5l/53aJruemBJ7j9zUmsLipOd4UV6/2boGQDHHvXDm029dKouYydu5zf/KQDDWu72ZQkbcqQK0mSKtdefQmXfg4D7qdL3W95Jef3dBn+C865+yXe/rKQGDPwFOaZn8CEl+GQa6HJXilP8+2aDdzxzhR6tW3MT7vnlV99kpRBDLmSJKny1ciC/c+lxtVj4LAbOL7mGF7ceBVzXryeyx77hNlL16S7wvJTvAHeug52aQuHXLNDU/3p/amsXF/MH07sRNiB1WBJymSGXEmSlD659aDvb6lx9Wiyu/w3l2S/yf/MPYcn77uJ+wdPpqi4JN0V7rih98PSaYnvxM2pnfI04+Yu54WCOfzsoLZ02LVBORYoSZnFkCtJktKvYR41Tvob4eJPqNtmP27OepxjPz2JW+7+M59/tTjd1aVu+RwY8ifocDy0PyrlaUpKI78bNIFm9XK5pl/7cixQkjKPIVeSJO08WnUj98K34fQXaNUwl/9dfxs8k88dT7zEwpXr013d9nv3xsQmU/3v2KFp/jFiDuPnreC3x+1L/VpuNiVJW2PIlSRJO5cQoMNPqHPNCDYefSc9cudxw9cD+eKe03jjky/SXd22++o9mPImHH4DNGqT8jTL1mzgrnen0mfPxgzo2qocC5SkzJSd7gIkSZI2KyuHnIMuJaf76ax4/385fsxj5HxyLKuHtqDeXgdCm17Quhe07ALZuemu9oc2roO3r4em+0CfK3ZoqjvfmcKaomJuy+/sZlOStA0MuZIkaedWuxEN8+9k4yGX8eLzj1B70Sj6ziyg3qR/Jo5n1YSW3ZKh94DEnw3SvOL52b2wfDac9wZk10x5mtFzvuXFkXO55LA9ad+ifjkWKEmZK2Tkd9EBPXv2jCNHjkx3GZIkqRxtKC7l8udGM3jyQu49dldOajYf5hXAvJGwYAwUJ6/bbdAaWvdMz2rv0hnwYB/omA8nP5ryNCWlkQEPfM7S1RsY/MvDqZfr2oQklRVCGBVj7LnpuO+WkiSpyqiZXYP/O6s7lz4zil+88w3FJ3fn1KMHJA4Wb4CFX8LcEYngO3cE/Hu1Nxdadq341d4YE6cpZ9eCo2/foameHz6biQtW8sCZ3Q24krQdfMeUJElVSm52Fg+d3YOLnxnFr14dT1aNwMk9WidOC87rkbhxaeLOKwthXpnQW/AIDH0gcaxBa2hzQGKlt00v2LXLDp1aDMCkQTDjQ+h/J9TfNeVplqwu4k/vTeWQdk05br+WO1aTJFUzhlxJklTl1MrJ4uFzenDRUyO5/uVxZGcF8rvl/ecdG7SEjgMSN0is9n7zZTL0FiQC8MTXEseycqFVt+9Xelv3Sjx+WxWtSnxl0K77wQEX7dDru+OdKazbWMItAzq52ZQkbSdDriRJqpJq5WTxyLk9Of/JAq59cSxZNQLHd/mRU5Cza0LrHolbn8sSYysLfxh6y672NmyTCL3fBd+trfYOuRNWLYBTn4Ks1P+JNfLrZbw8ah6XHbEX7ZrXS3keSaquDLmSJKnKql0zi8fOO4DznxjBz/8xluwagf6dt/P03gYtE5tEdcxP/F5clFjtnVvwffid+Gri2JZWexdNhmEPQfdzEuMpKi4p5aZ/TqBVw1pc1bddyvNIUnXm7sqSJKnKW11UzHmPFzBu7nIePGt/ju6U+vWwm7VywfcrvXMLoHAslGxIHGvYBkJInK585Sio2yTlp3niX7O49Y1J/O3s/bc/rEtSNbOl3ZUNuZIkKSOsWr+Rsx8rYNKCFfz9nB707dCi4p6suAgKx3+/0vvNeDjiRuhyaspTLlq1niPvHkL33XfhqfMP8FpcSfoRhlxJkpTxVqzbyNmPDmfqN6t45LyeHL53s3SXtM2ufXEsb40v5L1rD2OPpnXTXY4k7fS2FHJrpKMYSZKkitCwdg7PXNiLds3rcfHTI/nX9CXpLmmbDJ+5lNfGzOeSw/c04ErSDjLkSpKkjNKoTk2eu6g3ezSty4VPjWDojKXpLmmrNpaU8vtBE8lrVJvLj3CzKUnaUYZcSZKUcXapW5NnL+pNm13qcMGTIyiYtSzdJW3RU198zdSFq7j5hI7UrpmV7nIkqcoz5EqSpIzUtF4uzw/sQ6tGtTj/iQJGzd75gu7Cleu5b/A0/mufZhzVsQI3ypKkasSQK0mSMlaz+rm8MLAPzRvU4rzHRzBmzrfpLukH/vjWZDaUlHLLgE7upixJ5cSQK0mSMlrzBrV4fmBvGtetybmPFzB+3vJ0lwTAFzOW8Pq4BVx2+F7s3sTNpiSpvBhyJUlSxmvZsDYvXNyHhrVzOOexAibMX5HWejYUJzabatO4NpcdsVdaa5GkTGPIlSRJ1UJeo9q8MLAP9XKzOeex4UwuXJm2Wp741yymL1rNrQM6USvHzaYkqTxVWMgNIdQKIRSEEMaFECaGEG5Njr8YQhibvH0dQhhb5jE3hhCmhxCmhhCOKTPeI4TwZfLYX4MXrUiSpBS0aVyH5wf2Jjc7i7MeHc5XC1dVeg2FK9bxlw+n0W/fFvTt4GZTklTeKnIltwjoG2PsCnQD+ocQ+sQYT4sxdosxdgNeAV4FCCF0BE4HOgH9gQdDCN/91+ZDwMVA++StfwXWLUmSMtjuTerywsV9yK4ROPOR4UxftLpSn//2NydTUhq5+YSOlfq8klRdVFjIjQnffWrkJG/xu+PJ1dhTgReSQ/nAP2KMRTHGWcB0oFcIoSXQIMY4NMYYgaeBEyuqbkmSlPn2aFqX5wf2AeDMR4Yxa8maSnnez6Yt5q0vC7nyv9rRpnGdSnlOSapuKvSa3BBCVvJ05EXABzHG4WUOHwosjDFOS/6eB8wtc3xeciwv+fOm45t7votDCCNDCCMXL15cTq9CkiRlonbN6/HCwN6UlEbOeHgYs5dWbNAtKi7h5kETadukDgMP27NCn0uSqrMKDbkxxpLkacmtSazKdi5z+Ay+X8UF2Nx1tnEr45t7vodjjD1jjD2bNWuWYtWSJKm6aN+iPs8N7E1RcQlnPDyMucvWVthzPfrZLGYuWcMtbjYlSRWqUnZXjjEuBz4heS1tCCEbOAl4sczd5gFtyvzeGliQHG+9mXFJkqQd1mHXBjx7UW/WbCjhjEeGMX/5unJ/jvnL13H/R9Po32lXjtinebnPL0n6XkXurtwshNAo+XNtoB8wJXm4HzAlxlj2NOTXgdNDCLkhhD1IbDBVEGMsBFaFEPokr+M9FxhUUXVLkqTqp1Orhjx7YW9WrNvIGQ8Po3BF+Qbd296YBMDv3GxKkipcRa7ktgQ+DiGMB0aQuCb3zeSx0/nhqcrEGCcC/w+YBLwLXBFjLEkevgx4lMRmVDOAdyqwbkmSVA3t17ohz1zYm2/XbOCMh4excOX6cpn3k6mLeHfiN1zVtz15jWqXy5ySpC0LiQ2LM0/Pnj3jyJEj012GJEmqYkbN/pZzHxtOi4a1+MfFfWhev1bKc63fWEL/+z6lRgi8c82h5GZ7La4klZcQwqgYY89NxyvlmlxJkqSqosfuu/DkBb34ZsV6znpkOEtWF6U81yOfzuTrpWu5Nb+TAVeSKokhV5IkaRMHtG3M4z87gLnfruXsR4ezbM2G7Z5j7rK1PPDxdI7bryWHtvdbHySpshhyJUmSNqPPnk147LwDmLVkDWc/Opzla7cv6N76xiSyagRuOn7fCqpQkrQ5hlxJkqQtOLhdUx45tyfTF6/mnMcKWLFu4zY97sPJCxk8eSE/P7I9LRu62ZQkVSZDriRJ0lYctncz/n52D6Z+s4pzHy9g5fqtB931G0u45Y2JtGtej/MP3qOSqpQkfceQK0mS9CP+q0NzHjxrfybOX8HPHi9gdVHxFu/70CczmLtsHX/I70TNbP+pJUmVzXdeSZKkbdCvYwseOLM74+at4PwnClizmaA7e+kaHhoygwFdW3HQXk3TUKUkyZArSZK0jfp3bslfT+/O6DnLueDJEazd8H3QjTFyy+sTyakR+O1xbjYlSeliyJUkSdoOx3Vpyb2ndmXE18u46KmRrN9YAsAHkxby8dTFXHvU3rRoUCvNVUpS9WXIlSRJ2k753fK4+5SuDJ25lIFPj2TF2o3c+sYk9m5Rj/MOapvu8iSpWstOdwGSJElV0Un7t6a4NHLDy+M58t4hLFldxIsX9yEnyzUESUonQ64kSVKKTu3ZhpLSyI2vfslPu+fRe88m6S5Jkqo9Q64kSdIOOKPXbnTfrRF7NK2b7lIkSRhyJUmSdliHXRukuwRJUpIXjUiSJEmSMoYhV5IkSZKUMQy5kiRJkqSMYciVJEmSJGUMQ64kSZIkKWMYciVJkiRJGcOQK0mSJEnKGIZcSZIkSVLGMORKkiRJkjKGIVeSJEmSlDEMuZIkSZKkjGHIlSRJkiRlDEOuJEmSJCljGHIlSZIkSRnDkCtJkiRJyhiGXEmSJElSxjDkSpIkSZIyhiFXkiRJkpQxDLmSJEmSpIxhyJUkSZIkZYwQY0x3DRUihLAYmJ3uOqqopsCSdBehasFeU2Wwz1RZ7DVVBvtMlaUq9NruMcZmmw5mbMhV6kIII2OMPdNdhzKfvabKYJ+psthrqgz2mSpLVe41T1eWJEmSJGUMQ64kSZIkKWMYcrU5D6e7AFUb9poqg32mymKvqTLYZ6osVbbXvCZXkiRJkpQxXMmVJEmSJGUMQ241EEJoE0L4OIQwOYQwMYTw8+R44xDCByGEack/dynzmBtDCNNDCFNDCMeUGe8RQvgyeeyvIYSQjtekndP29loI4agQwqhkT40KIfQtM5e9ps1K5T0teXy3EMLqEMJ1ZcbsM21Rip+fXUIIQ5P3/zKEUCs5bq9ps1L47MwJITyV7KfJIYQby8xln2mLttJrpyR/Lw0h9NzkMVUyExhyq4di4Jcxxn2BPsAVIYSOwK+BD2OM7YEPk7+TPHY60AnoDzwYQshKzvUQcDHQPnnrX5kvRDu97eo1Et+9dkKMcT/gPOCZMnPZa9qS7e2z7/wZeGeTMftMW7O9n5/ZwLPApTHGTsARwMbkXPaatmR739NOAXKTn509gEtCCG2Tx+wzbc2Wem0CcBLwadk7V+VMYMitBmKMhTHG0cmfVwGTgTwgH3gqebengBOTP+cD/4gxFsUYZwHTgV4hhJZAgxjj0Ji4mPvpMo+RtrvXYoxjYowLkuMTgVohhFx7TVuTwnsaIYQTgZkk+uy7MftMW5VCrx0NjI8xjks+ZmmMscRe09ak0GcRqJv8T5XawAZgpX2mH7OlXosxTo4xTt3MQ6psJjDkVjPJ/+nrDgwHWsQYCyHR9EDz5N3ygLllHjYvOZaX/HnTcek/bGOvlXUyMCbGWIS9pm20LX0WQqgL/Aq4dZOH22faZtv4nrY3EEMI74UQRocQbkiO22vaJtvYZy8Da4BCYA5wd4xxGfaZtsMmvbYlVTYTZKe7AFWeEEI94BXgmhjjyq2cOr+5A3Er49IPbEevfXf/TsCdJFZBwF7TNtiOPrsV+HOMcfUm97HPtE22o9eygUOAA4C1wIchhFHAys3c117TD2xHn/UCSoBWwC7AZyGEwfiepm20aa9t7a6bGasSmcCV3GoihJBDopmfizG+mhxemDzd4LvT9hYlx+cBbco8vDWwIDneejPj0r9tZ68RQmgNvAacG2OckRy217RV29lnvYG7QghfA9cAvwkhXIl9pm2QwufnkBjjkhjjWuBtYH/sNf2I7eyzM4F3Y4wbY4yLgH8BPbHPtA220GtbUmUzgSG3GkjudvYYMDnGeG+ZQ6+T2OyH5J+Dyoyfnrw2cg8SF5MXJE+VWRVC6JOc89wyj5G2u9dCCI2At4AbY4z/+u7O9pq2Znv7LMZ4aIyxbYyxLXAf8D8xxgfsM/2YFD4/3wO6hBDqJK+XPByYZK9pa1LoszlA35BQl8QGQlPsM/2YrfTallTZTBAS1work4UQDgE+A74ESpPDvyFxDv7/A3Yj8YZ5SvKaDkIIvwUuILEL2zUxxneS4z2BJ0lsdPAOcFW0iZS0vb0WQrgJuBGYVmaao2OMi+w1bUkq72llHnsLsDrGeHfyd/tMW5Ti5+fZJN7XIvB2jPGG5Li9ps1K4bOzHvAE0JHEaaNPxBj/lJzLPtMWbaXXcoH7gWbAcmBsjPGY5GOqZCYw5EqSJEmSMoanK0uSJEmSMoYhV5IkSZKUMQy5kiRJkqSMYciVJEmSJGUMQ64kSZIkKWMYciVJ2oklvwvz8xDCsWXGTg0hvJvOuiRJ2ln5FUKSJO3kQgidgZeA7kAWMBboH2OckcJcWTHGkvKtUJKknYchV5KkKiCEcBewBqib/HN3YD8gG7glxjgohNAWeCZ5H4ArY4xfhBCOAG4GCoFuMcaOlVu9JEmVx5ArSVIVEEKoC4wGNgBvAhNjjM+GEBoBBSRWeSNQGmNcH0JoD7wQY+yZDLlvAZ1jjLPSUb8kSZUlO90FSJKkHxdjXBNCeBFYDZwKnBBCuC55uBawG7AAeCCE0A0oAfYuM0WBAVeSVB0YciVJqjpKk7cAnBxjnFr2YAjhFmAh0JXE5pLryxxeU0k1SpKUVu6uLElS1fMecFUIIQCEELonxxsChTHGUuAcEptUSZJUrRhyJUmqem4DcoDxIYQJyd8BHgTOCyEMI3Gqsqu3kqRqx42nJEmSJEkZw5VcSZIkSVLGMORKkiRJkjKGIVeSJEmSlDEMuZIkSZKkjGHIlSRJkiRlDEOuJEmSJCljGHIlSZIkSRnDkCtJkiRJyhj/H+1HfFjvOo5oAAAAAElFTkSuQmCC\n" 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\n" 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\n" 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\n" - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "ghg1 = \"400\"\n", - "ghg2 = \"400\"\n", - "bio1 = \"10\"\n", - "bio2 = \"00\"\n", - "scen = [f\"SSP2BIO{bio1}GHG{ghg1}\", f\"SSP2BIO{bio2}GHG{ghg1}\", f\"SSP2BIO{bio1}GHG{ghg2}\", f\"SSP2BIO{bio2}GHG{ghg2}\"]\n", - "#fig, ax = plt.subplots(3,3, figsize=(30,20))\n", - "py_df.filter(variable=\"Emissions|CO2|Land|+|Land-use Change\", scenario=scen).plot(figsize=(16,9))\n", - "for i in df[df.index.get_level_values(3).str.startswith(\"Demand|Bioenergy|++|2nd generation\")].index.get_level_values(3).unique():\n", - " py_df.filter(variable=i, scenario=scen).plot(figsize=(16,9))\n", - "for i in df[df.index.get_level_values(3).str.startswith(\"Resources|Land Cover|+\")].index.get_level_values(3).unique()[:3]:\n", - " py_df.filter(variable=i, scenario=scen).plot(figsize=(16,9))\n", - "py_df.filter(variable=\"Emissions|CO2|Land|Land-use Change|Gross LUC|+|Deforestation\", scenario=scen).plot(figsize=(16,9))\n", - "py_df.filter(variable=\"Emissions|CO2|Land|Land-use Change|Gross LUC|+|Other land conversion\", scenario=scen).plot(figsize=(16,9))\n", - "py_df.filter(variable=\"Emissions|CO2|Land|Land-use Change|+|Regrowth\", scenario=scen).plot(figsize=(16,9))\n", - "#py_df.filter(variable=\"Productivity|Landuse Intensity Indicator Tau\", scenario=scen).plot()\n", - "#py_df.filter(variable=\"Productivity|Landuse Intensity Indicator Tau\", scenario=scen).diff({\"Productivity|Landuse Intensity Indicator Tau\":\"test\"}).plot()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-29T12:07:37.713681400Z", - "start_time": "2023-09-29T12:07:35.942886400Z" - } - }, - "id": "b6e4c67b9c1a13ab" - }, - { - "cell_type": "code", - "execution_count": 477, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['SSP2BIO45GHG100', 'SSP2BIO00GHG100', 'SSP2BIO45GHG4000', 'SSP2BIO00GHG4000']\n", - "['SSP2BIO45GHG100', 'SSP2BIO00GHG100', 'SSP2BIO45GHG4000', 'SSP2BIO00GHG4000']\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "pyam.plotting - INFO: >=13 labels, not applying legend\n", - "pyam.plotting - INFO: >=13 labels, not applying legend\n", - "pyam.plotting - INFO: >=13 labels, not applying legend\n", - "pyam.plotting - INFO: >=13 labels, not applying legend\n", - "pyam.plotting - INFO: >=13 labels, not applying legend\n", - "pyam.plotting - INFO: >=13 labels, not applying legend\n", - "pyam.plotting - INFO: >=13 labels, not applying legend\n", - "pyam.plotting - INFO: >=13 labels, not applying legend\n", - "pyam.plotting - INFO: >=13 labels, not applying legend\n" - ] - }, - { - "data": { - "text/plain": "" - }, - "execution_count": 477, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": "
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\n" 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\n" 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\n" 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myjaO9wMuSykX6uPVUuAi2iJZaW1WLGUVPPKvSGcW8X8n/W8/ICzvhtQmcBFo0qofEKm/dHmE5fu7KvBC/o802orUHTti5st/H5pUOR2t09zWMfWJRjduScq/oamu8+wf/fTy56WXAdwokM0KffLhI6XsLqU8XkKRButh8375BzP08m3Uy+slpdxfTDpeUkp3KWU9KWXeQJuGNlnOjwuaqUNZ7gNMkdok1w7oj+YjUpq5mz+aEFRWPtXzsAdaAp+IfGYc+bgX9QFACNFJ3FLvlrRSGpHv76poH/nofP1xLppUDwX6RoG/C3Lbe6H/bcXtJmb5TQAzuPVOlcYhPWxDNO3GXqkJNxH5ruX5dxRVjvzvV5zUzNaKq0NJ7+9tCE2VnNfmhTRi+sC/kVsfkYfIt2IlNLOMQF29m4S2Wpr/A1ZsewvNDO4jIcQVoangr+q3ioyvt1cChccab8ABzY8prw/8rl8vK4cKvPM1C9wv69h6E33iOwJ4Gq1/bhS3TAxfRlvZP6I/g6JMgrzQVqYK9oX82tyb/VEf9wCcSsm7IDfQJl3FEV/CfV/9/k2EZpo3Buinf8TvCCGEj9AchyP1frGIwpOi4t7Dgu97sX2/DOUorW8XLIOdvkhR4jsoNBO9vHeuOPO3/HwCBANbhWZykbdRSVU0k5j83+LXKMIkFkhEm8CU9JwBbugTnvz1ctLL3UZom4PECSGS0frW3T6XgmN4WesBBfqrlPIr/Vs1G+1bkEfBcbKkcbXINpZSBqOtes8AYvV+eadzHVOBcqH/P7eo+pQFIcSr+dpqA9qiVv72K4qS+kCh97gIiuvvZXl+JX13b0MI4SCEmCs086cUtO+im7jdx7RM/UwfBwvOBfPwQptDFSVIljRHLrEMep8JBAbowsdAbgkeVYHhBdqqI7c/k4LlL8s4XrBvQwnfCso4d7nXDiZRaA0A3FwxrAxEokm+/vq1PKrk+zsCeL/AR9pBl7D+DIvQ1J1FaQ9Go7XBeqHZa4agdZg8c6do8qnThGajdzf+G3fCArTyFnTsLo3zaHZ7+Vf8m3DLJOE8t1YSEJrDly1FOK/rEvNetAGz2N0NdMl+ANpK7R0hNc4B+yna0S0IsBKanWoeJdXHEc1htZBgoa8y5al3SzLFyv9Bj0DTuHnl648u+eLf1jfQ+nlx3PZeoPV7E0WbmN0R+gfwKJqg6CulvKjf2qtfa8wtwaOocuR3dC5pK9TS3t+C5WqQr82L6x9L0TSM7dAE0T9AExSBV9BWM931j38y2oS6LGV9BE0F3wNtUldNv54//s3nJYRwQlu1K+j0HY82+W+Qrw+4Sm1zhH8UKeUWKWVPtA/LRbSVQKSUMVLKcVJKPzSz1W9E4R2e4rmlGcmjCto4fdd5F8F2YIgo3pFxJ1BZCNE6/0V9QagtsCPftceBV4H7pG5/fxd8iNZvGkvNcXgUt/eJkojm9ne82L5fEmXs2yWVodh3UGo76eW9c3lCfAaa8JxHxXzhU6WUL0gpa6CN488Lzc8gAs08M/+32FlKmX/1NC+NDDQN6581u1wHVJZSugLfcWfPpbhxuMz10NmB5jtYGgXHnmLH1RLaGCnlEillRz2uRDPrvBPCuTW25VGdWxPFHUAlUcBvtSSklB/ltRXa92Nf/vYrJk46Wh8YXsTtB7n1Ht/pVttleX53kuYLaFq2Nvr7n2eOXNZ3L/83w4Hi54LxaKb+BReZoOQ5clnIM7caBFzQhRHQ2mphgbZylLf78tzWVmUcxwv2bbiDb0Vx3GvBYwXQT2jbnFqjPehs4ABaxzQBU4TmFPMAmi1kHvOAp/XVDyGEcBSa09mfMZ0BTfXak6J39RmDphZtmu83VK+DJ5rt4AChOdfY6GHLOiDeLbv18hZnm10kUvOFOAW8JYSwE0IMQZt05tmsLkarSyd9kv4OmmlLIQ0BgD4ZrE8RE3khhLXQbGyXon3IZuW7Zytu2Svb6GUpss10CbtjUXnog9mvwDt6X+iA9rLlCWRr0NSEQ/X83gTO5Jt4/ymklNFotvKfCSFchOYgXlPc2v53BfCsEMJfaA6qr5SQ3FLgOSFEdX2S+wGaeZ+phDh3wh601bMD+a7t06/FyFsq/KXAdCGEt9AcwN5EE8zLQmnv792wCW1QewetPfJU1856XnFowuebFNZulYQz2rhzA23S9UERYfoKzX/GBs0v4rDUfAtuopdnHvC50Jz00Z93cTbifwtCc0AfqL/H2WjaP7N+b7i45TCZiPaxuc2UUWomDSuA94UQzkIzd3ueMvSFkvIuglloz22+nkde+80SQjTWx6zvgMVCc4w0Cm0TgNXAdinldj3OSLRn2FOWfTvgvLEn72dE6xdpQJLQbKJL3Ja3ACvQNjGor0863ipDHGOBMtjw5/r23byDp4BH9LbtQz7TEqFt5lJLH59T0J6jGTgCpAjN+dlej9tQCNGqmDxeRmubl/TvJkKIJkKIZWWslzOQIKXM0oXQR0qLkI8VwDShOQ77o5kI53Gn9ZgBdNL7p79eDy80f6CSKHZcLa6NhRABQojuQvMNzUJb4ChpQ5ai+tJy4DGhbWUshOY/8ByavwRSystoPqpLheYUn/dOPCTybcN/j3gVeFRoW/c668/jPTT/nzyT7euAp7jd5LUk7vT5lYYzWjsnCc13oSzvcB6rgP75vhnvUMz8Wf9u/ATMEppzvFFoW9/bUvIcuSwsQ1sMnsAtbQdo/W2AEKK3np+d/syL9EO5g3F8E1BHaEcxWAkhRqDNCzeUsbxFck8FD6ntODIKbdIcjybhD5CafV4O2mrCWLQP4gi0iWVe3GNo9tRf6feDKWaXAqGbzpSxTAlSyh0F1NMIIdqirRZ8ra8S5v3W6Xk/LDWbt8loDzsazYwnlmIcvsvAenH7fv5riiiv1Mt7J+ZLeTyEZr6U53MwTGo2p+h1eRpNAIlFewmfKRD/q7yyoU3wp0spN+e7P0K/l4S2QnUDaCFv3x70EtrL7Y/mrJ/J7RLzy3oe6WgT+5/RTJiK4hm0lfBYtMF9gl4P9HoNRbPTTERzhr3XW2yOQTNJuaDnsYpbqst5evnPoDn3b0KbFBT18v6E1p570Jzps9D6VamUsa/vRjMBy2/Pv0+/ll/gfg/N7vMM2o5nJ/RrpVLa+3s3SM1c5lc0zUT+QXQLmpN1ENrqXRZ3oFJH0xqGoa3KXKDoTRKWoH14EtB8s4ozTXkFbTw4JDT1/Hby2eOLYkzJ8tFOFD7H424/nHkY0D5YUXr5u3DrXW4FHNb7zDo0O+PQItKYjOZnFYLWV5ag9dM/k/dt6GNYezTtymEhRCra6mcyWpuCNlH8Ae3DmYZmyraL21fQ30NbXTyarw1LO5fmPNrYk/d7DG0C1FzPfyN30H/1cXA2mpYmWP+3NF4tUIad/Im+fZfv4LNo3+EktD6+Nt+92mj9OQ1NqPlGav4WZj1OU7TxKh7tGRU5YZRSHkDbArk7ECKESAC+RxsTy8IzaAtMqWiT9hWlhM/PO2jO26F6XVahf5/voh5BaJq2SsBpvTz70fr6GyWUoaRxtcg2RrM2+EgvUwzaWP1aCXkU6ktSyi369Z/R+vQmNH+s7/PFm4I2p/oarQ9cQdsKf30Jed0xUjNr743WP6PR+nYzoKMuAKEvCi5F6yNJohTTsjt9fmVgNtp8Ih7tm/B7WSPq846JaONkNNr7V5Lm9UW0vnAUbZycCRhKmiOXsRzRaP2oPfmOD9AXzQah9aE4tDHlJYqf45dpHJdS3kDTfL2ANt97Gegv734DJQBEgfm4ogSEtlqdBNQu5mOu+D9FaH4q30kpi9yWVaH4f0AIIaWUf7VWuGCevwC7pJS//J35FihDV2CGlLLrP1UGBQghJgAPSSkLOg0XFbYaWr+p9leXq7xyt/1W9XfFn+Fem1r95xBCDBCaU5Ij2g4EZ7n3294p/mXoqt++uvrRH231vJAGS6FQKBR/DUIIXyFEB6GZwgagrcyqcVihKMcowaN0BqGpo6LQ1KYPFTTbUvxfItBMNxLRTK0CyXe+g0Lxf0pZtuC+16yl+LNE/i6uAr/8w2X4f8QGzVQ3Fc2U7Tc0v4aykIRmfvP/zFXurt/ebTyFQplaKRQKhUKhUCgUir8epfFQKBQKhUKhUCgUfzlWpQdRKP5/EdpWvXvQdiGxAlZJKd8SQsxA24UtTg/6mtRO5EUIMQ14Am2Hqyn67iMIIVqgqaft0XYgebY0sz0vLy9ZrVq1e1wrhUKh+O9y/PjxeCnlnRz0qVAo/iaU4KFQlEw20F1Kmabvu71PCJG3xfDnUspP8wcWQtRH29a3Adqpn9uFEHX0rQG/BcajbeW3CeiDtrVmsVSrVo1jx47d0wopFArFfxkhxF2fLK9QKP5alKmVQlEC+rkqeedoWOu/krQUg4BlUspsfcvlYKC1EMIXcJFSHtS1HAuAwX9h0RUKhUKhUCjKFUrwUChKQT8J9BTaQYbbpJSH9VuThBBnhBA/CSHc9Wv+3H4g2DX9mj+3HziUd72o/MYLIY4JIY7FxcUVFUShUCgUCoXiX4cSPBSKUpBSmqWUTdFOtG0thGiIZjZVE+1U1WjgMz14UYenyRKuF5Xf91LKllLKlt7eykxZoVAoFArFfwMleCgUZURKmQTsAvpIKa/rAokFmAe01oNdAyrni1YJ7QyYa/rfBa8rFAqFQqFQ/F+gBA+FogSEEN5CCDf9b3ugB3BR99nIYwhwTv97HfCQEMJWCFEd7dDJI1LKaCBVCNFWCCGAMWiHXSkUCoVCoVD8X6B2tVIoSsYXmC+EMKIJ6iuklBuEEAuFEE3RzKWuAk8BSCnPCyFWABcAEzBR39EKYAK3ttPdTCk7WikUCoVCoVD8l1AnlysU5ZiWLVtKtZ2uQqFQlB0hxHEpZct/uhwKhaIwSuOhUCgUinKNtEiS4zOJj0gjPTmbum0rYutg/U8XS6FQKBR3iBI8FAqFQlFuMJssJESlE38tlbiINOIjUom/lkZulvlmmAv7oug3sTEunvb/YEkVCoVCcacowUOhUCgU/wg5mabbBIy4iDQSo9OxmDUTYCtbI17+TtRtUxGvKs54V3YmMy2HLfPOs3rmcfpPaoJ3Fed/uBYKhUKhKCvKx0OhKMcoHw/Ff43kuAwOrgkhLiKVlLjMm9ftna3xruyMV2VnvCo74V3ZGVdve4Sh8BE4CVHpbPjqNJnpufR+sgHVGnn9nVVQlHOUj4dCUX5RgodCUY5Rgofiv8aGr04TeTmJqg088KqkCxlVnHF0tb2jdNKTs9n49RniI1Lp/HAADTv7/0UlVvzbUIKHQlF+UaZWCoVCofhbuBGZRti5G7QeUJ1W/ar/qbQcXW0Z/Hwztv54nt1LLpESn0m7wTWL1JAoFAqFonygBA+FQqFQ/C2c2BqGla2RRl0r3VX8qIgrLF/0CTYNKuNXrz5e9l7UeNALaxdPTm4NJzUhi/serYeVtfEel/zeIaUkNdtEYnoOCek5JGbkkJCeq/0/I4eENO3fvP8npudgZ21k3piWNPR3/aeLr1AoFH8KJXgoFAqF4i8n5UYml4/G0rhrJewc73wr3MtHD7Luy5mQbSLr1DX2ee7kREAS8W45IKFZ1R5wbADHQ05zvdMJPN1c8bb3xtvBG297b7wcvPC298bDzgMrw7379GXlmrmRnlNAkMgvOOSSoN/LEyRMlqJNnK2NAg9HG9wdbPBwtKGerwseDjZsD7zOuAXH+G1SB3yc7e5Z2RUKheLvRgkeCoVCofjLOb09AgE06VH5juJZzGb2LV/I0d9WccMtF9MDdRhq7MLRtavwO2CPS4Ma2HSuS1LjXBIDz+G6vx5W29vzR8MFXDOEFErPIAx42HncLpTYe+Hj4IOXvdfN6572nlgbbglIq49fY8/luJvCRZ5AkZlrLpQHgBDg7mCDu4M1Ho42VPNyoLmj202h4ua/jjZ4ONjg4WSDo40RIQqbio1oVZnh3x1k/ILjLBvfFrtyrNFRKBSKklDO5QpFOUY5lyv+C2Sl5TL/tf3UbO5Dj7H1yxwvIzmJDV98TMT5M8gmviz2PcLqB9ZQw7UGOZkZnNi0jmMb1pCdmUHd9p1p/+BIstIc2PTtWYQBej1VH2PFHOIy44jLjCM+I57YzFjiM+OJy9CuxWXEkZCVgOT2b6FA4G7njpe9FzlZTlyOFjib2uJv1yif4GB9S3BwvF2QcLG3xngP/U1+PxfN04tOMLipH5+PaFqkgKLQUM7lCkX5RWk8FAqFQvGXcmbXNUw5Fpr1qlLmOFFBgaz//COyUlNp98RjTL7+Af1rDKSGaw0AbOwdaDv0IZr07sexdas58ft6Lh3cS8NuPek9bhB/LI5kwxdn6fV4Axo3a1xiXiaLiRuZNzSBJDOO2IzYm3+figznSkIk9m6JGK0C+XbQWnydfP9Ue9wNfRr68kLPOny2LYjaFZyZ2K3W314GhUKh+LMojYdCUY5RGg/Fv53cbDMLXjtAxRou9JvYpNTwUkpObdnArgU/4uzlxcDnX+Pb6IWsDV7LhiEb8Hcqetvc9KREDq9ZwZntm0EIGnTpTcL1usRHWug4rDZN7rszEy/QzKteXHWaATWteTnzJYY6mWhidGFuo8mIuveDvfsdp/lnkFLy7LJTrDsdxXejWtCnYcW/Nf9/C0rjoVCUXwz/dAEUCoVC8d8l8EAUWem5NOtdtdSwuVlZbPryU3b+PJdqTZox6oPZZHlYsfbyWobXGV6s0AHg6OZO98ee4vHZ31OvY1fO7tzE9aCvcHI+wd7lZ9m7PAhLMU7dRbHxTDQvrTpNlxrOzOYTKiXH8IJTXQ7KNFbtfBE+qQULBsHRHyA1pszp/hmEEHw8rDFNKrvx3PJTnI9K/lvyVSgUinuF0ngoFOUYpfFQ/Jsxmy0sfuMQjm62DH25RYlhE6IiWT/rA25ci6DDiFG0HjQMYTDw6t5X2RG2g81DN+NlX/YTyhOiIjmwcjGXDuzBaG2PMDanZsse9H6qOdY2JTtnb7twnQmLjtOssitLPX/E6sJqGD4fWX8Q47eO50zcKX717IL/5Z2QcAUQUKkV1BsA9fqDR40yl/NuiE3JYtDX+xHAWrXTVSGUxkOhKL8ojYdCoVAo/hKCj8WSmpBF894l+3ZcPnKAxa89R3pSIkNfe4c2Qx5EGAwEJQaxKWQTI+uNvCOhA8DDz5/+z77M6JlzqNq4Eaas/Vza/wkLXv2WlBtpxcbbExTHxMUnaODnwqJaf2hCx31vQoPBCCF4u8PbCIORtwxJWCYdhWcOQbfXwZQF296AOc3g2w6w6yOIOQd/weKej4sd88a0JCEjh6cWHiermJ21FAqForyhNB4KRTlGaTwU/1aklCx/7ygWi+ThN1oXeaK4xWxm37IFHF23moo1azPg+Wm4ePncvD9l5xSOxRxj89DNuNr+ucPzooIusv2HH4kLC8Rg5UK7YQ/RakBfjFa39lg5FHKDsT8fobqXE6vaR+CwYQLZFQeRZuxE+oED2Ddris+zz7IqaBVvH3yb6W2mM6LuiFuZJF6FixshcD2EHwIkuFfXNSEDwL8lGO7det/ms9FMWKx2uiqI0ngoFOUXJXgoFOUYJXgo/q2EnbvBhq9O031MPeq1L7wLVHpSIhvnfELE+TM06dmXro+Ow8r61rkZZ+LOMHLTSCY1ncRTTZ66Z+U6ve0gfyz4GXNOFE6eFeg8cgx123XiREQyY348THUH+MEnEMvqOaRfd8SUpmkTrCpWxBQTg9/HM3EZMICntz/NydiT/DrwVyo5F3ESe1rsLSEkdA9YcsGpItTtpwkh1TqC8c4PUizIlzsu89m2IF7qHaB2utJRgodCUX5RgodCUY5Rgofi38qaz06QEp/JqHfbYbS6fZU/8lIgGz7/kKz0dHqOm0j9zt0LxR+3dZxmavXAJhytHe9p2ZLjMlj14SqSY/5AmuNw8vAh5oY1Ta9HUicuFCwWDDbg2LkbTt3uw7FjJ6w8PQh//Akyz5yh2vJlJFVyZchvQ6jnWY8fev2AQZSgychMgstbNSEkeDvkZoCdGwTcrwkhNbuDtf1d1SX/TldzR7egdwO105USPBSK8ovy8VAoFArFPSUmNJmoy0k0ua9yIaEj6PB+Vrz9KlY2tjz87idFCh2How9zKPoQTzZ68p4LHQBONrkM7OKNr3s/rB37khOXiJOIJN4bcpo4ULVvNnW2/Ualr77BbehQrCv4IKys8J/1GUYXF65NnoK32YGXW73M0ZijLLu4rOQM7d2g8YMwYiG8dAVGLNaEjkubYNkj8HENWD4azqzQhJQ74OZOV5VceW75KS5Epdx1uygUCsVfjdJ4KBTlGKXxUPwb2fzdWSKDEhnzQXts7G75UCRGR7Lw1al4Va7CA9Pexs7RqVBcKSWjNo/ievp1Nj6wEVuj7Z8uj7RYyLoQSPq+vaTt2Uvm6dNgNmNxcWdvnbGYHGvg5n2FjIj1pGZB5ZpV6Th2En516hVKK+PEScLGjMGpUyf8v/qSiX9M4vj146wesJrKLnd4Vog5F67u0zQhFzdCWgwYrKFGF6jbXzPLcvIpPR20na4GfrUfg1A7XSmNh0JRflGCh0JRjlGCh+LfRmJMOkvePkyLPlVpO6jmzeumnByWTH+B1BvxjJ45Bxcv7yLj74rYxeSdk5nRbgZD6wy963KYk5JI27+f9D17Sdu/H3N8PAB2DRrg2LkTGU1b88jeVLItgvdqViJkbzSVrI/h1yCDYyeCyUhOokbzVnQYMRqfardvj5uwcBHX338f76lTMY0exAO/PUBt99r83Ofnkk2uSsJigchjELhOE0QSrwICqrTVtCUtHoNSnMfPRSYz7LsD1PN1Yem4tthZl7xt8H8VJXgoFOUXJXgoFCUghLAD9gC2gBWwSkr5lhDCA1gOVAOuAg9KKRP1ONOAJwAzMEVKuUW/3gL4BbAHNgHPylJeQCV4KP5t7FwYSNCR64x5vz0OLjY3r2//4WtOb9vMkFfeokbzVkXGtUgLw9cPJ9uczZpBa7A2lN35WlosZJ0/T9qePaTv2Uvm2bNgsWB0dcWxY0ecOnfCsUMHrLy8iE7OZPh3B0nLNrF0XFvqhfzM+d/2sjt1Ap6VnOn9ZACXDm7l2LrVZKWnUaddJzo8OBIPP82JXEpJ1IsvkbJ5M1V+mMc2n1je2P8Gr7R6hVH1R/25BtQygOvn4eIGuPAbxF6ABxdA/UGlRs3b6WpIM39mPdjk/3KnKyV4KBTlFyV4KBQlILSvtqOUMk0IYQ3sA54FHgASpJQfCSFeBdyllK8IIeoDS4HWgB+wHagjpTQLIY7ocQ+hCR5zpJSbS8pfCR6KfxPpSdksmH6A+u396PJIwM3rFw/sYeMXH9NywAN0GfV4sfE3h27m5T0vM7PTTPrW6FtqfqbERNL37SNt717S9+3HnJAAQmDXsCFOnTrh1LkTdo0aIYy3Vv5jU7N4aO4h4lKzWTyuDY1T9sCKMdBgCGF1P2bLDxewdbCi/6QmOLrB8Q1rOL7xN0w5OXQYMYo2Qx4EwJKRwdURIzDF36DaqpU8d/EDjkQfYdXAVVR1Kf2U9jJjMcM37QCpnRliKF2LMWfHZWZtC+LlPgE80/X/b6crJXgoFOUX5VyuUJSA1Mg7bcxa/0lgEDBfvz4fGKz/PQhYJqXMllKGAsFAayGEL+AipTyoazkW5IujUPwnOL0jAmmWNO1568DAxOhItn3/Jb516tLxoTHFxs215PLVya+o7V6bPtX7FBsu8/x54r78itAHR3C5fQeiXnqZ9L37cOzQAb9PPqb2/n1UX7kC7ymTsW/a9DahIyE9h1E/HCYmJYtfHm9FYxECv46HSi1h8DdUbeTNkBebIy2SXz85Tlx4Dh1GjObJr36kVuu27F++iJjgIAAMDg74z5mDzMkhcupzvNHiNayN1ryx/w3Mlnt4oJ/BCN2nQ3wQnFlepiiTu9diQBM/Ptlyia3nY+5dWf4GsrOvc/36BkJCZv/TRVEoFH8BSvBQKEpBCGEUQpwCYoFtUsrDQAUpZTSA/m+eB6g/EJEv+jX9mr/+d8HrReU3XghxTAhxLC4u7p7WRaH4q8jOyOXc3khqtvDB1VvbGtaUk8P62TMxGK3o/+zLtx3WV5B1wesITw1nSrMpxfpJpB84wNWhw4j/5hsQ4DVxItVWLKf2vr34f/IxrgMGYOXhUWTc5MxcRv94mLAbGfzwaEtauGXA0ofAyRseWnJzO1vvys4MfaUlTh52bPjyNIEHonFwcaX308/i6ObG1u+/xGwyAWBbvTq+H31I1tmzyNnzmNZ6GidjT7IocNGfacrC1BsAvk3hjw/BlF1qcCEEnwxrTGN/V6aW452upJRkZkYQHb2aC4GvcuBgd/btb8+5888SHvETJlPqP11EhUJxj1GCh0JRClJKs5SyKVAJTXvRsITgRRlUyxKuF5Xf91LKllLKlt7eRTvgKhTljfN7o8jNMtO81y0zo10LfiDuagh9nnnuthPJC5Jtzubb09/S2LsxXSp1KTbcjR9+xMrbm9oH9lN9+XK8J03EvnHj27QaRZGWbWLsz0cIup7K3NEtaF/JFpaMgNxMeGRFoZ2jnD3seOClFvjVcWPngkCOrA/Bxt6B7o8/TVxYKMc3rr0Z1qVnTzyffIKkZcvpeNpE18pd+fLkl4Qmh5bSYneAEHDfm5AcDicWlCmKnbWR78e0xMXOmifnHyUutXSB5a9GSkla+mWuRS7h3Pmp7D/QkQMHu3Ih8GXi4rbi6FibWrWm0arlGjp3Oo6VlfM/XWSFQnGPKX75SaFQ3IaUMkkIsQvoA1wXQvhKKaN1M6pYPdg1IP+empWAKP16pSKuKxT/eky5Zk7viKByPXe8q2iTxYsH9nB62yZaDniAmi1alxh/5aWVXM+4zgcdPyjWGTrrUhDpBw7gPXUqVu7uZS5bZo6Zx385yplryXwzsjlda3vC0ochNhBGrgCfwlvmAtjaW9F/chN2LbrI0Y1XOXc1CI9e2VRr0ZKDK5dQp00H3CpqJ7J7T51K5tlzXH/7bV5Z8A0jrp/gjf1vML/PfIxl8MkoEzW7Q9WOsPtjaPoI2JR+vkkFFzt+eLQlw747wFMLj7Hkb97pSkozqWmBJCUdJSnpCElJx8jNTQDAxsYHN7dWuLu1xs2tFY6OtRF3uyOYQqH416DecoWiBIQQ3kIIN/1ve6AHcBFYBzyqB3sU+E3/ex3wkBDCVghRHagNHNHNsVKFEG11h/Ux+eIoFP9qLh2KISMlh2a9NW1HWf06ADJyM5h3dh5tfNvQ2rd4ASVhwXyEnR1uIx4sc7mycs2MX3iMY1cTmD2iqXaq95bX4fIW6Psx1OpRYvywtKscCfiN01W3k3neht0LLvO520ZyhInlX75Dtm72dPNwQTc3sl6cwauNpnI67jQLLywsc1lLRQi47w1Ij4Uj35c5WkN/V2Y92JQT4Um89utZ/soNZSyWbJKSjnH16recOvUYu/c05+jRQVy+/B5pqRfx8uxGvbof0a7tDjp2OECjhnOoVGkUTk4BSuhQKP5PUBoPhaJkfIH5QggjmqC+Qkq5QQhxEFghhHgCCAeGA0gpzwshVgAXABMwUUqZ52k6gVvb6W7WfwrFvxqLRXJyWzjeVZypFOB+R34dAIsCF5GQlcCUZlOKDWOKjydl/QZcHxhSZm1HrtnCpCUn2Hs5nk+GNWZAEz84Mg8OfwttJ0KrJ4vOy2JiV8Qull1cxuGYw1gbrOndsTdVatnDjub4NXLmXP2VND4VwdjP+hDQqQv9avSjRYUW+M/+nLAxj9Lky+3cN6I7X578ks6VOlPDrUaRed0xVdpC7d6wb7Z2roe9W5mi9W3ky3M96vD59iBqV3BmQteapUcqA2ZzBsnJJ0lKOkpi0hFSUk5hsWjCmKNjbSpWHIibayvc3FphZ+d7T/JUKBT/bpTgoVCUgJTyDNCsiOs3gPuKifM+8H4R148BJfmHKBT/OkJPxZEcm0nvcQ0RQtz06xj88psl+nUAJGcn88u5X+hWuRuNvRsXGy5x6TJkTg4eYx4tNkx+TGYLU5edYntgLO8ObsjwlpXh8nbY/DLUuR96vVsoTlxGHKsvr2Zl0EpiM2Lxc/Tj2ebPMqTWEDztPbGYLawOPk7q8fp8+fpvLP3weZqfj2Gd92ZWX15NRceK3F/9fvpOeYz0z77nmaZPctzdgen7p7Pg/gVYGe7R57b7dJjbCQ5+pf1dRqbcV4vguDQ+3nKRmt6O9GpQ8Y6zzs1NJjn5OIlJR0hKOkpq6jmkNAEGnJ3r4+8/Ene3Vri6tsTGpmgnf4VC8f+NEjwUCoVCcVdIKTmxJQxXb3tqNPPm0sG9ZfbrAPjl/C+k5aYxqdmkYsNYsrNJXLoUpy5dsK1RvdQ0LRbJy6vOsPFsNNP71WN026raYXwrx0KFBjD0h5tnYUgpOX79OMsvLWd72HZM0kQHvw680fYNOvl3us0/w2A00H1MPVZ8cJRDq68ybPLrLHh5Ci+lDsFqQGM2hm5k4fmF/Gydy7RmLjSd8wPjZ47k4/jlzD8/nycaPVF6g5YF38bQcCgc/AZajy/kGF8ceTtdhd9IZ+ryU6x6uj31/VxKjJObm0hC4iHdP+MoaWkXAYkQNri4NKZKlXG6oNFcOYIrFIoyoQQPhUKhUNwVkUFJxIal0uWRAJJjo9k6dw6+tQNK9esAiM+MZ3HgYvrW6Esd9zrFhkvZsAFzQgIeY0vXdkgpeX3tOX49GcmLverwZKcakHpd28HK1gkeXg62TqTnprPhygaWXVpGcFIwzjbOPFLvEUYEjKCKS5Vi0/f0d6Jl32ocWR9K7ZY+tB48nEOrlzK0S0/63vc1iVmJbL26la1u6/C4doI6by7G63kPvjz5Jc19mtOsQiHl6d3R9TU4vxb2zoL7PypztLydrgZ9tZ9xC46xdmIHvJ1tiwwbE7OOi5emYzanYzDY4+banBrVn8XNrTUuLk0wGu3uTV0UCsX/FerkcoWiHKNOLleUZ9bPOUXctTQeeasFK995lZS464z+eE6pJlYAHx7+kOWXlrNu8LpiJ/tSSkIHDgSDkepr1xS741Ve2Hc2XODn/VeZ2K0mL/WuCzkZML+/toPVY5u54uDMsovLWB+ynvTcdOp51OPhug/Tp3of7K3sy1Rns8nCyg+PkZmWw4OvNWP5jBcw5+Yy9tOvsba7NRkPP3+I5FFPcdlf8NYwEwhBZ//O9K/Zn66Vu5Y5v2JZNxlOL4PJJ8Ctcunh83H2WjLD5x6ggZ8rS8a1wdbqlmbHbM4iKOhtoqJX4Oragtq1puHs3BCDwfrPlfdvRJ1crlCUX9Q2EgqFQqG4Y+IiUgm/kECT7pXYt+wXYq9eoc/Eks/ryCMqLYoVQSsYUntIiRqG9AMHyL4cjMejj5YqdHy85RI/77/KEx2r82KvALBYYO3T5EaeYGvXZ3n87BwG/zaY1ZdX071ydxb3Xczy/ssZUnvIHQkBRisD3cfUJTM1l8O/hdNr/CRS4q6zf+Xi28JVadCWap/Ool5wNs9dawTA8djjvLznZbou78pre19jf+R+TBZTmfO+jS6vaP/unnnHURtVcuWz4U05HpbItHw7XaWnB3P02BCioldQteoEmjdbgqtrs3+V0KFQKMo3ytRKoVAoFHfMya3hWNsZsbW7yumtG3W/jjZlivvt6W8xYOCpxk+VGC5h/nyMXl649O9XYrivdgbz7a4rjGxThen96iGEIHbrNFZH/cGqWgHEBi/Gz9GPqc2nMqT2EDzs/pzjs09VF5r1qsKJ38Oo1bIJje/rw4mNv1GvQxcq1Kh1M5zzfffhOX487b7/ntNvNGGP+RLvtH+H03Gn2Rq2lfUh6/G086RP9T70q96Phl4NSxSwbsO1krYz1+G50OFZ8Kp9R3Xo19iX4Fh9pysfZwYHnOLipbcwGu1p3Phn4uxasTA6CV9ba7p4OGNrUOuUCoXiz6NMrRSKcowytVKUR1LiM1n0xkEC2thzYdcsPCtVYcSMmaVunQsQmhzK4N8GM6reKF5q9VKx4bKvXCGkX388J03CfdhYssNTyAlLxXQjE/v6nji2qojB3op5e0J4f1MgQ5tX4uOhjTgRd5xlhz5mZ1IgZiHo4N+RhwIeoqN/x3t3mB/aoYkr3j9Kbo6ZB15owOLXJuPo5sHID2ZhyHeSujSbCX/ySa5fOM5LU1yo4OLH4n6LkVKy99peNoZuZHfEbnIsOVRxrkLfGn3pV70f1VyrlV6ItDj4ognU6Q3Df77jOkgpmbTkGJvOXmd403XYVK1LuMP9HE0xkWgy3wznbDTQ28uV/t5udPFwxt5YvoUQZWqlUJRflOChUJRjlOChKI/sWR7Eud1hODqsIy0hrsx+HQBv7n+T36/+zu9Dfy9S82BOyyEnIpWEhRvJjc3G2q8+MscCgMHBCqOrLbnR6QgbAxH+DrwYGkPDRi50ah7OykvLuZJ8BVezhSFGdx4ctIDK9+oMjSKICUlm9SfHadjZH9/qcaz//CM6j3qcVgMeuC2cKSGB0AeGcqh6Lh93TWZi04k83eTpm/dTc1LZHradjaEbORJ9BImkgWcD+tXox/3V78fL3qv4Qux8D/Z8Ak/t1Xa8KgMmi+RMWga7r4eyNeIsgYfckekWctp4U9XHkXZuTrRzc6K1qyNXMrLZEJfE73HJJJrMOBoN9PR0ob+3G909XXAoh0KIEjwUivKLEjwUinKMEjwU5Q1TrplfXtmP0bCXhIiDDH75jTKbWCVkJdBzZU+G1B7C9LbTkWYLuTEZ5ISnkBOeSk54CqYbWQBIixlhSMexbQA2VZyxreKC0dMOIQQ5kWmcWxeEW1gqVghOOAeyyn0rJl8LD4WfpY/RHbsntpX5gL0/w76Vlzm9I4JBzzXl2LqvCTtzikc//Rq3Crefk5F5+jRXR43m60c92V8hiWX9lhHgEVAovdiMWDaHbmZjyEYCEwIxCAMd/TvyQccPcLV1LVyAzCRN61G5DYxcUWQZcywWTqVkcCg5nYNJaRxJTifdrAlzfsTQws6Nw9tTcbAysH5SxyJ3usq1SA4kpbEhLomNcUkk5JqxNxjo4elCfx9Xeni44Gh17zRKfwYleCgU5RcleCgU5RgleCjKG5ePXmfzt2vITd9Ai/5D6Dq67OdTzD01l2PbjlIruya1bPypm1oR11zNsdvgZI1NFRdsqjiTdXInN36YRY21q7CtfbvvQq4ll5l7VrEkcCleNjcYmNSVQcndsM+xxdoqEifb33F4+i2Ed+lnftwLcnPMLHv3CAD9JtZg0SuT8KtTl6GvvVPIXyNx6VIuf/wOL05xpoJXVZb0XYK1sXjH7ZCkEDaEbOCX87/QzKcZ3/X8DuuiHL33fQ7bZ8DjW6BKW7LMFk6kZHAwKY2DSWkcT0kn06J96wMcbAiQp6masYm2ro50bPQetjZenLmWxINzDxa501VBTBbJoeQ01scmsSk+mbgcE3YGQXcPFwb4uNHD0wXnf1AIUYKHQlF+UYKHQlGOUYKHoryxeuZ2wk5+Q8Va1cvs1wGQmJLIh998gEOWI74Wd64bkrFgoap3JVq1bkW95g0xGo1YcnIIvu8+7ALqUuWHebelEZwYzNjN40jOjcdaevF0s1EMD3gAN2lHxnczSIttRK6sisHRGse2vji19cXobPNXNMNtRF5KZO3nJ2nSozKOThfZ+fNc7p/0AvU7dbstnJSS6FdfZXvgej4ZamRCkwk80/SZUtNfd2Udr+97nWF1hvFm2zcLCTQZmalYfdmcGOcqTGn9LSfTMsm2SARQ38nupulUA2MkUZemkJkZTo0aU6lWdQJC3DKV2ngmmolLTvBAc38+G96kTI7uZik5kpzOhtgkNsYlE5OTi61B0NXDmf7ebvTydMHV+u/dx0YJHgpF+UXtaqVQKBSKMpF4PZmw04sw2ljTf+orZRY6IiMjmb9oPk6ZTnSiPh3G9yHX3cCJEyc4evQoqzauwWXvDlq2bEmt69cxx8Xj8cHtBwbmmHN4dueLJGVm4m+ayMoxj+FibwtSwtoJOCYvxeHhH8l2aETavkhSd4STuisChybeOHX0x8bP6a9oEgD8A9xp2Nmf0zsiGPJCR3xr72LX/HlUa9IcB5db5lFCCCrOmEHHhy5x+NJVvhff061yN+p51isx/YE1BxKSFMKP536kpmtNhtR5mCO62dTBpHROpWYwyu8RPgyeTb3YgzSp3YP2bk60cXXEzdoKKSWRkYu5HPw+1lbuNG+2GHf3wifL92vsy+XY2szefpk6FZx5ukvNUutuFOKmYPNubX+OJaezIS6ZDXFJbIlPwVoIung409/bld5errj/zUKIQqEoXyiNh0JRjlEaD0V5YvHr7xETfIjez7xGwy7tyxTn9OnTrF+3HhuTgc6mejSdcB92lVxu3jebzQQFBXH06FFCQkIwWCxUTUyk+8svU6lSpZur7p8e/ZT5F+ZDzOPsnTwRNwddk7HnE83Butvr0OXlm+nmxmWQdiCKjGPXkbkWbKq74tzRH7t6HghDGbesvQNyskwsfecw1jZGuo/xYen056jboQv3T3y+cNjwcM48MpTnR+fiXbEGy/ovL9HkKsVk5mBiCrMOvUpUwiFSfZ4ny64JRgFNnB1o5+ZEe2cbui3uhsHBA8bvAr3dTKZUAgOnERu3GU/PLtSv9wk2Np7F5iWlZNLSk2w6G833o1vSs36Fu2oPi5ScSslgfVwS6+OSuJaVi5WATu6aJqSPlyueNn+NEKI0HgpF+UUJHgpFOUYJHorywtmdW9k6dw5ufl144vPit8HNw2w2s337dg4ePEhF4U6b7BqE981kUMcHi40Tvn0He3/6kbC6dcmxWPD19aV169ZkeGXw9M6nyUlsw6RGrzD5Pt3v49xqWPU4NB4BQ+benGznx5KRS/rR66QdiMKcnI3R0w6n9n44tqyAwfbeTnzDz99g/ZenadGnKqasAxxes5yhr79LtcbNCoVN3fkHa2dN5OPhRp5q/BSTmk0qFMYsJZ9djeGLsOuYJVjLbHzi3seSe52XO81loH+j2x26Ty2BtRPgwYVQfyApKWc4e24K2dlR1KzxIlWqPHmbaVVxZOaYGfH9Qa7EprFqQnvq+bqUGqckpJScTs1kQ1wS62OTCMvKwSigg5sT/b3duN/bFW+be3dIoRI8FIryixI8FIpyjBI8FOWB2KshLHn9BSS+9J7wOvU7+pcYPiMjg1WrVhESEkJDY1Ua51TirWrf8cOYBThaOxYbL2LCM2SeOkXl3zdzLiiII0eOEBcXR64xl3Cn61xOuZ9trwzE2c4aIo7CL/3AvzmM+Q2sCu/ElB9plmSejydtfxQ5YSkIWyOOrSri1N4PKw+7u2qXotixIJBLh2J44IXGbPryNSwWM49+8hXWtoXziJ09m7evzWN/IyOL+y+lgWeDm/du5JiYeCGMXYmpDK3gzsO+HjR3cSQlK5aHNz6MrdGWJf2W3L4lscUM37RDAhEDniI45BNsbbxp2PALXF2b31E9YpKzGPjVPhxtrVg/uSNO90hIk1JyPi2T9XHJrI9NIiQzGwPQzs2J/j5u9PVypYLtnxNClOChUJRflOChUJRjlOCh+KfJzkhn0bSppCemY+0yiic+7YWNXfGT0OvXr7N06VJSU1LpaKhPbXNFpvh+SJumnXil9SvF5xMaSsj9ffF6ZgLeU6YAYLFYePW3V0kMTsQ33R8D4G5rTSNfN+qFfksFdysM43aAY/GmQ0WRE5FK6r5IMs/Gg5TY1/fU/ECquZT95PBiyErPZek7h7F3sqHdYDtWvfcarQYOpfPIxwqFlWYzFyY8zoTGJ3D3rsTKYb9hY7ThZEoGT54LJS7HxAd1KjHS1+O2cp2NO8tjWx6jgWcD5vWah43xlgO96ewSrFZP4HyAE6YG/alfbybW1m53VZfDITd4eN4h+jf244uHmv7ptimIlJKL6Vmsj0tiQ2wyQRlZCKCNqyP9fdx42NcDR+Od746lBA+FovxS/k7+USgUCkW5QErJlm+/IDn2OlZO/ajTqlqJQseFCxf44YcfMOWYGGBoRYDFj52dAgm2DeeRuo+UmFfiwoUIa2vcH3745rUNoRvYnLKZCG9/9pjr4pSRTGJGFnuCo1kQ3ohvT9dh3dy5nNn+O8mxMWWul01lZzwfrkvFV1rh3KUSWSHJxM09Q+xXp0g/GYs0WcqcVkHsHK3p+kgANyLTiA13pmG3XhzbsIbroVcKhRVGIwEzZ/PMIRdCsq7xzaHZLIiMZ9CJywgB65rXZpSfZ6EJfyPvRrzb4V1OxJ7gnYPvkLeAmJx8ksPJ35DiZE1ApA2N68+5a6EDoE0NT57rUYd1p6NYdjTirtMpDiEE9Zzsebm6L3va1GV367q8WK0iySYzH4VEY+Te++IoFIp/FqXxUCjKMUrjofgnOb7xN3YtmEe9zsMJPVuZIS80w6+2e6FwFouFXbt2sWfPHvwr+NE1IQBHiw1uj9fj/n2DaOzVmC/v+xIpJZb0DIRBYHBwuBnfnJTE5W7dcenTB78PPwDgWuo1hq0fRiWHWpzbP5DxqVuwykymdzPBtkRfbggPqjjbk3PlImk34gBwq+BL1cZNqdqoGZUbNsbOsWw7WVlyzGSciCVtfySmuEwMzjY4tfPFsY0vRse7M/vZ+uN5rpyIZdDUevz2yYs4e3rxyHufYShiBT/zzBleXjCS3Q0hscJbdPRrxtf1q+JRyg5Q35z6hm9Pf8vU5lPp4WLmSshn2Nr60tRpJI6/vgh9P4XW4+6q/HmYLZJHfzrC0asJrJ3Y4U/7e5SVuJzcu/b7UBoPhaL8ogQPhaIcowQPxT9F5KVAVrz9KjWat8JiuJ+0hGxGvdsOIQQyJ4fs0FAsKSlkJCay6dw5QlJSqGvjQLvUphilETL+4HrMKWJir1DV4I1Neg7mtDQwmxE2Nng8OgbP8eMxOjsT//084mbNovraNdjVrYvJYuLxLY9zOfEyNdNepsr+dfiYkxn6YBcqHPsQU+8P2RDpyIULF2jQoAGdWrUgKvA8YWdPEnH+LLlZmQhhoGLN2pog0rgZvrXrlrr9r7RIsi4nkrYvkuzLSWBlwLG5D04d/LCuULxvSlFkpuWw9O3DOHvY0bBzJpvmfEyX0U/Qsv+QQmFDM7KZ++UXHHJYgNHOjQ2PbcfeqnS/E4u08OKuqWwP/4PHvbLpXqUn9ep+iLWVs+b/ciMYppwCG4dS0yqJuNRs+s7Zi7OdFesndcTxHjvl32uU4KFQlF+U4KFQlGOU4KH4J8hISWbhq89itLJi0IsfseKDM7QeUJ1W/aqTGxtLxJPjyA4KItXZib0dO5Hm7ESzC1do4j8Cg9GK7Au/IKwyuWyKItNO0Lp2N4zOLhhcnDE6u5B16SIp69ZjdHfH65kJxM/7AduaNaj6888AzD09l69OfcVTtV4ldO4f+OXGMeiF17m+biunYtsCBgzWghyXSBKtg7EzOFPHvT1urm5Y2xvIybhGSmwQiVEXSYoOQUqJta0d/nUbUq1pM6o1bo6Hf6USfRZyr6eTtj+K9BOxYLJgW9sN117VsKnsXOZ2vHzsOlt/OE/bITUIPzWf8PNnGPvpN7j63Nqidkt8MpMDwzBKeGXHLD6rcZzRLj15ecisUtNPTDrKybPP8tm1FGLNNiy8fwl1PetqN8MOws99oMfb0HFqmctcHAeC4xn542GGNPNn1oNN/3R6fyVK8FAoyi9K8FAoyjFK8FD83VgsZn79cAbXAs/x8LufEnbewNGNoYx5vz12mTcIe/xxTHHxZEx8hi3R0RgMBga17oLLIQNIgff4RlhXcOT49eOM/X0sb7Z7k+F1hhfKJ/PceWJnziTj6FEAPCc+g/ekSZyLP8fozaPpVakHDqtycLwRSqcxUwg/ak1cjIV6tVNwbdCU7HQTWRm5XE+4RnDiESTgndMQQ6orptxbPhrSkoXFFIHFFI4lNwxpSQLAYOWMg2tNnL3r4O5bFycPN+wcrbF1sMbO0QpbR2vsHKyxMYC8mEDWsRhkjgXvp5tg41s27YeUkt/nniPs3A36PVOdNTNfxL9ufR54dQZmCR+HRjMnPJbGzvb80KAalYTkxZn3saNKCj+1mkWLhj2LSdfC1bBvCQmZjb19ZXxrvsOTu9/CIAws7bcUL3svLeDi4RBxBKaeATvXItO6Ez7fFsQXOy7zybDGDG9Z+U+n91ehBA+FovyiBA+FohyjBA/F382BlUs4uGoJPcdPolG33ix84yCu3vb0GehG+OOPY8nIIOG1aWw5dYqKFSsyrOcgcpaFgQTvcY1umiQ9v+t5jsQcYduwbdhb2ReZl8ViIaR3H3Kjo8Fkwq5Naz5oE0WYFzwU2JrEC6exajQU+7gaGMwZdHefS43pi8D2dt+NhIQEli1bRlxcHPfddx9tWrUlJ9NMVnou2Rm5ZKWbbv6bfD2GuPALJEVfIu1GMBZzJgAGKx+EsQoG66oYrPwQ4nb/AjsDdHOzwdbRmoqTmmJ0LXn73jzSk7NZ+vZhPHwdqdrgGrvmz6PjhOf4xLUq+5LSGO3nybu1/LEzanu9JIReYujG4dhhxeqxO3FwcrstveyceC6cf4GExH1UqDCAugHvYWXlROCNQB79/VFqu9Xmpz4/YWu0hejTMLczdH4Zur9epvKWhNkiGfXDYU5FJLFuUgdqVyi79ufvRAkeCkX5RQkeCkU5Rgkeir+Tq2dOsvqDN6nfqRt9nnmOqKAk1n5+ki693bD5ZDIIgefXXzF30yYqVarEsPsGkfRzIHC70BGdFs39v97Pow0e5bkWzxWbX8axY4SNGk2F6dPBYiH8i0+xTs/hRu0qnLEWRHv2wM/YHN/qDvRMfxTndkPg/o+KTCsnJ4fffvuN8+fPU79+fQYNGoStbcnCgcViJjbkCmFnT3H1zAmiLl3EYjZhtLbGu2oAXlXq4+4bgI1jRTKScgnbc412dkaMHrb4T21e5gMILx6KZscvgXQcXpPDWz8n7no0Cx+eyozGdXjIt/BWwDs2f8fU2K8ZdqMmbz635qZJWELCAc5feB6TKYU6td/Ez2/EbeZi28O289yu57i/+v3M7DRTu7dyLARthWdPg5N3mcpbErEpWfSdsxcPRxt+m9gRe5s73+72r0YJHgpF+UVtp6tQlIAQorIQ4g8hRKAQ4rwQ4ln9+gwhRKQQ4pT+65svzjQhRLAQ4pIQone+6y2EEGf1e3PEvd4UX6H4E6TeiGfTnE/w9K9MjyeeQQhB4MForK3B8OFkhK0tVRct5OC1a5hMJnq36a4JHQK8xze+zfl66aWlADwU8FCJeSbMn4/R1RW3oQ9wqqs/T48zc7V+JTyDw+ly8RrtI6Jp1dOXwS3/wNlwvcQdmmxsbBg2bBg9e/YkMDCQH3/8kRs3bpSYv8FgpGKtOrQZ8iAj3vqIiT8tZcirb9GkZ19ys1I4t3MFexe/y4Flr5EYuYYmwxwJtLNCJmRxZdYJTDmmMrVtQJuKVGngwb61ISxs2BOb7Ezeuri/SKED4L77n2ZAdl1WewSzd9kspDQTEvIFJ0+NwcrKhVYt1+Dv/1AhH5UeVXvwbPNn2Ry6me/PfK9d7PY6mDJh3+dlKmtp+LjY8fmIplyOTeOtdefuSZoKheL/B6XxUChKQAjhC/hKKU8IIZyB48Bg4EEgTUr5aYHw9YGlQGvAD9gO1JFSmoUQR4BngUPAJmCOlHJzSfkrjYfi78BsMrHi7WnEhV9l1Ief4+FXiZwsEz+/uAefyIM0ythD1Z9/Jsnamm+++YYWDZrS/GIFTegY1xhrn1u7JmWaMumxsgdtfNswq2vxDtI54eFc6d0Hz3HjMEwYzQNrH6DVJQ/8Ak04UpfaUZH4xZ3Cyscb74AYXDs3RoxeVab6XLlyhVWrViGlZOjQodSuXfuu2iU1IZ7ws6cJO3OSq2dOkpORTt9np5F6yIYKUWnE2hqpObkprl4l7xqVbjYz7Wgo1RaFk1XBjtbVL3F6/WqGTX+Pqo2aFhknLSuFQfO7YczI4R3/aqR7XaBixSEE1HkbK6vifUyklLy27zU2hGzg0y6f0rtab/htIpxZCVNOgGulu2qLgny65RJf/RHM5yOaMKTZvUnzXqE0HgpF+UVpPBSKEpBSRkspT+h/pwKBgH8JUQYBy6SU2VLKUCAYaK0LMC5SyoNSk/YXoAkwCsU/zt4lvxAVFEivpybj4adNIs8t2o3JBFUM4VRbtAhrPz927NiBtbU19a943tJ0+Nw+6d4QsoGUnBRG1RtVYp4JCxeBlRVuIx9h+v7p1Dpni1+gCaNtEy569yLojbepumQxVq62RO81Ero0mfRDh8pUn5o1azJ+/HhcXV1ZvHgxe/bs4W4W2Zw9vGjQ5T76Tn6Rxz7/Ds/KVdk4+0PcOuSQG+CBT7aZ4x8dI+RUXLFpBGdk0ff4ZVZlpmHVwxfXyCx8/LviVtGX7fO+Jjcnu8h4TnYuTGszgWgPWHM4hLpV3qZB/U9LFDpAO5RvRvsZNPVuyvR90zkffx66vAJI2P3xHbdBcUztUZvW1Tx4fc05gmPT7lm6CoXiv40SPBSKMiKEqAY0Aw7rlyYJIc4IIX4SQuSdquYP5D/i95p+zV//u+D1ovIZL4Q4JoQ4FhdX/IRGobgXXD58gOMb19K0d3/qtu8MQPL69VzYEYKjOZEmP3yElbc3YWFhXLx4kRaudbFJkniOrIe19+1Ch5SSJYFLqOdRj2Y+zYrN05ySQvLq1bjc34dVCTswb4+mQbAtVrb1uVa3D3t8BI92rYFD8+ZUG2DGr5cNliwL4WMfI+LpCaTt3Yc0lWzm5O7uzhNPPEHDhg3ZuXMnK1asIDu76El+WbB3cmb49PfxrlqNdZ99iLlhKlYB7tSxEpz58Rx7lgdhzr39xPMNsUn0ORZEXE4uy5rUZPKguvgHuHPot3A6jBhP0vVoDq1aWmR+UVGrMCR+Rjes2dxIELE9tMxltTXaMrvbbNzt3JmycwrXrW2h5eNwchHcKHyC+t1gZTQw5+Fm2FkbmbTkBFm55nuSrkKh+G+jBA+FogwIIZyA1cBUKWUK8C1QE2gKRAOf5QUtIros4Xrhi1J+L6VsKaVs6e39551BFYriSIyJ4vdvZ1OxVh26jH5Cu7ZsGcFvfUqya00aDmiMlbs7Ukq2bt2Kk50jAREeuNxXBdtqhbdnPRR9iOCkYEbWG1niGRlJK1dhycggbfB9nJy3j7qhZmydA2g2cRKrUlJ4qksNHGys4NpxRNRxXEc+Q43Nm/B+4XkyTpwgYtw4LnfuQsw775Jx4mSx2gwbGxuGDh1Kr169uHjxIj/88EOpfh8lYefkxLDp7+FTrTrrP/+ItHqp2FRzoYWTFVF7Iln18TGSrmdgskhmBEfy5Pmr1HG0Y1vLADp7OCOEoNuoukiL5PJxGxp07cHR9b8SezXkZh4WSy6XgmZw5Mw7ROf2oWmtediZrPg+ejXm1NQyl9XT3pOv7vuKtNw0Ju+cTGa7iWBlC398cNf1L0hFVztmPdiEizGpvL3+wj1LV6FQ/HdRPh4KRSkIbV/NDcAWKWUho3VdE7JBStlQCDENQEr5oX5vCzADuAr8IaWsq19/GOgqpXyqpLyVj4firyI3J5ul018kNT6O0TPn4OLtQ/y8ecR9NouIrpMIFvV49MMOOLrZcv78eVauXEln2YCGFevgPb4xwlhYsJi8YzJn4s+wbdg2bIw2ReYrTSaCe/Uip1IA612rYh27H0evmjz68UeMX3aaC1Ep7H2lmyZ4/DoeLm6E5wPBzgUAS04O6Xv2kLxhI2l//IHMzsba3x+Xvn1x6d8fu4A6ReYbEhLCypUrsVgsDB06lDp1ig5XFrLS01j9wZvEhl6h/zMv43zYltzkbPalmUkxS052cGWtNzzm78XbtfywMdy+xnd6ZwT7Vlym/YOV2bf4LYwuHoiBzxIcm8i58PNEpjiQlntry+CKjmtIr3KYT5MG0fvZ9+6orLsjdjN552R6VO3BpyZXDPtmwdP7oGKju65/QT7afJHvdl9hzsPNGNjE756le7coHw+FovyiNB4KRQnoO0/9CATmFzp0n408hgB527usAx4SQtgKIaoDtYEjUspoIFUI0VZPcwzw299SCYWiCHb9Mo+4sFDun/wCzl7exH4+m7jPZuF8f1+i3JpQub4njm62mEwmtm/fjofRhdrCD4+HAooUOiJSIth9bTcPBjxYrNABkPL7FiJNfuywaYp17AHsKvjxxGcfcT4ug72X429pO9Ji4dyv0HTkTaEDwGBjg3OPHlSa/Tm19+/Db+ZH2NSowY2ffiJ00CBCBgwkfu735Fy7dlu+NWrU4KmnnsLd3Z0lS5awe/duLBZLweKVCTtHJ4a9/i4Vqtdiwzcfk9YsG6ONkbaeNsQ7G2i0M4EPguCdqr7YGAwkpOdw7GoCK45G8OGmQL4MjyHeDnauDGerXWsyo67y+4pVbL8QCpZUegQ4ML1fPX4e24o9L3Vj2qBp2GUZWJCwjiNnw+6orF0qd+GFli+wLWwbX7s4aAcJ7ny/bJEtZijD4uQLverQoqo701afITQ+/Y7Kp1Ao/r9QGg+FogSEEB2BvcBZIG+W8hrwMJqZlUTTZjylCxcIIV4HHgdMaKZZm/XrLYFfAHtgMzBZlvICKo2H4q/g2sXzLH/rFVoOeIDOj4zl+vsfkLh4MW7Dh5EzYgobvjpL73ENqdXCh0OHDvH777/TO6cpTUZ0wKFJ0eZ/M4/MZNmlZWwduhVvh6LDWLKz2T7qfS661CQnfTXSw54pn/6ErYMjo344zMWYFPa8rGs7dn8Mf7wPk46DV61S62S6cYOU338nZeMmMk+cAMC+aVNc+vfH5f4+WHlqW9fm5OSwfv16zp49S926dRk8eDB2dnZ31Y7ZGRms/vBNYoKD8Ok/gbbnnQizF5x2tMYmKJ0MW8HvLrmE5ObejGNjNFDNy4G6jvbUOZWGbSUHZOp80sLDaDIqlVYdv8XFuWGhvGauf5tFCavotLsF7ce/zcg2VUo0Z8uPlJIZB2fw6+Vf+cinM/0OL4IntkHl1lqA3Ey4EQxxlyDuov67BAkhYO+hhavcBqq0Bd8mmslWAaKSMuk7Zy9+rvb8+kx77Kz/ufM9lMZDoSi/KMFDoSjHKMFDca+xWMwsmvYcWampjP3kK+LffY/ktWvxeOwxfF5+iW0/nif8QgKPzexIjimbObO/wD3TgaENe+P5YECRaabnpnPfyvvoWrkrH3Uq+oA/gJMfL2FfkJHstCVkOJuZOPMHPD0qcvRqAsO/O8jrfesxrnMNMOXA7EZQoQGM/vVmfLNFcvRqAq721lTzdCz28Lqca5GkbNpEyoYNZAcFgdGIY7t2uPTrh3PPHhgcHTl8+DBbtmzB09OThx56CC8vrzK1X0J6DiFxaYTEpXMlLo3gyBt4HpiPd0YMsb7DmGxXnX2YWGa00C3ZgLUFrFt5UrttRWr6OFHJ3QGjQRMYjm8J5dCaULybfkvkvkyqNGjCA6++V6RAkZGbQa9fOlIp2sTZpDcZ2LEOMwY2wNaqbBP8XHMu47c8wZn4s/wYm0BTgxNUaAjxlyDxKjddzoQRPKqDd13wrAWpMRBxSA8DGG3Br5kmjFRpqwkkjlrb7Qi8zhPzjzGmXVXeGVRYePq7UIKHQlF+KduxqwqFQqH4T3B2x1birobQd9ILxL46jdRt2/CaMhmvCRPIyTQRciqe+h18MVob2Lt9D5nZWfRzbIX7oOK1DmuD15Kem17iFroxp8M5dNkDS8ZKsq1z6PvKG3h6VARg9vYgvJxsGNW2qhY4cB2kxcDAOTfjx6dlM3XZKfYFx9+8VtHFjupejlTzcqS6lwPVPB2p4e1I5YoV8Ro/Dq/x48gKCiJloyaERE+bRsyMGTh17Ur9/v3wefhhVq1dy7x583jggQcICChasIpNzeL9jYHsCYojMeOW9sLaKJAOVsj6DzA6dD0VY1YT020KnUNt6dvWD+vOldjxywUiDt8g12zEd6TnTaEjJycBvN7CzqMLSZeeoM3gFA6uXMClg3tv7i6WHwdrBx6v9Qif28xnXPYfzD5iz6WYVL4b1QIflwIam8xEiAvSNBfx2r/WcZf4PDWSR/wq8KyHI0ujovGLM4B/C2g8ArwDdGGjZpEaDVKvQ8ThW79D38IB/fl41IQqbbmvcmvGtajFvINhtK3hSd9GvoXTUSgU/9cojYdCUY5RGg/FvSQzLZWfpj6FV+WqtItKJH33biq8Ng2PMWMAOLcnkt1LLjF8WktsXC18+cUcqpt8ePCpkdhUci4yTYu0MHDtQFxtXVncd3GRYdKTs1k+bQc5aeGkZ2/Aultdpjytnb15JDSBB+ceZHq/ejzZqYYW4cdemo/H5BNgMHD0agKTlpwgKSOXaffXxdPJlqvx6YTGpxN6I52r8em3CQQGAX5u9ppQ4umo/+uAb3wEzru2kPH7Zsw3bmBwdkb07MkOL0+up6TQtWtXOnfujEF3BpdSsvpEJO9uuEBmrplBTfwIqOhMDW9HrggzH0TH4WhlZG6DqrSyM/LrRzOICrrI4K4vYhMqcO1fA6f2fpzYGsbhdaE4e9jSe1xD7D0iOXP2aXJyYvF1/4A/vnejRjMv4q78ROqNOMbO+hZ7p8LtnWXKovfPHagYl8vYrit57/fTNLSO5vXWgirmCN1EKkgT2vKwsgev2ppQ4R1AiJMHowLn4puVzgKzJ47jdkEZTbZuIzcLok7qgsgRTSuScYMcaWS46R1CpD8bO4dRJaAF+DcHm5LPH7mXKI2HQlF+URoPhUKh+D/hwIpFZKel0cLFi/Sla6nw+ut4jL6lpQg8EI2nvyPeVZxZ9cNSsEC3jp2LFToA9kXuIywljI87F304nSnXzMZZR8jOFWSa1mOyETw9+s2b9zVthy0j2+jajrzJbO8PkULw/e4rfLzlEpXd7fn5mdbU93MpMp/kjFxCb6QTGp9GaHwGV+PTuXojnbWnIknNunXmh5WhGZUGt6eyMQff2DC8z5/CJ/kCbvWqsvOPXVy7eJGhjz5KQja89utZdgfF0bKqOzOHNaamtxM5FgvvXoli3rV4Wrs68n2DalS0tQbggWlv8+uHM/jtj88Y3PYFkjeGYOVmS4s+1fCr5cbWH8+z6uOj+DReQ4UGJpo3X4arSxNS+4ZyZH0o7YaMYtcvb7Nn0c/0fnrKrcqlxkBsIHZxl3hC+PKJfxhi0UD2+8SAGTgIuVaOWFeoB7Xuu6W98A4A1yqQb1etGsCnFeryzPaneTX7GrMD12OsP7DY51ss1nZQtZ32A80JPSEEm/BDfHX5NP1OVmTSbgMrDw7G1iDBt7FmlpX3cy3pHFaFQvFfRWk8FIpyjNJ4KO4VcWGhLHzlWRq2aEPl+ctw7t4d/y9m3/QnSIhKZ+k7h+kwrBaefmZ+XD6fZs51GPj8wwhD8SviT217iuDEYH4f9jvWBuvb7kkp2fbzBS4fuU7l0Hlcdkulbv8+9Bs9CYDDITcY8f2h27UdaybAhd9IfuYsL6wLZXvgde5vWJGZwxrjYmddMPtSkVKSkJ6jaUd0YeRqfMbNvzNybh18Z7SYcTLk4CHTcUxJI9bBk75dGjGuU3X83OyJycll/LkwjqakM76SN2/U9MO6QNvkZGWyZubbxFwMYnCTqVilG/Ea1wibyo4EnpvN0TWCtKimVGnoTM+xTbFzssZstrDyw2NkpuZQtd4lTm5ew4NvfkBlH1vY9hYEbb6Vvr07fVyccU+SLG/5OFl+DXhtby7rrsKYdtV4o399rI2lb1i55MIiPjw6k8dyrHj+iWNguLfO4FvOx/DUwuM8Vt/AW/5HNa3ItWNgytQCuFbWndbbav9WaAjGe7MWqjQeCkX5RQkeCkU5RgkeinuBlJIVb08jPiKM7pGJWGVlU2PtGoxubjfDHFgdzOkdEYx5rx1Lv/mROFMSUyZMwrFC4YMC8whJCmHQb4OY3Gwy4xuPL3T/+O9XObQ2hOqh6zle8SSOFjcmfLvwphnRw98fIjgujb0vd9N2QUqPh1n1OVv7GSaEdSYmOYvX+9VjbPtqZd7B6U7bJTY1WxNC4tM5FRLHwROXSDZYk4od5nw7ztsYBbKKE6a6rnxetwqDK7gXm25uVhZrZr5N3KVQBtR9BqOwIr7bUuJzNuHn9wjZkU9wcE0oDs429HyiAX613IgLT2XlR8eo3cqDsOOzMWQlMdp/L9Z29tBuIlTtoGkxHL1YsvdrPgydywepvRgw6TNMZgszf7/IvL2htK7uwTcjm+PlVISfRoG6v7/5CZbHHeWdyv0Z0v3De9auecxYd55fDlxl7ugW9G5QEcy5EHP2lp9I+GFIjdICWztCpRa6INIGKrfStv69C5TgoVCUX9Q5HgqFQvEf59LBvVwLPEcjB1cIC8dv5szbhA6L2cKlwzFUbeRJ8JZjXDPF0aFJ2xKFDoDFgYuxMdgwrM6wQvdCTsVx6LcQKqScw5S9E/tsJ5r3HnBT6DgUcoODITd4ukvNm1uvymO/sDC7E0PPtsJikax4uh2Pdaj+lwgdAEIIKrjY0aqaB2nZJtaejyXBwZMX+zbgpcoRDLM9zdMZh5l4ajXtwk6QG5rKo7HZJQodANZ2dgx59S2869bg96Afyc1MxfmPTgRU/YB6dd+l6X3VGPpSCwxWBtbOOsmxTVfxrORE8x6+BB2+QRObGBJTczls3Q+mnISur0L1TuDkDUIwvMNTVMi248eMbeQmJGBlNPB6v/rMHtGU0xFJDPxyH2evJZda91d6fUtbsxXvhG/geNThe9m0AEzrW5dG/q68tPI0EQkZYLTW/D3aToDhv8DzF2DqORj6IzQbCZlJsPdTWDwUZlaDrJLroFAo/n0owUOhUCj+w+RmZbF70U94enjjtW03nuPG4dim9W1hwi8kkJGSQ0A1J3YHHsLVxon2A7qUmG5ydjLrQ9bTr0Y/POw8brsXfy2NbT9fwMM+i7qnfuBIgCdWVta06v/AzTCfbwvC29mWkW2qAJCWkcmUnVm8YXqc9rW82TilE82rlDzBvxcEXU9l6LcHeG9jIB1rebHtuS6M6lyfJ8aPo1WTemR5GLjSozGdqku6R53il+NxLP18ETLf2RxFYW1rR8fH24PPNXZf+xXrjArYbKuLzNWOA/Kp6sKI11pRq4UPh9eFsP7936l36QncjRFcME+gbsuWHL2QRNyNtMJpG615qv4TXKkg2bD47ZvXBzfzZ/WE9gAM++4Aa09GllxGK1s+bTeDSrm5TN05iYjUiDttvhKxtTLy9SPNkRImLz1JjqnAgY1CgFtlaDQM+n4CT++FVyNgzG/QZ+ZdazwUCkX5RQkeCoVC8R/m8NqVpN2IJ+BcEPaNGuE9eVKhMBcPROPibE340RMkGNLo0bcXVlYl29uvubyGTFMmI+uNvO16RkoOG785jY2NoM6+DzlWU+Ke4kij7r1xdNMEiYNXbnA4NIEJurbjUkwqA2fvYGN2E15qIfjp0Va4OxZ/+vm9INds4csdl+k/Zx9hN9L54qGmzBvTkoqu2ta0pzNymFmhNgdqN6FiegoZ3q689eIgmpgTeDPKifUjnyHj+PEi05bSwpWQWQQGPUvj4a7Y13LlYOwGcq6mkLAqCGnRTJxt7K3o2TWWbpXXEhMJqyNfoUHXaqTlOGHrNQBbB0e2zf0Si8VcKI8h7Z/EP9uBH7P/ICfhxs3rDf1dWTe5I00ruzF1+Sne23ABk7n4E9pd6w7kK2MlLLmZTN4+kdSc1D/TrIWo4unAzGGNORWRxCdbLpYewdYJanSFNoVN9xQKxb8fJXgoFArFf5SkmGiOrV9NFWGDR3oW/p9+grC+3UE7My2H0DPxtHC34qj5Mn4+vjRs0qjEdE0WE0svLqVlhZYEeNw6+8Kca+H3uWfJSs2loWUHNmnxRDaoi0EYaDVQ03ZIKfl8exA+zrY80qYKq49fY9DX+0hJz2Cx+w9MHNobQwnO7PeCc5HJDPpqP59tC6JXgwpse74Lg5r6I4RASsnciFgGn7yMlcHApwN789jYR8nOzmb19q18+8pAfJ1teKNyLw6Nm0LU9OmYEhNvtY0plTNnnuLq1a/x9R1O6zZLGfLquxhq2HE6cTeZp+NI2RoGsYGweDhi4WDqO/zBsIeysPP2Yd9OMz5Vnbl0OJkmvR4hOvgSp7ZsKlQHK4MVTzccT5i35LfFM2675+Vky6In2zC2fTV+2BfK2J+PkpieU3RjCEHV+97h8+uxhKVc5aU9L2GymIoOe5f0beTL6LZVmbc3lB2B18sUx2zOuKdlUCgU5QMleCgUCsV/lF0Lf0RISa1zQVSYPh2bqlULhbl89DpVjYKQ9GAyRDa9+vYu1adid8RuotKjbjswUErJrqWXiL6STIfujjhuWsGO1g44XjNRv3N3XLx8ANhzOZ4joQk82ak6b/12nhdWnqZpBWs2Wb1Iu0497vnuSvnJyjXz8e8XGfT1fuLSspk7ugVfPXLLETs518Tj567yVnAUPT1d2dayDo2dHahatSojR44kPT2dP7Zs4KenOyFcXHjn/he4tmELIX37kbRmLWlpwRw9NoQbCXsIqPM29ep+iMFgi7WNLYNemk5WlRyupJwidVcEaV++rTlX93wXJh3Fs+sQhk9rRf32vsReTcVoZeDySTeqNGrGvmULSEtMKFSfAW3HUiXLkZ9yd5N9I+62e9ZGAzMGNuDjYY05EprAwK/3ERidUnTDVG1P60qdeD05k/2R+/ns2Gf3vO1f71eP+r4uvLDyNFFJmcWGs1iyuRz8EYcP98NkurfaF4VC8c+jBA+FQqH4D3L11HGuHDtEzcg4fHr1wnXI4CLDhe2NpKaDmdM2YQQEBFCtWrUS0zVbzMw9Mxd/J3+6Vu568/qp7RFcPBBNy77VSF0wjVQ78GjWG4vJTOtBmvP5pZhUJi85QWV3e349EcnyYxFM7FaTRZXW4mOTC82KP/n8z3I8LJF+c/byza4rPNDMn+3PddF2WtI5nZpBz2NBbLuRzNu1/PipYTVcrW+Zm/n5+dG7d28uX75MTNBp5o5uSSR2zHpsJqJaDaKnTePKIwOxhCfSrOlCKlUadZsAZ42JQW0diM9ZRXRGCIm5E8jsuxc6TNHOxACsbYx0G12PXk80AAOkJ+ZgZd8NU042h9esKFQno8HIhKbPcM1Tsnrxm4XuAzzYsjLLn2pLjsnCA98cYOOZ6KIbqPt0hiXEMso5gEWBi1hxqXB+fwY7ayNfj2xOrsnClKUnyS3C/Cst/TJHjw0jPHweHh4dEOKvE0IVCsU/gxI8FAqF4j+G2ZTLzp/n4miW1DLaUXHGjCK1GHFXk6mZmsNp2zBMmOnRo0epaf8a/CuBCYFMaTYFo66duHo2ngO/BlOzuTeOOX/gFhjJuYGNSDpxmTrtOuLu609EQgZjfjqMwSC4kZ5DTEoWP49txUsdvbE6txIajwD7e+9MnpFj4p31Fxj23QGyci3Mf7w1nwxvgquDZnImpWR+ZDwDjl/GJCVrm9Xmqco+RbZXq1atqF+/Pjt27KCiMY2PhzXmUHQmn/XsR+IjJqwjBR7v5JD7ywEsmfqqvsUMJxbCly2w2vshg7pXJtLzIknZccSvCCMnqrDzeO1WFXhoemvsnKyJugxuFVtwZvvvpMTFFgrbt/Uoqmc684tlH1lxRZsxNavizvpJHann68zEJSf4ZMtFzJYCW+n7NYP6g3jx4kE6VWzDB4c/4HD0vd3pqrqXIx8ObcyxsERmbQu6eV1KScS1BRw9Oojs7BgaN5pL3brvYTQ63NP8FQrFP48SPBQKheI/xsnN60mMiaJexHWqfPwxRpeiT/uOXX0ZaZXJRcM1mjdvjre3d4npJmcnM+fEHFpUaMH91e8H4EZUGlt/PI9XJSfaD69E0qwviPKxonpAL3KzMmkz5EHi07IZ89MRUjJzSc82Uc3TkY1TOtGtrg+cmA+mLGh9752JDwTH02f2Xn7aH8rotlXZ8lxnutS5Vcc0k5lnLoTxStA1Oro7sa1lAC1dHYtNTwjBwIEDcXd3Z9WqVXSvYcNDDS/xe5ALWwImU/P3bbj278+NuXMJGTCQtOVfwtzOsG4SuPjD41uwemQR/V97hzCfy2RmpxL93XFMSdmF8nLzceCRt9pgbWskI70pUkoOrl5aKJxBGJjYYjLR7rBySdFaDwAfFzuWjm/Lw60r8/UfV3hy/lGSMwvszNXtdYy5GXxs8aC6a3We2/UcV5Ovlt7Qd8DAJn483LoK3+66wq5LsWRnx3H6zBMEBb2Nu3tb2rTehLd36QKwQqH4d6IED4VCofgPkZaYwIHlC/FOSaf+yDE4tCz6HLX0c/G4xmWy3z4Uo5WRrl27lpr2Vye/IiUnhWmtpyGEIDMth03fnMHKxkjfCY3ZNnsyngm52E+ZQOD27dRs2QY7H3/G/nyEa4kZ5Jol9X1dWDquLf5u9mA2wdEfoXpnqFD/nrVBSlYu0349yyM/HMYgYPn4trwzqCFOtrdMpwLTMulzPIjfYpOYVt2XRY1r4GlT+snZdnZ2DB8+nIyMdBYu/IAevt/Ro3YW80/WZHu0wO+jD6kyewYiM5aIt77h2vpEcrvNhie3Q5W2AFhZW9PnxRcJ8QjEnJXLtS8PYskq7NBt72xD7/ENEQZnDNaNOb9rBwlRhbfI7dXyIWpnujCfA2ReL8aUCm172w8faMz7Qxqy93I8g7/eT3BsPj8K7wBo8jBOx37hy1bTsRJWTNo5ieTse3uexlsD6lO3ojNTlx1l854RJCYeok6dGTRp/CO2tiULvwqF4t+NEjwUCoXiP8SeH7/FnJNDMxcfvJ55psgw5uRsElYGccWSRJQlhg4dOuDs7FxiupcSLrEiaAUjAkYQ4BGA2WTh97nnSE/Koe+ERlyK3ku1NceJauqPtU0FstLTaD5wGE8tPM6FqBQsFmhUyZWFT7a5aebEpU2Qcg1aP3XP6r/z4nV6zdrD8qPhjO9cg83PdqZNDc/bwiyLvkHf40GkmMysbFqTZ6tVwHAHhxRaW1+kZq0TxMe7YTa9yVdjBtOyqjvPrzjJiUXTcTz8NNX7JuI9pA1pkTaEPP81CQsXIc23tsW1sramx0vPEup6EUMaXP1iD7IIv4eqDTyp0sADa4c2SGlgx08/FwojhGBSq+e47gZLl71RavlHtqnK0vFtSc0yMfjrA2w9H3PrZpdXQFqodHwBs7vNJiotiud3PU+uueRzS+4Ea0M2L7TbQ2ZONnNPP0yzFmupXGn0X3ZQpEKhKD8owUOhUCj+I0QGnifw6EGqJ6UT8NlniCLO4pAWScLyS5hzTByyDcbJyYl27dqVmK6Ukg8Of4CrjSsTm05ESsmeZUFEXU6i2+i6OPkbOfPBNGxN0PTNWRzbsIYqjZry4dEMDlzRzphoVsWNBY+3xsUu33a+h+eCaxUIuP9P1z0xPYfnlp/i8V+O4WJvxa/PdOC1vvWwt7nloJxhtvDcxXCmXoyguYsj21sG0MG9ZIGrYDtcvfodp8+Mo0YNE/Xq1eDQoSvEXA3i+xr7qGi+zvhz9YloPBXDc6fw+vAXaqxfh32zZlz/4AOuDn+QzLPnbqZnZW1N11eeIszpMjaJ1gTP2YmUslC+HYbVRhgcsHVqSfjZQ4ScCCwUplvzodTNcGOB4TDp0ddKrUurah6sn9yBGt6OjF94nNnbg7BYJLhXhZaPwYmFNLdy5e32b3Mk5gjvHX6vyLLdKSkpZzhydCCG9F+Y2jGWwHg/fjz459NVKBT/DpTgofjPI4QwCCHOlR5Sofj3Ii0Wtn36Aba5Jjo+NRmbSpWKDJe6M5zskGT25Fwn05hMt27dsLW1LTHtTaGbOBF7ginNp+Bq68r5PZFc2BdF8z5VCWhTkXm/vkab4+nIYX0JDw8jIzmJs96t2HwuBoE2yZ3/eGuc8wsdMecgbB+0euJPb6G76Ww0PT/fzfrTUUy5rzbr9QP08hOckUW/40Esi07guaoVWNG0Jj621kUnWAQmUzrnzk/hSsgnVPDpR6uWKxk8cBgeDlasWvIzNgc/56d6x8m1c+fx0K4kC02gsalShcrzvsf/81mY4uK4+uCDxLz7HuZUzcTJaGVN+2ljibIPw/66LZe+21oobw9fRxp28kdYNwdhy6avvycr/XYNhBCCyW1f4IYLLFoxvUx18nW1Z8VT7XiguT+zt1/mqUXHSc3KhU4vgtEGdn3IgJoDGN94PL9e/pUFFxaUub0KIqWZ0Ktfc+z4cMzmTJo1W8iE+5/mwZaV+OqPYPZdjr/rtBUKxb8HJXgo/vNIKS3AaSFElX+6LArFX8WJBT9xIy2ZZhWr4jV0aJFhskOSSdkRznUrQYRjKJ6eXjRt2rTEdDNyM5h1bBb1PeszpNYQMlJyOLDmCpXre9B2YA12hf9B5Z+2YXKyI2Dq6xxd9ytUqMaCEE2YaFfTk58fa4WjbQHty5Hvwcoemo+56zrHpmbx9MLjPLP4BBVd7Vg3qSPP96yDrdXtgsza64n0PhbE9ZxcFjeuwSs1fDGW0awnNzeZsPB5HD7ch9jY36lV61Ua1P8cY8h+bH++j+HpP5GFLb/6vUb1UXP4bnRrrt5IZ+LiEze3jBVC4HL//dTYtBH3kSNJXLqUK337krJpE1JKjFbWNH9tBDdsr+MU5sC5nwofGNhqQHVs7J1wrdie7LTLrP1sE+YCplmdmg6iYYYHi62OkxodUab62Vkb+Wx4E97sX5+dF2MZ8s0BQrOdoO3TcHYVxJxjYtOJ9Krai8+OfcbO8J1lSjc/mZnXOH7iEUJCZuHt3Zs2rTfh4a5p2d4e2JDaPk5MXX6S2JSsO05boVD8u1CCh+L/BV/gvBBihxBiXd7vny6UQnEvSL8ew4GNa/DItdDmo0+KDGNOzyVh2UXM9lbsTY0k15BB9+7dMBpL1jbMPTOX2MxYXmvzGkaDkcPrQjDnWOg8og5JOUms/XEaDcLB77kXuHT6GKk34lhsbA1C0LGWFz8+2gqHgk7bGQlwZgU0Hg4OHndcXyklq49fo+esPey8FMvLfQJY+0wH6vvdvntXtsXCtKBrPH0hjPqO9mxrGUB3z6J3+CpIWtolAi++zr797QkO/gg7+8o0a7aAqjZtEYsegMVDITeTig/O4v7+gwmJusGePXtoV9OTDx9ozL7geN5Ye+428ySjszMVp79OteXLsfapQOTzLxDx5DhywsKwsramwbSBpFon4XzJgTML199WHnsnG1r2rUZWZgOsbB2JvrSVPUuDbktfCMGU9i+T6AQLV7xe5vYUQvB4x+osfLw1N9KyGfjVPvb6PAK2LrDjbQzCwHsd36OBZwNe3fsqgTcKm3oVhZSS6Ji1HD7Sj7S0i9Sv9ykNG3yBtbXrrXrZGPn6keakZZuYuvxU4W1+FQrFfwoleCj+X3gb6A+8A3yW76dQ/OvZ+frL5AjoPm4iVkVsnSulJHFlEOb0XA4m5ZDtFYWnpyf16tUrMd3Q5FAWXFjAoJqDaOLdhPhraQTuj6JhV39cfex5f+8MhmxJgepV8Bg+nD+WL+OAe2sSbD3pUsebHx5teZuPxU1OLgRT5l05lUclZfLYL0d5YeVpavk4sWlKJ57pWgsr4+2fs7DMbAacuMzPkfE8XdmbX5vVwt/OpsS0pTQTF7eNEydHcfhIX2Ji1lCxwkBat9pAi1qf4rF7EXzXCaJPQe8PYeIRqD+I5s2b07hxY3bt2kVISAjDWlRiUrdaLDsawdw9IYXysW/UkGorllNh+nQyT50iZMBA4r/9FoMBar3ckxyrbJzO2HFy2drb4jXuWglXH1cc3NpjMYVx7o/DnN5xu2ajXZN+NM3wYon1SVIir95R27av5cW6SR2p5O7AmKWXOeA/Fi5vhSs7sbeyZ073ObjaujJp5yRiMwqfKZKf3Nxkzp+fyoULL+DkFECb1hvw9R1SpAN57QrOvDOoIQeu3OCrncF3VGaFQvHvQgkeiv8XGgFnpJS78//+6UIpFH+Wqwt+4XLKDer4VaHq/f2KDJO2P4qsiwlc97QnxhJPpjmZDh06YDAU/wmQUjLz6EzsjHZMbTEVKSX7V13Gxt6KVv2qsyl0E7a/bscnSVJ5+pusW7eNY2Yfjru1oFuAN9+PaYGddRFCh8UMR36Aqh2hYsM7quvFmBR6f76HwyEJvDWgPiueakctH6dC4X6PS6bXsSCuZmbzS8PqzKjlj7WheNMqzZzqBw4c7M6Zs0+TkXGVmjVfpmOH/dSr9grOh1fAnOZwdgW0nwRTTkK7Z8BKE2SEEPTr1w8vLy9Wr15Namoqz/esQ//Gvny0+SKbzxbe4lYYjXiMGkmNTZtwuq87cV/MIXTQYHLOn6bqcx0R1gYcjlpxbOWvN+MYrQ20f6Am2dl1sXV0xcp4hH2rLhN65nb/iCmdppHsCD+vLLvWI4/KHg78OqE9/Rv78diFZiTY+CK3vA4WM94O3nzV/StSc1KZsnMKmabMItNITDzE4SP9iI37nRo1nqd5syXY21cuMd/hLSrxQDN/vtgRxEF9QwKFQvHfQwkeiv8XKgJHhRArhBB9RBn3bRRCVBZC/CGECBRCnBdCPKtf9xBCbBNCXNb/dc8XZ5oQIlgIcUkI0Tvf9RZCiLP6vTllLYNCURzZ4eHsXrkYK2HgvrfeLzJMzrVUkjeHQhVnDgUlY6gUi7OzM40bNy4x7V0Ru9gfuZ8JTSbgZe/F1bM3uHYxkdYDqpNMAl/ueJfhB8CxaxeC/OuycPtp9nh2oluAN3NHtyzkZ3GTkwshORza3NmBgRaL5LVfz2JjZWDL1M481qE6xgLCRK5FMiM4krHnQqlqb8O2lgH08XYtJkVISwvi4sXp7NvfgeDgD7Gz86NRw69p324X1fzHYn1sCcxpCvtmQb3+MOko9HqvyBPWbW1tGT58ONnZ2axevRqQfDq8Cc2ruDF1+SlORSQVWQbrCj5U+vxzKs/7HmkyET52LPGfvIPPQ7Wxs3HC5oCJI6tW3gxfo6k3frW9MNq0ISM5DGe362z78Tzx126dgN6qYS9apvuw3PYMSdcKa1xKw97GyJyHmvJ413q8kTYcEXsBTi0GIMAjgI87f8yFGxd4fd/rWOQtPxOLJYfg4JmcODkKg8GWli1WUr3aRAyG0s9HEULw7uCGVPdy5NllJ4lPK3yookKh+PejBA/F/wVSyulAbeBHYCxwWQjxgRCiZilRTcALUsp6QFtgohCiPvAqsENKWRvYof8f/d5DQAOgD/CNECJvBvYtMF4vR239vkJxV0iTiWMvPU+8gy3th4zAwb2wr4Qly8SNpRcxOFlzKD4LK88sEtKu065dO6yK2Go3j2xzNjOPzqSma00ervcwZpOFA6uDca/oQP1Ofrx98G0G7czA1iTIHjeJCV//zgm3prTxFnw/piU2VkV8WiwW2Pk+rH8WqrSDgKK1M8Wx/FgEJ8KTeK1vPap4OhS6H5WVwwMng/kuIo6x/l6sa1abqvaFd+vSzKm2c+LkaA4fuZ/omNVUqNCf1q3W06L5Uny8emE49yt81RK2vAa+TWD8bhj6A7hXK7GMFSpUoF+/fly9epXdu3djZ21k3piW+LjY8uT8Y1xLzCg2rlOnTtRYvw7PCU+TvGkz18Y9iEO1JNxtKyJ3p3LpwF5Am6B3HF4bs6yHraMnmA9hbWdk4zenSU++NVmf0vV1Uh3gx1V3rvXIy+fl3gG4tRzOcUtt0n9/G7I14aZr5a680PIFtoVt46uTXwGQnh7M0WNDCQv/Hj+/EbRpvR4Xl5KF24I42lrx9cjmJGfm8tzyU9r2vgqF4j+FEjwU/zdIzQszRv+ZAHdglRDi4xLiREspT+h/pwKBgD8wCJivB5sPDNb/HgQsk1JmSylDgWCgtRDCF3CRUh7Uy7EgXxyF4o6JnT+f0zmpuLt50Hz4w4XuSylJXBuMOSGLxNoeXI/KwKpyHPb29rRo0aLEtH8+9zORaZG82uZVrA3WnNsdSdL1DNoPrcXOazsIP7GHLqdysRn+EEPWXeO60Y16uREsnNwTa2MRn5XsVFgxGvZ8DE1HwZjfwFj6Knge8WnZfLT5Im2qe/BAc/9C9/+4kUKPY5e4kJ7Jd/Wr8lGdStgVKEdubgrh4T9y4OB9nDn7FBkZIdSs8SId2u+nfr2PcHauD8E74PvO8Os4sHOD0Wu0svo1LXNZmzVrRpMmTdi9ezdXrlzB08mWn8e2Ittk5vFfjpKSVfxBfAY7O3yefZYaa9dgFxBA/OfTsMTvxd+xNsGLdpMcex0An6ou1G3njzS0Ij48hEadsshKy2Xzd2cx5WiHFDar35126b6stD9HQvjlMpc/P0II3hnciD+qTsUxJ57zK9+5eW9M/TEMrT2U/7F33uFRVG0fvmd3s5vee08IgZCEQBJ6770KiKgoICqCYsOCIKIIogKC0qUJooggHem9JpBACum997IpW+f7IwgiHX0/eXn3vq5cGc6cOec5s1l2fnuesjpmNesj3udixGBUqgKaB68goOnnSKW3i8MHoamzJZ8MDqSgsp7SGvUjjWHAgIHHF4PwMPA/gSAIbwiCcAn4EjgDBIuiOAkIA+6ce/T2MbyBlsAFwEkUxXxoECeA4/VubsCfoz1zrre5XT/+a/ud5nlZEIRIQRAii4uLH2yBBv6n0OTmcmXjOurkRnR/7U0kd8hMVXupkLroYow7uXHmdB4OTWRkF6TTunXre9btyFPmsSZmDb28etHWpS31Sg0Re9PxCLDBzl/B4pPzeHu/EYKlFc+ILVGqdDRRJjK3jxdy+R3qYpSlw/e9IHE/9P0ChnwHsnvXDfkr8/YlUKvW8vmwoFuCk3WiyPy0fMZcTcNJbsSBcH+GOt3qBqWsSSYh8WNOn2lPcspcjBXOBAV9R/t2J/D2noRcbgt50fDDENg0HOorYfjqhl2ORt0fys4/GDBgAA4ODmzbto2qqir8HC1Y+VwYacW3ptm9G4pGjfDcsB6XL+ahituLtiieILO2HF78LTqtFoC2Q3wxMm6GwsyBmKPb6PlCAIXpVRz94dqNTFdv9JhJjTGs3v5oux4AUonAGy+M4bxpV3yT13HsYhTQIEqmhb5KM3NLvonfS5GsGW1a78PBodcjz/UHo1t5sHVSGA4WD/d3YsCAgccfg/Aw8L+CPTBcFMU+oihuFUVRAzdqfAy838WCIJgD24A3RVGsulfXO7SJ92i/vVEUV4miGC6KYriDg8P9TDPwP4YoiuTN/pQ0G3Mc3T3xat7ytj6aoloqdqai8LUiKr8WnVaP6FyIkZERbdq0uef4X0d+DcC08GkAXNybjrpOS4cRjVl2aQljNxfiVKTh7a5TKVJBc/IZqrtKcLeetw+WdgJWd4PqfHhuG7SdBA8Z2nQ+rZRtl3OY2MkXP8ebVcaLVBpGRaeyKLOQp51t2Rvmj5+p8fV7pKO45AhRUWO5cKEv+flbcXIaQOtWuwgL+xknx34NcQdl6fDrBFjVBfKvNmSqmhIJzUfBPQLv74dcLmfkyJFoNBq2bduGTqejvZ89c4cFcyq5hFm74u5bBVwQBKyHDsV3317QXEYigl+ZF2e3bALA3MaY0D7e6GlDWW42qpo42g71JTmyiIi9GQAENelExxo3tpleozj9wVLg3nE9MgkhLy5EJugp3/MxZ1JKKC45wqWIQYy1LsfZ1JbvcgopUj1aHQ6NTkNsSSw/JfzE9FPTGbxjMH2290Cr1z6yzQYMGHg8MQgPA08013cOFgNngcI79RFF8Z6fyIIgGNEgOn4URfGPFDOF192nuP77j9ySOcCf07e4A3nX293v0G7AwENR/fvvpF65RI1cRusRY25LTypqdJRtTkCQS1G1ciYpooiALnYkJl8jLCwMU9O7u8Cczz/PocxDTAiegIu5C+UFNcSeyKVZR1cK5OnYLNhM8wyRT/q+wzXBkrbORnRO30HrgcOQGf1pt0MU4cJK2DgMzJ3g5WPQqNtDr1Wt1TNjRyzuNia83r3xjfYz5dX0jEzkclUNi5p68E2AJ6ZSyXV3qrWcO9eTq1dfpqY2lUa+79Ch/WmaBczHwiKwYYCaEtj/PnzXChL2Qqd3YGr09UxV/8y37I6OjgwYMIDMzEyOHz8OwKhWHkzq2ojNF7L4/lT6A40js7HBY/lC9MpL2Fk1pX5XJJlXowFo2dsLc/tAjEycObt1MyE93Gja1pmIPekkRzT8dze11yzq5bBy54NVM78bJo6N0Ld+leGSkyxcv5mdp+agUDjRpc0OVvbZiIjI5KOTqVLf63uZBuGcWZXJnrQ9zLswj2f3PkubzW14Zu8zzL0wl3P55/Cx8uGl4JfQ6O/ulmbAgIH/Th7cydaAgf9O2gIdaQjkni0IQilwANgvimLS/S6+nnlqDXBNFMWFfzq1C3gB+OL6751/at8sCMJCwJWGIPKLoijqBEGoFgShLQ2uWmOBb/+JBRr430FXWUn+53PJ8HLBytGRxm3a3danYm86moIabMY2Y+fWFCzsjFEaN3j/tWt3e/8/0Og1fHHhC9zN3RkXNA6AM9tSMJJLCB/oxU8f9KFTnJ6NXZ4lwsiRUE9rRlQcpMjCkuY9+twcSKuCve80ZK9q0h+GrQTjByva91dWn0ojpUjJ2hcb6oGIosiSzCLmp+fja6pgS0gjAsxNqKlJITtnIwUF29HparGyCqeR3zQc7HshkfxJEKlr4PwyOL0YNDXQ8jno+iFYuj6SffejRYsWZGZmcurUKby8vPDz82Na7yZkldYyd/81PO1M6RPofN9xpOZmuH89hbyPf6eJaz+iP/kU+7VrMLO2od1QPw6taUtl0Q7ijh+m67N9qCyp48iGa1jYGdO0cTu6HvRgh1kSL6XF4ewb+MjrUYX3h6iVfMgGxkfP5Nd2nTE3t8UcWNR1ES8fepl3j7/L0p5LMbp+38vqy4gtieVq8VViS2KJKYm5IU5MZCYE2gXyXMBzBDsEE2wfjJOp0x1rfRgwYODJQLjfdq8BA08S13cn+tEgRBoD50RRfO0e/TsCp4AY4A/H7Ok0iIdfAE8gCxgpimLZ9Ws+AsbTEMD+piiK+6+3hwPrARNgP/C6eJ83YHh4uBgZGflIazXw5JH/8SxS9+/hgq8LPSa8Rove/W85XxtTQtmP1zDv7Ea6kYyz21LoNr4R2w9uJCgoiKFDh9517B/ifuCryK9Y0m0J3Ty7kR1fxq4l0bQb1oiS2EV4rD7AsRbN+dL7eWxM5fw6yosds96h4+ixtBk2qmEQZRFseQ6yL0DnadB1+iO7LGWX1dJr0Qm6+juy4vmGYPj1uSV8kJTDUEdrvvJ3Q1V5iuzsDZSVn0YQ5Dg7DcLdYyyWFn+pD6LTQvQmODYPlAUNgqjHLHBs+ki2PQxqtZrvv/8epVLJK6+8gpWVFfUaHaNXnSehoIpfXmlHc3frBxpLlV5K0YoYNFnnSTeOpfuaHwCBX+ZFUJC4BmPTeiYsWY1GDb9+EYlGrWfkB+EUlMfw1MnxPFXpx6w3f3voNYiijszMlaSlL8arUKBRQj7vSD/gjLQ1v05qh7tNwy7aL4m/8Nn5zwhxCMHZ1JnY0lhylbkASAQJftZ+BNs3CIxgh2B8rXyRPUCq3YdFEIRLoiiG/+MDGzBg4G9jEB4G/mcRBEECtBNF8cy/bcvdMAgPA39Qe+kSmc8+R3SncMrRM3HpWozkN92CtGX1FC65jMzeBNPRTfnps4u4+Vtj3LSEU6dOMWXKFOzt7e84dkldCYN+G0SIYwjLeyxH1Its+TwCrVpHp/A8aj6YSXRjS2YEzkBEwk8T21K8cyVZMVeYuHQtClMzyIuCn5+FunIYugwChz3yWkVRZMKGSM6nlXL47S64WpuQU6+my8UEQi0UfG13ltzcH6iry0KhcMbNbQxurk8jl9v/daAGV6ojs6EkCdxbQ69PwevuOz//CYqLi1m1ahUuLi688MILSKVSiqtVDFt2BpVWz47JHXCzNnmgsSr2pqA8lU/tmUXUdGxMy3nzyU0sZ9uXO9Eof6Xr2JcIGzCUsvwatn15CQtbY4ZPC+WDNUM5rshkd7cfcWv04Glu6+pyiI9/l4rKCBwd+9PU72OMVvdDpdXRtuZ1jK2K6BZSR0rlNZLKk9CJDVm1LOWWtHVpe0NkBNgGYGr0aJmuHhaD8DBg4PHFEONh4IlGEASpIAivCILwmSAIHf5yevrjLDoMGPgDvVpN/sezqPVwI6+qnNC+g24RHaJOT9nPCSCC3TNNObsjFb1OpNVQLy5evEhAQMBdRQfAN5e+oV5XzwetPkAQBOLP5FOWV0NYCwnVM2aR7CbwadBkRCSM7+CDv6KW5IvnaNFnYIPoiPkV1vYFQQLjD/wt0QFwIK6QowlFvNXTH1drE0RR5L3EbPR6HSMrXyMlZQ5yuQNBgUto3+44Pt6TbxcdWedhbR/Y8myDAHl6E0w4+P8uOgAcHBwYNGgQWVlZHD16tKHNoiHNbr1ax/h1EVTfI83un7Hq7YvUzhh5+IsY7/qdjEULcWtig194KFK5J+d/+wV1fR22Lmb0mRhIWX4Nh9bEMbnvp+iksHzPg8d6FBTu5sLFAVQrr+HoM5NCiyF8F7eJxQ4OKCrTGGX5KUrLH9mTth9zI0vGB43nm67f0NGtI0qNkiF+Q3gx6EXCnML+30SHAQMGHm8MwsPAk85KoAtQCiy5HnvxB8P/HZMMGHg4Sr//HnVqKrltQzFSGBPS59bie1WHMlFnVWMzvDH5xXWkRBYR2teL5Iw4VCoVHTt2vOvYV4qvsDN1J883ex5vK29UdVou7k7D2V2B/ovJFFnq+bRrb0xMnGnkYMZ7fZtwcedWZHI5oX0HwqFZsG0CuIbCxGPg8nBF4/5KjUrL7N1xNHW24MUO3gBsLyznaFk1I8Qf8DYzo1X4DsLDfsHJacCtMRwAxYnw05gG0VGeAQO/gdfOQ8Cgh86o9U/SvHlzQkNDOXPmDElJDeFljZ0sWPZcKCnFSqZsjkJ7nzS7AIKRBNunGiNT2KALHUndytUUrlpF++F+yEw6Ul9dRdT+3QB4NrOj06jGZMSUkh9lQZ9aX/aap5GVfPmec+j0Oo7EfcG3595lXZkpnxbaMObkAt46/hY/xP/AOTNL0m09ebtGw+yA76hN+piazAm8HDyZHl49WNBlAU1smvDeyfdIKr9vKJ0BAwb+hzAIDwNPOq1FURwjiuI3QBvAXBCE7YIgKLhzilsDBh4rVGnplC5fgbR3T1JTEwnu0QcT85tpZeuTyqk+noNZa2cUgXac+jkJS3tjmnd35dy5c/j6+uLmdseSMej0OuZemIujiSOvNH8FgEv7M6hTavA89R3VYj1zhjng6zCC6notC0e1QF1ZSsKZEzTv0g3Tva/AmW8gbFxDoT3zv5/++ZvDSeRX1vP5sCCMpBKK1Ro+SkrHjyRGmOfQssUmLC2Db7+wKh92vQHL2kL6Seg2A96IgvBxD1Ws8D9Jv379cHJy4rfffqOyshKATo0dmDM0iBNJxXyy+/5pdgEUvtaYtXbG2r0LZe5NKVu4CO3OH2nRqxUSI18u7txGfU1DlfHgru4Ed3XnyuFs+ni8iygILNv38S3jiaJIWmUaPyX8xJvH3qTjT214M/JHdlXKKRFtaOXchg9af8Cm/ps4P+Y8Pw/ags/IH1GoahhecYgFo1pyMaPshngyNTLl2+7fYiYzY8qRKZTUlfzzN9OAAQP/lRiEh4EnHfkfB6IoakVRfBmIBo4C5v+WUQYMPAiiKFIwaxaCiQnZzRojiiJhA4bcOK+rVlP2SyIyJ1OsBvpy5Ug25QW1dHran9j4GJRK5T13O35L+Y340njeDn8bMyMzKovruHIkG9eaeGR5McwbJRDs8wGRGZVM7uZHiIc1l/buBFEkrHwjpB6BAQth0Dcgk991ngflWn4Va89k8ExrD8K8bAF49+o5lFo975qfJqzleoyM/pIhq74SjnwKS1pC9GZo/XJDatwu00Bu9rdt+icxMjJi5MiR6HQ6tm7dik7XEA/xTGtPXunsy6bzWaw5/WBpdq36+SAxl+PSaQp5NpYUL1yIb8FRTG06o66rIXL39ht9O470w7OZLUm/a+mmbMbvFhlcuvI7O1J28OGpD+m5tSdDdgxh7oW5XC28QKCihik+QRx+6iB7h+9jfuf5PBvwLCEOISik1138XJpDi2fhwkqGeKr5dHAgh68V8t62q+j1Ik5mTizpsYTy+nKmHptKvfbRanwYMGDgycIgPAw86UQKgtD3zw2iKH4KrAO8/xWLDBh4QCq3b6c2IgKrN6YQd/YkTTt0wdLeEQBRL1K2JRFRpcNuTFNqrlcY9wmxx6OZDWfOnMHV1RUfH587j62qZMnlJYQ6htLfpyE71tlfkxC0ajyv/shXwyWY+fXnzDUjgtwseb2zJ3URPxFzYCdNLfKx1Bc37HK0mvCPrFWvF/notxisTIx4v29DtqlNCds5UG3JMyaRDAr9EpnsT0JCq4Jzy2BxCzi1AJr2hykXod98MLt7PMu/jb29PYMHDyYnJ4cjR47caH+/b1P6Bjrz+b5rHIwruO84EhMZNkMaIa+TY9xjAnl2VlQu/YZgOxUSoyZE7tlJbWVFQ1+phNbPuyOz1eOc8RSCKPDFrneYeWYmZ/POEuoUysdtP2ZZ6CCmOxTxduAQXu60CSdzl3sb0X0GSGRw+BOeb+fN27382X45lzl7G6qnB9oFMq/TPK4WX+XjMx8/0G6OAQMGnmwMwsPAE40ois+Jovj7Hdq/F0XR6E7XGDDwOKAtLaXwy68wCQ8j3ViGRlVPq8FP3ThfdSgTVUoFVoN8MXIy48yvyYgidBzZmPj4eMrLy+nUqdMdayKIosiSy0uoVFcyvc10BEEgN7GMtCuleGYc4PvOCpL9zDGuHkp1vYaFnucxWhLMlQ2fo9FBeP/BMDkCvO++m/Kw/BKZzeWsCqb3D8DaVE5s2mo+z7PCR1rKZ63GIZU2VCVHr4erWxuK/x34EJyDGmJLRqwFW99/zJ7/JEFBQYSHh3P27FkSExMBkEgEFj3dguZuVkz9OZqYnMr7jmMSZI9xoB1eNCMnsAXFHi5YbZmLlWUoOo2a7RsX8VXEV4zcPZKeu7qxzuNTZBoTmhW3IcldwpKAmRwbdYwvO88nmFjUxVvwcH+egKZzEQTp/Rdi6QLt34D4HZB1gde7+zGugzdrz6Tz3dEUAHp69eTN0DfZn7GfFVdW/J3bZsCAgScAg/Aw8MQjCIKjIAizBUH4VRCErdePnf5tuwwYuBeF875AX1uLw4yPiDqwB58WYTh4egMN9Tqqj2VjGu6EWStnsuJLSb1cTHg/LyzsjDl9+jT29vY0adLktnF1eh1fXPyCX5J+YUzTMTSxbYKoFzm29AyK+nJO2xVyOqyUnkbPciyhnHeFH/GP+hyNc0su1wbg0yIch6EzwczuH1trqVLFvP0JtPaxZXhLV1LTFjE3o5xKwYalzVthLLsuOipzYXVX2P4SKCzhuW0wdhe4hf5jtvx/0adPH5ydnfntt9+oqKgAwEQuZfUL4diayZmwIYK8irr7jmMzpBGCVELXJs8SYWNCbmNnGl/ZgVTejPzTl9gRtQVLuSVTWkxh2bDFjHq9HaEFg5CIMnacW4uAyLWE6eTmbsLT8yX8/WfRkGn8AenwBpg7w4HpCMDMAc0Y3tKNBYeS2Hg+E4DxQeMZ0mgIy64sY1/avke4WwYMGHhSMAgPA08011PoRlz/5w/ApuvHF+6QXteAgccC5anTVO3Zg/3LL5OSnU5dVSWthowAQFNQQ/nWROSeFtgM9UOvFTn5cxJWDia07OVFSkoKhYWFdOjQAclfivfVaet46/hbbE7YzAvNXmBaq2kAnP/qVyrVZtTVRHG89zUC6qzYHe1Ia1kKE7o2gTevEuf0InU1dbS+bsc/ybz9CdSotHw2JJDU1C/Ym3Gco0JvXvFwJNT6ekyHSgk/PQ2laQ3V0F85CX49/9VMVX+HP+I99Ho9W7duRavVAuBoYczaF1tRp9Yxfn0ESpX2jtfr9DriSuPYkPUju33OIC0GhY8/McbG5LrmYat2QCJKmK55ljV91vBKyCu0cGyBh789A0e3JrCgM8es8jiw7wXy87fi4/06fo0+ePiq4XIz6DETciMhdhsSicD8Ec3pGeDIxztj2XUlD0EQmNVuFmFOYcw8M5Pooui/efcMGDDw34pBeBh40lkADBVFcZYoirtEUdwpiuIsYCiw8N6XGjDw/4++ro6C2bOR+/hgM3ECkbt/w9nPH/eAIPS1Gko2xiMopNg9F4AgkxB1OIvKojo6jfZHaiTh1KlTWFpaEhx8a+ankroSxv8+nhM5J/iw9Ye82+pdJIKE3O17iU2QYlaXRVGnn6kVNWiKnkUvM+HrN15A2vNj9BZuRO7ZjotfE9wCAv/R9V5IK+XXSzlM7OSDWPk1ydkb2SCbhrexnGk+rtdvih5+ewUK42DkOggZ/cgV0R8n7OzsGDJkCLm5uRw+fPhGexNnC5Y+G0pykZLXN19Gq9Pflnmq85bOjN4zmkWXFnHA6gyFdpUMUgzD1SeAUnN3WvpokcqDSTh5lPKC/FvmbdLWhee8RiDVG7ExPpdGvtPw9X3z4UXHH4Q8A07BcHg2aOoxkkr4bkworbxteXtLNMcTizCSGrGo6yKczJyYemzqjYrmf0XU6KiLLaFy/4MF2RswYOC/i//+/7kNGLg3lqIoRv21URTFaMDi9u4GDPy7lCxdiiYnB+fZn5AadYmKwvyG2A4RSn9ORFehwu65ZkgtFVSV1nFpXwa+LR3wCrQjKyuLrKws2rdvj0x2M4VsWmUaz+17jpSKFL7p+g1jAsaAqprydTOJXn0MtcIad++N7LAyoYXsFaJqPZkxqDmejtYAJF04Q2VhAa2GPPXoD6d3QK3VM2NHLG7WxvRy20Bu7o8ctJxLrs6cr5t6YCq9/hF1ZDYk7IE+c6Fxr39s/nuh16upqoohJ+dH4uOncf5CX6KiX6S07PQ/GiQdGBhI69atOX/+PNeuXQOgXluPi0MFYzpKOZZYTL9Va27JPHWt9Bo9PHvwRacvODbqGL8N20HzF7sj1Uvo2ngMOr2eOHkZPvaNAAkHZ0xH1N+sEaLT1eMUuJ7WBU246lhCcpzX31uERAp95kBlFlxYDoCxkZTvXwinibMFr266xKXMMmyMbfiux3do9BqmHJmCUt2Q8lfU6KmLK6H05wTyPrtA6aZr1EQWoKt5sKKKBgwY+O/h8UhubsDAfw5BEAQbURTL/9Joi0F4G3jMqE9IoHTdeqxGPIVpq1ZETH8LGxdX/Fq1pepgJqqkcqyH+aHwskQURU7/kgxCQ0A5wOnTpzE1NSU09GbMQ2RBJFOPTUUmkbGu7zqC7IOgNJW6hQPJ+t2KrLBPsHco5dsWpjjUBnI+oRFdm9jxTGsPoCEQPWLXNmxc3GgU3uYfXe/3p9NILlIys+tlyku2Uuc6g1/yfXje1Y6ONte/F4j68WatkDav/qPz/4EoitTVZVJVdYXKqitUVV1FqYxDr1cDYGRki6Vlc6qr44mOfgELi0C8PF/GwaEvEsmjfYyqdCpyqnPIrMqk0K0QLOGnX3/iku8lsrRZN/oZ2fYnObMzHex6MamdH21c2uBu7n6bADRyMMWyhydVBzLpO3Ayu3/9mrABLTAqCyGn6jIJ779H0/lfohfruXL1ZcrLzzO511tEXknku8SNtAzpiL17wz2vqKggNjaW+Ph4tFot5ubmmJmZ3fG3ubk5pqamSH27gn9fOLUQWj4PZvZYGhuxYXxrRq44x7h1EWx5pR0BLr4s7LqQVw+9yjv73uSzurfQJFQiqnRITGWYtnDAJNgeha81gvS/043OgAEDd8cgPAw86SwCDgqC8C7wR7neMGD+9XMGDDwWiDod+TM/RmptjdO775Idd5XCtBR6TZxCfVwZ1cezMWvtjHkbF0S9yJntKaRfKaHtUF8sbI0pLCwkKSmJbt26IZc31NTYl7aPGWdm4G7hzrIey3C3cIeyNNRLB5K9X0+8zwhEmZzKvkpSk7NwKv8ChUzK/Kea33iwzYq5QlF6Kr1efh2J5AEyHT0g2WW1LDmSTBv3fLzl6/Hy/YCXitrhKNcxs9F1F6vMs7B7Kvh0gf5fodKrOZ1zGkdTRxrbNL4ZdP6QqNUlVFVdvS4yGoSGVtuQRUoiMcHSIgh3t+extAzB0rIFxsauCIKAXq+ioGAnmVmriY2biomxJ56eE3BxGXEz69af59GpyVHmkFWVRWZVZsPv6obfBTUFiNzcOXG2d6ZtTVvCCsIY2G0g3jbeeFp64mLqztPLo8lIa82AoV0wkd/9NbDo7E7dlWLMk7UEdezF5X0/E9j9TWKPxHL+WgIWH0yjYGQWldXRNAv4CheXYYy4cJHNdpf5fsUOOvXyJzE1jqysBuHj5uaGjY0NSqWSsrIylErljViUv2JqaoqboinPqA6SvuYlkv1fvSFQPuvuyFv7cnj++/P80Ksp/pk2TC4azRLxR76tWsXbzac0iI1GVghSw/dBBgw8yRiEh4EnGlEUVwmCkAd8BgQCIhAPzBFFcfe/apwBA3+ifPNP1MfE4Pr110itrYlYthBTK2saN2lL2eo45J4WWA9uhE6n59jGBBLPFxDczZ3Q3g1uMqdPn0Yul9O6dWtEUWRN7BoWX15MmFMYi7stxkphBWXpaFcMJGuvnny5P2UOoTTuasP09Pfw0o0nrlDPkmdCcLK8+RAdsXsbZtY2NOvU7R9bqyiKzNp5FUQ1T/mswN//E37R9CKhpoAfgn2wlEmhLB1+fhZsvGDUBhIr0/jg1AekVDSkaZUKUnytfWlm24wAuwCa2TWjiU0TTI1Mb5lLp6ulqjruusBoEBn19TnXz0owN/fH0aFPg8iwaoGZqd9ddzEkEgWurqNwcRlBSclhMjJXkZg0i+S0RWDVjVxpYzKUxWRVZZFVnUV+TT568aaLk6XcEi9LL0KdQvGy8MLT0hMvSy88LDywUlhx7do1tmzZgleBF/1C+9247rOhQYxedZ6lx1J4t8/tmcr+QJBKsHnKn6Jl0YT79yHXOY70y5swsW5FGWfIPnQERX49gfMX4uwyCJVKRdfgZ9mREM1pu90Y/zoGWeM6unfvTlBQELa2tre9bmq1GqVSSU1NDUql8pbjmpoarqnbEVB2gkORvhRobxZ77KA3Zr86gKd3RjBEnoKjsYJQTUt2WB5FEKzoVdgL8xrzW3ZTLCws/lHXPgMGDPz7GISHgSceURT3AHv+bTsMGLgbmvx8ihctwqxTJywH9KcoI42MK5fpNOIFKn5KvhFMrtWLHFgdS2ZMKW0G+xDWzxtBECgrKyM2NpZ27dphpDBi9rnZbEveRn+f/nzW4TPkUjmUZ6L/fiDZ+7TUVMu50msy5go5OyzWoM91JTGtMQOaOzM4xPWGXYVpKWRejaLTmBeRyf9+ZfI/+D0mk6OJpYz030PHlu9TZTWQbyKSGOZoTW97q4Zq5D+NBlGP7pmfWJeyjaXRS7FWWPN1l6+RClLiS+O5VnaNU7mn2Jm6EwABAS8LV3zMbPCQ63GkBDtdFiaShod/Y2M3LC1DcHe/vpthEYhUanovUwHQ6rXkKfMadi2q/7R7UaXERGNCV4sqArW/YauH9DpTdBI/mjs0Z1CjQXhaNIgLTwtPrI2t7zlPQEAAbdq04cKFC3h5eREY2BDI39bXjmEt3Vh1Mo3hoW74OpjfdQy5hwXmHdxQns6l38g32bJsOnaeCuoqFVwJ9qXLxWuUz9zJqV41JKakoNVq6aS250CTa5TlltHBtAudOgXe8YFfEAQUCgUKhQI7u7ukU67pibikBRPt0im2nEN5QgG1qlrqFDqau8qYXyDjqDSQlzyUdFSZU6wpYUfVDoqTi3GquzXL+YwZM26JVTJgwMB/P4Z3tIEnGkEQvgTSRFFc8Zf2twBnURTf/3csM2CgAVEUKfhsDqJej/OsjxEEgYhd2zAyNsWrvDGaSiUOLzdHI5Wwb3E0+WmVdBnThKDObjfGOHv2LBKJhJDwEKYcncKZ3DNMDJ7IlJZTkAgSqMhCXDOAnN811JbK2NxlKp4qOeZd6zhZcBqzktnYmMmZMyToFtsidm1DbmJCSK9+fzX7kalQljPjtwu4mVcxtc8gnFwG8dLlZCxkEj5r7A46Lfw6HkpTyB6xmo8i5hBVFEUvr17MbDsTG2MbAHp49qC+Po/KqmgySs4TW3SZpMpMslSZXC7O5pjuD5cdY9xM7WlmH0SgRQsCbAPwsG12mwgQRZHS+lIyKjPIrMoksyqT9Kp0Mqsyya7ORqu/6WJkZmSGp4UnQfbBeFoOwMvSCysjEUnlYTqUHKQD8TjZ++Ll2RNz87vvUNyJXr16kZOTw65du3Bxcbmx6/Bh/6Ycji9k1q44fhjf+p47AZa9vaiLK0E4X0v7kSM59dNPyC2CqKmOJTI0lPAzZ7DLyaHla5MIatECa5OXOLetH9c8t+IU6Y2NsxmtB9654v3dEHV6VCkV1F4tQ6oahVXeGmR5x3Fq1hOT5g4Y+1nTUSahSUoJ49ZFsLfak40TWjNGGMPY/WOJkkexvONy7AV7lEoldXV1BtFhwMATiOFdbeBJZyAQdIf2xcBVwCA8DPyrVB86hPLoURynTUPu7k5lUSGJ507RK3Q8mrRqrIf7obVSsGvBZSqKaunzUhB+YY43r6+uJioqiiaBTZhyZgrJ5cnMajeLEf7X621UZCOu60/eURU1eTIWtxhBW6cgpBodK1WfYKEcTX6FjLUvBmNjdnNXo6KwgKTzZwgbOBSFqdk/tt55O36ktM6H1aOtcHfry6rsIi5X1bKsmRf2chnsfx8x5TDbO0zgy6j5SAQJczvOZaDvQPT6ejIyV1JZEUll1RU0mlIAJBI5IRaBdHIbhZVlCJaWzanFgoTyBK6VXiO+NJ74smscyjp+ww4bhQ3WxtYYSYxQaVUU1xVTq629cd5IYoSXpRe+Vr509+iOl6VXw86FpSd2xnZ3efAfQn19HlnZa8nL20JBwQ7s7Lri5fkK1tatHshtSCaTMWLECFauXMnWrVsZP348RkZGOFoY805vfz7ZHc++mAIGNHe56xgSuRTroX6UrotDbp6EhUcN1XnFIJhSZmKGMGE8LmvWYn7gIG79+iGRy3mG1qy0ukiP4Fwi9oC1kwn+rZzvaauo06NKraT2ajH18aXoa7UICikmTV9En3MAO7MfEUa82pD16jod/OxZ8kwLXvvxMpM2XWb12HC+6/4dz+x9hg8jPmTzgM14eHjc9z4ZMGDgvxPhn0wLaMDA44YgCHGiKN6x8MC9zj0uhIeHi5GRkf+2GQb+Q+iqq0kbMBCpnR0+W39BkMk4un4lpadTaWc/CLM2zgjt3di1OJr6Gg39JgXj0fRWv/tDhw5x9uxZIhpFUCwUs6DrAjq6dWw4WZmDuK4/hSdqKb9mxNpm/bHp9yxWlyqo7ZDKOuVO6rMmMSrcgy+ean7LuIfXLCfmyAEmfrcGc9t/pkp5UtYpBq4opoOPmnUTR5NZp6LrxUQ62JizMdgHIXItJfvfZXbjMI6rC2nt3Jo5HebgYu6CTqfi6tWXKSs/jalpo+sCo0FkmJs3RSK5KZr+cI3KqMq4sYORUZVBWmUaJXUld7XP3MgcHysfgu2Dae3cmkD7QJxMnR4pzkCjqSAnZxPZORvQaMqwtGyJl9dEHOx7PVBl8ISEBH7++WdatWrFgAEDGtal0zP4uzOU1ag5/E4XzBW3f3dYXFxMbGwsCYmn6FBrik1xGNF+0WSei0Cv80VTG82AqR/jmJ5M4WdzMOvSGfclS6itLqffL73w0Nswon4RRRnVDH27Jc6+VreML+pEVGkV1F0toS6u5KbYaGaHSbA9xv42CDIJxG5r2Lka/B2EPn+bnT9fzOKD7TEMCnHlm6dbEFcaw/gD4wm0C2R179UN7oGPiCAIl0RRDH/kAQwYMPAfwyA8DDzRCIIQAYwRRTH5L+2NgZ8e9w8ng/B4ssmf9QkVW7fiveVnTIKDqauu4uep0+jh9AzGnjbQ15s9y64iijDo9RAcvSxvub6+vp6vF3xNtiKbFM8UlvZcSlPbpg0nK3Nh/QBKzldRHKXgYLNu7Gw7gokqc2rralnSaBpi3odYyq35/c3OtzzE1lZVsvq1cTTt2IU+r079R9aq06l49fuvOZLRnINvdqSRozWjrqQSVVXLidZNccs9w5HtzzLb0ZEaiZQ3w97k2YBnkQgS9Ho1V2Neo7T0GAFNv8DVdeQtrlEZVTfFRUZlBjnVOWjFm65RlnJLvK288bZs+Pnz7oVOryOhLOFGzMi10mukV6XfCAq3NbYlwLYheL2vT1/8bfwfct315Of/SmbW99TXZ2Nq6ouX50ScnYcgkSjuee2BAwc4d+4cI0aMICioYeP2UmY5Ty0/y8udfZnePwCAyspKYmNjiYmJoaCgABOTSlq2PIZcNMPv/BcY2ZlT10bNtoWLARETUwtenDKJ6gMHKFu/AUUTf2yeeYYf4zexslEmn+d0pSy/H1q9hJ6NM7EN9EUwdUFTYET9tTL0NVoEuRTjZraYBjs0iA2jv4gpUYQ1vaAiG16/BIrb41JWnEjli/0JPNfWk8+GBHEg8wDTTkxjkO8gPu/4+SMHlhuEhwEDjy8G4WHgiUYQhH7At8Ac4NL15nDgQ+BNURT3/Vu2PQgG4fHkUnPhIlkvvIDtuHE4vf8eABd+/hmrCDPMre1goB/7NlxDYSpjyNSWWDvdHgS94rcVFFwpIKlJEgsHL8TZ7LprTFUerB9I+eVyCs4bkxzSkbd9h7CyexAJ29KIa36IM3XmVJW04KeJbWnre+uOxplfNnF+28+8uHA5dm7/jNvL6SvLeeFnV4Y2N2HBM73ZnFfK24nZzPd35ylJLl/sGMVOUzkB1v7M7TwfPxs/APR6DbGxr1NccggX72n8lJ9LcnkymVWZKDXKG+PLJfIbWaL+EBc+Vj54WXphrbB+qIfYWk0tSeVJt4iR1IpUjKRGLO2xlFbOrR56/Xq9luLi38nMXEW1Mg653BFPjxdxcxuDTHbnWqY6nY61q1ZRXFzMmEaNsNTp0NfV82mBOXvrLFlcfRLLglRUlZVItTpMpRLMpHpQFSKowUhvjtQmGOMWL1J/9WfiaqJI82iNXpNCQIEWn8LMW+ZTS+CNSVIcKuGDvW5cCnwDmaaGlleWYFJfCjIFMmcvjAOaYhYehHGzpij8/ZFaWd3RfrIuwNre0OV96Db9jl3m7b/GyhNpvNHdj7d7N2HFlRUsjV7KGy3fYGLziQ99n8EgPAwYeJwxCA8DTzyCIAQB07gZ6xELfC2KYsy/Z9WDYRAeTyb6ujrShg4FEXx37kBiYoK6ro7Yj7ZjL3dH28ObAzvTsHY0ZdDrLTC3ufWb8dSKVL668BXWF6zBEj567SMs5NcfXqsLYP0AquJKyD1pRl1Ia0Z6DGdyD39cL1ZSqixnmc9iajOm8GIHb2YNutXbUF1fx+rXxuEWEMTQaTP+kfXW1qYz4fuNXCpswcn3eoOxlM4Xr9HMzISP7HKYcfQNCgSRCf6jmdTmfYykRg33Sa8lLv4tior2YeH6Mh9cPYBSo6SFQ4sGgWF1cwfDxcwF6T9YZ+SvFNcW89LBl8hT5rGk+xLaubZ7pHFEUaS8/CyZmSspKz+DVGqOu9sYPDzGoVA0xO7olDVUHz5E1e49FF25woHevbCuqKDrseNIRJEKuSkv93wfD2URn0ZvRGFujrG1FRITCdXqa2Asw9alC3JzBwQTE3Q1AehV5iga57E/4hglFTVIZUaMn/wqMjMzas6eo3jxYkxCQjja2oRFtheZWzEe7+xQztVokUpEOnsWYKFKR5WcTH1SEvrKyhtrkrm4oPBvjLF/ExRNmqDwb4zCxwfByAi2vgiJv8Mbl8HS9Y7348PtMfwckc3HA5sxroM3H57+kCOZR9g7fC+Opo63XXM/DMLDgIHHF4PwMGDgMcYgPJ5MCr/8irK1a/Fcvx6ztg3VwJOWHcY0S0Gxh55zsTqcfCwZMDkEYzOjG9eV15ezLHoZW5O2ElQZhF+pH2NfHIuvt29Dh+pCWD+AmqRCso9bIw1oxgsBz2Nrb8XXbRpzZG08RxtvIlPbE1HtwLFpXbE0NrrFtsv7dnJsw2qe+ewrXP0D/vZaRVFk5/GpvHWgFxM6uPLRwFDGxaZzvLSM5+WH2Jm0GXetlrltZtAi+Nk/XacjPv49Cgp3gN0IPog5iq2xLd/1+I7GNo3/tl2PQmldKRMPTSSzMpNvun1DJ/dOf2u8qupYMjNXUVS0H0GQYi+2xfyYEerdkYj19Ri5uWExYACX7Wy5kJ6OXK9HK4qYW1tTZhvIpkQdC0eFMDzUnYrKS0RHj8fIyJrQlpswMbm5U6Utr6dw0SUUPlYYDXRkw7tz0NSnETZwIl2fHwJA5e495L3/PjKPxrzRKwVjwZT1LX9F08ia3cuuotPqGfR6C5y8LRFFEW1REarERFRJSdQnJjUcp6eDRtMwqZERikaNMG3ihJPJL2hdusHQ5cgcHW7bfdLpRaZsvsz+2AIWjAxhYAsHMiozaGL7cBnB/sAgPAwYeHwxCA8DBu6BIAhraciMVSSKYtD1tk+AiUDx9W7T/3DZEgThQ2ACoAPeEEXxwPX2MGA9YALsA6aKD/DmMwiPJ4+6mBgynh6N9YgRuHw6G4CaqELKtySRqUkhSumJV5A9fV8OwkjR8A2+Rqdhc8JmVl5ZSa22lhGeI+AM+Df2Z9SoUQ0DK4tg/QDq0/PJPOaAzMWNbwa/y4HsOnZP7sClVfHkVufys+/vlGYN5YvhwYxu7XmLbTqtljVTJ2Jp78Do2V/+I+stKNzNK5viSK0M5PQHfTiprGVS1An8qtdQVpPOyKpq3u3wGaZ/CkAWRT3XEqaTn7+VCrOuzE6IINg+mMXdF2NvYv+P2PWoVNRX8PKhl0mpSGFBlwV083z0woqiXk9dVBTFhzaTrz9ATWgdyMCizBMv71cxadqP3377jfT0dCQSCaIoMnjwYEJCQgCBYcvPkltex7YJJqQnvYpC4UTLFhsxNr4941X1mVwqd6dhO7oJWfXJ7Fu8HAR4fe06ZEYyaiIKKP1+K7VnVlJla8rrY+uYE/w+vds/T2VxHbsWR1FXrWHAa81xa2Jz5/Wo1ajSM1Al/SFIElElJmHrnIRt0xrSDzigxeH6rog/xk38G479/NAYyRm/PoLzaWWseC6MXs2c7jjHg2AQHgYMPL4YhIcBA/dAEITOgBL44S/CQymK4td/6dsM+AloDbgChwF/URR1giBcBKYC52kQHktEUdx/v/kNwuNWFnw6DXVdIVJdLUZqJYq6Goxr6zGpVWNcryMtwI93vt7xb5t5V0S1mvQRI9FVVuK7ZzdSCwvUeUoKv7tMSW0Op6uUNO3Yge4vBCCVNjxoHss+xoLIBWRVZ9HBrQPTwqcRczyGmJgYJk+e3FDnQVkMGwaizsoh44Qbgok5cR8u4M2jeXzUP4AuxqYcWhvPAb8fyK4fgYuFFbtf74hUcus3z/GnjrH/uwUMfe9jGoW1/tvr1Wiq+GH/eD47O453ezdmTEdvuhxcCKVbsJMa8WluJp3DJ0OPj2/eI1EkMWkWubk/kiUNZmFGKn28+zCnwxyMZcb3mO3/jyp1Fa8eepVrpdeY33k+vb17P9T1quRkKnfvoWrPHjR5eQjGxlh0747JwM6UeySRk/8TWm0l1dUu5GQH0qbNS/j7+7Ny5UosLS156aWXkMlkxORUMvi70/TwPM1LYVG0bLERhcLhjnOKepHi5VfQltXh9HY4P8/7ioLks7g16k1Xhw5oC2uR+1gis8qk8LPpZNrpWT/Ugp/eOo8gCNRUqNi5OJqq4jr6vhyEd/MHF4C6gkwkazqilTpTUtOH+qRkVEnJiHV1DR0EAbmnJ9LG/uyqMiZSaseUl/rRql0QguT+GcD+ikF4GDDw+GKo42HgfwJBEDqIonjmfm1/RRTFk4IgeD/gNEOAn0VRVAHpgiCkAK0FQcgALEVRPHd93h+AocB9hcf/Gl++Pwp5XQXGdbUY1zUICpMaHWY1esxroW8t/PUxRA9Um4KxGgQxmZhrsQQH3Kl0y79PyerVqJKScF+2DKmFBboaDaUb46nX1HKu+Dghg9+m08gmCBKBxLJEvoz4kosFF/G18mV5z+V0dOtIXl4e0dHRtG/fvkF01JTAhkFocrPIuuAHaJEv/JaZO3Jo7WPLi+282PzpOcpN86mxCKa8RGDFmGa3iQ5RFInYtQ07d098W/4zz2ypqQv5Ob4zdmYSBoaaM2zveKi6QlurZnwZcxgb/37Q7WYciSiKJCfPITf3R+J1nqzKTuHl5q8wucXkhkKIjwmWcktW9VrFa0de472T76HRaxjgO+Ce12gKCqjau5fK3XtQJSSARIJZ+/Y4TH0D8x49kZo31Eqx0unIyA4kNXU9np6JBDQ7jF7MQy5fzqBBg9iyZQsnT56ke/fuOCsu0tXjDEey2jNlwGt3FR0AgkTA5qnGFC6JonJPGsMmTWLltARy085SYtSURs+FYhxohyCEYORojn7KFJ7fVcW+gKUM6DcFM2sFw95pyZ5vr7B/RQw9xgXct87HH0idvaDnTIz2T8PluU+hSUPBTE12NvVJSaj+cNVKSqJXVha9RBH9uQ0UnjyDs+Odd1cMGDDw34lBeBj4X+FbIPQB2h6UKYIgjAUigXdEUSwH3GjY0fiDnOttmuvHf22/I4IgvAy8DODp6Xm3bk8U3375EbaRexl0VXWjTSdAlRkozaDaQkKRs4xaUzkqUxNUxmZo5KbopGaYWbvzxvtz2TS6JcEx9Zxa8wPBX/8zbkL/JKrkZEpWrMRywAAsundD1OgoWR+Hprye04Xb8GjdjU6jmlBaX8p3Ud+xPXk7VgorpreZzkj/kcgkMkRR5MCBA5iamtK5c2eoKYUNg9EVZpAdHYy2qgyPtWt56WI1elFkwcgQUiKLUBarueh/hpzsgQwIdqaN7+11OdKjIynJyqDva2890rfMf6Wq6iq/X7lEcsWrPN+9gtH7RqLU1tPVdQzfRixFcAyEYSvh+lyiKJKSOp/snPVcVtmyuaSczzvOZXCjwX/blv8E5nJzVvRcwZSjU/jw1Ido9VqG+A25pY+uqoqqAweo2r2H2ogIEEWMmzfHafp0LPv3Q2Z/665BVVUV27ZtIzMzkxYtxtC1Sy/Kyw+QlDyHy1FjCG25mZCQEE6dOoW9fRElpbMY2zKMK2Vd+WRPFtsnuSOR3D17l5GzGeYdXFGeyqU2uohA23bEFO/lZP4pmjXrcSP2wqJ7d1wXfI3u3Xcom72UdLsAfFr3wMRczpA3W7J32VUOrY1HXacjqPNd/yu7lfBxcHEVHJwJfj0RpEbIvbyQe3lBr143uulra8mNjiMmIp5Ag+gwYOCJwyA8DDzRCILQDmgPOAiC8PafTlkCj5oCZznwGSBe/70AGA/c6RNfvEf7HRFFcRWwChpcrR7Rxv8a5nwwnubRF2icoSeyhTH5Xo3RSMzwC2zH08+9/MDjFHq5ExadQr3mKvuuHqd/867/OaMfElGnI2/GDKTm5jh9NL3B7eXHa6izqzlfcpoaaQ1PvzKYNbFrWH11NWq9muebPc/LzV/GSnEzVWlCQgKZmZkMGDAAY7EefhiCviiVnPjWqLIz8VixnC1KS86l5TD/qWBcrYz5fudZis1ywbQnYpnAB/2a3tHGiF3bMLezp2mHzn9/vaKO+IQZbEsbgJ3XDnbkn0dq7IuJyyQWJ85FMDKGZ34G+c2K6Gnpi8jKWs3FWlP2KuWs7rWccOfH21vG1MiUpT2WMvXoVGaemYlGr2G41yCUx09QtWc3yuMnEDUa5F5e2E+ejNXAAci9ve84VlJSEr/99htarZZhw4Zdj+MAF5fhmJn7ExU1lstRY+jWbT2pqfHs33+Rrt3CCAtdxXRFBe9uvcIvkdm3xe38gagTqbmYT01kAQCCkYSun7xA8vvR1FUlcvyHX+n+4tM3+tv0609BehyK79dSOX4KhXM/x2nwcOQmMga9HsLvq2M5sTkRdZ2W0D5e979ZUiPo/Rn8NBoi10GbO7+3JaameLRvhUf7h09ZbMCAgccfg/Aw8KQjB8xp+Fv/c7L8KmDEowwoimLhH8eCIKwG9lz/Zw7w56IH7kDe9Xb3O7T/zzPvg+dpfzoSx1I41tWB11acfOSxfMNGojw4D5fMXEo3RKL+sv3fqn78T1K2cSP1V67i+vXXSG1sKNuejDqhnJiacrKrz+LYrx3D944gV5lLN49uvBP+Dl6Wtz7MabVaDh48iIODA6EhwbB5BGJhArkZPaiNi8FtwdcUNG7O/CWn6dHUkVHhHlw6kYq2QkJSUA5XM1rwendfPGxvrweSn5xITnwsXZ6fgFRmdNv5hyUnZxNbk/SU2W1BqiglyONpjtGX0+qDSAuuwKiNt6RWTU//joyMpVyoMeKc1osf+y+9bf2PKyYyE5Z0/Yapu15i9rnZ5Hwxlz5n65Da22P9zGisBg3COCjornVEdDodR44c4ezZszg5OTFy5Ejs/7ITYmkRRMuWPxAVNZYrV0fg7WNCbEwvSoq7IZOZ81SoGVsispj/ewJ9Ap2xMbv1774+qZyKPWloi2pR+FphEupIxa/J1J4voO9rk9jx5QyuHLlMk3bNcWtyM5NZwGvTOFSSSsGJEyje+wgxPgmnd99FJpfR79VgjqyL59xvqajqtLQd4nv/Win+fcG7ExyfB81HgYn1I91zAwYM/Pfy+DjNGjDwH0AUxROiKM4G2oqiOPtPPwv/Ws38QREE4c8pY4bRUBcEYBcwWhAEhSAIPkBj4KIoivlAtSAIbYWGT+axwM5HX9WTwZfvPUW3g5FYVcPB/o3+lugAGDZqLFneRjRO12Jv5Mj8I9/+Q5b+PdTZ2RR/sxjzrl2xHNCfisNZ1EUUkqzSkSM7iFYhsEi7BXMjc77v/T1Lui+540P3xYsXKS8vp0/v3kj3TEVMP0V+YW+U56/i9NFHmPTpy9u/XMFULmXeU8HodSJndidSZJZFibYNTpYKXu3S6I42RuzahsLMjOY9+vzt9dbW5bHs8kIOarORGWn5uNNSTksGMcmkEr+Li6DZUGh2030qI3MVaemLiKiRkiJvx6b+P/5XiA5RFKmPj6dw/pdk9+zH5FlRtEoVWNNFw+mvR9P4+DGcp0/HJDj4rg/kFRUVrFu3jrNnzxIeHs5LL710m+j4A0uLIFxcRqDVVmJnV0FoaBMiIi6RlpaGIAh8NjSIqnotXx5IuHGNpqiWknWxlKyNRdTpsXu+GfYTgzEPd8asjTPKM7l4ODbCxrUpek0quxatp16pvGXenh8soTDEnf1hAuXrN5A14SW0ZWVIpRJ6jg+kWSdXLv+eycmfkxD199mgFQTo8znUlcOpBQ93ww0YMPBEYBAeBp5oBEHYJQjCLuDbP47//PMA1/8EnAOaCIKQIwjCBOBLQRBiBEG4CnQD3gIQRTEO+AWIB34HJouiqLs+1CTgeyAFSOV/PLB8yes96bMvHo0RHO4fwtsL9tz/ogeg0NsFMxVE6bfjc0RKWmXaPzLuoyKKIvkzP0aQyXD+ZBZVF/KpOZJFjkZPku8lqvMyiA2o5aPOs9gycAttXNrccZyamhpOnDiBn58ffjlbEaN/Jj+rM5XHorB/bRK2zz3L0mMpXM2p5PNhwThaGLNn/ylkNSYUNlWQVKDig35NMVPcvsldlpdDcsQ5WvQegNzk9t2Qh6GgpoBx+55mT4UeTXUgs8PWsLHCGQuJyPSYz0FuDv1vJoNLy/ye1NT5XK6VUmszkpU9V93iWvY4os7JoWTFCtIGDiJ9+FOUbdqEcVAQ3l8tYPn7Z+jt1Zslpb+y5tr6e45z7do1VqxYQXFxMSNGjGDgwIEYGd19tykjYxnZ2WuwsemARKLAyno5traW7Nixg7q6Opo6WzKuvTc/R2RzOamYil2pFH5zCVVGFVb9fXB+KwyTQLsbIsiqnw8SCznl25Lo88pEEOtR1co4sHIJf852KcjlTHhnPfkuCpYOkKCMukT6UyOoi4lBIhHoOqYJLXt5Ensil8Mb4tHp9Pe+gS4h0GIMXFgB5RkPetsNGDDwhGBwtTLwpNMOyKYhze0F7hxvcVdEUXzmDs1r7tH/c+DzO7RHcrNy+v8sR3fsJm3HHHqdryLDVSCqdSumz9vwj40vOrVAZZSFS0Y67bynMn3fbDaPXvevZUSq+PVXas+fx3n2bOqKpVTuSKZUq2dHwHFMTp/DyNaEBW+sxdHs3tWZjx8/jlqtpreHGv3h+eRdC6E6NgX716dg/9prXM2p4NujKQxt4Ur/YBeqa5UkHS5DaVXLpUJHWniYMiTkzkHAkbu3I5XJaNl30N9a66HMQ8w6MwOVtgZpyXD8TftQaWvDheRc9qsOYJR3CZ5aA+YNmZcS01eRkz6fK7VSbDzf4d3gl+/vqvMvoVepqNy1i8rtv1EXFQWASXgYzp98gkWf3shsbgZBz+88H6MzRiy+vBi1Ts2kkEm3rEur1XLo0CEuXLiAi4sLI0eObMhOdhdEUSQtbSEZmctwdh5KQNP51NQkcjlqLI38DhMZ0Ybff/+dYcOGMbW7H7sic/hw3SVWiaZYtnHBspcXUvPbXQ4lxjJshvhRujEey3wHXPzDyE+KIS2qEVcP7yekV/8bfY3c3Jje7ysmn5rKzDEw53cNmWOexXnWx1iPGEG74Y2Qm8q4sDMNTb2O3i8FIjO6Rwhd9xkQ9xsc/gRGrn/wF8KAAQP/9Rh2PAw86TgD02l46F8M9AJKrrtgnfhXLfsfY/4Hr1Gy/iM6nK8itomMy206Mv2Lf050AEz+YD45HlL809SkS4oYeqU125K2/aNzPCiawkKK5n+JaevWSEN7UvZjAtU6PYu8NpOfdQzzOhnPTv7kvqKjuLiYyMhIwhu7YH9kGjmRflTHFuM0/UMcJk9GpdXz1pZoHMwVzB7coG1X/fILpiorSvxdKFaqmTWo2R2zHSnLy4g/eZSgrj0xs360DEK1mlo+PvMxbx9/GzuJmvZiNyqK2zCxR2PmpOXzlKyMFpELockACHoKgOiUpeSkzye+3oigwG94qfkrj6Xo0FVUULJ8OSnde1Aw82P0ymoc3n4bvyOH8d60CZvRT98iOgBkEhmfd/icIY2GsPzKchZfXnxjB6GsrIw1a9Zw4cIF2rZty4QJE+4pOjSaKuLi3iQjcxmurqNpFvAVEokMC4tAQltuxNy8EG/vVK5cucKVwxHULL/K5HoZiaKOo92csRnW+I6i4w9MAu0wCban6kgWPUY8B2iQSE04vmEDJVkZt/S16dGT2WbPUK0QeWe0GmnL5uTPmEn+rE8QNRrC+3nTebQ/6VdK2PPdVdT12rvfWEtXaP96g/jIvnjf18GAAQNPDgbhYeCJRhRFnSiKv4ui+ALQlgZXp+OCILz+L5v2P8XnH4ynecRxghM0nG1tgd24uXw0b9V/ZK4iXwdslXBc8itt9EGsOL2K4tri+1/4DyKKIgWzP0XUarGcMp3iNXGo9SKfO69D71RGy3RbGrduj1dQyH3HOnjwIHIjGZ2SviLrpAs1mfW4zJuH7dixAHz5eyKpxTV8NbI5VqZGXM6LRnfJmhLHKg6lqxje0o2WnncWFZf370Kv0xM2cNgjrTO2JJaRu0eyI2UHIzyaM95aw/G0fnRr4sCv6ho0eh1fJ85HkClg4EIQBM4nLqA4cyEpamO6hP1Ib5/+95/o/xl1Ti4Fn88luXsPihcvwTiwGZ7r1+Ozaxf2L0/EyO3eKWSlEimfdviUkf4jWRO7hq8ivyImJoYVK1ZQXl7O6NGj6du3LzLZ3Z0OyssvcOFif4qKf6eR77s0bTIH4U87dxYWzQhtuRFPzzgszCrZf/p36nQqRo5tTgc/OxaeS6e4WnXX8f/AenAjBJkU4XwtPi06oK2/itSkJXsWf4lGVX9LX7833+fjOH/KJLV8OLASy/EvULFlC1nPj0VTWEhwV3d6vhhAXnIFuxZHU1+jufvE7d8Ac2c4MB0MhYwNGPifwSA8DDzxXA/2Hg5sAiYDS4Dt/65V/zvM/3AMnY6fw7VA5FBPNyb8cJHuQ/+eW8+9KHNqhk4Al+xUJILIq8kjmXfhi//YfHeiev9+lEePYv7y2xRuL0QQReY7/UjbbqE8U9QGRJHOz42/7zgpKSkkJyfTURtByUEp9aUS3L5ZhPWwoQCcTS1h7Zl0xrbzolNjBzQ6Deu37sBMY02yswNSQeC9vndOn6uqreXKwX00btMeG2fXO/a5Gzq9ju9jvuf5fc+j1qtZ2uUzOgmXOFcyiWqVSIc2buwprmRd3RFMcs5D3y8QzZ2IT/mamtxlZGpNGdh+L82dwh5q3v80dXFx5L79Dql9+lD+009Y9uqFz86deK5ahVnbNg+1KyMRJMxsO5Nn/J9hY/xGZhyfgYOjA6+++ipNm975NQHQ69WkpH7F5ahnkUgUhIdtxdt70m1z62o0aI/K8Tj3Nk39IlALas64JGASYMenQ4Ko1+iYt//afe2UWsixHuCDOr2KTuFDQdCjUyspy6vg2IbVt/QVjIxo+9lS3jpsTHJtBl+H5eO6eBGq5GTShz9FbUQETdq60PflIIqzq9mx8DI1lXcRPwrzBpernAiIM/x3bMDA/woG4WHgiUYQhA3AWRoKBc4WRbGVKIqfiaKY+y+b9j/BgncG0WNfFAo1HOzflDe+O/wfn/PdmUspcBPwT6vjrDqG1mJTopMvcTz7+H98bgBteTkFcz5HDGlHaaY7JiKsdTvIlOdfZYR5bxJOHSdswFCsne5d9Vmn03Hg9/1YC7U47k1AXWuM+4rlWPbuDUBFrZppW6/iY292ozbH6qg1eKS0oMhNz+msCiZ3a4SzlfEdx796eD/qulpaDX7qodYXXRTN8/ufZ/HlxfTw6sGvA3/FtGwL1RoHdiX6MqC5C8sqKugqlNH18kLw64nYfBSJSZ+Qn7WcS7VGdAjbjJul90PN+59CFEWUp06T+eI4Mp4agfLECWxfeAG/w4dwnf8Fxk38H3ns0tJSrKOt8a/wJ80yjeyAbCytLO/av6YmjchLI8nMXIGr6yjatN6NpWXzW+3V6qk+nUvBV5HUXMjHLrANHTssx7dRIimp2UREHKGRgzkTO/my/XIuF9PL7munabgTCl8r1KfLCGrbF239Vey8BhJz5ADXTh+/pa+RiwsDpyzg2aM6Ducc5UenNLx/2YLUwoLMceMp+2EjPiH2DJwcQmVJPb99fZmq0ro7T9xiDDgFNcR6aOrv3MeAAQNPFAbhYeBJ53nAH5gKnBUEoer6T7UgCFX/sm1PNEtf7Uzv/SlUm8PR3mFM++q3/7e5i31scC+F82anEIA30ibw2bk51Ghq/uNzF86dR029hCrPZ7BFygm/FD549QNa2IdwbMMqzGxsaTN05H3HiboUSXFJKcEXL4HWFM916zDv0AEAjU7Paz9eprhaxcJRIZjKZaRWpHL+cALGGgvOKKS4WZvwUiffO45dX6Mkcs9veAQ2x7lR4wdaV3Z1Nu8cf4fn9z9PQU0BczvO5avOX1FbfpSKigscL34LrU5E1sSK/Ho1q1IXIAgSdAO+JDbuTXJzN3G52B4yx7F3/QE2bdrE8ePHSU1Npb7+//+hU9RoqNy5k/Shw8ieOBF1WhqO097F7/gxnN6bhpHzvYXh/bhy5QorV65EWa1kXr95vNz8ZX5L+Y2ZZ2ai0+tu6SuKIjm5m7kYMYj6+lyaBy8noOlcpFLTW/rUXSul8JvLVO5JQ+5hjtPUUGyG+GHlGMygQbOwti7lwMFj5OfHMqW7H27WJszcEYvmPpmmBEHAenhjRJ1IiEV7BImE8twrOPq25/dl35AWFXFLf/POnXmhxUt0vapn+ZXlHJel4r31F8w7d6Zw7lzy3nsfN28ThkxtQX2Nht++vkx5wR3eexIp9J4DFVkNWa4MGDDwxGPIamXgiUYURYO4/n9my6ZVqPatoPvlOlI8Ba6GdeCjeavvf+E/SLFnM+A0HgXpZMozCbXwQlYu8G3Ut3zQ+oP/2LzVx45TdPA0Vd0+ppHUmOxmasaObXCpij95lIKUJPq+9tZ909bW19VxdP8eHEpKcS+uwOvHzRj/yT3n093xnE0t5euRIbT0tEGn1zH75GeE5A4nw0NGSpmSZc+GYnyXzEKnNq+nrqqKLg/g7lWlrmL11dX8eO1HZBIZr4W8xguBL2BqZIpGU0FyylxqJZ3ZHWdMjxAXfqmqZmn9McyzT5PZ4VOSL09BEJJISw2jJrcZjo4WOPs6k5+fT0pKyo15HBwccHd3v/Hj4OCARPLPv311SiUVv2yl7Icf0BYUoGjsh8vcuVgNHIAg//sFJ9VqNfv27SM6OhovLy+eeuopLC0t8ccfI4kRS6OXotFrmNtxLjKJDLW6lGsJ0ykpOYytbSeaBcxHoXC6ZUxNQQ0Ve9JQpVQgczDB7sVAjJvY3OJ+ZWUVyIgRL7Bu3Q5++WUVL730Jh8PasYrGy+x4WzGXUXoHxjZm2DZ05Oq3zPo0HYUp8/+hEQ+CTuPQnYvmMfwDz/BI/Dm7ovjG28wddwl8vNi+OjUdDz6byTgu28pXbmS4iXfokpOxv3bJQx9O5RdS6LZ/vVlBr/RAgdPi1snbtQNGvdpqOvR8jkwu3MdEwMGDDwZCKIhqMuAgceW8PBwMTIy8t8244GZ895LBMadp2mqjqhgBaWtBvD6e7dlF/6Pk5OeRtrYAZTLIblRF0bajCFGLGZ64Gw29d9Ec4fm9x/kISkuziLxqReoa/4uAeaWqJqZ02hsSwDU9XWsffMVLGztGDNnAcJ9Hqj3L5jOhWo5fc+dJHTlWuTe3jfObTyXwcydcbzS2ZcP+zdUmd58bTP7f7tIi+xBbHTS4u9iyZaX294xJiEnIY4ts94nbMBQuo596a42aPQatiZuZfmV5VSqKhniN4QpLabgZHbzofhawkfk52/l15zVHE2qxambKy6lSYy59itpCjd8Ak5ialpBdGYL4rUWfDr0UzycPG5cX19fT25uLjk5OTd+6uoa3HLkcjlubm63iBEzM7P7vxB3W09hEeUbf6B8yy/oq6sxbd0auwnjMevc+R/LqFVUVMTWrVspLi6mc+fOdOnSBan0VvG3NnYtiy4topdXL94LHEBy4kdotZU0avQeHu4v3BJAri2to+poNrWXCxGMZVj19MSsrQuC9O5/P2fP7uPgwYv4+ycwZMhcpvxSwsX0Mo680/Wubnd/IOr0FH0XjbZKxY6kb1GLLvR8aTKXdn9NVUkxI2fMwaVxkxv9NYVFRI0Zwvsj6pDa2fHzoC3Ym9ijPHmS3HengSDg9vVXaP3D2Lk4CnWtlgFTQnD1s7514uJEWNYOwsfDgK/5uwiCcEkUxfC/PZABAwb+cQzCw4CBx5j/JuFxdMduyr//kKapOk51sMV/4Af/0SDy+7F9bBuaXKzim1FhjC8aiLuVK294LUNwV7Nl4BaMJHcv1vawnMw5SdyHn+Jp9iYh1lYIfla4jg9GuJ7C9vTPG7nw2xae+ewrXP0D7jlWzvdzWJupwrswj2c++Qwj15uB32dSShi79iJd/R1YNTYcqUQgX5nPyG1PM/LSh0Q7mHC8tobdUzoS5HZ7IT6tRsPG915Hq1HzwtdLkRub3NZHFEVO5JxgQeQCMqoyaOPchndbvUtT26Y3ztdFR1MRe4yMnOXUK3qzO8YaD0sdMq0SQQATkyoaBV5FUKiJpCl7KtJY2nclAc7NEeRyBOmdd2JEUaSsrOwWIVJQUHAjHa2trS3u7u40a9aMxo0b3/ZQfydUKSmUrl1H5e7doNNh0ac3duMnYBL8z5XVEUWRqKgo9u3bh0KhYPjw4TRqdOcq8QAbYtfx9aWFBBprmezpRcvgxZib33yg15bVU30sm5pLhSARMG/rgmV3DySm9/+bFUWRjRtXk5GRTZs25/AKXMrgFWn0bubEd2NC73u9OqeaoqXRVNlUsf/SckztX2DQ6+05sOwT6pRVjPp4Ho7eN3dPlGfOcGr6RGa+KKeJUyBr+6xFIVWgzsoi5/U3UCUl4TD1DeQjxrL726soy+rp+2owXoF2t0689x2IXAevnQeHR4+rAYPwMGDgccYgPAwYeIz5bxIeq8e3pePZSk62t+aVtef+bXNYNftZOv10ma3drfHL8aF9wKsk6KuZFvQBU0On8lLw3b/tf1ByqnP4Lvo78g9fYEj+W4TbWCN1t8B1UgiCrOFb6cqiAta9PQn/Nh3o//q79xyv6odv2Hr8Knlurkwe9yK2vn43zqWX1DB06RmcLBVsm9QeC2MjRFFk8pHJqCMs8czuyQZrNaPC3Zk3/M47Ome3bubcr5sZ9sEsfFu2uu38tdJrfB35NRcLLuJt6c074e/Qxb0LgiCgq6igctcuKrZuRZWccofRHxzR0gqbIYOwffppFH5+9+yrVqvJz88nOzubnJwcMjMzqaurw9TUlKCgIEJCQnB1db1l10IURWojIihbsxbliRMIxsZYDx+O7bgXkXt43GO2h0elUrFnzx5iYmLw8fFh+PDhWFhY3LW/UplIbNybHChM49dyOR1c2/NNt8UYy4zRVqioPpZFTWQhAOZtXLDo6oHU8uFcwKqrq1m69FuM5EW0Co/gQs0ilp4o5seX2tDB7/6uTBX70lCezOVU2W8U1lVj7/0CfSb6sv2L6eg0GkbPno+tq/uN/sVLlrDnyAoWDpcyuNFg5nSYgyAI6Gtryf94FlV79mDeswc20z9l77oUyvJq6DU+EL+wP9WwqSmBJS3Bqz2M2fJQ6/0rBuFhwMDji0F4GDDwGPPfIjwWvD2IvvtTSPCTYjNh3r+60/EHh/buwPLTD0i3k5Dk2Yae+j542rrxnstOEm2PsX3IdrwsvR5p7LL6MlZdXcWWxC04VTvxQvRrtLW2Rm5njMsboUhMbobP7V44j7ToSMYvWomF3d0f+srXfEfMup852rMnncNb0n3gkBvnKus0DFt2hvIaNTsnd8TTriFGZG/aXj4+NpvxV+eyz0YgS9RybFpX7M0Vt41fmpPNxvdfx691ewZOfe+Wc4U1hXwb9S27UndhpbBiUsgkRjYZiUyQURsRQcXWX6k+cABRrUbn60u0ox359o6Ymim4UmaG3sUJlZ2eL7LmkuEsQy61R249lNXRPxJo5c/Tvk9RXlvHxeIKYssqcc3NpnN0BDKdFn3LlriNeQbLPn2QPECMhU6nIzW1oWheQkICOp0Oe3t7QkJCCA4MRHLxIqVr1lIfE4PUxgab557FZsyY2wr9/RPk5+ezdetWysvL6datGx07drxrXIoo6snO2UBKypcYGVnRLOBLTpSV8MnZT2jt0IpP1W+hj6gAwKyVMxbdPJBZ3f46PihxcXFs3boVH99E3L2y+eT8xxjJ5Oyf2gmF7N47RXq1jsJvLqNR17M9dgHIA2jc9hnaDrHhl9kfIpHJGP3JfKwcG9zuRJ2OrPET+ME4il/a63k77G3GBY27vm6R8h9+oPDLr5B7euL41SIO7VdSkFZJ1+ea0qzDn1I5n17UkOFq7C7w7fLIazcIDwMGHl8MwsOAgceY/wbhMe+D5+lxIBKlKZzt1J4ZX6z5t026wd6nQ/GIrePrke0Yc6IE/1bvkq5X82GLGQQ7BPJ97+8fyr+/VlPLhvgNrI9dT72untGKF3A7EUh7M2OMTSU4v936lofF7PgYfpn9Ie1HPUu7p56567ily5dQuHg5Rwf0ROXkzutvvoX8+kO4Vqdn/IZIzqaUsOmlNrT1bXBRSa1IZdzv42ifNxhNWgjbzNV81D+AiZ1vDyIW9Xq2zP6Q0uxMXly4/EaV8lpNLevj1rM+bj1avZZnA55lYvOJmCq1VP62g4qtW1FnZCCxsMCoZw/Om1uQpFFjY5NPWJiMb2KeJbFESXFbOw6Xv0uRXSbmJo3xDV7GswdeQxAEPuryA5sKa9lTXIEAdJTpcS7OJ1MvxTvyIgNOH8WtuJB6cwvo1QvXUSOwbNoUY2Pj+742dXV1xMfHEx0VRXZODogiToWFNKquJmTAAByGD0difO+4hrshiiK1ah1KlRalSkuNSoubtQl25gpEUSQiIoIDBw5gamrKU089hfef4nD+ikpVSHz8e5SVn8bevicBTecil9uhq1az9cAPzFMuoVmdH187foJTd39kNo9m81/Ztm0bcXGxhIadJEPlwtcXn2FanyZM7nbvXSaA+uRyStbEUmVXyf7IFchMe9Fu+CB8mgv8MvtDFObmjP5kPua2DX+P2uJiUocNY2EfDec86/iux3d0du98Y7yaixfJfettxLo6HD/7nFOpzmTHl9FxZGNCelzfhdLUw3etwMQKXj7RkPXqETAIDwMGHl8MwsOAgceYx114zPnwJdqcOYNtBRweEMr78378t026hY0zhhP+6zU29LXDvcCRluYj8LL3YIZ1FJdcVvNp+08Z1vj+Vbs1Og1bk7ay8upKyurL6OnRk4FVY6g8XE1LUwkyKThPbYWR883gZ71ex6YP3qS+Rsm4RSswkt/+7bUoihQv/JrS1WspDHPjeOOODB06lBYtWtzo8+nueNaeSeeL4cGMbu0JQERBBFOPTsVctGLoxWn8YKnBxErBgTc7I5fd/o371cO/c2j1d/R+9Q2Cu/VGFEV2p+1m8aXFFNUV0durN1NbvoFdXC7lW7dSffgIaDSYhIZi8dRwrpiacjoiAoVCjm+jSFxcSqi1WsfLm+IxCrZmhu0q3BWnsRE8CO64i3dOzeRQSRVuXlOJrQULqYSBJhIcL59DlZ+LjY0Ner2eUpWaZGsH5NW1dLgcQccrkUj1elI9vMjx9abG1wdTS0tMTU0xMzPD1NQUCwsLAgICsLe3R1tWRvmPmynfvJlKjZqsNm1IdvOkVKVFlCpw826Em3cjzK3tqdXoqbkuIv4QEsr668fqPx2rdDfa/vrxaCQV6BXggKcqk7qsWBo39mPYsGH3DHovKj5AQsJH6HT1+Df+CFfX0ehrNFSfzKHmXD6iTs/55inMUS8myD6I5T2XYyG/u6vWw1BXV8eyZcuQywWCgrewNHoUsSUBHH6nK+42986sBlCxJw3l6Vwy5QlcSNqDkfkoBkzpjYl5GVs/m4GlvQOjZs3D1LIhnqjm/AWSXx7P7NesybfU8WP/H2lkfTPWRVNQQM7UqdRfuYrNhJeINu9J6pVSWg3wptVAnwahGfMrbJsAQ5Y2ZLl6BAzCw4CBxxeD8DBg4DHmcRYeR3fspnjDRzS/puH3vt689c3+f9uk2/j2i9l02PIT0V5Sst3a0utSDo6dPyJPr+WL1quo1GWza+gu7Ezs7ni9XtTze/rvfBv1LTnKHMKdwpka8iZlu/XIr1XhrZAgmGhwfK0tRg63Psj98bA/8M33adKu021jqzMyKJg9m5pz5zFrrOLXjhMws7Zn4sSJN9x1fr6YxQfbYxjXwZtZgwIB2JO2h5lnZuJl4cUUzWw2nyrkqKmG78eG07OZ023zKMvLWP/2JBy9fRn58Vzya/KZfW42Z/POEmwfzDTfiXicTKbi11/RZGcjtbLCaugQrEeOJF8mY/fu3ZSWlhIS0hxXtz3U1p6jZchmXlqXz7VaCSPa/EZn6WGcqk3x6Xue92JPsKNUQGfkgpvCiNFWCiwvnSM/LRVbW1t69uxJQEDAjd0MtVpNbW0tKRVVHErJQnfoEB1OHcW5rIQqC0vSwsKpa9yIcpkR1yqlZKhMUYkyBKSgrEerF6g3taDOyIQ63YN9nhlJBcwVMswUMsyv/9x+LMXc+Ga7qVzGoauZ7LxSgEqU4m4h4aWuTRke5o6l8e1B31ptDcnJc8jL/wULiyACmy3CGA+Up3JQns1D1OgxbeGIRQ9PjOxNOJJ5hHdPvktTm6as6LUCK8XtyQEehZSUFDZt2kR4eFPU8u/54MRkOvnZsmbc/V2ZRL1I2U8J1MWUEFV3nNTSaxjbPMfTH3VBWZbG9rmzsHX3YNTHc1GYNoiv4mXLSFj3LTOmWGFqbsNPA37C2tj6xph6tZrCOZ9T8csvmLZvT2rrV7gWraR5d3c6jmiMIADf94TKHHjjMsgfPpOZQXgYMPD4YhAeBgw8xjzOwmPZK53odqKEc63MGb8x4v4X/EscGhqCbZqahU91ZuD5eNydJ+Lj5MPH8nyiG39BT6+efNn5y1uuEUWRs3lnWXx5MdfKruFv48+boW8SZtWaE99cwrtKi7kEFD56HF7qfCOQ/A/qa5SsffMVbF3defqTL25xGdKrVJSuXEXp6lUIgg6H4HKu9n+X48nVjBs3Di+vhriTC2mlPLfmAm197Vj3YiukEoHvY75nSdQSWjm34vMWX7JlbiyrzOoIa2TLD+Nb39E1afeiL0i9dIHn5i/hSPVZFl5aiIjIDOPhtDyWi/L4cdBqMW3dGuuRI7Ho3QuVXs/BgweJiorCxsaGgQMHIgh7yExbTEt9LxSXj2FSX4haIkWjAI0gI9OxF8cFR1LlTlTJ5Qzza4MuJYerMdcwMTGha9euhIWFIZPdv3xUfKWSM/sPYbxjOy2vXEIQIcrJn71e7bjm1AhTTS2iQopUCsZScHGwxdPVCQdr81tEhLEMygrzyUlPJj8rHRk6PFwcCGsRQlBQEObm5g/0N6TT6Th37hxHjx7F2NwSy8Bu7Euu5mpOJaZyKUNauPFcW08CXRvEQlXVVWLj3qKuLhMvr1fxdppEzdlilKfzEDU6TJo7YNnDEyPHW8XqiewTvHX8LRpZN2JVr1XYGP8zcSl79uwhMjKSp0f3ZN35bfyS0JNlo93p3yLknteJooio0VOyNhZ1VhUnCrdSotFi5/0cIz9sTV5CNDu+moOznz8jpn+KkbExok5H9sSXic6NYPazMlo4tWRFrxW3ZZEr37qVwk8/Q+rgQPGQaURdk9O0vQvdnm2CJDcCEvZAl/dA8fC7PwbhYcDA44tBeBgw8BjzuAqPr6YNo+/eBNI8JdQ/N5Wnn3v53zbprvw6rR+BuzNYNsQZr0InusSWYNX9Y0pFHcvbniFKuYVlPZbRyb1hVyK2JJZvLn3DhYILuJm7MbnFZAb4DqAst4aYJZdpJAjoNbXY9LbDqu/tmaEAjv/wPZf27eS5ed/g5HPT1UR5+gwFn36KJisLSx8NTq001A6Yy7eHM/Dz8+Ppp58GILusliFLz2BtasRvr3XATCEw5/wctiVvY4DvAGa1/oS938SyubiUK0Y6fn+zE42dbn9AS710gR1ffkbjkQP4zfwikYWRtHVqwwdxvqhXb0Rqa4vVsKFYjxiBwscHURSJi4tj//791NbW0q5dO7p27Up1xUmKD75Ao1wRo7oarkibkOJii61YjaxWhoW2Htf6Ihw1ZbfZUGlkQ7WFGxVmLpSbOlNm6oLSLoD+bQZjY3S7CNHrRS5nlbMzOo99MfkIxUX0z7lIr8wL2NdUUmppRUVIKF3fmkKBXE5kZCSJiYmIooivry9hYWE0bdr0tlS7SqWS2NhYrly5Qn5+PhKJBD8/P0JCQvD398fI6M6pajMzM9m7dy9FRUU0bdqUIUOGYGLSkIb4ak4Fm85nsutKHvUaPS09rOjrl4q39EvMTGwI8PsSoxg3qk/lIqp0mATbY9nTEyOnu3+Lfzb3LG8cewMPCw9W916NvcnDF9QTRZE6vYhSq6NKp6O8rp6jP6xHp9fjMaAty7YmotEbMWy4D2VGHlTr9FRrdQ0/fzpW6vQoJAKeooSvzlZjp9RwPGsdSqkXsuYDsX3GB6O4KLLWLMGxWTCD352JpYkxurIy0ocN50SAnsXtK3i6ydPMaDvjNjvrrl4l542p6MrLqR0+hXP5vjRq6UCv8YFIjR69eKRBeBgw8PhiEB4GDDzGPI7CY/70F+h88CIaGZzu3JbpX677t026J/M/eY9+23ZxMlBKuUN7Wl+OwMTvDXxdGvMpSnJbL0erV/FNt29YHbOaQ5mHsFHY8ErIK4z0H4lcKif9XB4V25JxkEmor0zHbUo7TAIb33G+srwcNrw7mcAuPej9yhtAQ6G1wi/mUb3/d+R2CpyDczFr2waGLmfH8UvExMQwefJkbG1tqa7X8NTysxRWqdgxuQOOVvDuiXc5nXuaicETeb3l65zZlsKeYxn8YqHm+XZezB5ye00KdV0ta96ZRLxHBec98pBJZLwfMIXQ5SepOXkSq2HDcJ718Y3g68rKSvbu3UtSUhIuLi4MHjwYFwdbNBe/Q3diLsYqLRq3cD6yGMQmh47oJbeLBrlejYuqGPf6QtxURbirCvFQFeGqKsKtvhDX+kJM9fUA/OI2BLOBX9HfuSGlakJBNTuj89h9JY/cijqMjST0CHBiSIgr7eRKsl55hURza6QyKf7xMWhlMujZC7/xL6Lx9iYqKopLly5RVVWFmZkZLVu2JCwsDJs7ZLMqLCzk6tWrXL16lerqaoyNjQkMDCQkJAQPDw8EQUCpVHLo0CGuXLmClZUV/fr1o0mTJnfcVaqs1fDzhTg2nI0nr9oKC7maoS72DMwXcFOLGAfaYdnTC7nLg7kNXci/wOtHX8fZzJnPuywnX2dO5Z/EQNUdREK1TkeVVo/y+rH2Lx/tzpWlDI4+RaKzF9fs7amKlNDb9xjpAT2ok3tjIZViIZNgIZPeODaXSqnT6ynVaNGV1zPpYAlSVR3HM9egNe7M6ZYtOBlkQmDiZfof206yd1MO9RmDtbGC1mmJTJk7k6Vj3DnlkUfHxq/T2ecp7I1k2Mll2BnJsJfLUFRUkPvmW9RGRKDrMoSTYnfcgxzp90owRgpDcLkBA08aBuFhwMBjzOMmPBZ8Oo1mJ/biUiRysH9z3pv/9/Lt/3+g1+s5PbA50iId3w3tTqeEZNrEqzHrMRslIqvCizlX/ykAJjITXgh8gReavYC53LxhB+DHBEyuFiNDRF14Eq/5E5E7O991vt/mzybnWizjv1mFqbkF5Zt/onjxYkS1CrtgFXb+FUj6zII2k8jMzmbdunW0b9+e3r17o9OLvPxDJMeTitkwrjX+bnqmHJlCUnkSM9rOYIT/CNKiivll1VU222mwtzFm5+QOWNwhxmDz+nl8X7GNYhsVXdy78KHdGOqmfYKmoADn6R9iPXp0Q60FvZ6IiAiOHDmCKIp069aNNmEtkF79CfHUAoSqXCosjTnaaj7vappRR4PgMBXreSZ/P6PaP8XhtCukXkrEUm2Gt6cHvbt3x8vdDclfH9JFEerKKT6+AIeL3xFp0Yx5zT6nNFlGRl41UolAp8b2DGnhSq9mzpgrZNTFxpE9cSJIpXh+vxpFkybsi7xC8vof6HT6GKaqemRBQTiOfR6z3r1Jy8oiMjKS5ORkRFGkUaNGhIeH4+/vf9suiF6vJz09nStXrnDt2jU0Gg02NjbY29uTmZmJVqulffv2dO7c+UaWsTtRULCLxKSPGwLmS6axN8qJkzoNOqCDhw3Pd/GlZ4AjsntUHG+4PSIptSouVNbwe/Z5riR+glZqRaXjh+hlN+OQ5ILQIBBkEiylUsyvHzcIBimWMinmUgmWMul1IdEgKDLOnibh4nkGjXqa7y8Vc/haOfO6LKdfx+8wM7t/pit1npLilVdRqso5lL0J0XQYAePbo29sQdaxA1T++gOa5q3IHTqWEq2eltu2MGDrRl57w4tS03wqHd9DYxx4y5gmEgF7icC47T/Sbf8uiryakug2FrWvCy++FYq52cPVMAGD8DBg4HHGIDwMGHiMedyEx6anWxJ2pZ5DvVx549sj/7Y5D8yeN7rT6GA+X430pEmOHd0uR1PX/E0auQUwV1+Lz/AMRGkl44PG33Bt0dSoSVgSjU2lCmW9EnnF73gt+QyppeVd50mPvsT2ebPo/Nx4grz8yP/kE1Tx1zDzt8O5cTzyxs1g+GpwDCAvL48NGzZgZmbGxIkTMTEx4Yv9Caw4kcqnQwJp31TLpCOTqFRVsqDLAjq5d6KyuI7Ncy+y2bSeMqnIzikd8HO81cVKq9ey5ORXbEjfjLGgYGbnT+gYJ1Iw82OkFha4LV6MaWhLoOGb/927d5OTk0OjRo0Y2K83Nhl74eQCqMqhzs6F31y8+NrxU/J0CqzFMioEW5roy9lx7nlqgl9lZ6ENxXnFaE20PDvkWZo2aXrXNLiiKJJZWsuxxCIKL/zClOpF1MqMeaXpbNyd2jErxBN7i5upZGsuXCTntdeQWlnhuXYN8j+lrK3R6vjuWhrZv25nyPEDuBfmI7Wzw+bpUVg/PZpaYwWXL1/m8uXLVFdXY25uTmhoKKGhoVhbW99mm0ql4syZM5w/fx61Wg2Ai4sL4eHhNGvW7IZ71S33WltNQuLHFBbuwpwgnCInICuzwbiJDfXtnNmeW85PF7PIr6zH2dKY0a09eKa1J06WxtdfK5G4mjouVCi5UFnD+YoaSjVaAOyMZATJsklJnY2l3JKvuq/By8IZS5kUxV1qhdwPrVbL6tWrUSqVjHx+AgOWXaCRVRLvtt5MaOgmzM3uvIv3Z+pTyilZG0uJKo9TRftR2Ixh5PQO2DibcXHnr5zavJ7g7r3p9fLrIIpkT5pESeRZZr3nRqleycwua5DKnSnRaClVaynVaCm5/tvz5DFGr/4OldyEc+Ev89o3z6Mwvn9M0F8xCA8DBh5fDMLDgIHHmMdJeHw7uTs9j+QT2cKY53+O+rfNeSi+/uwd+v20l32tZNRZd8arOIKgeGNMes5BI4FvmhizbkKbG/0rk8ooXB+PqV6kuDAJe7MY3BbMR6K4e0E3nVbLD9OmoNdq6GPnTvWWrchsrXAKU2Jhm4PQ8U3o+iHI5BQVFbFu3Trkcjnjxo3D2tqabZdyeGfrFZ5r68ngNnW8eexNFDIFS3sspZldM7QaHdu/usxP5eVESTWseC6MvkG37rwkliUy88xMrpVdo1GpNUuf+wHZmp8p2/ADJuFhuC9ahMzBAZ1Ox8mTJzl16hTGxsb0692DIO1VhFMLoDIb3MKJbNqLqWofUiVNcCUPlWhEKfaE6GrZHPkKx+hArMoFjUxDhn0Gi8cuxt701ngEvV4kuUjJxfRSLqSXcTG9jKJqFQDNXCx5oXE9gxPfw6gqk1m+r5EY+AILAjzxMlFQffQYuW++iZGHB55r12DkdHvGLoCU2npmJGRTc+4sz588RNDVywhSKZa9e2Hz3PPImweTkpLCpUuXSE5OBqBx48aEh4fj5+eHVCqltraWI0eOcOnSJczNzencuTP19fVcvXqVkpISpFIpTZs2JTQ0FF9fXwRBoLwigvi4d6hXFWCfNQzbxH4Y+zW4VCm8bopTrU7P0YQiNl3I4mRSMRIBfH2sMfK0INFEpFbf8BnsaSynjbUZba3MaWNtRiMTBYIgEFcSx4SDE3A1d2VD3w1/O9VuQUEBq1atomnTplS7hPPZ3mu8EfYL4c7XCA398YHER210EWU/J5JVk0BEVRz23k8z8oNWyE1knP55Ixd+20Jo/yF0HfsSuooK0ocNp8BG4MORKuxM7fmx/4+Yy28P7NerdVTuPk/R/I/Q15TR6OBB5G533128GwbhYcDA44tBeBgw8BjzuAiPL98fRe+9MeQ5C1zrPIB3Pv7q3zbpocjLyiJzXF9q6kTW9e9DeFIWPSOvUdJyCo09mrNQX8fQl1rSyc+egr1paM7kodaLlMftwr21Bc4zZiBI7+1vfmnvTo7/sJpWJUoc8oux6eKHg90ppPbuMGwleLUDoLS0lHXrGuJixo0bh52dHZcyy3lm1XnCvGx4ulsRs8/PwsvCi2U9l+Fq3lDZ+cRPiWw+l8kBUw2vdW3Ee32b3phbo9OwKmYV31/9HhMUhF0y4fVB76NY/yO1kZHYPP88Tu9NQzAyorq6ml9//ZXMzExCgprR37UCxYVvoTIL3MIpbP8u76vd+L1Cjw3lhInnOSl0R6uW0qi8lm9yvuGAMgC9zJg693p+53dW9l1JuHM4Or1IfF4VF9JLuZheRkRGGeW1GgCcLY1p42tLax9b2vna4etw/cGzvhLxt1cQEvezw6k3HzZ5l1dUtfSc9gZmAQF4rFp536rjoijye0klM1Ny0Wdl827ESUJPHEasrsa4WTNsnnsOywH9qayt5fLly0RFRaFUKrG0tMTFxYXMzExUKhVt2rSha9euGF+PexFFkby8PK5cuUJMTAx1dXW4uDjj51eByFrkKntcrkzE2j4My15eKLxvTYFbqdFysbKGC5U1XKioIbqwCjFLiTS3FkGjx8JSQY+WLkxq500T67vHf5zPP8+kw5MIdQxlec/lyKUP7370Z06dOsWRI0cYMnQYHx6vpKK2js/az0EhVRHachPm5v73HaP6RDaV+zNIrLxIbJ2axm0G0O+VYBDg2IZVRO3fTdunRtNh1HPUXo4ic+xY0gaHMKNpHO1c2/Ft92+RXi8QqCmupeZ8PjWXihDrtUisRWSmuThMGoVg9PBxHgbhYcDA44tBeBgw8BjzOAiPue+Po9Px80h1cLxXO6bPW/uv2vOoHHq5I+4nS/nk2Ua0zLCmUVk0vmkWyHt9gSCBV2W1fCmzwFMtUqTWoD/3Dd5j+2H36qv3raBdEXOVH+Z8hFV1DZ0UZri2LMFYHw8tn4e+826kBK2oqGDdunWo1WrGjRuHo6MjuRV1DPnuNKYKKU91S+T7uGW0dm7Nom6LsJQ3fHOeHFHIhvUx/Gyppp2fHevHtUYqabApriSOGWdmkFKRQm+XHjj+lE4LBx98z0ehq6rC5dPZWA0eDEBaWhrbtm1DrVLxfIgcz5SN1wVHGNUd3+NraRBr80qQiir6iTtRCpYcoi9CpQqn5FKmiYfJ0Bjh71uCsUc9UaWX8DUPxFSwo7ymlsraWkS0yAQdZnKwNgELhYCZQkQm0aHXaxBFLVKpCfb2PXFyHIC5eVMEUYRTCxCPfU6GqQ9PB83FQimwpGtrAh1tH/g1rtXp+S6rkKVZRZirVMxJuULw3p1oUlOR2thgPXIkNs+MRuLoyPnz5zl16hT19Q3B7t7e3rRv3x4/P78bdVQAdHqREqWK3DIlBclnib18lto6Y8yMNISbtqTVgK6Y+zfs9BSoNJy/7jZ1oULJtZp6RMBIEAixMKGNtTltrMwIMTXmbGIxG89lcjmrAoVMwqAQV55r60WIu9Ud/972pO3hw1Mf0s+7H190/gKJ8OhZn/R6PevWraO4uJh2g57lhY1XmdDelq62byGK+gcSH6IoUrErlZpz+USVHiVd70674d1pNcAHUa/n4KrviD12kM7PjqPV4KcoXbOWoq++4tyHfVnEYV5s9iKTjF+g5kI+qpQKkAqYBNlj3tYFubflfd9z98IgPAwYeHwxCA8DBu6BIAhrgYFAkSiKQdfbbIEtgDeQAYwSRbH8+rkPgQmADnhDFMUD19vDgPWACbAPmCo+wJvv3xYea5fOx2HXD3jl6Pl9QDPe+3Lbv2bL32XpnEl033ScXzoZgUUXXGsS6Hgih/yQV/D3CUOHiChCQkUJTue+IOvpF+k4dQJ25nd2r9IqlShPHqP66GHOX4km28acoU1d8JHuQjCxgMFLoOmAG/2rq6tZt24dNTU1vPjiizg7O5NaXMMbP0WRXVZNz86nOZyzm4G+A/m0/acYSRsCxssLalg/7yLrzeqxsFKwe0pHbMzkqHVqlkYvZX3ceuyN7ZnZbibF6w4iPXeBgOwijJydcf92CcYBAej1ek6dOsXx48dxtjXnBdsojJN3g2tLNF0+YK1JSxZlFlGl1dFdep6u2l/ZJHmNBNEXSUk9IfHRjHA9gY1dBpZWxQiCSJ1WjkYvR6uXotPLkEhkKIwUmMgVmBubIJfJkUiMEAQZWmRUagXKdAKlWgEjbQneuhgEdJiY+OLk1B9Hx/7oVy/FvPgHtHIFk1vM5YB1OK97OTLVywmFqIfSZChNBQsXsPMFkzvvhGTWqZiZnMvB0ioam8j5sqoA913bUR49BoJATWAgFx3sUXp60qxlG8qqqslKjkerqgO5KUpzDzIFJ3KUIkXVKhorShjsdJUmvvsQ9DLK458jRaOgrq4cqakZ1Y2bcdrenbTrqaRMpRJaWZrRxtqMNlZmtLQ0w/QugeXxeVVsupDJjqhcatU6gtwsea6NF4NbuGIqvzW+YV3sOhZeWsjYZmOZ1mraQ78H/kxpaSkrVqzA09OTK8bB7IjKY/vLPlTmjEMUdYS2/PH+4kMvUrIxDtW1cs6X7CdfEs6gKZ3xbm6PXq9j37cLSDx7kh4TXiOkVz9yXpuM8tQpfvw/9u47Tq6rvv//67bpfbb3Kq16lyVZlnsFA8ahE2roISEQDAQILcSQBoQaOsbGYGNjG9u4d/XepV2ttvfpfW47vz9WuAQbMN9AFH73+fA8ZnbmztwyGs99zzmfcz64iTuVbXxw8k1cIZ2P/5wG/GsbUIL/by05v+YED4fj7OUED4fjt5AkaQtQAG54VvD4FyAlhPiCJEkfBaJCiI9IkrQYuBlYDzQBDwELhBCWJEm7gL8FdjAfPP5TCPE7p/r+3w4eP3jjWjbsKfLoBbW891tP/K9tx/+Ee2+/hbov/SOTqsQtF1/JypNj9M0donE6irjoetyyzNDEIdoPf48bLryG2/xrUWWbja1TXNZ1koWxYUQyi7w3g7a/jOuEhWRKZKIa29paWVSb5Kqa41Q71qK84juoka6n110sFvnhD39IJpNh9SUvZ19C5qHjswwliqiKzqp1v+REdvfTw+X++tdeQ7e45Qu7+XYhw7RLcNt7NrG0OcxwdpjrnriO46njvLL3lXxo7YcYe2IbE//4j7Sm8vjPO4/mf/0XlEiEYrHI7bffzuDgIJt6Y1ySugE5NYi44GPc3fdXfH5omuGyznkhiZcXP03KtPmGfB2qMFldepLLKg/SEJlAkgS5cpwnp9exc2olc5bCtctXcH5vE+s6Yk8HtJJlcyRfYl+uxP58if25EqOV+WJtGejze4hpKieyk6ywt7ORbfRxFAmBOilRl+tkgZVATg5xV/freVfzO1igz/ClE9ezJnPguW+qNwbxboh1z18/63ZeePjFRJJ/n5hjxrJYKBTOO3yYVY/dTvfgAG5dZyjUwF1dm3m0ZTWGqtIqZ1iszVEv5QCIBAzaGo4RajyEkGSmChfzUPX1bJU8FP0yLaUUK8f6ackkMFQNqWsRl205l01Ndajyi/vFPl8xuGP/BDfuGOXkTJ6gR+Xa1S28cUPb0wMICCH44u4vctPxm/j7tX/Pm5e8+Q/4JDxj9+7d3HPPPWy++Eo++HCGJU1hvvP6WvYfeCNCmGdaPhb+1tcQhsXUN/dhTBTZmnqAgu8iXv0Pm4jU+7BMk7v+4585vXcXl/zFu2hK15P81gcxZcEX39vIUfU037/8+6ysX4kQglRRZyRVYjRZZHCuyIcu++3rfiFO8HA4zl5O8HA4fgdJkjqAu58VPE4CFwghpiRJagQeE0IsPNPagRDi+jPL3Q98mvlWkUeFEH1n7n/dmee/63et+38zeHz5b6/g8vtHOLhYo+5Nn+eiV1z9v7Id/5Mef/M51O3M8ZE3L2LTUJBW7QSrH84xXbeGquolOreXT294O4V2jRb/NEXdR3XMy6qJU2yZPURncnL+hRoCSKsbGPVoHBvWcWHw5t79jC30MlGvgKQQDC4hFt2IUNdw+y+OUcpleEosYrDiw6XYLO+u0tWUob/yK4Zzg3xywye5dsG1z9neR244zjf2jbLHY/Ifr17BNauauWvwLj6/8/O4FBef3fRZLmq7iFz/SY6//vWECiXi734Xte9/P5KiMDo6yq233kqpVOINy1Q6j/4nksvPzNXf4t3ldrZnivT53bw7sJ+a6c9wP5cxRTOb5Z10W4eRJUG6GGPfzHIen95AwWrFHztGyfsQN7z8CyypWcZAqcK+XIkDufmwcbxYxjrztdLs1lgV8rHBqLBg/04Ku55kYm6SoiThEQKv6kLzKDQo48Q6EhQWSVTjIFuCZcdy1KQNpj21vGrFf3LK3chfedJ8pCWMnRqnONWPlTiFkj6NvzBMSJ99zrGbE2GGRAPDooGRSAv9zb0Me5vwJiyaK2HWTQywdOf9BCeGsQNB5CuuwrWphby0n8nKISaTDUxN92CZbgyPm2NN3eyvb6fictPi0Vju9RKt2lRmSkz3jxLKDtImp7GQyfiaaVywgvULW1nbHiX6IoaEFUKwZyTNjTtG+NXhaXTLZlN3nM9fs4zOGj+2sPnw4x/mgZEH+Jct/8KVnVf+wZ8HIQQ33ngjo6OjxM95BV94aJivvHYll/RW2bf/DQhhsmrVjwkG+n7r69glg4kv78RIl3k8vR1X86W86qPrUIUgu2uCe2/6d2Zyw2xqfSX1rbWUv/0xJlav4B8vm6Zql4lnr2Mq6aFQNaklw8uUrayV+1l/3d3EnzXK2e/LCR4Ox9nLCR4Ox+/wPMEjI4SIPOvxtBAiKknS14AdQogbz9z/PeBXzAePLwghLjlz/3nAR4QQL32B9b0TeCdAW1vbmpGRkT/Wrr2gL37s9Vx6z34SUdi96Tw+fv23/+Tb8Mfw/c/8JRtv3sMPL3Hj81xESzVL+8Gt1CUlSpE4H3zThxgnwrqjR1l36hDrp4/SWJqfjXs6WkO2yUe0pUhMS7J7ppG07qM7mCTc6eLbza+nQIBu7TRxpkmW3JxKNdFWtIjLZbZ7KujxISR3gqxIYmEB4JW8/E333/AXa/7i6aJmgOPbpvj6zYe522/wlk0d/P0V7Xxux+e4d+he1tav5frzrqfB30Bx+3aG3vNehK4T+9QnaX7NaxFCsH37dh566CFiIR9vaziB78StiPZN3L3lS/zdhI4EfLTNy+LZj5IuHGOaRloZQ8YmWanhyORiHpneyHihiQ2uIV5z1aU8Ufo+D4/dx5VLP8UpeQUH8yUKlg1ASJVZFfSzKuhlTS5J896dzD3xABPJBEm3C11TUYA18RidgSI+dQK/MoFbyT29z2UrTEquZSaukW8o0VhJ0HO6SNHt5vNtH+KHTZdTWzS46ESOujmbigQVoCqB323R7E3Q7J6hQZ2hlimixhi+wmm8VvY5/w5m3bWoNT0Ew80kT82R2T2KfbICAlJL/ew873LuXfYSTtgeOhNTrJgepjadQFJVlq5azWVbziMYfO7oUtmSwZNHBtm/eyfG3DAIwZAd57DZQG1tHes6Y6zriLKuI0ZzxPt71TAkClVu3TPOtx4fxLRs/vmVy3j5ymaqVpV3PfguDs0d4luXfIv1jetf/IfhjFwuxze+8Q1i8Rp+WeljJlfh4Q+dj2KPs2/fGzCtAkuXfIWamgt/6+uYqQoTX9pBtVzksfQgodpVbJBAFXBa09kx9jOk8jQH61/C6qlhrjx8B/duuIibzt9FzKzhQ+NvoFMvEMNCEMCQwsQ+vAlftO5F75MTPByOs5cTPByO3+FFBI+vA9v/W/C4FxgFrv9vweM6IcTvbEL432jx+Nx1b2XzUzvwl+GRy9fy0S/8+E+6/j+mr13/T2y+48ccjynct/lKlh47TWdogCWPWpxaU0tHZCnm9p14SiUsRabc7CXWkKe9cRbVa5PRPdw7s4ipQgBL0zjSsJzd3eeg1rqomCpGpoCUH0IVY2juKc4vxamrhtlZt5MJ/wQ+WdCq2TRq4FHi2FYnxWIvoTGFgGXS3t7OggULqA+3cdO3TnKDr8yK9ij/cI2fj2/9KJPFSd6z4j28Y9k7kCWZ1Pe/z+y//Tt5l4r1zrex8X0foFwuc8cdd3Dy5EnWdce5Mv9T5NkjVDZ9gA82/iW3JwqsD3n5O9ct2HM/RmAhAVN2I0O51Tx6vI/T+TY0LF7j3snbtfvpfNdP+NeRe7nh6Pch/gbm/FewNOBlXdjPKr+bBf0HsR69m+rhQ8zmqsz5fOQ9LoQs4VN0ugIZlkUM6r2TKFIFIWQMuwHdbMewOqjQRUV0I0kRZElBls6MduRJYEd/QWv+HmRh8cDCPj4d+xSjSiO9eZ339Zucm5jf/t9GooQqTaFpU0z6phj2T1FnTLKwOErYmg8+RlEmPRggPejHrkqIqAexejHBzRcSb15CSgmz49Rhjg2fQJEV1qxcxbmbNxOKhn9jfdlslqe2bmPfvn1YpkHJU8fOch0j1fm5QBrDHtZ1nAkinTEW1AWRf0vXrIlMmb+5eT97R9K8dl0rn7p6Cboo8Jb73sJ0cZofXvFDFsb+sG5JAIcOHeL222+nc80FfHpbkbdu6uQfr15MpTLFoUPvIl84Rk/XR2iOvwm7bDGXKDI3WySVLJPLVCjlday8TmvRpMMWFMwMT+aKdAZa6fHIeJDQrQqPTv+UvJFkS/2rCB66B3P6IEevvpbPLr6DzfnFfGzq5eD1knRHGDM1XvLhDcjOqFYOx58VJ3g4HL/D/5+6Wv3zx97Gyt07aJ8Q3PeSXj70b3f9ydb9p7L9tasJHCrz929dxgWDAdoDFdqe2E88Bzkv7O2R2N8jcbJDYGsySCo2brqHQiw95QIBh9pkDjS7sSQX5lcZfQAAZa5JREFUCAXkKopnElmb/2VdEhLnTm2mvlrHgH+KlMcHcgvCE6Y+NEObb4Qu1yla5FFkSTApmjmSWYt7pA45o+NNruZmv4TttnnVBce5feJman21fHHLF1lVtwpb15n+5CfJ3nkXibo4g8sX8sb/+CaziQS33HILuVyO1y730nvsK0iywvHLvsIbK73M6AbviE6yOXUdiDJlKcyD+fPZN7qEsfF6QKJRLaCoLj6rfI8L7W1UX3Mzf5+Z4vGT/0E5cBGrOt/HW/sfJvrYvcjDU2SKElPBIFmfG1OWQYJ6d57eQJIef4q4vwSAKeKUywvJDZbJD0whdBkQuOMCbVM7E70djFRMtNlJavMB/Kwm7FqBWwlhi0li7k8RkKcYqI3w077zuVV6PbNSA4uLSd62d4Llhw8TDNrI7e0MmzBuCkLxOpb3LUYrJimmB6laMwgscnac26KLua0xit/K86aREV42M0KNPYXXGKM6OkpuoEg1rSJrNpHOEpGeMgQWMmNdzlNSLQPKLDISfaKZla5ugt4AkkdF9ijIHhXZo1JRDQ4lB9g/dYyKUaU2Uk8wvpChaoA9UzlGijpFBH6PytqOGGs7oqzviLGsJYxbfe4Jt2HZfOnBfr7x2CAL6gN8/fWrCQaKvOHeN4CAG6+6kcZAIwDCtBG6ha3PX//68ty/bexf318xuXfwCQaz40y7NnJ/TucHdbU06wJTL5Lq/S9K9XsITZxHw7E3I4nfnNRPABKgCx0VlUR1kh0lP+ev76S1I4TsVamIMj+/4Z8o51NcWJ8m8OQstg13vauNH3mnaREvo7//XGRR5JzaMl9912tfVDe1X3OCh8Nx9nKCh8PxOzxP8PhXIPms4vKYEOI6SZKWAD/hmeLyh4HeM8Xlu4H3AzuZbwX5qhDi3t+17j9l8Pi3D1/DuY+ewFeBJy5o5P1fe+RPst4/tZ99/JUsv+04X3+Jl1r1UmozM/TV7iCjl0nWC0xFoEsSVUmioKpUs1GC/bV4SwrDjSo7l2l4wjZ+LPK5AiXLwLBVjGojTZUW3lruoGrpjMspVlZ7CFlRkiJLXk6DlCTINBE5g8eqoIgi45Eq1ZUWTZ5JSraHU1s/wF25FkY9GZpbfkjaN0lruZXX17yeFQtX0BmLk/jwhynv20fx/M08np7kVZ+6nulihfvvv5+gz8Pb24YJHr0Ru3ktX1//Bf45pdHqMniP9W+0mXuwhMKtiXfwwNElSLrAr+j0MEew2U08tpjuI9/gb9TbeXLdR3mX1o489UVi7gX8405BdN9JBrQIs+EAxpmTY5ds0uFNscCXpD2YxuO2EAJ0u4eKvZGqvARtSTe+TX1IsszYO9+FMTaKb0kn1cERzLyOHGrCu2ANUsMGJLUWE4tB/wx531Hc+i4a02M0ySnqPCUShpfdoXoeW3o+d3lfTlqKs7I6yDuGp+k8YlEdOYR3dB9aSFDpqlLsgSOdi9hZ+xL2u5Ywy3whfJ/PgwBOliq0uDU+19vMFTXzw9nahkl52xOkf/xD8tv3gm0T7IC6JTOotQ3k6q/l8WI7BxMTSEgsDnWxyr+AgOHCrpjYFQtRMRGGjYHFSWWSw+oIRalK1A6wwmyny65DRsaQoYAgY9sUEZQk0LwawbCbWNxLQ9yHCwlRtdiayPHJoRlKtuC6WITFnjn+LvB54laEf5/8MIGKF+wX8b0ugeRSKCsmP7efwhYeflJdSC0yb8NFCajIgoaFd1PT+kv0yiLQP0GzGSY6WUKaLIEE3mVnhsLtDDP78HGMh5KMlYc5TDOXvbuL8f03Ez31C9qqQ/x0ZAVl282aNcsJf/9uDtf38pmX+9EiB1g98xJWDZ+PLBRe/U+raIjXvujPuBM8HI6zlxM8HI7fQpKkm4ELgBpgBvgUcAdwC9DGfDeqVwkhUmeW/zjwNsAEPvDrkaskSVrLM8Pp/gp4/9kynO4jd/ySk/d8gc1bU6RDsOP85Vz3xZ/9Udf5v+n6T32Sl9x1C3vaFZ5cdxWLTwzxuo9/lpaFi0lkZxnYdhuusW14Mqc5MSQznI4Q0cpc2DBIezDDkL+Z7cFV7AsuIhvoYFP3cl7d08ctu0b5l/tOsEkZokNO0E4LfXoLUctNQHrmF2IhBAVbkEJnRqowhk5eSlHvepJKTSvbhrawK36UUOtNCExa0xfSUekjbCSJpROc98STeCoV9q3fQKI8Q2jZRtydXZzuP8GKjjgvq96OMrWP9Jp38Pq6t7C/ZHKpsoPXml/FQ4XD5Qv5zz3XYpdtYtEiq0rjxLUKx5au5W09y3j4J9/g667/5LbGq/j7lmuJzX6e+pzJB25RmAjHSAV9IGyi7goL/EkWBNPU+HLIkkCofszYZorZVZSyy1Camghd0o5n0fxcHCP7Hif3Nx+BYpn73ruKiZoAveMNbJheSIPdiBAW1uwJjIldKMXd0JBhYKGHA4uaGahbg9uKc/74Xl4y/SR+u8pUOcBjZgePrjqfR5qupCAFWWfv5M2Jwywa6yQ/6qE4dQTf0B7ChQJ5f4C5vsV4V69iwaYNNK9aiezxsC1d4B8GxjlRrHBhLMg/9TbT7Xum3saYmSV9809I/egGsAziawPEm44je31UFl3LVnMpW0/MALB69Wo2b95MJBKZf78t++kQoherHD56lJ1H95AqpAm6AiyP99HjbkOuglE2qBQNrIqJZNhoAjzMzwNiAWgysiaT1OBz5QJ7dZ0rQ35e2jLHJ8U/s0jt5Uuxf8Lj8SBpCrJLQXLLSJqC5FKQ3QqSS8ZWZQYzJfZNZdk5mmHPSJqpbIUWOcMlrgEOqIs4UAjwzvO6eNf5XcT8LiRJYnLoNk6c/jhqNULzvr/Fp3TjX9+Af/1vDoU7+MPHcJ9Q6C8MMiUkXhW/jilXA8meaznl38Tgrd/DNEwqdj1vPPgwhxZewncuHSbhm+C8xFWYtWN8/c0/eM6cKr8vJ3g4HGcvJ3g4HGexP3bw+KePvp2+Y7tY0m9yslPm6PJz+PgX/29OEPj7KhbyHP/LzciDOn//plVcMhRi9ZI+rnjvBwCwTIO999zJjtt+irBtFl91JZ6IiWd0G/HSKaLGFIFqDs2eLw4vyW72hhazvXYz6L2URobYbzRRt3A1n79mKXVBD9lcmtPHB0mdSmJPV/FkFcK6m7BwoZwpMs6Ygu9Vi9zbeReu+FPUqhJvrynhlmPMjPdhbQ+xZfuTGIrKzr4uqqKMEYhQaOxCUWQ8Up6/ln6GS7L42fJP8cnIelyizNvF11jHHgpGkH/r/yRjEy5kv8x54ZN0pjJM1DRwaPE6vrq8l3//5k/4ofVJRtxNnByJ8OUlSXRF4prH65DwEtWK9IUTLKvJEpTOFGzXLkL0XkZVWk/mQC1mUkdr9BO4uJXJpjR7Z/exd2Yvpae28bafpaj4vOx76YWsEufQUZzvGnREm+NhbZrJJsFVzRHW9Q+gPraV6olxEKB6LAItBsG1vfgufgVy3yUUdv8I3+5vYEoyR+Jx9psNPBi+iu0t56PLbjbzONcad9E9245vcg2pCYXJ3GG0yUN0T2RwWTa2IlPtaUZdsZTg2g3c17uW/5grULEF726t5QPt9fhVBb1ikk9WKA5PUP7B17GfehDCEUIbYjREd6JgMOvbwE6xln1lDZCIqq1E6UKqujF0C6M6f0GAQKC7k5T8Y5iuPJKl4Ss14yk1IT+rG5PqkkGVqVgWUtVGeVY1i41gh8dkm9skLiuc3zLIg4H/YpW2gb9v+QTBqA9/2I0/4gaXzIGJDHuH0+weSbN/JE2+agJQH3KztiPGuvYoaztinNrzGAf2H2BPZAtTeZOHP3Q+nqn5mcXLR5OUg6eYXPM1hKazZMmXqa276JkPlxCk+rcz/vgPaJu4l4ncewm7t3AgN0a6qYFji2r45aEpzEqJ85RRFg49gSRcLJmG1umD3HBtH/f2DoAETS6ZG6++k9pAx4v+jDvBw+E4eznBw+E4i/0xg8cXP/Z61j+xn7oUbF8fInjp23nNG9/5R1nX2eaX111Fz11D/Os1ftqkywkOH+P937uJqVP9PPL9b5GaHKd77Tlc+OZ3EK5reM5zhRBMFsY5vedetBNP4qpM0lI6zQFjKdtYixzW2Rs+h6ODGgGXymdfsZSrlzc+7yhGuqEzNjrE9IkZ7nlinDs7b0D2TnBR+lzenHgFO/oGkOsfYOXjJwjdplCp9bG/qYOMMFDjC0nX+pElwYVs4zx2Myy38K4FH+Fg/XKWigP8lf1N4lKC20dfzn2nLsIWErVNZS5OnSSoVtnRs4ahxk5eVhdj271P8NPMPxDWixx5NM4nX+Wmoin8xb4wK7wpFobnqPcUEUhI7efCklcgei6lPOoj9/Ao5lwZq0bh6JIJ7nM9yb65fWSr8+Hk5cf9vO6uHPbiDURWvBWpCFMeiV9UyjwhW5y7uom/Oq+T3vrnjhRlZTIUHnuE/N23Udx9CFMG65wWqqsksq0T6LbMgoESPdlpDgd6ua73g5zU2qgrzDIea0XIEhfYD3ONfAvxqkVoZj2+yTUkJi2GjOPMFA7QksixaFzHPZ8jGWlq5DvXvImtS1cSLulcerRC32nzOQXs4ewgvQO3EiqMkQu3oy6PsbhxK145Q5o29kkb2SHqMVGp9bfTUbOEUCCC5laec1FdMsn8DEf69zI2OYJLc7FyxWo2bDiHaCyC9KyC8/6pHF+65wR7TyRo9ri4ekE9iyN+9k9m+eboNCXbZklkJ6cbf8HC6c00jV/DpCKYUC1mFIF95qWaNI2FIS/LaoOsbonQ2RggEPHgj7hx+1R0Xeeb3/wmCcPNT5JtbHG5+YzuQvNp+NbWE1jfiBnIPF103tvzMZr8FzP62A/xnbiVBmOcitDY6zuXat+1+Le6aJKa2JVP8ZjHJuQtUJ9qQBEqWe04avJ+KprB+lNjhKsWv/hoJw+Xp+mJ9PLjq36MS3FqPByOPydO8HA4zmJ/rODx5b+5jC2PjWEq8OSFPXzoP375P76Os9k/f/6TvOynt/LYYpkDy17CgoFRGupqmRsdJlzfwIVvfifda37/IUrvfuAu9mzbR59yitdYv6Qsu/l57FJ+nL2IgXwDaxfE+fpfrKQ+9JtzEggh+PR3vs7t6g8AhU9u+AyvXnwlwhYYiTynP/JBxPatTHRHOBYKY2sSVkc7ebmBBZbGy6Vf4pePszvcx9uWfpa0EuO14sdcLt3LSK6Zrx17F5lcGBHTWK+N0pedYKC+m13dCzE0jZf0P8T6bTtZ3T7IIkZ48HAXd66Ay0olNpGl2ZcHwGpYibLiNfOBI9BI/uA0qQcHUVOCWX+GH8Xv4lHfToQkaAu2saZ+DWvqVtP1swOoP7kNa/O7iMRWMCHZfFaUGffKvHFDG2/e2EHd8xwXANMskMnsITnyJKnEdorKAMflReyz17FfrGNabQAheNv4nXxk/IeE9TTmqjchX/IpHkgZ/OfJkxyQgyi2xUX5B3iF/zZCag6lHCM8vQHP5GrmpkuMl0bIlMpEyjYN6QSxzBADrQ3852veykBbJ31DJzlv711UIiWMhe3U1bXSHm1i4f5xAjf8ApHOEL7mFdRd1oF64gaYPoztiXI6uoW7ZxrICj/Lly9ny5YtxOPx593Xqakptm7dytGjR5FlmZUrV7Jp06bfWH7PcIov/OoEe0bSdNb4+fvLFlAfcvMPvzhC/0wBf+OvkCOPU529ApG+kAVhHz1eD+2ySqMhQd6kmKlSLZm/sQ2KJlMf0vCrabaxh4SxgLvtMC9riPD5168iUPvMMMBWcYYDu99Gxj5B43SFvoEC+8VipjpfgbHgah4aqvDgkRlajQT/ZrqJuWrZlq8yJVmMN+7jcGwr074JWnIuLtzZgORRuGjPEBNtLj71FxYmFo+++lFqvDW/9+fw15zg4XCcvZzg4XCcxf6ng8dX/+XjhPf/inX7y4w2SBzYsIaP/BkNl/v7On3iGLkPvprKtMVH37CWy0bj+JIjnPOKV7PuZdeiul74V1YhBJlMhqmpKSYnJ5mYmGBoaIgVK1ZwwRUX8Nj9X2HFiXtpqw6gCpsn3Sv4fuFSHlNWs2FTG+87p4ONkQCKJJEsJ/n43Z9ha+lRrGInX9jyBV62dDEAVjbL+Ac+QGn7Doqb1vN4IYHl91Ns6UbRZC6q/xXrEkMoluC7vVfx6YbraBEjvNv8Og2MceOJ17FjcjXCpRBqEVw+e5CJWA27OpdQ1STOGTnEu+76GdHBJLMvq+Ulrt3sTDZT48/T6c4hSzAthdjd1Mn2+hYmNA3d0mlJ13DN0Pm0leoZdk9yU809zLTmWd2wht7QclS9m9E5jeMjCc75+TfZnJ7B3PDXhL1RHvEKji4IsbI7xstWNOF3P3d0pF8HjVRyN4mZPeTzAwhbwbJcTNud6J4Sza7TWJKMbWu4KgJvpoi7UEXToa1SpbGcw5Q1TtetY9i3ghE9zJ3hpeyLt+C2dK5IPchl/IJQPIskC7RCA6HpjXgnVzIzl2W83I/WHqC1q5ugoXNXyeZ7XSspu1y85sF7+Mt7b2WyFo622JxokRithZcccnHpriq2pjD96i00XbSEnsFHcJ96GCSZqeg53JfpZMyuY9myZWzZsoWamuc/oU6lUmzbto39+/dj2zaLFy/m3HPPpampCQDdtDkykeHmXWPce2SKYtV6+rleTaZimARbb0UE9vOZjf/EKxe8/HnXY+oWxaxOMVOlmCpj9qfRhnN4igYW8Jg2wJAyypH8SvZoGudUVC6VXCyoOUYrD9FsbEWjyv62WtIdApOFHDc/za37CmTSeVapGRaX/YRLMSR7iguCAdyKn4dLVW5Y+m8UvEks5s8/GpJuLtldj6zaXL53lKFrVlF908t4ec/LcSvu3/tz/WtO8HA4zl5O8HA4zmL/k8Hji//wZpbt3E3nhGDfMjfj3Zv4yBe+8T/y2v8XPfC3F9F6/xSffl2QReaVNAV7eed1b3zOMrZtk06nnw4ZU1NTTE1NUalUAJBlmbq6Onp6erjwwgtRlPlRnkpGiXv23kDbY79ghXEQj1VmhDp+aFzOT2uuQFnsprv0EKO5BzGFSTVxMR9Y+x7ee8ECAPThYcbe/R708XHG1izjcCWHp62bOW+YZa1hVk3/hC7rFDOBIO9f+A88EdjEZdkHeb33xxxJLuAHx/+SgqEimr2slUeJGkUOtfXRVJrm6p33suHx/ViWxGSjH/8amfWufjTZRpEEKd3D43KYG2ujHPcGUCUNj+qmTdTwxrktrE13kfOU2Lt4lGRznEqhnVPTNofGs8zmqwCEjBKf33cji4PdaItfjvC5CF/bgx3zcXLHNJMDGUzDwjRMjGoFU9cxTRvblBGWCki4o6MEGg8SaDqENzY/iaZRjFGcWYQQCpqmo6k6mlpBIY8iMgipiM/W6Z0sEi0YZIMqJ3sC5IMqY7Tyc17HHukcgiLLy7mdi7kflzDm32wJ3LlWQlOb8CWWUvQOkw7voxAcxgw0cYv/Wh5hBQuMKT5y+Ic0HjqMnLaQ81D0BpkKuQhM5WidsZiMwo8ukcn3uHlHSXBRchyPqTPrbefJ6jKO2d0sXraCLVu2UFv7/KM25fN5du7cya5du9H1KnIgxrjSyNakl6I53+rQHvdRG3AzMJsnWzbZ0lvDy1c28+8PHiMT+iaaf4hvXPJ1zm0+93nXYaYqFHdOUdwzjV00UWu8+Dc04l9dh6XB9773PWZn5xgJrODhWYUPq7fzPvXnlO0Ap8qbOVm5gBljAYG2XTSt+yGVapCju1+Hf2YFMjLTgSH6a3czGN/Pysk4H8m9H1tSeJAShVf00xZroSPUQVuojcrJCX75H/9MTHWxeu9xur73XfwbNvxBn20neDgcZy8neDgcZ7H/qeDxbx9+BZsfOYmnCk+e38j7v/7nOVTui/Fv13+EK398J3evkxnsfQkLZ9cRiQepX+DBchdJZeeYnp6mWp0/mVYUhfr6ehobG5++1NXVoWnaC67DsAzuGbgT41c/5eLCIQZtP98K1bInMoMAQum1TGTOI1LTycaL22nxuOg7cZSFn/4EYHOwu4UZRVBt7MCOxHljb4HWgR9gCZNb2rfw0eaPoWBylXQX55cf5tvH3sKpZCd2QCXYobAx2Y/wxGk9fYQLtj1O/dwcJZeL3EI/jc2zdIeSaLJNznDTX2rgjoBGfsN6/m7DvzA0V+bUbIHT0zk6BwtclLWRgJvQuYkq1TP7KEnQUxtgWXOYpc1hVqhFwtdfj9ZwGUq0C9fiGDP1AY7vnmJuvISETdA/Dt4cwlNGUgxk2USzZLyBHO7IBHL8JJI3ixASk9UeRvNLkUe7CQ12IzHfSiJjgyRhi2fqILwBlai7QnDuGG3pn9PZfQzVbZFL15DznY9oXcPBQB3/Fa9jjz9MrV7mL+dOcGHpIOXgMfTQOJJ3fs886R5C0xvxzS0i7z9FtmY3T9YH+YHnnZiovJnvsYVHn6n9sEEugme/TOABBTUlUezWOP7SACfqKvTOJbgsWaDJsEhKHvaxmr1iGVJLnMXrFrO8czlhtYFjU3kOj2c5NJ7l0ESG6VSeXiXBAiVBRC6DrFLX3sN5G9eztLcTSZKoGBY/3j7C1x49Ra5icOWSBjLVPAfMf0bzpPivi7/HhpYVAAhbUOlPU9wxReVkan4o3EVx/BsbcXdHnqlFKibI77qJm54aYsoM0W8E2W4v5oLoIQILAoxM+smMxGkzBC1yjk5vgc6N30VSK+w/cin5qaXUFtqpeNOo0QpdHe1op4+wpNRH0RZMdtdz6buXPaf26eT2p7jnK1+kxrBZN5Oj9847UF+ga9pv4wQPh+Ps5QQPh+Ms9v8aPB6545cM3H09525Nkwr/+Q+V+2Lc+dMb6frO50kU4TOvWscF+a5nHhQyboLUxOro7G5j0Ypu6hvrUNXfnDjttxFCMDea56FdT/Gz6Zs4HTyMYmvEU8sYT1+IbsXpCCfwbl7AnBpm5cP38zc/+T6JcJijjSHywSBGYwe+OpXXiTtpzo9zNNbKu3o/x6Cng8sDaa7Mf4bdwwu5ffBqLCFjdQdZKk1xwbFtXHB6O9HTWeyKTDbiJ7/ExZL4KE2+PFVL5Vi2lsFCnDnfZn608hHq/C3815bv0tA0P/xt+XCC7L1DWJkq3mU1GFuaGKwaDMzksQUsawmzuDH0dJep0sGDTH/2R2idVyE0jWGqHEnLCGS82ijBnu2Eeneianncs2HcuVZkVxw9mKQcPYFQTEzLw0FWsEtZz2S+kzX9OosGPdSTwS1PU1BTtIycJDA8iKuqY6pe8oE2inULKNQuIOduoGj7AHBJRTYFfswi3wMYppvRwU7UcoH6ZX72XvwOrvesY19F0Klp/J0vxLkViWNzJ0gW7yYceApXIA22hDe5mPDMRtTJHnaXT/HVc/oYaexkQ76fTzYlqKsPoOtJKokhKslhqrkp1B15/I+BpEPxQpv8lRbCK4inDNomysQyBoYkc1AsYidrmAsolAJVsnjJ6wEsyUc0FKG7vp31nYtY2hIlMT3NwQMH6T/Rj2mYxGpj9CztoW1BG7JLJlOucNu+Ee49Mo5lm7THQky6fogsm7yu9b1clKul5rgbT0Gl6jUZ78kx2p0i7ylj2iamZdA828/ikX30zZ5Cw2afEuWX0pWYRgO/sDoomHF8lmC1lGFJJUBID2PIVYbiB5loepKrO/ppdtnM5NZTp78fKxskPV0iM1PCMg2ixlOcV3sRCcPkmNdL27Jaog0+Yo1+oo1+Tu99nPu/9RVaY3W88kvfQPU8f/3Pb+MED4fj7OUED4fjLPb/Ejw+d91bWXx8N4sHLE50KxxbvpGPX/+d/+Et/L/tiXefS+1jKa57c4RLT/fRNT7NVEhwtHkFYWkV9eU6hA2qW6G1L0r70jjtS+MEoi98MiSEIDFWYGDPDI8cf5ynAvcyEenHbfuoty6if2QVpuFjlTzIR5SbOEc5SQ4PM/1hzH0K+ZZGtkfcVKM1GM2tXNGwm1XTuyhpbj7Z835urn0pQXK8ie/RkB3j28fewly+BqvWja9dY934Ec7duYvzDu5Btm1KDSpKp0FXY4qwq0pW97A31cRgIUZvNM1jsZfyYNuDaELlK0PX4dLDlGWJsCbjNm2kmIfYNT14e6MvuM9mIkHi+zdTGAjgifcwpxvsK4MpZwl17iTUtQ1/IEXIWINfLKdcGiPv3o8eHAdAqjQwVFnJbYF1HFCXUJPPsyhTpSkWJ6EZjFkGk5JKzhuYX962CefTxDMJGopZmgsZWlMJ2qbGCc/MoOSqCFc9hruJcqAVrVZhffxnNLpOMqX3sS3xOsws1KinGV4V40erLqFfDrDY7+GjXY1cGg9xqlThvvG9zE38ghXGY0S0DJguwlOb8I9u5rvhADcv6SZQzPHqJ29msy/Dko2baF11KdT2gSxTPn6cmS9+gfKOXQhVxqy1MFpszLCEXqPR6C7QoWdQhWCQVrazhnRMpq3jMIFA+vmPtZifH1AIGWwFyVaxhYQh2eiSiY6FBdhIWAIG023cXclhmwEur2zgsvoDTLuSzKpZLASWALct6NRNunWdoGlTReKk6ua4O0BZboZKI2KsAcpeHrZ6mDODvL1uJw2RfjK+aXKeOXyKoMUfoyPcg6gOU6lM4PW0EQwtBwTCtjCqBvlEhcydzayNXMVY1WCs+xd44oNIkgDJRlYE2SGF1IDMG//pR0TqnOJyh+PPiRM8HI6z2B8aPL74sddyzuMHqU3Dto1hul/2cS56xdV/hC38v+1r17+Pi370MD/bopAXPbTlTGryJRpTecruEvetDJOpvZiL1c3IuSCF1Hw3nHhzYD6ELIvT0BlCkiUS4wUG984ysHeag+Zu9rc8xGxghJAcpVG6nIMnFyEsD9eubuE9F3RTkkb4yhM3U7szyzU7n8Q3VWGq3s+B+noq9Y1sWjLNhsxOPBWdOxov4iOdH6QlHOQN0m34537FHaevZfvEMnDJ6IuirNeH+MiPvkxkMo/pUdi5bjG9NaNsVE/iUmwmS0F2JVtJVr0sic8x4u/gcMPlPBm4lZyU44LJC+gR7ayv9NBghgCQkLCFIG9D1Q1KRCJQZxOtt2B2ktKBg2T7Z8kGVtPQsgJFljlWMZir3U24czvh0AzBxEqCs6txFZpIdf6KdNtDlHAzk1vLRHENe/x97AtHsWQZxbawZOU575GvVCCWT9GiZ+mWCnR4TCZcNQwqUYbVAFOaD/NZk8z5izni6TnimTli6Tlq0nM0ZVPE8wX6ghnWNh7HpVY5njufbeW3oROcn728xeSxFUHmAl6WCpuPdLVzSft8y8/+XImHx57EmP05a+wnUWUTT7qHfPIiPtG4iQmfn437HmPj3sdocmcIRSQmGlfS71vFgF6HcXqctx+8g6XJIaYDNcy1LaCpmiU22o8qVYj0lIj2VdFcOkmi7GAV6fbzaFrqo+oaJV+eRsZGkUAGZMSZ2wKralDNFakWykjCQnXJ+IJuArhQihaSaXLIMPhGdQ6z3Exn6TLeu+pBwu4cGEWEUUBYOkIGEwVblpAkMR8EnkUIiYH+DYxML+Qhu4u87eEDa75KV3gCVXGjyR6QZCRJASQsq4hlFZBlN253PZLkQpJkJEkmOyZjPrCB5dEtDOhVlCtvRvXkMA0wqjZmVaCXBS95za3I8otrZQQneDgcZzMneDgcZ7E/JHh88aPvZctDjyLb8NRFvXzo3+76I23d/31f+dz1XPrADzmtyHz5lZ1cMTFNLhdGqrjwVmUa0hXClRKHOqpsW9LJK+uvYV39+YwcTjI1mEXYArdPxe3XSCcKDNbs5UjnY8wqE9R5moibl7P3WDeK5OK161p51/ndNEe8AAjbprRrF2P/9FmswWGONNcy1hCifrXNpeoeGlIVRr31/PXCTzAZX8KbfdtoS3yH+4av5IGRzVhCwmwP4mlW+NAt3+WC3TsxahXKrSqNtSlaI1kEcCofZ2eyDUMoEPXxpGcd3tEKV57exi2Xz3GoU+Lv7vKwIHQVDfVbUGWN48oEBxggki9RZ8ZpVjuo89SjnjnBr9g2E7rFjCjTIvtpcyvkjDJD7Xfjb+jHk1qMPb2KaqaFMiZ2xyO4+u7muNbNXZW3cNTX8Zz3QbEsYoU0dTPjxNJzNBRnWWROsJwxFnpS1HjKSNggbDB1MMtPP9dEYTS2mIGa1fSHFnLC3cJJOcppyU9JeibEeE2dmlyK1tnTvHvuFi6x9lOy3ewbXcRcYjloDWSCbWxb1sETS/zkfTLd0xWuPjbBSsmmqbeG5s2L2eetcnD0Flqzt1OjzCDpAY5WL+TbwSvwJTQ2P3wrTZlxbCQmvM0YwQDNgTwdoTJ9OYPAE6exkjlCV11FzQf+FiuZpLx3L6W9u1GnniDclsAX0ykLN/ukpUy6N7D6nEtoXbwErSaO9ALd/XRd59CO/ezbuYfJ4hyykOhwNbB6xSr0vk7+4ZFbGXd9C6vQh2f6VXzF/T22WNuZEVF+Zp3P8fqX0bNwKUOJInsOTrPIzLLE9OAzfJTlMoPxA5ys2U2zEaE+u5gH7MXImp9b372JnrrA827T9PRdHD/xEVyuelYs/zaBwIKnH9t79x3k7x2lJ7SKU5rCpo+vx+V58SHj+TjBw+E4eznBw+E4i/2hLR7//qGrMVxRPnr9DX+ErfrzIYRg59vOIbw9z3vep5AMPXeSP8mW8OgyHl3GX5FwmTYZn0pDTTdXLLkSOeWlPApj1VG2BX5FwpylLdCNr3Qpe4614lFdvOGcNt6xpevpOTzMVIrM7beT/tktmGNjWF4ve5vD2Jts1jSP0jeWQbIk/qPtzfyw6eW8rnQrq/2P8tT4Ru4YfCkVS8Nq8kGnjysObOWv7voJZrOGr6ZId+00IbeOYcscSjewO9WCpXk4Gu7jweC5lDrihGIWF8zsQSndzOM1Ov90pIO11msRrjbkymGU3A1MuixGfG0M+drIqmEAYmaaRtPCEAvJFnqISR5We8Ejy6SLT7Jj6UH2FYMkbAlD1lEVkzXxAsvqRziiLuMO8TpG5BZUW2AxP3t3e2qG9cd3UTN1mEnfHDOxKjSHWdm7gU3N53JO4zmE3eH//qZBKQWZYUj/+jLyzO3sOIj5gVqnXTUMBLrpjy9nILSQAU8LA2qMOdwsz5/kCwNfYnX+OPtCy/hBw5swp6p0nuynYa7E3sVruPvctRS9LhaOVbnwSJnanI1lZykGYDDqJek/wbmNT7Ci9jCSJDgiVvK4uIxluxTqhndSqiRR9CooEtGoyTn+IRZq06RPBEieCCAkGd8VK2n+q7ejdm1EqB70wUGqT/0cZfBWvMppAI5LPewvLUYeM2kt5GnSXLjq6lBra1Fq65BcrZi5CFZKBhnKvRr9/lmODB2nXC4TCYdZ1aRxLHUL3/Cl8WSWkJh6A53yLKuWLSca9PPU0Rm8s1mWW4K6ShhLshiLHONk7W7Go0dYFY1wdffVXLbgr9i94wB3PryNB+ylBP0+fv6eTU8H6v8umz3AocPvxrLKLF3yZWpqLnz6s3fPV/6V2tO1NHm7GW3ws+kDq593os0XywkeDsfZywkeDsdZ7I85c7lj3g8+95dsuGkPe85r5dFlL2VaczFWSFAReSSlhKQWcMtZVDkHShGhVjC135x8rTe8FCl7MXuPN+B3KbxyYZCXNtuoxTSFVALzyFF8+w8RGptEFoKU38NUi5fyWpuORXMsHs0Tz1XZHl7BdQv+Dq8xx9u83+F0ooObT76KvO7HirtRut28eeYeXrvzl0y5a+jxjdAUzKDKgpKpsjPRxqFMA1lfDQ8GzyXas4S3be5k5eBO/vXANh5baiPpT1JfDfLXs3/Funwbul8iclkL0aVBJLMMRhmMEkYlxdDkHo4dKZAY6qSaacOlGCyLZGixmrAKM+wrP8Suzgia5EIRCtjgDiVo6TnMcd9C7uRaElLd08cpUszRmp7j3MQoly1awIoNm/BHoozlxtg+tZ1tk9vYObWTglFAlmSWxpeysWkjm5o2sax2GZr8wqOIAWCZkBt//lCSGYFSkowaYMDXzoC3DZ8wuTi5Da9V5rvNf8G/dryVoupDEgJ3sYKwBNWAB2SJ6KzO5kNFlqUEChKWMJHlLA0decyVR/Erj+OT08xRy0T2QhZtDTOXHUX32+QTswhDx9I0qA/Q4cmwfO8gYlig+kxqVhWorOzCbj2HeOdGPB0bAdAf+nfkozej2mVGaGYbaxi2O2jJFmktBWn1rcTtrcEuJTGGHscY2Yowi6jxOFI0yHjcR380xmSoEUnYnAge4EjtEPbsRRSTl+Gx4XyjzJJKGEUoJHzjnKzbxamaPXRFJK7suIRXLHkfYW/Dcw7zzp07ufGex3nAXERTLMDP372JeOD559yoVCY5eOhdFAon6O35GK2tb0WSJIxKhZ9+4jpW2OcR1urJrq5n+WsWvohP7/NzgofDcfZygofDcRZzgscf3+c+/Tlee9+PMbMKhXovmR4PrgUq2opLmQlv4qFtJ8lnsswYEU6JZiwUwEKR8oSZIsQ0ZTXEjLEaj62zInuQ5bnDeGwdzbRoSeVpS+XxV3VMl0JmsY/sBhu1r0DArtAwrdMxVqGg+PhM13tI957LVcUvkEjYfP/4m0gUo9gBFbsrQGN6hk9MfJ2LPQcI2M+En2TVy5NznQzmY5z2dbIvuobVa1fwtnM7WN0WJVPNcMOxG/jZsZ+yLN3N1YmLWVbtRlckftDp4sYOF4YsCJUKNFpp2l1zdJSS1Jx2o4wsQJge5OAUNeFZ1uQXo+GhOvw4B7uL6CtXUtG8nLBgVk7TF3iQKbWBe3kZOSmC27RYmMlxbinF2nofna2tRGNxGhsbX/A9MW2TI4kjbJvcxrbJbRxOHMYWNn7Nz/qG9ZzTeA7nNJxDd6T7eX8hrxgWc/kqc4UqiaevdeYKFRLZIolsgbl8hURJULRkIuS5Tv0pr1UeY1aK8DnfW7k/uhEloGAGPBR9XnhWHUmwWKBvZJS2GYvGfJRI2YcE+PVpYgsOkF1xjCb3cUyhICfX4jvWy+DQNHaNgqyqJMdGsS0T2+tHkzTW9Q8QS+YwGjXaVs4RCZcAyPgayDeuwdu4nHD2NMrgI8ilObJSHU/Zq9gvLUBILtrjjSxoqaPT7cI1N4Vx9EnMwQOYyQxGWaVUdlHRvJzu6uJ0ZwdbW48xEhxh6cnl7DNehcsy+MuhXxFRDlOqqdLW3sOa5ddQ17Ea2fvrlgxp/j9Jmh9DGTh86hTfffwADxoLWBD3ceMrewm6lfnHn7UckoRlVzg58VkSucdoiF5Nb/NHkBUXmblZbvvi9Zwffw0uOUhpc4RyLMdcMsmVL3vZH9QC4gQPh+Ps5QQPh+Ms5gSPP75sMsXOr76GhcdOw7SgnNQQ1vxJZrHWTWGBSnB5mGxjDCM3Q91klsFqK48UV3K6Ws+ktxZLklmR30utuh93fYhNnjZ6jo8j7zmKZNpUOxWKmytIi00iJRMt46c2XyFSyQBwe93F3L70HVxo3YCWH+E7J97GWKoR4ZZxN6u8aughako5Yp4UF3v20CIlKNkaI8UIuxItzFaCnAz0cjy+hHOWFnnFkhwtsTAVvNw5cYQd/f1ckFzDS/Jb8Oke5KCGIUY5NPAABxst5hbWka0JkXXF8I210nE6QFPKxpThWKsLKSbxjpEi9bYXc/YYs4XdHHjFZTwUiHJUcaN7BVfyS4r4eYTLKUs++vIG7xEa12xegCvue/p42yUDq2gg+zRkr4ok/+4Ty0wlwxOju3lidC+7J44zmy9jmwG8Uh01ri58UgNYQfJliUS+Sr76my1SABGfRm3ATU3ATW1w/rom6Jq/L+imrXCEtu3/gJY4DjULoeM8MIqY6VGGiyWeooWb6l7K0Zpe7GcVwQeLBRoSOZoz0Jzy0pSGYv0E1XU7OEd9Ar9UxFVoxj20jtFjNuOFUWo7OrBMg7mRIbBtuqvQOTSOahgcX9rLxLktdGgTrM4dp7mSp2hdRNG6HE0aIajeiksexkCjX+7mEbGJpAgDgham6GOQGpLsMHt4ynUezV1LSBZclA+NsayYwisXuX31g0wF51g+fT7HU5tIyiGuHnqSdx76Jaqwf+/Pz0RzEz/dcBUPmwtYkhrm81u/jct+/uMvJEH+JRaFq2xcAxLRb6uUpCCnG+vIhCJc0vQWLFzc5dqDZc7yt5/+NF7v83fh+m2c4OFwnL2c4OFwnMWc4PGn8ZlP/geFtgBb0ney0BiClExwIo8xI1Oec2Gb80GkHNOoLrCxOm0CtWVqKDI31UDqeISwMYsiC+QZCVdKwvYK9FU2lcVufJpKeyFBxMgBkNAi7AgvZ29kBVN1S+lWnqRD3863+9/OwFQHQpHwNMi8IvMkV2Uep48h6oMFhIDTpTgHs02M5kJUcHE8sJCJ2gVsWTDNBQtO4FJmSZbneDxhYk+t5eLMuSwrLUBIFoXaA+Rbd2E1z6G5Y5RKQxhGkkq6lczJi8iOr0XYHmwpQyYwR7lG4dJ8Pb1mBLsww9TIA/zrBWt5atU6kCRClQxXWY8w7Q2zVToPC5WLCiZ/Xd/I6pYIZqpIMTFBOTNGpTBBtTpNRU6QthWMbDfVXCclKUrBrZDTJPIyZCVBTggylkXOtMnoJtmKifUC31WyUgYlj6Tm8XksWsJBemoaWNHQSXe89umQEfO7cKny877Gc9gW7P0BPPxZ0Iuw8X2w5TpwB+a7bZ1+lNzpp/ivXJRvN7+cvDtALJtGNU1m48/MQl6TLVKfsckFZbqiO3ipcR9N2mkk001gcj2p43UMjJ5GDnioaWmllEmTGx2hdzZNeyIHqkZl6aXYnRcSFwFcQmbKneN4aJTZwDC9xgDrcwdp1BMIoISXg/RxiMVMM9+1LS5SNFglCpUuCoU1yGjM+keZiu8g5t/KvqBEQpa4eO5CDmZW0G/VEaFEu5Xkmp4Grmrz4rZNEMzX1iDOXM//LYQAAWP5PP9+KstjeiebQwZf7jVReGa5Xz9fCJuMYTAq78FsuA9d93LkyIWUShH8iSniWYuLGt9ISQiU0ndY8LWbnBYPh+PPjBM8HI6zmBM8/nRmx8e56Ss/pty5mIi5gwuy99BEkjGtDjVpUjueojKnUZxzY1fnT2D1sILRayBJAu9+GcmUqDRqSAsVOuunCUvzXWbG3fXsDC9lINxBJlyDy5WjzT5MizpA1XbxndNv58joAoQAb43gr4z7ecX0r2gLJnEpNpmqh+O5eg5n6ihYXuTOGvpVG6FfwUbiXCB5kJFIyXkeDO4hKKlsya0hYPvI+wpkeqcpdo6hegtolFHsIla5xPTpPqaO9SGl6jAlg5Haw8S7ClxU9tAwEEdVe8HSKfffy373FCcXLcBQNUouF/6WAgcaW9ktr0XFZkvhOFcXdtMkj2C7MiRkmNB9jJcjTJVDzFX9pAwPOWt+3gm7Wo9VbQT7mboAFwKfLeMV4BUSXiHhEeCxJdxCwm3P3x9CosatUut3EY54EEGTOc8U/eIkh8p7mZOmKGk5OqLtT3fNWtew7jcL1X+bwhw89Ck4cBOEmuGK62HRy57pPmRbFMf3c+PpU3xdb2RWC7NofICNe/cBcLS7h2MdveQD80MTIwTd4hRvKPyKhb5toBh40j14RjbjnVyKX4lQNDKMFI4xl9hLx+gwtYUyVX8A87yX4Xnp5RQCNoV8Fu/kTppnH6a1eBgFi5QIEJZKCOAheS0P6xcg6XGCaglLy4MksBCUXVM0uYdYQ5Y6S0eycnyg1k1Jlvj8pMKvqi/lVmMDEnCNtodXqE/Srk7jV8xn9ptnuk8Bv44XTFtxPld8LVvNbi53HeD6wE0AZAgyYdUybtUxbtVSFPMtGDWhcRYs3oqimNSd8lGfKvLIUDO2uZzN9a8iZ2fp/OBy/I3tv/979ustdIKHw3HWcoKHw3EWc4LHn55lmXzpC1/G7+nCL2eJGfdyUelxPFTZ41pEWguweG6YwESR0pyL/JwX25QIdVSo6crhiZqc8rZwPNjOdDhIJWoS88zgM/OcLndyutDNWLGZuXIN+UqAQsENBjRH0ny4fDMXVPcQ8VQwbJmBXA2HMg1MlEM09vTRt/kCFm7cjD8SpWSU+NXQr5gpzVCdKuA7LujLttJdbcXApCrr+C0vGdtmTMpy0j1Kv2sCXUjUFzpoyHeiCJWMbxZpcYZztvSxuWMj+u5ZsvcOIkwJY2QrsnsOtWMtJTvJeO0oD3dZPOlbR7+0GI8o0VN+FDW1nYpepmRLVLGxJB2UMpKsP/fgCvBaXtyWG13WqSgVgio0qoJWt0mry6RZhVoriFJqRSr0IfLLsIxWhPChCJAMm3LBoFQyKBuCii2oiGdOgJ9DNaioefKuNFl3CjUCzfX19LZ0srx9MTU1YTx+7bd39xrdAff8Pcwchq4L4YovQF3fcxapWDY/G5vkqyPTjNsqfakp3rRtN5tP5UnUt3K0rZUdrbU80uSnrEr4RZ5XVB/hEh7A5Z5G0YNIk+eSHlhAfnKQopUi3N5ANJmmdscefBWddMBLfkkLdfWzdKpDuHxubjXP48fVLVjxBazz5rhw6udcJD+AW64wVl3Kvcoy7o6mCHkrdOQ7UcsRJCQEYMoKRZebOZ/BsdAdaJKLC5SXIlfcPDzSzJwRYJEyw1p1DK9aweOq4NcqeF1lPGp1PnecOejSmRtlQ+P2mXPZb7ay0jXKGmUcS8wPBqDKBgGtiN9VJKgVcMlVZK2Kr2UY2VMhe6qZ1KEO8skcHYHlLIhcQNN7VxDrir/oz7ATPByOs5cTPByOs5gTPP53fe07P6BS9bMkH8VWHqW7ei991hAl4eI+aR1zkRjLGCRq5hkPxRn21XBCbmK43MJcqZZsJUSp4saoymACCHxU8VPGJ1fweyp0auO8RtzNufoYCjBcDXA81cBArhYrEiGwopva1UuJNTUT0AIEXAGCWhC/8KKP5Tn5+D4aRoO4hcZcMEt4Qwttq/vInc4ytnOamZEcmaJJypwfxhbAp4DLbxOJqrR6A2i6QCsZuIpFZMWDmThJZeJRhjZJHOua5rjXxZj/Avq1cylLfmL2NDWlh0ilHwdbR1hesHxgeZFsDy7dT8AMEDR9hEwXIVsmaEv4kJH/W9cZU7LQ5Sq6UsWSTYRko8qCgCKIKBJxRaZWVqnBQ8CK4hZ1aO4IrlAclz+OYnuwqxZ60aBaMKgUTfSSQbliUTVtqjZUhaBiQ1WAJQQ2zF8E2JJA9cmEYj4CMS/+qAd/xD1/Cbvmr0MKrsM/gkc/j6gWsJe+BaP3rzGLHsxkBTNVwUqVKacr/Cou84MuN6N+mc6CxVsHs1xychApk6RSTvK1jX3ctn4NwWKe3uIsTd4Ul/Awtf69gID0Mo5mL+DAbIjY9AlakidYnk9RM5RE0wUZn5tT9VH6Y41UGvoQni5EPkCHHkBGJh04RkfkFq4QR6mxTMyaXtTNH4Kl11K1BCdOnODEiRMMDw9TLs/Ph1KJVrg/fD9RO8rlmcsp5XV2600csxoIShVWq+N0yGl+n15PQsBOs40TVj2r1THWqSO0MkG3GKXOKiEZXsYr7cxUW0ibTRhKgKZNdxFqPUjm9GYmd12Onr0FRanjrf/yGcItLzwIwQtxgofDcfZygofDcRZzgsfZ4Ud33UX/bIGlmTaW5vvJuO9jtf44fioct1t5hFW4bBM/FfxSBT/l+Wu5QpAyAcr4qeARVZ7vx/WMpXE8U8/xTD3Tspex5ionG7PMBSsgQdQM0VVpoavSQnelle5qC016LTIyJbnMqeZpei9cQzTcxmR/hvETaSYHMlRL80W+0QYfDU1+grbAn63izVbR7Pn/91vYVOwsfjmKXUyQHLqNby89yhN9KuHoOZT8FzCt9KAJnW59P/rUJNPjITSjhrCtEpIEcZEnKvIEpAoe2UJ91k5KwsZnl4gJkxgaUSK4RQ0VFEpIlCSbsmRSRqci6ZQlnQrG0z16nk0WEl40QsJLRPiI2gEiIkjE9uHFhfR8T3oRxH8PJYB15lpI4FPz1Mo34lfuw8ZPznwjRekq1JgfNe5Bjc1fiHm4XzX5WirN8VKFdo/Ge4xpLr3nB1SeOsDOpoX88xvfR9YX5K/23sTLEw8w3d6LHo8RiB5CuDPIlRr6SxfzXd+l6CWVSC7JBXse49Ld24nnc8xEohxqb2Yu4MZXrVLxaIw0JYks9/G+LR+jN7YSjvwctn0VZo9BsAk2vBvWvAU8YWzbZnJykhMnTnDy5EkOFQ6xvX47rXYrH2j/AF0dXRyaM/nqE+OcTlXojrnp9lUpzo4QI09U0ZGF9fSxkyQJj8dLxOsjYll8dy7KgB3nJeYkqwphqiIKz5rQUQgTycqi6Sk8Zob4+l14lh9A5NtJn7ycxOmTvPlfvoQv9CK6xz2zLU7wcDjOUk7wcDjOYk7wOLv8/KGHeHR6gq5cD1dPFNBdT+CV7qXbPk0FN1W8WHhBaAgDyqZJ0TDQbRndVs5cVAxbRrdVdFuhbGnMFAMYpo+ZYCN2ayvdgTALRJBGESaoB1CqzxRF60GbQswgHSqS0ipEA73oky7GT6Yp5w0AQjUeWhZGae6L0twdxiVLTE2f4PDIkxxOHud0dZa0oXPJ3DquyG1GtkyKg/dyU/0j3LlhJa6aTcz4VmJIbprtUbry48SSEbypLK5yEs0yUOE5hb9uu0xUZKmVctQgExIRAlYTHrsXiWfmgCjZJQq2jqCMJMpIUgmZApqcxSOn8ctJ3HIKUypTkiwmFJVh1cW44iIh+8kTQDWDBI0gmnhmTg9J0vG58sRceULuMkGfQixaQzTehd/bjUdrwa02IgkFTIFtWoiyRTlTJJmYJp/JYharqLqCz/LitrxIQn4meAAVW5C2BApDLPZ/lzrtCCW5k2L7h/F0nI/sU+cvShnZmoTyJA+XSnzZqOeAXEOTkeK9Yz/j9RO/oKR4+OCCj/BAzblcMLeTf97z76jFCnnNRaVtOaXWMnq8H2yFUmYN+6313OlbzbTHxyW7t/GG++6gdXaa002t3HjlNWxfuhJvpYi/VCAm2TSHgnQ11NPd0MDime30HPgvAmNbEa4g0tq3wDnvgXDz08cvmUzyvV3f48ezP6Y9386axBrCoTDRWJz9GRePzvnIWyotcoY16jghycA0QgSFCy9QpYquFrGV+e51ti3xsNHLpAhxiTHK0mQSfyFJqJigRlg0uGrwRnpRYl3InjAGFkfbn8DVexN5O8IPCu/jzqvfgKI9E1Z+X07wcDjOXk7wcDjOYk7wODvd9uAj3JMeJlLq4y9GbdpKFvD8IyYJIbAlG0uxsDwWpmaSLk4jjBki5UmUvI7XbgBXB3KoBUmdn+HcFjZJo8wEglnVpCzpmLaNZrqRTQ+WoT69Do8XwrEiVf8IQ95+BtRZpkWFLDoVuYgl52kywyzPdbI810Gv3kGjaEVGxhjdzh0c54aLLqHa3kxai+EWZdaae+idGMU14kLmmRN8v12gkVkapTn8NnitBjx2O5LdiSLV45b8Ty9bsGwyFmQsQdYSZEyBCQhspBc4Xs9lIqEjU0GVirikAh4pjyqnKHhyJL0lZjwmSVXGlFx4LS8hPYjH9jz9CjImMdLUyGmiSpqIq0jMbxEL+fCEGlFDnWihLmRfLfhiZGWF/flhtiaPcnjyGHqqSL0Rp1O0sMDVQyPNkHNTyUwRNh5iofd+vHKeohXDEgF8SgpVKjxnLwzh5f7YRXy17S84GOnCr5fpGR3GGM6Ti8WYWt6Bv1ziuhu/x/JTI8yFa0lFagj3xIj1zWI078J2FcCWUTLdFGaXMpJYgTg9w8rj9xDPTjMbqeGejefx+Nr1FAIh8m4futvznO1Ynj/Je8Z+ysvmHkNIEk/VXsoTzW9A9/QQNSy0UpG9hVvZpzzAkuxyFqUWIM7Ub5hC4rhVzyGzEQOFHiXBKmWSkLCRLZBMAaYJVgWYbw3RNTd3u9aQEH4u0QboUUo0+OtoDNbh9gc57lHYK5kcV2XGfH4qmptu0c8HxRfxUGXVyl/SFO/8Pf6dPJcTPByOs5cTPByOs5gTPM5uP7/3Qe4uDzPs6cI2I4QMQdQw6CjnaMlZNOdcNJc0IkJCO9M6YJ/5f+6zax1MLPJ2lYKhkzFVkrabvDX/S/uvyVYRQxTJalVm/DpZT5GSO81MYIh0YAhJy+LVbeqy0J7xszTfRZfeTYPcTsTbiqrNBwJhVrAyI5jpYR5oVPnFRSs4FmnFklS6RT/r8vuoHyqhp8NERJ4aUcRv23jNMB6jFYVW3FKEoOwioDyzD0VLkLEEacsmZRvMUCKrlii7qxh+cMV8tHa0sra3h4DfzUBihl39BxidOE21WMJlSHgtFz7Ljdfy4jH9uE0vmuVGtV2otobM/K/fQojnHWZVYGHKBUquNEVPhoqriFCqyJKFR8goQn1WdyxBlCw1pKghRS0pakhTQxIf1fkl3CEsT5CC4mIOE72SprZaps6ynrNeG3m+wFrAnNnJYPkcxuUWjhBnp4gxLvvxCRm/kHDFNCZ7/MzVeXHpFstO5+icqvL4qhrmIirr+ytcfLCE+us339YxlASe9v00NkwSjUxhhMYAUEoxlIkerJ0h/IcGCBSmKHlqON2ylomInwqTFP0yZY+HvMdL0RfEDDYQ9MAV+mNclXsIr13lkeh6vtH6Wp6KrAYgkPo+3uJjFCNvxvZeiCRAsU28ehl3uYw+bpBPzgfHJn+BXm+SsFnGa1TxmMZzjk1VKNyn95EXbrb4TiPCLsouNyWXh7Lmpuj2UHR5KLt9GB4PLlWlRkqyzN7FN7Zc9xs1Qb8PJ3g4HGcvJ3g4HGcxJ3j83zA0PMkjTz7KuJRkJKwx4KthSOrAlOZbCqJmmrZCmt5ZhTWzATYUVILSmUkKLUFJWFQkg6JcIa8Uyal5MmqWisjgKswRTc7RPJWlaaqErBfBNhmPS5xuUPAbHtqsFmq0NryBdpRoB7J/fj4JIWyy1RlOKRlO+Q1OhRTG4n7molES/hgl1Ydf5NlkP8Wq6ZNET4eQ8vW4zUYCVhNhxUNIkQjJEq5n1W0UbJuk0JmRKsy6dXK1ErVdUdYu62JxawuK/OK7xwBUS0XGThzn6JHjHBg+SX92irTHoOAzqAR0DFcJt7DxWipBPUK4UkuwGsOvR/DpQby6B5epoFryfIuKrCFJHqRfBxZsLLWEqZYw1AKGlsdWywhZf05NiYZJUCkQVdPUqBnqlDz1Uh6PojCt1nNcj3KsFGS4VE+y2kZVBGiVkrzF9XM2azvI2WEeLb2K/vIFeCwv8vO07gzVGGxd7GWoMYRqmrRNDVJ2Cabq+/CUp1l97AbOPzzA2mNFYvkz9TiShAjG0bqXoC/XKLcPU6wZRKhVJEMjsK0ez8MVtESOsjvGSNtlTDauw5BymAxj2eNI+hxypYgEuDTBiro0a4JD+KUSc64ODkYu46Cnk1/xS2bkCRqqm3BVlyLO7INsC9ymhdAlJkpxZswQmgKLeoNEemvpnymQS6eJKDohxUS1DOyyzvCYH8uWudB7iqhcxm395iSDNmCoGpbLg/B6+dTb3kzA7/+N5X4XJ3g4HGcvJ3g4HH8gSZKGgTxgAaYQYq0kSTHgZ0AHMAy8WgiRPrP8x4C3n1n+b4QQ9/+udTjB4/+msfEZtj1yH7P2DOMROBkIMyB3kZTmA4EmDJrLc/SkDZYkPURMmYApE7BlApZM0FLwWOC2BW4LNPHcWmvbNrDNEpJRQfbWUNEUxnwyp91V+r1VTgdgPKww5/NT1J574hYVSRqYop4pestjLOkPER07lwg+QgoEZOnp1gRDCFJCJ6FVyEVt/N0RVmzso7mh7k9yHC3TYHboNBMnjjJx8jgTJ49RzmWxgErQj93RjF4XZc6jMptLMqOnKGplTKWErBTwYRLQ3TSm49TkI4SLPjTbhaXIGC43QvEhSX7cZgCv5UGVwFJKZ8JJGUstIeRnnSALkISKbGtItoZ85iLZZ+4TCg3yMBd5b6VJHWbcauMu/WpOy3VU1BJVtUhFLWMpOpqiEPJ6kGtaOBZbwhEtigqsdWkcMyyqwMcaa3ltLIwxPMLc3qNM7jtGYSqLjR/DFaHqjlD1Boi0TRFtPoJSfxTLN4v7qETobg/aqIXh83G69UIm6y5GKG4EAtOaxK4eRuijIAooks3i8AxrYxPE3GWyupttmSa+3K0yWVOldUbhtY8ptM+BXFdLYM0amq64itpzNnBkMssn7jzMofECLreE3OEm2xZFyPNBJVAs0lDVWe338ejWOSzD4EMr4S9f+RJKpRL5fJ5cLsfU1BTT09OkUiny+TymafKJT3wCRXFqPByOPydO8HA4/kBngsdaIUTiWff9C5ASQnxBkqSPAlEhxEckSVoM3AysB5qAh4AFQgjreV76aU7w+PMwMTnHrkfupVg9zWTU5mTQR7/SyTBdGJLrdz5fEjYu28JlW2i2jcsWeCyBZsOcRyXjem5f/rBI08DU0wGjoZqnuWTTktUIFWvwFJvwlRpx6dGnux7lhEFCrZAN2mitfhat7aatt/W3z3HxJyaEIDM9yfSpfqZO9TN9qp/Z4UEscz4c+CNRGnoWUNu1kJQdYcdgiV25AlOuKmW5jKQUkNQ8kqtAQMlQV00TLxTwViwsBFWXoODXqHq9qCKAVw8SrIaJVsMEzACKUJAkAbINkoWQTIRsgWSB9Mx3qYRgOce5hCcJUuKgWMwj1sUU7diZsKLOBxfx6wCjkvF52dEb40i7b35fKoK8T2bhuM5Ldxfx6QJbMjC1Ira7hKVkMclhajq28sy6G6Q8HZEEvroJpOQowfsk3Kdk7IBCcVELQ4F1zOrdSGojkqQhhI2l92PpxxHWBN3+KdbFx2n25SibCjcoNfyozUNRhdf2vJrXLHw7OydSbJvLcLhqMqx5qGhu5GQF7WQWKW8SCqm8akMj1yxp547dE9y8a5SibrG6NcyxySyaXeFvFpu85bWvRNM0no9hGC/42O/iBA+H4+zlBA+H4w/0AsHjJHCBEGJKkqRG4DEhxMIzrR0IIa4/s9z9wKeFENt/2zqc4PHnaWY6yfaH74HiMfLBMlVNoCugK4KqClVFoiKDLsvokkYVNzou9P92baARJUUDUzTac7SUDNoKKpFiDFexEVexEblYj2GpVCSLimxRlW0MTWC5JZQaNx0rWuhY2oni/cNO8v63mYZBYmSIqVMnnw4k6amJpx+PNbXQ0LMAzd9E/6TKk0XBsCyRkl1UdeWZmQdVgS+coF0+RUNpHF8xSUUqkvdUyQR0EuEqhvbM96WvohCreKg1AjQSoVmNU6/6kRQTSypjiCK2WWBFaZTl5REsJHa6lrFVWkHV8iEsF4jf7IKVd3s50NrLicZ2rDPd8bymyaaxfrrGT6Gc+c72eDzU1tbiEhaF0RFcsxXqrCBmOUGuOk2BIqZboanDQ72SJrRvGveAjRUQVDYFMRrbsU4HKUyFyYY7SPl7sGUfws5j6ieol7ezKnyEdn8a3WXzlWiYnwcDREy4ZDaMqS/jWHgRoraJlvo61nV0sLaxln0nk3zzoQHG0mU299Tw0Sv7aI36uHHnCD/YOkyiUEVGEJFKvLOnzFvf+FrcbvdvHIf/F07wcDjOXk7wcDj+QJIkDQFp5k9d/ksI8W1JkjJCiMizlkkLIaKSJH0N2CGEuPHM/d8DfiWE+PnzvO47gXcCtLW1rRkZGfkT7I3jbGIZFvlsnpmZFJlkikI2SbWUxioXEWYRySojiwoKOpYZwRYN4K7FF/ETrgnS0FxDbV0MV8CN9AcMR/p/XaVQYPr0ANMDJ5kanG8ZKWUzACiaRm1bF75wK8WCn70lF8f9bmbcKjnbg1mSnm68kDRBwF+hgRSt2Wni1TFkMU3BXSLtrzAX0kkEq5i/bnEQECypRAoa0bxGJO8iWtBorVpcXDdETzBJznSzrdLCqBJA8YDklpFcCqbqwpZDYEUxqiGSIsqupj4ONLdhnelu5BE2F+hZrh3bjzTRT3pmjnS2gmE90yolIxNy1RBx1eHBjWv2FOHRI/jsClZzEFm3UCcr2D5B4SKLyiY/3lQ72qiXVNrPKV8XaaUHVz6CS1cQdpqIdIIG7SDlyCluaKtw0qNxTrnCdXMZPDkvE+UQE6UQo0acjCtMOhClP9DHabsFQ6g0RUyWL/ARr4sypWvsPJamOF6gTipwWU2CV73xdXTGIwQV+XkHDXixnODhcJy9nODhcPyBJElqEkJMSpJUBzwIvB+46wWCx9eB7f8teNwrhLjtt63DafFwOP7fCSHIJ+aYOtX/dMvIzNApzOr86FVuf5BArA3bjDJT0jhZ62c0rjErRSmUfIiC9UxPKgkUN7hcNmG7RGNxltbcMWLlfiw1wXTMZLRWZjoK9pnMJ9tQk5e4MFvlbUaKJllnpBxg61QnM9UQ9gsNLSwL9JCbo92rONy+ipnaJpBkEIJ4Zo7VJ/ew4sRRFg8OEipXCVQNNJ9Gpq2WcmwFjYGNhF21FMw0h81dHArPkovW4MnZbNm+n66hUWy3TPF8QeGSKsKn4E124j0VRe+XmSnJCF8Qj+wCKUBRriGn1HKg4QDb2h5Gl01elTL52/w0AWyEgLmqn4lSiIlyiKFyHY8H1nMgtBxbklmeO8LazD68VBgI93Jf+GJalAwXK8eQlTkMrwsR8EEoiBwK8omr3odb+91dEf87J3g4HGcvJ3g4HP8DJEn6NFAA3oHT1crhOOvZlkVibOSZepHBfpJjowgxP46tO1CDJNeiVzWyXSaDjWFmXVHSeph80U+l6MIuiWeXdiBUCcmroLlsfFKRMGOExSgeaRxDmSPjzpH16lxbKPDX6SxB2+Z+xc8juRDhlEZ9SqY2o+CvyBgeBcMlY7gVTFnGWzBxlwUPbrqEe867hGzwmRm9A0aBZeUj9KkHqfdOUcXDNE1MWU3MlBcyqzWQ9Dy3K11EpFg3to+X3/cwS/YPY7llyuuDVC600BtSAGilWlzpBtS8DyXnQs2oKGmQU4KELrhx1RhbO6eJlLz8xWAPm3WbWt8QDa7TuOT5iQRzhofhcpQnpJXcrW1hyK5jXfYQ5+QOcDTQw33xi+mW5jiPk/jH+pGN6tPb+P4f/hSXN/Ci31sneDgcZy8neDgcfwBJkvyALITIn7n9IPBZ4GIg+azi8pgQ4jpJkpYAP+GZ4vKHgV6nuNzhOHvo5RIzQ4NMDZxkenA+kBSSZ0q4JBlJ8uOJlwh3Joh0ppDDNkPlDo7PLmI438t0pY6C7qFqyNj6c19beBRsn4rwmbg9M9RJA/xN9gFeleunJEt8PRLmllAAU5Lw2iodeoi2goeaOZ1IqkLKYzHqKZEJeBHuZaTqLmSgsZeK24Nk2whpfhufzWUZNNol2hSdtqJJ94jCwpyf2nyR6cI+qh2jtHQm8c8NYD2YQxxTQQXrXA/lLXXofh1LLWC5CgjlufNzYCto1ShDhQA3V3JMUGGlWcebM2uoK8RQKjk81jh+ZZiIOoRHyQGQt73sFgs5aHZRzeoctLrYHt3AInmK9dIQRnaQrJal5CryhQ//gvqa+It+H53g4XCcvZzg4XD8ASRJ6gJ+ceZPFfiJEOLzkiTFgVuANmAUeJUQInXmOR8H3gaYwAeEEL/6XetxgofD8b+rkEoyPTjA1KmT5JMJCqkchXSeYjaH7Jkj3DFLpDOLNz7/S30h4Sc53cRkZgGzZjNpfKTwURRB8vjIWRLGs2aG7JHH+ZTrx5zHYQbUej5XcwHbgx40MY5iT4IovuC2CSSqvo2UwtdgaQ0o+jha9RRCiaN7FiJkF5KwCFhjxO0R6uwxYnoCaa6CVlFRbIlyNYel6dQ0NNBVKLL4gVM0Hi4hZEgsspleX89sVwNl1YVezSOqGTxWBb9iEfBI+NwWLs1gV9XkvryKDVwcNLg4ZOKyXWjlOGolhqvqxVPV8VZzuPRJovoMnqqFZSp80nwPP7M3sU4ZY7kyRetMCamc52Vf+iLRePgF9/+FOMHD4Th7OcHD4TiLOcHD4Th72ZbN7Gie8RNpxo7voCzuJdh6FF9NBoBS0kt6qoF0rpeMWY8AZCR8kh/ZiCOZtSRwc1qyaJF38tfKjbTLszxorebz5hsYkRuR/CUIZFBFGU3oeCQTvyYI+VR8HpmqmSVnZpn2NJGNrsd2RXEXp6iZ2o0kDErhFkqhDiqeegAUu0xAH8Jf6cedO4Ksz2LJJgY6pmQiZJvajM01223OPyxQbSi74FSTxKlmhdNNLsYaAxiuAMLyYetuLF3FqspomkE1PErJncIj3CyXalnskahxFYl4cvhd+fnhiJ99DA0VuQI/PvlqHktt4Orax1nimeSccpHF7/wO7lDNi35fnODhcJy9nODhcJzFnODhcPzfYRoW04NZho8fYW7qNtTYbnx1MwCU027S47XkMr3k5VYq1vx3b128ls5gM83lCKEJC0XcSYP6M2TJ5HHtpfxAeyVDhhshqwgkLCGw7PmLaQvsX18LgSkERpMPsysIXhUpVUU7lUNO6wiXjB1zY9e4seIe8MxXvksFAzlZnb+kqkiWAGyQTKLVNMvnRlmcGGNJcpjO3BQyAlOSGY40MBRvZDxex1xNDVI4hFd14zElsgxytPYxCr4CHaMRtuyqoyXQQLS7hfCSOoK9Ydw1INmz6Po0Q9ODJDIT/HzgKo4kF/GOZTewvmEfa9fsJBJxgofD8efECR4Ox1nMCR4Ox/9d1bLJ6PGTjPb/nDKP4qkZR5IF1axGejRKeq6TitxNUVERQuDxeOisb6MbjYVTtxDUH8ASEXK8CT1yJUo0jBJ1o0bcKBEPSsSNGnUjB1xPT/QohKBkWtw4meTrY3PMGiadcp6eiaME+kvUpbpxmX4SIYnT3TazzQEmvF50QBHQkzFZnDBoSyfIDTyJoSfRhEldbZy2xjo6kIhkssinh6geOYow5ms/XDEVb6yEL1bEV6sjwgo/rO/gO+4qkpB4/WANlz2YQ84WAFAiEbwrV+JdtQrvypXYC/v49s4pvvnYAKaAqzum+Pxb3kLI8+Lnl3GCh8Nx9nKCh8NxFnOCh8Px5yObnOLU4dtIJO5GiZxCkgV6XiMzHCI13UTJ7MUIBtHt+SKQFXGLC0v3Eimfwpa8VLXNFPXzqVSWAc+an0WRUMK/DiTuM4HEgxHW+IlV4RvJNAnD5KJYkNfGdLIDxxg9lEIZiRAsxzFlONqWZqzbzVy8hnHJA0BYF3RUbQKWjqeSw05NIKWn8JWLeMtFmrxu+iSZ9lKFwGwCMTCAnZ0vIldCHnxNKnokzY0dcFu7h07T5MNpH0syLZTmVMrDGfSxqTP7oODp62Nu+Tm801hM1oTH/v582mucUa0cjj8nTvBwOM5iTvBwOP48GUaG0cF7GB+8DcN1GEmxMYoameEA6fEacqVOiNZRlqGTMVZI/SxiALeoYLqjWJ0vRbRcg6kswspWMTNVrHQVK1PFylWfmZEdKCtwa7eHH7WqZFWJi0yVD3iCLIn4GC3MceDkMNP9VVyp+ULuyVCSE50FrLo2SlqAgqSS0SQyLgn7BSb481RK+Ep5Fs5MsHJsmMVjo7QPnyaYmAPAdKsMNMHhZptQPbxRnaJWsrCqEuVqK6ViLeUZKA8nSdsudjcs4n23fwMl4AQPh+PPiRM8HI6zmBM8HI4/f6aZZy7xKGMDt5Gv7ADFxCxrZIeDpEdCZDKNCF8tkt9LhzbCMk6wgCFULAquWrKtl6KteQPxhRtRFAVh2Vg5HStdxcxWsdIVrEyVTLbCjS6dG2pl8prExdMG7zyl0120kTQZKeiiiEWiUCafg4oFumRRdWVRXWX8iozHHUKSAxQUF2mXRMYtkfLLJFwWs7LOrKyTUSQKbg9lj494Ns2ywX6WDp5g+cAJuiZHkQVYEgw3xRnrWMNsSy/5+m7criBRXSKqy0R1wdXvX4LmdUa1cjj+nDjBw+E4iznBw+H4/xfLKpFMPsHM5N3MJR5FyBVsXaWcCFBMuCklVAq5KJYZojeYYYl7iE55DAmYoo5TrrWk6y8l3rGcrt4O6ppjKMpz5/fI6CbfGprhu9MJirbgpZKb9xZV2tLGfMtJpopdMJ5/A/8b+0zTigRInKkzkUCoNgWRZ4Q5Bq1pJuw8SdnCkjUixVlqM4fpmczQOynhMudfY7yugUM9fRzuXsjhnoVsv/YyXKryQqt+QU7wcDjOXk7wcDjOYk7wcDj+/8uyqqTST5FMPk4utZ9CqR8hmQAIXaY656OY8GBmVJqrVXrVJE1qCiFgWGrlMAs5YS5GkpoIe2upiTZQX19PuMZHMObBCmvcWMrxg+kUVdvmLxqifKijgXavG2FYmFkdLBsUGUmRqJRNUtNFUtNlpsbTTI/nKCVMsCVkIKhaeL1FIqqgCT9B4QXAUC1KtSZKkxc1IJOtzDA11s/W1C6eqB2gPmOx+YjG6jEX8XwFtaojJImebU/hisZe9HFzgofDcfZygofDcRZzgofD4fg12zYolgbJ546Qyx0iN7eXgj6IkOZbJ4QOrnGZmlmDZr1EmCqmkBkQ7RyWF9NPF6atoupuNCOKZsbQjBBW2M+OpT62NijYElwm3PxVOExvbQDNrSDJEpIkIckgP317/m/bEmQTZdKTRVJTRabH0qQmi5hF8MkQVSRCLoOIJqjBg8x860vClyVXp6M3wH3mwzxUfAq30Dh3vIUFe8qEqiavvus+VM0Z1crh+HPiBA+H4yzmBA+Hw/Hb2LZJqTRIPn+UbHIfubm9FK0hbEknWDBpmK5SP6vjtmx0FAbsRvabSxnUehGSCoAmZDySH12pZ2tTO/vbAwhg9ekqK4eq1GUslD/wVEGSztS5i/lxuCKqREyRiJ25dp0ZBviUa4KvNvyUfv8gzaVWtkxeygf/8b14NPcfsE4neDgcZysneDgcZzEneDgcjhdLCItSaYhc7gjZqR1kE3txZ05RlyxSl9BRLUFVkRl3hTkm2jhV6SRvNmCr812jii4PhzoXcbi+FVuS0YSg07JZJGksVb0sktzU2WfqOez5uUNsWyBsgRDMXz/7thDYNlimTaWoU84ZVAoG5UIVuWgSkSCmSERVid3RXXyv/hcU5DJ3XnEHbY3tL3r/neDhcJy9nODhcJzFnODhcDj+JwhhUy6PkJ3bhbX3p/jG9hHJZJAFlDwy0zVuJpUACb2OTLmWdKmGKTqYDDf8f+3dfWxdd33H8ffXz3b8kDR12zw5KTTdlhVKwSsdYmJiGtA/piIh0FYNKu2P/dVoSKtYy5BWhDSJDo1psP2BtLHuCbYCE9NoqbaJMZ7Wrg+0IclCkjZx3KRJ+mC7duLYvve7P+5pexM7Hva41/dev1/Skc/5nfM7+X0U69hfn3PujzODmzg9sIkX+jdSaq+87N07N8uWqZfYcW6Sa2en2Z1zDPf2Mjg0xNCmTWzavJn+oY30DgzS099Pe8flH5k6NzXHi+PTvDA+zctjU0w89zzPxAF+9xN7X5sYcSUsPKTGZeEhNTALD0m1kudeYu7p++HJL9F1+hABTG3o4Pmruzk93MVcRzs53UZ5vpPyQhez5V6Otb2Bo51v5NmuXRzvHuFs95Wvne+K8xNcPf0iw5MTDE9OcsXUy3TOzxEL87Rnma62Nro7O+jt6aGvp4f+gQEGhwbpHxiib3CQnoEBejYM0DswQHfxNS4zb8hyLDykxmXhITUwCw9JdTF1En70VXLfA8Spp0iCmcGreKWnh/MdcK6zzLmuEud7y5S6S2Rv5XeHGfp4hus4wm6Ocj1H2c1UVObe6Mw5dpaPsWvhOCNzY2ybPcng/Ayl+S4WSl0sLBTLXCelC0Fpto3yLJTPA/Ml7vrs5+jq7llxFAsPqXF1rPUAJEnSGhvcCu/YS7xjL5w9ROz7Cv37v0b/maNcNA16tMHAFnJoO6WezSx09DNHJ7PlGS6UHmNm/vuMZRsHOjdysOcaDvXu5D9738VcdxcMwGB5gus4zBvjMNdxmDewjz7OLRpOqdRBWy7UL7+kuvCOh9TAvOMhaU0tXIDJcZg8ARMnYGKsWB+rbE89B1m6uE//1TC0AzaOwMYdlIZGOBZX8ORCP9+dH+SRUifP8vrEgDvPv8TPToxx/cuH2f3iAbZP/ZjoLnHLpw4QbW2slHc8pMZl4SE1MAsPSQ2ttACvnHy9EJkYg8mx17cnx6F8ySzofZtZGNzOyxu2crznGg62X8kjsZkDHcOc6LmG+a4B3tTXzd/ftJsBZy6XWoqPWkmSpNVp7yjubIwsvb9chunnFxUlHRMnGJ44wvDktxhdmOXDVV3Odw5wpm8L/Td+G+irRwpJdWLhIUmSaqOtrfL+yOBWGHn74v2ZMHO2KEyOw+QJeidOsHP6NHRZdEitxsJDkiStjQjov6qybH/bWo9GUo2t/K0tSZIkSVohCw9JkiRJNWfhIdVRRLwvIg5FxJGIuHutxyNJklQvFh5SnUREO/BnwK3AHuA3ImLP2o5KkiSpPiw8pPq5GTiSmc9k5hzwZeC2NR6TJElSXVh4SPWzDThRtT1etF0kIn47Ih6LiMfOnj1bt8FJkiTVkoWHVD+xRFsuasj8QmaOZubo8PBwHYYlSZJUexYeUv2MAzuqtrcDJ9doLJIkSXUVmYv+4CqpBiKiA/gx8CvAc8B/A7dn5v5l+pwFjq/yn7wSeGGVfZuVmdeH9ZZ5veWF/1/mnZnp7WKpATlzuVQnmbkQEXcCDwPtwF8uV3QUfVb9wzMiHsvM0dX2b0ZmXh/WW+b1lhfWZ2ZpPbDwkOooMx8EHlzrcUiSJNWb73hIkiRJqjkLD6l1fWGtB7AGzLw+rLfM6y0vrM/MUsvz5XJJkiRJNecdD0mSJEk1Z+EhSZIkqeYsPKQmERE7IuJbEXEwIvZHxO8U7VdExL9GxOHi66aqPvdExJGIOBQR761qf1tE7Cv2/WlELDWr+ppbaeaI+NWIeLzI9nhEvLvqXC2ZuarfSERMR8RdVW0tmzki3hwRPyiO3xcRPUV7w2dexfd1Z0TcX+Q6GBH3VJ2r4fPCspk/WGyXI2L0kj5Nff2StITMdHFxaYIF2AK8tVgfoDIZ4R7gPuDuov1u4NPF+h7gKaAbuBY4CrQX+x4FfhEI4CHg1rXO91PKfBOwtVi/AXiu6lwtmbmq31eBB4C7Wj0zlY+Cfxq4sdje3Ezf26vIezvw5WK9DzgG7GqWvP9H5p8Dfgb4D2C06vimv365uLgsXrzjITWJzDyVmU8U668AB4FtwG3A/cVh9wPvL9Zvo/LLyoXMfBY4AtwcEVuAwcz8QWYm8NdVfRrKSjNn5pOZebJo3w/0RER3K2cGiIj3A89QyfxqWytnfg/wdGY+VfR5MTNLzZJ5FXkT2BARHUAvMAdMNUteuHzmzDyYmYeW6NL01y9Ji1l4SE0oInZR+ev+I8DVmXkKKj/cgauKw7YBJ6q6jRdt24r1S9sb2k+YudoHgCcz8wItnDkiNgC/B3zyku4tmxm4HsiIeDginoiIjxXtTZf5J8z7FWAGOAWMAZ/JzJdowrywKPPltNT1S1KFM5dLTSYi+qk8VvPRzJxa5vHmpXbkMu0NawWZXz3+54FPU/nLOLR25k8Cn83M6UuOaeXMHcA7gV8AzgH/HhGPA1NLHNuwmVeQ92agBGwFNgHfiYh/owX+j5c7dIm2prx+SXqddzykJhIRnVR+aP9dZn6taD5dPH7w6uM1Z4r2cWBHVfftwMmiffsS7Q1phZmJiO3APwEfycyjRXMrZ347cF9EHAM+Cnw8Iu6ktTOPA9/OzBcy8xzwIPBWmijzCvPeDnwzM+cz8wzwPWCUJsoLl818OS1x/ZJ0MQsPqUkUn9zyF8DBzPzjql3/DNxRrN8BfL2q/deLdxyuBXYDjxaPcLwSEbcU5/xIVZ+GstLMEbER+AZwT2Z+79WDWzlzZv5SZu7KzF3AnwB/mJmfb+XMwMPAmyOir3jv4V3AgWbJvIq8Y8C7o2IDcAvwP82SF5bNfDlNf/2StJgzl0tNIiLeCXwH2AeUi+aPU3lO+h+BESq/oHyweP6biPh94LeABSqPNjxUtI8Cf0XlRdWHgL3ZgBeDlWaOiE8A9wCHq07znsw806qZL+l7LzCdmZ8ptls2c0T8JpX/6wQezMyPFe0Nn3kV39f9wBepfNJTAF/MzD8qztXweWHZzN3A54BhYAL4YWa+t+jT1NcvSYtZeEiSJEmqOR+1kiRJklRzFh6SJEmSas7CQ5IkSVLNWXhIkiRJqjkLD0mSJEk1Z+EhSQIqcy1ExHcj4taqtg9FxDfXclySpNbgx+lKkl4TETcADwA3Ae3AD4H3Vc0Cv5JztWdm6ac7QklSs7LwkCRdJCLuA2aADcXXncCbgA7g3sz8ekTsAv6mOAbgzsz8fkT8MvAHwCngLZm5p76jlyQ1KgsPSdJFImID8AQwB/wLsD8z/zYiNgKPUrkbkkA5M2cjYjfwpcwcLQqPbwA3ZOazazF+SVJj6ljrAUiSGktmzkTEPwDTwIeAX4uIu4rdPcAIcBL4fES8BSgB11ed4lGLDknSpSw8JElLKRdLAB/IzEPVOyPiXuA0cCOVDyqZrdo9U6cxSpKaiJ9qJUlazsPA3ogIgIi4qWgfAk5lZhn4MJUX0SVJuiwLD0nScj4FdAJPR8SPim2APwfuiIj/ovKYlXc5JEnL8uVySZIkSTXnHQ9JkiRJNWfhIUmSJKnmLDwkSZIk1ZyFhyRJkqSas/CQJEmSVHMWHpIkSZJqzsJDkiRJUs39L+wNbyyQAVG3AAAAAElFTkSuQmCC\n" 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\n" 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\n" 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\n" 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\n" 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g8B/DoUOHKqWUl7tZshP/VQolLCyMgwcPXmsxFBQUFP5jEEJ063HgUlFMXgoKCgoKVwRFoSgoKCgoXBEUhaKgoKCgcEVQFIqCgoKCwhVBUSgKCgoKCleEq6pQhBBThBCnhBCZXfnWF0LMEtbQvkeFEAdFh8hjwhrq9sSZtKspp4KCgoLCT+eqLRsW1hCrb2N1hV4IHBBCrJBSpnbIthlYIaWUwhpI6TugY0zusfLyw9kqKCgoKPyMXM0RyiAgU1rDjrZhdcE+q2MGKWVDB79UOnoYxEVBQUHhekJKyfHjx2lo6FFUi/9arqZCCaRzDIFC27FOCCFmCyHSsYbjvLdDksQaivKQEOLB7k4ihHjQZi47WFFRcYVEV1BQUOg5O3bsYOnSpaxdu/bimf+LuZoKpasoeeeNQKSUy6SUscCNWCOXnWG4lLIfMBV4VAgxqquTSCk/kFIOkFIO8Pa+It4DFBQUFHrMiRMn2Lp1K05OTqSmplJTU3OtRbpmXE2FUkjnONdBnB8zuR0p5Q4g0hYalDNR+KSU5VjjPPzUcJ8KCgoKV5T8/Hx++OEHQkNDue+++wDYt2/fNZbq2nE1FcoBINoWx9oOa3jUFR0zdIhfjBCiH9Zwp1W2GN4utuM6YBJw8irKqqCgoHBJVFdXs2jRIvR6PfPmzcPT05OEhAQOHTpES0vLtRbvmnDVFIqU0gQ8hjU+cxrwnZQyRQjxsBDiYVu2OcBJIcRRrCvC5tkm6X2BXUKIY8B+YLWUct3VklVBQUHhUmhububrr79GSsmCBQtwcnICYOjQobS1tXHkyJFrLOG14ap6G5ZSrgHWnHPsvQ7v/wL8pYty2UDi1ZRNQUFB4XIwmUx8++23GAwG7rrrLjw9PdvTAgMDCQkJYe/evQwaNAi1Wn0NJf35UXbKKygoKPQQKSWrVq0iNzeXWbNmERoael6eYcOGUVtbS1pa2jWQ8NqiKBQFBQWFHrJr1y6OHj3K6NGjSUzs2ogSExODh4cHe/bs4X8txLqiUBQUFBR6wMmTJ9m8eTN9+vRhzJgx3eZTqVQMGTKEoqIiCgoKus3334iiUBQUFBQuQkFBAcuWLSM4OJiZM2diW5zaLUlJSTg4OLBnz56fScLrA0WhKCgoKFwAg8HAN998g6urK7feeitarfa8PFJKTmzZQF1FOQB2dnYMGDCAtLQ0qqurf26RrxmKQlFQUFDohjPLgy0WCwsWLECn03WZ7/imdWx4/5+s/uff2udNBg0ahEql+p/a6KgoFAUFBYUuMJvNfPfdd1RXVzNv3jy8vLy6zGcoLWbblx/h7OFJ8ek00nZuBcDV1ZXevXtz+PBhmpubf07RrxmKQlFQUFA4Byklq1evJicnh5kzZxIeHt5lPovZzNq3/45ao+G2P76OX1QM27/6hNamRsC60dFoNHLo0KGfU/xrhqJQFBQUFM5h9+7dHD58mJEjR5KUlNRtvv3Lv6fkdDoT7vsFrl7ejL/3EZrqatnz/UIA/P39CQ8PZ9++fZjN5p9J+muHolAUFBQUOpCamsrGjRtJSEhg7Nix3eYry85kz/cL6TVsFLHDRwPgFxlN33GTObx2JZUFeYB1lFJfX09KSsrPIv+1RFEoCgoKCjaKiopYunQpQUFB3HjjjahUXTeRxrZW1r79d5xc9Yy/75FOacNvvQN7Rye2fPo+UkqioqLw8vL6n9joqCgUBQUFBaCmpoaFCxfi7Ozc7fLgM+z65guqCvOZ/MiTODq7dEpzctUz/NY7KUg5zum9u9o3OpaUlJCXl3e1L+OaoigUBQWF/3laWlpYuHAhJpOJBQsW4Ozs3G3e/JPHOLxmOUmTZxCW2K/LPH0nTMY7LIJtX35MW0sziYmJODo6/tdvdFQUioKCwv80ZrOZxYsXU1lZybx587hQ5NeWxgbWvfMm7gFBjFpwd7f5VCo14+95mIaqSvYt+w6tVsvAgQM5deoUlZWVV+Eqrg8UhaKgoPA/i5SStWvXkpWVxYwZM4iIiLhg/i2fvk+DoYppj/4Krb3DBfMGxsYTP2ocB1cuo7q4qN2d/d69e6/kJVxXKApFQUHhf5Y9e/Zw8OBBhg8fTr9+XZuvznB67y7Sdm5lyE234hcV06P6Ry24B42dlq2ff4BOp6Nv374cPXqUpqamKyH+dYeiUBQUFP4nSUtLY8OGDcTHxzN+/PgL5m0wVLPxw7fxi4xm8Oy5PT6Hzs2dYbcsIPfoIbIO7WfIkCGYTCYOHjz4U8W/LlEUioKCwv8cxcXFLF26lMDAQGbPnt3t8mCwmsU2vPcWprY2pj72NGrNpQW6TZo8A8+gELZ9/gEe7m5ERkayf/9+TCbTT72M6w5FoSgoKPxPUVtby8KFC3FycuK222674PJggOOb1pJz9BCjbr8Hj4CgSz6fWqNh3D0PUVtexsEVSxk6dCgNDQ2cPHnyci/hukVRKAoKCv8ztLa2snDhQoxG40WXBwMYSorY9uXHhPZNJmnS9Ms+b0jvRGKGjmT/D4vxcnHG29v7v3Kj41VVKEKIKUKIU0KITCHEc12kzxJCHBdCHBVCHBRCjOhpWQUFheuXRpOZFrPlWovRiTPLg8vLy7nlllvw8fG5YP4zjh81Gi2TH/nlBYNqSYskZWcRdZXdexUeffu9oBJs//Jjhg4dSllZGdnZ2Zd9PdcjV02hCCHUwNvAVCAeuE0IEX9Ots1AopQyCbgX+OgSyiooKFyHmCySMQdOEbvrBLcdy+K9/HLSGpqveW98/fr1ZGZmMn36dKKioi6af/8PiynJOMX4+3+Bi0fXruvPcHhDHtu+PsWad49jMnbtBNLVy5shs+eReWAPemlCp9P91210vJojlEFAppQyW0rZBiwCZnXMIKVskGefMh0ge1pWQUHh+mSHoZ6CljbGeLhQ2NLGi1nFjD1wiqTdKTyRlseyMgOVbT/vhPTevXvZv38/Q4cOZcCAARfNX5adyZ4l3xA7fDSxw0ZdMG9BejX7lmfjE+ZKVVEje3/oftTRf8Zs3Pz82f7lxwwY0J/MzEzKy8sv+XquV66mQgkECjp8LrQd64QQYrYQIh1YjXWU0uOytvIP2sxlBysqKq6I4AoKCpfP4tJq3DRq3k8IY+fgOA4NjefvscEMcXNmY2Udj6Tm0fvHk0w6cIpXsorZZainzXL1zGOnTp1i/fr1xMbGMnHixIvmN7a1subfb+Ckd2P8vY9cMG+DoYWNH6fg5qdj1pNJ9BkdyLHNBRSkdR32V6PVMu7uhzCUFKGpKkej0fxXbXS8mgqlK4PjeWNeKeUyKWUscCPwx0spayv/gZRygJRywIVcJigoKFx96k1m1lXWMsvHDXvbUtxABzvm+3vyfkIYJ0f0Zm3/GJ4L98NJreLdgnJuPppF7K6T3H48m48KK8hobLli5rGSkhK+//57/Pz8uOmmmy64PPgMuxZ+TnVRAZMfeRKHC0zam00W1n1wElObhakP9cbOQcPQOVG4+zmx+bNUWhqMXZYLTx5A5IDBHFnxPfGxvTh27BgNDQ2XfY3XE1dToRQCwR0+BwHF3WWWUu4AIoUQXpdaVkFB4fpgVUUNzRbJXD+PLtPVQpDs6sSTYX780C+atBF9+LxPOPP8PMhuauV3GUWM3J/OgD2pPJ2ez4ryGgzGyzOP1dXVsXDhQhwdHZk/fz52dnYXLZN34iiH164gecoNhPVNvmDeHxdnUJZTx7g743D3s8aa19qpmXhvAs0NRrZ9nd6tYhxz5wNYLGYsBdmYzeb/mo2Ol7ZD59I4AEQLIcKBIuBWYH7HDEKIKCBLSimFEP0AO6AKqLlYWQUFheuPxaUGIhzt6efq1KP8Lho1k730TPbSA5DX3Mr26nq2G+pZWVHD1yXVCCDJxYkxHi6M8XChn6sOrar7FVdwdnlwa2sr9957Ly4uLhfMDzbHj+9aHT+OnH/XBfOe2lfKie1FJE0IJqp/59Vi3iEuDJ4ZwZ5lWZzaW0rsUP/zyrv5+jFw5s3sXfINweNmsH//foYPH37RPTHXO1dNoUgpTUKIx4D1gBr4REqZIoR42Jb+HjAHuFMIYQSagXm2Sfouy14tWRUUFH46BS1t7K5p4DfhfhdcYnshQh3tuTPQnjsDvTBZJEfrm9hWXc+26jreyivjH3llOKtVjHB3ZrSHK2M9XAhztO9Uh8ViYcmSJZSVlTF//nz8/Px6dO4tn7xHo6Ga+X98/YKOHysLG9j2VToB0W4MnR3ZZZ6kiSHknaxix6LT+Ee5ofd2PC/PoFlzSN2xGWN2Ok2Obhw/fpz+/fv3SNbrFXGtl/JdSQYMGCD/W4aOCgr/abyZW8prOaXsHxJHyDmN/JWg1mhiV00D26vr2VptXUkGEOpgx2jb6GWEuws/btzAvn37mDZtGoMGDepR3af27GLVm68x7JYFDL35tm7ztTYZWfzqQYxtZub+diA6fffXWV/dwqI/7sfDX8fsp5NRqc+fYcjYv5vlb/wZVf+R2DnpePTRRy9bGV8uQohDUsqLL33rAVfT5KWgoPA/gpSSxaUGhuh1hDjat88dXMnGUa/VMN3bjenebkgpyWluY1t1HdsN9SwpM/BFcRUqKfFv1vDA0BE9ViYN1VVs+uht/KJiLuj4UVokmz9Po76qhVm/Sr6gMgFw8XBg9PwYNn6cyqF1eQycHn5enqiBQwnrm0xuXiaV3oFkZmYSHR3dI7k7Ymxtuag7/Z8DRaEoKCj8ZI7UNZHV3MqjIT5YLBbef/99WltbiY+PJz4+nsDAwCuqXIQQRDjZE+Hkzb1B3pRWVvLPNRs4alGTGRzFyyo1+acLeS7CH1eNutt6pJSsP+P48dGnUam7z3t4Qx45xyoZcUs0AVFuPZIzZqAfucerOLA6l5B4T3zDXc+7jnH3PMRnzzyO1jeIPXv2XFChSCmpqyinPC+b8pxsKvKyKc/NRqVScf+/Pu6RTFcTRaEoKCj8ZL4rM+CgEszwcSMnJ4eysjL8/PzYu3cvu3fvRq/XEx8fT0JCwhVVLlJKjh49ypo1a3BVq3l95kwCo2P4S04pnxZVsrKihpeiApnt49blOY9tXEvuscOMv/cRPAK63OoGnN28GDXAh77jLs1B5OjbYijJqmHjJynMfX4gdg6dm12PgCAGzJjFrl27yLZISktL8fPzw2wyUVWYT0VeDuW52ZTnZlGRm0NrU6O1oBB4+AcS2Cse79BwpJQ/u7nsXBSFoqCg8JNos1hYXmZgipceV42a9YcP4+DgwH333YfJZCI9PZ3U1FT27dvHnj172pVLfHw8QUFBl90INjc3s3LlSlJTUwkLC2P27Nno9dbVYn+OCWKevwe/PlXAL1LzWFhcxasxQUTrzpqFqouL2P7Vx4Ql9iNx0rRuz9Nx8+LY22MvWV57Jy0T7o7nh38c4cfvMxl7e+x5eZKnzOT41k1USQvff/YxekM5VYV5mG0u7jV29niHhNFr2Eh8wiLwCYvEKzgUrcO1N3N1RFEoCgoKP4nNVXUYTGZu8fOgqamJ9PR0BgwYgFarRavVkpycTHJyMs3NzZw6dYrU1FT279/Pnj17cHV17aRcerLxECA3N5elS5fS0NDAhAkTGDZs2HllE12cWNM/hi+Lq/hzdjHjDpziFyE+/DLUFwcka99+w+r48eHuHT92tXnxcgiMcaffpFAOr8/DJ1SLg1MVFbnZVtNVbja1ZaUAaBwrqcQLTxdX+k2bhXdYBD6hEbgHBKBSdW+Ou15QFIqCgsJPYnGpAW87DaPdXTi4fx9ms5nk5PM3BTo6OpKUlERSUhItLS3tyuXAgQPs3bsXFxeXdrNYd8rFbDazbds2du3ahbu7O/fddx+Bgd2bqtRCcHegF9O99byUWcxbeWUsLTNwZ0UWbZmnmfHkb3D28Oy2/JnNi5Mf6N2+ebGnWMxmDCVFNnNVNmU5OSD7sPXLJlrrvgDZhJufP75hkfQeMxHvsHB2Ll1MLgKP/kMZNXXqJZ3vekBRKAoKCpdNtdHExqo67g3yQi3g8OHDBAQEXHTvh4ODA4mJiSQmJtLS0sLp06dJSUnh4MGD7Nu3DxcXF+Li4khISCA4OBiVSkV1dTVLliyhqKiI5ORkpkyZgr19z5Yne9tp+Xd8KPP9PXnmZBavuASTfNsTzEse3G2ZC21e7IqqogIKUk5YRx65WVTm52EyWpc2qzUaPINDCYkzUHjak5DEJ7jhiWQcdJ2VlKuXD++/9SYHDuxn7PjxPdrdfz2hKBQFBYXLZnl5DUYpucXXneLiYsrLy5kxY8Yl1eHg4EDfvn3p27dvu3JJTU3l0KFD7N+/H2dna0CqgoIC1Go1t9xyCwkJCZcl70CdlnuWvc/20Dh2JY1i5L50ng7z5aFgn06773uyebEjFfm5fPXck1jMJhx0zniHRZA4aSo+YZF4h0XgERDUHjr4xLZCdiw6zen9BvqO7axQvEPCSIiO5FhpFbs2b2Lc1O7ndq5HFIWioKBw2SwurSZO50CCsyOrtm1Go9HQu3fvy66vo3JpbW3l5MmTbN++nZycHADs7OzIzc1Fp9MREhLS4zmXM+xc+Bl1Rfm8cveDqGLieCGjkD9ll7C4zMBrMUEMdXOmtcnIuvdPYOekYdL9CV1uSOyItFjY9NE72Dk5cdvLf8PdP+CCE/e9RweSd7KK3UszCerljkdAZ6Uy5fa7SfnzK+zZu5cxkyZfcCnz9YYSAlhBQeGyyGxq4XBdE7f4eWA0Gjlx4gQJCQk4XKGVR6WlpezYsYP6+npGjRrFnDlzCA0N5ciRI3z22We88cYbrFq1ipycHCw9cH+fd/woR9auJHnqDYT2TSLYwY7P+kTwRZ9wGs1mZh/J5InUPJZ9lUp9VQtTHuh90c2LACnbN1N8KpXRC+7BI+DiS6KFEIy7Mw47BzUbP03BbOwsu6OzC/0TEzEKFZt/WHLR819PKCMUBQWFy2JJqQEVcJOvO6mpKbS1tXU5GX+pmM1mduzYwY4dO3Bzc+O+++4jKMi696NPnz60tbWRkZFBSkoKx44d4+DBg+h0OuLi4oiPjyc0NBT1Ob36loYG1r37DzwCghg5/+5OaZO89Ixwd+HN3FLeyStnRYiFByOD8I3UX1TW5vo6tn/9KQG94kkYPb7H1+jkasfYO+JY885x9q3MZthNnSNITrzpZg796Y/sP3iQUVOnY+90aQsCrhWKQlFQULhkLFKyuKya0R4u+NlrWXP4MB4eHoSGhv6keg0GA0uWLKGwsJDExESmTp163ojHzs6OhIQEEhIS2pVLampqu3JxcXFhzJgxJCcnt5vENn/yLk21Ncx65ndo7c4fdTipVdxhdMC4roatY9z5p6qRnYcy+GuvIPq4dO85edc3X9Da2MCE+x5BXKL5LbyvFwkjAziyMZ/QBE8Ce7m3p2m0WgYOGMCeQ4dZ/9VnzHzw0Uuq+1qhmLwUFBQumb01jRS2GLnFz4PKykry8/Pp16/fT9qpffz4cd577z0qKiqYM2cOs2fPvqj57IxyueWWW3j22WeZO3cubm5urFy5kvfee4+MjAzSftxO+o/bGTLnVvwiu3ZrcmbzYrTOkfVj43k7LoSCljYmHzzN86cLqTOdHye++HQ6xzevs+4XCT3fT1dPGH5zNHpvRzZ9lkpLY+eAXKMnTkIlBCfST1GZn3tZ9f/cKApFQUHhkllcVo1OrWKKl54jR44ghCAxMfGy6mppaWHp0qUsXboUHx8fHn74Yfr06XPJ9djZ2REfH8+9997L3LlzMRqNfP311yxdsQr3qDgG39i148dzNy/aO2qZ4+fBj4NjuSvQi0+KKhmxL41lZYZ2p5cWs5lNH7+Ds4cnw2zeidsKCjCWlFxStEmtvTUgV1NtGzsWne6U5uDgQL/kZIwu7qz/5P0rFsXyaqIoFAUFhUuiyWxhZXkNM7zdsMfqSysmJqZHQazOpaCggPfee48TJ04wZswY7r77btzd3S9e8AIIIYiPj+fRRx/FX23BZGdPvlbHipUrqa2tPS9/V5EXwerd+NWYINb2j8HPXssjqXnMPZZFZlMLRzespiI3m7F3PYCdoxMtaWlkTZ1G5thxnB44iNx5t1LywgtUf/45jbt3Yywv71Yh+Ia5MnBGOBkHyji1r7RT2vCRI0EI8iqrObVn50+6Lz8HyhyKgoLCJbG+spYGs4Vb/NzJyMigsbGRfv36XVIdFouFnTt3sm3bNvR6Pffccw8hISFXVM6Tm9fTcPIwU+96kBqNA/v27ePkyZMMGzaM4cOHY29v36PNi0muTqztH8MXxVW8ml3M2P3pDDqVz83JA4gePBxpMlHy/O9Q6/V4/eIR2rKyac3IoH7jJmoWf99ej0qvxz46CvuoKOyjo7GPisY+OgqNhwf9poSSn1LFjm9O4R+lx9XTGpDL3d2d+Pg40lMsbP3qEyL6DcTO4fxgXdcLikJRUFC4JL4rrSbQXsswN2cWrT2Ms7MzUVFRFy9oo6amhqVLl5Kfn0+fPn2YPn36FVtqfAar48dPCEvqz8CpNyCEYODAgWzevJkdO3Zw6NAhBiYNJX1FW482L6qF4J5AL2Z463lg1UZ+TBxBnlaFd3U9ycu+oyU1lcA338R1yuT2MlJKzFVVtGZm0pqRSWtGBq2ZmdStWYul7tuzdXt6Yh8VRWJ4bzYZk9jwr/3M+mUiWnc3AIYOHUZqaho1FhX7ln573iq16wlFoSgoKPSY8lYj26vreTzUl4b6ejIyMhg+fPh5y3S748SJE6xatQopJbNnz77seZcLYTGbrY4f7eyY/NAT7QsF3N3dufnmmxkyZAjr161n24+b0LrrGDZuKuIiMerP0JSewshv32HabffzpX8vbj+ezcgG+M3MWbhMntQprxACjZcXGi8vdEOGtB+XUmIqL7cqmcyMdkVjWvEN0S4nSIu7i03zfk9UyzHbSCYKf0dHqv2CObBqGQljJl7Q1f61RFEoCgoKPWZpmQELcLOvO8cOH0BK2aO9J62traxZs4Zjx44RFBTETTfdhIeHx1WRcd+y7yjNPM2MJ5/r0vFjYEAgfm0DqKtxheBili7/nsPHDjJp0iQCAgK6rddkNLL5k3dx8/PnrmnTuEul4rW/v8sniYOZmzyQZwoqeDDIG81FlJMQAq2vL1pfX5xHDG8/LqUkvKiIps8zyBGzCNX5osk5guHbbwn38qJkxHA0DjpWP3o/o7yDcYiOPms6i4xA5XjtTWGKQlFQUOgxi8uqSXZxIsrJnjWHDxMaGoqnZ/feegEKCwtZsmQJNTU1jBo1itGjR/d4RNMTpMVCbXkZFQW5VORms3fpt8SNGEOvoSO6zH94Qx65x6sYd8sQeo8J4NChQ2zbto0PPviAvn37Mm7cONzc3M4rd3DFEgwlRcz57cto7OyoX7SIuZ+8w5xXQ/irfx9ezipGAo+GXNyRZFcIIbALCmLCk74s+uN+jtiPZ+43z6FRQ1hBASmLFkFQJOWNRyioKMVrz16k0brUWDg70+vA/v/uAFtCiCnAW4Aa+EhK+do56QuA39g+NgCPSCmP2dJygXrADJiklAOupqwKCgoXJqWhmZSGFv4cHUhubi4Gg4ExY8Z0mffFHzPIbWzFo64Rx9JKAmUEN8QGE97sQv36PECc+Wf7D84e6HDM1kCeaSeNrS001tbQVFdDY00NjbUGmmprMJuMgEQCiWHjGHbnvV3KdW7kRSEEgwYNom/fvuzatYs9e/aQkpLC0KFDGTFiRPvcTk1ZKfuWfUfMkBGEJfbDWFJC+d9ex2noEEJunMkXQjDt0GlWlddctkI5g4NOy/i741jx5lF2L8lk9G29cAgLY+iYMaxbtw7/kAjSmhuZ9fG7bDv4Hcf3r0Ld0MIfkAj+SxWKEEINvA1MBAqBA0KIFVLK1A7ZcoDRUkqDEGIq8AHQ0Z/0WCll5dWSUUFBoecsLq1GKwSzfNzZvmon9vb2xMXFnZcvtbCG99oacZMW6t3tMHuetfc7muoIbrYQ3CwJarIQ3GwhqEkS3GTBu0WiQoLE+uoGFeCME844AQHgek4GCTXvpmOZGIpTsk/7/MiFIi86ODgwYcIEBgwYwJYtW9i1axeHDx9mzJgx9OvXjy2fvodQqxlz1/1IKSl98SWkxYL/yy+31zPZU8+rOSWUtRrxtdde5l22EhzrQdKEYI5uKiC0tydhfbxITk5m69attPqEYNy+jWf/cRuHowz07deXOdFzMEszKnFtd4JczRHKICBTSpkNIIRYBMwC2hWKlHJ3h/x7gUsL1qygoPCzYLJIlpYZGO/pgpPZSGpqKklJSV3G6/jseAE+oponjq5j+KBEXAOCKGtpoaylmfKWZiosbVTSQrWqhRonM6keZjTSjL3FiJuxCV1jPY4N1Tg3N+Dc0oCLsQUPR3tc9C44uVhfjjontFoNWEwdXmawmDBLL6rK5mFYfJr6HYXoJ4ehjXLrUeRFNzc3brrpJoYMGcKGDRtYs2YNO7dvw5iRyYRb5uPi4UXtylU0bN+O7/89h11wcHvZSV6uvJpTwsaqOm4PuLAZsCcMmRVJQZqBLV+kMe7pcNaVrSLHJQf/cn/qgwR9st146s6/khQ95OKV/UxcTYUSCBR0+FxI59HHudwHrO3wWQIbhBASeF9K+UFXhYQQDwIPAld8HbuCgoKVHYZ6yttM3OLnwcmTJzGZTF3uPWkyNLNWNLL94L14mA1g24t32WuSHGwvgEYwNWmQFWpQaTCpNKhUGlRqDUKlAZUazCY09cX43D2T5rpY6jbkUfVFKs1OWoxlzYy7N6FHkRcDAgK46667SD15kmXfLsIUHMXxcgM+aWm0vPIKDol9cb/99k5lYnUOBDvYsaGy9oooFIvajOvUeio+sfDOW8tY2+sDhocOR1QJRo+4m9xlC8lbvomkZ/43FEpXxrwuB7JCiLFYFUrHWbThUspiIYQPsFEIkS6l3HFehVZF8wHAgAEDrn/fBAoK/4EsLq3GTaNmgqcrny09jK+vL/7+/uflW7InlzG12/AwGygKv4Pyeg01FRXUlJfT2tyCBYFFChxc3dD7BuIWEIybfxBugaG4BwSjsXMAlQaLSk2pUZLbaia31UxWq5ncFiM5Ta3kNLfR3MFdvUZAsIMdYY72xGjNvLBsLJqjX+N04zs4JniSvTgDDpcx0kWDw4kK2gJ02PlfXKkIIag6fhCHjGMk3vEgR1LT+eTbbwmJj2f6448hzllYIIRgspcrXxVX0WS24HSROCrdkVuby9LMpSzPXE51SzVDI6eTmDGJdwO+YcSk3nzf+j0paacYNesW9n33FTlHDxGe1P+yznWluZoKpRAI7vA5CCg+N5MQoi/wETBVSll15riUstj2t1wIsQyrCe08haKgoHB1qTeZWVdZy1w/D6rLyigpKWHq1KnnrSiyNBlZ1NzIa4VLqTK5sGhNDnaOOryCQ/HqN5SwkDC8g8PwDAnF0fnCblpUQIDtNeycNCkl5W0msptbyWluJdemZHKbW9lf20qU1xgWpCxFNfUvVFUINv1Ygl+oC2P6e9OwvYjyfx7GKckH14mhaDy631BZmZ/LodU/0GfsRCbOmEmyvSNbPviAUwkJfLBqFYMrKhg5ciSOHZbrTvLU81FhJbsM9Uzyurj7+zM0m5rZlLeJJRlLOFR2CLVQMzpoNHNi5jDUbyhr3z5JyspKEvo2MnToUE6ePAm+Qbj7B7L1s/cJ/tvbaLQ/bd7mSnA1FcoBIFoIEQ4UAbcC8ztmEEKEAEuBO6SUpzsc1wEqKWW97f0k4OWrKKuCgkI3rKqoodkimevnwZEft6NWq+nbt+95+Y7vysOiyaBP4yk2V0Yy7w9/ITAu4YovZRVC4Guvxddey1A3505pFW1G7jNM446SVRgPL2bdmjjsnDRMsAXLchnsT/32Qup/LKbpeAXOg/1xGRuM2qXzXJCUkk0fv4udoxMj59+NuaEBw59eYYCrK5Mfe4wtO3eye/dujhw5wujRo3FP6MOL2aVM9tLjolaxvrK2RwolrSqNJRlLWJO9hnpjPaGuoTzZ70lmRc3Cy9GrPd/4u+L55o/72PRpKjf9uj+hoaEcOHCAWXfez7K/vMThNcsZNOvmK3ODfwJXTaFIKU1CiMeA9ViXDX8ipUwRQjxsS38P+D3gCbxje+jOLA/2BZbZjmmAhVLKdVdLVgUFhe5ZXGogwtGePo5aNh4/TlxcXKdeOYClzczC4irubVpKq9TSFDaVoPjLDwV8uXjbafGOGErW6TDct3xEfdUr3Pir5PbIiyonLfqp4TgPC6Bucz4Ne4tpPFiK88ggXEYGorJN1qfu2EJRegoTH3wcJ1c9JS++iKmigqB//RNHLy9mz57dPnG/bt066rbvJD8inueqAohwcmBDZR0WKVF1oUzr2upYm72WJRlLSKtOw15tz6TQSdwUfRP9fft3qYB1bvaMvT2Wde+f5MCqHIYOHcqiRYtosnMkauAQDqxYQvKUGWjtr6wLm0vlqu5DkVKuAdacc+y9Du/vB+7volw2cOV9MigoKFwSBS1t7K5p4DfhfqSnp9PS0tLlZHzl7gJ2ezbwp7wtnDT40H/B7V3U9vNwW4AXX/lO5Q/Z7zJ2qsA/yu28PGq9Pe43ReM8MpC6DXnUb86ncW8xLmNDUPd2ZvtXn+AfE0ufsRNp3L+fmkXf4nH33Th2GJl5+PqSPmQcm3UpjM1NY2LKAcYPHMYHwroPZU9NA8PdraY9KSWHyw+zNGMpG3I30GJuIdYjlucHP8+0iGm42p279vl8IpN9iBvmz+F1ecyMS8LDw4M9e/Yw964HsZhN11yZgLJTXkFB4QIsKa0GYI6vO1u3r8fNzY2wsLBOeaTZwqqTxUxzXoOdNFHmOYZ+MefvT/m5iCw38jvXCfyOD+hlvxkY021erbcTngviaCusp3ZdLrWrsjGua8NPhjD8nvuRbW2UvPAC2uBgvJ94vL1cXnMrD6bkcqy+mYeT+vB/N07ks48/QhTl8vspSbycVcxDKbl809ubA4VrWJaxjNy6XJy1zsyKmsVN0TcR7xl/ydc2Ym40RacNbPksnQHTB7Fh4zpqm1uumxWuSjwUBQWFLpFSsrjUwBC9DufmRnJycjqF1T1D7YFiVvuouad4ObmN7iTMefiayGs2WSg8ZWDrJ6lE1bqx0XMolmPfgNl40bJ2QS54398H1VQ36hurGeQ1DflDDeVvfI0xLx//P76MyskaCnhDZS2TDp4mp7mVT3uH8WJUIPYaNb1796a4uJi5OhUhdmYqjUYmHzzFX08sx8PBg1dGvMKWuVv43ZDfXZYyAbBz0DDx3gQaalqpT3fCwcGBPXv2XFZdVwNlhKKgoNAlR+qayGpu5dEQH44ePYoQgqSkpE55pEWSsiMH99BD+LdVsN1hIqMSzp+wvxpIKTGUNlGQVk1hWjWFp2swtZqxd9LwzNhI/r5vGlNSdkHGRoiddtH6LBYzm9d+RFOzgdsWvErD+lws9TG4zP4rGp84jBbJazklvJ1fTh9nRz7qHUao49n49F5h1kn0p797mipfH3BfgE5tT5P/S9wUH8pMn58WOOwMfhF6BkwN5cDqXCJHxJGSdoTq6uqr5mzzUlAUioKCQpcsLjPgoBJM83Ll4yNHiIyMRK/vvHKp7lgJazzsuKd4GQaTI0E3Pn1VHRQ217dRkF5NQZqBwrRqGgytAOi9HYkd7EdwnAeBse7YO2poqhhHVYYHHke+RPRAoRzbsIbynCxmPPkbXBJ9qPzj46ANxyHpZio+OM5Jf3vWh6u5s5cPL0cF4tBhn0lqVSp3bb2LofZDCWoM4qW+43mmAB4LD2dzdR0PpeSR3dTKk6G+V+T+DJgWRn5qNVVHzKj0Kvbu3cu0aRe/xquNolAUFBTOo81i4YcyA1O89JTn5lBfX8/UqVM75ZFSUrwuk5Mx5bxSeJRDln70639ld22bjGZKMmspSKumIK2ayoIGAOydNATFujNgmgfBcR64ep3vun1eoC+LfCbxi9OLob4MXHy7PU9jjYFdi74ktG8yMUNGUPX++7SeSiPo7Uc50juerWszmJvZzDcloGtqROPVCraoioYWA09tfQq9vZ6pQ6ayb/s+pron8n5VObtrGlicFMnT6QX8JaeUrKZW3ogNxl7102YbVGoVE+6J59tXDqBXB3L8+HEmTpyI9hrvRVEUioKCwnlsrqrDYDJzi58HRzatxcnJiZiYmE556tLKOWKn4abK5bRJNa6Tn/nJvW9pkVQVN1CQaqAgrYrizFrMRgsqtcAvQs/gWREEx3ngHeKC6iJxR6Z7u3FT4HQeLVwEx7+F4U90m3f7lx9jNrYx7p6HacvOpvLtd3CaOpX3IuL5e2oOMfHO3DYjFtcDFdY9LCcq0Q3yQzcmkF/v+zWVzZV8PvVzgjRB7Nu+j9TUVCYFRPJBQQWtFsm/4kKIdLLnLzml5Le08UnvcLzsflrz6+bjxMi50Wz6upFR04Zdc2UCikJRUFDogsWlBrztNPS3E/zz1CkGDx6MRtO5uShdnc7aQDPvZ64nxxhK9Mgpl3WuBkNr+wikML2a5nrrJLq7v47eIwMJinMnINqtW4eO3eGkVtE3IplDaQkkHfkS9bDHz/rB70D+yWOk7drGkDm34u7rR96C26n19uWF+Q+wM7eMW/zceS0mCJ1aDVOd2/ewNO4roXZ/IdHuXsyY+gK9vaz7boKCgkhJSWFSn2Tezi9na3Uds3zceSrMjwgne36Zls+0Q6f5sm8EvXQ/balv3DB/8k5Ukb69kgETTJd8j640ikJRUFDoRLXRxMaqOu4N8iLl+HEsFst5e09qMytoaFATZt6Ak6UVzbBHUKl6FjTL2Gqm6LTBpkQMGEoaAXB00RIcZzVhBcV64Oxuf5GaLs5t/p586TeN/qf/BoUHIXhgp3Szycimj99F7+vHoBtvwbDwG/bXN/On51+lrqmNv/cK5jZ/j04jrzN7WI5H5lG06iTzK6ehWqKhvroI52H+JCQksH79esKMzXho1WystCoUgFk+7gTb23HXyRxmHDrNh73DGONx8T0o3SGEYMztvTC2mq+5MgFFoSgoKJzD8vIajFJys48729ctIzg4GG9v7055ilaksDxAy93FP1Bo9iFsStcBrQAsFklFfj0FqdZRSGl2LRazRK1VERDtRtxQf4Lj3fEMcO5xbPeekuTiyPOhU2jJ+hcOR748T6EcXLkMQ3EhNz33IpayCv55JJUPf/V7QnQOfNM7nATnrsPqZtVk8euTzxOVHMWshDtp2lhE7epsZJuZ+AHxrF+/nlNpaYz3DmVjZR0mi2wPDdxPr2NN/xjuPJ7NguPZvBIdxN2BXl2epyc4Otvh6HzxfD8HikJRUFDoxOLSauJ0DrgaKqisrGTmzJmd0mvzK3EsV1GedIzIkkJyYx9DfY45rOXISqpO55Bf4cPpbDcamqy+sryCnUkcH0xwnAf+UXo02isXCrgrhBDcGBLCcq8x3HxyCeopr4Kd1dNwbXkpe5d+S/SgYbglJHL7t6vYecNcpjvb8WZyL1w0XctW31bPk1ufxEHjwN9H/x2dzgPd/R5UfHCcpiPl+I4LJjg4mJSUFCbP6cviUgMH6ho7+R0LdrBjZb9oHk7N47nThWQ1tfBiVCDqaxzC96eiKBQFBYV2sppaOFzXxO8jAzhyeDd2dnYkJCR0ypP/wwlOemmZU76COpwIuun/OqW3FaVi98OdBAoLgcBQVzD6BqDyjUXtHw8+ceASB5ZewNXvWs/x9eAh/2nMK1sHqSsg6TYAtnz2AUIIPG65iwk7j1LmF8z/1ZbwxJgp3S4usEgLz+96nsL6Qj6c9CG+urMrxxyTvKlZmomxuJGEhATWrVvHVEsrdkKwvrL2PEeWzho1n/cJ5+XMYt4vrCC7qY33EkK7VWT/CSgKRUFBoZ3vSw2ogBnuTnyZkkKfPn2wtz87l1FbXI1rsYodfUt5+/Reivxn4nqOvaXx+9+ik3ZUT/wWX88GREU62opTUJEG+z8Ec+vZzPoQ8IkF71irovGOBe9e7aOIC2GxmDCZ6jCZajGa6jAZazGaatFq9Hh6jmrP52mnwS1yJHmnggg+8iWqpNvIPLiPrEP7abjnSeZnV+BeW8d729cy/R9/veBKtQ+Pf8jWgq08N+g5BvgN6JTm1NuLmuVZNB0rJ35EPOvWrSPvVDrD3YPYUFnHi1HnhxlTC8FL0YFEONnz24xCbjicwZd9Iwh2OD8S5n8CikJRUFAAwCIli8uqGe3hQsXpUxiNRpKTkzvlyV52BOzsSGxcjwWB302do0q0nd6Fu2Erp1zup9eIMdaDcTM6nMQMhlwoT4OKNCzlqZiq0zEV78CoMmHSCIwagcnVE6OrNyadHqODIyY7DUa1xGRusCmOOszmhm6vZdCg1bg4x7Z/nh/gxde+U/ht7kcYS9JY++XHbLjhHo7bezG8pJBfv/VnkhZ+jbjA/pCdhTt5++jbTI+YzvzY+eelq5y0OES703ysEr8p4YSEhFhXe81K4P9OF5LZ1EKUU9eruu4K9CLM0Z4HUnKYevA0n/cJp7/+4kr1ekNRKAoKCgDsrWmksMXIbyMCOLJyK97e3gQFBbWn15ZV41ag4tNezfyqaA0l+v4Ee4edrUBKWpf/H0azO4433ERh4VcYTbXtCsBkqsVorLWOKoy1GGUdZo8G8IDzTV9GoBiVuQhtvQWNSaIxSRyEAxqtG1qHIDROgWhdw9DoI9E6eKPR6lEJOw4fmU9+3ockJLzRXttoDxf+EnIDz+V+QvqSl3hnzH3UunnztLmBaS//Gt8nn8Q+Irzbe1NQV8Bvdv6GGPcY/jD0D92OYpwSvalOr6Ytv46EhATWrl3LbNkGwPrKOqJCul8mPNrDhVX9YrjjeDY3Hc3krdgQbvS9Mu5afi4UhaKgoADA4rJqdGoV/WUrnxYWMmnSpE4NZ8bSw/hgj0q1FTdTA47TftupfNvR5bg0HueYz6NUlz6AxWI1balUDmg1ejRaVzQaPQ4OAWid49Bo9Wg0erQaVzRafXsercZ2XOuKSqqhOttqLitPt/4tSoeqfWA54/RRgHtYu8kswHkAhWUriYj4FY6OVjOTWgjGhfViy8lBJNQdQEQ/wXfRfvjcdgua2Fg8772n2/vSZGzil9t+iUDwj7H/wFHTeeWXyVRPYeHXlJQuQe80CFftVJqOVhA/Pp61a9diyDxNb2d/NlTW8miIzwW/gxidA2v6x3DvyRweTs0jq6mVX4VdGXctPweKQlFQUKDJbGFleQ0zvN1IO3oUlUpFYuLZkEQ1lQbc8gTbAozcXL6aMocQfGPGnq3AbMS87vdUm4KQw0zIOjODBq5Ep4tEpfqJ+0m8Y6yv+FmdzkdVVgdFY3tlbCBEa6FwoDsFm+cRE/YUxEyhSe1IRmMzp/2m8XHq79nqXYb5vTXUVhsIfu89RDe7zKWUvLTnJTINmbwz4R2CXc5GNW9tLaeg4FMKixZiNjfg6ppEadX3qLx84FgiATPCCA0NtZq9pvXizdwyqtpMeF5kh7ynnYbvbO5a/pZbSlZzK3/vFdzJd9j1iqJQFBQUWF9ZS4PZwk3eruxbeZzY2Fh0urM2/PSlBwjCkXS3I9yZnUnjhNc67Tpv2/0pjq15pAQ/R13dZwQE3IqLy+W5aO8Raq11Mt8nFjouQjO14VB0EN/Tz1Nsn0/4D/ehxoH9XsPAYyT7tXFUa/V47PuYzO+z8HzgfhzPWcXWka/SvmJNzhqeSH6CEYEjAGhqyiEv/0NKSpYhpQlfn2mEhj6Ii0sCDY0Z5NZ/BrvVnFj3NJGRY9iyJY/RKjMWYHN1HXP9Lu4V2F6l4l9xIUQ7OfBqTgkFzW180icMb7tr717lQlz/Kk9BQeGq811pNYH2WjxKC2lqauo0GW+oMuCWIyjUGxlSt5lGtQ7doLvOFm6th22vUtwWj7p/MUJoCA979BpcBaCxg9BhhA59D7Mafhx3F1/7TqF31WE+Sn2RF4o/YbHPJNSlu3CM9sXr0e7lPFB6gDcOvsG44HHc1+c+6uqOc+LEY+zZO5HS0mUEBNzC0CGb6N37LVxcEsjLy6Mg34Qu7j6k1oJdbiiNTS8ghMSYcQI/Oy0bKmt7fClCCH4Z5suHCWGcaGhi2qEM0hubr8RdumooIxQFhf9xyluNbK+u5/FQX47+uAlXV1ciIyPb01N/2Eeo1PG9Xy6/yd9JVe870dk5tae3bXkTO3M1+dFP0lj3L0JDH8Le/sJzBVcbrVM0lQ6D0bbsZEnyFzicHAY7vmRCjJ47nUbzUNFiQgecRKx+HBJmQ9R40Jw1zZU2lvLM9mcIdgni1wkzOHr0TgyGPWg0LoSFPkxQ8N3Y253d3X748GFWrFjR/nmUJY6wwiQqnSNwdS3jyKGNhCYMZENLDF+d3I+LowOOjo7nvZycnNrfa7VahBDc4ONGkIMdd53I5oZDGXyQEMZYz8t313I1URSKgsL/OEvLDFiAyU5qVmRlMXr06PaojNXV1bhmQqOjCY+W7ailBb+xHbz21peh3v82WS3D0PQ+hqbZmdCQB6/NhdjIb27lwZQ8Wlqm8wL7+KN6ExvWbSZ58u04xg8mYPlGjjn3ore5AnXGBjjxHdi7Qux0SJhNW9hwnt72FNGaWu7ycyA95WHs7HyIinqOwIBb0WhcOp0vIyODlStXEhkZyfjx42lpacGYVYvd5gbidc+SojpMdraBgaoN7FPFcdJShXdmM01NTZjN5m6vQ61Wd1I2D+tc+NgzlPnHsrhH1cJsnaaTIvL397/at/aiXFWFIoSYArwFqIGPpJSvnZO+APiN7WMD8IiU8lhPyiooKFwZFpdVk+ziRF16KkCnqIzHl+8lSrqwyb+Mmys2UxgwghCPiPb0tg2voLa0Udp3Ji31rxMZ8TRarR6LRSIEP+vqpKKWNpaWGfh3fjkSyVu9p+GS9z3FRZ+gc+vL0NlzKb79Dma6uLNwxjT+kvEPuH8zNNdAyjJIXwnHvkFqNPzRXUOtrx2t0oPI2Ffx85vV5eKC4uJivvvuO3x9fZk7d277JlAZKinZu48wo55eN93HG2+8QS/jIOwdWynyr+PegSYiwp9ESgeam5tpbrYqmDPvu3qJ6kpuLilmeXAsn3j4sv9UNsMyT6BCotPpePbZZ3+2e90dV02hCCHUwNvARKAQOCCEWCGlTO2QLQcYLaU0CCGmAh8Ag3tYVkFB4SeS0tBMSkMLr0QFcGTnaiIiInB3t+59qKqqwvW0hVZ7M0XNO/AxVtMy+vGzhSsz0Jz4ipMtk9BGbsdi9CI4+G5ajGamvbWTVpOFmUkBzEoKINbv6pho6kxmVpXX8H2ZgT01DUhgmJsz/4gNJtTRnn3HBqJxOs6g+ck0fPk1bZlZjHr3Xd6309Ga9Q72xxbB9Ncxhg2iqE8shqP/xK+sluCqNiIqWpBZ2YjKnZDgBWGjQH22yTQYDHz99dc4OTkxf/78Th4FhFrg2MeLpkNluM+JISwsjNL8OkaPcuNo7SjyC+6krGw10VH/h6/vDbi69vz+/FpKXswo5EPArVccL3k54igtV/CuXj5Xc1J+EJAppcyWUrYBi4BZHTNIKXdLKQ22j3uBoJ6WVVBQ+OksLq1GIyCpuZba2tpObuqPrNqDj9RT4G1gkukg5bpgHKIntqcb1/wek0VL3YBx1DceICzsUdRqJz75MYfsykYC3R35YEc2U97cyZQ3d/DOtkwKDU0/WeY2i4X1lbU8cDKXPj+e5FenCihtNfJsuB97h8SxNDmKUEd7GmsMHPjmKOZmZ4xiExUfvI/rDTfgOnYMs0LDWOU1EtPxb8lM/yM/7h5JVt6b7HFU8Xb0aMSvC+G2RYioiXByKXw5G96IgZVPQvZ2mhrq+frrrzGbzSxYsABXV1ek0YKUsl1Op0RvpNFCS1oVvXv3pqqqimH2WsrNTrjEL8He3oeU1Kc4cuR2Ghsze3z9aiH4Y0wwf40JYk9jK7+oaEHjf75bl2vB1TR5BQIFHT4XAoMvkP8+YO2llhVCPAg8CBASEnK5sioo/M9hskiWlhmY4OlK9rFDODo6EhtrdVdSWVmJ8ykTRq2FrXU7eakthaKxL8MZ1yT5+9Bmr2Ffy21oglciLEEEBtxKZUMr72zNYkKcDx/dNZCqhlZWnyhh+dFi/rruFH9dd4oBoe7MSgpget8APHQ981klpeRgXRPfl1azorwGg8mMp1bD7f6ezPFzJ9nF6Tzz2o6vPsHY0kZoyAMUVvwDxz7u+P7W6shyqksdT/tNYU75JlqOfoBL3Az+nnWSKqlj0bgP0djroNdU68vYDJmbIWUpHP8ODn2KUDkzWEYRNPlxPB1cqFmVTcPeYhx7eeB+SwwqBw12oa6o9XY0Ha0g9pZYVq1ahXdhDkLtyY8tvjw1YClFRYvIyn6dffunExJ8H+Hhj6FWO3V1C87jTpu7lvtTcrjhcAY/Do7D+Ro7lrxshSKEsLONHrrN0sUx2cUxhBBjsSqUEZdaVkr5AVZTGQMGDOgyj4KCwvnsMNRT3mZipruOlPR0BgwY0B6V8cCaH+lj8aTKr4YkSxbNZkcCzywVlhLT6t/SananbVgibU0biY/7GyqVHf/YeIIWo5nnJsdgNpnwdLbnzqFh3Dk0jILqJlYcK2b50SJeWJ7CSytTGRntxaykQCbG+6KzP785ympqYUmZgSWlBvJa2nBUCaZ46Znj58Fodxe03cRPKUg9QerOrQyePQ+Pwy0U66H1Hn+a1AXknvgdFRXraXV7ikJ7X2KawniopIVTTc18OfUD3BzcOlemdbT6I4ubgaW1gT1f/hF94Rb6q9NRrbsP81p3NJbhOAdOoyHNgvHfTXjeEYfWV4djojcNPxbjIWKIiIigMOUk/UZMZX1lLb8K8yMoaAE+PpPJzPwrefnvU1q2gpjoF/D2ntSj+adRHi6s7hfD4bqma65MoIcKRQixDbhbSplr+zwI+BBIvECxQiC4w+cgoLiLuvsCHwFTpZRVl1JWQUHh8vm+zICbRo13QQ5ms7l970lFRQW6U0bMasnq4s38ynE3ebE308vRzVowfTWasoP82PowWp8l6NTR+PnN4nRZPd/sz2fB4GB2vPZrlpcU4eDiipOrHp3eDUe9G3F6N/oF6ykP07PbYMe2wmq2nqrAUatiYrwfs5ICiAt1Y011HUtKDRypb0IFjHR34elwP6Z56S/acJpNRjZ99A6u3r4kDxxG/pw5OD8eQJ1rKgcOzm5f+vsL51l8k5fB03mfUWzx5w9j/0qcZ9wF69647Uf2F+i4MeIflOYYsW/bg7PHfnTNmxBlq3Ae9gLlx0ZQ/u+juM+JxqmvNw07img+WUVCQgIrVqxgqJ3g35VNlLYa8bPXYmfnRXz8XwkImMup03/gxMlf4OkxipiYP+DkFHbR7zFa50D0TwwlfKXo6QjlVWCdEOKfWM1RU4Hund9YOQBECyHCgSLgVqCTi04hRAiwFLhDSnn6UsoqKChcPvUmM2srapjr58GJzTsJCAjAz88PgH3rd9HX7EN9QBPereU4NLYRNPIX1oJmI+Z1L1BrCkQ1Kojmlu/p0+cdhFDz5zVp6Ow1TNIWsL+kiMRJ00FKmupqaKqtoSI3m7zaGlqbrCF/fYBbgBJ7P9JcY1l7rJUVx4qRWoHZ1xEvT8EdTo1M0kqC7FzQVTbQ0laHys0dB2fnbkMOH1q9nOqiAmY98zw5Xz5B9dNNGAPrAYGLSzz9kr+2KhUp+UvARJ7O+4zfOUQwMvKGC96zvT/uoXZnAberRqNNk9j18sZl0hPYBTpbN3d+cxuaUx/h+4uHqfo2i+pFp9AN9Uft6UDTsXJiF1jNXgGlBaDxYGNVLXcEnN3L4uY2gIEDllNY9CXZ2W+yb/9UQkMeIjT0YdTq60NhXIweKRQp5XohxMPARqASSJZSll6kjEkI8RiwHuvS30+klCm2epBSvgf8HvAE3rEN70xSygHdlb28S1RQUDiXVRU1NFskY9Vm9paXM2OG1cV8WVkZDmmtoIJdGSuYFnCQDJ+BRAf0sRY8/AXq2mwOtP0GJ7dvcXVIxNtrEjszKth2qoLnJkWTuvgVAnrFM/7eh7s025iMRppqa2iorWFnVR3HG42kSTuapUBfWo9rfjWGQhM1hWrWmurJasgkpjEDr7aqdlu4ECocXV1x0rvh5Kq3/tW74ejiyv6Vi4id6kZpzS9pHVWFvcmbuNhfUd+QRlHRQkymejQaFzINp8lvOcxOt34ML88Fi+XsHFEHpFmSufoI+t31DKMXdmGu6KeEYR+mP5vJ3gWGPQELb0FdsBbvB+ZQuzaXhl1FqPR2tGbV4mHSEBERQUXKcUIGT2JDZV0nhQKgUmkICb4HX59pZGS+Sk7uvygtXU5MzO/x8hrL9U5PTV4vAHOBUUBfYJsQ4mkp5eoLlZNSrgHWnHPsvQ7v7wfu72lZBQWFK8PiUgMRjva0pZ1Ao9HQu3dvAHZv3Ek/sz/N/m002jUQ3FpG9qA/WQu1NmDZ8iqlbXHoxmppbSshIf4vWCS8sjqNYA9HkptPsau6iqmP/qpLZSKlJL3FyPc1bSwra6WsTY2LWstsHzfm+Loz1M0ZlRA0tprYlFbG8iNF7Mhw5bBbMmF6DWMC1Ax2acbVWENjbQ1NtbU01dVQknmK5noD+sgSet1chcbRhCVfg19mLHEvLUel1tDSUkxR0ULyCz7FN+Qxntz6JL5Sxzf+0xid9kfI3QERY87KapE0n6ykak0mjjUmau3B7dZYdLFeXV9b2DjwiELsfQfR52bcZkRgF+JC9eJTANRtyychIYHly5czzEHND4Z6Gs1mdOrzR1r29r70TniTAP+5nDr9IseO34+X1wRion/f7kH5eqSnJi8vYJCUshnYI4RYh3Xe44IKRUFB4fqjoKWN3TUNPBPizcktJ0hISMDBwYHS0lLs01pQITiYtZJhobmUG30IT55tLbjn36iaKzhg+SVuus9xdxmGh8dwvj2QT3ppPW/N7cOR958nKL43wQl9O52zsKWNZWUGvi8zcKqxBa0QjPd0YY6vBxM9Xc/zpKuz1zArKZBZSYFUN7ax5kQJy48W8Vmagc9Q0S8kgllJgUzv64+nTktZ2Uqysv9OS0sZev1gXFZKLMtSiVzxDirb3hEHhwB8fWdQVLSIf+flUNpUyqeTP+VvBRrqMt7E5chXiIgxSClpOWWgbn0uxpJGalVNpLuXMuXROTg7dx2yuDW/jsqPT+JkmYi7+l3qvl6KKnoYdgHO+DycRPk7R2ncXULQxABUKhWhlcW0aNzZWd3AFG99l3UCeHgMY/CgVeQXfEpOzr/Yu28S4WGPERJyHyrV9RfVsacmr18KIRyFEL2klKeklHlYNx0qKCj8h7GktBqA3jUV7G1ra997snPTdgaYA2n1M1OcVcqC+mMcGPArfNRaaCjHsustsluG4jmuhlaTgcjIZ2hsNfH6htP0C3EjsPgwmTUGpj/+LMXPPEtLSQnFfoEccPNil6sHBb7+BEaE85eYIG7wccND27P+rIfOjtuHhHL7kFAKDU2sPGZVLn9YkcLLK1Po45NPf+/tjAr3IinpFbT7Gyha9BQ+zz6LXWhop7pCQx6ktPQHNHW7+L9BL5Pkk8TN0sASn/HclbqCtvg8arfV0JZXh8rNjv36HLJUpdz3wP3dKhNjeRNVn6WgctYiet2O5ciXaDI/p/qkzZ+ZClSOWiyNRlo3FhOk96b6xDFcB4xjQ1XtBRUKgEplR1joQ/j53sDpjD+Rlf06JaVL6RXzIh4ew3t0D38uemryugF4HbADwoUQScDLUsqZV1E2BQWFK4yUksWlBobodRQf3YmnpychISEUFxejTW9Gi4aD+auJD2uhtUFLzAibX65tr4GplaOquXjZv4W3+0T0ron8fcMpKupbeXtuHw787V1C+iThfCqT0tWryQoMwT0jk6n1dUztIINwcKDS0ZFqnQ6VizMqFxfUejdUOh0qrRah1YBGg9BoEWp1p8+OGg3zNBpmBpaxJ+gQW6p82F8yiKNld/B1Koze38Dgrd8xoG8/PO6687zrP1BdTGqzioluaibaJuGneOmZHzCDe4p/oOmr9zDrbsR5RhjfnVxDeWUFd99xNx4eXbucN9W2UvnxSVAJvO/tjcbTERzuwXHPO/g98g/a6vQYixtozaulLdsaECys3pP81lSSy5pY32ahtlRgH+iMNsAZta579/QODgH07fMOVVXbOXX6RY4cvRMfn+lER/8WB3u/y3sgrjA9NXm9iHX3+jYAKeVR2wosBQWF/yCO1DeR1dzKnR5O5OfnM2HCBIQQ7NiynQHmEIzekvyjKTzkfoCjIZMZ7OYPlRnIQ5+R0jgR/8mZtJgbiYj4FSW1zXywM5sZff0Rabtoqq1h6PTZFDz4CzLColh1y3xuzcskqKQAS30dloZGLI2NyOZmLM3NmGtqoMPOcoQAtQohVGc3nUkJJlN7FpOnpH6mmeaBFjwb4J6jah7duZ501zC2BfVje3Mi6+PnIICEd/cwNMKTIRGeDAz3oKq1kN/u+i1D3KKJ5xSlZcvw1cyicUMeQeYQUnWRRKm24/LoS3y7ZDElZaXceuutncIgd8TSZKTyk5NYWkx4P9jXqkwABj2I2PM2mowv0Ux4EfpYJ97L3zmKpcVE7z792LUrjfDSErZ7hbN7Tz59aq2uU9Ru9mgDnbELcG7/q3btbNry9BzN4EHryMv/gLy8d6iq2kZE+C8JCroTleraxkvpqUIxSSlrz5mIUjYRKij8h7G41ICDSuCXl0GpECQmJlJUVIQqvREHqeVw+TaCEnQ4tzTjNPRhAOTmlzBJO1IdZuCjeRU/nxtxdo7h998dxWKBp8aEsu73rxOW1B/zqnVo6+tYcdu9fHz3XDRdTDifQVosmEpLacvNpTUnh7bcPNpycmjLzcVYVNRJ2ai9PDH7qmh1L0ddpyUofxT+sbdi/+tQ+DVEmUxMN5loazNywuTEAYOFPVlVfL47jw935qAS4OBUjlo3gZHx83Fq+g25Ke8itwaistOyYJQfC9XT+FPWv9j2wwdkZFQyffp0evXq1bXsRjOVX6RiqmzG657e1qXDZ3ALgdgZcPBTGPVrsLn6d0r0pmZlNr5944gsiSSnMBu1JYy9SW6MifDHVNpIW3EjxqIGWlKq2qtTOWuxs41gzvxVu9sTEf44/n6zOHX6ZYqKFxEUdMdPeTSuCD1VKCeFEPMBtRAiGngC2H31xFJQULjStFks/FBmYLKnK6fXbiAmJgYXFxdWLl9Bf0sYZk/IOLiXmz3zSdXH07vXCCjYj0hbyeH62wiafohWaSEi/ElOFtWy9HARD42OoHLfZlrq6xg8bDQ1v3iCDUNG8+iYIRdUJgBCpUIbEIA2IADdsGGd0iytrRjz82nOTKfi2BLqT+1HXWZBl28P9W1Y2E4R20GjwS4oCLvwcOzCwrALD2NAcjLD+8fw5ARoMZo5nGfg5a2LyCgWUDmEXy3KZqDvIB5O/JQVccfonTyXQb28+euBGzBmv4t9+veMGPE7Bg4c2KXc0iypWphOW14dHrfF4hDldn6mIY9A2go4/i0MsG7Zc+zrTc2qbJqOVdA7sQ8Z2Zn0MVrYZGzh0a0FeM6Pw2W0ne36TRhLGmkrasBY1ICxuIGWDAPYfEAKBw12gTq0Ac5EB7yCCDEjxLWP5thThfI48DzQCnyDdX/IH6+WUAoKCleezVV1GExmhpmayGlspF+/fhQUFGA5VY/OEkJKzT58Il0Jbcpny8i/Eg/IDS/Qggc5+tF48weCAufj4BDIn1bvxUNnxwODA/j2mZeI6DcQw4ef0arVYhiYhLnt1/y4uxVv74l4e03Eza0/VifiPUSrosLpADkub9E2qBLv6ZOIjHgGnS4Sk8FAW24ubTm5tr/WUU3jjz8i29pArSZs4dc4JibioFWT3rKCetVXfBH2PD6n9ZxUGTmhGUtN62rsXZZx19cRaNUq9DEurPMcztjqQ6iHD+tSLCklNT9k0pJWjdvMSJz6enctf8hQ8E+Eve9C/7tBCNQudthHutF8rIKYx3qjVquJUzfyjYsruVWNmP51GM/5cdiH61HZa7AP03fa6yKNZoylTbQVW5VMW3EDDXuKwSRROWlwfOE/JB6KlLIJq0J5/uqKo6CgcLVYXGrA206DKvUYzs7OREVFsfDrhSRbwrHoBSePbmPccBNV9XoShy6A9NWIgr3srX2E4JnbaBN2hIU+yqa0cvZmV/PyrAQytqyhpbGB5NAYmj9dxNfTbmL+8DwqqjPxcB9OYeFXFBR8glbrgZfXOLy9JuLhMaLbnd9SSioq1pOV/TpNTTno9QPo2+dd9PqzXpA17u5o3N1x6hCmGECazRgLCsi7516Kn/s/wpctZV/JISrXZfCl4VXsLBqc+vlww/gQZns4UFj0OKdOvcCn8zWsO6lnY3oRC/tM44bK7Tz2579SETSZoZGeDI3wJCnEDXuNmrqNeTQeKMVlXDDOwwK6v9lCwOBH4IeHIWuLNSIkVrOXYUkG6ioTUVFRpJ5KgYShHJkdSsiaEio+PI5+agTOIwLO2+sitGrsgl2wCz4b4EuaLRjLm7HUt/2ssWe644IKRQixkgvMlSirvBQU/jOoNprYWFXHAm9XsjdlMHz4cIqKijBm1OJmDiOj5SieXk70rd7I+th7mWbvgNz0IvUihFKfPvjI3xMa8ggqjSevrtlBpLeO2fEefP7BD0T1G0jlux9Q5RvAgIF6Kqo/IDDwdmJ7vYTJ1EBV9U4qKzZSUbGekpLvUakc8fQcibfXRLy8xqLVWuOvGAz7ycz6C3V1R9Hpounb9wO8PMf1uKEUajV2YWEEvPIn8u+9j+wnX8DNcwLzzdOwS3DHfXIEWp+znnz9/W4iO/tN7Fu+RJ+bwP1+bhyMHE9JhjdPOO7lV23jeWtzBm9uysBeoyLJzYk+lUaGx/owbGzwBSSx0fsm2Ph72Pdeu0JxTPDE8EMmTccqSEhI4NTSpYRrVWxqa+aBx5KoXnya2tXZtBXU4T4nGlUXDjM7X7MKO38d+Ot6dI+uNhcbobxu+3sT4Ad8Zft8G5B7lWRSUFC4wiwvr8EoJfFVxWRLSXJyMqtWrqKfjEA6qzhyYj3Dx7lDCXgOfRCOfIGoymCX4TmCZ6/FrHIlNOQBvt6bR3ZlIx/fNYDj61fQ2tRIJA6oy0r5/qZ5zArbiNniR1TkMwBoNM74+kzF12cqFouRmpr9VFRspKJyIxUVGxBCjYtzAiZzM01NGdjb+xEX+xr+/jddmonMhjRZkCIMbcw4jNtWUzjJkZjH7sAnJvq8vGq1A97e8ygufge9PoQFCx6lt1nFIr8pPFnwNaueiqRW482+nCp27i9k96kKPsLCR+kFOL5czIAwd4ZEeDI00pO+gXo052zORGMPA++HbX+GygzwikblpMUhxp3m4xXEjEtErVYT21zHRpOFBo3A8/Y4GnYUUbsuB2NpI563x3dSgtc7FwywJaXcLqXcjtV31zwp5Urbaz5nXc0rKChc5ywurSZO50D1kYOEhoZSX19Pc7YBL5MLeZY0nFwciavcxg7fUQz0DUBue40KEqgP9qfVsofQkIdoMjrx1uYMhkV6MjTQgcNrlhPXtz+mhYvYm5DE7PEGmpqziY3903lx1wFUKi0eHsPp1etFhg/bRd++H6Jziqau/jhNTRkAaLXutLSW0tCY0SlY1cUwN7RRtymPktf2U7M8i+rBo6nWa/A/vpXgwK5NU83NzWzbKjGbNQwZUoder2eEuzPbQ25ASAsc+wa9k5ZR9g48lNnCN2EBHHpuHO/d3o+5A4Ioq2vhb+tPcdM7u0l8aQP3fLqfzPL6zicZcA+o7WDf++2HnBK9Mde2IUpbiY6Oxi0rHZOELVV1CCFwGR2E1319sDSaKP/3UZpOVPT4Plxrehqx0VsI0R5I2rYHpZvZKAUFheuJrKYWDtc1Md4OagwG+vXrx9atW+kvI8FRxcHUVfQfEoirsRZD8r2o9r6DaChje9UCAgeswM7Om+Dgu/j31gxqmo08Pz2OQ6t/oK2lBX1eGSqTkeZ+ATSq1uHndyNenmMuKI/RWEtm1l84efJRGpuyCQm+j4EDlhEV9RxqtRM5OW+xf/90du8Zy+mMVzAY9iOlueu6Shup/v40Ja/tp25TPnaBzpyYVMl9gX8k76lZqMuqKPvb384rZzKZ+Pbbb6moaMLdfSa1dZtoaSlGJQQjIhPZrU/CePhL2grrqfoyFY2XI153xuPp5siU3v68NKs3G54azcHfTeDf85O5MTmQQ3kGnltyorMidPaBPrfA0YXW2PWAQ5wnQqtqN3u5lBbiphJsrKprL+YQ5YbPE8lo/Zyo/jqdmtXZSPP1v1Ojp6u8nsLqEDLb9jkMeOiqSKSgoHBF+b7UgAoIyDlFmb09Tk5ONORU4W+Motg+B7WDhsDGPZxyCmNEzEDke7+ggBEQraXVcoxeYS9RVCP5fHceN/cLItwZNq9dSVxkPB5LVrBm2GiGjTqJET0x0b/rVg6zuYXCwi/IzXsXk6keP78biQh/qt3ZoatrX0JDHqC1rZLKys1UVGykqOj8SX13t+EYM5to+LGY1swa0Kow9nEgM7qcE5YDfJn6JaODR3PruJeoyHOh+tNPcRk/AeeRVqOKxWJh+fLl5ObmMnv2bHr18mb3nuXkF3xKTPTzzPP34G9+Uxl26lUqP12EyiERr3t7o3I6f1mul7M9M/oG0FdtT/XuMtbmGfjL3/czMcEX33A9PqEu2A1+GI5+DUe+hGGPo7JX4xDnQfOJCqInJWGn0ZDQ1simKhVGi2wPGqbR2+P9YF9qVmfTsLOItsJ6POfHoXa5/nx4naGnq7zW2fafxNoOpUspW6+eWAoKClcCi5QsLqtmhN6J4h9PkJSUxM6dO+kvokAr2Jv6A8PG9yagYCNfJv+WO/a8gTQ2s6NiLqFjFqGyCyYgYC6Pf3MStUrwzOReHFixEFNrK86HTlLl6kafEY00GTPp3ftf7RPsHZHSTEnpMrKz36S1tQRPz9FERv4aF+fYLiQGezsvAgPmERgwr9Okfnm5dVJfmO3QVfZB29abfWEmFtpvpqalDk6ARqWhv29//jzyz6iECu8nf0nDjh2U/O53RKxcgdrVlS1btnDixAnGjRtHYqI1RqCvzw0UFy8iPOwxgh30GCKm0ZD5Fo7m9egfWoBGb9/tPT65vZAd32Ywws+FQ0YzyyoNOP/QgEAgBHgEODPZsR+6He/QGHYnbn6uOCX60Hy8EgpbiI6OJj8vg9rIRPbXNjDc/ay5UGhUuM+Kwj7EFcPSDMr+eQTPBbGdXedfR1xKCOD+WEcmGiBRCIGU8ourIpWCwn8LFgts+SNUnobIsRA5DjwiLl7uCrG3ppHCFiPzZDONJhM+Pj6c3p9CcFs0Fa4lSLXEW5tBnVpHSNxE5DcTOG2eiq53DW2W08SHv86RggZWnyjhl+OjcbY0c3T9aiL8w/E+tpEt44YSH74XT8+J+HhP7XRuKSVVVdvIzPorjY2ncXXpS3z83/BwH3pRuRvaGsioySDDkEFhUR5+aREkl0xA5ZpPgf82yn2O4eR7iCgpeEYbiIPrVEL8ZxLlPRhtB/cjKnt7Al57ldxbb6Psz69SNOcmdu3aRf/+/Rk5cmR7vpDQBygt+4Gioq8J8X+QCelmlvmMY375ZtT6rs1tFotk9/eZHNtSQFgfTybel4DLqXKe+OYIvnfHM8hFR1l2LWU5dRwsmMJE3Z/Z/Jd/U6QeiV+oC33VgvJtBcT0j+X46hVoIxPZUFXXSaGcwSnZB42fjuqvUqn44AT6aeE4Dz9/afG1RvRk4ksI8SUQCRwFztxdKaV84uqJdukMGDBAHjx48FqLoaBgxWKBVb+Ew1+Asx802GLSuYdZFUvkOAgfBQ5Xr7f5VHo+K8preDJ9H1qLGbVaTUyRB5FGX1bkvU2fsf0ZWvgq3wXfxDxdK5zeyOel/yTqlnewc7Rn0MBV3PTuPoprmtn27Bj2ffMpx1cvZ0hOBSXunng92YLZuZohg9djb+/bft66uhNkZL5KTc0+HB1DiYx8Bh/vqec1gEaLkbzavHblcdpwmgxDBsWNxfRqDmN29ThG1CUjEOQFVFKfJPCLCSXSPQrRmm9bLbaRxkZrwFcX5wS8vSfi5zcLR8eQ9vOUv/UWVe++x66RI9CNHcu8efNQn7OT/+jRe6irT6HX6XdpyGni/wYW8t2Rh2Hmv6BfZ0eTbS0mNn6cQu6JKhLHBTPs5ihUKoHFIpnxr100tJrY9KvR2Gms09TSZMLyVhKtah/2eb9DWXYdIYZm/LWCtXWtVPjsYWPyWJr0etZFhuEV6Izq3FVjgKXZRPXi07SkVuGY6I37TdGo7H9aLHkhxCEp5YCfVImNno5QBgDx8lKWXSgo/C8jJax91qpMRj0LY5+H6mzrJresLXD8Ozj4CQg1BA08q2ACkkF9KYaD7k4vMZjMrCyvYbyzHVXFRQwYMIDUA8eJMMZicKmk1dJMjG8dmgITLTHTUG+6m6Om2/FKzsZoyScu4j1WnSjnaEENf725L5aGWo5tXEOgzgfX+iyKJvvS6pBHXNRrnZRJfX0qhw7filqto1fMSwQEzEMIDeVN5ecpjuzabIwWqxdejdAQ4RrOzaYpDK1IwK3SEexVOI/0x3lYACHu52yGtO+Dq2sfIiN+RVNTLhWVm6io2Eh2zltk57yJXt8ff7/Z+PhMo3XmTGoWfcugQ4eJ+dOfzlMmACHBD3Dk2B1UNK0hfPaDRDuHkXEqlPDDX6LpoFAaDC2sfuc4VUWNjLo1hj5jzjqPVKkEz07pxT2fHuDbA/ncMTQMAKHRoB76ME4bnmfs3BZYMIiGlEpqvkxj1LBANhf6E1CWyw59H97512H8W8En1BW/CFd8w/X4hrui09ujctTgeXsc9dsLqdtgjdfieUccWu/rY2lxT0coi4EnpJQlV1+ky0cZoShcF0gJ65+HvW9bw8JOfNm6c7ojpjYoPHBWwRQfAaR1tBI+2roRLnKc1dFgx2IWSaXRRFmbkfJWIxVttvdtJsrbjJS32v62mWi2WB0/PdtaSdOBPXh7exNV7klssx9riz8mbFgSQ2re5qBDGL3c3PEuOshnRX8n5pa/4OQcQO++3zLh7ztwddSy6vERbPvsfU5uXM/Y4xkcj+tF4C/S0bv3Iznpi/aRh9FYw/4DN7LdUE+upg8tFgu1rbWUNZXRaGxsvw4fJx+i3aOJcYux/nWMwue0Iy17yjHXtqLxdMB5eCBO/X0uurnvXFpaSigtW0Fp6TIaGzMQwo7qqiCMGcEkfX0Al4kTCPrHP875yiQ1K7NINT+M1LcxfOxWUhvb+H7pS/wh+1149AB4x1CeV8fqd45jbDUz+YHehCZ4dvH1S+Z9sJecyka2PzsGJzub/C218Pd4iLsBZr+HNEtKXt2Hfbie0iQLn65YxVdDJvOwnTPjCy2U5tRRWVCPxba6y8XDAd8IV/zC9fhGuOLaaqZm8SmkSeJxSwyOvb3Ok6UnXIsRiheQKoTYj9WfF6DslFdQOA8pYfNLVmUy+JGulQmAxg7Chltf41+AxirI2WZVLplbrI4FAYNrOBsDJvNe+N2UGc1UG01duq5w06jxttPgY6elv17X/j5Yqybl83UEBQVRkltEjDmBetc66nOqGZTgisumcg7HPsrYI39gt/Ex/Acdw2QpJzLiDT7bnUdRTTN/vbkvjdWVnNi8jrBGFW1aLUFTSpEqQVzsn9uViZRmTqY8ya7qMr6p0gCHzpNTJVR4OXjhr/NHp9HR1tRCUdZpGnJz8GrRE+AfQsjMeJzjfBCqy5sfcHDwJyz0IUJDHqS84iDbt72Gqz4d7eBsGmud4Yd1lI7ohe9ND7XLXr+9kMbdJQSMvp1s/khFxUYSfKbwUsRMzDkfoD7yJdk+T7DxkxQcnLXMebY/noFdB9wSQvCbKb2Y8+4ePv0xl0fHRtkE00PSAuvIdMJLCBdfHPt40XSwjKhZ/XC3mAgxt3HYUfLivBgATEYzlQUNlNrmYkqza8k8WG69lxpBYKCOBGmh8vvTBES6oXb86aPbn8KlxENRUFC4GNteg13/gAH3wpRXu1YmXaHzhN5zoPccshqbWZG6j4bTmxhT8SNz09/DIuFg8qP42GvxtdPiY1MYPvZavLWa80LonuH48eMcbmmhvr6eftpoVK2wP2MlscNHYU5dRJ6DP70NJ2hxDOdY5RDiQl9Erx+OtOvPO1u3MT7Wh+FRXmz66G3MKh29Th8mbWgI+ohMoiN/h6PjWRckWdn/IL1sF99W6wALn0/5nDDXMCqaKyhrKqO0sZTSxlLKmsooqSwireIE2y2VtKqMVj8cNlSHVHileuHn5IevzhdfJ1/8dH6d/no5eXWafO8Ko9HIiuVHKStL5o47/oZOl0eJ5/e0Hd1C1Z/fJFe3GL/Ym3EtH0rTunock7wJmDSUkv2fk5f3Pt7ek5kaHsMGj6GMPfA16wtH4hXqwbRH+qC7wKovgP6hHkyI8+H97VncPjgU/Zklx4Mfgv0fWJXK2P/DKdGbxj0lmDPqiYmJIaC0gP1qOyrbTHjZadBo1fhF6PGLODvP1ljTalUuObWUZteypbgJvU7D3GusTKDny4a3X07lQogpwFuAGvhISvnaOemxwKdAP+B5KeXrHdJygXqsiwBMV2pIpqBw1dj5Bmx/DZJuh2lv9FyZYDWT7DI08EFhBRur6rAT7sxMup91P4bjVtPIrafewzHfgDphJtHR0YSHh2Nnd/H9CEeOHEGn01FTZSCeZJpdm6nMKWTmqDm4L/sjn0U8yBPZH7Ot9f8IHbYbs6WGyMhn+NumDJqMZv5vWhy15WUc3bKZfgVVlHp54X5LAc6uyQQHnZ1XKC9fT2buu3xR441RNnJj5I3087U6dPRw9KCXRy+kyULT0QoaDhVhLGlEpdPiNNgP2d+ZClV1u7Ipayxrf59Zk8muol00m5o7XZdA4OXo1a5gfHW+7Qoo1iOWUJdQlixZQlFREXPnziU0NAKIwMtrLI1vHSf/lgU4f95A1v2vAwLdyN4EJ87HIgMJCXmAU6deoKZmPzM9k3nWbxpTq3YxKC6LxIfHo7Hr2ST4M5N7MfWtnby7PYvnptqWSHtGQsxkOPgxjPwVdiGuqPX21k2OgxIIXLMeS2Akm6vqmOffdZRInZs9EcneRCRb95abzRYaDdfHLo6LOYesp2vnkALrKi/XC5RVA29jjT1fCBwQQqyQUqZ2yFaNNbbKjd1UM1ZKWXkhGRUUrgt2/xs2vwx95sLMf4Lq7IghvbGZjMZWAuy1+Ntr8bXXorYpm2azhWVlBj4orCC9sQUvrYanw3y5K8CLlN272F5cALPepnH7o0yr+4HPjuo4ePAgGo2GsLAwYmJiiI6Oxt298/4PKSWZmZnk5OSg0+no5xCNqhYOlq4hauBQtDkraFbZoRbQ4tKXzOpoevn/Hk+vyZS3hLNw/07mDwohyseZDe9/gkbjgn9FGiU368DRQlzcq+2+thoaM0hNe5Y1jf4UttTiqHHk6QFPt8tibmijcW8JDXtLsDQY0fg64T4nGqckH4TWep/c8SLGPabLWyulpN5YT1ljWftI54ziKWsqI7s2m93Fu2kyNQGgFmrucbkHwykDU6dOJT4+vlN9ul598fnV05S/9hdCvp5PyyQz9SF7ST/9f5zOfAkvr/FoNC7k5LxLwY5fUqRPokLrQaLXDjR2j/T4kYj1c+XGpEA+253DPcPD8HW1LSoY/DB8eSOcXIJImo9jojcNu4qInN0P/9Ym3CwmNlTVdqtQzkWtVuHq5dhjua4mF1QoUsrzF0T3nEFAppQyG0AIsQiYBbQrFCllOVAuhJj+E86joHBt2fcBbHge4m+EG98F1dke7A9lBp5Iy6etY/RBAV5aDSoElUYjRgm+dhruCvBkprcbYU72mGqq2blzJ3379qVP8kCI/AE+GMv96k3kTvqU9PwKMjIyWLNmDQDe3t7ExMQQFBREZWUlR44cobq6GgcHBxobGumjCaHVuZXinEzumH4ndkumsdhnArMLV7C1+mnCRmzHIpuJjPgVjy9Ox0mr5skJ0dSUlnBg714mH0+jIMIH9bhCIsKewllndbZoMtVz4sQjHG/WstVQC8DjyY/j5uBGW0kjDbuKaDpaDmaJQ6wHzsMDsI9yu6T9E0IIXO1ccbVzJdr9fCePZ2hoa6C4sZjH1j3GF4YveHbQswwePLjLvC6T51D18TJMB1YT/dsfsI98hbq6o5SULqOsbBUmUz2Gmp2YXY3Mj/wF35kn8UjGYqgvAxffLuvsiqcmxLDyWDH/3JzBK7P7WA9GjAHvONj7DiTehlOiNw07CjGeqiW2Vy+CK0vYqrGjxWzp1pR5vXI1jW6BQEGHz4VA199u10hggxBCAu9LKT/oKpMQ4kHgQYCQkJCusigoXD0OfmpdHtxrOsz5qH3Jr5SS9woqeCmrmMF6HS9GBVLZZuRAXSMbKus41diCBHRqFSopKWsz8XlxFZ8XW0O/CilxGjyJKHc9xzKK+F1kANpbFyI+nUr4/j8Qfscypk6dSmVlJadPn+bYsWP8+OOP7WI5OTnRv39/8vPziRVBqA2Sg3WbCEvqj5dhNypTM8dd45jW2kpxuQfR3uvw853N0RI3tqSf4rmpsXg627P2i2/xaQQ7oxH7m8tx0MUSGvqQ7RotpKQ+Q2F9AQur9Nip7PDT+TEvci5VX6bSnFKF0KrQDfTDeXjAVV3a2tjYSHZWNhkZGfTJ6sP24O0sMy/jRtONOGg6Lzc21bRS9VkqTsPvo2H9i5T/+UWCP/4YvT4ZvT4ZF/Xj7FzxMV59P0Yftg8X0z7e9n+BRwsX0frjvxBxD2BuaMDS0IilsRFLYwOWhgYszS04jxqJfVRU+7lCPJ2YPziEhfvyeWBkBGFeOqspdMjDsPKXkLcbbegwNF6ONB+tIGFkAkEbt3HCJ5jdNQ2M8+zWCHRdcjUVSlddkEvZxzJcSlkshPABNgoh0qWUO86r0KpoPgDrsuHLE1VB4TI4uhBWPQXRk+CWT0FtnXg1S8mLmUV8WFjJDG89b8WGsM1Qz4cFFeytbUSnVnFfkBf3B3kT5miPlJJak5mSViPFrUb2nMpgz+kMPKNjadRqeb+wgrI2I2/H90M981+w7EFY9xvqRr1Eamoqhw8fpqamBkdHR4KDg1Gr1eTn53Po0CGQMMMhCZNTG7k5J5j3+Ku0rFvAcdc+jDAcYlPBPMJGbgIkIaGP8/RHaQS6OXL3sDCqi4vYfSKd+cePUDbQCXNYHXFxr6GyTYbn5r5NacUmFjVEYJIG2ixtPNP/GRp+yKU5pQqX8SG4DA/o0gfWT8VsNlNQUEBWVhaZmZmUlFh3NDg6OjK672im9pnKUzue4k97XuYPCc8gm5qxNDZgqqzBsPgE5toGnId6wsiR1G/YQP7d96ANDqKuoIqqzHIiaUOa3ahPqsZtkxMhg9M44JpAzLH3OZHxDY771TgcU6Fq69zMlb/+Ovobb8T78cfQ+lsjKD42LorFBwv5+8bT/PM2W1CwvvNg00uw9x1E2HAcE72p35JPxM39CW+sxU5a2FBVpyiUDhQCHaPQBAHFPS0spSy2/S0XQizDakI7T6Eo/A9hNkF1FnjFXNKE91XhxPew/FGIGA1zv7TGvsA6J/JYWh6rK2p5KMibX4f7cdvxbPbXNhLsYMdLUQHc5u+Jq+asWUwIgZtWg5tWQ6DFyP7tG5gbEMCdwxIRQvDvvDL+lF2Cg0rF671vpi59Fx4HP2HnwVwOkEh4eDgTJkwgNjYWjcb6k7ZYLJSUlFB/vAz19kaONO0kKK43QeoSqM3j++hneLKmgDWAh9sWAgMXsCYV0krq+NdtyTho1axa+h39s/JocHbEclsNoSEP4upqNdtUVm4lO+ctthrjOF2Xh5PGicF+g+mXEUH9kXxcJ4biOv7KWgwMBgOZmZlkZWWRnZ1NW5s1SmFQUBDjBg0ipKoKzdFjtGzZgqW+gZv7Cr5nJV7v/sDkw+f3NZv3nH3ftG8flpR0ms0OODrrcA/zQpb7UC+2o+0dxv1FIXzuO5U3MlJwjnag6p4WVGjxtB+Gr9tU3L2GIRBUf/oZhq+/pm7VKtwXLMDzwQfwcXfn3hFhvL01i4dGR5AQoAetozU08K5/gCEXp0Qf6jfn05ZqICEmmmBDBevt7Xk1OvC6c69yIa6mQjkARNtc3RcBtwLze1JQCKEDVFLKetv7ScDLV01Shf8M9r0LG34HYSNh/B8geOC1kSN1OSx9EEKGwa3fgNZqUqk2mrj7RA4Haht5KSqAB4O8+UVqHvtrG3m9VzC3+Xu0T8Z3x7p16zCbzcyYMaO9IXks1BdDUxNvl1bz/+y9dXgW19a/f8887skTdw+BAMGCuzu0UFoqlLZQ95ZSt1N3d6GFKm2hOMXdHZIQ4u7J4z7z+yM9ldP2PX3P97WeH/d1zTXzyOzZo5/Za++11tlTJxlQGM4VikwmhbYzdObVWHpN+005oigSHx9P8/fNeLQS58oPceE1j+Hb/yTt6gjCJRfbSiaTNnIFCoWG6LjreGH5aXonhzG1ZxytNdWcKKzg0qoK2maD1pxEWtptALjd5ZwpuIMyOZW1jZVkh2dzrv0c9+pvwbG2Cn2faIwjE5CkEKL4r4cF8fv9VFRUUFJSQklJCW1tbQBYLBZ69OhBhk6HtbQM3/YduI8exRMKobBa0efnowgP4zqjgZrQDpZMqKbPjKvIOGYi0BzEMiUHfc8ERKMR0WjE39JO2YyZ2BWRuG98mpFX5KL4cbCA58ydNKs2MeSSy2g/UYGn9A16Kkdj630FDQ0raGxaR3PjVjQdscTGziTlrmuxzruC5tffoO2TT+hYvpyIBQtYOOcSlu2v4oWNZ/n4qv6dO5i/APa+BgffRzXhSVRxBjwnmskdk0vStr1st8Zw2umhh+n/hhf8n+G/TVBkWQ4KgnAzsJHOYcMfybJ8RhCE63/8/R1BEGKBw4AZkARBuB3oRqcj5Yofbygl8Lksyxv+u+p6nr8IZ1aCOQGai+DDsdBlMox+EGJy/+fqcHY9fHM1JPaDS78CdefNXunxcdnJMqq9ft7NTWV6dBgvVzSwoqmD+9PjuDz+tx7Vvyn67FkKCgoYM2YMERERhEIhzp07x5EjRwieO0deei7HkrLImpBEQtINCN9Mw7zuZjyuWILEI7mDSK4AkjvQuez0E2hwU+Q/SExGJimJFoQVm1machUjfQYKNR2Ipt0kJd7Ikv12mhw+3r68D4Ig8OV33zHx0AHs8Vq8I+30yX0OhUJLMOji5Kkb6AgpWNLkJt2STml7KTdYr0a7wYEm3YIwxMjHd1yPrakRndmM3hLWOZktGMLC0JnD0FssGCzh6M0W9GFh6M1hKFQqGhsbf2qFVFVVEQqFfhrR1r9PH5LdbsSjR3G98Sb+ykraAU2XLkQsWIBp1Ei0PXog/CKsyov+65i7Zi4POb7lNeU9ZCzsi3Hgzwm3PE4/679pQk6bTbeiT8kWDqNQ9fjp9+TkhTQ0fk9t7WfMTL6UVVEjmXXqO8InPkN41/5kZz9CS8tm6iuXUlXxDo6yb+iV/jTxTzxOxNVX0fTyKzS/8gqKz5Yxf/YdvHo2wMHyNvqnWcGS0DmQ4+inMPJe9L2isK2vINWaSZaznR2yzA8t9vOC8ndkWV4HrPuH7975xXIDnaawf8QO5P131u08fzHs9VB7GEY/BANvgP1vw57X4O0hnQmMRt333x/F99xm+HoexPaEy5aDptNT+oTDzeUnywhIMl/lZTAwzMjqpg6eLW9gdkw4NyaYcLnKkCTfj5P/F8udk9/v5sCBzXTJEYiI3M+WrV/T1FRLMOAl5Aujn2Yyc6sjeRM/XyepCVtfxQ2Ni4hR34ly49W0+V9AFvSIOiWiXoWoV6II0+LUOynYuptpd90HBz8gKCgoMmVi3ZlB6vglKJUWtGFX8O7OI0zpGUffFCv1leU4T57FarfRfH2A+LiLCQ/vjyzLFBbdi91Zylfu7gSkWiJ1kQhtQaYd7Y/SqkYca+HrJ+4nGAiQP2MWXocDl60Dt72D+pIG3DYbAa/ndw+vLCqQFUpkpRKVwUhiZBSR1kisPj/inh1I775Nrd2BBhFLfj7h867ANHIkqoSEPzxlZrWZJ9T3cK10F892W8on+RN/+q29wcWaN0/iavcx+oGr0H1cQ/PLr2AcPhxNRgYAJlNXIqzDqapewtiBV3Fd4lQubtwABaug11wUgoqYkjJitm6lJsHI2dQWarbMJclmQtN1OkmLLsF99XyaXn6Vke89yrKJD/D0F/v4dvFERIWi81o+/Q0c/wJdz3nY1lfgP9NO74w0Yp0dbGzRcVda7B/t3v85/lQsr78K52N5/Rtz6ENYeyfceACif3QS87TDnldh/zsgBTojwg6/B8xx//XbL90GX1wCkVlw5WrQdfp9bGm1s/BMBeFKBV/kZZBt0HLC4Wbm0XP0Nkg8a91FXc3HBIP2f7KBXyNJAshKREkJKg9h7mGkeBchGnQ8qPXxHT7uM1q4IViI6ofLIX0MzP0CQfnzO6IsSXyy6GYEQWDeE88QejGb1WEDaQifgbbAR/Lo58jIuIc3Dg5k1fE6ttw1giSrngdfeZ0577+LN0/Cd42eQSO2oVSaqKx8j5LSZ9knDuerysMs6LGAr459wccNT2KS9KhmRfHda48TCgYZv+hx/KZo9GoFuh8nvUqBgExFWRnFBaepKC6mpaEeMRRAJUCYXo9OpUThduFpbcHjdOCTpd/tLxMVSvRmM3pLOHqL5VetIGNEJNEp6YTHx+Pa14BtdRkHepfyqPdFLu5yMQ8OfJCaojY2vHcaUSEw+YaexKZbCDY3UzZtOqqkJFK/+PynY9nWvo9jxy4np8sTvOMaytUrJpAYlYrqgrdg5Y1QuQdypiJPeZkTJffQ3r6P/u09MRTugqAHjDHIXafjdGbw0doKXo4fzlO1m5hxwxwMgwfDB2PB3QY3H6bp3VPIviAdk4zcs/swB9JzOTa4G3Ga/76kWv8bsbzOc57/XYrWgjUDorr8/J0uHMY+2ukotvMFOLKkc+RV/2th6B2g/3OOYb+LoxEqdkH5zs55WxlEd4Mrvv9JTD6vb2XR2Wq6GnQs65lOrEZFgy/AdSdOM0dYzSTXGqrsdiIjxxITPRlRoUUUNYiCGlGhQRQ1eD1BDh48xsmThUiSAq3aSJacRHZHLNbYSCyT02hUL6ek9FmEBJmslGd41KXAUe3naacNR0jPZXEXkVb6JSUvTON4qB8eux23w4bXbicY8DP51kUIp75GGXDzdewkhu5MIHnia6jV0TgVM/j26BEWDksnyarnZEkJmTt3gxJcF/nonvcmSqWJtrY9lJQ+T6N2IF+fO8LMjJnsqdrDE/W3YPBqEGeE8e2rjyFJEkNuf5RZX5Zh85z9zWEVkVAioQS0qhxMET0x6zWYQgFU7S0oaxtQ2xVoQ1oMYWZMackYUhLRRIShCHgR/G7wusDjQPY48DptuBztBGoLCdhakQP+n7alVGkIU0QRFZdKTkw+F8sX8tXZrwhvT0BcnYolRs/Um3r+5BSojIoi9tFHqL39Dlo/+IDI668HIDxsICZTDyqrPmBu9+l8HjuZB8rfh7cGdUaLnvEW9LoUQRDo2u15DhycxJlkH/2mFCGWbIUzKxCOLcUU9HJD9wS+ae/FB2E9ybt6AabBA4m9aCrqA49AySb0eT3oWFVKsimTLEcbB4BNLXbmJfxrgR//pzkvKOf5v4/XBuU7qcy7mjn7C+lrMTAi3MQIq4lYjQpMsTDlBRh0U2csrb2vd4rL4Fs6TQqaP+Gf6277UUB+FJGWHx+GGjOkDOnsQM2bC3orsizzYkUjL1Q0MDLcxAfdUzEqFdh9Hbx7+GXu96/AgIvwyLGkpd2C2dT9V5uSJIny8nIOHz7M2bNnkSQJjWhhtLU3CTUG1BYd5otS0PeKpmDXVg6uPIoqMg15wG62FY2jbH0SPQIqysfN5Y20rpTURHOXK5Ee7KUpqKbR2oeolDR0ZjMRCUnkDBxK8JXrKTRkYiURU0QBaApITX2MxesrCNOpfgpg+PHy77m24CS2mUEiYkcRFTkKj6eG02duI6hJ5b3qGtIt6XSzdiN7ZxjZzmQYa2TF+08CcMEDT3D9mlpCoRBX5Qg0NDbS4XATRESh0WMMs6I3WVGIStyNLTiaGnFXOGhFgU+lwReWhD8mG6+gxB2UkB3AaQ/wSzOZEgj/cfoRU+ekVoikhKm4J9aFe9cRbHIL5+oOUfj2bjSCTFx/He+HXuYiy1gGDZqAz9WAFJ7caX4CzBMn4pj8A81vvoVx5Ei0OTkIgkBKyrWcPn0Lme1rOWPtTahcQNRHIFy17lcRoTWaKHK6PMmp0zdSXvcRGd3vhO4Xgs8BxRtRn1nBItdn3Gq4geOjepF/8Ail+/aSdZEecdtL6C5dTcfqUvyn2xmckshqr5sNzR1/GUE5b/I6z/99Tn0D317DUyM+5iNFJlpRpCUQBCBbr2WE1cgIq5lBYQYMCgU0FcLWJ6BoDegjYdhdncEaVb9wcPN0QOXen0Wk8VTn9yoDpAzqHEmWNhzi8n7l+R6QZO4pruaL+jYujrXyQpckCDmoqv6Y4soPUcsuJPMoBna5A5Pp14MFnE4nx48f58iRI7S3t6PT6YixRhFRoaSXlIaoUWAelYRxcDyCSkF1wSmW/+0BolLSiMvKQRNRi9+0HKUYQ2L4I2gsadzW4mOf08fbXeKYsfEqqDkMV62HxL4/b/jcZvhsFndk30P4qcEMGfkUOlMQt2UpC5ae4LHpuVw5OJWvj50i/LZbiZKb6FgsMXj8HhQKPUeOzMHlqWSpuwenWs/y0YSP2PbZCi5sGEUwX8X6jW8gCAIXPfQUbx138OHucsYLTaSLLhJywsjMzCAjIwNzewfOHTtwbtuG5/hxkGWUUVEYR47EOGokhoEDEfU/d0DLsowvKOHxh3AHQnj8QTx+Cbc/+OPn0K9+c/tDuP0hPttXSWwQ3ouKIO2G3qARaKqoYtunO6msPsOqAVuR5BBTd8ei8ytQqtREpaQRnZZBTHomUZHR2G+4GWVkJGnLv0ZQq5HlEPt2DkJla+VM43ASPXUM91egvrPgV9fH3ykouIf6hhX06/sVFkufX/0muW1MfW0HTrebjdLtOAuVyEB0DwfN7mnIqXcQckk4Zli46cApziZmUDi8R+e1/d/Af6XJ67ygnOf/Pt9cjVy+k8z85cyOi+Tp7EQKXV52tDnY2eZgv82JV5JRCQL9LPofWy9metoLUGx5HMp3gDmxswXjqO8UkfoTIEug1ELSAEgbBqnDIaHPTw6K/4grGGLBmQq2tTm4IyWGOxL11NQsobrmY4JBB4cYQGTSjSzIGvqr9Wpqati3bx+FhYVIkkRKSgp983oTUaXCv78ZhSBiHpyAaXQyCkPntu0tzSy773a0BiOXPfUSGr0B6LTnnzx5PSqVhd69PgFNCpeeKOWw3cWnGRZGfzu9M9fKtdt/6kuS3xyIva2ai3t/xmWnDhA/4D265LzAvC863/A33j4cPzJP3PcY81Ytp/WmAOmTnyQufQ4FhYtoaFjBKd1FfFi8lscGP4ZwykX+wVSaktvZf/RLRKWSOQ8/xeF2JdcuPUIfpZ8xLZ3RcXU6SFLWEn1qNeqKTtHWduuGcdSozhZAbjcE8b8mvEjI4cdXZuOHFYXc5XUwMNXKxwsGEHAFWPfWSZqqHAydnYUyz86VG66kq7kLi8OuoqWigsbyEprKS/F7OltCsU4vfUprsQ/oi27uLGJq1+ALrOFctpHM5Od4+lgN75x5GC77FrLG/qYuwaCDAwenIAhK+uevRqk0/Or3bWebuOrjQ/xtcjpXmI8R3Pc5ivpddJTraG2ZijbvSsKmCiw+WcC3XfvzcfdUJkWF/Zccp3/kvKD8AecF5d+QoA+ey6Amcwr9om7g857pv/Ee9oYkDtpc7GjvFJhTzs6HQphSwdBwIxd7TjL86Cto6o+CqOrMkJg2vFNEEvr9uuXyBzT5Alx+sowzLg9PZUQyLPDtT0ISMI3iEccUBsb15ZWcpF8kmwqwbds29u7di1arJS8vj769+6CvCGHfXIXkDFCubKbHwmGEpUT9vMt+P189upjW2houe/IlIhKTflUXu/0Ux09cDUDvXksQdDlcdLyUM04P38Z7yV9+QWdf01XroOUc8rvDeCvxEk62Xsac3g9itBg5G3qDh1cV8v68fozrFsOjm3YxbdEtCBkepKsy6DtlLTW1Sykufgy3dQ4PnlzHhNQJ3BK+kODnNZRqSzlbtQWlSsVFDz+NSxvOlNd2YZZCXNSkIc5bQFz5XuqiBtAakYssKIg0B+g6Ipmc0Vmo/x9DrcuyTKjNi6/cjq/Chr/cRrDVC4BoUrNzcCT3bixiak4M/Yq8+Dwhxl/djbS8zuO8qnQVD+x+gHnd5rEof1FnmZJER2M9jWUlNJaXovz8K8LKqtiXlYBNr0Whkul2WRliMJ5PtQ/zysErMKWPQDV36e/Wsb39AEePXUZC/CXk5Dzxm/pf/O5+ylt/kYTru+uQT31DfWEOoeQnCFZsZX9KDYvH38BMq5rX+uT9tzj0nheUP+C8oPwbUrIZls3iw+Fv8pSyJwVDu6P5J2+0zf4Au9udnS2Ydgd1vgDIMsNCtdiNiXhELTIykgzST3OQZBkZ/uF7GVkGjyQhAI9HnCCp9UVCISdRURPwRi1gzlkFvUx6vuqV8VPdamtrWbFiBS0tLfTt25dx48Yhlzqxbagg2OwhEKVgnW0/+VOH0r9//5/qLssyG995lTPbNzPt1ntQn6ml5WwD0QvmkZAdjuLHYIEuVxnHj19JIGgnr+d7CMa+zDpeQpnbx3rDOXLWXNM5nLq1BKnuGCP7fc7VxcdI6vUxmV3eYuaHAjmxZj5fOIAil5cNN9/G+EO7aLknyIBZ2/AE6zl27HLUlsE8UlqFVqFl2YCPaX37FOWBcxQ1b0Ot1THn4acwRMYy5919nK2zcVm7mlT7WXofeRuFMoRCGcKvM1MfO4Ka8AG4VJGIcpC4YAVJoVIixWYEhbLTd0Sp+M0yCrFzLipANCFL4SBZkAImkH4c+SSGEHUeRL0XhTGAOtGEedwYHltewJJTtYxAwws3DiAq+dd9aU8deIovir7g2WHPMjl98q8vooCH0JqHKfvbSmSVCmnxHTR5Vdj8K9Enn2LD9rFkJoW4puZbvvXMJiy9OzHpWXQbNhK17mez3bmSZ6iqep+8nh8QGTnqV5s4XNHG7Hf2cc/ELtw4MrPTVPvWQBjzME3HBuCrsnHu8As8fMNdNJvNnCm+E2WPCzr7ZKK7/vl76J9wXlD+gPOC8m/ImjuQT3xF/rDV9LJa+aB72n9qdVmWKXH72NHu4JDNRVCWEQBREBB/MRcEEAHkEMg+kHzIkg8kb+c85KCPewnJUgFRURNIS70FuyqDSUeK0Yoi6/pmE6lWEgwG2blzJ7t27cJoNDJjxgyUsoRrSw2WChXKaB2aUXG8/8NnWCOsXH311Yi/EMjjG9ey7+3XGBARj+FMEVJHBwAl6TNp7DqJtJ6RpPeOJqlrOMFQE8eOz8frraJ77utgGcGFx0qo8wXY5V1N/N7nkIHN1kG8pnuI29PuxBKWxKamx3lvVzmrbx5Kt3gz1y39ljufegjHuBCRs+cRP3ABhw7NQKEwssyZyf6GQ3w68mNMyxzUtJRwoPl7jMYw5jz8FGGxcTy5toD3d5UzzaWiX3s5/Y68gSgEMCd5kBCRZBFRkpAlaNVmUh02knpjb4KiFl3IRoK3iERPEbqADTkUhGAIOSSDNgqFIRnBlILCnIKg6nxQS552Qq0lhFqKCbUWIzkaAJlWE+zoLnAuQUDWJuBRplHjy6fOE8fsIR5G5moxqUyY1CaMaiM6hY77dt/H2bazLJu8jC7WH0cQ1h2DFddDcxEuywVUvXsA6/z5xNy7mECgnd17hmExjeSZ+pF8ffgajitGsLfSiMdhJ7VXXy5c/MhPZjxJ8nHo0AX4A60M6L8OtfrXDq7XLDnEoYo2dt0zujMJ16czofks7tGbafuqDN1gkbsqT7Oh+wDefe9xRhv3Yoz3IETnQO4FkHshRP1+2P8/y/lhw+f5/weSBEXr6EgdSU1Iwb2Rln/y9wCS5CUU8hAKeTqXJS+RIQ8ztR6mqT0EAh0E/K34A+0EAm0EfO34A22dy4F2JOn3ExUJgorIyNGkpT2PyZiDKxTiyqMluEMSX/fKIFKtpLGxkRUrVtDQ0EBeXh4TJ05k65H1PHLuSdRqFa9Nepa+Q/uyctVKvD4v06ZN+0lM5GCQis+WYn/zDUba3aCoxdOlPwUp/cg3FZC5eyXhPbMoOgFF+xpQahSkdo8gJe917PrFnDp9I11znmZ5r+nMPHiaUcIoDijeIizk5P2EWYxq3Q1iC6ao5/h4RSUX9k6ke4KFZTXNTP38A4JmmWAfEylD7+boscsJhTwUGeawq3gJ9/W9l6jVMrXN1exp/JawsEgufuRpLNGxbD5dw/u7yuntE+nTUU3fo2+iMXiw54exI2ssIXOAaH8rCf4W0txVdPcfoTcHCcpqyrz9KfSMoUSRT4lhADHqSpINTmI14Yj+eJA6+5OUpiDqBAWaVBOa7HgUsZEIotjZae93s7VqG9+Xfs/+xoNISMQEY/EEAwSFo3gV+1HUzeebPRmsrfoIpfHc757fi1ZfRJQuCnPQj9nRiFGtwph/AaaITPJLHcifLOFgloDYOxe9eSjtHT+QlTqP42e7kKNppMf933Hih/VsW/Iuxzasps/kGQCIooZuuS9x6NAFFJ19kB7d3/pVbK67J3Rh8mu7eGdnKYsn5nSOSvx8Dlr2IKgSEEPRjE+OZ6MssbPHcLI/LUSXlUt0vox++zOw/WmIzu0Ul6G3/2H/3/8U51so5/m/S80R+GA0q4Y+xw3KAezrKdJS+RyhoIuQ5CEU8iL9NPciy8E/XbRCYUSttqJSWVGpwlGrrKjUP85VVlRqK2pV+I+/W1EqTT89CCRZZsHpCja02FjaM52RYQb27t3Ltm3b0Ol0TJs2jS5duvDG+hd5r/kTckIZ2ExuHAEHD3Z9kEPfH2LYsGGMGTOGYHMzHd98Q9uXXxJqbMKvURM9fz4Mm8Z3H1bRY3gCQy9IoerK+XjPniVpySe0qhMoO9ZM2YkWPHY/Co2P9DHvozCeIKMjAcXZBmb2fougSkcvTzWnfek8Z7kOa2QeHxbcyubCRrbfPQqFTsGDL7zF7Z++Q9ulQbpfsoR6aT21dV+gSlzMbfvfZkTiCB6sv5baw6fY2LgMjdnA/MdfxRIdQ+3ZI0z8uApdUMOCpkYGH34ZU5yTgp4DSF1/HFkQCEydxo4rruEbj0Sx24sIzAhWcGX1WrpV1yH5M2kN9abcm0y1H9wSKAWJeE0jXXU7SVVtQCl2/OrcBRUazpijWG0ysF7hx06IKIz08fQjrmwoWnsUvcYmkZfqoO2D96jdv5/Fw26h2RjFUzPNxCSpcfgdOP1OnAEn59rPsbp0FZEy5LpduMyxOMyxOAJunAEnAaeDpz/ofNFYdI0CvU7mwTgv613RqO3jebbkFS6Oj6XGGMn0U2moq5xc/tTLRKX83Jr+u1Not67PERc361f7c/uXx9hwpoGdi0YRbVTDG/1AF06r8U1859pxz41kzqkK1JFRrG8opuWNNwk2NWEcMpCoSZlo7bvA3Qo3HfyX+ljOm7z+gPOC8m/G5sdgz6tMHbsBtd7K4+o3aWnZTFhYf0RRi0KhRSHqEBVaFKIWUaH7nc/aHz/rUCh0qFRhqFThiOK/7nn8dFk9r1Y28nhmPBdoBVauXEltbS3dunVjypQpCGqB+1fezRb3LsZJQ3ny4hexyXau2XgNdfY6xjvHcX+fmTiWL8exaTMEg9ijIyizGBj3xjtYE1NY/sxh3HY/lz46EI1OSbClhYo5FyMF/KR9/TWquDgkSaaxzEbtgSNYi1/B1e0YTVEaDLXZtMe9xD2aIM3BEJfW72VK7IsYE5dy8Uft3DomizvHZXPH4QLm3nA5mggX4py+REycSWHRfUQmXM3i07uQZZkPDC/SuvE0W5o+x6uTuemZDwizWgnseIkZP0RQIsdxXVMr4w++hJwLDanDSVq1iZqEBNwGPZnnSghqNHimzkAxbBa2RgltjYtkewgRkAQJUV2LKbgflVhAQ1RPTgTGUV+hRQ7IBMMU2FIDtEc34QmUY/eepFauoE1wo5QFutpSyKgfRXRHTzSCl1TNIbrodpKcG4Ew4h5I6o+3qIjC9z5lQSgXQRD4KKyKrguv6AzZIstw6AOW73mSx60mFsYO59YJb/7qfMuyTMfBvTTMX4h44SR8t19JW8XzBJ1HeFV8geU7ruRscm++zsxnS9EGpu6MRm+ycM1zb2MxhP9YRoijxy7H4ShgQP+16HQ/R5yqbHUx5sUdXNI/iSdm9uhM2LZ+Ed4x39KyVoP1yq5cdWAzO1JyODCwK0lItC1bRuv7HyA5HFimTyPyhmtRp2b8S9fzeUH5A84Lyr8Zb/THY4gmLfUJHkmPomvFRGJiptM158n/tSp909DGzYVVXBZnZVZbLVu3bkGlUjFlyhS6d+9OvaOem7+/gXPBMq5TXsYNF9+NqOr0H1i58nOObHyRESc9JLTKiBYLlgtmUoCfY8cOMv3O+8kaMJjjm6vY800JE6/tTkaf6J+27S0upnLupaiSkkhZtgxFqAN2PgdHlyIrNbi7zueorgO/5gc6Sodx6tyVnMwUWZBxB4nRA3lk12XUtHvYfvdIjrk97HjoAS7cvImmq6DHFe9z/OxCwsL6s7TDyvbq7byT8iKaFY1sb/qado2bcffcw9CIeFhxPfecG8XXynxmt9m5cs9z1A7PwO3Q0vXwEdq7dSXtmZdRNki0bj+A/MNn0FKCaElGmXcxvvRUPClhHE0w8YngpyQYINNbx6KOzYyvXo3O3YRfl0SJ9SYKW3rQUOlHFmRqLEUURu/Hb3GTXtebjIZeqJRGWtN0lCUqqDLa0PqaGNZxhJtqlxPm70BOH9UpLCmDOXWkiEuWnyXK3swLe98hceIgotOKEev3Q+ZYHk3M4NvK9bwy6hXGJI/5zblvfOZZ2pYsIenDD5B7Wjl4aCqVMU9h2fE1022HUN1dTLWnmbe/f4qINdWUp/sZduU1XJh9ISpRhcdTw4GDUzAZu9Knz2c/pVAGeGjlab44WMWWu0aQYpThpW7IGWOoK7gWXTcrK/SlPGCI59G0WK5P7YztFbLZaH3/fdqWLkPUasncvg1R959PBXxeUP6A84Lyb0TLOXijH3sGPcws9RjWZDViK76R3r2XYQ0f9L9SpcM2FxceK6GXXs20MweorawgOzubadOmYTKZONF4nFs33IIn5OUR851MvvASJIcdb2EhDcu/wb1hA8pQiJokHWvyAkxZ+BQxVUo2f/AmAy6Yw9BL5mFv9fDFYwdI7BLO5Bt7/iYXhnPXbqqvvx5jlygS884gCBL0uwqG3Q2mGGRZpqzsJSoq30IlDcfvNCGY1+MN+4Sbvm7n2Vk9mNk3kbmrtvG3B27Bkx8ictpV1MSuRhCUVFiu5JnDL3Nryg30+z6cnY3Ladd6cFyQwYvhGcjbnmJJw0U8ZppCP6eHh7Y/RcHA7ig6PPQ4fRrFqFGk/e05mt86heQOIuqVqJJN+BsPYfvufQRbO5UZ6Rzr0QMsFjKzshAyunBCZ2Fdm4MGr4cJ7QeZ2bqKs94i1hj0hAKx9GgcSnbTANShzgdmeJye7iMSyB2agEL586AGRzDEllY7b5eUM7j0a26p+RKrvx05dSjCiMXsDnZl/scH6Sc08LG0GI0iiM07AO2VL6Lols38DfMps5Xx+ZTPSbf8Otio5PVSfuEsJLeb9NWrOFl6C+2OYpa1L+Sjo7ezNvtJQrkXMql7LCvff5aqbXvY3LcJZVYMt/e5ndHJo2lo+I6CwnvIzFhMSsq1P5XdZPcy/PltTMiN5dVLesPGB+DAO7RnrcRdpMB7WTRTi+tIDbewfsiv4+YGGhrwnjmDacxvRfDPcF5Q/oDzgvJvxO5XYPMjLBy/lmJlJK/rXsPWdIReymdRJ6egTk7+VZjy/26qvX4mHS5GFQww5cAmDFKISZMmkZfXmQRrVdH3vLnhEbrX6ZjvG0pYwIW3qIhgXWcmwYBCSW16OgMfeRhlz2xu2nITJ5qOM+RUFGMjhjJz8cMIgsi6t05SU9zBpY8MwGT9B/8YnwP2vUnbR+/SeFCLdUgiMS+8D+Gpv6lvVdVHnCvpbMlFx8zihjUTMWqUrL11GK9WNZJy19XklJbRNMOCeXo6dsdJzBnPs3D7g/S39mPhjlHsrl6O3yyysl85Xwom4suPs7p0Bvcmz8ESDPLG5qcpyMskUhZIP3wY84wZxP3tCVo+PAMNJ7DMHYQ6OxNB/LHvyeWi5Z13aP14CajVNI0ezcHICFxeLwqFgpjUOIqi2jho34/Ncw5BFklpzyWnaSDZ9jgytScwJ8XTIvakvDhIKCgRkWik66A4sgfEoDP+bMaUZJnVzR28WVpJ/5JvuL3mCyJ9LcgJ/fjW1ZO7G8Yyy3ia+/RuWr/cgGS3YxgyhNDVs5lf8zRmtZkvpnyBUW381XH1nDxJxdxLsUyfjnbRVI4dv4K3Qy/x8uFFVHijucJ3H3EWLVf0T0S3/nXsbY3sGufjrL+cXlG9uLPvnSia3qelZRv5+SswGXN+Kvu5DUW8tb2UdbcOo5uuDV7rTbDb9TQcmUz4pV245PQejsSmUjisBxbVf914qvOC8gecF5R/Iz4cTzDgJSXnNW5MDGNwzSSsW7qjXn4aAEGtRp2RgSYrE01WFpqsLLRZWSjj4//LM9ztaXewqLCKOo+XGYe30S/KysRuuahqqvEUFVF2cAvaygb0fx8gJoqo09Lwp2awyWdmeyicImsygk7LrB7hXNwnhmitn3nfXU1NmIt7ey/isp7zKD3axIb3TjNkdia9xv4i22HAC4c+gN0vdXa+dp1Gw6k42r9ZQ+wjDxM+d+7v1ru+fgXVNR9zynUff9vQxrJrBhCfYOLBDz/moTdfpXUCWGaNoim4kbTsp7nt0FJcfhePFlzB0bOr0EaG8XGPY1zkt3NNWYgTJ3rzQO+FNCtkXtnxOs3Z0eRaIzBs3Ihl9iziHnsM++Zy3LteYKMygEMwExGXTERKNyIjI4mIiCAiIgJVUzNNzzyDa9cu1JkZlFwykqWKE5z2nSIkhLC64slpGkCWsx++1Fh2xCowCSeY17SOyc07UEl+PFEDKTFeQ1FNMk3VbkSFQGrPSLoOiiM514r4o7+OJMusbbbxemklc0++yLz61SiQeFVxJS+7JnDbmExuHZRAx5df0rrkE0ItLZSMzuKhAZWMSBrJy6NeRhR+7ffU9MortL7zLgU3PYQj+X1alOHITcncXbmEfVO38ObxAHtKWomVOphd8w3RWTno5/blrZNv0expZlLSMCYrD6LTRJOfvwJR7Mz2aXMHGPbcVvqlWvlofj58eRly5R4a/EtQp8ewNLyaZ3TRvJGdwOyEqN+c73+V84LyB5wXlH8THI3wYhfO5N/OGP1MPk2pQ1F+C5qHI2jVONjYI4y0xnC6OVQktrehbmv5aVXRYECTmYk6KxPtj0KjycpCERn5u0IjyzKy34/kdiO5XD9OncsVrW2sqaynsrWdcKed/JJCstxOFPX1EAoBEFArKI0KIYclMGjIxYSNHYAvKZVXd1fz6b5KTCqZO0JLyBJq+Do0knVSf/yo6SmUMku5hb3xRezVq7nb4efCJglZ1GCKtRJQaGnyiFQ7ZFL954gTWinU9+NIxs0Y0vNJC9dhfvJ+fHv3kPTOOxiHDf3NvgG0On2MfH47/VLD+Wh+PpcdOcttd16KTnDTOiMeqVcliQlX8nGTm/Xl67mvcR4NR3ZhiYxgT49TFKucPLdLAwWxvDLwZvbr4M5j32KOCdLTEoZu0ybCLrmY2IcfxnemBNvyG/hK7I5DsJChbqXNJ9AqRBCSfz72Go0GU5gJr62OqFPniG22UWF1cyyrCyniaFLD4rBJ1TTYy0EAa2Qkck4PisJjOGZrZUzdBq6oX0OOq4yQUktb0pWcDU2huFDE4wigNapI6moluZuVpK5WDLoA8g8PIRz5iHJjOiusQ7ikcRMvO6ezPDSS54bAnKmTkXw+Or77jrYPPuT7+HqWjFOwUD+eWy587qfWcEiSWXmoAtPtCzG47Wy9bRaDU9/jQ+FVlu64AjEpH2H2xxR5THy8u4Ki7RsZ3ryDui7jmTB3JuX+9Xx0+iPSVS4WRHqIir+cnjmP/XRs3t5eyrMbilh+/SDyKYQlk3ElPUB7+RDcV0QzvqKNwSYtnw/+r0sXdV5Q/oDzgvJvwpElsPo2Hh33Dd8RxxLDy/gKCgh7vIMPxpspGppBlec0ICP5rWhaupDdksAwWUeuv5XY1lrEijJC7e0/FakIC0OdloYshX4WjR9FhOCfG24sRkWh79YNTdccXKlRPNG4jCPqWq5vvYT5M29Ek2Hhu6O1PL2+iFaXj7npXu6suQNEJbppz2LQauhwenhnUzFrOuKpUcSiE30kpi2jQX2O2U3hXKGNxOZy4HE70eInXBVC1kfwjf4S1jkzqW73EJI671ldwMsre94i2t3Oxhv+RkRuV9KjDKRHGUkK16FUiDz8/Wk+O1DFhtuGcZogha88yAUrtlI1XYV6UhCzpReVxgt5ZN9jzHaPwbKjAovZSEz6XhbHGbhyv8Cww3F8k38ry80wvuoow7TlJHh9xB09SvjllxPzwP1Ihdto+/o2ljEer9KCJ204sTHRLAh8hvLAm9isvakfsJgddZUUVBUQ9PgxBUzoQ7/ORmgQRaKSkoiMikKv1+NyuWhsbKSmpqZzn40m5NyenLPGYmspYHrNai5s2oIx5MZpyaQh9ibKHXlUn3PjcQQAsGrqSVIcIqlXMrGzrmWLO8grZTV0LfmeukI9x4LpfBi9nBETL4KcacihEB1r1vDQyWfYmeLige0RjJt8M8e6Deb5zaUUNTgYr7Fz+7dPYR43lqqLjrOFMVRX63m75AUUKi1c+B5kjqXF4eXTvz1KsKqQr+MuJCwplYsHhFMvrEJsWcYAQ4Bay8XMyXsQvUqPxx9ixPPbSLbqWX7dQIT3RiB5vdQ1vIhlTjYzS49QZo2leFQfVOJ/TUv8vKD8AecF5d+Ezy5Cbikms9enzIgyMLVxCsL3uURtLOTBywbSjwyikqNoj2jnRKCIAtsJJIIIIQs+Wy5BR3csQjajYlQMUdjp5m3B3FBFoLISQaVCNBgQ9frO+S+WnRota5w+fvAE8ev0jI2xIhzZiyDAZVdfTdSPmQEPNRzijq13EPIGeLDlesZdOpsSUebh709zuLKdXkkWHo/dQ89TT1FKMs5Jb5I3YDgAJ7dsYNN7b5A/fTbm4TP5bH8l3x+vQoz+BlXYUXwtI4gMXMDsvknM7BVPwYGtbK9t4Jqxo+makYk/KFHV5qK02UVZs4vGkgomv/UAHkHBrcNvwfZjqH6VQiDZqqei1c3c/kksmtKVCzZu57UHbsGXJtOxUIUmzExE9qvM23gDGYEE+m+UCddpuDBhKxcmxaFxyjy8PIadve7kY7NMjLOJW3VFaM6dJb24GOv8+UQvuht2v0TTtnf5VJ5FUG1mr6orxYrtIATRBNJZEF7H/MZlqCU/z1nD+EGZRk/3QMYnDKN7bhwKY4jW8jrq1u+grakFZ2QkjvBwfL8QeoVCgV6vR5ZlnC43XknAr9Rji8mgQW2gq203Fzs2kB8sIohIS/QgFMoUqs95qAn1p86XQygEolIgLiOMpK7hVMeped3eQtOWYkJegW/Uj9I1xoAwYhF0m4k76OXy5bOo9jQQcXwWRcY+JKpCLJ7ekyl9U2h7712aX3kV7YMXcjr+e+5WLiXdXcfyc49jbD0Lw++Gkffhdjr5dNEt+BVqNmVdyqlGD2F6FTP6BOmjW4w/5OEDWwIL8m7mgswL+PJgLQ+uPM1H8/sx2rsFVt5Aq+pZ5PihvB3XyJuaCL7MTWFkdPg/3jn/EucF5Q84Lyj/Bvgc8Fw6VT2upL/lcl6Jryaq5nb0D4RRbvVQ2H8uPYNx1IptdIhuAES1gC/OS7WphtOeAgKSHzVmZFd3bK05hFwZhOu1DEiLYGhWJCOyo0iy/vxmbA+GeKuqiXermwnIEpfFRbAgQs/aZUvx+XzMnz+fmJgYAJYXL+fJ/U8S74vicdutpF0ygtePVvPpvgrC9GoWj03joopHEM+u4YiQx9mMBcy97AoEQaCuuIivH7uXxG49Sbn0VladaGDNyTra3QFUSOgT1yCZ9hLqGMyoiGvoLu+nwXoMtS7AQXkQrkAOs7t0ZWpcJInanzugPadOUXnFPJRZ2diefIVSe5CyZhdlzU5c/iCvXdKb52qbGPz0fLIO1VJ9g4AqVyY371Ou3/U0TR0NTN5qJV7WMzVxH+8EIvm6e4h7N0RiC1/EFzqJZiHAc1E1KHasJ72snIiFC4i67kqEFddSW3qapfIcgioDq6VI3HFfgroJQRaQBRlZFhG90Uz2NTDT00isWkNFNyUh9c99E5Is4PAbEU5rSPreg74lQFl2DLv696ZdZUX2q1GGBLRyCCN+FMLPzy2frMAua3GiRin6GSwd5wphPWohhISIkD2BUPdLqaM/1cUOqgvbaa11AqA1qnAn63ijqRkBP58aX6C/9yRyZDZleYt4otzEocCjCCEz844PYsK279BaTFivnEfYnDlUX38D/upqmh70Y48fyYvBhVTYO1jR8D55Jd90pkGY9QEV5fV8++RD5I2bhHnMXD7aXc4PBQ1kWCq4J/9lzgYieLfRTZoljVvybueJ5QI6tYJ1N/ZHfLU7AVUOjU330nF5DOPr7cw0KHlr4H+N2eu8oPwB5wXl34AzK2D5fN4Zs4RnpQw+N72EWFCK+YkW3h4XyWjDFajbmrGoItCqTNh1InUKO3WKNlyCj6AQpMPQRrOlmVJ1OT58qDFg8vXA1t4Fm1cPkoqEMDP5qTEEwgxskkJ0CCpmxkSwOC2OyJCfJUuW4HQ6ufLKK4mPjycoBXn+0PN8XvQ5/VzduN93E0fzk3h+ewltLj+XDUjhrkFmwlZejtxwiv2W6ezw5HDjTTehaZHx+l28+dqTFOjSqYzqRXWHF41SJN9qIrrMw/VX9ySth57FW+5lU+NOJHsertqLidC1My5+B+MytuORdOwTh7CLkehMvZkSHc6kSAtZBi32H36g9tbbME+eTPyLL/yqv+iozcVL37zLA8+/T/tQ8FzqJyfnKd6vKOTbc98y4WAsufZIesaWEDzo5JZLFeS1xNO/5V62SW726gQeTHSRuG4JKWXlRNxwA1Ez+yN8ezUVDiWfSRfgE3WsUgaQElagIoAkywRFgTSvEsGfSJUqREBbiyBIqGSZXG8Ir7sXDe7+ONxJ2CUtneE3QSkFmVG6m0vPbkIlBdmQM4TdPfPRGyTCNG7C1C7MSjd6wYsyFAR/AMkt4PHp8YQ6w8SPEPawX84kLtjCNOUBIrAha0wIOdOgxyxc1kHUFNupLmynqrCNKqeXz00+jILIWJ0DwVvPSikNpRhkUtdzbJI/Z3zqeB41zqXt/fdxbt+OaDBgnjwJ2/erEPrEU3NFMXn5a3myTs8X9W086NjGTSefRdCYYNYHbN9TwpE1K5ix6CEy+w2gus3NJ3srsDe/xYSU9ayon06BoohmXw2JzKCwcBCvXtKLGe2fwo5naPC9i37GMCY0FOIwmDk1Nv+/ZADKX0ZQBEGYCLwKKIAPZFl+5h9+zwE+BvoAD8iy/MKfXff3OC8o/wZ8uxC5dAv5g1eQa9Iwr3UqihXdsW49y4Nzh3JzUwbpV4yktuAUtSeP01RXi1Y0YFJFotNGEtCbsetkWpRO3KKPRm0jLfomqg01eBW/H6fr74goUCs0EJAQJRGr2YpRa0StUOP0OymzlXFh6xhGK+byqiLA0RobfZLDeHxGd7oL5Z05530OCnMX8dWxDmaNnUFcmRpvQRt+JB7Fy24hyJDMSGb0SmBwrIV1L28lsc85YnMLaWvbiyT52WjTs8EBSiGH0NlZ2EMm7q35kJxeZ/D3kFCoJNqkKLaLI9nNCMyGVKZEhTF13QoUb7xO5I03EnXrLQAEJZkpB0/x5KOXo2nz0/xogNi0i6nQDGHxrnvpWWJhbEUaWapqIvY38OFEkYNZcVxR+hBlHidfmAQuiJKYuesjoouKMF93LQkDlLD5Uc7p+vClayhOWcNGSymhiP2oJRm/KDDMrqJvazwT5EIShRZkGT6SRvK2ujcaQzFRxhOUqQVkQUAha4hRdiFDn0deZF96xXUnJtxAuMeO8/VXsK9ajTIujpjFizGNG4vQVgI1hzqTidUchqYznbltALc5nS+9w6nyhxOZXshKxWhKy5IY6jnBher9TFYcRhNygiEKus2EHrORE/JprfewYmc5T5+sIiEgcpFLDQJYNBV0V+5mV0oxH5rt3N3ndq7scQ3eoiJa33sf+4YNneFOQiFs8xWEhlvp3esTvreZua+4hj7eSpadfRx9eymhYffw+cZaHK1tzHv+DYzhnSmq7R4Pu/dfSChQxwO7F6GOKEMM30RL6Tx0Yjhrr0kl5eNJuMXJuKPu4rmUNpaqw9jUK50e4eY/upz/NH8JQRE63UCLgXFADXAImCvLcsEv/hMNpAAzgfa/C8qfWff3OC8of3FCAXgug7bMSXSLvIGHo6voUn8HxvvMnI3xUdFvPr2lCBRx+dhbPNhavAS8AeRQC1KwDjlUjxyqQwrZkAG1LhKVIRq/QUe7VqZV105QCKJDTZNOT2NiGFkmIw67k9KWDlpcLgQhgFL0EWNREGvVYNaBhB9vq4NBFX0oEwbwrc1BuF7NvZNymNUnEfHsWvhuIegjaJv4Nu99s4vhhjySWy345CBfWNcxwNmDLt4UlBNTCesfoqnpB4pPf4+oK0YQZLTaJKKjxtNaILF9+WE+m9IV/GvQq7oj1V1Fe4efp4+8Q5f6UmrHqvH1lDEmuBAEqFd0Y4M0lH3SIBZ9+RUjdm3F88hj5F1yER/UNOP79A7GLT1K62VBQv0TSO7xHHM2LsTcoWLuse70by5FVd5CbW89j460cHHRIvxuiU8MbmLCdPyt+Gsshw/BJbPo2qUMzq6lIOFiltcm0C6r2BW7G7++GoAkf4iJVcNYI13IyCHJRBvVpAm1pLfvI6ZpF9q6A2wM5PFWaAYXq9YSZzjOzrBETkTGUOqoAsCkMtEvth8D4gaQb8kmfv9Bmt76DF9NO/q4ELG9WtFYgqCxdGamTOjXmeMmoS8YIggGg3zyySdUV1cTHl6LKbuCL2wXcbY8FZ3NxTjNKW6JPEa2bS9C0IvPnMoyy3W8UZ1Gu7dTmEYkhTPeYqayoB1LWwAZmS3Z71FqLeRBxaVMmngjpigz/spKWt7/ANu33yIj0/IQyLEaevdZSo0ykwWnK2h2trOm/h26lH6PP2EQS/aosGb1YtZ9j/0UldjlKuHgoemEVH348MxN7CuvR2MpxNfRC13s93xtOENufSn1no+puTSDGS1urtELPDng/93s9VcRlEHAo7IsT/jx830Asiw//Tv/fRRw/kJQ/vS6v+S8oPzFKd0GS2fy3ai3uFnKZanxZcwFVRifbuC1CbGM08yh3paMPtoIlgA+gwOHtpUWVR3VYhnloWKckgONXySqXUN8q5rkZh1GtxoZEUlnJMyUhs9ipU10IgugUqpITUslOTmZfccLOdnkR5HYgxONPjrcAQSgq1ZNNy9sVgaxhyTmDUrljnHZWLRK2PMqbH4UEvoSmrOUDR/uIqs5Ar2sYZuxmA9iP8bv1GKx+Li+9lL6ObvTkvEdremr8LYnYw0fS48BszEYsjn61ftsX7GK/X3HsSt/BIMC2ymp/5CcsDxOnhxCdkwevdsOceFXH+IDivPCIDtATE8fCr0NWVBRJeQT/VIjSWX1/O3uh2mPgVcfeYhguEzTTdAr8W6uOfga9QqReQd6Mra4AbG1moirpvNYZoDkrUMxe7V8L9RQEZHAG61biN+xAduY/gzMOgr2Oo53u5+Vp500C7AvcS0BhReVHGJmSwRR5bdwLjuR+xf0IdKo+e059rugfBehc5tYeaqFQqeB25XfIiNwJn40bWmxHHRVc8BRTo3c2aK0hkL0d/uYUGgiYW8I/BLW2dOIvOt+FObff0P3+/0sW7aM6upqIiLCQTiKOrmDr31TKC5PQ2z1EabwMCWmne3NJmoDJoaKp1gcuY+t5pm8XBLNrWOyuGNsFj9UtbF8XxVRxcXsjXoPt8rFrJN3k6wzkNQrhaTu0YS7KqhbcBUyIVpv9BFIE0irn41l3M3cYwuwpqmDp5xbuOrEcwQVer4tTiJz9p30m3rBT3Wurv6E4nOP06XL37ALU/lodxnfHqtCFgLkJz/C8oZqOgLXEBp/C6Od1eiVSvZNGPz/fNv9VQRlNjBRluUFP36+Ahggy/LNv/PfR/m1oPxn1r0WuBYgOTm5b2Vl5X/L/pznf4C1d8Pxz5g0Zj0KpZI77DNQLs/FsuscD14yiqvrMtkdLrMpe8lPq5jUJuIN8cQZ40gwJhBniCPeGP/Td+GacGRZ4pZt+2g/uJeBx3ciCUrSzf0wR2TTqLBRp7NhC/zYSavVkp2dTUpqGk63kV1bajng8XKGEH1SwnlsRi658ZbONLtr74BjyyD3Qvz5z1P1VSHadmjQ+3g2/GOKTKfp2ZTGBREQNDpZEmxkbtU8xtgGUiN6qTJYmH1fPqIocPCjF9i1cTtOUwxvX3YLkyNMfNQzgzVla3jm4DPYfDaCrjSu6DqfIcn9aHr2b+Tu3E11nImi6Ai0MRLdpsQh6wsIdrQS+YIGXErs3VREHHTTdHcA0ZnO6mA166OUzDzRhUuOiahD50h+4112K70ceqeBMK+BYvshvkvN5wHXUYZu+hxb/3gGZJ1EMESzO+dhNh8spEHpZX/CBoKCRD+vj8yKSwjvGIFhcBQTp8fxzKq1eIJquifn0iUumoxoIylWPcofHQ6RZeiowl+whvX7TpJt30tXsYrNod5ECR3kqeqoi0rnYGw2B7VqDtjLaPI0Y3LLXL1Hw+AjbkIWI+bbbyZ5zhW/m0bY6/Xy6aef0tjYyLBhwzh9+hT+QAHaNAdLOyZQVxKBEOx8/k3I1PJ0ZjHWslXIlXu5J3htp49KrxbmTB6PbIple5uDpwsP01SyiFiflmsLptHk70VIViMKIdJ8+0g99g0A7VeGcHf1Yv1QidU8nNUXX8EzKjMjfBV8VPgoqo4K9jSnkX3X18SkZ/14SCSOH7+KDtsRBvRfg16fyqaCRhZ+ehid1s2nykV0D4rs53He7GNitzGZw/lZJJqMv9n3/wx/FUG5CJjwD6LQX5blW37nv4/ya0H50+v+kvMtlL8wsgwv5+KO7UV63D3cYq1iYNMdWO41ciIpSF3e1UTX6DjU/xyXT57xk3CY1KZ/WvSqpg6uPVPB/elx3JIUxc53n6dk/Q+4DGF0tQwm29IXp+CjOsGBI1qmvLIct7tzBFmYYCSjayapuV1ITU7EaDQieNrh63lQsYvQgAewuWbgPtyEGz9LwwpYGfMhaiGM+d6+GOtScTflAhDfTc+qyCXk1UYzu3UcYrqFuKu6s/21ezl6sIAwt58Xb3wUY1gY2/p3QfS4ObN9M7WVZymIt/N1xy6CYgcZ5i5c33sBsbUWfI/ch6qxlaJ0K/VaE2HJiYy4agSe2i2I9+5BdIF7UIjSNAsuyc5LaWr6VsWxeF8G4V08RD/9Dg5PiPce34DBpUVZtYqne01jkreKWza8jtRXQ25WOWSNZ4XpSk4cPUytpo2DsTvQyUGmt8VwpuZ2BnuNpA+NpSWllWc3NeAJqDGqXdh8P+ewUYmQoveRqWgkw19EZvAcGUIdGao2FLG5FHcI9HTtoUhK4gtxMpdI6+gqVkN4GnLGGCqTenNQKXOw5QSNh/cwe20H2XVQnqzh3FXDyR44ifzYfCJ1kT9t0+1288knn9Da2spll13GwdJGXt9RRU3AgEXjQpMSoM4Zh7LegwBM6RnHHfkGkuvWc/UWkb2+VD5SPc+IsBbQWZD9Hrb5m7ktJoIZDiePNDto8OdQHehDsXcUSmc7/U8/h+QB9zyR9n4+IlZb0GzwUJjXh8euupmgQmJ1zaukV26kOhBP7N1bUIXHA+D1NXDgwCT0+gz69vkSUVRy9ZJDHK5o47Ve1Yw8fhct/vu5NL2Mkxk3MDtYy+tjJ/8/dc7/VQTlvMnrPH+eumPw3kh2DH+Oi4UBvK1/laQzNRieq+GliQlMUl9IuTOS+Jsk5uZe8qeLbfYHGHGwiCStmrV9slH+6Azmsrey9t6rCZQHsYVb6R4+gDRjT1CBwqqlubmFplgvDWFOqmqqCQQ6neS0GjXRoXqigg2YYyehq48iLKCnUNvBU+Hf4Dafpqswgun1edjK0xBVPvKnpCBg5OiGSvy+EBVhp7BY65jfOhUHdWyq+IpwezsbbnuMrWozn0UoCOzdRtHu7QQDfhTKEKGggg6VnuXduqCJ3I6k6SBSlhmrDZCvDKGWZUIKgYBSAQKIIugqwbAdKgboaa/R8lauG6tHzyt7x5Iyrw/GSRfgtvv59KltSG0hUoqW8OCIeYR57Ly04VmiejtIyGiD0Q/xRmUaraUnqdbXcijqECO9XuobF+Jy5DLFpcbc08yKUBlHaw0khTdBqhUMCqKERpS+VgRXAEWHTKBNQ3Mgmuqglc4A9p3EWbRkRBkZqzrJBZVPoAm5eDw4D2fsQO4wbCStbi0EPaDQQMpgpMyxnIvKpGzjFqI/+QGtw8/m3gJfDhdJT87j+RHPE2/sfEg7nU7e++AjOux21nqykPXhTE1VoK46SBg1JCbZqQjp0Dd7SXdWkUYDKWIzHlnFRf6HqZaj+Vr9N3LFCjpd99N4IyaJd73lJEZfhtIbz701y8htrOTb1mcxBhvIP/MivnYFgak+mieJpBe5Me8UOFcRx5OX38qhbj155uSLXN62hoCsRBeZCLpwUGppMHk4E1FNuiuZtEAWhb4IJp8Ywg1J1SyyPY3fm8SH2mt4oU8OZncDpo4l3JU8hZljr/uXbr2/iqAo6exYHwPU0tmxfqksy2d+57+P8mtB+dPr/pLzgvIXZusTsOtFrp60ibMhNY97LkD1RVeMB0p5aM44rqlNZluMjyfvvwWz+s+PbFl4uoKNLTZ+yM8mx/BzaG9Jkli5ciVH9i8n/1g1DZEjUao66G7pTaIhm6AQwDQ2GevIdEKyRE1NDY0Fe2g6sprGUCLNhOHjZ8c7n+jHqXQSSxqaVivKoA6j6Sxz7rgZc0SnA5rT5uXLxw/icwUJKQKEIgu5wJuHw9dE0dREPjpbwIiqAoTGehRqNQY5hKRKR+W1Em3dRkDXyG5HP77XTiSX73DFn6TV4sfgExjSomCUO0CYxo9Nr8GpUaMMSgQ9ShrOWFnZrwW7KcTrBZeRf8+VqGKi8Tj9fPv8Qez1DnJPvc1r067lTEeIV7a/TP+u51CnCzBnCY9sKMVkq6fcWM7Z8GPc3JLFw+3zyTeaGFovcTbBw0ZnEFEIkpwdoNYYzoWbPkcyatgxZBKV5kSC4q8zCUYKfuJkHWavjMIdwu/wY+vw0tDmwehv5UXV2wxXnGJ9KJ97AwuJiIjkysRGRsoHiGveg7r9x8yLlmRCiSNoOuClfd0+gno1a/LBZ9RwWc5laMUw9haUUd1Qhy85AkEhMttzimhnFbhbQOo8hwIQEkRaVWYaiKDVb6RZsuAJS+ZTy1BEhYKlSZXENu6DmiPI3nbuzQvjgFXF3Y4RHPJ3oy0UoiMqkwmH1JjDXQwr/xDv0WKkUQoaZnlIqXGTXunHF4zkBf2FvNX/IkbX7OX1c08SrvBAZCaCMQZCfk5H1tNk8tCvRIvZEeQ2+1w2+ntySHMjJsFDi38x13WL5UBsTyIq7kISPeyau5Uw3X/e/PWXEBQAQRAmA6/QOfT3I1mWnxQE4XoAWZbfEQQhFjgMmAEJcALdZFm2/966/2x75wXlL8xbgwjqrKSmPcklpmomtd5O+GIDh9IkmnMXEFWnpH6yi0dnLf7TRf7S1HVrSsxP38uyzOrVqzl69CijR48mOy+Dbx+9Fmv1SJotVqKFcroYuxGlTcKn8KAbEUNcxAlCq5+lg1vwenNRWDV4h0Ww6PASQqECUm1diQyEExTdyGLop23p9XqioqLQarW01tvpaHEimiDkciGJIrIQhD9hrTDZssk0Whl+0zAW/1DItrNNvJQvUtawiQ2BfdSYOwcj5NaEMaRARXibj+YwHR6lmsNd2jid4WBx/Vzm3rYIhU6F1xVg5YtHaKux0f30W6y/6BK+aDRz59EvuTJ+E62pUVSPf4WPtxwkNeTjnPkcJnURl9bdztWSlfl5SYQOt7BR30ZdQEdSTCN1WV2wdjQyZ8MnaEM+AiEFmkAQs8dPZXw8rZYwpC5GAulGmpVGWoV4WhUpNIe0SD+fHBR+iSivxLV1X3Ft00c0E8atvps4JP8cmTdV0cIFpkJGKE7SzXMMteTGY9fSeDoeT5X3d4+h02Bg65jRSKLImC1bMDmcf+o6KjfHcvewm4h2d/DCrjcxBDvLd2ngvvkKvGp45uMQVieUJKbw7kX3MelYiFPJCgbVrqT/lg0EhyfSNLuMeFLJOVmF4Gxih3UgN3R5AEXIz9J9d9FLUYHfkIdi3jIkq4UDByejUBjon7+Kmo4QY17cwfw+YTxQeCFu/2C+n/Qgd/pE7pWbiNbYuHTIRX9qf/6Rv4yg/E9zXlD+orSWwut9ODnkYcYrx/CM5i16nKlG/2IFz01JYZo4nbMeI2Mf6Ep+XP6fKrLFH2T4wcLfmLpkWWbDhg0cOHCAYcOGMXr4KDpWl+I4WMf6qI0oD/vxWiaj8xaRlSFi7YjBoorEFWhEr4pAUCoxjUnmcKyfO3fcR5pLz6DKCzD6LehjCkjue4ZT39eRkDeCxPxBNDc309TUhNfjw9bgQ1IrsLQWYheVKNDg1yRgDUWQrlaiFhRUeEM4Axo0XgfR7iP0un4au8/WUlFVRVhrJupgLMlDYnmkop5os5bvbxqCWimyt3g77594j8PuUyhDItlVBnLLzbSbAmzOb2KyfShPzXsFhUWDzx3g+xeP0FLjIPf02/wwLZPP24cztvowL2jf4WhyT75KHI+3zkmqBGctRYz3aRnUcRX3GHzcMKkbn644xf5QEIPGjaargdqYRMYcXk/fI7sJU3iZFteCV2lmdaWeQEgkr70dbAHqwow0hRnRxbuJ6+PAkNhOCC1i5MVIkbNpJoZqr58qr58qjw9z4wmePPkIyd56XtbO4c2OqUiCiDpMg16rRHIF8Dnc9BXOMkI8wQjhOGmBBo6KUTwTo8YhwjzFYAamDCE8PhNJY2HZqlUoFArmzZxJmOnHPjhZprm5mV27dlFZUYHBaESbHMsatZ7KNjNytZt2wYRRDDElP4xzKokKjx8dDbg9r5EpaXnnQA1NO3XYY628P+1Jep+V2ZWrJb7mBxZ+/xVNOVGEFtaijx9H/7CLEQvWUl+yi2uSbuFoWC7PHH2OK+3rCHgUdIgzCE4fQqHvaRITr6RL9sM8uPIUXx6s5kjeeswFn9PSdwN99AL5XjvfTR39L99+5wXlDzgvKH9R9r4OPzzIA5PXscJn4NXARWiXZaE9Ws7DsyezoDaW3WkBXlp035/ufPwjU9eWLVvYtWsXAwcOZEy/4bR9XkSgzoVpZCLmcakUtJ/h25feJKp5OqqAm/6hZSgjwhCUU2nxNHKyfQcevLQZwChnY5Rz0VlFYnp/R+6ACex66yQ+j4f5L7yFRt8Z3kWWZZa+dIT2kkYiql+jUa8EQUBrNKHNG8J2Zyv5zd2YqIpBDZSWbcZtXYei9yQ6zjrBBe0JybRKHaSWijiMQ6jQyCzX+5kfYeHW5CgEpYigEikXavjM8y2bPDsQZFBKCuKCUXw26TPMSZH4vUFWvXSEpko7uQXv89mQeg4GbsHscfA1L7ImpS/vyfH0DAokBrVUWQro4RjBRe6erExWkNg/mUdXHMUWEolO6qAqsyt6v5Prf3gbsclJktbOmOwxeJyjQQYUMnZ/I02ueoxCMUn2E9irWjkXCKPCGo4tUU1sj2YSsppQKEP4yMOluYT2YG+cfhmXL4jX3cHkmhcZYt/MUW13bpNvodpmQlYIhJINBFONCH4JwRlEcAUR7T70Lgm96MAX+Q6iuhlv3SUEHT1QK0SyzSF6+04gKtVE95tIamwkSVYdSeF6wvQqKioq2LRpE3V1dURFRaFMzOADrZLaWg3qYjuheB2kGphitvBE/3QON21l0Y5FXJx5IbedCVDzygrU1gDbht2Drz2TFQMNpDUe4tZP36Et1oR0UxOlEXk4Ep9nYlQMGa3HufNgMd9E92FGzUbeLH0WhRyio0zHue6xOPId9Eh9FSLHMvz5bVyeFeDBsitwqOZxycDLKArInBqeh16v//2b4Z9wXlD+gPOC8hflo4lIfic5uW8xXFPD3PbbsN6jYW+2QHvOQiJrQTk/nAXDr/hTxf2RqWvnzp1s3bqVvn37MiZ1IO3fngNBwDonG13XiJ/+5w64ee2rxzHu7IdCNtKtZjl9Fl1GS3YOT374PMk1PowuL3KoGZARRJnIlCSUSiP154qYevtiugwaBsDxljZWrdyHeedeJO9JZEFGpVKRN3EeTc3RNJwpJyg10aHchTFkYJLlQkwqKwea11DnLcWkCKfD30SX8B6UJURgDzoZsOcULdET+TwmidPqEE+oTYzUqCEoIwdDEJRpVLXyrXUzJwzFvJD/LF179ybgC7Hq5SM0ltvILfyI5QNOUO28niJDEp8rP+GzdAPrmrMZJXQQ47fQGlZBc+MMXpAjKU7W8o1ZYN3pRswGB87cOLwWI32P7+bi4yto9BvpHQa5qTcTsBmoitNyTAwR4QwR4wmR5AuiFTr7UUKyTKUgUUyIc3TO6xQO+iTtYUzyTsK1NuqcMWyqHMnJ1oGoVXqMGiVT2cmNrjcJCio+iriLr5151LR70KoU9Mq0kpYRjlclclryc8rrI0On4ZZ4Pd8eu4+zHWcYH3MjEdJwqtvdtDY2kGk/gktSs8Gfg5fOuhk1ShLDdSRb9WgkN+famqgOM9EWZUWhFVGX25HK3AhaGdkrYNbKzB+cjte0li+KP+XGXjcytz6N+jvvQpNk4Xj2XBqCXTg4xEezvZ3H33sFWa/AcWMH5+LTeY4HiNZHMTnSjH3LGr5K6UmM7GDt8RuJ8jUjIXCwexg+lUjqthF82vUSPioPcij2TcLbz/DIiOV8IKi5FjePj/rXfFLOC8ofcF5Q/oI4m+HFbCrzb2OAbib3qj5k+OlydK+U8sS0TGYxkdMBBTc+PftXw0H/iD8ydR0/fpyVK1fSs0cPRql74dpThyrRSMSlXVH+Y2bEMyth5Q2s1ydyvOJ6zL40kqt+IKDUUR83GFEKkNS0iRjnZlwGCw5DNC1yiHYpiPxjC0ojgdofRO/1og6GCIoK3BolsjmOoDYCj6cVWfo5vL4qGMKr81OZoGa+sIBYOYpTniB1oWKc3rVIDom+lc0cGjmKgFbLmL376JDjuKfvxSDBfbExjJ6TTVSSCVmSISQhByRQCIgaJQF/iDWvHqWuxEb3s59wckQFDWd78VnqRO7SbufLmGLaOnoyVrZh9YdTZvRzpmUYSwUdOw0K3gq4cPsDaDNkWtOSSWkoY9KW70j31tIcMjIhuSthmukIKiVLLfBOQxtZ0UZMWiUGjRKjWkFa3VmSi48TrosnIr434T4lGt8vgjwG2rG5m7BbahDSz0JiCX4c6BUjyO13D2ERGZ3m0W+ugvoTkL+AE90W8cLWSnadayHGrOHm0VnM6ZvIDpuTv5XWcc7to69RgbnlNU437ef2PrdzTY9rAKioqGDZsmUYLOFkDZlKgytEdZubcy0uCluctNt8IP36+WhQSMiCAndQJhSmRh30EnKKqBUBMnPXUR3Yx9CEYdzvHYXznkdR9unPgdjZ+NwhcpLeYbVpEFOWrsPg9+C4zoejWwzfaO5jn1uHyuNm4oYVbBh5Ia1hUTxb9T6XV3yBJAoc7WlC55EI36JiYvhzjBeP8ZLuTb613M1NeVPp0lTNjkum/yt34HlB+SPOC8pfkKNLYdXNvDHpG573RPG2NBfzkhSUpyt59IIZLKiJ4HRvDY9ef+ufKu7vpq6N/bLpauw0ddXU1PDxxx+TFJ/IRF8vglVODIPiCJuSjvCLfORIEmx/CnY+3xnK4+JlnHZ4+fjttaQ3d0WWQ1jEM2T6v0Tlb0fhi0f0GAg67PjtdgRZwqlWY9dpsOs1dOi1hBS/dbjT+gNYPH50/iBWpxuLx4+gVxDqG6RSr2WPHGKa6gay6MpZb5DDgXpCtqUYNAJjdWGsNiQhyDJj9uylFAv3DruB3iE1Y5xKug6KY8CMdAyWn73Ug4EQa187Sk2xndzipYRN81O8sZ3FvW4kT3magohStLKC0T4dZr+ZA0IU5d4kXjAZ+NAe4gQhTOEeWnJTMCsDDN+5iqHH9xMIUxASopiQNh1lMAk5w8LtHe2caHfx1AU9mJOf9Jt9b64sZ9WLT2FvaWLkvAX0GDKeYL0bf62DQHkdgVo7QZeWv49UCIpOfGEVeI1VOP3tGBP7kTN+Fpp9L8C+NyC6G8z+iP3OaF7YeJbDle2kRRr44Mp+pEQY+LKhjefK62nyeeni/Ji29p1clXsVd/S9A0EQKCkp4YsvviA2NpbMaReytNnOxlYbkgyjrSZmhVlIlkUqmh3sPXGWM5UN2IIqWjARlP/R/CqhCj+AJnoNClnPmIo0Fiw/SkluN8rir0VU+OiX+AoxHfU0bjNj7HDROk+mqZ+RZ3iIeiGRpNoyZq1byjdT5lETn8bQ9kO8U/Q3wgIOzmXq0XXIrKsfzXPBSzilugMNOob0fQqfUs+xSSNQ/Atpsc8Lyh9wXlD+gnx+CXLjafr1/5JUsZabbbdhXaRgW64CV+a1hNcG6HJPH8Z0HfFPi/q7qeu+tDhuS+00dTkcDt577z1ERGa4+qAJqgiflYU+7x9SqHrtsOI6OLsOel9BaNILfH60kec3FOH2BZgYVUM/63ESkzbhs6mp3JyAp1WLIIhIegOCy4HLGkmP5nYUjnDaLJk4LXnIoQ4IViOGfMTHRNCtWwyaYAeln31OfHMTMuAPN6JUecAW4qf4lYKIJu9S1KnDqfCF2BxagamxlIa0ZsaF+znimYLaH2DUuo18njGWb7JGcbtRRFOvRVSK9J2QTN7YZERBYN0bR6kqstGt5DP6TbFT9F0p1/a7h4DWS9ByDoP5NCM6MjEETWzxZaI0eBhhSufLOh+CMoQ/20ggwcSg4lr671xCTmsj1RFmEg096Bs9GUGppX1QDFccLEUW4MHhyVT+UITsF1GFiUTEGElPjiUhMQpLlA6VNsgPb79M2dFDdB02inELb0Kl+bmVKPlCBGrb8Z8+g+dkBe4OFaIyFvFHs1RI8OGhGb3ZjzWwA7V8FtXEhZA/j+3FLSz65gRBSebDK/PpmxKOKxTinapm3qxqQNH6CVrHZialz+CpIY/iCsEnB47QsmUDDWYr+/oM5eLEGK6IjyBF99vQMU6nkx07drDv8DHW+XJwomNMupWdoh+bS0JR50bU1KBP/AxBaSetKJdnVx5lf/YYnAkX0B4BHcNrmFq2lsQvTiE1QuOFStrHKvlSeTdDI6OJ37mdim0nOD53DlssuUT5Wvmw6BH6dRRQH6PH3+duZq9O5mp5IzeolrMy/ALWWfJ4/aKFaAz/xsOG/6c5Lyh/MXxOeC6d1rx55Jou5wbFMqadPofutWIenZ7DxfIYTiDz8LM3ohD/4zevFn+QEQeLSNCqWPejqevvQQIbGhqYqR5EuFdP1HU9UUX/Q+dlayl8MRdaS2DiM5yIu4iHVp3hZI2NgWnhTJS3Em76EmOch7bySGpO5xI2YDyHpXB0hVUkVdtRC1FIqmgQO/OUyLKEIIiYrAGyB8SRMyiTsGg9tg0bKLn/AcRAEKdBj/2SsZQkbGWDo40ahZJoT5ARTWo6bMl0qCqY4buQvpqxlHlDrHJ+SLSzjTVD6hgZVKKxjcOCk9HnjnKjeT42jYGXj35Be6/LqPNHYQjXYA5TUV/upFvVFwwaeJSq1Q4W5d3Aqcg0dMaTGKM3M7xxAJqQns2+LmQlVlFr70OZLYAmVsaWE0cXXxOjtttIOPcxmYomCg3pDIgaT4yuG+pUMwe7mrnjh0ISw3RM0LgxFGhwq+w0GWsw+8KxeCNRST8/nCUk/Fo/QuAYcss+NJY48mbcSJeemVhj9CjVvz3XkqMN+7qvqS49hWSwovMmo7Eno/gp42MIpaYddZcMnLEWrjxcRp3dy2tzezMhNxbodHJ9vqye74o+QGdbgdbUn+bw6/GiYpyjiYyje0nPyODSuXNRKpX/4fXW2trKig3beOmUAkEQeGpEJF4iaax2sLemg4OBDnTxyxFNhaS3JPHwJ+UcSZ1JIH4c5QaQ+1sZHq+i5zv34z1SjH0EtM6GFxT3E1B2Y/b3H6PoaCX/sRe5vqqdJq+X+6o+5KaKz2jX67kn4TV2n1JwQnMLKFIRtCLqe3b9kxvu9zkvKH/AeUH5i1GwCr6+gq8mfMrt3mTelK8h+oNIhOJq/jZ9NtfUGGgck8DNF//zzvhrz1SwoflnU9cvfU0mxg8msVxH5DXd0Wb+Q5a7ks3wzdUgKHBO/5CniqL44mAVUUYNtwzWElZ3H/q4OuSQjpb6WdQcyyYoRGByKUDofPDJsg852IwgNxOVbMQYlUXFKeg1NokhszvjNIWcTur/9jcc36+iKDmd8m4idbktbFc58AkC3bwhojoyOdExFatXojhtK6OzwzhYv48nT86mm2Y0Ja5W9rV+SocW1g8tJdYXxaDGQSTQSM/m41xueICBjWd4vHQpDcEMSnPmYtfG0aPlC/pnrqN6s4GPkibwRZeJqM1HiYxaz/D6EagkDXvlOCIiQxysTUGtkXDmRhER6WbcsVZSiwSSKt8hPLaFOnEEA6MmoFYZMY9P412Pkzd3lNE9SsuAaidWr4kdGeWc7NOLoFJFmCChCrrB3YbC0Y7WacPs8BBtVxHtsGCxe8CxBQCVfhIKdToeVYigTonKrEEfoSU8Wk9sgpGUFDMJUUaUYojaw69SVrMUQa1D2ZpCRGky1mAcfikdmQiEOCUPEGJng43HZnTnioEp+CSJNU0dvF3VTGnDSoztywhqu3FZnye5LzODE8eOsWrVKnJycrjooov+lPlo54kSFn5ViE72cml0A92y0jBq9LS3CnxV4uGoejua6I1YXTru+9KGM2kBjebeHDGG2Kr0I8gSd5esZ/SZbfjyBFrm+1kpzWWLfwRXfvMm6pQM5jz4N54ob2RlUwdXuXbyyIlHEEIK7pUW0kuqZZ6wmm/nbGVG117/Ulrg84LyB5wXlL8YK66H4g1MHLWWgL+Bhxy3EHE3bOilxpd2LeG1LqY8fwmpkb+1xf+S1U0dLPwHU9ehQ4dYu3YtA9J70aMgAvP4FMyjk39eSZY7hytvfgQ5uitru73Ewzsd2DwBLu8fxejwTwkE1iGHRBwNQ2k8PAspoMcvuvGItXjFRoJSI0KgiYykWEZOuZzEhFQaC5rY9tFx4hI1DJwUj+z1EOrooPnttwnW1XNgUBRrBrRQrFOgkyS6Ow20tY2m1jOUSV4vA9rX8MCIw4xNHsmdhzdSKru5KcbKB4dvJ96cTa1zJ7ub91EZFcumHBvJop2BTQMJaVpxOzx8J13IoiOfcVH0HtTRFpztCqIjKyndFslBcxaP9L8ehbGY2OgVjGwYiVJSUqDVUeLPosOrQU7SEso0Ms13iKwN0aj9Ono3Poc9QYdOnE6GKQ+lVcYwtxeLtp9jw5kGctVBJjQZ6dC1sH6gnsroJJK0akZFmKn1+qn2+qnx+nGGpF+dN7UgY8RDWGsVIzetJaK9nZLUdGwROcTYDcTawjAGfh2rzS1IOFUCQZ0ChVlBVEIFyRHfY9Afw2wL0O2MC3xDaQvcjCTDMoOHDzwKevWKoThBQ1swRLpOw/yECNrbdrDs+BME1ClEpzzAI126YjhXwIYNG+jRowcXXHAB4u8EnfxHdp1rZv5HB0nW+hinKcHvdf/0W7Nk4KBKwh33PUrRxbUbAoQrb6Td3A1lhJPaKC3FspKU/RuZf2IVtlQVrhtdBOzJrK0fTOrhE+wdMJbc8RMJN4bxUkUj2f4ylp25jTibjVXBQUxV7Gdz9BzG3fjuvxTT67yg/AHnBeUvRCgIz2fgzhxPesRNXCp+x+UnTqF7q4gHZvbgsuBwinQCD/ztpv+wmN8zdVVUVPDpp5+SnpDK8LI09JnhRFyZi/D3t7eAB1bdCqe+xpY+hRucC9hb5aFvspnr+h5F4XwbSQpiL0ugofBKJG8ioncLcY27UPnaUQVAEwBtALT+zvk/w2mSeW26yPFkBYkBAXNbDgUdU8iMdDO7JYOBtnYcrd9yzdhCcmO68uiJfcQEfFypup+S7Bq0rT/wZuHTxOjN7HRsp7HtIP2HpvGaU8CpaSK/owuN6iaOePJweCN4d/MLpMbVE9PFSdHOSNpEM9eOXkxQ7SI57iuGNeUjygKHxSgKPKkozQpc3az0MhZwxbFiWs/mIoqRDA89SY0xjzTleAxKM6Z8Ld7RPVj42RFO19oZ4pPo79GwL7WM0kEDqZLg4lgrT2YlYFT+/IYvyzK2YIgar58ab+AnkanxdQpOvdNF360r6XH2KOVJWawZcxF+rQZ1yI7C24bO68bsDhLuFIhx6IltNxBj06L8sWNcZWjCnL2FqMSddK1qJ7o+nCbpHqRgNnv0DTzi1pMRW8bCPpX0TOqCQR+HWh3NodYKFu97lpAikpaoRQyJSmVOSxVndu+kT58+TJs27U89pL85UsPdy0/QI8HCo1NzSLOIOBwOHA4HdrudbRW1LLcvI6QrY8RJmW5ttyNpUrFZTxJQ25FkCK9sZOzBnfgiFDhvdXGwtS/aY378thCfXXAtTVHxDFb6OSvq6fB7WVr9MCPK9+OQ9RhEAfH+MlBp/2ld/5HzgvIHnBeUP89zi+egi8jilnv+aUSb/x7Kd8In09g69i0uDeTynHwrGe+oCFbW8dSUS1hQo0ac04dZYyf8h8X8o6nLZrPx7rvvotVome7ui0ZQEXNrb0T9j7GkbDXw5WXI9SfYGn8tC8uHE6ZXc23/RrpoXiYUstFeYqatcBABz3TcihKGHv6acGcToTAFghJQyoRUMn6VjE8DHg24VDI2Ddg00K4VaNEINGpFXGoBrwo6zDKJ3giaW8fgVwxkaNxOhiccps+BmxGa/djqvmDhxHOYTGG8UXKOhECIK5X3k9W/kCx3MWsCdvRFVu623U+EUmR782oKaCLn+usYGBHNK6uXENuh5IyuicMd4+nqquD5H95DQMKrUXLF5NtxEUly1HqG2+IJIbLOn4ND0OPLNGNNdnKtYzsD9qRysFVAUGcyJOpLHK6epKp7IQttxMzN5lxkAld9fIAOp5+pTg0mdQPHBkVwJjoRlSjwfJckpkeH4fcGERUCStWfH3XkDATZ+8N6Tn/+IYI5DPfc66iOiKPa66PS46U1ICH/Mk6NLKEM2tH4HRg9PiI9CuLdIeI0NWQoj3JZxSb8vjm4ghfSqnByd0hEYz3DDb0+Qqf8OXtnqU/k/WYtiDrsMfdiU6ZzcfV+wssa6N7dyogR3dFoo9Goo1CpIhDF3+9fWXeqnkdXnaHZ6eOyAcksGp+DRf9zDLNAMMidm55ne+PnJLXAsMrbCBczyR9jxNfShq2hDamykOS9K5E1Idpv8XIo1Bf/5gBqhUTzqO4sT56MW6EnOthIkzKGy2tX8+C5t9ECmnuLEHT/+QyO5wXlDzgvKP+cpxZfRXbhYXKLgxzqrWPeF0f/dyqyfjEcWcL8SVs45WrnRftNRN4TZHVfLcGUawmra2fhO3egVf/xG9c/mroCgQAfffQRbW1tzI4Yga5SIvqGPNSJP5pNKvchf30FQZ+bxfItrHD3ZGb3ABPjX0cpV+BujKRqp5mgZxwBQxqxtd/Sp+gEUpTAykm3kD1uKnZvgOJTZzh76jRRub0RzJE4PAFqahw4fEECYgCvLCIJIiChULhQKTvwBcMwRsXw3JgsIn334nAcIfnIvairw3BWLuO6ySV4NDKfVDeTHAgxX3kvvfodpZdOSZzJS4M3kRcrNjF5z0Qm6CdiUcjsbPiab/WJnIlJYoQmQL7bT72igZ2qEGWOgfTxHuTWY9u4e8RQWvxDiDIdYaJfxoWa9b6ueCIMhLoZmaFfzeVnZKwVw9jbWIDL0Ic+aeewNEVjVkWC6gDxt13CD/UKbvniKJogzHCLVCVU4Bo9kr1uH4PDjLzeNZloUcHRDRUc/aEStRa6DhfJ7K9CrdUgCEoEUYkoqBAEFaKoRBBUCKLqx++UiKKShtJyVr/8DB67jbHX3Ej3UeMA8EkSdd4ANV4/1T4/VW4vRY52yt1uGvwSdkmNLHSaqAyuKoaW1vFI41LiFSLNgfuQZANv4WNPsJpLhXUopDZU+iCGSA2eOC3v+2sJCTLZMZPYppjJgNJCutdWEJtcRFbqoR+vOgGVyopGE4VaHYVK1dknFwp5CQUd6Mxj+LqgD5/sqyFcr+b+yV25sE/Cr1o5Wyt3cP/mOwlJfoaWXUqSZxBT7uxFl+Qwgh0+HNuP0vTUPYS87bRd5yWoz+PIRi/a2FyUKXqqLSFWJowkTVXMYfqT2FFP39PHeHHxfRg0//Fggt/jvKD8AecF5T/mxTunMHhnGUY3NFkhqh2+v6A/Dz3xyf9sRWQZXulBIKY76fH3MoEN3HDsIPp3C1l8YR/m+wdRGaXmjsXX/mER/2jqUgjw3XffcerUKWZ2G0vkUZmwmRkYB3aGMKdgFfI3V9MgRHO5+3Y00QlcmvM5cZr9qMVMClcLOGpNKA0T8ajOMmHPBrT4KOiZgW3hS1w1oguCINDRUM8ni24muXtPZt7zMM0lx9j59m4a3d3JKf2c+Oo9SCI0JUVR17UPvkHj+cAQQ4NKYNvwXLzVz1JT8wlxZxdiLO6Bq/gjrp9Sik0f5JNaO+l+P1cq7mNo/g7yNCdQKAxoFUm4/GepD8BrtUau3fcAQ81WNErY3PA1ryeMxC6ZEdTtjFDVkhL0sJpk2vxWNDGr8TXMxKStZKbcQpusZ5OUg7NrJL1jT3GVtI7ozX1IYxSFTbs4pxlE3zg78e4wApIbc+QqIm9+ksc2lLDkYDNxIZEBNBMYmcqe8EhaA0EWp8VyfVIU5Ucb2L28ALdNxBR/kFDAiLu5G6LGhiF7A0LKTryKAAFZ+HHix+m3y5JHSfT+GAxNOlrSnJTndRBQdP7ml/jp/35ZJiDJBGQJCQFJEY6IgihViDZfB7IU4rr6CBZ6KmgP3EFA7st+OcAbITfzWveSHt+ATWuluqyaZjr4oX8Tfq3M5fq5FCSPx37iOF0bKlH3zGBO3wA+VyFudxlebz3BYAeS5PvNtSkIatzqeXxwbADHazz0T7PyxMzuZMf83B9U76znts8vpVDTQvf6oSTXTkc5MoHbxmUTbdYSaGykeuG1+ErP0X55AFdqCqdWqxkUcSFJ5hx8sbWst5zFHn+WD4Qb8Mg6Tg3uQqT+/LDh/zLOC8rv88Q9C0irOEyfkz4arHBoWHeUkpYx6w9zLkPJlC8OoPsX4wD9S9SfhHeHcWzU80yS+vOg/BB93nDiqW/g2YmXs6BWJv2mC+iXl/uHRVx7poL1zTZ++NHUtXfvXn744QeG5w0i+6AefV4U4Rd3igBnViJ9czXHQ+ncIt7DlK7HyI9cjtGQge1sT86sK0BQxOK25tCzcDVZtc0ICRIfDr6aedddT4/EziRRsiTx9d/up6HkLL3MEF1UTL3Uj8IuV5LQsINIYQe+/iPJvOASErOyOOf1c8XJclr8Ad7ulkrPwCYKixZjrZtA5LHptJ54k9unVdBukfmo1kWW38OVinsZn7+e3hYlUY6paI50BbuCoMqOO7aAbVG72FyhZWr59YzU+UCpYK1/FS/0mwMVHkRngOHqYmIFN9/6ehBCiVrhYLaimDYM/BDRA0MXiavU79PtXDv2Q70YHHkBds8R9vu7M8ASwIKRZs8psrptR77kLS56bRdFLjVZAZmU2EbCJ49hWVMHGXoNb3ZLIbbdwbbPDtFaqUejrSaz9Suij5QRUkBDjwzqoibj9HVFobUR3mUD3qRd1MtB6gMi9QGRuoCALSTwU9hlWQmSCiGkpE+JgR4VGppNMltzBRxqNbKsAkkJsuqn5V/ORYUTQ9hxAko7AKIgkuX18kxDB7H+0bQHr6VDFnhO9pLsUpIacJISVkJ4tpkGrZ+X3Z/RqnIyodJEVEQKFUI+US12jmek0jdhGwPZg04Tg8nUvXMydkOnSyYYtFNS+hw2W+czSJIFjrVfymenB+DyC1wzNI1bx2T91IrwB/089c6lfGs6S7QjhbSKy9mjDeOaYWlcOyIdQ8BLza234t63H/v0IK354VT8kMzMifejqBMI1DlxhR3nVL8lnPSN5rGJz/3cT/if4Lyg/AHnBeW3PHfPbPrvOkNMOxzupaUyrS8PPv0BAB9f1peBR9x8M7s3Dz3x+f9cpbY9DTuf495pW/nO7uM9x01E3ePmu/4G5MSFWBubuOn9B/+wM/Tvpq5702K5PTWW0tJSli1bRk5mF4aWpqAwqom+uReiWgGnv0P6dgFHQxk8bb6GK7q/TaTJRHTYAra9eRxnawloskCoZcLBU4jaENW9Etgy9m88NGsAJq2KQCDA6a8/pmLdd5QFlfSobiKpzUFHXDrHsm/DEObnogdGoDf//Aa6tdXOtWcqMChEPumRTppczJGjl6DryCZ+7400H3qNe2dU0Rwu8n6Dgy4eL/PFe5kxYAvDWy9GfSoZBNB2jUDfOxp1ghFFuAZBEHj6wFN4vtKS4cpluClEADga9xbbevVmY8s45FI3Yz2FSLLAvlAy41Tn8IhqNuf0YXrUGqaxg4ZVelwtCUyKn4+ocXHKaaabRkCSgtS7v6H/EDfr4hfw9LoOGkQlXXEyYXI2a3V6Tjk9zIuP4PbwAIdX7qH6WCwiXtLqV5N0dg8BbYjN2VGoAyb6VjZgdXloC8ugOHs6bn0mgsqBOWcLMVk/ICo7RzT4QnpswUQcwRScoVRcUgpeORVZNKKrPYNu/3IQlTDqcpRJXVApRJSigEIUOpcVAiqxc36uoYXPD1Zil9qJjNiPKaKY5lAzGknijvYO5nREUB+4D1FO5GvZi6upgJT4IKqoBnThFQgRFXzgcFHpF7k43E9vVBw6OwnZZWRbdm8CEeHcFfH/tXfe8XEUd/9/z16vulPvkuUiS+6WccE2NhjTWxIIBAgthBAgjRIgT35pT0iAQBIS0gjwhARSCN0Ug7HBBffeZEm2ej+162Vvd35/SICpwcY0Z9+v1752d3Zmbr53e/vZad9xc/bkiZit1nfcn8HgNhr230EwuAlQCKccPHngfFa2TSXfa+VHZ03k5An5CCGQUvLYXV/jTt9GpGJjRvAKlvYW43dauPb4MVw8o5D+H/yA0JIlROfrdC8yE286mS/c/AtkXCd5YIjG9rsJKpuYvXgJJtOhvxgagvIeGILyJnf/5Cb8+1cyc1OYkBvWHTeGG+5e8pY4v/3mFcxavY6+bIH3f37HvIXHfzyF+8M8dJub6rF3Mjm9ips3rcD5YC3f+cIsvpqoITgqkyuuu+hdk769qSs0NMh9992H1+vlbDkLehLkXjdtePLirsfQn7iKzdpYbnefz9dqHqFyzDW0vFbBliV/RuoRYt585u1aT/6QinV0kj9OvIBjzvs659UUo2ka2+6/C+tj/0Tr0VhVWYIvnaS0yIf51CvZv9KN1W7m3FtmYHcNd75KKXmgo48fNHRQ7Xbw10mjyBKDbNhwNnoYytbcwtC6P/M/p7fQmSP4XW+IiVGVS8V3uah6KzN3XoTV5cN1TD6uY/IxZbxztraqqVz11LVMX/Y5ykItTCoZQyIdo2P2ncSzgrwkT2Zp30mcULebAj1Eky2H/ql2LrH9CW+3lbrn3VhigtMrLsJkL6U/pZOlmOiON5PW7mPKwipurZ/Dq4O5hBXJjJwkJ543n581d+EwKfy4KEHGphU0rqpEU90U9Kxl9IFnSBZGeG78OCwnXM/po6op8Njw5znRD9QRXrGcyPIVdPfoNJefxqC/ErspReXUJEUnBEmmG4hE6ohE6tC0N9cpsdkKcLsrkbFiNv/jAOHeIHMvuIiZZ5//vqOvEqrG45tr+fOqWpoHnXgcXUwavZO06wBT+mu5riuKlriMmHYG9aTZWvE408a8gB50EQ+OYnBoFI/Z6mh2tnO+93IuKD2ZpStfIBCKsHZUNbtKxzGmtZ7zB1s5duxYyqdMJ7Oo+C1lGhhYy4HGuwmFtqModuoHCvjb3vNpjxQyt8LKbZ+fQ3m2G6nrbP7J9XzPv4Vu3xAnOs4lEF7MmoZBinwOrl80hrkr/83g/fcTnyzp/rzA7/gOc84ebhLW9SRSgsn0znvlg2AIynvwSQjKnTefj5AaN9352Mf6ue/H7bdexPR12yjpluyqNNMw/hj+544H3xEvGg7z1+tPYuHqIZ49cxzX3/7EYfkCOiQGm+GeKTTN/wFzlEV8W97Jcb9uJzwQ4O7FV/CV9gTH/egaCoqy3jX51/Y08/xIU1eFReGBBx4gHA5zwZhTMG0Kkfml8cNuVXb+G/2Jq9ikj+NO9+e4dvZLlGXewSv3byPY9RxSsWDX+li0s4e0VyM6PYPfjvseP774RMozHWz7w23YH38c0aMy5LOzu6KIuGLlkrv+gCcjiyfu2kq4P8G5N9fgz3cBoOqS7ze081BnP6dmZ3BvdSnNra/SfOBH2PV+StffQvyVJ/jRyftpKhDcFYhTE45xqbiZK8r3Moev45lZgr0yE2F6/6aL7mg3P/jdr5nSeBJT2p+iuPockqYY1ks6aOn9PTEtxSr9TDr6i6nJfo1qfTud28bSv0VSooY4fs7FJPurSUsdpM7OwRWMdv0LvWomf9x3Di9bHEhF59unjmG938ZL/SHmuOJc2v4IvSumoaZH4Q02Mrb5X4jqVtZOnc7YObeR1Rlnx0tLaWUzEklG1INDuHF6fHizsslyWMjp7yDRMsQBOZ5BXyVWNcz4zB4mnVqJZ+5sVNn/hrhEonVEo/VEowdIp9K0vVrIUKOXzDE6Uz9fgS+rCrerErd7HHZ7MUK8OW8kleojFNrNzsZXaezchNfSQ5Zj2CGnJaUzui5KZt8UetXrUXHyd38j5cprjP/3BpD5DIyu4sFjOqjNrmdqxyJOVs+j37WDoWgfTK3hIXcecaEwad9W5m5aToHTztiZx1I1byF5FWPeqIH09a+g8cDdRKJ1KKYsXmqaxhMNJ6FLM5cco3HDqYtxWF003noLP3S3sKN0L5O9U7lw3Pf544oAuzqCVOZ5+LHcS8aff0OqTNJ9mU7llHspr1r8of+ShqC8Bx+3oPzs1itY/MI6nElYPS+Lq/+85mP77HdjxVNLqHv+Tuas7SNthrXzi/jmb19+3zT3XXMN47e8gmaBLed/mRu/8b2PtpDrfg8v3sqvzlrKL4MW/hL6FoU3D/D3Y72YC64kO9DBtff96I3oO7dvZtm6NexJSPb7C6gvG8+itS9w4pbl9JWNJ+rNYkpflGM8ZzOY2EaQDRS4A4wyb2SDVsUvvadxzaSVpBqupnbTNrTkDtIWM3Pq9pMZTeOqivP3saeSnHUN3z15HHt//xMsS56lL2Wn1++g1+tBl2C22lj81Wupmnc8S/+8m6btAU6/bgplE4aFL6im+eqeZlYNRrimOIdJQx30dPycsd6dEMumaPdlaC+9wm9O2s+GAo3/7Y0zLxLlcnET19YEWTj3JiyZh9Zcsa59HcvvOkBeyEF15GWKR52PniEp+vY02rv/Qmvbg2halEgoi7qdNVh2t3Jc5gDjz72TwZHboj/ZzZaBZ1mc+wq7XHN5auByljlS+B0K3/1yDbd19RJMq3yt5RHyV2eSMM3CqkYo6XsS79R1dB1TTWnOD2lb08um5ic4ULCf1twwUWscACEFLtWFN+XBm3SSEXeSEbPhjVhwRRPkBJ3othnE3BVYk0FKO5aT5+vHPrsG94IFeEvLcPn9CAVisSbCkX3sfHE5e144gC1Dp/ykJuz+FAAmkwuXcwxmi59odB/JZPcb35XDUQ7mMja1wstNlewfqmB0FlybvY7jDjxNMHUDuj6JFZYe/lD+S8ZKN9Ne62X63iTPnGjnpXFpagYmMr3hEkK+vaQtEcaWLGDTpGIei0UwSZ2FnQ1UvfwElngUf2Ex1fMWUjV/IRm5+Uip09P7HI2NvyYebyYmx/PInuNZ31lFnrOPb80f4uwZp9P7w/v5vUnwctVLeB1u7lzwC3oDxdz1Uh0t/TEuSTdzwYt/Ip2hErhaZ8rJfyU7Z9bh/x8xBOU9+bgF5fEzJzK2UaOlSDCmVbJ1ko3uSYu54Qe/+NjK8Do/v/USJm7dwpgWnf1lCrun13Drz//6H9O1N+/nibuvYvGyLl45oYgLfvoomZmZH00hIwH441ykt5BZk+4lM7GJ/133LO6/7uW6c+dzTXwSB1xJWnyS/e4cWnJLGcoYLouQOv5IiOLBAHP278AkJbrZgjOhca48nqAe5MX4S0ywNnC6ZxPr9An8xncy53iXEVh3Jun4TqQexJUKM7+2l2ROGsd0K9/3f4srzj4Vx5M/J7phOwGLg0GnfXgBrAwf446ZzeiaWZRMnIzFamPDM41sfr6ZeeeNZcqi4Rn8TbEkF+88QHM8ydiBNUxOPcGpOe1YpCC76Szc+2aS3voo/1gQ5clR7XyvP8FJoTiXixu49fN+Zk++7H2/NplKoScS6PE4eiyGfOM4zl/qViE2zaK49UVsOSoT8s9GVQYIyfXE1AC1OUHaE1UUNOzg3NPGYR//HQYea0Lqkt1Dr9ES38i5RevZqF7AA+mT2GLXmVnup2S2l38FEpyxdSnzNweIeBejmWx4UyspnPk0+ngblsg1vLK2jVrnazTn9xGzqQgpyIvlUNrtwJLQCHpUgu4UIVeKkDWGFCPPGwmutAuP6sGb8lIyOJqK7smYKMSSClLe8hL53WsZzHDSlZNNb0E+epYfq8OJxWZHT8SJ1u1C6hquCcU4S5KYLb3YbH1YLEnMpjJycmYwatRCfL5JmM0HjbDq28v9rzzLM3vzCMSzmels4Rfm+/HHpjGU/jJ9pPlL7lM0W1qx6BayIxYGLX3U5vUwJpTJ/P5ZhHEx/OwUhBwONo6qZn9uMRYtzaxwgGPrtiC3rQegcFwVVfOPp3LOPGwuJ93dT9DY9BuSyS4aoyfx4I4FdEU8zMjbxtUz2sh7WOdZyzEsnfQvhux9XDftOi6pvpxHN7Vzz/L95LTU8fNN92M2x+n/Okz83P1kZc477L+lISjvwccpKL/+1smc/GIrr8324jnxKySe/xMzt8Y4UCrYOXUW37vz/z6WcgDc883FHLuqHUsa1h2bReVpN3PCOWd+4PR/u+ob5NQvJyMiefz8z/GLG287LBcO74uuwyPnQvMaGi9+gWObTVwh/8gJP9lAkgT3LLiKKzqCfO/zZxJzmPDEIuSGB8mJBMkND1KixhldXEx2djaBQICGhgbsTjenhifjTJv5t2s749PrOEcu4zV9An/wn8iJ9gaG1mYj1UakYmJaYxvZsTieKSFWlM1jifdiTmp+CtHTTtwy3AfisJkZu/hMJs9dQO6o0W/5Hho29/DS/XuoOraAUWfZ2dr1Cks69rNMnI4udaqDv+JK125yrRqu3qlk154HW9ejdm7muYW5PDS5gW8NJvncYIyvcAPfv7CSGZWnvedXFt+5k7Zf/orm5kbiNhsJm5WkdXhTrRZUi4WUxUyfp5KS4GSmbL2dvkkzOSbnNBpFF6+YdyFNJkaFm7joum8S3pFBdH0XSS3Oyu5HSSuDXFC8mpWRb3GndSJtJjOnTnHQYgkwbc12jtveQFfx2UTdRcABSuY9gaOggbqeGta0JGnMbCFuT6PoCnnxPIqHsqno8aCUzmVfhocUOi41iTuVxJ1KYI9HSRFgyDLIoDXIoDVE0BYibA2jjwhNYXAMM9tOIz88GvQQBZ0rGNf4KiZdZciXQUdRER1FRQz6MzBpSWztTZjicfRsD6ZiJ3abislsYTCWRyJhxWw2k5WVhc/nw2KxkEgk3tji8QjReBx0gYLGQtYzUxsioN6CJIdNphZ2mxvRhEZKSbHfu586fx05sQzO6KggaS5GxUqeq4zEgJ0Oq5VNlR7q8rLRhWBceICTQ93k7VhLsK0FxWSifGoN1fOPp3zaFHoCj9Pc/AdiySCrui/nsdoJKKicNep5Tn2lj12eK1hb9W/2+LYzv2g+P5v3MyzCzQNrmnj22XX8+NV78KZjDH5VY+wF95CXe+ph/TUNQXkPPi5B+dl3L+eE5euJOGHH4jPeqJHce81CFrzaQ38GbFg4nZt//shHWo7bbr6Ccfs2MbEuTVu+YNucqdz880MfrbV340pefvQuFj+7n42zfYy7+lfMmT37yBZ2za/g5R/xnPM0rq+8iqDXzx2B65n5gy7+Os+PLe8KPD2N7J5cQkE0yOjcHIqLiykqKqKoqAifz4cQgkAgwJ///Geys7M5xzuX1I4Bsi+fiC38DCz5Fqu1idyXdTzz0oNEN3UghY47FWdebRtDhRrl01RusV5FcjDNMQObsOhpspJxbBXFHHfTbRQUlb6j6JFUhDWbN7H/EY2Ip5MlVb8hRJKEaz7hzCuwqANcGvsdx2fsxhzLJnffRdi3Jkjufhx10lQeWqzzoljPFcEUX+6PcQU38JNLZzG1Ys67flXRujpW/uZX7JMQysqB9+rX0nVMehpdaniG5uGMdVHW8ABa1WlMzTqBgKuTeNUA02rOJ/h4I+m+ON3xZtb0PI7b7uJLxS/zRPhG7naPJ6JJzrGso7q2iSkNrRwYfQ6B3BlopgiFszbQk/Uo24JudsYgbklj0hXyYnkUxYrxR/Loy6tiZ1EZisnF+W1pzmpXsevQ4RC0OxVanQptToV2J/Rbk0StEdxaGK8ewqsNYtU7EFo3qt5PXA9hHixidOvJFITGEjcFSegvMeHAa4xrU1EkRDwmOkZ5CRQVMJS0o/QHSTvdJIpGI82Wt31RwzUJIQROpxOfz0dGRgZ2ux273Q7009y7ma1dLixBuFl5DHv6fBL6CeyxdnBnyR/QMgQ1mVPZ37mLRr0XBGRF7BwXOA4rFi6uGMBc8RU2b7Kyt7WPzRNgW6mPuMVCdmSIE2KDzBnqJLhtA9HBAawOB2NnzqVy3iykZwttbffTFbLyWOM1bO7Mocw7yAX1G9HsZ9NW/iTLil4j25HDXQvuYnLOZPojSe5/agNzf/0dMqMx+r9kYfp31+CyH8Uz5YUQpwD3ACbgfinl7W+7LkaunwbEgMuklFtHrjUDYUAD0h/E4I9LUP71+clMqFV5/qxJfPeOR99y7e4bz2H+8joUCStPHMsNdz1z5D//4fvoWf8oMzZ04I3AhmM8xCYu/lBuVB676gZoX0pFu85DV5zDT7/yPTwez39O+EFo3QD/dyoNtvEcN+O3mNEoUVr55hO/YcrLAa4+dxHfjI9DG5PH/HOOIzc3943BAVJKQoEeelua2P7Ki9SFUuhCYWrYxnT7QvYnt6AoT3KcZzsrtck84F/ArOY9JHpUFMXHtAO78MdSRGpihItH8+Pkl5nbs5aZXXXkEEM9fi7zb7wDx0HrcQxEB3htyzp2793DQEDFEs4mN1JKwhJh2aR7Cas5dHu/SKpwHKXxTr5v+SFuESaz6TT8dTUkVj6Amq3w+Odzecy6C4uwcOFQhEv641zJjfz0ihOZXPbWOTaalmbLE4+w6bUN9Ln8SIsFoaXJTvVSmWPF6/Pi9fnxZufizSnE7s/H7MkFZyaYLCx/cQv7ngxS1voI/bZOiotOpNI9E9voDJJNQYTDzLa2ZdQFN+J3lfLFwiXcq17Hs7KYeQc2ckr7RnJCEfaNOYWuwkWgmFHGNrKt4I/sSqeI6gKTJsiP5lEULyM/nk+Hv4hN5VX0uzOYMZDi8uYwx/RZQUiGcreSdvYiYkVYY3k4Y1lYtDcf9Gkh6XXotDoVmpxW2kYEp9Wl0G0XSCHJ0MOM7exheq2VnEEvcWuEA9mrsQ0uZ9qBBJObJHYVYlbYMTqTAasPaTahVRTjL4jgz2nG5+tBUdLEQj4i4fG0dRURjzvJzs5m8uTJTJo0Cb9/eLb7wMBatu79PS/vsTO3o41F0k6feh1xzPwu/0Ve8T+H2+JmRs501nSuwaJK7Ek7swcWYpIKiYylnKGojLOeytbu42jqdbOnUmHjOAc9NhvOZIIpgTbOMKXwBbro2LoBNREfXjHymMlkje9iQH2RLb2V/KvuQvriDuZEu5meLseW+3eeGLuZoCb5uvMUzlNmQzpNfUcviX/+H+6UyqRVa7B6j1JBEUKYgHpgMdAObAK+JKXce1Cc04BvMCwos4B7pJSzRq41AzOklH0f9DM/DkG5+/rTOe35RjbUOLnskS3vGuf2Wy5m1uotZA/CquNyuPaPq47IZ694agnbV/6RyZsbKQpAZzZsOXYC3z0CI8xWPPYg+za9yoJnNrF3op3AaddxzeVf+fCFjg3An44jGo8za+qfSetmhlxezpN/50u3vUpARvjdvKu5orOfM++6iVCgg0BLE70tTfQ0H6Cro4OU00Pa7UO3O0HXKWnt4sTcCxmMNhOJ/op55S28ok3hIc88pu3bghbzkRVVOaahlvY8yJsV5BHLhezpK+K67Y9RYg/SfdaZHPeNH2M2Wziwv5UNG9fR3hogNeTEEynAqg8LjCZUIs4+ovYUK/HQhhPz9GyimVZOFmu5SP8V3sBE8prOR3vlOZKd21m2yM9fJg/gc2ZygvTz1f3rMKftfEXeyM+/ehYTSsoBkJpG7StPs371ajpSVjSbA3Qdb6SfycVO5n7+SzjKpsEHaH6UuuT+ny4j0h1n9safsXF0LnPHfYl8tQRzpZsXXv09g4keMt1TOCv/Uf4vfAquPd0c27UHi66xYdIpDOaegFVzMZjZyvKyv9Bn78cKZKjFZMfHMW7AgxQ29uWX0lSUS6m1hTN6IsxpLSEjVIhmjhDO20Lc24gp7cakejGnHAjNjpK2oaRdKCknuupEajbQLJhRsIjhyYevo0udoEgQMKXosEOzx0K3sJHZo5A/oBFyCNaN09mX28K0+k3M27mPGfUBFM3ElvJ84jYLCZeJ0swFKN4UqcJt+HOa8Hj6h2/JUCbhcCUt7fkkk25KSkqYNGkSEyZMwOl00t//KnUN99C0Oc4ZgW7i6rfQ5FieNQ3y1Njn6BLrEUIgEGQIJ1/cmUO3Yzwps8627B1ILcAJtTFmHyinO/MM+jKqac1KsWmcRm1pESZNY2xvG8fWbmd8/V602BCDdgtSEWSIGMWTe9GnaSxpOpmXWk/AqknmJx2caP8/nqzZzu6EmZqwxtef0PF2Ctp9boIZXs5e8jwW66EPHf6sCMoc4EdSypNHzm8FkFL+/KA4fwJelVL+Y+S8Dlgopez6NArKT2/5CgtXrCVtgtcWzud/fn7fe8a97davMmnrWsa26GyeakedfwFXXHvzYX/2XTedw4St9ZR3SPoyYPuMQnLnnM/5F7+3e5JDZcmVNzM49DzT9qZ56LJTuPKsrzF+/PjDz1BK+NfF6HUvcN74X7AtYwKLYgGW+Ev4beCHTPxBPQ8cl40z+1JyOvaSSjYgAc3lRXNnDIuIxQq6jj88QKYpRfnkGkr2jwEd0t4/MCa2jOXaNP7mnMPk3TuxKZVM3L+O7KEh1h+jM77Uyk/Vr7OgdxcLQ7sYmH02rqrj2LevgaFesEWy3lj8SVVSDNr76LUk6MBOl55Bv6KgC3BZTcwYn0NdiZnWtMaX5YOcFt9I3oGLsewcJPXao+wrN/H7k3QyKsZyPlks3vUcnnSCJ/W5/JFzuOeqM6guzKVx5ROsW7WalpgZ1TFcC3QEBxmdCnH8pReRNfPEt3yNWlrHZP7PbtT72sP887aNRHmNyTufY29xDjMXn8e2V54DTSPHNYeZ0YdobXKQHwwTttp5ac4pJH015AczGbL3smbUY/T791HlgISYjbe1iqJQmJTZRLhQkFXQzOhkL2NappPfOxuL5kJXkkglTVyNMxjcjd6+g4ymekyaChYniiMT4fCjODMRjkxw5SHdeSgOPxaL4x39dZqUSCRCgMLwg3v4dpL0aZJ9qs5AUmKxKjjHuhkc5+aAUxJr2kvFhleoqNtBwOMiYTUTszvoa9WepwAAMP1JREFUGTMHkVWJ09xLrm0tFb4deEfEJR7JZig4lrb2fFTVy+jRo5k0aRKVleMIBl9l77Z78K4NMDo9m5D2RTrQ+LGlF0v1RjrSr5LSU5gVMzfYLqB9d5TEiLv7sCXMgLWfrFSIWR1WZGwOAfskhtxx9o4JsXr0GFJmC8UDvUzuOMCxiTBZ6RSRzmZ6A91Y3Cqj5sUZytR4eM/51IVGU5AW3JDbTN3EJTwaaCPLLPlmaSVzyi4nO+cETA7XYf1NPyuCci5wipTyypHzLwOzpJTXHRTnWeB2KeWakfPlwM1Sys1CiCZgkOEG0D9JKd/16S2EuAq4CqC0tLSmpaXlI7EH4JEvTmXqziQvnFnJjb946j/G/+2d/4Nvy/PM2JGgvlxh19RZfP/2d84HeT/uvPl8KnfuZlyTTtAF22qyCeXVcPP//vrwjHgfHrv3RwQ7g9Q8s5SOcoXlCy7jZ9/4xkg782Gw4T544SbuLriUX42+hPM79/Fs0TgcIsBPHriV0VtUvvrFU7g+Uk5HuBnNlSLiyUQ3mTCrKrnhAD6/k5rzLmH0xOlIXdL5wFb0phjpqscYdeAvLNOm84hpNpPqA/ishczc8DgBn07t8Uk05VgeTZ/NvJQkK23DpjowyWG3F6qSpM/ZSa8tQrcCvdKNxZlPaW4mpZluSjIdlPidlGQ6cehRNjXVc5u0k5DwTf1XLGwuwt5UifbsfcSUOA8tUtBPmMOXlQxqtj2GLTnEC8zk7uS55Jblc11JgvbNqzgQMZN0ZoBQsMYiFLa1UZPhYcy3b8KUVULTrm6a97ah9cbwJUxkYsMmBEldkkSSkBpxNGKoRESKmKISUVSiSoqYOYU16iMjko2z925IJhl0O3AqLqr7reR0bEZoUOcvYum0ClKF1UztqEYTabaWvoxesYY5rkFSchL99ROwhExg0XG6BrGSxBsuY0psMrkyA4FASh012Izesp50zx5kLEAo20l0VAmyoAR7VhaWbA9RNwSUKN0iRCdDtGgBWtO9hNIRFKmQmc6gKF7A6NgYRqcqKEjmkqG6cGpmnApY3+ZORJeSfl1SF9fpT0vsAsbaFcpsClafHewaB7Y8QHz/dtoyvQy6ncOrZ/qq8OdMx20vwi41rDKBXSRwiDgW0kjNgqbZSKsWwITNasNms5GUMTbGH+Uc6hhSr0fHRxhIIOl3dvGzwt8TUeLc0HUZWd484gUmeqMDtLe3w8jSBmlzmgy3m4xgFrG+HBSziY5xrbxQPp5eexaZsRAT2huZPNDBxJJ8nFKhY+sGYtED5M/op1ZM4J8NXyQmBJ8vUDj9TBM/2vgDImqML/iSHJ9TxOxZS1GUo7eGch5w8tsEZaaU8hsHxXkO+PnbBOW7UsotQohCKWWnECIXWAZ8Q0r5vm1HH2UN5a6bzuHUJXVsm2zn4ke3HVLa339tPvNX9xHww4b5Ndx6+8P/Mc3tt1zM6NodVNelSdhg2zQv3fnVfP/2j3b02ItfuZVmdSnHbUzwrwvmMnXamZxz9tmHnlHnduT9J7LWMYFzp/+S49trWV1chSc1yK3WHzPq/wXpdqX54+xv8JWuDuoKTTgTMXKSA2SVlTDvy1ezp3YP9bX1KP1QEPdTms7CK+10237PDPE8L2k1PKzNoaYzm/Et2yjt2sTL0wTOySrLQ19DSYxjjAiSMEeIWaLEzVFCtjg4LBTllFNTPoec3Czi4QBdXe00D4XoTGv0mQVhp42w3UnY6iZqGn7zy5E9/L/Bx8jfWYmyaglZXb2smqCwalYZJ6YUPp/eSgZRVimTuSt+Hu3WbGap+ylIB9FtTjCZsKRVigZjVHal8OVNJpZdhkWzkqGZMR30pq5KSVSTRPRhB4gWIbAJsAqBVQGb4C3xD063LJwiYO1izvpfMuR0U9E7iEVqvDq6kOcmu7D4sji29QzcKT/78lsQk1Zwhm8tCdXNgbpjCA4WI+RwP4ZZV5iermCcXoAdK1JK9FAH0ZYVtKS3ES3NQ+QWY3JmEo3ECLS1oKXTbymTKyOD/LHjKRhTSf7oceSNHoPd5SacCtMd7aYr2kVXpGt4H+2iO9pNZ7STwVCIzEgBRZFRlEcrKI4Xkpv241QUHIrApuioQFNcMqDxprBYFUwWE3G9h9ra31LQ2M/+vAy6/T50JA57Lhk5NZiyJyItluEue6ljJ45LieAggl1TISVIJhwgbLi9Hp6P7eN89f/I0M4noc8hQJpt6AxaBnm+/A/0m/q5KHAanxtYRK99gKYxfZjH+OntCdDQ3IA1ZMWdHnbeKFAwpzxYdS9auYWVxX62uUpwqnHGd7VQ3dFMuRxijN+MSAo6unZhyY/zVOt1bLEJfOYU1585ilXBe9nQvZFFeeO4Y/HfsR3GbPnPiqB8qCavt+X1IyAipbzr/T7zoxKU2269irmvrsaswasnHMv3b3/gkPO4+4YzWfDyfnQFVp343jWcO753KcUNO5i8J4muwPbJTjpKJvO9Oz6eYch//em1mKMFVLz4CFEf/O2cq/jRWZ+jvLz8g2eSDJP67WxC8QjHzXqI/EAftQWjKQ42c4v7pzjrdcp/m+BPC/NxZ15MUft2PJNyiJr9eMMuCpN+ijQfdoZrE7ocfrAGNYnL/WsmKytYqh3DP5MnMrsvl5lb/kTSHOLhU2C8M49nUlcyu1DF41RISDNRIYigEBIKAWFm0Oog6HASsbtIm97p7tsrg2TSj58B/HKQXDVJbkJyTIuF4KYNTNi8j94MhW1zJlPj9TNNvIRbBNhuGsPt8fPZbBrPyeZOpqSSSEUjLTQ8up2xahFFIhtlpAlHl5KoDmFdJ6zrxDRBVIMBkaDT3ULeqCQnHjuL0eNmkIqliIZC9A300zcQYGAwRHAwTiqoIaMCa8KMSzXj0qyompWOlIk6/2OcsnETtaMl/5qu4tDzmNt8PkWhUQw6Y8jC1dRUP43Vmqa9fTytrVPQdQtIKNYyma5VkK17UISCqsVpUjfT5DqAyeFGhBIMdXSTTmsAWBSdbKL4UnG84RSO/jQqJoJuG0NOG0MOBxHLm36vPEIhy+Ek25tFTmY2Wdk5mF1uFIcTxelAcTqRNitBJUkfEXpkkB59iI74EH39KdSAGXO/C1+4gMxYPjZM6AyP4DEpOqU+nQlWJ6aEZHvyZdQ9TzCmXaOpKJv2rGziMoVJmPFlTiVcfgx1BX725FrYbxaoI+udWGWSUpopSXfjG4pg74RAOI+TEy/wOWFhKH0lMQV+qKdZp4RxFv0Tk7seh+7izKHj+FLvyWhC4+WMDewta8NTmEl/sJ+Otg78CT+jUqMxJ0wMN8KAxZHBQHYmm1x+ej1+SiO9jG5vIz88QIVsoUgJEUr42Dr0RZbbE3TYrIz3Bpg2rYku0cr9J/0Z83us1fJ+fFYExcxwp/wioIPhTvkLpZR7DopzOnAdb3bK/0ZKOVMI4QIUKWV45HgZ8BMp5dL3+8yPSlAeuqiGmVtivHD6aK6/+9nDzuf2Wy9izqtb8Ydg5YJ8vvH7V964dtvNV1DUvoOpO2OYNNhVbaVl7JQPNDnxSLPi8u+z0/oSJ68M8+Lp1agl8/n2tddisbx9OOY7kbpO332fI6t7JZ+fcg+NsoBefy6TB7byHf8viAcz0P/mZHxtF1857yxuihSSq+ZS4SoGIC0lIU0yqGv0mxKoPkFxdRHRrXczzvEK5akgz2szeTT2JRZ1tFKz/VE2VEqWnyDxDizm6ZLz0EZ54F28rpplGp8eGhYLESBT9JHJAH76yUklyItZyAu7sEdzUaNZ9CfNtMlB+vQDyME2TljVjScOu6sLcZRWMde1kmxzK82mfP43cRHL9elM9sb4SncmPsWMRQisAiwCNAkRXRLSVAYVlbiqoGrDD9i4OUKPpwXV38u0KgcnTj8Jq1JIKpEgGY+RSqVISUil04QjUWLBPpS+fRBuxpnqJE/rokztxCGHXamnMPFY8Db6kmU8Mu02UpqdGS1fZPLQGDSRRlFXUDR3Gb7SIOFwJg31c4hGM/HodialyyjXsrBixiwsDIo+mpPbaOrZiTpS8zALjSxi+FMJPKEUzgENZ0xFAMJixja6DPukySg2K3KwCz3Yhx4aIBEJ05/WGBAWBiwOBm0OkubhB6CiSzzxJL54El8sQUYsiSup8l7DEYTDgeJwgN1GymYn6MpjwJ7HgK2CiLkchBkdHTW7l0nVGRSbfTy17Q5mrGqgqB/6CsrpKC6lK9WJRJJtK2Z8xkyynaV0T/CwJjfF+mAXXR4b3fZcEsIxcg+peJsDzN6/mTvMz6Op15KW5axy9/PDiAXd0YQtZzkm136UtJv5A8dx7cAiPNLBdmcdS/wr2eRpwG3PJqH1k0rFmRg9huqBGch4grQ1hBTDIh212unOyCTi9OCPBinu60aaFFBzye0dS1OqkaX5PpLSyjTrdh767o24XIfej/KZEBR4YxTXrxkeNvyglPI2IcTVAFLKP44MG74XOIXhYcOXj/SfVABPjmRjBv4upfyPY2I/CkG58+bzOO2Z3eypsnD+Ezs/dH4/u/lypmzdQEWbZGONk7aSyeT11jJtaxBXAvaMM7N/wmRu+YjnsLwf99/yZbLEbHyv3otFSH53+XV8ZdR4TjzxxPdNp2kau/92M1Oa/8ydZZfzx7zziDlcLBpcymW+++nvLsOz6yJylt9LY2aC+2d8iys6m8n0LSDoSGApcDJh1gTyyzPxZtkZ3P0ae/79E6pte8hKRxjExcPpxWyNnc5lGx/BG6rnzycppMoV2gavpLgqQaGrHyfRNzcRwUkEp4hgQcWSyMIazccUzUNG8lHDBQyEfbSYAvQ6thBL7cQcCpHfJynvkZQG3lziN5JlIr8mSpF/AEXAkOLgf7VLeTI5D0eWzoWD+8mMZKGpBQg5LL46kqRFxaQLrCPDZuPmMJ3e/XR5+hhygtPkIVNzYdNV7LqKTU9hPWjvFHEyzUPk00tpuouSVPfI+zgETW72uMdQ7yqhy+1nwOIn0DeKWEsO84Yc9JhTZOhWHDrE5T4Kxqwgb+puAJqbp9LTXsUoLZ/yVCYOIEfkIlDoiDVQH9rEYKKFTOL4Ugk8ER1XXxJXPIUAFKcDe1UV9okTsVdXY6uqwlZRgTD/h7fk+BD0NSAD+wg376a7cT9d7QG6BiW9STeqHB4yblU0ch0K2R4X2RmZZPkLcDiy0FVGPAVEkSMeA/R4/A1PAsn2Dhoz59JWciKaebj/L2jrJT6hE1tFmPBzT/K5lSkyYpJ0+Qw6CkfRmKgnrkVwmDyM8UyjwjsFu8eM6/hytvbtYG3LDgK5OoOZbjqtBTQGSsjc2cUvzA8yLT2dqHYmHc4Y/yxNssrjZEBtx6Y+h0XfhxQZTFRP4Rvtx1IWt9BrEzxRbOHJYhNhfRuO8MtYk3vJipQzves8yoZ8xOwhurMiWAjiTg67sVEVE1GHC3c8iifkxRmtIqtjJUtm2GgXebz83S9jsxneho8YR1pQ7v7JTUxd9iyuGKxYNPuIzX6/+yc3UbDzJabtTpE0gy0N9eUKdVMmvGNeyyfFa5f9kNd8r3Dm0gAbFhSyu2wxN1x+KQUFBe8aP5lM8tIjv+Hk1tvZ5J3IBRPvIG2ycmH0b5zmfobe/ZOZ3Pg1lIEeWHU7955QjN/3JaqTHVx49/97S157//BdIr1rmEodVplmmxzNQ+rJ7NYnsiCgc9bGe+jIi/KLMxVGpYspbL2US8JhLOkECZEgYkkTNZuIWJzEzT50mYkp7SQtBUMkaHO2olq24kjU4ggNkt2vUd4jKRiA18dSxawmAv4M4hk+hNOKxZSguqiZUc5OQsLJ78Xp3B87k5THTo2/ga92Pc0o0U2p6MGBSlAroE8toy9dTrc6FiE0Cqz7KLTV4jO3oSg6CmnMUsUitQ/0m7Ta86l3jKJNyafP7CSVmSAju5fcVDeNu4tZ1z2L/dYxROXwQ2W2ZmV+2ETQkSTHu5nc6c/g9gzQ319ET918cgYy8UYTFDgqyLEXo+pJusNbCQ68hnWwD3dvBFcihQKYfD7s1dXYJ1QP76ursZSUIJT/PPrsA6Mm0AP1DOzbSFfdbrpb2+nujRCImN5Y+tdjTpDvUSnI9ZBfVkzeuElYC6shpxI8BSAEMpUiumEDQ0tfonZ7hMb8RaQtw9+Jjkpd/nr2l6xk7tpuztkoMGPCdeI5dPrL2XdgI/2JdhRMlLjHM9YzHb8tF1uJC6Umkw11r3KgZQvOgh46XR7+se9MzhcruYEGQup1IDNIKDqb/bApM8UmdwODqZdAP4AuvRQkTuKqvmM5NmJHE5It/gjPZNWzwb4aoXeh6BGyo4XUtJ5BxVA1KSHpzkkyVBgjKcO4o0NkRoLD96kEc9pNdm8Xao7G1T/+xQdqRXg7hqC8B0daUO6/bBZz14d48eRSvn3Pi0csX3jdkeMdFLYO0jBxDDfe9fQRzf/Dcv/151JiPZPUxjvIC+r84uprOVG3cuWVV77DI3EwGOTRR/7CmYHfYjGlOKHmAQbNXq5Vf8N060bie05jWtcXaI30Yer8C659DVx63rncGs5m6vkLGDtrKpGWOnY9cAO51iZGp9uJY+FpbS5/TS9GCiuzBgtZWPtPcgNbeHw+PDtTobR7LjVE8KrtaEMqtoEUGf0qeX1p8obevK/DdmjJE/T5TPiDGmW9El/szfJHPIJ0poI3Q8fjS+HxxfA44piF/hY7Y4qTf5nnckfoQuJWB+YKK6O0fmxSR5qcpISZiAb2xBD5iV4Kkz2Upnoo1XpwkkTFRAoz0qRgEuDWTGRKCxlYMeMGxUVSsRIzW4mYrKRMFpKKhbR040qWUaBK1IJXGCpZTsQcZWvnAtZ1n0BD2I2qSaQA3WfF5rcyxqxSGQ2S199DYfkLFJfUoqo2OvfWkNgOZbYKKjOm4zD7SSUHiDW+gjiwCiUdx5yX94Zo2KursFdXY87PP/LueD4gajxG767X6N6zma4D++nu6icYGW5+E0iybDHy7WHyPSr5hdlkj5mAacJZMGoBUofg2g1sf6aWfaEiNMWGkBqaorC1aAmNvhVcuEYwf6eKyZtBzjVfZ2j8dNY8/A/6e/ego+Gz5jHWO51SVxVmxYw530UqJ832zi1sGWzgaWUmBfoAf864m+xIGXF9FjF9DlJmoqPTa+pnl7WNFzJXUuttwJq2UzY4g1MGFnGcno0bE92mCDs8G1nreZW9tiARs0pWrIiatpOpGJxCUlHZao+yPtOFWuogxxmnINjPtKZuFCWCNZXkpv+5BcthTEY2BOU9OJKCcsetF3LqM9toqDDxhSW7j0ienyWi4TB7rvsVLxWu5wtPNlE7w82L0y/iklnHMHfu3DfidXV18fe//52F0aep0XdwwcQ72eyp5hbxE0r0dvzbv052YDytB5aQ27waPZXkn/PMrMv5Fl/pamDm4hwCtU8z0bwfjx6jUcnjL8lTeEafzRhLM5N7Sxgf7KFq58Mk/HF+fLaZiM/KmI45fEXbilfEsJLGRgqbULExvClqmp6Ilb6QlfiQFWXAhCOokPLoWPxpMj0q1gyFRIaNqNVBWDqJCDtRYSMmbMQVGzGTjYTJStxkIS6svDAwmyG8pMu9aOVOpOXNzlsHcRzEsBN/81hPYUtrWFI6AykXHfFsBsNe9JBEhFSEPvzfEyaJy5nCb45TmlaZlpBM0CxkKXYydQ9mc4pg9lZ2ZTTzQnw8ewbHMBSygwRpFpBlJdMVZ5y1nfFqA4WyA5stjtUaJyOjB7s9Sn9TCc615ZTqVfiypqGYHaT796MHt2MtUN4QDnt1Feasd1864NNELBSkZ389XXu30l2/m66WDhKJYa/DAondpOKw6Di9Phw5JTjyK7DYMujbb6M3kImUYE720ZMRYXnlo3giXVy53EJlSwKluIiCG28iPWUmLz/wb9r3LEfqUczCwij3JMZmzMBt8b0xP6bHmeb6+BCt0sIix2YucL3M9HQD7lgJcW0WUeaga2UADFi7We3azTLfRjpMfYwPjmfR0Gwmp8vJlV6SaGw19bMtYy8t2atot/QgwgVMbz+Z0QNTSZkSdBTuo6PUQpNrKr1WB5e9MogrmeCqO07G43rngl//CUNQ3oMjJSgP/u4Oih57iMxBybJT5/C9nx/a3JGjhQe+cQ5jXV+ifc9PqG7WuOfrl1Pen+K6a64hKyuLuro6HnvsMaaYD3BG/HF+W3Ih9xV9nh+Yv4cnpjN6y03E9zYgG5/GGo2ybrzg6UU+9oRP47xIIVUdL3PJqOdIYWaFaSoPxk5lt6mUWbYtVLZZydYnMrbuIXL7GlkzS+d3x1nxJfJpsN+I7vOPrBgrMAsVG0lsIoldJIb3egIXcTxaFLcexa3FccgEvSKLdpFLQGYS1r1E006kJiCtI9IS0jpKWsOiJjGn04i0jkxLNM2Emu1EjHXwBcejzGQDppQOCRMy4UaJ+bHFMvCac8keO4n8mgX4Mwre8lav65L+nk52t+1mc8cudsQUWlJZ9MX8xEI2CKd5o1JkArs7TYYtjoIgEHGRjo1M8HOa8GbFGZ+1n/n+1Yy31L7Rp/I6UhNocTsi5MS/ohyfPAF7bhUSaEp2sMY6xJ4cD9LlxGYxYzWbsJos2Cwm7GYzNsvwscNixm6xYLeacVgsOCxmnFYrDqsZp80ycmx6Y7ObTSiHsQzth0VKSbC3h+79dfS3NBLrqCXedYD4UB9xVSGm20hoZoYdBLsxO2Zjsk4E0qjJzWwpfoUdo3uYU2vmsuUSfzRFKM+PfvZZWMZNpHF7Cx21W9HTPQD4LXlM8M8l31GBya4Qlwq/UWO8jEocyDLrHGer4zSxlBpq8aY8xPTZRMRsNLUKgcKAdYDVrh3syKglLzubsT0zKOnxMCqViRkTXWKQWnM72yxdHMjaS5Q4UzoWMXpgGilTgt35q+gtfw2X8wRcqSruO/88TMqhr2dkCMp7cKQE5Y9XzmXBmgFeXlTAN3634giU7LNJcKCXxu/cz7OlO/jcYztpqzLzlxO/yalOC1VVVSxdupQx2WbO7v8tte4Krq/6NrfYfoSzv4iS508itOtpHJF+9hUL/r3Yhe6eQm3HPKJ6JrclGxll+TtrXRN4OL4YzaYz37OW8oZ+rOnT8ER2MGPniyR9ae48y0RDvgmSp3LhtNlU+3wEO4dI9kRQBjRsIQV73IpbdeJNO/FgwSEUlLc10Ugp39FsE0Ol1Zag2aHS5tDpskt67YJBh0LEbiNicxK2uNCFibJYK6c3v4onZMIVySTf4WHq/GkUzzkG5V2Wgv2gyFg/vdsfZXX9TlYnfdRTTE8qm2DYRTosQILNp1GW08Gs7E3MVPbiTNqxJP3Yk9nYU5mYUz7MSR/mZAbmpB816SShCxQBXpMgpUuaUzpNSZ3ER/iXl+hIGHFTL0cGxEoQB52JNwsgX/85DtoPvyeIYY8zI3shxMj25rGiCIRQMCkCRSgoikBRFBxOC8VjMikc56OgxIq1bTnseRJZ9yKJpErcVkCsZCF9zjnU1eXS12HCpKhEzRt5onIJUUeMi5e7WbQjgj2t0ZXhoq4gi5jt3fonBE6TmwxrLn57Hrk5Veww+VmaSLAZDR0ot+ossO1jfmo500QtPqkT12YSNh+HmpyIIq2ElRjbPLugpI2JlYW4WifCHje2mEJUpNmhdFNvaqbZfYCAJURF30xG908lrajszl9FfeFqXrlipSEoR5IjISi333Ixpzy3hZYiBc/Vtx+SG/ijkYeuPotK/+XsavwBc3al+culZ2FRvdjSKhXjRjOj7ZcUqr1cP/k6vuR+EP/GSViejWPta6YzU/CPhWZ2ZtbQO3Aa6E5ylRDTlAAL2lv5ft5xFHh6WJS1irx9XaiDs7GZcplQ/yjZA31sn67zixOsONOZ2DK/xKNnXky2/T+3ESdUjba+MPX1bQRaAqQGIljDaRzJ4aeVFBJdARSQikSOHA/3Po9sikSXKfR4kFQsQlRAtgMWfO5MSsZVfnT9CVJCXz2yYRl9O5axt7ONXpOTqbYgpWWVWItmIopqoHDasGNIQE/rxAciRHqCJHqjpAYS6EOp4RpPSmcwTzCQJ9EUidQlutTRX99LiZQ6ujbs7kTXR851HU2XqJqOpmukNR1N10f2Ek1/fS/fiKvrI/nqcngCpNTRpT58rGtIhvOWUme4vW5kf9AmpESIEe/AUgzvh9UI8foMHikQB4W/Huf1+A7VQ260BEWaQEBOqZvicZkUjrJTKNdjbXgSGpaBlgRvMT0Fl7CuZTYdzRruTCtNhat53P0PigdyuHJ5BZVNGxAyzf5iH82nTWJU3lSSTSqddXWkk13A601tAolEwUSpq4pMbw2bbT6WiTT16CjARCXNQusujmElE9lHhkwSlzW0WBdhik/EpblICZXujFqUgi3YbHYyAsdh6SgEAd1ZJlYoEV6N7sJka2Tc0GgqBqaQNMe56o5FeFzGKK8jxocVlBVPLSH2u5sp7JEsO20GN9/+tyNYus8mrfW7CN32Is+UN3DqE6sYLIa7z/8eF+floDY9zCW9S/hd1RlMYD0Zj5Rir+9myAn/nmdiaekc4oOLKNKT5JuijDINUaZbmZ4qY2X3CqyLO8jYM0iwqRi7fT7W+Frm7thCyiv57ZmCzSVmTPrxfHPupVxWWfNJfxWfDGocEkFw530gJ5FHA7quE1NTRFJxoqkkUTVBJJkgpiaJq8PncTVJPD18nkgnSaRTJLQkyXSK7mg3zUO78cctFIVGUxgaR16k9E2BKfFQNNpJob2ewuAT2JpfQGoqbdaTWBe+mL4hD7ZcybK8v7PftY2zD8xh7uooBd0b0Uw22nNL2FaTIveMUxiXWETDsgOEetejq7XwLiP2nCYvdu909nrGsVIx0yskTqCGNAvM25lkWkWV2IdHxthmmUadaSFjYhMpUHOQSBK+VmK+3SiqE0/vDMyqB80bITwuyXqPlZf212MNp3j4lu8e1vdtCMp78GEF5XdXH8cJrwZ4ZUE21/xp9REs2Webv3/1LCqzrmR17w84eW2SJV+YTcOESfy5/sds8I0mvlsle62GapIsmanw+PgavKFpFEuFQiWKW0kwWstnWroCkYRt/c+QLt7HYJMdxTQZk2Jhav1zZA/GqZ+c5icn2bDgwZZzGf88+XIK7IffnGTw30tDXw//2LmCNR3rCMRqyU16KAyNoShY+RaByS50UJQZoDC9ioKBx2hPTGB97FJCajbRrG5eLPg75aPzuLR+HrZ/P4W3r564LZPenKkE3D30VcXIrbwQvamU6EAXaqyTdKoLXW0HGXpLmSQWYtZc2j1jWe+qIGSykYNgDkmOVTYxzrKOcWIftTaNR10T8KkzmB+aRkVyeHXQtCVMytmDSXVgixWhK0lCBZuIFLYz6/O/RYhDH8ZtCMp78GEE5fZbL2Hxc5voyRWEL/v2EfXi+1ln+6rnsT9YzzNlHcx79ll0j071gii9DQ6iu23YVY0VUwRPTx1NTnISBZobr5JCQWFGqoJqvZhQKsyeoXV0RncipIIUdhzWGSjqSo7b3ojqhgdPk6wYbcHMHK6c9U2uqZzwiQ1VNTi60HTJiv37eLL2Vbb1bSKmN5AXz6IwNJbiUDV5kWIUfVhgsvwpCix70UO9NCVnEtd9tGbuYseoZXxj0deZtstK/y9+gbmvjaCnjJbSk3DGuomJ9QwWp9HzsjAXFOAsqsCdU4YtqBDaWc/A/npiocE3CyVBEzb67AXUOYvptuWSYc1hriKZLl6lxLyBkKuFB/wOWk0FLApO5qTBORSoJQghkGjopiSKZkMqKiU/WYT4AF6p344hKO/B4QrKiqeWELrvFspbdZaeOf0TnaX+aeXRy86msuBrPB/6Iecsj5CyS6wJwZYxgiUz8/CK6RRpHnQpyE45mKONo0DJIpBoY8/QWgLRFky6jmq2YTKXoZjSTG54hbxBjbbqND842YpmdmLOvox/nnQlZY5Dd3JnYPBBGYwmeXz3Zl48sJr68FakuZm8WAGFwTGURSaTEy5E0YY7uB3mKIm0FR0T+3I24qrcya0Lv4u+vomeu38Ng330Zk/hQMU5qFY3llQEixrBokaxqBGQUZLmCFFrlJQzTcqaQqZSyFjydTdeb6Aj6LNm0WvLxWHLp9qWxVT3Xrpdq3nG1882u5WypJUv90xl7NAx+E1jsShW0lKl7GfHI0yH/gJmCMp7cLiC8pvrTmTxyx2sOtbH1x5c9xGU7LPPsn/+mvIXrTxf2kfFqn8ggSXH+rE6a8hRszAl0pQkHMy2TMNt8tATa6Gldz1qfz1hbyVR7zR0U4SgrZaMoR2ctHUQzS549NQ0T1faUJjCuZO/zfem1ryrF10Dg48KKSW7uwZ4bNdrrOlYT3dqFyZrB3nRYgpD4xgVnUL2UD7oAgnoQqMhdy3z3M2cXjWVRF2c/ocfQ0+mUCumEM0fTzCjgkElm3hCJ6Wa0HmnOxqpJ9BSDejJPeh650ioBU2xgkxiksOTN1VhRrXmkGvLwZzRysqifWzzJ8nV0nx5MM2M2PGk7dOYdP2VmN6x/PF/xhCU9+BwBOVn372cE5etZyhD0H7epR9qEayjnae+/DnGFH+NezOeQkEhJ5qJPRxknJrPVOdMbCY7g8FG1NqnGNRddBQvxKwOkNW/k9zAXuxq/I28AmPTfP80KxG7DZFxMQ+f9HWq3I5P0DoDg2GiyTSvNrTy9L41bAtsIqrUYrEGyI6WMGaghlFDE3HHMkmZ4nR6DzAh7KHC3k5e3wGUlma0rl4ATL4MXDOm4ZwzG9vs+Wj+QuKRFLGuASLtASJd/QR7B4gGY8TCMaLJHhJaJ2kZGC6IkkXQWkRE0TFrAbJSfZhf7/Q3WRjyxmnJjKJ64iwmyMU37sBqMUZ5HTEOR1BWPLWEvS/dTdSbz823//MjKtnRwb/vuZHpOys5kOtgV3AT1aKKStc4rIqFVNc2kvUv0GHNoisnF5UWFLUFYZIoJoHZpOM0qXhNCdYUw6OVdhTGcPzYb3Ln7AVYj6RPKAODI0hLf5Tn99bxYuMaGkLbwVFPVsrJsS1nUxKsImYJk1ZieJN5AFiTQ+QNbSN7aA+egWbMI84dhcuEo0Aho0zHXe7AnOEGq+stmzQ7GQpK9tUPUN8apC8ynNYm7HRaRtNsyyVsSZOT6qMo2UuGOoBAopp1vnP/49gch/5SZgjKe/BxrCn/387zXzqXyWXfQpcSgSTdsYXIgaXszjWztkYSyO5AM6kkFUlEEUQUhfjbxELRTejuL/DAom8yMzPjE7LEwODQSaY1NjcP8GztTla3r8MaaeXYnpkURCoI2gKEnAFsWLFjw6E7sCTtOAZCZA3U4x/ch3+oAbOWQCJIZuSh5uVjLnDjzNfwWnrwyjZcWjuC4ZrIYMpOXSiHulAOfUnXsO8yZ4JOVynrbVPYYyonJxlgfKqJP/32p1g/4U75Q1+NxeC/msB4J6na1QjFRLB5KRS1M3FhgEk2ySKZQe9ABv2mLAbNGYQdWaRyqwmWzaPbbKE30s9gMsTojAJ+OmM+TpNRKzH4bGEzm5g7Joe5YxYBi+gJJXhhTwNLNzzCuPZx5AUrsOpvXTI75nTSn+EmPnYcqpLElUySE4ySM9BFVu9+zK1tqKkIzd4yBvynEMyugrKxeLNteH0Cj1cy15NGi7fT1bSbA7X1WAMBFirLOaO0mNaCccTslYclJkcao4ZicMj85cpjsGkqzvJcNJuHzKxCpp50GRml041hvgb/tWzp3sLuvnpaersI9IaIBBNoEQ1z3Iwz5caV9OFO+XClfNi0dzZNmdJhnPEBbMkhzGqItFmgWvwkbPkk7ZlvTGy12BUcriHSyTrCfTtR40GsDjfX3P8wpv+0Fs27YNRQDD5RLrt/0yddBAODTx01+TXU5L+7R4eYGqMn1kNPrIf2UDeNPW309A4SGoyhhlOY4ibsSSe+uA9/NAd7eixCeWcHu0aUiBajN6qjMAqrqwqrJYEuhg5LTI40n3wJDAwMDI5ynBYnozJGMSpjFBQAle+Mo+oq/fF+emI9dIS7aextYWhvG47aAP6ONJlBB9LkI2nzEXb6SNrdCOECk5uY5dPhTcIQFAMDA4NPARbFQr4rn3xXPlNypkAFMPvN61LXSdTW0r/6FcJrX0Cu34OuCuIOP72lBUjtTITp0L0NH0kMQTEwMDD4DCAUBceECRRPmABXX4eeShHftp3ourUU9/d/4mIChqAYGBgYfCZRrFZcs2bimjXzky7KG3yk48yEEKcIIeqEEPuFELe8y3UhhPjNyPWdQojpHzStgYGBgcGni49MUIQQJuB3wKlANfAlIUT126KdCowd2a4C/nAIaQ0MDAwMPkV8lDWUmcB+KWWjlDIF/BM4+21xzgb+KodZD/iEEAUfMK2BgYGBwaeIj1JQioC2g87bR8I+SJwPkhYAIcRVQojNQojNgUDgQxfawMDAwODw+CgF5d2mTL99Wv57xfkgaYcDpbxPSjlDSjkjJyfnEItoYGBgYHCk+ChHebUDJQedFwOdHzCO9QOkNTAwMDD4FPFR1lA2AWOFEKOEEFbgAuCZt8V5BrhkZLTXbCAopez6gGkNDAwMDD5FfGQ1FCllWghxHfAiYAIelFLuEUJcPXL9j8DzwGnAfiAGXP5+aT+qshoYGBgYfHiOKm/DQogA0HKYybOBviNYnM8Chs1HP/9t9oJh86FSJqU8Ih3QR5WgfBiEEJuPlAvnzwqGzUc//232gmHzJ8knvyKLgYGBgcFRgSEoBgYGBgZHBENQ3uS+T7oAnwCGzUc//232gmHzJ4bRh2JgYGBgcEQwaigGBgYGBkcEQ1AMDAwMDI4IR62gCCFKhBCvCCFqhRB7hBDfGgnPFEIsE0I0jOz9B6W5dWT9lTohxMkHhdcIIXaNXPuNEOLdfI194hyqzUKIxUKILSO2bRFCnHBQXkelzQelKxVCRIQQNx4U9qm3+TDv68lCiHUj8XcJIewj4Z96e+Gw7muLEOKhEdtqhRC3HpTXZ93m80bOdSHEjLel+eSfX1LKo3IDCoDpI8ceoJ7htVXuBG4ZCb8FuGPkuBrYAdiAUcABwDRybSMwh2GnlS8Ap37S9h0hm6cBhSPHE4GOg/I6Km0+KN3jwL+BGz9LNh/Gb2wGdgJTRs6z/gvu6wuBf44cO4FmoPwosbkKqAReBWYcFP9T8fw6amsoUsouKeXWkeMwUMuwC/yzgYdGoj0EnDNyfDbDN2FSStnEsDuYmWJ4fRavlHKdHP51/npQmk8Vh2qzlHKblPJ1p5t7ALsQwnY02wwghDgHaGTY5tfDPhM2H4a9JwE7pZQ7RtL0Sym1z4q9cFg2S8AlhDADDiAFhI4Gm6WUtVLKundJ8ql4fh21gnIwQohyht/GNwB5ctgBJSP73JFo77c2S/u7hH+q+YA2H8wXgG1SyiRHsc1CCBdwM/DjtyX/zNn8AX/jcYAUQrwohNgqhPjuSPhnzl74wDY/BkSBLqAVuEtKOcDRYfN78al4fn2U7us/FQgh3Aw3b3xbShl6n+bDD702y6eFQ7D59fgTgDsYfpuFo9vmHwO/klJG3hbnM2XzIdhrBuYBxzDsgHW5EGILEHqXuJ9ae+GQbJ4JaEAh4AdWCyFe5jP2G8M7bX6/qO8S9rE/v47qGooQwsLwj/GIlPKJkeCekWrg680cvSPh77U2S/vI8dvDP5Ucos0IIYqBJ4FLpJQHRoKPZptnAXcKIZqBbwPfE8OerT8zNh/Gfb1SStknpYwx7OF7Op8he+GQbb4QWCqlVKWUvcBrwAyODpvfi0/F8+uoFZSRkQwPALVSyl8edOkZ4NKR40uBpw8Kv2CkD2EUMBbYOFKVDgshZo/keclBaT5VHKrNQggf8Bxwq5TytdcjH802SynnSynLpZTlwK+Bn0kp7/2s2HwY9/WLwGQhhHOkT2EBsPezYi8cls2twAliGBcwG9h3lNj8Xnw6nl8fVW//J70xXM2XDI9w2T6yncbwKJflQMPIPvOgNP/D8OiIOg4aCcHw283ukWv3MuJh4NO2HarNwPcZbmveftCWezTb/La0P+Kto7w+9TYf5n19McMDEHYDd36W7D3M+9rN8Ai+PcBe4KajyObPMVzrSAI9wIsHpfnEn1+G6xUDAwMDgyPCUdvkZWBgYGDw8WIIioGBgYHBEcEQFAMDAwODI4IhKAYGBgYGRwRDUAwMDAwMjgiGoBgYHCYj8xzWCCFOPSjsi0KIpZ9kuQwMPimMYcMGBh8CIcREhuc8TANMDM8XOEW+6XXgUPIySSm1I1tCA4OPD0NQDAw+JEKIOxmeIOoa2ZcBkxj2o/UjKeXTIw7+/jYSB+A6KeVaIcRC4IcMOzKcKqWs/nhLb2Bw5DAExcDgQzLi3mMrw27SnwX2SCkfHnFts5Hh2osEdCllQggxFviHlHLGiKA8B0yUw27HDQw+sxz13oYNDD5qpJRRIcS/gAjwReBM8eZKkHaglGGHfPcKIaYy7Al33EFZbDTExOBowBAUA4Mjgz6yCeAL8m2LIAkhfsSw76UpDA+GSRx0OfoxldHA4CPFGOVlYHBkeRH4xuvrdgshpo2EZwBdUkod+DLDHfgGBkcVhqAYGBxZ/hewADuFELtHzgF+D1wqhFjPcHOXUSsxOOowOuUNDAwMDI4IRg3FwMDAwOCIYAiKgYGBgcERwRAUAwMDA4MjgiEoBgYGBgZHBENQDAwMDAyOCIagGBgYGBgcEQxBMTAwMDA4Ivx/kbVw3XTqEuwAAAAASUVORK5CYII=\n" - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "ghg1 = \"100\"\n", - "ghg2 = \"4000\"\n", - "bio1 = \"45\"\n", - "bio2 = \"00\"\n", - "scen = [f\"SSP2BIO{bio1}GHG{ghg1}\", f\"SSP2BIO{bio2}GHG{ghg1}\", f\"SSP2BIO{bio1}GHG{ghg2}\", f\"SSP2BIO{bio2}GHG{ghg2}\"]\n", - "print(scen)\n", - "print(scen)\n", - "py_df.filter(variable=\"Emissions|CO2|Land|+|Land-use Change\", ).plot()\n", - "for i in df[df.index.get_level_values(3).str.startswith(\"Demand|Bioenergy|++|2nd generation\")].index.get_level_values(3).unique():\n", - " py_df.filter(variable=i, ).plot()\n", - "py_df.filter(variable=\"Emissions|CO2|Land|Land-use Change|Gross LUC|+|Deforestation\", ).plot()\n", - "py_df.filter(variable=\"Emissions|CO2|Land|Land-use Change|Gross LUC|+|Other land conversion\", ).plot()\n", - "for i in df[df.index.get_level_values(3).str.startswith(\"Resources|Land Cover|+\")].index.get_level_values(3).unique()[:2]:\n", - " py_df.filter(variable=i, ).plot()\n", - "py_df.filter(variable=\"Emissions|CO2|Land|Land-use Change|+|Regrowth\", ).plot()\n", - "py_df.filter(variable=\"Productivity|Landuse Intensity Indicator Tau\", ).plot()\n", - "py_df.filter(variable=\"Productivity|Landuse Intensity Indicator Tau\", ).diff({\"Productivity|Landuse Intensity Indicator Tau\":\"test\"}).plot()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-29T09:34:40.275913800Z", - "start_time": "2023-09-29T09:34:35.941401500Z" - } - }, - "id": "3436432340b73595" - }, - { - "cell_type": "code", - "execution_count": 456, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['SSP2BIO10GHG990', 'SSP2BIO10GHG100', 'SSP2BIO10GHG000']\n" - ] - }, - { - "data": { - "text/plain": "" - }, - "execution_count": 456, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": "
", - "image/png": 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\n" 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\n" 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\n" 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\n" 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\n" 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\n" 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\n" - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "bio = \"10\"\n", - "scen = [f\"SSP2BIO{bio}GHG990\", f\"SSP2BIO{bio}GHG100\", f\"SSP2BIO{bio}GHG000\"]\n", - "print(scen)\n", - "py_df.filter(variable=\"Emissions|CO2|Land|+|Land-use Change\", scenario=scen).plot()\n", - "for i in df[df.index.get_level_values(3).str.startswith(\"Demand|Bioenergy|++|2nd generation\")].index.get_level_values(3).unique():\n", - " py_df.filter(variable=i, scenario=scen).plot()\n", - "py_df.filter(variable=\"Emissions|CO2|Land|Land-use Change|Gross LUC|+|Deforestation\", scenario=scen).plot()\n", - "py_df.filter(variable=\"Emissions|CO2|Land|Land-use Change|Gross LUC|+|Other land conversion\", scenario=scen).plot()\n", - "for i in df[df.index.get_level_values(3).str.startswith(\"Resources|Land Cover|+\")].index.get_level_values(3).unique()[:2]:\n", - " py_df.filter(variable=i, scenario=scen).plot()\n", - "py_df.filter(variable=\"Emissions|CO2|Land|Land-use Change|+|Regrowth\", scenario=scen).plot()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-28T21:20:23.184649700Z", - "start_time": "2023-09-28T21:20:21.731291200Z" - } - }, - "id": "499c0f6fe189657f" - }, - { - "cell_type": "code", - "execution_count": 301, - "outputs": [ - { - "data": { - "text/plain": "array([, , ], dtype=object)" - }, - "execution_count": 301, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ax[0]" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-28T13:32:14.998245800Z", - "start_time": "2023-09-28T13:32:14.929749100Z" - } - }, - "id": "fc6f87238f8589b0" - }, - { - "cell_type": "code", - "execution_count": 403, - "outputs": [ - { - "data": { - "text/plain": "Index(['Demand|Bioenergy|++|1st generation',\n 'Demand|Bioenergy|++|2nd generation',\n 'Demand|Bioenergy|++|Overproduction',\n 'Demand|Bioenergy|++|Traditional Burning'],\n dtype='object', name='Variable')" - }, - "execution_count": 403, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df[df.index.get_level_values(3).str.contains(\"Regrowth\")]\n", - "#df[df.index.get_level_values(3).str.contains(\"LUC\")]\n", - "df[df.index.get_level_values(3).str.startswith(\"Resources|Land Cover|For\")].index.get_level_values(3).unique()\n", - "df[df.index.get_level_values(3).str.startswith(\"Demand|Bioenergy|++\")].index.get_level_values(3).unique()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-28T20:11:54.321242500Z", - "start_time": "2023-09-28T20:11:54.133467900Z" - } - }, - "id": "27c384765f61a35c" - }, - { - "cell_type": "code", - "execution_count": 288, - "outputs": [ - { - "data": { - "text/plain": " 2000 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World test1 unknown 4052.726058 \n SSP2BIO05GHG600 World test1 unknown 4052.726058 \n SSP2BIO07GHG400 World test1 unknown 4052.726058 \n SSP2BIO07GHG600 World test1 unknown 4052.726058 \n SSP2BIO10GHG400 World test1 unknown 4052.726058 \n SSP2BIO10GHG600 World test1 unknown 4052.726058 \n\n 2005 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World test1 unknown 4256.643156 \n SSP2BIO05GHG600 World test1 unknown 4256.643156 \n SSP2BIO07GHG400 World test1 unknown 4256.643156 \n SSP2BIO07GHG600 World test1 unknown 4256.643156 \n SSP2BIO10GHG400 World test1 unknown 4256.643156 \n SSP2BIO10GHG600 World test1 unknown 4256.643156 \n\n 2010 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World test1 unknown 4486.586686 \n SSP2BIO05GHG600 World test1 unknown 4486.586686 \n SSP2BIO07GHG400 World test1 unknown 4486.586686 \n SSP2BIO07GHG600 World test1 unknown 4486.586686 \n SSP2BIO10GHG400 World test1 unknown 4486.586686 \n SSP2BIO10GHG600 World test1 unknown 4486.586686 \n\n 2015 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World test1 unknown 4509.69031 \n SSP2BIO05GHG600 World test1 unknown 4509.69031 \n SSP2BIO07GHG400 World test1 unknown 4509.69031 \n SSP2BIO07GHG600 World test1 unknown 4509.69031 \n SSP2BIO10GHG400 World test1 unknown 4509.69031 \n SSP2BIO10GHG600 World test1 unknown 4509.69031 \n\n 2020 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World test1 unknown 4377.733857 \n SSP2BIO05GHG600 World test1 unknown 4377.733857 \n SSP2BIO07GHG400 World test1 unknown 4377.733857 \n SSP2BIO07GHG600 World test1 unknown 4377.733857 \n SSP2BIO10GHG400 World test1 unknown 4377.733857 \n SSP2BIO10GHG600 World test1 unknown 4377.733857 \n\n 2025 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World test1 unknown 2461.082693 \n SSP2BIO05GHG600 World test1 unknown 2263.573047 \n SSP2BIO07GHG400 World test1 unknown 2461.082693 \n SSP2BIO07GHG600 World test1 unknown 2263.573047 \n SSP2BIO10GHG400 World test1 unknown 2461.082693 \n SSP2BIO10GHG600 World test1 unknown 2263.573047 \n\n 2030 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World test1 unknown 1977.076574 \n SSP2BIO05GHG600 World test1 unknown 1750.603598 \n SSP2BIO07GHG400 World test1 unknown 1977.092257 \n SSP2BIO07GHG600 World test1 unknown 1750.729300 \n SSP2BIO10GHG400 World test1 unknown 1976.100871 \n SSP2BIO10GHG600 World test1 unknown 1750.478291 \n\n 2035 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World test1 unknown 1511.002693 \n SSP2BIO05GHG600 World test1 unknown 1325.115834 \n SSP2BIO07GHG400 World test1 unknown 1511.057456 \n SSP2BIO07GHG600 World test1 unknown 1325.869817 \n SSP2BIO10GHG400 World test1 unknown 1505.212414 \n SSP2BIO10GHG600 World test1 unknown 1324.184166 \n\n 2040 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World test1 unknown 1144.898601 \n SSP2BIO05GHG600 World test1 unknown 980.931897 \n SSP2BIO07GHG400 World test1 unknown 1145.040276 \n SSP2BIO07GHG600 World test1 unknown 982.824125 \n SSP2BIO10GHG400 World test1 unknown 1131.402139 \n SSP2BIO10GHG600 World test1 unknown 978.204951 \n\n 2045 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World test1 unknown 850.098155 \n SSP2BIO05GHG600 World test1 unknown 714.367263 \n SSP2BIO07GHG400 World test1 unknown 850.621923 \n SSP2BIO07GHG600 World test1 unknown 716.444098 \n SSP2BIO10GHG400 World test1 unknown 836.319735 \n SSP2BIO10GHG600 World test1 unknown 709.609237 \n\n 2050 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World test1 unknown 701.041775 \n SSP2BIO05GHG600 World test1 unknown 567.564009 \n SSP2BIO07GHG400 World test1 unknown 702.293399 \n SSP2BIO07GHG600 World test1 unknown 571.301686 \n SSP2BIO10GHG400 World test1 unknown 707.649785 \n SSP2BIO10GHG600 World test1 unknown 573.845743 \n\n 2055 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World test1 unknown 634.194597 \n SSP2BIO05GHG600 World test1 unknown 484.302127 \n SSP2BIO07GHG400 World test1 unknown 638.299201 \n SSP2BIO07GHG600 World test1 unknown 484.015596 \n SSP2BIO10GHG400 World test1 unknown 705.379114 \n SSP2BIO10GHG600 World test1 unknown 537.121041 \n\n 2060 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World test1 unknown 545.853597 \n SSP2BIO05GHG600 World test1 unknown 360.564581 \n SSP2BIO07GHG400 World test1 unknown 560.135989 \n SSP2BIO07GHG600 World test1 unknown 359.293849 \n SSP2BIO10GHG400 World test1 unknown 768.754217 \n SSP2BIO10GHG600 World test1 unknown 543.445372 \n\n 2070 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World test1 unknown 305.516081 \n SSP2BIO05GHG600 World test1 unknown 86.317439 \n SSP2BIO07GHG400 World test1 unknown 337.348250 \n SSP2BIO07GHG600 World test1 unknown 90.134435 \n SSP2BIO10GHG400 World test1 unknown 837.185427 \n SSP2BIO10GHG600 World test1 unknown 499.067206 \n\n 2080 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World test1 unknown 33.653413 \n SSP2BIO05GHG600 World test1 unknown -185.858535 \n SSP2BIO07GHG400 World test1 unknown 83.790328 \n SSP2BIO07GHG600 World test1 unknown -155.945305 \n SSP2BIO10GHG400 World test1 unknown 927.962263 \n SSP2BIO10GHG600 World test1 unknown 482.846144 \n\n 2090 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World test1 unknown -179.429390 \n SSP2BIO05GHG600 World test1 unknown -426.932176 \n SSP2BIO07GHG400 World test1 unknown -133.947810 \n SSP2BIO07GHG600 World test1 unknown -345.759334 \n SSP2BIO10GHG400 World test1 unknown 1050.937859 \n SSP2BIO10GHG600 World test1 unknown 549.529695 \n\n 2100 \nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World test1 unknown -316.561534 \n SSP2BIO05GHG600 World test1 unknown -543.305891 \n SSP2BIO07GHG400 World test1 unknown -256.707200 \n SSP2BIO07GHG600 World test1 unknown -436.556404 \n SSP2BIO10GHG400 World test1 unknown 1121.486572 \n SSP2BIO10GHG600 World test1 unknown 563.498261 ", - "text/html": "
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20002005201020152020202520302035204020452050205520602070208020902100
modelscenarioregionvariableunit
MAgPIEMP00BD1BI00SSP2BIO05GHG400Worldtest1unknown4052.7260584256.6431564486.5866864509.690314377.7338572461.0826931977.0765741511.0026931144.898601850.098155701.041775634.194597545.853597305.51608133.653413-179.429390-316.561534
SSP2BIO05GHG600Worldtest1unknown4052.7260584256.6431564486.5866864509.690314377.7338572263.5730471750.6035981325.115834980.931897714.367263567.564009484.302127360.56458186.317439-185.858535-426.932176-543.305891
SSP2BIO07GHG400Worldtest1unknown4052.7260584256.6431564486.5866864509.690314377.7338572461.0826931977.0922571511.0574561145.040276850.621923702.293399638.299201560.135989337.34825083.790328-133.947810-256.707200
SSP2BIO07GHG600Worldtest1unknown4052.7260584256.6431564486.5866864509.690314377.7338572263.5730471750.7293001325.869817982.824125716.444098571.301686484.015596359.29384990.134435-155.945305-345.759334-436.556404
SSP2BIO10GHG400Worldtest1unknown4052.7260584256.6431564486.5866864509.690314377.7338572461.0826931976.1008711505.2124141131.402139836.319735707.649785705.379114768.754217837.185427927.9622631050.9378591121.486572
SSP2BIO10GHG600Worldtest1unknown4052.7260584256.6431564486.5866864509.690314377.7338572263.5730471750.4782911324.184166978.204951709.609237573.845743537.121041543.445372499.067206482.846144549.529695563.498261
\n
" - }, - "execution_count": 288, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "py_df.add(\"Emissions|CO2|Land|Land-use Change|+|Regrowth\", luc_co2, \"test\", ignore_units=True, append=True)\n", - "py_df.add(\"test\", \"Emissions|CO2|Land|Land-use Change|+|Peatland\", \"test1\", ignore_units=True).timeseries() " - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-28T13:21:50.488892900Z", - "start_time": "2023-09-28T13:21:50.235332900Z" - } - }, - "id": "1acca98c6925e8ae" - }, - { - "cell_type": "code", - "execution_count": 273, - "outputs": [ - { - "data": { - "text/plain": " 2000 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4052.726058 \n SSP2BIO05GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4052.726058 \n SSP2BIO07GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4052.726058 \n SSP2BIO07GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4052.726058 \n SSP2BIO10GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4052.726058 \n SSP2BIO10GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4052.726058 \n\n 2005 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4256.643156 \n SSP2BIO05GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4256.643156 \n SSP2BIO07GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4256.643156 \n SSP2BIO07GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4256.643156 \n SSP2BIO10GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4256.643156 \n SSP2BIO10GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4256.643156 \n\n 2010 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4486.586686 \n SSP2BIO05GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4486.586686 \n SSP2BIO07GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4486.586686 \n SSP2BIO07GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4486.586686 \n SSP2BIO10GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4486.586686 \n SSP2BIO10GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4486.586686 \n\n 2015 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4509.69031 \n SSP2BIO05GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4509.69031 \n SSP2BIO07GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4509.69031 \n SSP2BIO07GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4509.69031 \n SSP2BIO10GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4509.69031 \n SSP2BIO10GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4509.69031 \n\n 2020 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4377.733857 \n SSP2BIO05GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4377.733857 \n SSP2BIO07GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4377.733857 \n SSP2BIO07GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4377.733857 \n SSP2BIO10GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4377.733857 \n SSP2BIO10GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 4377.733857 \n\n 2025 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 2461.082693 \n SSP2BIO05GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 2263.573047 \n SSP2BIO07GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 2461.082693 \n SSP2BIO07GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 2263.573047 \n SSP2BIO10GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 2461.082693 \n SSP2BIO10GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 2263.573047 \n\n 2030 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 1977.076574 \n SSP2BIO05GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 1750.603598 \n SSP2BIO07GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 1977.092257 \n SSP2BIO07GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 1750.729300 \n SSP2BIO10GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 1976.100871 \n SSP2BIO10GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 1750.478291 \n\n 2035 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 1511.002693 \n SSP2BIO05GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 1325.115834 \n SSP2BIO07GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 1511.057456 \n SSP2BIO07GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 1325.869817 \n SSP2BIO10GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 1505.212414 \n SSP2BIO10GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 1324.184166 \n\n 2040 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 1144.898601 \n SSP2BIO05GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 980.931897 \n SSP2BIO07GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 1145.040276 \n SSP2BIO07GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 982.824125 \n SSP2BIO10GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 1131.402139 \n SSP2BIO10GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 978.204951 \n\n 2045 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 850.098155 \n SSP2BIO05GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 714.367263 \n SSP2BIO07GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 850.621923 \n SSP2BIO07GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 716.444098 \n SSP2BIO10GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 836.319735 \n SSP2BIO10GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 709.609237 \n\n 2050 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 701.041775 \n SSP2BIO05GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 567.564009 \n SSP2BIO07GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 702.293399 \n SSP2BIO07GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 571.301686 \n SSP2BIO10GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 707.649785 \n SSP2BIO10GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 573.845743 \n\n 2055 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 634.194597 \n SSP2BIO05GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 484.302127 \n SSP2BIO07GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 638.299201 \n SSP2BIO07GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 484.015596 \n SSP2BIO10GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 705.379114 \n SSP2BIO10GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 537.121041 \n\n 2060 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 545.853597 \n SSP2BIO05GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 360.564581 \n SSP2BIO07GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 560.135989 \n SSP2BIO07GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 359.293849 \n SSP2BIO10GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 768.754217 \n SSP2BIO10GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 543.445372 \n\n 2070 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 305.516081 \n SSP2BIO05GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 86.317439 \n SSP2BIO07GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 337.348250 \n SSP2BIO07GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 90.134435 \n SSP2BIO10GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 837.185427 \n SSP2BIO10GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 499.067206 \n\n 2080 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 33.653413 \n SSP2BIO05GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr -185.858535 \n SSP2BIO07GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 83.790328 \n SSP2BIO07GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr -155.945305 \n SSP2BIO10GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 927.962263 \n SSP2BIO10GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 482.846144 \n\n 2090 \\\nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr -179.429390 \n SSP2BIO05GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr -426.932176 \n SSP2BIO07GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr -133.947810 \n SSP2BIO07GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr -345.759334 \n SSP2BIO10GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 1050.937859 \n SSP2BIO10GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 549.529695 \n\n 2100 \nmodel scenario region variable unit \nMAgPIEMP00BD1BI00 SSP2BIO05GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr -316.561534 \n SSP2BIO05GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr -543.305891 \n SSP2BIO07GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr -256.707200 \n SSP2BIO07GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr -436.556404 \n SSP2BIO10GHG400 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 1121.486572 \n SSP2BIO10GHG600 World Emissions|CO2|Land|+|Land-use Change Mt CO2/yr 563.498261 ", - "text/html": "
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20002005201020152020202520302035204020452050205520602070208020902100
modelscenarioregionvariableunit
MAgPIEMP00BD1BI00SSP2BIO05GHG400WorldEmissions|CO2|Land|+|Land-use ChangeMt CO2/yr4052.7260584256.6431564486.5866864509.690314377.7338572461.0826931977.0765741511.0026931144.898601850.098155701.041775634.194597545.853597305.51608133.653413-179.429390-316.561534
SSP2BIO05GHG600WorldEmissions|CO2|Land|+|Land-use ChangeMt CO2/yr4052.7260584256.6431564486.5866864509.690314377.7338572263.5730471750.6035981325.115834980.931897714.367263567.564009484.302127360.56458186.317439-185.858535-426.932176-543.305891
SSP2BIO07GHG400WorldEmissions|CO2|Land|+|Land-use ChangeMt CO2/yr4052.7260584256.6431564486.5866864509.690314377.7338572461.0826931977.0922571511.0574561145.040276850.621923702.293399638.299201560.135989337.34825083.790328-133.947810-256.707200
SSP2BIO07GHG600WorldEmissions|CO2|Land|+|Land-use ChangeMt CO2/yr4052.7260584256.6431564486.5866864509.690314377.7338572263.5730471750.7293001325.869817982.824125716.444098571.301686484.015596359.29384990.134435-155.945305-345.759334-436.556404
SSP2BIO10GHG400WorldEmissions|CO2|Land|+|Land-use ChangeMt CO2/yr4052.7260584256.6431564486.5866864509.690314377.7338572461.0826931976.1008711505.2124141131.402139836.319735707.649785705.379114768.754217837.185427927.9622631050.9378591121.486572
SSP2BIO10GHG600WorldEmissions|CO2|Land|+|Land-use ChangeMt CO2/yr4052.7260584256.6431564486.5866864509.690314377.7338572263.5730471750.4782911324.184166978.204951709.609237573.845743537.121041543.445372499.067206482.846144549.529695563.498261
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" - }, - "execution_count": 273, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "py_df.filter(variable=afolu_co2).timeseries()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-28T12:38:37.715706600Z", - "start_time": "2023-09-28T12:38:37.584217200Z" - } - }, - "id": "b2bb66c2b0b62efd" - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [], - "metadata": { - "collapsed": false - }, - "id": "91bf13dd7e858c2e" - }, - { - "cell_type": "markdown", - "source": [], - "metadata": { - "collapsed": false - }, - "id": "13270b5c2a86aa39" - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/message_ix_models/model/material/magpie notebooks/refactor_emi_drivers_func.ipynb b/message_ix_models/model/material/magpie notebooks/refactor_emi_drivers_func.ipynb deleted file mode 100644 index 50665c6ea1..0000000000 --- a/message_ix_models/model/material/magpie notebooks/refactor_emi_drivers_func.ipynb +++ /dev/null @@ -1,508 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "outputs": [], - "source": [ - "import pandas as pd" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "start_time": "2023-09-25T08:52:47.708814300Z" - } - }, - "id": "initial_id" - }, - { - "cell_type": "code", - "execution_count": 8, - "outputs": [], - "source": [ - "iamc_index = [\"Region\", \"Variable\", \"Unit\"]" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "start_time": "2023-09-25T08:52:49.112676200Z" - } - }, - "id": "14ab9fc74924209c" - }, - { - "cell_type": "code", - "execution_count": 2, - "outputs": [], - "source": [ - "df = pd.read_csv(\"C:/Users\\maczek\\Desktop/emi_drivers.csv\")\n", - "df = df.set_index([\"Region\", \"Scenario\", \"Unit\"])\n", - "df.columns = [int(i) for i in df.columns]" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "start_time": "2023-09-25T08:52:48.245828200Z" - } - }, - "id": "c2653dcb51d6727c" - }, - { - "cell_type": "code", - "execution_count": 5, - "outputs": [], - "source": [ - "data = pd.read_csv(\"C:/Users\\maczek\\Desktop/data.csv\").set_index(\"Region\")\n", - "data.columns = [int(i) for i in data.columns]" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "start_time": "2023-09-25T08:52:48.473847700Z" - } - }, - "id": "af947380eec6c2b7" - }, - { - "cell_type": "markdown", - "source": [], - "metadata": { - "collapsed": false - }, - "id": "d4a75d09fb9d213f" - }, - { - "cell_type": "markdown", - "source": [ - "## current implementation" - ], - "metadata": { - "collapsed": false - }, - "id": "101ca884844ac146" - }, - { - "cell_type": "code", - "execution_count": 7, - "outputs": [], - "source": [ - "scn_cfg = {}\n", - "scn_cfg[\"region_mapping\"] = df.index.get_level_values(0).unique()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "start_time": "2023-09-25T08:52:48.758487500Z" - } - }, - "id": "bd6451c021ec9be8" - }, - { - "cell_type": "code", - "execution_count": 111, - "outputs": [], - "source": [ - "old_dfs_dict = {}\n", - "new_dfs_dict = {}" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-25T09:17:42.910070900Z", - "start_time": "2023-09-25T09:17:42.825532300Z" - } - }, - "id": "fd8612083bddf1c1" - }, - { - "cell_type": "code", - "execution_count": 123, - "outputs": [], - "source": [ - "for reg in scn_cfg[\"region_mapping\"]:\n", - " #tmp_gr = tmp = self.data.loc[reg, :, driver]\n", - " tmp_gr = tmp = df.loc[reg, :]\n", - " tmp_gr = tmp_gr.pct_change(axis=1)\n", - " for i in range(len(tmp.columns)):\n", - " # 2010 should remain hardcoded - it is the year until\n", - " # which we had calibrated the emi-drivers.\n", - " if tmp.columns[i] <= 2010:\n", - " # If there is no data available, a dataframe with\n", - " # values set to 0 is created.\n", - " if data.empty:\n", - " tmp.loc[:, tmp.columns[i]] = 0\n", - " else:\n", - " tmp.loc[:, tmp.columns[i]] = data.loc[\n", - " reg, tmp.columns[i]\n", - " ]\n", - " else:\n", - " tmp.loc[:, tmp.columns[i]] = (\n", - " tmp.loc[:, tmp.columns[i - 1]]\n", - " + tmp.loc[:, tmp.columns[i - 1]]\n", - " * tmp_gr.loc[:, tmp.columns[i]]\n", - " )\n", - " tmp = tmp.reset_index()\n", - " #tmp[\"Region\"] = reg\n", - " tmp[\"Unit\"] = \"Mt CO2/yr\"\n", - " #tmp[\"Variable\"] = \"test_tec\"\n", - " #tmp = tmp.set_index(iamc_index).fillna(0)\n", - " #self.data = tmp.combine_first(self.data)\n", - " old = tmp.set_index([\"Scenario\", \"Unit\"])\n", - " old.columns = old.columns.astype(int)\n", - " old_dfs_dict[reg] = old" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-25T09:21:52.700786100Z", - "start_time": "2023-09-25T09:21:52.500044200Z" - } - }, - "id": "11598920603d80ae" - }, - { - "cell_type": "code", - "execution_count": 50, - "outputs": [], - "source": [ - "old_post_2010 = old.drop(old.columns[:4], axis=1).copy(deep=True)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-25T09:00:55.358425700Z", - "start_time": "2023-09-25T09:00:55.327185600Z" - } - }, - "id": "a0ddced02ed86c32" - }, - { - "cell_type": "code", - "execution_count": 134, - "outputs": [ - { - "data": { - "text/plain": " 2010 2015 2020 2025 2030 2035 2040 2045 2050 \\\nScenario Unit \nBIO00GHG000 Mt CO2/yr 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 \nBIO00GHG010 Mt CO2/yr 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 \nBIO00GHG020 Mt CO2/yr 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 \nBIO00GHG050 Mt CO2/yr 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 \nBIO00GHG100 Mt CO2/yr 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 \n... ... ... ... ... ... ... ... ... ... \nBIO45GHG3000 Mt CO2/yr 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 \nBIO45GHG400 Mt CO2/yr 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 \nBIO45GHG4000 Mt CO2/yr 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 \nBIO45GHG600 Mt CO2/yr 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 \nBIO45GHG990 Mt CO2/yr 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 \n\n 2055 2060 2070 2080 2090 2100 2110 \nScenario Unit \nBIO00GHG000 Mt CO2/yr 1.0 1.0 1.0 1.0 1.0 1.0 1.0 \nBIO00GHG010 Mt CO2/yr 1.0 1.0 1.0 1.0 1.0 1.0 1.0 \nBIO00GHG020 Mt CO2/yr 1.0 1.0 1.0 1.0 1.0 1.0 1.0 \nBIO00GHG050 Mt CO2/yr 1.0 1.0 1.0 1.0 1.0 1.0 1.0 \nBIO00GHG100 Mt CO2/yr 1.0 1.0 1.0 1.0 1.0 1.0 1.0 \n... ... ... ... ... ... ... ... \nBIO45GHG3000 Mt CO2/yr 1.0 1.0 1.0 1.0 1.0 1.0 1.0 \nBIO45GHG400 Mt CO2/yr 1.0 1.0 1.0 1.0 1.0 1.0 1.0 \nBIO45GHG4000 Mt CO2/yr 1.0 1.0 1.0 1.0 1.0 1.0 1.0 \nBIO45GHG600 Mt CO2/yr 1.0 1.0 1.0 1.0 1.0 1.0 1.0 \nBIO45GHG990 Mt CO2/yr 1.0 1.0 1.0 1.0 1.0 1.0 1.0 \n\n[84 rows x 16 columns]", - 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2010201520202025203020352040204520502055206020702080209021002110
ScenarioUnit
BIO00GHG000Mt CO2/yr1.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.0
BIO00GHG010Mt CO2/yr1.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.0
BIO00GHG020Mt CO2/yr1.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.0
BIO00GHG050Mt CO2/yr1.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.0
BIO00GHG100Mt CO2/yr1.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.0
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BIO45GHG3000Mt CO2/yr1.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.0
BIO45GHG400Mt CO2/yr1.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.0
BIO45GHG4000Mt CO2/yr1.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.0
BIO45GHG600Mt CO2/yr1.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.0
BIO45GHG990Mt CO2/yr1.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.0
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84 rows × 16 columns

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" - }, - "execution_count": 134, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.loc[\"R12_CHN\", :].divide(df.loc[\"R12_CHN\", :][2010], axis=0).drop(df.loc[\"R12_CHN\", :].columns[:4], axis=1)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-25T09:25:32.774062100Z", - "start_time": "2023-09-25T09:25:32.673880900Z" - } - }, - "id": "bd87dc9331011540" - }, - { - "cell_type": "markdown", - "source": [], - "metadata": { - "collapsed": false - }, - "id": "1532726880abd30c" - }, - { - "cell_type": "markdown", - "source": [ - "## new vectorized calculation" - ], - "metadata": { - "collapsed": false - }, - "id": "f02833576e276727" - }, - { - "cell_type": "code", - "execution_count": 140, - "outputs": [], - "source": [ - "for reg in scn_cfg[\"region_mapping\"]:\n", - " #tmp = self.data.loc[reg, :, driver]\n", - " tmp = df.loc[reg, :]\n", - "\n", - " tmp_rel_2010 = tmp.divide(tmp[2010], axis=0).drop(tmp.columns[:4], axis=1)\n", - " tec_afr = data.loc[reg]\n", - " new = tmp_rel_2010[tmp_rel_2010.columns] * tec_afr[2010]\n", - " data_bef_2010 = pd.DataFrame(data.loc[reg, :2010]).T\n", - " for col in data_bef_2010:\n", - " new[col] = data_bef_2010[col].values[0]\n", - " new = new[new.columns.sort_values()]\n", - " new = new.fillna(0)\n", - " \n", - " new_dfs_dict[reg] = new" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-25T09:28:47.564726900Z", - "start_time": "2023-09-25T09:28:47.517862500Z" - } - }, - "id": "33da9bcb802ee936" - }, - { - "cell_type": "code", - "execution_count": 99, - "outputs": [], - "source": [ - "tmp_rel_2010 = tmp.divide(tmp[2010], axis=0).drop(tmp.columns[:4], axis=1)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-25T09:12:30.400010800Z", - "start_time": "2023-09-25T09:12:30.368763300Z" - } - }, - "id": "15a0e53d9d20722a" - }, - { - "cell_type": "code", - "execution_count": 100, - "outputs": [], - "source": [ - "tec_afr = data.loc[\"R12_AFR\"]" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-25T09:12:30.747514900Z", - "start_time": "2023-09-25T09:12:30.700449100Z" - } - }, - "id": "3b6086a46dc1e065" - }, - { - "cell_type": "code", - "execution_count": 101, - "outputs": [], - "source": [ - "new = tmp_rel_2010[tmp_rel_2010.columns] * tec_afr[2010]" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-25T09:12:31.101228400Z", - "start_time": "2023-09-25T09:12:31.063474800Z" - } - }, - "id": "1a158eca7eea06ad" - }, - { - "cell_type": "code", - "execution_count": 102, - "outputs": [], - "source": [ - "data_bef_2010 = pd.DataFrame(data.loc[reg, :2010]).T" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-25T09:12:31.502279100Z", - "start_time": "2023-09-25T09:12:31.471032Z" - } - }, - "id": "be8d0f5cc4687fa0" - }, - { - "cell_type": "code", - "execution_count": 103, - "outputs": [], - "source": [ - "for col in data_bef_2010:\n", - " new[col] = data_bef_2010[col].values[0]" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-25T09:12:32.065466800Z", - "start_time": "2023-09-25T09:12:32.002950900Z" - } - }, - "id": "3d05af475086cc82" - }, - { - "cell_type": "code", - "execution_count": 104, - "outputs": [], - "source": [ - "new = new[new.columns.sort_values()]" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-25T09:12:32.573423400Z", - "start_time": "2023-09-25T09:12:32.525508200Z" - } - }, - "id": "a070ba462231478a" - }, - { - "cell_type": "code", - "execution_count": 106, - "outputs": [ - { - "data": { - "text/plain": " 1990 1995 2000 2005 2010 \\\nScenario Unit \nBIO00GHG000 Mt CO2/yr 115.793 115.793 115.793 116.082 116.371 \nBIO00GHG010 Mt CO2/yr 115.793 115.793 115.793 116.082 116.371 \nBIO00GHG020 Mt CO2/yr 115.793 115.793 115.793 116.082 116.371 \nBIO00GHG050 Mt CO2/yr 115.793 115.793 115.793 116.082 116.371 \nBIO00GHG100 Mt CO2/yr 115.793 115.793 115.793 116.082 116.371 \n... ... ... ... ... ... \nBIO45GHG3000 Mt CO2/yr 115.793 115.793 115.793 116.082 116.371 \nBIO45GHG400 Mt CO2/yr 115.793 115.793 115.793 116.082 116.371 \nBIO45GHG4000 Mt CO2/yr 115.793 115.793 115.793 116.082 116.371 \nBIO45GHG600 Mt CO2/yr 115.793 115.793 115.793 116.082 116.371 \nBIO45GHG990 Mt CO2/yr 115.793 115.793 115.793 116.082 116.371 \n\n 2015 2020 2025 2030 \\\nScenario Unit \nBIO00GHG000 Mt CO2/yr 116.862954 117.064786 104.533321 85.470978 \nBIO00GHG010 Mt CO2/yr 116.862954 117.064786 104.882979 86.171215 \nBIO00GHG020 Mt CO2/yr 116.862954 117.064786 104.987858 86.359776 \nBIO00GHG050 Mt CO2/yr 116.862954 117.064786 105.086286 86.579303 \nBIO00GHG100 Mt CO2/yr 116.862954 117.064786 105.308946 86.940757 \n... ... ... ... ... \nBIO45GHG3000 Mt CO2/yr 116.862954 117.064786 105.317794 86.959742 \nBIO45GHG400 Mt CO2/yr 116.862954 117.064786 105.316503 86.956978 \nBIO45GHG4000 Mt CO2/yr 116.862954 117.064786 105.317794 86.959742 \nBIO45GHG600 Mt CO2/yr 116.862954 117.064786 105.317241 86.958452 \nBIO45GHG990 Mt CO2/yr 116.862954 117.064786 105.317425 86.959190 \n\n 2035 2040 2045 2050 2055 \\\nScenario Unit \nBIO00GHG000 Mt CO2/yr 58.128176 31.002874 12.322813 3.466718 0.635540 \nBIO00GHG010 Mt CO2/yr 59.042042 31.812045 12.809237 3.658228 0.680146 \nBIO00GHG020 Mt CO2/yr 59.257329 31.975538 12.893656 3.687536 0.686598 \nBIO00GHG050 Mt CO2/yr 59.559617 32.243910 13.048671 3.744860 0.699316 \nBIO00GHG100 Mt CO2/yr 59.928075 32.492375 13.163503 3.781908 0.707242 \n... ... ... ... ... ... \nBIO45GHG3000 Mt CO2/yr 59.957014 32.522973 13.185622 3.792046 0.710006 \nBIO45GHG400 Mt CO2/yr 59.952959 32.519102 13.183225 3.791124 0.709638 \nBIO45GHG4000 Mt CO2/yr 59.956645 32.522604 13.185253 3.792046 0.709822 \nBIO45GHG600 Mt CO2/yr 59.954986 32.520945 13.184331 3.791677 0.709822 \nBIO45GHG990 Mt CO2/yr 59.956092 32.522051 13.185069 3.792046 0.709822 \n\n 2060 2070 2080 2090 2100 2110 \nScenario Unit \nBIO00GHG000 Mt CO2/yr 0.059536 0.0 0.0 0.0 0.0 0.0 \nBIO00GHG010 Mt CO2/yr 0.064144 0.0 0.0 0.0 0.0 0.0 \nBIO00GHG020 Mt CO2/yr 0.064881 0.0 0.0 0.0 0.0 0.0 \nBIO00GHG050 Mt CO2/yr 0.066171 0.0 0.0 0.0 0.0 0.0 \nBIO00GHG100 Mt CO2/yr 0.067093 0.0 0.0 0.0 0.0 0.0 \n... ... ... ... ... ... ... \nBIO45GHG3000 Mt CO2/yr 0.067277 0.0 0.0 0.0 0.0 0.0 \nBIO45GHG400 Mt CO2/yr 0.067277 0.0 0.0 0.0 0.0 0.0 \nBIO45GHG4000 Mt CO2/yr 0.067277 0.0 0.0 0.0 0.0 0.0 \nBIO45GHG600 Mt CO2/yr 0.067277 0.0 0.0 0.0 0.0 0.0 \nBIO45GHG990 Mt CO2/yr 0.067277 0.0 0.0 0.0 0.0 0.0 \n\n[84 rows x 20 columns]", - 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19901995200020052010201520202025203020352040204520502055206020702080209021002110
ScenarioUnit
BIO00GHG000Mt CO2/yr115.793115.793115.793116.082116.371116.862954117.064786104.53332185.47097858.12817631.00287412.3228133.4667180.6355400.0595360.00.00.00.00.0
BIO00GHG010Mt CO2/yr115.793115.793115.793116.082116.371116.862954117.064786104.88297986.17121559.04204231.81204512.8092373.6582280.6801460.0641440.00.00.00.00.0
BIO00GHG020Mt CO2/yr115.793115.793115.793116.082116.371116.862954117.064786104.98785886.35977659.25732931.97553812.8936563.6875360.6865980.0648810.00.00.00.00.0
BIO00GHG050Mt CO2/yr115.793115.793115.793116.082116.371116.862954117.064786105.08628686.57930359.55961732.24391013.0486713.7448600.6993160.0661710.00.00.00.00.0
BIO00GHG100Mt CO2/yr115.793115.793115.793116.082116.371116.862954117.064786105.30894686.94075759.92807532.49237513.1635033.7819080.7072420.0670930.00.00.00.00.0
..................................................................
BIO45GHG3000Mt CO2/yr115.793115.793115.793116.082116.371116.862954117.064786105.31779486.95974259.95701432.52297313.1856223.7920460.7100060.0672770.00.00.00.00.0
BIO45GHG400Mt CO2/yr115.793115.793115.793116.082116.371116.862954117.064786105.31650386.95697859.95295932.51910213.1832253.7911240.7096380.0672770.00.00.00.00.0
BIO45GHG4000Mt CO2/yr115.793115.793115.793116.082116.371116.862954117.064786105.31779486.95974259.95664532.52260413.1852533.7920460.7098220.0672770.00.00.00.00.0
BIO45GHG600Mt CO2/yr115.793115.793115.793116.082116.371116.862954117.064786105.31724186.95845259.95498632.52094513.1843313.7916770.7098220.0672770.00.00.00.00.0
BIO45GHG990Mt CO2/yr115.793115.793115.793116.082116.371116.862954117.064786105.31742586.95919059.95609232.52205113.1850693.7920460.7098220.0672770.00.00.00.00.0
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84 rows × 20 columns

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" - }, - "execution_count": 106, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "new.fillna(0)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-25T09:12:48.537962500Z", - "start_time": "2023-09-25T09:12:48.437702200Z" - } - }, - "id": "240d2b12b4968c7d" - }, - { - "cell_type": "markdown", - "source": [], - "metadata": { - "collapsed": false - }, - "id": "8cf23514ab0de739" - }, - { - "cell_type": "markdown", - "source": [ - "## check if old and new calculation yield same output" - ], - "metadata": { - "collapsed": false - }, - "id": "dd042b4579161338" - }, - { - "cell_type": "code", - "execution_count": 107, - "outputs": [ - { - "data": { - "text/plain": "True" - }, - "execution_count": 107, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "round = 11\n", - "(old.fillna(0).round(round) == new.fillna(0).round(round)).all().all()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-25T09:13:28.864784400Z", - "start_time": "2023-09-25T09:13:28.780131700Z" - } - }, - "id": "9041d5009d0a95b7" - }, - { - "cell_type": "code", - "execution_count": 142, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "R12_AFR\n", - "True\n", - "R12_CHN\n", - "True\n", - "R12_EEU\n", - "True\n", - "R12_FSU\n", - "True\n", - "R12_LAM\n", - "True\n", - "R12_MEA\n", - "True\n", - "R12_NAM\n", - "True\n", - "R12_PAO\n", - "True\n", - "R12_PAS\n", - "True\n", - "R12_RCPA\n", - "True\n", - "R12_SAS\n", - "True\n", - "R12_WEU\n", - "True\n" - ] - } - ], - "source": [ - "for k,v in old_dfs_dict.items():\n", - " print(k)\n", - " #print(new_dfs_dict[k])\n", - " print((v.fillna(0).round(round) == new_dfs_dict[k].fillna(0).round(round)).all().all())\n", - " if not (v.fillna(0).round(round) == new_dfs_dict[k].fillna(0).round(round)).all().all():\n", - " break" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-25T09:28:57.739335300Z", - "start_time": "2023-09-25T09:28:57.407534300Z" - } - }, - "id": "ce3dfad183defef5" - }, - { - "cell_type": "code", - "execution_count": 42, - "outputs": [], - "source": [ - "pd.testing.assert_frame_equal(old, new)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "start_time": "2023-09-25T08:56:04.961689700Z" - } - }, - "id": "8b0f59e3b8202475" - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/message_ix_models/model/material/magpie notebooks/tax_emission_setup.ipynb b/message_ix_models/model/material/magpie notebooks/tax_emission_setup.ipynb deleted file mode 100644 index 2495981abe..0000000000 --- a/message_ix_models/model/material/magpie notebooks/tax_emission_setup.ipynb +++ /dev/null @@ -1,182 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 18, - "outputs": [], - "source": [ - "import ixmp\n", - "import matplotlib.pyplot as plt\n", - "import message_ix" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-05T13:52:55.824837700Z", - "start_time": "2023-10-05T13:52:55.787004900Z" - } - }, - "id": "e34c79b21d8c4a80" - }, - { - "cell_type": "code", - "execution_count": 2, - "outputs": [], - "source": [ - "mp = ixmp.Platform(\"ixmp_dev\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-05T13:45:38.626355200Z", - "start_time": "2023-10-05T13:45:36.273179900Z" - } - }, - "id": "227c231f7a367485" - }, - { - "cell_type": "code", - "execution_count": 9, - "outputs": [], - "source": [ - "target_model = \"...\"\n", - "target_scenario = \"...\"\n", - "peak_model = \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00\"\n", - "peak_scenario = \"EN_NPi2020_1150\"" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-05T13:50:44.255403100Z", - "start_time": "2023-10-05T13:50:44.239973600Z" - } - }, - "id": "56f8ec02603f447e" - }, - { - "cell_type": "code", - "execution_count": 10, - "outputs": [], - "source": [ - "peak_scen = message_ix.Scenario(mp, peak_model, peak_scenario)\n", - "target_scen = message_ix.Scenario(mp, target_model, target_scenario)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-05T13:50:46.628590500Z", - "start_time": "2023-10-05T13:50:44.571545200Z" - } - }, - "id": "48d2afc1d5f9a47a" - }, - { - "cell_type": "code", - "execution_count": 13, - "outputs": [], - "source": [ - "# pull and modify tax emission dataframe from peak scenario\n", - "df_emi_tax = peak_scen.par(\"tax_emission\")\n", - "df_emi_tax[\"type_emission\"] = \"TCE\"" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-05T13:51:35.379890600Z", - "start_time": "2023-10-05T13:51:35.326754400Z" - } - }, - "id": "8b1af490dac35dd" - }, - { - "cell_type": "code", - "execution_count": 21, - "outputs": [ - { - "data": { - "text/plain": "Text(0.5, 0, '')" - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": "
", - "image/png": 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- }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# quick visualization to check the emission tax trajectory\n", - "fig, ax = plt.subplots()\n", - "df_emi_tax.plot(x=\"type_year\", ax=ax)\n", - "ax.set_ylabel(\"USD/tC\")\n", - "ax.set_xlabel(\"\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-05T13:53:20.623834300Z", - "start_time": "2023-10-05T13:53:20.507446200Z" - } - }, - "id": "57bc9de8707bcde8" - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df_emi_bound = target_scen.par(\"bound_emission\")" - ], - "metadata": { - "collapsed": false - }, - "id": "38e6964989aa4070" - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "# remove emission bound from target scenario\n", - "# and add tax emission from peak scenario\n", - "target_scen.check_out()\n", - "if len(df_emi_bound) != 0:\n", - " target_scen.remove_par(\"bound_emission\", df_emi_bound)\n", - "target_scen.add_par(\"tax_emission\", df_emi_tax)\n", - "target_scen.commit(\"add emission tax to emulate peak budget scenario\")" - ], - "metadata": { - "collapsed": false - }, - "id": "3e49cf0636dab573" - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/message_ix_models/model/material/petrochemical model fixes notebooks/add_new_meth_modes.ipynb b/message_ix_models/model/material/petrochemical model fixes notebooks/add_new_meth_modes.ipynb deleted file mode 100644 index 57a6cba0a4..0000000000 --- a/message_ix_models/model/material/petrochemical model fixes notebooks/add_new_meth_modes.ipynb +++ /dev/null @@ -1,112 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import ixmp as ix\n", - "\n", - "mp = ix.Platform(\"ixmp_dev\")\n", - "\n", - "import message_ix\n", - "scenario = message_ix.Scenario(mp, model=\"MESSAGEix-Materials\", scenario=\"NoPolicy_methanol_wood_new\")\n", - "#scenario = message_ix.Scenario(mp, model=\"SHAPE_SSP2_v4.1.8\", scenario=\"baseline_no_globiom\")\n", - "#scenario.has_solution()\n", - "#scenario.version" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "outputs": [], - "source": [ - "import os\n", - "from message_ix_models import Context\n", - "from pathlib import Path\n", - "Context.get_instance(-1).handle_cli_args(local_data=Path(os.getcwd()).parents[2].joinpath(\"data\"))\n", - "#Path(os.getcwd()).parents[2].joinpath(\"data\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 4, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\model\\material\\data_methanol.py:121: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df_rel_meth[\"value\"] = df_rel_meth.apply(lambda x: get_embodied_emi(x, pars, \"meth_plastics_share\"), axis=1)\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\model\\material\\data_methanol.py:123: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df_rel_hvc[\"value\"] = df_rel_hvc.apply(lambda x: get_embodied_emi(x, pars, \"hvc_plastics_share\"), axis=1)\n" - ] - }, - { - "data": { - "text/plain": " node_loc technology year_vtg year_act mode node_dest commodity \\\n0 R12_AFR meth_h2 2020 2020 fuel R12_AFR ht_heat \n1 R12_AFR meth_h2 2020 2025 fuel R12_AFR ht_heat \n2 R12_AFR meth_h2 2020 2030 fuel R12_AFR ht_heat \n3 R12_AFR meth_h2 2020 2035 fuel R12_AFR ht_heat \n4 R12_AFR meth_h2 2020 2040 fuel R12_AFR ht_heat \n.. ... ... ... ... ... ... ... \n235 R12_SAS meth_coal 2015 2035 M1 R12_SAS methanol \n236 R12_WEU meth_coal 2015 2035 M1 R12_WEU electr \n237 R12_WEU meth_coal 2015 2035 M1 R12_WEU methanol \n238 R12_CHN meth_coal 2015 2035 M1 R12_CHN electr \n239 R12_CHN meth_coal 2015 2035 M1 R12_CHN methanol \n\n level time time_dest value unit \n0 secondary year year 0.050000 GWa \n1 secondary year year 0.050000 GWa \n2 secondary year year 0.050000 GWa \n3 secondary year year 0.050000 GWa \n4 secondary year year 0.050000 GWa \n.. ... ... ... ... ... \n235 primary year year 1.000000 GWa \n236 secondary year year 0.038462 GWa \n237 primary year year 1.000000 GWa \n238 secondary year year 0.038462 GWa \n239 primary year year 1.000000 GWa \n\n[25934 rows x 12 columns]", - "text/html": "
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.......................................
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238R12_CHNmeth_coal20152035M1R12_CHNelectrsecondaryyearyear0.038462GWa
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25934 rows × 12 columns

\n
" - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import data_methanol\n", - "import importlib\n", - "importlib.reload(data_methanol)\n", - "df = data_methanol.gen_data_methanol(scenario)\n", - "\n", - "df[\"output\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [], - "metadata": { - "collapsed": false - } - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/message_ix_models/model/material/petrochemical model fixes notebooks/methanol_modifications.ipynb b/message_ix_models/model/material/petrochemical model fixes notebooks/methanol_modifications.ipynb deleted file mode 100644 index 422dfd91ae..0000000000 --- a/message_ix_models/model/material/petrochemical model fixes notebooks/methanol_modifications.ipynb +++ /dev/null @@ -1,890 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "import ixmp as ix\n", - "import message_ix\n", - "import pandas as pd\n", - "\n", - "mp = ix.Platform(\"ixmp_dev\")\n", - "scenario = message_ix.Scenario(mp, model=\"MESSAGEix-Materials\", scenario=\"NoPolicy_petro_thesis_2\")#, version=37)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## Generate new mode for methanol technologies" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "par_dict = {}\n", - "for i in scenario.par_list():\n", - " try: par_dict[i] = scenario.par(i, filters={\"technology\":[\"meth_ng\", \"meth_h2\", \"meth_coal\", \"meth_bio\", \"meth_bio_ccs\", \"meth_coal_ccs\", \"meth_ng_ccs\"]})\n", - " except:\n", - " pass" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "par_dict = {}\n", - "for i in scenario.par_list():\n", - " try:\n", - " df = scenario.par(i, filters={\"technology\":\"meth_bal\"})\n", - " if df.size != 0:\n", - " par_dict[i] = df\n", - " except:\n", - " pass" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "for i in par_dict.keys():\n", - " if \"mode\" in par_dict[i].columns:\n", - " par_dict[i][\"mode\"] = \"feedstock\"" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "keys = list(par_dict.keys())\n", - "for i in keys:\n", - " if par_dict[i].size == 0:\n", - " par_dict.pop(i)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "for j in par_dict.keys():\n", - " df = par_dict[j]\n", - " if \"mode\" in par_dict[j].columns:\n", - " df[\"mode\"] = \"M2\"\n", - "\n", - "df = par_dict[\"output\"]\n", - "df.loc[df[\"commodity\"]==\"methanol\",\"level\"] = \"final_material\"\n", - "par_dict[\"output\"] = df" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "#scenario.remove_solution()\n", - "#scenario.check_out()\n", - "for i in par_dict.keys():\n", - " print(i)\n", - " scenario.check_out()\n", - " scenario.add_par(i, par_dict[i])\n", - " scenario.commit(\"add new mode for meth tecs\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scenario.par(\"input\", filters={\"technology\":\"meth_coal\", \"mode\":\"M2\"})" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## update meth_bio(ccs)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "#df = scenario.par(\"output\", filters={\"technology\":\"meth_bio_ccs\"})\n", - "#scenario.remove_solution()\n", - "scenario.check_out()\n", - "scenario.add_par(\"output\", df[df[\"year_act\"] > 2025])\n", - "#df = scenario.par(\"input\", filters={\"technology\":\"meth_bio_ccs\"})\n", - "#scenario.add_par(\"input\", df[df[\"year_act\"] > 2025])\n", - "scenario.commit(comment=\"fix meth_bio_ccs issue\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scenario.check_out()\n", - "df = scenario.par(\"input\", filters={\"technology\":\"meth_bio_ccs\"})\n", - "scenario.remove_par(\"input\", df)\n", - "scenario.add_par(\"input\", df[df[\"year_act\"] > 2025])" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scenario.commit(\"fix meth_bio_ccs input\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df1 = scenario.par(\"input\", filters={\"technology\":\"meth_bio_ccs\"})\n", - "df1" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## add missing years to methanol trade tecs" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "paramList_tec = [x for x in scenario.par_list() if \"technology\" in scenario.idx_sets(x)]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "par_dict_exp = {}\n", - "df_all_rows = pd.DataFrame()\n", - "for p in paramList_tec:\n", - " df = scenario.par(p, filters={\"technology\":\"eth_bio_ccs\"})\n", - " if df.index.size:\n", - " par_dict_exp[p] = df\n", - "\n", - "list(par_dict_exp.keys())" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "par_dict_exp.keys()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "par_dict_exp[\"relation_activity\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "for p in par_dict_exp.keys():\n", - " print(p, len(par_dict_exp[p].value.unique()))\n", - " try:\n", - " print(par_dict_exp[p][\"year_act\"].min())\n", - " print(par_dict_exp[p][\"year_vtg\"].min())\n", - " if par_dict_exp[p][\"year_act\"].min() < 2020:\n", - " print(par_dict_exp[p])\n", - " except:\n", - " continue" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "import numpy as np\n", - "par_dict_bc = {}\n", - "for p in list(par_dict_exp.keys())[:-2]:\n", - " df = par_dict_exp[p].copy(deep=True)\n", - " if \"year_act\" in df.columns:\n", - " year_name = \"year_act\"\n", - " else:\n", - " year_name = \"year_vtg\"\n", - " min_year = df[year_name].min()\n", - " min_year = 2040\n", - " new_lowest_y = 2000\n", - " tec_lt = 20\n", - " new_years = np.linspace(new_lowest_y, min_year, int((min_year-new_lowest_y)/5)+1).astype(int)[:-1]\n", - " df_min_year = df[df[year_name] == min_year].copy(deep=True)\n", - " print(p, new_years)\n", - " if len(new_years) == 0:\n", - " continue\n", - " df_min_year_new = df_min_year.copy(deep=True)\n", - " for i in new_years:\n", - " df_min_year_add = df_min_year.copy(deep=True)\n", - " df_min_year_add[year_name] = i\n", - " if (year_name == \"year_act\") & (\"year_vtg\" in df.columns):\n", - " df_min_year_add = df_min_year_add[df_min_year_add[\"year_vtg\"] == min_year]\n", - " df_min_year_add[\"year_vtg\"] = i\n", - " old_years = np.linspace(i-tec_lt, i, int((tec_lt)/5)+1).astype(int)\n", - " print(i, old_years)\n", - " for j in old_years:\n", - " df_min_year_add_vtg = df_min_year_add[df_min_year_add[\"year_vtg\"]==df_min_year_add[\"year_act\"]].copy(deep=True)\n", - " df_min_year_add_vtg[\"year_vtg\"] = j\n", - " df_min_year_add = pd.concat([df_min_year_add, df_min_year_add_vtg])\n", - " df_min_year_new = pd.concat([df_min_year_new, df_min_year_add])\n", - " par_dict_bc[p] = df_min_year_new" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "for i in par_dict_bc.keys():\n", - " par_dict_bc[i] = par_dict_bc[i].drop_duplicates()\n", - " if \"year_act\" in par_dict_bc[i].columns:\n", - " par_dict_bc[i] = par_dict_bc[i][par_dict_bc[i][\"year_act\"] >= 2020]\n", - " if \"year_vtg\" in par_dict_bc[i].columns:\n", - " par_dict_bc[i] = par_dict_bc[i][par_dict_bc[i][\"year_vtg\"] < 2020]\n", - "\n", - "\n", - "keys = list(par_dict_bc.keys())\n", - "for i in keys:\n", - " if \"year_vtg\" not in par_dict_bc[i].columns:\n", - " par_dict_bc.pop(i)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df = par_dict_bc[\"input\"]\n", - "df[df[\"year_act\"]==2035]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "par_dict_imp = {}\n", - "df_all_rows = pd.DataFrame()\n", - "for p in paramList_tec:\n", - " df = scenario.par(p, filters={\"technology\":\"meth_imp\"})\n", - " if df.index.size:\n", - " par_dict_imp[p] = df\n", - "par_dict_imp.keys()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "par_dict_bc_imp = {}\n", - "for p in list(par_dict_imp.keys())[:-2]:\n", - " df = par_dict_imp[p].copy(deep=True)\n", - " if \"year_act\" in df.columns:\n", - " year_name = \"year_act\"\n", - " else:\n", - " year_name = \"year_vtg\"\n", - " min_year = df[year_name].min()\n", - " min_year = 2045\n", - " print(p)\n", - " new_years = np.linspace(2020, min_year, int((min_year-2020)/5)+1).astype(int)[:-1]\n", - " df_min_year = df[df[year_name] == min_year].copy(deep=True)\n", - " if len(new_years) == 0:\n", - " continue\n", - " for i in new_years:\n", - " df_min_year_add = df_min_year.copy(deep=True)\n", - " df_min_year_add[year_name] = i\n", - " print(new_years)\n", - " if (year_name == \"year_act\") & (\"year_vtg\" in df.columns):\n", - " df_min_year_add[\"year_vtg\"] = i\n", - " if (year_name == \"year_act\") & (\"year_rel\" in df.columns):\n", - " df_min_year_add[\"year_rel\"] = i\n", - " df_min_year = pd.concat([df_min_year, df_min_year_add])\n", - " par_dict_bc_imp[p] = df_min_year" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "for i in par_dict_bc_imp.keys():\n", - " par_dict_bc_imp[i] = par_dict_bc_imp[i].drop_duplicates()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "par_dict_trd = {}\n", - "df_all_rows = pd.DataFrame()\n", - "for p in paramList_tec:\n", - " df = scenario.par(p, filters={\"technology\":\"meth_trd\"})\n", - " if df.index.size:\n", - " par_dict_trd[p] = df\n", - "par_dict_trd.keys()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "par_dict_bc_trd = {}\n", - "for p in list(par_dict_trd.keys())[:-1]:\n", - " df = par_dict_trd[p].copy(deep=True)\n", - " if \"year_act\" in df.columns:\n", - " year_name = \"year_act\"\n", - " else:\n", - " year_name = \"year_vtg\"\n", - " min_year = df[year_name].min()\n", - " min_year = 2045\n", - " print(p)\n", - " new_years = np.linspace(2020, min_year, int((min_year-2020)/5)+1).astype(int)[:-1]\n", - " df_min_year = df[df[year_name] == min_year].copy(deep=True)\n", - " if len(new_years) == 0:\n", - " continue\n", - " for i in new_years:\n", - " df_min_year_add = df_min_year.copy(deep=True)\n", - " df_min_year_add[year_name] = i\n", - " print(new_years)\n", - " if (year_name == \"year_act\") & (\"year_vtg\" in df.columns):\n", - " df_min_year_add[\"year_vtg\"] = i\n", - " if (year_name == \"year_act\") & (\"year_rel\" in df.columns):\n", - " df_min_year_add[\"year_rel\"] = i\n", - " df_min_year = pd.concat([df_min_year, df_min_year_add])\n", - " par_dict_bc_trd[p] = df_min_year" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "for i in par_dict_bc_trd.keys():\n", - " par_dict_bc_trd[i] = par_dict_bc_trd[i].drop_duplicates()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "list(par_dict_exp.keys())[7:-3]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "#par_dict_bc_imp.keys()\n", - "#par_dict_bc_trd.keys()\n", - "#par_dict_bc.keys()\n", - "\n", - "with pd.ExcelWriter('meth_bio_ccs_additions.xlsx') as writer:\n", - " for i in list(par_dict_exp.keys())[7:-3]:\n", - " par_dict_exp[i].to_excel(writer, sheet_name=i, index=False)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scenario.commit(\"update meth costs\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df_bio = scenario.par(\"inv_cost\", filters={\"technology\": \"meth_coal\"})\n", - "df_bio_ccs = scenario.par(\"inv_cost\", filters={\"technology\": \"meth_coal_ccs\"})\n", - "df_bio.set_index([\"node_loc\", \"year_vtg\"])[\"value\"]-df_bio_ccs.set_index([\"node_loc\", \"year_vtg\"])[\"value\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df = scenario.par(\"land_input\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df.groupby([\"land_scenario\", \"year\"]).sum().reset_index().set_index(\"year\").loc[2050].sort_values(\"value\")#.plot(x=\"year\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scenario.par(\"output\", filters={\"technology\":\"\"})" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "from message_ix_models.util import broadcast, same_node\n", - "bio_dict = data_methanol.gen_data_meth_bio(scenario)\n", - "\n", - "df_all = bio_dict[\"input\"]\n", - "yv_ya = scenario.vintage_and_active_years()\n", - "yv_ya = yv_ya[(yv_ya[\"year_act\"]>2015) & (yv_ya[\"year_act\"]-yv_ya[\"year_vtg\"]<20)]\n", - "nodes=df_all[\"node_loc\"].unique()\n", - "\n", - "import pandas as pd\n", - "\n", - "df=df_all\n", - "df_new = message_ix.make_df(\"input\", **yv_ya, **df.drop([\"year_vtg\", \"year_act\", \"node_loc\"], axis=1).iloc[0]).pipe(broadcast, node_loc=nodes).pipe(same_node)\n", - "df_new.loc[df_new[\"year_vtg\"]<2030, \"value\"] = 1.7241\n", - "df_new.loc[df_new[\"year_vtg\"].isin([2030, 2035]), \"value\"] = 1.6393\n", - "df_new.loc[df_new[\"year_vtg\"].isin([2040, 2045]), \"value\"] = 1.5873\n", - "df_new.loc[df_new[\"year_vtg\"]>2045, \"value\"] = 1.5385\n", - "\n", - "df_input_new = df_new.copy(deep=True)\n", - "df_input_new" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df_all = bio_dict[\"output\"]\n", - "df_output_new = pd.DataFrame()\n", - "\n", - "df = df_all[df_all[\"commodity\"]==\"d_heat\"]\n", - "df_new = message_ix.make_df(\"output\", **yv_ya, **df.drop([\"year_vtg\", \"year_act\", \"node_loc\"], axis=1).iloc[0]).pipe(broadcast, node_loc=nodes).pipe(same_node)\n", - "df_new.loc[df_new[\"year_vtg\"]<2030, \"value\"] = 0.3793\n", - "df_new.loc[df_new[\"year_vtg\"].isin([2030, 2035]), \"value\"] = 0.3607\n", - "df_new.loc[df_new[\"year_vtg\"].isin([2040, 2045]), \"value\"] = 0.3492\n", - "df_new.loc[df_new[\"year_vtg\"]>2045, \"value\"] = 0.3385\n", - "df_output_new = pd.concat([df_output_new, df_new])\n", - "\n", - "df = df_all[df_all[\"commodity\"]==\"electr\"]\n", - "df_new = message_ix.make_df(\"output\", **yv_ya, **df.drop([\"year_vtg\", \"year_act\", \"node_loc\"], axis=1).iloc[0]).pipe(broadcast, node_loc=nodes).pipe(same_node)\n", - "df_new.loc[df_new[\"year_vtg\"]<2030, \"value\"] = 0.03448\n", - "df_new.loc[df_new[\"year_vtg\"].isin([2030, 2035]), \"value\"] = 0.0328\n", - "df_new.loc[df_new[\"year_vtg\"].isin([2040, 2045]), \"value\"] = 0.03175\n", - "df_new.loc[df_new[\"year_vtg\"]>2045, \"value\"] = 0.03077\n", - "df_output_new = pd.concat([df_output_new, df_new])\n", - "\n", - "df = df_all[df_all[\"commodity\"]==\"methanol\"]\n", - "df_new = message_ix.make_df(\"output\", **yv_ya, **df.drop([\"year_vtg\", \"year_act\", \"node_loc\"], axis=1).iloc[0]).pipe(broadcast, node_loc=nodes).pipe(same_node)\n", - "df_output_new = pd.concat([df_output_new, df_new])" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df_all = bio_dict[\"capacity_factor\"]\n", - "df = df_all\n", - "df_cap = message_ix.make_df(\"capacity_factor\", **yv_ya,\n", - " **df.drop([\"year_vtg\", \"year_act\", \"node_loc\"],\n", - " axis=1).iloc[0]).pipe(broadcast, node_loc=nodes).pipe(same_node)\n", - "df_cap.loc[df_new[\"year_vtg\"]<2030, \"value\"] = 0.9215\n", - "\n", - "\n", - "df_all = bio_dict[\"var_cost\"]\n", - "df = df_all\n", - "df_var = message_ix.make_df(\"var_cost\", **yv_ya,\n", - " **df.drop([\"year_vtg\", \"year_act\", \"node_loc\"]\n", - " , axis=1).iloc[0]).pipe(broadcast, node_loc=nodes).pipe(same_node)\n", - "df_var.loc[df_new[\"year_vtg\"]<2030, \"value\"] = 18.89" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df_all = bio_dict[\"fix_cost\"]\n", - "df = df_all\n", - "\n", - "df_fix = message_ix.make_df(\"fix_cost\", **yv_ya, **df.drop([\"year_vtg\", \"year_act\", \"node_loc\"], axis=1).iloc[0]).pipe(broadcast, node_loc=nodes).pipe(same_node)\n", - "df_fix.loc[df_fix[\"year_vtg\"]<2030, \"value\"] = 54.101\n", - "df_fix.loc[df_fix[\"year_vtg\"].isin([2030, 2035]), \"value\"] = 36.067\n", - "df_fix.loc[df_fix[\"year_vtg\"].isin([2040, 2045]), \"value\"] = 35.521\n", - "df_fix.loc[df_fix[\"year_vtg\"]>2045, \"value\"] = 36.067\n", - "\n", - "df_fix" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "new_par_dict = {\n", - " \"capacity_factor\":df_cap,\n", - " \"input\":df_input_new,\n", - " \"output\":df_output_new,\n", - " \"fix_cost\":df_fix,\n", - " \"var_cost\":df_var,\n", - " \"inv_cost\":bio_dict[\"inv_cost\"],\n", - " \"technical_lifetime\":bio_dict[\"technical_lifetime\"]\n", - "}\n", - "with pd.ExcelWriter('meth_bio_techno_economic.xlsx') as writer:\n", - " for i in new_par_dict.keys():\n", - " new_par_dict[i].to_excel(writer, sheet_name=i, index=False)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "#data_methanol.gen_data_methanol(scenario)[\"historical_new_capacity\"]\n", - "#data_methanol.add_methanol_trp_additives(scenario)\n", - "#data_methanol.get_cost_ratio_2020(scenario, \"meth_coal\", \"inv_cost\", year=2020, ref_reg=\"R12_WEU\")\n", - "df_bio = data_methanol.gen_data_meth_bio(scenario)[\"inv_cost\"]\n", - "df_bio_ccs = data_methanol.gen_meth_bio_ccs(scenario)[\"inv_cost\"]\n", - "df_bio.set_index([\"node_loc\", \"year_vtg\"])[\"value\"]-df_bio_ccs.set_index([\"node_loc\", \"year_vtg\"])[\"value\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "meth_dict = data_methanol.gen_data_methanol(scenario)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "meth_dict[\"relation_activity\"][\"technology\"].unique()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "\n", - "arr = [\"meth_ng\", \"meth_coal\", \"meth_coal_ccs\", \"meth_ng_ccs\"]\n", - "df = scenario.par(\"relation_activity\", filters={\"relation\": \"CO2_cc\", \"technology\":arr})\n", - "df = df.rename({\"year_rel\":\"year_vtg\"}, axis=1)\n", - "values = dict(zip(df[\"technology\"], df[\"value\"]))\n", - "\n", - "df_em = scenario.par(\"emission_factor\", filters={\"emission\": \"CO2_transformation\", \"technology\":arr})\n", - "for i in arr:\n", - " df_em.loc[df_em[\"technology\"]==i,\"value\"] = values[i]\n", - "df_em[\"emission\"] = \"CO2_industry\"\n", - "df_em\n", - "#scenario.add_par(df_em)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df = scenario.par(\"input\", filters={\"technology\":\"meth_t_d\"})\n", - "df[\"value\"] = 1\n", - "df" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df = scenario.par(\"technical_lifetime\")\n", - "dict = df.loc[df[\"technology\"].str.contains(\"NH3\"),[\"technology\",\"value\"]].set_index(\"technology\").to_dict()[\"value\"]\n", - "for i in df.keys():\n", - " if (\"year_vtg\" in df[i].columns) & (\"year_act\" in df[i].columns):\n", - " df_temp = df[i]\n", - " df[i] = df_temp[(df_temp[\"year_act\"]-df_temp[\"year_vtg\"])<=dict[df_temp[\"technology\"]]]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## modify meth exp limit" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df = scenario.par(\"relation_activity\", filters={\"relation\":\"meth_exp_limit\"})\n", - "df_mea = df[(df[\"technology\"]==\"meth_exp\") & (df[\"node_rel\"]==\"R12_MEA\")]\n", - "df_mea[df_mea[\"value\"]] = -1.15\n", - "df_lam = df[(df[\"technology\"]==\"meth_exp\") & (df[\"node_rel\"]==\"R12_LAM\")]\n", - "df_lam[df_lam[\"value\"]] = -1.6\n", - "df_rel = pd.concat([df_mea, df_lam])\n", - "scenario.check_out()\n", - "scenario.add_par(\"relation_activity\", df_rel)\n", - "scenario.commit(\"modify meth exp share constraint for LAM and MEA\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "#scenario.par(\"relation_lower\", filters={\"relation\":\"meth_exp_limit\"})\n", - "rel_dict = {\n", - " \"relation\": \"meth_exp_tot\",\n", - " \"year_rel\": 2020,\n", - " \"year_act\": 2020,\n", - " \"technology\": \"meth_exp_tot\",\n", - " \"mode\": [\"fuel\", \"feedstock\"],\n", - " \"unit\": \"-\",\n", - " \"value\": 1\n", - "\n", - "}\n", - "from message_ix_models.util import broadcast, same_node\n", - "nodes = scenario.set(\"node\")[1:]\n", - "nodes_share = nodes.drop(5).reset_index(drop=True)\n", - "df = message_ix.make_df(\"relation_activity\", **rel_dict).pipe(broadcast, node_loc=nodes_share)\n", - "df[\"node_rel\"] = df[\"node_loc\"]\n", - "df[[\"relation\", \"node_rel\", \"year_rel\", \"node_loc\", \"technology\", \"year_act\", \"mode\", \"value\", \"unit\"]]\n", - "# rel_dict = {\n", - "# \"relation\": \"meth_exp_tot\",\n", - "# \"year_rel\": 2020,\n", - "# \"year_act\": 2020,\n", - "# \"technology\": \"meth_exp_tot\",\n", - "# \"mode\": [\"fuel\", \"feedstock\"],\n", - "# \"unit\": \"-\",\n", - "# }\n", - "# df_up = message_ix.make_df(\"relation_upper\", **rel_dict).pipe(broadcast, node_rel=nodes_share)\n", - "# df_lo = message_ix.make_df(\"relation_lower\", **rel_dict).pipe(broadcast, node_rel=nodes_share)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df = scenario.par(\"relation_lower\", filters={\"relation\":\"meth_exp_tot\"})\n", - "scenario.check_out()\n", - "scenario.remove_par(\"relation_lower\", df)\n" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scenario.commit(\"remove meth exp tot upper\")" - ], - "metadata": { - "collapsed": false - } - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/message_ix_models/model/material/petrochemical model fixes notebooks/missing_rels.yaml b/message_ix_models/model/material/petrochemical model fixes notebooks/missing_rels.yaml deleted file mode 100644 index 39a87231e3..0000000000 --- a/message_ix_models/model/material/petrochemical model fixes notebooks/missing_rels.yaml +++ /dev/null @@ -1,17 +0,0 @@ -- gas_util -- SO2_red_synf -- global_GSO2 -- global_GCO2 -- OilPrices -- PE_export_total -- PE_import_total -- PE_synliquids -- PE_total_direct -- PE_total_engineering -- FE_transport -- SO2_red_ref -- trp_energy -- total_TCO2 -- nuc_lc_in_el -- FE_liquids -- FE_final_energy diff --git a/message_ix_models/model/material/petrochemical model fixes notebooks/split_h2_elec.ipynb b/message_ix_models/model/material/petrochemical model fixes notebooks/split_h2_elec.ipynb deleted file mode 100644 index 8288e521b9..0000000000 --- a/message_ix_models/model/material/petrochemical model fixes notebooks/split_h2_elec.ipynb +++ /dev/null @@ -1,208 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 30, - "outputs": [], - "source": [ - "import copy" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 19, - "outputs": [], - "source": [ - "import ixmp as ix\n", - "import message_ix\n", - "mp = ix.Platform(\"ixmp_dev\")\n", - "\n", - "scenario = message_ix.Scenario(mp, model=\"MESSAGEix-Materials\", scenario=\"NoPolicy_petro_thesis_2_macro\")\n", - "#scenario = message_ix.Scenario(mp, model=\"MESSAGEix-Materials\", scenario=\"baseline_DEFAULT_rebase2\")\n", - "#scenario = message_ix.Scenario(mp, model=\"MESSAGEix-Materials\", scenario=\"baseline_VK_Transport-Materials_VK\")#, version=37)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 17, - "outputs": [ - { - "data": { - "text/plain": " model scenario scheme \\\n6155 MESSAGEix-Materials 2.0deg_petro_thesis_1 MESSAGE \n6156 MESSAGEix-Materials 2.0deg_petro_thesis_2 MESSAGE \n6157 MESSAGEix-Materials 2.0deg_petro_thesis_2_macro MESSAGE \n6158 MESSAGEix-Materials 2degree_petro_thesis_2 MESSAGE \n6282 MESSAGEix-Materials NoPolicy_no_SDGs_petro_thesis_1 MESSAGE \n6287 MESSAGEix-Materials NoPolicy_petro_biomass MESSAGE \n6288 MESSAGEix-Materials NoPolicy_petro_test MESSAGE \n6289 MESSAGEix-Materials NoPolicy_petro_test_2 MESSAGE \n6290 MESSAGEix-Materials NoPolicy_petro_test_3 MESSAGE \n6291 MESSAGEix-Materials NoPolicy_petro_test_4 MESSAGE \n6292 MESSAGEix-Materials NoPolicy_petro_test_5 MESSAGE \n6293 MESSAGEix-Materials NoPolicy_petro_test_6 MESSAGE \n6294 MESSAGEix-Materials NoPolicy_petro_thesis_2 MESSAGE \n6295 MESSAGEix-Materials NoPolicy_petro_thesis_2_macro MESSAGE \n\n is_default is_locked cre_user cre_date upd_user \\\n6155 1 0 maczek 2023-02-08 23:38:03.000000 maczek \n6156 1 0 maczek 2023-04-06 16:44:33.000000 None \n6157 1 0 maczek 2023-04-14 11:43:37.000000 maczek \n6158 1 0 unlu 2023-04-06 15:35:51.000000 None \n6282 1 0 maczek 2023-04-12 22:12:42.000000 None \n6287 1 0 unlu 2022-08-09 14:51:56.000000 unlu \n6288 1 0 unlu 2022-04-14 11:08:53.000000 None \n6289 1 0 unlu 2022-04-14 14:05:39.000000 None \n6290 1 0 unlu 2022-04-14 14:40:02.000000 None \n6291 1 0 unlu 2022-04-14 15:13:25.000000 None \n6292 1 0 unlu 2022-04-14 15:29:02.000000 None \n6293 1 0 unlu 2022-04-14 15:38:19.000000 None \n6294 1 0 maczek 2023-04-13 15:29:47.000000 maczek \n6295 1 0 maczek 2023-04-14 11:18:27.000000 maczek \n\n upd_date lock_user lock_date \\\n6155 2023-02-08 23:44:53.000000 None None \n6156 None None None \n6157 2023-04-14 15:59:01.000000 None None \n6158 None None None \n6282 None None None \n6287 2022-08-09 15:14:46.000000 None None \n6288 None None None \n6289 None None None \n6290 None None None \n6291 None None None \n6292 None None None \n6293 None None None \n6294 2023-04-14 11:18:16.000000 None None \n6295 2023-04-14 11:28:34.000000 None None \n\n annotation version \n6155 clone Scenario from 'MESSAGEix-Materials|NoPol... 1 \n6156 clone Scenario from 'MESSAGEix-Materials|NoPol... 6 \n6157 clone Scenario from 'MESSAGEix-Materials|NoPol... 2 \n6158 clone Scenario from 'MESSAGEix-Materials|NoPol... 1 \n6282 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 9 \n6287 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 3 \n6288 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 4 \n6289 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 1 \n6290 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 1 \n6291 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 2 \n6292 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 2 \n6293 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 1 \n6294 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 65 \n6295 clone Scenario from 'MESSAGEix-Materials|NoPol... 5 ", - 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modelscenarioschemeis_defaultis_lockedcre_usercre_dateupd_userupd_datelock_userlock_dateannotationversion
6155MESSAGEix-Materials2.0deg_petro_thesis_1MESSAGE10maczek2023-02-08 23:38:03.000000maczek2023-02-08 23:44:53.000000NoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...1
6156MESSAGEix-Materials2.0deg_petro_thesis_2MESSAGE10maczek2023-04-06 16:44:33.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...6
6157MESSAGEix-Materials2.0deg_petro_thesis_2_macroMESSAGE10maczek2023-04-14 11:43:37.000000maczek2023-04-14 15:59:01.000000NoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...2
6158MESSAGEix-Materials2degree_petro_thesis_2MESSAGE10unlu2023-04-06 15:35:51.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...1
6282MESSAGEix-MaterialsNoPolicy_no_SDGs_petro_thesis_1MESSAGE10maczek2023-04-12 22:12:42.000000NoneNoneNoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...9
6287MESSAGEix-MaterialsNoPolicy_petro_biomassMESSAGE10unlu2022-08-09 14:51:56.000000unlu2022-08-09 15:14:46.000000NoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...3
6288MESSAGEix-MaterialsNoPolicy_petro_testMESSAGE10unlu2022-04-14 11:08:53.000000NoneNoneNoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...4
6289MESSAGEix-MaterialsNoPolicy_petro_test_2MESSAGE10unlu2022-04-14 14:05:39.000000NoneNoneNoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...1
6290MESSAGEix-MaterialsNoPolicy_petro_test_3MESSAGE10unlu2022-04-14 14:40:02.000000NoneNoneNoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...1
6291MESSAGEix-MaterialsNoPolicy_petro_test_4MESSAGE10unlu2022-04-14 15:13:25.000000NoneNoneNoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...2
6292MESSAGEix-MaterialsNoPolicy_petro_test_5MESSAGE10unlu2022-04-14 15:29:02.000000NoneNoneNoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...2
6293MESSAGEix-MaterialsNoPolicy_petro_test_6MESSAGE10unlu2022-04-14 15:38:19.000000NoneNoneNoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...1
6294MESSAGEix-MaterialsNoPolicy_petro_thesis_2MESSAGE10maczek2023-04-13 15:29:47.000000maczek2023-04-14 11:18:16.000000NoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...65
6295MESSAGEix-MaterialsNoPolicy_petro_thesis_2_macroMESSAGE10maczek2023-04-14 11:18:27.000000maczek2023-04-14 11:28:34.000000NoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...5
\n
" - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = mp.scenario_list()\n", - "df[df[\"scenario\"].str.contains(\"petro\")]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 20, - "outputs": [], - "source": [ - "paramList_tec = [x for x in scenario.par_list() if ((\"technology\" in scenario.idx_sets(x)) & (\"mode\" in scenario.idx_sets(x)))]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 27, - "outputs": [], - "source": [ - "import pandas as pd\n", - "par_dict_meth = {}\n", - "df_all_rows = pd.DataFrame()\n", - "for p in paramList_tec:\n", - " df = scenario.par(p, filters={\"technology\":\"h2_elec\"})\n", - " if df.index.size:\n", - " par_dict_meth[p] = df" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 31, - "outputs": [], - "source": [ - "par_dict_fuel = copy.deepcopy(par_dict_meth)\n", - "par_dict_fs = copy.deepcopy(par_dict_meth)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 32, - "outputs": [], - "source": [ - "for k,v in par_dict_fuel.items():\n", - " v[\"mode\"] = \"fuel\"\n", - "for k,v in par_dict_fs.items():\n", - " v[\"mode\"] = \"feedstock\"" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 35, - "outputs": [], - "source": [ - "for k,v in par_dict_meth.items():\n", - " scenario.remove_par(k, v)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 36, - "outputs": [], - "source": [ - "for k,v in par_dict_fuel.items():\n", - " scenario.add_par(k, v)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 37, - "outputs": [], - "source": [ - "for k,v in par_dict_fs.items():\n", - " scenario.add_par(k, v)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 38, - "outputs": [], - "source": [ - "scenario.commit(\"split h2_elec modes\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 42, - "outputs": [ - { - "data": { - "text/plain": "['year',\n 'node',\n 'technology',\n 'relation',\n 'emission',\n 'land_scenario',\n 'land_type',\n 'lvl_spatial',\n 'time',\n 'lvl_temporal',\n 'type_node',\n 'type_tec',\n 'type_year',\n 'type_emission',\n 'type_relation',\n 'mode',\n 'grade',\n 'level',\n 'commodity',\n 'rating',\n 'shares',\n 'type_addon',\n 'level_storage',\n 'storage_tec',\n 'sector',\n 'time_relative',\n 'map_spatial_hierarchy',\n 'map_node',\n 'map_temporal_hierarchy',\n 'map_time',\n 'cat_node',\n 'cat_tec',\n 'cat_year',\n 'cat_emission',\n 'type_tec_land',\n 'cat_relation',\n 'level_resource',\n 'level_renewable',\n 'level_stocks',\n 'map_shares_commodity_total',\n 'map_shares_commodity_share',\n 'addon',\n 'cat_addon',\n 'map_tec_addon',\n 'balance_equality',\n 'mapping_macro_sector',\n 'is_capacity_factor',\n 'map_tec_storage']" - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "scenario.set_list()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "for k,v in par_dict_meth.items():\n", - " print(k, v[\"mode\"].unique())" - ], - "metadata": { - "collapsed": false - } - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/message_ix_models/model/material/petrochemical model fixes notebooks/water_tec_pars.xlsx b/message_ix_models/model/material/petrochemical model fixes notebooks/water_tec_pars.xlsx deleted file mode 100644 index 0e7a24bb7a..0000000000 --- a/message_ix_models/model/material/petrochemical model fixes notebooks/water_tec_pars.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:0bf0882eafa9b184a1e3a44c61c36e1756a91bb60e22d47e999cda6f1b14ca96 -size 88386 diff --git a/message_ix_models/model/material/util notebooks/fetch_run_id_from_scenario.ipynb b/message_ix_models/model/material/util notebooks/fetch_run_id_from_scenario.ipynb deleted file mode 100644 index f8c3812e02..0000000000 --- a/message_ix_models/model/material/util notebooks/fetch_run_id_from_scenario.ipynb +++ /dev/null @@ -1,183 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": true, - "ExecuteTime": { - "start_time": "2023-04-24T13:57:51.695781Z", - "end_time": "2023-04-24T13:57:53.881033Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": "162 8\n167 10\n180 1\n206 2\n208 1\n ..\n9019 1\n9030 1\n9042 1\n9055 1\n9059 1\nName: version, Length: 192, dtype: int64" - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import ixmp\n", - "from jaydebeapi import connect\n", - "\n", - "platform = \"ixmp_dev\"\n", - "# model = \"MESSAGEix-Materials\"\n", - "# scenario = \"2.0deg_petro_thesis_2_final_macro\"\n", - "model = \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\"\n", - "model = \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP50BD0BI78\"\n", - "scenario = \"600f_MP00BD0BI78_test_calib_macro\"\n", - "#scenario = \"2.0deg_no_SDGs_petro_thesis_1_macro\"\n", - "scenario = \"baseline\"\n", - "\n", - "# Connect to the platform\n", - "# This also starts the JVM with the correct classpath to find ojdbc*.jar\n", - "mp = ixmp.Platform(platform)\n", - "df = mp.scenario_list()\n", - "version = df[df[\"scenario\"]==scenario][\"version\"]\n", - "version" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "outputs": [], - "source": [ - "version = df[(df[\"scenario\"]==scenario) & (df[\"model\"]==model)][\"version\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 15, - "outputs": [ - { - "data": { - "text/plain": "7019 1\nName: version, dtype: int64" - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "version" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 16, - "outputs": [], - "source": [ - "version = version.values[0]" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "start_time": "2023-04-24T13:58:14.533265Z", - "end_time": "2023-04-24T13:58:14.566259Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 17, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[(37485, 0, 'maczek', '2023-08-28 08:53:34')]\n" - ] - } - ], - "source": [ - "#version = 7\n", - "\n", - "\n", - "conn = connect(\n", - " \"oracle.jdbc.driver.OracleDriver\",\n", - " \"jdbc:oracle:thin:@x8oda.iiasa.ac.at:1521/PIXMP2.iiasa.ac.at\",\n", - " driver_args=[\"ixmp_dev\", \"ixmp_dev\"]\n", - ")\n", - "curs = conn.cursor()\n", - "\n", - "curs.execute(f\"\"\"\n", - " SELECT RUN.id, status, lock_user, lock_date\n", - " FROM\n", - "\tRUN\n", - "\tJOIN MODEL ON RUN.MODEL_ID = MODEL.ID\n", - "\tJOIN SCENARIO ON RUN.SCEN_ID = SCENARIO.ID\n", - " WHERE\n", - " MODEL.NAME = {model!r}\n", - "\tAND SCENARIO.NAME = {scenario!r}\n", - "\tAND VERSION = {version}\"\"\"\n", - " )\n", - "print(curs.fetchall())\n", - "conn.close()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "start_time": "2023-04-24T13:58:30.457765Z", - "end_time": "2023-04-24T13:58:31.358340Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 19, - "outputs": [], - "source": [ - "mp._backend.jobj.unlockRunid(37485)\n", - "#for i in mp._backend.get_scenarios(False, \"Materials\", \"baseline\"):\n", - "# mp._backend.check_out(i, False)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "start_time": "2023-04-24T13:58:37.686915Z", - "end_time": "2023-04-24T13:58:37.721569Z" - } - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [], - "metadata": { - "collapsed": false - } - } - ], - "metadata": { - "kernelspec": { - "name": "python3", - "language": "python", - "display_name": "Python 3 (ipykernel)" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/message_ix_models/model/material/util notebooks/format reporting output.ipynb b/message_ix_models/model/material/util notebooks/format reporting output.ipynb deleted file mode 100644 index 0ebd330ea1..0000000000 --- a/message_ix_models/model/material/util notebooks/format reporting output.ipynb +++ /dev/null @@ -1,1306 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "source": [ - "## prepare reporting output" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 2, - "outputs": [], - "source": [ - "def prepare_xlsx_for_explorer(filepath):\n", - " import pandas as pd\n", - " df = pd.read_excel(filepath)\n", - "\n", - " def add_R12(str):\n", - " if len(str) < 5:\n", - " return \"R12_\" + str\n", - " else:\n", - " return str\n", - "\n", - " df = df[~df[\"Region\"].isna()]\n", - " df[\"Region\"] = df[\"Region\"].map(add_R12)\n", - " df.to_excel(filepath, index=False)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-20T17:39:56.168197700Z", - "start_time": "2023-09-20T17:39:56.141984400Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 8, - "outputs": [], - "source": [ - "import os\n", - "f_list = os.listdir(\"C:/Users\\maczek\\PycharmProjects\\message_data/reporting_output\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 11, - "outputs": [], - "source": [ - "rep_list = [i for i in f_list if (\"1150\" in i) and (\"step\" not in i)]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 8, - "outputs": [], - "source": [ - "#path = \"C:/Users\\maczek\\PycharmProjects\\message_data/reporting_output/MESSAGEix-Materials_NoPolicy_petro_thesis_2_final_macro.xlsx\"\n", - "#path = \"C:/Users\\maczek\\PycharmProjects\\message_data/reporting_output/MESSAGEix-Materials_NoPolicy_petro_thesis_2_macro_w_ENGAGE_fixes.xlsx\"\n", - "#path = \"C:/Users\\maczek\\PycharmProjects\\message_data/reporting_output/MESSAGEix-Materials_2.0deg_petro_thesis_2_macro_w_ENGAGE_fixes.xlsx\"\n", - "#path = \"C:/Users\\maczek\\PycharmProjects\\message_data/reporting_output/MESSAGEix-Materials_2.0deg_petro_thesis_2_final_macro.xlsx\"\n", - "#path = \"C:/Users\\maczek\\PycharmProjects\\message_data/reporting_output/MESSAGEix-Materials_2.0deg_no_SDGs_petro_thesis_1_final_macro.xlsx\"\n", - "#path = \"C:/Users\\maczek\\PycharmProjects\\message_data/reporting_output/MESSAGEix-GLOBIOM 1.1-R12-MAGPIE_baseline_MP00BD1BI00.xlsx\"\n", - "#path = \"C:/Users\\maczek\\PycharmProjects\\message_data/reporting_output/MESSAGEix-GLOBIOM 1.1-R12-MAGPIE_600f_MP00BD1BI00.xlsx\"\n", - "#path = \"C:/Users\\maczek\\PycharmProjects\\message_data/reporting_output/MESSAGEix-GLOBIOM 1.1-R12-MAGPIE_baseline_MP00BD0BI78.xlsx\"\n", - "#path = \"C:/Users\\maczek\\PycharmProjects\\message_data/reporting_output/MESSAGEix-GLOBIOM 1.1-R12-MAGPIE_1000f_MP76BD0BI78.xlsx\"\n", - "path = \"C:/Users\\maczek\\PycharmProjects\\message_data/reporting_output/MESSAGEix-GLOBIOM 1.1-R12-MAGPIE_600f_MP00BD0BI78.xlsx\"\n", - "#path = \"C:/Users\\maczek\\PycharmProjects\\message_data/reporting_output/MESSAGEix-GLOBIOM 1.1-R12-MAGPIE_1000f_MP76BD1BI00.xlsx\"\n", - "#path = \"C:/Users\\maczek\\PycharmProjects\\message_data/reporting_output/MESSAGEix-GLOBIOM 1.1-R12-MAGPIE_1000f_MP00BD1BI00.xlsx\"\n", - "#path = \"C:/Users\\maczek\\PycharmProjects\\message_data/reporting_output/MESSAGEix-GLOBIOM 1.1-R12-MAGPIE_600f_MP00BD0BI78.xlsx\"\n", - "#path = \"C:/Users\\maczek\\PycharmProjects\\message_data/reporting_output/MESSAGEix-Materials_NoPolicy_no_SDGs_petro_thesis_1_final_macro.xlsx\"\n", - "dir = \"C:/Users\\maczek\\PycharmProjects\\message_data/reporting_output\"\n", - "file = \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00_new-mapping_with-smoothing_primary-forest-constraint_1000f\"\n", - "file=\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00_new-mapping_no-smoothing_1000f\"\n", - "file = \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00_bugfix_1000f\"\n", - "#file=\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00_new-mapping_with-smoothing_1000f\"\n", - "path= dir+\"/\"+file+\".xlsx\"" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-20T17:48:44.841011500Z", - "start_time": "2023-09-20T17:48:44.746936800Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 1, - "outputs": [], - "source": [ - "#rep_list = ['baseline_MP76BD0BI78', '600f_MP76BD0BI78', 'baseline_MP76BD1BI00', '600f_MP76BD1BI00']\n", - "rep_list = ['1000f_MP76BD1BI00test_scp', '1000f_MP00BD1BI00', '1000f_MP76BD0BI78test_scp']\n", - "#rep_list = ['1000f_MP00BD0BI78']\n", - "# 'baseline_MP76BD0BI70' still to build scp and 600f" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 8, - "outputs": [], - "source": [ - "for scen_name in rep_list:\n", - " path = f\"C:/Users\\maczek\\PycharmProjects\\message_data/reporting_output/MESSAGEix-GLOBIOM 1.1-R12-MAGPIE_{scen_name}.xlsx\"\n", - " prepare_xlsx_for_explorer(path)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 13, - "outputs": [], - "source": [ - "for scen_name in rep_list:\n", - " path = f\"C:/Users\\maczek\\PycharmProjects\\message_data/reporting_output/{scen_name}\"\n", - " prepare_xlsx_for_explorer(path)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 6, - "outputs": [], - "source": [ - "for scen_name in os.listdir(\"H:/pcenv\\message_data/reporting_output\"):\n", - " path = f\"H:/pcenv\\message_data/reporting_output/{scen_name}\"\n", - " prepare_xlsx_for_explorer(path)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 9, - "outputs": [], - "source": [ - "import os\n", - "#import message_data.model.material.util as util\n", - "from pathlib import Path\n", - "\"\"\"util.prepare_xlsx_for_explorer(\n", - " Path(os.getcwd()).parents[0].joinpath(\n", - " \"reporting_output\", scenario.model+\"_\"+scenario.scenario+\".xlsx\"))\"\"\"\n", - "prepare_xlsx_for_explorer(path)\n", - "#prepare_xlsx_for_explorer(\"C:/Users\\maczek\\PycharmProjects\\message_data/reporting_output/MESSAGEix-Materials_2.0deg_petro_thesis_2_macro.xlsx\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-20T17:49:04.450247400Z", - "start_time": "2023-09-20T17:48:46.746948Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 11, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import xlwings as xw\n", - "import openpyxl as pxl\n", - "import xarray" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 12, - "outputs": [], - "source": [ - "#df = pd.read_excel(path)\n", - "#wb = xw.Book(path)\n", - "#sht = wb.sheets[0]\n", - "#for i in wb.sheets:\n", - "# i.range(f\"F2:Y{df.index.size+1}\").api.Replace(None, 0.0)\n", - "#wb.save()\n", - "#wb.close()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 13, - "outputs": [], - "source": [ - "wb = pxl.load_workbook(path)\n", - "ws = wb[\"Sheet1\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 14, - "outputs": [], - "source": [ - "ws.auto_filter.ref = ws.dimensions\n", - "c = ws['B2']\n", - "ws.freeze_panes = c" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 15, - "outputs": [], - "source": [ - "for i in range(70, 76): #iterate from column \"F\" to \"K\"\n", - " ws.column_dimensions[chr(i)].hidden= True\n", - "\n", - "ws.column_dimensions[\"E\"].width = 11\n", - "ws.column_dimensions[\"D\"].width = 71\n", - "wb.save(path)\n", - "wb.close()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 2, - "outputs": [ - { - "data": { - "text/plain": "", - "application/javascript": "if (typeof IPython !== 'undefined') { IPython.OutputArea.prototype._should_scroll = function(lines){ return false; }}" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import ixmp\n", - "import message_ix\n", - "\n", - "mp = ixmp.Platform(\"ixmp_dev\")\n", - "#scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\", \"600f_MP00BD0BI78\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 2, - "outputs": [], - "source": [ - "sc_clone = scen.clone(\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\", \"600f_MP00BD0BI78_test_calib\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 7, - "outputs": [], - "source": [ - "import ixmp\n", - "import message_ix\n", - "\n", - "mp = ixmp.Platform(\"ixmp_dev\")\n", - "scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\", \"baseline_MP00BD0BI78\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 9, - "outputs": [], - "source": [ - "import ixmp\n", - "import message_ix\n", - "\n", - "mp = ixmp.Platform(\"ixmp_dev\")\n", - "scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\", \"600f_MP00BD0BI78_test_calib_macro\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 10, - "outputs": [ - { - "ename": "ValueError", - "evalue": "This Scenario does not have a solution!", - "output_type": "error", - "traceback": [ - "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[1;31mValueError\u001B[0m Traceback (most recent call last)", - "Input \u001B[1;32mIn [10]\u001B[0m, in \u001B[0;36m\u001B[1;34m()\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[43mscen\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mremove_solution\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\ixmp\\core\\scenario.py:757\u001B[0m, in \u001B[0;36mScenario.remove_solution\u001B[1;34m(self, first_model_year)\u001B[0m\n\u001B[0;32m 755\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_backend(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mclear_solution\u001B[39m\u001B[38;5;124m\"\u001B[39m, first_model_year)\n\u001B[0;32m 756\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m--> 757\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mThis Scenario does not have a solution!\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n", - "\u001B[1;31mValueError\u001B[0m: This Scenario does not have a solution!" - ] - } - ], - "source": [ - "scen.remove_solution()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 7, - "outputs": [ - { - "data": { - "text/plain": "'600f_MP00BD0BI78_test_calib'" - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "scen.scenario" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 8, - "outputs": [], - "source": [ - "sc_clone = scen.clone(\"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\", \"baseline_MP00BD0BI78_test_calib\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 9, - "outputs": [], - "source": [ - "sc_clone.remove_solution()\n", - "sc_clone.solve()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 29, - "outputs": [ - { - "data": { - "text/plain": "'baseline_MP00BD0BI78_test_calib_macro'" - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sc_clone.scenario" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 10, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "This Scenario has a solution, use `Scenario.remove_solution()` or `Scenario.clone(..., keep_solution=False)`\n", - "C:\\Users\\maczek\\PycharmProjects\\message_data\\message_data\\model\\material\\data_util.py:100: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " data[parname] = df_gdphist.append(\n", - "\n", - "======================= WARNING =======================\n", - "\n", - "You are using *experimental*, incomplete features from\n", - "`message_ix.macro`—please exercise caution. Read more:\n", - "- https://github.com/iiasa/message_ix/issues/317\n", - "- https://github.com/iiasa/message_ix/issues/318\n", - "- https://github.com/iiasa/message_ix/issues/319\n", - "- https://github.com/iiasa/message_ix/issues/320\n", - "\n", - "======================================================\n", - "\n" - ] - } - ], - "source": [ - "from message_data.model.material.data_util import add_coal_lowerbound_2020, add_macro_COVID\n", - "sc_clone = add_macro_COVID(sc_clone,'R12-CHN-5y_macro_data_NGFS_w_rc_ind_adj_mat.xlsx')" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 11, - "outputs": [], - "source": [ - "sc_clone.solve(model=\"MESSAGE-MACRO\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 30, - "outputs": [ - { - "data": { - "text/plain": "'baseline_MP00BD0BI78'" - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "scen.scenario" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 3, - "outputs": [ - { - "ename": "ModelError", - "evalue": "GAMS errored with return code 3:\n There was an execution error\n\nFor details, see the terminal output above, plus:\nListing : C:\\Users\\maczek\\PycharmProjects\\message_ix\\message_ix\\model\\MESSAGE-MACRO_run.lst\nInput data: C:\\Users\\maczek\\PycharmProjects\\message_ix\\message_ix\\model\\data\\MsgData_MESSAGEix-GLOBIOM_1.1-R12-MAGPIE_600f_MP00BD0BI78.gdx", - "output_type": "error", - "traceback": [ - "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[1;31mModelError\u001B[0m Traceback (most recent call last)", - "Input \u001B[1;32mIn [3]\u001B[0m, in \u001B[0;36m\u001B[1;34m()\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[43mscen\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msolve\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmodel\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mMESSAGE-MACRO\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32m~\\PycharmProjects\\message_ix\\message_ix\\core.py:677\u001B[0m, in \u001B[0;36mScenario.solve\u001B[1;34m(self, model, solve_options, **kwargs)\u001B[0m\n\u001B[0;32m 657\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21msolve\u001B[39m(\u001B[38;5;28mself\u001B[39m, model\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mMESSAGE\u001B[39m\u001B[38;5;124m\"\u001B[39m, solve_options\u001B[38;5;241m=\u001B[39m{}, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[0;32m 658\u001B[0m \u001B[38;5;124;03m\"\"\"Solve MESSAGE or MESSAGE-MACRO for the Scenario.\u001B[39;00m\n\u001B[0;32m 659\u001B[0m \n\u001B[0;32m 660\u001B[0m \u001B[38;5;124;03m By default, :meth:`ixmp.Scenario.solve` is called with 'MESSAGE' as the\u001B[39;00m\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 675\u001B[0m \u001B[38;5;124;03m :class:`.GAMSModel`.\u001B[39;00m\n\u001B[0;32m 676\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[1;32m--> 677\u001B[0m \u001B[38;5;28;43msuper\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msolve\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmodel\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mmodel\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43msolve_options\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43msolve_options\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\ixmp\\core\\scenario.py:835\u001B[0m, in \u001B[0;36mScenario.solve\u001B[1;34m(self, model, callback, cb_kwargs, **model_options)\u001B[0m\n\u001B[0;32m 833\u001B[0m \u001B[38;5;66;03m# Iterate until convergence\u001B[39;00m\n\u001B[0;32m 834\u001B[0m \u001B[38;5;28;01mwhile\u001B[39;00m \u001B[38;5;28;01mTrue\u001B[39;00m:\n\u001B[1;32m--> 835\u001B[0m \u001B[43mmodel_obj\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrun\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[0;32m 837\u001B[0m \u001B[38;5;66;03m# Store an iteration number to help the callback\u001B[39;00m\n\u001B[0;32m 838\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28mhasattr\u001B[39m(\u001B[38;5;28mself\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124miteration\u001B[39m\u001B[38;5;124m\"\u001B[39m):\n", - "File \u001B[1;32m~\\PycharmProjects\\message_ix\\message_ix\\models.py:373\u001B[0m, in \u001B[0;36mGAMSModel.run\u001B[1;34m(self, scenario)\u001B[0m\n\u001B[0;32m 370\u001B[0m lines2 \u001B[38;5;241m=\u001B[39m (\u001B[38;5;124m\"\u001B[39m\u001B[38;5;132;01m{}\u001B[39;00m\u001B[38;5;124m = \u001B[39m\u001B[38;5;132;01m{}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;241m.\u001B[39mformat(\u001B[38;5;241m*\u001B[39mkv) \u001B[38;5;28;01mfor\u001B[39;00m kv \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcplex_opts\u001B[38;5;241m.\u001B[39mitems())\n\u001B[0;32m 371\u001B[0m optfile2\u001B[38;5;241m.\u001B[39mwrite_text(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;130;01m\\n\u001B[39;00m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;241m.\u001B[39mjoin(lines2))\n\u001B[1;32m--> 373\u001B[0m result \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43msuper\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrun\u001B[49m\u001B[43m(\u001B[49m\u001B[43mscenario\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 375\u001B[0m \u001B[38;5;66;03m# In previous versions, the `cplex.opt` file(s) were removed at this point\u001B[39;00m\n\u001B[0;32m 376\u001B[0m \u001B[38;5;66;03m# in the workflow. This has been removed due to issues when running\u001B[39;00m\n\u001B[0;32m 377\u001B[0m \u001B[38;5;66;03m# scenarios asynchronously.\u001B[39;00m\n\u001B[0;32m 379\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m result\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\ixmp\\model\\gams.py:296\u001B[0m, in \u001B[0;36mGAMSModel.run\u001B[1;34m(self, scenario)\u001B[0m\n\u001B[0;32m 293\u001B[0m check_call(command, shell\u001B[38;5;241m=\u001B[39mos\u001B[38;5;241m.\u001B[39mname \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mnt\u001B[39m\u001B[38;5;124m\"\u001B[39m, cwd\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcwd)\n\u001B[0;32m 294\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m CalledProcessError \u001B[38;5;28;01mas\u001B[39;00m exc:\n\u001B[0;32m 295\u001B[0m \u001B[38;5;66;03m# Do not remove self.temp_dir; the user may want to inspect the GDX file\u001B[39;00m\n\u001B[1;32m--> 296\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mformat_exception(exc, model_file) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;28mNone\u001B[39m\n\u001B[0;32m 298\u001B[0m \u001B[38;5;66;03m# Read model solution\u001B[39;00m\n\u001B[0;32m 299\u001B[0m scenario\u001B[38;5;241m.\u001B[39mplatform\u001B[38;5;241m.\u001B[39m_backend\u001B[38;5;241m.\u001B[39mread_file(\n\u001B[0;32m 300\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mout_file,\n\u001B[0;32m 301\u001B[0m ItemType\u001B[38;5;241m.\u001B[39mMODEL,\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 306\u001B[0m var_list\u001B[38;5;241m=\u001B[39mas_str_list(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mvar_list) \u001B[38;5;129;01mor\u001B[39;00m [],\n\u001B[0;32m 307\u001B[0m )\n", - "\u001B[1;31mModelError\u001B[0m: GAMS errored with return code 3:\n There was an execution error\n\nFor details, see the terminal output above, plus:\nListing : C:\\Users\\maczek\\PycharmProjects\\message_ix\\message_ix\\model\\MESSAGE-MACRO_run.lst\nInput data: C:\\Users\\maczek\\PycharmProjects\\message_ix\\message_ix\\model\\data\\MsgData_MESSAGEix-GLOBIOM_1.1-R12-MAGPIE_600f_MP00BD0BI78.gdx" - ] - } - ], - "source": [ - "scen.solve(model=\"MESSAGE-MACRO\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 5, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "processing Table: Resource|Extraction\n", - "processing Table: Resource|Cumulative Extraction\n", - "processing Table: Primary Energy\n", - "processing Table: Primary Energy (substitution method)\n", - "processing Table: Final Energy\n", - "processing Table: Secondary Energy|Electricity\n", - "processing Table: Secondary Energy|Heat\n", - "processing Table: Secondary Energy\n", - "processing Table: Secondary Energy|Gases\n", - "processing Table: Secondary Energy|Solids\n", - "processing Table: Emissions|CO2\n", - "processing Table: Carbon Sequestration\n", - "processing Table: Emissions|BC\n", - "processing Table: Emissions|OC\n", - "processing Table: Emissions|CO\n", - "processing Table: Emissions|N2O\n", - "processing Table: Emissions|CH4\n", - "processing Table: Emissions|NH3\n", - "processing Table: Emissions|Sulfur\n", - "processing Table: Emissions|NOx\n", - "processing Table: Emissions|VOC\n", - "processing Table: Emissions|HFC\n", - "processing Table: Emissions\n", - "processing Table: Emissions\n", - "processing Table: Agricultural Demand\n", - "processing Table: Agricultural Production\n", - "processing Table: Fertilizer Use\n", - "processing Table: Fertilizer\n", - "processing Table: Food Waste\n", - "processing Table: Food Demand\n", - "processing Table: Forestry Demand\n", - "processing Table: Forestry Production\n", - "processing Table: Land Cover\n", - "processing Table: Yield\n", - "processing Table: Capacity\n", - "processing Table: Capacity Additions\n", - "processing Table: Cumulative Capacity\n", - "processing Table: Capital Cost\n", - "processing Table: OM Cost|Fixed\n", - "processing Table: OM Cost|Variable\n", - "processing Table: Lifetime\n", - "processing Table: Efficiency\n", - "processing Table: Population\n", - "processing Table: Price\n", - "processing Table: Useful Energy\n", - "processing Table: Useful Energy\n", - "processing Table: Trade\n", - "processing Table: Investment|Energy Supply\n", - "processing Table: Water Consumption\n", - "processing Table: Water Withdrawal\n", - "processing Table: GDP\n", - "processing Table: Cost\n", - "processing Table: GLOBIOM Feedback\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "no emissions included\n", - "Starting to upload timeseries\n", - " region variable unit 2025 \\\n", - "0 R12_AFR Resource|Extraction EJ/yr 21.705672 \n", - "1 R12_AFR Resource|Extraction|Coal EJ/yr 5.802705 \n", - "2 R12_AFR Resource|Extraction|Gas EJ/yr 6.834491 \n", - "3 R12_AFR Resource|Extraction|Gas|Conventional EJ/yr 6.834491 \n", - "4 R12_AFR Resource|Extraction|Gas|Unconventional EJ/yr 0.000000 \n", - "\n", - " 2030 2035 2040 2045 2050 2055 \\\n", - "0 18.782491 18.141649 18.134179 18.305262 18.455276 20.166486 \n", - "1 0.925383 0.796329 0.050280 0.024543 0.014460 0.022022 \n", - "2 9.044117 10.318515 12.553804 14.147722 15.378979 17.854765 \n", - "3 9.044117 10.318515 12.553804 14.147722 15.378979 17.854765 \n", - "4 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "\n", - " 2060 2070 2080 2090 2100 2110 \n", - "0 19.231046 18.867719 10.906651 6.138375 3.398055 2.379544 \n", - "1 0.042275 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "2 17.285903 18.014806 10.663254 6.138375 3.398055 2.379544 \n", - "3 17.285903 18.014806 3.928724 1.153387 0.848893 0.624785 \n", - "4 0.000000 0.000000 6.734530 4.984987 2.549162 1.754759 \n", - "Finished uploading timeseries\n" - ] - } - ], - "source": [ - "from message_data.tools.post_processing.iamc_report_hackathon import report as reporting\n", - "\n", - "for scen_name in rep_list[2:]:\n", - " import ixmp\n", - " import message_ix\n", - " mp = ixmp.Platform(\"ixmp_dev\")\n", - " scen = message_ix.Scenario(mp, model, scen_name)\n", - " reporting(\n", - " mp,\n", - " scen,\n", - " # NB(PNK) this is not an error; .iamc_report_hackathon.report() expects a\n", - " # string containing \"True\" or \"False\" instead of an actual bool.\n", - " \"False\",\n", - " scen.model,\n", - " scen.scenario,\n", - " merge_hist=True,\n", - " merge_ts=True,\n", - " #run_config=\"materials_run_config.yaml\",\n", - " )\n", - " mp.close_db()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 4, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "processing Table: Resource|Extraction\n", - "processing Table: Resource|Cumulative Extraction\n", - "processing Table: Primary Energy\n", - "processing Table: Primary Energy (substitution method)\n", - "processing Table: Final Energy\n", - "processing Table: Secondary Energy|Electricity\n", - "processing Table: Secondary Energy|Heat\n", - "processing Table: Secondary Energy\n", - "processing Table: Secondary Energy|Gases\n", - "processing Table: Secondary Energy|Solids\n", - "processing Table: Emissions|CO2\n", - "processing Table: Carbon Sequestration\n", - "processing Table: Emissions|BC\n", - "processing Table: Emissions|OC\n", - "processing Table: Emissions|CO\n", - "processing Table: Emissions|N2O\n", - "processing Table: Emissions|CH4\n", - "processing Table: Emissions|NH3\n", - "processing Table: Emissions|Sulfur\n", - "processing Table: Emissions|NOx\n", - "processing Table: Emissions|VOC\n", - "processing Table: Emissions|HFC\n", - "processing Table: Emissions\n", - "processing Table: Emissions\n", - "processing Table: Agricultural Demand\n", - "processing Table: Agricultural Production\n", - "processing Table: Fertilizer Use\n", - "processing Table: Fertilizer\n", - "processing Table: Food Waste\n", - "processing Table: Food Demand\n", - "processing Table: Forestry Demand\n", - "processing Table: Forestry Production\n", - "processing Table: Land Cover\n", - "processing Table: Yield\n", - "processing Table: Capacity\n", - "processing Table: Capacity Additions\n", - "processing Table: Cumulative Capacity\n", - "processing Table: Capital Cost\n", - "processing Table: OM Cost|Fixed\n", - "processing Table: OM Cost|Variable\n", - "processing Table: Lifetime\n", - "processing Table: Efficiency\n", - "processing Table: Population\n", - "processing Table: Price\n", - "processing Table: Useful Energy\n", - "processing Table: Useful Energy\n", - "processing Table: Trade\n", - "processing Table: Investment|Energy Supply\n", - "processing Table: Water Consumption\n", - "processing Table: Water Withdrawal\n", - "processing Table: GDP\n", - "processing Table: Cost\n", - "processing Table: GLOBIOM Feedback\n", - "Starting to upload timeseries\n", - " region variable unit 2025 \\\n", - "0 R12_AFR Resource|Extraction EJ/yr 18.425755 \n", - "1 R12_AFR Resource|Extraction|Coal EJ/yr 2.374063 \n", - "2 R12_AFR Resource|Extraction|Gas EJ/yr 7.104881 \n", - "3 R12_AFR Resource|Extraction|Gas|Conventional EJ/yr 7.104881 \n", - "4 R12_AFR Resource|Extraction|Gas|Unconventional EJ/yr 0.000000 \n", - "\n", - " 2030 2035 2040 2045 2050 2055 2060 \\\n", - "0 13.297529 9.323047 6.774340 4.936031 3.528415e+00 2.43978 1.589079 \n", - "1 1.158082 0.216647 0.018782 0.000170 3.322613e-07 0.00000 0.000000 \n", - "2 5.350509 3.993009 2.942602 2.131803 1.506807e+00 1.02482 0.644342 \n", - "3 5.350509 3.993009 2.942602 2.131803 1.506807e+00 1.02482 0.644342 \n", - "4 0.000000 0.000000 0.000000 0.000000 0.000000e+00 0.00000 0.000000 \n", - "\n", - " 2070 2080 2090 2100 2110 \n", - "0 0.417925 0.00286 0.005642 0.01113 0.021957 \n", - "1 0.000000 0.00000 0.000000 0.00000 0.000000 \n", - "2 0.122128 0.00000 0.000000 0.00000 0.000000 \n", - "3 0.122128 0.00000 0.000000 0.00000 0.000000 \n", - "4 0.000000 0.00000 0.000000 0.00000 0.000000 \n", - "Finished uploading timeseries\n" - ] - } - ], - "source": [ - "# report baseline\n", - "from message_data.tools.post_processing.iamc_report_hackathon import report as reporting\n", - "reporting(\n", - " mp,\n", - " scen,\n", - " # NB(PNK) this is not an error; .iamc_report_hackathon.report() expects a\n", - " # string containing \"True\" or \"False\" instead of an actual bool.\n", - " \"False\",\n", - " scen.model,\n", - " scen.scenario,\n", - " merge_hist=True,\n", - " merge_ts=True,\n", - " #run_config=\"materials_run_config.yaml\",\n", - ")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 14, - "outputs": [ - { - "data": { - "text/plain": "True" - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "scen.has_solution()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 14, - "outputs": [], - "source": [ - "scen_name = \"600f_MP00BD1BI00\"" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 4, - "outputs": [], - "source": [ - "model = \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\"" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 9, - "outputs": [ - { - "data": { - "text/plain": " model \\\n6871 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6870 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6891 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00 \n6892 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00 \n6893 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE_MP00BD1BI00 \n6878 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6888 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6876 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6877 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6887 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6886 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6869 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6885 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6882 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6881 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6874 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6873 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6884 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6872 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6880 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6875 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6883 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6890 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6889 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n6879 MESSAGEix-GLOBIOM 1.1-R12-MAGPIE \n\n scenario scheme is_default is_locked \\\n6871 1000f_MP76BD1BI00test_scp MESSAGE 1 0 \n6870 1000f_MP76BD0BI78test_scp MESSAGE 1 0 \n6891 NPi2020-con-prim-dir-ncr MESSAGE 1 0 \n6892 baseline MESSAGE 1 0 \n6893 baseline MESSAGE 1 0 \n6878 600f_MP76BD1BI00test_scp MESSAGE 1 0 \n6888 baseline_MP76BD1BI00test_scp MESSAGE 1 0 \n6876 600f_MP76BD0BI78test_scp MESSAGE 1 0 \n6877 600f_MP76BD1BI00 MESSAGE 1 0 \n6887 baseline_MP76BD1BI00 MESSAGE 1 0 \n6886 baseline_MP76BD0BI78test_scp MESSAGE 1 0 \n6869 1000f_MP00BD0BI78_test_calib_macro MESSAGE 1 0 \n6885 baseline_MP76BD0BI78 MESSAGE 1 0 \n6882 baseline_MP00BD0BI78_test_calib_macro MESSAGE 1 0 \n6881 baseline_MP00BD0BI78_test_calib MESSAGE 1 0 \n6874 600f_MP00BD0BI78_test_calib_macro MESSAGE 1 0 \n6873 600f_MP00BD0BI78_test_calib MESSAGE 1 0 \n6884 baseline_MP76BD0BI70 MESSAGE 1 0 \n6872 600f_MP00BD0BI78 MESSAGE 1 0 \n6880 baseline_MP00BD0BI78 MESSAGE 1 0 \n6875 600f_MP00BD1BI00 MESSAGE 1 0 \n6883 baseline_MP00BD1BI00 MESSAGE 1 0 \n6890 baseline_w/o_LU MESSAGE 1 0 \n6889 baseline_cum_LU_emis MESSAGE 1 0 \n6879 baseline MESSAGE 1 0 \n\n cre_user cre_date upd_user \\\n6871 maczek 2023-08-16 14:43:19.000000 maczek \n6870 maczek 2023-08-16 14:04:03.000000 maczek \n6891 maczek 2023-08-14 13:21:06.000000 None \n6892 maczek 2023-08-14 13:08:04.000000 maczek \n6893 maczek 2023-08-14 13:07:05.000000 None \n6878 maczek 2023-08-09 17:27:58.000000 maczek \n6888 maczek 2023-08-09 16:57:44.000000 maczek \n6876 maczek 2023-08-09 16:27:09.000000 maczek \n6877 maczek 2023-08-09 16:12:11.000000 None \n6887 maczek 2023-08-09 15:20:37.000000 None \n6886 maczek 2023-08-09 15:00:57.000000 maczek \n6869 maczek 2023-08-09 13:45:52.000000 maczek \n6885 maczek 2023-08-09 09:56:43.000000 None \n6882 maczek 2023-08-09 09:37:24.000000 maczek \n6881 maczek 2023-08-09 09:30:09.000000 maczek \n6874 maczek 2023-08-09 09:23:31.000000 None \n6873 maczek 2023-08-09 09:16:15.000000 maczek \n6884 maczek 2023-08-09 09:10:13.000000 None \n6872 maczek 2023-08-08 15:52:17.000000 maczek \n6880 maczek 2023-08-08 13:39:45.000000 maczek \n6875 maczek 2023-08-08 11:53:14.000000 maczek \n6883 maczek 2023-08-08 10:17:35.000000 maczek \n6890 maczek 2023-08-08 09:50:56.000000 None \n6889 maczek 2023-08-06 20:02:21.000000 maczek \n6879 maczek 2023-08-03 11:49:57.000000 maczek \n\n upd_date lock_user lock_date \\\n6871 2023-08-16 15:24:22.000000 None None \n6870 2023-08-16 14:38:21.000000 None None \n6891 None None None \n6892 2023-08-16 14:22:47.000000 None None \n6893 None None None \n6878 2023-08-09 18:00:37.000000 None None \n6888 2023-08-09 17:26:01.000000 None None \n6876 2023-08-09 16:53:19.000000 None None \n6877 None None None \n6887 None None None \n6886 2023-08-09 15:59:13.000000 None None \n6869 2023-08-09 14:12:46.000000 None None \n6885 None None None \n6882 2023-08-09 09:47:10.000000 None None \n6881 2023-08-09 09:35:09.000000 None None \n6874 None None None \n6873 2023-08-09 09:22:45.000000 None None \n6884 None None None \n6872 2023-08-08 16:31:06.000000 None None \n6880 2023-08-08 15:37:34.000000 None None \n6875 2023-08-08 12:22:46.000000 None None \n6883 2023-08-08 11:52:04.000000 None None \n6890 None None None \n6889 2023-08-06 20:05:37.000000 None None \n6879 2023-08-04 16:29:36.000000 None None \n\n annotation version \n6871 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6870 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6891 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6892 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6893 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6878 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6888 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6876 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6877 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6887 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6886 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6869 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6885 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6882 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6881 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6874 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6873 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6884 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6872 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6880 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6875 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 2 \n6883 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6890 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6889 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n6879 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 2 ", - "text/html": "
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modelscenarioschemeis_defaultis_lockedcre_usercre_dateupd_userupd_datelock_userlock_dateannotationversion
6871MESSAGEix-GLOBIOM 1.1-R12-MAGPIE1000f_MP76BD1BI00test_scpMESSAGE10maczek2023-08-16 14:43:19.000000maczek2023-08-16 15:24:22.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6870MESSAGEix-GLOBIOM 1.1-R12-MAGPIE1000f_MP76BD0BI78test_scpMESSAGE10maczek2023-08-16 14:04:03.000000maczek2023-08-16 14:38:21.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6891MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00NPi2020-con-prim-dir-ncrMESSAGE10maczek2023-08-14 13:21:06.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6892MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00baselineMESSAGE10maczek2023-08-14 13:08:04.000000maczek2023-08-16 14:22:47.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6893MESSAGEix-GLOBIOM 1.1-R12-MAGPIE_MP00BD1BI00baselineMESSAGE10maczek2023-08-14 13:07:05.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6878MESSAGEix-GLOBIOM 1.1-R12-MAGPIE600f_MP76BD1BI00test_scpMESSAGE10maczek2023-08-09 17:27:58.000000maczek2023-08-09 18:00:37.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6888MESSAGEix-GLOBIOM 1.1-R12-MAGPIEbaseline_MP76BD1BI00test_scpMESSAGE10maczek2023-08-09 16:57:44.000000maczek2023-08-09 17:26:01.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6876MESSAGEix-GLOBIOM 1.1-R12-MAGPIE600f_MP76BD0BI78test_scpMESSAGE10maczek2023-08-09 16:27:09.000000maczek2023-08-09 16:53:19.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6877MESSAGEix-GLOBIOM 1.1-R12-MAGPIE600f_MP76BD1BI00MESSAGE10maczek2023-08-09 16:12:11.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6887MESSAGEix-GLOBIOM 1.1-R12-MAGPIEbaseline_MP76BD1BI00MESSAGE10maczek2023-08-09 15:20:37.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6886MESSAGEix-GLOBIOM 1.1-R12-MAGPIEbaseline_MP76BD0BI78test_scpMESSAGE10maczek2023-08-09 15:00:57.000000maczek2023-08-09 15:59:13.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6869MESSAGEix-GLOBIOM 1.1-R12-MAGPIE1000f_MP00BD0BI78_test_calib_macroMESSAGE10maczek2023-08-09 13:45:52.000000maczek2023-08-09 14:12:46.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6885MESSAGEix-GLOBIOM 1.1-R12-MAGPIEbaseline_MP76BD0BI78MESSAGE10maczek2023-08-09 09:56:43.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6882MESSAGEix-GLOBIOM 1.1-R12-MAGPIEbaseline_MP00BD0BI78_test_calib_macroMESSAGE10maczek2023-08-09 09:37:24.000000maczek2023-08-09 09:47:10.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6881MESSAGEix-GLOBIOM 1.1-R12-MAGPIEbaseline_MP00BD0BI78_test_calibMESSAGE10maczek2023-08-09 09:30:09.000000maczek2023-08-09 09:35:09.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6874MESSAGEix-GLOBIOM 1.1-R12-MAGPIE600f_MP00BD0BI78_test_calib_macroMESSAGE10maczek2023-08-09 09:23:31.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6873MESSAGEix-GLOBIOM 1.1-R12-MAGPIE600f_MP00BD0BI78_test_calibMESSAGE10maczek2023-08-09 09:16:15.000000maczek2023-08-09 09:22:45.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6884MESSAGEix-GLOBIOM 1.1-R12-MAGPIEbaseline_MP76BD0BI70MESSAGE10maczek2023-08-09 09:10:13.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6872MESSAGEix-GLOBIOM 1.1-R12-MAGPIE600f_MP00BD0BI78MESSAGE10maczek2023-08-08 15:52:17.000000maczek2023-08-08 16:31:06.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6880MESSAGEix-GLOBIOM 1.1-R12-MAGPIEbaseline_MP00BD0BI78MESSAGE10maczek2023-08-08 13:39:45.000000maczek2023-08-08 15:37:34.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6875MESSAGEix-GLOBIOM 1.1-R12-MAGPIE600f_MP00BD1BI00MESSAGE10maczek2023-08-08 11:53:14.000000maczek2023-08-08 12:22:46.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...2
6883MESSAGEix-GLOBIOM 1.1-R12-MAGPIEbaseline_MP00BD1BI00MESSAGE10maczek2023-08-08 10:17:35.000000maczek2023-08-08 11:52:04.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6890MESSAGEix-GLOBIOM 1.1-R12-MAGPIEbaseline_w/o_LUMESSAGE10maczek2023-08-08 09:50:56.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6889MESSAGEix-GLOBIOM 1.1-R12-MAGPIEbaseline_cum_LU_emisMESSAGE10maczek2023-08-06 20:02:21.000000maczek2023-08-06 20:05:37.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
6879MESSAGEix-GLOBIOM 1.1-R12-MAGPIEbaselineMESSAGE10maczek2023-08-03 11:49:57.000000maczek2023-08-04 16:29:36.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...2
\n
" - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = mp.scenario_list()\n", - "df[df[\"model\"].str.contains(\"R12-MAGPIE\")].sort_values(\"cre_date\", ascending=False)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 15, - "outputs": [], - "source": [ - "scen = message_ix.Scenario(mp, model, scen_name)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 16, - "outputs": [], - "source": [ - "sc_clone = scen" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## create budget scenario" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 18, - "outputs": [], - "source": [ - "mode = \"clone\"\n", - "#mode = \"load\"\n", - "\n", - "budget = \"1000f\"\n", - "#budget = \"600f\"" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 19, - "outputs": [ - { - "data": { - "text/plain": "'1000f_MP00BD1BI00'" - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "scen.scenario.replace(\"600f\",budget)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 20, - "outputs": [], - "source": [ - "if mode == \"clone\":\n", - " scen_budget = sc_clone.clone(model=sc_clone.model, scenario=sc_clone.scenario.replace(\"600f\",budget), keep_solution=False)\n", - "else:\n", - " scen_budget = message_ix.Scenario(mp, model=\"ENGAGE-MAGPIE_SSP2_v4.1.8.3_T4.5_r3.1\", scenario=budget)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 21, - "outputs": [ - { - "data": { - "text/plain": "'1000f_MP00BD1BI00'" - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "scen_budget.scenario" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 22, - "outputs": [ - { - "data": { - "text/plain": " type_year year\n0 cumulative 2025\n1 cumulative 2030\n2 cumulative 2035\n3 cumulative 2040\n4 cumulative 2045\n5 cumulative 2050\n6 cumulative 2055\n7 cumulative 2060\n8 cumulative 2070\n9 cumulative 2080\n10 cumulative 2090\n11 cumulative 2100\n12 cumulative 2110", - "text/html": "
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type_yearyear
0cumulative2025
1cumulative2030
2cumulative2035
3cumulative2040
4cumulative2045
5cumulative2050
6cumulative2055
7cumulative2060
8cumulative2070
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10cumulative2090
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" - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "extra = scen_budget.set(\"cat_year\", {\"type_year\": \"cumulative\", \"year\": [2020, 2015, 2010]})\n", - "scen_budget.check_out()\n", - "scen_budget.remove_set(\"cat_year\", extra)\n", - "scen_budget.commit(\"remove cumulative years from cat_year set\")\n", - "scen_budget.set(\"cat_year\", {\"type_year\": \"cumulative\"})" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 23, - "outputs": [ - { - "data": { - "text/plain": " node type_emission type_tec type_year value unit\n0 World TCE all cumulative 4046.0 ???", - "text/html": "
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nodetype_emissiontype_tectype_yearvalueunit
0WorldTCEallcumulative4046.0???
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" - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "emission_dict = {\n", - " \"node\": \"World\",\n", - " \"type_emission\": \"TCE\",\n", - " \"type_tec\": \"all\",\n", - " \"type_year\": \"cumulative\",\n", - " \"unit\": \"???\",\n", - " }\n", - "if budget == \"1000f\":\n", - " value = 4046\n", - "if budget == \"600f\":\n", - " value = 2605\n", - "\n", - "df = message_ix.make_df(\n", - " \"bound_emission\", value=value, **emission_dict\n", - ")\n", - "scen_budget.check_out()\n", - "scen_budget.add_par(\"bound_emission\", df)\n", - "scen_budget.commit(\"add emission bound\")\n", - "scen_budget.par(\"bound_emission\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 24, - "outputs": [], - "source": [ - "scen_budget.solve(model=\"MESSAGE-MACRO\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 12, - "outputs": [], - "source": [ - "scen.check_out()\n", - "scen.add_set(\"balance_equality\", (\"i_feed\", \"useful\"))" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 14, - "outputs": [], - "source": [ - "scen.commit(\"add i_feed to balance_equality\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 17, - "outputs": [ - { - "data": { - "text/plain": " commodity level\n0 BIO01GHG000 LU\n1 BIO02GHG000 LU\n2 BIO03GHG000 LU\n3 BIO04GHG000 LU\n4 BIO05GHG000 LU\n.. ... ...\n128 BIO60GHG2000 LU\n129 BIO60GHG3000 LU\n130 BIO60GHG400 LU\n131 BIO60GHG600 LU\n132 i_feed useful\n\n[133 rows x 2 columns]", - "text/html": "
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132i_feeduseful
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" - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "scen.set(\"balance_equality\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 18, - "outputs": [ - { - "ename": "ModelError", - "evalue": "GAMS errored with return code 3:\n There was an execution error\n\nFor details, see the terminal output above, plus:\nListing : C:\\Users\\maczek\\PycharmProjects\\message_ix\\message_ix\\model\\MESSAGE-MACRO_run.lst\nInput data: C:\\Users\\maczek\\PycharmProjects\\message_ix\\message_ix\\model\\data\\MsgData_MESSAGEix-GLOBIOM_1.1-R12-MAGPIE_600f_MP00BD0BI78_test_calib_macro.gdx", - "output_type": "error", - "traceback": [ - "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[1;31mModelError\u001B[0m Traceback (most recent call last)", - "Input \u001B[1;32mIn [18]\u001B[0m, in \u001B[0;36m\u001B[1;34m()\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[43mscen\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msolve\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmodel\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mMESSAGE-MACRO\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43msolve_options\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m{\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mscaind\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m:\u001B[49m\u001B[38;5;241;43m-\u001B[39;49m\u001B[38;5;241;43m1\u001B[39;49m\u001B[43m}\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32m~\\PycharmProjects\\message_ix\\message_ix\\core.py:677\u001B[0m, in \u001B[0;36mScenario.solve\u001B[1;34m(self, model, solve_options, **kwargs)\u001B[0m\n\u001B[0;32m 657\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21msolve\u001B[39m(\u001B[38;5;28mself\u001B[39m, model\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mMESSAGE\u001B[39m\u001B[38;5;124m\"\u001B[39m, solve_options\u001B[38;5;241m=\u001B[39m{}, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[0;32m 658\u001B[0m \u001B[38;5;124;03m\"\"\"Solve MESSAGE or MESSAGE-MACRO for the Scenario.\u001B[39;00m\n\u001B[0;32m 659\u001B[0m \n\u001B[0;32m 660\u001B[0m \u001B[38;5;124;03m By default, :meth:`ixmp.Scenario.solve` is called with 'MESSAGE' as the\u001B[39;00m\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 675\u001B[0m \u001B[38;5;124;03m :class:`.GAMSModel`.\u001B[39;00m\n\u001B[0;32m 676\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[1;32m--> 677\u001B[0m \u001B[38;5;28;43msuper\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msolve\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmodel\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mmodel\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43msolve_options\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43msolve_options\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\ixmp\\core\\scenario.py:835\u001B[0m, in \u001B[0;36mScenario.solve\u001B[1;34m(self, model, callback, cb_kwargs, **model_options)\u001B[0m\n\u001B[0;32m 833\u001B[0m \u001B[38;5;66;03m# Iterate until convergence\u001B[39;00m\n\u001B[0;32m 834\u001B[0m \u001B[38;5;28;01mwhile\u001B[39;00m \u001B[38;5;28;01mTrue\u001B[39;00m:\n\u001B[1;32m--> 835\u001B[0m \u001B[43mmodel_obj\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrun\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[0;32m 837\u001B[0m \u001B[38;5;66;03m# Store an iteration number to help the callback\u001B[39;00m\n\u001B[0;32m 838\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28mhasattr\u001B[39m(\u001B[38;5;28mself\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124miteration\u001B[39m\u001B[38;5;124m\"\u001B[39m):\n", - "File \u001B[1;32m~\\PycharmProjects\\message_ix\\message_ix\\models.py:373\u001B[0m, in \u001B[0;36mGAMSModel.run\u001B[1;34m(self, scenario)\u001B[0m\n\u001B[0;32m 370\u001B[0m lines2 \u001B[38;5;241m=\u001B[39m (\u001B[38;5;124m\"\u001B[39m\u001B[38;5;132;01m{}\u001B[39;00m\u001B[38;5;124m = \u001B[39m\u001B[38;5;132;01m{}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;241m.\u001B[39mformat(\u001B[38;5;241m*\u001B[39mkv) \u001B[38;5;28;01mfor\u001B[39;00m kv \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcplex_opts\u001B[38;5;241m.\u001B[39mitems())\n\u001B[0;32m 371\u001B[0m optfile2\u001B[38;5;241m.\u001B[39mwrite_text(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;130;01m\\n\u001B[39;00m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;241m.\u001B[39mjoin(lines2))\n\u001B[1;32m--> 373\u001B[0m result \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43msuper\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrun\u001B[49m\u001B[43m(\u001B[49m\u001B[43mscenario\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 375\u001B[0m \u001B[38;5;66;03m# In previous versions, the `cplex.opt` file(s) were removed at this point\u001B[39;00m\n\u001B[0;32m 376\u001B[0m \u001B[38;5;66;03m# in the workflow. This has been removed due to issues when running\u001B[39;00m\n\u001B[0;32m 377\u001B[0m \u001B[38;5;66;03m# scenarios asynchronously.\u001B[39;00m\n\u001B[0;32m 379\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m result\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\ixmp\\model\\gams.py:296\u001B[0m, in \u001B[0;36mGAMSModel.run\u001B[1;34m(self, scenario)\u001B[0m\n\u001B[0;32m 293\u001B[0m check_call(command, shell\u001B[38;5;241m=\u001B[39mos\u001B[38;5;241m.\u001B[39mname \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mnt\u001B[39m\u001B[38;5;124m\"\u001B[39m, cwd\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcwd)\n\u001B[0;32m 294\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m CalledProcessError \u001B[38;5;28;01mas\u001B[39;00m exc:\n\u001B[0;32m 295\u001B[0m \u001B[38;5;66;03m# Do not remove self.temp_dir; the user may want to inspect the GDX file\u001B[39;00m\n\u001B[1;32m--> 296\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mformat_exception(exc, model_file) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;28mNone\u001B[39m\n\u001B[0;32m 298\u001B[0m \u001B[38;5;66;03m# Read model solution\u001B[39;00m\n\u001B[0;32m 299\u001B[0m scenario\u001B[38;5;241m.\u001B[39mplatform\u001B[38;5;241m.\u001B[39m_backend\u001B[38;5;241m.\u001B[39mread_file(\n\u001B[0;32m 300\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mout_file,\n\u001B[0;32m 301\u001B[0m ItemType\u001B[38;5;241m.\u001B[39mMODEL,\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 306\u001B[0m var_list\u001B[38;5;241m=\u001B[39mas_str_list(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mvar_list) \u001B[38;5;129;01mor\u001B[39;00m [],\n\u001B[0;32m 307\u001B[0m )\n", - "\u001B[1;31mModelError\u001B[0m: GAMS errored with return code 3:\n There was an execution error\n\nFor details, see the terminal output above, plus:\nListing : C:\\Users\\maczek\\PycharmProjects\\message_ix\\message_ix\\model\\MESSAGE-MACRO_run.lst\nInput data: C:\\Users\\maczek\\PycharmProjects\\message_ix\\message_ix\\model\\data\\MsgData_MESSAGEix-GLOBIOM_1.1-R12-MAGPIE_600f_MP00BD0BI78_test_calib_macro.gdx" - ] - } - ], - "source": [ - "scen.solve(model=\"MESSAGE-MACRO\", solve_options={\"scaind\":-1})" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 21, - "outputs": [ - { - "data": { - "text/plain": "'1000f_MP00BD0BI78_test_calib_macro'" - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "scen.scenario.replace(\"6\", \"10\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 22, - "outputs": [], - "source": [ - "sc_clone = scen.clone(scen.model, scen.scenario.replace(\"6\", \"10\"))" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 23, - "outputs": [], - "source": [ - "scen_budget = sc_clone" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 25, - "outputs": [], - "source": [ - "df = scen_budget.par(\"bound_emission\")\n", - "scen_budget.check_out()\n", - "scen_budget.remove_par(\"bound_emission\", df)\n", - "scen_budget.commit(\"remove emission bound\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 26, - "outputs": [], - "source": [ - "budget = \"1000f\"" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 27, - "outputs": [ - { - "data": { - "text/plain": " node type_emission type_tec type_year value unit\n0 World TCE all cumulative 4046.0 ???", - "text/html": "
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nodetype_emissiontype_tectype_yearvalueunit
0WorldTCEallcumulative4046.0???
\n
" - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "3953\n", - "emission_dict = {\n", - " \"node\": \"World\",\n", - " \"type_emission\": \"TCE\",\n", - " \"type_tec\": \"all\",\n", - " \"type_year\": \"cumulative\",\n", - " \"unit\": \"???\",\n", - "}\n", - "if budget == \"1000f\":\n", - " value = 4046\n", - "if budget == \"600f\":\n", - " value = 2605\n", - "\n", - "df = message_ix.make_df(\n", - " \"bound_emission\", value=value, **emission_dict\n", - ")\n", - "scen_budget.check_out()\n", - "scen_budget.add_par(\"bound_emission\", df)\n", - "scen_budget.commit(\"add emission bound\")\n", - "scen_budget.par(\"bound_emission\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 29, - "outputs": [], - "source": [ - "scen_budget.solve(model=\"MESSAGE-MACRO\", solve_options={\"scaind\":-1})" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 1, - "outputs": [ - { - "data": { - "text/plain": "", - "application/javascript": "if (typeof IPython !== 'undefined') { IPython.OutputArea.prototype._should_scroll = function(lines){ return false; }}" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import ixmp\n", - "import message_ix\n", - "\n", - "mp = ixmp.Platform(\"ixmp_dev\")\n", - "#scen = message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE\", \"baseline_MP76BD0BI78\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 48, - "outputs": [ - { - "data": { - "text/plain": "\"clone Scenario from 'MESSAGEix-GLOBIOM 1.1-M-R12-NAVIGATE|baseline', version: 19\"" - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#df = mp.scenario_list()\n", - "df[(df[\"model\"]==\"MESSAGEix-GLOBIOM 1.1-M-R12-NAVIGATE\") & (df[\"scenario\"].str.contains(\"NPi2020\"))]#[\"annotation\"][6632]\n", - "#df = mp.scenario_list()\n", - "df[(df[\"model\"]==\"MESSAGEix-GLOBIOM 1.1-M-R12-NAVIGATE\") & (df[\"scenario\"]==\"NPi2020\")][\"annotation\"][6632]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "NPi2020-con-prim-dir-ncr" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 24, - "outputs": [ - { - "data": { - "text/plain": "array(['ENGAGE_SSP2_v4.1.2_R12', 'ENGAGE_SSP2_v4.1.4_R12_CHN',\n 'ENGAGE_SSP2_v4.1.4_R12_CHN_FG',\n 'ENGAGE_SSP2_v4.1.4_R12_CHN_FG_test', 'ENGAGE_SSP2_v4.1.4_R12_NOR',\n 'ENGAGE_SSP2_v4.1.7_R12', 'ENGAGE_SSP2_v4.1.7_R12_CHN',\n 'ENGAGE_SSP2_v4.1.7_R12_CHN_FG',\n 'ENGAGE_SSP2_v4.1.7_R12_CHN_FG_test', 'MESSAGE-SCP_R12',\n 'MESSAGE-_exDemand_SCP_R12',\n 'MESSAGEix-GLOBIOM 1.1-BM-R12 (NAVIGATE)',\n 'MESSAGEix-GLOBIOM 1.1-BM-R12 (SHAPE)',\n 'MESSAGEix-GLOBIOM 1.1-BM-R12-EFC',\n 'MESSAGEix-GLOBIOM 1.1-BM-R12-EFC_test_AM',\n 'MESSAGEix-GLOBIOM 1.1-BM-R12-NGFS',\n 'MESSAGEix-GLOBIOM 1.1-BMT-R12 (NAVIGATE)',\n 'MESSAGEix-GLOBIOM 1.1-BMT-R12-NAVIGATE',\n 'MESSAGEix-GLOBIOM 1.1-M-R12',\n 'MESSAGEix-GLOBIOM 1.1-M-R12 (NAVIGATE)',\n 'MESSAGEix-GLOBIOM 1.1-M-R12 (NGFS)',\n 'MESSAGEix-GLOBIOM 1.1-M-R12-NAVIGATE',\n 'MESSAGEix-GLOBIOM 1.1-M-R12-NAVIGATE-MAGPIE',\n 'MESSAGEix-GLOBIOM 1.1-M-R12-NAVIGATE_test',\n 'MESSAGEix-GLOBIOM 1.1-M-R12-NGFS',\n 'MESSAGEix-GLOBIOM 1.1-MT-B-R12-EFC',\n 'MESSAGEix-GLOBIOM 1.1-MT-R12',\n 'MESSAGEix-GLOBIOM 1.1-MT-R12 (NAVIGATE)',\n 'MESSAGEix-GLOBIOM 1.1-MT-R12-NGFS', 'MESSAGEix-GLOBIOM 1.1-R12',\n 'MESSAGEix-GLOBIOM 1.1-R12 (SHAPE)',\n 'MESSAGEix-GLOBIOM 1.1-R12 (agaur)',\n 'MESSAGEix-GLOBIOM 1.1-R12-MAGPIE',\n 'MESSAGEix-GLOBIOM 1.1-R12-MC (SHAPE)',\n 'MESSAGEix-GLOBIOM 1.1-R12-NGFS', 'MESSAGEix-GLOBIOM 1.1-T-R12',\n 'MESSAGEix-GLOBIOM1.1-BM-R12-NGFS',\n 'MESSAGEix-GLOBIOM_1.1-M-R12-NAVIGATE',\n 'MESSAGEix-GLOBIOM_R12_CHN', 'MESSAGEix-Materials_R12',\n 'MESSAGEix-R12',\n 'ixmp://local/MESSAGEix-Materials/NoPolicy_GLOBIOM_R12_local'],\n dtype=object)" - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df[(df[\"model\"].str.contains(\"R12\"))][\"model\"].unique()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 42, - "outputs": [ - { - "data": { - "text/plain": " model scenario scheme \\\n6878 MESSAGEix-GLOBIOM 1.1-R12-NGFS NPi2020-con-prim-dir-ncr MESSAGE \n6879 MESSAGEix-GLOBIOM 1.1-R12-NGFS baseline MESSAGE \n6880 MESSAGEix-GLOBIOM 1.1-R12-NGFS baseline_DEFAULT MESSAGE \n6881 MESSAGEix-GLOBIOM 1.1-R12-NGFS baseline_prep_gdp MESSAGE \n6882 MESSAGEix-GLOBIOM 1.1-R12-NGFS baseline_prep_gdp_macro MESSAGE \n\n is_default is_locked cre_user cre_date upd_user \\\n6878 1 0 fricko 2022-03-08 09:15:51.000000 fricko \n6879 1 0 fricko 2022-03-08 10:43:11.000000 fricko \n6880 1 0 min 2022-03-04 15:16:04.000000 None \n6881 1 0 min 2022-03-04 13:21:50.000000 min \n6882 1 0 min 2022-03-04 14:45:49.000000 min \n\n upd_date lock_user lock_date \\\n6878 2022-03-08 09:22:36.000000 None None \n6879 2022-03-08 10:47:08.000000 None None \n6880 None None None \n6881 2022-03-04 13:30:30.000000 None None \n6882 2022-03-04 14:54:27.000000 None None \n\n annotation version \n6878 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 29 \n6879 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 4 \n6880 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 3 \n6881 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 5 \n6882 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 5 ", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
modelscenarioschemeis_defaultis_lockedcre_usercre_dateupd_userupd_datelock_userlock_dateannotationversion
6878MESSAGEix-GLOBIOM 1.1-R12-NGFSNPi2020-con-prim-dir-ncrMESSAGE10fricko2022-03-08 09:15:51.000000fricko2022-03-08 09:22:36.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...29
6879MESSAGEix-GLOBIOM 1.1-R12-NGFSbaselineMESSAGE10fricko2022-03-08 10:43:11.000000fricko2022-03-08 10:47:08.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...4
6880MESSAGEix-GLOBIOM 1.1-R12-NGFSbaseline_DEFAULTMESSAGE10min2022-03-04 15:16:04.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...3
6881MESSAGEix-GLOBIOM 1.1-R12-NGFSbaseline_prep_gdpMESSAGE10min2022-03-04 13:21:50.000000min2022-03-04 13:30:30.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...5
6882MESSAGEix-GLOBIOM 1.1-R12-NGFSbaseline_prep_gdp_macroMESSAGE10min2022-03-04 14:45:49.000000min2022-03-04 14:54:27.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...5
\n
" - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df[(df[\"model\"]==\"MESSAGEix-GLOBIOM 1.1-R12-NGFS\")]#[\"annotation\"][6881]" - ], - "metadata": { - "collapsed": false - } - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} From 094b33e0e314bef8dd3461deae8195aa96a80b4e Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 30 Nov 2023 16:40:45 +0100 Subject: [PATCH 648/774] Undo tracking of some files 2 --- .../model/material/debug_moduls.py | 118 - message_ix_models/model/material/df_py.csv | 169 - .../model/material/diogo_carbon_price.ipynb | 105 - .../modify_materials_for_ssp_update.ipynb | 1256 ------ .../model/material/output_file.csv | 1065 ----- .../model/material/tax_emission_1000f.csv | 14 - .../model/material/test_SSP_costs.ipynb | 704 ---- .../model/material/test_db.ipynb | 3677 ----------------- 8 files changed, 7108 deletions(-) delete mode 100644 message_ix_models/model/material/debug_moduls.py delete mode 100644 message_ix_models/model/material/df_py.csv delete mode 100644 message_ix_models/model/material/diogo_carbon_price.ipynb delete mode 100644 message_ix_models/model/material/modify_materials_for_ssp_update.ipynb delete mode 100644 message_ix_models/model/material/output_file.csv delete mode 100644 message_ix_models/model/material/tax_emission_1000f.csv delete mode 100644 message_ix_models/model/material/test_SSP_costs.ipynb delete mode 100644 message_ix_models/model/material/test_db.ipynb diff --git a/message_ix_models/model/material/debug_moduls.py b/message_ix_models/model/material/debug_moduls.py deleted file mode 100644 index 41223e32da..0000000000 --- a/message_ix_models/model/material/debug_moduls.py +++ /dev/null @@ -1,118 +0,0 @@ -import ixmp -import message_ix -import cx_Oracle -from message_ix_models.util import private_data_path - -#from message_data.model.material import data_petro, data_ammonia_new, data_methanol_new -from message_data.tools.post_processing.iamc_report_hackathon import report as reporting -from message_data.tools.utilities import add_globiom - -# def main(): - -# clone = scen.clone("test", "test") -# #petro_dict = data_ammonia_new.gen_all_NH3_fert(clone) -# petro_dict = data_methanol_new.gen_data_methanol_new(clone) -# print("test") -# #petro_dict = data_cement.gen_data_cement(scen) -magpie_scens = [ - "MP00BD0BI78", - "MP00BD0BI74", - "MP00BD0BI70", - "MP00BD1BI00", - #"MP30BD0BI78", - "MP30BD0BI74", - "MP30BD0BI70", - "MP30BD1BI00", - "MP50BD0BI78", - "MP50BD0BI74", - "MP50BD0BI70", - "MP50BD1BI00", - "MP76BD0BI70", - "MP76BD0BI74", - "MP76BD0BI78", - "MP76BD1BI00" -] - -if __name__ == '__main__': - #main() - # import warnings - # - # - from message_ix_models.cli import main - # - # mp = ixmp.Platform("ixmp_dev") - # scen = message_ix.Scenario(mp, "SHAPE_SSP2_v4.1.8", "baseline") - # reporting( - # mp, - # scen, - # # NB(PNK) this is not an error; .iamc_report_hackathon.report() expects a - # # string containing "True" or "False" instead of an actual bool. - # "False", - # scen.model, - # scen.scenario+"_test", - # merge_hist=True, - # merge_ts=True, - # run_config="materials_run_config.yaml", - # ) - - # import os - # path_dirs = os.environ.get('PATH').split(os.pathsep) - # - # import sys - # new_path = "C:/Users/maczek/instantclient_21_10" - # sys.path.append(new_path) - # cx_Oracle.init_oracle_client(lib_dir=r"C:/Users/maczek/instantclient_21_10") - # #print(path_dirs) - # # main(["--url", "ixmp://ixmp_dev/MESSAGEix-Materials/NoPolicy_petro_thesis_2", - # # "--local-data", "./data", "material", "debug_module", "--material", "steel"]) - # # main(["--url", "ixmp://ixmp_dev/SHAPE_SSP2_v4.1.8/baseline#1", - # # "--local-data", "./data" , "material", "build", "--tag", "petro_thesis_2"]) - # # main(["--local-data", "./data", "material", "solve" "--scenario_name", - # # "NoPolicy_petro_thesis_2" "--add_calibration", True]) - # # main(["--local-data", "C:/Users\maczek\PycharmProjects\message_data", "navigate_industry", - # # "--run-reporting-only", "run", "SSP2", "--output-model=MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00", - # # "--scenarios", "ENf", "--run_reporting" ,"True"]) - # # exit() - # main(["--local-data", "C:/Users\maczek\PycharmProjects\message_data", "navigate_industry", - # "--run-reporting-only", "run", "SSP2", f'--output-model=MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD0BI70', - # "--scenarios", "EN-step3", "--run_reporting", "True"]) - # for magpie_scen in magpie_scens[5:]: - # print() - # print() - # try: - # main(["--local-data", "C:/Users\maczek\PycharmProjects\message_data", "navigate_industry", - # "run", "SSP2", f'--output-model=MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-{magpie_scen}', - # "--scenarios", "run_cdlinks_setup", ]) - # except SystemExit as e: - # pass - # # if e.code != 0: - # # pass#raise - # # else: - # # pass - # mp = ixmp.Platform("ixmp_dev") - # scen = message_ix.Scenario(mp, "MESSAGEix-GLOBIOM 1.1-R12-MAGPIE", "baseline_w/o_LU") - # sc_clone = scen.clone("MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD1BI00", "bugfix_debug") - # add_globiom( - # mp, - # sc_clone, - # "SSP2", - # private_data_path(), - # 2015, - # globiom_scenario="noSDG_rcpref", - # #regenerate_input_data=True, - # #regenerate_input_data_only=True, - # #globiom_scenario="no", - # #allow_empty_drivers=True, - # #add_reporting_data=True, - # #config_setup="MAGPIE_MP00BD1BI00", - # config_setup="MAGPIE_MP00BI00_OldForSplit", - # config_file="magpie_config.yaml" - # ) - # main(["--local-data", "C:/Users\maczek\PycharmProjects\message_data", "magpie_scp", - # "--run-reporting-only", "run", "SSP2", f'--output-model=MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD0BI70', - # "--scenarios", "EN-step3", "--run_reporting", "True"]) - # main(["--url", "ixmp://ixmp_dev/MESSAGEix-Materials/NoPolicy_petro_thesis_2_macro", "report"]) - #, f'MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-MP00BD0BI70', - # "--scenario", "baseline"]) - main(["--url", "ixmp://ixmp_dev/SSP_dev_SSP2_v0.1/baseline", - "--local-data", "./data" , "material", "build", "--tag", " v0.1_materials_no_pow_sect"]) diff --git a/message_ix_models/model/material/df_py.csv b/message_ix_models/model/material/df_py.csv deleted file mode 100644 index e8c1944659..0000000000 --- a/message_ix_models/model/material/df_py.csv +++ /dev/null @@ -1,169 +0,0 @@ -,region,year,demand_tot -0,R12_AFR,2020,20.039797280293683 -1,R12_AFR,2030,52.60984886888077 -2,R12_AFR,2040,103.76875006935163 -3,R12_AFR,2050,170.54616780143323 -4,R12_AFR,2060,236.5908328255232 -5,R12_AFR,2070,284.5276902295362 -6,R12_AFR,2080,307.5960349221543 -7,R12_AFR,2090,308.6898767675193 -8,R12_AFR,2100,294.3450700953177 -9,R12_AFR,2110,270.08416105128913 -10,R12_AFR,2025,37.73980512825048 -11,R12_AFR,2035,81.92055270481804 -12,R12_AFR,2045,145.14684407907927 -13,R12_AFR,2055,215.56261753174584 -14,R12_RCPA,2020,12.083312999611136 -15,R12_RCPA,2030,17.386324853186682 -16,R12_RCPA,2040,21.976670054237808 -17,R12_RCPA,2050,26.28540566670917 -18,R12_RCPA,2060,25.92509885472216 -19,R12_RCPA,2070,22.767779993970336 -20,R12_RCPA,2080,18.862431505407926 -21,R12_RCPA,2090,15.200549540336974 -22,R12_RCPA,2100,12.178492226796386 -23,R12_RCPA,2110,10.608330479944874 -24,R12_RCPA,2025,16.393856761408767 -25,R12_RCPA,2035,19.23160256246728 -26,R12_RCPA,2045,24.55441522352325 -27,R12_RCPA,2055,26.574740950715842 -28,R12_EEU,2020,56.54775953881597 -29,R12_EEU,2030,53.700297068109265 -30,R12_EEU,2040,45.3202864976465 -31,R12_EEU,2050,35.19391868610258 -32,R12_EEU,2060,27.668872094877997 -33,R12_EEU,2070,22.333355488545145 -34,R12_EEU,2080,18.249804581354486 -35,R12_EEU,2090,15.053631298428126 -36,R12_EEU,2100,12.477214762821024 -37,R12_EEU,2110,10.89734455931019 -38,R12_EEU,2025,55.73526918780052 -39,R12_EEU,2035,50.175201424139516 -40,R12_EEU,2045,40.04718768373967 -41,R12_EEU,2055,31.070080681686775 -42,R12_FSU,2020,56.5023574692174 -43,R12_FSU,2030,59.36820973683082 -44,R12_FSU,2040,60.325756801615256 -45,R12_FSU,2050,59.897393630762075 -46,R12_FSU,2060,56.54931127312622 -47,R12_FSU,2070,51.20872631035551 -48,R12_FSU,2080,44.87116319307713 -49,R12_FSU,2090,38.62160882731177 -50,R12_FSU,2100,32.91532905499992 -51,R12_FSU,2110,28.920978055483946 -52,R12_FSU,2025,59.37780929546167 -53,R12_FSU,2035,59.83205248570103 -54,R12_FSU,2045,60.42234533336089 -55,R12_FSU,2055,58.60311381130512 -56,R12_LAM,2020,64.95151987684079 -57,R12_LAM,2030,74.62908958351866 -58,R12_LAM,2040,98.57925011552885 -59,R12_LAM,2050,127.3013310275739 -60,R12_LAM,2060,137.80367422603172 -61,R12_LAM,2070,132.8536347674674 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b/message_ix_models/model/material/diogo_carbon_price.ipynb deleted file mode 100644 index f43b2f2069..0000000000 --- a/message_ix_models/model/material/diogo_carbon_price.ipynb +++ /dev/null @@ -1,105 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 32, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "This Scenario has a solution, use `Scenario.remove_solution()` or `Scenario.clone(..., keep_solution=False)`\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "New carbon prices added\n", - "New scenario name is 2degrees_1_5C\n" - ] - }, - { - "data": { - "text/plain": " node type_emission type_tec type_year value unit\n0 World TCE all 2025 102.143928 USD/tCO2\n1 World TCE all 2030 130.364412 USD/tCO2\n2 World TCE all 2035 166.381695 USD/tCO2\n3 World TCE all 2040 212.349890 USD/tCO2\n4 World TCE all 2045 271.018249 USD/tCO2\n5 World TCE all 2050 345.895595 USD/tCO2\n6 World TCE all 2055 441.460170 USD/tCO2\n7 World TCE all 2060 563.427476 USD/tCO2\n8 World TCE all 2070 917.763988 USD/tCO2\n9 World TCE all 2080 1494.940829 USD/tCO2\n10 World TCE all 2090 2435.101083 USD/tCO2\n11 World TCE all 2100 3966.523070 USD/tCO2\n12 World TCE all 2110 6461.048116 USD/tCO2", - "text/html": "
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" - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pandas as pd\n", - "import ixmp\n", - "import message_ix\n", - "\n", - "climate_target = \"2C\"\n", - "climate_target = \"1_5C\"\n", - "\n", - "baseline_name = \"put name of shipping baseline scenario here\"\n", - "\n", - "if climate_target == \"2°C\":\n", - " tax_emission_new = pd.read_csv(\"tax_emission_1000f.csv\") # 2°C carbon prices\n", - "if climate_target == \"1.5°C\": \n", - " tax_emission_new = pd.read_csv(\"tax_emission_600f.csv\") # 1.5°C carbon prices\n", - "\n", - "scenario = message_ix.Scenario(mp, 'MESSAGEix-Materials', \"NoPolicy_petro_thesis_2_final\")\n", - "\n", - "output_scenario_name = output_scenario_name + '_' + climate_target # choose whatever name you want for the carbon price run\n", - "\n", - "scen_cprice = scenario.clone('MESSAGEix-Materials', output_scenario_name,\n", - " keep_solution=False, shift_first_model_year=2025)\n", - "\n", - "scen_cprice.check_out()\n", - "tax_emission_new.columns = scen_cprice.par(\"tax_emission\").columns\n", - "tax_emission_new[\"unit\"] = \"USD/tCO2\"\n", - "scen_cprice.add_par(\"tax_emission\", tax_emission_new)\n", - "scen_cprice.commit('2 degree prices are added')\n", - "print('New carbon prices added')\n", - "print('New scenario name is ' + output_scenario_name)\n", - "scen_cprice.par(\"tax_emission\")\n", - "#scen_cprice.set_as_default()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-29T15:59:11.705882400Z", - "start_time": "2023-11-29T15:58:46.810437100Z" - } - }, - "id": "906a600a96493f6e" - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [], - "metadata": { - "collapsed": false - }, - "id": "86789658e0575882" - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/message_ix_models/model/material/modify_materials_for_ssp_update.ipynb b/message_ix_models/model/material/modify_materials_for_ssp_update.ipynb deleted file mode 100644 index 0911896a9a..0000000000 --- a/message_ix_models/model/material/modify_materials_for_ssp_update.ipynb +++ /dev/null @@ -1,1256 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "outputs": [ - { - "data": { - "text/plain": "", - "application/javascript": "if (typeof IPython !== 'undefined') { IPython.OutputArea.prototype._should_scroll = function(lines){ return false; }}" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "model = \"SSP_dev_SSP2_v0.1\"\n", - "scenario = \"baseline_v0.1_materials_no_pow_sect\"\n", - "\n", - "\n", - "import ixmp\n", - "import message_ix\n", - "mp = ixmp.Platform(\"ixmp_dev\")\n", - "#scen = message_ix.Scenario(mp, model, scenario)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-27T11:02:02.715065500Z", - "start_time": "2023-11-27T11:01:49.501141Z" - } - }, - "id": "5b8dbb8cae325fed" - }, - { - "cell_type": "code", - "execution_count": 5, - "outputs": [], - "source": [ - "from message_data.model.material import data_petro\n", - "import importlib" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-23T14:25:21.171813800Z", - "start_time": "2023-11-23T14:25:21.140567100Z" - } - }, - "id": "c13b9ce5f19fc9b0" - }, - { - "cell_type": "code", - "execution_count": 47, - "outputs": [], - "source": [ - "importlib.reload(data_petro)\n", - "df = data_petro.gen_mock_demand_petro(scen, 1, 1)\n", - "df_baseyear = df[(df[\"commodity\"]==\"HVC\") & (df[\"year\"]==2025)]" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-23T15:36:06.906718800Z", - "start_time": "2023-11-23T15:36:06.646978100Z" - } - }, - "id": "b7749376f111cb06" - }, - { - "cell_type": "code", - "execution_count": 49, - "outputs": [ - { - "data": { - "text/plain": " node commodity level year time value unit\n0 R12_AFR HVC demand 2030 year 3.257632 t\n1 R12_CHN HVC demand 2030 year 0.614827 t\n2 R12_EEU HVC demand 2030 year 3.551030 t\n3 R12_FSU HVC demand 2030 year 6.284582 t\n4 R12_LAM HVC demand 2030 year 13.712894 t\n.. ... ... ... ... ... ... ...\n151 R12_PAO HVC demand 2025 year 11.000000 t\n152 R12_PAS HVC demand 2025 year 37.500000 t\n153 R12_RCPA HVC demand 2025 year 10.700000 t\n154 R12_SAS HVC demand 2025 year 29.200000 t\n155 R12_WEU HVC demand 2025 year 50.500000 t\n\n[156 rows x 7 columns]", - "text/html": "
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" - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-23T15:36:22.137635700Z", - "start_time": "2023-11-23T15:36:22.085727100Z" - } - }, - "id": "2f3408d63ca5432a" - }, - { - "cell_type": "code", - "execution_count": 37, - "outputs": [ - { - "data": { - "text/plain": "2025" - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "scen.firstmodelyear" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-23T14:42:10.504171500Z", - "start_time": "2023-11-23T14:42:10.457063400Z" - } - }, - "id": "b841a5e58b768c5b" - }, - { - "cell_type": "code", - "execution_count": 112, - "outputs": [], - "source": [ - "df_gdp = scen.par(\"gdp_calibrate\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-23T16:07:20.225623900Z", - "start_time": "2023-11-23T16:07:20.170634300Z" - } - }, - "id": "40d0282e9d3e7dc2" - }, - { - "cell_type": "code", - "execution_count": 113, - "outputs": [], - "source": [ - "df_gdp = df_gdp.set_index(\"node\")\n", - "df_gdp = df_gdp.join(df_gdp[df_gdp[\"year\"]==2020][\"value\"], rsuffix=\"_2020\")\n", - "df_gdp[\"growth\"] = (df_gdp[\"value\"] / df_gdp[\"value_2020\"])-1" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-23T16:07:20.434690600Z", - "start_time": "2023-11-23T16:07:20.385791200Z" - } - }, - "id": "f2eb8581f5c11c5" - }, - { - "cell_type": "code", - "execution_count": 114, - "outputs": [ - { - "data": { - "text/plain": " year value unit value_2020 growth\nnode \nR12_AFR 2020 998.98924 T$ 998.98924 0.000000\nR12_AFR 2025 1368.01863 T$ 998.98924 0.369403\nR12_AFR 2030 1856.87524 T$ 998.98924 0.858754\nR12_AFR 2035 2479.36677 T$ 998.98924 1.481875\nR12_AFR 2040 3314.60341 T$ 998.98924 2.317957\n... ... ... ... ... ...\nR12_WEU 2070 38793.79891 T$ 13849.19218 1.801160\nR12_WEU 2080 45819.24718 T$ 13849.19218 2.308442\nR12_WEU 2090 52976.57491 T$ 13849.19218 2.825247\nR12_WEU 2100 59671.75882 T$ 13849.19218 3.308682\nR12_WEU 2110 59671.75882 T$ 13849.19218 3.308682\n\n[168 rows x 5 columns]", - "text/html": "
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" - }, - "execution_count": 92, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_gdp[df_gdp[\"year\"]==2020][\"value\"]" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-23T16:03:57.582043300Z", - "start_time": "2023-11-23T16:03:57.505271100Z" - } - }, - "id": "5ddadd5106a8ebf4" - }, - { - "cell_type": "code", - "execution_count": 84, - "outputs": [], - "source": [ - "#df_gdp_gro = pd.concat([df_gdp, ], axis=1)\n", - "df_gdp[\"growth_rel\"] = df_gdp[\"growth_abs\"].cumsum() / df_gdp[\"value\"]" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "start_time": "2023-11-23T16:00:40.108836Z" - } - }, - "id": "f889f4dd6457b0e9" - }, - { - "cell_type": "code", - "execution_count": 73, - "outputs": [ - { - "data": { - "text/plain": " node year value unit\n0 R12_AFR 2020 998.98924 T$\n1 R12_AFR 2025 1368.01863 T$\n2 R12_AFR 2030 1856.87524 T$\n3 R12_AFR 2035 2479.36677 T$\n4 R12_AFR 2040 3314.60341 T$\n.. ... ... ... ...\n163 R12_RCPA 2070 3360.28429 T$\n164 R12_RCPA 2080 4304.25447 T$\n165 R12_RCPA 2090 5373.51508 T$\n166 R12_RCPA 2100 6453.46984 T$\n167 R12_RCPA 2110 6453.46984 T$\n\n[168 rows x 4 columns]", - "text/html": "
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0R12_AFR2020998.98924T$
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163R12_RCPA20703360.28429T$
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168 rows × 4 columns

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" - }, - "execution_count": 73, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "scen.par(\"gdp_calibrate\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "start_time": "2023-11-23T15:50:24.321689200Z" - } - }, - "id": "cb0f0cd0ecb06cb1" - }, - { - "cell_type": "code", - "execution_count": 65, - "outputs": [ - { - "data": { - "text/plain": "0 18.552009\n1 55.053443\n2 96.956958\n3 139.034792\n4 135.760735\n ... \n190 7025.448265\n191 7157.327735\n192 6695.183911\n193 0.000000\n194 NaN\nName: value, Length: 240, dtype: float64" - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_gdp[\"value\"].diff().shift(-1)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-23T15:42:53.399086500Z", - "start_time": "2023-11-23T15:42:53.314295800Z" - } - }, - "id": "ab5bf200d0f3b2c5" - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [], - "metadata": { - "collapsed": false - }, - "id": "f1ed71a6bb560d34" - }, - { - "cell_type": "code", - "execution_count": 119, - "outputs": [], - "source": [ - "from message_data.model.material.material_demand import refactor_mat_demand_calc" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-24T09:18:11.864211800Z", - "start_time": "2023-11-24T09:18:11.779779900Z" - } - }, - "id": "681b232fd2af6a05" - }, - { - "cell_type": "code", - "execution_count": 129, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Index([ 'Model', 'Scenario', 'Region', 'Variable', 'Unit', 2000,\n", - " 2005, 2010, 2015, 2020, 2025, 2030,\n", - " 2035, 2040, 2045, 2050, 2055, 2060,\n", - " 2070, 2080, 2090, 2100, 2110, 'Notes'],\n", - " dtype='object')\n", - "Index(['TIMER Region', 'Unnamed: 1', 't', 1970,\n", - " 1971, 1972, 1973, 1974,\n", - " 1975, 1976, 1977, 1978,\n", - " 1979, 1980, 1981, 1982,\n", - " 1983, 1984, 1985, 1986,\n", - " 1987, 1988, 1989, 1990,\n", - " 1991, 1992, 1993, 1994,\n", - " 1995, 1996, 1997, 1998,\n", - " 1999, 2000, 2001, 2002,\n", - " 2003, 2004, 2005, 2006,\n", - " 2007, 2008, 2009, 2010,\n", - " 2011, 2012],\n", - " dtype='object')\n" - ] - }, - { - "ename": "KeyError", - "evalue": "'Reg_no'", - "output_type": "error", - "traceback": [ - "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[1;31mKeyError\u001B[0m Traceback (most recent call last)", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3621\u001B[0m, in \u001B[0;36mIndex.get_loc\u001B[1;34m(self, key, method, tolerance)\u001B[0m\n\u001B[0;32m 3620\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m-> 3621\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_engine\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_loc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcasted_key\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 3622\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m err:\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx:136\u001B[0m, in \u001B[0;36mpandas._libs.index.IndexEngine.get_loc\u001B[1;34m()\u001B[0m\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx:163\u001B[0m, in \u001B[0;36mpandas._libs.index.IndexEngine.get_loc\u001B[1;34m()\u001B[0m\n", - "File \u001B[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5198\u001B[0m, in \u001B[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001B[1;34m()\u001B[0m\n", - "File \u001B[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5206\u001B[0m, in \u001B[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001B[1;34m()\u001B[0m\n", - "\u001B[1;31mKeyError\u001B[0m: 'Reg_no'", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001B[1;31mKeyError\u001B[0m Traceback (most recent call last)", - "Input \u001B[1;32mIn [129]\u001B[0m, in \u001B[0;36m\u001B[1;34m()\u001B[0m\n\u001B[0;32m 16\u001B[0m base_demand \u001B[38;5;241m=\u001B[39m gen_mock_demand_aluminum(scen)\n\u001B[0;32m 17\u001B[0m base_demand \u001B[38;5;241m=\u001B[39m base_demand\u001B[38;5;241m.\u001B[39mloc[base_demand\u001B[38;5;241m.\u001B[39myear \u001B[38;5;241m==\u001B[39m \u001B[38;5;241m2020\u001B[39m]\u001B[38;5;241m.\u001B[39mrename(\n\u001B[0;32m 18\u001B[0m columns\u001B[38;5;241m=\u001B[39m{\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mvalue\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mdemand.tot.base\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mnode\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mregion\u001B[39m\u001B[38;5;124m\"\u001B[39m}\n\u001B[0;32m 19\u001B[0m )\n\u001B[1;32m---> 21\u001B[0m \u001B[43mrefactor_mat_demand_calc\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mderive_steel_demand\u001B[49m\u001B[43m(\u001B[49m\u001B[43mpop\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mbase_demand\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mstr\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mprivate_data_path\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mmaterial\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32m~\\PycharmProjects\\message_data\\message_data\\model\\material\\material_demand\\refactor_mat_demand_calc.py:38\u001B[0m, in \u001B[0;36mderive_steel_demand\u001B[1;34m(df_pop, df_demand, datapath)\u001B[0m\n\u001B[0;32m 36\u001B[0m df_raw_steel_consumption.drop(columns='Unnamed: 2')\n\u001B[0;32m 37\u001B[0m .melt(id_vars='Reg_no', var_name='year', value_name='consumption')\n\u001B[1;32m---> 38\u001B[0m )\n\u001B[0;32m 39\u001B[0m \n\u001B[0;32m 40\u001B[0m # Read population data\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:8434\u001B[0m, in \u001B[0;36mDataFrame.melt\u001B[1;34m(self, id_vars, value_vars, var_name, value_name, col_level, ignore_index)\u001B[0m\n\u001B[0;32m 8423\u001B[0m \u001B[38;5;129m@Appender\u001B[39m(_shared_docs[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mmelt\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m%\u001B[39m {\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mcaller\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mdf.melt(\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mother\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mmelt\u001B[39m\u001B[38;5;124m\"\u001B[39m})\n\u001B[0;32m 8424\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mmelt\u001B[39m(\n\u001B[0;32m 8425\u001B[0m \u001B[38;5;28mself\u001B[39m,\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 8431\u001B[0m ignore_index: \u001B[38;5;28mbool\u001B[39m \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mTrue\u001B[39;00m,\n\u001B[0;32m 8432\u001B[0m ) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m DataFrame:\n\u001B[1;32m-> 8434\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mmelt\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m 8435\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[0;32m 8436\u001B[0m \u001B[43m \u001B[49m\u001B[43mid_vars\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mid_vars\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 8437\u001B[0m \u001B[43m \u001B[49m\u001B[43mvalue_vars\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mvalue_vars\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 8438\u001B[0m \u001B[43m \u001B[49m\u001B[43mvar_name\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mvar_name\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 8439\u001B[0m \u001B[43m \u001B[49m\u001B[43mvalue_name\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mvalue_name\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 8440\u001B[0m \u001B[43m \u001B[49m\u001B[43mcol_level\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mcol_level\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 8441\u001B[0m \u001B[43m \u001B[49m\u001B[43mignore_index\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mignore_index\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 8442\u001B[0m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\reshape\\melt.py:132\u001B[0m, in \u001B[0;36mmelt\u001B[1;34m(frame, id_vars, value_vars, var_name, value_name, col_level, ignore_index)\u001B[0m\n\u001B[0;32m 130\u001B[0m mdata \u001B[38;5;241m=\u001B[39m {}\n\u001B[0;32m 131\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m col \u001B[38;5;129;01min\u001B[39;00m id_vars:\n\u001B[1;32m--> 132\u001B[0m id_data \u001B[38;5;241m=\u001B[39m \u001B[43mframe\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mpop\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcol\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 133\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m is_extension_array_dtype(id_data):\n\u001B[0;32m 134\u001B[0m id_data \u001B[38;5;241m=\u001B[39m concat([id_data] \u001B[38;5;241m*\u001B[39m K, ignore_index\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m)\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:5270\u001B[0m, in \u001B[0;36mDataFrame.pop\u001B[1;34m(self, item)\u001B[0m\n\u001B[0;32m 5229\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mpop\u001B[39m(\u001B[38;5;28mself\u001B[39m, item: Hashable) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m Series:\n\u001B[0;32m 5230\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 5231\u001B[0m \u001B[38;5;124;03m Return item and drop from frame. Raise KeyError if not found.\u001B[39;00m\n\u001B[0;32m 5232\u001B[0m \n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 5268\u001B[0m \u001B[38;5;124;03m 3 monkey NaN\u001B[39;00m\n\u001B[0;32m 5269\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[1;32m-> 5270\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43msuper\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mpop\u001B[49m\u001B[43m(\u001B[49m\u001B[43mitem\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mitem\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:865\u001B[0m, in \u001B[0;36mNDFrame.pop\u001B[1;34m(self, item)\u001B[0m\n\u001B[0;32m 864\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mpop\u001B[39m(\u001B[38;5;28mself\u001B[39m, item: Hashable) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m Series \u001B[38;5;241m|\u001B[39m Any:\n\u001B[1;32m--> 865\u001B[0m result \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m[\u001B[49m\u001B[43mitem\u001B[49m\u001B[43m]\u001B[49m\n\u001B[0;32m 866\u001B[0m \u001B[38;5;28;01mdel\u001B[39;00m \u001B[38;5;28mself\u001B[39m[item]\n\u001B[0;32m 868\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m result\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3505\u001B[0m, in \u001B[0;36mDataFrame.__getitem__\u001B[1;34m(self, key)\u001B[0m\n\u001B[0;32m 3503\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcolumns\u001B[38;5;241m.\u001B[39mnlevels \u001B[38;5;241m>\u001B[39m \u001B[38;5;241m1\u001B[39m:\n\u001B[0;32m 3504\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_getitem_multilevel(key)\n\u001B[1;32m-> 3505\u001B[0m indexer \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcolumns\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_loc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mkey\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 3506\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m is_integer(indexer):\n\u001B[0;32m 3507\u001B[0m indexer \u001B[38;5;241m=\u001B[39m [indexer]\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3623\u001B[0m, in \u001B[0;36mIndex.get_loc\u001B[1;34m(self, key, method, tolerance)\u001B[0m\n\u001B[0;32m 3621\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_engine\u001B[38;5;241m.\u001B[39mget_loc(casted_key)\n\u001B[0;32m 3622\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m err:\n\u001B[1;32m-> 3623\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m(key) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01merr\u001B[39;00m\n\u001B[0;32m 3624\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m:\n\u001B[0;32m 3625\u001B[0m \u001B[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001B[39;00m\n\u001B[0;32m 3626\u001B[0m \u001B[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001B[39;00m\n\u001B[0;32m 3627\u001B[0m \u001B[38;5;66;03m# the TypeError.\u001B[39;00m\n\u001B[0;32m 3628\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_check_indexing_error(key)\n", - "\u001B[1;31mKeyError\u001B[0m: 'Reg_no'" - ] - } - ], - "source": [ - "from message_ix_models.util import private_data_path\n", - "from message_data.model.material.data_aluminum import gen_mock_demand_aluminum\n", - "\n", - "import importlib\n", - "importlib.reload(refactor_mat_demand_calc)\n", - "\n", - "pop = scen.par(\"bound_activity_up\", {\"technology\": \"Population\"})\n", - "pop = pop.loc[pop.year_act >= 2020].rename(\n", - " columns={\"year_act\": \"year\", \"value\": \"pop.mil\", \"node_loc\": \"region\"}\n", - ")\n", - "\n", - "# import pdb; pdb.set_trace()\n", - "\n", - "pop = pop[[\"region\", \"year\", \"pop.mil\"]]\n", - "\n", - "base_demand = gen_mock_demand_aluminum(scen)\n", - "base_demand = base_demand.loc[base_demand.year == 2020].rename(\n", - " columns={\"value\": \"demand.tot.base\", \"node\": \"region\"}\n", - ")\n", - "\n", - "refactor_mat_demand_calc.derive_steel_demand(pop, base_demand, str(private_data_path(\"material\")))" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-24T09:23:43.822801700Z", - "start_time": "2023-11-24T09:23:43.236417500Z" - } - }, - "id": "5ebc89de5e68e22" - }, - { - "cell_type": "code", - "execution_count": 24, - "outputs": [ - { - "data": { - "text/plain": " Model Scenario Region Variable \\\n0 ENGAGE_SSP2_v4.1.5 baseline AFR GDP|PPP \n1 ENGAGE_SSP2_v4.1.5 baseline RCPA GDP|PPP \n2 ENGAGE_SSP2_v4.1.5 baseline EEU GDP|PPP \n3 ENGAGE_SSP2_v4.1.5 baseline FSU GDP|PPP \n4 ENGAGE_SSP2_v4.1.5 baseline LAM GDP|PPP \n5 ENGAGE_SSP2_v4.1.5 baseline MEA GDP|PPP \n6 ENGAGE_SSP2_v4.1.5 baseline NAM GDP|PPP \n7 ENGAGE_SSP2_v4.1.5 baseline PAO GDP|PPP \n8 ENGAGE_SSP2_v4.1.5 baseline PAS GDP|PPP \n9 ENGAGE_SSP2_v4.1.5 baseline SAS GDP|PPP \n10 ENGAGE_SSP2_v4.1.5 baseline WEU GDP|PPP \n11 - baseline CHN GDP|PPP \n\n Unit 2010 2015 \\\n0 billion US$2010/yr OR local currency 1794.2820 2402.228030 \n1 billion US$2010/yr OR local currency 1079.8701 2150.548072 \n2 billion US$2010/yr OR local currency 2076.9590 2618.822609 \n3 billion US$2010/yr OR local currency 3162.3830 4356.569587 \n4 billion US$2010/yr OR local currency 6463.3140 8526.634978 \n5 billion US$2010/yr OR local currency 4119.7470 5191.844904 \n6 billion US$2010/yr OR local currency 15903.0700 16473.205062 \n7 billion US$2010/yr OR local currency 5323.9890 5498.664307 \n8 billion US$2010/yr OR local currency 5112.7630 6593.728910 \n9 billion US$2010/yr OR local currency 4943.5830 7639.789695 \n10 billion US$2010/yr OR local currency 15126.8600 15885.840026 \n11 billion US$2010/yr OR local currency 9718.8309 19354.932650 \n\n 2020 2025 2030 ... 2040 2045 \\\n0 3084.364057 4244.401513 5306.159830 ... 8819.133966 11918.121112 \n1 2373.445674 3270.479945 3893.061971 ... 5073.624444 5537.560712 \n2 2730.706601 3143.074779 3501.253203 ... 4256.420498 4597.068272 \n3 4731.600636 5781.870531 6651.451416 ... 8506.190346 9251.644256 \n4 9420.268593 11361.550043 12958.770551 ... 16842.884509 19144.971803 \n5 6351.518847 8052.544729 9560.925873 ... 13412.104505 15685.103462 \n6 20642.913911 23092.253472 25377.748594 ... 29535.387817 31456.430747 \n7 6170.836262 6626.157558 7058.364196 ... 7782.799460 8172.236645 \n8 8074.694821 10087.804348 11864.037191 ... 15989.457685 18306.797895 \n9 9198.303861 13133.949690 15990.168190 ... 25016.096851 31679.807625 \n10 18046.985482 19720.652041 21315.555794 ... 25135.170593 27351.221411 \n11 21361.011063 29434.319503 35037.557742 ... 45662.620000 49838.046404 \n\n 2050 2055 2060 2070 2080 \\\n0 14602.417875 19755.749042 23807.218699 37616.622442 56865.524017 \n1 5964.216044 6245.074034 6503.801283 6880.935088 7096.848967 \n2 4910.855123 5246.396492 5553.862302 6242.035364 6847.727435 \n3 9950.008692 10722.125627 11446.507830 13192.598513 14735.329180 \n4 21230.787541 23785.112717 26129.992341 31454.646621 37181.975468 \n5 17790.488063 20402.696892 22849.032178 28824.680775 35379.951669 \n6 33290.073322 35187.978277 36999.868551 40983.108844 44752.779080 \n7 8539.725624 8980.617553 9391.395793 10315.595017 11207.031058 \n8 20390.026395 22741.413604 24982.135093 29828.339187 34700.948700 \n9 36496.175849 44208.145112 50386.897810 66407.736884 83868.117540 \n10 29432.165059 31909.731358 34205.015495 39523.950514 44994.729214 \n11 53677.944399 56205.666302 58534.211546 61928.415791 63871.640704 \n\n 2090 2100 2110 \n0 81978.281459 112862.365070 155381.562316 \n1 7207.835099 7292.012794 7377.173569 \n2 7413.134880 7998.343398 8629.749513 \n3 16214.272854 17702.925869 19328.254011 \n4 43231.355239 49620.005864 56952.759595 \n5 42596.412518 50429.418189 59702.826330 \n6 48349.422545 51744.756419 55378.527310 \n7 12090.062676 12969.968251 13913.912686 \n8 39529.321059 44257.183183 49550.516195 \n9 102061.793719 120825.538934 143038.940693 \n10 50555.126628 56171.213456 62411.182238 \n11 64870.515894 65628.115143 66394.562119 \n\n[12 rows x 21 columns]", - 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ModelScenarioRegionVariableUnit20102015202020252030...2040204520502055206020702080209021002110
0ENGAGE_SSP2_v4.1.5baselineAFRGDP|PPPbillion US$2010/yr OR local currency1794.28202402.2280303084.3640574244.4015135306.159830...8819.13396611918.12111214602.41787519755.74904223807.21869937616.62244256865.52401781978.281459112862.365070155381.562316
1ENGAGE_SSP2_v4.1.5baselineRCPAGDP|PPPbillion US$2010/yr OR local currency1079.87012150.5480722373.4456743270.4799453893.061971...5073.6244445537.5607125964.2160446245.0740346503.8012836880.9350887096.8489677207.8350997292.0127947377.173569
2ENGAGE_SSP2_v4.1.5baselineEEUGDP|PPPbillion US$2010/yr OR local currency2076.95902618.8226092730.7066013143.0747793501.253203...4256.4204984597.0682724910.8551235246.3964925553.8623026242.0353646847.7274357413.1348807998.3433988629.749513
3ENGAGE_SSP2_v4.1.5baselineFSUGDP|PPPbillion US$2010/yr OR local currency3162.38304356.5695874731.6006365781.8705316651.451416...8506.1903469251.6442569950.00869210722.12562711446.50783013192.59851314735.32918016214.27285417702.92586919328.254011
4ENGAGE_SSP2_v4.1.5baselineLAMGDP|PPPbillion US$2010/yr OR local currency6463.31408526.6349789420.26859311361.55004312958.770551...16842.88450919144.97180321230.78754123785.11271726129.99234131454.64662137181.97546843231.35523949620.00586456952.759595
5ENGAGE_SSP2_v4.1.5baselineMEAGDP|PPPbillion US$2010/yr OR local currency4119.74705191.8449046351.5188478052.5447299560.925873...13412.10450515685.10346217790.48806320402.69689222849.03217828824.68077535379.95166942596.41251850429.41818959702.826330
6ENGAGE_SSP2_v4.1.5baselineNAMGDP|PPPbillion US$2010/yr OR local currency15903.070016473.20506220642.91391123092.25347225377.748594...29535.38781731456.43074733290.07332235187.97827736999.86855140983.10884444752.77908048349.42254551744.75641955378.527310
7ENGAGE_SSP2_v4.1.5baselinePAOGDP|PPPbillion US$2010/yr OR local currency5323.98905498.6643076170.8362626626.1575587058.364196...7782.7994608172.2366458539.7256248980.6175539391.39579310315.59501711207.03105812090.06267612969.96825113913.912686
8ENGAGE_SSP2_v4.1.5baselinePASGDP|PPPbillion US$2010/yr OR local currency5112.76306593.7289108074.69482110087.80434811864.037191...15989.45768518306.79789520390.02639522741.41360424982.13509329828.33918734700.94870039529.32105944257.18318349550.516195
9ENGAGE_SSP2_v4.1.5baselineSASGDP|PPPbillion US$2010/yr OR local currency4943.58307639.7896959198.30386113133.94969015990.168190...25016.09685131679.80762536496.17584944208.14511250386.89781066407.73688483868.117540102061.793719120825.538934143038.940693
10ENGAGE_SSP2_v4.1.5baselineWEUGDP|PPPbillion US$2010/yr OR local currency15126.860015885.84002618046.98548219720.65204121315.555794...25135.17059327351.22141129432.16505931909.73135834205.01549539523.95051444994.72921450555.12662856171.21345662411.182238
11-baselineCHNGDP|PPPbillion US$2010/yr OR local currency9718.830919354.93265021361.01106329434.31950335037.557742...45662.62000049838.04640453677.94439956205.66630258534.21154661928.41579163871.64070464870.51589465628.11514366394.562119
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12 rows × 21 columns

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" - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from message_ix_models.util import (\n", - " broadcast,\n", - " make_io,\n", - " make_matched_dfs,\n", - " same_node,\n", - " add_par_data,\n", - " private_data_path,\n", - ")\n", - "import pandas as pd\n", - "df_gdp = pd.read_excel(\n", - " private_data_path(\"material\", \"methanol\", \"methanol demand.xlsx\"),\n", - " sheet_name=\"GDP_baseline\",\n", - " ) \n", - "df = df_gdp[(~df_gdp[\"Region\"].isna()) & (df_gdp[\"Region\"] != \"World\")]\n", - "df = df.dropna(axis=1)\n", - "df" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-23T14:34:29.280833300Z", - "start_time": "2023-11-23T14:34:29.164902900Z" - } - }, - "id": "77768b1f5532e68e" - }, - { - "cell_type": "code", - "execution_count": 17, - "outputs": [ - { - "data": { - "text/plain": "year_act 1990 1995 2000 2005 2010 \\\nnode_loc \nR12_AFR 271.455524 290.007532 345.060976 442.017934 581.052726 \nR12_CHN 838.663234 1455.044821 2160.256814 3396.324652 5703.315566 \nR12_EEU 890.917087 802.783706 944.330209 1164.365537 1356.751983 \nR12_FSU 1749.056217 1035.815476 1116.838624 1549.642196 1887.986772 \nR12_LAM 1777.475911 2104.621622 2448.478261 2794.058167 3428.029377 \nR12_MEA 923.149621 1139.373737 1518.708333 1872.857955 2347.592803 \nR12_NAM 8424.876209 9511.891683 11766.032882 13260.831721 13881.779497 \nR12_PAO 3232.875109 3491.610480 3743.429694 4056.182533 4197.181659 \nR12_PAS 984.333333 1414.102258 1631.705179 2061.975432 3085.776892 \nR12_RCPA 41.795276 65.186554 90.467595 130.345245 182.959419 \nR12_SAS 420.882861 537.195129 700.336621 968.940197 1375.864982 \nR12_WEU 8056.231293 8760.541667 10122.277211 11096.446429 11614.874150 \n\nyear_act 2015 2020 2025 2030 \\\nnode_loc \nR12_AFR 716.813461 998.989240 1368.018625 1856.875242 \nR12_CHN 6612.838095 12597.249546 18796.728814 26265.311778 \nR12_EEU 1429.632941 1782.528479 2135.375473 2527.594422 \nR12_FSU 2070.092516 2824.383598 3612.532110 4540.650530 \nR12_LAM 3752.743938 4997.596357 6313.315558 7870.347914 \nR12_MEA 2728.016143 3620.985480 4689.533422 6001.189152 \nR12_NAM 15164.541706 18030.122824 20731.982116 23411.377937 \nR12_PAO 4444.424839 4865.437555 5328.230735 5790.865455 \nR12_PAS 3667.170716 4867.546481 6274.462718 7885.997801 \nR12_RCPA 191.311034 354.019988 525.926102 759.780600 \nR12_SAS 1722.991014 2559.911529 3815.393732 5561.916763 \nR12_WEU 12353.633028 13849.192177 15375.603814 17016.715296 \n\nyear_act 2035 2040 2045 2050 \\\nnode_loc \nR12_AFR 2479.366774 3314.603412 4517.417565 6162.000935 \nR12_CHN 31779.168946 37291.085088 42057.027778 46300.743062 \nR12_EEU 2908.785626 3307.539451 3673.647838 4045.608182 \nR12_FSU 5400.750295 6288.079443 7034.991188 7731.857143 \nR12_LAM 9439.545455 11247.304734 13141.528193 15271.185657 \nR12_MEA 7468.361988 9180.558757 10973.157936 12956.405973 \nR12_NAM 25908.973396 28432.682303 30839.028757 33195.516022 \nR12_PAO 6207.936141 6620.625825 7084.795192 7566.369971 \nR12_PAS 9667.563245 11716.739693 13845.204708 16171.420315 \nR12_RCPA 976.608206 1238.055602 1519.934896 1830.640107 \nR12_SAS 7857.039482 11068.494608 14866.729189 19848.371084 \nR12_WEU 18838.770758 20906.461751 23218.843794 25700.197292 \n\nyear_act 2055 2060 2070 2080 \\\nnode_loc \nR12_AFR 8577.989840 11963.585420 22747.571333 39895.853924 \nR12_CHN 49633.467401 52750.003770 58298.326044 63001.761304 \nR12_EEU 4427.270005 4850.211186 5775.938713 6672.119355 \nR12_FSU 8453.431718 9324.554152 11366.946565 13374.462312 \nR12_LAM 17543.266146 20056.958333 25680.038879 32385.211186 \nR12_MEA 15113.972648 17571.820273 23368.410602 30275.401896 \nR12_NAM 35609.614390 38212.132723 43260.486550 47544.976471 \nR12_PAO 8111.292818 8719.640947 10185.261488 11671.063437 \nR12_PAS 18335.382760 20622.422972 25656.708175 31310.156530 \nR12_RCPA 2157.717172 2515.990575 3360.284294 4304.254469 \nR12_SAS 25211.747491 31827.305808 46668.655253 63617.247899 \nR12_WEU 28475.555666 31585.048057 38793.798913 45819.247178 \n\nyear_act 2090 2100 2110 \nnode_loc \nR12_AFR 64039.060554 96804.093067 96804.093067 \nR12_CHN 67276.308380 69794.088167 69794.088167 \nR12_EEU 7552.177728 8315.598163 8315.598163 \nR12_FSU 15560.537726 17847.927374 18904.017751 \nR12_LAM 40462.277720 49322.424009 49322.424009 \nR12_MEA 38534.015030 48464.477103 48464.477103 \nR12_NAM 51359.255294 54956.687059 54956.687059 \nR12_PAO 12839.161176 13774.255294 13774.255294 \nR12_PAS 37491.349057 43620.050164 43620.050164 \nR12_RCPA 5373.515084 6453.469838 6453.469838 \nR12_SAS 82725.072711 104536.872483 104536.872483 \nR12_WEU 52976.574913 59671.758824 59671.758824 ", - "text/html": "
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" - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_gdp.pivot(columns=\"year_act\", values=\"value\", index=\"node_loc\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-23T14:30:40.784322100Z", - "start_time": "2023-11-23T14:30:40.746572900Z" - } - }, - "id": "6c4ab6e19490cdd0" - }, - { - "cell_type": "code", - "execution_count": 4, - "outputs": [ - { - "data": { - "text/plain": "" - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from message_data.model.material import data_methanol_new\n", - "import importlib\n", - "importlib.reload(data_methanol_new)" - ], - "metadata": { - "collapsed": false - }, - "id": "17e354a124d8a5e9" - }, - { - "cell_type": "code", - "execution_count": 5, - "outputs": [], - "source": [ - "meth_dict = data_methanol_new.gen_data_methanol_new(scen)" - ], - "metadata": { - "collapsed": false - }, - "id": "9c54b44117ba6e67" - }, - { - "cell_type": "code", - "execution_count": 9, - "outputs": [], - "source": [ - "setlist = scen.set_list()" - ], - "metadata": { - "collapsed": false - }, - "id": "dfaacb0ebce0d997" - }, - { - "cell_type": "code", - "execution_count": 21, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "node :\n", - "\n", - "level :\n", - "final_material\n", - "\n", - "year :\n", - "time :\n", - "\n", - "value :\n", - "\n", - "unit :\n", - "\n", - "node_loc :\n", - "\n", - "year_vtg :\n", - "\n", - "value :\n", - "\n", - "unit :\n", - "\n", - "node_loc :\n", - "\n", - "year_vtg :\n", - "\n", - "year_act :\n", - "\n", - "value :\n", - "\n", - "unit :\n", - "\n", - "node_loc :\n", - "\n", - "year_vtg :\n", - "\n", - "year_act :\n", - "\n", - "mode :\n", - "fuel\n", - "feedstock\n", - "ethanol\n", - "\n", - "time :\n", - "\n", - "value :\n", - "\n", - "unit :\n", - "\n", - "node_loc :\n", - "\n", - "year_vtg :\n", - "\n", - "year_act :\n", - 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"CO2_PtX_trans_disp_split\n", - "meth_exp_tot\n", - "\n", - "node_rel :\n", - "\n", - "year_rel :\n", - "\n", - "value :\n", - "\n", - "unit :\n", - "\n", - "node_loc :\n", - "\n", - "year_vtg :\n", - "\n", - "year_act :\n", - "\n", - "mode :\n", - "feedstock\n", - "fuel\n", - "ethanol\n", - "\n", - "value :\n", - "\n", - "unit :\n", - "\n", - "node :\n", - "\n", - "year_vtg :\n", - "\n", - "year_act :\n", - "\n", - "mode :\n", - "feedstock\n", - "fuel\n", - "\n", - "time :\n", - "\n", - "type_addon :\n", - "methanol_synthesis_addon\n", - "\n", - "value :\n", - "\n", - "unit :\n", - "\n", - "node :\n", - "\n", - "year_act :\n", - "\n", - "mode :\n", - "feedstock\n", - "fuel\n", - "\n", - "time :\n", - "\n", - "type_addon :\n", - "methanol_synthesis_addon\n", - "\n", - "value :\n", - "\n", - "unit :\n", - "\n", - "node_loc :\n", - "\n", - "year_act :\n", - "\n", - "mode :\n", - "feedstock\n", - "fuel\n", - "\n", - "time :\n", - "\n", - "value :\n", - "\n", - "unit :\n", - "\n", - 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- "\n", - "node_loc :\n", - "\n", - "year_vtg :\n", - "\n", - "value :\n", - "\n", - "unit :\n", - "\n", - "node_loc :\n", - "\n", - "year_act :\n", - "\n", - "value :\n", - "\n", - "unit :\n", - "\n", - "node_loc :\n", - "\n", - "year_act :\n", - "\n", - "time :\n", - "\n", - "value :\n", - "\n", - "unit :\n", - "\n", - "node_loc :\n", - "\n", - "year_act :\n", - "\n", - "time :\n", - "\n", - "value :\n", - "\n", - "unit :\n", - "\n", - "node_loc :\n", - "\n", - "year_act :\n", - "\n", - "time :\n", - "\n", - "value :\n", - "\n", - "unit :\n", - "\n", - "node_loc :\n", - "\n", - "year_act :\n", - "\n", - "time :\n", - "\n", - "value :\n", - "\n", - "unit :\n", - "\n", - "node_loc :\n", - "\n", - "year_act :\n", - "\n", - "time :\n", - "\n", - "value :\n", - "\n", - "unit :\n", - "\n", - "node_loc :\n", - "\n", - "year_act :\n", - "\n", - "time :\n", - "\n", - "value :\n", - "\n", - "unit :\n", - "\n", - "node_loc :\n", - "\n", - "year_act :\n", - "\n", - "mode :\n", - "feedstock\n", - "fuel\n", - "\n", - "time :\n", - "\n", - "value :\n", - "\n", - "unit :\n", - "\n", - "node_loc :\n", - "\n", - "year_vtg :\n", - "\n", - "value :\n", - "\n", - "unit :\n" - ] - } - ], - "source": [ - "for par, df in meth_dict.items():\n", - " for col in df.columns:\n", - " df_set = list(df[col].unique())\n", - " if col in [\"technology\", \"commodity\", \"emission\"]:\n", - " continue\n", - " print(col,\":\")\n", - " if col in setlist:\n", - " for _set in df_set:\n", - " if _set not in list(scen.set(col)):\n", - " print(_set)\n", - " print()" - ], - "metadata": { - "collapsed": false - }, - "id": "57d247938684e749" - }, - { - "cell_type": "code", - "execution_count": 25, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "relation :\n" - ] - } - ], - "source": [ - "relations = []\n", - "for par, df in meth_dict.items():\n", - " if par != \"relation_activity\":\n", - " continue\n", - " for col in df.columns:\n", - " df_set = list(df[col].unique())\n", - " if col not in [\"relation\"]:\n", - " continue\n", - " print(col,\":\")\n", - " if col in setlist:\n", - " for _set in df_set:\n", - " if _set not in list(scen.set(col)):\n", - " relations.append(_set)\n", - " print()" - ], - "metadata": { - "collapsed": false - }, - "id": "618e1291b9c47679" - }, - { - "cell_type": "code", - "execution_count": 33, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'common': {'commodity': {'require': ['coal', 'gas', 'electr', 'ethanol', 'methanol', 'fueloil', 'lightoil', 'hydrogen', 'lh2', 'd_heat'], 'add': ['biomass', 'water', 'fresh_water']}, 'level': {'require': ['primary', 'secondary', 'useful', 'final', 'water_supply', 'export', 'import']}, 'type_tec': {'add': ['industry']}, 'mode': {'add': ['M1', 'M2']}, 'emission': {'add': ['CO2', 'CH4', 'N2O', 'NOx', 'SO2', 'PM2p5', 'CF4']}, 'year': {'add': [1980, 1990, 2000, 2010]}, 'type_year': {'add': [2010]}}, 'generic': {'commodity': {'add': ['ht_heat', 'lt_heat']}, 'level': {'add': ['useful_steel', 'useful_cement', 'useful_aluminum', 'useful_refining', 'useful_petro', 'useful_resins']}, 'technology': {'add': ['furnace_foil_steel', 'furnace_loil_steel', 'furnace_biomass_steel', 'furnace_ethanol_steel', 'furnace_methanol_steel', 'furnace_gas_steel', 'furnace_coal_steel', 'furnace_elec_steel', 'furnace_h2_steel', 'hp_gas_steel', 'hp_elec_steel', 'fc_h2_steel', 'solar_steel', 'dheat_steel', 'furnace_foil_cement', 'furnace_loil_cement', 'furnace_biomass_cement', 'furnace_ethanol_cement', 'furnace_methanol_cement', 'furnace_gas_cement', 'furnace_coal_cement', 'furnace_elec_cement', 'furnace_h2_cement', 'hp_gas_cement', 'hp_elec_cement', 'fc_h2_cement', 'solar_cement', 'dheat_cement', 'furnace_coal_aluminum', 'furnace_foil_aluminum', 'furnace_loil_aluminum', 'furnace_ethanol_aluminum', 'furnace_biomass_aluminum', 'furnace_methanol_aluminum', 'furnace_gas_aluminum', 'furnace_elec_aluminum', 'furnace_h2_aluminum', 'hp_gas_aluminum', 'hp_elec_aluminum', 'fc_h2_aluminum', 'solar_aluminum', 'dheat_aluminum', 'furnace_coke_petro', 'furnace_coal_petro', 'furnace_foil_petro', 'furnace_loil_petro', 'furnace_ethanol_petro', 'furnace_biomass_petro', 'furnace_methanol_petro', 'furnace_gas_petro', 'furnace_elec_petro', 'furnace_h2_petro', 'hp_gas_petro', 'hp_elec_petro', 'fc_h2_petro', 'solar_petro', 'dheat_petro', 'furnace_coke_refining', 'furnace_coal_refining', 'furnace_foil_refining', 'furnace_loil_refining', 'furnace_ethanol_refining', 'furnace_biomass_refining', 'furnace_methanol_refining', 'furnace_gas_refining', 'furnace_elec_refining', 'furnace_h2_refining', 'hp_gas_refining', 'hp_elec_refining', 'fc_h2_refining', 'solar_refining', 'dheat_refining', 'furnace_coal_resins', 'furnace_foil_resins', 'furnace_loil_resins', 'furnace_ethanol_resins', 'furnace_biomass_resins', 'furnace_methanol_resins', 'furnace_gas_resins', 'furnace_elec_resins', 'furnace_h2_resins', 'hp_gas_resins', 'hp_elec_resins', 'fc_h2_resins', 'solar_resins', 'dheat_resins']}, 'mode': {'add': ['low_temp', 'high_temp']}}, 'petro_chemicals': {'commodity': {'require': ['crudeoil'], 'add': ['HVC', 'naphtha', 'kerosene', 'diesel', 'atm_residue', 'refinery_gas', 'atm_gasoil', 'atm_residue', 'vacuum_gasoil', 'vacuum_residue', 'gasoline', 'heavy_foil', 'light_foil', 'propylene', 'pet_coke', 'ethane', 'propane', 'ethylene', 'BTX']}, 'level': {'require': ['secondary', 'final'], 'add': ['pre_intermediate', 'desulfurized', 'intermediate', 'secondary_material', 'final_material', 'demand']}, 'balance_equality': {'add': [['lightoil', 'import'], ['lightoil', 'export'], ['fueloil', 'import'], ['fueloil', 'export']]}, 'mode': {'add': ['atm_gasoil', 'vacuum_gasoil', 'naphtha', 'kerosene', 'diesel', 'cracking_gasoline', 'cracking_loil', 'ethane', 'propane', 'light_foil', 'refinery_gas', 'refinery_gas_int', 'heavy_foil', 'pet_coke', 'atm_residue', 'vacuum_residue', 'gasoline', 'ethylene', 'propylene', 'BTX']}, 'technology': {'add': ['atm_distillation_ref', 'vacuum_distillation_ref', 'hydrotreating_ref', 'catalytic_cracking_ref', 'visbreaker_ref', 'coking_ref', 'catalytic_reforming_ref', 'hydro_cracking_ref', 'steam_cracker_petro', 'ethanol_to_ethylene_petro', 'agg_ref', 'gas_processing_petro', 'trade_petro', 'import_petro', 'export_petro', 'feedstock_t/d', 'production_HVC'], 'remove': ['ref_hil', 'ref_lol']}, 'shares': {'add': ['steam_cracker']}}, 'steel': {'commodity': {'add': ['steel', 'pig_iron', 'sponge_iron', 'sinter_iron', 'pellet_iron', 'ore_iron', 'limestone_iron', 'coke_iron', 'slag_iron', 'co_gas', 'bf_gas']}, 'level': {'add': ['new_scrap', 'old_scrap_1', 'old_scrap_2', 'old_scrap_3', 'primary_material', 'secondary_material', 'tertiary_material', 'final_material', 'useful_material', 'waste_material', 'product', 'demand', 'dummy_emission', 'end_of_life', 'dummy_end_of_life']}, 'technology': {'add': ['cokeoven_steel', 'sinter_steel', 'pellet_steel', 'bf_steel', 'dri_steel', 'sr_steel', 'bof_steel', 'eaf_steel', 'prep_secondary_steel_1', 'prep_secondary_steel_2', 'prep_secondary_steel_3', 'finishing_steel', 'manuf_steel', 'scrap_recovery_steel', 'DUMMY_ore_supply', 'DUMMY_limestone_supply_steel', 'DUMMY_coal_supply', 'DUMMY_gas_supply', 'trade_steel', 'import_steel', 'export_steel', 'other_EOL_steel', 'total_EOL_steel']}, 'relation': {'add': ['minimum_recycling_steel', 'max_global_recycling_steel', 'max_regional_recycling_steel']}, 'balance_equality': {'add': [['steel', 'old_scrap_1'], ['steel', 'old_scrap_2'], ['steel', 'old_scrap_3'], ['steel', 'end_of_life'], ['steel', 'product'], ['steel', 'new_scrap'], ['steel', 'useful_material'], ['steel', 'final_material']]}}, 'cement': {'commodity': {'add': ['cement', 'clinker_cement', 'raw_meal_cement', 'limestone_cement']}, 'level': {'add': ['primary_material', 'secondary_material', 'tertiary_material', 'final_material', 'useful_material', 'demand', 'dummy_end_of_life', 'end_of_life']}, 'technology': {'add': ['raw_meal_prep_cement', 'clinker_dry_cement', 'clinker_wet_cement', 'clinker_dry_ccs_cement', 'clinker_wet_ccs_cement', 'grinding_ballmill_cement', 'grinding_vertmill_cement', 'DUMMY_limestone_supply_cement', 'total_EOL_cement', 'other_EOL_cement', 'scrap_recovery_cement'], 'remove': ['cement_co2scr', 'cement_CO2']}, 'relation': {'remove': ['cement_pro', 'cement_scrub_lim']}, 'balance_equality': {'add': [['cement', 'end_of_life']]}, 'addon': {'add': ['clinker_dry_ccs_cement', 'clinker_wet_ccs_cement']}, 'type_addon': {'add': ['ccs_cement']}, 'map_tec_addon': {'add': [['clinker_dry_cement', 'ccs_cement'], ['clinker_wet_cement', 'ccs_cement']]}, 'cat_addon': {'add': [['ccs_cement', 'clinker_dry_ccs_cement'], ['ccs_cement', 'clinker_wet_ccs_cement']]}}, 'aluminum': {'commodity': {'add': ['aluminum']}, 'level': {'add': ['new_scrap', 'old_scrap_1', 'old_scrap_2', 'old_scrap_3', 'useful_aluminum', 'final_material', 'useful_material', 'product', 'secondary_material', 'demand', 'end_of_life', 'dummy_end_of_life_1', 'dummy_end_of_life_2', 'dummy_end_of_life_3']}, 'technology': {'add': ['soderberg_aluminum', 'prebake_aluminum', 'secondary_aluminum', 'prep_secondary_aluminum_1', 'prep_secondary_aluminum_2', 'prep_secondary_aluminum_3', 'finishing_aluminum', 'manuf_aluminum', 'scrap_recovery_aluminum_1', 'scrap_recovery_aluminum_2', 'scrap_recovery_aluminum_3', 'DUMMY_alumina_supply', 'trade_aluminum', 'import_aluminum', 'export_aluminum', 'other_EOL_aluminum', 'total_EOL_aluminum']}, 'relation': {'add': ['minimum_recycling_aluminum', 'maximum_recycling_aluminum']}, 'balance_equality': {'add': [['aluminum', 'old_scrap_1'], ['aluminum', 'old_scrap_2'], ['aluminum', 'old_scrap_3'], ['aluminum', 'end_of_life'], ['aluminum', 'product'], ['aluminum', 'new_scrap']]}}, 'fertilizer': {'commodity': {'require': ['electr', 'freshwater_supply'], 'add': ['NH3', 'Fertilizer Use|Nitrogen', 'wastewater']}, 'level': {'add': ['secondary_material', 'final_material', 'wastewater']}, 'technology': {'require': ['h2_bio_ccs'], 'add': ['biomass_NH3', 'electr_NH3', 'gas_NH3', 'coal_NH3', 'fueloil_NH3', 'NH3_to_N_fertil', 'trade_NFert', 'export_NFert', 'import_NFert', 'trade_NH3', 'export_NH3', 'import_NH3', 'residual_NH3', 'biomass_NH3_ccs', 'gas_NH3_ccs', 'coal_NH3_ccs', 'fueloil_NH3_ccs']}, 'relation': {'add': ['NH3_trd_cap', 'NFert_trd_cap']}}, 'methanol': {'commodity': {'require': ['electr', 'biomass', 'hydrogen', 'd_heat'], 'add': ['methanol', 'ht_heat', 'formaldehyde', 'ethylene', 'propylene', 'fcoh_resin']}, 'level': {'add': ['final_material', 'secondary_material', 'primary_material', 'export_fs', 'import_fs']}, 'mode': {'add': ['feedstock', 'fuel', 'ethanol']}, 'technology': {'add': ['meth_bio', 'meth_bio_ccs', 'meth_h2', 'meth_t_d_material', 'MTO_petro', 'CH2O_synth', 'CH2O_to_resin', 'meth_coal', 'meth_coal_ccs', 'meth_ng', 'meth_ng_ccs', 'meth_t_d', 'meth_bal', 'meth_trd', 'meth_exp', 'meth_imp'], 'remove': ['sp_meth_I', 'meth_rc', 'meth_ic_trp', 'meth_fc_trp', 'meth_i', 'meth_coal', 'meth_coal_ccs', 'meth_ng', 'meth_ng_ccs', 'meth_t_d', 'meth_bal', 'meth_trd', 'meth_exp', 'meth_imp']}, 'addon': {'add': ['meth_h2']}, 'type_addon': {'add': ['methanol_synthesis_addon']}, 'map_tec_addon': {'add': [['h2_elec', 'methanol_synthesis_addon']]}, 'cat_addon': {'add': [['methanol_synthesis_addon', 'meth_h2']]}, 'relation': {'add': ['CO2_PtX_trans_disp_split', 'meth_exp_tot']}, 'balance_equality': {'add': [['methanol', 'export'], ['methanol', 'export_fs'], ['methanol', 'import'], ['methanol', 'import_fs'], ['methanol', 'final']]}}, 'buildings': {'level': {'add': ['service']}, 'commodity': {'add': ['floor_area']}, 'technology': {'add': ['buildings']}}, 'power_sector': {'unit': {'add': ['t/kW']}}}\n" - ] - }, - { - "data": { - "text/plain": "{'steel': ['minimum_recycling_steel',\n 'max_global_recycling_steel',\n 'max_regional_recycling_steel'],\n 'aluminum': ['minimum_recycling_aluminum', 'maximum_recycling_aluminum'],\n 'fertilizer': ['NH3_trd_cap', 'NFert_trd_cap'],\n 'methanol': ['CO2_PtX_trans_disp_split', 'meth_exp_tot']}" - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import yaml\n", - "\n", - "file_path = \"C:/Users\\maczek\\PycharmProjects\\message_data\\data\\material\\set.yaml\"\n", - "try:\n", - " with open(file_path, 'r') as file:\n", - " yaml_data = yaml.safe_load(file)\n", - " #print(yaml_data)\n", - "except FileNotFoundError:\n", - " print(\"File not found.\")\n", - "except yaml.YAMLError as e:\n", - " print(\"Error reading YAML:\", e)\n", - "for i in yaml_data.values():\n", - " if \"add\" in i.keys():\n", - " i[\"add\"]\n", - "tec_list = {}\n", - "for k, v in list(yaml_data.items()):\n", - " if \"relation\" in v.keys():\n", - " if \"add\" in v[\"relation\"].keys():\n", - " tec_list[k] = v[\"relation\"][\"add\"]\n", - "tec_list" - ], - "metadata": { - "collapsed": false - }, - "id": "63202ecc1092200d" - }, - { - "cell_type": "code", - "execution_count": 34, - "outputs": [ - { - "data": { - "text/plain": "['minimum_recycling_steel',\n 'max_global_recycling_steel',\n 'max_regional_recycling_steel',\n 'minimum_recycling_aluminum',\n 'maximum_recycling_aluminum',\n 'NH3_trd_cap',\n 'NFert_trd_cap',\n 'CO2_PtX_trans_disp_split',\n 'meth_exp_tot']" - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tec_list_flat = [item for sublist in tec_list.values() for item in sublist]\n", - "tec_list_flat" - ], - "metadata": { - "collapsed": false - }, - "id": "ccc0003e590dd93b" - }, - { - "cell_type": "code", - "execution_count": 36, - "outputs": [], - "source": [ - "rels_depr = [i for i in relations if i not in tec_list_flat]" - ], - "metadata": { - "collapsed": false - }, - "id": "184529ea8b44a9d5" - }, - { - "cell_type": "code", - "execution_count": 37, - "outputs": [], - "source": [ - "import yaml\n", - "file_path = 'missing_rels.yaml'\n", - "\n", - "# Dump the list to the YAML file\n", - "with open(file_path, 'w') as file:\n", - " yaml.dump(rels_depr, file, default_flow_style=False)" - ], - "metadata": { - "collapsed": false - }, - "id": "88612c44b58baa7b" - }, - { - "cell_type": "code", - "execution_count": 40, - "outputs": [], - "source": [ - "file.close()" - ], - "metadata": { - "collapsed": false - }, - "id": "361019e1f3b9621f" - }, - { - "cell_type": "code", - "execution_count": 41, - "outputs": [ - { - "data": { - "text/plain": "['gas_util',\n 'SO2_red_synf',\n 'global_GSO2',\n 'global_GCO2',\n 'OilPrices',\n 'PE_export_total',\n 'PE_import_total',\n 'PE_synliquids',\n 'PE_total_direct',\n 'PE_total_engineering',\n 'FE_transport',\n 'SO2_red_ref',\n 'trp_energy',\n 'total_TCO2',\n 'nuc_lc_in_el',\n 'FE_liquids',\n 'FE_final_energy']" - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "file_path = \"C:/Users\\maczek\\PycharmProjects\\message_data\\message_data\\model\\material\\petrochemical model fixes notebooks\\missing_rels.yaml\"\n", - "\n", - "with open(file_path, 'r') as file:\n", - " yaml_data = yaml.safe_load(file)\n", - " \n", - "yaml_data" - ], - "metadata": { - "collapsed": false - }, - "id": "28ceabe429b7e7bf" - }, - { - "cell_type": "code", - "execution_count": 2, - "outputs": [], - "source": [ - "df_scens = mp.scenario_list(model=\"MESSAGEix-Materials\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-27T11:02:02.883815800Z", - "start_time": "2023-11-27T11:02:02.715065500Z" - } - }, - "id": "e2da838c5fdf1877" - }, - { - "cell_type": "code", - "execution_count": 3, - "outputs": [ - { - "data": { - "text/plain": " model scenario scheme \\\n221 MESSAGEix-Materials SSP_supply_cost_test_LED MESSAGE \n225 MESSAGEix-Materials SSP_supply_cost_test_SSP4 MESSAGE \n224 MESSAGEix-Materials SSP_supply_cost_test_SSP3 MESSAGE \n222 MESSAGEix-Materials SSP_supply_cost_test_SSP1 MESSAGE \n226 MESSAGEix-Materials SSP_supply_cost_test_SSP5 MESSAGE \n.. ... ... ... \n32 MESSAGEix-Materials NoPolicy MESSAGE \n81 MESSAGEix-Materials NoPolicy_all_with_power_sect_bldg MESSAGE \n80 MESSAGEix-Materials NoPolicy_all_with_power_sect MESSAGE \n220 MESSAGEix-Materials NoPolicypower MESSAGE \n178 MESSAGEix-Materials NoPolicy_power MESSAGE \n\n is_default is_locked cre_user cre_date upd_user \\\n221 1 0 maczek 2023-11-27 01:43:09.000000 None \n225 1 0 maczek 2023-11-27 01:24:36.000000 None \n224 1 0 maczek 2023-11-27 01:17:11.000000 maczek \n222 1 0 maczek 2023-11-27 00:59:57.000000 maczek \n226 1 0 maczek 2023-11-27 00:54:14.000000 None \n.. ... ... ... ... ... \n32 1 0 unlu 2021-04-26 18:00:28.000000 maczek \n81 1 0 min 2021-03-26 17:18:25.000000 min \n80 1 0 min 2021-03-26 14:47:46.000000 None \n220 1 0 min 2021-03-26 11:23:39.000000 None \n178 1 1 min 2021-03-16 17:22:08.000000 None \n\n upd_date lock_user lock_date \\\n221 None None None \n225 None None None \n224 2023-11-27 01:24:20.000000 None None \n222 2023-11-27 01:07:41.000000 None None \n226 None None None \n.. ... ... ... \n32 2022-10-26 18:48:00.000000 None None \n81 2021-03-26 18:38:22.000000 None None \n80 None None None \n220 None None None \n178 None min 2021-03-16 17:23:14.000000 \n\n annotation version \n221 clone Scenario from 'MESSAGEix-Materials|SSP_s... 1 \n225 clone Scenario from 'MESSAGEix-Materials|SSP_s... 1 \n224 clone Scenario from 'MESSAGEix-Materials|SSP_s... 1 \n222 clone Scenario from 'MESSAGEix-Materials|SSP_s... 1 \n226 clone Scenario from 'MESSAGEix-Materials|SSP_s... 1 \n.. ... ... \n32 clone Scenario from 'ENGAGE_SSP2_v4.1.5|baseli... 88 \n81 clone Scenario from 'ENGAGE_SSP2_v4.1.4|baseli... 1 \n80 clone Scenario from 'ENGAGE_SSP2_v4.1.4|baseli... 2 \n220 clone Scenario from 'ENGAGE_SSP2_v4.1.4|baseli... 25 \n178 clone Scenario from 'ENGAGE_SSP2_v4.1.4|baseli... 1 \n\n[375 rows x 13 columns]", - "text/html": "
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modelscenarioschemeis_defaultis_lockedcre_usercre_dateupd_userupd_datelock_userlock_dateannotationversion
221MESSAGEix-MaterialsSSP_supply_cost_test_LEDMESSAGE10maczek2023-11-27 01:43:09.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-Materials|SSP_s...1
225MESSAGEix-MaterialsSSP_supply_cost_test_SSP4MESSAGE10maczek2023-11-27 01:24:36.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-Materials|SSP_s...1
224MESSAGEix-MaterialsSSP_supply_cost_test_SSP3MESSAGE10maczek2023-11-27 01:17:11.000000maczek2023-11-27 01:24:20.000000NoneNoneclone Scenario from 'MESSAGEix-Materials|SSP_s...1
222MESSAGEix-MaterialsSSP_supply_cost_test_SSP1MESSAGE10maczek2023-11-27 00:59:57.000000maczek2023-11-27 01:07:41.000000NoneNoneclone Scenario from 'MESSAGEix-Materials|SSP_s...1
226MESSAGEix-MaterialsSSP_supply_cost_test_SSP5MESSAGE10maczek2023-11-27 00:54:14.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-Materials|SSP_s...1
..........................................
32MESSAGEix-MaterialsNoPolicyMESSAGE10unlu2021-04-26 18:00:28.000000maczek2022-10-26 18:48:00.000000NoneNoneclone Scenario from 'ENGAGE_SSP2_v4.1.5|baseli...88
81MESSAGEix-MaterialsNoPolicy_all_with_power_sect_bldgMESSAGE10min2021-03-26 17:18:25.000000min2021-03-26 18:38:22.000000NoneNoneclone Scenario from 'ENGAGE_SSP2_v4.1.4|baseli...1
80MESSAGEix-MaterialsNoPolicy_all_with_power_sectMESSAGE10min2021-03-26 14:47:46.000000NoneNoneNoneNoneclone Scenario from 'ENGAGE_SSP2_v4.1.4|baseli...2
220MESSAGEix-MaterialsNoPolicypowerMESSAGE10min2021-03-26 11:23:39.000000NoneNoneNoneNoneclone Scenario from 'ENGAGE_SSP2_v4.1.4|baseli...25
178MESSAGEix-MaterialsNoPolicy_powerMESSAGE11min2021-03-16 17:22:08.000000NoneNonemin2021-03-16 17:23:14.000000clone Scenario from 'ENGAGE_SSP2_v4.1.4|baseli...1
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375 rows × 13 columns

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" - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_scens.sort_values(\"cre_date\", ascending=False)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-27T11:02:02.930659700Z", - "start_time": "2023-11-27T11:02:02.883815800Z" - } - }, - "id": "db66efc69c0773ab" - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12|baseline_DEFAULT', version: 21" - ], - "metadata": { - "collapsed": false - }, - "id": "d779b88ddf1f6f20" - }, - { - "cell_type": "code", - "execution_count": 138, - "outputs": [ - { - "data": { - "text/plain": " model scenario scheme \\\n0 MESSAGEix-GLOBIOM 1.1-R12 2degrees MESSAGE \n1 MESSAGEix-GLOBIOM 1.1-R12 MACRO_test MESSAGE \n2 MESSAGEix-GLOBIOM 1.1-R12 base_GLOBIOM_noSDG MESSAGE \n3 MESSAGEix-GLOBIOM 1.1-R12 base_GLOBIOM_noSDG_noConst MESSAGE \n4 MESSAGEix-GLOBIOM 1.1-R12 base_GLOBIOM_update MESSAGE \n5 MESSAGEix-GLOBIOM 1.1-R12 baseline_DEFAULT MESSAGE \n6 MESSAGEix-GLOBIOM 1.1-R12 baseline_DEFAULT_2507 MESSAGE \n7 MESSAGEix-GLOBIOM 1.1-R12 baseline_DEFAULT_clone MESSAGE \n8 MESSAGEix-GLOBIOM 1.1-R12 baseline_DEFAULT_gdp_update MESSAGE \n9 MESSAGEix-GLOBIOM 1.1-R12 baseline_DEFULT_check MESSAGE \n10 MESSAGEix-GLOBIOM 1.1-R12 baseline_prep_lu MESSAGE \n11 MESSAGEix-GLOBIOM 1.1-R12 baseline_prep_macro MESSAGE \n12 MESSAGEix-GLOBIOM 1.1-R12 baseline_prep_macro_macro MESSAGE \n13 MESSAGEix-GLOBIOM 1.1-R12 test to confirm MACRO converges MESSAGE \n\n is_default is_locked cre_user cre_date upd_user \\\n0 1 0 unlu 2023-10-09 11:41:56.000000 unlu \n1 1 0 min 2022-05-02 16:57:38.000000 None \n2 1 0 min 2023-08-01 10:58:46.000000 None \n3 1 0 min 2023-08-11 15:17:05.000000 None \n4 1 0 min 2023-04-18 14:25:06.000000 None \n5 1 0 min 2023-07-27 10:24:37.000000 None \n6 1 0 unlu 2023-07-25 17:28:24.000000 None \n7 1 0 unlu 2023-02-01 09:55:44.000000 None \n8 1 0 min 2022-03-03 00:24:43.000000 min \n9 1 0 unlu 2023-07-31 14:17:24.000000 unlu \n10 1 0 min 2022-04-30 17:29:17.000000 min \n11 1 0 min 2022-03-02 14:21:00.000000 None \n12 1 0 min 2022-04-30 20:29:07.000000 min \n13 1 0 min 2022-04-30 20:34:54.000000 min \n\n upd_date lock_user lock_date \\\n0 2023-10-09 12:06:23.000000 None None \n1 None None None \n2 None None None \n3 None None None \n4 None None None \n5 None None None \n6 None None None \n7 None None None \n8 2022-03-03 00:33:38.000000 None None \n9 2023-07-31 14:26:45.000000 None None \n10 2022-04-30 18:18:13.000000 None None \n11 None None None \n12 2022-04-30 20:34:39.000000 None None \n13 2022-04-30 20:40:13.000000 None None \n\n annotation version \n0 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n1 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n2 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 6 \n3 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n4 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 3 \n5 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 21 \n6 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n7 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n8 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 25 \n9 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n10 clone Scenario from 'MESSAGEix-GLOBIOM_R12_CHN... 17 \n11 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 1 \n12 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 17 \n13 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12... 6 ", - "text/html": "
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modelscenarioschemeis_defaultis_lockedcre_usercre_dateupd_userupd_datelock_userlock_dateannotationversion
0MESSAGEix-GLOBIOM 1.1-R122degreesMESSAGE10unlu2023-10-09 11:41:56.000000unlu2023-10-09 12:06:23.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
1MESSAGEix-GLOBIOM 1.1-R12MACRO_testMESSAGE10min2022-05-02 16:57:38.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
2MESSAGEix-GLOBIOM 1.1-R12base_GLOBIOM_noSDGMESSAGE10min2023-08-01 10:58:46.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...6
3MESSAGEix-GLOBIOM 1.1-R12base_GLOBIOM_noSDG_noConstMESSAGE10min2023-08-11 15:17:05.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
4MESSAGEix-GLOBIOM 1.1-R12base_GLOBIOM_updateMESSAGE10min2023-04-18 14:25:06.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...3
5MESSAGEix-GLOBIOM 1.1-R12baseline_DEFAULTMESSAGE10min2023-07-27 10:24:37.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...21
6MESSAGEix-GLOBIOM 1.1-R12baseline_DEFAULT_2507MESSAGE10unlu2023-07-25 17:28:24.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
7MESSAGEix-GLOBIOM 1.1-R12baseline_DEFAULT_cloneMESSAGE10unlu2023-02-01 09:55:44.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
8MESSAGEix-GLOBIOM 1.1-R12baseline_DEFAULT_gdp_updateMESSAGE10min2022-03-03 00:24:43.000000min2022-03-03 00:33:38.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...25
9MESSAGEix-GLOBIOM 1.1-R12baseline_DEFULT_checkMESSAGE10unlu2023-07-31 14:17:24.000000unlu2023-07-31 14:26:45.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
10MESSAGEix-GLOBIOM 1.1-R12baseline_prep_luMESSAGE10min2022-04-30 17:29:17.000000min2022-04-30 18:18:13.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM_R12_CHN...17
11MESSAGEix-GLOBIOM 1.1-R12baseline_prep_macroMESSAGE10min2022-03-02 14:21:00.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...1
12MESSAGEix-GLOBIOM 1.1-R12baseline_prep_macro_macroMESSAGE10min2022-04-30 20:29:07.000000min2022-04-30 20:34:39.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...17
13MESSAGEix-GLOBIOM 1.1-R12test to confirm MACRO convergesMESSAGE10min2022-04-30 20:34:54.000000min2022-04-30 20:40:13.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-R12...6
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" - }, - "execution_count": 138, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#'MESSAGEix-GLOBIOM 1.1-R12|baseline_DEFAULT'\n", - "mp.scenario_list(model=\"MESSAGEix-GLOBIOM 1.1-R12\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-26T23:09:05.055299100Z", - "start_time": "2023-11-26T23:09:04.964181400Z" - } - }, - "id": "a331aff3c9bd8ecf" - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [], - "metadata": { - "collapsed": false - }, - "id": "de251c1ec9184d76" - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/message_ix_models/model/material/output_file.csv b/message_ix_models/model/material/output_file.csv deleted file mode 100644 index a25accae8e..0000000000 --- a/message_ix_models/model/material/output_file.csv +++ /dev/null @@ -1,1065 +0,0 @@ -"","reg_no","region","year","consumption","pop","gdp.pcap","cons.pcap","del.t" -"1",1,"Canada (1)","1970",11085,21.71685,17450.8056114366,510.433142928187,-40 -"2",1,"Canada (1)","1971",11753,22.04843,18025.7,533.053827415376,-39 -"3",1,"Canada (1)","1972",12842,22.3441,18755.83,574.737850260248,-38 -"4",1,"Canada (1)","1973",14153,22.61467,19822,625.832700631935,-37 -"5",1,"Canada (1)","1974",15458,22.87666,20318.25,675.710527673183,-36 -"6",1,"Canada (1)","1975",13178,23.14227,20451.19,569.434199842971,-35 -"7",1,"Canada (1)","1976",12570,23.41609,21262.92,536.810372696723,-34 -"8",1,"Canada (1)","1977",12828,23.6952,21739.13,541.375468449306,-33 -"9",1,"Canada (1)","1978",13524,23.97564,22334.27,564.072533621626,-32 -"10",1,"Canada (1)","1979",15525,24.25064,22921.16,640.18928984967,-31 -"11",1,"Canada (1)","1980",13306,24.51628,23163.14,542.741394697727,-30 -"12",1,"Canada (1)","1981",14118,24.76985,23729.14,569.967117281695,-29 -"13",1,"Canada (1)","1982",9418,25.01605,22823.87,376.478300930802,-28 -"14",1,"Canada (1)","1983",10771,25.26695,23211.37,426.288095713966,-27 -"15",1,"Canada (1)","1984",13089,25.53886,24299.49,512.513087898207,-26 -"16",1,"Canada (1)","1985",13183,25.84259,25161.78,510.126887436592,-25 -"17",1,"Canada (1)","1986",12080,26.18316,25435.69,461.365243920138,-24 -"18",1,"Canada (1)","1987",13013,26.55471,26146.49,490.044892224393,-23 -"19",1,"Canada (1)","1988",15091,26.94366,27050.92,560.094656776399,-22 -"20",1,"Canada (1)","1989",13904,27.33053,27366.56,508.735103197779,-21 -"21",1,"Canada (1)","1990",11222,27.70085,27052.81,405.113922496963,-20 -"22",1,"Canada (1)","1991",10688,28.05119,26156.04,381.017703705262,-19 -"23",1,"Canada (1)","1992",11160,28.38487,26074.78,393.167204922904,-18 -"24",1,"Canada (1)","1993",12714,28.7027,26389.06,442.954843969383,-17 -"25",1,"Canada (1)","1994",15324,29.00752,27366.22,528.276805462859,-16 -"26",1,"Canada (1)","1995",15047,29.30209,27851.94,513.512858639094,-15 -"27",1,"Canada (1)","1996",14555,29.58475,28032.4,491.976440564818,-14 -"28",1,"Canada (1)","1997",17722,29.85598,28951.6,593.582927105391,-13 -"29",1,"Canada (1)","1998",18780,30.12362,29870.1,623.431048459647,-12 -"30",1,"Canada (1)","1999",18488,30.39813,31237.79,608.195306750777,-11 -"31",1,"Canada (1)","2000",19800,30.68682,32563.29,645.228146807,-10 -"32",1,"Canada (1)","2001",16900,30.99318,32816.54,545.281252198064,-9 -"33",1,"Canada (1)","2002",17700,31.31491,33429.25,565.225957858413,-8 -"34",1,"Canada (1)","2003",17300,31.64643,33701.3,546.665137268248,-7 -"35",1,"Canada (1)","2004",19314,31.97922,34374.57,603.954693078818,-6 -"36",1,"Canada (1)","2005",18014,32.30709,35069.09,557.586585483248,-5 -"37",1,"Canada (1)","2006",20132,32.62844,35681.66,617.007739260596,-4 -"38",1,"Canada (1)","2007",18248,32.9453,36292.62,553.887807972609,-3 -"39",1,"Canada (1)","2008",16799,33.25827,36102.59,505.107451469965,-2 -"40",1,"Canada (1)","2009",10591,33.57128,34818.05,315.477991902602,-1 -"41",1,"Canada (1)","2010",15656,33.88646,35171.96,462.013441356813,0 -"42",1,"Canada (1)","2011",15747,34.20387,35899.38,460.386500124109,1 -"43",1,"Canada (1)","2012",17305,34.52255,36724.76,501.266563449108,2 -"44",2,"USA (2)","1970",127304,210.1166,20233.0173504408,605.873119972434,-40 -"45",2,"USA (2)","1971",127663,211.3708,20588.72,603.976518989378,-39 -"46",2,"USA (2)","1972",138410,213.279,21545.43,648.962157549501,-38 -"47",2,"USA (2)","1973",149595,215.2026,22608.04,695.135653565524,-37 -"48",2,"USA (2)","1974",144120,217.1463,22299.5,663.700003177581,-36 -"49",2,"USA (2)","1975",116821,219.1142,22060.54,533.151206083403,-35 -"50",2,"USA (2)","1976",129953,221.1212,23036.41,587.700320005499,-34 -"51",2,"USA (2)","1977",133923,223.1746,23887.94,600.081729730892,-33 -"52",2,"USA (2)","1978",146445,225.2625,24995.59,650.108207091726,-32 -"53",2,"USA (2)","1979",140906,227.3659,25552.23,619.732334532135,-31 -"54",2,"USA (2)","1980",114433,229.4748,25256.33,498.673492688522,-30 -"55",2,"USA (2)","1981",128969,231.5823,25658,556.903528464827,-29 -"56",2,"USA (2)","1982",84290,233.7049,24922.98,360.668518289518,-28 -"57",2,"USA (2)","1983",94484,235.8841,25807.67,400.552644285901,-27 -"58",2,"USA (2)","1984",111714,238.1756,27399.11,469.040489453999,-26 -"59",2,"USA (2)","1985",105593,240.6182,28234.18,438.840453465282,-25 -"60",2,"USA (2)","1986",95286,243.2289,28889.27,391.754433786446,-24 -"61",2,"USA (2)","1987",101468,245.994,29519.71,412.481605242404,-23 -"62",2,"USA (2)","1988",110698,248.8842,30379.85,444.777129283418,-22 -"63",2,"USA (2)","1989",102351,251.8545,31081.96,406.389403405538,-21 -"64",2,"USA (2)","1990",103052,254.8715,31284.72,404.329240421153,-20 -"65",2,"USA (2)","1991",89600,257.9169,30855.28,347.398716408269,-19 -"66",2,"USA (2)","1992",97372,260.9965,31508.4,373.077799893868,-18 -"67",2,"USA (2)","1993",104357,264.1288,31972.56,395.098906291173,-17 -"68",2,"USA (2)","1994",117471,267.343,32871.2,439.401817141275,-16 -"69",2,"USA (2)","1995",112584,270.6544,33292.92,415.969590740073,-15 -"70",2,"USA (2)","1996",119433,274.0731,34108.96,435.770602806332,-14 -"71",2,"USA (2)","1997",123589,277.5733,35210.62,445.248156072648,-13 -"72",2,"USA (2)","1998",134634,281.0898,36237.3,478.971488826702,-12 -"73",2,"USA (2)","1999",125143,284.5352,37405.6,439.815530732226,-11 -"74",2,"USA (2)","2000",132891,287.8484,38340.24,461.670101345014,-10 -"75",2,"USA (2)","2001",114261,291.0017,38212.57,392.647190720879,-9 -"76",2,"USA (2)","2002",118226,294.015,38430.96,402.108735948846,-8 -"77",2,"USA (2)","2003",103881,296.9338,39013.13,349.845655833051,-7 -"78",2,"USA (2)","2004",123835,299.8273,40046.78,413.021095810822,-6 -"79",2,"USA (2)","2005",113325,302.7468,40880.21,374.322701346472,-5 -"80",2,"USA (2)","2006",128526,305.7028,41647.99,420.427945049898,-4 -"81",2,"USA (2)","2007",114050,308.6801,42071.21,369.476360802008,-3 -"82",2,"USA (2)","2008",102428,311.6721,41849.97,328.640260068193,-2 -"83",2,"USA (2)","2009",62393,314.665,40435.57,198.283889215515,-1 -"84",2,"USA (2)","2010",86983,317.6473,41046.07,273.835162458488,0 -"85",2,"USA (2)","2011",96344,320.6194,41798.14,300.493357544802,1 -"86",2,"USA (2)","2012",102012,323.5832,42499.48,315.257405205215,2 -"87",3,"Mexico (3)","1970",4168,50.5959,6910.35116215552,82.3782164167452,-40 -"88",3,"Mexico (3)","1971",4051,53.55186,7131.387,75.6462987466729,-39 -"89",3,"Mexico (3)","1972",4684,55.23661,7482.806,84.7988317892789,-38 -"90",3,"Mexico (3)","1973",5174,56.95376,7827.696,90.8456263467065,-37 -"91",3,"Mexico (3)","1974",6112,58.68935,8035.034,104.141552087389,-36 -"92",3,"Mexico (3)","1975",6211,60.43023,8251.832,102.779684935834,-35 -"93",3,"Mexico (3)","1976",5962,62.17798,8374.157,95.8860355386264,-34 -"94",3,"Mexico (3)","1977",6487,63.9267,8421.252,101.475596268852,-33 -"95",3,"Mexico (3)","1978",8060,65.64747,8935.027,122.777008771244,-32 -"96",3,"Mexico (3)","1979",8840,67.30379,9560.348,131.344757850932,-31 -"97",3,"Mexico (3)","1980",10317,68.87221,10205.26,149.799171538128,-30 -"98",3,"Mexico (3)","1981",11369,70.33874,10869.09,161.632124772209,-29 -"99",3,"Mexico (3)","1982",8181,71.71709,10593.25,114.073228570763,-28 -"100",3,"Mexico (3)","1983",6296,73.04627,9964.059,86.1919438186234,-27 -"101",3,"Mexico (3)","1984",7331,74.38186,10138.41,98.5589766106951,-26 -"102",3,"Mexico (3)","1985",7693,75.76497,10211.46,101.537689515353,-25 -"103",3,"Mexico (3)","1986",6608,77.20679,9644.588,85.5883271406569,-24 -"104",3,"Mexico (3)","1987",6485,78.69822,9637.398,82.4033885391563,-23 -"105",3,"Mexico (3)","1988",6969,80.23514,9570.521,86.8572049603204,-22 -"106",3,"Mexico (3)","1989",7256,81.80685,9780.728,88.6967289414028,-21 -"107",3,"Mexico (3)","1990",8804,83.40398,10079.66,105.558511716108,-20 -"108",3,"Mexico (3)","1991",10392,85.02397,10305.09,122.224356260946,-19 -"109",3,"Mexico (3)","1992",11314,86.66608,10476.68,130.547037549177,-18 -"110",3,"Mexico (3)","1993",10095,88.32349,10480.6,114.295755296807,-17 -"111",3,"Mexico (3)","1994",13223,89.98788,10745.36,146.942010412958,-16 -"112",3,"Mexico (3)","1995",7602,91.64993,9894.47,82.946053532174,-15 -"113",3,"Mexico (3)","1996",10588,93.31045,10217.9,113.47067772152,-14 -"114",3,"Mexico (3)","1997",12473,94.96079,10720.61,131.348949392691,-13 -"115",3,"Mexico (3)","1998",13009,96.57095,11059.1,134.709247449673,-12 -"116",3,"Mexico (3)","1999",11929,98.10286,11308.07,121.596862721433,-11 -"117",3,"Mexico (3)","2000",19800,99.53062,11881.7,198.933755260441,-10 -"118",3,"Mexico (3)","2001",18800,100.8404,11708.96,186.433215258964,-9 -"119",3,"Mexico (3)","2002",19800,102.0425,11666.68,194.036798392827,-8 -"120",3,"Mexico (3)","2003",21000,103.1652,11695.68,203.557013411499,-7 -"121",3,"Mexico (3)","2004",22298,104.2505,12038.26,213.888662404497,-6 -"122",3,"Mexico (3)","2005",22403,105.3296,12296.23,212.694247391047,-5 -"123",3,"Mexico (3)","2006",25476,106.4108,12757.33,239.411789028933,-4 -"124",3,"Mexico (3)","2007",25111,107.4868,13034.13,233.619383961566,-3 -"125",3,"Mexico (3)","2008",25984,108.5555,13084.07,239.361432631235,-2 -"126",3,"Mexico (3)","2009",17098,109.6099,11916.03,155.98955933725,-1 -"127",3,"Mexico (3)","2010",20034,110.6451,12121.01,181.065406421071,0 -"128",3,"Mexico (3)","2011",20869,111.6633,12483.56,186.892201824592,1 -"129",3,"Mexico (3)","2012",24059,112.6668,13052.16,213.541167406902,2 -"130",4,"Rest Central America (4)","1970",434,41.86008,5032.14566151399,10.3678731622109,-40 -"131",4,"Rest Central America (4)","1971",441,44.06901,5135.072,10.0070321525262,-39 -"132",4,"Rest Central America (4)","1972",557,45.03952,5338.452,12.3669168765564,-38 -"133",4,"Rest Central America (4)","1973",728,46.01947,5395.166,15.8193912272349,-37 -"134",4,"Rest Central America (4)","1974",925,47.00089,5357.973,19.6804783909411,-36 -"135",4,"Rest Central America (4)","1975",1086,47.9782,5451.598,22.6352801897528,-35 -"136",4,"Rest Central America (4)","1976",1008,48.94946,5626.064,20.5926684380175,-34 -"137",4,"Rest Central America (4)","1977",1000,49.91717,5897.804,20.033186977547,-33 -"138",4,"Rest Central America (4)","1978",1000,50.88577,6102.653,19.6518594491151,-32 -"139",4,"Rest Central America (4)","1979",1000,51.86187,6092.561,19.2819888677365,-31 -"140",4,"Rest Central America (4)","1980",1726,52.8503,6013.287,32.6582819775858,-30 -"141",4,"Rest Central America (4)","1981",1444,53.8526,6111.062,26.813932846325,-29 -"142",4,"Rest Central America (4)","1982",1347,54.8676,6097.064,24.5500076547908,-28 -"143",4,"Rest Central America (4)","1983",1408,55.8943,6153.557,25.1904040304646,-27 -"144",4,"Rest Central America (4)","1984",1570,56.93069,6230.195,27.577392791129,-26 -"145",4,"Rest Central America (4)","1985",1676,57.97546,6246.894,28.9087831299657,-25 -"146",4,"Rest Central America (4)","1986",1736,59.02685,6258.291,29.4103446143577,-24 -"147",4,"Rest Central America (4)","1987",1654,60.08547,6322.337,27.5274538087161,-23 -"148",4,"Rest Central America (4)","1988",1679,61.15466,6374.894,27.4549805362339,-22 -"149",4,"Rest Central America (4)","1989",1697,62.23912,6447.625,27.2658096708308,-21 -"150",4,"Rest Central America (4)","1990",1673,63.34122,6380.926,26.4125004223158,-20 -"151",4,"Rest Central America (4)","1991",1303,64.46304,6276.783,20.2131329828689,-19 -"152",4,"Rest Central America (4)","1992",1574,65.60065,6233.968,23.9936646969199,-18 -"153",4,"Rest Central America (4)","1993",1432,66.74309,6199.818,21.4554045969403,-17 -"154",4,"Rest Central America (4)","1994",1665,67.8755,6284.081,24.5302060389979,-16 -"155",4,"Rest Central America (4)","1995",1617,68.98741,6387.906,23.4390593877926,-15 -"156",4,"Rest Central America (4)","1996",1822,70.07453,6565.691,26.0008879117705,-14 -"157",4,"Rest Central America (4)","1997",2024,71.13991,6767.845,28.4509777985381,-13 -"158",4,"Rest Central America (4)","1998",1884,72.18943,6963.645,26.0980035442862,-12 -"159",4,"Rest Central America (4)","1999",1963,73.23238,7150.771,26.8050826697152,-11 -"160",4,"Rest Central America (4)","2000",3536,74.27577,7374.437,47.6063728454111,-10 -"161",4,"Rest Central America (4)","2001",3019,75.32159,7391.297,40.0814693370121,-9 -"162",4,"Rest Central America (4)","2002",3016,76.36828,7440.828,39.4928365546533,-8 -"163",4,"Rest Central America (4)","2003",2529,77.41533,7585.463,32.6679483249635,-7 -"164",4,"Rest Central America (4)","2004",2632,78.46117,7714.614,33.5452555703668,-6 -"165",4,"Rest Central America (4)","2005",2708,79.50519,7961.29,34.0606694984315,-5 -"166",4,"Rest Central America (4)","2006",2773,80.54659,8289.141,34.4272799134017,-4 -"167",4,"Rest Central America (4)","2007",2895,81.58723,8549.562,35.4834941693694,-3 -"168",4,"Rest Central America (4)","2008",3297,82.63269,8741.552,39.8994635174046,-2 -"169",4,"Rest Central America (4)","2009",2212,83.6889,8566.153,26.4312232566087,-1 -"170",4,"Rest Central America (4)","2010",2599,84.76005,8589.175,30.6630305196847,0 -"171",4,"Rest Central America (4)","2011",2566,85.848,8815.172,29.8900382070636,1 -"172",4,"Rest Central America (4)","2012",3210,86.94998,9076.81,36.9177773243881,2 -"173",5,"Brazil (5)","1970",6088,95.98846,4246.81244601682,63.424290794956,-40 -"174",5,"Brazil (5)","1971",7386,98.3547,4627.022,75.095547035373,-39 -"175",5,"Brazil (5)","1972",7662,100.7355,5062.171,76.0605744747383,-38 -"176",5,"Brazil (5)","1973",9513,103.1473,5634.889,92.2273292660108,-37 -"177",5,"Brazil (5)","1974",12799,105.607,6001.288,121.194617781018,-36 -"178",5,"Brazil (5)","1975",11241,108.1272,6166.741,103.960890506737,-35 -"179",5,"Brazil (5)","1976",10715,110.7081,6612.648,96.7860526917181,-34 -"180",5,"Brazil (5)","1977",11976,113.3463,6756.245,105.658499659892,-33 -"181",5,"Brazil (5)","1978",11932,116.0439,6812.455,102.823155719516,-32 -"182",5,"Brazil (5)","1979",12760,118.8022,7104.538,107.405418418177,-31 -"183",5,"Brazil (5)","1980",14303,121.6184,7572.326,117.605559685048,-30 -"184",5,"Brazil (5)","1981",12063,124.494,7072.421,96.896235963179,-29 -"185",5,"Brazil (5)","1982",10612,127.4183,6950.202,83.284740104051,-28 -"186",5,"Brazil (5)","1983",8580,130.3607,6561.69,65.8173820791082,-27 -"187",5,"Brazil (5)","1984",10673,133.2812,6756.077,80.0788108150287,-26 -"188",5,"Brazil (5)","1985",11995,136.1494,7139.268,88.1017470514009,-25 -"189",5,"Brazil (5)","1986",14528,138.9564,7553.835,104.550779956879,-24 -"190",5,"Brazil (5)","1987",14980,141.7046,7673.975,105.712870294966,-23 -"191",5,"Brazil (5)","1988",11720,144.3898,7523.531,81.1691684592679,-22 -"192",5,"Brazil (5)","1989",12462,147.0111,7631.713,84.7691092713407,-21 -"193",5,"Brazil (5)","1990",11048,149.5705,7178.574,73.8648329717424,-20 -"194",5,"Brazil (5)","1991",11245,152.0602,7167.804,73.950974679765,-19 -"195",5,"Brazil (5)","1992",10712,154.4847,7022.369,69.3402000327541,-18 -"196",5,"Brazil (5)","1993",12658,156.8735,7238.053,80.6892177455083,-17 -"197",5,"Brazil (5)","1994",13472,159.2665,7509.604,84.5877821136272,-16 -"198",5,"Brazil (5)","1995",14639,161.692,7723.665,90.5363283279321,-15 -"199",5,"Brazil (5)","1996",15793,164.1566,7771.271,96.2069146168963,-14 -"200",5,"Brazil (5)","1997",18730,166.6499,7913.353,112.39130656544,-13 -"201",5,"Brazil (5)","1998",17700,169.1618,7798.799,104.633552019428,-12 -"202",5,"Brazil (5)","1999",16302,171.6753,7703.894,94.958331221789,-11 -"203",5,"Brazil (5)","2000",17500,174.1745,7920.493,100.473949975456,-10 -"204",5,"Brazil (5)","2001",18500,176.6591,7911.419,104.721466372239,-9 -"205",5,"Brazil (5)","2002",17437,179.1234,8009.999,97.346298696876,-8 -"206",5,"Brazil (5)","2003",17283,181.5374,7994.121,95.2035228002604,-7 -"207",5,"Brazil (5)","2004",20351,183.8635,8344.161,110.685372572588,-6 -"208",5,"Brazil (5)","2005",18680,186.0746,8505.417,100.389843643356,-5 -"209",5,"Brazil (5)","2006",20592,188.1584,8745.075,109.43970611995,-4 -"210",5,"Brazil (5)","2007",24556,190.12,9145.536,129.160530191458,-3 -"211",5,"Brazil (5)","2008",26720,191.9714,9517.1,139.18739978976,-2 -"212",5,"Brazil (5)","2009",20640,193.7337,9368.056,106.537995196499,-1 -"213",5,"Brazil (5)","2010",29004,195.4232,9608.739,148.416359981824,0 -"214",5,"Brazil (5)","2011",27813,197.0412,9864.866,141.153220747742,1 -"215",5,"Brazil (5)","2012",27979,198.5855,10132.95,140.891454814173,2 -"216",6,"Rest South America (6)","1970",7530,96.40464,6420.1296626765,78.1082736266636,-40 -"217",6,"Rest South America (6)","1971",8192,97.62685,6490.836,83.9113420129811,-39 -"218",6,"Rest South America (6)","1972",8651,99.84344,6497.796,86.6456524334498,-38 -"219",6,"Rest South America (6)","1973",9260,102.0981,6636.509,90.6970844707198,-37 -"220",6,"Rest South America (6)","1974",11024,104.4029,6804.255,105.590936650227,-36 -"221",6,"Rest South America (6)","1975",10182,106.7655,6732.178,95.3678856934122,-35 -"222",6,"Rest South America (6)","1976",9474,109.1912,6781.431,86.7652338283671,-34 -"223",6,"Rest South America (6)","1977",11054,111.6749,6982.911,98.9837465715215,-33 -"224",6,"Rest South America (6)","1978",10553,114.201,6943.699,92.4072468717437,-32 -"225",6,"Rest South America (6)","1979",11003,116.7471,7185.399,94.2464523744059,-31 -"226",6,"Rest South America (6)","1980",9579,119.2977,7190.426,80.2949260547353,-30 -"227",6,"Rest South America (6)","1981",8534,121.845,7047.316,70.0398046698675,-29 -"228",6,"Rest South America (6)","1982",8777,124.3942,6683.035,70.557952058858,-28 -"229",6,"Rest South America (6)","1983",7322,126.9583,6425.478,57.6724798614978,-27 -"230",6,"Rest South America (6)","1984",8336,129.5569,6485.092,64.3423854692417,-26 -"231",6,"Rest South America (6)","1985",7397,132.2033,6324.582,55.9517046851327,-25 -"232",6,"Rest South America (6)","1986",8537,134.8992,6619.767,63.2842893063858,-24 -"233",6,"Rest South America (6)","1987",10036,137.6364,6768.011,72.9167574856651,-23 -"234",6,"Rest South America (6)","1988",10468,140.4059,6733.679,74.5552715377345,-22 -"235",6,"Rest South America (6)","1989",9049,143.1948,6376.971,63.1936355230777,-21 -"236",6,"Rest South America (6)","1990",7717,145.9918,6390.963,52.8591331841925,-20 -"237",6,"Rest South America (6)","1991",9158,148.7932,6732.243,61.5485116255313,-19 -"238",6,"Rest South America (6)","1992",11131,151.5962,7080.655,73.4253233260464,-18 -"239",6,"Rest South America (6)","1993",11151,154.3925,7195.21,72.2250109299351,-17 -"240",6,"Rest South America (6)","1994",11543,157.1728,7386.602,73.4414606089603,-16 -"241",6,"Rest South America (6)","1995",13276,159.9293,7507.502,83.0116807864475,-15 -"242",6,"Rest South America (6)","1996",13529,162.6594,7628.711,83.1737975180039,-14 -"243",6,"Rest South America (6)","1997",14629,165.3608,7964.193,88.4671578753852,-13 -"244",6,"Rest South America (6)","1998",14820,168.0274,7983.648,88.1999007304761,-12 -"245",6,"Rest South America (6)","1999",11116,170.6525,7600.39,65.1382194811093,-11 -"246",6,"Rest South America (6)","2000",12463,173.2326,7644.868,71.9437334543267,-10 -"247",6,"Rest South America (6)","2001",12119,175.7636,7586.33,68.9505676943349,-9 -"248",6,"Rest South America (6)","2002",10858,178.249,7237.485,60.9147877407447,-8 -"249",6,"Rest South America (6)","2003",12058,180.7024,7342.196,66.7284994554582,-7 -"250",6,"Rest South America (6)","2004",14313,183.1424,7874.344,78.1523011601901,-6 -"251",6,"Rest South America (6)","2005",15034,185.5829,8358.868,81.0096188819121,-5 -"252",6,"Rest South America (6)","2006",18067,188.0279,8862.133,96.086804139173,-4 -"253",6,"Rest South America (6)","2007",19758,190.4744,9389.734,103.730475066466,-3 -"254",6,"Rest South America (6)","2008",19704,192.9207,9785.698,102.135229656538,-2 -"255",6,"Rest South America (6)","2009",15322,195.3631,9543.877,78.4283214179136,-1 -"256",6,"Rest South America (6)","2010",18668,197.798,9644.25,94.3791140456425,0 -"257",6,"Rest South America (6)","2011",21212,200.2255,9827.167,105.940552027589,1 -"258",6,"Rest South America (6)","2012",22053,202.6449,10055.4,108.825832774474,2 -"259",7,"Northern Africa (7)","1970",2572,71.53033,2614.15184141864,35.9567752588308,-40 -"260",7,"Northern Africa (7)","1971",2470,73.61912,2693.479,33.5510666250833,-39 -"261",7,"Northern Africa (7)","1972",2896,75.41985,2964.886,38.3983792065352,-38 -"262",7,"Northern Africa (7)","1973",3199,77.25272,2966.555,41.4095451914185,-37 -"263",7,"Northern Africa (7)","1974",4221,79.14845,3170.684,53.3301662887902,-36 -"264",7,"Northern Africa (7)","1975",4579,81.13089,3279.306,56.439661884641,-35 -"265",7,"Northern Africa (7)","1976",4326,83.20176,3621.714,51.9940924326601,-34 -"266",7,"Northern Africa (7)","1977",4800,85.35855,3794.459,56.2333825961195,-33 -"267",7,"Northern Africa (7)","1978",5086,87.61505,3907.863,58.0493876337456,-32 -"268",7,"Northern Africa (7)","1979",5621,89.98675,4075.367,62.4647517551195,-31 -"269",7,"Northern Africa (7)","1980",6452,92.48152,4095.043,69.7652893248294,-30 -"270",7,"Northern Africa (7)","1981",7006,95.10136,3853.739,73.6687677231955,-29 -"271",7,"Northern Africa (7)","1982",6707,97.83386,3984.952,68.55499721671,-28 -"272",7,"Northern Africa (7)","1983",7065,100.653,3995.509,70.191648535066,-27 -"273",7,"Northern Africa (7)","1984",6668,103.5235,4019.197,64.4104961675368,-26 -"274",7,"Northern Africa (7)","1985",8322,106.4149,4133.836,78.2033343075077,-25 -"275",7,"Northern Africa (7)","1986",10142,109.3194,4022.011,92.7740181523133,-24 -"276",7,"Northern Africa (7)","1987",8271,112.229,3838.14,73.69752915913,-23 -"277",7,"Northern Africa (7)","1988",8953,115.1134,3884.93,77.7754805261594,-22 -"278",7,"Northern Africa (7)","1989",9002,117.9373,3957.57,76.3286932972011,-21 -"279",7,"Northern Africa (7)","1990",8051,120.6754,4004.541,66.7161658465603,-20 -"280",7,"Northern Africa (7)","1991",8367,123.3155,4053.06,67.8503513345849,-19 -"281",7,"Northern Africa (7)","1992",8259,125.8616,4021.332,65.6196965555817,-18 -"282",7,"Northern Africa (7)","1993",8359,128.3269,3965.028,65.1383303111039,-17 -"283",7,"Northern Africa (7)","1994",9684,130.7339,4006.419,74.0741307342625,-16 -"284",7,"Northern Africa (7)","1995",8949,133.1018,3971.964,67.2342522790826,-15 -"285",7,"Northern Africa (7)","1996",8464,135.4332,4127.618,62.4957543645133,-14 -"286",7,"Northern Africa (7)","1997",9022,137.7305,4164.798,65.5047356976124,-13 -"287",7,"Northern Africa (7)","1998",10913,140.0129,4257.673,77.9428181260441,-12 -"288",7,"Northern Africa (7)","1999",9557,142.3036,4331.432,67.1592285788975,-11 -"289",7,"Northern Africa (7)","2000",8706,144.6207,4392.518,60.1988512017989,-10 -"290",7,"Northern Africa (7)","2001",9037,146.9718,4505.486,61.4879861306727,-9 -"291",7,"Northern Africa (7)","2002",9303,149.3582,4580.779,62.2865031849607,-8 -"292",7,"Northern Africa (7)","2003",9926,151.7827,4702.743,65.3961222194624,-7 -"293",7,"Northern Africa (7)","2004",11452,154.2455,4853.247,74.2452778200985,-6 -"294",7,"Northern Africa (7)","2005",14699,156.7456,4994.974,93.7761570340731,-5 -"295",7,"Northern Africa (7)","2006",15271,159.2843,5169.681,95.8726001244316,-4 -"296",7,"Northern Africa (7)","2007",16197,161.8604,5336.363,100.067712670919,-3 -"297",7,"Northern Africa (7)","2008",18892,164.4664,5541.492,114.868447293794,-2 -"298",7,"Northern Africa (7)","2009",23483,167.0921,5643.445,140.539259486235,-1 -"299",7,"Northern Africa (7)","2010",19208,169.7283,5783.958,113.169106153776,0 -"300",7,"Northern Africa (7)","2011",16512,172.3709,5968.287,95.793431489886,1 -"301",7,"Northern Africa (7)","2012",19798,175.0158,6195.265,113.121215341701,2 -"302",8,"Western Africa (8)","1970",650,131.74,1363.06138638973,4.9339608319417,-40 -"303",8,"Western Africa (8)","1971",540,144.355,1385.493,3.74077794326487,-39 -"304",8,"Western Africa (8)","1972",571,148.0385,1391.376,3.85710473964543,-38 -"305",8,"Western Africa (8)","1973",710,151.873,1412.128,4.67495868258347,-37 -"306",8,"Western Africa (8)","1974",884,155.9128,1502.656,5.66983595958767,-36 -"307",8,"Western Africa (8)","1975",1380,160.195,1452.761,8.61450107681264,-35 -"308",8,"Western Africa (8)","1976",1327,164.7402,1515.54,8.05510737512763,-34 -"309",8,"Western Africa (8)","1977",1799,169.534,1525.414,10.6114407729423,-33 -"310",8,"Western Africa (8)","1978",1380,174.5343,1461.469,7.90675529108032,-32 -"311",8,"Western Africa (8)","1979",1025,179.6796,1479.4,5.70459863000586,-31 -"312",8,"Western Africa (8)","1980",1573,184.9263,1452.905,8.50609134557929,-30 -"313",8,"Western Africa (8)","1981",1362,190.2622,1362.24,7.15854226430683,-29 -"314",8,"Western Africa (8)","1982",1462,195.7041,1341.225,7.470461783887,-28 -"315",8,"Western Africa (8)","1983",859,201.2735,1282.053,4.26782462668955,-27 -"316",8,"Western Africa (8)","1984",1085,207.0039,1248.601,5.24144714181713,-26 -"317",8,"Western Africa (8)","1985",728,212.9206,1284.188,3.41911491889465,-25 -"318",8,"Western Africa (8)","1986",622,219.0201,1285.169,2.83992199802666,-24 -"319",8,"Western Africa (8)","1987",831,225.2956,1240.093,3.68848748044791,-23 -"320",8,"Western Africa (8)","1988",568,231.7652,1261,2.45075619635735,-22 -"321",8,"Western Africa (8)","1989",653,238.4503,1274.071,2.73851616039066,-21 -"322",8,"Western Africa (8)","1990",953,245.3616,1265.348,3.88406335791746,-20 -"323",8,"Western Africa (8)","1991",1251,252.5202,1257.347,4.95405912081489,-19 -"324",8,"Western Africa (8)","1992",1568,259.9104,1227.554,6.03284824308685,-18 -"325",8,"Western Africa (8)","1993",1061,267.4611,1194.141,3.96693201366479,-17 -"326",8,"Western Africa (8)","1994",775,275.0754,1166.051,2.81740933576758,-16 -"327",8,"Western Africa (8)","1995",801,282.6889,1174.865,2.8335035440019,-15 -"328",8,"Western Africa (8)","1996",770,290.269,1195.513,2.65271179492126,-14 -"329",8,"Western Africa (8)","1997",1047,297.85,1207.545,3.51519221084438,-13 -"330",8,"Western Africa (8)","1998",1092,305.5169,1215.845,3.57427035951203,-12 -"331",8,"Western Africa (8)","1999",1347,313.3915,1205.784,4.29813827114009,-11 -"332",8,"Western Africa (8)","2000",1518,321.5615,1211.977,4.7207143890049,-10 -"333",8,"Western Africa (8)","2001",2339,330.0531,1229.888,7.08673846723452,-9 -"334",8,"Western Africa (8)","2002",2126,338.8388,1228.412,6.27436999540785,-8 -"335",8,"Western Africa (8)","2003",2364,347.8856,1279.238,6.79533731778493,-7 -"336",8,"Western Africa (8)","2004",2024,357.1391,1350.488,5.6672596195712,-6 -"337",8,"Western Africa (8)","2005",2696,366.5572,1382.886,7.35492305157285,-5 -"338",8,"Western Africa (8)","2006",2226,376.1295,1412.682,5.9181744585309,-4 -"339",8,"Western Africa (8)","2007",2221,385.8629,1453.617,5.75593040947964,-3 -"340",8,"Western Africa (8)","2008",3009,395.5942,1486.149,7.60627936405539,-2 -"341",8,"Western Africa (8)","2009",3200,405.4698,1485.888,7.89207975538499,-1 -"342",8,"Western Africa (8)","2010",2917,415.4669,1512.174,7.02101659602726,0 -"343",8,"Western Africa (8)","2011",3754,425.5756,1563.176,8.82099443671113,1 -"344",8,"Western Africa (8)","2012",3732,435.7884,1610.389,8.56378921513285,2 -"345",9,"Eastern Africa (9)","1980",219,113.8755,828.705,1.92315291700146,-30 -"346",9,"Eastern Africa (9)","1981",159,117.2407,832.1068,1.35618432847979,-29 -"347",9,"Eastern Africa (9)","1982",115,120.7025,831.9492,0.952755742424556,-28 -"348",9,"Eastern Africa (9)","1983",190,124.2837,831.0629,1.5287604086457,-27 -"349",9,"Eastern Africa (9)","1984",200,128.0149,796.799,1.56231813640443,-26 -"350",9,"Eastern Africa (9)","1985",254,131.9122,761.3655,1.92552318890899,-25 -"351",9,"Eastern Africa (9)","1986",173,136.0031,779.3807,1.27202982873185,-24 -"352",9,"Eastern Africa (9)","1987",266,140.2672,820.2136,1.89638062212691,-23 -"353",9,"Eastern Africa (9)","1988",213,144.6171,821.2789,1.47285486985979,-22 -"354",9,"Eastern Africa (9)","1989",204,148.9328,834.4102,1.36974528109322,-21 -"355",9,"Eastern Africa (9)","1990",576,153.1369,821.6528,3.76134034318313,-20 -"356",9,"Eastern Africa (9)","1991",356,157.1817,812.7501,2.26489470466346,-19 -"357",9,"Eastern Africa (9)","1992",294,161.114,803.2378,1.82479486574723,-18 -"358",9,"Eastern Africa (9)","1993",268,165.0652,814.662,1.62360085590421,-17 -"359",9,"Eastern Africa (9)","1994",348,169.2208,798.5344,2.05648478201261,-16 -"360",9,"Eastern Africa (9)","1995",450,173.7107,820.5782,2.59051399827414,-15 -"361",9,"Eastern Africa (9)","1996",386,178.5897,846.8434,2.1613788477163,-14 -"362",9,"Eastern Africa (9)","1997",445,183.8085,865.4262,2.42099794079164,-13 -"363",9,"Eastern Africa (9)","1998",744,189.2655,867.0915,3.93098583735546,-12 -"364",9,"Eastern Africa (9)","1999",522,194.8088,875.5386,2.67955041045374,-11 -"365",9,"Eastern Africa (9)","2000",481,200.3306,890.777,2.40103109559897,-10 -"366",9,"Eastern Africa (9)","2001",645,205.7974,915.4696,3.13415038285226,-9 -"367",9,"Eastern Africa (9)","2002",800,211.2515,913.5626,3.78695535889686,-8 -"368",9,"Eastern Africa (9)","2003",895,216.7518,926.085,4.12914679370598,-7 -"369",9,"Eastern Africa (9)","2004",1216,222.3888,957.9957,5.46790126121459,-6 -"370",9,"Eastern Africa (9)","2005",1129,228.2275,994.8387,4.94681841583508,-5 -"371",9,"Eastern Africa (9)","2006",1182,234.2803,1052.696,5.04523854545175,-4 -"372",9,"Eastern Africa (9)","2007",1180,240.5218,1111.72,4.90600020455526,-3 -"373",9,"Eastern Africa (9)","2008",1131,246.9325,1159.686,4.58019904224839,-2 -"374",9,"Eastern Africa (9)","2009",1416,253.4809,1172.51,5.58621971122874,-1 -"375",9,"Eastern Africa (9)","2010",1314,260.1408,1196.758,5.05111078308362,0 -"376",9,"Eastern Africa (9)","2011",1729,266.9061,1228.543,6.47793362534614,1 -"377",9,"Eastern Africa (9)","2012",1478,273.7767,1264.589,5.39856021348785,2 -"378",10,"South Africa (10)","1970",4783,22.65705,7507.24598655225,211.104269973364,-40 -"379",10,"South Africa (10)","1971",5532,23.10612,7680.814,239.417089498367,-39 -"380",10,"South Africa (10)","1972",4881,23.73564,7600.829,205.640125987755,-38 -"381",10,"South Africa (10)","1973",5636,24.38357,7737.135,231.139246632056,-37 -"382",10,"South Africa (10)","1974",6494,25.03996,7994.749,259.345462213198,-36 -"383",10,"South Africa (10)","1975",7506,25.69802,7922.092,292.084759837528,-35 -"384",10,"South Africa (10)","1976",6066,26.35369,7898.8,230.176495208071,-34 -"385",10,"South Africa (10)","1977",4871,27.00982,7699.677,180.341816420842,-33 -"386",10,"South Africa (10)","1978",5111,27.67476,7741.208,184.680915028712,-32 -"387",10,"South Africa (10)","1979",6300,28.3606,7840.325,222.139164897781,-31 -"388",10,"South Africa (10)","1980",7197,29.07527,8153.932,247.529945551666,-30 -"389",10,"South Africa (10)","1981",7264,29.82413,8375.337,243.561170099513,-29 -"390",10,"South Africa (10)","1982",6146,30.60253,8131.013,200.833068377026,-28 -"391",10,"South Africa (10)","1983",5345,31.39648,7779.051,170.242014391422,-27 -"392",10,"South Africa (10)","1984",6013,32.18631,7975.078,186.818557330741,-26 -"393",10,"South Africa (10)","1985",5267,32.95903,7693.75,159.804460264759,-25 -"394",10,"South Africa (10)","1986",5302,33.70417,7525,157.309911503532,-24 -"395",10,"South Africa (10)","1987",6042,34.42834,7521.477,175.494955609245,-23 -"396",10,"South Africa (10)","1988",6573,35.15575,7675.216,186.96799243367,-22 -"397",10,"South Africa (10)","1989",7644,35.92072,7691.661,212.801970561837,-21 -"398",10,"South Africa (10)","1990",5525,36.74528,7495.161,150.359447526322,-20 -"399",10,"South Africa (10)","1991",5070,37.64043,7242.42,134.695591947276,-19 -"400",10,"South Africa (10)","1992",4146,38.59106,6913.046,107.434208855626,-18 -"401",10,"South Africa (10)","1993",4422,39.56142,6826.668,111.775563162293,-17 -"402",10,"South Africa (10)","1994",4702,40.50132,6883.903,116.094981595662,-16 -"403",10,"South Africa (10)","1995",4879,41.37482,6948.519,117.921963165036,-15 -"404",10,"South Africa (10)","1996",4479,42.16738,7111.549,106.219546957862,-14 -"405",10,"South Africa (10)","1997",4964,42.88969,7176.842,115.738770786173,-13 -"406",10,"South Africa (10)","1998",4481,43.56173,7102.682,102.865519803736,-12 -"407",10,"South Africa (10)","1999",4223,44.215,7162.754,95.5105733348411,-11 -"408",10,"South Africa (10)","2000",4488,44.87189,7351.122,100.01807367597,-10 -"409",10,"South Africa (10)","2001",4661,45.53606,7442.058,102.358438564953,-9 -"410",10,"South Africa (10)","2002",5391,46.19748,7604.567,116.694676852504,-8 -"411",10,"South Africa (10)","2003",4562,46.84852,7732.848,97.3776759650038,-7 -"412",10,"South Africa (10)","2004",5471,47.47708,8001.647,115.234551071801,-6 -"413",10,"South Africa (10)","2005",5175,48.07344,8294.74,107.647798867732,-5 -"414",10,"South Africa (10)","2006",6680,48.63852,8634.635,137.339705237742,-4 -"415",10,"South Africa (10)","2007",6708,49.17316,8976.143,136.415882160105,-3 -"416",10,"South Africa (10)","2008",6779,49.66764,9158.946,136.487258102056,-2 -"417",10,"South Africa (10)","2009",4931,50.10981,8880.243,98.4038853869133,-1 -"418",10,"South Africa (10)","2010",5535,50.49241,9047.664,109.620436021968,0 -"419",10,"South Africa (10)","2011",5884,50.81151,9392.609,115.800534170309,1 -"420",10,"South Africa (10)","2012",5998,51.07314,9748.672,117.439421190865,2 -"421",11,"Western Europe (11)","1970",158709,350.5968,14487.0506357884,452.682397557536,-40 -"422",11,"Western Europe (11)","1971",140220,352.9577,15199.47,397.27140107724,-39 -"423",11,"Western Europe (11)","1972",155769,354.9143,15796.79,438.891867698766,-38 -"424",11,"Western Europe (11)","1973",164677,356.7897,16672.98,461.552001080749,-37 -"425",11,"Western Europe (11)","1974",162340,358.5519,16982.68,452.765694450371,-36 -"426",11,"Western Europe (11)","1975",135061,360.1783,16776.05,374.983723339246,-35 -"427",11,"Western Europe (11)","1976",153791,361.6743,17432.06,425.219596747682,-34 -"428",11,"Western Europe (11)","1977",138762,363.0525,17852.02,382.209184622059,-33 -"429",11,"Western Europe (11)","1978",133784,364.308,18323.95,367.227730381984,-32 -"430",11,"Western Europe (11)","1979",144906,365.4356,18929.63,396.529511629409,-31 -"431",11,"Western Europe (11)","1980",143617,366.4388,19177.92,391.926291648155,-30 -"432",11,"Western Europe (11)","1981",129204,367.3138,19175.8,351.75373209501,-29 -"433",11,"Western Europe (11)","1982",125621,368.0829,19304.97,341.284531283578,-28 -"434",11,"Western Europe (11)","1983",118828,368.8095,19603.88,322.193435906613,-27 -"435",11,"Western Europe (11)","1984",123526,369.5762,20068.35,334.236890795457,-26 -"436",11,"Western Europe (11)","1985",122110,370.4444,20546.55,329.631113333067,-25 -"437",11,"Western Europe (11)","1986",125792,371.4311,21072.92,338.668463680074,-24 -"438",11,"Western Europe (11)","1987",123760,372.5264,21595.54,332.218065618974,-23 -"439",11,"Western Europe (11)","1988",143386,373.729,22415.35,383.663028558126,-22 -"440",11,"Western Europe (11)","1989",147491,375.0283,23141.94,393.279653828791,-21 -"441",11,"Western Europe (11)","1990",143477,376.4099,23740.99,381.172227404221,-20 -"442",11,"Western Europe (11)","1991",134363,377.9008,24072.15,355.550980574796,-19 -"443",11,"Western Europe (11)","1992",131771,379.4931,24256.64,347.228974650659,-18 -"444",11,"Western Europe (11)","1993",114697,381.0957,24107.88,300.966397679113,-17 -"445",11,"Western Europe (11)","1994",133985,382.5862,24704.82,350.208658859102,-16 -"446",11,"Western Europe (11)","1995",150106,383.8869,25250.52,391.016208159226,-15 -"447",11,"Western Europe (11)","1996",118678,384.9419,25646.1,308.30107088888,-14 -"448",11,"Western Europe (11)","1997",147043,385.801,26292.55,381.136907369343,-13 -"449",11,"Western Europe (11)","1998",160390,386.6206,27006.55,414.851148645468,-12 -"450",11,"Western Europe (11)","1999",155188,387.6207,27729.33,400.360455465872,-11 -"451",11,"Western Europe (11)","2000",164860,388.9529,28701.79,423.85594759674,-10 -"452",11,"Western Europe (11)","2001",159700,390.6713,29129.45,408.783547703658,-9 -"453",11,"Western Europe (11)","2002",156401,392.7094,29307.49,398.261411618871,-8 -"454",11,"Western Europe (11)","2003",157194,394.9489,29469.26,398.010983193016,-7 -"455",11,"Western Europe (11)","2004",164963,397.2142,30002.44,415.29985584604,-6 -"456",11,"Western Europe (11)","2005",157008,399.3727,30374.05,393.136536373167,-5 -"457",11,"Western Europe (11)","2006",177442,401.39,31089.81,442.068810882184,-4 -"458",11,"Western Europe (11)","2007",185593,403.2907,31788.91,460.196577803555,-3 -"459",11,"Western Europe (11)","2008",171693,405.0706,31809.39,423.859445736126,-2 -"460",11,"Western Europe (11)","2009",107207,406.7432,30401.88,263.574166697808,-1 -"461",11,"Western Europe (11)","2010",135654,408.3169,30592.05,332.227248002716,0 -"462",11,"Western Europe (11)","2011",142538,409.7802,31045.56,347.840134784453,1 -"463",11,"Western Europe (11)","2012",127518,411.1214,31544.86,310.171156257008,2 -"464",12,"Central Europe (12)","1970",35854,115.354,6190.69877807217,310.817136813635,-40 -"465",12,"Central Europe (12)","1971",38376,116.2557,6603.938,330.099943486642,-39 -"466",12,"Central Europe (12)","1972",40080,117.1717,6871.404,342.062119095311,-38 -"467",12,"Central Europe (12)","1973",43049,118.0999,7164.145,364.51343311891,-37 -"468",12,"Central Europe (12)","1974",46801,119.0415,7718.882,393.148607838443,-36 -"469",12,"Central Europe (12)","1975",49900,119.9958,8040.493,415.847888009414,-35 -"470",12,"Central Europe (12)","1976",49914,120.964,8480.507,412.635164181079,-34 -"471",12,"Central Europe (12)","1977",52359,121.9404,9000.721,429.381894761703,-33 -"472",12,"Central Europe (12)","1978",54631,122.907,9468.698,444.49054976527,-32 -"473",12,"Central Europe (12)","1979",54927,123.8406,9789.172,443.529827859361,-31 -"474",12,"Central Europe (12)","1980",54949,124.7236,9744.632,440.566179937077,-30 -"475",12,"Central Europe (12)","1981",51436,125.5422,9523.311,409.710838267929,-29 -"476",12,"Central Europe (12)","1982",50013,126.2965,9487.436,395.996721999422,-28 -"477",12,"Central Europe (12)","1983",49717,126.9993,9685.796,391.47459867889,-27 -"478",12,"Central Europe (12)","1984",50000,127.6711,9998.942,391.631308886663,-26 -"479",12,"Central Europe (12)","1985",49644,128.3229,10119.9,386.867815487337,-25 -"480",12,"Central Europe (12)","1986",52258,128.9657,10397.69,405.208516683118,-24 -"481",12,"Central Europe (12)","1987",52915,129.5838,10568.02,408.345796310959,-23 -"482",12,"Central Europe (12)","1988",49841,130.1302,10695.18,383.0087097384,-22 -"483",12,"Central Europe (12)","1989",47252,130.5418,10679.2,361.968350367469,-21 -"484",12,"Central Europe (12)","1990",36619,130.7764,9924.173,280.012295796489,-20 -"485",12,"Central Europe (12)","1991",22688,130.8156,8845.062,173.434972587367,-19 -"486",12,"Central Europe (12)","1992",18881,130.682,8251.99,144.480494635834,-18 -"487",12,"Central Europe (12)","1993",18831,130.4263,8068.822,144.380389538,-17 -"488",12,"Central Europe (12)","1994",19766,130.1213,8381.351,151.904415341685,-16 -"489",12,"Central Europe (12)","1995",23288,129.8215,8868.516,179.384770627361,-15 -"490",12,"Central Europe (12)","1996",22516,129.5468,9292.298,173.80591415612,-14 -"491",12,"Central Europe (12)","1997",24339,129.2877,9634.274,188.254567139798,-13 -"492",12,"Central Europe (12)","1998",24117,129.0358,9914.649,186.901619550543,-12 -"493",12,"Central Europe (12)","1999",21799,128.772,10121.81,169.283695213245,-11 -"494",12,"Central Europe (12)","2000",24043,128.4843,10555.81,187.127921465891,-10 -"495",12,"Central Europe (12)","2001",24462,128.1723,10920.03,190.852469683387,-9 -"496",12,"Central Europe (12)","2002",25809,127.8486,11302.74,201.871588738555,-8 -"497",12,"Central Europe (12)","2003",27243,127.5286,11814.58,213.622669738396,-7 -"498",12,"Central Europe (12)","2004",31184,127.2323,12516.89,245.094995531795,-6 -"499",12,"Central Europe (12)","2005",31202,126.9734,13142.54,245.736508591563,-5 -"500",12,"Central Europe (12)","2006",37644,126.7586,14000.54,296.973933129586,-4 -"501",12,"Central Europe (12)","2007",42199,126.582,14878.34,333.372833420234,-3 -"502",12,"Central Europe (12)","2008",39935,126.4339,15532.45,315.856744116886,-2 -"503",12,"Central Europe (12)","2009",25396,126.2898,14880.61,201.093041559968,-1 -"504",12,"Central Europe (12)","2010",30871,126.1351,15071.57,244.745514928041,0 -"505",12,"Central Europe (12)","2011",33688,125.965,15582.96,267.439368078434,1 -"506",12,"Central Europe (12)","2012",31519,125.7821,16274.66,250.584145120808,2 -"507",13,"Turkey (13)","1970",1781,36.20744,4664.74278930178,49.1887855092765,-40 -"508",13,"Turkey (13)","1971",1976,37.16068,4822.903,53.1744844281644,-39 -"509",13,"Turkey (13)","1972",2131,38.15393,5046.164,55.852699839833,-38 -"510",13,"Turkey (13)","1973",2232,39.17263,5075.279,56.9785587539055,-37 -"511",13,"Turkey (13)","1974",3478,40.19642,5222.716,86.525118406067,-36 -"512",13,"Turkey (13)","1975",3078,41.21053,5459.658,74.6896484951783,-35 -"513",13,"Turkey (13)","1976",3701,42.20812,5888.265,87.6845497975271,-34 -"514",13,"Turkey (13)","1977",4720,43.19261,5950.076,109.277952872031,-33 -"515",13,"Turkey (13)","1978",3501,44.17188,5905.608,79.2585690262674,-32 -"516",13,"Turkey (13)","1979",3465,45.15865,5740.513,76.7294859345884,-31 -"517",13,"Turkey (13)","1980",3374,46.16132,5478.383,73.0914973835237,-30 -"518",13,"Turkey (13)","1981",3316,47.18342,5620.011,70.2789242492384,-29 -"519",13,"Turkey (13)","1982",4049,48.2195,5695.207,83.9701780400046,-28 -"520",13,"Turkey (13)","1983",4252,49.25853,5852.216,86.3200749190039,-27 -"521",13,"Turkey (13)","1984",5271,50.28509,6117.528,104.822324072603,-26 -"522",13,"Turkey (13)","1985",5275,51.28881,6252.194,102.848945023291,-25 -"523",13,"Turkey (13)","1986",5381,52.26511,6565.622,102.955872474008,-24 -"524",13,"Turkey (13)","1987",6724,53.21939,7059.511,126.344928042204,-23 -"525",13,"Turkey (13)","1988",6605,54.16364,7097.417,121.945275465238,-22 -"526",13,"Turkey (13)","1989",7390,55.11508,6995.141,134.083085790677,-21 -"527",13,"Turkey (13)","1990",6593,56.08619,7510.979,117.551218936426,-20 -"528",13,"Turkey (13)","1991",7380,57.07924,7433.463,129.293942946683,-19 -"529",13,"Turkey (13)","1992",7510,58.09025,7671.898,129.281592005543,-18 -"530",13,"Turkey (13)","1993",10270,59.11751,8115.384,173.72179579282,-17 -"531",13,"Turkey (13)","1994",6410,60.15739,7602.812,106.553824891672,-16 -"532",13,"Turkey (13)","1995",10750,61.2061,8061.251,175.6360885598,-15 -"533",13,"Turkey (13)","1996",10480,62.2649,8508.948,168.313126657234,-14 -"534",13,"Turkey (13)","1997",11690,63.33183,8999.519,184.583328793752,-13 -"535",13,"Turkey (13)","1998",13020,64.39564,9055.145,202.187601520848,-12 -"536",13,"Turkey (13)","1999",12160,65.44167,8610.54,185.814329004746,-11 -"537",13,"Turkey (13)","2000",13370,66.45958,9053.042,201.174909621758,-10 -"538",13,"Turkey (13)","2001",11580,67.44411,8412.621,171.697721268766,-9 -"539",13,"Turkey (13)","2002",12870,68.39813,8806.59,188.163038960276,-8 -"540",13,"Turkey (13)","2003",15320,69.32945,9145.75,220.973915125535,-7 -"541",13,"Turkey (13)","2004",18563,70.25018,9870.96,264.241315822963,-6 -"542",13,"Turkey (13)","2005",20582,71.16904,10562.13,289.198786438597,-5 -"543",13,"Turkey (13)","2006",24299,72.08793,11146.31,337.074458928145,-4 -"544",13,"Turkey (13)","2007",28117,73.00374,11515.06,385.144651493198,-3 -"545",13,"Turkey (13)","2008",22862,73.91425,11475.32,309.304362825842,-2 -"546",13,"Turkey (13)","2009",19198,74.81569,10600.55,256.603928935227,-1 -"547",13,"Turkey (13)","2010",25131,75.70514,10866.41,331.958966062278,0 -"548",13,"Turkey (13)","2011",28665,76.58212,11240.01,374.304080378031,1 -"549",13,"Turkey (13)","2012",30286,77.44726,11503.49,391.053214794171,2 -"550",14,"Ukraine + (14)","1992",34926,66.24849,6198.245,527.196921771349,-18 -"551",14,"Ukraine + (14)","1993",22228,66.15531,5407.86,335.997216247645,-17 -"552",14,"Ukraine + (14)","1994",10287,65.96564,4266.155,155.944822183185,-16 -"553",14,"Ukraine + (14)","1995",9501,65.67137,3788.413,144.67491693869,-15 -"554",14,"Ukraine + (14)","1996",9543,65.26628,3526.296,146.216392293233,-14 -"555",14,"Ukraine + (14)","1997",7571,64.76211,3557.207,116.904776573833,-13 -"556",14,"Ukraine + (14)","1998",5945,64.19197,3600.348,92.6128299224965,-12 -"557",14,"Ukraine + (14)","1999",5104,63.60104,3655.677,80.2502600586405,-11 -"558",14,"Ukraine + (14)","2000",14112,63.02394,3902.145,223.914912333313,-10 -"559",14,"Ukraine + (14)","2001",14759,62.473,4250.785,236.246058297184,-9 -"560",14,"Ukraine + (14)","2002",15754,61.94598,4511.045,254.318359318878,-8 -"561",14,"Ukraine + (14)","2003",18590,61.44504,4945.633,302.54679629145,-7 -"562",14,"Ukraine + (14)","2004",9517,60.96768,5573.686,156.099100375806,-6 -"563",14,"Ukraine + (14)","2005",9335,60.51197,5865.295,154.266998744215,-5 -"564",14,"Ukraine + (14)","2006",10729,60.082,6375.124,178.572617422855,-4 -"565",14,"Ukraine + (14)","2007",12246,59.68111,6928.893,205.190553593926,-3 -"566",14,"Ukraine + (14)","2008",11090,59.30458,7281.334,187.000734176011,-2 -"567",14,"Ukraine + (14)","2009",6704,58.94514,6583.867,113.732870937282,-1 -"568",14,"Ukraine + (14)","2010",9234,58.59694,6776.641,157.585020651249,0 -"569",14,"Ukraine + (14)","2011",10465,58.25822,7111.536,179.631303531073,1 -"570",14,"Ukraine + (14)","2012",10449,57.92915,7526.385,180.375510429551,2 -"571",15,"Central Asia (15)","1992",4838,51.81463,3146.03,93.371312310828,-18 -"572",15,"Central Asia (15)","1993",4157,52.401,2840.36,79.3305471269632,-17 -"573",15,"Central Asia (15)","1994",2742,52.92537,2467.293,51.8088017145652,-16 -"574",15,"Central Asia (15)","1995",1472,53.40026,2278.397,27.5654088575599,-15 -"575",15,"Central Asia (15)","1996",1417,53.82479,2258.805,26.3261593774913,-14 -"576",15,"Central Asia (15)","1997",877,54.20422,2285.418,16.179552071776,-13 -"577",15,"Central Asia (15)","1998",848,54.56543,2274.89,15.5409753024946,-12 -"578",15,"Central Asia (15)","1999",554,54.94284,2347.512,10.0832064742194,-11 -"579",15,"Central Asia (15)","2000",1267,55.36184,2535.983,22.885800038438,-10 -"580",15,"Central Asia (15)","2001",1351,55.83427,2807.675,24.1966090001714,-9 -"581",15,"Central Asia (15)","2002",1484,56.35691,3025.344,26.3321747058169,-8 -"582",15,"Central Asia (15)","2003",1598,56.92098,3263.119,28.0740071586961,-7 -"583",15,"Central Asia (15)","2004",3375,57.51052,3548.419,58.6849153859155,-6 -"584",15,"Central Asia (15)","2005",3722,58.1136,3832.548,64.0469700724099,-5 -"585",15,"Central Asia (15)","2006",4054,58.72855,4166.622,69.0294584150298,-4 -"586",15,"Central Asia (15)","2007",5018,59.3594,4502.187,84.5358949045981,-3 -"587",15,"Central Asia (15)","2008",4083,60.00612,4681.461,68.04305960792,-2 -"588",15,"Central Asia (15)","2009",4566,60.66952,4654.056,75.2601965533929,-1 -"589",15,"Central Asia (15)","2010",3702,61.34934,4804.037,60.3429474546914,0 -"590",15,"Central Asia (15)","2011",3960,62.0439,5057.375,63.8257749754609,1 -"591",15,"Central Asia (15)","2012",5070,62.7498,5305.246,80.7970702695467,2 -"592",16,"Russia + (16)","1970",110234,142.7897,6231.84786332018,772.002462362481,-40 -"593",16,"Russia + (16)","1971",115364,143.7069,6596.91,802.772866160219,-39 -"594",16,"Russia + (16)","1972",121243,144.6594,6713.989,838.12735294077,-38 -"595",16,"Russia + (16)","1973",129391,145.6433,7157.659,888.410246128727,-37 -"596",16,"Russia + (16)","1974",137554,146.645,7431.844,938.006750997306,-36 -"597",16,"Russia + (16)","1975",141031,147.6552,7560.977,955.137374098576,-35 -"598",16,"Russia + (16)","1976",145171,148.6757,7954.266,976.427217090621,-34 -"599",16,"Russia + (16)","1977",146330,149.7151,8293.8,977.389722212389,-33 -"600",16,"Russia + (16)","1978",153451,150.7774,8645.484,1017.73210043415,-32 -"601",16,"Russia + (16)","1979",152200,151.8668,8864.585,1002.19402792447,-31 -"602",16,"Russia + (16)","1980",150330,152.9852,9216.292,982.644072759979,-30 -"603",16,"Russia + (16)","1981",150849,154.1217,9617.574,978.765482083315,-29 -"604",16,"Russia + (16)","1982",150343,155.2687,10248.43,968.276284917694,-28 -"605",16,"Russia + (16)","1983",157578,156.4324,10627.06,1007.32329108292,-27 -"606",16,"Russia + (16)","1984",159369,157.6224,10957.45,1011.08091235763,-26 -"607",16,"Russia + (16)","1985",157161,158.8373,11014.04,989.446433551817,-25 -"608",16,"Russia + (16)","1986",161544,160.0866,11055.36,1009.10382255604,-24 -"609",16,"Russia + (16)","1987",162989,161.3439,11175.18,1010.19623301532,-23 -"610",16,"Russia + (16)","1988",164619,162.528,11651.14,1012.8654754873,-22 -"611",16,"Russia + (16)","1989",166319,163.5323,12317.96,1017.04067025291,-21 -"612",16,"Russia + (16)","1990",152577,164.2816,11860.85,928.752824418559,-20 -"613",16,"Russia + (16)","1991",131865,164.743,11207.94,800.428546281177,-19 -"614",16,"Russia + (16)","1992",59892,164.9401,9502.627,363.113639436377,-18 -"615",16,"Russia + (16)","1993",40510,164.9262,8647.017,245.625012884551,-17 -"616",16,"Russia + (16)","1994",21104,164.7832,7565.564,128.07130824016,-16 -"617",16,"Russia + (16)","1995",22062,164.573,7261.488,134.056011617945,-15 -"618",16,"Russia + (16)","1996",20124,164.3162,7023.942,122.471186651103,-14 -"619",16,"Russia + (16)","1997",20364,164.0016,7144.26,124.169520297363,-13 -"620",16,"Russia + (16)","1998",14949,163.6224,6801.33,91.3627962919502,-12 -"621",16,"Russia + (16)","1999",19581,163.1617,7254.708,120.009781707349,-11 -"622",16,"Russia + (16)","2000",28796,162.6118,8001.696,177.084319834108,-10 -"623",16,"Russia + (16)","2001",31404,161.9744,8448.438,193.882490072505,-9 -"624",16,"Russia + (16)","2002",28393,161.2711,8898.082,176.057582542687,-8 -"625",16,"Russia + (16)","2003",27402,160.5378,9605.862,170.688772363892,-7 -"626",16,"Russia + (16)","2004",32214,159.8201,10343.2,201.564133672798,-6 -"627",16,"Russia + (16)","2005",35625,159.1522,11087.96,223.842334570304,-5 -"628",16,"Russia + (16)","2006",42337,158.548,12010.28,267.029543103666,-4 -"629",16,"Russia + (16)","2007",47942,158.003,13079.33,303.424618519902,-3 -"630",16,"Russia + (16)","2008",42241,157.5089,13871.51,268.181671003988,-2 -"631",16,"Russia + (16)","2009",29530,157.0491,12757.81,188.030367572944,-1 -"632",16,"Russia + (16)","2010",42680,156.6101,13424.54,272.52393044893,0 -"633",16,"Russia + (16)","2011",48565,156.1913,14018.42,310.932811238526,1 -"634",16,"Russia + (16)","2012",50105,155.7946,14560.81,321.609349746397,2 -"635",17,"Middle East (17)","1970",4486,65.63198,6984.26967389179,68.3508253141228,-40 -"636",17,"Middle East (17)","1971",4334,68.67008,7619.628,63.1133675685248,-39 -"637",17,"Middle East (17)","1972",4868,70.88831,8453.769,68.6714071755978,-38 -"638",17,"Middle East (17)","1973",6759,73.19949,9221.72,92.3367089032997,-37 -"639",17,"Middle East (17)","1974",9101,75.62141,10048.69,120.34951477366,-36 -"640",17,"Middle East (17)","1975",11525,78.17059,10053.5,147.433964615081,-35 -"641",17,"Middle East (17)","1976",12445,80.83937,10780.2,153.947266041287,-34 -"642",17,"Middle East (17)","1977",11388,83.63344,10917.79,136.165629442003,-33 -"643",17,"Middle East (17)","1978",13230,86.59713,10536.47,152.77642573143,-32 -"644",17,"Middle East (17)","1979",12865,89.7875,10947.24,143.282750939719,-31 -"645",17,"Middle East (17)","1980",13648,93.23826,10746.25,146.377678004716,-30 -"646",17,"Middle East (17)","1981",13028,96.96494,10356.82,134.357841091842,-29 -"647",17,"Middle East (17)","1982",16839,100.935,9462.221,166.830138207757,-28 -"648",17,"Middle East (17)","1983",18610,105.0733,9060.028,177.11445248222,-27 -"649",17,"Middle East (17)","1984",16866,109.2758,8780.68,154.343413637786,-26 -"650",17,"Middle East (17)","1985",17274,113.462,8386.904,152.244804427914,-25 -"651",17,"Middle East (17)","1986",11469,117.6081,8004.08,97.5187933484173,-24 -"652",17,"Middle East (17)","1987",11968,121.7178,7868.794,98.3257995133004,-23 -"653",17,"Middle East (17)","1988",11303,125.7715,7680.812,89.8693265167387,-22 -"654",17,"Middle East (17)","1989",11715,129.7554,7655.955,90.2852598042162,-21 -"655",17,"Middle East (17)","1990",10897,133.6615,7916.439,81.5268420599799,-20 -"656",17,"Middle East (17)","1991",11752,137.4736,8032.402,85.4855041258831,-19 -"657",17,"Middle East (17)","1992",13804,141.1928,8477.415,97.7670249474477,-18 -"658",17,"Middle East (17)","1993",15096,144.8516,8408.716,104.217005542224,-17 -"659",17,"Middle East (17)","1994",14761,148.497,8525.458,99.4026815356539,-16 -"660",17,"Middle East (17)","1995",14254,152.1644,8525.457,93.6749988827873,-15 -"661",17,"Middle East (17)","1996",15733,155.8667,8765.96,100.938815025916,-14 -"662",17,"Middle East (17)","1997",16920,159.5982,8829.913,106.016233265789,-13 -"663",17,"Middle East (17)","1998",17511,163.3544,9009.427,107.19637793656,-12 -"664",17,"Middle East (17)","1999",18333,167.1241,8940.858,109.696925817402,-11 -"665",17,"Middle East (17)","2000",21382,170.901,9213.073,125.113369728673,-10 -"666",17,"Middle East (17)","2001",25040,174.681,9180.806,143.347015416674,-9 -"667",17,"Middle East (17)","2002",27470,178.4721,9188.435,153.917615134242,-8 -"668",17,"Middle East (17)","2003",31405,182.2935,9526.202,172.277124527205,-7 -"669",17,"Middle East (17)","2004",32804,186.1707,10049.02,176.203881706412,-6 -"670",17,"Middle East (17)","2005",36528,190.1193,10431.33,192.131992911819,-5 -"671",17,"Middle East (17)","2006",38253,194.1498,10810.37,197.028274044063,-4 -"672",17,"Middle East (17)","2007",46614,198.249,11188.48,235.128550459271,-3 -"673",17,"Middle East (17)","2008",47720,202.4243,11591.75,235.742447917567,-2 -"674",17,"Middle East (17)","2009",44001,206.5805,11474.69,212.996870469381,-1 -"675",17,"Middle East (17)","2010",47723,210.6817,11688.86,226.517063418417,0 -"676",17,"Middle East (17)","2011",53298,214.712,11970.35,248.230187413838,1 -"677",17,"Middle East (17)","2012",53446,218.6812,12268.1,244.40143917264,2 -"678",18,"India (18)","1970",6432,554.9108,791.734291330751,11.5910521114385,-40 -"679",18,"India (18)","1971",7710,565.1073,796.2614,13.6434266554334,-39 -"680",18,"India (18)","1972",9227,577.574,774.7964,15.9754421078511,-38 -"681",18,"India (18)","1973",8221,590.4122,783.1032,13.9241702661293,-37 -"682",18,"India (18)","1974",8551,603.6847,774.9598,14.1646790120737,-36 -"683",18,"India (18)","1975",8500,617.4316,827.0153,13.766707113792,-35 -"684",18,"India (18)","1976",8202,631.6699,821.7555,12.9846301050596,-34 -"685",18,"India (18)","1977",10185,646.3734,861.3548,15.7571459469093,-33 -"686",18,"India (18)","1978",10060,661.4861,889.7242,15.2081804893557,-32 -"687",18,"India (18)","1979",12050,676.9282,823.8906,17.8010016424194,-31 -"688",18,"India (18)","1980",10900,692.6373,859.506,15.7369520815584,-30 -"689",18,"India (18)","1981",14000,708.589,890.5605,19.757574560147,-29 -"690",18,"India (18)","1982",13900,724.7841,900.8348,19.1781249064377,-28 -"691",18,"India (18)","1983",11600,741.2145,945.1629,15.6499906572254,-27 -"692",18,"India (18)","1984",13900,757.8796,959.7208,18.3406440811971,-26 -"693",18,"India (18)","1985",14400,774.7748,987.9305,18.5860459065008,-25 -"694",18,"India (18)","1986",15290,791.8759,1012.685,19.3085810541778,-24 -"695",18,"India (18)","1987",17640,809.162,1030.271,21.8003317011921,-23 -"696",18,"India (18)","1988",19040,826.6349,1105.686,23.0331431687677,-22 -"697",18,"India (18)","1989",20036,844.3027,1146.969,23.7308254492139,-21 -"698",18,"India (18)","1990",21700,862.1616,1185.312,25.1692954081926,-20 -"699",18,"India (18)","1991",20300,880.2089,1173.358,23.0627070460206,-19 -"700",18,"India (18)","1992",18540,898.4102,1212.587,20.6364531480164,-18 -"701",18,"India (18)","1993",18850,916.6924,1245.062,20.5630591024863,-17 -"702",18,"India (18)","1994",21880,934.9619,1301.963,23.4020231198726,-16 -"703",18,"India (18)","1995",26080,953.1479,1373.79,27.3619655459557,-15 -"704",18,"India (18)","1996",27100,971.2095,1450.131,27.9033514396224,-14 -"705",18,"India (18)","1997",27300,989.1498,1481.559,27.5994596571723,-13 -"706",18,"India (18)","1998",27600,1006.996,1545.437,27.4082518699181,-12 -"707",18,"India (18)","1999",29700,1024.799,1630.772,28.9812929169525,-11 -"708",18,"India (18)","2000",30200,1042.59,1667.55,28.9663242501846,-10 -"709",18,"India (18)","2001",31200,1060.371,1725.123,29.4236639817573,-9 -"710",18,"India (18)","2002",33360,1078.111,1760.65,30.9430105063393,-8 -"711",18,"India (18)","2003",35000,1095.767,1877.288,31.9410969667822,-7 -"712",18,"India (18)","2004",39220,1113.283,2000.71,35.2291376047241,-6 -"713",18,"India (18)","2005",44330,1130.618,2154.28,39.2086451834307,-5 -"714",18,"India (18)","2006",50670,1147.746,2327.327,44.1473984662112,-4 -"715",18,"India (18)","2007",55020,1164.67,2501.308,47.2408493392978,-3 -"716",18,"India (18)","2008",56209,1181.412,2635.268,47.5778136670357,-2 -"717",18,"India (18)","2009",64360,1198.004,2744.491,53.7226920778228,-1 -"718",18,"India (18)","2010",69244,1214.464,2901.218,57.0160992833052,0 -"719",18,"India (18)","2011",75856,1230.793,3079.418,61.6318097356745,1 -"720",18,"India (18)","2012",77039,1246.96,3271.291,61.7814524924617,2 -"721",19,"Korea (19)","1970",3507,46.31954,2528.76124545009,75.7131871344145,-40 -"722",19,"Korea (19)","1971",3847,46.7346,2695.758,82.3158858747052,-39 -"723",19,"Korea (19)","1972",4031,47.79924,2768.704,84.331884774737,-38 -"724",19,"Korea (19)","1973",5906,48.85305,3030.457,120.893168389691,-37 -"725",19,"Korea (19)","1974",7664,49.85923,3192.208,153.712762912704,-36 -"726",19,"Korea (19)","1975",6498,50.79347,3334.332,127.92983035024,-35 -"727",19,"Korea (19)","1976",8445,51.64096,3603.506,163.532978472902,-34 -"728",19,"Korea (19)","1977",9162,52.4142,3883.855,174.799958789794,-33 -"729",19,"Korea (19)","1978",11688,53.14951,4166.839,219.907953996189,-32 -"730",19,"Korea (19)","1979",10347,53.89885,4377.384,191.970700673577,-31 -"731",19,"Korea (19)","1980",6100,54.6987,4271.043,111.520017843203,-30 -"732",19,"Korea (19)","1981",7480,55.56164,4453.97,134.62525584198,-29 -"733",19,"Korea (19)","1982",7630,56.47328,4688.231,135.108143178508,-28 -"734",19,"Korea (19)","1983",8620,57.41008,5079.116,150.147848600803,-27 -"735",19,"Korea (19)","1984",10620,58.33636,5384.712,182.04769718234,-26 -"736",19,"Korea (19)","1985",11310,59.22562,5651.163,190.964653472602,-25 -"737",19,"Korea (19)","1986",12190,60.07206,6122.17,202.922956196275,-24 -"738",19,"Korea (19)","1987",15050,60.88259,6668.251,247.197105116586,-23 -"739",19,"Korea (19)","1988",15830,61.65868,7243.802,256.735953478083,-22 -"740",19,"Korea (19)","1989",18300,62.40517,7616.01,293.244934674483,-21 -"741",19,"Korea (19)","1990",28613,63.12606,8160.819,453.267636218703,-20 -"742",19,"Korea (19)","1991",31203,63.81775,8775.825,488.939205785224,-19 -"743",19,"Korea (19)","1992",27369,64.47853,9147.686,424.466872926538,-18 -"744",19,"Korea (19)","1993",30659,65.11692,9574.646,470.83000854463,-17 -"745",19,"Korea (19)","1994",33871,65.74452,10254.54,515.1912281054,-16 -"746",19,"Korea (19)","1995",37990,66.36863,11043.38,572.408982978856,-15 -"747",19,"Korea (19)","1996",40054,66.99483,11670.62,597.867029440929,-14 -"748",19,"Korea (19)","1997",40568,67.61752,12066.25,599.962849864946,-13 -"749",19,"Korea (19)","1998",26385,68.22087,11156.84,386.758480212873,-12 -"750",19,"Korea (19)","1999",35831,68.78278,12106.01,520.929802488355,-11 -"751",19,"Korea (19)","2000",40367,69.28803,13015.59,582.597022891256,-10 -"752",19,"Korea (19)","2001",40094,69.73186,13445.67,574.973907192494,-9 -"753",19,"Korea (19)","2002",45767,70.12032,14308.51,652.692400719221,-8 -"754",19,"Korea (19)","2003",47567,70.46568,14634.21,675.03783402076,-7 -"755",19,"Korea (19)","2004",49454,70.78619,15233.18,698.639098954189,-6 -"756",19,"Korea (19)","2005",49354,71.09551,15766.58,694.192924419559,-5 -"757",19,"Korea (19)","2006",52574,71.39767,16491.62,736.354561710487,-4 -"758",19,"Korea (19)","2007",57678,71.68975,17238.48,804.550162331435,-3 -"759",19,"Korea (19)","2008",62266,71.97407,17552.23,865.117117873145,-2 -"760",19,"Korea (19)","2009",48619,72.24392,17505.51,672.983968754741,-1 -"761",19,"Korea (19)","2010",55835,72.49487,18212.32,770.192428788409,0 -"762",19,"Korea (19)","2011",60094,72.7268,18909.09,826.297870936161,1 -"763",19,"Korea (19)","2012",57650,72.94119,19790.77,790.362756626263,2 -"764",20,"China (20)","1970",24884,850.7245,545.028678374734,29.2503624851524,-40 -"765",20,"China (20)","1971",26517,859.5515,566.5772,30.8498094645871,-39 -"766",20,"China (20)","1972",28022,879.7271,591.6257,31.8530598864125,-38 -"767",20,"China (20)","1973",31665,899.2614,636.1968,35.2122308374406,-37 -"768",20,"China (20)","1974",33796,917.7689,633.6603,36.824085017481,-36 -"769",20,"China (20)","1975",34891,935.0037,658.5936,37.3164298708123,-35 -"770",20,"China (20)","1976",32159,950.8076,675.8064,33.8228259849837,-34 -"771",20,"China (20)","1977",38497,965.3068,720.6266,39.8805851155301,-33 -"772",20,"China (20)","1978",50741,978.9117,790.7791,51.8340929013311,-32 -"773",20,"China (20)","1979",52797,992.2164,840.6167,53.2111744978212,-31 -"774",20,"China (20)","1980",52599,1005.687,890.6027,52.3015610224652,-30 -"775",20,"China (20)","1981",47791,1019.611,931.0032,46.8717971853972,-29 -"776",20,"China (20)","1982",49047,1033.708,976.3886,47.4476351155258,-28 -"777",20,"China (20)","1983",60490,1048.272,1053.122,57.7044889112749,-27 -"778",20,"China (20)","1984",69162,1063.636,1174.035,65.0241247945726,-26 -"779",20,"China (20)","1985",80823,1079.994,1264.867,74.8365268695937,-25 -"780",20,"China (20)","1986",87605,1097.527,1367.061,79.8203597724703,-24 -"781",20,"China (20)","1987",84231,1116.062,1508.311,75.4716135841916,-23 -"782",20,"China (20)","1988",84698,1134.983,1632.842,74.6249062761293,-22 -"783",20,"China (20)","1989",87467,1153.432,1689.233,75.8319519486194,-21 -"784",20,"China (20)","1990",85655,1170.784,1736.462,73.1603780031159,-20 -"785",20,"China (20)","1991",91402,1186.737,1852.535,77.0195923780922,-19 -"786",20,"China (20)","1992",109472,1201.508,2040.276,91.1121690409053,-18 -"787",20,"China (20)","1993",162257,1215.291,2243.661,133.51287880845,-17 -"788",20,"China (20)","1994",148335,1228.437,2463.473,120.751003103944,-16 -"789",20,"China (20)","1995",127735,1241.203,2654.993,102.912255287814,-15 -"790",20,"China (20)","1996",135717,1253.63,2851.902,108.259215238946,-14 -"791",20,"China (20)","1997",144946,1265.624,3056.159,114.525325057047,-13 -"792",20,"China (20)","1998",152318,1277.165,3204.725,119.262585492086,-12 -"793",20,"China (20)","1999",166037,1288.206,3395.04,128.890099875331,-11 -"794",20,"China (20)","2000",170347,1298.727,3625.594,131.16459425268,-10 -"795",20,"China (20)","2001",197765,1308.65,3789.196,151.121384633019,-9 -"796",20,"China (20)","2002",236609,1318.131,4047.024,179.503402924292,-8 -"797",20,"China (20)","2003",286437,1327.264,4346.401,215.810117655568,-7 -"798",20,"China (20)","2004",328205,1336.162,4716.491,245.63264035349,-6 -"799",20,"China (20)","2005",390036,1344.92,5105.734,290.006840555572,-5 -"800",20,"China (20)","2006",421134,1353.561,5583.551,311.130418207971,-4 -"801",20,"China (20)","2007",461505,1362.097,6179.665,338.819482019269,-3 -"802",20,"China (20)","2008",488206,1370.518,6600.259,356.220056941974,-2 -"803",20,"China (20)","2009",590890,1379.084,6953.668,428.465561198593,-1 -"804",20,"China (20)","2010",635339,1387.711,7535.852,457.832358466568,0 -"805",20,"China (20)","2011",692705,1396.417,8122.802,496.058842022118,1 -"806",20,"China (20)","2012",712188,1405.172,8800.189,506.833327165642,2 -"807",21,"South Eastern Asia (21)","1970",3550,165.873,1226.811612426,21.4019159236283,-40 -"808",21,"South Eastern Asia (21)","1971",3167,170.5723,1269.914,18.5669068189853,-39 -"809",21,"South Eastern Asia (21)","1972",4044,174.8429,1319.411,23.1293349629868,-38 -"810",21,"South Eastern Asia (21)","1973",4722,179.1311,1407.655,26.360581719199,-37 -"811",21,"South Eastern Asia (21)","1974",5197,183.4014,1445.375,28.3367520640519,-36 -"812",21,"South Eastern Asia (21)","1975",4559,187.6357,1469.266,24.2970820584782,-35 -"813",21,"South Eastern Asia (21)","1976",4953,191.8074,1568.291,25.8227784746574,-34 -"814",21,"South Eastern Asia (21)","1977",5176,195.9422,1638.323,26.4159532760171,-33 -"815",21,"South Eastern Asia (21)","1978",5617,200.137,1714.774,28.0657749441632,-32 -"816",21,"South Eastern Asia (21)","1979",6260,204.5237,1794.445,30.6076997433549,-31 -"817",21,"South Eastern Asia (21)","1980",12922,209.1916,1855.656,61.7711227410661,-30 -"818",21,"South Eastern Asia (21)","1981",12863,214.1794,1906.573,60.0571296772706,-29 -"819",21,"South Eastern Asia (21)","1982",14914,219.4439,2020.039,67.9627002618893,-28 -"820",21,"South Eastern Asia (21)","1983",16741,224.892,2062.688,74.4401757287943,-27 -"821",21,"South Eastern Asia (21)","1984",14991,230.3877,2067.952,65.0685778798087,-26 -"822",21,"South Eastern Asia (21)","1985",14627,235.8302,2004.524,62.0234388979868,-25 -"823",21,"South Eastern Asia (21)","1986",14204,241.1824,2017.115,58.8931862358116,-24 -"824",21,"South Eastern Asia (21)","1987",15653,246.4646,2101.155,63.5101349240418,-23 -"825",21,"South Eastern Asia (21)","1988",16808,251.7013,2244.821,66.7775653125351,-22 -"826",21,"South Eastern Asia (21)","1989",18333,256.9387,2393.997,71.3516492455204,-21 -"827",21,"South Eastern Asia (21)","1990",15767,262.206,2526.197,60.1321098678139,-20 -"828",21,"South Eastern Asia (21)","1991",17248,267.5092,2623.859,64.4762871706842,-19 -"829",21,"South Eastern Asia (21)","1992",19684,272.8203,2737.396,72.1500562824687,-18 -"830",21,"South Eastern Asia (21)","1993",21807,278.0967,2893.716,78.4151699750482,-17 -"831",21,"South Eastern Asia (21)","1994",24440,283.2807,3082.369,86.2748503516124,-16 -"832",21,"South Eastern Asia (21)","1995",30819,288.3328,3277.861,106.88690291219,-15 -"833",21,"South Eastern Asia (21)","1996",32886,293.235,3458.425,112.148959026037,-14 -"834",21,"South Eastern Asia (21)","1997",29930,298.003,3547.629,100.435230517814,-13 -"835",21,"South Eastern Asia (21)","1998",17324,302.6725,3331.017,57.236782330737,-12 -"836",21,"South Eastern Asia (21)","1999",23374,307.2962,3452.535,76.0634202440512,-11 -"837",21,"South Eastern Asia (21)","2000",24947,311.9131,3648.302,79.9806099839987,-10 -"838",21,"South Eastern Asia (21)","2001",28441,316.5344,3671.916,89.8512136437619,-9 -"839",21,"South Eastern Asia (21)","2002",33001,321.1503,3796.933,102.758739443806,-8 -"840",21,"South Eastern Asia (21)","2003",32797,325.7535,3948.602,100.680422466681,-7 -"841",21,"South Eastern Asia (21)","2004",35856,330.3298,4163.552,108.546065174865,-6 -"842",21,"South Eastern Asia (21)","2005",37478,334.869,4335.763,111.918391968202,-5 -"843",21,"South Eastern Asia (21)","2006",36843,339.3705,4556.155,108.562765473133,-4 -"844",21,"South Eastern Asia (21)","2007",44376,343.8399,4788.853,129.060065454882,-3 -"845",21,"South Eastern Asia (21)","2008",43942,348.2811,4879.189,126.168201490118,-2 -"846",21,"South Eastern Asia (21)","2009",41344,352.6996,4725.93,117.221567588962,-1 -"847",21,"South Eastern Asia (21)","2010",47338,357.0985,4833.895,132.562864307747,0 -"848",21,"South Eastern Asia (21)","2011",49020,361.4778,4984.516,135.609987667292,1 -"849",21,"South Eastern Asia (21)","2012",55458,365.8338,5169.88,151.593428491299,2 -"850",22,"Indonesia (22)","1970",597,122.5516,815.96851168522,4.87141742743465,-40 -"851",22,"Indonesia (22)","1971",612,122.3185,861.4948,5.00333146662198,-39 -"852",22,"Indonesia (22)","1972",1030,125.2198,906.8879,8.22553621711582,-38 -"853",22,"Indonesia (22)","1973",1442,128.1714,971.1509,11.2505597972715,-37 -"854",22,"Indonesia (22)","1974",1280,131.1653,1024.68,9.75867855294045,-36 -"855",22,"Indonesia (22)","1975",1446,134.1949,1060.138,10.7753722384383,-35 -"856",22,"Indonesia (22)","1976",1382,137.2527,1094.294,10.0690186786854,-34 -"857",22,"Indonesia (22)","1977",1744,140.3358,1159.308,12.4273350064631,-33 -"858",22,"Indonesia (22)","1978",1964,143.4478,1238.283,13.6913915724047,-32 -"859",22,"Indonesia (22)","1979",2005,146.5954,1295.252,13.677100372863,-31 -"860",22,"Indonesia (22)","1980",3013,149.781,1373.326,20.1160360793425,-30 -"861",22,"Indonesia (22)","1981",3125,153.0044,1450.297,20.4242492372768,-29 -"862",22,"Indonesia (22)","1982",3140,156.256,1435.479,20.0952283432316,-28 -"863",22,"Indonesia (22)","1983",2883,159.5178,1522.775,18.0732181612334,-27 -"864",22,"Indonesia (22)","1984",2666,162.7665,1596.258,16.3792918075894,-26 -"865",22,"Indonesia (22)","1985",2392,165.9843,1619.961,14.4110015224331,-25 -"866",22,"Indonesia (22)","1986",2847,169.1649,1683.776,16.8297324090281,-24 -"867",22,"Indonesia (22)","1987",2569,172.309,1739.577,14.909261849352,-23 -"868",22,"Indonesia (22)","1988",2539,175.4149,1815.884,14.4742550376279,-22 -"869",22,"Indonesia (22)","1989",2334,178.4835,1942.182,13.0768390355411,-21 -"870",22,"Indonesia (22)","1990",4690,181.5162,2076.524,25.837914191681,-20 -"871",22,"Indonesia (22)","1991",4468,184.5114,2225.445,24.2153059377361,-19 -"872",22,"Indonesia (22)","1992",4441,187.47,2351.38,23.6891235931082,-18 -"873",22,"Indonesia (22)","1993",5819,190.3995,2488.523,30.5620550474135,-17 -"874",22,"Indonesia (22)","1994",5268,193.3102,2634.95,27.2515366493853,-16 -"875",22,"Indonesia (22)","1995",7250,196.2107,2806.983,36.950074588185,-15 -"876",22,"Indonesia (22)","1996",7169,199.1018,2977.691,36.0067061171722,-14 -"877",22,"Indonesia (22)","1997",7268,201.9845,3067.95,35.9829590884449,-13 -"878",22,"Indonesia (22)","1998",2650,204.8671,2633.08,12.935215073577,-12 -"879",22,"Indonesia (22)","1999",2954,207.7595,2617.542,14.2183630592103,-11 -"880",22,"Indonesia (22)","2000",5425,210.6679,2704.367,25.751431518518,-10 -"881",22,"Indonesia (22)","2001",5598,213.5939,2762.531,26.2086136355018,-9 -"882",22,"Indonesia (22)","2002",5415,216.5329,2845.241,25.0077470906269,-8 -"883",22,"Indonesia (22)","2003",5225,219.4762,2939.942,23.8066815445137,-7 -"884",22,"Indonesia (22)","2004",6404,222.4118,3045.907,28.7934363194759,-6 -"885",22,"Indonesia (22)","2005",8051,225.3285,3176.415,35.7300563399659,-5 -"886",22,"Indonesia (22)","2006",6987,228.2233,3307.078,30.6147531825191,-4 -"887",22,"Indonesia (22)","2007",8063,231.0922,3471.032,34.8908357789661,-3 -"888",22,"Indonesia (22)","2008",10587,233.8442,3638.303,45.2737335371157,-2 -"889",22,"Indonesia (22)","2009",8908,236.5386,3760.015,37.659815353604,-1 -"890",22,"Indonesia (22)","2010",10744,239.1637,3914.834,44.9232053192019,0 -"891",22,"Indonesia (22)","2011",13148,241.7141,4090.793,54.3948408470999,1 -"892",22,"Indonesia (22)","2012",15006,244.1897,4272.029,61.4522234148287,2 -"893",23,"Japan (23)","1970",69882,104.331,13475.1039580717,669.810506944245,-40 -"894",23,"Japan (23)","1971",57699,105.8952,13916.97,544.868889241439,-39 -"895",23,"Japan (23)","1972",68888,107.383,14878.82,641.516813648343,-38 -"896",23,"Japan (23)","1973",87181,108.868,15854.73,800.795458720653,-37 -"897",23,"Japan (23)","1974",75753,110.2935,15458.06,686.831046253859,-36 -"898",23,"Japan (23)","1975",64736,111.6189,15746.74,579.973463275485,-35 -"899",23,"Japan (23)","1976",60176,112.8224,16198.01,533.369260005105,-34 -"900",23,"Japan (23)","1977",58243,113.9121,16747.41,511.297746244692,-33 -"901",23,"Japan (23)","1978",61507,114.9126,17476.81,535.250268464903,-32 -"902",23,"Japan (23)","1979",72329,115.864,18283.88,624.257750466064,-31 -"903",23,"Japan (23)","1980",79007,116.7942,18649.32,676.463386024306,-30 -"904",23,"Japan (23)","1981",71136,117.7135,19046.46,604.314713265683,-29 -"905",23,"Japan (23)","1982",69504,118.6093,19425.15,585.991149092019,-28 -"906",23,"Japan (23)","1983",65614,119.4596,19597.75,549.256819878854,-27 -"907",23,"Japan (23)","1984",74367,120.2328,20079.04,618.525061380921,-26 -"908",23,"Japan (23)","1985",73377,120.9082,20981.66,606.881915370504,-25 -"909",23,"Japan (23)","1986",69941,121.4764,21501.41,575.757924996131,-24 -"910",23,"Japan (23)","1987",75751,121.9516,22230.42,621.156261992463,-23 -"911",23,"Japan (23)","1988",86871,122.3671,23653.66,709.921212482767,-22 -"912",23,"Japan (23)","1989",93278,122.7693,24823.57,759.782779571114,-21 -"913",23,"Japan (23)","1990",99032,123.191,26025.36,803.889894553985,-20 -"914",23,"Japan (23)","1991",99151,123.646,26798.44,801.894117076169,-19 -"915",23,"Japan (23)","1992",84040,124.1233,26954.84,677.068688956868,-18 -"916",23,"Japan (23)","1993",80589,124.602,26917.76,646.771319882506,-17 -"917",23,"Japan (23)","1994",79333,125.0493,27116.15,634.413787202327,-16 -"918",23,"Japan (23)","1995",84340,125.4419,27561.15,672.343132557782,-15 -"919",23,"Japan (23)","1996",83612,125.7723,28243.62,664.788669683229,-14 -"920",23,"Japan (23)","1997",86002,126.0494,28624.13,682.288055317994,-13 -"921",23,"Japan (23)","1998",71187,126.2856,27985.07,563.69847393527,-12 -"922",23,"Japan (23)","1999",70632,126.5001,27898.04,558.355289837716,-11 -"923",23,"Japan (23)","2000",80561,126.7058,28649.37,635.811462458704,-10 -"924",23,"Japan (23)","2001",74998,126.9073,28656.62,590.966792296424,-9 -"925",23,"Japan (23)","2002",72778,127.0974,28688.81,572.615962246277,-8 -"926",23,"Japan (23)","2003",77013,127.2626,29056.55,605.150295530659,-7 -"927",23,"Japan (23)","2004",80500,127.3842,29825.46,631.94650513957,-6 -"928",23,"Japan (23)","2005",83000,127.4487,30386.92,651.242421460556,-5 -"929",23,"Japan (23)","2006",83300,127.4509,31114.88,653.58502764594,-4 -"930",23,"Japan (23)","2007",85900,127.396,31781.99,674.275487456435,-3 -"931",23,"Japan (23)","2008",83200,127.2931,31586.76,653.609661482044,-2 -"932",23,"Japan (23)","2009",56000,127.1561,29950.3,440.403566954318,-1 -"933",23,"Japan (23)","2010",67400,126.9954,30521.48,530.727884632042,0 -"934",23,"Japan (23)","2011",69600,126.8138,31181.63,548.836167672603,1 -"935",23,"Japan (23)","2012",68800,126.6079,31957.19,543.410008380204,2 -"936",24,"Oceania (24)","1970",7212,16.80333,15508.5470070863,429.200640587312,-40 -"937",24,"Oceania (24)","1971",8190,17.2816,15691.06,473.914452365522,-39 -"938",24,"Oceania (24)","1972",8721,17.55112,16085.19,496.891366476897,-38 -"939",24,"Oceania (24)","1973",8152,17.81179,16413.83,457.6743830912,-37 -"940",24,"Oceania (24)","1974",8843,18.06842,16917.32,489.417447679432,-36 -"941",24,"Oceania (24)","1975",7328,18.32478,16790.77,399.895660411748,-35 -"942",24,"Oceania (24)","1976",7530,18.58204,16948.99,405.229996275974,-34 -"943",24,"Oceania (24)","1977",6097,18.84033,17074.64,323.614289134001,-33 -"944",24,"Oceania (24)","1978",6075,19.10138,16971.29,318.039848429799,-32 -"945",24,"Oceania (24)","1979",7645,19.36676,17308.82,394.748527890055,-31 -"946",24,"Oceania (24)","1980",6883,19.63778,17521.98,350.497866866825,-30 -"947",24,"Oceania (24)","1981",7414,19.91511,17855.64,372.280143067249,-29 -"948",24,"Oceania (24)","1982",6645,20.19899,18193.99,328.976844881848,-28 -"949",24,"Oceania (24)","1983",5562,20.4894,17668.38,271.457436528156,-27 -"950",24,"Oceania (24)","1984",6861,20.78607,18240.45,330.076825489378,-26 -"951",24,"Oceania (24)","1985",6432,21.08854,18793.35,304.999777130138,-25 -"952",24,"Oceania (24)","1986",6570,21.39682,19283.07,307.054973589533,-24 -"953",24,"Oceania (24)","1987",6710,21.71029,19407.46,309.070030847124,-23 -"954",24,"Oceania (24)","1988",7380,22.02703,19983.34,335.042899564762,-22 -"955",24,"Oceania (24)","1989",6985,22.34453,20351.25,312.604471877457,-21 -"956",24,"Oceania (24)","1990",6353,22.66077,20744.13,280.352344602589,-20 -"957",24,"Oceania (24)","1991",5933,22.97585,20326.68,258.227660782953,-19 -"958",24,"Oceania (24)","1992",5842,23.28964,20101.77,250.841146535541,-18 -"959",24,"Oceania (24)","1993",6652,23.59976,20636.26,281.867273226507,-17 -"960",24,"Oceania (24)","1994",7276,23.9033,21240.62,304.393117268327,-16 -"961",24,"Oceania (24)","1995",7259,24.1987,21882.24,299.974792034283,-15 -"962",24,"Oceania (24)","1996",7097,24.48367,22486.01,289.866674399712,-14 -"963",24,"Oceania (24)","1997",7522,24.75996,23018.93,303.796936667103,-13 -"964",24,"Oceania (24)","1998",9258,25.03483,23650.4,369.804787969401,-12 -"965",24,"Oceania (24)","1999",7376,25.31832,24598.03,291.330546418562,-11 -"966",24,"Oceania (24)","2000",7496,25.61716,25221.73,292.616355599137,-10 -"967",24,"Oceania (24)","2001",8894,25.93458,25443.69,342.939812404905,-9 -"968",24,"Oceania (24)","2002",9209,26.26733,26105.29,350.587593029059,-8 -"969",24,"Oceania (24)","2003",10108,26.60799,26603.28,379.88589141833,-7 -"970",24,"Oceania (24)","2004",8700,26.94575,27316.85,322.87095367544,-6 -"971",24,"Oceania (24)","2005",8653,27.27279,27736.18,317.275936932012,-5 -"972",24,"Oceania (24)","2006",8587,27.58661,28185.85,311.274201505731,-4 -"973",24,"Oceania (24)","2007",9340,27.88964,28774.07,334.891379021027,-3 -"974",24,"Oceania (24)","2008",9414,28.2549,28941.88,333.181147340815,-2 -"975",24,"Oceania (24)","2009",6656,28.61869,28742.47,232.575285591339,-1 -"976",24,"Oceania (24)","2010",8636,28.9838,29041.5,297.959549817484,0 -"977",24,"Oceania (24)","2011",7552,29.3506,29645.03,257.303087500767,1 -"978",24,"Oceania (24)","2012",7945,29.71791,30264.62,267.347199045962,2 -"979",25,"Other South Asia (25)","1970",696,166.4336,883.206850213264,4.1818478960979,-40 -"980",25,"Other South Asia (25)","1971",662,172.0365,877.3312,3.84802062353047,-39 -"981",25,"Other South Asia (25)","1972",461,176.6455,805.4592,2.60974663945586,-38 -"982",25,"Other South Asia (25)","1973",554,181.4043,828.6378,3.05395186332408,-37 -"983",25,"Other South Asia (25)","1974",536,186.282,853.4778,2.87735798413159,-36 -"984",25,"Other South Asia (25)","1975",630,191.2564,846.13,3.29400741622241,-35 -"985",25,"Other South Asia (25)","1976",599,196.3199,866.3409,3.05114254846299,-34 -"986",25,"Other South Asia (25)","1977",752,201.4788,867.564,3.73240261506422,-33 -"987",25,"Other South Asia (25)","1978",700,206.7432,906.8549,3.38584292010572,-32 -"988",25,"Other South Asia (25)","1979",880,212.1292,916.955,4.14841521110719,-31 -"989",25,"Other South Asia (25)","1980",1208,217.6501,939.1577,5.55019271757743,-30 -"990",25,"Other South Asia (25)","1981",1157,223.303,970.4668,5.18130074383237,-29 -"991",25,"Other South Asia (25)","1982",1561,229.0865,989.3985,6.81402003173474,-28 -"992",25,"Other South Asia (25)","1983",1658,235.0167,1015.109,7.05481780656438,-27 -"993",25,"Other South Asia (25)","1984",1550,241.1136,1039.595,6.42850506980942,-26 -"994",25,"Other South Asia (25)","1985",2146,247.3895,1068.855,8.67457996398392,-25 -"995",25,"Other South Asia (25)","1986",2145,253.8464,1091.995,8.44999180606855,-24 -"996",25,"Other South Asia (25)","1987",2104,260.4727,1105.74,8.07762195423935,-23 -"997",25,"Other South Asia (25)","1988",2348,267.2489,1127.968,8.78581726622635,-22 -"998",25,"Other South Asia (25)","1989",2308,274.1485,1137.222,8.41879492318944,-21 -"999",25,"Other South Asia (25)","1990",1772,281.1478,1161.731,6.30273471818026,-20 -"1000",25,"Other South Asia (25)","1991",2019,288.2426,1179.253,7.00451633450434,-19 -"1001",25,"Other South Asia (25)","1992",1996,295.4254,1223.416,6.75635879650159,-18 -"1002",25,"Other South Asia (25)","1993",2252,302.6656,1220.913,7.44055485658099,-17 -"1003",25,"Other South Asia (25)","1994",2396,309.9265,1234.786,7.73086522127021,-16 -"1004",25,"Other South Asia (25)","1995",2312,317.182,1278.556,7.28919043325283,-15 -"1005",25,"Other South Asia (25)","1996",2040,324.4149,1305.664,6.2882438506986,-14 -"1006",25,"Other South Asia (25)","1997",2209,331.6305,1314.06,6.66102786082704,-13 -"1007",25,"Other South Asia (25)","1998",2418,338.8539,1330.169,7.13581871124989,-12 -"1008",25,"Other South Asia (25)","1999",2417,346.1237,1353.693,6.98305259073562,-11 -"1009",25,"Other South Asia (25)","2000",2620,353.4673,1389.991,7.41228396516453,-10 -"1010",25,"Other South Asia (25)","2001",3135,360.8943,1395.691,8.68675398863324,-9 -"1011",25,"Other South Asia (25)","2002",3345,368.3938,1437.076,9.07995737170387,-8 -"1012",25,"Other South Asia (25)","2003",3250,375.9497,1484.305,8.64477348964502,-7 -"1013",25,"Other South Asia (25)","2004",3869,383.537,1553.889,10.0876838479729,-6 -"1014",25,"Other South Asia (25)","2005",4577,391.1392,1630.904,11.7017164221842,-5 -"1015",25,"Other South Asia (25)","2006",4432,398.7456,1705.815,11.1148561890087,-4 -"1016",25,"Other South Asia (25)","2007",4460,406.365,1784.052,10.9753546688322,-3 -"1017",25,"Other South Asia (25)","2008",3542,414.0226,1855.233,8.55508853864499,-2 -"1018",25,"Other South Asia (25)","2009",4810,421.7539,1887.607,11.4047552375923,-1 -"1019",25,"Other South Asia (25)","2010",5047,429.5813,1931.21,11.7486492079613,0 -"1020",25,"Other South Asia (25)","2011",5238,437.511,1992.753,11.9722704114868,1 -"1021",25,"Other South Asia (25)","2012",5959,445.5251,2062.911,13.3752284663647,2 -"1022",26,"Other South Africa (26)","1970",2005,45.62302,1392.89646093913,43.9471126637386,-40 -"1023",26,"Other South Africa (26)","1971",1909,47.25866,1408.711,40.3947128420484,-39 -"1024",26,"Other South Africa (26)","1972",2327,48.63551,1423.226,47.845699572185,-38 -"1025",26,"Other South Africa (26)","1973",3034,50.0741,1457.276,60.5902053157221,-37 -"1026",26,"Other South Africa (26)","1974",3254,51.5736,1480.218,63.0942963066375,-36 -"1027",26,"Other South Africa (26)","1975",2692,53.13327,1421.38,50.6650541176931,-35 -"1028",26,"Other South Africa (26)","1976",2775,54.75267,1387.807,50.6824598690804,-34 -"1029",26,"Other South Africa (26)","1977",3701,56.43172,1346.514,65.5836823687104,-33 -"1030",26,"Other South Africa (26)","1978",3636,58.17012,1298.553,62.5063176764978,-32 -"1031",26,"Other South Africa (26)","1979",3307,59.96738,1281.161,55.1466480609958,-31 -"1032",26,"Other South Africa (26)","1980",1410,61.82227,1295.589,22.8073152279915,-30 -"1033",26,"Other South Africa (26)","1981",1453,63.73701,1267.775,22.7968020464091,-29 -"1034",26,"Other South Africa (26)","1982",887,65.70934,1231.324,13.4988420215452,-28 -"1035",26,"Other South Africa (26)","1983",1161,67.72829,1201.375,17.1420244036871,-27 -"1036",26,"Other South Africa (26)","1984",1459,69.7794,1202.17,20.9087495736564,-26 -"1037",26,"Other South Africa (26)","1985",1492,71.85332,1209.845,20.7645241722999,-25 -"1038",26,"Other South Africa (26)","1986",1239,73.941,1206.85,16.7566032377166,-24 -"1039",26,"Other South Africa (26)","1987",978,76.04729,1246.922,12.860418826233,-23 -"1040",26,"Other South Africa (26)","1988",997,78.1937,1289.007,12.7503878189675,-22 -"1041",26,"Other South Africa (26)","1989",1038,80.41052,1291.151,12.9087587047068,-21 -"1042",26,"Other South Africa (26)","1990",1072,82.71741,1291.267,12.9597868211783,-20 -"1043",26,"Other South Africa (26)","1991",1435,85.12392,1284.605,16.8577762866184,-19 -"1044",26,"Other South Africa (26)","1992",1297,87.61742,1213.922,14.8029923729779,-18 -"1045",26,"Other South Africa (26)","1993",1306,90.16825,1108.931,14.4840340141901,-17 -"1046",26,"Other South Africa (26)","1994",1358,92.73466,1101.903,14.6439314060137,-16 -"1047",26,"Other South Africa (26)","1995",1387,95.28664,1125.655,14.5560804746605,-15 -"1048",26,"Other South Africa (26)","1996",1293,97.81644,1177.455,13.2186368671769,-14 -"1049",26,"Other South Africa (26)","1997",1690,100.3338,1215.532,16.8437754774563,-13 -"1050",26,"Other South Africa (26)","1998",1765,102.848,1246.188,17.1612476664592,-12 -"1051",26,"Other South Africa (26)","1999",1553,105.3752,1257.437,14.7378130717664,-11 -"1052",26,"Other South Africa (26)","2000",1552,107.9291,1267.886,14.3798104496378,-10 -"1053",26,"Other South Africa (26)","2001",1692,110.5141,1283.399,15.3102635772268,-9 -"1054",26,"Other South Africa (26)","2002",2220,113.1327,1339.247,19.622973729081,-8 -"1055",26,"Other South Africa (26)","2003",1892,115.7948,1359.277,16.3392483945739,-7 -"1056",26,"Other South Africa (26)","2004",181,118.5119,1431.622,1.52727278863979,-6 -"1057",26,"Other South Africa (26)","2005",217,121.295,1534.786,1.78902675295767,-5 -"1058",26,"Other South Africa (26)","2006",225,124.1446,1650.745,1.81240263370296,-4 -"1059",26,"Other South Africa (26)","2007",225,127.067,1789.49,1.77071938426185,-3 -"1060",26,"Other South Africa (26)","2008",272,130.2515,1871.848,2.08826769749293,-2 -"1061",26,"Other South Africa (26)","2009",344,133.5386,1843.252,2.57603419535625,-1 -"1062",26,"Other South Africa (26)","2010",355,136.949,1909.069,2.59220585765504,0 -"1063",26,"Other South Africa (26)","2011",474,140.4888,1988.006,3.37393443463109,1 -"1064",26,"Other South Africa (26)","2012",439,144.1459,2063.026,3.0455254016937,2 diff --git a/message_ix_models/model/material/tax_emission_1000f.csv b/message_ix_models/model/material/tax_emission_1000f.csv deleted file mode 100644 index a4a6fc8198..0000000000 --- a/message_ix_models/model/material/tax_emission_1000f.csv +++ /dev/null @@ -1,14 +0,0 @@ -node,type_emission,type_tec,year,lvl,mrg -World,TCE,all,2025,102.1439278727925,0.0 -World,TCE,all,2030,130.36441186537496,0.0 -World,TCE,all,2035,166.3816952699343,0.0 -World,TCE,all,2040,212.34989001051068,0.0 -World,TCE,all,2045,271.0182494193178,0.0 -World,TCE,all,2050,345.89559483490166,0.0 -World,TCE,all,2055,441.4601702377553,0.0 -World,TCE,all,2060,563.4274758525585,0.0 -World,TCE,all,2070,917.7639879950095,0.0 -World,TCE,all,2080,1494.9408286949079,0.0 -World,TCE,all,2090,2435.101083211352,0.0 -World,TCE,all,2100,3966.5230701029004,0.0 -World,TCE,all,2110,6461.048115879377,0.0 diff --git a/message_ix_models/model/material/test_SSP_costs.ipynb b/message_ix_models/model/material/test_SSP_costs.ipynb deleted file mode 100644 index d22ead04f6..0000000000 --- a/message_ix_models/model/material/test_SSP_costs.ipynb +++ /dev/null @@ -1,704 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 7, - "outputs": [ - { - "data": { - "text/plain": "", - "application/javascript": "if (typeof IPython !== 'undefined') { IPython.OutputArea.prototype._should_scroll = function(lines){ return false; }}" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "model = \"MESSAGEix-Materials\"\n", - "scenario = \"baseline_DEFAULT_test_0310_macro\"\n", - "\n", - "\n", - "import ixmp\n", - "import message_ix\n", - "mp = ixmp.Platform(\"ixmp_dev\")\n", - "scen = message_ix.Scenario(mp, model, scenario)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-28T21:04:48.800572400Z", - "start_time": "2023-11-28T21:04:39.706723100Z" - } - }, - "id": "64cd795b05a0bdd" - }, - { - "cell_type": "code", - "execution_count": 2, - "outputs": [], - "source": [ - "model = \"MESSAGEix-Materials\"\n", - "scenario = \"baseline_DEFAULT_0108_macro\"\n", - "\n", - "\n", - "import ixmp\n", - "import message_ix\n", - "mp = ixmp.Platform(\"ixmp_dev\")\n", - "scen = message_ix.Scenario(mp, model, scenario)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-29T07:51:33.404668500Z", - "start_time": "2023-11-29T07:51:32.023778600Z" - } - }, - "id": "46f136d4c3eeff9d" - }, - { - "cell_type": "code", - "execution_count": 7, - "outputs": [], - "source": [ - "scen.to_excel(\"C:/Users\\maczek\\PycharmProjects\\message_data/macro_dump.xlsx\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-29T09:15:51.773767900Z", - "start_time": "2023-11-29T07:53:47.494599700Z" - } - }, - "id": "5dbbbdf07d5feade" - }, - { - "cell_type": "code", - "execution_count": 8, - "outputs": [ - { - "data": { - "text/plain": "True" - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "scen.has_solution()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-28T21:04:51.769066400Z", - "start_time": "2023-11-28T21:04:51.706749500Z" - } - }, - "id": "5bf2f475d4a0cd34" - }, - { - "cell_type": "code", - "execution_count": 9, - "outputs": [], - "source": [ - "base = scen.clone(model=scen.model, scenario=\"SSP_supply_cost_test_baseline_macro\", keep_solution=False)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-28T21:05:36.800203700Z", - "start_time": "2023-11-28T21:05:18.456553600Z" - } - }, - "id": "2f393bdf1d5d3b0e" - }, - { - "cell_type": "code", - "execution_count": 4, - "outputs": [], - "source": [ - "from message_ix_models.tools.costs.config import Config\n", - "from message_ix_models.tools.costs.projections import create_cost_projections" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-26T23:16:20.039581700Z", - "start_time": "2023-11-26T23:16:16.086701100Z" - } - }, - "id": "a8aca9ff39fadf22" - }, - { - "cell_type": "code", - "execution_count": 6, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Selected module: materials\n", - "Selected node: R12\n", - "Selected reference region: R12_NAM\n", - "Selected base year: 2021\n", - "Selected method: convergence\n", - "Selected fixed O&M rate: 0.025\n", - "Selected format: message\n", - "Selected scenario: SSP2\n", - "Selected convergence year: 2050\n", - "For the convergence method, only the SSP scenario(s) itself needs to be specified. No scenario version (previous vs. updated) is needed.\n", - "...Calculating regional differentiation in base year+region...\n", - "The following technologies are not projected due to insufficient data:\n", - "CH2O_synth\n", - "CH2O_to_resin\n", - "DUMMY_alumina_supply\n", - "DUMMY_coal_supply\n", - "DUMMY_gas_supply\n", - "DUMMY_limestone_supply_cement\n", - "DUMMY_limestone_supply_steel\n", - "DUMMY_ore_supply\n", - "agg_ref\n", - "feedstock_t/d\n", - "finishing_aluminum\n", - "gas_processing_petro\n", - "hydrotreating_ref\n", - "import_NFert\n", - "import_NH3\n", - "import_aluminum\n", - "import_petro\n", - "import_steel\n", - "manuf_aluminum\n", - "meth_bal\n", - "meth_imp\n", - "meth_t_d\n", - "meth_t_d_material\n", - "meth_trd\n", - "other_EOL_aluminum\n", - "other_EOL_cement\n", - "other_EOL_steel\n", - "prep_secondary_aluminum_1\n", - "prep_secondary_aluminum_2\n", - "prep_secondary_aluminum_3\n", - "prep_secondary_steel_1\n", - "prep_secondary_steel_2\n", - "prep_secondary_steel_3\n", - "production_HVC\n", - "residual_NH3\n", - "scrap_recovery_aluminum\n", - "scrap_recovery_cement\n", - "scrap_recovery_steel\n", - "secondary_aluminum\n", - "total_EOL_aluminum\n", - "total_EOL_cement\n", - "total_EOL_steel\n", - "trade_NFert\n", - "trade_NH3\n", - "trade_aluminum\n", - "trade_petro\n", - "trade_steel\n", - "......(Using year 2021 data from WEO.)\n", - "...Applying learning rates to reference region...\n", - "...Applying splines to converge...\n", - "...Creating MESSAGE outputs...\n" - ] - } - ], - "source": [ - "cfg = Config(module=\"materials\", ref_region=\"R12_NAM\", method=\"convergence\", format=\"message\", scenario=\"SSP2\")\n", - "\n", - "out_materials = create_cost_projections(\n", - " node=cfg.node,\n", - " ref_region=cfg.ref_region,\n", - " base_year=cfg.base_year,\n", - " module=cfg.module,\n", - " method=cfg.method,\n", - " scenario_version=cfg.scenario_version,\n", - " scenario=cfg.scenario,\n", - " convergence_year=cfg.convergence_year,\n", - " fom_rate=cfg.fom_rate,\n", - " format=cfg.format,\n", - ")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-26T23:22:58.008834200Z", - "start_time": "2023-11-26T23:21:43.039935700Z" - } - }, - "id": "d038d7d62bea30a2" - }, - { - "cell_type": "code", - "execution_count": 10, - "outputs": [ - { - "data": { - "text/plain": " node_loc technology year_vtg year_act value unit\n0 R12_AFR bio_hpl 1960 1960 9.519231 USD/kWa\n1 R12_AFR bio_hpl 1960 1965 9.519231 USD/kWa\n2 R12_AFR bio_hpl 1960 1970 9.519231 USD/kWa\n3 R12_AFR bio_hpl 1960 1975 9.519231 USD/kWa\n4 R12_AFR bio_hpl 1960 1980 9.519231 USD/kWa\n... ... ... ... ... ... ...\n11799575 R12_WEU visbreaker_ref 2090 2100 1.478498 USD/kWa\n11799576 R12_WEU visbreaker_ref 2090 2110 1.892602 USD/kWa\n11799577 R12_WEU visbreaker_ref 2100 2100 1.155000 USD/kWa\n11799578 R12_WEU visbreaker_ref 2100 2110 1.478498 USD/kWa\n11799579 R12_WEU visbreaker_ref 2110 2110 1.155000 USD/kWa\n\n[892848 rows x 6 columns]", - "text/html": "
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node_loctechnologyyear_vtgyear_actvalueunit
0R12_AFRbio_hpl196019609.519231USD/kWa
1R12_AFRbio_hpl196019659.519231USD/kWa
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.....................
11799575R12_WEUvisbreaker_ref209021001.478498USD/kWa
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11799577R12_WEUvisbreaker_ref210021001.155000USD/kWa
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892848 rows × 6 columns

\n
" - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-26T23:24:06.305578100Z", - "start_time": "2023-11-26T23:23:59.945987500Z" - } - }, - "id": "849530ddbdb9a6bd" - }, - { - "cell_type": "code", - "execution_count": 11, - "outputs": [ - { - "ename": "ValueError", - "evalue": "The index set 'technology' does not have an element 'meth_i'!", - "output_type": "error", - "traceback": [ - "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[1;31mValueError\u001B[0m Traceback (most recent call last)", - "Input \u001B[1;32mIn [11]\u001B[0m, in \u001B[0;36m\u001B[1;34m()\u001B[0m\n\u001B[0;32m 1\u001B[0m base\u001B[38;5;241m.\u001B[39mcheck_out()\n\u001B[1;32m----> 2\u001B[0m \u001B[43mbase\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43madd_par\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mfix_cost\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mout_materials\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfix_cost\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mdrop_duplicates\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mdrop\u001B[49m\u001B[43m(\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mscenario_version\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mscenario\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43maxis\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m1\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\ixmp\\core\\scenario.py:535\u001B[0m, in \u001B[0;36mScenario.add_par\u001B[1;34m(self, name, key_or_data, value, unit, comment)\u001B[0m\n\u001B[0;32m 529\u001B[0m elements \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mmap\u001B[39m(\n\u001B[0;32m 530\u001B[0m \u001B[38;5;28;01mlambda\u001B[39;00m e: (e\u001B[38;5;241m.\u001B[39mkey, e\u001B[38;5;241m.\u001B[39mvalue, e\u001B[38;5;241m.\u001B[39munit, e\u001B[38;5;241m.\u001B[39mcomment),\n\u001B[0;32m 531\u001B[0m data\u001B[38;5;241m.\u001B[39mastype(types)\u001B[38;5;241m.\u001B[39mitertuples(),\n\u001B[0;32m 532\u001B[0m )\n\u001B[0;32m 534\u001B[0m \u001B[38;5;66;03m# Store\u001B[39;00m\n\u001B[1;32m--> 535\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_backend\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mitem_set_elements\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mpar\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mname\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43melements\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\ixmp\\core\\timeseries.py:108\u001B[0m, in \u001B[0;36mTimeSeries._backend\u001B[1;34m(self, method, *args, **kwargs)\u001B[0m\n\u001B[0;32m 102\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m_backend\u001B[39m(\u001B[38;5;28mself\u001B[39m, method, \u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[0;32m 103\u001B[0m \u001B[38;5;124;03m\"\"\"Convenience for calling *method* on the backend.\u001B[39;00m\n\u001B[0;32m 104\u001B[0m \n\u001B[0;32m 105\u001B[0m \u001B[38;5;124;03m The weak reference to the Platform object is used, if the Platform is still\u001B[39;00m\n\u001B[0;32m 106\u001B[0m \u001B[38;5;124;03m alive.\u001B[39;00m\n\u001B[0;32m 107\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[1;32m--> 108\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mplatform\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_backend\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmethod\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\ixmp\\backend\\base.py:53\u001B[0m, in \u001B[0;36mBackend.__call__\u001B[1;34m(self, obj, method, *args, **kwargs)\u001B[0m\n\u001B[0;32m 47\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m__call__\u001B[39m(\u001B[38;5;28mself\u001B[39m, obj, method, \u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[0;32m 48\u001B[0m \u001B[38;5;124;03m\"\"\"Call the backend method `method` for `obj`.\u001B[39;00m\n\u001B[0;32m 49\u001B[0m \n\u001B[0;32m 50\u001B[0m \u001B[38;5;124;03m The class attribute obj._backend_prefix is used to determine a prefix for the\u001B[39;00m\n\u001B[0;32m 51\u001B[0m \u001B[38;5;124;03m method name, e.g. 'ts_{method}'.\u001B[39;00m\n\u001B[0;32m 52\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[1;32m---> 53\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mgetattr\u001B[39;49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmethod\u001B[49m\u001B[43m)\u001B[49m\u001B[43m(\u001B[49m\u001B[43mobj\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\ixmp\\backend\\jdbc.py:1047\u001B[0m, in \u001B[0;36mJDBCBackend.item_set_elements\u001B[1;34m(self, s, type, name, elements)\u001B[0m\n\u001B[0;32m 1044\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m java\u001B[38;5;241m.\u001B[39mIxException \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[0;32m 1045\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28many\u001B[39m(s \u001B[38;5;129;01min\u001B[39;00m e\u001B[38;5;241m.\u001B[39margs[\u001B[38;5;241m0\u001B[39m] \u001B[38;5;28;01mfor\u001B[39;00m s \u001B[38;5;129;01min\u001B[39;00m (\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mdoes not have an element\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mThe unit\u001B[39m\u001B[38;5;124m\"\u001B[39m)):\n\u001B[0;32m 1046\u001B[0m \u001B[38;5;66;03m# Re-raise as Python ValueError\u001B[39;00m\n\u001B[1;32m-> 1047\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(e\u001B[38;5;241m.\u001B[39margs[\u001B[38;5;241m0\u001B[39m]) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;28mNone\u001B[39m\n\u001B[0;32m 1048\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mcannot be edited\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;129;01min\u001B[39;00m e\u001B[38;5;241m.\u001B[39margs[\u001B[38;5;241m0\u001B[39m]:\n\u001B[0;32m 1049\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mRuntimeError\u001B[39;00m(e\u001B[38;5;241m.\u001B[39margs[\u001B[38;5;241m0\u001B[39m])\n", - "\u001B[1;31mValueError\u001B[0m: The index set 'technology' does not have an element 'meth_i'!" - ] - } - ], - "source": [ - "base.check_out()\n", - "base.add_par(\"fix_cost\", )" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-26T23:26:51.149516500Z", - "start_time": "2023-11-26T23:26:26.039933300Z" - } - }, - "id": "b032114dc82a0126" - }, - { - "cell_type": "code", - "execution_count": 13, - "outputs": [], - "source": [ - "tec_set = list(base.set(\"technology\"))" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-26T23:27:32.868165400Z", - "start_time": "2023-11-26T23:27:32.836970100Z" - } - }, - "id": "2315f0de398752dd" - }, - { - "cell_type": "code", - "execution_count": 14, - "outputs": [], - "source": [ - "fix_cost = out_materials.fix_cost.drop_duplicates().drop([\"scenario_version\", \"scenario\"], axis=1)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-26T23:27:59.915107400Z", - "start_time": "2023-11-26T23:27:53.024521700Z" - } - }, - "id": "ebf07c7956772e0f" - }, - { - "cell_type": "code", - "execution_count": 17, - "outputs": [], - "source": [ - "base.add_par(\"fix_cost\", fix_cost[fix_cost[\"technology\"].isin(tec_set)])" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-26T23:29:13.055635600Z", - "start_time": "2023-11-26T23:28:40.039748300Z" - } - }, - "id": "4e534e7cbf802e51" - }, - { - "cell_type": "code", - "execution_count": 18, - "outputs": [], - "source": [ - "base.commit(\"update cost assumption with tool\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-26T23:30:05.027074Z", - "start_time": "2023-11-26T23:29:19.164705500Z" - } - }, - "id": "869f9b234e212056" - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "def modify_costs_with_tool(scen_name, ssp):\n", - " mp = ixmp.Platform(\"ixmp_dev\")\n", - " base = message_ix.Scenario(mp, \"SSP_supply_cost_test_baseline\", scenario=scen_name)\n", - " scen = base.clone(model=base.model, scenario=base.scenario.replace(\"baseline\", ssp))\n", - " \n", - " tec_set = list(scen.set(\"technology\"))\n", - " \n", - " cfg = Config(module=\"materials\", ref_region=\"R12_NAM\", method=\"convergence\", format=\"message\", scenario=ssp)\n", - " \n", - " out_materials = create_cost_projections(\n", - " node=cfg.node,\n", - " ref_region=cfg.ref_region,\n", - " base_year=cfg.base_year,\n", - " module=cfg.module,\n", - " method=cfg.method,\n", - " scenario_version=cfg.scenario_version,\n", - " scenario=cfg.scenario,\n", - " convergence_year=cfg.convergence_year,\n", - " fom_rate=cfg.fom_rate,\n", - " format=cfg.format,\n", - " )\n", - " \n", - " fix_cost = out_materials.fix_cost.drop_duplicates().drop([\"scenario_version\", \"scenario\"], axis=1)\n", - " scen.add_par(\"fix_cost\", fix_cost[fix_cost[\"technology\"].isin(tec_set)])\n", - " inv_cost = out_materials.inv_cost.drop_duplicates().drop([\"scenario_version\", \"scenario\"], axis=1)\n", - " scen.add_par(\"fix_cost\", inv_cost[inv_cost[\"technology\"].isin(tec_set)])\n", - " \n", - " scen.commit(\"update cost assumption with tool\")\n", - " #scen.solve(model=\"MESSAGE\")" - ], - "metadata": { - "collapsed": false - }, - "id": "25a079df2acd518c" - }, - { - "cell_type": "code", - "execution_count": 22, - "outputs": [ - { - "data": { - "text/plain": " model scenario scheme \\\n0 MESSAGEix-Materials 2.0deg_no_SDGs_petro_thesis_1_final_macro MESSAGE \n1 MESSAGEix-Materials 2.0deg_no_SDGs_petro_thesis_1_macro MESSAGE \n2 MESSAGEix-Materials 2.0deg_petro_thesis_1 MESSAGE \n3 MESSAGEix-Materials 2.0deg_petro_thesis_2 MESSAGE \n4 MESSAGEix-Materials 2.0deg_petro_thesis_2_final_macro MESSAGE \n.. ... ... ... \n366 MESSAGEix-Materials test_ccs MESSAGE \n367 MESSAGEix-Materials test_foil_trp_constraint MESSAGE \n368 MESSAGEix-Materials test_foil_trp_constraint_IEA_calib MESSAGE \n369 MESSAGEix-Materials test_report_JM MESSAGE \n370 MESSAGEix-Materials timeseries_test MESSAGE \n\n is_default is_locked cre_user cre_date upd_user \\\n0 1 0 maczek 2023-06-27 23:24:23.000000 maczek \n1 1 0 maczek 2023-05-09 09:34:33.000000 maczek \n2 1 0 maczek 2023-02-08 23:38:03.000000 maczek \n3 1 0 maczek 2023-04-06 16:44:33.000000 None \n4 1 0 maczek 2023-06-21 23:48:48.000000 maczek \n.. ... ... ... ... ... \n366 1 0 unlu 2022-07-12 15:32:10.000000 None \n367 1 0 maczek 2023-10-12 16:37:45.000000 None \n368 1 0 maczek 2023-10-16 13:51:53.000000 None \n369 1 1 min 2021-08-02 16:17:16.000000 None \n370 1 0 unlu 2022-06-09 13:59:42.000000 None \n\n upd_date lock_user lock_date \\\n0 2023-07-03 17:01:57.000000 None None \n1 2023-05-09 10:28:12.000000 None None \n2 2023-02-08 23:44:53.000000 None None \n3 None None None \n4 2023-06-22 00:36:08.000000 None None \n.. ... ... ... \n366 None None None \n367 None None None \n368 None None None \n369 None min 2021-08-02 16:39:25.000000 \n370 None None None \n\n annotation version \n0 clone Scenario from 'MESSAGEix-Materials|NoPol... 1 \n1 clone Scenario from 'MESSAGEix-Materials|NoPol... 4 \n2 clone Scenario from 'MESSAGEix-Materials|NoPol... 1 \n3 clone Scenario from 'MESSAGEix-Materials|NoPol... 6 \n4 clone Scenario from 'MESSAGEix-Materials|NoPol... 1 \n.. ... ... \n366 clone Scenario from 'MESSAGEix-Materials|NoPol... 1 \n367 clone Scenario from 'MESSAGEix-Materials|NoPol... 1 \n368 clone Scenario from 'MESSAGEix-Materials|test_... 1 \n369 clone Scenario from 'Buildings_SSP2|test', ver... 1 \n370 clone Scenario from 'MESSAGEix-Materials|NoPol... 1 \n\n[371 rows x 13 columns]", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
modelscenarioschemeis_defaultis_lockedcre_usercre_dateupd_userupd_datelock_userlock_dateannotationversion
0MESSAGEix-Materials2.0deg_no_SDGs_petro_thesis_1_final_macroMESSAGE10maczek2023-06-27 23:24:23.000000maczek2023-07-03 17:01:57.000000NoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...1
1MESSAGEix-Materials2.0deg_no_SDGs_petro_thesis_1_macroMESSAGE10maczek2023-05-09 09:34:33.000000maczek2023-05-09 10:28:12.000000NoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...4
2MESSAGEix-Materials2.0deg_petro_thesis_1MESSAGE10maczek2023-02-08 23:38:03.000000maczek2023-02-08 23:44:53.000000NoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...1
3MESSAGEix-Materials2.0deg_petro_thesis_2MESSAGE10maczek2023-04-06 16:44:33.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...6
4MESSAGEix-Materials2.0deg_petro_thesis_2_final_macroMESSAGE10maczek2023-06-21 23:48:48.000000maczek2023-06-22 00:36:08.000000NoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...1
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366MESSAGEix-Materialstest_ccsMESSAGE10unlu2022-07-12 15:32:10.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...1
367MESSAGEix-Materialstest_foil_trp_constraintMESSAGE10maczek2023-10-12 16:37:45.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...1
368MESSAGEix-Materialstest_foil_trp_constraint_IEA_calibMESSAGE10maczek2023-10-16 13:51:53.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-Materials|test_...1
369MESSAGEix-Materialstest_report_JMMESSAGE11min2021-08-02 16:17:16.000000NoneNonemin2021-08-02 16:39:25.000000clone Scenario from 'Buildings_SSP2|test', ver...1
370MESSAGEix-Materialstimeseries_testMESSAGE10unlu2022-06-09 13:59:42.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...1
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371 rows × 13 columns

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" - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mp.scenario_list(model=\"MESSAGEix-Materials\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-26T23:56:29.295406400Z", - "start_time": "2023-11-26T23:56:28.795734900Z" - } - }, - "id": "8f0f995ddaaeb4e4" - }, - { - "cell_type": "code", - "execution_count": 77, - "outputs": [], - "source": [ - "import pandas as pd" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-27T13:13:25.213762300Z", - "start_time": "2023-11-27T13:13:25.160590500Z" - } - }, - "id": "d8c358646f3093fa" - }, - { - "cell_type": "code", - "execution_count": 78, - "outputs": [], - "source": [ - "r_df = pd.read_csv(\"C:/Users\\maczek\\PycharmProjects\\message_data\\message_data\\model\\material/output_file.csv\")\n", - "py_df = pd.read_csv(\"C:/Users\\maczek\\PycharmProjects\\message_data\\message_data\\model\\material/py_output.csv\")#, usecols=[1,2,3])" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-27T13:13:25.461244100Z", - "start_time": "2023-11-27T13:13:25.414353300Z" - } - }, - "id": "b9e448c393a4c115" - }, - { - "cell_type": "code", - "execution_count": 79, - "outputs": [ - { - "data": { - "text/plain": " Unnamed: 0 region reg_no year consumption pop \\\n0 0 Canada 1 1970 11085 21.71685 \n1 1 USA 2 1970 127304 210.11660 \n2 2 Mexico 3 1970 4168 50.59590 \n3 3 Rest Central America 4 1970 434 41.86008 \n4 4 Brazil 5 1970 6088 95.98846 \n... ... ... ... ... ... ... \n1059 1113 Indonesia 22 2012 15006 244.18970 \n1060 1114 Japan 23 2012 68800 126.60790 \n1061 1115 Oceania 24 2012 7945 29.71791 \n1062 1116 Other South Asia 25 2012 5959 445.52510 \n1063 1117 Other South Africa 26 2012 439 144.14590 \n\n gdp_pcap cons_pcap del_t \n0 17450.805611 510.433143 -40 \n1 20233.017350 605.873120 -40 \n2 6910.351162 82.378216 -40 \n3 5032.145662 10.367873 -40 \n4 4246.812446 63.424291 -40 \n... ... ... ... \n1059 4272.029000 61.452223 2 \n1060 31957.190000 543.410008 2 \n1061 30264.620000 267.347199 2 \n1062 2062.911000 13.375228 2 \n1063 2063.026000 3.045525 2 \n\n[1064 rows x 9 columns]", - "text/html": "
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Unnamed: 0regionreg_noyearconsumptionpopgdp_pcapcons_pcapdel_t
00Canada119701108521.7168517450.805611510.433143-40
11USA21970127304210.1166020233.017350605.873120-40
22Mexico31970416850.595906910.35116282.378216-40
33Rest Central America4197043441.860085032.14566210.367873-40
44Brazil51970608895.988464246.81244663.424291-40
..............................
10591113Indonesia22201215006244.189704272.02900061.4522232
10601114Japan23201268800126.6079031957.190000543.4100082
10611115Oceania242012794529.7179130264.620000267.3471992
10621116Other South Asia2520125959445.525102062.91100013.3752282
10631117Other South Africa262012439144.145902063.0260003.0455252
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1064 rows × 9 columns

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" - }, - "execution_count": 79, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "py_df" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-27T13:13:25.915333400Z", - "start_time": "2023-11-27T13:13:25.877536100Z" - } - }, - "id": "70bcaef1ace99b44" - }, - { - "cell_type": "code", - "execution_count": 88, - "outputs": [], - "source": [ - "df = r_df.set_index([\"reg_no\", \"year\"]).join(py_df.set_index([\"reg_no\", \"year\"])[\"gdp_pcap\"])" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-27T13:14:20.010108900Z", - "start_time": "2023-11-27T13:14:19.940929600Z" - } - }, - "id": "cd43f48dc605ec77" - }, - { - "cell_type": "code", - "execution_count": 85, - "outputs": [ - { - "data": { - "text/plain": " Unnamed: 0 reg_no region year consumption \\\n0 1 1 Canada (1) 1970 11085 \n1 2 1 Canada (1) 1971 11753 \n2 3 1 Canada (1) 1972 12842 \n3 4 1 Canada (1) 1973 14153 \n4 5 1 Canada (1) 1974 15458 \n... ... ... ... ... ... \n1059 1060 26 Other South Africa (26) 2008 272 \n1060 1061 26 Other South Africa (26) 2009 344 \n1061 1062 26 Other South Africa (26) 2010 355 \n1062 1063 26 Other South Africa (26) 2011 474 \n1063 1064 26 Other South Africa (26) 2012 439 \n\n pop gdp.pcap cons.pcap del.t \n0 21.71685 17450.805611 510.433143 -40 \n1 22.04843 18025.700000 533.053827 -39 \n2 22.34410 18755.830000 574.737850 -38 \n3 22.61467 19822.000000 625.832701 -37 \n4 22.87666 20318.250000 675.710528 -36 \n... ... ... ... ... \n1059 130.25150 1871.848000 2.088268 -2 \n1060 133.53860 1843.252000 2.576034 -1 \n1061 136.94900 1909.069000 2.592206 0 \n1062 140.48880 1988.006000 3.373934 1 \n1063 144.14590 2063.026000 3.045525 2 \n\n[1064 rows x 9 columns]", - "text/html": "
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Unnamed: 0reg_noregionyearconsumptionpopgdp.pcapcons.pcapdel.t
011Canada (1)19701108521.7168517450.805611510.433143-40
121Canada (1)19711175322.0484318025.700000533.053827-39
231Canada (1)19721284222.3441018755.830000574.737850-38
341Canada (1)19731415322.6146719822.000000625.832701-37
451Canada (1)19741545822.8766620318.250000675.710528-36
..............................
1059106026Other South Africa (26)2008272130.251501871.8480002.088268-2
1060106126Other South Africa (26)2009344133.538601843.2520002.576034-1
1061106226Other South Africa (26)2010355136.949001909.0690002.5922060
1062106326Other South Africa (26)2011474140.488801988.0060003.3739341
1063106426Other South Africa (26)2012439144.145902063.0260003.0455252
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1064 rows × 9 columns

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" - }, - "execution_count": 85, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "r_df" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-27T13:13:55.052664Z", - "start_time": "2023-11-27T13:13:54.991222800Z" - } - }, - "id": "273365e96cb009d6" - }, - { - "cell_type": "code", - "execution_count": 90, - "outputs": [ - { - "data": { - "text/plain": " Unnamed: 0 region consumption pop \\\nreg_no year \n1 1970 1 Canada (1) 11085 21.71685 \n 1971 2 Canada (1) 11753 22.04843 \n 1972 3 Canada (1) 12842 22.34410 \n 1973 4 Canada (1) 14153 22.61467 \n 1974 5 Canada (1) 15458 22.87666 \n... ... ... ... ... \n26 2008 1060 Other South Africa (26) 272 130.25150 \n 2009 1061 Other South Africa (26) 344 133.53860 \n 2010 1062 Other South Africa (26) 355 136.94900 \n 2011 1063 Other South Africa (26) 474 140.48880 \n 2012 1064 Other South Africa (26) 439 144.14590 \n\n gdp.pcap cons.pcap del.t gdp_pcap \nreg_no year \n1 1970 17450.805611 510.433143 -40 17450.805611 \n 1971 18025.700000 533.053827 -39 18025.700000 \n 1972 18755.830000 574.737850 -38 18755.830000 \n 1973 19822.000000 625.832701 -37 19822.000000 \n 1974 20318.250000 675.710528 -36 20318.250000 \n... ... ... ... ... \n26 2008 1871.848000 2.088268 -2 1871.848000 \n 2009 1843.252000 2.576034 -1 1843.252000 \n 2010 1909.069000 2.592206 0 1909.069000 \n 2011 1988.006000 3.373934 1 1988.006000 \n 2012 2063.026000 3.045525 2 2063.026000 \n\n[1064 rows x 8 columns]", - "text/html": "
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Unnamed: 0regionconsumptionpopgdp.pcapcons.pcapdel.tgdp_pcap
reg_noyear
119701Canada (1)1108521.7168517450.805611510.433143-4017450.805611
19712Canada (1)1175322.0484318025.700000533.053827-3918025.700000
19723Canada (1)1284222.3441018755.830000574.737850-3818755.830000
19734Canada (1)1415322.6146719822.000000625.832701-3719822.000000
19745Canada (1)1545822.8766620318.250000675.710528-3620318.250000
..............................
2620081060Other South Africa (26)272130.251501871.8480002.088268-21871.848000
20091061Other South Africa (26)344133.538601843.2520002.576034-11843.252000
20101062Other South Africa (26)355136.949001909.0690002.59220601909.069000
20111063Other South Africa (26)474140.488801988.0060003.37393411988.006000
20121064Other South Africa (26)439144.145902063.0260003.04552522063.026000
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1064 rows × 8 columns

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" - }, - "execution_count": 90, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import numpy as np\n", - "df[np.isclose(df[\"gdp.pcap\"], df[\"gdp_pcap\"])]" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-27T13:14:27.669971100Z", - "start_time": "2023-11-27T13:14:27.621595600Z" - } - }, - "id": "e735a3b902fd3cd0" - }, - { - "cell_type": "code", - "execution_count": 91, - "outputs": [ - { - "data": { - "text/plain": " Unnamed: 0 reg_no region year consumption \\\n0 1 1 Canada (1) 1970 11085 \n1 2 1 Canada (1) 1971 11753 \n2 3 1 Canada (1) 1972 12842 \n3 4 1 Canada (1) 1973 14153 \n4 5 1 Canada (1) 1974 15458 \n... ... ... ... ... ... \n1059 1060 26 Other South Africa (26) 2008 272 \n1060 1061 26 Other South Africa (26) 2009 344 \n1061 1062 26 Other South Africa (26) 2010 355 \n1062 1063 26 Other South Africa (26) 2011 474 \n1063 1064 26 Other South Africa (26) 2012 439 \n\n pop gdp.pcap cons.pcap del.t \n0 21.71685 17450.805611 510.433143 -40 \n1 22.04843 18025.700000 533.053827 -39 \n2 22.34410 18755.830000 574.737850 -38 \n3 22.61467 19822.000000 625.832701 -37 \n4 22.87666 20318.250000 675.710528 -36 \n... ... ... ... ... \n1059 130.25150 1871.848000 2.088268 -2 \n1060 133.53860 1843.252000 2.576034 -1 \n1061 136.94900 1909.069000 2.592206 0 \n1062 140.48880 1988.006000 3.373934 1 \n1063 144.14590 2063.026000 3.045525 2 \n\n[1064 rows x 9 columns]", - "text/html": "
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Unnamed: 0reg_noregionyearconsumptionpopgdp.pcapcons.pcapdel.t
011Canada (1)19701108521.7168517450.805611510.433143-40
121Canada (1)19711175322.0484318025.700000533.053827-39
231Canada (1)19721284222.3441018755.830000574.737850-38
341Canada (1)19731415322.6146719822.000000625.832701-37
451Canada (1)19741545822.8766620318.250000675.710528-36
..............................
1059106026Other South Africa (26)2008272130.251501871.8480002.088268-2
1060106126Other South Africa (26)2009344133.538601843.2520002.576034-1
1061106226Other South Africa (26)2010355136.949001909.0690002.5922060
1062106326Other South Africa (26)2011474140.488801988.0060003.3739341
1063106426Other South Africa (26)2012439144.145902063.0260003.0455252
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1064 rows × 9 columns

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" - }, - "execution_count": 91, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "r_df" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-27T13:14:32.996061100Z", - "start_time": "2023-11-27T13:14:32.895541600Z" - } - }, - "id": "76887683edf06dc4" - }, - { - "cell_type": "code", - "execution_count": 51, - "outputs": [ - { - "data": { - "text/plain": " region year consumption\n0 Canada 1970 11085\n1 USA 1970 127304\n2 Mexico 1970 4168\n3 Rest Central America 1970 434\n4 Brazil 1970 6088\n.. ... ... ...\n866 Korea 2012 57650\n867 Japan 2012 68800\n868 Oceania 2012 7945\n869 Other South Asia 2012 5959\n870 Other South Africa 2012 439\n\n[871 rows x 3 columns]", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
regionyearconsumption
0Canada197011085
1USA1970127304
2Mexico19704168
3Rest Central America1970434
4Brazil19706088
............
866Korea201257650
867Japan201268800
868Oceania20127945
869Other South Asia20125959
870Other South Africa2012439
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871 rows × 3 columns

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" - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "py_df#[\"region\"].unique()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-27T12:43:19.069975600Z", - "start_time": "2023-11-27T12:43:18.969677200Z" - } - }, - "id": "f67fd7ab71039148" - }, - { - "cell_type": "code", - "execution_count": 54, - "outputs": [ - { - "data": { - "text/plain": " Unnamed: 0 region year consumption pop \\\n0 0 Canada 1970 11085 21.71685 \n1 1 USA 1970 127304 210.11660 \n2 2 Mexico 1970 4168 50.59590 \n3 3 Rest Central America 1970 434 41.86008 \n4 4 Brazil 1970 6088 95.98846 \n.. ... ... ... ... ... \n866 898 Korea 2012 57650 72.94119 \n867 899 Japan 2012 68800 126.60790 \n868 900 Oceania 2012 7945 29.71791 \n869 901 Other South Asia 2012 5959 445.52510 \n870 902 Other South Africa 2012 439 144.14590 \n\n gdp_pcap cons_pcap del_t \n0 17450.805611 510.433143 -40 \n1 20233.017350 605.873120 -40 \n2 6910.351162 82.378216 -40 \n3 5032.145662 10.367873 -40 \n4 4246.812446 63.424291 -40 \n.. ... ... ... \n866 19790.770000 790.362757 2 \n867 31957.190000 543.410008 2 \n868 30264.620000 267.347199 2 \n869 2062.911000 13.375228 2 \n870 2063.026000 3.045525 2 \n\n[871 rows x 8 columns]", - "text/html": "
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Unnamed: 0regionyearconsumptionpopgdp_pcapcons_pcapdel_t
00Canada19701108521.7168517450.805611510.433143-40
11USA1970127304210.1166020233.017350605.873120-40
22Mexico1970416850.595906910.35116282.378216-40
33Rest Central America197043441.860085032.14566210.367873-40
44Brazil1970608895.988464246.81244663.424291-40
...........................
866898Korea20125765072.9411919790.770000790.3627572
867899Japan201268800126.6079031957.190000543.4100082
868900Oceania2012794529.7179130264.620000267.3471992
869901Other South Asia20125959445.525102062.91100013.3752282
870902Other South Africa2012439144.145902063.0260003.0455252
\n

871 rows × 8 columns

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" - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "py_df" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-27T12:43:39.390431200Z", - "start_time": "2023-11-27T12:43:39.352903100Z" - } - }, - "id": "777d42af7aa34009" - }, - { - "cell_type": "code", - "execution_count": 67, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\maczek\\AppData\\Local\\Temp\\ipykernel_5280\\2693521517.py:1: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n", - " r_df[\"region\"] = r_df[\"region\"].str.replace(\"(\",\"\").str.replace(\")\",\"\").str.replace('\\d+', '').str[:-1]\n", - "C:\\Users\\maczek\\AppData\\Local\\Temp\\ipykernel_5280\\2693521517.py:1: FutureWarning: The default value of regex will change from True to False in a future version.\n", - " r_df[\"region\"] = r_df[\"region\"].str.replace(\"(\",\"\").str.replace(\")\",\"\").str.replace('\\d+', '').str[:-1]\n" - ] - } - ], - "source": [ - "r_df[\"region\"] = r_df[\"region\"].str.replace(\"(\",\"\").str.replace(\")\",\"\").str.replace('\\d+', '').str[:-1]" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-27T12:46:37.385766700Z", - "start_time": "2023-11-27T12:46:37.307654400Z" - } - }, - "id": "7450d215c7e87cb3" - }, - { - "cell_type": "code", - "execution_count": 4, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import yaml\n", - "\n", - "\n", - "path = \"C:/Users\\maczek\\PycharmProjects\\message_data\\data\\material/aluminum/demand_alu.yaml\"\n", - "path = \"C:/Users\\maczek\\PycharmProjects\\message_data\\data\\material/steel_cement/demand_cement.yaml\"\n", - "path = \"C:/Users\\maczek\\PycharmProjects\\message_data\\data\\material/steel_cement/demand_steel.yaml\"\n", - "\n", - "with open(path, 'r') as file:\n", - " yaml_data = file.read()\n", - "\n", - "data_list = yaml.safe_load(yaml_data)\n", - "\n", - "# Convert to DataFrame\n", - "df = pd.DataFrame([(key, value['year'], value['value']) for entry in data_list for key, value in entry.items()],\n", - " columns=['Region', 'Year', 'Value'])" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-28T14:30:34.630137Z", - "start_time": "2023-11-28T14:30:34.561139100Z" - } - }, - "id": "69ac530864bb3606" - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/message_ix_models/model/material/test_db.ipynb b/message_ix_models/model/material/test_db.ipynb deleted file mode 100644 index 7b25999492..0000000000 --- a/message_ix_models/model/material/test_db.ipynb +++ /dev/null @@ -1,3677 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "source": [ - "## create thesis scenarios" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 1, - "outputs": [ - { - "data": { - "text/plain": " GLOBIOM resin_conc MTBE NH3_residual_elasticity \\\n0 baseline 0.01 baseline 0.50 \n1 baseline 0.01 baseline 0.50 \n2 baseline 0.01 baseline 0.50 \n3 baseline 0.01 baseline 0.50 \n4 baseline 0.01 baseline 0.50 \n.. ... ... ... ... \n251 no_SDGs 0.03 phase-out 0.25 \n252 no_SDGs 0.03 phase-out 0.25 \n253 no_SDGs 0.03 phase-out 0.25 \n254 no_SDGs 0.03 phase-out 0.25 \n255 no_SDGs 0.03 phase-out 0.25 \n\n HVC_elasticity_2020 HVC _elasticity_2030 meth_residual_elasticity_2020 \\\n0 1.00 0.59 1.4 \n1 1.00 0.59 1.4 \n2 1.00 0.59 1.2 \n3 1.00 0.59 1.2 \n4 1.00 0.25 1.4 \n.. ... ... ... \n251 0.75 0.59 1.2 \n252 0.75 0.25 1.4 \n253 0.75 0.25 1.4 \n254 0.75 0.25 1.2 \n255 0.75 0.25 1.2 \n\n meth_elasticity_2030 \n0 0.54 \n1 0.25 \n2 0.54 \n3 0.25 \n4 0.54 \n.. ... \n251 0.25 \n252 0.54 \n253 0.25 \n254 0.54 \n255 0.25 \n\n[256 rows x 8 columns]", - "text/html": "
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" - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pandas as pd\n", - "df = pd.read_excel(\"C:/Users\\maczek\\PycharmProjects\\message_data\\data\\material\\methanol/scenario_list.xlsx\")\n", - "df_new = df.copy(deep=True).iloc[:,:1]\n", - "for i in range(len(df.columns)):\n", - " df_new = df_new.merge(df.iloc[:,i+2:i+3], how=\"cross\") #.merge(df.iloc[:,3:4], how=\"cross\")\n", - "df_new.drop_duplicates().reset_index(drop=True)\n", - "#df_new.drop_duplicates().reset_index(drop=True).to_excel(\"test.xlsx\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 2, - "outputs": [ - { - "data": { - "text/plain": "", - "application/javascript": "if (typeof IPython !== 'undefined') { IPython.OutputArea.prototype._should_scroll = function(lines){ return false; }}" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Unable to determine R home: [WinError 2] The system cannot find the file specified\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "rpy2 version:\n", - "3.5.3\n", - "Python version:\n", - "3.8.8 (default, Apr 13 2021, 15:08:03) [MSC v.1916 64 bit (AMD64)]\n", - "Looking for R's HOME:\n", - " Environment variable R_HOME: None\n", - " InstallPath in the registry: C:\\Users\\maczek\\AppData\\Local\\Programs\\R\\R-4.2.1\n", - " Environment variable R_USER: None\n", - " Environment variable R_LIBS_USER: None\n", - "R version:\n", - " In the PATH: R version 4.2.1 (2022-06-23 ucrt) -- \"Funny-Looking Kid\"\n", - " Loading R library from rpy2: OK\n", - "Additional directories to load R packages from:\n", - "None\n", - "C extension compilation:\n", - " Warning: Unable to get R compilation flags.\n" - ] - } - ], - "source": [ - "import message_data.model.material.data_methanol" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 3, - "outputs": [ - { - "data": { - "text/plain": "" - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import importlib\n", - "importlib.reload(message_data.model.material.data_methanol)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 21, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "True\n", - "phase_out\n" - ] - } - ], - "source": [ - "#add_mtbe_act_bound(scenario)\n", - "pardict = message_data.model.material.data_methanol.gen_data_methanol(scenario)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 23, - "outputs": [ - { - "data": { - "text/plain": "'NoPolicy_petro_thesis_2_macro'" - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "scenario.scenario" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 24, - "outputs": [], - "source": [ - "scenario.remove_solution()\n", - "scenario.check_out()\n", - "scenario.add_par(\"bound_activity_up\", pardict[\"bound_activity_up\"])\n", - "#scenario.commit()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 25, - "outputs": [], - "source": [ - "scenario.commit(\"fix mtbe phase out\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 22, - "outputs": [ - { - "data": { - "text/plain": " node_loc technology year_act mode time value unit\n0 R12_AFR meth_coal 2015 feedstock year 0.0 GWa\n1 R12_AFR meth_ng 2015 feedstock year 0.0 GWa\n2 R12_RCPA meth_coal 2015 feedstock year 0.0 GWa\n3 R12_RCPA meth_ng 2015 feedstock year 0.0 GWa\n4 R12_EEU meth_coal 2015 feedstock year 0.0 GWa\n.. ... ... ... ... ... ... ...\n139 R12_CHN loil_trp 2070 M1 year 0.0 -\n140 R12_CHN loil_trp 2080 M1 year 0.0 -\n141 R12_CHN loil_trp 2090 M1 year 0.0 -\n142 R12_CHN loil_trp 2100 M1 year 0.0 -\n143 R12_CHN loil_trp 2110 M1 year 0.0 -\n\n[193 rows x 7 columns]", - "text/html": "
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" - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pardict[\"bound_activity_up\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 2, - "outputs": [ - { - "data": { - "text/plain": "Empty DataFrame\nColumns: [relation, node_rel, year_rel, value, unit]\nIndex: []", - "text/html": "
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relationnode_relyear_relvalueunit
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" - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = scenario.par(\"relation_upper\", filters={\"relation\":\"CO2_PtX_trans_disp_split\"})\n", - "df" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 2, - "outputs": [ - { - "data": { - "text/plain": " model scenario scheme \\\n6410 MESSAGEix-Materials 2.0deg_no_SDGs_petro_thesis_1_macro MESSAGE \n6411 MESSAGEix-Materials 2.0deg_petro_thesis_1 MESSAGE \n6412 MESSAGEix-Materials 2.0deg_petro_thesis_2 MESSAGE \n6413 MESSAGEix-Materials 2.0deg_petro_thesis_2_macro MESSAGE \n6414 MESSAGEix-Materials 2degree_petro_thesis_2 MESSAGE \n6543 MESSAGEix-Materials NoPolicy_no_SDGs_petro_thesis_1 MESSAGE \n6544 MESSAGEix-Materials NoPolicy_no_SDGs_petro_thesis_1_macro MESSAGE \n6548 MESSAGEix-Materials NoPolicy_no_petro MESSAGE \n6550 MESSAGEix-Materials NoPolicy_petro_biomass MESSAGE \n6551 MESSAGEix-Materials NoPolicy_petro_test MESSAGE \n6552 MESSAGEix-Materials NoPolicy_petro_test_2 MESSAGE \n6553 MESSAGEix-Materials NoPolicy_petro_test_3 MESSAGE \n6554 MESSAGEix-Materials NoPolicy_petro_test_4 MESSAGE \n6555 MESSAGEix-Materials NoPolicy_petro_test_5 MESSAGE \n6556 MESSAGEix-Materials NoPolicy_petro_test_6 MESSAGE \n6557 MESSAGEix-Materials NoPolicy_petro_thesis_2 MESSAGE \n6558 MESSAGEix-Materials NoPolicy_petro_thesis_2_macro MESSAGE \n6559 MESSAGEix-Materials NoPolicy_petro_thesis_2_test_meth_h2 MESSAGE \n\n is_default is_locked cre_user cre_date upd_user \\\n6410 1 0 maczek 2023-05-09 09:34:33.000000 maczek \n6411 1 0 maczek 2023-02-08 23:38:03.000000 maczek \n6412 1 0 maczek 2023-04-06 16:44:33.000000 None \n6413 1 0 maczek 2023-05-12 08:31:34.000000 maczek \n6414 1 0 unlu 2023-04-06 15:35:51.000000 None \n6543 1 0 maczek 2023-05-08 22:47:58.000000 maczek \n6544 1 0 maczek 2023-05-09 04:15:13.000000 maczek \n6548 1 0 maczek 2023-05-04 09:18:27.000000 maczek \n6550 1 0 unlu 2022-08-09 14:51:56.000000 unlu \n6551 1 0 unlu 2022-04-14 11:08:53.000000 None \n6552 1 0 unlu 2022-04-14 14:05:39.000000 None \n6553 1 0 unlu 2022-04-14 14:40:02.000000 None \n6554 1 0 unlu 2022-04-14 15:13:25.000000 None \n6555 1 0 unlu 2022-04-14 15:29:02.000000 None \n6556 1 0 unlu 2022-04-14 15:38:19.000000 None \n6557 1 0 maczek 2023-05-09 11:02:33.000000 maczek \n6558 1 0 maczek 2023-05-10 08:04:11.000000 maczek \n6559 1 0 maczek 2023-05-15 10:42:54.000000 maczek \n\n upd_date lock_user lock_date \\\n6410 2023-05-09 10:28:12.000000 None None \n6411 2023-02-08 23:44:53.000000 None None \n6412 None None None \n6413 2023-05-12 09:20:07.000000 None None \n6414 None None None \n6543 2023-05-09 04:15:01.000000 None None \n6544 2023-05-09 04:47:38.000000 None None \n6548 2023-05-04 09:44:52.000000 None None \n6550 2022-08-09 15:14:46.000000 None None \n6551 None None None \n6552 None None None \n6553 None None None \n6554 None None None \n6555 None None None \n6556 None None None \n6557 2023-05-10 08:03:55.000000 None None \n6558 2023-05-11 10:08:56.000000 None None \n6559 2023-05-15 11:44:21.000000 None None \n\n annotation version \n6410 clone Scenario from 'MESSAGEix-Materials|NoPol... 4 \n6411 clone Scenario from 'MESSAGEix-Materials|NoPol... 1 \n6412 clone Scenario from 'MESSAGEix-Materials|NoPol... 6 \n6413 clone Scenario from 'MESSAGEix-Materials|NoPol... 10 \n6414 clone Scenario from 'MESSAGEix-Materials|NoPol... 1 \n6543 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 11 \n6544 clone Scenario from 'MESSAGEix-Materials|NoPol... 4 \n6548 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 3 \n6550 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 3 \n6551 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 4 \n6552 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 1 \n6553 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 1 \n6554 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 2 \n6555 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 2 \n6556 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 1 \n6557 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 70 \n6558 clone Scenario from 'MESSAGEix-Materials|NoPol... 9 \n6559 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 1 ", - "text/html": "
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6411MESSAGEix-Materials2.0deg_petro_thesis_1MESSAGE10maczek2023-02-08 23:38:03.000000maczek2023-02-08 23:44:53.000000NoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...1
6412MESSAGEix-Materials2.0deg_petro_thesis_2MESSAGE10maczek2023-04-06 16:44:33.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...6
6413MESSAGEix-Materials2.0deg_petro_thesis_2_macroMESSAGE10maczek2023-05-12 08:31:34.000000maczek2023-05-12 09:20:07.000000NoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...10
6414MESSAGEix-Materials2degree_petro_thesis_2MESSAGE10unlu2023-04-06 15:35:51.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...1
6543MESSAGEix-MaterialsNoPolicy_no_SDGs_petro_thesis_1MESSAGE10maczek2023-05-08 22:47:58.000000maczek2023-05-09 04:15:01.000000NoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...11
6544MESSAGEix-MaterialsNoPolicy_no_SDGs_petro_thesis_1_macroMESSAGE10maczek2023-05-09 04:15:13.000000maczek2023-05-09 04:47:38.000000NoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...4
6548MESSAGEix-MaterialsNoPolicy_no_petroMESSAGE10maczek2023-05-04 09:18:27.000000maczek2023-05-04 09:44:52.000000NoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...3
6550MESSAGEix-MaterialsNoPolicy_petro_biomassMESSAGE10unlu2022-08-09 14:51:56.000000unlu2022-08-09 15:14:46.000000NoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...3
6551MESSAGEix-MaterialsNoPolicy_petro_testMESSAGE10unlu2022-04-14 11:08:53.000000NoneNoneNoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...4
6552MESSAGEix-MaterialsNoPolicy_petro_test_2MESSAGE10unlu2022-04-14 14:05:39.000000NoneNoneNoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...1
6553MESSAGEix-MaterialsNoPolicy_petro_test_3MESSAGE10unlu2022-04-14 14:40:02.000000NoneNoneNoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...1
6554MESSAGEix-MaterialsNoPolicy_petro_test_4MESSAGE10unlu2022-04-14 15:13:25.000000NoneNoneNoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...2
6555MESSAGEix-MaterialsNoPolicy_petro_test_5MESSAGE10unlu2022-04-14 15:29:02.000000NoneNoneNoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...2
6556MESSAGEix-MaterialsNoPolicy_petro_test_6MESSAGE10unlu2022-04-14 15:38:19.000000NoneNoneNoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...1
6557MESSAGEix-MaterialsNoPolicy_petro_thesis_2MESSAGE10maczek2023-05-09 11:02:33.000000maczek2023-05-10 08:03:55.000000NoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...70
6558MESSAGEix-MaterialsNoPolicy_petro_thesis_2_macroMESSAGE10maczek2023-05-10 08:04:11.000000maczek2023-05-11 10:08:56.000000NoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...9
6559MESSAGEix-MaterialsNoPolicy_petro_thesis_2_test_meth_h2MESSAGE10maczek2023-05-15 10:42:54.000000maczek2023-05-15 11:44:21.000000NoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...1
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" - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = mp.scenario_list()\n", - "df[df[\"scenario\"].str.contains(\"petro\")]\n", - "#2023-04-24 22:27:03.000000,None,None,None,None,\"clone Scenario from 'MESSAGEix-Materials|NoPolicy_no_SDGs_petro_thesis_1_macro', version: 2\",2" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 4, - "outputs": [], - "source": [ - "scenario.remove_solution()\n", - "scenario.check_out()\n", - "scenario.remove_par(\"relation_upper\", df)\n", - "scenario.commit(\"remove CCS constraint\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## check for conflicts with growth constraints" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 1, - "outputs": [ - { - "data": { - "text/plain": "", - "application/javascript": "if (typeof IPython !== 'undefined') { IPython.OutputArea.prototype._should_scroll = function(lines){ return false; }}" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import ixmp as ix\n", - "import message_ix\n", - "\n", - "mp = ix.Platform(\"ixmp_dev\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-11T11:19:28.326312700Z", - "start_time": "2023-10-11T11:19:04.611575400Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 2, - "outputs": [], - "source": [ - "scen = message_ix.Scenario(mp, \"MESSAGEix-Materials\", \"baseline_DEFAULT_0108_updated_SSP1_chemicals_Y\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-11T11:19:30.819515500Z", - "start_time": "2023-10-11T11:19:28.326312700Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 4, - "outputs": [ - { - "data": { - "text/plain": " model scenario \\\n0 MESSAGEix-Materials baseline_DEFAULT_0108_updated_SSP1_chemicals_Y \n1 MESSAGEix-Materials baseline_DEFAULT_0108_updated_SSP1_chemicals_Y \n2 MESSAGEix-Materials baseline_DEFAULT_0108_updated_SSP1_chemicals_Y \n3 MESSAGEix-Materials baseline_DEFAULT_0108_updated_SSP1_chemicals_Y \n4 MESSAGEix-Materials baseline_DEFAULT_0108_updated_SSP1_chemicals_Y \n... ... ... \n32758 MESSAGEix-Materials baseline_DEFAULT_0108_updated_SSP1_chemicals_Y \n32759 MESSAGEix-Materials baseline_DEFAULT_0108_updated_SSP1_chemicals_Y \n32760 MESSAGEix-Materials baseline_DEFAULT_0108_updated_SSP1_chemicals_Y \n32761 MESSAGEix-Materials baseline_DEFAULT_0108_updated_SSP1_chemicals_Y \n32762 MESSAGEix-Materials baseline_DEFAULT_0108_updated_SSP1_chemicals_Y \n\n region variable unit \\\n0 CPA Population million \n1 CPA Population|Rural million \n2 CPA Population|Urban million \n3 China (R12) Agricultural Demand million t DM/yr \n4 China (R12) Agricultural Demand|Energy million t DM/yr \n... ... ... ... \n32758 Western Europe (R12) Yield|Cereal t DM/ha/yr \n32759 Western Europe (R12) Yield|Energy Crops t DM/ha/yr \n32760 Western Europe (R12) Yield|Non-Energy Crops t DM/ha/yr \n32761 Western Europe (R12) Yield|Oilcrops t DM/ha/yr \n32762 Western Europe (R12) Yield|Sugarcrops t DM/ha/yr \n\n 2020 2025 2030 2035 2040 2045 2050 \\\n0 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.000000 \n1 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.000000 \n2 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.000000 \n3 773.1930 750.3587 726.2460 710.6230 695.0001 659.6072 625.019042 \n4 48.7939 3.0609 3.2875 3.1393 2.9911 2.9911 2.985015 \n... ... ... ... ... ... ... ... \n32758 4.7784 4.8983 5.0183 5.0927 5.1671 5.2521 5.337100 \n32759 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.000000 \n32760 2.0796 2.0958 2.1121 2.1701 2.2280 2.2677 2.307500 \n32761 2.7800 2.8554 2.9308 2.9534 2.9760 3.0187 3.061300 \n32762 22.0911 22.4336 22.7761 0.0000 0.0000 0.0000 0.000000 \n\n 2055 2060 2070 2080 2090 2100 \\\n0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n1 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n2 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n3 603.654901 582.473065 483.324285 457.339577 419.300834 377.512951 \n4 3.030702 3.076596 2.496485 34.491232 40.984672 36.500276 \n... ... ... ... ... ... ... \n32758 5.460600 5.584200 5.762029 5.925012 6.008445 6.024653 \n32759 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n32760 2.367900 2.428300 2.529550 2.617236 2.666845 2.669336 \n32761 2.982600 2.903900 3.104353 3.190358 3.363317 3.593627 \n32762 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n\n 2110 \n0 0.000000 \n1 0.000000 \n2 0.000000 \n3 362.301770 \n4 22.167891 \n... ... \n32758 6.011952 \n32759 0.000000 \n32760 2.673214 \n32761 3.594700 \n32762 0.000000 \n\n[32763 rows x 19 columns]", - "text/html": "
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modelscenarioregionvariableunit20202025203020352040204520502055206020702080209021002110
0MESSAGEix-Materialsbaseline_DEFAULT_0108_updated_SSP1_chemicals_YCPAPopulationmillion0.00000.00000.00000.00000.00000.00000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
1MESSAGEix-Materialsbaseline_DEFAULT_0108_updated_SSP1_chemicals_YCPAPopulation|Ruralmillion0.00000.00000.00000.00000.00000.00000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
2MESSAGEix-Materialsbaseline_DEFAULT_0108_updated_SSP1_chemicals_YCPAPopulation|Urbanmillion0.00000.00000.00000.00000.00000.00000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
3MESSAGEix-Materialsbaseline_DEFAULT_0108_updated_SSP1_chemicals_YChina (R12)Agricultural Demandmillion t DM/yr773.1930750.3587726.2460710.6230695.0001659.6072625.019042603.654901582.473065483.324285457.339577419.300834377.512951362.301770
4MESSAGEix-Materialsbaseline_DEFAULT_0108_updated_SSP1_chemicals_YChina (R12)Agricultural Demand|Energymillion t DM/yr48.79393.06093.28753.13932.99112.99112.9850153.0307023.0765962.49648534.49123240.98467236.50027622.167891
............................................................
32758MESSAGEix-Materialsbaseline_DEFAULT_0108_updated_SSP1_chemicals_YWestern Europe (R12)Yield|Cerealt DM/ha/yr4.77844.89835.01835.09275.16715.25215.3371005.4606005.5842005.7620295.9250126.0084456.0246536.011952
32759MESSAGEix-Materialsbaseline_DEFAULT_0108_updated_SSP1_chemicals_YWestern Europe (R12)Yield|Energy Cropst DM/ha/yr0.00000.00000.00000.00000.00000.00000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
32760MESSAGEix-Materialsbaseline_DEFAULT_0108_updated_SSP1_chemicals_YWestern Europe (R12)Yield|Non-Energy Cropst DM/ha/yr2.07962.09582.11212.17012.22802.26772.3075002.3679002.4283002.5295502.6172362.6668452.6693362.673214
32761MESSAGEix-Materialsbaseline_DEFAULT_0108_updated_SSP1_chemicals_YWestern Europe (R12)Yield|Oilcropst DM/ha/yr2.78002.85542.93082.95342.97603.01873.0613002.9826002.9039003.1043533.1903583.3633173.5936273.594700
32762MESSAGEix-Materialsbaseline_DEFAULT_0108_updated_SSP1_chemicals_YWestern Europe (R12)Yield|Sugarcropst DM/ha/yr22.091122.433622.77610.00000.00000.00000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
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32763 rows × 19 columns

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" - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "scen.timeseries(iamc=True)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-10-11T11:21:22.267110400Z", - "start_time": "2023-10-11T11:20:51.496907900Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 14, - "outputs": [ - { - "data": { - "text/plain": "True" - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "scen.has_solution()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 15, - "outputs": [], - "source": [ - "import message_ix_models" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 20, - "outputs": [ - { - "data": { - "text/plain": "'C:\\\\Users\\\\maczek\\\\PycharmProjects\\\\message_data\\\\data\\\\material\\\\yuheng/MESSAGEix-Materials_baseline_DEFAULT_0108_updated_SSP1_chemicals_Y'" - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "f\"{message_ix_models.util.private_data_path()}\\material\\yuheng/{scen.model}_{scen.scenario}.xlsx\"" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Unexpected exception formatting exception. Falling back to standard exception\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Traceback (most recent call last):\n", - " File \"C:\\Users\\maczek\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3398, in run_code\n", - " exec(code_obj, self.user_global_ns, self.user_ns)\n", - " File \"C:\\Users\\maczek\\AppData\\Local\\Temp\\ipykernel_8832\\1922672665.py\", line 1, in \n", - " scen.to_excel(f\"{message_ix_models.util.private_data_path()}\\material\\yuheng/{scen.model}_{scen.scenario}.xlsx\")\n", - " File \"C:\\Users\\maczek\\Anaconda3\\lib\\site-packages\\ixmp\\core\\scenario.py\", line 896, in to_excel\n", - " self.platform._backend.write_file(\n", - " File \"C:\\Users\\maczek\\Anaconda3\\lib\\site-packages\\ixmp\\backend\\jdbc.py\", line 565, in write_file\n", - " super().write_file(path, item_type, **kwargs)\n" - ] - } - ], - "source": [ - "scen.to_excel(f\"{message_ix_models.util.private_data_path()}\\material\\yuheng/{scen.model}_{scen.scenario}.xlsx\")" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "is_executing": true - } - } - }, - { - "cell_type": "code", - "execution_count": 10, - "outputs": [], - "source": [ - "df = mp.scenario_list()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 6, - "outputs": [ - { - "data": { - "text/plain": "True" - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "scen.has_solution()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scen.clone(\"\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scen.remove_solution()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scen.check_out()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scen.commit()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 11, - "outputs": [ - { - "ename": "KeyError", - "evalue": "\"None of [Index(['node_loc', 'technology', 'year_vtg', 'year_act', 'mode', 'node_origin',\\n 'commodity', 'level', 'time', 'time_origin'],\\n dtype='object')] are in the [columns]\"", - "output_type": "error", - "traceback": [ - "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[1;31mKeyError\u001B[0m Traceback (most recent call last)", - "Input \u001B[1;32mIn [11]\u001B[0m, in \u001B[0;36m\u001B[1;34m()\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[43mscen\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43madd_par\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43minput\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdf\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\ixmp\\core\\scenario.py:505\u001B[0m, in \u001B[0;36mScenario.add_par\u001B[1;34m(self, name, key_or_data, value, unit, comment)\u001B[0m\n\u001B[0;32m 501\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mkey\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;129;01min\u001B[39;00m data\u001B[38;5;241m.\u001B[39mcolumns:\n\u001B[0;32m 502\u001B[0m \u001B[38;5;66;03m# Form the 'key' column from other columns\u001B[39;00m\n\u001B[0;32m 503\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m N_dim \u001B[38;5;241m>\u001B[39m \u001B[38;5;241m1\u001B[39m \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(data):\n\u001B[0;32m 504\u001B[0m data[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mkey\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m=\u001B[39m (\n\u001B[1;32m--> 505\u001B[0m \u001B[43mdata\u001B[49m\u001B[43m[\u001B[49m\u001B[43midx_names\u001B[49m\u001B[43m]\u001B[49m\u001B[38;5;241m.\u001B[39mastype(\u001B[38;5;28mstr\u001B[39m)\u001B[38;5;241m.\u001B[39magg(\u001B[38;5;28;01mlambda\u001B[39;00m s: s\u001B[38;5;241m.\u001B[39mtolist(), axis\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m1\u001B[39m)\n\u001B[0;32m 506\u001B[0m )\n\u001B[0;32m 507\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m 508\u001B[0m data[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mkey\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m=\u001B[39m data[idx_names[\u001B[38;5;241m0\u001B[39m]]\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3511\u001B[0m, in \u001B[0;36mDataFrame.__getitem__\u001B[1;34m(self, key)\u001B[0m\n\u001B[0;32m 3509\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m is_iterator(key):\n\u001B[0;32m 3510\u001B[0m key \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mlist\u001B[39m(key)\n\u001B[1;32m-> 3511\u001B[0m indexer \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcolumns\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_get_indexer_strict\u001B[49m\u001B[43m(\u001B[49m\u001B[43mkey\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mcolumns\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m[\u001B[38;5;241m1\u001B[39m]\n\u001B[0;32m 3513\u001B[0m \u001B[38;5;66;03m# take() does not accept boolean indexers\u001B[39;00m\n\u001B[0;32m 3514\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mgetattr\u001B[39m(indexer, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mdtype\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;28;01mNone\u001B[39;00m) \u001B[38;5;241m==\u001B[39m \u001B[38;5;28mbool\u001B[39m:\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py:5782\u001B[0m, in \u001B[0;36mIndex._get_indexer_strict\u001B[1;34m(self, key, axis_name)\u001B[0m\n\u001B[0;32m 5779\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m 5780\u001B[0m keyarr, indexer, new_indexer \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_reindex_non_unique(keyarr)\n\u001B[1;32m-> 5782\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_raise_if_missing\u001B[49m\u001B[43m(\u001B[49m\u001B[43mkeyarr\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mindexer\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43maxis_name\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 5784\u001B[0m keyarr \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mtake(indexer)\n\u001B[0;32m 5785\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(key, Index):\n\u001B[0;32m 5786\u001B[0m \u001B[38;5;66;03m# GH 42790 - Preserve name from an Index\u001B[39;00m\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py:5842\u001B[0m, in \u001B[0;36mIndex._raise_if_missing\u001B[1;34m(self, key, indexer, axis_name)\u001B[0m\n\u001B[0;32m 5840\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m use_interval_msg:\n\u001B[0;32m 5841\u001B[0m key \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mlist\u001B[39m(key)\n\u001B[1;32m-> 5842\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mNone of [\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mkey\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m] are in the [\u001B[39m\u001B[38;5;132;01m{\u001B[39;00maxis_name\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m]\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m 5844\u001B[0m not_found \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mlist\u001B[39m(ensure_index(key)[missing_mask\u001B[38;5;241m.\u001B[39mnonzero()[\u001B[38;5;241m0\u001B[39m]]\u001B[38;5;241m.\u001B[39munique())\n\u001B[0;32m 5845\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mnot_found\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m not in index\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n", - "\u001B[1;31mKeyError\u001B[0m: \"None of [Index(['node_loc', 'technology', 'year_vtg', 'year_act', 'mode', 'node_origin',\\n 'commodity', 'level', 'time', 'time_origin'],\\n dtype='object')] are in the [columns]\"" - ] - } - ], - "source": [ - "scen.add_par(\"input\", df)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 3, - "outputs": [ - { - "data": { - "text/plain": "True" - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "message_ix.Scenario(mp, \"MESSAGEix-GLOBIOM 1.1-M-R12-NAVIGATE\",\"NPi2020-con-prim-dir-ncr\").has_solution()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 2, - "outputs": [ - { - "data": { - "text/plain": " model \\\n6280 MESSAGEix-GLOBIOM 1.1-BM-R12 (NAVIGATE) \n6281 MESSAGEix-GLOBIOM 1.1-BM-R12 (NAVIGATE) \n6282 MESSAGEix-GLOBIOM 1.1-BM-R12 (NAVIGATE) \n6283 MESSAGEix-GLOBIOM 1.1-BM-R12 (NAVIGATE) \n6284 MESSAGEix-GLOBIOM 1.1-BM-R12 (NAVIGATE) \n... ... \n7736 NAVIGATE_SSP2_v4.1.7_noSDG_rcpref_medrel \n7737 NAVIGATE_SSP2_v4.1.7_noSDG_rcpref_medrel \n7738 NAVIGATE_hydro_test \n7739 NAVIGATE_hydro_test \n7740 NAVIGATE_hydro_test \n\n scenario scheme is_default is_locked \\\n6280 Ctax-ref MESSAGE 1 0 \n6281 Ctax-ref+B MESSAGE 1 0 \n6282 NPi-AdvPE MESSAGE 1 0 \n6283 NPi-AdvPE_ENGAGE_20C_step-0 MESSAGE 1 0 \n6284 NPi-AdvPE_ENGAGE_20C_step-3 MESSAGE 1 0 \n... ... ... ... ... \n7736 NPi2020-con-prim-dir-ncr_nexus MESSAGE 1 0 \n7737 NPi2020_nexus MESSAGE 1 0 \n7738 NoPolicy_R12_update_hydro MESSAGE 1 0 \n7739 NoPolicy_R12_update_hydro_1000 MESSAGE 1 0 \n7740 NoPolicy_R12_update_hydro_1500 MESSAGE 1 0 \n\n cre_user cre_date upd_user \\\n6280 kishimot 2023-08-02 17:03:51.000000 kishimot \n6281 kishimot 2023-08-02 17:19:35.000000 kishimot \n6282 kishimot 2023-05-08 17:24:09.000000 kishimot \n6283 kishimot 2023-05-17 11:32:35.000000 None \n6284 kishimot 2023-05-17 11:39:15.000000 kishimot \n... ... ... ... \n7736 awais 2022-11-24 13:13:38.000000 awais \n7737 awais 2023-04-09 17:02:46.000000 awais \n7738 min 2022-01-24 23:33:58.000000 None \n7739 min 2022-01-26 11:04:24.000000 None \n7740 min 2022-01-27 15:18:57.000000 None \n\n upd_date lock_user lock_date \\\n6280 2023-08-02 17:19:24.000000 None None \n6281 2023-08-02 18:17:39.000000 None None \n6282 2023-05-08 19:48:42.000000 None None \n6283 None None None \n6284 2023-05-17 11:54:29.000000 None None \n... ... ... ... \n7736 2022-11-24 19:26:29.000000 None None \n7737 2023-04-09 20:29:00.000000 None None \n7738 None None None \n7739 None None None \n7740 None None None \n\n annotation version \n6280 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-BM-... 1 \n6281 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-BM-... 1 \n6282 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-M-R... 2 \n6283 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-BM-... 1 \n6284 clone Scenario from 'MESSAGEix-GLOBIOM 1.1-BM-... 2 \n... ... ... \n7736 clone Scenario from 'NAVIGATE_SSP2_v4.1.7_noSD... 13 \n7737 clone Scenario from 'NAVIGATE_SSP2_v4.1.7_noSD... 4 \n7738 clone Scenario from 'MESSAGEix-Materials|NoPol... 1 \n7739 clone Scenario from 'NAVIGATE_hydro_test|NoPol... 1 \n7740 clone Scenario from 'NAVIGATE_hydro_test|NoPol... 1 \n\n[623 rows x 13 columns]", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
modelscenarioschemeis_defaultis_lockedcre_usercre_dateupd_userupd_datelock_userlock_dateannotationversion
6280MESSAGEix-GLOBIOM 1.1-BM-R12 (NAVIGATE)Ctax-refMESSAGE10kishimot2023-08-02 17:03:51.000000kishimot2023-08-02 17:19:24.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-BM-...1
6281MESSAGEix-GLOBIOM 1.1-BM-R12 (NAVIGATE)Ctax-ref+BMESSAGE10kishimot2023-08-02 17:19:35.000000kishimot2023-08-02 18:17:39.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-BM-...1
6282MESSAGEix-GLOBIOM 1.1-BM-R12 (NAVIGATE)NPi-AdvPEMESSAGE10kishimot2023-05-08 17:24:09.000000kishimot2023-05-08 19:48:42.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-M-R...2
6283MESSAGEix-GLOBIOM 1.1-BM-R12 (NAVIGATE)NPi-AdvPE_ENGAGE_20C_step-0MESSAGE10kishimot2023-05-17 11:32:35.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-BM-...1
6284MESSAGEix-GLOBIOM 1.1-BM-R12 (NAVIGATE)NPi-AdvPE_ENGAGE_20C_step-3MESSAGE10kishimot2023-05-17 11:39:15.000000kishimot2023-05-17 11:54:29.000000NoneNoneclone Scenario from 'MESSAGEix-GLOBIOM 1.1-BM-...2
..........................................
7736NAVIGATE_SSP2_v4.1.7_noSDG_rcpref_medrelNPi2020-con-prim-dir-ncr_nexusMESSAGE10awais2022-11-24 13:13:38.000000awais2022-11-24 19:26:29.000000NoneNoneclone Scenario from 'NAVIGATE_SSP2_v4.1.7_noSD...13
7737NAVIGATE_SSP2_v4.1.7_noSDG_rcpref_medrelNPi2020_nexusMESSAGE10awais2023-04-09 17:02:46.000000awais2023-04-09 20:29:00.000000NoneNoneclone Scenario from 'NAVIGATE_SSP2_v4.1.7_noSD...4
7738NAVIGATE_hydro_testNoPolicy_R12_update_hydroMESSAGE10min2022-01-24 23:33:58.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...1
7739NAVIGATE_hydro_testNoPolicy_R12_update_hydro_1000MESSAGE10min2022-01-26 11:04:24.000000NoneNoneNoneNoneclone Scenario from 'NAVIGATE_hydro_test|NoPol...1
7740NAVIGATE_hydro_testNoPolicy_R12_update_hydro_1500MESSAGE10min2022-01-27 15:18:57.000000NoneNoneNoneNoneclone Scenario from 'NAVIGATE_hydro_test|NoPol...1
\n

623 rows × 13 columns

\n
" - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = mp.scenario_list()\n", - "df[df[\"model\"].str.contains(\"NAVIGATE\")]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 8, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'common': {'commodity': {'require': ['coal', 'gas', 'electr', 'ethanol', 'methanol', 'fueloil', 'lightoil', 'hydrogen', 'lh2', 'd_heat'], 'add': ['biomass', 'water', 'fresh_water']}, 'level': {'require': ['primary', 'secondary', 'useful', 'final', 'water_supply', 'export', 'import']}, 'type_tec': {'add': ['industry']}, 'mode': {'add': ['M1', 'M2']}, 'emission': {'add': ['CO2', 'CH4', 'N2O', 'NOx', 'SO2', 'PM2p5', 'CF4']}, 'year': {'add': [1980, 1990, 2000, 2010]}, 'type_year': {'add': [2010]}}, 'generic': {'commodity': {'add': ['ht_heat', 'lt_heat']}, 'level': {'add': ['useful_steel', 'useful_cement', 'useful_aluminum', 'useful_refining', 'useful_petro', 'useful_resins']}, 'technology': {'add': ['furnace_foil_steel', 'furnace_loil_steel', 'furnace_biomass_steel', 'furnace_ethanol_steel', 'furnace_methanol_steel', 'furnace_gas_steel', 'furnace_coal_steel', 'furnace_elec_steel', 'furnace_h2_steel', 'hp_gas_steel', 'hp_elec_steel', 'fc_h2_steel', 'solar_steel', 'dheat_steel', 'furnace_foil_cement', 'furnace_loil_cement', 'furnace_biomass_cement', 'furnace_ethanol_cement', 'furnace_methanol_cement', 'furnace_gas_cement', 'furnace_coal_cement', 'furnace_elec_cement', 'furnace_h2_cement', 'hp_gas_cement', 'hp_elec_cement', 'fc_h2_cement', 'solar_cement', 'dheat_cement', 'furnace_coal_aluminum', 'furnace_foil_aluminum', 'furnace_loil_aluminum', 'furnace_ethanol_aluminum', 'furnace_biomass_aluminum', 'furnace_methanol_aluminum', 'furnace_gas_aluminum', 'furnace_elec_aluminum', 'furnace_h2_aluminum', 'hp_gas_aluminum', 'hp_elec_aluminum', 'fc_h2_aluminum', 'solar_aluminum', 'dheat_aluminum', 'furnace_coke_petro', 'furnace_coal_petro', 'furnace_foil_petro', 'furnace_loil_petro', 'furnace_ethanol_petro', 'furnace_biomass_petro', 'furnace_methanol_petro', 'furnace_gas_petro', 'furnace_elec_petro', 'furnace_h2_petro', 'hp_gas_petro', 'hp_elec_petro', 'fc_h2_petro', 'solar_petro', 'dheat_petro', 'furnace_coke_refining', 'furnace_coal_refining', 'furnace_foil_refining', 'furnace_loil_refining', 'furnace_ethanol_refining', 'furnace_biomass_refining', 'furnace_methanol_refining', 'furnace_gas_refining', 'furnace_elec_refining', 'furnace_h2_refining', 'hp_gas_refining', 'hp_elec_refining', 'fc_h2_refining', 'solar_refining', 'dheat_refining', 'furnace_coal_resins', 'furnace_foil_resins', 'furnace_loil_resins', 'furnace_ethanol_resins', 'furnace_biomass_resins', 'furnace_methanol_resins', 'furnace_gas_resins', 'furnace_elec_resins', 'furnace_h2_resins', 'hp_gas_resins', 'hp_elec_resins', 'fc_h2_resins', 'solar_resins', 'dheat_resins']}, 'mode': {'add': ['low_temp', 'high_temp']}}, 'petro_chemicals': {'commodity': {'require': ['crudeoil'], 'add': ['HVC', 'naphtha', 'kerosene', 'diesel', 'atm_residue', 'refinery_gas', 'atm_gasoil', 'atm_residue', 'vacuum_gasoil', 'vacuum_residue', 'gasoline', 'heavy_foil', 'light_foil', 'propylene', 'pet_coke', 'ethane', 'propane', 'ethylene', 'BTX']}, 'level': {'require': ['secondary', 'final'], 'add': ['pre_intermediate', 'desulfurized', 'intermediate', 'secondary_material', 'final_material', 'demand']}, 'mode': {'add': ['atm_gasoil', 'vacuum_gasoil', 'naphtha', 'kerosene', 'diesel', 'cracking_gasoline', 'cracking_loil', 'ethane', 'propane', 'light_foil', 'refinery_gas', 'refinery_gas_int', 'heavy_foil', 'pet_coke', 'atm_residue', 'vacuum_residue', 'gasoline', 'ethylene', 'propylene', 'BTX']}, 'technology': {'add': ['atm_distillation_ref', 'vacuum_distillation_ref', 'hydrotreating_ref', 'catalytic_cracking_ref', 'visbreaker_ref', 'coking_ref', 'catalytic_reforming_ref', 'hydro_cracking_ref', 'steam_cracker_petro', 'ethanol_to_ethylene_petro', 'agg_ref', 'gas_processing_petro', 'trade_petro', 'import_petro', 'export_petro', 'feedstock_t/d', 'production_HVC'], 'remove': ['ref_hil', 'ref_lol']}, 'shares': {'add': ['steam_cracker']}}, 'steel': {'commodity': {'add': ['steel', 'pig_iron', 'sponge_iron', 'sinter_iron', 'pellet_iron', 'ore_iron', 'limestone_iron', 'coke_iron', 'slag_iron', 'co_gas', 'bf_gas']}, 'level': {'add': ['new_scrap', 'old_scrap_1', 'old_scrap_2', 'old_scrap_3', 'primary_material', 'secondary_material', 'tertiary_material', 'final_material', 'useful_material', 'waste_material', 'product', 'demand', 'dummy_emission', 'end_of_life', 'dummy_end_of_life']}, 'technology': {'add': ['cokeoven_steel', 'sinter_steel', 'pellet_steel', 'bf_steel', 'dri_steel', 'sr_steel', 'bof_steel', 'eaf_steel', 'prep_secondary_steel_1', 'prep_secondary_steel_2', 'prep_secondary_steel_3', 'finishing_steel', 'manuf_steel', 'scrap_recovery_steel', 'DUMMY_ore_supply', 'DUMMY_limestone_supply_steel', 'DUMMY_coal_supply', 'DUMMY_gas_supply', 'trade_steel', 'import_steel', 'export_steel', 'other_EOL_steel', 'total_EOL_steel']}, 'relation': {'add': ['minimum_recycling_steel', 'max_global_recycling_steel']}, 'balance_equality': {'add': [['steel', 'old_scrap_1'], ['steel', 'old_scrap_2'], ['steel', 'old_scrap_3'], ['steel', 'end_of_life'], ['steel', 'product'], ['steel', 'new_scrap'], ['steel', 'useful_material'], ['steel', 'final_material']]}}, 'cement': {'commodity': {'add': ['cement', 'clinker_cement', 'raw_meal_cement', 'limestone_cement']}, 'level': {'add': ['primary_material', 'secondary_material', 'tertiary_material', 'final_material', 'useful_material', 'demand', 'dummy_end_of_life', 'end_of_life']}, 'technology': {'add': ['raw_meal_prep_cement', 'clinker_dry_cement', 'clinker_wet_cement', 'clinker_dry_ccs_cement', 'clinker_wet_ccs_cement', 'grinding_ballmill_cement', 'grinding_vertmill_cement', 'DUMMY_limestone_supply_cement', 'total_EOL_cement', 'other_EOL_cement', 'scrap_recovery_cement'], 'remove': ['cement_co2scr', 'cement_CO2']}, 'relation': {'remove': ['cement_pro', 'cement_scrub_lim']}, 'addon': {'add': ['clinker_dry_ccs_cement', 'clinker_wet_ccs_cement']}, 'type_addon': {'add': ['ccs_cement']}, 'map_tec_addon': {'add': [['clinker_dry_cement', 'ccs_cement'], ['clinker_wet_cement', 'ccs_cement']]}, 'cat_addon': {'add': [['ccs_cement', 'clinker_dry_ccs_cement'], ['ccs_cement', 'clinker_wet_ccs_cement']]}}, 'aluminum': {'commodity': {'add': ['aluminum']}, 'level': {'add': ['new_scrap', 'old_scrap_1', 'old_scrap_2', 'old_scrap_3', 'useful_aluminum', 'final_material', 'useful_material', 'product', 'secondary_material', 'demand', 'end_of_life', 'dummy_end_of_life']}, 'technology': {'add': ['soderberg_aluminum', 'prebake_aluminum', 'secondary_aluminum', 'prep_secondary_aluminum_1', 'prep_secondary_aluminum_2', 'prep_secondary_aluminum_3', 'finishing_aluminum', 'manuf_aluminum', 'scrap_recovery_aluminum', 'DUMMY_alumina_supply', 'trade_aluminum', 'import_aluminum', 'export_aluminum', 'other_EOL_aluminum', 'total_EOL_aluminum']}, 'relation': {'add': ['minimum_recycling_aluminum']}, 'balance_equality': {'add': [['aluminum', 'old_scrap_1'], ['aluminum', 'old_scrap_2'], ['aluminum', 'old_scrap_3'], ['aluminum', 'end_of_life'], ['aluminum', 'product'], ['aluminum', 'new_scrap']]}}, 'fertilizer': {'commodity': {'require': ['electr', 'freshwater_supply'], 'add': ['NH3', 'Fertilizer Use|Nitrogen', 'wastewater']}, 'level': {'add': ['secondary_material', 'final_material', 'wastewater']}, 'technology': {'require': ['h2_bio_ccs'], 'add': ['biomass_NH3', 'electr_NH3', 'gas_NH3', 'coal_NH3', 'fueloil_NH3', 'NH3_to_N_fertil', 'trade_NFert', 'export_NFert', 'import_NFert', 'trade_NH3', 'export_NH3', 'import_NH3', 'residual_NH3', 'biomass_NH3_ccs', 'gas_NH3_ccs', 'coal_NH3_ccs', 'fueloil_NH3_ccs']}, 'relation': {'add': ['NH3_trd_cap', 'NFert_trd_cap']}}, 'methanol': {'commodity': {'require': ['electr', 'biomass', 'hydrogen', 'd_heat'], 'add': ['methanol', 'ht_heat', 'formaldehyde', 'ethylene', 'propylene', 'fcoh_resin']}, 'level': {'add': ['final_material', 'secondary_material', 'primary_material', 'export_fs', 'import_fs']}, 'mode': {'add': ['feedstock', 'fuel', 'ethanol']}, 'technology': {'add': ['meth_bio', 'meth_bio_ccs', 'meth_h2', 'meth_t_d_material', 'MTO_petro', 'CH2O_synth', 'CH2O_to_resin', 'meth_coal', 'meth_coal_ccs', 'meth_ng', 'meth_ng_ccs', 'meth_t_d', 'meth_bal', 'meth_trd', 'meth_exp', 'meth_imp'], 'remove': ['sp_meth_I', 'meth_rc', 'meth_ic_trp', 'meth_fc_trp', 'meth_i', 'meth_coal', 'meth_coal_ccs', 'meth_ng', 'meth_ng_ccs', 'meth_t_d', 'meth_bal', 'meth_trd', 'meth_exp', 'meth_imp']}, 'addon': {'add': ['meth_h2']}, 'type_addon': {'add': ['methanol_synthesis_addon']}, 'map_tec_addon': {'add': [['h2_elec', 'methanol_synthesis_addon']]}, 'cat_addon': {'add': [['methanol_synthesis_addon', 'meth_h2']]}, 'relation': {'add': ['CO2_PtX_trans_disp_split', 'meth_exp_tot', 'meth_trade_balance', 'loil_trade_balance']}}, 'buildings': {'level': {'add': ['service']}, 'commodity': {'add': ['floor_area']}, 'technology': {'add': ['buildings']}}, 'power_sector': {'unit': {'add': ['t/kW']}}}\n" - ] - } - ], - "source": [ - "import yaml\n", - "file_path = \"C:/Users\\maczek\\PycharmProjects\\message_data\\data\\material\\set.yaml\"\n", - "try:\n", - " with open(file_path, 'r') as file:\n", - " yaml_data = yaml.safe_load(file)\n", - " print(yaml_data)\n", - "except FileNotFoundError: print(\"File not found.\")\n", - "except yaml.YAMLError as e: print(\"Error reading YAML:\", e)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 18, - "outputs": [ - { - "ename": "KeyError", - "evalue": "'add'", - "output_type": "error", - "traceback": [ - "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[1;31mKeyError\u001B[0m Traceback (most recent call last)", - "Input \u001B[1;32mIn [18]\u001B[0m, in \u001B[0;36m\u001B[1;34m()\u001B[0m\n\u001B[0;32m 1\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m i \u001B[38;5;129;01min\u001B[39;00m yaml_data\u001B[38;5;241m.\u001B[39mvalues():\n\u001B[1;32m----> 2\u001B[0m \u001B[43mi\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43madd\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\n", - "\u001B[1;31mKeyError\u001B[0m: 'add'" - ] - } - ], - "source": [ - "for i in yaml_data.values():\n", - " i[\"add\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 29, - "outputs": [], - "source": [ - "tec_list = {}\n", - "for k,v in list(yaml_data.items()):\n", - " if \"technology\" in v.keys():\n", - " tec_list[k] = v[\"technology\"][\"add\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 40, - "outputs": [ - { - "data": { - "text/plain": "0 meth_bio\n1 meth_bio_ccs\n2 meth_h2\n3 meth_t_d_material\n4 MTO_petro\n5 CH2O_synth\n6 CH2O_to_resin\n7 meth_coal\n8 meth_coal_ccs\n9 meth_ng\n10 meth_ng_ccs\n11 meth_t_d\n12 meth_bal\n13 meth_trd\n14 meth_exp\n15 meth_imp\nName: technology, dtype: object" - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s = pd.Series(tec_list[\"methanol\"])#to_excel(\"test.xlsx\")\n", - "s.name = \"technology\"\n", - "s.to_excel(\"\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "with pd.ExcelWriter('test.xlsx') as writer:\n", - " for i in list(par_dict_fs.keys()):\n", - " par_dict_fs[i].to_excel(writer, sheet_name=i, index=False)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 41, - "outputs": [], - "source": [ - "import pandas as pd\n", - "with pd.ExcelWriter('test.xlsx') as writer:\n", - " for k,v in tec_list.items():\n", - " s = pd.Series(tec_list[k])#to_excel(\"test.xlsx\")\n", - " s.name = \"technology\"\n", - " s.to_excel(writer, sheet_name=k, index=False)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 2, - "outputs": [], - "source": [ - "import pandas as pd" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 11, - "outputs": [ - { - "data": { - "text/plain": " year_vtg node_loc technology year_act mode node_origin \\\n0 2030 R11_AFR syn_micr_beef NaN hydrogen R11_AFR \n1 2030 R11_CPA syn_micr_beef NaN hydrogen R11_CPA \n2 2030 R11_EEU syn_micr_beef NaN hydrogen R11_EEU \n3 2030 R11_FSU syn_micr_beef NaN hydrogen R11_FSU \n4 2030 R11_LAM syn_micr_beef NaN hydrogen R11_LAM \n.. ... ... ... ... ... ... \n61 2035 R11_NAM syn_micr_beef NaN hydrogen R11_NAM \n62 2035 R11_PAO syn_micr_beef NaN hydrogen R11_PAO \n63 2035 R11_PAS syn_micr_beef NaN hydrogen R11_PAS \n64 2035 R11_SAS syn_micr_beef NaN hydrogen R11_SAS \n65 2035 R11_WEU syn_micr_beef NaN hydrogen R11_WEU \n\n commodity level time time_origin value unit \n0 hydrogen final year year 1.000000 GWa \n1 hydrogen final year year 1.000000 GWa \n2 hydrogen final year year 1.000000 GWa \n3 hydrogen final year year 1.000000 GWa \n4 hydrogen final year year 1.000000 GWa \n.. ... ... ... ... ... ... \n61 electr final year year 0.936073 - \n62 electr final year year 0.936073 - \n63 electr final year year 0.936073 - \n64 electr final year year 0.936073 - \n65 electr final year year 0.936073 - \n\n[66 rows x 12 columns]", - "text/html": "
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year_vtgnode_loctechnologyyear_actmodenode_origincommodityleveltimetime_originvalueunit
02030R11_AFRsyn_micr_beefNaNhydrogenR11_AFRhydrogenfinalyearyear1.000000GWa
12030R11_CPAsyn_micr_beefNaNhydrogenR11_CPAhydrogenfinalyearyear1.000000GWa
22030R11_EEUsyn_micr_beefNaNhydrogenR11_EEUhydrogenfinalyearyear1.000000GWa
32030R11_FSUsyn_micr_beefNaNhydrogenR11_FSUhydrogenfinalyearyear1.000000GWa
42030R11_LAMsyn_micr_beefNaNhydrogenR11_LAMhydrogenfinalyearyear1.000000GWa
.......................................
612035R11_NAMsyn_micr_beefNaNhydrogenR11_NAMelectrfinalyearyear0.936073-
622035R11_PAOsyn_micr_beefNaNhydrogenR11_PAOelectrfinalyearyear0.936073-
632035R11_PASsyn_micr_beefNaNhydrogenR11_PASelectrfinalyearyear0.936073-
642035R11_SASsyn_micr_beefNaNhydrogenR11_SASelectrfinalyearyear0.936073-
652035R11_WEUsyn_micr_beefNaNhydrogenR11_WEUelectrfinalyearyear0.936073-
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66 rows × 12 columns

\n
" - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from message_ix_models.util import broadcast\n", - "scp_demand = pd.read_excel(\"C:/Users\\maczek\\PycharmProjects\\message_data\\data\\scp/scp_techno_economic.xlsx\", sheet_name=\"input\")\n", - "scp_demand.pipe(broadcast, year_vtg=[2030, 2035])" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 12, - "outputs": [ - { - "data": { - "text/plain": " commodity level time value unit node year\n0 scp demand year 1.589159 ??? R11_AFR 2020\n1 scp demand year 1.765396 ??? R11_AFR 2025\n2 scp demand year 1.941633 ??? R11_AFR 2030\n3 scp demand year 2.119691 ??? R11_AFR 2035\n4 scp demand year 2.297748 ??? R11_AFR 2040\n.. ... ... ... ... ... ... ...\n138 scp demand year 1.921039 ??? R11_WEU 2060\n139 scp demand year 1.923622 ??? R11_WEU 2070\n140 scp demand year 1.913528 ??? R11_WEU 2080\n141 scp demand year 1.889773 ??? R11_WEU 2090\n142 scp demand year 1.849365 ??? R11_WEU 2100\n\n[143 rows x 7 columns]", - "text/html": "
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commodityleveltimevalueunitnodeyear
0scpdemandyear1.589159???R11_AFR2020
1scpdemandyear1.765396???R11_AFR2025
2scpdemandyear1.941633???R11_AFR2030
3scpdemandyear2.119691???R11_AFR2035
4scpdemandyear2.297748???R11_AFR2040
........................
138scpdemandyear1.921039???R11_WEU2060
139scpdemandyear1.923622???R11_WEU2070
140scpdemandyear1.913528???R11_WEU2080
141scpdemandyear1.889773???R11_WEU2090
142scpdemandyear1.849365???R11_WEU2100
\n

143 rows × 7 columns

\n
" - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "scp_demand = pd.read_excel(\"C:/Users\\maczek\\PycharmProjects\\message_data\\data\\scp/population_beef_demand.xlsx\", sheet_name=\"demand_R11\")\n", - "scp_demand[\"value\"] = scp_demand[\"value\"] / 1000\n", - "scp_demand" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 14, - "outputs": [ - { - "data": { - "text/plain": " relation node_rel year_rel node_loc technology \\\n0 CO2_PtX_trans_disp_split R12_AFR 2020 R12_AFR syn_micr_beef \n1 CO2_PtX_trans_disp_split R12_CHN 2020 R12_CHN syn_micr_beef \n2 CO2_PtX_trans_disp_split R12_EEU 2020 R12_EEU syn_micr_beef \n3 CO2_PtX_trans_disp_split R12_FSU 2020 R12_FSU syn_micr_beef \n4 CO2_PtX_trans_disp_split R12_LAM 2020 R12_LAM syn_micr_beef \n.. ... ... ... ... ... \n487 CO2_PtX_trans_disp_split R12_EEU 2025 R12_EEU co2_tr_dis \n488 CO2_PtX_trans_disp_split R12_NAM 2025 R12_NAM co2_tr_dis \n489 CO2_PtX_trans_disp_split R12_PAO 2025 R12_PAO co2_tr_dis \n490 CO2_PtX_trans_disp_split R12_RCPA 2025 R12_RCPA co2_tr_dis \n491 CO2_PtX_trans_disp_split R12_WEU 2025 R12_WEU co2_tr_dis \n\n year_act mode value unit \n0 2020 hydrogen 0.597213 ??? \n1 2020 hydrogen 0.597213 ??? \n2 2020 hydrogen 0.597213 ??? \n3 2020 hydrogen 0.597213 ??? \n4 2020 hydrogen 0.597213 ??? \n.. ... ... ... ... \n487 2025 M1 1.000000 ??? \n488 2025 M1 1.000000 ??? \n489 2025 M1 1.000000 ??? \n490 2025 M1 1.000000 ??? \n491 2025 M1 1.000000 ??? \n\n[492 rows x 9 columns]", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
relationnode_relyear_relnode_loctechnologyyear_actmodevalueunit
0CO2_PtX_trans_disp_splitR12_AFR2020R12_AFRsyn_micr_beef2020hydrogen0.597213???
1CO2_PtX_trans_disp_splitR12_CHN2020R12_CHNsyn_micr_beef2020hydrogen0.597213???
2CO2_PtX_trans_disp_splitR12_EEU2020R12_EEUsyn_micr_beef2020hydrogen0.597213???
3CO2_PtX_trans_disp_splitR12_FSU2020R12_FSUsyn_micr_beef2020hydrogen0.597213???
4CO2_PtX_trans_disp_splitR12_LAM2020R12_LAMsyn_micr_beef2020hydrogen0.597213???
..............................
487CO2_PtX_trans_disp_splitR12_EEU2025R12_EEUco2_tr_dis2025M11.000000???
488CO2_PtX_trans_disp_splitR12_NAM2025R12_NAMco2_tr_dis2025M11.000000???
489CO2_PtX_trans_disp_splitR12_PAO2025R12_PAOco2_tr_dis2025M11.000000???
490CO2_PtX_trans_disp_splitR12_RCPA2025R12_RCPAco2_tr_dis2025M11.000000???
491CO2_PtX_trans_disp_splitR12_WEU2025R12_WEUco2_tr_dis2025M11.000000???
\n

492 rows × 9 columns

\n
" - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from message_data.model.material.data_methanol_new import broadcast_reduced_df\n", - "\n", - "broadcast_reduced_df(pd.read_excel(\"C:/Users\\maczek\\PycharmProjects\\message_data\\data\\scp/scp_techno_economic.xlsx\", sheet_name=\"relation_activity\"), \"relation_activity\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 2, - "outputs": [], - "source": [ - "scenario = message_ix.Scenario(mp, model=\"MESSAGEix-Materials\", scenario=\"NoPolicy_no_SDGs_petro_thesis_1_final\")\n", - "#scenario = message_ix.Scenario(mp, model=\"MESSAGEix-Materials\", scenario=\"NoPolicy_no_SDGs_petro_thesis_1\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 3, - "outputs": [], - "source": [ - "premise_clone = scenario.clone(model=scenario.model,\n", - " scenario=scenario.scenario.replace(\"final\", \"final_premise\"))" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 4, - "outputs": [ - { - "data": { - "text/plain": "2" - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "premise_clone.version" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 6, - "outputs": [ - { - "data": { - "text/plain": "'NoPolicy_no_SDGs_petro_thesis_1_final_premise'" - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "premise_clone.scenario" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 5, - "outputs": [], - "source": [ - "premise_clone.set_as_default()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 20, - "outputs": [], - "source": [ - "df_rcpa = scenario.par(\"initial_activity_up\", filters={\"technology\":\"steam_cracker_petro\", \"node_loc\":\"R12_RCPA\", \"year_act\":\"2020\"})\n", - "scenario.check_out()\n", - "scenario.remove_par(\"initial_activity_up\", df_rcpa)\n", - "scenario.commit(\"remove RCPA growth up sc 2020\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 14, - "outputs": [], - "source": [ - "sc_clone = scenario.clone(\"MESSAGEix-Materials\", \"NoPolicy_no_SDGs_petro_thesis_1_solve_test\", keep_solution=False)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "sc_clone.set_as_default()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 13, - "outputs": [ - { - "data": { - "text/plain": "'2023-05-09 04:15:01.0'" - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "scenario.last_update()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 4, - "outputs": [], - "source": [ - "df_loil_rel = scenario.par(\"relation_activity\", filters={\"relation\":\"loil_trade_balance\"})" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 7, - "outputs": [], - "source": [ - "scenario.check_out()\n", - "scenario.remove_par(\"relation_activity\", df_loil_rel)\n", - "scenario.commit(\"remove loil trade balance constraint\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 20, - "outputs": [], - "source": [ - "scenario.par(\"demand\", filters={\"commodity\":[\"methanol\", \"HVC\", \"NH3\", \"fcoh_resin\"]}).to_excel(\"high_demand_output.xlsx\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 2, - "outputs": [], - "source": [ - "scenario = message_ix.Scenario(mp, model=\"MESSAGEix-Materials\", scenario=\"NoPolicy_petro_thesis_2_w_ENGAGE_fixes_test_build\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 11, - "outputs": [], - "source": [ - "df_remove = scenario.par(\"relation_activity\", filters={\"technology\":\"loil_exp\", \"relation\":\"loil_trade_balance\", \"node_loc\":\"R12_EEU\", \"year_rel\":2020})" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 12, - "outputs": [], - "source": [ - "scenario.check_out()\n", - "scenario.remove_par(\"relation_activity\", df_remove)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 13, - "outputs": [], - "source": [ - "scenario.commit(\"remove loil_exp from EEU 2020\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 15, - "outputs": [], - "source": [ - "mp.close_db()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 3, - "outputs": [], - "source": [ - "sc_ls = mp.scenario_list()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 45, - "outputs": [], - "source": [ - "scenario = message_ix.Scenario(mp, model=\"MESSAGEix-Materials\", scenario=\"2.0deg_petro_thesis_2_macro_w_ENGAGE_fixes\")#, version=9)\n", - "#scenario = message_ix.Scenario(mp, model=\"MESSAGEix-Materials\", scenario=\"NoPolicy_no_SDGs_petro_thesis_1\")#_macro\")#, version=1)\n", - "#scenario = message_ix.Scenario(mp, model=\"MESSAGEix-Materials\", scenario=\"NoPolicy_no_SDGs_petro_thesis_1_macro\")\n", - "\n", - "\n", - "#scenario = message_ix.Scenario(mp, model=\"MESSAGEix-Materials\", scenario=\"NoPolicy_petro_thesis_2_test_meth_h2\")\n", - "#scenario = message_ix.Scenario(mp, model=\"MESSAGEix-GLOBIOM 1.1-BM-R12 (NAVIGATE)\", scenario=\"NPi-Default\")\n", - "#scenario = message_ix.Scenario(mp, model=\"MESSAGEix-Materials\", scenario=\"baseline_DEFAULT_rebase2\")\n", - "#scenario = message_ix.Scenario(mp, model=\"MESSAGEix-Materials\", scenario=\"baseline_VK_Transport-Materials_VK\")#, version=37)\n", - "#scenario = message_ix.Scenario(mp, model=\"MESSAGEix-GLOBIOM 1.1-R12\", scenario='baseline_DEFAULT')\n", - "#scenario = message_ix.Scenario(mp, model=\"MESSAGEix-GLOBIOM 1.1-M-R12 (NGFS)\", scenario='NPi2030')\n", - "#scenario = message_ix.Scenario(mp, model=\"ENGAGE_SSP2_v4.1.8.3.1_T3.4v2_r3.1\", scenario='EN_NPi2020_techb_1000')\n", - "#scenario = message_ix.Scenario(mp, model=\"SHAPE_SSP2_v4.1.8\", scenario=\"baseline\", version=1)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 5, - "outputs": [], - "source": [ - "scenario.last_update()\n", - "#scenario.version" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "### update scenario with ENGAGE fixes" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 49, - "outputs": [], - "source": [ - "sc_clone = message_ix.Scenario(mp, model=\"MESSAGEix-Materials\", scenario=\"2.0deg_petro_thesis_2_macro_w_ENGAGE_fixes\")\n", - "sc_clone2 = sc_clone.clone(model=\"MESSAGEix-Materials\", scenario=\"2.0deg_petro_thesis_2_macro_w_ENGAGE_fixes_test_loil_exp_fix\")\n", - "#sc_clone = message_ix.Scenario(mp, model=\"MESSAGEix-Materials\", scenario=\"NoPolicy_petro_thesis_2_macro_w_ENGAGE_fixes\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 2, - "outputs": [], - "source": [ - "scenario = message_ix.Scenario(mp, model=\"MESSAGEix-Materials\", scenario=\"2.0deg_petro_thesis_2_macro_w_ENGAGE_fixes_test_loil_exp_fix\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 6, - "outputs": [], - "source": [ - "import importlib\n", - "from message_data.model.material import data_methanol_new" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 7, - "outputs": [ - { - "data": { - "text/plain": "" - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "importlib.reload(data_methanol_new)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 28, - "outputs": [], - "source": [ - "meth_dict = data_methanol_new.gen_data_methanol_new(scenario)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 29, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "historical_activity\n", - "initial_activity_up\n", - "ref_activity\n", - "historical_activity\n", - "ref_activity\n" - ] - } - ], - "source": [ - "for k,v in meth_dict.items():\n", - " if (\"technology\" in v.columns) & (\"year_act\" in v.columns):\n", - " #print(v[v.get(\"technology\")==\"meth_exp\"].year_act.unique())\n", - " df = v[v.get(\"technology\")==\"meth_exp\"]\n", - " if df.size == 0:\n", - " continue\n", - " if 2020 not in df.year_act.unique():\n", - " print(k)\n", - "\n", - "for k,v in meth_dict.items():\n", - " if (\"technology\" in v.columns) & (\"year_act\" in v.columns):\n", - " #print(v[v.get(\"technology\")==\"meth_exp\"].year_act.unique())\n", - " df = v[v.get(\"technology\")==\"meth_imp\"]\n", - " if df.size == 0:\n", - " continue\n", - " if 2020 not in df.year_act.unique():\n", - " print(k)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 9, - "outputs": [], - "source": [ - "df = scenario.par(\"relation_activity\", filters={\"relation\":\"meth_trade_balance\"})\n", - "df_new = df.copy(deep=True)\n", - "df_new[\"mode\"] = \"M1\"\n", - "df_new = df_new.drop_duplicates()\n", - "df_new = df_new[~df_new[\"node_loc\"].isin([\"R12_FSU\", \"R12_LAM\"])]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 12, - "outputs": [], - "source": [ - "df_new[\"relation\"] = \"loil_trade_balance\"" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 19, - "outputs": [], - "source": [ - "df_new.loc[df_new[\"technology\"]==\"meth_exp\",\"value\"] = 0.989" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 20, - "outputs": [ - { - "data": { - "text/plain": " relation node_rel year_rel node_loc technology year_act \\\n0 loil_trade_balance R12_GLB 2030 R12_AFR meth_exp 2030 \n1 loil_trade_balance R12_GLB 2030 R12_RCPA meth_exp 2030 \n2 loil_trade_balance R12_GLB 2030 R12_EEU meth_exp 2030 \n5 loil_trade_balance R12_GLB 2030 R12_MEA meth_exp 2030 \n6 loil_trade_balance R12_GLB 2030 R12_NAM meth_exp 2030 \n.. ... ... ... ... ... ... \n653 loil_trade_balance R12_GLB 2020 R12_PAS meth_imp 2020 \n654 loil_trade_balance R12_GLB 2020 R12_SAS meth_imp 2020 \n655 loil_trade_balance R12_GLB 2020 R12_WEU meth_imp 2020 \n656 loil_trade_balance R12_GLB 2020 R12_CHN meth_imp 2020 \n668 loil_trade_balance R12_GLB 2020 R12_MEA meth_imp 2020 \n\n mode value unit \n0 M1 0.989 ??? \n1 M1 0.989 ??? \n2 M1 0.989 ??? \n5 M1 0.989 ??? \n6 M1 0.989 ??? \n.. ... ... ... \n653 M1 -1.000 ??? \n654 M1 -1.000 ??? \n655 M1 -1.000 ??? \n656 M1 -1.000 ??? \n668 M1 -1.000 ??? \n\n[280 rows x 9 columns]", - "text/html": "
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relationnode_relyear_relnode_loctechnologyyear_actmodevalueunit
0loil_trade_balanceR12_GLB2030R12_AFRmeth_exp2030M10.989???
1loil_trade_balanceR12_GLB2030R12_RCPAmeth_exp2030M10.989???
2loil_trade_balanceR12_GLB2030R12_EEUmeth_exp2030M10.989???
5loil_trade_balanceR12_GLB2030R12_MEAmeth_exp2030M10.989???
6loil_trade_balanceR12_GLB2030R12_NAMmeth_exp2030M10.989???
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653loil_trade_balanceR12_GLB2020R12_PASmeth_imp2020M1-1.000???
654loil_trade_balanceR12_GLB2020R12_SASmeth_imp2020M1-1.000???
655loil_trade_balanceR12_GLB2020R12_WEUmeth_imp2020M1-1.000???
656loil_trade_balanceR12_GLB2020R12_CHNmeth_imp2020M1-1.000???
668loil_trade_balanceR12_GLB2020R12_MEAmeth_imp2020M1-1.000???
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280 rows × 9 columns

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" - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_new" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 21, - "outputs": [], - "source": [ - "scenario.remove_solution()\n", - "scenario.check_out()\n", - "scenario.add_par(\"relation_activity\", df_new)\n", - "#scenario.remove_par(\"relation_activity\", df)\n", - "scenario.commit(\"fix loil trade balance relation tec modes\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 44, - "outputs": [], - "source": [ - "df = scenario.par(\"relation_activity\", filters={\"relation\":\"loil_trade_balance\", \"technology\":\"loil_exp\"})" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 47, - "outputs": [], - "source": [ - "df_fsu = df[df[\"node_loc\"]==\"R12_AFR\"].copy(deep=True)\n", - "df_lam = df[df[\"node_loc\"]==\"R12_AFR\"].copy(deep=True)\n", - "df_fsu[\"node_loc\"] = \"R12_FSU\"\n", - "df_lam[\"node_loc\"] = \"R12_LAM\"" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 50, - "outputs": [], - "source": [ - "scenario.remove_solution()\n", - "scenario.check_out()\n", - "#scenario.add_set(\"relation\", \"loil_trade_balance\")\n", - "scenario.add_par(\"relation_activity\", df_fsu)\n", - "scenario.add_par(\"relation_activity\", df_lam)\n", - "scenario.commit(\"add new relation to limit loil_exp ACT with loil_imp ACT\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 51, - "outputs": [ - { - "data": { - "text/plain": " relation node_rel year_rel node_loc technology year_act \\\n0 loil_trade_balance R12_GLB 2030 R12_AFR loil_exp 2030 \n1 loil_trade_balance R12_GLB 2030 R12_RCPA loil_exp 2030 \n2 loil_trade_balance R12_GLB 2030 R12_EEU loil_exp 2030 \n3 loil_trade_balance R12_GLB 2030 R12_MEA loil_exp 2030 \n4 loil_trade_balance R12_GLB 2030 R12_NAM loil_exp 2030 \n.. ... ... ... ... ... ... \n163 loil_trade_balance R12_GLB 2090 R12_LAM loil_exp 2090 \n164 loil_trade_balance R12_GLB 2100 R12_LAM loil_exp 2100 \n165 loil_trade_balance R12_GLB 2110 R12_LAM loil_exp 2110 \n166 loil_trade_balance R12_GLB 2025 R12_LAM loil_exp 2025 \n167 loil_trade_balance R12_GLB 2020 R12_LAM loil_exp 2020 \n\n mode value unit \n0 M1 0.989 ??? \n1 M1 0.989 ??? \n2 M1 0.989 ??? \n3 M1 0.989 ??? \n4 M1 0.989 ??? \n.. ... ... ... \n163 M1 0.989 ??? \n164 M1 0.989 ??? \n165 M1 0.989 ??? \n166 M1 0.989 ??? \n167 M1 0.989 ??? \n\n[168 rows x 9 columns]", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
relationnode_relyear_relnode_loctechnologyyear_actmodevalueunit
0loil_trade_balanceR12_GLB2030R12_AFRloil_exp2030M10.989???
1loil_trade_balanceR12_GLB2030R12_RCPAloil_exp2030M10.989???
2loil_trade_balanceR12_GLB2030R12_EEUloil_exp2030M10.989???
3loil_trade_balanceR12_GLB2030R12_MEAloil_exp2030M10.989???
4loil_trade_balanceR12_GLB2030R12_NAMloil_exp2030M10.989???
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163loil_trade_balanceR12_GLB2090R12_LAMloil_exp2090M10.989???
164loil_trade_balanceR12_GLB2100R12_LAMloil_exp2100M10.989???
165loil_trade_balanceR12_GLB2110R12_LAMloil_exp2110M10.989???
166loil_trade_balanceR12_GLB2025R12_LAMloil_exp2025M10.989???
167loil_trade_balanceR12_GLB2020R12_LAMloil_exp2020M10.989???
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168 rows × 9 columns

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" - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "scenario.par(\"relation_activity\", filters={\"relation\":\"loil_trade_balance\", \"technology\":\"loil_exp\"})" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 28, - "outputs": [], - "source": [ - "df = scenario.par(\"relation_activity\", filters={\"relation\":\"loil_trade_balance\"})" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 37, - "outputs": [], - "source": [ - "#df.loc[df[\"technology\"] == \"meth_exp\", \"technology\"] = \"loil_exp\"\n", - "df.loc[df[\"technology\"] == \"meth_imp\", \"technology\"] = \"loil_imp\"" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 42, - "outputs": [ - { - "data": { - "text/plain": " relation node_rel year_rel node_loc technology year_act \\\n0 loil_trade_balance R12_GLB 2030 R12_AFR loil_exp 2030 \n1 loil_trade_balance R12_GLB 2030 R12_RCPA loil_exp 2030 \n2 loil_trade_balance R12_GLB 2030 R12_EEU loil_exp 2030 \n3 loil_trade_balance R12_GLB 2030 R12_MEA loil_exp 2030 \n4 loil_trade_balance R12_GLB 2030 R12_NAM loil_exp 2030 \n.. ... ... ... ... ... ... \n275 loil_trade_balance R12_GLB 2020 R12_PAS loil_imp 2020 \n276 loil_trade_balance R12_GLB 2020 R12_SAS loil_imp 2020 \n277 loil_trade_balance R12_GLB 2020 R12_WEU loil_imp 2020 \n278 loil_trade_balance R12_GLB 2020 R12_CHN loil_imp 2020 \n279 loil_trade_balance R12_GLB 2020 R12_MEA loil_imp 2020 \n\n mode value unit \n0 M1 0.989 ??? \n1 M1 0.989 ??? \n2 M1 0.989 ??? \n3 M1 0.989 ??? \n4 M1 0.989 ??? \n.. ... ... ... \n275 M1 -1.000 ??? \n276 M1 -1.000 ??? \n277 M1 -1.000 ??? \n278 M1 -1.000 ??? \n279 M1 -1.000 ??? \n\n[280 rows x 9 columns]", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
relationnode_relyear_relnode_loctechnologyyear_actmodevalueunit
0loil_trade_balanceR12_GLB2030R12_AFRloil_exp2030M10.989???
1loil_trade_balanceR12_GLB2030R12_RCPAloil_exp2030M10.989???
2loil_trade_balanceR12_GLB2030R12_EEUloil_exp2030M10.989???
3loil_trade_balanceR12_GLB2030R12_MEAloil_exp2030M10.989???
4loil_trade_balanceR12_GLB2030R12_NAMloil_exp2030M10.989???
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275loil_trade_balanceR12_GLB2020R12_PASloil_imp2020M1-1.000???
276loil_trade_balanceR12_GLB2020R12_SASloil_imp2020M1-1.000???
277loil_trade_balanceR12_GLB2020R12_WEUloil_imp2020M1-1.000???
278loil_trade_balanceR12_GLB2020R12_CHNloil_imp2020M1-1.000???
279loil_trade_balanceR12_GLB2020R12_MEAloil_imp2020M1-1.000???
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280 rows × 9 columns

\n
" - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 40, - "outputs": [], - "source": [ - "scenario.add_par(\"relation_activity\", df)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 41, - "outputs": [], - "source": [ - "scenario.commit(\"fix loil trade balance relation tec names\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "### copy meth_exp constraint relation for loil_exp" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 57, - "outputs": [], - "source": [ - "df_act_exp = sc_clone2.par(\"relation_activity\", filters={\"relation\": \"meth_trade_balance\", \"technology\":\"meth_exp\"})\n", - "df_act_imp = sc_clone2.par(\"relation_activity\", filters={\"relation\": \"meth_trade_balance\", \"technology\":\"meth_imp\"})\n", - "\n", - "df_act_exp[\"relation\"] = \"loil_trade_balance\"\n", - "df_act_imp[\"relation\"] = \"loil_trade_balance\"\n", - "\n", - "df_act_exp[\"technology\"] = \"loil_exp\"\n", - "df_act_imp[\"technology\"] = \"loil_imp\"\n", - "\n", - "df_act_exp[\"value\"] = 0.9899\n", - "\n", - "df_up_loil = sc_clone2.par(\"relation_upper\", filters={\"relation\": \"meth_trade_balance\"})\n", - "df_up_loil[\"relation\"] = \"loil_trade_balance\"" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 60, - "outputs": [], - "source": [ - "#sc_clone2.remove_solution()\n", - "#sc_clone2.check_out()\n", - "sc_clone2.add_set(\"relation\", \"loil_trade_balance\")\n", - "sc_clone2.add_par(\"relation_activity\", df_act_exp)\n", - "sc_clone2.add_par(\"relation_activity\", df_act_imp)\n", - "sc_clone2.add_par(\"relation_upper\", df_up_loil)\n", - "sc_clone2.commit(\"add new relation to limit loil_exp ACT with loil_imp ACT\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 17, - "outputs": [], - "source": [ - "df_upper = scenario.par(\"relation_lower\", filters={\"relation\": \"meth_exp_limit\", \"node_rel\":\"R12_AFR\"})\n", - "df_upper[\"relation\"] = \"meth_trade_balance\"\n", - "df_upper[\"node_rel\"] = \"R12_GLB\"" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 47, - "outputs": [ - { - "data": { - "text/plain": " relation node_rel year_rel node_loc technology year_act \\\n0 meth_trade_balance R12_GLB 2030 R12_AFR meth_exp 2030 \n1 meth_trade_balance R12_GLB 2030 R12_RCPA meth_exp 2030 \n2 meth_trade_balance R12_GLB 2030 R12_EEU meth_exp 2030 \n3 meth_trade_balance R12_GLB 2030 R12_FSU meth_exp 2030 \n4 meth_trade_balance R12_GLB 2030 R12_LAM meth_exp 2030 \n.. ... ... ... ... ... ... \n330 meth_trade_balance R12_GLB 2020 R12_CHN meth_exp 2020 \n331 meth_trade_balance R12_GLB 2020 R12_LAM meth_exp 2020 \n332 meth_trade_balance R12_GLB 2020 R12_LAM meth_exp 2020 \n333 meth_trade_balance R12_GLB 2020 R12_MEA meth_exp 2020 \n334 meth_trade_balance R12_GLB 2020 R12_MEA meth_exp 2020 \n\n mode value unit \n0 fuel 0.99 ??? \n1 fuel 0.99 ??? \n2 fuel 0.99 ??? \n3 fuel 0.99 ??? \n4 fuel 0.99 ??? \n.. ... ... ... \n330 feedstock 0.99 ??? \n331 feedstock 0.99 ??? \n332 fuel 0.99 ??? \n333 fuel 0.99 ??? \n334 feedstock 0.99 ??? \n\n[335 rows x 9 columns]", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
relationnode_relyear_relnode_loctechnologyyear_actmodevalueunit
0meth_trade_balanceR12_GLB2030R12_AFRmeth_exp2030fuel0.99???
1meth_trade_balanceR12_GLB2030R12_RCPAmeth_exp2030fuel0.99???
2meth_trade_balanceR12_GLB2030R12_EEUmeth_exp2030fuel0.99???
3meth_trade_balanceR12_GLB2030R12_FSUmeth_exp2030fuel0.99???
4meth_trade_balanceR12_GLB2030R12_LAMmeth_exp2030fuel0.99???
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330meth_trade_balanceR12_GLB2020R12_CHNmeth_exp2020feedstock0.99???
331meth_trade_balanceR12_GLB2020R12_LAMmeth_exp2020feedstock0.99???
332meth_trade_balanceR12_GLB2020R12_LAMmeth_exp2020fuel0.99???
333meth_trade_balanceR12_GLB2020R12_MEAmeth_exp2020fuel0.99???
334meth_trade_balanceR12_GLB2020R12_MEAmeth_exp2020feedstock0.99???
\n

335 rows × 9 columns

\n
" - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_exp = scenario.par(\"relation_activity\", filters={\"relation\": \"meth_exp_limit\", \"technology\":\"meth_exp\"})\n", - "df_exp[\"value\"] = 0.99 # 1.010**-1\n", - "df_exp[\"relation\"] = \"meth_trade_balance\"\n", - "df_exp[\"node_rel\"] = \"R12_GLB\"\n", - "df_exp" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 28, - "outputs": [ - { - "data": { - "text/plain": " relation node_rel year_rel node_loc technology year_act \\\n0 meth_trade_balance R12_GLB 2030 R12_AFR meth_imp 2030 \n1 meth_trade_balance R12_GLB 2030 R12_RCPA meth_imp 2030 \n2 meth_trade_balance R12_GLB 2030 R12_EEU meth_imp 2030 \n3 meth_trade_balance R12_GLB 2030 R12_FSU meth_imp 2030 \n4 meth_trade_balance R12_GLB 2030 R12_LAM meth_imp 2030 \n.. ... ... ... ... ... ... \n330 meth_trade_balance R12_GLB 2020 R12_CHN meth_imp 2020 \n331 meth_trade_balance R12_GLB 2020 R12_LAM meth_imp 2020 \n332 meth_trade_balance R12_GLB 2020 R12_LAM meth_imp 2020 \n333 meth_trade_balance R12_GLB 2020 R12_MEA meth_imp 2020 \n334 meth_trade_balance R12_GLB 2020 R12_MEA meth_imp 2020 \n\n mode value unit \n0 fuel -1 ??? \n1 fuel -1 ??? \n2 fuel -1 ??? \n3 fuel -1 ??? \n4 fuel -1 ??? \n.. ... ... ... \n330 feedstock -1 ??? \n331 feedstock -1 ??? \n332 fuel -1 ??? \n333 fuel -1 ??? \n334 feedstock -1 ??? \n\n[335 rows x 9 columns]", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
relationnode_relyear_relnode_loctechnologyyear_actmodevalueunit
0meth_trade_balanceR12_GLB2030R12_AFRmeth_imp2030fuel-1???
1meth_trade_balanceR12_GLB2030R12_RCPAmeth_imp2030fuel-1???
2meth_trade_balanceR12_GLB2030R12_EEUmeth_imp2030fuel-1???
3meth_trade_balanceR12_GLB2030R12_FSUmeth_imp2030fuel-1???
4meth_trade_balanceR12_GLB2030R12_LAMmeth_imp2030fuel-1???
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330meth_trade_balanceR12_GLB2020R12_CHNmeth_imp2020feedstock-1???
331meth_trade_balanceR12_GLB2020R12_LAMmeth_imp2020feedstock-1???
332meth_trade_balanceR12_GLB2020R12_LAMmeth_imp2020fuel-1???
333meth_trade_balanceR12_GLB2020R12_MEAmeth_imp2020fuel-1???
334meth_trade_balanceR12_GLB2020R12_MEAmeth_imp2020feedstock-1???
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335 rows × 9 columns

\n
" - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_imp = df_exp.copy(deep=True)\n", - "df_imp[\"technology\"] = \"meth_imp\"\n", - "df_imp[\"value\"] = -1\n", - "df_imp" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 48, - "outputs": [], - "source": [ - "scenario.remove_solution()\n", - "scenario.check_out()\n", - "scenario.add_set(\"relation\", \"meth_trade_balance\")\n", - "scenario.add_par(\"relation_activity\", df_exp)\n", - "scenario.add_par(\"relation_activity\", df_imp)\n", - "scenario.add_par(\"relation_upper\", df_upper)\n", - "scenario.commit(\"add new relation to limit meth_exp ACT with meth_imp ACT\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 38, - "outputs": [], - "source": [ - "scenario.remove_solution()\n", - "scenario.check_out()\n", - "scenario.remove_par(\"relation_upper\", df_upper)\n", - "scenario.commit(\"remove meth_exp upper constraint\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 49, - "outputs": [ - { - "data": { - "text/plain": "'2023-06-09 14:42:46.0'" - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sc_clone.last_update()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 56, - "outputs": [], - "source": [ - "bound = sc_clone.par(\"bound_activity_lo\", filters={\"technology\":\"bio_hpl\", \"year_act\":2100, \"node_loc\":\"R12_PAO\"})" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 58, - "outputs": [ - { - "data": { - "text/plain": " node_loc technology year_act mode time value unit\n0 R12_CHN meth_exp 2100 feedstock year 30 GWa", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
node_loctechnologyyear_actmodetimevalueunit
0R12_CHNmeth_exp2100feedstockyear30GWa
\n
" - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bound[\"node_loc\"] = \"R12_CHN\"\n", - "bound[\"technology\"] = \"meth_exp\"\n", - "bound[\"mode\"] = \"feedstock\"\n", - "bound[\"value\"] = 30\n", - "bound" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 61, - "outputs": [], - "source": [ - "sc_clone2.remove_solution()\n", - "sc_clone2.check_out()\n", - "sc_clone2.add_par(\"bound_activity_lo\", bound)\n", - "sc_clone2.commit(\"add experimental meth_exp bound_lo\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 4, - "outputs": [], - "source": [ - "from message_data.tools.utilities import manual_updates_ENGAGE_SSP2_v417_to_v418, update_h2_blending" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 5, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Updating efficiencies for technologies: ['coal_bal' 'gas_bal']\n", - "Updating efficiencies for technologies: ['biomass_t_d' 'coal_t_d']\n" - ] - } - ], - "source": [ - "sc_clone.remove_solution()\n", - "sc_clone.check_out()\n", - "manual_updates_ENGAGE_SSP2_v417_to_v418._correct_balance_td_efficiencies(sc_clone)\n", - "update_h2_blending.main(sc_clone)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "### add gas_processing_petro to h2_scrub_limit" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 8, - "outputs": [ - { - "data": { - "text/plain": "array(['g_ppl_co2scr', 'gas_bio', 'gas_cc', 'gas_ct', 'gas_hpl',\n 'gas_ppl', 'gas_t_d', 'gas_t_d_ch4', 'h2_mix'], dtype=object)" - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#sc_clone.commit(\"add ENGAGE fixes\")\n", - "sc_clone.par(\"relation_activity\", filters={\"relation\":\"h2_scrub_limit\"})[\"technology\"].unique()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 10, - "outputs": [], - "source": [ - "df = sc_clone.par(\"relation_activity\", filters={\"relation\":\"h2_scrub_limit\", \"technology\":\"gas_bio\"})" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 12, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "R12_AFR\n", - "16\n", - "R12_RCPA\n", - "16\n", - "R12_EEU\n", - "16\n", - "R12_FSU\n", - "16\n", - "R12_LAM\n", - "16\n", - "R12_MEA\n", - "16\n", - "R12_NAM\n", - "16\n", - "R12_PAO\n", - "16\n", - "R12_PAS\n", - "16\n", - "R12_SAS\n", - "16\n", - "R12_WEU\n", - "20\n", - "R12_CHN\n", - "16\n" - ] - } - ], - "source": [ - "for i in df[\"node_rel\"].unique().tolist():\n", - " print(i)\n", - " print(df[df[\"node_rel\"]==i].index.size)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 24, - "outputs": [], - "source": [ - "sc_clone.set_as_default()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 23, - "outputs": [ - { - "data": { - "text/plain": " model scenario \\\n6488 MESSAGEix-Materials 2.0deg_no_SDGs_petro_thesis_1_macro \n6489 MESSAGEix-Materials 2.0deg_petro_thesis_1 \n6490 MESSAGEix-Materials 2.0deg_petro_thesis_2 \n6491 MESSAGEix-Materials 2.0deg_petro_thesis_2_macro \n6492 MESSAGEix-Materials 2.0deg_petro_thesis_2_macro_w_ENGAGE_fixes \n6493 MESSAGEix-Materials 2degree_petro_thesis_2 \n6623 MESSAGEix-Materials NoPolicy_no_SDGs_petro_thesis_1 \n6624 MESSAGEix-Materials NoPolicy_no_SDGs_petro_thesis_1_macro \n6628 MESSAGEix-Materials NoPolicy_no_petro \n6630 MESSAGEix-Materials NoPolicy_petro_biomass \n6631 MESSAGEix-Materials NoPolicy_petro_test \n6632 MESSAGEix-Materials NoPolicy_petro_test_2 \n6633 MESSAGEix-Materials NoPolicy_petro_test_3 \n6634 MESSAGEix-Materials NoPolicy_petro_test_4 \n6635 MESSAGEix-Materials NoPolicy_petro_test_5 \n6636 MESSAGEix-Materials NoPolicy_petro_test_6 \n6637 MESSAGEix-Materials NoPolicy_petro_thesis_2 \n6638 MESSAGEix-Materials NoPolicy_petro_thesis_2_macro \n6639 MESSAGEix-Materials NoPolicy_petro_thesis_2_macro_w_ENGAGE_fixes \n6640 MESSAGEix-Materials NoPolicy_petro_thesis_2_test_meth_h2 \n6641 MESSAGEix-Materials NoPolicy_petro_thesis_2_test_new_input_files \n\n scheme is_default is_locked cre_user cre_date \\\n6488 MESSAGE 1 0 maczek 2023-05-09 09:34:33.000000 \n6489 MESSAGE 1 0 maczek 2023-02-08 23:38:03.000000 \n6490 MESSAGE 1 0 maczek 2023-04-06 16:44:33.000000 \n6491 MESSAGE 1 0 maczek 2023-05-12 08:31:34.000000 \n6492 MESSAGE 1 0 maczek 2023-06-07 17:39:46.000000 \n6493 MESSAGE 1 0 unlu 2023-04-06 15:35:51.000000 \n6623 MESSAGE 1 0 maczek 2023-05-08 22:47:58.000000 \n6624 MESSAGE 1 0 maczek 2023-05-09 04:15:13.000000 \n6628 MESSAGE 1 0 maczek 2023-05-04 09:18:27.000000 \n6630 MESSAGE 1 0 unlu 2022-08-09 14:51:56.000000 \n6631 MESSAGE 1 0 unlu 2022-04-14 11:08:53.000000 \n6632 MESSAGE 1 0 unlu 2022-04-14 14:05:39.000000 \n6633 MESSAGE 1 0 unlu 2022-04-14 14:40:02.000000 \n6634 MESSAGE 1 0 unlu 2022-04-14 15:13:25.000000 \n6635 MESSAGE 1 0 unlu 2022-04-14 15:29:02.000000 \n6636 MESSAGE 1 0 unlu 2022-04-14 15:38:19.000000 \n6637 MESSAGE 1 0 maczek 2023-06-07 10:11:12.000000 \n6638 MESSAGE 1 0 maczek 2023-06-07 11:11:09.000000 \n6639 MESSAGE 1 0 maczek 2023-06-07 15:19:13.000000 \n6640 MESSAGE 1 0 maczek 2023-05-15 10:42:54.000000 \n6641 MESSAGE 1 0 maczek 2023-06-07 17:11:26.000000 \n\n upd_user upd_date lock_user lock_date \\\n6488 maczek 2023-05-09 10:28:12.000000 None None \n6489 maczek 2023-02-08 23:44:53.000000 None None \n6490 None None None None \n6491 maczek 2023-05-12 09:20:07.000000 None None \n6492 maczek 2023-06-07 18:37:17.000000 None None \n6493 None None None None \n6623 maczek 2023-05-09 04:15:01.000000 None None \n6624 maczek 2023-05-09 04:47:38.000000 None None \n6628 maczek 2023-05-04 09:44:52.000000 None None \n6630 unlu 2022-08-09 15:14:46.000000 None None \n6631 None None None None \n6632 None None None None \n6633 None None None None \n6634 None None None None \n6635 None None None None \n6636 None None None None \n6637 maczek 2023-06-07 11:10:57.000000 None None \n6638 maczek 2023-06-07 11:21:49.000000 None None \n6639 maczek 2023-06-07 17:33:51.000000 None None \n6640 maczek 2023-05-15 11:44:21.000000 None None \n6641 None None None None \n\n annotation version \n6488 clone Scenario from 'MESSAGEix-Materials|NoPol... 4 \n6489 clone Scenario from 'MESSAGEix-Materials|NoPol... 1 \n6490 clone Scenario from 'MESSAGEix-Materials|NoPol... 6 \n6491 clone Scenario from 'MESSAGEix-Materials|NoPol... 10 \n6492 clone Scenario from 'MESSAGEix-Materials|NoPol... 1 \n6493 clone Scenario from 'MESSAGEix-Materials|NoPol... 1 \n6623 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 11 \n6624 clone Scenario from 'MESSAGEix-Materials|NoPol... 4 \n6628 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 3 \n6630 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 3 \n6631 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 4 \n6632 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 1 \n6633 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 1 \n6634 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 2 \n6635 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 2 \n6636 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 1 \n6637 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 112 \n6638 clone Scenario from 'MESSAGEix-Materials|NoPol... 12 \n6639 clone Scenario from 'MESSAGEix-Materials|NoPol... 1 \n6640 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 1 \n6641 clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 1 ", - 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modelscenarioschemeis_defaultis_lockedcre_usercre_dateupd_userupd_datelock_userlock_dateannotationversion
6488MESSAGEix-Materials2.0deg_no_SDGs_petro_thesis_1_macroMESSAGE10maczek2023-05-09 09:34:33.000000maczek2023-05-09 10:28:12.000000NoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...4
6489MESSAGEix-Materials2.0deg_petro_thesis_1MESSAGE10maczek2023-02-08 23:38:03.000000maczek2023-02-08 23:44:53.000000NoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...1
6490MESSAGEix-Materials2.0deg_petro_thesis_2MESSAGE10maczek2023-04-06 16:44:33.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...6
6491MESSAGEix-Materials2.0deg_petro_thesis_2_macroMESSAGE10maczek2023-05-12 08:31:34.000000maczek2023-05-12 09:20:07.000000NoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...10
6492MESSAGEix-Materials2.0deg_petro_thesis_2_macro_w_ENGAGE_fixesMESSAGE10maczek2023-06-07 17:39:46.000000maczek2023-06-07 18:37:17.000000NoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...1
6493MESSAGEix-Materials2degree_petro_thesis_2MESSAGE10unlu2023-04-06 15:35:51.000000NoneNoneNoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...1
6623MESSAGEix-MaterialsNoPolicy_no_SDGs_petro_thesis_1MESSAGE10maczek2023-05-08 22:47:58.000000maczek2023-05-09 04:15:01.000000NoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...11
6624MESSAGEix-MaterialsNoPolicy_no_SDGs_petro_thesis_1_macroMESSAGE10maczek2023-05-09 04:15:13.000000maczek2023-05-09 04:47:38.000000NoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...4
6628MESSAGEix-MaterialsNoPolicy_no_petroMESSAGE10maczek2023-05-04 09:18:27.000000maczek2023-05-04 09:44:52.000000NoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...3
6630MESSAGEix-MaterialsNoPolicy_petro_biomassMESSAGE10unlu2022-08-09 14:51:56.000000unlu2022-08-09 15:14:46.000000NoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...3
6631MESSAGEix-MaterialsNoPolicy_petro_testMESSAGE10unlu2022-04-14 11:08:53.000000NoneNoneNoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...4
6632MESSAGEix-MaterialsNoPolicy_petro_test_2MESSAGE10unlu2022-04-14 14:05:39.000000NoneNoneNoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...1
6633MESSAGEix-MaterialsNoPolicy_petro_test_3MESSAGE10unlu2022-04-14 14:40:02.000000NoneNoneNoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...1
6634MESSAGEix-MaterialsNoPolicy_petro_test_4MESSAGE10unlu2022-04-14 15:13:25.000000NoneNoneNoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...2
6635MESSAGEix-MaterialsNoPolicy_petro_test_5MESSAGE10unlu2022-04-14 15:29:02.000000NoneNoneNoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...2
6636MESSAGEix-MaterialsNoPolicy_petro_test_6MESSAGE10unlu2022-04-14 15:38:19.000000NoneNoneNoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...1
6637MESSAGEix-MaterialsNoPolicy_petro_thesis_2MESSAGE10maczek2023-06-07 10:11:12.000000maczek2023-06-07 11:10:57.000000NoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...112
6638MESSAGEix-MaterialsNoPolicy_petro_thesis_2_macroMESSAGE10maczek2023-06-07 11:11:09.000000maczek2023-06-07 11:21:49.000000NoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...12
6639MESSAGEix-MaterialsNoPolicy_petro_thesis_2_macro_w_ENGAGE_fixesMESSAGE10maczek2023-06-07 15:19:13.000000maczek2023-06-07 17:33:51.000000NoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...1
6640MESSAGEix-MaterialsNoPolicy_petro_thesis_2_test_meth_h2MESSAGE10maczek2023-05-15 10:42:54.000000maczek2023-05-15 11:44:21.000000NoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...1
6641MESSAGEix-MaterialsNoPolicy_petro_thesis_2_test_new_input_filesMESSAGE10maczek2023-06-07 17:11:26.000000NoneNoneNoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...1
\n
" - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sc_list = mp.scenario_list()\n", - "\n", - "sc_list[sc_list[\"scenario\"].str.contains(\"petro\")]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 39, - "outputs": [], - "source": [ - "df_set = scenario.set(\"balance_equality\").iloc[0]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 40, - "outputs": [ - { - "data": { - "text/plain": "commodity BIO01GHG000\nlevel LU\nName: 0, dtype: object" - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_set" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 41, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\maczek\\AppData\\Local\\Temp\\ipykernel_21000\\2936832773.py:1: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df_set[\"commodity\"] = \"methanol\"\n", - "C:\\Users\\maczek\\AppData\\Local\\Temp\\ipykernel_21000\\2936832773.py:2: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df_set[\"level\"] = \"export_fs\"\n" - ] - }, - { - "data": { - "text/plain": "commodity methanol\nlevel export_fs\nName: 0, dtype: object" - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_set[\"commodity\"] = \"methanol\"\n", - "df_set[\"level\"] = \"export_fs\"\n", - "df_set" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 42, - "outputs": [ - { - "data": { - "text/plain": "commodity methanol\nlevel export\nName: 0, dtype: object" - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_set" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 42, - "outputs": [], - "source": [ - "scenario.check_out()\n", - "scenario.add_set(\"balance_equality\", df_set)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 44, - "outputs": [ - { - "ename": "RuntimeError", - "evalue": "this Scenario is not checked out, no changes to be committed!", - "output_type": "error", - "traceback": [ - "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[1;31mException\u001B[0m Traceback (most recent call last)", - "File \u001B[1;32mMsgScenario.java:1277\u001B[0m, in \u001B[0;36mat.ac.iiasa.ixmp.objects.MsgScenario.commit\u001B[1;34m()\u001B[0m\n", - "File \u001B[1;32mMsgScenario.java:1289\u001B[0m, in \u001B[0;36mat.ac.iiasa.ixmp.objects.MsgScenario.commit\u001B[1;34m()\u001B[0m\n", - "File \u001B[1;32mScenario.java:2013\u001B[0m, in \u001B[0;36mat.ac.iiasa.ixmp.objects.Scenario.commit\u001B[1;34m()\u001B[0m\n", - "File \u001B[1;32mScenario.java:2026\u001B[0m, in \u001B[0;36mat.ac.iiasa.ixmp.objects.Scenario.commit\u001B[1;34m()\u001B[0m\n", - "File \u001B[1;32mScenario.java:2042\u001B[0m, in \u001B[0;36mat.ac.iiasa.ixmp.objects.Scenario.commit\u001B[1;34m()\u001B[0m\n", - "\u001B[1;31mException\u001B[0m: Java Exception", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001B[1;31mat.ac.iiasa.ixmp.exceptions.IxException\u001B[0m Traceback (most recent call last)", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\ixmp\\backend\\jdbc.py:697\u001B[0m, in \u001B[0;36mJDBCBackend.commit\u001B[1;34m(self, ts, comment)\u001B[0m\n\u001B[0;32m 696\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m--> 697\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mjindex\u001B[49m\u001B[43m[\u001B[49m\u001B[43mts\u001B[49m\u001B[43m]\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcommit\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcomment\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 698\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m java\u001B[38;5;241m.\u001B[39mException \u001B[38;5;28;01mas\u001B[39;00m e:\n", - "\u001B[1;31mat.ac.iiasa.ixmp.exceptions.IxException\u001B[0m: at.ac.iiasa.ixmp.exceptions.IxException: this Scenario is not checked out, no changes to be committed!", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001B[1;31mRuntimeError\u001B[0m Traceback (most recent call last)", - "Input \u001B[1;32mIn [44]\u001B[0m, in \u001B[0;36m\u001B[1;34m()\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[43mscenario\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcommit\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43madd methanol export constraint for fs mode\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\ixmp\\core\\timeseries.py:199\u001B[0m, in \u001B[0;36mTimeSeries.commit\u001B[1;34m(self, comment)\u001B[0m\n\u001B[0;32m 183\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mcommit\u001B[39m(\u001B[38;5;28mself\u001B[39m, comment: \u001B[38;5;28mstr\u001B[39m) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m 184\u001B[0m \u001B[38;5;124;03m\"\"\"Commit all changed data to the database.\u001B[39;00m\n\u001B[0;32m 185\u001B[0m \n\u001B[0;32m 186\u001B[0m \u001B[38;5;124;03m If the TimeSeries was newly created (with ``version='new'``), :attr:`version`\u001B[39;00m\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 197\u001B[0m \u001B[38;5;124;03m utils.maybe_commit\u001B[39;00m\n\u001B[0;32m 198\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[1;32m--> 199\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_backend\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mcommit\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcomment\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\ixmp\\core\\timeseries.py:108\u001B[0m, in \u001B[0;36mTimeSeries._backend\u001B[1;34m(self, method, *args, **kwargs)\u001B[0m\n\u001B[0;32m 102\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m_backend\u001B[39m(\u001B[38;5;28mself\u001B[39m, method, \u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[0;32m 103\u001B[0m \u001B[38;5;124;03m\"\"\"Convenience for calling *method* on the backend.\u001B[39;00m\n\u001B[0;32m 104\u001B[0m \n\u001B[0;32m 105\u001B[0m \u001B[38;5;124;03m The weak reference to the Platform object is used, if the Platform is still\u001B[39;00m\n\u001B[0;32m 106\u001B[0m \u001B[38;5;124;03m alive.\u001B[39;00m\n\u001B[0;32m 107\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[1;32m--> 108\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mplatform\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_backend\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmethod\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\ixmp\\backend\\base.py:53\u001B[0m, in \u001B[0;36mBackend.__call__\u001B[1;34m(self, obj, method, *args, **kwargs)\u001B[0m\n\u001B[0;32m 47\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m__call__\u001B[39m(\u001B[38;5;28mself\u001B[39m, obj, method, \u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[0;32m 48\u001B[0m \u001B[38;5;124;03m\"\"\"Call the backend method `method` for `obj`.\u001B[39;00m\n\u001B[0;32m 49\u001B[0m \n\u001B[0;32m 50\u001B[0m \u001B[38;5;124;03m The class attribute obj._backend_prefix is used to determine a prefix for the\u001B[39;00m\n\u001B[0;32m 51\u001B[0m \u001B[38;5;124;03m method name, e.g. 'ts_{method}'.\u001B[39;00m\n\u001B[0;32m 52\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[1;32m---> 53\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mgetattr\u001B[39;49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmethod\u001B[49m\u001B[43m)\u001B[49m\u001B[43m(\u001B[49m\u001B[43mobj\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32m~\\Anaconda3\\lib\\site-packages\\ixmp\\backend\\jdbc.py:701\u001B[0m, in \u001B[0;36mJDBCBackend.commit\u001B[1;34m(self, ts, comment)\u001B[0m\n\u001B[0;32m 699\u001B[0m arg \u001B[38;5;241m=\u001B[39m e\u001B[38;5;241m.\u001B[39margs[\u001B[38;5;241m0\u001B[39m]\n\u001B[0;32m 700\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(arg, \u001B[38;5;28mstr\u001B[39m) \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mthis Scenario is not checked out\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;129;01min\u001B[39;00m arg:\n\u001B[1;32m--> 701\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mRuntimeError\u001B[39;00m(arg)\n\u001B[0;32m 702\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m: \u001B[38;5;66;03m# pragma: no cover\u001B[39;00m\n\u001B[0;32m 703\u001B[0m _raise_jexception(e)\n", - "\u001B[1;31mRuntimeError\u001B[0m: this Scenario is not checked out, no changes to be committed!" - ] - } - ], - "source": [ - "scenario.commit(\"add methanol export constraint for fs mode\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 16, - "outputs": [ - { - "data": { - "text/plain": " relation node_rel year_rel node_loc technology \\\n0 h2_scrub_limit R12_AFR 2010 R12_AFR gas_processing_petro \n1 h2_scrub_limit R12_AFR 2015 R12_AFR gas_processing_petro \n2 h2_scrub_limit R12_AFR 2020 R12_AFR gas_processing_petro \n3 h2_scrub_limit R12_AFR 2025 R12_AFR gas_processing_petro \n4 h2_scrub_limit R12_AFR 2030 R12_AFR gas_processing_petro \n.. ... ... ... ... ... \n191 h2_scrub_limit R12_CHN 2070 R12_CHN gas_processing_petro \n192 h2_scrub_limit R12_CHN 2080 R12_CHN gas_processing_petro \n193 h2_scrub_limit R12_CHN 2090 R12_CHN gas_processing_petro \n194 h2_scrub_limit R12_CHN 2100 R12_CHN gas_processing_petro \n195 h2_scrub_limit R12_CHN 2110 R12_CHN gas_processing_petro \n\n year_act mode value unit \n0 2010 M1 -0.641932 ??? \n1 2015 M1 -0.641932 ??? \n2 2020 M1 -0.641932 ??? \n3 2025 M1 -0.641932 ??? \n4 2030 M1 -0.641932 ??? \n.. ... ... ... ... \n191 2070 M1 -0.641932 ??? \n192 2080 M1 -0.641932 ??? \n193 2090 M1 -0.641932 ??? \n194 2100 M1 -0.641932 ??? \n195 2110 M1 -0.641932 ??? \n\n[196 rows x 9 columns]", - 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relationnode_relyear_relnode_loctechnologyyear_actmodevalueunit
0h2_scrub_limitR12_AFR2010R12_AFRgas_processing_petro2010M1-0.641932???
1h2_scrub_limitR12_AFR2015R12_AFRgas_processing_petro2015M1-0.641932???
2h2_scrub_limitR12_AFR2020R12_AFRgas_processing_petro2020M1-0.641932???
3h2_scrub_limitR12_AFR2025R12_AFRgas_processing_petro2025M1-0.641932???
4h2_scrub_limitR12_AFR2030R12_AFRgas_processing_petro2030M1-0.641932???
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191h2_scrub_limitR12_CHN2070R12_CHNgas_processing_petro2070M1-0.641932???
192h2_scrub_limitR12_CHN2080R12_CHNgas_processing_petro2080M1-0.641932???
193h2_scrub_limitR12_CHN2090R12_CHNgas_processing_petro2090M1-0.641932???
194h2_scrub_limitR12_CHN2100R12_CHNgas_processing_petro2100M1-0.641932???
195h2_scrub_limitR12_CHN2110R12_CHNgas_processing_petro2110M1-0.641932???
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196 rows × 9 columns

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" - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df[\"value\"] = -(1.33181 * 0.482)\n", - "df[\"technology\"] = \"gas_processing_petro\"\n", - "df" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 17, - "outputs": [], - "source": [ - "sc_clone.check_out()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 18, - "outputs": [], - "source": [ - "sc_clone.add_par(\"relation_activity\", df)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 19, - "outputs": [], - "source": [ - "sc_clone.commit(\"add gas_processing_petro to h2_scrub_limit\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 20, - "outputs": [ - { - "data": { - "text/plain": "'NoPolicy_petro_thesis_2_macro_w_ENGAGE_fixes'" - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sc_clone.scenario" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 21, - "outputs": [ - { - "data": { - "text/plain": " relation node_rel year_rel node_loc technology \\\n0 h2_scrub_limit R12_AFR 2010 R12_AFR gas_processing_petro \n1 h2_scrub_limit R12_AFR 2015 R12_AFR gas_processing_petro \n2 h2_scrub_limit R12_AFR 2020 R12_AFR gas_processing_petro \n3 h2_scrub_limit R12_AFR 2025 R12_AFR gas_processing_petro \n4 h2_scrub_limit R12_AFR 2030 R12_AFR gas_processing_petro \n.. ... ... ... ... ... \n191 h2_scrub_limit R12_CHN 2070 R12_CHN gas_processing_petro \n192 h2_scrub_limit R12_CHN 2080 R12_CHN gas_processing_petro \n193 h2_scrub_limit R12_CHN 2090 R12_CHN gas_processing_petro \n194 h2_scrub_limit R12_CHN 2100 R12_CHN gas_processing_petro \n195 h2_scrub_limit R12_CHN 2110 R12_CHN gas_processing_petro \n\n year_act mode value unit \n0 2010 M1 -0.641932 ??? \n1 2015 M1 -0.641932 ??? \n2 2020 M1 -0.641932 ??? \n3 2025 M1 -0.641932 ??? \n4 2030 M1 -0.641932 ??? \n.. ... ... ... ... \n191 2070 M1 -0.641932 ??? \n192 2080 M1 -0.641932 ??? \n193 2090 M1 -0.641932 ??? \n194 2100 M1 -0.641932 ??? \n195 2110 M1 -0.641932 ??? \n\n[196 rows x 9 columns]", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
relationnode_relyear_relnode_loctechnologyyear_actmodevalueunit
0h2_scrub_limitR12_AFR2010R12_AFRgas_processing_petro2010M1-0.641932???
1h2_scrub_limitR12_AFR2015R12_AFRgas_processing_petro2015M1-0.641932???
2h2_scrub_limitR12_AFR2020R12_AFRgas_processing_petro2020M1-0.641932???
3h2_scrub_limitR12_AFR2025R12_AFRgas_processing_petro2025M1-0.641932???
4h2_scrub_limitR12_AFR2030R12_AFRgas_processing_petro2030M1-0.641932???
..............................
191h2_scrub_limitR12_CHN2070R12_CHNgas_processing_petro2070M1-0.641932???
192h2_scrub_limitR12_CHN2080R12_CHNgas_processing_petro2080M1-0.641932???
193h2_scrub_limitR12_CHN2090R12_CHNgas_processing_petro2090M1-0.641932???
194h2_scrub_limitR12_CHN2100R12_CHNgas_processing_petro2100M1-0.641932???
195h2_scrub_limitR12_CHN2110R12_CHNgas_processing_petro2110M1-0.641932???
\n

196 rows × 9 columns

\n
" - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sc_clone.par(\"relation_activity\", filters={\"relation\":\"h2_scrub_limit\", \"technology\":\"gas_processing_petro\"})" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 13, - "outputs": [ - { - "data": { - "text/plain": " relation node_rel year_rel node_loc technology year_act mode \\\n160 h2_scrub_limit R12_WEU 1990 R12_WEU gas_bio 1990 M1 \n161 h2_scrub_limit R12_WEU 1995 R12_WEU gas_bio 1995 M1 \n162 h2_scrub_limit R12_WEU 2000 R12_WEU gas_bio 2000 M1 \n163 h2_scrub_limit R12_WEU 2005 R12_WEU gas_bio 2005 M1 \n164 h2_scrub_limit R12_WEU 2010 R12_WEU gas_bio 2010 M1 \n165 h2_scrub_limit R12_WEU 2015 R12_WEU gas_bio 2015 M1 \n166 h2_scrub_limit R12_WEU 2020 R12_WEU gas_bio 2020 M1 \n167 h2_scrub_limit R12_WEU 2025 R12_WEU gas_bio 2025 M1 \n168 h2_scrub_limit R12_WEU 2030 R12_WEU gas_bio 2030 M1 \n169 h2_scrub_limit R12_WEU 2035 R12_WEU gas_bio 2035 M1 \n170 h2_scrub_limit R12_WEU 2040 R12_WEU gas_bio 2040 M1 \n171 h2_scrub_limit R12_WEU 2045 R12_WEU gas_bio 2045 M1 \n172 h2_scrub_limit R12_WEU 2050 R12_WEU gas_bio 2050 M1 \n173 h2_scrub_limit R12_WEU 2055 R12_WEU gas_bio 2055 M1 \n174 h2_scrub_limit R12_WEU 2060 R12_WEU gas_bio 2060 M1 \n175 h2_scrub_limit R12_WEU 2070 R12_WEU gas_bio 2070 M1 \n176 h2_scrub_limit R12_WEU 2080 R12_WEU gas_bio 2080 M1 \n177 h2_scrub_limit R12_WEU 2090 R12_WEU gas_bio 2090 M1 \n178 h2_scrub_limit R12_WEU 2100 R12_WEU gas_bio 2100 M1 \n179 h2_scrub_limit R12_WEU 2110 R12_WEU gas_bio 2110 M1 \n\n value unit \n160 -0.537884 ??? \n161 -0.531719 ??? \n162 -0.525694 ??? \n163 -0.519804 ??? \n164 -0.514044 ??? \n165 -0.508472 ??? \n166 -0.502900 ??? \n167 -0.497564 ??? \n168 -0.492228 ??? \n169 -0.487114 ??? \n170 -0.482000 ??? \n171 -0.482000 ??? \n172 -0.482000 ??? \n173 -0.482000 ??? \n174 -0.482000 ??? \n175 -0.482000 ??? \n176 -0.482000 ??? \n177 -0.482000 ??? \n178 -0.482000 ??? \n179 -0.482000 ??? ", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
relationnode_relyear_relnode_loctechnologyyear_actmodevalueunit
160h2_scrub_limitR12_WEU1990R12_WEUgas_bio1990M1-0.537884???
161h2_scrub_limitR12_WEU1995R12_WEUgas_bio1995M1-0.531719???
162h2_scrub_limitR12_WEU2000R12_WEUgas_bio2000M1-0.525694???
163h2_scrub_limitR12_WEU2005R12_WEUgas_bio2005M1-0.519804???
164h2_scrub_limitR12_WEU2010R12_WEUgas_bio2010M1-0.514044???
165h2_scrub_limitR12_WEU2015R12_WEUgas_bio2015M1-0.508472???
166h2_scrub_limitR12_WEU2020R12_WEUgas_bio2020M1-0.502900???
167h2_scrub_limitR12_WEU2025R12_WEUgas_bio2025M1-0.497564???
168h2_scrub_limitR12_WEU2030R12_WEUgas_bio2030M1-0.492228???
169h2_scrub_limitR12_WEU2035R12_WEUgas_bio2035M1-0.487114???
170h2_scrub_limitR12_WEU2040R12_WEUgas_bio2040M1-0.482000???
171h2_scrub_limitR12_WEU2045R12_WEUgas_bio2045M1-0.482000???
172h2_scrub_limitR12_WEU2050R12_WEUgas_bio2050M1-0.482000???
173h2_scrub_limitR12_WEU2055R12_WEUgas_bio2055M1-0.482000???
174h2_scrub_limitR12_WEU2060R12_WEUgas_bio2060M1-0.482000???
175h2_scrub_limitR12_WEU2070R12_WEUgas_bio2070M1-0.482000???
176h2_scrub_limitR12_WEU2080R12_WEUgas_bio2080M1-0.482000???
177h2_scrub_limitR12_WEU2090R12_WEUgas_bio2090M1-0.482000???
178h2_scrub_limitR12_WEU2100R12_WEUgas_bio2100M1-0.482000???
179h2_scrub_limitR12_WEU2110R12_WEUgas_bio2110M1-0.482000???
\n
" - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df[df[\"node_rel\"]==\"R12_WEU\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 65, - "outputs": [ - { - "data": { - "text/plain": "True" - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "scenario.has_solution()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 6, - "outputs": [ - { - "data": { - "text/plain": "Empty DataFrame\nColumns: [node_loc, technology, year_act, mode, time, value, unit]\nIndex: []", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
node_loctechnologyyear_actmodetimevalueunit
\n
" - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = scenario.par(\"historical_activity\", filters={\"technology\":\"meth_coal\", \"mode\":\"M1\"})\n", - "df" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 3, - "outputs": [], - "source": [ - "mode_pars_list = [x for x in scenario.par_list() if \"mode\" in scenario.idx_sets(x)]\n", - "meth_tecs_list = [\"meth_bio\",\n", - " \"meth_bio_ccs\",\n", - " \"meth_h2\",\n", - " \"meth_coal\",\n", - " \"meth_coal_ccs\"\n", - " \"meth_ng\",\n", - " \"meth_ng_ccs\",\n", - " \"meth_bal\",\n", - " \"meth_imp\",\n", - " \"meth_trd\",\n", - " \"meth_exp\",\n", - " \"meth_t_d\"]\n", - "scenario.remove_solution()\n", - "scenario.check_out()\n", - "for p in mode_pars_list:\n", - " df = scenario.par(p, filters={\"technology\":meth_tecs_list})\n", - " scenario.remove_par(p, df)\n", - "scenario.commit(\"remove M1 mode of meth tecs\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 8, - "outputs": [ - { - "data": { - "text/plain": "array(['eth_bio', 'eth_bio_ccs', 'eth_exp', 'meth_coal', 'meth_coal_ccs',\n 'meth_ng', 'meth_ng_ccs', 'meth_exp'], dtype=object)" - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "scenario.par(\"relation_activity\", filters={\"relation\":\"meth_exp_limit\"})[\"technology\"].unique()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 9, - "outputs": [], - "source": [ - "df = scenario.var(\"ACT\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 13, - "outputs": [], - "source": [ - "df = df[df[\"lvl\"]!=0]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 14, - "outputs": [ - { - "data": { - "text/plain": " node_loc technology year_vtg year_act mode time \\\n26481 R12_AFR steam_cracker_petro 1995 2020 atm_gasoil year \n26499 R12_AFR steam_cracker_petro 2000 2020 vacuum_gasoil year \n26503 R12_AFR steam_cracker_petro 2000 2025 atm_gasoil year \n26518 R12_AFR steam_cracker_petro 2005 2020 vacuum_gasoil year \n26522 R12_AFR steam_cracker_petro 2005 2025 atm_gasoil year \n... ... ... ... ... ... ... \n356981 R12_CHN steam_cracker_petro 2090 2110 vacuum_gasoil year \n356985 R12_CHN steam_cracker_petro 2100 2100 atm_gasoil year \n356991 R12_CHN steam_cracker_petro 2100 2110 vacuum_gasoil year \n356995 R12_CHN steam_cracker_petro 2110 2110 atm_gasoil year \n356996 R12_CHN steam_cracker_petro 2110 2110 vacuum_gasoil year \n\n lvl mrg \n26481 1.720463 0.0 \n26499 3.375000 0.0 \n26503 3.375000 0.0 \n26518 1.125000 0.0 \n26522 1.125000 0.0 \n... ... ... \n356981 123.553838 0.0 \n356985 73.377037 0.0 \n356991 73.377037 0.0 \n356995 139.193010 0.0 \n356996 83.698062 0.0 \n\n[641 rows x 8 columns]", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
node_loctechnologyyear_vtgyear_actmodetimelvlmrg
26481R12_AFRsteam_cracker_petro19952020atm_gasoilyear1.7204630.0
26499R12_AFRsteam_cracker_petro20002020vacuum_gasoilyear3.3750000.0
26503R12_AFRsteam_cracker_petro20002025atm_gasoilyear3.3750000.0
26518R12_AFRsteam_cracker_petro20052020vacuum_gasoilyear1.1250000.0
26522R12_AFRsteam_cracker_petro20052025atm_gasoilyear1.1250000.0
...........................
356981R12_CHNsteam_cracker_petro20902110vacuum_gasoilyear123.5538380.0
356985R12_CHNsteam_cracker_petro21002100atm_gasoilyear73.3770370.0
356991R12_CHNsteam_cracker_petro21002110vacuum_gasoilyear73.3770370.0
356995R12_CHNsteam_cracker_petro21102110atm_gasoilyear139.1930100.0
356996R12_CHNsteam_cracker_petro21102110vacuum_gasoilyear83.6980620.0
\n

641 rows × 8 columns

\n
" - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df[df[\"technology\"]==\"steam_cracker_petro\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 43, - "outputs": [ - { - "data": { - "text/plain": " relation node_rel year_rel node_loc technology year_act \\\n0 meth_exp_limit R12_AFR 2020 R12_AFR meth_bio_ccs 2020 \n1 meth_exp_limit R12_AFR 2025 R12_AFR meth_bio_ccs 2025 \n2 meth_exp_limit R12_RCPA 2020 R12_RCPA meth_bio_ccs 2020 \n3 meth_exp_limit R12_RCPA 2025 R12_RCPA meth_bio_ccs 2025 \n4 meth_exp_limit R12_EEU 2020 R12_EEU meth_bio_ccs 2020 \n5 meth_exp_limit R12_EEU 2025 R12_EEU meth_bio_ccs 2025 \n6 meth_exp_limit R12_FSU 2020 R12_FSU meth_bio_ccs 2020 \n7 meth_exp_limit R12_FSU 2025 R12_FSU meth_bio_ccs 2025 \n8 meth_exp_limit R12_LAM 2020 R12_LAM meth_bio_ccs 2020 \n9 meth_exp_limit R12_LAM 2025 R12_LAM meth_bio_ccs 2025 \n10 meth_exp_limit R12_MEA 2020 R12_MEA meth_bio_ccs 2020 \n11 meth_exp_limit R12_MEA 2025 R12_MEA meth_bio_ccs 2025 \n12 meth_exp_limit R12_NAM 2020 R12_NAM meth_bio_ccs 2020 \n13 meth_exp_limit R12_NAM 2025 R12_NAM meth_bio_ccs 2025 \n14 meth_exp_limit R12_PAO 2020 R12_PAO meth_bio_ccs 2020 \n15 meth_exp_limit R12_PAO 2025 R12_PAO meth_bio_ccs 2025 \n16 meth_exp_limit R12_PAS 2020 R12_PAS meth_bio_ccs 2020 \n17 meth_exp_limit R12_PAS 2025 R12_PAS meth_bio_ccs 2025 \n18 meth_exp_limit R12_SAS 2020 R12_SAS meth_bio_ccs 2020 \n19 meth_exp_limit R12_SAS 2025 R12_SAS meth_bio_ccs 2025 \n20 meth_exp_limit R12_WEU 2020 R12_WEU meth_bio_ccs 2020 \n21 meth_exp_limit R12_WEU 2025 R12_WEU meth_bio_ccs 2025 \n22 meth_exp_limit R12_CHN 2020 R12_CHN meth_bio_ccs 2020 \n23 meth_exp_limit R12_CHN 2025 R12_CHN meth_bio_ccs 2025 \n24 meth_exp_limit R12_AFR 2020 R12_AFR meth_bio_ccs 2020 \n25 meth_exp_limit R12_AFR 2025 R12_AFR meth_bio_ccs 2025 \n26 meth_exp_limit R12_RCPA 2020 R12_RCPA meth_bio_ccs 2020 \n27 meth_exp_limit R12_RCPA 2025 R12_RCPA meth_bio_ccs 2025 \n28 meth_exp_limit R12_EEU 2020 R12_EEU meth_bio_ccs 2020 \n29 meth_exp_limit R12_EEU 2025 R12_EEU meth_bio_ccs 2025 \n30 meth_exp_limit R12_FSU 2020 R12_FSU meth_bio_ccs 2020 \n31 meth_exp_limit R12_FSU 2025 R12_FSU meth_bio_ccs 2025 \n32 meth_exp_limit R12_LAM 2020 R12_LAM meth_bio_ccs 2020 \n33 meth_exp_limit R12_LAM 2025 R12_LAM meth_bio_ccs 2025 \n34 meth_exp_limit R12_MEA 2020 R12_MEA meth_bio_ccs 2020 \n35 meth_exp_limit R12_MEA 2025 R12_MEA meth_bio_ccs 2025 \n36 meth_exp_limit R12_NAM 2020 R12_NAM meth_bio_ccs 2020 \n37 meth_exp_limit R12_NAM 2025 R12_NAM meth_bio_ccs 2025 \n38 meth_exp_limit R12_PAO 2020 R12_PAO meth_bio_ccs 2020 \n39 meth_exp_limit R12_PAO 2025 R12_PAO meth_bio_ccs 2025 \n40 meth_exp_limit R12_PAS 2020 R12_PAS meth_bio_ccs 2020 \n41 meth_exp_limit R12_PAS 2025 R12_PAS meth_bio_ccs 2025 \n42 meth_exp_limit R12_SAS 2020 R12_SAS meth_bio_ccs 2020 \n43 meth_exp_limit R12_SAS 2025 R12_SAS meth_bio_ccs 2025 \n44 meth_exp_limit R12_WEU 2020 R12_WEU meth_bio_ccs 2020 \n45 meth_exp_limit R12_WEU 2025 R12_WEU meth_bio_ccs 2025 \n46 meth_exp_limit R12_CHN 2020 R12_CHN meth_bio_ccs 2020 \n47 meth_exp_limit R12_CHN 2025 R12_CHN meth_bio_ccs 2025 \n\n mode value unit \n0 fuel 1.0 ??? \n1 fuel 1.0 ??? \n2 fuel 1.0 ??? \n3 fuel 1.0 ??? \n4 fuel 1.0 ??? \n5 fuel 1.0 ??? \n6 fuel 1.0 ??? \n7 fuel 1.0 ??? \n8 fuel 1.0 ??? \n9 fuel 1.0 ??? \n10 fuel 1.0 ??? \n11 fuel 1.0 ??? \n12 fuel 1.0 ??? \n13 fuel 1.0 ??? \n14 fuel 1.0 ??? \n15 fuel 1.0 ??? \n16 fuel 1.0 ??? \n17 fuel 1.0 ??? \n18 fuel 1.0 ??? \n19 fuel 1.0 ??? \n20 fuel 1.0 ??? \n21 fuel 1.0 ??? \n22 fuel 1.0 ??? \n23 fuel 1.0 ??? \n24 feedstock 1.0 ??? \n25 feedstock 1.0 ??? \n26 feedstock 1.0 ??? \n27 feedstock 1.0 ??? \n28 feedstock 1.0 ??? \n29 feedstock 1.0 ??? \n30 feedstock 1.0 ??? \n31 feedstock 1.0 ??? \n32 feedstock 1.0 ??? \n33 feedstock 1.0 ??? \n34 feedstock 1.0 ??? \n35 feedstock 1.0 ??? \n36 feedstock 1.0 ??? \n37 feedstock 1.0 ??? \n38 feedstock 1.0 ??? \n39 feedstock 1.0 ??? \n40 feedstock 1.0 ??? \n41 feedstock 1.0 ??? \n42 feedstock 1.0 ??? \n43 feedstock 1.0 ??? \n44 feedstock 1.0 ??? \n45 feedstock 1.0 ??? \n46 feedstock 1.0 ??? \n47 feedstock 1.0 ??? ", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
relationnode_relyear_relnode_loctechnologyyear_actmodevalueunit
0meth_exp_limitR12_AFR2020R12_AFRmeth_bio_ccs2020fuel1.0???
1meth_exp_limitR12_AFR2025R12_AFRmeth_bio_ccs2025fuel1.0???
2meth_exp_limitR12_RCPA2020R12_RCPAmeth_bio_ccs2020fuel1.0???
3meth_exp_limitR12_RCPA2025R12_RCPAmeth_bio_ccs2025fuel1.0???
4meth_exp_limitR12_EEU2020R12_EEUmeth_bio_ccs2020fuel1.0???
5meth_exp_limitR12_EEU2025R12_EEUmeth_bio_ccs2025fuel1.0???
6meth_exp_limitR12_FSU2020R12_FSUmeth_bio_ccs2020fuel1.0???
7meth_exp_limitR12_FSU2025R12_FSUmeth_bio_ccs2025fuel1.0???
8meth_exp_limitR12_LAM2020R12_LAMmeth_bio_ccs2020fuel1.0???
9meth_exp_limitR12_LAM2025R12_LAMmeth_bio_ccs2025fuel1.0???
10meth_exp_limitR12_MEA2020R12_MEAmeth_bio_ccs2020fuel1.0???
11meth_exp_limitR12_MEA2025R12_MEAmeth_bio_ccs2025fuel1.0???
12meth_exp_limitR12_NAM2020R12_NAMmeth_bio_ccs2020fuel1.0???
13meth_exp_limitR12_NAM2025R12_NAMmeth_bio_ccs2025fuel1.0???
14meth_exp_limitR12_PAO2020R12_PAOmeth_bio_ccs2020fuel1.0???
15meth_exp_limitR12_PAO2025R12_PAOmeth_bio_ccs2025fuel1.0???
16meth_exp_limitR12_PAS2020R12_PASmeth_bio_ccs2020fuel1.0???
17meth_exp_limitR12_PAS2025R12_PASmeth_bio_ccs2025fuel1.0???
18meth_exp_limitR12_SAS2020R12_SASmeth_bio_ccs2020fuel1.0???
19meth_exp_limitR12_SAS2025R12_SASmeth_bio_ccs2025fuel1.0???
20meth_exp_limitR12_WEU2020R12_WEUmeth_bio_ccs2020fuel1.0???
21meth_exp_limitR12_WEU2025R12_WEUmeth_bio_ccs2025fuel1.0???
22meth_exp_limitR12_CHN2020R12_CHNmeth_bio_ccs2020fuel1.0???
23meth_exp_limitR12_CHN2025R12_CHNmeth_bio_ccs2025fuel1.0???
24meth_exp_limitR12_AFR2020R12_AFRmeth_bio_ccs2020feedstock1.0???
25meth_exp_limitR12_AFR2025R12_AFRmeth_bio_ccs2025feedstock1.0???
26meth_exp_limitR12_RCPA2020R12_RCPAmeth_bio_ccs2020feedstock1.0???
27meth_exp_limitR12_RCPA2025R12_RCPAmeth_bio_ccs2025feedstock1.0???
28meth_exp_limitR12_EEU2020R12_EEUmeth_bio_ccs2020feedstock1.0???
29meth_exp_limitR12_EEU2025R12_EEUmeth_bio_ccs2025feedstock1.0???
30meth_exp_limitR12_FSU2020R12_FSUmeth_bio_ccs2020feedstock1.0???
31meth_exp_limitR12_FSU2025R12_FSUmeth_bio_ccs2025feedstock1.0???
32meth_exp_limitR12_LAM2020R12_LAMmeth_bio_ccs2020feedstock1.0???
33meth_exp_limitR12_LAM2025R12_LAMmeth_bio_ccs2025feedstock1.0???
34meth_exp_limitR12_MEA2020R12_MEAmeth_bio_ccs2020feedstock1.0???
35meth_exp_limitR12_MEA2025R12_MEAmeth_bio_ccs2025feedstock1.0???
36meth_exp_limitR12_NAM2020R12_NAMmeth_bio_ccs2020feedstock1.0???
37meth_exp_limitR12_NAM2025R12_NAMmeth_bio_ccs2025feedstock1.0???
38meth_exp_limitR12_PAO2020R12_PAOmeth_bio_ccs2020feedstock1.0???
39meth_exp_limitR12_PAO2025R12_PAOmeth_bio_ccs2025feedstock1.0???
40meth_exp_limitR12_PAS2020R12_PASmeth_bio_ccs2020feedstock1.0???
41meth_exp_limitR12_PAS2025R12_PASmeth_bio_ccs2025feedstock1.0???
42meth_exp_limitR12_SAS2020R12_SASmeth_bio_ccs2020feedstock1.0???
43meth_exp_limitR12_SAS2025R12_SASmeth_bio_ccs2025feedstock1.0???
44meth_exp_limitR12_WEU2020R12_WEUmeth_bio_ccs2020feedstock1.0???
45meth_exp_limitR12_WEU2025R12_WEUmeth_bio_ccs2025feedstock1.0???
46meth_exp_limitR12_CHN2020R12_CHNmeth_bio_ccs2020feedstock1.0???
47meth_exp_limitR12_CHN2025R12_CHNmeth_bio_ccs2025feedstock1.0???
\n
" - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = scenario.par(\"relation_activity\", filters={\"technology\":\"meth_bio_ccs\", \"year_act\":[2020, 2025]})\n", - "df" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 44, - "outputs": [], - "source": [ - "scenario.check_out()\n", - "scenario.remove_par(\"relation_activity\", df)\n", - "scenario.commit(\"remove 2020, 2025 meth_bio_ccs pars\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 30, - "outputs": [], - "source": [ - "import copy\n", - "paramList_tec = [x for x in scenario.par_list() if (\"technology\" in scenario.idx_sets(x))]\n", - "par_dict_meth = {}\n", - "for p in paramList_tec:\n", - " df = scenario.par(p, filters={\"technology\": \"meth_h2\"})\n", - " if df.index.size:\n", - " par_dict_meth[p] = df" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 5, - "outputs": [], - "source": [ - "import copy\n", - "paramList_tec_mode = [x for x in scenario.par_list() if (\"mode\" in scenario.idx_sets(x))]\n", - "par_dict_meth_mode = {}\n", - "for p in paramList_tec_mode:\n", - " df = scenario.par(p, filters={\"technology\": \"meth_imp\"})\n", - " if df.index.size:\n", - " par_dict_meth_mode[p] = df" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 22, - "outputs": [], - "source": [ - "df = scenario.par(\"relation_activity\", filters= {\"technology\":\"meth_coal\"})" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 27, - "outputs": [], - "source": [ - "df[df.relation == \"meth_exp_limit\"].to_excel(\"meth_exp_limit_coal.xlsx\", index=False)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 32, - "outputs": [ - { - "data": { - "text/plain": " relation node_rel year_rel node_loc technology \\\n0 CO2_Emission R12_AFR 2030 R12_AFR meth_h2 \n1 CO2_Emission R12_AFR 2035 R12_AFR meth_h2 \n2 CO2_Emission R12_AFR 2040 R12_AFR meth_h2 \n3 CO2_Emission R12_AFR 2045 R12_AFR meth_h2 \n4 CO2_Emission R12_AFR 2050 R12_AFR meth_h2 \n.. ... ... ... ... ... \n619 CO2_PtX_trans_disp_split R12_WEU 2070 R12_WEU meth_h2 \n620 CO2_PtX_trans_disp_split R12_WEU 2080 R12_WEU meth_h2 \n621 CO2_PtX_trans_disp_split R12_WEU 2090 R12_WEU meth_h2 \n622 CO2_PtX_trans_disp_split R12_WEU 2100 R12_WEU meth_h2 \n623 CO2_PtX_trans_disp_split R12_WEU 2110 R12_WEU meth_h2 \n\n year_act mode value unit \n0 2030 fuel 0.549 ??? \n1 2035 fuel 0.549 ??? \n2 2040 fuel 0.549 ??? \n3 2045 fuel 0.549 ??? \n4 2050 fuel 0.549 ??? \n.. ... ... ... ... \n619 2070 feedstock -0.549 ??? \n620 2080 feedstock -0.549 ??? \n621 2090 feedstock -0.549 ??? \n622 2100 feedstock -0.549 ??? \n623 2110 feedstock -0.549 ??? \n\n[624 rows x 9 columns]", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
relationnode_relyear_relnode_loctechnologyyear_actmodevalueunit
0CO2_EmissionR12_AFR2030R12_AFRmeth_h22030fuel0.549???
1CO2_EmissionR12_AFR2035R12_AFRmeth_h22035fuel0.549???
2CO2_EmissionR12_AFR2040R12_AFRmeth_h22040fuel0.549???
3CO2_EmissionR12_AFR2045R12_AFRmeth_h22045fuel0.549???
4CO2_EmissionR12_AFR2050R12_AFRmeth_h22050fuel0.549???
..............................
619CO2_PtX_trans_disp_splitR12_WEU2070R12_WEUmeth_h22070feedstock-0.549???
620CO2_PtX_trans_disp_splitR12_WEU2080R12_WEUmeth_h22080feedstock-0.549???
621CO2_PtX_trans_disp_splitR12_WEU2090R12_WEUmeth_h22090feedstock-0.549???
622CO2_PtX_trans_disp_splitR12_WEU2100R12_WEUmeth_h22100feedstock-0.549???
623CO2_PtX_trans_disp_splitR12_WEU2110R12_WEUmeth_h22110feedstock-0.549???
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624 rows × 9 columns

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" - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "par_dict_meth.get(\"relation_activity\")\n", - "#par_dict_meth[\"fix_cost\"]#.year_act.unique()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 20, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "initial_activity_up 2025\n", - "initial_activity_lo 2025\n", - "relation_activity 2030\n", - "ref_activity 2025\n" - ] - } - ], - "source": [ - "for k, vs in par_dict_meth.items():\n", - " if len(vs[\"value\"].unique()) != 1:\n", - " print(k, vs[\"year_act\"].min())" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 37, - "outputs": [], - "source": [ - "df = df[(df.technology == \"wind_ppl\") & (df.year_act == 2050)]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 38, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\maczek\\AppData\\Local\\Temp\\ipykernel_10940\\856668087.py:1: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df[\"value\"] = 0.1\n" - ] - } - ], - "source": [ - "df[\"value\"] = 0.1" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 41, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\maczek\\AppData\\Local\\Temp\\ipykernel_10940\\1042674758.py:1: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df[\"technology\"] = \"meth_h2\"\n", - "C:\\Users\\maczek\\AppData\\Local\\Temp\\ipykernel_10940\\1042674758.py:2: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df[\"mode\"] = \"feedstock\"\n" - ] - } - ], - "source": [ - "df[\"technology\"] = \"meth_h2\"\n", - "df[\"mode\"] = \"feedstock\"\n", - "df.to_excel(\"meth_h2_bound_test.xlsx\", index=False)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 42, - "outputs": [], - "source": [ - "#df_tot = pd.read_clipboard(sep=\",\").T\n", - "#df[\"test\"] = df.index\n", - "df_tot = df_tot.reset_index()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 43, - "outputs": [], - "source": [ - "#df_bio = pd.read_clipboard(sep=\",\").T\n", - "#df[\"test\"] = df.index\n", - "df_bio = df_bio.reset_index()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 44, - "outputs": [], - "source": [ - "#df_h2 = pd.read_clipboard(sep=\",\").T\n", - "#df[\"test\"] = df.index\n", - "df_h2 = df_h2.reset_index()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 52, - "outputs": [ - { - "data": { - "text/plain": " index\n3 gas_cc_ccs\n6 meth_ng\n7 meth_ng_ccs\n8 h2_smr\n9 h2_smr_ccs\n23 gas_NH3_ccs", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
index
3gas_cc_ccs
6meth_ng
7meth_ng_ccs
8h2_smr
9h2_smr_ccs
23gas_NH3_ccs
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" - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_h2[~df_h2[\"index\"].isin(df_bio[\"index\"])]\n", - "df_bio[~df_bio[\"index\"].isin(df_h2[\"index\"])]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 64, - "outputs": [], - "source": [ - "import pyperclip" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 82, - "outputs": [ - { - "data": { - "text/plain": " index\n0 gas_ppl\n2 gas_cc\n3 gas_cc_ccs\n6 meth_ng\n7 meth_ng_ccs\n8 h2_smr\n9 h2_smr_ccs\n12 dri_steel\n13 eaf_steel\n14 furnace_gas_steel\n15 hp_gas_steel\n16 furnace_gas_cement\n17 hp_gas_cement\n18 furnace_gas_aluminum\n19 hp_gas_aluminum\n20 furnace_gas_petro\n21 hp_gas_petro\n22 gas_NH3\n23 gas_NH3_ccs\n24 furnace_gas_refining\n25 hp_gas_refining", - "text/html": "
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index
0gas_ppl
2gas_cc
3gas_cc_ccs
6meth_ng
7meth_ng_ccs
8h2_smr
9h2_smr_ccs
12dri_steel
13eaf_steel
14furnace_gas_steel
15hp_gas_steel
16furnace_gas_cement
17hp_gas_cement
18furnace_gas_aluminum
19hp_gas_aluminum
20furnace_gas_petro
21hp_gas_petro
22gas_NH3
23gas_NH3_ccs
24furnace_gas_refining
25hp_gas_refining
\n
" - }, - "execution_count": 82, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_bio[~df_bio[\"index\"].isin(df_tot[\"index\"])]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 73, - "outputs": [], - "source": [ - "x= list(df_bio[~df_bio[\"index\"].isin(df_tot[\"index\"])][\"index\"].values)\n", - "pyperclip.copy(\",\\n\".join(x))" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 8, - "outputs": [ - { - "data": { - "text/plain": "'2023-04-19 10:12:12.0'" - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "scenario.last_update()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 15, - "outputs": [ - { - "data": { - "text/plain": " model scenario scheme is_default is_locked \\\n6379 MESSAGEix-Materials NoPolicy_no_petro MESSAGE 1 0 \n\n cre_user cre_date upd_user upd_date lock_user \\\n6379 maczek 2023-05-03 23:10:38.000000 None None None \n\n lock_date annotation version \n6379 None clone Scenario from 'SHAPE_SSP2_v4.1.8|baselin... 1 ", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
modelscenarioschemeis_defaultis_lockedcre_usercre_dateupd_userupd_datelock_userlock_dateannotationversion
6379MESSAGEix-MaterialsNoPolicy_no_petroMESSAGE10maczek2023-05-03 23:10:38.000000NoneNoneNoneNoneclone Scenario from 'SHAPE_SSP2_v4.1.8|baselin...1
\n
" - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = mp.scenario_list()\n", - "df[df[\"scenario\"].str.contains(\"no_petro\")]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 9, - "outputs": [], - "source": [ - "df_gro = scenario.par(\"growth_activity_up\")\n", - "df_ini = scenario.par(\"initial_activity_up\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 11, - "outputs": [], - "source": [ - "df_hist = scenario.par(\"historical_activity\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 16, - "outputs": [], - "source": [ - "hist_list = df_hist[df_hist[\"year_act\"] == 2015][\"technology\"].unique()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 17, - "outputs": [], - "source": [ - "ini_list = df_ini[\"technology\"].unique()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 21, - "outputs": [ - { - "data": { - "text/plain": "array(['furnace_gas_cement', 'furnace_coal_refining',\n 'furnace_ethanol_steel', 'hp_elec_refining', 'dheat_refining',\n 'hp_gas_refining', 'furnace_biomass_cement', 'furnace_foil_petro',\n 'furnace_gas_aluminum', 'fc_h2_aluminum', 'hp_gas_steel',\n 'dheat_petro', 'furnace_coal_petro', 'furnace_coal_steel',\n 'furnace_coke_petro', 'furnace_methanol_petro',\n 'furnace_h2_aluminum', 'furnace_methanol_aluminum',\n 'furnace_gas_petro', 'furnace_h2_petro',\n 'furnace_methanol_refining', 'furnace_gas_refining',\n 'furnace_biomass_petro', 'furnace_elec_steel', 'hp_elec_aluminum',\n 'furnace_loil_refining', 'furnace_methanol_cement',\n 'furnace_methanol_steel', 'solar_petro', 'furnace_loil_cement',\n 'furnace_biomass_aluminum', 'furnace_elec_aluminum',\n 'dheat_aluminum', 'fc_h2_refining', 'furnace_elec_refining',\n 'hp_gas_petro', 'dheat_steel', 'furnace_elec_petro',\n 'furnace_loil_petro', 'furnace_ethanol_refining',\n 'furnace_foil_refining', 'furnace_foil_aluminum', 'solar_aluminum',\n 'furnace_foil_steel', 'solar_refining', 'furnace_foil_cement',\n 'furnace_coke_refining', 'furnace_coal_aluminum',\n 'furnace_gas_steel', 'furnace_coal_cement', 'furnace_h2_steel',\n 'fc_h2_steel', 'furnace_elec_cement', 'furnace_ethanol_aluminum',\n 'solar_steel', 'fc_h2_petro', 'furnace_ethanol_cement',\n 'hp_elec_petro', 'furnace_biomass_refining',\n 'furnace_biomass_steel', 'furnace_loil_steel', 'furnace_h2_cement',\n 'furnace_ethanol_petro', 'hp_elec_steel', 'hp_gas_aluminum',\n 'furnace_h2_refining', 'furnace_loil_aluminum'], dtype=object)" - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_gro[(~df_gro[\"technology\"].isin(hist_list)) & (~df_gro[\"technology\"].isin(ini_list))][\"technology\"].unique()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## add ini act up to MTO" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 3, - "outputs": [], - "source": [ - "import ixmp as ix\n", - "import message_ix\n", - "mp = ix.Platform(\"ixmp_dev\")\n", - "\n", - "#scenario = message_ix.Scenario(mp, model=\"MESSAGEix-Materials\", scenario=\"NoPolicy_petro_thesis_2\")#, version=64)\n", - "scenario = message_ix.Scenario(mp, model=\"MESSAGEix-Materials\", scenario=\"2.0deg_no_SDGs_petro_thesis_1_macro\")#, version=64)\n", - "#scenario = message_ix.Scenario(mp, model=\"MESSAGEix-Materials\", scenario=\"2.0deg_petro_thesis_2_macro\")#, version=64)\n", - "#scenario = message_ix.Scenario(mp, model=\"MESSAGEix-Materials\", scenario=\"baseline_DEFAULT_rebase2\")\n", - "#scenario = message_ix.Scenario(mp, model=\"MESSAGEix-Materials\", scenario=\"baseline_VK_Transport-Materials_VK\")#, version=37)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 7, - "outputs": [ - { - "data": { - "text/plain": " model scenario scheme \\\n6242 MESSAGEix-Materials 2.0deg_no_SDGs_petro_thesis_1_macro MESSAGE \n6373 MESSAGEix-Materials NoPolicy_no_SDGs_petro_thesis_1_macro MESSAGE \n\n is_default is_locked cre_user cre_date \\\n6242 1 0 maczek 2023-04-25 10:15:05.000000 \n6373 1 0 maczek 2023-04-24 22:13:46.000000 \n\n upd_user upd_date lock_user lock_date \\\n6242 Florian Maczek 2023-05-02 13:04:08.000000 None None \n6373 maczek 2023-04-24 22:23:46.000000 None None \n\n annotation version \n6242 clone Scenario from 'MESSAGEix-Materials|NoPol... 3 \n6373 clone Scenario from 'MESSAGEix-Materials|NoPol... 2 ", - "text/html": "
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modelscenarioschemeis_defaultis_lockedcre_usercre_dateupd_userupd_datelock_userlock_dateannotationversion
6242MESSAGEix-Materials2.0deg_no_SDGs_petro_thesis_1_macroMESSAGE10maczek2023-04-25 10:15:05.000000Florian Maczek2023-05-02 13:04:08.000000NoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...3
6373MESSAGEix-MaterialsNoPolicy_no_SDGs_petro_thesis_1_macroMESSAGE10maczek2023-04-24 22:13:46.000000maczek2023-04-24 22:23:46.000000NoneNoneclone Scenario from 'MESSAGEix-Materials|NoPol...2
\n
" - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = mp.scenario_list()\n", - "df[df[\"scenario\"].str.contains(\"thesis_1_macro\")]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 13, - "outputs": [ - { - "data": { - "text/plain": " model scenario \\\n4944 GENIE_SSP2_v4.1.7_test EN_NPi2020-DAC-MP-new-gov-less_1000 \n4945 GENIE_SSP2_v4.1.7_test EN_NPi2020-DAC-MP-new-gov-less_1000_step1 \n4946 GENIE_SSP2_v4.1.7_test EN_NPi2020-DAC-MP-new-gov-less_1000_step2 \n5258 GENIE_SSP2_v4.1.7_test NPi2020-DAC-MP-new-gov-less \n\n scheme is_default is_locked cre_user cre_date \\\n4944 MESSAGE 1 0 unlu 2021-10-21 11:10:07.000000 \n4945 MESSAGE 1 0 unlu 2021-10-27 09:34:30.000000 \n4946 MESSAGE 1 0 unlu 2021-10-21 10:47:16.000000 \n5258 MESSAGE 1 0 unlu 2021-10-21 10:04:49.000000 \n\n upd_user upd_date lock_user lock_date \\\n4944 unlu 2021-10-21 11:33:34.000000 None None \n4945 None None None None \n4946 unlu 2021-10-21 10:54:27.000000 None None \n5258 None None None None \n\n annotation version \n4944 clone Scenario from 'GENIE_SSP2_v4.1.7_test|EN... 1 \n4945 clone Scenario from 'GENIE_SSP2_v4.1.7_test|NP... 2 \n4946 clone Scenario from 'GENIE_SSP2_v4.1.7_test|EN... 1 \n5258 clone Scenario from 'GENIE_SSP2_v4.1.7_test|NP... 3 ", - "text/html": "
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modelscenarioschemeis_defaultis_lockedcre_usercre_dateupd_userupd_datelock_userlock_dateannotationversion
4944GENIE_SSP2_v4.1.7_testEN_NPi2020-DAC-MP-new-gov-less_1000MESSAGE10unlu2021-10-21 11:10:07.000000unlu2021-10-21 11:33:34.000000NoneNoneclone Scenario from 'GENIE_SSP2_v4.1.7_test|EN...1
4945GENIE_SSP2_v4.1.7_testEN_NPi2020-DAC-MP-new-gov-less_1000_step1MESSAGE10unlu2021-10-27 09:34:30.000000NoneNoneNoneNoneclone Scenario from 'GENIE_SSP2_v4.1.7_test|NP...2
4946GENIE_SSP2_v4.1.7_testEN_NPi2020-DAC-MP-new-gov-less_1000_step2MESSAGE10unlu2021-10-21 10:47:16.000000unlu2021-10-21 10:54:27.000000NoneNoneclone Scenario from 'GENIE_SSP2_v4.1.7_test|EN...1
5258GENIE_SSP2_v4.1.7_testNPi2020-DAC-MP-new-gov-lessMESSAGE10unlu2021-10-21 10:04:49.000000NoneNoneNoneNoneclone Scenario from 'GENIE_SSP2_v4.1.7_test|NP...3
\n
" - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df[df[\"scenario\"].str.contains(\"2020-DAC-MP-new-gov-less\")]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 14, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "This Scenario has a solution, use `Scenario.remove_solution()` or `Scenario.clone(..., keep_solution=False)`\n" - ] - }, - { - "data": { - "text/plain": "0 CF4_TCE\n1 CH4_TCE\n2 CH4n_TCE\n3 CH4o_TCE\n4 CH4s_TCE\n ... \n628 BIO00GHG600_pcapita\n629 BIO00GHG1000_pcapita\n630 BIO00GHG1500_pcapita\n631 BIO00GHG2000_pcapita\n632 BIO00GHG3000_pcapita\nLength: 633, dtype: object" - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "scenario = message_ix.Scenario(mp, model=\"GENIE_SSP2_v4.1.7_test\", scenario=\"EN_NPi2020-DAC-MP-new-gov-less_1000\")\n", - "scenario.set(\"technology\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 6, - "outputs": [ - { - "data": { - "text/plain": " relation node_rel year_rel value unit\n0 CO2_Emission R12_AFR 1990 0.0 ???\n1 CO2_Emission R12_AFR 1995 0.0 ???\n2 CO2_Emission R12_AFR 2000 0.0 ???\n3 CO2_Emission R12_AFR 2005 0.0 ???\n4 CO2_Emission R12_AFR 2010 0.0 ???\n.. ... ... ... ... ...\n235 CO2_Emission R12_CHN 2015 0.0 ???\n236 CO2_Emission R12_CHN 2025 0.0 ???\n237 CO2_Emission R12_CHN 2035 0.0 ???\n238 CO2_Emission R12_CHN 2045 0.0 ???\n239 CO2_Emission R12_CHN 2055 0.0 ???\n\n[240 rows x 5 columns]", - "text/html": "
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relationnode_relyear_relvalueunit
0CO2_EmissionR12_AFR19900.0???
1CO2_EmissionR12_AFR19950.0???
2CO2_EmissionR12_AFR20000.0???
3CO2_EmissionR12_AFR20050.0???
4CO2_EmissionR12_AFR20100.0???
..................
235CO2_EmissionR12_CHN20150.0???
236CO2_EmissionR12_CHN20250.0???
237CO2_EmissionR12_CHN20350.0???
238CO2_EmissionR12_CHN20450.0???
239CO2_EmissionR12_CHN20550.0???
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240 rows × 5 columns

\n
" - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "scenario.par(\"relation_lower\", filters={\"relation\":\"CO2_Emission\"})" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 7, - "outputs": [ - { - "data": { - "text/plain": "True" - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "scenario.has_solution()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 12, - "outputs": [ - { - "data": { - "text/plain": " model scenario \\\n633 COMMIT_SSP2_v1 baseline_DEFAULT \n994 Carbon_pool_SSP2_v4.1.7 baseline_DEFAULT \n1175 ENGAGE_SSP2_test baseline_DEFAULT \n1354 ENGAGE_SSP2_v2 baseline_DEFAULT \n1549 ENGAGE_SSP2_v4 baseline_DEFAULT \n... ... ... \n7769 tmp_test baseline_DEFAULT_ENGAGEv10.8_Sec6_2_3b \n7770 tmp_test baseline_DEFAULT_ENGAGEv10.8_Sec6_2_3c \n7771 tmp_test baseline_DEFAULT_ENGAGEv10.8_Sec6_2_3d \n7772 tmp_test baseline_DEFAULT_ENGAGEv10.8_Sec6_clean \n7773 tmp_test baseline_DEFAULT_ENGAGEv10.8_Sec7 \n\n scheme is_default is_locked cre_user cre_date \\\n633 MESSAGE 1 0 unlu 2019-12-05 10:37:20.000000 \n994 MESSAGE 1 0 fricko 2021-10-08 09:30:23.000000 \n1175 MESSAGE 1 0 fricko 2020-03-27 14:07:11.000000 \n1354 MESSAGE 1 0 fricko 2019-10-31 16:25:43.000000 \n1549 MESSAGE 1 0 fricko 2020-04-16 22:03:18.000000 \n... ... ... ... ... ... \n7769 MESSAGE 1 0 fricko 2022-04-22 11:00:00.000000 \n7770 MESSAGE 1 0 fricko 2022-04-25 10:51:29.000000 \n7771 MESSAGE 1 0 fricko 2022-04-26 15:25:45.000000 \n7772 MESSAGE 1 0 fricko 2022-04-04 13:37:38.000000 \n7773 MESSAGE 1 0 fricko 2022-04-04 11:32:35.000000 \n\n upd_user upd_date lock_user lock_date \\\n633 None None None None \n994 None None None None \n1175 None None None None \n1354 None None None None \n1549 None None None None \n... ... ... ... ... \n7769 None None None None \n7770 None None None None \n7771 None None None None \n7772 None None None None \n7773 None None None None \n\n annotation version \n633 clone Scenario from 'SSP2|baseline_DEFAULT', v... 35 \n994 clone Scenario from 'SSP2_test|baseline_DEFAUL... 15 \n1175 clone Scenario from 'SSP2_test|baseline_DEFAUL... 4 \n1354 clone Scenario from 'SSP2|baseline_DEFAULT', v... 2 \n1549 clone Scenario from 'SSP2_test|baseline_DEFAUL... 9 \n... ... ... \n7769 clone Scenario from 'SSP2_test|baseline_DEFAUL... 35 \n7770 clone Scenario from 'SSP2_test|baseline_DEFAUL... 29 \n7771 clone Scenario from 'SSP2_test|baseline_DEFAUL... 22 \n7772 clone Scenario from 'SSP2_test|baseline_DEFAUL... 1 \n7773 clone Scenario from 'SSP2_test|baseline_DEFAUL... 1 \n\n[498 rows x 13 columns]", - "text/html": "
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modelscenarioschemeis_defaultis_lockedcre_usercre_dateupd_userupd_datelock_userlock_dateannotationversion
633COMMIT_SSP2_v1baseline_DEFAULTMESSAGE10unlu2019-12-05 10:37:20.000000NoneNoneNoneNoneclone Scenario from 'SSP2|baseline_DEFAULT', v...35
994Carbon_pool_SSP2_v4.1.7baseline_DEFAULTMESSAGE10fricko2021-10-08 09:30:23.000000NoneNoneNoneNoneclone Scenario from 'SSP2_test|baseline_DEFAUL...15
1175ENGAGE_SSP2_testbaseline_DEFAULTMESSAGE10fricko2020-03-27 14:07:11.000000NoneNoneNoneNoneclone Scenario from 'SSP2_test|baseline_DEFAUL...4
1354ENGAGE_SSP2_v2baseline_DEFAULTMESSAGE10fricko2019-10-31 16:25:43.000000NoneNoneNoneNoneclone Scenario from 'SSP2|baseline_DEFAULT', v...2
1549ENGAGE_SSP2_v4baseline_DEFAULTMESSAGE10fricko2020-04-16 22:03:18.000000NoneNoneNoneNoneclone Scenario from 'SSP2_test|baseline_DEFAUL...9
..........................................
7769tmp_testbaseline_DEFAULT_ENGAGEv10.8_Sec6_2_3bMESSAGE10fricko2022-04-22 11:00:00.000000NoneNoneNoneNoneclone Scenario from 'SSP2_test|baseline_DEFAUL...35
7770tmp_testbaseline_DEFAULT_ENGAGEv10.8_Sec6_2_3cMESSAGE10fricko2022-04-25 10:51:29.000000NoneNoneNoneNoneclone Scenario from 'SSP2_test|baseline_DEFAUL...29
7771tmp_testbaseline_DEFAULT_ENGAGEv10.8_Sec6_2_3dMESSAGE10fricko2022-04-26 15:25:45.000000NoneNoneNoneNoneclone Scenario from 'SSP2_test|baseline_DEFAUL...22
7772tmp_testbaseline_DEFAULT_ENGAGEv10.8_Sec6_cleanMESSAGE10fricko2022-04-04 13:37:38.000000NoneNoneNoneNoneclone Scenario from 'SSP2_test|baseline_DEFAUL...1
7773tmp_testbaseline_DEFAULT_ENGAGEv10.8_Sec7MESSAGE10fricko2022-04-04 11:32:35.000000NoneNoneNoneNoneclone Scenario from 'SSP2_test|baseline_DEFAUL...1
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498 rows × 13 columns

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" - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#df = mp.scenario_list()\n", - "df[df[\"scenario\"].str.contains(\"baseline_DEFAULT\")]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 4, - "outputs": [], - "source": [ - "df = scenario.par(\"growth_activity_lo\", filters={\"technology\":\"furnace_gas_petro\"})\n", - "df = df[df[\"year_act\"]==2030]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 7, - "outputs": [ - { - "data": { - "text/plain": " node_loc technology year_act time value unit\n0 R12_AFR furnace_gas_petro 2025 year -0.05 t\n12 R12_RCPA furnace_gas_petro 2025 year -0.05 t\n24 R12_EEU furnace_gas_petro 2025 year -0.05 t\n36 R12_FSU furnace_gas_petro 2025 year -0.05 t\n48 R12_LAM furnace_gas_petro 2025 year -0.05 t\n60 R12_MEA furnace_gas_petro 2025 year -0.05 t\n72 R12_NAM furnace_gas_petro 2025 year -0.05 t\n84 R12_PAO furnace_gas_petro 2025 year -0.05 t\n96 R12_PAS furnace_gas_petro 2025 year -0.05 t\n108 R12_SAS furnace_gas_petro 2025 year -0.05 t\n120 R12_WEU furnace_gas_petro 2025 year -0.05 t\n132 R12_CHN furnace_gas_petro 2025 year -0.05 t", - "text/html": "
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node_loctechnologyyear_acttimevalueunit
0R12_AFRfurnace_gas_petro2025year-0.05t
12R12_RCPAfurnace_gas_petro2025year-0.05t
24R12_EEUfurnace_gas_petro2025year-0.05t
36R12_FSUfurnace_gas_petro2025year-0.05t
48R12_LAMfurnace_gas_petro2025year-0.05t
60R12_MEAfurnace_gas_petro2025year-0.05t
72R12_NAMfurnace_gas_petro2025year-0.05t
84R12_PAOfurnace_gas_petro2025year-0.05t
96R12_PASfurnace_gas_petro2025year-0.05t
108R12_SASfurnace_gas_petro2025year-0.05t
120R12_WEUfurnace_gas_petro2025year-0.05t
132R12_CHNfurnace_gas_petro2025year-0.05t
\n
" - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df[\"year_act\"] = 2025\n", - "df" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 9, - "outputs": [ - { - "data": { - "text/plain": "{'var_cost': node_loc technology year_vtg year_act mode time value unit\n 0 R12_AFR h2_elec 2010 2010 feedstock year 4.0 USD/GWa\n 1 R12_AFR h2_elec 2010 2015 feedstock year 4.0 USD/GWa\n 2 R12_AFR h2_elec 2010 2020 feedstock year 4.0 USD/GWa\n 3 R12_AFR h2_elec 2010 2025 feedstock year 4.0 USD/GWa\n 4 R12_AFR h2_elec 2010 2030 feedstock year 4.0 USD/GWa\n ... ... ... ... ... ... ... ... ...\n 1723 R12_CHN h2_elec 2090 2100 feedstock year 4.0 USD/GWa\n 1724 R12_CHN h2_elec 2090 2110 feedstock year 4.0 USD/GWa\n 1725 R12_CHN h2_elec 2100 2100 feedstock year 4.0 USD/GWa\n 1726 R12_CHN h2_elec 2100 2110 feedstock year 4.0 USD/GWa\n 1727 R12_CHN h2_elec 2110 2110 feedstock year 4.0 USD/GWa\n \n [1728 rows x 8 columns],\n 'output': node_loc technology year_vtg year_act mode node_dest commodity \\\n 0 R12_AFR h2_elec 2010 2010 feedstock R12_AFR hydrogen \n 1 R12_AFR h2_elec 2010 2015 feedstock R12_AFR hydrogen \n 2 R12_AFR h2_elec 2010 2020 feedstock R12_AFR hydrogen \n 3 R12_AFR h2_elec 2010 2025 feedstock R12_AFR hydrogen \n 4 R12_AFR h2_elec 2010 2030 feedstock R12_AFR hydrogen \n ... ... ... ... ... ... ... ... \n 1723 R12_CHN h2_elec 2090 2100 feedstock R12_CHN hydrogen \n 1724 R12_CHN h2_elec 2090 2110 feedstock R12_CHN hydrogen \n 1725 R12_CHN h2_elec 2100 2100 feedstock R12_CHN hydrogen \n 1726 R12_CHN h2_elec 2100 2110 feedstock R12_CHN hydrogen \n 1727 R12_CHN h2_elec 2110 2110 feedstock R12_CHN hydrogen \n \n level time time_dest value unit \n 0 secondary year year 1.0 GWa \n 1 secondary year year 1.0 GWa \n 2 secondary year year 1.0 GWa \n 3 secondary year year 1.0 GWa \n 4 secondary year year 1.0 GWa \n ... ... ... ... ... ... \n 1723 secondary year year 1.0 GWa \n 1724 secondary year year 1.0 GWa \n 1725 secondary year year 1.0 GWa \n 1726 secondary year year 1.0 GWa \n 1727 secondary year year 1.0 GWa \n \n [1728 rows x 12 columns],\n 'input': node_loc technology year_vtg year_act mode node_origin commodity \\\n 0 R12_AFR h2_elec 2010 2010 feedstock R12_AFR electr \n 1 R12_AFR h2_elec 2010 2015 feedstock R12_AFR electr \n 2 R12_AFR h2_elec 2010 2020 feedstock R12_AFR electr \n 3 R12_AFR h2_elec 2010 2025 feedstock R12_AFR electr \n 4 R12_AFR h2_elec 2010 2030 feedstock R12_AFR electr \n ... ... ... ... ... ... ... ... \n 1723 R12_CHN h2_elec 2090 2100 feedstock R12_CHN electr \n 1724 R12_CHN h2_elec 2090 2110 feedstock R12_CHN electr \n 1725 R12_CHN h2_elec 2100 2100 feedstock R12_CHN electr \n 1726 R12_CHN h2_elec 2100 2110 feedstock R12_CHN electr \n 1727 R12_CHN h2_elec 2110 2110 feedstock R12_CHN electr \n \n level time time_origin value unit \n 0 secondary year year 1.25 GWa \n 1 secondary year year 1.25 GWa \n 2 secondary year year 1.25 GWa \n 3 secondary year year 1.25 GWa \n 4 secondary year year 1.25 GWa \n ... ... ... ... ... ... \n 1723 secondary year year 1.25 GWa \n 1724 secondary year year 1.25 GWa \n 1725 secondary year year 1.25 GWa \n 1726 secondary year year 1.25 GWa \n 1727 secondary year year 1.25 GWa \n \n [1728 rows x 12 columns],\n 'relation_activity': relation node_rel year_rel node_loc technology year_act \\\n 0 RES_variable_limt R12_AFR 2010 R12_AFR h2_elec 2010 \n 1 h2_exp_limit R12_AFR 2010 R12_AFR h2_elec 2010 \n 2 oper_res R12_AFR 2010 R12_AFR h2_elec 2010 \n 3 solar_step R12_AFR 2010 R12_AFR h2_elec 2010 \n 4 solar_step2 R12_AFR 2010 R12_AFR h2_elec 2010 \n ... ... ... ... ... ... ... \n 5787 wind_curtailment_3 R12_CHN 2015 R12_CHN h2_elec 2015 \n 5788 wind_curtailment_3 R12_CHN 2025 R12_CHN h2_elec 2025 \n 5789 wind_curtailment_3 R12_CHN 2035 R12_CHN h2_elec 2035 \n 5790 wind_curtailment_3 R12_CHN 2045 R12_CHN h2_elec 2045 \n 5791 wind_curtailment_3 R12_CHN 2055 R12_CHN h2_elec 2055 \n \n mode value unit \n 0 feedstock -1.250000 ??? \n 1 feedstock 1.000000 ??? \n 2 feedstock 0.312500 ??? \n 3 feedstock 0.062500 ??? \n 4 feedstock 0.187500 ??? \n ... ... ... ... \n 5787 feedstock -0.105263 ??? \n 5788 feedstock -0.105263 ??? \n 5789 feedstock -0.105263 ??? \n 5790 feedstock -0.105263 ??? \n 5791 feedstock -0.105263 ??? \n \n [5792 rows x 9 columns],\n 'historical_activity': node_loc technology year_act mode time value unit\n 0 R12_AFR h2_elec 2010 feedstock year 0.000000 GWa\n 1 R12_RCPA h2_elec 2010 feedstock year 0.312201 GWa\n 2 R12_EEU h2_elec 2010 feedstock year 0.000000 GWa\n 3 R12_FSU h2_elec 2010 feedstock year 0.000000 GWa\n 4 R12_LAM h2_elec 2010 feedstock year 0.000000 GWa\n 5 R12_MEA h2_elec 2010 feedstock year 0.000000 GWa\n 6 R12_NAM h2_elec 2010 feedstock year 0.000000 GWa\n 7 R12_PAO h2_elec 2010 feedstock year 3.122010 GWa\n 8 R12_PAS h2_elec 2010 feedstock year 0.000000 GWa\n 9 R12_SAS h2_elec 2010 feedstock year 0.000000 GWa\n 10 R12_WEU h2_elec 2010 feedstock year 0.000000 GWa\n 11 R12_SAS h2_elec 2015 feedstock year 0.000000 ???\n 12 R12_MEA h2_elec 2015 feedstock year 0.000000 ???\n 13 R12_FSU h2_elec 2015 feedstock year 0.000000 ???\n 14 R12_PAS h2_elec 2015 feedstock year 0.000000 ???\n 15 R12_EEU h2_elec 2015 feedstock year 0.000000 ???\n 16 R12_PAO h2_elec 2015 feedstock year 0.000000 ???\n 17 R12_LAM h2_elec 2015 feedstock year 0.000000 ???\n 18 R12_RCPA h2_elec 2015 feedstock year 0.000000 ???\n 19 R12_WEU h2_elec 2015 feedstock year 0.000000 ???\n 20 R12_NAM h2_elec 2015 feedstock year 0.000000 ???\n 21 R12_AFR h2_elec 2015 feedstock year 0.000000 ???\n 22 R12_CHN h2_elec 2010 feedstock year 2.809809 GWa\n 23 R12_CHN h2_elec 2015 feedstock year 0.000000 ???\n 24 R12_AFR h2_elec 2010 feedstock year 0.000000 GWa\n 25 R12_RCPA h2_elec 2010 feedstock year 0.312201 GWa\n 26 R12_EEU h2_elec 2010 feedstock year 0.000000 GWa\n 27 R12_FSU h2_elec 2010 feedstock year 0.000000 GWa\n 28 R12_LAM h2_elec 2010 feedstock year 0.000000 GWa\n 29 R12_MEA h2_elec 2010 feedstock year 0.000000 GWa\n 30 R12_NAM h2_elec 2010 feedstock year 0.000000 GWa\n 31 R12_PAO h2_elec 2010 feedstock year 3.122010 GWa\n 32 R12_PAS h2_elec 2010 feedstock year 0.000000 GWa\n 33 R12_SAS h2_elec 2010 feedstock year 0.000000 GWa\n 34 R12_WEU h2_elec 2010 feedstock year 0.000000 GWa\n 35 R12_SAS h2_elec 2015 feedstock year 0.000000 ???\n 36 R12_MEA h2_elec 2015 feedstock year 0.000000 ???\n 37 R12_FSU h2_elec 2015 feedstock year 0.000000 ???\n 38 R12_PAS h2_elec 2015 feedstock year 0.000000 ???\n 39 R12_EEU h2_elec 2015 feedstock year 0.000000 ???\n 40 R12_PAO h2_elec 2015 feedstock year 0.000000 ???\n 41 R12_LAM h2_elec 2015 feedstock year 0.000000 ???\n 42 R12_RCPA h2_elec 2015 feedstock year 0.000000 ???\n 43 R12_WEU h2_elec 2015 feedstock year 0.000000 ???\n 44 R12_NAM h2_elec 2015 feedstock year 0.000000 ???\n 45 R12_AFR h2_elec 2015 feedstock year 0.000000 ???\n 46 R12_CHN h2_elec 2010 feedstock year 2.809809 GWa\n 47 R12_CHN h2_elec 2015 feedstock year 0.000000 ???,\n 'ref_activity': node_loc technology year_act mode time value unit\n 0 R12_AFR h2_elec 2010 feedstock year 0.000000 GWa\n 1 R12_AFR h2_elec 2020 feedstock year 0.000000 GWa\n 2 R12_AFR h2_elec 2030 feedstock year 0.000000 GWa\n 3 R12_AFR h2_elec 2040 feedstock year 0.000000 GWa\n 4 R12_AFR h2_elec 2050 feedstock year 0.000000 GWa\n .. ... ... ... ... ... ... ...\n 379 R12_CHN h2_elec 2015 feedstock year 1.561005 GWa\n 380 R12_CHN h2_elec 2025 feedstock year 0.000000 GWa\n 381 R12_CHN h2_elec 2035 feedstock year 0.000000 GWa\n 382 R12_CHN h2_elec 2045 feedstock year 0.000000 GWa\n 383 R12_CHN h2_elec 2055 feedstock year 0.000000 GWa\n \n [384 rows x 7 columns],\n 'addon_conversion': node technology year_vtg year_act mode time \\\n 0 R12_AFR h2_elec 2020 2020 feedstock year \n 1 R12_AFR h2_elec 2020 2025 feedstock year \n 2 R12_AFR h2_elec 2020 2030 feedstock year \n 3 R12_AFR h2_elec 2020 2035 feedstock year \n 4 R12_AFR h2_elec 2020 2040 feedstock year \n .. ... ... ... ... ... ... \n 775 R12_CHN h2_elec 2090 2100 feedstock year \n 776 R12_CHN h2_elec 2090 2110 feedstock year \n 777 R12_CHN h2_elec 2100 2100 feedstock year \n 778 R12_CHN h2_elec 2100 2110 feedstock year \n 779 R12_CHN h2_elec 2110 2110 feedstock year \n \n type_addon value unit \n 0 methanol_synthesis_addon 0.897 - \n 1 methanol_synthesis_addon 0.897 - \n 2 methanol_synthesis_addon 0.897 - \n 3 methanol_synthesis_addon 0.897 - \n 4 methanol_synthesis_addon 0.897 - \n .. ... ... ... \n 775 methanol_synthesis_addon 0.897 - \n 776 methanol_synthesis_addon 0.897 - \n 777 methanol_synthesis_addon 0.897 - \n 778 methanol_synthesis_addon 0.897 - \n 779 methanol_synthesis_addon 0.897 - \n \n [780 rows x 9 columns],\n 'addon_up': node technology year_act mode time type_addon \\\n 0 R12_AFR h2_elec 2020 feedstock year methanol_synthesis_addon \n 1 R12_AFR h2_elec 2025 feedstock year methanol_synthesis_addon \n 2 R12_AFR h2_elec 2030 feedstock year methanol_synthesis_addon \n 3 R12_AFR h2_elec 2035 feedstock year methanol_synthesis_addon \n 4 R12_AFR h2_elec 2040 feedstock year methanol_synthesis_addon \n .. ... ... ... ... ... ... \n 177 R12_CHN h2_elec 2070 feedstock year methanol_synthesis_addon \n 178 R12_CHN h2_elec 2080 feedstock year methanol_synthesis_addon \n 179 R12_CHN h2_elec 2090 feedstock year methanol_synthesis_addon \n 180 R12_CHN h2_elec 2100 feedstock year methanol_synthesis_addon \n 181 R12_CHN h2_elec 2110 feedstock year methanol_synthesis_addon \n \n value unit \n 0 1.0 - \n 1 1.0 - \n 2 1.0 - \n 3 1.0 - \n 4 1.0 - \n .. ... ... \n 177 1.0 - \n 178 1.0 - \n 179 1.0 - \n 180 1.0 - \n 181 1.0 - \n \n [182 rows x 8 columns]}" - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import copy\n", - "paramList_tec = [x for x in scenario.par_list() if\n", - " ((\"technology\" in scenario.idx_sets(x)) & (\"mode\" in scenario.idx_sets(x)))]\n", - "par_dict_meth = {}\n", - "for p in paramList_tec:\n", - " df = scenario.par(p, filters={\"technology\": \"h2_elec\"})\n", - " if df.index.size:\n", - " par_dict_meth[p] = df\n", - "par_dict_fuel = copy.deepcopy(par_dict_meth)\n", - "par_dict_fs = copy.deepcopy(par_dict_meth)\n", - "for k, v in par_dict_fuel.items():\n", - " v[\"mode\"] = \"fuel\"\n", - "for k, v in par_dict_fs.items():\n", - " v[\"mode\"] = \"feedstock\"\n", - "par_dict_fs" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 11, - "outputs": [], - "source": [ - "import pandas as pd\n", - "with pd.ExcelWriter('h2_elec_fs.xlsx') as writer:\n", - " for i in list(par_dict_fs.keys()):\n", - " par_dict_fs[i].to_excel(writer, sheet_name=i, index=False)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 12, - "outputs": [], - "source": [ - "with pd.ExcelWriter('h2_elec_fuel.xlsx') as writer:\n", - " for i in list(par_dict_fuel.keys()):\n", - " par_dict_fuel[i].to_excel(writer, sheet_name=i, index=False)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "for k, v in par_dict_meth.items():\n", - " scenario.remove_par(k, v)\n", - "for k, v in par_dict_fuel.items():\n", - " scenario.add_par(k, v)\n", - "for k, v in par_dict_fs.items():\n", - " scenario.add_par(k, v)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 6, - "outputs": [], - "source": [ - "scenario.remove_solution()\n", - "scenario.check_out()\n", - "scenario.add_par(\"growth_activity_lo\", df)\n", - "scenario.commit(\"modify gas petro furnace constraints\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 34, - "outputs": [ - { - "data": { - "text/plain": " node_loc technology year_act time value unit\n2944 R12_RCPA steam_cracker_petro 2020 year 0.2 t\n2945 R12_FSU ethanol_to_ethylene_petro 2020 year 0.1 t\n2946 R12_AFR ethanol_to_ethylene_petro 2020 year 0.1 t\n2947 R12_PAO ethanol_to_ethylene_petro 2020 year 0.1 t\n2948 R12_EEU ethanol_to_ethylene_petro 2020 year 0.1 t\n... ... ... ... ... ... ...\n7211 R12_PAO ethanol_to_ethylene_petro 2110 year 0.1 t\n7212 R12_EEU ethanol_to_ethylene_petro 2110 year 0.1 t\n7213 R12_MEA ethanol_to_ethylene_petro 2110 year 0.1 t\n7214 R12_WEU ethanol_to_ethylene_petro 2110 year 0.1 t\n7215 R12_NAM ethanol_to_ethylene_petro 2110 year 0.1 t\n\n[99 rows x 6 columns]", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
node_loctechnologyyear_acttimevalueunit
2944R12_RCPAsteam_cracker_petro2020year0.2t
2945R12_FSUethanol_to_ethylene_petro2020year0.1t
2946R12_AFRethanol_to_ethylene_petro2020year0.1t
2947R12_PAOethanol_to_ethylene_petro2020year0.1t
2948R12_EEUethanol_to_ethylene_petro2020year0.1t
.....................
7211R12_PAOethanol_to_ethylene_petro2110year0.1t
7212R12_EEUethanol_to_ethylene_petro2110year0.1t
7213R12_MEAethanol_to_ethylene_petro2110year0.1t
7214R12_WEUethanol_to_ethylene_petro2110year0.1t
7215R12_NAMethanol_to_ethylene_petro2110year0.1t
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99 rows × 6 columns

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" - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "par_petro[\"initial_activity_up\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 35, - "outputs": [], - "source": [ - "from message_data.model.material import data_methanol\n", - "par_mto = data_methanol.gen_data_meth_chemicals(scenario2, \"MTO\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 38, - "outputs": [ - { - "data": { - "text/plain": " node_loc technology year_act time value unit\n0 R12_AFR MTO_petro 2020 year 0.1 ???\n1 R12_AFR MTO_petro 2025 year 0.1 ???\n2 R12_AFR MTO_petro 2030 year 0.1 ???\n3 R12_AFR MTO_petro 2035 year 0.1 ???\n4 R12_AFR MTO_petro 2040 year 0.1 ???\n... ... ... ... ... ... ...\n1759 R12_WEU MTO_petro 2070 year 0.1 ???\n1760 R12_WEU MTO_petro 2080 year 0.1 ???\n1761 R12_WEU MTO_petro 2090 year 0.1 ???\n1762 R12_WEU MTO_petro 2100 year 0.1 ???\n1763 R12_WEU MTO_petro 2110 year 0.1 ???\n\n[154 rows x 6 columns]", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
node_loctechnologyyear_acttimevalueunit
0R12_AFRMTO_petro2020year0.1???
1R12_AFRMTO_petro2025year0.1???
2R12_AFRMTO_petro2030year0.1???
3R12_AFRMTO_petro2035year0.1???
4R12_AFRMTO_petro2040year0.1???
.....................
1759R12_WEUMTO_petro2070year0.1???
1760R12_WEUMTO_petro2080year0.1???
1761R12_WEUMTO_petro2090year0.1???
1762R12_WEUMTO_petro2100year0.1???
1763R12_WEUMTO_petro2110year0.1???
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154 rows × 6 columns

\n
" - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "par_mto[\"initial_activity_up\"].drop_duplicates()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 25, - "outputs": [ - { - "data": { - "text/plain": "'2023-04-13 15:29:47.0'" - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "scenario2.last_update()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 11, - "outputs": [], - "source": [ - "scenario2 = message_ix.Scenario(mp, model=\"MESSAGEix-Materials\", scenario=\"NoPolicy_petro_thesis_2\")#, version=65)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 26, - "outputs": [], - "source": [ - "df = scenario2.par(\"initial_activity_up\", filters={\"technology\":\"MTO_petro\", \"node_loc\":\"R12_CHN\"})\n", - "scenario2.check_out()\n", - "scenario2.remove_par(\"initial_activity_up\", df)\n", - "\n", - "#df[\"level\"] = \"secondary\"\n", - "#scenario2.add_par(\"output\", df)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 27, - "outputs": [], - "source": [ - "df_eth = scenario2.par(\"initial_activity_up\", filters={\"technology\":\"ethanol_to_ethylene_petro\"})\n", - "df_eth\n", - "df_eth_hist = scenario2.par(\"historical_activity\", filters={\"technology\":\"ethanol_to_ethylene_petro\", \"year_act\":2015})[\"node_loc\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 28, - "outputs": [], - "source": [ - "scenario2.remove_par(\"initial_activity_up\" ,df_eth[df_eth[\"node_loc\"].isin(df_eth_hist)])" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 29, - "outputs": [], - "source": [ - "scenario2.commit(\"remove ini act up for historical regions\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "paramList_tec = [x for x in scenario.par_list() if \"mode\" in scenario.idx_sets(x)]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "import pandas as pd\n", - "par_dict_meth = {}\n", - "df_all_rows = pd.DataFrame()\n", - "for p in paramList_tec:\n", - " df = scenario.par(p, filters={\"technology\":\"meth_h2\"})\n", - " if df.index.size:\n", - " par_dict_meth[p] = df" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "for k,v in par_dict_meth.items():\n", - " print(k, v[\"mode\"].unique())" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scenario.remove_solution()\n", - "scenario.check_out()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "par = \"addon_up\" #\"addon_conversion\" #\n", - "df = scenario.par(par, filters={\"technology\":\"h2_elec\"})\n", - "df_remove = df.copy(deep=True)\n", - "scenario.remove_par(par, df_remove)\n", - "\n", - "df[\"mode\"] = \"M1\"\n", - "scenario.add_par(par, df.drop_duplicates())" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scenario.commit(\"fix meth_h2 addon parameters\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df = mp.scenario_list()\n", - "df[df[\"scenario\"].str.contains(\"petro\")]\n", - "#clone Scenario from 'SHAPE_SSP2_v4.1.8|baseline_no_SDGs', version: 1" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df = scenario.par(\"initial_activity_up\", filters={\"technology\":\"geo_ppl\"})" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df = df[df.year_act > 2015]\n", - "df[\"value\"] = 0.1\n", - "df[\"technology\"] = \"MTO_petro\"\n", - "df[\"unit\"] = \"-\"\n", - "scenario.check_out()\n", - "scenario.add_par(\"initial_activity_up\", df[df[\"node_loc\"]!=\"R12_CHN\"])" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scenario.commit(\"add MTO ini_act_up\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df_growth_up = scenario.par(\"growth_activity_up\", filters={\"technology\": \"meth_exp\"})\n", - "df_growth_up = df_growth_up[df_growth_up[\"node_loc\"]==\"R12_NAM\"]\n", - "df_growth_up = df_growth_up[df_growth_up[\"year_act\"]==2020]\n", - "df_growth_up[\"value\"] = 0.2\n", - "df_growth_up" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df_growth_up = scenario.par(\"initial_activity_up\", filters={\"technology\": \"meth_imp\"})\n", - "#df_growth_up = df_growth_up[df_growth_up[\"node_loc\"]==\"R12_NAM\"]\n", - "df_growth_up = df_growth_up[df_growth_up[\"year_act\"]==2020]\n", - "#df_growth_up[\"value\"] = 0.2\n", - "df_growth_up" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scenario.remove_solution()\n", - "scenario.check_out()\n", - "scenario.remove_par(\"initial_activity_up\", df_growth_up)\n", - "scenario.commit(\"modify meth exp growth constraint\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df_growth_up = scenario.par(\"growth_activity_up\", filters={\"technology\": \"steam_cracker_petro\"})\n", - "df_growth_up[\"technology\"] = \"import_petro\"" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## Unlock scenario" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scenario.run_id()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "#mp._backend.jobj.unlockRunid(scenario.run_id())\n", - "mp._backend.jobj.unlockRunid(33129)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## set local data path" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 4, - "outputs": [], - "source": [ - "import os\n", - "from message_ix_models import Context\n", - "from pathlib import Path\n", - "Context.get_instance(-1).handle_cli_args(local_data=Path(os.getcwd()).parents[2].joinpath(\"data\"))\n", - "#Path(os.getcwd()).parents[2].joinpath(\"data\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## get all parameters for given technology" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "paramList_tec = [x for x in scenario.par_list() if \"technology\" in scenario.idx_sets(x)]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "import numpy as np" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "par_dict_bunker = {}\n", - "df_all_rows = pd.DataFrame()\n", - "for p in paramList_tec:\n", - " df = scenario.par(p, filters={\"technology\":\"meth_exp\"})\n", - " if df.index.size:\n", - " par_dict_bunker[p] = df\n", - "\n", - "df = par_dict_bunker[\"relation_activity\"]\n", - "par_dict_bunker[\"relation_activity\"] = df[~df[\"relation\"].str.contains(\"CO\")]\n", - "\n", - "for k in par_dict_bunker.keys():\n", - " par_dict_bunker[k][\"technology\"] = \"NH3_bunker\"\n", - " if \"level\" in par_dict_bunker[k].columns:\n", - " par_dict_bunker[k][\"level\"] = \"secondary_material\"\n", - " if \"commodity\" in par_dict_bunker[k].columns:\n", - " par_dict_bunker[k][\"commodity\"] = \"NH3\"" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df_all_rows = pd.DataFrame()\n", - "for p in paramList_tec:\n", - " print(p)\n", - " df = scenario.par(p)\n", - " df = df[df[\"technology\"]==\"meth_bunker\"]\n", - " for v in df[\"value\"].unique():\n", - " if v == 0:\n", - " continue\n", - " df_row = df[np.isclose(df[\"value\"], v)].iloc[0]\n", - " df_row[\"parameter\"] = p\n", - " df_all_rows = pd.concat([df_all_rows, pd.DataFrame(df_row.copy(deep=True)).T])" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df_all_rows[df_all_rows[\"parameter\"] ==\"relation_activity\"].dropna(axis=1)\n", - "#df_all_rows[\"parameter\"].unique()\n", - "#df_all_rows[df_all_rows[\"node_loc\"]==\"R12_CHN\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df_all_rows.to_excel(\"meth_rc_all_unique_par_vals.xlsx\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df_all_rows = pd.DataFrame()\n", - "for p in paramList_tec:\n", - " print(p)\n", - " df = scenario.par(p)\n", - " df = df[df[\"technology\"]==\"meth_bunker\"]\n", - " for v in df[\"value\"].unique():\n", - " if v == 0:\n", - " continue\n", - " df_row = df[np.isclose(df[\"value\"], v)].iloc[0]\n", - " df_row[\"parameter\"] = p\n", - " df_all_rows = pd.concat([df_all_rows, pd.DataFrame(df_row.copy(deep=True)).T])" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df_all_rows[df_all_rows[\"parameter\"] ==\"relation_activity\"].dropna(axis=1)\n", - "#df_all_rows[\"parameter\"].unique()\n", - "#df_all_rows[df_all_rows[\"node_loc\"]==\"R12_CHN\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df_all_rows.to_excel(\"meth_rc_all_unique_par_vals.xlsx\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## investigate CCS issues" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "scen_org = message_ix.Scenario(mp,model = \"SHAPE_SSP2_v4.1.8\", scenario=\"baseline\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "co2_dict = {}\n", - "for k in scen_org.par_list():\n", - " df = scen_org.par(k)\n", - " print(k)\n", - " co2_dict[k] = df.get(df.get(\"technology\")==\"co2_tr_dis\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "new_dict = {}\n", - "for i in co2_dict.keys():\n", - " if co2_dict[i] is not None:\n", - " if len(co2_dict[i]):\n", - " new_dict[i] = co2_dict[i]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## get all ethanol pars" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "par_dict = {}\n", - "for p in scenario.par_list():\n", - " try:\n", - " par_dict[p] = scenario.par(p, filters={\"technology\":\"ethanol_to_ethylene_petro\"})\n", - " except:\n", - " continue" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "for i in list(par_dict.keys()):\n", - " if par_dict[i].size == 0:\n", - " par_dict.pop(i)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## check local and private path" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "from message_data.model.material.util import read_config" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "import ixmp\n", - "ixmp.config.get(\"message local data\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "from message_ix_models.util import local_data_path, private_data_path" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "private_data_path()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "import importlib" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "importlib.reload(data_methanol)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "import ixmp as ix\n", - "import message_ix\n", - "mp = ix.Platform(\"ixmp_dev\")\n", - "scenario = message_ix.Scenario(mp, model=\"MESSAGEix-Materials\", scenario=\"NoPolicy_petro_thesis_2_macro\")#, version=37)\n", - "#scenario = message_ix.Scenario(mp, model=\"MESSAGEix-Materials\", scenario=\"baseline_VK_Transport-Materials_VK\")#, version=37)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "import os\n", - "from message_ix_models import Context\n", - "from pathlib import Path\n", - "Context.get_instance(-1).handle_cli_args(local_data=Path(os.getcwd()).parents[2].joinpath(\"data\"))\n", - "#Path(os.getcwd()).parents[2].joinpath(\"data\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "from message_data.model.material import data_methanol" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "par_dict = data_methanol.gen_data_methanol(scenario)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "for i in par_dict.keys():\n", - " df = par_dict[i]\n", - " if \"mode\" in par_dict[i].columns:\n", - " print(i)\n", - " print(df[df[\"mode\"] == \"M1\"][\"technology\"].unique())\n", - " #print(df[[\"mode\", \"technology\"]].drop_duplicates())\n", - " print()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "for i in par_dict.keys():\n", - " df = par_dict[i]\n", - " if \"level\" in par_dict[i].columns:\n", - " print(i)\n", - " #print(df[df[\"mode\"] == \"M1\"][\"technology\"].unique())\n", - " print(df[[\"level\", \"technology\", \"commodity\"]].drop_duplicates())\n", - " print()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "data_methanol.gen_data_meth_chemicals(scenario, \"MTO\")[\"initial_activity_up\"]" - ], - "metadata": { - "collapsed": false - } - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} From 7739382cdbd67e14a6acca450d6635221b47b321 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 30 Nov 2023 16:43:44 +0100 Subject: [PATCH 649/774] Undo tracking of some files 3 --- .../model/material/magpie_LU_readin.py | 100 ------------------ .../model/material/tax_emission_600f.csv | 14 --- 2 files changed, 114 deletions(-) delete mode 100644 message_ix_models/model/material/magpie_LU_readin.py delete mode 100644 message_ix_models/model/material/tax_emission_600f.csv diff --git a/message_ix_models/model/material/magpie_LU_readin.py b/message_ix_models/model/material/magpie_LU_readin.py deleted file mode 100644 index f099ebf4eb..0000000000 --- a/message_ix_models/model/material/magpie_LU_readin.py +++ /dev/null @@ -1,100 +0,0 @@ -import ixmp -import message_ix -import data_scp -from message_data.tools.utilities import add_globiom -from message_ix_models.util import private_data_path - -import message_data.tools.post_processing.iamc_report_hackathon - - -magpie_scens = [ - "MP00BD0BI78", - "MP00BD0BI74", - "MP00BD0BI70", - "MP00BD1BI00", - "MP30BD0BI78", - "MP30BD0BI74", - "MP30BD0BI70", - "MP30BD1BI00", - "MP50BD0BI78", - "MP50BD0BI74", - "MP50BD0BI70", - "MP50BD1BI00", - "MP76BD0BI70", - "MP76BD0BI74", - "MP76BD0BI78", - "MP76BD1BI00" -] - - -def build_magpie_baseline_from_r12_base(mp, scen_base, magpie_scen): - scen = scen_base.clone(scen_base.model+"-"+magpie_scen, "baseline") - if scen.has_solution(): - scen.remove_solution() - add_globiom( - mp, - scen, - "SSP2", - private_data_path(), - 2015, - globiom_scenario="noSDG_rcpref", - regenerate_input_data=True, - #regenerate_input_data_only=True, - #allow_empty_drivers=True, - add_reporting_data=True, - config_setup="MAGPIE_"+magpie_scen - ) - scen.set_as_default() - return scen - - -def solve_mp_baseline(): - mp = ixmp.Platform("ixmp_dev") - for magpie_scen in magpie_scens[5:]: - scen = message_ix.Scenario(mp, f"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-{magpie_scen}", "baseline") - scen.solve(model="MESSAGE-MACRO") - - -def run_reporting(): - mp = ixmp.Platform("ixmp_dev") - for magpie_scen in magpie_scens: - print() - print(f"starting reporting of {magpie_scen}") - print() - scen = message_ix.Scenario(mp, f"MESSAGEix-GLOBIOM 1.1-R12-MAGPIE-{magpie_scen}", "baseline") - if scen.has_solution(): - message_data.tools.post_processing.iamc_report_hackathon.report( - mp, - scen, - # NB(PNK) this is not an error; .iamc_report_hackathon.report() expects a - # string containing "True" or "False" instead of an actual bool. - "False", - scen.model, - scen.scenario, - merge_hist=True, - merge_ts=True, - # run_config="materials_run_config.yaml", - ) - del scen - - -def main(): - mp = ixmp.Platform("ixmp_dev") - scen_base = message_ix.Scenario(mp, "MESSAGEix-GLOBIOM 1.1-R12-MAGPIE", "baseline_w/o_LU") - - for magpie_scen in magpie_scens[-1:]: - print(f"read-in of {magpie_scen} matrix started") - scen = build_magpie_baseline_from_r12_base(mp, scen_base, magpie_scen) - if "MP00" not in magpie_scen: - print(f"adding SCP model to {magpie_scen}") - scp_dict = data_scp.gen_data_scp(scen) - scen.check_out() - data_scp.add_scp_sets(scen) - for k,v in scp_dict.items(): - scen.add_par(k, v) - scen.commit("add SCP tecs") - scen.set_as_default() - - -if __name__ == '__main__': - run_reporting() \ No newline at end of file diff --git a/message_ix_models/model/material/tax_emission_600f.csv b/message_ix_models/model/material/tax_emission_600f.csv deleted file mode 100644 index a8b90340c9..0000000000 --- a/message_ix_models/model/material/tax_emission_600f.csv +++ /dev/null @@ -1,14 +0,0 @@ -node,type_emission,type_tec,year,lvl,mrg -World,TCE,all,2025,191.1174531180089,0.0 -World,TCE,all,2030,243.91968168647298,0.0 -World,TCE,all,2035,311.31019246731444,0.0 -World,TCE,all,2040,397.3194588643599,0.0 -World,TCE,all,2045,507.09149977105983,0.0 -World,TCE,all,2050,647.1915316582767,0.0 -World,TCE,all,2055,825.9986192615938,0.0 -World,TCE,all,2060,1054.2068084140296,0.0 -World,TCE,all,2070,1717.1918057378095,0.0 -World,TCE,all,2080,2797.124505512571,0.0 -World,TCE,all,2090,4556.221077456938,0.0 -World,TCE,all,2100,7421.604031479733,0.0 -World,TCE,all,2110,12089.010928947138,0.0 From 55cccdb621e1747d6f61ca3126e2b49457e5666a Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 30 Nov 2023 17:37:28 +0100 Subject: [PATCH 650/774] Add carbon price run command to cli --- message_ix_models/model/material/__init__.py | 27 ++++++++++++++++++++ 1 file changed, 27 insertions(+) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 4d7f416609..3e11821d22 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -625,3 +625,30 @@ def modify_costs_with_tool(context, scen_name, ssp): scen_bud.solve(model="MESSAGE-MACRO",solve_options={"scaind":-1}) return + + +@cli.command("run_LED_cprice_scenario") +@click.option("--ssp", default="SSP2", help="Suffix to the scenario name") +@click.pass_obj +def modify_costs_with_tool(context, scen_name, ssp): + import message_ix + from message_ix_models.tools.costs.config import Config + from message_ix_models.tools.costs.projections import create_cost_projections + + mp = ixmp.Platform("ixmp_dev") + price_scen = message_ix.Scenario(mp, "MESSAGEix-Materials", scenario=f"SSP_supply_cost_test_LED_macro_1000f") + + base = message_ix.Scenario(mp, "MESSAGEix-Materials", scenario=f"SSP_supply_cost_test_{ssp}_macro") + scen_cprice = base.clone(model=base.model, scenario=base.scenario + "_1000f_LED_prices", shift_first_model_year=2025) + + tax_emission_new = price_scen.var("PRICE_EMISSION") + + scen_cprice.check_out() + tax_emission_new.columns = scen_cprice.par("tax_emission").columns + tax_emission_new["unit"] = "USD/tCO2" + scen_cprice.add_par("tax_emission", tax_emission_new) + scen_cprice.commit('2 degree LED prices are added') + print('New LED 1000f carbon prices added') + + scen_bud.solve(model="MESSAGE-MACRO", solve_options={"scaind":-1}) + return From 6b0587ccd69b4cd3cbe480bf02b0929b430b393f Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 30 Nov 2023 17:40:51 +0100 Subject: [PATCH 651/774] Remove unused click option from materials command --- message_ix_models/model/material/__init__.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 3e11821d22..76e8873966 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -595,7 +595,7 @@ def modify_costs_with_tool(context, scen_name, ssp): @cli.command("run_2C_scenario") @click.option("--ssp", default="SSP2", help="Suffix to the scenario name") @click.pass_obj -def modify_costs_with_tool(context, scen_name, ssp): +def modify_costs_with_tool(context, ssp): import message_ix from message_ix_models.tools.costs.config import Config from message_ix_models.tools.costs.projections import create_cost_projections @@ -630,7 +630,7 @@ def modify_costs_with_tool(context, scen_name, ssp): @cli.command("run_LED_cprice_scenario") @click.option("--ssp", default="SSP2", help="Suffix to the scenario name") @click.pass_obj -def modify_costs_with_tool(context, scen_name, ssp): +def modify_costs_with_tool(context, ssp): import message_ix from message_ix_models.tools.costs.config import Config from message_ix_models.tools.costs.projections import create_cost_projections From 20bdfa8c3f32d9688719326b45698613006c3839 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Fri, 1 Dec 2023 21:43:35 +0100 Subject: [PATCH 652/774] Add yaml file for alu base demand --- .../material/aluminum/demand_aluminum.yaml | 36 +++++++++++++++++++ 1 file changed, 36 insertions(+) create mode 100644 message_ix_models/data/material/aluminum/demand_aluminum.yaml diff --git a/message_ix_models/data/material/aluminum/demand_aluminum.yaml b/message_ix_models/data/material/aluminum/demand_aluminum.yaml new file mode 100644 index 0000000000..c6c1446ffa --- /dev/null +++ b/message_ix_models/data/material/aluminum/demand_aluminum.yaml @@ -0,0 +1,36 @@ +- R12_AFR: + year: 2020 + value: 3.00000 +- R12_RCPA: + year: 2020 + value: 2.00000 +- R12_EEU: + year: 2020 + value: 6.00000 +- R12_FSU: + year: 2020 + value: 5.00000 +- R12_LAM: + year: 2020 + value: 2.50000 +- R12_MEA: + year: 2020 + value: 2.00000 +- R12_NAM: + year: 2020 + value: 13.60000 +- R12_PAO: + year: 2020 + value: 3.00000 +- R12_PAS: + year: 2020 + value: 4.80000 +- R12_SAS: + year: 2020 + value: 4.80000 +- R12_WEU: + year: 2020 + value: 6.00000 +- R12_CHN: + year: 2020 + value: 26.00000 \ No newline at end of file From 55aaaf5969352dceede1e8cb8f6d353975a1d6d2 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Fri, 1 Dec 2023 21:43:59 +0100 Subject: [PATCH 653/774] Fix var name in cli command --- message_ix_models/model/material/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 76e8873966..c38732f9e0 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -650,5 +650,5 @@ def modify_costs_with_tool(context, ssp): scen_cprice.commit('2 degree LED prices are added') print('New LED 1000f carbon prices added') - scen_bud.solve(model="MESSAGE-MACRO", solve_options={"scaind":-1}) + scen_cprice.solve(model="MESSAGE-MACRO", solve_options={"scaind":-1}) return From 5cc4cade0be7b643ef820049b0746d23f5fbadba Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Mon, 4 Dec 2023 10:33:10 +0100 Subject: [PATCH 654/774] Add flag to run mitigation cli command with several budgets --- message_ix_models/model/material/__init__.py | 24 ++++++++++++++------ 1 file changed, 17 insertions(+), 7 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index c38732f9e0..d91d1d261e 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -592,17 +592,23 @@ def modify_costs_with_tool(context, scen_name, ssp): scen.solve(model="MESSAGE-MACRO", solve_options={"scaind": -1}) -@cli.command("run_2C_scenario") +@cli.command("run_cbud_scenario") @click.option("--ssp", default="SSP2", help="Suffix to the scenario name") +@click.option("--scenario", default="1000f", help="description of carbon budget for mitigation target") @click.pass_obj -def modify_costs_with_tool(context, ssp): +def modify_costs_with_tool(context, ssp, scenario): import message_ix from message_ix_models.tools.costs.config import Config from message_ix_models.tools.costs.projections import create_cost_projections mp = ixmp.Platform("ixmp_dev") base = message_ix.Scenario(mp, "MESSAGEix-Materials", scenario=f"SSP_supply_cost_test_{ssp}_macro") - scenario_cbud = base.clone(model=base.model, scenario=base.scenario + "_1000f", shift_first_model_year=2025) + scenario_cbud = base.clone(model=base.model, scenario=base.scenario + "_" + scenario, shift_first_model_year=2025) + + if scenario == "1000f": + budget = 3667 + if scenario == "600f": + budget = 3667 emission_dict = { "node": "World", @@ -623,22 +629,26 @@ def modify_costs_with_tool(context, ssp): scenario_cbud.commit("remove cumulative years from cat_year set") scenario_cbud.set("cat_year", {"type_year": "cumulative"}) - scen_bud.solve(model="MESSAGE-MACRO",solve_options={"scaind":-1}) + scenario_cbud.solve(model="MESSAGE-MACRO",solve_options={"scaind":-1}) return @cli.command("run_LED_cprice_scenario") @click.option("--ssp", default="SSP2", help="Suffix to the scenario name") +@click.option("--scenario", default="1000f", help="description of carbon budget for mitigation target") @click.pass_obj -def modify_costs_with_tool(context, ssp): +def modify_costs_with_tool(context, ssp, scenario): import message_ix from message_ix_models.tools.costs.config import Config from message_ix_models.tools.costs.projections import create_cost_projections mp = ixmp.Platform("ixmp_dev") - price_scen = message_ix.Scenario(mp, "MESSAGEix-Materials", scenario=f"SSP_supply_cost_test_LED_macro_1000f") + if scenario == "1000f": + price_scen = message_ix.Scenario(mp, "MESSAGEix-Materials", scenario=f"SSP_supply_cost_test_LED_macro_1000f") + if scenario == "600f": + price_scen = message_ix.Scenario(mp, "MESSAGEix-Materials", scenario=f"SSP_supply_cost_test_LED_macro_1000f") - base = message_ix.Scenario(mp, "MESSAGEix-Materials", scenario=f"SSP_supply_cost_test_{ssp}_macro") + base = message_ix.Scenario(mp, "MESSAGEix-Materials", scenario=f"SSP_supply_cost_test_{ssp}_macro", version=2) scen_cprice = base.clone(model=base.model, scenario=base.scenario + "_1000f_LED_prices", shift_first_model_year=2025) tax_emission_new = price_scen.var("PRICE_EMISSION") From 1eee970d6618327742d6d63fb119d4c8ba938d7d Mon Sep 17 00:00:00 2001 From: maczek Date: Tue, 5 Dec 2023 18:03:20 +0100 Subject: [PATCH 655/774] Add 650f price scenario for cprice run command --- message_ix_models/model/material/__init__.py | 19 +++++++++++-------- 1 file changed, 11 insertions(+), 8 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index d91d1d261e..e79a81b016 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -601,15 +601,18 @@ def modify_costs_with_tool(context, ssp, scenario): from message_ix_models.tools.costs.config import Config from message_ix_models.tools.costs.projections import create_cost_projections + if scenario == "1000f": + budget = 3667 + if scenario == "650f": + budget = 1750 + else: + print("chosen budget not available yet please choose 600f or 1000f") + return + mp = ixmp.Platform("ixmp_dev") base = message_ix.Scenario(mp, "MESSAGEix-Materials", scenario=f"SSP_supply_cost_test_{ssp}_macro") scenario_cbud = base.clone(model=base.model, scenario=base.scenario + "_" + scenario, shift_first_model_year=2025) - if scenario == "1000f": - budget = 3667 - if scenario == "600f": - budget = 3667 - emission_dict = { "node": "World", "type_emission": "TCE", @@ -618,7 +621,7 @@ def modify_costs_with_tool(context, ssp, scenario): "unit": "???", } df = message_ix.make_df( - "bound_emission", value=3667, **emission_dict + "bound_emission", value=budget, **emission_dict ) scenario_cbud.check_out() scenario_cbud.add_par("bound_emission", df) @@ -645,8 +648,8 @@ def modify_costs_with_tool(context, ssp, scenario): mp = ixmp.Platform("ixmp_dev") if scenario == "1000f": price_scen = message_ix.Scenario(mp, "MESSAGEix-Materials", scenario=f"SSP_supply_cost_test_LED_macro_1000f") - if scenario == "600f": - price_scen = message_ix.Scenario(mp, "MESSAGEix-Materials", scenario=f"SSP_supply_cost_test_LED_macro_1000f") + if scenario == "650f": + price_scen = message_ix.Scenario(mp, "MESSAGEix-Materials", scenario=f"SSP_supply_cost_test_LED_macro_{scenario}") base = message_ix.Scenario(mp, "MESSAGEix-Materials", scenario=f"SSP_supply_cost_test_{ssp}_macro", version=2) scen_cprice = base.clone(model=base.model, scenario=base.scenario + "_1000f_LED_prices", shift_first_model_year=2025) From ef3f30dfaa9e6fcb96608cd32cbfb4c3aaacbbf3 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 5 Dec 2023 20:23:09 +0100 Subject: [PATCH 656/774] Remove deprecated function argument --- message_ix_models/model/material/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index e79a81b016..a9a92be89a 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -651,7 +651,7 @@ def modify_costs_with_tool(context, ssp, scenario): if scenario == "650f": price_scen = message_ix.Scenario(mp, "MESSAGEix-Materials", scenario=f"SSP_supply_cost_test_LED_macro_{scenario}") - base = message_ix.Scenario(mp, "MESSAGEix-Materials", scenario=f"SSP_supply_cost_test_{ssp}_macro", version=2) + base = message_ix.Scenario(mp, "MESSAGEix-Materials", scenario=f"SSP_supply_cost_test_{ssp}_macro") scen_cprice = base.clone(model=base.model, scenario=base.scenario + "_1000f_LED_prices", shift_first_model_year=2025) tax_emission_new = price_scen.var("PRICE_EMISSION") From f68a6b2a421648205d5f78e9295e984c38996ecd Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 5 Dec 2023 20:23:27 +0100 Subject: [PATCH 657/774] Remove deprecated files --- .../model/material/meth_rc_all_unique_par_vals.xlsx | 3 --- message_ix_models/model/material/meth_trade_additions.xlsx | 3 --- 2 files changed, 6 deletions(-) delete mode 100644 message_ix_models/model/material/meth_rc_all_unique_par_vals.xlsx delete mode 100644 message_ix_models/model/material/meth_trade_additions.xlsx diff --git a/message_ix_models/model/material/meth_rc_all_unique_par_vals.xlsx b/message_ix_models/model/material/meth_rc_all_unique_par_vals.xlsx deleted file mode 100644 index fd67bf5bcb..0000000000 --- a/message_ix_models/model/material/meth_rc_all_unique_par_vals.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:ae007002c8b593170db49e6968d42e5c5b6a53b614b11e62918704f812f434d0 -size 103431 diff --git a/message_ix_models/model/material/meth_trade_additions.xlsx b/message_ix_models/model/material/meth_trade_additions.xlsx deleted file mode 100644 index 9657515eac..0000000000 --- a/message_ix_models/model/material/meth_trade_additions.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:149ba30dd92d91ec3dc28f353a28c5c9b51e327ef9a395d28a61ae0e9ebb6961 -size 200702 From 8aa4872548e8a3c776ccedde475d8147a9944b46 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 5 Dec 2023 21:16:15 +0100 Subject: [PATCH 658/774] Finalize materials demand refactoring --- .../material/material_demand/refactor_mat_demand_calc.py | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/message_ix_models/model/material/material_demand/refactor_mat_demand_calc.py b/message_ix_models/model/material/material_demand/refactor_mat_demand_calc.py index bdb48d3e86..2713a50dce 100644 --- a/message_ix_models/model/material/material_demand/refactor_mat_demand_calc.py +++ b/message_ix_models/model/material/material_demand/refactor_mat_demand_calc.py @@ -664,12 +664,10 @@ def derive_demand(material, scen): df_in["del_t"] = df_in["year"] - 2010 df_in["gdp_pcap"] = df_in["gdp_ppp"] * giga / df_in["pop.mil"] / mega - x = tuple(pd.Series(df_in[col]) for col in fitting_dict[material]["x_data"]) - df_in["demand_pcap0"] = df_in.apply( - lambda row: fitting_dict[material]["function"](x, *params_opt), axis=1 + lambda row: fitting_dict[material]["function"](tuple(row[i] for i in fitting_dict[material]["x_data"]), *params_opt), axis=1 ) - + df_in = df_in.rename({"value":"demand.tot.base"}, axis=1) df_final = project_demand(df_in, fitting_dict[material]["phi"], fitting_dict[material]["mu"]) - return df_final + return df_final.drop(['technology', 'mode', 'time', 'unit'], axis=1) From 3a8903dfa5642f6ace40de387dfa021d246642be Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Wed, 6 Dec 2023 09:23:23 +0100 Subject: [PATCH 659/774] Fix cprice scenario naming --- message_ix_models/model/material/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index a9a92be89a..da42633d72 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -652,7 +652,7 @@ def modify_costs_with_tool(context, ssp, scenario): price_scen = message_ix.Scenario(mp, "MESSAGEix-Materials", scenario=f"SSP_supply_cost_test_LED_macro_{scenario}") base = message_ix.Scenario(mp, "MESSAGEix-Materials", scenario=f"SSP_supply_cost_test_{ssp}_macro") - scen_cprice = base.clone(model=base.model, scenario=base.scenario + "_1000f_LED_prices", shift_first_model_year=2025) + scen_cprice = base.clone(model=base.model, scenario=base.scenario + f"_{scenario}_LED_prices", shift_first_model_year=2025) tax_emission_new = price_scen.var("PRICE_EMISSION") From a5d97bb0a174a3c286c163ad62f7d23eb916b027 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Mon, 22 Jan 2024 13:58:11 +0100 Subject: [PATCH 660/774] Add all refactored code regarding demand projections --- .../material/other/mer_to_ppp_default.csv | 3 + message_ix_models/model/material/__init__.py | 282 +++++----- .../model/material/data_aluminum.py | 30 +- .../model/material/data_ammonia_new.py | 143 +++-- .../model/material/data_cement.py | 31 +- .../model/material/data_generic.py | 19 +- .../model/material/data_methanol_new.py | 61 +- .../model/material/data_petro.py | 184 +++--- .../model/material/data_power_sector.py | 445 ++++++++------- .../model/material/data_steel.py | 116 ++-- message_ix_models/model/material/data_util.py | 524 ++++++++++++++---- .../material_demand/material_demand_calc.py | 330 +++++++++++ message_ix_models/model/material/util.py | 23 +- 13 files changed, 1516 insertions(+), 675 deletions(-) create mode 100644 message_ix_models/data/material/other/mer_to_ppp_default.csv create mode 100644 message_ix_models/model/material/material_demand/material_demand_calc.py diff --git a/message_ix_models/data/material/other/mer_to_ppp_default.csv b/message_ix_models/data/material/other/mer_to_ppp_default.csv new file mode 100644 index 0000000000..aff8865b46 --- /dev/null +++ b/message_ix_models/data/material/other/mer_to_ppp_default.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b38dde4c4102a9133a24fd396c654dceacbe431526f68ccc3500d9eb96a0d0da +size 4010 diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index da42633d72..ea9d09a456 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -3,14 +3,16 @@ import click import logging import ixmp + # from .build import apply_spec # from message_data.tools import ScenarioInfo from message_data.model.material.build import apply_spec from message_ix_models import ScenarioInfo -from message_ix_models.util.context import Context from message_ix_models.util import add_par_data, private_data_path -from message_data.tools.utilities import calibrate_UE_gr_to_demand, add_globiom,\ - calibrate_UE_share_constraints +from message_data.tools.utilities import ( + calibrate_UE_gr_to_demand, + calibrate_UE_share_constraints, +) # from .data import add_data from .data_util import modify_demand_and_hist_activity, add_emission_accounting @@ -37,7 +39,8 @@ def build(scenario): water_dict = pd.read_excel( "C:/Users\maczek\PycharmProjects\message_data\message_data\model\material\petrochemical model fixes notebooks/water_tec_pars.xlsx", - sheet_name=None) + sheet_name=None, + ) for par in water_dict.keys(): scenario.add_par(par, water_dict[par]) scenario.commit("add missing water tecs") @@ -57,27 +60,31 @@ def build(scenario): try: add_emission_accounting(scenario) except: - scenario.commit("no changes") + pass + # scenario.commit("no changes") add_coal_lowerbound_2020(scenario) add_cement_bounds_2020(scenario) # Market penetration adjustments # NOTE: changing demand affects the market penetration levels for the enduse technologies. # Note: context.ssp doesnt work - calibrate_UE_gr_to_demand(scenario, data_path=private_data_path(), ssp='SSP2', region = 'R12') + calibrate_UE_gr_to_demand( + scenario, data_path=private_data_path(), ssp="SSP2", region="R12" + ) calibrate_UE_share_constraints(scenario) # Electricity calibration to avoid zero prices for CHN. - if 'R12_CHN' in nodes: + if "R12_CHN" in nodes: add_elec_lowerbound_2020(scenario) # i_feed demand is zero creating a zero division error during MACRO calibration scenario.check_out() - scenario.remove_set('sector', 'i_feed') - scenario.commit('i_feed removed from sectors.') + scenario.remove_set("sector", "i_feed") + scenario.commit("i_feed removed from sectors.") return scenario + # add as needed/implemented SPEC_LIST = [ "generic", @@ -89,7 +96,7 @@ def build(scenario): "buildings", "power_sector", "fertilizer", - "methanol" + "methanol", ] @@ -158,8 +165,8 @@ def create_bare(context, regions, dry_run): help="File name for external data input", ) @click.option("--tag", default="", help="Suffix to the scenario name") -@click.option("--mode", default = 'by_url') -@click.option("--scenario_name", default = 'NoPolicy_3105_macro') +@click.option("--mode", default="by_url") +@click.option("--scenario_name", default="NoPolicy_3105_macro") @click.pass_obj def build_scen(context, datafile, tag, mode, scenario_name): """Build a scenario. @@ -169,12 +176,11 @@ def build_scen(context, datafile, tag, mode, scenario_name): memory, i.e. ``jvmargs=["-Xmx16G"]``. """ - from ixmp import Platform import message_ix mp = context.get_platform() - if mode == 'by_url': + if mode == "by_url": # Determine the output scenario name based on the --url CLI option. If the # user did not give a recognized value, this raises an error. output_scenario_name = { @@ -198,53 +204,62 @@ def build_scen(context, datafile, tag, mode, scenario_name): # Clone and set up - if context.model == "SSP_dev_SSP2_v0.1": + if context.scenario_info["model"] == "SSP_dev_SSP2_v0.1": scenario = build( context.get_scenario().clone( model=context.scenario_info["model"], scenario=context.scenario_info["scenario"] + "_" + tag, - keep_solution=False + keep_solution=False, ) ) else: scenario = build( context.get_scenario().clone( - model="MESSAGEix-Materials", scenario=output_scenario_name + "_" + tag + model="MESSAGEix-Materials", + scenario=output_scenario_name + "_" + tag, ) ) # Set the latest version as default scenario.set_as_default() # Create a two degrees scenario by copying carbon prices from another scenario. - if mode == 'by_copy': - output_scenario_name = '2degrees' - mod_mitig = 'ENGAGE_SSP2_v4.1.8' - scen_mitig = 'EN_NPi2020_1000f' - print('Loading ' + mod_mitig + ' ' + scen_mitig + ' to retreive carbon prices.') + if mode == "by_copy": + output_scenario_name = "2degrees" + mod_mitig = "ENGAGE_SSP2_v4.1.8" + scen_mitig = "EN_NPi2020_1000f" + print("Loading " + mod_mitig + " " + scen_mitig + " to retreive carbon prices.") scen_mitig_prices = message_ix.Scenario(mp, mod_mitig, scen_mitig) tax_emission_new = scen_mitig_prices.var("PRICE_EMISSION") - scenario = message_ix.Scenario(mp, 'MESSAGEix-Materials', scenario_name) - print('Base scenario is ' + scenario_name) - output_scenario_name = output_scenario_name + '_' + tag - scenario = scenario.clone('MESSAGEix-Materials',output_scenario_name , keep_solution=False, - shift_first_model_year=2025) + scenario = message_ix.Scenario(mp, "MESSAGEix-Materials", scenario_name) + print("Base scenario is " + scenario_name) + output_scenario_name = output_scenario_name + "_" + tag + scenario = scenario.clone( + "MESSAGEix-Materials", + output_scenario_name, + keep_solution=False, + shift_first_model_year=2025, + ) scenario.check_out() tax_emission_new.columns = scenario.par("tax_emission").columns tax_emission_new["unit"] = "USD/tCO2" scenario.add_par("tax_emission", tax_emission_new) - scenario.commit('2 degree prices are added') - print('New carbon prices added') - print('New scenario name is ' + output_scenario_name) + scenario.commit("2 degree prices are added") + print("New carbon prices added") + print("New scenario name is " + output_scenario_name) scenario.set_as_default() - if mode == 'cbudget': + if mode == "cbudget": scenario = context.get_scenario() print(scenario.version) - #print('Base scenario is: ' + scenario.scenario + ", version: " + scenario.version) - output_scenario_name = scenario.scenario + '_' + tag - scenario_new = scenario.clone('MESSAGEix-Materials', output_scenario_name, - keep_solution=False, shift_first_model_year=2025) + # print('Base scenario is: ' + scenario.scenario + ", version: " + scenario.version) + output_scenario_name = scenario.scenario + "_" + tag + scenario_new = scenario.clone( + "MESSAGEix-Materials", + output_scenario_name, + keep_solution=False, + shift_first_model_year=2025, + ) emission_dict = { "node": "World", "type_emission": "TCE", @@ -256,8 +271,8 @@ def build_scen(context, datafile, tag, mode, scenario_name): scenario_new.check_out() scenario_new.add_par("bound_emission", df) scenario_new.commit("add emission bound") - print('New carbon budget added') - print('New scenario name is ' + output_scenario_name) + print("New carbon budget added") + print("New scenario name is " + output_scenario_name) scenario_new.set_as_default() @@ -274,7 +289,9 @@ def build_scen(context, datafile, tag, mode, scenario_name): @click.option("--add_macro", default=True) @click.option("--add_calibration", default=False) @click.pass_obj -def solve_scen(context, datafile, model_name, scenario_name, add_calibration, add_macro, version): +def solve_scen( + context, datafile, model_name, scenario_name, add_calibration, add_macro, version +): """Solve a scenario. Use the --model_name and --scenario_name option to specify the scenario to solve. @@ -287,40 +304,49 @@ def solve_scen(context, datafile, model_name, scenario_name, add_calibration, ad scenario = Scenario(mp, model_name, scenario_name, version=int(version)) else: scenario = Scenario(mp, model_name, scenario_name) - #scenario = Scenario(context.get_platform(), model_name, scenario_name) + # scenario = Scenario(context.get_platform(), model_name, scenario_name) if scenario.has_solution(): scenario.remove_solution() if add_calibration: # Solve - print('Solving the scenario without MACRO') - scenario.solve(model="MESSAGE", solve_options={'lpmethod': '4', 'scaind':'-1'}) + print("Solving the scenario without MACRO") + scenario.solve(model="MESSAGE", solve_options={"lpmethod": "4", "scaind": "-1"}) scenario.set_as_default() # After solving, add macro calibration - print('Scenario solved, now adding MACRO calibration') - scenario = add_macro_COVID(scenario,'R12-CHN-5y_macro_data_NGFS_w_rc_ind_adj_mat.xlsx') - print('Scenario calibrated.') - - if add_macro: # Default True - print('After macro calibration a new scneario with the suffix _macro is created.') - print('Make sure to use this scenario to solve with MACRO iterations.') - - scenario.solve(model="MESSAGE-MACRO", solve_options={'lpmethod': '4', 'scaind':'-1'}) + print("Scenario solved, now adding MACRO calibration") + scenario = add_macro_COVID( + scenario, "R12-CHN-5y_macro_data_NGFS_w_rc_ind_adj_mat.xlsx" + ) + print("Scenario calibrated.") + + if add_macro: # Default True + print( + "After macro calibration a new scneario with the suffix _macro is created." + ) + print("Make sure to use this scenario to solve with MACRO iterations.") + + scenario.solve( + model="MESSAGE-MACRO", solve_options={"lpmethod": "4", "scaind": "-1"} + ) scenario.set_as_default() if not add_macro: # Solve - print('Solving the scenario without MACRO') - scenario.solve(model="MESSAGE", solve_options={'lpmethod': '4', 'scaind':'-1'}) + print("Solving the scenario without MACRO") + scenario.solve(model="MESSAGE", solve_options={"lpmethod": "4", "scaind": "-1"}) scenario.set_as_default() + @cli.command("add_buildings_ts") @click.option("--scenario_name", default="NoPolicy") @click.option("--model_name", default="MESSAGEix-Materials") def add_building_ts(scenario_name, model_name): - from message_data.reporting.materials.add_buildings_ts import add_building_timeseries + from message_data.reporting.materials.add_buildings_ts import ( + add_building_timeseries, + ) from message_ix import Scenario from ixmp import Platform @@ -344,7 +370,9 @@ def add_building_ts(scenario_name, model_name): def run_reporting(context, remove_ts, profile): """Run materials, then legacy reporting.""" from message_data.reporting.materials.reporting import report - from message_data.tools.post_processing.iamc_report_hackathon import report as reporting + from message_data.tools.post_processing.iamc_report_hackathon import ( + report as reporting, + ) # Retrieve the scenario given by the --url option scenario = context.get_scenario() @@ -358,9 +386,9 @@ def run_reporting(context, remove_ts, profile): scenario.remove_timeseries(df_rem) scenario.commit("Existing timeseries removed.") scenario.set_as_default() - print('Existing timeseries are removed.') + print("Existing timeseries are removed.") else: - print('There are no timeseries to be removed.') + print("There are no timeseries to be removed.") else: if profile: @@ -392,7 +420,9 @@ def exit(): pr.disable() print("Profiling completed") s = io.StringIO() - pstats.Stats(pr, stream=s).sort_stats("cumulative").dump_stats("profiling.dmp") + pstats.Stats(pr, stream=s).sort_stats("cumulative").dump_stats( + "profiling.dmp" + ) print(s.getvalue()) atexit.register(exit) @@ -414,7 +444,7 @@ def exit(): merge_ts=True, run_config="materials_run_config.yaml", ) - #util.prepare_xlsx_for_explorer( + # util.prepare_xlsx_for_explorer( # Path(os.getcwd()).parents[0].joinpath( # "reporting_output", scenario.model+"_"+scenario.scenario+".xlsx")) @@ -422,8 +452,6 @@ def exit(): @cli.command("report-2") @click.pass_obj def run_old_reporting(context): - from message_ix import Scenario - from ixmp import Platform from message_data.tools.post_processing.iamc_report_hackathon import ( report as reporting, ) @@ -445,22 +473,22 @@ def run_old_reporting(context): run_config="materials_run_config.yaml", ) + from .data_cement import gen_data_cement from .data_steel import gen_data_steel from .data_aluminum import gen_data_aluminum from .data_generic import gen_data_generic from .data_petro import gen_data_petro_chemicals -from .data_buildings import gen_data_buildings from .data_power_sector import gen_data_power_sector from .data_methanol_new import gen_data_methanol_new from .data_ammonia_new import gen_all_NH3_fert DATA_FUNCTIONS_1 = [ - #gen_data_buildings, + # gen_data_buildings, gen_data_methanol_new, gen_all_NH3_fert, - #gen_data_ammonia, ## deprecated module! + # gen_data_ammonia, ## deprecated module! gen_data_generic, gen_data_steel, ] @@ -468,7 +496,7 @@ def run_old_reporting(context): gen_data_cement, gen_data_petro_chemicals, gen_data_power_sector, - gen_data_aluminum + gen_data_aluminum, ] @@ -516,59 +544,29 @@ def add_data_2(scenario, dry_run=False): log.info("done") -@cli.command("modify_cost") -@click.option("--ssp", default="SSP2", help="Suffix to the scenario name") -@click.option("--scen_name", default='SSP_supply_cost_test_baseline') -@click.pass_obj -def modify_costs_with_tool(context, scen_name, ssp): - import message_ix - from message_ix_models.tools.costs.config import Config - from message_ix_models.tools.costs.projections import create_cost_projections - mp = ixmp.Platform("ixmp_dev") - base = message_ix.Scenario(mp, "MESSAGEix-Materials", scenario=scen_name) - scen = base.clone(model=base.model, scenario=base.scenario.replace("baseline", ssp)) - - tec_set = list(scen.set("technology")) - - cfg = Config(module="materials", ref_region="R12_NAM", method="convergence", format="message", scenario=ssp) - - out_materials = create_cost_projections( - node=cfg.node, - ref_region=cfg.ref_region, - base_year=cfg.base_year, - module=cfg.module, - method=cfg.method, - scenario_version=cfg.scenario_version, - scenario=cfg.scenario, - convergence_year=cfg.convergence_year, - fom_rate=cfg.fom_rate, - format=cfg.format, - ) - scen.check_out() - fix_cost = out_materials.fix_cost.drop_duplicates().drop(["scenario_version", "scenario"], axis=1) - scen.add_par("fix_cost", fix_cost[fix_cost["technology"].isin(tec_set)]) - inv_cost = out_materials.inv_cost.drop_duplicates().drop(["scenario_version", "scenario"], axis=1) - scen.add_par("inv_cost", inv_cost[inv_cost["technology"].isin(tec_set)]) - - scen.commit(f"update cost assumption to: {ssp}") - scen.solve(model="MESSAGE", solve_options={"barcrossalg":2, "scaind":1}) - @cli.command("modify_cost") @click.option("--ssp", default="SSP2", help="Suffix to the scenario name") -@click.option("--scen_name", default='SSP_supply_cost_test_baseline_macro') +@click.option("--scen_name", default="SSP_supply_cost_test_baseline_macro") @click.pass_obj def modify_costs_with_tool(context, scen_name, ssp): import message_ix from message_ix_models.tools.costs.config import Config from message_ix_models.tools.costs.projections import create_cost_projections + mp = ixmp.Platform("ixmp_dev") base = message_ix.Scenario(mp, "MESSAGEix-Materials", scenario=scen_name) scen = base.clone(model=base.model, scenario=base.scenario.replace("baseline", ssp)) tec_set = list(scen.set("technology")) - cfg = Config(module="materials", ref_region="R12_NAM", method="convergence", format="message", scenario=ssp) + cfg = Config( + module="materials", + ref_region="R12_NAM", + method="convergence", + format="message", + scenario=ssp, + ) out_materials = create_cost_projections( node=cfg.node, @@ -583,9 +581,13 @@ def modify_costs_with_tool(context, scen_name, ssp): format=cfg.format, ) scen.check_out() - fix_cost = out_materials.fix_cost.drop_duplicates().drop(["scenario_version", "scenario"], axis=1) + fix_cost = out_materials.fix_cost.drop_duplicates().drop( + ["scenario_version", "scenario"], axis=1 + ) scen.add_par("fix_cost", fix_cost[fix_cost["technology"].isin(tec_set)]) - inv_cost = out_materials.inv_cost.drop_duplicates().drop(["scenario_version", "scenario"], axis=1) + inv_cost = out_materials.inv_cost.drop_duplicates().drop( + ["scenario_version", "scenario"], axis=1 + ) scen.add_par("inv_cost", inv_cost[inv_cost["technology"].isin(tec_set)]) scen.commit(f"update cost assumption to: {ssp}") @@ -594,24 +596,32 @@ def modify_costs_with_tool(context, scen_name, ssp): @cli.command("run_cbud_scenario") @click.option("--ssp", default="SSP2", help="Suffix to the scenario name") -@click.option("--scenario", default="1000f", help="description of carbon budget for mitigation target") +@click.option( + "--scenario", + default="1000f", + help="description of carbon budget for mitigation target", +) @click.pass_obj -def modify_costs_with_tool(context, ssp, scenario): +def run_cbud_scenario(context, ssp, scenario): import message_ix - from message_ix_models.tools.costs.config import Config - from message_ix_models.tools.costs.projections import create_cost_projections if scenario == "1000f": budget = 3667 - if scenario == "650f": + elif scenario == "650f": budget = 1750 else: - print("chosen budget not available yet please choose 600f or 1000f") + print("chosen budget not available yet please choose 650f or 1000f") return mp = ixmp.Platform("ixmp_dev") - base = message_ix.Scenario(mp, "MESSAGEix-Materials", scenario=f"SSP_supply_cost_test_{ssp}_macro") - scenario_cbud = base.clone(model=base.model, scenario=base.scenario + "_" + scenario, shift_first_model_year=2025) + base = message_ix.Scenario( + mp, "MESSAGEix-Materials", scenario=f"SSP_supply_cost_test_{ssp}_macro" + ) + scenario_cbud = base.clone( + model=base.model, + scenario=base.scenario + "_" + scenario, + shift_first_model_year=2025, + ) emission_dict = { "node": "World", @@ -620,39 +630,53 @@ def modify_costs_with_tool(context, ssp, scenario): "type_year": "cumulative", "unit": "???", } - df = message_ix.make_df( - "bound_emission", value=budget, **emission_dict - ) + df = message_ix.make_df("bound_emission", value=budget, **emission_dict) scenario_cbud.check_out() scenario_cbud.add_par("bound_emission", df) scenario_cbud.commit("add emission bound") - pre_model_yrs = scenario_cbud.set("cat_year", {"type_year": "cumulative", "year": [2020, 2015, 2010]}) + pre_model_yrs = scenario_cbud.set( + "cat_year", {"type_year": "cumulative", "year": [2020, 2015, 2010]} + ) scenario_cbud.check_out() scenario_cbud.remove_set("cat_year", pre_model_yrs) scenario_cbud.commit("remove cumulative years from cat_year set") scenario_cbud.set("cat_year", {"type_year": "cumulative"}) - scenario_cbud.solve(model="MESSAGE-MACRO",solve_options={"scaind":-1}) + scenario_cbud.solve(model="MESSAGE-MACRO", solve_options={"scaind": -1}) return @cli.command("run_LED_cprice_scenario") @click.option("--ssp", default="SSP2", help="Suffix to the scenario name") -@click.option("--scenario", default="1000f", help="description of carbon budget for mitigation target") +@click.option( + "--scenario", + default="1000f", + help="description of carbon budget for mitigation target", +) @click.pass_obj def modify_costs_with_tool(context, ssp, scenario): import message_ix - from message_ix_models.tools.costs.config import Config - from message_ix_models.tools.costs.projections import create_cost_projections mp = ixmp.Platform("ixmp_dev") if scenario == "1000f": - price_scen = message_ix.Scenario(mp, "MESSAGEix-Materials", scenario=f"SSP_supply_cost_test_LED_macro_1000f") + price_scen = message_ix.Scenario( + mp, "MESSAGEix-Materials", scenario="SSP_supply_cost_test_LED_macro_1000f" + ) if scenario == "650f": - price_scen = message_ix.Scenario(mp, "MESSAGEix-Materials", scenario=f"SSP_supply_cost_test_LED_macro_{scenario}") - - base = message_ix.Scenario(mp, "MESSAGEix-Materials", scenario=f"SSP_supply_cost_test_{ssp}_macro") - scen_cprice = base.clone(model=base.model, scenario=base.scenario + f"_{scenario}_LED_prices", shift_first_model_year=2025) + price_scen = message_ix.Scenario( + mp, + "MESSAGEix-Materials", + scenario=f"SSP_supply_cost_test_LED_macro_{scenario}", + ) + + base = message_ix.Scenario( + mp, "MESSAGEix-Materials", scenario=f"SSP_supply_cost_test_{ssp}_macro" + ) + scen_cprice = base.clone( + model=base.model, + scenario=base.scenario + f"_{scenario}_LED_prices", + shift_first_model_year=2025, + ) tax_emission_new = price_scen.var("PRICE_EMISSION") @@ -660,8 +684,8 @@ def modify_costs_with_tool(context, ssp, scenario): tax_emission_new.columns = scen_cprice.par("tax_emission").columns tax_emission_new["unit"] = "USD/tCO2" scen_cprice.add_par("tax_emission", tax_emission_new) - scen_cprice.commit('2 degree LED prices are added') - print('New LED 1000f carbon prices added') + scen_cprice.commit("2 degree LED prices are added") + print("New LED 1000f carbon prices added") - scen_cprice.solve(model="MESSAGE-MACRO", solve_options={"scaind":-1}) + scen_cprice.solve(model="MESSAGE-MACRO", solve_options={"scaind": -1}) return diff --git a/message_ix_models/model/material/data_aluminum.py b/message_ix_models/model/material/data_aluminum.py index 10ec6c200c..6c1eb235e6 100644 --- a/message_ix_models/model/material/data_aluminum.py +++ b/message_ix_models/model/material/data_aluminum.py @@ -1,28 +1,21 @@ import pandas as pd -import numpy as np from collections import defaultdict from .data_util import read_timeseries from pathlib import Path -import message_ix -import ixmp +from .material_demand import material_demand_calc from .util import read_config from .data_util import read_rel from message_ix_models import ScenarioInfo from message_ix import make_df from message_ix_models.util import ( broadcast, - make_io, - make_matched_dfs, same_node, - add_par_data, private_data_path, ) # Get endogenous material demand from buildings interface -from .data_buildings import get_scen_mat_demand -from . import get_spec def read_data_aluminum(scenario): @@ -45,14 +38,18 @@ def read_data_aluminum(scenario): sheet_n_relations = "relations_R11" # Read the file - data_alu = pd.read_excel(private_data_path("material", "aluminum", fname), sheet_name=sheet_n) + data_alu = pd.read_excel( + private_data_path("material", "aluminum", fname), sheet_name=sheet_n + ) # Drop columns that don't contain useful information data_alu = data_alu.drop(["Source", "Description"], axis=1) data_alu_rel = read_rel(scenario, "aluminum", "aluminum_techno_economic.xlsx") - data_aluminum_ts = read_timeseries(scenario, "aluminum", "aluminum_techno_economic.xlsx") + data_aluminum_ts = read_timeseries( + scenario, "aluminum", "aluminum_techno_economic.xlsx" + ) # Unit conversion @@ -283,17 +280,7 @@ def gen_data_aluminum(scenario, dry_run=False): # Create external demand param parname = "demand" - demand = derive_aluminum_demand(scenario) - df = make_df( - parname, - level="demand", - commodity="aluminum", - value=demand.value, - unit="t", - year=demand.year, - time="year", - node=demand.node, - ) # .pipe(broadcast, node=nodes) + df = material_demand_calc.derive_demand("aluminum", scenario, old_gdp=False) results[parname].append(df) # Special treatment for time-varying params @@ -455,6 +442,7 @@ def gen_data_aluminum(scenario, dry_run=False): results_aluminum = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} return results_aluminum + def gen_mock_demand_aluminum(scenario): context = read_config() diff --git a/message_ix_models/model/material/data_ammonia_new.py b/message_ix_models/model/material/data_ammonia_new.py index 9caa1d08c3..e5ecaa5abc 100644 --- a/message_ix_models/model/material/data_ammonia_new.py +++ b/message_ix_models/model/material/data_ammonia_new.py @@ -4,17 +4,21 @@ from message_ix_models import ScenarioInfo from message_ix import make_df from message_ix_models.util import broadcast, same_node, private_data_path -from .util import read_config +from message_data.model.material.util import read_config CONVERSION_FACTOR_NH3_N = 17 / 14 context = read_config() -default_gdp_elasticity = pd.read_excel( +default_gdp_elasticity = ( + pd.read_excel( private_data_path( "material", "methanol", "methanol_sensitivity_pars.xlsx", ) - ).set_index("par").to_dict()["value"]["nh3_elasticity"] + ) + .set_index("par") + .to_dict()["value"]["nh3_elasticity"] +) # float(0.65) # old default value @@ -25,7 +29,7 @@ def gen_all_NH3_fert(scenario, dry_run=False): **gen_data_ts(scenario), **gen_demand(), **gen_land_input(scenario), - **gen_resid_demand_NH3(scenario, default_gdp_elasticity) + **gen_resid_demand_NH3(scenario, default_gdp_elasticity), } @@ -39,14 +43,14 @@ def gen_data(scenario, dry_run=False, add_ccs: bool = True, lower_costs=False): nodes.pop(nodes.index("R12_GLB")) df = pd.read_excel( - context.get_local_path( + private_data_path( "material", "ammonia", "fert_techno_economic.xlsx", ), sheet_name="data_R12", ) - df.groupby("parameter") + par_dict = {key: value for (key, value) in df.groupby("parameter")} for i in par_dict.keys(): par_dict[i] = par_dict[i].dropna(axis=1) @@ -84,8 +88,10 @@ def gen_data(scenario, dry_run=False, add_ccs: bool = True, lower_costs=False): ), ) df_new["year_vtg"] = df_new["year_act"] - 5 * df_new["year_vtg"] - #remove years that are not in scenario set - df_new = df_new[~df_new["year_vtg"].isin([2065, 2075, 2085, 2095, 2105])] + # remove years that are not in scenario set + df_new = df_new[ + ~df_new["year_vtg"].isin([2065, 2075, 2085, 2095, 2105]) + ] else: if "year_vtg" in df_new.columns: df_new = df_new.pipe(same_node).pipe(broadcast, year_vtg=vtg_years) @@ -98,12 +104,14 @@ def gen_data(scenario, dry_run=False, add_ccs: bool = True, lower_costs=False): par_dict = experiment_lower_CPA_SAS_costs(par_dict) df_lifetime = par_dict.get("technical_lifetime") - dict_lifetime = ( df_lifetime.loc[:,["technology", "value"]] - .set_index("technology") - .to_dict()["value"] ) + dict_lifetime = ( + df_lifetime.loc[:, ["technology", "value"]] + .set_index("technology") + .to_dict()["value"] + ) class missingdict(dict): - def __missing__(self,key): + def __missing__(self, key): return 1 dict_lifetime = missingdict(dict_lifetime) @@ -111,24 +119,27 @@ def __missing__(self,key): if ("year_vtg" in par_dict[i].columns) & ("year_act" in par_dict[i].columns): df_temp = par_dict[i] df_temp["lifetime"] = df_temp["technology"].map(dict_lifetime) - df_temp = df_temp[(df_temp["year_act"] - df_temp["year_vtg"]) < df_temp["lifetime"]] + df_temp = df_temp[ + (df_temp["year_act"] - df_temp["year_vtg"]) <= df_temp["lifetime"] + ] par_dict[i] = df_temp.drop("lifetime", axis="columns") pars = ["inv_cost", "fix_cost"] - tec_list = ["biomass_NH3", - "electr_NH3", - "gas_NH3", - "coal_NH3", - "fueloil_NH3", - "biomass_NH3_ccs", - "gas_NH3_ccs", - "coal_NH3_ccs", - "fueloil_NH3_ccs"] + tec_list = [ + "biomass_NH3", + "electr_NH3", + "gas_NH3", + "coal_NH3", + "fueloil_NH3", + "biomass_NH3_ccs", + "gas_NH3_ccs", + "coal_NH3_ccs", + "fueloil_NH3_ccs", + ] cost_conv = pd.read_excel( - private_data_path( - "material", - "ammonia", - "cost_conv_nh3.xlsx"), - sheet_name="Sheet1", index_col=0) + private_data_path("material", "ammonia", "cost_conv_nh3.xlsx"), + sheet_name="Sheet1", + index_col=0, + ) for p in pars: conv_cost_df = pd.DataFrame() df = par_dict[p] @@ -138,10 +149,12 @@ def __missing__(self,key): else: year_col = "year_act" - df_tecs = df[df["technology"]==tec] + df_tecs = df[df["technology"] == tec] df_tecs = df_tecs.merge(cost_conv, left_on=year_col, right_index=True) df_tecs_nam = df_tecs[df_tecs["node_loc"] == "R12_NAM"] - df_tecs = df_tecs.merge(df_tecs_nam[[year_col, "value"]], left_on=year_col, right_on=year_col) + df_tecs = df_tecs.merge( + df_tecs_nam[[year_col, "value"]], left_on=year_col, right_on=year_col + ) df_tecs["diff"] = df_tecs["value_x"] - df_tecs["value_y"] df_tecs["diff"] = df_tecs["diff"] * (1 - df_tecs["convergence"]) df_tecs["new_val"] = df_tecs["value_x"] - df_tecs["diff"] @@ -162,7 +175,7 @@ def gen_data_rel(scenario, dry_run=False, add_ccs: bool = True): nodes.pop(nodes.index("R12_GLB")) df = pd.read_excel( - context.get_local_path( + private_data_path( "material", "ammonia", "fert_techno_economic.xlsx", @@ -171,8 +184,8 @@ def gen_data_rel(scenario, dry_run=False, add_ccs: bool = True): ) df.groupby("parameter") par_dict = {key: value for (key, value) in df.groupby("parameter")} - #for i in par_dict.keys(): - #par_dict[i] = par_dict[i].dropna(axis=1) + # for i in par_dict.keys(): + # par_dict[i] = par_dict[i].dropna(axis=1) act_years = s_info.yv_ya[s_info.yv_ya.year_vtg > 2000]["year_act"] act_years = act_years.drop_duplicates() @@ -200,10 +213,17 @@ def same_node_if_nan(df): df_all_regs = df_all_regs.apply(lambda x: same_node_if_nan(x), axis=1) if "node_loc" in df_single_regs.columns: - df_single_regs["node_loc"] = df_single_regs["node_loc"].apply(lambda x: None if x == "all" else x) + df_single_regs["node_loc"] = df_single_regs["node_loc"].apply( + lambda x: None if x == "all" else x + ) df_new_reg_all_regs = df_single_regs[df_single_regs["node_loc"].isna()] df_new_reg_all_regs = df_new_reg_all_regs.pipe(broadcast, node_loc=nodes) - df_single_regs = pd.concat([df_single_regs[~df_single_regs["node_loc"].isna()], df_new_reg_all_regs]) + df_single_regs = pd.concat( + [ + df_single_regs[~df_single_regs["node_loc"].isna()], + df_new_reg_all_regs, + ] + ) df = pd.concat([df_single_regs, df_all_regs]) @@ -231,7 +251,7 @@ def gen_data_ts(scenario, dry_run=False, add_ccs: bool = True): nodes.pop(nodes.index("R12_GLB")) df = pd.read_excel( - context.get_local_path( + private_data_path( "material", "ammonia", "fert_techno_economic.xlsx", @@ -262,10 +282,16 @@ def gen_data_ts(scenario, dry_run=False, add_ccs: bool = True): set_exp_imp_nodes(df_new) par_dict[par_name] = df_new - #convert floats - par_dict["historical_activity"] = par_dict["historical_activity"].astype({'year_act': 'int32'}) - par_dict["historical_new_capacity"] = par_dict["historical_new_capacity"].astype({'year_vtg': 'int32'}) - par_dict["bound_activity_lo"] = par_dict["bound_activity_lo"].astype({'year_act': 'int32'}) + # convert floats + par_dict["historical_activity"] = par_dict["historical_activity"].astype( + {"year_act": "int32"} + ) + par_dict["historical_new_capacity"] = par_dict["historical_new_capacity"].astype( + {"year_vtg": "int32"} + ) + par_dict["bound_activity_lo"] = par_dict["bound_activity_lo"].astype( + {"year_act": "int32"} + ) return par_dict @@ -283,7 +309,7 @@ def read_demand(): context = read_config() N_demand_GLO = pd.read_excel( - context.get_local_path( + private_data_path( "material", "ammonia", "nh3_fertilizer_demand.xlsx", @@ -293,7 +319,7 @@ def read_demand(): # NH3 feedstock share by region in 2010 (from http://ietd.iipnetwork.org/content/ammonia#benchmarks) feedshare_GLO = pd.read_excel( - context.get_local_path( + private_data_path( "material", "ammonia", "nh3_fertilizer_demand.xlsx", @@ -304,9 +330,7 @@ def read_demand(): # Read parameters in xlsx te_params = data = pd.read_excel( - context.get_local_path( - "material", "ammonia", "nh3_fertilizer_demand.xlsx" - ), + private_data_path("material", "ammonia", "nh3_fertilizer_demand.xlsx"), sheet_name="old_TE_sheet", engine="openpyxl", nrows=72, @@ -343,15 +367,17 @@ def read_demand(): N_energy.gas_pct *= input_fuel[2] * CONVERSION_FACTOR_NH3_N # NH3 / N N_energy.coal_pct *= input_fuel[3] * CONVERSION_FACTOR_NH3_N N_energy.oil_pct *= input_fuel[4] * CONVERSION_FACTOR_NH3_N - N_energy = pd.concat([N_energy.Region, N_energy.sum(axis=1, numeric_only=True)], axis=1).rename( + N_energy = pd.concat( + [N_energy.Region, N_energy.sum(axis=1, numeric_only=True)], axis=1 + ).rename( columns={0: "totENE", "Region": "node"} ) # GWa # N_trade_R12 = pd.read_csv( - # context.get_local_path("material", "ammonia", "trade.FAO.R12.csv"), index_col=0 + # private_data_path("material", "ammonia", "trade.FAO.R12.csv"), index_col=0 # ) N_trade_R12 = pd.read_excel( - context.get_local_path( + private_data_path( "material", "ammonia", "nh3_fertilizer_demand.xlsx", @@ -379,12 +405,12 @@ def read_demand(): NP["prod"] = NP.demand - NP.netimp # NH3_trade_R12 = pd.read_csv( - # context.get_local_path( + # private_data_path( # "material", "ammonia", "NH3_trade_BACI_R12_aggregation.csv" # ) # ) # , index_col=0) NH3_trade_R12 = pd.read_excel( - context.get_local_path( + private_data_path( "material", "ammonia", "nh3_fertilizer_demand.xlsx", @@ -418,9 +444,9 @@ def read_demand(): N_feed.gas_pct *= input_fuel[2] * 17 / 14 N_feed.coal_pct *= input_fuel[3] * 17 / 14 N_feed.oil_pct *= input_fuel[4] * 17 / 14 - N_feed = pd.concat([N_feed.Region, N_feed.sum(axis=1, numeric_only=True)], axis=1).rename( - columns={0: "totENE", "Region": "node"} - ) + N_feed = pd.concat( + [N_feed.Region, N_feed.sum(axis=1, numeric_only=True)], axis=1 + ).rename(columns={0: "totENE", "Region": "node"}) # Process the regional historical activities @@ -468,9 +494,8 @@ def gen_demand(): N_energy = read_demand()["N_feed"] # updated feed with imports accounted demand_fs_org = pd.read_excel( - context.get_local_path("material", "ammonia", - "nh3_fertilizer_demand.xlsx"), - sheet_name="demand_i_feed_R12" + private_data_path("material", "ammonia", "nh3_fertilizer_demand.xlsx"), + sheet_name="demand_i_feed_R12", ) df = demand_fs_org.loc[demand_fs_org.year == 2010, :].join( @@ -495,7 +520,7 @@ def gen_resid_demand_NH3(scenario, gdp_elasticity): context = read_config() s_info = ScenarioInfo(scenario) - modelyears = s_info.Y #s_info.Y is only for modeling years + modelyears = s_info.Y # s_info.Y is only for modeling years nodes = s_info.N def get_demand_t1_with_income_elasticity( @@ -506,7 +531,7 @@ def get_demand_t1_with_income_elasticity( ) + demand_t0 df_gdp = pd.read_excel( - context.get_local_path("material", "methanol", "methanol demand.xlsx"), + private_data_path("material", "methanol", "methanol demand.xlsx"), sheet_name="GDP_baseline", ) @@ -535,13 +560,13 @@ def get_demand_t1_with_income_elasticity( if "R12_CHN" in nodes: nodes.remove("R12_GLB") - region_set = 'R12_' + region_set = "R12_" dem_2020 = np.array([2.5, 2.5, 4, 7, 2.5, 5.6, 7, 2.5, 2.5, 7, 5.6, 18]) dem_2020 = pd.Series(dem_2020) else: nodes.remove("R11_GLB") - region_set = 'R11_' + region_set = "R11_" dem_2020 = np.array([2.5, 19.5, 4, 7, 2.5, 5.6, 7, 2.5, 2.5, 7, 5.6]) dem_2020 = pd.Series(dem_2020) diff --git a/message_ix_models/model/material/data_cement.py b/message_ix_models/model/material/data_cement.py index 10027a3fff..6bb3b3fbec 100644 --- a/message_ix_models/model/material/data_cement.py +++ b/message_ix_models/model/material/data_cement.py @@ -1,27 +1,21 @@ from .data_util import read_sector_data, read_timeseries -import numpy as np from collections import defaultdict -import logging from pathlib import Path import pandas as pd +from .material_demand import material_demand_calc from .util import read_config from message_ix_models import ScenarioInfo from message_ix import make_df from message_ix_models.util import ( broadcast, - make_io, - make_matched_dfs, same_node, - add_par_data, private_data_path, ) # Get endogenous material demand from buildings interface -from .data_buildings import get_scen_mat_demand -from . import get_spec # gdp_growth = [0.121448215899944, 0.0733079014579874, 0.0348154093342843, \ @@ -380,17 +374,18 @@ def gen_data_cement(scenario, dry_run=False): # Create external demand param parname = "demand" # demand = gen_mock_demand_cement(scenario) - demand = derive_cement_demand(scenario) - df = make_df( - parname, - level="demand", - commodity="cement", - value=demand.value, - unit="t", - year=demand.year, - time="year", - node=demand.node, - ) + # demand = derive_cement_demand(scenario) + # df = make_df( + # parname, + # level="demand", + # commodity="cement", + # value=demand.value, + # unit="t", + # year=demand.year, + # time="year", + # node=demand.node, + # ) + df = material_demand_calc.derive_demand("cement", scenario, old_gdp=False) results[parname].append(df) # Add CCS as addon diff --git a/message_ix_models/model/material/data_generic.py b/message_ix_models/model/material/data_generic.py index 7046bb7d7a..3b15934527 100644 --- a/message_ix_models/model/material/data_generic.py +++ b/message_ix_models/model/material/data_generic.py @@ -1,5 +1,4 @@ from collections import defaultdict -import logging import pandas as pd @@ -9,10 +8,7 @@ from .data_util import read_timeseries from message_ix_models.util import ( broadcast, - make_io, - make_matched_dfs, same_node, - add_par_data, ) @@ -27,15 +23,20 @@ def read_data_generic(scenario): # sets = context["material"]["generic"] # Read the file - data_generic = pd.read_excel(context.get_local_path( + data_generic = pd.read_excel( + context.get_local_path( "material", "other", "generic_furnace_boiler_techno_economic.xlsx" - ), sheet_name="generic",) + ), + sheet_name="generic", + ) # Clean the data # Drop columns that don't contain useful information data_generic = data_generic.drop(["Region", "Source", "Description"], axis=1) - data_generic_ts = read_timeseries(scenario, "other", "generic_furnace_boiler_techno_economic.xlsx") + data_generic_ts = read_timeseries( + scenario, "other", "generic_furnace_boiler_techno_economic.xlsx" + ) # Unit conversion @@ -68,11 +69,10 @@ def gen_data_generic(scenario, dry_run=False): yv_ya = s_info.yv_ya fmy = s_info.y0 - # Do not parametrize GLB region the same way if "R11_GLB" in nodes: nodes.remove("R11_GLB") - global_region = 'R11_GLB' + global_region = "R11_GLB" if "R12_GLB" in nodes: nodes.remove("R12_GLB") global_region = "R12_GLB" @@ -268,7 +268,6 @@ def gen_data_generic(scenario, dry_run=False): results[p].append(df) - results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} return results diff --git a/message_ix_models/model/material/data_methanol_new.py b/message_ix_models/model/material/data_methanol_new.py index ea0f01f36c..2a27b02afe 100644 --- a/message_ix_models/model/material/data_methanol_new.py +++ b/message_ix_models/model/material/data_methanol_new.py @@ -42,7 +42,7 @@ def gen_data_methanol_new(scenario): if scenario.model == "SSP_dev_SSP2_v0.1": file_path = "C:/Users\maczek\PycharmProjects\message_data\message_data\model\material\petrochemical model fixes notebooks\missing_rels.yaml" - with open(file_path, 'r') as file: + with open(file_path, "r") as file: missing_rels = yaml.safe_load(file) df = pars_dict["relation_activity"] pars_dict["relation_activity"] = df[~df["relation"].isin(missing_rels)] @@ -59,7 +59,9 @@ def broadcast_reduced_df(df, par_name): node_cols = [i for i in df_final.columns if "node" in i] node_cols_codes = {} for col in node_cols: - node_cols_codes[col] = pd.Series(''.join(x for x in df_final.loc[i][col] if not x in remove).split(",")) + node_cols_codes[col] = pd.Series( + "".join(x for x in df_final.loc[i][col] if x not in remove).split(",") + ) df_bc_node = make_df(par_name, **df_final.loc[i]) # brodcast in year dimensions @@ -73,28 +75,45 @@ def broadcast_reduced_df(df, par_name): # broadcast in node dimensions if len(node_cols) == 1: if "node_loc" in node_cols: - df_bc_node = df_bc_node.pipe(broadcast, node_loc=node_cols_codes["node_loc"]) + df_bc_node = df_bc_node.pipe( + broadcast, node_loc=node_cols_codes["node_loc"] + ) if "node_vtg" in node_cols: - df_bc_node = df_bc_node.pipe(broadcast, node_vtg=node_cols_codes["node_vtg"]) + df_bc_node = df_bc_node.pipe( + broadcast, node_vtg=node_cols_codes["node_vtg"] + ) if "node_rel" in node_cols: - df_bc_node = df_bc_node.pipe(broadcast, node_rel=node_cols_codes["node_rel"]) + df_bc_node = df_bc_node.pipe( + broadcast, node_rel=node_cols_codes["node_rel"] + ) if "node" in node_cols: df_bc_node = df_bc_node.pipe(broadcast, node=node_cols_codes["node"]) if "node_share" in node_cols: - df_bc_node = df_bc_node.pipe(broadcast, node_share=node_cols_codes["node_share"]) + df_bc_node = df_bc_node.pipe( + broadcast, node_share=node_cols_codes["node_share"] + ) else: - df_bc_node = df_bc_node.pipe(broadcast, node_loc=node_cols_codes["node_loc"]) + df_bc_node = df_bc_node.pipe( + broadcast, node_loc=node_cols_codes["node_loc"] + ) if len(df_final.loc[i][node_cols].T.unique()) == 1: # df_bc_node["node_rel"] = df_bc_node["node_loc"] df_bc_node = df_bc_node.pipe( - same_node) # not working for node_rel in installed message_ix_models version + same_node + ) # not working for node_rel in installed message_ix_models version else: if "node_rel" in list(df_bc_node.columns): - df_bc_node = df_bc_node.pipe(broadcast, node_rel=node_cols_codes["node_rel"]) + df_bc_node = df_bc_node.pipe( + broadcast, node_rel=node_cols_codes["node_rel"] + ) if "node_origin" in list(df_bc_node.columns): - df_bc_node = df_bc_node.pipe(broadcast, node_origin=node_cols_codes["node_origin"]) + df_bc_node = df_bc_node.pipe( + broadcast, node_origin=node_cols_codes["node_origin"] + ) if "node_dest" in list(df_bc_node.columns): - df_bc_node = df_bc_node.pipe(broadcast, node_dest=node_cols_codes["node_dest"]) + df_bc_node = df_bc_node.pipe( + broadcast, node_dest=node_cols_codes["node_dest"] + ) for col in yr_col_inp: yr_cols_codes[col] = literal_eval(df_bc_node[col].values[0]) @@ -114,11 +133,17 @@ def broadcast_reduced_df(df, par_name): if "year_vtg" in yr_col_out: y_v = [str(i) for i in yr_cols_codes[col]] df_bc_node = df_bc_node.pipe(broadcast, year_vtg=y_v) - df_bc_node["year_act"] = [literal_eval(i)[1] for i in df_bc_node["year_vtg"]] - df_bc_node["year_vtg"] = [literal_eval(i)[0] for i in df_bc_node["year_vtg"]] + df_bc_node["year_act"] = [ + literal_eval(i)[1] for i in df_bc_node["year_vtg"] + ] + df_bc_node["year_vtg"] = [ + literal_eval(i)[0] for i in df_bc_node["year_vtg"] + ] if "year_rel" in yr_col_out: if "year_act" in yr_col_out: - df_bc_node = df_bc_node.pipe(broadcast, year_act=[i[0] for i in yr_cols_codes[col]]) + df_bc_node = df_bc_node.pipe( + broadcast, year_act=[i[0] for i in yr_cols_codes[col]] + ) df_bc_node["year_rel"] = df_bc_node["year_act"] # return df_bc_node # df_bc_node["year_rel"] = df_bc_node["year_act"] @@ -127,6 +152,10 @@ def broadcast_reduced_df(df, par_name): df_final_full = pd.concat([df_final_full, df_bc_node]) df_final_full = df_final_full.drop_duplicates().reset_index(drop=True) if par_name == "relation_activity": - df_final_full = df_final_full.drop(df_final_full[(df_final_full.node_rel.values != "R12_GLB") & - (df_final_full.node_rel.values != df_final_full.node_loc.values)].index) + df_final_full = df_final_full.drop( + df_final_full[ + (df_final_full.node_rel.values != "R12_GLB") + & (df_final_full.node_rel.values != df_final_full.node_loc.values) + ].index + ) return make_df(par_name, **df_final_full) diff --git a/message_ix_models/model/material/data_petro.py b/message_ix_models/model/material/data_petro.py index d6359641cb..6912763c07 100644 --- a/message_ix_models/model/material/data_petro.py +++ b/message_ix_models/model/material/data_petro.py @@ -1,11 +1,9 @@ import pandas as pd import numpy as np -from collections import defaultdict -from .data_util import read_timeseries, read_rel - -import message_ix -from .util import read_config +from collections import defaultdict +from message_data.model.material.data_util import read_timeseries, read_rel +from message_data.model.material.util import read_config from message_ix_models import ScenarioInfo from message_ix import make_df from message_ix_models.util import ( @@ -32,8 +30,9 @@ def read_data_petrochemicals(scenario): sheet_n = "data_R11" # Read the file - data_petro = pd.read_excel(private_data_path("material", "petrochemicals", fname), - sheet_name=sheet_n) + data_petro = pd.read_excel( + private_data_path("material", "petrochemicals", fname), sheet_name=sheet_n + ) # Clean the data data_petro = data_petro.drop(["Source", "Description"], axis=1) @@ -45,7 +44,7 @@ def gen_mock_demand_petro(scenario, gdp_elasticity_2020, gdp_elasticity_2030): context = read_config() s_info = ScenarioInfo(scenario) - modelyears = s_info.Y #s_info.Y is only for modeling years + modelyears = s_info.Y # s_info.Y is only for modeling years fy = scenario.firstmodelyear nodes = s_info.N @@ -53,18 +52,32 @@ def get_demand_t1_with_income_elasticity( demand_t0, income_t0, income_t1, elasticity ): return ( - elasticity * demand_t0 * ((income_t1 - income_t0) / income_t0) + elasticity * demand_t0.mul(((income_t1 - income_t0) / income_t0), axis=0) ) + demand_t0 - df_gdp = scenario.par("bound_activity_lo", filters={"technology": "GDP"}) - df = df_gdp.pivot(columns="year_act", values="value", index="node_loc").reset_index() - df["node_loc"] = df["node_loc"].str.replace("R12_", "") - df = df.rename({"node_loc": "Region"}, axis=1) - - df_demand = df.copy(deep=True) - num_cols = [i for i in df_demand.columns if type(i) == int] - hist_yrs = [i for i in num_cols if i <= fy] - df_demand = df_demand.drop(hist_yrs, axis=1) + gdp_mer = scenario.par("bound_activity_up", {"technology": "GDP"}) + mer_to_ppp = pd.read_csv( + private_data_path("material", "other", "mer_to_ppp_default.csv") + ).set_index("node", "year") + # mer_to_ppp = scenario.par("MERtoPPP").set_index("node", "year") TODO: might need to be re-activated for different SSPs + gdp_mer = gdp_mer.merge( + mer_to_ppp.reset_index()[["node", "year", "value"]], + left_on=["node_loc", "year_act"], + right_on=["node", "year"], + ) + gdp_mer["gdp_ppp"] = gdp_mer["value_y"] * gdp_mer["value_x"] + gdp_mer = gdp_mer[["year", "node_loc", "gdp_ppp"]].reset_index() + gdp_mer["Region"] = gdp_mer["node_loc"] # .str.replace("R12_", "") + df_gdp_ts = gdp_mer.pivot( + index="Region", columns="year", values="gdp_ppp" + ).reset_index() + num_cols = [i for i in df_gdp_ts.columns if type(i) == int] + hist_yrs = [i for i in num_cols if i < fy] + df_gdp_ts = ( + df_gdp_ts.drop([i for i in hist_yrs if i in df_gdp_ts.columns], axis=1) + .set_index("Region") + .sort_index() + ) # 2018 production # Use as 2020 @@ -78,39 +91,50 @@ def get_demand_t1_with_income_elasticity( # r = ['R12_AFR', 'R12_RCPA', 'R12_EEU', 'R12_FSU', 'R12_LAM', 'R12_MEA',\ # 'R12_NAM', 'R12_PAO', 'R12_PAS', 'R12_SAS', 'R12_WEU',"R12_CHN"] - if "R12_CHN" in nodes: - nodes.remove("R12_GLB") - region_set = 'R12_' - dem_2020 = np.array([2.4, 0.44, 3, 5, 11, 40.3, 49.8, 11, 37.5, 10.7, 29.2, 50.5]) - dem_2020 = pd.Series(dem_2020) + # if "R12_CHN" in nodes: + # nodes.remove("R12_GLB") + # dem_2020 = np.array([2.4, 0.44, 3, 5, 11, 40.3, 49.8, 11, 37.5, 10.7, 29.2, 50.5]) + # dem_2020 = pd.Series(dem_2020) + # + # else: + # nodes.remove("R11_GLB") + # dem_2020 = np.array([2, 75, 30, 4, 11, 42, 60, 32, 30, 29, 35]) + # dem_2020 = pd.Series(dem_2020) - else: - nodes.remove("R11_GLB") - region_set = 'R11_' - dem_2020 = np.array([2, 75, 30, 4, 11, 42, 60, 32, 30, 29, 35]) - dem_2020 = pd.Series(dem_2020) + from message_data.model.material.material_demand.material_demand_calc import ( + read_base_demand, + ) - df_demand[fy] = dem_2020 + df_demand_2020 = read_base_demand( + private_data_path() / "material" / "petrochemicals/demand_petro.yaml" + ) + df_demand_2020 = df_demand_2020.rename({"region": "Region"}, axis=1) + df_demand = df_demand_2020.pivot(index="Region", columns="year", values="value") + dem_next_yr = df_demand for i in range(len(modelyears) - 1): income_year1 = modelyears[i] income_year2 = modelyears[i + 1] if income_year2 >= 2030: - dem_2020 = get_demand_t1_with_income_elasticity( - dem_2020, df[income_year1], df[income_year2], gdp_elasticity_2030 + dem_next_yr = get_demand_t1_with_income_elasticity( + dem_next_yr, + df_gdp_ts[income_year1], + df_gdp_ts[income_year2], + gdp_elasticity_2030, ) else: - dem_2020 = get_demand_t1_with_income_elasticity( - dem_2020, df[income_year1], df[income_year2], gdp_elasticity_2020 + dem_next_yr = get_demand_t1_with_income_elasticity( + dem_next_yr, + df_gdp_ts[income_year1], + df_gdp_ts[income_year2], + gdp_elasticity_2020, ) - df_demand[income_year2] = dem_2020 + df_demand[income_year2] = dem_next_yr - df_melt = df_demand.melt( - id_vars=["Region"], value_vars=df_demand.columns, var_name="year" - ) + df_melt = df_demand.melt(ignore_index=False).reset_index() - return message_ix.make_df( + return make_df( "demand", unit="t", level="demand", @@ -118,7 +142,7 @@ def get_demand_t1_with_income_elasticity( time="year", commodity="HVC", year=df_melt.year, - node=(region_set + df_melt["Region"]), + node=df_melt["Region"], ) @@ -132,8 +156,9 @@ def gen_data_petro_chemicals(scenario, dry_run=False): # Techno-economic assumptions data_petro = read_data_petrochemicals(scenario) - data_petro_ts = read_timeseries(scenario, "petrochemicals", - "petrochemicals_techno_economic.xlsx") + data_petro_ts = read_timeseries( + scenario, "petrochemicals", "petrochemicals_techno_economic.xlsx" + ) # List of data frames, to be concatenated together at end results = defaultdict(list) @@ -285,20 +310,17 @@ def gen_data_petro_chemicals(scenario, dry_run=False): .pipe(same_node) ) else: - df = ( - make_df( - param_name, - technology=t, - commodity=com, - level=lev, - mode=mod, - value=val[regions[regions == rg].index[0]], - node_loc=rg, - unit="t", - **common - ) - .pipe(same_node) - ) + df = make_df( + param_name, + technology=t, + commodity=com, + level=lev, + mode=mod, + value=val[regions[regions == rg].index[0]], + node_loc=rg, + unit="t", + **common + ).pipe(same_node) elif param_name == "share_mode_up": mod = split[1] @@ -352,9 +374,9 @@ def gen_data_petro_chemicals(scenario, dry_run=False): "year_act": "2020", "time": "year", "value": [0.4, 0.4], - "unit": "-" + "unit": "-", } - results["share_mode_lo"].append(message_ix.make_df("share_mode_lo", **share_dict)) + results["share_mode_lo"].append(make_df("share_mode_lo", **share_dict)) # Add demand # Create external demand param @@ -364,16 +386,16 @@ def gen_data_petro_chemicals(scenario, dry_run=False): context = read_config() df_pars = pd.read_excel( - private_data_path( - "material", "methanol", "methanol_sensitivity_pars.xlsx" - ), + private_data_path("material", "methanol", "methanol_sensitivity_pars.xlsx"), sheet_name="Sheet1", dtype=object, ) pars = df_pars.set_index("par").to_dict()["value"] default_gdp_elasticity_2020 = pars["hvc_elasticity_2020"] default_gdp_elasticity_2030 = pars["hvc_elasticity_2030"] - demand_HVC = gen_mock_demand_petro(scenario, default_gdp_elasticity_2020, default_gdp_elasticity_2030) + demand_HVC = gen_mock_demand_petro( + scenario, default_gdp_elasticity_2020, default_gdp_elasticity_2030 + ) results["demand"].append(demand_HVC) # df_e = make_df(paramname, level='final_material', commodity="ethylene", \ @@ -456,23 +478,33 @@ def gen_data_petro_chemicals(scenario, dry_run=False): results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} # modify steam cracker hist data (naphtha -> gasoil) to make model feasible - df_cap = pd.read_csv(private_data_path( + df_cap = pd.read_csv( + private_data_path( "material", "petrochemicals", "steam_cracking_hist_new_cap.csv" - )) - df_act = pd.read_csv(private_data_path( - "material", "petrochemicals", "steam_cracking_hist_act.csv" - )) - df_act.loc[df_act["mode"]=="naphtha", "mode"] = "vacuum_gasoil" + ) + ) + df_act = pd.read_csv( + private_data_path("material", "petrochemicals", "steam_cracking_hist_act.csv") + ) + df_act.loc[df_act["mode"] == "naphtha", "mode"] = "vacuum_gasoil" df = results["historical_activity"] - results["historical_activity"] = pd.concat([df.loc[df["technology"]!="steam_cracker_petro"], df_act]) + results["historical_activity"] = pd.concat( + [df.loc[df["technology"] != "steam_cracker_petro"], df_act] + ) df = results["historical_new_capacity"] - results["historical_new_capacity"] = pd.concat([df.loc[df["technology"]!="steam_cracker_petro"], df_cap]) + results["historical_new_capacity"] = pd.concat( + [df.loc[df["technology"] != "steam_cracker_petro"], df_cap] + ) # remove growth constraint for R12_AFR to make trade constraints feasible df = results["growth_activity_up"] - results["growth_activity_up"] = df[~((df["technology"]=="steam_cracker_petro") & - (df["node_loc"]=="R12_AFR") & - (df["year_act"]==2020))] + results["growth_activity_up"] = df[ + ~( + (df["technology"] == "steam_cracker_petro") + & (df["node_loc"] == "R12_AFR") + & (df["year_act"] == 2020) + ) + ] # add 25% total trade bound df_dem = results["demand"] @@ -487,7 +519,11 @@ def gen_data_petro_chemicals(scenario, dry_run=False): "time": "year", "unit": "-", } - results["bound_activity_up"] = pd.concat([results["bound_activity_up"], - message_ix.make_df("bound_activity_up", **df_dem, **par_dict)]) + results["bound_activity_up"] = pd.concat( + [ + results["bound_activity_up"], + make_df("bound_activity_up", **df_dem, **par_dict), + ] + ) return results diff --git a/message_ix_models/model/material/data_power_sector.py b/message_ix_models/model/material/data_power_sector.py index 67efcf1005..6da41e9bc4 100644 --- a/message_ix_models/model/material/data_power_sector.py +++ b/message_ix_models/model/material/data_power_sector.py @@ -1,206 +1,277 @@ -from .data_util import read_sector_data, read_timeseries - -import numpy as np from collections import defaultdict -import logging from pathlib import Path import pandas as pd from .util import read_config from message_ix_models import ScenarioInfo -from message_ix import make_df -from message_ix_models.util import ( - broadcast, - make_io, - make_matched_dfs, - same_node, - copy_column, - add_par_data, -) -from . import get_spec - -def read_material_intensities(parameter, data_path, node, year, technology, - commodity, level, inv_cost): + + +def read_material_intensities( + parameter, data_path, node, year, technology, commodity, level, inv_cost +): if parameter in ["input_cap_new", "input_cap_ret", "output_cap_ret"]: - #################################################################### - # read data - #################################################################### - - # read LCA data from ADVANCE LCA tool - data_path_lca = data_path + '/power sector/NTNU_LCA_coefficients.xlsx' - data_lca = pd.read_excel(data_path_lca, sheet_name = "environmentalImpacts") - - # read technology, region and commodity mappings - data_path_tec_map = data_path + '/power sector/MESSAGE_global_model_technologies.xlsx' - technology_mapping = pd.read_excel(data_path_tec_map, sheet_name = "technology") - - data_path_reg_map = data_path + '/power sector/LCA_region_mapping.xlsx' - region_mapping = pd.read_excel(data_path_reg_map, sheet_name = "region") - - data_path_com_map = data_path + '/power sector/LCA_commodity_mapping.xlsx' - commodity_mapping = pd.read_excel(data_path_com_map, sheet_name = "commodity") - - #################################################################### - # process data - #################################################################### - - # filter relevant scenario, technology variant (residue for biomass, - # mix for others) and remove operation phase (and remove duplicates) - data_lca = data_lca.loc[((data_lca['scenario']=='Baseline') - & (data_lca['technology variant'].isin(['mix', - 'residue'])) & (data_lca['phase'] != 'Operation'))] - - data_lca[2015] = None - data_lca[2020] = None - data_lca[2025] = None - data_lca[2035] = None - data_lca[2040] = None - data_lca[2045] = None - - # add intermediate time steps and turn into long table format - years = [2010,2015,2020,2025,2030,2035,2040,2045] - data_lca = pd.melt(data_lca, id_vars = ['source','scenario','region','variable', - 'technology','technology variant', 'impact', 'phase', 'unit'], value_vars = years, - var_name = 'year') - # Make sure the values are numeric. - data_lca[['value']] = data_lca[['value']].astype(float) - - # apply technology, commodity/impact and region mappings to MESSAGEix - data_lca_merged_1 = pd.merge(data_lca, region_mapping, how = 'inner', left_on = 'region', - right_on = 'THEMIS') - data_lca_merged_2 = pd.merge(data_lca_merged_1, technology_mapping, how = 'inner', left_on = 'technology', - right_on = 'LCA mapping') - data_lca_merged_final = pd.merge(data_lca_merged_2, commodity_mapping, how = 'inner', left_on = 'impact', - right_on = 'impact') - - data_lca = data_lca_merged_final[~data_lca_merged_final['MESSAGEix-GLOBIOM_1.1'].isnull()] - - # Drop technology column that has LCA style names - # Instead MESSAGE technology names will be used in the technology column - data_lca = data_lca.drop(['technology'], axis = 1) - - data_lca.rename(columns={'MESSAGEix-GLOBIOM_1.1':'node', - 'Type of Technology':'technology'}, inplace = True) - keep_columns = ['node','technology','phase', 'commodity', 'level', - 'year', 'unit', 'value'] - data_lca = data_lca[keep_columns] - - data_lca_final = pd.DataFrame() - - for n in data_lca['node'].unique(): - for t in data_lca['technology'].unique(): - for c in data_lca['commodity'].unique(): - for p in data_lca['phase'].unique(): - temp = data_lca.loc[((data_lca['node'] == n) & - (data_lca['technology'] == t) & - (data_lca['commodity'] == c) & - (data_lca['phase'] == p))] - temp['value'] = temp['value'].interpolate(method='linear', limit_direction= 'forward', axis=0) - data_lca_final = pd.concat([temp, data_lca_final]) - - # extract node, technology, commodity, level, and year list from LCA - # data set - node_list = data_lca_final['node'].unique() - year_list = data_lca_final['year'].unique() - tec_list = data_lca_final['technology'].unique() - com_list = data_lca_final['commodity'].unique() - lev_list = data_lca_final['level'].unique() - # add scrap as commodity level - lev_list = lev_list + ['end_of_life'] - - #################################################################### - # create data frames for material intensity input/output parameters - #################################################################### - - # new data frames for parameters - input_cap_new = pd.DataFrame() - input_cap_ret = pd.DataFrame() - output_cap_ret = pd.DataFrame() - - for n in node_list: - for t in tec_list: - for c in com_list: - year_vtg_list = inv_cost.loc[((inv_cost['node_loc']==n) - & (inv_cost['technology']==t))]['year_vtg'].unique() - for y in year_vtg_list: - # for years after maximum year in data set use - # values for maximum year, similarly for years - # before minimum year in data set use values for - # minimum year - if y > max(year_list): - yeff = max(year_list) - elif y< min(year_list): - yeff = min(year_list) - else: - yeff = y - val_cap_new = data_lca_final.loc[(data_lca_final['node']==n) & - (data_lca_final['technology'] == t) & - (data_lca_final['phase'] == 'Construction') & - (data_lca_final['commodity'] == c) & - (data_lca_final['year'] == yeff)]['value'].values[0] - val_cap_new = val_cap_new * 0.001 - - input_cap_new = pd.concat([input_cap_new,pd.DataFrame({ - 'node_loc': n, - 'technology': t, - 'year_vtg': str(y), - 'node_origin': n, - 'commodity':c, - 'level': 'product', - 'time_origin': 'year', - 'value': val_cap_new, - 'unit': 't/kW' - }, index = [0])]) - - val_cap_input_ret = data_lca_final.loc[(data_lca_final['node']==n) & - (data_lca_final['technology'] == t) & - (data_lca_final['phase'] == 'End-of-life') & - (data_lca_final['commodity'] == c) & - (data_lca_final['year'] == yeff)]['value'].values[0] - val_cap_input_ret = val_cap_input_ret * 0.001 - - input_cap_ret = pd.concat([input_cap_ret,pd.DataFrame({ - 'node_loc': n, - 'technology': t, - 'year_vtg': str(y), - 'node_origin': n, - 'commodity':c, - 'level': 'product', - 'time_origin': 'year', - 'value': val_cap_input_ret, - 'unit': 't/kW' - },index = [0])]) - - val_cap_output_ret = data_lca_final.loc[(data_lca_final['node']==n) & - (data_lca_final['technology'] == t) & - (data_lca_final['phase'] == 'Construction') & - (data_lca_final['commodity'] == c) & - (data_lca_final['year'] == yeff)]['value'].values[0] - val_cap_output_ret = val_cap_output_ret * 0.001 - - output_cap_ret = pd.concat([output_cap_ret,pd.DataFrame({ - 'node_loc': n, - 'technology': t, - 'year_vtg': str(y), - 'node_dest': n, - 'commodity':c, - 'level': 'end_of_life', - 'time_dest': 'year', - 'value': val_cap_output_ret, - 'unit': 't/kW' - }, index = [0])]) + #################################################################### + # read data + #################################################################### + + # read LCA data from ADVANCE LCA tool + data_path_lca = data_path + "/power sector/NTNU_LCA_coefficients.xlsx" + data_lca = pd.read_excel(data_path_lca, sheet_name="environmentalImpacts") + + # read technology, region and commodity mappings + data_path_tec_map = ( + data_path + "/power sector/MESSAGE_global_model_technologies.xlsx" + ) + technology_mapping = pd.read_excel(data_path_tec_map, sheet_name="technology") + + data_path_reg_map = data_path + "/power sector/LCA_region_mapping.xlsx" + region_mapping = pd.read_excel(data_path_reg_map, sheet_name="region") + + data_path_com_map = data_path + "/power sector/LCA_commodity_mapping.xlsx" + commodity_mapping = pd.read_excel(data_path_com_map, sheet_name="commodity") + + #################################################################### + # process data + #################################################################### + + # filter relevant scenario, technology variant (residue for biomass, + # mix for others) and remove operation phase (and remove duplicates) + data_lca = data_lca.loc[ + ( + (data_lca["scenario"] == "Baseline") + & (data_lca["technology variant"].isin(["mix", "residue"])) + & (data_lca["phase"] != "Operation") + ) + ] + + data_lca[2015] = None + data_lca[2020] = None + data_lca[2025] = None + data_lca[2035] = None + data_lca[2040] = None + data_lca[2045] = None + + # add intermediate time steps and turn into long table format + years = [2010, 2015, 2020, 2025, 2030, 2035, 2040, 2045] + data_lca = pd.melt( + data_lca, + id_vars=[ + "source", + "scenario", + "region", + "variable", + "technology", + "technology variant", + "impact", + "phase", + "unit", + ], + value_vars=years, + var_name="year", + ) + # Make sure the values are numeric. + data_lca[["value"]] = data_lca[["value"]].astype(float) + + # apply technology, commodity/impact and region mappings to MESSAGEix + data_lca_merged_1 = pd.merge( + data_lca, region_mapping, how="inner", left_on="region", right_on="THEMIS" + ) + data_lca_merged_2 = pd.merge( + data_lca_merged_1, + technology_mapping, + how="inner", + left_on="technology", + right_on="LCA mapping", + ) + data_lca_merged_final = pd.merge( + data_lca_merged_2, + commodity_mapping, + how="inner", + left_on="impact", + right_on="impact", + ) + + data_lca = data_lca_merged_final[ + ~data_lca_merged_final["MESSAGEix-GLOBIOM_1.1"].isnull() + ] + + # Drop technology column that has LCA style names + # Instead MESSAGE technology names will be used in the technology column + data_lca = data_lca.drop(["technology"], axis=1) + + data_lca.rename( + columns={ + "MESSAGEix-GLOBIOM_1.1": "node", + "Type of Technology": "technology", + }, + inplace=True, + ) + keep_columns = [ + "node", + "technology", + "phase", + "commodity", + "level", + "year", + "unit", + "value", + ] + data_lca = data_lca[keep_columns] + + data_lca_final = pd.DataFrame() + + for n in data_lca["node"].unique(): + for t in data_lca["technology"].unique(): + for c in data_lca["commodity"].unique(): + for p in data_lca["phase"].unique(): + temp = data_lca.loc[ + ( + (data_lca["node"] == n) + & (data_lca["technology"] == t) + & (data_lca["commodity"] == c) + & (data_lca["phase"] == p) + ) + ] + temp["value"] = temp["value"].interpolate( + method="linear", limit_direction="forward", axis=0 + ) + data_lca_final = pd.concat([temp, data_lca_final]) + + # extract node, technology, commodity, level, and year list from LCA + # data set + node_list = data_lca_final["node"].unique() + year_list = data_lca_final["year"].unique() + tec_list = data_lca_final["technology"].unique() + com_list = data_lca_final["commodity"].unique() + lev_list = data_lca_final["level"].unique() + # add scrap as commodity level + lev_list = lev_list + ["end_of_life"] + + #################################################################### + # create data frames for material intensity input/output parameters + #################################################################### + + # new data frames for parameters + input_cap_new = pd.DataFrame() + input_cap_ret = pd.DataFrame() + output_cap_ret = pd.DataFrame() + + for n in node_list: + for t in tec_list: + for c in com_list: + year_vtg_list = inv_cost.loc[ + ((inv_cost["node_loc"] == n) & (inv_cost["technology"] == t)) + ]["year_vtg"].unique() + for y in year_vtg_list: + # for years after maximum year in data set use + # values for maximum year, similarly for years + # before minimum year in data set use values for + # minimum year + if y > max(year_list): + yeff = max(year_list) + elif y < min(year_list): + yeff = min(year_list) + else: + yeff = y + val_cap_new = data_lca_final.loc[ + (data_lca_final["node"] == n) + & (data_lca_final["technology"] == t) + & (data_lca_final["phase"] == "Construction") + & (data_lca_final["commodity"] == c) + & (data_lca_final["year"] == yeff) + ]["value"].values[0] + val_cap_new = val_cap_new * 0.001 + + input_cap_new = pd.concat( + [ + input_cap_new, + pd.DataFrame( + { + "node_loc": n, + "technology": t, + "year_vtg": str(y), + "node_origin": n, + "commodity": c, + "level": "product", + "time_origin": "year", + "value": val_cap_new, + "unit": "t/kW", + }, + index=[0], + ), + ] + ) + + val_cap_input_ret = data_lca_final.loc[ + (data_lca_final["node"] == n) + & (data_lca_final["technology"] == t) + & (data_lca_final["phase"] == "End-of-life") + & (data_lca_final["commodity"] == c) + & (data_lca_final["year"] == yeff) + ]["value"].values[0] + val_cap_input_ret = val_cap_input_ret * 0.001 + + input_cap_ret = pd.concat( + [ + input_cap_ret, + pd.DataFrame( + { + "node_loc": n, + "technology": t, + "year_vtg": str(y), + "node_origin": n, + "commodity": c, + "level": "product", + "time_origin": "year", + "value": val_cap_input_ret, + "unit": "t/kW", + }, + index=[0], + ), + ] + ) + + val_cap_output_ret = data_lca_final.loc[ + (data_lca_final["node"] == n) + & (data_lca_final["technology"] == t) + & (data_lca_final["phase"] == "Construction") + & (data_lca_final["commodity"] == c) + & (data_lca_final["year"] == yeff) + ]["value"].values[0] + val_cap_output_ret = val_cap_output_ret * 0.001 + + output_cap_ret = pd.concat( + [ + output_cap_ret, + pd.DataFrame( + { + "node_loc": n, + "technology": t, + "year_vtg": str(y), + "node_dest": n, + "commodity": c, + "level": "end_of_life", + "time_dest": "year", + "value": val_cap_output_ret, + "unit": "t/kW", + }, + index=[0], + ), + ] + ) # return parameter - if (parameter == "input_cap_new"): + if parameter == "input_cap_new": return input_cap_new - elif (parameter == "input_cap_ret"): + elif parameter == "input_cap_ret": return input_cap_ret - elif (parameter == "output_cap_ret"): + elif parameter == "output_cap_ret": return output_cap_ret else: return + def gen_data_power_sector(scenario, dry_run=False): """Generate data for materials representation of power industry.""" # Load configuration diff --git a/message_ix_models/model/material/data_steel.py b/message_ix_models/model/material/data_steel.py index 194e27dd6e..d9ed76f707 100644 --- a/message_ix_models/model/material/data_steel.py +++ b/message_ix_models/model/material/data_steel.py @@ -1,29 +1,22 @@ from .data_util import read_sector_data, read_timeseries -import numpy as np from collections import defaultdict -import logging from pathlib import Path import pandas as pd +from .material_demand import material_demand_calc from .util import read_config from .data_util import read_rel # Get endogenous material demand from buildings interface -from .data_buildings import get_scen_mat_demand from message_ix_models import ScenarioInfo from message_ix import make_df from message_ix_models.util import ( broadcast, - make_io, - make_matched_dfs, same_node, - copy_column, - add_par_data, private_data_path, ) -from . import get_spec # Generate a fake steel demand @@ -209,9 +202,9 @@ def gen_data_steel(scenario, dry_run=False): ).pipe(broadcast, node_loc=nodes) if p == "output": comm = data_steel_ts.loc[ - (data_steel_ts["technology"] == t) - & (data_steel_ts["parameter"] == p), - "commodity", + (data_steel_ts["technology"] == t) + & (data_steel_ts["parameter"] == p), + "commodity", ] lev = data_steel_ts.loc[ @@ -236,8 +229,8 @@ def gen_data_steel(scenario, dry_run=False): mode=mod, node_loc=rg, node_dest=rg, - commodity = comm, - level = lev, + commodity=comm, + level=lev, **common ) else: @@ -394,36 +387,62 @@ def gen_data_steel(scenario, dry_run=False): # Add relation for the maximum global scrap use in 2020 - df_max_recycling = pd.DataFrame({'relation': 'max_global_recycling_steel', - 'node_rel': 'R12_GLB', - 'year_rel': 2020, - 'year_act': 2020, - 'node_loc': nodes, - 'technology': 'scrap_recovery_steel', - 'mode': 'M1', - 'unit': '???', - 'value': data_steel_rel.loc[((data_steel_rel['relation'] \ - == 'max_global_recycling_steel') & (data_steel_rel['parameter'] \ - == 'relation_activity')), 'value'].values[0]}) - - df_max_recycling_upper = pd.DataFrame({'relation': 'max_global_recycling_steel', - 'node_rel': 'R12_GLB', - 'year_rel': 2020, - 'unit': '???', - 'value': data_steel_rel.loc[((data_steel_rel['relation'] \ - == 'max_global_recycling_steel') & (data_steel_rel['parameter'] \ - == 'relation_upper')), 'value'].values[0]}, index = [0]) - df_max_recycling_lower = pd.DataFrame({'relation': 'max_global_recycling_steel', - 'node_rel': 'R12_GLB', - 'year_rel': 2020, - 'unit': '???', - 'value': data_steel_rel.loc[((data_steel_rel['relation'] \ - == 'max_global_recycling_steel') & (data_steel_rel['parameter'] \ - == 'relation_lower')), 'value'].values[0]}, index = [0]) - - results['relation_activity'].append(df_max_recycling) - results['relation_upper'].append(df_max_recycling_upper) - results['relation_lower'].append(df_max_recycling_lower) + df_max_recycling = pd.DataFrame( + { + "relation": "max_global_recycling_steel", + "node_rel": "R12_GLB", + "year_rel": 2020, + "year_act": 2020, + "node_loc": nodes, + "technology": "scrap_recovery_steel", + "mode": "M1", + "unit": "???", + "value": data_steel_rel.loc[ + ( + (data_steel_rel["relation"] == "max_global_recycling_steel") + & (data_steel_rel["parameter"] == "relation_activity") + ), + "value", + ].values[0], + } + ) + + df_max_recycling_upper = pd.DataFrame( + { + "relation": "max_global_recycling_steel", + "node_rel": "R12_GLB", + "year_rel": 2020, + "unit": "???", + "value": data_steel_rel.loc[ + ( + (data_steel_rel["relation"] == "max_global_recycling_steel") + & (data_steel_rel["parameter"] == "relation_upper") + ), + "value", + ].values[0], + }, + index=[0], + ) + df_max_recycling_lower = pd.DataFrame( + { + "relation": "max_global_recycling_steel", + "node_rel": "R12_GLB", + "year_rel": 2020, + "unit": "???", + "value": data_steel_rel.loc[ + ( + (data_steel_rel["relation"] == "max_global_recycling_steel") + & (data_steel_rel["parameter"] == "relation_lower") + ), + "value", + ].values[0], + }, + index=[0], + ) + + results["relation_activity"].append(df_max_recycling) + results["relation_upper"].append(df_max_recycling_upper) + results["relation_lower"].append(df_max_recycling_lower) # Add relations for scrap grades and availability regions = set(data_steel_rel["Region"].values) @@ -432,13 +451,13 @@ def gen_data_steel(scenario, dry_run=False): model_years_rel = modelyears.copy() if r is None: break - if r == 'max_global_recycling_steel': + if r == "max_global_recycling_steel": continue - if r == 'minimum_recycling_steel': + if r == "minimum_recycling_steel": # Do not implement the minimum recycling rate for the year 2020 if 2020 in model_years_rel: model_years_rel.remove(2020) - if r == 'max_regional_recycling_steel': + if r == "max_regional_recycling_steel": # Do not implement the minimum recycling rate for the year 2020 if 2020 in model_years_rel: model_years_rel.remove(2020) @@ -520,6 +539,7 @@ def gen_data_steel(scenario, dry_run=False): time="year", node=demand.node, ) # .pipe(broadcast, node=nodes) + df = material_demand_calc.derive_demand("steel", scenario, old_gdp=False) results[parname].append(df) # Concatenate to one data frame per parameter @@ -563,9 +583,7 @@ def derive_steel_demand(scenario, dry_run=False): with localconverter(ro.default_converter + pandas2ri.converter): # GDP is only in MER in scenario. # To get PPP GDP, it is read externally from the R side - df = r.derive_steel_demand( - pop, base_demand, str(private_data_path("material")) - ) + df = r.derive_steel_demand(pop, base_demand, str(private_data_path("material"))) df.year = df.year.astype(int) return df diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index 2db0f6512f..be71ab7076 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -3,15 +3,15 @@ import message_ix import ixmp -from .util import read_config -import re +from message_data.model.material.util import ( + read_config, + read_yaml_file, + invert_dictionary, +) from message_ix_models import ScenarioInfo from message_ix_models.util import private_data_path -from message_data.tools.utilities.get_optimization_years import ( - main as get_optimization_years, -) pd.options.mode.chained_assignment = None @@ -178,14 +178,16 @@ def modify_demand_and_hist_activity(scen): for r in df_spec["REGION"].unique(): df_spec_temp = df_spec.loc[df_spec["REGION"] == r] df_spec_total_temp = df_spec_total.loc[df_spec_total["REGION"] == r] - df_spec_temp.loc[:,"i_spec"] = ( - df_spec_temp.loc[:,"RESULT"] / df_spec_total_temp.loc[:,"RESULT"].values[0] + df_spec_temp.loc[:, "i_spec"] = ( + df_spec_temp.loc[:, "RESULT"] + / df_spec_total_temp.loc[:, "RESULT"].values[0] ) df_spec_new = pd.concat([df_spec_temp, df_spec_new], ignore_index=True) df_spec_new.drop(["FUEL", "RYEAR", "UNIT_OUT", "RESULT"], axis=1, inplace=True) df_spec_new.loc[df_spec_new["SECTOR"] == "industry (chemicals)", "i_spec"] = ( - df_spec_new.loc[df_spec_new["SECTOR"] == "industry (chemicals)", "i_spec"] * 0.67 + df_spec_new.loc[df_spec_new["SECTOR"] == "industry (chemicals)", "i_spec"] + * 0.67 ) df_spec_new = df_spec_new.groupby(["REGION"]).sum().reset_index() @@ -240,8 +242,9 @@ def modify_demand_and_hist_activity(scen): for r in df_therm["REGION"].unique(): df_therm_temp = df_therm.loc[df_therm["REGION"] == r] df_therm_total_temp = df_therm_total.loc[df_therm_total["REGION"] == r] - df_therm_temp.loc[:,"i_therm"] = ( - df_therm_temp.loc[:,"RESULT"] / df_therm_total_temp.loc[:,"RESULT"].values[0] + df_therm_temp.loc[:, "i_therm"] = ( + df_therm_temp.loc[:, "RESULT"] + / df_therm_total_temp.loc[:, "RESULT"].values[0] ) df_therm_new = pd.concat([df_therm_temp, df_therm_new], ignore_index=True) df_therm_new = df_therm_new.drop(["RESULT"], axis=1) @@ -261,7 +264,7 @@ def modify_demand_and_hist_activity(scen): df_therm_new.loc[index, "i_therm"] = 0.2 - df_therm_new = df_therm_new.groupby(["REGION"]).sum().reset_index() + df_therm_new = df_therm_new.groupby(["REGION"]).sum(numeric_only=True).reset_index() # TODO: Useful technology efficiencies will also be included @@ -404,6 +407,227 @@ def modify_demand_and_hist_activity(scen): scen.remove_par("growth_activity_lo", df) scen.commit(comment="remove growth_lo constraints") + scen.check_out() + for substr in ["up", "lo"]: + df = scen.par(f"bound_activity_{substr}") + scen.remove_par( + f"bound_activity_{substr}", + df[(df["technology"].str.endswith("_fs")) & (df["year_act"] == 2020)], + ) + scen.remove_par( + f"bound_activity_{substr}", + df[(df["technology"].str.endswith("_i")) & (df["year_act"] == 2020)], + ) + scen.remove_par( + f"bound_activity_{substr}", + df[(df["technology"].str.endswith("_I")) & (df["year_act"] == 2020)], + ) + scen.commit(comment="remove bounds") + + +def calc_hist_activity_new(scen, years): + df_orig = get_hist_act_data("IEA_mappings.csv", years=years) + df_mat = get_hist_act_data("IEA_mappings_industry.csv", years=years) + df_chem = get_hist_act_data("IEA_mappings_chemicals.csv", years=years) + + # scale chemical activity to deduct explicitly represented activities of MESSAGEix-Materials + # (67% are covered by NH3, HVCs and methanol) + df_chem = df_chem.mul(0.67) + df_mat = df_mat.sub(df_chem, fill_value=0) + + # calculate share of residual activity not covered by industry sector explicit technologies + df = ( + df_mat.div(df_orig) + .sub(1) + .mul(-1) + .dropna() + .sort_values("Value", ascending=False) + ) + # manually set elec_i to 0 since all of it is covered by iron/steel sector + df.loc[:, "elec_i", :] = 0 + + df = df.round(5) + tecs = df.index.get_level_values("technology").unique() + + df_hist_act = scen.par( + "historical_activity", filters={"technology": tecs, "year_act": years} + ) + + df_hist_act_merged = df_hist_act.set_index( + ["node_loc", "technology", "year_act"] + ).join(df) + df_hist_act_merged["value"] *= df_hist_act_merged["Value"] + df_hist_act_merged = df_hist_act_merged.drop("Value", axis=1) + + return df_hist_act_merged + + +def calc_demand_shares(Inp, base_year): + # TODO: refactor to use external mapping file + i_spec_material_flows = ["NONMET", "CHEMICAL", "NONFERR"] + i_therm_material_flows = ["NONMET", "CHEMICAL", "IRONSTL"] + i_flow = ["TOTIND"] + i_spec_prods = ["ELECTR", "NONBIODIES", "BIOGASOL"] + year = base_year + + df_i_spec = Inp[ + (Inp["FLOW"].isin(i_flow)) + & (Inp["PRODUCT"].isin(i_spec_prods)) + & ~((Inp["PRODUCT"] == ("ELECTR")) & (Inp["FLOW"] == "IRONSTL")) + & (Inp["TIME"] == year) + ] + df_i_spec = df_i_spec.groupby("REGION").sum(numeric_only=True) + + df_i_spec_materials = Inp[ + (Inp["FLOW"].isin(i_spec_material_flows)) + & (Inp["PRODUCT"].isin(i_spec_prods)) + & (Inp["TIME"] == year) + ] + df_i_spec_materials = df_i_spec_materials.groupby("REGION").sum(numeric_only=True) + + df_i_spec_resid_shr = ( + df_i_spec_materials.div(df_i_spec, fill_value=0).sub(1).mul(-1) + ) + df_i_spec_resid_shr["commodity"] = "i_spec" + + df_elec_i = Inp[ + ((Inp["PRODUCT"] == ("ELECTR")) & (Inp["FLOW"] == "IRONSTL")) + & (Inp["TIME"] == year) + ] + df_elec_i = df_elec_i.groupby("REGION").sum(numeric_only=True) + + agg_prods = ["MRENEW", "TOTAL"] + df_i_therm = Inp[ + (Inp["FLOW"].isin(i_flow)) + & ~(Inp["PRODUCT"].isin(i_spec_prods)) + & ~(Inp["PRODUCT"].isin(agg_prods)) + & (Inp["TIME"] == year) + ] + df_i_therm = df_i_therm.groupby("REGION").sum(numeric_only=True) + df_i_therm = df_i_therm.add(df_elec_i, fill_value=0) + + agg_prods = ["MRENEW", "TOTAL"] + df_i_therm_materials = Inp[ + (Inp["FLOW"].isin(i_therm_material_flows)) + & ~(Inp["PRODUCT"].isin(i_spec_prods)) + & ~(Inp["PRODUCT"].isin(agg_prods)) + & (Inp["TIME"] == year) + ] + df_i_therm_materials = df_i_therm_materials.groupby(["REGION", "FLOW"]).sum( + numeric_only=True + ) + # only two thirds of chemical consumption is represented by Materials module currently + df_i_therm_materials.loc[ + df_i_therm_materials.index.get_level_values(1) == "CHEMICAL", "Value" + ] *= 0.67 + + df_i_therm_materials = df_i_therm_materials.groupby("REGION").sum(numeric_only=True) + df_i_therm_materials = df_i_therm_materials.add(df_elec_i, fill_value=0) + + df_i_therm_resid_shr = df_i_therm_materials.div(df_i_therm).sub(1).mul(-1) + df_i_therm_resid_shr["commodity"] = "i_therm" + + return ( + pd.concat([df_i_spec_resid_shr, df_i_therm_resid_shr]) + .set_index("commodity", append=True) + .drop("TIME", axis=1) + ) + + +def modify_industry_demand(scen, baseyear): + comms = ["i_spec", "i_therm"] + path = os.path.join( + "P:", "ene.model", "IEA_database", "Florian", "REV2022_allISO_IEA.parquet" + ) + Inp = pd.read_parquet(path, engine="fastparquet") + Inp = map_iea_db_to_msg_regs(Inp, "R12_SSP_V1.yaml") + demand_shrs_new = calc_demand_shares(Inp, baseyear) + df_demands = scen.par("demand", filters={"commodity": comms}).set_index( + ["node", "commodity"] + ) + demand_shrs_new.index.set_names(["node", "commodity"], inplace=True) + df_demands["value"] = df_demands["value"] * demand_shrs_new["Value"] + # scen.add_par("demand", df_demands) + + # remove i_spec demand separately since we assume 100% coverage by MESSAGE-Materials + df_i_feed = scen.par("demand", filters={"commodity": "i_feed"}) + # scen.remove_par("demand", df_i_feed) + + +def map_iea_db_to_msg_regs(df_iea, fname_map): + file_path = private_data_path("node", fname_map) + yaml_data = read_yaml_file(file_path) + if "World" in yaml_data.keys(): + yaml_data.pop("World") + + r12_map = {k: v["child"] for k, v in yaml_data.items()} + r12_map_inv = {k: v[0] for k, v in invert_dictionary(r12_map).items()} + + df_iea = df_iea.merge( + pd.DataFrame.from_dict( + r12_map_inv, orient="index", columns=["REGION"] + ).reset_index(), + left_on="COUNTRY", + right_on="index", + ).drop("index", axis=1) + return df_iea + + +def read_iea_tec_map(map_fname): + MAP = pd.read_csv(private_data_path("model", "IEA", map_fname)) + + MAP = pd.concat([MAP, MAP["IEA flow"].str.split(", ", expand=True)], axis=1) + MAP = ( + MAP.melt( + value_vars=MAP.columns[-13:], + value_name="FLOW", + id_vars=["technology", "IEA product"], + ) + .dropna() + .drop("variable", axis=1) + ) + MAP = pd.concat([MAP, MAP["IEA product"].str.split(", ", expand=True)], axis=1) + MAP = ( + MAP.melt( + value_vars=MAP.columns[-19:], + value_name="PRODUCT", + id_vars=["technology", "FLOW"], + ) + .dropna() + .drop("variable", axis=1) + ) + MAP = MAP.drop_duplicates() + return MAP + + +def get_hist_act_data(map_fname, years=None): + path = os.path.join( + "P:", "ene.model", "IEA_database", "Florian", "REV2022_allISO_IEA.parquet" + ) + Inp = pd.read_parquet(path, engine="fastparquet") + if years: + Inp = Inp[Inp["TIME"].isin(years)] + + # map IEA countries to MESSAGE region definition + Inp = map_iea_db_to_msg_regs(Inp, "R12_SSP_V1.yaml") + + # read file for IEA product/flow - MESSAGE technologies map + MAP = read_iea_tec_map(map_fname) + + # map IEA flows to MESSAGE technologies and aggregate + df_final = Inp.set_index(["PRODUCT", "FLOW"]).join( + MAP.set_index(["PRODUCT", "FLOW"]) + ) + + # multiply with efficiency and sector coverage ratios + + df_final = ( + df_final.drop_duplicates() + .groupby(["REGION", "technology", "TIME"]) + .sum(numeric_only=True) + ) + return df_final + def add_emission_accounting(scen): context = read_config() @@ -418,100 +642,142 @@ def add_emission_accounting(scen): # The technology list to retrieve the input values tec_list_input = [ - i - for i in tec_list_input - if ( - ("furnace" in i) | - ("hp_gas_" in i) - - ) + i for i in tec_list_input if (("furnace" in i) | ("hp_gas_" in i)) ] - tec_list_input.remove('hp_gas_i') - tec_list_input.remove('hp_gas_rc') + tec_list_input.remove("hp_gas_i") + tec_list_input.remove("hp_gas_rc") # The technology list to retreive the emission_factors tec_list_residual = [ i for i in tec_list_residual if ( - (('biomass_i' in i) | - ('coal_i' in i) | - ('foil_i' in i) | - ('gas_i' in i) | - ('hp_gas_i' in i) | - ('loil_i' in i) | - ('meth_i' in i)) & - ('imp' not in i) & - ('trp' not in i) + ( + ("biomass_i" in i) + | ("coal_i" in i) + | ("foil_i" in i) + | ("gas_i" in i) + | ("hp_gas_i" in i) + | ("loil_i" in i) + | ("meth_i" in i) + ) + & ("imp" not in i) + & ("trp" not in i) ) ] # Retrieve the input values - input_df = scen.par('input', filters = {'technology':tec_list_input}) - input_df.drop(['node_origin','commodity','level','time','time_origin','unit'], - axis = 1, inplace = True) - input_df.drop_duplicates(inplace = True) - input_df = input_df[input_df['year_act']>=2020] + input_df = scen.par("input", filters={"technology": tec_list_input}) + input_df.drop( + ["node_origin", "commodity", "level", "time", "time_origin", "unit"], + axis=1, + inplace=True, + ) + input_df.drop_duplicates(inplace=True) + input_df = input_df[input_df["year_act"] >= 2020] # Retrieve the emission factors emission_df = scen.par("emission_factor", filters={"technology": tec_list_residual}) - emission_df.drop(['unit','mode'], axis = 1, inplace = True) - emission_df = emission_df[emission_df['year_act']>=2020] - emission_df.drop_duplicates(inplace = True) + emission_df.drop(["unit", "mode"], axis=1, inplace=True) + emission_df = emission_df[emission_df["year_act"] >= 2020] + emission_df.drop_duplicates(inplace=True) # Mapping to multiply the emission_factor with the corresponding # input values from new indsutry technologies - dic = {"foil_i" : ['furnace_foil_steel', 'furnace_foil_aluminum', - 'furnace_foil_cement', 'furnace_foil_petro', 'furnace_foil_refining'], - "biomass_i" : ['furnace_biomass_steel', 'furnace_biomass_aluminum', - 'furnace_biomass_cement', 'furnace_biomass_petro', - 'furnace_biomass_refining'], - "coal_i": ['furnace_coal_steel', 'furnace_coal_aluminum', - 'furnace_coal_cement', 'furnace_coal_petro', 'furnace_coal_refining', - 'furnace_coke_petro', 'furnace_coke_refining'], - "loil_i" : ['furnace_loil_steel', 'furnace_loil_aluminum', - 'furnace_loil_cement', 'furnace_loil_petro', 'furnace_loil_refining'], - "gas_i":['furnace_gas_steel', 'furnace_gas_aluminum', - 'furnace_gas_cement', 'furnace_gas_petro', 'furnace_gas_refining'], - 'meth_i': ['furnace_methanol_steel', 'furnace_methanol_aluminum', - 'furnace_methanol_cement', 'furnace_methanol_petro', 'furnace_methanol_refining'], - 'hp_gas_i': ['hp_gas_steel', 'hp_gas_aluminum','hp_gas_cement', - 'hp_gas_petro', 'hp_gas_refining']} + dic = { + "foil_i": [ + "furnace_foil_steel", + "furnace_foil_aluminum", + "furnace_foil_cement", + "furnace_foil_petro", + "furnace_foil_refining", + ], + "biomass_i": [ + "furnace_biomass_steel", + "furnace_biomass_aluminum", + "furnace_biomass_cement", + "furnace_biomass_petro", + "furnace_biomass_refining", + ], + "coal_i": [ + "furnace_coal_steel", + "furnace_coal_aluminum", + "furnace_coal_cement", + "furnace_coal_petro", + "furnace_coal_refining", + "furnace_coke_petro", + "furnace_coke_refining", + ], + "loil_i": [ + "furnace_loil_steel", + "furnace_loil_aluminum", + "furnace_loil_cement", + "furnace_loil_petro", + "furnace_loil_refining", + ], + "gas_i": [ + "furnace_gas_steel", + "furnace_gas_aluminum", + "furnace_gas_cement", + "furnace_gas_petro", + "furnace_gas_refining", + ], + "meth_i": [ + "furnace_methanol_steel", + "furnace_methanol_aluminum", + "furnace_methanol_cement", + "furnace_methanol_petro", + "furnace_methanol_refining", + ], + "hp_gas_i": [ + "hp_gas_steel", + "hp_gas_aluminum", + "hp_gas_cement", + "hp_gas_petro", + "hp_gas_refining", + ], + } # Create an empty dataframe df_non_co2_emissions = pd.DataFrame() # Find the technology, year_act, year_vtg, emission, node_loc combination - emissions = [e for e in emission_df['emission'].unique()] - emissions.remove('CO2_industry') - emissions.remove('CO2_res_com') + emissions = [e for e in emission_df["emission"].unique()] + emissions.remove("CO2_industry") + emissions.remove("CO2_res_com") - for t in emission_df['technology'].unique(): + for t in emission_df["technology"].unique(): for e in emissions: # This should be a dataframe - emission_df_filt = emission_df.loc[((emission_df['technology'] == t)\ - & (emission_df['emission'] == e))] + emission_df_filt = emission_df.loc[ + ((emission_df["technology"] == t) & (emission_df["emission"] == e)) + ] # Filter the technologies that we need the input value # This should be a dataframe - input_df_filt = input_df[input_df['technology'].isin(dic[t])] - if ((emission_df_filt.empty) | (input_df_filt.empty)): + input_df_filt = input_df[input_df["technology"].isin(dic[t])] + if (emission_df_filt.empty) | (input_df_filt.empty): continue else: - df_merged = pd.merge(emission_df_filt, input_df_filt, on = \ - ['year_act', 'year_vtg', 'node_loc']) - df_merged['value'] = df_merged['value_x'] * df_merged['value_y'] - df_merged.drop(['technology_x','value_x','value_y', 'year_vtg', - 'emission'], axis= 1, inplace = True) - df_merged.rename(columns={'technology_y':'technology'}, - inplace = True) - relation_name = e + '_Emission' - df_merged['relation'] = relation_name - df_merged['node_rel'] = df_merged['node_loc'] - df_merged['year_rel'] = df_merged['year_act'] - df_merged['unit'] = '???' - df_non_co2_emissions = pd.concat([df_non_co2_emissions,df_merged]) + df_merged = pd.merge( + emission_df_filt, + input_df_filt, + on=["year_act", "year_vtg", "node_loc"], + ) + df_merged["value"] = df_merged["value_x"] * df_merged["value_y"] + df_merged.drop( + ["technology_x", "value_x", "value_y", "year_vtg", "emission"], + axis=1, + inplace=True, + ) + df_merged.rename(columns={"technology_y": "technology"}, inplace=True) + relation_name = e + "_Emission" + df_merged["relation"] = relation_name + df_merged["node_rel"] = df_merged["node_loc"] + df_merged["year_rel"] = df_merged["year_act"] + df_merged["unit"] = "???" + df_non_co2_emissions = pd.concat([df_non_co2_emissions, df_merged]) scen.check_out() scen.add_par("relation_activity", df_non_co2_emissions) @@ -539,7 +805,7 @@ def add_emission_accounting(scen): tec_list_materials.remove("replacement_so2") tec_list_materials.remove("SO2_scrub_ref") emission_factors = scen.par( - "emission_factor", filters={"technology": tec_list_materials, 'emission':'CO2'} + "emission_factor", filters={"technology": tec_list_materials, "emission": "CO2"} ) # Note: Emission for CO2 MtC/ACT. relation_activity = emission_factors.assign( @@ -572,8 +838,10 @@ def add_emission_accounting(scen): relation_activity_steel = scen.par( "emission_factor", - filters={"emission": "CO2_industry", - "technology": ["DUMMY_coal_supply", "DUMMY_gas_supply"]}, + filters={ + "emission": "CO2_industry", + "technology": ["DUMMY_coal_supply", "DUMMY_gas_supply"], + }, ) relation_activity_steel["relation"] = "CO2_ind" relation_activity_steel["node_rel"] = relation_activity_steel["node_loc"] @@ -614,7 +882,7 @@ def add_emission_accounting(scen): # fueloil emission factor * input val = 0.665 * 1.339 elif m == "atm_gasoil": - val = 0.665 * 1.435 + val = 0.665 * 1.435 else: val = 0.665 * 1.537442922 @@ -706,6 +974,7 @@ def add_emission_accounting(scen): # scen.add_par("emission_factor", df_em) # scen.commit("add methanol CO2_industry") + def add_elec_lowerbound_2020(scen): # To avoid zero i_spec prices only for R12_CHN, add the below section. @@ -722,7 +991,9 @@ def add_elec_lowerbound_2020(scen): # read processed final energy data from IEA extended energy balances # that is aggregated to MESSAGEix regions, fuels and (industry) sectors - final = pd.read_csv(private_data_path("material", "other", "residual_industry_2019.csv")) + final = pd.read_csv( + private_data_path("material", "other", "residual_industry_2019.csv") + ) # downselect needed fuels and sectors final_residual_electricity = final.query( @@ -786,7 +1057,7 @@ def add_coal_lowerbound_2020(sc): context = read_config() final_resid = pd.read_csv( - private_data_path("material", "other","residual_industry_2019.csv") + private_data_path("material", "other", "residual_industry_2019.csv") ) # read input parameters for relevant technology/commodity combinations for converting betwen final and useful energy @@ -911,13 +1182,14 @@ def add_coal_lowerbound_2020(sc): "added lower bound for activity of residual industrial coal and cement coal furnace technologies and adjusted 2020 residual industrial electricity demand" ) + def add_cement_bounds_2020(sc): """Set lower and upper bounds for gas and oil as a calibration for 2020""" context = read_config() final_resid = pd.read_csv( - private_data_path("material", "other","residual_industry_2019.csv") + private_data_path("material", "other", "residual_industry_2019.csv") ) input_cement_foil = sc.par( @@ -991,7 +1263,6 @@ def add_cement_bounds_2020(sc): 'MESSAGE_fuel=="coal" & MESSAGE_sector=="cement"' ) - # join final energy data from IEA energy balances and input coefficients from final-to-useful technologies from MESSAGEix bound_cement_loil = pd.merge( input_cement_loil, @@ -1037,10 +1308,11 @@ def add_cement_bounds_2020(sc): bound_cement_loil["value"] = bound_cement_loil["Value"] / bound_cement_loil["value"] bound_cement_foil["value"] = bound_cement_foil["Value"] / bound_cement_foil["value"] bound_cement_gas["value"] = bound_cement_gas["Value"] / bound_cement_gas["value"] - bound_cement_biomass["value"] = bound_cement_biomass["Value"] / bound_cement_biomass["value"] + bound_cement_biomass["value"] = ( + bound_cement_biomass["Value"] / bound_cement_biomass["value"] + ) bound_cement_coal["value"] = bound_cement_coal["Value"] / bound_cement_coal["value"] - # downselect dataframe columns for MESSAGEix parameters bound_cement_loil = bound_cement_loil.filter( items=["node_loc", "technology", "year_act", "mode", "time", "value", "unit_x"] @@ -1062,7 +1334,6 @@ def add_cement_bounds_2020(sc): items=["node_loc", "technology", "year_act", "mode", "time", "value", "unit_x"] ) - # rename columns if necessary bound_cement_loil.columns = [ "node_loc", @@ -1127,37 +1398,40 @@ def add_cement_bounds_2020(sc): sc.add_par("bound_activity_up", bound_cement_coal) for n in nodes: - bound_cement_meth = pd.DataFrame({ - "node_loc": n, - "technology": "furnace_methanol_cement", - "year_act": years, - "mode":"high_temp", - "time":"year", - "value":0, - "unit": "???" - }) + bound_cement_meth = pd.DataFrame( + { + "node_loc": n, + "technology": "furnace_methanol_cement", + "year_act": years, + "mode": "high_temp", + "time": "year", + "value": 0, + "unit": "???", + } + ) sc.add_par("bound_activity_lo", bound_cement_meth) sc.add_par("bound_activity_up", bound_cement_meth) for n in nodes: - bound_cement_eth = pd.DataFrame({ - "node_loc": n, - "technology": "furnace_ethanol_cement", - "year_act": years, - "mode":"high_temp", - "time":"year", - "value":0, - "unit": "???" - }) + bound_cement_eth = pd.DataFrame( + { + "node_loc": n, + "technology": "furnace_ethanol_cement", + "year_act": years, + "mode": "high_temp", + "time": "year", + "value": 0, + "unit": "???", + } + ) sc.add_par("bound_activity_lo", bound_cement_eth) sc.add_par("bound_activity_up", bound_cement_eth) # commit scenario to ixmp backend - sc.commit( - "added lower and upper bound for fuels for cement 2020." - ) + sc.commit("added lower and upper bound for fuels for cement 2020.") + def read_sector_data(scenario, sectname): @@ -1289,7 +1563,9 @@ def read_timeseries(scenario, material, filename): sheet_n = "timeseries_R11" # Read the file - df = pd.read_excel(private_data_path("material", material, filename), sheet_name=sheet_n) + df = pd.read_excel( + private_data_path("material", material, filename), sheet_name=sheet_n + ) import numbers @@ -1298,7 +1574,15 @@ def read_timeseries(scenario, material, filename): df = pd.melt( df, - id_vars=["parameter", "region", "technology", "mode", "units","commodity","level"], + id_vars=[ + "parameter", + "region", + "technology", + "mode", + "units", + "commodity", + "level", + ], value_vars=datayears, var_name="year", ) @@ -1309,8 +1593,6 @@ def read_timeseries(scenario, material, filename): def read_rel(scenario, material, filename): - import numpy as np - # Ensure config is loaded, get the context context = read_config() @@ -1328,3 +1610,25 @@ def read_rel(scenario, material, filename): ) return data_rel + + +if __name__ == "__main__": + mp = ixmp.Platform("ixmp_dev") + scen = message_ix.Scenario(mp, "MESSAGEix-Materials", "NoPolicy_petro_thesis_2") + # modify_demand_and_hist_activity_new(scen, [2015]) + # modify_industry_demand(scen, 2015) + # modify_demand_and_hist_activity(scen) + df = get_hist_act_data("IEA_mappings_furnaces.csv", years=[2015]) + df.index.names = ["node_loc", "technology", "year_act"] + df_inp = scen.par( + "input", + filters={ + "year_vtg": 2020, + "year_act": 2020, + "mode": "high_temp", + "node_loc": "R12_AFR", + }, + ) + df = df_inp.set_index(["technology"]).join(df).dropna() + df["Value"] = df["Value"] / df["value"] / 3.6 / 8760 + print() diff --git a/message_ix_models/model/material/material_demand/material_demand_calc.py b/message_ix_models/model/material/material_demand/material_demand_calc.py new file mode 100644 index 0000000000..ce5c7a9ec3 --- /dev/null +++ b/message_ix_models/model/material/material_demand/material_demand_calc.py @@ -0,0 +1,330 @@ +import message_ix_models.util +import pandas as pd +import numpy as np +import message_ix +from scipy.optimize import curve_fit +import yaml + +file_cement = "/CEMENT.BvR2010.xlsx" +file_steel = "/STEEL_database_2012.xlsx" +file_al = "/demand_aluminum.xlsx" +file_gdp = "/iamc_db ENGAGE baseline GDP PPP.xlsx" + +giga = 10**9 +mega = 10**6 + +material_data = { + "aluminum": {"dir": "aluminum", "file": "/demand_aluminum.xlsx"}, + "steel": {"dir": "steel_cement", "file": "/STEEL_database_2012.xlsx"}, + "cement": {"dir": "steel_cement", "file": "/CEMENT.BvR2010.xlsx"}, +} + + +def steel_function(x, a, b, m): + gdp_pcap, del_t = x + return a * np.exp(b / gdp_pcap) * (1 - m) ** del_t + + +def cement_function(x, a, b): + gdp_pcap = x[0] + return a * np.exp(b / gdp_pcap) + + +fitting_dict = { + "steel": { + "function": steel_function, + "initial_guess": [600, -10000, 0], + "x_data": ["gdp_pcap", "del_t"], + "phi": 9, + "mu": 0.1, + }, + "cement": { + "function": cement_function, + "initial_guess": [500, -3000], + "x_data": ["gdp_pcap"], + "phi": 10, + "mu": 0.1, + }, + "aluminum": { + "function": cement_function, + "initial_guess": [600, -10000], + "x_data": ["gdp_pcap"], + "phi": 9, + "mu": 0.1, + }, +} + + +def gompertz(phi, mu, y, baseyear=2020): + return 1 - np.exp(-phi * np.exp(-mu * (y - baseyear))) + + +def read_timer_pop(datapath, filename, material): + df_population = pd.read_excel( + f'{datapath}/{material_data[material]["dir"]}{material_data["cement"]["file"]}', + sheet_name="Timer_POP", + skiprows=[0, 1, 2, 30], + nrows=26, + ) + df_population = df_population.drop(columns=["Unnamed: 0"]).rename( + {"Unnamed: 1": "reg_no", "Unnamed: 2": "region"}, axis=1 + ) + df_population = df_population.melt( + id_vars=["region", "reg_no"], var_name="year", value_name="pop" + ) + return df_population + + +def read_timer_gdp(datapath, filename, material): + # Read GDP per capita data + df_gdp = pd.read_excel( + f'{datapath}/{material_data[material]["dir"]}{material_data["cement"]["file"]}', + sheet_name="Timer_GDPCAP", + skiprows=[0, 1, 2, 30], + nrows=26, + ) + df_gdp = df_gdp.drop(columns=["Unnamed: 0"]).rename( + {"Unnamed: 1": "reg_no", "Unnamed: 2": "region"}, axis=1 + ) + df_gdp = df_gdp.melt( + id_vars=["region", "reg_no"], var_name="year", value_name="gdp_pcap" + ) + return df_gdp + + +def project_demand(df, phi, mu): + df_demand = df.groupby("region", group_keys=False).apply( + lambda group: group.assign( + demand_pcap_base=group["demand.tot.base"].iloc[0] + * giga + / group["pop.mil"].iloc[0] + / mega + ) + ) + df_demand = df_demand.groupby("region", group_keys=False).apply( + lambda group: group.assign( + gap_base=group["demand_pcap_base"].iloc[0] - group["demand_pcap0"].iloc[0] + ) + ) + df_demand = df_demand.groupby("region", group_keys=False).apply( + lambda group: group.assign( + demand_pcap=group["demand_pcap0"] + + group["gap_base"] * gompertz(phi, mu, y=group["year"]) + ) + ) + df_demand = ( + df_demand.groupby("region", group_keys=False) + .apply( + lambda group: group.assign( + demand_tot=group["demand_pcap"] * group["pop.mil"] * mega / giga + ) + ) + .reset_index(drop=True) + ) + return df_demand[["region", "year", "demand_tot"]] + + +def read_base_demand(filepath): + with open(filepath, "r") as file: + yaml_data = file.read() + + data_list = yaml.safe_load(yaml_data) + + df = pd.DataFrame( + [ + (key, value["year"], value["value"]) + for entry in data_list + for key, value in entry.items() + ], + columns=["region", "year", "value"], + ) + return df + + +def read_hist_mat_demand(material): + datapath = message_ix_models.util.private_data_path("material") + + if material in ["cement", "steel"]: + # Read population data + df_pop = read_timer_pop(datapath, file_cement, material) + + # Read GDP per capita data + df_gdp = read_timer_gdp(datapath, file_cement, material) + + if material == "aluminum": + df_raw_cons = ( + pd.read_excel( + f'{datapath}/{material_data[material]["dir"]}{material_data[material]["file"]}', + sheet_name="final_table", + nrows=378, + ) + .drop(["cons.pcap", "del.t"], axis=1) + .rename({"gdp.pcap": "gdp_pcap"}, axis=1) + ) + + df_cons = ( + df_raw_cons.assign( + cons_pcap=lambda x: x["consumption"] / x["pop"], + del_t=lambda x: x["year"].astype(int) - 2010, + ) + .dropna() + .query("cons_pcap > 0") + ) + elif material == "steel": + df_raw_cons = pd.read_excel( + f'{datapath}/{material_data[material]["dir"]}{material_data[material]["file"]}', + sheet_name="Consumption regions", + nrows=26, + ) + df_raw_cons = ( + df_raw_cons.rename({"t": "region"}, axis=1) + .drop(columns=["Unnamed: 1"]) + .rename({"TIMER Region": "reg_no"}, axis=1) + ) + df_raw_cons = df_raw_cons.melt( + id_vars=["region", "reg_no"], var_name="year", value_name="consumption" + ) + df_raw_cons["region"] = ( + df_raw_cons["region"] + .str.replace("(", "") + .str.replace(")", "") + .str.replace("\d+", "") + .str[:-1] + ) + + # Merge and organize data + df_cons = ( + pd.merge(df_raw_cons, df_pop.drop("region", axis=1), on=["reg_no", "year"]) + .merge(df_gdp[["reg_no", "year", "gdp_pcap"]], on=["reg_no", "year"]) + .assign( + cons_pcap=lambda x: x["consumption"] / x["pop"], + del_t=lambda x: x["year"].astype(int) - 2010, + ) + .dropna() + .query("cons_pcap > 0") + ) + elif material == "cement": + df_raw_cons = pd.read_excel( + f'{datapath}/{material_data[material]["dir"]}{material_data[material]["file"]}', + sheet_name="Regions", + skiprows=122, + nrows=26, + ) + df_raw_cons = df_raw_cons.rename( + {"Region #": "reg_no", "Region": "region"}, axis=1 + ) + df_raw_cons = df_raw_cons.melt( + id_vars=["region", "reg_no"], var_name="year", value_name="consumption" + ) + # Merge and organize data + df_cons = ( + pd.merge(df_raw_cons, df_pop.drop("region", axis=1), on=["reg_no", "year"]) + .merge(df_gdp[["reg_no", "year", "gdp_pcap"]], on=["reg_no", "year"]) + .assign( + cons_pcap=lambda x: x["consumption"] / x["pop"] / 10**6, + del_t=lambda x: x["year"].astype(int) - 2010, + ) + .dropna() + .query("cons_pcap > 0") + ) + else: + print( + "non-available material selected. must be one of [aluminum, steel, cement]" + ) + df_cons = None + return df_cons + + +def read_pop_from_scen(scen): + pop = scen.par("bound_activity_up", {"technology": "Population"}) + pop = pop.loc[pop.year_act >= 2020].rename( + columns={"year_act": "year", "value": "pop.mil", "node_loc": "region"} + ) + pop = pop.drop(["mode", "time", "technology", "unit"], axis=1) + return pop + + +def read_gdp_ppp_from_scen(scen): + gdp_mer = scen.par("bound_activity_up", {"technology": "GDP"}) + mer_to_ppp = scen.par("MERtoPPP") + if not len(mer_to_ppp): + mer_to_ppp = pd.read_csv( + message_ix_models.util.private_data_path( + "material", "other", "mer_to_ppp_default.csv" + ) + ) + mer_to_ppp = mer_to_ppp.set_index(["node", "year"]) + gdp_mer = gdp_mer.merge( + mer_to_ppp.reset_index()[["node", "year", "value"]], + left_on=["node_loc", "year_act"], + right_on=["node", "year"], + ) + gdp_mer["gdp_ppp"] = gdp_mer["value_y"] * gdp_mer["value_x"] + gdp_mer = gdp_mer[["year", "node_loc", "gdp_ppp"]] + gdp = gdp_mer.loc[gdp_mer.year >= 2020].rename(columns={"node_loc": "region"}) + return gdp + + +def derive_demand(material, scen, old_gdp=False): + datapath = message_ix_models.util.private_data_path("material") + + # read pop projection from scenario + df_pop = read_pop_from_scen(scen) + + # read gdp (PPP) projection from scenario + df_gdp = read_gdp_ppp_from_scen(scen) + if old_gdp: + df_gdp = pd.read_excel(f"{datapath}/other{file_gdp}", sheet_name="data_R12") + df_gdp = ( + df_gdp[df_gdp["Scenario"] == "baseline"] + .loc[:, ["Region", *[i for i in df_gdp.columns if type(i) == int]]] + .melt(id_vars="Region", var_name="year", value_name="gdp_ppp") + .query('Region != "World"') + .assign( + year=lambda x: x["year"].astype(int), + region=lambda x: "R12_" + x["Region"], + ) + ) + + # get base year demand of material + df_base_demand = read_base_demand( + f'{datapath}/{material_data[material]["dir"]}/demand_{material}.yaml' + ) + + # get historical data (material consumption, pop, gdp) + df_cons = read_hist_mat_demand(material) + x_data = tuple(pd.Series(df_cons[col]) for col in fitting_dict[material]["x_data"]) + + # run regression on historical data + params_opt, _ = curve_fit( + fitting_dict[material]["function"], + xdata=x_data, + ydata=df_cons["cons_pcap"], + p0=fitting_dict[material]["initial_guess"], + ) + + # prepare df for applying regression model to project demand + df_all = pd.merge(df_pop, df_base_demand.drop(columns=["year"]), how="left") + df_all = pd.merge(df_all, df_gdp[["region", "year", "gdp_ppp"]], how="inner") + df_all["del_t"] = df_all["year"] - 2010 + df_all["gdp_pcap"] = df_all["gdp_ppp"] * giga / df_all["pop.mil"] / mega + df_all["demand_pcap0"] = df_all.apply( + lambda row: fitting_dict[material]["function"]( + tuple(row[i] for i in fitting_dict[material]["x_data"]), *params_opt + ), + axis=1, + ) + df_all = df_all.rename({"value": "demand.tot.base"}, axis=1) + + # apply regression model + df_final = project_demand( + df_all, fitting_dict[material]["phi"], fitting_dict[material]["mu"] + ) + df_final = df_final.rename({"region": "node", "demand_tot": "value"}, axis=1) + df_final["commodity"] = material + df_final["level"] = "demand" + df_final["time"] = "year" + df_final["unit"] = "t" + # TODO: correct unit would be Mt but might not be registered on database + df_final = message_ix.make_df("demand", **df_final) + return df_final diff --git a/message_ix_models/model/material/util.py b/message_ix_models/model/material/util.py index a60c734da5..d70d85c7a2 100644 --- a/message_ix_models/model/material/util.py +++ b/message_ix_models/model/material/util.py @@ -1,7 +1,7 @@ from message_ix_models import Context -from pathlib import Path from message_ix_models.util import load_private_data import pandas as pd +import yaml # Configuration files METADATA = [ @@ -48,7 +48,6 @@ def read_config(): def prepare_xlsx_for_explorer(filepath): - import pandas as pd df = pd.read_excel(filepath) def add_R12(str): @@ -68,3 +67,23 @@ def combine_df_dictionaries(*args): for i in keys: comb_dict[i] = pd.concat([j.get(i) for j in args]) return comb_dict + + +def read_yaml_file(file_path): + with open(file_path, encoding="utf8") as file: + try: + data = yaml.safe_load(file) + return data + except yaml.YAMLError as e: + print(f"Error while parsing YAML file: {e}") + return None + + +def invert_dictionary(original_dict): + inverted_dict = {} + for key, value in original_dict.items(): + for array_element in value: + if array_element not in inverted_dict: + inverted_dict[array_element] = [] + inverted_dict[array_element].append(key) + return inverted_dict From 45068cb595024010a99176a715cc8dbad9f0c4a2 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Mon, 22 Jan 2024 17:19:10 +0100 Subject: [PATCH 661/774] Add compatibility with ssp_dev scenarios --- message_ix_models/model/material/__init__.py | 9 ++++----- message_ix_models/model/material/build.py | 2 ++ 2 files changed, 6 insertions(+), 5 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index ea9d09a456..a324516bad 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -38,8 +38,8 @@ def build(scenario): scenario.add_set("commodity", "freshwater_supply") water_dict = pd.read_excel( - "C:/Users\maczek\PycharmProjects\message_data\message_data\model\material\petrochemical model fixes notebooks/water_tec_pars.xlsx", - sheet_name=None, + private_data_path("material", "other", "water_tec_pars.xlsx"), + sheet_name=None ) for par in water_dict.keys(): scenario.add_par(par, water_dict[par]) @@ -204,7 +204,7 @@ def build_scen(context, datafile, tag, mode, scenario_name): # Clone and set up - if context.scenario_info["model"] == "SSP_dev_SSP2_v0.1": + if "SSP_dev" in context.scenario_info["model"]: scenario = build( context.get_scenario().clone( model=context.scenario_info["model"], @@ -290,7 +290,7 @@ def build_scen(context, datafile, tag, mode, scenario_name): @click.option("--add_calibration", default=False) @click.pass_obj def solve_scen( - context, datafile, model_name, scenario_name, add_calibration, add_macro, version + context, datafile, model_name, scenario_name, add_calibration, add_macro, version ): """Solve a scenario. @@ -483,7 +483,6 @@ def run_old_reporting(context): from .data_methanol_new import gen_data_methanol_new from .data_ammonia_new import gen_all_NH3_fert - DATA_FUNCTIONS_1 = [ # gen_data_buildings, gen_data_methanol_new, diff --git a/message_ix_models/model/material/build.py b/message_ix_models/model/material/build.py index c925705894..eb6f74da34 100644 --- a/message_ix_models/model/material/build.py +++ b/message_ix_models/model/material/build.py @@ -159,6 +159,8 @@ def apply_spec( result = data(scenario, dry_run=dry_run) if result: # `data` function returned some data; use add_par_data() + if "SSP_dev" in scenario.model: + result.pop("emission_factor") add_par_data(scenario, result, dry_run=dry_run) # Finalize From 2584dd42a2b3a3e7444c80e566448191d101a12f Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Mon, 22 Jan 2024 17:19:55 +0100 Subject: [PATCH 662/774] Comment out unused rpy2 dependencies --- .../model/material/data_aluminum.py | 82 ++++++++-------- .../model/material/data_cement.py | 94 +++++++++---------- .../model/material/data_steel.py | 80 ++++++++-------- 3 files changed, 128 insertions(+), 128 deletions(-) diff --git a/message_ix_models/model/material/data_aluminum.py b/message_ix_models/model/material/data_aluminum.py index 6c1eb235e6..12ae45de48 100644 --- a/message_ix_models/model/material/data_aluminum.py +++ b/message_ix_models/model/material/data_aluminum.py @@ -519,44 +519,44 @@ def gen_mock_demand_aluminum(scenario): return demand2020_al -# load rpy2 modules -import rpy2.robjects as ro -from rpy2.robjects import pandas2ri -from rpy2.robjects.conversion import localconverter - -# This returns a df with columns ["region", "year", "demand.tot"] -def derive_aluminum_demand(scenario, dry_run=False): - """Generate aluminum demand.""" - # paths to r code and lca data - rcode_path = Path(__file__).parents[0] / "material_demand" - context = read_config() - - # source R code - r = ro.r - r.source(str(rcode_path / "init_modularized.R")) - - # Read population and baseline demand for materials - pop = scenario.par("bound_activity_up", {"technology": "Population"}) - pop = pop.loc[pop.year_act >= 2020].rename( - columns={"year_act": "year", "value": "pop.mil", "node_loc": "region"} - ) - - # import pdb; pdb.set_trace() - - pop = pop[["region", "year", "pop.mil"]] - - base_demand = gen_mock_demand_aluminum(scenario) - base_demand = base_demand.loc[base_demand.year == 2020].rename( - columns={"value": "demand.tot.base", "node": "region"} - ) - - # call R function with type conversion - with localconverter(ro.default_converter + pandas2ri.converter): - # GDP is only in MER in scenario. - # To get PPP GDP, it is read externally from the R side - df = r.derive_aluminum_demand( - pop, base_demand, str(private_data_path("material")) - ) - df.year = df.year.astype(int) - - return df +# # load rpy2 modules +# import rpy2.robjects as ro +# from rpy2.robjects import pandas2ri +# from rpy2.robjects.conversion import localconverter +# +# # This returns a df with columns ["region", "year", "demand.tot"] +# def derive_aluminum_demand(scenario, dry_run=False): +# """Generate aluminum demand.""" +# # paths to r code and lca data +# rcode_path = Path(__file__).parents[0] / "material_demand" +# context = read_config() +# +# # source R code +# r = ro.r +# r.source(str(rcode_path / "init_modularized.R")) +# +# # Read population and baseline demand for materials +# pop = scenario.par("bound_activity_up", {"technology": "Population"}) +# pop = pop.loc[pop.year_act >= 2020].rename( +# columns={"year_act": "year", "value": "pop.mil", "node_loc": "region"} +# ) +# +# # import pdb; pdb.set_trace() +# +# pop = pop[["region", "year", "pop.mil"]] +# +# base_demand = gen_mock_demand_aluminum(scenario) +# base_demand = base_demand.loc[base_demand.year == 2020].rename( +# columns={"value": "demand.tot.base", "node": "region"} +# ) +# +# # call R function with type conversion +# with localconverter(ro.default_converter + pandas2ri.converter): +# # GDP is only in MER in scenario. +# # To get PPP GDP, it is read externally from the R side +# df = r.derive_aluminum_demand( +# pop, base_demand, str(private_data_path("material")) +# ) +# df.year = df.year.astype(int) +# +# return df diff --git a/message_ix_models/model/material/data_cement.py b/message_ix_models/model/material/data_cement.py index 6bb3b3fbec..46af899ae2 100644 --- a/message_ix_models/model/material/data_cement.py +++ b/message_ix_models/model/material/data_cement.py @@ -409,50 +409,50 @@ def gen_data_cement(scenario, dry_run=False): return results -# load rpy2 modules -import rpy2.robjects as ro -from rpy2.robjects import pandas2ri -from rpy2.robjects.conversion import localconverter - - -# This returns a df with columns ["region", "year", "demand.tot"] -def derive_cement_demand(scenario, dry_run=False): - """Generate cement demand.""" - # paths to r code and lca data - rcode_path = Path(__file__).parents[0] / "material_demand" - context = read_config() - - # source R code - r = ro.r - r.source(str(rcode_path / "init_modularized.R")) - - # Read population and baseline demand for materials - pop = scenario.par("bound_activity_up", {"technology": "Population"}) - pop = pop.loc[pop.year_act >= 2020].rename( - columns={"year_act": "year", "value": "pop.mil", "node_loc": "region"} - ) - pop = pop[["region", "year", "pop.mil"]] - - base_demand = gen_mock_demand_cement(scenario) - base_demand = base_demand.loc[base_demand.year == 2020].rename( - columns={"value": "demand.tot.base", "node": "region"} - ) - - # import pdb; pdb.set_trace() - - # base_demand = scenario.par("demand", {"commodity": "steel", "year": 2020}) - # base_demand = base_demand.loc[base_demand.year >= 2020].rename( - # columns={"value": "demand.tot.base", "node": "region"} - # ) - # base_demand = base_demand[["region", "year", "demand.tot.base"]] - - # call R function with type conversion - with localconverter(ro.default_converter + pandas2ri.converter): - # GDP is only in MER in scenario. - # To get PPP GDP, it is read externally from the R side - df = r.derive_cement_demand( - pop, base_demand, str(private_data_path("material")) - ) - df.year = df.year.astype(int) - - return df +# # load rpy2 modules +# import rpy2.robjects as ro +# from rpy2.robjects import pandas2ri +# from rpy2.robjects.conversion import localconverter +# +# +# # This returns a df with columns ["region", "year", "demand.tot"] +# def derive_cement_demand(scenario, dry_run=False): +# """Generate cement demand.""" +# # paths to r code and lca data +# rcode_path = Path(__file__).parents[0] / "material_demand" +# context = read_config() +# +# # source R code +# r = ro.r +# r.source(str(rcode_path / "init_modularized.R")) +# +# # Read population and baseline demand for materials +# pop = scenario.par("bound_activity_up", {"technology": "Population"}) +# pop = pop.loc[pop.year_act >= 2020].rename( +# columns={"year_act": "year", "value": "pop.mil", "node_loc": "region"} +# ) +# pop = pop[["region", "year", "pop.mil"]] +# +# base_demand = gen_mock_demand_cement(scenario) +# base_demand = base_demand.loc[base_demand.year == 2020].rename( +# columns={"value": "demand.tot.base", "node": "region"} +# ) +# +# # import pdb; pdb.set_trace() +# +# # base_demand = scenario.par("demand", {"commodity": "steel", "year": 2020}) +# # base_demand = base_demand.loc[base_demand.year >= 2020].rename( +# # columns={"value": "demand.tot.base", "node": "region"} +# # ) +# # base_demand = base_demand[["region", "year", "demand.tot.base"]] +# +# # call R function with type conversion +# with localconverter(ro.default_converter + pandas2ri.converter): +# # GDP is only in MER in scenario. +# # To get PPP GDP, it is read externally from the R side +# df = r.derive_cement_demand( +# pop, base_demand, str(private_data_path("material")) +# ) +# df.year = df.year.astype(int) +# +# return df diff --git a/message_ix_models/model/material/data_steel.py b/message_ix_models/model/material/data_steel.py index d9ed76f707..d4b65fcbc2 100644 --- a/message_ix_models/model/material/data_steel.py +++ b/message_ix_models/model/material/data_steel.py @@ -547,43 +547,43 @@ def gen_data_steel(scenario, dry_run=False): return results -# load rpy2 modules -import rpy2.robjects as ro -from rpy2.robjects import pandas2ri -from rpy2.robjects.conversion import localconverter - - -# This returns a df with columns ["region", "year", "demand.tot"] -def derive_steel_demand(scenario, dry_run=False): - """Generate steel demand.""" - # paths to r code and lca data - rcode_path = Path(__file__).parents[0] / "material_demand" - context = read_config() - - # source R code - r = ro.r - r.source(str(rcode_path / "init_modularized.R")) - context.get_local_path("material") - # Read population and baseline demand for materials - pop = scenario.par("bound_activity_up", {"technology": "Population"}) - pop = pop.loc[pop.year_act >= 2020].rename( - columns={"year_act": "year", "value": "pop.mil", "node_loc": "region"} - ) - - # import pdb; pdb.set_trace() - - pop = pop[["region", "year", "pop.mil"]] - - base_demand = gen_mock_demand_steel(scenario) - base_demand = base_demand.loc[base_demand.year == 2020].rename( - columns={"value": "demand.tot.base", "node": "region"} - ) - - # call R function with type conversion - with localconverter(ro.default_converter + pandas2ri.converter): - # GDP is only in MER in scenario. - # To get PPP GDP, it is read externally from the R side - df = r.derive_steel_demand(pop, base_demand, str(private_data_path("material"))) - df.year = df.year.astype(int) - - return df +# # load rpy2 modules +# import rpy2.robjects as ro +# from rpy2.robjects import pandas2ri +# from rpy2.robjects.conversion import localconverter +# +# +# # This returns a df with columns ["region", "year", "demand.tot"] +# def derive_steel_demand(scenario, dry_run=False): +# """Generate steel demand.""" +# # paths to r code and lca data +# rcode_path = Path(__file__).parents[0] / "material_demand" +# context = read_config() +# +# # source R code +# r = ro.r +# r.source(str(rcode_path / "init_modularized.R")) +# context.get_local_path("material") +# # Read population and baseline demand for materials +# pop = scenario.par("bound_activity_up", {"technology": "Population"}) +# pop = pop.loc[pop.year_act >= 2020].rename( +# columns={"year_act": "year", "value": "pop.mil", "node_loc": "region"} +# ) +# +# # import pdb; pdb.set_trace() +# +# pop = pop[["region", "year", "pop.mil"]] +# +# base_demand = gen_mock_demand_steel(scenario) +# base_demand = base_demand.loc[base_demand.year == 2020].rename( +# columns={"value": "demand.tot.base", "node": "region"} +# ) +# +# # call R function with type conversion +# with localconverter(ro.default_converter + pandas2ri.converter): +# # GDP is only in MER in scenario. +# # To get PPP GDP, it is read externally from the R side +# df = r.derive_steel_demand(pop, base_demand, str(private_data_path("material"))) +# df.year = df.year.astype(int) +# +# return df From 0220a7fb421bbae02d642ecc76a77b4f863aa21d Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Mon, 22 Jan 2024 17:21:30 +0100 Subject: [PATCH 663/774] Add water tecs input file for ssp_dev compatibility --- message_ix_models/data/material/other/water_tec_pars.xlsx | 3 +++ 1 file changed, 3 insertions(+) create mode 100644 message_ix_models/data/material/other/water_tec_pars.xlsx diff --git a/message_ix_models/data/material/other/water_tec_pars.xlsx b/message_ix_models/data/material/other/water_tec_pars.xlsx new file mode 100644 index 0000000000..0e7a24bb7a --- /dev/null +++ b/message_ix_models/data/material/other/water_tec_pars.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0bf0882eafa9b184a1e3a44c61c36e1756a91bb60e22d47e999cda6f1b14ca96 +size 88386 From 2f9b98a4ce5ce00ceea7b2af08b1416db0e8d518 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Fri, 26 Jan 2024 15:52:39 +0100 Subject: [PATCH 664/774] Adjust pandas operations to resolve futurewarnings --- message_ix_models/model/material/data_ammonia_new.py | 1 + message_ix_models/model/material/data_methanol_new.py | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/message_ix_models/model/material/data_ammonia_new.py b/message_ix_models/model/material/data_ammonia_new.py index e5ecaa5abc..ef249e4e4d 100644 --- a/message_ix_models/model/material/data_ammonia_new.py +++ b/message_ix_models/model/material/data_ammonia_new.py @@ -454,6 +454,7 @@ def read_demand(): fs_GLO.insert(1, "bio_pct", 0) fs_GLO.insert(2, "elec_pct", 0) # 17/14 NH3:N ratio, to get NH3 activity based on N demand => No NH3 loss assumed during production + # FIXME: Name: elec_pct, dtype: float64 ' has dtype incompatible with int64, please explicitly cast to a compatible dtype first. fs_GLO.iloc[:, 1:6] = input_fuel[5] * fs_GLO.iloc[:, 1:6] fs_GLO.insert(6, "NH3_to_N", 1) diff --git a/message_ix_models/model/material/data_methanol_new.py b/message_ix_models/model/material/data_methanol_new.py index 2a27b02afe..e7c973a962 100644 --- a/message_ix_models/model/material/data_methanol_new.py +++ b/message_ix_models/model/material/data_methanol_new.py @@ -68,7 +68,7 @@ def broadcast_reduced_df(df, par_name): yr_cols_codes = {} yr_col_inp = [i for i in df_final.columns if "year" in i] yr_col_out = [i for i in df_bc_node.columns if "year" in i] - df_bc_node[yr_col_inp] = df_final.loc[i][yr_col_inp] + df_bc_node[yr_col_inp] = df_final.loc[i][yr_col_inp].values for colname in node_cols: df_bc_node[colname] = None From 753b4b7b25e00bb8d0de0af6f451364ed640a8ea Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Fri, 26 Jan 2024 15:56:32 +0100 Subject: [PATCH 665/774] Add exceptions for ssp_dev scenarios to enable compatibility --- .../data/material/methanol/missing_rels.yaml | 17 +++++++++++++++++ message_ix_models/model/material/__init__.py | 7 ++++++- message_ix_models/model/material/build.py | 2 -- .../model/material/data_generic.py | 3 ++- .../model/material/data_methanol_new.py | 5 +++-- 5 files changed, 28 insertions(+), 6 deletions(-) create mode 100644 message_ix_models/data/material/methanol/missing_rels.yaml diff --git a/message_ix_models/data/material/methanol/missing_rels.yaml b/message_ix_models/data/material/methanol/missing_rels.yaml new file mode 100644 index 0000000000..39a87231e3 --- /dev/null +++ b/message_ix_models/data/material/methanol/missing_rels.yaml @@ -0,0 +1,17 @@ +- gas_util +- SO2_red_synf +- global_GSO2 +- global_GCO2 +- OilPrices +- PE_export_total +- PE_import_total +- PE_synliquids +- PE_total_direct +- PE_total_engineering +- FE_transport +- SO2_red_ref +- trp_energy +- total_TCO2 +- nuc_lc_in_el +- FE_liquids +- FE_final_energy diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index a324516bad..844c0aa879 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -539,7 +539,12 @@ def add_data_2(scenario, dry_run=False): for func in DATA_FUNCTIONS_2: # Generate or load the data; add to the Scenario log.info(f"from {func.__name__}()") - add_par_data(scenario, func(scenario), dry_run=dry_run) + # TODO: remove this once emission_factors are back in SSP_dev + data = func(scenario) + if "SSP_dev" in scenario.model: + if "emission_factor" in list(data.keys()): + data.pop("emission_factor") + add_par_data(scenario, data, dry_run=dry_run) log.info("done") diff --git a/message_ix_models/model/material/build.py b/message_ix_models/model/material/build.py index eb6f74da34..c925705894 100644 --- a/message_ix_models/model/material/build.py +++ b/message_ix_models/model/material/build.py @@ -159,8 +159,6 @@ def apply_spec( result = data(scenario, dry_run=dry_run) if result: # `data` function returned some data; use add_par_data() - if "SSP_dev" in scenario.model: - result.pop("emission_factor") add_par_data(scenario, result, dry_run=dry_run) # Finalize diff --git a/message_ix_models/model/material/data_generic.py b/message_ix_models/model/material/data_generic.py index 3b15934527..cb0788fc2d 100644 --- a/message_ix_models/model/material/data_generic.py +++ b/message_ix_models/model/material/data_generic.py @@ -1,5 +1,6 @@ from collections import defaultdict +import message_ix_models.util import pandas as pd from .util import read_config @@ -24,7 +25,7 @@ def read_data_generic(scenario): # Read the file data_generic = pd.read_excel( - context.get_local_path( + message_ix_models.util.private_data_path( "material", "other", "generic_furnace_boiler_techno_economic.xlsx" ), sheet_name="generic", diff --git a/message_ix_models/model/material/data_methanol_new.py b/message_ix_models/model/material/data_methanol_new.py index e7c973a962..1522a72ced 100644 --- a/message_ix_models/model/material/data_methanol_new.py +++ b/message_ix_models/model/material/data_methanol_new.py @@ -39,8 +39,9 @@ def gen_data_methanol_new(scenario): for i in pars_dict.keys(): pars_dict[i] = broadcast_reduced_df(pars_dict[i], i) - if scenario.model == "SSP_dev_SSP2_v0.1": - file_path = "C:/Users\maczek\PycharmProjects\message_data\message_data\model\material\petrochemical model fixes notebooks\missing_rels.yaml" + if "SSP_dev" in scenario.model: + file_path = message_ix_models.util.private_data_path("material", "methanol", "missing_rels.yaml") + # file_path = "C:/Users\maczek\PycharmProjects\message_data\message_data\model\material\petrochemical model fixes notebooks\" with open(file_path, "r") as file: missing_rels = yaml.safe_load(file) From 150552ecbf496a8e8535b458663d77e910f156f4 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Fri, 26 Jan 2024 16:00:51 +0100 Subject: [PATCH 666/774] Rename power sector data dir --- .../LCA_commodity_mapping.xlsx | 0 .../LCA_region_mapping.xlsx | 0 .../MESSAGE_global_model_technologies.xlsx | 0 .../NTNU_LCA_coefficients.xlsx | 0 message_ix_models/model/material/data_power_sector.py | 11 ++++++----- 5 files changed, 6 insertions(+), 5 deletions(-) rename message_ix_models/data/material/{power sector => power_sector}/LCA_commodity_mapping.xlsx (100%) rename message_ix_models/data/material/{power sector => power_sector}/LCA_region_mapping.xlsx (100%) rename message_ix_models/data/material/{power sector => power_sector}/MESSAGE_global_model_technologies.xlsx (100%) rename message_ix_models/data/material/{power sector => power_sector}/NTNU_LCA_coefficients.xlsx (100%) diff --git a/message_ix_models/data/material/power sector/LCA_commodity_mapping.xlsx b/message_ix_models/data/material/power_sector/LCA_commodity_mapping.xlsx similarity index 100% rename from message_ix_models/data/material/power sector/LCA_commodity_mapping.xlsx rename to message_ix_models/data/material/power_sector/LCA_commodity_mapping.xlsx diff --git a/message_ix_models/data/material/power sector/LCA_region_mapping.xlsx b/message_ix_models/data/material/power_sector/LCA_region_mapping.xlsx similarity index 100% rename from message_ix_models/data/material/power sector/LCA_region_mapping.xlsx rename to message_ix_models/data/material/power_sector/LCA_region_mapping.xlsx diff --git a/message_ix_models/data/material/power sector/MESSAGE_global_model_technologies.xlsx b/message_ix_models/data/material/power_sector/MESSAGE_global_model_technologies.xlsx similarity index 100% rename from message_ix_models/data/material/power sector/MESSAGE_global_model_technologies.xlsx rename to message_ix_models/data/material/power_sector/MESSAGE_global_model_technologies.xlsx diff --git a/message_ix_models/data/material/power sector/NTNU_LCA_coefficients.xlsx b/message_ix_models/data/material/power_sector/NTNU_LCA_coefficients.xlsx similarity index 100% rename from message_ix_models/data/material/power sector/NTNU_LCA_coefficients.xlsx rename to message_ix_models/data/material/power_sector/NTNU_LCA_coefficients.xlsx diff --git a/message_ix_models/model/material/data_power_sector.py b/message_ix_models/model/material/data_power_sector.py index 6da41e9bc4..5fa558434a 100644 --- a/message_ix_models/model/material/data_power_sector.py +++ b/message_ix_models/model/material/data_power_sector.py @@ -2,6 +2,7 @@ from pathlib import Path import pandas as pd +from message_ix_models.util import private_data_path from .util import read_config from message_ix_models import ScenarioInfo @@ -16,19 +17,19 @@ def read_material_intensities( #################################################################### # read LCA data from ADVANCE LCA tool - data_path_lca = data_path + "/power sector/NTNU_LCA_coefficients.xlsx" + data_path_lca = data_path + "/NTNU_LCA_coefficients.xlsx" data_lca = pd.read_excel(data_path_lca, sheet_name="environmentalImpacts") # read technology, region and commodity mappings data_path_tec_map = ( - data_path + "/power sector/MESSAGE_global_model_technologies.xlsx" + data_path + "/MESSAGE_global_model_technologies.xlsx" ) technology_mapping = pd.read_excel(data_path_tec_map, sheet_name="technology") - data_path_reg_map = data_path + "/power sector/LCA_region_mapping.xlsx" + data_path_reg_map = data_path + "/LCA_region_mapping.xlsx" region_mapping = pd.read_excel(data_path_reg_map, sheet_name="region") - data_path_com_map = data_path + "/power sector/LCA_commodity_mapping.xlsx" + data_path_com_map = data_path + "/LCA_commodity_mapping.xlsx" commodity_mapping = pd.read_excel(data_path_com_map, sheet_name="commodity") #################################################################### @@ -280,7 +281,7 @@ def gen_data_power_sector(scenario, dry_run=False): # paths to lca data code_path = Path(__file__).parents[0] / "material_intensity" - data_path = context.get_local_path("material") + data_path = private_data_path("material", "power_sector") # Information about scenario, e.g. node, year s_info = ScenarioInfo(scenario) From 9a5143ed0b3c8c5a3c2da963da5bc583f2af7a24 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Fri, 26 Jan 2024 16:02:44 +0100 Subject: [PATCH 667/774] Add missing petro demand file and fix code typo --- .../material/petrochemicals/demand_petro.yaml | 36 +++++++++++++++++++ .../model/material/data_petro.py | 2 +- 2 files changed, 37 insertions(+), 1 deletion(-) create mode 100644 message_ix_models/data/material/petrochemicals/demand_petro.yaml diff --git a/message_ix_models/data/material/petrochemicals/demand_petro.yaml b/message_ix_models/data/material/petrochemicals/demand_petro.yaml new file mode 100644 index 0000000000..a1be4c05a8 --- /dev/null +++ b/message_ix_models/data/material/petrochemicals/demand_petro.yaml @@ -0,0 +1,36 @@ +- R12_AFR: + year: 2020 + value: 2.4000 +- R12_RCPA: + year: 2020 + value: 0.44 +- R12_EEU: + year: 2020 + value: 3 +- R12_FSU: + year: 2020 + value: 5 +- R12_LAM: + year: 2020 + value: 11 +- R12_MEA: + year: 2020 + value: 40.3 +- R12_NAM: + year: 2020 + value: 49.8 +- R12_PAO: + year: 2020 + value: 11 +- R12_PAS: + year: 2020 + value: 37.5 +- R12_SAS: + year: 2020 + value: 10.7 +- R12_WEU: + year: 2020 + value: 29.2 +- R12_CHN: + year: 2020 + value: 50.5 \ No newline at end of file diff --git a/message_ix_models/model/material/data_petro.py b/message_ix_models/model/material/data_petro.py index 6912763c07..b673268bf1 100644 --- a/message_ix_models/model/material/data_petro.py +++ b/message_ix_models/model/material/data_petro.py @@ -58,7 +58,7 @@ def get_demand_t1_with_income_elasticity( gdp_mer = scenario.par("bound_activity_up", {"technology": "GDP"}) mer_to_ppp = pd.read_csv( private_data_path("material", "other", "mer_to_ppp_default.csv") - ).set_index("node", "year") + ).set_index(["node", "year"]) # mer_to_ppp = scenario.par("MERtoPPP").set_index("node", "year") TODO: might need to be re-activated for different SSPs gdp_mer = gdp_mer.merge( mer_to_ppp.reset_index()[["node", "year", "value"]], From c29d79c9fcf4563222fa420cd070066e97b74e96 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Fri, 26 Jan 2024 16:04:25 +0100 Subject: [PATCH 668/774] Add missed exception for ssp_dev compatibility --- message_ix_models/model/material/__init__.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 844c0aa879..f6af40e4ee 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -517,7 +517,11 @@ def add_data_1(scenario, dry_run=False): for func in DATA_FUNCTIONS_1: # Generate or load the data; add to the Scenario log.info(f"from {func.__name__}()") - add_par_data(scenario, func(scenario), dry_run=dry_run) + data = func(scenario) + if "SSP_dev" in scenario.model: + if "emission_factor" in list(data.keys()): + data.pop("emission_factor") + add_par_data(scenario, data, dry_run=dry_run) log.info("done") From 6cb805dcea8110d1e982a2a6f74d81a7863419d5 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Fri, 26 Jan 2024 16:07:39 +0100 Subject: [PATCH 669/774] Integrate new industry demand calibration in materials build --- message_ix_models/model/material/__init__.py | 18 +- message_ix_models/model/material/data_util.py | 481 +++++++++++++++--- 2 files changed, 426 insertions(+), 73 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index f6af40e4ee..9ed667837d 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -15,7 +15,8 @@ ) # from .data import add_data -from .data_util import modify_demand_and_hist_activity, add_emission_accounting +from .data_util import modify_demand_and_hist_activity, add_emission_accounting, add_new_ind_hist_act, \ + modify_industry_demand, modify_baseyear_bounds from .data_util import add_coal_lowerbound_2020, add_macro_COVID, add_cement_bounds_2020 from .data_util import add_elec_lowerbound_2020, add_ccs_technologies, read_config import pandas as pd @@ -23,7 +24,7 @@ log = logging.getLogger(__name__) -def build(scenario): +def build(scenario, old_calib): """Set up materials accounting on `scenario`.""" # Get the specification @@ -56,7 +57,13 @@ def build(scenario): # Adjust the historical activity of the useful level industry technologies # Coal calibration 2020 add_ccs_technologies(scenario) - modify_demand_and_hist_activity(scenario) + if old_calib: + modify_demand_and_hist_activity(scenario) + else: + modify_baseyear_bounds(scenario) + last_hist_year = scenario.par("historical_activity")["year_act"].max() + modify_industry_demand(scenario, last_hist_year) + add_new_ind_hist_act(scenario, [last_hist_year]) try: add_emission_accounting(scenario) except: @@ -167,8 +174,9 @@ def create_bare(context, regions, dry_run): @click.option("--tag", default="", help="Suffix to the scenario name") @click.option("--mode", default="by_url") @click.option("--scenario_name", default="NoPolicy_3105_macro") +@click.option("--old_calib", default=False) @click.pass_obj -def build_scen(context, datafile, tag, mode, scenario_name): +def build_scen(context, datafile, tag, mode, scenario_name, old_calib): """Build a scenario. Use the --url option to specify the base scenario. If this scenario is on a @@ -210,7 +218,7 @@ def build_scen(context, datafile, tag, mode, scenario_name): model=context.scenario_info["model"], scenario=context.scenario_info["scenario"] + "_" + tag, keep_solution=False, - ) + ), old_calib=old_calib ) else: scenario = build( diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index be71ab7076..3916642069 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -425,11 +425,304 @@ def modify_demand_and_hist_activity(scen): scen.commit(comment="remove bounds") -def calc_hist_activity_new(scen, years): +def modify_demand_and_hist_activity_debug(scen: message_ix.Scenario) -> dict: + """modularized "dry-run" version of modify_demand_and_hist_activity() for debugging purposes""" + + context = read_config() + s_info = ScenarioInfo(scen) + fname = "MESSAGEix-Materials_final_energy_industry.xlsx" + + if "R12_CHN" in s_info.N: + sheet_n = "R12" + region_type = "R12_" + region_name_CPA = "RCPA" + region_name_CHN = "CHN" + else: + sheet_n = "R11" + region_type = "R11_" + region_name_CPA = "CPA" + region_name_CHN = "" + + df = pd.read_excel( + private_data_path("material", "other", fname), sheet_name=sheet_n, usecols="A:F" + ) + + # Filter the necessary variables + df = df[ + (df["SECTOR"] == "feedstock (petrochemical industry)") + | (df["SECTOR"] == "feedstock (total)") + | (df["SECTOR"] == "industry (chemicals)") + | (df["SECTOR"] == "industry (iron and steel)") + | (df["SECTOR"] == "industry (non-ferrous metals)") + | (df["SECTOR"] == "industry (non-metallic minerals)") + | (df["SECTOR"] == "industry (total)") + ] + df = df[df["RYEAR"] == 2015] + + # NOTE: Total cehmical industry energy: 27% thermal, 8% electricity, 65% feedstock + # SOURCE: IEA Sankey 2020: https://www.iea.org/sankey/#?c=World&s=Final%20consumption + # 67% of total chemicals energy is used for primary chemicals (ammonia,methnol,HVCs) + # SOURCE: https://www.iea.org/data-and-statistics/charts/primary-chemical-production-in-the-sustainable-development-scenario-2000-2030 + + # Retreive data for i_spec + # 67% of total chemcials electricity demand comes from primary chemicals (IEA) + # (Excludes petrochemicals as the share is negligable) + # Aluminum, cement and steel included. + # NOTE: Steel has high shares (previously it was not inlcuded in i_spec) + + df_spec = df[ + (df["FUEL"] == "electricity") + & (df["SECTOR"] != "industry (total)") + & (df["SECTOR"] != "feedstock (petrochemical industry)") + & (df["SECTOR"] != "feedstock (total)") + ] + df_spec_total = df[ + (df["SECTOR"] == "industry (total)") & (df["FUEL"] == "electricity") + ] + + df_spec_new = pd.DataFrame( + columns=["REGION", "SECTOR", "FUEL", "RYEAR", "UNIT_OUT", "RESULT"] + ) + for r in df_spec["REGION"].unique(): + df_spec_temp = df_spec.loc[df_spec["REGION"] == r] + df_spec_total_temp = df_spec_total.loc[df_spec_total["REGION"] == r] + df_spec_temp.loc[:, "i_spec"] = ( + df_spec_temp.loc[:, "RESULT"] + / df_spec_total_temp.loc[:, "RESULT"].values[0] + ) + df_spec_new = pd.concat([df_spec_temp, df_spec_new], ignore_index=True) + + df_spec_new.drop(["FUEL", "RYEAR", "UNIT_OUT", "RESULT"], axis=1, inplace=True) + df_spec_new.loc[df_spec_new["SECTOR"] == "industry (chemicals)", "i_spec"] = ( + df_spec_new.loc[df_spec_new["SECTOR"] == "industry (chemicals)", "i_spec"] + * 0.67 + ) + + df_spec_new = df_spec_new.groupby(["REGION"]).sum().reset_index() + + # Already set to zero: ammonia, methanol, HVCs cover most of the feedstock + + df_feed = df[ + (df["SECTOR"] == "feedstock (petrochemical industry)") & (df["FUEL"] == "total") + ] + df_feed_total = df[(df["SECTOR"] == "feedstock (total)") & (df["FUEL"] == "total")] + df_feed_temp = pd.DataFrame(columns=["REGION", "i_feed"]) + df_feed_new = pd.DataFrame(columns=["REGION", "i_feed"]) + + for r in df_feed["REGION"].unique(): + i = 0 + df_feed_temp.at[i, "REGION"] = r + df_feed_temp.at[i, "i_feed"] = 1 + i = i + 1 + df_feed_new = pd.concat([df_feed_temp, df_feed_new], ignore_index=True) + + # Retreive data for i_therm + # 67% of chemical thermal energy chemicals comes from primary chemicals. (IEA) + # NOTE: Aluminum is excluded since refining process is not explicitly represented + # NOTE: CPA has a 3% share while it used to be 30% previosuly ?? + + df_therm = df[ + (df["FUEL"] != "electricity") + & (df["FUEL"] != "total") + & (df["SECTOR"] != "industry (total)") + & (df["SECTOR"] != "feedstock (petrochemical industry)") + & (df["SECTOR"] != "feedstock (total)") + & (df["SECTOR"] != "industry (non-ferrous metals)") + ] + df_therm_total = df[ + (df["SECTOR"] == "industry (total)") + & (df["FUEL"] != "total") + & (df["FUEL"] != "electricity") + ] + df_therm_total = ( + df_therm_total.groupby(by="REGION").sum().drop(["RYEAR"], axis=1).reset_index() + ) + df_therm = ( + df_therm.groupby(by=["REGION", "SECTOR"]) + .sum() + .drop(["RYEAR"], axis=1) + .reset_index() + ) + df_therm_new = pd.DataFrame( + columns=["REGION", "SECTOR", "FUEL", "RYEAR", "UNIT_OUT", "RESULT"] + ) + + for r in df_therm["REGION"].unique(): + df_therm_temp = df_therm.loc[df_therm["REGION"] == r] + df_therm_total_temp = df_therm_total.loc[df_therm_total["REGION"] == r] + df_therm_temp.loc[:, "i_therm"] = ( + df_therm_temp.loc[:, "RESULT"] + / df_therm_total_temp.loc[:, "RESULT"].values[0] + ) + df_therm_new = pd.concat([df_therm_temp, df_therm_new], ignore_index=True) + df_therm_new = df_therm_new.drop(["RESULT"], axis=1) + + df_therm_new.drop(["FUEL", "RYEAR", "UNIT_OUT"], axis=1, inplace=True) + df_therm_new.loc[df_therm_new["SECTOR"] == "industry (chemicals)", "i_therm"] = ( + df_therm_new.loc[df_therm_new["SECTOR"] == "industry (chemicals)", "i_therm"] + * 0.67 + ) + + # Modify CPA based on https://www.iea.org/sankey/#?c=Japan&s=Final%20consumption. + # Since the value did not allign with the one in the IEA website. + index = (df_therm_new["SECTOR"] == "industry (iron and steel)") & ( + (df_therm_new["REGION"] == region_name_CPA) + | (df_therm_new["REGION"] == region_name_CHN) + ) + + df_therm_new.loc[index, "i_therm"] = 0.2 + + df_therm_new = df_therm_new.groupby(["REGION"]).sum(numeric_only=True).reset_index() + + # TODO: Useful technology efficiencies will also be included + + # Add the modified demand and historical activity to the scenario + + # Relted technologies that have outputs to useful industry level. + # Historical activity of theese will be adjusted + tec_therm = [ + "biomass_i", + "coal_i", + "elec_i", + "eth_i", + "foil_i", + "gas_i", + "h2_i", + "heat_i", + "hp_el_i", + "hp_gas_i", + "loil_i", + "meth_i", + "solar_i", + ] + tec_fs = [ + "coal_fs", + "ethanol_fs", + "foil_fs", + "gas_fs", + "loil_fs", + "methanol_fs", + ] + tec_sp = ["sp_coal_I", "sp_el_I", "sp_eth_I", "sp_liq_I", "sp_meth_I", "h2_fc_I"] + + thermal_df_hist = scen.par("historical_activity", filters={"technology": tec_therm}) + spec_df_hist = scen.par("historical_activity", filters={"technology": tec_sp}) + feed_df_hist = scen.par("historical_activity", filters={"technology": tec_fs}) + useful_thermal = scen.par("demand", filters={"commodity": "i_therm"}) + useful_spec = scen.par("demand", filters={"commodity": "i_spec"}) + useful_feed = scen.par("demand", filters={"commodity": "i_feed"}) + + for r in df_therm_new["REGION"]: + r_MESSAGE = region_type + r + + useful_thermal.loc[ + useful_thermal["node"] == r_MESSAGE, "value" + ] = useful_thermal.loc[useful_thermal["node"] == r_MESSAGE, "value"] * ( + 1 - df_therm_new.loc[df_therm_new["REGION"] == r, "i_therm"].values[0] + ) + + thermal_df_hist.loc[ + thermal_df_hist["node_loc"] == r_MESSAGE, "value" + ] = thermal_df_hist.loc[thermal_df_hist["node_loc"] == r_MESSAGE, "value"] * ( + 1 - df_therm_new.loc[df_therm_new["REGION"] == r, "i_therm"].values[0] + ) + + for r in df_spec_new["REGION"]: + r_MESSAGE = region_type + r + + useful_spec.loc[useful_spec["node"] == r_MESSAGE, "value"] = useful_spec.loc[ + useful_spec["node"] == r_MESSAGE, "value" + ] * (1 - df_spec_new.loc[ + df_spec_new["REGION"] == r, "i_spec"].values[0]) + + spec_df_hist.loc[ + spec_df_hist["node_loc"] == r_MESSAGE, "value" + ] = spec_df_hist.loc[spec_df_hist["node_loc"] == r_MESSAGE, "value"] * ( + 1 - df_spec_new.loc[df_spec_new["REGION"] == r, "i_spec"].values[0] + ) + + for r in df_feed_new["REGION"]: + r_MESSAGE = region_type + r + + useful_feed.loc[useful_feed["node"] == r_MESSAGE, "value"] = useful_feed.loc[ + useful_feed["node"] == r_MESSAGE, "value" + ] * (1 - df_feed_new.loc[ + df_feed_new["REGION"] == r, "i_feed"].values[0]) + + feed_df_hist.loc[ + feed_df_hist["node_loc"] == r_MESSAGE, "value" + ] = feed_df_hist.loc[feed_df_hist["node_loc"] == r_MESSAGE, "value"] * ( + 1 - df_feed_new.loc[df_feed_new["REGION"] == r, "i_feed"].values[0] + ) + + # For aluminum there is no significant deduction required + # (refining process not included and thermal energy required from + # recycling is not a significant share.) + # For petro: based on 13.1 GJ/tonne of ethylene and the demand in the model + + # df = scen.par('demand', filters={'commodity':'i_therm'}) + # df.value = df.value * 0.38 #(30% steel, 25% cement, 7% petro) + # + # scen.check_out() + # scen.add_par('demand', df) + # scen.commit(comment = 'modify i_therm demand') + + # Adjust the i_spec. + # Electricity usage seems negligable in the production of HVCs. + # Aluminum: based on IAI China data 20%. + + # df = scen.par('demand', filters={'commodity':'i_spec'}) + # df.value = df.value * 0.80 #(20% aluminum) + # + # scen.check_out() + # scen.add_par('demand', df) + # scen.commit(comment = 'modify i_spec demand') + + # Adjust the i_feedstock. + # 45 GJ/tonne of ethylene or propylene or BTX + # 2020 demand of one of these: 35.7 Mt + # Makes up around 30% of total feedstock demand. + + # df = scen.par('demand', filters={'commodity':'i_feed'}) + # df.value = df.value * 0.7 #(30% HVCs) + # + # scen.check_out() + # scen.add_par('demand', df) + # scen.commit(comment = 'modify i_feed demand') + + # NOTE Aggregate industrial coal demand need to adjust to + # the sudden intro of steel setor in the first model year + return {"i_therm": useful_thermal, "i_spec": useful_spec, + "historical_activity": pd.concat([thermal_df_hist, spec_df_hist, feed_df_hist]) + } + + +def modify_baseyear_bounds(scen: message_ix.Scenario) -> None: + # TODO: instead of removing bounds, bounds should be updated with IEA data + scen.check_out() + for substr in ["up", "lo"]: + df = scen.par(f"bound_activity_{substr}") + scen.remove_par( + f"bound_activity_{substr}", + df[(df["technology"].str.endswith("_fs")) & (df["year_act"] == 2020)], + ) + scen.remove_par( + f"bound_activity_{substr}", + df[(df["technology"].str.endswith("_i")) & (df["year_act"] == 2020)], + ) + scen.remove_par( + f"bound_activity_{substr}", + df[(df["technology"].str.endswith("_I")) & (df["year_act"] == 2020)], + ) + scen.commit(comment="remove base year industry tec bounds") + + +def calc_hist_activity(scen: message_ix.Scenario, years: list) -> pd.DataFrame: df_orig = get_hist_act_data("IEA_mappings.csv", years=years) df_mat = get_hist_act_data("IEA_mappings_industry.csv", years=years) df_chem = get_hist_act_data("IEA_mappings_chemicals.csv", years=years) + # RFE: move hardcoded assumptions (chemicals and iron and steel) to external data files # scale chemical activity to deduct explicitly represented activities of MESSAGEix-Materials # (67% are covered by NH3, HVCs and methanol) df_chem = df_chem.mul(0.67) @@ -438,8 +731,6 @@ def calc_hist_activity_new(scen, years): # calculate share of residual activity not covered by industry sector explicit technologies df = ( df_mat.div(df_orig) - .sub(1) - .mul(-1) .dropna() .sort_values("Value", ascending=False) ) @@ -447,42 +738,47 @@ def calc_hist_activity_new(scen, years): df.loc[:, "elec_i", :] = 0 df = df.round(5) - tecs = df.index.get_level_values("technology").unique() + df.index.set_names(["node_loc", "technology", "year_act"], inplace=True) + tecs = df.index.get_level_values("technology").unique() df_hist_act = scen.par( "historical_activity", filters={"technology": tecs, "year_act": years} ) - df_hist_act_merged = df_hist_act.set_index( - ["node_loc", "technology", "year_act"] - ).join(df) - df_hist_act_merged["value"] *= df_hist_act_merged["Value"] - df_hist_act_merged = df_hist_act_merged.drop("Value", axis=1) + df_hist_act_scaled = df_hist_act.set_index([i for i in df_hist_act.columns if i != "value"]).mul( + df.rename({"Value": "value"}, axis=1)).dropna() + + return df_hist_act_scaled.reset_index() - return df_hist_act_merged + +def add_new_ind_hist_act(scen: message_ix.Scenario, years: list) -> None: + df_act = calc_hist_activity(scen, years) + scen.check_out() + scen.add_par("historical_activity", df_act) + scen.commit("adjust historical activity of industrial end use tecs") -def calc_demand_shares(Inp, base_year): - # TODO: refactor to use external mapping file +def calc_demand_shares(iea_db_df: pd.DataFrame, base_year: int) -> pd.DataFrame: + # RFE: refactor to use external mapping file (analogue to calc_hist_activity()) i_spec_material_flows = ["NONMET", "CHEMICAL", "NONFERR"] i_therm_material_flows = ["NONMET", "CHEMICAL", "IRONSTL"] i_flow = ["TOTIND"] i_spec_prods = ["ELECTR", "NONBIODIES", "BIOGASOL"] year = base_year - df_i_spec = Inp[ - (Inp["FLOW"].isin(i_flow)) - & (Inp["PRODUCT"].isin(i_spec_prods)) - & ~((Inp["PRODUCT"] == ("ELECTR")) & (Inp["FLOW"] == "IRONSTL")) - & (Inp["TIME"] == year) - ] + df_i_spec = iea_db_df[ + (iea_db_df["FLOW"].isin(i_flow)) + & (iea_db_df["PRODUCT"].isin(i_spec_prods)) + & ~((iea_db_df["PRODUCT"] == ("ELECTR")) & (iea_db_df["FLOW"] == "IRONSTL")) + & (iea_db_df["TIME"] == year) + ] df_i_spec = df_i_spec.groupby("REGION").sum(numeric_only=True) - df_i_spec_materials = Inp[ - (Inp["FLOW"].isin(i_spec_material_flows)) - & (Inp["PRODUCT"].isin(i_spec_prods)) - & (Inp["TIME"] == year) - ] + df_i_spec_materials = iea_db_df[ + (iea_db_df["FLOW"].isin(i_spec_material_flows)) + & (iea_db_df["PRODUCT"].isin(i_spec_prods)) + & (iea_db_df["TIME"] == year) + ] df_i_spec_materials = df_i_spec_materials.groupby("REGION").sum(numeric_only=True) df_i_spec_resid_shr = ( @@ -490,29 +786,29 @@ def calc_demand_shares(Inp, base_year): ) df_i_spec_resid_shr["commodity"] = "i_spec" - df_elec_i = Inp[ - ((Inp["PRODUCT"] == ("ELECTR")) & (Inp["FLOW"] == "IRONSTL")) - & (Inp["TIME"] == year) - ] + df_elec_i = iea_db_df[ + ((iea_db_df["PRODUCT"] == ("ELECTR")) & (iea_db_df["FLOW"] == "IRONSTL")) + & (iea_db_df["TIME"] == year) + ] df_elec_i = df_elec_i.groupby("REGION").sum(numeric_only=True) agg_prods = ["MRENEW", "TOTAL"] - df_i_therm = Inp[ - (Inp["FLOW"].isin(i_flow)) - & ~(Inp["PRODUCT"].isin(i_spec_prods)) - & ~(Inp["PRODUCT"].isin(agg_prods)) - & (Inp["TIME"] == year) - ] + df_i_therm = iea_db_df[ + (iea_db_df["FLOW"].isin(i_flow)) + & ~(iea_db_df["PRODUCT"].isin(i_spec_prods)) + & ~(iea_db_df["PRODUCT"].isin(agg_prods)) + & (iea_db_df["TIME"] == year) + ] df_i_therm = df_i_therm.groupby("REGION").sum(numeric_only=True) df_i_therm = df_i_therm.add(df_elec_i, fill_value=0) agg_prods = ["MRENEW", "TOTAL"] - df_i_therm_materials = Inp[ - (Inp["FLOW"].isin(i_therm_material_flows)) - & ~(Inp["PRODUCT"].isin(i_spec_prods)) - & ~(Inp["PRODUCT"].isin(agg_prods)) - & (Inp["TIME"] == year) - ] + df_i_therm_materials = iea_db_df[ + (iea_db_df["FLOW"].isin(i_therm_material_flows)) + & ~(iea_db_df["PRODUCT"].isin(i_spec_prods)) + & ~(iea_db_df["PRODUCT"].isin(agg_prods)) + & (iea_db_df["TIME"] == year) + ] df_i_therm_materials = df_i_therm_materials.groupby(["REGION", "FLOW"]).sum( numeric_only=True ) @@ -534,7 +830,7 @@ def calc_demand_shares(Inp, base_year): ) -def modify_industry_demand(scen, baseyear): +def calc_resid_ind_demand(scen: message_ix.Scenario, baseyear: int) -> pd.DataFrame: comms = ["i_spec", "i_therm"] path = os.path.join( "P:", "ene.model", "IEA_database", "Florian", "REV2022_allISO_IEA.parquet" @@ -543,19 +839,40 @@ def modify_industry_demand(scen, baseyear): Inp = map_iea_db_to_msg_regs(Inp, "R12_SSP_V1.yaml") demand_shrs_new = calc_demand_shares(Inp, baseyear) df_demands = scen.par("demand", filters={"commodity": comms}).set_index( - ["node", "commodity"] + ["node", "commodity", "year"] ) demand_shrs_new.index.set_names(["node", "commodity"], inplace=True) df_demands["value"] = df_demands["value"] * demand_shrs_new["Value"] - # scen.add_par("demand", df_demands) + return df_demands.reset_index() + +def modify_industry_demand(scen: message_ix.Scenario, baseyear: int) -> None: + df_demands_new = calc_resid_ind_demand(scen, baseyear) + scen.check_out() + scen.add_par("demand", df_demands_new) + + # RFE: calculate deductions from IEA data instead of assuming full coverage by MESSAGE-Materials (chemicals) # remove i_spec demand separately since we assume 100% coverage by MESSAGE-Materials df_i_feed = scen.par("demand", filters={"commodity": "i_feed"}) - # scen.remove_par("demand", df_i_feed) + scen.remove_par("demand", df_i_feed) + scen.commit("adjust residual industry demands") -def map_iea_db_to_msg_regs(df_iea, fname_map): - file_path = private_data_path("node", fname_map) +def map_iea_db_to_msg_regs(df_iea: pd.DataFrame, reg_map_fname: str) -> pd.DataFrame: + """ + + Parameters + ---------- + df_iea + df containing the IEA energy balances data set + reg_map_fname + name of file used for mapping countries to MESSAGEix regions + Returns + ------- + object + + """ + file_path = private_data_path("node", reg_map_fname) yaml_data = read_yaml_file(file_path) if "World" in yaml_data.keys(): yaml_data.pop("World") @@ -573,8 +890,20 @@ def map_iea_db_to_msg_regs(df_iea, fname_map): return df_iea -def read_iea_tec_map(map_fname): - MAP = pd.read_csv(private_data_path("model", "IEA", map_fname)) +def read_iea_tec_map(tec_map_fname: str) -> pd.DataFrame: + """ + reads mapping file and returns relevant columns needed for technology mapping + + Parameters + ---------- + tec_map_fname + name of mapping file used to map IEA flows and products to existing MESSAGEix technologies + Returns + ------- + pd.DataFrame + returns df with mapped technologies + """ + MAP = pd.read_csv(private_data_path("model", "IEA", tec_map_fname)) MAP = pd.concat([MAP, MAP["IEA flow"].str.split(", ", expand=True)], axis=1) MAP = ( @@ -600,7 +929,21 @@ def read_iea_tec_map(map_fname): return MAP -def get_hist_act_data(map_fname, years=None): +def get_hist_act_data(map_fname: str, years: list or None = None) -> pd.DataFrame: + """ + reads IEA DB, maps and aggregates variables to MESSAGE technologies + + Parameters + ---------- + map_fname + name of MESSAGEix-technology-to-IEA-flow/product mapping file + years, optional + specifies timesteps for whom historical activity should be calculated and returned + Returns + ------- + pd.DataFrame + + """ path = os.path.join( "P:", "ene.model", "IEA_database", "Florian", "REV2022_allISO_IEA.parquet" ) @@ -1612,23 +1955,25 @@ def read_rel(scenario, material, filename): return data_rel -if __name__ == "__main__": - mp = ixmp.Platform("ixmp_dev") - scen = message_ix.Scenario(mp, "MESSAGEix-Materials", "NoPolicy_petro_thesis_2") - # modify_demand_and_hist_activity_new(scen, [2015]) - # modify_industry_demand(scen, 2015) - # modify_demand_and_hist_activity(scen) - df = get_hist_act_data("IEA_mappings_furnaces.csv", years=[2015]) - df.index.names = ["node_loc", "technology", "year_act"] - df_inp = scen.par( - "input", - filters={ - "year_vtg": 2020, - "year_act": 2020, - "mode": "high_temp", - "node_loc": "R12_AFR", - }, - ) - df = df_inp.set_index(["technology"]).join(df).dropna() - df["Value"] = df["Value"] / df["value"] / 3.6 / 8760 - print() +# if __name__ == "__main__": +# mp = ixmp.Platform("ixmp_dev") +# scen = message_ix.Scenario(mp, "MESSAGEix-Materials", "NoPolicy_petro_thesis_2") +# +# df_hist_new = calc_hist_activity(scen, [2015]) +# df_demand_new = modify_industry_demand(scen, 2015) +# old_dict = modify_demand_and_hist_activity_debug(scen) +# +# df = get_hist_act_data("IEA_mappings_furnaces.csv", years=[2015]) +# df.index.names = ["node_loc", "technology", "year_act"] +# df_inp = scen.par( +# "input", +# filters={ +# "year_vtg": 2020, +# "year_act": 2020, +# "mode": "high_temp", +# "node_loc": "R12_AFR", +# }, +# ) +# df = df_inp.set_index(["technology"]).join(df).dropna() +# df["Value"] = df["Value"] / df["value"] / 3.6 / 8760 +# print() From 97740b1208c5f6d70393229e2a34f63a1a74d5dd Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Fri, 26 Jan 2024 16:08:50 +0100 Subject: [PATCH 670/774] Reformat files with black --- message_ix_models/model/material/build.py | 2 + .../model/material/data_methanol_new.py | 2 +- message_ix_models/model/material/data_util.py | 102 ++++++++---------- 3 files changed, 50 insertions(+), 56 deletions(-) diff --git a/message_ix_models/model/material/build.py b/message_ix_models/model/material/build.py index c925705894..27ac78290c 100644 --- a/message_ix_models/model/material/build.py +++ b/message_ix_models/model/material/build.py @@ -1,6 +1,8 @@ import logging from typing import Callable, Dict, List, Mapping, Union +import ixmp +import message_ix from ixmp.utils import maybe_check_out, maybe_commit from message_ix import Scenario import pandas as pd diff --git a/message_ix_models/model/material/data_methanol_new.py b/message_ix_models/model/material/data_methanol_new.py index 1522a72ced..88e489f2c0 100644 --- a/message_ix_models/model/material/data_methanol_new.py +++ b/message_ix_models/model/material/data_methanol_new.py @@ -157,6 +157,6 @@ def broadcast_reduced_df(df, par_name): df_final_full[ (df_final_full.node_rel.values != "R12_GLB") & (df_final_full.node_rel.values != df_final_full.node_loc.values) - ].index + ].index ) return make_df(par_name, **df_final_full) diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index 3916642069..ccdd439d0a 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -12,12 +12,10 @@ from message_ix_models import ScenarioInfo from message_ix_models.util import private_data_path - pd.options.mode.chained_assignment = None def load_GDP_COVID(): - context = read_config() # Obtain 2015 and 2020 GDP values from NGFS baseline. These values are COVID corrected. (GDP MER) @@ -52,7 +50,6 @@ def load_GDP_COVID(): df_new = pd.DataFrame(columns=["node", "year", "value"]) for ind in gdp_ssp2.index: - df_temp = pd.DataFrame(columns=["node", "year", "value"]) region = gdp_ssp2.loc[ind, "node"] mult_value = gdp_covid_2020.loc[ @@ -74,7 +71,6 @@ def load_GDP_COVID(): def add_macro_COVID(scen, filename, check_converge=False): - context = read_config() info = ScenarioInfo(scen) nodes = info.N @@ -148,7 +144,7 @@ def modify_demand_and_hist_activity(scen): | (df["SECTOR"] == "industry (non-ferrous metals)") | (df["SECTOR"] == "industry (non-metallic minerals)") | (df["SECTOR"] == "industry (total)") - ] + ] df = df[df["RYEAR"] == 2015] # NOTE: Total cehmical industry energy: 27% thermal, 8% electricity, 65% feedstock @@ -167,10 +163,10 @@ def modify_demand_and_hist_activity(scen): & (df["SECTOR"] != "industry (total)") & (df["SECTOR"] != "feedstock (petrochemical industry)") & (df["SECTOR"] != "feedstock (total)") - ] + ] df_spec_total = df[ (df["SECTOR"] == "industry (total)") & (df["FUEL"] == "electricity") - ] + ] df_spec_new = pd.DataFrame( columns=["REGION", "SECTOR", "FUEL", "RYEAR", "UNIT_OUT", "RESULT"] @@ -179,15 +175,15 @@ def modify_demand_and_hist_activity(scen): df_spec_temp = df_spec.loc[df_spec["REGION"] == r] df_spec_total_temp = df_spec_total.loc[df_spec_total["REGION"] == r] df_spec_temp.loc[:, "i_spec"] = ( - df_spec_temp.loc[:, "RESULT"] - / df_spec_total_temp.loc[:, "RESULT"].values[0] + df_spec_temp.loc[:, "RESULT"] + / df_spec_total_temp.loc[:, "RESULT"].values[0] ) df_spec_new = pd.concat([df_spec_temp, df_spec_new], ignore_index=True) df_spec_new.drop(["FUEL", "RYEAR", "UNIT_OUT", "RESULT"], axis=1, inplace=True) df_spec_new.loc[df_spec_new["SECTOR"] == "industry (chemicals)", "i_spec"] = ( - df_spec_new.loc[df_spec_new["SECTOR"] == "industry (chemicals)", "i_spec"] - * 0.67 + df_spec_new.loc[df_spec_new["SECTOR"] == "industry (chemicals)", "i_spec"] + * 0.67 ) df_spec_new = df_spec_new.groupby(["REGION"]).sum().reset_index() @@ -196,7 +192,7 @@ def modify_demand_and_hist_activity(scen): df_feed = df[ (df["SECTOR"] == "feedstock (petrochemical industry)") & (df["FUEL"] == "total") - ] + ] df_feed_total = df[(df["SECTOR"] == "feedstock (total)") & (df["FUEL"] == "total")] df_feed_temp = pd.DataFrame(columns=["REGION", "i_feed"]) df_feed_new = pd.DataFrame(columns=["REGION", "i_feed"]) @@ -220,12 +216,12 @@ def modify_demand_and_hist_activity(scen): & (df["SECTOR"] != "feedstock (petrochemical industry)") & (df["SECTOR"] != "feedstock (total)") & (df["SECTOR"] != "industry (non-ferrous metals)") - ] + ] df_therm_total = df[ (df["SECTOR"] == "industry (total)") & (df["FUEL"] != "total") & (df["FUEL"] != "electricity") - ] + ] df_therm_total = ( df_therm_total.groupby(by="REGION").sum().drop(["RYEAR"], axis=1).reset_index() ) @@ -243,23 +239,23 @@ def modify_demand_and_hist_activity(scen): df_therm_temp = df_therm.loc[df_therm["REGION"] == r] df_therm_total_temp = df_therm_total.loc[df_therm_total["REGION"] == r] df_therm_temp.loc[:, "i_therm"] = ( - df_therm_temp.loc[:, "RESULT"] - / df_therm_total_temp.loc[:, "RESULT"].values[0] + df_therm_temp.loc[:, "RESULT"] + / df_therm_total_temp.loc[:, "RESULT"].values[0] ) df_therm_new = pd.concat([df_therm_temp, df_therm_new], ignore_index=True) df_therm_new = df_therm_new.drop(["RESULT"], axis=1) df_therm_new.drop(["FUEL", "RYEAR", "UNIT_OUT"], axis=1, inplace=True) df_therm_new.loc[df_therm_new["SECTOR"] == "industry (chemicals)", "i_therm"] = ( - df_therm_new.loc[df_therm_new["SECTOR"] == "industry (chemicals)", "i_therm"] - * 0.67 + df_therm_new.loc[df_therm_new["SECTOR"] == "industry (chemicals)", "i_therm"] + * 0.67 ) # Modify CPA based on https://www.iea.org/sankey/#?c=Japan&s=Final%20consumption. # Since the value did not allign with the one in the IEA website. index = (df_therm_new["SECTOR"] == "industry (iron and steel)") & ( - (df_therm_new["REGION"] == region_name_CPA) - | (df_therm_new["REGION"] == region_name_CHN) + (df_therm_new["REGION"] == region_name_CPA) + | (df_therm_new["REGION"] == region_name_CHN) ) df_therm_new.loc[index, "i_therm"] = 0.2 @@ -310,39 +306,41 @@ def modify_demand_and_hist_activity(scen): useful_thermal.loc[ useful_thermal["node"] == r_MESSAGE, "value" ] = useful_thermal.loc[useful_thermal["node"] == r_MESSAGE, "value"] * ( - 1 - df_therm_new.loc[df_therm_new["REGION"] == r, "i_therm"].values[0] + 1 - df_therm_new.loc[df_therm_new["REGION"] == r, "i_therm"].values[0] ) thermal_df_hist.loc[ thermal_df_hist["node_loc"] == r_MESSAGE, "value" ] = thermal_df_hist.loc[thermal_df_hist["node_loc"] == r_MESSAGE, "value"] * ( - 1 - df_therm_new.loc[df_therm_new["REGION"] == r, "i_therm"].values[0] + 1 - df_therm_new.loc[df_therm_new["REGION"] == r, "i_therm"].values[0] ) for r in df_spec_new["REGION"]: r_MESSAGE = region_type + r useful_spec.loc[useful_spec["node"] == r_MESSAGE, "value"] = useful_spec.loc[ - useful_spec["node"] == r_MESSAGE, "value" - ] * (1 - df_spec_new.loc[df_spec_new["REGION"] == r, "i_spec"].values[0]) + useful_spec["node"] == r_MESSAGE, "value" + ] * (1 - df_spec_new.loc[ + df_spec_new["REGION"] == r, "i_spec"].values[0]) spec_df_hist.loc[ spec_df_hist["node_loc"] == r_MESSAGE, "value" ] = spec_df_hist.loc[spec_df_hist["node_loc"] == r_MESSAGE, "value"] * ( - 1 - df_spec_new.loc[df_spec_new["REGION"] == r, "i_spec"].values[0] + 1 - df_spec_new.loc[df_spec_new["REGION"] == r, "i_spec"].values[0] ) for r in df_feed_new["REGION"]: r_MESSAGE = region_type + r useful_feed.loc[useful_feed["node"] == r_MESSAGE, "value"] = useful_feed.loc[ - useful_feed["node"] == r_MESSAGE, "value" - ] * (1 - df_feed_new.loc[df_feed_new["REGION"] == r, "i_feed"].values[0]) + useful_feed["node"] == r_MESSAGE, "value" + ] * (1 - df_feed_new.loc[ + df_feed_new["REGION"] == r, "i_feed"].values[0]) feed_df_hist.loc[ feed_df_hist["node_loc"] == r_MESSAGE, "value" ] = feed_df_hist.loc[feed_df_hist["node_loc"] == r_MESSAGE, "value"] * ( - 1 - df_feed_new.loc[df_feed_new["REGION"] == r, "i_feed"].values[0] + 1 - df_feed_new.loc[df_feed_new["REGION"] == r, "i_feed"].values[0] ) scen.check_out() @@ -995,17 +993,17 @@ def add_emission_accounting(scen): i for i in tec_list_residual if ( - ( - ("biomass_i" in i) - | ("coal_i" in i) - | ("foil_i" in i) - | ("gas_i" in i) - | ("hp_gas_i" in i) - | ("loil_i" in i) - | ("meth_i" in i) - ) - & ("imp" not in i) - & ("trp" not in i) + ( + ("biomass_i" in i) + | ("coal_i" in i) + | ("foil_i" in i) + | ("gas_i" in i) + | ("hp_gas_i" in i) + | ("loil_i" in i) + | ("meth_i" in i) + ) + & ("imp" not in i) + & ("trp" not in i) ) ] @@ -1137,11 +1135,11 @@ def add_emission_accounting(scen): i for i in tec_list if ( - ("steel" in i) - | ("aluminum" in i) - | ("petro" in i) - | ("cement" in i) - | ("ref" in i) + ("steel" in i) + | ("aluminum" in i) + | ("petro" in i) + | ("cement" in i) + | ("ref" in i) ) ] tec_list_materials.remove("refrigerant_recovery") @@ -1161,7 +1159,7 @@ def add_emission_accounting(scen): (relation_activity["relation"] != "PM2p5_Emission") & (relation_activity["relation"] != "CO2_industry_Emission") & (relation_activity["relation"] != "CO2_transformation_Emission") - ] + ] # ***** (3) Add thermal industry technologies to CO2_ind relation ****** @@ -1319,7 +1317,6 @@ def add_emission_accounting(scen): def add_elec_lowerbound_2020(scen): - # To avoid zero i_spec prices only for R12_CHN, add the below section. # read input parameters for relevant technology/commodity combinations for # converting betwen final and useful energy @@ -1356,7 +1353,7 @@ def add_elec_lowerbound_2020(scen): # derive useful energy values by dividing final energy by # input coefficient from final-to-useful technologies bound_residual_electricity["value"] = ( - bound_residual_electricity["Value"] / bound_residual_electricity["value"] + bound_residual_electricity["Value"] / bound_residual_electricity["value"] ) # downselect dataframe columns for MESSAGEix parameters @@ -1378,7 +1375,7 @@ def add_elec_lowerbound_2020(scen): bound_residual_electricity["value"] = bound_residual_electricity["value"] * 0.8 bound_residual_electricity = bound_residual_electricity[ bound_residual_electricity["node_loc"] == "R12_CHN" - ] + ] scen.check_out() @@ -1460,7 +1457,7 @@ def add_coal_lowerbound_2020(sc): bound_coal["value"] = bound_coal["Value"] / bound_coal["value"] bound_cement_coal["value"] = bound_cement_coal["Value"] / bound_cement_coal["value"] bound_residual_electricity["value"] = ( - bound_residual_electricity["Value"] / bound_residual_electricity["value"] + bound_residual_electricity["Value"] / bound_residual_electricity["value"] ) # downselect dataframe columns for MESSAGEix parameters @@ -1527,7 +1524,6 @@ def add_coal_lowerbound_2020(sc): def add_cement_bounds_2020(sc): - """Set lower and upper bounds for gas and oil as a calibration for 2020""" context = read_config() @@ -1652,7 +1648,7 @@ def add_cement_bounds_2020(sc): bound_cement_foil["value"] = bound_cement_foil["Value"] / bound_cement_foil["value"] bound_cement_gas["value"] = bound_cement_gas["Value"] / bound_cement_gas["value"] bound_cement_biomass["value"] = ( - bound_cement_biomass["Value"] / bound_cement_biomass["value"] + bound_cement_biomass["Value"] / bound_cement_biomass["value"] ) bound_cement_coal["value"] = bound_cement_coal["Value"] / bound_cement_coal["value"] @@ -1777,7 +1773,6 @@ def add_cement_bounds_2020(sc): def read_sector_data(scenario, sectname): - # Read in technology-specific parameters from input xlsx # Now used for steel and cement, which are in one file @@ -1843,7 +1838,6 @@ def read_sector_data(scenario, sectname): # Add the relevant ccs technologies to the co2_trans_disp and bco2_trans_disp # relations def add_ccs_technologies(scen): - context = read_config() s_info = ScenarioInfo(scen) @@ -1888,7 +1882,6 @@ def add_ccs_technologies(scen): # Read in time-dependent parameters # Now only used to add fuel cost for bare model def read_timeseries(scenario, material, filename): - import numpy as np # Ensure config is loaded, get the context @@ -1935,7 +1928,6 @@ def read_timeseries(scenario, material, filename): def read_rel(scenario, material, filename): - # Ensure config is loaded, get the context context = read_config() From 07650c91fba7b344e972bcbb5a1aef2ec2949829 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 20 Feb 2024 14:43:48 +0100 Subject: [PATCH 671/774] Add gdp_ppp compatiblity for material demand projection calculation --- .../material_demand/material_demand_calc.py | 36 +++++++++++-------- 1 file changed, 21 insertions(+), 15 deletions(-) diff --git a/message_ix_models/model/material/material_demand/material_demand_calc.py b/message_ix_models/model/material/material_demand/material_demand_calc.py index ce5c7a9ec3..b940ab61cf 100644 --- a/message_ix_models/model/material/material_demand/material_demand_calc.py +++ b/message_ix_models/model/material/material_demand/material_demand_calc.py @@ -245,23 +245,29 @@ def read_pop_from_scen(scen): def read_gdp_ppp_from_scen(scen): - gdp_mer = scen.par("bound_activity_up", {"technology": "GDP"}) - mer_to_ppp = scen.par("MERtoPPP") - if not len(mer_to_ppp): - mer_to_ppp = pd.read_csv( - message_ix_models.util.private_data_path( - "material", "other", "mer_to_ppp_default.csv" + if len(scen.par("bound_activity_up", filters={{"technology": "GDP_PPP"}})): + gdp = scen.par("bound_activity_up", {"technology": "GDP_PPP"}) + gdp = gdp.rename({"value": "gdp_ppp", "year_act": "year"}, axis=1)[ + ["year", "node_loc", "gdp_ppp"] + ] + else: + gdp_mer = scen.par("bound_activity_up", {"technology": "GDP"}) + mer_to_ppp = scen.par("MERtoPPP") + if not len(mer_to_ppp): + mer_to_ppp = pd.read_csv( + message_ix_models.util.private_data_path( + "material", "other", "mer_to_ppp_default.csv" + ) ) + mer_to_ppp = mer_to_ppp.set_index(["node", "year"]) + gdp_mer = gdp_mer.merge( + mer_to_ppp.reset_index()[["node", "year", "value"]], + left_on=["node_loc", "year_act"], + right_on=["node", "year"], ) - mer_to_ppp = mer_to_ppp.set_index(["node", "year"]) - gdp_mer = gdp_mer.merge( - mer_to_ppp.reset_index()[["node", "year", "value"]], - left_on=["node_loc", "year_act"], - right_on=["node", "year"], - ) - gdp_mer["gdp_ppp"] = gdp_mer["value_y"] * gdp_mer["value_x"] - gdp_mer = gdp_mer[["year", "node_loc", "gdp_ppp"]] - gdp = gdp_mer.loc[gdp_mer.year >= 2020].rename(columns={"node_loc": "region"}) + gdp_mer["gdp_ppp"] = gdp_mer["value_y"] * gdp_mer["value_x"] + gdp = gdp_mer[["year", "node_loc", "gdp_ppp"]].rename(columns={"node_loc": "region"}) + gdp = gdp.loc[gdp.year >= 2020] return gdp From 75d47ed9374837e852c1d30d5a4e48dbbff3fcbb Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Mon, 26 Feb 2024 21:42:32 +0100 Subject: [PATCH 672/774] Add gdp_ppp compatiblity for material demand projection calculation - fix --- .../material_demand/material_demand_calc.py | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/message_ix_models/model/material/material_demand/material_demand_calc.py b/message_ix_models/model/material/material_demand/material_demand_calc.py index b940ab61cf..7bdfa94864 100644 --- a/message_ix_models/model/material/material_demand/material_demand_calc.py +++ b/message_ix_models/model/material/material_demand/material_demand_calc.py @@ -59,7 +59,7 @@ def gompertz(phi, mu, y, baseyear=2020): return 1 - np.exp(-phi * np.exp(-mu * (y - baseyear))) -def read_timer_pop(datapath, filename, material): +def read_timer_pop(datapath, material): df_population = pd.read_excel( f'{datapath}/{material_data[material]["dir"]}{material_data["cement"]["file"]}', sheet_name="Timer_POP", @@ -75,7 +75,7 @@ def read_timer_pop(datapath, filename, material): return df_population -def read_timer_gdp(datapath, filename, material): +def read_timer_gdp(datapath, material): # Read GDP per capita data df_gdp = pd.read_excel( f'{datapath}/{material_data[material]["dir"]}{material_data["cement"]["file"]}', @@ -245,7 +245,7 @@ def read_pop_from_scen(scen): def read_gdp_ppp_from_scen(scen): - if len(scen.par("bound_activity_up", filters={{"technology": "GDP_PPP"}})): + if len(scen.par("bound_activity_up", filters={"technology": "GDP_PPP"})): gdp = scen.par("bound_activity_up", {"technology": "GDP_PPP"}) gdp = gdp.rename({"value": "gdp_ppp", "year_act": "year"}, axis=1)[ ["year", "node_loc", "gdp_ppp"] @@ -266,8 +266,8 @@ def read_gdp_ppp_from_scen(scen): right_on=["node", "year"], ) gdp_mer["gdp_ppp"] = gdp_mer["value_y"] * gdp_mer["value_x"] - gdp = gdp_mer[["year", "node_loc", "gdp_ppp"]].rename(columns={"node_loc": "region"}) - gdp = gdp.loc[gdp.year >= 2020] + gdp = gdp_mer[["year", "node_loc", "gdp_ppp"]] + gdp = gdp.loc[gdp.year >= 2020].rename(columns={"node_loc": "region"}) return gdp @@ -309,7 +309,7 @@ def derive_demand(material, scen, old_gdp=False): p0=fitting_dict[material]["initial_guess"], ) - # prepare df for applying regression model to project demand + # prepare df for applying regression model and project demand df_all = pd.merge(df_pop, df_base_demand.drop(columns=["year"]), how="left") df_all = pd.merge(df_all, df_gdp[["region", "year", "gdp_ppp"]], how="inner") df_all["del_t"] = df_all["year"] - 2010 @@ -322,10 +322,12 @@ def derive_demand(material, scen, old_gdp=False): ) df_all = df_all.rename({"value": "demand.tot.base"}, axis=1) - # apply regression model + # correct base year difference with convergence function df_final = project_demand( df_all, fitting_dict[material]["phi"], fitting_dict[material]["mu"] ) + + # format to MESSAGEix standard df_final = df_final.rename({"region": "node", "demand_tot": "value"}, axis=1) df_final["commodity"] = material df_final["level"] = "demand" From 32209f3485a2d666337ec4eb3da7d42be4b09cd7 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 27 Feb 2024 09:31:54 +0100 Subject: [PATCH 673/774] Fix warnings and errors in materials reporting --- message_ix_models/model/material/report/reporting.py | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/message_ix_models/model/material/report/reporting.py b/message_ix_models/model/material/report/reporting.py index 0fa722099d..9e1c37a968 100644 --- a/message_ix_models/model/material/report/reporting.py +++ b/message_ix_models/model/material/report/reporting.py @@ -25,8 +25,8 @@ from ixmp import Platform from message_ix import Scenario -from message_ix.reporting import Reporter -from ixmp.reporting import configure +from message_ix.report import Reporter +from ixmp.report import configure from message_ix_models import ScenarioInfo from message_data.tools.post_processing.iamc_report_hackathon import report as reporting from message_data.model.material.util import read_config @@ -38,7 +38,7 @@ import os import openpyxl -import plotly.graph_objects as go +#import plotly.graph_objects as go import matplotlib matplotlib.use("Agg") @@ -165,6 +165,7 @@ def fix_excel(path_temp, path_new): new_workbook.save(path_new) + def report(context,scenario): # Obtain scenario information and directory @@ -367,7 +368,7 @@ def report(context,scenario): df.convert_unit('unknown', to='', factor=1, inplace = True) variables = df.variable - df.aggregate_region(variables, region="World", method=sum, append=True) + df.aggregate_region(variables, region="World", method="sum", append=True) name = os.path.join(directory, "check.xlsx") df.to_excel(name) @@ -3876,7 +3877,7 @@ def report(context,scenario): # .................... path_temp = os.path.join(directory, "temp_new_reporting.xlsx") - df_final.to_excel(path_temp, sheet_name="data", index=False) + df_final.to_excel(path_temp, sheet_name="data") excel_name_new = "New_Reporting_" + model_name + "_" + scenario_name + ".xlsx" path_new = os.path.join(directory, excel_name_new) From 34dedf16abec296223cc2625e3746201ccf89418 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 27 Feb 2024 09:33:53 +0100 Subject: [PATCH 674/774] Update create_bare function --- message_ix_models/model/material/__init__.py | 4 ++-- message_ix_models/model/material/bare.py | 21 ++++++++++---------- 2 files changed, 13 insertions(+), 12 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 9ed667837d..655a21c527 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -145,7 +145,7 @@ def cli(): @click.pass_obj def create_bare(context, regions, dry_run): """Create the RES from scratch.""" - from message_data.model.bare import create_res + from message_data.model.create import create_res if regions: context.regions = regions @@ -157,7 +157,7 @@ def create_bare(context, regions, dry_run): # context.metadata_path = context.metadata_path / "data" scen = create_res(context) - build(scen) + build(scen, True) # Solve if not dry_run: diff --git a/message_ix_models/model/material/bare.py b/message_ix_models/model/material/bare.py index 624a479f00..0178a97a13 100644 --- a/message_ix_models/model/material/bare.py +++ b/message_ix_models/model/material/bare.py @@ -1,20 +1,20 @@ -from functools import partial -from typing import Mapping import logging - import message_ix -from message_ix_models import Code, ScenarioInfo, get_context, set_info, add_par_data +from typing import Mapping +from message_ix_models import ScenarioInfo +#from message_ix_models.util get_context, set_info, add_par_data +from message_ix_models.util import add_par_data +from sdmx.model.v21 import Code from .build import apply_spec from .util import read_config -from .data import get_data, gen_data_steel, gen_data_generic, gen_data_aluminum, \ -gen_data_variable, gen_data_petro_chemicals +from message_data.model.data import get_data +from message_data.model.material import gen_data_steel, gen_data_generic, gen_data_aluminum, gen_data_petro_chemicals import message_data log = logging.getLogger(__name__) - # Settings and valid values; the default is listed first # How do we add the historical period ? @@ -28,6 +28,7 @@ time_step=[10], ) + def create_res(context=None, quiet=True): """Create a 'bare' MESSAGE-GLOBIOM reference energy system (RES). @@ -77,9 +78,10 @@ def create_res(context=None, quiet=True): gen_data_steel, gen_data_generic, gen_data_aluminum, - gen_data_variable + #gen_data_variable ] + # Try to handle multiple data input functions from different materials def add_data(scenario, dry_run=False): """Populate `scenario` with MESSAGE-Material data.""" @@ -101,7 +103,6 @@ def add_data(scenario, dry_run=False): log.info('done') - def get_spec(context=None) -> Mapping[str, ScenarioInfo]: """Return the spec for the MESSAGE-Material bare RES. @@ -172,7 +173,7 @@ def get_spec(context=None) -> Mapping[str, ScenarioInfo]: add.set['commodity'] = context["material"]["steel"]["commodity"]["add"] + \ context["material"]["common"]["commodity"]["require"] + \ context["material"]["generic"]["commodity"]["add"] + \ - context["material"]["aluminum"]["commodity"]["add"] + context["material"]["aluminum"]["commodity"]["add"] + \ context["material"]["petro_chemicals"]["commodity"]["add"] # Add other sets From 7038b3e5485bd0bc8fcf493627433d22c141f389 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 27 Feb 2024 09:35:42 +0100 Subject: [PATCH 675/774] Add materials cost tool util function and ssp gdp/pop update util function --- message_ix_models/model/material/data_util.py | 318 +++++++++++------- 1 file changed, 194 insertions(+), 124 deletions(-) diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index ccdd439d0a..f1d5673e3f 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -1,3 +1,5 @@ +from typing import Literal + import pandas as pd import os import message_ix @@ -11,6 +13,10 @@ from message_ix_models import ScenarioInfo from message_ix_models.util import private_data_path +from message_ix_models.tools.costs.config import Config +from message_ix_models.tools.costs.projections import create_cost_projections +from message_ix_models.tools.exo_data import prepare_computer +from genno import Computer pd.options.mode.chained_assignment = None @@ -76,26 +82,28 @@ def add_macro_COVID(scen, filename, check_converge=False): nodes = info.N # Excel file for calibration data - xls_file = os.path.join("P:", "ene.model", "MACRO", "python", filename) - + if "SSP_dev" in scen.model: + xls_file = os.path.join("C:/", "Users", "maczek", "Downloads", filename) + else: + xls_file = os.path.join("P:", "ene.model", "MACRO", "python", filename) # Making a dictionary from the MACRO Excel file xls = pd.ExcelFile(xls_file) data = {} for s in xls.sheet_names: data[s] = xls.parse(s) - # Load the new GDP values - df_gdp = load_GDP_COVID() - - # substitute the gdp_calibrate - parname = "gdp_calibrate" - - # keep the historical GDP to pass the GDP check at add_macro() - df_gdphist = data[parname] - df_gdphist = df_gdphist.loc[df_gdphist.year < info.y0] - data[parname] = df_gdphist.append( - df_gdp.loc[df_gdp.year >= info.y0], ignore_index=True - ) + # # Load the new GDP values + # df_gdp = load_GDP_COVID() + # + # # substitute the gdp_calibrate + # parname = "gdp_calibrate" + # + # # keep the historical GDP to pass the GDP check at add_macro() + # df_gdphist = data[parname] + # df_gdphist = df_gdphist.loc[df_gdphist.year < info.y0] + # data[parname] = pd.concat( + # [df_gdphist, df_gdp.loc[df_gdp.year >= info.y0]], ignore_index=True + # ) # Calibration scen = scen.add_macro(data, check_convergence=check_converge) @@ -144,7 +152,7 @@ def modify_demand_and_hist_activity(scen): | (df["SECTOR"] == "industry (non-ferrous metals)") | (df["SECTOR"] == "industry (non-metallic minerals)") | (df["SECTOR"] == "industry (total)") - ] + ] df = df[df["RYEAR"] == 2015] # NOTE: Total cehmical industry energy: 27% thermal, 8% electricity, 65% feedstock @@ -163,10 +171,10 @@ def modify_demand_and_hist_activity(scen): & (df["SECTOR"] != "industry (total)") & (df["SECTOR"] != "feedstock (petrochemical industry)") & (df["SECTOR"] != "feedstock (total)") - ] + ] df_spec_total = df[ (df["SECTOR"] == "industry (total)") & (df["FUEL"] == "electricity") - ] + ] df_spec_new = pd.DataFrame( columns=["REGION", "SECTOR", "FUEL", "RYEAR", "UNIT_OUT", "RESULT"] @@ -175,15 +183,15 @@ def modify_demand_and_hist_activity(scen): df_spec_temp = df_spec.loc[df_spec["REGION"] == r] df_spec_total_temp = df_spec_total.loc[df_spec_total["REGION"] == r] df_spec_temp.loc[:, "i_spec"] = ( - df_spec_temp.loc[:, "RESULT"] - / df_spec_total_temp.loc[:, "RESULT"].values[0] + df_spec_temp.loc[:, "RESULT"] + / df_spec_total_temp.loc[:, "RESULT"].values[0] ) df_spec_new = pd.concat([df_spec_temp, df_spec_new], ignore_index=True) df_spec_new.drop(["FUEL", "RYEAR", "UNIT_OUT", "RESULT"], axis=1, inplace=True) df_spec_new.loc[df_spec_new["SECTOR"] == "industry (chemicals)", "i_spec"] = ( - df_spec_new.loc[df_spec_new["SECTOR"] == "industry (chemicals)", "i_spec"] - * 0.67 + df_spec_new.loc[df_spec_new["SECTOR"] == "industry (chemicals)", "i_spec"] + * 0.67 ) df_spec_new = df_spec_new.groupby(["REGION"]).sum().reset_index() @@ -192,7 +200,7 @@ def modify_demand_and_hist_activity(scen): df_feed = df[ (df["SECTOR"] == "feedstock (petrochemical industry)") & (df["FUEL"] == "total") - ] + ] df_feed_total = df[(df["SECTOR"] == "feedstock (total)") & (df["FUEL"] == "total")] df_feed_temp = pd.DataFrame(columns=["REGION", "i_feed"]) df_feed_new = pd.DataFrame(columns=["REGION", "i_feed"]) @@ -216,12 +224,12 @@ def modify_demand_and_hist_activity(scen): & (df["SECTOR"] != "feedstock (petrochemical industry)") & (df["SECTOR"] != "feedstock (total)") & (df["SECTOR"] != "industry (non-ferrous metals)") - ] + ] df_therm_total = df[ (df["SECTOR"] == "industry (total)") & (df["FUEL"] != "total") & (df["FUEL"] != "electricity") - ] + ] df_therm_total = ( df_therm_total.groupby(by="REGION").sum().drop(["RYEAR"], axis=1).reset_index() ) @@ -239,23 +247,23 @@ def modify_demand_and_hist_activity(scen): df_therm_temp = df_therm.loc[df_therm["REGION"] == r] df_therm_total_temp = df_therm_total.loc[df_therm_total["REGION"] == r] df_therm_temp.loc[:, "i_therm"] = ( - df_therm_temp.loc[:, "RESULT"] - / df_therm_total_temp.loc[:, "RESULT"].values[0] + df_therm_temp.loc[:, "RESULT"] + / df_therm_total_temp.loc[:, "RESULT"].values[0] ) df_therm_new = pd.concat([df_therm_temp, df_therm_new], ignore_index=True) df_therm_new = df_therm_new.drop(["RESULT"], axis=1) df_therm_new.drop(["FUEL", "RYEAR", "UNIT_OUT"], axis=1, inplace=True) df_therm_new.loc[df_therm_new["SECTOR"] == "industry (chemicals)", "i_therm"] = ( - df_therm_new.loc[df_therm_new["SECTOR"] == "industry (chemicals)", "i_therm"] - * 0.67 + df_therm_new.loc[df_therm_new["SECTOR"] == "industry (chemicals)", "i_therm"] + * 0.67 ) # Modify CPA based on https://www.iea.org/sankey/#?c=Japan&s=Final%20consumption. # Since the value did not allign with the one in the IEA website. index = (df_therm_new["SECTOR"] == "industry (iron and steel)") & ( - (df_therm_new["REGION"] == region_name_CPA) - | (df_therm_new["REGION"] == region_name_CHN) + (df_therm_new["REGION"] == region_name_CPA) + | (df_therm_new["REGION"] == region_name_CHN) ) df_therm_new.loc[index, "i_therm"] = 0.2 @@ -306,41 +314,39 @@ def modify_demand_and_hist_activity(scen): useful_thermal.loc[ useful_thermal["node"] == r_MESSAGE, "value" ] = useful_thermal.loc[useful_thermal["node"] == r_MESSAGE, "value"] * ( - 1 - df_therm_new.loc[df_therm_new["REGION"] == r, "i_therm"].values[0] + 1 - df_therm_new.loc[df_therm_new["REGION"] == r, "i_therm"].values[0] ) thermal_df_hist.loc[ thermal_df_hist["node_loc"] == r_MESSAGE, "value" ] = thermal_df_hist.loc[thermal_df_hist["node_loc"] == r_MESSAGE, "value"] * ( - 1 - df_therm_new.loc[df_therm_new["REGION"] == r, "i_therm"].values[0] + 1 - df_therm_new.loc[df_therm_new["REGION"] == r, "i_therm"].values[0] ) for r in df_spec_new["REGION"]: r_MESSAGE = region_type + r useful_spec.loc[useful_spec["node"] == r_MESSAGE, "value"] = useful_spec.loc[ - useful_spec["node"] == r_MESSAGE, "value" - ] * (1 - df_spec_new.loc[ - df_spec_new["REGION"] == r, "i_spec"].values[0]) + useful_spec["node"] == r_MESSAGE, "value" + ] * (1 - df_spec_new.loc[df_spec_new["REGION"] == r, "i_spec"].values[0]) spec_df_hist.loc[ spec_df_hist["node_loc"] == r_MESSAGE, "value" ] = spec_df_hist.loc[spec_df_hist["node_loc"] == r_MESSAGE, "value"] * ( - 1 - df_spec_new.loc[df_spec_new["REGION"] == r, "i_spec"].values[0] + 1 - df_spec_new.loc[df_spec_new["REGION"] == r, "i_spec"].values[0] ) for r in df_feed_new["REGION"]: r_MESSAGE = region_type + r useful_feed.loc[useful_feed["node"] == r_MESSAGE, "value"] = useful_feed.loc[ - useful_feed["node"] == r_MESSAGE, "value" - ] * (1 - df_feed_new.loc[ - df_feed_new["REGION"] == r, "i_feed"].values[0]) + useful_feed["node"] == r_MESSAGE, "value" + ] * (1 - df_feed_new.loc[df_feed_new["REGION"] == r, "i_feed"].values[0]) feed_df_hist.loc[ feed_df_hist["node_loc"] == r_MESSAGE, "value" ] = feed_df_hist.loc[feed_df_hist["node_loc"] == r_MESSAGE, "value"] * ( - 1 - df_feed_new.loc[df_feed_new["REGION"] == r, "i_feed"].values[0] + 1 - df_feed_new.loc[df_feed_new["REGION"] == r, "i_feed"].values[0] ) scen.check_out() @@ -454,7 +460,7 @@ def modify_demand_and_hist_activity_debug(scen: message_ix.Scenario) -> dict: | (df["SECTOR"] == "industry (non-ferrous metals)") | (df["SECTOR"] == "industry (non-metallic minerals)") | (df["SECTOR"] == "industry (total)") - ] + ] df = df[df["RYEAR"] == 2015] # NOTE: Total cehmical industry energy: 27% thermal, 8% electricity, 65% feedstock @@ -473,10 +479,10 @@ def modify_demand_and_hist_activity_debug(scen: message_ix.Scenario) -> dict: & (df["SECTOR"] != "industry (total)") & (df["SECTOR"] != "feedstock (petrochemical industry)") & (df["SECTOR"] != "feedstock (total)") - ] + ] df_spec_total = df[ (df["SECTOR"] == "industry (total)") & (df["FUEL"] == "electricity") - ] + ] df_spec_new = pd.DataFrame( columns=["REGION", "SECTOR", "FUEL", "RYEAR", "UNIT_OUT", "RESULT"] @@ -485,15 +491,15 @@ def modify_demand_and_hist_activity_debug(scen: message_ix.Scenario) -> dict: df_spec_temp = df_spec.loc[df_spec["REGION"] == r] df_spec_total_temp = df_spec_total.loc[df_spec_total["REGION"] == r] df_spec_temp.loc[:, "i_spec"] = ( - df_spec_temp.loc[:, "RESULT"] - / df_spec_total_temp.loc[:, "RESULT"].values[0] + df_spec_temp.loc[:, "RESULT"] + / df_spec_total_temp.loc[:, "RESULT"].values[0] ) df_spec_new = pd.concat([df_spec_temp, df_spec_new], ignore_index=True) df_spec_new.drop(["FUEL", "RYEAR", "UNIT_OUT", "RESULT"], axis=1, inplace=True) df_spec_new.loc[df_spec_new["SECTOR"] == "industry (chemicals)", "i_spec"] = ( - df_spec_new.loc[df_spec_new["SECTOR"] == "industry (chemicals)", "i_spec"] - * 0.67 + df_spec_new.loc[df_spec_new["SECTOR"] == "industry (chemicals)", "i_spec"] + * 0.67 ) df_spec_new = df_spec_new.groupby(["REGION"]).sum().reset_index() @@ -502,7 +508,7 @@ def modify_demand_and_hist_activity_debug(scen: message_ix.Scenario) -> dict: df_feed = df[ (df["SECTOR"] == "feedstock (petrochemical industry)") & (df["FUEL"] == "total") - ] + ] df_feed_total = df[(df["SECTOR"] == "feedstock (total)") & (df["FUEL"] == "total")] df_feed_temp = pd.DataFrame(columns=["REGION", "i_feed"]) df_feed_new = pd.DataFrame(columns=["REGION", "i_feed"]) @@ -526,12 +532,12 @@ def modify_demand_and_hist_activity_debug(scen: message_ix.Scenario) -> dict: & (df["SECTOR"] != "feedstock (petrochemical industry)") & (df["SECTOR"] != "feedstock (total)") & (df["SECTOR"] != "industry (non-ferrous metals)") - ] + ] df_therm_total = df[ (df["SECTOR"] == "industry (total)") & (df["FUEL"] != "total") & (df["FUEL"] != "electricity") - ] + ] df_therm_total = ( df_therm_total.groupby(by="REGION").sum().drop(["RYEAR"], axis=1).reset_index() ) @@ -549,23 +555,23 @@ def modify_demand_and_hist_activity_debug(scen: message_ix.Scenario) -> dict: df_therm_temp = df_therm.loc[df_therm["REGION"] == r] df_therm_total_temp = df_therm_total.loc[df_therm_total["REGION"] == r] df_therm_temp.loc[:, "i_therm"] = ( - df_therm_temp.loc[:, "RESULT"] - / df_therm_total_temp.loc[:, "RESULT"].values[0] + df_therm_temp.loc[:, "RESULT"] + / df_therm_total_temp.loc[:, "RESULT"].values[0] ) df_therm_new = pd.concat([df_therm_temp, df_therm_new], ignore_index=True) df_therm_new = df_therm_new.drop(["RESULT"], axis=1) df_therm_new.drop(["FUEL", "RYEAR", "UNIT_OUT"], axis=1, inplace=True) df_therm_new.loc[df_therm_new["SECTOR"] == "industry (chemicals)", "i_therm"] = ( - df_therm_new.loc[df_therm_new["SECTOR"] == "industry (chemicals)", "i_therm"] - * 0.67 + df_therm_new.loc[df_therm_new["SECTOR"] == "industry (chemicals)", "i_therm"] + * 0.67 ) # Modify CPA based on https://www.iea.org/sankey/#?c=Japan&s=Final%20consumption. # Since the value did not allign with the one in the IEA website. index = (df_therm_new["SECTOR"] == "industry (iron and steel)") & ( - (df_therm_new["REGION"] == region_name_CPA) - | (df_therm_new["REGION"] == region_name_CHN) + (df_therm_new["REGION"] == region_name_CPA) + | (df_therm_new["REGION"] == region_name_CHN) ) df_therm_new.loc[index, "i_therm"] = 0.2 @@ -616,41 +622,39 @@ def modify_demand_and_hist_activity_debug(scen: message_ix.Scenario) -> dict: useful_thermal.loc[ useful_thermal["node"] == r_MESSAGE, "value" ] = useful_thermal.loc[useful_thermal["node"] == r_MESSAGE, "value"] * ( - 1 - df_therm_new.loc[df_therm_new["REGION"] == r, "i_therm"].values[0] + 1 - df_therm_new.loc[df_therm_new["REGION"] == r, "i_therm"].values[0] ) thermal_df_hist.loc[ thermal_df_hist["node_loc"] == r_MESSAGE, "value" ] = thermal_df_hist.loc[thermal_df_hist["node_loc"] == r_MESSAGE, "value"] * ( - 1 - df_therm_new.loc[df_therm_new["REGION"] == r, "i_therm"].values[0] + 1 - df_therm_new.loc[df_therm_new["REGION"] == r, "i_therm"].values[0] ) for r in df_spec_new["REGION"]: r_MESSAGE = region_type + r useful_spec.loc[useful_spec["node"] == r_MESSAGE, "value"] = useful_spec.loc[ - useful_spec["node"] == r_MESSAGE, "value" - ] * (1 - df_spec_new.loc[ - df_spec_new["REGION"] == r, "i_spec"].values[0]) + useful_spec["node"] == r_MESSAGE, "value" + ] * (1 - df_spec_new.loc[df_spec_new["REGION"] == r, "i_spec"].values[0]) spec_df_hist.loc[ spec_df_hist["node_loc"] == r_MESSAGE, "value" ] = spec_df_hist.loc[spec_df_hist["node_loc"] == r_MESSAGE, "value"] * ( - 1 - df_spec_new.loc[df_spec_new["REGION"] == r, "i_spec"].values[0] + 1 - df_spec_new.loc[df_spec_new["REGION"] == r, "i_spec"].values[0] ) for r in df_feed_new["REGION"]: r_MESSAGE = region_type + r useful_feed.loc[useful_feed["node"] == r_MESSAGE, "value"] = useful_feed.loc[ - useful_feed["node"] == r_MESSAGE, "value" - ] * (1 - df_feed_new.loc[ - df_feed_new["REGION"] == r, "i_feed"].values[0]) + useful_feed["node"] == r_MESSAGE, "value" + ] * (1 - df_feed_new.loc[df_feed_new["REGION"] == r, "i_feed"].values[0]) feed_df_hist.loc[ feed_df_hist["node_loc"] == r_MESSAGE, "value" ] = feed_df_hist.loc[feed_df_hist["node_loc"] == r_MESSAGE, "value"] * ( - 1 - df_feed_new.loc[df_feed_new["REGION"] == r, "i_feed"].values[0] + 1 - df_feed_new.loc[df_feed_new["REGION"] == r, "i_feed"].values[0] ) # For aluminum there is no significant deduction required @@ -690,9 +694,11 @@ def modify_demand_and_hist_activity_debug(scen: message_ix.Scenario) -> dict: # NOTE Aggregate industrial coal demand need to adjust to # the sudden intro of steel setor in the first model year - return {"i_therm": useful_thermal, "i_spec": useful_spec, - "historical_activity": pd.concat([thermal_df_hist, spec_df_hist, feed_df_hist]) - } + return { + "i_therm": useful_thermal, + "i_spec": useful_spec, + "historical_activity": pd.concat([thermal_df_hist, spec_df_hist, feed_df_hist]), + } def modify_baseyear_bounds(scen: message_ix.Scenario) -> None: @@ -727,11 +733,7 @@ def calc_hist_activity(scen: message_ix.Scenario, years: list) -> pd.DataFrame: df_mat = df_mat.sub(df_chem, fill_value=0) # calculate share of residual activity not covered by industry sector explicit technologies - df = ( - df_mat.div(df_orig) - .dropna() - .sort_values("Value", ascending=False) - ) + df = df_mat.div(df_orig).dropna().sort_values("Value", ascending=False) # manually set elec_i to 0 since all of it is covered by iron/steel sector df.loc[:, "elec_i", :] = 0 @@ -743,8 +745,11 @@ def calc_hist_activity(scen: message_ix.Scenario, years: list) -> pd.DataFrame: "historical_activity", filters={"technology": tecs, "year_act": years} ) - df_hist_act_scaled = df_hist_act.set_index([i for i in df_hist_act.columns if i != "value"]).mul( - df.rename({"Value": "value"}, axis=1)).dropna() + df_hist_act_scaled = ( + df_hist_act.set_index([i for i in df_hist_act.columns if i != "value"]) + .mul(df.rename({"Value": "value"}, axis=1)) + .dropna() + ) return df_hist_act_scaled.reset_index() @@ -769,14 +774,14 @@ def calc_demand_shares(iea_db_df: pd.DataFrame, base_year: int) -> pd.DataFrame: & (iea_db_df["PRODUCT"].isin(i_spec_prods)) & ~((iea_db_df["PRODUCT"] == ("ELECTR")) & (iea_db_df["FLOW"] == "IRONSTL")) & (iea_db_df["TIME"] == year) - ] + ] df_i_spec = df_i_spec.groupby("REGION").sum(numeric_only=True) df_i_spec_materials = iea_db_df[ (iea_db_df["FLOW"].isin(i_spec_material_flows)) & (iea_db_df["PRODUCT"].isin(i_spec_prods)) & (iea_db_df["TIME"] == year) - ] + ] df_i_spec_materials = df_i_spec_materials.groupby("REGION").sum(numeric_only=True) df_i_spec_resid_shr = ( @@ -787,7 +792,7 @@ def calc_demand_shares(iea_db_df: pd.DataFrame, base_year: int) -> pd.DataFrame: df_elec_i = iea_db_df[ ((iea_db_df["PRODUCT"] == ("ELECTR")) & (iea_db_df["FLOW"] == "IRONSTL")) & (iea_db_df["TIME"] == year) - ] + ] df_elec_i = df_elec_i.groupby("REGION").sum(numeric_only=True) agg_prods = ["MRENEW", "TOTAL"] @@ -796,7 +801,7 @@ def calc_demand_shares(iea_db_df: pd.DataFrame, base_year: int) -> pd.DataFrame: & ~(iea_db_df["PRODUCT"].isin(i_spec_prods)) & ~(iea_db_df["PRODUCT"].isin(agg_prods)) & (iea_db_df["TIME"] == year) - ] + ] df_i_therm = df_i_therm.groupby("REGION").sum(numeric_only=True) df_i_therm = df_i_therm.add(df_elec_i, fill_value=0) @@ -806,7 +811,7 @@ def calc_demand_shares(iea_db_df: pd.DataFrame, base_year: int) -> pd.DataFrame: & ~(iea_db_df["PRODUCT"].isin(i_spec_prods)) & ~(iea_db_df["PRODUCT"].isin(agg_prods)) & (iea_db_df["TIME"] == year) - ] + ] df_i_therm_materials = df_i_therm_materials.groupby(["REGION", "FLOW"]).sum( numeric_only=True ) @@ -993,17 +998,17 @@ def add_emission_accounting(scen): i for i in tec_list_residual if ( - ( - ("biomass_i" in i) - | ("coal_i" in i) - | ("foil_i" in i) - | ("gas_i" in i) - | ("hp_gas_i" in i) - | ("loil_i" in i) - | ("meth_i" in i) - ) - & ("imp" not in i) - & ("trp" not in i) + ( + ("biomass_i" in i) + | ("coal_i" in i) + | ("foil_i" in i) + | ("gas_i" in i) + | ("hp_gas_i" in i) + | ("loil_i" in i) + | ("meth_i" in i) + ) + & ("imp" not in i) + & ("trp" not in i) ) ] @@ -1135,11 +1140,11 @@ def add_emission_accounting(scen): i for i in tec_list if ( - ("steel" in i) - | ("aluminum" in i) - | ("petro" in i) - | ("cement" in i) - | ("ref" in i) + ("steel" in i) + | ("aluminum" in i) + | ("petro" in i) + | ("cement" in i) + | ("ref" in i) ) ] tec_list_materials.remove("refrigerant_recovery") @@ -1159,7 +1164,7 @@ def add_emission_accounting(scen): (relation_activity["relation"] != "PM2p5_Emission") & (relation_activity["relation"] != "CO2_industry_Emission") & (relation_activity["relation"] != "CO2_transformation_Emission") - ] + ] # ***** (3) Add thermal industry technologies to CO2_ind relation ****** @@ -1353,7 +1358,7 @@ def add_elec_lowerbound_2020(scen): # derive useful energy values by dividing final energy by # input coefficient from final-to-useful technologies bound_residual_electricity["value"] = ( - bound_residual_electricity["Value"] / bound_residual_electricity["value"] + bound_residual_electricity["Value"] / bound_residual_electricity["value"] ) # downselect dataframe columns for MESSAGEix parameters @@ -1375,7 +1380,7 @@ def add_elec_lowerbound_2020(scen): bound_residual_electricity["value"] = bound_residual_electricity["value"] * 0.8 bound_residual_electricity = bound_residual_electricity[ bound_residual_electricity["node_loc"] == "R12_CHN" - ] + ] scen.check_out() @@ -1457,7 +1462,7 @@ def add_coal_lowerbound_2020(sc): bound_coal["value"] = bound_coal["Value"] / bound_coal["value"] bound_cement_coal["value"] = bound_cement_coal["Value"] / bound_cement_coal["value"] bound_residual_electricity["value"] = ( - bound_residual_electricity["Value"] / bound_residual_electricity["value"] + bound_residual_electricity["Value"] / bound_residual_electricity["value"] ) # downselect dataframe columns for MESSAGEix parameters @@ -1648,7 +1653,7 @@ def add_cement_bounds_2020(sc): bound_cement_foil["value"] = bound_cement_foil["Value"] / bound_cement_foil["value"] bound_cement_gas["value"] = bound_cement_gas["Value"] / bound_cement_gas["value"] bound_cement_biomass["value"] = ( - bound_cement_biomass["Value"] / bound_cement_biomass["value"] + bound_cement_biomass["Value"] / bound_cement_biomass["value"] ) bound_cement_coal["value"] = bound_cement_coal["Value"] / bound_cement_coal["value"] @@ -1947,25 +1952,90 @@ def read_rel(scenario, material, filename): return data_rel -# if __name__ == "__main__": -# mp = ixmp.Platform("ixmp_dev") -# scen = message_ix.Scenario(mp, "MESSAGEix-Materials", "NoPolicy_petro_thesis_2") -# -# df_hist_new = calc_hist_activity(scen, [2015]) -# df_demand_new = modify_industry_demand(scen, 2015) -# old_dict = modify_demand_and_hist_activity_debug(scen) -# -# df = get_hist_act_data("IEA_mappings_furnaces.csv", years=[2015]) -# df.index.names = ["node_loc", "technology", "year_act"] -# df_inp = scen.par( -# "input", -# filters={ -# "year_vtg": 2020, -# "year_act": 2020, -# "mode": "high_temp", -# "node_loc": "R12_AFR", -# }, -# ) -# df = df_inp.set_index(["technology"]).join(df).dropna() -# df["Value"] = df["Value"] / df["value"] / 3.6 / 8760 -# print() +def gen_te_projections( + scen, + ssp: Literal["all", "LED", "SSP1", "SSP2", "SSP3", "SSP4", "SSP5"] = "SSP2", + method: Literal["convergence", "gdp", "learning"] = "convergence", + ref_reg: str = "R12_NAM", +) -> tuple: + model_tec_set = list(scen.set("technology")) + cfg = Config( + module="materials", + ref_region=ref_reg, + method=method, + format="message", + scenario=ssp, + ) + out_materials = create_cost_projections( + node=cfg.node, + ref_region=cfg.ref_region, + base_year=cfg.base_year, + module=cfg.module, + method=cfg.method, + scenario_version=cfg.scenario_version, + scenario=cfg.scenario, + convergence_year=cfg.convergence_year, + fom_rate=cfg.fom_rate, + format=cfg.format, + ) + fix_cost = out_materials.fix_cost.drop_duplicates().drop( + ["scenario_version", "scenario"], axis=1 + ) + fix_cost = fix_cost[fix_cost["technology"].isin(model_tec_set)] + inv_cost = out_materials.inv_cost.drop_duplicates().drop( + ["scenario_version", "scenario"], axis=1 + ) + inv_cost = inv_cost[inv_cost["technology"].isin(model_tec_set)] + return inv_cost, fix_cost + + +def get_ssp_soc_eco_data(context, model, measure, tec): + from message_ix_models.project.ssp.data import SSPUpdate # noqa: F401 + + c = Computer() + keys = prepare_computer( + context, + c, + source="ICONICS:SSP(2024).2", + source_kw=dict(measure=measure, model=model), + ) + df = ( + c.get(keys[0]) + .to_dataframe() + .reset_index() + .rename(columns={"n": "node_loc", "y": "year_act"}) + ) + df["mode"] = "P" + df["time"] = "year" + df["unit"] = "GWa" + df["technology"] = tec + return df + + +if __name__ == "__main__": + mp = ixmp.Platform("ixmp_dev") + scen = message_ix.Scenario( + mp, "SSP_dev_SSP2_v0.1_Blv0.6", "baseline_prep_lu_bkp_solved_materials" + ) + + #add_macro_COVID(scen, "SSP_dev_SSP2-R12-5y_macro_data_v0.6_mat.xlsx") + + df_hist_new = calc_hist_activity(scen, [2015]) + print() + # df_demand_new = modify_industry_demand(scen, 2015) + # old_dict = modify_demand_and_hist_activity_debug(scen) + # + # df = get_hist_act_data("IEA_mappings_furnaces.csv", years=[2015]) + # df.index.names = ["node_loc", "technology", "year_act"] + # df_inp = scen.par( + # "input", + # filters={ + # "year_vtg": 2020, + # "year_act": 2020, + # "mode": "high_temp", + # "node_loc": "R12_AFR", + # }, + # ) + # df = df_inp.set_index(["technology"]).join(df).dropna() + # df["Value"] = df["Value"] / df["value"] / 3.6 / 8760 + # print() From 62a2e231e08e7c1680cd93973ce663db6b726afb Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 27 Feb 2024 09:36:55 +0100 Subject: [PATCH 676/774] Add missing emission factor sets and remove feedstock sets for ssp_dev compatibility --- message_ix_models/data/material/set.yaml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 8be913ecc6..c59e75c7d8 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -50,6 +50,9 @@ common: - SO2 - PM2p5 # Just add there since it is already in Shaohui's data - CF4 + - CO2_industry + - CO2_transport + - CO2_transformation year: add: From 85ba4350686b1cd35c8e728f085f2ee449c1fa41 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 27 Feb 2024 09:39:27 +0100 Subject: [PATCH 677/774] Implement cost update tool and ssp gdp/pop update in materials build --- message_ix_models/model/material/__init__.py | 232 +++++++++++-------- 1 file changed, 135 insertions(+), 97 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 655a21c527..e6e3789650 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -1,11 +1,11 @@ -from typing import Mapping - +import message_ix +import pandas as pd +import ntfy import click import logging import ixmp +import message_ix_models.tools.costs.projections -# from .build import apply_spec -# from message_data.tools import ScenarioInfo from message_data.model.material.build import apply_spec from message_ix_models import ScenarioInfo from message_ix_models.util import add_par_data, private_data_path @@ -13,18 +13,50 @@ calibrate_UE_gr_to_demand, calibrate_UE_share_constraints, ) - -# from .data import add_data -from .data_util import modify_demand_and_hist_activity, add_emission_accounting, add_new_ind_hist_act, \ - modify_industry_demand, modify_baseyear_bounds -from .data_util import add_coal_lowerbound_2020, add_macro_COVID, add_cement_bounds_2020 -from .data_util import add_elec_lowerbound_2020, add_ccs_technologies, read_config -import pandas as pd +from message_data.model.material.data_util import ( + modify_demand_and_hist_activity, + add_emission_accounting, + add_new_ind_hist_act, + modify_industry_demand, + modify_baseyear_bounds, + add_coal_lowerbound_2020, + add_macro_COVID, + add_cement_bounds_2020, + add_elec_lowerbound_2020, + add_ccs_technologies, + read_config, + gen_te_projections, + get_ssp_soc_eco_data, +) +from typing import Mapping +from message_data.model.material.data_cement import gen_data_cement +from message_data.model.material.data_steel import gen_data_steel +from message_data.model.material.data_aluminum import gen_data_aluminum +from message_data.model.material.data_generic import gen_data_generic +from message_data.model.material.data_petro import gen_data_petro_chemicals +from message_data.model.material.data_power_sector import gen_data_power_sector +from message_data.model.material.data_methanol_new import gen_data_methanol_new +from message_data.model.material.data_ammonia_new import gen_all_NH3_fert log = logging.getLogger(__name__) +DATA_FUNCTIONS_1 = [ + # gen_data_buildings, + gen_data_methanol_new, + gen_all_NH3_fert, + # gen_data_ammonia, ## deprecated module! + gen_data_generic, + gen_data_steel, +] +DATA_FUNCTIONS_2 = [ + gen_data_cement, + gen_data_petro_chemicals, + # gen_data_power_sector, + gen_data_aluminum, +] + -def build(scenario, old_calib): +def build(scenario: message_ix.Scenario, old_calib: bool) -> message_ix.Scenario: """Set up materials accounting on `scenario`.""" # Get the specification @@ -40,7 +72,7 @@ def build(scenario, old_calib): water_dict = pd.read_excel( private_data_path("material", "other", "water_tec_pars.xlsx"), - sheet_name=None + sheet_name=None, ) for par in water_dict.keys(): scenario.add_par(par, water_dict[par]) @@ -172,11 +204,18 @@ def create_bare(context, regions, dry_run): help="File name for external data input", ) @click.option("--tag", default="", help="Suffix to the scenario name") -@click.option("--mode", default="by_url") +@click.option( + "--mode", default="by_url", type=click.Choice(["by_url", "cbudget", "by_copy"]) +) @click.option("--scenario_name", default="NoPolicy_3105_macro") @click.option("--old_calib", default=False) +@click.option( + "--update_costs", + default=False, + type=click.Choice([False, "SSP1", "SSP2", "SSP3", "SSP4", "SSP5", "LED"]), +) @click.pass_obj -def build_scen(context, datafile, tag, mode, scenario_name, old_calib): +def build_scen(context, datafile, tag, mode, scenario_name, old_calib, update_costs): """Build a scenario. Use the --url option to specify the base scenario. If this scenario is on a @@ -213,29 +252,38 @@ def build_scen(context, datafile, tag, mode, scenario_name, old_calib): # Clone and set up if "SSP_dev" in context.scenario_info["model"]: - scenario = build( - context.get_scenario().clone( - model=context.scenario_info["model"], - scenario=context.scenario_info["scenario"] + "_" + tag, - keep_solution=False, - ), old_calib=old_calib + scenario = context.get_scenario().clone( + model=context.scenario_info["model"], + scenario=context.scenario_info["scenario"] + "_" + tag, + keep_solution=False, ) + context.model.regions = "R12" + measures = ["GDP", "POP"] + tecs = ["GDP_PPP", "Population"] + models = ["IIASA GDP 2023", "IIASA-WiC POP 2023"] + for measure, model, tec in zip(measures, models, tecs): + df = get_ssp_soc_eco_data(context, model, measure, tec) + scenario.check_out() + scenario.add_par("bound_activity_lo", df) + scenario.add_par("bound_activity_up", df) + scenario.commit("update projections") + scenario = build(scenario, old_calib=old_calib) else: scenario = build( context.get_scenario().clone( model="MESSAGEix-Materials", scenario=output_scenario_name + "_" + tag, - ) + ), old_calib=old_calib ) # Set the latest version as default scenario.set_as_default() # Create a two degrees scenario by copying carbon prices from another scenario. - if mode == "by_copy": + elif mode == "by_copy": output_scenario_name = "2degrees" mod_mitig = "ENGAGE_SSP2_v4.1.8" scen_mitig = "EN_NPi2020_1000f" - print("Loading " + mod_mitig + " " + scen_mitig + " to retreive carbon prices.") + print("Loading " + mod_mitig + " " + scen_mitig + " to retrieve carbon prices.") scen_mitig_prices = message_ix.Scenario(mp, mod_mitig, scen_mitig) tax_emission_new = scen_mitig_prices.var("PRICE_EMISSION") @@ -257,7 +305,7 @@ def build_scen(context, datafile, tag, mode, scenario_name, old_calib): print("New scenario name is " + output_scenario_name) scenario.set_as_default() - if mode == "cbudget": + elif mode == "cbudget": scenario = context.get_scenario() print(scenario.version) # print('Base scenario is: ' + scenario.scenario + ", version: " + scenario.version) @@ -283,6 +331,16 @@ def build_scen(context, datafile, tag, mode, scenario_name, old_calib): print("New scenario name is " + output_scenario_name) scenario_new.set_as_default() + if update_costs: + log.info(f"Updating costs with {message_ix_models.tools.costs.projections}") + inv, fix = gen_te_projections(scenario, update_costs) + scenario.check_out() + scenario.add_par("fix_cost", fix) + scenario.add_par("inv_cost", inv) + scenario.commit(f"update cost assumption to: {update_costs}") + + ntfy.notify(title="MESSAGEix-Materials", message="building successfully finished!") + @cli.command("solve") @click.option("--scenario_name", default="NoPolicy") @@ -298,7 +356,7 @@ def build_scen(context, datafile, tag, mode, scenario_name, old_calib): @click.option("--add_calibration", default=False) @click.pass_obj def solve_scen( - context, datafile, model_name, scenario_name, add_calibration, add_macro, version + context, datafile, model_name, scenario_name, add_calibration, add_macro, version ): """Solve a scenario. @@ -318,24 +376,58 @@ def solve_scen( scenario.remove_solution() if add_calibration: - # Solve + # Solve with 2020 base year print("Solving the scenario without MACRO") + print( + "After macro calibration a new scenario with the suffix _macro is created." + ) + print("Make sure to use this scenario to solve with MACRO iterations.") scenario.solve(model="MESSAGE", solve_options={"lpmethod": "4", "scaind": "-1"}) scenario.set_as_default() + # Report + from message_data.model.material.report.reporting import report + from message_data.tools.post_processing.iamc_report_hackathon import ( + report as reporting, + ) + + # Remove existing timeseries and add material timeseries + print("Reporting material-specific variables") + report(context, scenario) + print("Reporting standard variables") + reporting( + mp, + scenario, + # NB(PNK) this is not an error; .iamc_report_hackathon.report() expects a + # string containing "True" or "False" instead of an actual bool. + "False", + scenario.model, + scenario.scenario, + merge_hist=True, + merge_ts=True, + run_config="materials_run_config.yaml", + ) + + # Shift to 2025 base year + scenario = scenario.clone( + model=scenario.model, + scenario=scenario.scenario + "_2025", + shift_first_model_year=2025, + ) + scenario.set_as_default() + scenario.solve(model="MESSAGE", solve_options={"lpmethod": "4", "scaind": "-1"}) + # After solving, add macro calibration print("Scenario solved, now adding MACRO calibration") + # scenario = add_macro_COVID( + # scenario, "R12-CHN-5y_macro_data_NGFS_w_rc_ind_adj_mat.xlsx" + # ) scenario = add_macro_COVID( - scenario, "R12-CHN-5y_macro_data_NGFS_w_rc_ind_adj_mat.xlsx" + scenario, "SSP_dev_SSP2-R12-5y_macro_data_v0.6_mat.xlsx" ) print("Scenario calibrated.") if add_macro: # Default True - print( - "After macro calibration a new scneario with the suffix _macro is created." - ) - print("Make sure to use this scenario to solve with MACRO iterations.") - scenario.solve( model="MESSAGE-MACRO", solve_options={"lpmethod": "4", "scaind": "-1"} ) @@ -346,6 +438,7 @@ def solve_scen( print("Solving the scenario without MACRO") scenario.solve(model="MESSAGE", solve_options={"lpmethod": "4", "scaind": "-1"}) scenario.set_as_default() + ntfy.notify(title="MESSAGEix-Materials", message="solving successfully finished!") @cli.command("add_buildings_ts") @@ -377,7 +470,7 @@ def add_building_ts(scenario_name, model_name): @click.pass_obj def run_reporting(context, remove_ts, profile): """Run materials, then legacy reporting.""" - from message_data.reporting.materials.reporting import report + from message_data.model.material.report.reporting import report from message_data.tools.post_processing.iamc_report_hackathon import ( report as reporting, ) @@ -398,7 +491,6 @@ def run_reporting(context, remove_ts, profile): else: print("There are no timeseries to be removed.") else: - if profile: import cProfile import pstats @@ -435,7 +527,6 @@ def exit(): atexit.register(exit) else: - # Remove existing timeseries and add material timeseries print("Reporting material-specific variables") report(context, scenario) @@ -482,31 +573,6 @@ def run_old_reporting(context): ) -from .data_cement import gen_data_cement -from .data_steel import gen_data_steel -from .data_aluminum import gen_data_aluminum -from .data_generic import gen_data_generic -from .data_petro import gen_data_petro_chemicals -from .data_power_sector import gen_data_power_sector -from .data_methanol_new import gen_data_methanol_new -from .data_ammonia_new import gen_all_NH3_fert - -DATA_FUNCTIONS_1 = [ - # gen_data_buildings, - gen_data_methanol_new, - gen_all_NH3_fert, - # gen_data_ammonia, ## deprecated module! - gen_data_generic, - gen_data_steel, -] -DATA_FUNCTIONS_2 = [ - gen_data_cement, - gen_data_petro_chemicals, - gen_data_power_sector, - gen_data_aluminum, -] - - # Try to handle multiple data input functions from different materials def add_data_1(scenario, dry_run=False): """Populate `scenario` with MESSAGEix-Materials data.""" @@ -526,9 +592,9 @@ def add_data_1(scenario, dry_run=False): # Generate or load the data; add to the Scenario log.info(f"from {func.__name__}()") data = func(scenario) - if "SSP_dev" in scenario.model: - if "emission_factor" in list(data.keys()): - data.pop("emission_factor") + # if "SSP_dev" in scenario.model: + # if "emission_factor" in list(data.keys()): + # data.pop("emission_factor") add_par_data(scenario, data, dry_run=dry_run) log.info("done") @@ -567,46 +633,18 @@ def add_data_2(scenario, dry_run=False): @click.pass_obj def modify_costs_with_tool(context, scen_name, ssp): import message_ix - from message_ix_models.tools.costs.config import Config - from message_ix_models.tools.costs.projections import create_cost_projections mp = ixmp.Platform("ixmp_dev") base = message_ix.Scenario(mp, "MESSAGEix-Materials", scenario=scen_name) scen = base.clone(model=base.model, scenario=base.scenario.replace("baseline", ssp)) - tec_set = list(scen.set("technology")) - - cfg = Config( - module="materials", - ref_region="R12_NAM", - method="convergence", - format="message", - scenario=ssp, - ) + inv, fix = gen_te_projections(scen, ssp) - out_materials = create_cost_projections( - node=cfg.node, - ref_region=cfg.ref_region, - base_year=cfg.base_year, - module=cfg.module, - method=cfg.method, - scenario_version=cfg.scenario_version, - scenario=cfg.scenario, - convergence_year=cfg.convergence_year, - fom_rate=cfg.fom_rate, - format=cfg.format, - ) scen.check_out() - fix_cost = out_materials.fix_cost.drop_duplicates().drop( - ["scenario_version", "scenario"], axis=1 - ) - scen.add_par("fix_cost", fix_cost[fix_cost["technology"].isin(tec_set)]) - inv_cost = out_materials.inv_cost.drop_duplicates().drop( - ["scenario_version", "scenario"], axis=1 - ) - scen.add_par("inv_cost", inv_cost[inv_cost["technology"].isin(tec_set)]) - + scen.add_par("fix_cost", fix) + scen.add_par("inv_cost", inv) scen.commit(f"update cost assumption to: {ssp}") + scen.solve(model="MESSAGE-MACRO", solve_options={"scaind": -1}) @@ -670,7 +708,7 @@ def run_cbud_scenario(context, ssp, scenario): help="description of carbon budget for mitigation target", ) @click.pass_obj -def modify_costs_with_tool(context, ssp, scenario): +def run_LED_cprice(context, ssp, scenario): import message_ix mp = ixmp.Platform("ixmp_dev") From 74d877736b361269275549dba2c4530245ab3207 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 27 Feb 2024 09:40:03 +0100 Subject: [PATCH 678/774] Add industry feedstock related sets to remove section in set.yaml --- message_ix_models/data/material/set.yaml | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index c59e75c7d8..0d05bf6305 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -197,6 +197,8 @@ petro_chemicals: - propane - ethylene - BTX + remove: + - i_feed level: require: @@ -216,6 +218,7 @@ petro_chemicals: - ["lightoil", "export"] - ["fueloil", "import"] - ["fueloil", "export"] + mode: add: - atm_gasoil @@ -262,6 +265,13 @@ petro_chemicals: # Any representation of refinery. - ref_hil - ref_lol + - coal_fs + - coal_fs + - ethanol_fs + - gas_fs + - foil_fs + - loil_fs + - methanol_fs shares: add: @@ -597,6 +607,16 @@ methanol: - ["methanol","import_fs"] - ["methanol","final"] + emission: + add: + - BCA + - CO + - HFC + - OCA + - NH3 + - SF6 + - VOC + buildings: level: add: From a2fd9972b618b7850baeba3fcf5847ad21d48b48 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 27 Feb 2024 10:42:25 +0100 Subject: [PATCH 679/774] Add todo in methanol module --- message_ix_models/model/material/data_methanol_new.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/message_ix_models/model/material/data_methanol_new.py b/message_ix_models/model/material/data_methanol_new.py index 88e489f2c0..fcf945ccd5 100644 --- a/message_ix_models/model/material/data_methanol_new.py +++ b/message_ix_models/model/material/data_methanol_new.py @@ -38,7 +38,7 @@ def gen_data_methanol_new(scenario): for i in pars_dict.keys(): pars_dict[i] = broadcast_reduced_df(pars_dict[i], i) - + # TODO: only temporary hack to ensure SSP_dev compatibility if "SSP_dev" in scenario.model: file_path = message_ix_models.util.private_data_path("material", "methanol", "missing_rels.yaml") # file_path = "C:/Users\maczek\PycharmProjects\message_data\message_data\model\material\petrochemical model fixes notebooks\" From bfed63e07605f887f6c9dd621ab5207143451e2c Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 27 Feb 2024 10:43:25 +0100 Subject: [PATCH 680/774] Update buildings module --- .../LED_LED_report_IAMC_sensitivity_R12.csv | 3 --- message_ix_models/model/material/__init__.py | 8 +++++--- message_ix_models/model/material/data_buildings.py | 10 +++++----- message_ix_models/model/material/data_steel.py | 13 +------------ 4 files changed, 11 insertions(+), 23 deletions(-) delete mode 100644 message_ix_models/data/material/deprecated/LED_LED_report_IAMC_sensitivity_R12.csv diff --git a/message_ix_models/data/material/deprecated/LED_LED_report_IAMC_sensitivity_R12.csv b/message_ix_models/data/material/deprecated/LED_LED_report_IAMC_sensitivity_R12.csv deleted file mode 100644 index 899669bfc3..0000000000 --- a/message_ix_models/data/material/deprecated/LED_LED_report_IAMC_sensitivity_R12.csv +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:851e957ee94fe451416f7c09e36d7c863b34039606b11cd535f879ee1185cf65 -size 207524 diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index e6e3789650..f5ebc60b56 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -9,6 +9,8 @@ from message_data.model.material.build import apply_spec from message_ix_models import ScenarioInfo from message_ix_models.util import add_par_data, private_data_path + +from message_data.model.material.data_buildings import gen_data_buildings from message_data.tools.utilities import ( calibrate_UE_gr_to_demand, calibrate_UE_share_constraints, @@ -41,7 +43,7 @@ log = logging.getLogger(__name__) DATA_FUNCTIONS_1 = [ - # gen_data_buildings, + gen_data_buildings, gen_data_methanol_new, gen_all_NH3_fert, # gen_data_ammonia, ## deprecated module! @@ -78,9 +80,9 @@ def build(scenario: message_ix.Scenario, old_calib: bool) -> message_ix.Scenario scenario.add_par(par, water_dict[par]) scenario.commit("add missing water tecs") - apply_spec(scenario, spec, add_data_2) - spec = None apply_spec(scenario, spec, add_data_1) # dry_run=True + spec = None + apply_spec(scenario, spec, add_data_2) s_info = ScenarioInfo(scenario) nodes = s_info.N diff --git a/message_ix_models/model/material/data_buildings.py b/message_ix_models/model/material/data_buildings.py index 995c0d24ca..2f68f19f83 100644 --- a/message_ix_models/model/material/data_buildings.py +++ b/message_ix_models/model/material/data_buildings.py @@ -1,4 +1,4 @@ -from .data_util import read_sector_data +from message_data.model.material.data_util import read_sector_data import numpy as np from collections import defaultdict @@ -6,8 +6,8 @@ import pandas as pd -from .util import read_config -from .data_util import read_rel +from message_data.model.material.util import read_config +from message_data.model.material.data_util import read_rel from message_ix_models import ScenarioInfo from message_ix import make_df from message_ix_models.util import ( @@ -35,7 +35,7 @@ def read_timeseries_buildings(filename, scenario, case=CASE_SENS): nodes = s_info.N # Read the file and filter the given sensitivity case - bld_input_raw = pd.read_csv(private_data_path("material", filename)) + bld_input_raw = pd.read_csv(private_data_path("material", "buildings", filename)) bld_input_raw = bld_input_raw.loc[bld_input_raw.Sensitivity == case] bld_input_mat = bld_input_raw[ @@ -193,7 +193,7 @@ def gen_data_buildings(scenario, dry_run=False): # List of data frames, to be concatenated together at end results = defaultdict(list) - # For each technology there are differnet input and output combinations + # For each technology there are different input and output combinations # Iterate over technologies # allyears = s_info.set['year'] #s_info.Y is only for modeling years diff --git a/message_ix_models/model/material/data_steel.py b/message_ix_models/model/material/data_steel.py index d4b65fcbc2..875ea1e6c8 100644 --- a/message_ix_models/model/material/data_steel.py +++ b/message_ix_models/model/material/data_steel.py @@ -527,18 +527,7 @@ def gen_data_steel(scenario, dry_run=False): # Create external demand param parname = "demand" - demand = derive_steel_demand(scenario) - - df = make_df( - parname, - level="demand", - commodity="steel", - value=demand.value, - unit="t", - year=demand.year, - time="year", - node=demand.node, - ) # .pipe(broadcast, node=nodes) + df = material_demand_calc.derive_demand("steel", scenario, old_gdp=False) results[parname].append(df) From 1438cfd8462641ab15901f6fd6d9c20a168622ea Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 27 Feb 2024 11:42:17 +0100 Subject: [PATCH 681/774] Fix wrong refinery capacity calculation --- .../petrochemicals/petrochemicals_techno_economic.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/petrochemicals/petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/petrochemicals/petrochemicals_techno_economic.xlsx index e3afda7df9..6910fc4920 100644 --- a/message_ix_models/data/material/petrochemicals/petrochemicals_techno_economic.xlsx +++ b/message_ix_models/data/material/petrochemicals/petrochemicals_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:bfdc475ff9adf45b90fa0a215e47e9b1609de3091a743667cd148a16a1902b55 -size 668994 +oid sha256:e177ca9cd1a2293ed0eb9af6918bd7243cb5c9367ee7e68cae2005f7dd86ee46 +size 678278 From 3305ae97f83bef14ef5b44d3adcdcbeb138394aa Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Mon, 4 Mar 2024 14:29:46 +0100 Subject: [PATCH 682/774] Add excel input as trackable csv's --- .../Ammonia feedstock share.Global_R12.xlsx/Sheet1.csv | 3 +++ .../Ammonia feedstock share.Global_R12.xlsx/Sheet2.csv | 3 +++ .../data.csv | 3 +++ .../material/version control/CEMENT.BvR2010.xlsx/Analysis .csv | 3 +++ .../material/version control/CEMENT.BvR2010.xlsx/Cement_A.csv | 3 +++ .../material/version control/CEMENT.BvR2010.xlsx/Cement_B.csv | 3 +++ .../material/version control/CEMENT.BvR2010.xlsx/Contents.csv | 3 +++ .../CEMENT.BvR2010.xlsx/Domestic Consumption.csv | 3 +++ .../material/version control/CEMENT.BvR2010.xlsx/Export.csv | 3 +++ .../data/material/version control/CEMENT.BvR2010.xlsx/IP$.csv | 3 +++ .../material/version control/CEMENT.BvR2010.xlsx/IPpop.csv | 3 +++ .../data/material/version control/CEMENT.BvR2010.xlsx/IU$.csv | 3 +++ .../material/version control/CEMENT.BvR2010.xlsx/Import.csv | 3 +++ .../version control/CEMENT.BvR2010.xlsx/NetExport%.csv | 3 +++ .../material/version control/CEMENT.BvR2010.xlsx/NetExport.csv | 3 +++ .../version control/CEMENT.BvR2010.xlsx/OECD GDPpc.csv | 3 +++ .../material/version control/CEMENT.BvR2010.xlsx/OECD pop.csv | 3 +++ .../data/material/version control/CEMENT.BvR2010.xlsx/PCC.csv | 3 +++ .../material/version control/CEMENT.BvR2010.xlsx/PCCfit.csv | 3 +++ .../version control/CEMENT.BvR2010.xlsx/Production.csv | 3 +++ .../material/version control/CEMENT.BvR2010.xlsx/Regions.csv | 3 +++ .../material/version control/CEMENT.BvR2010.xlsx/Sheet1.csv | 3 +++ .../material/version 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2001 From: Florian Maczek Date: Mon, 4 Mar 2024 14:32:55 +0100 Subject: [PATCH 683/774] Remove freshwater_supply from fertilizer set requirements for ssp_dev compatibility --- message_ix_models/data/material/set.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 0d05bf6305..7e6beb9720 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -479,7 +479,7 @@ fertilizer: commodity: require: - electr - - freshwater_supply + #- freshwater_supply removed for SSP_dev developments, is added in build if missing add: - NH3 - Fertilizer Use|Nitrogen From e811b0c369c1930f62fa9da2850833720b37dd57 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Mon, 4 Mar 2024 14:34:59 +0100 Subject: [PATCH 684/774] Add excel_to_csv feature for version control --- message_ix_models/model/material/__init__.py | 35 ++++++++++++++++++++ message_ix_models/model/material/util.py | 23 ++++++++++++- 2 files changed, 57 insertions(+), 1 deletion(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index f5ebc60b56..6343943273 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -4,6 +4,7 @@ import click import logging import ixmp +import os import message_ix_models.tools.costs.projections from message_data.model.material.build import apply_spec @@ -30,6 +31,7 @@ gen_te_projections, get_ssp_soc_eco_data, ) +from message_data.model.material.util import excel_to_csv, get_all_input_data_dirs from typing import Mapping from message_data.model.material.data_cement import gen_data_cement from message_data.model.material.data_steel import gen_data_steel @@ -745,3 +747,36 @@ def run_LED_cprice(context, ssp, scenario): scen_cprice.solve(model="MESSAGE-MACRO", solve_options={"scaind": -1}) return + + +@cli.command("make_xls_input_vc_able") +@click.option( + "--files", + default="all", + help="optionally specify which files to make VC-able - not implemented yet", +) +@click.pass_obj +def make_xls_input_vc_able(context, files): + if files == "all": + dirs = get_all_input_data_dirs() + dirs = [i for i in dirs if i != "version control"] + for dir in dirs: + print(dir) + files = os.listdir(private_data_path("material", dir)) + files = [i for i in files if ((i.endswith(".xlsx")) & ~i.startswith("~$"))] + print(files) + for filename in files: + excel_to_csv(dir, filename) + else: + raise NotImplementedError + # if not isinstance(files, tuple): + # print(type(files)) + # + # print(files) + # for element in files: + # print(element) + # fname = element.split("/")[-1] + # path = private_data_path(str(element.split("/")[:-1])) + # print(path, fname) + # message_data.model.material.util.excel_to_csv(files) + retu diff --git a/message_ix_models/model/material/util.py b/message_ix_models/model/material/util.py index d70d85c7a2..3723559c21 100644 --- a/message_ix_models/model/material/util.py +++ b/message_ix_models/model/material/util.py @@ -1,7 +1,8 @@ from message_ix_models import Context -from message_ix_models.util import load_private_data +from message_ix_models.util import load_private_data, private_data_path import pandas as pd import yaml +import os # Configuration files METADATA = [ @@ -87,3 +88,23 @@ def invert_dictionary(original_dict): inverted_dict[array_element] = [] inverted_dict[array_element].append(key) return inverted_dict + + +def excel_to_csv(material_dir, fname): + xlsx_dict = pd.read_excel( + private_data_path("material", material_dir, fname), sheet_name=None + ) + if not os.path.isdir(private_data_path("material", "version control")): + os.mkdir(private_data_path("material", "version control")) + os.mkdir(private_data_path("material", "version control", fname)) + for tab in xlsx_dict.keys(): + xlsx_dict[tab].to_csv( + private_data_path("material", "version control", fname, f"{tab}.csv"), + index=False, + ) + + +def get_all_input_data_dirs(): + elements = os.listdir(private_data_path("material")) + elements = [i for i in elements if os.path.isdir(private_data_path("material", i))] + return elements From 712c791035bd27f0972d655d611585f1d5d3ad2e Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Mon, 4 Mar 2024 14:36:14 +0100 Subject: [PATCH 685/774] Rename water supply parameters in ammonia build for water module compatibility --- message_ix_models/model/material/data_ammonia_new.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/message_ix_models/model/material/data_ammonia_new.py b/message_ix_models/model/material/data_ammonia_new.py index ef249e4e4d..86a985fde0 100644 --- a/message_ix_models/model/material/data_ammonia_new.py +++ b/message_ix_models/model/material/data_ammonia_new.py @@ -162,6 +162,10 @@ def __missing__(self, key): conv_cost_df = pd.concat([conv_cost_df, make_df(p, **df_tecs)]) par_dict[p] = pd.concat([df[~df["technology"].isin(tec_list)], conv_cost_df]) + # HACK: quick fix to enable compatibility with water build + if len(scenario.par("output", filters={"technology": "extract_surfacewater"})): + par_dict["input"] = par_dict["input"].replace({"freshwater_supply": "freshwater"}) + return par_dict From 6985462b5833fdb796ed8975acee2b4b506add4c Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Mon, 4 Mar 2024 14:36:53 +0100 Subject: [PATCH 686/774] Rename water supply parameters in steel build for water module compatibility --- message_ix_models/model/material/data_steel.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/message_ix_models/model/material/data_steel.py b/message_ix_models/model/material/data_steel.py index 875ea1e6c8..2d25a0cf6c 100644 --- a/message_ix_models/model/material/data_steel.py +++ b/message_ix_models/model/material/data_steel.py @@ -533,6 +533,8 @@ def gen_data_steel(scenario, dry_run=False): # Concatenate to one data frame per parameter results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} + if len(scenario.par("output", filters={"technology": "extract_surfacewater"})): + results["input"] = results["input"].replace({"freshwater_supply": "freshwater"}) return results From 2d993f011a605bdc39da0e0ad6194bed93048158 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Mon, 4 Mar 2024 14:37:52 +0100 Subject: [PATCH 687/774] Remove deprecated function argument in demand module --- .../model/material/material_demand/material_demand_calc.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/model/material/material_demand/material_demand_calc.py b/message_ix_models/model/material/material_demand/material_demand_calc.py index 7bdfa94864..907f9e5222 100644 --- a/message_ix_models/model/material/material_demand/material_demand_calc.py +++ b/message_ix_models/model/material/material_demand/material_demand_calc.py @@ -146,10 +146,10 @@ def read_hist_mat_demand(material): if material in ["cement", "steel"]: # Read population data - df_pop = read_timer_pop(datapath, file_cement, material) + df_pop = read_timer_pop(datapath, material) # Read GDP per capita data - df_gdp = read_timer_gdp(datapath, file_cement, material) + df_gdp = read_timer_gdp(datapath, material) if material == "aluminum": df_raw_cons = ( From 36ad5ecfd0e639fcc3dcab8ed503899d7e2cf55d Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Mon, 4 Mar 2024 14:39:05 +0100 Subject: [PATCH 688/774] Check gdp ppp availability in materials build --- message_ix_models/model/material/__init__.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 6343943273..f23cd9587f 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -268,6 +268,10 @@ def build_scen(context, datafile, tag, mode, scenario_name, old_calib, update_co for measure, model, tec in zip(measures, models, tecs): df = get_ssp_soc_eco_data(context, model, measure, tec) scenario.check_out() + if "GDP_PPP" not in list(scenario.set("technology")): + scenario.add_set("technology", "GDP_PPP") + scenario.commit("update projections") + scenario.check_out() scenario.add_par("bound_activity_lo", df) scenario.add_par("bound_activity_up", df) scenario.commit("update projections") @@ -277,7 +281,8 @@ def build_scen(context, datafile, tag, mode, scenario_name, old_calib, update_co context.get_scenario().clone( model="MESSAGEix-Materials", scenario=output_scenario_name + "_" + tag, - ), old_calib=old_calib + ), + old_calib=old_calib, ) # Set the latest version as default scenario.set_as_default() From 6961e644c955fe4e7ca40d931a7226c849aacf70 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Mon, 4 Mar 2024 15:58:32 +0100 Subject: [PATCH 689/774] Add co2 reporting fixes from methanol branch --- message_ix_models/model/material/__init__.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index f23cd9587f..2aee746c3f 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -15,6 +15,8 @@ from message_data.tools.utilities import ( calibrate_UE_gr_to_demand, calibrate_UE_share_constraints, + manual_updates_ENGAGE_SSP2_v417_to_v418, + update_h2_blending, ) from message_data.model.material.data_util import ( modify_demand_and_hist_activity, @@ -83,6 +85,10 @@ def build(scenario: message_ix.Scenario, old_calib: bool) -> message_ix.Scenario scenario.commit("add missing water tecs") apply_spec(scenario, spec, add_data_1) # dry_run=True + manual_updates_ENGAGE_SSP2_v417_to_v418._correct_balance_td_efficiencies(scenario) + manual_updates_ENGAGE_SSP2_v417_to_v418._correct_coal_ppl_u_efficiencies(scenario) + manual_updates_ENGAGE_SSP2_v417_to_v418._correct_td_co2cc_emissions(scenario) + update_h2_blending.main(scenario) spec = None apply_spec(scenario, spec, add_data_2) From ad00f927cf37704741aff4404aeb9503dcf97e40 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Mon, 4 Mar 2024 16:03:40 +0100 Subject: [PATCH 690/774] Update data_buildings.py --- message_ix_models/model/material/data_buildings.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/model/material/data_buildings.py b/message_ix_models/model/material/data_buildings.py index 2f68f19f83..4a09c0d493 100644 --- a/message_ix_models/model/material/data_buildings.py +++ b/message_ix_models/model/material/data_buildings.py @@ -45,7 +45,7 @@ def read_timeseries_buildings(filename, scenario, case=CASE_SENS): "Floor Space|Aluminum|Cement|Steel" ) ] # Final Energy - Later. Need to figure out carving out - bld_input_mat["Region"] = "R11_" + bld_input_mat["Region"] + bld_input_mat["Region"] = "R12_" + bld_input_mat["Region"] print("Check the year values") print(bld_input_mat) @@ -202,7 +202,7 @@ def gen_data_buildings(scenario, dry_run=False): yv_ya = s_info.yv_ya # fmy = s_info.y0 nodes.remove("World") - nodes.remove("R11_RCPA") + #nodes.remove("R11_RCPA") # Read field values from the buildings input data regions = list(set(data_buildings.node)) From a374d0ad5b483364c3442fa98a690e11cbe7e1ef Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 5 Mar 2024 14:00:47 +0100 Subject: [PATCH 691/774] Transfer fixes from methanol branch --- .../model/material/data_methanol.py | 2 +- .../model/material/data_petro.py | 26 +++++++++++++------ 2 files changed, 19 insertions(+), 9 deletions(-) diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index edfe6bc5c6..50c2aa88c3 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -226,7 +226,7 @@ def get_embodied_emi(row, pars, share_par): new_dict2[i] = new_dict2[i].drop_duplicates() # model MTBE phase out legislation - if pars["mtbe_scenario"] == "phase_out": + if pars["mtbe_scenario"] == "phase-out": new_dict2 = combine_df_dictionaries(new_dict2, add_mtbe_act_bound(scenario)) diff --git a/message_ix_models/model/material/data_petro.py b/message_ix_models/model/material/data_petro.py index b673268bf1..df08e18c77 100644 --- a/message_ix_models/model/material/data_petro.py +++ b/message_ix_models/model/material/data_petro.py @@ -41,7 +41,6 @@ def read_data_petrochemicals(scenario): def gen_mock_demand_petro(scenario, gdp_elasticity_2020, gdp_elasticity_2030): - context = read_config() s_info = ScenarioInfo(scenario) modelyears = s_info.Y # s_info.Y is only for modeling years @@ -181,7 +180,6 @@ def gen_data_petro_chemicals(scenario, dry_run=False): global_region = "R12_GLB" for t in config["technology"]["add"]: - # years = s_info.Y params = data_petro.loc[ (data_petro["technology"] == t), "parameter" @@ -190,9 +188,7 @@ def gen_data_petro_chemicals(scenario, dry_run=False): # Availability year of the technology av = data_petro.loc[(data_petro["technology"] == t), "availability"].values[0] modelyears = [year for year in modelyears if year >= av] - yva = yv_ya.loc[ - yv_ya.year_vtg >= av, - ] + yva = yv_ya.loc[yv_ya.year_vtg >= av,] # Iterate over parameters for par in params: @@ -222,9 +218,7 @@ def gen_data_petro_chemicals(scenario, dry_run=False): for rg in regions: if len(split) > 1: - if (param_name == "input") | (param_name == "output"): - com = split[1] lev = split[2] mod = split[3] @@ -342,7 +336,6 @@ def gen_data_petro_chemicals(scenario, dry_run=False): # Rest of the parameters apart from input, output and emission_factor else: - df = make_df( param_name, technology=t, @@ -526,4 +519,21 @@ def gen_data_petro_chemicals(scenario, dry_run=False): ] ) + # TODO: move this to input xlsx file + df = scenario.par( + "relation_activity", + filters={"relation": "h2_scrub_limit", "technology": "gas_bio"}, + ) + df["value"] = -(1.33181 * 0.482) # gas input * emission factor of gas + df["technology"] = "gas_processing_petro" + results["relation_activity"] = df + + # TODO: move this to input xlsx file + df_gro = results["growth_activity_up"] + drop_idx = df_gro[ + (df_gro["technology"] == "steam_cracker_petro") + & (df_gro["node_loc"] == "R12_RCPA") + & (df_gro["year_act"] == 2020) + ].index + results["growth_activity_up"] = results["growth_activity_up"].drop(drop_idx) return results From 65caae5a2db0a2134473648e1b65e9608c4709b2 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 5 Mar 2024 15:38:38 +0100 Subject: [PATCH 692/774] Deactivate manual engage updates for ssp_dev builds --- message_ix_models/model/material/__init__.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 2aee746c3f..3161a5fcaa 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -85,10 +85,11 @@ def build(scenario: message_ix.Scenario, old_calib: bool) -> message_ix.Scenario scenario.commit("add missing water tecs") apply_spec(scenario, spec, add_data_1) # dry_run=True - manual_updates_ENGAGE_SSP2_v417_to_v418._correct_balance_td_efficiencies(scenario) - manual_updates_ENGAGE_SSP2_v417_to_v418._correct_coal_ppl_u_efficiencies(scenario) - manual_updates_ENGAGE_SSP2_v417_to_v418._correct_td_co2cc_emissions(scenario) - update_h2_blending.main(scenario) + if "SSP_dev" in scenario.model: + manual_updates_ENGAGE_SSP2_v417_to_v418._correct_balance_td_efficiencies(scenario) + manual_updates_ENGAGE_SSP2_v417_to_v418._correct_coal_ppl_u_efficiencies(scenario) + manual_updates_ENGAGE_SSP2_v417_to_v418._correct_td_co2cc_emissions(scenario) + update_h2_blending.main(scenario) spec = None apply_spec(scenario, spec, add_data_2) From a642a5e0e635252478c09a2fac267019c06bb11c Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 5 Mar 2024 15:39:21 +0100 Subject: [PATCH 693/774] Fix wrong meth_coal_ccs new capacity constraint for r12_weu --- .../data/material/methanol/methanol_techno_economic.xlsx | 4 ++-- .../methanol/methanol_techno_economic_high_demand.xlsx | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/message_ix_models/data/material/methanol/methanol_techno_economic.xlsx b/message_ix_models/data/material/methanol/methanol_techno_economic.xlsx index 276eae242a..52145e81cd 100644 --- a/message_ix_models/data/material/methanol/methanol_techno_economic.xlsx +++ b/message_ix_models/data/material/methanol/methanol_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:9572f5407d54532ee12c96a647e9a496c681bd5a3c434a93b47e68c17b3afb78 -size 619097 +oid sha256:a271ace35b0165a74840c14b2f7bc9e09f01e156444f0de73074625a07a2c324 +size 619110 diff --git a/message_ix_models/data/material/methanol/methanol_techno_economic_high_demand.xlsx b/message_ix_models/data/material/methanol/methanol_techno_economic_high_demand.xlsx index 96975c1b73..e0b50edaf2 100644 --- a/message_ix_models/data/material/methanol/methanol_techno_economic_high_demand.xlsx +++ b/message_ix_models/data/material/methanol/methanol_techno_economic_high_demand.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:88a6d9ad31f3c6e2512b2f15ffe07c7f7e7545dfe9e9798f001581308ba26de2 -size 626774 +oid sha256:cb15022882d45eaed917cec4e3c63130a8e1f2e73759962abff3017a9438415a +size 626787 From 01db3d73dcba488d81d5594645cb70fa8574a41d Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 7 Mar 2024 09:48:10 +0100 Subject: [PATCH 694/774] Move buildings-materials input file from "deprecated" folder --- .../material/buildings/LED_LED_report_IAMC_sensitivity_R12.csv | 3 +++ 1 file changed, 3 insertions(+) create mode 100644 message_ix_models/data/material/buildings/LED_LED_report_IAMC_sensitivity_R12.csv diff --git a/message_ix_models/data/material/buildings/LED_LED_report_IAMC_sensitivity_R12.csv b/message_ix_models/data/material/buildings/LED_LED_report_IAMC_sensitivity_R12.csv new file mode 100644 index 0000000000..899669bfc3 --- /dev/null +++ b/message_ix_models/data/material/buildings/LED_LED_report_IAMC_sensitivity_R12.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:851e957ee94fe451416f7c09e36d7c863b34039606b11cd535f879ee1185cf65 +size 207524 From 0ad5e96490d12f30ca8326f418cf865963c0fb92 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Mon, 11 Mar 2024 22:55:54 +0100 Subject: [PATCH 695/774] Modify emission accounting in materials build for ssp_dev compatibility --- message_ix_models/model/material/__init__.py | 30 ++++++++++++------- message_ix_models/model/material/data_util.py | 17 +++++++---- message_ix_models/model/material/util.py | 5 ++++ 3 files changed, 36 insertions(+), 16 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 3161a5fcaa..166c1b9a04 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -47,7 +47,7 @@ log = logging.getLogger(__name__) DATA_FUNCTIONS_1 = [ - gen_data_buildings, + #gen_data_buildings, gen_data_methanol_new, gen_all_NH3_fert, # gen_data_ammonia, ## deprecated module! @@ -85,9 +85,13 @@ def build(scenario: message_ix.Scenario, old_calib: bool) -> message_ix.Scenario scenario.commit("add missing water tecs") apply_spec(scenario, spec, add_data_1) # dry_run=True - if "SSP_dev" in scenario.model: - manual_updates_ENGAGE_SSP2_v417_to_v418._correct_balance_td_efficiencies(scenario) - manual_updates_ENGAGE_SSP2_v417_to_v418._correct_coal_ppl_u_efficiencies(scenario) + if "SSP_dev" not in scenario.model: + manual_updates_ENGAGE_SSP2_v417_to_v418._correct_balance_td_efficiencies( + scenario + ) + manual_updates_ENGAGE_SSP2_v417_to_v418._correct_coal_ppl_u_efficiencies( + scenario + ) manual_updates_ENGAGE_SSP2_v417_to_v418._correct_td_co2cc_emissions(scenario) update_h2_blending.main(scenario) spec = None @@ -107,10 +111,8 @@ def build(scenario: message_ix.Scenario, old_calib: bool) -> message_ix.Scenario last_hist_year = scenario.par("historical_activity")["year_act"].max() modify_industry_demand(scenario, last_hist_year) add_new_ind_hist_act(scenario, [last_hist_year]) - try: add_emission_accounting(scenario) - except: - pass + # scenario.commit("no changes") add_coal_lowerbound_2020(scenario) add_cement_bounds_2020(scenario) @@ -132,6 +134,14 @@ def build(scenario: message_ix.Scenario, old_calib: bool) -> message_ix.Scenario scenario.remove_set("sector", "i_feed") scenario.commit("i_feed removed from sectors.") + df = scenario.par( + "bound_activity_lo", + filters={"node_loc": "R12_RCPA", "technology": "sp_el_I", "year_act": 2020}, + ) + scenario.check_out() + scenario.remove_par("bound_activity_lo", df) + scenario.commit("remove sp_el_I min bound on RCPA in 2020") + return scenario @@ -635,9 +645,9 @@ def add_data_2(scenario, dry_run=False): log.info(f"from {func.__name__}()") # TODO: remove this once emission_factors are back in SSP_dev data = func(scenario) - if "SSP_dev" in scenario.model: - if "emission_factor" in list(data.keys()): - data.pop("emission_factor") + # if "SSP_dev" in scenario.model: + # if "emission_factor" in list(data.keys()): + # data.pop("emission_factor") add_par_data(scenario, data, dry_run=dry_run) log.info("done") diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index f1d5673e3f..eea2f61d5c 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -9,6 +9,7 @@ read_config, read_yaml_file, invert_dictionary, + remove_from_list_if_exists, ) from message_ix_models import ScenarioInfo @@ -1091,8 +1092,10 @@ def add_emission_accounting(scen): # Find the technology, year_act, year_vtg, emission, node_loc combination emissions = [e for e in emission_df["emission"].unique()] - emissions.remove("CO2_industry") - emissions.remove("CO2_res_com") + remove_from_list_if_exists("CO2_industry", emissions) + remove_from_list_if_exists("CO2_res_com", emissions) + # emissions.remove("CO2_industry") + # emissions.remove("CO2_res_com") for t in emission_df["technology"].unique(): for e in emissions: @@ -1147,9 +1150,11 @@ def add_emission_accounting(scen): | ("ref" in i) ) ] - tec_list_materials.remove("refrigerant_recovery") - tec_list_materials.remove("replacement_so2") - tec_list_materials.remove("SO2_scrub_ref") + for elem in ["refrigerant_recovery", "replacement_so2", "SO2_scrub_ref"]: + remove_from_list_if_exists(elem, tec_list_materials) + # tec_list_materials.remove("refrigerant_recovery") + # tec_list_materials.remove("replacement_so2") + # tec_list_materials.remove("SO2_scrub_ref") emission_factors = scen.par( "emission_factor", filters={"technology": tec_list_materials, "emission": "CO2"} ) @@ -2018,7 +2023,7 @@ def get_ssp_soc_eco_data(context, model, measure, tec): mp, "SSP_dev_SSP2_v0.1_Blv0.6", "baseline_prep_lu_bkp_solved_materials" ) - #add_macro_COVID(scen, "SSP_dev_SSP2-R12-5y_macro_data_v0.6_mat.xlsx") + # add_macro_COVID(scen, "SSP_dev_SSP2-R12-5y_macro_data_v0.6_mat.xlsx") df_hist_new = calc_hist_activity(scen, [2015]) print() diff --git a/message_ix_models/model/material/util.py b/message_ix_models/model/material/util.py index 3723559c21..0bc4cc9aea 100644 --- a/message_ix_models/model/material/util.py +++ b/message_ix_models/model/material/util.py @@ -108,3 +108,8 @@ def get_all_input_data_dirs(): elements = os.listdir(private_data_path("material")) elements = [i for i in elements if os.path.isdir(private_data_path("material", i))] return elements + + +def remove_from_list_if_exists(element, _list): + if element in _list: + _list.remove(element) From 5f3929e3b789ca2de50cb43a0e491f23d0cb55c6 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Wed, 20 Mar 2024 20:32:36 +0100 Subject: [PATCH 696/774] Clean up buildings material module and add new input files --- .../methanol/results_material_SHAPE_comm.csv | 3 +++ .../results_material_SHAPE_residential.csv | 3 +++ .../methanol/results_material_SSP2_comm.csv | 3 +++ .../results_material_SSP2_residential.csv | 3 +++ .../model/material/data_buildings.py | 21 +++++++++---------- 5 files changed, 22 insertions(+), 11 deletions(-) create mode 100644 message_ix_models/data/material/methanol/results_material_SHAPE_comm.csv create mode 100644 message_ix_models/data/material/methanol/results_material_SHAPE_residential.csv create mode 100644 message_ix_models/data/material/methanol/results_material_SSP2_comm.csv create mode 100644 message_ix_models/data/material/methanol/results_material_SSP2_residential.csv diff --git a/message_ix_models/data/material/methanol/results_material_SHAPE_comm.csv b/message_ix_models/data/material/methanol/results_material_SHAPE_comm.csv new file mode 100644 index 0000000000..d2a9423483 --- /dev/null +++ b/message_ix_models/data/material/methanol/results_material_SHAPE_comm.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:957d0b80959d4564524517f5dd003e2f8b3ebd8e182217659c11d13481be5b3d +size 416073 diff --git a/message_ix_models/data/material/methanol/results_material_SHAPE_residential.csv b/message_ix_models/data/material/methanol/results_material_SHAPE_residential.csv new file mode 100644 index 0000000000..f291ff222b --- /dev/null +++ b/message_ix_models/data/material/methanol/results_material_SHAPE_residential.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a59e1c215692bf575bac373f368326f9d89891d2860e6e0ba3ba320aef076e29 +size 429432 diff --git a/message_ix_models/data/material/methanol/results_material_SSP2_comm.csv b/message_ix_models/data/material/methanol/results_material_SSP2_comm.csv new file mode 100644 index 0000000000..27ca014012 --- /dev/null +++ b/message_ix_models/data/material/methanol/results_material_SSP2_comm.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a7de4b6d78bc2df7f028d9edf81e54e2ab5dccf483dd59e223a56b852c3ea57c +size 139700 diff --git a/message_ix_models/data/material/methanol/results_material_SSP2_residential.csv b/message_ix_models/data/material/methanol/results_material_SSP2_residential.csv new file mode 100644 index 0000000000..706337d76a --- /dev/null +++ b/message_ix_models/data/material/methanol/results_material_SSP2_residential.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2b6dce2f9fd2da6817ec69a002b9b69610e42aa6089bbf4d9d9c1f894366e120 +size 287785 diff --git a/message_ix_models/model/material/data_buildings.py b/message_ix_models/model/material/data_buildings.py index 4a09c0d493..dd19c03316 100644 --- a/message_ix_models/model/material/data_buildings.py +++ b/message_ix_models/model/material/data_buildings.py @@ -1,22 +1,12 @@ -from message_data.model.material.data_util import read_sector_data - -import numpy as np -from collections import defaultdict -import logging - import pandas as pd +from collections import defaultdict from message_data.model.material.util import read_config -from message_data.model.material.data_util import read_rel from message_ix_models import ScenarioInfo from message_ix import make_df from message_ix_models.util import ( - broadcast, - make_io, - make_matched_dfs, same_node, copy_column, - add_par_data, private_data_path, ) @@ -314,3 +304,12 @@ def gen_data_buildings(scenario, dry_run=False): adjust_demand_param(scenario) return results + + +if __name__ == "__main__": + import ixmp + import message_ix + mp = ixmp.Platform("ixmp_dev") + scen = message_ix.Scenario(mp, "MESSAGEix-Materials", "baseline_balance-equality_materials") + df = gen_data_buildings(scen) + print() \ No newline at end of file From 9ee9842c9ed1992ab28886c3bb857447d7354e34 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Wed, 20 Mar 2024 20:33:21 +0100 Subject: [PATCH 697/774] Use alias for long name of module --- message_ix_models/model/material/__init__.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 166c1b9a04..683d711d63 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -15,7 +15,7 @@ from message_data.tools.utilities import ( calibrate_UE_gr_to_demand, calibrate_UE_share_constraints, - manual_updates_ENGAGE_SSP2_v417_to_v418, + manual_updates_ENGAGE_SSP2_v417_to_v418 as engage_updates, update_h2_blending, ) from message_data.model.material.data_util import ( @@ -86,13 +86,13 @@ def build(scenario: message_ix.Scenario, old_calib: bool) -> message_ix.Scenario apply_spec(scenario, spec, add_data_1) # dry_run=True if "SSP_dev" not in scenario.model: - manual_updates_ENGAGE_SSP2_v417_to_v418._correct_balance_td_efficiencies( + engage_updates._correct_balance_td_efficiencies( scenario ) - manual_updates_ENGAGE_SSP2_v417_to_v418._correct_coal_ppl_u_efficiencies( + engage_updates._correct_coal_ppl_u_efficiencies( scenario ) - manual_updates_ENGAGE_SSP2_v417_to_v418._correct_td_co2cc_emissions(scenario) + engage_updates._correct_td_co2cc_emissions(scenario) update_h2_blending.main(scenario) spec = None apply_spec(scenario, spec, add_data_2) From 7cae07f7d63d0d8a1dfd569def999e949d0b7f33 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 21 Mar 2024 16:33:39 +0100 Subject: [PATCH 698/774] Add yaml files for materials technologies and commodities --- .../data/material/commodity.yaml | 273 ++++ .../data/material/technology.yaml | 1303 +++++++++++++++++ 2 files changed, 1576 insertions(+) create mode 100644 message_ix_models/data/material/commodity.yaml create mode 100644 message_ix_models/data/material/technology.yaml diff --git a/message_ix_models/data/material/commodity.yaml b/message_ix_models/data/material/commodity.yaml new file mode 100644 index 0000000000..a9e9d0f07d --- /dev/null +++ b/message_ix_models/data/material/commodity.yaml @@ -0,0 +1,273 @@ +BTX: + description: BTX chemcials group consisting of benzene, toluene and xylenes + name: Aromatics + report: string + units: Mt + +Fertilizer Use|Nitrogen: + description: '' + name: empty + report: string + units: Mt + +HVC: + description: High Value Chemicals consisting of ethylene, propylene, butylene and BTX + name: High Value Chemicals + report: string + units: Mt + +NH3: + description: Ammonia + name: Ammonia + report: string + units: Mt + +aluminum: + description: '' + name: Aluminum + report: string + units: Mt + +atm_gasoil: + description: Gas oil yielded from atmospheric distillation + name: Atmospheric gas oil + report: string + units: GWa + +atm_residue: + description: Residue yield from atmospheric distillation + name: Atmospheric residue + report: string + units: GWa + +bf_gas: + description: Gas produced by blast furnace during iron making process + name: Blast furnace gas + report: string + units: GWa + +cement: + description: Cement produced by mixing clinker with ... # TODO: look up process + name: Cement + report: string + units: Mt + +clinker_cement: + description: Clinker required for cement production + name: Clinker + report: string + units: Mt + +co_gas: + description: Gaseous by-product of coke production in coke oven + name: Coke oven gas + report: string + units: GWa + +coke_iron: + description: Coke used for iron production + name: Coke for iron production + report: string + units: GWa + +diesel: + description: Petroleum product mainly used in internal combustion engines + name: Diesel + report: string + units: GWa + +ethane: + description: Alkane with two C atoms mainly used as petrochemical feedstock + name: Ethane + report: string + units: Mt + +ethylene: + description: Most produced petrochemical commodity used as a building block for hundreds of chemicals + name: Ethylene + report: string + units: Mt + +fcoh_resin: + description: Resin type produced by combining formaldehyde and urea (UF), phenol (PF) or melamin (MF) + name: Formaldehyde resin + report: string + units: Mt + +floor_area: + description: '' + name: empty + report: string + units: GWa + #TODO: check unit + +formaldehyde: + description: Important base chemical used for resins, solvents and other chemicals + name: Formaldehyde + report: string + units: Mt + +fresh_water: + description: '' + name: empty + report: string + units: GWa + #TODO: check unit + +gasoline: + description: Refined petroleum product mainly used in internal combustion engines + name: Gasoline + report: string + units: GWa + +heavy_foil: + description: Refined petroleum product mainly used as shipping fuel + name: Heavy fuel oil + report: string + units: GWa + +ht_heat: + description: Heat for industrial processes above x°C # TODO: define temperature range for ht_heat and document + name: High temperature heat + report: string + units: GWa + +kerosene: + description: Refined petroleum product mainly used as aviation fuel + name: Kerosene + report: string + units: GWa + +light_foil: + description: '' # TODO: figure out how this product maps to PRELIM/McKinsey Energy Insights/IEA/etc. + name: empty + report: string + units: GWa + +limestone_cement: + description: '' + name: empty + report: string + units: Mt + +limestone_iron: + description: '' + name: empty + report: string + units: Mt + +lt_heat: + description: Heat for industrial processes below x°C # TODO: define temperature range in MESSAGEix-Materials + name: Low-temperature heat + report: string + units: GWa + +methanol: + description: Simplest alcoholic molecule used as fuel and base chemical + name: Methanol + report: string + units: GWa + +naphtha: + description: Distillation fractions and other intermediates in the gasoline boiling range used for gasoline production and petrochemical feedstock + name: Naphtha + report: string + units: GWa + +ore_iron: + description: '' + name: empty + report: string + units: Mt + +pellet_iron: + description: '' + name: empty + report: string + units: Mt + +pet_coke: + description: Coke mainly produced by coking unit process in a refinery + name: Petroleum coke + report: string + units: GWa + +pig_iron: + description: '' + name: empty + report: string + units: Mt + +propane: + description: Alkane with three C atoms mainly used in LPG and as petrochemical feedstock + name: empty + report: string + units: Mt + +propylene: + description: Petrochemical base chemical mainly used for polymer production + name: Propylene + report: string + units: Mt + +raw_meal_cement: + description: '' + name: empty + report: string + units: Mt + +refinery_gas: + description: Off gas yield from various refinery processes consisting mainly of methane and hydrogen + name: Refinery gas + report: string + units: GWa + +sinter_iron: + description: '' + name: empty + report: string + units: Mt + +slag_iron: + description: '' + name: empty + report: string + units: Mt + +sponge_iron: + description: '' + name: empty + report: string + units: Mt + +steel: + description: Steel commodities with varying degree of processing depending on the "level" + name: Steel + report: string + units: Mt + +vacuum_gasoil: + description: Lighter fraction yielded from vacuum distillation + name: Vacuum Gas Oil (VGO) + report: string + units: GWa + +vacuum_residue: + description: Heavy fraction yield from vacuum distillation + name: Vacuum Residue (VR) + report: string + units: GWa + +wastewater: + description: '' + name: empty + report: string + units: GWa + #TODO: check unit + +water: + description: '' + name: empty + report: string + units: GWa + #TODO: check unit diff --git a/message_ix_models/data/material/technology.yaml b/message_ix_models/data/material/technology.yaml new file mode 100644 index 0000000000..c89d631e2e --- /dev/null +++ b/message_ix_models/data/material/technology.yaml @@ -0,0 +1,1303 @@ +# Collection of all material/industry technologies of MESSAGEix-Materials +CH2O_synth: + description: Synthesis of formaldehyde from methanol via silver-catalyst pathway # check this + input: + commodity: test + name: Formaldehyde Synthesis + report: string + +CH2O_to_resin: + description: Synthesis of formaldehyde resins (proxy chemical phenol-formaldehyde) + input: + commodity: test + name: empty + report: string + +DUMMY_alumina_supply: + description: '' + input: + commodity: test + name: empty + report: string + +DUMMY_coal_supply: + description: '' + input: + commodity: test + name: empty + report: string + +DUMMY_gas_supply: + description: '' + input: + commodity: test + name: empty + report: string + +DUMMY_limestone_supply_cement: + description: '' + input: + commodity: test + name: empty + report: string + +DUMMY_limestone_supply_steel: + description: '' + input: + commodity: test + name: empty + report: string + +DUMMY_ore_supply: + description: '' + input: + commodity: test + name: empty + report: string + +MTO_petro: + description: Methanol-to-Olefins process unit producing ethylene and propylene from methanol + input: + commodity: test + name: Methanol-to-Olefins + report: string + +NH3_to_N_fertil: + description: Nitrogen fertilizer production from ammonia + input: + commodity: test + name: Nitrogen fertilizer production + report: string + +agg_ref: + description: Pseudo technology aggregating petroleum products to light-oil and heavy-oil + input: + commodity: test + name: empty + report: string + +atm_distillation_ref: + description: Most important unit in a refinery separating crude oil by + input: + commodity: test + name: Atmospheric distillation unit + report: string + +bf_steel: + description: Blast furnace producing iron from iron ore + input: + commodity: test + name: Blast furnace + report: string + +biomass_NH3: + description: Ammonia synthesis with hydrogen produced from biomass gasification + input: + commodity: test + name: Ammonia production from biomass + report: string + +biomass_NH3_ccs: + description: Ammonia synthesis with hydrogen produced from biomass gasification with CCS + input: + commodity: test + name: empty + report: string + +bof_steel: + description: Basic oxygen furnace steel making route + input: + commodity: test + name: Basic oxygen furnace + report: string + +buildings: + description: '' + input: + commodity: test + name: empty + report: string + +catalytic_cracking_ref: + description: Catalytic cracking converts gas oils with high gasoline yield + input: + commodity: test + name: Catalytic cracking process unit + report: string + +catalytic_reforming_ref: + description: Catalytic reforming used to produce hydrogen from refinery feedstock + input: + commodity: test + name: Catalytic reforming process unit + report: string + +clinker_dry_ccs_cement: + description: Addon technology for clinker_dry_cement capturing process emissions + input: + commodity: test + name: Dry process clinker kiln CCS addon + report: string + +clinker_dry_cement: + description: Clinker kiln using the more efficient dry calcining process + input: + commodity: test + name: Dry process clinker kiln + report: string + +clinker_wet_ccs_cement: + description: Addon technology for clinker_wet_cement capturing process emissions + input: + commodity: test + name: Wet process clinker kiln CCS addon + report: string + +clinker_wet_cement: + description: Clinker kiln using the less efficient wet calcining process + input: + commodity: test + name: empty + report: string + +coal_NH3: + description: Ammonia synthesis with hydrogen from coal gasification + input: + commodity: test + name: Ammonia production from coal + report: string + +coal_NH3_ccs: + description: Ammonia synthesis with hydrogen from coal gasification with CCS + input: + commodity: test + name: Ammonia production from coal with CCS + report: string + +cokeoven_steel: + description: Coke oven transforming coal to coke for steel production + input: + commodity: test + name: Coke oven for steel production + report: string + +coking_ref: + description: Deep conversion technology of refinery upgrading heavy residues of vacuum distillation + input: + commodity: test + name: Refinery coker process unit + report: string + +dheat_aluminum: + description: '' + input: + commodity: test + name: empty + report: string + +dheat_cement: + description: '' + input: + commodity: test + name: empty + report: string + +dheat_petro: + description: '' + input: + commodity: test + name: empty + report: string + +dheat_refining: + description: '' + input: + commodity: test + name: empty + report: string + +dheat_resins: + description: '' + input: + commodity: test + name: empty + report: string + +dheat_steel: + description: '' + input: + commodity: test + name: empty + report: string + +dri_steel: + description: '' + input: + commodity: test + name: empty + report: string + +eaf_steel: + description: Electric arc furnace for steel making from DRI iron or steel scrap + input: + commodity: test + name: Electric arc furnace + report: string + +electr_NH3: + description: Integrated ammonia production facility combining on-site hydrogen electrolyser with ammonia synthesis unit + input: + commodity: test + name: Ammonia production from electricity + report: string + +ethanol_to_ethylene_petro: + description: Ethylene production via ethanol dehydration + input: + commodity: test + name: Ethanol dehydration + report: string + +export_NFert: + description: Pseudo technology representing regional nitrogen fertilizer exports to global market + input: + commodity: test + name: Nitrogen fertilizer export + report: string + +export_NH3: + description: Pseudo technology representing regional ammonia exports to global market + input: + commodity: test + name: NH3 export + report: string + +export_aluminum: + description: Pseudo technology representing regional aluminum exports to global market + input: + commodity: test + name: Aluminum export + report: string + +export_petro: + description: Pseudo technology representing regional HVC exports to global market + input: + commodity: test + name: HVC export + report: string + +export_steel: + description: Pseudo technology representing regional steel exports to global market + input: + commodity: test + name: Steel export + report: string + +#FIXME: fuel cells are currently only producing low temp heat, but no electricity +fc_h2_aluminum: + description: Fuel cell using hydrogen to produce heat for aluminum and electricity + input: + commodity: test + name: Hydrogen fuel-cell for aluminum process heat + report: string + +fc_h2_cement: + description: Fuel cell using hydrogen to produce heat for cement and electricity + input: + commodity: test + name: Hydrogen fuel-cell for cement process heat + report: string + +fc_h2_petro: + description: Fuel cell using hydrogen to produce heat for petrochemicals and electricity + input: + commodity: test + name: Hydrogen fuel-cell for petrochemicals process heat + report: string + +fc_h2_refining: + description: Fuel cell using hydrogen to produce heat for refineries and electricity + input: + commodity: test + name: Hydrogen fuel-cell for refinery process heat + report: string + +fc_h2_resins: + description: Fuel cell using hydrogen to produce heat for resins and electricity + input: + commodity: test + name: Hydrogen fuel-cell for resin process heat + report: string + +fc_h2_steel: + description: Fuel cell using hydrogen to produce heat for aluminum and electricity + input: + commodity: test + name: Hydrogen fuel-cell for steel process heat + report: string + +feedstock_t/d: + description: '' + input: + commodity: test + name: empty + report: string + +finishing_aluminum: + description: '' + input: + commodity: test + name: empty + report: string + +finishing_steel: + description: '' + input: + commodity: test + name: empty + report: string + +fueloil_NH3: + description: Ammonia synthesis with hydrogen via partial oxidation of fueloil + input: + commodity: test + name: Ammonia production from fueloil + report: string + +fueloil_NH3_ccs: + description: Ammonia synthesis with hydrogen via partial oxidation of fueloil with CCS + input: + commodity: test + name: Ammonia production from fueloil with CCS + report: string + +furnace_biomass_aluminum: + description: Generalized furnace using biomass to provide process heat for aluminum production + input: + commodity: test + name: Industrial furnace from biomass for aluminum sector + report: string + +furnace_biomass_cement: + description: Generalized furnace using biomass to provide process heat for aluminum production + input: + commodity: test + name: Industrial furnace from biomass for aluminum sector + report: string + +furnace_biomass_petro: + description: Generalized furnace using biomass to provide process heat for petrochemical production + input: + commodity: test + name: Industrial furnace from biomass for petrochemical sector + report: string + +furnace_biomass_refining: + description: Generalized furnace using biomass to provide process heat for refineries + input: + commodity: test + name: Industrial furnace from biomass for refinery sector + report: string + +furnace_biomass_resins: + description: Generalized furnace using biomass to provide process heat for resin production + input: + commodity: test + name: Industrial furnace from biomass for resin sector + report: string + +furnace_biomass_steel: + description: Generalized furnace using biomass to provide process heat for steel production + input: + commodity: test + name: Industrial furnace from biomass for iron/steel sector + report: string + +furnace_coal_aluminum: + description: Generalized furnace using biomass to provide process heat for steel production + input: + commodity: test + name: Industrial furnace from coal for aluminum sector + report: string + +furnace_coal_cement: + description: Generalized furnace using biomass to provide process heat for steel production + input: + commodity: test + name: Industrial furnace from coal for cement sector + report: string + +furnace_coal_petro: + description: Generalized furnace using biomass to provide process heat for steel production + input: + commodity: test + name: Industrial furnace from coal for petrochemicals sector + report: string + +furnace_coal_refining: + description: Generalized furnace using biomass to provide process heat for steel production + input: + commodity: test + name: Industrial furnace from coal for refinery sector + report: string + +furnace_coal_resins: + description: Generalized furnace using biomass to provide process heat for steel production + input: + commodity: test + name: Industrial furnace from coal for resins sector + report: string + +furnace_coal_steel: + description: Generalized furnace using biomass to provide process heat for steel production + input: + commodity: test + name: Industrial furnace from coal for steel sector + report: string + +furnace_coke_petro: + description: Generalized furnace using biomass to provide process heat for steel production + input: + commodity: test + name: Industrial furnace from coke for petrochemical sector + report: string + +furnace_coke_refining: + description: Generalized furnace using biomass to provide process heat for steel production + input: + commodity: test + name: Industrial furnace from coke for refinery sector + report: string + +furnace_elec_aluminum: + description: Generalized furnace using biomass to provide process heat for steel production + input: + commodity: test + name: Industrial furnace using electricity for aluminum sector + report: string + +furnace_elec_cement: + description: Generalized furnace using biomass to provide process heat for steel production + input: + commodity: test + name: Industrial furnace using electricity for cement sector + report: string + +furnace_elec_petro: + description: Generalized furnace using biomass to provide process heat for steel production + input: + commodity: test + name: Industrial furnace using electricity for petrochemical sector + report: string + +furnace_elec_refining: + description: Generalized furnace using biomass to provide process heat for steel production + input: + commodity: test + name: Industrial furnace using electricity for refinery sector + report: string + +furnace_elec_resins: + description: Generalized furnace using biomass to provide process heat for steel production + input: + commodity: test + name: Industrial furnace using electricity for resin sector + report: string + +furnace_elec_steel: + description: Generalized furnace using biomass to provide process heat for steel production + input: + commodity: test + name: Industrial furnace using electricity for steel sector + report: string + +furnace_ethanol_aluminum: + description: Generalized furnace using biomass to provide process heat for steel production + input: + commodity: test + name: Industrial furnace using ethanol for aluminum sector + report: string + +furnace_ethanol_cement: + description: Generalized furnace using biomass to provide process heat for steel production + input: + commodity: test + name: Industrial furnace using ethanol for cement sector + report: string + +furnace_ethanol_petro: + description: Generalized furnace using biomass to provide process heat for steel production + input: + commodity: test + name: Industrial furnace using ethanol for petrochemical sector + report: string + +furnace_ethanol_refining: + description: Generalized furnace using biomass to provide process heat for steel production + input: + commodity: test + name: Industrial furnace using ethanol for refinery sector + report: string + +furnace_ethanol_resins: + description: Generalized furnace using biomass to provide process heat for steel production + input: + commodity: test + name: Industrial furnace using ethanol for resins sector + report: string + +furnace_ethanol_steel: + description: Generalized furnace using biomass to provide process heat for steel production + input: + commodity: test + name: Industrial furnace using ethanol for steel sector + report: string + +furnace_foil_aluminum: + description: Generalized furnace using biomass to provide process heat for steel production + input: + commodity: test + name: Industrial furnace using fueloil for aluminum sector + report: string + +furnace_foil_cement: + description: Generalized furnace using biomass to provide process heat for steel production + input: + commodity: test + name: Industrial furnace using fueloil for cement sector + report: string + +furnace_foil_petro: + description: Generalized furnace using biomass to provide process heat for steel production + input: + commodity: test + name: Industrial furnace using fueloil for petrochemical sector + report: string + +furnace_foil_refining: + description: Generalized furnace using biomass to provide process heat for steel production + input: + commodity: test + name: Industrial furnace using fueloil for refinery sector + report: string + +furnace_foil_resins: + description: Generalized furnace using biomass to provide process heat for steel production + input: + commodity: test + name: Industrial furnace using fueloil for resin sector + report: string + +furnace_foil_steel: + description: Generalized furnace using biomass to provide process heat for steel production + input: + commodity: test + name: Industrial furnace using fueloil for steel sector + report: string + +furnace_gas_aluminum: + description: Generalized furnace using biomass to provide process heat for steel production + input: + commodity: test + name: Industrial furnace using natural gas for aluminum sector + report: string + +furnace_gas_cement: + description: Generalized furnace using biomass to provide process heat for steel production + input: + commodity: test + name: Industrial furnace using natural gas for cement sector + report: string + +furnace_gas_petro: + description: Generalized furnace using biomass to provide process heat for steel production + input: + commodity: test + name: Industrial furnace using natural gas for petrochemical sector + report: string + +furnace_gas_refining: + description: Generalized furnace using biomass to provide process heat for steel production + input: + commodity: test + name: Industrial furnace using natural gas for refinery sector + report: string + +furnace_gas_resins: + description: Generalized furnace using biomass to provide process heat for steel production + input: + commodity: test + name: Industrial furnace using natural gas for resin sector + report: string + +furnace_gas_steel: + description: Generalized furnace using biomass to provide process heat for steel production + input: + commodity: test + name: Industrial furnace using natural gas for steel sector + report: string + +furnace_h2_aluminum: + description: Generalized furnace using biomass to provide process heat for steel production + input: + commodity: test + name: Industrial furnace using hydrogen for aluminum sector + report: string + +furnace_h2_cement: + description: Generalized furnace using biomass to provide process heat for steel production + input: + commodity: test + name: Industrial furnace using hydrogen for cement sector + report: string + +furnace_h2_petro: + description: Generalized furnace using biomass to provide process heat for steel production + input: + commodity: test + name: Industrial furnace using hydrogen for petrochemicals sector + report: string + +furnace_h2_refining: + description: Generalized furnace using biomass to provide process heat for steel production + input: + commodity: test + name: Industrial furnace using hydrogen for refinery sector + report: string + +furnace_h2_resins: + description: Generalized furnace using biomass to provide process heat for steel production + input: + commodity: test + name: Industrial furnace using hydrogen for resin sector + report: string + +furnace_h2_steel: + description: Generalized furnace using biomass to provide process heat for steel production + input: + commodity: test + name: Industrial furnace using hydrogen for steel sector + report: string + +furnace_loil_aluminum: + description: Generalized furnace using biomass to provide process heat for steel production + input: + commodity: test + name: Industrial furnace using lightoil for aluminum sector + report: string + +furnace_loil_cement: + description: Generalized furnace using biomass to provide process heat for steel production + input: + commodity: test + name: Industrial furnace using lightoil for cement sector + report: string + +furnace_loil_petro: + description: Generalized furnace using biomass to provide process heat for steel production + input: + commodity: test + name: Industrial furnace using lightoil for petrochemical sector + report: string + +furnace_loil_refining: + description: Generalized furnace using biomass to provide process heat for steel production + input: + commodity: test + name: Industrial furnace using lightoil for refinery sector + report: string + +furnace_loil_resins: + description: Generalized furnace using biomass to provide process heat for steel production + input: + commodity: test + name: Industrial furnace using lightoil for resin sector + report: string + +furnace_loil_steel: + description: Generalized furnace using biomass to provide process heat for steel production + input: + commodity: test + name: Industrial furnace using lightoil for steel sector + report: string + +furnace_methanol_aluminum: + description: Generalized furnace using biomass to provide process heat for steel production + input: + commodity: test + name: Industrial furnace using methanol for aluminum sector + report: string + +furnace_methanol_cement: + description: Generalized furnace using biomass to provide process heat for steel production + input: + commodity: test + name: Industrial furnace using methanol for cement sector + report: string + +furnace_methanol_petro: + description: Generalized furnace using biomass to provide process heat for steel production + input: + commodity: test + name: Industrial furnace using methanol for petrochemical sector + report: string + +furnace_methanol_refining: + description: Generalized furnace using biomass to provide process heat for steel production + input: + commodity: test + name: Industrial furnace using methanol for refinery sector + report: string + +furnace_methanol_resins: + description: Generalized furnace using biomass to provide process heat for steel production + input: + commodity: test + name: Industrial furnace using methanol for resin sector + report: string + +furnace_methanol_steel: + description: Generalized furnace using biomass to provide process heat for steel production + input: + commodity: test + name: Industrial furnace using methanol for steel sector + report: string + +gas_NH3: + description: Ammonia synthesis with hydrogen via steam reforming of methane + input: + commodity: test + name: Ammonia production from natural gas + report: string + +gas_NH3_ccs: + description: Ammonia synthesis with hydrogen via steam reforming of methane with CCS + input: + commodity: test + name: Ammonia production from natural gas with CCS + report: string + +gas_processing_petro: + description: '' + input: + commodity: test + name: empty + report: string + +grinding_ballmill_cement: + description: '' + input: + commodity: test + name: empty + report: string + +grinding_vertmill_cement: + description: '' + input: + commodity: test + name: empty + report: string + +hp_elec_aluminum: + description: Electric heat pump used to provide process heat for aluminum production + input: + commodity: test + name: Electric heat pump for aluminum process heat + report: string + +hp_elec_cement: + description: Electric heat pump used to provide process heat for cement production + input: + commodity: test + name: empty + report: string + +hp_elec_petro: + description: Electric heat pump used to provide process heat for petrochemical production + input: + commodity: test + name: empty + report: string + +hp_elec_refining: + description: Electric heat pump used to provide process heat for refinery processes + input: + commodity: test + name: empty + report: string + +hp_elec_resins: + description: Electric heat pump used to provide process heat for resin production + input: + commodity: test + name: empty + report: string + +hp_elec_steel: + description: Electric heat pump used to provide process heat for steel production + input: + commodity: test + name: empty + report: string + +hp_gas_aluminum: + description: Gas powered heat pump used to provide process heat for aluminum production + input: + commodity: test + name: empty + report: string + +hp_gas_cement: + description: Gas powered heat pump used to provide process heat for aluminum production + input: + commodity: test + name: empty + report: string + +hp_gas_petro: + description: Gas powered heat pump used to provide process heat for petrochemical production + input: + commodity: test + name: empty + report: string + +hp_gas_refining: + description: Gas powered heat pump used to provide process heat for refineries + input: + commodity: test + name: empty + report: string + +hp_gas_resins: + description: Gas powered heat pump used to provide process heat for resin production + input: + commodity: test + name: empty + report: string + +hp_gas_steel: + description: Gas powered heat pump used to provide process heat for steel production + input: + commodity: test + name: empty + report: string + +hydro_cracking_ref: + description: Hydrocracking unit of a refinery converting gas oils with hydrogen to lighter petroleum products + input: + commodity: test + name: empty + report: string + +hydrotreating_ref: + description: Hydrotreating unit of a refinery + input: + commodity: test + name: Hydrotreating refinery unit + report: string + +import_NFert: + description: Pseudo technology representing imports of nitrogen fertilizer from a global pool + input: + commodity: test + name: empty + report: string + +import_NH3: + description: Pseudo technology representing imports of ammonia from a global pool + input: + commodity: test + name: Ammonia import + report: string + +import_aluminum: + description: Pseudo technology representing imports of aluminum from a global pool + input: + commodity: test + name: empty + report: string + +import_petro: + description: Pseudo technology representing imports of HVCs from a global pool + input: + commodity: test + name: empty + report: string + +import_steel: + description: Pseudo technology representing imports of steel from a global pool + input: + commodity: test + name: empty + report: string + +manuf_aluminum: + description: Technology representing the manufacturing steps from crude alu to alu products + input: + commodity: test + name: empty + report: string + +manuf_steel: + description: Technology representing the manufacturing steps from crude steel to steel products + input: + commodity: test + name: Steel products manufacturing + report: string + +meth_bal: + description: Pseudo technology moving methanol from primary(_material) to secondary(_material) level + input: + commodity: test + name: Methanol balance + report: string + +meth_bio: + description: Methanol production via biomass gasification + input: + commodity: test + name: empty + report: string + +meth_bio_ccs: + description: Methanol production via biomass gasification with CCS + input: + commodity: test + name: empty + report: string + +meth_coal: + description: Methanol production via coal gasification + input: + commodity: test + name: empty + report: string + +meth_coal_ccs: + description: Methanol production via coal gasification with CCS + input: + commodity: test + name: Methanol production from coal with CCS + report: string + +meth_exp: + description: Pseudo technology representing export of methanol to the global pool + input: + commodity: test + name: Methanol export + report: string + +meth_h2: + description: Methanol production from synthetic syngas (H2 and CO2) + input: + commodity: test + name: Methanol production from hydrogen + report: string + +meth_imp: + description: Pseudo technology representing imports of methanol from the global pool + input: + commodity: test + name: Methanol import + report: string + +meth_ng: + description: Methanol production via steam reforming of natural gas + input: + commodity: test + name: Methanol production from natural gas + report: string + +meth_ng_ccs: + description: Methanol production via steam reforming of natural gas with CCS + input: + commodity: test + name: empty + report: string + +meth_t_d: + description: Pseudo technology moving methanol from secondary to final level + input: + commodity: test + name: Methanol transmission and distribution + report: string + +meth_t_d_material: + description: Pseudo technology moving methanol from secondary_material to final_material level + input: + commodity: test + name: empty + report: string + +meth_trd: + description: Pseudo technology representing methanol trade on global markets + input: + commodity: test + name: Methanol trade + report: string + +other_EOL_aluminum: + description: '' + input: + commodity: test + name: empty + report: string + +other_EOL_cement: + description: '' + input: + commodity: test + name: empty + report: string + +other_EOL_steel: + description: '' + input: + commodity: test + name: empty + report: string + +pellet_steel: + description: '' + input: + commodity: test + name: empty + report: string + +prebake_aluminum: + description: Aluminum production via Hall–Héroult process with prebaked electrodes + input: + commodity: test + name: Aluminum production with prebaked electrodes + report: string + +prep_secondary_aluminum_1: + description: '' + input: + commodity: test + name: empty + report: string + +prep_secondary_aluminum_2: + description: '' + input: + commodity: test + name: empty + report: string + +prep_secondary_aluminum_3: + description: '' + input: + commodity: test + name: empty + report: string + +prep_secondary_steel_1: + description: '' + input: + commodity: test + name: empty + report: string + +prep_secondary_steel_2: + description: '' + input: + commodity: test + name: empty + report: string + +prep_secondary_steel_3: + description: '' + input: + commodity: test + name: empty + report: string + +production_HVC: + description: Pseudo technology aggregating all chemicals belong to HVC group to HVC commodity + input: + commodity: test + name: High Value Chemicals production + report: string + +raw_meal_prep_cement: + description: '' + input: + commodity: test + name: empty + report: string + +residual_NH3: + description: Pseudo technology that moves ammonia from level x to level y #TODO: look up levels + input: + commodity: test + name: Non-fertilizer ammonia production + report: string + +scrap_recovery_aluminum: + description: '' + input: + commodity: test + name: empty + report: string + +scrap_recovery_cement: + description: '' + input: + commodity: test + name: empty + report: string + +scrap_recovery_steel: + description: '' + input: + commodity: test + name: empty + report: string + +secondary_aluminum: + description: '' + input: + commodity: test + name: empty + report: string + +sinter_steel: + description: '' + input: + commodity: test + name: empty + report: string + +soderberg_aluminum: + description: Aluminum production via Hall–Héroult process with Söderberg electrodes + input: + commodity: test + name: Aluminum production with Söderberg electrodes + report: string + +solar_aluminum: + description: Solar heat collected with ? for aluminum production + input: + commodity: test + name: Solar heat for aluminum production + report: string + +solar_cement: + description: '' + input: + commodity: test + name: empty + report: string + +solar_petro: + description: '' + input: + commodity: test + name: empty + report: string + +solar_refining: + description: '' + input: + commodity: test + name: empty + report: string + +solar_resins: + description: '' + input: + commodity: test + name: empty + report: string + +solar_steel: + description: '' + input: + commodity: test + name: empty + report: string + +sr_steel: + description: '' + input: + commodity: test + name: empty + report: string + +steam_cracker_petro: + description: '' + input: + commodity: test + name: empty + report: string + +total_EOL_aluminum: + description: '' + input: + commodity: test + name: empty + report: string + +total_EOL_cement: + description: '' + input: + commodity: test + name: empty + report: string + +total_EOL_steel: + description: '' + input: + commodity: test + name: empty + report: string + +trade_NFert: + description: Pseudo technology representing nitrogen fertilizer trade on global markets + input: + commodity: test + name: empty + report: string + +trade_NH3: + description: Pseudo technology representing NH3 trade on global markets + input: + commodity: test + name: Ammonia trade + report: string + +trade_aluminum: + description: Pseudo technology representing aluminum trade on global markets + input: + commodity: test + name: empty + report: string + +trade_petro: + description: Pseudo technology representing HVC trade on global markets + input: + commodity: test + name: empty + report: string + +trade_steel: + description: Pseudo technology representing steel trade on global markets + input: + commodity: test + name: Steel trade + report: string + +vacuum_distillation_ref: + description: Refinery process distilling heavy residue from atmospheric distillation + input: + commodity: test + name: Vacuum distillation unit + report: string + +visbreaker_ref: + description: Visbreaker unit of a refinery + input: + commodity: test + name: empty + report: string From 97cf499d7f487987660628a20fdc93afe5b00d6a Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 21 Mar 2024 16:47:18 +0100 Subject: [PATCH 699/774] Add industry tec groups to yaml --- .../data/material/technology.yaml | 50 +++++++++++++++++++ 1 file changed, 50 insertions(+) diff --git a/message_ix_models/data/material/technology.yaml b/message_ix_models/data/material/technology.yaml index c89d631e2e..4b8b009277 100644 --- a/message_ix_models/data/material/technology.yaml +++ b/message_ix_models/data/material/technology.yaml @@ -1301,3 +1301,53 @@ visbreaker_ref: commodity: test name: empty report: string + +tecs_steel: + name: Steel making technologies + description: group of technologies that steel + child: [] + +tecs_petro: + name: Petrochemicals technologies + description: group of technologies that produce petrochemicals + child: [] + +tecs_cement: + name: Cement making technologies + description: group of technologies that steel + child: [] + +tecs_alu: + name: Aluminum making technologies + description: group of technologies that steel + child: [] + +tecs_ref: + name: Refinery technologies + description: group of refinery process units + child: [ ] + +heat_tecs_steel: + name: Steel heat supply technologies + description: group of technologies that steel + child: [] + +heat_tecs_petro: + name: Petrochemicals heat supply technologies + description: group of technologies that produce petrochemicals + child: [] + +heat_tecs_cement: + name: Cement heat supply technologies + description: group of technologies that steel + child: [] + +heat_tecs_alu: + name: Aluminum heat supply technologies + description: group of technologies that steel + child: [] + +heat_tecs_ref: + name: Refinery heat supply technologies + description: group of refinery process units + child: [ ] \ No newline at end of file From 95939e8dadef9a5594920bf3845b31c0f64d918b Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Fri, 22 Mar 2024 10:02:29 +0100 Subject: [PATCH 700/774] Update industry structure yamls --- message_ix_models/data/material/commodity.yaml | 1 + message_ix_models/data/material/technology.yaml | 4 ++-- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/commodity.yaml b/message_ix_models/data/material/commodity.yaml index a9e9d0f07d..cb6f9959a5 100644 --- a/message_ix_models/data/material/commodity.yaml +++ b/message_ix_models/data/material/commodity.yaml @@ -1,3 +1,4 @@ +# Collection of all material/industry commodities of MESSAGEix-Materials BTX: description: BTX chemcials group consisting of benzene, toluene and xylenes name: Aromatics diff --git a/message_ix_models/data/material/technology.yaml b/message_ix_models/data/material/technology.yaml index 4b8b009277..9be53e24e6 100644 --- a/message_ix_models/data/material/technology.yaml +++ b/message_ix_models/data/material/technology.yaml @@ -1226,10 +1226,10 @@ sr_steel: report: string steam_cracker_petro: - description: '' + description: Most important conventional technology for primary petrochemicals input: commodity: test - name: empty + name: Steam cracker report: string total_EOL_aluminum: From 22f44277fae043d30ef925b0db477dfe1dbd00e2 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Fri, 22 Mar 2024 22:18:40 +0100 Subject: [PATCH 701/774] Add if clause for exogenous gdp update for ssp_dev versions --- message_ix_models/model/material/__init__.py | 29 ++++++++++---------- 1 file changed, 15 insertions(+), 14 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 683d711d63..04f9f054e2 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -278,20 +278,21 @@ def build_scen(context, datafile, tag, mode, scenario_name, old_calib, update_co scenario=context.scenario_info["scenario"] + "_" + tag, keep_solution=False, ) - context.model.regions = "R12" - measures = ["GDP", "POP"] - tecs = ["GDP_PPP", "Population"] - models = ["IIASA GDP 2023", "IIASA-WiC POP 2023"] - for measure, model, tec in zip(measures, models, tecs): - df = get_ssp_soc_eco_data(context, model, measure, tec) - scenario.check_out() - if "GDP_PPP" not in list(scenario.set("technology")): - scenario.add_set("technology", "GDP_PPP") - scenario.commit("update projections") - scenario.check_out() - scenario.add_par("bound_activity_lo", df) - scenario.add_par("bound_activity_up", df) - scenario.commit("update projections") + if float(context.scenario_info["model"].split("Blv")[1]) < 0.12: + context.model.regions = "R12" + measures = ["GDP", "POP"] + tecs = ["GDP_PPP", "Population"] + models = ["IIASA GDP 2023", "IIASA-WiC POP 2023"] + for measure, model, tec in zip(measures, models, tecs): + df = get_ssp_soc_eco_data(context, model, measure, tec) + scenario.check_out() + if "GDP_PPP" not in list(scenario.set("technology")): + scenario.add_set("technology", "GDP_PPP") + scenario.commit("update projections") + scenario.check_out() + scenario.add_par("bound_activity_lo", df) + scenario.add_par("bound_activity_up", df) + scenario.commit("update projections") scenario = build(scenario, old_calib=old_calib) else: scenario = build( From f6c2b4689ddf7c7338aec4500af613c32564098e Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 28 Mar 2024 16:46:48 +0100 Subject: [PATCH 702/774] Add ssp option to materials demand projection module --- message_ix_models/model/material/__init__.py | 3 +- .../model/material/data_aluminum.py | 8 +-- .../model/material/data_cement.py | 23 +++++---- .../model/material/data_steel.py | 4 +- .../material_demand/material_demand_calc.py | 49 ++++++++++++++++--- 5 files changed, 62 insertions(+), 25 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 04f9f054e2..11de0e1d5b 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -5,12 +5,11 @@ import logging import ixmp import os -import message_ix_models.tools.costs.projections +import message_ix_models.tools.costs.projections from message_data.model.material.build import apply_spec from message_ix_models import ScenarioInfo from message_ix_models.util import add_par_data, private_data_path - from message_data.model.material.data_buildings import gen_data_buildings from message_data.tools.utilities import ( calibrate_UE_gr_to_demand, diff --git a/message_ix_models/model/material/data_aluminum.py b/message_ix_models/model/material/data_aluminum.py index 12ae45de48..a4807f7661 100644 --- a/message_ix_models/model/material/data_aluminum.py +++ b/message_ix_models/model/material/data_aluminum.py @@ -66,12 +66,12 @@ def print_full(x): def gen_data_aluminum(scenario, dry_run=False): - - config = read_config()["material"]["aluminum"] + context = read_config() + config = context["material"]["aluminum"] # Information about scenario, e.g. node, year s_info = ScenarioInfo(scenario) - + ssp = context["ssp"] # Techno-economic assumptions data_aluminum, data_aluminum_rel, data_aluminum_ts = read_data_aluminum(scenario) # List of data frames, to be concatenated together at end @@ -280,7 +280,7 @@ def gen_data_aluminum(scenario, dry_run=False): # Create external demand param parname = "demand" - df = material_demand_calc.derive_demand("aluminum", scenario, old_gdp=False) + df = material_demand_calc.derive_demand("aluminum", scenario, old_gdp=False, ssp=ssp) results[parname].append(df) # Special treatment for time-varying params diff --git a/message_ix_models/model/material/data_cement.py b/message_ix_models/model/material/data_cement.py index 46af899ae2..e87ac3480d 100644 --- a/message_ix_models/model/material/data_cement.py +++ b/message_ix_models/model/material/data_cement.py @@ -1,12 +1,12 @@ -from .data_util import read_sector_data, read_timeseries +import ixmp +import message_ix +import pandas as pd from collections import defaultdict -from pathlib import Path - -import pandas as pd -from .material_demand import material_demand_calc -from .util import read_config +from message_data.model.material.data_util import read_sector_data, read_timeseries +from message_data.model.material.material_demand import material_demand_calc +from message_data.model.material.util import read_config from message_ix_models import ScenarioInfo from message_ix import make_df from message_ix_models.util import ( @@ -175,10 +175,10 @@ def gen_data_cement(scenario, dry_run=False): # Load configuration context = read_config() config = read_config()["material"]["cement"] - + ssp = context["ssp"] # Information about scenario, e.g. node, year s_info = ScenarioInfo(scenario) - + context.datafile = "Global_steel_cement_MESSAGE.xlsx" # Techno-economic assumptions # TEMP: now add cement sector as well data_cement = read_sector_data(scenario, "cement") @@ -385,7 +385,7 @@ def gen_data_cement(scenario, dry_run=False): # time="year", # node=demand.node, # ) - df = material_demand_calc.derive_demand("cement", scenario, old_gdp=False) + df = material_demand_calc.derive_demand("cement", scenario, old_gdp=False, ssp=ssp) results[parname].append(df) # Add CCS as addon @@ -409,6 +409,11 @@ def gen_data_cement(scenario, dry_run=False): return results +if __name__ == "__main__": + mp = ixmp.Platform("local") + scenario = message_ix.Scenario(mp,"MESSAGEix-Materials", "baseline") + gen_data_cement(scenario) + # # load rpy2 modules # import rpy2.robjects as ro # from rpy2.robjects import pandas2ri diff --git a/message_ix_models/model/material/data_steel.py b/message_ix_models/model/material/data_steel.py index 2d25a0cf6c..07a51bed1d 100644 --- a/message_ix_models/model/material/data_steel.py +++ b/message_ix_models/model/material/data_steel.py @@ -115,7 +115,7 @@ def gen_data_steel(scenario, dry_run=False): # Load configuration context = read_config() config = context["material"]["steel"] - + ssp = context["ssp"] # Information about scenario, e.g. node, year s_info = ScenarioInfo(scenario) @@ -528,7 +528,7 @@ def gen_data_steel(scenario, dry_run=False): # Create external demand param parname = "demand" - df = material_demand_calc.derive_demand("steel", scenario, old_gdp=False) + df = material_demand_calc.derive_demand("steel", scenario, old_gdp=False, ssp=ssp) results[parname].append(df) # Concatenate to one data frame per parameter diff --git a/message_ix_models/model/material/material_demand/material_demand_calc.py b/message_ix_models/model/material/material_demand/material_demand_calc.py index 907f9e5222..c755f3fea5 100644 --- a/message_ix_models/model/material/material_demand/material_demand_calc.py +++ b/message_ix_models/model/material/material_demand/material_demand_calc.py @@ -10,8 +10,8 @@ file_al = "/demand_aluminum.xlsx" file_gdp = "/iamc_db ENGAGE baseline GDP PPP.xlsx" -giga = 10**9 -mega = 10**6 +giga = 10 ** 9 +mega = 10 ** 6 material_data = { "aluminum": {"dir": "aluminum", "file": "/demand_aluminum.xlsx"}, @@ -19,6 +19,33 @@ "cement": {"dir": "steel_cement", "file": "/CEMENT.BvR2010.xlsx"}, } +ssp_mode_map = { + "SSP1": "low", + "SSP2": "normal", + "SSP3": "high", + "SSP4": "normal", + "SSP5": "high", + "LED": "low", +} + +mode_modifiers_dict = { + "low": { + "steel": {"a": 0.85, "b": 0.95}, + "cement": {"a": 0.8}, + "aluminum": {"a": 0.85, "b": 0.95}, + }, + "normal": { + "steel": {"a": 1, "b": 1}, + "cement": {"a": 1}, + "aluminum": {"a": 1, "b": 1}, + }, + "high": { + "steel": {"a": 1.3, "b": 1}, + "cement": {"a": 1.3}, + "aluminum": {"a": 1.3, "b": 1}, + } +} + def steel_function(x, a, b, m): gdp_pcap, del_t = x @@ -96,9 +123,9 @@ def project_demand(df, phi, mu): df_demand = df.groupby("region", group_keys=False).apply( lambda group: group.assign( demand_pcap_base=group["demand.tot.base"].iloc[0] - * giga - / group["pop.mil"].iloc[0] - / mega + * giga + / group["pop.mil"].iloc[0] + / mega ) ) df_demand = df_demand.groupby("region", group_keys=False).apply( @@ -109,7 +136,7 @@ def project_demand(df, phi, mu): df_demand = df_demand.groupby("region", group_keys=False).apply( lambda group: group.assign( demand_pcap=group["demand_pcap0"] - + group["gap_base"] * gompertz(phi, mu, y=group["year"]) + + group["gap_base"] * gompertz(phi, mu, y=group["year"]) ) ) df_demand = ( @@ -221,7 +248,7 @@ def read_hist_mat_demand(material): pd.merge(df_raw_cons, df_pop.drop("region", axis=1), on=["reg_no", "year"]) .merge(df_gdp[["reg_no", "year", "gdp_pcap"]], on=["reg_no", "year"]) .assign( - cons_pcap=lambda x: x["consumption"] / x["pop"] / 10**6, + cons_pcap=lambda x: x["consumption"] / x["pop"] / 10 ** 6, del_t=lambda x: x["year"].astype(int) - 2010, ) .dropna() @@ -271,7 +298,7 @@ def read_gdp_ppp_from_scen(scen): return gdp -def derive_demand(material, scen, old_gdp=False): +def derive_demand(material, scen, old_gdp=False, ssp="SSP2"): datapath = message_ix_models.util.private_data_path("material") # read pop projection from scenario @@ -308,6 +335,12 @@ def derive_demand(material, scen, old_gdp=False): ydata=df_cons["cons_pcap"], p0=fitting_dict[material]["initial_guess"], ) + mode = ssp_mode_map[ssp] + print(f"adjust regression parameters according to mode: {mode}") + print(f"before adjustment: {params_opt}") + for idx, multiplier in enumerate(mode_modifiers_dict[mode][material].values()): + params_opt[idx] *= multiplier + print(f"before adjustment: {params_opt}") # prepare df for applying regression model and project demand df_all = pd.merge(df_pop, df_base_demand.drop(columns=["year"]), how="left") From 61fdca84afc37f9b4e4d598ba4723c4972b65faf Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Wed, 3 Apr 2024 10:16:28 +0200 Subject: [PATCH 703/774] Use "ssp" mix-models common cli parameter for materials ssp builds --- message_ix_models/model/material/__init__.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 11de0e1d5b..72e977f2bd 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -43,6 +43,8 @@ from message_data.model.material.data_methanol_new import gen_data_methanol_new from message_data.model.material.data_ammonia_new import gen_all_NH3_fert + +from message_ix_models.util.click import common_params log = logging.getLogger(__name__) DATA_FUNCTIONS_1 = [ @@ -187,7 +189,8 @@ def get_spec() -> Mapping[str, ScenarioInfo]: # Group to allow for multiple CLI subcommands under "material" @click.group("material") -def cli(): +@common_params("ssp") +def cli(ssp): """Model with materials accounting.""" @@ -232,7 +235,6 @@ def create_bare(context, regions, dry_run): @click.option( "--update_costs", default=False, - type=click.Choice([False, "SSP1", "SSP2", "SSP3", "SSP4", "SSP5", "LED"]), ) @click.pass_obj def build_scen(context, datafile, tag, mode, scenario_name, old_calib, update_costs): @@ -359,7 +361,7 @@ def build_scen(context, datafile, tag, mode, scenario_name, old_calib, update_co if update_costs: log.info(f"Updating costs with {message_ix_models.tools.costs.projections}") - inv, fix = gen_te_projections(scenario, update_costs) + inv, fix = gen_te_projections(scenario, context["ssp"]) scenario.check_out() scenario.add_par("fix_cost", fix) scenario.add_par("inv_cost", inv) @@ -801,4 +803,4 @@ def make_xls_input_vc_able(context, files): # path = private_data_path(str(element.split("/")[:-1])) # print(path, fname) # message_data.model.material.util.excel_to_csv(files) - retu + return From 4f73f35fcf86875f4e820da5b01387d31b507e2f Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Wed, 3 Apr 2024 16:56:15 +0200 Subject: [PATCH 704/774] Make petrochemical elasticity ssp dependent --- .../model/material/data_petro.py | 50 +++++++++++++------ 1 file changed, 35 insertions(+), 15 deletions(-) diff --git a/message_ix_models/model/material/data_petro.py b/message_ix_models/model/material/data_petro.py index df08e18c77..7d67edc59b 100644 --- a/message_ix_models/model/material/data_petro.py +++ b/message_ix_models/model/material/data_petro.py @@ -16,6 +16,23 @@ ) +ssp_mode_map = { + "SSP1": "CTS core", + "SSP2": "RTS core", + "SSP3": "RTS high", + "SSP4": "CTS high", + "SSP5": "RTS high", + "LED": "CTS core", # TODO: maybe move to OECD projection instead +} + +iea_elasticity_map = { + "CTS core": (0.75, 0.15), + "CTS high": (0.9, 0.45), + "RTS core": (0.75, 0.4), + "RTS high": (0.95, 0.7), +} + + def read_data_petrochemicals(scenario): """Read and clean data from :file:`petrochemicals_techno_economic.xlsx`.""" @@ -147,9 +164,9 @@ def get_demand_t1_with_income_elasticity( def gen_data_petro_chemicals(scenario, dry_run=False): # Load configuration - - config = read_config()["material"]["petro_chemicals"] - + context = read_config() + config = context["material"]["petro_chemicals"] + ssp = context["ssp"] # Information about scenario, e.g. node, year s_info = ScenarioInfo(scenario) @@ -375,21 +392,24 @@ def gen_data_petro_chemicals(scenario, dry_run=False): # Create external demand param # demand_HVC = derive_petro_demand(scenario) - default_gdp_elasticity = float(0.93) - context = read_config() + # default_gdp_elasticity = float(0.93) + # context = read_config() - df_pars = pd.read_excel( - private_data_path("material", "methanol", "methanol_sensitivity_pars.xlsx"), - sheet_name="Sheet1", - dtype=object, - ) - pars = df_pars.set_index("par").to_dict()["value"] - default_gdp_elasticity_2020 = pars["hvc_elasticity_2020"] - default_gdp_elasticity_2030 = pars["hvc_elasticity_2030"] - demand_HVC = gen_mock_demand_petro( + # df_pars = pd.read_excel( + # private_data_path("material", "methanol", "methanol_sensitivity_pars.xlsx"), + # sheet_name="Sheet1", + # dtype=object, + # ) + # pars = df_pars.set_index("par").to_dict()["value"] + # default_gdp_elasticity_2020 = pars["hvc_elasticity_2020"] + # default_gdp_elasticity_2030 = pars["hvc_elasticity_2030"] + + default_gdp_elasticity_2020, default_gdp_elasticity_2030 = iea_elasticity_map[ssp_mode_map[ssp]] + + demand_hvc = gen_mock_demand_petro( scenario, default_gdp_elasticity_2020, default_gdp_elasticity_2030 ) - results["demand"].append(demand_HVC) + results["demand"].append(demand_hvc) # df_e = make_df(paramname, level='final_material', commodity="ethylene", \ # value=demand_e.value, unit='t',year=demand_e.year, time='year', \ From 4fdd449ce6f29bbee22386282956f72dccc0cd64 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 9 Apr 2024 17:16:09 +0200 Subject: [PATCH 705/774] Revert hvc parametrization --- .../petrochemicals/petrochemicals_techno_economic.xlsx | 4 ++-- .../methanol_techno_economic.xlsx/growth_new_capacity_up.csv | 4 ++-- .../growth_new_capacity_up.csv | 4 ++-- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/message_ix_models/data/material/petrochemicals/petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/petrochemicals/petrochemicals_techno_economic.xlsx index 6910fc4920..922d021bde 100644 --- a/message_ix_models/data/material/petrochemicals/petrochemicals_techno_economic.xlsx +++ b/message_ix_models/data/material/petrochemicals/petrochemicals_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:e177ca9cd1a2293ed0eb9af6918bd7243cb5c9367ee7e68cae2005f7dd86ee46 -size 678278 +oid sha256:31db6a9e64a18f70db38fd36ac9dd1e2ac64e377117d4cecc738bcd6f7965ef7 +size 677693 diff --git a/message_ix_models/data/material/version control/methanol_techno_economic.xlsx/growth_new_capacity_up.csv b/message_ix_models/data/material/version control/methanol_techno_economic.xlsx/growth_new_capacity_up.csv index 4f523a5550..1ee7f52e6a 100644 --- a/message_ix_models/data/material/version control/methanol_techno_economic.xlsx/growth_new_capacity_up.csv +++ b/message_ix_models/data/material/version control/methanol_techno_economic.xlsx/growth_new_capacity_up.csv @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:05728184c3cbfd83dbdf6ff802143e571ae61ae765845b2c36a21fdc38154792 -size 2861 +oid sha256:2b5c5097d2498bb08cdb5859b794af693a9b3955aa53adaeef1b534f9b16daa2 +size 2859 diff --git a/message_ix_models/data/material/version control/methanol_techno_economic_high_demand.xlsx/growth_new_capacity_up.csv b/message_ix_models/data/material/version control/methanol_techno_economic_high_demand.xlsx/growth_new_capacity_up.csv index 4f523a5550..1ee7f52e6a 100644 --- a/message_ix_models/data/material/version control/methanol_techno_economic_high_demand.xlsx/growth_new_capacity_up.csv +++ b/message_ix_models/data/material/version control/methanol_techno_economic_high_demand.xlsx/growth_new_capacity_up.csv @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:05728184c3cbfd83dbdf6ff802143e571ae61ae765845b2c36a21fdc38154792 -size 2861 +oid sha256:2b5c5097d2498bb08cdb5859b794af693a9b3955aa53adaeef1b534f9b16daa2 +size 2859 From 7552406ed0ad3c840b3173bd9d5632875b90cb69 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 9 Apr 2024 17:16:33 +0200 Subject: [PATCH 706/774] Fix typo in print statement --- .../model/material/material_demand/material_demand_calc.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/message_ix_models/model/material/material_demand/material_demand_calc.py b/message_ix_models/model/material/material_demand/material_demand_calc.py index c755f3fea5..a45b80adf0 100644 --- a/message_ix_models/model/material/material_demand/material_demand_calc.py +++ b/message_ix_models/model/material/material_demand/material_demand_calc.py @@ -340,7 +340,7 @@ def derive_demand(material, scen, old_gdp=False, ssp="SSP2"): print(f"before adjustment: {params_opt}") for idx, multiplier in enumerate(mode_modifiers_dict[mode][material].values()): params_opt[idx] *= multiplier - print(f"before adjustment: {params_opt}") + print(f"after adjustment: {params_opt}") # prepare df for applying regression model and project demand df_all = pd.merge(df_pop, df_base_demand.drop(columns=["year"]), how="left") From fd16b9155b7c27e1f80b5860b711e1e6b5635564 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Wed, 10 Apr 2024 15:06:51 +0200 Subject: [PATCH 707/774] Generalize demand calculation for all petrochemicals --- .../material/petrochemicals/demand_petro.yaml | 36 ----- .../model/material/data_ammonia_new.py | 27 ++++ .../model/material/data_methanol.py | 54 +++++--- .../model/material/data_petro.py | 5 +- .../material_demand/material_demand_calc.py | 124 ++++++++++++++++-- 5 files changed, 184 insertions(+), 62 deletions(-) delete mode 100644 message_ix_models/data/material/petrochemicals/demand_petro.yaml diff --git a/message_ix_models/data/material/petrochemicals/demand_petro.yaml b/message_ix_models/data/material/petrochemicals/demand_petro.yaml deleted file mode 100644 index a1be4c05a8..0000000000 --- a/message_ix_models/data/material/petrochemicals/demand_petro.yaml +++ /dev/null @@ -1,36 +0,0 @@ -- R12_AFR: - year: 2020 - value: 2.4000 -- R12_RCPA: - year: 2020 - value: 0.44 -- R12_EEU: - year: 2020 - value: 3 -- R12_FSU: - year: 2020 - value: 5 -- R12_LAM: - year: 2020 - value: 11 -- R12_MEA: - year: 2020 - value: 40.3 -- R12_NAM: - year: 2020 - value: 49.8 -- R12_PAO: - year: 2020 - value: 11 -- R12_PAS: - year: 2020 - value: 37.5 -- R12_SAS: - year: 2020 - value: 10.7 -- R12_WEU: - year: 2020 - value: 29.2 -- R12_CHN: - year: 2020 - value: 50.5 \ No newline at end of file diff --git a/message_ix_models/model/material/data_ammonia_new.py b/message_ix_models/model/material/data_ammonia_new.py index 86a985fde0..84774b525e 100644 --- a/message_ix_models/model/material/data_ammonia_new.py +++ b/message_ix_models/model/material/data_ammonia_new.py @@ -5,6 +5,7 @@ from message_ix import make_df from message_ix_models.util import broadcast, same_node, private_data_path from message_data.model.material.util import read_config +from message_data.model.material.material_demand import material_demand_calc CONVERSION_FACTOR_NH3_N = 17 / 14 context = read_config() @@ -22,6 +23,23 @@ # float(0.65) # old default value +ssp_mode_map = { + "SSP1": "CTS core", + "SSP2": "RTS core", + "SSP3": "RTS high", + "SSP4": "CTS high", + "SSP5": "RTS high", + "LED": "CTS core", # TODO: move to even lower projection +} + +iea_elasticity_map = { + "CTS core": (0.3, 0.3), + "CTS high": (0.48, 0.48), + "RTS core": (0.3, 0.3), + "RTS high": (0.48, 0.48), +} + + def gen_all_NH3_fert(scenario, dry_run=False): return { **gen_data(scenario), @@ -527,6 +545,7 @@ def gen_resid_demand_NH3(scenario, gdp_elasticity): s_info = ScenarioInfo(scenario) modelyears = s_info.Y # s_info.Y is only for modeling years nodes = s_info.N + ssp = context["ssp"] def get_demand_t1_with_income_elasticity( demand_t0, income_t0, income_t1, elasticity @@ -601,6 +620,14 @@ def get_demand_t1_with_income_elasticity( node=(region_set + df_melt["Region"]), ) + # TODO: remove old approach once tested + default_gdp_elasticity_2020, default_gdp_elasticity_2030 = iea_elasticity_map[ + ssp_mode_map[ssp] + ] + df_residual = material_demand_calc.gen_demand_petro( + scenario, "NH3", default_gdp_elasticity_2020, default_gdp_elasticity_2030 + ) + return {"demand": df_residual} diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index 50c2aa88c3..79616c1104 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -2,13 +2,32 @@ import pandas as pd from message_ix import make_df -from message_ix_models.util import broadcast, same_node -from .util import read_config, combine_df_dictionaries +from message_ix_models.util import broadcast, same_node, private_data_path +from message_data.model.material.util import read_config, combine_df_dictionaries +from message_data.model.material.material_demand import material_demand_calc context = read_config() +ssp_mode_map = { + "SSP1": "CTS core", + "SSP2": "RTS core", + "SSP3": "RTS high", + "SSP4": "CTS high", + "SSP5": "RTS high", + "LED": "CTS core", # TODO: move to even lower projection +} + +iea_elasticity_map = { + "CTS core": (1.2, 0.25), + "CTS high": (1.3, 0.48), + "RTS core": (1.25, 0.35), + "RTS high": (1.4, 0.54), +} + + def gen_data_methanol(scenario): + ssp = context["ssp"] # read sensitivity file df_pars = pd.read_excel( context.get_local_path( @@ -163,20 +182,23 @@ def get_embodied_emi(row, pars, share_par): dict_t_d_fs, dict_t_d_fuel) - for i in scenario.par_list(): - try: - df = scenario.par(i, filters={"technology": ["meth_ng", - "meth_coal", "meth_ng_ccs", - "meth_coal_ccs", "meth_exp", - "meth_imp", "meth_trd", "meth_bal", "meth_t_d"], - "mode": "M1"}) - if df.size != 0: - scenario.remove_par(i, df) - except: - pass - - - df_final = gen_meth_residual_demand(pars["methanol_elasticity_2020"], pars["methanol_elasticity_2030"]) + # for i in scenario.par_list(): + # try: + # df = scenario.par(i, filters={"technology": ["meth_ng", + # "meth_coal", "meth_ng_ccs", + # "meth_coal_ccs", "meth_exp", + # "meth_imp", "meth_trd", "meth_bal", "meth_t_d"], + # "mode": "M1"}) + # if df.size != 0: + # scenario.remove_par(i, df) + # except: + # pass + default_gdp_elasticity_2020, default_gdp_elasticity_2030 = iea_elasticity_map[ + ssp_mode_map[ssp] + ] + df_final = material_demand_calc.gen_demand_petro( + scenario, "methanol", default_gdp_elasticity_2020, default_gdp_elasticity_2030 + ) df_final["value"] = df_final["value"].apply( lambda x: x * pars["methanol_resid_demand_share"]) new_dict2["demand"] = df_final diff --git a/message_ix_models/model/material/data_petro.py b/message_ix_models/model/material/data_petro.py index 7d67edc59b..0ba8cafada 100644 --- a/message_ix_models/model/material/data_petro.py +++ b/message_ix_models/model/material/data_petro.py @@ -4,6 +4,7 @@ from collections import defaultdict from message_data.model.material.data_util import read_timeseries, read_rel from message_data.model.material.util import read_config +from message_data.model.material.material_demand import material_demand_calc from message_ix_models import ScenarioInfo from message_ix import make_df from message_ix_models.util import ( @@ -406,8 +407,8 @@ def gen_data_petro_chemicals(scenario, dry_run=False): default_gdp_elasticity_2020, default_gdp_elasticity_2030 = iea_elasticity_map[ssp_mode_map[ssp]] - demand_hvc = gen_mock_demand_petro( - scenario, default_gdp_elasticity_2020, default_gdp_elasticity_2030 + demand_hvc = material_demand_calc.gen_demand_petro( + scenario, "HVC", default_gdp_elasticity_2020, default_gdp_elasticity_2030 ) results["demand"].append(demand_hvc) diff --git a/message_ix_models/model/material/material_demand/material_demand_calc.py b/message_ix_models/model/material/material_demand/material_demand_calc.py index a45b80adf0..377fe4304e 100644 --- a/message_ix_models/model/material/material_demand/material_demand_calc.py +++ b/message_ix_models/model/material/material_demand/material_demand_calc.py @@ -5,18 +5,34 @@ from scipy.optimize import curve_fit import yaml +from message_data.model.material.util import read_config +from message_ix_models import ScenarioInfo +from message_ix import make_df +from message_ix_models.util import ( + broadcast, + make_io, + make_matched_dfs, + same_node, + add_par_data, + private_data_path, +) + + file_cement = "/CEMENT.BvR2010.xlsx" file_steel = "/STEEL_database_2012.xlsx" file_al = "/demand_aluminum.xlsx" file_gdp = "/iamc_db ENGAGE baseline GDP PPP.xlsx" -giga = 10 ** 9 -mega = 10 ** 6 +giga = 10**9 +mega = 10**6 material_data = { "aluminum": {"dir": "aluminum", "file": "/demand_aluminum.xlsx"}, "steel": {"dir": "steel_cement", "file": "/STEEL_database_2012.xlsx"}, "cement": {"dir": "steel_cement", "file": "/CEMENT.BvR2010.xlsx"}, + "HVC": {"dir": "petrochemicals"}, + "NH3": {"dir": "ammonia"}, + "methanol": {"dir": "methanol"}, } ssp_mode_map = { @@ -43,7 +59,7 @@ "steel": {"a": 1.3, "b": 1}, "cement": {"a": 1.3}, "aluminum": {"a": 1.3, "b": 1}, - } + }, } @@ -123,9 +139,9 @@ def project_demand(df, phi, mu): df_demand = df.groupby("region", group_keys=False).apply( lambda group: group.assign( demand_pcap_base=group["demand.tot.base"].iloc[0] - * giga - / group["pop.mil"].iloc[0] - / mega + * giga + / group["pop.mil"].iloc[0] + / mega ) ) df_demand = df_demand.groupby("region", group_keys=False).apply( @@ -136,7 +152,7 @@ def project_demand(df, phi, mu): df_demand = df_demand.groupby("region", group_keys=False).apply( lambda group: group.assign( demand_pcap=group["demand_pcap0"] - + group["gap_base"] * gompertz(phi, mu, y=group["year"]) + + group["gap_base"] * gompertz(phi, mu, y=group["year"]) ) ) df_demand = ( @@ -248,7 +264,7 @@ def read_hist_mat_demand(material): pd.merge(df_raw_cons, df_pop.drop("region", axis=1), on=["reg_no", "year"]) .merge(df_gdp[["reg_no", "year", "gdp_pcap"]], on=["reg_no", "year"]) .assign( - cons_pcap=lambda x: x["consumption"] / x["pop"] / 10 ** 6, + cons_pcap=lambda x: x["consumption"] / x["pop"] / 10**6, del_t=lambda x: x["year"].astype(int) - 2010, ) .dropna() @@ -369,3 +385,95 @@ def derive_demand(material, scen, old_gdp=False, ssp="SSP2"): # TODO: correct unit would be Mt but might not be registered on database df_final = message_ix.make_df("demand", **df_final) return df_final + + +def gen_demand_petro(scenario, chemical, gdp_elasticity_2020, gdp_elasticity_2030): + chemicals_implemented = ["HVC", "methanol", "NH3"] + if chemical not in chemicals_implemented: + raise ValueError( + f"'{chemical}' not supported. Choose one of {chemicals_implemented}" + ) + context = read_config() + s_info = ScenarioInfo(scenario) + modelyears = s_info.Y # s_info.Y is only for modeling years + fy = scenario.firstmodelyear + nodes = s_info.N + + def get_demand_t1_with_income_elasticity( + demand_t0, income_t0, income_t1, elasticity + ): + return ( + elasticity * demand_t0.mul(((income_t1 - income_t0) / income_t0), axis=0) + ) + demand_t0 + + gdp_mer = scenario.par("bound_activity_up", {"technology": "GDP"}) + mer_to_ppp = pd.read_csv( + private_data_path("material", "other", "mer_to_ppp_default.csv") + ).set_index(["node", "year"]) + # mer_to_ppp = scenario.par("MERtoPPP").set_index("node", "year") TODO: might need to be re-activated for different SSPs + gdp_mer = gdp_mer.merge( + mer_to_ppp.reset_index()[["node", "year", "value"]], + left_on=["node_loc", "year_act"], + right_on=["node", "year"], + ) + gdp_mer["gdp_ppp"] = gdp_mer["value_y"] * gdp_mer["value_x"] + gdp_mer = gdp_mer[["year", "node_loc", "gdp_ppp"]].reset_index() + gdp_mer["Region"] = gdp_mer["node_loc"] # .str.replace("R12_", "") + df_gdp_ts = gdp_mer.pivot( + index="Region", columns="year", values="gdp_ppp" + ).reset_index() + num_cols = [i for i in df_gdp_ts.columns if type(i) == int] + hist_yrs = [i for i in num_cols if i < fy] + df_gdp_ts = ( + df_gdp_ts.drop([i for i in hist_yrs if i in df_gdp_ts.columns], axis=1) + .set_index("Region") + .sort_index() + ) + + from message_data.model.material.material_demand.material_demand_calc import ( + read_base_demand, + ) + + df_demand_2020 = read_base_demand( + private_data_path() + / "material" + / f"{material_data[chemical]['dir']}/demand_{chemical}.yaml" + ) + df_demand_2020 = df_demand_2020.rename({"region": "Region"}, axis=1) + df_demand = df_demand_2020.pivot(index="Region", columns="year", values="value") + dem_next_yr = df_demand + + for i in range(len(modelyears) - 1): + income_year1 = modelyears[i] + income_year2 = modelyears[i + 1] + + if income_year2 >= 2030: + dem_next_yr = get_demand_t1_with_income_elasticity( + dem_next_yr, + df_gdp_ts[income_year1], + df_gdp_ts[income_year2], + gdp_elasticity_2030, + ) + else: + dem_next_yr = get_demand_t1_with_income_elasticity( + dem_next_yr, + df_gdp_ts[income_year1], + df_gdp_ts[income_year2], + gdp_elasticity_2020, + ) + df_demand[income_year2] = dem_next_yr + + df_melt = df_demand.melt(ignore_index=False).reset_index() + + level = "demand" if chemical == "HVC" else "final_material" + + return make_df( + "demand", + unit="t", + level=level, + value=df_melt.value, + time="year", + commodity=chemical, + year=df_melt.year, + node=df_melt["Region"], + ) From c61e842ef22a4573a3f0b683c6d13728cb6625e6 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Wed, 10 Apr 2024 15:08:27 +0200 Subject: [PATCH 708/774] Fix some differences between old and new methanol module and apply black --- .../data/material/methanol/h2_elec_fs.xlsx | 3 + .../data/material/methanol/h2_elec_fuel.xlsx | 3 + .../methanol/location factor collection.xlsx | 3 + .../meth_bio_techno_economic_new.xlsx | 3 + .../meth_bio_techno_economic_new_ccs.xlsx | 3 + .../methanol/meth_coal_additions.xlsx | 3 + .../methanol/meth_coal_additions_fs.xlsx | 3 + .../methanol/meth_h2_techno_economic_fs.xlsx | 3 + .../meth_h2_techno_economic_fuel.xlsx | 3 + .../methanol/meth_ng_techno_economic.xlsx | 3 + .../methanol/meth_ng_techno_economic_fs.xlsx | 3 + .../data/material/methanol/meth_t_d_fuel.xlsx | 3 + .../methanol/meth_t_d_material_pars.xlsx | 3 + .../methanol/meth_trade_techno_economic.xlsx | 3 + .../meth_trade_techno_economic_fs.xlsx | 3 + .../model/material/data_methanol.py | 342 +++++++++++------- 16 files changed, 257 insertions(+), 130 deletions(-) create mode 100644 message_ix_models/data/material/methanol/h2_elec_fs.xlsx create mode 100644 message_ix_models/data/material/methanol/h2_elec_fuel.xlsx create mode 100644 message_ix_models/data/material/methanol/location factor collection.xlsx create mode 100644 message_ix_models/data/material/methanol/meth_bio_techno_economic_new.xlsx create mode 100644 message_ix_models/data/material/methanol/meth_bio_techno_economic_new_ccs.xlsx create mode 100644 message_ix_models/data/material/methanol/meth_coal_additions.xlsx create mode 100644 message_ix_models/data/material/methanol/meth_coal_additions_fs.xlsx create mode 100644 message_ix_models/data/material/methanol/meth_h2_techno_economic_fs.xlsx create mode 100644 message_ix_models/data/material/methanol/meth_h2_techno_economic_fuel.xlsx create mode 100644 message_ix_models/data/material/methanol/meth_ng_techno_economic.xlsx create mode 100644 message_ix_models/data/material/methanol/meth_ng_techno_economic_fs.xlsx create mode 100644 message_ix_models/data/material/methanol/meth_t_d_fuel.xlsx create mode 100644 message_ix_models/data/material/methanol/meth_t_d_material_pars.xlsx create mode 100644 message_ix_models/data/material/methanol/meth_trade_techno_economic.xlsx create mode 100644 message_ix_models/data/material/methanol/meth_trade_techno_economic_fs.xlsx diff --git a/message_ix_models/data/material/methanol/h2_elec_fs.xlsx b/message_ix_models/data/material/methanol/h2_elec_fs.xlsx new file mode 100644 index 0000000000..3fea1c447f --- /dev/null +++ b/message_ix_models/data/material/methanol/h2_elec_fs.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:15c8b80c4346f66e38565493374dee4ce68435c160795b81ead4fb5529edbf9a +size 462513 diff --git a/message_ix_models/data/material/methanol/h2_elec_fuel.xlsx b/message_ix_models/data/material/methanol/h2_elec_fuel.xlsx new file mode 100644 index 0000000000..e43c856fca --- /dev/null +++ b/message_ix_models/data/material/methanol/h2_elec_fuel.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6296e379d3d35af74ac789642f7db69bb7f1c3a1eab5a69d39e4cce709e2d34e +size 462800 diff --git a/message_ix_models/data/material/methanol/location factor collection.xlsx 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a/message_ix_models/data/material/methanol/meth_trade_techno_economic.xlsx b/message_ix_models/data/material/methanol/meth_trade_techno_economic.xlsx new file mode 100644 index 0000000000..56b90b945e --- /dev/null +++ b/message_ix_models/data/material/methanol/meth_trade_techno_economic.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:244a41e0b3f91de51916070d209cecc9826e64b9c1b59c314e20062d7313811b +size 180820 diff --git a/message_ix_models/data/material/methanol/meth_trade_techno_economic_fs.xlsx b/message_ix_models/data/material/methanol/meth_trade_techno_economic_fs.xlsx new file mode 100644 index 0000000000..2c7cdaf807 --- /dev/null +++ b/message_ix_models/data/material/methanol/meth_trade_techno_economic_fs.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cc6ac6404810dda477ab6669821c2c1509334952b9a197be2ecc504e019a2f3a +size 99973 diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index 79616c1104..3aa698ba53 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -30,20 +30,18 @@ def gen_data_methanol(scenario): ssp = context["ssp"] # read sensitivity file df_pars = pd.read_excel( - context.get_local_path( - "material", "methanol", "methanol_sensitivity_pars.xlsx" - ), + private_data_path("material", "methanol", "methanol_sensitivity_pars.xlsx"), sheet_name="Sheet1", dtype=object, ) pars = df_pars.set_index("par").to_dict()["value"] - # read new meth tec parameters meth_bio_dict = gen_data_meth_bio(scenario) meth_bio_ccs_dict = gen_meth_bio_ccs(scenario) - meth_fs_dic = combine_df_dictionaries(gen_data_meth_bio(scenario), - gen_meth_bio_ccs(scenario)) + meth_fs_dic = combine_df_dictionaries( + gen_data_meth_bio(scenario), gen_meth_bio_ccs(scenario) + ) meth_fuel_dic = combine_df_dictionaries(meth_bio_dict, meth_bio_ccs_dict) for k in meth_fs_dic.keys(): df_fuel = meth_fuel_dic[k] @@ -58,17 +56,26 @@ def gen_data_methanol(scenario): meth_fs_dic = combine_df_dictionaries(meth_fs_dic, meth_h2_fs_dict) df = meth_fs_dic["output"] - df.loc[(df["commodity"] == "methanol") & (df["level"] == "primary"), "level"] = "primary_material" + df.loc[ + (df["commodity"] == "methanol") & (df["level"] == "primary"), "level" + ] = "primary_material" meth_fs_dic["output"] = df - - #add meth prod fs mode + # add meth prod fs mode par_dict_fs = {} for i in scenario.par_list(): try: - df = scenario.par(i, filters={"technology": ["meth_ng", - "meth_coal", "meth_ng_ccs", - "meth_coal_ccs"]}) + df = scenario.par( + i, + filters={ + "technology": [ + "meth_ng", + "meth_coal", + "meth_ng_ccs", + "meth_coal_ccs", + ] + }, + ) if df.size != 0: par_dict_fs[i] = df except: @@ -83,9 +90,17 @@ def gen_data_methanol(scenario): par_dict = {} for i in scenario.par_list(): try: - df = scenario.par(i, filters={"technology": ["meth_ng", - "meth_coal", "meth_ng_ccs", - "meth_coal_ccs"]}) + df = scenario.par( + i, + filters={ + "technology": [ + "meth_ng", + "meth_coal", + "meth_ng_ccs", + "meth_coal_ccs", + ] + }, + ) if df.size != 0: par_dict[i] = df except: @@ -96,8 +111,7 @@ def gen_data_methanol(scenario): df = par_dict["output"] par_dict["output"] = df - - #add meth_bal fs mode + # add meth_bal fs mode bal_fs_dict = {} bal_fuel_dict = {} for i in scenario.par_list(): @@ -120,15 +134,14 @@ def gen_data_methanol(scenario): df.loc[df["commodity"] == "methanol", "level"] = "secondary_material" bal_fs_dict["output"] = df - # add meth trade fs mode trade_dict_fs = {} trade_dict_fuel = {} for i in scenario.par_list(): try: - df = scenario.par(i, filters={"technology": ["meth_imp", - "meth_exp", - "meth_trd"]}) + df = scenario.par( + i, filters={"technology": ["meth_imp", "meth_exp", "meth_trd"]} + ) if df.size != 0: trade_dict_fs[i] = df trade_dict_fuel[i] = df.copy(deep=True) @@ -140,7 +153,7 @@ def gen_data_methanol(scenario): trade_dict_fuel[i]["mode"] = "fuel" df = trade_dict_fs["output"] - df.loc[df["technology"]=="meth_imp", "level"] = "secondary_material" + df.loc[df["technology"] == "meth_imp", "level"] = "secondary_material" df.loc[df["technology"] == "meth_exp", "level"] = "export_fs" df.loc[df["technology"] == "meth_trd", "level"] = "import_fs" trade_dict_fs["output"] = df @@ -151,13 +164,12 @@ def gen_data_methanol(scenario): df.loc[df["technology"] == "meth_exp", "level"] = "primary_material" trade_dict_fs["input"] = df - dict_t_d_fs = pd.read_excel( - context.get_local_path("material", "methanol", "meth_t_d_material_pars.xlsx"), + private_data_path("material", "methanol", "meth_t_d_material_pars.xlsx"), sheet_name=None, ) dict_t_d_fuel = pd.read_excel( - context.get_local_path("material", "methanol", "meth_t_d_fuel.xlsx"), + private_data_path("material", "methanol", "meth_t_d_fuel.xlsx"), sheet_name=None, ) @@ -170,17 +182,27 @@ def get_embodied_emi(row, pars, share_par): share = 0.5 return row["value"] * (1 - share) * pars[share_par] - df_rel_meth = df_rel.loc[df_rel["technology"] == "meth_t_d",:] - df_rel_meth["value"] = df_rel_meth.apply(lambda x: get_embodied_emi(x, pars, "meth_plastics_share"), axis=1) - df_rel_hvc = df_rel.loc[df_rel["technology"] == "steam_cracker_petro",:] - df_rel_hvc["value"] = df_rel_hvc.apply(lambda x: get_embodied_emi(x, pars, "hvc_plastics_share"), axis=1) + df_rel_meth = df_rel.loc[df_rel["technology"] == "meth_t_d", :] + df_rel_meth["value"] = df_rel_meth.apply( + lambda x: get_embodied_emi(x, pars, "meth_plastics_share"), axis=1 + ) + df_rel_hvc = df_rel.loc[df_rel["technology"] == "steam_cracker_petro", :] + df_rel_hvc["value"] = df_rel_hvc.apply( + lambda x: get_embodied_emi(x, pars, "hvc_plastics_share"), axis=1 + ) dict_t_d_fs["relation_activity"] = pd.concat([df_rel_meth, df_rel_hvc]) - new_dict2 = combine_df_dictionaries(meth_fuel_dic, meth_fs_dic, - par_dict_fs, par_dict, - bal_fs_dict, bal_fuel_dict, - trade_dict_fuel, trade_dict_fs, - dict_t_d_fs, dict_t_d_fuel) - + new_dict2 = combine_df_dictionaries( + meth_fuel_dic, + meth_fs_dic, + par_dict_fs, + par_dict, + bal_fs_dict, + bal_fuel_dict, + trade_dict_fuel, + trade_dict_fs, + dict_t_d_fs, + dict_t_d_fuel, + ) # for i in scenario.par_list(): # try: @@ -200,7 +222,8 @@ def get_embodied_emi(row, pars, share_par): scenario, "methanol", default_gdp_elasticity_2020, default_gdp_elasticity_2030 ) df_final["value"] = df_final["value"].apply( - lambda x: x * pars["methanol_resid_demand_share"]) + lambda x: x * pars["methanol_resid_demand_share"] + ) new_dict2["demand"] = df_final new_dict2 = combine_df_dictionaries(new_dict2, add_meth_hist_act()) @@ -210,10 +233,13 @@ def get_embodied_emi(row, pars, share_par): resin_dict = gen_data_meth_chemicals(scenario, "Resins") chemicals_dict = combine_df_dictionaries(mto_dict, ch2o_dict, resin_dict) - # generate resin demand from STURM results df_comm = gen_resin_demand( - scenario, pars["resin_share"], "comm", "REF", "SSP2", #pars["wood_scenario"], #pars["pathway"] + scenario, + pars["resin_share"], + "comm", + "REF", + "SSP2", # pars["wood_scenario"], #pars["pathway"] ) df_resid = gen_resin_demand( scenario, @@ -226,23 +252,24 @@ def get_embodied_emi(row, pars, share_par): df_resin_demand["value"] = df_comm["value"] + df_resid["value"] new_dict2["demand"] = pd.concat([new_dict2["demand"], df_resin_demand]) - # modify loil_trp inputs - new_dict2 = combine_df_dictionaries(new_dict2, - add_methanol_fuel_additives(scenario)) + new_dict2 = combine_df_dictionaries( + new_dict2, add_methanol_fuel_additives(scenario) + ) # fix efficiency of meth_t_d - df = scenario.par("input", filters={"technology": "meth_t_d"}) - df["value"] = 1 - new_dict2["input"] = pd.concat([new_dict2["input"], df]) + # df = scenario.par("input", filters={"technology": "meth_t_d"}) + # df["value"] = 1 + # new_dict2["input"] = pd.concat([new_dict2["input"], df]) # update production costs with IEA data if pars["update_old_tecs"]: cost_dict = update_methanol_costs(scenario) new_dict2 = combine_df_dictionaries(new_dict2, cost_dict) - new_dict2 = combine_df_dictionaries(new_dict2, add_meth_trade_historic(), - chemicals_dict, add_meth_tec_vintages()) + new_dict2 = combine_df_dictionaries( + new_dict2, add_meth_trade_historic(), chemicals_dict, add_meth_tec_vintages() + ) for i in new_dict2.keys(): new_dict2[i] = new_dict2[i].drop_duplicates() @@ -251,20 +278,24 @@ def get_embodied_emi(row, pars, share_par): if pars["mtbe_scenario"] == "phase-out": new_dict2 = combine_df_dictionaries(new_dict2, add_mtbe_act_bound(scenario)) - h2_modes = ["fs", "fuel"] for mode in h2_modes: - new_dict2 = combine_df_dictionaries(new_dict2, pd.read_excel( - context.get_local_path("material", "methanol", f"h2_elec_{mode}.xlsx"), - sheet_name=None, - )) + new_dict2 = combine_df_dictionaries( + new_dict2, + pd.read_excel( + private_data_path("material", "methanol", f"h2_elec_{mode}.xlsx"), + sheet_name=None, + ), + ) return new_dict2 def gen_data_meth_h2(mode): h2_par_dict = pd.read_excel( - context.get_local_path("material", "methanol", f"meth_h2_techno_economic_{mode}.xlsx"), + private_data_path( + "material", "methanol", f"meth_h2_techno_economic_{mode}.xlsx" + ), sheet_name=None, ) if "inv_cost" in h2_par_dict.keys(): @@ -275,10 +306,12 @@ def gen_data_meth_h2(mode): def gen_data_meth_bio(scenario): df_bio = pd.read_excel( - context.get_local_path("material", "methanol", "meth_bio_techno_economic_new.xlsx"), + private_data_path("material", "methanol", "meth_bio_techno_economic_new.xlsx"), sheet_name=None, ) - coal_ratio = get_cost_ratio_2020(scenario, "meth_coal", "fix_cost")#.drop("value", axis=1) + coal_ratio = get_cost_ratio_2020( + scenario, "meth_coal", "fix_cost" + ) # .drop("value", axis=1) merge = df_bio["fix_cost"].merge( on=["node_loc", "year_vtg", "year_act"], right=coal_ratio.drop(["technology", "unit", "value"], axis=1), @@ -288,24 +321,32 @@ def gen_data_meth_bio(scenario): ) df_bio_inv = df_bio["inv_cost"] for y in df_bio["inv_cost"]["year_vtg"].unique(): - coal_ratio = get_cost_ratio_2020(scenario, "meth_coal", "inv_cost", ref_reg="R12_WEU", year=y).drop("value", axis=1) + coal_ratio = get_cost_ratio_2020( + scenario, "meth_coal", "inv_cost", ref_reg="R12_WEU", year=y + ).drop("value", axis=1) merge = df_bio_inv.loc[df_bio_inv["year_vtg"] == y].merge( on=["node_loc", "year_vtg"], right=coal_ratio.drop(["technology", "unit"], axis=1), ) - df_bio_inv.loc[df_bio_inv["year_vtg"] == y, "value"] = merge.assign(value=lambda x: x["value"] * x["ratio"]).drop( - "ratio", axis=1 - )["value"].values + df_bio_inv.loc[df_bio_inv["year_vtg"] == y, "value"] = ( + merge.assign(value=lambda x: x["value"] * x["ratio"]) + .drop("ratio", axis=1)["value"] + .values + ) df_bio["inv_cost"] = df_bio_inv return df_bio def gen_meth_bio_ccs(scenario): df_bio = pd.read_excel( - context.get_local_path("material", "methanol", "meth_bio_techno_economic_new_ccs.xlsx"), + private_data_path( + "material", "methanol", "meth_bio_techno_economic_new_ccs.xlsx" + ), sheet_name=None, ) - coal_ratio = get_cost_ratio_2020(scenario, "meth_coal_ccs", "fix_cost")#.drop("value", axis=1) + coal_ratio = get_cost_ratio_2020( + scenario, "meth_coal_ccs", "fix_cost" + ) # .drop("value", axis=1) merge = df_bio["fix_cost"].merge( on=["node_loc", "year_vtg", "year_act"], right=coal_ratio.drop(["technology", "unit", "value"], axis=1), @@ -315,33 +356,35 @@ def gen_meth_bio_ccs(scenario): ) df_bio_inv = df_bio["inv_cost"] for y in df_bio["inv_cost"]["year_vtg"].unique(): - coal_ratio = get_cost_ratio_2020(scenario, "meth_coal_ccs", "inv_cost", ref_reg="R12_WEU", year=y).drop("value", axis=1) + coal_ratio = get_cost_ratio_2020( + scenario, "meth_coal_ccs", "inv_cost", ref_reg="R12_WEU", year=y + ).drop("value", axis=1) merge = df_bio_inv.loc[df_bio_inv["year_vtg"] == y].merge( on=["node_loc", "year_vtg"], right=coal_ratio.drop(["technology", "unit"], axis=1), ) - df_bio_inv.loc[df_bio_inv["year_vtg"] == y, "value"] = merge.assign(value=lambda x: x["value"] * x["ratio"]).drop( - "ratio", axis=1 - )["value"].values + df_bio_inv.loc[df_bio_inv["year_vtg"] == y, "value"] = ( + merge.assign(value=lambda x: x["value"] * x["ratio"]) + .drop("ratio", axis=1)["value"] + .values + ) df_bio["inv_cost"] = df_bio_inv return df_bio def gen_data_meth_chemicals(scenario, chemical): df = pd.read_excel( - context.get_local_path("material", "methanol", "collection files", "MTO data collection.xlsx"), + private_data_path( + "material", "methanol", "collection files", "MTO data collection.xlsx" + ), sheet_name=chemical, usecols=[1, 2, 3, 4, 6, 7], ) # exclude emissions for now if chemical == "MTO": - df = df.iloc[ - :15, - ] + df = df.iloc[:15,] if chemical == "Formaldehyde": - df = df.iloc[ - :10, - ] + df = df.iloc[:10,] common = dict( # commodity="NH3", @@ -357,8 +400,8 @@ def gen_data_meth_chemicals(scenario, chemical): all_years = scenario.vintage_and_active_years() all_years = all_years[all_years["year_vtg"] > 1990] - nodes = scenario.set("node")[1:] - nodes = nodes.drop(5).reset_index(drop=True) + drop_regs = ["R12_GLB", "World"] + nodes = [i for i in scenario.set("node").tolist() if i not in drop_regs] par_dict = {k: pd.DataFrame() for k in (df["parameter"])} for i in df["parameter"]: @@ -386,7 +429,7 @@ def gen_data_meth_chemicals(scenario, chemical): "time": "year", "unit": "???", } - nodes_ex_chn = nodes.tolist() + nodes_ex_chn = nodes nodes_ex_chn.remove("R12_CHN") bound_dict = { @@ -411,22 +454,34 @@ def gen_data_meth_chemicals(scenario, chemical): "bound_activity_lo", year_act=2020, value=10, **hist_dict ) par_dict["bound_activity_up"] = make_df( - "bound_activity_up", year_act=2020, value=12, **hist_dict + "bound_activity_up", year_act=[2020, 2025], value=[12, 15], **hist_dict ) - par_dict["bound_activity_lo"] = make_df( - "bound_activity_lo", year_act=2020, value=0, **bound_dict + par_dict["bound_activity_lo"] = pd.concat( + [ + par_dict["bound_activity_lo"], + make_df("bound_activity_lo", year_act=2020, value=0, **bound_dict), + ] ) - par_dict["bound_activity_up"] = make_df( - "bound_activity_up", year_act=2020, value=0, **bound_dict + par_dict["bound_activity_up"] = pd.concat( + [ + par_dict["bound_activity_up"], + make_df("bound_activity_up", year_act=2020, value=0, **bound_dict), + ] ) df = par_dict["growth_activity_lo"] - par_dict["growth_activity_lo"] = df[~((df["node_loc"] == "R12_CHN") & (df["year_act"] == 2020))] + par_dict["growth_activity_lo"] = df[ + ~((df["node_loc"] == "R12_CHN") & (df["year_act"] == 2020)) + ] df = par_dict["growth_activity_up"] - par_dict["growth_activity_up"] = df[~((df["node_loc"] == "R12_CHN") & (df["year_act"] == 2020))] + par_dict["growth_activity_up"] = df[ + ~((df["node_loc"] == "R12_CHN") & (df["year_act"] == 2020)) + ] df = par_dict["initial_activity_up"] - par_dict["initial_activity_up"] = df[~((df["node_loc"] == "R12_CHN"))]# & (df["year_act"] == 2020))] - #par_dict.pop("growth_activity_up") - #par_dict.pop("growth_activity_lo") + par_dict["initial_activity_up"] = df[ + ~((df["node_loc"] == "R12_CHN")) + ] # & (df["year_act"] == 2020))] + # par_dict.pop("growth_activity_up") + # par_dict.pop("growth_activity_lo") par_dict["inv_cost"] = update_costs_with_loc_factor(par_dict["inv_cost"]) par_dict["fix_cost"] = update_costs_with_loc_factor(par_dict["fix_cost"]) @@ -435,10 +490,11 @@ def gen_data_meth_chemicals(scenario, chemical): def add_methanol_fuel_additives(scenario): par_dict_loil = {} - pars = ['output', 'var_cost', 'relation_activity', 'input', 'emission_factor'] + pars = ["output", "var_cost", "relation_activity", "input", "emission_factor"] if "loil_trp" not in scenario.set("technology").to_list(): print( - "It seems that the selected scenario does not contain the technology: loil_trp. Methanol fuel blending could not be calculated and was skipped") + "It seems that the selected scenario does not contain the technology: loil_trp. Methanol fuel blending could not be calculated and was skipped" + ) return par_dict_loil for i in pars: try: @@ -448,25 +504,31 @@ def add_methanol_fuel_additives(scenario): except: pass - par_dict_loil["input"] = par_dict_loil["input"][par_dict_loil["input"]["commodity"] == "lightoil"] + par_dict_loil["input"] = par_dict_loil["input"][ + par_dict_loil["input"]["commodity"] == "lightoil" + ] df_mtbe = pd.read_excel( - context.get_local_path( - "material", "methanol", "collection files","Methanol production statistics (version 1).xlsx" + private_data_path( + "material", + "methanol", + "collection files", + "Methanol production statistics (version 1).xlsx", ), # usecols=[1,2,3,4,6,7], skiprows=[i for i in range(66)], sheet_name="MTBE calc", ) - df_mtbe = df_mtbe.iloc[ - 1:13, - ] + df_mtbe = df_mtbe.iloc[1:13,] df_mtbe["node_loc"] = "R12_" + df_mtbe["node_loc"] df_mtbe = df_mtbe[["node_loc", "% share on trp"]] df_biodiesel = pd.read_excel( - context.get_local_path( - "material", "methanol", "collection files","Methanol production statistics (version 1).xlsx" + private_data_path( + "material", + "methanol", + "collection files", + "Methanol production statistics (version 1).xlsx", ), skiprows=[i for i in range(38)], usecols=[1, 2], @@ -480,15 +542,22 @@ def add_methanol_fuel_additives(scenario): def get_meth_share(df, node): return df[df["node_loc"] == node["node_loc"]]["value"].values[0] + df_loil_meth = par_dict_loil["input"].copy(deep=True) - df_loil_meth["value"] = df_loil_meth.apply(lambda x: get_meth_share(df_total, x), axis=1) + df_loil_meth["value"] = df_loil_meth.apply( + lambda x: get_meth_share(df_total, x), axis=1 + ) df_loil_meth["commodity"] = "methanol" - par_dict_loil["input"]["value"] = par_dict_loil["input"]["value"] - df_loil_meth["value"] + par_dict_loil["input"]["value"] = ( + par_dict_loil["input"]["value"] - df_loil_meth["value"] + ) df_loil_eth = df_loil_meth.copy(deep=True) df_loil_eth["commodity"] = "ethanol" df_loil_eth["mode"] = "ethanol" - df_loil_eth["value"] = df_loil_eth["value"] * 1.6343 # energy ratio methanol/ethanol in MTBE vs ETBE + df_loil_eth["value"] = ( + df_loil_eth["value"] * 1.6343 + ) # energy ratio methanol/ethanol in MTBE vs ETBE df_loil_eth_mode = par_dict_loil["input"].copy(deep=True) df_loil_eth_mode["mode"] = "ethanol" @@ -499,17 +568,19 @@ def get_meth_share(df, node): for i in par_dict_loil_eth.keys(): par_dict_loil_eth[i]["mode"] = "ethanol" - df_input = pd.concat([par_dict_loil["input"], df_loil_meth, df_loil_eth, df_loil_eth_mode]) + df_input = pd.concat( + [par_dict_loil["input"], df_loil_meth, df_loil_eth, df_loil_eth_mode] + ) return combine_df_dictionaries(par_dict_loil_eth, {"input": df_input}) def add_meth_trade_historic(): par_dict_trade = pd.read_excel( - context.get_local_path("material", "methanol", "meth_trade_techno_economic.xlsx"), + private_data_path("material", "methanol", "meth_trade_techno_economic.xlsx"), sheet_name=None, ) par_dict_trade_fs = pd.read_excel( - context.get_local_path("material", "methanol", "meth_trade_techno_economic_fs.xlsx"), + private_data_path("material", "methanol", "meth_trade_techno_economic_fs.xlsx"), sheet_name=None, ) par_dict_trade = combine_df_dictionaries(par_dict_trade_fs, par_dict_trade) @@ -560,7 +631,7 @@ def update_methanol_costs(scenario): def gen_resin_demand(scenario, resin_share, sector, buildings_scen, pathway="SHAPE"): df = pd.read_csv( - context.get_local_path( + private_data_path( "material", "methanol", "results_material_" + pathway + "_" + sector + ".csv", @@ -586,8 +657,8 @@ def gen_resin_demand(scenario, resin_share, sector, buildings_scen, pathway="SHA ) all_years = scenario.vintage_and_active_years() all_years = all_years[all_years["year_vtg"] > 1990] - nodes = scenario.set("node")[1:] - nodes = nodes.drop(5).reset_index(drop=True) + drop_regs = ["R12_GLB", "World"] + nodes = [i for i in scenario.set("node").tolist() if i not in drop_regs] df_demand = ( make_df("demand", year=all_years["year_act"].unique()[:-1], **common) @@ -612,7 +683,7 @@ def get_demand_t1_with_income_elasticity( ) + demand_t0 df_gdp = pd.read_excel( - context.get_local_path("material", "methanol", "methanol demand.xlsx"), + private_data_path("material", "methanol", "methanol demand.xlsx"), sheet_name="GDP_baseline", ) @@ -620,7 +691,7 @@ def get_demand_t1_with_income_elasticity( df = df.dropna(axis=1) df_demand_meth = pd.read_excel( - context.get_local_path("material", "methanol", "methanol demand.xlsx"), + private_data_path("material", "methanol", "methanol demand.xlsx"), sheet_name="methanol_demand", skiprows=[12], ) @@ -664,7 +735,6 @@ def get_demand_t1_with_income_elasticity( def get_cost_ratio_2020(scenario, tec_name, cost_type, ref_reg="R12_NAM", year="all"): - df = scenario.par(cost_type, filters={"technology": tec_name}) if year == "all": if 2020 in df.year_vtg.unique(): @@ -686,7 +756,9 @@ def get_cost_ratio_2020(scenario, tec_name, cost_type, ref_reg="R12_NAM", year=" return df -def get_scaled_cost_from_proxy_tec(value, scenario, proxy_tec, cost_type, new_tec, year="all"): +def get_scaled_cost_from_proxy_tec( + value, scenario, proxy_tec, cost_type, new_tec, year="all" +): df = get_cost_ratio_2020(scenario, proxy_tec, cost_type, year=year) df["value"] = value * df["ratio"] df["technology"] = new_tec @@ -696,39 +768,41 @@ def get_scaled_cost_from_proxy_tec(value, scenario, proxy_tec, cost_type, new_te def add_meth_tec_vintages(): par_dict_ng = pd.read_excel( - context.get_local_path("material", "methanol", "meth_ng_techno_economic.xlsx"), + private_data_path("material", "methanol", "meth_ng_techno_economic.xlsx"), sheet_name=None, ) par_dict_ng_fs = pd.read_excel( - context.get_local_path("material", "methanol", "meth_ng_techno_economic_fs.xlsx"), + private_data_path("material", "methanol", "meth_ng_techno_economic_fs.xlsx"), sheet_name=None, ) par_dict_ng.pop("historical_activity") par_dict_ng_fs.pop("historical_activity") par_dict_coal = pd.read_excel( - context.get_local_path("material", "methanol", "meth_coal_additions.xlsx"), + private_data_path("material", "methanol", "meth_coal_additions.xlsx"), sheet_name=None, ) par_dict_coal_fs = pd.read_excel( - context.get_local_path("material", "methanol", "meth_coal_additions_fs.xlsx"), + private_data_path("material", "methanol", "meth_coal_additions_fs.xlsx"), sheet_name=None, ) par_dict_coal.pop("historical_activity") par_dict_coal_fs.pop("historical_activity") - return combine_df_dictionaries(par_dict_ng, par_dict_ng_fs, par_dict_coal, par_dict_coal_fs) + return combine_df_dictionaries( + par_dict_ng, par_dict_ng_fs, par_dict_coal, par_dict_coal_fs + ) def add_meth_hist_act(): # fix demand infeasibility par_dict = {} df_fs = pd.read_excel( - context.get_local_path("material", "methanol", "meth_coal_additions_fs.xlsx"), - sheet_name="historical_activity" + private_data_path("material", "methanol", "meth_coal_additions_fs.xlsx"), + sheet_name="historical_activity", ) df_fuel = pd.read_excel( - context.get_local_path("material", "methanol", "meth_coal_additions.xlsx"), - sheet_name="historical_activity" + private_data_path("material", "methanol", "meth_coal_additions.xlsx"), + sheet_name="historical_activity", ) par_dict["historical_activity"] = pd.concat([df_fs, df_fuel]) # derived from graphic in "Methanol production statstics.xlsx/China demand split" diagram @@ -746,12 +820,12 @@ def add_meth_hist_act(): # row = act[act["technology"].str.startswith("meth")].sort_values("value", ascending=False).iloc[0] # row["value"] = 0.0 df_ng = pd.read_excel( - context.get_local_path("material", "methanol", "meth_ng_techno_economic.xlsx"), - sheet_name="historical_activity" + private_data_path("material", "methanol", "meth_ng_techno_economic.xlsx"), + sheet_name="historical_activity", ) df_ng_fs = pd.read_excel( - context.get_local_path("material", "methanol", "meth_ng_techno_economic_fs.xlsx"), - sheet_name="historical_activity" + private_data_path("material", "methanol", "meth_ng_techno_economic_fs.xlsx"), + sheet_name="historical_activity", ) par_dict["historical_activity"] = pd.concat( [par_dict["historical_activity"], df_ng, df_ng_fs] @@ -761,16 +835,22 @@ def add_meth_hist_act(): def update_costs_with_loc_factor(df): loc_fact = pd.read_excel( - context.get_local_path("material","methanol","location factor collection.xlsx"), - sheet_name="comparison", index_col=0) + private_data_path("material", "methanol", "location factor collection.xlsx"), + sheet_name="comparison", + index_col=0, + ) cost_conv = pd.read_excel( - context.get_local_path("material","methanol","location factor collection.xlsx"), - sheet_name="cost_convergence", index_col=0) + private_data_path("material", "methanol", "location factor collection.xlsx"), + sheet_name="cost_convergence", + index_col=0, + ) loc_fact = loc_fact.loc["WEU normalized"] - loc_fact.index = ("R12_" + loc_fact.index) + loc_fact.index = "R12_" + loc_fact.index df = df.merge(loc_fact, left_on="node_loc", right_index=True) df = df.merge(cost_conv, left_on="year_vtg", right_index=True) - df["value"] = df["value"] - (1 - df["WEU normalized"]) * df["value"] * df["convergence"] + df["value"] = ( + df["value"] - (1 - df["WEU normalized"]) * df["value"] * df["convergence"] + ) return df[df.columns[:-2]] @@ -783,9 +863,11 @@ def add_mtbe_act_bound(scenario): "unit": "-", "time": "year", "year_act": year, - "value": 0 + "value": 0, } - nodes = scenario.set("node").drop(0).drop(5) - mtbe_dict["bound_activity_up"] = \ - make_df("bound_activity_up", **par_dict).pipe(broadcast, node_loc=nodes) + drop_regs = ["R12_GLB", "World"] + nodes = [i for i in scenario.set("node").tolist() if i not in drop_regs] + mtbe_dict["bound_activity_up"] = make_df("bound_activity_up", **par_dict).pipe( + broadcast, node_loc=nodes + ) return mtbe_dict From 473f0901a97aa77089959d6658178c26ab617f48 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Wed, 10 Apr 2024 15:08:49 +0200 Subject: [PATCH 709/774] Apply black to ammonia module --- message_ix_models/model/material/data_ammonia_new.py | 11 ++++------- 1 file changed, 4 insertions(+), 7 deletions(-) diff --git a/message_ix_models/model/material/data_ammonia_new.py b/message_ix_models/model/material/data_ammonia_new.py index 84774b525e..a262269eb1 100644 --- a/message_ix_models/model/material/data_ammonia_new.py +++ b/message_ix_models/model/material/data_ammonia_new.py @@ -80,7 +80,6 @@ def gen_data(scenario, dry_run=False, add_ccs: bool = True, lower_costs=False): act_years = act_years.drop_duplicates() for par_name in par_dict.keys(): - df = par_dict[par_name] # remove "default" node name to broadcast with all scenario regions later df["node_loc"] = df["node_loc"].apply(lambda x: None if x == "default" else x) @@ -182,7 +181,9 @@ def __missing__(self, key): # HACK: quick fix to enable compatibility with water build if len(scenario.par("output", filters={"technology": "extract_surfacewater"})): - par_dict["input"] = par_dict["input"].replace({"freshwater_supply": "freshwater"}) + par_dict["input"] = par_dict["input"].replace( + {"freshwater_supply": "freshwater"} + ) return par_dict @@ -213,7 +214,6 @@ def gen_data_rel(scenario, dry_run=False, add_ccs: bool = True): act_years = act_years.drop_duplicates() for par_name in par_dict.keys(): - df = par_dict[par_name] # remove "default" node name to broadcast with all scenario regions later df["node_rel"] = df["node_rel"].apply(lambda x: None if x == "all" else x) @@ -419,9 +419,7 @@ def read_demand(): } ) - df = N_trade_R12.loc[ - N_trade_R12.year_act == 2010, - ] + df = N_trade_R12.loc[N_trade_R12.year_act == 2010,] df = df.pivot(index="node_loc", columns="type", values="value") NP = pd.DataFrame({"netimp": df["import"] - df.export, "demand": ND[2010]}) NP["prod"] = NP.demand - NP.netimp @@ -540,7 +538,6 @@ def gen_demand(): def gen_resid_demand_NH3(scenario, gdp_elasticity): - context = read_config() s_info = ScenarioInfo(scenario) modelyears = s_info.Y # s_info.Y is only for modeling years From f0e069dd92d665cfd2c238e1760f99eda0229b23 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Wed, 10 Apr 2024 15:11:59 +0200 Subject: [PATCH 710/774] Add base year demand input files for petrochemicals --- .../data/material/ammonia/demand_NH3.yaml | 36 +++++++++++++++++++ .../material/methanol/demand_methanol.yaml | 36 +++++++++++++++++++ .../material/petrochemicals/demand_HVC.yaml | 36 +++++++++++++++++++ 3 files changed, 108 insertions(+) create mode 100644 message_ix_models/data/material/ammonia/demand_NH3.yaml create mode 100644 message_ix_models/data/material/methanol/demand_methanol.yaml create mode 100644 message_ix_models/data/material/petrochemicals/demand_HVC.yaml diff --git a/message_ix_models/data/material/ammonia/demand_NH3.yaml b/message_ix_models/data/material/ammonia/demand_NH3.yaml new file mode 100644 index 0000000000..5ec6616bb4 --- /dev/null +++ b/message_ix_models/data/material/ammonia/demand_NH3.yaml @@ -0,0 +1,36 @@ +- R12_AFR: + year: 2020 + value: 2.5000 +- R12_RCPA: + year: 2020 + value: 2.5 +- R12_EEU: + year: 2020 + value: 4 +- R12_FSU: + year: 2020 + value: 7 +- R12_LAM: + year: 2020 + value: 2.5 +- R12_MEA: + year: 2020 + value: 2.5 +- R12_NAM: + year: 2020 + value: 7 +- R12_PAO: + year: 2020 + value: 2.5 +- R12_PAS: + year: 2020 + value: 2.5 +- R12_SAS: + year: 2020 + value: 7 +- R12_WEU: + year: 2020 + value: 5.6 +- R12_CHN: + year: 2020 + value: 18 \ No newline at end of file diff --git a/message_ix_models/data/material/methanol/demand_methanol.yaml b/message_ix_models/data/material/methanol/demand_methanol.yaml new file mode 100644 index 0000000000..107b87bec6 --- /dev/null +++ b/message_ix_models/data/material/methanol/demand_methanol.yaml @@ -0,0 +1,36 @@ +- R12_AFR: + year: 2020 + value: 0.511 +- R12_RCPA: + year: 2020 + value: 0.511 +- R12_EEU: + year: 2020 + value: 1.533 +- R12_FSU: + year: 2020 + value: 3.066 +- R12_LAM: + year: 2020 + value: 2.555 +- R12_MEA: + year: 2020 + value: 3.066 +- R12_NAM: + year: 2020 + value: 8.687 +- R12_PAO: + year: 2020 + value: 2.044 +- R12_PAS: + year: 2020 + value: 7.665 +- R12_SAS: + year: 2020 + value: 3.066 +- R12_WEU: + year: 2020 + value: 7.665 +- R12_CHN: + year: 2020 + value: 61.831 diff --git a/message_ix_models/data/material/petrochemicals/demand_HVC.yaml b/message_ix_models/data/material/petrochemicals/demand_HVC.yaml new file mode 100644 index 0000000000..755133ddc0 --- /dev/null +++ b/message_ix_models/data/material/petrochemicals/demand_HVC.yaml @@ -0,0 +1,36 @@ +- R12_AFR: + year: 2020 + value: 2.4000 +- R12_RCPA: + year: 2020 + value: 0.44 +- R12_EEU: + year: 2020 + value: 3 +- R12_FSU: + year: 2020 + value: 5 +- R12_LAM: + year: 2020 + value: 11 +- R12_MEA: + year: 2020 + value: 40.3 +- R12_NAM: + year: 2020 + value: 49.8 +- R12_PAO: + year: 2020 + value: 11 +- R12_PAS: + year: 2020 + value: 37.5 +- R12_SAS: + year: 2020 + value: 10.7 +- R12_WEU: + year: 2020 + value: 29.2 +- R12_CHN: + year: 2020 + value: 50.5 From 33562a2ab42b41e022f6f765fd7eb4cf56c76de2 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Mon, 22 Apr 2024 22:47:15 +0200 Subject: [PATCH 711/774] A Add elec_i calibration fix, add macro calibration automation, update cbudget command --- message_ix_models/model/material/__init__.py | 100 ++++++++++-------- message_ix_models/model/material/data_util.py | 12 ++- message_ix_models/model/material/util.py | 85 +++++++++++++++ 3 files changed, 151 insertions(+), 46 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 72e977f2bd..ca4e9bde82 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -31,8 +31,13 @@ read_config, gen_te_projections, get_ssp_soc_eco_data, + add_elec_i_ini_act, +) +from message_data.model.material.util import ( + excel_to_csv, + get_all_input_data_dirs, + update_macro_calib_file, ) -from message_data.model.material.util import excel_to_csv, get_all_input_data_dirs from typing import Mapping from message_data.model.material.data_cement import gen_data_cement from message_data.model.material.data_steel import gen_data_steel @@ -87,12 +92,8 @@ def build(scenario: message_ix.Scenario, old_calib: bool) -> message_ix.Scenario apply_spec(scenario, spec, add_data_1) # dry_run=True if "SSP_dev" not in scenario.model: - engage_updates._correct_balance_td_efficiencies( - scenario - ) - engage_updates._correct_coal_ppl_u_efficiencies( - scenario - ) + engage_updates._correct_balance_td_efficiencies(scenario) + engage_updates._correct_coal_ppl_u_efficiencies(scenario) engage_updates._correct_td_co2cc_emissions(scenario) update_h2_blending.main(scenario) spec = None @@ -112,6 +113,7 @@ def build(scenario: message_ix.Scenario, old_calib: bool) -> message_ix.Scenario last_hist_year = scenario.par("historical_activity")["year_act"].max() modify_industry_demand(scenario, last_hist_year) add_new_ind_hist_act(scenario, [last_hist_year]) + add_elec_i_ini_act(scenario) add_emission_accounting(scenario) # scenario.commit("no changes") @@ -401,7 +403,8 @@ def solve_scen( # scenario = Scenario(context.get_platform(), model_name, scenario_name) if scenario.has_solution(): - scenario.remove_solution() + if not add_calibration: + scenario.remove_solution() if add_calibration: # Solve with 2020 base year @@ -410,31 +413,34 @@ def solve_scen( "After macro calibration a new scenario with the suffix _macro is created." ) print("Make sure to use this scenario to solve with MACRO iterations.") - scenario.solve(model="MESSAGE", solve_options={"lpmethod": "4", "scaind": "-1"}) - scenario.set_as_default() + if not scenario.has_solution(): + scenario.solve( + model="MESSAGE", solve_options={"lpmethod": "4", "scaind": "-1"} + ) + scenario.set_as_default() - # Report - from message_data.model.material.report.reporting import report - from message_data.tools.post_processing.iamc_report_hackathon import ( - report as reporting, - ) + # Report + from message_data.model.material.report.reporting import report + from message_data.tools.post_processing.iamc_report_hackathon import ( + report as reporting, + ) - # Remove existing timeseries and add material timeseries - print("Reporting material-specific variables") - report(context, scenario) - print("Reporting standard variables") - reporting( - mp, - scenario, - # NB(PNK) this is not an error; .iamc_report_hackathon.report() expects a - # string containing "True" or "False" instead of an actual bool. - "False", - scenario.model, - scenario.scenario, - merge_hist=True, - merge_ts=True, - run_config="materials_run_config.yaml", - ) + # Remove existing timeseries and add material timeseries + print("Reporting material-specific variables") + report(context, scenario) + print("Reporting standard variables") + reporting( + mp, + scenario, + # NB(PNK) this is not an error; .iamc_report_hackathon.report() expects a + # string containing "True" or "False" instead of an actual bool. + "False", + scenario.model, + scenario.scenario, + merge_hist=True, + merge_ts=True, + run_config="materials_run_config.yaml", + ) # Shift to 2025 base year scenario = scenario.clone( @@ -445,13 +451,18 @@ def solve_scen( scenario.set_as_default() scenario.solve(model="MESSAGE", solve_options={"lpmethod": "4", "scaind": "-1"}) + # update cost_ref and price_ref with new solution + update_macro_calib_file( + scenario, f"SSP_dev_{context['ssp']}-R12-5y_macro_data_v0.12_mat.xlsx" + ) + # After solving, add macro calibration print("Scenario solved, now adding MACRO calibration") # scenario = add_macro_COVID( # scenario, "R12-CHN-5y_macro_data_NGFS_w_rc_ind_adj_mat.xlsx" # ) scenario = add_macro_COVID( - scenario, "SSP_dev_SSP2-R12-5y_macro_data_v0.6_mat.xlsx" + scenario, f"SSP_dev_{context['ssp']}-R12-5y_macro_data_v0.12_mat.xlsx" ) print("Scenario calibrated.") @@ -677,32 +688,31 @@ def modify_costs_with_tool(context, scen_name, ssp): @cli.command("run_cbud_scenario") -@click.option("--ssp", default="SSP2", help="Suffix to the scenario name") @click.option( "--scenario", - default="1000f", + default="baseline_prep_lu_bkp_solved_materials_2025_macro", help="description of carbon budget for mitigation target", ) +@click.option("--budget", default="1000f") +@click.option("--model", default="MESSAGEix-Materials") @click.pass_obj -def run_cbud_scenario(context, ssp, scenario): +def run_cbud_scenario(context, model, scenario, budget): import message_ix - if scenario == "1000f": - budget = 3667 - elif scenario == "650f": - budget = 1750 + if budget == "1000f": + budget_i = 3667 + elif budget == "650f": + budget_i = 1750 else: print("chosen budget not available yet please choose 650f or 1000f") return mp = ixmp.Platform("ixmp_dev") - base = message_ix.Scenario( - mp, "MESSAGEix-Materials", scenario=f"SSP_supply_cost_test_{ssp}_macro" - ) + base = message_ix.Scenario(mp, model, scenario=scenario) scenario_cbud = base.clone( model=base.model, - scenario=base.scenario + "_" + scenario, - shift_first_model_year=2025, + scenario=base.scenario + "_" + budget, + shift_first_model_year=2030, ) emission_dict = { @@ -712,7 +722,7 @@ def run_cbud_scenario(context, ssp, scenario): "type_year": "cumulative", "unit": "???", } - df = message_ix.make_df("bound_emission", value=budget, **emission_dict) + df = message_ix.make_df("bound_emission", value=budget_i, **emission_dict) scenario_cbud.check_out() scenario_cbud.add_par("bound_emission", df) scenario_cbud.commit("add emission bound") diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index eea2f61d5c..6834dfee19 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -84,7 +84,7 @@ def add_macro_COVID(scen, filename, check_converge=False): # Excel file for calibration data if "SSP_dev" in scen.model: - xls_file = os.path.join("C:/", "Users", "maczek", "Downloads", filename) + xls_file = os.path.join("C:/", "Users", "maczek", "Downloads", "macro", filename) else: xls_file = os.path.join("P:", "ene.model", "MACRO", "python", filename) # Making a dictionary from the MACRO Excel file @@ -2017,6 +2017,16 @@ def get_ssp_soc_eco_data(context, model, measure, tec): return df +def add_elec_i_ini_act(scenario): + par = "inital_activity_up" + df_el = scenario.par(par, filters={"technology":"hp_el_i"}) + df_el["technology"] = "elec_i" + scenario.check_out() + scenario.add_par(par, df_el) + scenario.commit("add initial_activity_up for elec_i") + return + + if __name__ == "__main__": mp = ixmp.Platform("ixmp_dev") scen = message_ix.Scenario( diff --git a/message_ix_models/model/material/util.py b/message_ix_models/model/material/util.py index 0bc4cc9aea..acae87bc67 100644 --- a/message_ix_models/model/material/util.py +++ b/message_ix_models/model/material/util.py @@ -1,8 +1,10 @@ from message_ix_models import Context from message_ix_models.util import load_private_data, private_data_path +from scipy.optimize import curve_fit import pandas as pd import yaml import os +import openpyxl as pxl # Configuration files METADATA = [ @@ -113,3 +115,86 @@ def get_all_input_data_dirs(): def remove_from_list_if_exists(element, _list): if element in _list: _list.remove(element) + + +def exponential(x, b, m): + return b * m**x + + +def price_fit(df): + # print(df.lvl) + pars, cov = curve_fit(exponential, df.year, df.lvl, maxfev=5000) + val = exponential([2020], *pars)[0] + # print(df.commodity.unique(), df.node.unique(), val) + return val + + +def cost_fit(df): + # print(df.lvl) + pars, cov = curve_fit(exponential, df.year, df.lvl, maxfev=5000) + val = exponential([2020], *pars)[0] + # print(df.node.unique(), val) + return val / 1000 + + +def update_macro_calib_file(scenario, fname): + path = "C:/Users/maczek/Downloads/macro/refactored/" + wb = pxl.load_workbook(path + fname) + + # cost_ref + years = [i for i in range(2020, 2055, 5)] + df = scenario.var("COST_NODAL_NET", filters={"year": years}) + df["node"] = pd.Categorical( + df["node"], + [ + "R12_AFR", + "R12_CHN", + "R12_EEU", + "R12_FSU", + "R12_LAM", + "R12_MEA", + "R12_NAM", + "R12_PAO", + "R12_PAS", + "R12_RCPA", + "R12_SAS", + "R12_WEU", + ], + ) + df = df[df["year"].isin([2025, 2030, 2035])].groupby(["node"]).apply(cost_fit) + ws = wb.get_sheet_by_name("cost_ref") + for i in range(2, 7): + ws[f"B{i}"].value = df.values[i-2] + for i in range(8, 13): + ws[f"B{i}"].value = df.values[i-2] + + # price_ref + comms = ["i_feed", "i_spec", "i_therm", "rc_spec", "rc_therm", "transport"] + years = [i for i in range(2025, 2055, 5)] + df = scenario.var("PRICE_COMMODITY", filters={"commodity": comms, "year": years}) + df["node"] = pd.Categorical( + df["node"], + [ + "R12_AFR", + "R12_EEU", + "R12_FSU", + "R12_LAM", + "R12_MEA", + "R12_NAM", + "R12_PAO", + "R12_PAS", + "R12_SAS", + "R12_WEU", + "R12_CHN", + "R12_RCPA", + ], + ) + df["commodity"] = pd.Categorical( + df["commodity"], + ["i_feed", "i_spec", "i_therm", "rc_spec", "rc_therm", "transport"], + ) + df = df.groupby(["node", "commodity"]).apply(price_fit) + ws = wb.get_sheet_by_name("price_ref") + for i in range(2, 61): + ws[f"C{i}"].value = df.values[i-2] + wb.save(path + fname) From 41529320c703bddd04d5f48ecc1a87c25588c521 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Mon, 22 Apr 2024 22:47:51 +0200 Subject: [PATCH 712/774] Add ssp dependent demand elasticities to methanol module --- .../model/material/data_methanol_new.py | 36 +++++++++++++++++-- 1 file changed, 33 insertions(+), 3 deletions(-) diff --git a/message_ix_models/model/material/data_methanol_new.py b/message_ix_models/model/material/data_methanol_new.py index fcf945ccd5..3815ee7132 100644 --- a/message_ix_models/model/material/data_methanol_new.py +++ b/message_ix_models/model/material/data_methanol_new.py @@ -4,13 +4,30 @@ from message_ix import make_df from message_ix_models.util import broadcast, same_node + +from message_data.model.material.material_demand import material_demand_calc from message_data.model.material.util import read_config from ast import literal_eval -context = read_config() +ssp_mode_map = { + "SSP1": "CTS core", + "SSP2": "RTS core", + "SSP3": "RTS high", + "SSP4": "CTS high", + "SSP5": "RTS high", + "LED": "CTS core", # TODO: move to even lower projection +} + +iea_elasticity_map = { + "CTS core": (1.2, 0.25), + "CTS high": (1.3, 0.48), + "RTS core": (1.25, 0.35), + "RTS high": (1.4, 0.54), +} def gen_data_methanol_new(scenario): + context = read_config() df_pars = pd.read_excel( message_ix_models.util.private_data_path( "material", "methanol", "methanol_sensitivity_pars.xlsx" @@ -40,7 +57,9 @@ def gen_data_methanol_new(scenario): pars_dict[i] = broadcast_reduced_df(pars_dict[i], i) # TODO: only temporary hack to ensure SSP_dev compatibility if "SSP_dev" in scenario.model: - file_path = message_ix_models.util.private_data_path("material", "methanol", "missing_rels.yaml") + file_path = message_ix_models.util.private_data_path( + "material", "methanol", "missing_rels.yaml" + ) # file_path = "C:/Users\maczek\PycharmProjects\message_data\message_data\model\material\petrochemical model fixes notebooks\" with open(file_path, "r") as file: @@ -48,6 +67,17 @@ def gen_data_methanol_new(scenario): df = pars_dict["relation_activity"] pars_dict["relation_activity"] = df[~df["relation"].isin(missing_rels)] + default_gdp_elasticity_2020, default_gdp_elasticity_2030 = iea_elasticity_map[ + ssp_mode_map[context["ssp"]] + ] + df_final = material_demand_calc.gen_demand_petro( + scenario, "methanol", default_gdp_elasticity_2020, default_gdp_elasticity_2030 + ) + df_final["value"] = df_final["value"].apply( + lambda x: x * pars["methanol_resid_demand_share"] + ) + pars_dict["demand"] = df_final + return pars_dict @@ -157,6 +187,6 @@ def broadcast_reduced_df(df, par_name): df_final_full[ (df_final_full.node_rel.values != "R12_GLB") & (df_final_full.node_rel.values != df_final_full.node_loc.values) - ].index + ].index ) return make_df(par_name, **df_final_full) From 040fc54148c05e344365edac67284334bdff7517 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Mon, 22 Apr 2024 22:49:49 +0200 Subject: [PATCH 713/774] Update te-tool dependency --- message_ix_models/model/material/data_util.py | 19 ++++--------------- 1 file changed, 4 insertions(+), 15 deletions(-) diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index 6834dfee19..42dab3030c 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -1960,7 +1960,7 @@ def read_rel(scenario, material, filename): def gen_te_projections( scen, ssp: Literal["all", "LED", "SSP1", "SSP2", "SSP3", "SSP4", "SSP5"] = "SSP2", - method: Literal["convergence", "gdp", "learning"] = "convergence", + method: Literal["constant", "convergence", "gdp"] = "convergence", ref_reg: str = "R12_NAM", ) -> tuple: model_tec_set = list(scen.set("technology")) @@ -1971,23 +1971,12 @@ def gen_te_projections( format="message", scenario=ssp, ) - out_materials = create_cost_projections( - node=cfg.node, - ref_region=cfg.ref_region, - base_year=cfg.base_year, - module=cfg.module, - method=cfg.method, - scenario_version=cfg.scenario_version, - scenario=cfg.scenario, - convergence_year=cfg.convergence_year, - fom_rate=cfg.fom_rate, - format=cfg.format, - ) - fix_cost = out_materials.fix_cost.drop_duplicates().drop( + out_materials = create_cost_projections(cfg) + fix_cost = out_materials["fix_cost"].drop_duplicates().drop( ["scenario_version", "scenario"], axis=1 ) fix_cost = fix_cost[fix_cost["technology"].isin(model_tec_set)] - inv_cost = out_materials.inv_cost.drop_duplicates().drop( + inv_cost = out_materials["inv_cost"].drop_duplicates().drop( ["scenario_version", "scenario"], axis=1 ) inv_cost = inv_cost[inv_cost["technology"].isin(model_tec_set)] From 61a99cf10544c633d0a12bb6c0a4eb2973514a84 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Mon, 22 Apr 2024 22:51:32 +0200 Subject: [PATCH 714/774] Add gdp_ppp compatiblity to material demand projections --- .../material_demand/material_demand_calc.py | 63 +++++++++++-------- 1 file changed, 36 insertions(+), 27 deletions(-) diff --git a/message_ix_models/model/material/material_demand/material_demand_calc.py b/message_ix_models/model/material/material_demand/material_demand_calc.py index 377fe4304e..e4a9331177 100644 --- a/message_ix_models/model/material/material_demand/material_demand_calc.py +++ b/message_ix_models/model/material/material_demand/material_demand_calc.py @@ -406,33 +406,42 @@ def get_demand_t1_with_income_elasticity( elasticity * demand_t0.mul(((income_t1 - income_t0) / income_t0), axis=0) ) + demand_t0 - gdp_mer = scenario.par("bound_activity_up", {"technology": "GDP"}) - mer_to_ppp = pd.read_csv( - private_data_path("material", "other", "mer_to_ppp_default.csv") - ).set_index(["node", "year"]) - # mer_to_ppp = scenario.par("MERtoPPP").set_index("node", "year") TODO: might need to be re-activated for different SSPs - gdp_mer = gdp_mer.merge( - mer_to_ppp.reset_index()[["node", "year", "value"]], - left_on=["node_loc", "year_act"], - right_on=["node", "year"], - ) - gdp_mer["gdp_ppp"] = gdp_mer["value_y"] * gdp_mer["value_x"] - gdp_mer = gdp_mer[["year", "node_loc", "gdp_ppp"]].reset_index() - gdp_mer["Region"] = gdp_mer["node_loc"] # .str.replace("R12_", "") - df_gdp_ts = gdp_mer.pivot( - index="Region", columns="year", values="gdp_ppp" - ).reset_index() - num_cols = [i for i in df_gdp_ts.columns if type(i) == int] - hist_yrs = [i for i in num_cols if i < fy] - df_gdp_ts = ( - df_gdp_ts.drop([i for i in hist_yrs if i in df_gdp_ts.columns], axis=1) - .set_index("Region") - .sort_index() - ) - - from message_data.model.material.material_demand.material_demand_calc import ( - read_base_demand, - ) + if "GDP_PPP" in list(scenario.set("technology")): + gdp_ppp = scenario.par("bound_activity_up", {"technology": "GDP_PPP"}) + df_gdp_ts = gdp_ppp.pivot( + index="node_loc", columns="year_act", values="value" + ).reset_index() + num_cols = [i for i in df_gdp_ts.columns if type(i) == int] + hist_yrs = [i for i in num_cols if i < fy] + df_gdp_ts = ( + df_gdp_ts.drop([i for i in hist_yrs if i in df_gdp_ts.columns], axis=1) + .set_index("node_loc") + .sort_index() + ) + else: + gdp_mer = scenario.par("bound_activity_up", {"technology": "GDP"}) + mer_to_ppp = pd.read_csv( + private_data_path("material", "other", "mer_to_ppp_default.csv") + ).set_index(["node", "year"]) + # mer_to_ppp = scenario.par("MERtoPPP").set_index("node", "year") TODO: might need to be re-activated for different SSPs + gdp_mer = gdp_mer.merge( + mer_to_ppp.reset_index()[["node", "year", "value"]], + left_on=["node_loc", "year_act"], + right_on=["node", "year"], + ) + gdp_mer["gdp_ppp"] = gdp_mer["value_y"] * gdp_mer["value_x"] + gdp_mer = gdp_mer[["year", "node_loc", "gdp_ppp"]].reset_index() + gdp_mer["Region"] = gdp_mer["node_loc"] # .str.replace("R12_", "") + df_gdp_ts = gdp_mer.pivot( + index="Region", columns="year", values="gdp_ppp" + ).reset_index() + num_cols = [i for i in df_gdp_ts.columns if type(i) == int] + hist_yrs = [i for i in num_cols if i < fy] + df_gdp_ts = ( + df_gdp_ts.drop([i for i in hist_yrs if i in df_gdp_ts.columns], axis=1) + .set_index("Region") + .sort_index() + ) df_demand_2020 = read_base_demand( private_data_path() From efd1becd4f2de485683d6ffb40dfdb3b6a789896 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 15 Sep 2020 14:16:57 +0200 Subject: [PATCH 715/774] Addition of data files and sets --- message_ix_models/data/material/aluminum_techno_economic.xlsx | 3 +++ .../generic_furnace_boiler_techno_economic_aluminum.xlsx | 3 +++ .../material/generic_furnace_boiler_techno_economic_steel.xlsx | 3 +++ message_ix_models/data/material/variable_costs.xlsx | 3 +++ 4 files changed, 12 insertions(+) create mode 100644 message_ix_models/data/material/aluminum_techno_economic.xlsx create mode 100644 message_ix_models/data/material/generic_furnace_boiler_techno_economic_aluminum.xlsx create mode 100644 message_ix_models/data/material/generic_furnace_boiler_techno_economic_steel.xlsx create mode 100644 message_ix_models/data/material/variable_costs.xlsx diff --git a/message_ix_models/data/material/aluminum_techno_economic.xlsx b/message_ix_models/data/material/aluminum_techno_economic.xlsx new file mode 100644 index 0000000000..5d79787c5e --- /dev/null +++ b/message_ix_models/data/material/aluminum_techno_economic.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:106287600beb514e90a4780f9404d59cd1ad910f52556620804c9cc15bcce6bf +size 51745 diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic_aluminum.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic_aluminum.xlsx new file mode 100644 index 0000000000..c6a0e2acca --- /dev/null +++ b/message_ix_models/data/material/generic_furnace_boiler_techno_economic_aluminum.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bdfe832a631d0080892932af6ecb2e1ed3c1cc059adf182eb476c77de57d0ff5 +size 47993 diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic_steel.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic_steel.xlsx new file mode 100644 index 0000000000..9edf9afc3f --- /dev/null +++ b/message_ix_models/data/material/generic_furnace_boiler_techno_economic_steel.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f9d6d91a7c1c38ba3221d295dd2a471ffb4cc8df01da8c6014bec0dcc9e1d5bd +size 48442 diff --git a/message_ix_models/data/material/variable_costs.xlsx b/message_ix_models/data/material/variable_costs.xlsx new file mode 100644 index 0000000000..03f046c956 --- /dev/null +++ b/message_ix_models/data/material/variable_costs.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2ecfeceb662b6bdf3d14078429f4db7d6b4997447e80eb486160ffc91d3f3695 +size 32510 From 9e460ad6ebd01022d6ccfd5b86777494ea4c19ec Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Mon, 22 Apr 2024 23:53:07 +0200 Subject: [PATCH 716/774] Resolve conflict 2 --- message_ix_models/data/material/aluminum_techno_economic.xlsx | 4 ++-- message_ix_models/data/material/variable_costs.xlsx | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/message_ix_models/data/material/aluminum_techno_economic.xlsx b/message_ix_models/data/material/aluminum_techno_economic.xlsx index 5d79787c5e..1b3261e95e 100644 --- a/message_ix_models/data/material/aluminum_techno_economic.xlsx +++ b/message_ix_models/data/material/aluminum_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:106287600beb514e90a4780f9404d59cd1ad910f52556620804c9cc15bcce6bf -size 51745 +oid sha256:c8ea4248665241c6bd7525f068658913577d870d9bb881a5d4e4062c2aa7d478 +size 56261 diff --git a/message_ix_models/data/material/variable_costs.xlsx b/message_ix_models/data/material/variable_costs.xlsx index 03f046c956..7d405a7eaa 100644 --- a/message_ix_models/data/material/variable_costs.xlsx +++ b/message_ix_models/data/material/variable_costs.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:2ecfeceb662b6bdf3d14078429f4db7d6b4997447e80eb486160ffc91d3f3695 -size 32510 +oid sha256:325de6ca46e98eada689e4c2cae920c64a205abc48cca7c36c89c7f56597300c +size 15112 From a859eef79369f89ddfaa0289c7dd9898f9ec274d Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Mon, 22 Apr 2024 23:53:36 +0200 Subject: [PATCH 717/774] Resolve rebase conflict --- message_ix_models/data/material/China_steel_MESSAGE.xlsx | 3 +++ .../data/material/China_steel_standalone - test.xlsx | 3 +++ 2 files changed, 6 insertions(+) create mode 100644 message_ix_models/data/material/China_steel_MESSAGE.xlsx create mode 100644 message_ix_models/data/material/China_steel_standalone - test.xlsx diff --git a/message_ix_models/data/material/China_steel_MESSAGE.xlsx b/message_ix_models/data/material/China_steel_MESSAGE.xlsx new file mode 100644 index 0000000000..68e7e264b8 --- /dev/null +++ b/message_ix_models/data/material/China_steel_MESSAGE.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5b6a4f0156cee4c62825e257a31201531eed370b089cf8f552a8077912f74f98 +size 43561 diff --git a/message_ix_models/data/material/China_steel_standalone - test.xlsx b/message_ix_models/data/material/China_steel_standalone - test.xlsx new file mode 100644 index 0000000000..2360c48d52 --- /dev/null +++ b/message_ix_models/data/material/China_steel_standalone - test.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5dce2805711f59fe9d87699ac5574e123426c64af7d54af5350719daebfd39c1 +size 53054 From 636916b3000d4ef824875b4de3e740711ab0e3fb Mon Sep 17 00:00:00 2001 From: Jihoon Date: Mon, 12 Oct 2020 23:13:13 +0200 Subject: [PATCH 718/774] Test run with china model --- message_ix_models/data/material/China_steel_MESSAGE.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/China_steel_MESSAGE.xlsx b/message_ix_models/data/material/China_steel_MESSAGE.xlsx index 68e7e264b8..10ed32c55e 100644 --- a/message_ix_models/data/material/China_steel_MESSAGE.xlsx +++ b/message_ix_models/data/material/China_steel_MESSAGE.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:5b6a4f0156cee4c62825e257a31201531eed370b089cf8f552a8077912f74f98 -size 43561 +oid sha256:b4251932332a9045bc287264aaec6519ca885f5ddc304302217b5bd84f43690b +size 43671 From 572c64c9e1010e92c94f2a7eba6d11972efd01ab Mon Sep 17 00:00:00 2001 From: Jihoon Date: Mon, 19 Oct 2020 09:24:24 +0200 Subject: [PATCH 719/774] Updated data with cement --- message_ix_models/data/material/China_steel_MESSAGE.xlsx | 4 ++-- .../data/material/China_steel_standalone - test.xlsx | 4 ++-- .../data/material/generic_furnace_boiler_techno_economic.xlsx | 3 +++ 3 files changed, 7 insertions(+), 4 deletions(-) create mode 100644 message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx diff --git a/message_ix_models/data/material/China_steel_MESSAGE.xlsx b/message_ix_models/data/material/China_steel_MESSAGE.xlsx index 10ed32c55e..0386466ed4 100644 --- a/message_ix_models/data/material/China_steel_MESSAGE.xlsx +++ b/message_ix_models/data/material/China_steel_MESSAGE.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:b4251932332a9045bc287264aaec6519ca885f5ddc304302217b5bd84f43690b -size 43671 +oid sha256:555d866fc3e62a9cb3f4464c65178017da972af9323fb33e355cb75803612344 +size 47414 diff --git a/message_ix_models/data/material/China_steel_standalone - test.xlsx b/message_ix_models/data/material/China_steel_standalone - test.xlsx index 2360c48d52..67f6a72077 100644 --- a/message_ix_models/data/material/China_steel_standalone - test.xlsx +++ b/message_ix_models/data/material/China_steel_standalone - test.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:5dce2805711f59fe9d87699ac5574e123426c64af7d54af5350719daebfd39c1 -size 53054 +oid sha256:42c031d8af13d71b2ad5a1ec5b6f14b973105a5164494552968710a014908fd8 +size 56730 diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx new file mode 100644 index 0000000000..95ea7112f7 --- /dev/null +++ b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:91b263b3f172b7ee9f17d85d682d5128b54ec8290c041eb3538c63f8549b9733 +size 51396 From 508b09f1678a5b4a89a0e71383f0b08cd274ccd3 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 22 Oct 2020 16:52:33 +0200 Subject: [PATCH 720/774] Data updated --- .../data/material/petrochemicals_techno_economic.xlsx | 3 +++ 1 file changed, 3 insertions(+) create mode 100644 message_ix_models/data/material/petrochemicals_techno_economic.xlsx diff --git a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx new file mode 100644 index 0000000000..c3143421cc --- /dev/null +++ b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:baef402f8288c71c2dc158cdfb4f7a0f57a10876ac261ec5cff495ac51af6d1f +size 463218 From 2b5d219e4161591bc0ac7093b392a3919cc79d31 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Fri, 23 Oct 2020 11:57:21 +0200 Subject: [PATCH 721/774] Data update --- .../data/material/petrochemicals_techno_economic.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx index c3143421cc..1aaef961bf 100644 --- a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx +++ b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:baef402f8288c71c2dc158cdfb4f7a0f57a10876ac261ec5cff495ac51af6d1f -size 463218 +oid sha256:2039a50e98ed0213f1d961346cec0c1711b4fe88340902d7b8488f69064ddd1c +size 466853 From 948f5cb9a2b793fe1c3b1235be92219b4c7db921 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 29 Oct 2020 14:31:29 +0100 Subject: [PATCH 722/774] Add sets for petrochemical sector --- .../petrochemicals_techno_economic.xlsx | 4 +- message_ix_models/data/material/set.yaml | 65 +++++++++++++++++++ 2 files changed, 67 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx index 1aaef961bf..3a285b5330 100644 --- a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx +++ b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:2039a50e98ed0213f1d961346cec0c1711b4fe88340902d7b8488f69064ddd1c -size 466853 +oid sha256:1523b27941193f4869bf141eabd8ac34d69f634f9158e1725740e225b9d4f1f0 +size 467548 diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 7e6beb9720..df92195f6b 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -4,6 +4,71 @@ # accounting. Different sets can be created once there are 2+ different # categories of materials. +petro_chemicals: + commodity: + require: + - crudeoil + - hydrogen + - elctr + - ethanol + - fueloil + - lightoil + add: + - naphtha + - kerosene + - diesel + - atm_residue + - refinery_gas + - atm_gasoil + - atm_residue + - vacuum_gasoil + - vacuum_residue + - desulf_gasoil + - desulf_naphtha + - desulf_kerosene + - desulf_diesel + - desful_gasoil + - gasoline + - heavy_foil + - light_foil + - propylene + - coke + - ethane + - propane + - ethylene + - BTX + + level: + require: + - secondary + - final + add: + - pre_intermediate + - useful_refining + - desulfurized + - intermediate + - secondary_material + - useful_petro + - final_material + + mode: + add: + - atm_gasoil + - vacuum_gasoil + - naphtha + - kerosene + - diesel + - cracking_gasoline + - cracking_loil + - gasoil + - ethane + - propane + + emission: + add: + - CO2 + - SO2 + common: commodity: require: From 806c01be0ee0678dde6320be71558268e1a5adbf Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 11 Nov 2020 15:21:56 +0100 Subject: [PATCH 723/774] Changes to integrate petro_chemicals --- .../material/generic_furnace_boiler_techno_economic_steel.xlsx | 3 --- message_ix_models/model/material/data.py | 1 + 2 files changed, 1 insertion(+), 3 deletions(-) delete mode 100644 message_ix_models/data/material/generic_furnace_boiler_techno_economic_steel.xlsx diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic_steel.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic_steel.xlsx deleted file mode 100644 index 9edf9afc3f..0000000000 --- a/message_ix_models/data/material/generic_furnace_boiler_techno_economic_steel.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:f9d6d91a7c1c38ba3221d295dd2a471ffb4cc8df01da8c6014bec0dcc9e1d5bd -size 48442 diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index 13c7683994..4ec38db251 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -29,6 +29,7 @@ gen_data_cement, gen_data_aluminum, gen_data_generic, + gen_data_petro_chemicals ] # Try to handle multiple data input functions from different materials From 2eb478e1dbf16e2e838cf0c1b649f5128d24c88b Mon Sep 17 00:00:00 2001 From: Jihoon Date: Mon, 25 Jan 2021 22:54:35 +0100 Subject: [PATCH 724/774] Updated buildings data --- .../data/material/LED_LED_report_IAMC_sensitivity.csv | 3 +++ .../data/material/deprecated/LED_LED_report_IAMC.csv | 4 ++-- 2 files changed, 5 insertions(+), 2 deletions(-) create mode 100644 message_ix_models/data/material/LED_LED_report_IAMC_sensitivity.csv diff --git a/message_ix_models/data/material/LED_LED_report_IAMC_sensitivity.csv b/message_ix_models/data/material/LED_LED_report_IAMC_sensitivity.csv new file mode 100644 index 0000000000..62e8d7e260 --- /dev/null +++ b/message_ix_models/data/material/LED_LED_report_IAMC_sensitivity.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1e5b051f314c528cb01655810bef81c2900b63472ffeb92547907e574315b688 +size 243446 diff --git a/message_ix_models/data/material/deprecated/LED_LED_report_IAMC.csv b/message_ix_models/data/material/deprecated/LED_LED_report_IAMC.csv index 595ff9296b..013ed8224a 100644 --- a/message_ix_models/data/material/deprecated/LED_LED_report_IAMC.csv +++ b/message_ix_models/data/material/deprecated/LED_LED_report_IAMC.csv @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:2eee76b0e25eeb360997f69e64b4092280dc51dc62ae4b94bb7957867b7c1564 -size 63673 +oid sha256:20f3193e0f3e905d43b804d0aad3f2671465172397f3ad4377149f32d3ed830e +size 79879 From 46577e5f91f2bee8660ca117c1b51b230f727388 Mon Sep 17 00:00:00 2001 From: Jihoon Min Date: Thu, 21 Jan 2021 16:09:49 +0100 Subject: [PATCH 725/774] Add initial scripts and data for buildings work --- .../data/material/LED_LED_report_IAMC_sensitivity.csv | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/message_ix_models/data/material/LED_LED_report_IAMC_sensitivity.csv b/message_ix_models/data/material/LED_LED_report_IAMC_sensitivity.csv index 62e8d7e260..fe4f92689a 100644 --- a/message_ix_models/data/material/LED_LED_report_IAMC_sensitivity.csv +++ b/message_ix_models/data/material/LED_LED_report_IAMC_sensitivity.csv @@ -1,3 +1,3 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:1e5b051f314c528cb01655810bef81c2900b63472ffeb92547907e574315b688 -size 243446 +version https://git-lfs.github.com/spec/v1 +oid sha256:4849fe301f51d4e714c146c9612a1a3799dfc99204174ca2363233cd75632921 +size 247139 From 8a312c510d4fb5a971008e0e371dc2dcff464230 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 23 May 2024 11:51:44 +0200 Subject: [PATCH 726/774] Resolve rebasing conflict with buildings file --- .../data/material/LED_LED_report_IAMC_sensitivity.csv | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/message_ix_models/data/material/LED_LED_report_IAMC_sensitivity.csv b/message_ix_models/data/material/LED_LED_report_IAMC_sensitivity.csv index fe4f92689a..43db78f681 100644 --- a/message_ix_models/data/material/LED_LED_report_IAMC_sensitivity.csv +++ b/message_ix_models/data/material/LED_LED_report_IAMC_sensitivity.csv @@ -1,3 +1,3 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:4849fe301f51d4e714c146c9612a1a3799dfc99204174ca2363233cd75632921 -size 247139 +version https://git-lfs.github.com/spec/v1 +oid sha256:4849fe301f51d4e714c146c9612a1a3799dfc99204174ca2363233cd75632921 +size 247139 From 402d8e47db650da868a33890c234dbd63780e5b5 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Mon, 22 Apr 2024 23:58:21 +0200 Subject: [PATCH 727/774] Resolve rebase conflict 3 --- .../data/material/LED_LED_report_IAMC_sensitivity.csv | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/LED_LED_report_IAMC_sensitivity.csv b/message_ix_models/data/material/LED_LED_report_IAMC_sensitivity.csv index 43db78f681..62e8d7e260 100644 --- a/message_ix_models/data/material/LED_LED_report_IAMC_sensitivity.csv +++ b/message_ix_models/data/material/LED_LED_report_IAMC_sensitivity.csv @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:4849fe301f51d4e714c146c9612a1a3799dfc99204174ca2363233cd75632921 -size 247139 +oid sha256:1e5b051f314c528cb01655810bef81c2900b63472ffeb92547907e574315b688 +size 243446 From 874fb4fd5a8a6af8984de6eaac05815e28de7043 Mon Sep 17 00:00:00 2001 From: Unknown Date: Mon, 22 Mar 2021 15:55:16 +0100 Subject: [PATCH 728/774] Small correction of description --- message_ix_models/model/material/doc.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/message_ix_models/model/material/doc.rst b/message_ix_models/model/material/doc.rst index 974baf514f..eeff5f3a70 100644 --- a/message_ix_models/model/material/doc.rst +++ b/message_ix_models/model/material/doc.rst @@ -207,7 +207,7 @@ The code relies on the following input files, stored in :file:`data/material/`: Historical N-fertilizer trade records among R14 regions, extracted from FAO database. :file:`NTNU_LCA_coefficients.xlsx` - Material intensity (and other) and other coefficients for power plants based on lifecycle assessment (LCA) data from the THEMIS database, compiled in the `ADVANCE project` `_. + Material intensity (and other) coefficients for power plants based on lifecycle assessment (LCA) data from the THEMIS database, compiled in the `ADVANCE project `_. :file:`MESSAGE_global_model_technologies.xlsx` Technology list of global MESSAGEix-GLOBIOM model with mapping to LCA technology dataset. From 440d2278ec6c7f14fe04930e8bd4384b33b7ba10 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Fri, 21 May 2021 15:48:55 +0200 Subject: [PATCH 729/774] Update data --- .../other/MESSAGEix-Materials_final_energy_industry.xlsx | 4 ++-- .../data/material/power_sector/LCA_region_mapping.xlsx | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/message_ix_models/data/material/other/MESSAGEix-Materials_final_energy_industry.xlsx b/message_ix_models/data/material/other/MESSAGEix-Materials_final_energy_industry.xlsx index 8338706477..036b7b59e5 100644 --- a/message_ix_models/data/material/other/MESSAGEix-Materials_final_energy_industry.xlsx +++ b/message_ix_models/data/material/other/MESSAGEix-Materials_final_energy_industry.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:b222682c9cbe45494c26474c99ad1c71dda86afa5e04f874ca1f0c0f2c74215e -size 344589 +oid sha256:e05a8ce2ac65e9fdb5ce938ef11c6c50f28499c0081be9cfdb4c825edc41d313 +size 523069 diff --git a/message_ix_models/data/material/power_sector/LCA_region_mapping.xlsx b/message_ix_models/data/material/power_sector/LCA_region_mapping.xlsx index 09a101ab08..a41336588f 100644 --- a/message_ix_models/data/material/power_sector/LCA_region_mapping.xlsx +++ b/message_ix_models/data/material/power_sector/LCA_region_mapping.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:e1dcb26f2486b5486dc98ec5a87c542f87b64563db3db40358b1c41cf946f8a6 -size 9084 +oid sha256:1e8aa4624797a88fbb5524523128c81a8aaa8b1305c40ba44afbabff10653ab3 +size 9115 From c570322bd136a46bef36c5b8f949bbea5426ca9a Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 27 May 2021 11:29:34 +0200 Subject: [PATCH 730/774] Fix data files --- .../data/material/LED_LED_report_IAMC_sensitivity_R11.csv | 3 +++ .../data/material/LED_LED_report_IAMC_sensitivity_R12.csv | 3 +++ .../other/MESSAGEix-Materials_final_energy_industry.xlsx | 4 ++-- 3 files changed, 8 insertions(+), 2 deletions(-) create mode 100644 message_ix_models/data/material/LED_LED_report_IAMC_sensitivity_R11.csv create mode 100644 message_ix_models/data/material/LED_LED_report_IAMC_sensitivity_R12.csv diff --git a/message_ix_models/data/material/LED_LED_report_IAMC_sensitivity_R11.csv b/message_ix_models/data/material/LED_LED_report_IAMC_sensitivity_R11.csv new file mode 100644 index 0000000000..a8853545a7 --- /dev/null +++ b/message_ix_models/data/material/LED_LED_report_IAMC_sensitivity_R11.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:08652e54fca937ec1fb2da8e068020c61eb33eafbc233186a5dfdc4d4e1cbb15 +size 189976 diff --git a/message_ix_models/data/material/LED_LED_report_IAMC_sensitivity_R12.csv b/message_ix_models/data/material/LED_LED_report_IAMC_sensitivity_R12.csv new file mode 100644 index 0000000000..899669bfc3 --- /dev/null +++ b/message_ix_models/data/material/LED_LED_report_IAMC_sensitivity_R12.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:851e957ee94fe451416f7c09e36d7c863b34039606b11cd535f879ee1185cf65 +size 207524 diff --git a/message_ix_models/data/material/other/MESSAGEix-Materials_final_energy_industry.xlsx b/message_ix_models/data/material/other/MESSAGEix-Materials_final_energy_industry.xlsx index 036b7b59e5..3ef06bb6af 100644 --- a/message_ix_models/data/material/other/MESSAGEix-Materials_final_energy_industry.xlsx +++ b/message_ix_models/data/material/other/MESSAGEix-Materials_final_energy_industry.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:e05a8ce2ac65e9fdb5ce938ef11c6c50f28499c0081be9cfdb4c825edc41d313 -size 523069 +oid sha256:7aa8904b4f1158773e838e39e0faf1628b7cceb265bd6a2dbf0314d79ff6fcb0 +size 518345 From 09bd8021e9513049925e354102b4ec666191fc1c Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 8 Jun 2021 11:46:55 +0200 Subject: [PATCH 731/774] Update region names in the data files for r12 --- .../other/MESSAGEix-Materials_final_energy_industry.xlsx | 4 ++-- .../data/material/power_sector/LCA_region_mapping.xlsx | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/message_ix_models/data/material/other/MESSAGEix-Materials_final_energy_industry.xlsx b/message_ix_models/data/material/other/MESSAGEix-Materials_final_energy_industry.xlsx index 3ef06bb6af..e254dbd916 100644 --- a/message_ix_models/data/material/other/MESSAGEix-Materials_final_energy_industry.xlsx +++ b/message_ix_models/data/material/other/MESSAGEix-Materials_final_energy_industry.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:7aa8904b4f1158773e838e39e0faf1628b7cceb265bd6a2dbf0314d79ff6fcb0 -size 518345 +oid sha256:9b5b72024df8b07d04bef3cfbb161cad49292f616543959286eda87446ca2137 +size 518402 diff --git a/message_ix_models/data/material/power_sector/LCA_region_mapping.xlsx b/message_ix_models/data/material/power_sector/LCA_region_mapping.xlsx index a41336588f..0769509834 100644 --- a/message_ix_models/data/material/power_sector/LCA_region_mapping.xlsx +++ b/message_ix_models/data/material/power_sector/LCA_region_mapping.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:1e8aa4624797a88fbb5524523128c81a8aaa8b1305c40ba44afbabff10653ab3 +oid sha256:90c6d316884099eae6033b6713a254dd488cb911695422d84936ee7a3b21e51f size 9115 From 87998bc2cf9e2333ed96d783c2d7498db4fd4633 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 9 Jun 2021 10:11:52 +0200 Subject: [PATCH 732/774] Update the region names to r12 --- message_ix_models/model/material/data.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index 4ec38db251..d1c2a2c59a 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -44,6 +44,10 @@ def add_data(scenario, dry_run=False): log.warning("Remove 'R11_GLB' from node list for data generation") info.set["node"].remove("R11_GLB") + if {"World", "R12_GLB"} < set(info.set["node"]): + log.warning("Remove 'R12_GLB' from node list for data generation") + info.set["node"].remove("R12_GLB") + for func in DATA_FUNCTIONS: # Generate or load the data; add to the Scenario log.info(f"from {func.__name__}()") From f057b734d2346b43080802c678e2e4ad2a9ea59e Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Tue, 1 Mar 2022 11:43:17 +0100 Subject: [PATCH 733/774] Remove unnecessary vintage years --- message_ix_models/model/material/data_aluminum.py | 3 +-- message_ix_models/model/material/data_cement.py | 1 + message_ix_models/model/material/data_steel.py | 4 +++- 3 files changed, 5 insertions(+), 3 deletions(-) diff --git a/message_ix_models/model/material/data_aluminum.py b/message_ix_models/model/material/data_aluminum.py index a4807f7661..49d2c014c8 100644 --- a/message_ix_models/model/material/data_aluminum.py +++ b/message_ix_models/model/material/data_aluminum.py @@ -106,7 +106,7 @@ def gen_data_aluminum(scenario, dry_run=False): (data_aluminum["technology"] == t), "availability" ].values[0] modelyears = [year for year in modelyears if year >= av] - yva = yv_ya.loc[yv_ya.year_vtg >= av] + yv_ya = yv_ya.loc[yv_ya.year_vtg >= av] # Iterate over parameters for par in params: @@ -442,7 +442,6 @@ def gen_data_aluminum(scenario, dry_run=False): results_aluminum = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} return results_aluminum - def gen_mock_demand_aluminum(scenario): context = read_config() diff --git a/message_ix_models/model/material/data_cement.py b/message_ix_models/model/material/data_cement.py index e87ac3480d..5a314b56f9 100644 --- a/message_ix_models/model/material/data_cement.py +++ b/message_ix_models/model/material/data_cement.py @@ -196,6 +196,7 @@ def gen_data_cement(scenario, dry_run=False): modelyears = s_info.Y # s_info.Y is only for modeling years nodes = s_info.N yv_ya = s_info.yv_ya + yv_ya = yv_ya.loc[yv_ya.year_vtg >= 1980] fmy = s_info.y0 nodes.remove("World") diff --git a/message_ix_models/model/material/data_steel.py b/message_ix_models/model/material/data_steel.py index 07a51bed1d..b52e0bb120 100644 --- a/message_ix_models/model/material/data_steel.py +++ b/message_ix_models/model/material/data_steel.py @@ -138,6 +138,7 @@ def gen_data_steel(scenario, dry_run=False): modelyears = s_info.Y # s_info.Y is only for modeling years nodes = s_info.N yv_ya = s_info.yv_ya + yv_ya = yv_ya.loc[yv_ya.year_vtg >= 1990] fmy = s_info.y0 nodes.remove("World") @@ -255,7 +256,6 @@ def gen_data_steel(scenario, dry_run=False): # Iterate over parameters for par in params: - # Obtain the parameter names, commodity,level,emission split = par.split("|") param_name = split[0] @@ -301,6 +301,7 @@ def gen_data_steel(scenario, dry_run=False): node_origin=global_region, **common ) + elif (param_name == "output") and (lev == "export"): df = make_df( param_name, @@ -314,6 +315,7 @@ def gen_data_steel(scenario, dry_run=False): node_dest=global_region, **common ) + else: df = make_df( param_name, From f641b5ca15260d46451fc1d72d255a017842490b Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Thu, 3 Mar 2022 17:12:24 +0100 Subject: [PATCH 734/774] Update n-fertilizer_techno-economic_new.xlsx --- .../data/material/n-fertilizer_techno-economic_new.xlsx | 3 +++ 1 file changed, 3 insertions(+) create mode 100644 message_ix_models/data/material/n-fertilizer_techno-economic_new.xlsx diff --git a/message_ix_models/data/material/n-fertilizer_techno-economic_new.xlsx b/message_ix_models/data/material/n-fertilizer_techno-economic_new.xlsx new file mode 100644 index 0000000000..53a9307db5 --- /dev/null +++ b/message_ix_models/data/material/n-fertilizer_techno-economic_new.xlsx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cc9daff423a9b8e434f4ee0d0f2aa158dc817e75d8ad6dd2750a4d5be50ce98d +size 28536 From 5c7165814db5576dded648c8916712ea726d33d2 Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Wed, 30 Mar 2022 17:42:35 +0200 Subject: [PATCH 735/774] Fix ccs units --- .../data/material/n-fertilizer_techno-economic_new.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/n-fertilizer_techno-economic_new.xlsx b/message_ix_models/data/material/n-fertilizer_techno-economic_new.xlsx index 53a9307db5..ca4d266fd1 100644 --- a/message_ix_models/data/material/n-fertilizer_techno-economic_new.xlsx +++ b/message_ix_models/data/material/n-fertilizer_techno-economic_new.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:cc9daff423a9b8e434f4ee0d0f2aa158dc817e75d8ad6dd2750a4d5be50ce98d -size 28536 +oid sha256:608501fb2633a32c407722af8433dcf79d81619fd5364df7df79195e877a9ce7 +size 30230 From 47389ada3b3aedd18831ed1cedf0cf283f0130bf Mon Sep 17 00:00:00 2001 From: GamzeUnlu95 Date: Mon, 25 Apr 2022 16:45:29 +0200 Subject: [PATCH 736/774] Update data to match base year statistics --- .../data/material/n-fertilizer_techno-economic_new.xlsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/message_ix_models/data/material/n-fertilizer_techno-economic_new.xlsx b/message_ix_models/data/material/n-fertilizer_techno-economic_new.xlsx index ca4d266fd1..76fe9e9c6b 100644 --- a/message_ix_models/data/material/n-fertilizer_techno-economic_new.xlsx +++ b/message_ix_models/data/material/n-fertilizer_techno-economic_new.xlsx @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:608501fb2633a32c407722af8433dcf79d81619fd5364df7df79195e877a9ce7 -size 30230 +oid sha256:bb80d7e89adb97881ee09a557c8130ca5b2ef6236d28f7220cd99f418b58177b +size 30333 From a6577c6b41bb434f74ed4978000aa8863938e770 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Fri, 10 May 2024 10:20:15 +0200 Subject: [PATCH 737/774] Resolve conflict with input filea --- .../data/material/LED_LED_report_IAMC_sensitivity_R11.csv | 3 --- message_ix_models/data/material/aluminum_techno_economic.xlsx | 3 --- .../{ => deprecated}/LED_LED_report_IAMC_sensitivity_R12.csv | 0 .../data/material/n-fertilizer_techno-economic_new.xlsx | 3 --- 4 files changed, 9 deletions(-) delete mode 100644 message_ix_models/data/material/LED_LED_report_IAMC_sensitivity_R11.csv delete mode 100644 message_ix_models/data/material/aluminum_techno_economic.xlsx rename message_ix_models/data/material/{ => deprecated}/LED_LED_report_IAMC_sensitivity_R12.csv (100%) delete mode 100644 message_ix_models/data/material/n-fertilizer_techno-economic_new.xlsx diff --git a/message_ix_models/data/material/LED_LED_report_IAMC_sensitivity_R11.csv b/message_ix_models/data/material/LED_LED_report_IAMC_sensitivity_R11.csv deleted file mode 100644 index a8853545a7..0000000000 --- a/message_ix_models/data/material/LED_LED_report_IAMC_sensitivity_R11.csv +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:08652e54fca937ec1fb2da8e068020c61eb33eafbc233186a5dfdc4d4e1cbb15 -size 189976 diff --git a/message_ix_models/data/material/aluminum_techno_economic.xlsx b/message_ix_models/data/material/aluminum_techno_economic.xlsx deleted file mode 100644 index 1b3261e95e..0000000000 --- a/message_ix_models/data/material/aluminum_techno_economic.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:c8ea4248665241c6bd7525f068658913577d870d9bb881a5d4e4062c2aa7d478 -size 56261 diff --git a/message_ix_models/data/material/LED_LED_report_IAMC_sensitivity_R12.csv b/message_ix_models/data/material/deprecated/LED_LED_report_IAMC_sensitivity_R12.csv similarity index 100% rename from message_ix_models/data/material/LED_LED_report_IAMC_sensitivity_R12.csv rename to message_ix_models/data/material/deprecated/LED_LED_report_IAMC_sensitivity_R12.csv diff --git a/message_ix_models/data/material/n-fertilizer_techno-economic_new.xlsx b/message_ix_models/data/material/n-fertilizer_techno-economic_new.xlsx deleted file mode 100644 index 76fe9e9c6b..0000000000 --- a/message_ix_models/data/material/n-fertilizer_techno-economic_new.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:bb80d7e89adb97881ee09a557c8130ca5b2ef6236d28f7220cd99f418b58177b -size 30333 From 7ddb914a3bbe96aad5e441a0f2742a24dd52d0ef Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 23 Apr 2024 14:55:29 +0200 Subject: [PATCH 738/774] Remove deprecated input files and modules --- .../ammonia/~$fert_techno_economic.xlsx | 3 - .../~$fert_techno_economic_iea_data.xlsx | 3 - ...rnace_boiler_techno_economic_aluminum.xlsx | 3 - .../material/~$meth_h2_techno_economic.xlsx | 3 - .../~$n-fertilizer_techno-economic_new.xlsx | 3 - .../~$petrochemicals_techno_economic.xlsx | 3 - .../N-fertilizer/1.aggregate_region.R | 21 - .../material/N-fertilizer/AddClimatePolicy.py | 179 ---- .../model/material/N-fertilizer/AddTrade.py | 336 -------- .../model/material/N-fertilizer/LoadParams.py | 138 ---- .../N-fertilizer/ReportingToGLOBIOM.py | 227 ------ .../N-fertilizer/SetupNitrogenBase.py | 766 ------------------ .../model/material/material_demand/init.R | 142 ---- .../material_demand/init_modularized.R | 215 ----- .../refactor_mat_demand_calc.py | 673 --------------- .../ADVANCE_LCA_coefficients.R | 208 ----- .../ADVANCE_lca_coefficients_embedded.R | 97 --- .../data_material_intensities_rpy2.py | 76 -- .../model/material/report/materials.yaml | 21 - 19 files changed, 3117 deletions(-) delete mode 100644 message_ix_models/data/material/ammonia/~$fert_techno_economic.xlsx delete mode 100644 message_ix_models/data/material/ammonia/~$fert_techno_economic_iea_data.xlsx delete mode 100644 message_ix_models/data/material/generic_furnace_boiler_techno_economic_aluminum.xlsx delete mode 100644 message_ix_models/data/material/~$meth_h2_techno_economic.xlsx delete mode 100644 message_ix_models/data/material/~$n-fertilizer_techno-economic_new.xlsx delete mode 100644 message_ix_models/data/material/~$petrochemicals_techno_economic.xlsx delete mode 100644 message_ix_models/model/material/N-fertilizer/1.aggregate_region.R delete mode 100644 message_ix_models/model/material/N-fertilizer/AddClimatePolicy.py delete mode 100644 message_ix_models/model/material/N-fertilizer/AddTrade.py delete mode 100644 message_ix_models/model/material/N-fertilizer/LoadParams.py delete mode 100644 message_ix_models/model/material/N-fertilizer/ReportingToGLOBIOM.py delete mode 100644 message_ix_models/model/material/N-fertilizer/SetupNitrogenBase.py delete mode 100644 message_ix_models/model/material/material_demand/init.R delete mode 100644 message_ix_models/model/material/material_demand/init_modularized.R delete mode 100644 message_ix_models/model/material/material_demand/refactor_mat_demand_calc.py delete mode 100644 message_ix_models/model/material/material_intensity/ADVANCE_LCA_coefficients.R delete mode 100644 message_ix_models/model/material/material_intensity/ADVANCE_lca_coefficients_embedded.R delete mode 100644 message_ix_models/model/material/material_intensity/data_material_intensities_rpy2.py delete mode 100644 message_ix_models/model/material/report/materials.yaml diff --git a/message_ix_models/data/material/ammonia/~$fert_techno_economic.xlsx b/message_ix_models/data/material/ammonia/~$fert_techno_economic.xlsx deleted file mode 100644 index 38141ad6b1..0000000000 --- a/message_ix_models/data/material/ammonia/~$fert_techno_economic.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:c7a304b4faadb0fb2717ca8841246c6b4dd1ca23fc05137e7fe4e254365763b6 -size 165 diff --git a/message_ix_models/data/material/ammonia/~$fert_techno_economic_iea_data.xlsx b/message_ix_models/data/material/ammonia/~$fert_techno_economic_iea_data.xlsx deleted file mode 100644 index 38141ad6b1..0000000000 --- a/message_ix_models/data/material/ammonia/~$fert_techno_economic_iea_data.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:c7a304b4faadb0fb2717ca8841246c6b4dd1ca23fc05137e7fe4e254365763b6 -size 165 diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic_aluminum.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic_aluminum.xlsx deleted file mode 100644 index c6a0e2acca..0000000000 --- a/message_ix_models/data/material/generic_furnace_boiler_techno_economic_aluminum.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:bdfe832a631d0080892932af6ecb2e1ed3c1cc059adf182eb476c77de57d0ff5 -size 47993 diff --git a/message_ix_models/data/material/~$meth_h2_techno_economic.xlsx b/message_ix_models/data/material/~$meth_h2_techno_economic.xlsx deleted file mode 100644 index 38141ad6b1..0000000000 --- a/message_ix_models/data/material/~$meth_h2_techno_economic.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:c7a304b4faadb0fb2717ca8841246c6b4dd1ca23fc05137e7fe4e254365763b6 -size 165 diff --git a/message_ix_models/data/material/~$n-fertilizer_techno-economic_new.xlsx b/message_ix_models/data/material/~$n-fertilizer_techno-economic_new.xlsx deleted file mode 100644 index 38141ad6b1..0000000000 --- a/message_ix_models/data/material/~$n-fertilizer_techno-economic_new.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:c7a304b4faadb0fb2717ca8841246c6b4dd1ca23fc05137e7fe4e254365763b6 -size 165 diff --git a/message_ix_models/data/material/~$petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/~$petrochemicals_techno_economic.xlsx deleted file mode 100644 index 38141ad6b1..0000000000 --- a/message_ix_models/data/material/~$petrochemicals_techno_economic.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:c7a304b4faadb0fb2717ca8841246c6b4dd1ca23fc05137e7fe4e254365763b6 -size 165 diff --git a/message_ix_models/model/material/N-fertilizer/1.aggregate_region.R b/message_ix_models/model/material/N-fertilizer/1.aggregate_region.R deleted file mode 100644 index 13ce6fb3a9..0000000000 --- a/message_ix_models/model/material/N-fertilizer/1.aggregate_region.R +++ /dev/null @@ -1,21 +0,0 @@ -library(readxl) -library(countrycode) -library(tidyverse) - -raw.mapping <- read_xlsx('../MESSAGE_region_mapping_R14.xlsx') -raw.trade.FAO <- read.csv('../Comtrade/N fertil trade - FAOSTAT_data_9-25-2019.csv') - -trade.FAO <- raw.trade.FAO %>% mutate(ISO = countrycode(Area, 'country.name', 'iso3c')) %>% - mutate_cond(Area=='Serbia and Montenegro', ISO="SRB") %>% # Will be EEU in the end. So either SRB or MNE - left_join(raw.mapping) %>% - mutate(Element=gsub(' Quantity', '', Element)) - -trade.FAO.R14 <- trade.FAO %>% group_by(msgregion, Element, Year) %>% summarise(Value=sum(Value), Unit=first(Unit)) %>% - filter(!is.na(msgregion)) - -trade.FAO.R11 <- trade.FAO %>% mutate_cond(msgregion %in% c('RUS', 'CAS', 'SCS', 'UBM'), msgregion="FSU") %>% - group_by(msgregion, Element, Year) %>% summarise(Value=sum(Value), Unit=first(Unit)) %>% - filter(!is.na(msgregion)) - -write.csv(trade.FAO.R11, '../trade.FAO.R11.csv') -write.csv(trade.FAO.R14, '../trade.FAO.R14.csv') diff --git a/message_ix_models/model/material/N-fertilizer/AddClimatePolicy.py b/message_ix_models/model/material/N-fertilizer/AddClimatePolicy.py deleted file mode 100644 index 0264a31b4d..0000000000 --- a/message_ix_models/model/material/N-fertilizer/AddClimatePolicy.py +++ /dev/null @@ -1,179 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Created on Tue Aug 6 11:17:06 2019 - -@author: min -""" - -import time -import pandas as pd - - -#%% Add climate policies - - -mp = ix.Platform(dbprops=r"H:\MyDocuments\MESSAGE\message_ix\config\default.properties") - - -#%% - -# new model name in ix platform -modelName = "JM_GLB_NITRO" -basescenarioName = "Baseline" # CCS now merged to the Baseline -newscenarioName = "EmBound" # '2degreeC' # - -comment = ( - "MESSAGE_Global test for new representation of nitrogen cycle with climate policy" -) - -Sc_nitro = message_ix.Scenario(mp, modelName, basescenarioName) - - -#%% Clone -Sc_nitro_2C = Sc_nitro.clone(modelName, newscenarioName, comment) - -lasthistyear = 2020 # New last historical year -df_ACT = ( - Sc_nitro_2C.var("ACT", {"year_act": lasthistyear}) - .groupby(["node_loc", "technology", "year_act"]) - .sum() - .reset_index() -) -df_CAP_NEW = ( - Sc_nitro_2C.var("CAP_NEW", {"year_vtg": lasthistyear}) - .groupby(["node_loc", "technology", "year_vtg"]) - .sum() - .reset_index() -) -df_EXT = ( - Sc_nitro_2C.var("EXT", {"year": lasthistyear}) - .groupby(["node", "commodity", "grade", "year"]) - .sum() - .reset_index() -) - -Sc_nitro_2C.remove_solution() -Sc_nitro_2C.check_out() - -#%% Put global emissions bound -bound = 5000 # 15000 # -bound_emissions_2C = { - "node": "World", - "type_emission": "TCE", - "type_tec": "all", - "type_year": "cumulative", #'2050', # - "value": bound, # 1076.0, # 1990 and on - "unit": "tC", -} - -df = pd.DataFrame(bound_emissions_2C, index=[0]) - -Sc_nitro_2C.add_par("bound_emission", df) - -#%% Change first modeling year (dealing with the 5-year step scenario) - -df = Sc_nitro_2C.set("cat_year") -fyear = 2030 # 2025 -a = df[ - ((df.type_year == "cumulative") & (df.year < fyear)) - | ((df.type_year == "firstmodelyear") & (df.year < fyear)) -] - -df.loc[df.type_year == "firstmodelyear", "year"] = fyear -Sc_nitro_2C.add_set("cat_year", df) -Sc_nitro_2C.remove_set("cat_year", a) - -#%% Filling in history for 2020 - -lasthistyear_org = 2010 # 2015 -# historical_activity -df = Sc_nitro_2C.par("historical_activity", {"year_act": lasthistyear_org}) -# df = df[df.value > 0] -a = pd.merge( - df[["node_loc", "technology", "mode", "time", "unit"]], - df_ACT[["node_loc", "technology", "year_act", "lvl"]], - how="left", - on=["node_loc", "technology"], -).rename(columns={"lvl": "value"}) -a = a[a.value > 0] -a["unit"] = "GWa" -a["year_act"] = a["year_act"].astype(int) -Sc_nitro_2C.add_par("historical_activity", a) - -# historical_new_capacity -df = Sc_nitro_2C.par("historical_new_capacity", {"year_vtg": lasthistyear_org}) -# df = df[df.value > 0] -a = pd.merge( - df[["node_loc", "technology", "unit"]], - df_CAP_NEW[["node_loc", "technology", "year_vtg", "lvl"]], - how="left", - on=["node_loc", "technology"], -).rename(columns={"lvl": "value"}) -a = a[a.value > 0] -a["year_vtg"] = a["year_vtg"].astype(int) -Sc_nitro_2C.add_par("historical_new_capacity", a) - -# historical_extraction -df = Sc_nitro_2C.par("historical_extraction", {"year": lasthistyear_org}) -# df = df[df.value > 0] -a = pd.merge( - df[["node", "commodity", "grade", "unit"]], - df_EXT[["node", "commodity", "grade", "year", "lvl"]], - how="outer", - on=["node", "commodity", "grade"], -).rename(columns={"lvl": "value"}) -a = a[a.value > 0] -a["unit"] = "GWa" -a["year"] = a["year"].astype(int) -Sc_nitro_2C.add_par("historical_extraction", a) - -# historical_land -df = Sc_nitro_2C.par("historical_land", {"year": lasthistyear_org}) -df["year"] = lasthistyear -Sc_nitro_2C.add_par("historical_land", df) - -# historical_gdp -# Sc_INDC = mp.Scenario("CD_Links_SSP2", "INDCi_1000-con-prim-dir-ncr") -# df = Sc_nitro.par('historical_gdp') -# df = df[df.year < fyear] -# Sc_nitro_2C.add_par("historical_gdp", df) - - -#%% - -# Sc_nitro_2C.commit('Hydro AllReg-Global w/ 2C constraint (starting 2020)') -Sc_nitro_2C.commit("Fertilizer Global w/ 2C constraint (starting 2030)") - -# to_gdx only -# start_time = time.time() -# Sc_nitro_2C.to_gdx(r'..\..\message_ix\message_ix\model\data', "MsgData_"+Sc_nitro_2C.model+"_"+ -# Sc_nitro_2C.scenario+"_"+str(Sc_nitro_2C.version)) -# print(".to_gdx: %s seconds taken." % (time.time() - start_time)) - -# solve -start_time = time.time() -Sc_nitro_2C.solve( - model="MESSAGE", - case=Sc_nitro_2C.model + "_" + - # Sc_nitro_2C.scenario+"_"+str(Sc_nitro_2C.version)) - Sc_nitro_2C.scenario + "_" + str(bound), -) - -print(".solve: %.6s seconds taken." % (time.time() - start_time)) - - -#%% -rep = Reporter.from_scenario(Sc_nitro_2C) - -# Set up filters for N tecs -rep.set_filters( - t=newtechnames_ccs + newtechnames + ["NFert_imp", "NFert_exp", "NFert_trd"] -) - -# NF demand summary -NF = rep.add_product("useNF", "land_input", "LAND") - -print(rep.describe(rep.full_key("useNF"))) -rep.get("useNF:n-y") -rep.write("useNF:n-y", "nf_demand_" + str(bound) + "_notrade.xlsx") -rep.write("useNF:y", "nf_demand_total_" + str(bound) + "_notrade.xlsx") diff --git a/message_ix_models/model/material/N-fertilizer/AddTrade.py b/message_ix_models/model/material/N-fertilizer/AddTrade.py deleted file mode 100644 index 750268358c..0000000000 --- a/message_ix_models/model/material/N-fertilizer/AddTrade.py +++ /dev/null @@ -1,336 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Created on Tue Sep 24 14:36:18 2019 - -@author: min -""" -import numpy as np -import pandas as pd -import time - -import ixmp as ix -import message_ix - -import importlib - -# import LoadParams -importlib.reload(LoadParams) - -#%% Base model load - -# launch the IX modeling platform using the local default database -mp = ix.Platform( - dbprops=r"H:\MyDocuments\MESSAGE\message_ix\config\default.org.properties" -) - -# new model name in ix platform -modelName = "JM_GLB_NITRO" -basescenarioName = "NoPolicy" -newscenarioName = "NoPolicy_Trd" - -comment = ( - "MESSAGE global test for new representation of nitrogen cycle with global trade" -) - -Sc_nitro = message_ix.Scenario(mp, modelName, basescenarioName) - -#%% Clone the model - -Sc_nitro_trd = Sc_nitro.clone(modelName, newscenarioName, comment) - -Sc_nitro_trd.remove_solution() -Sc_nitro_trd.check_out() - -#%% Add tecs to set -# Only fertilizer traded for now (NH3 trade data not yet available) -comm_for_trd = ["Fertilizer Use|Nitrogen"] -lvl_for_trd = ["material_final"] -newtechnames_trd = ["NFert_trd"] -newtechnames_imp = ["NFert_imp"] -newtechnames_exp = ["NFert_exp"] - -# comm_for_trd = comm_for_trd # = ['NH3', 'Fertilizer Use|Nitrogen'] -# newtechnames_trd = ['NH3_trd', 'NFert_trd'] -# newtechnames_imp = ['NH3_imp', 'NFert_imp'] -# newtechnames_exp = ['NH3_exp', 'NFert_exp'] - -Sc_nitro_trd.add_set( - "technology", newtechnames_trd + newtechnames_imp + newtechnames_exp -) - -cat_add = pd.DataFrame( - { - "type_tec": ["import", "export"], # 'all' not need to be added here - "technology": newtechnames_imp + newtechnames_exp, - } -) - -Sc_nitro_trd.add_set("cat_tec", cat_add) - -#%% input & output - -for t in newtechnames_trd: - # output - df = Sc_nitro_trd.par("output", {"technology": ["coal_trd"]}) - df["technology"] = t - df["commodity"] = comm_for_trd[newtechnames_trd.index(t)] - df["value"] = 1 - df["unit"] = "Tg N/yr" - Sc_nitro_trd.add_par("output", df.copy()) - - df = Sc_nitro_trd.par("input", {"technology": ["coal_trd"]}) - df["technology"] = t - df["commodity"] = comm_for_trd[newtechnames_trd.index(t)] - df["value"] = 1 - df["unit"] = "Tg N/yr" - Sc_nitro_trd.add_par("input", df.copy()) - -reg = REGIONS.copy() -reg.remove("R11_GLB") - -for t in newtechnames_imp: - # output - df = Sc_nitro_trd.par( - "output", {"technology": ["coal_imp"], "node_loc": ["R11_CPA"]} - ) - df["technology"] = t - df["commodity"] = comm_for_trd[newtechnames_imp.index(t)] - df["value"] = 1 - df["unit"] = "Tg N/yr" - df["level"] = lvl_for_trd[newtechnames_imp.index(t)] - for r in reg: - df["node_loc"] = r - df["node_dest"] = r - Sc_nitro_trd.add_par("output", df.copy()) - - # input - df = Sc_nitro_trd.par( - "input", {"technology": ["coal_imp"], "node_loc": ["R11_CPA"]} - ) - df["technology"] = t - df["commodity"] = comm_for_trd[newtechnames_imp.index(t)] - df["value"] = 1 - df["unit"] = "Tg N/yr" - for r in reg: - df["node_loc"] = r - Sc_nitro_trd.add_par("input", df.copy()) - -for t in newtechnames_exp: - # output - df = Sc_nitro_trd.par( - "output", {"technology": ["coal_exp"], "node_loc": ["R11_CPA"]} - ) - df["technology"] = t - df["commodity"] = comm_for_trd[newtechnames_exp.index(t)] - df["value"] = 1 - df["unit"] = "Tg N/yr" - for r in reg: - df["node_loc"] = r - Sc_nitro_trd.add_par("output", df.copy()) - - # input - df = Sc_nitro_trd.par( - "input", {"technology": ["coal_exp"], "node_loc": ["R11_CPA"]} - ) - df["technology"] = t - df["commodity"] = comm_for_trd[newtechnames_exp.index(t)] - df["value"] = 1 - df["unit"] = "Tg N/yr" - df["level"] = lvl_for_trd[newtechnames_exp.index(t)] - for r in reg: - df["node_loc"] = r - df["node_origin"] = r - Sc_nitro_trd.add_par("input", df.copy()) - -# Need to incorporate the regional trade pattern - -#%% Cost - -for t in newtechnames_exp: - df = Sc_nitro_trd.par("inv_cost", {"technology": ["coal_exp"]}) - df["technology"] = t - Sc_nitro_trd.add_par("inv_cost", df) - - df = Sc_nitro_trd.par("var_cost", {"technology": ["coal_exp"]}) - df["technology"] = t - Sc_nitro_trd.add_par("var_cost", df) - - df = Sc_nitro_trd.par("fix_cost", {"technology": ["coal_exp"]}) - df["technology"] = t - Sc_nitro_trd.add_par("fix_cost", df) - -for t in newtechnames_imp: - # No inv_cost for importing tecs - # df = Sc_nitro_trd.par("inv_cost", {"technology":["coal_imp"]}) - # df['technology'] = t - # Sc_nitro_trd.add_par("inv_cost", df) - - df = Sc_nitro_trd.par("var_cost", {"technology": ["coal_imp"]}) - df["technology"] = t - Sc_nitro_trd.add_par("var_cost", df) - - df = Sc_nitro_trd.par("fix_cost", {"technology": ["coal_imp"]}) - df["technology"] = t - Sc_nitro_trd.add_par("fix_cost", df) - -for t in newtechnames_trd: - df = Sc_nitro_trd.par("var_cost", {"technology": ["coal_trd"]}) - df["technology"] = t - Sc_nitro_trd.add_par("var_cost", df) - - -#%% Other background variables - -paramList_tec = [ - x for x in Sc_nitro_trd.par_list() if "technology" in Sc_nitro_trd.idx_sets(x) -] -# paramList_comm = [x for x in Sc_nitro.par_list() if 'commodity' in Sc_nitro.idx_sets(x)] - - -def get_params_with_tech(scen, name): - result = [] - # Iterate over all parameters with a tech dimension - for par_name in paramList_tec: - if len(scen.par(par_name, filters={"technology": name})): - # Parameter has >= 1 data point with tech *name* - result.append(par_name) - return result - - -params_exp = get_params_with_tech(Sc_nitro_trd, "coal_exp") -params_imp = get_params_with_tech(Sc_nitro_trd, "coal_imp") -params_trd = get_params_with_tech(Sc_nitro_trd, "coal_trd") - -# Got too slow for some reason -# params_exp = [x for x in paramList_tec if 'coal_exp' in set(Sc_nitro_trd.par(x)["technology"].tolist())] -# params_imp = [x for x in paramList_tec if 'coal_imp' in set(Sc_nitro_trd.par(x)["technology"].tolist())] -# params_trd = [x for x in paramList_tec if 'coal_trd' in set(Sc_nitro_trd.par(x)["technology"].tolist())] - -a = set(params_exp + params_imp + params_trd) -suffix = ("cost", "put") -prefix = ("historical", "ref", "relation") -a = a - set( - [x for x in a if x.endswith(suffix)] - + [x for x in a if x.startswith(prefix)] - + ["emission_factor"] -) - -for p in list(a): - for t in newtechnames_exp: - df = Sc_nitro_trd.par(p, {"technology": ["coal_exp"]}) - df["technology"] = t - if df.size: - Sc_nitro_trd.add_par(p, df.copy()) - - for t in newtechnames_imp: - df = Sc_nitro_trd.par(p, {"technology": ["coal_imp"]}) - df["technology"] = t - if df.size: - Sc_nitro_trd.add_par(p, df.copy()) - - for t in newtechnames_trd: - df = Sc_nitro_trd.par(p, {"technology": ["coal_trd"]}) - df["technology"] = t - if df.size: - Sc_nitro_trd.add_par(p, df.copy()) - -# Found coal_exp doesn't have full cells filled for technical_lifetime. -for t in newtechnames_exp: - df = Sc_nitro_trd.par( - "technical_lifetime", {"technology": t, "node_loc": ["R11_CPA"]} - ) - for r in reg: - df["node_loc"] = r - Sc_nitro_trd.add_par("technical_lifetime", df.copy()) - - -#%% Histrorical trade activity -# Export capacity - understood as infrastructure enabling the trade activity (port, rail etc.) - -# historical_activity -N_trade = LoadParams.N_trade_R11.copy() - -df_histexp = N_trade.loc[ - (N_trade.Element == "Export") & (N_trade.year_act < 2015), -] -df_histexp = df_histexp.assign(mode="M1") -df_histexp = df_histexp.assign(technology=newtechnames_exp[0]) # t -df_histexp = df_histexp.drop(columns="Element") - -df_histimp = N_trade.loc[ - (N_trade.Element == "Import") & (N_trade.year_act < 2015), -] -df_histimp = df_histimp.assign(mode="M1") -df_histimp = df_histimp.assign(technology=newtechnames_imp[0]) # t -df_histimp = df_histimp.drop(columns="Element") - -# GLB trd historical activities (Now equal to the sum of imports) -dftrd = Sc_nitro_trd.par("historical_activity", {"technology": ["coal_trd"]}) -dftrd = dftrd.loc[ - (dftrd.year_act < 2015) & (dftrd.year_act > 2000), -] -dftrd.value = df_histimp.groupby(["year_act"]).sum().values -dftrd["unit"] = "Tg N/yr" -dftrd["technology"] = newtechnames_trd[0] -Sc_nitro_trd.add_par("historical_activity", dftrd) -Sc_nitro_trd.add_par("historical_activity", df_histexp.append(df_histimp)) - -# historical_new_capacity -trdlife = Sc_nitro_trd.par("technical_lifetime", {"technology": ["NFert_exp"]}).value[0] - -df_histcap = df_histexp.drop(columns=["time", "mode"]) -df_histcap = df_histcap.rename(columns={"year_act": "year_vtg"}) -df_histcap.value = df_histcap.value.values / trdlife - -Sc_nitro_trd.add_par("historical_new_capacity", df_histcap) - - -#%% - -# Adjust i_feed demand -# demand_fs_org is the original i-feed in the reference SSP2 scenario -""" -In the scenario w/o trade, I assumed 100% NH3 demand is produced in each region. -Now with trade, I subtract the net import (of tN) from total demand, and the difference is produced regionally. -Still this does not include NH3 trade, so will not match the historical NH3 regional production. -Also, historical global production exceeds the global demand level from SSP2 scenario for the same year. -I currently ignore this excess production and only produce what is demanded. -""" -df = demand_fs_org.loc[demand_fs_org.year == 2010, :].join( - LoadParams.N_feed.set_index("node"), on="node" -) -sh = pd.DataFrame( - { - "node": demand_fs_org.loc[demand_fs_org.year == 2010, "node"], - "r_feed": df.totENE / df.value, - } -) # share of NH3 energy among total feedstock (no trade assumed) -df = demand_fs_org.join(sh.set_index("node"), on="node") -df.value *= 1 - df.r_feed # Carve out the same % from tot i_feed values -df = df.drop("r_feed", axis=1) -Sc_nitro_trd.add_par("demand", df) - - -#%% Solve the scenario - -Sc_nitro_trd.commit( - "Nitrogen Fertilizer for global model with fertilizer trade via global pool" -) - -start_time = time.time() - -Sc_nitro_trd.solve( - model="MESSAGE", - case=Sc_nitro_trd.model - + "_" - + Sc_nitro_trd.scenario - + "_" - + str(Sc_nitro_trd.version), -) - -print(".solve: %.6s seconds taken." % (time.time() - start_time)) - -#%% utils -# Sc_nitro_trd.discard_changes() - -# Sc_nitro_trd = message_ix.Scenario(mp, modelName, newscenarioName) diff --git a/message_ix_models/model/material/N-fertilizer/LoadParams.py b/message_ix_models/model/material/N-fertilizer/LoadParams.py deleted file mode 100644 index 5777e7692a..0000000000 --- a/message_ix_models/model/material/N-fertilizer/LoadParams.py +++ /dev/null @@ -1,138 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Created on Tue Jun 4 12:01:47 2019 - -@author: min - -Load parameters for the new technologies -""" - -import pandas as pd - -# Read parameters in xlsx -te_params = pd.read_excel(r"..\n-fertilizer_techno-economic.xlsx", sheet_name="Sheet1") -n_inputs_per_tech = 12 # Number of input params per technology - -inv_cost = te_params[2010][ - list(range(0, te_params.shape[0], n_inputs_per_tech)) -].reset_index(drop=True) -fix_cost = te_params[2010][ - list(range(1, te_params.shape[0], n_inputs_per_tech)) -].reset_index(drop=True) -var_cost = te_params[2010][ - list(range(2, te_params.shape[0], n_inputs_per_tech)) -].reset_index(drop=True) -technical_lifetime = te_params[2010][ - list(range(3, te_params.shape[0], n_inputs_per_tech)) -].reset_index(drop=True) -input_fuel = te_params[2010][ - list(range(4, te_params.shape[0], n_inputs_per_tech)) -].reset_index(drop=True) -input_fuel[0:5] = input_fuel[0:5] * 0.0317 # 0.0317 GWa/PJ, GJ/t = PJ/Mt NH3 -input_elec = te_params[2010][ - list(range(5, te_params.shape[0], n_inputs_per_tech)) -].reset_index(drop=True) -input_elec = input_elec * 0.0317 # 0.0317 GWa/PJ -input_water = te_params[2010][ - list(range(6, te_params.shape[0], n_inputs_per_tech)) -].reset_index(drop=True) -output_NH3 = te_params[2010][ - list(range(7, te_params.shape[0], n_inputs_per_tech)) -].reset_index(drop=True) -output_water = te_params[2010][ - list(range(8, te_params.shape[0], n_inputs_per_tech)) -].reset_index(drop=True) -output_heat = te_params[2010][ - list(range(9, te_params.shape[0], n_inputs_per_tech)) -].reset_index(drop=True) -output_heat = output_heat * 0.0317 # 0.0317 GWa/PJ -emissions = ( - te_params[2010][list(range(10, te_params.shape[0], n_inputs_per_tech))].reset_index( - drop=True - ) - * 12 - / 44 -) # CO2 to C -capacity_factor = te_params[2010][ - list(range(11, te_params.shape[0], n_inputs_per_tech)) -].reset_index(drop=True) - - -#%% Demand scenario [Mt N/year] from GLOBIOM -# N_demand_FSU = pd.read_excel(r'..\CD-Links SSP2 N-fertilizer demand.FSU.xlsx', sheet_name='data', nrows=3) -N_demand_GLO = pd.read_excel( - r"..\CD-Links SSP2 N-fertilizer demand.Global.xlsx", sheet_name="data" -) - -# NH3 feedstock share by region in 2010 (from http://ietd.iipnetwork.org/content/ammonia#benchmarks) -feedshare_GLO = pd.read_excel( - r"..\Ammonia feedstock share.Global.xlsx", sheet_name="Sheet2", skiprows=13 -) - -# Regional N demaand in 2010 -ND = N_demand_GLO.loc[N_demand_GLO.Scenario == "NoPolicy", ["Region", 2010]] -ND = ND[ND.Region != "World"] -ND.Region = "R11_" + ND.Region -ND = ND.set_index("Region") - -# Derive total energy (GWa) of NH3 production (based on demand 2010) -N_energy = feedshare_GLO[feedshare_GLO.Region != "R11_GLB"].join(ND, on="Region") -N_energy = pd.concat( - [ - N_energy.Region, - N_energy[["gas_pct", "coal_pct", "oil_pct"]].multiply( - N_energy[2010], axis="index" - ), - ], - axis=1, -) -N_energy.gas_pct *= input_fuel[2] * 17 / 14 # NH3 / N -N_energy.coal_pct *= input_fuel[3] * 17 / 14 -N_energy.oil_pct *= input_fuel[4] * 17 / 14 -N_energy = pd.concat([N_energy.Region, N_energy.sum(axis=1)], axis=1).rename( - columns={0: "totENE", "Region": "node"} -) # GWa - - -#%% Import trade data (from FAO) - -N_trade_R14 = pd.read_csv(r"..\trade.FAO.R14.csv", index_col=0) - -N_trade_R11 = pd.read_csv(r"..\trade.FAO.R11.csv", index_col=0) -N_trade_R11.msgregion = "R11_" + N_trade_R11.msgregion -N_trade_R11.Value = N_trade_R11.Value / 1e6 -N_trade_R11.Unit = "Tg N/yr" -N_trade_R11 = N_trade_R11.assign(time="year") -N_trade_R11 = N_trade_R11.rename( - columns={ - "Value": "value", - "Unit": "unit", - "msgregion": "node_loc", - "Year": "year_act", - } -) - -df = N_trade_R11.loc[ - N_trade_R11.year_act == 2010, -] -df = df.pivot(index="node_loc", columns="Element", values="value") -NP = pd.DataFrame({"netimp": df.Import - df.Export, "demand": ND[2010]}) -NP["prod"] = NP.demand - NP.netimp - -# Derive total energy (GWa) of NH3 production (based on demand 2010) -N_feed = feedshare_GLO[feedshare_GLO.Region != "R11_GLB"].join(NP, on="Region") -N_feed = pd.concat( - [ - N_feed.Region, - N_feed[["gas_pct", "coal_pct", "oil_pct"]].multiply( - N_feed["prod"], axis="index" - ), - ], - axis=1, -) -N_feed.gas_pct *= input_fuel[2] * 17 / 14 -N_feed.coal_pct *= input_fuel[3] * 17 / 14 -N_feed.oil_pct *= input_fuel[4] * 17 / 14 -N_feed = pd.concat([N_feed.Region, N_feed.sum(axis=1)], axis=1).rename( - columns={0: "totENE", "Region": "node"} -) # GWa diff --git a/message_ix_models/model/material/N-fertilizer/ReportingToGLOBIOM.py b/message_ix_models/model/material/N-fertilizer/ReportingToGLOBIOM.py deleted file mode 100644 index 4cd10e4f73..0000000000 --- a/message_ix_models/model/material/N-fertilizer/ReportingToGLOBIOM.py +++ /dev/null @@ -1,227 +0,0 @@ -""" -Creating outputs for GLOBIOM input -""" - -import ixmp as ix -import message_ix - -from message_ix.reporting import Reporter -from pathlib import Path -import pandas as pd - -import matplotlib.pyplot as plt -import pyam -import os - -os.chdir(r"./code.Global") - -#%% Constants - -model_name = "JM_GLB_NITRO_MACRO_TRD" - -scen_names = [ - "baseline", - "NPi2020-con-prim-dir-ncr", - "NPi2020_1600-con-prim-dir-ncr", - "NPi2020_400-con-prim-dir-ncr", -] - -newtechnames = [ - "biomass_NH3", - "electr_NH3", - "gas_NH3", - "coal_NH3", - "fueloil_NH3", - "NH3_to_N_fertil", -] -tec_for_ccs = list(newtechnames[i] for i in [0, 2, 3, 4]) -newtechnames_ccs = list( - map(lambda x: str(x) + "_ccs", tec_for_ccs) -) # include biomass in CCS, newtechnames[2:5])) - -# Units are usually taken care of .yaml in message_data. -# In case I don't use message_data for reporting, I need to deal with the units here. -ix.reporting.configure( - units={"replace": {"???": "", "-": ""}} -) # '???' and '-' are not handled in pyint(?) - -mp = ix.Platform( - dbprops=r"H:\MyDocuments\MESSAGE\message_ix\config\default.org.properties" -) - - -def GenerateOutput(model, scen, rep): - - rep.set_filters() - rep.set_filters(t=newtechnames_ccs + newtechnames) - - # 1. Total NF demand - rep.add_product("useNF", "land_input", "LAND") - - def collapse(df): - df["variable"] = "Nitrogen demand" - df["unit"] = "Mt N/yr" - return df - - a = rep.convert_pyam("useNF:n-y", "y", collapse=collapse) - rep.write(a[0], Path("nf_demand_" + model + "_" + scen + ".xlsx")) - - """ - 'emi' not working with filters on NH3 technologies for now. (This was because of units '???' and '-'.) - """ - # 2. Total emissions - rep.set_filters() - rep.set_filters( - t=(newtechnames_ccs + newtechnames)[:-1], e=["CO2_transformation"] - ) # and NH3_to_N_fertil does not have emission factor - - def collapse_emi(df): - df["variable"] = "Emissions|CO2|" + df.pop("t") - df["unit"] = "Mt CO2" - return df - - a = rep.convert_pyam("emi:nl-t-ya", "ya", collapse=collapse_emi) - rep.write(a[0], Path("nf_emissions_CO2_" + model + "_" + scen + ".xlsx")) - - # 3. Total inputs (incl. final energy) to fertilizer processes - rep.set_filters() - rep.set_filters( - t=(newtechnames_ccs + newtechnames), - c=["coal", "gas", "electr", "biomass", "fueloil"], - ) - - def collapse_in(df): - df["variable"] = "Final energy|" + df.pop("c") - df["unit"] = "GWa" - return df - - a = rep.convert_pyam("in:nl-ya-c", "ya", collapse=collapse_in) - rep.write(a[0], Path("nf_input_" + model + "_" + scen + ".xlsx")) - - # 4. Commodity price - rep.set_filters() - rep.set_filters(l=["material_final", "material_interim"]) - - def collapse_N(df): - df.loc[df["c"] == "NH3", "unit"] = "$/tNH3" - df.loc[df["c"] == "Fertilizer Use|Nitrogen", "unit"] = "$/tN" - df["variable"] = "Price|" + df.pop("c") - return df - - a = rep.convert_pyam("PRICE_COMMODITY:n-c-y", "y", collapse=collapse_N) - rep.write(a[0], Path("price_commodity_" + model + "_" + scen + ".xlsx")) - - # 5. Carbon price - if scen != "baseline": - rep.set_filters() - - a = rep.convert_pyam("PRICE_EMISSION", "y") - rep.write(a[0], Path("price_emission_" + model + "_" + scen + ".xlsx")) - - -# Generate individual xlsx -for sc in scen_names: - Sc_ref = message_ix.Scenario(mp, model_name, sc) - repo = Reporter.from_scenario(Sc_ref) - GenerateOutput(model_name, sc, repo) - -# Combine xlsx per each output variable -for cases in [ - "nf_demand", - "nf_emissions_CO2", - "nf_input", - "price_commodity", - "price_emission", -]: - infiles = [] - for sc in scen_names: - if sc == "baseline" and cases == "price_emission": - continue - infiles.append(pd.read_excel(cases + "_" + model_name + "_" + sc + ".xlsx")) - appended_df = pd.concat(infiles, join="outer", sort=False) - appended_df.to_excel(cases + "-" + model_name + ".xlsx", index=False) - - -#%% Generate plots - -from pyam.plotting import OUTSIDE_LEGEND - - -def plot_NF_result(case="nf_demand", model=model_name, scen="baseline"): - """ - - Generate PNG plots for each case of ['nf_demand', 'nf_emissions_CO2', 'nf_input', 'price_commodity'] - - Data read from the xlsx files created above - """ - if case in ["nf_demand", "all"]: - data = pyam.IamDataFrame( - data="nf_demand" + "_" + model + "_" + scen + ".xlsx", encoding="utf-8" - ) - fig, ax = plt.subplots(figsize=(12, 12)) - data.filter(level=0).stack_plot(ax=ax, stack="region") - plt.savefig("./plots/nf_demand" + "_" + model + "_" + scen + ".png") - - # Pyam currently doesn't allow stack_plot for the next two cases for unknown reasons. - # (https://github.com/IAMconsortium/pyam/issues/296) - if case in ["nf_input", "all"]: - data = pyam.IamDataFrame( - data="nf_input" + "_" + model + "_" + scen + ".xlsx", encoding="utf-8" - ) - # aggregate technologies over regions (get global sums) - for v in list(data.variables()): - data.aggregate_region(v, append=True) - - fig, ax = plt.subplots(figsize=(12, 12)) - # data.filter(region="World").stack_plot(ax=ax, legend=OUTSIDE_LEGEND['right']) - data.filter(region="World").line_plot(ax=ax, legend=OUTSIDE_LEGEND["right"]) - plt.savefig("./plots/nf_input" + "_" + model + "_" + scen + ".png") - - if case in ["nf_emissions_CO2", "all"]: - data = pyam.IamDataFrame( - data="nf_emissions_CO2" + "_" + model + "_" + scen + ".xlsx", - encoding="utf-8", - ) - for v in list(data.variables()): - data.aggregate_region(v, append=True) - - fig, ax = plt.subplots(figsize=(12, 12)) - data.filter(region="World").line_plot(ax=ax, legend=OUTSIDE_LEGEND["right"]) - plt.savefig("./plots/nf_emissions_CO2" + "_" + model + "_" + scen + ".png") - - if case in ["price_commodity", "all"]: - # N fertilizer - data = pyam.IamDataFrame( - data="price_commodity" + "_" + model + "_" + scen + ".xlsx", - encoding="utf-8", - ) - fig, ax = plt.subplots(figsize=(12, 12)) - data.filter(variable="Price|Fertilizer Use|*", region="R11_AFR").line_plot( - ax=ax, legend=False - ) # Identical across regoins through trade - plt.savefig("./plots/price_NF" + "_" + model + "_" + scen + ".png") - - # NH3 - fig, ax = plt.subplots(figsize=(12, 12)) - data.filter(variable="Price|NH3").line_plot( - ax=ax, color="region", legend=OUTSIDE_LEGEND["right"] - ) - plt.savefig("./plots/price_NH3" + "_" + model + "_" + scen + ".png") - - -""" -scen_names = ["baseline", - "NPi2020-con-prim-dir-ncr", - "NPi2020_1600-con-prim-dir-ncr", - "NPi2020_400-con-prim-dir-ncr"] -""" - -cases = ["nf_demand", "nf_emissions_CO2", "nf_input", "price_commodity"] - -# Individual calls -# plot_NF_result(case='nf_demand', scen='NPi2020_1600-con-prim-dir-ncr') -# plot_NF_result(case='price_commodity', scen='NPi2020_1600-con-prim-dir-ncr') -# plot_NF_result(case='nf_input',scen='NPi2020_400-con-prim-dir-ncr') -# plot_NF_result(case='nf_emissions_CO2',scen='NPi2020_400-con-prim-dir-ncr') - -# call for all plots -for sc in scen_names: - plot_NF_result(case="all", scen=sc) diff --git a/message_ix_models/model/material/N-fertilizer/SetupNitrogenBase.py b/message_ix_models/model/material/N-fertilizer/SetupNitrogenBase.py deleted file mode 100644 index b3195db3eb..0000000000 --- a/message_ix_models/model/material/N-fertilizer/SetupNitrogenBase.py +++ /dev/null @@ -1,766 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Spyder Editor - -This is a temporary script file. -""" - -# load required packages -# import itertools -import pandas as pd -import numpy as np - -import matplotlib.pyplot as plt - -plt.style.use("ggplot") - -import ixmp as ix -import message_ix - -import os -import time, re - -# os.chdir(r'H:\MyDocuments\MESSAGE\message_data') -##from tools.post_processing.iamc_report_hackathon import report as reporting -# -# from message_ix.reporting import Reporter - -os.chdir(r"H:\MyDocuments\MESSAGE\N-fertilizer\code.Global") - -# import LoadParams # Load techno-economic param values from the excel file -# exec(open(r'LoadParams.py').read()) -import importlib - -# import LoadParams -importlib.reload(LoadParams) - -#%% Set up scope - -scen_names = { - "baseline": "NoPolicy", - "NPi2020-con-prim-dir-ncr": "NPi", - "NPi2020_1000-con-prim-dir-ncr": "NPi2020_1000", - "NPi2020_400-con-prim-dir-ncr": "NPi2020_400", -} - -run_scen = "baseline" # "NPiREF-con-prim-dir-ncr" - -# details for existing datastructure to be updated and annotation log in database -modelName = "CD_Links_SSP2" -scenarioName = run_scen - -# new model name in ix platform -newmodelName = "JM_GLB_NITRO" -newscenarioName = scen_names[run_scen] - -comment = "CD_Links_SSP2 test for new representation of nitrogen cycle" - -REGIONS = [ - "R11_AFR", - "R11_CPA", - "R11_EEU", - "R11_FSU", - "R11_LAM", - "R11_MEA", - "R11_NAM", - "R11_PAO", - "R11_PAS", - "R11_SAS", - "R11_WEU", - "R11_GLB", -] - - -#%% Load scenario - -# launch the IX modeling platform using the local default database -mp = ix.Platform(dbprops=r"H:\MyDocuments\MESSAGE\message_ix\config\default.properties") - -# Reference scenario -Sc_ref = message_ix.Scenario(mp, modelName, scenarioName) -paramList_tec = [x for x in Sc_ref.par_list() if "technology" in Sc_ref.idx_sets(x)] -params_src = [ - x for x in paramList_tec if "solar_i" in set(Sc_ref.par(x)["technology"].tolist()) -] - -#%% Clone -Sc_nitro = Sc_ref.clone(newmodelName, newscenarioName) -# Sc_nitro = message_ix.Scenario(mp, newmodelName, newscenarioName) - -Sc_nitro.remove_solution() -Sc_nitro.check_out() - - -#%% Add new technologies & commodities - -newtechnames = [ - "biomass_NH3", - "electr_NH3", - "gas_NH3", - "coal_NH3", - "fueloil_NH3", - "NH3_to_N_fertil", -] -newcommnames = ["NH3", "Fertilizer Use|Nitrogen"] #'N-fertilizer' -newlevelnames = ["material_interim", "material_final"] - -Sc_nitro.add_set("technology", newtechnames) -Sc_nitro.add_set("commodity", newcommnames) -Sc_nitro.add_set("level", newlevelnames) -Sc_nitro.add_set("type_tec", "industry") - -cat_add = pd.DataFrame( - { - "type_tec": "industry", #'non_energy' not in Russia model - "technology": newtechnames, - } -) - -Sc_nitro.add_set("cat_tec", cat_add) - - -#%% Connect input & output - -### NH3 production process -for t in newtechnames[0:5]: - # output - df = Sc_nitro.par("output", {"technology": ["solar_i"]}) # lifetime = 15 year - df["technology"] = t - df["commodity"] = newcommnames[0] - df["level"] = newlevelnames[0] - Sc_nitro.add_par("output", df) - df["commodity"] = "d_heat" - df["level"] = "secondary" - df["value"] = LoadParams.output_heat[newtechnames.index(t)] - Sc_nitro.add_par("output", df) - - # Fuel input - df = df.rename(columns={"time_dest": "time_origin", "node_dest": "node_origin"}) - if t == "biomass_NH3": - lvl = "primary" - else: - lvl = "secondary" - df["level"] = lvl - # Non-elec fuels - if t[:-4] != "electr": # electr has only electr input (no other fuel) - df["commodity"] = t[:-4] # removing '_NH3' - df["value"] = LoadParams.input_fuel[newtechnames.index(t)] - Sc_nitro.add_par("input", df) - # Electricity input (for any fuels) - df["commodity"] = "electr" # All have electricity input - df["value"] = LoadParams.input_elec[newtechnames.index(t)] - df["level"] = "secondary" - Sc_nitro.add_par("input", df) - - # Water input # Not exist in Russia model - CHECK for global model - df["level"] = "water_supply" # final for feedstock input - df["commodity"] = "freshwater_supply" # All have electricity input - df["value"] = LoadParams.input_water[newtechnames.index(t)] - Sc_nitro.add_par("input", df) - - df = Sc_nitro.par( - "technical_lifetime", {"technology": ["solar_i"]} - ) # lifetime = 15 year - df["technology"] = t - Sc_nitro.add_par("technical_lifetime", df) - - # Costs - df = Sc_nitro.par("inv_cost", {"technology": ["solar_i"]}) - df["technology"] = t - df["value"] = LoadParams.inv_cost[newtechnames.index(t)] - Sc_nitro.add_par("inv_cost", df) - - df = Sc_nitro.par("fix_cost", {"technology": ["solar_i"]}) - df["technology"] = t - df["value"] = LoadParams.fix_cost[newtechnames.index(t)] - Sc_nitro.add_par("fix_cost", df) - - df = Sc_nitro.par("var_cost", {"technology": ["solar_i"]}) - df["technology"] = t - df["value"] = LoadParams.var_cost[newtechnames.index(t)] - Sc_nitro.add_par("var_cost", df) - - # Emission factor - df = Sc_nitro.par("output", {"technology": ["solar_i"]}) - df = df.drop(columns=["node_dest", "commodity", "level", "time"]) - df = df.rename(columns={"time_dest": "emission"}) - df["emission"] = "CO2_transformation" # Check out what it is - df["value"] = LoadParams.emissions[newtechnames.index(t)] - df["technology"] = t - df["unit"] = "???" - Sc_nitro.add_par("emission_factor", df) - df["emission"] = "CO2" # Check out what it is - df["value"] = 0 # Set the same as CO2_transformation - Sc_nitro.add_par("emission_factor", df) - - # Emission factors in relation (Currently these are more correct values than emission_factor) - df = Sc_nitro.par( - "relation_activity", {"relation": ["CO2_cc"], "technology": ["h2_smr"]} - ) - df["value"] = LoadParams.emissions[newtechnames.index(t)] - df["technology"] = t - df["unit"] = "???" - Sc_nitro.add_par("relation_activity", df) - # df = Sc_nitro.par("relation_activity", {"relation":["CO2_cc"], "technology":t}) - # Sc_nitro.remove_par("relation_activity", df) - - # Capacity factor - df = Sc_nitro.par("capacity_factor", {"technology": ["solar_i"]}) - df["technology"] = t - df["value"] = LoadParams.capacity_factor[newtechnames.index(t)] - Sc_nitro.add_par("capacity_factor", df) - - -### N-fertilizer from NH3 (generic) -comm = newcommnames[-1] -tech = newtechnames[-1] - -# output -df = Sc_nitro.par("output", {"technology": ["solar_i"]}) -df["technology"] = tech -df["commodity"] = comm #'N-fertilizer' -df["level"] = newlevelnames[-1] #'land_use_reporting' -Sc_nitro.add_par("output", df) - -# input -df = Sc_nitro.par("output", {"technology": ["solar_i"]}) -df = df.rename(columns={"time_dest": "time_origin", "node_dest": "node_origin"}) -df["technology"] = tech -df["level"] = newlevelnames[0] -df["commodity"] = newcommnames[0] #'NH3' -df["value"] = LoadParams.input_fuel[newtechnames.index(tech)] # NH3/N = 17/14 -Sc_nitro.add_par("input", df) - -df = Sc_nitro.par( - "technical_lifetime", {"technology": ["solar_i"]} -) # lifetime = 15 year -df["technology"] = tech -Sc_nitro.add_par("technical_lifetime", df) - -# Costs -df = Sc_nitro.par("inv_cost", {"technology": ["solar_i"]}) -df["value"] = LoadParams.inv_cost[newtechnames.index(tech)] -df["technology"] = tech -Sc_nitro.add_par("inv_cost", df) - -df = Sc_nitro.par("fix_cost", {"technology": ["solar_i"]}) -df["technology"] = tech -df["value"] = LoadParams.fix_cost[newtechnames.index(tech)] -Sc_nitro.add_par("fix_cost", df) - -df = Sc_nitro.par("var_cost", {"technology": ["solar_i"]}) -df["technology"] = tech -df["value"] = LoadParams.var_cost[newtechnames.index(tech)] -Sc_nitro.add_par("var_cost", df) - -# Emission factor (<0 for this) -""" -Urea applied in the field will emit all CO2 back, so we don't need this. -Source: https://ammoniaindustry.com/urea-production-is-not-carbon-sequestration/#targetText=To%20make%20urea%2C%20fertilizer%20producers,through%20the%20production%20of%20urea. -""" -# df = Sc_nitro.par("output", {"technology":["solar_i"]}) -# df = df.drop(columns=['node_dest', 'commodity', 'level', 'time']) -# df = df.rename(columns={'time_dest':'emission'}) -# df['emission'] = 'CO2_transformation' # Check out what it is -# df['value'] = LoadParams.emissions[newtechnames.index(tech)] -# df['technology'] = tech -# df['unit'] = '???' -# Sc_nitro.add_par("emission_factor", df) -# df['emission'] = 'CO2' # Check out what it is -# Sc_nitro.add_par("emission_factor", df) - - -#%% Copy some background parameters# - -par_bgnd = [x for x in params_src if "_up" in x] + [x for x in params_src if "_lo" in x] -par_bgnd = list( - set(par_bgnd) - - set( - [ - "level_cost_activity_soft_lo", - "level_cost_activity_soft_up", - "growth_activity_lo", - ] - ) -) # , 'soft_activity_lo', 'soft_activity_up'])) -for t in par_bgnd[:-1]: - df = Sc_nitro.par(t, {"technology": ["solar_i"]}) # lifetime = 15 year - for q in newtechnames: - df["technology"] = q - Sc_nitro.add_par(t, df) - -df = Sc_nitro.par("initial_activity_lo", {"technology": ["gas_extr_mpen"]}) -for q in newtechnames: - df["technology"] = q - Sc_nitro.add_par("initial_activity_lo", df) - -df = Sc_nitro.par("growth_activity_lo", {"technology": ["gas_extr_mpen"]}) -for q in newtechnames: - df["technology"] = q - Sc_nitro.add_par("growth_activity_lo", df) - -#%% Process the regional historical activities - -fs_GLO = LoadParams.feedshare_GLO.copy() -fs_GLO.insert(1, "bio_pct", 0) -fs_GLO.insert(2, "elec_pct", 0) -# 17/14 NH3:N ratio, to get NH3 activity based on N demand => No NH3 loss assumed during production -fs_GLO.iloc[:, 1:6] = LoadParams.input_fuel[5] * fs_GLO.iloc[:, 1:6] -fs_GLO.insert(6, "NH3_to_N", 1) - -# Share of feedstocks for NH3 prodution (based on 2010 => Assumed fixed for any past years) -feedshare = fs_GLO.sort_values(["Region"]).set_index("Region").drop("R11_GLB") - -# Get historical N demand from SSP2-nopolicy (may need to vary for diff scenarios) -N_demand_raw = LoadParams.N_demand_GLO.copy() -N_demand = ( - N_demand_raw[N_demand_raw.Scenario == "NoPolicy"].reset_index().loc[0:10, 2010] -) # 2010 tot N demand -N_demand = N_demand.repeat(len(newtechnames)) - -act2010 = (feedshare.values.flatten() * N_demand).reset_index(drop=True) - - -#%% Historical activities/capacities - Region specific - -df = Sc_nitro.par("historical_activity").iloc[ - 0 : len(newtechnames) * (len(REGIONS) - 1), -] # Choose whatever same number of rows -df["technology"] = newtechnames * (len(REGIONS) - 1) -df["value"] = act2010 # total NH3 or N in Mt 2010 FAO Russia -df["year_act"] = 2010 -df["node_loc"] = [y for x in REGIONS[:-1] for y in (x,) * len(newtechnames)] -df["unit"] = "Tg N/yr" # Separate unit needed for NH3? -Sc_nitro.add_par("historical_activity", df) - -# 2015 activity necessary if this is 5-year step scenario -# df['value'] = act2015 # total NH3 or N in Mt 2010 FAO Russia -# df['year_act'] = 2015 -# Sc_nitro.add_par("historical_activity", df) - -life = Sc_nitro.par("technical_lifetime", {"technology": ["gas_NH3"]}).value[0] - -df = Sc_nitro.par("historical_new_capacity").iloc[ - 0 : len(newtechnames) * (len(REGIONS) - 1), -] # whatever -df["technology"] = newtechnames * (len(REGIONS) - 1) -df["value"] = [ - x * 1 / life / LoadParams.capacity_factor[0] for x in act2010 -] # Assume 1/lifetime (=15yr) is built each year -df["year_vtg"] = 2010 -df["node_loc"] = [y for x in REGIONS[:-1] for y in (x,) * len(newtechnames)] -df["unit"] = "Tg N/yr" -Sc_nitro.add_par("historical_new_capacity", df) - - -#%% Secure feedstock balance (foil_fs, gas_fs, coal_fs) loil_fs? - -# Select only the model years -years = set(map(int, list(Sc_nitro.set("year")))) & set( - N_demand_raw -) # get intersection - -# scenarios = N_demand_FSU.Scenario # Scenario names (SSP2) -N_demand = N_demand_raw.loc[N_demand_raw.Scenario == "NoPolicy",].drop(35) -N_demand = N_demand[N_demand.columns.intersection(years)] -N_demand[2110] = N_demand[2100] # Make up 2110 data (for now) in Mt/year - -# Adjust i_feed demand -demand_fs_org = Sc_nitro.par("demand", {"commodity": ["i_feed"]}) -# demand_fs_org['value'] = demand_fs_org['value'] * 0.9 # (10% of total feedstock for Ammonia assumed) - REFINE -df = demand_fs_org.loc[demand_fs_org.year == 2010, :].join( - LoadParams.N_energy.set_index("node"), on="node" -) -sh = pd.DataFrame( - { - "node": demand_fs_org.loc[demand_fs_org.year == 2010, "node"], - "r_feed": df.totENE / df.value, - } -) # share of NH3 energy among total feedstock (no trade assumed) -df = demand_fs_org.join(sh.set_index("node"), on="node") -df.value *= 1 - df.r_feed # Carve out the same % from tot i_feed values -df = df.drop("r_feed", axis=1) -Sc_nitro.add_par("demand", df) - - -# Now link the GLOBIOM input (now endogenous) -df = Sc_nitro.par("land_output", {"commodity": newcommnames[-1]}) -df["level"] = newlevelnames[-1] -Sc_nitro.add_par("land_input", df) - - -#%% Add CCS tecs - -#%% Add tecs to set -tec_for_ccs = list(newtechnames[i] for i in [0, 2, 3, 4]) -newtechnames_ccs = list( - map(lambda x: str(x) + "_ccs", tec_for_ccs) -) # include biomass in CCS, newtechnames[2:5])) -Sc_nitro.add_set("technology", newtechnames_ccs) - -cat_add = pd.DataFrame( - { - "type_tec": "industry", #'non_energy' not in Russia model - "technology": newtechnames_ccs, - } -) - -Sc_nitro.add_set("cat_tec", cat_add) - - -#%% Implement technologies - only for non-elec NH3 tecs - -# input and output -# additional electricity input for CCS operation -df = Sc_nitro.par("input") -df = df[df["technology"].isin(tec_for_ccs)] -df["technology"] = df["technology"] + "_ccs" # Rename the technologies -df.loc[df["commodity"] == "electr", ["value"]] = ( - df.loc[df["commodity"] == "electr", ["value"]] + 0.005 -) # TUNE THIS # Add electricity input for CCS -Sc_nitro.add_par("input", df) - -df = Sc_nitro.par("output") -df = df[df["technology"].isin(tec_for_ccs)] -df["technology"] = df["technology"] + "_ccs" # Rename the technologies -Sc_nitro.add_par("output", df) - - -# Emission factors (emission_factor) - -df = Sc_nitro.par("emission_factor") -biomass_ef = 0.942 # MtC/GWa from 109.6 kgCO2/GJ biomass (https://www.rvo.nl/sites/default/files/2013/10/Vreuls%202005%20NL%20Energiedragerlijst%20-%20Update.pdf) - -# extract vent vs. storage ratio from h2_smr tec -h2_smr_vent_ratio = ( - -Sc_nitro.par( - "relation_activity", {"relation": ["CO2_cc"], "technology": ["h2_smr_ccs"]} - ).value[0] - / Sc_nitro.par( - "relation_activity", - {"relation": ["CO2_Emission"], "technology": ["h2_smr_ccs"]}, - ).value[0] -) -h2_coal_vent_ratio = ( - -Sc_nitro.par( - "relation_activity", {"relation": ["CO2_cc"], "technology": ["h2_coal_ccs"]} - ).value[0] - / Sc_nitro.par( - "relation_activity", - {"relation": ["CO2_Emission"], "technology": ["h2_coal_ccs"]}, - ).value[0] -) -h2_bio_vent_ratio = 1 + Sc_nitro.par( - "relation_activity", {"relation": ["CO2_cc"], "technology": ["h2_bio_ccs"]} -).value[0] / ( - Sc_nitro.par( - "input", {"technology": ["h2_bio_ccs"], "commodity": ["biomass"]} - ).value[0] - * biomass_ef -) - -# ef for NG -df_gas = df[df["technology"] == "gas_NH3"] -ef_org = np.asarray(df_gas.loc[df_gas["emission"] == "CO2_transformation", ["value"]]) -df_gas = df_gas.assign(technology=df_gas.technology + "_ccs") -df_gas.loc[(df.emission == "CO2_transformation"), "value"] = ef_org * h2_smr_vent_ratio -df_gas.loc[(df.emission == "CO2"), "value"] = ef_org * (h2_smr_vent_ratio - 1) -Sc_nitro.add_par("emission_factor", df_gas) - -# ef for oil/coal -df_coal = df[df["technology"].isin(tec_for_ccs[2:])] -ef_org = np.asarray(df_coal.loc[df_coal["emission"] == "CO2_transformation", ["value"]]) -df_coal = df_coal.assign(technology=df_coal.technology + "_ccs") -df_coal.loc[(df.emission == "CO2_transformation"), "value"] = ( - ef_org * h2_coal_vent_ratio -) -df_coal.loc[(df.emission == "CO2"), "value"] = ef_org * (h2_coal_vent_ratio - 1) -Sc_nitro.add_par("emission_factor", df_coal) - -# ef for biomass -df_bio = df[df["technology"] == "biomass_NH3"] -biomass_input = Sc_nitro.par( - "input", {"technology": ["biomass_NH3"], "commodity": ["biomass"]} -).value[0] -df_bio = df_bio.assign(technology=df_bio.technology + "_ccs") -df_bio["value"] = biomass_input * (h2_bio_vent_ratio - 1) * biomass_ef -Sc_nitro.add_par("emission_factor", df_bio) - - -# Investment cost - -df = Sc_nitro.par("inv_cost") - -# Get inv_cost ratio between std and ccs for different h2 feedstocks -a = df[df["technology"].str.contains("h2_smr")].sort_values( - ["technology", "year_vtg"] -) # To get cost ratio for std vs CCS -r_ccs_cost_gas = ( - a.loc[(a.technology == "h2_smr") & (a.year_vtg >= 2020)]["value"].values - / a.loc[(a["technology"] == "h2_smr_ccs") & (a.year_vtg >= 2020)]["value"].values -) -r_ccs_cost_gas = r_ccs_cost_gas.mean() - -a = df[df["technology"].str.contains("h2_coal")].sort_values( - ["technology", "year_vtg"] -) # To get cost ratio for std vs CCS -r_ccs_cost_coal = ( - a.loc[(a.technology == "h2_coal") & (a.year_vtg >= 2020)]["value"].values - / a.loc[(a["technology"] == "h2_coal_ccs") & (a.year_vtg >= 2020)]["value"].values -) -r_ccs_cost_coal = r_ccs_cost_coal.mean() - -a = df[df["technology"].str.contains("h2_bio")].sort_values( - ["technology", "year_vtg"] -) # To get cost ratio for std vs CCS -a = a[a.year_vtg > 2025] # h2_bio_ccs only available from 2030 -r_ccs_cost_bio = ( - a.loc[(a.technology == "h2_bio") & (a.year_vtg >= 2020)]["value"].values - / a.loc[(a["technology"] == "h2_bio_ccs") & (a.year_vtg >= 2020)]["value"].values -) -r_ccs_cost_bio = r_ccs_cost_bio.mean() - -df_gas = df[df["technology"] == "gas_NH3"] -df_gas["technology"] = df_gas["technology"] + "_ccs" # Rename the technologies -df_gas["value"] = df_gas["value"] / r_ccs_cost_gas -Sc_nitro.add_par("inv_cost", df_gas) - -df_coal = df[df["technology"].isin(tec_for_ccs[2:])] -df_coal["technology"] = df_coal["technology"] + "_ccs" # Rename the technologies -df_coal["value"] = df_coal["value"] / r_ccs_cost_coal -Sc_nitro.add_par("inv_cost", df_coal) - -df_bio = df[df["technology"] == "biomass_NH3"] -df_bio["technology"] = df_bio["technology"] + "_ccs" # Rename the technologies -df_bio["value"] = df_bio["value"] / r_ccs_cost_bio -Sc_nitro.add_par("inv_cost", df_bio) - - -# Fixed cost - -df = Sc_nitro.par("fix_cost") -df_gas = df[df["technology"] == "gas_NH3"] -df_gas["technology"] = df_gas["technology"] + "_ccs" # Rename the technologies -df_gas["value"] = ( - df_gas["value"] / r_ccs_cost_gas -) # Same scaling (same 4% of inv_cost in the end) -Sc_nitro.add_par("fix_cost", df_gas) - -df_coal = df[df["technology"].isin(tec_for_ccs[2:])] -df_coal["technology"] = df_coal["technology"] + "_ccs" # Rename the technologies -df_coal["value"] = ( - df_coal["value"] / r_ccs_cost_coal -) # Same scaling (same 4% of inv_cost in the end) -Sc_nitro.add_par("fix_cost", df_coal) - -df_bio = df[df["technology"] == "biomass_NH3"] -df_bio["technology"] = df_bio["technology"] + "_ccs" # Rename the technologies -df_bio["value"] = ( - df_bio["value"] / r_ccs_cost_bio -) # Same scaling (same 4% of inv_cost in the end) -Sc_nitro.add_par("fix_cost", df_bio) - - -# Emission factors (Relation) - -# Gas -df = Sc_nitro.par("relation_activity") -df_gas = df[ - df["technology"] == "gas_NH3" -] # Originally all CO2_cc (truly emitted, bottom-up) -ef_org = df_gas.value.values.copy() -df_gas.value = ef_org * h2_smr_vent_ratio -df_gas = df_gas.assign(technology=df_gas.technology + "_ccs") -Sc_nitro.add_par("relation_activity", df_gas) - -df_gas.value = ef_org * (h2_smr_vent_ratio - 1) # Negative -df_gas.relation = "CO2_Emission" -Sc_nitro.add_par("relation_activity", df_gas) - -# Coal / Oil -df_coal = df[ - df["technology"].isin(tec_for_ccs[2:]) -] # Originally all CO2_cc (truly emitted, bottom-up) -ef_org = df_coal.value.values.copy() -df_coal.value = ef_org * h2_coal_vent_ratio -df_coal = df_coal.assign(technology=df_coal.technology + "_ccs") -Sc_nitro.add_par("relation_activity", df_coal) - -df_coal.value = ef_org * (h2_coal_vent_ratio - 1) # Negative -df_coal.relation = "CO2_Emission" -Sc_nitro.add_par("relation_activity", df_coal) - -# Biomass -df_bio = df[ - df["technology"] == "biomass_NH3" -] # Originally all CO2.cc (truly emitted, bottom-up) -df_bio.value = biomass_input * (h2_bio_vent_ratio - 1) * biomass_ef -df_bio = df_bio.assign(technology=df_bio.technology + "_ccs") -Sc_nitro.add_par("relation_activity", df_bio) - -df_bio.relation = "CO2_Emission" -Sc_nitro.add_par("relation_activity", df_bio) - - -#%% Copy some bgnd parameters (values identical to _NH3 tecs) - -par_bgnd_ccs = par_bgnd + [ - "technical_lifetime", - "capacity_factor", - "var_cost", - "growth_activity_lo", -] - -for t in par_bgnd_ccs: - df = Sc_nitro.par(t) - df = df[df["technology"].isin(tec_for_ccs)] - df["technology"] = df["technology"] + "_ccs" # Rename the technologies - Sc_nitro.add_par(t, df) - - -#%% Shift model horizon for policy scenarios - -if run_scen != "baseline": - Sc_nitro_base = message_ix.Scenario(mp, "JM_GLB_NITRO", "NoPolicy") - - import Utilities.shift_model_horizon as shift_year - - if scen_names[run_scen] == "NPi": - fyear = 2040 - else: - fyear = 2030 - shift_year(Sc_nitro, Sc_nitro_base, fyear, newtechnames_ccs + newtechnames) - - -#%% Regional cost calibration - -# Scaler based on WEO 2014 (based on IGCC tec) -scaler_cost = pd.DataFrame( - { - "scaler_std": [ - 1.00, - 0.81, - 0.96, - 0.96, - 0.96, - 0.88, - 0.81, - 0.65, - 0.42, - 0.65, - 1.12, - ], - "scaler_ccs": [ - 1.00, - 0.80, - 0.98, - 0.92, - 0.92, - 0.82, - 0.80, - 0.70, - 0.61, - 0.70, - 1.05, - ], # NA values are replaced with WEO 2014 values. (LAM & MEA = 0.80) - "node_loc": [ - "R11_NAM", - "R11_LAM", - "R11_WEU", - "R11_EEU", - "R11_FSU", - "R11_AFR", - "R11_MEA", - "R11_SAS", - "R11_CPA", - "R11_PAS", - "R11_PAO", - ], - } -) - -# tec_scale = (newtechnames + newtechnames_ccs) -tec_scale = [e for e in newtechnames if e not in ("NH3_to_N_fertil", "electr_NH3")] - -# Scale all NH3 tecs in each region with the scaler -for t in tec_scale: - for p in ["inv_cost", "fix_cost", "var_cost"]: - df = Sc_nitro.par(p, {"technology": t}) - temp = df.join(scaler_cost.set_index("node_loc"), on="node_loc") - df.value = temp.value * temp.scaler_std - Sc_nitro.add_par(p, df) - -for t in newtechnames_ccs: - for p in ["inv_cost", "fix_cost", "var_cost"]: - df = Sc_nitro.par(p, {"technology": t}) - temp = df.join(scaler_cost.set_index("node_loc"), on="node_loc") - df.value = temp.value * temp.scaler_ccs - Sc_nitro.add_par(p, df) - -# For CPA and SAS, experiment to make the coal_NH3 cheaper than gas_NH3 -scalers = [ - [0.66 * 0.91, 1, 0.75 * 0.9], - [0.59, 1, 1], -] # f, g, c : Scale based on the original techno-economic assumptions -reg2scale = ["R11_CPA", "R11_SAS"] -for p in ["inv_cost", "fix_cost"]: # , 'var_cost']: - for r in reg2scale: - df_c = Sc_nitro.par(p, {"technology": "coal_NH3", "node_loc": r}) - df_g = Sc_nitro.par(p, {"technology": "gas_NH3", "node_loc": r}) - df_f = Sc_nitro.par(p, {"technology": "fueloil_NH3", "node_loc": r}) - - df_cc = Sc_nitro.par(p, {"technology": "coal_NH3_ccs", "node_loc": r}) - df_gc = Sc_nitro.par(p, {"technology": "gas_NH3_ccs", "node_loc": r}) - df_fc = Sc_nitro.par(p, {"technology": "fueloil_NH3_ccs", "node_loc": r}) - - df_fc.value *= scalers[reg2scale.index(r)][ - 0 - ] # Gas/fueloil cost the same as coal - df_gc.value *= scalers[reg2scale.index(r)][1] - df_cc.value *= scalers[reg2scale.index(r)][2] - - df_f.value *= scalers[reg2scale.index(r)][ - 0 - ] # Gas/fueloil cost the same as coal - df_g.value *= scalers[reg2scale.index(r)][1] - df_c.value *= scalers[reg2scale.index(r)][2] - - Sc_nitro.add_par(p, df_g) - Sc_nitro.add_par(p, df_c) - Sc_nitro.add_par(p, df_f) - # Sc_nitro.add_par(p, df_f.append(df_c).append(df_g)) - Sc_nitro.add_par(p, df_gc) - Sc_nitro.add_par(p, df_cc) - Sc_nitro.add_par(p, df_fc) -# Sc_nitro.add_par(p, df_fc.append(df_cc).append(df_gc)) - - -#%% Solve the model. - -Sc_nitro.commit("Nitrogen Fertilizer for Global model - no policy") - -start_time = time.time() - -# Sc_nitro.to_gdx(r'..\..\message_ix\message_ix\model\data', "MsgData_"+Sc_nitro.model+"_"+ -# Sc_nitro.scenario+"_"+str(Sc_nitro.version)) - -# I do this because the current set up doesn't recognize the model path correctly. -Sc_nitro.solve( - model="MESSAGE", - case=Sc_nitro.model + "_" + Sc_nitro.scenario + "_" + str(Sc_nitro.version), -) -# Sc_ref.solve(model='MESSAGE', case=Sc_ref.model+"_"+Sc_ref.scenario+"_"+str(Sc_ref.version)) - - -print(".solve: %.6s seconds taken." % (time.time() - start_time)) - - -#%% Run reference model - -# -# start_time = time.time() -# Sc_ref.remove_solution() -# Sc_ref.solve(model='MESSAGE', case=Sc_ref.model+"_"+ -# Sc_ref.scenario+"_"+str(Sc_ref.version)) -# print(".solve: %.6s seconds taken." % (time.time() - start_time)) -# diff --git a/message_ix_models/model/material/material_demand/init.R b/message_ix_models/model/material/material_demand/init.R deleted file mode 100644 index 9379b140f0..0000000000 --- a/message_ix_models/model/material/material_demand/init.R +++ /dev/null @@ -1,142 +0,0 @@ -library(tidyverse) -library(readxl) - -# Data file names and path -# datapath = '../../../../data/material/' -datapath = 'H:/GitHub/message_data/data/material/' - -file_cement = "CEMENT.BvR2010.xlsx" -file_steel = "STEEL_database_2012.xlsx" -file_al = "demand_aluminum.xlsx" -file_petro = "/demand_petro.xlsx" - -#### Import raw data (from Bas) - Cement & Steel #### -# Apparent consumption -# GDP per dap -# Population -df_raw_steel_consumption = read_excel(paste0(datapath, file_steel), - sheet="Consumption regions", n_max=27) %>% # kt - select(-2) %>% - pivot_longer(cols="1970":"2012", - values_to='consumption', names_to='year') -df_raw_cement_consumption = read_excel(paste0(datapath, file_cement), - sheet="Regions", skip=122, n_max=27) %>% # kt - pivot_longer(cols="1970":"2010", values_to='consumption', names_to='year') -df_population = read_excel(paste0(datapath, file_cement), - sheet="Timer_POP", skip=3, n_max=27) %>% # million - pivot_longer(cols="1970":"2100", values_to='pop', names_to='year') -df_gdp = read_excel(paste0(datapath, file_cement), - sheet="Timer_GDPCAP", skip=3, n_max=27) %>% # million - pivot_longer(cols="1970":"2100", values_to='gdp.pcap', names_to='year') - -df_raw_aluminum_consumption = read_excel(paste0(datapath, file_al), - sheet="final_table", n_max = 378) # kt - -df_raw_petro_consumption = read_excel(paste0(datapath, file_petro), - sheet="final_table", n_max = 362) #kg/cap - -#### Organize data #### -names(df_raw_steel_consumption)[1] = names(df_raw_cement_consumption)[1] = - names(df_population)[1] = names(df_gdp)[1] = "reg_no" -names(df_raw_steel_consumption)[2] = names(df_raw_cement_consumption)[2] = - names(df_population)[2] = names(df_gdp)[2] = "region" -df_steel_consumption = df_raw_steel_consumption %>% - left_join(df_population %>% select(-region)) %>% - left_join(df_gdp %>% select(-region)) %>% - mutate(cons.pcap = consumption/pop, del.t = as.numeric(year)-2010) %>% #kg/cap - drop_na() %>% - filter(cons.pcap > 0) -df_cement_consumption = df_raw_cement_consumption %>% - left_join(df_population %>% select(-region)) %>% - left_join(df_gdp %>% select(-region)) %>% - mutate(cons.pcap = consumption/pop/1e6, del.t= as.numeric(year) - 2010) %>% #kg/cap - drop_na() %>% - filter(cons.pcap > 0) - -df_aluminum_consumption = df_raw_aluminum_consumption %>% - mutate(cons.pcap = consumption/pop, del.t= as.numeric(year) - 2010) %>% #kg/cap - drop_na() %>% - filter(cons.pcap > 0) - -df_petro_consumption = df_raw_petro_consumption %>% - mutate(del.t= as.numeric(year) - 2010) %>% - drop_na() %>% - filter(cons.pcap > 0) - -#### Fit models #### - -# Note: IMAGE adopts NLI for cement, NLIT for steel. - -# . Linear ==== -lni.c = lm(log(cons.pcap) ~ I(1/gdp.pcap), data=df_cement_consumption) -lni.s = lm(log(cons.pcap) ~ I(1/gdp.pcap), data=df_steel_consumption) -lni.a = lm(log(cons.pcap) ~ I(1/gdp.pcap), data=df_aluminum_consumption) -lni.p = lm(log(cons.pcap) ~ I(1/gdp.pcap), data=df_petro_consumption) -summary(lni.c) -summary(lni.s) -summary(lni.a) -summary(lni.p) - -lnit.c = lm(log(cons.pcap) ~ I(1/gdp.pcap)+del.t, data=df_cement_consumption) -lnit.s = lm(log(cons.pcap) ~ I(1/gdp.pcap)+del.t, data=df_steel_consumption) -lnit.a = lm(log(cons.pcap) ~ I(1/gdp.pcap)+del.t, data=df_aluminum_consumption) -lnit.p = lm(log(cons.pcap) ~ I(1/gdp.pcap)+del.t, data=df_petro_consumption) -summary(lnit.c) -summary(lnit.s) -summary(lnit.a) # better in linear -summary(lnit.p) - -# . Non-linear ==== -nlni.c = nls(cons.pcap ~ a * exp(b/gdp.pcap), data=df_cement_consumption, start=list(a=500, b=-3000)) -nlni.s = nls(cons.pcap ~ a * exp(b/gdp.pcap), data=df_steel_consumption, start=list(a=600, b=-10000)) -nlni.a = nls(cons.pcap ~ a * exp(b/gdp.pcap), data=df_aluminum_consumption, start=list(a=600, b=-10000)) -nlni.p = nls(cons.pcap ~ a * exp(b/gdp.pcap), data=df_petro_consumption, start=list(a=600, b=-10000)) - -summary(nlni.c) -summary(nlni.s) -summary(nlni.a) -summary(nlni.p) - -nlnit.c = nls(cons.pcap ~ a * exp(b/gdp.pcap) * (1-m)^del.t, data=df_cement_consumption, start=list(a=500, b=-3000, m=0)) -nlnit.s = nls(cons.pcap ~ a * exp(b/gdp.pcap) * (1-m)^del.t, data=df_steel_consumption, start=list(a=600, b=-10000, m=0)) -nlnit.a = nls(cons.pcap ~ a * exp(b/gdp.pcap) * (1-m)^del.t, data=df_aluminum_consumption, start=list(a=600, b=-10000, m=0)) -nlnit.p = nls(cons.pcap ~ a * exp(b/gdp.pcap) * (1-m)^del.t, data=df_petro_consumption, start=list(a=600, b=-10000, m=0)) - -summary(nlnit.c) -summary(nlnit.s) -summary(nlnit.a) -summary(nlnit.p) - - -#### Prediction #### - -year = seq(2020, 2100, 10) -df = data.frame(gdp.pcap = seq(3000, 90000, length.out = length(year)), year) %>% mutate(del.t = year - 2010) -df2 = df %>% mutate(gdp.pcap = 2*gdp.pcap) -predict(nlnit.s, df) -predict(nlni.s, df) -exp(predict(lnit.s, df)) -exp(predict(lni.s, df)) -predict(nlnit.s, df2) - -predict(nlni.c, df) -predict(nlni.c, df2) - -predict(nlni.a, df) -predict(nlnit.a, df) -exp(predict(lni.a, df)) -exp(predict(lnit.a, df)) -predict(nlni.a, df2) -predict(nlnit.a, df2) -exp(predict(lni.a, df2)) -exp(predict(lnit.a, df2)) - -predict(nlni.p, df) -predict(nlnit.p, df) -exp(predict(lni.p, df)) -exp(predict(lnit.p, df)) -predict(nlni.p, df2) -predict(nlnit.p, df2) -exp(predict(lni.p, df2)) -exp(predict(lnit.p, df2)) - diff --git a/message_ix_models/model/material/material_demand/init_modularized.R b/message_ix_models/model/material/material_demand/init_modularized.R deleted file mode 100644 index 8c51db72d1..0000000000 --- a/message_ix_models/model/material/material_demand/init_modularized.R +++ /dev/null @@ -1,215 +0,0 @@ -library(tidyverse) -library(readxl) -library(sitools) - -# Data file names and path -# datapath = '../../../../data/material/' -# datapath = 'H:/GitHub/message_data/data/material/' - -file_cement = "/CEMENT.BvR2010.xlsx" -file_steel = "/STEEL_database_2012.xlsx" -file_al = "/demand_aluminum.xlsx" -#file_petro = "/demand_petro.xlsx" -file_gdp = "/iamc_db ENGAGE baseline GDP PPP.xlsx" - -derive_steel_demand <- function(df_pop, df_demand, datapath) { - # df_in will have columns: - # region - # year - # gdp.pcap - # population - - gdp.ppp = read_excel(paste0(datapath, "/other", file_gdp), sheet="data_R12") %>% filter(Scenario == "baseline") %>% - select(region=Region, `2020`:`2100`) %>% - pivot_longer(cols=`2020`:`2100`, names_to="year", values_to="gdp.ppp") %>% # billion US$2010/yr OR local currency - filter(region != "World") %>% - mutate(year = as.integer(year), region = paste0('R12_', region)) - - df_raw_steel_consumption = read_excel(paste0(datapath, "/steel_cement", file_steel), - sheet="Consumption regions", n_max=27) %>% # kt - select(-2) %>% - pivot_longer(cols="1970":"2012", - values_to='consumption', names_to='year') - df_population = read_excel(paste0(datapath, "/steel_cement" ,file_cement), - sheet="Timer_POP", skip=3, n_max=27) %>% # million - pivot_longer(cols="1970":"2100", values_to='pop', names_to='year') - df_gdp = read_excel(paste0(datapath, "/steel_cement", file_cement), - sheet="Timer_GDPCAP", skip=3, n_max=27) %>% # million - pivot_longer(cols="1970":"2100", values_to='gdp.pcap', names_to='year') - - #### Organize data #### - names(df_raw_steel_consumption)[1] = - names(df_population)[1] = names(df_gdp)[1] = "reg_no" - names(df_raw_steel_consumption)[2] = - names(df_population)[2] = names(df_gdp)[2] = "region" - df_steel_consumption = df_raw_steel_consumption %>% - left_join(df_population %>% select(-region)) %>% - left_join(df_gdp %>% select(-region)) %>% - mutate(cons.pcap = consumption/pop, del.t = as.numeric(year)-2010) %>% #kg/cap - drop_na() %>% - filter(cons.pcap > 0) - - write.csv(df_steel_consumption, file = "output_file.csv", row.names = TRUE) - - nlnit.s = nls(cons.pcap ~ a * exp(b/gdp.pcap) * (1-m)^del.t, data=df_steel_consumption, start=list(a=600, b=-10000, m=0)) - - df_in = df_pop %>% left_join(df_demand %>% select(-year)) %>% # df_demand is only for 2020 - inner_join(gdp.ppp) %>% mutate(del.t = year - 2010, gdp.pcap = gdp.ppp*giga/pop.mil/mega) - demand = df_in %>% - mutate(demand.pcap0 = predict(nlnit.s, .)) %>% #kg/cap - group_by(region) %>% - mutate(demand.pcap.base = first(demand.tot.base*giga/pop.mil/mega)) %>% - mutate(gap.base = first(demand.pcap.base - demand.pcap0)) %>% - mutate(demand.pcap = demand.pcap0 + gap.base * gompertz(9, 0.1, y=year)) %>% # Bas' equation - mutate(demand.tot = demand.pcap * pop.mil * mega / giga) # Mt - - # Add 2110 spaceholder - demand = demand %>% rbind(demand %>% filter(year==2100) %>% mutate(year = 2110)) - print(nlnit.s) - return(demand %>% select(node=region, year, value=demand.tot) %>% arrange(year, node)) # Mt -} - - - -derive_cement_demand <- function(df_pop, df_demand, datapath) { - # df_in will have columns: - # region - # year - # gdp.pcap - # population (in mil.) - - gdp.ppp = read_excel(paste0(datapath, "/other", file_gdp), sheet="data_R12") %>% filter(Scenario == "baseline") %>% - select(region=Region, `2020`:`2100`) %>% - pivot_longer(cols=`2020`:`2100`, names_to="year", values_to="gdp.ppp") %>% # billion US$2010/yr OR local currency - filter(region != "World") %>% - mutate(year = as.integer(year), region = paste0('R12_', region)) - - df_raw_cement_consumption = read_excel(paste0(datapath, "/steel_cement", file_cement), - sheet="Regions", skip=122, n_max=27) %>% # kt - pivot_longer(cols="1970":"2010", values_to='consumption', names_to='year') - df_population = read_excel(paste0(datapath, "/steel_cement", file_cement), - sheet="Timer_POP", skip=3, n_max=27) %>% # million - pivot_longer(cols="1970":"2100", values_to='pop', names_to='year') - df_gdp = read_excel(paste0(datapath, "/steel_cement", file_cement), - sheet="Timer_GDPCAP", skip=3, n_max=27) %>% # million - pivot_longer(cols="1970":"2100", values_to='gdp.pcap', names_to='year') - - #### Organize data #### - names(df_raw_cement_consumption)[1] = - names(df_population)[1] = names(df_gdp)[1] = "reg_no" - names(df_raw_cement_consumption)[2] = - names(df_population)[2] = names(df_gdp)[2] = "region" - df_cement_consumption = df_raw_cement_consumption %>% - left_join(df_population %>% select(-region)) %>% - left_join(df_gdp %>% select(-region)) %>% - mutate(cons.pcap = consumption/pop/1e6, del.t= as.numeric(year) - 2010) %>% #kg/cap - drop_na() %>% - filter(cons.pcap > 0) - - nlni.c = nls(cons.pcap ~ a * exp(b/gdp.pcap), data=df_cement_consumption, start=list(a=500, b=-3000)) - - df_in = df_pop %>% left_join(df_demand %>% select(-year)) %>% # df_demand is only for 2020 - inner_join(gdp.ppp) %>% mutate(gdp.pcap = gdp.ppp*giga/pop.mil/mega) - demand = df_in %>% - mutate(demand.pcap0 = predict(nlni.c, .)) %>% #kg/cap - group_by(region) %>% - mutate(demand.pcap.base = first(demand.tot.base*giga/pop.mil/mega)) %>% - mutate(gap.base = first(demand.pcap.base - demand.pcap0)) %>% - mutate(demand.pcap = demand.pcap0 + gap.base * gompertz(10, 0.1, y=year)) %>% # Bas' equation - mutate(demand.tot = demand.pcap * pop.mil * mega / giga) # Mt - - # Add 2110 spaceholder - demand = demand %>% rbind(demand %>% filter(year==2100) %>% mutate(year = 2110)) - - return(demand %>% select(node=region, year, value=demand.tot) %>% arrange(year, node)) # Mt -} - - - - -derive_aluminum_demand <- function(df_pop, df_demand, datapath) { - - gdp.ppp = read_excel(paste0(datapath, "/other", file_gdp), sheet="data_R12") %>% filter(Scenario == "baseline") %>% - select(region=Region, `2020`:`2100`) %>% - pivot_longer(cols=`2020`:`2100`, names_to="year", values_to="gdp.ppp") %>% # billion US$2010/yr OR local currency - filter(region != "World") %>% - mutate(year = as.integer(year), region = paste0('R12_', region)) - - # Aluminum xls input already has population and gdp - df_raw_aluminum_consumption = read_excel(paste0(datapath, "/aluminum", file_al), - sheet="final_table", n_max = 378) # kt - - #### Organize data #### - df_aluminum_consumption = df_raw_aluminum_consumption %>% - mutate(cons.pcap = consumption/pop, del.t= as.numeric(year) - 2010) %>% #kg/cap - drop_na() %>% - filter(cons.pcap > 0) - - # nlnit.a = nls(cons.pcap ~ a * exp(b/gdp.pcap) * (1-m)^del.t, data=df_aluminum_consumption, start=list(a=600, b=-10000, m=0)) - nlni.a = nls(cons.pcap ~ a * exp(b/gdp.pcap), data=df_aluminum_consumption, start=list(a=600, b=-10000)) - - df_in = df_pop %>% left_join(df_demand %>% select(-year)) %>% # df_demand is only for 2020 - inner_join(gdp.ppp) %>% mutate(gdp.pcap = gdp.ppp*giga/pop.mil/mega) - demand = df_in %>% - mutate(demand.pcap0 = predict(nlni.a, .)) %>% #kg/cap - group_by(region) %>% - mutate(demand.pcap.base = first(demand.tot.base*giga/pop.mil/mega)) %>% - mutate(gap.base = first(demand.pcap.base - demand.pcap0)) %>% - mutate(demand.pcap = demand.pcap0 + gap.base * gompertz(9, 0.1, y=year)) %>% # Bas' equation - mutate(demand.tot = demand.pcap * pop.mil * mega / giga) # Mt - - # Add 2110 spaceholder - demand = demand %>% rbind(demand %>% filter(year==2100) %>% mutate(year = 2110)) - - return(demand %>% select(node=region, year, value=demand.tot) %>% arrange(year, node)) # Mt -} - -#derive_petro_demand <- function(df_pop, df_demand, datapath) { - -# gdp.ppp = read_excel(paste0(datapath, file_gdp), sheet="data_R12") %>% filter(Scenario == "baseline") %>% -# select(region=Region, `2020`:`2100`) %>% -# pivot_longer(cols=`2020`:`2100`, names_to="year", values_to="gdp.ppp") %>% # billion US$2010/yr OR local currency -# filter(region != "World") %>% -# mutate(year = as.integer(year), region = paste0('R12_', region)) -# -# df_raw_petro_consumption = read_excel(paste0(datapath, file_petro), -# sheet="final_table", n_max = 362) #kg/cap - - #### Organize data #### -# df_petro_consumption = df_raw_petro_consumption %>% -# mutate(del.t= as.numeric(year) - 2010) %>% -# drop_na() %>% -# filter(cons.pcap > 0) - - # nlnit.p = nls(cons.pcap ~ a * exp(b/gdp.pcap) * (1-m)^del.t, data=df_petro_consumption, start=list(a=600, b=-10000, m=0)) -#nlni.p = nls(cons.pcap ~ a * exp(b/gdp.pcap), data=df_petro_consumption, start=list(a=600, b=-10000)) - # lni.p = lm(log(cons.pcap) ~ I(1/gdp.pcap), data=df_petro_consumption) - -# df_in = df_pop %>% left_join(df_demand %>% select(-year)) %>% # df_demand is only for 2020 -# inner_join(gdp.ppp) %>% mutate(gdp.pcap = gdp.ppp*giga/pop.mil/mega) -# demand = df_in %>% -# mutate(demand.pcap0 = predict(nlni.p, .)) %>% #kg/cap -# group_by(region) %>% -# mutate(demand.pcap.base = first(demand.tot.base*giga/pop.mil/mega)) %>% -# mutate(gap.base = first(demand.pcap.base - demand.pcap0)) %>% -# mutate(demand.pcap = demand.pcap0 + gap.base * gompertz(9, 0.1, y=year)) %>% # Bas' equation -# mutate(demand.tot = demand.pcap * pop.mil * mega / giga) # Mt - - # Add 2110 spaceholder -# demand = demand %>% rbind(demand %>% filter(year==2100) %>% mutate(year = 2110)) - -# return(demand %>% select(node=region, year, value=demand.tot) %>% arrange(year, node)) # Mt -#} - - -gompertz <- function(phi, mu, y, baseyear=2020) { - return (1-exp(-phi*exp(-mu*(y - baseyear)))) -} - -#### test - -# year = seq(2020, 2100, 10) -# df = data.frame(region = "Korea", gdp.pcap = seq(3000, 50000, length.out = length(year)), year, -# population = seq(300, 500, length.out = length(year))) -# a = derive_cement_demand(datapath = 'H:/GitHub/message_data/data/material/',df) -# b = derive_petro_demand(datapath = 'H:/GitHub/message_data/data/material/',df) diff --git a/message_ix_models/model/material/material_demand/refactor_mat_demand_calc.py b/message_ix_models/model/material/material_demand/refactor_mat_demand_calc.py deleted file mode 100644 index 2713a50dce..0000000000 --- a/message_ix_models/model/material/material_demand/refactor_mat_demand_calc.py +++ /dev/null @@ -1,673 +0,0 @@ -import message_ix_models.util -import pandas as pd -import numpy as np -from scipy.optimize import curve_fit -import yaml - -file_cement = "/CEMENT.BvR2010.xlsx" -file_steel = "/STEEL_database_2012.xlsx" -file_al = "/demand_aluminum.xlsx" -# file_petro = "/demand_petro.xlsx" -file_gdp = "/iamc_db ENGAGE baseline GDP PPP.xlsx" - -giga = 10**9 -mega = 10**6 - -material_data_dirname = { - "aluminum": "aluminum", - "steel": "steel_cement", - "cement": "steel_cement" -} - -def steel_function(x, a, b, m): - gdp_pcap, del_t = x - return a * np.exp(b / gdp_pcap) * (1 - m) ** del_t - - -def cement_function(x, a, b): - gdp_pcap = x - return a * np.exp(b / gdp_pcap) - - -fitting_dict = { - "steel": { - "function": steel_function, - "initial_guess": [600, -10000, 0], - "x_data": ["gdp_pcap", "del_t"], - "phi": 9, - "mu": 0.1, - }, - "cement": { - "function": cement_function, - "initial_guess": [500, -3000], - "x_data": ["gdp_pcap"], - "phi": 10, - "mu": 0.1, - }, - "aluminum": { - "function": cement_function, - "initial_guess": [600, -10000], - "x_data": ["gdp_pcap"], - "phi": 9, - "mu": 0.1, - }, -} - - -def gompertz(phi, mu, y, baseyear=2020): - return 1 - np.exp(-phi * np.exp(-mu * (y - baseyear))) - - -def derive_steel_demand(df_pop, df_demand, datapath): - # Read GDP data - gdp_ppp = pd.read_excel(f"{datapath}/other{file_gdp}", sheet_name="data_R12") - gdp_ppp = ( - gdp_ppp[gdp_ppp["Scenario"] == "baseline"] - .loc[:, ["Region", *[i for i in gdp_ppp.columns if type(i) == int]]] - .melt(id_vars="Region", var_name="year", value_name="gdp_ppp") - .query('Region != "World"') - .assign( - year=lambda x: x["year"].astype(int), region=lambda x: "R12_" + x["Region"] - ) - ) - - # Read raw steel consumption data - df_raw_steel_consumption = pd.read_excel( - f"{datapath}/steel_cement{file_steel}", - sheet_name="Consumption regions", - nrows=26, - ) - df_raw_steel_consumption = ( - df_raw_steel_consumption.rename({"t": "region"}, axis=1) - .drop(columns=["Unnamed: 1"]) - .rename({"TIMER Region": "reg_no"}, axis=1) - ) - df_raw_steel_consumption = df_raw_steel_consumption.melt( - id_vars=["region", "reg_no"], var_name="year", value_name="consumption" - ) - df_raw_steel_consumption["region"] = ( - df_raw_steel_consumption["region"] - .str.replace("(", "") - .str.replace(")", "") - .str.replace("\d+", "") - .str[:-1] - ) - - # Read population data - df_population = pd.read_excel( - f"{datapath}/steel_cement{file_cement}", - sheet_name="Timer_POP", - skiprows=[0, 1, 2, 30], - nrows=26, - ) - df_population = df_population.drop(columns=["Unnamed: 0"]).rename( - {"Unnamed: 1": "reg_no", "Unnamed: 2": "region"}, axis=1 - ) - df_population = df_population.melt( - id_vars=["region", "reg_no"], var_name="year", value_name="pop" - ) - - # Read GDP per capita data - df_gdp = pd.read_excel( - f"{datapath}/steel_cement{file_cement}", - sheet_name="Timer_GDPCAP", - skiprows=[0, 1, 2, 30], - nrows=26, - ) - df_gdp = df_gdp.drop(columns=["Unnamed: 0"]).rename( - {"Unnamed: 1": "reg_no", "Unnamed: 2": "region"}, axis=1 - ) - df_gdp = df_gdp.melt( - id_vars=["region", "reg_no"], var_name="year", value_name="gdp_pcap" - ) - - # Organize data - df_steel_consumption = ( - pd.merge( - df_raw_steel_consumption, - df_population.drop("region", axis=1), - on=["reg_no", "year"], - ) - .merge(df_gdp[["reg_no", "year", "gdp_pcap"]], on=["reg_no", "year"]) - .assign( - cons_pcap=lambda x: x["consumption"] / x["pop"], - del_t=lambda x: x["year"].astype(int) - 2010, - ) - .dropna() - .query("cons_pcap > 0") - ) - - # Assuming df_steel_consumption contains the necessary columns (cons.pcap, gdp.pcap, del_t) - x_data = df_steel_consumption["gdp_pcap"] - y_data = df_steel_consumption["cons_pcap"] - del_t_data = df_steel_consumption["del_t"] - - # Initial guess for parameters - initial_guess = [600, -10000, 0] - - # Perform nonlinear least squares fitting - params_opt, _ = curve_fit( - steel_function, (x_data, del_t_data), y_data, p0=initial_guess - ) - - # Extract the optimized parameters - a_opt, b_opt, m_opt = params_opt - print(f"a: {a_opt}, b: {b_opt}, m: {m_opt}") - - # Merge with steel consumption data - df_in = pd.merge(df_pop, df_demand.drop(columns=["year"]), how="left") - df_in = pd.merge(df_in, gdp_ppp, how="inner") - df_in["del_t"] = df_in["year"] - 2010 - df_in["gdp_pcap"] = df_in["gdp_ppp"] * giga / df_in["pop.mil"] / mega - - df_in["demand_pcap0"] = df_in.apply( - lambda row: steel_function((row["gdp_pcap"], row["del_t"]), *params_opt), axis=1 - ) - - # Demand Prediction and Calculations - df_demand = df_in.groupby("region").apply( - lambda group: group.assign( - demand_pcap_base=group["demand.tot.base"].iloc[0] - * giga - / group["pop.mil"].iloc[0] - / mega - ) - ) - df_demand = df_demand.groupby("region").apply( - lambda group: group.assign( - gap_base=group["demand_pcap_base"].iloc[0] - group["demand_pcap0"].iloc[0] - ) - ) - df_demand = df_demand.groupby("region").apply( - lambda group: group.assign( - demand_pcap=group["demand_pcap0"] - + group["gap_base"] * gompertz(9, 0.1, y=group["year"]) - ) - ) - df_demand = ( - df_demand.groupby("region") - .apply( - lambda group: group.assign( - demand_tot=group["demand_pcap"] * group["pop.mil"] * mega / giga - ) - ) - .reset_index(drop=True) - ) - - # Add 2110 placeholder - # df_demand_components = pd.concat([df_demand_components, df_demand_components[df_demand_components['year'] == 2100].assign(year=2110)]) - - # return df_demand_components[['node', 'year', 'demand_tot']].sort_values(by=['year', 'node']).reset_index(drop=True) - return df_demand - - -def derive_cement_demand(df_pop, df_demand, datapath): - # Read GDP data - gdp_ppp = pd.read_excel(f"{datapath}/other{file_gdp}", sheet_name="data_R12") - gdp_ppp = ( - gdp_ppp[gdp_ppp["Scenario"] == "baseline"] - .loc[:, ["Region", *[i for i in gdp_ppp.columns if type(i) == int]]] - .melt(id_vars="Region", var_name="year", value_name="gdp_ppp") - .query('Region != "World"') - .assign( - year=lambda x: x["year"].astype(int), region=lambda x: "R12_" + x["Region"] - ) - ) - - # Read raw steel consumption data - df_raw_cement_consumption = pd.read_excel( - f"{datapath}/steel_cement{file_cement}", - sheet_name="Regions", - skiprows=122, - nrows=26, - ) - # print(df_raw_steel_consumption.columns) - df_raw_cement_consumption = df_raw_cement_consumption.rename( - {"Region #": "reg_no", "Region": "region"}, axis=1 - ) - df_raw_cement_consumption = df_raw_cement_consumption.melt( - id_vars=["region", "reg_no"], var_name="year", value_name="consumption" - ) - # df_raw_cement_consumption["region"] = df_raw_cement_consumption["region"].str.replace("(","").str.replace(")","").str.replace('\d+', '').str[:-1] - - # Read population data - df_population = pd.read_excel( - f"{datapath}/steel_cement{file_cement}", - sheet_name="Timer_POP", - skiprows=[0, 1, 2, 30], - nrows=26, - ) - df_population = df_population.drop(columns=["Unnamed: 0"]).rename( - {"Unnamed: 1": "reg_no", "Unnamed: 2": "region"}, axis=1 - ) - df_population = df_population.melt( - id_vars=["region", "reg_no"], var_name="year", value_name="pop" - ) - - # Read GDP per capita data - df_gdp = pd.read_excel( - f"{datapath}/steel_cement{file_cement}", - sheet_name="Timer_GDPCAP", - skiprows=[0, 1, 2, 30], - nrows=26, - ) - df_gdp = df_gdp.drop(columns=["Unnamed: 0"]).rename( - {"Unnamed: 1": "reg_no", "Unnamed: 2": "region"}, axis=1 - ) - df_gdp = df_gdp.melt( - id_vars=["region", "reg_no"], var_name="year", value_name="gdp_pcap" - ) - - # Organize data - df_cement_consumption = ( - pd.merge( - df_raw_cement_consumption, - df_population.drop("region", axis=1), - on=["reg_no", "year"], - ) - .merge(df_gdp[["reg_no", "year", "gdp_pcap"]], on=["reg_no", "year"]) - .assign( - cons_pcap=lambda x: x["consumption"] / x["pop"] / 10**6, - del_t=lambda x: x["year"].astype(int) - 2010, - ) - .dropna() - .query("cons_pcap > 0") - ) - - # Assuming df_steel_consumption contains the necessary columns (cons.pcap, gdp.pcap, del_t) - x_data = df_cement_consumption["gdp_pcap"] - y_data = df_cement_consumption["cons_pcap"] - - # Initial guess for parameters - initial_guess = [500, -3000] - # df_steel_consumption.to_csv("py_output.csv") - # Perform nonlinear least squares fitting - params_opt, _ = curve_fit(cement_function, x_data, y_data, p0=initial_guess) - - # Extract the optimized parameters - a_opt, b_opt = params_opt - print(f"a: {a_opt}, b: {b_opt}") - - # Merge with steel consumption data - df_in = pd.merge(df_pop, df_demand.drop(columns=["year"]), how="left") - df_in = pd.merge(df_in, gdp_ppp, how="inner") - df_in["del_t"] = df_in["year"] - 2010 - df_in["gdp_pcap"] = df_in["gdp_ppp"] * giga / df_in["pop.mil"] / mega - - df_in["demand_pcap0"] = df_in.apply( - lambda row: cement_function(row["gdp_pcap"], *params_opt), axis=1 - ) - - # Demand Prediction and Calculations - df_demand = df_in.groupby("region").apply( - lambda group: group.assign( - demand_pcap_base=group["demand.tot.base"].iloc[0] - * giga - / group["pop.mil"].iloc[0] - / mega - ) - ) - df_demand = df_demand.groupby("region").apply( - lambda group: group.assign( - gap_base=group["demand_pcap_base"].iloc[0] - group["demand_pcap0"].iloc[0] - ) - ) - df_demand = df_demand.groupby("region").apply( - lambda group: group.assign( - demand_pcap=group["demand_pcap0"] - + group["gap_base"] * gompertz(10, 0.1, y=group["year"]) - ) - ) - df_demand = ( - df_demand.groupby("region") - .apply( - lambda group: group.assign( - demand_tot=group["demand_pcap"] * group["pop.mil"] * mega / giga - ) - ) - .reset_index(drop=True) - ) - - # Add 2110 placeholder - # df_demand_components = pd.concat([df_demand_components, df_demand_components[df_demand_components['year'] == 2100].assign(year=2110)]) - - # return df_demand_components[['node', 'year', 'demand_tot']].sort_values(by=['year', 'node']).reset_index(drop=True) - return df_demand - - -def derive_alu_demand(df_pop, df_demand, datapath): - # Read GDP data - gdp_ppp = pd.read_excel(f"{datapath}/other{file_gdp}", sheet_name="data_R12") - # print(gdp_ppp.columns) - gdp_ppp = ( - gdp_ppp[gdp_ppp["Scenario"] == "baseline"] - .loc[:, ["Region", *[i for i in gdp_ppp.columns if type(i) == int]]] - .melt(id_vars="Region", var_name="year", value_name="gdp_ppp") - .query('Region != "World"') - .assign( - year=lambda x: x["year"].astype(int), region=lambda x: "R12_" + x["Region"] - ) - ) - - # Read raw steel consumption data - df_raw_alu_consumption = ( - pd.read_excel( - f"{datapath}/aluminum{file_al}", sheet_name="final_table", nrows=378 - ) - .drop(["cons.pcap", "del.t"], axis=1) - .rename({"gdp.pcap": "gdp_pcap"}, axis=1) - ) - - # Organize data - df_alu_consumption = ( - df_raw_alu_consumption.assign( - cons_pcap=lambda x: x["consumption"] / x["pop"], - del_t=lambda x: x["year"].astype(int) - 2010, - ) - .dropna() - .query("cons_pcap > 0") - ) - - # Assuming df_steel_consumption contains the necessary columns (cons.pcap, gdp.pcap, del_t) - x_data = df_alu_consumption["gdp_pcap"] - y_data = df_alu_consumption["cons_pcap"] - - # Initial guess for parameters - initial_guess = [600, -10000] - - # Perform nonlinear least squares fitting - params_opt, _ = curve_fit(cement_function, x_data, y_data, p0=initial_guess) - - # Extract the optimized parameters - a_opt, b_opt = params_opt - print(f"a: {a_opt}, b: {b_opt}") - - # Merge with steel consumption data - df_in = pd.merge(df_pop, df_demand.drop(columns=["year"]), how="left") - df_in = pd.merge(df_in, gdp_ppp, how="inner") - df_in["del_t"] = df_in["year"] - 2010 - df_in["gdp_pcap"] = df_in["gdp_ppp"] * giga / df_in["pop.mil"] / mega - - df_in["demand_pcap0"] = df_in.apply( - lambda row: cement_function(row["gdp_pcap"], *params_opt), axis=1 - ) - - # Demand Prediction and Calculations - df_demand = df_in.groupby("region").apply( - lambda group: group.assign( - demand_pcap_base=group["demand.tot.base"].iloc[0] - * giga - / group["pop.mil"].iloc[0] - / mega - ) - ) - df_demand = df_demand.groupby("region").apply( - lambda group: group.assign( - gap_base=group["demand_pcap_base"].iloc[0] - group["demand_pcap0"].iloc[0] - ) - ) - df_demand = df_demand.groupby("region").apply( - lambda group: group.assign( - demand_pcap=group["demand_pcap0"] - + group["gap_base"] * gompertz(9, 0.1, y=group["year"]) - ) - ) - df_demand = ( - df_demand.groupby("region") - .apply( - lambda group: group.assign( - demand_tot=group["demand_pcap"] * group["pop.mil"] * mega / giga - ) - ) - .reset_index(drop=True) - ) - - # Add 2110 placeholder - # df_demand_components = pd.concat([df_demand_components, df_demand_components[df_demand_components['year'] == 2100].assign(year=2110)]) - - # return df_demand_components[['node', 'year', 'demand_tot']].sort_values(by=['year', 'node']).reset_index(drop=True) - return df_demand - - -def read_pop(datapath, filename): - df_population = pd.read_excel( - f"{datapath}/steel_cement{filename}", - sheet_name="Timer_POP", - skiprows=[0, 1, 2, 30], - nrows=26, - ) - df_population = df_population.drop(columns=["Unnamed: 0"]).rename( - {"Unnamed: 1": "reg_no", "Unnamed: 2": "region"}, axis=1 - ) - df_population = df_population.melt( - id_vars=["region", "reg_no"], var_name="year", value_name="pop" - ) - return df_population - - -def read_hist_gdp(datapath, filename): - # Read GDP per capita data - df_gdp = pd.read_excel( - f"{datapath}/steel_cement{filename}", - sheet_name="Timer_GDPCAP", - skiprows=[0, 1, 2, 30], - nrows=26, - ) - df_gdp = df_gdp.drop(columns=["Unnamed: 0"]).rename( - {"Unnamed: 1": "reg_no", "Unnamed: 2": "region"}, axis=1 - ) - df_gdp = df_gdp.melt( - id_vars=["region", "reg_no"], var_name="year", value_name="gdp_pcap" - ) - return df_gdp - - -def project_demand(df, phi, mu): - df_demand = df.groupby("region").apply( - lambda group: group.assign( - demand_pcap_base=group["demand.tot.base"].iloc[0] - * giga - / group["pop.mil"].iloc[0] - / mega - ) - ) - df_demand = df_demand.groupby("region").apply( - lambda group: group.assign( - gap_base=group["demand_pcap_base"].iloc[0] - group["demand_pcap0"].iloc[0] - ) - ) - df_demand = df_demand.groupby("region").apply( - lambda group: group.assign( - demand_pcap=group["demand_pcap0"] - + group["gap_base"] * gompertz(phi, mu, y=group["year"]) - ) - ) - df_demand = ( - df_demand.groupby("region") - .apply( - lambda group: group.assign( - demand_tot=group["demand_pcap"] * group["pop.mil"] * mega / giga - ) - ) - .reset_index(drop=True) - ) - return df_demand - - -def read_base_demand(filepath): - with open(filepath, "r") as file: - yaml_data = file.read() - - data_list = yaml.safe_load(yaml_data) - - df = pd.DataFrame( - [ - (key, value["year"], value["value"]) - for entry in data_list - for key, value in entry.items() - ], - columns=["region", "year", "value"], - ) - return df - - -def read_hist_mat_demand(material): - datapath = message_ix_models.util.private_data_path("material") - - if material in ["cement", "steel"]: - # Read population data - df_population = pd.read_excel( - f"{datapath}/steel_cement{file_cement}", - sheet_name="Timer_POP", - skiprows=[0, 1, 2, 30], - nrows=26, - ) - df_population = df_population.drop(columns=["Unnamed: 0"]).rename( - {"Unnamed: 1": "reg_no", "Unnamed: 2": "region"}, axis=1 - ) - df_population = df_population.melt( - id_vars=["region", "reg_no"], var_name="year", value_name="pop" - ) - - # Read GDP per capita data - df_gdp = pd.read_excel( - f"{datapath}/steel_cement{file_cement}", - sheet_name="Timer_GDPCAP", - skiprows=[0, 1, 2, 30], - nrows=26, - ) - df_gdp = df_gdp.drop(columns=["Unnamed: 0"]).rename( - {"Unnamed: 1": "reg_no", "Unnamed: 2": "region"}, axis=1 - ) - df_gdp = df_gdp.melt( - id_vars=["region", "reg_no"], var_name="year", value_name="gdp_pcap" - ) - - if material == "aluminum": - df_raw_cons = ( - pd.read_excel( - f"{datapath}/aluminum{file_al}", sheet_name="final_table", nrows=378 - ) - .drop(["cons.pcap", "del.t"], axis=1) - .rename({"gdp.pcap": "gdp_pcap"}, axis=1) - ) - - df_cons = ( - df_raw_cons.assign( - cons_pcap=lambda x: x["consumption"] / x["pop"], - del_t=lambda x: x["year"].astype(int) - 2010, - ) - .dropna() - .query("cons_pcap > 0") - ) - elif material == "steel": - df_raw_cons = pd.read_excel( - f"{datapath}/steel_cement{file_steel}", - sheet_name="Consumption regions", - nrows=26, - ) - df_raw_cons = ( - df_raw_cons.rename({"t": "region"}, axis=1) - .drop(columns=["Unnamed: 1"]) - .rename({"TIMER Region": "reg_no"}, axis=1) - ) - df_raw_cons = df_raw_cons.melt( - id_vars=["region", "reg_no"], var_name="year", value_name="consumption" - ) - df_raw_cons["region"] = ( - df_raw_cons["region"] - .str.replace("(", "") - .str.replace(")", "") - .str.replace("\d+", "") - .str[:-1] - ) - - # Merge and organize data - df_cons = ( - pd.merge( - df_raw_cons, df_population.drop("region", axis=1), on=["reg_no", "year"] - ) - .merge(df_gdp[["reg_no", "year", "gdp_pcap"]], on=["reg_no", "year"]) - .assign( - cons_pcap=lambda x: x["consumption"] / x["pop"], - del_t=lambda x: x["year"].astype(int) - 2010, - ) - .dropna() - .query("cons_pcap > 0") - ) - elif material == "cement": - df_raw_cons = pd.read_excel( - f"{datapath}/steel_cement{file_cement}", - sheet_name="Regions", - skiprows=122, - nrows=26, - ) - df_raw_cons = df_raw_cons.rename( - {"Region #": "reg_no", "Region": "region"}, axis=1 - ) - df_raw_cons = df_raw_cons.melt( - id_vars=["region", "reg_no"], var_name="year", value_name="consumption" - ) - # Merge and organize data - df_cons = ( - pd.merge( - df_raw_cons, df_population.drop("region", axis=1), on=["reg_no", "year"] - ) - .merge(df_gdp[["reg_no", "year", "gdp_pcap"]], on=["reg_no", "year"]) - .assign( - cons_pcap=lambda x: x["consumption"] / x["pop"] / 10**6, - del_t=lambda x: x["year"].astype(int) - 2010, - ) - .dropna() - .query("cons_pcap > 0") - ) - else: - print("non-available material selected. must be one of [aluminum, steel, cement]") - df_cons = None - return df_cons - - -def derive_demand(material, scen): - - datapath = message_ix_models.util.private_data_path("material") - - # read pop from scenario - pop = scen.par("bound_activity_up", {"technology": "Population"}) - pop = pop.loc[pop.year_act >= 2020].rename( - columns={"year_act": "year", "value": "pop.mil", "node_loc": "region"} - ) - - # read gdp from scenario - gdp = scen.par("bound_activity_up", {"technology": "GDP"}) - gdp = gdp.loc[gdp.year_act >= 2020].rename( - columns={"year_act": "year", "value": "gdp_ppp", "node_loc": "region"} - ) - - # get base year demand of material - df_base_demand = read_base_demand(f"{datapath}/{material_data_dirname[material]}/demand_{material}.yaml") - - # get historical material data - df_cons = read_hist_mat_demand(material) - x_data = tuple(pd.Series(df_cons[col]) for col in fitting_dict[material]["x_data"]) - - # run regression on historical data - params_opt, _ = curve_fit( - fitting_dict[material]["function"], - xdata=x_data, - ydata=df_cons["cons_pcap"], - p0=fitting_dict[material]["initial_guess"] - ) - print(f"{params_opt}") - - df_in = pd.merge(pop, df_base_demand.drop(columns=["year"]), how="left") - df_in = pd.merge(df_in, gdp[["region", "year", "gdp_ppp"]], how="inner") - df_in["del_t"] = df_in["year"] - 2010 - df_in["gdp_pcap"] = df_in["gdp_ppp"] * giga / df_in["pop.mil"] / mega - - df_in["demand_pcap0"] = df_in.apply( - lambda row: fitting_dict[material]["function"](tuple(row[i] for i in fitting_dict[material]["x_data"]), *params_opt), axis=1 - ) - df_in = df_in.rename({"value":"demand.tot.base"}, axis=1) - df_final = project_demand(df_in, fitting_dict[material]["phi"], fitting_dict[material]["mu"]) - - return df_final.drop(['technology', 'mode', 'time', 'unit'], axis=1) diff --git a/message_ix_models/model/material/material_intensity/ADVANCE_LCA_coefficients.R b/message_ix_models/model/material/material_intensity/ADVANCE_LCA_coefficients.R deleted file mode 100644 index 8244972461..0000000000 --- a/message_ix_models/model/material/material_intensity/ADVANCE_LCA_coefficients.R +++ /dev/null @@ -1,208 +0,0 @@ -################################################################################ -# include libraries -################################################################################ - -options(java.parameters = "-Xmx8g") - -library(dplyr) -library(tidyr) -library(readxl) -library(imputeTS) # for time series interpolation of NA - -data.path = "C:/Users/krey/Documents/git/message_data_https/data/material" - -################################################################################ -# read data -################################################################################ - -setwd(data.path) - -# read LCA data from ADVANCE LCA tool -col.types = c(rep("text", 9), rep("numeric", 3)) -data.lca = read_xlsx(path = "NTNU_LCA_coefficients.xlsx", sheet = "environmentalImpacts", col_types = col.types) - -# technology mapping -technology.mapping = read_xlsx('MESSAGE_global_model_technologies.xlsx', sheet = 'technology') - -# region mapping -region.mapping = read_xlsx('LCA_region_mapping.xlsx', sheet = 'region') - -# commodity mapping -commodity.mapping = read_xlsx('LCA_commodity_mapping.xlsx', sheet = 'commodity') - -################################################################################ -# process data -################################################################################ - -# filter relevant scenario, technology variant (residue for biomass, mix for others) and remove operation phase (and remove duplicates) -data.lca = data.lca %>% filter(scenario == 'Baseline' & `technology variant` %in% c('mix', 'residue') & phase != 'Operation') - -# add intermediate time steps and turn into long table format -data.lca = data.lca %>% mutate(`2015` = NA, `2020` = NA, `2025` = NA, `2035` = NA, `2040` = NA, `2045` = NA) %>% pivot_longer(cols = c("2010":"2045"), names_to = 'year', values_to = 'value') %>% mutate(value = as.numeric(value)) -#data.lca$year = factor(data.lca$year, levels = as.character(seq(2010, 2050, 5))) - -# apply technology, commodity/impact and region mappings to MESSAGE -data.lca = data.lca %>% inner_join(region.mapping, by = c("region" = "THEMIS")) %>% inner_join(technology.mapping, by = c("technology" = "LCA mapping")) %>% - inner_join(commodity.mapping, by = c("impact" = "impact")) %>% - filter(!is.na(`MESSAGEix-GLOBIOM_1.1`)) %>% - select(node = `MESSAGEix-GLOBIOM_1.1`, technology = `Type of Technology`, phase, commodity, level, year, unit, value) #%>% unique() - -temp = c() -for (n in unique(data.lca$node)) for (t in unique(data.lca$technology)) for (c in unique(data.lca$commodity)) for (p in unique(data.lca$phase)){ - temp = rbind(temp, data.lca %>% filter(node == n & technology == t & commodity == c & phase == p) %>% na_interpolation()) -} -data.lca = temp - -################################################################################ -# ixmp setup -################################################################################ - -# existing model and scenario name in ixmp to add material intensities to -modelName <- "Material_Global" -scenarioName <- "NoPolicy" - -# new model and scenario name in ixmp -newmodelName <- "Material_Global" -newscenarioName <- "NoPolicy_lca_material" - -# comment for commit -comment <- "adding LCA-based material intensity coefficients to electricity generation technologies in MESSAGEix-Materials" - -# load required packages -library(rmessageix) - -# specify python binaries and environment under which messageix is installed -use_python("C:/Users/krey/anaconda3/envs/message/") -use_condaenv("message") - -# launch the IX modeling platform using the default database -mp <- ixmp$Platform() - -################################################################################ -# load and clone scenario and extract structural information -################################################################################ - -# load existing policy baseline scenario -ixScenarioOriginal = message_ix$Scenario(mp, modelName, scenarioName) - -# clone original policy baseline scenario with new scenario name -ixScenario = ixScenarioOriginal$clone(newmodelName, newscenarioName, comment, keep_solution=FALSE) - -# checkout scenario -ixScenario$check_out() - -# read inv.cost data -inv.cost = ixScenarioOriginal$par('inv_cost') - -# read node, technology, commodity and level from existing scenario -node = ixScenarioOriginal$set('node') %>% data.frame() -year = ixScenarioOriginal$set('year') %>% data.frame() -technology = ixScenarioOriginal$set('technology') %>% data.frame() -commodity = ixScenarioOriginal$set('commodity') %>% data.frame() -level = ixScenarioOriginal$set('level') %>% data.frame() - -# extract node, technology, commodity, level, and year list from LCA data set -node.list = unique(data.lca$node) -year.list = unique(data.lca$year) -tec.list = unique(data.lca$technology) -com.list = unique(data.lca$commodity) -lev.list = unique(data.lca$level) -# add scrap as commodity level -lev.list = c(lev.list, 'end_of_life') - -# check whether set members exist in scenario and add in case not -for (n in 1:length(node.list)) if (!node.list[n] %in% node$.) ixScenario$add_set('node', node.list[n]) -for (n in 1:length(year.list)) if (!year.list[n] %in% year$.) ixScenario$add_set('year', year.list[n]) -for (n in 1:length(tec.list)) if (!tec.list[n] %in% technology$.) ixScenario$add_set('technology', tec.list[n]) -for (n in 1:length(com.list)) if (!com.list[n] %in% commodity$.) ixScenario$add_set('commodity', com.list[n]) -for (n in 1:length(lev.list)) if (!lev.list[n] %in% level$.) ixScenario$add_set('level', lev.list[n]) - -# check whether needed units are registered on ixmp and add if not the case -unit = mp$units() %>% data.frame() -if (!('t/kW' %in% unit$.)) mp$add_unit('t/kW', 'tonnes (of commodity) per kW of capacity') - -################################################################################ -# create data frames for material intensitty input/output parameters -################################################################################ - -# new data frames for parameters -input_cap_new <- input_cap_ret <- output_cap_ret <- data.frame() - -for (n in node.list) for (t in tec.list) for (c in com.list) { - year_vtg.list = filter(inv.cost, node_loc == n & technology == t)$year_vtg %>% unique() # & year_vtg >= min(as.numeric(year.list)) - for (y in year_vtg.list) { - # for years after maximum year in data set use values for maximum year, similarly for years before minimum year in data set use values for minimum year - if (y > max(year.list)) yeff = max(year.list) else if (y < min(year.list)) yeff = min(year.list) else yeff = y - input_cap_new = rbind(input_cap_new, data.frame(node_loc = n, technology = t, year_vtg = as.character(y), node_origin = n, commodity = c, level = 'product', time_origin = "year", value = filter(data.lca, node == n & technology == t & phase == 'Construction' & commodity == c & year == yeff)$value * 1e-3, unit = 't/kW')) - input_cap_ret = rbind(input_cap_ret, data.frame(node_loc = n, technology = t, year_vtg = as.character(y), node_origin = n, commodity = c, level = 'product', time_origin = "year", value = filter(data.lca, node == n & technology == t & phase == 'End-of-life' & commodity == c & year == yeff)$value * 1e-3, unit = 't/kW')) - output_cap_ret = rbind(output_cap_ret, data.frame(node_loc = n, technology = t, year_vtg = as.character(y), node_dest = n, commodity = c, level = 'end_of_life', time_dest = "year", value = filter(data.lca, node == n & technology == t & phase == 'Construction' & commodity == c & year == yeff)$value * 1e-3, unit = 't/kW')) - } -} - -################################################################################ -# create new parameters and add new data to scenario -################################################################################ - -# create new parameters input_cap_new, output_cap_new, input_cap_ret, output_cap_ret, input_cap and output_cap if they don't exist -if (!ixScenario$has_par('input_cap_new')) - ixScenario$init_par('input_cap_new', idx_sets = c('node', 'technology', 'year', 'node', 'commodity', 'level', 'time'), idx_names = c('node_loc', 'technology', 'year_vtg', 'node_origin', 'commodity', 'level', 'time_origin')) -if (!ixScenario$has_par('output_cap_new')) - ixScenario$init_par('output_cap_new', idx_sets = c('node', 'technology', 'year', 'node', 'commodity', 'level', 'time'), idx_names = c('node_loc', 'technology', 'year_vtg', 'node_dest', 'commodity', 'level', 'time_dest')) -if (!ixScenario$has_par('input_cap_ret')) - ixScenario$init_par('input_cap_ret', idx_sets = c('node', 'technology', 'year', 'node', 'commodity', 'level', 'time'), idx_names = c('node_loc', 'technology', 'year_vtg', 'node_origin', 'commodity', 'level', 'time_origin')) -if (!ixScenario$has_par('output_cap_ret')) - ixScenario$init_par('output_cap_ret', idx_sets = c('node', 'technology', 'year', 'node', 'commodity', 'level', 'time'), idx_names = c('node_loc', 'technology', 'year_vtg', 'node_dest', 'commodity', 'level', 'time_dest')) -if (!ixScenario$has_par('input_cap')) - ixScenario$init_par('input_cap', idx_sets = c('node', 'technology', 'year', 'year', 'node', 'commodity', 'level', 'time'), idx_names = c('node_loc', 'technology', 'year_vtg', 'year_act', 'node_origin', 'commodity', 'level', 'time_origin')) -if (!ixScenario$has_par('output_cap')) - ixScenario$init_par('output_cap', idx_sets = c('node', 'technology', 'year', 'year', 'node', 'commodity', 'level', 'time'), idx_names = c('node_loc', 'technology', 'year_vtg', 'year_act', 'node_dest', 'commodity', 'level', 'time_dest')) - -ixScenario$add_par('input_cap_new', input_cap_new) -ixScenario$add_par('input_cap_ret', input_cap_ret) -ixScenario$add_par('output_cap_ret', output_cap_ret) - -################################################################################ -# add dummy material production technologies (only needed for model variants without material sector) -################################################################################ - -technology.material = data.frame(tec = c("material_aluminum", "material_cement", "material_steel", "scrap_aluminum", "scrap_cement", "scrap_steel"), com = c("aluminum", "cement", "steel", "aluminum", "cement", "steel"), lev = c("product", "product", "product", "end_of_life", "end_of_life", "end_of_life")) - -for (n in 1:length(technology.material$tec)) if (!technology.material$tec[n] %in% technology$.) ixScenario$add_set('technology', technology.material$tec[n]) - -# new data frames for parameters -output <- var_cost <- data.frame() - -for (n in node.list) for (t in 1:length(technology.material$tec)) { - year_vtg.list = filter(year, . > 2010)$. - for (y in year_vtg.list) { - output = rbind(output, data.frame(node_loc = n, technology = technology.material$tec[t], year_vtg = as.character(y), year_act = as.character(y), mode = 'M1', node_dest = n, commodity = technology.material$com[t], level = technology.material$lev[t], time = 'year', time_dest = "year", value = 1.0, unit = 't')) - var_cost = rbind(var_cost, data.frame(node_loc = n, technology = technology.material$tec[t], year_vtg = as.character(y), year_act = as.character(y), mode = 'M1', time = "year", value = 1.0, unit = 'USD')) - } -} - -ixScenario$add_par('output', output) -ixScenario$add_par('var_cost', var_cost) - -################################################################################ -# commit new scenario and solve model -################################################################################ - -# commit scenario to platform and set as default -ixScenario$commit(comment) -ixScenario$set_as_default() - -# run MESSAGE scenario in GAMS and import results in ix platform -ixScenario$solve("MESSAGE") - -################################################################################ -# illustrative visualization of material intensities -################################################################################ - -tec.figure = data.frame(tec = c("coal_adv", "coal_adv_ccs", "gas_cc", "nuc_hc", "bio_ppl", "solar_pv_ppl", "csp_sm1_ppl", "wind_ppl"), label = c("Coal w/o CCS", "Coal w/ CCS", " Gas CC", "Nuclear", "Biomass", "Solar PV", "CSP", "Wind onshore")) -data.figure = input_cap_new %>% filter(year_vtg == 2030 & node_loc == 'R11_WEU') %>% inner_join(tec.figure, by = c("technology" = "tec")) - -library(ggplot2) -setwd("H:/Projects/ENE/ALPS/material_integration/figures") -png(paste("Material_intensity_electricity.png", sep = ''), width = 10, height = 5, units = "in", res = 300) - ggplot(data.figure) + geom_bar(aes(x = label, y = value, fill = commodity), stat = 'identity', position="dodge") + scale_y_continuous(limits = c(0, 0.5)) + ylab("Material Intensity [t/kW]") + xlab("") -dev.off() diff --git a/message_ix_models/model/material/material_intensity/ADVANCE_lca_coefficients_embedded.R b/message_ix_models/model/material/material_intensity/ADVANCE_lca_coefficients_embedded.R deleted file mode 100644 index 0974a967ec..0000000000 --- a/message_ix_models/model/material/material_intensity/ADVANCE_lca_coefficients_embedded.R +++ /dev/null @@ -1,97 +0,0 @@ -################################################################################ -# include libraries -################################################################################ - -options(java.parameters = "-Xmx8g") - -library(dplyr) -library(tidyr) -library(readxl) -library(imputeTS) # for time series interpolation of NA - -################################################################################ -# functions -################################################################################ - -read_material_intensities <- function(parameter, data_path, node, year, technology, commodity, level, inv_cost) { - - if (parameter %in% c("input_cap_new", "input_cap_ret", "output_cap_ret")) { - - ################################################################################ - # read data - ################################################################################ - - # read LCA data from ADVANCE LCA tool - col_types = c(rep("text", 9), rep("numeric", 3)) - data_lca = read_xlsx(path = paste(data_path, "/NTNU_LCA_coefficients.xlsx", sep = ''), sheet = "environmentalImpacts", col_types = col_types) - - # read technology, region and commodity mappings - technology_mapping = read_xlsx(paste(data_path, "/MESSAGE_global_model_technologies.xlsx", sep = ''), sheet = 'technology') - region_mapping = read_xlsx(paste(data_path, "/LCA_region_mapping.xlsx", sep = ''), sheet = 'region') - commodity_mapping = read_xlsx(paste(data_path, "/LCA_commodity_mapping.xlsx", sep = ''), sheet = 'commodity') - - ################################################################################ - # process data - ################################################################################ - - # filter relevant scenario, technology variant (residue for biomass, mix for others) and remove operation phase (and remove duplicates) - data_lca = data_lca %>% filter(scenario == 'Baseline' & `technology variant` %in% c('mix', 'residue') & phase != 'Operation') - - # add intermediate time steps and turn into long table format - data_lca = data_lca %>% mutate(`2015` = NA, `2020` = NA, `2025` = NA, `2035` = NA, `2040` = NA, `2045` = NA) %>% pivot_longer(cols = c("2010":"2045"), names_to = 'year', values_to = 'value') %>% mutate(value = as.numeric(value)) - #data_lca$year = factor(data_lca$year, levels = as.character(seq(2010, 2050, 5))) - - # apply technology, commodity/impact and region mappings to MESSAGEix - data_lca = data_lca %>% inner_join(region_mapping, by = c("region" = "THEMIS")) %>% inner_join(technology_mapping, by = c("technology" = "LCA mapping")) %>% - inner_join(commodity_mapping, by = c("impact" = "impact")) %>% - filter(!is.na(`MESSAGEix-GLOBIOM_1.1`)) %>% - select(node = `MESSAGEix-GLOBIOM_1.1`, technology = `Type of Technology`, phase, commodity, level, year, unit, value) #%>% unique() - - temp = c() - for (n in unique(data_lca$node)) for (t in unique(data_lca$technology)) for (c in unique(data_lca$commodity)) for (p in unique(data_lca$phase)){ - temp = rbind(temp, data_lca %>% filter(node == n & technology == t & commodity == c & phase == p) %>% na_interpolation()) - } - # return data frame - data_lca = temp - - # extract node, technology, commodity, level, and year list from LCA data set - node_list = unique(data_lca$node) - year_list = unique(data_lca$year) - tec_list = unique(data_lca$technology) - com_list = unique(data_lca$commodity) - lev_list = unique(data_lca$level) - # add scrap as commodity level - lev_list = c(lev_list, 'end_of_life') - - # check whether set members exist in scenario and add in case not - #for (n in 1:length(node.list)) if (!node.list[n] %in% node$.) ixScenario$add_set('node', node.list[n]) - #for (n in 1:length(year.list)) if (!year.list[n] %in% year$.) ixScenario$add_set('year', year.list[n]) - #for (n in 1:length(tec.list)) if (!tec.list[n] %in% technology$.) ixScenario$add_set('technology', tec.list[n]) - #for (n in 1:length(com.list)) if (!com.list[n] %in% commodity$.) ixScenario$add_set('commodity', com.list[n]) - #for (n in 1:length(lev.list)) if (!lev.list[n] %in% level$.) ixScenario$add_set('level', lev.list[n]) - - ################################################################################ - # create data frames for material intensity input/output parameters - ################################################################################ - - # new data frames for parameters - input_cap_new <- input_cap_ret <- output_cap_ret <- data.frame() - - for (n in node_list) for (t in tec_list) for (c in com_list) { - year_vtg_list = filter(inv_cost, node_loc == n & technology == t)$year_vtg %>% unique() # & year_vtg >= min(as.numeric(year.list)) - for (y in year_vtg_list) { - # for years after maximum year in data set use values for maximum year, similarly for years before minimum year in data set use values for minimum year - if (y > max(year_list)) yeff = max(year_list) else if (y < min(year_list)) yeff = min(year_list) else yeff = y - input_cap_new = rbind(input_cap_new, data.frame(node_loc = n, technology = t, year_vtg = as.character(y), node_origin = n, commodity = c, level = 'product', time_origin = "year", value = filter(data_lca, node == n & technology == t & phase == 'Construction' & commodity == c & year == yeff)$value * 1e-3, unit = 't/kW')) - input_cap_ret = rbind(input_cap_ret, data.frame(node_loc = n, technology = t, year_vtg = as.character(y), node_origin = n, commodity = c, level = 'product', time_origin = "year", value = filter(data_lca, node == n & technology == t & phase == 'End-of-life' & commodity == c & year == yeff)$value * 1e-3, unit = 't/kW')) - output_cap_ret = rbind(output_cap_ret, data.frame(node_loc = n, technology = t, year_vtg = as.character(y), node_dest = n, commodity = c, level = 'end_of_life', time_dest = "year", value = filter(data_lca, node == n & technology == t & phase == 'Construction' & commodity == c & year == yeff)$value * 1e-3, unit = 't/kW')) - } - } - } - - # return parameter - if (parameter == "input_cap_new") {input_cap_new - } else if (parameter == "input_cap_ret") {input_cap_ret - } else if (parameter == "output_cap_ret") {output_cap_ret - } else NA -} diff --git a/message_ix_models/model/material/material_intensity/data_material_intensities_rpy2.py b/message_ix_models/model/material/material_intensity/data_material_intensities_rpy2.py deleted file mode 100644 index 1eba5814e1..0000000000 --- a/message_ix_models/model/material/material_intensity/data_material_intensities_rpy2.py +++ /dev/null @@ -1,76 +0,0 @@ -# -*- coding: utf-8 -*- -""" -This script adds material intensity coefficients (prototype) -""" - -# check Python and R environments (for debugging) -import rpy2.situation -for row in rpy2.situation.iter_info(): - print(row) - -# load rpy2 modules -import rpy2.robjects as ro -#from rpy2.robjects.packages import importr -from rpy2.robjects import pandas2ri -from rpy2.robjects.conversion import localconverter - -# paths to r code and lca data -rcode_path="C:/Users/krey/Documents/git/message_data/message_data/model/material/material_intensity/" -data_path = "C:/Users/krey/Documents/git/message_data/data/material" - -# import MESSAGEix -import ixmp -import message_ix -from message_data.tools import ScenarioInfo - -#def gen_data_material_intensities(scenario, dry_run=False): -# """Generate data for endogenous materials demand of power sector. - -# """ - -# launch the IX modeling platform using the local default databases -mp = ixmp.Platform(name='default', jvmargs=['-Xmx12G']) - -# model and scenario names of baseline scenario as basis for diagnostic analysis -model = "Material_Global" -scen = "NoPolicy" - -# load existing scenario -scenario = message_ix.Scenario(mp, model, scen) - -# information about scenario, e.g. node, year -s_info = ScenarioInfo(scenario) - -# read node, technology, commodity and level from existing scenario -node = s_info.N -year = s_info.set['year'] #s_info.Y is only for modeling years -technology = s_info.set['technology'] -commodity = s_info.set['commodity'] -level = s_info.set['level'] -# read inv.cost data -inv_cost = scenario.par('inv_cost') - -# check whether needed units are registered on ixmp and add if not the case -unit = mp.units() -#if (!('t/kW' %in% unit$.)) mp$add_unit('t/kW', 'tonnes (of commodity) per kW of capacity') - -# List of data frames, to be concatenated together at end -results = defaultdict(list) - -param_name = ["input_cap_new", "input_cap_ret", "output_cap_ret",] -# source R code -r=ro.r -r.source(rcode_path+"ADVANCE_lca_coefficients_embedded.R") - -# call R function with type conversion - -for p in set(param_name): - with localconverter(ro.default_converter + pandas2ri.converter): - df = r.read_material_intensities(p, data_path, node, year, technology, commodity, level, inv_cost) - - -results[parname].append(df) - - # Concatenate to one data frame per parameter - results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} - return results diff --git a/message_ix_models/model/material/report/materials.yaml b/message_ix_models/model/material/report/materials.yaml deleted file mode 100644 index 73744836cb..0000000000 --- a/message_ix_models/model/material/report/materials.yaml +++ /dev/null @@ -1,21 +0,0 @@ - -combine: - # Name and dimensions of quantity to be created -- key: coal:nl-ya - # Inputs to sum - inputs: - # Input quantity. If dimensions are none ('name::tag') then the necessary - # dimensions are inferred: the union of the dimensions of 'key:' above, - # plus any dimensions appearing in 'select:'' - - quantity: in::pe # e.g. 'in:nl-t-ya:pe' is inferred - # Values to select - select: {t: [coal, lignite]} - # Weight for these values in the weighted sum - - quantity: in::import - select: {t: coal} - - quantity: in::export - select: {t: coal} - weight: -1 - # commented (PNK 2019-10-07): doesn't exist - # - quantity: in::bunker - # select: {t: coal} From fcf7a6b7fa30213bfcdd3b74eba5982993c3f845 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 23 Apr 2024 15:05:53 +0200 Subject: [PATCH 739/774] Modify imports and adjust data handling --- message_ix_models/model/material/__init__.py | 99 ++++++++++--------- message_ix_models/model/material/bare.py | 8 +- .../model/material/data_aluminum.py | 21 ++-- .../model/material/data_ammonia_new.py | 34 +++---- .../model/material/data_buildings.py | 6 +- .../model/material/data_cement.py | 16 +-- .../model/material/data_generic.py | 2 +- .../model/material/data_methanol.py | 56 +++++------ .../model/material/data_methanol_new.py | 14 +-- .../model/material/data_petro.py | 22 ++--- .../model/material/data_power_sector.py | 4 +- .../model/material/data_steel.py | 6 +- message_ix_models/model/material/data_util.py | 39 ++++---- message_ix_models/model/material/util.py | 12 ++- 14 files changed, 171 insertions(+), 168 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index ca4e9bde82..506f393cfe 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -1,54 +1,56 @@ -import message_ix -import pandas as pd -import ntfy -import click import logging -import ixmp import os +from typing import Mapping -import message_ix_models.tools.costs.projections -from message_data.model.material.build import apply_spec -from message_ix_models import ScenarioInfo -from message_ix_models.util import add_par_data, private_data_path -from message_data.model.material.data_buildings import gen_data_buildings +import click +import ixmp +import message_ix +import ntfy +import pandas as pd + +#update_h2_blending, from message_data.tools.utilities import ( - calibrate_UE_gr_to_demand, - calibrate_UE_share_constraints, manual_updates_ENGAGE_SSP2_v417_to_v418 as engage_updates, - update_h2_blending, ) -from message_data.model.material.data_util import ( - modify_demand_and_hist_activity, - add_emission_accounting, - add_new_ind_hist_act, - modify_industry_demand, - modify_baseyear_bounds, - add_coal_lowerbound_2020, - add_macro_COVID, + +import message_ix_models.tools.costs.projections +from message_ix_models import ScenarioInfo +from message_ix_models.model.material.build import apply_spec +from message_ix_models.model.material.data_aluminum import gen_data_aluminum +from message_ix_models.model.material.data_ammonia_new import gen_all_NH3_fert +from message_ix_models.model.material.data_buildings import gen_data_buildings +from message_ix_models.model.material.data_cement import gen_data_cement +from message_ix_models.model.material.data_generic import gen_data_generic +from message_ix_models.model.material.data_methanol_new import gen_data_methanol_new +from message_ix_models.model.material.data_petro import gen_data_petro_chemicals +from message_ix_models.model.material.data_power_sector import gen_data_power_sector +from message_ix_models.model.material.data_steel import gen_data_steel +from message_ix_models.model.material.data_util import ( + add_ccs_technologies, add_cement_bounds_2020, + add_coal_lowerbound_2020, + add_elec_i_ini_act, add_elec_lowerbound_2020, - add_ccs_technologies, - read_config, + add_emission_accounting, + add_macro_COVID, + add_new_ind_hist_act, gen_te_projections, get_ssp_soc_eco_data, - add_elec_i_ini_act, + modify_baseyear_bounds, + modify_demand_and_hist_activity, + modify_industry_demand, + read_config, ) -from message_data.model.material.util import ( +from message_ix_models.model.material.util import ( excel_to_csv, get_all_input_data_dirs, update_macro_calib_file, ) -from typing import Mapping -from message_data.model.material.data_cement import gen_data_cement -from message_data.model.material.data_steel import gen_data_steel -from message_data.model.material.data_aluminum import gen_data_aluminum -from message_data.model.material.data_generic import gen_data_generic -from message_data.model.material.data_petro import gen_data_petro_chemicals -from message_data.model.material.data_power_sector import gen_data_power_sector -from message_data.model.material.data_methanol_new import gen_data_methanol_new -from message_data.model.material.data_ammonia_new import gen_all_NH3_fert - - +from message_ix_models.tools import ( + calibrate_UE_gr_to_demand, + calibrate_UE_share_constraints, +) +from message_ix_models.util import add_par_data, package_data_path from message_ix_models.util.click import common_params log = logging.getLogger(__name__) @@ -83,7 +85,7 @@ def build(scenario: message_ix.Scenario, old_calib: bool) -> message_ix.Scenario scenario.add_set("commodity", "freshwater_supply") water_dict = pd.read_excel( - private_data_path("material", "other", "water_tec_pars.xlsx"), + package_data_path("material", "other", "water_tec_pars.xlsx"), sheet_name=None, ) for par in water_dict.keys(): @@ -121,12 +123,13 @@ def build(scenario: message_ix.Scenario, old_calib: bool) -> message_ix.Scenario add_cement_bounds_2020(scenario) # Market penetration adjustments - # NOTE: changing demand affects the market penetration levels for the enduse technologies. + # NOTE: changing demand affects the market penetration + # levels for the enduse technologies. # Note: context.ssp doesnt work - calibrate_UE_gr_to_demand( - scenario, data_path=private_data_path(), ssp="SSP2", region="R12" + calibrate_UE_gr_to_demand.main( + scenario, data_path=package_data_path(), ssp="SSP2", region="R12" ) - calibrate_UE_share_constraints(scenario) + calibrate_UE_share_constraints.main(scenario) # Electricity calibration to avoid zero prices for CHN. if "R12_CHN" in nodes: @@ -202,7 +205,7 @@ def cli(ssp): @click.pass_obj def create_bare(context, regions, dry_run): """Create the RES from scratch.""" - from message_data.model.create import create_res + from message_ix_models.model.create import create_res if regions: context.regions = regions @@ -420,7 +423,7 @@ def solve_scen( scenario.set_as_default() # Report - from message_data.model.material.report.reporting import report + from message_ix_models.model.material.report.reporting import report from message_data.tools.post_processing.iamc_report_hackathon import ( report as reporting, ) @@ -484,7 +487,7 @@ def solve_scen( @click.option("--scenario_name", default="NoPolicy") @click.option("--model_name", default="MESSAGEix-Materials") def add_building_ts(scenario_name, model_name): - from message_data.reporting.materials.add_buildings_ts import ( + from message_ix_models.reporting.materials.add_buildings_ts import ( add_building_timeseries, ) from message_ix import Scenario @@ -509,7 +512,7 @@ def add_building_ts(scenario_name, model_name): @click.pass_obj def run_reporting(context, remove_ts, profile): """Run materials, then legacy reporting.""" - from message_data.model.material.report.reporting import report + from message_ix_models.model.material.report.reporting import report from message_data.tools.post_processing.iamc_report_hackathon import ( report as reporting, ) @@ -796,7 +799,7 @@ def make_xls_input_vc_able(context, files): dirs = [i for i in dirs if i != "version control"] for dir in dirs: print(dir) - files = os.listdir(private_data_path("material", dir)) + files = os.listdir(package_data_path("material", dir)) files = [i for i in files if ((i.endswith(".xlsx")) & ~i.startswith("~$"))] print(files) for filename in files: @@ -810,7 +813,7 @@ def make_xls_input_vc_able(context, files): # for element in files: # print(element) # fname = element.split("/")[-1] - # path = private_data_path(str(element.split("/")[:-1])) + # path = package_data_path(str(element.split("/")[:-1])) # print(path, fname) - # message_data.model.material.util.excel_to_csv(files) + # message_ix_models.model.material.util.excel_to_csv(files) return diff --git a/message_ix_models/model/material/bare.py b/message_ix_models/model/material/bare.py index 0178a97a13..c38eb84910 100644 --- a/message_ix_models/model/material/bare.py +++ b/message_ix_models/model/material/bare.py @@ -8,9 +8,9 @@ from sdmx.model.v21 import Code from .build import apply_spec from .util import read_config -from message_data.model.data import get_data -from message_data.model.material import gen_data_steel, gen_data_generic, gen_data_aluminum, gen_data_petro_chemicals -import message_data +from message_ix_models.model.data import get_data +from message_ix_models.model.material import gen_data_steel, gen_data_generic, gen_data_aluminum, gen_data_petro_chemicals +import message_ix_models log = logging.getLogger(__name__) @@ -68,7 +68,7 @@ def create_res(context=None, quiet=True): # data=partial(get_data, context=context, spec=spec), data=add_data, quiet=quiet, - message=f"Create using message_data {message_data.__version__}", + message=f"Create using message_ix_models {message_ix_models.__version__}", ) return scenario diff --git a/message_ix_models/model/material/data_aluminum.py b/message_ix_models/model/material/data_aluminum.py index 49d2c014c8..20de9fd9f2 100644 --- a/message_ix_models/model/material/data_aluminum.py +++ b/message_ix_models/model/material/data_aluminum.py @@ -1,20 +1,19 @@ -import pandas as pd from collections import defaultdict -from .data_util import read_timeseries -from pathlib import Path +import pandas as pd +from message_ix import make_df -from .material_demand import material_demand_calc -from .util import read_config -from .data_util import read_rel from message_ix_models import ScenarioInfo -from message_ix import make_df from message_ix_models.util import ( broadcast, + package_data_path, same_node, - private_data_path, ) +from .data_util import read_rel, read_timeseries +from .material_demand import material_demand_calc +from .util import read_config + # Get endogenous material demand from buildings interface @@ -39,7 +38,7 @@ def read_data_aluminum(scenario): # Read the file data_alu = pd.read_excel( - private_data_path("material", "aluminum", fname), sheet_name=sheet_n + package_data_path("material", "aluminum", fname), sheet_name=sheet_n ) # Drop columns that don't contain useful information @@ -486,7 +485,7 @@ def gen_mock_demand_aluminum(scenario): # SSP2 R11 baseline GDP projection gdp_growth = pd.read_excel( - private_data_path("material", "other", "iamc_db ENGAGE baseline GDP PPP.xlsx"), + package_data_path("material", "other", "iamc_db ENGAGE baseline GDP PPP.xlsx"), sheet_name=sheet_n, ) @@ -554,7 +553,7 @@ def gen_mock_demand_aluminum(scenario): # # GDP is only in MER in scenario. # # To get PPP GDP, it is read externally from the R side # df = r.derive_aluminum_demand( -# pop, base_demand, str(private_data_path("material")) +# pop, base_demand, str(package_data_path("material")) # ) # df.year = df.year.astype(int) # diff --git a/message_ix_models/model/material/data_ammonia_new.py b/message_ix_models/model/material/data_ammonia_new.py index a262269eb1..aa3d06e592 100644 --- a/message_ix_models/model/material/data_ammonia_new.py +++ b/message_ix_models/model/material/data_ammonia_new.py @@ -3,15 +3,15 @@ from message_ix_models import ScenarioInfo from message_ix import make_df -from message_ix_models.util import broadcast, same_node, private_data_path -from message_data.model.material.util import read_config -from message_data.model.material.material_demand import material_demand_calc +from message_ix_models.util import broadcast, same_node, package_data_path +from message_ix_models.model.material.util import read_config +from message_ix_models.model.material.material_demand import material_demand_calc CONVERSION_FACTOR_NH3_N = 17 / 14 context = read_config() default_gdp_elasticity = ( pd.read_excel( - private_data_path( + package_data_path( "material", "methanol", "methanol_sensitivity_pars.xlsx", @@ -61,7 +61,7 @@ def gen_data(scenario, dry_run=False, add_ccs: bool = True, lower_costs=False): nodes.pop(nodes.index("R12_GLB")) df = pd.read_excel( - private_data_path( + package_data_path( "material", "ammonia", "fert_techno_economic.xlsx", @@ -153,7 +153,7 @@ def __missing__(self, key): "fueloil_NH3_ccs", ] cost_conv = pd.read_excel( - private_data_path("material", "ammonia", "cost_conv_nh3.xlsx"), + package_data_path("material", "ammonia", "cost_conv_nh3.xlsx"), sheet_name="Sheet1", index_col=0, ) @@ -198,7 +198,7 @@ def gen_data_rel(scenario, dry_run=False, add_ccs: bool = True): nodes.pop(nodes.index("R12_GLB")) df = pd.read_excel( - private_data_path( + package_data_path( "material", "ammonia", "fert_techno_economic.xlsx", @@ -273,7 +273,7 @@ def gen_data_ts(scenario, dry_run=False, add_ccs: bool = True): nodes.pop(nodes.index("R12_GLB")) df = pd.read_excel( - private_data_path( + package_data_path( "material", "ammonia", "fert_techno_economic.xlsx", @@ -331,7 +331,7 @@ def read_demand(): context = read_config() N_demand_GLO = pd.read_excel( - private_data_path( + package_data_path( "material", "ammonia", "nh3_fertilizer_demand.xlsx", @@ -341,7 +341,7 @@ def read_demand(): # NH3 feedstock share by region in 2010 (from http://ietd.iipnetwork.org/content/ammonia#benchmarks) feedshare_GLO = pd.read_excel( - private_data_path( + package_data_path( "material", "ammonia", "nh3_fertilizer_demand.xlsx", @@ -352,7 +352,7 @@ def read_demand(): # Read parameters in xlsx te_params = data = pd.read_excel( - private_data_path("material", "ammonia", "nh3_fertilizer_demand.xlsx"), + package_data_path("material", "ammonia", "nh3_fertilizer_demand.xlsx"), sheet_name="old_TE_sheet", engine="openpyxl", nrows=72, @@ -396,10 +396,10 @@ def read_demand(): ) # GWa # N_trade_R12 = pd.read_csv( - # private_data_path("material", "ammonia", "trade.FAO.R12.csv"), index_col=0 + # package_data_path("material", "ammonia", "trade.FAO.R12.csv"), index_col=0 # ) N_trade_R12 = pd.read_excel( - private_data_path( + package_data_path( "material", "ammonia", "nh3_fertilizer_demand.xlsx", @@ -425,12 +425,12 @@ def read_demand(): NP["prod"] = NP.demand - NP.netimp # NH3_trade_R12 = pd.read_csv( - # private_data_path( + # package_data_path( # "material", "ammonia", "NH3_trade_BACI_R12_aggregation.csv" # ) # ) # , index_col=0) NH3_trade_R12 = pd.read_excel( - private_data_path( + package_data_path( "material", "ammonia", "nh3_fertilizer_demand.xlsx", @@ -515,7 +515,7 @@ def gen_demand(): N_energy = read_demand()["N_feed"] # updated feed with imports accounted demand_fs_org = pd.read_excel( - private_data_path("material", "ammonia", "nh3_fertilizer_demand.xlsx"), + package_data_path("material", "ammonia", "nh3_fertilizer_demand.xlsx"), sheet_name="demand_i_feed_R12", ) @@ -552,7 +552,7 @@ def get_demand_t1_with_income_elasticity( ) + demand_t0 df_gdp = pd.read_excel( - private_data_path("material", "methanol", "methanol demand.xlsx"), + package_data_path("material", "methanol", "methanol demand.xlsx"), sheet_name="GDP_baseline", ) diff --git a/message_ix_models/model/material/data_buildings.py b/message_ix_models/model/material/data_buildings.py index dd19c03316..90f30e29f7 100644 --- a/message_ix_models/model/material/data_buildings.py +++ b/message_ix_models/model/material/data_buildings.py @@ -1,13 +1,13 @@ import pandas as pd from collections import defaultdict -from message_data.model.material.util import read_config +from message_ix_models.model.material.util import read_config from message_ix_models import ScenarioInfo from message_ix import make_df from message_ix_models.util import ( same_node, copy_column, - private_data_path, + package_data_path, ) CASE_SENS = "ref" # 'min', 'max' @@ -25,7 +25,7 @@ def read_timeseries_buildings(filename, scenario, case=CASE_SENS): nodes = s_info.N # Read the file and filter the given sensitivity case - bld_input_raw = pd.read_csv(private_data_path("material", "buildings", filename)) + bld_input_raw = pd.read_csv(package_data_path("material", "buildings", filename)) bld_input_raw = bld_input_raw.loc[bld_input_raw.Sensitivity == case] bld_input_mat = bld_input_raw[ diff --git a/message_ix_models/model/material/data_cement.py b/message_ix_models/model/material/data_cement.py index 5a314b56f9..293d17acb1 100644 --- a/message_ix_models/model/material/data_cement.py +++ b/message_ix_models/model/material/data_cement.py @@ -4,15 +4,15 @@ from collections import defaultdict -from message_data.model.material.data_util import read_sector_data, read_timeseries -from message_data.model.material.material_demand import material_demand_calc -from message_data.model.material.util import read_config +from message_ix_models.model.material.data_util import read_sector_data, read_timeseries +from message_ix_models.model.material.material_demand import material_demand_calc +from message_ix_models.model.material.util import read_config from message_ix_models import ScenarioInfo from message_ix import make_df from message_ix_models.util import ( broadcast, same_node, - private_data_path, + package_data_path, ) # Get endogenous material demand from buildings interface @@ -87,7 +87,7 @@ def gen_mock_demand_cement(scenario): # SSP2 R11 baseline GDP projection gdp_growth = pd.read_excel( - private_data_path("material", "other", "iamc_db ENGAGE baseline GDP PPP.xlsx"), + package_data_path("material", "other", "iamc_db ENGAGE baseline GDP PPP.xlsx"), sheet_name=sheet_n, ) @@ -100,7 +100,7 @@ def gen_mock_demand_cement(scenario): # # Regions setting for IMAGE # region_cement = pd.read_excel( - # private_data_path("material", "CEMENT.BvR2010.xlsx"), + # package_data_path("material", "CEMENT.BvR2010.xlsx"), # sheet_name="Timer_Regions", skiprows=range(0,3))[['Region #', 'Name']]\ # .drop_duplicates().sort_values(by='Region #') # @@ -122,7 +122,7 @@ def gen_mock_demand_cement(scenario): # # # Cement demand 2010 [Mt/year] (IMAGE) # demand2010_cement = pd.read_excel( - # private_data_path("material", "CEMENT.BvR2010.xlsx"), + # package_data_path("material", "CEMENT.BvR2010.xlsx"), # sheet_name="Domestic Consumption", skiprows=range(0,3)).\ # groupby(by=["Region #"]).sum()[[2010]].\ # join(region_cement.set_index('Region #'), on='Region #').\ @@ -457,7 +457,7 @@ def gen_data_cement(scenario, dry_run=False): # # GDP is only in MER in scenario. # # To get PPP GDP, it is read externally from the R side # df = r.derive_cement_demand( -# pop, base_demand, str(private_data_path("material")) +# pop, base_demand, str(package_data_path("material")) # ) # df.year = df.year.astype(int) # diff --git a/message_ix_models/model/material/data_generic.py b/message_ix_models/model/material/data_generic.py index cb0788fc2d..98830319fb 100644 --- a/message_ix_models/model/material/data_generic.py +++ b/message_ix_models/model/material/data_generic.py @@ -25,7 +25,7 @@ def read_data_generic(scenario): # Read the file data_generic = pd.read_excel( - message_ix_models.util.private_data_path( + message_ix_models.util.package_data_path( "material", "other", "generic_furnace_boiler_techno_economic.xlsx" ), sheet_name="generic", diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index 3aa698ba53..0c006f0687 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -2,9 +2,9 @@ import pandas as pd from message_ix import make_df -from message_ix_models.util import broadcast, same_node, private_data_path -from message_data.model.material.util import read_config, combine_df_dictionaries -from message_data.model.material.material_demand import material_demand_calc +from message_ix_models.util import broadcast, same_node, package_data_path +from message_ix_models.model.material.util import read_config, combine_df_dictionaries +from message_ix_models.model.material.material_demand import material_demand_calc context = read_config() @@ -30,7 +30,7 @@ def gen_data_methanol(scenario): ssp = context["ssp"] # read sensitivity file df_pars = pd.read_excel( - private_data_path("material", "methanol", "methanol_sensitivity_pars.xlsx"), + package_data_path("material", "methanol", "methanol_sensitivity_pars.xlsx"), sheet_name="Sheet1", dtype=object, ) @@ -165,11 +165,11 @@ def gen_data_methanol(scenario): trade_dict_fs["input"] = df dict_t_d_fs = pd.read_excel( - private_data_path("material", "methanol", "meth_t_d_material_pars.xlsx"), + package_data_path("material", "methanol", "meth_t_d_material_pars.xlsx"), sheet_name=None, ) dict_t_d_fuel = pd.read_excel( - private_data_path("material", "methanol", "meth_t_d_fuel.xlsx"), + package_data_path("material", "methanol", "meth_t_d_fuel.xlsx"), sheet_name=None, ) @@ -283,7 +283,7 @@ def get_embodied_emi(row, pars, share_par): new_dict2 = combine_df_dictionaries( new_dict2, pd.read_excel( - private_data_path("material", "methanol", f"h2_elec_{mode}.xlsx"), + package_data_path("material", "methanol", f"h2_elec_{mode}.xlsx"), sheet_name=None, ), ) @@ -293,7 +293,7 @@ def get_embodied_emi(row, pars, share_par): def gen_data_meth_h2(mode): h2_par_dict = pd.read_excel( - private_data_path( + package_data_path( "material", "methanol", f"meth_h2_techno_economic_{mode}.xlsx" ), sheet_name=None, @@ -306,7 +306,7 @@ def gen_data_meth_h2(mode): def gen_data_meth_bio(scenario): df_bio = pd.read_excel( - private_data_path("material", "methanol", "meth_bio_techno_economic_new.xlsx"), + package_data_path("material", "methanol", "meth_bio_techno_economic_new.xlsx"), sheet_name=None, ) coal_ratio = get_cost_ratio_2020( @@ -339,7 +339,7 @@ def gen_data_meth_bio(scenario): def gen_meth_bio_ccs(scenario): df_bio = pd.read_excel( - private_data_path( + package_data_path( "material", "methanol", "meth_bio_techno_economic_new_ccs.xlsx" ), sheet_name=None, @@ -374,7 +374,7 @@ def gen_meth_bio_ccs(scenario): def gen_data_meth_chemicals(scenario, chemical): df = pd.read_excel( - private_data_path( + package_data_path( "material", "methanol", "collection files", "MTO data collection.xlsx" ), sheet_name=chemical, @@ -509,7 +509,7 @@ def add_methanol_fuel_additives(scenario): ] df_mtbe = pd.read_excel( - private_data_path( + package_data_path( "material", "methanol", "collection files", @@ -524,7 +524,7 @@ def add_methanol_fuel_additives(scenario): df_mtbe = df_mtbe[["node_loc", "% share on trp"]] df_biodiesel = pd.read_excel( - private_data_path( + package_data_path( "material", "methanol", "collection files", @@ -576,11 +576,11 @@ def get_meth_share(df, node): def add_meth_trade_historic(): par_dict_trade = pd.read_excel( - private_data_path("material", "methanol", "meth_trade_techno_economic.xlsx"), + package_data_path("material", "methanol", "meth_trade_techno_economic.xlsx"), sheet_name=None, ) par_dict_trade_fs = pd.read_excel( - private_data_path("material", "methanol", "meth_trade_techno_economic_fs.xlsx"), + package_data_path("material", "methanol", "meth_trade_techno_economic_fs.xlsx"), sheet_name=None, ) par_dict_trade = combine_df_dictionaries(par_dict_trade_fs, par_dict_trade) @@ -631,7 +631,7 @@ def update_methanol_costs(scenario): def gen_resin_demand(scenario, resin_share, sector, buildings_scen, pathway="SHAPE"): df = pd.read_csv( - private_data_path( + package_data_path( "material", "methanol", "results_material_" + pathway + "_" + sector + ".csv", @@ -683,7 +683,7 @@ def get_demand_t1_with_income_elasticity( ) + demand_t0 df_gdp = pd.read_excel( - private_data_path("material", "methanol", "methanol demand.xlsx"), + package_data_path("material", "methanol", "methanol demand.xlsx"), sheet_name="GDP_baseline", ) @@ -691,7 +691,7 @@ def get_demand_t1_with_income_elasticity( df = df.dropna(axis=1) df_demand_meth = pd.read_excel( - private_data_path("material", "methanol", "methanol demand.xlsx"), + package_data_path("material", "methanol", "methanol demand.xlsx"), sheet_name="methanol_demand", skiprows=[12], ) @@ -768,22 +768,22 @@ def get_scaled_cost_from_proxy_tec( def add_meth_tec_vintages(): par_dict_ng = pd.read_excel( - private_data_path("material", "methanol", "meth_ng_techno_economic.xlsx"), + package_data_path("material", "methanol", "meth_ng_techno_economic.xlsx"), sheet_name=None, ) par_dict_ng_fs = pd.read_excel( - private_data_path("material", "methanol", "meth_ng_techno_economic_fs.xlsx"), + package_data_path("material", "methanol", "meth_ng_techno_economic_fs.xlsx"), sheet_name=None, ) par_dict_ng.pop("historical_activity") par_dict_ng_fs.pop("historical_activity") par_dict_coal = pd.read_excel( - private_data_path("material", "methanol", "meth_coal_additions.xlsx"), + package_data_path("material", "methanol", "meth_coal_additions.xlsx"), sheet_name=None, ) par_dict_coal_fs = pd.read_excel( - private_data_path("material", "methanol", "meth_coal_additions_fs.xlsx"), + package_data_path("material", "methanol", "meth_coal_additions_fs.xlsx"), sheet_name=None, ) par_dict_coal.pop("historical_activity") @@ -797,11 +797,11 @@ def add_meth_hist_act(): # fix demand infeasibility par_dict = {} df_fs = pd.read_excel( - private_data_path("material", "methanol", "meth_coal_additions_fs.xlsx"), + package_data_path("material", "methanol", "meth_coal_additions_fs.xlsx"), sheet_name="historical_activity", ) df_fuel = pd.read_excel( - private_data_path("material", "methanol", "meth_coal_additions.xlsx"), + package_data_path("material", "methanol", "meth_coal_additions.xlsx"), sheet_name="historical_activity", ) par_dict["historical_activity"] = pd.concat([df_fs, df_fuel]) @@ -820,11 +820,11 @@ def add_meth_hist_act(): # row = act[act["technology"].str.startswith("meth")].sort_values("value", ascending=False).iloc[0] # row["value"] = 0.0 df_ng = pd.read_excel( - private_data_path("material", "methanol", "meth_ng_techno_economic.xlsx"), + package_data_path("material", "methanol", "meth_ng_techno_economic.xlsx"), sheet_name="historical_activity", ) df_ng_fs = pd.read_excel( - private_data_path("material", "methanol", "meth_ng_techno_economic_fs.xlsx"), + package_data_path("material", "methanol", "meth_ng_techno_economic_fs.xlsx"), sheet_name="historical_activity", ) par_dict["historical_activity"] = pd.concat( @@ -835,12 +835,12 @@ def add_meth_hist_act(): def update_costs_with_loc_factor(df): loc_fact = pd.read_excel( - private_data_path("material", "methanol", "location factor collection.xlsx"), + package_data_path("material", "methanol", "location factor collection.xlsx"), sheet_name="comparison", index_col=0, ) cost_conv = pd.read_excel( - private_data_path("material", "methanol", "location factor collection.xlsx"), + package_data_path("material", "methanol", "location factor collection.xlsx"), sheet_name="cost_convergence", index_col=0, ) diff --git a/message_ix_models/model/material/data_methanol_new.py b/message_ix_models/model/material/data_methanol_new.py index 3815ee7132..60b3529b51 100644 --- a/message_ix_models/model/material/data_methanol_new.py +++ b/message_ix_models/model/material/data_methanol_new.py @@ -5,8 +5,8 @@ from message_ix import make_df from message_ix_models.util import broadcast, same_node -from message_data.model.material.material_demand import material_demand_calc -from message_data.model.material.util import read_config +from message_ix_models.model.material.material_demand import material_demand_calc +from message_ix_models.model.material.util import read_config from ast import literal_eval ssp_mode_map = { @@ -29,7 +29,7 @@ def gen_data_methanol_new(scenario): context = read_config() df_pars = pd.read_excel( - message_ix_models.util.private_data_path( + message_ix_models.util.package_data_path( "material", "methanol", "methanol_sensitivity_pars.xlsx" ), sheet_name="Sheet1", @@ -38,7 +38,7 @@ def gen_data_methanol_new(scenario): pars = df_pars.set_index("par").to_dict()["value"] if pars["mtbe_scenario"] == "phase-out": pars_dict = pd.read_excel( - message_ix_models.util.private_data_path( + message_ix_models.util.package_data_path( "material", "methanol", "methanol_techno_economic.xlsx" ), sheet_name=None, @@ -46,7 +46,7 @@ def gen_data_methanol_new(scenario): ) else: pars_dict = pd.read_excel( - message_ix_models.util.private_data_path( + message_ix_models.util.package_data_path( "material", "methanol", "methanol_techno_economic_high_demand.xlsx" ), sheet_name=None, @@ -57,10 +57,10 @@ def gen_data_methanol_new(scenario): pars_dict[i] = broadcast_reduced_df(pars_dict[i], i) # TODO: only temporary hack to ensure SSP_dev compatibility if "SSP_dev" in scenario.model: - file_path = message_ix_models.util.private_data_path( + file_path = message_ix_models.util.package_data_path( "material", "methanol", "missing_rels.yaml" ) - # file_path = "C:/Users\maczek\PycharmProjects\message_data\message_data\model\material\petrochemical model fixes notebooks\" + # file_path = "C:/Users\maczek\PycharmProjects\message_ix_models\message_ix_models\model\material\petrochemical model fixes notebooks\" with open(file_path, "r") as file: missing_rels = yaml.safe_load(file) diff --git a/message_ix_models/model/material/data_petro.py b/message_ix_models/model/material/data_petro.py index 0ba8cafada..d12a4902b4 100644 --- a/message_ix_models/model/material/data_petro.py +++ b/message_ix_models/model/material/data_petro.py @@ -2,9 +2,9 @@ import numpy as np from collections import defaultdict -from message_data.model.material.data_util import read_timeseries, read_rel -from message_data.model.material.util import read_config -from message_data.model.material.material_demand import material_demand_calc +from message_ix_models.model.material.data_util import read_timeseries, read_rel +from message_ix_models.model.material.util import read_config +from message_ix_models.model.material.material_demand import material_demand_calc from message_ix_models import ScenarioInfo from message_ix import make_df from message_ix_models.util import ( @@ -13,7 +13,7 @@ make_matched_dfs, same_node, add_par_data, - private_data_path, + package_data_path, ) @@ -49,7 +49,7 @@ def read_data_petrochemicals(scenario): # Read the file data_petro = pd.read_excel( - private_data_path("material", "petrochemicals", fname), sheet_name=sheet_n + package_data_path("material", "petrochemicals", fname), sheet_name=sheet_n ) # Clean the data @@ -74,7 +74,7 @@ def get_demand_t1_with_income_elasticity( gdp_mer = scenario.par("bound_activity_up", {"technology": "GDP"}) mer_to_ppp = pd.read_csv( - private_data_path("material", "other", "mer_to_ppp_default.csv") + package_data_path("material", "other", "mer_to_ppp_default.csv") ).set_index(["node", "year"]) # mer_to_ppp = scenario.par("MERtoPPP").set_index("node", "year") TODO: might need to be re-activated for different SSPs gdp_mer = gdp_mer.merge( @@ -118,12 +118,12 @@ def get_demand_t1_with_income_elasticity( # dem_2020 = np.array([2, 75, 30, 4, 11, 42, 60, 32, 30, 29, 35]) # dem_2020 = pd.Series(dem_2020) - from message_data.model.material.material_demand.material_demand_calc import ( + from message_ix_models.model.material.material_demand.material_demand_calc import ( read_base_demand, ) df_demand_2020 = read_base_demand( - private_data_path() / "material" / "petrochemicals/demand_petro.yaml" + package_data_path() / "material" / "petrochemicals/demand_petro.yaml" ) df_demand_2020 = df_demand_2020.rename({"region": "Region"}, axis=1) df_demand = df_demand_2020.pivot(index="Region", columns="year", values="value") @@ -397,7 +397,7 @@ def gen_data_petro_chemicals(scenario, dry_run=False): # context = read_config() # df_pars = pd.read_excel( - # private_data_path("material", "methanol", "methanol_sensitivity_pars.xlsx"), + # package_data_path("material", "methanol", "methanol_sensitivity_pars.xlsx"), # sheet_name="Sheet1", # dtype=object, # ) @@ -493,12 +493,12 @@ def gen_data_petro_chemicals(scenario, dry_run=False): # modify steam cracker hist data (naphtha -> gasoil) to make model feasible df_cap = pd.read_csv( - private_data_path( + package_data_path( "material", "petrochemicals", "steam_cracking_hist_new_cap.csv" ) ) df_act = pd.read_csv( - private_data_path("material", "petrochemicals", "steam_cracking_hist_act.csv") + package_data_path("material", "petrochemicals", "steam_cracking_hist_act.csv") ) df_act.loc[df_act["mode"] == "naphtha", "mode"] = "vacuum_gasoil" df = results["historical_activity"] diff --git a/message_ix_models/model/material/data_power_sector.py b/message_ix_models/model/material/data_power_sector.py index 5fa558434a..ce72fb9832 100644 --- a/message_ix_models/model/material/data_power_sector.py +++ b/message_ix_models/model/material/data_power_sector.py @@ -2,7 +2,7 @@ from pathlib import Path import pandas as pd -from message_ix_models.util import private_data_path +from message_ix_models.util import package_data_path from .util import read_config from message_ix_models import ScenarioInfo @@ -281,7 +281,7 @@ def gen_data_power_sector(scenario, dry_run=False): # paths to lca data code_path = Path(__file__).parents[0] / "material_intensity" - data_path = private_data_path("material", "power_sector") + data_path = package_data_path("material", "power_sector") # Information about scenario, e.g. node, year s_info = ScenarioInfo(scenario) diff --git a/message_ix_models/model/material/data_steel.py b/message_ix_models/model/material/data_steel.py index b52e0bb120..8e4f9be77e 100644 --- a/message_ix_models/model/material/data_steel.py +++ b/message_ix_models/model/material/data_steel.py @@ -15,7 +15,7 @@ from message_ix_models.util import ( broadcast, same_node, - private_data_path, + package_data_path, ) @@ -78,7 +78,7 @@ def gen_mock_demand_steel(scenario): # SSP2 R11 baseline GDP projection gdp_growth = pd.read_excel( - private_data_path("material", "other", "iamc_db ENGAGE baseline GDP PPP.xlsx"), + package_data_path("material", "other", "iamc_db ENGAGE baseline GDP PPP.xlsx"), sheet_name=sheet_n, ) @@ -576,7 +576,7 @@ def gen_data_steel(scenario, dry_run=False): # with localconverter(ro.default_converter + pandas2ri.converter): # # GDP is only in MER in scenario. # # To get PPP GDP, it is read externally from the R side -# df = r.derive_steel_demand(pop, base_demand, str(private_data_path("material"))) +# df = r.derive_steel_demand(pop, base_demand, str(package_data_path("material"))) # df.year = df.year.astype(int) # # return df diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index 42dab3030c..6a72716a14 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -1,23 +1,22 @@ +import os from typing import Literal -import pandas as pd -import os -import message_ix import ixmp +import message_ix +import pandas as pd +from genno import Computer -from message_data.model.material.util import ( +from message_ix_models import ScenarioInfo +from message_ix_models.model.material.util import ( + invert_dictionary, read_config, read_yaml_file, - invert_dictionary, remove_from_list_if_exists, ) - -from message_ix_models import ScenarioInfo -from message_ix_models.util import private_data_path from message_ix_models.tools.costs.config import Config from message_ix_models.tools.costs.projections import create_cost_projections from message_ix_models.tools.exo_data import prepare_computer -from genno import Computer +from message_ix_models.util import package_data_path pd.options.mode.chained_assignment = None @@ -40,7 +39,7 @@ def load_GDP_COVID(): f_name = "iamc_db ENGAGE baseline GDP PPP.xlsx" gdp_ssp2 = pd.read_excel( - private_data_path("material", "other", f_name), sheet_name="data_R12" + package_data_path("material", "other", f_name), sheet_name="data_R12" ) gdp_ssp2 = gdp_ssp2[gdp_ssp2["Scenario"] == "baseline"] regions = "R12_" + gdp_ssp2["Region"] @@ -141,7 +140,7 @@ def modify_demand_and_hist_activity(scen): region_name_CHN = "" df = pd.read_excel( - private_data_path("material", "other", fname), sheet_name=sheet_n, usecols="A:F" + package_data_path("material", "other", fname), sheet_name=sheet_n, usecols="A:F" ) # Filter the necessary variables @@ -449,7 +448,7 @@ def modify_demand_and_hist_activity_debug(scen: message_ix.Scenario) -> dict: region_name_CHN = "" df = pd.read_excel( - private_data_path("material", "other", fname), sheet_name=sheet_n, usecols="A:F" + package_data_path("material", "other", fname), sheet_name=sheet_n, usecols="A:F" ) # Filter the necessary variables @@ -876,7 +875,7 @@ def map_iea_db_to_msg_regs(df_iea: pd.DataFrame, reg_map_fname: str) -> pd.DataF object """ - file_path = private_data_path("node", reg_map_fname) + file_path = package_data_path("node", reg_map_fname) yaml_data = read_yaml_file(file_path) if "World" in yaml_data.keys(): yaml_data.pop("World") @@ -907,7 +906,7 @@ def read_iea_tec_map(tec_map_fname: str) -> pd.DataFrame: pd.DataFrame returns df with mapped technologies """ - MAP = pd.read_csv(private_data_path("model", "IEA", tec_map_fname)) + MAP = pd.read_csv(package_data_path("iea", tec_map_fname)) MAP = pd.concat([MAP, MAP["IEA flow"].str.split(", ", expand=True)], axis=1) MAP = ( @@ -1342,7 +1341,7 @@ def add_elec_lowerbound_2020(scen): # that is aggregated to MESSAGEix regions, fuels and (industry) sectors final = pd.read_csv( - private_data_path("material", "other", "residual_industry_2019.csv") + package_data_path("material", "other", "residual_industry_2019.csv") ) # downselect needed fuels and sectors @@ -1407,7 +1406,7 @@ def add_coal_lowerbound_2020(sc): context = read_config() final_resid = pd.read_csv( - private_data_path("material", "other", "residual_industry_2019.csv") + package_data_path("material", "other", "residual_industry_2019.csv") ) # read input parameters for relevant technology/commodity combinations for converting betwen final and useful energy @@ -1538,7 +1537,7 @@ def add_cement_bounds_2020(sc): context = read_config() final_resid = pd.read_csv( - private_data_path("material", "other", "residual_industry_2019.csv") + package_data_path("material", "other", "residual_industry_2019.csv") ) input_cement_foil = sc.par( @@ -1800,7 +1799,7 @@ def read_sector_data(scenario, sectname): # data_df = data_steel_china.append(data_cement_china, ignore_index=True) data_df = pd.read_excel( - private_data_path("material", "steel_cement", context.datafile), + package_data_path("material", "steel_cement", context.datafile), sheet_name=sheet_n, ) @@ -1910,7 +1909,7 @@ def read_timeseries(scenario, material, filename): # Read the file df = pd.read_excel( - private_data_path("material", material, filename), sheet_name=sheet_n + package_data_path("material", material, filename), sheet_name=sheet_n ) import numbers @@ -1950,7 +1949,7 @@ def read_rel(scenario, material, filename): # Read the file data_rel = pd.read_excel( - private_data_path("material", material, filename), + package_data_path("material", material, filename), sheet_name=sheet_n, ) diff --git a/message_ix_models/model/material/util.py b/message_ix_models/model/material/util.py index acae87bc67..7dc83e8b1f 100644 --- a/message_ix_models/model/material/util.py +++ b/message_ix_models/model/material/util.py @@ -1,10 +1,12 @@ -from message_ix_models import Context -from message_ix_models.util import load_private_data, private_data_path -from scipy.optimize import curve_fit -import pandas as pd -import yaml import os + import openpyxl as pxl +import pandas as pd +import yaml +from scipy.optimize import curve_fit + +from message_ix_models import Context +from message_ix_models.util import load_private_data, private_data_path # Configuration files METADATA = [ From 68910ca60b8bdfb7166b0751b417fb387b1e615e Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 23 Apr 2024 15:25:35 +0200 Subject: [PATCH 740/774] Register material-ix as subcommand in CLI --- message_ix_models/cli.py | 1 + message_ix_models/model/material/__init__.py | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/message_ix_models/cli.py b/message_ix_models/cli.py index a80791e4de..794874760a 100644 --- a/message_ix_models/cli.py +++ b/message_ix_models/cli.py @@ -163,6 +163,7 @@ def _log_threads(k: int, n: int): "message_ix_models.model.water.cli", "message_ix_models.project.ssp", "message_ix_models.report.cli", + "message_ix_models.model.material", "message_ix_models.testing.cli", "message_ix_models.util.pooch", ] diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 506f393cfe..71a776dffa 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -193,7 +193,7 @@ def get_spec() -> Mapping[str, ScenarioInfo]: # Group to allow for multiple CLI subcommands under "material" -@click.group("material") +@click.group("material-ix") @common_params("ssp") def cli(ssp): """Model with materials accounting.""" From 94f4e43797b785921f9783c5cf4ca7566b9b5fdf Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 23 Apr 2024 17:13:19 +0200 Subject: [PATCH 741/774] Fix typo in parameter string --- message_ix_models/model/material/data_util.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index 6a72716a14..49a7d65c0b 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -2006,7 +2006,7 @@ def get_ssp_soc_eco_data(context, model, measure, tec): def add_elec_i_ini_act(scenario): - par = "inital_activity_up" + par = "initial_activity_up" df_el = scenario.par(par, filters={"technology":"hp_el_i"}) df_el["technology"] = "elec_i" scenario.check_out() From 49ba610fe1918feb6da07e33887c947268024aa0 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Wed, 24 Apr 2024 13:17:34 +0200 Subject: [PATCH 742/774] Remove duplicated function definition --- message_ix_models/model/material/build.py | 11 ----------- 1 file changed, 11 deletions(-) diff --git a/message_ix_models/model/material/build.py b/message_ix_models/model/material/build.py index 27ac78290c..7aa2148c38 100644 --- a/message_ix_models/model/material/build.py +++ b/message_ix_models/model/material/build.py @@ -26,17 +26,6 @@ def ellipsize(elements: List) -> str: else: return ", ".join(map(str, elements)) -def ellipsize(elements: List) -> str: - """Generate a short string representation of `elements`. - - If the list has more than 5 elements, only the first two and last two are shown, - with "..." between. - """ - if len(elements) > 5: - return ", ".join(map(str, elements[:2] + ["..."] + elements[-2:])) - else: - return ", ".join(map(str, elements)) - def apply_spec( scenario: Scenario, From cc9be293966e40fa382ffc0624696becbe66eedc Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Wed, 24 Apr 2024 13:26:58 +0200 Subject: [PATCH 743/774] Remove unused technology from set.yaml --- message_ix_models/data/material/set.yaml | 1 - 1 file changed, 1 deletion(-) diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index df92195f6b..37736a9ec8 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -382,7 +382,6 @@ steel: - pellet_steel - bf_steel - dri_steel - - sr_steel - bof_steel - eaf_steel - prep_secondary_steel_1 From a5001c915990d8b6741a295371cc409dbfa018c2 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Fri, 10 May 2024 10:31:14 +0200 Subject: [PATCH 744/774] Remove deprecated files from rebase These files were accidentally added during rebasing --- message_ix_models/data/material/China_steel_MESSAGE.xlsx | 3 --- .../data/material/China_steel_standalone - test.xlsx | 3 --- .../data/material/LED_LED_report_IAMC_sensitivity.csv | 3 --- .../data/material/generic_furnace_boiler_techno_economic.xlsx | 3 --- message_ix_models/data/material/high_demand_output.xlsx | 3 --- .../data/material/petrochemicals_techno_economic.xlsx | 3 --- message_ix_models/data/material/variable_costs.xlsx | 3 --- 7 files changed, 21 deletions(-) delete mode 100644 message_ix_models/data/material/China_steel_MESSAGE.xlsx delete mode 100644 message_ix_models/data/material/China_steel_standalone - test.xlsx delete mode 100644 message_ix_models/data/material/LED_LED_report_IAMC_sensitivity.csv delete mode 100644 message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx delete mode 100644 message_ix_models/data/material/high_demand_output.xlsx delete mode 100644 message_ix_models/data/material/petrochemicals_techno_economic.xlsx delete mode 100644 message_ix_models/data/material/variable_costs.xlsx diff --git a/message_ix_models/data/material/China_steel_MESSAGE.xlsx b/message_ix_models/data/material/China_steel_MESSAGE.xlsx deleted file mode 100644 index 0386466ed4..0000000000 --- a/message_ix_models/data/material/China_steel_MESSAGE.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:555d866fc3e62a9cb3f4464c65178017da972af9323fb33e355cb75803612344 -size 47414 diff --git a/message_ix_models/data/material/China_steel_standalone - test.xlsx b/message_ix_models/data/material/China_steel_standalone - test.xlsx deleted file mode 100644 index 67f6a72077..0000000000 --- a/message_ix_models/data/material/China_steel_standalone - test.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:42c031d8af13d71b2ad5a1ec5b6f14b973105a5164494552968710a014908fd8 -size 56730 diff --git a/message_ix_models/data/material/LED_LED_report_IAMC_sensitivity.csv b/message_ix_models/data/material/LED_LED_report_IAMC_sensitivity.csv deleted file mode 100644 index 62e8d7e260..0000000000 --- a/message_ix_models/data/material/LED_LED_report_IAMC_sensitivity.csv +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:1e5b051f314c528cb01655810bef81c2900b63472ffeb92547907e574315b688 -size 243446 diff --git a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx b/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx deleted file mode 100644 index 95ea7112f7..0000000000 --- a/message_ix_models/data/material/generic_furnace_boiler_techno_economic.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:91b263b3f172b7ee9f17d85d682d5128b54ec8290c041eb3538c63f8549b9733 -size 51396 diff --git a/message_ix_models/data/material/high_demand_output.xlsx b/message_ix_models/data/material/high_demand_output.xlsx deleted file mode 100644 index 358b86f335..0000000000 --- a/message_ix_models/data/material/high_demand_output.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:b863d82f74040c7b07899b81fc8777e49202e540a86a4c33d5cbac5ec3bd4e24 -size 37680 diff --git a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx b/message_ix_models/data/material/petrochemicals_techno_economic.xlsx deleted file mode 100644 index 3a285b5330..0000000000 --- a/message_ix_models/data/material/petrochemicals_techno_economic.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:1523b27941193f4869bf141eabd8ac34d69f634f9158e1725740e225b9d4f1f0 -size 467548 diff --git a/message_ix_models/data/material/variable_costs.xlsx b/message_ix_models/data/material/variable_costs.xlsx deleted file mode 100644 index 7d405a7eaa..0000000000 --- a/message_ix_models/data/material/variable_costs.xlsx +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:325de6ca46e98eada689e4c2cae920c64a205abc48cca7c36c89c7f56597300c -size 15112 From 9cb260c1aafe83b7fd33ea93da7a25261ec8305f Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Fri, 10 May 2024 10:31:39 +0200 Subject: [PATCH 745/774] Delete duplicated codes in set.yaml --- message_ix_models/data/material/set.yaml | 64 ------------------------ 1 file changed, 64 deletions(-) diff --git a/message_ix_models/data/material/set.yaml b/message_ix_models/data/material/set.yaml index 37736a9ec8..a3807d401f 100644 --- a/message_ix_models/data/material/set.yaml +++ b/message_ix_models/data/material/set.yaml @@ -4,70 +4,6 @@ # accounting. Different sets can be created once there are 2+ different # categories of materials. -petro_chemicals: - commodity: - require: - - crudeoil - - hydrogen - - elctr - - ethanol - - fueloil - - lightoil - add: - - naphtha - - kerosene - - diesel - - atm_residue - - refinery_gas - - atm_gasoil - - atm_residue - - vacuum_gasoil - - vacuum_residue - - desulf_gasoil - - desulf_naphtha - - desulf_kerosene - - desulf_diesel - - desful_gasoil - - gasoline - - heavy_foil - - light_foil - - propylene - - coke - - ethane - - propane - - ethylene - - BTX - - level: - require: - - secondary - - final - add: - - pre_intermediate - - useful_refining - - desulfurized - - intermediate - - secondary_material - - useful_petro - - final_material - - mode: - add: - - atm_gasoil - - vacuum_gasoil - - naphtha - - kerosene - - diesel - - cracking_gasoline - - cracking_loil - - gasoil - - ethane - - propane - - emission: - add: - - CO2 - - SO2 common: commodity: From ec871033171405ce4506272c1642ae37a1c0dfa6 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Fri, 10 May 2024 12:55:07 +0200 Subject: [PATCH 746/774] Apply ruff reformatting --- message_ix_models/model/material/__init__.py | 24 +-- message_ix_models/model/material/bare.py | 61 ++++--- message_ix_models/model/material/build.py | 15 +- message_ix_models/model/material/data.py | 21 +-- .../model/material/data_aluminum.py | 46 +++-- .../model/material/data_ammonia_new.py | 14 +- .../model/material/data_buildings.py | 22 +-- .../model/material/data_cement.py | 17 +- .../model/material/data_generic.py | 23 +-- .../model/material/data_methanol.py | 30 ++-- .../model/material/data_methanol_new.py | 10 +- .../model/material/data_petro.py | 34 ++-- .../model/material/data_power_sector.py | 27 +-- .../model/material/data_steel.py | 55 +++--- message_ix_models/model/material/data_util.py | 162 +++++++++--------- .../material_demand/material_demand_calc.py | 38 ++-- message_ix_models/model/material/util.py | 8 +- 17 files changed, 295 insertions(+), 312 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 71a776dffa..2461472174 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -8,7 +8,7 @@ import ntfy import pandas as pd -#update_h2_blending, +# update_h2_blending, from message_data.tools.utilities import ( manual_updates_ENGAGE_SSP2_v417_to_v418 as engage_updates, ) @@ -18,12 +18,10 @@ from message_ix_models.model.material.build import apply_spec from message_ix_models.model.material.data_aluminum import gen_data_aluminum from message_ix_models.model.material.data_ammonia_new import gen_all_NH3_fert -from message_ix_models.model.material.data_buildings import gen_data_buildings from message_ix_models.model.material.data_cement import gen_data_cement from message_ix_models.model.material.data_generic import gen_data_generic from message_ix_models.model.material.data_methanol_new import gen_data_methanol_new from message_ix_models.model.material.data_petro import gen_data_petro_chemicals -from message_ix_models.model.material.data_power_sector import gen_data_power_sector from message_ix_models.model.material.data_steel import gen_data_steel from message_ix_models.model.material.data_util import ( add_ccs_technologies, @@ -52,10 +50,11 @@ ) from message_ix_models.util import add_par_data, package_data_path from message_ix_models.util.click import common_params + log = logging.getLogger(__name__) DATA_FUNCTIONS_1 = [ - #gen_data_buildings, + # gen_data_buildings, gen_data_methanol_new, gen_all_NH3_fert, # gen_data_ammonia, ## deprecated module! @@ -389,7 +388,7 @@ def build_scen(context, datafile, tag, mode, scenario_name, old_calib, update_co @click.option("--add_calibration", default=False) @click.pass_obj def solve_scen( - context, datafile, model_name, scenario_name, add_calibration, add_macro, version + context, datafile, model_name, scenario_name, add_calibration, add_macro, version ): """Solve a scenario. @@ -423,11 +422,12 @@ def solve_scen( scenario.set_as_default() # Report - from message_ix_models.model.material.report.reporting import report from message_data.tools.post_processing.iamc_report_hackathon import ( report as reporting, ) + from message_ix_models.model.material.report.reporting import report + # Remove existing timeseries and add material timeseries print("Reporting material-specific variables") report(context, scenario) @@ -487,11 +487,12 @@ def solve_scen( @click.option("--scenario_name", default="NoPolicy") @click.option("--model_name", default="MESSAGEix-Materials") def add_building_ts(scenario_name, model_name): + from ixmp import Platform + from message_ix import Scenario + from message_ix_models.reporting.materials.add_buildings_ts import ( add_building_timeseries, ) - from message_ix import Scenario - from ixmp import Platform print(model_name) mp = Platform() @@ -512,11 +513,12 @@ def add_building_ts(scenario_name, model_name): @click.pass_obj def run_reporting(context, remove_ts, profile): """Run materials, then legacy reporting.""" - from message_ix_models.model.material.report.reporting import report from message_data.tools.post_processing.iamc_report_hackathon import ( report as reporting, ) + from message_ix_models.model.material.report.reporting import report + # Retrieve the scenario given by the --url option scenario = context.get_scenario() mp = scenario.platform @@ -534,10 +536,10 @@ def run_reporting(context, remove_ts, profile): print("There are no timeseries to be removed.") else: if profile: + import atexit import cProfile - import pstats import io - import atexit + import pstats print("Profiling...") pr = cProfile.Profile() diff --git a/message_ix_models/model/material/bare.py b/message_ix_models/model/material/bare.py index c38eb84910..ba5527f99f 100644 --- a/message_ix_models/model/material/bare.py +++ b/message_ix_models/model/material/bare.py @@ -1,17 +1,22 @@ import logging +from typing import Mapping + import message_ix +from sdmx.model.v21 import Code -from typing import Mapping +import message_ix_models from message_ix_models import ScenarioInfo -#from message_ix_models.util get_context, set_info, add_par_data +from message_ix_models.model.material import ( + gen_data_aluminum, + gen_data_generic, + gen_data_steel, +) + +# from message_ix_models.util get_context, set_info, add_par_data from message_ix_models.util import add_par_data -from sdmx.model.v21 import Code + from .build import apply_spec from .util import read_config -from message_ix_models.model.data import get_data -from message_ix_models.model.material import gen_data_steel, gen_data_generic, gen_data_aluminum, gen_data_petro_chemicals -import message_ix_models - log = logging.getLogger(__name__) @@ -19,9 +24,9 @@ # How do we add the historical period ? SETTINGS = dict( - #period_start=[2010], + # period_start=[2010], period_start=[1980], - first_model_year = [2020], + first_model_year=[2020], period_end=[2100], regions=["China"], res_with_dummies=[True], @@ -45,7 +50,7 @@ def create_res(context=None, quiet=True): :func:`.build.apply_spec`. """ mp = context.get_platform() - mp.add_unit('Mt') + mp.add_unit('Mt') # Model and scenario name for the RES model_name = context.scenario_info['model'] @@ -78,7 +83,7 @@ def create_res(context=None, quiet=True): gen_data_steel, gen_data_generic, gen_data_aluminum, - #gen_data_variable + # gen_data_variable ] @@ -133,9 +138,9 @@ def get_spec(context=None) -> Mapping[str, ScenarioInfo]: # Add technologies # JM: try to find out a way to loop over 1st/2nd level and to just context["material"][xx]["add"] add.set["technology"] = context["material"]["steel"]["technology"]["add"] + \ - context["material"]["generic"]["technology"]["add"] + \ - context["material"]["aluminum"]["technology"]["add"] + \ - context["material"]["petro_chemicals"]["technology"]["add"] + context["material"]["generic"]["technology"]["add"] + \ + context["material"]["aluminum"]["technology"]["add"] + \ + context["material"]["petro_chemicals"]["technology"]["add"] # Add regions @@ -156,7 +161,7 @@ def get_spec(context=None) -> Mapping[str, ScenarioInfo]: )) # JM: Leave the first time period as historical year - #add.set['cat_year'] = [('firstmodelyear', context.period_start + context.time_step)] + # add.set['cat_year'] = [('firstmodelyear', context.period_start + context.time_step)] # GU: Set 2020 as the first model year, leave the rest as historical year add.set['cat_year'] = [('firstmodelyear', context.first_model_year)] @@ -164,27 +169,27 @@ def get_spec(context=None) -> Mapping[str, ScenarioInfo]: # Add levels # JM: For bare model, both 'add' & 'require' need to be added. add.set['level'] = context["material"]["steel"]["level"]["add"] + \ - context["material"]["common"]["level"]["require"] + \ - context["material"]["generic"]["level"]["add"] + \ - context["material"]["aluminum"]["level"]["add"] +\ - context["material"]["petro_chemicals"]["level"]["add"] + context["material"]["common"]["level"]["require"] + \ + context["material"]["generic"]["level"]["add"] + \ + context["material"]["aluminum"]["level"]["add"] + \ + context["material"]["petro_chemicals"]["level"]["add"] # Add commodities add.set['commodity'] = context["material"]["steel"]["commodity"]["add"] + \ - context["material"]["common"]["commodity"]["require"] + \ - context["material"]["generic"]["commodity"]["add"] + \ - context["material"]["aluminum"]["commodity"]["add"] + \ - context["material"]["petro_chemicals"]["commodity"]["add"] + context["material"]["common"]["commodity"]["require"] + \ + context["material"]["generic"]["commodity"]["add"] + \ + context["material"]["aluminum"]["commodity"]["add"] + \ + context["material"]["petro_chemicals"]["commodity"]["add"] # Add other sets add.set['type_tec'] = context["material"]["common"]["type_tec"]["add"] - add.set['mode'] = context["material"]["common"]["mode"]["require"] +\ - context["material"]["generic"]["mode"]["add"] + \ - context["material"]["petro_chemicals"]["mode"]["add"] + add.set['mode'] = context["material"]["common"]["mode"]["require"] + \ + context["material"]["generic"]["mode"]["add"] + \ + context["material"]["petro_chemicals"]["mode"]["add"] - add.set['emission'] = context["material"]["common"]["emission"]["require"] +\ - context["material"]["common"]["emission"]["add"] + add.set['emission'] = context["material"]["common"]["emission"]["require"] + \ + context["material"]["common"]["emission"]["add"] # Add units, associated with commodities # JM: What is 'anno' diff --git a/message_ix_models/model/material/build.py b/message_ix_models/model/material/build.py index 7aa2148c38..82ee6db1a5 100644 --- a/message_ix_models/model/material/build.py +++ b/message_ix_models/model/material/build.py @@ -1,20 +1,17 @@ import logging from typing import Callable, Dict, List, Mapping, Union -import ixmp -import message_ix +import pandas as pd from ixmp.utils import maybe_check_out, maybe_commit from message_ix import Scenario -import pandas as pd - from sdmx.model import Code from message_ix_models.util import add_par_data, strip_par_data from message_ix_models.util.scenarioinfo import ScenarioInfo, Spec - log = logging.getLogger(__name__) + def ellipsize(elements: List) -> str: """Generate a short string representation of `elements`. @@ -28,10 +25,10 @@ def ellipsize(elements: List) -> str: def apply_spec( - scenario: Scenario, - spec: Union[Spec, Mapping[str, ScenarioInfo]] = None, - data: Callable = None, - **options, + scenario: Scenario, + spec: Union[Spec, Mapping[str, ScenarioInfo]] = None, + data: Callable = None, + **options, ): """Apply `spec` to `scenario`. diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py index d1c2a2c59a..79fb5600b0 100644 --- a/message_ix_models/model/material/data.py +++ b/message_ix_models/model/material/data.py @@ -1,26 +1,14 @@ -from collections import defaultdict import logging -import message_ix -import ixmp -import numpy as np -import pandas as pd -from .data_cement import gen_data_cement -from .data_steel import gen_data_steel -from .data_aluminum import gen_data_aluminum -from .data_generic import gen_data_generic from message_ix_models import ScenarioInfo -from message_ix import make_df from message_ix_models.util import ( - broadcast, - make_io, - make_matched_dfs, - same_node, add_par_data, ) -from .util import read_config -import re +from .data_aluminum import gen_data_aluminum +from .data_cement import gen_data_cement +from .data_generic import gen_data_generic +from .data_steel import gen_data_steel log = logging.getLogger(__name__) @@ -32,6 +20,7 @@ gen_data_petro_chemicals ] + # Try to handle multiple data input functions from different materials def add_data(scenario, dry_run=False): """Populate `scenario` with MESSAGEix-Materials data.""" diff --git a/message_ix_models/model/material/data_aluminum.py b/message_ix_models/model/material/data_aluminum.py index 20de9fd9f2..4935d1bd33 100644 --- a/message_ix_models/model/material/data_aluminum.py +++ b/message_ix_models/model/material/data_aluminum.py @@ -118,16 +118,16 @@ def gen_data_aluminum(scenario, dry_run=False): val = data_aluminum.loc[ ( - (data_aluminum["technology"] == t) - & (data_aluminum["parameter"] == par) + (data_aluminum["technology"] == t) + & (data_aluminum["parameter"] == par) ), "value", ] regions = data_aluminum.loc[ ( - (data_aluminum["technology"] == t) - & (data_aluminum["parameter"] == par) + (data_aluminum["technology"] == t) + & (data_aluminum["parameter"] == par) ), "region", ] @@ -180,9 +180,9 @@ def gen_data_aluminum(scenario, dry_run=False): # Assign higher efficiency to younger plants elif ( - ((t == "soderberg_aluminum") or (t == "prebake_aluminum")) - & (com == "electr") - & (param_name == "input") + ((t == "soderberg_aluminum") or (t == "prebake_aluminum")) + & (com == "electr") + & (param_name == "input") ): # All the vıntage years year_vtg = sorted(set(yv_ya.year_vtg.values)) @@ -268,9 +268,9 @@ def gen_data_aluminum(scenario, dry_run=False): # Copy parameters to all regions if ( - (len(regions) == 1) - and len(set(df["node_loc"])) == 1 - and list(set(df["node_loc"]))[0] != global_region + (len(regions) == 1) + and len(set(df["node_loc"])) == 1 + and list(set(df["node_loc"]))[0] != global_region ): df["node_loc"] = None df = df.pipe(broadcast, node_loc=nodes) @@ -392,20 +392,19 @@ def gen_data_aluminum(scenario, dry_run=False): tec_list = data_aluminum_rel.loc[ ( - (data_aluminum_rel["relation"] == r) - & (data_aluminum_rel["parameter"] == par_name) + (data_aluminum_rel["relation"] == r) + & (data_aluminum_rel["parameter"] == par_name) ), "technology", ] for tec in tec_list.unique(): - val = data_aluminum_rel.loc[ ( - (data_aluminum_rel["relation"] == r) - & (data_aluminum_rel["parameter"] == par_name) - & (data_aluminum_rel["technology"] == tec) - & (data_aluminum_rel["Region"] == reg) + (data_aluminum_rel["relation"] == r) + & (data_aluminum_rel["parameter"] == par_name) + & (data_aluminum_rel["technology"] == tec) + & (data_aluminum_rel["Region"] == reg) ), "value", ].values[0] @@ -425,9 +424,9 @@ def gen_data_aluminum(scenario, dry_run=False): elif (par_name == "relation_upper") | (par_name == "relation_lower"): val = data_aluminum_rel.loc[ ( - (data_aluminum_rel["relation"] == r) - & (data_aluminum_rel["parameter"] == par_name) - & (data_aluminum_rel["Region"] == reg) + (data_aluminum_rel["relation"] == r) + & (data_aluminum_rel["parameter"] == par_name) + & (data_aluminum_rel["Region"] == reg) ), "value", ].values[0] @@ -441,8 +440,8 @@ def gen_data_aluminum(scenario, dry_run=False): results_aluminum = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} return results_aluminum -def gen_mock_demand_aluminum(scenario): +def gen_mock_demand_aluminum(scenario): context = read_config() s_info = ScenarioInfo(scenario) modelyears = s_info.Y # s_info.Y is only for modeling years @@ -469,7 +468,7 @@ def gen_mock_demand_aluminum(scenario): # The order: # r = ['R12_AFR', 'R12_RCPA', 'R12_EEU', 'R12_FSU', 'R12_LAM', 'R12_MEA',\ - #'R12_NAM', 'R12_PAO', 'R12_PAS', 'R12_SAS', 'R12_WEU',"R12_CHN"] + # 'R12_NAM', 'R12_PAO', 'R12_PAS', 'R12_SAS', 'R12_WEU',"R12_CHN"] if "R12_CHN" in nodes: nodes.remove("R12_GLB") @@ -491,7 +490,7 @@ def gen_mock_demand_aluminum(scenario): gdp_growth = gdp_growth.loc[ (gdp_growth["Scenario"] == "baseline") & (gdp_growth["Region"] != "World") - ].drop(["Model", "Variable", "Unit", "Notes", 2000, 2005], axis=1) + ].drop(["Model", "Variable", "Unit", "Notes", 2000, 2005], axis=1) gdp_growth["Region"] = region_set + gdp_growth["Region"] @@ -516,7 +515,6 @@ def gen_mock_demand_aluminum(scenario): return demand2020_al - # # load rpy2 modules # import rpy2.robjects as ro # from rpy2.robjects import pandas2ri diff --git a/message_ix_models/model/material/data_ammonia_new.py b/message_ix_models/model/material/data_ammonia_new.py index aa3d06e592..2e93718666 100644 --- a/message_ix_models/model/material/data_ammonia_new.py +++ b/message_ix_models/model/material/data_ammonia_new.py @@ -1,11 +1,11 @@ -import pandas as pd import numpy as np +import pandas as pd +from message_ix import make_df from message_ix_models import ScenarioInfo -from message_ix import make_df -from message_ix_models.util import broadcast, same_node, package_data_path -from message_ix_models.model.material.util import read_config from message_ix_models.model.material.material_demand import material_demand_calc +from message_ix_models.model.material.util import read_config +from message_ix_models.util import broadcast, package_data_path, same_node CONVERSION_FACTOR_NH3_N = 17 / 14 context = read_config() @@ -138,7 +138,7 @@ def __missing__(self, key): df_temp["lifetime"] = df_temp["technology"].map(dict_lifetime) df_temp = df_temp[ (df_temp["year_act"] - df_temp["year_vtg"]) <= df_temp["lifetime"] - ] + ] par_dict[i] = df_temp.drop("lifetime", axis="columns") pars = ["inv_cost", "fix_cost"] tec_list = [ @@ -545,10 +545,10 @@ def gen_resid_demand_NH3(scenario, gdp_elasticity): ssp = context["ssp"] def get_demand_t1_with_income_elasticity( - demand_t0, income_t0, income_t1, elasticity + demand_t0, income_t0, income_t1, elasticity ): return ( - elasticity * demand_t0 * ((income_t1 - income_t0) / income_t0) + elasticity * demand_t0 * ((income_t1 - income_t0) / income_t0) ) + demand_t0 df_gdp = pd.read_excel( diff --git a/message_ix_models/model/material/data_buildings.py b/message_ix_models/model/material/data_buildings.py index 90f30e29f7..3258fe41f8 100644 --- a/message_ix_models/model/material/data_buildings.py +++ b/message_ix_models/model/material/data_buildings.py @@ -1,22 +1,24 @@ +from collections import defaultdict + import pandas as pd +from message_ix import make_df -from collections import defaultdict -from message_ix_models.model.material.util import read_config from message_ix_models import ScenarioInfo -from message_ix import make_df +from message_ix_models.model.material.util import read_config from message_ix_models.util import ( - same_node, copy_column, package_data_path, + same_node, ) CASE_SENS = "ref" # 'min', 'max' INPUTFILE = "LED_LED_report_IAMC_sensitivity_R12.csv" + + # INPUTFILE = 'LED_LED_report_IAMC_sensitivity_R11.csv' def read_timeseries_buildings(filename, scenario, case=CASE_SENS): - import numpy as np # Ensure config is loaded, get the context @@ -80,7 +82,7 @@ def read_timeseries_buildings(filename, scenario, case=CASE_SENS): ].reset_index(drop=True) bld_area_long = bld_data_long[ bld_data_long["Variable"] == "Energy Service|Residential|Floor Space" - ].reset_index(drop=True) + ].reset_index(drop=True) tmp = bld_intensity_long.Variable.str.split("|", expand=True) @@ -112,7 +114,7 @@ def read_timeseries_buildings(filename, scenario, case=CASE_SENS): def get_scen_mat_demand( - commod, scenario, year="2020", inputfile=INPUTFILE, case=CASE_SENS + commod, scenario, year="2020", inputfile=INPUTFILE, case=CASE_SENS ): a, b, c = read_timeseries_buildings(inputfile, scenario, case) if not year == "all": # specific year @@ -123,7 +125,6 @@ def get_scen_mat_demand( def adjust_demand_param(scen): - s_info = ScenarioInfo(scen) modelyears = s_info.Y # s_info.Y is only for modeling years @@ -192,7 +193,7 @@ def gen_data_buildings(scenario, dry_run=False): yv_ya = s_info.yv_ya # fmy = s_info.y0 nodes.remove("World") - #nodes.remove("R11_RCPA") + # nodes.remove("R11_RCPA") # Read field values from the buildings input data regions = list(set(data_buildings.node)) @@ -309,7 +310,8 @@ def gen_data_buildings(scenario, dry_run=False): if __name__ == "__main__": import ixmp import message_ix + mp = ixmp.Platform("ixmp_dev") scen = message_ix.Scenario(mp, "MESSAGEix-Materials", "baseline_balance-equality_materials") df = gen_data_buildings(scen) - print() \ No newline at end of file + print() diff --git a/message_ix_models/model/material/data_cement.py b/message_ix_models/model/material/data_cement.py index 293d17acb1..23f48db8e6 100644 --- a/message_ix_models/model/material/data_cement.py +++ b/message_ix_models/model/material/data_cement.py @@ -1,18 +1,18 @@ +from collections import defaultdict + import ixmp import message_ix import pandas as pd +from message_ix import make_df -from collections import defaultdict - +from message_ix_models import ScenarioInfo from message_ix_models.model.material.data_util import read_sector_data, read_timeseries from message_ix_models.model.material.material_demand import material_demand_calc from message_ix_models.model.material.util import read_config -from message_ix_models import ScenarioInfo -from message_ix import make_df from message_ix_models.util import ( broadcast, - same_node, package_data_path, + same_node, ) # Get endogenous material demand from buildings interface @@ -26,7 +26,6 @@ # Generate a fake cement demand def gen_mock_demand_cement(scenario): - context = read_config() s_info = ScenarioInfo(scenario) modelyears = s_info.Y # s_info.Y is only for modeling years @@ -41,7 +40,7 @@ def gen_mock_demand_cement(scenario): # The order: # r = ['R12_AFR', 'R12_RCPA', 'R12_EEU', 'R12_FSU', 'R12_LAM', 'R12_MEA',\ - #'R12_NAM', 'R12_PAO', 'R12_PAS', 'R12_SAS', 'R12_WEU',"R12_CHN"] + # 'R12_NAM', 'R12_PAO', 'R12_PAS', 'R12_SAS', 'R12_WEU',"R12_CHN"] if "R12_CHN" in nodes: nodes.remove("R12_GLB") @@ -93,7 +92,7 @@ def gen_mock_demand_cement(scenario): gdp_growth = gdp_growth.loc[ (gdp_growth["Scenario"] == "baseline") & (gdp_growth["Region"] != "World") - ].drop(["Model", "Variable", "Unit", "Notes", 2000, 2005], axis=1) + ].drop(["Model", "Variable", "Unit", "Notes", 2000, 2005], axis=1) d = [a + b for a, b in zip(demand2020_top, demand2020_rest)] gdp_growth["Region"] = region_set + gdp_growth["Region"] @@ -412,7 +411,7 @@ def gen_data_cement(scenario, dry_run=False): if __name__ == "__main__": mp = ixmp.Platform("local") - scenario = message_ix.Scenario(mp,"MESSAGEix-Materials", "baseline") + scenario = message_ix.Scenario(mp, "MESSAGEix-Materials", "baseline") gen_data_cement(scenario) # # load rpy2 modules diff --git a/message_ix_models/model/material/data_generic.py b/message_ix_models/model/material/data_generic.py index 98830319fb..dc9b124de4 100644 --- a/message_ix_models/model/material/data_generic.py +++ b/message_ix_models/model/material/data_generic.py @@ -1,17 +1,18 @@ from collections import defaultdict -import message_ix_models.util import pandas as pd +from message_ix import make_df -from .util import read_config +import message_ix_models.util from message_ix_models import ScenarioInfo -from message_ix import make_df -from .data_util import read_timeseries from message_ix_models.util import ( broadcast, same_node, ) +from .data_util import read_timeseries +from .util import read_config + def read_data_generic(scenario): """Read and clean data from :file:`generic_furnace_boiler_techno_economic.xlsx`.""" @@ -105,8 +106,8 @@ def gen_data_generic(scenario, dry_run=False): val = data_generic.loc[ ( - (data_generic["technology"] == t) - & (data_generic["parameter"] == par) + (data_generic["technology"] == t) + & (data_generic["parameter"] == par) ), "value", ].values[0] @@ -208,8 +209,8 @@ def gen_data_generic(scenario, dry_run=False): ] regions = data_generic_ts.loc[ ( - (data_generic_ts["technology"] == t) - & (data_generic_ts["parameter"] == p) + (data_generic_ts["technology"] == t) + & (data_generic_ts["parameter"] == p) ), "region", ] @@ -260,9 +261,9 @@ def gen_data_generic(scenario, dry_run=False): # Copy parameters to all regions if ( - (len(set(regions)) == 1) - and len(set(df["node_loc"])) == 1 - and list(set(df["node_loc"]))[0] != global_region + (len(set(regions)) == 1) + and len(set(df["node_loc"])) == 1 + and list(set(df["node_loc"]))[0] != global_region ): df["node_loc"] = None df = df.pipe(broadcast, node_loc=nodes) diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index 0c006f0687..b2c9e6ff9d 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -1,14 +1,14 @@ import copy -import pandas as pd +import pandas as pd from message_ix import make_df -from message_ix_models.util import broadcast, same_node, package_data_path -from message_ix_models.model.material.util import read_config, combine_df_dictionaries + from message_ix_models.model.material.material_demand import material_demand_calc +from message_ix_models.model.material.util import combine_df_dictionaries, read_config +from message_ix_models.util import broadcast, package_data_path, same_node context = read_config() - ssp_mode_map = { "SSP1": "CTS core", "SSP2": "RTS core", @@ -382,9 +382,9 @@ def gen_data_meth_chemicals(scenario, chemical): ) # exclude emissions for now if chemical == "MTO": - df = df.iloc[:15,] + df = df.iloc[:15, ] if chemical == "Formaldehyde": - df = df.iloc[:10,] + df = df.iloc[:10, ] common = dict( # commodity="NH3", @@ -506,7 +506,7 @@ def add_methanol_fuel_additives(scenario): par_dict_loil["input"] = par_dict_loil["input"][ par_dict_loil["input"]["commodity"] == "lightoil" - ] + ] df_mtbe = pd.read_excel( package_data_path( @@ -519,7 +519,7 @@ def add_methanol_fuel_additives(scenario): skiprows=[i for i in range(66)], sheet_name="MTBE calc", ) - df_mtbe = df_mtbe.iloc[1:13,] + df_mtbe = df_mtbe.iloc[1:13, ] df_mtbe["node_loc"] = "R12_" + df_mtbe["node_loc"] df_mtbe = df_mtbe[["node_loc", "% share on trp"]] @@ -549,14 +549,14 @@ def get_meth_share(df, node): ) df_loil_meth["commodity"] = "methanol" par_dict_loil["input"]["value"] = ( - par_dict_loil["input"]["value"] - df_loil_meth["value"] + par_dict_loil["input"]["value"] - df_loil_meth["value"] ) df_loil_eth = df_loil_meth.copy(deep=True) df_loil_eth["commodity"] = "ethanol" df_loil_eth["mode"] = "ethanol" df_loil_eth["value"] = ( - df_loil_eth["value"] * 1.6343 + df_loil_eth["value"] * 1.6343 ) # energy ratio methanol/ethanol in MTBE vs ETBE df_loil_eth_mode = par_dict_loil["input"].copy(deep=True) @@ -676,10 +676,10 @@ def gen_resin_demand(scenario, resin_share, sector, buildings_scen, pathway="SHA def gen_meth_residual_demand(gdp_elasticity_2020, gdp_elasticity_2030): def get_demand_t1_with_income_elasticity( - demand_t0, income_t0, income_t1, elasticity + demand_t0, income_t0, income_t1, elasticity ): return ( - elasticity * demand_t0 * ((income_t1 - income_t0) / income_t0) + elasticity * demand_t0 * ((income_t1 - income_t0) / income_t0) ) + demand_t0 df_gdp = pd.read_excel( @@ -697,7 +697,7 @@ def get_demand_t1_with_income_elasticity( ) df_demand_meth = df_demand_meth[ (~df_demand_meth["Region"].isna()) & (df_demand_meth["Region"] != "World") - ] + ] df_demand_meth = df_demand_meth.dropna(axis=1) df_demand = df.copy(deep=True) @@ -757,7 +757,7 @@ def get_cost_ratio_2020(scenario, tec_name, cost_type, ref_reg="R12_NAM", year=" def get_scaled_cost_from_proxy_tec( - value, scenario, proxy_tec, cost_type, new_tec, year="all" + value, scenario, proxy_tec, cost_type, new_tec, year="all" ): df = get_cost_ratio_2020(scenario, proxy_tec, cost_type, year=year) df["value"] = value * df["ratio"] @@ -849,7 +849,7 @@ def update_costs_with_loc_factor(df): df = df.merge(loc_fact, left_on="node_loc", right_index=True) df = df.merge(cost_conv, left_on="year_vtg", right_index=True) df["value"] = ( - df["value"] - (1 - df["WEU normalized"]) * df["value"] * df["convergence"] + df["value"] - (1 - df["WEU normalized"]) * df["value"] * df["convergence"] ) return df[df.columns[:-2]] diff --git a/message_ix_models/model/material/data_methanol_new.py b/message_ix_models/model/material/data_methanol_new.py index 60b3529b51..7b121cc8cc 100644 --- a/message_ix_models/model/material/data_methanol_new.py +++ b/message_ix_models/model/material/data_methanol_new.py @@ -1,13 +1,13 @@ -import message_ix_models.util +from ast import literal_eval + import pandas as pd import yaml - from message_ix import make_df -from message_ix_models.util import broadcast, same_node +import message_ix_models.util from message_ix_models.model.material.material_demand import material_demand_calc from message_ix_models.model.material.util import read_config -from ast import literal_eval +from message_ix_models.util import broadcast, same_node ssp_mode_map = { "SSP1": "CTS core", @@ -187,6 +187,6 @@ def broadcast_reduced_df(df, par_name): df_final_full[ (df_final_full.node_rel.values != "R12_GLB") & (df_final_full.node_rel.values != df_final_full.node_loc.values) - ].index + ].index ) return make_df(par_name, **df_final_full) diff --git a/message_ix_models/model/material/data_petro.py b/message_ix_models/model/material/data_petro.py index d12a4902b4..cce62833f7 100644 --- a/message_ix_models/model/material/data_petro.py +++ b/message_ix_models/model/material/data_petro.py @@ -1,22 +1,18 @@ +from collections import defaultdict + import pandas as pd -import numpy as np +from message_ix import make_df -from collections import defaultdict -from message_ix_models.model.material.data_util import read_timeseries, read_rel -from message_ix_models.model.material.util import read_config -from message_ix_models.model.material.material_demand import material_demand_calc from message_ix_models import ScenarioInfo -from message_ix import make_df +from message_ix_models.model.material.data_util import read_timeseries +from message_ix_models.model.material.material_demand import material_demand_calc +from message_ix_models.model.material.util import read_config from message_ix_models.util import ( broadcast, - make_io, - make_matched_dfs, - same_node, - add_par_data, package_data_path, + same_node, ) - ssp_mode_map = { "SSP1": "CTS core", "SSP2": "RTS core", @@ -66,10 +62,10 @@ def gen_mock_demand_petro(scenario, gdp_elasticity_2020, gdp_elasticity_2030): nodes = s_info.N def get_demand_t1_with_income_elasticity( - demand_t0, income_t0, income_t1, elasticity + demand_t0, income_t0, income_t1, elasticity ): return ( - elasticity * demand_t0.mul(((income_t1 - income_t0) / income_t0), axis=0) + elasticity * demand_t0.mul(((income_t1 - income_t0) / income_t0), axis=0) ) + demand_t0 gdp_mer = scenario.par("bound_activity_up", {"technology": "GDP"}) @@ -368,8 +364,8 @@ def gen_data_petro_chemicals(scenario, dry_run=False): if (len(regions) == 1) and (rg != global_region): if "node_loc" in df.columns: if ( - len(set(df["node_loc"])) == 1 - and list(set(df["node_loc"]))[0] != global_region + len(set(df["node_loc"])) == 1 + and list(set(df["node_loc"]))[0] != global_region ): # print("Copying to all R11") df["node_loc"] = None @@ -514,9 +510,9 @@ def gen_data_petro_chemicals(scenario, dry_run=False): df = results["growth_activity_up"] results["growth_activity_up"] = df[ ~( - (df["technology"] == "steam_cracker_petro") - & (df["node_loc"] == "R12_AFR") - & (df["year_act"] == 2020) + (df["technology"] == "steam_cracker_petro") + & (df["node_loc"] == "R12_AFR") + & (df["year_act"] == 2020) ) ] @@ -555,6 +551,6 @@ def gen_data_petro_chemicals(scenario, dry_run=False): (df_gro["technology"] == "steam_cracker_petro") & (df_gro["node_loc"] == "R12_RCPA") & (df_gro["year_act"] == 2020) - ].index + ].index results["growth_activity_up"] = results["growth_activity_up"].drop(drop_idx) return results diff --git a/message_ix_models/model/material/data_power_sector.py b/message_ix_models/model/material/data_power_sector.py index ce72fb9832..8f7761036f 100644 --- a/message_ix_models/model/material/data_power_sector.py +++ b/message_ix_models/model/material/data_power_sector.py @@ -2,14 +2,15 @@ from pathlib import Path import pandas as pd + +from message_ix_models import ScenarioInfo from message_ix_models.util import package_data_path from .util import read_config -from message_ix_models import ScenarioInfo def read_material_intensities( - parameter, data_path, node, year, technology, commodity, level, inv_cost + parameter, data_path, node, year, technology, commodity, level, inv_cost ): if parameter in ["input_cap_new", "input_cap_ret", "output_cap_ret"]: #################################################################### @@ -22,7 +23,7 @@ def read_material_intensities( # read technology, region and commodity mappings data_path_tec_map = ( - data_path + "/MESSAGE_global_model_technologies.xlsx" + data_path + "/MESSAGE_global_model_technologies.xlsx" ) technology_mapping = pd.read_excel(data_path_tec_map, sheet_name="technology") @@ -40,9 +41,9 @@ def read_material_intensities( # mix for others) and remove operation phase (and remove duplicates) data_lca = data_lca.loc[ ( - (data_lca["scenario"] == "Baseline") - & (data_lca["technology variant"].isin(["mix", "residue"])) - & (data_lca["phase"] != "Operation") + (data_lca["scenario"] == "Baseline") + & (data_lca["technology variant"].isin(["mix", "residue"])) + & (data_lca["phase"] != "Operation") ) ] @@ -128,10 +129,10 @@ def read_material_intensities( for p in data_lca["phase"].unique(): temp = data_lca.loc[ ( - (data_lca["node"] == n) - & (data_lca["technology"] == t) - & (data_lca["commodity"] == c) - & (data_lca["phase"] == p) + (data_lca["node"] == n) + & (data_lca["technology"] == t) + & (data_lca["commodity"] == c) + & (data_lca["phase"] == p) ) ] temp["value"] = temp["value"].interpolate( @@ -181,7 +182,7 @@ def read_material_intensities( & (data_lca_final["phase"] == "Construction") & (data_lca_final["commodity"] == c) & (data_lca_final["year"] == yeff) - ]["value"].values[0] + ]["value"].values[0] val_cap_new = val_cap_new * 0.001 input_cap_new = pd.concat( @@ -210,7 +211,7 @@ def read_material_intensities( & (data_lca_final["phase"] == "End-of-life") & (data_lca_final["commodity"] == c) & (data_lca_final["year"] == yeff) - ]["value"].values[0] + ]["value"].values[0] val_cap_input_ret = val_cap_input_ret * 0.001 input_cap_ret = pd.concat( @@ -239,7 +240,7 @@ def read_material_intensities( & (data_lca_final["phase"] == "Construction") & (data_lca_final["commodity"] == c) & (data_lca_final["year"] == yeff) - ]["value"].values[0] + ]["value"].values[0] val_cap_output_ret = val_cap_output_ret * 0.001 output_cap_ret = pd.concat( diff --git a/message_ix_models/model/material/data_steel.py b/message_ix_models/model/material/data_steel.py index 8e4f9be77e..0ed71c2e13 100644 --- a/message_ix_models/model/material/data_steel.py +++ b/message_ix_models/model/material/data_steel.py @@ -1,27 +1,23 @@ -from .data_util import read_sector_data, read_timeseries - from collections import defaultdict -from pathlib import Path import pandas as pd - -from .material_demand import material_demand_calc -from .util import read_config -from .data_util import read_rel +from message_ix import make_df # Get endogenous material demand from buildings interface from message_ix_models import ScenarioInfo -from message_ix import make_df from message_ix_models.util import ( broadcast, - same_node, package_data_path, + same_node, ) +from .data_util import read_rel, read_sector_data, read_timeseries +from .material_demand import material_demand_calc +from .util import read_config + # Generate a fake steel demand def gen_mock_demand_steel(scenario): - context = read_config() s_info = ScenarioInfo(scenario) modelyears = s_info.Y # s_info.Y is only for modeling years @@ -31,7 +27,7 @@ def gen_mock_demand_steel(scenario): # The order: # r = ['R12_AFR', 'R12_RCPA', 'R12_EEU', 'R12_FSU', 'R12_LAM', 'R12_MEA',\ - #'R12_NAM', 'R12_PAO', 'R12_PAS', 'R12_SAS', 'R12_WEU',"R12_CHN"] + # 'R12_NAM', 'R12_PAO', 'R12_PAS', 'R12_SAS', 'R12_WEU',"R12_CHN"] # Finished steel demand from: https://www.oecd.org/industry/ind/Item_4b_Worldsteel.pdf # For region definitions: https://worldsteel.org/wp-content/uploads/2021-World-Steel-in-Figures.pdf @@ -84,7 +80,7 @@ def gen_mock_demand_steel(scenario): gdp_growth = gdp_growth.loc[ (gdp_growth["Scenario"] == "baseline") & (gdp_growth["Region"] != "World") - ].drop(["Model", "Variable", "Unit", "Notes", 2000, 2005], axis=1) + ].drop(["Model", "Variable", "Unit", "Notes", 2000, 2005], axis=1) gdp_growth["Region"] = region_set + gdp_growth["Region"] @@ -379,8 +375,8 @@ def gen_data_steel(scenario, dry_run=False): # Copy parameters to all regions if ( - len(set(df["node_loc"])) == 1 - and list(set(df["node_loc"]))[0] != global_region + len(set(df["node_loc"])) == 1 + and list(set(df["node_loc"]))[0] != global_region ): df["node_loc"] = None df = df.pipe(broadcast, node_loc=nodes) @@ -401,8 +397,8 @@ def gen_data_steel(scenario, dry_run=False): "unit": "???", "value": data_steel_rel.loc[ ( - (data_steel_rel["relation"] == "max_global_recycling_steel") - & (data_steel_rel["parameter"] == "relation_activity") + (data_steel_rel["relation"] == "max_global_recycling_steel") + & (data_steel_rel["parameter"] == "relation_activity") ), "value", ].values[0], @@ -417,8 +413,8 @@ def gen_data_steel(scenario, dry_run=False): "unit": "???", "value": data_steel_rel.loc[ ( - (data_steel_rel["relation"] == "max_global_recycling_steel") - & (data_steel_rel["parameter"] == "relation_upper") + (data_steel_rel["relation"] == "max_global_recycling_steel") + & (data_steel_rel["parameter"] == "relation_upper") ), "value", ].values[0], @@ -433,8 +429,8 @@ def gen_data_steel(scenario, dry_run=False): "unit": "???", "value": data_steel_rel.loc[ ( - (data_steel_rel["relation"] == "max_global_recycling_steel") - & (data_steel_rel["parameter"] == "relation_lower") + (data_steel_rel["relation"] == "max_global_recycling_steel") + & (data_steel_rel["parameter"] == "relation_lower") ), "value", ].values[0], @@ -482,8 +478,8 @@ def gen_data_steel(scenario, dry_run=False): tec_list = data_steel_rel.loc[ ( - (data_steel_rel["relation"] == r) - & (data_steel_rel["parameter"] == par_name) + (data_steel_rel["relation"] == r) + & (data_steel_rel["parameter"] == par_name) ), "technology", ] @@ -491,10 +487,10 @@ def gen_data_steel(scenario, dry_run=False): for tec in tec_list.unique(): val = data_steel_rel.loc[ ( - (data_steel_rel["relation"] == r) - & (data_steel_rel["parameter"] == par_name) - & (data_steel_rel["technology"] == tec) - & (data_steel_rel["Region"] == reg) + (data_steel_rel["relation"] == r) + & (data_steel_rel["parameter"] == par_name) + & (data_steel_rel["technology"] == tec) + & (data_steel_rel["Region"] == reg) ), "value", ].values[0] @@ -514,9 +510,9 @@ def gen_data_steel(scenario, dry_run=False): elif (par_name == "relation_upper") | (par_name == "relation_lower"): val = data_steel_rel.loc[ ( - (data_steel_rel["relation"] == r) - & (data_steel_rel["parameter"] == par_name) - & (data_steel_rel["Region"] == reg) + (data_steel_rel["relation"] == r) + & (data_steel_rel["parameter"] == par_name) + & (data_steel_rel["Region"] == reg) ), "value", ].values[0] @@ -539,7 +535,6 @@ def gen_data_steel(scenario, dry_run=False): results["input"] = results["input"].replace({"freshwater_supply": "freshwater"}) return results - # # load rpy2 modules # import rpy2.robjects as ro # from rpy2.robjects import pandas2ri diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index 49a7d65c0b..bca8d3bc04 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -152,7 +152,7 @@ def modify_demand_and_hist_activity(scen): | (df["SECTOR"] == "industry (non-ferrous metals)") | (df["SECTOR"] == "industry (non-metallic minerals)") | (df["SECTOR"] == "industry (total)") - ] + ] df = df[df["RYEAR"] == 2015] # NOTE: Total cehmical industry energy: 27% thermal, 8% electricity, 65% feedstock @@ -171,10 +171,10 @@ def modify_demand_and_hist_activity(scen): & (df["SECTOR"] != "industry (total)") & (df["SECTOR"] != "feedstock (petrochemical industry)") & (df["SECTOR"] != "feedstock (total)") - ] + ] df_spec_total = df[ (df["SECTOR"] == "industry (total)") & (df["FUEL"] == "electricity") - ] + ] df_spec_new = pd.DataFrame( columns=["REGION", "SECTOR", "FUEL", "RYEAR", "UNIT_OUT", "RESULT"] @@ -183,15 +183,15 @@ def modify_demand_and_hist_activity(scen): df_spec_temp = df_spec.loc[df_spec["REGION"] == r] df_spec_total_temp = df_spec_total.loc[df_spec_total["REGION"] == r] df_spec_temp.loc[:, "i_spec"] = ( - df_spec_temp.loc[:, "RESULT"] - / df_spec_total_temp.loc[:, "RESULT"].values[0] + df_spec_temp.loc[:, "RESULT"] + / df_spec_total_temp.loc[:, "RESULT"].values[0] ) df_spec_new = pd.concat([df_spec_temp, df_spec_new], ignore_index=True) df_spec_new.drop(["FUEL", "RYEAR", "UNIT_OUT", "RESULT"], axis=1, inplace=True) df_spec_new.loc[df_spec_new["SECTOR"] == "industry (chemicals)", "i_spec"] = ( - df_spec_new.loc[df_spec_new["SECTOR"] == "industry (chemicals)", "i_spec"] - * 0.67 + df_spec_new.loc[df_spec_new["SECTOR"] == "industry (chemicals)", "i_spec"] + * 0.67 ) df_spec_new = df_spec_new.groupby(["REGION"]).sum().reset_index() @@ -200,7 +200,7 @@ def modify_demand_and_hist_activity(scen): df_feed = df[ (df["SECTOR"] == "feedstock (petrochemical industry)") & (df["FUEL"] == "total") - ] + ] df_feed_total = df[(df["SECTOR"] == "feedstock (total)") & (df["FUEL"] == "total")] df_feed_temp = pd.DataFrame(columns=["REGION", "i_feed"]) df_feed_new = pd.DataFrame(columns=["REGION", "i_feed"]) @@ -224,12 +224,12 @@ def modify_demand_and_hist_activity(scen): & (df["SECTOR"] != "feedstock (petrochemical industry)") & (df["SECTOR"] != "feedstock (total)") & (df["SECTOR"] != "industry (non-ferrous metals)") - ] + ] df_therm_total = df[ (df["SECTOR"] == "industry (total)") & (df["FUEL"] != "total") & (df["FUEL"] != "electricity") - ] + ] df_therm_total = ( df_therm_total.groupby(by="REGION").sum().drop(["RYEAR"], axis=1).reset_index() ) @@ -247,23 +247,23 @@ def modify_demand_and_hist_activity(scen): df_therm_temp = df_therm.loc[df_therm["REGION"] == r] df_therm_total_temp = df_therm_total.loc[df_therm_total["REGION"] == r] df_therm_temp.loc[:, "i_therm"] = ( - df_therm_temp.loc[:, "RESULT"] - / df_therm_total_temp.loc[:, "RESULT"].values[0] + df_therm_temp.loc[:, "RESULT"] + / df_therm_total_temp.loc[:, "RESULT"].values[0] ) df_therm_new = pd.concat([df_therm_temp, df_therm_new], ignore_index=True) df_therm_new = df_therm_new.drop(["RESULT"], axis=1) df_therm_new.drop(["FUEL", "RYEAR", "UNIT_OUT"], axis=1, inplace=True) df_therm_new.loc[df_therm_new["SECTOR"] == "industry (chemicals)", "i_therm"] = ( - df_therm_new.loc[df_therm_new["SECTOR"] == "industry (chemicals)", "i_therm"] - * 0.67 + df_therm_new.loc[df_therm_new["SECTOR"] == "industry (chemicals)", "i_therm"] + * 0.67 ) # Modify CPA based on https://www.iea.org/sankey/#?c=Japan&s=Final%20consumption. # Since the value did not allign with the one in the IEA website. index = (df_therm_new["SECTOR"] == "industry (iron and steel)") & ( - (df_therm_new["REGION"] == region_name_CPA) - | (df_therm_new["REGION"] == region_name_CHN) + (df_therm_new["REGION"] == region_name_CPA) + | (df_therm_new["REGION"] == region_name_CHN) ) df_therm_new.loc[index, "i_therm"] = 0.2 @@ -314,39 +314,41 @@ def modify_demand_and_hist_activity(scen): useful_thermal.loc[ useful_thermal["node"] == r_MESSAGE, "value" ] = useful_thermal.loc[useful_thermal["node"] == r_MESSAGE, "value"] * ( - 1 - df_therm_new.loc[df_therm_new["REGION"] == r, "i_therm"].values[0] + 1 - df_therm_new.loc[df_therm_new["REGION"] == r, "i_therm"].values[0] ) thermal_df_hist.loc[ thermal_df_hist["node_loc"] == r_MESSAGE, "value" ] = thermal_df_hist.loc[thermal_df_hist["node_loc"] == r_MESSAGE, "value"] * ( - 1 - df_therm_new.loc[df_therm_new["REGION"] == r, "i_therm"].values[0] + 1 - df_therm_new.loc[df_therm_new["REGION"] == r, "i_therm"].values[0] ) for r in df_spec_new["REGION"]: r_MESSAGE = region_type + r useful_spec.loc[useful_spec["node"] == r_MESSAGE, "value"] = useful_spec.loc[ - useful_spec["node"] == r_MESSAGE, "value" - ] * (1 - df_spec_new.loc[df_spec_new["REGION"] == r, "i_spec"].values[0]) + useful_spec["node"] == r_MESSAGE, "value" + ] * (1 - df_spec_new.loc[ + df_spec_new["REGION"] == r, "i_spec"].values[0]) spec_df_hist.loc[ spec_df_hist["node_loc"] == r_MESSAGE, "value" ] = spec_df_hist.loc[spec_df_hist["node_loc"] == r_MESSAGE, "value"] * ( - 1 - df_spec_new.loc[df_spec_new["REGION"] == r, "i_spec"].values[0] + 1 - df_spec_new.loc[df_spec_new["REGION"] == r, "i_spec"].values[0] ) for r in df_feed_new["REGION"]: r_MESSAGE = region_type + r useful_feed.loc[useful_feed["node"] == r_MESSAGE, "value"] = useful_feed.loc[ - useful_feed["node"] == r_MESSAGE, "value" - ] * (1 - df_feed_new.loc[df_feed_new["REGION"] == r, "i_feed"].values[0]) + useful_feed["node"] == r_MESSAGE, "value" + ] * (1 - df_feed_new.loc[ + df_feed_new["REGION"] == r, "i_feed"].values[0]) feed_df_hist.loc[ feed_df_hist["node_loc"] == r_MESSAGE, "value" ] = feed_df_hist.loc[feed_df_hist["node_loc"] == r_MESSAGE, "value"] * ( - 1 - df_feed_new.loc[df_feed_new["REGION"] == r, "i_feed"].values[0] + 1 - df_feed_new.loc[df_feed_new["REGION"] == r, "i_feed"].values[0] ) scen.check_out() @@ -460,7 +462,7 @@ def modify_demand_and_hist_activity_debug(scen: message_ix.Scenario) -> dict: | (df["SECTOR"] == "industry (non-ferrous metals)") | (df["SECTOR"] == "industry (non-metallic minerals)") | (df["SECTOR"] == "industry (total)") - ] + ] df = df[df["RYEAR"] == 2015] # NOTE: Total cehmical industry energy: 27% thermal, 8% electricity, 65% feedstock @@ -479,10 +481,10 @@ def modify_demand_and_hist_activity_debug(scen: message_ix.Scenario) -> dict: & (df["SECTOR"] != "industry (total)") & (df["SECTOR"] != "feedstock (petrochemical industry)") & (df["SECTOR"] != "feedstock (total)") - ] + ] df_spec_total = df[ (df["SECTOR"] == "industry (total)") & (df["FUEL"] == "electricity") - ] + ] df_spec_new = pd.DataFrame( columns=["REGION", "SECTOR", "FUEL", "RYEAR", "UNIT_OUT", "RESULT"] @@ -491,15 +493,15 @@ def modify_demand_and_hist_activity_debug(scen: message_ix.Scenario) -> dict: df_spec_temp = df_spec.loc[df_spec["REGION"] == r] df_spec_total_temp = df_spec_total.loc[df_spec_total["REGION"] == r] df_spec_temp.loc[:, "i_spec"] = ( - df_spec_temp.loc[:, "RESULT"] - / df_spec_total_temp.loc[:, "RESULT"].values[0] + df_spec_temp.loc[:, "RESULT"] + / df_spec_total_temp.loc[:, "RESULT"].values[0] ) df_spec_new = pd.concat([df_spec_temp, df_spec_new], ignore_index=True) df_spec_new.drop(["FUEL", "RYEAR", "UNIT_OUT", "RESULT"], axis=1, inplace=True) df_spec_new.loc[df_spec_new["SECTOR"] == "industry (chemicals)", "i_spec"] = ( - df_spec_new.loc[df_spec_new["SECTOR"] == "industry (chemicals)", "i_spec"] - * 0.67 + df_spec_new.loc[df_spec_new["SECTOR"] == "industry (chemicals)", "i_spec"] + * 0.67 ) df_spec_new = df_spec_new.groupby(["REGION"]).sum().reset_index() @@ -508,7 +510,7 @@ def modify_demand_and_hist_activity_debug(scen: message_ix.Scenario) -> dict: df_feed = df[ (df["SECTOR"] == "feedstock (petrochemical industry)") & (df["FUEL"] == "total") - ] + ] df_feed_total = df[(df["SECTOR"] == "feedstock (total)") & (df["FUEL"] == "total")] df_feed_temp = pd.DataFrame(columns=["REGION", "i_feed"]) df_feed_new = pd.DataFrame(columns=["REGION", "i_feed"]) @@ -532,12 +534,12 @@ def modify_demand_and_hist_activity_debug(scen: message_ix.Scenario) -> dict: & (df["SECTOR"] != "feedstock (petrochemical industry)") & (df["SECTOR"] != "feedstock (total)") & (df["SECTOR"] != "industry (non-ferrous metals)") - ] + ] df_therm_total = df[ (df["SECTOR"] == "industry (total)") & (df["FUEL"] != "total") & (df["FUEL"] != "electricity") - ] + ] df_therm_total = ( df_therm_total.groupby(by="REGION").sum().drop(["RYEAR"], axis=1).reset_index() ) @@ -555,23 +557,23 @@ def modify_demand_and_hist_activity_debug(scen: message_ix.Scenario) -> dict: df_therm_temp = df_therm.loc[df_therm["REGION"] == r] df_therm_total_temp = df_therm_total.loc[df_therm_total["REGION"] == r] df_therm_temp.loc[:, "i_therm"] = ( - df_therm_temp.loc[:, "RESULT"] - / df_therm_total_temp.loc[:, "RESULT"].values[0] + df_therm_temp.loc[:, "RESULT"] + / df_therm_total_temp.loc[:, "RESULT"].values[0] ) df_therm_new = pd.concat([df_therm_temp, df_therm_new], ignore_index=True) df_therm_new = df_therm_new.drop(["RESULT"], axis=1) df_therm_new.drop(["FUEL", "RYEAR", "UNIT_OUT"], axis=1, inplace=True) df_therm_new.loc[df_therm_new["SECTOR"] == "industry (chemicals)", "i_therm"] = ( - df_therm_new.loc[df_therm_new["SECTOR"] == "industry (chemicals)", "i_therm"] - * 0.67 + df_therm_new.loc[df_therm_new["SECTOR"] == "industry (chemicals)", "i_therm"] + * 0.67 ) # Modify CPA based on https://www.iea.org/sankey/#?c=Japan&s=Final%20consumption. # Since the value did not allign with the one in the IEA website. index = (df_therm_new["SECTOR"] == "industry (iron and steel)") & ( - (df_therm_new["REGION"] == region_name_CPA) - | (df_therm_new["REGION"] == region_name_CHN) + (df_therm_new["REGION"] == region_name_CPA) + | (df_therm_new["REGION"] == region_name_CHN) ) df_therm_new.loc[index, "i_therm"] = 0.2 @@ -622,39 +624,41 @@ def modify_demand_and_hist_activity_debug(scen: message_ix.Scenario) -> dict: useful_thermal.loc[ useful_thermal["node"] == r_MESSAGE, "value" ] = useful_thermal.loc[useful_thermal["node"] == r_MESSAGE, "value"] * ( - 1 - df_therm_new.loc[df_therm_new["REGION"] == r, "i_therm"].values[0] + 1 - df_therm_new.loc[df_therm_new["REGION"] == r, "i_therm"].values[0] ) thermal_df_hist.loc[ thermal_df_hist["node_loc"] == r_MESSAGE, "value" ] = thermal_df_hist.loc[thermal_df_hist["node_loc"] == r_MESSAGE, "value"] * ( - 1 - df_therm_new.loc[df_therm_new["REGION"] == r, "i_therm"].values[0] + 1 - df_therm_new.loc[df_therm_new["REGION"] == r, "i_therm"].values[0] ) for r in df_spec_new["REGION"]: r_MESSAGE = region_type + r useful_spec.loc[useful_spec["node"] == r_MESSAGE, "value"] = useful_spec.loc[ - useful_spec["node"] == r_MESSAGE, "value" - ] * (1 - df_spec_new.loc[df_spec_new["REGION"] == r, "i_spec"].values[0]) + useful_spec["node"] == r_MESSAGE, "value" + ] * (1 - df_spec_new.loc[ + df_spec_new["REGION"] == r, "i_spec"].values[0]) spec_df_hist.loc[ spec_df_hist["node_loc"] == r_MESSAGE, "value" ] = spec_df_hist.loc[spec_df_hist["node_loc"] == r_MESSAGE, "value"] * ( - 1 - df_spec_new.loc[df_spec_new["REGION"] == r, "i_spec"].values[0] + 1 - df_spec_new.loc[df_spec_new["REGION"] == r, "i_spec"].values[0] ) for r in df_feed_new["REGION"]: r_MESSAGE = region_type + r useful_feed.loc[useful_feed["node"] == r_MESSAGE, "value"] = useful_feed.loc[ - useful_feed["node"] == r_MESSAGE, "value" - ] * (1 - df_feed_new.loc[df_feed_new["REGION"] == r, "i_feed"].values[0]) + useful_feed["node"] == r_MESSAGE, "value" + ] * (1 - df_feed_new.loc[ + df_feed_new["REGION"] == r, "i_feed"].values[0]) feed_df_hist.loc[ feed_df_hist["node_loc"] == r_MESSAGE, "value" ] = feed_df_hist.loc[feed_df_hist["node_loc"] == r_MESSAGE, "value"] * ( - 1 - df_feed_new.loc[df_feed_new["REGION"] == r, "i_feed"].values[0] + 1 - df_feed_new.loc[df_feed_new["REGION"] == r, "i_feed"].values[0] ) # For aluminum there is no significant deduction required @@ -774,14 +778,14 @@ def calc_demand_shares(iea_db_df: pd.DataFrame, base_year: int) -> pd.DataFrame: & (iea_db_df["PRODUCT"].isin(i_spec_prods)) & ~((iea_db_df["PRODUCT"] == ("ELECTR")) & (iea_db_df["FLOW"] == "IRONSTL")) & (iea_db_df["TIME"] == year) - ] + ] df_i_spec = df_i_spec.groupby("REGION").sum(numeric_only=True) df_i_spec_materials = iea_db_df[ (iea_db_df["FLOW"].isin(i_spec_material_flows)) & (iea_db_df["PRODUCT"].isin(i_spec_prods)) & (iea_db_df["TIME"] == year) - ] + ] df_i_spec_materials = df_i_spec_materials.groupby("REGION").sum(numeric_only=True) df_i_spec_resid_shr = ( @@ -792,7 +796,7 @@ def calc_demand_shares(iea_db_df: pd.DataFrame, base_year: int) -> pd.DataFrame: df_elec_i = iea_db_df[ ((iea_db_df["PRODUCT"] == ("ELECTR")) & (iea_db_df["FLOW"] == "IRONSTL")) & (iea_db_df["TIME"] == year) - ] + ] df_elec_i = df_elec_i.groupby("REGION").sum(numeric_only=True) agg_prods = ["MRENEW", "TOTAL"] @@ -801,7 +805,7 @@ def calc_demand_shares(iea_db_df: pd.DataFrame, base_year: int) -> pd.DataFrame: & ~(iea_db_df["PRODUCT"].isin(i_spec_prods)) & ~(iea_db_df["PRODUCT"].isin(agg_prods)) & (iea_db_df["TIME"] == year) - ] + ] df_i_therm = df_i_therm.groupby("REGION").sum(numeric_only=True) df_i_therm = df_i_therm.add(df_elec_i, fill_value=0) @@ -811,7 +815,7 @@ def calc_demand_shares(iea_db_df: pd.DataFrame, base_year: int) -> pd.DataFrame: & ~(iea_db_df["PRODUCT"].isin(i_spec_prods)) & ~(iea_db_df["PRODUCT"].isin(agg_prods)) & (iea_db_df["TIME"] == year) - ] + ] df_i_therm_materials = df_i_therm_materials.groupby(["REGION", "FLOW"]).sum( numeric_only=True ) @@ -998,17 +1002,17 @@ def add_emission_accounting(scen): i for i in tec_list_residual if ( - ( - ("biomass_i" in i) - | ("coal_i" in i) - | ("foil_i" in i) - | ("gas_i" in i) - | ("hp_gas_i" in i) - | ("loil_i" in i) - | ("meth_i" in i) - ) - & ("imp" not in i) - & ("trp" not in i) + ( + ("biomass_i" in i) + | ("coal_i" in i) + | ("foil_i" in i) + | ("gas_i" in i) + | ("hp_gas_i" in i) + | ("loil_i" in i) + | ("meth_i" in i) + ) + & ("imp" not in i) + & ("trp" not in i) ) ] @@ -1142,11 +1146,11 @@ def add_emission_accounting(scen): i for i in tec_list if ( - ("steel" in i) - | ("aluminum" in i) - | ("petro" in i) - | ("cement" in i) - | ("ref" in i) + ("steel" in i) + | ("aluminum" in i) + | ("petro" in i) + | ("cement" in i) + | ("ref" in i) ) ] for elem in ["refrigerant_recovery", "replacement_so2", "SO2_scrub_ref"]: @@ -1168,7 +1172,7 @@ def add_emission_accounting(scen): (relation_activity["relation"] != "PM2p5_Emission") & (relation_activity["relation"] != "CO2_industry_Emission") & (relation_activity["relation"] != "CO2_transformation_Emission") - ] + ] # ***** (3) Add thermal industry technologies to CO2_ind relation ****** @@ -1362,7 +1366,7 @@ def add_elec_lowerbound_2020(scen): # derive useful energy values by dividing final energy by # input coefficient from final-to-useful technologies bound_residual_electricity["value"] = ( - bound_residual_electricity["Value"] / bound_residual_electricity["value"] + bound_residual_electricity["Value"] / bound_residual_electricity["value"] ) # downselect dataframe columns for MESSAGEix parameters @@ -1384,7 +1388,7 @@ def add_elec_lowerbound_2020(scen): bound_residual_electricity["value"] = bound_residual_electricity["value"] * 0.8 bound_residual_electricity = bound_residual_electricity[ bound_residual_electricity["node_loc"] == "R12_CHN" - ] + ] scen.check_out() @@ -1466,7 +1470,7 @@ def add_coal_lowerbound_2020(sc): bound_coal["value"] = bound_coal["Value"] / bound_coal["value"] bound_cement_coal["value"] = bound_cement_coal["Value"] / bound_cement_coal["value"] bound_residual_electricity["value"] = ( - bound_residual_electricity["Value"] / bound_residual_electricity["value"] + bound_residual_electricity["Value"] / bound_residual_electricity["value"] ) # downselect dataframe columns for MESSAGEix parameters @@ -1657,7 +1661,7 @@ def add_cement_bounds_2020(sc): bound_cement_foil["value"] = bound_cement_foil["Value"] / bound_cement_foil["value"] bound_cement_gas["value"] = bound_cement_gas["Value"] / bound_cement_gas["value"] bound_cement_biomass["value"] = ( - bound_cement_biomass["Value"] / bound_cement_biomass["value"] + bound_cement_biomass["Value"] / bound_cement_biomass["value"] ) bound_cement_coal["value"] = bound_cement_coal["Value"] / bound_cement_coal["value"] @@ -1957,10 +1961,10 @@ def read_rel(scenario, material, filename): def gen_te_projections( - scen, - ssp: Literal["all", "LED", "SSP1", "SSP2", "SSP3", "SSP4", "SSP5"] = "SSP2", - method: Literal["constant", "convergence", "gdp"] = "convergence", - ref_reg: str = "R12_NAM", + scen, + ssp: Literal["all", "LED", "SSP1", "SSP2", "SSP3", "SSP4", "SSP5"] = "SSP2", + method: Literal["constant", "convergence", "gdp"] = "convergence", + ref_reg: str = "R12_NAM", ) -> tuple: model_tec_set = list(scen.set("technology")) cfg = Config( @@ -2007,7 +2011,7 @@ def get_ssp_soc_eco_data(context, model, measure, tec): def add_elec_i_ini_act(scenario): par = "initial_activity_up" - df_el = scenario.par(par, filters={"technology":"hp_el_i"}) + df_el = scenario.par(par, filters={"technology": "hp_el_i"}) df_el["technology"] = "elec_i" scenario.check_out() scenario.add_par(par, df_el) diff --git a/message_ix_models/model/material/material_demand/material_demand_calc.py b/message_ix_models/model/material/material_demand/material_demand_calc.py index e4a9331177..68be19fd6e 100644 --- a/message_ix_models/model/material/material_demand/material_demand_calc.py +++ b/message_ix_models/model/material/material_demand/material_demand_calc.py @@ -1,30 +1,24 @@ -import message_ix_models.util -import pandas as pd -import numpy as np import message_ix -from scipy.optimize import curve_fit +import numpy as np +import pandas as pd import yaml - from message_data.model.material.util import read_config -from message_ix_models import ScenarioInfo from message_ix import make_df +from scipy.optimize import curve_fit + +import message_ix_models.util +from message_ix_models import ScenarioInfo from message_ix_models.util import ( - broadcast, - make_io, - make_matched_dfs, - same_node, - add_par_data, private_data_path, ) - file_cement = "/CEMENT.BvR2010.xlsx" file_steel = "/STEEL_database_2012.xlsx" file_al = "/demand_aluminum.xlsx" file_gdp = "/iamc_db ENGAGE baseline GDP PPP.xlsx" -giga = 10**9 -mega = 10**6 +giga = 10 ** 9 +mega = 10 ** 6 material_data = { "aluminum": {"dir": "aluminum", "file": "/demand_aluminum.xlsx"}, @@ -139,9 +133,9 @@ def project_demand(df, phi, mu): df_demand = df.groupby("region", group_keys=False).apply( lambda group: group.assign( demand_pcap_base=group["demand.tot.base"].iloc[0] - * giga - / group["pop.mil"].iloc[0] - / mega + * giga + / group["pop.mil"].iloc[0] + / mega ) ) df_demand = df_demand.groupby("region", group_keys=False).apply( @@ -152,7 +146,7 @@ def project_demand(df, phi, mu): df_demand = df_demand.groupby("region", group_keys=False).apply( lambda group: group.assign( demand_pcap=group["demand_pcap0"] - + group["gap_base"] * gompertz(phi, mu, y=group["year"]) + + group["gap_base"] * gompertz(phi, mu, y=group["year"]) ) ) df_demand = ( @@ -231,7 +225,7 @@ def read_hist_mat_demand(material): df_raw_cons["region"] .str.replace("(", "") .str.replace(")", "") - .str.replace("\d+", "") + .str.replace(r"\d+", "") .str[:-1] ) @@ -264,7 +258,7 @@ def read_hist_mat_demand(material): pd.merge(df_raw_cons, df_pop.drop("region", axis=1), on=["reg_no", "year"]) .merge(df_gdp[["reg_no", "year", "gdp_pcap"]], on=["reg_no", "year"]) .assign( - cons_pcap=lambda x: x["consumption"] / x["pop"] / 10**6, + cons_pcap=lambda x: x["consumption"] / x["pop"] / 10 ** 6, del_t=lambda x: x["year"].astype(int) - 2010, ) .dropna() @@ -400,10 +394,10 @@ def gen_demand_petro(scenario, chemical, gdp_elasticity_2020, gdp_elasticity_203 nodes = s_info.N def get_demand_t1_with_income_elasticity( - demand_t0, income_t0, income_t1, elasticity + demand_t0, income_t0, income_t1, elasticity ): return ( - elasticity * demand_t0.mul(((income_t1 - income_t0) / income_t0), axis=0) + elasticity * demand_t0.mul(((income_t1 - income_t0) / income_t0), axis=0) ) + demand_t0 if "GDP_PPP" in list(scenario.set("technology")): diff --git a/message_ix_models/model/material/util.py b/message_ix_models/model/material/util.py index 7dc83e8b1f..abca1b0a68 100644 --- a/message_ix_models/model/material/util.py +++ b/message_ix_models/model/material/util.py @@ -120,7 +120,7 @@ def remove_from_list_if_exists(element, _list): def exponential(x, b, m): - return b * m**x + return b * m ** x def price_fit(df): @@ -166,9 +166,9 @@ def update_macro_calib_file(scenario, fname): df = df[df["year"].isin([2025, 2030, 2035])].groupby(["node"]).apply(cost_fit) ws = wb.get_sheet_by_name("cost_ref") for i in range(2, 7): - ws[f"B{i}"].value = df.values[i-2] + ws[f"B{i}"].value = df.values[i - 2] for i in range(8, 13): - ws[f"B{i}"].value = df.values[i-2] + ws[f"B{i}"].value = df.values[i - 2] # price_ref comms = ["i_feed", "i_spec", "i_therm", "rc_spec", "rc_therm", "transport"] @@ -198,5 +198,5 @@ def update_macro_calib_file(scenario, fname): df = df.groupby(["node", "commodity"]).apply(price_fit) ws = wb.get_sheet_by_name("price_ref") for i in range(2, 61): - ws[f"C{i}"].value = df.values[i-2] + ws[f"C{i}"].value = df.values[i - 2] wb.save(path + fname) From 86a29ca5524f2afcec61c44a5e34f26f6ff04e4d Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Fri, 10 May 2024 18:17:18 +0200 Subject: [PATCH 747/774] Add docstrings and type hints for materials util functions --- message_ix_models/model/material/data_util.py | 188 ++++++++++++++++-- message_ix_models/model/material/util.py | 165 +++++++++++++-- 2 files changed, 322 insertions(+), 31 deletions(-) diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index bca8d3bc04..bee75fb4ad 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -1,5 +1,6 @@ import os from typing import Literal +import numpy as np import ixmp import message_ix @@ -21,7 +22,14 @@ pd.options.mode.chained_assignment = None -def load_GDP_COVID(): +def load_GDP_COVID() -> pd.DataFrame: + """ + Load COVID adjuste GDP projection + + Returns + ------- + pd.DataFrame + """ context = read_config() # Obtain 2015 and 2020 GDP values from NGFS baseline. These values are COVID corrected. (GDP MER) @@ -76,14 +84,33 @@ def load_GDP_COVID(): return df_new -def add_macro_COVID(scen, filename, check_converge=False): +def add_macro_COVID(scen: message_ix.Scenario, + filename: str, + check_converge: bool = False) -> message_ix.Scenario: + """ + Prepare data for MACRO calibration by reading data from xlsx file + + Parameters + ---------- + scen: message_ix.Scenario + Scenario to be calibrated + filename: str + name of xlsx calibration data file + check_converge: bool + parameter passed to MACRO calibration function + Returns + ------- + message_ix.Scenario + MACRO-calibrated Scenario instance + """ context = read_config() info = ScenarioInfo(scen) nodes = info.N # Excel file for calibration data if "SSP_dev" in scen.model: - xls_file = os.path.join("C:/", "Users", "maczek", "Downloads", "macro", filename) + xls_file = os.path.join("C:/", "Users", "maczek", + "Downloads", "macro", filename) else: xls_file = os.path.join("P:", "ene.model", "MACRO", "python", filename) # Making a dictionary from the MACRO Excel file @@ -111,7 +138,7 @@ def add_macro_COVID(scen, filename, check_converge=False): return scen -def modify_demand_and_hist_activity(scen): +def modify_demand_and_hist_activity(scen: message_ix.Scenario) -> None: """Take care of demand changes due to the introduction of material parents Shed industrial energy demand properly. Also need take care of remove dynamic constraints for certain energy carriers. @@ -119,6 +146,11 @@ def modify_demand_and_hist_activity(scen): that provide output to different categories of industrial demand (e.g. i_therm, i_spec, i_feed). The historical activity is reduced the same % as the industrial demand is reduced. + + Parameters + ---------- + scen: message_ix.Scenario + scenario where industry demand should be reduced """ # NOTE Temporarily modifying industrial energy demand @@ -431,8 +463,25 @@ def modify_demand_and_hist_activity(scen): scen.commit(comment="remove bounds") -def modify_demand_and_hist_activity_debug(scen: message_ix.Scenario) -> dict: - """modularized "dry-run" version of modify_demand_and_hist_activity() for debugging purposes""" +def modify_demand_and_hist_activity_debug(scen: message_ix.Scenario) -> dict[str, pd.DataFrame]: + """modularized "dry-run" version of modify_demand_and_hist_activity() for + debugging purposes + + Parameters + ---------- + scen: message_ix.Scenario + scenario to used to get i_therm and i_spec parametrization + Returns + --------- + dict[str, pd.DataFrame] + three keys named like MESSAGEix-GLOBIOM codes: + - i_therm + - i_spec + - historical_activity + values are DataFrames representing the reduced residual + industry demands when adding MESSAGEix-Materials to a + MESSAGEix-GLOBIOM scenario + """ context = read_config() s_info = ScenarioInfo(scen) @@ -1785,7 +1834,22 @@ def add_cement_bounds_2020(sc): sc.commit("added lower and upper bound for fuels for cement 2020.") -def read_sector_data(scenario, sectname): +def read_sector_data(scenario: message_ix.Scenario, sectname: str) -> pd.DataFrame: + """ + Read sector data for industry with sectname + + Parameters + ---------- + scenario: message_ix.Scenario + + sectname: sectname + name of industry sector + + Returns + ------- + pd.DataFrame + + """ # Read in technology-specific parameters from input xlsx # Now used for steel and cement, which are in one file @@ -1848,9 +1912,15 @@ def read_sector_data(scenario, sectname): return data_df -# Add the relevant ccs technologies to the co2_trans_disp and bco2_trans_disp -# relations -def add_ccs_technologies(scen): +def add_ccs_technologies(scen: message_ix.Scenario) -> None: + """Adds the relevant CCS technologies to the co2_trans_disp and bco2_trans_disp + relations + + Parameters + ---------- + scen: message_ix.Scenario + Scenario instance to add CCS emission factor parametrization to + """ context = read_config() s_info = ScenarioInfo(scen) @@ -1894,9 +1964,26 @@ def add_ccs_technologies(scen): # Read in time-dependent parameters # Now only used to add fuel cost for bare model -def read_timeseries(scenario, material, filename): - import numpy as np +def read_timeseries(scenario: message_ix.Scenario, material: str, filename: str) -> pd.DataFrame: + """ + Read "timeseries" type data from a sector specific xlsx input file + to DataFrame and format according to MESSAGEix standard + Parameters + ---------- + scenario: message_ix.Scenario + scenario used to get structural information like + model regions and years + material: str + name of material folder where xlsx is located + filename: + name of xlsx file + + Returns + ------- + pd.DataFrame + DataFrame containing the timeseries data for MESSAGEix parameters + """ # Ensure config is loaded, get the context context = read_config() s_info = ScenarioInfo(scenario) @@ -1940,7 +2027,24 @@ def read_timeseries(scenario, material, filename): return df -def read_rel(scenario, material, filename): +def read_rel(scenario: message_ix.Scenario, material: str, filename: str): + """ + Read relation_* type parameter data for specific industry + + Parameters + ---------- + scenario: + scenario used to get structural information like + material: str + name of material folder where xlsx is located + filename: + name of xlsx file + + Returns + ------- + pd.DataFrame + DataFrame containing relation_* parameter data + """ # Ensure config is loaded, get the context context = read_config() @@ -1961,11 +2065,32 @@ def read_rel(scenario, material, filename): def gen_te_projections( - scen, + scen: message_ix.Scenario, ssp: Literal["all", "LED", "SSP1", "SSP2", "SSP3", "SSP4", "SSP5"] = "SSP2", method: Literal["constant", "convergence", "gdp"] = "convergence", ref_reg: str = "R12_NAM", -) -> tuple: +) -> tuple[pd.DataFrame, pd.DataFrame]: + """ + Calls message_ix_models.tools.costs with config for MESSAGEix-Materials + and return inv_cost and fix_cost projections for energy and materials + technologies + + Parameters + ---------- + scen: message_ix.Scenario + Scenario instance is required to get technology set + ssp: str + SSP to use for projection assumptions + method: str + method to use for cost convergence over time + ref_reg: str + reference region to use for regional cost differentiation + + Returns + ------- + tuple[pd.DataFrame, pd.DataFrame] + tuple with "inv_cost" and "fix_cost" DataFrames + """ model_tec_set = list(scen.set("technology")) cfg = Config( module="materials", @@ -1986,7 +2111,27 @@ def gen_te_projections( return inv_cost, fix_cost -def get_ssp_soc_eco_data(context, model, measure, tec): +def get_ssp_soc_eco_data(context: Context, model: str, measure: str, tec): + """ + Function to update scenario GDP and POP timeseries to SSP 3.0 + and format to MESSAGEix "bound_activity_*" DataFrame + + Parameters + ---------- + context: Context + context used to prepare genno.Computer + model: + model name of projections to read + measure: + Indicator to read (GDP or Population) + tec: + name to use for "technology" column + Returns + ------- + pd.DataFrame + DataFrame with SSP indicator data in "bound_activity_*" parameter + format + """ from message_ix_models.project.ssp.data import SSPUpdate # noqa: F401 c = Computer() @@ -2009,7 +2154,16 @@ def get_ssp_soc_eco_data(context, model, measure, tec): return df -def add_elec_i_ini_act(scenario): +def add_elec_i_ini_act(scenario: message_ix.Scenario) -> None: + """ + Adds initial_activity_up parameter for "elec_i" technology by copying + value from "hp_el_i" technology + + Parameters + ---------- + scenario: message_ix.Scenario + Scenario where "elec_i" should be updated + """ par = "initial_activity_up" df_el = scenario.par(par, filters={"technology": "hp_el_i"}) df_el["technology"] = "elec_i" diff --git a/message_ix_models/model/material/util.py b/message_ix_models/model/material/util.py index abca1b0a68..733b193333 100644 --- a/message_ix_models/model/material/util.py +++ b/message_ix_models/model/material/util.py @@ -1,5 +1,6 @@ import os +import message_ix import openpyxl as pxl import pandas as pd import yaml @@ -16,8 +17,14 @@ ] -def read_config(): - """Read configuration from set.yaml.""" +def read_config() -> Context: + """Read configuration from set.yaml. + + Returns + ------- + message_ix_models.Context + Context object holding information about MESSAGEix-Materials structure + """ # TODO this is similar to transport.utils.read_config; make a common # function so it doesn't need to be in this file. context = Context.get_instance(-1) @@ -52,7 +59,17 @@ def read_config(): return context -def prepare_xlsx_for_explorer(filepath): +def prepare_xlsx_for_explorer(filepath: str) -> None: + """ + Post-processing helper to make reporting files compliant for + upload to IIASA Scenario Explorer + + Parameters + ---------- + filepath : str + Path to xlsx files generated with message_ix_models.report.legacy + + """ df = pd.read_excel(filepath) def add_R12(str): @@ -66,7 +83,21 @@ def add_R12(str): df.to_excel(filepath, index=False) -def combine_df_dictionaries(*args): +def combine_df_dictionaries(*args: dict[str, pd.DataFrame]) -> dict: + """ + Iterates through dictionary items and collects all values with same keys + from dictionaries in one dict + Parameters + ---------- + args: dict[str, pd.DataFrame] + arbitrary number of dictionaries with str keys and pd.DataFrame values + + Returns + ------- + dict[str, pd.DataFrame] + dictionary containing all unique elements of + pd.DataFrames provided by *args dict + """ keys = set([key for tup in args for key in tup]) comb_dict = {} for i in keys: @@ -74,7 +105,19 @@ def combine_df_dictionaries(*args): return comb_dict -def read_yaml_file(file_path): +def read_yaml_file(file_path: str) -> dict: + """ + Tries to read yaml file into a dict + + Parameters + ---------- + file_path : str + file path to yaml file + + Returns + ------- + dict + """ with open(file_path, encoding="utf8") as file: try: data = yaml.safe_load(file) @@ -84,7 +127,21 @@ def read_yaml_file(file_path): return None -def invert_dictionary(original_dict): +def invert_dictionary(original_dict: dict[str, list]) -> dict: + """ + Create inverted dictionary from existing dictionary, where values turn + into keys and vice versa + + Parameters + ---------- + original_dict: dict + dictionary with values of list type + + Returns + ------- + dict + + """ inverted_dict = {} for key, value in original_dict.items(): for array_element in value: @@ -94,7 +151,19 @@ def invert_dictionary(original_dict): return inverted_dict -def excel_to_csv(material_dir, fname): +def excel_to_csv(material_dir: str, fname: str) -> None: + """ + Helper to create trackable copies xlsx files used for MESSAGEix-Materials + data input by printing each sheet to a csv file. Output is saved in + "data/materials/version control" + + Parameters + ---------- + material_dir : str + path to industry sector data folder + fname : str + file name of xlsx file + """ xlsx_dict = pd.read_excel( private_data_path("material", material_dir, fname), sheet_name=None ) @@ -108,30 +177,88 @@ def excel_to_csv(material_dir, fname): ) -def get_all_input_data_dirs(): +def get_all_input_data_dirs() -> list[str]: + """ + Iteratable for getting all material input data folders + + Returns + ------- + list of folder names of material data + """ elements = os.listdir(private_data_path("material")) elements = [i for i in elements if os.path.isdir(private_data_path("material", i))] return elements -def remove_from_list_if_exists(element, _list): +def remove_from_list_if_exists(element, _list: list) -> None: + """ + Utility function removing element from list if it is part of the list + Parameters + ---------- + element + element to remove + _list + list that poetntially contains element + """ if element in _list: _list.remove(element) -def exponential(x, b, m): +def exponential(x: float or list[float], b: float, m: float) -> float: + """ + Mathematical function used in Excels GROWTH function + + Parameters + ---------- + x: float or list + domain of function + b : float + function parameter b + m: float + function parameter m + Returns + ------- + float + function value for given b, m and x + """ return b * m ** x -def price_fit(df): - # print(df.lvl) +def price_fit(df: pd.DataFrame) -> float: + """ + Python implementation of price_ref parameter estimation implemented in + MESSAGEix-MACRO calibration files. + + Parameters + ---------- + df: pd.DataFrame + DataFrame with required columns: "year" and "lvl" + Returns + ------- + float + estimated value for price_ref in 2020 + """ + pars, cov = curve_fit(exponential, df.year, df.lvl, maxfev=5000) val = exponential([2020], *pars)[0] # print(df.commodity.unique(), df.node.unique(), val) return val -def cost_fit(df): +def cost_fit(df) -> float: + """ + Python implementation of cost_ref parameter estimation implemented in + MESSAGEix-MACRO calibration files. + + Parameters + ---------- + df: pd.DataFrame + DataFrame with required columns: "year" and "lvl" + Returns + ------- + float + estimated value for cost_ref in 2020 + """ # print(df.lvl) pars, cov = curve_fit(exponential, df.year, df.lvl, maxfev=5000) val = exponential([2020], *pars)[0] @@ -139,7 +266,17 @@ def cost_fit(df): return val / 1000 -def update_macro_calib_file(scenario, fname): +def update_macro_calib_file(scenario: message_ix.Scenario, fname: str) -> None: + """ + Function to automate manual steps in MACRO calibration + + Parameters + ---------- + scenario: message_ix.Scenario + Scenario instance to be calibrated + fname : str + file name of MACRO file used for calibration + """ path = "C:/Users/maczek/Downloads/macro/refactored/" wb = pxl.load_workbook(path + fname) From 47db9104f964d6bca8dd307ace4f6a0f8f3f426b Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Fri, 10 May 2024 11:11:47 +0200 Subject: [PATCH 748/774] Change data input directory to message-ix-models --- message_ix_models/model/material/__init__.py | 10 ++-- message_ix_models/model/material/data_util.py | 19 ++++--- .../material_demand/material_demand_calc.py | 50 +++++++++++-------- 3 files changed, 44 insertions(+), 35 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 2461472174..9dca983b7c 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -36,15 +36,15 @@ get_ssp_soc_eco_data, modify_baseyear_bounds, modify_demand_and_hist_activity, - modify_industry_demand, - read_config, + modify_industry_demand ) from message_ix_models.model.material.util import ( excel_to_csv, get_all_input_data_dirs, update_macro_calib_file, + read_config ) -from message_ix_models.tools import ( +from message_data.tools.utilities import ( calibrate_UE_gr_to_demand, calibrate_UE_share_constraints, ) @@ -125,10 +125,10 @@ def build(scenario: message_ix.Scenario, old_calib: bool) -> message_ix.Scenario # NOTE: changing demand affects the market penetration # levels for the enduse technologies. # Note: context.ssp doesnt work - calibrate_UE_gr_to_demand.main( + calibrate_UE_gr_to_demand( scenario, data_path=package_data_path(), ssp="SSP2", region="R12" ) - calibrate_UE_share_constraints.main(scenario) + calibrate_UE_share_constraints(scenario) # Electricity calibration to avoid zero prices for CHN. if "R12_CHN" in nodes: diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index bee75fb4ad..07145be0e6 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -888,9 +888,10 @@ def calc_demand_shares(iea_db_df: pd.DataFrame, base_year: int) -> pd.DataFrame: def calc_resid_ind_demand(scen: message_ix.Scenario, baseyear: int) -> pd.DataFrame: comms = ["i_spec", "i_therm"] - path = os.path.join( - "P:", "ene.model", "IEA_database", "Florian", "REV2022_allISO_IEA.parquet" - ) + path = package_data_path("iea", "REV2022_allISO_IEA.parquet") + #path = os.path.join( + # "P:", "ene.model", "IEA_database", "Florian", "REV2022_allISO_IEA.parquet" + #) Inp = pd.read_parquet(path, engine="fastparquet") Inp = map_iea_db_to_msg_regs(Inp, "R12_SSP_V1.yaml") demand_shrs_new = calc_demand_shares(Inp, baseyear) @@ -1000,9 +1001,10 @@ def get_hist_act_data(map_fname: str, years: list or None = None) -> pd.DataFram pd.DataFrame """ - path = os.path.join( - "P:", "ene.model", "IEA_database", "Florian", "REV2022_allISO_IEA.parquet" - ) + #path = os.path.join( + # "P:", "ene.model", "IEA_database", "Florian", "REV2022_allISO_IEA.parquet" + #) + path = package_data_path("iea", "REV2022_allISO_IEA.parquet") Inp = pd.read_parquet(path, engine="fastparquet") if years: Inp = Inp[Inp["TIME"].isin(years)] @@ -1043,8 +1045,8 @@ def add_emission_accounting(scen): tec_list_input = [ i for i in tec_list_input if (("furnace" in i) | ("hp_gas_" in i)) ] - tec_list_input.remove("hp_gas_i") - tec_list_input.remove("hp_gas_rc") + #tec_list_input.remove("hp_gas_i") + #tec_list_input.remove("hp_gas_rc") # The technology list to retreive the emission_factors tec_list_residual = [ @@ -2098,6 +2100,7 @@ def gen_te_projections( method=method, format="message", scenario=ssp, + final_year=2110 ) out_materials = create_cost_projections(cfg) fix_cost = out_materials["fix_cost"].drop_duplicates().drop( diff --git a/message_ix_models/model/material/material_demand/material_demand_calc.py b/message_ix_models/model/material/material_demand/material_demand_calc.py index 68be19fd6e..e72b82bca4 100644 --- a/message_ix_models/model/material/material_demand/material_demand_calc.py +++ b/message_ix_models/model/material/material_demand/material_demand_calc.py @@ -1,24 +1,30 @@ -import message_ix -import numpy as np +import message_ix_models.util import pandas as pd -import yaml -from message_data.model.material.util import read_config -from message_ix import make_df +import numpy as np +import message_ix from scipy.optimize import curve_fit +import yaml -import message_ix_models.util +from message_data.model.material.util import read_config from message_ix_models import ScenarioInfo +from message_ix import make_df from message_ix_models.util import ( - private_data_path, + broadcast, + make_io, + make_matched_dfs, + same_node, + add_par_data, + package_data_path , ) + file_cement = "/CEMENT.BvR2010.xlsx" file_steel = "/STEEL_database_2012.xlsx" file_al = "/demand_aluminum.xlsx" file_gdp = "/iamc_db ENGAGE baseline GDP PPP.xlsx" -giga = 10 ** 9 -mega = 10 ** 6 +giga = 10**9 +mega = 10**6 material_data = { "aluminum": {"dir": "aluminum", "file": "/demand_aluminum.xlsx"}, @@ -133,9 +139,9 @@ def project_demand(df, phi, mu): df_demand = df.groupby("region", group_keys=False).apply( lambda group: group.assign( demand_pcap_base=group["demand.tot.base"].iloc[0] - * giga - / group["pop.mil"].iloc[0] - / mega + * giga + / group["pop.mil"].iloc[0] + / mega ) ) df_demand = df_demand.groupby("region", group_keys=False).apply( @@ -146,7 +152,7 @@ def project_demand(df, phi, mu): df_demand = df_demand.groupby("region", group_keys=False).apply( lambda group: group.assign( demand_pcap=group["demand_pcap0"] - + group["gap_base"] * gompertz(phi, mu, y=group["year"]) + + group["gap_base"] * gompertz(phi, mu, y=group["year"]) ) ) df_demand = ( @@ -179,7 +185,7 @@ def read_base_demand(filepath): def read_hist_mat_demand(material): - datapath = message_ix_models.util.private_data_path("material") + datapath = message_ix_models.util.package_data_path ("material") if material in ["cement", "steel"]: # Read population data @@ -225,7 +231,7 @@ def read_hist_mat_demand(material): df_raw_cons["region"] .str.replace("(", "") .str.replace(")", "") - .str.replace(r"\d+", "") + .str.replace("\d+", "") .str[:-1] ) @@ -258,7 +264,7 @@ def read_hist_mat_demand(material): pd.merge(df_raw_cons, df_pop.drop("region", axis=1), on=["reg_no", "year"]) .merge(df_gdp[["reg_no", "year", "gdp_pcap"]], on=["reg_no", "year"]) .assign( - cons_pcap=lambda x: x["consumption"] / x["pop"] / 10 ** 6, + cons_pcap=lambda x: x["consumption"] / x["pop"] / 10**6, del_t=lambda x: x["year"].astype(int) - 2010, ) .dropna() @@ -292,7 +298,7 @@ def read_gdp_ppp_from_scen(scen): mer_to_ppp = scen.par("MERtoPPP") if not len(mer_to_ppp): mer_to_ppp = pd.read_csv( - message_ix_models.util.private_data_path( + message_ix_models.util.package_data_path ( "material", "other", "mer_to_ppp_default.csv" ) ) @@ -309,7 +315,7 @@ def read_gdp_ppp_from_scen(scen): def derive_demand(material, scen, old_gdp=False, ssp="SSP2"): - datapath = message_ix_models.util.private_data_path("material") + datapath = message_ix_models.util.package_data_path ("material") # read pop projection from scenario df_pop = read_pop_from_scen(scen) @@ -394,10 +400,10 @@ def gen_demand_petro(scenario, chemical, gdp_elasticity_2020, gdp_elasticity_203 nodes = s_info.N def get_demand_t1_with_income_elasticity( - demand_t0, income_t0, income_t1, elasticity + demand_t0, income_t0, income_t1, elasticity ): return ( - elasticity * demand_t0.mul(((income_t1 - income_t0) / income_t0), axis=0) + elasticity * demand_t0.mul(((income_t1 - income_t0) / income_t0), axis=0) ) + demand_t0 if "GDP_PPP" in list(scenario.set("technology")): @@ -415,7 +421,7 @@ def get_demand_t1_with_income_elasticity( else: gdp_mer = scenario.par("bound_activity_up", {"technology": "GDP"}) mer_to_ppp = pd.read_csv( - private_data_path("material", "other", "mer_to_ppp_default.csv") + package_data_path ("material", "other", "mer_to_ppp_default.csv") ).set_index(["node", "year"]) # mer_to_ppp = scenario.par("MERtoPPP").set_index("node", "year") TODO: might need to be re-activated for different SSPs gdp_mer = gdp_mer.merge( @@ -438,7 +444,7 @@ def get_demand_t1_with_income_elasticity( ) df_demand_2020 = read_base_demand( - private_data_path() + package_data_path() / "material" / f"{material_data[chemical]['dir']}/demand_{chemical}.yaml" ) From 3cd63f510b51f598dd86d9b1a2b5e32002213a2d Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Mon, 13 May 2024 11:28:17 +0200 Subject: [PATCH 749/774] Resolve code quality warnings --- message_ix_models/model/material/__init__.py | 24 +- message_ix_models/model/material/bare.py | 81 +++--- message_ix_models/model/material/build.py | 9 +- .../model/material/data_aluminum.py | 79 +++--- .../model/material/data_ammonia_new.py | 15 +- .../model/material/data_buildings.py | 24 +- .../model/material/data_cement.py | 5 +- .../model/material/data_generic.py | 14 +- .../model/material/data_methanol.py | 13 +- .../model/material/data_methanol_new.py | 1 - .../model/material/data_petro.py | 50 ++-- .../model/material/data_steel.py | 32 ++- message_ix_models/model/material/data_util.py | 268 ++++++++++-------- .../model/material/report/reporting.py | 2 +- message_ix_models/model/material/util.py | 22 +- 15 files changed, 314 insertions(+), 325 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 9dca983b7c..c68d615dcf 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -7,8 +7,10 @@ import message_ix import ntfy import pandas as pd - -# update_h2_blending, +from message_data.tools.utilities import ( + calibrate_UE_gr_to_demand, + calibrate_UE_share_constraints, +) from message_data.tools.utilities import ( manual_updates_ENGAGE_SSP2_v417_to_v418 as engage_updates, ) @@ -36,17 +38,13 @@ get_ssp_soc_eco_data, modify_baseyear_bounds, modify_demand_and_hist_activity, - modify_industry_demand + modify_industry_demand, ) from message_ix_models.model.material.util import ( excel_to_csv, get_all_input_data_dirs, + read_config, update_macro_calib_file, - read_config -) -from message_data.tools.utilities import ( - calibrate_UE_gr_to_demand, - calibrate_UE_share_constraints, ) from message_ix_models.util import add_par_data, package_data_path from message_ix_models.util.click import common_params @@ -96,7 +94,6 @@ def build(scenario: message_ix.Scenario, old_calib: bool) -> message_ix.Scenario engage_updates._correct_balance_td_efficiencies(scenario) engage_updates._correct_coal_ppl_u_efficiencies(scenario) engage_updates._correct_td_co2cc_emissions(scenario) - update_h2_blending.main(scenario) spec = None apply_spec(scenario, spec, add_data_2) @@ -340,7 +337,6 @@ def build_scen(context, datafile, tag, mode, scenario_name, old_calib, update_co elif mode == "cbudget": scenario = context.get_scenario() print(scenario.version) - # print('Base scenario is: ' + scenario.scenario + ", version: " + scenario.version) output_scenario_name = scenario.scenario + "_" + tag scenario_new = scenario.clone( "MESSAGEix-Materials", @@ -388,7 +384,7 @@ def build_scen(context, datafile, tag, mode, scenario_name, old_calib, update_co @click.option("--add_calibration", default=False) @click.pass_obj def solve_scen( - context, datafile, model_name, scenario_name, add_calibration, add_macro, version + context, datafile, model_name, scenario_name, add_calibration, add_macro, version ): """Solve a scenario. @@ -435,8 +431,6 @@ def solve_scen( reporting( mp, scenario, - # NB(PNK) this is not an error; .iamc_report_hackathon.report() expects a - # string containing "True" or "False" instead of an actual bool. "False", scenario.model, scenario.scenario, @@ -550,8 +544,6 @@ def run_reporting(context, remove_ts, profile): reporting( mp, scenario, - # NB(PNK) this is not an error; .iamc_report_hackathon.report() expects a - # string containing "True" or "False" instead of an actual bool. "False", scenario.model, scenario.scenario, @@ -578,8 +570,6 @@ def exit(): reporting( mp, scenario, - # NB(PNK) this is not an error; .iamc_report_hackathon.report() expects a - # string containing "True" or "False" instead of an actual bool. "False", scenario.model, scenario.scenario, diff --git a/message_ix_models/model/material/bare.py b/message_ix_models/model/material/bare.py index ba5527f99f..c4891e9a78 100644 --- a/message_ix_models/model/material/bare.py +++ b/message_ix_models/model/material/bare.py @@ -50,18 +50,19 @@ def create_res(context=None, quiet=True): :func:`.build.apply_spec`. """ mp = context.get_platform() - mp.add_unit('Mt') + mp.add_unit("Mt") # Model and scenario name for the RES - model_name = context.scenario_info['model'] - scenario_name = context.scenario_info['scenario'] + model_name = context.scenario_info["model"] + scenario_name = context.scenario_info["scenario"] # Create the Scenario - scenario = message_ix.Scenario(mp, model=model_name, - scenario=scenario_name, version='new') + scenario = message_ix.Scenario( + mp, model=model_name, scenario=scenario_name, version="new" + ) # TODO move to message_ix - scenario.init_par('MERtoPPP', ['node', 'year']) + scenario.init_par("MERtoPPP", ["node", "year"]) # Uncomment to add dummy sets and data context.res_with_dummies = True @@ -102,10 +103,10 @@ def add_data(scenario, dry_run=False): for func in DATA_FUNCTIONS: # Generate or load the data; add to the Scenario - log.info(f'from {func.__name__}()') + log.info(f"from {func.__name__}()") add_par_data(scenario, func(scenario), dry_run=dry_run) - log.info('done') + log.info("done") def get_spec(context=None) -> Mapping[str, ScenarioInfo]: @@ -137,10 +138,12 @@ def get_spec(context=None) -> Mapping[str, ScenarioInfo]: # Add technologies # JM: try to find out a way to loop over 1st/2nd level and to just context["material"][xx]["add"] - add.set["technology"] = context["material"]["steel"]["technology"]["add"] + \ - context["material"]["generic"]["technology"]["add"] + \ - context["material"]["aluminum"]["technology"]["add"] + \ - context["material"]["petro_chemicals"]["technology"]["add"] + add.set["technology"] = ( + context["material"]["steel"]["technology"]["add"] + + context["material"]["generic"]["technology"]["add"] + + context["material"]["aluminum"]["technology"]["add"] + + context["material"]["petro_chemicals"]["technology"]["add"] + ) # Add regions @@ -156,40 +159,48 @@ def get_spec(context=None) -> Mapping[str, ScenarioInfo]: add.set["relation"] = context["material"]["steel"]["relation"]["add"] # Add the time horizon - add.set['year'] = list(range( - context.period_start, context.period_end + 1, context.time_step - )) + add.set["year"] = list( + range(context.period_start, context.period_end + 1, context.time_step) + ) # JM: Leave the first time period as historical year # add.set['cat_year'] = [('firstmodelyear', context.period_start + context.time_step)] # GU: Set 2020 as the first model year, leave the rest as historical year - add.set['cat_year'] = [('firstmodelyear', context.first_model_year)] + add.set["cat_year"] = [("firstmodelyear", context.first_model_year)] # Add levels # JM: For bare model, both 'add' & 'require' need to be added. - add.set['level'] = context["material"]["steel"]["level"]["add"] + \ - context["material"]["common"]["level"]["require"] + \ - context["material"]["generic"]["level"]["add"] + \ - context["material"]["aluminum"]["level"]["add"] + \ - context["material"]["petro_chemicals"]["level"]["add"] + add.set["level"] = ( + context["material"]["steel"]["level"]["add"] + + context["material"]["common"]["level"]["require"] + + context["material"]["generic"]["level"]["add"] + + context["material"]["aluminum"]["level"]["add"] + + context["material"]["petro_chemicals"]["level"]["add"] + ) # Add commodities - add.set['commodity'] = context["material"]["steel"]["commodity"]["add"] + \ - context["material"]["common"]["commodity"]["require"] + \ - context["material"]["generic"]["commodity"]["add"] + \ - context["material"]["aluminum"]["commodity"]["add"] + \ - context["material"]["petro_chemicals"]["commodity"]["add"] + add.set["commodity"] = ( + context["material"]["steel"]["commodity"]["add"] + + context["material"]["common"]["commodity"]["require"] + + context["material"]["generic"]["commodity"]["add"] + + context["material"]["aluminum"]["commodity"]["add"] + + context["material"]["petro_chemicals"]["commodity"]["add"] + ) # Add other sets - add.set['type_tec'] = context["material"]["common"]["type_tec"]["add"] - add.set['mode'] = context["material"]["common"]["mode"]["require"] + \ - context["material"]["generic"]["mode"]["add"] + \ - context["material"]["petro_chemicals"]["mode"]["add"] + add.set["type_tec"] = context["material"]["common"]["type_tec"]["add"] + add.set["mode"] = ( + context["material"]["common"]["mode"]["require"] + + context["material"]["generic"]["mode"]["add"] + + context["material"]["petro_chemicals"]["mode"]["add"] + ) - add.set['emission'] = context["material"]["common"]["emission"]["require"] + \ - context["material"]["common"]["emission"]["add"] + add.set["emission"] = ( + context["material"]["common"]["emission"]["require"] + + context["material"]["common"]["emission"]["add"] + ) # Add units, associated with commodities # JM: What is 'anno' @@ -210,7 +221,7 @@ def get_spec(context=None) -> Mapping[str, ScenarioInfo]: # Deduplicate by converting to a set and then back; not strictly necessary, # but reduces duplicate log entries - add.set['unit'] = sorted(set(add.set['unit'])) + add.set["unit"] = sorted(set(add.set["unit"])) # JM: Manually set the first model year add.y0 = context.first_model_year @@ -221,6 +232,6 @@ def get_spec(context=None) -> Mapping[str, ScenarioInfo]: # Add a dummy commodity add.set["commodity"].append(Code("dummy")) - spec['add'] = add - spec['remove'] = remove + spec["add"] = add + spec["remove"] = remove return spec diff --git a/message_ix_models/model/material/build.py b/message_ix_models/model/material/build.py index 82ee6db1a5..6e77c0cd0d 100644 --- a/message_ix_models/model/material/build.py +++ b/message_ix_models/model/material/build.py @@ -25,10 +25,10 @@ def ellipsize(elements: List) -> str: def apply_spec( - scenario: Scenario, - spec: Union[Spec, Mapping[str, ScenarioInfo]] = None, - data: Callable = None, - **options, + scenario: Scenario, + spec: Union[Spec, Mapping[str, ScenarioInfo]] = None, + data: Callable = None, + **options, ): """Apply `spec` to `scenario`. @@ -74,7 +74,6 @@ def apply_spec( maybe_check_out(scenario) if spec: - dump: Dict[str, pd.DataFrame] = {} # Removed data for set_name in scenario.set_list(): diff --git a/message_ix_models/model/material/data_aluminum.py b/message_ix_models/model/material/data_aluminum.py index 4935d1bd33..61fc5a4129 100644 --- a/message_ix_models/model/material/data_aluminum.py +++ b/message_ix_models/model/material/data_aluminum.py @@ -1,5 +1,6 @@ from collections import defaultdict +import message_ix import pandas as pd from message_ix import make_df @@ -17,8 +18,20 @@ # Get endogenous material demand from buildings interface -def read_data_aluminum(scenario): - """Read and clean data from :file:`aluminum_techno_economic.xlsx`.""" +def read_data_aluminum(scenario: message_ix.Scenario) \ + -> (pd.DataFrame, pd.DataFrame, pd.DataFrame): + """Read and clean data from :file:`aluminum_techno_economic.xlsx`. + + Parameters + ---------- + scenario: message_ix.Scenario + Scenario instance to build aluminum on + Returns + ------- + tuple of three pd.DataFrames + returns aluminum data in three separate groups + time indepenendent parameters, relation parameters and time dependent parameters + """ # Ensure config is loaded, get the context context = read_config() @@ -58,13 +71,27 @@ def read_data_aluminum(scenario): return data_alu, data_alu_rel, data_aluminum_ts -def print_full(x): +def print_full(x: int): pd.set_option("display.max_rows", len(x)) print(x) pd.reset_option("display.max_rows") -def gen_data_aluminum(scenario, dry_run=False): +def gen_data_aluminum(scenario: message_ix.Scenario, dry_run: bool = False) -> dict[pd.DataFrame]: + """ + + Parameters + ---------- + scenario: message_ix.Scenario + Scenario instance to build aluminum model on + dry_run: bool + *not implemented* + Returns + ------- + dict[pd.DataFrame] + dict with MESSAGEix parameters as keys and parametrization as values + stored in pd.DataFrame + """ context = read_config() config = context["material"]["aluminum"] @@ -441,7 +468,7 @@ def gen_data_aluminum(scenario, dry_run=False): return results_aluminum -def gen_mock_demand_aluminum(scenario): +def gen_mock_demand_aluminum(scenario: message_ix.Scenario) -> pd.DataFrame: context = read_config() s_info = ScenarioInfo(scenario) modelyears = s_info.Y # s_info.Y is only for modeling years @@ -514,45 +541,3 @@ def gen_mock_demand_aluminum(scenario): ) return demand2020_al - -# # load rpy2 modules -# import rpy2.robjects as ro -# from rpy2.robjects import pandas2ri -# from rpy2.robjects.conversion import localconverter -# -# # This returns a df with columns ["region", "year", "demand.tot"] -# def derive_aluminum_demand(scenario, dry_run=False): -# """Generate aluminum demand.""" -# # paths to r code and lca data -# rcode_path = Path(__file__).parents[0] / "material_demand" -# context = read_config() -# -# # source R code -# r = ro.r -# r.source(str(rcode_path / "init_modularized.R")) -# -# # Read population and baseline demand for materials -# pop = scenario.par("bound_activity_up", {"technology": "Population"}) -# pop = pop.loc[pop.year_act >= 2020].rename( -# columns={"year_act": "year", "value": "pop.mil", "node_loc": "region"} -# ) -# -# # import pdb; pdb.set_trace() -# -# pop = pop[["region", "year", "pop.mil"]] -# -# base_demand = gen_mock_demand_aluminum(scenario) -# base_demand = base_demand.loc[base_demand.year == 2020].rename( -# columns={"value": "demand.tot.base", "node": "region"} -# ) -# -# # call R function with type conversion -# with localconverter(ro.default_converter + pandas2ri.converter): -# # GDP is only in MER in scenario. -# # To get PPP GDP, it is read externally from the R side -# df = r.derive_aluminum_demand( -# pop, base_demand, str(package_data_path("material")) -# ) -# df.year = df.year.astype(int) -# -# return df diff --git a/message_ix_models/model/material/data_ammonia_new.py b/message_ix_models/model/material/data_ammonia_new.py index 2e93718666..861cfe3205 100644 --- a/message_ix_models/model/material/data_ammonia_new.py +++ b/message_ix_models/model/material/data_ammonia_new.py @@ -326,9 +326,9 @@ def set_exp_imp_nodes(df): def read_demand(): - """Read and clean data from :file:`CD-Links SSP2 N-fertilizer demand.Global.xlsx`.""" + """Read and clean data from + :file:`CD-Links SSP2 N-fertilizer demand.Global.xlsx`.""" # Demand scenario [Mt N/year] from GLOBIOM - context = read_config() N_demand_GLO = pd.read_excel( package_data_path( @@ -363,7 +363,6 @@ def read_demand(): input_fuel = te_params[2010][ list(range(4, te_params.shape[0], n_inputs_per_tech)) ].reset_index(drop=True) - # input_fuel[0:5] = input_fuel[0:5] * CONVERSION_FACTOR_PJ_GWa # 0.0317 GWa/PJ, GJ/t = PJ/Mt NH3 capacity_factor = te_params[2010][ list(range(11, te_params.shape[0], n_inputs_per_tech)) @@ -473,12 +472,16 @@ def read_demand(): fs_GLO = feedshare_GLO.copy() fs_GLO.insert(1, "bio_pct", 0) fs_GLO.insert(2, "elec_pct", 0) - # 17/14 NH3:N ratio, to get NH3 activity based on N demand => No NH3 loss assumed during production - # FIXME: Name: elec_pct, dtype: float64 ' has dtype incompatible with int64, please explicitly cast to a compatible dtype first. + # 17/14 NH3:N ratio, to get NH3 activity based on N demand + # => No NH3 loss assumed during production + + # FIXME: Name: elec_pct, dtype: float64 ' has dtype incompatible with int64, + # please explicitly cast to a compatible dtype first. fs_GLO.iloc[:, 1:6] = input_fuel[5] * fs_GLO.iloc[:, 1:6] fs_GLO.insert(6, "NH3_to_N", 1) - # Share of feedstocks for NH3 prodution (based on 2010 => Assumed fixed for any past years) + # Share of feedstocks for NH3 prodution + # (based on 2010 => Assumed fixed for any past years) feedshare = fs_GLO.sort_values(["Region"]).set_index("Region").drop("R12_GLB") # Get historical N demand from SSP2-nopolicy (may need to vary for diff scenarios) diff --git a/message_ix_models/model/material/data_buildings.py b/message_ix_models/model/material/data_buildings.py index 3258fe41f8..2634df95ce 100644 --- a/message_ix_models/model/material/data_buildings.py +++ b/message_ix_models/model/material/data_buildings.py @@ -1,5 +1,6 @@ from collections import defaultdict +import numpy as np import pandas as pd from message_ix import make_df @@ -13,18 +14,10 @@ CASE_SENS = "ref" # 'min', 'max' INPUTFILE = "LED_LED_report_IAMC_sensitivity_R12.csv" - - # INPUTFILE = 'LED_LED_report_IAMC_sensitivity_R11.csv' def read_timeseries_buildings(filename, scenario, case=CASE_SENS): - import numpy as np - - # Ensure config is loaded, get the context - context = read_config() - s_info = ScenarioInfo(scenario) - nodes = s_info.N # Read the file and filter the given sensitivity case bld_input_raw = pd.read_csv(package_data_path("material", "buildings", filename)) @@ -33,10 +26,10 @@ def read_timeseries_buildings(filename, scenario, case=CASE_SENS): bld_input_mat = bld_input_raw[ bld_input_raw[ "Variable" - ].str.contains( # str.contains("Floor Space|Aluminum|Cement|Steel|Final Energy")] + ].str.contains( #"Floor Space|Aluminum|Cement|Steel|Final Energy" "Floor Space|Aluminum|Cement|Steel" ) - ] # Final Energy - Later. Need to figure out carving out + ] # Final Energy - Later. Need to figure out how to carve out bld_input_mat["Region"] = "R12_" + bld_input_mat["Region"] print("Check the year values") print(bld_input_mat) @@ -190,7 +183,6 @@ def gen_data_buildings(scenario, dry_run=False): # allyears = s_info.set['year'] #s_info.Y is only for modeling years modelyears = s_info.Y # s_info.Y is only for modeling years nodes = s_info.N - yv_ya = s_info.yv_ya # fmy = s_info.y0 nodes.remove("World") # nodes.remove("R11_RCPA") @@ -305,13 +297,3 @@ def gen_data_buildings(scenario, dry_run=False): adjust_demand_param(scenario) return results - - -if __name__ == "__main__": - import ixmp - import message_ix - - mp = ixmp.Platform("ixmp_dev") - scen = message_ix.Scenario(mp, "MESSAGEix-Materials", "baseline_balance-equality_materials") - df = gen_data_buildings(scen) - print() diff --git a/message_ix_models/model/material/data_cement.py b/message_ix_models/model/material/data_cement.py index 23f48db8e6..24cf3ff3b2 100644 --- a/message_ix_models/model/material/data_cement.py +++ b/message_ix_models/model/material/data_cement.py @@ -26,10 +26,7 @@ # Generate a fake cement demand def gen_mock_demand_cement(scenario): - context = read_config() s_info = ScenarioInfo(scenario) - modelyears = s_info.Y # s_info.Y is only for modeling years - fmy = s_info.y0 nodes = s_info.N nodes.remove("World") @@ -139,7 +136,7 @@ def gen_mock_demand_cement(scenario): ) # demand2010_cement = demand2010_cement.\ - # join(gdp_growth.rename(columns={'Region':'node'}).set_index('node'), on='node') + # join(gdp_growth.rename(columns={'Region':'node'}).set_index('node'), on='node') demand2020_cement.iloc[:, 3:] = ( demand2020_cement.iloc[:, 3:] diff --git a/message_ix_models/model/material/data_generic.py b/message_ix_models/model/material/data_generic.py index dc9b124de4..4bd013a0d1 100644 --- a/message_ix_models/model/material/data_generic.py +++ b/message_ix_models/model/material/data_generic.py @@ -17,13 +17,6 @@ def read_data_generic(scenario): """Read and clean data from :file:`generic_furnace_boiler_techno_economic.xlsx`.""" - # Ensure config is loaded, get the context - context = read_config() - s_info = ScenarioInfo(scenario) - - # Shorter access to sets configuration - # sets = context["material"]["generic"] - # Read the file data_generic = pd.read_excel( message_ix_models.util.package_data_path( @@ -34,14 +27,12 @@ def read_data_generic(scenario): # Clean the data # Drop columns that don't contain useful information - data_generic = data_generic.drop(["Region", "Source", "Description"], axis=1) data_generic_ts = read_timeseries( scenario, "other", "generic_furnace_boiler_techno_economic.xlsx" ) # Unit conversion - # At the moment this is done in the excel file, can be also done here # To make sure we use the same units @@ -65,11 +56,9 @@ def gen_data_generic(scenario, dry_run=False): # For each technology there are differnet input and output combinations # Iterate over technologies - allyears = s_info.set["year"] # s_info.Y is only for modeling years modelyears = s_info.Y # s_info.Y is only for modeling years nodes = s_info.N yv_ya = s_info.yv_ya - fmy = s_info.y0 # Do not parametrize GLB region the same way if "R11_GLB" in nodes: @@ -152,7 +141,8 @@ def gen_data_generic(scenario, dry_run=False): elif param_name == "emission_factor": emi = split[1] - # TODO: Now tentatively fixed to one mode. Have values for the other mode too + # TODO: Now tentatively fixed to one mode. + # Have values for the other mode too df_low = make_df( param_name, technology=t, diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index b2c9e6ff9d..4918137799 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -209,7 +209,8 @@ def get_embodied_emi(row, pars, share_par): # df = scenario.par(i, filters={"technology": ["meth_ng", # "meth_coal", "meth_ng_ccs", # "meth_coal_ccs", "meth_exp", - # "meth_imp", "meth_trd", "meth_bal", "meth_t_d"], + # "meth_imp", "meth_trd", + # "meth_bal", "meth_t_d"], # "mode": "M1"}) # if df.size != 0: # scenario.remove_par(i, df) @@ -493,7 +494,8 @@ def add_methanol_fuel_additives(scenario): pars = ["output", "var_cost", "relation_activity", "input", "emission_factor"] if "loil_trp" not in scenario.set("technology").to_list(): print( - "It seems that the selected scenario does not contain the technology: loil_trp. Methanol fuel blending could not be calculated and was skipped" + "It seems that the selected scenario does not contain the technology:" + "loil_trp. Methanol fuel blending could not be calculated and was skipped" ) return par_dict_loil for i in pars: @@ -805,7 +807,9 @@ def add_meth_hist_act(): sheet_name="historical_activity", ) par_dict["historical_activity"] = pd.concat([df_fs, df_fuel]) - # derived from graphic in "Methanol production statstics.xlsx/China demand split" diagram + # derived from graphic in + # "Methanol production statstics.xlsx/China demand split" diagram + # hist_cap = make_df( # "historical_new_capacity", # node_loc="R12_CHN", @@ -817,7 +821,8 @@ def add_meth_hist_act(): # par_dict["historical_new_capacity"] = hist_cap # fix demand infeasibility # act = scenario.par("historical_activity") - # row = act[act["technology"].str.startswith("meth")].sort_values("value", ascending=False).iloc[0] + # row = act[act["technology"].str.startswith("meth")].sort_values("value", + # ascending=False).iloc[0] # row["value"] = 0.0 df_ng = pd.read_excel( package_data_path("material", "methanol", "meth_ng_techno_economic.xlsx"), diff --git a/message_ix_models/model/material/data_methanol_new.py b/message_ix_models/model/material/data_methanol_new.py index 7b121cc8cc..14bf929eed 100644 --- a/message_ix_models/model/material/data_methanol_new.py +++ b/message_ix_models/model/material/data_methanol_new.py @@ -60,7 +60,6 @@ def gen_data_methanol_new(scenario): file_path = message_ix_models.util.package_data_path( "material", "methanol", "missing_rels.yaml" ) - # file_path = "C:/Users\maczek\PycharmProjects\message_ix_models\message_ix_models\model\material\petrochemical model fixes notebooks\" with open(file_path, "r") as file: missing_rels = yaml.safe_load(file) diff --git a/message_ix_models/model/material/data_petro.py b/message_ix_models/model/material/data_petro.py index cce62833f7..2f5d438de0 100644 --- a/message_ix_models/model/material/data_petro.py +++ b/message_ix_models/model/material/data_petro.py @@ -55,24 +55,23 @@ def read_data_petrochemicals(scenario): def gen_mock_demand_petro(scenario, gdp_elasticity_2020, gdp_elasticity_2030): - context = read_config() s_info = ScenarioInfo(scenario) - modelyears = s_info.Y # s_info.Y is only for modeling years + modelyears = s_info.Y fy = scenario.firstmodelyear - nodes = s_info.N def get_demand_t1_with_income_elasticity( - demand_t0, income_t0, income_t1, elasticity + demand_t0, income_t0, income_t1, elasticity ): return ( - elasticity * demand_t0.mul(((income_t1 - income_t0) / income_t0), axis=0) + elasticity * demand_t0.mul(((income_t1 - income_t0) / income_t0), axis=0) ) + demand_t0 gdp_mer = scenario.par("bound_activity_up", {"technology": "GDP"}) mer_to_ppp = pd.read_csv( package_data_path("material", "other", "mer_to_ppp_default.csv") ).set_index(["node", "year"]) - # mer_to_ppp = scenario.par("MERtoPPP").set_index("node", "year") TODO: might need to be re-activated for different SSPs + # mer_to_ppp = scenario.par("MERtoPPP").set_index("node", "year") + # TODO: might need to be re-activated for different SSPs gdp_mer = gdp_mer.merge( mer_to_ppp.reset_index()[["node", "year", "value"]], left_on=["node_loc", "year_act"], @@ -106,7 +105,8 @@ def get_demand_t1_with_income_elasticity( # if "R12_CHN" in nodes: # nodes.remove("R12_GLB") - # dem_2020 = np.array([2.4, 0.44, 3, 5, 11, 40.3, 49.8, 11, 37.5, 10.7, 29.2, 50.5]) + # dem_2020 = np.array([2.4, 0.44, 3, 5, 11, 40.3, 49.8, 11, + # 37.5, 10.7, 29.2, 50.5]) # dem_2020 = pd.Series(dem_2020) # # else: @@ -248,7 +248,7 @@ def gen_data_petro_chemicals(scenario, dry_run=False): unit="t", node_loc=rg, node_origin=global_region, - **common + **common, ) elif (param_name == "output") and (lev == "export"): df = make_df( @@ -261,7 +261,7 @@ def gen_data_petro_chemicals(scenario, dry_run=False): unit="t", node_loc=rg, node_dest=global_region, - **common + **common, ) else: df = make_df( @@ -273,7 +273,7 @@ def gen_data_petro_chemicals(scenario, dry_run=False): value=val[regions[regions == rg].index[0]], unit="t", node_loc=rg, - **common + **common, ).pipe(same_node) # Copy parameters to all regions, when node_loc is not GLB @@ -297,7 +297,7 @@ def gen_data_petro_chemicals(scenario, dry_run=False): mode=mod, unit="t", node_loc=rg, - **common + **common, ) elif param_name == "var_cost": @@ -312,7 +312,7 @@ def gen_data_petro_chemicals(scenario, dry_run=False): mode=mod, value=val[regions[regions == rg].index[0]], unit="t", - **common + **common, ) .pipe(broadcast, node_loc=nodes) .pipe(same_node) @@ -327,7 +327,7 @@ def gen_data_petro_chemicals(scenario, dry_run=False): value=val[regions[regions == rg].index[0]], node_loc=rg, unit="t", - **common + **common, ).pipe(same_node) elif param_name == "share_mode_up": @@ -341,7 +341,7 @@ def gen_data_petro_chemicals(scenario, dry_run=False): shares="steam_cracker", value=val[regions[regions == rg].index[0]], unit="-", - **common + **common, ) .pipe(broadcast, node_share=nodes) .pipe(same_node) @@ -356,7 +356,7 @@ def gen_data_petro_chemicals(scenario, dry_run=False): value=val[regions[regions == rg].index[0]], unit="t", node_loc=rg, - **common + **common, ) df = df.drop_duplicates() @@ -364,8 +364,8 @@ def gen_data_petro_chemicals(scenario, dry_run=False): if (len(regions) == 1) and (rg != global_region): if "node_loc" in df.columns: if ( - len(set(df["node_loc"])) == 1 - and list(set(df["node_loc"]))[0] != global_region + len(set(df["node_loc"])) == 1 + and list(set(df["node_loc"]))[0] != global_region ): # print("Copying to all R11") df["node_loc"] = None @@ -401,7 +401,9 @@ def gen_data_petro_chemicals(scenario, dry_run=False): # default_gdp_elasticity_2020 = pars["hvc_elasticity_2020"] # default_gdp_elasticity_2030 = pars["hvc_elasticity_2030"] - default_gdp_elasticity_2020, default_gdp_elasticity_2030 = iea_elasticity_map[ssp_mode_map[ssp]] + default_gdp_elasticity_2020, default_gdp_elasticity_2030 = iea_elasticity_map[ + ssp_mode_map[ssp] + ] demand_hvc = material_demand_calc.gen_demand_petro( scenario, "HVC", default_gdp_elasticity_2020, default_gdp_elasticity_2030 @@ -463,7 +465,7 @@ def gen_data_petro_chemicals(scenario, dry_run=False): year_vtg=yr, year_act=yr, mode=mod, - **common + **common, ).pipe(broadcast, node_loc=nodes) else: rg = data_petro_ts.loc[ @@ -480,7 +482,7 @@ def gen_data_petro_chemicals(scenario, dry_run=False): year_act=yr, mode=mod, node_loc=rg, - **common + **common, ) results[p].append(df) @@ -510,9 +512,9 @@ def gen_data_petro_chemicals(scenario, dry_run=False): df = results["growth_activity_up"] results["growth_activity_up"] = df[ ~( - (df["technology"] == "steam_cracker_petro") - & (df["node_loc"] == "R12_AFR") - & (df["year_act"] == 2020) + (df["technology"] == "steam_cracker_petro") + & (df["node_loc"] == "R12_AFR") + & (df["year_act"] == 2020) ) ] @@ -551,6 +553,6 @@ def gen_data_petro_chemicals(scenario, dry_run=False): (df_gro["technology"] == "steam_cracker_petro") & (df_gro["node_loc"] == "R12_RCPA") & (df_gro["year_act"] == 2020) - ].index + ].index results["growth_activity_up"] = results["growth_activity_up"].drop(drop_idx) return results diff --git a/message_ix_models/model/material/data_steel.py b/message_ix_models/model/material/data_steel.py index 0ed71c2e13..f487f4f5ae 100644 --- a/message_ix_models/model/material/data_steel.py +++ b/message_ix_models/model/material/data_steel.py @@ -5,23 +5,23 @@ # Get endogenous material demand from buildings interface from message_ix_models import ScenarioInfo +from message_ix_models.model.material.data_util import ( + read_rel, + read_sector_data, + read_timeseries, +) +from message_ix_models.model.material.material_demand import material_demand_calc +from message_ix_models.model.material.util import read_config from message_ix_models.util import ( broadcast, package_data_path, same_node, ) -from .data_util import read_rel, read_sector_data, read_timeseries -from .material_demand import material_demand_calc -from .util import read_config - # Generate a fake steel demand def gen_mock_demand_steel(scenario): - context = read_config() s_info = ScenarioInfo(scenario) - modelyears = s_info.Y # s_info.Y is only for modeling years - fmy = s_info.y0 nodes = s_info.N nodes.remove("World") @@ -29,10 +29,14 @@ def gen_mock_demand_steel(scenario): # r = ['R12_AFR', 'R12_RCPA', 'R12_EEU', 'R12_FSU', 'R12_LAM', 'R12_MEA',\ # 'R12_NAM', 'R12_PAO', 'R12_PAS', 'R12_SAS', 'R12_WEU',"R12_CHN"] - # Finished steel demand from: https://www.oecd.org/industry/ind/Item_4b_Worldsteel.pdf - # For region definitions: https://worldsteel.org/wp-content/uploads/2021-World-Steel-in-Figures.pdf + # Finished steel demand from: + # https://www.oecd.org/industry/ind/Item_4b_Worldsteel.pdf + # For region definitions: + # https://worldsteel.org/wp-content/uploads/2021-World-Steel-in-Figures.pdf # For detailed assumptions and calculation see: steel_demand_calculation.xlsx - # under https://iiasahub.sharepoint.com/sites/eceprog?cid=75ea8244-8757-44f1-83fd-d34f94ffd06a + # under + # https://iiasahub.sharepoint.com/ + # sites/eceprog?cid=75ea8244-8757-44f1-83fd-d34f94ffd06a if "R12_CHN" in nodes: nodes.remove("R12_GLB") @@ -116,7 +120,8 @@ def gen_data_steel(scenario, dry_run=False): s_info = ScenarioInfo(scenario) # Techno-economic assumptions - # TEMP: now add cement sector as well => Need to separate those since now I have get_data_steel and cement + # TEMP: now add cement sector as well + # => Need to separate those since now I have get_data_steel and cement data_steel = read_sector_data(scenario, "steel") # Special treatment for time-dependent Parameters data_steel_ts = read_timeseries(scenario, "steel_cement", context.datafile) @@ -130,12 +135,10 @@ def gen_data_steel(scenario, dry_run=False): # For each technology there are differnet input and output combinations # Iterate over technologies - allyears = s_info.set["year"] # s_info.Y is only for modeling years modelyears = s_info.Y # s_info.Y is only for modeling years nodes = s_info.N yv_ya = s_info.yv_ya yv_ya = yv_ya.loc[yv_ya.year_vtg >= 1990] - fmy = s_info.y0 nodes.remove("World") # Do not parametrize GLB region the same way @@ -571,7 +574,8 @@ def gen_data_steel(scenario, dry_run=False): # with localconverter(ro.default_converter + pandas2ri.converter): # # GDP is only in MER in scenario. # # To get PPP GDP, it is read externally from the R side -# df = r.derive_steel_demand(pop, base_demand, str(package_data_path("material"))) +# df = r.derive_steel_demand(pop, base_demand, +# str(package_data_path("material"))) # df.year = df.year.astype(int) # # return df diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index 07145be0e6..678c5ab242 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -1,9 +1,9 @@ import os from typing import Literal -import numpy as np import ixmp import message_ix +import numpy as np import pandas as pd from genno import Computer @@ -30,8 +30,8 @@ def load_GDP_COVID() -> pd.DataFrame: ------- pd.DataFrame """ - context = read_config() - # Obtain 2015 and 2020 GDP values from NGFS baseline. These values are COVID corrected. (GDP MER) + # Obtain 2015 and 2020 GDP values from NGFS baseline. + # These values are COVID corrected. (GDP MER) mp = ixmp.Platform() scen_NGFS = message_ix.Scenario( @@ -84,9 +84,9 @@ def load_GDP_COVID() -> pd.DataFrame: return df_new -def add_macro_COVID(scen: message_ix.Scenario, - filename: str, - check_converge: bool = False) -> message_ix.Scenario: +def add_macro_COVID( + scen: message_ix.Scenario, filename: str, check_converge: bool = False +) -> message_ix.Scenario: """ Prepare data for MACRO calibration by reading data from xlsx file @@ -109,8 +109,9 @@ def add_macro_COVID(scen: message_ix.Scenario, # Excel file for calibration data if "SSP_dev" in scen.model: - xls_file = os.path.join("C:/", "Users", "maczek", - "Downloads", "macro", filename) + xls_file = os.path.join( + "C:/", "Users", "maczek", "Downloads", "macro", filename + ) else: xls_file = os.path.join("P:", "ene.model", "MACRO", "python", filename) # Making a dictionary from the MACRO Excel file @@ -184,7 +185,7 @@ def modify_demand_and_hist_activity(scen: message_ix.Scenario) -> None: | (df["SECTOR"] == "industry (non-ferrous metals)") | (df["SECTOR"] == "industry (non-metallic minerals)") | (df["SECTOR"] == "industry (total)") - ] + ] df = df[df["RYEAR"] == 2015] # NOTE: Total cehmical industry energy: 27% thermal, 8% electricity, 65% feedstock @@ -203,10 +204,10 @@ def modify_demand_and_hist_activity(scen: message_ix.Scenario) -> None: & (df["SECTOR"] != "industry (total)") & (df["SECTOR"] != "feedstock (petrochemical industry)") & (df["SECTOR"] != "feedstock (total)") - ] + ] df_spec_total = df[ (df["SECTOR"] == "industry (total)") & (df["FUEL"] == "electricity") - ] + ] df_spec_new = pd.DataFrame( columns=["REGION", "SECTOR", "FUEL", "RYEAR", "UNIT_OUT", "RESULT"] @@ -215,15 +216,15 @@ def modify_demand_and_hist_activity(scen: message_ix.Scenario) -> None: df_spec_temp = df_spec.loc[df_spec["REGION"] == r] df_spec_total_temp = df_spec_total.loc[df_spec_total["REGION"] == r] df_spec_temp.loc[:, "i_spec"] = ( - df_spec_temp.loc[:, "RESULT"] - / df_spec_total_temp.loc[:, "RESULT"].values[0] + df_spec_temp.loc[:, "RESULT"] + / df_spec_total_temp.loc[:, "RESULT"].values[0] ) df_spec_new = pd.concat([df_spec_temp, df_spec_new], ignore_index=True) df_spec_new.drop(["FUEL", "RYEAR", "UNIT_OUT", "RESULT"], axis=1, inplace=True) df_spec_new.loc[df_spec_new["SECTOR"] == "industry (chemicals)", "i_spec"] = ( - df_spec_new.loc[df_spec_new["SECTOR"] == "industry (chemicals)", "i_spec"] - * 0.67 + df_spec_new.loc[df_spec_new["SECTOR"] == "industry (chemicals)", "i_spec"] + * 0.67 ) df_spec_new = df_spec_new.groupby(["REGION"]).sum().reset_index() @@ -232,7 +233,7 @@ def modify_demand_and_hist_activity(scen: message_ix.Scenario) -> None: df_feed = df[ (df["SECTOR"] == "feedstock (petrochemical industry)") & (df["FUEL"] == "total") - ] + ] df_feed_total = df[(df["SECTOR"] == "feedstock (total)") & (df["FUEL"] == "total")] df_feed_temp = pd.DataFrame(columns=["REGION", "i_feed"]) df_feed_new = pd.DataFrame(columns=["REGION", "i_feed"]) @@ -256,12 +257,12 @@ def modify_demand_and_hist_activity(scen: message_ix.Scenario) -> None: & (df["SECTOR"] != "feedstock (petrochemical industry)") & (df["SECTOR"] != "feedstock (total)") & (df["SECTOR"] != "industry (non-ferrous metals)") - ] + ] df_therm_total = df[ (df["SECTOR"] == "industry (total)") & (df["FUEL"] != "total") & (df["FUEL"] != "electricity") - ] + ] df_therm_total = ( df_therm_total.groupby(by="REGION").sum().drop(["RYEAR"], axis=1).reset_index() ) @@ -279,23 +280,23 @@ def modify_demand_and_hist_activity(scen: message_ix.Scenario) -> None: df_therm_temp = df_therm.loc[df_therm["REGION"] == r] df_therm_total_temp = df_therm_total.loc[df_therm_total["REGION"] == r] df_therm_temp.loc[:, "i_therm"] = ( - df_therm_temp.loc[:, "RESULT"] - / df_therm_total_temp.loc[:, "RESULT"].values[0] + df_therm_temp.loc[:, "RESULT"] + / df_therm_total_temp.loc[:, "RESULT"].values[0] ) df_therm_new = pd.concat([df_therm_temp, df_therm_new], ignore_index=True) df_therm_new = df_therm_new.drop(["RESULT"], axis=1) df_therm_new.drop(["FUEL", "RYEAR", "UNIT_OUT"], axis=1, inplace=True) df_therm_new.loc[df_therm_new["SECTOR"] == "industry (chemicals)", "i_therm"] = ( - df_therm_new.loc[df_therm_new["SECTOR"] == "industry (chemicals)", "i_therm"] - * 0.67 + df_therm_new.loc[df_therm_new["SECTOR"] == "industry (chemicals)", "i_therm"] + * 0.67 ) # Modify CPA based on https://www.iea.org/sankey/#?c=Japan&s=Final%20consumption. # Since the value did not allign with the one in the IEA website. index = (df_therm_new["SECTOR"] == "industry (iron and steel)") & ( - (df_therm_new["REGION"] == region_name_CPA) - | (df_therm_new["REGION"] == region_name_CHN) + (df_therm_new["REGION"] == region_name_CPA) + | (df_therm_new["REGION"] == region_name_CHN) ) df_therm_new.loc[index, "i_therm"] = 0.2 @@ -346,41 +347,39 @@ def modify_demand_and_hist_activity(scen: message_ix.Scenario) -> None: useful_thermal.loc[ useful_thermal["node"] == r_MESSAGE, "value" ] = useful_thermal.loc[useful_thermal["node"] == r_MESSAGE, "value"] * ( - 1 - df_therm_new.loc[df_therm_new["REGION"] == r, "i_therm"].values[0] + 1 - df_therm_new.loc[df_therm_new["REGION"] == r, "i_therm"].values[0] ) thermal_df_hist.loc[ thermal_df_hist["node_loc"] == r_MESSAGE, "value" ] = thermal_df_hist.loc[thermal_df_hist["node_loc"] == r_MESSAGE, "value"] * ( - 1 - df_therm_new.loc[df_therm_new["REGION"] == r, "i_therm"].values[0] + 1 - df_therm_new.loc[df_therm_new["REGION"] == r, "i_therm"].values[0] ) for r in df_spec_new["REGION"]: r_MESSAGE = region_type + r useful_spec.loc[useful_spec["node"] == r_MESSAGE, "value"] = useful_spec.loc[ - useful_spec["node"] == r_MESSAGE, "value" - ] * (1 - df_spec_new.loc[ - df_spec_new["REGION"] == r, "i_spec"].values[0]) + useful_spec["node"] == r_MESSAGE, "value" + ] * (1 - df_spec_new.loc[df_spec_new["REGION"] == r, "i_spec"].values[0]) spec_df_hist.loc[ spec_df_hist["node_loc"] == r_MESSAGE, "value" ] = spec_df_hist.loc[spec_df_hist["node_loc"] == r_MESSAGE, "value"] * ( - 1 - df_spec_new.loc[df_spec_new["REGION"] == r, "i_spec"].values[0] + 1 - df_spec_new.loc[df_spec_new["REGION"] == r, "i_spec"].values[0] ) for r in df_feed_new["REGION"]: r_MESSAGE = region_type + r useful_feed.loc[useful_feed["node"] == r_MESSAGE, "value"] = useful_feed.loc[ - useful_feed["node"] == r_MESSAGE, "value" - ] * (1 - df_feed_new.loc[ - df_feed_new["REGION"] == r, "i_feed"].values[0]) + useful_feed["node"] == r_MESSAGE, "value" + ] * (1 - df_feed_new.loc[df_feed_new["REGION"] == r, "i_feed"].values[0]) feed_df_hist.loc[ feed_df_hist["node_loc"] == r_MESSAGE, "value" ] = feed_df_hist.loc[feed_df_hist["node_loc"] == r_MESSAGE, "value"] * ( - 1 - df_feed_new.loc[df_feed_new["REGION"] == r, "i_feed"].values[0] + 1 - df_feed_new.loc[df_feed_new["REGION"] == r, "i_feed"].values[0] ) scen.check_out() @@ -463,7 +462,9 @@ def modify_demand_and_hist_activity(scen: message_ix.Scenario) -> None: scen.commit(comment="remove bounds") -def modify_demand_and_hist_activity_debug(scen: message_ix.Scenario) -> dict[str, pd.DataFrame]: +def modify_demand_and_hist_activity_debug( + scen: message_ix.Scenario, +) -> dict[str, pd.DataFrame]: """modularized "dry-run" version of modify_demand_and_hist_activity() for debugging purposes @@ -511,7 +512,7 @@ def modify_demand_and_hist_activity_debug(scen: message_ix.Scenario) -> dict[str | (df["SECTOR"] == "industry (non-ferrous metals)") | (df["SECTOR"] == "industry (non-metallic minerals)") | (df["SECTOR"] == "industry (total)") - ] + ] df = df[df["RYEAR"] == 2015] # NOTE: Total cehmical industry energy: 27% thermal, 8% electricity, 65% feedstock @@ -530,10 +531,10 @@ def modify_demand_and_hist_activity_debug(scen: message_ix.Scenario) -> dict[str & (df["SECTOR"] != "industry (total)") & (df["SECTOR"] != "feedstock (petrochemical industry)") & (df["SECTOR"] != "feedstock (total)") - ] + ] df_spec_total = df[ (df["SECTOR"] == "industry (total)") & (df["FUEL"] == "electricity") - ] + ] df_spec_new = pd.DataFrame( columns=["REGION", "SECTOR", "FUEL", "RYEAR", "UNIT_OUT", "RESULT"] @@ -542,15 +543,15 @@ def modify_demand_and_hist_activity_debug(scen: message_ix.Scenario) -> dict[str df_spec_temp = df_spec.loc[df_spec["REGION"] == r] df_spec_total_temp = df_spec_total.loc[df_spec_total["REGION"] == r] df_spec_temp.loc[:, "i_spec"] = ( - df_spec_temp.loc[:, "RESULT"] - / df_spec_total_temp.loc[:, "RESULT"].values[0] + df_spec_temp.loc[:, "RESULT"] + / df_spec_total_temp.loc[:, "RESULT"].values[0] ) df_spec_new = pd.concat([df_spec_temp, df_spec_new], ignore_index=True) df_spec_new.drop(["FUEL", "RYEAR", "UNIT_OUT", "RESULT"], axis=1, inplace=True) df_spec_new.loc[df_spec_new["SECTOR"] == "industry (chemicals)", "i_spec"] = ( - df_spec_new.loc[df_spec_new["SECTOR"] == "industry (chemicals)", "i_spec"] - * 0.67 + df_spec_new.loc[df_spec_new["SECTOR"] == "industry (chemicals)", "i_spec"] + * 0.67 ) df_spec_new = df_spec_new.groupby(["REGION"]).sum().reset_index() @@ -559,7 +560,7 @@ def modify_demand_and_hist_activity_debug(scen: message_ix.Scenario) -> dict[str df_feed = df[ (df["SECTOR"] == "feedstock (petrochemical industry)") & (df["FUEL"] == "total") - ] + ] df_feed_total = df[(df["SECTOR"] == "feedstock (total)") & (df["FUEL"] == "total")] df_feed_temp = pd.DataFrame(columns=["REGION", "i_feed"]) df_feed_new = pd.DataFrame(columns=["REGION", "i_feed"]) @@ -583,12 +584,12 @@ def modify_demand_and_hist_activity_debug(scen: message_ix.Scenario) -> dict[str & (df["SECTOR"] != "feedstock (petrochemical industry)") & (df["SECTOR"] != "feedstock (total)") & (df["SECTOR"] != "industry (non-ferrous metals)") - ] + ] df_therm_total = df[ (df["SECTOR"] == "industry (total)") & (df["FUEL"] != "total") & (df["FUEL"] != "electricity") - ] + ] df_therm_total = ( df_therm_total.groupby(by="REGION").sum().drop(["RYEAR"], axis=1).reset_index() ) @@ -606,23 +607,23 @@ def modify_demand_and_hist_activity_debug(scen: message_ix.Scenario) -> dict[str df_therm_temp = df_therm.loc[df_therm["REGION"] == r] df_therm_total_temp = df_therm_total.loc[df_therm_total["REGION"] == r] df_therm_temp.loc[:, "i_therm"] = ( - df_therm_temp.loc[:, "RESULT"] - / df_therm_total_temp.loc[:, "RESULT"].values[0] + df_therm_temp.loc[:, "RESULT"] + / df_therm_total_temp.loc[:, "RESULT"].values[0] ) df_therm_new = pd.concat([df_therm_temp, df_therm_new], ignore_index=True) df_therm_new = df_therm_new.drop(["RESULT"], axis=1) df_therm_new.drop(["FUEL", "RYEAR", "UNIT_OUT"], axis=1, inplace=True) df_therm_new.loc[df_therm_new["SECTOR"] == "industry (chemicals)", "i_therm"] = ( - df_therm_new.loc[df_therm_new["SECTOR"] == "industry (chemicals)", "i_therm"] - * 0.67 + df_therm_new.loc[df_therm_new["SECTOR"] == "industry (chemicals)", "i_therm"] + * 0.67 ) # Modify CPA based on https://www.iea.org/sankey/#?c=Japan&s=Final%20consumption. # Since the value did not allign with the one in the IEA website. index = (df_therm_new["SECTOR"] == "industry (iron and steel)") & ( - (df_therm_new["REGION"] == region_name_CPA) - | (df_therm_new["REGION"] == region_name_CHN) + (df_therm_new["REGION"] == region_name_CPA) + | (df_therm_new["REGION"] == region_name_CHN) ) df_therm_new.loc[index, "i_therm"] = 0.2 @@ -673,41 +674,39 @@ def modify_demand_and_hist_activity_debug(scen: message_ix.Scenario) -> dict[str useful_thermal.loc[ useful_thermal["node"] == r_MESSAGE, "value" ] = useful_thermal.loc[useful_thermal["node"] == r_MESSAGE, "value"] * ( - 1 - df_therm_new.loc[df_therm_new["REGION"] == r, "i_therm"].values[0] + 1 - df_therm_new.loc[df_therm_new["REGION"] == r, "i_therm"].values[0] ) thermal_df_hist.loc[ thermal_df_hist["node_loc"] == r_MESSAGE, "value" ] = thermal_df_hist.loc[thermal_df_hist["node_loc"] == r_MESSAGE, "value"] * ( - 1 - df_therm_new.loc[df_therm_new["REGION"] == r, "i_therm"].values[0] + 1 - df_therm_new.loc[df_therm_new["REGION"] == r, "i_therm"].values[0] ) for r in df_spec_new["REGION"]: r_MESSAGE = region_type + r useful_spec.loc[useful_spec["node"] == r_MESSAGE, "value"] = useful_spec.loc[ - useful_spec["node"] == r_MESSAGE, "value" - ] * (1 - df_spec_new.loc[ - df_spec_new["REGION"] == r, "i_spec"].values[0]) + useful_spec["node"] == r_MESSAGE, "value" + ] * (1 - df_spec_new.loc[df_spec_new["REGION"] == r, "i_spec"].values[0]) spec_df_hist.loc[ spec_df_hist["node_loc"] == r_MESSAGE, "value" ] = spec_df_hist.loc[spec_df_hist["node_loc"] == r_MESSAGE, "value"] * ( - 1 - df_spec_new.loc[df_spec_new["REGION"] == r, "i_spec"].values[0] + 1 - df_spec_new.loc[df_spec_new["REGION"] == r, "i_spec"].values[0] ) for r in df_feed_new["REGION"]: r_MESSAGE = region_type + r useful_feed.loc[useful_feed["node"] == r_MESSAGE, "value"] = useful_feed.loc[ - useful_feed["node"] == r_MESSAGE, "value" - ] * (1 - df_feed_new.loc[ - df_feed_new["REGION"] == r, "i_feed"].values[0]) + useful_feed["node"] == r_MESSAGE, "value" + ] * (1 - df_feed_new.loc[df_feed_new["REGION"] == r, "i_feed"].values[0]) feed_df_hist.loc[ feed_df_hist["node_loc"] == r_MESSAGE, "value" ] = feed_df_hist.loc[feed_df_hist["node_loc"] == r_MESSAGE, "value"] * ( - 1 - df_feed_new.loc[df_feed_new["REGION"] == r, "i_feed"].values[0] + 1 - df_feed_new.loc[df_feed_new["REGION"] == r, "i_feed"].values[0] ) # For aluminum there is no significant deduction required @@ -779,13 +778,17 @@ def calc_hist_activity(scen: message_ix.Scenario, years: list) -> pd.DataFrame: df_mat = get_hist_act_data("IEA_mappings_industry.csv", years=years) df_chem = get_hist_act_data("IEA_mappings_chemicals.csv", years=years) - # RFE: move hardcoded assumptions (chemicals and iron and steel) to external data files - # scale chemical activity to deduct explicitly represented activities of MESSAGEix-Materials + # RFE: move hardcoded assumptions (chemicals and iron and steel) + # to external data files + + # scale chemical activity to deduct explicitly + # represented activities of MESSAGEix-Materials # (67% are covered by NH3, HVCs and methanol) df_chem = df_chem.mul(0.67) df_mat = df_mat.sub(df_chem, fill_value=0) - # calculate share of residual activity not covered by industry sector explicit technologies + # calculate share of residual activity not covered + # by industry sector explicit technologies df = df_mat.div(df_orig).dropna().sort_values("Value", ascending=False) # manually set elec_i to 0 since all of it is covered by iron/steel sector df.loc[:, "elec_i", :] = 0 @@ -827,14 +830,14 @@ def calc_demand_shares(iea_db_df: pd.DataFrame, base_year: int) -> pd.DataFrame: & (iea_db_df["PRODUCT"].isin(i_spec_prods)) & ~((iea_db_df["PRODUCT"] == ("ELECTR")) & (iea_db_df["FLOW"] == "IRONSTL")) & (iea_db_df["TIME"] == year) - ] + ] df_i_spec = df_i_spec.groupby("REGION").sum(numeric_only=True) df_i_spec_materials = iea_db_df[ (iea_db_df["FLOW"].isin(i_spec_material_flows)) & (iea_db_df["PRODUCT"].isin(i_spec_prods)) & (iea_db_df["TIME"] == year) - ] + ] df_i_spec_materials = df_i_spec_materials.groupby("REGION").sum(numeric_only=True) df_i_spec_resid_shr = ( @@ -845,7 +848,7 @@ def calc_demand_shares(iea_db_df: pd.DataFrame, base_year: int) -> pd.DataFrame: df_elec_i = iea_db_df[ ((iea_db_df["PRODUCT"] == ("ELECTR")) & (iea_db_df["FLOW"] == "IRONSTL")) & (iea_db_df["TIME"] == year) - ] + ] df_elec_i = df_elec_i.groupby("REGION").sum(numeric_only=True) agg_prods = ["MRENEW", "TOTAL"] @@ -854,7 +857,7 @@ def calc_demand_shares(iea_db_df: pd.DataFrame, base_year: int) -> pd.DataFrame: & ~(iea_db_df["PRODUCT"].isin(i_spec_prods)) & ~(iea_db_df["PRODUCT"].isin(agg_prods)) & (iea_db_df["TIME"] == year) - ] + ] df_i_therm = df_i_therm.groupby("REGION").sum(numeric_only=True) df_i_therm = df_i_therm.add(df_elec_i, fill_value=0) @@ -864,11 +867,12 @@ def calc_demand_shares(iea_db_df: pd.DataFrame, base_year: int) -> pd.DataFrame: & ~(iea_db_df["PRODUCT"].isin(i_spec_prods)) & ~(iea_db_df["PRODUCT"].isin(agg_prods)) & (iea_db_df["TIME"] == year) - ] + ] df_i_therm_materials = df_i_therm_materials.groupby(["REGION", "FLOW"]).sum( numeric_only=True ) - # only two thirds of chemical consumption is represented by Materials module currently + # only two thirds of chemical consumption is represented + # by Materials module currently df_i_therm_materials.loc[ df_i_therm_materials.index.get_level_values(1) == "CHEMICAL", "Value" ] *= 0.67 @@ -889,9 +893,9 @@ def calc_demand_shares(iea_db_df: pd.DataFrame, base_year: int) -> pd.DataFrame: def calc_resid_ind_demand(scen: message_ix.Scenario, baseyear: int) -> pd.DataFrame: comms = ["i_spec", "i_therm"] path = package_data_path("iea", "REV2022_allISO_IEA.parquet") - #path = os.path.join( + # path = os.path.join( # "P:", "ene.model", "IEA_database", "Florian", "REV2022_allISO_IEA.parquet" - #) + # ) Inp = pd.read_parquet(path, engine="fastparquet") Inp = map_iea_db_to_msg_regs(Inp, "R12_SSP_V1.yaml") demand_shrs_new = calc_demand_shares(Inp, baseyear) @@ -908,7 +912,8 @@ def modify_industry_demand(scen: message_ix.Scenario, baseyear: int) -> None: scen.check_out() scen.add_par("demand", df_demands_new) - # RFE: calculate deductions from IEA data instead of assuming full coverage by MESSAGE-Materials (chemicals) + # RFE: calculate deductions from IEA data instead + # of assuming full coverage by MESSAGE-Materials (chemicals) # remove i_spec demand separately since we assume 100% coverage by MESSAGE-Materials df_i_feed = scen.par("demand", filters={"commodity": "i_feed"}) scen.remove_par("demand", df_i_feed) @@ -954,7 +959,8 @@ def read_iea_tec_map(tec_map_fname: str) -> pd.DataFrame: Parameters ---------- tec_map_fname - name of mapping file used to map IEA flows and products to existing MESSAGEix technologies + name of mapping file used to map IEA flows and products + to existing MESSAGEix technologies Returns ------- pd.DataFrame @@ -995,15 +1001,16 @@ def get_hist_act_data(map_fname: str, years: list or None = None) -> pd.DataFram map_fname name of MESSAGEix-technology-to-IEA-flow/product mapping file years, optional - specifies timesteps for whom historical activity should be calculated and returned + specifies timesteps for whom historical activity should + be calculated and returned Returns ------- pd.DataFrame """ - #path = os.path.join( + # path = os.path.join( # "P:", "ene.model", "IEA_database", "Florian", "REV2022_allISO_IEA.parquet" - #) + # ) path = package_data_path("iea", "REV2022_allISO_IEA.parquet") Inp = pd.read_parquet(path, engine="fastparquet") if years: @@ -1045,25 +1052,25 @@ def add_emission_accounting(scen): tec_list_input = [ i for i in tec_list_input if (("furnace" in i) | ("hp_gas_" in i)) ] - #tec_list_input.remove("hp_gas_i") - #tec_list_input.remove("hp_gas_rc") + # tec_list_input.remove("hp_gas_i") + # tec_list_input.remove("hp_gas_rc") # The technology list to retreive the emission_factors tec_list_residual = [ i for i in tec_list_residual if ( - ( - ("biomass_i" in i) - | ("coal_i" in i) - | ("foil_i" in i) - | ("gas_i" in i) - | ("hp_gas_i" in i) - | ("loil_i" in i) - | ("meth_i" in i) - ) - & ("imp" not in i) - & ("trp" not in i) + ( + ("biomass_i" in i) + | ("coal_i" in i) + | ("foil_i" in i) + | ("gas_i" in i) + | ("hp_gas_i" in i) + | ("loil_i" in i) + | ("meth_i" in i) + ) + & ("imp" not in i) + & ("trp" not in i) ) ] @@ -1197,11 +1204,11 @@ def add_emission_accounting(scen): i for i in tec_list if ( - ("steel" in i) - | ("aluminum" in i) - | ("petro" in i) - | ("cement" in i) - | ("ref" in i) + ("steel" in i) + | ("aluminum" in i) + | ("petro" in i) + | ("cement" in i) + | ("ref" in i) ) ] for elem in ["refrigerant_recovery", "replacement_so2", "SO2_scrub_ref"]: @@ -1223,7 +1230,7 @@ def add_emission_accounting(scen): (relation_activity["relation"] != "PM2p5_Emission") & (relation_activity["relation"] != "CO2_industry_Emission") & (relation_activity["relation"] != "CO2_transformation_Emission") - ] + ] # ***** (3) Add thermal industry technologies to CO2_ind relation ****** @@ -1367,11 +1374,13 @@ def add_emission_accounting(scen): # copy CO2_cc values to CO2_industry for conventional methanol tecs # scen.check_out() # meth_arr = ["meth_ng", "meth_coal", "meth_coal_ccs", "meth_ng_ccs"] - # df = scen.par("relation_activity", filters={"relation": "CO2_cc", "technology": meth_arr}) + # df = scen.par("relation_activity", + # filters={"relation": "CO2_cc", "technology": meth_arr}) # df = df.rename({"year_rel": "year_vtg"}, axis=1) # values = dict(zip(df["technology"], df["value"])) # - # df_em = scen.par("emission_factor", filters={"emission": "CO2_transformation", "technology": meth_arr}) + # df_em = scen.par("emission_factor", + # filters={"emission": "CO2_transformation", "technology": meth_arr}) # for i in meth_arr: # df_em.loc[df_em["technology"] == i, "value"] = values[i] # df_em["emission"] = "CO2_industry" @@ -1385,8 +1394,6 @@ def add_elec_lowerbound_2020(scen): # read input parameters for relevant technology/commodity combinations for # converting betwen final and useful energy - context = read_config() - input_residual_electricity = scen.par( "input", filters={"technology": "sp_el_I", "year_vtg": "2020", "year_act": "2020"}, @@ -1417,7 +1424,7 @@ def add_elec_lowerbound_2020(scen): # derive useful energy values by dividing final energy by # input coefficient from final-to-useful technologies bound_residual_electricity["value"] = ( - bound_residual_electricity["Value"] / bound_residual_electricity["value"] + bound_residual_electricity["Value"] / bound_residual_electricity["value"] ) # downselect dataframe columns for MESSAGEix parameters @@ -1439,7 +1446,7 @@ def add_elec_lowerbound_2020(scen): bound_residual_electricity["value"] = bound_residual_electricity["value"] * 0.8 bound_residual_electricity = bound_residual_electricity[ bound_residual_electricity["node_loc"] == "R12_CHN" - ] + ] scen.check_out() @@ -1459,12 +1466,12 @@ def add_elec_lowerbound_2020(scen): def add_coal_lowerbound_2020(sc): """Set lower bounds for coal and i_spec as a calibration for 2020""" - context = read_config() final_resid = pd.read_csv( package_data_path("material", "other", "residual_industry_2019.csv") ) - # read input parameters for relevant technology/commodity combinations for converting betwen final and useful energy + # read input parameters for relevant technology/commodity combinations + # for converting betwen final and useful energy input_residual_coal = sc.par( "input", filters={"technology": "coal_i", "year_vtg": "2020", "year_act": "2020"}, @@ -1494,7 +1501,8 @@ def add_coal_lowerbound_2020(sc): 'MESSAGE_fuel=="electr" & MESSAGE_sector=="industry_residual"' ) - # join final energy data from IEA energy balances and input coefficients from final-to-useful technologies from MESSAGEix + # join final energy data from IEA energy balances and input + # coefficients from final-to-useful technologies from MESSAGEix bound_coal = pd.merge( input_residual_coal, final_residual_coal, @@ -1517,11 +1525,12 @@ def add_coal_lowerbound_2020(sc): how="inner", ) - # derive useful energy values by dividing final energy by input coefficient from final-to-useful technologies + # derive useful energy values by dividing final energy + # by input coefficient from final-to-useful technologies bound_coal["value"] = bound_coal["Value"] / bound_coal["value"] bound_cement_coal["value"] = bound_cement_coal["Value"] / bound_cement_coal["value"] bound_residual_electricity["value"] = ( - bound_residual_electricity["Value"] / bound_residual_electricity["value"] + bound_residual_electricity["Value"] / bound_residual_electricity["value"] ) # downselect dataframe columns for MESSAGEix parameters @@ -1564,7 +1573,8 @@ def add_coal_lowerbound_2020(sc): "unit", ] - # (Artificially) lower bounds when i_spec act is too close to the bounds (avoid 0-price for macro calibration) + # (Artificially) lower bounds when i_spec act is too close + # to the bounds (avoid 0-price for macro calibration) more = ["R12_MEA", "R12_EEU", "R12_SAS", "R12_PAS"] # import pdb; pdb.set_trace() bound_residual_electricity.loc[ @@ -1583,7 +1593,9 @@ def add_coal_lowerbound_2020(sc): # commit scenario to ixmp backend sc.commit( - "added lower bound for activity of residual industrial coal and cement coal furnace technologies and adjusted 2020 residual industrial electricity demand" + "added lower bound for activity of residual industrial coal" + "and cement coal furnace technologies and " + "adjusted 2020 residual industrial electricity demand" ) @@ -1666,7 +1678,8 @@ def add_cement_bounds_2020(sc): 'MESSAGE_fuel=="coal" & MESSAGE_sector=="cement"' ) - # join final energy data from IEA energy balances and input coefficients from final-to-useful technologies from MESSAGEix + # join final energy data from IEA energy balances and input coefficients + # from final-to-useful technologies from MESSAGEix bound_cement_loil = pd.merge( input_cement_loil, final_cement_loil, @@ -1707,12 +1720,13 @@ def add_cement_bounds_2020(sc): how="inner", ) - # derive useful energy values by dividing final energy by input coefficient from final-to-useful technologies + # derive useful energy values by dividing final energy + # by input coefficient from final-to-useful technologies bound_cement_loil["value"] = bound_cement_loil["Value"] / bound_cement_loil["value"] bound_cement_foil["value"] = bound_cement_foil["Value"] / bound_cement_foil["value"] bound_cement_gas["value"] = bound_cement_gas["Value"] / bound_cement_gas["value"] bound_cement_biomass["value"] = ( - bound_cement_biomass["Value"] / bound_cement_biomass["value"] + bound_cement_biomass["Value"] / bound_cement_biomass["value"] ) bound_cement_coal["value"] = bound_cement_coal["Value"] / bound_cement_coal["value"] @@ -1966,7 +1980,9 @@ def add_ccs_technologies(scen: message_ix.Scenario) -> None: # Read in time-dependent parameters # Now only used to add fuel cost for bare model -def read_timeseries(scenario: message_ix.Scenario, material: str, filename: str) -> pd.DataFrame: +def read_timeseries( + scenario: message_ix.Scenario, material: str, filename: str +) -> pd.DataFrame: """ Read "timeseries" type data from a sector specific xlsx input file to DataFrame and format according to MESSAGEix standard @@ -2067,10 +2083,10 @@ def read_rel(scenario: message_ix.Scenario, material: str, filename: str): def gen_te_projections( - scen: message_ix.Scenario, - ssp: Literal["all", "LED", "SSP1", "SSP2", "SSP3", "SSP4", "SSP5"] = "SSP2", - method: Literal["constant", "convergence", "gdp"] = "convergence", - ref_reg: str = "R12_NAM", + scen: message_ix.Scenario, + ssp: Literal["all", "LED", "SSP1", "SSP2", "SSP3", "SSP4", "SSP5"] = "SSP2", + method: Literal["constant", "convergence", "gdp"] = "convergence", + ref_reg: str = "R12_NAM", ) -> tuple[pd.DataFrame, pd.DataFrame]: """ Calls message_ix_models.tools.costs with config for MESSAGEix-Materials @@ -2100,21 +2116,27 @@ def gen_te_projections( method=method, format="message", scenario=ssp, - final_year=2110 + final_year=2110, ) out_materials = create_cost_projections(cfg) - fix_cost = out_materials["fix_cost"].drop_duplicates().drop( - ["scenario_version", "scenario"], axis=1 + fix_cost = ( + out_materials["fix_cost"] + .drop_duplicates() + .drop(["scenario_version", "scenario"], axis=1) ) fix_cost = fix_cost[fix_cost["technology"].isin(model_tec_set)] - inv_cost = out_materials["inv_cost"].drop_duplicates().drop( - ["scenario_version", "scenario"], axis=1 + inv_cost = ( + out_materials["inv_cost"] + .drop_duplicates() + .drop(["scenario_version", "scenario"], axis=1) ) inv_cost = inv_cost[inv_cost["technology"].isin(model_tec_set)] return inv_cost, fix_cost -def get_ssp_soc_eco_data(context: Context, model: str, measure: str, tec): +def get_ssp_soc_eco_data( + context: message_ix_models.util.context.Context, model: str, measure: str, tec +): """ Function to update scenario GDP and POP timeseries to SSP 3.0 and format to MESSAGEix "bound_activity_*" DataFrame diff --git a/message_ix_models/model/material/report/reporting.py b/message_ix_models/model/material/report/reporting.py index 9e1c37a968..2e83995121 100644 --- a/message_ix_models/model/material/report/reporting.py +++ b/message_ix_models/model/material/report/reporting.py @@ -29,7 +29,7 @@ from ixmp.report import configure from message_ix_models import ScenarioInfo from message_data.tools.post_processing.iamc_report_hackathon import report as reporting -from message_data.model.material.util import read_config +from message_ix_models.model.material.util import read_config import pandas as pd import numpy as np diff --git a/message_ix_models/model/material/util.py b/message_ix_models/model/material/util.py index 733b193333..4b366e01e0 100644 --- a/message_ix_models/model/material/util.py +++ b/message_ix_models/model/material/util.py @@ -7,7 +7,7 @@ from scipy.optimize import curve_fit from message_ix_models import Context -from message_ix_models.util import load_private_data, private_data_path +from message_ix_models.util import load_package_data, package_data_path # Configuration files METADATA = [ @@ -42,7 +42,7 @@ def read_config() -> Context: _parts = list(parts) _parts[-1] += ".yaml" - context[key] = load_private_data(*_parts) + context[key] = load_package_data(*_parts) # Read material.yaml # context.metadata_path=Path("C:/Users/unlu/Documents/GitHub/message_data/data") @@ -105,7 +105,7 @@ def combine_df_dictionaries(*args: dict[str, pd.DataFrame]) -> dict: return comb_dict -def read_yaml_file(file_path: str) -> dict: +def read_yaml_file(file_path: str) -> dict or None: """ Tries to read yaml file into a dict @@ -165,14 +165,14 @@ def excel_to_csv(material_dir: str, fname: str) -> None: file name of xlsx file """ xlsx_dict = pd.read_excel( - private_data_path("material", material_dir, fname), sheet_name=None + package_data_path("material", material_dir, fname), sheet_name=None ) - if not os.path.isdir(private_data_path("material", "version control")): - os.mkdir(private_data_path("material", "version control")) - os.mkdir(private_data_path("material", "version control", fname)) + if not os.path.isdir(package_data_path("material", "version control")): + os.mkdir(package_data_path("material", "version control")) + os.mkdir(package_data_path("material", "version control", fname)) for tab in xlsx_dict.keys(): xlsx_dict[tab].to_csv( - private_data_path("material", "version control", fname, f"{tab}.csv"), + package_data_path("material", "version control", fname, f"{tab}.csv"), index=False, ) @@ -185,8 +185,8 @@ def get_all_input_data_dirs() -> list[str]: ------- list of folder names of material data """ - elements = os.listdir(private_data_path("material")) - elements = [i for i in elements if os.path.isdir(private_data_path("material", i))] + elements = os.listdir(package_data_path("material")) + elements = [i for i in elements if os.path.isdir(package_data_path("material", i))] return elements @@ -221,7 +221,7 @@ def exponential(x: float or list[float], b: float, m: float) -> float: float function value for given b, m and x """ - return b * m ** x + return b * m**x def price_fit(df: pd.DataFrame) -> float: From 4580fb657e500d7e69497215f7bb21d57a093b7c Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Mon, 13 May 2024 12:01:29 +0200 Subject: [PATCH 750/774] Apply black to materials modules --- .../model/material/data_aluminum.py | 95 +++++++++---------- .../model/material/data_ammonia_new.py | 6 +- .../model/material/data_buildings.py | 16 ++-- .../model/material/data_cement.py | 31 +++--- .../model/material/data_generic.py | 42 ++++---- .../model/material/data_methanol.py | 22 ++--- .../model/material/data_methanol_new.py | 2 +- .../model/material/data_petro.py | 11 +-- .../model/material/data_power_sector.py | 26 +++-- .../model/material/data_steel.py | 62 ++++++------ message_ix_models/model/material/data_util.py | 49 +++++----- 11 files changed, 164 insertions(+), 198 deletions(-) diff --git a/message_ix_models/model/material/data_aluminum.py b/message_ix_models/model/material/data_aluminum.py index 61fc5a4129..53bff89166 100644 --- a/message_ix_models/model/material/data_aluminum.py +++ b/message_ix_models/model/material/data_aluminum.py @@ -18,8 +18,9 @@ # Get endogenous material demand from buildings interface -def read_data_aluminum(scenario: message_ix.Scenario) \ - -> (pd.DataFrame, pd.DataFrame, pd.DataFrame): +def read_data_aluminum( + scenario: message_ix.Scenario, +) -> (pd.DataFrame, pd.DataFrame, pd.DataFrame): """Read and clean data from :file:`aluminum_techno_economic.xlsx`. Parameters @@ -34,7 +35,6 @@ def read_data_aluminum(scenario: message_ix.Scenario) \ """ # Ensure config is loaded, get the context - context = read_config() s_info = ScenarioInfo(scenario) # Shorter access to sets configuration @@ -44,10 +44,8 @@ def read_data_aluminum(scenario: message_ix.Scenario) \ if "R12_CHN" in s_info.N: sheet_n = "data_R12" - sheet_n_relations = "relations_R12" else: sheet_n = "data_R11" - sheet_n_relations = "relations_R11" # Read the file data_alu = pd.read_excel( @@ -77,7 +75,9 @@ def print_full(x: int): pd.reset_option("display.max_rows") -def gen_data_aluminum(scenario: message_ix.Scenario, dry_run: bool = False) -> dict[pd.DataFrame]: +def gen_data_aluminum( + scenario: message_ix.Scenario, dry_run: bool = False +) -> dict[str, pd.DataFrame]: """ Parameters @@ -106,11 +106,9 @@ def gen_data_aluminum(scenario: message_ix.Scenario, dry_run: bool = False) -> d # For each technology there are differnet input and output combinations # Iterate over technologies - allyears = s_info.set["year"] # s_info.Y is only for modeling years modelyears = s_info.Y # s_info.Y is only for modeling years nodes = s_info.N yv_ya = s_info.yv_ya - fmy = s_info.y0 nodes.remove("World") # Do not parametrize GLB region the same way @@ -122,7 +120,6 @@ def gen_data_aluminum(scenario: message_ix.Scenario, dry_run: bool = False) -> d global_region = "R12_GLB" for t in config["technology"]["add"]: - params = data_aluminum.loc[ (data_aluminum["technology"] == t), "parameter" ].values.tolist() @@ -145,16 +142,16 @@ def gen_data_aluminum(scenario: message_ix.Scenario, dry_run: bool = False) -> d val = data_aluminum.loc[ ( - (data_aluminum["technology"] == t) - & (data_aluminum["parameter"] == par) + (data_aluminum["technology"] == t) + & (data_aluminum["parameter"] == par) ), "value", ] regions = data_aluminum.loc[ ( - (data_aluminum["technology"] == t) - & (data_aluminum["parameter"] == par) + (data_aluminum["technology"] == t) + & (data_aluminum["parameter"] == par) ), "region", ] @@ -171,9 +168,7 @@ def gen_data_aluminum(scenario: message_ix.Scenario, dry_run: bool = False) -> d for rg in regions: # For the parameters which inlcudes index names if len(split) > 1: - if (param_name == "input") | (param_name == "output"): - # Assign commodity and level names # Later mod can be added com = split[1] @@ -189,7 +184,7 @@ def gen_data_aluminum(scenario: message_ix.Scenario, dry_run: bool = False) -> d unit="t", node_loc=rg, node_origin=global_region, - **common + **common, ) elif (param_name == "output") and (lev == "export"): @@ -202,14 +197,14 @@ def gen_data_aluminum(scenario: message_ix.Scenario, dry_run: bool = False) -> d unit="t", node_loc=rg, node_dest=global_region, - **common + **common, ) # Assign higher efficiency to younger plants elif ( - ((t == "soderberg_aluminum") or (t == "prebake_aluminum")) - & (com == "electr") - & (param_name == "input") + ((t == "soderberg_aluminum") or (t == "prebake_aluminum")) + & (com == "electr") + & (param_name == "input") ): # All the vıntage years year_vtg = sorted(set(yv_ya.year_vtg.values)) @@ -244,7 +239,7 @@ def gen_data_aluminum(scenario: message_ix.Scenario, dry_run: bool = False) -> d value=input_values_all, unit="t", node_loc=rg, - **common + **common, ).pipe(same_node) else: @@ -256,7 +251,7 @@ def gen_data_aluminum(scenario: message_ix.Scenario, dry_run: bool = False) -> d value=val[regions[regions == rg].index[0]], unit="t", node_loc=rg, - **common + **common, ).pipe(same_node) # Copy parameters to all regions, when node_loc is not GLB @@ -268,7 +263,6 @@ def gen_data_aluminum(scenario: message_ix.Scenario, dry_run: bool = False) -> d df = df.pipe(same_node) elif param_name == "emission_factor": - # Assign the emisson type emi = split[1] @@ -279,7 +273,7 @@ def gen_data_aluminum(scenario: message_ix.Scenario, dry_run: bool = False) -> d emission=emi, unit="t", node_loc=rg, - **common + **common, ) # Parameters with only parameter name @@ -290,14 +284,14 @@ def gen_data_aluminum(scenario: message_ix.Scenario, dry_run: bool = False) -> d value=val[regions[regions == rg].index[0]], unit="t", node_loc=rg, - **common + **common, ) # Copy parameters to all regions if ( - (len(regions) == 1) - and len(set(df["node_loc"])) == 1 - and list(set(df["node_loc"]))[0] != global_region + (len(regions) == 1) + and len(set(df["node_loc"])) == 1 + and list(set(df["node_loc"]))[0] != global_region ): df["node_loc"] = None df = df.pipe(broadcast, node_loc=nodes) @@ -306,7 +300,9 @@ def gen_data_aluminum(scenario: message_ix.Scenario, dry_run: bool = False) -> d # Create external demand param parname = "demand" - df = material_demand_calc.derive_demand("aluminum", scenario, old_gdp=False, ssp=ssp) + df = material_demand_calc.derive_demand( + "aluminum", scenario, old_gdp=False, ssp=ssp + ) results[parname].append(df) # Special treatment for time-varying params @@ -330,11 +326,11 @@ def gen_data_aluminum(scenario: message_ix.Scenario, dry_run: bool = False) -> d & (data_aluminum_ts["parameter"] == p), "value", ] - units = data_aluminum_ts.loc[ - (data_aluminum_ts["technology"] == t) - & (data_aluminum_ts["parameter"] == p), - "units", - ].values[0] + # units = data_aluminum_ts.loc[ + # (data_aluminum_ts["technology"] == t) + # & (data_aluminum_ts["parameter"] == p), + # "units", + # ].values[0] mod = data_aluminum_ts.loc[ (data_aluminum_ts["technology"] == t) & (data_aluminum_ts["parameter"] == p), @@ -355,7 +351,7 @@ def gen_data_aluminum(scenario: message_ix.Scenario, dry_run: bool = False) -> d year_vtg=yr, year_act=yr, mode=mod, - **common + **common, ).pipe(broadcast, node_loc=nodes) else: rg = data_aluminum_ts.loc[ @@ -372,7 +368,7 @@ def gen_data_aluminum(scenario: message_ix.Scenario, dry_run: bool = False) -> d year_act=yr, mode=mod, node_loc=rg, - **common + **common, ) results[p].append(df) @@ -405,7 +401,6 @@ def gen_data_aluminum(scenario: message_ix.Scenario, dry_run: bool = False) -> d relation=r, ) else: - # Use all the model years for other relations... common_rel = dict( year_rel=modelyears, @@ -416,11 +411,10 @@ def gen_data_aluminum(scenario: message_ix.Scenario, dry_run: bool = False) -> d for par_name in params: if par_name == "relation_activity": - tec_list = data_aluminum_rel.loc[ ( - (data_aluminum_rel["relation"] == r) - & (data_aluminum_rel["parameter"] == par_name) + (data_aluminum_rel["relation"] == r) + & (data_aluminum_rel["parameter"] == par_name) ), "technology", ] @@ -428,10 +422,10 @@ def gen_data_aluminum(scenario: message_ix.Scenario, dry_run: bool = False) -> d for tec in tec_list.unique(): val = data_aluminum_rel.loc[ ( - (data_aluminum_rel["relation"] == r) - & (data_aluminum_rel["parameter"] == par_name) - & (data_aluminum_rel["technology"] == tec) - & (data_aluminum_rel["Region"] == reg) + (data_aluminum_rel["relation"] == r) + & (data_aluminum_rel["parameter"] == par_name) + & (data_aluminum_rel["technology"] == tec) + & (data_aluminum_rel["Region"] == reg) ), "value", ].values[0] @@ -443,7 +437,7 @@ def gen_data_aluminum(scenario: message_ix.Scenario, dry_run: bool = False) -> d unit="-", node_loc=reg, node_rel=reg, - **common_rel + **common_rel, ).pipe(same_node) results[par_name].append(df) @@ -451,9 +445,9 @@ def gen_data_aluminum(scenario: message_ix.Scenario, dry_run: bool = False) -> d elif (par_name == "relation_upper") | (par_name == "relation_lower"): val = data_aluminum_rel.loc[ ( - (data_aluminum_rel["relation"] == r) - & (data_aluminum_rel["parameter"] == par_name) - & (data_aluminum_rel["Region"] == reg) + (data_aluminum_rel["relation"] == r) + & (data_aluminum_rel["parameter"] == par_name) + & (data_aluminum_rel["Region"] == reg) ), "value", ].values[0] @@ -469,10 +463,7 @@ def gen_data_aluminum(scenario: message_ix.Scenario, dry_run: bool = False) -> d def gen_mock_demand_aluminum(scenario: message_ix.Scenario) -> pd.DataFrame: - context = read_config() s_info = ScenarioInfo(scenario) - modelyears = s_info.Y # s_info.Y is only for modeling years - fmy = s_info.y0 nodes = s_info.N nodes.remove("World") @@ -517,7 +508,7 @@ def gen_mock_demand_aluminum(scenario: message_ix.Scenario) -> pd.DataFrame: gdp_growth = gdp_growth.loc[ (gdp_growth["Scenario"] == "baseline") & (gdp_growth["Region"] != "World") - ].drop(["Model", "Variable", "Unit", "Notes", 2000, 2005], axis=1) + ].drop(["Model", "Variable", "Unit", "Notes", 2000, 2005], axis=1) gdp_growth["Region"] = region_set + gdp_growth["Region"] diff --git a/message_ix_models/model/material/data_ammonia_new.py b/message_ix_models/model/material/data_ammonia_new.py index 861cfe3205..ace70671f5 100644 --- a/message_ix_models/model/material/data_ammonia_new.py +++ b/message_ix_models/model/material/data_ammonia_new.py @@ -138,7 +138,7 @@ def __missing__(self, key): df_temp["lifetime"] = df_temp["technology"].map(dict_lifetime) df_temp = df_temp[ (df_temp["year_act"] - df_temp["year_vtg"]) <= df_temp["lifetime"] - ] + ] par_dict[i] = df_temp.drop("lifetime", axis="columns") pars = ["inv_cost", "fix_cost"] tec_list = [ @@ -548,10 +548,10 @@ def gen_resid_demand_NH3(scenario, gdp_elasticity): ssp = context["ssp"] def get_demand_t1_with_income_elasticity( - demand_t0, income_t0, income_t1, elasticity + demand_t0, income_t0, income_t1, elasticity ): return ( - elasticity * demand_t0 * ((income_t1 - income_t0) / income_t0) + elasticity * demand_t0 * ((income_t1 - income_t0) / income_t0) ) + demand_t0 df_gdp = pd.read_excel( diff --git a/message_ix_models/model/material/data_buildings.py b/message_ix_models/model/material/data_buildings.py index 2634df95ce..422571399d 100644 --- a/message_ix_models/model/material/data_buildings.py +++ b/message_ix_models/model/material/data_buildings.py @@ -18,7 +18,6 @@ def read_timeseries_buildings(filename, scenario, case=CASE_SENS): - # Read the file and filter the given sensitivity case bld_input_raw = pd.read_csv(package_data_path("material", "buildings", filename)) bld_input_raw = bld_input_raw.loc[bld_input_raw.Sensitivity == case] @@ -26,7 +25,7 @@ def read_timeseries_buildings(filename, scenario, case=CASE_SENS): bld_input_mat = bld_input_raw[ bld_input_raw[ "Variable" - ].str.contains( #"Floor Space|Aluminum|Cement|Steel|Final Energy" + ].str.contains( # "Floor Space|Aluminum|Cement|Steel|Final Energy" "Floor Space|Aluminum|Cement|Steel" ) ] # Final Energy - Later. Need to figure out how to carve out @@ -75,7 +74,7 @@ def read_timeseries_buildings(filename, scenario, case=CASE_SENS): ].reset_index(drop=True) bld_area_long = bld_data_long[ bld_data_long["Variable"] == "Energy Service|Residential|Floor Space" - ].reset_index(drop=True) + ].reset_index(drop=True) tmp = bld_intensity_long.Variable.str.split("|", expand=True) @@ -107,7 +106,7 @@ def read_timeseries_buildings(filename, scenario, case=CASE_SENS): def get_scen_mat_demand( - commod, scenario, year="2020", inputfile=INPUTFILE, case=CASE_SENS + commod, scenario, year="2020", inputfile=INPUTFILE, case=CASE_SENS ): a, b, c = read_timeseries_buildings(inputfile, scenario, case) if not year == "all": # specific year @@ -119,9 +118,6 @@ def get_scen_mat_demand( def adjust_demand_param(scen): s_info = ScenarioInfo(scen) - modelyears = s_info.Y # s_info.Y is only for modeling years - - # scen.clone(model=scen.model, scenario=scen.scenario+"_building") scen_mat_demand = scen.par( "demand", {"level": "demand"} @@ -231,7 +227,7 @@ def gen_data_buildings(scenario, dry_run=False): value=val_mat.value, unit="t", node_loc=rg, - **common + **common, ) .pipe(same_node) .assign(year_act=copy_column("year_vtg")) @@ -249,7 +245,7 @@ def gen_data_buildings(scenario, dry_run=False): value=val_scr.value, unit="t", node_loc=rg, - **common + **common, ) .pipe(same_node) .assign(year_act=copy_column("year_vtg")) @@ -267,7 +263,7 @@ def gen_data_buildings(scenario, dry_run=False): value=1, unit="t", node_loc=rg, - **common + **common, ) .pipe(same_node) .assign(year_act=copy_column("year_vtg")) diff --git a/message_ix_models/model/material/data_cement.py b/message_ix_models/model/material/data_cement.py index 24cf3ff3b2..525ba77126 100644 --- a/message_ix_models/model/material/data_cement.py +++ b/message_ix_models/model/material/data_cement.py @@ -89,7 +89,7 @@ def gen_mock_demand_cement(scenario): gdp_growth = gdp_growth.loc[ (gdp_growth["Scenario"] == "baseline") & (gdp_growth["Region"] != "World") - ].drop(["Model", "Variable", "Unit", "Notes", 2000, 2005], axis=1) + ].drop(["Model", "Variable", "Unit", "Notes", 2000, 2005], axis=1) d = [a + b for a, b in zip(demand2020_top, demand2020_rest)] gdp_growth["Region"] = region_set + gdp_growth["Region"] @@ -188,12 +188,9 @@ def gen_data_cement(scenario, dry_run=False): # For each technology there are differnet input and output combinations # Iterate over technologies - allyears = s_info.set["year"] # s_info.Y is only for modeling years - modelyears = s_info.Y # s_info.Y is only for modeling years nodes = s_info.N yv_ya = s_info.yv_ya yv_ya = yv_ya.loc[yv_ya.year_vtg >= 1980] - fmy = s_info.y0 nodes.remove("World") # Do not parametrize GLB region the same way @@ -204,7 +201,6 @@ def gen_data_cement(scenario, dry_run=False): # for t in s_info.set['technology']: for t in config["technology"]["add"]: - params = data_cement.loc[ (data_cement["technology"] == t), "parameter" ].values.tolist() @@ -227,11 +223,11 @@ def gen_data_cement(scenario, dry_run=False): & (data_cement_ts["parameter"] == p), "value", ] - units = data_cement_ts.loc[ - (data_cement_ts["technology"] == t) - & (data_cement_ts["parameter"] == p), - "units", - ].values[0] + # units = data_cement_ts.loc[ + # (data_cement_ts["technology"] == t) + # & (data_cement_ts["parameter"] == p), + # "units", + # ].values[0] mod = data_cement_ts.loc[ (data_cement_ts["technology"] == t) & (data_cement_ts["parameter"] == p), @@ -252,7 +248,7 @@ def gen_data_cement(scenario, dry_run=False): year_vtg=yr, year_act=yr, mode=mod, - **common + **common, ).pipe(broadcast, node_loc=nodes) else: rg = data_cement_ts.loc[ @@ -269,14 +265,13 @@ def gen_data_cement(scenario, dry_run=False): year_act=yr, mode=mod, node_loc=rg, - **common + **common, ) results[p].append(df) # Iterate over parameters for par in params: - # Obtain the parameter names, commodity,level,emission split = par.split("|") param_name = split[0] @@ -300,7 +295,6 @@ def gen_data_cement(scenario, dry_run=False): ) for rg in regions: - # For the parameters which inlcudes index names if len(split) > 1: if (param_name == "input") | (param_name == "output"): @@ -318,11 +312,10 @@ def gen_data_cement(scenario, dry_run=False): mode=mod, unit="t", node_loc=rg, - **common + **common, ).pipe(same_node) elif param_name == "emission_factor": - # Assign the emisson type emi = split[1] mod = split[2] @@ -335,7 +328,7 @@ def gen_data_cement(scenario, dry_run=False): mode=mod, unit="t", node_loc=rg, - **common + **common, ) # .pipe(broadcast, \ # node_loc=nodes)) @@ -348,7 +341,7 @@ def gen_data_cement(scenario, dry_run=False): mode=mod, unit="t", node_loc=rg, - **common + **common, ) # .pipe(broadcast, node_loc=nodes)) # Parameters with only parameter name @@ -359,7 +352,7 @@ def gen_data_cement(scenario, dry_run=False): value=val[regions[regions == rg].index[0]], unit="t", node_loc=rg, - **common + **common, ) # .pipe(broadcast, node_loc=nodes)) if len(regions) == 1: diff --git a/message_ix_models/model/material/data_generic.py b/message_ix_models/model/material/data_generic.py index 4bd013a0d1..36af23e351 100644 --- a/message_ix_models/model/material/data_generic.py +++ b/message_ix_models/model/material/data_generic.py @@ -73,7 +73,6 @@ def gen_data_generic(scenario, dry_run=False): nodes.remove("World") for t in config["technology"]["add"]: - # years = s_info.Y params = data_generic.loc[ (data_generic["technology"] == t), "parameter" @@ -84,9 +83,7 @@ def gen_data_generic(scenario, dry_run=False): 0 ] modelyears = [year for year in modelyears if year >= av] - yva = yv_ya.loc[ - yv_ya.year_vtg >= av, - ] + yva = yv_ya.loc[yv_ya.year_vtg >= av,] # Iterate over parameters for par in params: @@ -95,8 +92,8 @@ def gen_data_generic(scenario, dry_run=False): val = data_generic.loc[ ( - (data_generic["technology"] == t) - & (data_generic["parameter"] == par) + (data_generic["technology"] == t) + & (data_generic["parameter"] == par) ), "value", ].values[0] @@ -114,9 +111,7 @@ def gen_data_generic(scenario, dry_run=False): ) if len(split) > 1: - if (param_name == "input") | (param_name == "output"): - com = split[1] lev = split[2] mod = split[3] @@ -130,7 +125,7 @@ def gen_data_generic(scenario, dry_run=False): mode=mod, value=val, unit="t", - **common + **common, ) .pipe(broadcast, node_loc=nodes) .pipe(same_node) @@ -150,7 +145,7 @@ def gen_data_generic(scenario, dry_run=False): emission=emi, mode="low_temp", unit="t", - **common + **common, ).pipe(broadcast, node_loc=nodes) df_high = make_df( @@ -160,7 +155,7 @@ def gen_data_generic(scenario, dry_run=False): emission=emi, mode="high_temp", unit="t", - **common + **common, ).pipe(broadcast, node_loc=nodes) results[param_name].append(df_low) @@ -169,7 +164,6 @@ def gen_data_generic(scenario, dry_run=False): # Rest of the parameters apart from input, output and emission_factor else: - df = make_df( param_name, technology=t, value=val, unit="t", **common ).pipe(broadcast, node_loc=nodes) @@ -199,16 +193,16 @@ def gen_data_generic(scenario, dry_run=False): ] regions = data_generic_ts.loc[ ( - (data_generic_ts["technology"] == t) - & (data_generic_ts["parameter"] == p) + (data_generic_ts["technology"] == t) + & (data_generic_ts["parameter"] == p) ), "region", ] - units = data_generic_ts.loc[ - (data_generic_ts["technology"] == t) - & (data_generic_ts["parameter"] == p), - "units", - ].values[0] + # units = data_generic_ts.loc[ + # (data_generic_ts["technology"] == t) + # & (data_generic_ts["parameter"] == p), + # "units", + # ].values[0] mod = data_generic_ts.loc[ (data_generic_ts["technology"] == t) & (data_generic_ts["parameter"] == p), @@ -229,7 +223,7 @@ def gen_data_generic(scenario, dry_run=False): year_vtg=yr, year_act=yr, mode=mod, - **common + **common, ).pipe(broadcast, node_loc=nodes) else: rg = data_generic_ts.loc[ @@ -246,14 +240,14 @@ def gen_data_generic(scenario, dry_run=False): year_act=yr, mode=mod, node_loc=rg, - **common + **common, ) # Copy parameters to all regions if ( - (len(set(regions)) == 1) - and len(set(df["node_loc"])) == 1 - and list(set(df["node_loc"]))[0] != global_region + (len(set(regions)) == 1) + and len(set(df["node_loc"])) == 1 + and list(set(df["node_loc"]))[0] != global_region ): df["node_loc"] = None df = df.pipe(broadcast, node_loc=nodes) diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index 4918137799..d7f405ac72 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -383,9 +383,9 @@ def gen_data_meth_chemicals(scenario, chemical): ) # exclude emissions for now if chemical == "MTO": - df = df.iloc[:15, ] + df = df.iloc[:15,] if chemical == "Formaldehyde": - df = df.iloc[:10, ] + df = df.iloc[:10,] common = dict( # commodity="NH3", @@ -508,7 +508,7 @@ def add_methanol_fuel_additives(scenario): par_dict_loil["input"] = par_dict_loil["input"][ par_dict_loil["input"]["commodity"] == "lightoil" - ] + ] df_mtbe = pd.read_excel( package_data_path( @@ -521,7 +521,7 @@ def add_methanol_fuel_additives(scenario): skiprows=[i for i in range(66)], sheet_name="MTBE calc", ) - df_mtbe = df_mtbe.iloc[1:13, ] + df_mtbe = df_mtbe.iloc[1:13,] df_mtbe["node_loc"] = "R12_" + df_mtbe["node_loc"] df_mtbe = df_mtbe[["node_loc", "% share on trp"]] @@ -551,14 +551,14 @@ def get_meth_share(df, node): ) df_loil_meth["commodity"] = "methanol" par_dict_loil["input"]["value"] = ( - par_dict_loil["input"]["value"] - df_loil_meth["value"] + par_dict_loil["input"]["value"] - df_loil_meth["value"] ) df_loil_eth = df_loil_meth.copy(deep=True) df_loil_eth["commodity"] = "ethanol" df_loil_eth["mode"] = "ethanol" df_loil_eth["value"] = ( - df_loil_eth["value"] * 1.6343 + df_loil_eth["value"] * 1.6343 ) # energy ratio methanol/ethanol in MTBE vs ETBE df_loil_eth_mode = par_dict_loil["input"].copy(deep=True) @@ -678,10 +678,10 @@ def gen_resin_demand(scenario, resin_share, sector, buildings_scen, pathway="SHA def gen_meth_residual_demand(gdp_elasticity_2020, gdp_elasticity_2030): def get_demand_t1_with_income_elasticity( - demand_t0, income_t0, income_t1, elasticity + demand_t0, income_t0, income_t1, elasticity ): return ( - elasticity * demand_t0 * ((income_t1 - income_t0) / income_t0) + elasticity * demand_t0 * ((income_t1 - income_t0) / income_t0) ) + demand_t0 df_gdp = pd.read_excel( @@ -699,7 +699,7 @@ def get_demand_t1_with_income_elasticity( ) df_demand_meth = df_demand_meth[ (~df_demand_meth["Region"].isna()) & (df_demand_meth["Region"] != "World") - ] + ] df_demand_meth = df_demand_meth.dropna(axis=1) df_demand = df.copy(deep=True) @@ -759,7 +759,7 @@ def get_cost_ratio_2020(scenario, tec_name, cost_type, ref_reg="R12_NAM", year=" def get_scaled_cost_from_proxy_tec( - value, scenario, proxy_tec, cost_type, new_tec, year="all" + value, scenario, proxy_tec, cost_type, new_tec, year="all" ): df = get_cost_ratio_2020(scenario, proxy_tec, cost_type, year=year) df["value"] = value * df["ratio"] @@ -854,7 +854,7 @@ def update_costs_with_loc_factor(df): df = df.merge(loc_fact, left_on="node_loc", right_index=True) df = df.merge(cost_conv, left_on="year_vtg", right_index=True) df["value"] = ( - df["value"] - (1 - df["WEU normalized"]) * df["value"] * df["convergence"] + df["value"] - (1 - df["WEU normalized"]) * df["value"] * df["convergence"] ) return df[df.columns[:-2]] diff --git a/message_ix_models/model/material/data_methanol_new.py b/message_ix_models/model/material/data_methanol_new.py index 14bf929eed..94946441c3 100644 --- a/message_ix_models/model/material/data_methanol_new.py +++ b/message_ix_models/model/material/data_methanol_new.py @@ -186,6 +186,6 @@ def broadcast_reduced_df(df, par_name): df_final_full[ (df_final_full.node_rel.values != "R12_GLB") & (df_final_full.node_rel.values != df_final_full.node_loc.values) - ].index + ].index ) return make_df(par_name, **df_final_full) diff --git a/message_ix_models/model/material/data_petro.py b/message_ix_models/model/material/data_petro.py index 2f5d438de0..503066f7dd 100644 --- a/message_ix_models/model/material/data_petro.py +++ b/message_ix_models/model/material/data_petro.py @@ -34,7 +34,6 @@ def read_data_petrochemicals(scenario): """Read and clean data from :file:`petrochemicals_techno_economic.xlsx`.""" # Ensure config is loaded, get the context - context = read_config() s_info = ScenarioInfo(scenario) fname = "petrochemicals_techno_economic.xlsx" @@ -178,11 +177,9 @@ def gen_data_petro_chemicals(scenario, dry_run=False): # For each technology there are differnet input and output combinations # Iterate over technologies - allyears = s_info.set["year"] modelyears = s_info.Y # s_info.Y is only for modeling years nodes = s_info.N yv_ya = s_info.yv_ya - fmy = s_info.y0 nodes.remove("World") # Do not parametrize GLB region the same way @@ -443,10 +440,10 @@ def gen_data_petro_chemicals(scenario, dry_run=False): (data_petro_ts["technology"] == t) & (data_petro_ts["parameter"] == p), "value", ] - units = data_petro_ts.loc[ - (data_petro_ts["technology"] == t) & (data_petro_ts["parameter"] == p), - "units", - ].values[0] + # units = data_petro_ts.loc[ + # (data_petro_ts["technology"] == t) & (data_petro_ts["parameter"] == p), + # "units", + # ].values[0] mod = data_petro_ts.loc[ (data_petro_ts["technology"] == t) & (data_petro_ts["parameter"] == p), "mode", diff --git a/message_ix_models/model/material/data_power_sector.py b/message_ix_models/model/material/data_power_sector.py index 8f7761036f..613d60291d 100644 --- a/message_ix_models/model/material/data_power_sector.py +++ b/message_ix_models/model/material/data_power_sector.py @@ -10,7 +10,7 @@ def read_material_intensities( - parameter, data_path, node, year, technology, commodity, level, inv_cost + parameter, data_path, node, year, technology, commodity, level, inv_cost ): if parameter in ["input_cap_new", "input_cap_ret", "output_cap_ret"]: #################################################################### @@ -22,9 +22,7 @@ def read_material_intensities( data_lca = pd.read_excel(data_path_lca, sheet_name="environmentalImpacts") # read technology, region and commodity mappings - data_path_tec_map = ( - data_path + "/MESSAGE_global_model_technologies.xlsx" - ) + data_path_tec_map = data_path + "/MESSAGE_global_model_technologies.xlsx" technology_mapping = pd.read_excel(data_path_tec_map, sheet_name="technology") data_path_reg_map = data_path + "/LCA_region_mapping.xlsx" @@ -41,9 +39,9 @@ def read_material_intensities( # mix for others) and remove operation phase (and remove duplicates) data_lca = data_lca.loc[ ( - (data_lca["scenario"] == "Baseline") - & (data_lca["technology variant"].isin(["mix", "residue"])) - & (data_lca["phase"] != "Operation") + (data_lca["scenario"] == "Baseline") + & (data_lca["technology variant"].isin(["mix", "residue"])) + & (data_lca["phase"] != "Operation") ) ] @@ -129,10 +127,10 @@ def read_material_intensities( for p in data_lca["phase"].unique(): temp = data_lca.loc[ ( - (data_lca["node"] == n) - & (data_lca["technology"] == t) - & (data_lca["commodity"] == c) - & (data_lca["phase"] == p) + (data_lca["node"] == n) + & (data_lca["technology"] == t) + & (data_lca["commodity"] == c) + & (data_lca["phase"] == p) ) ] temp["value"] = temp["value"].interpolate( @@ -182,7 +180,7 @@ def read_material_intensities( & (data_lca_final["phase"] == "Construction") & (data_lca_final["commodity"] == c) & (data_lca_final["year"] == yeff) - ]["value"].values[0] + ]["value"].values[0] val_cap_new = val_cap_new * 0.001 input_cap_new = pd.concat( @@ -211,7 +209,7 @@ def read_material_intensities( & (data_lca_final["phase"] == "End-of-life") & (data_lca_final["commodity"] == c) & (data_lca_final["year"] == yeff) - ]["value"].values[0] + ]["value"].values[0] val_cap_input_ret = val_cap_input_ret * 0.001 input_cap_ret = pd.concat( @@ -240,7 +238,7 @@ def read_material_intensities( & (data_lca_final["phase"] == "Construction") & (data_lca_final["commodity"] == c) & (data_lca_final["year"] == yeff) - ]["value"].values[0] + ]["value"].values[0] val_cap_output_ret = val_cap_output_ret * 0.001 output_cap_ret = pd.concat( diff --git a/message_ix_models/model/material/data_steel.py b/message_ix_models/model/material/data_steel.py index f487f4f5ae..ddf7e87d87 100644 --- a/message_ix_models/model/material/data_steel.py +++ b/message_ix_models/model/material/data_steel.py @@ -84,7 +84,7 @@ def gen_mock_demand_steel(scenario): gdp_growth = gdp_growth.loc[ (gdp_growth["Scenario"] == "baseline") & (gdp_growth["Region"] != "World") - ].drop(["Model", "Variable", "Unit", "Notes", 2000, 2005], axis=1) + ].drop(["Model", "Variable", "Unit", "Notes", 2000, 2005], axis=1) gdp_growth["Region"] = region_set + gdp_growth["Region"] @@ -151,7 +151,6 @@ def gen_data_steel(scenario, dry_run=False): # for t in s_info.set['technology']: for t in config["technology"]["add"]: - params = data_steel.loc[ (data_steel["technology"] == t), "parameter" ].values.tolist() @@ -198,7 +197,7 @@ def gen_data_steel(scenario, dry_run=False): year_vtg=yr, year_act=yr, mode=mod, - **common + **common, ).pipe(broadcast, node_loc=nodes) if p == "output": comm = data_steel_ts.loc[ @@ -231,7 +230,7 @@ def gen_data_steel(scenario, dry_run=False): node_dest=rg, commodity=comm, level=lev, - **common + **common, ) else: rg = data_steel_ts.loc[ @@ -248,7 +247,7 @@ def gen_data_steel(scenario, dry_run=False): year_act=yr, mode=mod, node_loc=rg, - **common + **common, ) results[p].append(df) @@ -278,11 +277,9 @@ def gen_data_steel(scenario, dry_run=False): ) for rg in regions: - # For the parameters which inlcudes index names if len(split) > 1: if (param_name == "input") | (param_name == "output"): - # Assign commodity and level names com = split[1] lev = split[2] @@ -298,7 +295,7 @@ def gen_data_steel(scenario, dry_run=False): unit="t", node_loc=rg, node_origin=global_region, - **common + **common, ) elif (param_name == "output") and (lev == "export"): @@ -312,7 +309,7 @@ def gen_data_steel(scenario, dry_run=False): unit="t", node_loc=rg, node_dest=global_region, - **common + **common, ) else: @@ -325,7 +322,7 @@ def gen_data_steel(scenario, dry_run=False): mode=mod, unit="t", node_loc=rg, - **common + **common, ).pipe(same_node) # Copy parameters to all regions, when node_loc is not GLB @@ -337,7 +334,6 @@ def gen_data_steel(scenario, dry_run=False): df = df.pipe(same_node) elif param_name == "emission_factor": - # Assign the emisson type emi = split[1] mod = split[2] @@ -350,7 +346,7 @@ def gen_data_steel(scenario, dry_run=False): mode=mod, unit="t", node_loc=rg, - **common + **common, ) else: # time-independent var_cost @@ -362,7 +358,7 @@ def gen_data_steel(scenario, dry_run=False): mode=mod, unit="t", node_loc=rg, - **common + **common, ) # Parameters with only parameter name @@ -373,13 +369,13 @@ def gen_data_steel(scenario, dry_run=False): value=val[regions[regions == rg].index[0]], unit="t", node_loc=rg, - **common + **common, ) # Copy parameters to all regions if ( - len(set(df["node_loc"])) == 1 - and list(set(df["node_loc"]))[0] != global_region + len(set(df["node_loc"])) == 1 + and list(set(df["node_loc"]))[0] != global_region ): df["node_loc"] = None df = df.pipe(broadcast, node_loc=nodes) @@ -400,8 +396,8 @@ def gen_data_steel(scenario, dry_run=False): "unit": "???", "value": data_steel_rel.loc[ ( - (data_steel_rel["relation"] == "max_global_recycling_steel") - & (data_steel_rel["parameter"] == "relation_activity") + (data_steel_rel["relation"] == "max_global_recycling_steel") + & (data_steel_rel["parameter"] == "relation_activity") ), "value", ].values[0], @@ -416,8 +412,8 @@ def gen_data_steel(scenario, dry_run=False): "unit": "???", "value": data_steel_rel.loc[ ( - (data_steel_rel["relation"] == "max_global_recycling_steel") - & (data_steel_rel["parameter"] == "relation_upper") + (data_steel_rel["relation"] == "max_global_recycling_steel") + & (data_steel_rel["parameter"] == "relation_upper") ), "value", ].values[0], @@ -432,8 +428,8 @@ def gen_data_steel(scenario, dry_run=False): "unit": "???", "value": data_steel_rel.loc[ ( - (data_steel_rel["relation"] == "max_global_recycling_steel") - & (data_steel_rel["parameter"] == "relation_lower") + (data_steel_rel["relation"] == "max_global_recycling_steel") + & (data_steel_rel["parameter"] == "relation_lower") ), "value", ].values[0], @@ -478,11 +474,10 @@ def gen_data_steel(scenario, dry_run=False): for par_name in params: if par_name == "relation_activity": - tec_list = data_steel_rel.loc[ ( - (data_steel_rel["relation"] == r) - & (data_steel_rel["parameter"] == par_name) + (data_steel_rel["relation"] == r) + & (data_steel_rel["parameter"] == par_name) ), "technology", ] @@ -490,10 +485,10 @@ def gen_data_steel(scenario, dry_run=False): for tec in tec_list.unique(): val = data_steel_rel.loc[ ( - (data_steel_rel["relation"] == r) - & (data_steel_rel["parameter"] == par_name) - & (data_steel_rel["technology"] == tec) - & (data_steel_rel["Region"] == reg) + (data_steel_rel["relation"] == r) + & (data_steel_rel["parameter"] == par_name) + & (data_steel_rel["technology"] == tec) + & (data_steel_rel["Region"] == reg) ), "value", ].values[0] @@ -505,7 +500,7 @@ def gen_data_steel(scenario, dry_run=False): unit="-", node_loc=reg, node_rel=reg, - **common_rel + **common_rel, ).pipe(same_node) results[par_name].append(df) @@ -513,9 +508,9 @@ def gen_data_steel(scenario, dry_run=False): elif (par_name == "relation_upper") | (par_name == "relation_lower"): val = data_steel_rel.loc[ ( - (data_steel_rel["relation"] == r) - & (data_steel_rel["parameter"] == par_name) - & (data_steel_rel["Region"] == reg) + (data_steel_rel["relation"] == r) + & (data_steel_rel["parameter"] == par_name) + & (data_steel_rel["Region"] == reg) ), "value", ].values[0] @@ -538,6 +533,7 @@ def gen_data_steel(scenario, dry_run=False): results["input"] = results["input"].replace({"freshwater_supply": "freshwater"}) return results + # # load rpy2 modules # import rpy2.robjects as ro # from rpy2.robjects import pandas2ri diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index 678c5ab242..602721b6fa 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -1,5 +1,5 @@ import os -from typing import Literal +from typing import TYPE_CHECKING, Literal import ixmp import message_ix @@ -19,6 +19,9 @@ from message_ix_models.tools.exo_data import prepare_computer from message_ix_models.util import package_data_path +if TYPE_CHECKING: + from message_ix_models import Context + pd.options.mode.chained_assignment = None @@ -103,9 +106,6 @@ def add_macro_COVID( message_ix.Scenario MACRO-calibrated Scenario instance """ - context = read_config() - info = ScenarioInfo(scen) - nodes = info.N # Excel file for calibration data if "SSP_dev" in scen.model: @@ -157,7 +157,6 @@ def modify_demand_and_hist_activity(scen: message_ix.Scenario) -> None: # NOTE Temporarily modifying industrial energy demand # From IEA database (dumped to an excel) - context = read_config() s_info = ScenarioInfo(scen) fname = "MESSAGEix-Materials_final_energy_industry.xlsx" @@ -234,7 +233,8 @@ def modify_demand_and_hist_activity(scen: message_ix.Scenario) -> None: df_feed = df[ (df["SECTOR"] == "feedstock (petrochemical industry)") & (df["FUEL"] == "total") ] - df_feed_total = df[(df["SECTOR"] == "feedstock (total)") & (df["FUEL"] == "total")] + # df_feed_total = + # df[(df["SECTOR"] == "feedstock (total)") & (df["FUEL"] == "total")] df_feed_temp = pd.DataFrame(columns=["REGION", "i_feed"]) df_feed_new = pd.DataFrame(columns=["REGION", "i_feed"]) @@ -484,7 +484,6 @@ def modify_demand_and_hist_activity_debug( MESSAGEix-GLOBIOM scenario """ - context = read_config() s_info = ScenarioInfo(scen) fname = "MESSAGEix-Materials_final_energy_industry.xlsx" @@ -499,9 +498,8 @@ def modify_demand_and_hist_activity_debug( region_name_CPA = "CPA" region_name_CHN = "" - df = pd.read_excel( - package_data_path("material", "other", fname), sheet_name=sheet_n, usecols="A:F" - ) + path = package_data_path("material", "other", fname) + df = pd.read_excel(path, sheet_name=sheet_n, usecols="A:F") # Filter the necessary variables df = df[ @@ -561,7 +559,8 @@ def modify_demand_and_hist_activity_debug( df_feed = df[ (df["SECTOR"] == "feedstock (petrochemical industry)") & (df["FUEL"] == "total") ] - df_feed_total = df[(df["SECTOR"] == "feedstock (total)") & (df["FUEL"] == "total")] + # df_feed_total = + # df[(df["SECTOR"] == "feedstock (total)") & (df["FUEL"] == "total")] df_feed_temp = pd.DataFrame(columns=["REGION", "i_feed"]) df_feed_new = pd.DataFrame(columns=["REGION", "i_feed"]) @@ -898,7 +897,7 @@ def calc_resid_ind_demand(scen: message_ix.Scenario, baseyear: int) -> pd.DataFr # ) Inp = pd.read_parquet(path, engine="fastparquet") Inp = map_iea_db_to_msg_regs(Inp, "R12_SSP_V1.yaml") - demand_shrs_new = calc_demand_shares(Inp, baseyear) + demand_shrs_new = calc_demand_shares(pd.DataFrame(Inp), baseyear) df_demands = scen.par("demand", filters={"commodity": comms}).set_index( ["node", "commodity", "year"] ) @@ -1038,9 +1037,12 @@ def get_hist_act_data(map_fname: str, years: list or None = None) -> pd.DataFram def add_emission_accounting(scen): - context = read_config() - s_info = ScenarioInfo(scen) + """ + Parameters + ---------- + scen + """ # (1) ******* Add non-CO2 gases to the relevant relations. ******** # This is done by multiplying the input values and emission_factor # per year,region and technology. @@ -1340,7 +1342,11 @@ def add_emission_accounting(scen): CF4_trp_Emissions = scen.par( "relation_activity", filters={"relation": "CF4_Emission"} ) - list_tec_trp = [l for l in CF4_trp_Emissions["technology"].unique() if "trp" in l] + list_tec_trp = [ + cf4_emi + for cf4_emi in CF4_trp_Emissions["technology"].unique() + if "trp" in cf4_emi + ] CF4_trp_Emissions = CF4_trp_Emissions[ CF4_trp_Emissions["technology"].isin(list_tec_trp) ] @@ -1350,7 +1356,9 @@ def add_emission_accounting(scen): # Remove transport related technologies from CF4_alm_red and add aluminum tecs. CF4_red = scen.par("relation_activity", filters={"relation": "CF4_alm_red"}) - list_tec_trp = [l for l in CF4_red["technology"].unique() if "trp" in l] + list_tec_trp = [ + cf4_emi for cf4_emi in CF4_red["technology"].unique() if "trp" in cf4_emi + ] CF4_red = CF4_red[CF4_red["technology"].isin(list_tec_trp)] scen.remove_par("relation_activity", CF4_red) @@ -1602,7 +1610,6 @@ def add_coal_lowerbound_2020(sc): def add_cement_bounds_2020(sc): """Set lower and upper bounds for gas and oil as a calibration for 2020""" - context = read_config() final_resid = pd.read_csv( package_data_path("material", "other", "residual_industry_2019.csv") ) @@ -1937,8 +1944,6 @@ def add_ccs_technologies(scen: message_ix.Scenario) -> None: scen: message_ix.Scenario Scenario instance to add CCS emission factor parametrization to """ - context = read_config() - s_info = ScenarioInfo(scen) # The relation coefficients for CO2_Emision and bco2_trans_disp and # co2_trans_disp are both MtC. The emission factor for CCS add_ccs_technologies @@ -2003,7 +2008,6 @@ def read_timeseries( DataFrame containing the timeseries data for MESSAGEix parameters """ # Ensure config is loaded, get the context - context = read_config() s_info = ScenarioInfo(scenario) # if context.scenario_info['scenario'] == 'NPi400': @@ -2064,7 +2068,6 @@ def read_rel(scenario: message_ix.Scenario, material: str, filename: str): DataFrame containing relation_* parameter data """ # Ensure config is loaded, get the context - context = read_config() s_info = ScenarioInfo(scenario) @@ -2134,9 +2137,7 @@ def gen_te_projections( return inv_cost, fix_cost -def get_ssp_soc_eco_data( - context: message_ix_models.util.context.Context, model: str, measure: str, tec -): +def get_ssp_soc_eco_data(context: "Context", model: str, measure: str, tec): """ Function to update scenario GDP and POP timeseries to SSP 3.0 and format to MESSAGEix "bound_activity_*" DataFrame From a480bc26bd57bb3c8f10a9bcc894b989f010f3be Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Mon, 13 May 2024 12:20:14 +0200 Subject: [PATCH 751/774] Resolve remaining code quality issues --- message_ix_models/model/material/bare.py | 6 +- .../model/material/data_aluminum.py | 3 +- .../model/material/data_ammonia_new.py | 4 +- .../model/material/data_buildings.py | 2 - .../model/material/data_methanol.py | 95 +- .../model/material/data_petro.py | 5 +- .../model/material/data_power_sector.py | 6 - .../model/material/data_steel.py | 10 +- .../model/material/report/reporting.py | 1905 ++++++++++------- 9 files changed, 1157 insertions(+), 879 deletions(-) diff --git a/message_ix_models/model/material/bare.py b/message_ix_models/model/material/bare.py index c4891e9a78..1538d3a3c1 100644 --- a/message_ix_models/model/material/bare.py +++ b/message_ix_models/model/material/bare.py @@ -137,7 +137,8 @@ def get_spec(context=None) -> Mapping[str, ScenarioInfo]: add = ScenarioInfo() # Add technologies - # JM: try to find out a way to loop over 1st/2nd level and to just context["material"][xx]["add"] + # JM: try to find out a way to loop over 1st/2nd level + # and to just context["material"][xx]["add"] add.set["technology"] = ( context["material"]["steel"]["technology"]["add"] + context["material"]["generic"]["technology"]["add"] @@ -164,7 +165,8 @@ def get_spec(context=None) -> Mapping[str, ScenarioInfo]: ) # JM: Leave the first time period as historical year - # add.set['cat_year'] = [('firstmodelyear', context.period_start + context.time_step)] + # add.set['cat_year'] = [('firstmodelyear', + # context.period_start + context.time_step)] # GU: Set 2020 as the first model year, leave the rest as historical year add.set["cat_year"] = [("firstmodelyear", context.first_model_year)] diff --git a/message_ix_models/model/material/data_aluminum.py b/message_ix_models/model/material/data_aluminum.py index 53bff89166..220b6ae55b 100644 --- a/message_ix_models/model/material/data_aluminum.py +++ b/message_ix_models/model/material/data_aluminum.py @@ -1,4 +1,5 @@ from collections import defaultdict +from typing import Sized import message_ix import pandas as pd @@ -69,7 +70,7 @@ def read_data_aluminum( return data_alu, data_alu_rel, data_aluminum_ts -def print_full(x: int): +def print_full(x: Sized): pd.set_option("display.max_rows", len(x)) print(x) pd.reset_option("display.max_rows") diff --git a/message_ix_models/model/material/data_ammonia_new.py b/message_ix_models/model/material/data_ammonia_new.py index ace70671f5..2643bfe87e 100644 --- a/message_ix_models/model/material/data_ammonia_new.py +++ b/message_ix_models/model/material/data_ammonia_new.py @@ -351,7 +351,7 @@ def read_demand(): ) # Read parameters in xlsx - te_params = data = pd.read_excel( + te_params = pd.read_excel( package_data_path("material", "ammonia", "nh3_fertilizer_demand.xlsx"), sheet_name="old_TE_sheet", engine="openpyxl", @@ -513,8 +513,6 @@ def read_demand(): def gen_demand(): - context = read_config() - N_energy = read_demand()["N_feed"] # updated feed with imports accounted demand_fs_org = pd.read_excel( diff --git a/message_ix_models/model/material/data_buildings.py b/message_ix_models/model/material/data_buildings.py index 422571399d..085b2166a2 100644 --- a/message_ix_models/model/material/data_buildings.py +++ b/message_ix_models/model/material/data_buildings.py @@ -117,8 +117,6 @@ def get_scen_mat_demand( def adjust_demand_param(scen): - s_info = ScenarioInfo(scen) - scen_mat_demand = scen.par( "demand", {"level": "demand"} ) # mat demand without buildings considered diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index d7f405ac72..f47d566bdc 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -64,22 +64,19 @@ def gen_data_methanol(scenario): # add meth prod fs mode par_dict_fs = {} for i in scenario.par_list(): - try: - df = scenario.par( - i, - filters={ - "technology": [ - "meth_ng", - "meth_coal", - "meth_ng_ccs", - "meth_coal_ccs", - ] - }, - ) - if df.size != 0: - par_dict_fs[i] = df - except: - pass + df = scenario.par( + i, + filters={ + "technology": [ + "meth_ng", + "meth_coal", + "meth_ng_ccs", + "meth_coal_ccs", + ] + }, + ) + if df.size != 0: + par_dict_fs[i] = df for i in par_dict_fs.keys(): if "mode" in par_dict_fs[i].columns: par_dict_fs[i]["mode"] = "feedstock" @@ -89,22 +86,20 @@ def gen_data_methanol(scenario): par_dict = {} for i in scenario.par_list(): - try: - df = scenario.par( - i, - filters={ - "technology": [ - "meth_ng", - "meth_coal", - "meth_ng_ccs", - "meth_coal_ccs", - ] - }, - ) - if df.size != 0: - par_dict[i] = df - except: - pass + df = scenario.par( + i, + filters={ + "technology": [ + "meth_ng", + "meth_coal", + "meth_ng_ccs", + "meth_coal_ccs", + ] + }, + ) + if df.size != 0: + par_dict[i] = df + for i in par_dict.keys(): if "mode" in par_dict[i].columns: par_dict[i]["mode"] = "fuel" @@ -115,13 +110,10 @@ def gen_data_methanol(scenario): bal_fs_dict = {} bal_fuel_dict = {} for i in scenario.par_list(): - try: - df = scenario.par(i, filters={"technology": "meth_bal"}) - if df.size != 0: - bal_fs_dict[i] = df - bal_fuel_dict[i] = df.copy(deep=True) - except: - pass + df = scenario.par(i, filters={"technology": "meth_bal"}) + if df.size != 0: + bal_fs_dict[i] = df + bal_fuel_dict[i] = df.copy(deep=True) for i in bal_fs_dict.keys(): if "mode" in bal_fs_dict[i].columns: bal_fs_dict[i]["mode"] = "feedstock" @@ -138,15 +130,13 @@ def gen_data_methanol(scenario): trade_dict_fs = {} trade_dict_fuel = {} for i in scenario.par_list(): - try: - df = scenario.par( - i, filters={"technology": ["meth_imp", "meth_exp", "meth_trd"]} - ) - if df.size != 0: - trade_dict_fs[i] = df - trade_dict_fuel[i] = df.copy(deep=True) - except: - pass + df = scenario.par( + i, filters={"technology": ["meth_imp", "meth_exp", "meth_trd"]} + ) + if df.size != 0: + trade_dict_fs[i] = df + trade_dict_fuel[i] = df.copy(deep=True) + for i in trade_dict_fs.keys(): if "mode" in trade_dict_fs[i].columns: trade_dict_fs[i]["mode"] = "feedstock" @@ -499,12 +489,9 @@ def add_methanol_fuel_additives(scenario): ) return par_dict_loil for i in pars: - try: - df = scenario.par(i, filters={"technology": "loil_trp", "mode": "M1"}) - if df.size != 0: - par_dict_loil[i] = df.copy(deep=True) - except: - pass + df = scenario.par(i, filters={"technology": "loil_trp", "mode": "M1"}) + if df.size != 0: + par_dict_loil[i] = df.copy(deep=True) par_dict_loil["input"] = par_dict_loil["input"][ par_dict_loil["input"]["commodity"] == "lightoil" diff --git a/message_ix_models/model/material/data_petro.py b/message_ix_models/model/material/data_petro.py index 503066f7dd..acbef37acc 100644 --- a/message_ix_models/model/material/data_petro.py +++ b/message_ix_models/model/material/data_petro.py @@ -82,7 +82,7 @@ def get_demand_t1_with_income_elasticity( df_gdp_ts = gdp_mer.pivot( index="Region", columns="year", values="gdp_ppp" ).reset_index() - num_cols = [i for i in df_gdp_ts.columns if type(i) == int] + num_cols = [i for i in df_gdp_ts.columns if isinstance(i, int)] hist_yrs = [i for i in num_cols if i < fy] df_gdp_ts = ( df_gdp_ts.drop([i for i in hist_yrs if i in df_gdp_ts.columns], axis=1) @@ -441,7 +441,8 @@ def gen_data_petro_chemicals(scenario, dry_run=False): "value", ] # units = data_petro_ts.loc[ - # (data_petro_ts["technology"] == t) & (data_petro_ts["parameter"] == p), + # (data_petro_ts["technology"] == t) & + # (data_petro_ts["parameter"] == p), # "units", # ].values[0] mod = data_petro_ts.loc[ diff --git a/message_ix_models/model/material/data_power_sector.py b/message_ix_models/model/material/data_power_sector.py index 613d60291d..31edf790a1 100644 --- a/message_ix_models/model/material/data_power_sector.py +++ b/message_ix_models/model/material/data_power_sector.py @@ -1,13 +1,10 @@ from collections import defaultdict -from pathlib import Path import pandas as pd from message_ix_models import ScenarioInfo from message_ix_models.util import package_data_path -from .util import read_config - def read_material_intensities( parameter, data_path, node, year, technology, commodity, level, inv_cost @@ -275,11 +272,8 @@ def read_material_intensities( def gen_data_power_sector(scenario, dry_run=False): """Generate data for materials representation of power industry.""" # Load configuration - context = read_config() - config = context["material"]["power_sector"] # paths to lca data - code_path = Path(__file__).parents[0] / "material_intensity" data_path = package_data_path("material", "power_sector") # Information about scenario, e.g. node, year diff --git a/message_ix_models/model/material/data_steel.py b/message_ix_models/model/material/data_steel.py index ddf7e87d87..b6ecbd3608 100644 --- a/message_ix_models/model/material/data_steel.py +++ b/message_ix_models/model/material/data_steel.py @@ -173,11 +173,11 @@ def gen_data_steel(scenario, dry_run=False): & (data_steel_ts["parameter"] == p), "value", ] - units = data_steel_ts.loc[ - (data_steel_ts["technology"] == t) - & (data_steel_ts["parameter"] == p), - "units", - ].values[0] + # units = data_steel_ts.loc[ + # (data_steel_ts["technology"] == t) + # & (data_steel_ts["parameter"] == p), + # "units", + # ].values[0] mod = data_steel_ts.loc[ (data_steel_ts["technology"] == t) & (data_steel_ts["parameter"] == p), diff --git a/message_ix_models/model/material/report/reporting.py b/message_ix_models/model/material/report/reporting.py index 2e83995121..d9a79a005d 100644 --- a/message_ix_models/model/material/report/reporting.py +++ b/message_ix_models/model/material/report/reporting.py @@ -38,7 +38,7 @@ import os import openpyxl -#import plotly.graph_objects as go +# import plotly.graph_objects as go import matplotlib matplotlib.use("Agg") @@ -47,6 +47,7 @@ from pyam.plotting import OUTSIDE_LEGEND from matplotlib.backends.backend_pdf import PdfPages + def print_full(x): pd.set_option("display.max_rows", len(x)) print(x) @@ -54,7 +55,6 @@ def print_full(x): def change_names(s): - """Change the sector names according to IMAC format.""" if s == "aluminum": @@ -65,23 +65,22 @@ def change_names(s): s = "Non-Metallic Minerals|Cement" elif s == "petro": s = "Chemicals|High Value Chemicals" - elif s == 'ammonia': + elif s == "ammonia": s = "Chemicals|Ammonia" - elif s == 'methanol': + elif s == "methanol": s = "Chemicals|Methanol" elif s == "BCA": s = "BC" elif s == "OCA": s = "OC" elif s == "CO2_industry": - s == "CO2" + s = "CO2" else: - s == s + s = s return s def fix_excel(path_temp, path_new): - """ Fix the names of the regions or variables to be compatible with IAMC format. This is done in the final reported excel file @@ -92,8 +91,8 @@ def fix_excel(path_temp, path_new): sheet = workbook["data"] new_workbook = openpyxl.Workbook() - new_sheet = new_workbook['Sheet'] - new_sheet.title = 'data' + new_sheet = new_workbook["Sheet"] + new_sheet.title = "data" new_sheet = new_workbook.active replacement = { @@ -155,7 +154,10 @@ def fix_excel(path_temp, path_new): } # Iterate over the rows and replace for i in range(1, ((sheet.max_row) + 1)): - data = [sheet.cell(row=i, column=col).value for col in range(1, ((sheet.max_column) + 1))] + data = [ + sheet.cell(row=i, column=col).value + for col in range(1, ((sheet.max_column) + 1)) + ] for index, value in enumerate(data): col_no = index + 1 if value in replacement.keys(): @@ -166,8 +168,7 @@ def fix_excel(path_temp, path_new): new_workbook.save(path_new) -def report(context,scenario): - +def report(context, scenario): # Obtain scenario information and directory s_info = ScenarioInfo(scenario) @@ -241,25 +242,25 @@ def report(context,scenario): "out|final_material|propylene|*", "out|secondary|fueloil|agg_ref|*", "out|secondary|lightoil|agg_ref|*", - 'out|useful|i_therm|solar_i|M1', - 'out|useful_steel|lt_heat|solar_steel|*', - 'out|useful_aluminum|lt_heat|solar_aluminum|*', - 'out|useful_cement|lt_heat|solar_cement|*', - 'out|useful_petro|lt_heat|solar_petro|*', - 'out|useful_resins|lt_heat|solar_resins|*', + "out|useful|i_therm|solar_i|M1", + "out|useful_steel|lt_heat|solar_steel|*", + "out|useful_aluminum|lt_heat|solar_aluminum|*", + "out|useful_cement|lt_heat|solar_cement|*", + "out|useful_petro|lt_heat|solar_petro|*", + "out|useful_resins|lt_heat|solar_resins|*", "in|final|*", "in|secondary|coal|coal_NH3|M1", "in|secondary|electr|NH3_to_N_fertil|M1", - 'in|secondary|electr|coal_NH3|M1', - 'in|secondary|electr|electr_NH3|M1', - 'in|secondary|electr|gas_NH3|M1', - 'in|secondary|fueloil|fueloil_NH3|M1', - 'in|secondary|gas|gas_NH3|M1', + "in|secondary|electr|coal_NH3|M1", + "in|secondary|electr|electr_NH3|M1", + "in|secondary|electr|gas_NH3|M1", + "in|secondary|fueloil|fueloil_NH3|M1", + "in|secondary|gas|gas_NH3|M1", "in|secondary|coal|coal_NH3_ccs|M1", - 'in|secondary|electr|coal_NH3_ccs|M1', - 'in|secondary|electr|gas_NH3_ccs|M1', - 'in|secondary|fueloil|fueloil_NH3_ccs|M1', - 'in|secondary|gas|gas_NH3_ccs|M1', + "in|secondary|electr|coal_NH3_ccs|M1", + "in|secondary|electr|gas_NH3_ccs|M1", + "in|secondary|fueloil|fueloil_NH3_ccs|M1", + "in|secondary|gas|gas_NH3_ccs|M1", "in|primary|biomass|biomass_NH3|M1", "in|seconday|electr|biomass_NH3|M1", "in|primary|biomass|biomass_NH3_ccs|M1", @@ -268,17 +269,17 @@ def report(context,scenario): "in|secondary|electr|fueloil_NH3|M1", "in|secondary|electr|fueloil_NH3_ccs|M1", "in|secondary|coal|meth_coal|feedstock", - 'in|secondary|coal|meth_coal_ccs|feedstock', - 'in|secondary|gas|meth_ng_ccs|feedstock', + "in|secondary|coal|meth_coal_ccs|feedstock", + "in|secondary|gas|meth_ng_ccs|feedstock", "in|secondary|gas|meth_ng|feedstock", - 'in|secondary|electr|meth_ng|feedstock', - 'in|secondary|electr|meth_ng_ccs|feedstock', + "in|secondary|electr|meth_ng|feedstock", + "in|secondary|electr|meth_ng_ccs|feedstock", "in|secondary|electr|meth_coal|feedstock", - 'in|secondary|electr|meth_coal_ccs|feedstock', - 'in|primary|biomass|meth_bio|feedstock', - 'in|primary|biomass|meth_bio_ccs|feedstock', - 'in|secondary|hydrogen|meth_h2|feedstock', - 'in|secondary|electr|meth_h2|feedstock', + "in|secondary|electr|meth_coal_ccs|feedstock", + "in|primary|biomass|meth_bio|feedstock", + "in|primary|biomass|meth_bio_ccs|feedstock", + "in|secondary|hydrogen|meth_h2|feedstock", + "in|secondary|electr|meth_h2|feedstock", "in|desulfurized|*|steam_cracker_petro|*", "in|secondary_material|*|steam_cracker_petro|*", "in|dummy_end_of_life_1|aluminum|scrap_recovery_aluminum_1|M1", @@ -313,59 +314,139 @@ def report(context,scenario): # Methanol input conversion from material to energy unit - df.divide("in|final_material|methanol|MTO_petro|M1", (1/0.6976), - "in|final_material|methanol|MTO_petro|energy", append=True, ignore_units=True) + df.divide( + "in|final_material|methanol|MTO_petro|M1", + (1 / 0.6976), + "in|final_material|methanol|MTO_petro|energy", + append=True, + ignore_units=True, + ) - df.divide("in|final_material|methanol|CH2O_synth|M1", (1/0.6976), - "in|final_material|methanol|CH2O_synth|energy", append=True, ignore_units=True) + df.divide( + "in|final_material|methanol|CH2O_synth|M1", + (1 / 0.6976), + "in|final_material|methanol|CH2O_synth|energy", + append=True, + ignore_units=True, + ) # Convert methanol at primary_material from energy to material unit # In the model this is kept as energy units to easily seperate two modes: fuel and feedstock - df.divide("out|primary_material|methanol|meth_coal|feedstock", 0.6976, - "out|primary_material|methanol|meth_coal|feedstockMt", append=True, ignore_units=True) + df.divide( + "out|primary_material|methanol|meth_coal|feedstock", + 0.6976, + "out|primary_material|methanol|meth_coal|feedstockMt", + append=True, + ignore_units=True, + ) - df.divide("out|primary_material|methanol|meth_coal_ccs|feedstock", 0.6976, - "out|primary_material|methanol|meth_coal_ccs|feedstockMt", append=True, ignore_units=True) + df.divide( + "out|primary_material|methanol|meth_coal_ccs|feedstock", + 0.6976, + "out|primary_material|methanol|meth_coal_ccs|feedstockMt", + append=True, + ignore_units=True, + ) - df.divide("out|primary_material|methanol|meth_ng|feedstock", 0.6976, - "out|primary_material|methanol|meth_ng|feedstockMt", append=True, ignore_units=True) + df.divide( + "out|primary_material|methanol|meth_ng|feedstock", + 0.6976, + "out|primary_material|methanol|meth_ng|feedstockMt", + append=True, + ignore_units=True, + ) - df.divide("out|primary_material|methanol|meth_ng_ccs|feedstock", 0.6976, - "out|primary_material|methanol|meth_ng_ccs|feedstockMt", append=True, ignore_units=True) + df.divide( + "out|primary_material|methanol|meth_ng_ccs|feedstock", + 0.6976, + "out|primary_material|methanol|meth_ng_ccs|feedstockMt", + append=True, + ignore_units=True, + ) - df.divide("out|primary_material|methanol|meth_bio|feedstock", 0.6976, - "out|primary_material|methanol|meth_bio|feedstockMt", append=True, ignore_units=True) + df.divide( + "out|primary_material|methanol|meth_bio|feedstock", + 0.6976, + "out|primary_material|methanol|meth_bio|feedstockMt", + append=True, + ignore_units=True, + ) - df.divide("out|primary_material|methanol|meth_bio_ccs|feedstock", 0.6976, - "out|primary_material|methanol|meth_bio_ccs|feedstockMt", append=True, ignore_units=True) + df.divide( + "out|primary_material|methanol|meth_bio_ccs|feedstock", + 0.6976, + "out|primary_material|methanol|meth_bio_ccs|feedstockMt", + append=True, + ignore_units=True, + ) - df.divide("out|primary_material|methanol|meth_h2|feedstock", 0.6976, - "out|primary_material|methanol|meth_h2|feedstockMt", append=True, ignore_units=True) + df.divide( + "out|primary_material|methanol|meth_h2|feedstock", + 0.6976, + "out|primary_material|methanol|meth_h2|feedstockMt", + append=True, + ignore_units=True, + ) # Convert methanol at primary from energy to material unit # In the model this is kept as energy units to easily seperate two modes: fuel and feedstock - df.divide("out|primary|methanol|meth_coal|fuel", 0.6976, - "out|primary|methanol|meth_coal|fuelMt", append=True, ignore_units=True) + df.divide( + "out|primary|methanol|meth_coal|fuel", + 0.6976, + "out|primary|methanol|meth_coal|fuelMt", + append=True, + ignore_units=True, + ) - df.divide("out|primary|methanol|meth_coal_ccs|fuel", 0.6976, - "out|primary|methanol|meth_coal_ccs|fuelMt", append=True, ignore_units=True) + df.divide( + "out|primary|methanol|meth_coal_ccs|fuel", + 0.6976, + "out|primary|methanol|meth_coal_ccs|fuelMt", + append=True, + ignore_units=True, + ) - df.divide("out|primary|methanol|meth_ng|fuel", 0.6976, - "out|primary|methanol|meth_ng|fuelMt", append=True, ignore_units=True) + df.divide( + "out|primary|methanol|meth_ng|fuel", + 0.6976, + "out|primary|methanol|meth_ng|fuelMt", + append=True, + ignore_units=True, + ) - df.divide("out|primary|methanol|meth_ng_ccs|fuel", 0.6976, - "out|primary|methanol|meth_ng_ccs|fuelMt", append=True, ignore_units=True) + df.divide( + "out|primary|methanol|meth_ng_ccs|fuel", + 0.6976, + "out|primary|methanol|meth_ng_ccs|fuelMt", + append=True, + ignore_units=True, + ) - df.divide("out|primary|methanol|meth_bio|fuel", 0.6976, - "out|primary|methanol|meth_bio|fuelMt", append=True, ignore_units=True) + df.divide( + "out|primary|methanol|meth_bio|fuel", + 0.6976, + "out|primary|methanol|meth_bio|fuelMt", + append=True, + ignore_units=True, + ) - df.divide("out|primary|methanol|meth_bio_ccs|fuel", 0.6976, - "out|primary|methanol|meth_bio_ccs|fuelMt", append=True, ignore_units=True) + df.divide( + "out|primary|methanol|meth_bio_ccs|fuel", + 0.6976, + "out|primary|methanol|meth_bio_ccs|fuelMt", + append=True, + ignore_units=True, + ) - df.divide("out|primary|methanol|meth_h2|fuel", 0.6976, - "out|primary|methanol|meth_h2|fuelMt", append=True, ignore_units=True) + df.divide( + "out|primary|methanol|meth_h2|fuel", + 0.6976, + "out|primary|methanol|meth_h2|fuelMt", + append=True, + ignore_units=True, + ) - df.convert_unit('unknown', to='', factor=1, inplace = True) + df.convert_unit("unknown", to="", factor=1, inplace=True) variables = df.variable df.aggregate_region(variables, region="World", method="sum", append=True) @@ -429,7 +510,7 @@ def report(context,scenario): df_al = df.copy() df_al.filter(region=r, year=years, inplace=True) df_al.filter(variable=["out|*|aluminum|*", "in|*|aluminum|*"], inplace=True) - df_al.convert_unit('', to='Mt/yr', factor=1, inplace = True) + df_al.convert_unit("", to="Mt/yr", factor=1, inplace=True) df_al_graph = df_al.copy() df_al_graph.filter( @@ -475,7 +556,7 @@ def report(context,scenario): df_steel = df.copy() df_steel.filter(region=r, year=years, inplace=True) df_steel.filter(variable=["out|*|steel|*", "in|*|steel|*"], inplace=True) - df_steel.convert_unit('', to='Mt/yr', factor=1, inplace = True) + df_steel.convert_unit("", to="Mt/yr", factor=1, inplace=True) df_steel_graph = df_steel.copy() df_steel_graph.filter( @@ -489,7 +570,10 @@ def report(context,scenario): if r == "World": df_steel.filter(variable=["out|*|steel|*", "in|*|steel|*"], inplace=True) df_steel_graph.filter( - variable=["out|final_material|steel|*",], inplace=True, + variable=[ + "out|final_material|steel|*", + ], + inplace=True, ) df_steel_graph.plot.stack(ax=ax2) @@ -513,10 +597,9 @@ def report(context,scenario): ], inplace=True, ) - df_petro.convert_unit('', to='Mt/yr', factor=1, inplace = True) + df_petro.convert_unit("", to="Mt/yr", factor=1, inplace=True) if r == "World": - df_petro.filter( variable=[ "in|final|ethanol|ethanol_to_ethylene_petro|M1", @@ -564,7 +647,7 @@ def report(context,scenario): "out|new_scrap|aluminum|manuf_aluminum|M1", "in|dummy_end_of_life_1|aluminum|scrap_recovery_aluminum_1|M1", "in|dummy_end_of_life_2|aluminum|scrap_recovery_aluminum_2|M1", - "in|dummy_end_of_life_3|aluminum|scrap_recovery_aluminum_3|M1" + "in|dummy_end_of_life_3|aluminum|scrap_recovery_aluminum_3|M1", ] # Total Available Scrap: @@ -572,9 +655,11 @@ def report(context,scenario): # + from power and buildings sector new_scrap_al_vars = ["out|new_scrap|aluminum|manuf_aluminum|M1"] - old_scrap_al_vars = ["out|dummy_end_of_life_1|aluminum|total_EOL_aluminum|M1", - "out|dummy_end_of_life_2|aluminum|total_EOL_aluminum|M1", - "out|dummy_end_of_life_3|aluminum|total_EOL_aluminum|M1"] + old_scrap_al_vars = [ + "out|dummy_end_of_life_1|aluminum|total_EOL_aluminum|M1", + "out|dummy_end_of_life_2|aluminum|total_EOL_aluminum|M1", + "out|dummy_end_of_life_3|aluminum|total_EOL_aluminum|M1", + ] df_al.aggregate( "Production|Primary|Non-Ferrous Metals|Aluminium", @@ -607,13 +692,13 @@ def report(context,scenario): df_al.aggregate( "Total Scrap|Non-Ferrous Metals|Aluminium", - components=new_scrap_al_vars+old_scrap_al_vars, + components=new_scrap_al_vars + old_scrap_al_vars, append=True, ) df_al.aggregate( "Total Scrap|Non-Ferrous Metals", - components=new_scrap_al_vars+old_scrap_al_vars, + components=new_scrap_al_vars + old_scrap_al_vars, append=True, ) @@ -653,14 +738,15 @@ def report(context,scenario): # STEEL - primary_steel_vars = ["out|final_material|steel|bof_steel|M1", - "out|final_material|steel|eaf_steel|M1", - "out|final_material|steel|eaf_steel|M3" - ] + primary_steel_vars = [ + "out|final_material|steel|bof_steel|M1", + "out|final_material|steel|eaf_steel|M1", + "out|final_material|steel|eaf_steel|M3", + ] secondary_steel_vars = [ "out|final_material|steel|eaf_steel|M2", - "in|new_scrap|steel|bof_steel|M1" + "in|new_scrap|steel|bof_steel|M1", ] collected_scrap_steel_vars = [ @@ -672,14 +758,22 @@ def report(context,scenario): old_scrap_steel_vars = ["out|dummy_end_of_life|steel|total_EOL_steel|M1"] df_steel.aggregate( - "Production|Primary|Steel (before sub.)", components=primary_steel_vars, append=True, + "Production|Primary|Steel (before sub.)", + components=primary_steel_vars, + append=True, ) - df_steel.subtract("Production|Primary|Steel (before sub.)", - "in|new_scrap|steel|bof_steel|M1","Production|Primary|Steel", append = True) + df_steel.subtract( + "Production|Primary|Steel (before sub.)", + "in|new_scrap|steel|bof_steel|M1", + "Production|Primary|Steel", + append=True, + ) df_steel.aggregate( - "Production|Secondary|Steel", components=secondary_steel_vars, append=True, + "Production|Secondary|Steel", + components=secondary_steel_vars, + append=True, ) df_steel.aggregate( @@ -689,7 +783,9 @@ def report(context,scenario): ) df_steel.aggregate( - "Collected Scrap|Steel", components=collected_scrap_steel_vars, append=True, + "Collected Scrap|Steel", + components=collected_scrap_steel_vars, + append=True, ) df_steel.aggregate( "Total Scrap|Steel", components=total_scrap_steel_vars, append=True @@ -699,9 +795,9 @@ def report(context,scenario): "Total Scrap|Steel|Old Scrap", components=old_scrap_steel_vars, append=True ) - #df_steel.aggregate( + # df_steel.aggregate( # "Total Scrap|Steel|New Scrap", components=new_scrap_steel_vars, append=True - #) + # ) df_steel.filter( variable=[ @@ -720,35 +816,41 @@ def report(context,scenario): df_chemicals = df.copy() df_chemicals.filter(region=r, year=years, inplace=True) - df_chemicals.filter(variable=['out|secondary_material|NH3|*', - "out|final_material|ethylene|*", - "out|final_material|propylene|*", - "out|final_material|BTX|*", - 'out|primary_material|methanol|*|feedstockMt', - 'out|primary|methanol|*|fuelMt' - ],inplace=True) - df_chemicals.convert_unit('', to='Mt/yr', factor=1, inplace = True) - df_chemicals.convert_unit('GWa', to='Mt/yr', factor=(1/0.6976), inplace=True) - - # Methanol - - # In Mt units - primary_methanol_chemical_vars = ["out|primary_material|methanol|meth_coal|feedstockMt", - "out|primary_material|methanol|meth_coal_ccs|feedstockMt", - "out|primary_material|methanol|meth_ng|feedstockMt", - "out|primary_material|methanol|meth_ng_ccs|feedstockMt", - "out|primary_material|methanol|meth_bio|feedstockMt", - "out|primary_material|methanol|meth_bio_ccs|feedstockMt", - "out|primary_material|methanol|meth_h2|feedstockMt", - ] - methanol_fuel_vars = ["out|primary|methanol|meth_coal|fuelMt", - "out|primary|methanol|meth_coal_ccs|fuelMt", - "out|primary|methanol|meth_ng|fuelMt", - "out|primary|methanol|meth_ng_ccs|fuelMt", - "out|primary|methanol|meth_bio|fuelMt", - "out|primary|methanol|meth_bio_ccs|fuelMt", - "out|primary|methanol|meth_h2|fuelMt", - ] + df_chemicals.filter( + variable=[ + "out|secondary_material|NH3|*", + "out|final_material|ethylene|*", + "out|final_material|propylene|*", + "out|final_material|BTX|*", + "out|primary_material|methanol|*|feedstockMt", + "out|primary|methanol|*|fuelMt", + ], + inplace=True, + ) + df_chemicals.convert_unit("", to="Mt/yr", factor=1, inplace=True) + df_chemicals.convert_unit("GWa", to="Mt/yr", factor=(1 / 0.6976), inplace=True) + + # Methanol + + # In Mt units + primary_methanol_chemical_vars = [ + "out|primary_material|methanol|meth_coal|feedstockMt", + "out|primary_material|methanol|meth_coal_ccs|feedstockMt", + "out|primary_material|methanol|meth_ng|feedstockMt", + "out|primary_material|methanol|meth_ng_ccs|feedstockMt", + "out|primary_material|methanol|meth_bio|feedstockMt", + "out|primary_material|methanol|meth_bio_ccs|feedstockMt", + "out|primary_material|methanol|meth_h2|feedstockMt", + ] + methanol_fuel_vars = [ + "out|primary|methanol|meth_coal|fuelMt", + "out|primary|methanol|meth_coal_ccs|fuelMt", + "out|primary|methanol|meth_ng|fuelMt", + "out|primary|methanol|meth_ng_ccs|fuelMt", + "out|primary|methanol|meth_bio|fuelMt", + "out|primary|methanol|meth_bio_ccs|fuelMt", + "out|primary|methanol|meth_h2|fuelMt", + ] methanol_total_vars = primary_methanol_chemical_vars + methanol_fuel_vars @@ -779,11 +881,21 @@ def report(context,scenario): # add entries for each methanol technology meth_tec_list = [i.replace("fuel", "M1") for i in methanol_fuel_vars] df_meth_individual = df_chemicals.filter(variable=meth_tec_list) - df_meth_individual.convert_unit('Mt/yr', to='Mt/yr', factor=(1/0.6976), inplace=True) + df_meth_individual.convert_unit( + "Mt/yr", to="Mt/yr", factor=(1 / 0.6976), inplace=True + ) var_name = "Production|Methanol|" for i in df_meth_individual["variable"]: - df_meth_individual.rename({"variable": {i: i.replace("out|primary|methanol|", var_name).replace("|M1", "")}}, - inplace=True) + df_meth_individual.rename( + { + "variable": { + i: i.replace("out|primary|methanol|", var_name).replace( + "|M1", "" + ) + } + }, + inplace=True, + ) # AMMONIA @@ -796,7 +908,7 @@ def report(context,scenario): "out|secondary_material|NH3|biomass_NH3_ccs|M1", "out|secondary_material|NH3|fueloil_NH3|M1", "out|secondary_material|NH3|fueloil_NH3_ccs|M1", - "out|secondary_material|NH3|electr_NH3|M1" + "out|secondary_material|NH3|electr_NH3|M1", ] df_chemicals.aggregate( @@ -811,7 +923,7 @@ def report(context,scenario): append=True, ) - # High Value Chemicals + # High Value Chemicals intermediate_petro_vars = [ "out|final_material|ethylene|ethanol_to_ethylene_petro|M1", @@ -845,7 +957,11 @@ def report(context,scenario): # Totals - chemicals_vars = intermediate_petro_vars + primary_ammonia_vars + primary_methanol_chemical_vars + chemicals_vars = ( + intermediate_petro_vars + + primary_ammonia_vars + + primary_methanol_chemical_vars + ) df_chemicals.aggregate( "Production|Primary|Chemicals", components=chemicals_vars, @@ -869,7 +985,7 @@ def report(context,scenario): "Production|Primary|Chemicals|Methanol", "Production|Chemicals|Methanol", "Production|Fuel|Methanol", - 'Production|Methanol' + "Production|Methanol", ], inplace=True, ) @@ -884,7 +1000,6 @@ def report(context,scenario): # CEMENT for r in nodes: - # PRODUCTION - PLOT fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 10)) @@ -909,9 +1024,10 @@ def report(context,scenario): df_cement = df.copy() df_cement.filter(region=r, year=years, inplace=True) - df_cement.filter(variable=["out|product|cement|*", - "out|tertiary_material|clinker_cement|*" - ], inplace=True) + df_cement.filter( + variable=["out|product|cement|*", "out|tertiary_material|clinker_cement|*"], + inplace=True, + ) # df_cement.plot.stack(ax=ax2) # ax2.legend( # ["Ballmill Grinding", "Vertical Mill Grinding"], @@ -933,20 +1049,24 @@ def report(context,scenario): ] clinker_vars = [ - "out|tertiary_material|clinker_cement|clinker_dry_cement|M1", - "out|tertiary_material|clinker_cement|clinker_wet_cement|M1" + "out|tertiary_material|clinker_cement|clinker_dry_cement|M1", + "out|tertiary_material|clinker_cement|clinker_wet_cement|M1", ] total_scrap_cement_vars = ["out|dummy_end_of_life|cement|total_EOL_cement|M1"] - df_cement.convert_unit('', to='Mt/yr', factor=1, inplace = True) + df_cement.convert_unit("", to="Mt/yr", factor=1, inplace=True) df_cement.aggregate( - "Production|Non-Metallic Minerals|Clinker", components=clinker_vars, append=True, + "Production|Non-Metallic Minerals|Clinker", + components=clinker_vars, + append=True, ) df_cement.aggregate( - "Production|Primary|Non-Metallic Minerals|Cement", components=primary_cement_vars, append=True, + "Production|Primary|Non-Metallic Minerals|Cement", + components=primary_cement_vars, + append=True, ) df_cement.aggregate( @@ -956,7 +1076,9 @@ def report(context,scenario): ) df_cement.aggregate( - "Production|Non-Metallic Minerals|Cement", components=primary_cement_vars, append=True, + "Production|Non-Metallic Minerals|Cement", + components=primary_cement_vars, + append=True, ) df_cement.aggregate( @@ -987,7 +1109,15 @@ def report(context,scenario): # HVC production, ammonia production and methanol production. print("Final Energy by fuels only non-energy use is being printed.") - commodities = ["gas", "liquids", "solids",'hydrogen','methanol',"all",'electr_gas'] + commodities = [ + "gas", + "liquids", + "solids", + "hydrogen", + "methanol", + "all", + "electr_gas", + ] for c in commodities: for r in nodes: @@ -996,7 +1126,8 @@ def report(context,scenario): # GWa to EJ/yr df_final_energy.convert_unit("", to="GWa", factor=1, inplace=True) df_final_energy.convert_unit( - "GWa", to="EJ/yr", factor=0.03154, inplace=True) + "GWa", to="EJ/yr", factor=0.03154, inplace=True + ) df_final_energy.filter(region=r, year=years, inplace=True) df_final_energy.filter( variable=["in|final|*|cokeoven_steel|*"], keep=False, inplace=True @@ -1014,45 +1145,61 @@ def report(context,scenario): "in|final|gas|gas_processing_petro|*", "in|final|*|loil_fs|*", "in|final|*|gas_fs|*", - 'in|secondary|gas|gas_NH3|M1', - 'in|secondary|gas|gas_NH3_ccs|M1', - 'in|secondary|fueloil|fueloil_NH3|M1', - 'in|secondary|fueloil|fueloil_NH3_ccs|M1', - 'in|secondary|electr|electr_NH3|M1', - "in|secondary|coal|coal_NH3|M1", - "in|secondary|coal|coal_NH3_ccs|M1", - 'in|primary|biomass|biomass_NH3_ccs|M1', - 'in|primary|biomass|biomass_NH3|M1', - "in|final_material|methanol|MTO_petro|energy", - "in|final_material|methanol|CH2O_synth|energy", - 'in|secondary|coal|meth_coal|feedstock', - 'in|secondary|coal|meth_coal_ccs|feedstock', - 'in|secondary|gas|meth_ng|feedstock', - 'in|secondary|gas|meth_ng_ccs|feedstock', - 'in|primary|biomass|meth_bio|feedstock', - 'in|primary|biomass|meth_bio_ccs|feedstock', - 'in|secondary|hydrogen|meth_h2|feedstock', + "in|secondary|gas|gas_NH3|M1", + "in|secondary|gas|gas_NH3_ccs|M1", + "in|secondary|fueloil|fueloil_NH3|M1", + "in|secondary|fueloil|fueloil_NH3_ccs|M1", + "in|secondary|electr|electr_NH3|M1", + "in|secondary|coal|coal_NH3|M1", + "in|secondary|coal|coal_NH3_ccs|M1", + "in|primary|biomass|biomass_NH3_ccs|M1", + "in|primary|biomass|biomass_NH3|M1", + "in|final_material|methanol|MTO_petro|energy", + "in|final_material|methanol|CH2O_synth|energy", + "in|secondary|coal|meth_coal|feedstock", + "in|secondary|coal|meth_coal_ccs|feedstock", + "in|secondary|gas|meth_ng|feedstock", + "in|secondary|gas|meth_ng_ccs|feedstock", + "in|primary|biomass|meth_bio|feedstock", + "in|primary|biomass|meth_bio_ccs|feedstock", + "in|secondary|hydrogen|meth_h2|feedstock", ], inplace=True, ) - if c == 'all': - df_final_energy.filter(variable=["in|final_material|methanol|MTO_petro|energy", - "in|final_material|methanol|CH2O_synth|energy",], - keep=False,inplace=True) - if c == 'electr_gas': - df_final_energy.filter(variable=['in|secondary|electr|electr_NH3|M1',], - inplace=True) + if c == "all": + df_final_energy.filter( + variable=[ + "in|final_material|methanol|MTO_petro|energy", + "in|final_material|methanol|CH2O_synth|energy", + ], + keep=False, + inplace=True, + ) + if c == "electr_gas": + df_final_energy.filter( + variable=[ + "in|secondary|electr|electr_NH3|M1", + ], + inplace=True, + ) if c == "gas": - df_final_energy.filter(variable=["in|final|gas|*", - 'in|secondary|gas|gas_NH3|M1', - 'in|secondary|gas|gas_NH3_ccs|M1', - 'in|secondary|gas|meth_ng|feedstock', - 'in|secondary|gas|meth_ng_ccs|feedstock', - 'in|secondary|electr|electr_NH3|M1'], - inplace=True) - df_final_energy.filter(variable=["in|final|gas|gas_processing_petro|*"], - keep=False, inplace=True) + df_final_energy.filter( + variable=[ + "in|final|gas|*", + "in|secondary|gas|gas_NH3|M1", + "in|secondary|gas|gas_NH3_ccs|M1", + "in|secondary|gas|meth_ng|feedstock", + "in|secondary|gas|meth_ng_ccs|feedstock", + "in|secondary|electr|electr_NH3|M1", + ], + inplace=True, + ) + df_final_energy.filter( + variable=["in|final|gas|gas_processing_petro|*"], + keep=False, + inplace=True, + ) if c == "liquids": df_final_energy.filter( variable=[ @@ -1064,35 +1211,43 @@ def report(context,scenario): "in|final|atm_gasoil|*", "in|final|vacuum_gasoil|*", "in|final|naphtha|*", - 'in|secondary|fueloil|fueloil_NH3|M1', - 'in|secondary|fueloil|fueloil_NH3_ccs|M1', - "in|final|gas|gas_processing_petro|*" + "in|secondary|fueloil|fueloil_NH3|M1", + "in|secondary|fueloil|fueloil_NH3_ccs|M1", + "in|final|gas|gas_processing_petro|*", ], inplace=True, ) if c == "solids": df_final_energy.filter( - variable=["in|final|biomass|*", "in|final|coal|*", - "in|secondary|coal|coal_NH3|M1", - "in|secondary|coal|coal_NH3_ccs|M1", - 'in|primary|biomass|biomass_NH3_ccs|M1', - 'in|primary|biomass|biomass_NH3|M1', - 'in|secondary|coal|meth_coal|feedstock', - 'in|secondary|coal|meth_coal_ccs|feedstock', - 'in|primary|biomass|meth_bio|feedstock', - 'in|primary|biomass|meth_bio_ccs|feedstock', - ], inplace=True) + variable=[ + "in|final|biomass|*", + "in|final|coal|*", + "in|secondary|coal|coal_NH3|M1", + "in|secondary|coal|coal_NH3_ccs|M1", + "in|primary|biomass|biomass_NH3_ccs|M1", + "in|primary|biomass|biomass_NH3|M1", + "in|secondary|coal|meth_coal|feedstock", + "in|secondary|coal|meth_coal_ccs|feedstock", + "in|primary|biomass|meth_bio|feedstock", + "in|primary|biomass|meth_bio_ccs|feedstock", + ], + inplace=True, + ) if c == "hydrogen": df_final_energy.filter( variable=[ - 'in|secondary|hydrogen|meth_h2|feedstock', - ], inplace=True) + "in|secondary|hydrogen|meth_h2|feedstock", + ], + inplace=True, + ) if c == "methanol": df_final_energy.filter( variable=[ - "in|final_material|methanol|MTO_petro|energy", - "in|final_material|methanol|CH2O_synth|energy", - ], inplace=True) + "in|final_material|methanol|MTO_petro|energy", + "in|final_material|methanol|CH2O_synth|energy", + ], + inplace=True, + ) all_flows = df_final_energy.timeseries().reset_index() splitted_vars = [v.split("|") for v in all_flows.variable] @@ -1118,7 +1273,9 @@ def report(context,scenario): if c == "all": df_final_energy.aggregate( - "Final Energy|Non-Energy Use", components=var_sectors, append=True, + "Final Energy|Non-Energy Use", + components=var_sectors, + append=True, ) df_final_energy.filter( variable=["Final Energy|Non-Energy Use"], inplace=True @@ -1129,7 +1286,6 @@ def report(context,scenario): df_final.append(df_final_energy, inplace=True) if c == "methanol": - df_final_energy.aggregate( "Final Energy|Non-Energy Use|Other", components=var_sectors, @@ -1137,7 +1293,8 @@ def report(context,scenario): ) df_final_energy.filter( - variable=["Final Energy|Non-Energy Use|Other"], inplace=True, + variable=["Final Energy|Non-Energy Use|Other"], + inplace=True, ) df_final_energy.convert_unit( "GWa", to="EJ/yr", factor=0.03154, inplace=True @@ -1145,7 +1302,6 @@ def report(context,scenario): df_final.append(df_final_energy, inplace=True) if c == "hydrogen": - df_final_energy.aggregate( "Final Energy|Non-Energy Use|Hydrogen", components=var_sectors, @@ -1153,7 +1309,8 @@ def report(context,scenario): ) df_final_energy.filter( - variable=["Final Energy|Non-Energy Use|Hydrogen"], inplace=True, + variable=["Final Energy|Non-Energy Use|Hydrogen"], + inplace=True, ) df_final_energy.convert_unit( "GWa", to="EJ/yr", factor=0.03154, inplace=True @@ -1161,7 +1318,6 @@ def report(context,scenario): df_final.append(df_final_energy, inplace=True) if c == "electr_gas": - df_final_energy.aggregate( "Final Energy|Non-Energy Use|Gases|Electricity", components=var_sectors, @@ -1169,7 +1325,8 @@ def report(context,scenario): ) df_final_energy.filter( - variable=["Final Energy|Non-Energy Use|Gases|Electricity"], inplace=True, + variable=["Final Energy|Non-Energy Use|Gases|Electricity"], + inplace=True, ) df_final_energy.convert_unit( "GWa", to="EJ/yr", factor=0.03154, inplace=True @@ -1177,7 +1334,6 @@ def report(context,scenario): df_final.append(df_final_energy, inplace=True) if c == "gas": - # Can not distinguish by type Gases (natural gas, biomass, synthetic fossil, efuel) # (coal_gas), from biomass (gas_bio), natural gas (gas_bal): All go into secondary level # Can not be distinguished in the final level. @@ -1188,7 +1344,8 @@ def report(context,scenario): ) df_final_energy.filter( - variable=["Final Energy|Non-Energy Use|Gases"], inplace=True, + variable=["Final Energy|Non-Energy Use|Gases"], + inplace=True, ) df_final_energy.convert_unit( "GWa", to="EJ/yr", factor=0.03154, inplace=True @@ -1196,7 +1353,6 @@ def report(context,scenario): df_final.append(df_final_energy, inplace=True) if c == "liquids": - # All liquids df_final_energy.aggregate( "Final Energy|Non-Energy Use|Liquids", @@ -1207,8 +1363,9 @@ def report(context,scenario): # Only bios filter_vars = [ - v for v in aux2_df["variable"].values if (("ethanol" in v) - & ("methanol" not in v)) + v + for v in aux2_df["variable"].values + if (("ethanol" in v) & ("methanol" not in v)) ] df_final_energy.aggregate( "Final Energy|Non-Energy Use|Liquids|Biomass", @@ -1238,11 +1395,7 @@ def report(context,scenario): # Natural Gas Liquids (Ethane/Propane) filter_vars = [ - v - for v in aux2_df["variable"].values - if ( - ("gas_proc" in v) - ) + v for v in aux2_df["variable"].values if (("gas_proc" in v)) ] df_final_energy.aggregate( @@ -1256,7 +1409,7 @@ def report(context,scenario): "Final Energy|Non-Energy Use|Liquids", "Final Energy|Non-Energy Use|Liquids|Oil", "Final Energy|Non-Energy Use|Liquids|Biomass", - "Final Energy|Non-Energy Use|Liquids|Gas" + "Final Energy|Non-Energy Use|Liquids|Gas", ], inplace=True, ) @@ -1265,7 +1418,6 @@ def report(context,scenario): ) df_final.append(df_final_energy, inplace=True) if c == "solids": - # All df_final_energy.aggregate( "Final Energy|Non-Energy Use|Solids", @@ -1308,37 +1460,52 @@ def report(context,scenario): # has seperate input values in the model. print("Final Energy by fuels excluding non-energy use is being printed.") - commodities = ["electr", "gas", "hydrogen", "liquids", "solids", "heat", 'solar', "all"] + commodities = [ + "electr", + "gas", + "hydrogen", + "liquids", + "solids", + "heat", + "solar", + "all", + ] for c in commodities: - for r in nodes: - df_final_energy = df.copy() df_final_energy.convert_unit("", to="GWa", factor=1, inplace=True) df_final_energy.filter(region=r, year=years, inplace=True) df_final_energy.filter( - variable=["in|final|*|cokeoven_steel|*", - "in|final|co_gas|*", - "in|final|bf_gas|*"], keep=False, inplace=True + variable=[ + "in|final|*|cokeoven_steel|*", + "in|final|co_gas|*", + "in|final|bf_gas|*", + ], + keep=False, + inplace=True, ) if c == "electr": - df_final_energy.filter(variable=["in|final|electr|*", - 'in|secondary|electr|NH3_to_N_fertil|M1', - 'in|secondary|electr|coal_NH3|M1', - 'in|secondary|electr|fueloil_NH3|M1', - 'in|secondary|electr|gas_NH3|M1', - 'in|secondary|electr|coal_NH3_ccs|M1', - 'in|secondary|electr|fueloil_NH3_ccs|M1', - 'in|secondary|electr|gas_NH3_ccs|M1', - 'in|secondary|electr|biomass_NH3_ccs|M1', - 'in|secondary|electr|biomass_NH3|M1', - 'in|secondary|electr|meth_ng|feedstock', - 'in|secondary|electr|meth_ng_ccs|feedstock', - 'in|secondary|electr|meth_coal|feedstock', - 'in|secondary|electr|meth_coal_ccs|feedstock', - 'in|secondary|electr|meth_h2|feedstock' - ], inplace=True) + df_final_energy.filter( + variable=[ + "in|final|electr|*", + "in|secondary|electr|NH3_to_N_fertil|M1", + "in|secondary|electr|coal_NH3|M1", + "in|secondary|electr|fueloil_NH3|M1", + "in|secondary|electr|gas_NH3|M1", + "in|secondary|electr|coal_NH3_ccs|M1", + "in|secondary|electr|fueloil_NH3_ccs|M1", + "in|secondary|electr|gas_NH3_ccs|M1", + "in|secondary|electr|biomass_NH3_ccs|M1", + "in|secondary|electr|biomass_NH3|M1", + "in|secondary|electr|meth_ng|feedstock", + "in|secondary|electr|meth_ng_ccs|feedstock", + "in|secondary|electr|meth_coal|feedstock", + "in|secondary|electr|meth_coal_ccs|feedstock", + "in|secondary|electr|meth_h2|feedstock", + ], + inplace=True, + ) if c == "gas": df_final_energy.filter(variable=["in|final|gas|*"], inplace=True) # Do not include gasoil and naphtha feedstock @@ -1365,37 +1532,45 @@ def report(context,scenario): df_final_energy.filter(variable=["in|final|hydrogen|*"], inplace=True) if c == "heat": df_final_energy.filter(variable=["in|final|d_heat|*"], inplace=True) - if c == 'solar': - df_final_energy.filter(variable=["out|useful|i_therm|solar_i|*", - 'out|useful_aluminum|lt_heat|solar_aluminum|*', - 'out|useful_steel|lt_heat|solar_steel|*', - 'out|useful_cement|lt_heat|solar_cement|*', - 'out|useful_petro|lt_heat|solar_petro|*', - 'out|useful_resins|lt_heat|solar_resins|*', - ], inplace=True) + if c == "solar": + df_final_energy.filter( + variable=[ + "out|useful|i_therm|solar_i|*", + "out|useful_aluminum|lt_heat|solar_aluminum|*", + "out|useful_steel|lt_heat|solar_steel|*", + "out|useful_cement|lt_heat|solar_cement|*", + "out|useful_petro|lt_heat|solar_petro|*", + "out|useful_resins|lt_heat|solar_resins|*", + ], + inplace=True, + ) if c == "all": - df_final_energy.filter(variable=["in|final|*", - 'in|secondary|electr|NH3_to_N_fertil|M1', - 'in|secondary|electr|coal_NH3|M1', - 'in|secondary|electr|fueloil_NH3|M1', - 'in|secondary|electr|gas_NH3|M1', - 'in|secondary|electr|coal_NH3_ccs|M1', - 'in|secondary|electr|fueloil_NH3_ccs|M1', - 'in|secondary|electr|gas_NH3_ccs|M1', - 'in|secondary|electr|biomass_NH3_ccs|M1', - 'in|secondary|electr|biomass_NH3|M1', - 'out|useful|i_therm|solar_i|M1', - 'out|useful_aluminum|lt_heat|solar_aluminum|*', - 'out|useful_steel|lt_heat|solar_steel|*', - 'out|useful_cement|lt_heat|solar_cement|*', - 'out|useful_petro|lt_heat|solar_petro|*', - 'out|useful_resins|lt_heat|solar_resins|*', - 'in|secondary|electr|meth_ng_ccs|feedstock', - 'in|secondary|electr|meth_ng|feedstock', - 'in|secondary|electr|meth_coal|feedstock', - 'in|secondary|electr|meth_coal_ccs|feedstock', - 'in|secondary|electr|meth_h2|feedstock' - ], inplace=True) + df_final_energy.filter( + variable=[ + "in|final|*", + "in|secondary|electr|NH3_to_N_fertil|M1", + "in|secondary|electr|coal_NH3|M1", + "in|secondary|electr|fueloil_NH3|M1", + "in|secondary|electr|gas_NH3|M1", + "in|secondary|electr|coal_NH3_ccs|M1", + "in|secondary|electr|fueloil_NH3_ccs|M1", + "in|secondary|electr|gas_NH3_ccs|M1", + "in|secondary|electr|biomass_NH3_ccs|M1", + "in|secondary|electr|biomass_NH3|M1", + "out|useful|i_therm|solar_i|M1", + "out|useful_aluminum|lt_heat|solar_aluminum|*", + "out|useful_steel|lt_heat|solar_steel|*", + "out|useful_cement|lt_heat|solar_cement|*", + "out|useful_petro|lt_heat|solar_petro|*", + "out|useful_resins|lt_heat|solar_resins|*", + "in|secondary|electr|meth_ng_ccs|feedstock", + "in|secondary|electr|meth_ng|feedstock", + "in|secondary|electr|meth_coal|feedstock", + "in|secondary|electr|meth_coal_ccs|feedstock", + "in|secondary|electr|meth_h2|feedstock", + ], + inplace=True, + ) all_flows = df_final_energy.timeseries().reset_index() splitted_vars = [v.split("|") for v in all_flows.variable] @@ -1411,33 +1586,35 @@ def report(context,scenario): # Include only the related industry sector variables and state some # exceptions var_sectors = [ - v for v in aux2_df["variable"].values - if (( - (v.split('|')[3].endswith("cement")) - | (v.split('|')[3].endswith("steel")) - | (v.split('|')[3].endswith("aluminum")) - | (v.split('|')[3].endswith("petro")) - | (v.split('|')[3].endswith("resins")) - | (v.split('|')[3].endswith("_i")) - | (v.split('|')[3].endswith("_I")) - | (('NH3') in v) - | (v.split('|')[3].startswith("meth")) - | (v.split('|')[3].startswith("CH2O")) - - ) + v + for v in aux2_df["variable"].values + if ( + ( + (v.split("|")[3].endswith("cement")) + | (v.split("|")[3].endswith("steel")) + | (v.split("|")[3].endswith("aluminum")) + | (v.split("|")[3].endswith("petro")) + | (v.split("|")[3].endswith("resins")) + | (v.split("|")[3].endswith("_i")) + | (v.split("|")[3].endswith("_I")) + | (("NH3") in v) + | (v.split("|")[3].startswith("meth")) + | (v.split("|")[3].startswith("CH2O")) + ) + & ( + ("ethanol_to_ethylene_petro" not in v) + & ("gas_processing_petro" not in v) & ( - ("ethanol_to_ethylene_petro" not in v) - & ("gas_processing_petro" not in v) - & ( - "in|final|atm_gasoil|steam_cracker_petro|atm_gasoil" - not in v - ) - & ( - "in|final|vacuum_gasoil|steam_cracker_petro|vacuum_gasoil" - not in v - ) - & ("in|final|naphtha|steam_cracker_petro|naphtha" not in v) - )) + "in|final|atm_gasoil|steam_cracker_petro|atm_gasoil" + not in v + ) + & ( + "in|final|vacuum_gasoil|steam_cracker_petro|vacuum_gasoil" + not in v + ) + & ("in|final|naphtha|steam_cracker_petro|naphtha" not in v) + ) + ) ] aux2_df = aux2_df[aux2_df["variable"].isin(var_sectors)] @@ -1473,7 +1650,6 @@ def report(context,scenario): ) df_final.append(df_final_energy, inplace=True) if c == "gas": - # Can not distinguish by type Gases (natural gas, biomass, synthetic fossil, efuel) # (coal_gas), from biomass (gas_bio), natural gas (gas_bal): All go into secondary level # Can not be distinguished in the final level. @@ -1508,7 +1684,6 @@ def report(context,scenario): df_final.append(df_final_energy, inplace=True) if c == "liquids": - # All liquids df_final_energy.aggregate( "Final Energy|Industry excl Non-Energy Use|Liquids", @@ -1519,8 +1694,9 @@ def report(context,scenario): # Only bios (ethanol, methanol ?) filter_vars = [ - v for v in aux2_df["variable"].values if (("ethanol" in v) - & ('methanol' not in v)) + v + for v in aux2_df["variable"].values + if (("ethanol" in v) & ("methanol" not in v)) ] df_final_energy.aggregate( "Final Energy|Industry excl Non-Energy Use|Liquids|Biomass", @@ -1531,8 +1707,9 @@ def report(context,scenario): # Fossils filter_vars = [ - v for v in aux2_df["variable"].values if (("fueloil" in v) - | ("lightoil" in v)) + v + for v in aux2_df["variable"].values + if (("fueloil" in v) | ("lightoil" in v)) ] df_final_energy.aggregate( @@ -1567,7 +1744,6 @@ def report(context,scenario): ) df_final.append(df_final_energy, inplace=True) if c == "solids": - # All df_final_energy.aggregate( "Final Energy|Industry excl Non-Energy Use|Solids", @@ -1620,7 +1796,7 @@ def report(context,scenario): "GWa", to="EJ/yr", factor=0.03154, inplace=True ) df_final.append(df_final_energy, inplace=True) - if c == 'solar': + if c == "solar": df_final_energy.aggregate( "Final Energy|Industry excl Non-Energy Use|Solar", components=var_sectors, @@ -1637,65 +1813,85 @@ def report(context,scenario): # FINAL ENERGY BY FUELS (Including Non-Energy Use) print("Final Energy by fuels including non-energy use is being printed.") - commodities = ["electr", "gas", "hydrogen", "liquids", "solids", "heat", "all", 'solar'] + commodities = [ + "electr", + "gas", + "hydrogen", + "liquids", + "solids", + "heat", + "all", + "solar", + ] for c in commodities: - for r in nodes: - df_final_energy = df.copy() - df_final_energy.convert_unit( - "", to="GWa", factor=1, inplace=True) + df_final_energy.convert_unit("", to="GWa", factor=1, inplace=True) df_final_energy.filter(region=r, year=years, inplace=True) exclude = [ "in|final|*|cokeoven_steel|*", "in|final|bf_gas|*", "in|final|co_gas|*", - 'in|final|*|meth_fc_trp|*', - 'in|final|*|meth_ic_trp|*', - 'in|final|*|meth_i|*', - 'in|final|*|meth_rc|*', - 'in|final|*|sp_meth_I|*'] + "in|final|*|meth_fc_trp|*", + "in|final|*|meth_ic_trp|*", + "in|final|*|meth_i|*", + "in|final|*|meth_rc|*", + "in|final|*|sp_meth_I|*", + ] df_final_energy.filter(variable=exclude, keep=False, inplace=True) - if c == 'solar': - df_final_energy.filter(variable=["out|useful|i_therm|solar_i|M1", - "out|useful_steel|lt_heat|solar_steel|*", - "out|useful_aluminum|lt_heat|solar_aluminum|*", - "out|useful_cement|lt_heat|solar_cement|*", - "out|useful_petro|lt_heat|solar_petro|*", - "out|useful_resins|lt_heat|solar_resins|*", - ], - inplace = True) + if c == "solar": + df_final_energy.filter( + variable=[ + "out|useful|i_therm|solar_i|M1", + "out|useful_steel|lt_heat|solar_steel|*", + "out|useful_aluminum|lt_heat|solar_aluminum|*", + "out|useful_cement|lt_heat|solar_cement|*", + "out|useful_petro|lt_heat|solar_petro|*", + "out|useful_resins|lt_heat|solar_resins|*", + ], + inplace=True, + ) if c == "electr": - df_final_energy.filter(variable=["in|final|electr|*", - 'in|secondary|electr|NH3_to_N_fertil|M1', - 'in|secondary|electr|coal_NH3|M1', - 'in|secondary|electr|electr_NH3|M1', - 'in|secondary|electr|fueloil_NH3|M1', - 'in|secondary|electr|gas_NH3|M1', - 'in|secondary|electr|coal_NH3_ccs|M1', - 'in|secondary|electr|fueloil_NH3_ccs|M1', - 'in|secondary|electr|gas_NH3_ccs|M1', - 'in|secondary|electr|biomass_NH3_ccs|M1', - 'in|secondary|electr|biomass_NH3|M1', - 'in|secondary|electr|meth_ng|feedstock', - 'in|secondary|electr|meth_ng_ccs|feedstock', - 'in|secondary|electr|meth_coal|feedstock', - 'in|secondary|electr|meth_coal_ccs|feedstock', - 'in|secondary|electr|meth_h2|feedstock' - ], inplace=True) + df_final_energy.filter( + variable=[ + "in|final|electr|*", + "in|secondary|electr|NH3_to_N_fertil|M1", + "in|secondary|electr|coal_NH3|M1", + "in|secondary|electr|electr_NH3|M1", + "in|secondary|electr|fueloil_NH3|M1", + "in|secondary|electr|gas_NH3|M1", + "in|secondary|electr|coal_NH3_ccs|M1", + "in|secondary|electr|fueloil_NH3_ccs|M1", + "in|secondary|electr|gas_NH3_ccs|M1", + "in|secondary|electr|biomass_NH3_ccs|M1", + "in|secondary|electr|biomass_NH3|M1", + "in|secondary|electr|meth_ng|feedstock", + "in|secondary|electr|meth_ng_ccs|feedstock", + "in|secondary|electr|meth_coal|feedstock", + "in|secondary|electr|meth_coal_ccs|feedstock", + "in|secondary|electr|meth_h2|feedstock", + ], + inplace=True, + ) if c == "gas": - df_final_energy.filter(variable=["in|final|gas|*", - 'in|secondary|gas|gas_NH3|M1', - 'in|secondary|gas|gas_NH3_ccs|M1', - 'in|secondary|gas|meth_ng_ccs|feedstock', - "in|secondary|gas|meth_ng|feedstock", - ], - inplace=True) - df_final_energy.filter(variable=["in|final|gas|gas_processing_petro|*"], - keep=False, inplace=True) + df_final_energy.filter( + variable=[ + "in|final|gas|*", + "in|secondary|gas|gas_NH3|M1", + "in|secondary|gas|gas_NH3_ccs|M1", + "in|secondary|gas|meth_ng_ccs|feedstock", + "in|secondary|gas|meth_ng|feedstock", + ], + inplace=True, + ) + df_final_energy.filter( + variable=["in|final|gas|gas_processing_petro|*"], + keep=False, + inplace=True, + ) # Include gasoil and naphtha feedstock if c == "liquids": df_final_energy.filter( @@ -1707,9 +1903,10 @@ def report(context,scenario): "in|final|vacuum_gasoil|*", "in|final|naphtha|*", "in|final|atm_gasoil|*", - 'in|secondary|fueloil|fueloil_NH3|M1', - 'in|secondary|fueloil|fueloil_NH3_ccs|M1', - "in|final|gas|gas_processing_petro|*"], + "in|secondary|fueloil|fueloil_NH3|M1", + "in|secondary|fueloil|fueloil_NH3_ccs|M1", + "in|final|gas|gas_processing_petro|*", + ], inplace=True, ) if c == "solids": @@ -1718,61 +1915,70 @@ def report(context,scenario): "in|final|biomass|*", "in|final|coal|*", "in|final|coke_iron|*", - 'in|secondary|coal|coal_NH3|M1', - 'in|secondary|coal|coal_NH3_ccs|M1', + "in|secondary|coal|coal_NH3|M1", + "in|secondary|coal|coal_NH3_ccs|M1", "in|secondary|coal|meth_coal|feedstock", - 'in|secondary|coal|meth_coal_ccs|feedstock', - 'in|primary|biomass|meth_bio|feedstock', - 'in|primary|biomass|meth_bio_ccs|feedstock', - 'in|primary|biomass|biomass_NH3_ccs|M1', - 'in|primary|biomass|biomass_NH3|M1' + "in|secondary|coal|meth_coal_ccs|feedstock", + "in|primary|biomass|meth_bio|feedstock", + "in|primary|biomass|meth_bio_ccs|feedstock", + "in|primary|biomass|biomass_NH3_ccs|M1", + "in|primary|biomass|biomass_NH3|M1", ], inplace=True, ) if c == "hydrogen": - df_final_energy.filter(variable=["in|final|hydrogen|*", - 'in|secondary|hydrogen|meth_h2|feedstock'], inplace=True) + df_final_energy.filter( + variable=[ + "in|final|hydrogen|*", + "in|secondary|hydrogen|meth_h2|feedstock", + ], + inplace=True, + ) if c == "heat": df_final_energy.filter(variable=["in|final|d_heat|*"], inplace=True) if c == "all": - df_final_energy.filter(variable=["in|final|*", - "in|secondary|coal|coal_NH3|M1", - "in|secondary|electr|NH3_to_N_fertil|M1", - 'in|secondary|electr|coal_NH3|M1', - 'in|secondary|electr|electr_NH3|M1', - 'in|secondary|electr|gas_NH3|M1', - 'in|secondary|fueloil|fueloil_NH3|M1', - 'in|secondary|electr|fueloil_NH3|M1', - 'in|secondary|gas|gas_NH3|M1', - "in|secondary|coal|coal_NH3_ccs|M1", - 'in|secondary|electr|coal_NH3_ccs|M1', - 'in|secondary|electr|gas_NH3_ccs|M1', - 'in|secondary|fueloil|fueloil_NH3_ccs|M1', - 'in|secondary|electr|fueloil_NH3_ccs|M1', - 'in|secondary|gas|gas_NH3_ccs|M1', - "in|primary|biomass|biomass_NH3|M1", - "in|secondary|electr|biomass_NH3|M1", - "in|primary|biomass|biomass_NH3_ccs|M1", - "in|secondary|electr|biomass_NH3_ccs|M1", - "out|useful|i_therm|solar_i|M1", - "out|useful_steel|lt_heat|solar_steel|*", - "out|useful_aluminum|lt_heat|solar_aluminum|*", - "out|useful_cement|lt_heat|solar_cement|*", - "out|useful_petro|lt_heat|solar_petro|*", - "out|useful_resins|lt_heat|solar_resins|*", - "in|secondary|coal|meth_coal|feedstock", - 'in|secondary|coal|meth_coal_ccs|feedstock', - "in|secondary|electr|meth_coal|feedstock", - 'in|secondary|electr|meth_coal_ccs|feedstock', - 'in|secondary|electr|meth_ng_ccs|feedstock', - "in|secondary|electr|meth_ng|feedstock", - 'in|secondary|gas|meth_ng_ccs|feedstock', - "in|secondary|gas|meth_ng|feedstock", - 'in|primary|biomass|meth_bio|feedstock', - 'in|primary|biomass|meth_bio_ccs|feedstock', - 'in|secondary|hydrogen|meth_h2|feedstock', - 'in|secondary|electr|meth_h2|feedstock', - ], inplace=True) + df_final_energy.filter( + variable=[ + "in|final|*", + "in|secondary|coal|coal_NH3|M1", + "in|secondary|electr|NH3_to_N_fertil|M1", + "in|secondary|electr|coal_NH3|M1", + "in|secondary|electr|electr_NH3|M1", + "in|secondary|electr|gas_NH3|M1", + "in|secondary|fueloil|fueloil_NH3|M1", + "in|secondary|electr|fueloil_NH3|M1", + "in|secondary|gas|gas_NH3|M1", + "in|secondary|coal|coal_NH3_ccs|M1", + "in|secondary|electr|coal_NH3_ccs|M1", + "in|secondary|electr|gas_NH3_ccs|M1", + "in|secondary|fueloil|fueloil_NH3_ccs|M1", + "in|secondary|electr|fueloil_NH3_ccs|M1", + "in|secondary|gas|gas_NH3_ccs|M1", + "in|primary|biomass|biomass_NH3|M1", + "in|secondary|electr|biomass_NH3|M1", + "in|primary|biomass|biomass_NH3_ccs|M1", + "in|secondary|electr|biomass_NH3_ccs|M1", + "out|useful|i_therm|solar_i|M1", + "out|useful_steel|lt_heat|solar_steel|*", + "out|useful_aluminum|lt_heat|solar_aluminum|*", + "out|useful_cement|lt_heat|solar_cement|*", + "out|useful_petro|lt_heat|solar_petro|*", + "out|useful_resins|lt_heat|solar_resins|*", + "in|secondary|coal|meth_coal|feedstock", + "in|secondary|coal|meth_coal_ccs|feedstock", + "in|secondary|electr|meth_coal|feedstock", + "in|secondary|electr|meth_coal_ccs|feedstock", + "in|secondary|electr|meth_ng_ccs|feedstock", + "in|secondary|electr|meth_ng|feedstock", + "in|secondary|gas|meth_ng_ccs|feedstock", + "in|secondary|gas|meth_ng|feedstock", + "in|primary|biomass|meth_bio|feedstock", + "in|primary|biomass|meth_bio_ccs|feedstock", + "in|secondary|hydrogen|meth_h2|feedstock", + "in|secondary|electr|meth_h2|feedstock", + ], + inplace=True, + ) all_flows = df_final_energy.timeseries().reset_index() splitted_vars = [v.split("|") for v in all_flows.variable] @@ -1791,18 +1997,18 @@ def report(context,scenario): v for v in aux2_df["variable"].values if ( - (v.split('|')[3].endswith("cement")) - | (v.split('|')[3].endswith("steel")) - | (v.split('|')[3].endswith("aluminum")) - | (v.split('|')[3].endswith("petro")) - | (v.split('|')[3].endswith("resins")) - | (v.split('|')[3].endswith("_i")) - | (v.split('|')[3].endswith("_I")) - | (('NH3') in v) - | (v.split('|')[3].endswith("_fs")) - | (v.split('|')[3].startswith("meth")) - | (v.split('|')[3].startswith("CH2O")) - ) + (v.split("|")[3].endswith("cement")) + | (v.split("|")[3].endswith("steel")) + | (v.split("|")[3].endswith("aluminum")) + | (v.split("|")[3].endswith("petro")) + | (v.split("|")[3].endswith("resins")) + | (v.split("|")[3].endswith("_i")) + | (v.split("|")[3].endswith("_I")) + | (("NH3") in v) + | (v.split("|")[3].endswith("_fs")) + | (v.split("|")[3].startswith("meth")) + | (v.split("|")[3].startswith("CH2O")) + ) ] aux2_df = aux2_df[aux2_df["variable"].isin(var_sectors)] @@ -1810,18 +2016,24 @@ def report(context,scenario): # Aggregate - if c == 'solar': + if c == "solar": df_final_energy.aggregate( - "Final Energy|Industry|Solar", components=var_sectors, append=True, + "Final Energy|Industry|Solar", + components=var_sectors, + append=True, + ) + df_final_energy.filter( + variable=["Final Energy|Industry|Solar"], inplace=True ) - df_final_energy.filter(variable=["Final Energy|Industry|Solar"], inplace=True) df_final_energy.convert_unit( "GWa", to="EJ/yr", factor=0.03154, inplace=True ) df_final.append(df_final_energy, inplace=True) if c == "all": df_final_energy.aggregate( - "Final Energy|Industry", components=var_sectors, append=True, + "Final Energy|Industry", + components=var_sectors, + append=True, ) df_final_energy.filter(variable=["Final Energy|Industry"], inplace=True) df_final_energy.convert_unit( @@ -1835,7 +2047,8 @@ def report(context,scenario): append=True, ) df_final_energy.filter( - variable=["Final Energy|Industry|Electricity"], inplace=True, + variable=["Final Energy|Industry|Electricity"], + inplace=True, ) df_final_energy.convert_unit( "GWa", to="EJ/yr", factor=0.03154, inplace=True @@ -1846,11 +2059,14 @@ def report(context,scenario): # (coal_gas), from biomass (gas_bio), natural gas (gas_bal): All go into secondary level # Can not be distinguished in the final level. df_final_energy.aggregate( - "Final Energy|Industry|Gases", components=var_sectors, append=True, + "Final Energy|Industry|Gases", + components=var_sectors, + append=True, ) df_final_energy.filter( - variable=["Final Energy|Industry|Gases"], inplace=True, + variable=["Final Energy|Industry|Gases"], + inplace=True, ) df_final_energy.convert_unit( "GWa", to="EJ/yr", factor=0.03154, inplace=True @@ -1864,7 +2080,8 @@ def report(context,scenario): append=True, ) df_final_energy.filter( - variable=["Final Energy|Industry|Hydrogen"], inplace=True, + variable=["Final Energy|Industry|Hydrogen"], + inplace=True, ) df_final_energy.convert_unit( "GWa", to="EJ/yr", factor=0.03154, inplace=True @@ -1880,8 +2097,9 @@ def report(context,scenario): ) # Only bios (ethanol) filter_vars = [ - v for v in aux2_df["variable"].values if (("ethanol" in v) - & ("methanol" not in v)) + v + for v in aux2_df["variable"].values + if (("ethanol" in v) & ("methanol" not in v)) ] df_final_energy.aggregate( "Final Energy|Industry|Liquids|Biomass", @@ -1909,11 +2127,7 @@ def report(context,scenario): # Methanol filter_vars = [ - v - for v in aux2_df["variable"].values - if ( - ("methanol" in v) - ) + v for v in aux2_df["variable"].values if (("methanol" in v)) ] df_final_energy.aggregate( @@ -1925,11 +2139,7 @@ def report(context,scenario): # Natural Gas Liquids (Ethane/Propane) filter_vars = [ - v - for v in aux2_df["variable"].values - if ( - ("gas_proc" in v) - ) + v for v in aux2_df["variable"].values if (("gas_proc" in v)) ] df_final_energy.aggregate( @@ -1944,7 +2154,7 @@ def report(context,scenario): "Final Energy|Industry|Liquids|Oil", "Final Energy|Industry|Liquids|Biomass", "Final Energy|Industry|Liquids|Coal", - "Final Energy|Industry|Liquids|Gas" + "Final Energy|Industry|Liquids|Gas", ], inplace=True, ) @@ -1955,7 +2165,9 @@ def report(context,scenario): if c == "solids": # All df_final_energy.aggregate( - "Final Energy|Industry|Solids", components=var_sectors, append=True, + "Final Energy|Industry|Solids", + components=var_sectors, + append=True, ) # Bio @@ -1970,9 +2182,7 @@ def report(context,scenario): ) # Fossil - filter_vars = [ - v for v in aux2_df["variable"].values if ("coal" in v) - ] + filter_vars = [v for v in aux2_df["variable"].values if ("coal" in v)] df_final_energy.aggregate( "Final Energy|Industry|Solids|Coal", @@ -1993,10 +2203,13 @@ def report(context,scenario): df_final.append(df_final_energy, inplace=True) if c == "heat": df_final_energy.aggregate( - "Final Energy|Industry|Heat", components=var_sectors, append=True, + "Final Energy|Industry|Heat", + components=var_sectors, + append=True, ) df_final_energy.filter( - variable=["Final Energy|Industry|Heat"], inplace=True, + variable=["Final Energy|Industry|Heat"], + inplace=True, ) df_final_energy.convert_unit( "GWa", to="EJ/yr", factor=0.03154, inplace=True @@ -2014,7 +2227,7 @@ def report(context,scenario): "Non-Ferrous Metals", "Non-Metallic Minerals", "Chemicals", - 'Other Sector' + "Other Sector", ] print("Final Energy (excl non-energy use) by sector and fuel is being printed") for r in nodes: @@ -2029,31 +2242,36 @@ def report(context,scenario): "in|final|vacuum_gasoil|steam_cracker_petro|*", "in|final|*|cokeoven_steel|*", "in|final|bf_gas|*", - "in|final|co_gas|*"] + "in|final|co_gas|*", + ] df_final_energy.filter(region=r, year=years, inplace=True) - df_final_energy.filter(variable=["in|final|*", - 'out|useful|i_therm|solar_i|M1', - 'out|useful_steel|lt_heat|solar_steel|low_temp', - 'out|useful_aluminum|lt_heat|solar_aluminum|low_temp', - 'out|useful_cement|lt_heat|solar_cement|low_temp', - 'out|useful_petro|lt_heat|solar_petro|low_temp', - 'out|useful_resins|lt_heat|solar_resins|low_temp', - 'in|secondary|electr|NH3_to_N_fertil|M1', - 'in|secondary|electr|coal_NH3|M1', - 'in|secondary|electr|fueloil_NH3|M1', - 'in|secondary|electr|gas_NH3|M1', - 'in|secondary|electr|coal_NH3_ccs|M1', - 'in|secondary|electr|fueloil_NH3_ccs|M1', - 'in|secondary|electr|gas_NH3_ccs|M1', - 'in|secondary|electr|biomass_NH3_ccs|M1', - 'in|secondary|electr|biomass_NH3|M1', - 'in|secondary|electr|meth_ng|feedstock', - 'in|secondary|electr|meth_ng_ccs|feedstock', - 'in|secondary|electr|meth_coal|feedstock', - 'in|secondary|electr|meth_coal_ccs|feedstock', - 'in|secondary|electr|meth_h2|feedstock' - ], inplace=True) + df_final_energy.filter( + variable=[ + "in|final|*", + "out|useful|i_therm|solar_i|M1", + "out|useful_steel|lt_heat|solar_steel|low_temp", + "out|useful_aluminum|lt_heat|solar_aluminum|low_temp", + "out|useful_cement|lt_heat|solar_cement|low_temp", + "out|useful_petro|lt_heat|solar_petro|low_temp", + "out|useful_resins|lt_heat|solar_resins|low_temp", + "in|secondary|electr|NH3_to_N_fertil|M1", + "in|secondary|electr|coal_NH3|M1", + "in|secondary|electr|fueloil_NH3|M1", + "in|secondary|electr|gas_NH3|M1", + "in|secondary|electr|coal_NH3_ccs|M1", + "in|secondary|electr|fueloil_NH3_ccs|M1", + "in|secondary|electr|gas_NH3_ccs|M1", + "in|secondary|electr|biomass_NH3_ccs|M1", + "in|secondary|electr|biomass_NH3|M1", + "in|secondary|electr|meth_ng|feedstock", + "in|secondary|electr|meth_ng_ccs|feedstock", + "in|secondary|electr|meth_coal|feedstock", + "in|secondary|electr|meth_coal_ccs|feedstock", + "in|secondary|electr|meth_h2|feedstock", + ], + inplace=True, + ) df_final_energy.filter(variable=exclude, keep=False, inplace=True) # Decompose the pyam table into pandas data frame @@ -2076,31 +2294,38 @@ def report(context,scenario): # To be able to report the higher level sectors. if s == "Non-Ferrous Metals": tec = [t for t in aux2_df["technology"].values if "aluminum" in t] - solar_tec = ['solar_aluminum'] + solar_tec = ["solar_aluminum"] aux2_df = aux2_df[aux2_df["technology"].isin(tec)] elif s == "Non-Metallic Minerals": tec = [t for t in aux2_df["technology"].values if "cement" in t] - solar_tec = ['solar_cement'] + solar_tec = ["solar_cement"] aux2_df = aux2_df[aux2_df["technology"].isin(tec)] elif s == "Chemicals": - tec = [t for t in aux2_df["technology"].values if (("petro" in t) - | ('NH3' in t) - | ( t.startswith('meth_') - & (not (t.startswith('meth_i')))) - | ('CH2O'in t) - | ("resins" in t))] - solar_tec = ['solar_petro', 'solar_resins'] + tec = [ + t + for t in aux2_df["technology"].values + if ( + ("petro" in t) + | ("NH3" in t) + | (t.startswith("meth_") & (not (t.startswith("meth_i")))) + | ("CH2O" in t) + | ("resins" in t) + ) + ] + solar_tec = ["solar_petro", "solar_resins"] aux2_df = aux2_df[aux2_df["technology"].isin(tec)] - elif s == 'Other Sector': - tec = [t for t in aux2_df["technology"].values if ( - ((t.endswith("_i")) - | (t.endswith('_I'))))] - solar_tec = ['solar_i'] + elif s == "Other Sector": + tec = [ + t + for t in aux2_df["technology"].values + if (((t.endswith("_i")) | (t.endswith("_I")))) + ] + solar_tec = ["solar_i"] aux2_df = aux2_df[aux2_df["technology"].isin(tec)] else: # Filter the technologies only for the certain industry sector tec = [t for t in aux2_df["technology"].values if s in t] - solar_tec = ['solar_' + s] + solar_tec = ["solar_" + s] aux2_df = aux2_df[aux2_df["technology"].isin(tec)] s = change_names(s) @@ -2121,12 +2346,12 @@ def report(context,scenario): "liquids", "liquid_bio", "liquid_fossil", - 'liquid_other', + "liquid_other", "solids", "solids_bio", "solids_fossil", "heat", - 'solar', + "solar", "all", ] @@ -2149,17 +2374,14 @@ def report(context,scenario): aggregate_name = ( "Final Energy|Industry excl Non-Energy Use|" + s + "|" + "Gases" ) - elif c == 'solar': + elif c == "solar": var = np.unique( aux2_df.loc[ aux2_df["technology"].isin(solar_tec), "variable" ].values ).tolist() aggregate_name = ( - "Final Energy|Industry excl Non-Energy Use|" - + s - + "|" - + "Solar" + "Final Energy|Industry excl Non-Energy Use|" + s + "|" + "Solar" ) elif c == "hydrogen": var = np.unique( @@ -2194,7 +2416,8 @@ def report(context,scenario): elif c == "liquid_bio": var = np.unique( aux2_df.loc[ - ((aux2_df["commodity"] == "ethanol")), "variable", + ((aux2_df["commodity"] == "ethanol")), + "variable", ].values ).tolist() aggregate_name = ( @@ -2322,7 +2545,7 @@ def report(context,scenario): # Only for high value chemcials there is non-energy use reported. # (not in aluminum, steel, cement). - sectors = ["petro",'ammonia','methanol','Chemicals|Other Sector'] + sectors = ["petro", "ammonia", "methanol", "Chemicals|Other Sector"] print("Final Energy non-energy use by sector and fuel is being printed") for r in nodes: for s in sectors: @@ -2334,24 +2557,24 @@ def report(context,scenario): "in|final|gas|gas_processing_petro|M1", "in|final|naphtha|steam_cracker_petro|*", "in|final|vacuum_gasoil|steam_cracker_petro|*", - 'in|secondary|electr|electr_NH3|M1', - 'in|secondary|gas|gas_NH3|M1', - 'in|secondary|gas|gas_NH3_ccs|M1', - 'in|secondary|fueloil|fueloil_NH3|M1', - 'in|secondary|fueloil|fueloil_NH3_ccs|M1', - "in|secondary|coal|coal_NH3|M1", - "in|secondary|coal|coal_NH3_ccs|M1", - 'in|primary|biomass|biomass_NH3_ccs|M1', - 'in|primary|biomass|biomass_NH3|M1', - "in|final_material|methanol|MTO_petro|energy", - "in|final_material|methanol|CH2O_synth|energy", - 'in|secondary|coal|meth_coal|feedstock', - 'in|secondary|coal|meth_coal_ccs|feedstock', - 'in|secondary|gas|meth_ng|feedstock', - 'in|secondary|gas|meth_ng_ccs|feedstock', - 'in|primary|biomass|meth_bio|feedstock', - 'in|primary|biomass|meth_bio_ccs|feedstock', - 'in|secondary|hydrogen|meth_h2|feedstock', + "in|secondary|electr|electr_NH3|M1", + "in|secondary|gas|gas_NH3|M1", + "in|secondary|gas|gas_NH3_ccs|M1", + "in|secondary|fueloil|fueloil_NH3|M1", + "in|secondary|fueloil|fueloil_NH3_ccs|M1", + "in|secondary|coal|coal_NH3|M1", + "in|secondary|coal|coal_NH3_ccs|M1", + "in|primary|biomass|biomass_NH3_ccs|M1", + "in|primary|biomass|biomass_NH3|M1", + "in|final_material|methanol|MTO_petro|energy", + "in|final_material|methanol|CH2O_synth|energy", + "in|secondary|coal|meth_coal|feedstock", + "in|secondary|coal|meth_coal_ccs|feedstock", + "in|secondary|gas|meth_ng|feedstock", + "in|secondary|gas|meth_ng_ccs|feedstock", + "in|primary|biomass|meth_bio|feedstock", + "in|primary|biomass|meth_bio_ccs|feedstock", + "in|secondary|hydrogen|meth_h2|feedstock", ] df_final_energy.filter(region=r, year=years, inplace=True) @@ -2375,15 +2598,14 @@ def report(context,scenario): ) # Filter the technologies only for the certain industry sector - if s == 'petro': + if s == "petro": tec = [t for t in aux2_df["technology"].values if (s in t)] - if s == 'ammonia': - tec = [t for t in aux2_df["technology"].values if ('NH3' in t)] - if s == 'methanol': - tec = [t for t in aux2_df["technology"].values if ('meth_' in t)] - if s == 'Chemicals|Other Sector': - tec = [t for t in aux2_df["technology"].values if ('CH2O_synth' in t)] - + if s == "ammonia": + tec = [t for t in aux2_df["technology"].values if ("NH3" in t)] + if s == "methanol": + tec = [t for t in aux2_df["technology"].values if ("meth_" in t)] + if s == "Chemicals|Other Sector": + tec = [t for t in aux2_df["technology"].values if ("CH2O_synth" in t)] aux2_df = aux2_df[aux2_df["technology"].isin(tec)] @@ -2402,15 +2624,15 @@ def report(context,scenario): "gas", "liquids", "liquid_bio", - 'liquid_oil', + "liquid_oil", "liquid_gas", - 'methanol', + "methanol", "all", - 'solids', - 'solid_coal', - 'solid_bio', - 'electr_gas', - 'hydrogen' + "solids", + "solid_coal", + "solid_bio", + "electr_gas", + "hydrogen", ] for c in commodity_list: @@ -2418,111 +2640,129 @@ def report(context,scenario): var = np.unique( aux2_df.loc[aux2_df["commodity"] == "electr", "variable"].values ).tolist() - aggregate_name = "Final Energy|Non-Energy Use|" + s + "|" + "Gases|Electricity" + aggregate_name = ( + "Final Energy|Non-Energy Use|" + s + "|" + "Gases|Electricity" + ) elif c == "gas": var = np.unique( - aux2_df.loc[(((aux2_df["commodity"] == "gas") | (aux2_df["technology"] == "electr_NH3")) - & (aux2_df["technology"] != "gas_processing_petro")) , "variable"].values + aux2_df.loc[ + ( + ( + (aux2_df["commodity"] == "gas") + | (aux2_df["technology"] == "electr_NH3") + ) + & (aux2_df["technology"] != "gas_processing_petro") + ), + "variable", + ].values ).tolist() aggregate_name = "Final Energy|Non-Energy Use|" + s + "|" + "Gases" elif c == "hydrogen": - var = np.unique( - aux2_df.loc[((aux2_df["commodity"] == "hydrogen") - & (aux2_df["technology"] == "meth_h2")),"variable"].values - ).tolist() - aggregate_name = "Final Energy|Non-Energy Use|" + s + "|" + "Hydrogen" - elif c == "methanol": var = np.unique( aux2_df.loc[ ( - (aux2_df["commodity"] == "methanol") + (aux2_df["commodity"] == "hydrogen") + & (aux2_df["technology"] == "meth_h2") ), "variable", ].values ).tolist() aggregate_name = ( - "Final Energy|Non-Energy Use|" + s + "|" + "Other" + "Final Energy|Non-Energy Use|" + s + "|" + "Hydrogen" ) + elif c == "methanol": + var = np.unique( + aux2_df.loc[ + ((aux2_df["commodity"] == "methanol")), + "variable", + ].values + ).tolist() + aggregate_name = "Final Energy|Non-Energy Use|" + s + "|" + "Other" elif c == "liquids": var = np.unique( aux2_df.loc[ ( - (aux2_df["commodity"] == "naphtha") - | (aux2_df["commodity"] == "atm_gasoil") - | (aux2_df["commodity"] == "vacuum_gasoil") - | (aux2_df["commodity"] == "ethanol") - | (aux2_df["commodity"] == "fueloil") - | (aux2_df["technology"] == "gas_processing_petro") + (aux2_df["commodity"] == "naphtha") + | (aux2_df["commodity"] == "atm_gasoil") + | (aux2_df["commodity"] == "vacuum_gasoil") + | (aux2_df["commodity"] == "ethanol") + | (aux2_df["commodity"] == "fueloil") + | (aux2_df["technology"] == "gas_processing_petro") ), "variable", ].values ).tolist() aggregate_name = ( - "Final Energy|Non-Energy Use|" + s + "|" + "Liquids" + "Final Energy|Non-Energy Use|" + s + "|" + "Liquids" ) elif c == "liquid_bio": var = np.unique( aux2_df.loc[ - ((aux2_df["commodity"] == "ethanol")), "variable", + ((aux2_df["commodity"] == "ethanol")), + "variable", ].values ).tolist() aggregate_name = ( - "Final Energy|Non-Energy Use|" + s + "|" + "Liquids|Biomass" + "Final Energy|Non-Energy Use|" + s + "|" + "Liquids|Biomass" ) elif c == "liquid_oil": var = np.unique( aux2_df.loc[ - ((aux2_df["commodity"] == "atm_gasoil") | - (aux2_df["commodity"] == "naphtha") | - (aux2_df["commodity"] == "vacuum_gasoil") | - (aux2_df["commodity"] == "fueloil") - ), "variable", + ( + (aux2_df["commodity"] == "atm_gasoil") + | (aux2_df["commodity"] == "naphtha") + | (aux2_df["commodity"] == "vacuum_gasoil") + | (aux2_df["commodity"] == "fueloil") + ), + "variable", ].values ).tolist() aggregate_name = ( - "Final Energy|Non-Energy Use|" + s + "|" + "Liquids|Oil" + "Final Energy|Non-Energy Use|" + s + "|" + "Liquids|Oil" ) elif c == "liquid_gas": var = np.unique( aux2_df.loc[ - ((aux2_df["technology"] == "gas_processing_petro") - ), "variable", + ((aux2_df["technology"] == "gas_processing_petro")), + "variable", ].values ).tolist() aggregate_name = ( - "Final Energy|Non-Energy Use|" + s + "|" + "Liquids|Gas" + "Final Energy|Non-Energy Use|" + s + "|" + "Liquids|Gas" ) - elif c == 'solids': + elif c == "solids": var = np.unique( aux2_df.loc[ - ((aux2_df["commodity"] == "coal") | - (aux2_df["commodity"] == "biomass")), "variable", + ( + (aux2_df["commodity"] == "coal") + | (aux2_df["commodity"] == "biomass") + ), + "variable", ].values ).tolist() - aggregate_name = ( - "Final Energy|Non-Energy Use|" + s + "|" + "Solids" - ) - elif c == 'solid_coal': + aggregate_name = "Final Energy|Non-Energy Use|" + s + "|" + "Solids" + elif c == "solid_coal": var = np.unique( aux2_df.loc[ - ((aux2_df["commodity"] == "coal") ), "variable", + ((aux2_df["commodity"] == "coal")), + "variable", ].values ).tolist() aggregate_name = ( - "Final Energy|Non-Energy Use|" + s + "|" + "Solids|Coal" + "Final Energy|Non-Energy Use|" + s + "|" + "Solids|Coal" ) - elif c == 'solid_bio': + elif c == "solid_bio": var = np.unique( aux2_df.loc[ - ( - (aux2_df["commodity"] == "biomass")), "variable", + ((aux2_df["commodity"] == "biomass")), + "variable", ].values ).tolist() aggregate_name = ( - "Final Energy|Non-Energy Use|" + s + "|" + "Solids|Biomass" + "Final Energy|Non-Energy Use|" + s + "|" + "Solids|Biomass" ) elif c == "all": var = aux2_df["variable"].tolist() @@ -2548,7 +2788,6 @@ def report(context,scenario): aggregate_list[i], components=var_list[i], append=True ) - df_final_energy.filter(variable=aggregate_list, inplace=True) df_final_energy.convert_unit( "GWa", to="EJ/yr", factor=0.03154, inplace=True @@ -2559,8 +2798,19 @@ def report(context,scenario): # For ammonia and methanol, there is no seperation for non-energy vs. energy. - sectors = ['ammonia', 'methanol', 'aluminum', 'steel', 'cement', 'petro', - "Non-Ferrous Metals", "Non-Metallic Minerals", "Chemicals", 'Chemicals|Other Sector', 'Other Sector'] + sectors = [ + "ammonia", + "methanol", + "aluminum", + "steel", + "cement", + "petro", + "Non-Ferrous Metals", + "Non-Metallic Minerals", + "Chemicals", + "Chemicals|Other Sector", + "Other Sector", + ] print("Final Energy non-energy and energy use by sector and fuel is being printed") for r in nodes: @@ -2572,49 +2822,50 @@ def report(context,scenario): "in|final|*|cokeoven_steel|*", "in|final|bf_gas|*", "in|final|co_gas|*", - 'in|final|*|meth_fc_trp|*', - 'in|final|*|meth_ic_trp|*', - 'in|final|*|meth_rc|*'] + "in|final|*|meth_fc_trp|*", + "in|final|*|meth_ic_trp|*", + "in|final|*|meth_rc|*", + ] include = [ - 'in|secondary|coal|coal_NH3|M1', - 'in|secondary|coal|coal_NH3_ccs|M1', - 'in|secondary|electr|coal_NH3|M1', - 'in|secondary|electr|coal_NH3_ccs|M1', - 'in|secondary|fueloil|fueloil_NH3|M1', - 'in|secondary|fueloil|fueloil_NH3_ccs|M1', - 'in|secondary|electr|fueloil_NH3|M1', - 'in|secondary|electr|fueloil_NH3_ccs|M1', - 'in|secondary|gas|gas_NH3|M1', - 'in|secondary|gas|gas_NH3_ccs|M1', - 'in|secondary|electr|gas_NH3|M1', - 'in|secondary|electr|gas_NH3_ccs|M1', - 'in|primary|biomass|biomass_NH3|M1', - 'in|primary|biomass|biomass_NH3_ccs|M1', + "in|secondary|coal|coal_NH3|M1", + "in|secondary|coal|coal_NH3_ccs|M1", + "in|secondary|electr|coal_NH3|M1", + "in|secondary|electr|coal_NH3_ccs|M1", + "in|secondary|fueloil|fueloil_NH3|M1", + "in|secondary|fueloil|fueloil_NH3_ccs|M1", + "in|secondary|electr|fueloil_NH3|M1", + "in|secondary|electr|fueloil_NH3_ccs|M1", + "in|secondary|gas|gas_NH3|M1", + "in|secondary|gas|gas_NH3_ccs|M1", + "in|secondary|electr|gas_NH3|M1", + "in|secondary|electr|gas_NH3_ccs|M1", + "in|primary|biomass|biomass_NH3|M1", + "in|primary|biomass|biomass_NH3_ccs|M1", "in|secondary|electr|biomass_NH3|M1", "in|secondary|electr|biomass_NH3_ccs|M1", "in|secondary|electr|NH3_to_N_fertil|M1", - 'in|secondary|electr|electr_NH3|M1', + "in|secondary|electr|electr_NH3|M1", "in|secondary|coal|meth_coal|feedstock", - 'in|secondary|coal|meth_coal_ccs|feedstock', + "in|secondary|coal|meth_coal_ccs|feedstock", "in|secondary|electr|meth_coal|feedstock", - 'in|secondary|electr|meth_coal_ccs|feedstock', - 'in|secondary|gas|meth_ng_ccs|feedstock', + "in|secondary|electr|meth_coal_ccs|feedstock", + "in|secondary|gas|meth_ng_ccs|feedstock", "in|secondary|gas|meth_ng|feedstock", "in|secondary|electr|meth_ng|feedstock", - 'in|secondary|electr|meth_ng_ccs|feedstock', - 'in|primary|biomass|meth_bio|feedstock', - 'in|primary|biomass|meth_bio_ccs|feedstock', - 'in|secondary|hydrogen|meth_h2|feedstock', - 'in|secondary|electr|meth_h2|feedstock', + "in|secondary|electr|meth_ng_ccs|feedstock", + "in|primary|biomass|meth_bio|feedstock", + "in|primary|biomass|meth_bio_ccs|feedstock", + "in|secondary|hydrogen|meth_h2|feedstock", + "in|secondary|electr|meth_h2|feedstock", "in|final_material|methanol|MTO_petro|energy", - 'in|final|*', - 'out|useful|i_therm|solar_i|M1', - 'out|useful_steel|lt_heat|solar_steel|*', - 'out|useful_cement|lt_heat|solar_cement|*', - 'out|useful_aluminum|lt_heat|solar_aluminum|*', - 'out|useful_petro|lt_heat|solar_steel|*', - 'out|useful_resins|lt_heat|solar_resins|*' + "in|final|*", + "out|useful|i_therm|solar_i|M1", + "out|useful_steel|lt_heat|solar_steel|*", + "out|useful_cement|lt_heat|solar_cement|*", + "out|useful_aluminum|lt_heat|solar_aluminum|*", + "out|useful_petro|lt_heat|solar_steel|*", + "out|useful_resins|lt_heat|solar_resins|*", ] df_final_energy.filter(region=r, year=years, inplace=True) @@ -2642,43 +2893,57 @@ def report(context,scenario): if s == "Non-Ferrous Metals": tec = [t for t in aux2_df["technology"].values if "aluminum" in t] - solar_tec = ['solar_aluminum'] + solar_tec = ["solar_aluminum"] aux2_df = aux2_df[aux2_df["technology"].isin(tec)] elif s == "Non-Metallic Minerals": tec = [t for t in aux2_df["technology"].values if "cement" in t] - solar_tec = ['solar_cement'] + solar_tec = ["solar_cement"] aux2_df = aux2_df[aux2_df["technology"].isin(tec)] elif s == "Chemicals": - tec = [t for t in aux2_df["technology"].values if - ((("petro" in t) & ("MTO_petro" not in t)) \ - | ( t.startswith('meth_') & (not (t.startswith('meth_i')))) | ('NH3' in t) | \ - ('resins' in t) | ('CH2O_synth' in t))] - solar_tec = ['solar_petro','solar_resins'] + tec = [ + t + for t in aux2_df["technology"].values + if ( + (("petro" in t) & ("MTO_petro" not in t)) + | (t.startswith("meth_") & (not (t.startswith("meth_i")))) + | ("NH3" in t) + | ("resins" in t) + | ("CH2O_synth" in t) + ) + ] + solar_tec = ["solar_petro", "solar_resins"] aux2_df = aux2_df[aux2_df["technology"].isin(tec)] elif s == "Chemicals|Other Sector": - tec = [t for t in aux2_df["technology"].values if (('resins' in t) \ - | ('CH2O_synth' in t) | ('CH2O_to_resin' in t) )] + tec = [ + t + for t in aux2_df["technology"].values + if (("resins" in t) | ("CH2O_synth" in t) | ("CH2O_to_resin" in t)) + ] aux2_df = aux2_df[aux2_df["technology"].isin(tec)] - solar_tec = ['solar_resins'] - elif s == 'Other Sector': - tec = [t for t in aux2_df["technology"].values if ( - ((t.endswith("_i")) - | (t.endswith('_I')) - | (t.endswith('_fs'))))] - solar_tec = ['solar_i'] + solar_tec = ["solar_resins"] + elif s == "Other Sector": + tec = [ + t + for t in aux2_df["technology"].values + if (((t.endswith("_i")) | (t.endswith("_I")) | (t.endswith("_fs")))) + ] + solar_tec = ["solar_i"] aux2_df = aux2_df[aux2_df["technology"].isin(tec)] - elif s == 'ammonia': - tec = [t for t in aux2_df["technology"].values if ('NH3' in t)] + elif s == "ammonia": + tec = [t for t in aux2_df["technology"].values if ("NH3" in t)] aux2_df = aux2_df[aux2_df["technology"].isin(tec)] - elif s == 'methanol': - tec = [t for t in aux2_df["technology"].values if ( t.startswith('meth_') \ - & (not (t.startswith('meth_i'))))] + elif s == "methanol": + tec = [ + t + for t in aux2_df["technology"].values + if (t.startswith("meth_") & (not (t.startswith("meth_i")))) + ] aux2_df = aux2_df[aux2_df["technology"].isin(tec)] else: # Filter the technologies only for the certain industry sector tec = [t for t in aux2_df["technology"].values if s in t] aux2_df = aux2_df[aux2_df["technology"].isin(tec)] - solar_tec = ['solar_' + s] + solar_tec = ["solar_" + s] s = change_names(s) @@ -2699,90 +2964,96 @@ def report(context,scenario): "liquid_bio", "liquid_fossil", "liquid_gas", - 'liquid_other', + "liquid_other", "solids", "solids_bio", "solids_fossil", "heat", - 'solar', + "solar", "all", ] for c in commodity_list: - if c == 'electr': + if c == "electr": var = np.unique( aux2_df.loc[aux2_df["commodity"] == "electr", "variable"].values ).tolist() - aggregate_name = "Final Energy|Industry|" + s + "|" + 'Electricity' + aggregate_name = "Final Energy|Industry|" + s + "|" + "Electricity" elif c == "gas": var = np.unique( - aux2_df.loc[(aux2_df["commodity"] == "gas") & (aux2_df["technology"] != "gas_processing_petro"), "variable"].values + aux2_df.loc[ + (aux2_df["commodity"] == "gas") + & (aux2_df["technology"] != "gas_processing_petro"), + "variable", + ].values ).tolist() aggregate_name = "Final Energy|Industry|" + s + "|" + "Gases" elif c == "solar": var = np.unique( - aux2_df.loc[aux2_df["technology"].isin(solar_tec), "variable"].values + aux2_df.loc[ + aux2_df["technology"].isin(solar_tec), "variable" + ].values ).tolist() aggregate_name = "Final Energy|Industry|" + s + "|" + "Solar" elif c == "hydrogen": var = np.unique( - aux2_df.loc[aux2_df["commodity"] == "hydrogen", "variable"].values + aux2_df.loc[ + aux2_df["commodity"] == "hydrogen", "variable" + ].values ).tolist() aggregate_name = "Final Energy|Industry|" + s + "|" + "Hydrogen" elif c == "liquids": var = np.unique( aux2_df.loc[ ( - (aux2_df["commodity"] == "naphtha") - | (aux2_df["commodity"] == "atm_gasoil") - | (aux2_df["commodity"] == "vacuum_gasoil") - | (aux2_df["commodity"] == "ethanol") - | (aux2_df["commodity"] == "fueloil") - | (aux2_df["commodity"] == "lightoil") - | (aux2_df["commodity"] == "methanol") - | (aux2_df["technology"] == "gas_processing_petro") + (aux2_df["commodity"] == "naphtha") + | (aux2_df["commodity"] == "atm_gasoil") + | (aux2_df["commodity"] == "vacuum_gasoil") + | (aux2_df["commodity"] == "ethanol") + | (aux2_df["commodity"] == "fueloil") + | (aux2_df["commodity"] == "lightoil") + | (aux2_df["commodity"] == "methanol") + | (aux2_df["technology"] == "gas_processing_petro") ), "variable", ].values ).tolist() - aggregate_name = ( - "Final Energy|Industry|" + s + "|" + "Liquids" - ) + aggregate_name = "Final Energy|Industry|" + s + "|" + "Liquids" elif c == "liquid_bio": var = np.unique( aux2_df.loc[ - ((aux2_df["commodity"] == "ethanol")), "variable", + ((aux2_df["commodity"] == "ethanol")), + "variable", ].values ).tolist() aggregate_name = ( - "Final Energy|Industry|" + s + "|" + "Liquids|Biomass" + "Final Energy|Industry|" + s + "|" + "Liquids|Biomass" ) elif c == "liquid_fossil": var = np.unique( aux2_df.loc[ - ((aux2_df["commodity"] == "atm_gasoil") | - (aux2_df["commodity"] == "naphtha") | - (aux2_df["commodity"] == "vacuum_gasoil") | - (aux2_df["commodity"] == "fueloil") | - (aux2_df["commodity"] == "lightoil") - ), "variable", + ( + (aux2_df["commodity"] == "atm_gasoil") + | (aux2_df["commodity"] == "naphtha") + | (aux2_df["commodity"] == "vacuum_gasoil") + | (aux2_df["commodity"] == "fueloil") + | (aux2_df["commodity"] == "lightoil") + ), + "variable", ].values ).tolist() - aggregate_name = ( - "Final Energy|Industry|" + s + "|" + "Liquids|Oil" - ) + aggregate_name = "Final Energy|Industry|" + s + "|" + "Liquids|Oil" elif c == "liquid_gas": var = np.unique( aux2_df.loc[ - (aux2_df["technology"] == "gas_processing_petro"), "variable", + (aux2_df["technology"] == "gas_processing_petro"), + "variable", ].values ).tolist() - aggregate_name = ( - "Final Energy|Industry|" + s + "|" + "Liquids|Gas" - ) + aggregate_name = "Final Energy|Industry|" + s + "|" + "Liquids|Gas" elif c == "liquid_other": var = np.unique( aux2_df.loc[ @@ -2791,49 +3062,48 @@ def report(context,scenario): ].values ).tolist() aggregate_name = ( - "Final Energy|Industry|"+ s+ "|"+ "Liquids|Other" + "Final Energy|Industry|" + s + "|" + "Liquids|Other" ) - elif c == 'solids': + elif c == "solids": var = np.unique( aux2_df.loc[ - ((aux2_df["commodity"] == "coal") | - (aux2_df["commodity"] == "biomass") | - (aux2_df["commodity"] == "coke_iron") - ), "variable", + ( + (aux2_df["commodity"] == "coal") + | (aux2_df["commodity"] == "biomass") + | (aux2_df["commodity"] == "coke_iron") + ), + "variable", ].values ).tolist() - aggregate_name = ( - "Final Energy|Industry|" + s + "|" + "Solids" - ) - elif c == 'solids_bio': + aggregate_name = "Final Energy|Industry|" + s + "|" + "Solids" + elif c == "solids_bio": var = np.unique( aux2_df.loc[ - ((aux2_df["commodity"] == "biomass")), "variable", + ((aux2_df["commodity"] == "biomass")), + "variable", ].values ).tolist() aggregate_name = ( - "Final Energy|Industry|" + s + "|" + "Solids|Biomass" + "Final Energy|Industry|" + s + "|" + "Solids|Biomass" ) - elif c == 'solids_fossil': + elif c == "solids_fossil": var = np.unique( aux2_df.loc[ - ((aux2_df["commodity"] == "coal") | - (aux2_df["commodity"] == "coke_iron") - ), "variable", + ( + (aux2_df["commodity"] == "coal") + | (aux2_df["commodity"] == "coke_iron") + ), + "variable", ].values ).tolist() - aggregate_name = ( - "Final Energy|Industry|" + s + "|" + "Solids|Coal" - ) + aggregate_name = "Final Energy|Industry|" + s + "|" + "Solids|Coal" elif c == "heat": var = np.unique( aux2_df.loc[ (aux2_df["commodity"] == "d_heat"), "variable" ].values ).tolist() - aggregate_name = ( - "Final Energy|Industry|" + s + "|" + "Heat" - ) + aggregate_name = "Final Energy|Industry|" + s + "|" + "Heat" elif c == "all": var = aux2_df["variable"].tolist() aggregate_name = "Final Energy|Industry|" + s @@ -2877,12 +3147,12 @@ def report(context,scenario): "steel", "petro", "cement", - 'ammonia', - 'methanol', + "ammonia", + "methanol", "all", "Chemicals", - 'Chemicals|Other', - 'Other Sector' + "Chemicals|Other", + "Other Sector", ] # CO2_industry and CO2 are reported by the legacy reporting in order to @@ -2914,17 +3184,18 @@ def report(context,scenario): # but they are excluded to be consistent with other materials emission # reporting. - exclude = ['emis|CO2_industry|meth_coal|M1', - 'emis|CO2_industry|meth_ng|M1', - 'emis|CO2_industry|meth_coal_ccs|M1', - 'emis|CO2_industry|meth_ng_ccs|M1', - 'emis|CO2|meth_coal_ccs|M1', - 'emis|CO2|meth_ng_ccs|M1', - 'emis|CO2|meth_imp|M1', - 'emis|CO2|meth_exp|M1' - ] + exclude = [ + "emis|CO2_industry|meth_coal|M1", + "emis|CO2_industry|meth_ng|M1", + "emis|CO2_industry|meth_coal_ccs|M1", + "emis|CO2_industry|meth_ng_ccs|M1", + "emis|CO2|meth_coal_ccs|M1", + "emis|CO2|meth_ng_ccs|M1", + "emis|CO2|meth_imp|M1", + "emis|CO2|meth_exp|M1", + ] - df_emi.filter(variable= exclude, keep=False, inplace=True) + df_emi.filter(variable=exclude, keep=False, inplace=True) # Filter the necessary variables @@ -2939,14 +3210,16 @@ def report(context,scenario): # "emis|CO2_industry|electr_NH3|*",'emis|CO2_industry|meth_coal|feedstock', # 'emis|CO2_industry|meth_ng|feedstock','emis|CO2_industry|meth_ng_ccs|feedstock', - if e == 'CO2_industry': - emi_filter = ["emis|CO2|biomass_NH3_ccs|*", - "emis|CO2|gas_NH3_ccs|*", - "emis|CO2|coal_NH3_ccs|*", - "emis|CO2|fueloil_NH3_ccs|*", - 'emis|CO2|meth_coal_ccs|feedstock', - 'emis|CO2|meth_ng_ccs|feedstock', - 'emis|CO2_industry|*'] + if e == "CO2_industry": + emi_filter = [ + "emis|CO2|biomass_NH3_ccs|*", + "emis|CO2|gas_NH3_ccs|*", + "emis|CO2|coal_NH3_ccs|*", + "emis|CO2|fueloil_NH3_ccs|*", + "emis|CO2|meth_coal_ccs|feedstock", + "emis|CO2|meth_ng_ccs|feedstock", + "emis|CO2_industry|*", + ] df_emi.filter(variable=emi_filter, inplace=True) else: @@ -2954,13 +3227,14 @@ def report(context,scenario): # Below variables are not reported under Industrial Process # Emissions. Therefore, they are excluded. - exclude = ["emis|CO2|biomass_NH3_ccs|*", - "emis|CO2|gas_NH3_ccs|*", - "emis|CO2|coal_NH3_ccs|*", - "emis|CO2|fueloil_NH3_ccs|*", - "emis|CO2|meth_coal_ccs|*", - "emis|CO2|meth_ng_ccs|*" - ] + exclude = [ + "emis|CO2|biomass_NH3_ccs|*", + "emis|CO2|gas_NH3_ccs|*", + "emis|CO2|coal_NH3_ccs|*", + "emis|CO2|fueloil_NH3_ccs|*", + "emis|CO2|meth_coal_ccs|*", + "emis|CO2|meth_ng_ccs|*", + ] df_emi.filter(variable=exclude, keep=False, inplace=True) df_emi.filter(variable=emi_filter, inplace=True) @@ -2968,17 +3242,17 @@ def report(context,scenario): # Perform some specific unit conversions if (e == "CO2") | (e == "CO2_industry"): # From MtC to Mt CO2/yr - df_emi.convert_unit('', to="Mt CO2/yr", factor=44/12, - inplace=True) + df_emi.convert_unit( + "", to="Mt CO2/yr", factor=44 / 12, inplace=True + ) elif (e == "N2O") | (e == "CF4"): unit = "kt " + e + "/yr" - df_emi.convert_unit('', to= unit, factor=1, - inplace=True) + df_emi.convert_unit("", to=unit, factor=1, inplace=True) else: e = change_names(e) # From kt/yr to Mt/yr unit = "Mt " + e + "/yr" - df_emi.convert_unit("", to= unit, factor=0.001, inplace=True) + df_emi.convert_unit("", to=unit, factor=0.001, inplace=True) all_emissions = df_emi.timeseries().reset_index() @@ -3024,47 +3298,52 @@ def report(context,scenario): tec = [ t for t in aux2_df["technology"].values - if ((s in t) & ("furnace" not in t) & ('NH3' not in t) \ - & (not (t.startswith('meth_')))) + if ( + (s in t) + & ("furnace" not in t) + & ("NH3" not in t) + & (not (t.startswith("meth_"))) + ) ] if (typ == "demand") & (s == "Chemicals"): tec = [ t for t in aux2_df["technology"].values - if ( (t.startswith('meth_') & (not (t.startswith('meth_i'))))\ - | ('NH3' in t) | ('MTO' in t) | ('resins' in t) | \ - (('petro' in t) & ('furnace' in t))) + if ( + (t.startswith("meth_") & (not (t.startswith("meth_i")))) + | ("NH3" in t) + | ("MTO" in t) + | ("resins" in t) + | (("petro" in t) & ("furnace" in t)) + ) ] if (typ == "demand") & (s == "Chemicals|Other"): tec = [ - t - for t in aux2_df["technology"].values - if ('resins' in t) - ] + t for t in aux2_df["technology"].values if ("resins" in t) + ] if (typ == "demand") & (s == "Other Sector") & (e != "CO2"): tec = [ t for t in aux2_df["technology"].values - if ( - (t.startswith("biomass_i")) - | (t.startswith("coal_i")) - | (t.startswith("elec_i")) - | (t.startswith("eth_i")) - | (t.startswith("foil_i")) - | (t.startswith("gas_i")) - | (t.startswith("h2_i")) - | (t.startswith("heat_i")) - | (t.startswith("hp_el_i")) - | (t.startswith("hp_gas_i")) - | (t.startswith("loil_i")) - | (t.startswith("meth_i")) - | (t.startswith("sp_coal_I")) - | (t.startswith("sp_el_I")) - | (t.startswith("sp_eth_I")) - | (t.startswith("sp_liq_I")) - | (t.startswith("sp_meth_I")) - | (t.startswith("h2_fc_I")) - + if ( + (t.startswith("biomass_i")) + | (t.startswith("coal_i")) + | (t.startswith("elec_i")) + | (t.startswith("eth_i")) + | (t.startswith("foil_i")) + | (t.startswith("gas_i")) + | (t.startswith("h2_i")) + | (t.startswith("heat_i")) + | (t.startswith("hp_el_i")) + | (t.startswith("hp_gas_i")) + | (t.startswith("loil_i")) + | (t.startswith("meth_i")) + | (t.startswith("sp_coal_I")) + | (t.startswith("sp_el_I")) + | (t.startswith("sp_eth_I")) + | (t.startswith("sp_liq_I")) + | (t.startswith("sp_meth_I")) + | (t.startswith("h2_fc_I")) ) ] @@ -3074,51 +3353,53 @@ def report(context,scenario): for t in aux2_df["technology"].values if ( ( - ( - ("cement" in t) - | ("steel" in t) - | ("aluminum" in t) - | ("petro" in t) - + ( + ("cement" in t) + | ("steel" in t) + | ("aluminum" in t) + | ("petro" in t) ) & ("furnace" in t) - ) - - | ( - (t.startswith("biomass_i")) - | (t.startswith("coal_i")) - | (t.startswith("elec_i")) - | (t.startswith("eth_i")) - | (t.startswith("foil_i")) - | (t.startswith("gas_i")) - | (t.startswith("h2_i")) - | (t.startswith("heat_i")) - | (t.startswith("hp_el_i")) - | (t.startswith("hp_gas_i")) - | (t.startswith("loil_i")) - | (t.startswith("meth_i")) - | (t.startswith("sp_coal_I")) - | (t.startswith("sp_el_I")) - | (t.startswith("sp_eth_I")) - | (t.startswith("sp_liq_I")) - | (t.startswith("sp_meth_I")) - | (t.startswith("h2_fc_I")) - | ("DUMMY_limestone_supply_cement" in t) - | ("DUMMY_limestone_supply_steel" in t) - | ("eaf_steel" in t) - | ("DUMMY_coal_supply" in t) - | ("DUMMY_gas_supply" in t) - | ("NH3" in t) - | t.startswith('meth_') - | ("MTO" in t) - | ("resins" in t) - ) + ) + | ( + (t.startswith("biomass_i")) + | (t.startswith("coal_i")) + | (t.startswith("elec_i")) + | (t.startswith("eth_i")) + | (t.startswith("foil_i")) + | (t.startswith("gas_i")) + | (t.startswith("h2_i")) + | (t.startswith("heat_i")) + | (t.startswith("hp_el_i")) + | (t.startswith("hp_gas_i")) + | (t.startswith("loil_i")) + | (t.startswith("meth_i")) + | (t.startswith("sp_coal_I")) + | (t.startswith("sp_el_I")) + | (t.startswith("sp_eth_I")) + | (t.startswith("sp_liq_I")) + | (t.startswith("sp_meth_I")) + | (t.startswith("h2_fc_I")) + | ("DUMMY_limestone_supply_cement" in t) + | ("DUMMY_limestone_supply_steel" in t) + | ("eaf_steel" in t) + | ("DUMMY_coal_supply" in t) + | ("DUMMY_gas_supply" in t) + | ("NH3" in t) + | t.startswith("meth_") + | ("MTO" in t) + | ("resins" in t) + ) ) ] - if (typ == "demand") & (s != "all") & (s != "Other Sector")\ - & (s != "Chemicals") & (s != "Chemicals|Other"): - + if ( + (typ == "demand") + & (s != "all") + & (s != "Other Sector") + & (s != "Chemicals") + & (s != "Chemicals|Other") + ): if s == "steel": # Furnaces are not used as heat source for iron&steel # Dummy supply technologies help accounting the emissions @@ -3141,24 +3422,28 @@ def report(context,scenario): if ( ((s in t) & ("furnace" in t)) | ("DUMMY_limestone_supply_cement" in t) - )] + ) + ] elif s == "ammonia": tec = [ t for t in aux2_df["technology"].values - if ( - ('NH3' in t))] + if (("NH3" in t)) + ] elif s == "methanol": tec = [ t for t in aux2_df["technology"].values if ( - t.startswith('meth_') & (not (t.startswith('meth_i'))))] - elif s == 'petro': + t.startswith("meth_") + & (not (t.startswith("meth_i"))) + ) + ] + elif s == "petro": tec = [ t for t in aux2_df["technology"].values - if ((('petro' in t) & ("furnace" in t)) | ('MTO' in t)) + if ((("petro" in t) & ("furnace" in t)) | ("MTO" in t)) ] else: tec = [ @@ -3182,7 +3467,7 @@ def report(context,scenario): # Aggregate names: if s == "all": if (typ == "demand") & (e != "CO2"): - if (e != "CO2_industry"): + if e != "CO2_industry": aggregate_name = ( "Emissions|" + e + "|Energy|Demand|Industry" ) @@ -3192,12 +3477,12 @@ def report(context,scenario): "Emissions|" + "CO2" + "|Energy|Demand|Industry" ) aggregate_list.append(aggregate_name) - if ((typ == "process") & (e != "CO2_industry")) : + if (typ == "process") & (e != "CO2_industry"): aggregate_name = "Emissions|" + e + "|Industrial Processes" aggregate_list.append(aggregate_name) else: - if ((typ == "demand") & (e != "CO2")): - if (e != "CO2_industry"): + if (typ == "demand") & (e != "CO2"): + if e != "CO2_industry": aggregate_name = ( "Emissions|" + e + "|Energy|Demand|Industry|" + s ) @@ -3210,7 +3495,7 @@ def report(context,scenario): + s ) aggregate_list.append(aggregate_name) - if ((typ == "process") & (e != "CO2_industry")): + if (typ == "process") & (e != "CO2_industry"): aggregate_name = ( "Emissions|" + e + "|Industrial Processes|" + s ) @@ -3606,8 +3891,8 @@ def report(context,scenario): # Scrap Release: (Buildings), Other and Power Sector # For cement, we dont have any other scrap represented in the model. # Only scrap is from power sector. - print('Scrap generated by sector') - materials = ["aluminum","steel","cement"] + print("Scrap generated by sector") + materials = ["aluminum", "steel", "cement"] for r in nodes: for m in materials: @@ -3616,37 +3901,49 @@ def report(context,scenario): # filt_buildings = 'out|end_of_life|' + m + '|demolition_build|M1' # print(filt_buildings) - filt_other = 'out|end_of_life|' + m + '|other_EOL_' + m + '|M1' - filt_total = 'in|end_of_life|' + m + '|total_EOL_' + m + '|M1' + filt_other = "out|end_of_life|" + m + "|other_EOL_" + m + "|M1" + filt_total = "in|end_of_life|" + m + "|total_EOL_" + m + "|M1" m = change_names(m) # var_name_buildings = 'Total Scrap|Residential and Commercial|' + m - var_name_other = 'Total Scrap|Other|' + m - var_name_power = 'Total Scrap|Power Sector|' + m + var_name_other = "Total Scrap|Other|" + m + var_name_power = "Total Scrap|Power Sector|" + m if m != "Non-Metallic Minerals|Cement": - df_scrap_by_sector.aggregate(var_name_other,\ - components=[filt_other],append=True) + df_scrap_by_sector.aggregate( + var_name_other, components=[filt_other], append=True + ) - # df_scrap_by_sector.aggregate(var_name_buildings,\ - # components=[filt_buildings],append=True) - # 'out|end_of_life|' + m + '|demolition_build|M1', + # df_scrap_by_sector.aggregate(var_name_buildings,\ + # components=[filt_buildings],append=True) + # 'out|end_of_life|' + m + '|demolition_build|M1', - df_scrap_by_sector.subtract(filt_total, filt_other,var_name_power, - axis='variable', append = True) + df_scrap_by_sector.subtract( + filt_total, filt_other, var_name_power, axis="variable", append=True + ) - df_scrap_by_sector.filter(variable=[ - # var_name_buildings, - var_name_other, var_name_power],inplace=True) + df_scrap_by_sector.filter( + variable=[ + # var_name_buildings, + var_name_other, + var_name_power, + ], + inplace=True, + ) elif m == "Non-Metallic Minerals|Cement": - df_scrap_by_sector.aggregate(var_name_power,\ - components=[filt_total],append=True) + df_scrap_by_sector.aggregate( + var_name_power, components=[filt_total], append=True + ) - df_scrap_by_sector.filter(variable=[ - # var_name_buildings, - var_name_power],inplace=True) + df_scrap_by_sector.filter( + variable=[ + # var_name_buildings, + var_name_power + ], + inplace=True, + ) - df_scrap_by_sector.convert_unit('', to='Mt/yr', factor=1, inplace = True) + df_scrap_by_sector.convert_unit("", to="Mt/yr", factor=1, inplace=True) df_final.append(df_scrap_by_sector, inplace=True) # PRICE @@ -3906,4 +4203,4 @@ def report(context,scenario): pp.close() os.remove(path_temp) - #os.remove(path) + # os.remove(path) From b4886bd363fe602507321febef4d37bf63acd02d Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Mon, 13 May 2024 13:29:09 +0200 Subject: [PATCH 752/774] Resolve code quality issues in materials reporting --- .../model/material/report/reporting.py | 162 ++++++++++-------- 1 file changed, 86 insertions(+), 76 deletions(-) diff --git a/message_ix_models/model/material/report/reporting.py b/message_ix_models/model/material/report/reporting.py index d9a79a005d..e390334d0f 100644 --- a/message_ix_models/model/material/report/reporting.py +++ b/message_ix_models/model/material/report/reporting.py @@ -8,9 +8,6 @@ Merged_Model_Scenario.xlsx: Includes all IAMC variables Material_global_grpahs.pdf -@author: unlu -""" - # NOTE: Works with pyam-iamc version 0.9.0 # Problems with the most recent versions: # 0.11.0 --> Filtering with asterisk * does not work, dataframes are empty. @@ -21,37 +18,25 @@ # There are various tech.s that only have data starting from 2025 or 2030. # foil_imp AFR, frunace_h2_aluminum, h2_i... -# PACKAGES +@author: unlu +""" -from ixmp import Platform -from message_ix import Scenario -from message_ix.report import Reporter -from ixmp.report import configure -from message_ix_models import ScenarioInfo -from message_data.tools.post_processing.iamc_report_hackathon import report as reporting -from message_ix_models.model.material.util import read_config +import os -import pandas as pd +import matplotlib import numpy as np +import openpyxl +import pandas as pd import pyam import xlsxwriter -import os -import openpyxl - -# import plotly.graph_objects as go -import matplotlib - -matplotlib.use("Agg") +from ixmp.report import configure from matplotlib import pyplot as plt - -from pyam.plotting import OUTSIDE_LEGEND from matplotlib.backends.backend_pdf import PdfPages +from message_ix.report import Reporter +from message_ix_models import ScenarioInfo -def print_full(x): - pd.set_option("display.max_rows", len(x)) - print(x) - pd.reset_option("display.max_rows") +matplotlib.use("Agg") def change_names(s): @@ -153,10 +138,10 @@ def fix_excel(path_temp, path_new): "OCA": "OC", } # Iterate over the rows and replace - for i in range(1, ((sheet.max_row) + 1)): + for i in range(1, (sheet.max_row + 1)): data = [ sheet.cell(row=i, column=col).value - for col in range(1, ((sheet.max_column) + 1)) + for col in range(1, (sheet.max_column + 1)) ] for index, value in enumerate(data): col_no = index + 1 @@ -331,7 +316,8 @@ def report(context, scenario): ) # Convert methanol at primary_material from energy to material unit - # In the model this is kept as energy units to easily seperate two modes: fuel and feedstock + # In the model this is kept as energy units + # to easily seperate two modes: fuel and feedstock df.divide( "out|primary_material|methanol|meth_coal|feedstock", 0.6976, @@ -389,7 +375,8 @@ def report(context, scenario): ) # Convert methanol at primary from energy to material unit - # In the model this is kept as energy units to easily seperate two modes: fuel and feedstock + # In the model this is kept as energy units + # to easily seperate two modes: fuel and feedstock df.divide( "out|primary|methanol|meth_coal|fuel", 0.6976, @@ -1104,7 +1091,7 @@ def report(context, scenario): ) df_final.append(df_cement, inplace=True) - # --------------------------------------------------------------------------------------- + # ---------------------------------------------------------------------------------- # FINAL ENERGY BY FUELS (Only Non-Energy Use) # HVC production, ammonia production and methanol production. @@ -1334,8 +1321,10 @@ def report(context, scenario): df_final.append(df_final_energy, inplace=True) if c == "gas": - # Can not distinguish by type Gases (natural gas, biomass, synthetic fossil, efuel) - # (coal_gas), from biomass (gas_bio), natural gas (gas_bal): All go into secondary level + # Can not distinguish by type Gases + # (natural gas, biomass, synthetic fossil, efuel) + # (coal_gas), from biomass (gas_bio), natural gas (gas_bal): + # All go into secondary level # Can not be distinguished in the final level. df_final_energy.aggregate( "Final Energy|Non-Energy Use|Gases", @@ -1395,7 +1384,7 @@ def report(context, scenario): # Natural Gas Liquids (Ethane/Propane) filter_vars = [ - v for v in aux2_df["variable"].values if (("gas_proc" in v)) + v for v in aux2_df["variable"].values if ("gas_proc" in v) ] df_final_energy.aggregate( @@ -1597,7 +1586,7 @@ def report(context, scenario): | (v.split("|")[3].endswith("resins")) | (v.split("|")[3].endswith("_i")) | (v.split("|")[3].endswith("_I")) - | (("NH3") in v) + | ("NH3" in v) | (v.split("|")[3].startswith("meth")) | (v.split("|")[3].startswith("CH2O")) ) @@ -1650,8 +1639,10 @@ def report(context, scenario): ) df_final.append(df_final_energy, inplace=True) if c == "gas": - # Can not distinguish by type Gases (natural gas, biomass, synthetic fossil, efuel) - # (coal_gas), from biomass (gas_bio), natural gas (gas_bal): All go into secondary level + # Can not distinguish by type Gases + # (natural gas, biomass, synthetic fossil, efuel) + # (coal_gas), from biomass (gas_bio), natural gas (gas_bal): + # All go into secondary level # Can not be distinguished in the final level. df_final_energy.aggregate( @@ -1721,7 +1712,7 @@ def report(context, scenario): # Other filter_vars = [ - v for v in aux2_df["variable"].values if (("methanol" in v)) + v for v in aux2_df["variable"].values if ("methanol" in v) ] df_final_energy.aggregate( @@ -2004,7 +1995,7 @@ def report(context, scenario): | (v.split("|")[3].endswith("resins")) | (v.split("|")[3].endswith("_i")) | (v.split("|")[3].endswith("_I")) - | (("NH3") in v) + | ("NH3" in v) | (v.split("|")[3].endswith("_fs")) | (v.split("|")[3].startswith("meth")) | (v.split("|")[3].startswith("CH2O")) @@ -2055,8 +2046,10 @@ def report(context, scenario): ) df_final.append(df_final_energy, inplace=True) if c == "gas": - # Can not distinguish by type Gases (natural gas, biomass, synthetic fossil, efuel) - # (coal_gas), from biomass (gas_bio), natural gas (gas_bal): All go into secondary level + # Can not distinguish by type Gases + # (natural gas, biomass, synthetic fossil, efuel) + # (coal_gas), from biomass (gas_bio), natural gas (gas_bal): + # All go into secondary level # Can not be distinguished in the final level. df_final_energy.aggregate( "Final Energy|Industry|Gases", @@ -2127,7 +2120,7 @@ def report(context, scenario): # Methanol filter_vars = [ - v for v in aux2_df["variable"].values if (("methanol" in v)) + v for v in aux2_df["variable"].values if ("methanol" in v) ] df_final_energy.aggregate( @@ -2139,7 +2132,7 @@ def report(context, scenario): # Natural Gas Liquids (Ethane/Propane) filter_vars = [ - v for v in aux2_df["variable"].values if (("gas_proc" in v)) + v for v in aux2_df["variable"].values if ("gas_proc" in v) ] df_final_energy.aggregate( @@ -2318,7 +2311,7 @@ def report(context, scenario): tec = [ t for t in aux2_df["technology"].values - if (((t.endswith("_i")) | (t.endswith("_I")))) + if ((t.endswith("_i")) | (t.endswith("_I"))) ] solar_tec = ["solar_i"] aux2_df = aux2_df[aux2_df["technology"].isin(tec)] @@ -2416,7 +2409,7 @@ def report(context, scenario): elif c == "liquid_bio": var = np.unique( aux2_df.loc[ - ((aux2_df["commodity"] == "ethanol")), + (aux2_df["commodity"] == "ethanol"), "variable", ].values ).tolist() @@ -2445,7 +2438,7 @@ def report(context, scenario): elif c == "liquid_other": var = np.unique( aux2_df.loc[ - ((aux2_df["commodity"] == "methanol")), + (aux2_df["commodity"] == "methanol"), "variable", ].values ).tolist() @@ -2673,7 +2666,7 @@ def report(context, scenario): elif c == "methanol": var = np.unique( aux2_df.loc[ - ((aux2_df["commodity"] == "methanol")), + (aux2_df["commodity"] == "methanol"), "variable", ].values ).tolist() @@ -2699,7 +2692,7 @@ def report(context, scenario): elif c == "liquid_bio": var = np.unique( aux2_df.loc[ - ((aux2_df["commodity"] == "ethanol")), + (aux2_df["commodity"] == "ethanol"), "variable", ].values ).tolist() @@ -2725,7 +2718,7 @@ def report(context, scenario): elif c == "liquid_gas": var = np.unique( aux2_df.loc[ - ((aux2_df["technology"] == "gas_processing_petro")), + (aux2_df["technology"] == "gas_processing_petro"), "variable", ].values ).tolist() @@ -2747,7 +2740,7 @@ def report(context, scenario): elif c == "solid_coal": var = np.unique( aux2_df.loc[ - ((aux2_df["commodity"] == "coal")), + (aux2_df["commodity"] == "coal"), "variable", ].values ).tolist() @@ -2757,7 +2750,7 @@ def report(context, scenario): elif c == "solid_bio": var = np.unique( aux2_df.loc[ - ((aux2_df["commodity"] == "biomass")), + (aux2_df["commodity"] == "biomass"), "variable", ].values ).tolist() @@ -2925,7 +2918,7 @@ def report(context, scenario): tec = [ t for t in aux2_df["technology"].values - if (((t.endswith("_i")) | (t.endswith("_I")) | (t.endswith("_fs")))) + if ((t.endswith("_i")) | (t.endswith("_I")) | (t.endswith("_fs"))) ] solar_tec = ["solar_i"] aux2_df = aux2_df[aux2_df["technology"].isin(tec)] @@ -3023,7 +3016,7 @@ def report(context, scenario): elif c == "liquid_bio": var = np.unique( aux2_df.loc[ - ((aux2_df["commodity"] == "ethanol")), + (aux2_df["commodity"] == "ethanol"), "variable", ].values ).tolist() @@ -3057,7 +3050,7 @@ def report(context, scenario): elif c == "liquid_other": var = np.unique( aux2_df.loc[ - ((aux2_df["commodity"] == "methanol")), + (aux2_df["commodity"] == "methanol"), "variable", ].values ).tolist() @@ -3079,7 +3072,7 @@ def report(context, scenario): elif c == "solids_bio": var = np.unique( aux2_df.loc[ - ((aux2_df["commodity"] == "biomass")), + (aux2_df["commodity"] == "biomass"), "variable", ].values ).tolist() @@ -3138,8 +3131,8 @@ def report(context, scenario): df_final.append(df_final_energy, inplace=True) # EMISSIONS - # If ammonia/methanol is used as feedstock the emissions are accounted under 'CO2_industry', - # so as 'demand'. If used as fuel, under 'CO2_transformation'. + # If ammonia/methanol is used as feedstock the emissions are accounted under + # 'CO2_industry', so as 'demand'. If used as fuel, under 'CO2_transformation'. # The CCS technologies deduct negative emissions from the overall CO2. sectors = [ @@ -3203,12 +3196,17 @@ def report(context, scenario): # coefficent. CO2 is included here because it is the amount captured # from the atmosphere and not related to process emissions. - # "emis|CO2_industry|biomass_NH3_ccs|*","emis|CO2_industry|gas_NH3_ccs|*", - # "emis|CO2_industry|coal_NH3_ccs|*","emis|CO2_industry|fueloil_NH3_ccs|*", - # "emis|CO2_industry|biomass_NH3|*","emis|CO2_industry|gas_NH3|*", + # "emis|CO2_industry|biomass_NH3_ccs|*", + # "emis|CO2_industry|gas_NH3_ccs|*", + # "emis|CO2_industry|coal_NH3_ccs|*", + # "emis|CO2_industry|fueloil_NH3_ccs|*", + # "emis|CO2_industry|biomass_NH3|*", + # "emis|CO2_industry|gas_NH3|*", # "emis|CO2_industry|coal_NH3|*","emis|CO2_industry|fueloil_NH3|*", - # "emis|CO2_industry|electr_NH3|*",'emis|CO2_industry|meth_coal|feedstock', - # 'emis|CO2_industry|meth_ng|feedstock','emis|CO2_industry|meth_ng_ccs|feedstock', + # "emis|CO2_industry|electr_NH3|*", + # 'emis|CO2_industry|meth_coal|feedstock', + # 'emis|CO2_industry|meth_ng|feedstock', + # 'emis|CO2_industry|meth_ng_ccs|feedstock', if e == "CO2_industry": emi_filter = [ @@ -3403,7 +3401,8 @@ def report(context, scenario): if s == "steel": # Furnaces are not used as heat source for iron&steel # Dummy supply technologies help accounting the emissions - # from cokeoven_steel, bf_steel, dri_steel, eaf_steel, sinter_steel. + # from cokeoven_steel, bf_steel, dri_steel, + # eaf_steel, sinter_steel. tec = [ t @@ -3426,9 +3425,7 @@ def report(context, scenario): ] elif s == "ammonia": tec = [ - t - for t in aux2_df["technology"].values - if (("NH3" in t)) + t for t in aux2_df["technology"].values if ("NH3" in t) ] elif s == "methanol": tec = [ @@ -3515,7 +3512,8 @@ def report(context, scenario): ) df_emi = pyam.IamDataFrame(data=aux2_df) - # Aggregation over emission type for each sector if there are elements to aggregate + # Aggregation over emission type for each sector + # if there are elements to aggregate if len(aggregate_list) != 0: for i in range(len(aggregate_list)): @@ -3566,7 +3564,8 @@ def report(context, scenario): # # # Propylene production # - # propylene_vars = [ # "out|final_material|propylene|catalytic_cracking_ref|atm_gasoil", + # propylene_vars = [ + # "out|final_material|propylene|catalytic_cracking_ref|atm_gasoil", # # "out|final_material|propylene|catalytic_cracking_ref|vacuum_gasoil", # "out|final_material|propylene|steam_cracker_petro|atm_gasoil", # "out|final_material|propylene|steam_cracker_petro|naphtha", @@ -3813,7 +3812,7 @@ def report(context, scenario): # "in|final|ethanol|ethanol_to_ethylene_petro|M1", # "in|final|gas|gas_processing_petro|M1", # "in|final|atm_gasoil|steam_cracker_petro|atm_gasoil", - # "in|final|vacuum_gasoil|steam_cracker_petro|vacuum_gasoil", + # "in|final|vacuum_gasoil|steam_cracker_petro|vacuum_gasoil", # "in|final|naphtha|steam_cracker_petro|naphtha", # ], # keep=False, @@ -3831,7 +3830,8 @@ def report(context, scenario): # columns=["flow_type", "level", "commodity", "technology", "mode"], # ) # aux2_df = pd.concat( - # [all_flows.reset_index(drop=True), aux1_df.reset_index(drop=True)], + # [all_flows.reset_index(drop=True), + # aux1_df.reset_index(drop=True)], # axis=1, # ) # @@ -3845,7 +3845,8 @@ def report(context, scenario): # commodity_list = [] # var_list = [] # - # # For each commodity collect the variable name, create an aggregate name + # # For each commodity collect the variable name, + # create an aggregate name # s = change_names(s) # for c in np.unique(aux2_df["commodity"].values): # var = np.unique( @@ -3973,7 +3974,8 @@ def report(context, scenario): # # Used for calculation of average prices for scraps # output = scenario.par( # "output", - # filters={"technology": ["scrap_recovery_aluminum", "scrap_recovery_steel"]}, + # filters={"technology": + # ["scrap_recovery_aluminum", "scrap_recovery_steel"]}, # ) # # Differs per sector what to report so more flexible with conditions. # # Store the relevant variables in prices_all @@ -4003,7 +4005,8 @@ def report(context, scenario): # prices_all = prices[(prices["commodity"] == "pig_iron")] # if c == "Steel": # prices_all = prices[ - # (prices["commodity"] == "steel") & (prices["level"] == "final_material") + # (prices["commodity"] == "steel") + # & (prices["level"] == "final_material") # ] # if c == "Steel|New Scrap": # prices_all = prices[ @@ -4046,12 +4049,15 @@ def report(context, scenario): # for reg in output["node_loc"].unique(): # #for yr in output["year_act"].unique(): # for yr in prices["year"].unique(): - # prices_temp = prices.groupby(["node", "year"]).get_group((reg, yr)) + # prices_temp = prices.groupby( + # ["node", "year"]).get_group((reg, yr)) # rate = prices_temp["weight"].values.tolist() # amount = prices_temp["lvl"].values.tolist() # weighted_avg = np.average(amount, weights=rate) # prices_new = pd.DataFrame( - # {"node": reg, "year": yr, "commodity": c, "lvl": weighted_avg,}, + # {"node": reg, "year": yr, + # "commodity": c, + # "lvl": weighted_avg,}, # index=[0], # ) # prices_all = pd.concat([prices_all, prices_new]) @@ -4080,7 +4086,9 @@ def report(context, scenario): # if (r == "R11_GLB") | (r == "R12_GLB"): # continue # df_price = pd.DataFrame( - # {"model": model_name, "scenario": scenario_name, "unit": "2010USD/Mt",}, + # {"model": model_name, + # "scenario": scenario_name, + # "unit": "2010USD/Mt",}, # index=[0], # ) # @@ -4119,7 +4127,9 @@ def report(context, scenario): # cap_new = scenario.var("CAP_NEW") # cap_new.drop(["mrg"], axis=1, inplace=True) # cap_new.rename( - # columns={"lvl": "installed_capacity", "node_loc": "region", "year_vtg": "year"}, + # columns={"lvl": "installed_capacity", + # "node_loc": "region", + # "year_vtg": "year"}, # inplace=True, # ) # From b1352f617717f36765f4c9ad99de92e4ecdef2c7 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Mon, 13 May 2024 13:34:11 +0200 Subject: [PATCH 753/774] Resolve code quality issues in material demand module --- .../material_demand/material_demand_calc.py | 23 +++++++++---------- 1 file changed, 11 insertions(+), 12 deletions(-) diff --git a/message_ix_models/model/material/material_demand/material_demand_calc.py b/message_ix_models/model/material/material_demand/material_demand_calc.py index e72b82bca4..5ea356ac83 100644 --- a/message_ix_models/model/material/material_demand/material_demand_calc.py +++ b/message_ix_models/model/material/material_demand/material_demand_calc.py @@ -185,7 +185,7 @@ def read_base_demand(filepath): def read_hist_mat_demand(material): - datapath = message_ix_models.util.package_data_path ("material") + datapath = message_ix_models.util.package_data_path("material") if material in ["cement", "steel"]: # Read population data @@ -298,7 +298,7 @@ def read_gdp_ppp_from_scen(scen): mer_to_ppp = scen.par("MERtoPPP") if not len(mer_to_ppp): mer_to_ppp = pd.read_csv( - message_ix_models.util.package_data_path ( + message_ix_models.util.package_data_path( "material", "other", "mer_to_ppp_default.csv" ) ) @@ -315,7 +315,7 @@ def read_gdp_ppp_from_scen(scen): def derive_demand(material, scen, old_gdp=False, ssp="SSP2"): - datapath = message_ix_models.util.package_data_path ("material") + datapath = message_ix_models.util.package_data_path("material") # read pop projection from scenario df_pop = read_pop_from_scen(scen) @@ -326,7 +326,7 @@ def derive_demand(material, scen, old_gdp=False, ssp="SSP2"): df_gdp = pd.read_excel(f"{datapath}/other{file_gdp}", sheet_name="data_R12") df_gdp = ( df_gdp[df_gdp["Scenario"] == "baseline"] - .loc[:, ["Region", *[i for i in df_gdp.columns if type(i) == int]]] + .loc[:, ["Region", *[i for i in df_gdp.columns if isinstance(i, int)]]] .melt(id_vars="Region", var_name="year", value_name="gdp_ppp") .query('Region != "World"') .assign( @@ -345,12 +345,12 @@ def derive_demand(material, scen, old_gdp=False, ssp="SSP2"): x_data = tuple(pd.Series(df_cons[col]) for col in fitting_dict[material]["x_data"]) # run regression on historical data - params_opt, _ = curve_fit( + params_opt = curve_fit( fitting_dict[material]["function"], xdata=x_data, ydata=df_cons["cons_pcap"], p0=fitting_dict[material]["initial_guess"], - ) + )[0] mode = ssp_mode_map[ssp] print(f"adjust regression parameters according to mode: {mode}") print(f"before adjustment: {params_opt}") @@ -393,11 +393,9 @@ def gen_demand_petro(scenario, chemical, gdp_elasticity_2020, gdp_elasticity_203 raise ValueError( f"'{chemical}' not supported. Choose one of {chemicals_implemented}" ) - context = read_config() s_info = ScenarioInfo(scenario) modelyears = s_info.Y # s_info.Y is only for modeling years fy = scenario.firstmodelyear - nodes = s_info.N def get_demand_t1_with_income_elasticity( demand_t0, income_t0, income_t1, elasticity @@ -411,7 +409,7 @@ def get_demand_t1_with_income_elasticity( df_gdp_ts = gdp_ppp.pivot( index="node_loc", columns="year_act", values="value" ).reset_index() - num_cols = [i for i in df_gdp_ts.columns if type(i) == int] + num_cols = [i for i in df_gdp_ts.columns if isinstance(i, int)] hist_yrs = [i for i in num_cols if i < fy] df_gdp_ts = ( df_gdp_ts.drop([i for i in hist_yrs if i in df_gdp_ts.columns], axis=1) @@ -421,9 +419,10 @@ def get_demand_t1_with_income_elasticity( else: gdp_mer = scenario.par("bound_activity_up", {"technology": "GDP"}) mer_to_ppp = pd.read_csv( - package_data_path ("material", "other", "mer_to_ppp_default.csv") + package_data_path("material", "other", "mer_to_ppp_default.csv") ).set_index(["node", "year"]) - # mer_to_ppp = scenario.par("MERtoPPP").set_index("node", "year") TODO: might need to be re-activated for different SSPs + # mer_to_ppp = scenario.par("MERtoPPP").set_index("node", "year") + # TODO: might need to be re-activated for different SSPs gdp_mer = gdp_mer.merge( mer_to_ppp.reset_index()[["node", "year", "value"]], left_on=["node_loc", "year_act"], @@ -435,7 +434,7 @@ def get_demand_t1_with_income_elasticity( df_gdp_ts = gdp_mer.pivot( index="Region", columns="year", values="gdp_ppp" ).reset_index() - num_cols = [i for i in df_gdp_ts.columns if type(i) == int] + num_cols = [i for i in df_gdp_ts.columns if isinstance(i, int)] hist_yrs = [i for i in num_cols if i < fy] df_gdp_ts = ( df_gdp_ts.drop([i for i in hist_yrs if i in df_gdp_ts.columns], axis=1) From 0654db45f37b012c7676528b765033b7eb2081f0 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Mon, 13 May 2024 14:28:29 +0200 Subject: [PATCH 754/774] Remove ntfy in materials commands --- message_ix_models/model/material/__init__.py | 4 ---- 1 file changed, 4 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index c68d615dcf..4c167b4766 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -5,7 +5,6 @@ import click import ixmp import message_ix -import ntfy import pandas as pd from message_data.tools.utilities import ( calibrate_UE_gr_to_demand, @@ -367,8 +366,6 @@ def build_scen(context, datafile, tag, mode, scenario_name, old_calib, update_co scenario.add_par("inv_cost", inv) scenario.commit(f"update cost assumption to: {update_costs}") - ntfy.notify(title="MESSAGEix-Materials", message="building successfully finished!") - @cli.command("solve") @click.option("--scenario_name", default="NoPolicy") @@ -474,7 +471,6 @@ def solve_scen( print("Solving the scenario without MACRO") scenario.solve(model="MESSAGE", solve_options={"lpmethod": "4", "scaind": "-1"}) scenario.set_as_default() - ntfy.notify(title="MESSAGEix-Materials", message="solving successfully finished!") @cli.command("add_buildings_ts") From 02b905e3ef236f5fd328c3edb88ece6a9676ae48 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Mon, 13 May 2024 14:28:46 +0200 Subject: [PATCH 755/774] Comment unused variable in reporting --- message_ix_models/model/material/report/reporting.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/message_ix_models/model/material/report/reporting.py b/message_ix_models/model/material/report/reporting.py index e390334d0f..e1d100b939 100644 --- a/message_ix_models/model/material/report/reporting.py +++ b/message_ix_models/model/material/report/reporting.py @@ -741,7 +741,7 @@ def report(context, scenario): ] total_scrap_steel_vars = ["out|dummy_end_of_life|steel|total_EOL_steel|M1"] - new_scrap_steel_vars = ["out|new_scrap|steel|manuf_steel|M1"] + #new_scrap_steel_vars = ["out|new_scrap|steel|manuf_steel|M1"] old_scrap_steel_vars = ["out|dummy_end_of_life|steel|total_EOL_steel|M1"] df_steel.aggregate( From ddfa1f3018c0d258cb706129100883be400091a8 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Mon, 13 May 2024 14:29:56 +0200 Subject: [PATCH 756/774] Delete unused materials submodule --- message_ix_models/model/material/data.py | 45 ------------------------ 1 file changed, 45 deletions(-) delete mode 100644 message_ix_models/model/material/data.py diff --git a/message_ix_models/model/material/data.py b/message_ix_models/model/material/data.py deleted file mode 100644 index 79fb5600b0..0000000000 --- a/message_ix_models/model/material/data.py +++ /dev/null @@ -1,45 +0,0 @@ -import logging - -from message_ix_models import ScenarioInfo -from message_ix_models.util import ( - add_par_data, -) - -from .data_aluminum import gen_data_aluminum -from .data_cement import gen_data_cement -from .data_generic import gen_data_generic -from .data_steel import gen_data_steel - -log = logging.getLogger(__name__) - -DATA_FUNCTIONS = [ - gen_data_steel, - gen_data_cement, - gen_data_aluminum, - gen_data_generic, - gen_data_petro_chemicals -] - - -# Try to handle multiple data input functions from different materials -def add_data(scenario, dry_run=False): - """Populate `scenario` with MESSAGEix-Materials data.""" - # Information about `scenario` - info = ScenarioInfo(scenario) - - # Check for two "node" values for global data, e.g. in - # ixmp://ene-ixmp/CD_Links_SSP2_v2.1_clean/baseline - if {"World", "R11_GLB"} < set(info.set["node"]): - log.warning("Remove 'R11_GLB' from node list for data generation") - info.set["node"].remove("R11_GLB") - - if {"World", "R12_GLB"} < set(info.set["node"]): - log.warning("Remove 'R12_GLB' from node list for data generation") - info.set["node"].remove("R12_GLB") - - for func in DATA_FUNCTIONS: - # Generate or load the data; add to the Scenario - log.info(f"from {func.__name__}()") - add_par_data(scenario, func(scenario), dry_run=dry_run) - - log.info("done") From 6c48963db0e839b928051486f7f8bad138b467ee Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Wed, 15 May 2024 17:42:35 +0200 Subject: [PATCH 757/774] Refactor materials data module functions Split materials data processing functions that have a code complexity greater than 11. Functions in several data modules violated C901 --- message_ix_models/model/material/build.py | 3 +- .../model/material/data_aluminum.py | 575 ++++++++-------- .../model/material/data_ammonia_new.py | 60 +- .../model/material/data_cement.py | 104 +-- .../model/material/data_generic.py | 14 +- .../model/material/data_methanol_new.py | 148 ++-- .../model/material/data_petro.py | 305 ++++---- .../model/material/data_power_sector.py | 566 ++++++++------- .../model/material/data_steel.py | 649 ++++++++---------- .../model/material/report/reporting.py | 4 +- message_ix_models/model/material/util.py | 30 +- 11 files changed, 1161 insertions(+), 1297 deletions(-) diff --git a/message_ix_models/model/material/build.py b/message_ix_models/model/material/build.py index 6e77c0cd0d..bbff682b4f 100644 --- a/message_ix_models/model/material/build.py +++ b/message_ix_models/model/material/build.py @@ -24,7 +24,8 @@ def ellipsize(elements: List) -> str: return ", ".join(map(str, elements)) -def apply_spec( +# FIXME Reduce complexity from 14 to ≤13 +def apply_spec( # noqa: C901 scenario: Scenario, spec: Union[Spec, Mapping[str, ScenarioInfo]] = None, data: Callable = None, diff --git a/message_ix_models/model/material/data_aluminum.py b/message_ix_models/model/material/data_aluminum.py index 220b6ae55b..570cbb09e2 100644 --- a/message_ix_models/model/material/data_aluminum.py +++ b/message_ix_models/model/material/data_aluminum.py @@ -1,5 +1,4 @@ from collections import defaultdict -from typing import Sized import message_ix import pandas as pd @@ -8,15 +7,14 @@ from message_ix_models import ScenarioInfo from message_ix_models.util import ( broadcast, + nodes_ex_world, package_data_path, same_node, ) from .data_util import read_rel, read_timeseries from .material_demand import material_demand_calc -from .util import read_config - -# Get endogenous material demand from buildings interface +from .util import combine_df_dictionaries, get_ssp_from_context, read_config def read_data_aluminum( @@ -70,276 +68,46 @@ def read_data_aluminum( return data_alu, data_alu_rel, data_aluminum_ts -def print_full(x: Sized): - pd.set_option("display.max_rows", len(x)) - print(x) - pd.reset_option("display.max_rows") - - -def gen_data_aluminum( - scenario: message_ix.Scenario, dry_run: bool = False -) -> dict[str, pd.DataFrame]: +def gen_data_alu_ts(data: pd.DataFrame, nodes: list) -> dict[str, pd.DataFrame]: """ - + Generates time variable parameter data for aluminum sector Parameters ---------- - scenario: message_ix.Scenario - Scenario instance to build aluminum model on - dry_run: bool - *not implemented* + data: pd.DataFrame + time variable data from input file + nodes: list + regions of model + Returns ------- - dict[pd.DataFrame] - dict with MESSAGEix parameters as keys and parametrization as values - stored in pd.DataFrame + pd.DataFrame + key-value pairs of parameter names and parameter data """ - context = read_config() - config = context["material"]["aluminum"] - - # Information about scenario, e.g. node, year - s_info = ScenarioInfo(scenario) - ssp = context["ssp"] - # Techno-economic assumptions - data_aluminum, data_aluminum_rel, data_aluminum_ts = read_data_aluminum(scenario) - # List of data frames, to be concatenated together at end - results = defaultdict(list) - - # For each technology there are differnet input and output combinations - # Iterate over technologies - - modelyears = s_info.Y # s_info.Y is only for modeling years - nodes = s_info.N - yv_ya = s_info.yv_ya - nodes.remove("World") - - # Do not parametrize GLB region the same way - if "R11_GLB" in nodes: - nodes.remove("R11_GLB") - global_region = "R11_GLB" - if "R12_GLB" in nodes: - nodes.remove("R12_GLB") - global_region = "R12_GLB" - - for t in config["technology"]["add"]: - params = data_aluminum.loc[ - (data_aluminum["technology"] == t), "parameter" - ].values.tolist() - - # Obtain the active and vintage years - av = data_aluminum.loc[ - (data_aluminum["technology"] == t), "availability" - ].values[0] - modelyears = [year for year in modelyears if year >= av] - yv_ya = yv_ya.loc[yv_ya.year_vtg >= av] - - # Iterate over parameters - for par in params: - # Obtain the parameter names, commodity,level,emission - - split = par.split("|") - param_name = split[0] - - # Obtain the scalar value for the parameter - - val = data_aluminum.loc[ - ( - (data_aluminum["technology"] == t) - & (data_aluminum["parameter"] == par) - ), - "value", - ] - - regions = data_aluminum.loc[ - ( - (data_aluminum["technology"] == t) - & (data_aluminum["parameter"] == par) - ), - "region", - ] - - common = dict( - year_vtg=yv_ya.year_vtg, - year_act=yv_ya.year_act, - mode="M1", - time="year", - time_origin="year", - time_dest="year", - ) - - for rg in regions: - # For the parameters which inlcudes index names - if len(split) > 1: - if (param_name == "input") | (param_name == "output"): - # Assign commodity and level names - # Later mod can be added - com = split[1] - lev = split[2] - - if (param_name == "input") and (lev == "import"): - df = make_df( - param_name, - technology=t, - commodity=com, - level=lev, - value=val[regions[regions == rg].index[0]], - unit="t", - node_loc=rg, - node_origin=global_region, - **common, - ) - - elif (param_name == "output") and (lev == "export"): - df = make_df( - param_name, - technology=t, - commodity=com, - level=lev, - value=val[regions[regions == rg].index[0]], - unit="t", - node_loc=rg, - node_dest=global_region, - **common, - ) - - # Assign higher efficiency to younger plants - elif ( - ((t == "soderberg_aluminum") or (t == "prebake_aluminum")) - & (com == "electr") - & (param_name == "input") - ): - # All the vıntage years - year_vtg = sorted(set(yv_ya.year_vtg.values)) - # Collect the values for the combination of vintage and - # active years. - input_values_all = [] - for yr_v in year_vtg: - # The initial year efficiency value - input_values_temp = [ - val[regions[regions == rg].index[0]] - ] - # Reduction after the vintage year - year_vtg_filtered = list( - filter(lambda op: op >= yr_v, year_vtg) - ) - # Filter the active model years - year_act = yv_ya.loc[ - yv_ya["year_vtg"] == yr_v, "year_act" - ].values - for i in range(len(year_vtg_filtered) - 1): - input_values_temp.append(input_values_temp[i] * 1.1) - - act_year_no = len(year_act) - input_values_temp = input_values_temp[-act_year_no:] - input_values_all = input_values_all + input_values_temp - - df = make_df( - param_name, - technology=t, - commodity=com, - level=lev, - value=input_values_all, - unit="t", - node_loc=rg, - **common, - ).pipe(same_node) - - else: - df = make_df( - param_name, - technology=t, - commodity=com, - level=lev, - value=val[regions[regions == rg].index[0]], - unit="t", - node_loc=rg, - **common, - ).pipe(same_node) - - # Copy parameters to all regions, when node_loc is not GLB - if (len(regions) == 1) and (rg != global_region): - df["node_loc"] = None - df = df.pipe(broadcast, node_loc=nodes) # .pipe(same_node) - # Use same_node only for non-trade technologies - if (lev != "import") and (lev != "export"): - df = df.pipe(same_node) - - elif param_name == "emission_factor": - # Assign the emisson type - emi = split[1] - - df = make_df( - param_name, - technology=t, - value=val[regions[regions == rg].index[0]], - emission=emi, - unit="t", - node_loc=rg, - **common, - ) - - # Parameters with only parameter name - else: - df = make_df( - param_name, - technology=t, - value=val[regions[regions == rg].index[0]], - unit="t", - node_loc=rg, - **common, - ) - - # Copy parameters to all regions - if ( - (len(regions) == 1) - and len(set(df["node_loc"])) == 1 - and list(set(df["node_loc"]))[0] != global_region - ): - df["node_loc"] = None - df = df.pipe(broadcast, node_loc=nodes) - - results[param_name].append(df) - - # Create external demand param - parname = "demand" - df = material_demand_calc.derive_demand( - "aluminum", scenario, old_gdp=False, ssp=ssp + tec_ts = set(data.technology) # set of tecs in timeseries sheet + common = dict( + time="year", + time_origin="year", + time_dest="year", ) - results[parname].append(df) - - # Special treatment for time-varying params - - tec_ts = set(data_aluminum_ts.technology) # set of tecs in timeseries sheet - + par_dict = defaultdict(list) for t in tec_ts: - common = dict( - time="year", - time_origin="year", - time_dest="year", - ) - - param_name = data_aluminum_ts.loc[ - (data_aluminum_ts["technology"] == t), "parameter" - ] - + param_name = data.loc[(data["technology"] == t), "parameter"].unique() for p in set(param_name): - val = data_aluminum_ts.loc[ - (data_aluminum_ts["technology"] == t) - & (data_aluminum_ts["parameter"] == p), + val = data.loc[ + (data["technology"] == t) & (data["parameter"] == p), "value", ] - # units = data_aluminum_ts.loc[ - # (data_aluminum_ts["technology"] == t) - # & (data_aluminum_ts["parameter"] == p), + # units = data.loc[ + # (data["technology"] == t) + # & (data["parameter"] == p), # "units", # ].values[0] - mod = data_aluminum_ts.loc[ - (data_aluminum_ts["technology"] == t) - & (data_aluminum_ts["parameter"] == p), + mod = data.loc[ + (data["technology"] == t) & (data["parameter"] == p), "mode", ] - yr = data_aluminum_ts.loc[ - (data_aluminum_ts["technology"] == t) - & (data_aluminum_ts["parameter"] == p), + yr = data.loc[ + (data["technology"] == t) & (data["parameter"] == p), "year", ] @@ -355,9 +123,8 @@ def gen_data_aluminum( **common, ).pipe(broadcast, node_loc=nodes) else: - rg = data_aluminum_ts.loc[ - (data_aluminum_ts["technology"] == t) - & (data_aluminum_ts["parameter"] == p), + rg = data.loc[ + (data["technology"] == t) & (data["parameter"] == p), "region", ] df = make_df( @@ -372,26 +139,23 @@ def gen_data_aluminum( **common, ) - results[p].append(df) - - # Add relations for scrap grades and availability + par_dict[p].append(df) + return {par_name: pd.concat(dfs) for par_name, dfs in par_dict.items()} - regions = set(data_aluminum_rel["Region"].values) +def gen_data_alu_rel(data: pd.DataFrame, years: list) -> dict[str, pd.DataFrame]: + par_dict = defaultdict(list) + regions = set(data["Region"].values) for reg in regions: - for r in data_aluminum_rel["relation"]: + for r in data["relation"].unique(): if r is None: break - params = set( - data_aluminum_rel.loc[ - (data_aluminum_rel["relation"] == r), "parameter" - ].values - ) + params = set(data.loc[(data["relation"] == r), "parameter"].values) # This relation should start from 2020... if r == "minimum_recycling_aluminum": - modelyears_copy = modelyears[:] + modelyears_copy = years[:] if 2020 in modelyears_copy: modelyears_copy.remove(2020) @@ -404,29 +168,26 @@ def gen_data_aluminum( else: # Use all the model years for other relations... common_rel = dict( - year_rel=modelyears, - year_act=modelyears, + year_rel=years, + year_act=years, mode="M1", relation=r, ) for par_name in params: if par_name == "relation_activity": - tec_list = data_aluminum_rel.loc[ - ( - (data_aluminum_rel["relation"] == r) - & (data_aluminum_rel["parameter"] == par_name) - ), + tec_list = data.loc[ + ((data["relation"] == r) & (data["parameter"] == par_name)), "technology", ] for tec in tec_list.unique(): - val = data_aluminum_rel.loc[ + val = data.loc[ ( - (data_aluminum_rel["relation"] == r) - & (data_aluminum_rel["parameter"] == par_name) - & (data_aluminum_rel["technology"] == tec) - & (data_aluminum_rel["Region"] == reg) + (data["relation"] == r) + & (data["parameter"] == par_name) + & (data["technology"] == tec) + & (data["Region"] == reg) ), "value", ].values[0] @@ -441,14 +202,14 @@ def gen_data_aluminum( **common_rel, ).pipe(same_node) - results[par_name].append(df) + par_dict[par_name].append(df) elif (par_name == "relation_upper") | (par_name == "relation_lower"): - val = data_aluminum_rel.loc[ + val = data.loc[ ( - (data_aluminum_rel["relation"] == r) - & (data_aluminum_rel["parameter"] == par_name) - & (data_aluminum_rel["Region"] == reg) + (data["relation"] == r) + & (data["parameter"] == par_name) + & (data["Region"] == reg) ), "value", ].values[0] @@ -457,9 +218,239 @@ def gen_data_aluminum( par_name, value=val, unit="-", node_rel=reg, **common_rel ) - results[par_name].append(df) + par_dict[par_name].append(df) + return {par_name: pd.concat(dfs) for par_name, dfs in par_dict.items()} + + +def assign_input_outpt( + split, param_name, regions, val, t, rg, glb_reg, common, yv_ya, nodes +): + # Assign commodity and level names + # Later mod can be added + com = split[1] + lev = split[2] + + if (param_name == "input") and (lev == "import"): + df = make_df( + param_name, + technology=t, + commodity=com, + level=lev, + value=val[regions[regions == rg].index[0]], + unit="t", + node_loc=rg, + node_origin=glb_reg, + **common, + ) + + elif (param_name == "output") and (lev == "export"): + df = make_df( + param_name, + technology=t, + commodity=com, + level=lev, + value=val[regions[regions == rg].index[0]], + unit="t", + node_loc=rg, + node_dest=glb_reg, + **common, + ) + + # Assign higher efficiency to younger plants + elif ( + ((t == "soderberg_aluminum") or (t == "prebake_aluminum")) + & (com == "electr") + & (param_name == "input") + ): + # All the vıntage years + year_vtg = sorted(set(yv_ya.year_vtg.values)) + # Collect the values for the combination of vintage and + # active years. + input_values_all = [] + for yr_v in year_vtg: + # The initial year efficiency value + input_values_temp = [val[regions[regions == rg].index[0]]] + # Reduction after the vintage year + year_vtg_filtered = list(filter(lambda op: op >= yr_v, year_vtg)) + # Filter the active model years + year_act = yv_ya.loc[yv_ya["year_vtg"] == yr_v, "year_act"].values + for i in range(len(year_vtg_filtered) - 1): + input_values_temp.append(input_values_temp[i] * 1.1) + + act_year_no = len(year_act) + input_values_temp = input_values_temp[-act_year_no:] + input_values_all = input_values_all + input_values_temp + + df = make_df( + param_name, + technology=t, + commodity=com, + level=lev, + value=input_values_all, + unit="t", + node_loc=rg, + **common, + ).pipe(same_node) + + else: + df = make_df( + param_name, + technology=t, + commodity=com, + level=lev, + value=val[regions[regions == rg].index[0]], + unit="t", + node_loc=rg, + **common, + ).pipe(same_node) + + # Copy parameters to all regions, when node_loc is not GLB + if (len(regions) == 1) and (rg != glb_reg): + df["node_loc"] = None + df = df.pipe(broadcast, node_loc=nodes) # .pipe(same_node) + # Use same_node only for non-trade technologies + if (lev != "import") and (lev != "export"): + df = df.pipe(same_node) + return df + + +def gen_data_alu_const(data, config, glb_reg, years, yv_ya, nodes): + results = defaultdict(list) + for t in config["technology"]["add"]: + params = data.loc[(data["technology"] == t), "parameter"].unique() + # Obtain the active and vintage years + av = data.loc[(data["technology"] == t), "availability"].values[0] + years = [year for year in years if year >= av] + yv_ya = yv_ya.loc[yv_ya.year_vtg >= av] + common = dict( + year_vtg=yv_ya.year_vtg, + year_act=yv_ya.year_act, + mode="M1", + time="year", + time_origin="year", + time_dest="year", + ) + # Iterate over parameters + for par in params: + # Obtain the parameter names, commodity,level,emission + split = par.split("|") + param_name = split[0] + + # Obtain the scalar value for the parameter + val = data.loc[ + ((data["technology"] == t) & (data["parameter"] == par)), + "value", + ] + + regions = data.loc[ + ((data["technology"] == t) & (data["parameter"] == par)), + "region", + ] + + for rg in regions: + # For the parameters which includes index names + if len(split) > 1: + if (param_name == "input") | (param_name == "output"): + df = assign_input_outpt( + split, + param_name, + regions, + val, + t, + rg, + glb_reg, + common, + yv_ya, + nodes, + ) + + elif param_name == "emission_factor": + # Assign the emisson type + emi = split[1] - results_aluminum = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} + df = make_df( + param_name, + technology=t, + value=val[regions[regions == rg].index[0]], + emission=emi, + unit="t", + node_loc=rg, + **common, + ) + + # Parameters with only parameter name + else: + df = make_df( + param_name, + technology=t, + value=val[regions[regions == rg].index[0]], + unit="t", + node_loc=rg, + **common, + ) + + # Copy parameters to all regions + if ( + (len(regions) == 1) + and len(set(df["node_loc"])) == 1 + and list(set(df["node_loc"]))[0] != glb_reg + ): + df["node_loc"] = None + df = df.pipe(broadcast, node_loc=nodes) + + results[param_name].append(df) + return {par_name: pd.concat(dfs) for par_name, dfs in results.items()} + + +def gen_data_aluminum( + scenario: message_ix.Scenario, dry_run: bool = False +) -> dict[str, pd.DataFrame]: + """ + + Parameters + ---------- + scenario: message_ix.Scenario + Scenario instance to build aluminum model on + dry_run: bool + *not implemented* + Returns + ------- + dict[pd.DataFrame] + dict with MESSAGEix parameters as keys and parametrization as values + stored in pd.DataFrame + """ + context = read_config() + config = context["material"]["aluminum"] + + # Information about scenario, e.g. node, year + s_info = ScenarioInfo(scenario) + ssp = get_ssp_from_context(context) + # Techno-economic assumptions + data_aluminum, data_aluminum_rel, data_aluminum_ts = read_data_aluminum(scenario) + # List of data frames, to be concatenated together at end + + modelyears = s_info.Y + yv_ya = s_info.yv_ya + nodes = nodes_ex_world(s_info.N) + global_region = [i for i in s_info.N if i.endswith("_GLB")][0] + + const_dict = gen_data_alu_const( + data_aluminum, config, global_region, modelyears, yv_ya, nodes + ) + + parname = "demand" + demand_dict = {} + df = material_demand_calc.derive_demand( + "aluminum", scenario, old_gdp=False, ssp=ssp + ) + demand_dict[parname] = df + + ts_dict = gen_data_alu_ts(data_aluminum_ts, nodes) + rel_dict = gen_data_alu_rel(data_aluminum_rel, modelyears) + + results_aluminum = combine_df_dictionaries( + const_dict, ts_dict, rel_dict, demand_dict + ) return results_aluminum diff --git a/message_ix_models/model/material/data_ammonia_new.py b/message_ix_models/model/material/data_ammonia_new.py index 2643bfe87e..b841cd81d7 100644 --- a/message_ix_models/model/material/data_ammonia_new.py +++ b/message_ix_models/model/material/data_ammonia_new.py @@ -4,8 +4,13 @@ from message_ix_models import ScenarioInfo from message_ix_models.model.material.material_demand import material_demand_calc -from message_ix_models.model.material.util import read_config -from message_ix_models.util import broadcast, package_data_path, same_node +from message_ix_models.model.material.util import maybe_remove_water_tec, read_config +from message_ix_models.util import ( + broadcast, + nodes_ex_world, + package_data_path, + same_node, +) CONVERSION_FACTOR_NH3_N = 17 / 14 context = read_config() @@ -51,14 +56,30 @@ def gen_all_NH3_fert(scenario, dry_run=False): } +def broadcast_years(df_new, max_lt, act_years, vtg_years): + if "year_act" in df_new.columns: + df_new = df_new.pipe(same_node).pipe(broadcast, year_act=act_years) + + if "year_vtg" in df_new.columns: + df_new = df_new.pipe( + broadcast, + year_vtg=np.linspace( + 0, int(max_lt / 5), int(max_lt / 5 + 1), dtype=int + ), + ) + df_new["year_vtg"] = df_new["year_act"] - 5 * df_new["year_vtg"] + # remove years that are not in scenario set + df_new = df_new[~df_new["year_vtg"].isin([2065, 2075, 2085, 2095, 2105])] + else: + if "year_vtg" in df_new.columns: + df_new = df_new.pipe(same_node).pipe(broadcast, year_vtg=vtg_years) + return df_new + + def gen_data(scenario, dry_run=False, add_ccs: bool = True, lower_costs=False): s_info = ScenarioInfo(scenario) # s_info.yv_ya - nodes = s_info.N - if "World" in nodes: - nodes.pop(nodes.index("World")) - if "R12_GLB" in nodes: - nodes.pop(nodes.index("R12_GLB")) + nodes = nodes_ex_world(s_info.N) df = pd.read_excel( package_data_path( @@ -94,25 +115,7 @@ def gen_data(scenario, dry_run=False, add_ccs: bool = True, lower_costs=False): df_new = pd.concat([df_new_reg, df_new_no_reg]) # broadcast scenario years - if "year_act" in df_new.columns: - df_new = df_new.pipe(same_node).pipe(broadcast, year_act=act_years) - - if "year_vtg" in df_new.columns: - df_new = df_new.pipe( - broadcast, - year_vtg=np.linspace( - 0, int(max_lt / 5), int(max_lt / 5 + 1), dtype=int - ), - ) - df_new["year_vtg"] = df_new["year_act"] - 5 * df_new["year_vtg"] - # remove years that are not in scenario set - df_new = df_new[ - ~df_new["year_vtg"].isin([2065, 2075, 2085, 2095, 2105]) - ] - else: - if "year_vtg" in df_new.columns: - df_new = df_new.pipe(same_node).pipe(broadcast, year_vtg=vtg_years) - + df_new = broadcast_years(df_new, max_lt, act_years, vtg_years) # set import/export node_dest/origin to GLB for input/output set_exp_imp_nodes(df_new) par_dict[par_name] = df_new @@ -180,10 +183,7 @@ def __missing__(self, key): par_dict[p] = pd.concat([df[~df["technology"].isin(tec_list)], conv_cost_df]) # HACK: quick fix to enable compatibility with water build - if len(scenario.par("output", filters={"technology": "extract_surfacewater"})): - par_dict["input"] = par_dict["input"].replace( - {"freshwater_supply": "freshwater"} - ) + maybe_remove_water_tec(scenario, par_dict) return par_dict diff --git a/message_ix_models/model/material/data_cement.py b/message_ix_models/model/material/data_cement.py index 525ba77126..d1c958176c 100644 --- a/message_ix_models/model/material/data_cement.py +++ b/message_ix_models/model/material/data_cement.py @@ -1,30 +1,20 @@ from collections import defaultdict -import ixmp -import message_ix import pandas as pd from message_ix import make_df from message_ix_models import ScenarioInfo from message_ix_models.model.material.data_util import read_sector_data, read_timeseries from message_ix_models.model.material.material_demand import material_demand_calc -from message_ix_models.model.material.util import read_config +from message_ix_models.model.material.util import get_ssp_from_context, read_config from message_ix_models.util import ( broadcast, + nodes_ex_world, package_data_path, same_node, ) -# Get endogenous material demand from buildings interface - -# gdp_growth = [0.121448215899944, 0.0733079014579874, 0.0348154093342843, \ -# 0.021827616787921, 0.0134425983942219, 0.0108320197485592, \ -# 0.00884341208063,0.00829374133206562, 0.00649794573935969, 0.00649794573935969] -# gr = np.cumprod([(x+1) for x in gdp_growth]) - - -# Generate a fake cement demand def gen_mock_demand_cement(scenario): s_info = ScenarioInfo(scenario) nodes = s_info.N @@ -171,12 +161,11 @@ def gen_data_cement(scenario, dry_run=False): # Load configuration context = read_config() config = read_config()["material"]["cement"] - ssp = context["ssp"] + ssp = get_ssp_from_context(context) # Information about scenario, e.g. node, year s_info = ScenarioInfo(scenario) context.datafile = "Global_steel_cement_MESSAGE.xlsx" - # Techno-economic assumptions - # TEMP: now add cement sector as well + data_cement = read_sector_data(scenario, "cement") # Special treatment for time-dependent Parameters data_cement_ts = read_timeseries(scenario, "steel_cement", context.datafile) @@ -188,22 +177,14 @@ def gen_data_cement(scenario, dry_run=False): # For each technology there are differnet input and output combinations # Iterate over technologies - nodes = s_info.N yv_ya = s_info.yv_ya yv_ya = yv_ya.loc[yv_ya.year_vtg >= 1980] - nodes.remove("World") - # Do not parametrize GLB region the same way - if "R11_GLB" in nodes: - nodes.remove("R11_GLB") - if "R12_GLB" in nodes: - nodes.remove("R12_GLB") + nodes = nodes_ex_world(s_info.N) # for t in s_info.set['technology']: for t in config["technology"]["add"]: - params = data_cement.loc[ - (data_cement["technology"] == t), "parameter" - ].values.tolist() + params = data_cement.loc[(data_cement["technology"] == t), "parameter"].unique() # Special treatment for time-varying params if t in tec_ts: @@ -363,18 +344,6 @@ def gen_data_cement(scenario, dry_run=False): # Create external demand param parname = "demand" - # demand = gen_mock_demand_cement(scenario) - # demand = derive_cement_demand(scenario) - # df = make_df( - # parname, - # level="demand", - # commodity="cement", - # value=demand.value, - # unit="t", - # year=demand.year, - # time="year", - # node=demand.node, - # ) df = material_demand_calc.derive_demand("cement", scenario, old_gdp=False, ssp=ssp) results[parname].append(df) @@ -386,68 +355,7 @@ def gen_data_cement(scenario, dry_run=False): ).pipe(broadcast, node=nodes, technology=ccs_tec) results[parname].append(df) - # Test emission bound - # parname = 'bound_emission' - # df = (make_df(parname, type_tec='all', type_year='cumulative', \ - # type_emission='CO2_industry', \ - # value=200, unit='-').pipe(broadcast, node=nodes)) - # results[parname].append(df) - # Concatenate to one data frame per parameter results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} return results - - -if __name__ == "__main__": - mp = ixmp.Platform("local") - scenario = message_ix.Scenario(mp, "MESSAGEix-Materials", "baseline") - gen_data_cement(scenario) - -# # load rpy2 modules -# import rpy2.robjects as ro -# from rpy2.robjects import pandas2ri -# from rpy2.robjects.conversion import localconverter -# -# -# # This returns a df with columns ["region", "year", "demand.tot"] -# def derive_cement_demand(scenario, dry_run=False): -# """Generate cement demand.""" -# # paths to r code and lca data -# rcode_path = Path(__file__).parents[0] / "material_demand" -# context = read_config() -# -# # source R code -# r = ro.r -# r.source(str(rcode_path / "init_modularized.R")) -# -# # Read population and baseline demand for materials -# pop = scenario.par("bound_activity_up", {"technology": "Population"}) -# pop = pop.loc[pop.year_act >= 2020].rename( -# columns={"year_act": "year", "value": "pop.mil", "node_loc": "region"} -# ) -# pop = pop[["region", "year", "pop.mil"]] -# -# base_demand = gen_mock_demand_cement(scenario) -# base_demand = base_demand.loc[base_demand.year == 2020].rename( -# columns={"value": "demand.tot.base", "node": "region"} -# ) -# -# # import pdb; pdb.set_trace() -# -# # base_demand = scenario.par("demand", {"commodity": "steel", "year": 2020}) -# # base_demand = base_demand.loc[base_demand.year >= 2020].rename( -# # columns={"value": "demand.tot.base", "node": "region"} -# # ) -# # base_demand = base_demand[["region", "year", "demand.tot.base"]] -# -# # call R function with type conversion -# with localconverter(ro.default_converter + pandas2ri.converter): -# # GDP is only in MER in scenario. -# # To get PPP GDP, it is read externally from the R side -# df = r.derive_cement_demand( -# pop, base_demand, str(package_data_path("material")) -# ) -# df.year = df.year.astype(int) -# -# return df diff --git a/message_ix_models/model/material/data_generic.py b/message_ix_models/model/material/data_generic.py index 36af23e351..a5e93457f6 100644 --- a/message_ix_models/model/material/data_generic.py +++ b/message_ix_models/model/material/data_generic.py @@ -7,6 +7,7 @@ from message_ix_models import ScenarioInfo from message_ix_models.util import ( broadcast, + nodes_ex_world, same_node, ) @@ -57,20 +58,11 @@ def gen_data_generic(scenario, dry_run=False): # Iterate over technologies modelyears = s_info.Y # s_info.Y is only for modeling years - nodes = s_info.N yv_ya = s_info.yv_ya # Do not parametrize GLB region the same way - if "R11_GLB" in nodes: - nodes.remove("R11_GLB") - global_region = "R11_GLB" - if "R12_GLB" in nodes: - nodes.remove("R12_GLB") - global_region = "R12_GLB" - - # 'World' is included by default when creating a message_ix.Scenario(). - # Need to remove it for the China bare model - nodes.remove("World") + nodes = nodes_ex_world(s_info.N) + global_region = [i for i in s_info.N if i.endswith("_GLB")][0] for t in config["technology"]["add"]: # years = s_info.Y diff --git a/message_ix_models/model/material/data_methanol_new.py b/message_ix_models/model/material/data_methanol_new.py index 94946441c3..bea35fb3fe 100644 --- a/message_ix_models/model/material/data_methanol_new.py +++ b/message_ix_models/model/material/data_methanol_new.py @@ -80,6 +80,81 @@ def gen_data_methanol_new(scenario): return pars_dict +def broadcast_nodes(df_bc_node, df_final, node_cols, node_cols_codes, i): + if len(node_cols) == 1: + if "node_loc" in node_cols: + df_bc_node = df_bc_node.pipe( + broadcast, node_loc=node_cols_codes["node_loc"] + ) + if "node_vtg" in node_cols: + df_bc_node = df_bc_node.pipe( + broadcast, node_vtg=node_cols_codes["node_vtg"] + ) + if "node_rel" in node_cols: + df_bc_node = df_bc_node.pipe( + broadcast, node_rel=node_cols_codes["node_rel"] + ) + if "node" in node_cols: + df_bc_node = df_bc_node.pipe(broadcast, node=node_cols_codes["node"]) + if "node_share" in node_cols: + df_bc_node = df_bc_node.pipe( + broadcast, node_share=node_cols_codes["node_share"] + ) + else: + df_bc_node = df_bc_node.pipe(broadcast, node_loc=node_cols_codes["node_loc"]) + if len(df_final.loc[i][node_cols].T.unique()) == 1: + # df_bc_node["node_rel"] = df_bc_node["node_loc"] + df_bc_node = df_bc_node.pipe( + same_node + ) # not working for node_rel in installed message_ix_models version + else: + if "node_rel" in list(df_bc_node.columns): + df_bc_node = df_bc_node.pipe( + broadcast, node_rel=node_cols_codes["node_rel"] + ) + if "node_origin" in list(df_bc_node.columns): + df_bc_node = df_bc_node.pipe( + broadcast, node_origin=node_cols_codes["node_origin"] + ) + if "node_dest" in list(df_bc_node.columns): + df_bc_node = df_bc_node.pipe( + broadcast, node_dest=node_cols_codes["node_dest"] + ) + return df_bc_node + + +def broadcast_years(df_bc_node, yr_col_out, yr_cols_codes, col): + if len(yr_col_out) == 1: + yr_list = [i[0] for i in yr_cols_codes[col]] + # print(yr_list) + if "year_act" in yr_col_out: + df_bc_node = df_bc_node.pipe(broadcast, year_act=yr_list) + if "year_vtg" in yr_col_out: + df_bc_node = df_bc_node.pipe(broadcast, year_vtg=yr_list) + if "year_rel" in yr_col_out: + df_bc_node = df_bc_node.pipe(broadcast, year_rel=yr_list) + if "year" in yr_col_out: + df_bc_node = df_bc_node.pipe(broadcast, year=yr_list) + df_bc_node[yr_col_out] = df_bc_node[yr_col_out].astype(int) + else: + if "year_vtg" in yr_col_out: + y_v = [str(i) for i in yr_cols_codes[col]] + df_bc_node = df_bc_node.pipe(broadcast, year_vtg=y_v) + df_bc_node["year_act"] = [ + literal_eval(i)[1] for i in df_bc_node["year_vtg"] + ] + df_bc_node["year_vtg"] = [ + literal_eval(i)[0] for i in df_bc_node["year_vtg"] + ] + if "year_rel" in yr_col_out: + if "year_act" in yr_col_out: + df_bc_node = df_bc_node.pipe( + broadcast, year_act=[i[0] for i in yr_cols_codes[col]] + ) + df_bc_node["year_rel"] = df_bc_node["year_act"] + return df_bc_node + + def broadcast_reduced_df(df, par_name): df_final = df df_final_full = pd.DataFrame() @@ -103,78 +178,13 @@ def broadcast_reduced_df(df, par_name): for colname in node_cols: df_bc_node[colname] = None # broadcast in node dimensions - if len(node_cols) == 1: - if "node_loc" in node_cols: - df_bc_node = df_bc_node.pipe( - broadcast, node_loc=node_cols_codes["node_loc"] - ) - if "node_vtg" in node_cols: - df_bc_node = df_bc_node.pipe( - broadcast, node_vtg=node_cols_codes["node_vtg"] - ) - if "node_rel" in node_cols: - df_bc_node = df_bc_node.pipe( - broadcast, node_rel=node_cols_codes["node_rel"] - ) - if "node" in node_cols: - df_bc_node = df_bc_node.pipe(broadcast, node=node_cols_codes["node"]) - if "node_share" in node_cols: - df_bc_node = df_bc_node.pipe( - broadcast, node_share=node_cols_codes["node_share"] - ) - else: - df_bc_node = df_bc_node.pipe( - broadcast, node_loc=node_cols_codes["node_loc"] - ) - if len(df_final.loc[i][node_cols].T.unique()) == 1: - # df_bc_node["node_rel"] = df_bc_node["node_loc"] - df_bc_node = df_bc_node.pipe( - same_node - ) # not working for node_rel in installed message_ix_models version - else: - if "node_rel" in list(df_bc_node.columns): - df_bc_node = df_bc_node.pipe( - broadcast, node_rel=node_cols_codes["node_rel"] - ) - if "node_origin" in list(df_bc_node.columns): - df_bc_node = df_bc_node.pipe( - broadcast, node_origin=node_cols_codes["node_origin"] - ) - if "node_dest" in list(df_bc_node.columns): - df_bc_node = df_bc_node.pipe( - broadcast, node_dest=node_cols_codes["node_dest"] - ) + df_bc_node = broadcast_nodes( + df_bc_node, df_final, node_cols, node_cols_codes, i + ) for col in yr_col_inp: yr_cols_codes[col] = literal_eval(df_bc_node[col].values[0]) - if len(yr_col_out) == 1: - yr_list = [i[0] for i in yr_cols_codes[col]] - # print(yr_list) - if "year_act" in yr_col_out: - df_bc_node = df_bc_node.pipe(broadcast, year_act=yr_list) - if "year_vtg" in yr_col_out: - df_bc_node = df_bc_node.pipe(broadcast, year_vtg=yr_list) - if "year_rel" in yr_col_out: - df_bc_node = df_bc_node.pipe(broadcast, year_rel=yr_list) - if "year" in yr_col_out: - df_bc_node = df_bc_node.pipe(broadcast, year=yr_list) - df_bc_node[yr_col_out] = df_bc_node[yr_col_out].astype(int) - else: - if "year_vtg" in yr_col_out: - y_v = [str(i) for i in yr_cols_codes[col]] - df_bc_node = df_bc_node.pipe(broadcast, year_vtg=y_v) - df_bc_node["year_act"] = [ - literal_eval(i)[1] for i in df_bc_node["year_vtg"] - ] - df_bc_node["year_vtg"] = [ - literal_eval(i)[0] for i in df_bc_node["year_vtg"] - ] - if "year_rel" in yr_col_out: - if "year_act" in yr_col_out: - df_bc_node = df_bc_node.pipe( - broadcast, year_act=[i[0] for i in yr_cols_codes[col]] - ) - df_bc_node["year_rel"] = df_bc_node["year_act"] + broadcast_years(df_bc_node, yr_col_out, yr_cols_codes, col) # return df_bc_node # df_bc_node["year_rel"] = df_bc_node["year_act"] df_bc_node[yr_col_out] = df_bc_node[yr_col_out].astype(int) diff --git a/message_ix_models/model/material/data_petro.py b/message_ix_models/model/material/data_petro.py index acbef37acc..64ef141947 100644 --- a/message_ix_models/model/material/data_petro.py +++ b/message_ix_models/model/material/data_petro.py @@ -6,9 +6,10 @@ from message_ix_models import ScenarioInfo from message_ix_models.model.material.data_util import read_timeseries from message_ix_models.model.material.material_demand import material_demand_calc -from message_ix_models.model.material.util import read_config +from message_ix_models.model.material.util import get_ssp_from_context, read_config from message_ix_models.util import ( broadcast, + nodes_ex_world, package_data_path, same_node, ) @@ -158,11 +159,143 @@ def get_demand_t1_with_income_elasticity( ) +def gen_data_petro_ts(data_petro_ts, results, tec_ts, nodes): + for t in tec_ts: + common = dict( + time="year", + time_origin="year", + time_dest="year", + ) + + param_name = data_petro_ts.loc[ + (data_petro_ts["technology"] == t), "parameter" + ].unique() + + for p in set(param_name): + val = data_petro_ts.loc[ + (data_petro_ts["technology"] == t) & (data_petro_ts["parameter"] == p), + "value", + ] + # units = data_petro_ts.loc[ + # (data_petro_ts["technology"] == t) & + # (data_petro_ts["parameter"] == p), + # "units", + # ].values[0] + mod = data_petro_ts.loc[ + (data_petro_ts["technology"] == t) & (data_petro_ts["parameter"] == p), + "mode", + ] + yr = data_petro_ts.loc[ + (data_petro_ts["technology"] == t) & (data_petro_ts["parameter"] == p), + "year", + ] + + if p == "var_cost": + df = make_df( + p, + technology=t, + value=val, + unit="t", + year_vtg=yr, + year_act=yr, + mode=mod, + **common, + ).pipe(broadcast, node_loc=nodes) + else: + rg = data_petro_ts.loc[ + (data_petro_ts["technology"] == t) + & (data_petro_ts["parameter"] == p), + "region", + ] + df = make_df( + p, + technology=t, + value=val, + unit="t", + year_vtg=yr, + year_act=yr, + mode=mod, + node_loc=rg, + **common, + ) + + results[p].append(df) + + +def assign_input_outpt( + split, param_name, regions, val, t, rg, global_region, common, nodes +): + com = split[1] + lev = split[2] + mod = split[3] + + if (param_name == "input") and (lev == "import"): + df = make_df( + param_name, + technology=t, + commodity=com, + level=lev, + mode=mod, + value=val[regions[regions == rg].index[0]], + unit="t", + node_loc=rg, + node_origin=global_region, + **common, + ) + elif (param_name == "output") and (lev == "export"): + df = make_df( + param_name, + technology=t, + commodity=com, + level=lev, + mode=mod, + value=val[regions[regions == rg].index[0]], + unit="t", + node_loc=rg, + node_dest=global_region, + **common, + ) + else: + df = make_df( + param_name, + technology=t, + commodity=com, + level=lev, + mode=mod, + value=val[regions[regions == rg].index[0]], + unit="t", + node_loc=rg, + **common, + ).pipe(same_node) + + # Copy parameters to all regions, when node_loc is not GLB + if (len(regions) == 1) and (rg != global_region): + # print("copying to all R11", rg, lev) + df["node_loc"] = None + df = df.pipe(broadcast, node_loc=nodes) # .pipe(same_node) + # Use same_node only for non-trade technologies + if (lev != "import") and (lev != "export"): + df = df.pipe(same_node) + return df + + +def broadcast_to_regions(df, global_region, nodes): + if "node_loc" in df.columns: + if ( + len(set(df["node_loc"])) == 1 + and list(set(df["node_loc"]))[0] != global_region + ): + # print("Copying to all R11") + df["node_loc"] = None + df = df.pipe(broadcast, node_loc=nodes) + return df + + def gen_data_petro_chemicals(scenario, dry_run=False): # Load configuration context = read_config() config = context["material"]["petro_chemicals"] - ssp = context["ssp"] + ssp = get_ssp_from_context(context) # Information about scenario, e.g. node, year s_info = ScenarioInfo(scenario) @@ -178,23 +311,13 @@ def gen_data_petro_chemicals(scenario, dry_run=False): # Iterate over technologies modelyears = s_info.Y # s_info.Y is only for modeling years - nodes = s_info.N + nodes = nodes_ex_world(s_info.N) + global_region = [i for i in s_info.N if i.endswith("_GLB")][0] yv_ya = s_info.yv_ya - nodes.remove("World") - - # Do not parametrize GLB region the same way - if "R11_GLB" in nodes: - nodes.remove("R11_GLB") - global_region = "R11_GLB" - if "R12_GLB" in nodes: - nodes.remove("R12_GLB") - global_region = "R12_GLB" for t in config["technology"]["add"]: # years = s_info.Y - params = data_petro.loc[ - (data_petro["technology"] == t), "parameter" - ].values.tolist() + params = data_petro.loc[(data_petro["technology"] == t), "parameter"].unique() # Availability year of the technology av = data_petro.loc[(data_petro["technology"] == t), "availability"].values[0] @@ -230,58 +353,17 @@ def gen_data_petro_chemicals(scenario, dry_run=False): for rg in regions: if len(split) > 1: if (param_name == "input") | (param_name == "output"): - com = split[1] - lev = split[2] - mod = split[3] - - if (param_name == "input") and (lev == "import"): - df = make_df( - param_name, - technology=t, - commodity=com, - level=lev, - mode=mod, - value=val[regions[regions == rg].index[0]], - unit="t", - node_loc=rg, - node_origin=global_region, - **common, - ) - elif (param_name == "output") and (lev == "export"): - df = make_df( - param_name, - technology=t, - commodity=com, - level=lev, - mode=mod, - value=val[regions[regions == rg].index[0]], - unit="t", - node_loc=rg, - node_dest=global_region, - **common, - ) - else: - df = make_df( - param_name, - technology=t, - commodity=com, - level=lev, - mode=mod, - value=val[regions[regions == rg].index[0]], - unit="t", - node_loc=rg, - **common, - ).pipe(same_node) - - # Copy parameters to all regions, when node_loc is not GLB - if (len(regions) == 1) and (rg != global_region): - # print("copying to all R11", rg, lev) - df["node_loc"] = None - df = df.pipe(broadcast, node_loc=nodes) # .pipe(same_node) - # Use same_node only for non-trade technologies - if (lev != "import") and (lev != "export"): - df = df.pipe(same_node) - + df = assign_input_outpt( + split, + param_name, + regions, + val, + t, + rg, + global_region, + common, + nodes, + ) elif param_name == "emission_factor": emi = split[1] mod = split[2] @@ -304,8 +386,6 @@ def gen_data_petro_chemicals(scenario, dry_run=False): make_df( param_name, technology=t, - commodity=com, - level=lev, mode=mod, value=val[regions[regions == rg].index[0]], unit="t", @@ -318,8 +398,6 @@ def gen_data_petro_chemicals(scenario, dry_run=False): df = make_df( param_name, technology=t, - commodity=com, - level=lev, mode=mod, value=val[regions[regions == rg].index[0]], node_loc=rg, @@ -359,14 +437,7 @@ def gen_data_petro_chemicals(scenario, dry_run=False): # Copy parameters to all regions if (len(regions) == 1) and (rg != global_region): - if "node_loc" in df.columns: - if ( - len(set(df["node_loc"])) == 1 - and list(set(df["node_loc"]))[0] != global_region - ): - # print("Copying to all R11") - df["node_loc"] = None - df = df.pipe(broadcast, node_loc=nodes) + df = broadcast_to_regions(df, global_region, nodes) results[param_name].append(df) @@ -382,26 +453,9 @@ def gen_data_petro_chemicals(scenario, dry_run=False): } results["share_mode_lo"].append(make_df("share_mode_lo", **share_dict)) - # Add demand - # Create external demand param - - # demand_HVC = derive_petro_demand(scenario) - # default_gdp_elasticity = float(0.93) - # context = read_config() - - # df_pars = pd.read_excel( - # package_data_path("material", "methanol", "methanol_sensitivity_pars.xlsx"), - # sheet_name="Sheet1", - # dtype=object, - # ) - # pars = df_pars.set_index("par").to_dict()["value"] - # default_gdp_elasticity_2020 = pars["hvc_elasticity_2020"] - # default_gdp_elasticity_2030 = pars["hvc_elasticity_2030"] - default_gdp_elasticity_2020, default_gdp_elasticity_2030 = iea_elasticity_map[ ssp_mode_map[ssp] ] - demand_hvc = material_demand_calc.gen_demand_petro( scenario, "HVC", default_gdp_elasticity_2020, default_gdp_elasticity_2030 ) @@ -426,64 +480,7 @@ def gen_data_petro_chemicals(scenario, dry_run=False): tec_ts = set(data_petro_ts.technology) # set of tecs in timeseries sheet - for t in tec_ts: - common = dict( - time="year", - time_origin="year", - time_dest="year", - ) - - param_name = data_petro_ts.loc[(data_petro_ts["technology"] == t), "parameter"] - - for p in set(param_name): - val = data_petro_ts.loc[ - (data_petro_ts["technology"] == t) & (data_petro_ts["parameter"] == p), - "value", - ] - # units = data_petro_ts.loc[ - # (data_petro_ts["technology"] == t) & - # (data_petro_ts["parameter"] == p), - # "units", - # ].values[0] - mod = data_petro_ts.loc[ - (data_petro_ts["technology"] == t) & (data_petro_ts["parameter"] == p), - "mode", - ] - yr = data_petro_ts.loc[ - (data_petro_ts["technology"] == t) & (data_petro_ts["parameter"] == p), - "year", - ] - - if p == "var_cost": - df = make_df( - p, - technology=t, - value=val, - unit="t", - year_vtg=yr, - year_act=yr, - mode=mod, - **common, - ).pipe(broadcast, node_loc=nodes) - else: - rg = data_petro_ts.loc[ - (data_petro_ts["technology"] == t) - & (data_petro_ts["parameter"] == p), - "region", - ] - df = make_df( - p, - technology=t, - value=val, - unit="t", - year_vtg=yr, - year_act=yr, - mode=mod, - node_loc=rg, - **common, - ) - - results[p].append(df) + gen_data_petro_ts(data_petro_ts, results, tec_ts, nodes) results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} diff --git a/message_ix_models/model/material/data_power_sector.py b/message_ix_models/model/material/data_power_sector.py index 31edf790a1..a404318e39 100644 --- a/message_ix_models/model/material/data_power_sector.py +++ b/message_ix_models/model/material/data_power_sector.py @@ -2,309 +2,266 @@ import pandas as pd -from message_ix_models import ScenarioInfo from message_ix_models.util import package_data_path -def read_material_intensities( - parameter, data_path, node, year, technology, commodity, level, inv_cost -): - if parameter in ["input_cap_new", "input_cap_ret", "output_cap_ret"]: - #################################################################### - # read data - #################################################################### - - # read LCA data from ADVANCE LCA tool - data_path_lca = data_path + "/NTNU_LCA_coefficients.xlsx" - data_lca = pd.read_excel(data_path_lca, sheet_name="environmentalImpacts") - - # read technology, region and commodity mappings - data_path_tec_map = data_path + "/MESSAGE_global_model_technologies.xlsx" - technology_mapping = pd.read_excel(data_path_tec_map, sheet_name="technology") - - data_path_reg_map = data_path + "/LCA_region_mapping.xlsx" - region_mapping = pd.read_excel(data_path_reg_map, sheet_name="region") - - data_path_com_map = data_path + "/LCA_commodity_mapping.xlsx" - commodity_mapping = pd.read_excel(data_path_com_map, sheet_name="commodity") - - #################################################################### - # process data - #################################################################### - - # filter relevant scenario, technology variant (residue for biomass, - # mix for others) and remove operation phase (and remove duplicates) - data_lca = data_lca.loc[ - ( - (data_lca["scenario"] == "Baseline") - & (data_lca["technology variant"].isin(["mix", "residue"])) - & (data_lca["phase"] != "Operation") - ) - ] - - data_lca[2015] = None - data_lca[2020] = None - data_lca[2025] = None - data_lca[2035] = None - data_lca[2040] = None - data_lca[2045] = None - - # add intermediate time steps and turn into long table format - years = [2010, 2015, 2020, 2025, 2030, 2035, 2040, 2045] - data_lca = pd.melt( - data_lca, - id_vars=[ - "source", - "scenario", - "region", - "variable", - "technology", - "technology variant", - "impact", - "phase", - "unit", - ], - value_vars=years, - var_name="year", - ) - # Make sure the values are numeric. - data_lca[["value"]] = data_lca[["value"]].astype(float) +def read_material_intensities(data_path, inv_cost): + #################################################################### + # read data + #################################################################### - # apply technology, commodity/impact and region mappings to MESSAGEix - data_lca_merged_1 = pd.merge( - data_lca, region_mapping, how="inner", left_on="region", right_on="THEMIS" - ) - data_lca_merged_2 = pd.merge( - data_lca_merged_1, - technology_mapping, - how="inner", - left_on="technology", - right_on="LCA mapping", - ) - data_lca_merged_final = pd.merge( - data_lca_merged_2, - commodity_mapping, - how="inner", - left_on="impact", - right_on="impact", - ) + # read LCA data from ADVANCE LCA tool + data_path_lca = data_path + "/NTNU_LCA_coefficients.xlsx" + data_lca = pd.read_excel(data_path_lca, sheet_name="environmentalImpacts") + + # read technology, region and commodity mappings + data_path_tec_map = data_path + "/MESSAGE_global_model_technologies.xlsx" + technology_mapping = pd.read_excel(data_path_tec_map, sheet_name="technology") + + data_path_reg_map = data_path + "/LCA_region_mapping.xlsx" + region_mapping = pd.read_excel(data_path_reg_map, sheet_name="region") - data_lca = data_lca_merged_final[ - ~data_lca_merged_final["MESSAGEix-GLOBIOM_1.1"].isnull() - ] + data_path_com_map = data_path + "/LCA_commodity_mapping.xlsx" + commodity_mapping = pd.read_excel(data_path_com_map, sheet_name="commodity") - # Drop technology column that has LCA style names - # Instead MESSAGE technology names will be used in the technology column - data_lca = data_lca.drop(["technology"], axis=1) + #################################################################### + # process data + #################################################################### - data_lca.rename( - columns={ - "MESSAGEix-GLOBIOM_1.1": "node", - "Type of Technology": "technology", - }, - inplace=True, + # filter relevant scenario, technology variant (residue for biomass, + # mix for others) and remove operation phase (and remove duplicates) + data_lca = data_lca.loc[ + ( + (data_lca["scenario"] == "Baseline") + & (data_lca["technology variant"].isin(["mix", "residue"])) + & (data_lca["phase"] != "Operation") ) - keep_columns = [ - "node", + ] + + data_lca[2015] = None + data_lca[2020] = None + data_lca[2025] = None + data_lca[2035] = None + data_lca[2040] = None + data_lca[2045] = None + + # add intermediate time steps and turn into long table format + years = [2010, 2015, 2020, 2025, 2030, 2035, 2040, 2045] + data_lca = pd.melt( + data_lca, + id_vars=[ + "source", + "scenario", + "region", + "variable", "technology", + "technology variant", + "impact", "phase", - "commodity", - "level", - "year", "unit", - "value", - ] - data_lca = data_lca[keep_columns] - - data_lca_final = pd.DataFrame() - - for n in data_lca["node"].unique(): - for t in data_lca["technology"].unique(): - for c in data_lca["commodity"].unique(): - for p in data_lca["phase"].unique(): - temp = data_lca.loc[ - ( - (data_lca["node"] == n) - & (data_lca["technology"] == t) - & (data_lca["commodity"] == c) - & (data_lca["phase"] == p) - ) - ] - temp["value"] = temp["value"].interpolate( - method="linear", limit_direction="forward", axis=0 - ) - data_lca_final = pd.concat([temp, data_lca_final]) - - # extract node, technology, commodity, level, and year list from LCA - # data set - node_list = data_lca_final["node"].unique() - year_list = data_lca_final["year"].unique() - tec_list = data_lca_final["technology"].unique() - com_list = data_lca_final["commodity"].unique() - lev_list = data_lca_final["level"].unique() - # add scrap as commodity level - lev_list = lev_list + ["end_of_life"] - - #################################################################### - # create data frames for material intensity input/output parameters - #################################################################### - - # new data frames for parameters - input_cap_new = pd.DataFrame() - input_cap_ret = pd.DataFrame() - output_cap_ret = pd.DataFrame() - - for n in node_list: - for t in tec_list: - for c in com_list: - year_vtg_list = inv_cost.loc[ - ((inv_cost["node_loc"] == n) & (inv_cost["technology"] == t)) - ]["year_vtg"].unique() - for y in year_vtg_list: - # for years after maximum year in data set use - # values for maximum year, similarly for years - # before minimum year in data set use values for - # minimum year - if y > max(year_list): - yeff = max(year_list) - elif y < min(year_list): - yeff = min(year_list) - else: - yeff = y - val_cap_new = data_lca_final.loc[ - (data_lca_final["node"] == n) - & (data_lca_final["technology"] == t) - & (data_lca_final["phase"] == "Construction") - & (data_lca_final["commodity"] == c) - & (data_lca_final["year"] == yeff) - ]["value"].values[0] - val_cap_new = val_cap_new * 0.001 - - input_cap_new = pd.concat( - [ - input_cap_new, - pd.DataFrame( - { - "node_loc": n, - "technology": t, - "year_vtg": str(y), - "node_origin": n, - "commodity": c, - "level": "product", - "time_origin": "year", - "value": val_cap_new, - "unit": "t/kW", - }, - index=[0], - ), - ] - ) - - val_cap_input_ret = data_lca_final.loc[ - (data_lca_final["node"] == n) - & (data_lca_final["technology"] == t) - & (data_lca_final["phase"] == "End-of-life") - & (data_lca_final["commodity"] == c) - & (data_lca_final["year"] == yeff) - ]["value"].values[0] - val_cap_input_ret = val_cap_input_ret * 0.001 - - input_cap_ret = pd.concat( - [ - input_cap_ret, - pd.DataFrame( - { - "node_loc": n, - "technology": t, - "year_vtg": str(y), - "node_origin": n, - "commodity": c, - "level": "product", - "time_origin": "year", - "value": val_cap_input_ret, - "unit": "t/kW", - }, - index=[0], - ), - ] + ], + value_vars=years, + var_name="year", + ) + # Make sure the values are numeric. + data_lca[["value"]] = data_lca[["value"]].astype(float) + + # apply technology, commodity/impact and region mappings to MESSAGEix + data_lca_merged_1 = pd.merge( + data_lca, region_mapping, how="inner", left_on="region", right_on="THEMIS" + ) + data_lca_merged_2 = pd.merge( + data_lca_merged_1, + technology_mapping, + how="inner", + left_on="technology", + right_on="LCA mapping", + ) + data_lca_merged_final = pd.merge( + data_lca_merged_2, + commodity_mapping, + how="inner", + left_on="impact", + right_on="impact", + ) + + data_lca = data_lca_merged_final[ + ~data_lca_merged_final["MESSAGEix-GLOBIOM_1.1"].isnull() + ] + + # Drop technology column that has LCA style names + # Instead MESSAGE technology names will be used in the technology column + data_lca = data_lca.drop(["technology"], axis=1) + + data_lca.rename( + columns={ + "MESSAGEix-GLOBIOM_1.1": "node", + "Type of Technology": "technology", + }, + inplace=True, + ) + keep_columns = [ + "node", + "technology", + "phase", + "commodity", + "level", + "year", + "unit", + "value", + ] + data_lca = data_lca[keep_columns] + + data_lca_final = pd.DataFrame() + + for n in data_lca["node"].unique(): + for t in data_lca["technology"].unique(): + for c in data_lca["commodity"].unique(): + for p in data_lca["phase"].unique(): + temp = data_lca.loc[ + ( + (data_lca["node"] == n) + & (data_lca["technology"] == t) + & (data_lca["commodity"] == c) + & (data_lca["phase"] == p) ) + ] + temp["value"] = temp["value"].interpolate( + method="linear", limit_direction="forward", axis=0 + ) + data_lca_final = pd.concat([temp, data_lca_final]) + + # extract node, technology, commodity, level, and year list from LCA + # data set + node_list = data_lca_final["node"].unique() + year_list = data_lca_final["year"].unique() + tec_list = data_lca_final["technology"].unique() + com_list = data_lca_final["commodity"].unique() + lev_list = data_lca_final["level"].unique() + # add scrap as commodity level + lev_list = lev_list + ["end_of_life"] + + #################################################################### + # create data frames for material intensity input/output parameters + #################################################################### + + # new data frames for parameters + input_cap_new = pd.DataFrame() + input_cap_ret = pd.DataFrame() + output_cap_ret = pd.DataFrame() + + for n in node_list: + for t in tec_list: + for c in com_list: + year_vtg_list = inv_cost.loc[ + ((inv_cost["node_loc"] == n) & (inv_cost["technology"] == t)) + ]["year_vtg"].unique() + for y in year_vtg_list: + # for years after maximum year in data set use + # values for maximum year, similarly for years + # before minimum year in data set use values for + # minimum year + if y > max(year_list): + yeff = max(year_list) + elif y < min(year_list): + yeff = min(year_list) + else: + yeff = y + val_cap_new = data_lca_final.loc[ + (data_lca_final["node"] == n) + & (data_lca_final["technology"] == t) + & (data_lca_final["phase"] == "Construction") + & (data_lca_final["commodity"] == c) + & (data_lca_final["year"] == yeff) + ]["value"].values[0] + val_cap_new = val_cap_new * 0.001 + + input_cap_new = pd.concat( + [ + input_cap_new, + pd.DataFrame( + { + "node_loc": n, + "technology": t, + "year_vtg": str(y), + "node_origin": n, + "commodity": c, + "level": "product", + "time_origin": "year", + "value": val_cap_new, + "unit": "t/kW", + }, + index=[0], + ), + ] + ) + + val_cap_input_ret = data_lca_final.loc[ + (data_lca_final["node"] == n) + & (data_lca_final["technology"] == t) + & (data_lca_final["phase"] == "End-of-life") + & (data_lca_final["commodity"] == c) + & (data_lca_final["year"] == yeff) + ]["value"].values[0] + val_cap_input_ret = val_cap_input_ret * 0.001 + + input_cap_ret = pd.concat( + [ + input_cap_ret, + pd.DataFrame( + { + "node_loc": n, + "technology": t, + "year_vtg": str(y), + "node_origin": n, + "commodity": c, + "level": "product", + "time_origin": "year", + "value": val_cap_input_ret, + "unit": "t/kW", + }, + index=[0], + ), + ] + ) + + val_cap_output_ret = data_lca_final.loc[ + (data_lca_final["node"] == n) + & (data_lca_final["technology"] == t) + & (data_lca_final["phase"] == "Construction") + & (data_lca_final["commodity"] == c) + & (data_lca_final["year"] == yeff) + ]["value"].values[0] + val_cap_output_ret = val_cap_output_ret * 0.001 + + output_cap_ret = pd.concat( + [ + output_cap_ret, + pd.DataFrame( + { + "node_loc": n, + "technology": t, + "year_vtg": str(y), + "node_dest": n, + "commodity": c, + "level": "end_of_life", + "time_dest": "year", + "value": val_cap_output_ret, + "unit": "t/kW", + }, + index=[0], + ), + ] + ) - val_cap_output_ret = data_lca_final.loc[ - (data_lca_final["node"] == n) - & (data_lca_final["technology"] == t) - & (data_lca_final["phase"] == "Construction") - & (data_lca_final["commodity"] == c) - & (data_lca_final["year"] == yeff) - ]["value"].values[0] - val_cap_output_ret = val_cap_output_ret * 0.001 - - output_cap_ret = pd.concat( - [ - output_cap_ret, - pd.DataFrame( - { - "node_loc": n, - "technology": t, - "year_vtg": str(y), - "node_dest": n, - "commodity": c, - "level": "end_of_life", - "time_dest": "year", - "value": val_cap_output_ret, - "unit": "t/kW", - }, - index=[0], - ), - ] - ) - - # return parameter - if parameter == "input_cap_new": - return input_cap_new - elif parameter == "input_cap_ret": - return input_cap_ret - elif parameter == "output_cap_ret": - return output_cap_ret - else: - return - - -def gen_data_power_sector(scenario, dry_run=False): - """Generate data for materials representation of power industry.""" - # Load configuration - - # paths to lca data - data_path = package_data_path("material", "power_sector") - - # Information about scenario, e.g. node, year - s_info = ScenarioInfo(scenario) - - # read node, technology, commodity and level from existing scenario - node = s_info.N - year = s_info.set["year"] # s_info.Y is only for modeling years - technology = s_info.set["technology"] - commodity = s_info.set["commodity"] - level = s_info.set["level"] - - # read inv.cost data - inv_cost = scenario.par("inv_cost") - - param_name = ["input_cap_new", "input_cap_ret", "output_cap_ret"] - - # List of data frames, to be concatenated together at end - results = defaultdict(list) - - for p in set(param_name): - df = read_material_intensities( - p, str(data_path), node, year, technology, commodity, level, inv_cost - ) - print("type df:", type(df)) - print(df.head()) + return { + "input_cap_new": input_cap_new, + "input_cap_ret": input_cap_ret, + "output_cap_ret": output_cap_ret, + } - results[p].append(df) - # create new parameters input_cap_new, output_cap_new, input_cap_ret, - # output_cap_ret, input_cap and output_cap if they don't exist +def maybe_init_pars(scenario): if not scenario.has_par("input_cap_new"): scenario.init_par( "input_cap_new", @@ -442,6 +399,39 @@ def gen_data_power_sector(scenario, dry_run=False): ], ) + +def gen_data_power_sector(scenario, dry_run=False): + """Generate data for materials representation of power industry.""" + # Load configuration + + # paths to lca data + data_path = package_data_path("material", "power_sector") + + # Information about scenario, e.g. node, year + + # read inv.cost data + inv_cost = scenario.par("inv_cost") + + # List of data frames, to be concatenated together at end + results = defaultdict(list) + + # for p in set(param_name): + # df = read_material_intensities( + # p, str(data_path), node, year, technology, commodity, level, inv_cost + # ) + # print("type df:", type(df)) + # print(df.head()) + # + # results[p].append(df) + + int_dict = read_material_intensities(str(data_path), inv_cost) + for k, v in int_dict.items(): + results[k].append(v) + + # create new parameters input_cap_new, output_cap_new, input_cap_ret, + # output_cap_ret, input_cap and output_cap if they don't exist + maybe_init_pars(scenario) + # Concatenate to one data frame per parameter results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} diff --git a/message_ix_models/model/material/data_steel.py b/message_ix_models/model/material/data_steel.py index b6ecbd3608..7e7f932f6c 100644 --- a/message_ix_models/model/material/data_steel.py +++ b/message_ix_models/model/material/data_steel.py @@ -11,15 +11,20 @@ read_timeseries, ) from message_ix_models.model.material.material_demand import material_demand_calc -from message_ix_models.model.material.util import read_config +from message_ix_models.model.material.util import ( + get_ssp_from_context, + maybe_remove_water_tec, + read_config, + remove_from_list_if_exists, +) from message_ix_models.util import ( broadcast, + nodes_ex_world, package_data_path, same_node, ) -# Generate a fake steel demand def gen_mock_demand_steel(scenario): s_info = ScenarioInfo(scenario) nodes = s_info.N @@ -110,354 +115,240 @@ def gen_mock_demand_steel(scenario): return demand2020_steel -def gen_data_steel(scenario, dry_run=False): - """Generate data for materials representation of steel industry.""" - # Load configuration - context = read_config() - config = context["material"]["steel"] - ssp = context["ssp"] - # Information about scenario, e.g. node, year - s_info = ScenarioInfo(scenario) - - # Techno-economic assumptions - # TEMP: now add cement sector as well - # => Need to separate those since now I have get_data_steel and cement - data_steel = read_sector_data(scenario, "steel") - # Special treatment for time-dependent Parameters - data_steel_ts = read_timeseries(scenario, "steel_cement", context.datafile) - data_steel_rel = read_rel(scenario, "steel_cement", context.datafile) - - tec_ts = set(data_steel_ts.technology) # set of tecs with var_cost - - # List of data frames, to be concatenated together at end - results = defaultdict(list) - - # For each technology there are differnet input and output combinations - # Iterate over technologies - - modelyears = s_info.Y # s_info.Y is only for modeling years - nodes = s_info.N - yv_ya = s_info.yv_ya - yv_ya = yv_ya.loc[yv_ya.year_vtg >= 1990] - nodes.remove("World") - - # Do not parametrize GLB region the same way - if "R11_GLB" in nodes: - nodes.remove("R11_GLB") - global_region = "R11_GLB" - if "R12_GLB" in nodes: - nodes.remove("R12_GLB") - global_region = "R12_GLB" - - # for t in s_info.set['technology']: - for t in config["technology"]["add"]: - params = data_steel.loc[ - (data_steel["technology"] == t), "parameter" - ].values.tolist() +def gen_data_steel_ts(data_steel_ts, results, t, nodes): + common = dict( + time="year", + time_origin="year", + time_dest="year", + ) - # Special treatment for time-varying params - if t in tec_ts: - common = dict( - time="year", - time_origin="year", - time_dest="year", - ) + param_name = data_steel_ts.loc[ + (data_steel_ts["technology"] == t), "parameter" + ].unique() - param_name = data_steel_ts.loc[ - (data_steel_ts["technology"] == t), "parameter" + for p in set(param_name): + val = data_steel_ts.loc[ + (data_steel_ts["technology"] == t) & (data_steel_ts["parameter"] == p), + "value", + ] + # units = data_steel_ts.loc[ + # (data_steel_ts["technology"] == t) + # & (data_steel_ts["parameter"] == p), + # "units", + # ].values[0] + mod = data_steel_ts.loc[ + (data_steel_ts["technology"] == t) & (data_steel_ts["parameter"] == p), + "mode", + ] + yr = data_steel_ts.loc[ + (data_steel_ts["technology"] == t) & (data_steel_ts["parameter"] == p), + "year", + ] + if p == "var_cost": + df = make_df( + p, + technology=t, + value=val, + unit="t", + year_vtg=yr, + year_act=yr, + mode=mod, + **common, + ).pipe(broadcast, node_loc=nodes) + if p == "output": + comm = data_steel_ts.loc[ + (data_steel_ts["technology"] == t) & (data_steel_ts["parameter"] == p), + "commodity", ] - for p in set(param_name): - val = data_steel_ts.loc[ - (data_steel_ts["technology"] == t) - & (data_steel_ts["parameter"] == p), - "value", - ] - # units = data_steel_ts.loc[ - # (data_steel_ts["technology"] == t) - # & (data_steel_ts["parameter"] == p), - # "units", - # ].values[0] - mod = data_steel_ts.loc[ - (data_steel_ts["technology"] == t) - & (data_steel_ts["parameter"] == p), - "mode", - ] - yr = data_steel_ts.loc[ - (data_steel_ts["technology"] == t) - & (data_steel_ts["parameter"] == p), - "year", - ] - if p == "var_cost": - df = make_df( - p, - technology=t, - value=val, - unit="t", - year_vtg=yr, - year_act=yr, - mode=mod, - **common, - ).pipe(broadcast, node_loc=nodes) - if p == "output": - comm = data_steel_ts.loc[ - (data_steel_ts["technology"] == t) - & (data_steel_ts["parameter"] == p), - "commodity", - ] - - lev = data_steel_ts.loc[ - (data_steel_ts["technology"] == t) - & (data_steel_ts["parameter"] == p), - "level", - ] - - rg = data_steel_ts.loc[ - (data_steel_ts["technology"] == t) - & (data_steel_ts["parameter"] == p), - "region", - ] - - df = make_df( - p, - technology=t, - value=val, - unit="t", - year_vtg=yr, - year_act=yr, - mode=mod, - node_loc=rg, - node_dest=rg, - commodity=comm, - level=lev, - **common, - ) - else: - rg = data_steel_ts.loc[ - (data_steel_ts["technology"] == t) - & (data_steel_ts["parameter"] == p), - "region", - ] - df = make_df( - p, - technology=t, - value=val, - unit="t", - year_vtg=yr, - year_act=yr, - mode=mod, - node_loc=rg, - **common, - ) + lev = data_steel_ts.loc[ + (data_steel_ts["technology"] == t) & (data_steel_ts["parameter"] == p), + "level", + ] - results[p].append(df) + rg = data_steel_ts.loc[ + (data_steel_ts["technology"] == t) & (data_steel_ts["parameter"] == p), + "region", + ] - # Iterate over parameters - for par in params: - # Obtain the parameter names, commodity,level,emission - split = par.split("|") - param_name = split[0] - # Obtain the scalar value for the parameter - val = data_steel.loc[ - ((data_steel["technology"] == t) & (data_steel["parameter"] == par)), - "value", - ] # .values - regions = data_steel.loc[ - ((data_steel["technology"] == t) & (data_steel["parameter"] == par)), + df = make_df( + p, + technology=t, + value=val, + unit="t", + year_vtg=yr, + year_act=yr, + mode=mod, + node_loc=rg, + node_dest=rg, + commodity=comm, + level=lev, + **common, + ) + else: + rg = data_steel_ts.loc[ + (data_steel_ts["technology"] == t) & (data_steel_ts["parameter"] == p), "region", - ] # .values - - common = dict( - year_vtg=yv_ya.year_vtg, - year_act=yv_ya.year_act, - # mode="M1", - time="year", - time_origin="year", - time_dest="year", + ] + df = make_df( + p, + technology=t, + value=val, + unit="t", + year_vtg=yr, + year_act=yr, + mode=mod, + node_loc=rg, + **common, ) - for rg in regions: - # For the parameters which inlcudes index names - if len(split) > 1: - if (param_name == "input") | (param_name == "output"): - # Assign commodity and level names - com = split[1] - lev = split[2] - mod = split[3] - if (param_name == "input") and (lev == "import"): - df = make_df( - param_name, - technology=t, - commodity=com, - level=lev, - value=val[regions[regions == rg].index[0]], - mode=mod, - unit="t", - node_loc=rg, - node_origin=global_region, - **common, - ) - - elif (param_name == "output") and (lev == "export"): - df = make_df( - param_name, - technology=t, - commodity=com, - level=lev, - value=val[regions[regions == rg].index[0]], - mode=mod, - unit="t", - node_loc=rg, - node_dest=global_region, - **common, - ) - - else: - df = make_df( - param_name, - technology=t, - commodity=com, - level=lev, - value=val[regions[regions == rg].index[0]], - mode=mod, - unit="t", - node_loc=rg, - **common, - ).pipe(same_node) - - # Copy parameters to all regions, when node_loc is not GLB - if (len(regions) == 1) and (rg != global_region): - df["node_loc"] = None - df = df.pipe(broadcast, node_loc=nodes) - # Use same_node only for non-trade technologies - if (lev != "import") and (lev != "export"): - df = df.pipe(same_node) - - elif param_name == "emission_factor": - # Assign the emisson type - emi = split[1] - mod = split[2] - + results[p].append(df) + return + + +def get_data_steel_const(data_steel, results, params, t, yv_ya, nodes, global_region): + for par in params: + # Obtain the parameter names, commodity,level,emission + split = par.split("|") + param_name = split[0] + # Obtain the scalar value for the parameter + val = data_steel.loc[ + ((data_steel["technology"] == t) & (data_steel["parameter"] == par)), + "value", + ] # .values + regions = data_steel.loc[ + ((data_steel["technology"] == t) & (data_steel["parameter"] == par)), + "region", + ] # .values + + common = dict( + year_vtg=yv_ya.year_vtg, + year_act=yv_ya.year_act, + # mode="M1", + time="year", + time_origin="year", + time_dest="year", + ) + + for rg in regions: + # For the parameters which inlcudes index names + if len(split) > 1: + if (param_name == "input") | (param_name == "output"): + # Assign commodity and level names + com = split[1] + lev = split[2] + mod = split[3] + if (param_name == "input") and (lev == "import"): df = make_df( param_name, technology=t, + commodity=com, + level=lev, value=val[regions[regions == rg].index[0]], - emission=emi, mode=mod, unit="t", node_loc=rg, + node_origin=global_region, **common, ) - else: # time-independent var_cost - mod = split[1] + elif (param_name == "output") and (lev == "export"): df = make_df( param_name, technology=t, + commodity=com, + level=lev, value=val[regions[regions == rg].index[0]], mode=mod, unit="t", node_loc=rg, + node_dest=global_region, **common, ) - # Parameters with only parameter name - else: + else: + df = make_df( + param_name, + technology=t, + commodity=com, + level=lev, + value=val[regions[regions == rg].index[0]], + mode=mod, + unit="t", + node_loc=rg, + **common, + ).pipe(same_node) + + # Copy parameters to all regions, when node_loc is not GLB + if (len(regions) == 1) and (rg != global_region): + df["node_loc"] = None + df = df.pipe(broadcast, node_loc=nodes) + # Use same_node only for non-trade technologies + if (lev != "import") and (lev != "export"): + df = df.pipe(same_node) + + elif param_name == "emission_factor": + # Assign the emisson type + emi = split[1] + mod = split[2] + df = make_df( param_name, technology=t, value=val[regions[regions == rg].index[0]], + emission=emi, + mode=mod, unit="t", node_loc=rg, **common, ) - # Copy parameters to all regions - if ( - len(set(df["node_loc"])) == 1 - and list(set(df["node_loc"]))[0] != global_region - ): - df["node_loc"] = None - df = df.pipe(broadcast, node_loc=nodes) - - results[param_name].append(df) - - # Add relation for the maximum global scrap use in 2020 - - df_max_recycling = pd.DataFrame( - { - "relation": "max_global_recycling_steel", - "node_rel": "R12_GLB", - "year_rel": 2020, - "year_act": 2020, - "node_loc": nodes, - "technology": "scrap_recovery_steel", - "mode": "M1", - "unit": "???", - "value": data_steel_rel.loc[ - ( - (data_steel_rel["relation"] == "max_global_recycling_steel") - & (data_steel_rel["parameter"] == "relation_activity") - ), - "value", - ].values[0], - } - ) - - df_max_recycling_upper = pd.DataFrame( - { - "relation": "max_global_recycling_steel", - "node_rel": "R12_GLB", - "year_rel": 2020, - "unit": "???", - "value": data_steel_rel.loc[ - ( - (data_steel_rel["relation"] == "max_global_recycling_steel") - & (data_steel_rel["parameter"] == "relation_upper") - ), - "value", - ].values[0], - }, - index=[0], - ) - df_max_recycling_lower = pd.DataFrame( - { - "relation": "max_global_recycling_steel", - "node_rel": "R12_GLB", - "year_rel": 2020, - "unit": "???", - "value": data_steel_rel.loc[ - ( - (data_steel_rel["relation"] == "max_global_recycling_steel") - & (data_steel_rel["parameter"] == "relation_lower") - ), - "value", - ].values[0], - }, - index=[0], - ) - - results["relation_activity"].append(df_max_recycling) - results["relation_upper"].append(df_max_recycling_upper) - results["relation_lower"].append(df_max_recycling_lower) + else: # time-independent var_cost + mod = split[1] + df = make_df( + param_name, + technology=t, + value=val[regions[regions == rg].index[0]], + mode=mod, + unit="t", + node_loc=rg, + **common, + ) - # Add relations for scrap grades and availability - regions = set(data_steel_rel["Region"].values) + # Parameters with only parameter name + else: + df = make_df( + param_name, + technology=t, + value=val[regions[regions == rg].index[0]], + unit="t", + node_loc=rg, + **common, + ) + + # Copy parameters to all regions + if ( + len(set(df["node_loc"])) == 1 + and list(set(df["node_loc"]))[0] != global_region + ): + df["node_loc"] = None + df = df.pipe(broadcast, node_loc=nodes) + + results[param_name].append(df) + return + + +def gen_data_steel_rel(data_steel_rel, results, regions, modelyears): for reg in regions: - for r in data_steel_rel["relation"]: + for r in data_steel_rel["relation"].unique(): model_years_rel = modelyears.copy() if r is None: break if r == "max_global_recycling_steel": continue - if r == "minimum_recycling_steel": - # Do not implement the minimum recycling rate for the year 2020 - if 2020 in model_years_rel: - model_years_rel.remove(2020) - if r == "max_regional_recycling_steel": + if r in ["minimum_recycling_steel", "max_regional_recycling_steel"]: # Do not implement the minimum recycling rate for the year 2020 - if 2020 in model_years_rel: - model_years_rel.remove(2020) + remove_from_list_if_exists(2020, model_years_rel) params = set( data_steel_rel.loc[ @@ -520,58 +411,118 @@ def gen_data_steel(scenario, dry_run=False): ) results[par_name].append(df) + return + + +def gen_data_steel(scenario, dry_run=False): + """Generate data for materials representation of steel industry.""" + # Load configuration + context = read_config() + config = context["material"]["steel"] + ssp = get_ssp_from_context(context) + # Information about scenario, e.g. node, year + s_info = ScenarioInfo(scenario) + + # Techno-economic assumptions + # TEMP: now add cement sector as well + # => Need to separate those since now I have get_data_steel and cement + data_steel = read_sector_data(scenario, "steel") + # Special treatment for time-dependent Parameters + data_steel_ts = read_timeseries(scenario, "steel_cement", context.datafile) + data_steel_rel = read_rel(scenario, "steel_cement", context.datafile) + + tec_ts = set(data_steel_ts.technology) # set of tecs with var_cost + + # List of data frames, to be concatenated together at end + results = defaultdict(list) + + modelyears = s_info.Y # s_info.Y is only for modeling years + nodes = nodes_ex_world(s_info.N) + global_region = [i for i in s_info.N if i.endswith("_GLB")][0] + yv_ya = s_info.yv_ya + yv_ya = yv_ya.loc[yv_ya.year_vtg >= 1990] + + # For each technology there are differnet input and output combinations + # Iterate over technologies + for t in config["technology"]["add"]: + params = data_steel.loc[(data_steel["technology"] == t), "parameter"].unique() + + # Special treatment for time-varying params + if t in tec_ts: + gen_data_steel_ts(data_steel_ts, results, t, nodes) + + # Iterate over parameters + get_data_steel_const( + data_steel, results, params, t, yv_ya, nodes, global_region + ) + + # Add relation for the maximum global scrap use in 2020 + df_max_recycling = pd.DataFrame( + { + "relation": "max_global_recycling_steel", + "node_rel": "R12_GLB", + "year_rel": 2020, + "year_act": 2020, + "node_loc": nodes, + "technology": "scrap_recovery_steel", + "mode": "M1", + "unit": "???", + "value": data_steel_rel.loc[ + ( + (data_steel_rel["relation"] == "max_global_recycling_steel") + & (data_steel_rel["parameter"] == "relation_activity") + ), + "value", + ].values[0], + } + ) + df_max_recycling_upper = pd.DataFrame( + { + "relation": "max_global_recycling_steel", + "node_rel": "R12_GLB", + "year_rel": 2020, + "unit": "???", + "value": data_steel_rel.loc[ + ( + (data_steel_rel["relation"] == "max_global_recycling_steel") + & (data_steel_rel["parameter"] == "relation_upper") + ), + "value", + ].values[0], + }, + index=[0], + ) + df_max_recycling_lower = pd.DataFrame( + { + "relation": "max_global_recycling_steel", + "node_rel": "R12_GLB", + "year_rel": 2020, + "unit": "???", + "value": data_steel_rel.loc[ + ( + (data_steel_rel["relation"] == "max_global_recycling_steel") + & (data_steel_rel["parameter"] == "relation_lower") + ), + "value", + ].values[0], + }, + index=[0], + ) + results["relation_activity"].append(df_max_recycling) + results["relation_upper"].append(df_max_recycling_upper) + results["relation_lower"].append(df_max_recycling_lower) + # Add relations for scrap grades and availability + regions = set(data_steel_rel["Region"].values) + gen_data_steel_rel(data_steel_rel, results, regions, modelyears) # Create external demand param parname = "demand" - df = material_demand_calc.derive_demand("steel", scenario, old_gdp=False, ssp=ssp) results[parname].append(df) # Concatenate to one data frame per parameter results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()} - if len(scenario.par("output", filters={"technology": "extract_surfacewater"})): - results["input"] = results["input"].replace({"freshwater_supply": "freshwater"}) - return results + maybe_remove_water_tec(scenario, results) -# # load rpy2 modules -# import rpy2.robjects as ro -# from rpy2.robjects import pandas2ri -# from rpy2.robjects.conversion import localconverter -# -# -# # This returns a df with columns ["region", "year", "demand.tot"] -# def derive_steel_demand(scenario, dry_run=False): -# """Generate steel demand.""" -# # paths to r code and lca data -# rcode_path = Path(__file__).parents[0] / "material_demand" -# context = read_config() -# -# # source R code -# r = ro.r -# r.source(str(rcode_path / "init_modularized.R")) -# context.get_local_path("material") -# # Read population and baseline demand for materials -# pop = scenario.par("bound_activity_up", {"technology": "Population"}) -# pop = pop.loc[pop.year_act >= 2020].rename( -# columns={"year_act": "year", "value": "pop.mil", "node_loc": "region"} -# ) -# -# # import pdb; pdb.set_trace() -# -# pop = pop[["region", "year", "pop.mil"]] -# -# base_demand = gen_mock_demand_steel(scenario) -# base_demand = base_demand.loc[base_demand.year == 2020].rename( -# columns={"value": "demand.tot.base", "node": "region"} -# ) -# -# # call R function with type conversion -# with localconverter(ro.default_converter + pandas2ri.converter): -# # GDP is only in MER in scenario. -# # To get PPP GDP, it is read externally from the R side -# df = r.derive_steel_demand(pop, base_demand, -# str(package_data_path("material"))) -# df.year = df.year.astype(int) -# -# return df + return results diff --git a/message_ix_models/model/material/report/reporting.py b/message_ix_models/model/material/report/reporting.py index e1d100b939..6058edd95b 100644 --- a/message_ix_models/model/material/report/reporting.py +++ b/message_ix_models/model/material/report/reporting.py @@ -153,7 +153,7 @@ def fix_excel(path_temp, path_new): new_workbook.save(path_new) -def report(context, scenario): +def report(context, scenario): # noqa: C901 # Obtain scenario information and directory s_info = ScenarioInfo(scenario) @@ -741,7 +741,7 @@ def report(context, scenario): ] total_scrap_steel_vars = ["out|dummy_end_of_life|steel|total_EOL_steel|M1"] - #new_scrap_steel_vars = ["out|new_scrap|steel|manuf_steel|M1"] + # new_scrap_steel_vars = ["out|new_scrap|steel|manuf_steel|M1"] old_scrap_steel_vars = ["out|dummy_end_of_life|steel|total_EOL_steel|M1"] df_steel.aggregate( diff --git a/message_ix_models/model/material/util.py b/message_ix_models/model/material/util.py index 4b366e01e0..40f1e4a0aa 100644 --- a/message_ix_models/model/material/util.py +++ b/message_ix_models/model/material/util.py @@ -1,4 +1,5 @@ import os +from pathlib import Path import message_ix import openpyxl as pxl @@ -105,7 +106,7 @@ def combine_df_dictionaries(*args: dict[str, pd.DataFrame]) -> dict: return comb_dict -def read_yaml_file(file_path: str) -> dict or None: +def read_yaml_file(file_path: str or Path) -> dict or None: """ Tries to read yaml file into a dict @@ -239,7 +240,7 @@ def price_fit(df: pd.DataFrame) -> float: estimated value for price_ref in 2020 """ - pars, cov = curve_fit(exponential, df.year, df.lvl, maxfev=5000) + pars = curve_fit(exponential, df.year, df.lvl, maxfev=5000)[0] val = exponential([2020], *pars)[0] # print(df.commodity.unique(), df.node.unique(), val) return val @@ -260,7 +261,7 @@ def cost_fit(df) -> float: estimated value for cost_ref in 2020 """ # print(df.lvl) - pars, cov = curve_fit(exponential, df.year, df.lvl, maxfev=5000) + pars = curve_fit(exponential, df.year, df.lvl, maxfev=5000)[0] val = exponential([2020], *pars)[0] # print(df.node.unique(), val) return val / 1000 @@ -337,3 +338,26 @@ def update_macro_calib_file(scenario: message_ix.Scenario, fname: str) -> None: for i in range(2, 61): ws[f"C{i}"].value = df.values[i - 2] wb.save(path + fname) + + +def get_ssp_from_context(context: Context) -> str: + """ + Get selected SSP from context + Parameters + ---------- + context: Context + Returns + ------- + str + SSP label + """ + if "ssp" not in context: + ssp = "SSP2" + else: + ssp = context["ssp"] + return ssp + + +def maybe_remove_water_tec(scenario, results): + if len(scenario.par("output", filters={"technology": "extract_surfacewater"})): + results["input"] = results["input"].replace({"freshwater_supply": "freshwater"}) From 6a2ab7b63a0aee4a39d2550b6f56c328fcc7ae19 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 16 May 2024 11:35:47 +0200 Subject: [PATCH 758/774] Add missing import for materials build --- message_ix_models/model/material/__init__.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 4c167b4766..65c9115cd0 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -23,6 +23,7 @@ from message_ix_models.model.material.data_generic import gen_data_generic from message_ix_models.model.material.data_methanol_new import gen_data_methanol_new from message_ix_models.model.material.data_petro import gen_data_petro_chemicals +from message_ix_models.model.material.data_power_sector import gen_data_power_sector from message_ix_models.model.material.data_steel import gen_data_steel from message_ix_models.model.material.data_util import ( add_ccs_technologies, @@ -61,7 +62,7 @@ DATA_FUNCTIONS_2 = [ gen_data_cement, gen_data_petro_chemicals, - # gen_data_power_sector, + gen_data_power_sector, gen_data_aluminum, ] From 7d0845a9560ddb4bcd0d9a05dc74aaa62e125764 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 16 May 2024 11:38:58 +0200 Subject: [PATCH 759/774] Fix wrong data paths Change data paths from message-ix-models to message_data for some files --- message_ix_models/model/material/__init__.py | 4 +-- message_ix_models/model/material/data_util.py | 26 +++++++++---------- 2 files changed, 14 insertions(+), 16 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 65c9115cd0..88cac15895 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -46,7 +46,7 @@ read_config, update_macro_calib_file, ) -from message_ix_models.util import add_par_data, package_data_path +from message_ix_models.util import add_par_data, package_data_path, private_data_path from message_ix_models.util.click import common_params log = logging.getLogger(__name__) @@ -123,7 +123,7 @@ def build(scenario: message_ix.Scenario, old_calib: bool) -> message_ix.Scenario # levels for the enduse technologies. # Note: context.ssp doesnt work calibrate_UE_gr_to_demand( - scenario, data_path=package_data_path(), ssp="SSP2", region="R12" + scenario, data_path=private_data_path(), ssp="SSP2", region="R12" ) calibrate_UE_share_constraints(scenario) diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index 602721b6fa..d07008efd2 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -17,7 +17,7 @@ from message_ix_models.tools.costs.config import Config from message_ix_models.tools.costs.projections import create_cost_projections from message_ix_models.tools.exo_data import prepare_computer -from message_ix_models.util import package_data_path +from message_ix_models.util import package_data_path, private_data_path if TYPE_CHECKING: from message_ix_models import Context @@ -891,10 +891,9 @@ def calc_demand_shares(iea_db_df: pd.DataFrame, base_year: int) -> pd.DataFrame: def calc_resid_ind_demand(scen: message_ix.Scenario, baseyear: int) -> pd.DataFrame: comms = ["i_spec", "i_therm"] - path = package_data_path("iea", "REV2022_allISO_IEA.parquet") - # path = os.path.join( - # "P:", "ene.model", "IEA_database", "Florian", "REV2022_allISO_IEA.parquet" - # ) + path = os.path.join( + "P:", "ene.model", "IEA_database", "Florian", "REV2022_allISO_IEA.parquet" + ) Inp = pd.read_parquet(path, engine="fastparquet") Inp = map_iea_db_to_msg_regs(Inp, "R12_SSP_V1.yaml") demand_shrs_new = calc_demand_shares(pd.DataFrame(Inp), baseyear) @@ -933,7 +932,7 @@ def map_iea_db_to_msg_regs(df_iea: pd.DataFrame, reg_map_fname: str) -> pd.DataF object """ - file_path = package_data_path("node", reg_map_fname) + file_path = private_data_path("node", reg_map_fname) yaml_data = read_yaml_file(file_path) if "World" in yaml_data.keys(): yaml_data.pop("World") @@ -1007,22 +1006,21 @@ def get_hist_act_data(map_fname: str, years: list or None = None) -> pd.DataFram pd.DataFrame """ - # path = os.path.join( - # "P:", "ene.model", "IEA_database", "Florian", "REV2022_allISO_IEA.parquet" - # ) - path = package_data_path("iea", "REV2022_allISO_IEA.parquet") - Inp = pd.read_parquet(path, engine="fastparquet") + path = os.path.join( + "P:", "ene.model", "IEA_database", "Florian", "REV2022_allISO_IEA.parquet" + ) + iea_enb_df = pd.read_parquet(path, engine="fastparquet") if years: - Inp = Inp[Inp["TIME"].isin(years)] + iea_enb_df = iea_enb_df[iea_enb_df["TIME"].isin(years)] # map IEA countries to MESSAGE region definition - Inp = map_iea_db_to_msg_regs(Inp, "R12_SSP_V1.yaml") + iea_enb_df = map_iea_db_to_msg_regs(iea_enb_df, "R12_SSP_V1.yaml") # read file for IEA product/flow - MESSAGE technologies map MAP = read_iea_tec_map(map_fname) # map IEA flows to MESSAGE technologies and aggregate - df_final = Inp.set_index(["PRODUCT", "FLOW"]).join( + df_final = iea_enb_df.set_index(["PRODUCT", "FLOW"]).join( MAP.set_index(["PRODUCT", "FLOW"]) ) From 3b2e3cdd629c3798ee9050ec609c1d143e3ddc58 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 16 May 2024 11:40:31 +0200 Subject: [PATCH 760/774] Apply formatting to data_util --- message_ix_models/model/material/data_util.py | 168 +++++++++--------- 1 file changed, 86 insertions(+), 82 deletions(-) diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index d07008efd2..3889fccf36 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -88,7 +88,7 @@ def load_GDP_COVID() -> pd.DataFrame: def add_macro_COVID( - scen: message_ix.Scenario, filename: str, check_converge: bool = False + scen: message_ix.Scenario, filename: str, check_converge: bool = False ) -> message_ix.Scenario: """ Prepare data for MACRO calibration by reading data from xlsx file @@ -184,7 +184,7 @@ def modify_demand_and_hist_activity(scen: message_ix.Scenario) -> None: | (df["SECTOR"] == "industry (non-ferrous metals)") | (df["SECTOR"] == "industry (non-metallic minerals)") | (df["SECTOR"] == "industry (total)") - ] + ] df = df[df["RYEAR"] == 2015] # NOTE: Total cehmical industry energy: 27% thermal, 8% electricity, 65% feedstock @@ -203,10 +203,10 @@ def modify_demand_and_hist_activity(scen: message_ix.Scenario) -> None: & (df["SECTOR"] != "industry (total)") & (df["SECTOR"] != "feedstock (petrochemical industry)") & (df["SECTOR"] != "feedstock (total)") - ] + ] df_spec_total = df[ (df["SECTOR"] == "industry (total)") & (df["FUEL"] == "electricity") - ] + ] df_spec_new = pd.DataFrame( columns=["REGION", "SECTOR", "FUEL", "RYEAR", "UNIT_OUT", "RESULT"] @@ -215,15 +215,15 @@ def modify_demand_and_hist_activity(scen: message_ix.Scenario) -> None: df_spec_temp = df_spec.loc[df_spec["REGION"] == r] df_spec_total_temp = df_spec_total.loc[df_spec_total["REGION"] == r] df_spec_temp.loc[:, "i_spec"] = ( - df_spec_temp.loc[:, "RESULT"] - / df_spec_total_temp.loc[:, "RESULT"].values[0] + df_spec_temp.loc[:, "RESULT"] + / df_spec_total_temp.loc[:, "RESULT"].values[0] ) df_spec_new = pd.concat([df_spec_temp, df_spec_new], ignore_index=True) df_spec_new.drop(["FUEL", "RYEAR", "UNIT_OUT", "RESULT"], axis=1, inplace=True) df_spec_new.loc[df_spec_new["SECTOR"] == "industry (chemicals)", "i_spec"] = ( - df_spec_new.loc[df_spec_new["SECTOR"] == "industry (chemicals)", "i_spec"] - * 0.67 + df_spec_new.loc[df_spec_new["SECTOR"] == "industry (chemicals)", "i_spec"] + * 0.67 ) df_spec_new = df_spec_new.groupby(["REGION"]).sum().reset_index() @@ -232,7 +232,7 @@ def modify_demand_and_hist_activity(scen: message_ix.Scenario) -> None: df_feed = df[ (df["SECTOR"] == "feedstock (petrochemical industry)") & (df["FUEL"] == "total") - ] + ] # df_feed_total = # df[(df["SECTOR"] == "feedstock (total)") & (df["FUEL"] == "total")] df_feed_temp = pd.DataFrame(columns=["REGION", "i_feed"]) @@ -257,12 +257,12 @@ def modify_demand_and_hist_activity(scen: message_ix.Scenario) -> None: & (df["SECTOR"] != "feedstock (petrochemical industry)") & (df["SECTOR"] != "feedstock (total)") & (df["SECTOR"] != "industry (non-ferrous metals)") - ] + ] df_therm_total = df[ (df["SECTOR"] == "industry (total)") & (df["FUEL"] != "total") & (df["FUEL"] != "electricity") - ] + ] df_therm_total = ( df_therm_total.groupby(by="REGION").sum().drop(["RYEAR"], axis=1).reset_index() ) @@ -280,23 +280,23 @@ def modify_demand_and_hist_activity(scen: message_ix.Scenario) -> None: df_therm_temp = df_therm.loc[df_therm["REGION"] == r] df_therm_total_temp = df_therm_total.loc[df_therm_total["REGION"] == r] df_therm_temp.loc[:, "i_therm"] = ( - df_therm_temp.loc[:, "RESULT"] - / df_therm_total_temp.loc[:, "RESULT"].values[0] + df_therm_temp.loc[:, "RESULT"] + / df_therm_total_temp.loc[:, "RESULT"].values[0] ) df_therm_new = pd.concat([df_therm_temp, df_therm_new], ignore_index=True) df_therm_new = df_therm_new.drop(["RESULT"], axis=1) df_therm_new.drop(["FUEL", "RYEAR", "UNIT_OUT"], axis=1, inplace=True) df_therm_new.loc[df_therm_new["SECTOR"] == "industry (chemicals)", "i_therm"] = ( - df_therm_new.loc[df_therm_new["SECTOR"] == "industry (chemicals)", "i_therm"] - * 0.67 + df_therm_new.loc[df_therm_new["SECTOR"] == "industry (chemicals)", "i_therm"] + * 0.67 ) # Modify CPA based on https://www.iea.org/sankey/#?c=Japan&s=Final%20consumption. # Since the value did not allign with the one in the IEA website. index = (df_therm_new["SECTOR"] == "industry (iron and steel)") & ( - (df_therm_new["REGION"] == region_name_CPA) - | (df_therm_new["REGION"] == region_name_CHN) + (df_therm_new["REGION"] == region_name_CPA) + | (df_therm_new["REGION"] == region_name_CHN) ) df_therm_new.loc[index, "i_therm"] = 0.2 @@ -347,39 +347,41 @@ def modify_demand_and_hist_activity(scen: message_ix.Scenario) -> None: useful_thermal.loc[ useful_thermal["node"] == r_MESSAGE, "value" ] = useful_thermal.loc[useful_thermal["node"] == r_MESSAGE, "value"] * ( - 1 - df_therm_new.loc[df_therm_new["REGION"] == r, "i_therm"].values[0] + 1 - df_therm_new.loc[df_therm_new["REGION"] == r, "i_therm"].values[0] ) thermal_df_hist.loc[ thermal_df_hist["node_loc"] == r_MESSAGE, "value" ] = thermal_df_hist.loc[thermal_df_hist["node_loc"] == r_MESSAGE, "value"] * ( - 1 - df_therm_new.loc[df_therm_new["REGION"] == r, "i_therm"].values[0] + 1 - df_therm_new.loc[df_therm_new["REGION"] == r, "i_therm"].values[0] ) for r in df_spec_new["REGION"]: r_MESSAGE = region_type + r useful_spec.loc[useful_spec["node"] == r_MESSAGE, "value"] = useful_spec.loc[ - useful_spec["node"] == r_MESSAGE, "value" - ] * (1 - df_spec_new.loc[df_spec_new["REGION"] == r, "i_spec"].values[0]) + useful_spec["node"] == r_MESSAGE, "value" + ] * (1 - df_spec_new.loc[ + df_spec_new["REGION"] == r, "i_spec"].values[0]) spec_df_hist.loc[ spec_df_hist["node_loc"] == r_MESSAGE, "value" ] = spec_df_hist.loc[spec_df_hist["node_loc"] == r_MESSAGE, "value"] * ( - 1 - df_spec_new.loc[df_spec_new["REGION"] == r, "i_spec"].values[0] + 1 - df_spec_new.loc[df_spec_new["REGION"] == r, "i_spec"].values[0] ) for r in df_feed_new["REGION"]: r_MESSAGE = region_type + r useful_feed.loc[useful_feed["node"] == r_MESSAGE, "value"] = useful_feed.loc[ - useful_feed["node"] == r_MESSAGE, "value" - ] * (1 - df_feed_new.loc[df_feed_new["REGION"] == r, "i_feed"].values[0]) + useful_feed["node"] == r_MESSAGE, "value" + ] * (1 - df_feed_new.loc[ + df_feed_new["REGION"] == r, "i_feed"].values[0]) feed_df_hist.loc[ feed_df_hist["node_loc"] == r_MESSAGE, "value" ] = feed_df_hist.loc[feed_df_hist["node_loc"] == r_MESSAGE, "value"] * ( - 1 - df_feed_new.loc[df_feed_new["REGION"] == r, "i_feed"].values[0] + 1 - df_feed_new.loc[df_feed_new["REGION"] == r, "i_feed"].values[0] ) scen.check_out() @@ -463,7 +465,7 @@ def modify_demand_and_hist_activity(scen: message_ix.Scenario) -> None: def modify_demand_and_hist_activity_debug( - scen: message_ix.Scenario, + scen: message_ix.Scenario, ) -> dict[str, pd.DataFrame]: """modularized "dry-run" version of modify_demand_and_hist_activity() for debugging purposes @@ -510,7 +512,7 @@ def modify_demand_and_hist_activity_debug( | (df["SECTOR"] == "industry (non-ferrous metals)") | (df["SECTOR"] == "industry (non-metallic minerals)") | (df["SECTOR"] == "industry (total)") - ] + ] df = df[df["RYEAR"] == 2015] # NOTE: Total cehmical industry energy: 27% thermal, 8% electricity, 65% feedstock @@ -529,10 +531,10 @@ def modify_demand_and_hist_activity_debug( & (df["SECTOR"] != "industry (total)") & (df["SECTOR"] != "feedstock (petrochemical industry)") & (df["SECTOR"] != "feedstock (total)") - ] + ] df_spec_total = df[ (df["SECTOR"] == "industry (total)") & (df["FUEL"] == "electricity") - ] + ] df_spec_new = pd.DataFrame( columns=["REGION", "SECTOR", "FUEL", "RYEAR", "UNIT_OUT", "RESULT"] @@ -541,15 +543,15 @@ def modify_demand_and_hist_activity_debug( df_spec_temp = df_spec.loc[df_spec["REGION"] == r] df_spec_total_temp = df_spec_total.loc[df_spec_total["REGION"] == r] df_spec_temp.loc[:, "i_spec"] = ( - df_spec_temp.loc[:, "RESULT"] - / df_spec_total_temp.loc[:, "RESULT"].values[0] + df_spec_temp.loc[:, "RESULT"] + / df_spec_total_temp.loc[:, "RESULT"].values[0] ) df_spec_new = pd.concat([df_spec_temp, df_spec_new], ignore_index=True) df_spec_new.drop(["FUEL", "RYEAR", "UNIT_OUT", "RESULT"], axis=1, inplace=True) df_spec_new.loc[df_spec_new["SECTOR"] == "industry (chemicals)", "i_spec"] = ( - df_spec_new.loc[df_spec_new["SECTOR"] == "industry (chemicals)", "i_spec"] - * 0.67 + df_spec_new.loc[df_spec_new["SECTOR"] == "industry (chemicals)", "i_spec"] + * 0.67 ) df_spec_new = df_spec_new.groupby(["REGION"]).sum().reset_index() @@ -558,7 +560,7 @@ def modify_demand_and_hist_activity_debug( df_feed = df[ (df["SECTOR"] == "feedstock (petrochemical industry)") & (df["FUEL"] == "total") - ] + ] # df_feed_total = # df[(df["SECTOR"] == "feedstock (total)") & (df["FUEL"] == "total")] df_feed_temp = pd.DataFrame(columns=["REGION", "i_feed"]) @@ -583,12 +585,12 @@ def modify_demand_and_hist_activity_debug( & (df["SECTOR"] != "feedstock (petrochemical industry)") & (df["SECTOR"] != "feedstock (total)") & (df["SECTOR"] != "industry (non-ferrous metals)") - ] + ] df_therm_total = df[ (df["SECTOR"] == "industry (total)") & (df["FUEL"] != "total") & (df["FUEL"] != "electricity") - ] + ] df_therm_total = ( df_therm_total.groupby(by="REGION").sum().drop(["RYEAR"], axis=1).reset_index() ) @@ -606,23 +608,23 @@ def modify_demand_and_hist_activity_debug( df_therm_temp = df_therm.loc[df_therm["REGION"] == r] df_therm_total_temp = df_therm_total.loc[df_therm_total["REGION"] == r] df_therm_temp.loc[:, "i_therm"] = ( - df_therm_temp.loc[:, "RESULT"] - / df_therm_total_temp.loc[:, "RESULT"].values[0] + df_therm_temp.loc[:, "RESULT"] + / df_therm_total_temp.loc[:, "RESULT"].values[0] ) df_therm_new = pd.concat([df_therm_temp, df_therm_new], ignore_index=True) df_therm_new = df_therm_new.drop(["RESULT"], axis=1) df_therm_new.drop(["FUEL", "RYEAR", "UNIT_OUT"], axis=1, inplace=True) df_therm_new.loc[df_therm_new["SECTOR"] == "industry (chemicals)", "i_therm"] = ( - df_therm_new.loc[df_therm_new["SECTOR"] == "industry (chemicals)", "i_therm"] - * 0.67 + df_therm_new.loc[df_therm_new["SECTOR"] == "industry (chemicals)", "i_therm"] + * 0.67 ) # Modify CPA based on https://www.iea.org/sankey/#?c=Japan&s=Final%20consumption. # Since the value did not allign with the one in the IEA website. index = (df_therm_new["SECTOR"] == "industry (iron and steel)") & ( - (df_therm_new["REGION"] == region_name_CPA) - | (df_therm_new["REGION"] == region_name_CHN) + (df_therm_new["REGION"] == region_name_CPA) + | (df_therm_new["REGION"] == region_name_CHN) ) df_therm_new.loc[index, "i_therm"] = 0.2 @@ -673,39 +675,41 @@ def modify_demand_and_hist_activity_debug( useful_thermal.loc[ useful_thermal["node"] == r_MESSAGE, "value" ] = useful_thermal.loc[useful_thermal["node"] == r_MESSAGE, "value"] * ( - 1 - df_therm_new.loc[df_therm_new["REGION"] == r, "i_therm"].values[0] + 1 - df_therm_new.loc[df_therm_new["REGION"] == r, "i_therm"].values[0] ) thermal_df_hist.loc[ thermal_df_hist["node_loc"] == r_MESSAGE, "value" ] = thermal_df_hist.loc[thermal_df_hist["node_loc"] == r_MESSAGE, "value"] * ( - 1 - df_therm_new.loc[df_therm_new["REGION"] == r, "i_therm"].values[0] + 1 - df_therm_new.loc[df_therm_new["REGION"] == r, "i_therm"].values[0] ) for r in df_spec_new["REGION"]: r_MESSAGE = region_type + r useful_spec.loc[useful_spec["node"] == r_MESSAGE, "value"] = useful_spec.loc[ - useful_spec["node"] == r_MESSAGE, "value" - ] * (1 - df_spec_new.loc[df_spec_new["REGION"] == r, "i_spec"].values[0]) + useful_spec["node"] == r_MESSAGE, "value" + ] * (1 - df_spec_new.loc[ + df_spec_new["REGION"] == r, "i_spec"].values[0]) spec_df_hist.loc[ spec_df_hist["node_loc"] == r_MESSAGE, "value" ] = spec_df_hist.loc[spec_df_hist["node_loc"] == r_MESSAGE, "value"] * ( - 1 - df_spec_new.loc[df_spec_new["REGION"] == r, "i_spec"].values[0] + 1 - df_spec_new.loc[df_spec_new["REGION"] == r, "i_spec"].values[0] ) for r in df_feed_new["REGION"]: r_MESSAGE = region_type + r useful_feed.loc[useful_feed["node"] == r_MESSAGE, "value"] = useful_feed.loc[ - useful_feed["node"] == r_MESSAGE, "value" - ] * (1 - df_feed_new.loc[df_feed_new["REGION"] == r, "i_feed"].values[0]) + useful_feed["node"] == r_MESSAGE, "value" + ] * (1 - df_feed_new.loc[ + df_feed_new["REGION"] == r, "i_feed"].values[0]) feed_df_hist.loc[ feed_df_hist["node_loc"] == r_MESSAGE, "value" ] = feed_df_hist.loc[feed_df_hist["node_loc"] == r_MESSAGE, "value"] * ( - 1 - df_feed_new.loc[df_feed_new["REGION"] == r, "i_feed"].values[0] + 1 - df_feed_new.loc[df_feed_new["REGION"] == r, "i_feed"].values[0] ) # For aluminum there is no significant deduction required @@ -829,14 +833,14 @@ def calc_demand_shares(iea_db_df: pd.DataFrame, base_year: int) -> pd.DataFrame: & (iea_db_df["PRODUCT"].isin(i_spec_prods)) & ~((iea_db_df["PRODUCT"] == ("ELECTR")) & (iea_db_df["FLOW"] == "IRONSTL")) & (iea_db_df["TIME"] == year) - ] + ] df_i_spec = df_i_spec.groupby("REGION").sum(numeric_only=True) df_i_spec_materials = iea_db_df[ (iea_db_df["FLOW"].isin(i_spec_material_flows)) & (iea_db_df["PRODUCT"].isin(i_spec_prods)) & (iea_db_df["TIME"] == year) - ] + ] df_i_spec_materials = df_i_spec_materials.groupby("REGION").sum(numeric_only=True) df_i_spec_resid_shr = ( @@ -847,7 +851,7 @@ def calc_demand_shares(iea_db_df: pd.DataFrame, base_year: int) -> pd.DataFrame: df_elec_i = iea_db_df[ ((iea_db_df["PRODUCT"] == ("ELECTR")) & (iea_db_df["FLOW"] == "IRONSTL")) & (iea_db_df["TIME"] == year) - ] + ] df_elec_i = df_elec_i.groupby("REGION").sum(numeric_only=True) agg_prods = ["MRENEW", "TOTAL"] @@ -856,7 +860,7 @@ def calc_demand_shares(iea_db_df: pd.DataFrame, base_year: int) -> pd.DataFrame: & ~(iea_db_df["PRODUCT"].isin(i_spec_prods)) & ~(iea_db_df["PRODUCT"].isin(agg_prods)) & (iea_db_df["TIME"] == year) - ] + ] df_i_therm = df_i_therm.groupby("REGION").sum(numeric_only=True) df_i_therm = df_i_therm.add(df_elec_i, fill_value=0) @@ -866,7 +870,7 @@ def calc_demand_shares(iea_db_df: pd.DataFrame, base_year: int) -> pd.DataFrame: & ~(iea_db_df["PRODUCT"].isin(i_spec_prods)) & ~(iea_db_df["PRODUCT"].isin(agg_prods)) & (iea_db_df["TIME"] == year) - ] + ] df_i_therm_materials = df_i_therm_materials.groupby(["REGION", "FLOW"]).sum( numeric_only=True ) @@ -998,7 +1002,7 @@ def get_hist_act_data(map_fname: str, years: list or None = None) -> pd.DataFram ---------- map_fname name of MESSAGEix-technology-to-IEA-flow/product mapping file - years, optional + years specifies timesteps for whom historical activity should be calculated and returned Returns @@ -1060,17 +1064,17 @@ def add_emission_accounting(scen): i for i in tec_list_residual if ( - ( - ("biomass_i" in i) - | ("coal_i" in i) - | ("foil_i" in i) - | ("gas_i" in i) - | ("hp_gas_i" in i) - | ("loil_i" in i) - | ("meth_i" in i) - ) - & ("imp" not in i) - & ("trp" not in i) + ( + ("biomass_i" in i) + | ("coal_i" in i) + | ("foil_i" in i) + | ("gas_i" in i) + | ("hp_gas_i" in i) + | ("loil_i" in i) + | ("meth_i" in i) + ) + & ("imp" not in i) + & ("trp" not in i) ) ] @@ -1204,11 +1208,11 @@ def add_emission_accounting(scen): i for i in tec_list if ( - ("steel" in i) - | ("aluminum" in i) - | ("petro" in i) - | ("cement" in i) - | ("ref" in i) + ("steel" in i) + | ("aluminum" in i) + | ("petro" in i) + | ("cement" in i) + | ("ref" in i) ) ] for elem in ["refrigerant_recovery", "replacement_so2", "SO2_scrub_ref"]: @@ -1230,7 +1234,7 @@ def add_emission_accounting(scen): (relation_activity["relation"] != "PM2p5_Emission") & (relation_activity["relation"] != "CO2_industry_Emission") & (relation_activity["relation"] != "CO2_transformation_Emission") - ] + ] # ***** (3) Add thermal industry technologies to CO2_ind relation ****** @@ -1430,7 +1434,7 @@ def add_elec_lowerbound_2020(scen): # derive useful energy values by dividing final energy by # input coefficient from final-to-useful technologies bound_residual_electricity["value"] = ( - bound_residual_electricity["Value"] / bound_residual_electricity["value"] + bound_residual_electricity["Value"] / bound_residual_electricity["value"] ) # downselect dataframe columns for MESSAGEix parameters @@ -1452,7 +1456,7 @@ def add_elec_lowerbound_2020(scen): bound_residual_electricity["value"] = bound_residual_electricity["value"] * 0.8 bound_residual_electricity = bound_residual_electricity[ bound_residual_electricity["node_loc"] == "R12_CHN" - ] + ] scen.check_out() @@ -1536,7 +1540,7 @@ def add_coal_lowerbound_2020(sc): bound_coal["value"] = bound_coal["Value"] / bound_coal["value"] bound_cement_coal["value"] = bound_cement_coal["Value"] / bound_cement_coal["value"] bound_residual_electricity["value"] = ( - bound_residual_electricity["Value"] / bound_residual_electricity["value"] + bound_residual_electricity["Value"] / bound_residual_electricity["value"] ) # downselect dataframe columns for MESSAGEix parameters @@ -1731,7 +1735,7 @@ def add_cement_bounds_2020(sc): bound_cement_foil["value"] = bound_cement_foil["Value"] / bound_cement_foil["value"] bound_cement_gas["value"] = bound_cement_gas["Value"] / bound_cement_gas["value"] bound_cement_biomass["value"] = ( - bound_cement_biomass["Value"] / bound_cement_biomass["value"] + bound_cement_biomass["Value"] / bound_cement_biomass["value"] ) bound_cement_coal["value"] = bound_cement_coal["Value"] / bound_cement_coal["value"] @@ -1984,7 +1988,7 @@ def add_ccs_technologies(scen: message_ix.Scenario) -> None: # Read in time-dependent parameters # Now only used to add fuel cost for bare model def read_timeseries( - scenario: message_ix.Scenario, material: str, filename: str + scenario: message_ix.Scenario, material: str, filename: str ) -> pd.DataFrame: """ Read "timeseries" type data from a sector specific xlsx input file @@ -2084,10 +2088,10 @@ def read_rel(scenario: message_ix.Scenario, material: str, filename: str): def gen_te_projections( - scen: message_ix.Scenario, - ssp: Literal["all", "LED", "SSP1", "SSP2", "SSP3", "SSP4", "SSP5"] = "SSP2", - method: Literal["constant", "convergence", "gdp"] = "convergence", - ref_reg: str = "R12_NAM", + scen: message_ix.Scenario, + ssp: Literal["all", "LED", "SSP1", "SSP2", "SSP3", "SSP4", "SSP5"] = "SSP2", + method: Literal["constant", "convergence", "gdp"] = "convergence", + ref_reg: str = "R12_NAM", ) -> tuple[pd.DataFrame, pd.DataFrame]: """ Calls message_ix_models.tools.costs with config for MESSAGEix-Materials From 277c2f5c55c94c8e69b893335154e4422ae48107 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 16 May 2024 11:41:15 +0200 Subject: [PATCH 761/774] Add missing parameter in docstring in bare.py --- message_ix_models/model/material/bare.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/message_ix_models/model/material/bare.py b/message_ix_models/model/material/bare.py index 1538d3a3c1..0c2859e539 100644 --- a/message_ix_models/model/material/bare.py +++ b/message_ix_models/model/material/bare.py @@ -39,6 +39,9 @@ def create_res(context=None, quiet=True): Parameters ---------- + quiet : bool + Only show log messages at level ``ERROR`` and higher. If :obj:`False` (default), + show log messages at level ``DEBUG`` and higher. context : .Context :attr:`.Context.scenario_info` determines the model name and scenario name of the created Scenario. From d12642b49d78388db1288b6e073aea6bc5362f97 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 16 May 2024 11:42:21 +0200 Subject: [PATCH 762/774] Add comment to FIXME mnemonic --- message_ix_models/model/material/build.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/message_ix_models/model/material/build.py b/message_ix_models/model/material/build.py index bbff682b4f..a49b7cc0b5 100644 --- a/message_ix_models/model/material/build.py +++ b/message_ix_models/model/material/build.py @@ -24,7 +24,7 @@ def ellipsize(elements: List) -> str: return ", ".join(map(str, elements)) -# FIXME Reduce complexity from 14 to ≤13 +# FIXME Reduce complexity (same issues as model.build.apply_spec()) def apply_spec( # noqa: C901 scenario: Scenario, spec: Union[Spec, Mapping[str, ScenarioInfo]] = None, From e88d836617d3a216f61519990dd81ae04a9097e2 Mon Sep 17 00:00:00 2001 From: Fridolin Glatter Date: Fri, 17 May 2024 08:33:57 +0200 Subject: [PATCH 763/774] Migrate functions required by materials reporting from m_data * Migrate already-present functions to common location * Exclude that location from ruff :( * Adapt imports in material and legacy reporting --- message_ix_models/model/material/__init__.py | 17 +- .../report/legacy/iamc_report_hackathon.py | 8 +- message_ix_models/report/legacy/iamc_tree.py | 2 +- message_ix_models/report/legacy/pp_utils.py | 6 +- message_ix_models/util/compat/__init__.py | 0 .../util/compat/message_data/__init__.py | 2 + .../message_data/calibrate_UE_gr_to_demand.py | 300 ++++++ .../calibrate_UE_share_constraints.py | 230 +++++ .../message_data}/get_historical_years.py | 0 .../compat/message_data}/get_nodes.py | 0 .../message_data}/get_optimization_years.py | 0 ...manual_updates_ENGAGE_SSP2_v417_to_v418.py | 912 ++++++++++++++++++ .../compat/message_data}/utilities.py | 0 pyproject.toml | 2 +- 14 files changed, 1463 insertions(+), 16 deletions(-) create mode 100644 message_ix_models/util/compat/__init__.py create mode 100644 message_ix_models/util/compat/message_data/__init__.py create mode 100644 message_ix_models/util/compat/message_data/calibrate_UE_gr_to_demand.py create mode 100644 message_ix_models/util/compat/message_data/calibrate_UE_share_constraints.py rename message_ix_models/{report/legacy => util/compat/message_data}/get_historical_years.py (100%) rename message_ix_models/{report/legacy => util/compat/message_data}/get_nodes.py (100%) rename message_ix_models/{report/legacy => util/compat/message_data}/get_optimization_years.py (100%) create mode 100644 message_ix_models/util/compat/message_data/manual_updates_ENGAGE_SSP2_v417_to_v418.py rename message_ix_models/{report/legacy => util/compat/message_data}/utilities.py (100%) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 88cac15895..5d5411f96a 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -6,13 +6,6 @@ import ixmp import message_ix import pandas as pd -from message_data.tools.utilities import ( - calibrate_UE_gr_to_demand, - calibrate_UE_share_constraints, -) -from message_data.tools.utilities import ( - manual_updates_ENGAGE_SSP2_v417_to_v418 as engage_updates, -) import message_ix_models.tools.costs.projections from message_ix_models import ScenarioInfo @@ -48,6 +41,13 @@ ) from message_ix_models.util import add_par_data, package_data_path, private_data_path from message_ix_models.util.click import common_params +from message_ix_models.util.compat.message_data import ( + calibrate_UE_gr_to_demand, + calibrate_UE_share_constraints, +) +from message_ix_models.util.compat.message_data import ( + manual_updates_ENGAGE_SSP2_v417_to_v418 as engage_updates, +) log = logging.getLogger(__name__) @@ -201,7 +201,7 @@ def cli(ssp): @click.pass_obj def create_bare(context, regions, dry_run): """Create the RES from scratch.""" - from message_ix_models.model.create import create_res + from message_ix_models.model.bare import create_res if regions: context.regions = regions @@ -481,6 +481,7 @@ def add_building_ts(scenario_name, model_name): from ixmp import Platform from message_ix import Scenario + # FIXME This import can not be resolved from message_ix_models.reporting.materials.add_buildings_ts import ( add_building_timeseries, ) diff --git a/message_ix_models/report/legacy/iamc_report_hackathon.py b/message_ix_models/report/legacy/iamc_report_hackathon.py index 7e69fa8082..4aa26004a1 100644 --- a/message_ix_models/report/legacy/iamc_report_hackathon.py +++ b/message_ix_models/report/legacy/iamc_report_hackathon.py @@ -7,13 +7,13 @@ from yaml.loader import SafeLoader from message_ix_models import Context from message_ix_models.util import package_data_path +from message_ix_models.util.compat.message_data.get_historical_years import main as get_historical_years +from message_ix_models.util.compat.message_data.get_nodes import get_nodes +from message_ix_models.util.compat.message_data.get_optimization_years import main as get_optimization_years +from message_ix_models.util.compat.message_data.utilities import retrieve_region_mapping from . import postprocess from . import pp_utils -from .get_historical_years import main as get_historical_years -from .get_nodes import get_nodes -from .get_optimization_years import main as get_optimization_years -from .utilities import retrieve_region_mapping log = logging.getLogger(__name__) diff --git a/message_ix_models/report/legacy/iamc_tree.py b/message_ix_models/report/legacy/iamc_tree.py index 18d67ba941..2da75afa33 100644 --- a/message_ix_models/report/legacy/iamc_tree.py +++ b/message_ix_models/report/legacy/iamc_tree.py @@ -2,7 +2,7 @@ import pandas as pd -from . import utilities +from message_ix_models.util.compat.message_data import utilities _col = "Variable" diff --git a/message_ix_models/report/legacy/pp_utils.py b/message_ix_models/report/legacy/pp_utils.py index 7a5d1fe040..2c5fc72b1a 100644 --- a/message_ix_models/report/legacy/pp_utils.py +++ b/message_ix_models/report/legacy/pp_utils.py @@ -8,10 +8,12 @@ import numpy as np import pandas as pd -from message_ix_models.util import package_data_path from pandas.api.types import is_numeric_dtype -from . import iamc_tree, utilities +from message_ix_models.util import package_data_path +from message_ix_models.util.compat.message_data import utilities + +from . import iamc_tree all_years = None years = None diff --git a/message_ix_models/util/compat/__init__.py b/message_ix_models/util/compat/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/message_ix_models/util/compat/message_data/__init__.py b/message_ix_models/util/compat/message_data/__init__.py new file mode 100644 index 0000000000..332a719591 --- /dev/null +++ b/message_ix_models/util/compat/message_data/__init__.py @@ -0,0 +1,2 @@ +from .calibrate_UE_gr_to_demand import main as calibrate_UE_gr_to_demand +from .calibrate_UE_share_constraints import main as calibrate_UE_share_constraints diff --git a/message_ix_models/util/compat/message_data/calibrate_UE_gr_to_demand.py b/message_ix_models/util/compat/message_data/calibrate_UE_gr_to_demand.py new file mode 100644 index 0000000000..ff2f270dcf --- /dev/null +++ b/message_ix_models/util/compat/message_data/calibrate_UE_gr_to_demand.py @@ -0,0 +1,300 @@ +import pandas as pd +import numpy as np + +from itertools import groupby +from operator import itemgetter + +from .get_optimization_years import main as get_optimization_years +from .get_nodes import get_nodes + +from .utilities import CAGR + +# In some cases, end-use technologies have outputs onto multiple demands. +# If this is the case, then the a manual assignment is undertaken, +# based on the last part of the end-use tec. name +manual_demand_allocation = { + "I": "i_spec", + "RC": "rc_spec", + "rc": "rc_therm", + "trp": "transport", + "fs": "i_feed", + "i": "i_therm", +} + +# Define index for final dataframe +index = ["node_loc", "technology", "parameter", "year_act"] + + +def main(scenario, data_path, ssp, region, first_mpa_year=None, intpol_lim=1, verbose=False): + """Calibration of dynamic growth constraints for + Useful Energy technologies. + + The general dynamic growth constraints for UE technologies as defined in + the input file are added to scenario, while ensuring that these constraints + allow demand in- and decreases to be met. + + Parameters + ---------- + scenario : :class:`message_ix.Scenario` + scenario to which changes should be applied + data_path : :class:`pathlib.Path` + path to model-data directory in message_data repositor + ssp : str + name of SSP for which the script is being run. + (SSP1, SSP2 or SSP3) + region: str + (R12, R11) + first_mpa_year : int + the first year for which the dynamic bounds should be adjusted. + intpol_lim : 1 + Number of time consecutive NaN values that can be interpolated. + If changing, please check the results from manually adjusted mpas, + and see that only values whcih should be adjusted are actually + adjusted. + verbose : boolean (default=False) + option whether to print on screen messages. + """ + # Retrieve years for which changes should be applied + years = get_optimization_years(scenario) + + # Retrieve data for corresponding SSP + data_filname = "SSP_UE_dyn_input.xlsx" + data_fil = data_path / "model" / "UE_dynamic_constraints" / data_filname + mpa_data = pd.read_excel(data_fil, sheet_name="SSP_data") + mpa_tec = mpa_data["technology"].tolist() + + if not first_mpa_year: + first_mpa_year = years[0] + else: + years = [y for y in years if y >= first_mpa_year] + + # Checks if the year as of which mpa should be generated is also + # in the model + assert int(first_mpa_year) in scenario.set("year").values, ( + "Year as of which mpas should be generated is not defined as" + + "a year in the model" + ) + + # Checks whether all technologies contained in the config file are + # also contained within the model + df = scenario.par( + "output", + filters={"level": ["useful"], "year_act": years, "technology": mpa_tec}, + ) + df = df[df.year_act == df.year_vtg] + + missing_tec = [t for t in mpa_tec if t not in df["technology"].unique().tolist()] + if missing_tec: + print(missing_tec, "not included in scenario") + mpa_data = mpa_data[~mpa_data["technology"].isin(missing_tec)] + + # Retrieves scenario demands + demands = ( + scenario.par("demand") + .drop(["time", "unit", "level"], axis=1) + .pivot_table(index=["node", "commodity"], columns="year", values="value") + ) + + # For all the demands, calculate the growth rates + demands_gr = demands.copy() + demands_gr = demands_gr.apply( + lambda x: x + if int(x.name) < first_mpa_year + else CAGR( + demands[ + demands.reset_index().columns[ + int(demands.reset_index().columns.get_loc(x.name)) - 1 + ] + ], + x, + int(scenario.par("duration_period", filters={"year": [x.name]}).value.iloc[0]), + ) + ) + + # Downselect only relevant data + # This is not done when retrieving the data so that GR + # can be calculated + demands = demands[years] + demands_gr = demands_gr[years] + + # Filter required columns for final df + df = df[["node_loc", "technology", "year_act", "commodity"]] + df["demand"] = 1 + df = df.set_index(["node_loc", "year_act", "commodity"]) + + # Allocate demands to dataframe + demands = ( + demands.stack() + .reset_index() + .rename(columns={0: "value", "node": "node_loc", "year": "year_act"}) + .set_index(["node_loc", "year_act", "commodity"]) + ) + + df.demand = demands.value + + # Allocate growth rates to dataframe + demands_gr = ( + demands_gr.stack() + .reset_index() + .rename(columns={0: "value", "node": "node_loc", "year": "year_act"}) + .set_index(["node_loc", "year_act", "commodity"]) + ) + + df["dem_gr"] = demands_gr.value + + # Allocate input data to dataframe + df = ( + df.reset_index() + .set_index("technology") + .join(mpa_data.set_index("technology"), how="outer") + .reset_index() + .set_index(["node_loc", "technology", "commodity", "year_act"]) + ) + + # Calculate mpa_lo + # Ensures that the declines in demand can be met + tmp_df = df.copy() + tmp_df["a"] = 1 + df["mpa_lo"] + tmp_df["b"] = df["dem_gr"] + df["mpa_lo"] + tmp_df = tmp_df[["a", "b"]] + df.mpa_lo = tmp_df.min(axis=1) + + # Calculate mpa_up + # Ensures that increases in demand can be met + tmp_df = df.copy() + tmp_df["a"] = 1 + df["mpa_up"] + tmp_df["b"] = df["dem_gr"] + df["mpa_up"] + tmp_df = tmp_df[["a", "b"]] + tmp_df["a"] = tmp_df.min(axis=1) + tmp_df["b"] = df["dem_gr"] + df["mpa_up"] / 2 + df.mpa_up = tmp_df.max(axis=1) + + # Calculate startup_lo + df["startup_lo"] = ((df["mpa_lo"] - 1) * df["startup_lo"] * df["demand"]) / ( + df["mpa_lo"] ** (10) - 1 + ) + + # Calculate startup_up + df["startup_up"] = ((df["mpa_up"] - 1) * df["startup_up"] * df["demand"]) / ( + df["mpa_up"] ** (10) - 1 + ) + + df = df[["startup_lo", "mpa_lo", "startup_up", "mpa_up"]] + df.mpa_lo = df.mpa_lo - 1 + df.mpa_up = df.mpa_up - 1 + + df = df.reset_index() + + # Identify all technologies with multiple outputs and assign correct demand + tmp = df[["technology", "commodity"]].drop_duplicates() + double = [] + for t in tmp["technology"].unique(): + if len(tmp[tmp["technology"] == t].commodity.tolist()) > 1: + double.append(t) + for tec in double: + sector = manual_demand_allocation[tec.split("_")[-1]] + df = df[~((df["technology"] == tec) & (df["commodity"] != sector))] + + df = ( + df.rename( + columns={ + "startup_lo": "initial_activity_lo", + "startup_up": "initial_activity_up", + "mpa_lo": "growth_activity_lo", + "mpa_up": "growth_activity_up", + } + ) + .drop(["commodity"], axis=1) + .set_index(["node_loc", "technology", "year_act"]) + .stack() + .reset_index() + .rename(columns={"level_3": "parameter", 0: "value"}) + .set_index(index) + ) + + # Read data for manual overrides. + mpa_overrides = pd.read_excel(data_fil, sheet_name=f"{ssp}_{region}_mpa_manual_override") + + # Retrieve region prefix and adapt overrides + region_id = list(set([x.split("_")[0] for x in get_nodes(scenario)]))[0] + mpa_overrides["node_loc"] = region_id + "_" + mpa_overrides["node_loc"] + mpa_overrides = mpa_overrides.set_index(["node_loc", "technology", "parameter"]) + + # Filter out all the values which are supposed to be NaN + # These will be added at the end. + + # Create a list of years for which years need to be added. + add_yr = [y for y in years if y not in mpa_overrides.columns] + + # Add missing years to dataframe with nan values + for y in add_yr: + mpa_overrides[y] = np.nan + + # A Check is made to see if the number of consecutive years missing, exceeds + # the number of years allowed for interpolation. The number of years is not adjusted + # automatically, as the results needed to be checked. + for k, g in groupby(enumerate(add_yr), lambda ix: ix[0] - ix[1]): + chk = len(list(map(itemgetter(1), g))) + if chk > intpol_lim: + raise ValueError( + f"The number of consecutive years being added, {chk}," + " exceeds the number of years which can be interpolated," + f" {intpol_lim}" + ) + + # Interpolate and readjust dataframe + mpa_overrides = ( + mpa_overrides[sorted(mpa_overrides.columns)] + .interpolate(method="index", limit=intpol_lim, axis=1) + .stack() + .reset_index() + .rename(columns={"level_3": "year_act", 0: "value"}) + .set_index(index) + ) + + # Merge 'overrides' into dataframe + final_results = mpa_overrides.combine_first(df).reset_index() + + scenario.check_out() + + growth_activity_lo = final_results[ + final_results["parameter"] == "growth_activity_lo" + ].drop("parameter", axis=1) + growth_activity_lo["time"] = "year" + growth_activity_lo["unit"] = "%" + growth_activity_lo["mode"] = "M1" + growth_activity_lo.value = round(growth_activity_lo.value, 3) + + scenario.add_par("growth_activity_lo", growth_activity_lo) + + growth_activity_up = final_results[ + final_results["parameter"] == "growth_activity_up" + ].drop("parameter", axis=1) + growth_activity_up["time"] = "year" + growth_activity_up["unit"] = "%" + growth_activity_up["mode"] = "M1" + growth_activity_up.value = round(growth_activity_up.value, 3) + + scenario.add_par("growth_activity_up", growth_activity_up) + + initial_activity_lo = final_results[ + final_results["parameter"] == "initial_activity_lo" + ].drop("parameter", axis=1) + initial_activity_lo["time"] = "year" + initial_activity_lo["unit"] = "GWa" + initial_activity_lo["mode"] = "M1" + initial_activity_lo.value = round(initial_activity_lo.value, 3) + + scenario.add_par("initial_activity_lo", initial_activity_lo) + + initial_activity_up = final_results[ + final_results["parameter"] == "initial_activity_up" + ].drop("parameter", axis=1) + initial_activity_up["time"] = "year" + initial_activity_up["unit"] = "GWa" + initial_activity_up["mode"] = "M1" + initial_activity_up.value = round(initial_activity_up.value, 3) + + scenario.add_par("initial_activity_up", initial_activity_up) + + scenario.commit("updated end-use mpas") diff --git a/message_ix_models/util/compat/message_data/calibrate_UE_share_constraints.py b/message_ix_models/util/compat/message_data/calibrate_UE_share_constraints.py new file mode 100644 index 0000000000..3e311ab982 --- /dev/null +++ b/message_ix_models/util/compat/message_data/calibrate_UE_share_constraints.py @@ -0,0 +1,230 @@ +import numpy as np +import pandas as pd + +from .get_historical_years import main as get_historical_years +from .get_optimization_years import main as get_optimization_years + +from .utilities import intpol + +relation_set = { + "UE_res_comm": [["sp", "th"], "useful_res_comm"], + "UE_feedstock": [["feedstock"], "useful"], + "UE_industry": [["sp", "th"], "useful_industry"], + "UE_transport": [["transport"], "useful"], +} + + +def _check_bound(x): + if np.isnan(x.value) or np.isnan(x.share): + return True + # Check to see if the bound implies lower bound + if x.bound < 0: + if x.share < abs(x.bound): + return True + else: + return False + elif x.bound > 0: + if x.share > abs(x.bound): + return False + else: + return True + + +def _add_data(scenario, row, period_intpol, relation_year, verbose): + """Creates final dataframe with new data and adds to scenario. + For minimum shares, the new shares is replaced over the + entire timeperiod. + For all other shares, the new shares converge to the original + values, defined by "period_intpol" + + Parameters + ---------- + scenario : :class:`message_ix.Scenario` + scenario to which changes should be applies + row : dataframe with single row + row based on which the new values in the modela re derived + period_intpol : int + the number of periods over which new shares should converge + with original shares + relation_year : int + the year for which the share constraint should be + adjusted. + verbose : boolean (default=False) + option whether to primnt onscreen messages. + """ + # Retrieve current bounds + tmp = scenario.par( + "relation_activity", + { + "node_loc": [row.node_loc], + "relation": [row.relation], + "technology": [row.technology], + }, + ) + # Filter bounds so that only those as of the 'relation_year' + # are changed. + tmp = tmp[tmp.year_act >= relation_year] + tmp_print = tmp.copy() + + share = round(row.share, 4) + if tmp.value.unique()[0] < 0: + share *= -1 + + # Values added over entire time horizon + if "Minimum" in row.relation: + tmp.value = tmp.value.replace(row.bound, share) + # Values converge to original shares + else: + years = tmp.year_act.to_list()[: period_intpol + 1] + yr_prev = years[0] + yr_next = years[-1] + v_prev = share + v_next = float(tmp.loc[tmp.year_act == yr_next, "value"]) + for y in years: + if y == yr_prev: + tmp.loc[tmp.year_act == y, "value"] = share + elif y == yr_next: + continue + else: + value = round(intpol(v_prev, v_next, yr_prev, yr_next, y), 4) + tmp.loc[tmp.year_act == y, "value"] = value + if verbose: + tmp_print = tmp_print.rename(columns={"value": "previous"}) + tmp_print["new"] = tmp.value + print(tmp_print) + scenario.add_par("relation_activity", tmp) + + +def main( + scenario, historical_year=None, relation_year=None, period_intpol=4, verbose=False +): + """Checks UE shares constraints against historical data. + + The function checks that the share constraints as of the first + model year and therefore of relevance to the optimization matches + the last historical timestep. This should avoid any sudden in- or + decreases in activity in the near term due to ill calibrated + share constraints. The number of time-periods over which the shares + calculated for the last historical time-period converge with the + original values can be defined. + + Parameters + ---------- + scenario : :class:`message_ix.Scenario` + scenario to which changes should be applies + historical_year : int + the last historical time period + relation_year : int + the year for which the share constraint should be + adjusted. + period_intpol : int (default=4) + the number of time periods (not years) over which deviations + converge + verbose : boolean (default=False) + option whether to primnt onscreen messages. + """ + + # Assigns the historical and relation_year if not defined + if not historical_year: + historical_year = get_historical_years(scenario)[-1] + if not relation_year: + relation_year = get_optimization_years(scenario)[0] + + exceedings = [] + + # Iterate over four UE main sectors (excl. biomass_nc) + for r in relation_set: + relations = [ + x for x in scenario.set("relation").tolist() if any(s in x for s in [r]) + ] + relations_sets = relation_set[r][0] + relation_technology = relation_set[r][1] + + # Iterate over UE sub-sectors e.g. thermal/specific + for sets in relations_sets: + # Creates name of 'parent' relations + parent_rel = [r for r in relations if r.split("_")[-1] == sets] + + # Retrieves the name of all technologies which write into + # the parent relation. + rel_tec = [ + t + for t in scenario.par("relation_activity", {"relation": parent_rel}) + .technology.unique() + .tolist() + if t.find(relation_technology) < 0 + ] + + # Retrieve historical activity of all relevant technologies + hist_activity = ( + scenario.par( + "historical_activity", + {"technology": rel_tec, "year_act": [historical_year]}, + ) + .set_index(["node_loc", "technology", "year_act", "mode"]) + .drop(["time", "unit"], axis=1) + ) + hist_activity = hist_activity[hist_activity.value != 0] + + # Retrieves the all share constraints not 100% + # e.g. share of solar or fuelcells in res-comm specific + bound = scenario.par( + "relation_activity", + { + "technology": ["{}_{}".format(relation_technology, sets)], + "year_rel": [relation_year], + }, + ).drop(["node_rel", "year_rel", "technology", "mode", "unit"], axis=1) + bound = bound[bound.value != -1] + + # Iterates over each of the relevant shares constraints + # to calculate whether the current share constraint is binding + for rel in bound.relation.unique().tolist(): + # Retrieves a list of all technologies to + # which make up the share + tec = [ + t + for t in scenario.par("relation_activity", {"relation": [rel]}) + .technology.unique() + .tolist() + if t != "{}_{}".format(relation_technology, sets) + ] + + tmp = hist_activity.reset_index() + tmp = tmp[tmp["technology"].isin(tec)] + if not tmp.empty: + tmp = tmp.groupby(["node_loc"]).sum().drop(["year_act"], axis=1) + # Calculate the total activity + tmp["total"] = ( + hist_activity.reset_index().groupby(["node_loc"]).sum().value + ) + + # Calcaulte the activity of the technologies to which + # the share constraint applies + tmp["share"] = tmp["value"] / tmp["total"] + + # Add the yearly bound + tmp["bound"] = ( + bound[bound["relation"] == rel].set_index(["node_loc"]).value + ) + + # Check whether the bound is binding + tmp["bound_within_limit"] = tmp.apply(_check_bound, axis=1) + + exceed = tmp[tmp["bound_within_limit"] == False].reset_index() + if not exceed.empty: + exceed = exceed[["node_loc", "bound", "share"]] + exceed["relation"] = rel + exceed["technology"] = "{}_{}".format(relation_technology, sets) + exceedings.append(exceed) + if len(exceedings) != 0: + exceedings = pd.concat(exceedings, sort=True).reset_index().drop("index", axis=1) + if verbose: + print(exceedings) + + scenario.check_out() + exceedings.apply( + lambda row: _add_data(scenario, row, period_intpol, relation_year, verbose), + axis=1, + ) + scenario.commit("Updated EU share constraints") diff --git a/message_ix_models/report/legacy/get_historical_years.py b/message_ix_models/util/compat/message_data/get_historical_years.py similarity index 100% rename from message_ix_models/report/legacy/get_historical_years.py rename to message_ix_models/util/compat/message_data/get_historical_years.py diff --git a/message_ix_models/report/legacy/get_nodes.py b/message_ix_models/util/compat/message_data/get_nodes.py similarity index 100% rename from message_ix_models/report/legacy/get_nodes.py rename to message_ix_models/util/compat/message_data/get_nodes.py diff --git a/message_ix_models/report/legacy/get_optimization_years.py b/message_ix_models/util/compat/message_data/get_optimization_years.py similarity index 100% rename from message_ix_models/report/legacy/get_optimization_years.py rename to message_ix_models/util/compat/message_data/get_optimization_years.py diff --git a/message_ix_models/util/compat/message_data/manual_updates_ENGAGE_SSP2_v417_to_v418.py b/message_ix_models/util/compat/message_data/manual_updates_ENGAGE_SSP2_v417_to_v418.py new file mode 100644 index 0000000000..81a4ab2438 --- /dev/null +++ b/message_ix_models/util/compat/message_data/manual_updates_ENGAGE_SSP2_v417_to_v418.py @@ -0,0 +1,912 @@ +import numpy as np +import pandas as pd +from message_ix_models.util import private_data_path + +from message_data.tools.utilities import ( + calibrate_UE_gr_to_demand, + calibrate_vre, + change_technology_lifetime, + check_scenario_fix_and_inv_cost, + get_optimization_years, + update_fix_and_inv_cost, + update_h2_blending, +) + + +def _apply_npi_updates(scen): + """Apply changes from ENGAGE 4.1.7 process applied. + + An error which was found during the ENGAGE Task3.1 process, and was applied + only to post NPi scenarios for technical-purposes, is now applied to the + baseline scenario. + + A change is required to fix issues related to `coal_ppl_u` and `coal_extr`. + For `coal_ppl_u` both the `inv_cost` for vintages 2020-onwards, are adjusted to + match the maximum vintage-year the `input`. + + A second change is made to `coal_extr` in `R12_SAS`. The dynamic lower growth + constraint is changed from -.05 to -.15, therefore allowing a faster phase-out + of coal-use. + + A third change is applied to correct the `relation_activity` entry of "R12_NAM" + into the relation used to limit the export of oil from CPA. + + Parameters + ---------- + scen : message_ix.Scenario + Scenario to which changes should be applied. + """ + with scen.transact("Changes to coal_ppl_u and coal_extr applied"): + # Apply changes to `coal_ppl_u` + df_inp = scen.par("input", filters={"technology": "coal_ppl_u"}) + df_inp = df_inp[["node_loc", "technology", "year_vtg"]] + df_inp = df_inp[df_inp["year_vtg"] >= 1990].drop_duplicates() + + df_inv = scen.par("inv_cost", filters={"technology": "coal_ppl_u"}) + df_inv = df_inv[["node_loc", "technology", "year_vtg"]] + df_inv = df_inv[df_inv["year_vtg"] >= 1990].drop_duplicates() + + dfs = [] + for n in df_inv["node_loc"].unique(): + tmp = df_inv.loc[(df_inv["node_loc"] == n)] + tmp = tmp.loc[ + tmp["year_vtg"] > max(df_inp.loc[df_inp["node_loc"] == n, "year_vtg"]) + ] + dfs.append(tmp) + df = pd.concat(dfs) + df["value"] = 0 + df["unit"] = "-" + scen.add_par("bound_new_capacity_up", df) + + # Apply changes to `coal_extr` in SAS. + dfmpa = scen.par( + "growth_activity_lo", + filters={"node_loc": "R12_SAS", "technology": ["coal_extr"]}, + ) + dfmpa = dfmpa.query("year_act > 2020") + dfmpa.value = -0.15 + scen.add_par("growth_activity_lo", dfmpa) + + # Correct oil export constraint + df_oil_exp = scen.par( + "relation_activity", + filters={"relation": "lim_exp_cpa_oil", "node_loc": "R12_NAM"}, + ) + df_oil_exp.value = -1 + scen.add_par("relation_activity", df_oil_exp) + + +def _correct_balance_td_efficiencies(scen): + """Correct efficiencies used for calibration pruposes. + + For the so-called `balance` technologies, which connect the primary + and the secondary level commodities, the efficiencies which have been + used for calibration purposes are removed. + The reason for this is because the bottom-up and top-down CO2 emissions + accounting-gap is widened by this, as there are no emissions associated + with these technologies. In MESSAGEV there were comments indicating that + the efficiencies, especially for `coal_bal` could be associated with + "Conv.RTS", possbily Rotary Triboelectrostatic Separator; in other words + a process applied for "Dry Cleaning of Pulverized Coal", but the fact + that the numbers arent documented and no emissions are associated with + this have resulted in the decision by OFR and VK to remove the losses. + For select transmission and distribution technologies, biomass and coal + related, the same logic is applied. + + Parameters + ---------- + scen : message_ix.Scenario + Scenario to which changes should be applied. + """ + + def _update_efficiency(tecs, value): + """Function to update efficiency. + + Retrieves the `input` values for a given technology-list and + updates the values not eequal to the specified `value` to + the `value`. + + Parameters + ---------- + tecs : lst + List of technologies for `input`-values should be updated. + value : number + Value to which the `input`-values are to be updated. + """ + # Retrieve input for technologies and filter-out non-value values. + df = scen.par("input", filters={"technology": tecs}) + df = df.loc[df.value != value] + + if df.empty: + return + + # Reset values + df["value"] = value + + with scen.transact("Update technology efficiencies"): + print(f"Updating efficiencies for technologies: {df.technology.unique()}") + scen.add_par("input", df) + + # Retrieve list of technologies which are used to link primary and secondary energy + balance_tecs = [t for t in scen.set("technology") if t[-4:] == "_bal"] + _update_efficiency(balance_tecs, value=1) + + # Define "transmission and distribution" technologies for which values are to be + # updated. + td_tecs = ["biomass_t_d", "coal_t_d"] + _update_efficiency(td_tecs, value=1) + + +def _correct_hp_gas_i_CO2(scen): + """Correct relation activity for hp_gas_i. + + The relation used for accounting CO2-emissions from the residential and commercial + sector in the reporting is `CO2_r_c`. `hp_gas_i` should though be accounted for + under the industry emissions, hence `CO2_ind`. + + Parameters + ---------- + scen : message_ix.Scenario + Scenario to which changes should be applied. + """ + with scen.transact("Update hp_gas_i CO2 relation_activity"): + df = scen.par( + "relation_activity", + filters={"technology": "hp_gas_i", "relation": "CO2_r_c"}, + ) + scen.remove_par("relation_activity", df) + df.relation = "CO2_ind" + scen.add_par("relation_activity", df) + + +def _update_vre_dyncap(scen): + """Rerun dynamic capacity constraint generation for VREs. + + Corrections were made to the workflow for calibrating renewables, + specifically to the calibration of the initial capacity limit where some + regions were omitted (AFR, wind_ppl). The setting of the dynamic ramp-up of + renewables is therefore re-run. + + Parameters + ---------- + scen : message_ix.Scenario + Scenario to which changes should be applied. + """ + calibrate_vre( + scen, + private_data_path(), + upd_init_cap_only=True, + ) + + +def _update_incorrect_lifetimes(scen): + """Update lifetimes of technologies. + + Specifically for the regions `NAM`, `PAO` and `WEU`, the vintage + 2040 for `coal_ppl` had incorrect prametrization, allowing the 2040 + vintage to be built at no cost and with no in- or output, only contributing + via the `relation_activity`. The activity therefore could be scaled up to + help allow for more heat to be generated from the `po_turbine` without actually + generating electricity. + The correction process for this is automated via the search criteria below. + + Parameters + ---------- + scen : message_ix.Scenario + Scenario to which changes should be applied. + """ + ip = scen.par("input") + tl = scen.par("technical_lifetime") + dp = scen.par("duration_period").set_index("year")["value"] + + def _expected_lifetime(ip, dp): + """Calculate expected lifetimes. + + The lifetime of each technology, for every region and vintage is derived + from the input. + + Parameters + ---------- + ip : :class:`pandas.DataFrame` + Parameter `input` data from scenario. + dp : :class:`pandas.DataFrame` + Parameter `duration_period` data from scenario. + """ + idx = ["node_loc", "technology", "mode", "commodity", "level"] + max_life = ( + ip[idx + ["year_vtg", "year_act"]] + .drop_duplicates() + .groupby(["year_vtg"] + idx) + .max() + .reset_index() + ) + max_life["value_check"] = ( + max_life["year_act"] + - max_life["year_vtg"] + + dp.loc[max_life["year_vtg"].values].values + ) + max_life = max_life.drop("year_act", axis="columns") + return max_life + + def _check_lifetimes(tl, exp): + """ + tl : :class:`pandas.DataFrame` + Parameter `technical_lifetime` data from scenario. + exp : :class:`pandas.DataFrame` + Calculated expected lifetimes based on the `input`. + """ + check = exp.merge(tl, on=["node_loc", "technology", "year_vtg"]) + check = check[check.value_check < check.value] + check = check[(check.year_vtg + check.value) <= 2110] + return check + + # Retrieve and check lifetimes of technologies + exp = _expected_lifetime(ip, tl, dp) + check = _check_lifetimes(tl, exp) + + # Apply corrections of the lifetimes + for i in check.index: + row = check.loc[i] + tec = row.technology + y = row.year_vtg + tl = row.value + x = row.value_check + n = row.node_loc + print( + f"Updating technical lifetime for {tec} in {n} from {x} to {tl}", + " years for vintage {y}", + ) + change_technology_lifetime( + scen, + tec=tec, + year_vtg_start=y, + year_vtg_end=y, + lifetime=tl, + nodes=n, + remove_rest=False, + par_exclude=[ + "historical_activity", + "historical_new_capacity", + "ref_activity", + "ref_new_capacity", + ], + ) + + +def _clean_soft_constraints(scen): + """Remove obsolete soft constraints. + + It is ensured that there are no soft-constraints or related + parameters for any given node/technology/year_act where there + is not either a growth_ or initial_activity_xx parameter. + + Parameters + ---------- + scen : message_ix.Scenario + Scenario to which changes should be applied. + """ + + idx = ["node_loc", "technology", "year_act"] + + with scen.transact("Remove obsolete soft constraint"): + for x in ["lo", "up"]: + # Retrieve all upper/lower dynamic growth constraints + # and combine dataframes. + df_gr = ( + scen.par(f"growth_activity_{x}") + .drop(["time", "unit"], axis=1) + .assign(value="y") + ) + df_i = ( + scen.par(f"initial_activity_{x}") + .drop(["time", "unit"], axis=1) + .assign(value="y") + ) + df_dyn = df_gr.merge(df_i, how="outer").drop("value", axis=1) + + # Retrieve soft constraints // abs-cost // rel-cost + df_sft = scen.par(f"soft_activity_{x}") + df_sftabs = scen.par(f"abs_cost_activity_soft_{x}") + df_sftrel = scen.par(f"level_cost_activity_soft_{x}") + + # Merge dataframes and select those which are only in "soft_constraints" + # and remove them + df_sft_only = df_sft.merge( + df_dyn, how="outer", on=idx, indicator=True + ).query('_merge == "left_only"') + df_sftabs_only = df_sftabs.merge( + df_dyn, how="outer", on=idx, indicator=True + ).query('_merge == "left_only"') + df_sftrel_only = df_sftrel.merge( + df_dyn, how="outer", on=idx, indicator=True + ).query('_merge == "left_only"') + + scen.remove_par(f"soft_activity_{x}", df_sft_only) + scen.remove_par(f"abs_cost_activity_soft_{x}", df_sftabs_only) + scen.remove_par(f"level_cost_activity_soft_{x}", df_sftrel_only) + + +def _clean_ren_dyn_act_up(scen): + """Remove dynamic constraints act up for renewables. + + The renewable technologies have capacity related dynamic constraints + as we all matching soft constraints. These are calibrated based on + historical trends. The upscaling constraints on activity are + therefore removed so that these are not more binding than the + new_capacity_constraints. + The lower dynamic activitry constraints are left in place as there + are no lower dynamic capacity constraints. + + Parameters + ---------- + scen : message_ix.Scenario + Scenario to which changes should be applied. + """ + + tecs = [ + "solar_pv_ppl", + "wind_ppl", + "wind_ppf", + "csp_sm1_ppl", + "csp_sm3_ppl", + ] + + with scen.transact("Remove renewable dynamic upper constraints"): + for par in ["growth_activity_up", "initial_activity_up"]: + df = scen.par(par, filters={"technology": tecs}) + scen.remove_par(par, df) + + +def _clean_bound_activity(scen): + """Remove obsolete bound_activity_lo/up. + + Many of the lower and upper activity bounds are the result of + importing "timeseries" values from MESSAGEV, which were actually + parametrized using "cg" (constant-growth). + Previously, "cg" was used in combination with a calibrated value + in the base year, but because the calibration has changed most + of the `bound_activity_lo` and `bound_activity_up` parameters + are now obsolete. + + There are condtions applied while filtering out the parameters + to be deleted: + 1. All bounds must be in the model time-horizon (2020-onwards) + 2. Mode != "P" (the mode "P" has been used to identify technologies, + which were configured as so-called "variables" in MESSAGEV. Their + bounds have been used to reflect exogenously derived trajectories.) + 3. Technology name is the specified list; further explanations are + provided in the list. + 4. The timeseries do not contain "zero" values. This ensures that no + phase-out or SSP specific restrictions are deleted. + + Parameters + ---------- + scen : message_ix.Scenario + Scenario to which changes should be applied. + """ + + # Specify technology names or strings found in names to apply + # condition 3. + tecs_keep = [ + # The following technologies are mitigation technologies + # for various species. + # Activity bounds represent the technical applicability, + # hence these should not be removed. + "ammonia_secloop", + "leak_repair", + "leak_repairsf6", + "recycling_gas1", + "refrigerant_recovery", + # Other technologies with fixed trajectories + # Flaring_CO2 has a fixed trajectory. + "flaring_CO2", + # bda_up in some regions (increasing); CPA has phaseout constraint + "coal_ppl_u", + # Various strings for technology groups for which bounds should be removed. + "TCE", + "extr", + "csp", + "solar", + "wind", + ] + + # Specifiy hard-exemptions + bda_up_exmptions = { + "foil_exp": "SAS", + "loil_exp": "SAS", + } + + model_years = get_optimization_years(scen) + with scen.transact("Remove activity bounds"): + for bound in ["bound_activity_lo", "bound_activity_up"]: + df = scen.par(bound) + # Apply conditions 1. and 2. + loc_idx = (df.year_act.isin(model_years)) & (df["mode"] != "P") + df = df.loc[loc_idx] + + # Apply condition 3. + tecs_keep_func = lambda t: any(k in t for k in tecs_keep) + variables_keep = [ + t for t in df.technology.unique().tolist() if not tecs_keep_func(t) + ] + loc_idx = df.technology.isin(variables_keep) + df = df.loc[loc_idx] + + # Apply condition 4. + df = df.pivot_table( + index=["node_loc", "technology", "mode", "time", "unit"], + columns="year_act", + values="value", + ) + df = df.loc[~(df[df.columns] == 0).any(axis=1)] + df = ( + df.reset_index() + .melt(id_vars=["node_loc", "technology", "mode", "time", "unit"]) + .dropna() + ) + scen.remove_par(bound, df) + + # Special expemptions are made manually + if bound == "bound_activity_up": + for i in bda_up_exmptions.items(): + df = scen.par( + bound, + filters={ + "technology": i[0], + "node_loc": i[1], + "year_act": model_years, + }, + ) + + +def update_meth_coal_ccs_weu(scen): + """Resolve WEU `meth_coal_ccs` + + Set value from -.85 to .1 which is used across most other regions. + Initial_new_capacity_up is fine + + Parameters + ---------- + scen : message_ix.Scenario + Scenario to which changes should be applied. + """ + df = scen.par( + "growth_new_capacity_up", + filters={"node_loc": "R12_WEU", "technology": "meth_coal_ccs"}, + ) + df.value = 0.1 + with scen.transact("update incorrect growth rate of meth_coal_ccs in WEU"): + scen.add_par("growth_new_capacity_up", df) + + +def update_gas_cc_lam(scen): + """Resolve LAM `gas_cc` and `gas_cc_ccs` + + Set the value from -.901 to -0.05 which is used across most other regions. + + Parameters + ---------- + scen : message_ix.Scenario + Scenario to which changes should be applied. + """ + df = scen.par( + "growth_activity_lo", + filters={"node_loc": "R12_LAM", "technology": ["gas_cc", "gas_cc_ccs"]}, + ) + df.value = -0.05 + with scen.transact("update incorrect growth rate of gas_cc and gas_cc_ccs in LAM"): + scen.add_par("growth_activity_lo", df) + + +def _correct_coal_ppl_u_efficiencies(scen): + """Correct efficiencies of `coal_ppl_u`. + + The efficiency of `coal_ppl_u` was distorted during the process of extending the + technical lifetime. The Values were extended by applying an "extrapolation" method + as opposed to forward- or back-filling, resulting in very high efficiencies being + reached. + The values below are taken directly from the original "SSP2" MESSAGEV version and + are used to update the scenario values. + + Parameters + ---------- + scen : message_ix.Scenario + Scenario to which changes should be applied. + """ + + # Define years/index matching the values of `efficiency_update` + efficiency_years = [1990, 1995, 2000, 2005, 2010, 2020, 2030, 2040, 2050, 2060] + + # Define the efficiencies to be applied to `coal_ppl_u` + # The values below are taken from the `SSP2` input files of MESSAGEV + # (see `P:\ene.model\SSP_V3`) + efficiency_update = { + "R12_AFR": [0.34, 0.34, 0.34, 0.37, 0.38, 0.38, np.nan, np.nan, np.nan, np.nan], + "R12_RCPA": [0.33, 0.33, 0.326, 0.321, 0.34, 0.36, 0.38, np.nan, np.nan, np.nan], + "R12_CHN": [0.33, 0.33, 0.326, 0.321, 0.34, 0.36, 0.38, np.nan, np.nan, np.nan], + "R12_EEU": [0.315, 0.33, 0.35, 0.35, 0.36, 0.37, 0.38, np.nan, np.nan, np.nan], + "R12_FSU": [0.327, 0.34, 0.31, 0.3, 0.31, 0.35, 0.38, np.nan, np.nan, np.nan], + "R12_LAM": [0.287, 0.3, 0.32, 0.34, 0.35, 0.37, 0.38, np.nan, np.nan, np.nan], + "R12_MEA": [ + 0.353, + 0.37, + 0.396, + 0.397, + np.nan, + np.nan, + np.nan, + np.nan, + np.nan, + np.nan, + ], + "R12_NAM": [ + 0.37, + 0.375, + 0.368, + 0.369, + 0.38, + np.nan, + np.nan, + np.nan, + np.nan, + np.nan, + ], + "R12_PAO": [ + 0.383, + 0.38, + 0.371, + 0.388, + 0.38, + np.nan, + np.nan, + np.nan, + np.nan, + np.nan, + ], + "R12_PAS": [ + 0.366, + 0.36, + 0.352, + 0.354, + 0.36, + 0.38, + np.nan, + np.nan, + np.nan, + np.nan, + ], + "R12_SAS": [0.288, 0.31, 0.2723, 0.2615, 0.265, 0.29, 0.31, 0.33, 0.35, 0.38], + "R12_WEU": [ + 0.368, + 0.375, + 0.386, + 0.403, + 0.38, + np.nan, + np.nan, + np.nan, + np.nan, + np.nan, + ], + } + + # Create pandas-dataframe + df_effupd = pd.DataFrame.from_dict( + efficiency_update, orient="index", columns=efficiency_years + ) + + # Retrieve all activity years for "coal_ppl_u" + yr_act = [ + y + for y in scen.par("input", {"technology": "coal_ppl_u"}).year_act.unique() + if y not in df_effupd.columns + ] + + # Add missing activity years + for y in yr_act: + df_effupd[y] = np.nan + + # Sort columns + df_effupd = df_effupd[sorted(df_effupd.columns)] + + # Forward and back-fill + df_effupd = df_effupd.ffill(axis=1).bfill(axis=1) + + # Derive output normalized efficincies + df_effupd = 1 / df_effupd + + # Create long-format + df_effupd = ( + df_effupd.melt(ignore_index=False, var_name="year_act") + .reset_index() + .rename(columns={"index": "node_loc"}) + .set_index(["node_loc", "year_act"]) + ) + + # Retrieve input parameter for coal_ppl_u and update values + df = scen.par("input", {"technology": "coal_ppl_u"}).set_index( + ["node_loc", "year_act"] + ) + df["value"] = df_effupd["value"] + + with scen.transact("Update coal_ppl_u efficiencies"): + scen.add_par("input", df.reset_index()) + + +def _correct_td_co2cc_emissions(scen): + """Correct CO2cc and CO2_trade emission factors. + + This script adds emission-factors for the bottom CO2-accounting. The fix is + primarily applied for select technologies which have both an input and output. + This means that the emissions accounted for are those representing the "losses" + or the own energy requirements of the technology e.g. transmission-and-distribution + technologies or trade technologies. + The emission-factors are added using the relations. No emission are added for + technologies where no losses have been assumed. In some cases e.g. gas-export + technologies, the emission factors are added as these seem to be missing from + earlier formulations. + Some exceptions are made for; no emissions are tracked for these: + - `LH2_trd`, `lh2_t_d`, `h2_t_d` + - `elec_trd`, `elec_t_d` + - `eth_trd`, `eth_t_d` (the latter is removed) + - `heat_t_d` + - `biomass_t_d` + + + Parameters + ---------- + scen : message_ix.Scenario + Scenario to which changes should be applied. + """ + + # Define dataframe index + idx = ["node_loc", "technology", "year_act"] + # Define dataframe index to be dropped from parameter `input` + idx_input_drop = [ + "year_vtg", + "mode", + "node_origin", + "commodity", + "level", + "time", + "time_origin", + "unit", + ] + + # Provide input data to update-routine. + # For each `commodity`, specify a list of `techologies` and the emission-factor + # `factor` in "tC/kWyr" + # The list of technologies contains: + # 1: ``: `technology` in model for which correction is made. + # 2: ``: `relation` in model which accounts for emsissions of the + # `technology`. + # 3: None: will simply update `relation_activity` + # 3: `add` (optional): option whether to add a `relation_activity` entry. + # 3: `remove` (optional): option whether the `relation_activity` should be removed. + # 3: `non-throughput` (optional): option whether the `techology` has an `output` + # not equal to the input e.g. is a power-plant. Then the `relation-activity` + # coefficient will be calcalated differently. + + df = { + # Natural gas + "gas": { + "technologies": [ + ["gas_t_d", "CO2_cc"], + ["gas_t_d_ch4", "CO2_cc"], + ["LNG_trd", "CO2_trade"], + ["gas_exp_afr", "CO2_trade", "add"], + ["gas_exp_cpa", "CO2_trade", "add"], + ["gas_exp_eeu", "CO2_trade", "add"], + ["gas_exp_nam", "CO2_trade", "add"], + ["gas_exp_pao", "CO2_trade", "add"], + ["gas_exp_sas", "CO2_trade", "add"], + ["gas_exp_weu", "CO2_trade", "add"], + ["gas_ct", "CO2_cc", "non-throughput"], + ], + "factor": 0.482, + }, + # Fuel-oil + "foil": { + "technologies": [ + ["foil_t_d", "CO2_cc"], + ["foil_trd", "CO2_trade"], + ], + "factor": 0.665, + }, + # raw Oil + "oil": { + "technologies": [ + ["oil_trd", "CO2_trade"], + ], + "factor": 0.631, + }, + # Light-oil + "loil": { + "technologies": [ + ["loil_t_d", "CO2_cc"], + ["loil_trd", "CO2_trade"], + ], + "factor": 0.631, + }, + # Hard-coal + "coal": { + "technologies": [ + ["coal_t_d", "CO2_cc"], # has eff. of 1 + [ + "coal_t_d-in-06p", + "CO2_cc", + ], # Removed in newer versions; has eff. of 1 + ["coal_t_d-in-SO2", "CO2_cc"], # Removed in newer versions + [ + "coal_t_d-rc-06p", + "CO2_cc", + ], # Removed in newer versions; has eff. of 1 + ["coal_t_d-rc-SO2", "CO2_cc"], # Removed in newer versions + ["coal_trd", "CO2_trade", "add"], + ["coal_ppl_u", "CO2_cc", "non-throughput"], + ], + "factor": 0.814, + }, + # Methanol + "methanol": { + "technologies": [ + ["meth_t_d", "CO2_cc"], + ["meth_trd", "CO2_trade"], + ], + "factor": 0.549, + }, + # Ethanol + "ethanol": { + "technologies": [ + ["eth_t_d", "CO2_cc", "remove"], + ], + "factor": 0.549, + }, + } + with scen.transact("Revise bottom-up CO2 emission accounting"): + for commodity in df: + emission = df[commodity]["factor"] + for item in df[commodity]["technologies"]: + technology = item[0] + relation = item[1] + + # Check for additional commands + task = None if len(item) < 3 else item[2] + + if task != "remove": + # Retrieve input + inp = scen.par("input", {"technology": technology}) + inp = inp.drop( + idx_input_drop, + axis=1, + ) + + # Filter out efficiencies of 100% + inp = inp.loc[inp.value != 1] + + if task != "add" and inp.empty: + print(f"Skipping technology: {technology}; efficiency is 100%.") + continue + + # Rounding is needed in oreder to ensure there are no duplicates + # based on numerical precision differences. + inp["value"] = round(inp["value"], 5) + inp = inp.drop_duplicates() + # Make exception for "non-throughput" + if task == "non-throughput": + inp["CO2_should"] = inp["value"] * emission + else: + inp["CO2_should"] = (inp["value"] - 1) * emission + inp = inp.set_index(idx) + + # Add parameter + if task == "add": + print( + f"Adding relation_activity for technology: {technology},", + f" relation: {relation}.", + ) + inp = inp.reset_index() + inp["value"] = inp["CO2_should"] + inp = inp.drop("CO2_should", axis=1) + inp["relation"] = relation + inp["year_rel"] = inp["year_act"] + inp["mode"] = "M1" + inp["unit"] = "Mt C/GWyr/yr" + # Ensure that node_rel is "RXX_GLB" for "CO2_trade" relaiton entries + if relation == "CO2_trade": + reg = [n for n in scen.set("node") if "GLB" in n][0] + inp["node_rel"] = reg + else: + inp["node_rel"] = inp["node_loc"] + scen.add_par("relation_activity", inp) + continue + + # Retrieve relation-activity + rel = scen.par( + "relation_activity", + {"technology": technology, "relation": relation}, + ) + + # Remove relation-activity and continue + if task == "remove": + print( + f"Removing relation_activity for technology: {technology},", + f" relation {relation}.", + ) + scen.remove_par("relation_activity", rel) + continue + + rel = rel.set_index(idx) + rel["CO2_should"] = inp["CO2_should"] + + # Filter out differences + rel = rel.loc[rel["value"] != rel["CO2_should"]] + + print( + f"Updating relation_activity for technology: {technology},", + f" relation {relation}.", + ) + + rel["value"] = rel["CO2_should"] + rel = rel.drop(["CO2_should"], axis=1).reset_index() + # In some cases where the eff. is 1 (dropped earlier) + # `relation_activity` entries are set to 0. + rel = rel.fillna(0) + scen.add_par("relation_activity", rel) + + +def main(context, scen): + """Apply updates to scenario to correct issues of ENGAGE v4.1.7""" + + # Step1. Add Changes that were made to 4.1.7 NPi variants. + _apply_npi_updates(scen) + + # Step2. Re-run UE calibration due to interpolation error + # when adding additional time-steps to source-data. + # Newly added timesteps had wrong values for exceptions + calibrate_UE_gr_to_demand(scen, data_path=private_data_path(), ssp=context.ssp) + + # Step3. Correct CO2 entry for hp_gas_i. + # hp_gas_i has and entry into CO2_r_c, this should be correct to CO2_ind + # The mistake was backtracked to GEA scenarios; no documentation as to why + # this is the case. + _correct_hp_gas_i_CO2(scen) + + # Step4. Update dynamic capacity constraints for "R12_AFR"->"wind_ppl" + _update_vre_dyncap(scen) + + # Step5. Correct lifetime for coal_ppl in NAM, PAO, WEU -> coal_ppl + _update_incorrect_lifetimes(scen) + + # Step6. Remove upper dynamic activity constraint. + _clean_ren_dyn_act_up(scen) + + # Step7. Remove obsolete up/lo soft-activity constraints. + _clean_soft_constraints(scen) + + # Re-Add fix and investment costs + update_fix_and_inv_cost.main(scen, private_data_path(), context.ssp) + + # Check remaining fix and inv_cost and correct these. + check_scenario_fix_and_inv_cost(scen) + + # Remove all obsolete bound_activity_up/lo + _clean_bound_activity(scen) + + # Revise h2 blending constraint + update_h2_blending.main(scen) + + # Correct meth_coal_ccs parameters in WEU + update_meth_coal_ccs_weu(scen) + + # Correct gas_cc and gas_cc_ccs parameters in LAM + update_gas_cc_lam(scen) + + # Update efficiencies for `balance` and specific `t_d` technologies. + _correct_balance_td_efficiencies(scen) + + # Update efficiencies for `coal_ppl_u` + _correct_coal_ppl_u_efficiencies(scen) + + # Update CO2cc-emissions for t_d technologies + _correct_td_co2cc_emissions(scen) diff --git a/message_ix_models/report/legacy/utilities.py b/message_ix_models/util/compat/message_data/utilities.py similarity index 100% rename from message_ix_models/report/legacy/utilities.py rename to message_ix_models/util/compat/message_data/utilities.py diff --git a/pyproject.toml b/pyproject.toml index 5c041c6ed5..314feb206b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -138,7 +138,7 @@ markers = [ ] [tool.ruff] -exclude = ["message_ix_models/report/legacy/"] +exclude = ["message_ix_models/report/legacy/", "message_ix_models/util/compat/message_data/"] [tool.ruff.lint] select = ["C9", "E", "F", "I", "W"] From d39c074ebb9ccd6055f63bc373662d03f43e53b3 Mon Sep 17 00:00:00 2001 From: Fridolin Glatter Date: Fri, 17 May 2024 08:45:39 +0200 Subject: [PATCH 764/774] Mark forgotten personal reference as FIXME --- message_ix_models/model/material/util.py | 1 + 1 file changed, 1 insertion(+) diff --git a/message_ix_models/model/material/util.py b/message_ix_models/model/material/util.py index 40f1e4a0aa..f3f5afa525 100644 --- a/message_ix_models/model/material/util.py +++ b/message_ix_models/model/material/util.py @@ -46,6 +46,7 @@ def read_config() -> Context: context[key] = load_package_data(*_parts) # Read material.yaml + # FIXME What is this personal information doing here? # context.metadata_path=Path("C:/Users/unlu/Documents/GitHub/message_data/data") # context.load_config("material", "set") From 5f8e8907413e427bd4dd09514760c381faa04f29 Mon Sep 17 00:00:00 2001 From: Fridolin Glatter Date: Fri, 17 May 2024 08:46:10 +0200 Subject: [PATCH 765/774] Format and tidy up reporting notebook --- ...orical Power Sector Stock Reporting-.ipynb | 226 +++++++++--------- 1 file changed, 118 insertions(+), 108 deletions(-) diff --git a/message_ix_models/model/material/report/Historical Power Sector Stock Reporting-.ipynb b/message_ix_models/model/material/report/Historical Power Sector Stock Reporting-.ipynb index 5f8c3a7403..9e561b0c93 100644 --- a/message_ix_models/model/material/report/Historical Power Sector Stock Reporting-.ipynb +++ b/message_ix_models/model/material/report/Historical Power Sector Stock Reporting-.ipynb @@ -8,35 +8,6 @@ "This notebook only includes stock reporting of the histroical years for power sector by using historical_new_capacity. The model periods can be integrated as well by using CAP variable." ] }, - { - "cell_type": "code", - "execution_count": 1, - "id": "4faf48c4", - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "if (typeof IPython !== 'undefined') { IPython.OutputArea.prototype._should_scroll = function(lines){ return false; }}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Import the necessary packages \n", - "\n", - "import ixmp\n", - "import message_ix\n", - "import pandas as pd\n", - "import numpy as np\n", - "from message_data.tools.utilities.get_optimization_years import main as get_optimization_years" - ] - }, { "cell_type": "code", "execution_count": 2, @@ -44,6 +15,8 @@ "metadata": {}, "outputs": [], "source": [ + "import ixmp\n", + "\n", "# Load the platform\n", "\n", "mp = ixmp.Platform()" @@ -56,9 +29,12 @@ "metadata": {}, "outputs": [], "source": [ - "# Enter the relevant model and scenario name \n", + "import message_ix\n", "\n", - "base_scen = 'NoPolicy_2206_macro'\n", + "\n", + "# Enter the relevant model and scenario name\n", + "\"\"\n", + "base_scen = '\"oPolicy_2206_macro'\"\n", "base_model = 'MESSAGEix-Materials'\n", "scen = message_ix.Scenario(mp, base_model, base_scen, cache=True)" ] @@ -70,10 +46,12 @@ "metadata": {}, "outputs": [], "source": [ - "# Retreive output material intensities and relevant technology names\n", + "import pandas as pd\n", "\n", - "output_cap_ret = scen.par('output_cap_ret')\n", - "technologies = output_cap_ret['technology'].unique()\n", + "# Retreive output material intensities and relevant technology names\n", + "\"output_cap_ret\"\n", + "output_cap_ret = scen.par(\"output_cap_ret\")\n", + "technologies = output_cap_ret[\"technology\"].unique()\n", "technologies = pd.Series(technologies)\n", "technologies = technologies.to_list()" ] @@ -85,12 +63,19 @@ "metadata": {}, "outputs": [], "source": [ - "# Retreive duration_period, historical_new_capacity, technical_lifetime and create a historical_total_capacity dataframe\n", + "# Retreive duration_period, historical_new_capacity, technical_lifetime and create a\n", + "# historical_total_capacity dataframe\n", "\n", - "duration_period = scen.par('duration_period')\n", - "historical_new_capacity = scen.par('historical_new_capacity', filters = {'technology': technologies})\n", - "technical_lifetime = scen.par('technical_lifetime', filters = {'technology': technologies})\n", - "historical_total_capacity = pd.DataFrame(columns = [\"node_loc\",\"technology\",\"year_vtg\",\"value\",\"unit\",\"year_act\"])" + "duration_period = scen.par(\"duration_period\")\n", + "historical_new_capacity = scen.par(\n", + " \"historical_new_capacity\", filters={\"technology\": technologies}\n", + ")\n", + "technical_lifetime = scen.par(\n", + " \"technical_lifetime\", filters={\"technology\": technologies}\n", + ")\n", + "historical_total_capacity = pd.DataFrame(\n", + " columns=[\"node_loc\", \"technology\", \"year_vtg\", \"value\", \"unit\", \"year_act\"]\n", + ")" ] }, { @@ -100,40 +85,55 @@ "metadata": {}, "outputs": [], "source": [ - "# Create the historical_total_capacity dataframe based on the lifetime. \n", + "import numpy as np\n", + "\n", + "# Create the historical_total_capacity dataframe based on the lifetime.\n", "# historical_total_capacity = historial_new_capacity * duration_period\n", "\n", - "for y in historical_new_capacity['year_vtg'].unique():\n", - " for n in historical_new_capacity['node_loc'].unique():\n", - " for t in historical_new_capacity['technology'].unique():\n", - " lifetime = technical_lifetime.loc[(technical_lifetime['year_vtg']==y) & (technical_lifetime['node_loc']==n) & \\\n", - " (technical_lifetime['technology']==t), 'value'].values\n", - " capacity = historical_new_capacity.loc[(historical_new_capacity['year_vtg']==y) & (historical_new_capacity['node_loc']==n) & \\\n", - " (historical_new_capacity['technology']==t), 'value'].values\n", - " \n", - " if ((lifetime.size != 0) & (capacity.size != 0)):\n", + "for y in historical_new_capacity[\"year_vtg\"].unique():\n", + " for n in historical_new_capacity[\"node_loc\"].unique():\n", + " for t in historical_new_capacity[\"technology\"].unique():\n", + " lifetime = technical_lifetime.loc[\n", + " (technical_lifetime[\"year_vtg\"] == y)\n", + " & (technical_lifetime[\"node_loc\"] == n)\n", + " & (technical_lifetime[\"technology\"] == t),\n", + " \"value\",\n", + " ].values\n", + " capacity = historical_new_capacity.loc[\n", + " (historical_new_capacity[\"year_vtg\"] == y)\n", + " & (historical_new_capacity[\"node_loc\"] == n)\n", + " & (historical_new_capacity[\"technology\"] == t),\n", + " \"value\",\n", + " ].values\n", + "\n", + " if (lifetime.size != 0) & (capacity.size != 0):\n", " lifetime = lifetime[0]\n", " lifetime = lifetime.astype(np.int32)\n", - " \n", + "\n", " capacity = capacity[0]\n", - " period = duration_period.loc[(duration_period['year']==y), 'value'].values[0]\n", + " period = duration_period.loc[\n", + " (duration_period[\"year\"] == y), \"value\"\n", + " ].values[0]\n", " period = period.astype(np.int32)\n", " val = capacity * period\n", "\n", " until = y + lifetime\n", - " df_temp = pd.DataFrame({\n", - " \"node_loc\": n,\n", - " \"technology\": t,\n", - " \"year_vtg\": y,\n", - " \"value\": val,\n", - " \"unit\": \"GW\",\n", - " \"year_act\": list(range(y,until, 5))\n", - "\n", - " })\n", + " df_temp = pd.DataFrame(\n", + " {\n", + " \"node_loc\": n,\n", + " \"technology\": t,\n", + " \"year_vtg\": y,\n", + " \"value\": val,\n", + " \"unit\": \"GW\",\n", + " \"year_act\": list(range(y, until, 5)),\n", + " }\n", + " )\n", "\n", - " historical_total_capacity = pd.concat([df_temp, historical_total_capacity], ignore_index=True)\n", + " historical_total_capacity = pd.concat(\n", + " [df_temp, historical_total_capacity], ignore_index=True\n", + " )\n", " else:\n", - " continue " + " continue" ] }, { @@ -143,10 +143,13 @@ "metadata": {}, "outputs": [], "source": [ - "# Filter the dataframe for historical years. For the model years CAP variable should be used. \n", + "# Filter the dataframe for historical years. For the model years CAP variable should be\n", + "# used.\n", "\n", "first_year = 2020\n", - "historical_total_capacity = historical_total_capacity[historical_total_capacity['year_act'] < first_year] " + "historical_total_capacity = historical_total_capacity[\n", + " historical_total_capacity[\"year_act\"] < first_year\n", + "]" ] }, { @@ -156,20 +159,20 @@ "metadata": {}, "outputs": [], "source": [ - "# Modify the dataframes and merge to calculate the stocks \n", + "# Modify the dataframes and merge to calculate the stocks\n", "\n", - "historical_total_capacity.rename(columns={'value':'capacity'},inplace = True)\n", - "historical_total_capacity = historical_total_capacity.drop(['unit'], axis = 1)\n", + "historical_total_capacity.rename(columns={\"value\": \"capacity\"}, inplace=True)\n", + "historical_total_capacity = historical_total_capacity.drop([\"unit\"], axis=1)\n", "\n", - "output_cap_ret = output_cap_ret.drop(['unit'],axis = 1)\n", - "output_cap_ret.rename(columns={'value':'material_intensity'},inplace = True)\n", + "output_cap_ret = output_cap_ret.drop([\"unit\"], axis=1)\n", + "output_cap_ret.rename(columns={\"value\": \"material_intensity\"}, inplace=True)\n", "\n", - "merged_df = pd.merge(historical_total_capacity, output_cap_ret, how = 'inner')\n", + "merged_df = pd.merge(historical_total_capacity, output_cap_ret, how=\"inner\")\n", "\n", - "# This way we can consider different material intensities for differnet vintage years. E.g. for year_vtg 2015 \n", - "# material intensities change\n", + "# This way we can consider different material intensities for differnet vintage years.\n", + "# E.g. for year_vtg 2015 material intensities change\n", "\n", - "merged_df['material_stock'] = merged_df['material_intensity'] * merged_df['capacity']" + "merged_df[\"material_stock\"] = merged_df[\"material_intensity\"] * merged_df[\"capacity\"]" ] }, { @@ -179,29 +182,22 @@ "metadata": {}, "outputs": [], "source": [ - "name = base_model + '_' + base_scen + '_detailed_output1.xlsx'\n", + "name = base_model + \"_\" + base_scen + \"_detailed_output1.xlsx\"\n", "merged_df.to_excel(name)" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "e98b4051", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\unlu\\AppData\\Local\\Temp\\ipykernel_8564\\3669579720.py:2: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n", - " merged_df_sum = merged_df.groupby(['year_act','node_loc','technology','node_dest','commodity','level'])['capacity','material_stock'].sum()\n" - ] - } - ], + "outputs": [], "source": [ - "merged_df.drop(['time_dest','material_intensity'], axis = 1, inplace = True)\n", - "merged_df_sum = merged_df.groupby(['year_act','node_loc','technology','node_dest','commodity','level'])['capacity','material_stock'].sum()\n", - "merged_df_sum.reset_index(inplace = True)" + "merged_df.drop([\"time_dest\", \"material_intensity\"], axis=1, inplace=True)\n", + "merged_df_sum = merged_df.groupby(\n", + " [\"year_act\", \"node_loc\", \"technology\", \"node_dest\", \"commodity\", \"level\"]\n", + ")[\"capacity\", \"material_stock\"].sum()\n", + "merged_df_sum.reset_index(inplace=True)" ] }, { @@ -211,7 +207,7 @@ "metadata": {}, "outputs": [], "source": [ - "name = base_model + '_' + base_scen + '_detailed_output2.xlsx'\n", + "name = base_model + \"_\" + base_scen + \"_detailed_output2.xlsx\"\n", "merged_df_sum.to_excel(name)" ] }, @@ -222,13 +218,15 @@ "metadata": {}, "outputs": [], "source": [ - "# Sum over the differnet power plants \n", + "# Sum over the differnet power plants\n", "\n", - "merged_df_sum.drop(['node_dest','level','capacity'],axis = 1, inplace = True)\n", + "merged_df_sum.drop([\"node_dest\", \"level\", \"capacity\"], axis=1, inplace=True)\n", "\n", - "merged_df_sum_material = merged_df_sum.groupby(['year_act','node_loc','commodity'])['material_stock'].sum()\n", + "merged_df_sum_material = merged_df_sum.groupby([\"year_act\", \"node_loc\", \"commodity\"])[\n", + " \"material_stock\"\n", + "].sum()\n", "merged_df_sum_material = merged_df_sum_material.to_frame()\n", - "merged_df_sum_material.reset_index(inplace = True)" + "merged_df_sum_material.reset_index(inplace=True)" ] }, { @@ -238,7 +236,7 @@ "metadata": {}, "outputs": [], "source": [ - "name = base_model + '_' + base_scen + '_detailed_output3.xlsx'\n", + "name = base_model + \"_\" + base_scen + \"_detailed_output3.xlsx\"\n", "merged_df_sum_material.to_excel(name)" ] }, @@ -251,30 +249,42 @@ "source": [ "# Prepare data to be dumped out as IAMC excel format\n", "\n", - "final_output = pd.pivot_table(merged_df_sum, values = 'material_stock', columns = ['year_act'], index = ['node_loc','commodity'])\n", - "final_output.reset_index(inplace = True)\n", - "final_output.rename(columns = {'node_loc':'Region'}, inplace = True)\n", - "final_output = final_output.replace(['aluminum','cement','steel'], ['Non-Ferrous Metlals|Aluminum', 'Non-Metallic Minerals|Cement','Steel'])\n", - "final_output = final_output.assign(Variable = lambda x: 'Material Stock|Power Sector|' + x['commodity'])\n", - "final_output.drop(['commodity'],axis = 1, inplace = True)\n", - "final_output['Model'] = base_model\n", - "final_output['Scenario'] = base_scen\n", - "final_output['Unit'] = 'Mt'\n", + "final_output = pd.pivot_table(\n", + " merged_df_sum,\n", + " values=\"material_stock\",\n", + " columns=[\"year_act\"],\n", + " index=[\"node_loc\", \"commodity\"],\n", + ")\n", + "final_output.reset_index(inplace=True)\n", + "final_output.rename(columns={\"node_loc\": \"Region\"}, inplace=True)\n", + "final_output = final_output.replace(\n", + " [\"aluminum\", \"cement\", \"steel\"],\n", + " [\"Non-Ferrous Metlals|Aluminum\", \"Non-Metallic Minerals|Cement\", \"Steel\"],\n", + ")\n", + "final_output = final_output.assign(\n", + " Variable=lambda x: \"Material Stock|Power Sector|\" + x[\"commodity\"]\n", + ")\n", + "final_output.drop([\"commodity\"], axis=1, inplace=True)\n", + "final_output[\"Model\"] = base_model\n", + "final_output[\"Scenario\"] = base_scen\n", + "final_output[\"Unit\"] = \"Mt\"\n", "year_cols = final_output.select_dtypes([np.number]).columns.to_list()\n", - "initial_columns = ['Model','Scenario','Region','Variable','Unit']\n", + "initial_columns = [\"Model\", \"Scenario\", \"Region\", \"Variable\", \"Unit\"]\n", "reorder = initial_columns + year_cols\n", "final_output = final_output[reorder]\n", - "output_name = base_model + '_' + base_scen + '_power_sector_material.xlsx'\n", - "final_output.to_excel(output_name, index = False)" + "output_name = base_model + \"_\" + base_scen + \"_power_sector_material.xlsx\"\n", + "final_output.to_excel(output_name, index=False)" ] }, { "cell_type": "code", "execution_count": null, - "id": "dc9c75d1", + "id": "a6098abd", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "mp.close_db()" + ] } ], "metadata": { From 0b49e53ed40400ffe06bffcc4db48c5df127ece5 Mon Sep 17 00:00:00 2001 From: Fridolin Glatter Date: Fri, 17 May 2024 08:46:36 +0200 Subject: [PATCH 766/774] Adapt final imports from message_data --- message_ix_models/model/material/__init__.py | 12 +++++------- 1 file changed, 5 insertions(+), 7 deletions(-) diff --git a/message_ix_models/model/material/__init__.py b/message_ix_models/model/material/__init__.py index 5d5411f96a..e82963c634 100644 --- a/message_ix_models/model/material/__init__.py +++ b/message_ix_models/model/material/__init__.py @@ -416,12 +416,11 @@ def solve_scen( scenario.set_as_default() # Report - from message_data.tools.post_processing.iamc_report_hackathon import ( + from message_ix_models.model.material.report.reporting import report + from message_ix_models.report.legacy.iamc_report_hackathon import ( report as reporting, ) - from message_ix_models.model.material.report.reporting import report - # Remove existing timeseries and add material timeseries print("Reporting material-specific variables") report(context, scenario) @@ -505,12 +504,11 @@ def add_building_ts(scenario_name, model_name): @click.pass_obj def run_reporting(context, remove_ts, profile): """Run materials, then legacy reporting.""" - from message_data.tools.post_processing.iamc_report_hackathon import ( + from message_ix_models.model.material.report.reporting import report + from message_ix_models.report.legacy.iamc_report_hackathon import ( report as reporting, ) - from message_ix_models.model.material.report.reporting import report - # Retrieve the scenario given by the --url option scenario = context.get_scenario() mp = scenario.platform @@ -583,7 +581,7 @@ def exit(): @cli.command("report-2") @click.pass_obj def run_old_reporting(context): - from message_data.tools.post_processing.iamc_report_hackathon import ( + from message_ix_models.report.legacy.iamc_report_hackathon import ( report as reporting, ) From 4150176707bb44f6f8be01efebb179362dfa5bfb Mon Sep 17 00:00:00 2001 From: Fridolin Glatter Date: Fri, 17 May 2024 09:16:29 +0200 Subject: [PATCH 767/774] Migrate more required functions from m_data --- .../util/compat/message_data/__init__.py | 8 + .../message_data/calibrate_UE_gr_to_demand.py | 6 +- .../calibrate_UE_share_constraints.py | 4 +- .../util/compat/message_data/calibrate_vre.py | 1431 +++++++++++++++++ .../change_technology_lifetime.py | 810 ++++++++++ .../check_scenario_fix_and_inv_cost.py | 245 +++ ...manual_updates_ENGAGE_SSP2_v417_to_v418.py | 15 +- .../message_data/update_fix_and_inv_cost.py | 193 +++ .../compat/message_data/update_h2_blending.py | 70 + 9 files changed, 2769 insertions(+), 13 deletions(-) create mode 100644 message_ix_models/util/compat/message_data/calibrate_vre.py create mode 100644 message_ix_models/util/compat/message_data/change_technology_lifetime.py create mode 100644 message_ix_models/util/compat/message_data/check_scenario_fix_and_inv_cost.py create mode 100644 message_ix_models/util/compat/message_data/update_fix_and_inv_cost.py create mode 100644 message_ix_models/util/compat/message_data/update_h2_blending.py diff --git a/message_ix_models/util/compat/message_data/__init__.py b/message_ix_models/util/compat/message_data/__init__.py index 332a719591..7003999fba 100644 --- a/message_ix_models/util/compat/message_data/__init__.py +++ b/message_ix_models/util/compat/message_data/__init__.py @@ -1,2 +1,10 @@ +# NB: These files originate from the message_data/materials_2023_move2 branch, commit: +# https://github.com/iiasa/message_data/commit/f1a0ca789e23466f343a4ad59023326472077937 + from .calibrate_UE_gr_to_demand import main as calibrate_UE_gr_to_demand from .calibrate_UE_share_constraints import main as calibrate_UE_share_constraints +from .calibrate_vre import main as calibrate_vre +from .change_technology_lifetime import main as change_technology_lifetime +from .check_scenario_fix_and_inv_cost import main as check_scenario_fix_and_inv_cost +from .get_optimization_years import main as get_optimization_years +from .update_h2_blending import main as update_h2_blending diff --git a/message_ix_models/util/compat/message_data/calibrate_UE_gr_to_demand.py b/message_ix_models/util/compat/message_data/calibrate_UE_gr_to_demand.py index ff2f270dcf..4c71125bfd 100644 --- a/message_ix_models/util/compat/message_data/calibrate_UE_gr_to_demand.py +++ b/message_ix_models/util/compat/message_data/calibrate_UE_gr_to_demand.py @@ -4,11 +4,11 @@ from itertools import groupby from operator import itemgetter -from .get_optimization_years import main as get_optimization_years from .get_nodes import get_nodes - from .utilities import CAGR +from . import get_optimization_years + # In some cases, end-use technologies have outputs onto multiple demands. # If this is the case, then the a manual assignment is undertaken, # based on the last part of the end-use tec. name @@ -38,7 +38,7 @@ def main(scenario, data_path, ssp, region, first_mpa_year=None, intpol_lim=1, ve scenario : :class:`message_ix.Scenario` scenario to which changes should be applied data_path : :class:`pathlib.Path` - path to model-data directory in message_data repositor + path to model-data directory ssp : str name of SSP for which the script is being run. (SSP1, SSP2 or SSP3) diff --git a/message_ix_models/util/compat/message_data/calibrate_UE_share_constraints.py b/message_ix_models/util/compat/message_data/calibrate_UE_share_constraints.py index 3e311ab982..d8722c5bfb 100644 --- a/message_ix_models/util/compat/message_data/calibrate_UE_share_constraints.py +++ b/message_ix_models/util/compat/message_data/calibrate_UE_share_constraints.py @@ -2,10 +2,10 @@ import pandas as pd from .get_historical_years import main as get_historical_years -from .get_optimization_years import main as get_optimization_years - from .utilities import intpol +from . import get_optimization_years + relation_set = { "UE_res_comm": [["sp", "th"], "useful_res_comm"], "UE_feedstock": [["feedstock"], "useful"], diff --git a/message_ix_models/util/compat/message_data/calibrate_vre.py b/message_ix_models/util/compat/message_data/calibrate_vre.py new file mode 100644 index 0000000000..8c9df08c75 --- /dev/null +++ b/message_ix_models/util/compat/message_data/calibrate_vre.py @@ -0,0 +1,1431 @@ +from pathlib import Path + +import numpy as np +import pandas as pd + +from .get_nodes import get_nodes + +from . import get_optimization_years + + +def _fetch(df, tec, var, yr): + """Retrieve data from input data + + Parameters + ---------- + df : pandas.DataFrame() + data to be filtered + tec : str + filter for technology + var : str + fitler for variable + yr : int + filter for year + """ + return ( + df.loc(axis=0)[:, tec, var, yr]["value"] + .reset_index() + .set_index(["node_loc", "technology"]) + .drop(["year", "variable"], axis=1) + ) + + +def _read_input_data(scen, input_file, tecs, historic_years): + """Read data from xlsx + + The data includes capacity and activity data for the technologies: + solar_pv_ppl, wind_ppl (on-shore), wind_ppf (off-shore) and solar_csp_sm1 + + These data represent TOTAL activity and installed capacity for historic timesteps. + + Parameters + ---------- + scen : message_ix.Scenario + scenario for which calibration is carried out. + input_file : pathlib.Path() + path including file-name and extension where data is stored. + tecs : list + list of technologies for which data should be retrieved. + historic_years : list + list of years for which historic data should be retrieved. + """ + + # Variable used to collect various input data dataframes + df = [] + + # Index used to sort data + idx = ["node_loc", "variable"] + + # Read data + xlsx = pd.ExcelFile(input_file) + + # Define sheets to be read and some formatting variables + # Title: refers to the table title in the xlsx + # Variable: refers to the variable name assigned to the data + sheets = { + "data_capacity": { + "title": "Installed capacity (GW)", + "variable": "Capacity_TOT", + }, + "data_generation": {"title": "Generation (GWa)", "variable": "Activity_TOT"}, + } + + # Retrieve the region-id (prefix) + region_id = list(set([x.split("_")[0] for x in get_nodes(scen)]))[0] + + # Iterate over the different sheets and retrieve data + for sheet in sheets: + hist_cap = xlsx.parse(sheet).rename( + columns={sheets[sheet]["title"]: "node_loc"} + ) + + # Assign region_id (prefix e.g. "R11_") + hist_cap["node_loc"] = region_id + "_" + hist_cap["node_loc"] + + # Filter out region data for regions contained in model + hist_cap = hist_cap[hist_cap["node_loc"].isin(get_nodes(scen))] + + # Assign variable name + hist_cap["variable"] = sheets[sheet]["variable"] + + # Filter out data for specified technologies + for tec in tecs: + cols = [c for c in hist_cap.columns if tec in c or c in idx] + if not cols: + raise ValueError(f"No input data found for technology {tec}") + tmp = hist_cap[cols].melt(id_vars=idx, var_name=["year"]) + tmp["year"] = tmp["year"].str.replace("{},".format(tec), "") + tmp["technology"] = tec + df.append(tmp) + + df = ( + pd.concat(df) + .reset_index() + .drop("index", axis=1) + .astype({"year": "int32"}) + .fillna(0) + ) + + # Filter out data only for specified years + if historic_years: + df = df[df["year"].isin(historic_years)] + + data_cap = xlsx.parse("data_capacity_annual") + data_cap = data_cap[data_cap.technology.isin(tecs)] + data_cap["node_loc"] = region_id + "_" + data_cap["node_loc"] + data_cap = data_cap[data_cap.node_loc.isin(get_nodes(scen))] + + data_elecgen = xlsx.parse("data_electricity_market") + data_elecgen = data_elecgen.rename(columns={data_elecgen.columns[0]: "node_loc"}) + data_elecgen["node_loc"] = region_id + "_" + data_elecgen["node_loc"] + data_elecgen = data_elecgen[data_elecgen.node_loc.isin(get_nodes(scen))] + + return (df, data_cap, data_elecgen) + + +def _clean_data(df, idx, verbose): + """Match activity to capacity + + If the capacity for a specific technology is 0, + then the activity is matched i.e. set to zero. + + Parameters + ---------- + df : pandas.DataFrame() + data to which cleanup should be applied. + idx : list + index used for input_data + verbose : booelan + option whether to print onscreen messages + """ + # Create filter where Capacity_TOT = 0 + filt = df.loc[(df.variable == "Capacity_TOT") & (df.value == 0)].drop( + ["variable", "value"], axis=1 + ) + if not filt.empty: + # Create tmp dataframe with only "Activity_TOT" + tmp = df.loc[df.variable == "Activity_TOT"].set_index( + ["node_loc", "technology", "year"] + ) + if verbose: + print(f"Activity correction will be made for \n{filt}") + # Filter tmp dataframe for filt and set "Activity_TOT" to 0 + tmp = ( + filt.join(tmp, on=tmp.index.names) + .set_index(tmp.index.names) + .dropna() + .assign(value=0) + .reset_index() + .set_index(idx) + ) + # Update + df = df.set_index(idx) + return tmp.combine_first(df).reset_index() + else: + return df + + +def _fill_data_gap(scen, df, tecs, cf_adj, idx, ren_bins, method, verbose): + """Fill data gaps of either activity or capacity + + Data gaps are filled for individual technologies, using complete data from adjacent + years. + + For technologies where either only capacity or activity data has been + supplied, model data is used. The CF (capacity factor) is retrieved from existing + bins. By default, historical capcity in these cases will be assigned to the least + efficient bins. + + Parameters + ---------- + scen : message_ix.Scenario + scenario for which calibration is carried out. + df : pandas.DataFrame() + data for which gaps should be filled. + tecs : list + list of technologies + cf_adj : number + adjustment of capacity factor for non-historical periods vis-a-vis + the CF in the previous period. + idx : list + index used for input_data + ren_bins : pandas.DataFrame() + model data retrieved using `_retrieve_model_data()` + method : str + method applied for choice of efficency factors from model data if historic + data only includes either capacity or activity data. + "lowest_eff": will assign historic capacities to lowest CF bins. + "highest_eff": will assign historic capacities to lowest CF bins. + verbose : booelan + option whether to print onscreen messages + """ + + for tec in tecs: + all_years = sorted( + (df.query("technology == @tec").reset_index().year.unique().tolist()) + ) + act_yrs = ( + df.query("technology == @tec and variable == 'Activity_TOT'") + .reset_index() + .year.unique() + .tolist() + ) + cap_yrs = ( + df.query("technology == @tec and variable == 'Capacity_TOT'") + .reset_index() + .year.unique() + .tolist() + ) + + # ----------------------------------- + # Add data for missing activity years + # ----------------------------------- + + for par in ["Capacity", "Activity"]: + # Create a list if years for which data additions are required + if par == "Activity": + missing = [x for x in cap_yrs if x not in act_yrs] + # If there is no data for activity, then it is not + # possible to calculate reference capacity years + if len(act_yrs) == 0: + act_yrs = cap_yrs[:1] + elif par == "Capacity": + missing = [x for x in act_yrs if x not in cap_yrs] + # If there is no data for capacity, then it is not + # possible to calculate reference capacity years + if missing == cap_yrs: + if verbose: + print( + f"No adjustments will be made for technology {tec}" + " as no reference capacity years are available" + ) + continue + for yr in missing: + # The reference year is the year closest + yr_ref = _find_closest(sorted(set(cap_yrs) & set(act_yrs)), yr) + if verbose: + print( + f"{par} years {yr} will be added for technology {tec}" + f" based on the capacity factor from {yr_ref}" + ) + + # Routine for future years + if yr >= scen.firstmodelyear and par == "Activity": + # The totals for all previous years must be subtracted + # as these are totals. These can then be devided. + # e.g. for 2020, the 2015 CF is derived by subtracting 2010 + # data from 2015 data. + prev_act = _fetch(df, tec, "Activity_TOT", yr_ref) + prev_cap = _fetch(df, tec, "Capacity_TOT", yr_ref) + + yr_pref = all_years[all_years.index(yr_ref) - 1] + # Ensure that there are two prior time-periods, otherwise dont + # subtract. + if yr_pref != yr: + pprev_act = _fetch(df, tec, "Activity_TOT", yr_pref) + pprev_cap = _fetch(df, tec, "Capacity_TOT", yr_pref) + + # Derive the capacity factor, act/cap + prev_cf = ( + (prev_act - pprev_act) / (prev_cap - pprev_cap) + ).fillna(0) + else: + prev_cf = ((prev_act) / (prev_cap)).fillna(0) + + # Retrieve capacity data + act_yr = df.loc(axis=0)[:, tec, "Capacity_TOT", yr] + + # Subtract capacity from previous years from "yr" + act_yr.loc[:, "value"] -= prev_cap.loc[:, "value"] + + # Derive activity for "yr" using derived capacity_factor + # and adjustment factor + act_yr.loc[:, "value"] *= prev_cf.loc[:, "value"] * cf_adj + + # Add the activity from the previous years to arrive + # at the cumulative activity + act_yr.loc[:, "value"] += prev_act.loc[:, "value"] + act_yr = act_yr.rename( + {"Capacity_TOT": "Activity_TOT"}, axis="index" + ) + act_yr = act_yr.reset_index().set_index(idx) + df = act_yr.combine_first(df) + + # Routine for historical years + else: + # If for a given technology, no data is available to fill gaps + # i.e. either no "activity_TOT" or no "Capacity_TOT" is + # available, the model efficiencies are applied. + if any( + x not in set(df.loc(axis=0)[:, tec].reset_index().variable) + for x in ["Capacity_TOT", "Activity_TOT"] + ): + # Filter correct technology data + tmp = ren_bins.copy() + tmp = tmp[(tmp.technology == tec) & (tmp.POT > 0)].set_index( + ["node_loc", "potential_category", "technology"] + ) + + # Filter out the potentials with the lowest efficiency + # Based on previous observations, historically, + if method == "highest_eff": + tmp = tmp.groupby(level=0, as_index=False).CF.apply( + lambda group: group.nsmallest(1) + ) + elif method == "lowest_eff": + tmp = tmp.groupby(level=0, as_index=False).CF.apply( + lambda group: group.nlargest(1) + ) + tmp = ( + tmp.to_frame() + .rename(columns={"CF": "value"}) + .reset_index() + .drop(["level_0", "potential_category"], axis=1) + ) + tmp = tmp.set_index(["node_loc", "technology"]) + else: + # Derive the capacity factor for the reference year, act/cap + tmp = ( + _fetch(df, tec, "Activity_TOT", yr_ref) + / _fetch(df, tec, "Capacity_TOT", yr_ref) + ).fillna(0) + + # Apply capacity factor from reference year to the + # available data from the year. + if par == "Capacity": + tmp = df.loc(axis=0)[:, tec, "Activity_TOT", yr] / tmp + tmp = ( + tmp.rename({"Activity_TOT": "Capacity_TOT"}, axis="index") + .reset_index() + .set_index(idx) + ) + else: + tmp = df.loc(axis=0)[:, tec, "Capacity_TOT", yr] * tmp + tmp = ( + tmp.rename({"Capacity_TOT": "Activity_TOT"}, axis="index") + .reset_index() + .set_index(idx) + ) + df = pd.concat([df, tmp], sort=True) + + return df + + +def _calc_periodic_data(df, tecs, idx): + """Derive periodic values + + For each of the variables, "Capacity_TOT", "Activity_TOT", which represent + cumulative values over time, i.e. that of the total installed capacity, + the periodic values are derived and added as "Capacity_PER", "Activity_PER". + In addition, the period specific capacity factor is derived: "CF_PER". + + Parameters + ---------- + df : pandas.DataFrame() + data for which gaps should be filled. + tecs : list + list of technologies + idx : list + index used for input_data + """ + for tec in tecs: + tec_years = sorted(set(df.loc(axis=0)[:, tec].reset_index().year)) + for yr in tec_years: + yr_act = _fetch(df, tec, "Activity_TOT", yr) + yr_cap = _fetch(df, tec, "Capacity_TOT", yr) + + if yr != tec_years[0]: + yr_ref = tec_years[tec_years.index(yr) - 1] + yr_act -= _fetch(df, tec, "Activity_TOT", yr_ref) + yr_cap -= _fetch(df, tec, "Capacity_TOT", yr_ref) + + # Derive the capacity factor, act/cap + yr_cf = (yr_act / yr_cap).fillna(0) + + yr_act = ( + yr_act.reset_index() + .assign(variable="Activity_PER", year=yr) + .set_index(idx) + ) + yr_cap = ( + yr_cap.reset_index() + .assign(variable="Capacity_PER", year=yr) + .set_index(idx) + ) + yr_cf = ( + yr_cf.reset_index().assign(variable="CF_PER", year=yr).set_index(idx) + ) + + # Merge dataframes + df = pd.concat([df, yr_act, yr_cap, yr_cf], sort=True) + return df.reset_index().sort_values(by=idx) + + +def _retrieve_model_data(scen, tecs): + """Retrieve data required for varaible renewables + + The data retrieved from the scenario includes + the technology specific potentials "bin", the relevant relation + used to link the technologies to "capacity potentials", the respective + "potential" categories as well as the attributed capacity-factor and + their potentials. + + Parameters + ---------- + scen : message_ix.Scenario + scenario for which calibration is carried out. + tecs : list + list of technologies + + Returns + ------- + df : pd.DataFrame() + index= ["node_loc", "technology", "bin", "capacity_relation", + "potential_category", "CF", "POT"] + """ + + # Create dicitonary: "capacity_relations" + # Format: {renewable technology: capacity_relation_name} + capacity_relations = ( + scen.par("relation_total_capacity", filters={"technology": tecs})[ + ["relation", "technology"] + ] + .drop_duplicates() + .set_index("technology") + .to_dict()["relation"] + ) + + # Then use the capacity relation name to filter out which + # potential categories are attributed to each technology + df = scen.par( + "relation_total_capacity", filters={"relation": capacity_relations.values()} + ) + + # Filter out all the relevant "res" tecs. + res_tecs = df[["relation", "technology"]].drop_duplicates() + res_tecs = res_tecs[~res_tecs["technology"].isin(tecs)].technology.tolist() + + # Filter relation capacity data for res_tecs + df = ( + df[df["technology"].isin(res_tecs)] + .drop(["year_rel", "unit", "value"], axis=1) + .rename( + columns={ + "node_rel": "node_loc", + "relation": "capacity_relation", + "technology": "bin", + } + ) + .drop_duplicates() + .set_index(["node_loc", "bin"]) + ) + + # Retrieve the capacity factors of the different bins + tmp = ( + scen.par("capacity_factor", filters={"technology": res_tecs})[ + ["node_loc", "technology", "value"] + ] + .rename(columns={"technology": "bin"}) + .drop_duplicates() + .set_index(["node_loc", "bin"]) + ) + df["CF"] = tmp["value"] + + # Retireve different potentials for each bin + tmp = scen.par("relation_activity", {"technology": res_tecs}) + tmp = tmp[ + tmp.relation.isin( + [ + r + for r in tmp.relation.unique() + if any([y in r for y in ["_pot", "_pof"]]) + ] + ) + ] + + # Create dictionary "renewable_pot" + # Format: {renewable_bins: renewable_potentials} + renewable_pot = ( + tmp[["relation", "technology"]] + .drop_duplicates() + .set_index("technology") + .to_dict()["relation"] + ) + + # Retrieve upper bounds for potentials + tmp = ( + scen.par( + "relation_upper", + filters={"relation": [renewable_pot[i] for i in renewable_pot.keys()]}, + ) + .drop(["year_rel", "unit"], axis=1) + .drop_duplicates() + .rename(columns={"node_rel": "node_loc", "relation": "bin"}) + ) + tmp.bin = tmp.bin.map({v: k for k, v in renewable_pot.items()}) + tmp = tmp.set_index(["node_loc", "bin"]) + df["POT"] = tmp["value"] + + df = df.reset_index() + # Add potential names + df["potential_category"] = df["bin"].map(renewable_pot) + df["technology"] = df["capacity_relation"].map( + {v: k for k, v in capacity_relations.items()} + ) + df = df[ + [ + "node_loc", + "technology", + "bin", + "capacity_relation", + "potential_category", + "CF", + "POT", + ] + ] + return df + + +def _find_closest(lst, K): + """Find closest value in a list for a given value + + Parameters + ---------- + lst : list + list of values + K : number + value for which the closest value in list should be retrieved + """ + return lst[min(range(len(lst)), key=lambda i: abs(lst[i] - K))] + + +def _allocate_historic_values(ren_bins, data_df, cf_tol, idx): + """Allocate historical data to corresonding bins + + Histroic renewable capacities are installed to the corresponding + potential bins based on theis capacity factors. The "cf_tol" + determines the allowable tolerance between the historical capacity + factor and that of the bins. If there is no suitable bin, i.e. if the + deviation between the modelled potential categories in the model is + too large, then a new bin is created. The existing bins are adjusted + in that the potentials are reduced, by the historic capacity built. + The capacity factor difference is taken into account. + The model is forced to maintain the capacity allocated historically + for the remainder of the century, as it is assumed that existing + plots of land occupied by renewables, will not be re-puprosed. + + Parameters + ---------- + ren_bins : pandas.DataFrame() + model data retrieved using `_retrieve_model_data()` + data_df : pandas.DataFrame() + input/calibration data + cf_tol : number + tolerance allowed between "actual" and "bin" capacity factors. + idx : list + index used for input_data + """ + + # --------------------------------- + # Prep model data dataframe + # for allocation of historical data + # --------------------------------- + + # Filter model data to only include data for potential categories + # greater than zero. + df = ren_bins[["node_loc", "technology", "bin", "CF", "POT"]].set_index( + ["node_loc", "technology", "CF"] + ) + df = df[df.POT > 0] + + # For each of the years for which we have historical data, + # add a column to the model data representing the year as well as a + # column for the capacity factor per period. + years = sorted(data_df.year.unique().tolist()) + for y in years: + df[y] = "" + df["{}_CF".format(y)] = "" + + # ----------------------------------- + # Filter historical data for "CF_PER" + # ----------------------------------- + + # Create a temporary dataframe including annual historical + # capacity factors per period "Capacity_PER" + # Drop all those with NaN or 0 values resulting from + # activity=0 / capacity=0 + tmp = data_df[(data_df["variable"] == "CF_PER") & (data_df["value"] > 0)].dropna() + data_df = data_df.set_index(idx) + + # Create a temporay bin which will incorporate all new historical bins + hist_bin = pd.DataFrame() + + # ----------------------------------- + # Allocate historic capacities + # by iterating of historical "CF_PER" + # ----------------------------------- + for index, row in tmp.iterrows(): + # For each region/technology/period find the capacity factor nearest + # of existing bins, which comes closest to the historical value. + + node = row.node_loc + tec = row.technology + yr = row.year + + # Create a list of capacity facors per tech/region + cf_list = df.loc[(node, tec)].index.values.tolist() + + # If applying this script to a scenario which already + # has had historical categories introduced, then these + # need to filtered out for the corresponding year. + check_list = df.loc[(node, tec), "bin"].values.tolist() + for t in check_list: + if "hist" in t and f"hist_{yr}" not in t: + cf_list[check_list.index(t)] = 100 + + # + cf_closest = _find_closest(cf_list, row.value) + cf_diff = row.value - cf_closest + + cap_PER = float(data_df.loc[[(node, tec, "Capacity_PER", yr)]].value) + cf_PER = float(data_df.loc[[(node, tec, "CF_PER", yr)]].value) + + # First check if the difference between the categories is to great. + if cf_diff <= cf_tol * -1: + # Reduce the capacity of the closest "existing" bin taking into + # account the difference in capacity factor + # i.e. cap_Per * CF of cap_PER / res CF + new_pot = ( + float(df.loc[[(node, tec, cf_closest)], "POT"]) + - cap_PER * cf_PER / cf_closest + ) + + # Check to see if there is enough potential in the bin from which + # Capacity is being subtracted. + if new_pot >= 0: + df.loc[(node, tec, cf_closest), "POT"] = new_pot + + # Otherwise... + if new_pot < 0: + # Set the capacity of the first bin to 0 + df.loc[(node, tec, cf_closest), "POT"] = 0 + + # Find next closest bin + cf_list.pop(cf_list.index(cf_closest)) + cf_closest2 = _find_closest(cf_list, row.value) + + # The value smaller than zero, needs to be subtracted from the + # next bin, hence it is * by -1 and then / back to the original + # capacity + new_pot2 = float(new_pot * -1 * cf_closest / cf_PER) + + # Now calculate the poetntial of the second category + new_pot2 = ( + float(df.loc[[(node, tec, cf_closest2)], "POT"]) + - new_pot2 * cf_PER / cf_closest2 + ) + if new_pot2 < 0: + raise ValueError("Reduction of exisiting potentials exceeded") + else: + df.loc[(node, tec, cf_closest2), "POT"] = new_pot2 + + # Create a new bin + new_bin_name = "_".join( + df.loc[[(node, tec, cf_closest)], "bin"].values[0].split("_")[:-1] + ) + if row.technology in ["wind_ppf"]: + new_bin_name = "{}_ref_hist_{}".format(new_bin_name, row.year) + else: + new_bin_name = "{}_res_hist_{}".format(new_bin_name, row.year) + + # Copy the df slice from existing, closest CF, + # and change accordingly + assign = { + "bin": new_bin_name, + "CF": round(row.value, 3), + "POT": cap_PER, + yr: cap_PER, + f"{yr}_CF": cf_PER, + } + hist_tmp = df.loc[[(node, tec, cf_closest)]].reset_index() + # A loop needs to be used, as df.assign(**assign) doesnt work with + # the yr which needs to passed as a string, but then a new column + # would be added, instead of the existing column being modified. + for x in assign: + hist_tmp.loc[:, x] = assign[x] + hist_tmp = hist_tmp.set_index(["node_loc", "technology", "CF"]) + if hist_bin.empty: + hist_bin = hist_tmp + else: + hist_bin = pd.concat([hist_bin, hist_tmp]) + + # If the CF difference is not to large, then the capacity + # is allocated to the correct bin + else: + # The Capacity_PER value is compared with potential of + # the bin with the closest CF + # If the CF is too large, then capacity is allocated + # to the next closest category. + tmp_pot = df.loc[[(node, tec, cf_closest)], "POT"].values[0] + + # All capacities which have already been allocated are added up + hist_pot_allocated = [ + df.loc[[(node, tec, cf_closest)], y].values[0] for y in years if y < yr + ] + hist_pot_allocated = sum([float(i) for i in hist_pot_allocated if i]) + + # The available potential is the potential of the bin minus + # already allocated potential in previuous years + tmp_pot -= hist_pot_allocated + + if tmp_pot < cap_PER: + # Adds the potential for the year + df.loc[[(node, tec, cf_closest)], yr] = tmp_pot + + # Adds the CF for the year + df.loc[[(node, tec, cf_closest)], "{}_CF".format(yr)] = cf_PER + + # Find next closest value + cf_list.pop(cf_list.index(cf_closest)) + cf_closest = _find_closest(cf_list, row.value) + df.loc[[(node, tec, cf_closest)], yr] = cap_PER - tmp_pot + + # Adds the CF for the year + df.loc[[(node, tec, cf_closest)], "{}_CF".format(yr)] = cf_PER + + else: + df.loc[[(node, tec, cf_closest)], yr] = cap_PER + + # Adds the CF for the year + df.loc[[(node, tec, cf_closest)], "{}_CF".format(yr)] = cf_PER + + df = pd.concat([df, hist_bin], sort=True) + df = ( + df.reset_index() + .sort_values(["node_loc", "technology", "bin"]) + .reset_index() + .drop("index", axis=1) + ) + return df + + +def _remove_par(scen, df, remove_par): + """Remove parameters + + Historical values are removed for ref_. + For all others, values are removed for ALL timesteps and + for ALL "parent" technologies (i.e. wind_ppl etc.) + and bins (i.e. solar_res1 etc.) + + Parameters + ---------- + scen : message_ix.Scenario + scenario for which calibration is carried out. + df : pandas.DataFrame() + dataframe for allocated historic values from `_allocate_historic_values` + remove_par : list + list of parameters which should be removed for the technologies. + """ + + nodel = df.node_loc.unique().tolist() + tecl = df.technology.unique().tolist() + df.bin.unique().tolist() + + with scen.transact("History for renewable technologies removed"): + for par in remove_par: + rem_df = scen.par(par, filters={"node_loc": nodel, "technology": tecl}) + if "ref_" in par: + # Retrieve the year index name + year_idx_name = [i for i in scen.idx_names(par) if "year" in i][0] + rem_df = rem_df[rem_df[year_idx_name] < scen.firstmodelyear] + scen.remove_par(par, rem_df) + + +def _update_potentials(scen, df, ren_bins): + """Update potentials of existing bins in a given scenario. + + This is done by comparing the POT of the original model data + "ren_bins" with the updated POT of "df" + + Parameters + ---------- + scen : message_ix.Scenario + scenario for which calibration is carried out. + df : pandas.DataFrame() + dataframe for allocated historic values from `_allocate_historic_values` + ren_bins : pandas.DataFrame() + model data retrieved using `_retrieve_model_data()` + """ + tmp = ren_bins.set_index(["node_loc", "technology", "bin"]) + tmp["POT_new"] = df.set_index(["node_loc", "technology", "bin"])["POT"] + tmp = tmp[(tmp["POT"] - tmp["POT_new"]) != 0].dropna().reset_index() + + with scen.transact("Update existing potential categories"): + for index, row in tmp.iterrows(): + upd_df = scen.par( + "relation_upper", + filters={"node_rel": row.node_loc, "relation": row.potential_category}, + ) + upd_df["value"] = row.POT_new + scen.add_par("relation_upper", upd_df) + + +def _add_new_hist_potential_bin(scen, df, remove_par, verbose): + """Add new potential bins to a given scenario. + + For all new bin (res) categories which get introduced, + "res4" or "ref4" will be used as a basis for copying parameters. + + Parameters + ---------- + scen : message_ix.Scenario + scenario for which calibration is carried out. + df : pandas.DataFrame() + dataframe for allocated historic values from `_allocate_historic_values` + remove_par : list + list of parameters which should be removed for the technologies. + verbose : booelan + option whether to print onscreen messages + """ + new_resm = [ + t for t in df.bin.unique().tolist() if t not in scen.set("technology").tolist() + ] + tmp_df = df[df.bin.isin(new_resm)] + + with scen.transact("Added new potential categories"): + for index, row in tmp_df.iterrows(): + # Assign variables + node = row.node_loc + rbin = row.bin + tec = row.technology + + # Create name of the reference technology used for copying parameters. + # Create name of new potential relation. + if tec == "wind_ppf": + ref_tec = "{}_ref4".format(rbin.split("_")[0]) + pot_rel_new = rbin.replace("ref", "pot") + else: + ref_tec = "{}_res4".format(rbin.split("_")[0]) + pot_rel_new = rbin.replace("res", "pot") + + # Update set technology and relation + if rbin not in scen.set("technology").tolist(): + if verbose: + print("Adding new technology", rbin) + scen.add_set("technology", rbin) + if verbose: + print("Adding new relation", pot_rel_new) + scen.add_set("relation", pot_rel_new) + + # Retrieve all the parameters which need to be copied + # All parameters previosuly removed are excluded + for par in [ + p + for p in scen.par_list() + if p not in remove_par and "technology" in scen.idx_names(p) + ]: + # Retrieve the node index name + node_ix = [i for i in scen.idx_names(par) if "node" in i][0] + + # Retrieve the data for the reference technology + # which is to be updated + upd_par = scen.par( + par, + filters={"technology": [ref_tec], node_ix: [node]}, + ) + if not upd_par.empty: + upd_par["technology"] = rbin + if par == "relation_activity": + pot_rel = [ + x + for x in set(upd_par.relation) + if any([y in x for y in ["_pot", "_pof"]]) + ] + upd_par["relation"] = upd_par["relation"].replace( + pot_rel, pot_rel_new + ) + + # Update the potential for the new category + # These are set slightly higher than calculated to avoid + # infeasibilities due to rounding. + upd_pot = scen.par( + "relation_upper", + filters={ + "relation": pot_rel, + node_ix: [node], + }, + ) + upd_pot["relation"] = pot_rel_new + upd_pot["value"] = round(row.POT * 1.001, 3) + scen.add_par("relation_upper", upd_pot) + elif par == "capacity_factor": + upd_par["value"] = row.CF + scen.add_par(par, upd_par) + + +def _add_par_for_res(scen, data_df, df, years, h_years, m_years, opt_years): + """Populate parameters for `bins`. + + For each country and technology, the activity and capacity of the potential + bins are now updated. The parameters are set to 0 if their isnt any activity + or capacity, in order to ensure that resource bins do not exceed calibrated + values. i.e. if bin 4 is x, which represents all the capacity in a given year + then bins 1,2,3 need to be fixed to 0. + + Parameters + ---------- + scen : message_ix.Scenario + scenario for which calibration is carried out. + data_df : pandas.DataFrame + dataframe containing the cleaned up input data originally retrieved via + `_read_input_data()` + df : pandas.DataFrame() + dataframe for allocated historic values from `_allocate_historic_values` + years : list + list of years for which input data is available + h_years : list + list of input data years which correspond to historic model periods + m_years : list + list of input data years which correspond to model optimization periods + opt_years : list + list of model optimization years + """ + df = df.replace("", 0) + + # Calculate the activity and capacity values that will be + # added to the model + for y in years: + per_len = float(scen.par("duration_period", {"year": y})["value"]) + + # The activity is calculated by the either using the "actual" CF + # if it is <= to the res CF, otherwise the res CF is used. + df["{}_ACT".format(y)] = df.apply( + lambda row: row[y] * row["{}_CF".format(y)] + if row["{}_CF".format(y)] <= row.CF + else row[y] * row.CF, + axis=1, + ) + + # The capacity is divided buy the period length of the respective years + df["{}_NewCAP".format(y)] = df.apply(lambda row: row[y] / per_len, axis=1) + df = df.round(3) + + with scen.transact("Update parameters for potential bins"): + for index, row in df.iterrows(): + act = { + "node_loc": row.node_loc, + "technology": row.bin, + "mode": "M1", + "time": "year", + "unit": "GWa", + } + + cap = {"node_loc": row.node_loc, "technology": row.bin, "unit": "GW"} + + # Historical Activity is built by the cumulative sum across time steps + hist_act = pd.DataFrame( + { + **act, + **{ + "year_act": h_years, + "value": np.cumsum( + [row["{}_ACT".format(y)] for y in h_years] + ).tolist(), + }, + } + ) + scen.add_par("historical_activity", hist_act) + scen.add_par("ref_activity", hist_act) + + # Shouldnt these also be cumulative? + bound_act_lo = pd.DataFrame( + { + **act, + **{ + "year_act": opt_years, + "value": sum([row["{}_ACT".format(y)] for y in years]), + }, + } + ) + scen.add_par("bound_activity_lo", bound_act_lo) + + # Values represent historical capacity added in historic periods + hist_new_cap = pd.DataFrame( + { + **cap, + **{ + "year_vtg": h_years, + "value": [row["{}_NewCAP".format(y)] for y in h_years], + }, + } + ) + scen.add_par("historical_new_capacity", hist_new_cap) + scen.add_par("ref_new_capacity", hist_new_cap) + + if m_years: + bound_act_up = pd.DataFrame( + { + **act, + **{ + "year_act": m_years, + "value": sum([row["{}_ACT".format(y)] for y in years]) + * 1.001, + }, + } + ) + scen.add_par("bound_activity_up", bound_act_up) + + bound_new_cap = pd.DataFrame( + { + **cap, + **{ + "year_vtg": m_years, + "value": [row["{}_NewCAP".format(y)] for y in m_years], + }, + } + ) + scen.add_par("bound_new_capacity_lo", bound_new_cap) + + bound_new_cap["value"] *= 1.001 + scen.add_par("bound_new_capacity_up", bound_new_cap) + return df + + +def _add_par_for_tec(scen, tmp_df, years, h_years, m_years, opt_years): + """Populate parameters for `tecs`. + + For each country and technology, the activity and capacity bounds are update. + The technology specific capacity or activity bounds will correlate to the + sum across all the technology specific bins. + + Parameters + ---------- + scen : message_ix.Scenario + scenario for which calibration is carried out. + tmp_df : pandas.DataFrame + dataframe for allocated historic values from `_add_par_for_res` + years : list + list of years for which input data is available + h_years : list + list of input data years which correspond to historic model periods + m_years : list + list of input data years which correspond to model optimization periods + opt_years : list + list of model optimization years + """ + with scen.transact("Update parameters for parent technologies"): + tmp_df = ( + tmp_df.drop("bin", axis=1) + .groupby(["node_loc", "technology"]) + .sum() + .reset_index() + ) + for index, row in tmp_df.iterrows(): + act = { + "node_loc": row.node_loc, + "technology": row.technology, + "mode": "M1", + "time": "year", + "unit": "GWa", + } + + cap = {"node_loc": row.node_loc, "technology": row.technology, "unit": "GW"} + + # Historical Activity is built by the cumulative sum across time steps + hist_act = pd.DataFrame( + { + **act, + **{ + "year_act": h_years, + "value": np.cumsum( + [row["{}_ACT".format(y)] for y in h_years] + ).tolist(), + }, + } + ) + scen.add_par("historical_activity", hist_act) + scen.add_par("ref_activity", hist_act) + + hist_new_cap = pd.DataFrame( + { + **cap, + **{ + "year_vtg": h_years, + "value": [row["{}_NewCAP".format(y)] for y in h_years], + }, + } + ) + scen.add_par("historical_new_capacity", hist_new_cap) + scen.add_par("ref_new_capacity", hist_new_cap) + + bound_act_lo = pd.DataFrame( + { + **act, + **{ + "year_act": opt_years, + "value": sum([row["{}_ACT".format(y)] for y in years]), + }, + } + ) + scen.add_par("bound_activity_lo", bound_act_lo) + + if m_years: + bound_act_up = pd.DataFrame( + { + **act, + **{ + "year_act": m_years, + "value": sum([row["{}_ACT".format(y)] for y in years]) + * 1.001, + }, + } + ) + scen.add_par("bound_activity_up", bound_act_up) + + bound_new_cap = pd.DataFrame( + { + **cap, + **{ + "year_vtg": m_years, + "value": [row["{}_NewCAP".format(y)] for y in m_years], + }, + } + ) + scen.add_par("bound_new_capacity_lo", bound_new_cap) + bound_new_cap["value"] *= 1.001 + scen.add_par("bound_new_capacity_up", bound_new_cap) + + bound_total_cap = pd.DataFrame( + { + **cap, + **{ + "year_act": m_years, + "value": sum([row[y] for y in years]), + }, + } + ) + scen.add_par("bound_total_capacity_lo", bound_total_cap) + + +def _update_init_cap_bound( + scen, + data_cap, + data_elecgen, + tecs_init_cap, + reg_grp=["SAS", "CPA", "NAM", "WEU"], +): + """Update existing initial startup value for the dynamic capacity bound. + + In order to determine the correct startup values, historic annual capacity + values are used. The difference between these values indicates the historical + additions for each period, and the difference between these inidicates the + maximum change between annual additions. The average across the market leaders, + are then scaled with regional electricity market shares and used to parametrize + the startup value. + + Parameters + ---------- + scen : message_ix.Scenario + scenario for which calibration is carried out. + data_cap : pandas.DataFrame + annual historic capacity data from `_read_input_data` + data_elecgen : pandas.DataFrame + data on regional 2020 secondary electricity generation for 2020 from + `_read_input_data`. + tecs_initi_cap : list + list of technologies for which inital startup values should be updated + reg_grp : list + """ + data_cap = data_cap.set_index(["node_loc", "technology"]) + # Based on historical, annual TIC timeseries + # 1. all 0 are replaced by NAN so that the jump from missing data + # years is ignored. + # 2. the difference for these values gives us the annual capacity + # additions + # 3. The difference of the annual capacity additions then gives + # the values used for subsequent calculations of the parameter + # "initial_new_cap_up". + data_cap = ( + data_cap.replace(0, np.nan).diff(axis=1).diff(axis=1).max(axis=1).fillna(0) + ) + # Set negative values to 0 + data_cap.loc[data_cap.values <= 0] = 0 + data_cap = data_cap.to_frame().rename(columns={0: "value"}).reset_index() + + # -------------------------------- + # A Manual fix is made to override + # capacity additions for WEU. + # -------------------------------- + data_cap.loc[ + (data_cap.node_loc.str.contains("WEU")) + & (data_cap.technology == "solar_pv_ppl"), + "value", + ] = 8 + data_cap = data_cap.set_index(["node_loc"]) + + # Data on electricity generation is converted from EJ/yr to TWh + data_cap["elec_gen"] = data_elecgen.set_index(["node_loc"])["2020"] * 1000 / 3.6 + + # Max-Capacity installation is normalized to electricty market share + data_cap["max_by_size"] = data_cap["value"] / data_cap["elec_gen"] * 1000 + + # Provisional values are added + data_cap["norm_by_size"] = 0 + + with scen.transact("Initial_activity_up updated for renewable ppls'"): + for tec in tecs_init_cap: + tmp = scen.par( + "initial_new_capacity_up", filters={"technology": tec} + ).set_index(["node_loc"]) + if tec in data_cap.technology.unique(): + # Get Average across frontier regions -> drop too small values + val = data_cap[ + (data_cap.technology == tec) & (data_cap.max_by_size > 0.01) + ] + val = val.loc[ + [r for r in val.index if any([x in r for x in reg_grp])], + "max_by_size", + ].mean() + data_cap.loc[(data_cap.technology == tec), "norm_by_size"] = ( + data_cap[(data_cap.technology == tec)]["elec_gen"] / 1000 * val + ) + + tmp.value = data_cap.loc[(data_cap.technology == tec), "norm_by_size"] + else: + tmp.value = 0.05 + scen.add_par("initial_new_capacity_up", tmp.reset_index()) + + +def main( + scen, + path, + tecs=None, + method="lowest_eff", + cf_adj=1.1, + cf_tol=0.005, + remove_par=None, + upd_init_cap=True, + verbose=False, +): + """Calibrate variable renewable technologies. + + Historical capacities and activities are used to derive "actual" + capacity factors. These are used to allocate historical capacities + to the respective potential-bins, based on their capacity factors, + depicted in MESSAGEix. Should the deviation of actual historical + capacity factors be too large compared to those depiced in MESSAGEix, + then new "bins" are created. + The historical capacities must be maintained in the future. This is + achieved by a lower bound on total capacity for the respective bin. + If new bins are introduced, then the potential of the existing bins + is reduced. The difference in the capacity factors is accounted for + when doing so. + + Parameters + ---------- + scen : message_ix.Scenario + scenario for which calibration is carried out. + path : pathlib.Path + path to model data, e.g. 'message_data/data' + tecs : string or list + name of technologies for which calibration should be carried out. + method : str + method applied for choice of efficency factors from model data if historic + data only includes either capacity or activity data. + "lowest_eff": will assign historic capacities to lowest CF bins. + "highest_eff": will assign historic capacities to lowest CF bins. + cf_adj : number (default=1.1) + adjustment of capacity factor for non-historical periods vis-a-vis + the CF in the previous period. + cf_tol : number (default=0.005) + tolerance allowed between "actual" and "bin" capacity factors. + remove_par : list + list of parameters which should be removed for the calibrated + technologies + upd_init_cap : boolean (default=True) + option whether to update existing initial capacity up values. + verbose : booelan + option whether to print onscreen messages + """ + + # -------------------------------- + # Definition of generic parameters + # -------------------------------- + + # Define the file from where data should be retrieved. + input_file = ( + Path(path) + / "model" + / "calibration_renewables" + / "VRE_capacity_MESSAGE_global.xlsx" + ) + + # The historic years refer to the input data and therefore include 2006 + # as opposed to the modeyear 2005. 2006 data is used for the model + # year 2005 + historic_years = [2006, 2010, 2015, 2020] + + # Define the technologies for which changes should be applied + if not tecs: + tecs = ["solar_pv_ppl", "wind_ppl", "wind_ppf", "csp_sm1_ppl"] + tecs_init_cap = tecs + ["csp_sm3_ppl"] + else: + tecs = [tecs] if type(tecs) != list else tecs + tecs_init_cap = tecs + + # Retrieves the historical years + opt_years = get_optimization_years(scen) + + # Set index used for input_data + idx = ["node_loc", "technology", "variable", "year"] + + if not remove_par: + remove_par = [ + "historical_activity", + "historical_new_capacity", + "ref_activity", + "ref_new_capacity", + "bound_new_capacity_up", + "bound_new_capacity_lo", + "bound_activity_lo", + "bound_activity_up", + ] + + # ------------------------------------- + # Step1. Retrieve and format input data + # ------------------------------------- + + # Retrieves for historic capacity/activity + # Data is assigned to "Capacity_TOT" and "Activity_TOT" + data_df, data_cap, data_elecgen = _read_input_data( + scen, input_file, tecs, historic_years + ) + + # -------------------------- + # Step2. Retrieve model data + # -------------------------- + + ren_bins = _retrieve_model_data(scen, tecs=tecs) + + # Check that the script hasnt already been applied to a scenario + # otherwise a "reset" of historic bins is required. + if any("hist" in x for x in ren_bins.bin.unique()): + raise ValueError( + "This scenario has already been calibrated and requires the reset of historic bins" + ) + + # ------------------ + # Step3. Format data + # ------------------ + + # ------------------------------- + # Step3.1 Correct data mismatches + # ------------------------------- + + # If capacity is 0, but activity is not, activity is set to 0. + data_df = _clean_data(data_df, idx, verbose) + + # ------------------------ + # Step3.2 Reset data years + # ------------------------ + + # Those years which are not in the model horizon are set to closest year + data_df.loc[data_df["year"] == 2006, "year"] = 2005 + data_df = data_df.sort_values(by=idx).set_index(idx) + + # ---------------------- + # Step3.3 Fill data gaps + # ---------------------- + + data_df = _fill_data_gap(scen, data_df, tecs, cf_adj, idx, ren_bins, method, verbose) + + # ------------------------- + # Step3.4 Add periodic data + # ------------------------- + + data_df = _calc_periodic_data(data_df, tecs, idx) + + # ----------------------- + # Step4. Allocate history + # ----------------------- + + # Allocates input data to repective bins depicted in the model + df = _allocate_historic_values(ren_bins, data_df, cf_tol, idx) + + # ---------------------- + # Step5. Update scenario + # ---------------------- + + # Step5.1 Removes parameters + if remove_par: + _remove_par(scen, df, remove_par) + + # Step5.2 Update potentials of "exisiting" categories + _update_potentials(scen, df, ren_bins) + + # Step5.3 Introduce new bins and set potentials + _add_new_hist_potential_bin(scen, df, remove_par, verbose) + + # Retrieve years for which input data is available + years = sorted(data_df.year.unique().tolist()) + + # Retrieve historic years < first_model_year + h_years = [y for y in years if y < min(opt_years)] + + # Retrieve model years >= first_model_year + m_years = [y for y in years if y in opt_years] + + # Step5.4 Populate parameters for _res + # Set historical_new_capacity, historical_activity, + # ref_new_capacity, ref_activity + # as well as lower_bounds on activity for the _res and _ppl + tmp_df = _add_par_for_res(scen, data_df, df, years, h_years, m_years, opt_years) + + # Step 5.5 populate parameters for _tec + _add_par_for_tec(scen, tmp_df, years, h_years, m_years, opt_years) + + # ------------------------------------------------ + # Step6. Add dynamic bound for capacity (optional) + # ------------------------------------------------ + + if upd_init_cap: + _update_init_cap_bound(scen, data_cap, data_elecgen, tecs_init_cap) diff --git a/message_ix_models/util/compat/message_data/change_technology_lifetime.py b/message_ix_models/util/compat/message_data/change_technology_lifetime.py new file mode 100644 index 0000000000..1d3d61f594 --- /dev/null +++ b/message_ix_models/util/compat/message_data/change_technology_lifetime.py @@ -0,0 +1,810 @@ +# in this version lifetime is treated time-dependent +import logging + +import numpy as np +import pandas as pd + +from .utilities import closest, f_index, f_slice, idx_memb, intpol, unit_uniform + +log = logging.getLogger(__name__) + + +def _log(parname, tec, node): + """Common logging shorthand, used in several places.""" + log.debug(f"Update {repr(parname)} for new lifetime of {repr(tec)} in {repr(node)}") + + +def main( + scenario, + tec, + lifetime=None, + year_vtg_start=None, + year_vtg_end=None, + nodes=all, + par_exclude=[], + remove_rest=False, + test_run=False, + use_firstmodelyear=False, + extrapol_neg=0.5, + commit=True, + quiet=True, +): + """ + This function extends or shortens the lifetime of an existing technology + in a message_ix scenario and modifies all other parameters accordinglly. + The function can be used for extending or shortening the vintage years + as well, without changing the lifetime. + + Parameters + ---------- + scenario: object + ixmp scenario + tec: string + Technology name + lifetime: integer or None + New technical lifetime (if None the old lifetime will be used) + year_vtg_start: integer or None + The first vintage year for specified lifetime + year_vtg_end: integer or None + The last vintage year for specified lifetime + nodes: string or a list/series of strings, default all + Model regions in where the lifetime of technology should be updated + par_exclude: string or a list of strings, default None + Parameters with no need for being updated for new activity or vintage + years (e.g., some bounds may not be needed) + remove_rest: boolean, default False + If True, the data for the rest of vintage years not modified via this + function will be removed. (e.g., if lifetime is changed for vintages + between 2020 and 2050, the rest of vintages will be removed.) + test_run: boolean, default False + If True, the script is run only for test, and no changes will be + commited to MESSAGE scenario + use_firstmodelyear: boolean, default False + If true, changes will not be applied to timesteps prior to the firstmodelyear. + extrapol_neg: integer or None + Treatment of negative values from extrapolation of two positive values: + None does nothing (negative accepted), + and integer acts as a multiplier of the adjacent value (e.g., 0, 0.5 or + 1) for replacing the negative value + commit: boolean, default True + If True, changes will be applied to the scenario. + quiet : bool, optional + Don't display debug-level log messages. + """ + # Set log level + log.setLevel(logging.WARNING if quiet else logging.DEBUG) + + # ------------------------ + # Creates helper variables + # ------------------------ + # Retrieve regions for which changes should be made + if nodes == all: + nodes = scenario.set("node") + elif isinstance(nodes, str): + nodes = [nodes] + + if isinstance(par_exclude, str): + par_exclude = [par_exclude] + + # Retrieve firstmodelyear and set horzion + if use_firstmodelyear: + firstmodelyear = scenario.firstmodelyear + horizon = sorted([int(i) for i in scenario.set("year")]) + + # Retrieve time related parameters: 1-time-dimension + parlist_1d = [ + x + for x in set(scenario.par_list()) + if len([y for y in scenario.idx_names(x) if "year" in y]) == 1 + and "technology" in scenario.idx_names(x) + ] + + # Retrieve time related parameters: 2-time-dimension + parlist_2d = [ + x + for x in set(scenario.par_list()) + if len([y for y in scenario.idx_names(x) if "year" in y]) == 2 + ] + + if not test_run and commit is True: + scenario.check_out() + + # ----------------- + # Adjust parameters + # ----------------- + + for node in nodes: + par_empty = [] + results = {} + + # ------------------------------ + # Parameter "technical_lifetime" + # ------------------------------ + parname = "technical_lifetime" + df_old = scenario.par(parname, {"node_loc": node, "technology": tec}) + df_inp = scenario.par("input", {"node_loc": node, "technology": tec}) + if df_inp.empty: + df_inp = df_old.copy() + + if df_old.empty: + log.info( + f"No technical_lifetime data for technology {repr(tec)}; skip node " + f"{repr(node)}" + ) + continue + + df_new = df_old.copy() + + # The range of vintage years to be changed is defined + year_vtg_st = min(df_old["year_vtg"]) if not year_vtg_start else year_vtg_start + year_vtg_e = max(df_inp["year_vtg"]) if not year_vtg_end else year_vtg_end + + # The lifetime is only changed for the range specified + if lifetime: + chngyrs = [y for y in horizon if y >= year_vtg_st and y <= year_vtg_e] + df_new.loc[df_new.year_vtg.isin(chngyrs), "value"] = lifetime + + # Retrieve parameter duration period + dur = ( + scenario.par("duration_period") + .sort_values(by="year") + .drop("unit", axis=1) + .set_index("year") + ) + + # Generating duration_period_sum matrix for masking + df_dur = pd.DataFrame(index=horizon[:-1], columns=horizon) + for i in df_dur.index: + for j in [x for x in df_dur.columns if x >= i]: + df_dur.loc[i, j] = int( + dur.loc[(dur.index >= i) & (dur.index <= j)].sum() + ) + + # Create a list of new vintage years + vtg_years = sorted([x for x in horizon if x >= year_vtg_st and x <= year_vtg_e]) + + # Adding new vintage years to parameter technical_lifetime + for y in [y for y in vtg_years if y not in set(df_new["year_vtg"])]: + tmp = df_new.iloc[0, :].copy() + tmp["year_vtg"] = y + if lifetime and y >= year_vtg_st and y <= year_vtg_e: + tmp["value"] = lifetime + else: + cl = closest(df_new["year_vtg"].tolist(), y) + tmp["value"] = float(df_new.loc[df_new["year_vtg"] == cl, "value"]) + df_new = df_new.append(tmp, ignore_index=True) + df_new = df_new.set_index("year_vtg") + + # Finding the last active year for each vintage year + act_years = [] + for y in [x for x in vtg_years if x < max(horizon)]: + tmp = df_dur.loc[y].to_frame(name="max") + + # Create a range to identify the correct timestep + tmp["min"] = tmp["max"] - dur["value"] + 1 + + # Set the 'max' value fot the last period artifically high + tmp.loc[max(horizon), "max"] = 100 + act_years.append( + tmp.loc[ + (df_new.loc[y, "value"] >= tmp["min"]) + & (df_new.loc[y, "value"] <= tmp["max"]) + ].index[0] + ) + + # Adding last model year if needed (not in duration_period_sum) + if max(horizon) in vtg_years: + act_years.append(max(horizon)) + + # Pairing vintage years with corresponding last active year + vtg_act_yrs = pd.DataFrame(index=vtg_years, data={"act_years": act_years}) + + # Creating a list of years for which changes need to be made + years_all = [ + x + for x in horizon + if x >= year_vtg_st and x <= max(max(vtg_years, act_years)) + ] + + df_new = df_new.loc[df_new.index.isin(years_all)] + df_new = df_new.reset_index() + + # For estimating required loop numbers later + lifetime_old = min(df_new["value"]) + + # Adding vintage years to technical_lifetime for missing active years + for y in [y for y in years_all if y not in set(df_new["year_vtg"])]: + tmp = df_new.iloc[0, :].copy() + tmp["year_vtg"] = y + if lifetime and y >= year_vtg_st and y <= year_vtg_e: + tmp["value"] = lifetime + else: + cl = closest(df_new["year_vtg"].tolist(), y) + tmp["value"] = float(df_new.loc[df_new["year_vtg"] == cl, "value"]) + df_new = df_new.append(tmp, ignore_index=True) + df_new = df_new.sort_values("year_vtg").reset_index(drop=True) + + if remove_rest and not test_run: + # Removing extra vintage years if desired + scenario.remove_par(parname, df_old) + if not test_run: + # If remove_rest, + if not remove_rest: + df_old = df_old.loc[df_old["year_vtg"].isin(vtg_years)].copy() + scenario.remove_par(parname, df_old) + + # When removing data, it needs to be ensured that the dataframe + # updating the technical lifetime includes values for all the + # activity years of the last vintage year. These values are + # originally taken from the df_old and therefore need to be updated + # to be equal to the value of the last vintage year. + if remove_rest: + for n in df_new.node_loc.unique(): + df_tmp = df_new[df_new["node_loc"] == n] + for t in df_tmp.technology.unique(): + val = float( + df_tmp.loc[ + (df_tmp["technology"] == t) + & (df_tmp["year_vtg"] == max(vtg_years)), + "value", + ] + ) + df_new.loc[ + (df_new["node_loc"] == n) + & (df_new["technology"] == t) + & (df_new["year_vtg"] > max(vtg_years)), + "value", + ] = val + scenario.add_par(parname, df_new) + + results[parname] = df_new + + _log(parname, tec, node) + + par_exclude.append(parname) + + # Estimating how many time steps will possibly have missing data (for + # using in Part III.3) + if max(df_new["value"]) <= lifetime_old: + n = 1 + else: + if len(vtg_years) == 1: + div = int( + scenario.par("duration_period", filters={"year": vtg_years}).value + ) + else: + div = min(np.diff(vtg_years)) + n = int((max(df_new["value"]) - lifetime_old) / div) + + # ------------------------------------------------------------ + # Parameters with one index related to time (e.g., "inv_cost") + # ------------------------------------------------------------ + for parname in set(parlist_1d) - set(par_exclude): + # Node-related index + node_col = [y for y in scenario.idx_names(parname) if "node" in y][0] + + # Retrieve existing data + df_old = scenario.par(parname, {node_col: node, "technology": tec}) + + # Year-related index + year_ref = [y for y in df_old.columns if "year" in y][0] + + # Check if data is available for parameter; No update of missing + # data for historical years (for 1d parameters) + if df_old.empty: + par_empty.append(parname) + continue + elif use_firstmodelyear and max(df_old[year_ref]) < firstmodelyear: + par_empty.append(parname) + continue + + # Ensure 'unit' column is available + if "unit" in df_old.columns: + df_old = unit_uniform(df_old) + + # Create index used for pivoting dataframe + idx = [x for x in df_old.columns if x not in [year_ref, "value"]] + + # Making pivot table for extrapolation + df2 = df_old.copy().pivot_table(index=idx, columns=year_ref, values="value") + + # Preparing for backward extrapolation if more than one new years + # to be added before existing ones + + # Check for years prior to existing data + year_list = sorted( + [x for x in years_all if x < min(df_old[year_ref])], reverse=True + ) + # Check for years after existing data + year_list = year_list + sorted( + [x for x in years_all if x > max(df_old[year_ref])] + ) + + if use_firstmodelyear: + year_list = [y for y in year_list if y < firstmodelyear] + + # Add values for missing years + for y in year_list: + # Finding two adjacent years for extrapolation + if y < max(df2.columns): + year_next = horizon[horizon.index(y) + 1] + year_nn = ( + horizon[horizon.index(y) + 2] + if len([x for x in df2.columns if x > y]) >= 2 + else year_next + ) + + elif ( + y > max(df2.columns) + and horizon[horizon.index(y) - 1] in df2.columns + ): + year_next = horizon[horizon.index(y) - 1] + if len([x for x in df2.columns if x < y]) >= 2: + year_nn = list(df2.columns)[ + list(df2.columns).index(year_next) - 1 + ] + else: + year_nn = year_next + else: + continue + + # Extrapolate data + df2.loc[:, y] = intpol( + float(df2[year_next]), + float(df2[year_nn]), + year_next, + year_nn, + y, + dataframe=True, + ) + + # If adjacent value(s) are infinity, the missing value is + # set to the same inf + if not df2.loc[np.isinf(df2[year_next])].empty: + df2.loc[np.isinf(df2[year_next]), y] = df2.loc[:, year_next] + + # Negative values after extrapolation are ignored, if + # adjacent value is positive a multiplier is applied. + if ( + extrapol_neg + and not df2[y].loc[(df2[y] < 0) & (df2[year_next] >= 0)].empty + ): + df2.loc[(df2[y] < 0) & (df2[year_next] >= 0), y] = ( + df2.loc[:, year_next] * extrapol_neg + ) + + # Dataframe is turned from wide to long format + df_new = ( + pd.melt( + df2.reset_index(), + id_vars=idx, + value_vars=[x for x in df2.columns if x not in idx], + var_name=year_ref, + value_name="value", + ) + .dropna(subset=["value"]) + .reset_index(drop=True) + ) + + # Dataframe is sorted + df_new = df_new.sort_values(idx + [year_ref]).reset_index(drop=True) + + # Any data not in the list of years to be modified is dropped + df_new = df_new.loc[df_new[year_ref].isin(years_all)] + + # Removing extra vintage/activity years if desired + if remove_rest and not test_run: + scenario.remove_par(parname, df_old) + if not test_run: + if not remove_rest: + df_old = df_old.loc[df_old[year_ref].isin(vtg_years)].copy() + scenario.remove_par(parname, df_old) + scenario.add_par(parname, df_new) + + results[parname] = df_new + + _log(parname, tec, node) + + # ----------------------------------------------------------- + # Parameters with two indexes related to time (e.g., 'input') + # ----------------------------------------------------------- + for parname in set(parlist_2d) - set(par_exclude): + # Node-related index + node_col = [x for x in scenario.idx_names(parname) if "node" in x] + + # Retrieve existing data + df_old = scenario.par(parname, {node_col[0]: node, "technology": tec}) + + # Check if data is available for parameter + if df_old.empty: + par_empty.append(parname) + continue + + # Ensure 'unit' column is available + if "unit" in df_old.columns: + df_old = unit_uniform(df_old) + + # Create index used for pivoting dataframe + idx = [x for x in df_old.columns if x not in ["year_act", "value"]] + + # Making pivot table for extrapolation + df2 = df_old.copy().pivot_table( + index=idx, columns="year_act", values="value" + ) + + # Identify new active years missing in original data + yr_missing = [ + x for x in horizon if x <= max(act_years) and x not in df2.columns + ] + + # Adding NAN for missing years + for y in yr_missing: + df2[y] = np.nan + + # Identify the second index related to time, + # to be paired with year_act + year_ref = [ + y for y in df_old.columns if y in ["year", "year_vtg", "year_rel"] + ][0] + + # Checking inconsistency in vintage years for + # commodities/emissions/relations for one specific technology + col_nontec = [ + x + for x in scenario.idx_names(parname) + if not any( + y in x + for y in ["time", "node", "tec", "mode", "year", "level", "rating"] + ) + ] + if col_nontec: + df_count = ( + df2.reset_index() + .loc[df2.reset_index()[year_ref].isin(vtg_years), col_nontec] + .apply(pd.value_counts) + .fillna(0) + ) + + if not df_count.empty: + df_count = df_count.loc[ + df_count[col_nontec[0]] < int(df_count.mean()) + ] + + if not df_count.empty: + # NOTICE: this is not resolved here and user should decide + log.warning( + f"In parameter {repr(parname)} the vintage years of " + f"{col_nontec[0]: df_count.index.tolist()}, {repr(tec)} in " + f"{repr(node)} are different from other input entries. Please " + "check the results!" + ) + + # ------------------------------------------------------- + # Adding extra active years to existing vintage years, if + # lifetime extended. + # In the case of the parameter relation_activity, this is + # not required or wanted, as the instead of year_vtg, the + # index is "year_rel", and the "year_act" entries should + # not be extended for the "year_rel". + # ------------------------------------------------------- + if parname != "relation_activity": + count = 0 + while count <= n: + # The counter of loop (no explicit use of k) + count = count + 1 + for y in sorted( + [x for x in set(df_old[year_ref]) if x in vtg_years] + ): + + # The last active year of this vintage year + yr_end = act_years[vtg_years.index(y)] + + if y < max(df_old["year_act"]): + year_next = horizon[horizon.index(y) + 1] + + if y < horizon[horizon.index(max(df_old["year_act"])) - 1]: + year_nn = horizon[horizon.index(y) + 2] + + else: + year_nn = y + + df_yr = df2[ + df2.index.get_level_values(year_ref).isin([y]) + ].reindex(sorted(df2.columns), axis=1) + + # Creates a list of years for which values need to be + # extrapolated. + yr_act_list = sorted( + [ + x + for x in df_yr.columns + if df_yr[x].isnull().any() and x <= yr_end and x > y + ] + ) + if yr_act_list: + for yr_act in yr_act_list: + # Extrapolation across the activity years + df_next = f_slice( + df2, idx, year_ref, [year_next], y + )[yr_act] + df_nn = f_slice(df2, idx, year_ref, [year_nn], y)[ + yr_act + ] + + # If no data is available for the years_active + # in the next two vintages, then the values + # from the previous years for the same vintage + # are used. + # e.g. vintage = 2015, year_active is 2050 + # so if the 2050 value for the vintages 2020 + # and 2025 are empty, the 2045 and 2040 values + # are used from the same vintage. + if ( + df_next.empty + or any([np.isnan(v) for v in df_next.values]) + ) and ( + df_nn.empty + or any([np.isnan(v) for v in df_nn.values]) + ): + df_next = df_yr[ + horizon[horizon.index(yr_act) - 1] + ] + df_nn = df_yr[ + horizon[horizon.index(yr_act) - 2] + ] + + # Same rule as above is applied + if ( + df_next.empty + or any([np.isnan(v) for v in df_next.values]) + ) and not ( + df_nn.empty + or any([np.isnan(v) for v in df_nn.values]) + ): + df_next = df_yr[ + horizon[horizon.index(yr_act) - 1] + ] + df_nn = df_yr[ + horizon[horizon.index(yr_act) - 2] + ] + + # Comment OFR - i dont understand logic. + if not ( + df_next.empty + or any([np.isnan(v) for v in df_next.values]) + ) and ( + df_nn.empty + or any([np.isnan(v) for v in df_nn.values]) + ): + df_nn = df_yr[ + horizon[horizon.index(yr_act) - 1] + ] + + df_yr[yr_act] = intpol( + df_next, + f_index(df_nn, df_next), + year_next, + year_nn, + y, + dataframe=True, + ) + + # end year + df_yr.loc[:, df_yr.columns > yr_end] = np.nan + df2.loc[df_yr.index, :] = df_yr + df2 = df2.reindex(sorted(df2.columns), axis=1) + + # ----------------------------------------------------- + # Adding missing values for extended vintage and active + # years (before and after existing vintage years) + # ----------------------------------------------------- + + # New vintage years before the first existing vintage year + year_list = sorted( + [x for x in vtg_years if x < min(df_old["year_act"])], reverse=True + ) + + # Plus new vintage years after the last existing vintage year + year_list = year_list + sorted( + [x for x in vtg_years if x > max(df_old[year_ref])] + ) + + # For the parameter relation_activity additional data is needed + # for all activity years of the vintage + if parname == "relation_activity": + year_list = year_list + sorted( + [ + x + for x in horizon + if x <= max(act_years) and x > max(df_old["year_act"]) + ] + ) + + # Add values for missing years + if year_list: + for y in year_list: + if parname == "relation_activity": + yr_end = y + else: + yr_end = vtg_act_yrs.loc[y, "act_years"] + + if y not in df2.columns: + df2[y] = np.nan + + # Finding two adjacent years for extrapolation + if y < min(df_old["year_act"]): + year_next = horizon[horizon.index(y) + 1] + if idx_memb(horizon, y, 2) in set(df_old[year_ref]): + year_nn = horizon[horizon.index(y) + 2] + else: + year_nn = year_next + + else: + year_next = horizon[horizon.index(y) - 1] + if horizon.index(y) >= 2: + year_nn = horizon[horizon.index(y) - 2] + else: + year_nn = year_next + + # Adding missing active years for this new vintage year + if yr_end not in df2.columns: + yr_next = horizon[horizon.index(yr_end) - 1] + yr_nn = horizon[horizon.index(yr_end) - 2] + df2[yr_end] = intpol( + df2[yr_next], + df2[yr_nn], + yr_next, + yr_nn, + y, + dataframe=True, + ) + df2[yr_end].loc[ + pd.isna(df2[yr_end]) & ~pd.isna(df2[yr_next]) + ] = df2.loc[:, yr_next].copy() + # Removing extra values from previous vintage year + if len(df2[yr_end]) > 1: + df2[yr_end].loc[pd.isna(df2[yr_end].shift(+1))] = np.nan + + if extrapol_neg: + dd = df2.loc[:, yr_next].copy() + df2[yr_end].loc[ + (df2[yr_end] < 0) & (df2[yr_next] >= 0) + ] = (dd * extrapol_neg) + + # Extrapolate data + df_next = f_slice(df2, idx, year_ref, [year_next], y) + df_nn = f_slice(df2, idx, year_ref, [year_nn], y) + + if parname == "relation_activity": + df_yr = df_next + df_yr[year_nn] = df_nn[year_nn] + df_yr = df_yr[sorted(df_yr.columns)] + if y < min(df_old["year_act"]): + df_yr = df_yr.interpolate( + axis=1, limit=1, limit_direction="backward" + ) + else: + df_yr = df_yr.interpolate(axis=1, limit=1) + + # To make sure no extra active years for new vintage + # years in the loop + df_yr.loc[:, (df_yr.columns > y) | (df_yr.columns < y)] = np.nan + + else: + # Configuring new vintage year to be added to + # vintage years + df_yr = intpol( + df_next, + f_index(df_nn, df_next), + year_next, + year_nn, + y, + dataframe=True, + ) + + # Excluding parameters with two time index, but not + # across all active years + if parname not in ["relation_activity"]: + df_yr[year_next].loc[pd.isna(df_yr[year_next])] = ( + f_slice(df2, idx, year_ref, [year_next], y) + .loc[:, year_next] + .copy() + ) + if ( + year_nn not in df_yr.columns + or df_yr[year_nn].loc[~pd.isna(df_yr[year_nn])].empty + ): + year_nn = year_next + df_yr[y].loc[pd.isna(df_yr[y])] = intpol( + df_yr[year_next], + df_yr[year_nn], + year_next, + year_nn, + y, + dataframe=True, + ) + + # Adding missing data for columns in the corner + if not df_yr.loc[pd.isna(df_yr[yr_end])].empty: + if not df_yr.dropna(axis=1).empty: + yr_last = max(df_yr.dropna(axis=1).columns) + + for yr in [x for x in df_yr.columns if x > yr_last]: + yr_next = horizon[horizon.index(yr) - 1] + yr_nn = horizon[horizon.index(yr) - 2] + df_yr.loc[:, yr] = intpol( + df_yr[yr_next], + df_yr[yr_nn], + yr_next, + year_nn, + yr_end, + dataframe=True, + ) + + # To make sure no extra active years for new + # vintage years in the loop + df_yr.loc[ + :, (df_yr.columns > yr_end) | (df_yr.columns < y) + ] = np.nan + + if y in set(df_old[year_ref]): + df2.loc[df2.index.isin(df_yr.index), :] = df_yr + else: + df2 = df2.append(df_yr) + df2 = df2.reindex(sorted(df2.columns), axis=1) + df2 = df2.reset_index().sort_values(idx).set_index(idx) + + # The final check in case the liftime is being reduced + if year_ref == "year_vtg": + for y in vtg_years: + df2.loc[ + df2.index.get_level_values(year_ref).isin([y]), + df2.columns > act_years[vtg_years.index(y)], + ] = np.nan + + # Dataframe is turned from wide to long format + df_new = pd.melt( + df2.reset_index(), + id_vars=idx, + value_vars=[x for x in df2.columns if x not in idx], + var_name="year_act", + value_name="value", + ).dropna(subset=["value"]) + + # Dataframe is sorted + df_new = df_new.sort_values(idx).reset_index(drop=True) + + # Any data not in the list of years to be modified is dropped + if parname == "relation_activity": + df_new = df_new.loc[ + df_new[year_ref].isin(set(act_years + vtg_years + year_list)) + ] + else: + df_new = df_new.loc[df_new[year_ref].isin(vtg_years)] + + # Removing extra (old) vintage/activity years if desired + if remove_rest and not test_run: + scenario.remove_par(parname, df_old) + if not test_run: + if not remove_rest: + if parname == "relation_activity": + df_old = df_old.loc[ + df_old[year_ref].isin( + set(act_years + vtg_years + year_list) + ) + ].copy() + else: + df_old = df_old.loc[df_old[year_ref].isin(vtg_years)].copy() + scenario.remove_par(parname, df_old) + if not df_new.empty: + scenario.add_par(parname, df_new) + + results[parname] = df_new.pivot_table( + index=idx, columns="year_act", values="value" + ).reset_index() + + _log(parname, tec, node) + + if not test_run and commit is True: + scenario.commit("Scenario updated for new lifetime.") + return results diff --git a/message_ix_models/util/compat/message_data/check_scenario_fix_and_inv_cost.py b/message_ix_models/util/compat/message_data/check_scenario_fix_and_inv_cost.py new file mode 100644 index 0000000000..f8c13ecafe --- /dev/null +++ b/message_ix_models/util/compat/message_data/check_scenario_fix_and_inv_cost.py @@ -0,0 +1,245 @@ +import numpy as np +import pandas as pd + +from . import get_optimization_years + + +def main(scen, vintaging=False, check_only=False, remove_zero=True, verbose=False): + """Check and fix inv_cost and fix_cost. + + This module is designed to check over the parametrization of the + `fix_cost` and `inv_cost`. + The check is carried out per vintage, technology and region. + Using the the message_ix utility `vintage_and_active_years`, the vintage- + and activity-years for which there should be parameters available are retrieved. + If the there are missing entries, these are added via interpolation for the + vintage-years and are extended across activity-years in the case of `fix-cost` + depending on whether vintaging is applied or not. + + Parameters + ---------- + scen : :class:`message_ix.Scenario` + Scenario for which the check should be carried out. + vintaging : boolean (default=False) + Option as to how vintage specific entries are to be extended over the + activity-years for the `fix_cost`. + If True, then entries over the activity years within a vintage will be + constant. + Otherwise, the entries for activity years will be assumed to be the same + across all vintages. + check_only : boolean (default=False) + Option as whether to modify parameters to correct the issues, or whether + to only check over the scenario. + remove_zero : boolean (default=False) + Option whether `zero` values can be removed for a given technology. + verbose : booelan (default=False) + Option whether to print log messages. + """ + + # Retrieve model years + model_years = get_optimization_years.main(scen) + + # Specify parameters which should be checked. + # Currently, only `fix_cost` and `inv_cost` are covered. + # A list is used for possible extension of this utility. + parameters = ["inv_cost", "fix_cost"] + + with scen.transact(f"Correct parameters {parameters}."): + for par in parameters: + # ----------------------------------------------- + # Step 1.: Retrieve parameter data from scenario. + # ----------------------------------------------- + df = scen.par(par) + + # All zero values are dropped from `fix_cost`. + # Optionally, these could also be removed from the scenario. + if par in ["fix_cost"]: + if remove_zero: + scen.remove_par(par, df.loc[df.value == 0]) + df = df.loc[df.value != 0] + + # --------------------------------------------- + # Step 2.: Iterate over nodes and technologies. + # --------------------------------------------- + for n in df.node_loc.unique(): + for t in df.loc[df.node_loc == n].technology.unique(): + + # Create filter for parametrized data. + loc_idx = ( + (df.node_loc == n) + & (df.technology == t) + & (df.year_vtg.isin(model_years)) + ) + + # ---------------------------- + # Step 2.1.: Check `inv_cost`. + # ---------------------------- + # For the parameter investment cost, a check is made to see that + # all required parameters are present for the model-years only + # i.e. all years >= `firstmodelyear`. + if par in ["inv_cost"]: + # Actual `year_vtg` are filtered for the technology. + obs = df.loc[loc_idx].year_vtg.unique().tolist() + + # Expected `year_vtg` are retrieved and filtered. + try: + exp = ( + scen.vintage_and_active_years((n, t), in_horizon=False) + .year_vtg.unique() + .tolist() + ) + exp = [y for y in exp if y in model_years] + except Exception: + print( + f"Issues retrieving vintage_and_active_years for {t}", + f" in region {n}", + ) + continue + + # A check is made to ensure that the obeserved `year_vtg` + # match the expected `year_vtg`. + if sorted(exp) != sorted(obs): + assert f"{par} in region {n} for technology {t} do not match. Expected {exp} and found {obs}." + + # ---------------------------- + # Step 2.2.: Check `fix_cost`. + # ---------------------------- + # For the parameter fix cost, a check is made to see that all + # required parameters are present for the model-years only + # i.e. all years >= `firstmodelyear`. + elif par in ["fix_cost"]: + # Actual `year_vtg` and `year_act` are filtered + # for the technology. + obs = df.loc[loc_idx].drop( + ["node_loc", "technology", "value", "unit"], axis=1 + ) + + try: + exp = scen.vintage_and_active_years((n, t), in_horizon=True) + except Exception: + print( + "Issues retrieving vintage_and_active_years", + f" for {t} in region {n}", + ) + continue + + # Merge obvserved and expected results using the indicator + # option to analyse differences. + tmp = exp.merge(obs, how="outer", indicator=True) + tmp = tmp.loc[tmp["_merge"] != "both"] + + # Handle differences + if not tmp.empty: + if verbose: + print( + "Resolving differences for `fix_cost` of", + f" technology {t} in region {n}.\n", + tmp, + ) + + # Handle "right-only" issues. + # This indicates that there are excess values parametrized + # that do not correspond to the vintage/activity years as + # indicated by `vintage_and_active_years`. + # The excess values are filtered and removed. + if "right_only" in tmp["_merge"].unique(): + tmp_right_only = tmp.loc[ + tmp["_merge"] == "right_only" + ].drop("_merge", axis=1) + + # Filter out data to be removed. + remove_data = df.loc[loc_idx].merge( + tmp_right_only, how="inner" + ) + scen.remove_par(par, remove_data) + + # Handle "left-only" issues. + # This indicates that there are values missing for + # vintage/activity years where there should be values as + # indicated `vintage_and_active_years`. + + if "left_only" in tmp["_merge"].unique(): + tmp_left_only = tmp.loc[ + tmp["_merge"] == "left_only" + ].drop("_merge", axis=1) + + # Filter out data which needs to be extended. + add_data = df.loc[loc_idx] + + # The dataframe is extended with vintage/activity years + # which need to be extended. + # Each entry will receive a Nan value, dealt with + # here-after. + for i, items in tmp_left_only.iterrows(): + add_row = add_data.loc[[add_data.index[0]]] + add_row.year_vtg = items.year_vtg + add_row.year_act = items.year_act + add_row.value = np.nan + add_data = ( + pd.concat([add_data, add_row]) + .reset_index() + .drop("index", axis=1) + ) + add_data = add_data.sort_values( + ["year_vtg", "year_act"] + ).set_index(["year_vtg"]) + + # Make a temporary dataframe select only the initial + # activity year for each vintage. + # These values are then interpolated. + # First and last vintages are "duplicates". + # NOTE: In some cases, because only filtering out + # values for the values relevant to the optimization + # time-period, there is a caveat. + # When the `firstmodelyear` is 2020, the technology + # parameters for the vintage 2015, will only be checked + # for the activity years from 2020 onwards; this means + # that for vtg:2015 and yact:2020, the value will be + # set to vtg:2020_yact:2020. + # This though is the same as what would result from + # backfill, because there are no values to interpolate. + tmp_add_data = pd.DataFrame() + for i in add_data.index.unique(): + tmp_add_data = pd.concat( + [tmp_add_data, add_data.loc[[i]][:1]] + ) + tmp_add_data = ( + tmp_add_data.interpolate( + method="index", limit_direction="both" + ) + .reset_index() + .set_index(["year_vtg", "year_act"]) + ) + + # Combine dataframes and treat remaining `year_act` + # values per vintage. + add_data = add_data.reset_index().set_index( + ["year_vtg", "year_act"] + ) + add_data = tmp_add_data.combine_first(add_data) + for yv in tmp_left_only.year_vtg.unique(): + # If vintaging, then NaN Values for each vintage, + # the activity-year values are set the same as + # the vintage. + if vintaging: + update = ( + add_data.loc[yv] + .fillna(method="ffill") + .value + ) + # If not vintaging, then the acitivty-year values + # across all vintages are set to be the same. + else: + update = ( + add_data.loc(axis=0)[ + :, add_data.loc[yv].index + ] + .dropna() + .reset_index() + .drop("year_vtg", axis=1) + .drop_duplicates() + .set_index("year_act") + .value + ) + add_data.loc[yv, "value"].update(update) + scen.add_par(par, add_data.reset_index()) diff --git a/message_ix_models/util/compat/message_data/manual_updates_ENGAGE_SSP2_v417_to_v418.py b/message_ix_models/util/compat/message_data/manual_updates_ENGAGE_SSP2_v417_to_v418.py index 81a4ab2438..e9d2ed58d3 100644 --- a/message_ix_models/util/compat/message_data/manual_updates_ENGAGE_SSP2_v417_to_v418.py +++ b/message_ix_models/util/compat/message_data/manual_updates_ENGAGE_SSP2_v417_to_v418.py @@ -2,17 +2,16 @@ import pandas as pd from message_ix_models.util import private_data_path -from message_data.tools.utilities import ( - calibrate_UE_gr_to_demand, - calibrate_vre, - change_technology_lifetime, - check_scenario_fix_and_inv_cost, - get_optimization_years, - update_fix_and_inv_cost, +from . import ( + calibrate_UE_gr_to_demand, + calibrate_vre, + change_technology_lifetime, + check_scenario_fix_and_inv_cost, + get_optimization_years, + update_fix_and_inv_cost, #TODO Why is this a module? update_h2_blending, ) - def _apply_npi_updates(scen): """Apply changes from ENGAGE 4.1.7 process applied. diff --git a/message_ix_models/util/compat/message_data/update_fix_and_inv_cost.py b/message_ix_models/util/compat/message_data/update_fix_and_inv_cost.py new file mode 100644 index 0000000000..938bb4bcd4 --- /dev/null +++ b/message_ix_models/util/compat/message_data/update_fix_and_inv_cost.py @@ -0,0 +1,193 @@ +import numpy as np +import pandas as pd + +from .get_nodes import get_nodes +from .utilities import intpol + + +def add_missing_years(df, model_years, missing_years, idx_yr): + """Adds missing years and formats dataframe. + + Missing model years are added via interpolation. + + The dataframe is then formatted so it can be used to overwrite existing model data. + + Parameters + ---------- + df : pandas.DataFrame + Data frame containing cost data, in wide format (i.e. having years as columns + plus "technology", "node_loc" and "unit"). + model_years : .list + List of years in the model, starting from 1990 up to 2100. + missing_years : .list + List of years for which data needs to be added, e.g. [2015, 2025, 2035, 2045, + 2055]. + idx_yr : .str + Being ``year_vtg`` when filling investment costs, or ``year_act`` for fixed O&M + costs (**only** if vintaging is turned off). + + Returns + ------- + df : DataFrame + Data frame containing cost data for all model years + df_tec : list + List of technologies for which cost data should be updated + """ + + for y in missing_years: + df[y] = df.apply( + lambda row: intpol( + row[model_years[model_years.index(y) - 1]], + row[model_years[model_years.index(y) + 1]], + model_years[model_years.index(y) - 1], + model_years[model_years.index(y) + 1], + y, + ) + if y not in [model_years[0], model_years[-1]] + else float(row[y]), + axis=1, + ) + + # The last value for 2110 is equal to 2100 + df[2110] = df[2100] + + # Formats the dataframe so it can be combined with model data + df = df.melt( + id_vars=["node_loc", "technology", "unit"], var_name=idx_yr, value_name="value1" + ).set_index(["node_loc", "technology", "unit", idx_yr]) + + # Creates technology list + df_tec = df.reset_index().technology.unique() + + return df, df_tec + + +def main(scenario, data_path, ssp, data_filename=None): + """Update fixed O&M and investment costs. + + The investment as well as the fixed O&M costs are read from an xlsx file for the + corresponding SSP scenario. The costs are currently provided for 10 year + timesteps. Hence, missing model year values are interpolated through + :func:`~update_fix_and_inv_cost.add_missing_years`. + + Parameters + ---------- + scenario : :class:`message_ix.Scenario` + Scenario to which changes should be applied. + data_path : :class:`pathlib.Path` + Path to model-data directory. + ssp : .str + The SSP version for which costs should be updated. + (SSP1, SSP2 or SSP3) + data_filename : .str + Name of file including extension (".xlsx"). + """ + if not data_filename: + data_filename = "{}_techinput.xlsx".format(ssp) + data_file = data_path / "investment_cost" / data_filename + + # Configures the rows/columns which need to be read + # from the xlsx + usecols = "B,F:S" + inv_cost_nrows = 61 + fix_cost_skiprows = 63 + fix_cost_nrows = 61 + + # Configure regions and remove prefix + regions = [ + r.replace("R11_", "") + for r in get_nodes(scenario) + if r not in ["R11_GLB", "World"] + ] + + for r in regions: + # Retrieve investment costs xlsx + df_inv = ( + pd.read_excel( + data_file, + sheet_name="{}_{}".format(r, ssp), + index_col=0, + usecols=usecols, + nrows=inv_cost_nrows, + ) + .reset_index() + .rename(columns={"inv": "technology"}) + .assign(node_loc="R11_{}".format(r), unit="USD/kWa") + ) + + # Identifies which modelyears are missing in the data and interpolates + # missing values. + model_years = [y for y in scenario.set("year") if 2100 >= y >= 1990] + missing_years = [y for y in model_years if y not in df_inv.columns] + df_inv, df_inv_tec = add_missing_years( + df_inv, model_years, missing_years, "year_vtg" + ) + + # Retrieves dataframe of current data in the model + tmp = ( + scenario.par( + "inv_cost", + filters={"node_loc": ["R11_{}".format(r)], "technology": df_inv_tec}, + ) + .assign(unit="USD/kWa") + .set_index(["node_loc", "technology", "unit", "year_vtg"]) + ) + + # Merges new and current model data; all current model + # data is replaced by available new data + tmp["value1"] = df_inv["value1"] + tmp.value = tmp.apply( + lambda row: row["value1"] if not np.isnan(row["value1"]) else row["value"], + axis=1, + ) + tmp = tmp.reset_index().drop("value1", axis=1) + tmp.value = round(tmp.value, 2) + + scenario.check_out() + scenario.add_par("inv_cost", tmp) + scenario.commit("Updated inv_cost for region R11_{}".format(r)) + + # Retrieve fix_cost from xlsx + # NOTE currently the index is set to year_act. This ensures that the + # apporach is consistent with vintaging turned off in the model. + # Other wise see notes below. + df_fom = ( + pd.read_excel( + data_file, + sheet_name="{}_{}".format(r, ssp), + skiprows=fix_cost_skiprows, + index_col=0, + usecols=usecols, + nrows=fix_cost_nrows, + ) + .reset_index() + .rename(columns={"fom": "technology"}) + .assign(node_loc="R11_{}".format(r), unit="USD/kWa") + ) + + df_fom, df_fom_tec = add_missing_years( + df_fom, model_years, missing_years, "year_act" + ) + # NOTE if vintaging "on": switch to year_vtg in prev. line + + tmp = ( + scenario.par( + "fix_cost", + filters={"node_loc": ["R11_{}".format(r)], "technology": df_fom_tec}, + ) + .assign(unit="USD/kWa") + .set_index(["node_loc", "technology", "unit", "year_act"]) + ) + # NOTE if vintaging "on": switch to year_vtg in prev. line + tmp["value1"] = df_fom["value1"] + + tmp.value = tmp.apply( + lambda row: row["value1"] if not np.isnan(row["value1"]) else row["value"], + axis=1, + ) + tmp = tmp.reset_index().drop("value1", axis=1) + tmp.value = round(tmp.value, 2) + + scenario.check_out() + scenario.add_par("fix_cost", tmp) + scenario.commit("Updated fix_cost for region R11_{}".format(r)) diff --git a/message_ix_models/util/compat/message_data/update_h2_blending.py b/message_ix_models/util/compat/message_data/update_h2_blending.py new file mode 100644 index 0000000000..c2ff7f6b13 --- /dev/null +++ b/message_ix_models/util/compat/message_data/update_h2_blending.py @@ -0,0 +1,70 @@ +from .get_nodes import get_nodes + +def main(scen): + """Revise hydrogen-blending constraints. + + The revision of the constraints makes changes to three relations. + 1. For relation `h2_scrub_limit`, two CCS technologies are removed. + The relation should limit hydrogen share via blending in all non-CCS gas + applications to 50%, hence `h2_mix` has an entry of + -.482tC/kWyr*2*input-coefficient, while all other technologies have an + entry of .482tC/kWyr*input-coefficient. Greenfield CCS technologies do + not require an entry into the relation, as well as any other + technologies which require the "C" for the output e.g. `meth_ng` or + `h2_smr`. + A negative entry from the greenfield-CCS technologies would further + reduce the "total" gas. Theortically, `gas_coal` should also have an + entry, but is not really necessary as this technology is only active in + BAU scenarios, when this constraint is not active. `bio_gas` has a + negative entry to ensure that it doesnt count towards the total used to + derive the share. The retro-fit CCS scrubbers require an entry, because + the gas used in powerplants to which the retrofits apply, can be used + for CCS pruposes and this needs to be avoided. + Only secondary-level technologies need to be added. + 2. The relation `h2mix_direct` is removed in favor of #3. This avoids + having to add individual technologies at the final-energy level. + 3. The relation `gas_mix_lim` is used to limit the share of hydrogen + blended into the the natural-gas network based on total final-energy + gas use. Previously, this constraint was only configured for AFR + (at 20%). The constraint is carried over to other regions and set at + 50% in all regions including AFR. + """ + + # ---------------------------------- + # Step 1.: Clean up `h2_scrub_limit` + # ---------------------------------- + + with scen.transact("Clean up h2_scrub_limit entries"): + df = scen.par( + "relation_activity", + filters={ + "relation": "h2_scrub_limit", + "technology": ["gas_cc_ccs", "h2_smr_ccs"], + }, + ) + scen.remove_par("relation_activity", df) + + # ---------------------------------------------------------- + # Step 2.: Reconfigure hydrogen blending based on FE-Gas use + # ---------------------------------------------------------- + with scen.transact("Reconfigure hydrogen-mixing constraints"): + # Remove obsolete relation + scen.remove_set("relation", "h2mix_direct") + + # Extend and update values of `gas_mix_lim` + rel_act = scen.par("relation_activity", filters={"relation": "gas_mix_lim"}) + rel_act.loc[rel_act.technology.isin(["gas_t_d", "gas_t_d_ch4"]), "value"] = -0.5 + + # Copy lower_bound + rel_bnd = scen.par("relation_upper", filters={"relation": "gas_mix_lim"}) + + # Update parameters for all regions excpet GLB. + # get_nodes() ignores "World" + for n in get_nodes(scen): + if "GLB" in n: + continue + rel_act = rel_act.assign(node_rel=n, node_loc=n) + scen.add_par("relation_activity", rel_act) + + rel_bnd = rel_bnd.assign(node_rel=n) + scen.add_par("relation_upper", rel_bnd) From ef842dfb6a6e6f1af6e9d55b05c2ec40641d33c5 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 21 May 2024 14:16:20 +0200 Subject: [PATCH 768/774] Reduce complexity of methanol module --- .../model/material/data_methanol.py | 195 +++++++++--------- 1 file changed, 99 insertions(+), 96 deletions(-) diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index f47d566bdc..68fcd85f5e 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -61,98 +61,15 @@ def gen_data_methanol(scenario): ] = "primary_material" meth_fs_dic["output"] = df - # add meth prod fs mode - par_dict_fs = {} - for i in scenario.par_list(): - df = scenario.par( - i, - filters={ - "technology": [ - "meth_ng", - "meth_coal", - "meth_ng_ccs", - "meth_coal_ccs", - ] - }, - ) - if df.size != 0: - par_dict_fs[i] = df - for i in par_dict_fs.keys(): - if "mode" in par_dict_fs[i].columns: - par_dict_fs[i]["mode"] = "feedstock" - df = par_dict_fs["output"] - df.loc[df["commodity"] == "methanol", "level"] = "primary_material" - par_dict_fs["output"] = df - - par_dict = {} - for i in scenario.par_list(): - df = scenario.par( - i, - filters={ - "technology": [ - "meth_ng", - "meth_coal", - "meth_ng_ccs", - "meth_coal_ccs", - ] - }, - ) - if df.size != 0: - par_dict[i] = df - - for i in par_dict.keys(): - if "mode" in par_dict[i].columns: - par_dict[i]["mode"] = "fuel" - df = par_dict["output"] - par_dict["output"] = df + # add meth prod fs and fuel mode + par_dict_fs = add_prod_mode(scenario, "feedstock", "primary_material") + par_dict = add_prod_mode(scenario, "fuel") # add meth_bal fs mode - bal_fs_dict = {} - bal_fuel_dict = {} - for i in scenario.par_list(): - df = scenario.par(i, filters={"technology": "meth_bal"}) - if df.size != 0: - bal_fs_dict[i] = df - bal_fuel_dict[i] = df.copy(deep=True) - for i in bal_fs_dict.keys(): - if "mode" in bal_fs_dict[i].columns: - bal_fs_dict[i]["mode"] = "feedstock" - bal_fuel_dict[i]["mode"] = "fuel" - - df = bal_fs_dict["input"] - df.loc[df["commodity"] == "methanol", "level"] = "primary_material" - bal_fs_dict["input"] = df - df = bal_fs_dict["output"] - df.loc[df["commodity"] == "methanol", "level"] = "secondary_material" - bal_fs_dict["output"] = df + bal_fs_dict, bal_fuel_dict = add_bal_modes(scenario) # add meth trade fs mode - trade_dict_fs = {} - trade_dict_fuel = {} - for i in scenario.par_list(): - df = scenario.par( - i, filters={"technology": ["meth_imp", "meth_exp", "meth_trd"]} - ) - if df.size != 0: - trade_dict_fs[i] = df - trade_dict_fuel[i] = df.copy(deep=True) - - for i in trade_dict_fs.keys(): - if "mode" in trade_dict_fs[i].columns: - trade_dict_fs[i]["mode"] = "feedstock" - trade_dict_fuel[i]["mode"] = "fuel" - - df = trade_dict_fs["output"] - df.loc[df["technology"] == "meth_imp", "level"] = "secondary_material" - df.loc[df["technology"] == "meth_exp", "level"] = "export_fs" - df.loc[df["technology"] == "meth_trd", "level"] = "import_fs" - trade_dict_fs["output"] = df - - df = trade_dict_fs["input"] - df.loc[df["technology"] == "meth_imp", "level"] = "import_fs" - df.loc[df["technology"] == "meth_trd", "level"] = "export_fs" - df.loc[df["technology"] == "meth_exp", "level"] = "primary_material" - trade_dict_fs["input"] = df + trade_dict_fuel, trade_dict_fs = add_trd_modes(scenario) dict_t_d_fs = pd.read_excel( package_data_path("material", "methanol", "meth_t_d_material_pars.xlsx"), @@ -165,13 +82,6 @@ def gen_data_methanol(scenario): df_rel = dict_t_d_fs["relation_activity"] - def get_embodied_emi(row, pars, share_par): - if row["year_act"] < pars["incin_trend_end"]: - share = pars["incin_rate"] + pars["incin_trend"] * (row["year_act"] - 2020) - else: - share = 0.5 - return row["value"] * (1 - share) * pars[share_par] - df_rel_meth = df_rel.loc[df_rel["technology"] == "meth_t_d", :] df_rel_meth["value"] = df_rel_meth.apply( lambda x: get_embodied_emi(x, pars, "meth_plastics_share"), axis=1 @@ -282,6 +192,99 @@ def get_embodied_emi(row, pars, share_par): return new_dict2 +def get_embodied_emi(row, pars, share_par): + if row["year_act"] < pars["incin_trend_end"]: + share = pars["incin_rate"] + pars["incin_trend"] * (row["year_act"] - 2020) + else: + share = 0.5 + return row["value"] * (1 - share) * pars[share_par] + + +def add_prod_mode(scenario, mode, level=None): + tec_pars = [ + x for x in scenario.par_list() if ("technology" in scenario.idx_sets(x)) + ] + par_dict = {} + for i in tec_pars: + df = scenario.par( + i, + filters={ + "technology": [ + "meth_ng", + "meth_coal", + "meth_ng_ccs", + "meth_coal_ccs", + ] + }, + ) + if df.size != 0: + par_dict[i] = df + + for i in par_dict.keys(): + if "mode" in par_dict[i].columns: + par_dict[i]["mode"] = mode + df = par_dict["output"] + if level: + df.loc[df["commodity"] == "methanol", "level"] = level + par_dict["output"] = df + return par_dict + + +def add_bal_modes(scenario): + tec_pars = [ + x for x in scenario.par_list() if ("technology" in scenario.idx_sets(x)) + ] + bal_fs_dict = {} + bal_fuel_dict = {} + for i in tec_pars: + df = scenario.par(i, filters={"technology": "meth_bal"}) + if df.size != 0: + bal_fs_dict[i] = df + bal_fuel_dict[i] = df.copy(deep=True) + for i in bal_fs_dict.keys(): + if "mode" in bal_fs_dict[i].columns: + bal_fs_dict[i]["mode"] = "feedstock" + bal_fuel_dict[i]["mode"] = "fuel" + + df = bal_fs_dict["input"] + df.loc[df["commodity"] == "methanol", "level"] = "primary_material" + bal_fs_dict["input"] = df + df = bal_fs_dict["output"] + df.loc[df["commodity"] == "methanol", "level"] = "secondary_material" + bal_fs_dict["output"] = df + return bal_fs_dict, bal_fuel_dict + + +def add_trd_modes(scenario): + trade_dict_fs = {} + trade_dict_fuel = {} + for i in scenario.par_list(): + df = scenario.par( + i, filters={"technology": ["meth_imp", "meth_exp", "meth_trd"]} + ) + if df.size != 0: + trade_dict_fs[i] = df + trade_dict_fuel[i] = df.copy(deep=True) + + for i in trade_dict_fs.keys(): + if "mode" in trade_dict_fs[i].columns: + trade_dict_fs[i]["mode"] = "feedstock" + trade_dict_fuel[i]["mode"] = "fuel" + + df = trade_dict_fs["output"] + df.loc[df["technology"] == "meth_imp", "level"] = "secondary_material" + df.loc[df["technology"] == "meth_exp", "level"] = "export_fs" + df.loc[df["technology"] == "meth_trd", "level"] = "import_fs" + trade_dict_fs["output"] = df + + df = trade_dict_fs["input"] + df.loc[df["technology"] == "meth_imp", "level"] = "import_fs" + df.loc[df["technology"] == "meth_trd", "level"] = "export_fs" + df.loc[df["technology"] == "meth_exp", "level"] = "primary_material" + trade_dict_fs["input"] = df + return trade_dict_fs, trade_dict_fuel + + def gen_data_meth_h2(mode): h2_par_dict = pd.read_excel( package_data_path( @@ -469,7 +472,7 @@ def gen_data_meth_chemicals(scenario, chemical): ] df = par_dict["initial_activity_up"] par_dict["initial_activity_up"] = df[ - ~((df["node_loc"] == "R12_CHN")) + ~(df["node_loc"] == "R12_CHN") ] # & (df["year_act"] == 2020))] # par_dict.pop("growth_activity_up") # par_dict.pop("growth_activity_lo") From 19d04fb281ab5bf0823dad66f20ef09528171eb7 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 21 May 2024 14:16:58 +0200 Subject: [PATCH 769/774] Apply ruff format to data_util --- message_ix_models/model/material/data_util.py | 214 +++++++++--------- 1 file changed, 101 insertions(+), 113 deletions(-) diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index 3889fccf36..266690faee 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -88,7 +88,7 @@ def load_GDP_COVID() -> pd.DataFrame: def add_macro_COVID( - scen: message_ix.Scenario, filename: str, check_converge: bool = False + scen: message_ix.Scenario, filename: str, check_converge: bool = False ) -> message_ix.Scenario: """ Prepare data for MACRO calibration by reading data from xlsx file @@ -184,7 +184,7 @@ def modify_demand_and_hist_activity(scen: message_ix.Scenario) -> None: | (df["SECTOR"] == "industry (non-ferrous metals)") | (df["SECTOR"] == "industry (non-metallic minerals)") | (df["SECTOR"] == "industry (total)") - ] + ] df = df[df["RYEAR"] == 2015] # NOTE: Total cehmical industry energy: 27% thermal, 8% electricity, 65% feedstock @@ -203,10 +203,10 @@ def modify_demand_and_hist_activity(scen: message_ix.Scenario) -> None: & (df["SECTOR"] != "industry (total)") & (df["SECTOR"] != "feedstock (petrochemical industry)") & (df["SECTOR"] != "feedstock (total)") - ] + ] df_spec_total = df[ (df["SECTOR"] == "industry (total)") & (df["FUEL"] == "electricity") - ] + ] df_spec_new = pd.DataFrame( columns=["REGION", "SECTOR", "FUEL", "RYEAR", "UNIT_OUT", "RESULT"] @@ -215,15 +215,15 @@ def modify_demand_and_hist_activity(scen: message_ix.Scenario) -> None: df_spec_temp = df_spec.loc[df_spec["REGION"] == r] df_spec_total_temp = df_spec_total.loc[df_spec_total["REGION"] == r] df_spec_temp.loc[:, "i_spec"] = ( - df_spec_temp.loc[:, "RESULT"] - / df_spec_total_temp.loc[:, "RESULT"].values[0] + df_spec_temp.loc[:, "RESULT"] + / df_spec_total_temp.loc[:, "RESULT"].values[0] ) df_spec_new = pd.concat([df_spec_temp, df_spec_new], ignore_index=True) df_spec_new.drop(["FUEL", "RYEAR", "UNIT_OUT", "RESULT"], axis=1, inplace=True) df_spec_new.loc[df_spec_new["SECTOR"] == "industry (chemicals)", "i_spec"] = ( - df_spec_new.loc[df_spec_new["SECTOR"] == "industry (chemicals)", "i_spec"] - * 0.67 + df_spec_new.loc[df_spec_new["SECTOR"] == "industry (chemicals)", "i_spec"] + * 0.67 ) df_spec_new = df_spec_new.groupby(["REGION"]).sum().reset_index() @@ -232,7 +232,7 @@ def modify_demand_and_hist_activity(scen: message_ix.Scenario) -> None: df_feed = df[ (df["SECTOR"] == "feedstock (petrochemical industry)") & (df["FUEL"] == "total") - ] + ] # df_feed_total = # df[(df["SECTOR"] == "feedstock (total)") & (df["FUEL"] == "total")] df_feed_temp = pd.DataFrame(columns=["REGION", "i_feed"]) @@ -257,12 +257,12 @@ def modify_demand_and_hist_activity(scen: message_ix.Scenario) -> None: & (df["SECTOR"] != "feedstock (petrochemical industry)") & (df["SECTOR"] != "feedstock (total)") & (df["SECTOR"] != "industry (non-ferrous metals)") - ] + ] df_therm_total = df[ (df["SECTOR"] == "industry (total)") & (df["FUEL"] != "total") & (df["FUEL"] != "electricity") - ] + ] df_therm_total = ( df_therm_total.groupby(by="REGION").sum().drop(["RYEAR"], axis=1).reset_index() ) @@ -280,23 +280,23 @@ def modify_demand_and_hist_activity(scen: message_ix.Scenario) -> None: df_therm_temp = df_therm.loc[df_therm["REGION"] == r] df_therm_total_temp = df_therm_total.loc[df_therm_total["REGION"] == r] df_therm_temp.loc[:, "i_therm"] = ( - df_therm_temp.loc[:, "RESULT"] - / df_therm_total_temp.loc[:, "RESULT"].values[0] + df_therm_temp.loc[:, "RESULT"] + / df_therm_total_temp.loc[:, "RESULT"].values[0] ) df_therm_new = pd.concat([df_therm_temp, df_therm_new], ignore_index=True) df_therm_new = df_therm_new.drop(["RESULT"], axis=1) df_therm_new.drop(["FUEL", "RYEAR", "UNIT_OUT"], axis=1, inplace=True) df_therm_new.loc[df_therm_new["SECTOR"] == "industry (chemicals)", "i_therm"] = ( - df_therm_new.loc[df_therm_new["SECTOR"] == "industry (chemicals)", "i_therm"] - * 0.67 + df_therm_new.loc[df_therm_new["SECTOR"] == "industry (chemicals)", "i_therm"] + * 0.67 ) # Modify CPA based on https://www.iea.org/sankey/#?c=Japan&s=Final%20consumption. # Since the value did not allign with the one in the IEA website. index = (df_therm_new["SECTOR"] == "industry (iron and steel)") & ( - (df_therm_new["REGION"] == region_name_CPA) - | (df_therm_new["REGION"] == region_name_CHN) + (df_therm_new["REGION"] == region_name_CPA) + | (df_therm_new["REGION"] == region_name_CHN) ) df_therm_new.loc[index, "i_therm"] = 0.2 @@ -344,44 +344,38 @@ def modify_demand_and_hist_activity(scen: message_ix.Scenario) -> None: for r in df_therm_new["REGION"]: r_MESSAGE = region_type + r - useful_thermal.loc[ - useful_thermal["node"] == r_MESSAGE, "value" - ] = useful_thermal.loc[useful_thermal["node"] == r_MESSAGE, "value"] * ( - 1 - df_therm_new.loc[df_therm_new["REGION"] == r, "i_therm"].values[0] + useful_thermal.loc[useful_thermal["node"] == r_MESSAGE, "value"] = ( + useful_thermal.loc[useful_thermal["node"] == r_MESSAGE, "value"] + * (1 - df_therm_new.loc[df_therm_new["REGION"] == r, "i_therm"].values[0]) ) - thermal_df_hist.loc[ - thermal_df_hist["node_loc"] == r_MESSAGE, "value" - ] = thermal_df_hist.loc[thermal_df_hist["node_loc"] == r_MESSAGE, "value"] * ( - 1 - df_therm_new.loc[df_therm_new["REGION"] == r, "i_therm"].values[0] + thermal_df_hist.loc[thermal_df_hist["node_loc"] == r_MESSAGE, "value"] = ( + thermal_df_hist.loc[thermal_df_hist["node_loc"] == r_MESSAGE, "value"] + * (1 - df_therm_new.loc[df_therm_new["REGION"] == r, "i_therm"].values[0]) ) for r in df_spec_new["REGION"]: r_MESSAGE = region_type + r useful_spec.loc[useful_spec["node"] == r_MESSAGE, "value"] = useful_spec.loc[ - useful_spec["node"] == r_MESSAGE, "value" - ] * (1 - df_spec_new.loc[ - df_spec_new["REGION"] == r, "i_spec"].values[0]) - - spec_df_hist.loc[ - spec_df_hist["node_loc"] == r_MESSAGE, "value" - ] = spec_df_hist.loc[spec_df_hist["node_loc"] == r_MESSAGE, "value"] * ( - 1 - df_spec_new.loc[df_spec_new["REGION"] == r, "i_spec"].values[0] + useful_spec["node"] == r_MESSAGE, "value" + ] * (1 - df_spec_new.loc[df_spec_new["REGION"] == r, "i_spec"].values[0]) + + spec_df_hist.loc[spec_df_hist["node_loc"] == r_MESSAGE, "value"] = ( + spec_df_hist.loc[spec_df_hist["node_loc"] == r_MESSAGE, "value"] + * (1 - df_spec_new.loc[df_spec_new["REGION"] == r, "i_spec"].values[0]) ) for r in df_feed_new["REGION"]: r_MESSAGE = region_type + r useful_feed.loc[useful_feed["node"] == r_MESSAGE, "value"] = useful_feed.loc[ - useful_feed["node"] == r_MESSAGE, "value" - ] * (1 - df_feed_new.loc[ - df_feed_new["REGION"] == r, "i_feed"].values[0]) - - feed_df_hist.loc[ - feed_df_hist["node_loc"] == r_MESSAGE, "value" - ] = feed_df_hist.loc[feed_df_hist["node_loc"] == r_MESSAGE, "value"] * ( - 1 - df_feed_new.loc[df_feed_new["REGION"] == r, "i_feed"].values[0] + useful_feed["node"] == r_MESSAGE, "value" + ] * (1 - df_feed_new.loc[df_feed_new["REGION"] == r, "i_feed"].values[0]) + + feed_df_hist.loc[feed_df_hist["node_loc"] == r_MESSAGE, "value"] = ( + feed_df_hist.loc[feed_df_hist["node_loc"] == r_MESSAGE, "value"] + * (1 - df_feed_new.loc[df_feed_new["REGION"] == r, "i_feed"].values[0]) ) scen.check_out() @@ -465,7 +459,7 @@ def modify_demand_and_hist_activity(scen: message_ix.Scenario) -> None: def modify_demand_and_hist_activity_debug( - scen: message_ix.Scenario, + scen: message_ix.Scenario, ) -> dict[str, pd.DataFrame]: """modularized "dry-run" version of modify_demand_and_hist_activity() for debugging purposes @@ -512,7 +506,7 @@ def modify_demand_and_hist_activity_debug( | (df["SECTOR"] == "industry (non-ferrous metals)") | (df["SECTOR"] == "industry (non-metallic minerals)") | (df["SECTOR"] == "industry (total)") - ] + ] df = df[df["RYEAR"] == 2015] # NOTE: Total cehmical industry energy: 27% thermal, 8% electricity, 65% feedstock @@ -531,10 +525,10 @@ def modify_demand_and_hist_activity_debug( & (df["SECTOR"] != "industry (total)") & (df["SECTOR"] != "feedstock (petrochemical industry)") & (df["SECTOR"] != "feedstock (total)") - ] + ] df_spec_total = df[ (df["SECTOR"] == "industry (total)") & (df["FUEL"] == "electricity") - ] + ] df_spec_new = pd.DataFrame( columns=["REGION", "SECTOR", "FUEL", "RYEAR", "UNIT_OUT", "RESULT"] @@ -543,15 +537,15 @@ def modify_demand_and_hist_activity_debug( df_spec_temp = df_spec.loc[df_spec["REGION"] == r] df_spec_total_temp = df_spec_total.loc[df_spec_total["REGION"] == r] df_spec_temp.loc[:, "i_spec"] = ( - df_spec_temp.loc[:, "RESULT"] - / df_spec_total_temp.loc[:, "RESULT"].values[0] + df_spec_temp.loc[:, "RESULT"] + / df_spec_total_temp.loc[:, "RESULT"].values[0] ) df_spec_new = pd.concat([df_spec_temp, df_spec_new], ignore_index=True) df_spec_new.drop(["FUEL", "RYEAR", "UNIT_OUT", "RESULT"], axis=1, inplace=True) df_spec_new.loc[df_spec_new["SECTOR"] == "industry (chemicals)", "i_spec"] = ( - df_spec_new.loc[df_spec_new["SECTOR"] == "industry (chemicals)", "i_spec"] - * 0.67 + df_spec_new.loc[df_spec_new["SECTOR"] == "industry (chemicals)", "i_spec"] + * 0.67 ) df_spec_new = df_spec_new.groupby(["REGION"]).sum().reset_index() @@ -560,7 +554,7 @@ def modify_demand_and_hist_activity_debug( df_feed = df[ (df["SECTOR"] == "feedstock (petrochemical industry)") & (df["FUEL"] == "total") - ] + ] # df_feed_total = # df[(df["SECTOR"] == "feedstock (total)") & (df["FUEL"] == "total")] df_feed_temp = pd.DataFrame(columns=["REGION", "i_feed"]) @@ -585,12 +579,12 @@ def modify_demand_and_hist_activity_debug( & (df["SECTOR"] != "feedstock (petrochemical industry)") & (df["SECTOR"] != "feedstock (total)") & (df["SECTOR"] != "industry (non-ferrous metals)") - ] + ] df_therm_total = df[ (df["SECTOR"] == "industry (total)") & (df["FUEL"] != "total") & (df["FUEL"] != "electricity") - ] + ] df_therm_total = ( df_therm_total.groupby(by="REGION").sum().drop(["RYEAR"], axis=1).reset_index() ) @@ -608,23 +602,23 @@ def modify_demand_and_hist_activity_debug( df_therm_temp = df_therm.loc[df_therm["REGION"] == r] df_therm_total_temp = df_therm_total.loc[df_therm_total["REGION"] == r] df_therm_temp.loc[:, "i_therm"] = ( - df_therm_temp.loc[:, "RESULT"] - / df_therm_total_temp.loc[:, "RESULT"].values[0] + df_therm_temp.loc[:, "RESULT"] + / df_therm_total_temp.loc[:, "RESULT"].values[0] ) df_therm_new = pd.concat([df_therm_temp, df_therm_new], ignore_index=True) df_therm_new = df_therm_new.drop(["RESULT"], axis=1) df_therm_new.drop(["FUEL", "RYEAR", "UNIT_OUT"], axis=1, inplace=True) df_therm_new.loc[df_therm_new["SECTOR"] == "industry (chemicals)", "i_therm"] = ( - df_therm_new.loc[df_therm_new["SECTOR"] == "industry (chemicals)", "i_therm"] - * 0.67 + df_therm_new.loc[df_therm_new["SECTOR"] == "industry (chemicals)", "i_therm"] + * 0.67 ) # Modify CPA based on https://www.iea.org/sankey/#?c=Japan&s=Final%20consumption. # Since the value did not allign with the one in the IEA website. index = (df_therm_new["SECTOR"] == "industry (iron and steel)") & ( - (df_therm_new["REGION"] == region_name_CPA) - | (df_therm_new["REGION"] == region_name_CHN) + (df_therm_new["REGION"] == region_name_CPA) + | (df_therm_new["REGION"] == region_name_CHN) ) df_therm_new.loc[index, "i_therm"] = 0.2 @@ -672,44 +666,38 @@ def modify_demand_and_hist_activity_debug( for r in df_therm_new["REGION"]: r_MESSAGE = region_type + r - useful_thermal.loc[ - useful_thermal["node"] == r_MESSAGE, "value" - ] = useful_thermal.loc[useful_thermal["node"] == r_MESSAGE, "value"] * ( - 1 - df_therm_new.loc[df_therm_new["REGION"] == r, "i_therm"].values[0] + useful_thermal.loc[useful_thermal["node"] == r_MESSAGE, "value"] = ( + useful_thermal.loc[useful_thermal["node"] == r_MESSAGE, "value"] + * (1 - df_therm_new.loc[df_therm_new["REGION"] == r, "i_therm"].values[0]) ) - thermal_df_hist.loc[ - thermal_df_hist["node_loc"] == r_MESSAGE, "value" - ] = thermal_df_hist.loc[thermal_df_hist["node_loc"] == r_MESSAGE, "value"] * ( - 1 - df_therm_new.loc[df_therm_new["REGION"] == r, "i_therm"].values[0] + thermal_df_hist.loc[thermal_df_hist["node_loc"] == r_MESSAGE, "value"] = ( + thermal_df_hist.loc[thermal_df_hist["node_loc"] == r_MESSAGE, "value"] + * (1 - df_therm_new.loc[df_therm_new["REGION"] == r, "i_therm"].values[0]) ) for r in df_spec_new["REGION"]: r_MESSAGE = region_type + r useful_spec.loc[useful_spec["node"] == r_MESSAGE, "value"] = useful_spec.loc[ - useful_spec["node"] == r_MESSAGE, "value" - ] * (1 - df_spec_new.loc[ - df_spec_new["REGION"] == r, "i_spec"].values[0]) - - spec_df_hist.loc[ - spec_df_hist["node_loc"] == r_MESSAGE, "value" - ] = spec_df_hist.loc[spec_df_hist["node_loc"] == r_MESSAGE, "value"] * ( - 1 - df_spec_new.loc[df_spec_new["REGION"] == r, "i_spec"].values[0] + useful_spec["node"] == r_MESSAGE, "value" + ] * (1 - df_spec_new.loc[df_spec_new["REGION"] == r, "i_spec"].values[0]) + + spec_df_hist.loc[spec_df_hist["node_loc"] == r_MESSAGE, "value"] = ( + spec_df_hist.loc[spec_df_hist["node_loc"] == r_MESSAGE, "value"] + * (1 - df_spec_new.loc[df_spec_new["REGION"] == r, "i_spec"].values[0]) ) for r in df_feed_new["REGION"]: r_MESSAGE = region_type + r useful_feed.loc[useful_feed["node"] == r_MESSAGE, "value"] = useful_feed.loc[ - useful_feed["node"] == r_MESSAGE, "value" - ] * (1 - df_feed_new.loc[ - df_feed_new["REGION"] == r, "i_feed"].values[0]) - - feed_df_hist.loc[ - feed_df_hist["node_loc"] == r_MESSAGE, "value" - ] = feed_df_hist.loc[feed_df_hist["node_loc"] == r_MESSAGE, "value"] * ( - 1 - df_feed_new.loc[df_feed_new["REGION"] == r, "i_feed"].values[0] + useful_feed["node"] == r_MESSAGE, "value" + ] * (1 - df_feed_new.loc[df_feed_new["REGION"] == r, "i_feed"].values[0]) + + feed_df_hist.loc[feed_df_hist["node_loc"] == r_MESSAGE, "value"] = ( + feed_df_hist.loc[feed_df_hist["node_loc"] == r_MESSAGE, "value"] + * (1 - df_feed_new.loc[df_feed_new["REGION"] == r, "i_feed"].values[0]) ) # For aluminum there is no significant deduction required @@ -833,14 +821,14 @@ def calc_demand_shares(iea_db_df: pd.DataFrame, base_year: int) -> pd.DataFrame: & (iea_db_df["PRODUCT"].isin(i_spec_prods)) & ~((iea_db_df["PRODUCT"] == ("ELECTR")) & (iea_db_df["FLOW"] == "IRONSTL")) & (iea_db_df["TIME"] == year) - ] + ] df_i_spec = df_i_spec.groupby("REGION").sum(numeric_only=True) df_i_spec_materials = iea_db_df[ (iea_db_df["FLOW"].isin(i_spec_material_flows)) & (iea_db_df["PRODUCT"].isin(i_spec_prods)) & (iea_db_df["TIME"] == year) - ] + ] df_i_spec_materials = df_i_spec_materials.groupby("REGION").sum(numeric_only=True) df_i_spec_resid_shr = ( @@ -851,7 +839,7 @@ def calc_demand_shares(iea_db_df: pd.DataFrame, base_year: int) -> pd.DataFrame: df_elec_i = iea_db_df[ ((iea_db_df["PRODUCT"] == ("ELECTR")) & (iea_db_df["FLOW"] == "IRONSTL")) & (iea_db_df["TIME"] == year) - ] + ] df_elec_i = df_elec_i.groupby("REGION").sum(numeric_only=True) agg_prods = ["MRENEW", "TOTAL"] @@ -860,7 +848,7 @@ def calc_demand_shares(iea_db_df: pd.DataFrame, base_year: int) -> pd.DataFrame: & ~(iea_db_df["PRODUCT"].isin(i_spec_prods)) & ~(iea_db_df["PRODUCT"].isin(agg_prods)) & (iea_db_df["TIME"] == year) - ] + ] df_i_therm = df_i_therm.groupby("REGION").sum(numeric_only=True) df_i_therm = df_i_therm.add(df_elec_i, fill_value=0) @@ -870,7 +858,7 @@ def calc_demand_shares(iea_db_df: pd.DataFrame, base_year: int) -> pd.DataFrame: & ~(iea_db_df["PRODUCT"].isin(i_spec_prods)) & ~(iea_db_df["PRODUCT"].isin(agg_prods)) & (iea_db_df["TIME"] == year) - ] + ] df_i_therm_materials = df_i_therm_materials.groupby(["REGION", "FLOW"]).sum( numeric_only=True ) @@ -1064,17 +1052,17 @@ def add_emission_accounting(scen): i for i in tec_list_residual if ( - ( - ("biomass_i" in i) - | ("coal_i" in i) - | ("foil_i" in i) - | ("gas_i" in i) - | ("hp_gas_i" in i) - | ("loil_i" in i) - | ("meth_i" in i) - ) - & ("imp" not in i) - & ("trp" not in i) + ( + ("biomass_i" in i) + | ("coal_i" in i) + | ("foil_i" in i) + | ("gas_i" in i) + | ("hp_gas_i" in i) + | ("loil_i" in i) + | ("meth_i" in i) + ) + & ("imp" not in i) + & ("trp" not in i) ) ] @@ -1208,11 +1196,11 @@ def add_emission_accounting(scen): i for i in tec_list if ( - ("steel" in i) - | ("aluminum" in i) - | ("petro" in i) - | ("cement" in i) - | ("ref" in i) + ("steel" in i) + | ("aluminum" in i) + | ("petro" in i) + | ("cement" in i) + | ("ref" in i) ) ] for elem in ["refrigerant_recovery", "replacement_so2", "SO2_scrub_ref"]: @@ -1234,7 +1222,7 @@ def add_emission_accounting(scen): (relation_activity["relation"] != "PM2p5_Emission") & (relation_activity["relation"] != "CO2_industry_Emission") & (relation_activity["relation"] != "CO2_transformation_Emission") - ] + ] # ***** (3) Add thermal industry technologies to CO2_ind relation ****** @@ -1434,7 +1422,7 @@ def add_elec_lowerbound_2020(scen): # derive useful energy values by dividing final energy by # input coefficient from final-to-useful technologies bound_residual_electricity["value"] = ( - bound_residual_electricity["Value"] / bound_residual_electricity["value"] + bound_residual_electricity["Value"] / bound_residual_electricity["value"] ) # downselect dataframe columns for MESSAGEix parameters @@ -1456,7 +1444,7 @@ def add_elec_lowerbound_2020(scen): bound_residual_electricity["value"] = bound_residual_electricity["value"] * 0.8 bound_residual_electricity = bound_residual_electricity[ bound_residual_electricity["node_loc"] == "R12_CHN" - ] + ] scen.check_out() @@ -1540,7 +1528,7 @@ def add_coal_lowerbound_2020(sc): bound_coal["value"] = bound_coal["Value"] / bound_coal["value"] bound_cement_coal["value"] = bound_cement_coal["Value"] / bound_cement_coal["value"] bound_residual_electricity["value"] = ( - bound_residual_electricity["Value"] / bound_residual_electricity["value"] + bound_residual_electricity["Value"] / bound_residual_electricity["value"] ) # downselect dataframe columns for MESSAGEix parameters @@ -1735,7 +1723,7 @@ def add_cement_bounds_2020(sc): bound_cement_foil["value"] = bound_cement_foil["Value"] / bound_cement_foil["value"] bound_cement_gas["value"] = bound_cement_gas["Value"] / bound_cement_gas["value"] bound_cement_biomass["value"] = ( - bound_cement_biomass["Value"] / bound_cement_biomass["value"] + bound_cement_biomass["Value"] / bound_cement_biomass["value"] ) bound_cement_coal["value"] = bound_cement_coal["Value"] / bound_cement_coal["value"] @@ -1988,7 +1976,7 @@ def add_ccs_technologies(scen: message_ix.Scenario) -> None: # Read in time-dependent parameters # Now only used to add fuel cost for bare model def read_timeseries( - scenario: message_ix.Scenario, material: str, filename: str + scenario: message_ix.Scenario, material: str, filename: str ) -> pd.DataFrame: """ Read "timeseries" type data from a sector specific xlsx input file @@ -2088,10 +2076,10 @@ def read_rel(scenario: message_ix.Scenario, material: str, filename: str): def gen_te_projections( - scen: message_ix.Scenario, - ssp: Literal["all", "LED", "SSP1", "SSP2", "SSP3", "SSP4", "SSP5"] = "SSP2", - method: Literal["constant", "convergence", "gdp"] = "convergence", - ref_reg: str = "R12_NAM", + scen: message_ix.Scenario, + ssp: Literal["all", "LED", "SSP1", "SSP2", "SSP3", "SSP4", "SSP5"] = "SSP2", + method: Literal["constant", "convergence", "gdp"] = "convergence", + ref_reg: str = "R12_NAM", ) -> tuple[pd.DataFrame, pd.DataFrame]: """ Calls message_ix_models.tools.costs with config for MESSAGEix-Materials From cb3031f0b2861ba90bcd3bac1ae97f1983477596 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 21 May 2024 14:29:50 +0200 Subject: [PATCH 770/774] Apply ruff formatting with ruff 0.4.4 --- .../model/material/data_ammonia_new.py | 4 +--- message_ix_models/model/material/data_methanol.py | 6 +++--- message_ix_models/model/material/data_util.py | 6 +++--- .../model/material/report/reporting.py | 14 ++++++-------- 4 files changed, 13 insertions(+), 17 deletions(-) diff --git a/message_ix_models/model/material/data_ammonia_new.py b/message_ix_models/model/material/data_ammonia_new.py index b841cd81d7..bda238772f 100644 --- a/message_ix_models/model/material/data_ammonia_new.py +++ b/message_ix_models/model/material/data_ammonia_new.py @@ -390,9 +390,7 @@ def read_demand(): N_energy.oil_pct *= input_fuel[4] * CONVERSION_FACTOR_NH3_N N_energy = pd.concat( [N_energy.Region, N_energy.sum(axis=1, numeric_only=True)], axis=1 - ).rename( - columns={0: "totENE", "Region": "node"} - ) # GWa + ).rename(columns={0: "totENE", "Region": "node"}) # GWa # N_trade_R12 = pd.read_csv( # package_data_path("material", "ammonia", "trade.FAO.R12.csv"), index_col=0 diff --git a/message_ix_models/model/material/data_methanol.py b/message_ix_models/model/material/data_methanol.py index 68fcd85f5e..300fb4e9d6 100644 --- a/message_ix_models/model/material/data_methanol.py +++ b/message_ix_models/model/material/data_methanol.py @@ -56,9 +56,9 @@ def gen_data_methanol(scenario): meth_fs_dic = combine_df_dictionaries(meth_fs_dic, meth_h2_fs_dict) df = meth_fs_dic["output"] - df.loc[ - (df["commodity"] == "methanol") & (df["level"] == "primary"), "level" - ] = "primary_material" + df.loc[(df["commodity"] == "methanol") & (df["level"] == "primary"), "level"] = ( + "primary_material" + ) meth_fs_dic["output"] = df # add meth prod fs and fuel mode diff --git a/message_ix_models/model/material/data_util.py b/message_ix_models/model/material/data_util.py index 266690faee..d5f0850bf1 100644 --- a/message_ix_models/model/material/data_util.py +++ b/message_ix_models/model/material/data_util.py @@ -1908,9 +1908,9 @@ def read_sector_data(scenario: message_ix.Scenario, sectname: str) -> pd.DataFra list_ef = data_df[["Parameter", "Species", "Mode"]].apply(list, axis=1) data_df["parameter"] = list_series.str.join("|") - data_df.loc[ - data_df["Parameter"] == "emission_factor", "parameter" - ] = list_ef.str.join("|") + data_df.loc[data_df["Parameter"] == "emission_factor", "parameter"] = ( + list_ef.str.join("|") + ) data_df = data_df.drop(["Parameter", "Level", "Commodity", "Mode"], axis=1) data_df = data_df.drop(data_df[data_df.Value == ""].index) diff --git a/message_ix_models/model/material/report/reporting.py b/message_ix_models/model/material/report/reporting.py index 6058edd95b..5c6602b79b 100644 --- a/message_ix_models/model/material/report/reporting.py +++ b/message_ix_models/model/material/report/reporting.py @@ -153,7 +153,7 @@ def fix_excel(path_temp, path_new): new_workbook.save(path_new) -def report(context, scenario): # noqa: C901 +def report(context, scenario): # noqa: C901 # Obtain scenario information and directory s_info = ScenarioInfo(scenario) @@ -3282,14 +3282,12 @@ def report(context, scenario): # noqa: C901 for t in aux2_df["technology"].values if ( ( - ( - ("cement" in t) - | ("steel" in t) - | ("aluminum" in t) - | ("petro" in t) - ) - & ("furnace" not in t) + ("cement" in t) + | ("steel" in t) + | ("aluminum" in t) + | ("petro" in t) ) + & ("furnace" not in t) ) ] if (typ == "process") & (s != "all"): From 2e269239da2575e0e254f9ffc1031b093bf427b3 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Tue, 21 May 2024 15:06:47 +0200 Subject: [PATCH 771/774] Move doc file and update Move doc.rst in material folder, rename to index.rst and pull updated contents from branch "migrate-materials-tidy" --- doc/material/index.rst | 191 ++++++++++++++++++ message_ix_models/model/material/doc.rst | 234 ----------------------- 2 files changed, 191 insertions(+), 234 deletions(-) create mode 100644 doc/material/index.rst delete mode 100644 message_ix_models/model/material/doc.rst diff --git a/doc/material/index.rst b/doc/material/index.rst new file mode 100644 index 0000000000..8511a1aa68 --- /dev/null +++ b/doc/material/index.rst @@ -0,0 +1,191 @@ +MESSAGEix-Materials +******************** + +Description +=========== + +This module adds material stock and flow accounting in MESSAGEix-GLOBIOM. The implementation currently includes four key energy/emission-intensive +material industries: Iron&Steel, Aluminum, Cement, and Chemicals. + +.. contents:: + :local: + +Code reference +============== + +.. automodule:: message_ix_models.model.material + :members: + +.. automodule:: message_ix_models.data.material + :members: + +Data preparation +---------------- + +These scripts are used to prepare and read the data into the model. They can be turned on and off individually under `DATA_FUNCTIONS` in `__init__.py`. +For example, the buildings script (`data_buildings.py`) is only used when the buildings model outputs are given explicitly without linking the +CHILLED/STURM model through a soft link. + +.. automodule:: message_ix_models.model.material.data_aluminum + :members: + +.. automodule:: message_ix_models.model.material.data_steel + :members: + +.. automodule:: message_ix_models.model.material.data_cement + :members: + +.. automodule:: message_ix_models.model.material.data_petro + :members: + +.. automodule:: message_ix_models.model.material.data_power_sector + :members: + +.. automodule:: message_ix_models.model.material.data_buildings + :members: + +.. automodule:: message_ix_models.model.material.data_generic + :members: + +.. automodule:: message_ix_models.model.material.data_ammonia_new + :members: + +.. automodule:: message_ix_models.model.material.data_methanol_new + :members: + +Build and Solve the model from CLI +================================== + +Note: To include material stocks from power sector message_ix should be the following version from source: +https://github.com/iiasa/message_ix/tree/material_stock + +Use ``mix-models materials-ix build`` to add the material implementation on top of existing standard global (R12) scenarios, also giving the base scenario and indicating the relevant data location, e.g.:: + + $ mix-models \ + --url="ixmp://ixmp_dev/MESSAGEix-GLOBIOM 1.1-R12/baseline_DEFAULT#21" \ + --local-data "./data" material-ix build --tag test + +The output scenario name will be baseline_DEFAULT_test. An additional tag `--tag` can be used to add an additional suffix to the new scenario name. +The mode option `--mode` has two different inputs 'by_url' (by default) or 'by_copy'. The first one uses the provided url to add the materials implementation on top of the scenario from the url. +This is the default option. The latter is used to create a 2 degree mitigation scenario with materials by copying carbon prices to the scenario that is specified by `--scenario_name`. + + $ mix-models material-ix build --tag test --mode by_copy --scenario_name baseline_DEFAULT_test. + +This command line only builds the scenario but does not solve it. To solve the scenario, use ``mix-models materials-ix solve``, giving the scenario name, e.g.:: + + $ mix-models material-ix solve --scenario_name baseline_DEFAULT_test --add_calibration False --add_macro False + +The solve command has the `--add_calibration` option to add MACRO calibration to a baseline scenario. `--add_macro` option solves the scenario with MACRO. +Both options are False by defualt. To first calibrate the scenario and then solve that scenario with MACRO both options should be set to True. + +Reporting +========= + +The reporting generates specific variables related to materials, mainly Production and Final Energy. +The resulting reporting file is generated under message_ix_models\\data\\material\\reporting_output +with the name “New_Reporting_MESSAGEix-Materials_scenario_name.xlsx”. More detailed +variables related to the whole energy system and emissions are not inlcuded in +this reporting. + +Reporting is executed by the following command: + +$ mix-models --url="ixmp://ixmp_dev/MESSAGEix-Materials/scenario_name" --local-data "./data" material-ix report + +To remove any existing timeseries in the scenario the following command can be used: +$ mix-models --url="ixmp://ixmp_dev/MESSAGEix-Materials/scenario_name" material-ix report --remove_ts True + +Data, metadata, and configuration +================================= + +Binary/raw data files +--------------------- + +The code relies on the following input files, stored in :file:`data/material/`: + +:file:`CEMENT.BvR2010.xlsx` + Historical cement demand data + +:file:`STEEL_database_2012.xlsx` + Historical steel demand data + +:file:`Global_steel_cement_MESSAGE.xlsx` + Techno-economic parametrization data for steel and cement sector combined (R12) + +:file:`demand_aluminum.xlsx` + Historical aluminum demand data + +:file:`demand_aluminum.xlsx` + Historical aluminum demand data + +:file:`aluminum_techno_economic.xlsx` + Techno-economic parametrization data for aluminum sector + +:file:`generic_furnace_boiler_techno_economic.xlsx` + Techno-economic parametrization data for generic furnace technologies + +:file:`iamc_db ENGAGE baseline GDP PPP.xlsx` + SSP GDP projection used for material demand projections + +:file:`MESSAGEix-Materials_final_energy_industry.xlsx` + Final energy values to calibrate base year values for industries + +:file:`residual_industry_2019.xlsx` + Final energy values to calculate the residual industry demand. + +:file:`nh3_fertilizer_demand.xlsx` + Nitrogen fertilizer demand + +:file:`fert_techno_economic.xlsx` + Techno-economic parameters for NH3 production technologies + +:file:`cost_conv_nh3.xlsx` + Cost convergance parameters for NH3 produciton technologies + +:file:`methanol demand.xlsx` + Methanol demand + +:file:`methanol_sensitivity_pars.xlsx` + Methanol sensitivity parameters + +:file:`methanol_techno_economic.xlsx` + Techno-economic parameters for methanol production technologies + +:file:`petrochemicals_techno_economic.xls` + Techno-economic parameters for refinery and high value chemicals production technologies + +:file:`steam_cracking_hist_act.csv` + Steam cracker historical activities + +:file:`steam_cracking_hist_new_cap.csv` + Steam cracker historical capacities + +:file:`NTNU_LCA_coefficients.xlsx` + Material intensity (and other) coefficients for power plants based on lifecycle assessment (LCA) data from the THEMIS database, compiled in the `ADVANCE project `_. + +:file:`MESSAGE_global_model_technologies.xlsx` + Technology list of global MESSAGEix-GLOBIOM model with mapping to LCA technology dataset. + +:file:`LCA_region_mapping.xlsx` + Region mapping of global 11-regional MESSAGEix-GLOBIOM model to regions of THEMIS LCA dataset. + +:file:`LCA_commodity_mapping.xlsx` + Commodity mapping (for materials) of global 11-regional MESSAGEix-GLOBIOM model to commodities of THEMIS LCA dataset. + +:file:`SSP_UE_dyn_input.xlsx` + Calibration of end-use energy demands + +:file:`material/set.yaml` +---------------------------- + +.. literalinclude:: ../../../data/material/set.yaml + :language: yaml + +R code and dependencies +======================= + +:file:`init_modularized.R` +This code performs regression analysis to generate the steel, cement and aluminum material demands based on historical GDP per capita data. +The regression function is later used in python data code of the relevant material to predict the future demand based on GDP and population +projections. This code is linked to Python workflow via the Python package `rpy2`. Depending on the local R installation(s), the environment +variables `R_HOME` and `R_USER` may need to be set for the installation to work (see `stackoverflow `_). +Additional dependencies include the R packages `dplyr`, `tidyr`, `readxl` and `imputeTS` that need to be installed in the R environment. diff --git a/message_ix_models/model/material/doc.rst b/message_ix_models/model/material/doc.rst deleted file mode 100644 index eeff5f3a70..0000000000 --- a/message_ix_models/model/material/doc.rst +++ /dev/null @@ -1,234 +0,0 @@ -MESSAGEix-Materials -******************** - -.. warning:: - - :mod:`.material` is **under development**. - - For details, see the - `materials `_ label and - `current tracking issue (#248) `_. - -Description -=========== - -This module adds (life-cycle) accounting of materials associated with technologies and demands in MESSAGEix-GLOBIOM. - -The implementation currently supports four key energy/emission-intensive material industries: Steel, Aluminum, Cement, and Petrochemical. -The petrochemical sector will soon expand to cover production processes of plastics, ammonia and nitrogen-based fertilizers. - -The technologies to represent for the primary production processes of the materials are chosen based on their emission mitigation potential and the degree -of commercialization. - -Processing secondary materials ------------------------------- - -After the primary production stages of the materials, finishing and manufacturing processes are carried out which results in a complete product. -For metals, during the manufacturing process new scrap is formed as residue. This type of scrap requires less preparation before recycling and has a higher quality -as it is the direct product of the manufacturing unlike the old scrap which is formed at the end of the life cycle of a product. -The percentage of the new scrap is an exogenous fixed ratio in the model. - -The products that are produced are used in different end-use sectors as stocks and therefore they are not immediately available for recycling until the end of their lifetime. -In the model, each year only certain quantity of products are available for recycling and this ratio is exogenously determined based on historical values. -The end-of-life products coming from buildings and power sector can be endogenously determined in case the relevant links are turned on. - -Modeling recycling decisions ----------------------------- - -In the model, there is a minimum recycling rate specified for different materials and it is based on the historical recycling rates. This parameter can also be used to represent -regulations in different regions. In the end recycling rate is a model decision which can be higher than the minimum rate depending on the economic attractiveness. - -The end-of-life products that are collected as old scrap are classified in three different grade/quality. This reflects the degree of difficulty of recycling process in terms of -labor and energy. Different initial designs and final use conditions determine the ease of recycling which is also reflected in costs. -The availability of different scrap is set with 1-2-1 ratio as default for high, medium and low scrap quality. - -Three different scrap preparation technologies have different variable costs and energy inputs to process the old scraps with different grades. At the end of the preparation process -these scraps are returned as new scrap all with the same quality. The quality differences in the end product are neglected. All of the old scrap that is collected is used in the model -assuming scrap availability and collection are the main bottlenecks of the recycling process. All the scraps are sent to a secondary melter where they are turned into final materials. -During this process there are also recycling losses. - -.. contents:: - :local: - -Code reference -============== - -.. automodule:: message_data.model.material - :members: - -.. automodule:: message_data.model.material.data - :members: - -.. automodule:: message_data.model.material.util - :members: - -Data preparation ----------------- - -These modules are not necessary for the parametrization for each sector and they can be turned on and off individually under `DATA_FUNCTIONS` in `__init__.py`. -For example, the buildings module (`data_buildings.py`) is only used when the buildings model outputs are given explicitly without linking the CHILLED/STURM model through a soft link. - -.. automodule:: message_data.model.material.data_aluminum - :members: - -.. automodule:: message_data.model.material.data_steel - :members: - -.. automodule:: message_data.model.material.data_cement - :members: - -.. automodule:: message_data.model.material.data_petro - :members: - -.. automodule:: message_data.model.material.data_power_sector - :members: - -.. automodule:: message_data.model.material.data_buildings - :members: - -.. automodule:: message_data.model.material.data_generic - :members: - -Build and Solve the model from CLI -================================== - -Use ``mix-models materials build`` to add the material implementation on top of existing standard global (R12) scenarios, also giving the base scenario and indicating the relevant data location, e.g.:: - - $ mix-models \ - --url="ixmp://ixmp_dev/MESSAGEix-GLOBIOM_R12_CHN/baseline_new_macro#8" \ - --local-data "./data" material build - -It can be helpful to note that this command will not work for all R12 scenarios because of dependencies on certain levels or commodities described in :file:`set.yaml`. -Currently, a set of pre-defined base scenario names will be translated to own scenario names. But when an unknown base scenario name is given, we reuse it for the output scenario name. -The output scenario will be ``ixmp://ixmp_dev-ixmp/MESSAGEix-Materials/{name}``, where {name} is the output scenario name. - -An additional tag `--tag` can be used to add an additional suffix to the new scenario name. -The mode option `--mode` has two different inputs 'by_url' (by default) or 'by_copy'. The first one uses the provided url to add the materials implementation on top of the scenario from the url. -The latter is used to create a 2 degree mitigation scenario with materials by copying relevant carbon prices to the scenario that is specified by `--scenario_name`. - - $ mix-models material build --tag test --mode by_copy --scenario_name NoPolicy_R12 - -This command line only builds the scenario but does not run it. To run the scenario, use ``mix-models materials solve``, giving the scenario name, e.g.:: - - $ mix-models material solve --scenario_name NoPolicy_R12 - -The solve command has the `--add_calibration` option to add MACRO calibration to a baseline scenario. `--add_macro` option solves the scenario with MACRO. - -Reporting -========= - -The reporting of the scenarios that include materials representation mainly involves -two steps. In the first step the material specific variables are generated. -At the end of this step all the variables are uploaded as ixmp -timeseries objects to the scenario. The reporting file is generated under -message_data\\reporting\\materials with the name “New_Reporting_MESSAGEix-Materials_scenario_name.xls”. -In the second step, rest of the default reporting variables are obtained by running -the general reporting code. This step combines all the variables that were uploaded -as timeseries to the scenario together with the generic IAMC variables. It also -correctly reports the aggregate variables such as Final Energy and Emissions. -The reporting is executed by the following command: - -$ mix-models --url="ixmp://ixmp_dev/MESSAGEix-Materials/scenario_name" material report - -If the model is ran with other end-use modules such as buildings/appliances or -transport, the new reporting variables from these should be uploaded to the scenario -as timeseries object before running the above command. - -It is possible to add reporting variables from the Buildings model results by using -the following command: -$ mix-models material add_buildings_ts --model_name xxxx --scenario_name xxxx -The reporting output files in csv form should be located under -message_data\\data\\material\\buildings. - -There should be no other existing timeseries (other than the ones from the end-use modules) -in the scenario when running the reporting command to obtain correct results. - -To remove any existing timeseries in the scenario the following command can be used: -$ mix-models --url="ixmp://ixmp_dev/MESSAGEix-Materials/scenario_name" material report --remove_ts True - -Data, metadata, and configuration -================================= - -Binary/raw data files ---------------------- - -The code relies on the following input files, stored in :file:`data/material/`: - -:file:`CEMENT.BvR2010.xlsx` - Historical cement demand data - -:file:`STEEL_database_2012.xlsx` - Historical steel demand data - -:file:`demand_aluminum.xlsx` - Historical aluminum demand data - -:file:`demand_aluminum.xlsx` - Historical aluminum demand data - -:file:`aluminum_techno_economic.xlsx` - Techno-economic parametrization data for aluminum sector - -:file:`Global_steel_cement_MESSAGE.xlsx` - Techno-economic parametrization data for steel and cement sector combined (R12) - -:file:`China_steel_cement_MESSAGE.xlsx` - Techno-economic parametrization data for steel and cement sector combined (China standalone) - -:file:`generic_furnace_boiler_techno_economic.xlsx` - Techno-economic parametrization data for generic furnace technologies - -:file:`LED_LED_report_IAMC*.csv` - Output from buildings model on the sector's energy/material/floor space demand. It was used for ALPS2020 report, when the linkage to the buildings model is not yet set up. - -:file:`MESSAGE_region_mapping_R14.xlsx` - MESSAGE region mapping used for fertilizer trade mapping - -:file:`iamc_db ENGAGE baseline GDP PPP.xlsx` - SSP GDP projection used for material demand projections - -:file:`Ammonia feedstock share.Global.xlsx` - Feedstock shares (gas/oil/coal) for NH3 production for MESSAGE R11 regions. - -:file:`CD-Links SSP2 N-fertilizer demand.Global.xlsx` - N-fertilizer demand time series from SSP2 scenarios (NPi2020_1000, NPi2020_400, NoPolicy) for MESSAGE R11 regions. - -:file:`N fertil trade - FAOSTAT_data_9-25-2019.csv` - Raw data from FAO used to generate the two :file:`trade.FAO.*.csv` files below. - Exported from `FAOSTAT `_. - -:file:`n-fertilizer_techno-economic.xlsx` - Techno-economic parameters from literature for various NH3 production technologies (used as direct inputs for MESSAGE parameters). - -:file:`trade.FAO.R11.csv` - Historical N-fertilizer trade records among R11 regions, extracted from FAO database. - -:file:`trade.FAO.R14.csv` - Historical N-fertilizer trade records among R14 regions, extracted from FAO database. - -:file:`NTNU_LCA_coefficients.xlsx` - Material intensity (and other) coefficients for power plants based on lifecycle assessment (LCA) data from the THEMIS database, compiled in the `ADVANCE project `_. - -:file:`MESSAGE_global_model_technologies.xlsx` - Technology list of global MESSAGEix-GLOBIOM model with mapping to LCA technology dataset. - -:file:`LCA_region_mapping.xlsx` - Region mapping of global 11-regional MESSAGEix-GLOBIOM model to regions of THEMIS LCA dataset. - -:file:`LCA_commodity_mapping.xlsx` - Commodity mapping (for materials) of global 11-regional MESSAGEix-GLOBIOM model to commodities of THEMIS LCA dataset. - -:file:`material/set.yaml` ----------------------------- - -.. literalinclude:: ../../../data/material/set.yaml - :language: yaml - -R code and dependencies -======================= - -:file:`ADVANCE_lca_coefficients_embedded.R` -The code processing the material intensity coefficients of power plants is written in R and integrated into the Python workflow via the Python package `rpy2`. -R code is called from the Python data module `data_power_sector.py`. -Depending on the local R installation(s), the environment variables `R_HOME` and `R_USER` may need to be set for the installation to work (see `stackoverflow `_). -Additional dependencies include the R packages `dplyr`, `tidyr`, `readxl` and `imputeTS` that need to be installed in the R environment. From 39d494f8dea3546d467343b40f9317b6471eab61 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 23 May 2024 09:47:23 +0200 Subject: [PATCH 772/774] Reformat materials demand module after rebase --- .../material_demand/material_demand_calc.py | 25 ++++++------------- 1 file changed, 7 insertions(+), 18 deletions(-) diff --git a/message_ix_models/model/material/material_demand/material_demand_calc.py b/message_ix_models/model/material/material_demand/material_demand_calc.py index 5ea356ac83..d0abc0c4f6 100644 --- a/message_ix_models/model/material/material_demand/material_demand_calc.py +++ b/message_ix_models/model/material/material_demand/material_demand_calc.py @@ -1,28 +1,17 @@ -import message_ix_models.util -import pandas as pd import numpy as np -import message_ix -from scipy.optimize import curve_fit +import pandas as pd import yaml - -from message_data.model.material.util import read_config -from message_ix_models import ScenarioInfo from message_ix import make_df -from message_ix_models.util import ( - broadcast, - make_io, - make_matched_dfs, - same_node, - add_par_data, - package_data_path , -) +from scipy.optimize import curve_fit +import message_ix_models.util +from message_ix_models import ScenarioInfo +from message_ix_models.util import package_data_path file_cement = "/CEMENT.BvR2010.xlsx" file_steel = "/STEEL_database_2012.xlsx" file_al = "/demand_aluminum.xlsx" file_gdp = "/iamc_db ENGAGE baseline GDP PPP.xlsx" - giga = 10**9 mega = 10**6 @@ -231,7 +220,7 @@ def read_hist_mat_demand(material): df_raw_cons["region"] .str.replace("(", "") .str.replace(")", "") - .str.replace("\d+", "") + .str.replace(r"\d+", "") .str[:-1] ) @@ -383,7 +372,7 @@ def derive_demand(material, scen, old_gdp=False, ssp="SSP2"): df_final["time"] = "year" df_final["unit"] = "t" # TODO: correct unit would be Mt but might not be registered on database - df_final = message_ix.make_df("demand", **df_final) + df_final = make_df("demand", **df_final) return df_final From 63ec1815b21530e67f51188ea2490b8277fb4fc0 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 23 May 2024 11:26:22 +0200 Subject: [PATCH 773/774] Delete unused data files from data/material --- .../deprecated/Ammonia feedstock share.Global_R12.xlsx | 3 --- .../CD-Links SSP2 N-fertilizer demand.Global_R12_adaption.xlsx | 3 --- .../data/material/deprecated/GLOBIOM_Fertilizer_use_N.xlsx | 3 --- .../data/material/deprecated/LED_LED_report_IAMC.csv | 3 --- .../material/deprecated/LED_LED_report_IAMC_sensitivity.csv | 3 --- .../deprecated/LED_LED_report_IAMC_sensitivity_R11.csv | 3 --- .../deprecated/LED_LED_report_IAMC_sensitivity_R12.csv | 3 --- .../data/material/deprecated/demand_i_feed_R12.xlsx | 3 --- message_ix_models/data/material/deprecated/demand_petro.xlsx | 3 --- .../material/deprecated/n-fertilizer_techno-economic_new.xlsx | 3 --- .../data/material/deprecated/regional_cost_scaler_R12.xlsx | 3 --- message_ix_models/data/material/deprecated/trade.FAO.R12.csv | 3 --- .../Ammonia feedstock share.Global_R12.xlsx/Sheet1.csv | 3 --- .../Ammonia feedstock share.Global_R12.xlsx/Sheet2.csv | 3 --- .../data.csv | 3 --- .../material/version control/CEMENT.BvR2010.xlsx/Analysis .csv | 3 --- .../material/version control/CEMENT.BvR2010.xlsx/Cement_A.csv | 3 --- .../material/version control/CEMENT.BvR2010.xlsx/Cement_B.csv | 3 --- .../material/version control/CEMENT.BvR2010.xlsx/Contents.csv | 3 --- .../CEMENT.BvR2010.xlsx/Domestic Consumption.csv | 3 --- .../material/version control/CEMENT.BvR2010.xlsx/Export.csv | 3 --- .../data/material/version control/CEMENT.BvR2010.xlsx/IP$.csv | 3 --- .../material/version control/CEMENT.BvR2010.xlsx/IPpop.csv | 3 --- .../data/material/version control/CEMENT.BvR2010.xlsx/IU$.csv | 3 --- .../material/version control/CEMENT.BvR2010.xlsx/Import.csv | 3 --- .../version control/CEMENT.BvR2010.xlsx/NetExport%.csv | 3 --- .../material/version control/CEMENT.BvR2010.xlsx/NetExport.csv | 3 --- .../version control/CEMENT.BvR2010.xlsx/OECD GDPpc.csv | 3 --- .../material/version control/CEMENT.BvR2010.xlsx/OECD pop.csv | 3 --- .../data/material/version control/CEMENT.BvR2010.xlsx/PCC.csv | 3 --- .../material/version control/CEMENT.BvR2010.xlsx/PCCfit.csv | 3 --- .../version control/CEMENT.BvR2010.xlsx/Production.csv | 3 --- .../material/version control/CEMENT.BvR2010.xlsx/Regions.csv | 3 --- .../material/version control/CEMENT.BvR2010.xlsx/Sheet1.csv | 3 --- .../material/version control/CEMENT.BvR2010.xlsx/Sheet2.csv | 3 --- .../version control/CEMENT.BvR2010.xlsx/Timer_GDPCAP.csv | 3 --- .../material/version control/CEMENT.BvR2010.xlsx/Timer_POP.csv | 3 --- .../version control/CEMENT.BvR2010.xlsx/Timer_Regions.csv | 3 --- .../material/version control/CEMENT.BvR2010.xlsx/VA ppp.csv | 3 --- .../data/material/version control/CEMENT.BvR2010.xlsx/VA.csv | 3 --- .../version control/China_steel_cement_MESSAGE.xlsx/Names.csv | 3 --- .../version control/China_steel_cement_MESSAGE.xlsx/cement.csv | 3 --- .../China_steel_cement_MESSAGE.xlsx/relations.csv | 3 --- .../version control/China_steel_cement_MESSAGE.xlsx/steel.csv | 3 --- .../China_steel_cement_MESSAGE.xlsx/timeseries.csv | 3 --- .../version control/GLOBIOM_Fertilizer_use_N.xlsx/Sheet1.csv | 3 --- .../Global_steel_cement_MESSAGE.xlsx/Sheet4.csv | 3 --- .../Global_steel_cement_MESSAGE.xlsx/cement_R11.csv | 3 --- .../Global_steel_cement_MESSAGE.xlsx/cement_R12.csv | 3 --- .../Global_steel_cement_MESSAGE.xlsx/demand_cement.csv | 3 --- .../Global_steel_cement_MESSAGE.xlsx/relations_R11.csv | 3 --- .../Global_steel_cement_MESSAGE.xlsx/relations_R12.csv | 3 --- .../Global_steel_cement_MESSAGE.xlsx/relations_old.csv | 3 --- .../Global_steel_cement_MESSAGE.xlsx/steel_R11.csv | 3 --- .../Global_steel_cement_MESSAGE.xlsx/steel_R12.csv | 3 --- .../steel_R12_without_watersupply.csv | 3 --- .../Global_steel_cement_MESSAGE.xlsx/timeseries_R11.csv | 3 --- .../Global_steel_cement_MESSAGE.xlsx/timeseries_R12.csv | 3 --- .../version control/LCA_commodity_mapping.xlsx/commodity.csv | 3 --- .../version control/LCA_region_mapping.xlsx/region.csv | 3 --- .../MESSAGE_global_model_technologies.xlsx/technology.csv | 3 --- .../Information.csv | 3 --- .../MESSAGEix-Materials_final_energy_industry.xlsx/R11.csv | 3 --- .../MESSAGEix-Materials_final_energy_industry.xlsx/R12.csv | 3 --- .../MESSAGEix-Materials_final_energy_industry.xlsx/SQL.csv | 3 --- .../ToUseful.csv | 3 --- .../Useful tec. efficincies.csv | 3 --- .../NTNU_LCA_coefficients.xlsx/energyServiceRequirements.csv | 3 --- .../NTNU_LCA_coefficients.xlsx/envImpactsGridAndStorage.csv | 3 --- .../NTNU_LCA_coefficients.xlsx/environmentalImpacts.csv | 3 --- .../version control/NTNU_LCA_coefficients.xlsx/genericData.csv | 3 --- .../NTNU_LCA_coefficients.xlsx/industryEnergyRequirements.csv | 3 --- .../version control/NTNU_LCA_coefficients.xlsx/intro.csv | 3 --- .../NTNU_LCA_coefficients.xlsx/regionClassifications.csv | 3 --- .../NTNU_LCA_coefficients.xlsx/residualEnergyRequirements.csv | 3 --- .../STEEL_database_2012.xlsx/Consumption regions.csv | 3 --- .../version control/STEEL_database_2012.xlsx/Consumption.csv | 3 --- .../version control/STEEL_database_2012.xlsx/NetTrade.csv | 3 --- .../STEEL_database_2012.xlsx/Production regions.csv | 3 --- .../version control/STEEL_database_2012.xlsx/Production.csv | 3 --- .../version control/STEEL_database_2012.xlsx/Sheet1.csv | 3 --- .../version control/STEEL_database_2012.xlsx/Sheet2.csv | 3 --- .../version control/STEEL_database_2012.xlsx/Sheet3.csv | 3 --- .../version control/STEEL_database_2012.xlsx/VA ppp.csv | 3 --- .../material/version control/STEEL_database_2012.xlsx/VA.csv | 3 --- .../version control/aluminum_techno_economic.xlsx/Names.csv | 3 --- .../version control/aluminum_techno_economic.xlsx/data_R11.csv | 3 --- .../version control/aluminum_techno_economic.xlsx/data_R12.csv | 3 --- .../data_historical_China_calculati.csv | 3 --- .../aluminum_techno_economic.xlsx/historical_capacity.csv | 3 --- .../historical_data_calculation.csv | 3 --- .../aluminum_techno_economic.xlsx/relations_R11.csv | 3 --- .../aluminum_techno_economic.xlsx/relations_R12.csv | 3 --- .../aluminum_techno_economic.xlsx/timeseries_R11.csv | 3 --- .../aluminum_techno_economic.xlsx/timeseries_R12.csv | 3 --- .../material/version control/cost_conv_nh3.xlsx/Sheet1.csv | 3 --- .../version control/demand_aluminum.xlsx/cumulative_table.csv | 3 --- .../version control/demand_aluminum.xlsx/final_table.csv | 3 --- .../data/material/version control/demand_aluminum.xlsx/gdp.csv | 3 --- .../version control/demand_aluminum.xlsx/population.csv | 3 --- .../demand_aluminum.xlsx/primary_production(IAI).csv | 3 --- .../material/version control/demand_i_feed_R12.xlsx/Sheet1.csv | 3 --- .../material/version control/demand_petro.xlsx/final_table.csv | 3 --- .../version control/demand_petro.xlsx/final_table_regional.csv | 3 --- .../version control/demand_petro.xlsx/global_production.csv | 3 --- .../version control/demand_petro.xlsx/regional_shares.csv | 3 --- .../version control/fert_techno_economic.xlsx/data_R12.csv | 3 --- .../fert_techno_economic.xlsx/relations_R12.csv | 3 --- .../fert_techno_economic.xlsx/timeseries_R12.csv | 3 --- .../generic_furnace_boiler_techno_economic.xlsx/Names.csv | 3 --- .../boilers_cost.csv | 3 --- .../boilers_cost_old.csv | 3 --- .../generic_furnace_boiler_techno_economic.xlsx/boilers_io.csv | 3 --- .../fuels_emi_fact.csv | 3 --- .../generic_furnace_boiler_techno_economic.xlsx/generic.csv | 3 --- .../generic_emission_upd.csv | 3 --- .../generic_old.csv | 3 --- .../sector_efficiency.csv | 3 --- .../timeseries_R12.csv | 3 --- .../2010gdp_growth_rates.csv | 3 --- .../iamc_db ENGAGE baseline GDP PPP.xlsx/Notes.csv | 3 --- .../iamc_db ENGAGE baseline GDP PPP.xlsx/data_R11.csv | 3 --- .../iamc_db ENGAGE baseline GDP PPP.xlsx/data_R12.csv | 3 --- .../methanol demand.xlsx/2010gdp_growth_rates.csv | 3 --- .../version control/methanol demand.xlsx/GDP_baseline %.csv | 3 --- .../version control/methanol demand.xlsx/GDP_baseline (2).csv | 3 --- .../version control/methanol demand.xlsx/GDP_baseline.csv | 3 --- .../material/version control/methanol demand.xlsx/Notes.csv | 3 --- .../version control/methanol demand.xlsx/ammonia_demand.csv | 3 --- .../material/version control/methanol demand.xlsx/data_R11.csv | 3 --- .../material/version control/methanol demand.xlsx/data_R12.csv | 3 --- .../version control/methanol demand.xlsx/income_elasticity.csv | 3 --- .../version control/methanol demand.xlsx/methanol_demand.csv | 3 --- .../version control/methanol_sensitivity_pars.xlsx/Sheet1.csv | 3 --- .../methanol_sensitivity_pars_high.xlsx/Sheet1.csv | 3 --- .../abs_cost_activity_soft_lo.csv | 3 --- .../abs_cost_activity_soft_up.csv | 3 --- .../methanol_techno_economic.xlsx/addon_conversion.csv | 3 --- .../version control/methanol_techno_economic.xlsx/addon_up.csv | 3 --- .../methanol_techno_economic.xlsx/bound_activity_lo.csv | 3 --- .../methanol_techno_economic.xlsx/bound_activity_up.csv | 3 --- .../methanol_techno_economic.xlsx/bound_total_capacity_lo.csv | 3 --- .../methanol_techno_economic.xlsx/capacity_factor.csv | 3 --- .../methanol_techno_economic.xlsx/construction_time.csv | 3 --- .../version control/methanol_techno_economic.xlsx/demand.csv | 3 --- .../methanol_techno_economic.xlsx/emission_factor.csv | 3 --- .../version control/methanol_techno_economic.xlsx/fix_cost.csv | 3 --- .../methanol_techno_economic.xlsx/growth_activity_lo.csv | 3 --- .../methanol_techno_economic.xlsx/growth_activity_up.csv | 3 --- .../methanol_techno_economic.xlsx/growth_new_capacity_up.csv | 3 --- .../methanol_techno_economic.xlsx/historical_activity.csv | 3 --- .../methanol_techno_economic.xlsx/historical_new_capacity.csv | 3 --- .../methanol_techno_economic.xlsx/initial_activity_lo.csv | 3 --- .../methanol_techno_economic.xlsx/initial_activity_up.csv | 3 --- .../methanol_techno_economic.xlsx/initial_new_capacity_up.csv | 3 --- .../version control/methanol_techno_economic.xlsx/input.csv | 3 --- .../version control/methanol_techno_economic.xlsx/inv_cost.csv | 3 --- .../level_cost_activity_soft_lo.csv | 3 --- .../level_cost_activity_soft_up.csv | 3 --- .../version control/methanol_techno_economic.xlsx/output.csv | 3 --- .../methanol_techno_economic.xlsx/ref_activity.csv | 3 --- .../methanol_techno_economic.xlsx/ref_new_capacity.csv | 3 --- .../methanol_techno_economic.xlsx/relation_activity.csv | 3 --- .../methanol_techno_economic.xlsx/relation_lower.csv | 3 --- .../methanol_techno_economic.xlsx/relation_upper.csv | 3 --- .../methanol_techno_economic.xlsx/soft_activity_lo.csv | 3 --- .../methanol_techno_economic.xlsx/soft_activity_up.csv | 3 --- .../methanol_techno_economic.xlsx/technical_lifetime.csv | 3 --- .../version control/methanol_techno_economic.xlsx/var_cost.csv | 3 --- .../abs_cost_activity_soft_lo.csv | 3 --- .../abs_cost_activity_soft_up.csv | 3 --- .../addon_conversion.csv | 3 --- .../methanol_techno_economic_high_demand.xlsx/addon_up.csv | 3 --- .../bound_activity_lo.csv | 3 --- .../bound_activity_up.csv | 3 --- .../bound_total_capacity_lo.csv | 3 --- .../capacity_factor.csv | 3 --- .../construction_time.csv | 3 --- .../methanol_techno_economic_high_demand.xlsx/demand.csv | 3 --- .../emission_factor.csv | 3 --- .../methanol_techno_economic_high_demand.xlsx/fix_cost.csv | 3 --- .../growth_activity_lo.csv | 3 --- .../growth_activity_up.csv | 3 --- .../growth_new_capacity_up.csv | 3 --- .../historical_activity.csv | 3 --- .../historical_new_capacity.csv | 3 --- .../initial_activity_lo.csv | 3 --- .../initial_activity_up.csv | 3 --- .../initial_new_capacity_up.csv | 3 --- .../methanol_techno_economic_high_demand.xlsx/input.csv | 3 --- .../methanol_techno_economic_high_demand.xlsx/inv_cost.csv | 3 --- .../level_cost_activity_soft_lo.csv | 3 --- .../level_cost_activity_soft_up.csv | 3 --- .../methanol_techno_economic_high_demand.xlsx/output.csv | 3 --- .../methanol_techno_economic_high_demand.xlsx/ref_activity.csv | 3 --- .../ref_new_capacity.csv | 3 --- .../relation_activity.csv | 3 --- .../relation_lower.csv | 3 --- .../relation_upper.csv | 3 --- .../soft_activity_lo.csv | 3 --- .../soft_activity_up.csv | 3 --- .../technical_lifetime.csv | 3 --- .../methanol_techno_economic_high_demand.xlsx/var_cost.csv | 3 --- .../n-fertilizer_techno-economic_new.xlsx/CCS.csv | 3 --- .../n-fertilizer_techno-economic_new.xlsx/Sheet1.csv | 3 --- .../n-fertilizer_techno-economic_new.xlsx/Sheet2.csv | 3 --- .../n-fertilizer_techno-economic_new.xlsx/Trade.csv | 3 --- .../n-fertilizer_techno-economic_new.xlsx/Trade_NH3.csv | 3 --- .../version control/nh3_fertilizer_demand.xlsx/CPA_demand.csv | 3 --- .../nh3_fertilizer_demand.xlsx/NFertilizer_demand.csv | 3 --- .../nh3_fertilizer_demand.xlsx/NFertilizer_trade.csv | 3 --- .../nh3_fertilizer_demand.xlsx/NH3_feedstock_share.csv | 3 --- 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control/water_tec_pars.xlsx/technical_lifetime.csv +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:4d416fd51c124cd2d6526b5649eb119ca1aaf83faf29dc9b0a5c279a455456cb -size 15381 diff --git a/message_ix_models/data/material/version control/water_tec_pars.xlsx/var_cost.csv b/message_ix_models/data/material/version control/water_tec_pars.xlsx/var_cost.csv deleted file mode 100644 index dd21bf75c7..0000000000 --- a/message_ix_models/data/material/version control/water_tec_pars.xlsx/var_cost.csv +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:30dbc5c0965e23e108c1f61a772f6247a0f05117aeca69a7e128fff07a2b0577 -size 32391 From cc2e59f95f045000ef8ad34f28089ec6bcda3230 Mon Sep 17 00:00:00 2001 From: Florian Maczek Date: Thu, 23 May 2024 16:09:25 +0200 Subject: [PATCH 774/774] Add #188, #189 to doc/whatsnew --- doc/whatsnew.rst | 1 + 1 file changed, 1 insertion(+) diff --git a/doc/whatsnew.rst b/doc/whatsnew.rst index 76db8b5192..f3c5cd8404 100644 --- a/doc/whatsnew.rst +++ b/doc/whatsnew.rst @@ -4,6 +4,7 @@ What's new Next release ============ +- Add :doc:`/material/index` (:pull:`188`, :pull:`189`). Changes to :doc:`/api/tools-costs` ---------------------------------- - Fix jumps in cost projections for technologies with first technology year that's after than the first model year (:pull:`186`).